pax_global_header00006660000000000000000000000064141354620520014514gustar00rootroot0000000000000052 comment=bd0924b10f3e4c9112ee7778950d88e3f5de920f openTSNE-0.6.1/000077500000000000000000000000001413546205200131535ustar00rootroot00000000000000openTSNE-0.6.1/.ci/000077500000000000000000000000001413546205200136245ustar00rootroot00000000000000openTSNE-0.6.1/.ci/build_wheels.sh000077500000000000000000000015041413546205200166310ustar00rootroot00000000000000#!/usr/bin/env bash set -ex echo "Building for ${PYBIN}..." # Install cython so that the files will be re-cythonized, to account for old numpy version ${PYBIN}/pip install --user cython # Numpy must be available for openTSNE to be built ${PYBIN}/pip install --user numpy==1.16.6 # List installed dependency versions ${PYBIN}/pip freeze # Force wheel to use old version of numpy, otherwise it tries to download latest version echo numpy==1.16.6 > requirements_numpy.txt # Compile openTSNE wheels ${PYBIN}/pip wheel -w wheelhouse/ -r requirements_numpy.txt . # Bundle external shared libraries into the wheels for whl in wheelhouse/openTSNE*.whl; do auditwheel repair "$whl" --plat $PLAT -w wheelhouse/ done # Make sure the wheel can be installed ${PYBIN}/pip install --user --force-reinstall --find-links wheelhouse openTSNE openTSNE-0.6.1/.github/000077500000000000000000000000001413546205200145135ustar00rootroot00000000000000openTSNE-0.6.1/.github/ISSUE_TEMPLATE.md000066400000000000000000000001271413546205200172200ustar00rootroot00000000000000##### Expected behaviour ##### Actual behaviour ##### Steps to reproduce the behavioropenTSNE-0.6.1/.github/PULL_REQUEST_TEMPLATE.md000066400000000000000000000003311413546205200203110ustar00rootroot00000000000000##### Issue ##### Description of changes ##### Includes - [X] Code changes - [ ] Tests - [ ] Documentation openTSNE-0.6.1/.gitignore000066400000000000000000000001261413546205200151420ustar00rootroot00000000000000.idea/ venv/ build/ *.so *.egg-info/ __pycache__/ dist/ .ipynb_checkpoints/ .DS_STORE openTSNE-0.6.1/LICENSE000066400000000000000000000027531413546205200141670ustar00rootroot00000000000000BSD 3-Clause License Copyright (c) 2018, Pavlin Poličar All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. openTSNE-0.6.1/MANIFEST.in000066400000000000000000000002131413546205200147050ustar00rootroot00000000000000recursive-include openTSNE/ *.py *.h *.c *.cpp *.pyx *.pxd include LICENSE include README.rst include docs/source/images/macosko_2015.png openTSNE-0.6.1/README.rst000066400000000000000000000142341413546205200146460ustar00rootroot00000000000000openTSNE ======== |Build Status| |ReadTheDocs Badge| |License Badge| openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE) [1]_, a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings [2]_, massive speed improvements [3]_ [4]_, enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations [5]_. .. figure:: docs/source/images/macosko_2015.png :alt: Macosko 2015 mouse retina t-SNE embedding :align: center A visualization of 44,808 single cell transcriptomes obtained from the mouse retina [6]_ embedded using the multiscale kernel trick to better preserve the global aligment of the clusters. - `Documentation `__ - `User Guide and Tutorial `__ - Examples: `basic `__, `advanced `__, `preserving global alignment `__, `embedding large data sets `__ - `Speed benchmarks `__ Installation ------------ openTSNE requires Python 3.7 or higher in order to run. Conda ~~~~~ openTSNE can be easily installed from ``conda-forge`` with :: conda install --channel conda-forge opentsne `Conda package `__ PyPi ~~~~ openTSNE is also available through ``pip`` and can be installed with :: pip install opentsne `PyPi package `__ Installing from source ~~~~~~~~~~~~~~~~~~~~~~ If you wish to install openTSNE from source, please run :: python setup.py install in the root directory to install the appropriate dependencies and compile the necessary binary files. Please note that openTSNE requires a C/C++ compiler to be available on the system. Additionally, ``numpy`` must be pre-installed in the active environment. In order for openTSNE to utilize multiple threads, the C/C++ compiler must support ``OpenMP``. In practice, almost all compilers implement this with the exception of older version of ``clang`` on OSX systems. To squeeze the most out of openTSNE, you may also consider installing FFTW3 prior to installation. FFTW3 implements the Fast Fourier Transform, which is heavily used in openTSNE. If FFTW3 is not available, openTSNE will use numpy’s implementation of the FFT, which is slightly slower than FFTW. The difference is only noticeable with large data sets containing millions of data points. A hello world example --------------------- Getting started with openTSNE is very simple. First, we'll load up some data using scikit-learn .. code:: python from sklearn import datasets iris = datasets.load_iris() x, y = iris["data"], iris["target"] then, we'll import and run .. code:: python from openTSNE import TSNE embedding = TSNE().fit(x) Citation -------- If you make use of openTSNE for your work we would appreciate it if you would cite the paper .. code:: @article {Poli{\v c}ar731877, author = {Poli{\v c}ar, Pavlin G. and Stra{\v z}ar, Martin and Zupan, Bla{\v z}}, title = {openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding}, year = {2019}, doi = {10.1101/731877}, publisher = {Cold Spring Harbor Laboratory}, URL = {https://www.biorxiv.org/content/early/2019/08/13/731877}, eprint = {https://www.biorxiv.org/content/early/2019/08/13/731877.full.pdf}, journal = {bioRxiv} } openTSNE implements two efficient algorithms for t-SNE. Please consider citing the original authors of the algorithm that you use. If you use FIt-SNE (default), then the citation is [4]_ below, but if you use Barnes-Hut the citation is [3]_. References ---------- .. [1] Van Der Maaten, Laurens, and Hinton, Geoffrey. `“Visualizing data using t-SNE.” `__ Journal of Machine Learning Research 9.Nov (2008): 2579-2605. .. [2] Poličar, Pavlin G., Martin Stražar, and Blaž Zupan. `“Embedding to Reference t-SNE Space Addresses Batch Effects in Single-Cell Classification.” `__ Machine Learning (2021): 1-20. .. [3] Van Der Maaten, Laurens. `“Accelerating t-SNE using tree-based algorithms.” `__ Journal of Machine Learning Research 15.1 (2014): 3221-3245. .. [4] Linderman, George C., et al. `"Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data." `__ Nature Methods 16.3 (2019): 243. .. [5] Kobak, Dmitry, and Berens, Philipp. `“The art of using t-SNE for single-cell transcriptomics.” `__ Nature Communications 10, 5416 (2019). .. [6] Macosko, Evan Z., et al. \ `“Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.” `__ Cell 161.5 (2015): 1202-1214. .. |Build Status| image:: https://dev.azure.com/pavlingp/openTSNE/_apis/build/status/Test?branchName=master :target: https://dev.azure.com/pavlingp/openTSNE/_build/latest?definitionId=1&branchName=master .. |ReadTheDocs Badge| image:: https://readthedocs.org/projects/opentsne/badge/?version=latest :target: https://opentsne.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status .. |License Badge| image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg :target: https://opensource.org/licenses/BSD-3-Clause openTSNE-0.6.1/azure-pipelines-release.yml000066400000000000000000000165521413546205200204410ustar00rootroot00000000000000variables: AZURE_BUILD: true trigger: tags: include: - v* pr: none jobs: - job: 'PackageWinOsx' timeoutInMinutes: 0 cancelTimeoutInMinutes: 10 displayName: 'Build wheels ::' pool: vmImage: $(image.name) strategy: matrix: osx - python36: image.name: 'macos-10.14' python.version: '3.6' osx - python37: image.name: 'macos-10.14' python.version: '3.7' osx - python38: image.name: 'macos-10.14' python.version: '3.8' osx - python39: image.name: 'macos-10.14' python.version: '3.9' windows - python36: image.name: 'vs2017-win2016' python.version: '3.6' windows - python37: image.name: 'vs2017-win2016' python.version: '3.7' windows - python38: image.name: 'vs2017-win2016' python.version: '3.8' windows - python39: image.name: 'vs2017-win2016' python.version: '3.9' steps: - task: UsePythonVersion@0 inputs: versionSpec: '$(python.version)' architecture: 'x64' displayName: 'Use Python $(python.version)' - script: env displayName: 'List enviromental variables' - script: | python -m pip install --upgrade pip python -m pip install setuptools wheel pytest python -m pip install cython python -m pip install numpy==1.16.6 displayName: 'Install job dependencies' - script: python -m pip freeze displayName: 'List dependency versions' - script: python setup.py bdist_wheel displayName: 'Build wheel' # Since Python automatically adds `cwd` to `sys.path`, it's important we remove the local folder # containing our code from the working directory. Otherwise, the tests will use the local copy # instead of the installed package. We can easily achieve this by renaming the source folder. - bash: mv openTSNE src displayName: 'Remove source files from path' - bash: ls -lRh dist displayName: 'List built files' - bash: python -m pip install -vv --force-reinstall --find-links dist openTSNE displayName: 'Install wheel' - script: pip install pynndescent displayName: 'Install optional dependencies - pynndescent' condition: ne(variables['python.version'], '3.6') # llvm-lite doesn't support py36 from v0.37 onwards - script: pip install hnswlib displayName: 'Install optional dependencies - hnswlib' - script: pytest -v timeoutInMinutes: 15 displayName: 'Run unit tests' - task: CopyFiles@2 condition: eq(variables['Agent.JobStatus'], 'Succeeded') inputs: contents: dist/** targetFolder: $(Build.ArtifactStagingDirectory) - task: PublishBuildArtifacts@1 condition: eq(variables['Agent.JobStatus'], 'Succeeded') inputs: artifactName: 'build' pathtoPublish: $(Build.ArtifactStagingDirectory) - job: 'PackageLinux' timeoutInMinutes: 0 cancelTimeoutInMinutes: 10 displayName: 'Build wheels :: linux -' pool: vmImage: 'ubuntu-latest' strategy: matrix: python36: python: '/opt/python/cp36-cp36m/bin' python.version: '3.6' python37: python: '/opt/python/cp37-cp37m/bin' python.version: '3.7' python38: python: '/opt/python/cp38-cp38/bin' python.version: '3.8' python39: python: '/opt/python/cp39-cp39/bin' python.version: '3.9' container: image: quay.io/pypa/manylinux2010_x86_64:latest options: -e PLAT=manylinux2010_x86_64 steps: - bash: ls -R /opt/python displayName: 'List available Python binaries' - bash: $(python)/pip install --user pytest displayName: 'Install job dependencies' # Build and install the wheel - bash: .ci/build_wheels.sh displayName: 'Build wheels' env: PYBIN: $(python) # Since Python automatically adds `cwd` to `sys.path`, it's important we remove the local folder # containing our code from the working directory. Otherwise, the tests will use the local copy # instead of the installed package. We can easily achieve this by renaming the source folder. - bash: mv openTSNE src displayName: 'Remove source files from path' - script: $(python)/pip install pynndescent displayName: 'Install optional dependencies - pynndescent' condition: false # llvm-lite doesn't support py36 from v0.37 onwards, and the llvmlite version wasn't built for manylinux2010 - script: $(python)/pip install hnswlib displayName: 'Install optional dependencies - hnswlib' - script: $(python)/python -m pytest -v timeoutInMinutes: 15 displayName: 'Run unit tests' - bash: | ls -lRh wheelhouse mkdir -p dist cp wheelhouse/openTSNE*manylinux*.whl dist/ displayName: 'Copy files to dist folder' - task: CopyFiles@2 condition: eq(variables['Agent.JobStatus'], 'Succeeded') inputs: contents: dist/** targetFolder: $(Build.ArtifactStagingDirectory) - task: PublishBuildArtifacts@1 condition: eq(variables['Agent.JobStatus'], 'Succeeded') inputs: artifactName: 'build' pathtoPublish: $(Build.ArtifactStagingDirectory) - job: 'sdist' timeoutInMinutes: 0 cancelTimeoutInMinutes: 10 displayName: 'Package source distribution' pool: vmImage: 'ubuntu-latest' steps: - task: UsePythonVersion@0 displayName: 'Use Python 3.7' inputs: versionSpec: '3.7' - script: | python -m pip install --upgrade pip python -m pip install setuptools wheel pytest python -m pip install cython python -m pip install numpy==1.16.6 displayName: 'Install job dependencies' - script: python -m pip freeze displayName: 'List dependency versions' - script: python setup.py sdist displayName: 'Build sdist' # Since Python automatically adds `cwd` to `sys.path`, it's important we remove the local folder # containing our code from the working directory. Otherwise, the tests will use the local copy # instead of the installed package. We can easily achieve this by renaming the source folder. - bash: mv openTSNE src displayName: 'Remove source files from path' - bash: ls -lRh dist displayName: 'List built files' - bash: python -m pip install --force-reinstall --find-links dist openTSNE displayName: 'Install package' - script: | python -m pip install pynndescent python -m pip install hnswlib displayName: 'Install optional dependencies' - script: pytest -v timeoutInMinutes: 15 displayName: 'Run unit tests' - task: CopyFiles@2 condition: eq(variables['Agent.JobStatus'], 'Succeeded') inputs: contents: dist/** targetFolder: $(Build.ArtifactStagingDirectory) - task: PublishBuildArtifacts@1 condition: eq(variables['Agent.JobStatus'], 'Succeeded') inputs: artifactName: 'build' pathtoPublish: $(Build.ArtifactStagingDirectory) openTSNE-0.6.1/azure-pipelines.yml000066400000000000000000000044221413546205200170140ustar00rootroot00000000000000variables: AZURE_BUILD: true trigger: - master jobs: - job: 'Test' displayName: 'Unit tests' pool: vmImage: $(image.name) strategy: matrix: linux-python37: image.name: 'ubuntu-latest' python.version: '3.7' linux-python38: image.name: 'ubuntu-latest' python.version: '3.8' linux-python39: image.name: 'ubuntu-latest' python.version: '3.9' osx-python37: image.name: 'macos-10.14' python.version: '3.7' osx-python38: image.name: 'macos-10.14' python.version: '3.8' osx-python39: image.name: 'macos-10.14' python.version: '3.9' windows-python37: image.name: 'vs2017-win2016' python.version: '3.7' windows-python38: image.name: 'vs2017-win2016' python.version: '3.8' windows-python39: image.name: 'vs2017-win2016' python.version: '3.9' steps: - task: UsePythonVersion@0 inputs: versionSpec: '$(python.version)' architecture: 'x64' displayName: 'Use Python $(python.version)' - script: env displayName: 'List enviromental variables' - script: | python -m pip install --upgrade pip python -m pip install flake8 pytest python -m pip install numpy displayName: 'Install job dependencies' # stop the build if there are Python syntax errors or undefined names - script: flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics displayName: 'Check for syntax errors' - script: pip install -vv . displayName: 'Install package' - script: pip install pynndescent displayName: 'Install optional dependencies - pynndescent' condition: ne(variables['python.version'], '3.9') - script: pip install hnswlib displayName: 'Install optional dependencies - hnswlib' # Since Python automatically adds `cwd` to `sys.path`, it's important we remove the local folder # containing our code from the working directory. Otherwise, the tests will use the local copy # instead of the installed package. We can easily achieve this by renaming the source folder. - bash: mv openTSNE src displayName: 'Remove source files from path' - script: pytest -v timeoutInMinutes: 15 displayName: 'Run unit tests' openTSNE-0.6.1/benchmarks/000077500000000000000000000000001413546205200152705ustar00rootroot00000000000000openTSNE-0.6.1/benchmarks/.gitignore000066400000000000000000000000161413546205200172550ustar00rootroot00000000000000logs/ FIt-SNE openTSNE-0.6.1/benchmarks/__init__.py000066400000000000000000000000001413546205200173670ustar00rootroot00000000000000openTSNE-0.6.1/benchmarks/benchmark.ipynb000066400000000000000000006651031413546205200203000ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from os import path, listdir\n", "import re\n", "from types import SimpleNamespace as simplenamespace\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "atlas_train_all_neighbors\topenTSNEapprox_1000000.log\n", "FItSNE_1000000.log\t\topenTSNEapprox_100000.log\n", "FItSNE_100000.log\t\topenTSNEapprox_10000.log\n", "FItSNE_10000.log\t\topenTSNEapprox_1000.log\n", "FItSNE_1000.log\t\t\topenTSNEapprox_250000.log\n", "FItSNE_250000.log\t\topenTSNEapprox_500000.log\n", "FItSNE_500000.log\t\topenTSNEapprox_5000.log\n", "FItSNE_5000.log\t\t\topenTSNEapprox_750000.log\n", "FItSNE_750000.log\t\topenTSNEapprox8core_1000000.log\n", "FItSNE8core_1000000.log\t\topenTSNEapprox8core_100000.log\n", "FItSNE8core_100000.log\t\topenTSNEapprox8core_10000.log\n", "FItSNE8core_10000.log\t\topenTSNEapprox8core_1000.log\n", "FItSNE8core_1000.log\t\topenTSNEapprox8core_250000.log\n", "FItSNE8core_250000.log\t\topenTSNEapprox8core_500000.log\n", "FItSNE8core_500000.log\t\topenTSNEapprox8core_5000.log\n", "FItSNE8core_5000.log\t\topenTSNEapprox8core_750000.log\n", "FItSNE8core_750000.log\t\tsklearn_1000000.log\n", "MulticoreTSNE_1000000.log\tsklearn_100000.log\n", "MulticoreTSNE_100000.log\tsklearn_10000.log\n", "MulticoreTSNE_10000.log\t\tsklearn_1000.log\n", "MulticoreTSNE_1000.log\t\tsklearn_250000.log\n", "MulticoreTSNE_250000.log\tsklearn_500000.log\n", "MulticoreTSNE_500000.log\tsklearn_5000.log\n", "MulticoreTSNE_5000.log\t\tsklearn_750000.log\n", "MulticoreTSNE_750000.log\tUMAP_1000000.log\n", "MulticoreTSNE8core_1000000.log\tUMAP_100000.log\n", "MulticoreTSNE8core_100000.log\tUMAP_10000.log\n", "MulticoreTSNE8core_10000.log\tUMAP_1000.log\n", "MulticoreTSNE8core_1000.log\tUMAP_250000.log\n", "MulticoreTSNE8core_250000.log\tUMAP_500000.log\n", "MulticoreTSNE8core_500000.log\tUMAP_5000.log\n", "MulticoreTSNE8core_5000.log\tUMAP_750000.log\n", "MulticoreTSNE8core_750000.log\n" ] } ], "source": [ "!ls logs" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "LOG_DIR = path.join(path.abspath(\".\"), \"logs\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "n_points = [1000, 5000, 10_000, 100_000, 250_000, 500_000, 750_000, 1_000_000]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def make_regex_func(pattern, fname):\n", " \n", " def _wrapper(n_points):\n", " matches = [re.findall(pattern, line) for line in\n", " open(path.join(LOG_DIR, f\"{fname}_{n_points}.log\"))]\n", " matches = list(filter(len, matches))\n", " matches = list(map(lambda x: float(x[0]), matches))\n", "\n", " return np.array(matches)\n", " \n", " return _wrapper" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[228.33709931 225.79553986 226.98500681]\n" ] } ], "source": [ "parse_opentsne = make_regex_func(r\"openTSNE: Full (\\d+\\.\\d+)\", \"openTSNEapprox\")\n", "parse_opentsne_nn = make_regex_func(r\"openTSNE: NN search (\\d+\\.\\d+)\", \"openTSNEapprox\")\n", "parse_opentsne_optimization = make_regex_func(r\"openTSNE: Optimization (\\d+\\.\\d+)\", \"openTSNEapprox\")\n", "\n", "opentsne = simplenamespace(full=parse_opentsne, nn=parse_opentsne_nn, optim=parse_opentsne_optimization)\n", " \n", "print(opentsne.full(100000))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[104.89250898 104.21664405 104.45669413]\n" ] } ], "source": [ "parse_opentsne8 = make_regex_func(r\"openTSNE: Full (\\d+\\.\\d+)\", \"openTSNEapprox8core\")\n", "parse_opentsne_nn8 = make_regex_func(r\"openTSNE: NN search (\\d+\\.\\d+)\", \"openTSNEapprox8core\")\n", "parse_opentsne_optimization8 = make_regex_func(r\"openTSNE: Optimization (\\d+\\.\\d+)\", \"openTSNEapprox8core\")\n", "\n", "opentsne8 = simplenamespace(full=parse_opentsne8, nn=parse_opentsne_nn8, optim=parse_opentsne_optimization8)\n", " \n", "print(opentsne8.full(100000))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[177.11653399 176.98441291 177.97437263]\n" ] } ], "source": [ "parse_fitsne = make_regex_func(r\"FIt-SNE: (\\d+\\.\\d+)\", \"FItSNE\")\n", "parse_fitsne_nn = make_regex_func(r\"100\\% (\\d+\\.\\d+)\", \"FItSNE\")\n", "\n", "def parse_fitsne_optimization(n_points):\n", " full_times = parse_fitsne(n_points)\n", " nn_times = parse_fitsne_nn(n_points)\n", " return full_times - nn_times\n", "\n", "fitsne = simplenamespace(full=parse_fitsne, nn=parse_fitsne_nn, optim=parse_fitsne_optimization)\n", "\n", "print(fitsne.full(100000))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[13.23060322 13.45884466 12.84704351]\n" ] } ], "source": [ "parse_fitsne8 = make_regex_func(r\"FIt-SNE: (\\d+\\.\\d+)\", \"FItSNE8core\")\n", "parse_fitsne_nn8 = make_regex_func(r\"100\\% (\\d+\\.\\d+)\", \"FItSNE8core\")\n", "\n", "def parse_fitsne_optimization8(n_points):\n", " full_times = parse_fitsne8(n_points)\n", " nn_times = parse_fitsne_nn8(n_points)\n", " return full_times - nn_times\n", "\n", "fitsne8 = simplenamespace(full=parse_fitsne8, nn=parse_fitsne_nn8, optim=parse_fitsne_optimization8)\n", "\n", "print(fitsne8.full(1000))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[953.92509246 936.90797281 933.63834786]\n" ] } ], "source": [ "parse_multicore = make_regex_func(r\"Multicore t-SNE: (\\d+\\.\\d+)\", \"MulticoreTSNE\")\n", "parse_multicore_nn = make_regex_func(r\"Done in (\\d+\\.\\d+) seconds\", \"MulticoreTSNE\")\n", "parse_multicore_optimization = make_regex_func(r\"Fitting performed in (\\d+\\.\\d+) seconds\", \"MulticoreTSNE\")\n", "\n", "multicore = simplenamespace(full=parse_multicore, nn=parse_multicore_nn, optim=parse_multicore_optimization)\n", "\n", "print(multicore.full(100000))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2.9090538 2.66580534 2.82796788]\n" ] } ], "source": [ "parse_multicore8 = make_regex_func(r\"Multicore t-SNE: (\\d+\\.\\d+)\", \"MulticoreTSNE8core\")\n", "parse_multicore_nn8 = make_regex_func(r\"Done in (\\d+\\.\\d+) seconds\", \"MulticoreTSNE8core\")\n", "parse_multicore_optimization8 = make_regex_func(r\"Fitting performed in (\\d+\\.\\d+) seconds\", \"MulticoreTSNE8core\")\n", "\n", "multicore8 = simplenamespace(full=parse_multicore8, nn=parse_multicore_nn8, optim=parse_multicore_optimization8)\n", "\n", "print(multicore8.full(1000))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[65.73254728 66.63277197 67.05821729]\n" ] } ], "source": [ "parse_sklearn = make_regex_func(r\"scikit-learn t-SNE: (\\d+\\.\\d+)\", \"sklearn\")\n", "parse_sklearn_nn = make_regex_func(r\"neighbors for .* samples in (\\d+\\.\\d+)s\", \"sklearn\")\n", "\n", "def parse_sklearn_optimization(n_points):\n", " full_times = parse_sklearn(n_points)\n", " nn_times = parse_sklearn_nn(n_points)\n", " return full_times - nn_times\n", "\n", "sklearn = simplenamespace(full=parse_sklearn, nn=parse_sklearn_nn, optim=parse_sklearn_optimization)\n", "\n", "print(sklearn.full(10000))" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[25.80454087 21.35700297 21.26818299]\n" ] } ], "source": [ "parse_umap = make_regex_func(r\"UMAP: (\\d+\\.\\d+)\", \"UMAP\")\n", "\n", "umap = simplenamespace(full=parse_umap, nn=lambda *a, **kw: ..., optim=lambda *a, **kw: ...)\n", "\n", "print(umap.full(10000))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ppolicar/.local/lib/python3.7/site-packages/pandas/core/frame.py:6211: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", "of pandas will change to not sort by default.\n", "\n", "To accept the future behavior, pass 'sort=False'.\n", "\n", "To retain the current behavior and silence the warning, pass 'sort=True'.\n", "\n", " sort=sort)\n" ] } ], "source": [ "import warnings\n", "\n", "df = pd.DataFrame(columns=[\"time\", \"nn_time\", \"optim_time\", \"n_samples\", \"method\"])\n", "\n", "\n", "for method_name, method in [(\"openTSNE (1 core)\", opentsne),\n", " (\"openTSNE (8 cores)\", opentsne8),\n", " (\"MulticoreTSNE (1 core)\", multicore),\n", " (\"MulticoreTSNE (8 cores)\", multicore8),\n", " (\"FIt-SNE (1 core)\", fitsne),\n", " (\"FIt-SNE (8 cores)\", fitsne8),\n", " (\"scikit-learn (1 core)\", sklearn),\n", " (\"UMAP (1 core)\", umap)]:\n", " for n in n_points:\n", " try:\n", " tmp_df = pd.DataFrame({\n", " \"time\": method.full(n),\n", " #\"nn_time\": method.nn(n),\n", " #\"optim_time\": method.optim(n),\n", " })\n", " tmp_df[\"n_samples\"] = n\n", " tmp_df[\"method\"] = method_name\n", " \n", " df = df.append(tmp_df, ignore_index=True)\n", " \n", " except FileNotFoundError as e:\n", " warnings.warn(str(e))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
methodn_samplesnn_timeoptim_timetime
0openTSNE (1 core)1000NaNNaN15.150531
1openTSNE (1 core)1000NaNNaN28.151521
2openTSNE (1 core)1000NaNNaN21.724179
3openTSNE (1 core)5000NaNNaN30.766439
4openTSNE (1 core)5000NaNNaN25.733601
\n", "
" ], "text/plain": [ " method n_samples nn_time optim_time time\n", "0 openTSNE (1 core) 1000 NaN NaN 15.150531\n", "1 openTSNE (1 core) 1000 NaN NaN 28.151521\n", "2 openTSNE (1 core) 1000 NaN NaN 21.724179\n", "3 openTSNE (1 core) 5000 NaN NaN 30.766439\n", "4 openTSNE (1 core) 5000 NaN NaN 25.733601" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
nn_timeoptim_timetime
methodn_samples
FIt-SNE (1 core)1000003
5000003
10000003
100000003
250000003
500000003
750000003
1000000003
FIt-SNE (8 cores)1000003
5000003
10000003
100000003
250000003
500000003
750000003
1000000003
MulticoreTSNE (1 core)1000003
5000003
10000003
100000003
250000003
500000003
750000003
1000000003
MulticoreTSNE (8 cores)1000003
5000003
10000003
100000003
250000003
500000003
...............
UMAP (1 core)10000003
100000003
250000003
500000003
750000003
1000000003
openTSNE (1 core)1000003
5000003
10000003
100000003
250000003
500000003
750000003
1000000003
openTSNE (8 cores)1000003
5000003
10000003
100000003
250000003
500000003
750000003
1000000003
scikit-learn (1 core)1000003
5000003
10000003
100000003
250000003
500000003
750000003
1000000003
\n", "

64 rows × 3 columns

\n", "
" ], "text/plain": [ " nn_time optim_time time\n", "method n_samples \n", "FIt-SNE (1 core) 1000 0 0 3\n", " 5000 0 0 3\n", " 10000 0 0 3\n", " 100000 0 0 3\n", " 250000 0 0 3\n", " 500000 0 0 3\n", " 750000 0 0 3\n", " 1000000 0 0 3\n", "FIt-SNE (8 cores) 1000 0 0 3\n", " 5000 0 0 3\n", " 10000 0 0 3\n", " 100000 0 0 3\n", " 250000 0 0 3\n", " 500000 0 0 3\n", " 750000 0 0 3\n", " 1000000 0 0 3\n", "MulticoreTSNE (1 core) 1000 0 0 3\n", " 5000 0 0 3\n", " 10000 0 0 3\n", " 100000 0 0 3\n", " 250000 0 0 3\n", " 500000 0 0 3\n", " 750000 0 0 3\n", " 1000000 0 0 3\n", "MulticoreTSNE (8 cores) 1000 0 0 3\n", " 5000 0 0 3\n", " 10000 0 0 3\n", " 100000 0 0 3\n", " 250000 0 0 3\n", " 500000 0 0 3\n", "... ... ... ...\n", "UMAP (1 core) 10000 0 0 3\n", " 100000 0 0 3\n", " 250000 0 0 3\n", " 500000 0 0 3\n", " 750000 0 0 3\n", " 1000000 0 0 3\n", "openTSNE (1 core) 1000 0 0 3\n", " 5000 0 0 3\n", " 10000 0 0 3\n", " 100000 0 0 3\n", " 250000 0 0 3\n", " 500000 0 0 3\n", " 750000 0 0 3\n", " 1000000 0 0 3\n", "openTSNE (8 cores) 1000 0 0 3\n", " 5000 0 0 3\n", " 10000 0 0 3\n", " 100000 0 0 3\n", " 250000 0 0 3\n", " 500000 0 0 3\n", " 750000 0 0 3\n", " 1000000 0 0 3\n", "scikit-learn (1 core) 1000 0 0 3\n", " 5000 0 0 3\n", " 10000 0 0 3\n", " 100000 0 0 3\n", " 250000 0 0 3\n", " 500000 0 0 3\n", " 750000 0 0 3\n", " 1000000 0 0 3\n", "\n", "[64 rows x 3 columns]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby([\"method\", \"n_samples\"]).count()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "\n", "#sns.set(\"notebook\", \"whitegrid\")" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
methodn_samplesnn_timeoptim_timetimetime_min
0openTSNE (1 core)1000NaNNaN15.1505310.252509
1openTSNE (1 core)1000NaNNaN28.1515210.469192
2openTSNE (1 core)1000NaNNaN21.7241790.362070
3openTSNE (1 core)5000NaNNaN30.7664390.512774
4openTSNE (1 core)5000NaNNaN25.7336010.428893
\n", "
" ], "text/plain": [ " method n_samples nn_time optim_time time time_min\n", "0 openTSNE (1 core) 1000 NaN NaN 15.150531 0.252509\n", "1 openTSNE (1 core) 1000 NaN NaN 28.151521 0.469192\n", "2 openTSNE (1 core) 1000 NaN NaN 21.724179 0.362070\n", "3 openTSNE (1 core) 5000 NaN NaN 30.766439 0.512774\n", "4 openTSNE (1 core) 5000 NaN NaN 25.733601 0.428893" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"time_min\"] = df[\"time\"] / 60\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6))\n", "#g = sns.lineplot(x=\"n_samples\", y=\"time_min\", hue=\"method\", data=df, ax=ax)\n", "\n", "sns.despine(offset=20)\n", "\n", "ax.set_title(\"t-SNE implementation benchmarks\", loc=\"Left\", fontdict={\n", " \"fontsize\": \"13\"\n", "})\n", "ax.set_xlabel(\"$n$ samples\")\n", "ax.set_ylabel(\"Time (in minutes)\")\n", "\n", "ax.grid(color=\"0.9\", linestyle=\"--\", linewidth=1)\n", "\n", "# Lines\n", "d = df.groupby([\"method\", \"n_samples\"]).mean().reset_index()\n", "d_std = df.groupby([\"method\", \"n_samples\"]).std().reset_index()\n", "\n", "# openTSNE\n", "which = \"openTSNE (1 core)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#4C72B0\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#4C72B0\")\n", "\n", "which = \"openTSNE (8 cores)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#4C72B0\", linestyle=\"dashed\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#4C72B0\")\n", "\n", "# FIt-SNE\n", "which = \"FIt-SNE (1 core)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#DD8452\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#DD8452\")\n", "\n", "which = \"FIt-SNE (8 cores)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#DD8452\", linestyle=\"dashed\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#DD8452\")\n", "\n", "# MulticoreTSNE\n", "which = \"MulticoreTSNE (1 core)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#55A868\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#55A868\")\n", "\n", "which = \"MulticoreTSNE (8 cores)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#55A868\", linestyle=\"dashed\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#55A868\")\n", "\n", "# sklearn\n", "which = \"scikit-learn (1 core)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#C44E52\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#C44E52\")\n", "\n", "ax.set_xlim(0, 1_000_000)\n", "ax.set_ylim(0, 130)\n", "ax.set_yticks(range(0, 130, 15))\n", "\n", "ax.get_xaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(\n", " lambda x, p: format(int(x), ',').replace(\",\", \".\")))\n", "\n", "handles, labels = ax.get_legend_handles_labels()\n", "ax.legend(frameon=False, loc='upper right')\n", "\n", "plt.savefig(\"benchmarks.png\", dpi=300, transparent=True)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6))\n", "#g = sns.lineplot(x=\"n_samples\", y=\"time_min\", hue=\"method\", data=df, ax=ax)\n", "\n", "sns.despine(offset=20)\n", "\n", "ax.set_title(\"t-SNE implementation benchmarks\", loc=\"Left\", fontdict={\n", " \"fontsize\": \"13\"\n", "})\n", "ax.set_xlabel(\"$n$ samples\")\n", "ax.set_ylabel(\"Time (in minutes)\")\n", "\n", "ax.grid(color=\"0.9\", linestyle=\"--\", linewidth=1)\n", "\n", "# Lines\n", "d = df.groupby([\"method\", \"n_samples\"]).mean().reset_index()\n", "d_std = df.groupby([\"method\", \"n_samples\"]).std().reset_index()\n", "\n", "# openTSNE\n", "which = \"openTSNE (1 core)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#4C72B0\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#4C72B0\")\n", "\n", "which = \"openTSNE (8 cores)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#4C72B0\", linestyle=\"dashed\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#4C72B0\")\n", "\n", "# FIt-SNE\n", "which = \"FIt-SNE (1 core)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#DD8452\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#DD8452\")\n", "\n", "which = \"FIt-SNE (8 cores)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#DD8452\", linestyle=\"dashed\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#DD8452\")\n", "\n", "# MulticoreTSNE\n", "which = \"MulticoreTSNE (1 core)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#55A868\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#55A868\")\n", "\n", "which = \"MulticoreTSNE (8 cores)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#55A868\", linestyle=\"dashed\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#55A868\")\n", "\n", "# sklearn\n", "#which = \"scikit-learn (1 core)\"\n", "#tmp = d[d[\"method\"] == which]\n", "#tmp1 = d_std[d_std[\"method\"] == which]\n", "#ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#C44E52\", label=which)\n", "#ax.fill_between(tmp1[\"n_samples\"],\n", "# tmp[\"time_min\"] + tmp1[\"time_min\"],\n", "# tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#C44E52\")\n", "\n", "# UMAP\n", "which = \"UMAP (1 core)\"\n", "tmp = d[d[\"method\"] == which]\n", "tmp1 = d_std[d_std[\"method\"] == which]\n", "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#C44E52\", label=which)\n", "ax.fill_between(tmp1[\"n_samples\"],\n", " tmp[\"time_min\"] + tmp1[\"time_min\"],\n", " tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#C44E52\")\n", "\n", "ax.set_xlim(0, 1_000_000)\n", "ax.set_ylim(0, 130)\n", "ax.set_yticks(range(0, 130, 15))\n", "\n", "ax.get_xaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(\n", " lambda x, p: format(int(x), ',').replace(\",\", \".\")))\n", "\n", "handles, labels = ax.get_legend_handles_labels()\n", "ax.legend(frameon=False, loc='upper right')\n", "\n", "#plt.savefig(\"benchmarks.png\", dpi=300, transparent=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } openTSNE-0.6.1/benchmarks/benchmark.py000066400000000000000000000212211413546205200175720ustar00rootroot00000000000000"""Bencharking module Run something like this: Choose one of the available methods: --> openTSNENNDescent | openTSNEBH | openTSNEFFT_numpy | MulticoreTSNE | FItSNE | sklearn METHOD="openTSNEFFT_8core"; SAMPLE_SIZES=(1000 100000 250000 500000 750000 1000000); REPETITIONS=1; for size in ${SAMPLE_SIZES[@]}; do cmd="OMP_NUM_THREADS=8 NUMBA_NUM_THREADS=8 python benchmark.py $METHOD run_multiple --n-samples $size --n $REPETITIONS 2>&1 | tee -a logs/${METHOD}_${size}.log"; echo "$cmd"; eval "$cmd"; done; """ import gzip import pickle import time from os import path import fire import numpy as np from MulticoreTSNE import MulticoreTSNE as MulticoreTSNE_ from sklearn.manifold import TSNE as SKLTSNE from sklearn.utils import check_random_state import openTSNE import openTSNE.callbacks class TSNEBenchmark: perplexity = 30 learning_rate = 200 n_jobs = 1 def run(self, n_samples=1000, random_state=None): raise NotImplementedError() def run_multiple(self, n=5, n_samples=1000): for idx in range(n): self.run(n_samples=n_samples, random_state=idx) def load_data(self, n_samples=None): with gzip.open(path.join("data", "10x_mouse_zheng.pkl.gz"), "rb") as f: data = pickle.load(f) x, y = data["pca_50"], data["CellType1"] if n_samples is not None: indices = np.random.choice( list(range(x.shape[0])), n_samples, replace=False ) x, y = x[indices], y[indices] return x, y class openTSNENNDescent(TSNEBenchmark): def run(self, n_samples=1000, random_state=None): x, y = self.load_data(n_samples=n_samples) print("-" * 80) print("Random state", random_state) print("-" * 80, flush=True) random_state = check_random_state(random_state) start = time.time() start_aff = time.time() affinity = openTSNE.affinity.PerplexityBasedNN( x, perplexity=self.perplexity, method="pynndescent", n_jobs=self.n_jobs, random_state=random_state, verbose=True, ) print("openTSNE: NN search", time.time() - start_aff, flush=True) init = openTSNE.initialization.random( x, n_components=2, random_state=random_state, verbose=True ) start_optim = time.time() embedding = openTSNE.TSNEEmbedding( init, affinity, learning_rate=self.learning_rate, n_jobs=self.n_jobs, negative_gradient_method="fft", random_state=random_state, verbose=True, ) embedding.optimize(250, exaggeration=12, momentum=0.8, inplace=True) embedding.optimize(750, momentum=0.5, inplace=True) print("openTSNE: Optimization", time.time() - start_optim) print("openTSNE: Full", time.time() - start, flush=True) class openTSNENNDescent_8core(openTSNENNDescent): n_jobs = 8 class openTSNEBH(TSNEBenchmark): def run(self, n_samples=1000, random_state=None): x, y = self.load_data(n_samples=n_samples) print("-" * 80) print("Random state", random_state) print("-" * 80, flush=True) random_state = check_random_state(random_state) start = time.time() start_aff = time.time() affinity = openTSNE.affinity.PerplexityBasedNN( x, perplexity=self.perplexity, method="annoy", n_jobs=self.n_jobs, random_state=random_state, verbose=True, ) print("openTSNE: NN search", time.time() - start_aff, flush=True) init = openTSNE.initialization.random( x, n_components=2, random_state=random_state, verbose=True ) start_optim = time.time() embedding = openTSNE.TSNEEmbedding( init, affinity, learning_rate=self.learning_rate, n_jobs=self.n_jobs, negative_gradient_method="bh", random_state=random_state, verbose=True, ) embedding.optimize(250, exaggeration=12, momentum=0.8, inplace=True) embedding.optimize(750, momentum=0.5, inplace=True) print("openTSNE: Optimization", time.time() - start_optim) print("openTSNE: Full", time.time() - start, flush=True) class openTSNEBH_8core(openTSNEBH): n_jobs = 8 class openTSNEFFT(TSNEBenchmark): def run(self, n_samples=1000, random_state=None): x, y = self.load_data(n_samples=n_samples) print("-" * 80) print("Random state", random_state) print("-" * 80, flush=True) random_state = check_random_state(random_state) start = time.time() start_aff = time.time() affinity = openTSNE.affinity.PerplexityBasedNN( x, perplexity=self.perplexity, method="annoy", n_jobs=self.n_jobs, random_state=random_state, verbose=True, ) print("openTSNE: NN search", time.time() - start_aff, flush=True) init = openTSNE.initialization.random( x, n_components=2, random_state=random_state, verbose=True, ) start_optim = time.time() embedding = openTSNE.TSNEEmbedding( init, affinity, learning_rate=self.learning_rate, n_jobs=self.n_jobs, negative_gradient_method="fft", random_state=random_state, verbose=True, ) embedding.optimize(250, exaggeration=12, momentum=0.8, inplace=True) embedding.optimize(750, momentum=0.5, inplace=True) print("openTSNE: Optimization", time.time() - start_optim) print("openTSNE: Full", time.time() - start, flush=True) class openTSNEFFT_8core(openTSNEFFT): n_jobs = 8 class MulticoreTSNE(TSNEBenchmark): def run(self, n_samples=1000, random_state=None): x, y = self.load_data(n_samples=n_samples) print("-" * 80, flush=True) start = time.time() tsne = MulticoreTSNE_( early_exaggeration=12, learning_rate=self.learning_rate, perplexity=self.perplexity, n_jobs=self.n_jobs, angle=0.5, verbose=True, random_state=random_state, ) tsne.fit_transform(x) print("Multicore t-SNE:", time.time() - start, flush=True) class MulticoreTSNE_8core(MulticoreTSNE): n_jobs = 8 class FItSNE(TSNEBenchmark): def run(self, n_samples=1000, random_state=-1): import sys; sys.path.append("FIt-SNE") from fast_tsne import fast_tsne x, y = self.load_data(n_samples=n_samples) print("-" * 80) print("Random state", random_state) print("-" * 80, flush=True) if random_state == -1: init = openTSNE.initialization.random(x, n_components=2) else: init = openTSNE.initialization.random( x, n_components=2, random_state=random_state ) start = time.time() fast_tsne( x, map_dims=2, initialization=init, perplexity=self.perplexity, stop_early_exag_iter=250, early_exag_coeff=12, nthreads=self.n_jobs, seed=random_state, ) print("FIt-SNE:", time.time() - start, flush=True) class FItSNE_8core(FItSNE): n_jobs = 8 class sklearn(TSNEBenchmark): def run(self, n_samples=1000, random_state=None): x, y = self.load_data(n_samples=n_samples) print("-" * 80) print("Random state", random_state) print("-" * 80, flush=True) init = openTSNE.initialization.random( x, n_components=2, random_state=random_state ) start = time.time() SKLTSNE( early_exaggeration=12, learning_rate=self.learning_rate, angle=0.5, perplexity=self.perplexity, init=init, verbose=True, random_state=random_state, n_jobs=self.n_jobs, ).fit_transform(x) print("scikit-learn t-SNE:", time.time() - start, flush=True) class sklearn_8core(sklearn): n_jobs = 8 class UMAP(TSNEBenchmark): def run(self, n_samples=1000, random_state=None): import umap x, y = self.load_data(n_samples=n_samples) print("-" * 80) print("Random state", random_state) print("-" * 80, flush=True) start = time.time() umap.UMAP(random_state=random_state).fit_transform(x) print("UMAP:", time.time() - start, flush=True) class UMAP_8core(UMAP): n_jobs = 8 if __name__ == "__main__": fire.Fire() openTSNE-0.6.1/benchmarks/benchmark_nns.py000066400000000000000000000034071413546205200204560ustar00rootroot00000000000000import gzip import pickle from os import path from time import time import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns from openTSNE import nearest_neighbors from openTSNE import utils with utils.Timer("Loading data...", verbose=True): with gzip.open(path.join("data", "macosko_2015.pkl.gz"), "rb") as f: data = pickle.load(f) x = data["pca_50"] y, cluster_ids = data["CellType1"], data["CellType2"] results = [] n_reps = 5 for sample_size in range(1000, 8_001, 1000): print("Sample size:", sample_size) indices = np.random.choice(range(x.shape[0]), size=sample_size) sample = x[indices] for i in range(n_reps): start = time() nn = nearest_neighbors.BallTree(metric="euclidean", n_jobs=1) nn.build(sample, k=15) results.append(("Ball Tree (1 core)", sample_size, time() - start)) for i in range(n_reps): start = time() nn = nearest_neighbors.Annoy(metric="euclidean", n_jobs=1) nn.build(sample, k=15) results.append(("Annoy (1 core)", sample_size, time() - start)) for i in range(n_reps): start = time() nn = nearest_neighbors.BallTree(metric="euclidean", n_jobs=4) nn.build(sample, k=15) results.append(("Ball Tree (4 cores)", sample_size, time() - start)) for i in range(n_reps): start = time() nn = nearest_neighbors.Annoy(metric="euclidean", n_jobs=4) nn.build(sample, k=15) results.append(("Annoy (4 cores)", sample_size, time() - start)) df = pd.DataFrame(results, columns=["method", "size", "time"]) df.to_csv("benchmark_nns.csv") ax = sns.lineplot(data=df, x="size", y="time", hue="method") ax.set_ylim(0, ax.get_ylim()[1]) ax.legend(bbox_to_anchor=(1.1, 1)) plt.show() openTSNE-0.6.1/benchmarks/data/000077500000000000000000000000001413546205200162015ustar00rootroot00000000000000openTSNE-0.6.1/benchmarks/data/.gitignore000066400000000000000000000000241413546205200201650ustar00rootroot00000000000000* !.gitignore !.keepopenTSNE-0.6.1/benchmarks/macosko.py000066400000000000000000000021101413546205200172700ustar00rootroot00000000000000import gzip import pickle from os import path import openTSNE from openTSNE import utils with utils.Timer("Loading data...", verbose=True): with gzip.open(path.join("data", "macosko_2015.pkl.gz"), "rb") as f: data = pickle.load(f) x = data["pca_50"] y, cluster_ids = data["CellType1"], data["CellType2"] # import sys; sys.path.append("FIt-SNE") # from fast_tsne import fast_tsne # # with Timer("Running fast_tsne..."): # fast_tsne(x, nthreads=1) affinities = openTSNE.affinity.PerplexityBasedNN( x, perplexity=30, metric="cosine", method="approx", n_jobs=-1, random_state=0, verbose=True, ) init = openTSNE.initialization.spectral(affinities.P, verbose=True) embedding = openTSNE.TSNEEmbedding( init, affinities, negative_gradient_method="fft", n_jobs=-1, random_state=0, verbose=True, ) embedding.optimize(n_iter=250, exaggeration=12, momentum=0.5, inplace=True) embedding.optimize(n_iter=500, momentum=0.8, inplace=True) import matplotlib.pyplot as plt plt.scatter(embedding[:, 0], embedding[:, 1], s=1) plt.show() openTSNE-0.6.1/docs/000077500000000000000000000000001413546205200141035ustar00rootroot00000000000000openTSNE-0.6.1/docs/Makefile000066400000000000000000000011101413546205200155340ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build SOURCEDIR = source BUILDDIR = build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)openTSNE-0.6.1/docs/make.bat000066400000000000000000000013641413546205200155140ustar00rootroot00000000000000@ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=source set BUILDDIR=build if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% :end popd openTSNE-0.6.1/docs/requirements-doc.txt000066400000000000000000000003271413546205200201340ustar00rootroot00000000000000numpy sphinx sphinx-rtd-theme ipython # For whatever reason, the pip installation on readthedocs doesn't download the wheel fror 0.24, # and attempts to build the package from source; failing. scikit-learn==0.23.2 openTSNE-0.6.1/docs/source/000077500000000000000000000000001413546205200154035ustar00rootroot00000000000000openTSNE-0.6.1/docs/source/api/000077500000000000000000000000001413546205200161545ustar00rootroot00000000000000openTSNE-0.6.1/docs/source/api/affinity.rst000066400000000000000000000002531413546205200205170ustar00rootroot00000000000000Affinity ======== .. automodule:: openTSNE.affinity :members: Affinities, PerplexityBasedNN, MultiscaleMixture, Multiscale, FixedSigmaNN, Uniform :undoc-members: openTSNE-0.6.1/docs/source/api/callbacks.rst000066400000000000000000000002461413546205200206270ustar00rootroot00000000000000Callbacks ========= .. automodule:: openTSNE.callbacks .. autoclass:: Callback :members: optimization_about_to_start :special-members: __call__ openTSNE-0.6.1/docs/source/api/index.rst000066400000000000000000000003631413546205200200170ustar00rootroot00000000000000API Reference ============= .. toctree:: :maxdepth: 2 initialization affinity callbacks sklearn .. automodule:: openTSNE :members: TSNE, TSNEEmbedding, PartialTSNEEmbedding, OptimizationInterrupt :undoc-members: openTSNE-0.6.1/docs/source/api/initialization.rst000066400000000000000000000001411413546205200217310ustar00rootroot00000000000000Initialization ============== .. automodule:: openTSNE.initialization :members: pca, random openTSNE-0.6.1/docs/source/api/sklearn.rst000066400000000000000000000001311413546205200203400ustar00rootroot00000000000000sklearn ======= .. automodule:: openTSNE.sklearn :members: TSNE :undoc-members: openTSNE-0.6.1/docs/source/benchmarks.rst000066400000000000000000000136271413546205200202630ustar00rootroot00000000000000Benchmarks ========== Comparison to other t-SNE implementations ----------------------------------------- We benchmark openTSNE (v0.6.0) against three popular open-source implementations from scikit-learn (v0.24.2), MulticoreTSNE (v0.1), and FIt-SNE (v1.2.1). The benchmarks were run on a server-grade Intel Xeon E5-2650 processor. To generate benchmark data sets of different sizes, we subsampled data from the 10X Genomics 1.3 million mouse brain data set five times, resulting in five different data sets for each size. In total, we run each implementation on 30 different data sets. For each run, five separate datasets were generated by sampling from the 10X 1.3 Million Brain Cells dataset (available `here `_). We reduce the number of dimensions to 50 principal components, following the standard single-cell pipeline. Each t-SNE implementation was then run on every dataset using the following parameters: ``perplexity=30, learning_rate=200`` for 250 early exaggeration iterations with exaggeration 12 and 750 standard iterations with exaggeration 1. Other parameters were set to their default values. This was then repeated for different sample sizes (1,000, 100k, 250k, 500k, 750k, 1mln) resulting in 30 runs for each implementation. .. figure:: images/benchmarks.png :width: 100% :align: center We can immediately see the differences between the different t-SNE approximation schemes used by different algorithms. Both MulticoreTSNE and scikit-learn use the Barnes-Hut approximation by default with O(N log N) scaling. On the other hand, FIt-SNE and openTSNE both make use of the FIt-SNE approximation algorithm with linear time complexity, which is clearly visible from the figure. While the Python runtime inevtiably incurrs some overhead, making openTSNE slightly slower than FIt-SNE, both implementations have comparable runtimes. openTSNE makes this slight performance trade-off for the benefit of ease of installation and a much more flexible and familliar API. Comparison to UMAP ------------------------------- UMAP is another widely popular dimensionality reduction technique. We here benchmark openTSNE (v0.6.0) to umap-learn (v0.5.1), the most popular publicly available Python UMAP implementation. We use the same setup as before, downsampling the 10X Genomics mouse brain data set five times. Because UMAP makes use of numba and requires JIT compilation, we add an additional warmup run to ensure a fair comparison. Different from before, we run t-SNE with the default parameters, decreasing the number of total iterations to 750 and using the automatic learning rate. .. figure:: images/benchmarks-umap.png :width: 100% :align: center We see that while UMAP in indeed faster in the single-threaded case, openTSNE tends to perform much better in the multi-threaded scenario. This is likely due to the fact that the optimization phase of UMAP is not parallelizable, while openTSNE makes heavy use of parallelism in every stage of the algorithm. We see that for large data sets, openTSNE is almost twice as fast as UMAP with clear linear scaling. Somewhat surprising is the single-threaded UMAP trend. While it is clearly faster than openTSNE, the scaling does not appear to be linear, like the scaling exhibited by openTSNE, indicating that openTSNE may be faster than UMAP even in the single-threaded case for larger data sets. Another caveat is that for large data sets, it is highly recommended to increase the `exaggeration` parameter in t-SNE. This creates cleaner embeddings with good separation. These embeddings are fairly similar to the embeddings produced by UMAP. However, openTSNE actually runs `faster` when increasing the exaggeration. Therefore, in practice, the speed differences between openTSNE and UMAP are likely even greater. However, we run the algorithms here with default parameters to ensure a fair comparison. In practice, virtually every computer contains multiple cores. For example, consumer grade Intel i7 processors commonly have 8 cores, making the multi-threaded case more relevant to the majority of users. Caveats when running benchmarks ------------------------------- When running benchmarks on machines with multiple cores using Intel's Math Kernel Library (MKL), care must be taken to properly limit the number of threads available. The number of threads should be limited by setting the environmental variable ``OMP_NUM_THREADS=X``, where ``X`` is the number of threads. This is important when using a numpy distribution linked against the MKL. Both openTSNE and scikit-learn make heavy use of numpy. By default, the MKL will use all available cores by default, ignoring the user-defined ``n_jobs`` parameters. Failure to do so will allow MKL to use all available cores, resulting in an unfair comparison. Similarly, care must be taken when benchmarking against numba-dependent libraries e.g. umap-learn. In addition to specifying ``OMP_NUM_THREADS=X``, we must also specify the ``NUMBA_NUM_THREADS=X`` environmental variable. Failure to do so will allow numba to use all available cores, resulting in an unfair comparison. Additionally, each numba-dependent library should be run for a *warm-up* round so numba can perform its bytecode compilation without negatively affecting the benchmarks. Failure to do so will result in the first run being noticeably slower, skewing the overall results. Reproducibility --------------- All benchmarks were run on an Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz processor. We also ran a subset of these benchmarks on a consumer-grade Intel Core i7-7700HQ processor found in laptop computers. The general trends were similar. All benchmarks were run using the provided benchmark script in the openTSNE repository ``openTSNE/benchmarks/benchmark.py``. The data set used can be found in the example notebooks. A direct link to the preprocessed pickled matrix file is available at ``http://file.biolab.si/opentsne/10x_mouse_zheng.pkl.gz``. openTSNE-0.6.1/docs/source/conf.py000066400000000000000000000127131413546205200167060ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # Configuration file for the Sphinx documentation builder. # # This file does only contain a selection of the most common options. For a # full list see the documentation: # http://www.sphinx-doc.org/en/master/config # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. import os import sys sys.path.insert(0, os.path.abspath('openTSNE')) # -- Project information ----------------------------------------------------- project = 'openTSNE' copyright = u'2020, Pavlin Poličar' author = u'Pavlin Poličar' # The short X.Y version version = '' # The full version, including alpha/beta/rc tags release = '0.3.13' # -- General configuration --------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.todo', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The master toctree document. master_doc = 'index' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = [] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # # html_theme = 'alabaster' html_theme = 'sphinx_rtd_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Custom sidebar templates, must be a dictionary that maps document names # to template names. # # The default sidebars (for documents that don't match any pattern) are # defined by theme itself. Builtin themes are using these templates by # default: ``['localtoc.html', 'relations.html', 'sourcelink.html', # 'searchbox.html']``. # # html_sidebars = {} # -- Options for HTMLHelp output --------------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = 'openTSNEdoc' # -- Options for LaTeX output ------------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'openTSNE.tex', 'openTSNE Documentation', u'Pavlin Poličar', 'manual'), ] # -- Options for manual page output ------------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'opentsne', 'openTSNE Documentation', [author], 1) ] # -- Options for Texinfo output ---------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'openTSNE', 'openTSNE Documentation', author, 'openTSNE', 'One line description of project.', 'Miscellaneous'), ] # -- Options for Epub output ------------------------------------------------- # Bibliographic Dublin Core info. epub_title = project # The unique identifier of the text. This can be a ISBN number # or the project homepage. # # epub_identifier = '' # A unique identification for the text. # # epub_uid = '' # A list of files that should not be packed into the epub file. epub_exclude_files = ['search.html'] # -- Extension configuration ------------------------------------------------- # -- Options for todo extension ---------------------------------------------- # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = True openTSNE-0.6.1/docs/source/examples/000077500000000000000000000000001413546205200172215ustar00rootroot00000000000000openTSNE-0.6.1/docs/source/examples/01_simple_usage/000077500000000000000000000000001413546205200221765ustar00rootroot00000000000000openTSNE-0.6.1/docs/source/examples/01_simple_usage/01_simple_usage.rst000066400000000000000000000122661413546205200257140ustar00rootroot00000000000000Simple usage ============ This notebook demonstrates basic usage of the *openTSNE* library. This is sufficient for almost all use-cases. .. code:: ipython3 from openTSNE import TSNE from examples import utils import numpy as np from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt Load data --------- In most of the notebooks, we will be using the Macosko 2015 mouse retina data set. This is a fairly well-known and well explored data set in the single-cell literature making it suitable as an example. .. code:: ipython3 import gzip import pickle with gzip.open("data/macosko_2015.pkl.gz", "rb") as f: data = pickle.load(f) x = data["pca_50"] y = data["CellType1"].astype(str) .. code:: ipython3 print("Data set contains %d samples with %d features" % x.shape) .. parsed-literal:: Data set contains 44808 samples with 50 features Create train/test split ----------------------- .. code:: ipython3 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.33, random_state=42) .. code:: ipython3 print("%d training samples" % x_train.shape[0]) print("%d test samples" % x_test.shape[0]) .. parsed-literal:: 30021 training samples 14787 test samples Run t-SNE --------- We’ll first create an embedding on the training data. .. code:: ipython3 tsne = TSNE( perplexity=30, metric="euclidean", n_jobs=8, random_state=42, verbose=True, ) .. code:: ipython3 %time embedding_train = tsne.fit(x_train) .. parsed-literal:: -------------------------------------------------------------------------------- TSNE(n_jobs=8, random_state=42, verbose=True) -------------------------------------------------------------------------------- ===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance... --> Time elapsed: 3.89 seconds ===> Calculating affinity matrix... --> Time elapsed: 0.44 seconds ===> Calculating PCA-based initialization... --> Time elapsed: 0.10 seconds ===> Running optimization with exaggeration=12.00, lr=2501.75 for 250 iterations... Iteration 50, KL divergence 5.8046, 50 iterations in 1.7123 sec Iteration 100, KL divergence 5.2268, 50 iterations in 1.8265 sec Iteration 150, KL divergence 5.1357, 50 iterations in 2.0626 sec Iteration 200, KL divergence 5.0977, 50 iterations in 2.0250 sec Iteration 250, KL divergence 5.0772, 50 iterations in 1.9598 sec --> Time elapsed: 9.59 seconds ===> Running optimization with exaggeration=1.00, lr=2501.75 for 500 iterations... Iteration 50, KL divergence 3.5741, 50 iterations in 1.9948 sec Iteration 100, KL divergence 3.1653, 50 iterations in 1.8672 sec Iteration 150, KL divergence 2.9612, 50 iterations in 2.2518 sec Iteration 200, KL divergence 2.8342, 50 iterations in 3.2478 sec Iteration 250, KL divergence 2.7496, 50 iterations in 4.2982 sec Iteration 300, KL divergence 2.6901, 50 iterations in 5.4970 sec Iteration 350, KL divergence 2.6471, 50 iterations in 7.1508 sec Iteration 400, KL divergence 2.6138, 50 iterations in 8.1424 sec Iteration 450, KL divergence 2.5893, 50 iterations in 9.8184 sec Iteration 500, KL divergence 2.5699, 50 iterations in 10.3756 sec --> Time elapsed: 54.65 seconds CPU times: user 7min 53s, sys: 20.6 s, total: 8min 14s Wall time: 1min 8s .. code:: ipython3 utils.plot(embedding_train, y_train, colors=utils.MACOSKO_COLORS) .. image:: output_11_0.png Transform --------- openTSNE is currently the only library that allows embedding new points into an existing embedding. .. code:: ipython3 %time embedding_test = embedding_train.transform(x_test) .. parsed-literal:: ===> Finding 15 nearest neighbors in existing embedding using Annoy approximate search... --> Time elapsed: 1.12 seconds ===> Calculating affinity matrix... --> Time elapsed: 0.03 seconds ===> Running optimization with exaggeration=4.00, lr=0.10 for 0 iterations... --> Time elapsed: 0.00 seconds ===> Running optimization with exaggeration=1.50, lr=0.10 for 250 iterations... Iteration 50, KL divergence 214688.6176, 50 iterations in 0.3767 sec Iteration 100, KL divergence 213210.5159, 50 iterations in 0.3881 sec Iteration 150, KL divergence 212270.1679, 50 iterations in 0.3898 sec Iteration 200, KL divergence 211592.6686, 50 iterations in 0.3881 sec Iteration 250, KL divergence 211074.3288, 50 iterations in 0.3814 sec --> Time elapsed: 1.92 seconds CPU times: user 19.2 s, sys: 650 ms, total: 19.8 s Wall time: 3.89 s .. code:: ipython3 utils.plot(embedding_test, y_test, colors=utils.MACOSKO_COLORS) .. image:: output_14_0.png Together -------- We superimpose the transformed points onto the original embedding with larger opacity. .. code:: ipython3 fig, ax = plt.subplots(figsize=(8, 8)) utils.plot(embedding_train, y_train, colors=utils.MACOSKO_COLORS, alpha=0.25, ax=ax) utils.plot(embedding_test, y_test, colors=utils.MACOSKO_COLORS, alpha=0.75, ax=ax) .. image:: output_16_0.png openTSNE-0.6.1/docs/source/examples/01_simple_usage/output_11_0.png000066400000000000000000004323631413546205200247770ustar00rootroot00000000000000PNG  IHDRb6;9tEXtSoftwareMatplotlib version3.3.3, https://matplotlib.org/ȗ pHYs  IDATxwx? 7)* `X[ٻƺ޻ѯ;vmXQq wHB8w݈{'Oݙ;)y}ƮŦ K,,,,,,,, YXXXXXXX4,h$X"faaaaaaaHD¢`E#1 F%bK,,,,,,,, YXXXXXXX4,h$X"faaaaaaaHD¢`E#1 F%bK,,,,,,,, YXXXXXXX4,h$X"faaaaaaaHD¢`E#1 F%ba8y ] 1 00 '/1eaaaa!$7v,,,:ӻ2'op'/-pr  $  'o:nݕNL^Nx`%p53a~P8*maaa1 ) <8Z@>9"4p7#d-XF, lȶXXXXlI /.n \H,,,,,3"faDkP@!00۶G!Nf(\#3)%I$/ eVgeaaaaaWXX;q3R#?o%H5?(NGgLg+k԰߁Y@Pg~eWf[faaaIM&̗ oNqAƬߡK@~ ֭OKD֪HlcI-"!>,,,,,3lhbFK@ ;jvN~PXS],~DU  =pQw"c+٬m0ՠ0`BSPm4 s򓅀(Ezb%bL8  ; ̦ه'DBT~hr&`3 ',z|f7ke @ M64i" EGQdKQx H=Bʕ' eEϐ0l5"2d|\}^aaaaa^aŦlM3CJ1ANMDuc>2FgD[=j<8&]̶(?BmQ~ahaaa¦ؤP %({+ )EE@(?(-p6278 ]:Un6H&cфqQf$`+~-_t'"oH|fNn\jR]\0>X1Mfhm%(ԗf>"] )}RB4# (\?KGH@ؔq.n~PCp0ʗio6&x ?(|haaa1< .HȖfK(U3E$4[p"?'o$/o1p割{BB^^7&(GX3kXkDLS+(T=dm$9 GbS@ 2g{Ԁ׉ht71 ~+M(8x/O`63GD.p RTg; a:p5E&:DNGDmŦYl HB7>7uB$J/q302 N^pa~P8sѺ3 d%kBK )MPT`*J*1TO*bfP^5ʛU-Ge(Rz"R`K9F$ei|ܬ3 6CmK @Ja)Mb(C3G_BK(] )pZ4hK-,,,61X"f MF tRx-]e~ mN^:2' D {\d t_b~/G }߀&"F32GkKNB-]"5/-,,,,lhbSӹ8|V<l~x *{|^%fMB+VD)R~F*Z*J}1flDF"v5yLӁOo,,,,6moN^)nc\Z B3=DĬ8!'%CD|>`kD D+@.{"eۣ, (d eͯ@[<aYXl k|d֟]*;"^MrV᧠+Lȼ' RFl㐪RXl.FG}:GbG:zi* [(x I.D8?|4C0$DcA¢aagMZX P+#FNhFe"liȐ, 3!Bj/ȤԴRnZcĐN͑mA#gXX"fa/D͎LFN(lڢ)\P~?l6R^2ύ#tŴ3_  J\n~D3)5]@ȴ RN<F (lլKmοySVe_K,,}XSL.\ա;ѡnkSvn2HhR{Ge> ,mjj+361-,,,֬oa/C~P8)p>G}fGރ z  [ЃɃTʲCM^HMj gWפ_Jy 9G,ozo 1@접3' 2jaaaYXKQm)uV~P;)G;vno4N&יah5= !5{m!.En{ș'7ru,,,,"fa(pDIQ hjT#+A?F-тީ@`ۢ叶%ːFHwW+djaaao%bks*Z$1ef]ڢ*|K'/- h2kY{DΦS} lvIldYX&!|ucUC&/ZOW[rv)cIGv?52^Df[߀p~PXHMG ఃFάmXXXXoX"fR|+ ׭>}G:6Y7<4zۯV6 ߍF C7uގM/*L[K.رU)}Sʫv͢ ֖Heh}xc!6bH4BTQf#Wb1U4( wp8@d0. ?wk/\69ڬ.UYƪ@ߗoPF+<][kVڹV |H@&"62uF#Fiԟq l0@1U+`G͸.\q;Hx _ v=D4^R2WY4l$0A'9jye{wȭNO/#FI,URu]>iyfѢ <NC:Wc9s@`aaa`J/.[OFP% a\O*]f@Iȷw y󺵹EU6OXu |R׮s:ɞPXr R5@^E#ީ'4)YDN3e"E* Y ٖC: 4r#tB 4r҆Ec.BTJyF# ;R=FU+>z?. ǕhyoКgŢB8ơd;D*;1< KA)3O?ǽ>(z% :`ڔSm0 ˀIfYۑp/J%|͠~G <  ^zF t:5hblA#gewR9!RvR?.ǨiZ2#)@%.pzT3,g֯h4r;,vh$< 軦Ùؔk?0xyV'ڪp:8yCYs;z"vyS !^e~ChDD*f5/K~P8yCs!N77jIŦK8^F/"(lRn@1g"5@nʍK Ou=-Sv_^mz~s M٘ce!bPg¥Ɉ |V/|:-Zz83TE5]q# &8yAai莊F߭viuaAҒ^*m䝬Kuj DT8 QhL }7Q sș/orB(eǛ襣knvqن8E}X"1?Y/)|_z~%t=;AobXԲx׽sL'a 9zh$mLQϪKYH5 3 8r`W.Hڪ2wn(O; 3Bmw8nuZ!PUsKMrH JNZVu1p.Rn4^LA! 6?Y?O1SF\ JtCE7`:Cpp|rIݍu,v2Qh$RR )k(u7"eH, !"5.EDZ@4^OF«|%#kx7uǡp ~`mU#КTU[rEM;'`PUmRre]˪el>KӚA\c B5UV'U9„ EE#PX>N y\ U*bBh$\#4xx.@Ca Z{p vhf߁@42SPE\wVg` .0^u4s=7< }DOWEn1|Q2 N( "h$F'/^s`,oU<ʧƬ#[/{uKRG9 #t8.):Jy`A#g6J1NkrWXpog#b?}gB2P;%3!5h$\z /px a J3PԌn(ak! |Z+h$\z~;6uAD* Ot=? LuCHy  ϼA3^GC):p=?1/,obk.kL65?Mn|zңhjcĐN#t9oA#g-$($zsnv5 }l U*bNFyVzG5߮EA\ѠsZ$ l)l戸,Gkhf %hp2 r z=(Yz~+doTGMwA0\oelФ&HY;){QI CJ,>&s|_vs^q֓ v(s,#Q4^UU-+ˬ.MC/6?G'F5 74b9kNG%btB3r=XW ^SǪyh!烀pptME}@Ck,"Wh2O@!Ih@%r6C52'^>2}BOD#ᥦ 7$Ǚ}Cdn wD~|a-sф@޳loJ+=X`2 WJqK4hsL{nCmjG3rl3vA5ɷ4`I²[=3:yV:QbCm:ڳݢ_F};v(ӜD9@\-HKob#%b|[ː=zHԭWO-Z4-3Ai)Z!"# ]֏=+pK]j ь$<4AjکV@=25e ̌F# y(Lfu2pӆ4DZ!eu=gDW}"E#e>vwg#LiL[T^$}D#᪂7 U8Y\Qs[Rem~91[նxrzB'oÖǠYM#rǮ=rb#%b9Lh 䑺eBtIf:{Ey԰"`cҵ"FňT9h}Qhf  )WӐ? L@JSZ(z~I3"h x6ZAShF(FLqB b ̱Ckint=e4iV 8~2Gjё0_O=!yYE~QSǥ-^:;?(;.ަGMJ+14-9w?7^02*F<7\- E.@g,,,mp=3DDDpv;HҐ?ݚ =HhN\[̑SЀQ<5ˀ/L:ϐf =UK~"kij\Ϗ Ĕ40@9HxLYi"9˴C(\SGm釋lX|*+dWFulٚP .wj&93fqɒ^8)_'ߠvYkZ]5:"fh{77W\^bfiK .gaaHDl# ?C>Re# DƎCt+\Td$pPK4r3ͷhǡeH2D #8}Qx|"R1E\DnD*2R̒ь+y;Hxڴ90ޘB~בm"]~uxvw- }Sh$-331~74`'F#z~ iFk- #bHzm6+_~)wkQYrT%Nu68tjΦ*Tީi6Mґ2zmoBQ,gg"9KQHs/\chPS Ak]7j$ۦNkH뎈`L& ZU4[U;ꪝ*Z$&L+zIf8U5Nm]Rө_M:tZ߀{u3E:5?댢XS]A]j\} =:ZXX4,PzO0m5if({g]|jqDE$P8UD z'h$|!iטG#:DGL}z#r)7otf@XήSgRص&f0Mb 4FF#m<"I,a)f#%-DH&#u+ >CbHE42Ǔ^&| (tvHm !4E$%" (OX+Olx Z d"h&(6TvDaьƛL[&z`HxCN K8#80 O]v+ 4 [ަ_=oGLOD(ѹAa%&1߉ yn&7x* ـ0^K@anvX>,&@5^>!7ƩņU}4 #\ozy\~mPq,/tOC+1|ԣL7;yvF> 18ՔlPD.3BjMW%DMєȷUj,Ct2>1eES "DG "z "馾KѠg JG*"oA0 KyӔ9?HYyx* HxĪH9/#qUۭ$+3 5C: "{#b:u⿛DG.DbѼeb@ԐCB^pQ_ K:=@$ޗIl'Ha=SQXUDb!163 eژ@ |Xd}\;!TbmIÁ(G_ӦaK^q=q,]zf$2EHZCPD"!(.H{ K\? )ng\MU!4~7C5}wHr輤Xs8@dT4aW#L߆-H_gV6@ōUU`RA$9Af&R:C/g7f4B=-,,6 FYz8>ӯ?=TmAn(>XL@ћ,% (2ڏ1.@#hv3=`z.(IBm!(no4RJ@()fL[P3MoS@h$|"!_"B7)x!VݪI_3Qlh;Ca7 A5&1D/EotRy8=7ƫҪaԯ9<)flaz (,kdiWB(!BMqh01_g&hySM0J3]w Z–[MZ铿h$ ¦K+ٞ5>6 # YYf<ǥq&yPMPZtWr2Px97Krǯaq{kCˇցD:gҭ( b# u@ H4+H>)^k""!%%fWHH1ݑQ}M?E$+yyET9h%jAbb"_KRu"mPMV8z!FQ 凾0m@ҾHU\nW&"eH}@(w7uNԃJz~5V KsE)c I+ԗ)Dh̤]ZrPɬ~}Swu)v (LCQ,gxQ,jӝrw8~#D M\5=] '!0B*Q[4ބ}(E'`g_ zO0ef Czfp_D!ė yˎF3滛!4SӆB#1|­H-kRRH4&"6m{ T[ M`?oAcp"isM{ -"fO:rD̞7Q3нW Or+央XN::qTUmMC/*6XXXlܰɵIq0=\WWEoDZ#w1"--iR@~EיtFw8%i5$} #p#'(<ҎD1 r4l>{Ĭy(J1]MYHrG9ɷ+_YHj KL[L*#r)gwz>4}DWb&4竩OMƘ02H[QըC?P;F$A+sCH۶ulEE^nE=_9x QC5z(;F.So2zyhW2{;}d"Ki𪅅 KhNC!A5"P}ѠZG",ȱ- pRfvD&@kڏZ7Bmm*)CUlhzL@iH}7m-'d5m[˯\dTR) %< D`G#a4- `BLBEq5{SLyg#os<6BיB ̾1nD^iL9 Ih$|"9xtގ#?zHn0frC}Bր:< ZoffFe}97lu„&B$4/GQAB3EfDDC& 3M9jwm5u3o ԧ>d4?@js^B*^2w@k5I(-26b R gD#\? d3P5D@6wc9& &erҐѝ*{kHv # Mm zΈL@aو|^A9|x F(/&)B4HT Sx5 "=R]C) }Dj~% 纞$"j#RrƽrD>3mlf{vs=Nf)Mk[I"%[%c1 QNL4A(|9O5 ݾgtE޷7PGP.D(s=Z_%%7iHX2z^+ sr\ ƅ50%ai$^'"e9"6OP HGw@$D!vHͰ;doyvDd4"w"Ex"BH=낈\l GdS2u0@y.4݉Hxӎ6T bM\nH2dEH{R\Hm+Q(=f50ǾApg.Hfsך^TL?Eĸ)lGfF> Xρ0!(]ןf)u@b^ MV[0"BHx@g'4qD6FaT 5b]:/MoR:P֣fH=KG$ "4~nK: "ay{|+ CN4 @ p61DL/C>RLz>i~暲HdwL{:6bws]Q2=26}:8 z,j]`]JYHxh?ú;~A/ǣ>0ʝ8v(dʜfHzQHl9V*v4~,MT5G^fX!%P4O1u`de)g7W#E<>uज़icU|z FU]?KHx>iz sSoz~*"mb7t-$dt>*Hy U(Cb [tMl^陛]Z)g'?Ѥӵ,b#%b &ԶaTgv?"gKDxh  W؏Ѭafm'iyæ* դD1 ;}I8DޭD4A$ D]wDƗ+e_%#aDeF_pn|>ˬT'7۪WD~6eTȟu 2V$@~6Cn)"!?r DvBJ\?4>o XD|>z~ UvgP™ծo'4vu0t=c4^osetBCԠ599{GD9{!CK\IyXN_DP3b9o!(PXRЂ\3780ɴ?(x(E%XX"pҲɩo҂Pjmkg )mIFf4Y?@pfY QnD'!R'~F)"(G G2妠`8Sh l?M& G HeӾLo?43$9ntRfжy$NDs/6}z"us4  gGv3m^2ߏHOF"/$D2 s1}?o)Hx 1>̩G B )S;G9R> =hu=ֽې%UD#u=dhkx-Dr^-b9{"B?D.^ⶹoY_.i 93֦#D4#q7R@4=B;G`rPQMwp#QX gJ)"qo#aȫ:u+Crk"uR#"J$x [S)ԅ畋LJ T߃_Qkf;M #L,eYه04GmgnU4-M do Ifh$ϘSH%ۦBVf&3$EwJ: >`*%sG"-Q?)Ɣwrnf72As_^PJWFVXN7C$WQ,gop&8{%ӟ~"F b>JiR yH舼OW"D CZRYBKDbHzb ;jA ϗ(tM/!CL N4G~]>rX}F Z2ԟ^9ھ ɽ&L6X:=ߌz's/eS:[,0zdcb9XNfQ,砢XNUmcp;PVS仞g,isyZ؈`AgDt680LEch>ʺ k]?2G~B])k,GZN=` 3GgT!ӮQqʄ̙(F|E\^ňԡ0E9+Sb-^W( )Y_nDLhڻ+R>AbJQ{5 yʐ:9D2w0?o"52]Qh$h%a9RvnƜI!AlBQ8 k"s -5rXiֈr*| frK t݆ .Bѫ [t}ƉaS<(s_R6oy0G#7leOBuӹס K"R[`䣷_?" !b1N‡}A戜LF*C!a9H[ۢfDDaPFb¬G< Ko1ve!HA9hK e> "y韥(no|$2Ǔ$v>0WSv]˦YD0D#Em뒊TCM/G#yיjcH<9sfRD \BQbВaDdVD\"2 rB*#uoBV0Ъ(Ԯ)ޤh(6׎MAbKtMw@U(\* 0e4]E}C 5NM()H7e-,,64aN5}D>v1y@Y(eBپ)y f[ 7u\޽HdggHċ(e-(͂=D!h4r HꖘzHb)RcӈdE#[ TP|mHk~!I `!MEkoD˴2f߉His_ZަouM.ug.>2Rbz, 3s%!E]sG ?N@Gu?h$ScFmMd\F!CKʇ -yYmВX=?Y1 7LwFbB@#h4 4t}_U tV}"§-ZGk=!Blaa*b!z'5 򮤸^E BVD)ȇr4H:=r=cDr)E ܗ/A*RGD~@ẓq|!b s HM[8DBY''g u)K4,]T"6LQ#H%+ߏUz~{ 1\4G0&Le3Q3/E$xsDwB/ 1KV]5 5]Ϗ^^F•lG81# "''ڪL7hCu,Ȑ^zrnCw"}E orеٌB1,.^Yb="5-ivuU].%54ÆK/-gP^\j#pF|=Ey<%CoLk("l3k`xHaǜ>(h9;; v1~72"u<"oߘz<xDF3BN^۲+*pf8yC]S6#uv ƈJb9&OAA$iu ֖&\q߇LqJ+|5 8{^$?q$Ap&a,[M4˚~%R*WM4ş]q=nF b? FWPXDAĬED,.1 (RfDTDvFoU,pa9NHf>BcȄ~ Eb(h$|;"c#Bf-͐Tm9o:c{D.F•HZ<#~[+AM fɡ#o?-2bW?0O4kDB{."oG# $ QmBDw}c7V},"ERaݻ E)w>R#uo;Ҧ5&Ƈ1JbmʢXEX9橢Xꄗ^=(s<3Hq9|惫 NǢ{ϚIyFs9"~Kr_^[lp ;8o83q9q8?:`qvg8:>^8OmpHy88;98;qwgT:8.+iG8dw|iqwgv SƓǹx} M&Jw֢f`ŽcKeGoZ-z?eah`_9ɔq #(?N7MBfCs`R|>CiH{C="NM~l4a9:OR暟G P,f z={v!E衺cNA@h$sg_L$ Ӯu!:\C3KWD9{搡%)q:g!eEt R]•$P!H~d#[t\xj /1)9`v Ґ5հD qʫb]@DRd\D# { h$شR2 (2D2) a? ]_hj0=lw \_ϡ4"9k$ ?} @*X<뮦!0XuA'PS@on~1?H)f~葈PfI!5 /9ka3ttw=QALaNޥV\aM:7QXyzaSJE}yaݯ3jF[(s_2}Et ֢EDzs8[W˹y&wq4㴬"z[t߬jw8Dt@C/ 8o$&N-5QR %-EZVE>ƠxY#miAi} o8}s7&g0qKF Kx2R_L40oW|])!%z(FGH QR !tt^p=h$5A2Iوl45]z~R<i|)]!2=gvqsz i{DFEHa󻱖ltVmjRP١:1A!㉈4A?%Z萡% rC˫[uh0f(zj*+Gvŵ躚^ыIR@{F wŭ=g'Of?]PnvA-Al^yfIL9>zɵ(/JI6۴BC[qv28JA O_ն;,k@D#xFy$dNBaYE&ۢ7fHQH +jFbא zĂKvܑ߼&Xn8tM+끗ß 4I2;x e L2 ;g!ǪqЋF \0d0^vA T Do@A3A2@jI6f[0 Z ZR=rXub>AT^i}*?bf㝝 :(s RfO7F#BǠg=􂕉gqw[4oh RJ?_,6~ArwA kښKB ᄇ Hߵ]ߡ?yeu_Շ%bCvFcQfD@@rd: 2O!5#@;ЃR^j{I h5S%؝͐ǩ9Ra ejW:/ ׹_,4H"5h$\fu~Q2"{y.3m{e[eKG7D M"o< }&D#04aڔhx\6۩AR.Db_5yfx^BS{&}ZNhp}Qx7{-65?DomM9 G-ѵiCF$UbF3\Ͽl, xBF“bQ}wRŶ@_fϘ[5[թ7Fze(shnvaEr!uE^d}x:OXԟޯ.S9] Xt-@tdǁ=O{pZ/NDpS}IBϾH:CR?\9-'1sDz!Yxq=hׇRz)js5@Dr8")pL4:(Ioh`~cz,"]LCw=D2ߌ)"%`"' ,v篸CN%_.X:Npv)냈` VQ,gʩ&Kt_/ITYp8o 㓘6 >6ހň/Bl^&GD,xjO״F]bm$&=X \!2T\vpfŽȗTԭn0$p#0=p}r|()z{EfqLF#s"l; siCa#t Ru&V6E[U4߭&kpC[S+?⡫Q? N1Dh:`Kӏ5h?{M4.5~($Di!"B; p;SPި8!ma.E$R6G163HL9HLEl!R̶@Y|M&~z u}[lLYWA2/W'Qp/ Z0$yM]b9at^NDJ ޘ]JBP?x#zڏf7އiW5f?]΄2%NL,,,֬0^QBҳV$a\: ?fHX|V24.BhĎ(Lq(Rh8F|EHxf4EC94߇rhjH爠V(RJР 9hTK;ͬzSp`^M›*2z/BĤ7"s_!SʛYMW! MFBaIml; yж6?4Ǫ15A4/(ts orw@[D('1GSV "!M$h@~b-I-Wt9݆/v>Q[]J3 tIJ z_8rXkSQjtq[KA@ܳ}o}N<:E!E-XrB4HN9&壢XJ}3Ed4-D:#dt?S^uYXazYg* C4~8 ; !v1~D|&>Q|{zj_5 !uC$ G#%>G"Ҹ~:RgD,/湞}G H)` 靦(D9~-(G $@6{]a+Dv R>#a6> E a0ϔ5ss6RړX/~KFc<|s/̶р?Zk(t]ԹWkMq - D!cVf'D$5inMf~6=k?q6k[:c[t/άKkTuP]]]OG#Yz_;zyU Jfg Bsb9hUgIJtl&DAi%qH$v''y?f4m<* hQh$)/ U,EaOqV4^zfցyf"tDt>"sD<4D&srh3"_yHaI>ՈM6eԤ3u zh@۴#wSv% -3ȋm$4Ů@I4zbkgp t [RߥD݌( =)HJBt s | "a(:Zի#feJsLE#QxA0dhɔú蜴Co̿o wtGTIvI *j8ND#֥^Z5~@(T{/3w콴h$  /;%'UmѦٴ3Z5CPH<>xN+rgr~%%y@XWPhf&:k|%ǰHPCmaPǙ8kwf+ uU߯8sH\XEl%0!(tm}G!_f8r{4D(F'%@!t@AfDB"|]­ ͑z7TvDDasl{Rn( RҀ!fekLZ/: @d0biNK .3@4~(:D\.0,ɴkI9#_;"R w0Ӗ6p.F)\N4ob9a/e 'nѭjg#zԍư[wfӢsfsWBՋ!؃?HxiBKÐj',jhFwP~;r5RegrE1{3Zzc.+ӟC~+W@$d}]΃]Q,'(`Q,J r*场Xl8ʕ}^^-V[9NAmfۗԖ?_ \?D97HI4=\?h? "7߀/Hxo' Qb/}f_A0aAPtBD%RnF3]?$BPέô1)aelH\G/> 2hH㢑p#ÖϻU)?zߘdW3mHGD;z6>2Jڡ$RS|H;Ҵ;S6 7QXuʉ="o3H@$,ɿ3 Զ4" -YrI^׹a%I:]egwVnz͞7$B/)e-1qW/^ѽm_>}//H\w^o/HUMئgARn"/]o .z|ŶMZ-m][IC^hښG"$uYg#{/( []VBj@RQ,2k*.68=@;3 xEɈd}18q< x2]f? xO'`qAv4d8%RqnG  <,[9|tgD#3>ͻV¬i8x/ 21 ŷOAᵭJr@Db"Cg{B2ԝ"clA7 Ry6B''oӀ mPfHEE+n/Gi+E OHꌔЍHEā=/QgDRB [哩-j xؔy"xL2Db"i)"U[!kzE]FjR;d a +٦:Sn[DZCnE fAd ]hF#HLXPUݬWeBu|6eZq]>W;Utp˔"<MEC3(} _ M^n*"rZgD{t/ D.TI00w6h| qhyquCVA28g("YA)xHEpL =+DzDa1fY/"H{]XYv3* W =}bԤ$ oA7R9n)OUwCD~6ODfsT]oikkʋgįF£]Ͽ T嚂TiyI c9"['A: v7/\va-FfMQ̔d)dTi9o'#YRK)97K'H{}&"nLDH1 g#bA`HAy ``ۺPo(3/H ULM)8AKDp;\u'VZ;Uצ?P[{/{~;w ڶxzUuju]LIz%:jjCSSR 087m^]Y\־YϟݱiniW̼/7xřuez@/ EMx1 EtCf>*GD, cРH5KE#Xū4[4<i^rs'@)9EIq QV c`L VVE/5q'EP,k@D#7mhBzBh> 戌k~_ϡPhoԝHx?DϙC36'$yRnE5Ǫ %HY@!f~2C+>?);QH2eD2P!Rٴh$<$S=J,D[D67}"'aC2itF%=fb.zܴ3=$'sTӖ٦L^2o{9u(8 v=)"xi\H{HYVt|?~nyk_yйy]c$Hg|ۦIAՇc.fm̿-CVJW6?:mUyV;xk z-j:k(@Q,'٪iZ&2kr9(·\Vʶ{,ڬvrP ݋%ԛY8o5@wn~w`X[%c "텮CQwRnvkc |^@ς0 ѳA~?=_f{>DEVk .Lg?0JV02GICφjqR ^IYg9"9ެu *y]o+gTǤrg ~u' ]k3Qq@/`zN -I-/6ہcFfdjMl3 b֊|)522zwBX'v36@QL&ڇ( "lnt%6hHBxo L-Q&@ݮmp 1;ҡ"7}4@͹h 3] rQKde]\ϟn|qu݂@s\)MM~jL` r_VIs^QE# !O,gqX]X=`YhJݦvLQ"ǯ6n:怄}m B[o6{ۦ]ʮ=ᾗ_ܩIFy=:|5qN;~dA9$ |PG>3YdyCAϕe]kM'#RrG`6G eoBW Rl}7tރd74c4>vSQ U(Yb=cJ9yv d2Ee4nep=@䮋F£r7S4^e7ۍ:q^g-CVڂ}z|T^tfv㧧T<ڎwwk)9;l@w~8vΔ۵lɩS襣2$?Ëʞ#KK;pWdOB/gh1"_W{dYnvHES#:m-/ V3A8FqA\u*b {#v*fQ(DVWوO*R/z~ 4OG᳷;xͬmy2z(Ѡ\z荺RՎ7(y'z7 GL6$""> q"ܫ('z~h$!acSH-7w u"2J?7N4~ xܝn'HI!"V lH(\X zdžԄ^M5R~6eט}[xh$<' \ MJF%矂T+R9R`I4FvWՔ9m]ܬvV4bbVPz8ҁ)A+'"`矃LbbO64ҢMY.۶d'UhһqzYU.٬c ;^6mv]8) RojQ,e5YɳvѲvgoYүEּ>E{s]osЎצT&NoٹūUiQ蚜$(.^O[$Yd$m6&8zYoWǛȐkh$5RV,%lkS֡qRJY}Q ^0eԷQJHH*g!R$FٯoϢ\}E$BCE*߯'f D1m~%"SL='D*w?PI jme "RO"U w=B5jSJgU࡜|Lq=.n`Ǟ@h$vf|A/ O"%m9G|e5z~[D@(RQfw7uQXӞ2 -juY ' mtǖC#'u8ʏ޺uyt,0E t.G2p;$-,_Ӷ?Og=[onXuwiv%іiur0NjN}5VZ+e孧j6{M*^8}]S56)Ӯ$4#M\նQY+SMsg⯨j:4vAu/tD]2x(DŽ0a+. %s+"'h4x_C!M#&k9"xMݚ ?ԃHt3["9_";H4.t=AĤ1~+47 &5 ';*>rz튈L{JԿ 7FϘuL͉?{g^GߜhSww6聋`A |.0b`CbMrՠ5.-d=O$6{]+_F cg̲g'HjQTNYͣ\/ȴlc ]Di}v 3dZoF~}#fΧ!C(w'ƚ}RHy*i҅d4r{6DNnt0$rs'7uGmӪ[|7aMI2b2WjL'3V CjKWv~bUUY{á OmTn^YE:iiaeuvYs ?і 2wɼƬ ]y2%B9y@߂dQ::}Glk(@L@5=1GmA,vHgpQW]j2cn-е@lM>2IC2݅جDJ{~6ZB,V9:C1ǾD`){#2Ĉص#7 f]wybeeY"D[ӹtM܀LAO!P)WBp_d&Z"3 fYF"eȌt1RFկ !f6Z",dbmqV۩1f !G-ڒG98T}D-dd;:ݑohǩK/wa4WXUkS^sCEfvƎV5<$k)pOz(g}kl3nٿe-4JZ…ɼNhx2?p=.'%vъ=[\Y3>^5N:Flp֖r7RyyJ]/C !X;"Njy [Z^P'"t䉏Z"gkg1_kĸDlTJٖb OF`'*C$G# 9UD1A܌Fp{DegYvB .Pl"?D Drx=gKa5gvʮ Cʛc@U\ sk_(E:21Gfb,˾CX&Z! 1jϳ6Ѽxo}v3։#s{_Xަ # CG@0LdEOQ?{U'Zա2'3$Î&Բ ^3Ǣ 'ɱXV2kV7{٭-|WuR'uIbJ<2]?6fK_Ktc?I(Y}"g W~"8CТB"2O|bW'!Sa&r y@,SSR'!P6Cٖ-zO?s` bZ! ܢfJoϘhJ#}vx^p(b>E ?z HP4ޗ>olqg ^Gs*bsJ˲vbanVFỳڗKc׀Fɼs '$ڢihNo63 y AM:YĜ!euwZX-K\/hء~g-I)oGjS.|}2w3\/h'ӰcFNw]OįцzHLOԨkyws~n3s[W",H8P;g"Zu9ڡ߉u?t>ȹYRTD@d A [V#`vRP#`?bc J~V@ltlVyA LU~"]%o #0Y sٮ;1Zy/ kK$>'⟸^3: uJ~zw<#h-@s b~"~">&N~;IWyL!@|5y{+{ gl"L[v-ܵ bD~4#23˳ En EA2/ǣyVԉ>A?uIJBZw~Nfg`mm8Nb0 ?qpO,wM ~ͽY]708.ag<{c: &zX_+_\ر&H whPF>?(uW:5b^_d~KG j ~[׾/Gf`o{j@;2P#_Pj=,N@ඃ%R]c9_f ME{ vj Q t J/"S_=sƾծҖ ?VH{!2Kve@D~r'X.~͵; )ρa묟H~Mz!1; 6.B`6~CFn$sJP\fa&}h]/hމ~*y? Ր/IGJgʫ3) +\` V# ykG\شӖ9M[!bi ʬ^Jj p9 yh$0 _'Bꀘnd})7q]IO{Oyř~`4 ZP dOb vBf&Pzݑ#($Ǐ9n%b*I%<]8;g#K[[ Pn_߫H!6Gl6δv\o,GfIV3tfa@֦o0þˮ=%>g#]Y5R-=㫭>X'oZ4js"зC6wyCJr. XcB{PٔҳL'gǜrU,jVs g@UXް(Ո Oj:$3d~|ͅ}cޗֈ92/WO{deo}I};+bRA~Y'4ֵZ, hg0ήaxwixSlkV(j%YmPhӹ;r,0 836B / D>: 9]u2r} -BG@՝Rç,r'W[BjLte\~*{h¯\/k^†\<"?GHy^Nh@)tU 0x:+/UvMckwӑ281_+T9m֖-0=1Hc }fX`gd[lV" sKF"|4F1,A{O^p Z^:Wv>O we2HQQ#6s'2!CN!y+d ,<_N5K6l\qt]=j٥Wz¾7%@9o'HQ d4(5c͎(H]6l[d/o3DG'5@`T"s/JQ'sk4mYw\4V qg7C>sgd_ո88 UF;pt9ּ#08N#1? X6vǎDgM. co'u@e"4XM{E`  "%Jq:r/Dr#P2dz )vY! Vn!3 d6F)';spm2 ,E}Gq f,M6izk "V`թ/[E/G̱!56f"[>5 }'e;y{ jYĞ5b#el5rjϿ 11km@dD|^s `?jy' _$$mJFZًN6r@%ygĜp_R_)bZ eh r\6%q,䯓큧黖0 uC.W0,`,nv'OI263 0 WY{ljL} 8γ0ܴ"u@vX}]D1ZEL\,1Hc\/8~$䣗j9 #MB9|횭p'lƢpjwUt,tRe}m]|-{ſ!Qvr2S^\ ,/xnF3Τ{11fߚ r}gE5h[m[k:9߅X3ݛQJ{.7_9\loy]_⧢3I5̬Ўg)[.b:!+5bZx9z  m%0*翟v:dM74( tDA8Ow3 (<|iQc J]r e:[X:Ir> -4EH15- lcшV;݉F'+$3QR\4.#pEX']d)6X]\/hc$ĶE"H^I$@Xn9YwZ_d޼-Ȕ ):S J;bc^Bum@ +H=/Hh̫\@@bB~nzT_]̙Ma[};w͠^V w]E6-H5ܔ¯ y#$iOB5(uY|@aV)GDYsq H-0PJ7Ba;s_q!]bS_Zbr|_ 9_5Yo;gĀ6 [B|O+]O‚L9`rMqs>>h|-=j|#4ʑfb/"r߁Vo9NC/+~"^m)(tBA @'*4@GLVlE2C'⯹^p&@Pz^@J^ v]e"]H%vVo6'>OmߺSA2/'fQR>nsf/̀%/$d-!fS~nZ׾[I(k9n+Ѧ5/G0 ~:lYDuK#E Cb}r02w`R _WCH|v$[ȿ}\>b"zHU2mQ[^0Ābk"r YD]/h'?0*:N509RR\v8GaLz}K ?G(qSRgebj`&jHH]V~dŮ?@S Vף3#]%62+y|)Ab/CLNQ|zj/AyTy@KOM\/xH\!PF1DhS0Nq_zo:y9+?@hUWs+cW_`RH8pEg/Xµ -t;WWpvfORd^on)(fXyim~CWHW:У8d-ܐdYkM\'Lb?/]B#; [!6HazAd)x*a[ p4QuzAdrvKPz2)'.ȯib] h3RϬt]Kkͳ'I-3PαJ=N P*ET+" F ~ ' |ZzBh~4#j Z"@1e2f>W 1\ň%@I3Bg"j#H!DdcKʫ@n&"ư,gSz?Yy$!Oc>+AlGyq8bGFe}p<c}&e˗i?uUQKmhE̽sqWdXU6;h3&X].-\VyEe94Ys y>]KCl4Q9[{ˑijY%{{Ums iɼmIr1wآ0 @:Dnv51z<\>9׎ 1 Ȍ;Y?b3ϐt7 |u)HE>,[@S]D|ː8 Ѣid:D:eGhR"/AD[ uEs">t5R^7b)BW" WS;1I?p"2~AQp]Ĵ}@L[4Cf [ = 1{le7ưj 9ќHCN}0~\`VWm!@̔g s+u}9H~nɼFVCӛHLi9ψm[5,a,VYU0'%a|3dתب'jlʪ+UTԻ~7m8!sOn|a+UMw[22JXBXd^pM=b`7^}y;@9UU"hGsܿ:uR'kGwl :3k~V|-_}@XMN9?nxL*#FHYs]_|iդGH)b@Rr1:)NsUcO@O! E-B j:'"6wX% 63h=N:e>sZ~X#GQGXOX'[_WzvOO~,Qn,? W|N5X5Pk+v0Ӟ6E={[ F9kVQ#3kjmDӟMn]~qf擟:oϧƮr5?'5O& Ij=RS\cR9JiKl9ٓoBv0);sMl,شۜdE>Lޝ>>nڮ -\Q{Y=ྂdp@p ',_ղ-tj4)m:eQrWckOA6PǙiv!!ЛuR'ukdⲆoŪ~}@HAg#6{ ȟ5F;8ב92inӮl›#?nOrufc/4"w!-k7)5̱7"v5RM0 3%DmS YzrοߞB&Y, AZ;s{>:"sΈ{XaQ\A3;A"Rj`q9 -]̆cp9j}~bƢ!ؖ2RBG  b4g[!׈Y4(ޱ3K#0b$>P̋޵Vو]^ơ7#էQ28c23J?(_K 1bR9Kg ev&qmX"-s y"ieʴjgMgwI6`S{U-Bso#+͡~:e v|>tb@a #2RQK1H{ "$+|:!$D pJV^/b"s̉Hٽa_#6zT!eI'9vsCLٍH9!꼟] ^Ȥ5)YHBN"b6: Yَ+n~ZV^pB;vXө=}R|nE`m0M)&@g J+]d T(#/kl/6Ytǎ*As60Yn;ƴ=1]҆dUQ˷)USV?6出oդbNF!bE+6h@doU1]Nna7N#c3?kf ^Xvrd^+ 33z\1ʝ*m ј`-o}Z'ed-,_~,=saUE_OV'د r½&J MCJs'xA \3Mvb N"ߪːE",3SBs hn' mf\`iwq+gVE`{$o'@댂 7TZv@L-v<ȿmRfd.\ޣ7Պ]9mq}:ؾgs $*-*h&ʜS5wչ7.H!2Q vEӚ=So3,WIɼIHޝF8D_VoW(w_X+P:HueyCxo׽|kkq mл8/ ?s[lR/ Ƣ~WYKk8זHe֟Z@/(Q7vHIspk;7Ğ'zqCa-v6e El(c0fz;lہYQm;po %ͷr^A긤(z2)ERxD`NO}( YvZ]OQ4gfo .GVาf9*?,$J+r/ȼu-j&𜂘: 9YǑ_Xp ==W #cڿ'b F[/%[*D (gnm)&vOطˈ xy-z#CKs`J-v5dlpk-prz2I#'@ci'5;YC۹:q1L" zzBZMB@?\ )͟h<`0k'Q@wIJt-kK[Ҏh; YVZޠ8#dE 2B3}Bɼcn;VVNYS/XWVv,_R4FkjqE75z}-# yAcq  yG:Q OE?a8֑܎1 %hpL8~j`ߟ|qA~͕7૓?Vֱs؞("?c~!{"UhnfdBE [`)YHRdGgcu)n\/XH#vm0R!u{H6g쏔U)uT͎vQ5댅t-׶=rE~> utP8O`|߽ .Cha\' y|sDkoµ)9 9ЛI3eFbQKWu 3!ףq| Օ/Z,۾쉐VJ0Io[6N"  eww,26?h;l`uR'- %ZSnw}Z6GjGq2 d_ laɟ uOCk9^ 4Py"'a<bU.G.~icx"o*]/OWbO/Xk0ru"VG uRG&#y?Ұ[ A` <#,3W2#QaF9#&xDoE=ar >{O_t,4gQ.kY+ ƧyO1i3"dX&lF~wh!߶轪Ȳk'[^%3ZLoߛ?]D>E]/8܅:Uz4bz9U63>o[8C>]E"8`訠~s kcj|qhj`Iz.k͢nE9k{ޒth9mLzȬ+ y4y*3[/|_Eidw:-\UBqF5y pQ}ΨL#tx8R#gdbE׫Dm+uƴqH~"_ ]_zk+Gl)@983HEk%zq"QGh4p` bE/a5zAvPʾGYXΈY!XiϷ"9)@^EmGũZs. -!v(Jk2ɿje_g[@~֗ `ʪMĎl^?|4geU3#FZMLgQhw9qG[9QpIft9;g٤"oAhb.}%'/ =dB|@ <b8t.uUT呵?-,%u9ɼsI%z slusv-'ϊ bG>H9엡/ N-Y'us2Y-~HT&aZW8/ ʵ\W'^_֮N9aļ^p𱟈sP'ٽG}?%jUm"S4#Fs1iO>uJ`괊 7%81ֿk$b3 u1#}Syw)gȔ,?0~eUm [8 7ͷho|oZYg>Bm}8N pI䂒(;8ۇa*{MX0 ; i@Ku? !+pmA!SDs{0\6Np'K[q=2Ō7b:"%u?nC`!F5h!^Xfy5cv2C!p ڡL)ވu:*bgRq?myz M2 NAKq.{#𓉘W99bCR_yúu93@WסEc@?E`b?b[x6z"`c}8b޵\'\/L/.mz'/,>tq}ogI- 5{\/hPUU9H@kpv\| cvK,@ܰfgY]Z=nw+P'ckyS;4&u|Wsڗ]st1ouǜ[ oC8gLgN87?pپlN~n!38NyT'm1&l)6%qq! ~">澈 Zv#OYtf z"F9XEy'7lآȬ ^ ;Atfb`D6鈕kekG 7)c0ez Vg3RD"*յJШ^沯zu~a訫ENs! YBj~Z/F&(2[kkE):YX}֔OT3Sw=j+ß1QXd6+ruQz,Bk {n~pVKKz3;*JNrw(+kǖ;^oݮQja^˜juʴZ"pK1A.pyɂz?lqƹ?ɳ{i*~g4C!#?j`@>{o۝hA,+V'J;W r`` vmy1 yMs WM bLKX,?p]u?Hb 7$Yk,=sQ:4bʕ5(v'!ɭg߄cRNgP+du"̽OO;\/ȶY"02OįEv}&/ <eڱo11#+ VYݞAcQ*ƈavɭ(T[LCv^RUF n$ym@;m t33[bg p1] "sEE˳U, VxYBA"?YMKӲX,+\ۆ%2oδ/ùh`D|و~LyZ~{@qy #?\5ȷyiMlfUK춪͟ǐ_#<bdoFꪒ؄ '*ɼL7r"Ũ^甔7_gr߭_$~Z@X p'DO#iUW;-uC|_c ,He!nQiks㱴4!T0HRUQ& Cg}?uIM2bHn3DNd:bFXj?E-G,P:-q Og X9XR v@f$!2n.2O֝Ȍ&R!QBOg=L``'m]篧^k:E*Qeq;s&!Hcr-b ծ:cٔ9MDfz% 3s(F`/2맻^p32C ,xBL0K#A ̍Cggϯo}#-L;^#V}3]L]yp_,S38J^N'ڙh>6dĄ3 ͑ \^lwoSh6(=;\լI)Ү_o|1~"^nzߤg~wumȯ\^ظ'xߧTU0gc7v˥]46Q2˿{N.H r'ZzQr *bdeԧVN~n-~Ua1m`)ys {]?Z.H ,-ۘNwbiqgCBL;,QQD \}31c'#e0dmo@G3m?G`]"nCcR8 =;UVf9"@4Y1*)Vǻg#EV1͐V-boA+om}1'!fkWRVݬ]g* ^@1d^5b:=Ym]}\/xҾhcr=j v4u]G.:JW~^C,k;İ!sտ#:?UrR KH{"pD Dn^PD9OBOBK<"/a&'X:s +Q_űh%ɼ#s-֚OxPd ;39Y\]/-V< |8Z~q޶_oH=j$ǡ[6MO+qB .1gF,g vFr]dJWE 4s]/`P*g^O?FmlgRG~"*}}=bjv7`qbҐ}";#7 9XA(7ס9)뫪.^|MA\[Y )3xuV"з)dhV; %@Ի(̠sS~"^n9Vka|_XOZ9h=m[5ufzzV͍C&~C]bvNэrVl`hd_A7kH1 w9 (H -ה{HA2/ř]{ѷU:X5zARg'X vCĄw𘂘/R})Ov4bafzfDs6Zs8;s)2^cVIE+C"SAĻ9!?8R/#V=bZk ͋K~DefO>2[niN4/Cs{6C,ΖUYh>B!ìm!j4!bAOC ?_zAwk2X@ 9ET#ҳ3Nj%E`6D|L$@fs r8KukRb~8z%bA2oD51VN˦ӻ(-$GswkkK$@k=.#utP[&*ɼhm{sꃕ'ɼ@,?pjA2/LPF?DYaj8098# jq p8hHwSV<{i@ F}WRa!:#Ah|_Y:"5=?PE/פ. !-.24m 9îtvXĦm^ìˬ# ŮB.{-O!{:8 7X],t}n 0_Cukdء&?P!;NHغj W"Ez=6?ڲҞz7]@[Dig#_^HuE Y_#pqp`' @M7CR[k< δvl} F܅!<*n"C 0oea}VlRU}rV 2We#E*?Z?HXzѮ<@S51 )?ة3h]` zAk66?IzEiEr5>;J#hg6#xmR*!4UfcODs'J\\ d޵4ii`A0W(9taue4MBkOwCUE+ʪ+ʿ)Ïs 1r=cN5%I~na)pwA2A0$tƥ>zWoE ?ͬB+hCm<7Gkw)0 wG~na݆Lf)0 8Z}R[gaXd E}:0; ]]8)hڢqˣI]uU~MK8xE}u_d)G}1BnDnU!RP/"hA[3XhFfD^ÓHɜeם|t"_'D|gIOĖ@&ge<砳3_+F1J1)Ft D>RYT"ľD: @om!m%HhXMAJ*Enhc2>^*^D !г 4^:Et&>wQ/']͇ȗeM 䘣{?oLͮ:лrT"tVuֶ,.[Y7Z_bTyDhzjuS]/x8Oċ}7(*q"bo{8]}'r6\/p?j1@#W3~"!,2s ּ8?E3mQU1ʬ\̽+YY"ScU,FX I5MZ WA(([x5)3/T_.]nuzKU^(]#B;xm]кwkA2(??%6gGn_غqorq04shsye_Qq~a鑼l@ ZMM({)#"+QN[ 0s\oT{,RrQ4y:\ ,G )i0 Pэ@ʏBщB=xIGӣtyC+NNla;fp ~8#R$#-#@Ϭ~"1Mhbefc%+w!{ '[C <K' F b4# [h?e6': `G ilg>X56QSX[Z!0[kWݾk,s=+ll_$f9r62-_ֆ*#'[W deh3s?+aA2o);!)vkqjevff QPmL(HLȧ)l Lw>~+VX? $!@VЂd]EXr( rz}q0mkFht5#ia8-]a8:a8qj<ɦ .C#]/xmm뚅v}76\fЎ=/H?,Ha4 Q#R+фV3z8\!or7uRͼ-;g_@>GCbU.'Iq}ڎV^%ɲ Hic~εz싀ľ%hmΫynYbhQon}Y>T$e[+I%{=gxd"pw7RXB;Pt<4 p1'b{wM@h_\/U\%=SRP->?&PR"堻6cَ>InJ]4:g ?&(\'kG-;;ŸV}v#~M'[떾z xR}~bT PV"}cA2/zz?6~NCV2+/Kc#a~e+C!@{:)+Edl8N6 灇9 :خ' G7u$?hg]/xO]˥עdK!R,cc3ߒWG~ )fLmUHwA[#Sq_W$iHE#Y^{&X㬜\J\!giXN8 -\P NB *?m&7ZDzS)- cpm 6Hq@}C)[c\7`-Q^6#Ed={#} bδ~ke֏!F'a}vXkcZ`2[j,H0' ~ۑ6ro.Lё?, #0 D)j~6;v-RQܵ1?~_vZǽhdOgGt( _KU)qH7D|0 [G`}tr0R-mϙbB e"r35Owt`ƇZUŽ;f;e䄗 %14!0-"FXkK¶G;WGDʧ)Z\Xk\R@ދv5Q@X{X_ YzAE?OyQjYdvz/#HkY5HsHG#.yhx{2n7! c}2nm@;i7λ<Xn0X"xCuZ@^OĿw " $ v~z>kCʄs;#24b.1ҮA<$Yͪ^ q~\YFd^w#RƲZZc3Ze梾fbMg y~]Y|S̛֐ 4n@\$qm#u rN~!h~IR I'm$kM'ml2Gʌ𼟈?`uk*sw^~"]OhQBlʙHI@ŞgSsL]R]Dvu&}U֋RlWWNE.fߙi=1\!G.R '/[?\XHiO2P/{΋1 0ds홃{>샀_b+ v -xDk,bEaj%{zK؟⟴ ue6w v}w926E51z;i_}dY|LnvA|{V~F HI.0O_]v85$.B74?pu~"R)Hev|[XU^Yiil^UIq;fn@9ȱ|@ 4v#bDAkT 9I/М> m-| "]@*$X vtUɦ #l7ǹ^0)Hig"0)ȩRXb &+1X67"|21U!7ݞ V~">fCL)Hw1xKS=qv )[ 2uFwB@qZ\4]*Nd>b^ҭz5vokdmLo -M_t@;;X'@@,Bs͈FNo!P^lĀ,AqE3슀̖1 `9> x RiF"í^l/X@1b$A>?QtjYnGhsyw[YA ;k:Ȭʋwc]_\ ʋ֌_*4&~UYfō+g|vܠb"E4xe o]+ʕh-6X9@TZSGE>exbA2%ps w:"N䯒Mp`8N{q;Rt苜G,ȟ"c _#ZF@`lz)|F:"kYz_+kYĜXd]}%bg@T|C{92v `7[9" 00-Vڷ:c } pY/2el~ 6];H|A#{ߞsbFZ"ӣm JZa)ClLIߞ !WXtAgև#֩ (b ֗X 8ldm|l\^C)U9s0m\s>>;lY9o47WܮrAl?f&K@%5|DLW+4DLh-Wk_%x6n'"%ˀ(&O潽FٳKw}A2/ ߑ_I0F=+vO{oLA2/A*7ԍW%bA9R2:?E6%F 1A325H1H CYAVVcD ^C&49##VCĀ܄LE~"~iQO __\/hLtљ=Dz"pbGDdg29\wC;Ē @gMvvksD)EVW?\_V^o'[ƮC!ҘgY"^?Ϗ:ϐk;L=@p7]CJɝHkG]/8# R7.E`#69Gt@kdl_"۱qo6^fn򑕚y2=04кʮ+~F@a+0Tڠyc@ c.@0 |'W^h|;q5@\m5Jι5biE p[b~h@[)Ai;s$0\X]f {U#4vORagh\zǏZWۮ|ދ+M2ѵ>E`zא:^kG`|ɼ{М t>SM-F yIUhDk&6vJsW^g ˪6뻮NɽT?.Ha"֮\;?hNX46ClAɞlL|>|? 0臘aAlbGFgmnhm<͓H62>G8gY=кեL[waԏSb8$󜍌aEL]m ujZc^ &q0<qy + p횵;k8k0ڸeb4/Z-*e"9h7~5R)sI"E51X V&DL!鍜޻#.bG& #R6w -7Z]@lH %rA}~ȊrrCJnsdH~(gD91Bϣ-(y{?t@~@w:zd7-xڵҮ!}>#Dc ^_Rg5g pbV6k8)9y2@R]Re7G'x~uF h_k[rj{rHtC>y=QA)b*Mm_U3EsygČ,@=hΎrk)vMopfPXqZ}ӐJ4gZٻ"pgzdsԿQ{sU8 "/ Fx@~nɼSМX8Nz5&H?ebh"W{P>=#)i~"~IDATw 6RpBB+ZĚ#td9%(u)bxVWGLFf9%uDH퍘EK+qq1R KcdضH_PTh>)S+Ϫ_bHr3ߊG/PĈDk"ΛNG Rp &b@eDZ =!b.)>kA*e(kG[XIEA^kcTG`yYaUuڄҲҊ^m(X# t][aȪU3hhjϝa =hEvDdfxqa {|@v@6A,`z->߸:z ٴe ~=կ8 /cb @~n0dSl>C:x2?ּ0 gYR,qqk+㭜(0 >=ťkhgWaz~S qa8NKuΫq 8m+o0;FcmJOADrhhxrbؓh~7r~Oė^(5fJ.\r|XC]/(`;1ع|FV'XX{r8Y[;Y="Ss#LARNA HѾLJd|UTW˗--V!3d 2DJyyIRlR\4eU, r^`}w1#VT΅T:Ug.듮h?yhnD@n s P(B@Z:4jOGh޶Lj։laR'oW <5#nx2 '9w02apgv|!_ax8!7ڰ6sHQV:ppivp)mwp8pW9 mL\ t ð̥lR+" H'eGu'!\^=2bc1;MCr7Rt͐!JZzE,vz!z쎔sRbIxtBq )mL'#R蟄±VM\Pӟ( G.žDCfGcy-j" Zy"x, RLՈ)fey/-V֮dW!64qIh`]G#4 ]R+m ?_nwcd^WΝCÐj!tJx޿sI3 꿶ȄxІڨN 7G0֊_T:}Y ðq `~-ᓵ{"]0ogI4~6]adt0 H䡵z3Gq6a^QEhcIK+e->b㼃6ׯa_% #KeR; 2 )JRGw1Ff4Ja~"^zLpG`+kLF&~ @J-[e]t' V!{#R]/O'xc"5ŗ# p#@%R{ 7 Z!Ž~MC@ĕ @5C "iz@(p> +b~ wh|u껈݊"^p2bۢ^f8mˁoϯI u{gm[ˈX@쏘h \zs3{2_b1^XׅhѼ"6>Ϣҗi-k$CrmA*yi%cti3 jXrvh36=hC1-BI)cyV~gZ]6=]!-\Y;q:ihwQ4QC+#s ܮΤh\WLu N~58 ^0 7T]bh 2P8mJ~Ā0, ðr]oL1 vGlPMULi>Z.Ca)R0"E"YzEzD$ͷ);dz;gf"%l4z09 !3Jdn;Ȥx0RyM#GgjȖf)kwb#Z3wBBnR#=)ٞGw#@v3~( A d:CH|1Udv%]~">ݢQ/@@jbNfo7څ-G@s7{/b(v6@XIZqiӏ8eCkY[;"1=@W;2+h-Cfc.A;e {xĞ l4_gF%gTr#@;Ots\/oc>V("Wgk~ T^1|DzxĿIs:fWE]o)~">k8vDw;o߂6?d^ ^nvcѪ7N %O#04jy x< Ú׎opg`Uj"i ,2+Z~N>^q0 :, eldqa~lj08GrM!Ѐ \DY@ lO)ň-'Գ{!7H!LGr&&}DWg>&α `i'!&h`̞"2H-Fʪ*ña~">ծ3=!e9iDf4~-F/S#1 H*ґ~7dZjewFF}P󭎥@ A3>׳3^g߰{ٳ^p"CG]UavwFhѸWҬJkh뎍6m͕!p2m lPHqed3O{ظ>hqڑT24sg!G5 Ц$s|"4/:H*6&Zeczh+ ͑p{`]jk'XDU[#Y 1ne@q$,+_W8hnYr7~]6 -<: yɼ- hڬ^m*g*Z8W'9xq0gqhe>e vZ1s/FuǙ6pg<2OZǹ 8N̝6Y=Z|:Iq1z p=wcMGȿ)VȼS㖍'_ܫgy_N90їhDC (McD-cg Ñk9 B1 D iM*AJ4kj rV;!Ɉ 8AHN\~"zH~">؎mhy1bFb(ĶnU^a%2}|'XVYH=|*w|{w굩h+4wB;Ka}]ذխ?ӱHC%((2kE{Χ'/#yA1[ g"vi&2qB[ hfME Ħ"dށ+0k-;MM=47V%h! IX1'F<:]bwwPQ|U17D#+7[{Eȷrtz^JHA2 KN U[xGv4>+o?GNQ1cX̡#%XHQ2.E ;jn$#V7<⌯--IhD6ZokWGR E )Ї7!ː2l <'پZR;"ŶjĞd G4}y<"ț?)u \ɯ_]u0v2= 萕gnYǏvAX#'I@ZUu=VxG]/Nߏ!_d)+;5>o(^,no!))9HiIV53Djw")!Ȥz5R#-(_lf! 6p\/HgY92>T-+~">OćXE 4BƖX"02z h_3!v/"04tI !6.[mQ+i6'.GVCR-#DS86X\h}qjhB|[x#խyj힇B4ZY^XW{Eo>G#p̃'!`0>0†hnT]huWZݎDlHsٹ4_Y]O#Pe@x:RA]O&dq!k>Ʈj^:&;.][9u.)^my'`1@w+4WːYb ϑi]XFs~hőw9D]1zǪt@/e OwFާ!l)3ۘumm轿$"x=FO3T']2TY?ae?Y#v.ZxߜN5WTV""Uٻ/Z{#\ve܀"K[3[{T9gG9V!r<2l@p GPx=>^w`7kӐrDJJ3K O44R"Aݛf)*\ټ//m~Zn_+FM2Wjd8f]GY5eD|?2KwD^`6 )_ KrbV[uF*[kkw+e |DvmƼNh"[O? B@qgĦ9M쎀*R h^:T}'}u5eKcS2Ϥe-DsD# Cdn#~heCfemvctcsAj&ٳ#e3sCTN+l_ LBQ?a\zD X#_D]4!)_dY[=#͵ ,X5^Fv!`J*d")XRzX}Lx"f"H" 2KIO2{ubV@FXAlɘMWMZe*{^hKz֧Q*l@4"мq֯NmgȄ<ʞ= +I _-|ٸS}.1OnХzm*2 l#Ż) lRB۵I' &Mn,#e|7n XYh bK0"`(b 0ޞ 1;oL&RdygJ*$k,5)>YHfE>s;Fꜰ,  xeLA#E| bEv,OyZ9q'mG nkuk6f ?o^X^fcz kF[:?n~b]M"o!2d#0Lc"p!Oαf" \]@m1+sE란@wv$?qX}sywXP;{f̮kbɷ֎ֶNFê2VUVƎ;ֿ7"@ 3mMVwa c{۠ ڜ%#h4wDݐv"z?D^"dpZ2ՙԢA~N uk 蘟[Q3_e/$?_zWgQϰoBspz{WMO 7Շ~0 }qB`cN u vkvOGw}= #!pc/@]|̞9#6ÞqWڐ ' ú|x(i3f,r)7Ю@~7 C @(؆sO3?E5>v=kAV&@d&yxߞ2GQfwzV#_OClCH b}3δD^OU.duؔ)pD`12D12#5t&,~>-)R6 z6fG@cts\)C5!@Z[h^|%9wA aRfbڠt54^f,qlL@]8F(`F27Gj2gPlaأ)vn3A96&e1͋="\:\/8y_6E"}zQh^j@5#ņ(u"w!z%; )F/a%]I5W…R ~6 z~"e:NثAҌ5ӲgM3%{16Gs1Lͱ[htBjC<\8ΛR. ^u#0Î~"}9L Rli̲#`Eј6AJ>ʲ톀jRYw%e)#9eߒbV^OGI-CQ{!0OēϬol#[z5eu5O[[uUU?{:n H**cww^ݝXW(* RR" 5]3}_u33cZ{oX`;"4F/[ңՎ8B浑Na&## 6NqHAVacklrwgDFա>g?;v_yU9~7FRw=D|.RwiPEK;,?_=0`^r\"]yU*p)~iyt@q}w>d} ͑~e MlۣI 2ـG@ي5| Vu>ݭ}WH&!h b:Cի6?{!t^d o~g#:\tbжF.鈍M"$bǠVJ *q+M{3k[?kgF@}pnrkKk46±7m=k ί$Kb~e!Hg~{rZ<"o+urFU[7'CIYq̪z9Mʖ0GAQP0/ ۣy뛘+ 51WXty PsKP_-( `WzXAJ\/Ӝsϡy|Gq$8>o涁PrahAW~ewP[^\@}v 6 1 !BvW~ba"HPl%|;ܷo"d|7PVYrA);Du# 2ĵ܎Ȁ5C.iDsL6B"e3*ZD KtS1Z]!C~O.ϟB[kcn֘ vl8pԎF@0܍XYZ:V--NK;{An4A< 4|:Ւ^/NG4#9S/B\!bBvGzr1NΥt5mV#!AK! - OqHO%{ b@BD 3 Xg5҇L6wwF5@ [hl 1OF@ bBbz2j}6[Y@:?!=?J7W7^ϰՕHX9dDc;צ-=~;d¼+Yci].9pK-vkSvc¬nt̡~?ͽc0+ GYh}sW\a"hsco/?+A0'7yι ~u3MK X2s# ֤&LjrF17Ml,Edgذ:Ƞ썀 4׳QߒYe+݂:]H!Fs{ZA' {IVt>}ӕ %eɌܪ]:/؜|rYfyW #2DJЄYDCdt5qM w`9#6j:24 vLWT<+D|;#Ve۝JJb z!`;A]dZ[ iuڵZɅԲ>EWܮ1?E~-6@NխFEN( ֏!ЛMu[C/_rІs)? -PfXт!PKg%h2>H/#;["@'LgۻQ,!V&3P<5yBimY>GyI?f.qKMnh9dNxq&]k Lϯܝxtδ?T B~+Ր2m[dgC A Ъ4ҨKk :4%BaG45B IO ?X >Tb1A,Dsijxk I3-HgAFz(~74ghms\Uv_jTdnNg$+zbNCLH],נq )yU2T.˜9 -ֲT1 N[ Z3Hv]\=W7SV1ٽ@1\=XV]Q,*eI M!ra+͗#:YZ3y$ EQ4PsnBFzg,ZE@lfF@n(23̯||uRj1)i iS45B(7f##շ.2chR 5逘gb_@a=42mKPĬ|ieY ҊPdzɏv/xŴ/ϱI%K-)2 [Ve5@l#|bG3 A \Jmer>[X87տgmo@qj"+zAH?koӁ^Ϸ~@ȝȀej@ۈm(F=06C@/huG~>Xk;U\a|]% g&O?<ӭXQ' #~4"u9 ]O! ED|q O{8dF:K6tՋt- >C6AEڋծZJJg@ 8((Xs!}*BR"whNBcr]zʵFbWh!>/:}c]QPM'][TIldX*_ahX s b2NGF8E9|4FUhRCo\%Ŗpta?"c A+JĘi&%JF~UQCS@- Ir @F9{~gAמm/C J^G@?:i%d!!bDmZ&ZYS%̷z>M2g"<@GI@EeDlÛ7٩i|k+&.X%cP\c9&+Q{sk݇Td@`x!bJw&m?KM臑ot@) 8/F^\~vnWi2bb-C^X!ji}0`Vm e&fh-B݈W}CfYȰY>cu{QLec-Z Ac{Oq/gnfq B~ϐN<>=Sft=mGrUV4}[( XLtҲ%KwV%ok˘+ˎȕt[K³r~&i.bwZVюh [Wj1mRD|Dni2y(^{!#{*зN%"#Ĥ{IGd8 <%@`@XK4QYhz9bNhgUȇ|gۮ [}z_dmF"b2P5}=iv Ih2ӭ}y85O7wҩũD܎9`4?1K+锃ҵV?5O|dxzŵ(@=nh2H_mhygp 71EMFz50'~=ӣAs4Kw#bgV8 [5 ZLWif!0I%J$u¢9OyI/q?y9+ݼU#~֭Fs?d *t}b>ϮFZ]OF|$HZq-'?-ډCjRM[M⊣K"pC+/gB:vNf5iE L%OQ7oG#`\eC+1h¯cj(4]6;Tw^(/}#rxDK/Xst囇tvr?nnО/Dz;`#a@c˅(n b6݉{Zd XO@ j ڹ# ]]12ɞyPG4f(m/B<3Y1'Y][ZO%xIF`2wͬ_Z &d_'Q|W?sqVBQL8F A(@uX L~ÐzT*_ڱ!bg{&;"fa*_s|6 T4*и[ES 6džDl=h<Q/5&ӦAT|Z="V թ}\kߗ_5y+FJdh2C+*4.Fm!#D: 1fyGY>w!X MȨ]U ^H92޵Q[tO%̥T"/AFf:W+ H ߑȠvDFh\md  Dg2g#&d g{q3(.w\;g:Z䠱ɑVHf!iy]b7{X)>{ '(;-JXSc+>Wj}ΰz\۬N/%/Vާ3d~8砅jgt F%. < G[~ːNޏB`HQPp՞?;eT`RhU_N Pl\} v2^'!0QeuajwEGO^XyoJM'Y@C 0'CoZa_(Ѫ+t-A?2bQ\A+$QɗȐT^_&|G}1ѡH4D hdCpE|[i 7 --DzǷO!b$ ;?6GjUr}tFqWG s4߱2@,׵0\E7K/!h40ܯ[EFb"< "dHך+]]HK;H'|Yo Sj8OZ=wGnڥ8Rf-@r/%%QL[kwBgZ씧VVe4aoeDT|{CcYJW\i7uOA l+{Im˼CH"FҩhL66#z1a3 zITػg3Gcn?(B%]ݙU^9=}]cќ_+P=2c\zPTlo!H~7سuQiA Hg? Ú7[8r XssAA\G矘65 v.2ӑ1_ ډHV"V#BhnLؘ j:M+bwڻ D 'ζR\(+| Xl؝?Ҟ_6B.?dbORȀE2;o _[#fGZ韍VS 2d4Ȟ_X 6\H,dz>ȸ]% 1YJħ2?g#Cq r6AL_.xDN(-:Vo{JG˷؇빘* -\kx5b!&Tw0Yv]C:7!pXkx;[^DRz65̲t蘗592%_&t bՀu*\< kиi ^|ژ+* zh.n* 6Ԥ_GZp)"O36]}tZeA]t#h1_|ApzN&Xp2le(EQ*2,>:d|X2A6y<k|69[D}1UmR^@MV/^kBW=tn䎸EHbǣ3B$D:Z˾Ax!@( \)hȳZ=sgoVcCn躹Y^Zv3#>x:H[9<.eA>A,pνzιKi fibnAFX4^ v_\!MR4lMǢU\Q(d>E}[ Nu~VuV˼n2Zi";qs W༤і:UVen^r/!Њ(IUg;4CH7[t!2#P`:dbOe]h;֦D7F``}W/#v%a-&ېmkcmC% uMKvEthهrM7n,lsn^vl7mnϧ6kmi._HI*4AIkɎ(w2)^ҟ&GAJ2D3_ 4~8{mf噵&1#!2S0Nhrd*]# "WO(3@Ʈ}G#Jy+Ni~ 6oy]6ۗ輩xMu`mV{g홁M%˼=HVCugoE Hc WȠ5(](>=rQmX=n~ Ӗ,DLALjKYc )F8> q_d+LC~A֏{#<:K׵d,K'%A@3돎HBF>5]iu:!%V)GHgK{S@Gc}Ē G )VyH[X~нRy^ҿR_&b8/t Ӎ+Lt 쎴C`4Զ>v՞&*R.bF:9czWP!TPI9P+?&R|/AtjO((s@hVd((5ܦ1 .i`E 4ibhq@Gι!A;6c{sa\؅O A\-ImU3vuk-hà &yw+R*g$\={zI(@jd&! Ѥd wľK PسP"dhz;]Q|I}x瑡&<ɛp ,Myg"򌵣8%.[ +aAT"!tdAqKd P@^٧MwUh07ڹ񲵡Q1ZhvҍUZ۬/ og}}+inhEL] B@&I~vC,,qҍG+o2C@Rk_s^eի=cL"e"u;Ѣ#\G&5D|:hGֿ!0w:p>yK],|dӭ-z`β} 'T"V1b=PEvVH'ˉAl \SYa% 3b߮׵t1ƗVj_VJbͬlY2Q@/m"|ot~w񝐌Bc!\TQPP%g_d6\a`uQPk?4A0ŁuMB 6L$':bHssfoι'T-i +^ӸߝY=gnФ~q*K7 c~mxC=b:!7I92Tyhb BudNDK4h_TVwfF.gП i)"Fi+M ck?4A jXۉ8n -Dw/JĿ4!CFpէ~bBu\ma;Mxx6%#<hX5hB>716#p,֗<"<|}B@kk2J! _ G0 ?Z'##s=4b2 7ig j~-{Q_vB?(^|4q~ئHB{#;{+lo79_ienGxo퟉@1mkDgq!pd* ` 2Y@J뛥Vr4Z9!5h!UG!@Z3^ Ġ&omkauuY(uX陬Ec>npaOH1WLRy\㳑~68(( +O;ɿ}TsVA wM#vd072dԪPW zI2yh{MhŶ{3d"#N(>gw꽔Qg,s+w Q,ADgVMEJd|#_UU 2ȸ}v"@#@@ G N#"c3$u eŠJīgZ="ew\`Gs}Ni HT">K΍hڪsVi2h{!3词RdXk#sd/Cĵx:D6ۢLF^?dļMF8 fudoBgO럳h&=! ~%'AFnKM.PEro6ҡ:hXHwj[Y[5h2:h|hkwK2lۢaI[t2BUDgc.Z(] 3a1&Z;C,Uqzыф|#P}--()$2'[o?E'Ń;0J2-ػߘܻ#yXZwe)=uԷ CM9ғo/0祺5fmb^8rkS7jo]Y_!w}(F00M̰][!}~=_ii u4i܊CXdy5B`5l_">h'WkB`[`t*<Oj"=:T.'M7ʬc/E x"Z){00p]*/_b0dҟLԧT{'cC\ԤoMS@)J,Kqmtpt|j$ꢘЊ~ʳӖ(jwĄ hWMT͉.D^&Ő:ȵ23SYS {ANh\ )|cmD{! s;2f-)y H{#=~X8˧=4 K.0{;dԟ&p+k߭xC{I)=b:V|Xg['f֭vveoZ!:XSkf-ݺmhj١{x~A z2pR#&ǽ,Ytd}w}j{!ҝLMM,o9,Dd~hY߭ER4ڠH_Cg] csPagc_!~^eSK4 wx-n'L>-;31n-$5 XlZ+??타v';Z|ϯKդ1;ih|*O2TtEhR!sb!2fS'byEF92 G#`L Jd z5х“ p&kItR{w.Z d:2 4G{7Z9{Չ#$;*#&ۓ' ôM{!d{`vf? +ݙݬMmc[y{2{t;2 nգVhBoo޳~%Ӿf)wH%8T"ϑO!p 9u J^rŒve!Ow'ZYߌk<-F"^*k{U(:H!㸴ZFw~X+o?~l|aɵwF:oMw=7==W/t^ҿ͞w/d}V\ . H҈ )b179 &{fqryD)tcP+h2QdT\ ֤Tz#2YX]c7D V}9 @tcZ'/CFd:z(z;> ob|2$0О#-I~bhAaWdDsY־p7;\|G^/E~m/wGWU'rd䇛L~[yW C.NMcD`2-2y+z { r1fY%vBpɰb^F #m`(A{ X%7T; wS˿7#n66G`a4pv*뢷Yզ~qW׼# 9huV֧ìfu(f!{[t; _˃WxI{kw^o(6 ..C@p՞wֆ6 7bD|WZ^e../&ra[1OSTO<ļG@y;6:/iܮaeF3ty/7\ ((%bɷ߻SXPA /*uI~ԁ8EcGj<2&h?0{Zن1U\d!:{Gמ\ 9NNDc{XCm (~LCx0k؞i wE;Yo2P{,hף]]RDG;!YxТˑU\KSp@ׅXDݑ놎yK!p+MU&2aYLDb NAF #DtGgMtxb,5rϖ Z΢1ܵ]kޅ8D+jL6!dZr{Ck tb-Z]#;f2dYt`m*_n}Q:X.!02/AHO߳;A-XEc6AtFPXAVƕrwX^Xzq]ZL@:Y+.ɿ2Y mn*_Wߞ*l+(XPrU"=@ X+m_{EjүK\>s|T"nϟ@[lO"_A92`9M &h,Ga&2ȰϲT"Nn)Z_lb|&A#F2!u*29h?mX8a~`>ۣ UD/ߏؤY}1RTpȝÁSI^YwƉ=ňx}d&I"- c69OE@9drX˥wE,i(;@Z+K3흛B@d˯ +S 1 Hn s\g?X[CyIVZkCzؽ LS6~U QeB}OۻR}A٬T">H?G 5hE@ѽ]sa8 ]?ٹ{&ǝB .ƇM!6, s;sm֔4N5u>G9:ZK7A+࿎ͫ^nMScH/7}b'bLZLE:.\э Kuhl݊ $?X[5DЂ`kdʹ[X8QkSV﬌fZ;1O&rVLA9-V#w.G~Wak=^ƯD"OT"^)=eNsÊ s{A$|. bQ\a+/ι|ιQιqιs;v >uεsMҝsw9&:;q{39w v:5sh+[c_sYssrMP+]wߠ^ҟmcWX~>_ #Ydܺ7~hH%kM^&ɢ8gs.༤)OȽ6Su+y&UL Dtb|'dvCP>+ n91V;Y9!x(>/x(O6>t8dB#|[VtdCRxҡ 3J.B[ߦ!]M8>]##%;ZE:y02u.oI)??X]b53ٞ`mi< Hwϵoir܌(&:?M\=b>t;ܔPX+[춰<F&!V* $򗀰+dǕZ[CV~ιP~6Ϯ2|9ps:v@dh]OI.;@ v__zӥW!#0qߎ脘5hثH F#þM,Sф{" ԇ# ]>#bz!X\# ^! ݑn;FT"^K{̤ȨrVn}{/\kp ƣK%aX05M@F^_XwYB``'T"^%tMmu"'QWر|sb:]n= >P󵁽":nb'."Pգ,%'dg7XQȭs7VǗZb"쬝]#,!8io?"75,|v }I/?"0z{Kggw-$-A<43aL;h}oocwzCU@/B䝁u@T"M$\ac`PCAӲު*KkA] ![f-!-VW,5rb0[9.&z^PWgm6>M+ 0eA0eϷ!z %O\ `!s?Q'iS8\1.&()$@ ܏G,-&Wޯ :xN %܏I%Ꮋpgkl nGm/AH>.߁pmgu{1a/s`IĨRӬmאѫ4@BbNA,I 66xAu2Z1]hQ_~x伟(Î&dv/[n$_F}PtAflp&muMdR1oYB.gБ-@mh!A#[%b:_b2!x)# D6YwCUlio~foreukP|dҭFw6v[; L`P*JA<&#]hhC*8##2䮼&MyIV}7}NZ&TJB|Cd#lB4r*ˋ s*em-#^,͋rz5C Ԥ!}c \_f.N5Iͼ [@Y*weߟsIss~iI 1u>O%^M#1bC7h2o؊f!vbUdd!\ A.GDd'!2J;Z>!fi"#91X 294Fh}%)cm#2'#0tb@Ȟ(O%/'s{LAHdXOnM%Ql#c{`v2y-D~:{[r&9{Vy@L_57 z[]Z7h¶&[tdbUGw&O{7.[ J' C}:o30b:홭o#rֶPgX}ҭ=UH24Ffxy Gzv2٩D|5@p3.g]bFQV*q 'Fc$N`˳Ct%|s_n6k2Jď4*ҋGRFw逛>y|ٷ`-;H?cc.*8lڔd-&[CPd ux>Ue-jo?sdrѢr;dNJ 9w<+vAla+A{p V. (ahM"A|[oLeF^3lĄɾ&eȰ B.M@!+ jdFY3$BGwdx'2R /鿆 "dh[3ȸ#WLsd- 2:[ .DIḋ! ydץqG;^4Yf"#=h~ۣk$#C"x4YOGnCɭuXV/#ph<k8ʹȱ~I#*d\&ZG #z3dmR*ܵÐ,^k?0~|%/9VfVG{0!]֖(j(P}Ӑ^h}Xa}d7E J F[Z2:KXs/E^ҿ\~c" >+A.Hϳ< i{4;mBYߴ~ '^?+/$ tkQt6T(ەSr<3P\Į76"pn@lp ; 1Ph V':Gs=EAA+#$ X?K BY?퀘y]Jy ^E6mTÂifO^ M "Ҍ^o,R\s>FƲ@79DZcy vpHmXR6 G# Z4iD{7ͭj!F"1Y~ ߵELQ=CQxrCZ_@׬"r rQm}{WbCd_C/ 6&dܺ"PʛoU^a&g(y9ێNw xbƎC@hokjx@3 kH>M-B:Fϭ#WM#bH?G!ޛ3L1шthm}sҫthlcuα>%SRX/eO`HnO%7+l_T9DxT}Yˈn8|QPpi:7'W ]mBeDw ;((j?%Ow}U j6iFldv +NE[U@ŧq_DU^[ DI{sD|brdUD h2@;;~ b bhk|LC !M Bg1e" f!= 4+/bkqhrJ?\s!@Y!K!Wb k!#ژi]a?eȀ8VCa>1=8];߆Vj4I%7{IlkRdlZ!?$Xz!p|ys /鷴vE%nUyI?vXgL&4`:Y]: %cmawAzSe R4fA[/!\l2~It8x3!?-Z[O5YmbZgXz!d9 4N_wZKi}%l|k&h@c<}tCw+1cH`^.D,}7 ]+kFҦ~K _]c[1baڬkJhš\Iho%&f.DG mC+GͶhA,hdF`SihO%bB&]V^Ύh]"Jdj{u2YAO2ns eA,H#v+,$_S&~P/r!@z=U= 1dml& [ #e\ ٩D|{3M4>Jz[ET}Ui' I 22xbNDahWmy#Y\G@UXؤ{Ɛ,!2FPK9#b3/@*bFCrOGexU&1vu8Vuw9jTVr=fuz+~|ڴρH: f- -nI%߳ }ҺdI3>=jmF8^UfFnj 7Ti4Gݎ@$v@= g!{s[rIto1PA,fH+ #Qjҿ=m򌘗k+EJʗgNY^fLնl#(G4ȰljN9?ҞVˏ#`1eu eՙؕxȈ^bWC~-a|]/G w+:@2~hekXw,P? # !pBrg#vb2ёwXM,{Z3k&V!rj2q=w+ rͭC (Fh?/OC`caunX\HJ=]ڊwɰ٢/k%-XTY-;Oli:EߧZ@ ;Q&͐ѻt#r,l]rמ}\pߠ/[_~C^ƞ_j}_e7*ߧ~Ve_gիa}uh2"4"0%Eպh,EbĚ":tvKGU:U;7vo4?=rjektj&٭EA1WXخ^H?鳇RcϜh-h%FLd%T)muKG`Z>EAwyV۲c {"0ɤ6hB&Xخmъzѩ[ O%_"cN L4ۀS5?IȘkO G`,GPBT">1#9W큀OT"|l&ڄX(h9܀ t0BlK~R@@6 C121a= godˑڿUT+_Ej +2[ }8[W.oS>Ɏ+"29sSV믆@zf֪W7[6!g2t)HrZ 6ٜoU֖RZBzw#^^ZaʖQՖȐ\ʃMFw}X_hmjtg K J?EGsA,Y:[' -odzV֫өjXCjÍ'#k2OGc R8$b}64kpxQNUK+ *Үq}ڰ5( w98Ķ9hAh!x-cXJXt4.[!絨/Ds'PjRMI<#ESbﺡDh ? DVؔLdp!p#@ >hU~Nr+*;XkW*K^o&н="]&vDgQ=~ yI.b^~cӉVg2ch"sɩQt4HOAW{M##\G?d CF Y9وK"`q? gC 9a %~&-w8ݑ;\`k7 x5oot~ [vec y߱{cu:hih`2^\Ӑk}}`A U8OE#rg!wJ I'c/dO?kDUl@ַVp)ҙnp(6XhFN>-…NoA ,J78- v0}5;_GLb2#ehi?d`r~ ,( _\Dwv@sMQʢ`eL[PVl4X?+-,-[Ǔդ/m1b^ҿ1!A`bt*/@K1kDgv(! r:l>17Y?mxh@.@=/b/@ nr#= 1?(nD0t=ؽ^&CQ?;V{8(1#܂M{#] 1K컮>v!b 0doE+j#v b@NDKRD_>e\^Ghչ=F&nE_@{kvA:?UHx4eZ?݆mNg>mkgRHĮ#bq8>yb3سH/hr^ߣO~һcd*YWZ:![h`DzshBc!wUL ?4] aEA+<_]f#}nvVhq{gQP~nM s.-nN`]p8F~VK ;-XOFy *s"<s&<ι-9h2eVOs[A0ʾι==tAsw[#_ Lq=Oqι,d `sA9 s}<JgA09W )m@ROn !? u/Z_DTPM)2Nc"@ֈq Ðkg 40!gdV#R^ҿ=z voe^XȠ0=(~)ۅDnXm݀85D`i;׾&||}/2@gx/2[ lVۅ RތAbT"> x+TOzICƢ-b2ss=`v~eqr!ƪ=m YD`kxL!&jxIH,F5hܰ2HGx<~?lm9- RM@~}N} mk2DbĶXpCj ߗJ{I9xֱ<*A8/[9|t~w"}JC 2j4B5Jone߽YS9Ea)4+y(&n)9-W׫ i-6AssιNfn$B &:WPܩhhQkAsn9v@  s3B s30?J{v7`9Dņ0nK l6uf h`[d#:T44"cC|;\s0Wg*& #и Rc ؈^0;5KuFRw3ĊbE`yVF Rds 6OBL^k{32WY"&n 2 o \%\M鈍{; ?%/韆b>F`[yID;4aZӃ~,E,0cW !HorэsZڴLC-49](^jFzHE kV#>iT"~2Т$x}h|hg[۳Wعh=ie.3:Q#Sj?O,4#?5 9Wn͚0F-꯳C غO__8*ς -s:bh> \]= {so)Te, y=+[2bD| r%QL`DB/C`cWdlF!f@`g!Yd"Z G'E o 3 U]:bS?*[k [g#3I#jnǐVk{#Ci RvYhpov:6ft7sG(39)í&u T">K#eJmt hx%3,>jAY>Mdt5C2fVyyf%jWC d!pV*K'I-bE 1DLٶ&wxI0UM z2юk f{Y5C,_ NAw~K.h'3_!6~.';I1W!hQT*&>)ιsQ@s ^wŶ ;4֘ȫk7gιh kb\S4?ynT<%Z$1dW{ 9\897$rwsC ιȖ1bc^tdApJħڶVT"^%{>2ףU½HI/!#R/"@ JDGu(JC\kǁDl׃@T" (VvANG V1(XDWmneGi5Go!Ŝ*6'YOC@`ΰطB~WڳOEF[kmC[? 6jyI0{Q{3־99 OM?lR /@t~41oPJ;N*|n1㺹=Kc5WUNߒEAZfp Q&S7#[X$X=-}-K#j>бDbD M+//Y&җ?1c׀?'\a-Gt-R+X)q<GsW G_0kwށ\Wd4OB1VSsdm,];,@/snˎtp (sG&y:_\3u{2Ro c󘁰:(~T"^V-B4# - zbavB'~uF `8b3!ա- kC#Z[هA Z= F k<ȟ"UdCVz Ν$׫@<,C`vr.0ndJgmD(bg9$ޒJyI"`x蛋ZOG =PH,3d¬sO ئUh{cwpP4q@:3Y$B.3ƩeYtHOv&K 0g /N,|QMRFAaB􆢠ƿvs6dA8: 9J(x$<㟜֌]Rx~@FxJ* ?t MR+PLM..K%oUӳg4@{6 y~r& /18 P|Xѹ]#q02ufXL݈\ JNG.&X"ZQ 5V 3(jm<@](֬)b\#P.hBgVmgOC`7nV(|:!q1r~n&׬~?%/m5Фe^R> γ1?ڳo"`eϻfDimLPAUKӳN^wF`z[]2~6#ሱh?g ֳ c+3ꙁI^Aڎ֮-x~7Hsy %K Ec͓sт&)+-w̦?>RCnLl,^s΅^S~:no `:qտ@ŀcRI}زȀ@ρ)^o{*,CqRüPyZ7 QCRxH \ GԤ?3} 2((wjүHk XT4\fOC62_}qX }D|sDl@2ht^rvb PיhwoD@ dZ"1:EVNB @7qY#fMĨ4B)|TAw{o]Zvq#CUS U&I^,<#iJoXbrhdr0|2ޛ!`-rAVXOGb!:uG # NV"P^2_!7:hhCb#"}jb/:*%ZOA%",eg ZXqt++(fd˻-rEr0H Q6VE 0~jyؖTj_WJצ(o!28bddFnՖ:"$dHo@(oAM"hڙ B`zE z`ۮr}bܞ wuZ0AU۩buo]7`yY4vM4]hm h vDl-& iԔL>X~#k r3F*<2,E,!$;9 fgX%xI+K;+c`H%e\xO7 rGN!: Y1ks$9ϢV\;Ó]j} 1#@-OY{qL@kc;ҽN묟Ҭm[[?A $4vG1i+3d&kRMI5&m$Fy m % /%;K2T`Y- ܯE؀Ż!CrXڈxk)T^ CqP;#QSv{wP0< tI%f7hvAƼRJ+ [E&,Crv@nHJ"3rE]Xx.zsrϥ#cdr 2nM|'ش@ ]cSm }?n2k¾b&+L>y@T">1I]v xlhkU߱^үr=w9d] g{!]ٙ@]cG@u)SE#sk-O?-n bVV}1w[X ]YD6֤? \a^suI5&oS ^56[hV7 sRdPkPӑ `ƺ p-ymTY62 B,rb˺!}?.#J/黴tP˽}b2Y#W(.AX+rd@A9&7xvDE덀'c6d܉ 5;3nE~܌ci<eDKhFZsSj'mX\E8DV)͗My^~*vgֵ~ݟgz]8/xp#sVhҺj~]zE:3ol#d Xd_!Jх︚LnψjүO *6_OV6@li,  ,mwJQth[kKm+ 6*@e!f(#}dcH=bT"^e D{8p`*_ir;p)M@@db ;t Y)|w"&3k n5s<%wRx%Ւ pS/%b:x0in}V{[]DlpGVl}3-%{osFBZS"M&Aֆ8[?^E8֚u:y]cTZXnpr֤Z 89/QMӿBB'Œm^үڷU CBDcd"rb/GNP?No>7D`J#Vc}V/#<|-g C<.Y\-S م4C'$:1ĨCi,2ci=C-XGyIʽt#*}/?܊yd'>`WOg.:&k&LZ(` p61Tu0M!rEaYwD:9k M[.ZBzIiDw09e =j_%^ lob* l[0K}QMFjoL1W݅Y5:"ֈ(Cs‰A,wD@# \lGGԤ 끘D4bO4F}r◪3b>" 4:_%SQ\;"7嗏dx(inXID|7;Vb+t) ${^["0wS~֖_CdܦM 菭MFP &2dwD`P#󮵻Bah=;! b.!b(/M |D@T">KY}ئD^@(-4 Vp2Yg+%P,VC~4re:NoriX0%l{%ҡSvn =lC!K ?Kg.~1&+t23ؙb?+f?Ȼ< D>Pܝղ8/sY\m=H%K Df/ ܎\vaT">K"&<1< 1# /Kb<:##|b2 dxcC|Vg[#i+G`i .Ekd 7U_7G,Tջ)ZY @.r.Xă5ّq<;Ҧ#_6[]D XmI"`ɨ #& ̵~;WJjPcD{v ]|mzzILVӽ֧~ *і&󭯖7?B+I.!mDqp}dy[yVGlT"~^tdzGļxI@*_ q46bϪ+) \a:v4@L 1W x((Xw~Kݑ+;4((wkrsߢ <9u\=|}Zش+sA5l_W,yIs/bbRHd; ; %DƼ!(B"V)B,lW;@x˔(~*@o 4 2̳Q/鿅\Q{! Vg"U  M+n6rh`Gx* 'S$ Mس] y4ES'RX@Zb_(䯞yۚSUvQ~{AWQ&ˮ!r["uX/oOĦ=`7[\ 5_ܸgӱ7xJ#v|+˞\Cm+NކܰVZZAk*:L[c>o{?6\a6[1x( Fd0; 2wKΟ*6]T]tZ[vu]mSs91WGcQ5iƈEsYSθ|9VÚӿ!vm:5KRx^w#C[)ߌDa(`{4b?A')%sa];2Z>j wDc{+щ~2Rx 2h@/r݆hp2욢T"gF~O"fT">.H%SV̿oF:¸ v!!tq~=̺=Y@LT bgc.JUM;&͹UJkf"41NPlٺ)7hcD7>ZW]b!E S_ ێc2 A&0϶ Cg/9љl | G#g}{-bƾ@.+q֖i_iwd:c~X¾1b0s!2.ALpCĎ|s'\aooԡm Oؼ؝:m;9 hKqaGk[.+Jv,E~ DGԤ9Ay!-wݔ+s;G;Ӄ ٩פLפjlˆCخN~(>4*P@I(V%s}FeUD;!y0`cb"C$O>3i?Ow^>{-FFttVrNEG@:k׶\`!0X ȘKX1ɖ_hPLd!WU_kβeZwȵbvvDL[ؽ&-CF`ia |WeA ڍ&9ޮ^>OAv1DdKӑ@sS0]otC [`#k[CXVs$j7>@z-͕&{[y˼7NtOub𪢠`XL׵dFSGc![!C1"kds7=zI]S/|f=V-xfrZ>nz]KZ&^z i +H"<͞FcVc5i)qιhws.-ONvT GcshϫyMZ?큘mK&R/&9 vx5b3#75<totJ\3`֢ yvAF%5=1s#wD|tl^_D| ~k+{ VƁ}, ruqT* y5&)P> w9o:Cp)oK|6}6 cnHuW@erG5Ϊ[Udu.2|t`Z X>Bn׫gedD;v)9 ֖V5xܨX!FlbZ5=hHtzڋkpcxH_*vϫc:!F6{~ssLk۷Yf#ҪiĥWT- ?7UtS`wDlDWfeZ>iOC;G|0ɸ6\EAmjw,2i8gr, dMUNJi\ɟ%;#X1]%0Ѫ" \Eǖ#n[?k#c9MR"#݃GQ*!=2ʕ/An$ rnch%D[soJ+%eza59!C:ʹʊ##; (nnk! 2T!ƨկ4 Z+^L>[kULjh%͐m\%ػpY\b+3F.\1174֭`joT] .EF#r2HRq/Eta#pVh?]D.٣;vbXvFn;w42Hҭjsì.ݬLoyCK|eխ̞|W1W*WN `sL>, \)H@,C HV\@`oGC=: { 7V_6#jP +?M>@z\ttK4vYO fґ@:]RZF_sXkZFV 0I?%Λ1]* A+ _3Ѥ]( etTfh{&7896ȝJWyk<ä6|< QL`+sm֧5g59לӚS#YQQ B Hs`rsY 0k鮮pS0گ QgV<9ז:QiJng"yKݳv ۑU\\Աl|Fβm0QtC$znF/ck{0B(gx4%r~fHx4Rk~B"Qi<%s~mwmqȵxwv7rυ8eM&DhG tȑ0׿QGs<kuM5";y>ܗ-Q2/DO h$^BnݷHLF>cȁNY(v|rIA6!wLme_'<:fX3 4gȽufa2PܾvZșz9i%\o; 'Ӑ{^xԶѱ_C%&c7 lku3"Оc1y\%=/!C{lݱ#X=ɥNmGr`f0(:$zcζd\Þ7vp%@(;uV@eNIݏ-Ey֚9˒fe?P[9-ǡu ǣe)pg)t}tFawh6߰NAyN8$EoO hk]{&w"?@L1to ]t]yzEߵ\$+=)ݏ]cr8 7!69 Srnhd ĝcJ:®lae(&yD.t݆.C(#n@_#Pn覹r_G:vFbz}Ӯ^9DKHlp\q:8uXv5 "H^DhtN߿P6L| M@Tԩ46r uە( ~m䎲[xy?߫*gT֡=p=G6w!mGHvoSK_OwT<cotwka$F7]wga;{W6&E"9MȲvיT33L~9%u5?CvQpB[LSa^Ռ%M_>蜏@ݑ˺A43ld/䴵jcL2]% ䷯+,Q53H*o\8}}et]XWorm zF`z6r]5@YX[YuX{e~,qY2Xp;*+r Bv.ȒO,x@O_ģ$݀nh 7:jk T#` D"ӃB.\9OsГz8 e݄&E7[ UќrPFf ?uC?]mU !! WPh{lEMǶmYȱ %2?ܻP#hd0K)?*ߎuHrx+2AuK$6mw$0kml_6JmݑȈ|lIA8tӜrz!# ܌r}FBm/Ti7A3AUzʮ/9)q"P߾sv; rpZ]H 2ܿțH͵Hp\:3avXF2uٶ>9gwe6 9zA<Տ%Dd1rs=/ߣÐ8ȅ]d'8Lc}PxԠ|<$AD%Ñ2Ю\FcρXx4]ǿ|.hHk,{ eWO ,s$0:/˃ K|t}1'ApJr#G#Ydw$JBrI9x4 kH)4.ޕL|FNJ;$Bd$&:r?Q݀|^ {DXrɔ8~04pbǁ #72ӮQ$ţA~,`8' jnmۑ81A꿶-2DnE>rEsǑ}} HNc=ǿcF8uVC?}mo`<aF!Nk=ď%ND!V̱ft >cmzm/hÍkm^s!8-{&}P.&1HorS^zjJCSҹu?8е;W.MaAPf|S\S_9)o b0kb$R YZ L* P3;'$@߷uTuӭjjnJ 7m VRں}OY6֋@[${8WЃGF \0dA\r#}zʮȊpMbK6 PGz 7x4RA#CZTw"W'B$^@GN(\5rl*P&/4y7vjF `3$L~@9SAN@t!D7P0L26  NB)F~,]Ǣ`n;X Zq;$l>9Fǜrz#UCޥ-AZ"j6N*@xQ9Fp=G7Dx4r?;Q^Wc7۶V qSlCIh:ێIlDp28"9UnMbu.{׮9$h/p=GQV=wHFOa%p s޶K1rB~,ɆesP|$r|̓Mn4!0lb蟳v`YXt큮6djSBFբ ].7B &&+hSح3*G#煝J3(L]/  mR$hDa6K O ^e$OnK#K[ȔJXr& _A \c ѯ[!t}? pq˴zEy][ % [g>t1"N1A]= 4t/}.1Ap1=O1A`9*Aloc{Ȋ1=}_N +Pxb3򕦦_G#gsH`l9$N>DX2 %e_+Fy1#ݷ(P ~? Phg7$PG)0vnc]H G+Hy¶`{N&c&|3]Z~iJ1ǐ.E"T$ɬ 0p^8Ge۷pe<yж~,q,*p.xPb8$nDBf wMbjߴX}~f* 3٠:G`*]xxѤ~cO)@*/L}$f#g8'⦥ amcWQhDu_fйC#AY72Նm9Oc1g>#.D1Ai AP7,srڠ۴{m 8ܺlnς `JŴ `}5m~E@[2i'[c|Kd@^zD*vc7rᄘjt_֔.hd08u!J_׎(`>u59 uJ?脏@_ݑ%Aaht_M} mj[ر5$Pr+$:F"7-"bHtTܰÑReA݀\@,+3jVNC57ǣ fd#QXd9 MM5 mJ }Fn`$V$ض/FHDo<:l{.;rR(-rڒi7H)/NT-vs 힆nxo v溵lN`hzToo^NWx5zHף'j$:#oಬ.])1YLX69xWnjW=+|%zW@0jcz]# ѹ{ ]3z=N1:˻Ƙm c<I>y6>?:cG}:2SbKcL>J.I6lxyI8&1;B\JSѸJ6`ֱvi O.7G#߰d{SH7de!%C^}P]u:Ǒxٮ $HV.}Tz9& vild_# o $B#C E@ ᚜ܒbOy6G(\; e!wrAmݟ0nQ~,1& 2ކs~,1 ɪBۢw2]v A(f]l(,P/l{hd:;`aP'L_G$tG#}M Cĺ@KtG#%Eq3Гvk$F_zH(F\t鑲AhF}|7+٤߂ &HKPJV^C=v?^tSA3z$n= +pgуC%:v/9 =$eax&>]KB82APfGO>Ap9r:{ƘH}dн\F@{cLdi"d]OKp1Bm,@}Pw |=Do%˹Ƙσ 5F%1fůXa{!4X6\FaEGY\nhdKx(ܔByCaߎ:N(la=A`I$Z/H|}Pg2QRߑ{u? ܐ]g-D!(D]goVF¤={/ȕz omP(Y wlrzc鈄IG;qd^C9=glҿُ%6$}ĶȽnPbi}?{! [ Q9Wx4R=-%3hlFv#"t8ˏ%a_LcmPXȁ{ -_lH<` ^ alqˬ!G\nQskjW|]VQ]n#JkJQ T$[!Av 6u٥w4fTW=cdϿ?bsZeEalr+$Dv`;l$#3eW ~OD'hctO> ƘQ1^ k,AƘ3A|o=Na1Eσ (5<"0Y<[0B}KZ8imgiƘIUOc.BqI<f\7mZs~/ 5 V8`+ܿGfRPLD}Qzu9%9uNw"Q;(7k.r bUFO 6wG_Px4Rc݇mQ=70(OӖn.߉rn~GM. :'ٮsw2gmwE lFOp_/!H,U'8Z~n~.9;}<ϕd&h Hd1\mwY</K> B9촴eWI@NQLKO^]膜lS d;\:kkø] z~4j}yV5=e}yd@?E- 5]Z׾W5#o7TJ{lwO=<*E] |bhCTvܢ9'Ƙ`9Y X pvr%t16zirClJ47Qסߑz+Pm/ա !HF[Xb\$BF"A4 R$dHCurDaӷ(ϑpy~^{ls:=]MBp ՝nUK >bx=mm{HM¶a=m8DwHFL"m*JܵH%K$lLG#X2Ѯoa2 vCޟG#)?8W_ex& SAxv׮@c{tdJTl, փCoMzfO]#Rhs$.~6M}UU B[ykB ]XGf0}jqO{|&kk&k!isV?9,"q1{IđQ1Ț\DS,GV]h#hDB$W~z{r9f y<QY >f֫(y}mm>ܵBNbn'@=oUܙoHͶWs]'"!{r:d8l`qfvģ9KZGs3(<:/uEA9]"2 Q\kѹuZCSn#4y م Cj; 4זfo\޹*JavQ}Pzt8爭$,P"-Pҡ HǣjQꔾ^Fȁiޮ# ':Z P(Gg.dPL$X6Fv}9v{8_tEo V_53G"2t DI,;(ӑL "$~GX%P|%rR~A?hFJm;vYh J}d,$_˥Fyl[j|D0G#vnuJ:ž%D\":L5N:ĻgQZkʭcyo@Dk- —j< fNP5:k{=ܧjZS:Π@m*;_6w4u8вq,tH!%&Bǣx4;;jonDEHPrd:a$PAԙuuV(D*QZ$"֗C_*4 6ȭDam0`#'d{&u _8sx#J{}_¯'%XFkrF|]r.hj$j޲=rcE?]r/A`S䰜gg*?k۟}m"rcx66~u9_Q~(_n[P d/@ȩ]tO}aN*?~w?j^0l0ZQ4U~z(LUñ q/,aN?a=Q}F^CF@9GȝQ(,}FXb$jC>O AUFf/P2Q-5 (l܉c:W5'L՞ʏG#ШqH>a(G(oh K|`?Г"eW,!WģoX~$pضrNDܷh$h=<mP%pKܝShd݀L%eb,mȃh`+t;7B]k#.Ы۱>K@ ‰F}#GqU9jÿ{&] c 39:]iY"֩9kш"z;|ȸpU?:lpDr3(L1r^DbdKx4rKB75d u+ ?ܠ͐+VܸId&M> ubסNfHAm{||䔤; IL+ *] S(߬mǣQ8-Z9;hEnD_ٟ Xb*}/Tģ-Q: vZF(D<҅?@e$ZχF*Qsm.'{ŏHNLDp]dď%Nx4rr, ˱fej lFf.aG#o/~! Qx Q.=)|N-Q'ţt!֍LD_{\{(㑛uǶ113HG# ~,儽Fǵ /7~=۶iz=hIF5glrrNA9]l˟h?>ņXPFIe>@siդ%2#3RAx#/,ZйU oIn¿ѵ?x LlWM=`z ׺ q8kN$h$?ȳuÖg 02Q(^Ve;1K:P]rɦn:ϛXrh}BASa#'Q8)xۏ%NE^?Fz\rD[H8բNFR*xYBXiF+ա0w?B鶛`CuM=u XJX{"3$@c/CbT3$Gy=ѵ]f+YmuTG~B.baU`[&ڎX 8!,kB~,G#Qrxz}PW}*B5~,19M= 9SQ<>rXBx428ɏ%Bj r6D"b;܇{z[Rn4MD.ɵUF.o!Qu^yHP_\؏%QW2g_+B.B v?s k'zPHWom_9be)E( \%@Z.@ŋCMu  v=$C5 p%8!֌Kl ߡ)k{HX"=K;H#G)@WufE}G#o.fWE(glg2SME;䞍FX(;TFrD~x%s?Џ%ʀ;X" |Q+<9cnǫh0eK̟G#~,#Sc:W+y; v:YZ: ȱ}p;6ޘT^k$C(T86yϠ:JWkh8!ּi DEcJ 9+}ٽvA֋k}}7r <:n]M}{A =lr6FņGvg"Uz&yP*Pk`rȔvicߟF ]Wp[Į~,qe<學Xы($_ٯx4r7*W,A#h$݁=}wC A֨z~%k,>=OhPgp?8]?Ad@6ģP'Lh?,Z񿼂vHu;/Wh >UNϚ ' =5w!7@\4@!=(lx* sP(L*,O߳+8Tbgm(G]Hh8!IOcv+'U,^3nhtdT@Tt881{&yk7#"Im$o]C1N|9k[s;eNnsXE_Fz+VpSTϕe p (zΏ%_n׾/1wsOjd[3;_N5 <,2x&y2 ] sة̧Θrƣ4G[('; ȽKۣk@rgjz:C%AA ڽ3RAxӡ9EhMJ<9,[rV&_'fbno/ҟ)G[ţaPdH"" `OiQǶkK?ܖX@~A'x( Y= '06vjeP>\ ЀP׹6wkOnW*WTM 63l$OLVtF!ɗ^,~hf8Gl ҨP#3noSģv>cQ֋[l1Q"x4.mO9}ʱ>6( rwhcݠ6L2=TTIz$GċP>fV;Wm_B \hyhvٝQ*_ܰFNLr[4u TNs"Xp } nQcv9C~nU-?F^r8V;F(}*2|1z=qY$KہOSA]Zm@9m梺aϦX}ޞhYr-zX]<+H` p:ȯCW?ܽɳQx2f0p׵DS7}Z9j;"In{a*83ɗQ˹(%eV~õյ۶>J~jUS30t}< 0 _sOH?ّ^Jͯ=rRA y$Ask bZKlÏ%Iƣ侬x&*fvC=ԛ6а%iA_EI= _{]QX%Ն ^sBGrL]eh /*h_MN go}QR|)ȕ S=x %.$p5/4HIDAT#jo6\fݽ,;XW"P 3Vtc RR hhkzGRm lJ@N0Z'a]=<g\C7:ewEjjP~v u5⢬B;v*6) Ů_󺞬JS3%p8~d `$aʌ^|o&^v8.G$pYÊNS$;Bݑ.GyC-RAxO[s/1[tl աAC֗gk]ߥjfC;5:@J Z\Bۥt8ՉsMº t@P~A9GHm䞩 6,4bliª{~?-~,F'LյzM>M=QQW'p8<ѤmМ;V܆,${&rB9 {^CmD+uDMgz&aKA-J?M89a- (jiыgyIvl=w8eB31lA\0Y(DYorK•Wd5Ep7Aux&*=jd6vhd&98x;RAe|TC ,TbC BT#T<  ep,9k;Լ]R軙ԙyeP:,DRL+1p(oB"iO~+X+/BֲH& 'l-YFH`? j9bG3 qsBoRriꀗX s0ugp |`.GS霚 ~tgu `_Dm. g&RAxjSp,p4^FM|W|͹9cO 'I@EK/A4Hٓ ¥+2tBB A#A"l!rx&駂2E8,xy=+~fkT݆ !+F0$n|{ {&sUb*4&ǢD?H=M,f}8!p4RAxEpZzNh^C迅HPlx&3mR;(Qd[O 2P QzrJsQ+q>3}mҵTFNނAjh ZcMñq9bG33͐{ 9cHDGGa*HzI*hRA+@!_PX_`s(Lm~ٹAMV-*qV*|donim观pbMXq9bG3!Gz&y!p^IQ6H(g [(%JܵG/c`r$Fr M48v]+(QYQz&{*o#1v%%R4ўIn sV;#p4X#wœQ%W1D1o DiVQ:"9fy6!&/kse#'>Q:p(K3 d-TSF?hʣ$|c1yP ^9#5C($ER<vHeg0g9%eM$ իHWh(\T Lrc4jtT p$gGpI^ ܏ZMp]d}䱨BvsjQ@ a-Bh:$sV*L5~L&$$lہ7SAs{܃\HuC!PF9e!`Tp8G̱}qO<wH*߹&"_LrJ 6hROQiPSOT)_o@g/Yϡg!*T}p`O$OX]SAxg9%AAm$a !| :Grk3,?(eZYkZT^!rGycQnӾeHt FRbMDOm.f>Aah6ow-j r;x9Gㄘcm~nzm9=c~,ɭXb4~MM|$)'q7rei!8~| ːKV 6Dv4 뎪Зٟm_3ɶI>Ꙥ2p ¯d?y(T;(9 V@Lڟa(p8'?G#s;͏%cXbF PS1r.Y=JQFFL@"IQHB? h9șZ<U똏0@B.Wܱs<ñ\8!h\X"k&cpKl06W v!Ȥx4ry<9~,qK\z _P!UW4o,{ 6+ 0"! hrP1o@iyI*xU ruhڦB[t(.rL ;sgTm*.p8MbS3|Ώ%:EC~~,(?Xbzڅ적=*A9T.a;14zuEs@,/@N;U.p)̆() 7$F!17dL$$x&YsoDb%p Fa)&!FBHs[Rz&yȚañㄘFr !92_h;0 CP%J~ۑ?thB U93Ÿ;,[6ކgJG:R R'< Ӏѿ>Iv QUeAIQhÞ/P)# jρogSA"uh0[c} i@b Gs 8׽3 =3+lΙp4N9;!aKSDۢQ4;KV5(ifP}i_:𚬂ч]y϶;b/W"1Ʊy@޷}㑀;H|MFX 2nOy##bg 12dT??spE3ɳAad^¿; Q?!1 ͒UX!\1Gs3r1 %HoAw('@!W OJzWOmS{ܔZ- WnSRKlR1!/tXݟY4RA8LL@%vHVܯyH4lr(/*UQe?,4h݊YpBZ\H"+pSh>"+8"<wI;墝2,vEP}.S ߎTmhr<|%E_hBT&p8Glc^/D44o;K"g'yH܂jd@N\T'T: %W[~>MATQh$)( ͑K #t2rƪSAxgo UgYjH@VW0;As(,]?4]#hs)u_ss>"E46@.H;* 1Gď%h]?=脮HPφfT|CM](FDש <RчQHu^F}CC%>)`S$E֝I $DqBZe0h$i jwRA8]:$?9hTj+g[RA `O嵯A(`7[mx4p[jIIvg *]omp4cs4WA rvnFEBoEJWn_j 서nZ_4Yi9Fvc F}Gk'Or'6ͮhH$F i0*i{Pn= >EWT e۟$CT`wo7`3 G$d^ ];\yk(G.(`*;8.>:Ǫ9%~,AHd!Q0_3ȈBh;ݒY =4x4RG33letb#dO) ^jϣc—O"BO1=HF1haaQ.Z6ʿ #1sw6)} l}mZ|F&5e[ V`ƤI/P[sa(5#c9bLS6pgk7}3lȅiA 䈵o4G#HᅞI^O,D"!9J6O : ~Bܯb}$&:'ܲ HPSah`sj"$~Cgs686r :wn8u #h'(| -svfmPNY r|- ţvccC#f"w支8<F!\Z$C >,Dg!QA:ZظHgݚmJA[;tJX|o'dKT5i8ZbfK\ܮ4'h_,\&U7k5h$cG0̼YvˀȂOK \cWw $EwAb+?QP(d$.OCe@o"YHS EbH~n (,$avT>p8j\hќ( *P j!J.7'ov(' |]S`g{&,^H:zb<4 X7bըAnh  ]+hY~QleAh`C;:n(ly>HpeM#gl*ʛоڭ0ʦG) KSAxݑrsϟѶ&;zbfK$Q"~r[#Gf'Tb*I?!QA/T}~sɨHP,@QODȿ`d_xX!jf[Щȷ_lP<j;4|R&Am tm,`?9uHpA՗v[}IǢẃ r$?@#)GmLJMñ6*;~, ֨D  9?Ai74r"M@KQ~؇(<lmH?x !Ea*4; }PW3ь[Y1)g-*EN*#$ ȓP +&@|W!4 } TWݑH3ht7\c? :odr.F/ơM$ñVᄘYaF 뽨ϵoIfRԙ ѳ p;r—EE 1vs#7lʟz%WDrC:P^j; \db%HB <3CQ@"$$< 3)+IG< vPmB7G"pkԞ|z$[ lsTpJUDlqHv<:t9: C9^!T]<42;T H8ݍD͍\WkP $0NC"!]FisіosL<a$zE|.sX}QaMDi9r (Fsh;.{1j9G¸O.|ip8\%H,tAJ$f7N~<{[s? hFnA&@b1T]57 (v9r<)[ǼC4 \n? T uF9.ڿ bzwAa}Eئ\!1< /}{RA=rP Nk': M:%~,9*S(}H!@¶()G?rPEUBvu\rZNDnP$r < 3 E͎| LɵE"=rf)4=>( : %!l$j{ eTH{]E$sRA6 Q8Brgcp8A\h\9kk` !VBuH/Rv26Dc]:̶5/Q](Y ivId/H(?ЈH VmCܮg$b7T*6,${z&gK# KXpB\ۢp7HQ,f3ԶB{)},4Iu(|y eD'h1/u&";ʵyYa H֭hB)j'}#+`ʇ# Qȷl@-ZŮ:iѭE({!!40 $@Idkh8!hy)B›QX/}o}?hꆝm3ZehdHx؏%w_!B9Mum%kbT.K'RAxb M4r6BmcO$PG"(<( +C#(ܯ qcōt4wZ| `Q~AǁH4",~r+Py-r栰1s~,u5}7^D9M3Ш()\\ >Fy`# sP(Eyʭ{XsPB9Mt2YVo}4 µxcㄘrF!ˠEIO2c1~X4'<l&h_J\ @bwD %x4֋[?=hi?tUX/4*-NqTX6 8.Wf:f\1c~{7b0$7@#iLM{ñfpIGs9X]CvjRߑcJ 'D:ē(< W^1($w7]6 Qmg.Ə%D*i(?]B}[ܹ~IRAN2S{i+]QBT7"*P$Fas3 %7 f'6aoţH w*PFF&Yex&w&p 42DԾ 12wO; kU THSA8-z6Dw" 6 Uڸ]IZ-\{.nD S9lLk$hE"fg7; 1G&T :Q%hdr<Uӟꍍ%3e6ra҉CZĦݿ 8lbhd>rt~,q7rʑٮ < (oTDOBmA^H>f-8 te!2ߩLp38!XrtNc\rC%Fufţr2yLlY/:ui7|4"S[`5 ?ģx42Y坽 l\Ydj7|M䱞I~l vH #znĝzspsXgj4zT(%I=XX %D̫H4Bprǣz?Dס( w$#F QH.}FXmx& eiI!]D+DZ T4j:~]F`D&6E.ACn>LWá9j 1ZK샦-2hDc U oF9 ېܵܧwP.aHDHrq}-Q{Qȑ׮G#sVA34 < '!1?jGܱQX9f!?jP{֣:`j*q8Մbu? 1hfd!4bpA*Wv8Չ+Xcj$ơ2PI $NF= Y *ukFXHTDXqc%_Gzƒb卵BB,L}WTC4Gn*T:v-D5Jr7Pڵh&op8!X'c+H+QݪQزMb1!J QW;rm]( [4p?9[ɨ~O*Bn*zlT_ 05t^%p8fbu?FmܪXDbi VCߋmPד{wX) ;>EvP  4 шB:z+_XI@3)& M=Q(2@eϞZ"AjrԾE Qݰ9+ ,oM&v8zs<~,QY)H\ &?UU 4M2c { kQ}z.=[_G 5Q<Y p8'~,Ži#!H4\/Pݰi(yL<zf?$ V\OQEm3H3-#\Jp8W±^FjPjģss(94E$ rn@i. ֘_PUH`e#ĮV4 Q>(G47M~h8G̱a+oG#^d"3O4 %־oЈbT5tfx4Ėp8'?mC4(T#hd x sHj,pBX+N@ 3֓x4RwHժkp8!p,?8; |j~9ceqBXNX"F~8c 1p85+_p8ñpBp8c ᄘp8 1p85bp8k'p8N9p!s8XC8!p8ñpBp8c ᄘp8 1p85bp8k'p8N9p!s8XC8!p8ñpBp8c ᄘp8 1p85bp8k'p8N9p!s8XC?; l˥eNIENDB`openTSNE-0.6.1/docs/source/examples/01_simple_usage/output_12_0.png000066400000000000000000005674641413546205200250130ustar00rootroot00000000000000PNG  IHDRE6AsBIT|d pHYs  ~9tEXtSoftwarematplotlib version 2.2.2, http://matplotlib.org/ IDATxwTY"X@cDkL~FcFcc{ xal²xuEl{kٙ9|̙}} 0 0 0JVM0 0 0 &1QdaaFDaaa Eaaa4hLaaѠ1QdaaFDaaa Eaaa4hLaaѠ1QdaaFDaaa Eaaa4hLaaѠ1QdaaFDaaa Eaaa4hLaaѠ1QdaaFDaaa Eaaa4hLaaѠ1QdaaFDaaa Eaaa4hLaaѠ1QdaaFDaaa Eaaa4hrjza<:Eu#^Q1 09Ea]\1#An5=0 hhxa 0 I/8 2?QUKs&Si@w` J[ajtM0 0+>gm9r곀{嚬 X fI/vwDO0 0>gIzS^,b}Cb`p!X*B1 K.guߓ^ݶ;0 è(2 غ@\'ؤD aF 0CҋyI/#Uyl5<< Kz?{ǝsQM] 0T]kT4NXđaa6 èFҋ#f8prm|$LڢTai]!p(Jm+CNމ0½s~,@ϽQ\ <ܣH<$K^x˜ݙ\"Lݿuaa},}0 \ Cu<\$t.A5>cP7^l8jz96"z'NFN}ST(An(xQlTSTƔ | vGNP%jp7jͽ0u+uz"͵vE.@W-?,#sK1 0"0*$X{$dZ=0=Pb-ajt$p0rQzHdI`{T^Qg$vD DZiZ|CS>eЧl^!0 èXMa#ŊP4j廗e!<\ajvҋD|Ts4Z3QHT^{{J$~ t pB"LM]xOD)vF5EenǞ2aSdF%.Eb5v$9:O!1sjP^";wY"AT(G|T A|a`a D0;}FX޼daEa4d:"5#95#S5rlfZOEu@g+aꈤZ5H0a76UX9HڡPgU;D "2uIoaa[0L8/^EBe4YTܡfN}_~|$\vCP{6riR.A6u5hxSa|CN$ź$05u'^&^0 h0XMa BBh'<<ܜ}Q ]%J+G)s{Nq+PPm!tZpjAH 'e0r#PKݾ(n>rD1@:ڨ da K3 sIW'5/hDP.0 ̌R_$J>AuAnBT#$ QB䖺 Ԍa!PWП:}6BVZhw\z`ҋSM3 0"0:3Q=M&&PCw}HDWKܮ6$*Y{`d"L} bMpm]=7fPkL w^HAk'څW݇HحAd\50 èG(2:DҋWQ=La@8xxuS^B$j*P/ PyXSDv,PGR2^l%pr#79J+DO;"WcQCi{ZHo$T^̨!Eް1-o0 & #-s{2 %| S .g$ԣa*Lzƨ&h9JABhMsG0hFYHUZd9T{QvA.h -9iCjp?sܐS^+grnRv٣6fvaQ{0QdVș8Չ(5+^nKz'6SYC'D56IvD6QzZ$*vC.G"$Vv@nM>0)Z˪83s6ׁQUI/v>,.Nz?!MA t A)0 >gu; Y( 桵pBDzYGHzFרv J/-A3PnHAhK nEW`g($Xp'r7 x.rDzFJR7;ƑHaY"LkU5#j\"0 :& bMl'T23*/we'ojjuW jFPCikwڝHp*DR{03>NzPMMH@}<vչ2q^$gg'ԷI/VNE)qf Ck !Iw4QCr 3˚aQ9èEN*`D*Hz\/!*^6$ QGuJ[BnCϭTՉ0Zujȩ쯹g#AuHu¨!E9AB4]뷆}1 0Fd6wG]J_Tnҋ@]pfy0у'ԚD롨C)Zhz'X!pj}*Q'P3s rPDr|ͦ^ʨ!E݆]0 غ(2EoQx\#QK扨$Tl0zܘ23'TahM>rxvCk^(CMr yJ^Cey3s'Mh}PˋHH]޳I/4O?s Q+|T+t Zh [{zu'<>.;jHHAig 3aQW1QdDzg F=ү.KI/ OQ\ S%50ZEҋuC) Em"Gg5k^We<9Ga[Kei%eY30oT.0T 6U)((5;#r8$F kG(UmZҋU<`8P'8{M:gJg9rBn'xF "kPPۀ"^,(În yf6T'aal1LF-&VwyҋE+zXբKKvC&Դ6!CSs<u]jnEE'[]үâ9CzLw&9{c1EE]qJs[쎗 P?ԈaO$FFHPA2SRCi{)F]D*T#%o?"In^lxFzF )jCrD׮x50 (2:D"L댇G;6xiY."ZcWQ#U^v. uc늚*G&/F90Ѓ g,Pn?|OϷ[l4~"Lb'SP;wuvJP3 {x,L <3 b/!1rz1\< DkMض:.Dmj~13(La[EQǘ7S,Vt .S9`|^BKjhRV$!H\R:$>~1`;vc|T33.̬;'Ts$ {,#~XL!]\IhqfH J{Sx>CbkUY2 .6f^0 `jzal8Q?-)״j1*odR#WWҋe%S&X3w$:Q04j}J{w9r-Bs42F-X]b]C3RTa޽8/Vm-x3Չ0jHmzfOH 2 0F"g>.٥O]3Rl3.DŨ y_$d.LzkQꕗbq2rD,.J @$`qȍjV(mr!F ںqZm\I/}/t]7-C{s[kmRK=νnUAxk*"W0 0:& n:}ykz v=rWh~vBu4+QP R"|b1rI"L-Oz;PWhݧ~n.y4"ǐ[܏R禸Sk Srsr[hި&QCʐٍ+׹QM3jHQ!ǒ]n`t ^Dn[̞T4 0& :8e(oP$"`-CP0BkD&!)mg$&$Ti0]ҋMC^? >ǻt CJ_[ ,Mz,LF sk!|L{lO:y+-~>Zc zGF܂a °z=aCҋe'TEMck!-㒽5k|-0 芜Q܉aK֖Bmw.Z*y?sDZO0 09EIzKk^,??sܺ7uWc3 "j r!g?Ev+`k 605-ơaS_DsiФ;ùDaaLƖe5ZNUN-C"HjvT?K9A!h>b%X!pw"L\e,BI%0 0& c Sw&X*oX2nc x   nkf3.Z^{50`0 :*Ya^춆UΨ=;1htmto 0j=YaC"L݋jZ@Sؠ6c/xl:ca/oE7RVd_;K XگD 75--9vhȱC9vw@ 0*V 1 IDATه!x`iɃ؍9 sl௻O規/qGUfg_wj:cjWig4kVS/-yZ\,퍊i?gc?~EȠ 0VQ.Iҋՙ[$m;7.^ig>5Z٥# y6_ Fn+G c9vvIqD5320O .W+&yK/[u kd}o[Cz i>qʕ=k] ];e`'n;U}M쨜6Y+se c9vh[ `>:21hoal"& c+G^~PXSJ¦>~k_Yajnҋ]<:C.h^٫JWSƼ0'; B Feء4b4_0 LVk˒m̪l=M 9YU66|8{Y%s*҃ǛO^4E-V5 m_g;Y9Na̔M_02;#ZD !^k0 c`02ku(5gء  wPG aFDalȜÆG*7jzYn߮/om^cn~=_]ԿO8%%9Y c MVW& 0 }0zA"LݾxN*}bJg}=xuޭ4/hMyCGoy͒6SQj;1r#^ȈAKjt-(%n0'!\ tE]fuf]00"04Q?8=73^̿rt`e%--uQ?!; s7Ϻb-u@563ol)DMӀ֨B ۽%F1aXMa[ |&Vrסv|#9k4j~֊LPQ9݇q}gMnө=8&FZ4ۭ5faR.Fr_ @P{y#.5 02(2 cb砅%AkSo}G'7\oAnqE0oLe+gը}e~v>ws0P+z9Q9kZյt/5QߪjX*mw>+``/Wxd朻Ѱ9vhp߮Cqgm*EuD"̈AϪa?j}h>ӎ!XZ[awҋ펊s}tҋ#~pn:H Ci>́r8)I04[? W|>SQ?8*X-@ڢZIH  |Pur${?NHg5d@ݶwA>}Ӏ3k:bGԚ{RM0 )&Bu Cmm/N#P9,y˶ >Mz<ԴWV5IWTtQOO~pGfxo@_D:;#Wz{i݁ S~7H~:| A CJ2/@/`ZI#Fg8$dɵMEۥNjw7miիdz?Vj}eAޒss*A9#~.AVaEV%φCCȜ <ȑx km2n { |9P,D^,k./6]n|}U n9M#Ӂ~0 Exd{hJPzә@JoG6glu w Qasϥ_CHt.@['#wh"rpGC~pjF m!As` m.)mv +\S^X͔! ?8eyPǹkza/cȨM~Qp!1s @Z[#5~:;XG>ؘql,.]:5%Hʋ-m+%<hQ?8 _ŜP}:r'RUCݒc#E¤'Jm (~Qm]x8MHtCs5j2Pι!K䞜R^Di~xi9٫Gui3n/M)VdgZYz1haL)ݭ5^ۘFQ?89XeșҲTBĝf׷Nȑ lMΎ ϥU[a; |>-|` 0]rO#Q?8]oP]>Z!2V]A&$FnC $*GAH\jF-З 1~& Z]dC ǡ(q9T q7:tOHYqn.xd5DgAN{鮑co9o#X 0DQkpLlC_PQ?8}CIt@* +/q 0us na|w= ͜(t&ye vln/`|:[_9GHܱthD2 @N]H95 0jEƖfw47cKH#a.BAp_TQG?4Z2ojEh|K-({ى{~PAEv7>s4k&JWZe+;Ck\@|. $nsMY43G_Ch|䞍Fe 9EI5}?;EKuj:-FA)qdzcqz &twcCQ#QQ?8}Q׭(EC"kq:7#u{"=4t<2eaQ߰[-JrQ*˷xd:oR-m<ğJ!E95)]k]G՟[1 lJ*;Eq086ApuRvcQ[q..Z6Q?hP[RzHOFAro*y /ܦ'SwQ"֖^:v{L$hV"C$A14q? n=} $>EӫH{HI{; ˔&6ڧ'QÌ^ {?9I:o4 0+[Ds7FE+)GֻLv6Dl<=HQOD`t<2qCǏb81ƬGE` zYݽIˁc3fr-Q7QcPj{9bǣ@y=9--=J78!܉qrEh*JaFuۅuAJQk\s#;p7!rc:y̍!xJ j^vɼ]h9I 95Gr٪@׹( 0VO#7If^odMȆaFD/ 0Ьs(;<P_5mPv \sIAAU..itna!xd) :B@FÞ(u%JQ/9P7 uPaY/~ ]F }OP T E׿p{mp)0"w, -"랟QÃFQܿWIit< 8VFz]}98{ɟP: Rq? {H$=JoF HR 0 G(267 hG>@3<Wu RF^D$w bSkΡCj^(eh} @t<1K#?tstk# ޷F`1LރDʌjhG񟬝qqcZoK#uL5qMJqȑc[[{m 0"qEW<ܐGVlԗ z_`KY O;WjCQL Psy~0P' 6uj]bh=ͪ'805pb[]>as7>(A~8 4L T u}tCݻJya 7:/Q?u?r4F9ޯP˞'^޺!&D"|  ~  (DN9}~2Z`G23U*A&NmӐcrZ>& F!v&Fz׷A)~НXT˴JwG!W0 0~E +E~S,'@ `@A>޺ Uը4Yxd2LE`%w(D5 (J9BG|R~P0FǹKGN=]ʖ Ai%( WLR ׵f~Vi zԶxK+"a}f֖$;KxSP׸QSQ?KZnh"#j,!gO#t J9 |dDψ|E/Dng[t>D7GnYy~W?^w>o_De]89 AV6dTn (\#܊>Ka(2l$Cn͵HPԪq9 \nŵFK#Q?x9: Q[䜇 ˥5C3Q! 1,Dbܩ0P SJU@rT JsRB8CxEwMg~㑇~0h6.n'TUDu ؟q#Hh~0*8땨EU:Y\}xuwAcT;۷~@x]0G(xKZN.(,k{v&ލ=iܦÑx#Mu 4#nbk<{ 0%7gM)sw=wHU^|}DirOؕEYQ?~7 0(2ǹ(#KY tAt<2 }ѬtϺi- Q?xG{x$rz{r8PRDg!A<{a}LfTNTi"0 X&_ĵ~suL,yi'Lt G#+ IJP\fI#S~{')n ,L#)|<\%-Zy6X_VtCS\mht<]z qrԇQ?: w\88 "TYՇswCuP4cQ?x6LA6J?fۣڒv~7 g/''Y;-! g p x7oHݤaQ?0QdlZ/<6 5(: (y3̊X4< @" w>{T{@kW%byn,g#HDNP vfEkj qA:"(\9HxTcN\-ӍQ賚|\kPqn^tEjP %"9|BTK8 MV 2&DrwH\Nm5rm1J}[0 è"cp2(P9) 綜fy3P,(J5CN IJP)J9r`gl9>]&<$B&YQnLQ?ڮ\,xc J @ME5LX=+qeRJQ7( wP=QJҵQ?jl*Q9TW׾jAbEn Q^wےTmEPAHĮn̴ǎ}3d#r4rvEю3lX}_qNP_4P!<-KGIHLvMA) k%++к}Vzl6 06& )Hx(xɬ{M~%s3P$amAO,E} ?9AЌEH,uG_K D?=QVf1׸Tt\4Q'Q?O"YtA*wmkо;24/Ǣ1JW2?F~w{y rh P%՚#di> K}guqH|xQ?x"UCH\\޿]OD[9CK֮UrFWwYTN B/R0bukg,ĺXwumc]ŲZ׺jl2"* UKIs7"|I{y<眻n^hcy? 7Pl]1Zۡ5{Z{ rٱ%s.K-RKg))sٮH-G`0ߘD-;u[c=o׬j+XE -d`2|5/@;^-D1".UEc ހm@}"(j"n mʷ7:=\DlO$,tH\u.Y}:0=ݥVd3:RaZњHh%h=#ը)}$꫈F9|mj WފܣAHO"G;}3kK虵ZRI֟1xĊnRK-R8,%E.[5!^7 va0)%5A>BY5L軟z%":uȟ^m홂HH;RNX+@}[³^յe!Ok`VJq %\Gaq h:ZW!OE_@`08 5\\@Ո w[`Y]ͯhseYA/QNDp]TkK#bn Zr,6۶9RADPR}l* )sjH8)+ 8'{kvrTzw'3 z(Ciz軋H5Dq^uGa!2F)ÝZA.RKPH݃$ϕHuT@:r',Bl r,CN=PkQۖ z%ٻˊlKfܗکS[{RK-R8,%E.zA49 = Z;|u>h̫{%pDMɨ9ۢjPEA9RPFjH+Dl{B?(mPHBBc^C0f$> ݭ 5ߡiQ"rsRS B/[ߛ['"}vB)"<)s\w@&Э@`w5˲7ߡCn#2y`hm݄U5"{;ne{ FN_Yq,Dz%՟~6[p4Y'5FuМKJt7\$=r$LjZO`yAJ;^Czjs*H\)PQ>U:WNvy͇2k;prBj {#5;e^ }w^zAxD귱ϽNވG#PPX^_7ERpH-RKm# /nA%^MeGh[6!P݃rZбvew :U:?QH򢦐+h+^+V]MB0 Hj9E=?xJhd\ WG8!<ڏ$amhD >Bܚt[ݯz-NV.Mֈ,6E!CQ(}y]C@t0FY{Dr~@~dݍnj?L= U!r]J찳~^6rzL&&9_=As=O D軟 EwL TIV }w5> kLxr7H%E5Ds(n3kW[dD"~zBJihHx|w$RZ虰gGD@6#X-K-RKl))8 ˿DՈO>DPe(rDH9ygNHQ<\^ 0~aկ.h"NcЮ{#se˼")}~FPٚB>==m(z]=i(a;)76&c`k/rوAd#~M.QQA@k( %zH6EajDF>@$xW䉿̞.=l 7 UFFq*U~dKjZ XŲlZlvYHT{A9)hDGs}wߴȷN(z.lHPK;gOF S@lJz(L9To+OtR!D~ 6[kW;7Xw{>55Or r8!ތ೨H5 F^,3ZjYJ6NKpb ; f!O&܅Hkw.ʯI<{#P}DYQG";D/v"S1k?*b5a`/O{?- w[Bl"b0A)'l Ոt$D $ k@D#:Ү9:r7@OD(rm!ĵoV$ECv"fԃ+sQ @+DڡɆ<'WK/]>gjut`=``աN8R?^BD ,v:"Uh{u Z!t:U I5a(l"]HlD'D)\: W9>@b)"o"=OƠ4D!'`DvGᛃ çWѼx=ںRn5 #![Q.Zjz]ID@DϿ]Q^|RK-?9q>7F3sWo{F98T"0} ];]Ȩ@,BhD$>;¦Gː%JTnB.@Dd$mvD@j9S-iJ&ܭ )U#5[ EJHWDW "9!"u>" HK_رг]>vG❔ϲYؼ<"GKÃs1UmVRV1]W\XnAjFgRZ6| 4hw{*[HZe'sr J0v-. BBYSxA4yUSLU0|S^ȓ|FH1ATR Cy+=7.݅F2FX_Yg Br e  !xLv}m޻H>U:%76+:4GY4/n@k}& ݹ(qRE.@kkS h~ qZS|Xq>R #_\푢2$b+zp:R@d&Y{Y.Dsd\с֎s)Zo+]gr`߾egFcSFJۈQ#B{O3 +VacGf b+)+:ͭ^X\Xv}_'RKZJ6"+"!UI }a}=/#`"Oz*|<PS$F--BT P}5^Vdv Vh8 ,C6{CߝiCs# )D׼%98S5 y̓]r6"W#VߎxA PJ+囋HzpQNCGrG/2l~'+0,%0P-~ӈ B y[ @?}z!" 09}?i}<uX]w'QriZ{A\gxAC} ǭ_{*IivR?bkiޝl͓[QhQݗ摈N(lyAtc#2>Lxw+ *"S{ cwNRd$%@iG܊ دWd*%V?@`ͷzvHunr4qI)P^Pc{ RwXFDvC<#&#r="Y۶6BWD_CkJ;#z&% }wq_oQחF@{b.zo+GМ~WFjڏmѾ>͌A^.oH\"CEK}?1F"к"tDO.d# =3 Y9(>>J" q<}o<9֧֙PyXky[U݂ ԼWzA4m,[yAt]v? }@}3"}٣ȫEvfl^hFȫpngͭOy#yQ`ήU;d l<ؘ4]BD;ϊ\eV/DؽC:ٞ79g,@s;IUl +Ze9"Oy"Yt2%4n\)1%5(u:sQT5Ch4גrcH8X;vXl#89Zơ:ԙ3?.EZqai\RVt+zv[.)+s+%Dچ`))%_<ݑGuX_"FSQX&H=Xs{ ( #OwG%>? RD(Ll!{!@]kHᙍ$cC}2R6ErDbcLYMQhuHy`׸ΉkDzY_Y?0 mxy'h=E֮E;t)ʃz=ݳ #Ug1Vdk(h*;ZQ[?_oo9Qv?"`}"SwR_TyMV3 r OB,#Fm9ٚQw'oxA-(5?iϤ#QuoK79gBTJ}}&0A\|^m ZJ6@s Dd eF,f9j/=S"9IQzmni S. jରㆦ!u(ZIYQTµ KWfZjCRP,݉ =!%nIj +G۔LCQIT/ǀ " EfvBy)!=ʮ.\Tw _CLtC@-)`/*^TADI~%"oIl#"lރ8D(4mD;wk]k}l;DޒpP- ݻv΃['vnhVL{ge%vOE@ ͓߮Vn|Rv}wE^=Q;DY*K]P6m[׺"rZŶ&N|^|?hVq'w+fVP2?9}򜞀e*n9k rZg~oJnH5v8ݏw0}Z c 4m[mS UH%:9h !wӽ J~\!T>Ddy7mwg{28_c\R\?,j4yA[M+owͬ%^0"#W56ƮEkcwh R&#Gވ .h[g%Rk(4rϧԿ5˨=FVK(<зh10f]RSRVԟ5{!|5"Ϧ(RP-%EYHG|3y W"T|QbxORAWړ濏B %4,]юރHt@-UCazYZ{&9NE9#+x`{"uj>"W#vFyAl. 'lm$b"^5W(jk#<۪^ȔWbuB}ԎΈ"E``!`"/k3<ƻQPwzATɞC1@5к5(?~/~ eȲ>@4o:#q+47~v|my6"{!T'Z}O _Ö"/r_y"њkB4!} "Z/'*?*gWf5ۢy4ۣ7 x>ݕ^mP:kB Y@quR> }EUyAt" ]SQ:!դ?RJ>zAt#ʇn} %ַۭ}r!R_G$1 }mT"Έ,law"oIomg}No,DD;"b͉g-."}P.K23! 761^ylesгijf#BT-|m{UP 6d~ؿѺMJCk/ 1|9z?(wovu%L >N̊-NYp"Ѻyتafcj{[y'إ'r"d#Y"-CJBshȑ5t]2ZjAYZhað'Pп~,EJB *B P+D_V= ?ƎO ZTH:1փ̆g# # !9k%Su82E! Rv<!3PsLs@syW$R2B Wa 1 ԇd6126syLEeAk<{u>Au *X>&I>lyheco쭲~=Ո@Jnz| u: JBk[1V Bt>!%d4RŎG\SyAtptX1 !p9(mpwymhmBD=R[Zt }D"5r VgYOix߈5 [C跴WYm^_V\X~|hVRV܎?}$/.,~/s8Ŏ uyq'vg#8[}q~q>??J`?LDzB!P "Dׇ}nӐGc|@y[DV!#`ygpe胀WvzyA9sju!VճA]|*l$ "u_ {."Z-r~C w~;8$L\#Rq?׹>T%.1TKՃM-o _@]H1lDFaː:x17)2عFxMm|Ơ Q戤\`w :mF_BuV6VE3ڴFюOWbQ+/Z;{p GMa(C6{ݷ,?"X;ə4َ;8#aEKWΨeU[\co] C-%`O/& wXsB~5ȄZ9zv XXRVrli6M-5qf]{48ri+tg8?3|GQobdq|ou?9q:6}B߭T=T6-@E4LCоa8²;[)6"= ǰwv%.׮GN#2{lcQ8ՃFsP)CZ[_DmRXV!p~{ d#%=SȨR_)i|Y7X?rkuADf7Kd jhpa[I6aIz =5@8g9V<p͒e˾ȫnإkmڸ-R/Capo BozADR&VT=Dr*Q^^}ڇv30׊44(q[;d@$q #W6/w/ޣuyENR :v׺ )wEW\]bfUYKɭ~<jo~Yu^o 4(.,]D9-XaD~y֭:/١$D}vkF9G.;LdO78q{o[=8.t,@89qq;szZ64s6q5(Kǹa_-qsRrӏS3Jq:qwH|Xq RRسԿPۛU"@7)*QHUx H%fNgQ9ј)H9ZX4Nsl02vD}kT+ \B݊L!C'EA+" gH^ffvg^-{5PeJenƒL1ZkkFǢ>XH"RB߽zkcZWvHh6wYhhճ{D[#wjY5;Q6y.e<~*,^#^_nj&tp?Ď4W85VQwVtr&dgkɮnӰHH=_!u Zoy =@cJʊ/.,[RV r*d4/8ڗ5/umC]w1rZ\X:~}w:~M8ri9߲#0qV!hĎ$jV⬮5gqd!wWeWq\כֿ3y0U5Ԩ"c8bqEгT 6KB P ^ȳ1.L~"D,>@&IC (7(}w-k JtBwKM"TmmUź1<  H^^݇};܅F;yK!@!`jReGI>w@ $`U U{i3_-[(V.z{Jr}#;;l^-}tgyAͮ|PN!pUFz麕ʧfI6u[׻'۴){XgiC]jVɽw_FDn/k/rQ1(m'omuE9Io" " r n/Nh;+핓&)8 kf)\(4n]Qmc-s(^DWb)z˹ Q 3l.(n̈́n R;'L? }wDm,t$/-}3GP*jW0y"q ۗsVYz`ֿکm`f+)+FSs7DP K+Jʊ\+qrŷ& ).,]mmURV Q^5~Sx85p›tݶQK˧_Qzߟ[d]kZ Yq#<^q1{J~>y;5aRwPhq֢B wZ)ފkÎ$"ˆq,>DmԉrS[h% -ݑ0  :-JD*D_ԃ. rFx=myQеddR(D_\BD\POD^a(Wf"0/{]?"IHU#QN2b U*`TV>_MȐ }w[j)O4mߩ:oU1)H[}l\&; }[v^@agH>H{|d" =BBl5YNe.N\]]T-\gSon{篠0~<?@16>}C=r{D9T\ܭ!Jl Gޑ }F<{~a7CHj-1D[F$yA8 - )3sXSlhT\Xkm3{cY) KSu3Zqz9|? QjJцi{8;pD/|2 <СOQZhOA@;ڷeJ,"*{}]K/^kBDE9䕘f~IEn2!sZXێCxm bT -\kwv-Fe >HGޖV(G;6(i$^ji箣] >ҷO}S#*H0G/ 9U s&"eo{AJ@EvDr&>Q:J6ފcHkv/qNCb QTՈFU F$Q)ͽ+~$(44HkϕοZߒ91yBަ=i=9 }yޏkG_y8o846ZRVEZ⍼#%D\+.,]G=jZzNoZRV4jCڀѡEsyqp{)8^ϯ2ڟRRV[,gj8Ǡ@4 =V+>?O ځLDd9.D|FLSr?F Q% Hʃ"uӑԼyS iox%fm Tو~rVG@7h$vW-{Ǯ9x)t  &պJ+^[ܘׄ1iͭp6E&^uD9G}IRD~җL>BL[)$X7B38 &Qh^"38h~u }wwDQ)l.֋xY }# /61E]hΌ{]s nmD^snc/{-k8vo!cbߤx^ 9f N9<#4hX Hm#q%eEg,M*ZIYQC\pZmJ-6KIok'!a%(M""+ʩy yo@\V!!$>Qji&^T}#TPNs4#oy io~WRjc/ lSΰȳP|DJӭ] ߫3߮BTKD*J˧kV_2v!\Da_2D*vԖCQ566W!#KFdǍB`GD[߇Ըg}E R F1{v@D'ۘ_ D)=k>}/>B^"/DdyAt dHs7qDog5磐l/ }w,:M>ζum_FR, rZjZ+< T._`TލJ-6KIok!`7AHMn9,MQL ME cC)P~QHu3U3qM2e&{r+vCP8ܮ(tkua喿Q) of7CR䑜mNA4BDp -t̻ e, BNE$eS܉iX A$^'ENA׊CaIC߭ܬ PDkw Gy>M1@u l8qHKC}ƿ_'^fԑ=y*Th J:QK2Fss "%vLGD")U^t}3")Wo bD9Ѿnl/4Z~MjhsW\X9~s yЕŅг0!g~{RK-`)) JLS[-As<(aE`3/Y B>@jJQ ٬ .}wzl` j-F4FJDؚ[\yXy+&V%(_/RL.fV!"Ց>4!"o^d@u."@!e8(|mWgXs(W ڗ/ֶkBm [D!z=홓 ~vdc `H*6QH:Gdg\LA_k磡8 Fؿ|2 R"q}(D{A3_4vshM\.:Ϯ{wRrz"8"¹ݗ9 ElGD񜂈T.@_XuVlVRV9>,.,nϏ4 +.,]%eE[!~ k(u>vZjwKI`P3 EJHkDN&yA%oY`BhT.(!A@ 2JFWPPDgO"}=Ř73vR@"09T; f( -Zk`N-?@}(U!2]@,yA)XOZшC= sm@5H>6KAsgkߵ\dkb0D`qy҂SK/4o^Τ*)*7me#W%!eIL dIQ&#YRRT_lݮKs7y]}}wL*zYRv܎ )(qJOqu}eDnNF&憖Z8%eU|UHjW۵;!VSmb~^DZ:̮{?SrYmÑN(0tPW[bwo^,x$˪֮(؞vũF$n! "E@(h?JY f bVnOy؞u 2.A)DDž;pw=k!!)dDp/F5n1r #u&)@s t"rENS8$k&jq74?E(k\~P ,FVz|`@1 R` Db& iR?d&5't&1"ىYRToӢUK:΀CRiHIQjw_gK8OoqYrRc)ss҅yU˛QwgwxS YnALFؓSpYsf$2 /@݌6D@Zw,R!@;q^=|ι2gBX WXADC4sE|d)A]>#Zhnhp$tk_E^ Á ~muQ\/̋y'"+,Dx!e~;\amHs٪HݑB Yw'; Sj!A `W/Ɔ; AzAs.Ucd;~7]q"#g磹O] O :Y~v@$j"\Ri})CsEkb"Wٻ|L*RQVR,.0n9v&VV8uGJv;L ઒BΤ@V|duK. K*{__-I,s[uAYK˪I~ڸՌEL @8 ջYW:&7hDtm-F4D~H{8mDc0R=qBH1r?q9oq|=/=l]^qOq'a~qkȮм0q%2+ڼYjH'9Yw=ܟ&!WeP%e`eaȯ[Bɫ9r jChA dhvbd]ZQw vRU-mS~k[4"Vu1E75^5DVa(}QL7^ }wg("t#p q]QDf!+W"H#{"rW! h!q&05n{$ik۹(Ka"}7xA Cd#(iDg[ 9^x̱" YSt74g }wDY_܀6͐DvoF1d{],<';]=MIo;NBs^`A軗y* 3М@6~vMD 'a#/ZށɓQFΚ`"?[۽kSR^sU}x#p#r'(=Hth@` ¤-fYU j3P;Y0zATt+?@PVYd#m;9)w@d䝜!@(nճѡͭBa3V2D zx#Bl e ڸw><#bX_zZ_~,vX='D_[~o ͧCM#HG읶1Tg銈#b ֬́-:h&͖B~ "{j4c?4oힽkI ߺ}wʙO+e3)r!SXb+$EeK++-֫.MgR]Pִ3әTcp8rO:ito+)*UL!B]g?'/XoJcDHC^`oڏMgRyvP33I%YCG1YJWIª.Z'J׸qIQyΤ2h-xܦmf8u/>lQ+m}atWʉh<NDkqq W8jqqSyYq8Y}hY^ szq)ֽR!E(^5v[5 /*nkzﮐ'D"`}-Hy2{1  hs˹B YSIE6ڡ5"q"MxdZngigD"ϙ' qZdc]Od}iO'K8.A`%ovBwow{ŮBIvDD+"~CQ~Ѯ/ H <ݑKlȗ n].Z[/xA |=ӮrBf|{ 4J\B߽ ;wxAteI֦-힟}Nkgc1ۮ|pd͋6^&h=nnL½ `߄ иޅ4>"x }wDڳ ݀R>";XtG uh;,lVJM%ǵnEVYmA{[#󙟹'e3cP߾|"ť/"eP$ZRhL7lt&YK[!R}I:lCt&ah||}y lQh4DZ2HgR>Jސ0H lΤ^]57] IDATLYk~݆ ߦxCd]rj BFa<4V|2:;U0D4 ^ }wDcQ-<+O}}#*"q=P`ua>T!>kmoaneo!? >K{YBZػV"P: GiC.[)tu]hm y-E߮{Y&!B 6." m~;\(/^u4.B֯lk!֗g#W}سk"M4d]1D.Du{R<1rOYN$r-v#>oK+h@?ٸ΃}_!L+Ywǘje[!ޑkfh HZərܫF6yy<hr]0Lb-%L%88u6yX8NA+Sxd{iԉHxq-rkQO}&$,Pdz8αqh vkq:q89_Cr; h怨5<4)"% K~e~[vFDwx&Ӌ^ͷ͍PD:sC`t,&!4auCV.E$ta=@;@\4DޞB>Kibڜd# 4 n{sNfK}[!w_vCdd 2VhIb@:jyAzk#g o"[ ~o!8ڈ@x&6>"j3ծom|z'2uw{Uli#w]wD>L@ȳC8l\ T/]醈Xdv{Yߒ qL"WY?/ 5: y.;K<:;!oJL f!(ZxI$V --B貞܉,Iq8n (+#G3'w~)Hyh9Yjw q/t4Q>7ʲϭmډH+/!y,A̩# qƉ\A%^p\eK0[} Vt{xqi; x/_eli1_Y& 8+^+so?fKٿFMA-N܊Ys?ޞ՚,H~i+eZsHmeN/[ֳ]snO4&A7с"R;isIh`t5-}_0 G@|$DW:~,tڜ3K4α߾ב wBn2"Dr"/F{7"yd^,}[MYm()GE֢'}YCMr񂨻=`{${h ;p g:V:-fkD }?^F$!CDf!R :D;*!JTq(N+)kмmc<}@4S-gB K_]/}=;FtkwkD #Y.SO6K|r!WX.H5_)`K/ }wM܍-<(F1ey“K+Z^D([QVoqio tK_A _C <\RTņ~`IQLNN@{AH"3K#CZ},YTkw.ͫcIj{%mXeZ^$h0jw$qp&e1DY:Eq﷒Eѕ]+ZV9-Q]vD \a\JyX#Y!E$ᚅh*b?, /ը؟M6Ab]YHIl2O @y#@PH3t?۳^Fww+ z Ͻ >Bߝ<;hQ/Dks3Z :'258;{AtNosН~y oYaMN'u~Z rwg6AĪy#8 zl:1"=A$D w)Fr+)'0ޒQI6ظvl D]_8'r7[Œv;#D r =_1' N }wDril/J ̳#냀ˑh. G=X` # ]܎Fne(ƪڐt}hOyAT`;Y[hM:z軯yAz5B~ .YW6BťU Zd㺺VAsH(+ \Z\Z9b<PQVx"+YCpɒpop))*Τ>@pkW\LAxW?}rxY^;j%I]Τ+)*_eگBV|D#9%Ek,F~Cxken*z⭿nX Aw+M}8ӧ!-(6f)t}wyA">\TOTin6LC } a.@t v,V<(@AVK.\nCm״{#; Y 6EM[ dY&ͧ!д}ybDTЦ(2qCn?" WiO64 r2D>n@V2V//NAV9YܰX:7ֹZA:|{lAennzМ:@:YBD^2b[H\ѻqhw>G}]#r#=~t[pr 4кq$S­ } 8]o} r᪋ WagyTb݊zҴ%.> `>G)]3EZ8 ԖvU\FLD~!:vhD軡^Ǟ˥mf~f]#rch4yD}1`QK: Fv}ZMLM ?z{{2|{tIF&麗4DƥO׷"1= YBK|5,/ 8OIYS"w̄,n{F8ԉ?e#'"cgyJ5jdw'Y YʾCX?vgΣOAs4{l><,4[#x*_۟A,/nAs iOB#9rӫMmrDhm B\h? UN{N""\p Jo`ЪEvȊtſQV+ťK* {1IT^+4-Vg),u]&cd_?MgR{J(I˿=Z?/0=Hhf`L^'pp/tg9)ђ- l]Pꄼq(A z(6tУHym|rx,!>YCGE~'?BoB GHHMo AJd+FnX?b], }w5FM[o?8/F ':^ }w$=hS=gMv`I-^5dixayM-Յ0uf;OU ;8(ЀqwDLqgBwYp|~ww."U  "{# mWbRGgZ&c<+NͳkADMt0#~na}QOC>sܙjsfs\gm< m|mdj*96CBfR^@Ggyr-",rM|3瀋-^>ϒM0*DFGP|=TqԟsG 6H4Ɉ\lߏD 9zDspJ~D֖:/菉jnFK572Ye44OwJZh^X퀬[/ZEZfL/.B3ضv ^.Dd}j(FLpKP:͑io D!Br(r!M%[d2#9bre1Zgg'+g-?GZ"^hm8 ;O-y@.?qm &_\ٷmYc٦IS-kb㊲5.9þhmu^(.tc݋H&Ȫ½K+W0RRT>3I-Ds CG$;ۣνKʟMgRG帿AgL?XRT!7$h+^I٤,OWFjFRC~(t}w*lR1J{0Q?́$كJf}w`$ٺ;"C X셈R܌\vE֜˯}\ Ѷ;-֏akϹ=W]P֧Iw! ;(\1~ȕyAtp %X8Y3@\Ժ CZYCj!h軏asE4]Y[`%@ǡo1L\X,ʫ@]UzA46.IdMٌ K+60QBV"#L<Չ iV }/9pͥ_%hC`+D `4YBV+"7#hp߅$#OD[2C},!0pFh=kBB jlWUu2톔 v?d8?w]%TوVE͎y}ZoV!k`#~ٴ._ѲS>mx%Ym ͙hGkxd}}DK+* G{GhU:.oXŒΤ:@|~~4"ݓ*u"3 ΒE \IgRy TP괋~5QN%?Ԑ5dG r[l`,5!yO   {]d/>Go >3_Y-\0f"rChE(S{V4$]n:"RdB qyY^tAV!d혉@ %H"U/@?Gy!,O,CSΥ(jK"m :yAtrs<SU|  7Fd}^ii=kK?YKCߝbtSs\: ûo>g&f<n1? _Ip)re\HO}i MP„6hnGVY}PL_\otr9 ,8eQh4Z{A(ق[ I!@9ʞD <`+/Cc\ Y"% QJ`1hB s|eui\4yfbY k|uZ>o4]Ceh%Ϯ(+u'* 7ƛ ^}>(+lt? + E YťE*p<"Ij%"ȱ`4{>N()*Τ^Dʯp7ޚΤ"y%=eh3eh:uWn;u >W#@{/*Q#?q;a5%%s)w:.Sq6'zs8n.m*ތ㸫8q|YjHiZOEi+7,~7E͑w!KD!NRXFEQOCq+C!Dxp6ˇ lq^C`i."㐕yfma`L'[f=doATݽ bjʞh^ U#"srmj x mpנwǛBiCPs`=@=Iqսy͋m,EہCl}u8mLe,q#:.3X܏(4D|K1L!l>ilCqÐ^7^ecw*?W R$"]:<-Cd"Nè֡a X:n 2k%>)j#V#7O-4/#C}%hA3[00YǏzAt|tp{A^= ! dcʓQHk4/ǃ>y}On.M[obA(@6Bܿ=-#UP,$VLݩ%E+U h7[ JX!vP%EoVt&51ȚYE{d,*q3TjΜiM_(\Rqvsۻ6] K|1o1Cw8V) 8ɞq0:x82HWjd }wY6,Ae:i؋^qDB.HW!0pvMKGd1y1yh"kEH(\n48U~[BM*31B,Bk$ kPF܀iayˈAV!c]X#P;/gL- o I%4\[RT-F ^ V'J_OgR %yC:(HgR͑VxW;[tNLd\\8it۶}Xx_Y#^ǩPwKlVpYoq6]WIcLaPr:R4Ap8o.E5zq#4DgF3tn_kv킔cs~ڿ kHx;Rzq{TkT> 5h&P @"R #H\6G`v棅s7] \Xg jW}d jn9xA4KC߽ /%`DnBvBק4wח% w MȢ7rKZ;C,d{C5/ -AI%n +PCξ/(%=Bߝ3 0[7zAQ軹=D<.}77m_k@KwxJDs {lڬy*z {' 𧵏' P'!ϵ,6tBS߷Ѿ5}D8wG>J>q~S=Кrh h~B߭􂨩DIB\jibd"i@kn1"(֑T %F[*hmx( |~V֡ 쁬lkťVނ&esť K+s+/w(+8'oם2;9-έ(+ܪGޣuC߭w'%E0ŅX2oҙYh>?%buZ3gj.=ExZǟ3osbGȊ4;!ltn%xA^hv]ޭF6Z96] r9tϏf틺s+?ED{eR}㼅>sOǹQqvq-8 qjs88-g8q:@_qc1qٟ*%} )x% Cmp5g{At)܉04c@4\%`y|(f0{]{4nFD6lV)z7C&!?bQ${|D=߲{a'־^bepDĦ,C|;XF@zmNw7nco67o{A 0{wMgqzSpDȒ.љ9VA@F֩DF 9bEyЮCP<EMY݃,IȂ3 SC@H6m u"630B4{X4yqsM| WOCl:[B@s;; ղmEb8ͷ6h-2#YRH)c/{)"r/O^5B`ig"H) I;ݮX!2aY0emѺ~h f* \]* .;˩8u-P 2-@^NVRT>WәTmrY7Kʏ[uy9ڱ3ICҙԽ\H@~7N+)*^x)0f gMh\W:e+.,p\=麒8u/#f f]qZ{[l LL8m?qĭ !#Vr޼qgQU\R1݈\VU\ @B4o"Fp +ln4Z"L< d{ws޴w8""3}Ѷ^.`'OB #Dgٻ| y,r E(R)mH /o]$-;Ǒ(!y}> EG`["/{Y{~ Uy/.{/Y HkS!= IŲL1.APi7Asi3d"[ A=Ssc٦̽iDTAAZDhl|[X_,@3{,KY2ys/Yg^<)NAD/$ rD_=$(9  ROGwg5jme =2 MP_7FDuB`C_~ͺHqiK}c+ Fz d](+:r=eVۼ[~9p;&Nj:Y"Eܖh 1&ަ ]5ǕQ bY岐*pI8g֜+g*9KKǩ$=u*rq8‡rq|YEV8\J )юܵ F0ف3 ]7;ksN& ),/DD^5:@ۡnmjDwŹ^%YBdYY)׃(Avwp̵@=Kq:"=~{BZ.y# ڐ Q"H2 7~F; w FYvͬ_@EQRg."OqXKs60>Z1m[%nݾ.s_o jjtSf]X 7EqB̖Y_E>7#r҂-@cӜf;1*{ GuPZ V/d+LgRИFH;ߥI𻒗3|@ |>`1Cy86蜟FNqaq\ڗO:s;G+MFc5)sd8q}Y;o+*ާSǃ @SH )r 9 UɥH"MiBٱP4.-2Rs r>\Aq7"kb=(@M ^=@WvL6 ٿ3C hkދX>ܣ^kV'잍= j`/C-zk_R}F軻{Jkl rGC6Nh:!^MhqK4 Y^U}N6ʮ]ޯ2_DxnDZacPw"w}ɺBau ĈOg!똃69d|hc3Ylzsٹh޽=3Avo# xۢ9DMC]<\ZmlLDA>Jp"9 +DX~4;=! QbmAֻ8bDiXJw r!] QokùHQ0x2'hNδw.k%V{(N}l iP!Zs}Y xMi77}VC|t`CD{ݺo)L0|u_К_:qh֮y,=9홛!XIQyν)H΍=04;^$I%3K u~BqekӵV]5XyRuLκ8^p+H)<ۣD 8$= F8gIINj9Ґ/@'Zƺ)JiDlR47AjHц~@^]p)д}F֠DuA)_N5}"Jp>86|pr%Fq@x[!wQ,P ,27Ei\_U@#WUhc<-(60\+^ B>o DZ)hAf{At#rzϾ >ڔ\ѹ8=i3֤xDxNCZ[F2.~~;,'£HA3fU(C@lݯ 8Vs+1X^D3l4ZUnȿ"/xAͭ dD$(}K8}1&AS>Ks4KHY:xA-F8?8T80{Q(G=w""㬝αwj>dӜj|%ǽ *Bdlv軋un}zoZ$/n' -)F>k8 寧6).guNEnќ^UREz=4?j -i[o6&eM7iG7fЦpDaput&J׋{%ϸY@OA%EITx#YzhnF֕-~ΤFef}R6BkzI"cbj_߉Ť">?bFqҽ/JdLd|?! q߅΍7̰2YYRH[]?2+W#7k8hU3H )2>_p}^r܍Ȧn,y: F !Ҟ-Cf/JhQ](\fBV.j޾#F{r"w#'[I:A@|/t`c#1~{/"IA TyJ]dv%0{eqt`m\k9m\ںgyD&GVXB{"2w1KD8Vz",O^PuB }wJ"BWyu`eKH`Y*&|^6 @"] *{& "D eC!b E YF3ص uj׶ }^k[G/@k8ZgW!keȒ1wky<p'l݈,2;f}6U>R^]jJ!☏shmW,Qi}]'EaR^h:,^i1/N߳ögStĸh}>NJ.*t&5OZS3kQ:*&["Պe nhoO8b,iPwVt&-_h|yZ4@iKDxZd7;T+[.bzB:ki73$.e{dcϯq[xkeFdqz[[1k hB9Z]Ms[p|th iZ"?\1]w<h(1!F@ttA~{ޓK=Cgsd b/#{(c cl]v5b6V 04"HK({g1"1' c>n#oC%Cdm5C  !d-u`!vE(•ɛ1r;>TV-% }5OR@H +b{fwsEs|gC~v9o³9#yATl)^Fs31p?^bp7"W Fs%nBDDHs7Y>B`и?g}6R4!ki~mt }wD{ 7ѝ >]9l5'u=@6uJZsu=B4ooB샀3u:ba\YN-Aї΁mb'2Lx0]o.cK{AB={})퇈˷ȝLYkNG{f3FNGTCuy3J̞պ&n*pA^KиuG D{Pt.@ >lg䬒ץZPn3t\&h/'R6 A K'n5R#5>Rb.4":  /xH%Lh,sBKmDQK٬:# ҜO@ZV|pOHޥh7nI2X!4矣}v[# <띉^ Aa=+ǐ{wh,#~W$h+ IƾȊt="*#ul OjI/D.Cdgv@/((BDsruhYQwa軷?"rלl H" g!@r?Bߝ̊D?1I7UxAtS+sFANߝ ̵XVw$ qHYJF/?gyGuwzAOBkO ͓^h۠dke>Z 7^ @1~^լh ׫֠/W% mo؝2UwMK~ס=XF:>K؂!Wk*,`[g n@]^^xn(iEIQ"[:zŏq`0p图xP>M1Ne2+2TU-X֠:\p#.d2H_7hs_g<ZkYm,#; tTǑ'tP9Кϑ=rvC5QT4O1/+x쇤{nD׻ ~c^p3F#t_k5b/U.dsh{d"N!ܱNi}SUqc˟i**)hBDkWmʛm⬄[MDaF79#>Kׯ8xmo_̅>8m^ߴ]_|~ݮZsY{|@R`$E%10Nj4^//h_QO("aiQIA?`Xa~qXTRago̜JcFkE%A6ʼnKGIpo (:uy6+^pzyzzfMkG!K\ m-I WTRFa~稗z) C]k!UaC˳܏dž#{] |zA:$@HdEMdEwLa%Z?[9r)9 y`"KזXޓ-,,ĩzm~@ƓIGF]/@/EV/Xf pyR^A};NS6 z# e?݊>C(Z/vD2\4^}]/ʏǢCP^ҡ:}ڼBZVtFݫ4)]ֽ7 ~}i;;‘ #࢒4pi-*ffWlf(o>]ﻕ;ܓ^?[n-ީBbjESwm;lvݾXeT96 k3J2kGޕIVum+#Rd:gEKK[ca! DE%]1bFQ o/dMiu.#P d+>NyߧO,[xC.-L~ 9^ldО"dB?Ӏ%KˆŏRi#E yb| dGd,MцSڕvȭb 0i6m*M2B `ިƺ>=<-\/ꫨD/F7mQ(cU 2pyo>EXPx(j~qƕ:O X\TR&z(/p^uE6`݅E\8| J rQAcV ^DJEE%g"c`b9A; hO9dy?+1FURm?!#|~ OSy8;gf hi]{.:tƄG9jvϿHS Rԃ?,@~JEކQS%xlbU$ opUPXe{= Gtg,F c;D4IP[!W jXx1ڹq>ZNCO;9}/v/F! QR46 mPxV[jJ~7D&?&=WuhG7)GB @|MOT@me}y놐c;$)Ѣ*/./~k_~O=򚭘duk1?[ZsXUw;iX{W g>;lgMoװ􍢒ޅœJ Ak7h]y۠HCaO PZ_w=}y2~Ԣw7AԻԝW Ƭji i9'h.E{(# `2yQDޅ1znU*q''=3k@I6k{٣;8Wx0|"" %0nw0 Cq(D0 8s pF^~[EUqr˵Ya2x 5-;Żx'բkIQ^Rڄ~@F)6S ̩x Jj/J''OAz!Hy]*NdA m0sGRCy@"{(7Dy6ǡf)SHA.D7H6Nv\Ѱv뙂8Yߔg?zcH#A5G^H{G=SA;du€HYu)xQh݄0{>sqExSqk/&eVS_vG@Ȟ_s m:{BS ,grKPJ]0jy'&KeuQ@$mҼVNZv 8]/Kϭ/n~ii.;Nj~Csd/=@O Vy r)[::T30xpf߾SQ5 &7k4~QI> 1EhED)[jmMۘȰs)ZoӬUG##^#T `<2ru"i[ϵu!Z"]'5@yX5hlG P>RP_mk5 fLD;:npcE]#`* co_8NʸhqH:2 㜀D]0p)H=(; ѽ~е`&#UWg- vC@#R!OP,|ˏǦ>ώ+"L n@Jk~),~.2 - 6Z<Gclq`=6h#r4eֱ6z&~lcPVڠ{q=?Քs[\/X᫸5M(Z;Z 仢? )y!&w3Q3h>cgAƂQH)ʚ(LRrl6J)9t~Ԏ툈ދZ]/Hp~>(ЙtpR'˦y!@Օ-^AJxS鉌}Xr8ΉanE5o-~<zJ oU}:8N߾-Opy4aVEG_>sR8geTޱE1w9fpcN p78f/UEQ"yr+0dP\ӺAR퀀M1-O>ނJ4߯,*)8PRTR0Ž[4G(;/Ea~q?L/\hh F*YDa~h>W/ ޢkܺ~hu܅SG WȞa E,J0 8p-A+PD$t+㼃j wSf" %~Nϻ"l ISJX `B${|]e֔5%Ao^q s`WMMW"ięjj659#Ha !![ ,I+H%DE%M+*><{NYH v(`F(/3**)UY`Ŵy}wH qdhD?B  ~Kee͗O{Y^ÅCCOm+ڠ=.< 뾇ֈavӊJ [qh; .Hy]RTRga~d&E%wkH/QDF#>F^'s6Z&/: h2RM1(trS$א p-fS Pul&*VH)1=uu dV!ZKED`8S,Gz :"C<-hH"uH;? f!Ie Xd\|yԏƺ^p ]xOu0̏\/x1Mq\v$}9Yq#EKԠdt?zA#`N-$ m|C"u?\5s-D<~sxUb'dO7YUrև]/(}WsZ1W\/xIc6s&.^vjFC [T xnc>fmX۫pbDcvF¢gUE Ђ"H9BB͠3=L cuyzyW *y& (&UR= zAkk{ـֹ}лmJХHk Q}tD1ӏ֪YozC)uR3oPxE*F!}7jO!>V۵Ȉi0 XCm.Ff;zU\\Y}Y;nuyhMkߟfqϲNTfI3nbb_ʜʊs,63bV6-;t6hOfО2탋PDYg5mfa~o}4Ц:ZLN(*)<5BFh}ysJ .,/eδ"C({`Ia~T?cE%dytƄGf٣tj~SAyͱXG;Vq2@0 tg0sHaA/wB֕* Ke$%.xTSU,rD֥Nb]W Zy'M8t/԰Gj`CvvV&S5Y8eiYTX{}V}e~i#..}V"F)& }@0$&ufFe@J >C{+(/g(Y ?>70v`QIEhjfە`簚Nd| z@a8rǀf0κ IDAThuESǔΜP]SUyЯ6ǹcMD`BauNPa8.8Mm}پs0Dԃ_ %[v7Exl Nc :-4x"y2橊K <ӎmx dE܂DI.-~ 79Q7Hv x}JdzYa8NuMa3) p6MN|@,(;*{{M(::4YpҲVsS+;]/xʏ. exȏVR>>MuP?f(OJ.Pt4D f'&+ eD틢,:-ڌ^0" ˓X4#K34;яPJwm0 4; B]/耬CM  Y!OB< y#(? f=Rz?B fCR^ z#wKeݽ1 ԨF{IPxX{/_.Em5؉~<6-e BJoUH=PI&*"~:8E*ќX @uMpSyCSP2P8}wkf/ܤh@|']]/+='ܦіHEb,CJ;#ѻ% yBr6Vxlzuȅ^6^\FkR)%vₑiKc^zVp:~WU7첂 jwG!_#"\/4۞YjjF7͛@dWk;3আK'0s=ڝBVGuVJ .Ӂa_2W/1ˁn}׭: b&: KO|k섬/W !)Gou2RϺc ;\/?A_]rdX-@&X) )ٚ~UvCPS;U,(9 m3^> X|`zvJ "r"t:,]2R["xy= fJ+SI)jcg΁* CĉhW#Qq}/Pw]', $mg1E*Քi y5K`dQX}isvʘ(5`us{_U y]R"\:O跕OؕlucO8 IxYInĞZoRe1R-ZѠO G.[n1r.l_U6#;⤳h5[ョ]Z֍T90qUIy[W^uөneиڢ\.2<=K7sNQI(Y_VQI]=}@zz_xnQIkBD&YHMePPSP4'Q!2>Y֙nYZ" ш2<4N~J %z(-}?7R@UBW~k {k+"EI뉬o ZDm0V"vOD㚁зԾ;)=><46󞌼s7)J+=tjoϖG"$,"ha}]jϴ刍s-xbuMV)?+X2 ߞ㱋upYy+W,it֠O~0ݨ߶Ba,_ o>r}I"SXW2Vd~}c"`90Q8 >t-_jmwUVȂ7GaӃ6Dp/ C8g[kzȺ)i<|ꚬWlAHz> ߲C ]/ۖk ~`K}Ki._권f8a"/s6T ԝu@^R:"w6Ayqu2 V\/zPPއ: g_Gd/?;o7|FKFrS6@1l~1}@Wwu@!㦆=Px,bz&R =)'ZeB!>ʉj6FoI05&&)ːUnMf| HUkP|z"WWfq聀Z:FJ\?zdt8 y.FJt~=RXvͅ@CKئUi3a(e",?Wh'֋oդ6-#Lb8 !\բ&ҭD`vWكVlZ\/h'&˭h>RwvWy5s Z䕙O yhC/ ;v08JVpœ(| ,4h}][[{!g%2R~p cI2kB_H^lBd5?Fuv ӯi'V}rʶEz^^RvKHo#NDJ$lTy-tž% i< %XiUSvq9ύw:ڼH*«ÑVWaֆ],h_3y8qX/io9}:N܎D^磰d(*(&DU0^_M9._]P݀c\/|zG^ň%3z)[^p?|U(gPo@(t+=E$Z{mͥMUNZY;D`}+R9 U{3T 03SU! [+[V\Zb5: jnYN xQhg$jf'Ѻ4Y^U4􆵙u3)m?G X!eS7nAc^VHvCsCDտz735A*s[PcH}xN ;5#ANQ6n  ~u"z89h}_M8N k{S0qa |a:~\Z!h(`bԃdY7HYZoUcWT~Q yDuAch>6hn90G~<6W cO&S24ְ_kW/Wqv$c@GZgv]Td#wUaL\[FrZAzm&q̏>б~<[ב; =c?v/*96S¶>Ф%(HX#/$3겴ܬ&9! rAZGh(d;desg\/xyUg Rߵg9)y舛!5)uW")SȪb=Ͽ1W4v%H\,gY3 a)y=Rh?B^U}yak,'bGZ;!pt`J)z )tо?>8$xܔ3]s勁p3Dd? how*q Xc!k chnd "S yV!o vh ?JD:ۧm2*4-JoکR@3H;^0ѼA>7;mm޾W22ĿD!Z[:Sئx)B_q~ DA{~]ݭHNF&bAD`0ڰ3}pgBy^8NN& 5ML`87Gפ<8W} wal8$g0|8+(z BA8h0 oj]8#]P~Uc0?ԃ  ]1esRIcLhOE#&<5@ ԎHɩkboay39)糁Z ދ$ )PSUQ1zHzߍ@Mv0uCq @iƣ@Ljz6,䍨4@nZw~._"QM;=GȢ/A@l߯ n%ܔ{9E ?n8꾡X .%`#@=7gz-7lbXv}C]8ㅮwʝ>a8N0 8 ǹa8Mm1z1 +ig.NH~ XQb|K7FnF"$~ )X2RDxi^ (*"K̞Ha9 o`|0))b{ Y˧8=a!Df!ƨ3:7p)MG`3v-g"r-/9 )rM  < ToIHYn3(>:u#*sE ^0ͷ TM׬5"X흶u(Ķ8-GSl]8WuG\`w0Il@a_% X殇ۑZQ.ٴCϥepXA;g/~C;޻.m,/^K$fz]i[J׭6R Kw0\rK,J0 {oaX8[HyadY)XOAxm^mTy.7 D0?Ms`w vE@-FJW1:(4t ͽOQ88 0Ds%\ /d|)u/Dr?"v/GH?[zv+~F| 򌔢~+־9\>D0L^>C;3>IZ.FƋ;:xtw4^P vc!= p%mw!p7cֹkeer6x5Z[} )/}^eC2y*sHY-}]$PSR 5|kwðq@0 G랋 jh=q~?ԃ$~Ȃ8@^pS>ѽE!buwB1wxlF'NXbhCϓm_ޣn8z\\4dIyWTB"c]/׮W^\/@H#auȳg÷~Eܶx.z\ꂼjzAtc#OXHW_F::c={]? V׬=ױp>_7$YT yKS%L }D[;!=Ih\3y\n\B4^92b4yoaڝtť^uA`DW#{*! aNfl]<CRGHO/CB߽=YLɆ^c|roe3Hk"&+|;қ=i1"'ڼu"HC}>SXD*и\ȋ<4c2@R|긃̽M=P?}q1y #NHw:ӑ`u\<5_Y=d9k 툼G]M6;퐙EI޹8v?p,fXb/K<;1>0= Y?K1!Z4sdD&Y #Rp> 8 QF2-槡vҔ/j0ѝc!I=-u&0=(8#ZRnb5l@}ޓ͑ ;" *^0<: >^K=(P;&|Fu;_ypZ"3 QS_]ޢ,_xA4|=b4.4 1=R5h.*FyZK +dxp{NerC ZU[+DoFp4k6"+ťAZ 0qh)qƲl,,)BJ :&L-YW.ݽ :#8݅^%Fe|m$9ZD!W"`&C߽ y*xEnx!Cr1"6#Pz8 CX՜,CU ,SD Fe!"X wFajAas[@䑘‹pεdb[, Xe{zBw !@٘c3{@w'!P7"wX?nHBY֢p:(d16OB&󿇾;⮴@[+MOgFt$ߞt+ zވ~k㴧'߱wgI#p"@-ƕG y#k/.\K?ӧ@d֌ ,D`asDv^ZXI|Il`UyzΈ,`]0)KL^y ~?i]eYꌚ˽ :1慺Ξ$b-H ٻlU?;=knۭ LJ_81@ MZCV3H'=Y;! 蜨$(D.J2.W  }䩽osfwADhD^FyBݎEgP伏X_`}#\dg=(ț(BN߳+"'!R|鲐7bwO]㞜ZSʮfeg7ܯ /˓֟cduHG@yY#bPn}9ldVȈ h BOGƌa:*󲽊&{HJ2FDЎCĨ=J񅽣}_FAz1yrƘ\Fޘ_H ] ^!K6:P R%.EGh*Ͽ#h.& =u#<݋ ZI:GgyFXۨ&۬CWIe߯mե"ko"#q'Y).-K](FͳOll,*YR'pJ׺ "2 -~C}5{ڶR|.@+Y*"= ۈxgYd PF! ꉀ<{硝Bn "  )z&|>6(l(7Gac퀿oFe#p bﮏ@ږlֱ嘢a֩jmf^CmGP&XC->H~urOD0 W! pyxNG,2;ȳWs30ka gU謙| Sd my'>1FU"bcZauYz V^GgФW}8n<H|4|t~mIk_ '+YڽI#8Qp5\h{At)ѓW)7^D<@ck+QH/L>پy9fg%ғբTyL'ڻGf6қAv_ӑƈ]e2mo˷${X{ַWg!wkU#B&"t,}wyf6X=ZäxJHqJLRFSw[q6 -AyMDB4.D! dA$?뛌編Y+k8,AwK۩BXWg6FzdQA]Ƈ&kyD "vIfE嬬luW5pqy5NFy8Z#+' 'Ch2_A@E$)@)0CBY,"/`gMѢ̌3!(SK.I-!x3yy< 3 a}\BPнhh}e]3e("+["l5fKḪS;i*yW'k5;ԡ 8@kx@.vMcҟM!@!%"(kgWjD3Yגn8 h|hk: L#dy+N' w-Vv> 5Л(v0}uaO))B~ 0`Y_"2dN d Fw=(YhoE-+M&G 0ʞDo[j:|@c سY}H'lluGZ/֠}h,؎ }+O #<Q<tkZe5pe?'"m&V<wPXQBB 7YFdq% "27X:s'8 ;mXWиh|PH>\«^ADSwÐeϵ:? yATt1Sg[u9RK IDATi6]#}hDO %h޻bHEsPxyػIq ,O r#dhBī)ҩdz2k1i{ rDh_=!v' VB%= | ͑($_~돡!k}]oD jnf_7Y꼦_ϔv.?IDkh})B**(mNF g>d$꯵An[Yskժ7]j8ť[!ݾ9|4`QABz@󢂒:8/Qǩ͑~܎L8Nog8/}u1P돵qkqA11B)a(FH YK7zY5I*D=Ih`}k8Ʒ!@9F`5:퀬M[BM9/Ͳ]-ugo;)FM汽=s?t6ٗ "[,UȚ[CMV#2?>#tT˭/5%S-A^]Ѣ:E١N~}J:lm @E螏,ˑ_mucm^CZ.Dq9ҍH@@eH軉`!v xA[Z_=ɡ+ # ynfZd){+.EރعFOpoǬp|䅺9>eΘ( jb=H߯GdFD:wFLG{!ݟou|%yT-AR][t&d{eS[r83HܶSCߝob}x^̫X R:$%h^hLg}4_Y]AƍhWLa aKkQ*"\ǞB F$o>Hgsm}Lf+L~x]ֱV_"O@IRGu,ť~E% ť݀l:SuQ (*(Yo<=jV8tgsGdy@dvF軕^}d|JZbMv/愾;q6  !khK 8  $dj`fgL>{Mˬ@ pLpIHP PֈdMb֠×/ﰰFPh\6驏vMzK&!xflG-]J"l@y&"GYތ`Cw1cDD'l';ũ6^Lͮ׷z^kT)VʷDɷ%Zh1Xj:vk"nm`,4o29~ߓ$D1yLoA|GsxDw~>ht@~ m=_9䘲 H#"iWc>^}@ƎhNiV9~"]$3ig]&u;1 '#RC:C=|OLz^bA) }F~b$33K y5ҳh^d!ҥCJn:qisCf9f{#cڢӐ~_OM|}{OjBE+.-aF!ًz)4u 2 ,C^,4Fv(.-xZ: D,s'WO?ԵzD)# qNFƨC6Nqט8{8s>²q|8\DtCs`8H3 q8y50qBawx8.8?pZ\tBPOGBs$P.) y$&V7 PD?EkBdk<*E3.DbD =mo"2|ҖYNF DcH[_wh=Ӿ[:!!: Xi|amPfwB~( rC}YG&g|G]a|ly=7C+-9 ^{PIgުv{Ǭof%HΥh dˑNވtpwdoCi'3tB$nD/!4~$@>tAV^by k݂u!];ˌuGGW٧ABTmw4y&7h_!<~f.C M3e _!_@М`ϻtV(z)KLkSC4L4`QHVI 7 3Imh?HeNwҐ`z$=Ho }7e4'1uG:,NX"&_60HU95oN_Y_Avhn^qgT;4WZ_XͶ^4Ov$M~j/6 }/ j(?"&qv^ t]a^Ꝑ#ɒv6F4VťcL_^@kʰSv}c1o+VL%24FsShNB[Fc 4%GZ+FBc9$[K c4O.*()wo[oC_toSobyU슄|@:e~-8uq:#qug44s]FǕ@8 I'#)D}]_x3Iq了|_xq^!}IQ;pbP }{݊ ϑO&{Ac ̓q4Dvm')O@f^nt}mKt>}7Ca!  zd?m@"TC8dAHld䜞h=H6&w)7^2kmq؟auC 31y v򔰢9̵}j/0GMLfG /S1GguU N u>кdQO)"Mw&㏓+IHطGƗM]G#OډH/FzBcsf4w3Yc*ͨ?b"B,-.-|fh)As`zQAh_)p,N8$FR5 pCqiߊ JqLT^tx2|*7npS81ផװa,,ɚO M;jA/ rp8=~Sfӑw+>db:?[GZڵ{-4AD{`ήv+"-7#5 vz<`WE'g)/F#hfv#)mOV-(_ڢph= y{:#3BCgr!Ƞε.XRha@S(Ġ!=eJ:AA;x|]| D@tg'Y;>@nZ& ::ݱΐyy9PF т6 @?듫_ B 0>I?au?MC ں]^֖h|~%0#}lϪc(tGD!}=x!SHowg N<׎g3*nc*Z^5$JN47XnDG!?7^GZ@!]B$f""8x\Oy=YA᭑o%s뷏' "gEmKe9 ""5?ncLh^t{"  m<$)G~il<dg4G|vnj28 K1nͬ_5ւi&Ǿ֗kLnB,Tv"0#4ocL6yܑnh U!j.w&W-g gbLZ965$?|D;e,U\86kwǩ͏8Nj_-k߮o`A.'q2!C.Ǿ+ܜtzv~ y 브"]2̴Fnť9H' d<ɻ!4meս!3nԒ5L[YU(pnqyzqv[(w=c.Xh(_yxqqq-zAnpWǟn㤀q8HfCd9OiF@a":GZG:km?Pl; hrI^ /!0@v'>[T TC ld<gkuypb> fSc4gdn5V6!-ݹRng>Y7_^\bzWޯ|T3p] `mXQCl^#A*BR Ե4ΪNy rq08u!$5Bf5>^Hh O8GruӅ-6^2{o"{Ib6$q]-D_/l0o }w}~M;!W s֯ Q߈[D0u "\dbxLI(3g1is,sMU"ikk=C%ː>%iC^r>34 K󬽯ds/a/D$!IV>h,NFd.oB: 0/S=P|õ་HY❘LNw5]洎EKZga:2f(C>>!)x (RF[8l^̵OB!_f:"F" 7aʮ$lJe Qj4n;! ]mt\ϻ =#Y̓=_HqRg1xnu^) L+]&ophpyh-ƌb-&so/s}PSV[ZfdPQY/NgunNՐuʶAs9sxޅPe#L,zY.NkV)nG.;G~߾nty \ ylp?Mqia>_G*粻7̩7|i+sG$tC^8̼ȳȓ]ezs38g9S7!54yU6}qgoCp6P>{h:ӿTߪ%IQR<" }w\bnBĪ{+jk4F`($jOdM;H{l0dKe 9Y[۠:hR\@6zgbٻ~l֦Z| ^[MT6@3<ZnFTz"ғE^=8K[`r;)#/JrHeIFnB"65B o}0!kס:j/v19@&D̆ @ɤ70^iumBKyQH5!ć"oT}$ !Bg4^69LvMNNUYUA4 h1Xi>#p &b46_@^bu*%bs!R1Ep' xD "V7!štHo-(4.Pc&r!D_rG~iYZڏ&!n]wgM\S]ʂ-_Կe$P\QAq?ޥ[W]/ڀG).-֋ JF8(*(Y U^2)0>hc?]SZx.E!5^!I-@|.'{k#y!.2 UYE#X? @ @d55(#wB@9W!$@J> 0-*?uB^7S?"Pݬwef+FV2x,Gb90¿/^FːQg:"{"ɫ3yGuE鋈4O{ W"TbHOR6/:o&4oX4輯<>qIxr/ӷ i<7Dgky5%W큒 @$2t }wy">a8?dN8/{*odcʋK oJş֠2 3O Jn3C˖~Òֿ:?9Q}?E㸂enc4)%^fV䅙$FӖȵ%! nZ~o.D-L ~-=JX>GdjFhHR6-20zA+" h]?ɅMY=YM1D3v( syZkHFUd,{Yޛ &;MfIL1`zPoF1fq>=*`/^ XZ Fg!@ofo@zm] <^o:=O)'ڳ,?' Dr&Mญɣn?K첻+L h|J/jk|y#bꗥ7wW)퐮Nml]!$l1IӝX֛[M!SKPbnw:=]!H3=%_xS^u@c|M HCsgvJ dh'dD""A]4۫]z䜭CМ ;#qeИGh<`tNH_A ;"eIIFdbc"MfI⚋qھ_W4n#" 4OI[Cӑ.sP{G."#L ) yD8I$fEsg=NPoaޥ&w?_s1B;`^BD2ߢhLlX»:#, ).-YcS\ZuǜBPbE%SK +*(`VD_Z,Iя,#a!4E4׮[P! aY_#-۠Ethc!/ u`bF }zX4-xAО܃B5ͪ[P -'[=z#Q8xD`p/3B}ͽ5t,P$M!+f&}ukGxM(B`(҇-x׳?EUzt:ڇ6騟9mӘwW r|軃<[UmCI$a^@")e9>!n[U3(T.|M^~̮s 6B#$ B2s"]WUݾ7;`⹻MGho"}̮@o PH,=35C6!P"/TD&)NCގHGCz񞃈C m28 {];dzh MQr:L#DqҞUjx k |m|+ :} gyJc{0X>B`#v<գ/u!Dw>dRDf!md$EJ4+.s t;@~˩vD x:\x\X=g!d4wC=]&,D=m~iUim򽃌{SC}G5hl_F!|kExu_ԯPh¼ K 7ZW~B;X6?SHNGQ؛^k׀(9 yR&CJ ʾBh@ %¾E`r)H׭}uFWlZ? U&$t{'YޅP Fc_4F"}~;QxZ_B=2CדӗBj=M&:IqIwC|j73B"L?DR#9u+H<}M^@`4oOkW jr6XDGum0}jC4VZ:9@24 e{6HBq" ]@RD@>zDC]j@xPuMAY ҟd"۠e7ec}hQct_NȲ"񪟢w9Y#xP8^U"Y=C(U#z "6'پ ",ƹMw-6֎ 8<e)BPl|/F:NMۊ& q Z>Kk_D_> !,%Wk{VWjHZEE݌ĝBM6X.4"Y m"mzպE&A~gn!EcCzYh}SWh> y Zn5pgN|} M #r%DV׬k;srÉ',DƢFdи6ՎW.\G`vE^D )E%?yUQAť7!Vz?;3yD{ t*FLNխh}1<-<D~=;صYpٳ&#h}X/F"oq^=eO- ^-GI\X@N~<)و`&g'fw2R;򠜇?9KM"4`#yA"ap*?FAh4wR{o}YG"!1i /9Xd1Z!~gCaf.=g [/G^y<8ͭ!$)'PDZV߆=VMQXpf V6HrQ?7Gcww46kLٵkn[}#0kk ^сHl^ھ%2: M͓)= 0Ph;iF|:#j/f:f+Uh c[+9qfR#;$FI3bzQ }? ^@:x#iȻzϬ 6?$..-lXXTPR]\Zw#D 5@̽݋gX"wDۆV{#D#^ 32L u:L}i/ zž6:5o^ k<9Jaѭ(~.ӎMqjH[{D"|}R=DHʑ@eWOW,B(ϱC}yYP_lDmH=~NC۞>~"~t6Ff%&.hڝޔ:R?0yV 0YGħ5n"+h!O_D^<{^!>2d} p!Y`[mD#yy>arv@S P1pn{٫ވ^'\/w7:kiǓ)Ć[D{3Ii vdmʰdgPLO3mT4DG-{c`Y"`tFtu4&{@\o6m^x߅^ V::fû@W.퀈Yv9A&t;"g-_v *L~LVIgPfrx\tH# v Zso낀xlG!g]/>ۮ}IOy@"ojAHr#P,;v#[!BObs^oGƼ:'nCשSE2m~z"EY<\5j7+_,ۮB5f`M%u<a_N\+s[qz3%sҬ,x8Vd8N!6 !Ȩc3Cv8m7{ؾR &9BJbcj~\܋&χx̭(ZVv0do#"yJ½ `j7"b549G V!xsyWIEn c8@V-d-d5"q" ._!dm܎toMޟho&^2Jo+ֆjvMt&N1:x=k&wvD|4}Ikc|Oć^scұ9ʦX#=fr4 h@/"N"=1{[έ]_"bsd`ܞUڲ#Jp+D>sh;(8*4!/Nl/1;V|! RːCdtjy+4/vBD2emMz^#kJd*vVYyBj~~7XmfV,\c}su~"lkNbǢxEL Wmiza֡m> ߐ>;p(FN8 -"^3kNf&I4|㣹%4g8@0 /t':8y En]x7 +Yg!#J4׋ަ IDATaxm}x' Cqas0YOAb( Et"6ɬfJG#PTgoʽ"wVvF+4AȺ8VP\>bއֈLCuSB?8 * YÀaB1Fko?\/Xi]vzoB ffY͞y=6zD.CP]/e!N'"±?ڻUz;M2@?u*"Hw 07|#1YOl#r_>oO7Y,4)DLC$A9:Jo} ]},[]#"BD: Be6h'[jC^]/O]4N2B&qtU_47|iBm PhDd$9 H·JC홻#͇saZh2;~1W7nLfcИ.6\H2tD$FZD69>d= &Fr.שּׂ푮>h?EVH?zD$Ah{hk߾VrdDr"Yb阅U#/ hL1a2[RnyK~dכٮiodUWT/,VZC7 eƔ,| G+q뷒 2y(Mr* 5^撷7ւm+Ӓƹδ%rf s 㼃 mQFax8Ȑm8- q4GsG7> shd`OkP܁a 8p/h_)HyCB? v`dV"[ydҩV3Hr.'_6 ZԱS0Ėe=-GMxBuS';#E^o ') C[]%iq;!sӾ~ \/pD< Do߭<.. 6Aauv}=DA.$e KmFg],D h݂K#ks*Cљqz탈^k?Ѽ 0֠_+<ք+*[zI:~=5]ss*\uģ&S,kkG]KdIPuڻTlYW[U g'S޽N/걭j}[JnkDq #{[F`89akz#0\8΋hkȘ\ֽh>F~dS"pY2UuG YGoOvmR7mkW94R,e18.% Caah|Ȃ ͳ9O#v:B/K 8cD`H@_fvHrt#f4{Ky׮25GhYl<*0sqy@;#def!5&ƮK!}>(p }=f{GGg$P?YW 8"Cǘ it='ǑY(aBtML#$CM6d:^X;7jWoUR`#CH^J2E B\ YG:]ElϾuy9һ:(E1ҧ Z-v蜡ZgP\@KH!W4vC\@sZDV:!K[4gt2|bDk!Pdޞ:511̐{rј[{o䢄]ЙGΚ~"À\mzoby\d,G:Դ " 28"-AD\ǘtf?sm"B6cFcoջmnnk§Cr7R=j?^NOweȨːήEƴH[[Ed*a} !>]ƪNiA&ntk{<09+Թ*7V y^nX{NDzd B7+ELO/3f}6+“;SJ_ӊ8= y!?s7%y-"0x :xOg< g^P~"~*<]B z B#Ddh_Ol0Z{#0m¯'X뼡gS\m2/FO5K(񺌯ZT4T Dސ 3‰21@{t@H^Sub7 py=Q#P,ڳvFBhOIytu|d 2{?퐇d8'3 x}&"W ry֠>_ 6ᡧ"7hmmvpͬKrxe31L.ɷL+Q #}emdu]@Q9Hg"}4nvEH &6Bu)݆h\ |'oahaif`mX@k5 577GDf0NF:ɶv>_v4!Xkp"2.vug"xgg^=ϭ!"i\Yx^p"fo|`UYwGzHB+I떱epF >FB GNd|/(cdt༏yW_w :jq/&<4"{knEdkGDl!=ެ]d=]ys h>Yb77NA,J[G(Ϣ rEy+'{]5 V-Hbl_hwTkNr jXtD͂>pEB-CAz5o[6[Vay'3̤% K `e?\!۲iC?>yX:"@Kǩ5]YFd#Bl}'[m 1Z V 2=JvPGKPZ<."5 9+BR4Xޜ{'pmP=V(4xZ:PYDƠ.WltuB%`$E Ђ_ %8.: 5!@-`r`afDt{N:&ߊ|vD (ľF@Q)vm! zOėZ=XZ"2})"A" t&"<͑'k5"ܟ)\jr{D Nm@~!c~"G6v7yFP׮FT^k#^kKլ]21 %QЗ\orϫ̶zV!2ȪHa=`]/roc&"n!/?'íע"3Dvhiv@< Y(ZUs!2}' g@kV z$+"U>O:e2"TIwdi}ףPzR~൱Xho )\hEFX?bm6nibz@6=e^p024E^sјtXW4AVIEsTD3lϢd.Uh_ր[AQzNc2~ȨPț\1/gD.K2֣hmLh_uҗ0#{ޖ;:mvjTwz.ί<-*ڬyvݍ׭MxYԪфQW7fSA Wa TKQK;e, v"BU(\n>RXnDDlyȋ1-ۣ05spAtC ),<  aiVO:o#J{L9aDD`?]֕21PzQ[x0M:Zǘ%AdD2F{>A`e +M{D |= xݗ쏷Ϛ"3Yb!b "DӵFr!2SS]4UZ{Bi WA|tʡZ=J[;#.{V캦6iQXl@ t'l'!B@!4vΨJOw4jXݗ "d>y6~&`/8a_ +[#cFҭh}BLl"c 2dTGcd.}K ɱ&ҩ4&Wg)_gU p1܌=t8{ ~2"_G;۳ZitE %H^vruO#T5'lW-DȢsھ6s+4o.^<&Ao\z(Ko+DnCRBc$DNC㰛4Q=LV-F>_gU"O֭~"2J +Tlw {7rѼvdEeNʍ* }R..hN 홝3VGOy'Wyxū?3de!Y \/>ԌRV^v} w;mV0qXp!0 lc`Y_0/[[錧%vh& uH!E)߅[YꇓfQ'%9Z,]/e|z0zד s!Qz="I{Ӈz?#~%@@茱h}GrϭCUUN5-ZM{Lź"{WQad* Fm8TkbnαhdqhҫYoV˟^ס9`.{Vt8ub)v뷕͋#Ke81; #EʯSTGRg{YE7 ~EWu (Z"4<#/9٦=;^'dm{BО` (/Y[4qvMZ#Ђy.!)8g諅jd== =H>"OL#D5G!yWSn>wGzMBI[ďp](n8Y:unLhVள_@DDr3M.N6F~;k3,!9~" s-@y " 1u`G?j;IҷɮRRO$т|1O}M־Usk-t69"}}#G"җ2]Hl! Ovj@zUɚV׭n}po\C|]xO;Zl29j Y<$*A$lɲ"+=W"K AʈC4vݗvqhN|K+Nݑ~G!ѹѹJnvqCOv4BlD\ z"/{3 a kcɪ"kXnD)4.Dsw' o#MqYLCQִ =GW!Bį1euAi'`mCpAf3"0s7A`r=dQHdG 4DZOB(п8#ž(}w/ Yq`7kc6Vpg[gjnw=eD Eoүy[a40t$RD틀w\VaDg+<x{p?,> )y@z+؅YeUԭ~3| "!BdH8Oмck  h? Wk"+tE ND_X}tX< {?<Z0ҎCWzhnit1"򟙌в*K#=j w= ɍh(l H4.@=!=|:<ˮq{*{vOd8 yz;*P^1"ZԴg&Lv^: ᫏ѣ+zEY""hA^hhš?ߢF!Hd_ ɶōm8:VTX<'5ObUmj,dmtl@}>_Kn<_@AHn+hhJ4"S.Ƙg%aV?Bː")M$ۡEn ZЂ7k5t(!C :H2 t܅N?չ桅V`YW")(hx'Op7 |ukK, IDAT#p uG$J8~KP'/#"kYqUT\/x/l"ѩtEoT!2F&\ #@8;TBG`74Y3\/\4g5+ܱ19[]$O0|J%Aڬ^aT葑d*(Y<ʮ| rWÐe]>ozlDG(t[k&w>yn&CUԸ>g,mqy BqA=&D([[(DljpbD ʐn5Aao!{q6=,GuDhnOnh<^7@w}ү(Y@o-3>6[z~">DzK^L:iMU_t? cӐe!4vKqJϿ R4ֆ Dv~!rUN:4h|EƙHߓ%EF}6g: ɦ P?| ]^j]YGT,W Qg|wC Krڔ8:*S3Ct]iFBQFTY2 6gQiY^b756!IbhN#Qm+|LCFYT8r9V^@:FdkH*4zZ4 T:4+~Y}.ډ hYR??$'۳EvOD砄ޛ s`8 Y(jFͫL?Y1e}zH&#WC#k'g\^-"Y8ȞHλ `=wp|D"Bβɰ7һ`T#˙_g,t1 Amtf<"%'"2ǃD|om@;nƇqCrF:}"_iXh2a?QWe>y5Dufҝh_"ۡ1zҿ&>mLS,DޗבBmDB{04g\ t\jFhZV"(1;ȈTn@a}]##@[4YFsHM_D4KY&^HANSͶh|-!LD>AN9qÞ02tK28QA-$ȫ"@eG;KW\2aGEeT^2֊JQad*vlQaqE2\JDF:tmpvA /]yy V7ٸqXH~"۔לPUŽuj,\]ゼ5Pu).2%SҢeT##25@$Q(hǀ= ECm%H"3Q#r2 ;OģErsBUf_hOtJo\/X!J4Aͣ ȶRfy5Fd=D |`^UT׫gChsd@s2Ov$*tΓlR Lh <gS^Q:@ZH'>\-a\b\*KffWt9^đ.vvjj. D" #R>8Z뀼r!0w` [[!+CJDdk_J E䔋KE_3޷ϢmCѾl-Ds޷ 3k-"rHOٻ>㽬B]nDx 2ra~">߶ BYKz?dc45CŒRTX|Q2F0ފu] **,p˗F~f 9@Ӕ]}¬oX_kM ]RiN^6vUu&%K:ޤjmgžLꡱ,[TXUAkBS AFԧTm}O=Lt[VB^F*p}pt@ s?)r6A }RBEwFY&ۑe8?);"Bdd>ZE@XvEiG&%O!p C$@셮܏B)"+V xaiHm<j˱[{0w,6=e2ykz 97"m]D:QBkۮ$xOħ.J;vohu P,]08@ġe_>b!2X#E&Æֶ1!C䜢≯ ZfCކNV%Z7EV7Dٮ! vul%lJzv ݆`yz\@aN!^O۳#%9d"481L.ǡңЮa Up6֗!R`g ҟ<wg.Eb/`/3j@i,Iy}~V=8F ԃ< : `Q3\/ypFBC sddڽt[ў頰ZHytO"ǡ/>vC߉h Ե+~ie+cd׭/X=*AKM{V4$m }s۷]/Ð.F_eWG:XUTX|UprחeMpX/PƑNqͧuvҵ9JT-kEE6su5/:So/Ff~B$AG wm5iavϬ`mw;s3Pm[C. GٙhBPQQ$,BoZ,tI 1ؕ^pZ'!\/@c}U26DV>-X zܔ kxhuzAw?6DG%V,ύMW 5YI7 q,Vp?PZ Ƹ^w?K:Q&bJ]`7!v}(f`rhzA-?_'#/x7ɶ V`wvDV\/8/f:c17C'=dKjSk Ѽ#~"8h's\%X凤q6A7a,K#97}]/G%Z/5OaY-s8"#gd!]\k8u, V|&n 㘬>Jk` ўG]mUҊ!pDDDA DJQ"[װIa^lk8+ն:?Ì9fnO_Hޮ EƂBhIwKpDDSTn As4!"5_9g9GYeY}A+)u@qaK6-:;S`)qr0{0רϟUHQ50huA5-Z4zX˙h&96{PGؙa' )KDuYBnEOk^ "펬o5BFTIdQ;e;O3[!YwBRDf^FiZf…lRo/C^SOFL=~gmOL[hn΅8YihmrwY Q_TC~Nֆh(G !ҁ3D6iut Zn6Y_muDT]q:"ߐ%'QH^V((Ka;OǺ~:"$  ޤ]@EYqe|ŞQ<<MM 3 y9[d= /ZQ3?h ut0YAsw!Xsߨ$SЕd\>u oDrgQaq=!;Ek栟R5m"ҡ~_Z~S-DϓXs4!Lb/"?wXnnur~ G}[/l:Drn3Dsm/Z4o LD28 -"^3kNf&I4G%dScOpk6aQa>8~@[w귕۔ax<>a pN!ʦ!ET#}g{DZ+mõȳ҉!vWhMM;/lX-ר!XYxMD --E5RYw0"tQwY= EosDx{qlDd 406X[ B0,{FDno\&Z3q $DcM)wE%K%#pd&lM &{}#@\R7{*4˭]޾[ `?Dk ~3y^P7cW3"XXM~#PU@=IH@@{.HW~r}C`/M,h_jz흯 DgJU!]ww1 0}z ̷_0"ְ:YZ{=-c^l t|M}eg7鰭7OǻJ4Peu:Ndg HUv25F"RC쒧mCפX r b8 d KwuG^Ѹq; "4^/Es5D#e|Ѥ= GǗ5cD;ӷO" (f8=м2w%#B:Em Eť[@ND,;a؀Z i΢]v[[I!pYg:owaD"  +\/8O"0 Rd2Gp F #:&ZlOC ː[+{hqy29Fv&}fDWJDN{4J}z~ F!@v t LPvltq46M"푫&/B?t&Sx E@XZe2omiI}֮ N~̏ȮW\u 3^2yBe fSq D.֠ %v}%gJQ~MD>@8Y-Lh\5B^0yG@cd/Ϟ4XH Dc&&SޮE RE^F^Ez5ȣsҕhGީnO[{'#g#$^mPT-?M 7 &9"rF2[A QrD|W2Lqd2ҝ(mafzf …@쁗"/! (7ߙX"C.,#Y!WMFP?f9;.,GHǣE}>՞5KcD{^VZ8ݶ')@IT&({&Е:ZVEe#4Czxt&*5v>[Cg p_3xDs; 2E+ѿN#}ID&y`K]܇^0* ظY YHh@ivh_<@F D֣0ݭf IDAT3LF;Ƣ1|"H_uPbth^2g Zbbymw'e1ƺ^μC؁ע5dM:7Dh="⍐pm2x13.!iwҩ{@߲Un.LAQa?Y3v|d+4|3o?xC2;JD#ˑg2kpOYl֔d*2F?8ͅeAs#T[PfbA)"|CP~l/:`7*?{e-=?8a0 Y/Wpt O D|~"~vt-!kU}%2TDՆR ,vEB!4k[hߝwQƨ,`_Y`<-tH{7nEw 7\TW)lH quс{P,~3 ilx;Y>x I=5W#c{KMV#=jf?IBzN?TGt}Fz4g"vBn{w4׌~q #Y爐o6#C&ǭ&~RTX\Lddy ҩHl3c{^Ibimm?}P.*,jB Cuxg:s''SBZН/ou({C\ Ib[U%/P8DQmL>.*,?L)*,.Mb9C޳a ΀^h] "&S U^/a$3n23)aI%?2\ `eDewaVTIdrA͌FTڬҦaEĜ[?sV6a&ӘGD%0 VWw5yyf'9}TqFxߕQq8] 7;)&XIրEzoK?) }/@ :Gkm֥J,%-C֐VȢ#>S.0+AZФO܁,/;3EAZ# :i !L3zc.38{sd4Q;6:Z=|. vaʅF֤]))YI,ds{xA:9A_҉|mgmj#`v5QճMq3@4~lj:`Y6BdyoxDຓ=Fn1ru dū3YvV҈la'拨ꇾ&;@ڜXH$Df;mTo!p;"}/!(} |S$ZCs`"EHΧyA(eFc;#דfUcI,4*)euXAr5O c-2@]?Stj{&4& nԚuU+&zvDnh #SuŎHsmdSE84w"hN0H9 SR^Dv"*#:~lDq.ݭ{}BE׻iwG2`Ed}wO { D> ͉u?WQOQظw@k6EhX LYY󾼷Vfv?msH򴙽=w1Kko{/X{^E 0ol#KhRRV;3hg0# HUsD%eE'J[kܚqeU~K˛eί]'TY}F#K AXXG#%TA yq̒ll4.FkJ{ "+.,MRRVޞaHq:ZZek>qSVHQǫٳhT[WVP mԆ(q@_nPC DZ8{{q"lᒥ%܋*sH݆kJFÑ"LPtbA]Wk.ZWd)2.4@hs^2w?Iarxshy-Ώͻ"#=F=`8lgu\~gvn:}e@v5kM|P%4:(:;,62< 56L`}w,]g[D7%J{%rq8Hg?|,/X5FK@gD"rhm<_ʨORFXE$cr3f?o3!ݹ´mϳ@gYQ4wNY4D2^j?>fXg}{,6@C]n9ZY'J@oϰz^]ڿԮ)6|&"عv]Q/w=D:zyAtcrd]ڗȲ}Ӽ *O xB)9p'\g|&":[ B"εo{A甒3DkoiW N1?diWYq%["+.67 ,Zfn!EAkcsRWu.{^ 穀C#H)=ymVѲ{-Ukf{ݲf+&ܩnVT.ϩ@qa@`+&7%rle1Z[*)++ FxgOPRVTQ_x {[ r4UJ;f:3Dq/.yqE0{{-]/;Iw+g@)˓)2/BB5yA]>3Y<-i;!l)yĿ-Kф m`Gl RdB$iʫEƚK:(F 9Ut\e~vvp 쾎4Yh:)t,_ˁC%TȷM= {?BࡅS:Dk_ t#mjTːU `R'yiFhl֟FxtGTtsz(O}mG#3׬ckt4"܃{!Vϛ=CosfnAK<Ξ^4n"YNX@C{[A2w])pDׅ69(w̙:i@W Se־6"b$nL" d}^-E2P8͝FhGXVػ}w1D] ݈Pb.{<O"\Y'M>kX5vaE'A}YOgY1F" Y[z Pd$qY r*t&"Pv4hg[?>ls@q_ ؔx &( cVc;#|==^{nmd1z.H(HcRRxFMm,@ )Cp,;S){Hnѽvk q1ZB䶗䱑(9[6Zr)S K?=ʗqS\|܏Dd;4G.()+);G2RiTwNV]Fy9#ʡ/)+;+?$'hB3Wߗ=)EC"/Fww7K?-Y6ahN2BmRD:Fh>" ס{"GI@FJxA׈JҠ ;n"kw]u6I5"{j4:Ӽ HϛoN$ulh퍀t}"Fg=޶x w{1ZzA4k^H.@و$!+A!Dnwz 0%O| Ю ]I#5:#SIz }wD݀Z ,涱SÑK2lpy/H38TY #x ~klV ?D n_jyA\Aܮ[1d-ߐdgml|1ku&\T!@)@w;#{3;!Dׄ; 椑7 Vhc}VΡ܅" rʴ$lgu;ˈ*f;ǑFukRP>DL@2H軷{At'kHziG$CkGň, 9B f@ss}[$Y&Qec)GɅ^uzDat/_U(ɩ5Xv "n ,O] PasqFڲHN }мuE7Ԫy"shεLt Ǿ;Y۽gDFj/Av }w궈&I+HGUu|aǼUlr^*LtR^Bn?4#yMBޙqmAYmDkٻ߯"5͓íC^"d>kѤMcP 4_v@)DBA ٤g"5.Rl D=u/G SC}۬7j@ HEs$C;# {Yh\(}@+D -6 ̢P?%eEG+5OVۜ>k* U+տ(~*h.܁HC!Asx%?Kʊ@r"^j9B]Sj_HQ(;b3)HiG_yJ0iQ: (Z"3#T{Vu6ri-.# Q+>s8|PY XIRH{,rMgh&Z_G8A13o P檖oXyM]H* p=<ܷ5*7K_ẗ/CVۑkcA;bϜ@bͱdN B Pmk)hi#&$ ]R#u {o<ʼo$3Q~$gRDdwwB0՟T#uNӈ ߉dij@ˡ>aY ʕH޶4Bd pYEӉ+u(T{pF]/GD3%uj'lU1, /#MVNyVgԫZց]P:>)@Jy RO6AD,,9cB߽4ddn;"I^-ӗtD(bO }=Ik\Gֺrqjm}+_Z[ >"O]HVwd#'7Œh'.%F$:lAHFວI;$]@cy:Y+^=aúދs:MhG1 1Uf=WRV0wv)¥?pB+E5B iB$ho9?*6F+~šRSE@6hC 2\"0)4I,0AdA<QT3 Z m,']4hd^xA4 Ai@Twkk! lA\N3^1GYDmM*ioKGt/c{ ѹ,g_akgV! h+ e=jkdIJJPݛ4lOCgS"߱x/XVVs re|+h?6BSd<؅H+"kxߔynH>3ׯ,4KA67B#X_iߗ#kՈ8>feʏ:䣡N0h\ntVSRk©HޟAs8$?%Hg1p3縗s{ʷ%s%& qacq]<$ձsI<;+[F:!ݕ4@*nP 1pYAj{$"h=|ԀT̾OHxdz͋f֧ӑK\Dz!8\MGAs `lIֈIOtΫ/?UDsKkrdmץHrYB(2|wH5(%eEhtvߕMVtʯPo͖~PuМKr;mh kd RSj_HƊM3K^;Sm#դ i5%{ jў6%rVӽ:0u}lKTvȕ9zzA4Z=Z!i2iH #| DymΒ[ b{gr!D慾D9Ynd[D\ [ۡlSߐv?6GzJz$"@eg!W;HG<9p{8dڔV ^tTxmSg#8w n-Oa;!@z`-k#Í=7"% }DNf@/ZaD{ww{N)$\@Pr͑VR$'.Arm"vav{;#?= "4!#guREk.EDynBйQw"H=͇gY9 7,NExD|/yVH+D^y) IDATGf ͏VhӹǓg9AH Hu{-os(5#oY;ޞ5 )*IHβ{" ^S~FheC̟d8$d=oWRVtFqaiُMBX\X:d ))+*6ՈxDnϡ5tFsԔ)HR)R.yvN H(in *I H4M˙H`*ADBl뇢Xk#Y?^@Ǝ,!x5Х9 K2iMo! CC /Dzm*+H[]{ds1(i6@3m;oA!l]ORL66513ڐBdbӁn֧]f! M>?V{At;{nAKLY."_3="_6Jrt@p$ա7BqPA~5P{v_2GCsh rFP",DlՎ"7dyTО4HehM`\hMDĮd<j}|r :|9O!i@Z[neH[}% 葏_?\w8,-})Mv\!9iHMg$O?Adg ~b 0w*kǝcӦcs j垛ksADrRu{lom}97٤@jDn }r{h@vA;#-{ؙPI7xJx. DJ6*IS)U#΋ֿH?؀ NG`69 Yr}d_a" ѝh+D,t!4'i_/VX/gCPCթd!D$DFZ!F&; Y{oZ6 0ש_{k*j;Iqy z17$ \82Dv,Cm$MF<#ʑuH>Cd$; ^.ȒQb!+:0ڼ9^ })/!, }/EوH}{B$Z{Aa;{ޝhֳ Br4VXh3$3;5stBmD:"9s!qc'A$d ~)M:# @~l}G-ֶ)@_s *ze!Y6>#"2~e`++y=QRO9`W&~-EШwϮ]g0}!3$;ݥ^"cc؂@OЫ^ K?Ed;܎EednF2p=s7YwIh#ڞ4ik뗽9ޮy(]mַS Fr7"EȂ2Y@l4;/͍j{_B8Fspw<"WϯA[En4"6"RE^ؾeژ.Gkcʺ/fw0Ch䅾nhӋм;>b'IR=yյ?+Rjl"k"di%3zիA}ٵ[W4@s#{ dOznk )9^bׯ2nzh))+DRc*֔=*_R_3v? wBkB=t6mDqaVh_|F2߻hұ{GMݖ<7ЋYtfL8N8Nwq8w 0k>ط!Utqq\8Σvhێ R8 qOqg;gpўq8NßƿZݓPt3m Tv rX>bB * IzE.Z)Dl`ꌰj`x\5-ݱX#SE!i&! # t <l=ûȽ ~VX}Y Mtg]Kz֡iX{۵[!D{" JSDfj_A@&{44aґX)E_ }SyD=*Y6F]bc0\6b\3X<:x\>Cx7/}w+"õBZa6n3@֖\(WD$X?QS -ڵ+ j߿IyW,,tml펍ߍ.!r7YF$prm6haYHv v Ul/$wYgB{X`D軯{A"syh~@.k]| xX/:vEcl?zGY@wg :3H)9*o"{͗ވD܁dE*b{gbQ]lOQu$>ԝ:{(Z[,냄fOH>?CfvH!MUZtFu"Z[?1h=|`c ۭ#H)=xN[[oec9Ѻr\}/ZjhLe Ņ5Ak^%eEk-uM}NhUgc_+]Ǎ}K-qe;ȍ8|li*$g(x:@Nj[nx8u>tBHy 5'=)J?ĎCC5C`#["b&# 8 }]s!A憙1}/9 W?4M–&9=F@DxD"yv:kg[.WX} OD- ’WA5D\Sz +"K1Ym`Yމvo3mpFFdZ)w?E?A[!i-Bߍ}mͼ Z8&ۻ^Np<8*4wWٻdr]tZVy/EJM!|"ބ֤UI4D; ogdNdi-ZGrIkct]e/D[_c_dh$SS|t#9yĮ Yg/`{c:"s ֎(95W0 !_Lzyz->,]^[RF=ԅ5xصwo)ycj]i=qg8˟}-BޒD'#@iHҮ ry\4{mrJjE`noK{Aev\,BC+V 4Dڞ}xr+~x 3E @;HW 2]ŀ>)'hܿ@2HKAJF,2A.VHSzriaj@n DND@es;(B#kWKR+ѤMj1Dnww/:G .ѹr4{BSV8B*  6.C~{20?r/#k\1?B߭h&"p6E֮Zb[[ hw@NOw [g!EG4o +p/4{b zͩ$FM})J&Aeu`oWtAQY9Z RW실V-l' 2]֍yBr-?C9jtp&!T9C'IKW<6? ;f֭`+lڤ׊ KooGymeV_Lݵ К4)v$¿YRVԡt%5\yM`.tw̯䗷8Mqs1P/o=w8!}.݇#j9FqqF6x88qg"E+N"I@vgGCRKW`Bq,#=ڤ1P~ÿ cs a< *gE9-sjW'.hWg! Үފוџz%4Trh܏ADVSƈ$ mNšVuu S۩9"60@*q!>^uh8ogOEG9%!^yV3I-Z[wq޷:C66!Ux&Z{  \?*~d״B-$E'!kI bעkSl )ٱo9I-,յ:Y0́]B=<}/{ zҨzSL$90P^&%?gyAt&"5cQ,Q=Qr۳C$*@&QD""Ki,/X];Zۓ!"2f 4d`-rhhMH0{3hv-z73=Cl||օйE~od Y꠹" :®F֩֔ߦ3HPd4sp?ZrW8qmlut8cp%6^vs4i;z|Q\XQIYQ'VtBD#ՔGx8{jK4J>q_Z,8BR )@ =k6H =8iH)?ޛkߏw Sg5'}HR PލtWFAQGl|}u5u+IDVe!PY@ʬL:sz@T}DWO,iʬuo m_!8'E&@`voɯmi@ҳn(;X\_GuOUYs/8V@0{C} bErE E 0raR:?@cs볙@Ț=cwF },U-6fC= $~awzʑ'rt.; P_MH|i$\dg Znhss@|w)FUDKXrj,HvfI9tC ֎Q%J{d> )BkZZ "_cg{:3"ƭ5"{u8TͫI6XA`ǩb̝Έ:͵Hd\I?Go5zCoйYZAkcp}vg#Tf:{"ujn=9#b]Y[~CӁ=C]l'iD|o 3הO))+Y0ϳCHLj=qz5\m W3U))+* Kp5)y:bQiBkqnxߺ.g@r6Aň\a2D$I,/"zyRSB$]3(fw!hgїe:Fq~" n Hi!je$emkZh"?rCziO9MzcgNܚvCD"?xثַ~E:"8 =m=,{oA,qQ dyAkg#}w7-@l{o_d ~Ό~JO IDAT V3AnjeU>J8{!ZtOD`l\/* ,1HNyu WGDW{D|.L,d/yA4޾_Hfo{At "4 JB}S m<%Ig#± ;{8*t&v&j""?BuSD:uY19{$od룎nn;Yfv+ 7H~`/\h2q%Τ'Zrqa`ɩ7P_)*~fuq<#6vOA N_4BM@(sʑ X->#@,lYeA@= tG6pD;ηyVQP: }mOLJX=/ |0pDXb}5u7pD34IwVsroT=ET[#AR6ߐ7xodC֓M|? pi` kq fv|C3 Ya#{'0,lhnvEaUz]& r2v:Y!j\DZ{BdI Rhl֊/%q8ndk6Zsʷ6FPc0,W)R\X:eJw[ȚRS%k͚?. RYE|%=Pu^@}6PQw?4Ñ ߋΩtDiH[PB}ODf"-"} #= J! {A-N߇O@:! }c-j}}7'9xw= ";X ?DVBړPHCXl>GUDyoD/F#w,rgzlfyAevx H*ze31hk =Ƨ) }wD eV!Y:4Ȳ%&yRwMBYBe:v!"x4ធ>pp$3["Y: ʅ֏-O!`Y&'Z&_%YUuԮ3n<9wbhPz uwD҇bcfGGT,n]w5ߏ l}5r,$׬{{QRW~H&%kN{ˑA,dE" /JDT: d m ֶhT#E^:X X)B$w6C|$LJ C)B2>8N 44CQКRSjJM)57):R^BfzB6בh<'46ܜym6tBwQ4^=@=oCZlOD%QP/E@}iaGp$롃Ϋk~gS."գ/:"2٤lr9iC֪jD➵X,codI_1sλ VaM^F}] hdI⮵"Tgr mPB㞄 \O烒]C߽ Lϋ擆^`c]iKC驅@UT~o(rfr=ws Or(H=<7 Pw2d5ien#d1xԾf}}V Ð0usrO-[ݚqS;'n*m|#Ҳ.WP"*$o- =x<+7҉VڽHq#ooh;&.ʸg=2/g}wYhNAʅ+Y2VX5%O!H%H^^D{L"`dzNhCNGhl  C} #m,g|8X @vK̇;lDʮz] ^]ֲk Y_sK=< Í/Q:8 Kno]RSjJM)?^"P2M/@x ^\.sC!\m7{J: Y?"ik! 04EE N: z$/"`?Ȣ1 {,9 Wʅ\a ۛNX}@֛=iqw 0 o>'fm3s I}%ew"5i0_7Eɡf31E$X;DP!KN`0 0:@("wP"o"Eq"P!"kR#E·HKO\!^y}?ƪפּyAރpHo!4+`rB%},yC vH&w3b_uYvWt{OCz-mTϩW$dͳ伇o+ks]1rȇr("ٳȫ_a<͏1h-ͻ\D\d>4Ir.R$続[7+#Lmj$"8d+F8omK=h~Q+V~Wڨך78[4LʞwLJ{"YƗOhi5ԔRS%E|2"݌,"P [=-#-ud1"LKC Ζ\6:5EխL?I"ҵis\vFeG5o{A4 FH7`3[+n:017/;@`iZ# MtBgv6v0D@z KhUh2#R@A|lVϼ HUL$;;zAtf ZuAֺaf%u.Tɗroʹ̶1ia刬<w_D{'%hU٘C4w'sY#rA9ٷ1 fo#Hx$wMm#P42uq+ (ki(Ìĵ˱>,Gw4;!}GīZԷSP8^,@'{WD*kY]R=`y"<ޗo9 [2F޻V O"}JwwD$}~fy~$dbېGY\7{g#&O 2~g>N0Ʀȫ685\ Ơ8{m O>:m2<"BrkZ1(,/h}Ep(c,ݭKѺ UG:ˑg E4BTY3gͼ܂u+`֏x~X֯ UvF* 杹7-s8 yI\_u]:cQ6onrP#N#?1tAs7`}ޞF$ ͻlnZZ-7 )nݥ(`l%+8DQDJ?Qʣ( xJDQ4EQv.HHvB {W ,.*Y pІ3̗f0x6к!Hm@DpDcyyjwEwv_9-CtU7R~燭db%MǬ ͬ/"9X@eWRY#4ҞDb 燍o UR p"{aL|!mYJB,@awo =< vkDPv(Kh|ܾQ_w<+,!PVLu9n9Y; OIIi=1f\o;?06xM&!2s=[ޱƪ]/{ 0:|q"=n{w .AD*y3A21Yx@|'K L؞V6 evnf0-h3ir4oB?7s<]zc<}s>j"_H|wDsISGO#Dwmd^}2UŲ%[Y,B8@iϷ@u#4cz;QHb:{Ʊ1:y@M>ٱۗNxܼeHBmP` 1OHDz iа*=?=\[D%i%!)r%9(5N߀TEy֎AEdl+&?g)TbӢZߧ8rݐOhpDžOXÍqεBDƦW Es}ӱs4ddG{Q]ie=OXY?~Y"AFQts;2\Aӣ(#>8EQtQ=Ic =?uι(0EQIr2V=EQtsOhYB{X<E?_)'}/ƞ[.WmH!"yGr) p4 :ц6,Ȃ OGm0=qհUWCD5s#+/@ݵT"'X qNG e >$h^ vnALyo"&KarwA6J 偈&5BicY&!WĎڶgM=]'yys#o\#>ZмȲKGދfDm @KFK!9Ȳt_'l j<6 ,Z&X<4kGD_3S!Mi+L#W+;vaSH?#}:Ol<q6uyy@aoM r6 m,O tQo:!6~+Ѽ"D+k| mHk. I:Bc4_|Oiܣl]+쳢$37gÙTb3\bSM4qR[}/x]ls\bihMAvHdhlD( R3)PЅh.嚻V{%HNv|`>$Z(r+{:?A<%iz)J+@?g-(wDkC+/[9zBǫ"W_i}Ҏ[W yp{jVKҷuNv!`SH6RW9w_9sn_d!ͻmvFk:`s(fh\(6jrA}(*u=`(sMa^c؄ػzEQ494CGGQsUs90ן(Z;a36QVQ`uj.HlSQh]l |&Ěs7#b$C;h"s*bϠ8o QB6hEǗA`w^A f3?y?R{<V#Q$<<h"H&sגx;;L{ 냏$#Kz/2#SGy~X$#:r"S;qCҚ3"Cy8iLByX@s95"UV(|Й=?k$H&d 8c_QKW uE^Wl D^6F zJq.il{!¿öhQ_)_ T! 3N߅k = YMAW6-!bPm6A2h.* $P .=mf Gudbq%/^UGzyS^fy~N!xcP`} Jr'Xs}ZiF*>ȳ7O7ڨӱ#B ~hT"5>niuZCA{4[oUt0k#"W%(Gh] UhlH"[>ҫ W~c֢yH& _w"CWIu)-v6`L麶Qvy}Z07~/D+AEsߢHY1ιsӢ(*ߟGd& IDATW>5iػâ(Zj"16%(⳹qHaL;Bx8*#;})t΁0FD&ӀFginHNB_D*GLLz~z`MI6uA 5fH ?կ@@o4">7 {9ϐ WK D:"UP6Vnp!WF:yս :wt")Da: Czki텀t6 YϷnM2f2ƽcdb΀1hA$m\@W!08T*ڲ "\Zk[Zf d9iGg+ 燷#"Zds(7gg"DO)8kybA Lڛt_ohNFdbD^j1t燭8|K(lսʍS`GgSUhc5¶zZ6G!RW4-H&.@y'q{Ӡ?[Y^ݸC<;Z B/k[h~6@2{Hggʔ9ʻPtDf qZ'v$uC*6A׾$ ,VltWCh8e46t5_ jSZ^ ,]s wCkJ-)PYTM ǐGD w2J@F6tW˝# 8*Pw!?ŌC9;+ G@8|Gf0#Nq}O n}7RI-J֣H}5WJqV6>i-?y~x?L5Ydgdi (dϵ^lt F:s(ID}Y/͍_" פX=?y^t5C4 e\L 1oNC&mciOFĭy G"oa*`$6f[!%cw ҽnhҿ9zȓ2 `ctY}ڳ6F?iϯAkȫ3O3VΞ ߋQH FE^U b!v}726#X"W^DFVwC}}J4%Ƞ = "Bu4VqBUD}J}Jv-_wJ䕒>'dvE)ϖ(8.;Dι(ZkEꔺaz9 CSmEӑ%ZRW|k~?ms(a?=ι9+s+Qp(sM(^vMD,[RdtOG*SCd]]J|!y~=Ͳ-FEFH"Eni_myx`]ALFg{@Z>{AcIL rD#jDgBG.V(mX"B% Z@}WZ ʳz[Nw~ys>yF'"s*Dc>/@*huY`yfs9"{("Xeϣo"p,6{>Gn ҩhgG4g|DԵB:y#@s}+&۠_k\6ftFDl,ns$H&|X2-(=SA`dk{hi Yq= [Z>(' fFŋjͷitkV~Mhsύ)n㶹㦯[]2j]eg %Y ιQ=UIQMu}֊~Bcseϗۙws(|Zj$#DYss7#L_;x9'Zxޓ&D˱ιJhassG{=1i<ᜋ3w`E / A}#T#9Y\OF b{d~ Eg"&4H&s:7"%yH;!hoYqD(mB`%DjBw)ŗ0-'[j:>'!lnЄRohBZ$=?#V"2.H&x~x r~i6#c@{,$@DTV[F@0믷axYg#rXd}$bš`Ѐa( 8|RZm"=x&H&^:<䭪F^j!C$]QCO 9hL,u@ `6Y >Gd ؐ4B ,"xտ"ӾTTdϵAD%"!=+"['9w < rEHp2 2Fz;!"SD(nf# sO8rLwdGVYV1_#=:St1 @ƾ3?=\|'[A21ڶeΟU[_ߎ!];TvFD$xdp)֣ǭ3f|DoF>8P*IdH5ȕEw:J/PW|[G/΋oQJAƉVdKVjVAa3ιS>?WnLuuF#.K@d"oN"53y~x@E T{Ldhe}& o{֤&R>D~^6&v x2kk<\imx̑HyN$#{wBD$xL 7ߦ ;o萁n`{{ͅk=?| eG`%l9*yCvGd<HG[FpoR yFI ,߂:٘f[و FKوIYH*qB|k]4*^bR+R+I1 m˷D9k%Eȃ'0ESoB6ug&>+Bޣ `/E͡ ֡BR"bu'ڠ##[ A2燋^" :{T K]h^E`13]3C s+golճhEcف.|սx!K~3z L@:: .tejL,p";3mlG@-&;sq"6 /냒|nc])B`9"KG#k@`?K!|YO =?|/OIA!H6nc0mL'zvg)"z=z)k czJh'L6ĩ?GzH┚q&|hcy2=SظwDVhܷ1!c.wZ\W.vbk4; |wA^<]2̓ H7֐Xĕi?g!d"RZ=?dkp}j ֯"֧{K8\7,"!ҝS\JEqyK\IsRISfKɀU+R+R+[PȤD껝>gmF]8h{&n!kw@i xWVcٸ,Jd%Ğk@Ո톀߇@t/z DL|$ }%pȉY BpW&Al+B-# ;  gfr 4DOz>BޯEr{{_>8;Ч6Vl<_qFB[fٿBRY现r_"n7~k@n;!k;p @VvTex~-H&ax})ǫg~tzec ܹ 㬿%; O '>G=N*9ž}D6ZAaku3j$qXfXރȰyK=QJCdM#LH7FvCD͛ ٿ )td7}=Ns0ah= 2Dv#mSw4!R)Cw`~5On#ug-Me)Ҕku4F/>ȫ1vzqjg̛$<OvBdG(DDeaX{}gYL/!rYD#x ylAfDL|iaW YF@6thk{5"\ bq=g[}EIx D@!2`!N}ldL/ͳ>? "!Xˑ^C!m{~$"pn<\'y~x1udԲ=d=?,Cs4Bwnct*1;+oFUζhֆ(<*N> *W!z~*G=++΄ʿy@g8&Țix=,_#^d7y#R~YLJ됺tgժ ϋMow)" $w{Jp4ybO"oL?D^Z_Oz~n$o3W 0yv@IFd"ܶ"C.w!b>(H&z~xy)9k6J=Ɉ<֦KKͺJ[Ф(Ҩx=@r1C9ʼCv8&Jۜ4`Z O9%x~L,-0 38|yVJ;Tj޻ /vl~A2yf;؆6T}'bca(iD<&F.A$Z+nfGxDD`lr+"wɄoAeD'O>~3XW /U.wmdY5"<˭V!BLjvDz~::|[bii=?ys&]maz >p_ A>A2l@i Qtq6'X!G%o ?#EheX[#r Fadb燏#яCt;[Y_XCIX2x^=`]L$ CH֯&2a'g_8B+vBف,HZٸ\Bk%Cc lHZQ/r%}ҨG`?n 9GQd!Q-$fEԣVԒK$=(O /H|W^<nȺZЧ4+k<'@8.Co:d: Z_Bd /BQ6g!2?H&zl#Qm IDATR4 y\ u^ Y?G!N Do}s3ʸZױᛈx9 g"+a:׳P sI OBD= Dl3y_ڸlBbDGX "=1y{nC5#{:ݻm\v.-XFYӋhm,@: F876_AAdoOxv-ҁ p7dEshQ+io ҾV{ZiT/ݾZn9Cݸh%W͚89'/~MJyE;;B-RZJ-)ڈcA2QQ6)B^Da>ɽ|b?= YǗ!k39%hOO"t"cd}O"uLjؼ vYB/,Mߧm-Z°"Ì8C C/L 庆Խ* b-"P|Hb++m(t΂CRk^[Q݃? &+lW=1Nt_56 TwD[-!)r% .J\Iw]`y+iVHxFaQњ1{ #~l}rpg+9x4*~¼I K^+_qef ~5 [!p|Cs-%#ތ]z;??AhEuι[ x "h$mOՈ;$[ctAzwnBX"ܶrXy> AKzzȢã]"c>Yw@h_#V N$ϧb?>|-H&[ R2m!j߫:kSr f0k>lybg.DޞQ6YnIyW"0XW;B9JȞacOG#>' 8 zK3ITDCz|/Cp="o#>Ӑge:"T wqLiq*"5ӀF,S ([\A2 !R7ȋ{jLLw8Dd#{`ah 6T6ZUgpxb,DF4~֟e;v;As( "SrE 6R_@2^Zh(4%Et75}zjҨxžxrefA _΋N/@x)r%"~-#CR=d)D(!ejTFF?~ MC4&Ph9n 6V~ҷai;I;ͼ9 D͝~PK!|s1 +(rj4keRK~\jvxC`?y`: s=5"eRSep_Df"/$#x,C@ U@$i9sQxΈ G +C>_frh,F"ߗWyj(OF:HDsӴX[j3Y-MB~D1^'6w>SR;d12m}ȒK*r%3Nt;E,e]Dh# h b,Bc.]јǙ+\I;g26tC2Ocaj)EǮhO`|?k\Cиsqm7<7 h=97 M|s.zb9{_?)íѦQ`gJvBD둵ea]Dd" Ejo_8 H#@~v=?MHVhsεd&PFvJv7 Y'v{t[j7(ʋ&^֏-L{!D\ͷepsTld o}5YWD L렍5AgWW`$J|)Lz쇑r!"Nyh#mhY;#bTGqx]B=]]6"I&ב^Ɖzڿ#Fy~xTLaW"}P.]hm{DL^ tx"fZ] m,!/+/?He1̷cmZtbOD[RSW{"+& ~oDz'"4>3y0JG:xCO=; %)`ߏ_& x yƣbwא:gHA] l ƭFDi{DY mnoٕ2s4*JKq8lSKo-d{»޴1nRt~{|'+t9"+oXۮFZX+{_)lKa==$zԴ>҉tE JDH,Fj}`ʌCĎ.!k}ރt<ftқAlGoB}@)G|,y 巬z k?wQ:/GkZ_4+P8Lu8"kY@aL\j숈o;}P℅! [wj\Թ`Ie'V賅,EhXϢ1h;2ιW[!i_ 末˶DjYZd3{վ<"OE,A~,${~8&|ڀYod{`wzx~x+h 5Ȃ1"k!OB5"HH4F9J' s:RQЦj,Ȫ6V/Uil}qF 8!i], ^A䡍o\ysYD@)"% +IK p`Ga{OY!@A1kC ?7x%kco8kkAM  /4l pYQ9"?~?q7 lבu)ж~u].EUsc,gdh?Mkˈ,Eሬ"|1"FD}=&ujVhH =4.ki VVzƷHx_Fz1ҙ/,zi/C 皂TQhgKYsRUYx}vu&gy|Ý,j_A!cHЍ@"Wr\iT\nHӐ3dN?/ cM{/?^M.,Z\YJ`ǯ {/:']}TI!Dku@8goF,-r%;##K]rw4gCyq-"4/QL'r˾ x}Yh>n_*r%g!YZJE_8Z-.Mc SEQaK 6&7.eG~L6B@֡0X~/O]< G(g{~8?#ALA?dbiJ͛" F.(\7]NFk^πuocp;!Znp9ضA{=7@-DRbeDYKDa.ژB&"MVI nRwdZ6.Dmz26BG`l# wAx "ژtJ{vJ{!@OkkG{rZ6A2qnZ@zYEO!r;;2<:/H&F U#x#"3 F0q yq>6]DQ_fu "ˬOB1ܾ{pؒ/ +eL;viӬn^Cdi!zPSPd`Hn㪙ns+#| PyVk.Xz/Vb ,H&>y|"!$D٠=.|$wx~x2Pe}xM6i\] y~Xl}}P@s3Ӟ+C~DhkJ;peFob!Sg#2&j59_{aUq ۝u&/^ɛ*ռx{RD`:>r/P+_>#;j[VY9gT]]EDi%ZsY ,l[leKeb 2GVjdd!uޭ?:֩}ɚƲ;zv-ÙXZutNj,QYj#'Hp/ ;.6DlcE{ZL)UaLʿ˳hH]J:t h㘼z4m\vav^7]0t&>e{/:^qe_[.Rox~x\$g"/ݺ s*z1m{3Pj/H&#KJjYLL٩nD3 C{@k Fe74 Q a@Og Z)oD<>=;zߧ'"* B 0lJ{=(I*,䮙7v>Qձ#Sp,y{xiF&"Qo|2t 6x~Xki=>g:to ҟǞQ<%#}k<DoEsE 8ep=D@sen)kZ,\-H,P[OOBN#1{! KL~ 3ٸ\4 2,Nׁ }eeRVU,ɞ|BUnɲo>jyJQoJ8yj|z~3[RJN۝:M^)ȯh\&;'탈~ۯ)[;?YuʊeY~嫦-:bGu۠0u|HdԗݑqbRmE5OP㝐"ԝmyGKQe]w_~<"҂Ҙ:D4jsO1'Mn IDAT3eĀ0ڂЈ ˡ?)y>1YtWWݺuﭽ>( ?F8"`+X h)\9 ҩ($&0G@C,pjk1|Fz~ 9LB4SxM X 75,;Y;wF^( z~(RVB %=?S!(Cxy]m^xQ(QHj]AuD9^-ԫ~tId)R6B/}tjc+[Aլm"yA*¼D5H}OC G@[[_|]l 6 (g8C֟ O#eɶ{= |!(Lr m7D6 pq燯"$XYhס\JD^ַzDP:vx~~ vD"){{~x"Ypˁv[D9Bd"r aSv$h"&.q}GtAmG7m>;9Xg,HTqy^+yU.A:Tb" O Z_fwuq0ltʳQr~p"m pAe߼dݛ+ڸcjTָqEu3K)*o)Qșow(^5 'V\]<}eZv]X "~_UL@(BDDZ9qx(FɧZ5!Xy=(&9 ٽg\[th’E+012Q> VV EKQr%"Rz[=S^ b IN-V6UK;pUV6 ҩ'Z_S۾u"R{e^d!8c wz1 5vK=w Ӿ5Hf{ 7t~hAf<ʟ8ry"z?ڷIT5c=#ߺpͷgPHh/#DY6J>d,NfMzt iwܮ"nֻwh`Sd}U2P2(;GcSъ;P>̍YZ'{~x=IZcmg"5;R @و>`h "=Ո݆vT04i Z_Db; @ A~%,v-R3G)Mm0*-27+^WA GENa(9d7gfpXhEA(r[Mjhmb^fY^ jdmGJ\1&!ju*~GX:usmF(ټS7`ιJޫ?$sn8(4`@E'.ړM(ZbO|;( riQ.akd@ME+dVPV݅b\ Lߋ<;`/ hoPo:ôb1zC@{5"Uȋ:3bB_(&=r;wGVC )+emޖ\.c0 (9r!}w3BaQ uA:UawM7""upBNӹeoniJ&x#" ҩ='G>WA!"_sc8XSCyiۣrfr_"peRz~8/MݽL8Ui|R<<>؞vi8-v{x~lkxgiTr\z*Pv6h`c7UHֿhDv][tSxfc>k͍!֖ZkBdO/y=vDD{?늜60UQb"?xG{kKQH83B؆67&2>'%?=Ck\4#чqW:uCίh~^*-|6JV'\樊2=!ݐӰ1 1ܠRJʥ zVeZopEѨeM{>;V+hE;NCxW?9 h&|5:r2<|vĹ[_D^ Zx~$*{JZu}_cz܉@?rD0NҩPb:C/pšC&`>[%OTP}B8ҩS=?)=? }"Rs/67oeMFg̜w 㐲n^:!ME4D@Je0a(l,&=e6 h`BfDDGeR*hgz~Xe\#omڨ4=U!ԭ<ܞ?nqy"!o1Rk?q"TeADC4 ҩƝhn#_fvB@n4cJ l᫁6=n$oq#~{YNM TKZ| < Y hơ󇥎y?.is_^}B6J6aR'\U.#(B`0m7h)}.sw~>Xl\:Ĥ)%gArIBd`?̜s<'R]ec;z}O(.vΕEQ\{CFQs a ]ECιAS3~yFQtUQ W%(n"`4^M?H1QeLGVΊ(p9>Bs!\;!z '^p(vc/AQMsuGX(ι Q]{83ws݀w(k״6!,X>NZ E?0@v 2Bjr]N]ch ҩOGμ /("!S_{~>JՔQ'A:5N@2:7}jn/#B Bo]fr}9-m`S퉀{\S<ZF5ufhЫ?;Op{, 79 8Wah/&Y#{}i\ΈlynQ.?ʔ?kسfcLD2ngm~ al@o-k#<A '6="DHG4*zGqzYײe߿l"!H(?G㊭OG#Btn{G'?ym%y0c|iN*6\=ܵ\;l`?/"Pn?; ?L?M..%k.Ck}R-jqQDY%)FɺGE4D轵LNBw.3%/%H l[^ -mfp+O "Vas 2o^D8@ѳ8߮)"VG} p"sKa%Vz~x]k nWmtV7%gihxgٿZ{șqw1|;C71:\1&4z 9)20\F 98p3.qimVY}DiZ,%Ql=?,/Q=D8Ed|-GMi}6=M 'wޏ`Y뗠gU[_krZB;pscTiUD&F)ιm(֙q_E(.Wc<`Sq}=y"(>6pQ5GQ4g(»ˎmPt(u{0p)(DQ4 \VP~fS_.aA=#w( 4>vpsZy}ADt"ۡ/y6H=&ݎ(kD` j𿈼- O2Rv= "݀Vb3:xև'ľvޕыglNTJRNgz? oOAN=?g`U$aO"b>VLWW{~\eip K8rhdFd Da_y(Wl;BpD^#S$V1NES<ָL6J.6,W#(DlKr.SBQ?NѼ= bGq%4ztZ*f5DkrDX~#(!b{{e }>-2|˩h>=bJ`?5CwP}01L+f4 DQЪaӝFv@kι^쁇 IDAT(uE=s,|rNNt!YM1BDղ<9uE#/~E-g9B·3sEw.+ApmErr'!r$V Ec3 y'So{~W{<|U:UmhSsi}u]DNaO*F^}7d-lRdNi58ގi&x~Q46hQ>HA=U 3Lsպ ҩNFd=K%B^Y^Mzz$_QH#ر磂 tzz) ߚi_hd`5돇q?̯?{~dEU6]n9x~iQ%HydNe}i9-o"5fK"+c}`tOXmzkXEU3OzrU Ch bh :$vSOZF.? D _tjr^/H&Y^3݆,%-FѺ'IsтW-X wkr%VH)B7gg]MNvL'۸|N./xi@s]18b%;N)SYFyh:]_8H.#=kӁQ@ EQT~{ӽ$W}nFS#Mf '}Ph@W:b%+Gz EN(.mk-Z6 ;9$ֶe#g'|HZ+ sM -,NnP^>~)_<;9~+S7D?Ž=ZyF޴[" "`UtGL>䍻r-.f?8R : ŮIO#@\~l@g(XNl!g&kǥPy ,MDf /C\e-k\lRI`V N @%:2&H=">ymgО؍G4yM+~<\1]BBՁF<2KğN-H;=Hmn3)oO#u>TV}=ȗ nt ,25(8ȑS֣ȩp0푪SMst r>rXE/ˬW:z7p]A۳VG;V;u_x'\f7DFѻhSV`k)ZOQ~VPVu"; Z(ctj{5,z۹KwɼuC`oCTH } !z\)DӐ!h)1z] g"y_GLO""W:˾;-V_"" 3#Jd A|c Nk1XD$,OnB gj!g,]BpZ;A:u$k["Y+oP(sHo R4YoAazT@燿kgN4{՞';s%UϻϝЮ1V{y⳯eLPh_DmCePNƇH ~!ʸW[>:Quuj1=kS!bw9D~DťѭmW.F"45ڳh-h iBKQX44O3BSQHhG˄˼Q6J,.'/d䴯䊢(jr[[c]K^s}o.S7(y JeQK `K(jU ۴)ZS_y~xlznuK%av%ɩp4оs`ҩi^mdA:1"SBFZ4urAe*"&ME `tBb%s((D HQO{}HjB| Gg?v ʿ'=?|A:B8@!o +7UZt#EN{!q,"7{$R>#88*C$HH&W}9n'D оBؽи;)_" yůEcr06H bx~<".->ewMڶ-QiWPw]{N_ʆ7w:yWTz*y>1kמG\z|1 ҩ)־4,d%\hQrJe6BS.{6J6fdKeN@{)6Ywmsw&FwujZBa!Gi#4_+8rκq!Мp"%gq@.p-Кo0YJ7<2Od˼jddCe D&ʕW/X V `q89oP+D Vzw5Ϗay=vߌ!E zc[Gy=ɞ"oX1 L,?%HE e," ߈TOy5XTUfz"6)@U@KЮ=?vdQ^L"g3 7z~NaoDC d^ˮ)dؽ5=")*tEB@r=4C{ DlᎈMAdy"R69(A$m$Yo`Ϸ+"s'R-֗"p?r)X{g#%rya|Bck+;"bUO.Cc{h+`Bclk ll?F=%%.3Z:BgT :)I)!%I)9!+Ppׁ=Qъ/yFBWm_a"NG(eeU`?HČ,o[=?3H.z9NA:5BDl ¹DiKi]\!3#537 rk"`܈/! pRj`@<-y})0YܒP3%}l8\^1""5*eԊ(m$"CO[fLGu"FoйDѧ޻j~g+}͈$sG"Xe9"QA:5TɞB/֮Z fd^r'qmty%SQն˿"|#Qȉ|ޏִ9tT"QNa6z+ iK5NJ(X6JphE.rW tAa=wqE")k$\R)?CI+М? m=pF6J~pCFɩ6`+ i4]~o@(DLд(igyO ҩe+F,3QgQVƪMYD8:!U B/H5D`t[DjP"dwFe}DPP.Pp\ON9UvF$ :Dc[RU٘]Q A:u{f[?CD!h <yTwh{jifݰWKC<A:ň@df_!_ٮWlFql>|vlDb5%.=`}sUmc 4͵BJ\bx c„.Ei.`\mLn0e_>4*rŔ": 1q ۡKA; %gZ?ӽGkW"V}]q8!f[p7PXp%p>)(B`+X~V E@ ҩ=?<{_^磰yd՞NDĥV6@Sw1ўNn ҩr0RPl6TQNN ?1!z}']wma_W"UI> &(zԤx_jN+=1shU3yaDIet볿Z6G =&oΧ֟_^FK}V }~=3Hg#"ǫ:TT &"%K{ &UOe&(׮%qӖ CJL(#(T7#푢_ u)\67uh?. Kk׏n 99Ql,2[!)m V)Ux'Hz>SKaހ@sN}+J$˺:rq'c@BND78Ƈ r#"5R.!"?g\:tQx~+1I :)^Fa%!j4D ~8R#r1Huڵ "8/!u#hU4YDt*  q{yv'# :3H>pmD:9mmA:5*imjY(gјp%mHM(4n}ws}O`S ҩor_{z~XSӂtj)oF͸}Vp)4i{(/c|\R*`݆9%sg*HL{t@i6Jމ j,l/[ܟl*)DoZڣ1NU=uok4O(yb񆲕,4~rՈ#`+X~V E;k2߆ Pډ܎*vWwRntj燛aaOBҋ1mmZC^-NA:53઼ {A1uB9[[NB$mDܪQO;kNVnC! 2jD#=?|3#렪\!2w6 /ɼӬ=!b⬏&ӶM]~i ؝BҩE{'ȃ_߶4pRYmJ,Q~՞)j#X#,FysU[\14:G Ddi>@.g6Jk=hMANyH靇hδCnsU7ՈprV$)˺4-Ĝ}4#Ž&%t>[(],X VH܌^^aP2y\1PvE^͉HYiڛbƟPEH%-MNdlQ8J1E(U #ooGbo#0= @Uz"`0p0*<m@sG!ESox~wTbep=t.VnGH!=YgZ`l|4W!Vlσ ZF0!_#nXDbtjYuF8? )vX{:>7|E6)pu_4|u3[9{p>}}K(ǸY~kFc{roGi-Hf%Fomvg:7w.T(i)bᤲj*>+mXݥgc]eƋj^0d-vnbdf*(9 2Eh. Esi[09*r^lMg/V֯)ˬ{mg ?{Ӑ_A3 y3kZFaa+X [)b''ҩH-Z"RiD`sϒs-@jOswz{~(¦D%wEnn9R p 樖O#zewG̊Ol6wE!2? ,;s8+O+*I#bPnU332}NeI?vKڳh6)u@߄쌜*"RRh@јɗv%={Q7?@KGATn nOW`X+_Y{H #{^?#w%:@`>g!@q07p;pXD"B9.ZeQ9F$u/޲gS!2"Lz:~+J9;] b]:!Up et]Fu%\fmFEE--Qa%| 0:DEe]NDOb=2伙V4z^ zz;0'2f~eJ|6lޘv96p4.ڜ}T-l/KlO؄˜ߩ${6JO̭h*Ad-4_{#HGբsS&A퐳r>m̈aq:<\7h#MUi)*A\)ZFgȩ&\%+X k)Z3B4#>$г^Wv3u5y{!-dv^0cԲΤWl2XeVGíՔTP4Ypb#6\ EQKwORsSsi(rѳ4CUQ)KY>h9ybC6jDUeSgemrLTsCyD!5w*$ Y=\nHAQ `X>_Qv%ɬQU54k@N A*GJVh\K'+X V_(;X =B.@ ;"3QnӺZU64wn.^XT׳Q)2F!0aEX'Q8~]5v:z9 [ yD,뢪OGZUO G#Ū -Q٫nĹpʦ߉^Sci:iՆ jUR[SYԖhҨ\VyiD*c/~y.CI ,'vzhVej%#%vj.h!(j.:yuP`3T"/v 'W7lvryzzwL֮8xֿ2x~V^ y;%WdM RFݗcPckAyt$\f/mg9}sQ\y=]P;n,/RsEInBWz 6X VߘHѯvF/oFE9oGM ҩwѾx~?"87䅍B8"e3*^OQ?R 5 O#9ڳ CAj;yd_ի]C\OH!g~0=H!>֎'.7uvGBWB}|;Z 0ބfjjfӽKXü!!~uTpge IP{3?sQ 4\eԛf 雍K%yz.@x,R.Gϫll\Qm.X VHѯ~b*tj;ڷ6Sa.E@ؚ%WG*Ԍ =?R^B!t_[J燷wTmŪ̓e4"oy{ ߯<Ϣ(LwzA:u/"p8?H˻.(Dp,p)QKۀ6TnShA^쮏=?#u1Оo`sߌsr\T)m`V5|dZY?ϣHA)pH]hQe:=pV1]6\8r! 7:ME *|RޱH6@;![ ``_׼G~mP]eF=D^KHaVl`%i1QTp}9  .m6Jc^wI6^yy+P)yCj{L\kjzw+X k)*R͔&DJtjasKҩiB.!RrU@GhM& y~衜ǷKGwF L;{tjFSE@qJ ۽i뻼\o*7}aoDD{Vjmںۺ+VUsFU\qՅ:V=BHHHI|ޜ=9o^}a kGQی⢲d|,W?=WBr~6/.kg1(BNקH h=xhi8ui]q O s>W!m2"qW볐,_Y}>/b|GԂmW(/,$ޥYHrSz jRv@}6sPm'G1Rm#C #9ri|ۨkz!z.`Us&P],$S]@gޥGg!Y |}zA/!9۠ Haa E*ͪDs(P8xh:m ZzJzhNL'~xQr7Q$ล\b@QR\Z< *+< xZy ]>Yݟ\e@:VȥJ!ޥ]QQDB66BO{#)W]oɯ\YR\twirri[,$W*kX 0.,Jrpku@,$sK h9m@Nj>('y5 ?̈ǸNޥjyti/Rfԉu}JrHB2ͻ䭎Ed! ?gsPfqq}/!gol0Qd,7 )).zRGFU Z:hR9Z1DG--@3@wH,6b le)z /Ez?PR\4dՕnhZ硢%E ҫQlޥQbT(}a7)>]>:wC }z-+Ltm{߄q}ӫ3E[%"ޥ'R}"5*Z6"KF\"ޥEKoF?ߣ{yU@^ºr:Bx Yb"tBRVҵ{r3rCLJ\Lw4aEƊ04xhv4+EmEہ^n\;6K=V<'J[ҚR;}i,%\أ'ɭ(keEgV>(n_-x#ml$EϡM~ɯ5¯ƶ6#|DuDnA2ʙy/Ğz&,De^<$ B2ٻ$OBRX2(s;|r2TMG*bÕ-`I$U&#^eHDc5zo'"9Fre;;uu0 `X~ X](g`CKw[GQԭX*>CK>o]*).nq᱿@4mPyڹY__Yp%1P'"-SI)(B} ͉sQߞg m:AnC>.PvVw#ڴ?#W5xޭ6}!kp!?^ߣԔ]t8.xAe KOEB2]C^a8zAޫB^%$xUw^DNčC[%X05Er$!%>\줼mJVnر%E9'Z䢽cXrKlߋzF# r5*PBW4SkH_L>gRsO<].). q읨i&E~\Q9yljz}7/sKZ95JʪѦ 5Edw("&AA]碒 { 9a "api!ޥT|c^q8 7^PO%9S43\;sg,$cK[#a*2~ixB?+`Ce~\rk+TRw҅YHGܙdZC\aFcDJK.)).Zyࡥ"궥 Z'*+:z.:xh1dCKKF kwrukZ .V`6H|dl棣r,*ٻhwj P^ xһtO4( l*Q]fZN/ſ> ]rB*E67 {{>!]ɻHߊr(/e!.}X! qd,1Qd*DW{ࡥӥnW( "ܟ'-QB{j+.?!`ޥEw <'Cl d!ۻte&r:AwL$򁛼K/Esk*5*wZ~+6(Ta]zr`RYH>.MxZ󲖇8ڠ>xBQ;$^eq"'?q}azB2FL X* ?ij."ZޥK"^ޥYTx~kKjJ& 0-KX%>}ҵDXXwxA^G1z%EF.<6~ˊ \>B:^Xe+jK{p>ym?C%#Q[[{>jUDf/GB}cq߫p$ޤڥUt.{$^$ h%*',Go6D36EnH;4t>}< +Uwi'b,^zB +s7H_0.Gb/. 0 &5Qס(.kn-fZxIqQ]z#VOxm>i41:| m6_@=Bц ͧ>ChS?8ͭD\f^M>gu=!h["eP1`"r|@t4x-2 @oNYH2xQ@\ moJ^9r/G¨ ]ӈE*;>I1{{z䱺Pt~@3 JyCKy8*[~[ 0-&5WFdEzSС`yࡥ!%EmX" B&sum8@bb"r)Aoh=*=kzejp]QI8*j6"0PF׉\oVL-/{7,${vC5HhD 7 ǁR:${uM[&sGbT *䊴BRsH`]=wYH^ܓ|wז@_~Bs`ZCm/$H.ǤO|XzmvCeTP?T!p ^ ZʱC};k$,$+-DKwF3u2[Q \}Ue!Yf8n]aF#z 0VOxWRɨwmƯDhf!yݻpuZ U]bok>mGqyЗ+ͻt=/C[<׷ [k\QWtoG2^YHfz1ujR.BrwvG7 hSdxEk?4\@?S?@ӃH`ou|RI]d!IYHy.6@}Y-P P4:pXo 4~ KsMƻJ|5 ɰm/OH=~ 0e`0 c9.⶷@.xYWxTtI1B K6^ߢ>QhN;OC":pTܨ/,$_?6>pj,$sK_Ded,|$J{IHd޲Hir+j_aF#aY@{en4û&$resƻ W,nwGs2$8$*Q0Qr^byIh kxo/,$G}QYH[GiP \dBB\\o>f!h ĈrJmqa+9Exh6oz!2yEY㖲)=m_>CM#AU6W!a,:9VC)X6wH#W]z*[.K{ĻJ97Q7ͻ[j@ yݐpyXBR]! ~S0 X<&  ҆^t<#QYH>CM]6$m|$a"uB2% ӁYYHfeՕѐ.e=) <ȥ&tA g{> [}]~DQ>p \Oc`Yݓ 0a#ޥ}Р\iA/ǡ\ż^_4Wgc,$\ױ$bهh^B]BRUDYHbH8q]% ix9j7!$+93 0rSdQ&X4,nF3~%)@I *bٶ(]ëgwyht^7(آ߻=3+r覡Q%!Wvx}0 hSdx<.)`Yk]Ƹ6YH9~\G۵]b=ܡߠ?wd!߻t%()p!H*at0Qd@xz/R} ҫP:VuQBR*ϳ9_TVU Bus,XC%~S/0 hd(2 X.5ߞ&-87Fe! ޥgg!uG=ER|Wna460 c # s(a%E! ͌ǏӼKpPM~ n5>F4:+ޥ]Rkr 8.< 0f"0Ae#֐ ۮp"+@GU7R]$0 `A a4u8[`zao]P 3YHB*H :0 !00:]X$],kY@m;Οܒ*&|7eyk-ƚCٵ\]z S+3 h~(2 Nxխ{Vqo?vsy_cRfL)).zO;T3`m W-;tHF/: cQzaa4 V>gFBRպgMk3wu鰫^]˻vݼKw-?ӻOy]Zk|pYA3?+< 9N=#TQFCTp Noy}csx.=ϻJ^aaU Qw8맼*^ t0p{fYYHKD^ꪭxJIqQxzfxQaBJ^sM Zg-p<ˢ0 0V& X.K{" D8v)k\yV~۪}OhUe | Fe!Ӽ!M?_ |~9(P}  h߲{ݢ>BC?pP BbfӁuGgSYH,U0 XmXaK*Q^BLO` p=yvj~uԖܭlFn]u9yϐwiwDGSP[*`0]pa,/r eTࡥ=|I ,$wx"a%r4AdanLDP ;VQ]mlViiUyz~h+Gp%O AS鱇hG8x/GeqkoABn+~r#Qs(ttZi[MKwYHDqwBTDR]zp].# K |:܆ޥQhux2)).7/)\Py^sF?}oS,_Mge5#nYaFsĂ XaKQS',$﯇YHXyݐ(9x%=Lfg!ysnA=5Txܩ'cz, Iޥ큹1;cz_{dMP)Z܌aEj~ zE.Z.YhK3 0ǜ"0VooPL.K_ޥf!y T `r1kx L @ gzV!q8ۻ,$)h!Z|kޥcQrQ|pQ|xvFGoKP(Bz6* \7лJw%;"qPyO䐵0 0zĜ"0Vҍ?MFCp~ E2@JbBx (Aeo}Q4T~XsP\xkTurb"1Yz5>685 dYH>.R"SVAZ{w- :Cum||W{vOtv@¬E-3 0 SdF}%*G=@Ht@sѐSQZgRi9G *@N 7'cs6. _<=*A]/Pwl]LYH& ^Ϸ+rsnGAoe!M.m{'TW\x݇!k!rP빻{!_߻>$.ل 0 cRs3 0$KQ9\_= u;=vFc72VH6CrH܂ֻHlBweKl9#u!Cҫ1(hF=L"WmQoEHҝxf=}89`9 %εDz,2P)P88H4 0 c SdFdࡥy.F;-9 #ǣY>ۢ2HlܧHm\xܷ'.>$; N>H" 2g a!0ȻO#{5=* dȞH}d!yi1|r F{B){aa4ELQy -9*Dc *A}@%mHFB@$!spGyCPy߁qoü}#oLIƪ+k0 xҟz߸Ey7q͵y8ػtH'aC=`aa0UBj]9*kz0}1)]P2+(@`{``w韑ȲE-*g;ѵC")TqOބEG{<C푨9=Q8 wJ|ABRW5+ 0 Yb܆aAWcDF5*+%m\#?@ehFq{ icF7hPkKs<ߥ?Cۨ\ 3 =C5 g l(CU zk-f! ȡHl gm <]zwiNޥh(uR]wޥwi(^| n<70 0.gƪǻ7*G=YzH B'6YH/B[\] QBtEnB(0z!h89EcP%HEgS: ]6>պ PZ?r^@7@.!ezv֨nV<O Txw{>CrzdաqCmoaMEa6KD3x) I"9ٯBrj|7 (H*+C`<ԗY<~*ˀMg]: ?T wr\ڣEwޤ YHF< '.#&Zn}Trrވ@e9177>xL+Lǯu5}D߉h@wi;$E(wA}PÑkv# 8`ތk}^gN<]v.zwiga Z0 cNPhЃ( w쭹?{vDk_ Rw_e!]; a p (U`^]3?&MLGÐ+TvG"eT,ldw@,$F}RP[TvQL)x^D~}P@(01Ao*i$jʻt*{ KPIHMgy{vmݫrB޴{nAau 0 1a0ՎwikH$&DXҾohwY* %=܎h8 H̼dw陨x5<+y.< d䞴B2 ]5z>PCz{NCK" $HB2ۻj`'1YH. =uD+G~C5>]0 xCS#sgz6G FIYHɑh*x Q?^[{ OuO黯o`)aFDa AiWK.B2ɻ($ QZ˻t;4(*A벐|r_ܣ3+bP(4ԧ\$"7?@ۣDm\xV#_WU)E QCQۼxѨ̰K|ޥ/Ǒ;އ?uoÁ~ޥ#G,$OPҵ`%E?3 0ƌ- Djy,$! ֹjQ>ܽ6QyWџ]9*QV(6 }IDAT̙m"D1ԋsB"'ќq&{DCXZ"V^>* AbhB|l.AzOC"5_tT\{W$.EԸTC4^oa})+FB3JĀoAk{faMEa4%6@GjTzV<9UJ^Eb<4;u:gfGe>H:d! *)ޥ"1Cg! +@E=QcPy`.03:"^C5rz gxDU*?kZH}z JJQů@˥9$.@.' yoPoaMEa4ZK xy  * E= (0`&03x' wS3u+мPQYH- t9 "K;?ޣH<-nCnRa|[m}^o0:q(bs䂝"ϏwEG!wg!  G+41޺-C=ZX[/E`|z 0 )b0L$ >aH<6Cm(mC'r#IǢ-/?Ck-K-t$`F[<h_#!TRnn HDۮUF#WN>JkD7H\DW?ײk .YyhϡoB}?G(|a=݂JND]{$`*P9UJ>D "깚<+6DT5MB2˻t$߄J+pDTxOKz 0 )`0FMIXvQOM.z4) ND" qyC Q~G^%]KA!12ܹEŸQ_БGYH}J_?!qv_9{#m蘅"ЕIޥ{q}H Ӵ(dHG=MBwޥ0 0#y 0k$h"Q&IЦ87MoQJ2<}|=Zcf# ٹ.+mY Hܚ{r,rDB$!7]}܏CbYHaPXBq~yD2s+P8%<$&~&YJQW> < 0 as hR@U8h J$TjC;!ߣyOiIk( Ll+$@WȩB(g$*ۛzqʐp(]f#'}70" ޥDTp=`t.<ާQYHut7pkD`[5 |d8??/ $GNYLGמ=eaM+3 I2W.]8=/ ;- $h\㹞~)F`6+ÐsmL$\&(bC 0X .fANO,$xEm x +]* X᫵&!N ;^ Hli=/ި͑` ]/ ɗwj/j=ml0 hRXak1RW  \Qi؀r`"ѲN|TKzzq*0 ]2(.cfCq/0KO@M"tLH=P)^%tbF% ² ?}$Ga5(ޯx/FssHC",$ K@=>#g $@3Rt r+uv\h79EHuBs>CY} 9g!ӻx!72$^E,$ޥ0:x. 71|x =P)yYHʗ2 0F"0XK/FGͅ({m|JJyB2&^K4MDӨZ!A7 IE/#v*];K@R.F©3*;4epf' . Lx5sV6LoYH.[=_{w,$,-0 0&&s?y '˼ިMTb7 (53! ޥ"! nNEF}Ey%؝bşEe:3jFhs8W}Z\7zG =0!NERQTr~OMdJ|.0 hjSdFtGj[H4LB=D&Gd!Ww (RlZJB"&q?mbL"Ѵ6Jr*盌YH~0 X00fCޥ{>xnܞ(Dx:(: wFg,$io],$so|EemKK=':*? ۖh0Gq۴/r6k ggak8& hVQc:*Sdb^#r:/x~Z,$tWX Ro@w.(pg|4Sޙy0|b `TqTwSt7p"  G7qYXXXI5lT{v~Df@l`aSzqT0@qTZ^,וy0PRNk㲰X`Ck4J(d0ߚuPk3H bh QE(9 8ߔ 8)m@Z K,,,,HX"fcGZ!{hfUJUS KHɫAJ֝Ht ?vC*n/J E;p0X/lcXSa=bk$Jވ쏔[QI!_l40j3H_ WG/8E"v*UQR\]T{/끏l GH-,,,LXEbCS li!*Y$B!ʖ>H`mM5PyH {X϶6@_x`y`+,3(_CE`-,,,,V1,XPmTP+@*Q(SY?!X5" Aɕm.J,,,,hجI5 W;룐HL@ys :z_o!#tVk!: Fߕ8EDE9"n!XOD/#bz"{~q?7n73_QiXS`ŚSxe ~^43P~]P"fa!?W1 Onܞ耼_}oPˣ}:?GӑWO~)^* [XXXrX"f!?=RJ2B2.J"g~ |^\7! 56)|1̿eqT/F* =,X#PCe]to <.Ԕ8EY@řr!@{j}L(J72?1.B/k#p GVGQSUGI778E!ߎjqTcjXS0UhmyQMS4 wG8EmcQ9#P}Е흉*_lXXXXX:جI5 9;!2B0b^*qZčPU=`HqTC۠:hO8Eۣb!TE sQq SCa@gT?è$Twb*bkGG:F )OYHyn=ǣoG{L9Q%Nё |m[_]n,G `OJDؚӀK,,mPVHc:hXhp`/Q9j} R^Aj@!27LHJ`jYt aeĨ8*n]ג=[2wBaA![(-J+,Z=JZ䠆㖄YXXX4,dV%G-BEWE&{_t_VXTu%N Lhj= (@+bl ¢`_DqTS! x\]A6. #z(3냼bO Ui`֩cfaaaa aEE*'R\d_&J"h 37 \zNVQ%NQ P.MɇQCVaYXXXXaE~!Լ]LE `Tbv] tAj׃kWB/CْG%N0`E EqTZ ||\݁}.W9%Nq%Ns(yY2d?)a Qe)CqJhHڣ%Nf}1Qyk:,hٸH!O)zi Qa_Q'(`STb޼۬7JT;;xU淰hDX"faѲǁo Q\R($ #QqTZ^~XN(Jo}Wdk7qYXXXX`EKlj;&\8πPkP;J3`d"3({r 0?_Gbaaaa(K,,(q:G:9vV‘#c^qT:mgZe}t8X"faZ)Z0Z`![[(rԓro ;UoLAN[b@5*58<–?#ض\l` >imԼ CQxrTKn=bPಉƙYXPLy4==]\UI(D.E|><|saZXXa#8E*:~bږ}v D3+J\܅>JJl1O;5 =Z¢AT~=`A%NQQQX|_QZȠsqT "ir [hg3baC#GU lcMB}(QxjykSʜ8*},w;*m?8E"XuqTZՔǵ<1($y,ಉ-faaaho c/\Qv@W*bPfn$:)ISt9 kjYV6,F e\6qn3b1 @S]S4)܌èzQؼAQ#`t6jESYSIk* tD8c@P2l= UX1l2 ]>5]г݌ًwf%NQNqTZUTz)CbQy/,J+ @ K%+K,,V3kS^h׿[IVxcՁ(J+qC؁Sw/z5#F"doq1:i(emjX13Uǿő生TdWT\8*l>t~>j4#<^G .8daaaaŊ!]ëGc)R )q:?s0#/fU|w]g ypda%uj!ppySԮǽ p<=baaa(I C%Sk'ԚYTl$0~m`r.eN]<XRS>c_!'(.eg5,,,,YXPs{(Y,Gu tV_g"]bmkASt*oS=2$l, $bu%b+PhQqT:X iy6zT}*/ˠ?J*v2ಉ;7~-,,,ZGbp."*-Q8*臲ú˖8EJ\]C v16(sP4l✥haaawU,p PxfD: ^.BU]֘JmQg?3c,%`L]19l~\6ܯEK%bWihm7N8EmKfw qಉo6~-,,,Z ,Xn~9/sxzGA^NSt4p wqTEUk瀋V&޼{:bDzf{75x¥ȿEj&ܿE1eíQ?Q~>0+m͗Ѩbs BjS2j Avym~:zu~%Nў2?7% 3]Q~DǞ&~TchnX"f<8y2+a]Q\?H;G]~U#M=lZQ @SC# ͭ{?]2GӀ`U/v@WJӔHb‚d XaZ]gwZ^J\JmJPُVr+8 HAJjԕV=Gr%pqaAKtXXXX'ZʋES,Le~y)6gToR"acHovyklYm@S8XY㬝]r;`/ c3>+n6m`ݑde@0 |dlf** zjp+QjEYciTo55(Wpe_i6vڴjsl=靹Fb1(9 (GFG"AE/3ZXXX,> F随T MӁ/ lc Tq4iw@f `$8Xz(Գ4`i.eg:뭇4|T1SL}a4JX~}DU֯#=;+Svϟ[YA} ]M$6_X @"K,,Hהh^\w^:_Tlh"w}DZAdǠPdk`;ONl۽vAុ̺~ت~x|y]N>(T򝞿/\?xm`Dve䟎~Oa;>>ivܩz6}o_9lSD*JPh=&:x ݋ş xMo}ŧ~x;zԇ ɮV_lb\|L"3 o7^]ʍs+*@p &/(1Oވ;Q_^<*qfd9.:i^6cw0vzn9.hQ뎧M: DkZXX`X"7Q.NAeNX&^2c;x{-fTG sPH1a&fOGzz "BaʆPThmJSVl8]~}śxJXnϪzpf_!Q`d/gV/o@a̛F]/z?(N5TlS`jaAIe‚ D*L<[XߪE~ "?@َN=W~K#u 2E/~h";gS)H 5[Ʈ <:(+)ܘx̲?"Ů"WUD[v."i'pjhpw~W; |\{6XeDHbX޳‚DrPz_Ϣ'WŸ,,,647Ũ-MHjz>nIR~g3]YӵQ{EPAt?ǫ+uBk`_y. T(#CFSH_g-BFȧeVԸ GΊ]^~8+:}y ^f o//1d!C(x0:fP6feY3? |‡/>ÝQKf}P}WQA4\H]>}(2DD,=ܬ="y]Ͷupk* bƸBMhm盿#'",f"n"X8ZuDPTyu2͇ogLepj׆&8*jD*G#r~EyƗ]еSXK~s ]@_݆E˅ M|tA(FjW(\wpY}4DagͲ'~}A{C 澈C52@T+ }nkHŊ#.: ,Daa^;,wpuj/~?",^sU D~ơPE 3dz,0XniМPo$;|w\=( ́k?Wuԝi"S;!?z O~ ^kԁ<&ahMV)Z^|Rup)R":t=YH\DV>G Cf& H >Bd#}%RZ!B#⇢f!58DȦ~HD뎡;R P<k9#Php"\;"R5|.@aa(y a&AQY?L-Bz䓻 QM5FaA‚TM"gwtMؾK[.HJQK1%a/lhe ADJm/_ÔIhBH jCG#/QHP@` G*+2HrͼthronG#em9aCyB\?h{ *"v u i )_I7C~}}zơB˥P,.p贉aNNn] {D(y71Bp%2;w-D*Z˄YG?KOuaA>jKZ/^_aPۡLȴ ۴): )AFG 3;8ξ*\?l8#EYh=q|&"[^^.v.D"V?wB9؟0u^5`4Dk?l=TQ}%%$1B>_נ!lb5֖ߘpm:R@rT"soGm mCdtq޿3X[21,DD/D>mpcOt\_e"$jYXX4,kA0*z/!Xk^sPݮ|<&G/^h½:S"i2W!l"pR>B DTN. ћ}[ .z1M~9v̶*Q)}%Tdqmp 6'% |aRFf/u/_]gya跽eM>~/~WO~q6.ZN/6px>"y4 Yl4y0"a T3!EaՏEO4,,,$HxHQ )?"bo@dQA=TOVw_#[DF&;5Gˈ |h3\?|gTqD)/ԔM`zſsf¨~6`w7cF^YDqE}c2Ӎǯt 6Dz9vHA z/>Ϝʮ9g~|y!#xM>UjHX "RsCmKCt4!ɵИ$[L8¢aX3ިZ("5#T[seǘg^fa'u/mFZ1 "!JV<x×Pنȋ)d{E54p~BT*3H9y.G?P?-P:\?M$@V"s7(!wLSP(YѮ(k~x "@7fmHƨsG P\?,ᐯxF=1Foc#uG5Hj]jaTl?@sza%{<^X\r4UX0$b͚̓\A~D1LLEγ-~,j"dS6daz<]O&|d?LyfN  /g!J`l&4 :YdgB`s6 C5n@\[4I]b/4j%(|6}'!X`(t42? t1ß>}N*r$EY_$‚&FE9œFZ|k]zk,[~ Ym^:O^k&y^:#S"Qx =~Hۈ]HN7c`Qg뇗~8I#W |Ð˳[qNÞ(BIgDEfhRѲZ9WMVif'|õw!Uk];ށ271i5PɋYݎY"!U?#^W[T:o^DuIaArXaAF8,"J5\TH:$R=Cf;HB5(D*֫¢†&W mzDͫ׸"X!p:"@D[s{[T{:U_ۗQ>zGPXp-n6Q @QD`@{țt%m$6;stB+(ܹ "WglD~GK\?|3##4=gBS9Ȍ'S^i J2x%(DXy [X'\? < Zau폪jۻq‚lSc/е=N~Mb$$RMPywQMg\ ^Kidaa 3ZuJltTL-@.]FTk}!pm_5}#"+d:=(30 />U*CJ$Aj(/D *@I!5$iԸݑ+ T~7YԒ7q{~=0%7!7{ǛcF+8ޢS ! 6R?6% |){#\?M&lZ"5RYvMw+,HFaɈw*,Hn[XXX6뀮4#e‚HܝCm I^<ڞk".$[W< "pPJgh=Sn4> x,S@v(r"U'Uno {|]hBYƼouMBWcBVVՅ)W/'mO fcQnː tR4*8MqiB"`Bg|eX/6O؛_0$Nb!E t5+TlS`5d-Xa{M61\?< G"9%* j3Q]YH1xeNE " . R/Qa33Vdo"=&2mMnjmCT>%F/W¢*bMQl0p"*o -B W#Ӂ7AÎ0YcYC: y2<*}fOUKrqf8dZ-!2OCF(x S~ 2HD>1( -g6Lt"!]STD^!ZoF 6{x D{@*c"EVak G`[ 4?aB[ۡ q;RԱ-QDf d&)vWR D*__i~N]r*Ri-""Uw&|زr^U>i7ؔ|L^?E*lWf<xv]l8hBJV`Cr/R' ;C}Nm[9Yfd(9LzyRVka$R6hjÛ3s& ?l"l-i6R@|(Jߘ#bz9(,z݋TŇ16? g@‚-saa*bM+>PRD*5ȓt(d f!)"#%xDprňĽjaart RfcŒEP=Pz&{#iJ"27"HLnjRx]? ͶGaݵͿjL[T$ ÷u04? b?B xD65c;/2ˋ: G_D0JX;deEږ obP Õit0rP~Um %bM"%o~6Ϩ5/~k]?7A"J\@M#rQV=;Tވ G 9(T"!]iLgGrgD]?)mno(2 \fu6X'MZv06LD @,Mz IdԜ*g x֔k7v9#jvi š?K! A( rPDϗA`jD*x$%R R*% By M 5>BJ%j,}λ>!"3/~")@NŇ~:RGVu|~>",/^|Qn&!Bw,zӾZ²ۡ`lw56dMൌF EyʁMRT2cJZ~]B$3)V!` O/(̘ڏH >7q'^|Ɍ}MH v nI(֨Lozz3{ H.F!$RY4X"+,Hv5x *خE %bMSaDG?#V S7+HJ+N]QX7D٘dx\?^W#1oԥЄ XdXD*?Q+P1U\B2颴i^͑Nsn9(8 yh7DZ!l3敚}Q,ͨd3y9)t DSl'D0sp4"T^Wg Og|VXc*bMC@! M i+`7uߦߒ-6C8D: 96Am('lmyUȏ 5 enaof{:TOAoi@kQ] *2wԂjv>Rgڙ}.Ydl۞FMޟ;i(sL+MgTڣSiSpb#cypR,C l :nAٰ|lXZ^SqW"ع h e ^TĔI W6뮅Be:Z'RXkaa%bM^(/'PGb:vFj,"9!h_$R;m`bUeqZXXzX"ֈ0wO8|)7Ϣz =% e RoԆ' zwF0>c e.@>ېת[ռMPaӽ68ij_:Z"xt? >Ha'T!,SH m2:OEj9v97R+Vw5Di6"T/6ݟ LvIJpw͙鸪1h踯ʆH ]7r=1PX\Hź"2VY&XvN6SvY+$mJl|tM$R˗FX_?[X >@ϟK"sE3C7dx̬!ܰuG!f\?…X3Ad=Ti D$b[l[O0/Df{c^W~x"ӾH{Ԍd<@'gȁȼ}C=$t2RV6B$~&>$hi+DHuHKs~~E ت:6 xQ Bt q`Donyn24tܢa>RGsQ۫E(?e-a4$gU>距e~څ ,,H$R2tnn}HŲ lnЎX@^(b.9?-X#-PV3($(w_ /Y./>.,HV~"A#r ՠ7!﷐A|/枑w05sp ]^wyw"#鞋sQق/^e/) &/#nW]`aL+X( x u:fETQvM{: ɮ2fo@9^|:- E#*10M![`XGپdIX:%NZ<ʆ;Ԟď:2C šeɭ $R] RX,\EπޙұɚD*V@ RQ*#UANV!I‚d=k*,k$vKTA8"!r6|> l";?]jyRnA|PÁs/뇛!Uh xeDՁ@݂BWr e;Ԯ H"(CJxC'r~NB!L6R"M5"f/ב,{{f{{tSyesv (o6*ƺR6?g#BĀW?o8瀗L?I,? ] ˖a!@rq{D $R!(<ݿCjD*ux[ɪD*_St*,H[1oHE? 9iZXX4{+/^|;=r@(ПHǕ(5y">R:|,j[\HoJ;rrHP0?Hfz4PS9NC\?1_}xyE6Njٞ,zb=]?lgj}NY}vmm]oLjHaSouORM"H~zf-Z8Ts՗r7VsY˴( :3D']LLZK[ogTlD*ۘPf{{fr5:˃D*&H;)G, kʞ8Ny'7X-ZG x3JS;rޒ(O<~XwB%H"♍ .Mn07U nh"(C>J;MF¦ 1G$VDnF~R1xV=d>*A=1'Pՠ,f'"pB}&YH뇣/ƀ__1+Q3epN3c; Ml':0U/)y7;݅~pc~ցUu+stR=dQAawOqT„lX~6:nLٰGj{,"iBt=7; st܋ _Բ o @i"]~yJb{H:e+D*%?vʅX4*+jz]Q;֊q!p(VYͼ}MQt2_C(q()ИDl!BJEe{{Zxmo)2K^cc#%0DHGJQҬn=eތ !^:l/fv\4^<}큍5,PyRa9*^u3,d>)?dsњfB}Vs7#/ӵ QGXtN1Eõy{;ch#ͻP(7EVc_&̇2c{NW.\Bl|Z R[ wEpR~g#r5:7^eACU:aM@_lBB/-ϣ$:8fJy p";j. _{$tO<~ G#$p';Fy1\aت@ 8B~xR&!ܛ rФvR"/^|Y U/g/Y$]?<anڰ0FPr?4'*fجBe0Uhu`3~8VÑQXpGT~&ҽ̲#vJF8(X"PWf,~7I#kD^Q VBrCmȔ fY:Ӵmٰ J|k N2Q#F$[f2'Hr4OytBRFA9mt>R!gGݑ=3 d=-ghː(8szC(b{;]WDQ8 \Qw"B 4](>s(Nvqҵ7#a(-cqƘ~V:F7E}mƣ$=cԲPFaWPfWؤ+_&D^әv&C3/⣑tRn8`I4)l3) gy}ՌHI8($3([bxBD?U~{hHɺI1c1>¶!vxߘر ]&ۃDmeeT ҪU^NkGzV^D30lXptmG僆euй&aEk)>vEaAr0",'R[ᘁ;P79T]/keF)i}hH_/Mї(d?] eXZ-qZE?:3qm(Jgdo 4WEQ8gkcE8'!Qm8Nv'Qy̱>bqnD992qEs&ua(8N."EQ8yVQmnyNa˃%b R1xm =Ȱ z,m_B+Sy eGByfsSA.:~x*zO@U3-5uBoS#4taE7 <&cLm\?,@%'Pގ#3,n @DC`ᝄHnh=νx66K̾!. /b]BT31/1Ncz<[~x`M)*J(ӱ>5v1h踹eA6~FƲaC 7gq ?ج%}K7/q$Hbgdm^@/P'*x|aꀝ|ZX҄(OB ϯP#Mjz&b#qP )h SFta=(Q4>TPz ii8N(2o8zzY7xlǹ.E&(}_cҺK3V p PSt\JX L q7)M|߾fÇ2*ǐ[M꥛?oiݠ^|뇳M6#(fBhEvUM #83V4t\T6,zhZs,uTloӵ6CwD ]&R:mZ 9)yi<~?e^x ]#0(r.,~e3]-Fhz>˼o|7]E/R۶+s;'>uޭ( 0(ݥKWΫ(q ⨙a^+@/ ݁b9T£H1^o=A^q°fٍ,tD?kAYt2zn[uJ9f7Y({Ee\T^6H"K< [nw_t733~@8 \DDJV׃}HADw1clDH' 2EyUdi ?)ohq \xnB"ۏ!U‚dAjاa%pGaAA/CvR::|!!Sv~anN6z8ÿE A^ɕ5,֚5WSɢ}& 73x$#B4a0ُZT 3y & 21"TC?+D" pD:b17uC(sL[X/>3/2mTOwR>AZEo! ˟uo)"8j2NE٦ օ2f[sx5kW;TMl;4>ҵZ仺Ɍ=Dnh xulX~ ;܆j\oc Qr<6D`[ )7`2[ˆi>?+XoQs#sgwsvIַԛQvOb7)Er) )ID^B&MtF jڻ.QSSG %\TŝXt L'R u=-6ke`JS@FڜJZlhr?i_ HD"@d"S/^x3´5 Lm&q~(D ~^8DQ(z-C!3s:m lmmž#c )HSJq(t94yރ*4q\\ &#ttwCnΠ(HNތysN_GʣQ*+ FU?1h(`gDH )^ֺL+1wqn9. ?KqpSaArZaA`_ \BelSX D>ZY+,HObFeo }!"Ri ~?], &";:z ^_5L'GspgB|x&||( U#u1ՒI(t> k-fBo Cg2R@d o\e(Է+.E o ۢr Df{oF! Lsp9f"Rv'2?T 2^xt<܍a/7IF!|~."~JtB5FfJG~א뇟WFn<2 WQ6,(4/Z  nٰ5гCo]VC{m:Dr)hU%NbQ$gIժGq$Rg3UC"*0wErGzW"uկ)Ծ`]\XdnI=}]-ZrUT/ӬCʨʚ-KĚ>Э Dfe|6D$ad#PH:\}u`}ߗUHi*Xv+D4BjħH]Z!FdD !{"y0]S^fKbC[0&S}e{mBPj H6´ +,H~ A$Rn‚dCPPEZj6 _&Yk AM(Z"EQ]YαEтe-k0,k&^|&L^ilT7RtCԄ41rxd\iyy#S&uwAP72WVhY%݁zJ tEaH)C(MD!5l:R&"Bv&'d]\Pz#r6*M+͍'5ec*T l(r>MKGfɨ`D뇋! lK-\?<Œv|d̿u5uEh20doB[|.Y0ؘwވmXЙu*xw'zk¿ IgaAD*v&z9%=[Tl-t|\|-jvFV61xSTl1g܇r[-jת},ʩU}@5:򁑅fTltŀT,^% X4D#Qipʺ^ 9:Q,sг%bS+k XLWPH?,8UYfx?. e*#Aaƈ̴CDctE_Xby^hWm?(S3g9HŶj$n)]isP4`8Ub4g|T)B>:>.@ſkP3Qmc!t8δ(vwg/%n GQ4q x#F;ICX"xpQ;(x@ y= h\?|MDç/C:Ѥ*;oވ0nY~ҡ̿E4躺q;kK FhP0d |κ%{E+ೌA7d4!H~lc'ӮnG0D6^J^ xJ$J6L”x?C҂TLϮww_5exagDL@o9(PϨ?MX^"|M9(;ӵ $~TeԨBo>m 43{7lpBY\Mנ X0)f 'NBg \ؾ6d>`QM/-,HFo6?3O5\& `!ibVFN8oFIX82;i<|ǩD*i=hq>gI?%KEd5q>s%i]dyY1=fƬ8gF_TX"LY W=Ts\#ծHYX< 5yHކۘ^|FJfj cZM^T3\ӨmR|F7zO7Nnf{Px쫌'߁E쾌P8p/6C~\ȃoQ"Uz\_uWTW*o~{Hl]>x/>vo>:S',H6D/X7{,Oi@EmZ %43K4Pã(BeoGo#Y])0DSgB5cM렉O7>HB?Q5Ѿ@tӎ ՙ+|}miX0nWс3SAof~﵈4Ddj6 5\?C7f/Ba/k/UVk֐Qu&sM5J]XW '!?uRCD`!Rp#0:\V!4DPp4Q-!Om?z + !?H Uq[i~X]^Ah9 4]ՔuLH7[n>]m (e׺o?ߥӤ,s⚅kmeA\~km:NVHMzW6t!{k!{M qG*jX"֌0F;\?|?*gEvdp32—OT&CYRYdh~"uKZ!Fk/>&cVET| "D#,s> 7e6F [v MjHH+v&jDL7/;׻~vMsVD~xF5rD6틒 .GDrf_p-rsnR)2+#> &tY ^JT+YmkëO=mo0d1~HL7bSœ2jQEC&M赋u\r&IJ`V"*,Hu~Nm7f?D\+X!oG~^1I-Hb[SǜuG~a*,HV'RyuAoQ֖{Obc< YHՅ% cum[E=5/6EH3/YXZT\2jiDdZ6!+2^3_͗Nw_1oaEea&$'(<]f %CgD__#K6YM{tTlÌOgr{XT%]8^t`4ZX:,k&I8md CGLv; Y-ʖ{ Lo>Wi&݁L`<|q4*GG<}ԦSEm̹%⟹B} k9ϋL(1s. {뇛^<]}3t8j}R> ;z=GTQ27gv/ Z?\?7XJl7nrv4iv j.6EuRӗzeoD㝋*#VD97NwFa,ܿwG$ ^@$~ɱ ?{&{Y_ETD~ |tKvvD`wwIr=z.Nb +SbD`&;0YMh\g2 '|CaKMmPKqn3S\PҊҷHިØTԻVanϒޛzW"X^UͧZVeBW"y7TPɇ(-"W1°IVԶ =)v\? M$FW~Raz!2.Hs3tx5}."9gv59|2TnWǠ6M\?K'+~9~8"B<]}]?)_>6QaTrڡ YYXd?ɺ<׋A"Kg$/o5gOa<Gi-HN@@EMDnWn=4&_"|l*+,HGo‚"o,,&RQYX4%OX4\?<O2웇X!0wN@iҽ~7B*~^Yx"/ԳԆWg#t "t# *(-4VO8@ˮtHQJ^|yk$\?E/ 9 B5.(,HgfC"DQQEd/(o3‚fH >Alf!dz`? x-GzrD2DVK?_U!ed[2r0q3Tk#23(q*RG!ňX1~xS3'Le_K/eBcB^Q֗vXDҸל5߇&>X 0ÅV~x]UK#-#JP-LbMD="ohci5m5xxOJg<>9!:.96FgUrDE;PEwK%bћuKK.>EV}i~xR~Z6g2 pU>bx*WH 7:gCv[gM*ʋD!e|DT7Gt|`Ǡ3Wr5fz7ynPXp]BlIw@o6QדM8YE$%wn!&+C?ZHu1%E!,P7,^3mxW\?|3hE-Mx!g׷\aAeToN$Jbw"dT`%$r>R_6ߵEX$R‚dH%rf_>-, 3y|FQ4Uot8j$DQD Mp~rUlӁOr:31E ,%޽3kH7ȫ6u'VcӀERwRԶEDr$7#a& g=(n4?0ҧo s𠫶uN1u(IzxPiVvpv^ߨ!xd?^\/ImB!j,wz aQHŶFI0$g&R'2|R0p]aA&=9ljS#f*WpTECLǡgt'},+:!}Qݒ]ӎE.Eѵ-_EKCQ=q= GQtwC8RǍXE9pԙhۡPV/X- B +I4PT,uuB=>Á/W}.lT^w'cC+[ nd!Y氌]'ROD_v-"acn0iP&g+ݲ}d_i HX2o&Y/u9Jod>T+2kBŇD55 [{b~sU\?b{dV͟V?A#Ћݓ,qg&q!rsv'863w8Nz?(Au@lR[-ZEQ4!qq rzDf߳0ՐݑfY8&4u4q6ۈ|ug(2,Ys%bMcY4T4s럁ddMf PY[Ň@[Oo t԰xVcq.WĠecے:ϸg/~!"oѿ2> 8]/?lpZ=]όř-RC 2j4jf(bFWo!V|KͿʽ,gDAHEaTr!%D]H[?{Eնߤ{GDޣ:{/2klFQ"EhPz@N ׂ纸Μ9sys?Mf(rp&NUU9w:c_zt]8{u \^Тyٻ2|53S\ZXA!0zB طdH9b53FϬ8䍿WkQ؇〭0 k40bG;[zAdx7bO cslWjdLs]< dYdyJ˔5kq%J[{ )c]dWuv~21' >^2f ˱{M7&9ϛ'#Vc bo3_!I@U3htF δv@sk1]1kmbT~c_^@X$2v:Tݫlk`qiO6yfػR颂Sl <;fO4잎AXw v(x)0šUIxڟџ{HO&>q+?sP> O&ңpRqE}ҋ7@Lo!Q#z`%KވCzL1 3j__r۱uh싀c;䜿O&x3dS+Vǹ^pΪ&u}<.;\/+e,^ ʟU=Qꊩ/B @K[zA> `(~D` }ިbU54h%&PElf9$֗6(J(UGQAɛť!5%n{O3$,CkR!AYhuDQQk+ֲѼ$2czAYM햕I3it]n}ťK @땢*KpFuMNŕMOitQT}P`G02?O63 7 'H= Fd/Fc@-2,@&HF-܏gɿ?y)\ #4KB{)s1]Bt r- (XQj+C ֍7$E +1C}UL!Yb٠11*ߡZ"sE̾@,ַX^L, 3;z&2+A!*;؃ꚰjѲoP\1JVȬ8m (*(y&pd~ Jn{֧,39E(ʋAE%3K [!.5mڴ2f_>unZM>yWuF@9zGթ9KO7;_@wɳp2bF)#.BП\!ni4?XuLQ^2WFE"J.2k(Il1SjmI α1hzeYl\_ޙuIhK r'bc*b["'fȄVVYl?Hg '#E=zYw"h4-`hkog0E؃r@Nakdz`fBl$ aQJ^*.- =fv7!09tGa`zQAɧť9i7y+&]?Y1 軝FKZ4o\]cr~7 ={*^62!Xx0p| P\ZˎGť9"q6vC~),p&Ob3 JYs}|w"?:Eo4Lv=guvn'`:bA M)hc5!&E%ť@ZjDn/ J5H4ȿPGB$ m=Gfr6`Qj@@32='}?z?v9+M )bd"cX\̤zb>s` p7ȾA E,Cb%NA>Pz DQ͑twdޚE骶+iiBz P4 M 31@K{ecAEr []/1D8܁r݋2W!(܃/\9=O;RA<@rB\/갠1BNP`蹮LB`:_NjOٞ6']gҖwi0]_?X"*.-FhMp9߻p`QAI}h5\sOZRgFӪUd fHĘZ6mhEwWgu|&! ]/LL˫#mu]j t ڼKRDN]Xr)mR']/W3Q"\/~޵6Que &UC;2}VJ3pLUش{ %U F"0ᝇh @-qx>k'uLkA"C@Ldf>B&lDO4V\ pQ|aǯ2TTPئ94?VEC=儝;~+*().kּeY!xp+V9c؍13/MťPږhFe};vْܜ<0_ 5UK;|+=neq ma} 7 HO=P߶tq9ME&?E-pQttsd#zr ?]$1`#@V;d(bk ×O6À0  ;ߏ p8 R_~iXFP3b N_,YH4S+3G^f,ץs0U&X:y5h]/# f^1Lt2b@ sP6W,/QFOKk k2qaF7/UݨuM+6Ysz6D8 8?J́`lBjb'wv`"g!b>q>ݣ#d=HܕD f!A/.s84o2wS4O-.-|$J#󢂒F^#=nO&[hZSز JV[xQAmvNppdכpL<[A5t9n":%юd~8kz@3< Hg\ aX8N?^$& @gVlf3C__ˉL r@(~,lܽ")Qɜ#7#Ll!s^خ'b8*hAUTW$?ȹeY\۾W2p>?fcLČꪔӞs%]^P["F7j]i_5j]} 'ۿtEwʮR2jͦO2G=S -nJor(z^Q(GCc2 6U VZ9 ť{ uC {p,W5%TVh>jk5dDϰ6:);m84vkɎ aV Y aN=њ1XlR1F;:i&b r ̔ah7R7A8S>)1} Z!6n)KR1-d} ^.U" ]BlUc^2E(5y?9F1u"c,lle^&IE p8aҔ@ I[6Cٰ󓉕Uwm@O(X`Z* kL,rkR5ꓱϫs^}"6o ]'}L̳f#0P \-3?(3_l20;nx wH%cAćlZ}ף4]\/8h5{~\/h@T/+jw}_e)!w&u{1^pӮuOK"}TXSe̹=,\vӚ쳐\{`YqiaЫ_Qhc)WߚlČPsFvZ@'#l694b Qz ttg0 r'Y&08:тS4$ @lrzk#djdbg ɮljKh<(aJ0x 1)hĈzA"_ldͩYE?Sح Q)(FJ 'QD(3D.3SbvE?cc&p Az?U>ga0,;X5',J^7%]a|L_,C~x,6ϱ뼁0N\/h'kۇ+b\o U.{HqiaO`jTR(&gvPi6TUdW h-C3v+4/x7~7ge#Y6632[1DXԳ JV-n]\"I鏑ȗaNu'# p8.p8vi}ξs{0CoA 2RD+8W?tfyw?zXR솜q(c'K\/8]5rLDeZ"6bXf ;Xɟ=4̀/0UN5uLs||m}:`TL ixovv\ok?+Q!Ƭ 7|M'/(iqP^LKPlvGPȷkk|q﹨yǔ &+{?~,o|=a%D,C}.79ekޯIqi@&ŸqZ5]LcuE% M&VUgcM :!-мu-0:\] X>:>Y=BY5}$ 4].c?%#s.;lՔ f*7מU'7oوQi:sʑ瑲 .š|QzYr+|) HĪLD W'@ld9žnM&Pi#j{+f Jy19F~2p6]ƈ`9s=EKĆ;Y%G &xW676we:g^?OQ[A zO#fU8$IuMf4zl槥;1qWB];m98'b42CdZU[:"W.ť;#(HU:1,Cxm&hpp3/X&Q¶hz^AV_5o oѮy;b@ 1aMO&j~1!3i@,#L'NM1hTy` 7у7e7PY_A:ilr~ ){]/O&*]/XWv2s!SV(,E&(2ok c[_DYvElON}f;M]/x$\&^p )gW 1F@XX'wXYO&'Fϲ0.w#Ð+VdVSt1~@#pJy=+~_h_a4~ ߕ`F i|,A~2qKt2"mlqiaUC\\Z&bhCA'Įh;a˦W>{uߪɮY_4HRR`e)~H1zA',VO a#LMcu;wh؍\/Dk UG碈(igԯD9!v` ` Yܵ=ٖ|N<_3E&dԓiv\'RѣL\D>sOķ틘 P|MgzA͕9ۨ y!J]LLF_6f˿ )WH :s@MS:f6O3Y>YGC N55djo00SOO=U:?(*( /.-8𼢂qFr2m#>m!x?udzqi!@_Mc>Xn)ALObrP^+v1h d 5s7CcIgcCvl_gWpU"&r2s-a(zvKg4Z?e^sF4nli Iw󓉯]UHOPO+ RQ@Yqiavoqu;FJdPQ\ZxڀtCXW#`L;h^TPRQ\Z0]TPZ/.-QTPi?U0l#z:0myoE @G;kƪl;ƕ?>Vj[>PJXդt~nž2ܿ쌀_WH>Gr4CDbdRlmǏE j\/8IzgǾk,Z"&@Q3uA~d7DfQܮrE[ٸ_L/_"v'X_Gcx#d=Wyʢ5,*~;2'/-b7:J*jF]-Xm"PWJ@L2qdL_*.->= JصC[;2aE%63h޵^-*(9kER\Zb>dN*W꬟CeaU?Y *2q AffQXވA^6Afhe0cqN-bbtֈ嚁o#E(BSU@ [#&\oL~v k!x2`7~rKscrw<U)?C^O1b vEfӭc(Þ@E8gF%M˜G I.q Jp.Bc\$@d=LV"_kjM~,Q.k:)s:q,\#ĸLrM,/~c ceVR%^K  NALE%VE6}սoP-[qie75²ptDllQknB:*,STQ6\qrJZ&q~0 Z 07x; Í3ҧ+16db}̚MQچp"6Em$b:ZW 3ґȁ}߬\i~2_!jdBJB ܳF`akT*}RQQ2]m mcݮLG#s3ƦbLQ~28!%\,V$Ϟؾ/-uYiΟiv(6)߫P6c xO&kEܝlrU^@ϣLT*>g!8>ý߆@|k{k~D5u}>C,3ĊKR$\4 begR1" JB3?_"G!ss{Lť_]ѝ2Oz݇ttWx`WZsnGs+`Zqia/"]Iաͣl`pqiو9}Dt.bȦ^ ]TPR]_#rYi]ɶG> @lb蝍KH-"b]G)0ИH|6oDȌ7ZxڱM Ro?9ښLQQjA}c)1| WW0y8Ʈ2=M\/4Sl[v`oĠ=l[R F_D E&͓}.*{sp?8SxN)lўIӫɝvݡfMf_"@qmW;, s4c[4/V$;#x 00ENOF2=3R1) n,'[Z^Dob]ǭAB??\cuEQwF=Ѫ&r9ӷmXQA eȬƾdo|b}li戱FE*· H9m"?G-.-nhˋ8 0la[9_30qg8qdx, ûqGDŽa8΃=j a^eߌ6:U,}qѳ1U0 }G) fLȔ7%;]F --тX*A;;1D3!'uE#Ђ{ KgJ"~ᚇ&\i95R'uA[hqO&^v ̇m̿1f_"(P p\/xO&>7SnIvcg2dF-@x՘b> X쪒0n]V"-'X}c-siԶ՘òf! lzcW @{0,-ZvsAl1net?!%0=uG~|h>xEicU@ꗭ.veh.̧n$ERBtQw6m2oa&Dd}9vEs5}CkB._Ñv4?p+k(CWn%'6xI!ť J&X[#_݋K G|AQAIzɿA:3. B0 l{>q_81 A<"oOeav']NB1J1 ðq7pb / Jqn7Cs¥~2 Wc,:"QO@AU(qf;bR@⑤RG|d֮<^pzQ^Gx6276~'y9(VA*sJ"y.o^p JarO&fX?Fԛ#2Bs 2^95ځAL\Pڢb42vG@y >U4<"3Q=DO :Nr,d Nm]cW˟F2S=@x-1^oO&nM;LX,AQ~f(xR h?q/^pY^q5zS| v@cC},m5nN'j>\h_}&xU+E%̑ޱ5G>p}{հ=Ch M{JQAI۞sv&DzAm! QBg2`Jqi6W{(Lq) gq^@좏tEviG0 q&0! ?9VC9ᇮ%M9F;:Z #9܆h< d}9Hɝ|ΰvħ2  ) o߹vN<1p%6']F{y|?(3p#E\^'v~w" iQ]ʈ 6DscLzv=AkjLjXHDϋ J<,,.-Zj;`ou,MT xoqiaW4OG9T;Κivk앝[o^^=iC7xAx_4ñxdJU!ַm6C`о+(.-w0$}NF_X}8N҃ۄa8q&aV?h: zn9IkD_) uM,ùH1E Ā]~A e1r=ӘG-@ )WA%?UTB;d"b6vC nisJ=@|WwC&@@?l@,wkzP?xKdj)6vqH݈gLzI24莘~2)?d.^ 5b q,FϯbʾC\C^EG??#z'R!J^LMX/bIoRd<^mG5I"w͇ g}⫀|zd_x'^#2ˎ8cC挷_9(?xَͷ (G:v5-ʭzB37 י0ĄDR\ZA^߀jڳTU¦?޸ptO7chP7eɋ( o+(oËK E,v_\LfvDlܒzeR``43Ex|ُ"qg0 B 7=N+Ԟ8SqZ0|qAh3 p' @q@d. K-n|:ы ~^A̚XGJO& OZ<ۡ!^Zj-Z|FO7vh>do},Bd'UiE 7zQϡ|VӋi;Bd2_XZG@ = Y=B2 .{ ]/@u~2 0)bCFވi|ۦd03:6n@Lq|4K~2^~ЎAnXbx⦍*.^?h*0Sд~n24EJ-JU#9߱, 3@cԤޱ| =>ި< x]k!_F܊dqqia3fSTPZ9!Bkr4e4e2m#gcJ0n+"g'sG:3L %8< |yxqszqv=aV7w%󓉑*aRQ#PꇶqC`k(2movwS]TWpdg‰86?~ox%jdmukA[^ڛW!ׇ&kj"uP:Ȳ5~&J*Bt43nv#EyN"i$7ֈ̚!Qa!fdO&tCv?^p^F"<z6HQv 3,!0m1]~_PAf߷i *82 g}ε(Csw2O-ܾ9bcf-vXuK5RTPJ>zΚ*[z]Y\z@{ˉm^AhSrqp<ߞYe2-Ġk?y9 CǡT E"ߝdb];'Rȳ m)SR՞fF"ZC,ޞ~-C 4ٹ9s!vlc 3Q9$Fǭۘŋ8ͧJDm7w`:"SdƜ(suC6A1) X>[T7r`IJ!Ҳv^r.8zsXEf~?L0^x{gA#ehL#Pvtd]ю܊_Ϲ.U(;Ñ_=}@[qs  3dQ{zQpA\.6s|TiM4y/2ZGBd]Y=E%SAࣸTG]@œ} iJq@ ^pN=V]s.N?$^ZvȗH>(R{qpC%RPbl|=Lh5|mLs5gl$#ysPVXHϴ> 5kwdY?2~2UcYkvvmȇ?rzku"7d 6Dopzk׾{탐ol?e>(Zd \z-vƝocNELص>RL(]wllĒWCPژ7 Jf>S\Z9T ,"?IQAIEqi>@ ׊kT*J d2m/jBG%r=\:r=95Y&sQ]Jݎ||GOk>ƾ\^f:z_# e#e;jJ*-&!PL 392p(On2# t_W4y@L})z-P@ֺ0)lXg(qؤÐo36N S<;1 (>4E pv?ZX @}3SN*E`y}FL*ޭ~*FW2-3il}:Ns0_icCK4mز- sWyDs.28i n{O&H;ѤݏؼLo|=/VXGg(q}eVIK ["ehhd6fQՃܵeYk5 s)a ,[;t]q RI |8qaUY˵?a7>dh PJKr7^8ǹXaz#";lJ\ Cֹ|D~+'?p133v[-C9)ΥLrRmHZ&W~UTy %[b9n~W"&dbD >pQ򑓑nim/n.~Q:1sW!ڱYv.LQ8 }F ,~Aq+D{Y-q.LYX.k2)icЖh^bDBYsˈ9;#L/O(U,D~k}m5c fysk;߱lE/;4h@ҀڒvⲅKZj0Z͇iD M{I^)kbYϜTDzDP.OA/"3Dy.C&yHm oXl 5d pt02qL["%X .t?x+R$m05!gv !3M}L;`odbPߊX]Bϫ9rz=Z#(A;EhuGL\7(RC =B <,@l$=\'O3{{͵?hmEr[Xb6./ظLc#sd/i ʪIkVbTwſ+.-;ALc6P޼ɼӁL^'fY_4Hb@ 0 ,K>qQ&GN/(cֱ dmm(@ NaJ~ Qa8NGzv^ y6A)a[Č19CbVu:p4orO&jC ݋996$V0:j=党WfXlLc錘佊vb!Pz=*df(`Ȍ{_l? eR\e_1N xcr4z2:4C?d녀ilcDȏpK=w)2skT@3 @eO; Vgh F`T۝.;a*6v\'!f 旮ͮv/~α1swaL๾'cC=s֔*=b-~續kG+h..ElVIy{ 4 <#n <ӎ㜀ֵ@0 ʹ@8ZD0 ;NG-<6 p88FH p8짻;0qu+0 +\%3>#\7)mBkP .(=D*ɘ0heYH &_Ό4k!:2D>Ϸpcu](RE!6nkd>v~p8 #bT/ݮp`?վhgHM;VBc!fĄEV#WNh^] 옏Q7O&"36^7\/8c;z}#*\/ւ!е)CdfW"x1xK6;6fٽ4#UX#_;:2ثٮ<@}P@Umz{sͷD))?L[6L_GIc4"3<_nZť Jf}k,ȭ^;0 3Dq5 e숬9a8iea%P8t7:Snv\}nֿvh l8x}2 xqYA5Qv@B]5|m@ *r)V֨Mv]1ZO"{UQsd"]ñޱ(nhr^'_%B#Pxb?^ 3+4EJ3.ҶAc^p2٭BV؍Ea6n%T"&pG`}m3jxމyFhBMGY;7"eMGe<4ؘNGJm6[#Eؼ^(@a c?:!fO!}0D 7O&qqiukdD֎ҞˎX4"xF}5H];+L62VRTğx)ڨ܏xoUs`Iqi[TPRoµVE%6HFɴŀaJq51đhmʀxέdۅaXtm.sg0 Yv@ )H!Wp'_ .rM&1f_.A#V/xՀEGn9Bsb}$djH.v3vc$?bQ6Fs?"c: 3\c}G,'e$`?wCWEʫѳ TGdb *h|1ԭM穫9>#vSsi*bD$bܚ t \egLhgL"XhVcO&J+r2/̋W#@͋+b d b..+us i[8YΜcL,u`OdN{mO7!@9w7w)tɎ*eJoT6bW![*1ϖ&7k^p*D\~6f;Y:#3.E%2(.-1YR]\ZxHQAIF ADYuJ&yx2 9 0̌1AYV$9sGig؇hSx8a8"Sd08hZbsj:W-䠜u*r"?CҜ]!P9uKn_^0C-2?I$7m*<\1q.G F&@h+-ηG" d> @&HxFn[؛.@{=@s-IּFcGcBёڵv19nPSfWo5q쪚s{lbQK(lسƩ4#<%=as(*X7{}zPAk}Y);'sQebחY:6\h͘?*)oS̹(q \9`qz vz=)m0EH+9 xqocn;s8Bt[?rqZ=d9aXIMz55xJ~iʓMB,irsE+qRn^B| MVOF EgĀLG sX[$P'^,Ě(S8 =Y;&A ׈΍`-EرtGD6@b/F\KRE??]2u6U(Yi h=XopvkheO߱3ڻdxXk`8=OZLB,v+0?857udJ= #s6 D k"]h?8 JZ.F s<|c`A4s?(rs %.^3cCWm/s ѻ[TPR^9e0oaRi8Nyð^A]a\:O&ʁUTJ1=`F\/hLĎ@;^h1Qre\/p2{N*<2U Kb,v @(roCG1Ow!fbS]XjdFA,w"`2+U# !Rķñ?a0XZƶB͈n63g$zlLy4jC6bFs:.l o7 @` Gh>2G܃C:vlHVI}CnKdUch!^*@OhPiRz) Fz\vQyhAGT |!hLf!^~B }s%E]fonG)ElT,d]'!V?ah_0~SE1]þM> e !Έ{<̟+螱>C#`Vx͡.pzXkc-d2Eqn(f)ZY 爴!菞KiUHzh<{"hPkw]8V^p p7oonг>mkY ͆AswCRy .JQAɯť!hc bP(.- t/44-j"k<3 U+ RmQbjvrT>,UrU5G~'R;Yx Ziݞ= fmfŎ@r+C#M@J;O@ڬjQS/Rد٘lF e&D€!?F@TZ!~,ب >֗^9C n`wxڱ;"DRi*ħg X,*il+* x&0=Hhc96>_Z}"f^P~" m##;Q%w\?ʜ7Bwv֗^@k_K2b[#?[*ϯɈy?4މ=YlVoQiȴGh(z~i1{w沘-3 B27@b)B

F~GsbcAu?AZ])#+cg7NJ،pLŒmܷ.4g95_Ep0B$5[! }<5K5,ocƜ[7М!r+dbpSV!qȄQʹ?1U-ڙMJ\,DƢ Ċ_Q\Zx%z;! λgU"A$k,3ڝC~Do;4)vN 푲b ݤ^ Q'lG,qH9mzdzf=XTd2jR: yw#{[ؐh?-طDo!P1%F9#pEC@lj@H};' )s6R E\{S]تS1e <8wLylAؠ)@߰N8@RgQcC mEOst!ߝ >;!gL|zAc]Z_7;#7@HОq~?2C ;\/@=w`GTC}nl-+Ē]ӷqG{*-캣OD)j0cMO6Ddٳ펢78Pt#  N$#7*]"8"|*5 '+k {;-7srBE#,J6A)k,pؿ7@-bznsb3f#G8o"f%RX#3}mE;)njyx0su,sǢch"sa HMs$msŘvv͐"GJn'( p5Rspw"IFd}bLmMja)9w5phqidঢEK3:=mYXO  LA=`^ ).y]cM'qV-2Gmyq/q? ~UT;J9Ү@?,v^ oF@ T]{b~0sH1 s#v!R"ƈjigbE-{_7m!P266mKx&k!&LF"r#ĸ,.k!t<0IeW-}nH1?k~2QT{:r|oZBEȿ^L?"pǮ5ٮ;a6mQ .G~T-lCHA # zic6v6Dgf4`#?8Ҏ6h^L/$e}V^j@k gyؖ 5gxU0O?6FnL2_Q6Id\hNVV;uްՕh}WTPĀDkzheNy_WeM*;ƙ+=B_)X$aes0$ }0 ÛK؇O 0qwSή5Qt v#R\#֧SXp8zrhʯ;27} \kIT0R_\e7E jJIR_^ W#VR&P](mB\z*2+mf~Ha5F@r1R9| faCyX㧊W^.2$.*xDNi.B ZWacdz\מX;>wzqd~̾a\$< b^W{6L,7 q#W2MsV} nnY|ڠxO&<5kmc4UL_ٮeCl_iL-p`. hX ;;U?8UV[N}?z(4`6sQa\TήQCPk"3XPηd+_yĞp`{<!8)E~# e9Kjޤ;McFs`)vXPlL}o]/O&F" E 6m" 3Rd5A{JRw O?ڿj;h cĤDAcٴCsw*)]/oA`0Fq~21ULdbGlVW{NdNʮ^B[aXɭ0iŶu0 AVIt R gE)z_\/F Q9(@D.2oFdL~EƘ AY_ 4B!))CG"l_i[|' !P nG/HqR)t5>oe["%7)1v"֤Cak'EӭϕH)?UmgQM"N~ѯևы=ɜ;"0zko1r^? zX?dJ1e)zf;#S&5dв s#p7-x`U^p"2kBLh\ecYERϪߗZ½ݛ|~4c;DϮsy}Z&^pcS*aoGw;ڜ =*8o&'^q`'Ckm嗤B@J{wsAa~ kUR`zl_n#sɆv3R4jˋ㼍,5O`@,M> @q-Z p8~3um{g O,wrBij J 5  )1m`}-/ b&(!(gSd酔d!iv#Y$;!e$g"7{9F.A>I!3H1eNO&J옳v?R %X r_HvUXj>(a^܆GvϹ^pL; `C ;^ӡuCJc-{~~5aDr0JAK;g7u_Z IDATXd{ 5ݵ@(OX.T4B5~S 6u`7L tG?/PzA%sp 9̦ ;p9ݚJ9 mmia,67O!@5f4݀%Mm, |=>m2^0i{?ӥ5(2uEfhT#vw)Zmes[<yN ɽy症_@Y5kYAQAk?(qz"6rQ j`8m_0i|Sv^1Tq.rr8;ޭQ@ );?xÔ{ c)*>-9&!3!)ݫ:+v=@]l&Ɓ|n*LKP9ny4AH)`Hym|sz `$Z #/s,m]6^U1뀔ȷV ʓ06&m"W7B DX#\V3i{R"KBqd.~϶Gȇ%R:+ySd!oQ0AdصA#oVB`ClqڨDh~&;;?#ϏuEWLGAsk5a!̶vw#b@X3fh\t2<%rX)yKf,juI4E[-(9eS7[bn\~iՑ7.# W~8a.2&u RM)fm'3ގE-t`Kf,~21eg7y`vu ?(gl@L 311E")~,vcos:=H9WEn]')z1.bu ;Tv c' Lw#R96fو5iF) hwD I'I@&ٳ$ 7u!JE╳S9!&l[2$18n(گv6,ubdz912=iTu*cQ:h A4;lbi6  lzA7?d>m=_(Wyn@s)ktWE룔-ciu<_7ej3p?įE6Bb4`ȧ˥Fb$gUY̜#cc]۱~Fӟ7Z՘_c#yY=A3&.ho6E-g m~o2gz.CY+)[޽a<%SO9eCm󜲗닞L0 " o ei2]>Nuil {8־50@X># F1?(C|ؠ=:E R߸^p!Y[NiFMzI~*)(5 $Cke>cMT¸(X!HMA,Po"fg6by:ToXg[_.@JkodYb~2Al8.GJG6 2q EI/d5ss" K­gq ;Cz>Y^dIbzD=v q[RDZ_+n M&hnnX3 9-C@#Y P:4)&'Ho"h?@,sn6&[_DĚ9~2"&9́hb}kІCwI;;~! !b@`LsOLr`XF@}E|׊r3ҟ?Ah ,+hR6)K{1yzu1`[! ņyNY[ j6[y?)RR6ڣM5Hf p"zWW$8s"bOgRzwq$ p8л9=oiA iR砅G4E;udڊ03q <Ϣ|G9h2o*>ƣbKL p*x[k颓vtpo%ƻ^p.Șݹi}kAw+^LCjN{.F~T 1h] `LPdb} X6s6za{hȡ:30eR58W*Ri 'ž1ED9/db(b2s 6ؔ?ݱhBL]wv@xrO"eE(:/WQn5 /~\40(C3I45^t!*dB=ʮ޳9ߨ/C|0| + 7Iw3b@>'mXUR㮮߳^6 zTE;ȷr0g[?w 0[Ngmf1iM׮vkMuKF65Fh 6]s؎ے|G/rU`z~ | OТ3R6?v2Taf=k BW%q+Ct "3牮\ff+ǒ +/}XGѻbFB -řL I3򥛆%=:{J2@h@s6[5?u^cm\Kz!hkm"Eu#z"_LT .z#g9xnҾq58ѣa~YSz[#s!@5E@=b5mR%QsD~ =mU搽JҿXv ڤ^ԕ Q bsR# t9@f *ӎUvU \# w AJM;y"mĄEX]ѯjta+ 4 VFe92qѢo )UYl~2*ٳ2Yȏ,*A4 OI1Oݐ_u^[#6l& MF ,Ee2z}~ݨH.6w(?e,A46gae6J=- }9-!s٥DsqDOU盿=o7F F|_nt JYaYc玫f 9TQ6m%3}OlGFk'dmWlzAG?bHq_6HB 7ZBME 9<!P`5.TNm[CͲ's9)6nLBk<B,|'TDks@H*EO4?V AAx3d8 9yυZcJ) yBʙx9/\KUj\E U@L\أ[d~S4Sюt!wE 1R ;wOX[!b^m@S .-QA  ы[X~bꌜk[!s@v| d2f+ ,>/oIs^3i.5a ggWтl}-ֽ̍hn\sz~29V pQk4SNH;"CBRئronդ +·+RZLo9{d Y;iPdA Ҭm@se]4Dc,$9m&-:dETgzcU`k' >@P c>`MQ:~C~216'YD;+4ZD `:'msgd"ꑮ\gSFM:/rfx@ƎW0.CA@ o Z+08ͷ>ɳvB E]⻕T~21L^0)76Bߏǟ^vCH! {|Pr}1?#FnFA _z5VgTaG~H"E(7EFf\HD`lZpݴ{B} <aǦI626vZd>ؼ& ?x]/xvC: ķmYdLA~mJR|EstL(`b1?acz%A fs>45EldJ:Ӏa3cL1(Ld@5#Yy"y}ы/db]_YOqs4OGm|c9s`篋ش͟TQש{:@S"ƞ9ek9eS*Y a6͇!ڠ#G3[- ,"k$33|-gJ4RVdu:Y6op ^ALP2^'^&bj{b ~F |?jתB&=μ?ZM R]v[P݉88sd?i)Y1\7#uVh2ƿ9bLk>;jyu<#C*j!ZF ߥ1Lze6N' \Z;{]/h|!  63ާ?|db8 )kajM6[t3f I OUb@HIzGj?اhSs1J qbBTkh_#0荘qxW収B=)ADgG]ΘvwˮZ^0O&^/5#6ÐO]6"bTF>}J5;v[?ZY?Y a!6x2)6r@y?mMhCr>YyrSv#z.wZye sUT?ɟA7q扫0}sO&^cr.C\T})1/Zpg(;]HPh-23 c#HiOE ڏI{/^ p$O `mv}vhzLuvErO%$PR vRND&~tE C,|2onc L\Y(9.?]mL><`,D~21oG ב,'OoݐU;Nr`-ѳ?(ΜiyW//! D ^Fv#>}m lw|Mr!p|dhsOшO&v 2FLR$[ZۡgL @+/9 1\-_#Pz Vi dU/ev+kԾ oZ>ʡv*zHSr_7W_]oGrr=3O& Cɑm6FKSb_4gNFIo.Bka66CG a~u7hJ o̕w!azD*<Q9 pߕ V0 7^EǹX\ a}7hG5*CJd`@ xg&L$ u81Ehg|$R{9oqMl?8J^p^W!PO3k5d|b@IODegf 6} |Xm^.}SddW)5NGLO 襤!IELFr)2sn@c7;E#dUc6w}'lO6@N#V?x`9y36W_MLqhI^+yNΙLIqa.FI pW0rG5@LaGvE,GG;aezvcצaszMx4FLыpE)3fH,0"&-RRW@mkP G$1 Ñg,F b__l2V#%h;eB;(ߗ鹘l@M(btRJO^|SA ' 7'r*WsĮ˕Lr;Vw@[tͶ<hU; \a,b J8SdmK7n]`LLtCl i6!c [m/Cϛy'2l8E|7b>D1;3r;1|^,F6K,n{;%eW-mƪC%6k\/W2ldb綱[#D}bnc=ڬ5kEA~^ U3Ah01uDyͧ;>T# sʶD@PyNah]|s"gL>:^a8>SCVf5a+th䓖m_͑@egb|qD/pM5 K]%'w9gԵHQF nbi芔وC⑈Ӑ,\/x-r˲㫐 Qdb? f P(v(rbC6 t u9g&ELgv`[>B=}쮣LcWw`FsUMÍ\/O&>FJv+עqtcQlM;1v͑ݑͬ9 C깮1#3VbĘF^p]CJ`-R^"^u55A9(؞27Fl,ĦԭQ DɎg |"}<)_{rkWS4wDFfaMh>X ɀQk>neSic"{4:hK5sYg!`4B}7\B?؞]n \+c{zI՞]|@;!zX&! HQFfIhlrEX2;;9e3 ˋ㼍6(O`@7ȉ0 q10q(ߖaOpg-n֌OXZ.ax,e0-H?k>6sӀLkLC h:-md p2/DmA .]"r Y\h7Gu/`O3ɜLPoU 8m'z!R2db)k]/8O&ƹrveA6UHz.ݔh{\l 4C2zT!evHSe3ܯ M"*ԹhX1`>cSs2B 2"vyDnG ߣū) o,g\EU"}ίdbHMYJ Љ7*"7ٮ151:19 n=ɳҞŦvb]zA)H)ZBi3 <b ; lX{{ߡ_7mvnJs|NppiHAJ+ތCK?pe_G&V'7ڨtO\WX69I%k]I5bnA&v_~[@4 3Ē޺^}b)?CYsrr0^#DX\TDzN% r8by jk4'\/DL bm0 BdFhlw4?v(bzO/Vpy:ts򜲻3doֹoIۧc7Ȫ˛H7o$U1 "q0Η2M.t8Mм: f>]YUg2b %{PRX`Wu F7HaIkClv3}FO|E&fAhV6L'rDcP~h͐zUHY@Jhbj>.[1W="@c6&BQk`g &GG҆Tijo!6* h"<-u`)CYF C+_^b%P]B>^Tʍt0v#)?`qmqz^G'sy/RD7R&_-mʷ|xV!މm#8I"dt`3#FTə~2 x˯[>l!13;R'RhE]4onɵ_}M[h-ov}A162k Z`]4Zz>nE;[М1u#2ݐYo֊wԯ#zFD9eE`nsEc\yg硅HJ=&`}$" e3 3se6(uBL\k?)d̀ '\Way!r~d;ķAJa2 N2iֹvSn_!sH2rJhzA)_^p:1G[c׹l*I)D 124R D/E%t!p <R/m\l!]lz;6Fy3@dZ ΙMAsX|mq'<'? רhޑ csEF&jcqJ|̞u=hL7L^?'\Ujf*މTNpgj6{#{<#F 1u6y6_Oyltdlgl͇^6!"e_p?S3&dwE3 ;Hl?|F # 1,ٷi2OנHʥ"ўBz*CJ>Hx71;3v4? ~21?v6VѢ%(v\4X2h}d kd1ƫ,D`(C?k|? PqP6BasL܅{:HE5FQsdCW~#JHڽPRzA角B*^܍X!zf!nQ+dJ_m@6rW,wq0? ðE>%KiεʍArmHqX0kAWǮw\A2j?<'*M$ū膜[EG(\hfU"!ř@h\$$@B<=qL$8}gdfZNn QR"({"Lk-R4B{&6D&cQiCme;1vFJ]K6A}8@Mkפ N{ˎ8z>k=3|nǙ[pH zۖͭUf+TQr8Eފ>[İgO )s79 oOKQ>\};~Cp4M:ߠ CuW ##'PHqhRM^ֆKD+;vv)&]d>_ Y9Dd#Rf"G#p."izڳw}4Q3t$9o}RxF*˹IMAhVN,)aCĿR~@%,^H۞tU(]hNԣ$v_4Wۿg {7! Nr"4&!3Xv&Q>7Y x D&!u-Q6GFLխHE]N(:p1 O`rnև?cq.APG)B)jʎk=ތ =h}38|Xvĸ,z xmm:-h&Q=9Cª!v $(bճ*|6W6 }z ={o)HY۶NQ>W{*LKԼlGC0'3R]w?@䶀}>fN 3+ޟ5۰(a&bF1 ۍ̮hQw40l&`2W)5qhW@+QAr>{6}dy2#~ ll ^{47F}Iγ]h2AvG6%cv&*E&طwKJ=z1%!(4OC r(;#vޡ4AJBo41ooM{!"LMQyX+G x=DTܷ}{ɶn(%!vhbDr$+߀%߼)S @@uGoFDw-2A`}RHUѳE%~zBi?D#RS="KX㬐+MdM-'0ϑ)"~*0pe59ˎݿPDnގ]g Q>7=mA2;6Lo#29-~NNYH~7%aȸŠĢM8맘XX=9MǡkTg74q>̈K:qEH94aV 2ôB `94qtT0H'6P0M$E&h%2ɼ&⼝G5'ֻ~_MQlFv 3jD>SK!҄]ȼuh3՝tVEZ'dˢD: FXCweO-7"i? R(G(emYdht2ߩyv]뻉-vvݽ G1?Fv}ThLasQ>VK)Eq:pK_5|ު@ )r"a+"[wud^DyVB&H!2^YPAbmd)"i{ų}9nt*\]#s0_@ "T+~_\l}w)z^>X:E>)Gт`(FeLb(1SVs5]Bn!yg)hjD{y/_WK1kX،`+m98=R|02[@`ЊOR@'}Qύ  XG!|D&h"Y-۾[>a!EJI,?@[S} 4E 1AsOJM4EnD/SI.B3x|'Ճ0gEtJɱqύE`DE>LOǠJtY|.YOX܅Hj?>w̮3DD{ dJ(KMD%z֜@HiIn\mӡ܏a.".=?)fuqZ|0e3?f};-FHįs.|d(o,ZlɋH~WEz!RCGD{x %IJIf=r3Qٺ U\ .QZ(QS ѵc-0ȸBUǝ[nUQo~,(씌+NSeLޯ {e yf;9窼ucvpS,D >AzT-4F ND*H3D:haη|/Z[4aA_&ޫ)Z2 A*h MnKIMͨT itC^7@ehI"E&(Φ$;AY;3è( evFv'Pv!5dxϽ ft(hD@Ǜi4aWGuAokݗDW G7;"p k˰: u=Qo}'">; -~4nYH%xCjFs}i});od{׮qnfMTP`=X0ˮaPx=wI: -:!2իDd#cD@,?gy EBN@'^@O#b 원XDJHU^jYֈjST*}3Db' o>G`-ޞȸB4_Ͼ97G`wsd:.޿;-Ja՝sHm YCcЩ=s94u} ;-ډ֦ėzo]h`a&b Br zB՝hBЪDJEO@FH.&/Pֱߤ1"qAU9Mx! 2WV~' z齛 _;I~Lb}$~=5B$fT6$I| @ݴpcrI/4Թ8&(M|Zkە4:l?ٺQ+" cDJoq.AHls7$Lj^Q>nq=~3"q+"EI 0 )>Endױ*z^n x(~[C!< 'Df_̧hķ6,!H\smt]`Mιeq,z/Ou-w:Ẽ9N.SVAY};Ѹ_ݶk3z`BKl (9.&JߊJE5۷ 2VwM"KH~ONGsawJyA+h5~*)vtBRzei.A]r-:mU#;|;ՉXlHS(E椮Hyz${|>j YU}{?nS+d(4a1wZHy4㏁Zs.{"wY)"'hB QDsZ>/]xM"Wd6~7<&S@.w:) ݍiy±XaDCjZhRrT]c𜑮͑"x"ev.ȏ -TnB&ZP)9Jwc;^m"a޿fH_nWZ5ȟlm9Y Mcs zys:9-@GX`Vv ݼZ?3^߂^ 27ChQsgkmj4U#2 %׿QYNM2z@uw\d z=p~*@s1`Ch<9}C /Th>s6.,D sO@8Ahc4>^Fˆ򹷃0hl& sD[#ƿ:= ł0HŽ (w xylCʳAn;T`6]zב78q/^inHd^~&j5gZӛDW1zok#H7w^kW"G>\sX;+`LD-"3{7&"(-:ۙE($S7C `J> {8ώq  KH]ɇ5NGڍtJLWv/A$\^l,q-rĿ=U='b?v}ߠEVDf@{" wtUϐZ%N:p4)(Ev6A+h!"``lq>!}YiGg#?|X}?~#~<DไՁI?vέ㑻"1'P1=h^`ADž@οD4 _͑47D3EmV x*Z϶oԄ"@_&$H:FN d]̪D*'ZVa|RϽq@iVA$p kD2nFdd+a:Ž="<#f=sDNH!gHmVx6$r3p("s54:%PTn%& "!(6PM<0ḶR7 b4I > 6O_˖h)6Xю}W̆!uڱVFk10"kQy{t$f} =g[} U5t@2į!e%| 4 ¸E /a|pyv?)~X;"w9"9]g֯I#wF q(-/sZ|sHi[q(:?~g)NqΝιud89^b}ۗm?9W渗sxY%z'{w9z"fQ#w$ơ qHA;&$_V* w5IhXBU`}|m*;$zDCf4֠ w)4pRLˠ @MH! gf݂0naJG` @JE "p qmۿt5 e6l@wÑ݈@!1j?%30,5~^î~#L?R)+e%|6{\@#ϙYpOwR=+Z[*Yr*Ql-k@+ruqdd1!8X9#Kfh6F|BՖLk?=7[ꆖE;dň(DH{iFWSd;޳V=ޗ')HQs]JQ%A_Tqގb֤/V =#(4I):JloȸB_PK|46,Iɇuީ>[HqR9H}\ ݃-W{[@~ܺW"{w\yLι)虿~{X{߫l{mۯY"-;Mrc%b 1M&jZA|L537(BQ"i9g)|;2sdj{s) v却܊V}˲4uMjj)գG7R4F{bVa|/tAKD 2mQ.ȏ)ZA̙g uz -2kcc]G]8eaj JNHĩz"#tDTzxb;v dz= ?"[Q*z=*ʔ l8Phn|hڤȉ}l66ȇ6H ԣQ~@i[Z|b}AO@))!O|ns#ի+O)>%ҵ>ioBD$tY=hPD#%$J~L͐Yu Rh׶vF* Ձ;0>2CFR=Ԣu [8;)ՀmdvByOт51 G"j?-N/lvrE?;yBW޷ 轨}O>E fr74PʇY_d}Q\\?sMfՉ ¾ -FDfa+7ݜy%ߐym1ZvYnkB؏ֆ]O@ևK 7ˎ/|ʲ#({Jd_F'(Q- mYX-xYkZ6C}mǢ< ~NDc(>{m8>@7Z4fH({ݏE39_GQ7j'ULF&ϥ%#>ܺE {f}Why AֆZ4_M6fʜ&ZE֏^.%ow P2鬎Ԟz >UNFz%ӶhsHM[ADͰBHOs#!_|gLh -b.E& ?Gup|kkGr ADc3()H `2 )wgۼT߁73-vFប%6L\OuQi!"mm?.{ƂH 6Q1MH,HhMb]Ф"S$uCۑ)vS Tu0~ԔUx[ՑD(B6eTgLFlHT-O,wEۦrXTh>AH:&ݑ); mD| Q>70.C~ "( 3,H%LVۛZB&g0Miw֧Ivi )jQ?cOQ4tmCee|F2忀E(֯G9??uA71u]|jI*羶~s!"I̧!2u "S횚"2 zj== o_Z2"zX¢)@SƷa.cgha'y.n+z=O{ LtڷEՍ'-5'#¹d\a-ܟԮ8~_ݾ)6 /e\O>ίٺ^ف l[7E "v2 D$NBHi5!h=!&Jr?Zso@QMLA8qv wY{B 0~ MфmRNS uAm6{JKwېsL}i̕ D*J0MnZ"]`D>@ZE/ZE*>= y"㬟!EWt@$dsO|}v)R,>bT;r)=w|""*D*kiСsY)=E Ec2hk&|o+|_]ɢ>ky):/Eɷ2]:|wR;=ԧ~h》Rά۝TSΥ DL!4&gW #|n UxY;ݒvH9Q>75P ss|+4cZ8񻕎=sm wYs2pLC>\!r.JGM1ԞFQY/)_G$"$I2c~&HFnFgx]5qdnn}2~ֵvkA@(oţ|c#D݈+(-*ȸ5TOv۲-onFGg}_8fs= d+O#DHNSVH1o*b ]Yh%"e;%+mfh LIgHnA@OHm x1|̣Slw`M7pܔFmrIaQ>7!P;-9d|kjꭋ߾8puDfQM` Eft4?AcJd>Z^E$0>(%Q>%Zu|nHC#y$P2וALimc|k/UݪfDv2E&^v$sx@QR+ojG/:WnǑS9 kׯl"3=D?B`&yVGϦ=|%G9Q1"'"fعFχt6\|Hl=; v՝ѳ-"!36C {ζ@)U <^ ¸"PD7E>s}|nB8"PN[oֺW/hh(+%"!~}<~!}z@嘚Bf~sZ}0$=賽?E}+ Ṟ|E=1'O䩾G7tv[LZ}pՠ1 Yaޏ{?y{+oƹ,G\sns#_}0ӝT4}k`_,D.̏ WDoJٹ_FfÐ/Ҟڡ_CDD:wcrOr} U@ gm9 &!:s=G),1U=nj_JVwGJ)3?2 NY$a }I c]BvNoxSɫ둚;JQk3P']%RO"( TBd>Y/}[I>f<xj/?o' #"\?CD܍oS/jLϡMA)Ogt2RXPC (D>$ H>DS'#%vg9;t]Fjȿh @F\[UqLÚH!=M q<-_sf B)9 A u3 Mk  ("-_0>Ko`$X"v_D)]9mba eѻ]AT1(Ajvm+"ԱJ1ړdURE?޴~(2@W!Rʺٹ)J:% km>kUOQ ιUc 4^݁,>>rE\M8tD:p5C8{s.45"OC^rzae[2?39ko/z9]ks4ngw5Sw߲clek[z4}}X" m |6̖ALw**r݃LibyJ32{}|vAQ>/?j&[D79ϴs.&$=@I!sXl׽zH7zDAGDr`amg{ 5R'.ψ6=R5GpSkÿsJ%q݇W#aQYB2;0>})*46'$D .GUcr_ULu˟d} ?,T>DAF E>R6پH G tF$e D~Bm{)"Uf[~N 8 @'p*Pn(DdC:[_LsEkG}R~ Tڨ@ #?rV.N1q2) iK9WHսms`{aus'h4Nna5(G)qAsNFxWuέNɧBwskzܗzu|g_9EeVF$Gmi/ιFh \v0{s1s-Jy,Dv5ߟʮ*ckn(jd2 x;PQԴ "[dqGupwY)YY9^Qg-==Q)};qdm$[_D&ص|oN1#5rYzRN7D/t6sgUSLKf r8D!ɾ_ NQ>wuƇ y'z>bm=߻4P5A/"SA5F6\~Q>9 zƖG$X6!eۮctz6@+ X=S{\LIιo mxh5?+468VDFL9ypu/m?ܱhX Ey'wQ'眛h_~s'%&@hv`M\bl矨7;^o`&tm-l4l䧣 7l`+Uh|i)%i8} j[( !#ʂ~3RC$M TN Rƛi(^HjÐ1 Hȅh0mFT z3˂^iy2D U5smj 0>>?kRWFw ]F6CAb.00?ig۽]ێ֮QH~ AqG3YBI8a8)J!l+g d2,"{;E"sQ>w9ɯ|HJ bPнz? -:Af d Ӽ݃;v0~=[ CIʶwHn\I\:'H[3GՉ$|gCőlD>E+_WUvG&9"䷽[g<@;X/Zbk3 ?a*&ZtO0scb ͇fUH (֦e}i)tiB)1Ȭ"e Y0_4E+g1 x)x< 4Vٶ+Įi7"sH9Ec^HcOd{ۮa]$򹯂0~EjkWϽwH!udnH;~㪇\~ylsi l+(mߝbo$ǟ< HQvjI+so{U>R'p{W,i?9DG4y$"`[?% ;w"3u"alSCDGJ"ۙAi@i!?W~.]LDKNRAb!scm33fM#W>{ S-cf˜ќ{Y4&/bzFCӶƈg7]?Dfˁs D>sߣI ι9皢y}{4/ |mv(5ӝs۠hG`Q#b4u4dlɟmw1ʓuoSՅl=&1v.h@{Y5rJXyX?3OSoAgoGw)A#-hgz)3a_ *VvC:3~HW^mڪuCHun jMb$9>ax-ME")MR[r(B</XDnSC晵}}1;3yi4az"F(\L2j )fc=-P^KCksohkӑ3>!zCI$a-` JyĒnدnj?L"rĻ0~#)fHᜑ.(UY qf_0oQnewgO)e-[/SJ~V1ԯhMOͷ1%{x=7xn4hx'v-߾h>{?=ØE>כ6 >S;_"E}w7hBy0#sw0Ő-r~#U"DZ#" ;<kT% Z4E;F5R@ۈȽ|!U1=Zu8?6(B!SA_s*DRI1oY~Bj0d+8NJ D*/>>Nf]DgAYtdZ_F\ #RXGAed<^ B$AoiӇ^_HJA'])%_B4kÈCc{=sELfguK1gP٩W8@E%}MI@*7xV6rG )!d5Ohs2ι jETȞK}?DF"򹉁8-Q;ɄoHIWN R`]trU]0tDj(gۿÉT 7Em0B.(;,G+!!H9خgroVUHu60>V*rMۛ:ymPv$u]Sp+#aȉ.x30Enzڔo`dH׮7pm=T-II_)_od\s1s{|9nHw {"UO.:kر#n@HyAdݧ6@w3AplQFO,[d9֟CeGJV@aEUE9%<6PERS|&h/hY Tk"'Z2JM1"3l"ߢg/cz550Iq:E@F;!ܝȇ/" EHa䮳m;•tukq$I>Fc52,|6g}jlfX ]j J2_l}:%.NaT| )v"CHqK39H])"¹R^@$Fal} ŭ a| (8=9g=.HHIkSv3S6F^3M1Pʋbms)R3^J)e|z2F)QRBf9&*$6AQfUIeg[{n\" EdCYjhR-oÈ?\D!*"L[W_41_+R}'S: dc@];^R_G$D Gd\nR\~Hή@dd@jkQ nƺ!u-2M_ nqΈZ.L|k>NA teHۜRŃQd1s35Hdo_5 r֯+!%^]z3G;!}L@RHJ* ٗ9 5m"3! T^(|u>@>F(~o(4` E&/ `kv*5sj[#E `4C h~odo"5lkؼQ>oIOONU^ ?EdZ7F7_Ʈ D&Fu>@`#g5Ď7"d%5As"Ӿ?meDzg{D]ߩ (z1Q*SJ:`*"^ߖi#X| ?kT;^#"=ǡJ{X([## (Zs4J124h!zƾ́ݺpRX~ǖڼ"R7!U=cFς+ʢ?шT aG(8Gg>o#msFkfmogAȏp( P)]ck6>eQ֦1Q>kkJJz4z FOh(aQ“ݶt(pb́~x\8]#e& 9*:yUL'KQ?D bCD~B5 ϟLZ}pՠ1 9.˞s{# !6JCx@f϶}z!z/G6քyz1Z/!-ӧt1 z8"h=0~aXx2Da4ї34ܜ'!sal>mrhщG$J72392Ayϵ~^E]Pkd>Two?D¦X6=|кZ%!ō ]n/"[7\^rUD%U~"g]):~Qv]jWcTked]GA߇=vAjAG { b3S;r~zLnGq)R,(HJ{\=_[o#%b>ȹz6VIhbM?9^%Rņ"v2EC2wUq GZ-a|+Sibt G*MrQa"{' MjC)E$bmDH 4}s'_oTJZoGǙx]ws@G: R1:Z} GNH1Tŷ-S;[R}4Y~j ĜXq(#}s/sHmKJa\|]G+0 QoQTG|h l{=h0PC & '}) Ov۲0 ?\q)>:Ffh>{?9rwp#RGHXhS2 awUDb4yϽq6eԍKAhLy[" M\5]e=Y9aAW5hrH֥@@[oO|kk"d uj"3PDPSrH9(WRqD("d.L\.Cw(`o{gDh~Ajl $q(*%R Gc7djs]L2SY{v~h-0>.R!9ԏY O,pȒޖ#"K| n0~ =;o%f AY+ʊ<E错RyEE ϸ²hAS,|qT}v+4j>;Ovr94?GYRH4-)>)$V#p#J)M= ). hݱ /1(X/@QO@C5 H"H~AhY E}Lr r_?N_d[ܛн8#i__Ñ/H7J$%"-%DZmV2\%zU;[B|.52,cm#Su iH$Rf="zSǗ!,^d\%z~ޗU%=wF&Wv(j9Ԟjt/7mmޝ9f݁2prgM4|8.]C>Sx;NE*~snsns(4ι>:An.)OHb0 4D\}&9Фz"BLeBr(!) U! F%MzMVY4i6&rt9IbK!hҮ -;Gd&P3fA#LعH T_FZ@ (J)޶k|*vGwm/T{D"A#=0z=a)ya|Q*2{~Q]?/{SYTN4F)R)+*MHu,5{snc,!¶w?uk}!Ed\a)ܶ@ed\a*Gu_DvoA/5r͸ƒVWMW1VVFs#GcD(GlCT_ܐqлy]gBosј}p)e}3s3?~!ι)hyͼkyHL$NQv0^6CCՕ6 D2WHE({6-q*\و EΈTU8ۯ 24(O[ea)z0΢쭶5lyEu xd&sa2&% EJߪAoz0 Z|"zA7AAl4i4\n5"(콌趈(>o~kE#tt=? Һ(Wށh>S[-<kkݭN bmO`'{2RwT6;"^DCD)R1ơ1='=` )!9pwݫ7/ zeV{b{~2GpѳxzWGcEJfu>Vqz ݇QJb>yUX\ s 8?R1VhU9oLwG#eǸ RG84/7Q>v/4q-,ЋxlRD7R& =c Xڽ̏A *4M"[4a|;BDHOG39s4H$Az!unԈЀ$,-v>x_m׀2DnFE܅q(6L0>#t@3]FĜ;!ѳs3힍DnDG DW^(3Q8 /Sx]_mT@%>;/SV|D\=&Oѳ0̾T*tr}N^r:z/"xMCzEmM/Ž:z~#CtI:^\k2hT SRElq5b .BDtxDh׵}yi TaܓRrՑk4EjL42RH\ 8%-WV#gRUϽhw@(dȬ8.(Gv3BM1==iq#(ES\e#vAUWn1߮i0в]tC"S#jvF}\ӝq)u/l;gN'g3eg\m}3pR/¢~56R@`GDBc(jPU~{kdJDuDJRR"6&FRLnAdϣ} ۮRA#֥hbxmQ>7*h8:㇑ 7z:a> <ne!Q`0z?:)*0^4}Ϳ":۷Ck~:4_2o&jo A-26t@Dt볋)}mW#GϢ|no䐟 q)WY:eԩH[)h=݇HHc԰{( @@Dz7v*Hm}ywپ{u=Df\~Euocat;Q^j{1!rv4r6?YFf}xRSqǐhquzg˫`{zd\![_+TT6q-WvF4fT!T=ck#њbh YƎ V|:kd_-RHR"6w(G">۶>rrTVGe&OkD"V uG3Z}ɚDwA%EdN ¸uzsT3.̤[Zmu۵&>uox_[lUg"=;#ui3PZ_ވd@Nk!rOH)gP?kGg#k ȨYcZ@z}/VӻUo+<^;]q Em׸v*Ӹ]WW#Rgmok ʿlY)f) BD][$ djS?a|" !ZUKm7Bs3Du2"Mw!Ԉ22)8ڶY6@,|@VBpTIDXvu>o\2Ȥz52ŕגWG'&m4!B1cg76x_;6zBE`L@8%ImkDFlPjOj%nԶۮ RF!t'!u򑽈L]|<2$J9.)mD&LUdbթ( \F0^i]i@{:?g*zf ;wvG_~1]q}p["қ,z6D% 6EN F#Uώ,賵h'} Ǘac͸yv$޵̸:iEBԌ8-F#l=л/!w}>[1;?ծjTjf_E)~jOHlT b39oHP3QފS(m~Hl!4Y'b>"?P ?шH |s#b.ȧA]ѹh\QͰg@{#S!v#l w$QH%<zۣ N K9U2I>R\nq;V@DGDLK BK}GlkE5Y{6@(oϿ\pA߈XSnxm`(fG]\HDOC Yhu.jh&(ڸAY*wتfBeuӥ&?iCTbol+aK/1eD'% >]nA~C'5p^.0 lCJ5p+k؟˸B<rTRƣgy4"5סq\`:$,07rL;;fJ9pͲNVޟW0>[ޟa5Eȑ9O) D&Z {4M>tSw.1>Fټ&ծQ>m!Ҽ)" b"!PiX$1l9ηX["|(jTϾ ,Z1@3H]j"vNJICfPߋmWF6*Nh􎾂Rl*`WAٙtʸ­/Tu{_:0feۢ_} x-iMZ~BE,Z=Q0JyPPyι*6M,[56OiS"6gJh"6DܽAߗ#^|ߔ(;T oG)#%9̇ x)emOrv}h!SMc WF)\9E]W-"r*s|܈vh }b߈,n QwYS;F:%H]הc%T`9a3Bh<H}L݁yWa)RHEhKд賣ucqdzEE)^ GmMAj>ZT6i܉w8YtơR?@jfh5ZlTѻ WoBo20%kHϸқ\q'r+mOhzs~8swAS _ [srvг~u,8.AcwS4@}kB->ѻ:z.޿lJhQ<0eѢZFvr]:D ۣ9*k4Ὗsnkꜻ 뽿~ugh H|)/G ^)Ӹ h[_z &?7(=s 2A E.<jLFh\EaW ¸&Oy"4߁ޟЋ_g>]#z#(.\ x})ыs8P/@20tW@/|%[!i4iZMt!RLB`=eªI뭈\y_πL$m] %St‚+.v%ՔrS SHn>;Rl|F>^-jҝqѳZg+$w.'vG/H}MX;7e\S4wGL 4m_ޙWg]ιnge=kDBx9&rk 6mPE(p۽W8C$4ޯ_>z6tsνmIι{}9'F!o6#ι#ztνB9P9 ok |{p$DDtw4f9J7i!ӲEBf:`!G@/[\ӈ~vRѪiQ='Nnyhm#S G!r\^pz"5W4pJjeE eڮ{4oVh-~^֔_򹂩@s噼'!8T~3aQ?ohm\%d;H[ &HuL&/ }.sڮ1 H!N,f־Ash WX)"JHKт(CC۠H`J.m֩}*g ?:qV0=$ߖms{}> (M/_ֳhA12<;mJ-ιufsnaMSs"ՀRV}MnklW]]Kϴ]}?NsQ 0gs$.?N)x,1f/m-\_D*Y :P F Ӱ sd>Rz BYA߄ԯe0~>agNae\L`@9DZ>mlm3pEgɸl7pk)Rc-zAJɀDϣkg}\ D^w}B);hOir0snq+uQ"![NqΝb:)ŸZ[F9$ZtEJcg#~ J[Npν뽟KR!5O߿54#D0K W_DRo+0TǐsrkĸOCfʧ,=B:"T׋kSd{E@~[ޯ%RɶDNtB2]6kZkFak&ڥ|/@'"Bsg1$N*0<GD)٧R>c r4'X6:R ic^\20nLo$ FG${zh[_d\q邚>E \qm >[7u aA#Dz,H;賿f\auߨ0ha2u@k)RO!evwιoPI?J5[/W:B OsWv霻Y!7Y(:su6~mw۲OrΝMYssT46k'GؗyP3=~0g < 0p2"FU|'7 MC !oL:R`Tr4QM` x+ c(+O )T=)?29|l,:Iﹲ aAǢ|n9̣U4~fǭ@k" G|+;^+4m\k&S0Ԏy42KzsшX~f}*}nmãzZC`22;a}{gύvna[rvCDy"bYw0>ӕ'T!x-t=97+ȸB'djтj - o{"#B=zGd+ +y^(fF#U-j|%4Wl אp#*u\gQ {]gʒb!sb1nG Uf6A l8gVC| G*H@jWs/a *m)BAh+̓VQ>5؞6gDd Aٱ6@MٵBD\OkYhF)oAh)2[#:qlW"5kjE<[;?&9azнzkX;'LE ]ʛPQ0E_|Ezxݷw˫3/X05i]D8jⓤHԏ(2wd"sAarDqZS9qwA۬fQ>`Pʍص򹧃0T ~]])N#p8HVR`Y܆#GɄ+RM1H:&8p%4pQ(􇈠G)ju=m sRO%XwdQpJ4%h19ޗEf_ [|g]6hvNP,i6D֛հz׫nY;hY U'0 D~_BuXڴEZ( 5M&0>cs}DS& 嗺dtH迲c9Ӹz%ǘXUgeۭ>w" wFDfD| KVjσ0dm|L3WލL "@#5!vΟ'e9~)oGٹT)C"S¦lVHoFdm7-\Uקڼ93Y}ǮsJ] :-*">ctF*p28&qvϸYwI4&Qg־}B괢6HR-7ۑhq1ܔH[{ #>{nO)G>FiB_ Sza|.<90WB&$h"Xz~mdTxꅏ..a\QFdF"sZJYξ[!$\C~Z I05@fBr}&US>RVF&AL'Fnxo Oa ROCf$HɫFc0EvhԽ z0;O >J!½9TŘ~̹=]39"_g g -P.ȸBXىK ̔4#75sr%b~0#w#"xDgr$N f7Z)"R"6L|32k #VO4k4TRԛ@eގ׃0~yQK~B!V(s}PdH]JR0E|^TsMzFd2\esX;پhPPna:Ij>}WkQ2c]J)A a$cxH"2͕(EE͸qiަs 2E97 EV#"!v>Z,<46SsL1) So¡RHUYvH/ %b x_AYb/Gܛ:~z=/|GM]U@SK{R@A G뼿:)"1p˵R"\kQ| AA&?Y53JM1#1{"b6Q1WTD>|B"tWܽ}^`ϨG#"#W;RHPaCј E` Dܯ?F?a,50Ρ Qg4?|.)uq6"K#W)tu5x(0>X.㓑2+ "xI'HYԗ"?5R~Dß}d+%4k,h~|6< xm5,Eob^ e0NR9(PzSBēg[!_Ф2󽇔rEw/!sH?($Wˈli+F{!7nG>EA;w/ 0elWL#f} r^u@3߸nH)f " &(B?kV$R*Z}&ċYh9IT1k%HG!90t\]?/Fd@3#^ur8)e;j+֢,(# YDCYG[}+E}ݻihcE)RX0X`buQ>{6NI6:-|^puWo-Pߑ?ˀ(Yg%)<*¬@ec|.M cSl);a 28Jz;kHb*b hjUPڇi<1]^N0ڔL}P&zհQ>Wo^(3RA&qr> +\'Y ]zFBQ#a("2lq,oա3*.<'r) UH[ySH7Q8 9;6`e 78]{2(rnW3g^^DL\Ls{2KRXWDz d|]9/Ȣw "BF}vR} 28> Qu+\<\ن*tHb>"5MCaDȕe_e xn8_+TJhEp78a|RNd{RXuoU| ?EjnV̻7_ќ)R}?,ݟifoIeBC斺VS&0n L.9'aʸ(CQc>;& 룠2H1Tuw|*t:R?ir( aT6Tnq>;Hک(N˸EEĎ1"k!3X^B#wRa@)R,HX |n*E4/5?g`?; <|& k"*Z ǎƊ/"xnQ"w͂EO-0W;VsO{Ux4oEg (?(ݳ:I})R,xHXG}N?S}>SVGH?*;'r%fPtbr5t>ܞq[>6J93BMFH:#2 m};Ϯň{j>H`#K1q{dvy2zg! TgvՅ+E&A|LZ+  Vhk8]ˎWQUj~`c>;]N݋>P9)R,Hɤ{b6q]Q 9qA TQ^(*)8Tcj<܎LG}ORf}>;! "_됰(ȡpBzD&""E )K1s݃0>3⧘5mSн@jC39ѿ(7^૿q{PBdo ES"3lI`_U`x!2Y]T6b$s)Oz)}{=)R?HX(2۰s nmmAG,?ߩ}QMwPQP ̓XwKGn,W>;) "tv~̡)RXȐ)d\ PY>f\2 )R>E*}b`?={,$XhWv}/ٱ6 쏜; ~U( ( Ƣ~g_' A ߟI{SH"%b)R,<hqH8K`:? Ԫ=2@gK*͔G^;g\HpRߖX[(t+l^/lݚ^W8 U#}{!E 4Ҩ)d\a+*-=ȸ24T98=l瀭퀋fßsnoFXD>)7)g\4`GDڴ=)Vîl5-)RX0)R,ȸAȱ}p%0Zz#7DvR~F_:A_!S( kC(xأVSH1w&SHH’ꕮۀWNZ=Iɕ5Wx >߭9X_R[+jv@rGjZ#o@T[rU*RH"%b)Rga8Xߣ|n"GmLiaԑSQM7n7)#+wCvF>b?ʁ0^9 .G~`Pu!rA4 <m޾”H#%b)RTV5oX^y ܮ4^z U#~7SoFi M#fA7EN$gD "!O6G `'!uJ1unRSH1o)3.QSU6Jۊ*NXqOuݑl#G1Ec4{a'V|TnoXDl8nS'|Q3Lld\9WFK2AK}aۮ1>SB)RHX 2݈R[^/w?7)}ZTtd\!Fх#S( 'EB ]ٿH"E9*fI)6>;8C, #[Cw<(w(ZDªYlg s !jȑ@[We\mT #ngJEWfd~ ¸-p.JenCʹUDI@O+$Xg{f\ %͊OY)R4H1Cs;[g\/xzG'#edTop 5Q>wE݃as}o <(Cs|pY$T&32Ӯ]Vd{`K#29H"XvEQ]2e HK"Ey4"̸e!T E@V<׈1z`2x̥ jY26~C{rVT)RHX &!NM ܇2 |q݀E}sVNІ]&ҤDލ2Jmޚ6 "[Q=!{K"?X Vq$]򀭌ѻ(Hض1.n.muϱ{t\Mg[H)k1l(ϸJ(둈. ԷG>;l~1ELX ,7ջf~ Cj5դl 4EHt}z}stK --Q(Y*b>ݫ(OO3.ʙ54p 2O7_"E,RE,E WfG"]"l9drږDQSSs@՛\e%`7.Z+8D`W@IkC."=>;u50EHQ)R,@ȸ HD$!;'?F>G&5>"bԁ(8T:}<(0v<sYm)R), G+PI*pJP8r?+X"G6ȯ̘M>;OwE2s~`< 2IF J K"ETKbBgG hPUPTV(cH9{ bq JP %(,lzNu xȸBS(!Vfre\L)RH1W)0}#c-R~&#8gm_<`U|E2 3"R'/-M"?i2" [2>GmRd2rWO XH9@^Ȍ+  |/)e"˸E^@Xw}v|UXkHm{0eutƦHTK(b ks 0_4fB;GM d<) %}ɢe!6\ˠVCbG v,;&ݑn}N*HK"),ۼ "VH⟄X$q GL!pgG#ݫA[ nky@{T93 \d]4Eg9p9+l|ÆP>̼wuSHAjLP#Q3Q>7:߁0&fG>ZCik݋̸B6"*_,lOO>c A#};#(l׌+l2/h8%ʝA*a|B>d )R' MbFϣ t?䨾&p6pN}2Z80Bp!pNg+>[k_Ћ>;cFyj̗}Ɍ+Tm}̷JâϾT}?݈txۖUswnPdmr-{|N@C)RH -0n{T_q<2ѭd\-Y~?UT*p[- ;E`Z+ q>;V6Dz,M^WDq=P-Q"ضO9DCWϖAyoW}g^vA<xe+q"Es )K@L?G\9)_"Ⲷ}ncZ|7uLJ#7ݳ*]US$\㎣_*7zk([h R4IXJ|ZNɸG" d穈 Wv^˸ISE2p* !_u||nA d e_wS5,Es)KcSL8@&PbS)Z@g6|g-qZMz)lYr~ 3Sدu{ x^H5[Սc#_2gW_e\G]g ?EqGgk3zfDjj[ٕ3CFSHb"Lc Jδ<9+w~HkH)xGv=HH>;( k!+mJ&݈Ɖngo'z]ME}*8-"EX="Tax瑊0f18D{2G>LF*2 "NkyhԽ>;. W *+?^gt2,ֈMD)ɸ8__ّWUٮsCsv?$,Esi ,0^ |LoPҾ(@%!ߞQDNF:42)ZKYd?qȸ™H < <Ӂ>mW"1ߓU*/Yt Ft"ou`e!|gM`kLx9 Y@7sd\,Esi2ł>H 8M+!0vbz+( ;x+r6wEx2V'tD3;R3X,/YzK^32Q/(=`+C5k씇}EH4$n'wIQUG _W$Ҿ9@qPA"7CH-EF龗?*]/e0Z|/_0ơ!;],絾0&Č&Åδ_"a8Xae(ND=PU%jUV/wnD6Bc66Gooz U( 痣 ʱȜ!݌|hфPezf{8 wyfFӭˊ2x Ju&F0-Ctq/0 1n|-2JQi.ku`Pi78|Ɲs$HZ$,i1vM2 a^[Qqh{3T; XCyc:s&(; 6G#Pr,ڽs(ӪιÀw4H6WQP 0 cb9bF=!c(Tݹ JH?ޔ՜^dl?%7 TgQjNhb{U}Baxu;^p4v滨 9ȽXX=(:D$A38Wm'UU4 0:,V3ꙧ^}W"(NXY5EXxj3VTY*8GGIUnMeZwd͢ }/? mPq/4Ut?8m&qhIwQ_E"k`3 +E"m'u?J yиʫ([j}=a4&&Č%Q]"vChʚ/? %LW |_O8!Tq[T:@#tdDk˻c?]E'a'{[UF݃E꼝ܵQM0 h3Gф(*W  11\ |=JN4We%^ve%4W'fvDհy(~3$.w?XqVfEԞHaHڒ[Tu4Q;vF>C|/?ݵ1+ ~辿<c!7k}a46&Čzg:9GxQ˲{}fDIa"җX=w9R2XZДF۝|=r@j-2 ;KϢcCX'6-ʠd^A4 MOMl{Hm~>Q|F{y($~I֨aFGQ 8D,T(Ɏ8fEIv6- 7˦L?ǃ_{.\QQe^nr@+EyFCʣ;!/E?}vmD;fu",J iQX[ S䪷C{%#Qd^~hs[*tUQq_TY|d*u~"s.aFͱd}p-i`>f;S|n3Çl$tմO,]<TfcԆV_B`Gհhc(6 V{y_N#KQD4p;" /vpi1 hHgZ*V=$J47 (b40-nA>A]a4+^^Cկݑ=9>9o9TR2|#kFK7E ^CLZ|/_\[ 7Gb| a*bF=*3{#p(p?%Hmľ(W0gqwq8/PB VE/ Eʆ( n+ʠz` í*`iQC&nnEC]o# dWMGܛgu=06 hD"fiNd?js$j=*dd_$*V-v\=PFr:4T>?2*(}/?Ey_Q^~)JFE4^Z~QK|/U KiV?S|+{en0 bB̨K8|3JP%j>ڟx$Ҝ\QUf9ƩzϫyloDI{Ϯ@ݑ)a)wq|/=y@?/FϪ 0 (mv*6|/ep_DӘyPPau 1UE" $@{ C]m%ܧq8/J3pXDxXY5y.E|dDH,^@Bwc`$`OEm㳑GKe%󠝁+{gV\)P0 bB̨gnGSw}%V cmKQ;V()~%-6Msq5.MI (J~onȠ9Tc[_E+C7ݍa 0 hL.Htw5!B_љoX}P{(Ԋ#åQuPi%OG*"JMP8>,V 4(1 )Zt1aKѺ]7YN[[vA(;ϕ??&`COa&Čzfl&ؼ!5̗"OZ4+k6~DS$oC;,eV,4ŏP{nJ=T[U|M|70{?eAak%uMd!WMՂ|HF,w\[05".%Ñy̆'q+9-P euB(oTt΍w^׻X^*1@\&(~< άZ">RQ3 hXĘw[iz CqI'X@dC8ZnDI00J (ɦV\G1_xE^''SS'|/B^'XYOop0( 3QKyO# Y/DauOd-yƢ((X>Q] 最WR)JH F"^4a%/{m\tUѤ&|/&7AH&/tw6ÞT lRwBe"]Tv@e0?0ZaBh$~^`MίQ!hᗀiŅFH@܋{̫$h;u" smmTTNߣC0܉s(c7@hZ2a{Lh=x(E[/nAJ(tNJ$zqxpd]4Uʚr}Q6Y/L(ɆFI6c7GKÇ*\lƸGvQ.ha,Y<ڝ;&`~G?=3 h/3ZGhE ه}eɯop#hde=` }gOԉBbKP>(iB(h?oQyVE܂,>@x{49jKvEqCe(XfQ+L CFIp pEdIhj Gz$넖ϙ`/tYWQ6Q;q)2?|\|+*S GQH7ۃ! 25G_ԚbEV%,$P7CZ}QK VխH辀aF;Äшj4 [=#'!xo(ɾzȅ9p'e^;9z&{*c_Ě\aF и7"1W:< Uz龮ޚPk/J_s;)J0TuLr <"Fgzy: cP \Tъ2X*^pIrj|G3yޱn<p Z} )i~M4.r"JPrHp(ɺqGI? :}&Ã6 E,~%پHuFӐy _5fFGĄ.HpfdcP+rW|ꏦ&@Q<$ #Pm*{ص0؉U_-&G,wDIvD? GGhNT 0u[ieZ*_x=*\(~kAª9Jh.(ܱko"n G_B#!Q}%Ja UČ hh/Qk4A1z2J8{ET<^Kw1]|yҴ$哽뎝*l,/jQeuCQwi>aQϘ3i> <%oQ@-EhWabEI6|^-ƣ-;0jn] VWTZD@c{T^^p)*?abFĥOL$hyiuܳ7da44֚4*\~ؑ(MTTQ[r _S0kd["d`nsea 1X n'b, MT5<0 `IX=@+|EhѶRakU c )J8,_g0 W1!faQ#baQ#Laabaa5„aaF0!faQ#Laabaa5„aaF0!faQ#Laabaa5„aaF0!faQ#Laabaa5„aaF0!faQ#Laabaa5„aaF0!faQ#Laabaa5„aaF?Q_|IENDB`openTSNE-0.6.1/docs/source/examples/01_simple_usage/output_15_0.png000066400000000000000000004412201413546205200247730ustar00rootroot00000000000000PNG  IHDRE6AsBIT|d pHYs  ~9tEXtSoftwarematplotlib version 2.2.2, http://matplotlib.org/ IDATxwYzA"6$3jX(Q_b99X1ѨgXM:1ꊊ 2?a]{_^39}^/MS 0 0 0+%u=0 0 0DaaaEaaa4jLaaѨ1QdaaFDaaaEaaa4jLaaѨ1QdaaFDaaaEaaa4jLaaѨ1QdaaFDaaaEaaa4jLaaѨ1QdaaFDaaaEaaa4jLaaѨ1QdaaFDaaaEaaa4jLaaѨ1QdaaFDaaaEaaa4jLaaѨiZ0 h,̦@ @{tD`{lMi霗o6\7 0i]0 28! xݶ*[k94aa4dLa(WRONCgc`& \O+UX "D 4"0 hXaƺaeMK.~kV@w:h  ,^v:{#s^&MO1 0"0uÿy~7(Ϧor^h <vcQd90E9/n6OZWea K3 X r^PM*=Ϭll4^BѠA(E1`lg lTux>_ia6 è)y}s^fJ \\mVul_ i8hP$^@C1{wņa81QdQ=OL+ɕo~!SA!-g 8;ϧ N8f9G^{]aa4n,}0 r^(r3سr\54AћQ4.Ap=/E($` p*; pNS u tΦV 0 a0 g3$P~M%+l_2=Qh?<<7j:, vAV)C D6ͿzWnas3 !GBOHL9886yP ۿQ6\|2G(l`AR92+r^l2Y%lnaa4TLag+ܖH<ٶyi 슚@%sϧ4?*e~DQF 臢Dpi{〛ܹg̿V4?g] 0 h(2 !g}OEYr5AOί(Rt97Pi^4??.LȦil6/A5E$?^7 0Fa8(ҳ+Jek߃O+vmJ6VtC3ܣ@ 6NȦW+3D5F3ܩ4y- 4??eZ#ǺUaرHarN.D{oFtSQX =[8`ljLYHG6/y[Pg'a ói~l C>Ս)eꅄfa@odҰ+Jݻ8zaa4L)DY@5CݜKܙ!dH$<Anr/Q9/CѝyQĪE& (R p SԶXwSiM VaFDal62T?M_#?#h$ 3e sX\TiM'̦or^774ĝs_Լup)00-y MH-FyQ y5t0 h al`` 9( lVyi*wl/|DI(-VrγbdJw .mYa%h"pk6ߙ2Woi~{wPWL#5V w$Z>ʶ7 0)2 W;D) S&x&V邚 <_yl\nu@H,]SM*@CwIQj?cQjPxX.ɦ9/s2SMi سpzH4a (2 hBfnR4a]k ̦H30 2n.8e2$ͦPoFǰ= <2Øwɳr\aƆ"pywC_m6|T{b}i~38E\Cy  EQ(2H=4_4d('\wF r^E+%gWzl~џP$>wbMP{9/3" a{D{.@'Xeaf`0GNuQp*o6'u7A\,MPCROH A5>[]%e@G`t9/Q}ol$p'5{]7AϹ EwčH틚N]' 9 $v><|:aQoHa1M6͏MnB|{6Ϳ wy}i=ox h?R~Mg}!4d]X9 | .m0\[ \jE.mUu?Dh ?=7` 6uɣ:ჇO^V㎆aQ"è{.zQ4sfLy( @ E, FljMʫ'i 2RQz:rPt {/]Jwx@Bj;LV\igcD p&=KyJ vCu6g$dk<.eaOLFs;QAojg#ByH4U f*-yg+E+Tc4&-Ѥwy+v.gͽR"Ǻ 0 Se&L@]+eNBBx!y?(jpqSCa=jT{QaEQT<8Q)_hR{eTrI39/s~y(\ js'̬lc Of)~j.pwިb dzw+3;f?ѨlS%lێGOv0 XSh06@r^fc5%JzE|5MwAnt˦/<`#"eEi4$e!5Cb2wӲiM]Pq(wf6V>{we7x=-QhL6]H-v̦W2n=n'?QW3 0)2 l2P+B=Q cg#[u7fNC`wd M_.n5AQ(u}؜Kiw>S켋VbOC>Ycn س>6\OB5YOnaFCDalEKQCѾ(:F Pzn9/lʣ5}P,t_>Ld7/z=0 Q[5<2G"yZkRMyYՈ,{a{Fr7כa9è'L36wp?2 x}Ml}1[R~rf;y>( /?qy@ʦ )Pljey(l٭:]Fϰ=oNF>:a@Ha\;Q,4{>3<_%t73pL:uEV'lX|lgW;.~5z]܆V"7н-s 5 Yz,}1~ȝ 0 c`"è l6eA[du9K[MVm_oLk=y}殼Z&g r^#0ouDbdMط^G]q4?׳?2oX6g)JI A^>:aHa\Le%)O>Fi[ xjd\:`p. h\dH(/3B$A=}j2"MP.d3 P=|4x(:a1Qd>ă_AέV4`&"WrdE2e";NH,l,}6ݯxx#d%݁ݿ] Pj?FDM\Ƞl6eC%uy|C]4l`ϭ|=_ \h0 è+}06p(#˿ZNv5[Inp-p]6-ڦ#pj"/g9/:/^vE 7ƫi4*;ny=Ӊ'ڭ졏Veau"0W}Pڻ(R0 efv.rHKfPѢi`وwwh}Kً6Tjܱřc.1xQFI/}!2 06(LQkr^@ldϨГ]H v@Q1U)~n|tS'[-Y[2˦OzQ# 85X6 0& c~E%ʈCv%k1;G3iZ\8t;Ct%;tvUE'>ͤY?[ܶ/x=CGվMCJEZaF}Da#Я7fqHIE7. ߷s:F+Z6"8~&zbvwn6aБ6`b-95XP'4 h(2 AA<ձS7=e338*{-ؤإSPwOA_CJLae4Wa52fQ8 #1{|QCGE];g][c3 hȘ(2 QD_gnXBMmCJ>>~+a;LѠn>ӳiҽ'i~A˴.^Ȧg( 䀛[]h 9hGP5V%05x9:aa5Ea4V^L}&=| |\ЭMʗ~2f`uExZ-Pt2LE Cड#4l^p IDAT 0 c0A3}} `I{)ԪfǡJ%š_zI〣LJ0a5: Y^Tc4 0& hܺ/LK95J{KnvA 3'!y&usW<=Б6GX3iCJꍅaFCDakD|Ϧ9(iXeyFzyW~hҙN8l,KOۣyp;N7IǸÐҲX#5zt-fcSDaQGтakD,B$a6ͯ/ Jqa@G 90 5|v/vloD$uNC^%z }24:CG < >'S/{q!eSpxa`F?=|2ۭc9um >IG [Gai~ x4yA< <`X 9Б.:rP3fn`jaƊXakD6Oy}.kÀD(8Ǻm.p6ۓH6%+ġ?ߥmxJq?iTC%̀6q7;cc y(y=pҲCGjtRZ†aF`sa5r^fVl{m7A@)pc_Qҳ9o>vEf;ݼ2_@;E`4FqNCgc% 4MYq J!; T&p1J}lU%v-9}W;|x88@s$/.nRZv_ 0 èȦI`ǥZ?f93qetJɳ?Ytj[`<`cZҮY&cQ8#pW]( ۀ(^H =Q$? qǘ,C5׺ͺ{Y6LFi"훀 )-;mA-o` 01Qdks/[X_V'6⎭6X#QT[_DIi\Rl?~fi3F/%œq迼>Ʒ +"reVA DMG3cLR A<|?XjDoQr{twH)܋p$_"Sܳu4N0 (2i>kX;x+9&š_/l!%Ku& 7D W}S( \Dɩq X4m]M˂(il l&ǼAtE΁C88xۉ.'8s9h~?(}gmP,܀\^68;Cs~j=0 XL 7E~ PSh&nkrZkK49x|%oޮKW.B`G@V~հʊ%pKA:}q֜W A_ cw82 jZ98yLGp/Qt50L6*8 h~8Yo➻8?A 6 J: AtП[aEF} Ze,kFhE{m5JtE)HcVE?`Y(#u8#(P>Yw9ԁ.t2p H yfA3y'<p#2XL@lQίA\opޅR)Q JA-G_եmAlTWx(ճ]Nr=kx 0F"^ J~kFM*OER$jJ-#?O 긕mXO|PusVwIl \lW_d{$zF" (9ݏÐlIdy^ġA%CQX' PfO=(5;[!(y2q%{ġ?'!Ӆ!;YAłOܹ7B} 4 0fmM%GB=-pJ*NCY\ 4?8]TbQ8k5zjxQE7S$CBb!Ž]'tR=GFTtEeADɡ(5"v pBoNGcݮH=LG)!|=V`FMaF"E:Mz7`' J6:ġn]&K9̾/qWQ cR`u;Jwu&ϠHMtA)@q_Fx;NI8<:qzlLw@'w?1Dй탄Yq*FܸAHmP-^HvCFMDQᯀPʞaa"c}7 MUw%A z08AbnjJ6kp.j1kmo=cEi~8,,? E}:;QŻ(ڴNlN+z1$rP) n,M:o>@%k-ȝLjz/H{?BH \DS<0 bs:'MJ}he8W&ȥDmVlKk_/5}݌̨k( W7C;uA=*5hd1M!;Cx- xl!νo?@QOPzZ=vjRt~&w-_Ҿm BNc7a"cCUo[%Қʁ* Jzzǡ;ݻ12h>a>S-#]%R Cr%}k$c$>EQP*[? JAuHǣf_Դ0Rv@"k2T<QoAƘ8?q[[!2X C;i(J4ww;o=Wr=9}Qܽq藭5 0 >goZ>A'Qsk7E8('ks JZk7Y#$P!A졗"[kDS_׮|OPu]HdO iaA.En Y=tmO> %.%oE Ƙnka:&r-6,t-֨/>pJ.C_-( 17rρq迌 qPyM#V웲 $D)A8Q7`;Ԉ2_ h>Y6(b1E Nl5&"IIm jP 8Ǣ({H. gFh 5OP(Swġ_Q)G\tp=\vEn o_t\w|m@"?& 0bȨ-#Ma≵ɭ/JA *84[Z~ġJ%U dOrѥVɋ6װ:PPl |AdfQWR^[˓+A8؈I$|"w#!A$w賷ӚN J@N^@_$nA9Ո{?[f[zA:B0 (DQ+п T&P Bi}$,@l=Z\Rʀ2DI\I+SPjiH<ƓQ^y A.=/5mD;Y [7 07&U"ըV["'UMQ.h)E%'3RQ [ȱ)Aj#ėKJyuE6(\\xqlA5HAD1hC0gнM.>C@ i;I!e%T)r (E_:HQtfr{OK* 5>nqx )}hj?;R] 8 k J:š?!:} XnCs|(NtF 0&͕(.@%m7}x4F/ct ɽu0W/aSsu%Mܪy?Пrrd`/ݚ qӪmB5TKQ@k$JPE&}UDq8 |YGѷoEdF$~nC*̍]7EPG`v*EJ(h rn5ՓRCgDYwd %}iA=^ϠH(w J& a8͐YAlaF}D\JhE7WcW\ +=^E<-g itQ7>#hgPؿ q EI99}Dz+<'xn98o@})&_ ^p8 r*nt@_$@<ܾ AS: PTQ/0PWjBt<9d!S D&](߃Huz5_O-:-aOTd2 q3QԬZ 'Q2~8-vg)hDmNgx0QdTE+zM8nG=Vf(f#T##STNBԗ-Dbڬo.YЩ3&K_"'HPqc|+@U90-(+-Rl绔=H))yBZQ<{͢uZ`Z84HjAo;p JD8gvE{n{ᾛ\dw?EnQ,, ny0ƁEA^ ڠh d틊G.\{Kd $#Y_QY1P'td"@!J{MVп:R$jwQA>-FŚIC*MEbh,C JB:nфg 4AP}9(;v;7ADO59έg:;P44qZi7Y rDhR0$E Z,Ebu5szPm7C%ơ_0[ZE#A+i&Qұ(rr#)'[5JK rHo{uO`P%#Q߭; E.CeX{\_ JFhi%;ǡiU!&(Op"e=a8, dpcl= 0$&3쪞  W^%4u$*i*l.&:}]=5z;yQtĥ5EMڠTH݈,#M$sGT4ǡ?6n$zU^7VOrA"FuGs*Zǡ1q%/J]Wt/Z T&Ӄ(1jW J6Eu="Q#c{]:]{xŵEoQdI_sQisJ[,ݷ& 7Бf<RZVmaDQ=EG+OV'PMK$2QUNq&w T.0U>mNj-mۣ Fx}Ph̍X vvM E& )v^%=Urǻ ݃@җ #īФZJP$苸szEq=QuPGEVQr6H?8'Q$ܽ7{7>Fr8(wEk8?XV @n ;0= JBn(Jn/zB M=$rQrB:ȈwH*s\$ J.q#@QsaE*>Gw!\%עc}ݿG (Zk8?skAEw ܿù]!e 0DQ w-=KAl<Dq?[<\{4:o妯JUGE}L"@8 fM: 9Ѝ B:O'(%=/G)_X^4 .ADjY^G 8k~ @{GsQŹ6G+O,&W4Gc[#+=|{+V#ǽEpw* J.@;)Hh^4J,ݵo4K(2-,@,zo^wwQRX CA ̺rqװf5f]uwU,#fl e#ڄ J }4MO Z]]]}{gz<$x]Xn[Db~ARk;Wm$j?dmCf&!m z{*_yDm lӐ!r;W!Kʢ>I%H DBm|*JLβ2b5gEXz[N }jk=~A 5m}@`zA;9f܎w]Q uKB/AVPw(nO~#k*`AS6(; e6cPbdsC?[_PYkO'O3^B.(nL,(R?zrJa^; Q e**c \WY%Ct8t "X0A]]s FHxE軯zrU{>dUD#r x>_`Bx ]l psJV,V;GmyA1 UrDO+'KPUhX1Ut&հ|G,Tt&u݀I}=4t91~wPbI;g!J6lb?ˈV߷s1",l߻sߑoi(G!¾5/AۼgR軯8 $ +Bra^u&X oL֩r"I3mJ'H AEB~{UčVg:@|kEeq@|n2]Q}5h-k|#p kE軯xA k 4 w4q}dS3׼8W6jxf-Y:vlCyCߝ\i(=Ior^Xy |;~ WC*mD|5dY*:ן57GU*CKt>}Dz"yH7ɶgwxAɎɋ((. ~$zZgެ,Bߍ={R;͛>#fO;`e_~95nz:>D6'|.AmDėzj8;5` @ 7QB`"mgLwt.B50"m x?(\ە`-"I D?PRHgRU%H !E0>s-?݅z.v}9 탐\}~SQNQ__PϏ!S{!9n)z( h^,ۼ ꎪ;ٶ]bt,Ɏ/e`h*n QC/PCqD{bՔ*"DnB㩞ƈX}/Z|]&4V!XTj?>1,Yݼqhtͽ\ӹ}Fj)*a.g[;^ am P%+ Pv:[DuM:V)2HM AU(Br? PFHP@z`H^5-<6"gX'Fd9p-iC2IϢ`}(Km$]ENhwXٻƕU,y. /G:-maU)Y^=ap47qd,h F };/n@gDj#teu;F[x+t.RBߝhU :itM AP1my'k>q3g!h}かnv \]#9͞/8BD}'`<+Tݼ Q>*VPTtl[{?>"*oy>9"Bo$.`辻u:jZVZ&&G{eg&H DBaXflx*flG_ z mj} In?"q;_(gRD!g.H7 R݌zu ss+7!%w{A5UCyde*Ν\l)AR m;TyA "mWJ B{ھOG+o *wO&l"&3aA9˷@7Ci?>g1*[P#8E "^G@`E싈Wd IsX#QbcX.m_^ *xTɫMBrˆ >\t=DUvh(M:AU&wQ 28fEVΤaj:ۣ'H BB}2og;\F@&hLC?mCCT٨Y TYƯ)j*VCdcTe9erUfq#1w_\۞/gd]m3&Ӳ$d]}ZeDO.jQ ֈG9O,-wr PegnC$ Hw ,푬kd.WԗHҴ+"" I'~ z >qAΧy+oBQutA /rL/^ l~S=C}1E[I.b @wGLCP⧨:BI,> |U4MW$X[(+-ΤCäW$sk>p$H`"!E> VrPQO$qd! -h=^Dal~UjPno/ܪ; UCҫH,{ &xAt OCޝ;z_g&nǣڱ$9)1^&;:B r"΃ɮ#F}2me/*?Aou.om=cm{G{mr\ =Ͳi,k"BMxAd%((nxI) }YU_]M8:\E: SHr4@@TM컯ZxAt,";C߽Ӏؖ ;jhςڥ89%U׶`.ҙT7$[G $HH:OJZP&62)4 iQ(XɷF#ǩDMz0<?!X%Mmxp 5$kdVgP5/ spGyjq. '#22~ >~OTc#mmjSk\.ޱm8Ihhn_q殿 `'"{Osz:\|/}cOw=qyST-9vn0l bN|:Nm~ "?oQo+^=À_ z Bu}?cRWBыH8*Oy]-" dr9́s I2[BV'oQ7Z ,JcL.ΤC}$KgRG璃XcJ\ $ )Zp D'#1(O@DA>uù61(iTEY݆XVK!I9xm*JcQzAt8= A].nU>a*AFo񞈈}J54tʖ|/}<|:!,|f#y)3-݌ܛQcHv)Dx¶'ۑގQC!KPK8hЛ" rmy.Z`~F:񆽶>"t=.j`VA-Z A`jt[M^< H8Eז\Z n%(.@9(Aq3Daߍ;,FzaKPk{{~kֲ\7+nKz$H. !EA:YIV9 1;Jd0UR"1ɶ9|/Pu\U;"^݃4| (܌dG=P۠稡9 }796@P`DmP_["j*bXTIy IfiV{hR+dlp 5FQh榈h Xd|m^E쳅D'!]nIHZp'VX%x$k|,_ ~`pr9#2{ a޽O Y8NE?vM*ަO͑Hn9]dR=PS/TUNv]WIE˅>e+ԏ 8Mгr Hy&㕾&9 ]86:8΃-q8Cqq\h:cXB>?\>&c{Uz(`E E$Tg !YiV 1  U/ FD ")yAt!" Ѓy/Te.f߱ %DVYlWDF*Q>*\z.^mF@UdJKcDQ5k}G $k5߇nCiMz"T!in>a,TIʮ+HgǵCՠӀybT$fUF 5DFؕ\_Fv;Sw&f a_xA4* w?w~-}̠V%"6v}꿩^CT~moS5`7әT1J~}B`mq]6*zunKޱUQ#f9(:@AodN=)5}7$~c#c+) e~!ٻ9Qf'|? *,o=Q ? 7,DMYKdl ~$>";2e XNFjUI~LAR}9H!Uw n־/m$*_s>|mnF#!@#s{Z!Y]k/ w[XLnH#ԓ#sR6YLE3sV(ݱ>=o۲Σ3rcxYa<&$7&<ˡE7&(Jn\~ĝ]d}nw>?6셪K-Q 4 }.#I ojO[93$xW\Ԟ : ?BmϩP As)aYH5z7O@pmC-Ҥ5o;^HLDvCUm!Y݊kf{CL83{ AEtAHy! 1Y1q׏ 8#s+D6*Ei0j37]i]wp\ :t&a}|eW3Pb(IBڲ_(c;\#<:e *lzcZ55Wbݽ3M,UqC`P ] IDATh_(v 㬤}1U1S>RUe=@tydzs>{po}%9#l15|l8ΝHHZIZ58HϽɭ iwBAPI$je+Pc~=>r xYHGA{[wR"L,@qe7?YOwDCPVc*A|JO99(`Dň|RZkB)mZdiFIzAʎE%3ЈM}LdkgV tme B};j#VglKTq~0Dh Iw1USwHdCKnF,jNnEKәT)"țp80IIgRY[Js|TCJ˗\/LJB5Fv(sʔ.+-OQF9*Kaԧ3EljigTW?[E4qIkq\|Qbqwj&8wqOwg9x*{$hD-{o'МB6Y^2{ߩrDAxes{2Gws >j߾o""?1mWɾ w٨2Qs#>(r m &!f:z)eԯ@s۱ҶA{AU_ }7m2c?/z ӆ{:,C rK`/@Ǿ]~6C5i[w1UDU: qJ\~uHmKkY It&䒞<ΤPVZ^ΤζK=tLYiX{eW 3={oM%WB[S T\xq/I:J9r./ɩ-/^C8L8Ь|Ś=LNUUZS6N]gd)DFY\ǰGzhTe CT(?w~HW{AXB;mF؟v6>Xd.C=üAy.XzhD" 乨/ʒDr4g5W FDg@Ǵ2 Sr QEoyA^dː"#D CJNAmX[F.gzA49PyxVcΜj\ V(3]WD?9ȵ{A%"^HgM'){l]A)dWt&u 55-CJ˯W;QegkL$t&o HI0#wT5u'HV|qʑvּ;j6әs|=bʃV–79 O:;3I;w9gΊں>CqN9AqEha8p8쳷QFÎ/]pn 6 =>BA)Nh3@r J b|g_eZ޶n,cqDk `@]!isy!y{Ď5|GcT*:O[WQS3 n; U5)w=-:_G7m7xNaEݞ[ Uơ OMқ>X؉((K(A23ٱ>:z`uG7yk$&}R 3)Ie{ͯe>XXVZ^io)+-WҙT (+-ޞ9s6G U2(oI~XA^ hD9 )ӨrH`x3&'/>@D {A^6B~ߡ&N13P:$hl#ruvί tu/xAt-´Gdo"knj5!D 7An{[d?my3zzBeSm}gj#9hA؟ֶK'+kz >aiPt"T.t%{y~Wcҿ=M9宫p /5V 'P`w6"M"OAig!"٦dը?*A wNUlD؊|>?P*# :ҙT7VhΤ.ȳO8.s ~gHHQB_Tux.|Yɮ@2c4M+K\˼f4tGGP=rs# Zzm^} XN iٿAA跡fMĜNEASwg{?"u5\i~D#P<>ka^w)oO kcK5ǰ%M``bDZ78/BA ?w ԫU (PtCg|xA\QtI&~zvCzJ3,;< Z:+l[Bٵv/EDzh,ѱ||J A-].ΤsE4B AU/| jz!GsQD6C.|n,KeCcqU^D, EQ#.uDAW;B= M#I"@c/**Y.1E 90uAr|C^xAt"ׄruf159cmG\|@ *6sY|eۧr^k*<PCtܦj_'4hi$+a}N3 놶;+}$G"/ _" Ñ9a=&$H`z|)Vc;/AscS;sUgl}1WpEvY/"͹`'4­y@4}k؟+9ƕlS,nd&1 P_G}7i"J$M=; b96gPլZy&,s^r?_|<0/&ASz>>"SF8a*" 1qf&;E΂w#~t@vt| P6BF܁[W Z"yY93DmA ozA-$T$U?e$H`U 1Z\|`4 9y(H L{i FdL 9M }d/D9?FHQqUnGrW&lxdL8ꍪDDH0&^( } Τۖk{[V%ҙTUrI ARG{T .Hj KWD^z9ΰW:Roѽ~K*J[߄UjGa{yA4*Fyˮ ^}.CɾNE.p6bfvCtGdy~4F1qD ِdk=D^C=T C? P8रY=F̺#:^kZyAt^#̨95Bů(ۿlhu"'H r{נ ]vh(ZXuP?*3)zUqAǪdtB5=ѣ&P;'-z"KѦ8DU?!CH*ڞ~ЋМQ3p Xzѵѽ\Fc$^Et&pZgzt& FDAL&$OȱPV%8GnqWt8Wm&j]8AHHQ=e;*@Y_aްUX23IhfM>Gf]i}s= 5fM"bPV50^wvo ףL(DZVD SÁ{o3T*S>/" Mۇ :U! TykkȘD,A$e+D n!D= fc㑌-ܡ*1ٟ/yI!ر\ =9@/>\TMG kgDoh$q2qXzvD- L5h V]:U!g{y$gWx$d:!2?:撶}䩽VkqVlщgFhvӺt٥nz%l 灎+0 %YA-,/($h%zO0}w7 \i(?%㏢ {AV wы(;>"&~ࣳ /$F8# 튪M :I~}w1<!G_"[AFiAk#˼ ûBߝc,?[g}<D;"Cs0tZPr+A2Ml /ɛ4_ ZJw=sa;a]K*j_k3D˞輝*\r; R5Gյl?"2]Z'يQil&Dj[K[ϾȲ=mS$ٹo~S۳Ha:]~;ݿeN~cht&]ٙsCPdEӯ3M=({Uhu_G:}-JgR]yΤ^/+-kmn`q&%4x Ͷl}|OUq6\ъ8́^T ]8NGkbsqw9JGCUR8buQby8qmގ\FuF#9?38N-Xx6pQ!h8YkD,uXg13:e@EhP䓜׳ZFZG G#EI4 E H&-T!{mP/ȋnd/B՘Q*DH`RGȱNTC,3Gr/@&݁z/TYz,ݚ2VxAz*[#3<4#B~jꊬwA;"_%pFZZha=>PelW05ќG'~6Y9hFfܾD$өw20cEڞ 4%ɽ-9l@;P^Ʈt&dHd%n,+-6)Lpfw-3 'ks|KgRVDUVZ~ͪ .<`D>nqlx߹Ds*pY6uSxS jj(xZ Yz Drs bSwO=YgtK$L#OBVxܬ{)c'TYy^=l B4DD I2d96Fe@W/}w"¿k X*)TQ]F2l_k?]`HgR#7L^/$vLj=$  My?e{n]ֽ J05EݧIgR^EJU=kJfdt&U!mwd" 5Lf0)ޫ#z#S7?8N \#8]78q'}m1P8P6,{8V,"l=8 Yպ !E dP]UL>!/6@+(Kԏ!HB% НzKƣIмֶDl1{䙷pʳ~!\zTNANhDvߥE^uGV!HUF6l9ݏTm ׽(; Hj}$VYY` OͰ&^F}5oض4Q`z˓[tny&PD.wed'>+^5Fa7D46GD%D;u}h8q^ةF?>Di;/qi#{ tβ !y2-hc{}ZnR<ttJv6_=%7:BD~c6@W߭$|thΤ/%4iU`Τ#It&u2)+-_&Z d{Nc$*L#$qLj+s()r t&UPSJo[}菞37{(Z$ ߍat.{.=qY81zBGǹ%Bn+\iGq&; 7.#gqcϟmÀq RbhH$W"Lql-h2-؞'*ĕ( Q m<Ϣp޴Y@Mʴ}Vʕ #5ÏBAkmY%oq^}PG Mn]^D$@ŨjGଡ଼ [ϑ(f>"#p8v;^~C-AnÐg"yJ!fc~jUz0l@.((P} Un>l@A~cҳ}r؀N=z;fl| 0՝HzZrͦ3lAr:$`o;E,zrvJLRLKgRg(4*0u IDAT~gl{'hka߿(I[W׿t&y-zD% {rz/x 㼅5q81`8%q2f;igQ}nq:38& fa1[ǿ:3q8lq8[q<8!(IHQR}wDۯcʥ(S $$}Xf, MS{=d' t+TH߀F&y8LgR4/ZJcIsoCQ8<5B߽ˆ_u-p?Jk$킰_kwUl_e6-@-5 TD]tjwr؀N֡:J4GL=T976e2k:FN"#²܆*uEYit&="`AYi7L1w' K@I+ҙT Jg3Dݿ/+-݈i7I> &zuc:4rb-wo|2qE|8Ǩ!w'9s zeq3gPgzN?ҹ+"m'B)0q-^'G@H9 8;/ eGY[yue^툲/z6@k[dp HlTnl3;e[MU 5XxAUvv|B45iL,%=sF#"3>;}wEƑ@՞򷆾;H TeBxZ}eVYvZADb"rɻ =7o4`#-Cmd7(݋B߽ٓvH*Yo7DD쾴hOUdD:&!bu{ɯn?BGo` ʚ l2tFdB2O WP\jxA3 F }w L2^^L "ٷ@  Pҫرa:q7 M7OC6F)y#8ল)Lj8:s?Dڠ|>#gNH2J-83pk{m-"%y bF́ƕEn:*Inmt_X3=jJ,@vdaUt&r8I5*JHgRGt_)fA !q9%J zwFՖ,Y/H&kpT)Բ$4ϠT{G["4eǯ@fٲԃ@uYf}TFn*o&۲ s #oxiW *j+N$w&|=BkR(@ UYoovB$ihd?!R"F>Ck/?l~*iW Ԯ_P6*Q@?D S5𘁎E(PwGֶ=DŽ; msk5o[ Dv}Zeɝlg[.4X0DYfekFO%I#nUՉ^ViCWct:!Io5~~ئĊs Dη?J.ΤOOgRQ{!j1IәT*֏{(Fj?AF7$sՄ.}:~hd/d(5O0)^#D6AՅsF< i7/i#Ŝ}DK`=Q/G/j8j>GDdvۆq^Uv 2/n7㋦K]Y/F4bD ☝mevp'0"ꑙV4u7ȯQ.AnN;9 HR=/> qU^F\PrDg{'}k$ef1D.` Y&ĕ魱&O< )ިzX_I6OE^ݸCе)p6Eu6Sw$zW_EtU7跊 |"NgRO"Bq }Hr{)I}34{$ԏTΤ6F}^hZ0EfCm< ]{J )d%eBҠDuXG{nhLb R .@~CkSsT3)v^ޠau؂o aI|^=j9 -(jsi𝄂ZdVQL/ ew}%"cm?н]nBI'$[yAt82+X)L36GQoX؀Nzmox5cΤ6-+-m kDDIgRH{ Pv|:j4s03I9EŨB.M_3zɧwEDe+ Au )1EMP#_`rSG$~I\Ctl; (2aկ}PVǢrfu8:WEڷcө^vl6ED$@w3,ɑ2Yd,ʶuKivoVeZfP΅l}(Na YQ5߻"ü U"3 ued7BU(^fD-x U9OCsm^CYqD26 6C9 G= :+f8: ݯVb{#dk ;z8f{A":FVtU nEf%xM}LT]Jd]oua:] oZY]Yit&' ݗ?A3RT :5Y«t9UYd^VM}>!gUCDa ^u^˅laَa9]៯Բm }¾ ʂ_TUޫmsTM;ݶJݎ]PpU#>U[cpddQ3l{Of#"8+ul2Al>ՖvS!tFN^Q##nAD jAi+ۧ P >fo07Uΰ}YaRjFX?T*TY*:\ѿl`΂2Զ"Nj= zM{XDj$GzAa\gϚllCv\| ^B2m)2Wa:FUJTq:e؀N2A^Sa:js`J2nfH0+l]Dbz}ݘΤڤ3%ҙTwt^y/5d6 Au )^uFݱa^9 i2aAB(̙bġ0#RDmwBPQfe"t_s@Nm4DO~FwٍHZwD_!2ghEUMP=U@Yo(l^eOGg`{,X8 8uD:~E0;+G+ wA[NP"L-rjs I@۠s{Uz6K Qe/, ¼ ڸYtN[4q*D#mF?j<ѡe_:cB}>gAC<s7jf֦m44G+OYONxA5 ɗ~Qy J,[8nܢ\*Sk2vyЫlEa:C]D5l@wsޛ"Q\v{ Dc8jFcDג j ."EJ* M:x-w{ǝzқ)χQ6)ucu;ӈ9Sa~ ȼ&;+J"Df5/-33)hh. "%\  Jn${-LQ,mba^zKoMQՐQI{/LJr[Ur* -<S ðYeY݀aDzca]{gFeh'|HIQ QJQ!"e\pu~j:^_gڟq ~/h 'ͤV =.FJc=Q/Cќ41 "A"C,`Χ.)2\ԱL@dkK -佼͘Ff1F:^}W ")P$lXTU7QTQϨۢITKQ.E^Kz?Wɨט%J@ ȴ2B دheqO8j7 *"*:Sh,]{ 0񂿢Ա#r}nx'ߟogegʈE{niPnid+]"@#>\н@$ ɞe] A)(*.eYNC7vXf~RE֭9yfú5FwaDrm?d^HH|~,mffY/"UI3bQjj94vG9>9{J"/0*D4ypSvoNAo^# ,ghd7oM|~ S,0 wNA'oa.Fz˲,k4CIQc×m5'k5Fu/)H &uh20wO*s ::^0)=DEDsË0)P4IpPxwYkgQ}G9^p~ijFT4E.&R0mPd9.V4 w(SSCFHdIê Ld$uO)j^Il@sDfS!{w`D ibέg#IB4 ~Kߘm&:^p:٤UKI թkwk5wAADߵG;^pp9Ռ/v"c& z76&᫧#e* hRBd0:8E uf&#_Krs܆TH HeSߵ(*Cqc~;Ra݊[ȼucu; 4hDLټQ(Uk vnN!󲑆]%b]T8f19%+K"1upy9\*8"APFAtX"zJS튉p6i7Ǐe5h4=n60 iYV:jO\}K|. ;z7Q{2 y ˲z.5 G W)CN(xͻ G0L˲6 0 MegC0D ˗/8Es."go8ez<^LE(5[ 7n04_IB,kރPYF E:}x0־7<6'<8B-gr95\~eYdT=\rd~m95skqৈ% NG`i Qc,JߵR6ܤ{=;:/0yNr8^pn3D ̿K~ny/e_hf=˛̲6ȈJ%O#i];x ߵߪ漶砈IՌkl{yZ7E,l 2BF2]R' C=iǑZtƒvߍS^M>4>=oWD,mDK"J"#'a.%X"zTu}rДAg#J"lJy01}SM8)ԾOWc˲& T>IJѸ=eYa ᷖeYL8 q)cYV;7 O,jk0gYVC˲D06  9ȓ] C" @bG!- .B:Dn]eT4BDb:y1-yUKgj?R xAGߵ6(*65Hj N~yia)XH ޡ(<JK׍ݐj5"; ]܌Sv05AAfsf ݾ6rj_$R"/W3IH_MoakaNS`H[O ѭy{:^wkǨ>t8I:8 ǀ Lnu]n3^R4;ۊC˼mEǘ#*we Zѳ"E%DL4ζ^.% EN/b(|qT^/[Q%wNѷiqH͌>Seɰ^ `W0\(*ZݖeEѷwm* ,Iaπ,JDvV6~˲GW9?I.ߵ3hEkZ#9K<Ǭ)5BhY(2n'S"U-qY=_L70M (M^Vߵgub$IGPRT4a(;"dxcoDJ!#+Ջi a=6( B"HkoewZxgqG^cg4LrVQƦ=D:iZ-4I/]Yhd<fQDȻկ함]( VxfWn:,gj4rlʜP*/{5w3mcz4-I[~992zzМ(xL<۴֥s^]SDu6wxDJoCQ~G.Ccu{ESv݇DF#oiC`Ɩ ψ%OODU6ȼEcuc IDATE]8^жq5w4O+@\T !.Z EDhj%9NI$sX"܌.?;?K_4./1kc&>]O܄EhNL?Ww6\`7 íeͣ™US uDL*v:EN B yR }QsR)*+IͨHzsgkq! " !GoD2T:YTS}eC4F0DJ2;d<唃>@iAMd /xä SY#2Nڤ+{=XEEmIHa}",O؂?n\` SQγi~7х*ߵs}(/FJwooC.1(9")hтHޝ>coشEj4 tj~HUjz4)>F(,;"HqZQA1QCD(a;=UEJɨﱈe%EHF9be;Fl"g+=^M/xw5uH@hOposNMbvn܁9cz: K(:x }KX[. CHI3:F_ul<Qm`[ZJEeY`uIؖeAsҙ-[u Gs*Z !:VYe3sqG,zohv>{a8bjz]<)cўa?[חrѱ{ih!4wl3ǐjiNJߡChRKs`jq50wMaW+yeE4]{5; EI3ۇ7L@g.@IA^I#~ix2G}lG)2R!{)tݭ?߲xK2MlWlsߵ]jV_ߵWI{ӯޭG)-&þ쳉}~ܻax_г8Eg֙m ^!ihr`5"}W) CC)m}.7եq:$"#mDZ1X;)9(.8);ɌD_:^PDJAt)ؑ8ȕ@Bd;!0f}]I]/Zۧ {o*]C/8EWy1 6 [u;g~S9^`P~'-;Dz'DYsEI$&ގ2 Qdۖlj.wx]mDDctddF9# Me#Ч4Ą[ZYHaX&AJH Hha9QDTPHfE 3twi4/ƑC잒H|m,&ޑoㆉ챙L OQߵy{0 # aޏ˛~/2!SRrO-5R&0 805?bIQ߈EvF#jWdZ tByO-EkGd0}臠 \`"Fd52k r1|^ r`6{`_)g5C# /gR{xkIWH?sR(=!t/D:^M" h_Fk;te2z`_2חf/j~P I~ ٝ HxD"ӊPfpA t"=|HyiJtuF1nJ_xMߙߵg%Fa k]D;[#*j~ȼ떬:+14JV@oZ-ml X"z$Y{I$LuA'[&H&D/J:/~ m \;(]'N3~K m'#r=QMt)y S 10HU!O#VҿFD}' @Ʃja"2,B) mQ}zxiƦIto$Q-y{{/fkz+j?B7 HIxAoD_1?uGDk&+YP>җ yCT72*jҏ T,G) _t9\p9? mC`֢Bs+ɺ]5#ݑOQje3tZMl}AZ Fv-,FrsI;Ӥ\u)xNCAW!74&XtqTN(B' MGGRQd`ִBan@>!cuc丹ʖ\U}מaj6gy==w[|ގ: Dnu г@,-]ahfeׯ]4hva@g:\TE; GOEHHQKD?DΪZKD_@Q5%vѪX"E6DEE·miD,X"zmI$,jj9tͲ>GLm]غ*,(JO S G$~JYrȡ$ZKXp/J"ojq,+\j$%o~hN{դkou`"2 WS'k_UT&E#w^%+/4ny;^tKQvp3"h ϣ>g!'/w{M1Ȼ՜ӧ kPQEγ7"gcHV][doHk( R~hHL2%ˁ3b0|E6fKt$~>? ]H<*\FsSt'Mu:)ETNB^)YPm.-?%h{}54U9gV[ch`PI$^hoow[6G~-X"}k_6#JyQ;IQnh79/CY D2e/9AHG@99"A]qN"Ys6RZ&P*gȰާhtH8j&kOsRڙԓE)lC&}DNTV%+4!rd0Vf!~o +xjqO>?7HʣF $P-@UxGF{9@d568><&2Y"CeVM8K"T9hfE:P]1M[ i㝕vcv {Mj!H!ko٨Fr5!"))5z.VxfTlz'J_z~M&"Q{Tv?]{pGg:^B"[২<&c#ш<{wZ C;ߑQT9RNAta]<)k`,EgK"ƩhjϦ}2ZcDc_[J"Yah9rȆ(cC"ߦX"s4yR3 ̦$~;f?^ UnN<)y1 hx<\frSj(d ca}Nُ^EUȜ tFP~Fi1DRڎ6ي>W#fw/L$,FDߨ>% Q{FofREK/NOPTiҬ(-ߵ|E_ *)ܷ3  vu}ΐ~Ƽk>Bߦ-/C korfvZ(݆hZ݊uCz.B/Ki"200l~Hd%XgE^O?[F@R4(ZIDsԅ(B5&\Rřh^JL յ KDOKD_+8!ĈXI$rژBDu'Pt#!| raYV#[wٷm>MW̝~ՂY[uSZ~y6 ̲Pcӝ D24LL_T꩓dϢ vI9H:ߵH$-&w휻%ڧйNK'/eld2 }tߙLG* zՒ< %&]N!&)$11sa-PkZ]D@/;"z$1xAU|+6MQ-X6w}P}Z]jn(iׇ>w{MKD@uO$ZD߃P dߌl)E^L*}@g\[^D)6C8Ux:!`90@i&QckO]K# BBPb4iHS^q9^p>M6%DߵaGP[ec/.2 .1ǽi0<Yԙed o,_24]>Key&;^p/pI+Hiq7Jݸ>ړ}/}Ame^ޭr&7_Y1'_umPsVBBrLD鵨/N_~MQX"Ǘ;Kǩ7mi:lUwiVy:ochL+īj{ JR%i_T:NڲqK_,<А := xcBuX%MQuo$s'J%R:W*Ά[J:9֝-fԾkؤc q9.~bYV2`$eiYHp0 [u.J= wpeYj9hֈ|\ pEu|YR^DQ1ilu` jr;w2T^ & jMM@)5$4GއEc$}gӌ0W/xwC!1YqR7bE,mjm\X\嚤_CG7>nƹT2k)/*++fƶ\?/(Wdcm*ĽH|X"'r*1涣?\/t,xg"DPNFH݉u2K^V^ W=9j?<'FDUF2=r$q[,)=L-0bՂY/!5E*EiŻݖe]es-zGQW[0 ?,}{a8ڲތEˏyz f=;8^/dCw\0]WѯgPdDfJU 8.ݚ aO1JH{0ML9֯Q!|:^P|j=& z by@슢 Us|~u3l{ "~7QxAAg.ִfoĘzU(ڼE,WџHcnЫk{=3nKFذvߴm= C!*G=c\}v߯銹֯Z8lR3Җy.B 0 e[yAmYsa޳##7#ESf8^[9zQ 9eKUqU` NFjD :wT}$yToe= D7:^f&O1,̾k/wjL]\~PTMS+ BT;T _֔l̨ 9^/*D>g"a_W+x81; ]]9x*^9b2BԷAy&H{_H|Σ:g|0 f =ƓgLYY9eEZ6]z9OD4 {m^Ӿ:fryyqV8<4(n:O}rҲ0LCc%[67cRѯQOzϬ:?Jb{KI$04i4-IhD'X"ziw0 ?,koN)=P\) p1 !_2ay:9臉gVck_[ &|M|Pz9_0Fe^͞W_T"Lw {mrnML ~ {Ƞ fH"8m(uo*돒邹HtG٨(+YR-h0Qʓd$ )>z"RZr޷GDBʀאEOTjj S0mbtw;/tM5mF>FdO@֖y& W~ƚ'O֋%erT(ğ7vZ͝G/eDL]".@ g%Юm˅e)"VR]u(c55h~"PgR-ˣFj2Ǐ yRD}`z1ND h :^04YC[gD b~Jo Fv][sMz!GZ6;p6d97G io1|׾(@Eq'e^U-5Ps]P44r ~4MBߋ|׮O|Ũ<&E*bDW[jrS((ZheTO)2Qs48;ӫʙ< fnkCD~6]XHU`Cn:oDA$tpi{.]7ضKI1@W F2-6?D^.Ea~oDv>(j|u,.DGu<)/Lߵk(Ra|52*?Ao|^}IeHK(5( }6VӸ e ?3;IZ/;^py&bx(%n#nxq]ՙUs4W5ZTYᑂ)-k8:Ï?Eњ݀UE[vn?=ks\Q'rx3\ 1(/% D%D#< Ov=;^pڥ<7P8 .H5}ERұ A'])iiPtgYyLJ_sf Nf PIyAct"`:|מcۧ I6,9^#җ@ f P:k?2(ߵW,o[ǣk})z}j)A`^u ɵ9Wt M*W\}Ԉǡ P/ Ua2$lEyd鎞#}6vd25KR^f=( -}<E"=EZs?T|Z|"aekxA7ԇ9R~{ dEO"70ioh:E;Aw$=wvV#%Fc/xwL=FxBj֌ TBMD+Q'5u-Ilӟ/wO q 9l0tߵ7!5<ߵfM8"GHp}Uuu ߵgEw^n''lgWFI$L,2:+G;h}##Dwo/xI]נ$7REk@yH< x=KZH:'I@%sHW;*RJ|Qxkm"Sg\^@QUF42k#"o:D2iV` J+@J!5fn;k[պFI|5!wJք8^p< ޜ6\FDr?F)G/;g#Blm {D'=vVD?y~{<_} |a>yyR#!;' | 4cYcIFVun "kAVU/x}NL88wEHDpVP]@ )tkX7ڏцT9v5r -r0ʵcv? {n<=J33('cD|.xA;K"zU"\5M#3M=-m] վkFB_l $茚@]V#$蚔=/x Q$ߵ3JDinCbi^T[pz5Z1cY9;]IsJqZVx(\8U<@~ .UlZ#tj ![a/?D5WfKs4y9g>LM!'a;z>D$ '$AF `{ R==W"o>(!GZD@h}?D{Qȯ$S2/coZU'J/|:jYOXa65#}\|Mj/#ث;7I)}.ahYhHaT z˲FGa&ϡtRteYW![~w,J˄π-z?zIQ"ö#'Aa!Z8^hSmM,1y/qW#Lc8 EBcu2:^p0&ˎ\=Vu]L`{ߵQ.@?[&;ZT123Z+D^>];*IAkU׿"P5]{hKzێܳ/ho~!2DwD!8x-&WO#e"Bj2[8ߵigD4EI'(8wCQ6rcF~x .Lq^(08"DU-_]/|~-ʎz> ;joHQ~C}=82=xgC,=9v-wH.< d=5fyvjy.wߘl`YVw/ Ue aXjYV4sPq*O~gYqN<)GuOS<{]}4B*jBn:#Ln" Y%a>"g}Q|6\AL!ݐHBsI"ReQ8񂑨vcTQz]62, ]{62f@33hM7M`Cjz>/<ޗ`P \j~5־g>֜c!!8^p0"oLC)1ܑRr t~HԤ_QqPAj%1̍pM9]olއWQmϵGnB^3Ѻ)_S/A{z?eϪ@#]= .-oAVYn[<=\^6tW5sIP3h 9d--ҩqڨn&bG&@`NU8?r@18)2 j()"`};5`Hsmet!oW$@l Rз!{GU@S)"ӠI1PD<):?<)OMYf#:OElD)XZu/Q3qA3^bŞ+>~iלbVWF#vv37Pj<jʖTF+3޴oh,A153f̺*#0% YGukg]s"D h]K/@i!lWlRȠ y" wdX2~.9ѓ{ d)2D6J4J@Y+~ mN1|C><,(hюU,N;:9?KP$ `G<G2볋ќߎ`/ ͞'dެ>g:$3H\yLq mPOtjçu5s *y+V"p;ᆈBtËѼ߄Dm(sv~x8]۽E2H.dsF^76')]@U=[g^M-6CkX7 ҩb9җ35G( ҩ* OkC~ 0PBwE bD T!J kS[96j֖ 2FA4ΆZPGicAQ5Gg&꣖h>!CG6J4o\~3ȔpA:I搯!"dT&;(~Hq PǵB@3R )ᷨh{P<(FT ŮR:wGi)ɣr;z)%8Y~|Ls~x}Ob9DꋼG1mo𑄂8((14@)Q򉷑r̞Ɇ{wnk f+=~?7Gzߍ m0il1RbRDA{]7Y>z,]Bw>je-?r)Cϝ Lq~87_cOQ&y%)GEYϷ8ͯv<pzqDT'8e*{ڰ3R 6>ڱ7o^{#i OP4G3W"e~ Z0NHզnε%Rw@`jG5KLX1'ʕެ+jk (1_g"#ms㝹u3N_9gN]nl5غ{1{Qe gm<1mQQ{S&q"nh/:sdL=`dp_Oy T';;Wo4.nE{#Wyv^HAphNoCs"oց-XfWBDk$(`S;!86HG QMA:ĵ#Eשx}m%_avN[њluG^|leBbJ 4oEsCcKʊzZo/)+<> cMKʊ!VšExw-8)Um/Xرw[?XFE !E [|N jKm!KSЏ 2)#g+Zv{#KHYY&5,.+!QFvGJk@hYOB~<]e3%8HH+E /MDk;ꋐ.δ6 X:J&5>͐s~bhd@%;#Z%?6>S'yk2z9m6C#q_ rUw ,r*-YBJ\ *3d {:m:9?<=Hf9?эv )gƱd9q&OS^Iy)DZ2u(!F j9RR;q`: )VKP/==cŅly69O qM ͸QP=qH1#t~Ԓ,żX{ Fc(^l[?u &r%zx9H.[E ԉ'Nc8h-S" Ӌ$GИgN֞ז~̤]?sA@[4f"TpApj y_ft^,r~ 0 ҩa C#0:C3Pb K=jE]O&n+nM)]^#:"Ve%h5k^:Bf!EIYQ[oyMxw}6m5zaoȻRj^l ) e\k>G Kj3`)6!kKHOr\67=t݃14p=ۡ fL7E x+10YyKPF,i|o d~,HY؝ ,Uo%23`B HQ{y>z $5Cnz_d nQƛ'1y*zIa}vRXwK펔YWq*ؕwhCX[tc&a+LY>$pnÎ7*@d_qy{sG4>tCc @d51гIt{!yd2Ć#E?46!3(CtUZq~wd h'/A4Ǻ:?,BK;{ G\h$izG}{n}f6z#L2"%1Y+h0'Ji?R_KdG3,̳h4V:e3h\(8gʼns&&( Vw50tQݬAZiN=ubNdh(ݨDu)E.J260 &7/H+ z!lrԧw#؟c3H&;8y)"%$>>BVH (lW ഄ礆ܞ^5u0vAIȰ9];t);xzW9?tUY( Ҽ<=ѸOλmV^y,X$b:hs^tj2'M4oA@;?|mjb5Z[Gnvw~8G[ɺ6sR]#;Lh=|4Fc'u]*ȗpPNu~ږB#HQֶ$D pSXp:#P y]uI~*@JtL,)+s&ZӚtzm! =Kwj79zavfk4q]5_|{~rHM :5wʚumd:<Eу4~wgDQ-y yE{{SD6מ݁hyEQwy^??El:O77DQy=է9E W6\9HA_#h1]ƼFAͺ"H enK&m]Yҹ❠FպƮP|hTV־ WH?)#|LFޚ<%V(JYkRSfޥb'AH8l3{{`)ס %A4ǗRnKzxsfa1 d vE !`~hאz\C www^,!%oN]-ı$>/B|zA:uq6N0 pgwzP ܠD{#e%Ccx3^mJuBsN>Cqxɹȫ0r%\YysD{wWCƶk=c蔙[; CAϹ5̳S[M8$P tBzDں5B4 J#tleț%p6%!?<];B:֦}6m뇾m ZZX|@&>< 5!(ry|(Vz+m:yE `y3P17g79׾yU m=ϫ+i$y!%xPC55? CJpr=AYNh&\sGm\z۵rH^!7v>oԥ:iSA°-7) 9){{(LB Qۭ͛lrN-7+y9 aUM t ]f෪SKu`) pH6/KNosn6rOs~i EB`@"kh(^* )i*oY+G3LDCm@ \|@,Ǒ{ $Ñ2!R\_B`h|R}D|4z_g"0t5mҩڨ"4_݉S!'j J ̰v;bg=* 99b #o9}ޘ>ρhn Dޑd2Yq$!iDѸ-Nw!ISvA ӗ:Q*ќɕX Kϛ'W[ud@IYV@K`6-tjSVƘ2̕ɘ%xo#kDZ)8ːw)C:^) o()+AGy(wm%eE͋ K:5G/оBqaŅ)R-I/D>NCc ]ZdJ||k79Lo|Gqas˸ ˗,+ò,.l I(`%i .$# dMDxu%)jI2zsd Yu{Z?%!ڮ]iR+e&DY>Ngw%7aAa'GTO{n믠"6.[ipD!W9?,0yDN}Zv_ n6:35vՋNr ԑt|y#uA:5Nr'4j= gXNڞBs ZpG4?f!0͕ΰ4RWyyE ҩk{^GW!+d֦3Q*A?A:@OmfS pYF+e}2]W'(CNv wMyf&-ODI3! >`Yk57u3G&GUbF14TvAvTnp|6&6NE%-<;5k4^yy@lX '2}[S@1r;F KgS3Ki]-Z砘 p~^?[b:tS9"CFh,F;ڷ-/.,¯%yf[YNB7|r.._E꽭| ~!(J;ο=F;0ў]%ozw}Es$&]Ni"aHȶfg_gȀ-Et(턬H[)n.vAVSPk;Z1/X% Yv>蓐w0Ah{0˚z6]ϩ`螨NwS%qlP=cl[U[gjofZzy*O" ~/䝙o jD F  !P= m}eފgڴ"C-9  t(Z/y¯ZO|Z@&W `[9)h3qw>FF+xx=!2ϯD/6P*JݑU(*@pzy&*@{n[B`=(i"RAޮ_֊X%%eE"?GctRRV -?(.,]Q\XVh]Rd 89>y_7i:~yٶzo-nn1?}ܼs KGbwwD{ВS Ks[% yn4oۂV\2vqJIL t~8h{Ü .p/_ morOxc1-<ŗz!r@=ƄEf zo#pt?#޽ ԌM(-HV~u /| [8?uC1fO @ SmHdx=LUbFI>Lx-YdԈ13t&`|:-NJtjѕKa„͖[oZB}nGjҲw!mF +Ņ_C;mjry6$T&Q}yb1DY ukDQ41h[9h璃/:|\FQE7lDk>v(gNi\o`Zh1r )C+Z);~ V"H]x;?'H TE.F൵{BqR᫶4h\m\y䨭dr Rt#i |b JRI)JUhCL犟cݧ B USѫ P +ՃU1/Ds z*ۅHѻy>_kpiSX5I28{h!z5^^1@ԓ:LeoX^.])HDʹֱ19 y6@wc7}#daiC1Q{9ącK-)Fy޹>X#1zwG]Ł[TPߊGY;'q|0ǮD{{ b?bYsg\RE7FwNzΜj~u{nQfѷ=\pǂ1-cbHj NYyp=0!}PV+ ,Z匿QT&h#{#h(.,URVt uHj_1E2<\7LIOs6w&R<8R ZJ\6SÎ}Ը9Z6P&tEdWk1}XPaS(B#t rrdJ.{撉^-b?$$:'4gf_ӂtjYE}y"Ha=:HѾv=PZ1Tnoα#׃599:AQ$5ao&w~)ꔢMY˫ݖL?q"Rtb+l4~FrDzǣwR|'"p)D|_Y~ڙB4fA:Ug>8AHYw#*W,BO={e]D1+Ƶ E)Dc ?#A:u @'K&$P#vu~?Hj5$9?ldtW =P˂L/њL^!` zԩ>wT_WRN^ v~8zo; o %]oK%Xd-yHJUYj9w4<ʺ"_Ɯ"G9$ ];Tn NBs$!(ỳ("~x9_梨؞K9 NHIlJdQ_z D?2=mn}h!6r7d{gVkz;L&FiaI*$HT[SM=6~a׉Ao6 Y㚭9zu"" j=$}.Gnu }fGVUw21dl`SqЏQԠzS(5 ns(Cck4>A@84oUH hChıYx)=C"odΒy~>8.Ȫe џbO/|$HӫSEEs{J;YC1T!j|G/ ~ۡ~dbvB;B{[W@g*,Kѻyba{?k,훋6W쀌ϐHK맵M(.,K,|2Kk9f& mLn5Jv5LD^oLY~ Y}7DVAA:@ ^,@MB,ny(PgD1:2H cE;')Ʊ6d{ ً wH mܾr?xYGORGt8Mh5g)ȃFJb}^SU1_tͲ it4stqO)עa]: u zu)up9M [ *?5KQyqq&г4^EDcAcL4D!9"*:7 ziNT)m͇r[)} %o/]P=n-pty M=> ҩ9)#4Rh<=lE>He%NϛVm<l@ZG4yL <^y@T~hLp~88HVJpM \@qf8%%xyyUf {荌Sm(M8C^x/x,)+tFiFy5@lL~,"*Thühd͜J^f`-brS 2@6<ޅ<;MlQ<gd9RZ!ftȆ-unva劼V-߂Mtꢬkȉ!Ms!pv-THh]nqdR:g|_- 3gd(8?XaBת!Q 'F[|GMt jHkWq8Pg!qiN"9;┚7 q ^ ]ۮ-GY:Bq.h%O/0:Woh؏%>Hr~/L";(ȃçѺrrkPw j&XnkSBޘGO kz(DA&=DIi'6'ID]At6&=~ǩnXS?v' ӱ͔6!* bJ:+|{^PRVt$x2C d4h6J4oLA .*hQS>(Ms,-PgHQEImQ&O aI7@V!ekLmՖ}qJA JZFTv-/VBtjY#JDRpN Q  Cx]}>耨jy֧ J.6t~8 e[js0N;2B co s~xdPGj(H޴i}\#`ҩͧթ/Z^9): [!Ck~h;th<;>)cX(ҩR7xK.B: Jʊ|h=?hI6MV^I3.>6h/A &uD{T"%eE-:ܵQe-K#(T?d SBgt\ BUOBȭb~;,N"6i,ٴA:UU&5Y[u^8ӎ*S_9?싔EH@o0}݌_\da~ Y;>$;nuSFa4t ҩEԀͿ uM'A-kTI(; 4ks(Gmy擡MXu<AgALG[;*SmTSd2D}y,VW6Gk2z)t B PFJȠ JѺ;0RޖލŮHg{-°)5i"N Tz\3;ub GYA57,ō/$Nd8?.wk -{8-A:5)Fm  HYι_f;e+;]d=$P;vcD{4 䅊| R6Q<_AHXrgoEޕ rg^S]4ǗyPMOE8*bT|_"(]1u-~$b!iIgOwh.eZK5襵en}_a~y+6iӺ׊v$]3"0߼tmQU.Kf+ݤyew<o݈F _l|إd5e[ A:Ufi h}IqaŠ{yA-"J9im=9?)]7d ʞgyn%Z{ m 4pk##h숀FiN]iqzktykǿ:D/`dv?}`}rq<6n;T#iӭe}nPZv/E+_~$kH&]GI(>@)4gSg՝m6[3a先6,;#!T\ 燧"|.Jᝀٞ_yŅ %eEP"ҟXhIYQ+ U%;!2|8 y#C{ث1[ݺ)( Z1٤/O ἕ꾦##gQ=X``Ex Ft`2px9s^ǣn>2r^LS4xr!;[5FE)Z3yY QrsLE7[Xh_w#:h`8 (C(@DךpOġ'SÑz}P=ȋ5 >HC)},ԃI6jƼ,Dk( mP!Nix8?-hxΨQZ»:bދ>●(Υ/TJ2>@A1'+W' eĵBjJS\O! tyhQ튨Y'&큔ɣP;Dߡ9)-(6"@X5@^S'Z^gI:"R/GtwиekŇ)uG#zFiu1 q(3.4:;JynbiuJ?DQih}O&M~9 t=Q9] ?Ѓ'0P~WqB^B~dʖw(:&Ȁ3DQ4hfy(6,Bf}oF>QbՁ" +QA:V=vC.Pj7QAWad= yAEHQ)8  ףЄgCF!U|t9Qc׭$Cl|6ɾ{Є(o8YڢͦAS_"Wk1\be< kIA(D?,@ HyX<#;̀vi(vy9hpEsLiْB9? ,6iȻ1ĞaýDb?۠q/R8d]g8?|Ż,1y 2rJ}7 pÍtjb#wJK7䵙,r!d뷂tjŗ^NJp~x(RC a2;͵K|Ձ, Z4FVayNܸh~R׬4=_k.9={-wKPhAFf+>y/EeϺ9?<ͩ?#l;QP}k9)[/Ê;?_4ST_hϣ׿&قs/Ӭpz[.^ک1 8|FƀŅ^0`UqaiFYd^*F$o (Fz1gm9% `yIQņw(yޣdE\)%{zw62tD{p#(j@;MNPg#n.ksN ^diCFGOJPm<WjvԞvvV?[,pE.E'sDV9FO7űͧa߷E޳[مLZ)~D.?OGya\dh17;?;H^N\熬KiTO9K F"D{%h88;^&dEY7GCGao6jW唴6*9?<2HXNS4v^G@bW)7]RE};Gިvqml$=6P$N!OG&ޅhWa>ڜ5Ȝ AO" |{G=Y;Bmл@^-y[yD/fqyHOA}Z G[;qS: Y @y:?<8(r!.K+b#6>|6ۣ~"AK_# 4|8BJXYIYQgD~̀L KWQ:wCqľ_u^@ư=ȊmE]]<9hQjͦ(5CЬB!Y}MD=8O$YDN}N.ҩW)M0b H[㠿  :)FSMWed`LDCтASt6@V㊄ޛ'biP{A(dX-q#K{ہ6N1zZS ! y*eGdvwfJg˙[vŸ Ev=d#/Cz'^S%NMV@V֦)|)+] A(D{j<\/#OR9gϢ$q= t~xx⣟۹y#ɗHp(&/q+ 4۳O ,FBX6dRg8?쌨#$0d=IM8䡩V?)]\m9 LZOQr>B^־/@m9*~#Zq?CFP Y{eUʒSvġ(Ȣ;7u+NqCʞcѺ:k֛?R?,qZ#_M#k,)29 r`JG[HSv6>2'Ғ4]5vC@Y-i}O%| ȥ(3\b3ikB?yU}Y+r8 Ihݎ){0OmSYF8~EE+f"{zAo`J*)Nl][$돝U'""~5O vΉgq=b"kd n@2r2{ gfaY4Q$xJ4&B 'f ضrӊ[qH^pO4TrG"Dٿ_ad(w[Owc"e#s̠r3rF^7.BkT2NRrI)FD9JfcX&<['! mzy к*\9pI=3xaj9hm5T f!zJp^pG=j _P:흔9ڮ 2 =0i, Yfn}7Yxl^E4>h\Dv]hY<5VgIg%h\2KI^^DUr>wkGԑUfu[yM1PM $Il0FP$aqotSI6TiOu6IL0qnvc) ð' w G:AӑC90ɑtZ-1w0 {R+К$@dR4VE bDBd#}WfŔ9HKpvG'cot!4?p.!@F<[^0 8_;^z}*BbZQkUbp,i⿨h] J>ylp~RiwuDƢ"Vxqi2Mp:"2W*+sd)o Z]a B6Y_#Q6 _ܗ!z"JW&.D_d-rq X `7s_"QPz^*ǵo B dA#(&xrD_6 %ji(A7D)v ܄:wךF: 35doe>kWb)zE< Es1,UK9gXDA`!" z"B7kDY9[4s`?ChM 3|GJ xL,S?z|#RߞkWL 7kq x:XH Оs=4@dprjW1^ǐ?Ji}Z4D#RPHigrmW^乺I:ppY:`_oE]FQ_iVd>3yþsH-6zgvߘ#a6Om5mt&qLyYEqY=w\ةħWcfP!Hy=ב51| .Zd: p\\i`<9 }.E1"GBWAk;i}K. p5iŴwǾzed= 4Pފ^blzc[Q\֞kƾ FE^B^XSvdǯEx"XAq!O\N6Myp(Zi hVŮ?Yw *)p 5ndgN}fIz(yĨ:Wf, fÖ(v&feZSǯ0O% ZnB@/g#Fh M u> \9؞ĮO%wEnG!q)zGB? <RpJ4?&k,(E牻OF&z \/O%k R47"("ֶSW9eQ v:*HVSɯ׮Fow?Y YZ|٘O%vGhpc4f1eAXɆB (d3Ѻxח>1_g6cQlJg}6"! Y#FClJzdיa.Y'!8j{4"KKj_Trc<^2 9:e>^ވaK4vFl9z'Q0r5+j9ww>V$߂vL{l hW(3?qwAS:rP$.D'bFI~7fk_O~*yBKP rF-uֈ3i~*knHQMX]g!+YNF& ;?ڀ<;rb֒3n>Y(hsޞsE z~*U#{Seq~|d?s^Ed+D?AI([!B>׷d~NA}ZWb m:Ӿ"BǜkA\eԫ6p(?|7v\wD0nupg2v> 䂹ntCI#ǃ(`dr<*[]9*(~*4[lZrk rŒ@"Sfn0] -%&?\ WЈbf+z~[YH!ZE:6֦Gl35.,F6*(*/mBJh}ҙijFIde&E YNA|sx>Zf< ^FU(^ ܾ&bmR:;(68)xRE# ܎\ª]/:~*r f3i:up="N{ Ā,. |}h\'qWtZ_+r S|)5wE̒槒5IMRsg{{]6\P4 eYycRThܴB`%h!V!Zh~nz?B6EOT2mPSIbrzhn6v&nan~O4C[z~*9-jXEMȍ2~x Z/!5WQ1uf +]лY);K⧒CG4-zRzuG1-PntGa) f͎טzwSIgm*Ƙ{Uo,BdC~= Hg=y|V@LAVHi?"5o9ph:b9@_A|vGt&R|mbn(/hmum+әdD{!SәEroOI~-YInMZ!q#O%B{맒\/6o )iwF,_S*WCBcEDzcA2]CQZRm=O%Kaf膀<.:O&8ؿxw)Ly*Pi{_7>l +ddy8 >#P Y42](a{V.I$P^VQtO%v+W.YY,cͥC.(6w|s\M셁恨(HAp=/ sPת+}zUcbd 8)<@.o<,s`.ٺ:K)`x y#[.IaCqL 16CPW֩"vDe#PgWUPգ"[$I!سH~)3}&QViи[܎aoK0 iavr};`AtgP[>kR;a; ;7cdGq*w#Ҝ6ֺmO*۶ùqmІJ]e֋hb@ Vviͫe-F#j62z9cTrLY'!6O%sF7G8{} j4}d>m.eedc;w$R\Wk˵ͦR=m׶S1dOZ;[3K~*x׍8ma+g~*S\!'NUɂ K+(v=pCWAKFXho X| 6 7+Hq!2/{w3+BK?GL;v*]Z= ~f-RОN(Wqx[q/{`%"U^`du]&x7u`"GXgy"B HYTL>G_l~,O]/sYuFںv2*;^urY_Dz7>dD>CQ]/ s#Rv=R4nOiH ԈhZ|c'U $ڡ~ʠ>2 ۖ':jvdGbj>:C\ܒ l>oFK6IX^V=S ߖU-r^lJVe|~[:x^qJ)y;K[g8aLaӅ@_qZZ$95V8S {aؔdd$E(օe~*,á::7 ˱{3jGZF.C L?gt5c Ҙh}}:P?i{fP^`:\B&.q֎v\\/3 @?*oɜb}0z_Js5Gvdk@Z}QPt K5b"XwA֣Z;Dv^/cw"y{w(FpM(bZPg=0ꏀzAy!pku1?7\A@`v.㧒?^p@m1٘͏D vw8?iHYEHz+Sbzm@/:Jq/r}mz{6)Eh\y/aBisڳ/Wh'~*9 \̿uCkA4vD2F Z< 8$ Z9ns)hEaxN1"8N%aؔ0&k+)ܔ_T֥;ӐrCW_DZɓЦد M#(7">G#ױ=\/YrSws)F>nH.6U^b]c!Wc}+ڀ심N]ɮ, 7׻;U 47DHOh>ѻɵlI ^.F@;T ܌R юܬ!FT$.CݞbV \/¾D;JU# [#'tBVš]/8"7,k@9K1@5?|ӈvĒ@o^pJ2 k_\^ABg.e"Hbԯe6t_"r7HCbk:Evr_by:8HQDh C6fLEyYJrxw:}لt*ϴ#XOW92}#E@Na8́lj<1}0U~q К)(&k(;q[*&k%)S 4桹X\/i@d_D{SSr-nS/#y1b#oڰ"%[4G!zG#(7u;JCqk a-}Ś -pf 5I\/(D5VF3J=Z4ۡo~"a"db.}Q[q(+8uW-fx"Z>#O|*kݭS!2eY+CDr]< 2Ue`Hs#7!0?9g?3&uKFCLxMFk4wnQZjm%IֈѸ<x/\v!ZG(?BkbMUS.oY{!Ѯ9)Y#VTacD*9b؊JyYEe:8!r2WKqv0 '_CjO\ejeYK;4}_! qy'#\\f mwDu,v3BTKT h'#rYKŻ܊b.ʊ5\G$'V9@F~;"J^p1u>(6$G\6E}}~U(~h;_b] DQR¾G;Juk\_6x ~H\#&#W] =WFP"rJ~KP "&CЯn,bcDf6Bti3-@aJB:{w;Λ\/xWf9쪖Wͯ9OFn{y#d%iF]FJF:.:IE|c.`X@5 "tD~Bz8u-Sd$6CnQݷV SfEf+TTm-}1hy^vο\+k6 z`dAO%uN@-7>W^p ,f?5 TE3;ڟ P^V3Yi*Z!Ko}th3yv `xVH\ՂZO%__ 9dcEE~h|(I|͞B}3c-j!}Ngx ~eEcl*Y̸{㈻ cYX< [#0L@C8>y8q0\8NY|8N{H%"4qHhoEV{RҍniA4 (P'1qvk Ҹ_A&1myD2U~*y}#=έBoo־Ѽ}C.N7!\l{YC効9~*O% W-d-sr[r:(F; RsD$P$6F_Q5N ׍1: ,Q!kIcbR{әăpH)$fV򲊊t&qJ$#ZϞE{1(05 ht&q1qOѝN%s3+Sʦ{L&bgy4BX qd808HA8ᲂ0 :3aioRR.y5~*sǮ\FH$|.e++}F\ ^lpsr~ ifvyh9@G?\z(i=3%j)H=/&Nb#t>q9 R;0Kц CF8 (a\4ƺL;!Jz_g-tnkחh,mem<%{K.W>Fև IDAT.:8?%O%'B-J plmfٕ.uĈN1zWE;U5l(tnP! FRD雏vy7ACJI~*XWPt;QuE1մ]Ȧ_dsuXߘ>Yg^E}oŭ^1%6 7q\Zd"G~YeŜzh5"nں^pJ=V':ԟtuͼh?0SͿ*VX;h Y?>[әDOVФ3ZEX]< +T{" r=O%@cK ͅ mIg!+Hi2;BhXBL Lg T7iAyYE/rdͶQʜ*VDsK3ZcjF6UI1eD6AO%oyYEnu^1}b7RFB)FPV&,8¾oW$+.=)Z槒<D9#к&4؝SKi]'_Gm`z? ";'"kttf v Ans Bi;b Rd}Lmk 96w#F((tkY%2+a65O%kE ʐtQyW$"7DpѾy_;y< ifTTmDsz6[m)4w}O@ Sɯl w'<;M5s>tF({]^lzdN'q6hME+?i H3"!"ݘ@h,Y!E>"ס>]fc Y5_DS@X>vGD~" !Yl,i*Lb{LD~Bhnl,_l|+( W  Lb7{ȏs69z#WEm*ELl꣱Q\L:WL|qOkLF$ĩUKg?ULLg7kAkof6m6T~W5 2I~8zWV[6NzA/8F4f+hgI1ڬV gO:Xcnch7^ r9UdO) O%Z @M?oH,Q!Ql1w@7Dmл z糑djm`ߍG d~**ylzA=r)ϰE&]/X槒WY|ܟB@F\{nHCx"47vR)#̒4|৒c*t{5\ \/88q`kWDO|ߒXF2br\/pl b^GFwvV#Wcz(H_9v\/hS l@m:&ֈJ~zݰr)k?!m~9"="dֿG~TA{{`>@kT#q+LDJS(܏oB{|[,4b,J^;C`y)Ӏ[\/FO]H~|2<6C֌xG05_GIDfiK!+-t s?bD/Wv"?%o#X?.FD^ G(ATصAqt-F ":s"o2"WţrQH PQEv2"=o]׵_$Ҟ_\a/G fU{~WT2wv-Zws}$3ke^cf3-Q|#h\0f~Zj1kZ3/Lg畗U46>).K>ː{d~,<$~U{e򲊉Hi$M' ׄbfd r')Cđen6c]/ž戮_w{$QSgԵG g\7b!M(WH3 溬-cٓL\x։x/2;gYUd[GF}S],d82`J9}i E#d@+жwevQ3[dk^-EDɧϬJs}9"x~.<6Xb r[z|d̋,,9u@W;Ask u0? }BXmskv.1k՜>?'u88r'놔 ϣ1>eh\,;~)>4";\u2~hX z|1@wpȻ: /៓.AY ?#O9xiȹG;D^C֔CDqVF|0J?ֈ|p""ƳyOHH6X-%-FHG?OuWdqxXP1d3_FkhMl֬E #3rǾ)Q։+z#mO1={sQ[Ȏq&Τ)Ww17uodcnW'M7Yʏ@&IU\)WD-WXdmnވ Ch,O_t&ByYE^&i&Y;e%Em}Km-Bj{!0"wH93kflsoQ4Ү' Kh1|4"ף gYz9~*9qވ[t]~nQqٷjPtDZ~(2_)eHd{e1C~)D Y8mJLn{\Ū!'3VhL@]\36.\/ C`qG2bI 0G.FG$vKjWׇ?,XS,o`& O%2׷!r;!p"4PeϹGdG$ndicQLHM6)(!+6CzٳOt?щ= YB4R l槒^0Jzh0AvsWq6_!o AhDZ[^AkN .uwl,PC\/NbYxE/*9i!@&W$huw; zݐKg9MjŬX ~PCyYt&JfR$+/XтQAh] "K)$jWiBcSr;/A80 8CaVÀ+z^4^XRP=dV˙HZE~*eLGD<.`MDbNjiMdul0d:nb!`3]5D$E~*9U<*uY"m"^JހHDi"jJ)$rDZC-?zcQkσHKLܮ.%'hW4..Ȓ9ށ 4 |.]~NSN/<eֶKp=hxYf[[##"Sed)Sޅm(>וrDVR/D †F&X/*|9lVKc?$ ci\z+JB1Uhp5p1eG}G֐g𫨟Y>~F BPLwOː>_#r2$C.ۣ]w$&@-~6IdaY7𯘛Y5c7*!*&Kl[x3hl Gkd\lgDHk]EwI #әD򲊂&Y}qQN}_"14l#z8#f0 Y,g;w0 :s.F$RLqz r uA{"0 G:s$*P 0 7!k+):i;ɍh >6 i?.*^h?Ʈ%yiG]H{jnkqP^Zk" Bh켉Ĕ@&OyC*zKS| z&UeFZC#״A=b^bd Jg'"]dmGBMF\#Deөd<ӎ`%:Sگ;r= ܋ܡßa;u3xq:aX8 ;vD=q^^зga8q޳p8Vk֐" vZ Vhml9A@7ͻxD:נADE^{;~w1k=r2?|,v߹ 融#m*B uEul ³㐻R"s!`u ueHM(kۊ>>Zd{#VzAg_Y{ߌlf@O%n(RT<] =4Kl\秒 YgdXly5*QȲ 0z:#h_{|g)@yYt&gDNGddu T^VM: {uD:賖w][WOmFJKq"oihHOKUŸ+a0J6_oZ8NkLDȖxqz4RR4\KoFZ{? EXݩ=S8m7[) rHD-J.u;y;w,ls߂ڮR^|,6~+:PM7|Xi ` mz< F3$4?# IDAT6 fx,C^!ouӒ&e% gڌ#B1i4-0/ : J^(fˈh|tls8,8l\5"nroHY;5t @[ ^@֪vh^_JA [K K@b?c`};UyoCUKNqXaیk/c"e;kJyU9!h h>lOZ":^pJ27skau@"{Zt݆Q 6a{9*G4&"bgJuK_Pǔ 5R" AT[pMo?܌|M RiVHvCns5Le:8(^Q$e#ߥ3bLB{A2v<V51EyYQ^.]P@( 5"vlD1ƨ {,QclW5bc/؍q"ĉ ;EWJ}~|pg޻}#^{<3眓2y8I ,?7IF[$wι{"5?٘}s['I2V~NDc>[qnhBN '4d&(W[ݬ5m@E-}߂d 9^MU#a"b@+Kfs:#Fz;'xUZEad}!o٪AэUPty##G`EQ.xKt#vAf%"ԭV?? b#4=@ڙY*$_rc].eӑ<3;k=9͙=9#&~C#:ͭXWCN{۠h(|7-v+U7Dca(竰_Ve!0Κ1#P.v('fSS>w^ 8]ռ>H%|'hBUSmfv=gz ;k+\dJm)fIe[>F6-IuѪ}zzWCo.-OAK1UkrbMQN.>ԧRמw|d%t5ca!+Ayx9ws$;:$IRJ& e9Wϥl}z4f $I;js'I򔁲^I|${{ι}; `[Ua93r\;ůeD!Q. [Q$;Q'#Gkv(Ѿ[ʀ%IrpDN֙(j{EV(ǜKIpZ@ӓFl=i}iclϯIRȡy򝑴o~oco>us| /Nf{ŚA `SAm ~G˘GPHv*Hfk;yU e\hk#աAF>@R vmb[;r+P+H< M(_8֞|/nrAIf_!60"nO2;mj~Qj;gcvC|Ky;29z'ch9ѳ`j ,:b\F~6@͋u@ ^3m,:mm?iKK}Hv&6sGp=sDAќRʶB *F%g=;Eww[ŵIuo.矠g>ѧ{~ڣ(۲Xs?,\>眛$I=9w(z6C>П$ypyk -샞̀$ lJdz'IRcy(0ӵz$I.w=Z+!L~.l?'(A('q>DӤW}CTԢ\0Ld"G" 0-%o}&v_p% Ǹ=rL'ø9EH -08Խo\Q(3rg"'w8[XuD4 (ʼ:rD kD{zU;E*uQD W=uk)LTabK_-E]3=`chAI7l9QD!(x}`ѵ{s h=(!@HZccpHr7HH{#!a`b0Jw>[h*#6 ci~lR qz rػMN{oj~*i*zS$(Zfߋ4{a/z]qn丩 rll# ;\/TWlmն;hߣ̥ͪػ"IbI+kh#-JU(`#kUdĘ~ާ(AEho.=!6l,IsC5+OQ1@d? .$n`4 lsT`&[XXYٚf?Ptoa9(1 E>/@U7T[`_m472mXU]=`k;Q0'0Q4Eu'FNG䝇#L/{D֩ndrL1YJH~5ʤ?4ZUcc` x%׳blTcfcm[,iܜsmQ._n򨥒N>/>a:!Vdmt7Aj9tn&0IPJo a/VEL949==<@F(=0,_ty^Ǣ{3-Flvl f}|[Z z<*62[KnIiOGϝ傩&۫cZ"\KKXo T {{h?㣣r>WQeC\]Q.8ӫD9H7 ˲v3Ц]dh[h#"ط(p=6N\y߅=h&77x31 vX$sX*s?9GbDe0bn7*4MBF8߇CT6j@/7P>FK x 9,= |gdz)E;HXχUZU9͐s2=7o#`VHmAf`9G0nfoܠrٴV3>{j:lo)X&~@L z\$tCͣ\0և+(0~D"` 1k FylT(>&];݇cQ.8ԺE޶޹Q.U z/$<V*L _#z0+=]Z 9!F |e+mEdv+Sc41*ٹii M(a2NuzI#F,eg{P;Ƴ)O8,va|~ /ړPci\2uYIgt@5V܌yn(q3|_^c4(S 8_ip.gF~Ϩ#֫-@ʀ}ʶr+@, 2=#2*ZރޤDIԊ*Yle[Q$ɵXl?ς"$BP=<7)ʳd` EC ;SҟF7C !ac}̈T@KrCH[%JAH0a|\=@^.Cl[PDUQyQ.c P캻<.›v7m\Xvvk"d%4} x߳e[䈞Q(La2h@5m(_I>@P;-@ ћ@JS{'~ڎJ$e|+|_Vg nPg5Կquœ/Ƈ!`4Ivr atۯg ‏_ wa\d7/Ǝ3 r)§j$ٯ؆\0؇0ܔ*ٟ6A1GP7x/ hil{sp/>|(^,|y6&9-m Ȁ"7@ 1D`1}v@ X {,e@l?sr5WOG H([Vaˠ?ۅRP>C1f䭁ʌ8߁%=TE8=Aч(:4G`(|xƇ[s-`Q$l4#T쇤.ҭeXl`m!mu_7:I:yI]n$`6E0 )<f/"ʇqHU}~̴h21_|Y}j,I ^*5qr|9JQr^?2 niZLj:)0SM:-r0 ;x9۠/P4tAN" AoPE~Rpnf0nQ^gS\pI!f9FXlυ>ψrA/M-ט{H<1w#@[ {*}\ջH*;ɿv.Z"^qKƯn'fxLjaMǸ;=g`9S,$1Q,q>@sヂ#剨4i(0=o*|@L=%{/мݓ|.\@s6Fb="iRle[E@F"qJ&S7~ · xfö)?=* Kn9aȹ~E8>ߛO(]G備^EF[^5rj Xk6on>2y9>rA 04՛#'((2v;^BL@79JD #0·qkfӐ?9}P67roJvAg(̢#$ʣ6AyoGG ;Bkht?Q.0+lf>]?,k5'_d3Į>MNvx6}u0lн3b0a<Վm1Dϧ"=Nudbf٦(ok\% hkS evv?BLcQ.XhdJm{+@Z=Xݼ=ɸQ.ޖ ]cbg"P_ɷPH̲ x+Fe+[gAQS͜Qݾ:YkQ0rI%jkS<Çq3mP(\$>OT}m6 %Fu;Gl~ 4ftDZ{tydUJsCa|Ňx 9W!GWslw!v'xӘNym퇁^eSyc?SiQꨶGQWIx4٢\0ȇHV͌֨YnObm'(;r2w~gn:V<;AY?ĞB2Begt>Ak,sF/PH*V`6h.:o?ϳ/3FAMPi^r&"xX @(Pp"b[0O6=_5 GQ,uCCm,0d{~6J2}B&3R^l?SMP'[VEp=SE941AQ.Q>jBIb`9L=P(QbbgQzG>㴹m'mc<, QDJ9l]|3͐%bN@NPwTUy$-ȵEexB9W0ThgkeoC$ A: 0֫Jd;IjQ.xN^%Dz ѵ@R?U JDLR.gȾ]@Ɗ,&<hv6=LKv{U⛑˂s ?Dy7zR\"pu-άL-I4t f3}2 ݳ JHE 9^y7Rq~v6IrD_{ymg֊Df ܭ9*bF0MUEV3\kW$snRT $IJ9V$Ir6p[$~hY_sf&Irsr`P$>='IsEhQ4_ rDEH3Zdw",@#+x2#5EDkHԫlWW"3EZ#i#>)pp_a;v"VʇY@ub䝎r.a6rG鶨\6"|8A]I_{>Ѻ6WBʸļk|K;K'O#9O5ˇ 1*|{tm"$a<-BZsѕTw72%LaIӶQ#I>7"v?bLz4"6]korV*L9/uZ -URy^OFx$Q՝65P@b]4o7EEX:LI??m)4cA9f/" e]_"vMGMM˃k3f1m1BWiQ#$/[qojj%l snۖn`-*{Ul;vެp-WV`N$[>FAX~U$G IH?mIVA@ H q$rsr0>@C΄H^C(;[{9Y +ocYUo#F=Q=܄u5U`]b(:Rawb}&.xN]+!)QWiYٹiø"k堌*ܞͳ (0{̿:ܪ˂E㫣L(dTk<>w@MBvw|?傢<ס( E~ ף1Վja Vs)YFl$i @8?LK4^r1 60~a1_HP:+7k}O)b (nO^YSf*eSfKr1[' :c_Z]?UD|6 ]X,[ʶkݲ Ov޴Mq7~:^^Lm="Z${8>FUHDLGOܱȏh$9϶u7 2"VCgBAI9Iۓ$ɗxpރ$9`gl@J ӝ$<((wgv_ߓ$9O;K$IQq?_$Inn|5(ɞϑeQa|r#1@~K\1$] I95r=Na|B }]1!6R"ުhOE ̓J[ M^@O2'=P>Tfcܡы>r]>7DE,& V( u 04^ kB yfQ_!&ވ;\(j3|9KPqE+b.*tF,6V_;UGN5d IDATxuɅ,sI3FH&YEt':_=("C3Iíkt^Fl..DhvK m턂(L6ytcf^~}<z[WD@l @.*M h<@L-ߵH{/Wy:~43į?IhӬբ *.ޓljsy4C>O,LxrcϲQ(׫09_P]dm\0"U|A @n/C@$2(pF9)H+mؘbBQQFŋHd5(LL6u5zX>xj\LXl "c7:Ph&pY}9>ykr`}Iٲ.០oN{-b /"6]u| j\2QNW [7-U^Ҽ1&x'H T ɉ(=6S=cxw~Ufh\|]j&g݉Ib>M*W'6nj9>6@όoP~cVeZ-fZv^vkc}A~Yle? J.I$I8>G1!ι"~:IZq|sQe# $d)-eIr*)LACW=[$  DR9zT Fwu/ R@ s>@ xUNBkH,s$[tQhBކX('Vg"VWՑdg,6Qaz^#fk[:wٱm~fq$>@MK:E`mq >a|ȑ$mmcsNmMT8Jz茊 D azYiH6γFч1ȩRk(a(g&S$O̶]r}}Hr~@纵-Y>GvĘT3Gh(r"m EFyImGX9?1= En,0Ž#s^Gt+$횋Ks>Gih?k)(||@ k!ϣܽKo&[takԫpX[r{{U P &O6z"'0a&I^Lum{m^#v5rV{U ,2}p o19b-c೥Hsr`Sc#t^;!(zPf;Wh 傗f:慀̇<;D{_MF#Q.j]χ_Q!%`f|a{_9iQR(\5brSt |EW{{6YPjx_d㘋U4\dZ<]'*Wѳko|k{c:PUjD(t(nt}k\+[Vڞ;7&+{clj_Hqf,IιӁgsw!ts$If:Aػ|), @Osέ^k`/S-IQJz-ιw7I@racιWSmsuIcr$Vlι$Ik۲҂"sXt>y/9A;!g 2cC6d\Bshh=6*((`9Op\۵[ :#gt/¾ R5!^}Gڢu?n ׇqt-BQQ);S%W n @/& l24qs6H)`:;#_oDz90O!( lzO?9"=tBň¾E+z`@9 BEC=F1l8!s<]Q^e #yUm&ntMd?'pnø2 :3ޖ#֜"8&,+j`+Ҳjs f(f Q$GE 2/x`d&0*S^m?)N[exԨyz F wtf:\p'!fn0[YI]G,9ȉ?!0qhBήMle+2Z$ss{_ou[Tl֭eOG͛U3vy WP$ $ɣιMwL582IιsO)hl8熠`W .?rv^t͢z$1ۍ{sj,lcssO9B kl?U:B w8b{ĊvAmz=nߝD|ZT7rOTPSO+,[~$ιu>w} S$I}+ )>{~߭^M ݉$y9A7o_$I-6$I^FcXe Y|ݳ >gV.̠;td kX(Ux$ʧ3V E5Z$b0,E[TAN>+t2 EV[2'dc ޶[!bUڹmRWmnAdW%0C v[9S<]m\p$j r#&o'T_n0s sy< Ux*v6 U 0ޚ"S C]`6(R} _O۠󈑇mEhbUx(_!1=DsK&`-ح‡.Q. Y׶ު\M7x y3wY`]ѵ6~ =Z nCNTNe+[ٖ͒$rVs jViUkgYga+-(Te촂&0iG[#?-BNp' ,פ~ޫx%Zؤ"@(i[5@qSf c*Eף5bo$ ; ]yv]#ڿml܈$1<rO+Bĵltn1!Mg_13ˇ3(!yauz7ƝnCO l#Q=t6`+/E(*ٓo5Wp3L ǰ b1Gڹ@!V~cF ]bNt^b=3p~?sX~ØV&VuA?ag̸]5ȞlR#3s1@={NadwKW2+U3ncCnWJES7 ͱoQ0e44%'6r5P҆[e+[~^f}y7Ome҂ILܖ89d-$[$+O4 9{ *ëid-&}3yPtj㝀]Pկ6&Ǚ KSXy9"]K,^ER0w2J~5vCHVXOd HxB1)^ع9!l i1'Hg0L@ԫugPZA x߇SQ.! 8G0gBſZp-Ha(1ԲrǨHMcK٫靁\ƻ# Z?Ą^(,sHF# :ǒo {pF 淢l^O6n,2.&R$mh_dU GݎKkl׻/f*3f1)gƎE@?rӆ~(4_B%!@3(e8̴d!'{}7zɎ(c,4H5%gt&rh^?͋`} 9Iuz K6TjΠ኏e+[Vʠ̹93џQI^A27z9"0Ud0"_#G @jd[D`A AEO(S i$ܜֿ ՛("9a_0} Q߶GN9db56·>CDb?r.CB"F#=[En(ңa(-J{ؼ5s8իPQ.l>rjʴh礓]ș:|ZI_1HOKwT/6b _GrvyfK;m;cn*vN n1 @蚿kL?# gһ^li C0l$$r9a^WZcfQ?<\?ɾ{!ClPDg4/ \(lގuoȑɫe@3bK3%'#pvbDv(0#}ox2bRl&a*A]֪\M|DLdt}&sлGڤ^/le+Ok+5(*zpbJ y)IlxX"#4UL?3ۜ6GtmsGjDLnk82ygG'z:;Ď)D?@ҽ^}tGu^@ȚDf0 âu|Fw SVUB{I^ELBD^A#.uE ЕdIX~8˒}Vvn6rՅ܃v-}Cb0*(ݫA蚦6Tz}:!z^*휎*Eu8x pkz0,!L0>ǿ"{ b޷ua/:σLvyW5q_#r#a͏b܏G\cNCظbؿ:, =~xbT`?Q"16ˤ'#FBmv^:0a-B 娵~mIY@`g\TH"! Q,m# "o.{">5tV! 2DVdXW$5brhM8((NBV&mꂜ7ȉ~ IۮELC̯;&ߢ'/z(&DTسiȉokOr>, F@"=7ȱ7ҟ(Q>Gs1rF'u,O3\Px rLF]T0-WejdꂀSPNtF`kƄ.;\n*rͻqnx eYތ>{F3eo| gm{4nnGsaWL}f]K JJg>òK7|>l#t,="CE_TvE'w,djѳ`4߾Aظ;]< s'{GDã(z^K^zנ0 RE#QX z)ۣ͞zG!{Vdj'm!6?Mh\leimeE/ {^fH9rã\a`E @\^\mˤ]^%d6ٌ)hH T;}Ěo[~.zW2a͇Q.Rd"p7Ik=#}MxM"5-(/ƿCLg0hj'uŬ;iwD s_c|>}f/+n,^`Q.x| }߇pr<_" 9uDҠ:m,#_/u..D6vՁ6>rA/-@E,Apm0*Q</p99o[Ϋ*sBse$Ly? HIk.i&5r0b`b;fv|EtEJ 0 O!iGGGt/uᷗ'}QjήMG4eKV}z*'2\k@.[;*Ir?;IhyQo+;(J 6DBNt/޷ϖ4a>zaCUBKM,䌎R z!0292im/|? #4n̼ëO }_ܬ"=@ L2s( ǭm  E&À,+o!k lgk$iD2Q.Q.8I5/)mE%@x$3hqu:>yL ؘ!6 [K {5Q-lmcʂ%6>J|^}sJ:>{FR˔Dͣ\Xfw<+¢\phKtc ?{4'#l01BsP,}|67Ȏ>Eح;IXJ#Aup IDATH0LiȪ\MG`ZZ؟!݉HC EnVrެOArV-9m^bj]7TճfMι$y-9Ilax5gU`+5(r8[F}o>{D`lQ Է%ki( 9l4FnXP4h.a{'aGǫbBPD\:{B . 3Py͓&ߋXm OjS\qf[F\/ewŤv$1ߠd%R0-| "G6&\"-3cDN[JJ39>:%k2և9vfO.Z\q]sIm $ed>GEEJFu傢(܁E׍Zw޴$hazy>$b|f䔞h,5 aE%ʶ0^~x<>ߞAt'{ףsTz]BrLJ9@f[#c(od zCyR7 f}~c (knUhc#'UTaM-H/#" :9_}8܃0)iPsj)ǵ[Ek8+ݡ@ivvqDRŃPPG.$8QB}9a|#:7?Lm|? AȹydL/4rPQ%9֫"kl9+,OY L4ck Q<^p㻣\P:E>gF hkmmE(Lf,#Km/Ąt~EMNCLTTp^r4D%2L >BR4~ԕ肮55q|UhYGdR6m ,9_TϣHyEekqiS:Vj*3'6n(xVXƕ@=wdu7X,gSW\såW$'3snb7F5P ιN(^$I[uyS巕gt* CHβ.O0ǵdU '0~ -Q|oc]PlBȝPwZ*= }ζxCik8o^@xrwyz9TBx&/ gE̶wFE[<8-ODq4?db$/MF J.BP'Y)?vBPC]1XsUȯAeZ;e8#/9S|5b/V:o 9ڶ%&oӴT]7$34guB9SD>EWK1Z0?R)~JiBx?~R5ݿ!:˓_ڤz KkD 8tʶ86n ߂_WI۶~+8g#\M`@leѩgb_tڴO/Ƕ[;>F /Ǔ$Y|{d>{Ŗ5 w~VEE̤H;_B s'W'PDk*r|& ǰHk#3'#ui傯f=6a 7 d^(":sV ht7Rf1Y܅K&>ۑcK7PQpζܫ">oAMT/͡}Kib((W[\XiGX#OK7G"p'΋rv%>Wà5{Vy=k.U}_XaHi@b̵݈Y٭ոX-;/d,[S̀ʅ(v&ճ3 :;ty*~;k\T/r5]QaCPD~ 4CQUk[j󲑓j?ulRY.Id \KQ*$I:z##1-AR%Jo+QB[n* 3 =%\0!$RS}ԗH5$N!`WLW_ ޳mM~2,2P`zT1DzCU Q,*)r$_a|dh;@6ƣQ˘V;18"JWPuNBҔ]]濘U"6' >O"i8'h.o\Ӆ-JF" @䬯@KQ.AK@0%H8:CL8rN@r,yHr6_"pSoڙ>~3[73Yl4~Ul40o}_@HR4_An=gre7c"9-jU=aoAOggl|vڤnOIq>rl:cwޭMSbEx6Z-*/>>I\ɟܫ՜\Tm hc`LmR=6Zj.@, fuv pWy6Erಭ<1_1J6S~[.Kdst]HG+ Evy$)kI?ιX>V`ePx${zчNYy9 :4W8 &*̰ gy]j4 $UkFqGN-ik RCee^$U>ANނEmbCJ+7>OB(DE*ozmOe>6  e@D2D QWJ]觠r#PgKiCIm(JJSo1@r6z )~?LnG*'Gꗳ9: Yjߢkh 1FC.D!p l Ns/뎎?fs݉-喲3D*(_:;kӑCWGj*v),CG!&N>ҘI@ cWQ-N u*WspFvWhvմCc`pmRwiBe'WOx_e\x&۟m" dSY_Tլޗb/vlOUQQUڤq "ʳ} =Eϖ ] U+NBSQ$\ރzjn\ѩjӶj͚2˅{Y I!ι? fyILpGϟϑ"^x9 ^c*۲K6صø9Tjz;Dd >>rw{`^F2bG%]C1]}Qu#(Zd٥Ob2iXޥ0-D[`K"G@~2 Q.vwC9#~wT,"a6bMr973{ e5 F("[d?HrbF`Te;"ñ݀9-!L[M@FD~Dsv3D`(|~4o:F*sLK Zv@U4퉎o |+oT=zj*Q?@ſu[?3YTP/u7p'īns$ڤzx8f^k9Z=b.cy=.iZ-bH2wڤ*WsrM/IO 2ƫ RZ6n7G`78:1lU%z7!U%zf?WT,=3Mr5wH]-:3 jꓛzeyYOQw,w|5qz 5sW^ d/$@d/=r&Az*Xχʮl_ z)[ EadM0g*bT#a #-MRZ# ߴv542!aՠy(2I)AL#,ߎKu 0lYs2Nr_O@\0tivV9\pmvAMa;g9>rg zbJ{gEu]zƞbn45XQLLlk]Q{ (^.ww.|N93w}7[`n~QH"Ѭs()4&.ZRg[S-j Ց6-V lva/gO a1 ^)3QJ+;4C>֍qEuG-ny[p-ILcצܭkm3qP^6ԙ8ߚ7jG׬ vX3ΪH~tޟF×H^Yg,$(Ůvj,ZXT}23ɦ sM7x.u hA!u 6 xWGt)0[G6 wՕ4$FE=)bQA37Gjі* %?}ZUwե_UU 0nY=jKSBvBHEꌂYIڤ3}$1G 3}[RԷC;{D~꺠gNYPp*7ASOnEO tCuTc29MnQ UO B}%PI~R:"B2UND2E c!Qݵ a|$*Zvc&㭢|r tD&B*L8 E/00.G FыRENH-XR 59S)@䶹\F/rg#P>s0isQ>WhМt F hlZGգ|;nt@Ae5TK2u#DX HAGxq<sOa<MT}P@gD_tG #s ~vQ~(5Xn\sX$+0>x(rS41hh<a?~W+[~fhb14~팚'|n@)W{'DGoz&N?3ɅINLRu!d&&Ѧz& )#$HSdkt&!6qwTcZz&y e<&Cϛ:DpNM?3h3}}eȐYjZn<BU>*QȺ?CN\"B57 {3|&n!+uS(@t:CAE̵AAsJEQ(| hFxb~ =P.I VT㔊vIwiwt<՘|Y~<^z|&.l@V MDoP*Hi&Z4oѤݺTUM?3ɃhR m AflSTcin/z7*F}IM-RdQ.veGkݿ[lıaT &lϩ;}9cf~޶?p7mfwV0jԚ903ɞw[#uhtjjJ&-.Fgy&y)ɑw2dP12R-Q8H!yrQ[i=C%/Q "r۬B\ @c1蝆 a:z;P@S^j'"[("bAi.-X}Ƀ(88_¸?09*)Cdc 49L3~hvtoDGj;5w*M5d{nvf#ǽ(*2f_-@D"E\g 0?@)thFwڦ!Bݎ#=As/Hآc:ӴF鞝QB sb:E?!s{aܱLX-w|ۦt]ҎV9R79-1"d[#[!N4!=0fFyӪ"J`4~Sdv[Mw[3gAAlccA)oJjP|WːRԫBNH ^,ڷT'iFKAPeNQ>A!"2=>qg7"s+GQ>7 'zt4&pܫP:0Eϝsc)"nT IiyP\&a(OѸ'a@!Ddw>CƯ忡^F`A]4g bSL:c E%^tx" z v-t(5h}H4bqHR Q>e&)+1CQ[p)p]{H?w()P3}tM[SvnH9̯ZFq} ;Z{⎷}.Ic?g osqvtkmfWk9im6F IDATEF=f:aH!l8b1Ro GfQœ@Kye[a Xf0;6DuSw#R;J =qnRQή1\R y@W.R븦"E9p"'m?t)iUN͙qP O@=Qs<"2_7J 0ō"ǡnӁYA)jkҸqhW D?zwQsnܵsS0 H,U!^bSDCtRTV~Q?+){6TD/=䝝ꎣ >,8;wr͚s'&B6uVMHg|3o]vFDhrm0E06XRO@58bٷ:{8&jcԇwEL3sE9)Kc8?z±z(; sz]HgJTT(0?0f#(kcT(];`slQTÔ;βi<Uf"+,BDn&j s#0 #"1)5G w2ј%AC\Fn7g^?ьAⶱP*`!ԛOx?EƑO|@ߡwR(H,wuc-CC)H hgXLһݟk?jNFw$!WfBMN,x8e$j)n5gӱEuS ݃L>JLMtLߘ~){&/{&yE'/@)7(ʰӮ5 us>8i進b Xkmelt@1({h;koq< .9';-wkm%Z~HI)@SQ6O/cPܖCܢm&jTB61fv7hqskcLwmLkB11`v1f0p]cL7]kzNY:ȝ ݘZ9ڊK2Rtq z 4^m ~Έf3G[>>(E0nЍUג .<y;'މVXZT(h8ƥ=^7腔Q>w/%1>ՊwOQ? rx'3D6.۽>hg zvFuaǷmO}{T\ZٽQ/zQi)p?\};'eXqiU=Mz~_zER;:{ֺ8b!(Q!ݺM(md\Z&@[{BjI4m,2-s yíP ${͝8̰j>hUD؜{M׿M]cFuxOak5NuƘ;vv?cƘ(f8Z135ŕx7v;ƘGs-km:Xk?3$j}F(eVD]ƘhukƘ{S3Ɯ VxZGc5|"Yk_5CρMq(:8` S*56B5FKXQZ0ݍ9EA-AwrǴ7*nPB׃0~<vdw?%5% vCe,[Jcm(`W^8Ќ`~/5e/{aK,mx *ș6'iX:1/[kӌ1Sibdh)‘ { [l+Pshzu)zHiy# jtjYKaA&^#| CAAQQ$WﴐHHU(onξH ;9gjGܐ F9סV94*Ʒ' nuQ>B#B3 @jTAQӖ덍ql'#5\ jAgF_r,PmV vsz]ɠTN(s0L=CD f4_\w&s34S>]k;"rn;IE)_#St DƝhE4yKgjI,~*l?Mlk:5e}1%Õ| жtc̮(vK-+,7:*`v[\Q13l}CZ*G)RzN^W:s2RXd *4"wJTdbD܃n}Q)_IGvuW!A!pR]k2Jo ?bG 7Ե*G?@<F"uEu4E5?s0.Z.T-tA`bmIQG(U(O46ݱXNfw^L}F2DĹ jf0)lX9AȢp$ ]O qrmjT=Y$EuH 552);Sƨ>!K\6(zx]&-C` }B#S`aa:zU!fSdpW \6Fϡ$G3ѳe=fqhxKq8)Nh4΅.̧PF7b1kYk7LNDϴƘ]\'oN3T!i2CX"55ƘKk#^hKkmcC_cAY=zƘ #Ql44ٔC PFƘcгmT!sa oE" Jj \ ڭGNA39ܭ. S)JY ̑MLLENn. =&m̤jY6t;{ ft^>W6X]SK-sODAj6 j(/Rjs:)3|n8j`/SZW~; EMkSpQ΅,rKDF""6"UykgQ>mƻՅQ>%3ADOYCa/K)j)˔3(T <ºp.rP?TPms0kgT3<+hX/Ղ~ZDCdhg{& J_#N)~w9w^_@M"H=dH$w/xHga3Y7êklc^Czoc^c/[T1f+m\kC§:C\],&>FM)ƘO |$c̅(;!o8*!ݫos{J+-n7ƌ+-4Ӂ1ww6ƘфRW2Lx] Mn4%VQ>7`] O _#GQ`z kݺ͢ڟA(kV&g=!Q>79mgrAiVc8J mO|bLzt\e=\ D.B.hvTH݄7RzQSq%mRRmf6Uf(F3˷ۏGfEܑ(kszW%Q}ٟI6G&/{&9 &"@  6=Ny=wLz$`j=H ehYe.ꉌ88 nBGI0:`x. һp!`pss*ҺG.A'<q+g81ՊL^w?5km$9R4fE\mޭ )tau:B~A\=QiA|nvMj$m :#>JHq+w J7"U'8gw~ x(R}ύ tuG-EMĭ @5'.uO*ȰxpX_ =h k"|*A5R-Qۢ\Jb fۦ^Z=<+Ɓx&}Rx3dȐaUo a(}(䬌E*h6wI rތ+Ȏ{6"Esdfcȡu}I1JsZ0#s a>:aWNiA q PeQZNOPN2oFE}>2En0~.t% /:gp,R j_s[vvލ,(_S V[q"H7~aՁ#I7x&4u&R#=O~>HoQ%ƴp3i9BdR!C #E8| [({ 㿣T_H\Ϣ -*mʀ F_ԓN=-**:18ldºZhE96f9%*(+Ux)ߣyjۨ(񯁩Q>yƓ @'"҂{:PTQ>7)i]%Uƅ>A#0rJQ$A>9Nq˚9}k?*9Fa7v9fBkWjaBSXPK5]Ws=<(mog=T!3C W\IZ.8yfȐ!CKQzRۍH)jxx'*5)M/!r Ȯx L䲷D"n@yǫPZ̼s7P )Β @5"N{!}'gPȈDVڹqa^ƿCfO"WԩkQ> $? HU:;ru5"`!Bka9r8 R^ԵO2V9b.-'fm]%V!יu:s֞UAߌRD6콀Ao ;!:)wΞq+Z-0^yi#$9l?`9&L$G=&֭73IT3P !ôqj5]*2G 2dhY|Vʮ6$&"#rՏUpiUyTvBNyD&㚯EAn;m)Ԯ"$om}>(x(q@}A}oTt2U(, cG(E(;=+UΝ}F#ժEA_Aoƻ ':R(:(p3 m>c6hٳO_ w_/apIqt W8 KNZadr"nlݥ0ިr RM&9'X99OB6񭀃c9!"}":(;ŭs8"EӀ|nDƗ"W]`:EJ sp '"979uS ܘ/E$4n>곮F};7*DKwM=|sgU3,+x&錮7S_R>Mdˮz`ES< _oF6>]Rdtų3IWtG_2dȐay SVo!g 53p51!Rm Qv\}Af(+ꂂQT{1dӑN Br(M/0^o=f~g1qGtg<:7ReI| Eܭ:|;?!(rPrŇD? Da嶳.0ҙ,T~NKY )?q+ [ooRխh|=dI>軝Qy>M7DU,lo8RFcIWw~6%rJQCmm];ۼX;qq%GLj]h-t-oRPLGtI^A tz\癤;xjGXZ{$INrZ 2pHj 2w~ƻ@an@)S]tsAfYFA(40?΀5((;OF速:%JCnsQjP_v "DܧA_R;tF#j]d@ "&7! sR' ¸+J;{aDF>O Zj _AS󀪨 F &+0>]'h;7vh`,"{ E3SGٳ}߹T; lϛ $WLrB#M<77 Ö1R;)$$?GdtEgwS>#J g4(jjRg?C EI?c0C V0d(Q>F>-i RAJ(9ۢ0D@)Q)AtTs<"g#5↝Q>W!=~G/ZuW IDATRnPҫJQ iuaܥHXZG0 ;׼[G/ƌ1Ew8:9Rl Ќy(x,[sS,{Mv/Q =P6 Ո+|0[3wf3/}&^5n3vK AD]Rot&yGMcۡ{t4"Hx&˭?fI`K'I.H!gs=C3dȐ!C2RQ8ыdE zsw@OEd=T!ps] Cig0>Â0́`PΠYj4\Z]-4}&2fx"mFqN(e /C RWTI6_Ar(@ag@|@͓cf? Tq達ˑ27PP|VjdO|g2e[/#xg;FYꅧI6vpj]Vb_dلO(-"=a@iϠIsH*EjhB+a6ݿs;] Vgd(CjBεM,^:ԣkdAjjP>{,5NQLT|gS`@ύiyŮi(-LDFZxKT56"DCc\?h|JQlQ qlϥ ,.,.x7@Px7Q64)S d9r1g!D(*C3TCcba=&gMݳCOM>15SLl5)óފ<熢ɚ5En,ڜy3/J;ݜ2dȰ"#E8\z]]I)l|5IԤ(n8R&pA&ܺ(F[o̤ۇ\NwGԏFX\Mwr!)4ЩB_:IDDQ,}Dvȶ-M_ܸ0%R.s띅 M }#Df6h2}g45ńȩ^!+ Z}֌/Z85 Dqg۠)Q>R3Ik:~s˔_s7<[&~{ NQIFTPzCH9ꈜXz&9݄1{<Z 2dX K.x KQy(p}"o R\kT&YA|s縯¸*Q?!'>>hs;6{^Ǐ|n k*4R!El ;"aB\ƽug1r+vg~z  sT Rt*'\\Xq"%EW(sRL0h- a%$"3IMMnΟQlt foָVwT 'e*{uUk3I(Y/SO7?[f(ul5/>ݓ_W"z9@*k@y0Rz>@( hFnT37%`1k.:l^cvy uc+K0,QnOݸs/"bȭ;4R-bs6QriV>COP_DV _p6tzqVtL5?>gX)PR5_V7h$d9,+daT1q}= ]YR由!n b/ !y1J+):ݐ*7t%Ӓltm,@qY3ķR[ Xl}s;'9:>Yd;t$t]x=.)?}3& ]ZuYF KAy(Q=(Ӳ$! C ?'GpSP%oCVV8ki9Zghb`j!e֏aHTROL2Zy,?pt Qx&هP lǠgwqjg 2NȔ &.MRO>."(X#(tyQ>wm3 ,ja 3èNA#pSYG5`C_C T/3]Pp8)`߁eS6H? 3VHEkjoøL#rV 3#PQouY@ 2HQe&\q "&T?#넜iϕ[Zep}lvEdHi9܋kݾA5E}QC[Zڡ̯QZ߶(}jB~Zz&yA ;nBj`zx&iko% R M -sx& 8l~)\xgl3dȰZ!#E2SJ;W#\a 6YX=~2,}x&9T9~d*PEgݺۘ[RUT|/cBRNBnrcգ +]3ID|:x&9~(Q݈ZÁwl+fa|, ߕ- G硾KYn b8eX8!E.}Ζ9TR~o`${֟۴Zf?sˤSzv@K aAW9yb)^c~Hzv^#So=^B_s<[Z !L 5l]7 e\UQ+@uB)v2dȰZ!#EV(~H,S K:7yAzeXp*j$j$O 3k};"#EEcfC‘(y4U)<.JRHg p{BPdFoOP %mrzp}j¹ƽWrF`5p!p0Lr5r)n,6C %2 <0s(5A8]bQ = x-=xDs""8 )*퀇:Bj{&˫;gH:ϋn(-&u.; >_S/^D\\ ]RV(/PJ8dR '05~1 2dX)EVyD܇{ !BQ73Z(<Ʒ3Z~h{+EvvuGϟF)u;g6 O Al&J/C/R@h/DjMߤ>.0Tex(~i\.}BQE0)k{zDZ#}~2dȰ⠪E2dȰD}Q>7za\+ڠ*\lR 03IwDDdU` PwFdiJE빌]jNLrdILr/ "4ZYURAv"tt7= ɵKƻg=@ x8q 6C Vds2dhΨSM]cY 4C_oQٖ(HAZΥE_f SEq֟癤 PT7F rZfʞI:"bR;\q*å{=mEs"u=S_>Wx&9G3Zc~2dȰ*!#E2dP\'9Kc+]k Ԥ1l2)hjܠ͑l$뢚At;@/vA*GREi"T<(|Z#V5+_Ud bÁ e6A5AG5F3dȐaUFVS!C 9v R 2dȰ##EVzD,g@3dXJ4|]f;#;L$ߤPH1Lr Ȗ W#r6KΩ,!rF :uv/p&' ka 8n=Lr^'/-q,18IZjV͙K1Ȑ'5Λʻ#nx7%푣ߺ83I_@b2nE͈X LLْsѾO _)xw=ܲ";֟dz[<<c"QRVR庣RخI?{iW 2eȐ!C3}(]3 ڧ?Eϑɩj`[fYsgu䆞sOmxdRD8rczG#{Io4Ί${^Xc3}geyd]{!\S{88UiH/B <_Q 2G>!C P.p~K6k<F(? ~ш" վ 9-mtx!KԹ#t$ڧ(9-9O3 S_ IDATUsȢ,цcP@!0~[*%`c9g S(D/#vC^A7. F\s58>_6pyCڰh>hWIKs]-us)!lyF f]hчGGgbHCn{hn Qej 4uC˽\jr #hzOڕ^:WOwXp80/[<y=&>6u|fff9um];kU{@qh:]ǢC8 4 X-/-ufmxgz]꾷C' g ccH/B׹V6-=zZ6gff9$ZlWڧ;963C!}/6fe>t\MNCmwFkuئGjffݝcfօFwYZOby1^@kYơ@UƐkQ׌!L41!~c1'Ѵ{yjFmEa(=QX233{+EffKI\Q<.E{E vGI ` *M7^' dw90ռKV!M̬39Y{C` #h}{P(30Dff̬]\! ,WԹp5j|ԡnp&r 9Q#J< | cjUAs5q w igI`k! Kn=PԺ5cHOչjw@633HEf.1/+cHCP3o++ )u.y70 xcsUZ>l ޺y-&\Յ OoB<*IQp Ջ8 UZ ʦPsCbHsuG׎z""3kh܆hf@`{}?N6v!LwV絹+1mfl EOPv/ D[J9n@% \ \*jYr93[bC7n-?XMSj|9~}`o!Yufv举v UE}lDُWvWmw72O牥qRc\ҕc33ɡޕR?-z<:W5Umάsug; Tͪs59ꅵo<#Qc\k[|m¥cawgЂ3_{qeI i0433N0nk__07ܵ`nkEpSj- \)2wTvV~Zv Fh=ࡲƦК3_  uk860xU¢Ő Uϭ3 CKW\KS 3mus[333[rn`fJme`-wzL!5չz.0c4u1t>C?@i>jYԂi*rC}M-}&f{J\ i8akuly0\ s4Yw+Uzչrz "3W>\ScS5[\C0U!}Ӂϡ S_`:W)r'6ڇ4X i[T"6 }u2ڃ´%pb6)\V~FCE' [ \\Yұd>gf|x u; >GѦ`4оFK:WwŐBި(@FCMT):ՄŌ dGq~ekms5 ަ@ՠYuNmx?Kԍv\ ZuAGd,fff=+Efn. p8 Z3+ZӀ|8 x Ie4O44JuR_`%h1 Z/o`\=T]ck)mM lQΙ Efюga] 1sPg\ i`'QBnG&41(h=m[:5vEޮCl`U7p!jUE-? hʵD=[ U~]VO*a?*\^jA Zo5U̬l+S~,l7}{[8tNEN@ ^DmGk2VswG{UkcZ i?@SV *C"^atP`ދ@WC n!mݎ.6G '~P6ޫV{m<8lPdf&7\&1O.r?*>O!Aՙ6/cGOACJ>1@s5ZEs5Czfչz&Uywչz3+?0UW~Oda#uК1Qє#Ѻ5SXүk> ȭUZAmmmJT4g-t233[V9Yg1S9P @SߞFaTM5<Upֹ?ĐFa7r7=zǐs5.4V Ө:WSZ{!u^! Lmu5Lx-.?"*nfff7Z0NS>hGBQټ_xDi:WSbHף-*C5C;T}Pи 8Ujo0!Ca7p^.$=pᨣ׹z:>Ч߀5KPXXCz Z^'09h/BjL i*{0η 8 Tzoy=:ʽ]we-q(HMg Ci5z\jx[uNKR)3幙YPdfJ% A |սm3=5-QTyG iO6(}G Y&M\X<>ItexE.X}Usơ`te911?BAC;ݺ((M! CS O6!}shM[p33ơ̺ҞzW |<6cHNl/\Mo`5`xt;&t#jq!j0YC:_lڸAlvVg!팪C߬spy=gQuf{%&h*fe׷~&KRԥnh h^uKEf-p69PǶ4I(܍BAg`@VDa=R2@DǐiFCt5yPjjө/"2P&]* @hO+lC:cPl|yk0:j:j6 NW0J 部R6zu \!w33ʡ̺R/hEVG]P@H2 Xjzi:WGŐ&P yU^C{BWcC5{ :&_h-)5 oǗin^DfRF ePQ)eѦS\CQM(V88A!~]շf9YwPdf˃h[OF4եؿ5 ?/_`5mi[}`I1t{ZL{4\ ĎA!h}:WZ sV f S-;rf \p2UQ\57Lx3tڠuF5Bֹ\ZCaun㵘u ާ̖ 1UP | 5Q8mZ9giVD^?O,~ V؍އ6R U:uXYU>=E"ޮsuCH @Y:Wo/8z{333[V9YCZUsup ў?7չu )ϣ԰ar4:WהMg6s=l:{ 75jNqhԶr߅/ЦgչI1f}gIYwsfӬlCz/:WoԨ?U}ޥ=DkufuLWDWĐB{ǐLۢJ%u~iض4ZmFh lJ8rjI6`GQcֹ&0Ujby,uAuqNh u.*}k $~ h\ TÀDo&'仅vmO,,,,NXӤN`_tɫFw@2<1W|U@sp<싅 YXQ B֑@hS^@>a /2Dwo7 45=ػ`1 ? c^D?LMtR+2s.E]k`^'My@n=x.p@&v`)} NBcˀC^mv[q%Zfaaa`YXj%3P8y5l~7_xc}S ?仅_廅AxΗpZ% j6$\\ߖٻ_{^'FPE[(p:7K3x ㋐R|Í̑/ ]d)fH%h}B'faaaa1Nאy kPmWgO#38d|ݎ"$k ap94MyYE^ډka},J8y ȬʷuJ"d-ŻuQcd6 "u 0 ߫(EJiʊ0/{l="aKwx,,,,,"fwK(pBHQkX|H\+~lFCZ9WӑQqTE͑I)n/DGQ_ZC;faaaaXEbÉ(:6=Hu$-n]SP G{qE+X8 `_Ң8y/"/0B`Jc?Y?8yOb1 ?! (%")G/4T7(rp1q  9Ll3e 4-EI`P  }[+^/,,,,v8bou "3+ ?>滅n仅5 X>J|VBvÏH\c21&S~ vGHB`PeDHOSm'ۉ+a^CDr=}C{"79UCW_#RPEjRCPJ/|-Fds8mcS#< ED u;{/l1=F: ;rP@Q^DĬq*t?Y[x.nPGj$.H -\X#35(][; 9?,H`(ڟHۢ|о0,,,,v "f7 &Dh&"J.m* Į(Xp}̑?'o)"Y_$-p ўףm&į)#i: @Dl0Rٝ؛`G҃rG1ubd rMw N^cԞPۢZ!?h|*-09">D8< 3"a+T.NC v~-,,,.:x5w 7D#{Q~? QP4b?Yxw /`1 S/vlcI廅_Ǝ廅mlL(p" :w ɸw g8yE~gXWD.DI(OH ;E,DbBmZpOMS:&iΚMHc&l9ejk]uF|p9e$-\[Do P2y(@UA[7-B&١}Q0Fn_8s'/#-,}؛a^/|7&u+]z{66S |"8yGU1m+9̟ J(]="#2C1폈׉ +hiMG_~ZXXXհDbª!l孳nk& DZf;񍴷n +q$|ot7d!"NlY BZ3@1RDKCQ+odaaaaDbƒc/YCy N>(ۣF"D}!D<)"N^mQ.ZEz=w$Iz02?~#El=J:2m~ZXXX썰DbC(%43P HQ<-\GQ@*d|մwih`q[Y7eNDcjfmdZRd5-줾ZXXXU ]-,|8e%U %'}g0RD."/#j2QRN\ZTf8y]Z`2?,AdnSJ""0Ghaaaay,,~' CPVe_:c^(ۡ(WcͿDQNcLW仅S 4~[ofudشJOnVU(k+c1ʮ? QPL9qwJxkaaaGU,,~'oȑ;Z!.vqf06G'fU6~џ_h$'D\ba;Rݚ#Lj"wmCJgaaaa`},,~'VptڒM4jN RkQ,f͞^řښEI\)pB]UkMU+!HA`Hʷ{9E_^lw 78yciDw {{;ggg)\Qt-Fu{_\LBu#5ZD3r?7GEM~|Jr+PYr0<ݝ7B#*b7D>`Q5nam)Y= e(4(i (Q:s$=R"G(ErO廅~ZXXX앰Q7G>Y^)D@cMQrس E<-|Nw E{N"r6 EVS4; YXx<|B E9=G"e;pX[8 ~% Ci7԰y{gwbo%b1L$G(J^Ɍ0T$ixdv;;]od5@v-"qPؾ;Gbn仅s (2SDJV%l6D_1݅:hjdO[Xeaaaaan|ɻ)[`yJD#{##kB).5(`t[ˎm#s&,i08y-ސS:C1~ %}}9E)i>{1}v;m-fy܄%N7b`},,`Jyz=\ӯEnaS5r_y"/a-W#({ș, DB[D繳U^0vx}+wE;YXtDIˆu,=['dbl̋hO{Pc3Eg V6> )lOɩl^卓O&-OH.q=Mt7aI]TNa=nِ=J>GERx@ =jH|tv?/\XڪlDh렉"6ERk-MIHэ0vx|y$6bF8y]&DC?΅huRJD#(Akr\MײdAk/#p*H>(r 09lAMX!caaa`1 m@7m mX ԸPYwui9N^W'\>6RxHDj^?wA1 IX#i^K,,,$X"fam >fuk*RSW1oo="-\F K[Sy2ekjHhĺ:`I[[XHX5ڄb%bۆ5(aj0CVid]]ge,Yų9 (IMk̼|֓YiH|%v'7aI΀%b?v.PU*0h &6tN]3u{V2dl"4"(kMf]4򺴤m>ٱQJuUH>2?݊W@YNrv0wMXNCbg.A.҉`8 ߄&[Nv}6u̍:Cέ-s6 v!ښޝ]QY=Y^򯄪yz:DI[okjhW޶Ѣ9vCw!/眴0ilLaU@x{,Fg&rS&ӁpR2_y[jvJa?&z4JM+oxL[P}\ٶыkLlxM7!N7٫"c2'8Q.^?vxۿm7aZXXXXӤ& >>  {Ц5(U} xv;$%&nv!-\TF&zϪK:ͽIi3hu^#ʡud'zQz,@YbyKM.pҀ|p@<|38vx۾ K޶79tqϽaaaa`8L|ۥ8OZS~W*- |Q0<9;g{n(TzS'!ZKڟPNzUm n⃁[zbHL]]y7dπg8yI;mG-ӂcvF ,{  4vd`؋5PGMedTmm!"iNtm=IJdTNw k i H:mԱ\3)DxJӦ T%iaao?F`8 m3~Ԥ?Nz B͔=!; 3kD=<+ @ײZ5+{OHZ t.pfgC4f^`"P6@%E>ڭىQA4T7a;N Y/F()|(#0[!iȬw^0?NSߑ?V`g۱IOIKbM#0%m!7l ;xPnaj6O69(bc|Wi;MXR| _zo;mUýL |g䟁HUp{2FGyb>gh`xQ݁R`X`!0 fƛ:b2]M_nޭ^ߕuЋG8h%-p#Q:/"ip5RJwGc}3RF E1rE([_RAdaE(+WZDz 6@D2#"~ `py ˻7T֠ɻlw moLv'R ;ǻrHZ~Q}yr*Mc 8n&; cuP&,!vqEVx#d4 >/|QԎB`6DOB4i.1_ImpI(uĈQp1]"`u({`ʮ߶''eP832cnm!WBߌmit-pw ގ8w[v}ufƁ\=THlDׇ|6(t͏A7 ܈Գnam!f릁-k7av x)* vf*rRGu&"6"u(0 Q)/p?_#0ψܬ꧃(A&TOP*B_phpW-Kw [_ROʊ"U<6=F)ϧ/Y_IM8uQڽ?x3~+ `][zyvV?0,v:JrTrw=7-,,\X""U"S Ks5+ &H>4aBk$/RR!lR9Z`Pq?ۑ/ic?eo CnC 83K?~jW仅n7dڶS*J![A|z 5դzzQ tI]b0H|K' En̉֝3wA1En ;9iŦ`^PWaW`6N<TRn.F؇ȁ >9"U)x*C_- oRʀ/6n d:uh߃yMGϷv|pJR8ymBnR0P{J-nIOl_ީ'vM֌iYs|tMB'P`Q[85$gB?7Uܡ(*I5jBb? z[+9wh/ǍB>DRDz!߯h zs$R^D 'F`1jS' A{ jH[ԣ_j5}i OӐt3i)l˷4v;n9NdP  ~Wp\擖d%җolƴw?=:PDxJP>m/"x]X(XؤؙhNp@nvMkaa D쏉HͩC\$g2Pz՘ ["U3"WEHX`Ųg"|#? JwbD{S1N䡀os"? JpebJ26En {8isIiw%*uoqSrc>HIMܔ%s&9uP$m7Kہ"Qt7aNSCQIБհwv%bL!rRF~D^Ff\X6']kMDJ"4Q^4DɪHzoLDx"H' 8 q&;(;e@?>|~" 6Q"ӱ"j,(`2"lA`Pj}Vmf}zϔz'/!qo$Ovx\>vDw|oD:SSoH5H!r+p4JO ͬPh@LȈոom_/Su J"$!T@q WQjFȔLDx t@PODt&,NMfc|ˈoPl3ԟ8x5Hئ`M(ԺV.K~Vr&"mlP>7sΪL^%^ZwsyFt,qjՑ$o฾hE Lœ(|)c(=@QIa7_ fovZXX.Aݑ6eNnBb=HɏķZ|TUYuN:w_x%R./5."/1b%'rpMxǜx E\7u.|?67D*ۿ8)i(go!Zo#㕈-CSPDgG`l`N;9R8sprc}~G0} LԖT_Bu]㩉Mv.&'9s٫ڭnuUc9/uϸlҍkgMXά$'')H]$gZ ]|bUY`J0FƟ9V( AjR)HI2zj 귛f-*D9>k)Zyq,"c.R̺?XVJ:p@$,;wXy@~(F? $38ZMQ-M?:!hkg~By~L L0m`x_&68|S_/@$Q10}y_O++pzERVen}6N]QvmQ޲ ;Ѻgy6;Whuu.b7%b9hB)bu El:""'!rEdb9/DZ T" g!iH])w)#*% M~91fC0hH:O]HZ_."_7@Y J4= F~|Ҏך%`8 } Ɉ-f7%=ٷv9i7LCMd-9x<+C߾ X8P,w9p-RV=h!q cvE=%#"YXX"X"Ø#*9/D$')f G*7̃6B iH@PWU/hb R\DE1C`dg"uj*2s#%}d܏_G9f#RQADnCj\M9H5ᯈ1*G0djHIp"W jKҬ55͒6Tސ#<%ZHC`Ajiez~@[h $w`,,,0*"":ydw2`LA$AQ)WK~9d6l-@?X2[&X`A3`i?LIߋԪM[F4H&u֦ZpbĜ8ԗ̜+顀Xg5cX4@ c&/V=M-vny:NifhV,qU^آQźފ'.JZ2Ʃ_(YЫyTn8h"^qh=E%9P"јDŻbjt {Q'iaafa`&^HNP㟁&"1 9A#Bv;"c2YH`Gd"SXgd!Zݽ۷Yzpm6iIMmf3ovO]==E%9E%9+y|G_or]-i Y5kEC*b'GgC_ U 3d:R2a;א u 2]&_៑82tB>\ pDRV"q "0 u! 34 CL&"ȇm "h#N zDb*D`>Ef{*7 aͧ"uhR6mK$N")3;tLU9|{`H4`*`Y(+éDF;@l76V!8$y6Tό&%Lu=3sASV&N$x5<ꩪ{`a='Gǡ$ZdTDbO@.YDsɈ<H;лȧnHII2J &n,v?~&&|L4Ud?>08g}3l㕇^,&'G$Pݬ>J 'ѵB ukhq~IE1ףvB,,,4`CEn5u)&xk WoPbt/""K9v2+z3tMCᚚs& s24Bl!\T!bP^L:F7o:Cp )C>]A+O G"C䧚( Wj̞MY3J[L\Z\3f/ÃMWB:ӾLn p?n B!^xqvg,J:j= Nhr­x=6Mo\rmq # rBV+s$77u*8lp<5_(*遮[[qnv bU`8빰[jR鷕5>EN!R)F.۱52K8>}0T9"uI47?"[Ɉ܍&G#%5)Of> (Af(`ig7C52tAgڷxːڑL<3ɜ8|IdE_ L4mz;v22cXbAORǴ)U);hZu:Dе[02..+*9(ME9L#7碒h\T&7xyJr#շfkZkaaa?vpS_&)j >AZHFEg( q2b$,Ew7 S(雈, (_Y!RTRʐ_4EHazMCRF!izD(((RwhPL_]`gO/9!ŬI”YTbD |Lw Lǀ'6EfLcz?9?;mq"n 77=@7{h6^HXQIST33\ȯwuhv9bE%9iE%9hWddIŞ 5 !uP?Jt܂G*hmLCRL٭DF"2),'<)cL}+LP@A98M(eӏӐ &"Eakd6p)jdnԬH SbT)lv$DZאw7GSR,ô ӶALФLߡ9slEG亿 rͯWZ0ik RHҐj^j<Dަ bՌ+S_&2 .l-F!A}%R~w bטw:ZA-4 W =5 b!89D K;w*UǙeW~-5Vg`o{R'l`f6>Vgm׷:zMtu/eb[wyH)ԵCnAU#5FnGPZnvj0=ӁԢ/ B`Xy(ۺbـzr0<^PQz)ǔC&V?"4i-ɷܯE~DԒCaDHZ"%qtGG@e9hYd?'hҜُF~Yo2!)"E* ˑaeƠ(R܂|}Bg!+RNC,jc(2cڄ7IC߹lƄ DD:#ãinlHh̤e#&ẉ8C6;q7EW46M ѽY8o|-bP9oHM7SK&U_D&HG*,Gf# ddIF%   C diOchM~6V~4Y³,!9kM" Fj!h&Ŷ`*G[7| Hj/@^7{"rYk*ќL 5e~gWŅ2C. |? ǴhVc-~4 |(td{$ʜ8Ԡ{?5;f%-P](' -EH>=`6,,,v!,N$pm1uBAdYDG$d&s54WF"gC!U RN0/ED/&|DfRR4r7c9|@뒙qGZLNT]8sQ77x[Kd"eAĩvE%9ӁsnE9mEl =]*{w`-,,Q0 _7NmQTPDd<`Pb̩w& s\wDj ed:{AZ(DV#P%nJDz" VMg~ٔ_&7H]%IfJ DߔԿ n^dZ )"u32~Hck@0|"&Mg!VH{)| lTS$Mʇ#SH]k0ܔz#Qh0<ǑL%t/4ED{h;8.GdM0MLS W;G+&똶Ika댞==[Ts$6 =+`I$5{ɀ ];?"02^[Kh *DbY9_>Nb zCDSbBńHrw.C&H]:x Gk!n 9}HpPzO:H5}JCf1(btW|Bķ8CSL!X%f.E&u 2DFpӳ6fD.6n>Kg]%zQIEtEE%fow͠R k{p@QI#H]]\?qD=;OvͧW2v#o m; |mwbrG8(Y*FAa9$lԘ/#q<"G$_r="w"i!p? |Sg}ro&b :#ߴMvx0̯sՈAH!;ڌw3S}̘?iDDC?>UGkr_H+?L[8o!7LmOr/* NDsDȷXТcus+S3IH"5xȔSV!a-F D 2yT rD*"F>N "#'EJRDxQj Ae4KEh/E:D0+Lӑ2M5C.A~=m"BEuz,"1{0㘏̇K sL0j> >g̞}Xc2Ƕ\J|>q]({wGbQTs#[8zfYXX^N()[ L4jF.Y,ǜM6"SL(r.qRA*|9!ROtu,X18ѸLE͕R4܌ԱtGQa 4g]y#H!TPrEdD"b5RCd3F#v93uM4?Wk."M;6C@+=M[3KVק"PgS4LF&Ĵ$n*kkLsdf9!g]x D2EdbSGw4߂kddMzk V.8"}ݑ=3oLN |o%.SɈܝH'cs̪BMH+k dz("]cy15 XL ,Ք`ʸ|>!-5T74ٖfl%LYL7mu Od|b@왶 -Jj-2Ck;MsQ|b'NCd`PW?zay#wxlD\DJNG?"'ix1ImTLKL_V"%'S~#ddͣ,x_0O:ӶOۮei%jR\HlӇ!X#"Uj ZUJk_5RL"whcx9s ;|W9Ȅ*bP/q[#_~Qb(F-e(*ix7< {YXXl ,۹(G&Rd"d;42˟[f_~t1"5 8g-R#lU#suVev?̈+QSN>] R`ӼQ2fs@d>119/D82kHH ;Rʐi  dG#$F<~N־v9Rc^יqO$ɝvoYm.=>kLQӎa; WV3b7dzS&(dƟ[ҊJr^'dnvNmbς%b;ˁǐj1@JS$"bpR7o|(roj3 2翄LC "CPDfgw%ڊQDWԳ̔aL7m;xԡ:1+d뗶 BF=(ȼۋXu(("@ld #%iЌah8_zπ&*2L=H-\d 24~DjDf*7]YQI.D ?.lNED# 0EhvF}C!S BaD D#Uo e 3"*SA(9)/ @J\D0 -(?U7f#RR.B:Dۡ^sJad\"9"XZ(S̼DPFZL7d~ E`)WAQ"_UhC^אPz |sM0ǾDSM/@fx;a?R 2JrNBc3`nnvq-|2y{nv;| Uv ހq g!5)H{? JԔs>"LM9l_ dDvM} @DDd$f XfwD("BJd "'TFH: Ӑs9S?7A/1BD(m&qalK"Uk39ri9:f_."1EkPsVGyH0cPWƄ`LDR^G? & ݣ!"y&|(*9ӧ 4}8{zjaaaq7?8f7FjtFd# j4y=&zXh_H\L42iDH!s2A%R>DJY"#HD"akJ)C="BOl"m-a&-7QYrmښd"sHE$d{u<SLjD"[""51o|#6δ p?kJ\}gR ;o@w "hwrQ=Gf  |W!a3/Td2^k9m&'̛[(*ɹ, Jr^.^Hhpvq "o EKϷ| 8h_Ro]^eghB?j^!Ye( Bfhb?<(DXMlw(wGDRBR>BT:"?m.EbsAX+HEQ{?k!™|bITɼޛ2TlDb]3MPR%"sRD"Mc(CLx}-Em"bQ9 0gmAb'}M;ǚ:@%a&F(- mÖIH$nV!"R–qJ{.nv$`@)Xr7nd+W-ROp]f2!11ѼŞKvL660Mh4~ey8"0M1*(t8Dd/1c0s9#_m|~%"k3O+B_s-!BxFXB| (pc)/B洿wMykamGO ; f-7"s}-D'ЂP (G.8D64G(o7Ź[S!a{<[>b{Q;zm¬5 ?Q(+3BtUѤMUMw"SdH4e_'u_vL|'(TEh3}l !ū 7Ç(ѺEܡhB6GyI9"eRB3.w#"qU&!^mPc'TD x#)xo#Erb(e+DnGD4f2#P7VH&oy@EQ̨?l@oг5:e Ř:K^̫Dܛ_/o$$gvHبK(]f]\TPT޹gy3NϴhHXElGxdL_#YL+0h|p-" Ӕ9MI#rEZߌ|f#UbQxVyNY>>++g&V~@]ټߧn}H1M#j)=#tR>!siF9sĔ nU^ՍL'fD6U%"r43Q$@DbH)[#▀ҩD\x<)Ϙz6 3cƔ|7W5}E2D8:#/4>~ VU`ϴ o52ֿHØ=/uѝz+Ak,7Т!JrJ#vލ[cѽ5Z[T3ugUJrD"sxgv x ŮUv`xxǢȼx3RfiPA({HÝjmMj!$B\|V^F=r3J7IH̊6o:"ɛB|<h_:-uN^M``F>Q!#jCmB|Vqi24yW!DjqY&{/G> OYȅG$pEt-FJW(jRC*^#D^F*Jzb;tF3 s1PW"Tc;%H9uv0`d.CR.zJ%m&" xt߁E%9eE%9mkQT-j]]%>]l#](*i~7zyt̄{Jrz'4AuXXX X"`6q# |^U)RZQ0|?~FуPD82"U< FwMY ߕכDyfY=S"xVxҚnsM$ވ V#rA Ei&^x&'JÌQшc歷M]"U 2K!0MO .QK<](*Db;,9HGȁH=zH9B$4|c̔ $Hj5 )F,Z%g"b (Mۛ!rӸol7.rȾIDzG1Դq>"ȟI9HJDg7!b *u0|"dGښ>ôerҏ~DФ: !HD~@Px(Ym(q&_2f95$_H||t?䠅} ["Dnv<;of[ Kj=7FL[$"R fQIhL'g+f}䌪&Kz~..w-,,v1@30W盍S7U "[ ۛB[b5VL9CDW"!`L9 Ռgۂ# s+*nl(,EZ B׷z{Q +pdbA] e=noaaa?"g>2lu8M䝐Sx2}]ƘEf ӑbs"}10["j)b([kD܋HjD P,sv%ԙ@Ӻh)i[Ųឈm{sñn2M>DsQj`gD$;wL ̘6B6`8)o!_3nOgtաk0s&fc:#]l{^JrJx>}?W%)*ߜf{?c8/r[XXfDl"M+QbxQ:F>GZLi@b36ɜ)JӐ235A+ ^[,!`?O@jU :~ "k۔w"z 2??#h ~ hrQjJ9CQ-Af$6Ęz3+} VHJ,/ED"' vIN1o"\EDOfs<"!2|&R1NFs9fǛhӟ@0|.h(CJ@Yg9#okE'=R4 "[K h׶wd;GE%9k%wC˧(9=H**2s]13g԰A$67WfoK,Ie5V[:/1ɳ:zWb%b;Y DMdu`(A&%{거wϼɉýABFx\ 9xcNH1jP22o^L  RbшyB <hw9d5]W#u'08}W 'd$5,"]%#wm)IR-]ٔ5IA΃z!9ӍSW阘ʌ1m6#3"7O#8M-` IO2}mG`?X۠'~wUb0st&i5FFw=y3wu[Q?xm,*}P mt/Ƣ]wqYCE%9oʧʍ %"zDD~kJr]ތښh-q0uglk9XN>u]78x u-nG%b[K|x@F S* nsfN[^7 /Y&hb|M59|C/UhŕH&/P6o?^+H-kzy2"O!j$)%l|Rw1|Kh&#鋩WIxQ ~0cXM 6RQKuZo~^Aؽf|@Ha; ]&qr3z dZ=l||IX~ѽ;-mN# FzAA~ޝ1؁s}tVnvof_T z ųu!]Ķ}M9RǺGQINWtoR܎k,KM$lq:Z|8u]]U5HI*$p9òn~ԮI?Çːf f?d;9lMϕz +ijDbCH"_G/컑(:G(;ӞE8LGl{-"ȼx r4>tє3"}u9"9# w+KAψ$Ly:$/E36g(\2!#sS^Piqg"sp A\/-DO9ȏm "5xcQf\ڞ&< Ӑٷ݇ yFw>^bC;{ޝ6ˀkJr6{]vB$'m+E$A gJrzL"PKOY>eσQт#MK `_D-p'Cw`Cq7Ǚ8?9qq&;͜w8_;8:*V8Os'9sq8?:3q8Ώ|U87g&uq)χ~|8:)c8WqհV65XU5QMnmPTӽ[[^W[jHM(]Dt[d41vB+RrT|S^"K!9SH42C~`oZHu58XjY R)^_& e)n?6ç7DTV GȼXF@~(~R3ꈈ`d깗o"LG~aFD<2L~jܺ`,E?᷈Фz#╄Hb1wEcH19д߈v$ |`CIdہDc+W9 2U  d!Ɠ&dܩ(*IBzd-B?ٚ=!DM]h2=>LwZ_N+& %t2Eccj>F)p#I q1ܒ= \qՎ q];slRנ uǹ-^D\uljH_nqbQ׮ے| w]ם8NGdQ?.B@ui8N"zFqӐ{y@;u1mkuCC#*dDDd[`eɧ"c ^LqG!pL4r|VhB<(8 2փxߟ0wom&ۀUP0<Դ3L.20 |s1u"S-H|cy#D &JD0A]ҩz^w""y "!cY,G25JCHeCkz2טv2c'kۆi2;3EA(B;2CJpu(ۮ|pEwDǁiM?ȟ0|IAFw>6H }wgok]|V&&Ыѽw q ǠrR=hT11S76hyB5ѕ[98O hAKǙwΡFiJB tnlaa^ /ƴ!f8[e9n}ÁG\׭3qgdzDs<~4]\]|~nve <硰hb&U̬Dfd"2k]n³ $-'!;azBPPG B_ 1 >E @#?`/Z܊ԢC0n0|?~ =wG!1LÇXB,jX4Xo.n4`1[>:fHd'աo5":19.cSS(MF*_M{8/Ϡh,e`Ǧ*2M;t7+#_o7B:Gkm߄IOms؋|cȷ!D`:<2(F]\]<[;^2^S tZ?=my{ߌM Z<]\Rқ/Hz{D⻛{iO83-Nu⌧zr B| UnnWnl{@n#98z;ِHh+!a++"Bq:#'CO` s<)Chº=|hg~F (R#z!%bRžF+nszD2@0 )k?B`TD>@ǩ+u\h6MB,|z(g!2sRd_?G;HE9w:,4'#bP>/ĬYJz)bh0<PɋTЄ 7G~nykgۿi}Rl]T=G}oFfX~d3&Go6fw| b>A 5uҦn- t"o *$'~ͼRDP0s/|oyM"B[ql&inv8_g+V6UuE%9S= UQQIN<{)hotujq ?>pSm-Osvhx)I=Ud mč ;I4^Z8s_SeOu7nr9PkNJAH%yMֹ'f/tDĚ"eh"> jbMveh%H ̔/ 3D<@"I9dT Pz=p-HVMN,O0mJ@N2d';()R.FNgЋԷ.|}HM(A! 3eizֶBGYSH(sPlښW1zy'7C}3S_"Fd2DZ.H όӉ Gc둺53ˁj=q_䩣2i,"׀Uct>^3o-n^u;~Ý@#qSr`qŸ du]=g>D͡0q]mnPXEl &;;7RsOHEHꂈ9Ѥ|4REǔ APDN!0 |@X,d !{c`W ¸)1C&ܫS[\nLAg%"<Ӝ3 4}W1 |sӞRdB'Q9ޔI? kzYw9hq6s#uzRc뾉SƯȦBs BKzO\@l Hm?vʶ hҍXZ?E*HsdBH!/"t!rs>rBD"2V]|ʁBߏ8ʴ_4ُC$$+18脈,aǩm⺉\sο)ti`HpZ(; _&ͅe]&2j W) ([\dƴ9[YHQL@0  5T},2~̌Yw+5tCT(^J_1 Zav4u@7( lݶ8#Sաoc-8pxꚪ.)#:%+;֪[f85y, n@(O|/PеJrN.XM"JrCH.Rnܹgro$x`vix~c(C^$E%97]jϊDM$'VvAmvMWL^8s'O 3ޞޮGbg$kML,9{,~#%eh|'16 2д%tSnӏ`i_""bЇ# @jb#2u:y}0"翠SK֬k'7[y=&Wt7^/*).o_{AD6'9ui풓ؤTH{5@nv ]y0U/Y;uCJi\8fIݪmb %b mŶ=Xl "v9R6>C9l(mC,CLaC'q;g ڷfd4ese* b5FM0bQ+)("Ñj5eB  QԩOeU-n#pb"PK# 1Wo<)CoTӞiW-"BxTqPWkH@Ӈ<Vkah:X u,XHv9R:!v2 %Ctf#"`@>L?3'd&"d썔0״a)c(RZ08l%]Ō^B[a|JA\ $8L,_3ofP\t@"Nok0BQIN6qs3tE'^j7~QV]}9ljxc߿Ihq1M0O(YG3[Úf-D'L/pU,,Dl 'X.LgI#G$'nhjiF&ah?/}h~]*4L9(m @L`4a!'y@܈E2&>D_k"CMDD"3O"[UR&ʑ?Hj\\-L}و݈ԤgEf!+ 泛2>"[?=/6S^9'_[W0/!P 4="nc)? XtNڦ$X=7xjaaFMM0tT?"S"k t\&zAf/SR^C}p+d )Ȥv8" k01M}+`Dzb!D H#Hyh~"8-ÈxߥR|(z-"5$!sq(HxD65eC@2D`jDB&HqfÍU9M!u9EotsM1\e'wCPD*)MGufL3_f̧%me|B+F xhە6ci,?Ml㺞&[2+Ȱnu%fţں+g,ȉ蠔.\T|9pܷ?8h% &R'{憚S֔Pyv9hיx/|= k}7<9naa$E1;(,4qlDz &ǡ Cf!3[{QUgk:R"uDlTRD.Db4Y$!h~^݁"E DF6lj/7LJ#yDZ m!1\KeXha~)2cDt".z)2kst(sx4_h92Et-Rp 4m~\>ikt~1 q?R#K<YichRoCDVD(>0 f^ѝ] duCcz'QzzcV9*'?`_L}?/MU[ߥ$_8cEl1U |q"'N{x^unfj(7.lXSr (4m7 4o4ퟜX>("Kѵ=KYF]QINM]㛁smX$: lY?ZY&b& R7Bnf(ƔAs-2K]M<_%I: | $pk4!"%T5HY 5DxÐ"1&}Il~*RC&v EDdR z m!Rjbv02[ls%J8 ~H+BJbK8\"kg4Qm@z7c3KM"b)&k,گ?R+_Z"=_B)#FGލw%=)'s1Ɠd#Sj>#r я g"PSo Aïẅ́ѝx yCn -3껳O ž'Au8' m }=crڷ~ŪeyϘ03 _~tIO.?=QPZahq0wsu%&T z#7aˈVTVgNNI0? w"3g%;?.BEH\-qТ3~\׽{[ʶ\ mV`tnvv<(*ik%7:g`oB-Eݴ9܀Ϳq]wG䞻m,4!?rPPV̹ ®?GMF1I9jA4c=pKЄ"( pz([DµPW^d>Tjj-z>D&~4#l226AOhM:24}HM 4U`d R{ 8P,ٯ2HFdܴ{0R!a2bMKi]3K5O%'"婖S~D#'~InGO$Nt~zbϏa?d`a\n@ш@W!UREDHyf cHl<MpA Z84`~ͼ<}?;c_3od?8le/xpNu%~)릕W58NzlFQnǁW]l0=>qg뺇:sz纮[8?_|nN? ,4m0n i㑲p'4+$<)^OO9`HQohRCwiA<_+uFiS4ß RAJ_)#ZDwbfUnʛ["Ría[n26ՠq8 $C$ hFWs_Z=cڝT?|zoj4ǓH'8~4!#+"7 +)-NlMߙBQ:"nM q 1-5mdʛ7~7##rL= =YQ#ق لc6 |Kw9{q&3_`eE%9G IMQT3.RTPqi jҫYfjOM]JUjJ{JrR#zY[,?Үo-.ADYh ٹf _huFsMkBq)2&H'j;zd"Ba"-2"+Ru-"{/s,%_&Z> Kj]o{ewLOCۿQbz1cʺ E@"Yo0DBKFl RRuA혟Wi{Y_(;"AژvragS/pIڪW8MA q[8Ctq:5u)Oq]۔[>rװ 1ӵ"8MCfϪ)K) 1+nC]+K㡱B$enIokh#f-ty?uu_[V5c{$>aDL[O  q3>7x4!_: "|e:# #7$Ml|u2^t]7,7|x|{'=kiWH\OP0:xqz.'M?>M/#_|L^,UMЋy4;%|+zV!'5" BSHE%'՜M2@&0&0"h-Ed.Ţ*הzjgbje<&EjYhe*9v25 ZE)!n@Z3!!s(1BͩHenIqSHfQ,7USDx)Ҍ+ȼ :A |խ:# /;Yчucx|1͙ 'soc8-O]{_ x䓓9/֦7cky$D/O־=^M !wH/V6~> ~1D8eeJr% |>cWOHOY_WǺD=S:U}~|ch(*ԘEw&qˆ+#?cf}^(WzqsU!ËJr.ޡ4EᢅmZH?8ν>"'kR1뺫7D h{6|y,Dl"ɠ" PXly"qZTQ&W 6"brhR?2& G dW#'Sk>ST:8 R\`| <-xQIu_lRjLC H9{oDԎ@/ 3lp?0F sC/p3x?3Pd&9lUq=SDXGDo:r7̶Bw="[Gq7"WS P[ 0ܸbqbI?|A[,ՌqfmKܤ9ہ鯠mk9~W l["E?;Di~یknnI ulqӛia] 1jE%9woH?[;v٪^gF"nEUes@˝>[9[պ]'KrKz=iBa~LN <⫣sQ@Hto EztKs?GA'Է颕}'ݣÐZuJep=>'%bRZךmo]]?>0JV0uIFZq]TgŎ<Dj[yiu*qG땳-Vy!q;qt\̥89zwZԃ%b;h%HZ^sIj F "Vԣh"EJM#9;OYNG\k4v@Bsh"yPkoe +$x;nmpڌI'L%j=@#KKQ qF:_4D0}D۾hw#E2ߌuD>1罉^JM/WHͫ@D9njH=KD|oVL=ы[s |Щ;mMF K~y]EkO3&[  @قM۴Vh6⛰7CcU;]ہD]uejU\Yɸ_=_fqUd ~t7n_^b3_}~y)vf5/qheu.m~聮 L.^<9Ϩ9c׌KniJ&~H޹7.y.[Mq{l^C~Re=Lr{_cue{ u-n0y>Wc;K9\|6o:ޓWo[\-vsKca"B|1N!\2c"t88 *cЄR>6E)&EIHiV^W"`zf#h%1DWv"'Vh>쿈Pz>SWxDҐzr~oH_%JB0|2"2ϡ\c hԔ9'.n e~3HyMxW"rIG[EMJDl"2Gh "coGlΛiGf |p s-&Gj[wD`^GG+w ]ٜo1쨯X']5Oyݜ y=ZɇA͛=IZ~?Nj|ZݺftKz361>>iyӣ8#J7kPw=OqCzgvm4dh1fUT ->N:Jڼz +d)`wܮ5@Q n#w q2n쏷[q|77t[4XEl+aS{`f(]F?Vq 7u1d' "p,JU`bD4Ip6"rdj5d!&Z[)&E)_CiHB$L~ R"U;"@cLY HD&C>[A͕ ǎhhJDj}NNÕUN(wxEsNe yH"#=Å;882D06q9۲,+KְB8yHڝzn8:1(y[5g'6\bp_{N@[`NrJ0&!k g[BXKFL_zb6 :` C/EBp4 GʝLiQ_ķW_˄Eej''MlVmn8wKM7qIr~4f3|9"`ma)G*[|QXG@19h[Wn8]-b֡spt>YUVN;@g" őL$gRi=onȭ[N5jw6+E+A; t bG-bQ{9 6/CnzƚGn)*n!(*fF|7=Оڝ ,YW9ugs7!P\զP8n>qSIp[?r4@,&ĭ߮2_Gz2?!?Div jY]ףM[JKtN֥_ٹ6 Go%0<mG~Rw؟>-un8og}hdC 4oXBhjC$H̡8oKʫ,H mmYU35۽K5%:B'2az㐌6lD`-b0swm=;NO'+}@7}+=zX!gy*zGtyz@ xA^;=^UX_U2to"[& 'A(͖-gD5jg 6h E`0E#Š IPTͺSjeis 5Q]r D#p o, ps:#U H(nH~B}nD4"F*ĢȳxB{`igK- .ǣWvzb6IBFxKrʌy!!?6T >hCJg~@?$׶ޜ;xmŵ};TR^.gEɀhqR0P48r߳c&%(Ve6b@Dd_D Sиw$y\F nĚNug\g 2brNP,hބ!0Shke%@"HC}wڻKm/@Fbpv7XvEnWJ/NʕZG$21Rw8EFjm|I]\Ў7}*+;뇀7퇀$\㭑371팻*1]ql+w:q -h-#P-}sJ]}q7楬BGռ4.85=>jn˔q59WVKOٯk;n@+#iCYeۗj唖wxkWC/?m#Z$܃WaqK@3cLm]ZZjJIc\UaqvmRWYgܿA.+Zpg4IlYI be-{4 |W;h/`u$mmGL^ C`.8;P EB (&YeyئGIE>JXs}$2›KO(F {QPpO)SkڎVùh^ oe+I$P݉ۑe0xOU+{"~*bKЦy b"Mo/#t0nƮCRׂsNȥzLG.ؗ4H[솣[n.> ptWo<Ș Yp({s?cG9>|nj52+aՅ-W/[BWFBu{ūwzs J$\؄?\Y]Yv4S87Sc/!VK/-o6w`7'~,-,{wd,zϟ@kۅ؜t%यfCW'k&^9p4H3%sH.B(d~Zm \fA߾l=F{7n8H Mk61c9(YQ&b*ZI֎+QʊU"塈պ\tC I;Yv9he h ط(wF)!$@hG'zM˓KΪJkL!@{ ~{ }(g3=t_}ŝmmjLtI$놣DY6n ܤ_1s;pK$~UC൙kB޶~rCAKV}2 Nw Ac|?Xj'%=mKBÅ@]A^w#>phWAs˘V*Z=SND. ,l43;>KYu67I8^wuW8CM?AL_s(Vnա]O#r/c 1(A mЦ?X tkl($FlfvgnKR*AuPߔDB؜%ǽ@*8Jshq+#0^0Pi%lz`RLǁ$##]&q$TXK%޶"n;ms_w #:M #U >m y Ay(M@L2DΪH(hspfw~o DBsH0ƌDv"K9B ,p 7"H(Xo KFPJQшQ#h'%|T)Sǔ[|&3G+מG`܂_!p\i#q0>!`UtH(Xb^ㆣ1V6 %ewZYK7#+>Yݞ"f {N!1h,lBd,34k}P~Ps ,_#pTbYB| 5T_ץ~t_+_y GA}{q QWidy{O߻ޒjF#vK-Ab bXG\ŸDs-En*4>;M_`mLgrs4w+p@fwA$vMMn8zK#䤚M?s/KWb>E.}Q^m?l$҉Y{3ud{P](Y]C :dk5riñ.V3xCf텒onI3^Ak7X۽]Mݥ[}₼z~ſ\NxaqP}pM3O`"# =WwihO!;plV8T4t` V󐭪>u g") h")B+邀`ՅWC7d= U59MQ߱zY.!C't"`l>N<4wUr?˳޴VXvވw|#n8z8r!x >?X↣wDB7Kw/e^u]X<%|}])ү˄Dl q7GЏMHw>? G4|Ćbkr/ uQu/y^EGQ8䣾 ̤O<+GC]VBt ?yxe[oHfvWIZ CտĊ,F+FA2{7GrpL4ʈ%Z) v ;!H(x@^^ARR@H(XMZ|WOh?rSIf纥hU6CUC4ر<2(+qU#u+W)ypה$y" Xg pt0JC GW 3 G_Dp"K"sU_)vJ<9+aeؼȵ>tps}~"ڐ [5v$\Qg"Wi-=?07`f)cϳ?t&w߹'i$wN_<*%n)ZDV hcaq!hGo6ܢΒ˜ޯ @|kLay'ZKr7:ql;x7>[y]س=YEib_cTrqݗd %pmuQ҃zGFsAnKѾ1:e voD@"l>GK\iAЍ2VJyv`h (:o |h`2UC;"SQrj2z,1%=k[:ry)6Y[a;Ekbf4ꢞC\~j#[醣"n-p%3ל] O{~I$-GA 4Mȍ[@@ID}Jaq4&> ^-+*-,l)K.R.[3Ƽq?m/SX l/UldkOGc?%K{#'h֥8&qwg= %8NKj)܅$Xc lݖ:8&s.p+/[I8N/f3,?Z }yxj I41BȀ^s+"Vw ,`|sc#_hzDB%n8%5=BqZc߾@I9JJ[g*->d܎~/ݜתVD/(0x=Z:#,,X'+/ wA. (+%̕FTLl7b<}#y5Jh#vVوm F"1LEvo&J.M:?Ccw[zbF1D*>yqȍW_&V?'v ct +,οH+,Οtv8 pA^[{z9h䜒nq/yVE5َXR}6c [շ8_ Sd O/ R׸h (OBd|#б'չ#Bz/JQۗC.Pa(C|hkIkK-0^p, G?>8C'6 G,k0!빍 wsP7~1\o}q={Aeڳ6 Eȝz zspH(觼.IXG@x >m dzYEJ:f)^ jk?M (,ϳDCs$#O>e6WYۺ8^ĀʬSԧI~ĈmE٘, >w*xCCF@Iq"C޻3mQVN2(ˁxG%˸d*<'Py"C(2Y ̱d!&KWmlriҠ"C_QDqqC+pqxk ,)uXnR+ Ӗv?Vȷ-F$Hr;!f3,pCQ"<__'2usF쌃b} k] ٍ2:a rAB #ݷ;GLXkgGQߊ$7#P(bReQԤlѮ KC↣{"v9Nx6ZkSOAzLAlz?nZ"_Maqǡk7QgLlg<.OMIMMq>F^?Â߅_o ΋W&%\UW瘋v FOGcX=.Olk8k ~PI~p1bq+zt궮lgbc1m5pKbcn j:Uwo:jd|:0 G+sVㄱ%"`v5, bo_~=Hz=2w"#7)-z+b^ fy]MGN[#uR`hAmoZ٭kGq\̍W3/ 1QAZ"p G@ dzےHq3r~hu8 GOFl_ bڡՈ: 2 +1`zGߤq/>kC" Pyŧv]6(TļEY&11umSۡ n8zÎM7٦h$\ PWt}aq~'KJ%|3`9w(tB%@bΊe8C}W@Ԛ~)ͳW>4aڕ"{yYu뫪s2KSǢ0@v}w+KnxnHݻ{Rx!x8 pT,t|<hԺyzX{ĀxXRr|ǔ-M$M iխ߀Xm6k/X+AtA ϯP@Ȁ\dKJpb6gAf7+2Jo"u bF+:"2h7n8 T(by1sY$'#1O^t'~sѮV֖ H?,J=sCqN# Rkm4g5C{FcP){Xl4r q=+hy50B1l݀|7]Z k;9 T@XǩU 烏d7X͊to7eNmfM5jo|914`|spfVF #cc)'vc _p8תu%S>{̭ %uk/8̅d;/,3}JA^QMaq̒HoWs'_s˺]X[w^o|"dro䊚3kܦs) %h8E u$M$]F?Y~묻^9 4NJW\F ?t!@7][p}UMx;V1/GX:VL D{ t4q̅eՈKe #T ܱ㑛w fNu X!h#&, J!8bt%5^Gw'"/Q|\O@Ϙ40t=wouӑ{a?  kq=Ǎ+ rj,zEگ{B/)V~o6G`hNSZgкeVU^h톣=࿍^٭ES2Q]_>D ~ ៓88w7SyZޝ2+g!f:)uqiG/YvPqVsv?YC$KiԖ뺵^IYEMW pcgG6/+/,/QKZxY=>ԯER}.-fHjʚZawB Zdm^Ċh&iߥ@G>a F.]15_f;EHj2b wԗ]_@M/> ŇFĠ@!6m1D lrOmuC &7oQk !{Hkxn8:QhyEldG=.}(0v8r.Gt4J/Q@@37}> \>!6l(S%Wti@AzM?gpk$U ވ;?}@Bk0L'HvCncL?/`ښ#h}b"p×b>>ZJ;,\ҵ_:A;!(yE<&eP 2 ha11ů+8X5bcj3UD@n!#~KLn_];݃V G +w铖Rf %'2g8\ޟ\\4Yͩv{h1Q&HRJڪx}6ɩcu5:5/_~w@lGW E*l4@q-4pM4=~pq2b8ddDm2톣Bb 2mYKi{_C@7J! TS醣v֮;M h|nEڥy T?#[W wr#1-vo5D7tf{띓reOܽ,|bm@Kqx}L$χT>ǩS*y<( 2 .wQn8:r7uB1*":(o|P#Pkz=lȠDn\qnC`sXQ0Z k,Hl|w6v~KZu^S[, k bmo,+*+,ۮQ=KǹjSe2K_ CuzYEOoǀKD b2-aiI"1ȅrtth2bCN"f&QbP ";#@%y1_ZePŭ-c*,r39>J+rMKT;cVͻ~kV8h{W2)koefmr bP\թȕ鶵1 LkLǚ.@L6Y "zMzL2X]x&DBq"UVMG.%"hS d:r-D,etUpjGMNpG_RR?URJ2ƒu7wqM @{Kbmf˛g5\zv,NcZd-g"$vj&УMUG c?AY5Ye+jr;U$Tc9- wB9# O@d;v'M(Z(]<YĈ}[z荍b&)P^u?*+A ;Ծ;.n8z8r@gtޅDޫHCd bN6vļD{qs3z!+p/(l.bZ!@sxvڡkgX]A/D1>Y4?pXs1hb];[ǡ1؝$\k}.[|am.>[.&}8gAmi c6zնWS8rc?p$:bGtb!@'`?[0 GOPʼh,ZHomE,ubV V bk PwX?ӭ]]9l]֡"4޺ "q GXѾA9m6>vUcpE8qȞpVo=hSU{^YEZ.kpڍ/u8oĂYkyΨMi5~0b@YhݮʢQ/$Z7] n&Nxq­ (Abw(oG`7c^c}GP,ޗiL95CL|?hLtgZ1Y>~9D3֏bj]7b>4&v˝{`fƣ2o˷Kd3ÏWaqe1uBܬؽ>)9P[.k7wKn(Rq?Ǜ@ネ>'F}duµ;Ěd[/wD ~a6sq8p8C!drZ#bC,bUK(F;["|&kce<>(ƥ>n8:,b<򚏌OWAQrȝx<ڵ2OdؾB),Clbb#WϡhXVdsФI$vC6=\H(Xa}5PK`-M H ZlptH(85cXR}VzIReMnQ[S/>F.JD@p駋f|LA/+6cYd_ 놣ۆ.ԊG.8)+zΎ5;,['?5q9fX}9OLkx-N*--4`· /͈UX;91ůcv 0_2Iyރ Eyh 8y/ZvL76Zm;Atl}&\#< Krdİ]-]W ;滥^gU#WbGĮXre.C[# *힋 ( 3;/TYvG @T@@tNC`ǧ$!t鮙E/"]`&(%ǿP姚brpt9‹6؈!vښjנ>Ic =EcdAwT]Dy8z_OAk(f?wˣn8YN[m~ŘKw:seG~_Xڋ+6_N٭Y Vm1HOu >ۄlpgA^Qy/4:q{Kaq~3ƒ6oH,hnqA"py,syW6ICF&@Ȩr:؛p=N8tgZN|Ru^2d3A,VbZnː<4X4nGOV0ЄX)  / +{*;ZU5?#:ԺbrW=mx1) cgkX^^\1A+bvnE8u!@8" >kv@"1 SY4 W+!6s`]3ʳr PXcbVmi(ùyȬzbɅPk[ht]&tou q+.cfՊlg2t-b#@oۑ1#n-]p>r$G}v6C: 7nC 13~]&d;8g1`΢wq8Ac҂;hPҘ,oL̿ޱe|3j -|xaq~-l|h_xyUBOEHcff.n2_JCZ!26 \~uIn_#KRdg#hDvE:1R 14/BL{ȍ m;-Cr+%"@vgBY)a 6g2Y1n8:%$\(&#İtގCg)3PȈ}\wakg2\ÑcPAȀF#Val9$\@t7 +d#>Ib\{}~$C՛nF,7 'P߆#vD;mho}RomUII :Ny샼8%gxYC+عSe%3vOn[X@Cq.V}k8̊ZUW4 `r sMEK3?aV9$ŀ@@ֳ"q3 㧒^JM8\qH(XGs;X#F8?!8\v軶@Y ^o^)ɱV4<r $ޞd m_f ^ᆣ:\*F9rc GDk?#1xl>"F-łF6]du }g㗙فT':IծD@4m cN"`sf<@)Z_9͞- !phsmIҐ@7'5'|oգ*/6fuM7nz>Jx ⾋kL?CD=0 GC$QM@ H!E3a;e42y(ʎXfDHF&[&#RmuY\#w}Y;n FdWe\I92G|*^ R=q;G#

@1dxC\~6$}9p5@Ҙ| \ Go0݉1݁utYb)J&-7@s}WU_$v:%O $Ooz M dqhˡՠˑPpvCp#>sMop8xg\c=4hoV$"f!`]nG~s7)eun;o7-}=sb1cPZq4FKb=Jӓq6ܜ8eOMp[uKڶ-p++0q\ KNI\$'pb ;x&fI,e P,zcS_v8f]_JGz*+-~$iU 6DWIF / 6j9m: :d V$"4ik},#eɚ!{ ?kJALR|Ð@sF o#}l}9Ȏ@-g"x0 y^Aqggm(7>.("owݍ5J}\#m}kg厏 10ߖ:Dzxh\աZ93a *+ZđO5}<,,} h}aq@V]MlK-71knWmIA^UW{듢gWT^4O9Y1sEwGiw6m&i߅fX`eӑPpvBdШ\~?@FHS-jD.~!sTC Rn$ R\ NhgG$dr _hزȭ]gG2,!Psr=zm"BҞ(reBn D"#4q@lG f$,oc hB(.>+Eۺ~KtW $6E@t01W/!0TLd 솣"`c7gCE =~*Q>uVSXka}bֶ4ԟ9\Rj,9:ۘnchQ bƢR[w]l<o. Z/݊>vX! 0>kCl]_*E2_RX 8 'ܩ=_Ra'Vtk7m¶-+Z,,,? Dxr;cʻ=J &,,οhlO--Ўc@q)@ -+'yU=m'Ϋo'k7 ˟Ve0ꇌˈy&gQ\\=Vأ$f6" r7~qǰCG@f>Gskptowg?!#9#4P*@. #n8>nvb&'׫kw?خ2++2K>g!P2yt0v\ib.DCi@x2{<%~kܖ}C :nd8N@`[7bNvчePڳM>AR`>xE. k}AH)Ƞe gukե.  n\u$R v GY> Vdž`%lCC齶rhþ RN[Z~]aq~Oٱ%)+#`ڏ{;-9fĭIh1}{棟Z*Q_đ Il9-, bAꉵm󺫸o`ms8N z7y}Dz{7x3}hee5}h>t0EvJ0 ꄀIJ&uGzg oޏIkN ? ~HIBz?`4m6|: XW4G/Y<۹gwq\ʟBB& 13m'd̫\]hHA? d8ȈvC 52k#ƭ1n͹L_bFhn@nϮ(MbY#dzV$k +Ѫv![*d #Cn~?AiKI9t01S+ʭVW눲Zʉwt@FreNCz4gmqso=?_"Ɉ5`8ϭ~ Xn(sWS40gm },U ~(1ݭ$7-Dy,6#3}dEB)dL!z\meod$t8}θ1!񂼢[lj p{ޫv$c)8@,֡W]]nVWҩu9G]*B}+OlZ̜#xCOᎺ:Eߖ x8тc]8?{4,Wb}Kaq `iA^QN*KED8Sy^8. m0GKHqԡS<VN17 4:4x#Cx8BF7}c2@Hb_D 4Yd#6bMh ȈFI:~["C}.1eO"W(Ud}0ںw1b޲{F;ˑn҅h?`AC/bR2i& 7ȍ1%I{O^,]fچX<.zᏱz t⌙NݎpP]*B+M2;h%LM boFF`1;~?cGuu=ڭ9 G{*/#wm 4>gEBmEKRdEBqn8憣AtvbzY솣7ZBp7n59OpѼH(`q{ w\kƓ\V"YF龹V9| ^oܑ45V ${In \XU;=o6*=}ٚܶ$^-NDhl ΅oDGy}RdGs!< ~?Ĵ ,., )+! q =;y.F c' y^qahS8G  Wq4h#c"^x#v-BtdҐ4A24yþ S vNfrs Gkd m&nH!c<̮yVhZ[]Z!3LIȐGl `_7trB,pH(1JuD.A@4I~9' HMs?ܳzD.ώY:ݣ1~/#`=lW? 3V~s"7gnqWXWZl@Aw&bh<5mĜtFy|.hp;<3OnYONb}_3v b`+; ˦6^ZJŚXzmP Q߅eK и/ץ@.-σ-q^X<6emV zz- p` fkX7hC c[::dyQR;\릊&yE0y;NmqܺJG,?-DC゚c b[wO <^Xw%b4=k[Lw]O{?ѽ9}8N;B8@yo:3q q󼱎G l~K_+9pÿ![-K#'tr"FbF.D,}C9Ib=2PH[} Myh7ǰ Ǻhʲ Gٞy";&ٽcQV?H Iԥ!#? @i#CyĦt@z?G ^wGXAVFo{߈&C`xD&;}5r V#w- rk_KNE$Ѧp^?no˰{`;0nrdl:,V]kH(8 JH(8kz( c٦!c1:- Y1tKҍen|赍>xnXJ :y|ls;YuVX^[-*++x+\Ԥ.z0)+]%jg?3/kῐS zwiA޽q:&1h?y$=~; 7{O>wq;,G%6}1H="zsI 0M@4pbOPCсֿ7Y K>D+2bSvx =Qػ]zxNhӬ.Sd+Dy֖Z $&0s2ݰ]{,kV$% v% {W3ФeuKnbRGPa<$?i\H(?/-7^ JA^Tј ; OB$\[/➳IT!/M6˟_iެd+,ο"3ѼoI蝌54.g}ۼwgТrs3P7jt"7p&2PPCTH(X熣"#<,IPHB}ȅpľUm'Z22YxY|bT q-Y]g .G n(@қPFixyȈI"@ #. oR8}}J>F'bCC䖛^?U MC 7ɦrd(mȸgCbnF6-6ݟ@uVVV8d}@\4D5(~$܏Y{/j,at{#vpL?M~Ґ[hHnY^ْg_nۈyv\c&hO7=tvgH(8 ~zraAYwjr^&koc8NNk{خyE -3'AND6ʜtC亣N4)'={`wcmoov.^?}ʀ{p׏YVhG Eu؈&Ԇ;y4:#dVoOdVi_jgp LCn8:- ~m"z̰h+7؅A$NW!JALϝܷ!2(8Qdl/GlȞ߉j#W& 9:IC; G?@`2;IeZle|>ɝn8z",F|{ou&M?U{#sbv=&8L1 }K0x#)'= :kS3Vv5oIddhgf'4 -&cصw߈Ոa(݋"#;cD!)WN6vv[ ֱ$rCqF>NB!#ш5}?FGY~0upmdVڳV d[㻷q=oE>ј&W#"L ֟Y|!&.*+zo-'TdIN/ߴŧ1ĪjrZ֧N<}֗u[Rxّśq{f,>|YWlv7rZ(it%0XaqX4++Z4[XVfƜ5IW H?DG| GpL_zD K(Cs1 _QP bf!7,8/v^ȨBFz3 .-ML"Qe" V4=M@"E5=5ySXо1\h4!C;>s?`:lgjg?Dl;{z$7[;rEl]FQ=^?]l=w]"s'+~iE"H~ YJ""Y=?(47 f@MQteczOԯQZ(RB, ;z/dF~"j%PҸfcCz(#myEgGsěyE%撧 \u8ޘ;l'q3M!4 G"zf#J7mf.zD#Px=2xAw!:>#'lt!pnu<Zh1jۭr!'sy1'q*b<ČVٳCl@hCl:YCc`{΋vρ*801 #V!H(8 GX=c`t _LO}bOh #7zL9E(EmwPF!W9k$W}\죁zmW+m%yEk _ iII<5`l5 G_-;"C_$qDg(R{˗ G_Ehd# 28 * R92Q憣#A?\M#,d:=ji#$p @RP[=I<8@F1HG"7ˈ7w Ʃ $vG.Ld&"01 1bL>?{OB,$6?ie\uތH6#0u#YzXyVaTH5VK~NG`|+o~iuIm]Zsr]=Թ$=r&E̋3dFkoMJ+{Aل@qr`y#~ T`_v-b=|?qn8Nyy?؎/ye r/GcnʎNcF8gdWeu)b3rc*c!Q4߬uϻw{/d3{YYQ4Wl_ g^FOC ]yީAFWoKR<ĭ*$ 6Mr^戽NGZqJ򐫪+2_!ܮi&h2cpvEg,}F `K!PյojowFI=2>Sb˜k}ńGp&bnD}4o7cFF|): ]uީd8PiȍY~^m lfz []Cyչ/ph\ԠnVMw/u)ܿحE(N=%u53SCiE +3C{/NM6EcC3^j#F7Ҟ?fhLصF>nFאsdmi*{CZc)\Ư\,]8`彺 , q(-á6pԔGL+znpff,cIJF#Ox8g!(30󼘹ِhA 8e%G{W8Nk`8y[rg y:qZW<^\]WTpf!.&sg:qR6FlyO4(VYz}+Wundu)hQ~ \8=ϻkH~h.V8/QNBd<7'dd*!?t,@lG[(?+v)v`),u伎==Z]tF_! `V$62c%brC}ļhG+d.A.(bZ!ɞw0d oJAy> Vib$vdrBƬuGX큺^?|>u9oYӐK7lĸuF',qfq8.&q>Ƅ>=v5h1YɕTʢdhL7D8@ڽz־$hqi!"}RXBcs*!sw=c&ow\e{ّco[+{w [ pхhr8Ou4<:nlܪX, gW8/sk758qpHq5vcgvopJܺɳPš{fЎds=[4 y q2e ."(}G$y^u:oC;q!*ޙ_#`H  ?f2!Fhbz&pte$lZ)An8Vm p#2^]m" 734óEF92ʫv@$| Gg}͑|^n-CC _:38٦"c>Zٽ%~?ĤCK~6hgN @{4,r~A"0ūw9>V:k+gya<nx^my̔ںLqþ?D|%#:x{ j{z8*tb;@7]1|Z D~Wj}zݬM%h"$v^nm|# w14SbQdžv@_hL}"]//DBAջUq]8Ph8Wr7DηB>F@T qsIB? S}'/Y7cʚؑ&1q‚v89b@jnJ4ooQ G2biy<+sI "yGy(IJq4GtxV͖U^ݪBd,EAӇ#;hvDBF+홏 =Ȩ<"?Su; 4Į<>}a}5 -j>A}hϽCPp# e} wJ0AL/NlNZMҾ|pSPp ޽PR&2~VBZ[_GwbۦܹrK0l~ p8q>[!+`ϢUDN~ZU2/  VveMpKmrѲmboBp{kMH(^2,G@|9b͞C]N"L+w/v[\XE"`C/F B)"n }1g璘|ᔾvGO78ǎkߦ"17 X@{* v䶘r<bxeI(f*Ξ4YNZgo YY5Ș @ΈlP)`]/.sY9h՟ lH܊X @A &N@Xd=;.n8v {8EO@2҃moGi2#:&bvBL 0DFI GOpѬOM@٦CPLX-bs9 p4hu]n;$wO Nh|8־$Nvi?gr-IA^Q @aqd=@Hfh bV-;ry¤kyE<-h+V>aeO:$sW rcpG 1AN ~cR:Ge] cK"wxe{6\' NLEBq֮Dd>ꆣGho7mV~[d~ #bxsL/+juw#nC f$X_M1w$߇3V~y!0%Dक़{iբ>@[qv0ڮc}c;x} EeA-c'4F,0v"qSܟnqvD/R V@jYH@c-1;.ޢ]ɔ|OhMR{l&$E&HFM2@q؉|QS}2Zh&ŝ(=1A41#UH$n@.$b_^( ApˑAm_}=,2lZ$C `I$w!ͱpd3@:2CϵkbXLtACrZdlC`t Pp^?lMũ`\ @bo@z8oMi^;􆕳PLA?p ,^6MCF9X.'888,ߥ 8p>}KVH #.S0Ym@L \ch:>؈X3IfcM i§bIF(gNG[xu xbN;gQhA[/4>sHjN @vۉ5 vM(&v㻈uиA`:>A@{U}Q[0YyE_n3pe4a7I4/@~If)rA.ňLIW]s9PL|251 !"\ }& "c]h9#u"92!#5ȨFT @FsZQ/섌2a@X\@T3)NC72vdFtW{2]ÁY܃DPXfoˬ/cLU8楿jXe@bYpeRfCb{f/kFv̶~naw?֘VvBi C`x'~hf(9vi5>^NWf V{h\&b-֞4Schs$!flo4=kKY=wA LJM'[^޴suim -R^Cj4cV }&)$sD}odLA1Msq&(x>_"7xȎb: M@@d}%hԟ-ݐq=О ZֆdZ9'  1=kmO\6y=v#a#|bcDFws/k@ݲ@ p]pgr;լM|D#k"=oKɉ/&0FZd d!@"@[VGd$ @KU@ˡh<_d:wu/riv2#?k!1!1Ǡ1#BM<Ђ$T!V޵Uc Ax{:z2l$^6ehvDe=z/A# wr@}܏ĩscNg7=0 ~hm! ~Ҡm7m@$&yEcC橯S>C@7<&I6IZ]7ʼn5 b\zx]*4: r]nE"oO=ZA`Ծb<7݀VdHN@E҆Dpmd`D@cxwF Mv4`;Ry6x$rI FCԳgpCnz@|K dJtok偄헾h ?),o ėy떴>ͽ~| E,9W?{elzIC eݵk˵w{ŶvlAE:H7vɓd}93;sfI4s~ygږs^<8?!c<9w [aFy79>p_̮1m-@95;g<;M! ݈3ф}bVÑa0@netQX%ElV^!P:2. cعhC;iA@GB`I9Ihbmp(w Q84F 72~7hm27rmD dr'6B,^'{[ n s6ڮܡ8tnYtHpT.r=t k2k{Pf'"Wh¹{"~M=>X=XSa[H'vA3ΰ|>BBK[^M@)kW7I3-|;*rG HE ţE} Wɴ{ ._^wA5)7m+^@,U=;]!7-NM}uPfKMp6Uuh(Cl@eUI(^SJ]?#~]nrZ+S6#ӻ>.xCf~%+3;fcw9-?^6H_Gº< ?!'QcO߭KA*vMZt( |f-*׾rqJ:;w_41yh~GlY)۠u$@} S$=_sg2n=A] sӬ^4Q=hD|vA<k8ʞYYwYJhy v=KPǔT!001n!㹍dAvkʘqQTY]x&TEj*?g(ܸ YnwBnHpp뱨+px Kkp#YBF@t.Y-qArfF0cLf#UE|G݃&LFg;g!ҧrPXܨQnrb8SE&Kcݓ59Y.|S ndEL Z?ؓW1{QZ@QvpVh|_~ZG#=7݀4nx4<-H,ޛt*pEM r쏾8-=3t.@c\{a>oPSnqiޘ}mt?初-AwفWI92bhNee `Yyso6~mi`lhkJd(Z" MG# +j&kD"ǐJ C'H,qbG9ČAJ!2G"е >s12}S;"/ȮM@SjeX~O!Rz+;3Y~,Uۈ9jie Gm"q :hGū*>D,4>A;JR_~< N rJջ{BiZ#Ϭ;gdu!eˬb"~Jdf fll^^V7d}1`Ð,@ y@{zbb/C Y~,X3^UX_6CBҦnr}Ư?b. ξw.A `@u*N\;5&s7opM$"dNz9Y;"`:eYL@D\и]a ?-{Gc܄%TvG{GbawbVϯ.Ii bh C;h;{3,Z$]b;熡SȅyCsG=9w{q797-ڣy)[OLN&y.j62Ag[G=ɞ? l4B): і]nLmMfYV*ĦճWw6I ̴ߍ.~&Ѯ9(B P{P"6n\$x挅[RD>t`~Hvu'3Vka#Vh\Jv^JuMZZ3f/0 h !p0˸7! S@'P`u #vQ9y:@Gb(K>"j.tV<&8GA0z  _Y>.5ɩ7]]AUh#=%B<Ė,YD㭀Kc}.*F  njr%H5SSW~xlŇ)4)m8V\JXH9= ﴚrWe$//lsҟBnq9Z~6;=|!h(@!]]梺+y8ڣ6ģQxWw5{=}99v^32gj;۠9G!`3 /A͛!0;fD`BƦ-ps<n&&=fyL6=h54*sD%F"Wx4|| 2buhuE}讴<D7t sg{4{"6f%K&&l;HDb^C!mbZA xkoۍ͒G pUWƣ&S꺿%#cuo^-$+=!PR!TOEƼrőYĤوI#@tbG$hxc<Xs3$'x3+K6%8 .A)`enӤQNgBɺMD\x&B@J\]~#%t xYM'f4>dj}Umrn<wF;N}&4#0ň~ϞKA:i͑|?wFzݞy~ 3r 0]WX>AgZtc!Zk?k` -Mr " GqI m.r?ߌMdAV="mX}$ƯHXbH,q}okN>W={t# ~tThR]٤:ab?ޅ،bp]WxosѬ[5*pE *p}ͱ \QWW~uT]):|qΥ8(H "b0\bvk=+$(T~A]m_5%ѯ^ FImw@ʴ)49g 6bͤmZ YE52_nU~h%=NXHppl.2lUP48Up[V 2:mcԖb;Qa6vBNg|kHȀvDյܳĢXCxX2kS;˴OC.Pd&푡 1 w,0~]27F]<:UX)(=yB$Ñkn hH,EbTC%%4.!!"=3Ђ`2zk~U ҳш{ KzΑJ#/q`>VvC:Dm-7!y<~WzW3Y)$#hl.d+~Z<";3hN: C5?鲿$x|+t&!zhL@ ^@H,g</KB_6TA? \M!~DeU?pKl=w҅W4-ᅲAwN+ Ђ*4owA WpEWXǔy"4T's4fBcvzd[/`;B@cX,k31}]bTvDy,R$r]گqvk}?<6.Iy2e˵sh51<^&NAMr`NY䛡4?2f~쌿c2G? NjhA3xd 2!iy =Fo).##;hx2 k2vhgGiXZª&11j*^ۃ4f!v 3h 4IiuUY |!] {ОFc{.>_ey@,VoNF OG4vZB綈BlH&"5N߹߷~عh̵LG] =;H&"`v{M& <{C+bHE7]@ڴ'HÝz4]ڍ8rj 06~6Kb}W-Gè.Ƽ[=ť&ϟj~+zi.+*R|6 4p{S=I p 1s<`sSC2-C5K㯛<ϫF As-G{w=4/,Ϥs"Js|7վۭskϯnP]" finΈ⸲ЊA܎P+=R<.K|V[ dcNDir̠$U>arod"D`b·2rVL~ߥ!&eRUR.Z]^.ļ_qd$81dY|WlBrUX5ȐlD,hkiyAsX؁!zcGOGbˬNQD>Lr܋C?!27Ѱ%229V&1h_e>GAj>lX]W#flV#j{} #TaZeO ]X{ل W3\& !*^_ p6V,G`4'#ioH,1Hs8xZA&GH,q#ҝ $'}Ebq,',`/-d|!1EFgWiw2z)3qo<^`^OՏMAkSx\nZi6D^4OF@7?ַ/ PtKzh -4Y7ƣ X"- [fIZ5vU=n#8v A<#}  :r7Ac. @;`~TB}N}qfʆ5y'1_ Ιf^}?@E+X=Ə0 ԇ݁qW0Gݣg8Ŏn#VR{_%Ti5_Dz/xj@+$h B[`8 v=ܟF}4 Q' Ԗ`u]W *K%AF[:BvPLB FFTn@ơM6Ϡ@a E;Ćh!)2o Wמ#b#&n++1W-:٪ Nׯo?g} \!y|>]wηܱۗT}@mv KE4GxLf,G+k߅4ZKYGT܍tUh'Vކx48 WDb,+H>:)ioPl֞cFNG-A<[~F4 XtBkICӳҐaAN(6[&!hxbNG!;1}I s"YYO>ר^?2nEซQ/CLð4^>3~7ƣ֏ËCXOg:_M[֗ 7l]s:WLuy_#]NGs3v!GS8 hҚڠ}Ѽt.եE*8">T 7N=49>&Fd %ybuyMy轜6ߢw@g yf Cy0/ w;! {^&YؤG*{::7LYQ(%*zNx47An)P mS~n1hHpx..rvE@o|<.2F G!7 d"+b־n)}j؁|4ygXqjm6{M+7D`r~7Yct:oCe]`d[} ͞(ƭRx4<6K6*|'Ŷ-5_鐆.(С?О}(A@x!6ijmlm.ғ og]JpAvCvވGs#Dg bY#`7 -f0G`{2Mјb$|F2Y_Ogz6C8[Jp'?!Vj6G,C@ .h{dz->wu]Jg\ϙ,RѸтh>c~ͽX"&,whuDG#B{-1F5-0uӲ.M}+o Z44W L>;ai.uY55(X).wibX-Zɦ#;D 2G!ø ?jpѰ-^z(jx 2#ZXYK0W|n)M=Ю 27#Pw2P`g l*!0w%-C+vwC7@Mm<2 v.:Z 9("b?*($ %ͽI&ۅ*jҒYil"bgd`[G"VO3sX;Ўd$h oWv6y~iJ"Owx&/BdlwţH,)5ի)v hsG!'D<=KGz13 / ~^<8?13zWϮGy^_[/ߵ@yw:Fzo]Zj-E|##ܹȠEf?`T ξC+X P!%+&(k#bE@m@,VcAFlW4tGw5b7Gw{92p JMd~,bdP!YXP{41#v(b0fe Ѓj~ %&/Z`[VkB3YˆuppA?ډ@X:2~vƊ&>>xK6bj7yD`Vi/.^'$Ǯw}7kX#GY}1`:>K#Нhݰ[$BCou%B1cSnX؁RJl5A:|WXf p@hn2B@3VF%x}`\M[\8鳿I" t9eKVPy;ҟS[uiֿ_XZ?Boyr Cc>b6 `*mSd{=7Ih잏݀/"h7;XbJ刉0WH,1s)JmvQnssbO^a%?#dzSoj~sjNWL4}H^Fc}ҥe(tc>q*@w\yeh]{K5#`}4{KȀ6E.^ᯇT;9 q* sV)*wu8b c4O'&4cCFc?L%53?wma꼬 (xHry;ͯ wgfa b7OEs(dYtgH 70;d0Olڂ7U7Hβ=GFzg{Zkk(jkܸW[}n=g0XVcnBوX%:|1 =!K\O{l[yɶ50ܚ"Ptw AP .&W{t;XZ"џ>>*lXh2R ێk3x2Yn2hBgβ40η~ 4p_#@ս5  Ũ]ܪ rN7'8t&֢@E<^%,ˡ׾g=iam| 2nHw*hg ~#Rb-s{c \7H-QqI+z+BWdIM)ޢdQE\ԥ_<⼎Fsz:|C945ι،1B윻C1: }Sx4jZ'g%ɲ[#ěnh&Q@ ~ j83ZCr;O}ޠfE3K\ MRh%/lhTNҙwjhu@t=cW[bW&en\um ernvXk i61ό115OƣV$HI;>ߊM&Ց`;UɘOp9j{&؏h :2s 3 zʭvv@tE<%֣s"D9XhXrӑ,dƣH,6 7d$Qod^&T{Jweg̏7Ǘo^vG//ř3ի vaB<^A 1!9B #rѢh.+T띯 \l[XlŊҽϪ֧dw uץwFB@@%TL~:9<>rMno+iye"y }s[? Kz%Gh0Goh&BC.3M4N\6"&m9r܃&C @{s$y[ޯ @;3'h:{(6(ꎘC4SY-ܽ)EhվV.?~+!OkCT>&"ā(>gc^luX"X>#9 @o@AoY!F2dDS #!Ոy!m`:6ZG v6Z@$f_^<`>Xb6ţy0#D!b#[ۍ"r"S#cuw#l@cHz95d WnVNgXE1r>t04&/E=1 Eb؉IU;x<D:6 ~`` vGjud-<:8#8.֏pU$haM$XِVַ 6|3gh0Á \ѻXl?R7<9%{kNy'ke$u?IO<ϛjO9-~*}*\g~>psZ*rA*\=4U#̴M]ۘ#еȋGѤ_?֞ݭOj*4|lhxPo` B-B[C@{g  A6;9@ѽz# RJaw9Vo}iIƻ _Gb [S6!upcdIhO+nEh\n^NpMCsds-;nF6h guιѼYKH cGXS3lM*~ F?s=b"C$rAe!  |֫hQSIJQԿ D Ex!5Yha:?V۹h)hZXjDʽs(bX^GlpF 0}I<oIEFmO$`r֮Sz1V~H, 2^Ƞ NDn@Gn. LFmrU9Xo2]@Ne] l]t[{!wҙj/ve!G"sD btWX{#,&{ː ҹ#sЗ]#/e^?a,`umKp*zs>1!8+u>pi<xn{3٥Z}<{)@^V#yںͰt-[`qPLG!Gyc%\&ب1\:M\<6#o>r!fL~~ldg7FbFn켍zxl ҿh|5GP8ϭ29҉끜Wtouz<>lvŞ?n$=}?zc&xwmNUF֥-@W]$E#㶓?ZNݘK@Gpw)}w`&Dz}Qה ㉫[ {76=}";Z &49tD܂(T+/ Dl_Sd!EznX>(x9Aـ^)bgyj:4P"9b5>Z<^%3~ު5֗5D1chb.Cˁb2uh"#/e+peUGYX,䦼&!PbUmDo[]o SޏYkhtkĖXPh']1=7"hm Z܏\y8h|@FuOS1G{ \m}aynDzሽƢ#yҵRIN@`yv-LF̕/C tx-ڐB,Zk7APVf"j|ܰǣ2~ƣiC—c٤c3w}c \`@7V 9Z{svrsNVgqϺ(H|>%Ъ3X^4A0&Ǒ'pu܄*qIw-bvI\3U@3QM09xf?Id8(4B}1[Њ|}2L"JAp|\Cd4@`d(3ݬLw _LE_BKr6_M-|0sK^ R>iJ6 RC`k@`QOv'~=/ ag?ۑX"LEFvVSnEpZN\lq3h X8TgBHP H|oeDzk@iCqVH{#!#,?үMIH~k#88HV glo9KGpҙ/a03uMW zlB 2! D:&u֎&s杷o?:`бỳ?=˯%4h>Fz ]#U[+V!c$.𿔶F %ǣaϮmk rY=FbehEoZ@ ľh .BLL2Qi 0ǥJGq2sG;Z7F hZ# b%R&V`F`rwk_DCFedFF! Jd(FF 2(lb"V&xF@s?*D@<{)2ȧ=,N7ym(c-Z5i&c94gY[V xssc@_=e2"ƥ+IZAF8 í}ɯ-2Yk*i]1˵zbUT4@9ڭf2~^|il/+Pߧ#PCOLm(bkg2 F7 օ8uni+2SEhd,2{w2 YF˻( OYa&bmھ vg;,kV>+闑О˴:BF2M&PgȀ"j$ q82 J_2=sO[ٸY| 13ݴwf^E3( =OCN룯7yp4 ;X?`gnFd;o+.&ؙzb^x:]sP>v@:~s7o;ds}w"9oVn}amSB: -Zcwrso4MZd~ h{m}uwA;f{TRx4@9ٞv9r$L,و٪15%8 NoZC`|(]4 @y,k۹+TysLf#=em`L~z-@fXXGzhTǣrhE hþQwd]K?: YCFeb`n3ФdOD4FC>2ޅhB\d"`>>td|6l l+auAư-2 d!P2}+H+kĜEC+tdbZ#ÜIpf(^Z5_j7&2#ĞE죈euɣ-bn3MVXtn/{֏ػp+҇E6@!ݧ CAGW wn@\$TIg21ţa΋~n}F_yPS'@](FɗeP`{dSdVFa}Ѹrh,n@@(ݮytR{xv& ( 砅3 ݝxH и}˰< Vd1^_ly .Qn䇗|ץ6m@lbK@z<^eA-%~b heEM!F`a$+|4G1A,| . N!8h=̛"bd* k+-1KE(h9ZmLd"#'y.)K܄X㗂bJbEڏQIk W.#V~l #m=Nڶߵ4Gr>,Aً[Yg=di?bh.Gldf' 6rH,ѕ;J%8k>2f)Be@3J3,#HoZ ZlriePr/,61b\ء;-[dx\|WiV9{&HV\_3yQõ&FV7(q6MFsH -@@k5w¾YVvAzUWt3?_=US/xt1,XRrՠ0-Okv2H,1s2mvO=z2{;r5F L`4d#8+ R iɰ hrmͶt g2mXՈ VN.MM dҕ35V̦!>Htd>uV/?M|Z"1f/wGb;8:?Bo|.|O6TZP|عhdo bNvxh3Pߌt>Z#4 M"`4Gb/Qkm?eh^t{&dw"tmZdH,1`Em6CRYp.C^TyvιQ]?s.%tuwHKo1ÐO&qX ɞh7GFBF4Di"DP6~<2օJ:`W:qAY k92,4"w6b"K= (:(JC`k*k5dnOpq(cHrGB\|"<F! ΄- \<+L>Ȱ϶wDnfY[Z= p=$; >@`r@"ɲ: %8|&rvg" Rne)"ݬDqv G>b2AChoF9 +ux+S4794O6]n(gz3Yeϴbnë羛љ@ts/Z`m\X{QxC˻Ȯ2yC`~G{1r8ߧH,X! ИG&X?;uȜye\]t)^25'"4|tٺ x+,QҀk={9w)ιauwΥzy8&si;ټt?$4Oyw+g2bD:,"؆}\!1MM1L#8Sz-F.w@h ;(A}!j5ꕎAx6+0E_mϝh yiM<^d1[#*?ݞ ˗bM jdU%A 2B}ahvszpU*`y$Y%&hb $=bhЧ"ܴr]耴Αj\Nsv/owN |ɋne8ՖVyȈ&K;ivywҷ V^Lؖ-c֞Vw@@m2@P)r Id8O@q7aV=$)kdm91x%Hqt3v@{?ڞNhF7NhL9lidyH,M<m{xÜ{t)w-޿sp>8ţ T3ZW6=xk6f@r-aa+e+9H(pEo^b \+◼ceԁ8ىJSNvcsۣ}qεhj\C4r5ّDFH4! O.B~%:z'ES V .{WQؙ`IȠ/B`k O!ot4I'$"V%x4e$k`U@Z<=bRhz7Ϟ`m@[fbv#5|֊ UֆhBʛ>0?l4M$وXg.GKH_XpA`dCV?A@vғGbgH,dgB 'fr`Xg7EDOiZ!Fg ZyPi_ٽEzg:r54Cnթ5DnQ&&[d Wh+gb6!pb~psgwD{D@zZ/Ffok!=FpnF"vZL4#;Ebb+S[puպaKOaAAZ=dݪ i+V, ^ u4 A:?牨ӷTбPǪc:c.g@cI(<9۠LHx!#U8s=-mͅt=%Ytpݏl1 .n%ވG kcyAC %&PAdvF Tjg#Fihᘆ&s+SA|Oqb"6f7dJ#gxCb}7Mt] Vs 뎝=%3N=k,K!ꐆ^nLFH,qdX_7~%%[T25+uhxY$cV;[OCMf[' mu~ֆzHK-7mݑN'+넀PCD72@P >/5DMH,8G11`k Ǝ'1Zcu^&>h#vcK<@lSk[=+DpP=;y vCx~7#vzԟǛ6w/fY?cDzߞ悁u!d/kؔQOOvvO=j.sSCɩP~z2@,@2p x#R&EwQ?]״1{FmWL8fbpm+: p]s1b~˺GLTz*2\iȘF!p&=dۢU,mDF`lmy.|>2eh_Xb2zߏ!7 dKc7bL"x rص + + ؁C}g"KW[{l4:W 2wb`޴r 6!*cBKo8% ZdX6\fm@ȕ|1Xn!`WӫwVSPyV{ =`L3LE38& 6^8cz .O@zvokZ?a܂"}CpF&qgK#c1MsCzJdCH7&#P 1xV" e29䵛8"R*[{Z FZ}D%ay']<61hu@q "D'\&ddžRIVM}ԇm=^߷;9]x~;nFyyιޞM~kϵ';Qk+.Z #nylZG>#rmDlWhxm$ir鱨Sv%&ha3ꀝqv0vl! \vJx4Hoސzbuq;-rJlD_jVT} n"n$.Qx E]3 h} 1ϼD|㜫BcE,=fN`V^ґ2i dS/H[5G%Xg$HAF5M: cpZEV#h $Ƞ F/ `v$RM1,5켎&.F~2b!8l߿Z&[Ȑϴc19WjZ X"ةሱD pɢ#w2Ŝ4D ܗS 0۞F c9Yx|#6 iuM5v@q0OZ;s۴ ĽG#k"\XB:d| 049F}Wwz2zBsp#ptp M=t4q[Rfyk ܋MX9o!ނ ĸ5E:s0nC.϶YG Ur*1)ИA`mյZ%ј!8 7{[Ŏѿsڜ  O<\i'4R4._ASmD]ibS<1Ps *G)~hvB"F`l"BLkF<r~ -K+%>z5ÑdZf?4h@d!hb߷@(\ ?7ou}6 n_GjS8jnoFC bԂ\v#GFrnY{Y1aMCf+,+%b F"'-1f_IK Dnlw79l6 E/ѣlF)rX 7H,km]te?Jp%HwFFv-r׎3yu CQd[ 2m~7='b`(N7!lb]nG ]F%bvBk1Z-GHgDcxkKSD9 N'FbJ9hVGÓ"D 막5] FlCY w@2HGv:l tNC Ȁn؍(HnF41GF;&Mf-Y- }:VP@SV1b?m x t1Y_|M#BmiUm+S z NpB8 %!6=GG_9Y-*vq)Hw"hk2i2 ! ?% ;E2EH_#ú&qFm; ZenMFLhq,L" qMf"UHO 1iaH߻Y~cmO{Y۳Mݬ@~J=:]Ζ7ֶk]*5Ye甯S@U.[߄ 挎H.qq[ɿEI;/K;|yZ*yM dՒZiu_"SuN) b:N|u6iSsHI7<ĵT"1 '29hE>ȍC3M͐ ,tۈ@K6 ދ>KCksMy~fe24J! >Щ2 @ ϡI<ڗ$77#p4F| $&L:C+Vn'4okn}#9 'Kogn{<?u'Į.d"YYڥxf{cPdG ;pY$օhtPVg]B#<iӓbmy:LBsX"Px2%mMQl^kH_d?+w[ o)&~k#$W9 Q'nmG"؝kkչdUm1}b|@I;^~ϲ5y$]5e]:' TJ$IHn߄XH F1ɨUC28[75QvUG  Ձ5Su.ե6m@v%kh]!eO^i2pS{XCpTRd8Aj&X% BTd$8VhysSъ~܎މG7Eb@0;c4컩ҏ2ƣg"VȘx&dl^nodն%c@@6)WX[BƩ2<"p0B #=Q؂EDlU&C;nm+dFKPVv;dPS@kn2,vӈ ?}v@ַ|:"ދֶX0)җ+InJ^紪>9)VWH,qW$hSV s.NC:v$(F VTKZyϼ15GyH_>+>^d{b ֞..|@ gXbkoɹn7O{~H?GC;'\~O~O wd6\n`϶װcwqJO^[Ҡ[%OV*7^;ǐ_[O+|eX*$fh8²_H7>̪%KgB` #-S h%? d dP.Fc[d KB@7bKQ,VH,30 g>ҴλKZGH,ڰ[vV3JI03cWЎGmg_ 4YD;Ѥ)>_{yhl9 ηoѤ>MQHd wGl(@RHA?̣h5>Wt}ܭzClZֱ~.w|"K(UP^:/mrz^uշ3lθ#&* p Ӫ}<[V-CFp`"roV2,v!_1 U5Vk1%TST_QZ}HOB a=:~@mO;l'h|4h,G1bS=s?e4HnHS1 ?3o;ݭ1( {. 69-].އg[iaKZ9/(uS VwW+ |S)C +^ar5S#*6V6E1h,' e;[狑yVTBڪ2~Jn]SdPc2ރoV&x4⽲3QhXմQ%KvGwGbܱ`-/k.)@Ur}w_=d6w@,R5Z|wۢ S`F$HGÛǯ$|Kn;c2'r܁yA<HzvG@cnnelujXVW!>ҵVȕg~LFlV =y5-M5Uy]g$GjWdܧ#\1/oYF!Y}Eu獽 4MzrϬUmVnqNm, .39 Mp㩈GMV3y{CUuv/=/!0[dDƱ&!:!>WĺZ>[پ۱^gbUΊo/ƣ6F#=}e#&"=[ވ;t`-Fcn zQ<^[9y__ʴ|miY4 ~;2x쒲w+TVEv. v0YwܸF4NrGzYdQDPYVvOie맧Uɿtr[%Ii{8ٞh !c2(ّh5X>34Z#c;wC (1F,CVHVBh-b>F9[T Q%T#p=) "ZIJBYY o#č Y! k㙈e郘+Sf4#P=jmAF$bTZ;! |d`YL746vG"nhy j}uۅh2H,7ƣH,XCm6-P:(5\#0~&߉Cm>V"fC:bI c3ф|gg{8!w_s,{2b2{7\RZ1NX89伜`G]m29H@ NlV6}Wa/T.Bb߸ \^OWe;q*Kj#ܳhQuO<0y' }I[;{tS/57 WFbhU(vx{fFq6ȐBC2 Q(xd|so"&!i1b^"8Eg0ՅWM$\; /j~hDF13 1lrzhV]6q i0bRTو`ǾVskoe#ZIi֏5NBa1 :mў\#rE,m@L%y;T^5%+y&ڮc#1 ߴ Y[!x=\ͪv5Kz^ &^g:ΪEvig`2IJNC8K B,$MAKj!8דsՈ 8i z"U_k/D/C#8Px4|F$ldJ•=BNhAp*[{Bnh\vjW~t/{U+J+ jZB  䏲xuOKsyINCv@`W; 0cyq< rZy^sn9δDxGM.B[[sBs߭h<џhh~ ?V jy* GMst~H0J}\"#=d|ףƖMy:\ 2Z_#)P5r=Alۺ+*_YQ,ߣX  kirmeyA/ڲ2v+Ehb?̾12W")) oKMpAg`x.%g7ߵ0 HGbӫ`j>A b,G1o>1ÐqD. MLZ;GRY|CFGN+#h;1CM4jnf]$hn}b6Z7W4%˥4=/YVhW"RCtm0x-0n%g,ҙ hHd]컖hbJnGL~hQ%$p~᪆ f[_պ&5{J|21˧-r_ON^SMva@FWXUW+ZLFs2-F{֥?19Rм/(lku;&'JX}?q/LAlZE}yWߡ16\9@B:;pszpk!]Gл~x@7@oaVdXUm9lqQj1ҹcʖϴߢ,d4wLfw^2~a~o 7::!𑶥k0FospΝNp-]WwB }h\Eι;?h 2 p zwv@`TkAЗ_d^^Y/&|'!+emnlu #`tdևk.~ 8.-֮Vk8d\1%J&[j ڇqdV]}V+i[\M-+yq&L yҏ{!UU; Ed}k{}~H8|{Cu`J*e}\xL$pM7d,6Z>xȭi׺o~OVBH#X: '69G!9wy9b==ϛeyh ?D3\W4߀溍z\3dG¼n3f5d[ =hoh89yU9T9+d~.%fi=b*D+)w*B2!f`*b:" fdl|1"!( Q 6|dI3h܂"d}ז 1h]@a=LC!?)֮h!V߹v02ȈȽ8bKF7 x%;cfźhgSX34Ǭy/EL?{R2+-H,K"w\Ὀm ԏ] Yb2ZJpfUhwe%/ƣu斻\"Tb7CzgĀ& WM:g޷.Je{.r.w+SIN1gDblGiYHhxI$Dz@+c1cbE5[ rDb&f߅]ɶ#b2=|-03KA lb,߯@ \Q 4_) 3i]b kYP5<ϛdny؂м>~?n4&BcCwLC1Vs쳅^rl26;1a e?윛IUl!%*sG;粐큉jWc`OCޕ_s~i{1)_dd¨c";ѰϘ鄘@̴3RQlN%2-J^uP(<{p$xs2Bx @K+6T!ZS M(qhj1Zvъ;/.^c4FL(cI4Ic[kn;VZwY4:X}f2jU! ȍ ]G#W[־ӑQk &k{( t߀" ?G%G}sL ^|Xh  {q#7dP^Jfg/͸}/=mTĂx'\01Y p5>H;"~+A[b1nBnĐU &\k=VNx4+x4Oɾ.TN@}unWOG,kgGZ5+ o?~sĶdi͏m=ϫjl@JiF̌``V<ԟ?CT9bFģq UF#iX"΂C)99r] 95y.l*\!ÞZ>bhZ> $N"*MY5C,g!62MF1|^=!Co[#l%bڡ| pteuNo]_qb Xq$bs7yGlMlgy.Dq{{n:@t3'=cguģڻʐ<%A`>8bɞCW&B!Y;n\-<; mL6E@\mԻ|9}ď9":d2mbYoS ҙ (q[χ"ojL6n^@# !MϞh^ץ&8#~hѰC hf]Sz[%A)%%g' R[U\ٲ~:n/ Đ&sc;F@x4f!-po$8Ȑ#f"À 5Jnx?g 2퐡lOph:<2F~}?d8G- }L4;Fg! 2t#3d зr;$+=O$c51ۛݏ"&$muB4ؕ#Є}?B aW y/;!@9*3yLFnmcŵomGbg"{nbό6CxCCtFN1r ?+uR$nXj}s숣;nw[ǣᑑXwL_!Bs;c!{RXnrmX -hO;9о_tb#e~K3刑\ngoR4~9b?zxm7kZ_FdKq;`dnI%񐫲 ϓ\<#@ qy;4v(2Z!n[ /B+bbQ8)x`m! Gm=2^M% 7&<%6-JW5ʫ@`-dM ̰RLv7 LE {(H~0_"#,?57 3Lf|=! R$Qv߅@#x,q2 [Kpȵ仆EmS Ew{bg\9b.}wxt?޸(8Y оyWU6dz N^L/~:?p=Dpry&O\ڣX님( 8"`zzb{6"Ƭ0þBug["b.]\o#[v Cb3L;Gbhxf$]=/Kfmy;+!О@Vf3kV&3HO@wj2g!0Ub}}4bC'^M(m[ :;l]fm^Xŝ\C"-E(/M"mS$XuKu.y)ecZ`l$EbnX}k"c{ Mj5} crMUδ91[#n9bP:X"s~. duXj<38 @x4<=uU=ofjhXv2a늎OXd ?~FƬonhq)YQ eW!.i: X2f\B.ELf@x4Qb}zi5 F1^MivE.Z_@LF@+brY1 `Ċ$˓5Zn *ţˑgot7+Uoo2ta ҃-1`uɒ!S+J-pE] \QoZTҌOZ+"D+PB\X]mm:ţH,1 dEZ Qȕ7A,QE] Bd3F'1ݺ4 n"p#b^$]n0beխ rB$vrw^DF3nt&%+\Nɲedԓ4b: LWYG,rG`o5 p{ >ysC[yh!ЕGK#ĩL:[ƅ(jH, gu߄teɥ ґA^ E5he:reo7UA^]˶F"C$hܑ%)Y wJ@@YlK_k Xba}J#\M4t.#_H,q6[%)C_/H)i˒Օ?JM_^SU'K[N@l i6  D s1e۝S #D=vE.4ĖMF@LpL+nG]@#18刍wC.C֡xPk7d\c\L6zD; b߮@%!Vc&rdbVGnAY\UKZȅhߊ_"jP uէկYEX Ch0|%۟kxdʉ/+- y: 6ڻ5&A-ahxb$pmu*9?Hkט_vүJhx-a[OJVW6;?`mK9WXhty~˲6@̎}%>%"Rៜ^(xY@XXݔW!БJ?ehvG9D.ʹ;<KE &"!0 (m#lQy03sp[جw\]Хؽw x."C!MmNp&6y(B&÷i(+͐ar/CLַ^$,K$vZ.FqT(~0YVοBЌElT:mzm7G>&p_ϟ{&>2t@o9X݀nL8n%8cL"}d[}S`N<,pEHFk#gall?uOeZιgѹ75KuH!LvQX;ddǠݙ "Q hrߢئİ42mo^؞,Ik?#( 6?UĞtB%bGC@д<*肀P:} KgLAˑXv䶭}`X3Ư/bVZ{C3@޿oR {5bFBKxN_6܀hFrG *kO'x4KGX"c2m01X[ʞ[L^A,S5b'KйQ5Vߛ DxH?mrm)!&g uɳMF{Z"!Xc.A "{w Z X_hq} hW"@+:U(|NZ~׻"PfR#{M_#4=\ <9w= ]z79wG,kŽ\Pf}Bq"Glp HLlmBMlhF$MʆlVtvʆ6eie{ͩ,,Nw=s2E$! ǦA1fz$1-,1~xҽ a?vнƘs^ k\l7DM _ɵɏGïf >T}#hH,q'R^DH5Gd!ϭw4w?@*Ĝ uv@*"=HkH,R2AtKk]HɈTԎ1(5MKI9 ^FdWcWD$DE=tlƩ"!#2yR a\D,{#E@QejWB3"r|Ӛ#f6šef?t/2/{ Um܎4|*|R.!5"Gk!4y^E/篐_k?X>Ŕ$2K;<q·!|Y#55w̰ ;7ź&?z눮 p$Ǣ0h`i$lhanv7p[5`v 䨲`U\e]+`%)J?:lBƮ:|4ɡJrHи>0a)Ea!A>a}_3\`}wzb!bh> Gdx4 C DbH,N<^MG"S;H* 26"N4ނnT,@*4IW\KGz JDt&H,q"r}ɑ rC~&jֻ "_ '}J̆B`/QgG%rt\ tvB<^߭P4)?\Trz~zխN@-w "e<b~!H#%m G쀞BY1wr،\O,_u]/r HUML  X5柝 GH|MܾH[l(6Ζ4k[^Emӿ"GOE)(dz;]z )hxr$X&ԭb}wwc;=k~&,A p[ex'Ϗrbr쓨:O[ +_٩+ ޼ <-ңkw3)s~AiڍDʭz9zP)A5\WsDk RM%c)U<6Rұb|D S8S8mʔ -YǪ֊IAqz8msnuV>AedG?xݖ/>Z>`f|7cڞ%dos'H )D†#2^Hȅ)NB6m+[&d&ϋ!ZHA_D [QH+@ Ժٴi[Y!W$ /nߠlXD([H@|BPXTD`TIphFgyhºCqTH)=+@_&ق;YRc(A}ܸr Z$oAt8 ЫT|rŕNЈ =ۂ"pKc{0HI8 ).!bgr-[[ : +[!G!>BCBE7փЍ1R "B7詺Z+@{-{w9E ͑"SbDa} )uhy=\ƣș"Dx4 KLCI kq۽ ,zs e&?}\X} m )WY2=Wys[7>;#u_47u-FJ]P8NDbۢk>(j'9oh n犁/o1k++UaIN̝^kyN~Q(Q"e{r,:qY#5%ތAj%!JzLCJ^hP&6;n-t㻈4t]/]g;Kr{[gƽ>Mu>>,g=Q1/ٶPT4U}sEQڏK:Vlѷ:P/Kiw-L 65,.hWW| :%>r DL$8}0DAJ_#7\[DR|H;"(^)Ly%K3;vy~tD*j ޘk"~(KtRNG! F47^mw*lKM ([3'*l"u ҕ 7ZQ8۩OrnᲮ.%V Mv UySP]oݐ:םө(>GQ{[n[jt87ِQm~.4$Ԯ=u7)zf=OJjpcU~0ԖGb= vzkf~l}cUʙjkV:wQ'?/`ZBI{ ;#|4z(DtLkm^Clhx$4(U@DE^sY'@bjZb컻g)1uڹ20][9R"?{>LlhF$wMSN~>yjm'튈 hxz,5A rv "0o>GCa kDg$gtmMn{׫*"5H9K#U'B4QG_h&% Qh-yW"=c(jqeǵWy"fKv"}BoD".D8m15Hiy( "3i}~w̿rSB0!BnH %v8sYZZ3w)ڗ#2<̝srC5tΟC]nunrk#Np=۝~(4Vtv6"'+FK<"KL‘Xx4<c ouuP~WMrdʆeިڹ Yj+M>U7:{ tģYAܥbEzm˺C*VWBb*y5`%IDQ/'m-dK2\Q9pP\; akL.JP&h3P{Z*Rko/ U-(8X2UuYd<8 }%= } T+N ˥lhk*n'/z=X5O#V- Q#RN XFaPز><h4+*O"Ǜ^; Woߍ iƮȑ"p {0} ED'm'#+D X%ND5L,4@H-&:ܲ DTZUXYcr/p?фA  rYK RI(Yݸ BsTHS6l{"R{0M砉r{"7nL#) F!ا\!C1RNv.RE)?ATwǠI}U*@]H,G!"s=RD/[.`Jxͽ…ܧCuX #tt5*\]'>9(DQ㒮ӫ iC~Y𙯴0D! gH|WW]j}byfUgڢI )(Cv?tnWk}oG$Rya(ho0^ʆ̻XˣLӻ!W$oB$ϑv+&[{DgS|u-= 5Zkkn!%@Zcȯ޲^a逢, ѵv+rkws uku (t?jIf?SVvAk/:k?z[k>s.(EC1BƘ94!\ˠ %tї e>&.m~R^;2)D7ڧхyE_Wl;TUFN; t۝\9}w&"DrII<4Kw5,y*F%ϡ}i$nsX" e.F_I˕=vWAG*aD޲x q9`${WCZ˖4MT1(Cwz$(F5G^4k XG o.^2[{58$7s?2y@0ŕ3OBoQ+ZvsγXT25zeBf}_f"µ[RthH&eh`4,r^_?M}|~_ٸ=XXkc>C#/ӎѽc =2-ݪD(Ot˗+".e"%/ W?Zk3cL6zc'!2to>b9pcK ccA2kcL1-~\'sD@DtNXmD ]="ĄBd1=Ely"Dq<r0$]PMAG4=}gB?@Dfn@jDH PH}zJEO ekm15ܡՔ CW[]9|-83pY{:JEɅrq }: y@7ǵ9aPH,1vCʒB*W/2TԿ} 3}&'~lD03nlV} ~Nn 2‹-ݱMv۵j-YZ~>pkݹ BD_GbBD"r5e Г軱 zpIL:,Th U3=o祻t8`h>xk:)) 4 @O{l(4wu3][=uREߋ>"={kmr1X%ɐ OfyS1Dsz'2=p`e=v&lTL¬Ñ,=\/ 48cQz) #ӕfj Zfv٪ߺcnDj4IƯwbx4<)Esj&:ﳑbBO_y;?DaWi@DQwy$~I'n5ߓk 3)0Kh vdk77S>,ued?zpbQ<n*+[D+V$ܠ ݣ,"ȕV&:t&rE*ʪ~k}>ݪjnxn4ɉ:lAl UMt.Fd)F$DFOYo$DLuEc^@v1&Xk8s}6m=F"?x mSpcL1tpa˭i5I ַ/Wش$5ܾ\:6.=S x=Gói0im : a}H, MϠ|RBgPN-A  @_s#HaƘrD^3|DNy}]{W/49hoi+">$d[wօ71;hOkmC+P}| K })Iֵ/c޵cVSq0G~뱍 vOWv. 'ֲاkГ~Y< ǣə( DFPH鉈k!e:t`Ait*-G@QA3Z4KD H &ЄQuGK"A nq.7L!@I$(AxL#;n[cɹ9 UF 0uc4ۭ$Kp;丷ۇa4nBo;K>ut-wc6GۏLDGu"]?N֟v7|)|m\=^[jmn[:imGyp t>۠q/ی,(\Q[? fT?j^))  kܠIMrZ\T:V˝| ο tCΒGwm&oVzz@ o[]{/ 5~h4i{}.It: Z1+tN2FzXszxZ;s=zj [kcG_܀l$1p~qj[6*0c~Gά_o{kh^H{vOq!ubm.z& #>}Xnӑ|*%Q&U!4a=e_Qh?6yR>GLn/ku2>BDk EDH9=Ope W;_kwRKNwQ@w[o;DZ/뻈^n By+ɼn<ڭ(L{>- ~۟sR 3?C$<4npnr͵?B%8ߍp5q 0&  o> kPzb_wǣᵚ3`R<e\lr(Ga6z9"_o ". ݼ&n( O:rBW"b}z3(L7U&нA\Hbow\#PHwnn?GaבjD ww)wMDbR^[)pa=oJ0r7'~ͬI@eA "r޷E`5yw=K5D=%eCWK#QH%s#-4Jڧ,`f<;KamE`5UӋASGN? YzO޼)֚O޼u-+bH,q2?HU 5\n "t 1 +2#'ہH,&uh/'P쉔GDO"EU"Y_=HI]!L+QHLH|7퀕/"F@ĭ"s5pOt /4ۍh܋(ܸ?ehH,qHUAg%R~HDwA*ؾO" TKs %E|T>\,H, k598O sђ\މ>=LFd+[ҹUsj冔Qok4NygjSU*혛;gz4uq[ 0 SkkVz1日K7п{.zxx ^8&Cz6"C<M"ΈD(}dRDbZfBvFbSqvjyڽ#GNRC *yjX/G\x-|pe=V=oQXH,Gy8̭D:mvp˕!x>?̎Eh K\$BTD!UŽCb Gc G|4 Ou=Ŋx4Tl "Ri#TˠIT[6TĵXH]yZTl-/$Gߏ!9T߸ʹW`i[kdIUyvi`3y+Qͷ?bA4"yA< )fsdžçNoBI74aF<ģeH=y/ /BߣQȄH߽w"zGoʭ;vyr{>N>-w#a-,vCĸ>ϐbUHչ;u;x? u?#ڭg4 wEDhGD3KHU< RY%;O 3xGD4掫)sG0k6{ԻRܞk- yޑ%a

ֿ>Wlp`(=HM͆uqY'kc_L+t^/ho[7k&q=pp|ʆDHPUAv7|ředꐲ[L:/ -+ZެŠIX4g]BPp ײG#+bo"Rr =)l)KQ(-PMH ţ/eX\DbW*ibz=5~AA DD.BjDn a};Db n|[|ᮙ{7UU{Q*8 LDDVwR6\ Z'ﺢU>xE`2*ȰolhlW[ #eCM5dfzxx42<bW˸[gvSj3hBLt QkhGOY⛆;mpeeץ7<'uGd8Yirޯ$2H,1DfA.kdW{*QuFHʆФ}H׈LAsoqeԕzct"* ڠe "elbR@AC"^o^o=>DDԛ4"aAByHٺsl^Kj3~=D$[e)ژW|u+kٝR6ã +b'Qb,FbWX,KtDK\O#ժ_BQ𷈐|qx4%NY=}" (HׂH,1 @} #*8"K̎G e\T`&K‰d;}fR7J>HF.kY,^ݻ(LM:"nGj"7$?53urikXK˅ݹ(FX{z*2S.Yp M D>>kS6q$[s^2mxw$oD%)Z!l}^;}CLWxlc3D<~.Kꆭg~Vq(/"G,@*[~tR7^m6]ʄ8;V& ck+*eC@I n$ a6OyӲ}3gMq}=Hekyxx4}xXG$ 64JCGWlRT*ꉈB? ͐JfC/J]ص[g3n섞֏Ca<41:TI<4y[YT3, NGߞhg:Ru07 }zu#x# p5!K og:g`D<ޠeAlz r7QQD'7~7\وVFG&wnkX8օ_ˁk**8O-YR>`n@^L|AY 5a8_rYsx "y[GHt#,?|5Gօ޲*4`? yN=\-%Fm CaPɌfw-aWTJ"H`ǽj""}EJyVAp\c @?r" B%Y@,G0gMrzY "vF| d x\ h= A1KR6hԢVzW%E*gOA!Q}Â&7\ԓ2OĚ0\q(Z :""g?P| @Fl9ekģ#-h5aHq&]jmϠV?9U/3(\yQ< M#"I`BVIDATDb5H,.5vPw쇨_P^r NGdz~$E$ݔ 1= + yːry>d7gЃ1` q$,IPڕ%<4JIAdgʆMc)GnPvG'bMww}G9ƣٿ#Ļhl UO n.͌mV_9KeʐSd. /rQ7td+q+b@S\- ,mPCQ ePvvER?&]Z'1wj˽A,B6:"r?-JĎf^ eNEu n^FuG4;T<} k~ׂ&YHmo?Mr`ʆ 5jr sJuCwNb={xxl9x"JT-]CAU=  ?GO7W7@V6 E5#rU%ZqXmUx4@(sV_|JG#U"]PhMXczǣ%XuD@77 K9?'PDV l"wrܢo Zq[RjQFhgH1}W<1H)~8=$dP)nY DɸR/A,F]4#Y hDl#[YH,Qmyh+8j7D][M&np_<^NdU;<u2"`V@a6¦)m#D݄ ۀgen2HZڠIؔ ӈ8|}O~-<KEMPxL0tWְ zFӠIA`RȷXԪB՜*TeR Bm)Z4H5|<|#eC e/C /F["xXB ׳s(YORZIeSs:e]Tqgw,++[-:oO7ž %gÍ}"aAKקlhZc54ɾ8rԤlȎ]AX*kDRG7.44|]}_Bߗ|}W! ݇ŠM(r >y 6#B=I~Ն=<<^cs   -K~?_M~ve?SaP;Tt?s%MR{GVI4ɫ]@e, Xs`.E$vA<e"B!),BخFS9լeXb)&\k)j%jK սjrfv5|Y[>MCm[ @JSӐ)D4zdPx&eCn̊#0/eCoMrT-z) ~*lPdžurJvIPM֣cZõڤ;…+.zՂ@A]EmTU1]o#&M=ƤW,3{+S6$hF!Gul<^x%#ާ)Z_>nI^LϠ~}3"=?IVJ[_ãq)*6:M>I&&y T5@J0%mim;%sv[ v?0Jc*疱H5; 8K/(nozۃW%};ţA ]hdG#%NBt  MXgS60h_BĿB+*/Nt 4xB$;׶)dNvBG㬢v5差OGaGr$ :)<:SkWf jK$ˁ?S]4I-bE<A~~j\Tt} C)Hj1"yWg-jR&[^hdl_V `oՈH,po߻Ϯ.S%rcq@Mǣy(7*&y/yGʆV)&hDEQwV>!OQ|Mz$kϚhd&VĽg| #" r c; pdT#U@[]>[#&'Qhܽ-w?G.Q 3)ccCMd*QB֙tGp)ǐw*|R Cs\(k+@qF-hHTKէ ѽ5ӑ2A,dWET"up\!5$Eɀ^c 䡮XV jqDlǓF8M } #eCM!<&=<\39p^b!#jƽ^AlKN@~[k( ˀ@/?%?;@s0Y覔 b4ɾȔoa)w7h,xGkos E>(*Ig q$#8;S3d<}2`+%-[&i XM#{f4; 9G4ɱi^ Dã K䛼)[g^C^ӨBDޮ @md6-x"hQK Lך%Oo3LeRͽU%1׫fVM es] 6l!\9{&yڏቘG@$(z3k+DNUwE y* 17ʄ/ ]EJZ7 ,KS٢}Q'eC+71oJtb췚?^9+R|B ːҸ6 "Dãi ,[PHhFy^Өx)e+Q*"YDE'}tU#Q+Bmm$ \(?(呲I@Dj99p1,4EHAH!\f}&Ij~!R:"b5z=P!ؙ=)4"DOpfWdV 9P #"OZv 0 8UȮIP=|VH,:K%JsH,UapYAD " ӐI% )WQɅ bv? * 4Cޱ@K-GEp_~vA!ط# f'b["ӮZ0Kǰb%^##{H!ufoO^" !ǐ <q /H;?(r[/LGp߬(ER!wB?8Ecc[zl?|셨ޑXH,֑X04KtѨD!*2f!??k-dB# 7V6Q k/ި5(,d9IM]ϏEʆlʆƧlh۟<ϨUA[@!ݣP=t2GE$(-ǣjF-Ktq4FbN>g_G fQј%Xo$|bZ8D"eloT^5|o '\%+d/A*AHQp-Fᚣw"E)JvEFaϚhh%GYb &2)ࣩϴڭaFΤ/h0]ʮ8 .TΠ޶nߜZ)Az/ܝq %; +V#LO=Ϣdb(C ral. dV)ZZ&Y ׳1xW4*,TP@׍u?躪DcGjbլc+bM_1 ?/i_1.ORx 8vy`3 kwhR'Ka;C [̀ 4z.e _ ' EQ԰D8oF:Bn"m]3:K&ԟ@<)I냏Qf <́1@c)"ai4.(ty1"Q `\$>| &H,)@oǣ};~-'-^Pҡ`4Pce | (ұ% >16O\sA_Ƞ?CP 4tnӁU l&ޖd]ɝ;5U2 mHPM$#U~HT!cr! QfT= &Wtj@c<([\̪qU  @%A6}>gNFdqd-I[u踳?Aa+ Cp SbԔ --15@ax$b)Rf"R?m e*v˿~UВ ejk 3}"DA< hzBdUHlW "e"ep68YruC!*F3O׬[B*UQr CN@O{Md$hp߸ կ֤‰#&y "!Һ^"l"C"xG4n{xxlI&H,8):/7zYo0\GeK4Cl)R3rt) ,y]^AAyP`hS"aY)R6e݁mFl6˒<- S!"yѸ|`,j4DU!BA쎪mxXr8}24N[ݑVMy4IZ`]Q6c>pj)cx MPx D6@ 1&׀ e0hE0# 79'eCݿK;|ڜ|8 B \T`)r$S64~J}4lE#K[ajsXX^4>x큎 1c'bM"RUf#z) ]+A-{FقB|{L;r 1WRd!`S6T•bkr#cy"{#-ѽgY*2G q? hH$_Vlh{"ĭQqୠI4#궎x̋nFU+QgѸ<Íf'bMK"dY2{Z)0}+cпR4LjM"=MQ k8 MeEyY"+;x<H9\HRGe,ܲz&HG>/@~dg}= Һ{YU]ɦH7Ei&'bMQ, ׿"*4@$%+RcjPDVҨh)"&qx4ܤUGtBF4Tս*G*Բ";kDZoB |e "vceQO hmvD[edPFE$!ր{m y4e,FJ\>;yjp%AH)tH\m<;Kš5h\s2FDt< CTEȟ!nl "E&]xV ԺM"|tr-RѢH(YTd[6܍clHlA޲)by4eEdd>%BDpBk Uhpz YB0"KLGklq% Qx* o^V9 ( ~!:K6qdk4ڭ[ch_C>z/"ik"Er Uģ&F#PIoQ]=2" !\RƎsO@ͨBg̈Jd,xenٽ/Kd3WhlQlh*DD!9P5(| 5펣1늈Q'C)JX\ Cσ!u+rBʆ.J(a` |TH{ 3eCW"@$&Օؚq ߞ;+bMuHqOGp -2 E-%1AjW)D Tl nIʠIރÀWY/O4ۣcnyvC3ye\4kV“ MP%@$KPv `r$#)j`"_6~xxxl06ivH,MG "Ж\gxc|LPxl }>;Ϥ]9`JYCGLQ(WMA,B~Hʒ UW9ΛDLos/A?( ^ꆽ]+#"IDbȔD!6ֽ2eCuAl]V[i"єQshxq$8 RTnDBZ;""hKq`lႏ[t8hE~ÖF!i6 ?ecān O<fmO;me —lhi$t='vv@J[| C9{  "2<46)Z \[c'bMP%Zd&yeQ<^J(ԭ3^SܶnE H-_?k4Gb6qA~6h|<=m,D*as˚ &e.Bĵ(-@.܏hy "g-Q"Į DLDcs&y4IB}P* %J+#(8>KԿiȆʑ3"wS*Pu *{62 KbF™?B[P+sQ!HLA!S4enWd ҷ 3^ F `ez0ʀnLy^xҐ dwvh舔4x4ґX"k4Blh-~$'U"`M&§+TBy꛹tDǼH U,BehE% ,۳3[. ҈¸Ũ&VhaO ,Bv*"Ml(7I|YmwYUdhKfhTI&H,[^C |('2wA4e=Fhl&ׇ9rGƣc> )Kq;+jP( %,!H?E$x2I[~Wp3rO<"DkT^a7qWbOpZoTܵ )8m)"y-@d- Au~ֹOE ѽ@^$^F*PW8cU:%llMND .(T|}HxDQ}_GqO Ѻ#5lL{Daqh#"" dv(c'bM X"F̿Pp(R_,*0)`o! dE,Fx`:|<Xb26)HPXc D0v@/#hXIyE$u0eC&9)`29_Qh 4tZ)CwP!(D<sPj t~n̔ e6yxxx4UIj`e&Rz3mPyyZ]wZQWdFdDZ,u"`c6j 7^]uHrrEi?G(@jdA^PИ(Ib0wF ""x<:O_!nߦWlhۯH);uS ohp+Z Rt5"RRW#qDDe$BD`4l<SoEH@k=x8uE lDr[r,El"Si#BZV*3+EJX 9i>INLP&eCsPա'j4f#ã1l"c{x 7EBE_4Q!>n rWPo 3xGD 4R6T;hY'Fʆ d4єxyvCޥ2Hn E- %."NQ0lEKD^@$m,RƺGb2$ /YӎGb]|ZeOjGT^JW!Ru+pʸ< (4-:} FۨER-5eCf}&v:rLoYH/ꊰ (b0Rц̓&9U=<<<y4Yģ݈AJIaEPФ~7KT(Kd G/Wy* ފpD@0H,Qj5uTZ{MmD6F! ԱAc 2VW,;Vڹf}u0 GD>(y""zah|jMA, d(h7{xxx4 <hx $ׄr(5L,%ZmT"D':" ("pÇ\|`WeA,ቘGSlTG[sr>l|4OB!Q<{eH<\W\7 6HJX<%@>%(3 .JǡH,5̃(qm \f4D^B֩ȳ7)Y5hFݪP(6!2W"@П 4X*ft8K\ȣEQx1&F!EWh B5Ʋ| &MB[Mz4ipq("5fDv@D`7(8-? A⧨HDjQq?~Hmw?p r]Ec',۩`ꐰ[w *R4=<<<y4i8|HqJ+:= &@-uQg~C+veJG6z ) eJ%{Gblif"{䍊oaْ*任+FCDX_pE*D|P@l\( <!b=ϘǶO<EYxyV5.9)[/K>W֭ݷUl8oE$ 4w@U 3hN~H*TlRn BYu${M|yʆŨƭ"Wقm8aq v!K2| <ȯ=lLдr-jB!UH&')ʕ(@Һ/bU.BgbՠC"UPgy TAx=A>Al'H km"+@dm*Iyʦ1ADv@.}H%KCݟ9櫮||8Y["D{y"1Cֽ?›?G 3`݁ihxj)> z+K޷Q?Բ.ghx͇IUʆ0JvxeArt!u:!hH=\BS6_c[t3_.?cJucmDc+Z;9*z0|T([)1g"el! oVg+ U?;H,@$"܍iH</LCE4">I d>JĈ]Ab*蟉([4^v>Vʆ^DcG$E(L2[&s{#UB!0]¶E+[gD؊o*BPf!/䚊?Ye)i1 EFT_͠FY 2tnDdxkV1mX?/CaE($v8RP(RVcPr{j=TIHyTԠb 6"%R䲽&B!ӃhxܦI(xʆZ#eYfK![쟲=<<<%xVH,|ʾ0AfT"k@j ڼjGްȜ6䊐ˆ#E~)m)AVD|L::eC)c/_UM ,Bv)ԲlƶHp "aPEa[C6 Q G$/R2Ua(<;nWo"vAGDaY;v"c= ^ǶjBj>"b! |DAXo! cgEըH0iܶHe^1)<Ox4lQXtV;B R6~0 >4M  "(DX*LB 4W"k.2PɈ~wD[\o:d2 *TW,}jPX /K\|QvVmqؚ %YcB$耼a%( -fQOEZ}ߦ]P+)RKg ,R注| CJhTCk_x4 zxxxlDcC$w!(xpuj33XsgiAvsI1)EV6/Bӑ8OPjRJPزS<<<<<3x"#K@ơlPtf Uh-'9ka?!_Q y!?P<.#B-[ߛ R@yxxxxl'1})Cd(Qޔ#QS;Bd;"nPHsw+Tw?u٢">*ᱝgMzlp?l@j(4# z!aHuS–HJ f#S~=>C&}A]% 6 xxxxx4=ФvH,RʑcD@juF{B@b_<θFw!뇈V |iߢC#Ϭ̭{,pY<psGӂMzlWGS"Dh!_ <]UW bx9,DuȖ!-݇ EdsTu[#Jã+b%"Dk`y<ZWb*EpAa˙@U_Ӻ"em!RGahxf8$yx8Db ;R=Q{UYhx'bSB<2'-GçŨi4yxXbp.)Rªx4<1c'bH,a|UM O<<<<<<<< G#1F'bO<<<<<<<< yxxxxxxx4<h$x"HDãG#1F'bO<<<<<<<< yxxxxxxx4<h$x"HDãG#1F'bO<<<<<<<< yxxxxxxx4<h$x"HDã 3d IENDB`openTSNE-0.6.1/docs/source/examples/01_simple_usage/output_17_0.png000066400000000000000000005713501413546205200250050ustar00rootroot00000000000000PNG  IHDRE6AsBIT|d pHYs  ~9tEXtSoftwarematplotlib version 2.2.2, http://matplotlib.org/ IDATxwo7z]@ bDTt4F&bK1$j%LL{c:VĆ(]zY`Yؾxʆw} 7 9>}0 0 0J`aaј(2 0 0 Ec0 0 0"0 0 0Z4& 0 0 hј(2 0 0 Ec0 0 0"0 0 0Z4& 0 0 hј(2 0 0 Ec0 0 0"0 0 0Z4& 0 0 hј(2 0 0 Ec0 0 0"0 0 0Z4& 0 0 hј(2 0 0 Ec0 0 0"0 0 0Z4& 0 0 hј(2 0 0 Ec0 0 0"0 0 0Z4& 0 0 hј(2 0 0 Ec0 0 0"0 0 0Z4=0 ?;7ScOiaFKDaFvj{Oaa$<{ a-WC(rH#U BYUӁ'~b"0 00 c_?TRֻAʦgk|n &9{,`rK'P]S/c^8Q?a+]a(2 ļp!0)榜 ׶tt$ bHy=r7q+XzT v4 0=EaA {@ 'M#YDR*@߹ )\;SXJ4 v?OļpIO,ڥ/0 00La| N|k̓1/|J{ XyK??g!ǧ4'^ܡH(-@.Hx+ tBex~oda6`0 cb^8(@nJdV4慏BM+ ~bn /r;4Ȉ*`I {1/]HJPY !qt |7}8 3+1㨟KNaa(2 _C}6@7d' w@E A&xdޡ˜nrǼ9?g#nB GrAҳ (Hw͠ϐaaA(2 _?{S}`sv 0 cŜ"0Z,1/|rd#eJ~kzRH x2p82ߺעB6$,BvP^)rƣ@O|ർ}Fa>ca- Ead `.*[^rmRHT!as>XVEĵ1/<9F_!3at(Is==gcfy"$p]Fc}0 0ZaF#ZO,@es_ r&p.rjP9GJ桄|m =~vY \\ct9P@yOTG{b^{ ] 0 0-`=EahƬT&w"p, h5r_6 G8%X` a(PC_ a(-#PR/b^8D%sǡ43ԫ43waz@Ai㺢>HDm9*J:R}QP|}LMw⨟{ssm4*5BBBo(Xwsvt)@RQA _Xga- Ëy,6'9!s EoDnQp *k FA'#]$pNCek'u/ϐ 4#'Vż-@L51/A^/F.(rE GQ0=(hH =aa 9h&ļpM%݌ʻ^sl$hף~b} @uP[wH\Wlp2׎r){Yꋜxa:w+ x iP_RG6D 9`_Dԝr)Gw3 0Ɯ"htO45p=c^؜<iq䮄Q ``O|O?BrPtSO|s At$ڏTHݏDRg-5kr򑨪A=D+9U FV;?Gg00 %a0>9; n[&9ʌļ~H܂DE;v mJnw!41ļpk UOng~UJZ!'@O%HTqx$t@1$v^O<“[!7 6Ԣ?xaY,08d*gjMԋ`| .m,w$jPIb=g"Ht~FtÐHdEIJXJպծ<T ps@f#'rwtO!4/Q !wŨt}ru/yǐSE_D~K\"0 㿰U3hļp6gX/C#CQuh1/<"'e͇:!G>(w| Pս,QfOpkƅr\_qPiR87gQcQ?Qq$ \ 7B~ >'ܱ?8ܱsP?M\fDa00>mQIU)wCs!\'6ll 4p܆R^GnN[$`j3 /?Ιގu=EnܢAG~bշ ywDDm99n{w{!sd猵Ə,?xL\h% 2 0FM{ ~!w`C(vy Gˣ~b\# a95( n6ڛ'C:S Bex\%ѽJ{qt+˾8MQLmX4]4$2F +=9};f9U=xl%Ս:00 YbV p-^ v(''VƼb^'dlOLy( 9]1/<;'&5KhļpO6j]h4r:^B¨-{ymޮ(Hq(z_FŦ{mzvA e"4,/}#oļp>p8fOT~(-r~wQ?1/:݉J䪳Zz=nUm~!c^p%Ł\w <f /'5\; -z?W}E(1<ѸC2 0+& {7!ڛ& /&AۭoEilIT&@Cȅw|Bc,1e(9NwFĢVn ]^Au+zA=u{m: <"gDpܢv$pG0rnhm X5f⒚zDL\b׼aݘ(2KO9hr[4JzLjLyKCaI1 yDDt>3PLGHh֡`SRDޘ IM ?=|jԻ!Ѻ9O$nc+ؐ(r B%nl^O?$"*2d=Ǐ,06P& 0"h@)tkb^(MQ T p9p; M݃n \9ƞ em~")SW}Ҩi?rN@c/t^Ep$ Wi<*; 's =Z{uPWļ[P5LB7iBgM ߏ=e@{#A{rC8aHnhļp|z`~9aTNM?EN!8Nxk3}iGSrCfuݠ2eC9G˥&J:Ҷw9E[m(:"PR? U!w"ӨՏ%慇 zOuJn G@)żpkA{[ /'螱# 4՗ ƁL\ba; EDyVh> \SPIVz/CH} En?>v{V]08#dD F)t>r{A. n1/wxb^ TCWFS ۛگh( Jxx(nk~U~m G e/'Cdal|0(Q?.慟kPz,>d J 8͏2Zu)'ŨX<{8:/ !SzB pi3'>GnQ?1'慯A *sŶg媫gn"=-<>fJw?Da.Da43*;_ӭn{{D"41~NҲ~ .Fb#X̌:u?fgkCP:|/ D}P8w*GneEꆄX$*@=D9H但5Թ-$m @{;Y\r|="Aҵhߧ (9$xfNE>qPj\/BepG 11/\5d!3#}j;ވ9Q1/D` &K(P⿀ Ma s ۩0pZyl=HżoІ]Q̏*'s!51/{@nYx@4k;|䜨x F%lkXr!1T7\Q`“ȕD`*+s-\InY1FiQ(}{vK$?}܇뜺Q ].ړ0 0v9F_Ie ؃< $(@elsb^Lr"nGMmu_{6_x ǼV'XQyCĹ)H0UӁ]'(4=ƾxэC"G%v{mIt&T31ú=ήd_gpaSZLt/Fc&.Y1 09ELS?,îLF{>5 J 89 0%HFKD jnMHL]Jz0ߣn@VO| sinO8mPb$^^J8pC0C"qw)CE;pXwNFV^hC&V A%[4A r9ajW܏^Ax&kҬ[_ׯS~"¯l3@FߟۛÉLT EMg"!F~'gC~]E^MM&<7~dq:<~U)"m$p "0 11Qd̈́d$ -OQ?QBIkch\)C}:Q*\9*YF3K5uRCai "wOP(X(3Pݦixp_\k+MYƗ^; &?C'e<׵2VE5s* Sbԍ.::}zyQtAGz Uم* To< {8w{g6؃0 0 Ea4k~bW]09 "|9oGDRچǢ u7e]z~jE.l:??wA=N\uouUȅg9Q?1??^GTzgB́gʰϟ*h?UaT0Qdƞ@ǘx vOho!%ŭE=IQ\>g(;w(΅,Y27PoC8B/WIN{cň^W/n"tB th_ǥ>?ad(`bM#0 0XiNE%l SnzFqkP@k攢߻:>ڜ^+hުEÇKfF7C \;܂UEa1Qd; Axdz+pظ_OۨhRɸ|$ܵ(:x<'JǠ2c YN~#d$?p1/ /~%'aFDa$b^8MˁӁ(il 먟Fc8ѷ ~bT#iqD Cuܢx(n b1/|8Gc@2ܬ+Z kżp8avBEY8o{g t00*'^Ǽ鈨xacd7ǖPoPxaa>LN$'yN)hr܇ A' IDATnPY yH PA p5=?諵Q GԐl#n1/<%aƞ."慃@^\n!_0QdXa":v"#pԢcTuFVsSd Ȯ+nBSD1/ȨMjb]v`TΪEk2wymZm/KG5 ck7iTp2Lֱ440f9EX yW^SQVt9@"ѱ`I) ,.Z׻9^N^_AW{m45pJN\~Á)cOzx0z cs񉂒ݖz܍n|x5^}3V_pckkq]\CL66?sCK^ ,c'X~KQϸI:n A;|“560=+3]W-c ~ꌡg=`H3D76'eu*]͚~#W .|(еhW[F%粮 !&*7i s^Y W8aDabԕc unn/\4a^^mj0um?r݉cӹWɝXw|}w 1kyMghY4@)*k6#~3vqa{V>gA J{P[1ozW+[wF> Or}v'K1tV)+8_a0nҨˀ[T  { W6 0T,h0=8O)kOԦCE__,88r,%nN@Mvӗ=Ը0`ܤQQ,%o1QdÈ3w߳*/YWy}?XQQ[?'#4 M^A)s_!wH`^laXa{s貞m{V+x|pKo,E# |bGf끉HpV#hWPnAP@(>'6Ƹ 0t)2 c OJu}&(ySg9œ3^% 8&7K0@M %4Ek Mu80m(| >aV2gK1QdAVi塭f:_|s}t1v ]槁Tµ5Eهfu\_UQY:F~ ƸI4r`,c/p&m6 hX$a͞{3N%n=o_*ݒ}C'#Uzȟ]5.nW~[7ƕ=*E LsK! )}a`Na͜P<5Ȼ`%Y;r6Z[=h^%7gWyZS̴R@vIY^iY2Lt}X\#Q7еu .!+Pia"0v1/&Z*'viezH3.ݥyܹg=7PYWO~hyy%3nU6y5+?d߮C<^kWMK\sD1nҨ"0C@sy8 uaF=>gN!_FV3ZHo!1Ֆ2۸- )H2!WvKG Ї7&#AR4qF < ܍NfBd%R>90FDK S@d$X(!==9gfW~PP:+x(eG~MFYտhC{2> @@*YACd$xr3nҨ1 ^=wUF5f>aa9|՜ SJFK{0Dڅ~%pc#eb^x0(Fļ;7_(z! m㡟FŨGm`x5uE\1z y\j/w T׬Le/@BE 0壉lG|#OyHp[ P< ڜXvBKFMމ풓M`)ٯg-Vtހ\J0 `NK S}F/%#]-zxeW=ǎ* =TՇn噒<OMÁ(V;dV,\_]}NusфX8N_1x PWncѹ*@"wȝ%#Ex* ߕk/fZ r^HA>mo97l[#Q_$h ^A"rS 1%h&E(jw;'7k=>u./z]ykͮ_݀'9n0 c(2 l $#mDx5(CNS!)0M`&7lM}:*Ws$dkQZ$Vn%0hQSUқ^,Zk/;w]8vr`1 0 &&C2 SӶ1n6>=(~&`mPShVTtжe;f灹Hv?v숽/i?u}/hGd$J01 x?z/?G{\ҹgNU:7 6EzІ#H݃} C$jEw#qj%mHѦQHչ?KQ] 옍\P<rZeH0s k7iw$%rNG#)7)]wX 0DѤHF/ڷzIڱ]x걭qO\Yh(G}%+V9M[kh7o8 mpP=> BԁMgegX9 HtDB$&w )3 dA J{-EbE Qrl^B m7!0 MÁ;㻢 sV9ѷ8oyZ6-1;| |& 0"cJf$#o2 Myhbm$f P9ڪ;"hRy6%#rkQ=Q: #ëy턱J^F. sy] DhŸ ܏\ŻBԥHkQ!*"_AA{ \op[PRyrԃt>pt?EHtdPrϱ7oCB4*K#(]oGW< ;A1Bqמ_B߁@s(95 0EΦ#}4)}|swp+^2z{"f.Zq~Õ&jOXF71yx& .!Ou.@B)J': SYOJC]'vP.c=p Ћf}̌yn.iXiaHGd4)4qjb;rSYlʾ:r*}[b㋀x*ui=6#W#:ǵOpAn h 'w_ [PW݉ uc6o(tǞ X{h#\ļPr[6eh`R2An *C+Ё#HPe1qy<ÐۗF%yU(l}REC@Ľ(ٲEDa[9ENOFndžmD5dkNښHesN= lSlF S%#5[;~$~ |oKC #Hooj h~W-V{r^GeHI_$j/Cc> ƐRxBJf#gWrI\*t~]4Z BejGP=f{sP\>Y%rj!:4wv7i8rs^\LC_ԇfa_cݬDAx[pcrѤ]JǺV⩎P0nRVEכ? - 9 )hB%8..+2 VG 6KWJF|C sZkC >d$W>5. QMF{܃6&o9HF B4or⩃5D>;>9sm)T!'ZQA6O@(Ƞ\f^3y(p% JX3 @NRrzJ{K%k OP`'4UTpz/@.ӯP#[{N 0"cq! Z:| S?LFsܭ#gHkr4m'ȅ MƟVYHbddDHSԟmtr `N2^SSHkAPМd$_iUܰM:;!!t* VάA7C9&7 }z^m78;MyhI(ha{KƬ~c)fC(4]-rA sr`ppD.>kѾ\ g }{ER$|<27fm5H>DHezym<~5u@x&Cx & Ʒa- E7&#ñ 80O= ݑR4 rר[V~_:A;^O}>4݂&~59 x"O}&kQ(-y[]I_kQbT% ZW -0ܕ~}> 꾁ѨT} ١xʝ$AHRo*z;&h'L2pjܿiGpnYQ#Bq/#!<nDI rL!W) h3șp_2|xPG~T"!5(oevW~FwTvg?vc_vTnT#Nw^o.Zg'/8KDPaalEƷq6;d%,G\,o4tA%2o,穆ph,7^ס(^gOB87ǐ+E(-k Ze^׸ Sݱ'BT&rqHps{7awf& SsP:lt6=^-*&9C;C"hWԥ֣i.Ԥ?I0 C1Qd|k4ʸɹ͔b5mTjssi`Y(7px(*qu9Z M桦%.j)p0^J}9aȑKz'#[_he7jؿ.DGf栉L}(jzvg)O AuoU< u M+ssGqwBT5v'#:' Sx*וeg;kg15?:'3zPfR3=d:L#q(rK}$Wk]Kq>BеC ? 6 O;r5[D72sڷ^p~5蚿?2Pٺ 3~C5ZHXNIq O~ēz=\L3 09m|#ne9>ۜ0Yr4~0 yp!IFtF3fi &s9Q'57^B'\! -D!1uEÞPJFNtvZ%#xj%s}ؘMB;;V+,@}\ư2O&hZHpKkܪn].ڕQ9 w]RVs ^8n!rF "5fk{X$yC69n;VٸP}fOԧ7}nK~p@ķ]aQ_"Z7֍mʹ3QOn}V (}_|kh Ni;y(`d:izrGD?=OBSsBHaG`5{pa ۍl`վȱ_&0?F.P)O_$|F*Tb8 ֔ϏQto5ZN왨|,T&FÒ'(dO4 fM*odɒ9+lp_5~5W&p\&!' IDATܹ9 8M&LF}z>k0֨ҹZ"}P<=4~=~&qN1(E.Qrt$.B 9]#aPp3 ں>3Rh;Hp}Hৣ"$@_  6-@DRrYl/:ɖE {ǡy㑻 -@%|+ܹi^w+7a{ی[*bğ&]Є$MnR>@JBm8x9Vb4z ET~׍wȡK'#C8{!a7I毵x4'ȕ r}&+^,_&2bQpy)➿ MzǢЪb&vQ($fCPc9ZE_g{ Y~뒑755ګ9)$#x>$Z HFB#T7&#Ox zW#W6 |$5v "wuEe:9No:臜RY-\DLtMt}=o`q;mw800}GЩOOW35߽;Ѣ3ܬ SQj 0Z&MD4y>B"[&`+~&(E|4i4҆̊}^TWϏ眐DNNA9hr4MdXF{%#C $~WGh"W3圠b4qk\㦣=n,x/.=9[5xQSyt{Au ]~VƯt?ҽ^(s4 "O]!Q]^B%V zoMSv&ࢯЂȑ{( >R/2Vx緐@bPȽzپr퐘w\hJWGH(z92en,E"7~ܟȱ&5z V06~F<D 1qZD}>#=U}NFk*rf)0 h^(jA.h<  .|թ./*c OBL4)(a*]QiK%*ĭ5ҭEa.P5#As- F;;0+f.KG4 `YQ$h)/Ih&KƾXWi * Ҥ^wپ=A 8y;f-m[u==DxrxGڏ_Ӽ?ʟb7{!qdT;H=ԇV!Nލ<51Pwo63+<Ç:Ptfhmͮ:8ڕcmSg*wE1 5 و_#m4JҷYUBR#L<+A1P5hk6=Y(r&#GQxw7"ӄʵ?mhQPmka Bw*{Kڇ4I*Fa34=MyӎAd91jT'O,Kl籄}lWZ9.|!'=OVՍE%\ୌ D#d%F\?EIѵ,EiWuUYR`*S[[U~߮"B(4D^H+jíO#Q|z`c$t u)RD~V?DZ^!nD`DPOA]"ArPM(i-8EBBkS {և8E&Q`a8ZcQؼ-U[=h Dw=qh)'+?Z/wQ%"U)!9՗LD|vpKrTun 8O.̶,{f7sz89+f.~>ɑXb%؎oYۺ}ֈb+@B`D=on=m4'gxYR #oPd,G9@ ;<؎` msSs^G`?"sv~ԡpQWQ+驱Яquk%Q)6+=cyC=]?du %ٟP^LQol/uf4T;*"c@|PrS/m{["s(*6a]2 Ůw *6p6=R}^D{X I*EJMEDf"W#}$zv:^jRDF!{9˶cJvR7[X?~SLZ8;d rdP'!Z6(LQ~zٟ8;%Xb(E,{):Qb+y<y? ˙'Z"KxBjr O djnD@EB4VHݨCGu@j[ F=^$]P"XKZ D*ev\N>GW|7֦Km&K["D|g~g<X.FIW`(zNkPŮӜ,vjJp$ ;6>ƯQ`\5 6C9h&qx_2*"6:R)fD0'IV|fݛr7]p!uFV(Ofi VծD9 !o%BDf#q1R/"bCr yt^L-"=qaT)<T ߎzk[LEYomi ̲g=u@( N@9KBjF)B֤. C&R^E"/&D7!VmzڜeׯA R~(GkGUT 4!%)f(h^.D Fsg؏#3zvF 1N$7u6%R+ɘKmtvOQjzFz'yv&rXb%YBvB3iuF!"F2?(:)g YBV!x4R켑HLB |Dz~0=rS})U-Q^-"PMv|CjD@%"s –Xu i $^pJ^py:zD+mvňxqmnx!Ya3+Ӽ7HW[4^BhNCh!{bTd# vnWo}YOCf'7Wf%M^g$?9ԜhUh@hq;lDu߈M "TkZxe UӀf%rNl+@|wXW^9ۮ 7XK,ۉ,!E;]UWna( QZJ]CQSFޞ]?ax9^@+[!Hfڧ痮\fd!5%.G^* #wZg "4;%qhϢrnC^ꕤA&;p: e-}9kB]\?xTq7,\ ӘEjsHg6p8I"+Q(LíD]>iHwC /X덼=򥞶6%'/U\H$T /ZTow~03\hA91Mh]Dh# ⵈDD tصV }3خT>+drz|6۞hަ5}]QN67{/ e"Yi*qR(';dZ5hރ~+բ{֎C\?i&Xb%cYRhakQDypRr7 < PֱCߺ.Z߳C<[+=(t3rՇgq"b ֆvNU"lqi:PLwb/c]cABe ܜ ;f YHcPy~CIbꇣr7 WdF뻐Ur+r7Oߙbs>Gd,齓&Fơ`G!l^`0ⓍHJl}^!qKT%Ky߂B.Dқ/7ZxҊ% @ g/9Jƞ?f@$*0KQx\#g=rZ tD&ZRDV:uDADi)z߳v:Cl<7F2O"zFBL쏞C]??$9ZTbK݁1W3l}[/]Xb%N`N}3 y=^j;) ('B9w=ވR/V# /R^ T66ݐWۀ̰]'0H9y%v^yяFD#'o# s<:DrRԽƿ  na|RtEȋ?ή=G^\DJ&XnGCkSw16Ӏ_#RSH _mL*Z|U@sYx=@mݐ:8*.CPoL3 '{/_^ꯟvA Dդ70RC3f Dкv@MhMdROCmZ_3H"Az܀/ Ďy;cڟiGaNF[#?ɔx HLzbqqH^ ġs1lfLDnG$֏=J7Xye 8bhK,Β:"1hᮁZ`2!2 6HO@pD0C_Bx)W8[Ŕ^jKb)FZ!@Y)B!-(>m%"uho*RB!λގP zXu]ٵBV# mB.TU#Wةx0\۱E/V'&XNR aǽan%\.d tnfg#0zK*;Yp3" rs$y E.v2&D Ҝ2 D$0Dr\nv0=pVŤ'H}jBsy8*Ŏ{֨Dw!SO"8\>,k^hn@}ݏJxx\<=xj#K/ڷr45kcKl"%(xD^G@?*^).,"NIoT9U MkB(Ա3mN%',":S/5y0"PD( gH[wpjॖ ufALjOzcЛe׭ka ?L3">@4lPLyNhSv&#)KV#s֮1Ö4%Mҥ#Rb XdV\Yu/gMȉ䠩Ak""h#%2^-ƛ#wvZ3rAu9Ó3 SğGLٛX!ykYRK J,&# sș@ ҡ*o!`t:Q?@jR'^CarG8}inKͷ6tBSz^GYH! ~+R,DDw^68¯Mʶ{~C_̕c]lmԪ!`cy8d,F@#o Os;TcB@8_Ѫn/Dwp*R~6}>(&[#")sڜ,BdLDF*jDZ!~8KmpIm6EHQja4 7=o> 15K׭IM[gY"yHRBCk-DrHahޛI{E& BJ}! K!%wZ(ڎtY,JD& tr+.A*g7tȱ)Va2"ueň uEJơ.E yjbw9BSd'8k/Gh9 Y/)hsO}z ҄@ +0))Ж>2y 7^j)i.iDCSRNBks8R& }"pa]w/yM256cǗ y; [15_RqyPn uv˞_PluC ҕ8|p?'j뇣QIz+{ui%?*ǵ+sZ@V?;^UH-EdDB;eZӻ!gIh]eQ0D'ö0v^XUІ/~8:`MZJƮlAJ^F]kڵ7RBEkZY(u-RD+Uo*_{TH"R-YZӆ&Xb;%h3W;!`1mj<La羅!K3_o Uxn0BuuCk3WxPՉ%x徃6!(shitγhD h,OEk쳿=9"Ei#9HZP;f,ڄ4VbGD+%ZS|e TjDQkO%Z8k{ xZ)n@J`ܖX6U˰e?DO;Xyeٯ; CBK,R%Gӵ IDAT#,四\?E?͏[hq?ȁl(_nS]s)x}QYom#̼ 4~;a!r/4(6<LPu0 xP` fEnCav54R[( !?y@Jy`#d}Bwa Y-Ri:c tAf^ფrߵwH~K=l 2D`m\oEdgv9lem6ط1ԽqlN$M ^7aЊmox܇HgoVr>4Zv&rLT9t i@k"Djh[]Pe7ʱq ]FJ(nêgW|_^W3<2h=݃EM(rnG $b9R+TG1\/meY#K+..,!E;բЯQEXAzRn@_D0-GlfGjA b6uxbـԒ|%^ ,ݞBb 5cWr !g}>^ve!HZ&o8ڽt(( ŽkŬ%~U=ʗ =cUfc3Cjt>(iy=R\A@AD6!y@XZl^Sڼc,ڬ l!"6L*Uqa=@3 \ (*}e[ֿlRRTB!;}{fjf5G淉~8LB -ߌ*G ԳQ(A琲23`2@Cϡp,]cs?дPRoQ6G{(^l'njߛ@k;>*>q]s"nwR{xY^Gm]?,r+pO&xxSB265$DP7H켥k~YkaK[ :uK/~դ˙ ZHD_vg‹UE,BΑ'!~rѺ{Kvn[,wBѳ ߾F/5vĶ[uijb:3Yȹ%Xbۛ%JюiQxz @&\/G#^Pϴ_ax":N05;y߶;F!%j R1x.7~WktrQр5dJD#0}J?:wZ:JxGs&R\?kz"DN\^!0T)* 9*݈<%v" PxVT+.Xj΋I/@ otXQ<>n50e=N]ke"roA::xߖ^h @4ʵ~cE8/r]l~f*Ofl.9Ytxgk_v(m ymFdE7-l|لhr}"@4YnsYf*Y87 =MhMU#t.Vh^jyU(h/j+[b_lÜοkwZzqSVֹ{sЂ{{<?( g$4wQNH+ éy鵈@>*,g7zߍI)}~x))=ʚs7+jFVʲ,l߳ѻ޳נ~]O,,:޴pJ+ ? ȣ!w7 YB\D@ { yD`=ꁈGMv'Z9z3Iox%"_W*fg"eR/(7v|'eoD;kIh?QvwV!kocTE5;x Ho],ap"5+z<31@1)DasϢ́󞝟yrD|Yg6|C!k _]ߡ뫰Dskx% Ez46#?Q 4qh\~ %+c,V9,EZDT~GX@caH5Ҟ> I3 F7}}iYZ1Y^YR +,0s3h0yEr\ bY5v!KsYBK;2Qe3i9}܂ȡރPEț{!; ,Eu5dyڛ]~jr6T9k뇱{ i@j3H yˑaTCE*7U[֎ocg" SZ<ncךSAZ!o(Lp ,`e]?UIyt3:3Mh=xww'رĭkop#RN7/rx_ #$#۸!P>ob U6yReqTpbͅcYgR[4[ ~*|T|\gRjJSa""q?M/+h,x5F! bsJTYnksvqUk+l'ճ|LH+x3?"geFVcV D-z_[vb%[B{%¿_%Bg}1*ЌH|o{DH!b5"%/N3'ڟ}Uko{y["`= _Z`U#ED*!d6w/`\FD3PxD\DĎD$0.`/xk31Hh@WDlQH"4+QU *V#f}ʻ9=lF1"GC_k ǛhoηqzIx*:H19J3!H.F}ڛauEkcZ_۵>fV=?sO.L,wgs>'޳cۖHHFXon䮹vژ712Nh-LG᪹y-ZZ'%j4w)d=bN7]6? hj[7dFfVW{X};+&,YZXQ^YRע%q8O7KwD7h_(5(q^.h>kDzmxŮY"pkѾ539"x’BmV !q)R%vY֜H9ttt [Xrl@b/}8kA}{JՒZD: a[[ӭO b 8ͦ 8crÁɱBjjDε٘B N(!s!p3-y5m^FvԢy zI|R# V^r1R6"lmNnʉ^"z]mt[<Ů^Dz0O"El,\lzz~^:R}iimxXo-6"[Z>bӖ:}_tzV6KK՛>pc&U˶ȑR+ʲ)6);`IgXb_9^~֨{ۻILq(| |>G8Q7RX=qþȫ #Db~Lzу]?<v/q(rTٳ"\laKP^fUаPq#OT2{RJBcDb: 0}7Fdf{Z `_h"B8Ҏtr6bx6"{?Dջzi0av]ksSPIdMMy5R]?`}h=C+ʱb;#yfFDlt.Rn0R/EjyqO@fBᕫ:Xcs| Rrr!2gDG$O߳g}ϖ7\zArśއ2~뇃Qٸ-\+q67)exSOQN5v :447P2Uc Ru?/R<10ZW/Ұ6{@nQc~VjWe7thjpmh.G?IVfmݎoFk{#K+ʲsz7*o+JFVFmNjg 9]q7VsەG9*F!Gai{}>=3(rElqC<<=-v[PG\EǩBEF8s-kZ՚}QE7G6GEQt8މE EOl6;ȸnGeDKҹA8 B =lD`#ߒꫀ ~*gyWX}QNI]k܆D<=lD Y'Q G,]CvP<4#|')^B%vs B5zB~x"z ԼKs8K7'#rY6 [F/CCF_1/0d8R{&S.@!O!mD>mI+H#bKPXHUC 9yGm!PaqD3&=pJT`=J~9jB;D4fo،C/Iofoռ)M)6O9s(2 m]?ca됒l}g/"d CK,/c}]rK7IzkwQ>5XUqg6&V8a@E ob&XRAZ9SEQUƹ#FQh[8 B;7aO9@oqnA,!Eہ~-d]m?EhAF7[#k*AN\?)%{/þb4ΟlxO@/ BtDyE%wdsjP!զmcPFaA_2Hn?>z J^D('' {Cچ"tGy4͋6^x.c$ǮMz/֟:EcѮQ85vV6ݑGUl/,:DfD뇙age@}f~k-"}m"Q&6W#UvJ)WG~h4Bw߇ fvƴ+Zwyl?l+YhqOrg_{h1:]0CgD"Qöh-lm]?jrC&]@4yYcz/tDQ+YZ,vޭό, úwSx**XrV8 ӠKr޼%WlgLZ_g}'ns-Fq:rvH&k4JxBFQI Qμ{d!5:Q ,GQq{>*m @"bE1O:ȋ>Ѣ{= yPl*T>y]k{jC~)R/P.3ǐ SQDF<聚H^B ERFI(rDDnzcv֎ Tr.~ rZ;َC]0s:RBŒP٢,~x>ODRou'SgvN1AD"`?4r&"lN<"_ۘϳyԸ~xK핳sm>Ayl#wˏD.T"KMԋzivD@4;㳥Eh<xY[SADdڨ]㈺yO3wbۿ i IDAT uBZG%,DTmyeY#.`nyeٟd)FVdkFfe5٪F$?Τ\b3a];"|s(EQ(C>5J#-DQ/}{ { DŽOɱc"qaY8?&(ʓKy[m632>끒7~x]}p#`\P t _A}N5BS|)tH^yQ(9ӻ!(lg+As-~NGdh0: ūD_޻P  x߄}/#@D80ЋvP6nCѤnY,<9?jv89m萕5UȪE9uk/U`y($S=p6rbDƸ~x7"Ν6 ;@D.7}4-rpD :Mu%Eu:4]w=F!)F.m| (h Zwy6/]?\!H|x <۸M*~X6 }7ѳ(f\?x[׻Z?^VYf 6=R> cKMdD`J qڷN.pZ^ۼꡮtgF8W^YBNK3Upl4zTF`xyeʲ@Ҋ y\B:r0kdiGk,>x[rD$bQ:smWNk~KZxo}iUuG}G 2?#W9}{] 8IዓkX v'O;Ӏ -8 8NW,ea qXd!Inm/f^aBktv (ty.BJ~_Z[#RuvӛyyI[&䩘HN`#*@ ^8vm@_'/}/uۄ@L %aס B^ Tp  H] _<x95)Yk47zyW^F6"BɮR>?)ke  fH:b +R^C$4yn վ朜ek终4 (^a35_T4퍈 {׳l0_#b8ZGc"`i0BamsPQ<)B芁j q0 URheb۱Yхo%+: #K+-;8 lV^Y {di۸9mOQoU)JlD)>mO B!X"`YF _r!^AI Aۇ(;8CJvɨB? Da3PH-B tF:~ O!@Iफ़=*=b%v)c{Z QNŨc6qˉ߀T> q TfQD(~>]ljYʍo/ktp +ּ ;R\?eݱ(6Fl^: "N9YK}hsٜuDDgzg"Խ6wl?穫:9DqA$x87<#2 "`l2Jw5ۜ-1~!o"t0Zݭk ^ebBd^qWE@|jevNK=SLY`?|[:||a;ز˜,ysH,xľa 6~J,,}' g 6v}H(F@{$ߍPcWP2JyyTGl"{!%"6>߳ B{9i*J!0[PT#KAĮ Jz3]?)%DDnw"  ε޶1})POAlLHyMnM,-m6X9!ǬbZ?D9H ڗth(Lp7Dd o5"mC]?|?ބ6~XvKzh@HJtG92"VlqbD8~鰍OԯEXrdͱ뭵10+R D?B#Q xLᡋ)H%z)!wo%(.k51ֈ<ϥ־FLգa^j<z^\?,3 mAcmء ?+⍻D4573%'rĉ%Xb_RB'Qk:*uA|"6^J]?)6< vB=3UjsKMvBz?,"T[{Ώ6r})DZA]? P(b/kK]?_0ac~}zq6qPVVf,A*ZLF#Em"v5@? sv(i뇏YfcB,᭼57bYZQޅ_Y;M~vpÒv%ֳ}fwoOqABB%z E 8$–fdon@فzB y>kB`"KG1'r[aO,Bha}kg7~xg?ZXP܇WlE-{E'KgTOl rPP?u0FjJ{ksW1'p]"i=HkD'scXAA\?|gJZokXD8זYz"m)*tWZuZVXQ]?E^y i7gxDRvAN-"CW!\Dp"]iRaZ3!RMv#D碢 %OZ?#hrDCacC )"rx]k?6| #Gw DIiegzoWGV,[_YZ񭯹m$xb%WjRAx3C=]?TtFQU:-M3>{1R}@D8ڷ%]:C!G YrTUl!#،HF̞rYRۣ7}l>mX"`SRP doF9%q2.g~,ÐZ#ÑY+kݑ& C{gcQs/Io^_m9ƞ?rS СҲ,/!To~"y24'՟0+?B]Y$z->hNƢzq)̀w,3̋aI~Ds#@yڞHDE(b"0-98ق!Sg,vtGDXtxƻz#w&c Z5.OF}Hg)"ht%x%'hfH:rd,:iSmm "!d,yڰEԬ YBrA;m5UӐ5Dk_GEVÒB;)z9Lgkz(.x/e!+J'CnlXE,JƢ^Ok^$/GV^h|}N0@#r3~ՙh\_%nגs|R?b$h|?׿>Fͅhat7(erݼKHiB%EC"}$Rs{7Z/+)*_HEv%910|<;'T0[};W6%E Ho^RTޔ;"ι A^tm1dFA0v+h}3ສ Z6C۾jY^ K֗_$߰ 8n]E @BMy?toYVW" !~Xt2p?1(;#w %iۋPֳU߄4, S# 1QV"iFKHuҲ0c `Y=Xo$n,k+d, -B~d3e#!B0ŻW bu[U M\ C$G^?nBDB\Ϯ5 r_2]]xe GDzq|w`X2 CDsT2ZYh<`.9f}h\xvlGKu y2g""=Qyh<\Fˈh4?ƠyfZ|䃭Wػzqe2ASxi[TvGeo@EYa4VW0!WZ>3 ~^hPRT6) ƒ?$Rbw[ZJ"zWRT9@PRTMsѫYUǞ r~ߕ(Kl9s{ӛtʙSx-s˝s %۰ nqe̓ 霻,ιBn:纡W LBܑ 8A{ޅA{ Ksn8pI3sy~|\ۯY4m ?=LgϞKWoͳ̳KH. AnBS{Oe/ߋ6hCg1 Mgȥhmsh$ Lm1= *D}?xqt{#pƈD`C::{Z5 Z_ް,{.h鹭d,}=Xbڮ?AX!+w~ Z7ZV?= 1 ֵχ"kxEw!X3V :7{o+ ~ޫam:ZEk0۳L]{&^Cz\f#K4_/ܒ^_G~:4ww8oJ* [>oNGJ4FBC9ri+ O).ܮYJ7o%RIT$tyL"o HENBs;arf1ZZ+JWAD* |ϴXњf"zK>XPRTі`BD*p@N+ ZkhKz}oJݒ>ڢ/̕wcm,4f`qA,p=֩z<9AF{]3ٌ͊u(<3z9?=d3i"E[)^*X8cnU&=Qs2^rj(Z"2z>@S8@Zj;Yj`OD =_"9<W[?0.X >d: !+!+J+zDFY@Z,xCBDi˲6iE? +g :Vyqv ;.ιιоX,`UhKrt<09w@˿nyD^r+]_~yZ 8ȁ^E?oAZȊmBN<?⍪$]~{n8wAygY^i|YfyUs5 G>y"׋Y_/㼟fx)y%~b ag U`bzs6"Xc-M-R hVhs¾ ͧ+~ƥ e|T(m|Z4!6#u[9:hMGǡ=Q_!嚳94~H@4V}uVQvZY+Q2Z,E}2=#YpQ'[3Vyq_W{wwB(@էŏD: {F#IO[,B/~OH:i`EdGS'Τnmgm >p HjK$ZK^1؞HS+hќB nrY2=~筝^?xs{#ٻw$cq_z$f%c{)ZZ N(+<Ɵh޽ |i͊tE"փ6 D|G d'R!G7/m4fHE^C @\xʵn]}|N͹F"Kyݮ0 hx:{nh5w@ q NAtu}C>)W ̞1ـbVf%c_o֞.Å O^G,:N'8Wwꪝf 'q"|X;GQiYy 0vJl0~eeVCx U4xݷ*>fb!-p" a~d, 뱫z㝣)HZ{ -"VBDkzs*rsk\ƣhD;.m:@ZivooDPx}Yna.; E"# U0DPS**\<}h,GI+5Gff]'*~|c;V~ (+ 0VK+gTF?`/xf6Z{W#eť+D;l5F~C 9.?w^:|kHEAmB lZFQ-֝SRTH"f;fmqD*.Pkx\CR|xo|sqNQo2M)9w pg|RH3hdr"҆66$CE"<DGPy#kP;47hMa7)EցH%:/v+XtoF`m?%cщ uCw,", a\C2"л ioC״HӋZi,,%jp"4]>YUŝ;41Xh!$|bi?b;*DrqOAaQ'E:A%m86Ie{8ы{&cOY+rsF>NPpaO|XU@DE:2\YD^A䤕]_s(+`djhP6$ 8}\dmp0ҋϠ9WmeRu^~<$cxq@ij6HTD6 )4 4vLƢs2϶<O),S#JqiAe͓h,V~TQVx{kEY0Ϝ%V\ZYKPQVx7SoVJʟ׀sײnCi<&a[<#L4s\< Ϳx9h~QFFѸڳz!y4"7Ӭ-DCzd+OƢO7R,4Gݐq͡(! Эou4"0xa2]7~RQV͏^).,<hNʍlT@qiGG$'9n/P/Ѽc?h7Qh1u{@ldTF6J:L\]WnD*BD]=${3͕r44AqhċC/KƢ%_DZlDchnkxq|LO,5x8")Oqh0U{A㵽="B#jk7Ig7ИhhYeLE[!"ӨYHWgD{q``Y"׿S% Mw(󩈐_$FXޯ(+|?t2ӇP=Ct&, =ip\EYoBrsth.ܺh^#D*r[]NȲ O"mP D*r:R<]NFF 7{y|e"98z5tvw03 =MI#MhǓL<|k8#l DzG "$ZE`:e@P^rG@}2]clHV3Y^_,-i?HH 3PcV/:FPJv,{[Z[,@@zlL}oh\~V8Ƕޣ&DTٳ¸0$E7 pϵ^U(rD"&cљ6.r?݋݇bddx?FH1pN23.z(t`e!{ DAk ޞEWlm,k<'!Hp_ucNAK?E/p9-ťeAX=0hf60ʲЅZZ5nʉD]vpc.RRTHEBKi3dpzY[wM" YC1y୒8@`2Nr}VTIIcDv<5^m з%hcmvFOIwDMx*ұ>Yh- EWzq4ae"Z9b/~en|a0*"#aFx".L}<e{$o8sZ4T Nt~L0!+IOF K0~~"&-H{?}mHM"ᰣoQF]sclJq$>b^ȔfX{q !kRLҼwE.mM:SsDOyHk|Țt#l1x#" !%Nh"֮#G?7#y,Ca{BƲ5 RuMnz8h,ጬrNb`?B` ڈu2;'%c%^CnS%cя?F:NL2 Hu03#%" 0kE|/ M?̗3'cU^)NHbٳY+poDDy+<qhL։R+ؖmd,WOs >ߡhBs9rϛLgelG+˭ +CBdc?־ bEs"q֎U< Do''R%E8'R طe_8ΰ &i&H)B1@6-څ3smOYA$~'s쳽XL 2YfxO Pcuڈ!q/sEdQBnl}f -"ݐ22G0wGEem N<${*|LZKXE/bmr iڗ'>c-W!ZI]hguʾ3̫PT;mms rZh׎i9[X6(f}8!RXU;됋^ܟD &')@c˞`U!3;]h[k_iO:`~p2j.r4\KKTw^{nWU'Xˈl3?#=Y>U.M+3FOBc?ߋ{s@!d uk-C"hyqJ`d,:/N~ ,Um9t2W#8r P_:ޫYDS,4h}4:)h}ث)՛=g$𣊲4?(+l\O R6e#iDKS]4y i+sWTySRT^HE&=;ZGOK"7#Ksh e4z;)Hiw{ou̿IC>\? tSB%;jdN휫G><` F;wAŮț=*[SB ; o;4-Ӵ;!2+`dvA`_+/"p 3.t\H3r:m0?W"B!^ڶ%LZ[ H tCVZg['|_P/e.D$yF%}HJƢI+Rb^ ?+1L=M!w9=7V0>*\rI2|?#y|!ye2zV輣W+"-{!ɤh3r Ĝ=D2= 1E_Z놬fX3>~995 ݽGiQթ~[~7D;qmb-}\gmڀ~h~,r ^ȕȕ/+2,O}nV74heia͗;~W{J1U? oqmu;eٸ8l4X2iǑ;[R8ǭnw9ť+ Pd)SQVx>R^..̱{8: IDATh59PT-Y_~IE_IƢCGy rziB@.97d#@sϡMYyB`3" Oa,nb Gr CH*,>林6I'Թ:QLVcil =\dUMں49XW\A kѦ(Fi_NSC`"R9!p"YuBVEhzlܽ.D.AC A }d,Cդt*xAA2eYfK/EdRDXʀf9Þ#D{w&cR?_Bc3Z,h|a"v cG}[֮xn @`(+vw( t0⽏_Yz_%1"#eek{#Xje˟!-PGLזYx:b͎vىUHeȵ3@@߬lJ>>seg%񜊲Ѽ~jY~G`{D}vqHQtήkxąwk~/|w_FMJ"-fg8&mhk>jIQdYSRT_IQy_5f}"iHEzD╘+^^S~{6mZwN?^s/OdSq8 u屖u屖[;GaιfUsιyιs#s3s7dιsU#eo{9pa7osnsns.kvΕ9:8.8wMιV[B!ib,h>*_!U%r#3jiן4g&cB{WhS{_~_$Lho̵Ę 2Q/OƢý D$5`j+k^&H[[Ⱥ#03/'#Mh")+VX0f# QQoaڧy_am8 tG[""dY6@̠zoncPzah0CFe> iSl+%Xt0Sf;Ec"RwCX~M#>Bc :g!xYQFXʋ .ǐfywD6"Y ?! ,A@͌1s?˷V844@qo"+MKD~H0K d,:lrZ4iY? jdk,'g"4ʝo[ֽ݃S7e֠eW푻ev+ťӑ"SVE6?H-e}z>~ghM)%TLj0?[RT"h,oUf>K)fNL"()*_ȥᡩarV@D*bv Oj ;rRHIZtN^N.URTjڂُ^Z3lMݚdSۜ79GG ;ksnB]ҩ1)BJj`sn(?Ga{;9V\G]܋nAdf9pfcs`us(V F9ZZY~0Dv\4!.06#>5/HK%T+8G?<mZ/wFV椭<Ñ+u|zK!bŕYv[JG+xΕ`fd/;,/>43yh\:D ic7#sM Tcr--f L]Y!<~H~>&aW[҃I(6It槇Z̋F4@ Pκ2gJ#YX?Ϟ?"?Db3~ i!׵Qw˒R!ss{Cd8lHOk4CKvH6p{dYlejC=eaXԈl[dEY{DDR49y s2qˑ(4gSW]BbVFހkSј,YoG<9TWe8иl@m0,}YgNcrESF'"&}!M>/ CZG-G@P[?ƞ)D^DkEnnU{*^?[~4] MX br T!e\6zqLD%I4 К3{E΋ `s7$ >wBX?~rK;/̝>u:t6}k)Xd}r(t_Yrw:w_=#y/ҡQJe.bE\Jsh1#K̯#57Y@o_ R0u&:ͮHsV46kOL1jD!BDb럻8(neq."+7} D6zq,A"fo{X}r8 ,7Y#"ˎVA%#אB4ve06hAHye{ .bSIqi冊Q*[{O#9{8O ?()*\r`ܶV"'v p_"ܷw%)I+F9unkz"yh.Fo$?]u[l"5LXVK5Sc_'g`s)qA C%ZApYCVA|&R;v#V>+O\ͤٶvAziGQ* W@s0~CZ4[39 +}xvhUE9Ih9Nts4G{{V%-NɳLdU3Zuo8")2%kSx9w$ oLi,1Bt$ _&3ι>; sM{h? ==@g7&6nC.!83u2Ŋhs-HjV$Oϸg*SgwEgjML{~"0faεG#ùh҄yxɋ&mf;#z7 k|`ݻ(X=0ݫjOƢˬͬn @~>"%->C׮[@1,7C=@svJkzvKi0ӽI'^ Z/;s< G@hCg3&)=ZK5=[R+:o ݟM8pـFd9w 7H!(u$wDn"4V! ]&FZ(a~RgQwFA=i#lHu-YȂ"Z-Nyce\Z[2BMk"S  V&c'{? GG"Nj}Aco#-wcbC~[4&>ZϯڛU8ZHx[pNNy#׵/^:f oߙ9]Fϵ]TdߌCS삝KVRT(Ā+v@sCsNX957z7 H1Vl/EkU>?FyD*76wkS q ^5ӌ[0a]-SjoeZ0֜Am"aAp=B86'/9CKIIι3{, zQB n)C59\&R%5L^WvaMm/ӴVxgPPl(utxa}IGⰴǛ=v:Xt^y}Uhz@Ø=m;zqyq 'mdnNƢ5^ e)ZlY,+hRiD\k\^BLI+f]"A' hT΃Ly{bQc 亸FluKڭ7 uDP{qQDf ~~JWd!o$.@Yeh}Y/vsA{0ͳ_| RPnm[FtPZwf" /ׇY"0Z[y!hl G΋K<9okv1Ahl~ KGfG~+kr RFuh̍O*{a5|v^~] :f!6c\*).C֥ӑUt AK+* Ha2)"%$kϾ?4do !2gqMCkV6{9f鹽y%{ׯd^mtމT[+)*<}H$Dy6VRT>;/gg wkĬAY8`꒕_zDls G: ,WF1q'LO4FHOy_rm@bw}68(F= DI(  t柷vkL] p;ɔ᳂ c hoO1?s7uDٻ H }-8ҪCڊ[ ̇@ܒȢq! QHcP2R6.^ϱwC`{<ҮG`N`T%'ʽj6IƢq rdy?!kXdI "}\jt'W$cʌwce_2C`;H(/eitX0G:U&+(%G:so/@C ǣ"@~2@سEV#WϽ2F pذYhLkH;xN_tA{-ri|,1&͛8{ cHi+2ND{Pe# ԯfs&i'c06OG:yXt}56mJ:} -fcTAsdi4GsRt1ϞVZޖ>舃yکY2B$x0n3B" dx~IQ6MHEhD*,9ג}?r3I{f4)Rj>݆,vO ܮA1hGKe̔FBlg#HA3i9@]wOsApٷ]&ri"E@Ս|Xtgnz Ur6c.7dY,+h#B>h9ŰSa]dLNf4!!ӑmfw 90`uSVP2eӡ%l,G >"Y#vk^@Y>@DRFyIamk܁r9CnrjZ\s[!,E.@>8 -"D h~'U*hd.s;!Ҹr~ֱeA }}P׶[RTU(SGt?\yGʝx|N|$M]&!GraV҇f*ɞw[!pPhZ'ȸ|[!mx%Ҫ2|C ,226ͪ=tg +YX̞{Ҧ&kK"PK=o!"8a.G'cv}Zk %}FK{C轜tRs{_:P"Bt+C{"Y]{:6:_3?yȨ' Ԯ~5ҒvB@;Ϟ+"S3ߡϿhm8갓}&ڸQ7+^f2|~ [lm3 Y-"@65L<yQ]3nr\Z{HDM'!B 0 @ !hK5 ``c]eѮג!Cyޝs;Hx)Pj~h;Ҟ)zH , m} ֞hd: yek,P6(Bfc#k E@(,e(Q*n"(.s* Mr ݭhGޛ? ̪H?"ER@[b[]zM[ &bފK oHVО&P_\ZXc>{\\Z8 9V'(fheXɿu<>gŠcКh<âO1p#얿]eY40i:y'-l@QAIP\Z$z RX!P⹅>^"t%-&Q0A t OǢ04"a#HQ ?vꁓ㢸t&"Zx3"\VB!le=%l;#6 :y"P# Z7XuqJǢc檒는IFּޅECF,*Gsc"3QO7!1v\y2!cCe 0u?x<}2]O޽}PwOun4Wu|>O7TL2YԔ`Hw307{b! ~f!|qQAI˙e{ ȳ5H_row_QsE%K w ^mh-sqiaO(lO_{6.wj9p}~@Co0- S0?bU>/z΃ym ijh[qia?(>.*(i,.-îXXvCD 04º{>y~?US~4~AW\'o"ڼ ˑa2Vڷ7>h V{+yŀ)E%w$󺆜5g磙fs2ʵA/b;/p؀q]GLk So=?F#ջxe0$?cՄEs?5gnDޖT/.({%|DqbX/**(i6ʛQg<0:m=VukH"~0[uh^4dlMqi%E%ߪ.MvPE_!ee9RP[eCZQ|)[XS5n+,8H0f60v@4ͮ&W)SQ(@@!?%) >rLZMٍr𾅮 ~]i<}#{\ a7Ğ`d7jM74_}]߈6֋hAsއZ4s2S}gA,9MIC'z1 'ɶZ<}z~x6Qr%;s5 ٱ"港Lb*Ƚ7#RY*yOǢIwBDL֏"\~RR4H(C@GHɮDaVÐr"@QÐS:b@rUUG`K)G}RcדMz#[<`CrT2q`qj;n cc -b0¿U2lE,q=1mvc˸ؚư>n>VC:h|y"5f9Zx|&ޞ5Zr]>> sB#mOcѦx,|+ndeT9N&+j wv]w5qNmnݦu^ -L~s~%,d;K 8|<}̍2~ڭ=ן ,,*( ЭAJuzu$Xy_y9k/*Gv;pzOQAI #bQAI]qi{h,kK Nsc>ڷ=f)w27y½#`Y2 {ݣy]QfE%ť#6U-<~޳f ڹ zECf6>_ޭD;3PcP\Z2Z{zt `5{NCc8@mF,ϾlvP-H F!?r]a@"Zr=nЅdS*&S|#…Q(t,\`V>lA#z~2@K/+:1 \o69(8XcTHC:qȢyp %z_ҮMG'N 68i|_')YHf@g=Sc'r=Gݞv"EzBB{Z|Ď^87#PtS3+j6Dap,ZbpkRTPR^\Zx2&>f(w5tثD>an2-Fw۽D8i mܵe3/{8XMmA }6[,!UHJO: ?Gޛ;PVj CՒLҦnOp=Pc R-hKJ_h}XGU\t֏ Q8ʱ(翈~1ڹ?)&cс'G& 1<"qHjJ8Z䱺+ڤ-=Xw=$%/YW\dl)\a|, r z#[ڬv7g}?ֹE+ʞUgXOE6ӭ/F'3rҀWU9'7Ӫ6Yl:47q0ĉ2RCܻV3dH6|!Ww[Ƭ% zf6]k$43A s6_2Q3\ YWoBݿ!/hq/ngQyrBm(\/Ǣey|g 7:@0}OUvD܎9˻uϝfױ]"%7gekN 7HYIǢuDjS ;aVG 6RBw{\C^lĬ'!#yBk}mz]m̰}fhVJoBןDev?;NϮr O£4/>2yAVUQ w!poKb@ujU qp'𼡵2c#(n>''BF`>D9sk#/ *&jT|5,f|gk\լwEKA*37Iϒ X( \EZ 3~ug{T9}̍&_J7Z-2Ov\UJ,koW/#L?3W^z픧~{"Kt[=FTPw-YX95Z~「.ԯS<|t]}~EUegyGd^S3xXv dmIAO3ƁH}9'>>呢-.- 󦟵6 Y#k33~QytFH(.-ugF{?7!^XSo:>K%(*(٦1m־6x5eӇ|< x<&`8J4ㄅ+GOi!"i^A(lOBX4nwM"9 yLV{F9 yH1+F| Eu6 yjq {c2J^m xͽ6'sjpPNQr=ݬU% ig NIFv]ͲIX~hqϥ ysؼPl AG%RwEEOcѹvO>1:{?/D yM翄v5H/:M{̜}}mȸ"? ú퐸"59GM6&lkQ<&d8[їM'1*(_Vef'K 65vȮXq@P;+=3oCpi]O۵W 55fkxns"ޝqvU=޾ꐽi]\Z8r(w':;ː-]u_nJ!Nxxp?B^Tvǜ{t^,e%;ȁ:/~:o,*(J *זͨB:flfM!3QHrfҿ`fr (z$jqFA}^)}/%fLq5vM6 Bg%uxrZz_ޚ2jZT[1K萙Ūq=?ˀ5X""W;׻c^_GJq?~;Hzy]>@؜ݬzqҊY9 GԼ6'= FC'Xw1}<9ð y渞@<]EPdв~_#& J- %/AMqn&HA6&tE$X lV\OD蓙ϦFl5^g$-=rRObt/A!]z C&Ǹ[;$WIw8^+E!mǠpз;`gN.@YE&z{ &zyk'[|zE]7'5YuY3aģym׵kE*R@<ߦJ ڃI> uŖœX\ϟBKN@ _@.[\ZS#U=v_Ҍ H-^Ϣ=aA~7fmYoBp<o #oƞ'am}3~ʏ_zJi[}q=4-S[a. O7^؜9h..-a64fHO8#c&;%k@$2XC@{2LnK 3xTqi)d:t毵w J*-Qѭyj>N IDATMyv,13&~FdQy qAOqF!Y< =v=98@ALp]Ƿ4|$kSF гVdDRⲅl@QyzdB+]^h@}XdJn($9SnR%,^U?x,@7I܄eL?nȂyV#5C̀ Ė mg ?:ɛWuDQ5cIv2W56Yh^¹9 )}QLp򺭶q®}y\3Ώl+pyqW`Egz|B"Y'@Nu6d [Xac-^Px1P~&"8I3Z'0rӌFy66ݙS~.i8ȁV\(G5Zx2䥭Eg7x,z/4Ւ3C_>gUY0jS/WN'xpk o,' -ēѢ5{6"[k! -2w86s|v@^k7F Jj1\7u~{CQ? 9z}anffTGM6bA7Yh>ƠzpU#^ظ@߯kNNKaTUv qeqPZd:*89:-BOd[?봌!}>c^oXn֨Siœ]M*#Hs_dPhnUoN? 4HmAP812Kalfs2Fpp8C{BDw\ qZEA;w9 %w5}wAiE; (Z߷CJV4" Y8FJ]╍<5YŮOG.?A/Spˀ<(iUvq0R*?u=]\g@E3DV vͫ( a5 ;错eGVDP7J=r!8lh$T?D,`>D@""e4,G^dd޸07lʟ-<:{v]rkPaddhC^@o|JhEh# |-^VAX<] bl`]<g 40~71ﯶjo`徻D`INsYGk^FW*e?ؙ~w0&팁d5t%!R;2|y4:D5_tGkmZZgŎ}o2$}; Bѡt#ڿeeT?V=yK[^ួw}%Gca3+,YzD-$i)B9-*(`{~)wޟ21k-ہVTPRQ\Z1)2u8t 'H?lē46eds3z_.*(!z򢂒‡Q^(,d:ػp_v2 J%ìKHS鷦liCT(( 9U?׾'QJþle;z>7'sè U}KpoU\/nr=;,X \?/ WpWIJ(Wa19UYH#QXt!I{xF I/Rrg6i37QͤL8x,R2)en떿oZnvMsj|;Y7k6+x_D@Q*ٽch]7k| *Y?Uڸ[<|z#KB}?0WjM<\ϟл(*d,̳ 6?A`[pꐅ?[[7xG{]/V#o.$nCJߜq[ yj@P^>$qtG]#zWpʚiXf~FUEevnOuĠ$H\9q#}Al[(1+y lMY}SIAK!@qzv+g-icu\D+|єgUTxZکY?뗖p&p{ssZ╣0|+ G#)䭸cU鵫SÁ]}{AU/IrdĨ e/&+:9.`j.9𥳐vI_D*Ǣyg\ izLǴ5(DyvIɅ5&6̃M '{w 0-3=" 2jsWީk pxM|ys6[\TP҇^nQ}k;wqhgYHyť"i*B`+|[-.._|@Q^άަTǢR@„oBԶE00`bR (iaDl%[` Lyr=/R;#8'#tl@!bMES+; |Wl#RZ@RTW#Ż΅y w]?¦ TfvCEV&b;y@:x,:nI鄀<x)SýH@`=txL{u@xգh.oA^>[=?v֢5uQAܓdВb<(ea`AFZ_t=]VؐBTJ02jRBW ShRJ%}AnETyH<=;{m4ZߞIgPσ@yWZ+G +h]zZ_^H"Rho*RC_ih/:p4S䢂Ð~hg'V45̺|fϠ$s/w^k 99NgZ>R ߀xHYH4JSqiᛈj (>_CƱ hK|;9ǑhIR"_taJM$&I68%}6)D"b "QD&up~fIA`iCR(CotC|#(r Pk qI@c)ݻ 3y>JjFV*_yvBac{,[JF,_恫D(q)TXSnv4'\n;4ɞdAmYGJ2ot" ~5 ťtX媐B]?Yk:mulc w<W dnymɎhLZ{#E!J ըjznQo*:}䉨c!)DP+" <)?B=ͻ!YDx,Z<M~`n?ֈvfz+mlAg Oּx[D~<}.ڔj<B_@}riL|־w=:*Dյ2Phx,`t[̫E uFk!ha}vU]f4.y[hՀyh Ҹ"5]b.~ T;Q>M_{6gAƭPvivwSumM5̯G-M #Ch``g&+ ,lfGP_'-/L]7Fmaz DuGhA^7t&H=zui+p(W)ͮα:[L Zk y gVf n/4!2wG9J{(" )=WBy:Z})ؿvAt/G@(< HAzTlYky0l-BuZlm j fmM@e@+w"BjX?Wli56(Gލ@Ac׷rٓ6!_w >cA$;_֦![ ]ObmLQc ZW#/L6ϰGz6>"av=7RZZDlb)kXޥ6je%uU;k#oA{4m]ߴ>KK|c-]1mTA*n<u=)GBF87:EҸn]co<4-$d~Bϔl%ʁhk>Nތ1 y)w!- މǢuq1\繞?yXO? b D,&̡D&ЏDޥY1OԾ<"Cg@Bv=o:0{)GGZM(j]7/FGD>1 )GgUmwꑋW[Su27jg/F@(qAWgyk'8xGkmTƫ|FůaJvA퀨]'8N c/M8N:C9z ql lR#Zm >6O> `+7Am>)"l%_A<mǢSRI'Q3ؙ*  SiK"Aovj_W]ߏ3r:Y_B8r9#oxB(R7 d#%{Pk\g̼ e6FH؞J!lD`A JYˋ,:"VX 0u6ވvNGdoޱ6*`J<}%|]%=鐿f,YGVZw5էܵBN9&u$RuF&ôx-`g39y;YlBZj4Xخ#@ @ xG{!Aq S#IBama. r=(4?"@kf5iR~@7\K,Ci,Y IWբwCQI1:쟈{ a*.Ͱq8XkkZ?s=ETwso ?x&j2YhrWT"`}j`SS?ӳyvi8s@3c0}r(Zʪ{9&θm Nl!;oA|!NS^"h'=x,j.Cc%܁<b&HYE^7dmd6I 솔S^XK'!p=opzZfZFߐaOfn(ԩ3-%x%NEr )vo`hmEZ@W!M[!)dE^Rd}z6Qk/GrNGA{V +"cx )_ @w"~ى׮5D~RyWa R>@ٮ9ɶ^C`uF^df}8e:`Hf^ /X~/Qp#㿑e'wSSӐ4 o>bћ om0ԣ3h^ 辧%])x~LxwzfXtw%˦&wAnGz>O@d ۰NұU:?C%rZ]x uOH3ΌGI޶Pƶ~?ƭȎ0~C~B(ZF̅,9dAMK e>h,8}wFפEю㼏ۂ ]s#:W;[tv<tqC3j<_qC&9@<?##g#"Rz8נȃPv#0+쬱6K;(yR u=x,:.ND&R(|E,$ 8F weu@daߢsWE?%Ahl57cKzћyI4?' x,@̱W3L|m$*8{m M K6h}ꏧy+1uAZD)9Fނ\==Z]{ R>@!_1XZ"!)|H ,cPhֵ@Ik@a\Ww^@ej8O%N#ѽH6cgd*>E t A*mL NZfwإCf}a5w9LErU Ą֋oBJj"n=ɻZ{WD Z ^\OyV"o֖ @k/?tE)l74/+|B[C4o 5hMn};a ZCS2%֡:^O'=G:!"]|E'1wm2^ ۼޮ]E;yBu҂6Q4DIjam"ؾy+2Fe9@x'Beȡ۾[:峥ؘG? ])wMIQAɎRg5.0t0} P!}Z\A P8tTAEAP8γh~X{vݣ thYzqh2HxqѶ]hE;S`I_xw5kwEq'gJخ(26sވ}߸s<<1矌ǫb^N랪]| )[E(kҔÐRy_F.ې{8]PHi_"YCPB<'!%)++opm<+m\ p+RT!Xad}ClS)F!{N#4J YDt$rߺ"֙{{ù$7))FEl-ޛg3q=\$QĜަj!e.hmBhƂAKt>0# lBdW4g7[[%AkbZ]P8[$7xUޙ_Z!RzZgh]Ͽ+5%jkdh6#05 d!hm݈_:ZK[Z#D/KMqo!KnD`E9Pߧݮ֏؞n!|+Ҥ p{gx,Yr_vC֚ np=#6߾[A5+V!05Ѿ{|/GwFno^ɉ$<[ .NzF4ou#hY$)( $/CJ@Tn@CH3]n^+.Xd4ocr? BIBnW!+-7j:*#ƹ_[?OgO V-FHi Y߱g B5Hs23佊=)x>\>@%yEop= y2P  \?T?Q6kh"`4D\8>?^e;)6b-,T6sb[r'[@Jo1HCx"񓆬ѺrR!ǣz4*ORy^IYǰ`܀TBrjkm@y,Ss[.bc3.nr=hT"hCFt߰Z}[]:)lF3"/!,ߢP1G{}iA7lϸ)HE;8lg{Q@bcm%LLdbM%FMt4Ėml",R^m}s]yx;{SsSZ^+nûGW۲<ϫwgL) =dD3'k#g9Ry Gy#H-\e'\fM؀HEXV}5-lE E13 0>У"uS 6c[^6fsUFXEi ܊FKL{o~m,lۘ x7"%é(Yr]}ɞi2(tD뎽g[UP M<(c2FH|i6I8 F"Y݌'#`#m~!qVip`6T |3bn]cImjbY% *y*r Q!^42׏Q F| `cuH0{m ZDL |% jfeuRZfϟK`ELw]͓hiA{AGƾ dY*;YFU?|88D']V~iX-h_=E}̻󼷑2|& dU%|'4h{)r'|]H7\2p~ :Dwr"-鏁)Zͺf]ed6M|w<ZpŤOC}wm(Wi Z!r2i79Kl@W"lWR{b4[`O:)D5wm;e[Zq5{e@VSopĠqu/܈,|>8m^W"yBߎHpKI w T zE :~TV'b>wEh^: /KP,ֳHx~֓ڥ RNt mufi,U:FT`|;C{$6n3_%@ƬsK 57>g}# } `6fkɤ\#6oڙ㖖.B`ErXE*[_"YlO3a"֗6@w?Z1X$\0""ϡ56]g\>_ZEHYhOZKY"+qz. S[dwA‹Kl,%TAD${XMDS8%H&RmE O K,~l]o(L]܊E!`@7 ݅ )(X` Dp7F~"0컂,E`wlqk@C``l܊Uՙ@sl\&ˑ{+'ѸtJp% w" gm|!uY Jw n[Bֆ DN"bh Zrt]涻w|XhZ~֎2ƵO"᙮H\6w.@p<m֗_Ĕa;{X& @т#YouAg/!فp>"7K.`YDT/Y'mAk?("c)<  ms=pG .u-C'D66Y6u`b$?pjY{6V1D*!տ /!Wi6 hD  Ȋ͍7X[KWo$~iB]icgC`g5^HZ1\H} b[@斌) RT;H{U3c=6܃~rVO+@q7MU\7#: |`N:'0W>dzMD$.pDBе|vw`%Z3њ[q7mn;6Z%+0|b莢FDhD&E^}|C% /NbkM=wر>쾍߂H D6!_ڻ{ٸ\ѼNCV-?Ώ_n3ok4>(e"MR9 !0kBOB{TDʆ5}" 24V%Y]GT9!^V _=Hͳ砽*X$\F]Z@Hס$c8X$΍ƟB򟏬Ǡq0i|vtٱhGߣ!jr";`Xַ|/|q@f_w129e,g}{Ho=VJˋO~[i+m|C&EϠl>)z|}+~fv;ݑX$\Z_Z@ ǻ}I i1?xeRtש@t>(cwd L3i@TD~IGtH9 z*D 8ƞ9I6^gX\\Y`en4ݵ2 OtS#4մ!{2d X@Z@a<ܒeZSM:E/A%ڏd? F#kUynE~Ʌ֯Df#u%8"5לn۽:;/}uc!Km !"Uh`r,^1RNHxAcVv=zD(n(k"ۇ m=lu>4rW xEF2=OZPko5ҋ,9ADVf! u!ٿq3(BO _ ^֧f96 =e>{MȺ =Ywk!-߯qƸJ2Ql,G3 ֧ː5 ۍEAHOECl#2q&A淫\%dW ̳CeX$|nywwYD6ⲮZ1~ߣ֗""yLL1U5۬D3yz:'#./Dknho}9ܵ̏E qs-9L| m޾IT$D&?yL&JiyqPcYn&ZUHz;ڲϵVjX$U p x}Ѵ]]], &;o}W?$t08.7F/ICSU%_0@,t!1͚֞G^FC;kiA{%d uhwY^uHnn4 H{WRd\*kToBn<"h|@7ظb P{m%6W0Ґ/VAsG+9=0!0#oێf>H~Z6/mL"j{ϔ-ԣ +W#0LF VS6`[/1,<ǡ6>>+J lSlX֮D~h MoJ %'%͈OW}P[k 6a,:,5;,K]5a;sf~enHDR~TlVVJ[k)jU9Bp) yv&'Ո6ȝli3hF)`" i+п( Y:TC`b6r {ghz#u:KXF ,2RlriA 'E B$tE`v; )~c JDXj.#:ٍsy>խhٟ݈m6 b4!0tlLq6G$p H{opXF`JX$7jX9)}5"$_"--oٲGAJJ視D&|kɭe"zǐL ދk7Z;CdwOG{[H&Cry/"2ŝȢ"MG*6aO%bԚnD0fxNA !'U=Wsj=( ĿC8\F"u 0a"_(.GY"8HL^uD&Ni}c!#~"]AnoϦ!`Um &BkFDy':ڹ/A֧}ڀHL@ kRoC$qDv˽nvRxm<@/jbw)ZW!A"PHFz_ҭ`ڽaԈQ|<ɅeJ,^ ֍@@@h+.9ut/~^'% >֘hcl{$oRK6`#JCcw:t6@DCkk͕oE{Ёhi!`ow$gÑ!>`s oY]FqgmmIJ,^FR=ܪ'Iq,-/:}쾽mJˋ S[RR'iuiy1h"9Ʉ4hx쒛][%|oeIxcFM [{v)Xs"t.$g9hE :tͥ%Ee|8ΈvIO=qƻy޴ϮkWy}~_GW|&)j+\B|WL=7"1Br~,ΞȺ..?y:cцcp*틀X$"D 3LkWXC};,@eF f 0Tۍ  rtB|t̺s8f"D\.Gu "V#o2 mNv,9h_:_Dx0wg,D!"[!}Dd~,`!WVOX$?(ޥf_K|>y,/x,@I~f :hX$D^G}3"AR\Y6|w H{ruJso;U(m2 "?#\^ms[LzFD-Xߺz "j8mdHOԠ={G b\/܉)U=A$D>em9|_|{ ^$%%8. 9]~䎙KEAw${g"ֆ^z¯;Zw?UZ^׬m8NVz ,(yXaY}r\: =q2?/޾8lqp}8:388smVҿ:qu8/X=Sn^8΃82q.W :nq'?q99~q:98QŎ̳z C?)KKeDk@\7= o@EtHcjs1sME`yio3Fﻱe"k{6}-CwIss6Cbd?C6;ul>E[.v<] mH]|]=ko?n}k@c >ӍE@QA\0,>C%{XS(݁5@̒$٨Fi^6 ҕַaэd Z1O RS܆@3kϝbd}hBr@Gn!zDĢE^:!kH>{ќo@..~UF{~gm{J5ݒR#9;{Dkoav' [RO>Ƭ')6@R7&6:c.%3􊵽Sۑksimclnް~Y*ko$#sEd.[ߙ!U-Z#B;x-"Ӭu͹ m*Y\57tD =pƠ"*WX,+W%gYB2L̵o%Z@8$XːRf1˧m򐒣wl\3XR yct(:W[]b|PBaw>8~GC[w:bЋO"뎔@zHYPHFSQů"EׁhE=w9i~P9V7ė8ά;X}yZGNO Z pg,hy'83 ׾"a0s܃=JNzy*`n),kIUWUw9@n}YFATD`i;*3쳹5 mF Y])zߤfcgz%h-;:@Z[7#bA`x9tvj?cMG[A]1Yi؎d! Hx Cd+a\mYr3k">UȺGt knEn9" 0dI9!=hx!||+6~U֧"n4~r#V Hv^Y.Ad*Y)+jzW<`?@޶"KOzb W)}"ծhOY}RZw'tWoGmVKHAl^Eor3"=d3o>ƣ>ྷO䠡GOYY׹F9m>loEy{&llw39yמvS'RVduV!^h;F{evRch k@Pw;ō_}vgqN]&o)@bvhVZ^oNpdaَæ.ぼYI_I8|PRT]uR=?=Z”ODk#iǿ<Cg ϣx˪.^-i68/ w|H&gh ANwlbs3 j h@*D@|#+c!G`wvE6| +o&3 muH+Q/nm YWͷ:"@V@4@ZV H"]pK\im6tnnw<(i)@؍Ƨ$mc^V'LbGphkߏd0t.BWC|#pGwbMPI]ǍGdXn5kg"P"FAo#R3\2q+ԍG#  EIYI$kc;wYV:wDX`Z0tP 8SE`٘cmGVކ5TR-anOA,` R*_eh6m!oFV$C5A1G}LXc ɝO&Axɣz9pR,^fd62,2Ckf/c R@Z=\Gpif}hy-i<cm Cv6}#d3pb,H 5\Wm얪~ =#yAAf"xg}e//Dk  'm_I@Ɗ7p鎴[ $3GZDL}v;.,n4>  X$Í־|\4) M&f.>W:'& wAGsJiyW{j﮳JiLxY^"H)iC(o B@_|3*)*/-/]})`[IQYSiyqD_OKg농^YhOjC'Ɣk;RΫn܎#e^uv?.\x8ϓ]09cq ?AgվFV$"%:9EIj]~ηyw&gNFĩ'\G/~8uVG)W `98\cM""k kV39w4" dHEi.PtA0iϾmHx?f,2!22\Lk<D&~kyvݚC1GX ,4-"֡>Nh1Di z!V`YV\ 6AwelFೊum wZk5± $l\~4s}цi*# mn4^ds|W ?7i5m;&ȌE›<Dr֨<Ю3+ވn(Vj*]m\6ַ9D__XtQƯ"zʾ}o7DX$\q/ /oDMCf;]8rkk>q*H =l.Fq  HwGl~3Сׄ}OÍlRt98 $>! mȺq7Z"@O`A,䗖FF xsoTk!b7X'w`-^A鯧TG:>_v!Ll>W F rɎMLYmg4W!]g_nC;UԺD K7}mySH^km|Nݎlnl:we}ܥV" wC3/֥I.RήcV}FlI,4% }]"܄/=XvjlyoF GG'WT{*ηulzFIQVE{SPױj~{/ݾ?Hɑؒš][GpYyh}AERƻl'مnz6nD͏",3s|A!󒟝d8[!H:Rl2Bt8:> <8mmuY^C[]-qB@/r49|XwsPM@>GX8<g>hoDm盶|{!>-Bwp,,(>\,<<ߖ"@ޑ,CUn4^j}wh(nnJme4!~lFWgi&H7$Wwju9# > ,* e$NKH̱+:RLpqOA{j͗<* SjDU-ۚW=@D{_uq_)!f,H]QP:\:X}YHF!* (A :o E@*MFwAn4~ 0gK)紭}ӳAnOHmKT5D- IDAT`Y\7~E\/T!R˙H7 )bDRδ1؈R~B,a'wM?* 9yȲ5čƷ!RXޮkmnG `=ua`zIQƯP/J(%鞢hoZEŸ86:&q-Ң"p?^l.Fr.|4) [苀y l{_ޑ*w:0n:z3 GhEDHtm ,=C "U,F?bщݱHl,F Vi2#6{Ӳnua2>a־䒏W?~(}C "QEʕ"lJl_pDbuHj7# #֙j$# 7_cXe󌵥HTz4#rYanAr pg#ٽAk<_H|kY @{ijD!yAsnH629'co^r`#BG_2_ut߶|@v)58܂\|kV_D@|acz;ZWʒy)"=@W 6!T_yq R?`H!CF`JwN)6y]dXixhc??vi`O<^`u܄Ń"n4>ɏG|8"leFg ٞos;(d\N| `崹hn㵶Bd6 0%hr-֗FGđ[6zW6Dp]CQa[b\Y՛:;H6_iylR`́+L BSg==:}E!dZ\ WɻI ,]-4d;|k(x Go +9X$ؖ\?_,)* eiJ>vJ[!苔$\eo!H> Vt"7x fJ{A^b,YDnGws,^`4t߄#,kW6"=۝~;&K(p֖FX$ڄh;\F v(uq n%7ml| V__F,~ō@֔amL2E4|703I4'LFKv*>՞e \F>i.m}UZ[" vG"S%iA$ٍo !d/{lٸ= ҔiGNFY.G/Dd"i|mnףQv !"t͓V iD3,D\,l4e1_.ȽCk?F՟}&á"t ˑeOnqG a,!Rh۳h: CM+zm<dc+Yy &#Cb^q<sWdMlA7j\@w@j_&b,ȶL:l/[ Eֿ\kԠ?lOG{77".h`¡X$.+?PZ^<1@ǧ_y ,8X][ť} `ߚu$0KVw/Ph pWX,B, ɍGĢ) ww\y  ?F܂F֞h3߆N5rwCp:W#{L./+ ~yTn4~J,^ȷt Ύ'!S527xXB`, b!ހcwxP,qn4~:^EztiN,Eµcdd}ׇ(6^13r=@ϿʌEɄF\ZF+/ѡ ,1Gn}о`=(-ٸ<Ԏ2d-:FsŪvޙŧ+jyaUH0:= o%pamCN{"brm=TX SZ'#G$!u@j]nJd! k n~6 = )!ߚ1nbn4SdHwGOmND :I9.5ZSidM77[UV,76iև7)U tvOKIQv wrl$7AgAs+mUqq?奃S^AL 6,_Fس-耿\L .~]ijb$pFX$<ξؓtLͱ/@zX;{Q"z0ꫀ뷔g?ո-eJݍƯqͩ)s@KGD:6t !y끀|8e>ϯ!2x!9a}o~fm$qQ \Q֫#1X`Bt_m, kYwQy8ـf{% O#(! Y @hտ ik`YJmΪQִ6͞sJ1e{=Ύou쁬-K5gߏ.Mތ,#BP,'Oh5ak)/r0+s:Z7C2w(&;/5s_67gh<)Az!+L#YY'! 6FX@hvB p)F l6"b{ÍD#~u@db2zߋ6Ckm  W%rv%+ BI6E{ +C'U-}uڥ}O|OHg!ncwZcuxNqUJևhw8c ϷRVu.y6'AbXl-cť8#h5˟TJݷg7 o}E9쑦~hցk?揽g R$݌ƺ;P/SJ攖_@)b $p/Es Kˋە},W4du[W[[i+m_1E1F+Wo0Y,tX$@w2B.7'~iMo#pɏc8wCnX~)3A]s/@̿̿Δ~v[kz@^ixs-,G"е~oAVN"9@i,^*Eo1AȲb@`{<:*Xٝn4>~t=g=a?a< ?i_u*B8Ξ6 FBN;{?]@|b1Y6}1=a_$km<$[# ,Fl$"n~giFr~9"ܵl4ǡHHS{"Zׯ"] ?YDFX?fd[%ruoRHD{,rO|FVב0dyh["ͭ9?6krkuDil|SEtO՟cs ;N6IhF2rkcߛu"X=E?áHD;} YY3_q"kTPt4)'T.D|b8) αI?HxlQһ&#l,!rqۜh 6Nh]da<YodYɑ>r.gʭ5}!Ý.Yg<6M:9HКqy9<7r?jJˋǿ,$[SJ>UQ^2x,,-/ChG nY%Ee~,/W0#]YoV}@2&F&`M MNeܾ" I?{fUu߹SHz/0trŊ6Xch-1Fhc;"*X86F,R 3 x\"~yfSvY{]k/@ qDn#Ja$.F-Bop\M(.۫hA#6Į_GtNj yzlPgV(p['2Oil~='>6e^sH–~K 6#`рV]2Zxb0Kķv 'u1ho$,$ބU@ͻd,f~"Q:WA40Y˳lD7<Cy_ElE`9Jo܍AؕXErH$R{224Yȋ4ə<(+c{ ͟D# !=͞[auFs}~< XfuyMGd-"w}d1O֦>V h~@FpE=n=u=ws?Y]{-'#6ڼ66㑾DgF +87FV͆O;Un_w +{CD@@/Y*,qߋ${#hQyʈ@ {'4sў4dT@?yݤ*a% SlϪ/0Kˬhq>y7ǘwD#"yܤ^">M u@kK/w[}G`u#I ܄{0wM54! $ 7OA`p=].D0YF\ݭ#v29ֶТO\yON@YGTTg"ފ@53nsY'DnAƁlB(lۡ9cmm8WIƳgޱJͨB!Cs"ˮ1^Gr""`)=xKGnaoB;wZѢӵ3b߄~=K7VDs"\<|,dmJۿ*yvjDDBƉ!& IDAT_\WTR v)JX{db[}1ͳi @ZG{SA]iыN\w!ݕq-w xoRG9(LA\W,ߤ5.7@k$Ch`cŘmNXܤKķן9J) L0&guC hC^D6P#'"#z8>+C^'U; 5i~_w%KćIwv=^5 ǡ?)F;#KB}J G^03^G ܤ>HR M'!:7ƹ+usIw`}<9 a-ϡi|A/&n2Mw݂n@8ǃOr'\H@@=LNӖAv?Fg# 3) Ozmx/dӱ7Cs#?F04d|gMItxx_Gz˞{#Qyy䮲O D&A`y&;nҿfnƴ 0UY\&Wvs t|BQh7!u"eT \lz_BOC^">ՙ<@$3d`lYGsnկok8ai<|QckC)\#k/4GDFHֱCn:',l}ܬs "Zk$y6.`D~ | L-jݿ 4BD?f}}m j,O2@rUٷ1}&jr3~l9Us6vmն]deV5-\^S֦ 0׶࢒>hS ڰעMQIAZSklg6X 1<8 Bc;wviq';7 @3YHtWCqAp8zVbg@p8 } ҞuҡN 5/< s>d#vaӡ+ܤ Bժ6M@\1}K*W7rC6w!30+-w!`.$ P݅9HPh܉)hAi=%X7۬a> ]MȀHף : zNڂEd7D(s 8[fk"z(Htngm!Enc[#7]i_d;!eVa ևkPLg=B7:ClH71Z\W y&}NZ=ϱ> @=cm[jkB_,iD%w^H_E A 3BzNGC~ "3ʂ:E)g7$aZL4:!ZbB-^ faho& H܋oey%ܤڗn0Ŀy:xu7My<`wv@SGyc_z{ʀn kds`k2d ly6ϋ-H.wϷXj8qkc`m G]%di "Sη.KY{Ƞ5yyVFg_9w W&ҿ3egƆbMJ zWծ\7|Ʋ5ZY5`p NFzvvq-Z2/F]*۵no[ur`Na~q<$keoaΒt|VU>%/=_讴H!D 7]i^n>\88ZF)w 8Nҫ' >r3  㜀th=@Q Ӿ^wAp=8OUA"Hms7W"!Z[CiMcQY-YFD@2D:&"Utf!,F`5*d&wD| &5T7_QѶ%.@fvO/%ׯwDD 7ߊG_&Z4^])b]g^"^ ԹIEjȪ{5eS0uByXd{gWkބ Xe޻0\d!]2݀o"d6*G!cg!rt[Fhz S$[%n_J/D|ݤ\mps sPV E<4< Jg׻BhY?#/Sg) zt" 7Tc>$43 !^^mNF-Jg[ۺ"Y ܤzjBls3ɫ@ާvh,F"G"EKWIvf(t64ִM֎󣭍3;[gUgudEs4"E" vW[{aw="ؘ4zgс1C=T#`1hl̼ig C"]1͞Kď05~.F2|3c,f2֍TI?-=킅-rVX P[aT~l+jb.8ojQI;`Ϣҥkt[9Fue0m_-E%7Eea~wc|/K^tJ{{}tqz"C۫_qHA#P8Nk`qa2aAp)7Aj}<2&}U9q+5;I7,d(%l9BB3?!ZD|^zQƹ\~R eX{pDY>$ZDC y,vG!VR-I7w:=DjJ1Xܤ_&J7_h@cv%@hI"  =q[]I0 _ݤ2*#2BVۻ!4 wwlBacxbdA!=3Җw ̼MBqݬns .mbaf#K\$DA!~[9DޢWݤdB^*$<MDw/ ygD>D n^ FMBG"{d;WDn6f2y"JqQ(kZ [ ՞u=,D6@[d7#9>-޵pͩ(ѷm}c3R9nMA25͓N6IqAǖhoDhb(U{}&mL{H䩩6}lTkv}Bo֗#Fi)rsʑi}C-lNCk 6[W }1:&~A ='!Yd=m]C6 m_U]b$k' k)hdRsۑQQISTR],(evӺ xC|/8NZ7 dZY_}ΒZ~""+@+ \Byc^FVӋeYP@}x8\Mfz|;ZC(<-#^"I/"KeOdd@`ݤ?OSo)F^qk^E;m\m @de}K/wa=H 5>Cߦ#к"{h}~=g)JͼMݤ O: ahD>""=,wz:iy=D!p@.D`-\' ]gv`S̻Clͣ0tr4vՈ|ksktD@R_ Zp-ivYqZ,p9ZFsb ˝ A9(m 6O"|]Wrߖ@8bO{׾֎u6Drvf oP 볶C4_J8e #&JqF$X]?0d˟Z>Edm P&vS\"+p$"wgn糑 P#:X y_|~A7iy) %oF =لf-%^tpa:!{'ڛu}n[ommBܤlVU36G{{U֧ Bb>EtA=c/Lۼڽ޳Kķ:XJ Z"]PTRpua~qj6ii F ~d Hk8dUCզfc޻xuvɛiCESwX^E{t٫!-73s~1Yjf74[&Kv.'**)0uee2V=d!X@x껔GApgė\'m[J;Czt!}Ѽ9 A_VBqHz_qβMA"/ha;PEg*'<,['6 l6ou"YlXD ' pOD AsEx2L^"6!M-XHkmy:! x<#hBr2ګ4 zwDi/B @G?򅗈bim.q~Giz]/3"4hu+n| QvPj"yh%\ey<"D&B@`|w 27ڃs[y9y[#Z|TS-:[ @3,}Oyk< Z}!L ͫ@e+˪s4h9:$&3l\18Y/zD C7Ջ>Ei}岴l8޾6銫m h%Lfcw +Y?uA2S;Dg[ [^mt\;t`Y7AB$k~a2H&!D%xUy=cNCDzB`עS0P2$ۻʐ8 "ZdmC]kN "T䣿DTYW7yD[?tDD86P뻈B;Z{߶6jOż {!ݲ-*)hEhj|"DwhNeWgojӌ򺺆#6خzե*V SS)/>#!Qgm{eGl{,[H_~q%qFѕvQ5 4i:,t{W0 `8CkEh%qHzFqBب K;˷(;9E˜Z! <0{V9H`#:<Y^p}2Dʩ-W -ZϫiozAPD|EuO&7Ys/& JPgޛ]5 pVv!@co~&xOy dlB)V\gcr&0KğtiޮE쉖 jjdM hRRdks6k{ka{%n Pv&Qb#<YE5yhNGy|4c=;PMg#+X"y=hYʼDfr}M]M{[?Ld @DA> #NνNCy &#Rp'|Ѽ8ݐkyʘAA,Wسf\o `h}%tӳ?,dȷ\5G9 s G =]Q&SSHI`m{27h.I:Pt%h`2yD^m0 h^{f:\Mgηl@Dt. H 4DsDwC3֋R!Y(sb <7uge.2V®)6~Fjo. QDNErzNj֧=l\@W+BBD'@ w԰;n߳;-RxՐssXK9EZ)oqABzx$)&Ŭ o4-"4! 2Dv5G b1Z"9ZTF snz{[?>\@DaZY 0㗛 |Z%g6@@s :͉6kY3ZYڋӹ =9 (]^"~]k\:" +'!u PDd&֟O!y6Gqauy %ȳ𼓶[] 0D@w`#r1 Cc;~/@a֖R"pSQ"Q6V3S xzYf#% wG!_bldX?bm9 2$]s{L ڃ`]kii,eX{ xKKm/H{tWtƈV{^"瓐.xឱWg6^oz6.سCemH LFom~ #$) 9(# QjDҔzg{F!o?0D k%/<_ O"h^+D|ͳ#М)3g"=NCzgiͲ>ӧWCoFrSo c  U'4'Qƒ ij6-LHW#`YﮨjY ~z?x{ Fwڣ{:<΀GH_n>b;;#R|gqBa ڙb?W~s߲:fʬt?E.r! xU*d@d}KfޚijiuC7j:4F8Xk~Eɐ1T%cehq_@dYE@ yGϹI"F^ᄛG`7ppfdyYŏCa7D@D-<$/ x߼gݷ; B"C4p6fun%\Y#s KR-~F XJMc$XW!`DiHnGw="Z~vx$+IOmz \`sG 7zc yXtt-+ySgZz,ܛDuGY_^b"k&!({ Ju@Ȑ攃d B IDATr~L`(8YnFZ wkv ._T~4ȬBa2,О 3B^Rx?vhABj!lc#9<  y*^h;-p~KOɶ{F`a$cn5HY!u9vRcHϷ E`#>hϯCoa8O6S#hzQwϋ=]ifc&h m<xMM$$I2,3YPVUecz8%nrVʬ?F1(<$KЯC$p( !\mKCc(ko}w 1 7k}0x X}&Y$7{ny)jgI=4%"<:b 8%ƜWby"$aˑ!HNEH64O;&HڡXju/MvB-#BއO؟C+r64PnLCA]fmoY{`'6v9hG& %xl0BDmفȘhm^nuxQG6/ =zvCsmy{~p7"҅=u] /"]ߙheF)/^egʏ!t:B) /[O D u> qلaT hW[dG^`vTЬ&7oF =ym;:4LS$gJQ@`oCb #p4M +& k}xR5l D Xp~=a:saz]niamݤYE 5)lPi;3 ƛyݤ_Z0~KCZΊEnri3-zA &7&Zlo69z^E =64bMNooڊ?kNǞ1rTOq9E$~=Gi=kCW$K!5( 6dEDmVˁgD#Lf3/n_"CoB$(qH7%&]#La6~G#^O@yt^@%|"3?F$#<6^U1g2 ?܌dt3?u^"^7&7_,%^Aܤ$"U"YHi= y#(2˗Z_- fXt.$C2q(FV9R+P< 1m]^7QH+Y_denɄvE+7 eM]Eg ~ o bf5E%@]dqP\TRe0 <} yt` yJ "yi'9Yvo_~D!#P}-kr?-c"K_puȳ g?X%O&8ZDG-ܤm6^Oj{#l9t9#KuC8&^"MQh@%Kܤ7ZԜPąhh2WGw"GqQ9(oqtpft=^"^&l]k$"!D}o Si}_^lt] 6ظ ͋>W՘Akhwyv{V ۣ1x I#v$COuH?cQRTRЌ C=E;D{>C#< iA@~m}v3xџh M-śJ 7|n3ք6oiYu\,F<蕑VamM'kPT^"^9E%Na~qPTRп!抪65k23j@05@sZ, eЫّ50: ǖHA*I~E%O,;GAl0RskjVZoŰY Т㤄\-#WTPUO;®YW̬x9ML9:Ct(dIr@)h>&U^" lɔ8(mbJlE6= 8]e`8"dZ^iuH_%[nnó\WYic IGlks`u: mogMD{j[}&:J`(iQT=Jbl}HpX>wG Y "\&>R0n]?'cMݚ}y˫_ڹƒL.˭ +QFu,Hv}2V-_'B>BY3lj7OGr0ՀlMܽ50p/p&3A曬~guha5țԍ종Hg{xdRݤ%iȱ{Kn}F4~njH'+&<&Vj@=[(ao$-'܌At(iF^{W 0(n/A+gE;{"<2Feam> h>"y8 ],F%sYv[ )Z,P@?!=,}un׆ΈhA=LV}#'6!V/"ʘ ؖڻmJڋY?Bj0F ³؞ׄޟ_I/!/ *%0HZt/G& -\ajL|Bt7YxTEQlWoO{"q ARDY콗!Rs[GQHj8ɮYg㗅|ζwv^",Yz kǼ_BG*@\6ԫD'I@k'@zxwTkH~7yB5w@s7_m!fg)7aYb8lBb/"9q̇L.!D(DYڣ96X H-6+PS]&;i 8b]ʌWˈNO/C;OާCN_Gv -=iDᮋܤ6e}DYm[Cref}:#oC:u\ /9VӐ9s;7mDlh򆝉l9#yh]i"<3x"dzHF}o}Iߤzu?Dbː7~Ҁ˳š(Vb斮ᛗhNnA욌҂ʍ-VAGZ({c9 k<Ǹ{ꡅh#?{g~jh1"l =a"m(㐉 7#5E%7聯 3=_uWRRrAwN8N<tr~ͽ h8NO`\K] q wX~, Z9]68d]+_ ,@XHYsbYF?ّh@.:ԳM=E@'ѹ zD_&)LBc9DI# `;;nw hEQh4 g^"~b/ ?oo?ܤZІukl+|X[2:Rdi{ owB#Xݱ:gO{Jmi}P  7 e20B:Ն!4,Qx.6Y| 4I\wR< +J  <(-/5m|EA 82H+3[W8 q F?z8A4| ?QjQSdZdy=-31jnQֺc}谖ht_C+PBZH.Dd>?^9KďN=! ͿCaVx?BSșܟD<FDvE m1?Kē_ӧgYEvF5< /"pՋ(@w$xRWϲqBԲڸMw\v_Zh=/o4Yzx9 󟣰h1z =Ml`A>g9 Y;xv}D^3cD|^ v="yE^N!JZ4.B!Yg=1RUC<ur\e#`tpϵs߅DͦY;g"О\DZ`xKgJ m<; Zrv4wG٘BOaZtȸ $W؟fYh@Sx<hCfAY8Y>h?iu$0mJE4E)w#:D,ϟqI"[ S\kˮHd"OxY_Ɛ۽Iͷoew ž1l"fQIAxp|&DBRy>0!fp3P|0zyo ?t:Sٵ@0;y,h=O_C-29@l{W7:঎i ?Ax`qc6;{n~:k+\tNyhj)XW_O{v8Nv @-d!Eizٌ֓O y͹ 86Sc ` +@n7Yҍ]e;)&ǡ|F͓0Uk֮\EBR$Pbz#B5 k,B#W HOTצ!(dSi[# |E2d]ecbh{:$$@7Y,khbf/&HGem-g}@""'#׃YޝeTXpGL$H3k{6}<ѫ:{C슈 H{tԏAWP_\,p;3R,;L4Plڣ+J -/^naqr kA:'њvQIHgﰇ~h*d0?–NݴO[ F Yo = D!(C{J C:pca~qjDÎVFw}F*!@o.'Q߶<\88 B@cW)aF c]3[ $BpC@` (HEѫh_D?,Bn@d*QNLIQOgwt@ ÑacesЂ7sܤ*Gktk_%[,|[߽+dA2sc@s ZhGvMSLtVJO:3ǹhq?{6"s}c}>R/CY@kX0IH""3lmƹK` xU* DrU^t doliوkGSQӇ})x<;LӒ6G?F}%# >#e(PPV/*竑la'Kܤթ/nҟgcfDknUbG Cdw=yQx|#1F!u6FiAq"Q(%I30Ku2#6y\He0MCi-a{`l7w2{&k_9cQ~>"l0yyK?ٝQS IDAT 7 /0sy2WXXnHBZg&"=r$&S&:*f6kۮG WD|#rwMr#F:s>-l0͋hAI^ꢒj:뢒vDCkAOtΛ߼1mVnv-}j,$mQ(no:bme=YkyI[(G%E%C|c*3֢i82 GHז /:"D:E%ĨO/:n 뽽i `y{NG#A.H7%"+ 8<ұ_Uq IW䩙,Z*ob_H1E:?o6߉ t"-M}R4K)>eaLjG)`wo#!I:Iqi%q01taTee Fi|; )ݾև/=\`)o?ͿAh" )jD!kt@Vk9YȂ={2/o>ƸI}4mf}} )Sܤ)izx `wFCMo^)#Q('^J>w Ȣ>ͼyd=+ O)#<-q1pbL\inͷᘘRn*m.HZG&##S/ 8~_k@=" 6d^Uչ4b޾2 q}ӥZқڿElĮT{i_Z&lIY#b_ȳͫ04=",~"Pd8p>皭uȫJo?=${ǺIbd(Fq K "g!TAnno} "m7ZY?Dd{n;y֖0_O6pА`m JW3h%I)nҟ}94b :=[¦D~8^<z`PQ_<5|w eғ鎀zv3X]F`0.܏ Y[$  6 ԀH,w^"л[o}+Zh}zx>j5cPvD{zG@/Ka 0V7x#vGYlBrV3Crahhȓ1Z/J1#bap 3WBäm)hFfDGr3< 9ktagì9Sz "2"zvbt4X5kh=FvEFE@^ay̫Hgf=ezsRTeur2^w:e6_d ?_GfR]۬:/Gz_Q~3 -\Gb0 FgT$Le(V-ޱ>ۻq7BUhmUt{0;*Nw}oy)(CeF\;/ \AԎHHr06޷R0DuĞ2Z@ʐcb- ڛGJϱL}v]Y疳Efŷ7lVg;ǝ)#hNYH`+Pld4Fk`h=:~|Wa~qYQIA+Ca~LwcvV^Ӽ55Yۛ5^ __(J%S 1{=ϸݯ#@(-Eed,!7-/**)XL D}О;r5CӒ2.e{& ShO`SE"wy7 <ϛEQy}׉><ϕA8 @HIJ!2xA@k9 ;x<m[Qڳ +?>#P&1.ooF`})jr~4:ܫEddM؁ nk]f| 6V!L|$lG hb;BF? 2 Szf]g}6 Dϑ`,Y^j٥H KSD$ l Nދxfo`hv&>CK L"YFywi!N$LBվ]` D~g !/bY,U~ޘJ&Ej+ir&#A ;=CqY7 ؄&HxZF-ع@֗Eeg.r;5ztEUvp?,;h\ʀ J&8uwtC{(#dD}#iZn{2P~yg~!6<'w/4wZ! h#:| \H\sҶZRW?@8_J&;]mn6^谝x̩|)5q}d4zB9(.~q@ڕ3)A&!ќ/FXiaü~V$<#jG8`5&A8%s^׶#K>&"@E !T2q/!=; hF4H? <$퇴YHz /e~NMDN9_g]j"lf~۳γ>4y~cmu; peF"א@IJWq6_<;dODrf-&{1XLZģ=6/m7C 0Zg}Azߡp-m7#%ȋ&jsWl^] hm@qǠ58B6"omV C"[CV*X+6vm86>漩s]q:oQ^V4Aw6`Y5+} oqg0:!!R }uܨs Bϴ#47YՕgmX2N2 ]W/ZDGg\x}J z"s,*)`ŰӶhoHש,+Kc%v+/GO⸾i輸M墒{`5NhrKK˝eZ}P|hj]wEчuZ-hNQEK^E tE"4oDQTp5u._ъ(z$ADQtcJ:+n'A]"gOMo!z#:|eQz[i# 1 MaiDڹu4DJ&Efihbvx˸[w|Pۖf/2fvWSbot$OE`Rz^BG]޼a} )op!\`& V#+ә@T2qO5.7Lk]Vm} _홳Z_l[!kO#+L ^ĮSڸv8uH;@o]6" | )iۢ*/\ ,ja1"-|tx mOp¨#Ц߈F;WFx; kcaYCv@ Ax9d}|%~YNGVU&6Ct$p=Rшל+?=dS?/GcKdI9/o^7"yڸ)"=F<KÝF} Z?K db4JjyT#AYa ѾZw"%H@}OSǶV a5.]ln\ld;_!_/FȪt-֏?ݚN'hb!?F|Fs 8"&Ȃ`6w|df6*DUzC`%#'^_dN]wdq8?}nna~qe6Fֲ#3)2/%.Z]hދx7Ю8xo l /"ӮA_&^{{O.7;6 o֝+܏ՅIWfŮP_\ԝՍAbSDC%AQIHXr>cwi&|,svy-L(oyۭ"_T2¨_9Pn@#i"yG|E25-Be5ϔnm?Eo*fkӫsAqި!kU^jMb|R{(HnFA@#@ g[_D?K)KW۸^E`s/DYv )g #U"*7Sk aON"P.[C +' yhm0v :VliS*Vctx6qQ(Ts8s].B1(G֎HirũdYbEp xl&dlbY Zj"kHtuz 8,4v@{HH*{<3= o`[O@H5XT!W!E@?&WyGP}ݤv'cs ."B<ܞnhxsWB!2@˞Hqѳ%eyh% '^{#e69m}vIBJѽ_ڸZܴF.@s=2$vqI*J҆wĒ^ |ħ΋VCP߾v Jv8Z"% hM7B[FhSΆ!γw1W.R. W*wlٺ.Ud!-o)["Q*8W"f:~b ]1Ζ< nfog5dCvddrIe9 !mc+,#{=X4/78 En l"^jnʋ@nP-Ar>*Xƥz3 iz vv 1h\msؖPdn@;jVn4+BndO P[5\VYm09iWAx SDY;˜9H{z[&d̕HbB_EOsZQ()]=j;C'@mEwpZ*,S+3^`FWCb`'`g QL4$LFRnWCLG]E<UfNʋN.g#P+Y"fǟ0;" G-B| ]H!3εhTmEgtiϜ`Yh>55Y_G`=ZW#E~V37۸6 ^> Ud!:#e Y!AVOMN%c +퐂%^ckh5Ř=~oh-ƥe]Z{!Vֵ1W G|qZ7/"gub꼶sov;k mm54/s{'wE޳Ge %AgK'-*)=pڭ0u=y9C[}AJ(J%|^Ps,ؔV6Aߜ\$Z/\"hϼˮ)@u>des) DNO?}PďK3`Gn".GJ!Qc͌>7.#~|EЮhpֺ *|5ptQIAoߏ._7h-Fo_X<nF5==ZKlB5J +/^}IV~pm@./_H4׮ \,H@3Y#M<P0d` lQի8-u=){W-:!@i5 ٵZMh٣i DsŎYEċY kk@,B:d9F3)ONiԥj6A$cefe?Y2(#:kH^ܞCUd߿ VAxF֘Fv &O'x߫I_'vg0u{: erVAWߌx?#H2}U3`x>J%>{S$x;l}oiq3{s$k\"ܶ:? IDAT3["yocF|͞Q`d]9db@*;6CAx1{-^]ס ÐU$ ~ Z3?K%a<ߚ8X4O댮9}9b f;Y\Eh']>\!kfWmm?߁TEu3NF2{֡~A\`Z#z)UN3Zd ^8^J؈ho .V;hS d7KmOg'GF1hhSh>EASi֋K<oYSCbeA-Z# \c\-sW Di}Bb^)EUtLtb7da .[[}N(fF[ rqn3ax7" VHi`p!=6M|%('>,=/t5:d6!ͯKs4}h;Ge5ɩޚao܆ ru:8V}\5ktm֯{ן3LHt )}Z!-yl{`Y* +L.$ǩdiI%/A %mc? /`=#Ð@Y5KQJue0B ~GC){F%B t?Q;R:\xn} 9#ޞ@e7 dUFUe1p͖9ymjЦz&h Y[#` 5kM6#Yw?ƌ@@imhOD\)jd8Zc~T{_#dD dbYhG"ϟv# xxrlОwd˵ VsAVh,5ږ-^f؋Q}|s{?y)ՖOW{/YgmrkLd+@3"g7jƭ/}Pzh4V6H:amK~㷻M? )2,kJQI,܂ U5.[}o;vKȬ4g{,Hːִ> FZ1iz -ѡ?sIНJ&fY< T,0uINOGg&~r; ?h>,V. \ҌAg)0ـE j0IM1҄UTlC "" Y$8-@Z<ޮC!qܓ+Ӊӥ>wVF>{  NuhR#}(eCfZkP :tmm CNکY,L* ckLB͜)֟FRoqXwqB֏W- g5ZХ> T{!RZMNx>L\?"K AYF«Go{Bbϡ/m/I+ A(0i37FgWOŹCb}FCoKSˈ/G/t@Jupq*W b˥, My3)Z!!!dϐ  x9? s6H!p7I6ַ!nS?EV'2e}rqSɞww% ֞w紹Ga{MFFj)J%Sn[}|FѾ _ 7Y+*)h\ [ ީ%-sW5W=HQք8G{vy^> %y.K:ό+yKkE;w0߽}Ew;дm=ǚ!KS<\F4Yu>B{n;@BBc5-hgE5tj2鄴l]F]P"30G ~w>LC-N%}-6OSf`"!кhi\g#7{ޑ@ZUvOo{o@,^.Rd\L ԛ#yK@v$G Үb4}x5-^#rަFVg1kpI_29p.T2Ԝ[mZc)sl4H%es| ` /LJ&p3дMerǭ~KWw{#䧬K#{H`J+P߾yBGhGHW@("ںY~y×]mV/Eh&t:*/^oy~dٙq5UV?.^y6Wo󼧀(Zm=ˌK׸1(RR4B eŬTiVGi7"KvJdXPd%pRVΜex7?p6˹i=<RQǥ-dfNCj%^X 6_)GeWAx0p3焍=:UNd0A֊! \yvYBD$@Ce=USJ : M$ h<A6hk*nV|1t8YJ&j-#}YDg{NA .ŀ56Yݹ@P5T2qP&\ Iõw4~6oJo<8'Ғ<8V#㈗FqDDէs-H@8 *4g#8?0^zJ&D3™6<"uC%ַL޳p87L,kA)K>@ )K@mRLFkg/AYI"<'Mк(Gp`<Ⱦ?+l\Wkػٵg wfQs1w6gmxŶ@O?7 6_V* +r{\&?Kў$#ײ\=tp"{k@Y=fKP+dw$^=J}e0xKQIlTo/$ ׍h'J N}Y/m E3rrgBQa~z`}QIhFscٹB} yydYH@zdEozZMY+v_*l0W^\@5p˚mqdFnm?,i!r؄UH(:iJ#B 4IH;^# (>#. :-gThsSQ@h%|oX!Z>E +oE,8D3f<7x+)D <@RQs\M"4ggZC\cͺ1}S68~}4޲MÏx%Xek=,j\b@w{؜|ze7N *AȂ["!҈g=mNP.f$\h=J\e |Ud:x.C<(Z?db5Fmh-4y} K;hDp}Y߱`>ZߣɥhѾUV'"$By Zwu2I䵫F|w1qh^[i,[S?2]6%.j3J Dʷ/ϲٔfa}l[?g{uU}֭m_lݽҬgdw7Fʴloss{ R䢵|V_\WU߾δNH:&k!+OuwR 経';vEѻDQU5mF qGF[{ri`Y+"qIH?eқHrdis!0mc̵֏ N2] >F@ E'0|~jy6Cy+ < QN23xB|m?h9۸+r.Y]aRć(măyfK 7BZHHܾOl,F3@ mߏFU1d3-üUOߐc]J Qb4wVJ;>+hM kc_6#A=ij?U%mCml=~Rǣm$̵esҬJ~k4מY}!}>~oWwZ5Q1{ ^&f ^yr<ׯ %Ot6BkpD ٳGd? 8@1>raoŷ^i saoP~{m59yUKњ90xNwtTIoO8ͅ+J AU)*/S}Ϛy׏bMGqWי\;7DQtlSR%B⌅yQy^5<{:5y]Q <(^Iw<1_ݚEQGs(j+KjNGs".e3 Gف> `J&L{tCo_:m `i+$LCp=;OC\"o"qZX=NOC`RT$Qַ9HHr1+FQ-}[NW!p,xT6 :Y>D%ɲȒp=΂u)X-|7J%K A9gCҒTףy&rm!@8d)ݟ4mt*XeLtAaY\sN3= gdCvdEX?sa kOKăej >E~͈O#6ah vOGb9q--itAKRR$L?@\_i#~`X_!0}T2Qs 7Y5h[Ui}\Mp:E@{?i"]{!l}!w#GWov8/#e#$Ҿ9:V~@~f4Z<6'=њǾ·t5֞#N$aMFN²%9X׮z#r5KUH󦹽.E{;@?_Gإ&ct:܉y+Bwxkhw%W#%\[[ʹ˳l.o^η tXCZHs|ztcgX~$ c ,g,kho=Ҧu@l}l/??]lF~g}F Xyi ($l4ُ=p=d.]+d%4 65Hx--q>)M \Ax;k!"҂]JG"Z; ,^9H0P]aفxGȽi%"/pӕ(67Ȣ5F6ֿHϭ𘅬]!^ȵ_pH9m,!j/ M088񆫩}dщ,.i+Zc.-~fAx؛%voc`U*pZ,~F*@YJ&Vau1ZKϹt#r$D9ޯ@ Ug xYN~k{p R%0{~E6U6OXl"Xg4;#F^&{cbP'綪FK iuY/3B%h_jbo6:8nr{^iWM.Z2Wu>8߹dU{<zGSxtAvX_EÅQ^9ћVon&qC KpwQIt2=dX[0ŮlぱEZM(ZpyCH4j+hy =qEϛP6(h 0YQ/}}ѥCE4%H9Y۵Fj^u/:h>ii@:WXt L (a±xt)[;QC֗D\3d8 pnb%f!VW$i@ZG7.DV`9㑋ں?eَ6l0f:̝eϺh ܴ < iOC`l .F(>NCnJ#? dk_;cSɄK iZ{ J%ҲӹHk6Gŵ(k# !4a}bҜ /|޳`%&YF{z+D +4k}`,AB4īyz~#?L\jIu ՆZjV& k$mx^^f[A\mFrw%h4av f, hY. 5=Yo0Ļ{X'8S3}]\-CWC{Fci&f [%βy*R"Ѿ pe@lpOF{=Ȋ= S#_aZ*Y$N_ j3+uTqK볻{,g)RJ rzdo坅Qq? mZڲ҆ +Z wJ+6lVؘ6λJ @OњշoEQ#L8j>K-QͲoYr*}Pd~`Ag<<]c 7#~z 9i͡}3 ޛl\jtOQ$`ia;G׏U6GK$mp?'KJ^\p d4=M|Tʾd@}~_bQdF |znJ}2GBFHPyrLV^e'o{9sӐ`XC,$-1](&{lPe20i&dFBh04ugss1Scƒt 2#17k`xYVu! \_̌*>ɛniVd?eHQlyHB|? WXRj>mah55Z7e'6Aw3FC ?g%u}'(^NB{rF* kn-UtJ߶A{:xK ۗ6/շvoen]/%o,m\lwA @(̎xWz{ Sri,i`7! &\eW >~E֨LJ&7X"uSUm_AV9M$! vvMhgϹ ckc< [ |Ȟ9Yo5|x`ayslغHcYjkk \\*P!*ċ8"'% 9~~nN)Dd ďExF '{H0ks0:dYT\a]7khβrmW 5.-[R߾p+YB5 l D遹R#G Y(zLjA =AH-piŇ!+K-:KȅTm^HEG/$ ,1܋Ӕ >DP#|;"03$tB`{1F@|s&e#po,T]Hs#Y~J&>5eKǹ~'"+WkRawH?wL%mNS!|fVGyXT2icv܌@p,BMs\#+|_̴OAhUvT2qd 'RS6;/~}Fs{+hzۼC|ui}%1,Eׁ֘we7dz>Y@{&$Fެ/G!Z#]~s=!^FЕTAmocםעsB.yCą'S[}o־h/"@h oM@̉(E~Ty%!':G!&a("d Fڻ$탄\n(@@xdݮ{TWg3v ~|$<dby2B"A{5jSPD.ps?]M˟#XHuashk@c2?5zޛJ&YC r5۸:@->"0L}( v  *sC\tMJ/0xON?JdAbWjPZzE*oå4DAT? P 3~lO6GdT?["0; 户 >z#R?Og kĞh&Z;$!;Fm7hW7ꬱ7FOR D$d>wʍٵުfͶ>܇o{ w-.&$l1֚@4:3 E"au.7DfLx W"5~7I!!l. wF[Kf @BּXd^)A{X!Nz:R8WОmۈOJ͋4XkJ\i>\[}oOY(B #$L,A b_٪*F eB Fx1@l6 tGc0m7E&8´-Q!=N!V @2EBT[d399 /!NccIjյ9޵Hr:"!" GSk?g-~a׽Po+CrWP7Ⱥ1Yh ҊWJ_]~j\#P?ᕦaR;c.1@a?Z56'e4_28m'6O7qH ^}QLhךp.zXBB6Es ZlngV =MA@lO.Ff4qma?#AeUJ49O"%cݬr~<=[PE~>@__E.TKޥEGGmn>>OkhD`}.3@[a Ԝ{Tn7vG ^f5!m*ؔJ&Ax=Rp;wԞ}_ZKmY9Mjdld}k DK_lw֌}Ȓ_,}>d-D.BTGPG$~݆@nY:]ېc qFpTg=c=⯗l~Z!72=\/ 6]hYƆBls uJRƽYv l+Q82ȩڒ<]h gFkEM@M\AK˘ 5?o4mn5c1Zs7AZkE<Ξmll놬q Ο϶16t!Rv̾h&f?)S&3Aظ7jkXَ5ZϛHwz ~QwY7 ~([Z}oշ}o"P=?k| T"H=܍Qv̝4ۓP  a%ۄg@`!ҖH{$U#X!:P7"P@|5d P b!r4qm˳13[HH(K=߁AH^HI1){fEy߻]XzAXXXXc&MbQ1%јI-FD:b5 "ׅ~p+vLs]{93[ q)f?)@EkrLͶA뽐(ڸXDJ\_$EV$Sf(r 4Go@Co0䅷[WF>ՓvzG{M"ɤl}9Ji+1$6Xҵ.ssre1zN֌CxWĵHK\5XK#A] K\LXigS$MUܽѺ)sH(Ps -{Wf}?{gsW@ιW/p/>} E/g0Z, L1]?|h`DD\>`uB tQƃo  ERLk_>{4 סryaj撲4oX{F w-!id Z7H&SG2t4셈E^: ,A7k {vM~'u_h+kOWDw1L,QN-6WwdhaOD@EȪ3Şl\#~{NMUed0ł%(h\\[T@ Jobd/&׫h@Wkk.9( @98>J*:~X 8ՈD  @Ůecz`fel_syV_!S, Tg4>-$M%"#O[4GmE(QZmjjTcgc7~6^}5?"i/#9޽{sA2D W/ 0'%4 ́BN9ϔ Ed@0'$`>"G۶u͑fH1Y0f,uA.DZB{D`~,)5x(J#zo)54zWqQRBDc%HP.:4w>M2֡quYmM4&i%ιanK^^lqͭιQ)֕P>mP U6hJ"]rRd!qQy YtYH\Oja4]cPqn 5h2y"&c[Ps:Z{F ,ȷ\lUxo(lSݜ ϳv?K@jģ~c]FAQ\aM¤(}y!n[8g7P!YQ6H D瑵er˳Y@@l ѯpSakq*@.roxܴ.z̞8?t!WGoό/Cֶ6-dԡ4s!&6O!"bY> @[[ vBDywGsw?.D6a\~Rk6Dƿ'^Abkp-Z} L 8bw%6Wfw-DgRh<"W=ڝkWSe ,"`{J*^`:oDi7صDJS4@XWfrg >Cw¸ynsϻYCsc_&Ɲ! dmj!HJ"Zb#VyhГTQ:Ua/G "L#4sξw0:G!ވd%Z-ҙnֶ~rc7 @H&ށhB݃?#t=jijEX@^PhY_S/[sg!Y&civd:Fk;Ȳu"_h1b}Yp=;\#M/!Zd"͵~j@N"OY k<<\ն(Zmڀ~KGdoJk[-;ب$vjcF 6XDDT׀;0'KFndZ[i=X:ai[kבA/ҩix"Ɍ=ͷHd}V#ҩb5tjQuYvv;S4ipHir3|͏<`̦,vYG}8Ţ{s9k,N=Z9 Ҟ^RYY`=~C󬕍p4_޶w]H9Yy iBkGDYAA&aG"ظ=Pmʧ"߻O"6gAl~aRZ˝ B\b =* IMH h Bsd]:yl 7Wᗘ k4WHz.ib{ֳYiս>ǽ3;pj#q7_opνΤE)ι>L%EH:{?h9Y̳;/{Cxä?Pc'. )m0}Ahs ]+|"+C' mXrsHjD Ү;iT !iVa / -HsX^^:hf7#ф_Log{ &Y&[4𧑉6:7yr:93՞.>>Ո:5@4(wG"3(;8 E*-a*J; Fdd"M [HH&nCIypmAKQ:6!Sy6~ZoҤlGA#{R⯿Ai{n+F{[mڽ. 7^ E@vȭnݧh]@ѦY "d k~pƬBͲ\]̺u_A/hR%8ͭud"8eHY{F_y5 g3eJ?43DwtkvkAhODRBC>.Cs`Rʬ? 0[p3,Bn9"_3{֟G9PiYYAk`Bh+m_!Do&ͥ}a/ts"I+HS_ h/یeh\#4щh^hU#%h~&t#yCNCV*W&i7z3ZW; 7U3H~s9JpCt93g{ƒ Q9Jj~nO 'X[Գ?3>Jg|;`~ߏ&`}4!߇Y>T4!md> YǾj21D;x%#sCE~5J| W!0_z@BF~3uDf9 QPw'2׀Xݳv=,b#i)s!J L:ic 4%)i Qv]Ư ķz@p;]&㖈4@8Gs{V)"`"mA# Y{DA&.ڕhg >eGm< oD *D:kxd2S,FցJD"wUN&f:seq܌كdvFh4Z펯TȖhPa10B{-;L]|tMf];f+WMFq9dx6>"xa|S"2IJ$V0YhNT5!m9YRHL?ːK`Z{|faAhMLCfr,@z0$ڐMx9zvA$w_> TiԿȢ kV1$VVd?z?a@ʑ 2A{@{4&"Sv~gr_ZcBbM?4oE?I -dн:s ekX/>x8.tݎΒksz+s;=k,9q syG|6/ ZڃO)Mι'pzvO\{(ODb{ιqݳsn_$so9?BG>|eIPOq{APkz lqED`xƿF /LFt?%dbj4(m[q?kSUl)0$ k ʎWވ(!PL}6ѡ_K[h~L־T5Sȝ.(ΈM.XWymjCb3$-,(+CD q6"0t"U ~!٘4;&ưwl؜D" rcXM[|VYEn{58ԃmL; {[:Y f[;76`왽vt iO{ZYA^e#{buCf̳q(k3$\2ɬ{uCD(z!Pa҇'X FA'9޵y4joX;ӐwbI-FZI= 0%I"wژx_km+DѼH&5"҃x4 "ml!W(+F]mWV";4V%J@ֱ/Ry/v#O:hl[  Wv-hoy͞h|2=RM$MW9FcKֳYwTo.[\SW]G$ x:yp &Wp~snsnJdtG24TWZfܕd9~Խ8~ZkÆ~sl1\hqޏ"t\-Jps.nv%%!n^! 9(.*o|zfL&a@?@ F&I[a׍F`':8#m(z$P:" wb\k!o`1߇#` ڃ)zL hSa<-7GԪ BnQ(*{Jd,B#z1']JVk :6!YF;7=qyIbHN'!ٱAA?ܗh  _z,|`I 4^ҩyFx塹 !W;fwFq9%7c"(D7m""Fxu@ a&7" VGV!C|H2`(Zq3Vd]hSv]{bNMv&s<)'^@sv.܅~$\V;2)D$d3Z]&diXmv=,knA$Ha0bYE֜h_kmh@¢e,TmKߎ͕h^NX/DIZGUh\t}2n_Qo*YR=к):^&WG7]UK7I4琬:EEl+6 Ckg^yHQ}]O(#P@dK/ҩr,әYFo:wFdg]_7 g{6E}0r[\#Cg$"kw sHL3YI0 Y(*zTY, EnzF֎WƾCO;M-_trok,dmo#6 1h3[\y|/@Jn#hٸlA /@G# [BqgajC S?0W 970w VwgSaAhGԞAXmZHs4Ig"p-D^FEk 4FDdN!\wdD!x*f= ƋL¾#"L#Ry) n}Yr7 (zF/fAn<"#3v8lyh? t:DsQE5fažZkmzZ[4+Ѿ Yzbڡ5ր`4Q0>~ENM`4۠"O#b<i0mb(D֊ !_ ThudvA+Tl[K)%6=v"?FJr_ĕ-*7K\JD_Nzh-. xJN- L8*Qd2,K ¸؞5Ypw @]Md ig7@I *D̚% @ee$R=Jj# ňȽ]Bm{>vI( W٘T#dI."A {D~g}ķ!d*ww(h[eȲ ל? s>i_*vrYNFn?9,R!g8>x@֟a<qwɭydB}} н # uh-E.;}@|a}@ Of)Mr5"!|Lj2HB(Foݳͫh/x(JGDgky]eI-E zC{55~Ʒ){=3"(!Lx?"d/#DxZh!>XDc  >Y ;ZJ\Ysd< uD|DjRjBo*VtE[}i-$M$MŬB2{]#&_[RD\$uD2^B鮈oD@H!0^ptg i#׼Z2); WvhqANDs|.["0i=M n<I$dDhF C,~#L/ʗtj]""Tk}LQ*(#Pv(=2 x.h-;a\~b!%"O[6G$b=О?l2+W=bq,"(Gۘ* "ߴ`1Pf""Xw>{m}s{6F| y/ b1J;`<"4T l>qozYώ@`# (:n;MQ: J*B| Z_/XT! אh[$X1ĭ'( xPNmfhX'a[21gEtm"h},֙+,d\^D!2 ߊҩI&v6Ew0>JyLSNMB\HOȸEƱ;~NmMlDa`5/vE'9k d8~٤ĕ퉬3޽Y3 7'J\Y!7Mgq =rҴ?Z/?&i&i&Oי-C޼!,+"}vBoG! GMfs{D5a'D,qq\HЯF#t$)a|MNS#@ }0A@mDJj5ؿu0 xrIRDiu#*#7i0}'ˑ[K/g_Wb/d)1G -^6,D,[_HC"[_-G֏by&(XTqӻ+"q(z<_@:D_Ab9!JL@n~Oa g$6ڷ%,CDn]PŠ^umd:bv>\b2"9ʖ5As`vqy,9žXq(?ۭKq<= Q};b!"Hp}5Ryk.I6F#k`k3qyh: ͟Do Rv/Dk44 =ZFk`47V 7〱Q:uUMQ_ZZ6M[`WYN}ďktL})-iM$M$_Wڒ"O5%0jΟ;x\A=ͬ!#\$/v7!@[lҽEj6"wyҢOEֈ$%7!Al/tMi>#蟘^XU@(E Z̺ڸ`mUtFd``(kmZo^(m(ΡL? m|.v s]2DN#plƓL}3r(ň_(%K'܎%mgɔXnDI+rH(r4^G# kBB6E¹[Y "{sW<وBƠ8/#/@ݻk`g&$F=ʖ7>< ?J6ZF, ٴlKkh'.IsϓQ:U(M>뛋^y D"WENJ;Zy(cև.EnpO[Q:oCo2rlo(eo;wwH ¾lN݆bl$h=w/~W M#v)uec,Bs4;Bܼ)v/+Sk4I4I0ڒ"H)+GFv2@ƾddHܤ&#EI= `Gy("('#*J${@ f=P u݇M˝nm\@&̝(y2YU%Ia"dIkCYJ!sѐq?nw",P񣑕*2 F0bIVٳ!PfC= }jҩk'J!0bNx "Xkw(c[E`H>{Kt /Ֆ66=;OvƂsl ;!R1־97ˣ=վ'6<”Ov$d'IDǨT2.m;D~&cb# Y@5w/d=s1j ՈXF!.uȂ#\HImj'Sb!g#Rō(}< IDAT-F1{'Z_c},Dߴ|LlQدb d;LpFs!"1{"R)@5J.Cz{!h>nX7_CD.2[Û{eJKN(g)}*4M^󆛦:1r_ZĕVoUM$~]=SI/+qeaĸI>\J\Y{==)8j85I4I|!&EeM+ >؃KA, O#M(u @7!6CTqG榓gmBքIQӳhGLB %/SBFd( 6{'^YvF@:M]W{^4V[×# xn @d!Y,`/"( T$8"Sr3Xh?vm(Z4 ,=,r6}Y7Z: ҸZQn*[dU^LpX'9EYr_GD8~@dSoBYN19J`q_T2=@$n{퐫D ^dљb}|"c<YQ: V@%\dbm_҈( t*N8DF$G!D>(:YFJ}3" =0Z'/z!B "YC|ph~Yh/x.%TC>rJk{D>.}VZO,V>wT^QRdmJ\Y_ ܗ&vlFԪ]id;dxb\࿁V)qeܗW ۔μIKVJ_|ιzC8;Om9w/>)/2e5K!oC&c|G%cB~M46,_/}%JkۂLae֟ȝk ϣsC!{ Y!/_ҩ,vi),Gs F$'ILQ^[R-U*6Ǿ`+]v36zkJ\@]W AAEA?B_e)qe@}B"Wd3b`v?7h\ĕ!ύ9ĕWr_ZE_osnXn~ض/bM;97{?Rjϸ^C$Ui"Ep i0~i7v ?.V⤘&hfE}¸+:D@SHÞϬ@݃PzX톴S!273Y)^ҩA3p=e2"X[AɵϧeuQ{ ,W$U(hDF#B~#WG*IH]Sf:WzJQ:)(6*kNی׬=ظ5 U =  ng}j&JaDs~o׬=s׷,_c._u (:Zx\BxXcO2-6}L:XTKߍhl'"3hߨA뺵ݻ<wͲSUETNvCs“I̐-/P,k$ڃ>So:Lu$ܗcO=}X9g%}*8e^M#HP 2Wv s([#6M lr_Ch-C*7RD5ĕM&w}:'ι{m/so:"b+;t{Z#[YD9w=R/|]SaYyߴɊ4 h1܄bK2n4O~eKoX@aBy^@,zC5Q[Qa\\VD{t'#Yq-:\DK3 ijPfDRjwAlf=JEd' :gzx7|ɤ%NHQb d{Cx w 8\1g~|? F SXJvuy>Ql<CNCNnq}eƼIH•uS[nj{4kp릶8-hZ-ET׶7) {՚QEj<}ĕ%Ő yĴ  :퍷PaWSKW~u.n{8rov\p9hz"t_GJh:& aQ_iI2oM |YS>s3D4E*CsCS97:K8|H=-ejRGzkSO&+Q /@pdEv0)qҩidbpa|!x.J{ۡ"7 4\&ڽR 7fl#<'ɂz*4y;DDG\DJd돍HV zҊEEtvxH>kqbE_k۲|)qWTˣyN.#Dm]d|.:1ϵh}GVmAg\6٣ZcݞheKK4?X6?echNҩg0γ0M ]fDGz oi-&CЊ0|Ǯ`kkDpIևKH"q̖!H[kEGD]ID&2"""7\0I_fh]/Gʛ oN+C+ZzipaQªӑinѐ -Tڲ4e ўs4j+;+^ni~C]%m/4R>OU| wBJ+r_Zz9=<4_%Cs5' Hn; 8"ۜzy'l sz_!}/wc:pqRF[4֥3&SYxvRZ~.oq]<霻YvH9x$s@ssι |>$;@hڸ7o]l2rqJ[C  N ¸5dȿ ȟ]@V":7щw*"` ԋhDf~=(J~fwSl͊ҩ9;=%DV xZNMG~vr+KW:vGAsF6+9("|! gC*%Y@O"} ;A݀ 5ks*svDd}S`m[@f2YǮcSҬ(@dY?RmRmژ/w8s0ow;O!:Y2l7l6OGKtE9v>(N(, -(!@h. Mc FL^ay*j7-|E1;m c(}vRd("ɤo@s6~Q:5 @hWX[ "7AϷ%vr+Bs Ss ѾP`ϙQ:<XYwAd ]y90\fsllz2OV_CmgYkj7h㼂W{٘W&Yf[Qrƚ5yEr?ĕWĕTҭnFn7լ}O]G{@rޱ9yt 3vBH?#9!9,egK-h[{zNyișT^@;)kjBW΍hN\n,SZpwtV&<ϿpDpP磵'Rrxy]ZCMdU;R$M5}sn3myKvm|ug֌AIOuMGH8 ˼˝sO lJ2zr%Y/mj._;RdjtjEƏGLmhsO  u%r1"Q -c>bk&]0Gca.̇{n)4FHVָbߞς04J >%;a"mղP2ҩ [[lȭAe!Eb=;f}BAW \zW{^XSG|k; ZwH,K\Vr_M+/\vݖ1h]HW\ߦzaf9ܗ֔d,2+k7 ]va֬;Q zꏁh? YAOAsdG&עyYp#|Ԡ}>5HAS֦\_MBrMk"zmIt xWT,`j P6(ABKNG֎ւ(S%lcT]EG0$"B (8|X ((qYȍgj^&.sE@=%% Z{$V#)Ͳqd(@ֽQ9|6#MNXW?=4v"LӑEAbt Al.R#Xhti䪲FK,qb'˃0d`XfOm{罏֕66]Q0Dn@?frqw9~/fy{ "ߵgC/hr~kyEҩo~"DXGy*1HbD.(Z(d"^PhV4[Ȥ0cG{`6O5"|l,M<\_^dd IDAT7TuX1ג1-{N+zm#6e}}4.:!/r_Z]nR$_cW"Er4J)bk6@lj+X; mO#8eJz9i&ʔF6U"uD@m\[ؚ2Uj-r7/af,AV+2E7Y܏ :yȊlK&>]Y^_v=^25F.Ru|u)cYF3k֎ GJ;!?DnJA>*胈H?D̒ őCyَA ͅX<%֯ct ^k;7ur0v/w@kа'Q ic{ǡw5~%J(E)6!8DJ:o D<{tRJ20roL?21 !YU}%RM|Ĭ%hQ>sC5|*sR<"?k xt{xb{`OwF8KA 66M#%& A`{7T";;|+YS & Őy]*<.ai@R ?gS}3Sv(Gebj Hy3ێJ&&P{#9 IC-Rƒz? Hޮ5@^2,\aHG*wTLc_D|g"&v;Y<EsPkl"pSԉ6. W?\3`|*j}~G Yw(cx],Y6./}W$/>"tׁT 3PHTbqz7E/Kw@ Ԇ)?s1%1pI=aIҒ9EkW)[LGmX6%GLOvء.ݷW,z} ^o\9\wn5zZeIfG]f.3eh h ! V< u1,JQR-"(ڴt!ώl $@Y`fʊF|T"RcKHq'b4.ET"{y-Xp7rqZg&)!?vݙz gRw@ؘGxOO%xY6±tezvG/F,v($H!};g d , db@ 2֮^lC< 2٩DS Yb!=4#hb{pd !װtALϐ5ug?GZvA t`-?Ak ,=Zf~:!#h =#A! ܘcĈr\Cx 2њ(j(JQRMHmZ@H*_,>Sy ){O%Kh.coԄF{ 0y|ribLRݒH S쌬xJ5 E`OOfY~퉔F Ai\n} Tdٱ;#1%\g!e: ~or; 1nLLX߻"Rȣމ$ԠKA`Kk m ׭ z5)ERV\"P2|\@ű\XKQire?j t6b*b9|F3:" K<,䷠y}So{yjd{1+͎ϢyKJb8 @:2I%oyJ͏'K_.U#;|!.bOyu91ߎA -%#+-آ@!ofϏ1B kMF56&j}Y$&.s w +hm:2E)JQ^(ȨI%;!ER.*{(RZ `?)bІt R-X}3#pi>@v"r7/Wh75)ؽ=OT8ty; x=dcvR"_SCϮ)#+H=b&'1YJNh x bDgk1QvT"gYMCj߬{+ tϻ"S[!𵫍A h`D,#YwK%^2' ٸʹ^ob PZomjoVظ[ǡ-@n=x=J=;~3?Ŏ;:sd&(8{pS*_j͡x?"FI1T T"nnv.VZa(vj.Q)BY?Vu`mj %΅{s<0rLfč\+-9zN[[wRZ=ܙJlI4ND dt*Rfd J;S8J 0K@ %|Ӯ)?U`\!0cqD|%`3G9{^A*ᶈM2\Ʊ͏ uu؞om_5h-/L1Z))hRk~%2"&qER3 N7/z(9H9zg|B3tFu|X*:aY֏-hܨHȠ;F"πdXފ˺51.6Tg##ȍ1DJeB {\}?}үsQRr;^}Lg|mf=~k8}?cF_cWLa{gιst-r m|GCSG!k6w :)j"Ez "0@-^GH?Y גX*=ʇҽx5թT"~}w݇6ÃA;ׯ A< `4%q|vͷLSzȗܷ1z)SxV\*_|O,+رrKm0Gl   Dp!ttumR/>ژȳk2~/;em#$hg؃cKX xl\wULd^ ft߷{CsZ4E@#ީݑ{I}ͳ16Es(@a K%ⳍu 2զv{GYxye[ 6qͯD]{JF=\ܦPxv"H4i>=Д/T"pecUY?n! .Q9(2]јދː <@u92s}Ͽ5܏ZKh=yxjSwxCndhiyN{IǤ(_ qQBdA޷̈+BAr}\xG}2VG>DP)ڦJă҄P%G6s#ք(L+Aѻ*;~ 7"6e)R:;ҩT"MO))U@z3rB.;" kTw ;&?G3m1!*H< )د!F ^cT"~E?T3t?Czx.b>v;^<'XfT"%Ӂb=.E^w4ߋ,FJ}me|_Fk߀X=@2GU8^2"R"BV= lhsZD кT">S-vkXlf`J4K2͎A@oC NoFsx^9B.z"Z:94ͅ9c]{)pr֟% So!PzحňDJk¾_9ꢱ'Rٻ2;G#Žh:4bHX7*j߇?T{Tp)z߱5|g>~bRd= 2m֒^;p dTZ< uge2AݖuҬkg~1>n)D__qΕ]vb`q~cs?!c+}97)8ڢ8Nk2sU"h8Kh;cJO?Em|߿>Fއ"Cū BzQ7^1@k'p)CQ :׽s}?Ȏ#S9 K}}b!; ks |ߟ L}1K߶1lm*E{=p[O[E_1e/~B%n S?ڿk }Q0;nwd1o4br1x_JReٰ4WaH|$b<65dm*ЮbO d)w#tLKP +H)E:Kv!pm ,w֏]sRxLA[iCf]ŵDQBQ%F` 2أ~U}*^B(.Ab bkbbw~gF: -Y|.6C0(g]# )7B $0VY_ @{Aͦ=֞3ClbXBmYHiaȶE \0%pyxKiG\#ͫ=KXVKe!B@F,0h!1c,Fse"Y?urBxk%L:<>ZgZ.Y?uz!CS;dl ,FO[2𠱀H`Fe),uz ׽ݿv.ʗPK@N /"Y'(~sMd~ D;}_c+eG8:8J'w|9מ%rx97Vι8ιx} %A J1rikPGιsWA.~9w5cdD:zVPvB bwsI AH,CMT\B? p;["]:!Et M6Y^2Te%R2,׽h 6SFK +o0#h3 qBr9b+CH Jį06%OHm RV'#ZR섘R*z1p"Pp6^Ao"K@E: %3HHaM1 !-z0"=%RH?aDGOdz2R@#W "} ^{^}¹=:u%_s,I( b~QV \"{*~~bg?w?@|ZT~LFY@ͩȂ5k5۸Cn{>V4ΙJ"/`e7f:o-$2в M-;\GpJѼ1_LU͋}]\{dvyȕ==2m|1M1F JW.*[cJbF篗LF\Ks*uG3kB32MCh6C>h >Y?rS/[EUQzUfY4Pe;Oιs/(&"} ș-_8玷CF`cSpC}?Lu}Xkd} w!Gkࣁ|RIu IEks1P}$Wo(Eq߮3)p>;%ߎg^Ay p=IK?P6.h%ӵh;Ylk 0zafb5 -B󳰶 ( k_PJ#<ծLgbD K+</ώjR%ӿDq ߴ1@e4h fztrZM(35Yi"VeЏR rGl!pnCi}oK,-Aʻ6{YJXYaQL v;dL?kALԎvd6f[Tcژh$.*6\e%oxEVdĜ5XҎ^h d(#nLJ[eZӈm{|!֯\ѳ qc. 882r{8ˁ[X/G IDAT4e T}Eݡ8uY kah@sC EQBփ \>2>siF]:(h*z{.5>jge< wNJ򥓙YCkbVc_s^C7Zb6ƦF?8^?&6ul܁T~i)!`n侅g'35!-]7 ǥ=W9EP%!YSf4@#2R.#R "E" 9ѯ@q8=Sl,#%cyay5(xz[*_%UDd+Qbʯp\g#MO.REcr^;{Q?|vSm}kjzJ%O[GBKۦ{`CI(fLF,DT">w6@13c{_kD^2A%WצT#wF aT xtجrjA% u)^bw3rř,CF CL/]uDO=b%j/ޣonUݖ}us}o8G9r7KB6fZGp_EVT"^%Ӈ#qb/~!`NA ha[2EjJ%(P0= 3l%6N 3k @J<є>ElKR!IzXg 9 9c& r2/ݖm|is$Fg$zgKA뎀z?dP;[}tۚ/߼б){C&va%/@f%VNi>p QX>(pZOC.={(-:aӗns[7eM4ir< p}}3ps. ?$ nZ 3SWJkƖgss@1;.Ckl{s4훯=nsnu- -lD~{/#ɵι`{!:EXLI}"׈$x[\яG{OEm{>ÎIUz=K{:Sx2y,+KX{M(h2ڬQLFVUH.\κ#@VPpkgv ZlNF K;+r-RrE51;Q4cЛjT"Fc%?8)z*Cq1!=߱>-Q 6HI1 r5 e91QaMhis:Xt"hs#e.p q`/o`B'2;c=Gϴ4p (AhTuՖ"SE @L?J7k݄#XX@(=wx 8CTQEZ?Jzl*KGؽ*jDivgBHug"wvv<2dQof JzS8Oi@@ = }řԠg5~ [6e6ƽPc1ow6<ث0OC >;wD">xd)2υ5WcuyvlZ7lΗ-r6v"K^F&H2pŻɾ]n 2-~ 1+"w-R;}sMԿsveiƵ=RL6#ۣϽdN @ޡvLҽmC뵄9>yQ6#e,c+Z>E! d ]ֽ\kл= 'x,D2gfcxtIKm?>jWM)v@5BL`v+<6\Vgs˴Ay-| H(-Z>A|=kHQ;);S dW!0oML%0RwCҢ[ e!e 6 Td RBJ~+0Ħؒ$2 c0u}FXjSzHab]~ث?#8Y@0p`A*%'Z=uN7"HOyR>P l!lRЮ|n".f׻=/[u]ӕ-xZetD| J;ĈJDc*{9@:VԘ s^y= <,z3?ޱ _P=m7X{ ^2>r͞[=G 6O]K!2/]r>#1f806E]0\ߍY?1?2 ?c=KMoW) khK-J_%ӭ6wfI-,5d) "F{QIZʯr%rO H%A| [2YbźCX"Q=ſkSTEu):b}~ )iXGJk /-dY'q˺mD05䎳?b1bFBw έ>d/GdeL!8S+0bFnlU1J4Fٳ~ X;#0KY۟vȫ~L eCV& qknc}C cDq\bz91ўOh3`'KؒiE"=nvZ kMkӊ) њr&Mu7zzY!P͵;~&2u1nF^'fE]aےgR/Jo(t۟Rb֞?E3+JQRm&EPSԦn@KFyD|,ԿS*OEZD;ȂR#ŷ RnWn!BV+~HIAu)Qƺ eHK_A`ʎ)G"pĮyةX`1\ -TO9pCsP/@ #37`Kdx|GRF[{/vβgF`r+=^S[}E| 2ӄ{Tjmط^2R{>KoS3j#5ؿg#n(@@/c:kg&jKA 0#(\S1W PP ηsY6fW[[ظmv-9\8>f3e $\޼6m6NK.2 `]֏e6{g Y? 2;ŰբȕL/$ p}W sКzFOE&وL5f̺k2<px֏-̣@$2Y?Ƣ(EI}9%HqkN2e0dBB^dT">|؋`2V1=s))ì]Ț K}k=bA5;c ^2bCkbP}d-9?or-ҥܾ&1'R^2}v3qiߵC 3T"~L|_MȊ]\?{M6wؘerMu﵇G*s @m,{$kYX@ [(k xǞ=tL@sHH$_d%tE,qd=:GgϢrK#?F^f7ў=,{Վ=Ƴ+re|>DK/G`_ A /6. ~M_e$2Q`|+z$z{:ͫ;ryV u`ODWpn fq4">0n ޵.sT(_&)/g=Z_C kbVuy(KS=J_E懭"uBgL޵x+H uRF>D,=y9MCRN~KZ'#pr4r{bn%bW ˺C*VаktkδO%-߷]: bbvT<&Ue7JnwG]]\02L=gע3o4?"dpjSZ/XM&)"SN>\ ӁUȚǞ׿;4؅Ȫ]nc=c OݵXͰ% &pbtY1bj^2]]&|bL`y E]fpp&=S r+CJC@ߡ# 1[lAE~$JTX[~~R17uY?vzeKZC~](E)JQRL5/vKm ȥ)eJ䓐Rr*k )P,tB1c { CH9b-: Ys el_6g,D7vAyHzR^'p&ߧt䲟κ#*ErBG?Q __@(~6:V/byJ=0 ;1#c`| @D:,FL zԖRnv:bzG d9yY\鍒{,*8 J/AUDI/>ݞ @hȮ'JavKdepXJ*pYyƿo ԙâ.-d<7Z7m-AeBX\U+z1x-8'2\%9y >tD >AoBGLgdH8k#Eg?7.s-r֏aZ#=xQRl+)2E_1e:oS\Sx Y&RG}7rf l!r[Z`p,@lȜesz' Y8R."c2(C<TdU#E`Rt: m pw!R&#`8vA達uvn^2=؏7#1C:QĖP€ K*ĔBnGj"J3<ܿ#pYEyީDK_E 7(N7pXnڣb&g{yAJpc :xZܙJk YKRxM*og 6ٵsG('[SbԱK^̭|bW=6e.96Kحw}vSwhnpˤX科dh.e뫠[FxQ:Y?22fwߌ޳7ѻ ;Gsoc$FA^ܨܟc\[ ChjQc3aR 'EPՐ>^x_O~R׋PU`{沴 >sY%^2$b[q3s "]?وEm"z4(@ͯS OB0c~߱lh^2B*c!E‘rEiO@Xǐ4wF1o:rE6n(S6"zm߶LDud!~ H{ ) wj?#bK .%OXjdw6΅`+JQk'/ L%Ui+g&WO[Y7&>քuwX ?X%"0>%5tΖ]R2aeɫ("~llD#űQ+ ]]zA.uќދ!805D=ô-Aћh .дPv?@i!7,3>Q9>Ǟ݆,JQR/Aї@dz'dCf ?Zn2ZoL<hY&ww`]P6H)b ~U, d941@d G}K7yaLVz0Th R|c,KK{#- ֆo[&sT"~LB17/"v:qQLa`x'ZPCvCnB ftsӀn6>X\rN[jQ|T<(+lϷ!,0,RЯٸ!Ex"lvRޮqM v%oSKJ]=^Ҹ_TgIeׄhs6׹yu؋LAJo !RW"G|`݀ Cފ@ß?At"pbhlBt4 1G#xrY}l,KD{NH IDATakt*s ֎!6nО}q+eX2hgL`ÁzW_eu#u9;f.n[wf-ذ*t{~ms]цfPﯘRtZAd/C/cl?2/!7(W.Sޱ,k=(Z#jrf_l^LT#?M1_g Q(K)/7Ųu4m*_%h.X! bCl^IdI=%R~)ˑ; ^2}r[2:QdbGW@߽w|ҵΏw*-Dcuv6d64/"b#0Ė܁@}(KZdl_6׺j/ ZۇۘŃypƴP‚ C 0}Q֭Ru,ڽec:b"KU_!+\?`C >|כDz\37r\U= =Er;ԭ}p5]WۨmehX_II%~LH%s+JȆa_wd֏`3.|C?샌'[@/kxc:|}b8zoPKFVӚGq?b!Ipv`,JQR6HQbnX!˦f_%ӻ &jz bڣ8P"I)[BJ) {x~Y@<̧Q ۽\sdP]cn࿃ζ6,b6r1uq Qq((+jiɇ V+}(EuVDGH|qCms8j֞? zUǟ!?o>[kXȍ[]17aZR9dz'/%Qyi^2}1)/>֘/D]7ƒ;ʞuی`=P,^:\4K٫zv sK}L.ZJ龟|~?AS?Zvg[(_’Cl'v1F,T"> /-=r?y) c(\=3&nF}R8XH5"`.KP*_,/=Sx%s; IX5(3MXvD8^2]wP?"* 06nUDK_#N Tat#b~EA7^2ć]S|UHT"5#PUS|ӎ1AF6KdSm^2}ˎR'?6sc vI$^;$(JQRAWTLKk[ lH%|,xdq,?HPfH#.iSw4s4|(3ݦ%vh@oZnFKS"׻XTⰛ61^A Cm' K%3d?K;!kQUdw15YQԇS0F^,hZAqKW@ݭ(Ȣ1=u'V-~[94a yF.XD<őLU'/46 cþ`Ru(C͵UvOῡظuS4JJ;jVG,sFN4lJyz_wlUoTB~Pu\6̝'hcy$K?/e]tAϋY?&̟̏d2Ȝ4 '7]Zu;btݎѥ.tG_b^U(v(M]}D:%@b;IEPTk#EP՗Q7oLG HA #fch6RLYY :%#,1ڮ} R3=|ԯSvk Rhڠq7"kn|$-2bJ~GU;?~J{i\Zf(hTrUP1 Y֧P햲ʊwW*_a=H%'~' Zllrm;ˮן_}*ropԅ÷yƖH֏-d4uHAH)=|R`i֏-l.t7#7:͡F]&X[D O.7Ҏ rv@f"=pKenVOGuKw̋+ve#s1-r ݇! 1Ͼ] #7uk.jiӽ^ֶf߈\a5gވmQR|mr r$oGlHG*`LF1'('K+9=m& rʡLqg6}cGІڴ(A/d_@ߘtE^G/C2rkwTA>ur }Tk.pZ7f^7D|L(&^2]/8LAJx`ŮAuRAl,oSxul; MK%XjMm_8@uN͍QEsL4_н#ޗ ǯPI%D]O@l>p`&=@E KB~6bODyWV֥Cкťӑ`JeC [u>,kY?*0R\3g6lc=*h|:N{gY@*]XMF1~lâ(EH},M%כ[xk2 s R w@r(^m2GLF~~05!@XY^2DLΖj ,{2/@L!xmD[&۱Y6_Yw-9=jy5%(uz6z{cwK:QC"9HU^{7_EFEH <+i"d25\3Wv'U.ϯδ~fC0cÚ~msOABUuD߬5 w4Q\87rөR/\+t"&P\w}lvLU;0 Mah <g]M?lsd6Kq$FcqIQH@7zȊP6dF=Cg PhPwzqIOq-ZtJqIi.j>x~[[KMt^XtS5A}kzvI)D.ϒ "ڡOC8۫7=ȭyMO=~AJ6!kS5lE] *lFmȱ|:s^|ɳCnWP;kP֘yAK2?\\wR7%Y#ѵm9Ha=Ǜ"SvJuy{V9۫PԼ5"1ؓ06"c er#nW1M?x| z )=jb>.P?S;T٤|95gC6c{ ݸOҬ5m")[۰1VJ̚% x \JBb";:𽧆5ǟе">*zSf º[xiUC7ȋ/kjɦ ^_ NAKD.؟#n6#G,*-J̎\:*5y]o?灩{u*-}(V 4/Ez{(Y.1>mB,Ԡam EpPT\R:wkicME^R\mDt).)=l υn%M(.)mGКUW!!qvb^n43Ci*%#B[θt@B9mBjЧ=FD>|EAknVu"'l5~n_fEy={F ܒWKP (]_ꝛRCqkgD.Ǩ>\zF#u7Ɔ qrl>lgw!' yN: 2TFW4AaFkD=A -.)Sp7bt#d_B?ṲO|=jd}CM;dQ#gmawAng)K An@Pvb\ PT:8V"`8N ~u7W`?~HS ڈN|t LmA^6 'grzE#7-B=6@"qK' @?Ǎ _ Cei[G<a쎘(2 ff *y͗n'3(v{6ù@z}B;!Nn@ji=DэA(@bCed/_G.Yt22_yu='50`Es5G.>*{Ї W>>u^~xx 4'k-r;"Q497'#'j5T %^Cs/{e ~52'#h?R~5!ί }PmFk`~zcPirRTamEFk~/XP4K|P\R:{K~ f{fI5%yFR{Czd\;Ӳcwx{* ,P膽gP(R\$P_ J <ͽx$Lf wh@X3FZ$zf>+k5*yJN^L}_1Cý6=Ьs(,30\e"oƏPaэz$Z KiDbUԛ 2Ӂg]!țc;chKJ{gFoiDm&LRZ(;4INqIiJnoѣF-.) 5c@ѣF1tj=:o>KJ эۣG[̵ &u_WcTtGj31G}4B]~oǻ 9?LBNeC;707h9ݐjDc!:m}9Nko܄@ vmmm92+\p^C{f-dZ0Qd4Qd4gw$v:[4z*E{0i_%WHdNFBu% nKXC/ ..?ȥ(E/ AݜvG J9&Aק>^=p )yǍv"܆ܗorLB#!2 J}߈}6 sͅ =*=j_9H-Kq[ȑ)G[g3QF~%"1 /椂ڡ+Qo^!HPEQ۹(yy8qzvrPr%] ;ژO'>]?QWT$@Bt-os"ـբ\oZonF PM}]9>>!\9a\и׆a#&f!-MLqIi{$ B6̈c߹:G~,С1=KaM1L@[Dv}pirmyQ#gEDH`2H8EpF yd'4~ǢQcC;5T,% (\hhlp nEG#'q Ñ+vB\RKP)0۱۾ȁs^q@%'!EW#|&pY;pJaE](,PCE.q|LqI[j FҴZ(Q]]E hG"c ɄB_9d$<ƠҦ!h&O%'$ԍB>9U!ARn/"C&KG9~m H/ Un/GpǬ U>✯2N"7#t7GQ?Ce~ QtG~<L#D#w r P_5p{Ǎ\HM嗺!5^xc"4l8BB$2B0 bxoϧ>#}59*KJBe\IKJ}U{ Th~$j{O&?ƽS:CaE.9ųA%ll*rymBS Sgd *78(*F EYH8Ax mwS789_BMKs.za,(3 h(2v+Fyr/b)>n݌Nj#8|YT":,Fq{Q{H]e \ IDAT)XDp506"h:&59>7Z+XPLWTp7J RE.KH;&/I fJϺ ~8DPvѭd-FNؑ(`ӎ0[jGFDݝ8+kOE{,pa'r%SWuTBy B# ̮;8`9t@a%jD _ޘ3j?mamED5&AptjTJEefQ2ԗCHkN7cho(w0%o,U]RE7_AWB)fs4P7QTECix&K>~ Ӝ>@;&| rB2n)}F(h{CBQh>Ca>-a-EaH rNVf r-Ba+Чǖ>-xn3,ucFXZG;<kV*$й栾PIYd)XFNV*[R?ކv\r>v!Z% (M}aFkDam%}P6>\H(䡲T2J~+C? ,H}\7ֽr\D.zpΥϏ\䯩 ?wKeޜɯ)dg/ENč^C˚hz-.C7y(`rLo~ *T,\r8J\{DlByxݏB007 oeYDF` Wp J8`0 `06GY7=H}D䒋(yPэTT:G.YUf(f`Q䒿!$7f̬$Ҷ84rI΀/שWJՎ6mrnofE.GzQo_;r&7$"|;&p;$:5*y&rW( ( o/$A\[յ0 hNLYyuAC9OA= 뤰]%i]Ah3Evʽ5%"ͦ5<@傓QߔF3JB{7xv < ;ͼ*\/ z4QtN'rI.ziav`Na *5-ݘ/mF?%ǠSPo:`*+G l|HUЀf/ ΓKN4k"^fѪ1Qd-ѣF clQyυFeAB " }*2r2, ,H}\gфD.zǓcaHJ*QY#D.J>q#@hK:$"uCAаٮ/0rIw>9q1G<ܒaEaML!_%]9Կ ?a\on7%13B}R_E%6%FPS_TX`νOm ũhεa̮^aF[&ٛ?{l%yKi@\RY_EH$^F(O~4Sɇ ϗŎךa s 0vԮ+)KBC; Nn[ě P%(U`v'Q 0-a"BYs-@MU3c4Ü_\r`dzKDpDŽKB3:R+f;{aFDanFtݝAg\p6uh`^eᱵ ff\مa1QdaVCC{1oV(QPhr`qxqh%l?0 =0 Ux}㟧>~v+VnC!O?#۞ Fy(0 ͱ0 c%A<#n.z>"0x10 m`sanGC}P9 P|0` 9F%!bNx 0& 0}/_8bw䒡(NJ}4rsH-&+ŸraFz 0v3"F.鼫ױJ/5þPO˨:rivUqz-aFDaP)] ` *k\8b&T֬>^K?0~^9@etaFDa-4 t~.Cx籛Ox)Xj4@BrT:_Sz]uK~ uaFDan]^ [fTLlsvCeu/@~+!w 0"0ZeNZbMG 2lQߴbI7Ydͤ>^L}#|.| x/ "06"0M@uU;U篙ʦ'\? g@Z߫{#E%aF" hKReqܛ%Ce?%CKJlK7-= p8|?`m9al]aa|L(R{U^>U=qءUU8j0p"%7D.ɩAs#ApMs8U80KF.9caalEa4i^2  À`"p0 6'ʧ ]znCp1 0Ă rPJW 8x" ^X#%>^}m+"tOQ] TEKk`ׅUAO#xeA컺}}v\tw2w M8 e7<`8+5 0v/LhRWF. ?1bVJ; KnDK>6lC涫yr~h8ZV o<0X'}&]8_tʉgEA<\~wֆ?#0`9p3p&(ͼ0 0Eal+(3p;rY.sa \J;TQ35 }tkqF.)6 A"门|`: X 8<=n@R9gKĺΊKJѣF"t gq^r}aFDadTHQ ן܄f m@bjŦ%y**9mYDK&x.%C )rӆT5x \,*vg-0 Ţ+/xe@kFNJKJ3H7:wu4NÐS%Roڕ3 0& V!ޛU?zc\F5F.x?PO!O` X:" Q 0Dl,O}<Ӌ¾ LìORp5l\9F^օw[Qtp7ؤ>~ηL&Ҝš%?v˂li$p rĈ\rPƮw6G$0 0v >gv$ז\J̩Hx%MBecw> =GH*~J&>^cT3nu"?/'MԳR_O}p,* SoĹ klH}|Wܖ9 ٴ47yWlX;g;t;dC&랿'rq@eכ;@C먤"d`Jp 0 hnLD.\gCIǧy]p!*[x^3T$OR P!0;qUo>$ EH`F}NbSo|Ge`q=Lq6WOmWwrȞaaM#lDm'!q!rI , \3,r4`2 i(DelAeb?"P78#r%h^p4,gw@"%Ϣ2Q\wd6r+Dup9 Ql Kp.{ޡ .ps5ILaFcNa;L䒽P( (Gip/>^ȮJQI4 9;}¡=r!atSÚ\h~kHĨ5_,֦>^rʁx}vgo;; QO#tD%gE(5o?`ꎺVa0QdFd `f@ۧS#Z5%]  |zܢ596GNͭH8MN}|]4rrP8DNx GiYXx: a]᱊C >HUo P'"5$Dw#q0K.Fb9oy] p~7x0 h& h"@XTVC.PAǫCBܩHTuEId9%w i8*;9~OLB{ R|MDes[ﰯHtD!Ũ8.ˇ+B1>rɁWxzOo4K5Ꝅ{hVp޽uhbߨDvhx G#'kH%~f.k0 <& h2KJs]9*8$nGǟ\2ȅل:$>Az@ޥ9)(dPSaqD g?-|=ܰo29O}> *-Bg5syH䜎΍om rIqxO羳la0/\a̲OABh䰀n"'f92}6<~'|F+: 9Tn7G#cCVXUȥzŽy*ɏ]J^DkDV%sC?" 9UQ܄Fx4(o3GԮ7@vsA=K%_ 0 5a0d@&C5d|DG3dAD.&*BjͽGnAH݇E+?: G}#7i)IQ_С1Tjr} 9SPY $B=A"',<x/1 Wn`C?}9U~K^ >XJ#q8#z?{olz$w 0 0vLd5r Q^'4CHوGG3sΑKNB #P"\?(dGطg*A%H`Փ-DD7qpyaHd*P/*$ZFj&*DW~(Cx~!*-G U`rk<s U/E.p61x& 0ݎ71 .wz_уeAC+<m93DNC7O ~Fs/!72Dp ,_o-РH( \_mBb P>zjz+PpxDX.Pr C4Eg =x]=#QzsBKiu r/1)'0 hqSdF\P%PaɉD?K.H}( QG"&u T@q$ reHXA}L"7f)*Y{"%CDNLşC%s+PӁH8u ۗ!ATx1D>=@v; 9 yBpIzS\>7m1dRWcT"jamEa w:P(~: SQ?GHȤ%PyS.690'i/.C\(%(a 7rY k& Y{h60"]xm{~~_P`V,={9*{ r"$}xGׂ%]Qһz:rɃH(rDR=js 0 C(2 |Gn ȡAry:Ķl)X^[B CnHEepG gh8f6M48v:JQ{Nq#~<Jo`)\23qr"$CmӨnr0| Ua XDe n-(ÇHLfF$;\wI1$1rɾE3K9?>K뙝ēa1QdN#AHDt_D.LWu<*?DT$l@bf*![ܙm;SDu Y  J~ *lrF!!au84BZ8\?33NM ܖx] Q/$"wF{ GN98dԾP;'T*xTxMg$ ^]Kr@: deSV $^Fʲ5FL7ljMK:㨡\RW6aaLS W1IDATBu$D7Etÿ?ɞG{TVOx\ͪQYYWBg$FR6Ġ7*o+GNOÜ\RŇK:7WSo p5w:?YD>%*<9lQR~zsJ /Cp%zX/>%#wgny|+P'ywR5r})9T$0 hI(2 cWEW߇Rʀ8rI7}ҽWV#'l r}s݉X DT(\RTprnQiڅ(aŰ_sp3c7uHCUk4*!%r P/{ :rE=@) eY{N8/R\284r\$ZP 9Zsa7-Z9zHK3 0Z& 83-b%YQ\ֽa U$!(LႰe(]`qp:~홓M$]L䒯!' $O}<+<} r Bf+D?c_ ϟ}rsr# -[ܨA_ ?B_@36k'AB1St:8rT%$ )E勳x=d1reU뵮"bda-EatRWF.yn V rIn#K /DR$E+7\9fcQ1F(| lU8%\µRaFDa- }%܋ tSd<BNpFu Ddl)]l$* Z9= k쎜,`{#oI8'&kaQaeaH D3'\_0 hu(2 5 9.#s{6g!w7(e)4h,!Ѱ .AbPz)C 5Q;0u $xP)< ?P3#˷nT*X^B1CprH\p#&܅K9Wpz!1sL.yhHjj:"qH΅H=,~0 h(2 bB9+(x=n HF:#aT\$F=;-(GIl!p.rwBB+/zK|8wCtP EH|Fn8]ZDH^8n}I(rQޣH|QcHU!G)4ьS(хs%u s k3&DaFDa-t_3xz#э|<A%-@QrR@/6$ŠC#LE}:+ROI}|m=>^S59)PχMW)Gd9>ϣR$@^XW%]Gsqթ'dµ;3PHpq1*˄_ˁo*3 0V"0Z,!uITv=7)}28BkMCg$0@/Q*Tv8Ꝺ \u$-CnplC%ǡޟ U0dЄS>CU{{$FӿGqٓS (Xz^!QߎC" vHD;T p}v?trt֌\%{* DZ$V#t$*ӻD>$F 1 {0NP78sjض\7rv@6)\ pH!4 o#W%a0QdF!A{OC'K%*; d .Db,tAefP߁%"ש#PB /t]-HR:>=Qo'¬H-D|c6=*K}\d9VCUL݀kX(A>5'# /G*?k凯S,@KNL}0 h87k\m uT#(@a B)rKQMyx};aYJ9]>-u^ &99Ӑ`xAa !'' f5 \$z?f=VJ 1r1$JQR/:}<zOGtvhX$Emp\$"º g$_EBa^pu5s3 09Ea*R R(D_A1Htk9"/ q9}8t _{"Q5O!1 ^w 8ȱID.JKQ9ەHe C#FBc? | Z]w)IalB)uϬpy= ܨUH"-͠2 0"0v':Ptwo3-U׌({-5 )D s.EׅĘM((5lN k= G.ը.F PNAjS@Cj7 W"ç w0-[`T7=|BTF-\=?p>o0 h(2 cwb ?G! $DcR\7*d rI'hp!.Cu> (Z{:"7 pj$̲ir'S$Yg`c V j 0 cDam%l[=PiX6a9,ǢrӁ׃;}}LJ}Rgnm-tgQH]z 3J^ִD.9M:K"7=Ƭ0 0vg|06EP$Q2-H09*.QOA+QѶ|(Ͱln~#C=E7/ǀ{ܢ^OR۰N0 ح10 c }GUM(0%k[.7%C 1>^ٔk2 0"0BXUg >han"0V@I} Finding 90 nearest neighbors using Annoy approximate search using euclidean distance... --> Time elapsed: 3.78 seconds ===> Calculating affinity matrix... --> Time elapsed: 0.43 seconds CPU times: user 19.3 s, sys: 794 ms, total: 20.1 s Wall time: 4.22 s **2. Generate initial coordinates for our embedding** .. code:: ipython3 %time init_train = initialization.pca(x_train, random_state=42) .. parsed-literal:: CPU times: user 448 ms, sys: 88.3 ms, total: 536 ms Wall time: 86.9 ms **3. Construct the ``TSNEEmbedding`` object** .. code:: ipython3 embedding_train = TSNEEmbedding( init_train, affinities_train, negative_gradient_method="fft", n_jobs=8, verbose=True, ) **4. Optimize embedding** 1. Early exaggeration phase .. code:: ipython3 %time embedding_train_1 = embedding_train.optimize(n_iter=250, exaggeration=12, momentum=0.5) .. parsed-literal:: ===> Running optimization with exaggeration=12.00, lr=2501.75 for 250 iterations... Iteration 50, KL divergence 5.8046, 50 iterations in 1.8747 sec Iteration 100, KL divergence 5.2268, 50 iterations in 2.0279 sec Iteration 150, KL divergence 5.1357, 50 iterations in 1.9912 sec Iteration 200, KL divergence 5.0977, 50 iterations in 1.9626 sec Iteration 250, KL divergence 5.0772, 50 iterations in 1.9759 sec --> Time elapsed: 9.83 seconds CPU times: user 1min 11s, sys: 2.04 s, total: 1min 13s Wall time: 9.89 s .. code:: ipython3 utils.plot(embedding_train_1, y_train, colors=utils.MACOSKO_COLORS) .. image:: output_18_0.png 2. Regular optimization .. code:: ipython3 %time embedding_train_2 = embedding_train_1.optimize(n_iter=500, momentum=0.8) .. parsed-literal:: ===> Running optimization with exaggeration=1.00, lr=2501.75 for 500 iterations... Iteration 50, KL divergence 3.5741, 50 iterations in 1.9240 sec Iteration 100, KL divergence 3.1653, 50 iterations in 1.9942 sec Iteration 150, KL divergence 2.9612, 50 iterations in 2.3730 sec Iteration 200, KL divergence 2.8342, 50 iterations in 3.4895 sec Iteration 250, KL divergence 2.7496, 50 iterations in 4.7873 sec Iteration 300, KL divergence 2.6901, 50 iterations in 5.2739 sec Iteration 350, KL divergence 2.6471, 50 iterations in 6.9968 sec Iteration 400, KL divergence 2.6138, 50 iterations in 7.8137 sec Iteration 450, KL divergence 2.5893, 50 iterations in 9.5210 sec Iteration 500, KL divergence 2.5699, 50 iterations in 10.6958 sec --> Time elapsed: 54.87 seconds CPU times: user 6min 2s, sys: 20.3 s, total: 6min 23s Wall time: 55.1 s .. code:: ipython3 utils.plot(embedding_train_2, y_train, colors=utils.MACOSKO_COLORS) .. image:: output_21_0.png Transform --------- .. code:: ipython3 %%time embedding_test = embedding_train_2.prepare_partial( x_test, initialization="median", k=25, perplexity=5, ) .. parsed-literal:: ===> Finding 15 nearest neighbors in existing embedding using Annoy approximate search... --> Time elapsed: 1.11 seconds ===> Calculating affinity matrix... --> Time elapsed: 0.03 seconds CPU times: user 3 s, sys: 192 ms, total: 3.19 s Wall time: 1.15 s .. code:: ipython3 utils.plot(embedding_test, y_test, colors=utils.MACOSKO_COLORS) .. image:: output_24_0.png .. code:: ipython3 %time embedding_test_1 = embedding_test.optimize(n_iter=250, learning_rate=0.1, momentum=0.8) .. parsed-literal:: ===> Running optimization with exaggeration=1.00, lr=0.10 for 250 iterations... Iteration 50, KL divergence 226760.6820, 50 iterations in 0.3498 sec Iteration 100, KL divergence 221529.7066, 50 iterations in 0.4099 sec Iteration 150, KL divergence 215464.6854, 50 iterations in 0.4285 sec Iteration 200, KL divergence 211201.7247, 50 iterations in 0.4060 sec Iteration 250, KL divergence 209022.1241, 50 iterations in 0.4211 sec --> Time elapsed: 2.02 seconds CPU times: user 10.7 s, sys: 889 ms, total: 11.6 s Wall time: 2.74 s .. code:: ipython3 utils.plot(embedding_test_1, y_test, colors=utils.MACOSKO_COLORS) .. image:: output_26_0.png Together -------- We superimpose the transformed points onto the original embedding with larger opacity. .. code:: ipython3 fig, ax = plt.subplots(figsize=(8, 8)) utils.plot(embedding_train_2, y_train, colors=utils.MACOSKO_COLORS, alpha=0.25, ax=ax) utils.plot(embedding_test_1, y_test, colors=utils.MACOSKO_COLORS, alpha=0.75, ax=ax) .. image:: output_28_0.png openTSNE-0.6.1/docs/source/examples/02_advanced_usage/output_18_0.png000066400000000000000000002244021413546205200252540ustar00rootroot00000000000000PNG  IHDRb6;9tEXtSoftwareMatplotlib version3.3.3, https://matplotlib.org/ȗ pHYs  IDATxwx[EևQq齓@JHLYD ]4ӗ`S^PŲ`RwDUTJLI{w[|A[ӒJzj-5Țbi'X!f)q? ..HhZ//jp5xYp;Cs <`(D.sVR-aX,b1ˑ $ESti\9nĒێo@(G$`p wng؞'-;mX,-#f9x卖'9@AtѻSz?hP?Y9xRZZ< p8n nbiXe0X T5 mQ+HdQ?@h2 <[1;kJtHi.~j瞉2qlw?\ lbiX!)ųB4@&*TAUt5"RQ1|~P?a[+L>B~ '-[,b]g,hΝfi^酺 avR.~u+f[",c{3~e lbi%("琤E nRH\\s+ŧo]0qlρ;OZE-gkҔҼ6(TCY|=,Rı=!kFOZv{b[dXZVYgV+wG&wh]_]0 $c{>d4bY7k@m.>5l$g@% W52ֺƣdݑ3CEJ|e$Bpg{N]Q vey7gEҼCoZ;a~hLPm$񓖝&Y,6uMZF~ѭ~6mP F# mI[YDK{{DֻWƎn=VC&ɯ ҦBw PJ#d "U<^|>r]xYjw$#Knen.K [OZuV-eapx].U>s>6YÈ/ϨY6{@8 mX,뚴 .?úSKrׯXZ>VYZ=\‰Ys{s!]< A eSRxj6bi%(m줥rҁ31sQy+&=QR(pz#[&XZvJr ]X9gmƼY,-HP+,mú&-"U_ulU}j+-GX,;b>lZgSX,ێb^Օ*uL镽7xYdXv%eyIH^T҃"uc]VA*V ,R%{NI9䛥_Ba, .޵"b}Xpi$["1Zgiw_g7; X2%eyȀ?%UKZ,U}or/jOV=nҖ)) ,1Њ0e`-bK* Ty踓={νd~Lp3-6MIYޑ݈;8+?Ķbi;X!fi^"ޏp*x@`٧ǤyC{}4n;nnBkQJ.DVȺk*maR|5l.,~fJCDX 8͊0ecQ viijs6 ][uilXHЅDekC=m1:r3wp)pps( e))sHW ^g)s_\خ@R;3bIXX"dSbuCT]c}Fe]{=o4cIH@?Vώ ,ۏ쐾k<8>됖vĔ9| *sK$K IanI^SMMtq#/ | [g֝Ųt{M{+v72 IGXYΩբ#wU]B߻MxgbitI3PqMn,o8&?n,Jt,X}]S?MEě`V_v~WTIeXc /i\Rpe"gwE̲SîߜڻӉy?=3nʅwTʆ23PuW[,mY`aw(G4ceginݏI[ZEp0gt RNˎf?Pט[BnޱIN!𭉥x/_ts3} [30$ |I[aXkU@, X,qlBWv PPzfzc]~R/bX,mkl7pO$}v(Raee=⃬ iMu ͶX,%a1 HW``I;Ѵ\zuGP\JY^P7 ;L,bX!f C2ޗg+ 9_գųR ,t6؀}blw!PdFWN^[jMBfP |wX,Œ8EOB̾;%d&T(k䗙N Bߴ^~}E7T,Œ[^o"͛v X,Œ@kCkܖ f젖 rp?/#6Lr2&+N0|%$ %b.,bI$"f"u_l&H\`xGwttT7W+}/O@wwbX,-#f"`^ |qE.}7bX,YE `?z(O({x$>ToNobX,-klfc C0< Xքj |+M~  NX-bia`}˶(PY?0Tp*?0){ x{'bٮL?0iBؤ ?Nt[,K&(`8Iy(κ\@Z(2)3v7E'{맏'.*|퉐.~bi!܅KtC,KZ.'÷C]gm &,8 BߴΓ嵻C,d +)ٽ"mE:bfH3(6f0  ^2BDXɞ"`VE L6ʹ bq[e$eF K!X}?[:5\ko7Qn̝|'ZRcPk\VbmP XpA$ٞ\L |>Sd}TAgOcB]ՃB uh߀`dXZ*c%!v$;J)O;x l%EvX Nz Ľ70jE!#F8j$k?vحlˋӀ9fiL?xyܤ Ld{,T088R(^SJSJݡ:C)UN)5Ȕ;F)5Y)5E)RSR SvR$J)TJMSJ}ڻg))lRWKJfí*RJ}ow>ڜ7JwR=6s-J'M)ZZ7Tix |/7y([ \;O!k< *@F#KM$omZg? Y{lҖ >R0E,Xv&hg+*Fk1jsRW VJ  3+#R<Mے|A)u~i')X ZRR ^: y4@/Ӷ쭻-+Ķ0 Ɂz[Yddެkz#Iz  "UppQlrwu6bi L@DFegs:"t@#hRs獥) 8&6x; LyL|MڵhRJeh{(:bN)5;n`&́J{)ۢB()-)`<'[?JC_nS.~wbKDBM6כ0`70$uAH_9֛JԨMBn變ZzRjwp)lz֢BCb5 Ljϒ g!#af0)b)JGf~3w(o>}`!$E@?ĺ%"b5bin6 M[ ,xJk}A) iq:K4. RXŶWSZ *>r\ .J_WP 4h_RJB߭v-L!ȏ)Of$Iu\I}5?O&8* d41 o$!7cPW#~=2ugYj]r:pF^2۟:Z|H7}+pR{ĺwֲ).3uMG4F)bjʣP`RxDk]*:}b@/ 77۵&ۛ6{Թpjc@#ck`y8X 6 w.|f]WA`T$Xɯvq:V>%L? }ثXgRz+󈡵V[,hiK? <Ķ0gMjrqMwFDpO#2B$ h-QsݑXif?r/H`C 6;io7WbiLп3{dr[~7ɵrkz\+wF{,v%Lp~@0Z+ٮlo*q5 _XbM'#g!y< |ā̗3̒ECBi2g /2 -j=P6XZFWg#ɽnj%sؔiHl573o 4%3 zuBf4˜ 87/(R.$5ņ&bi 15"汅bXv#M16t.k{ɵ_"U[EFߨF끋B;Ѹ o@pRKXu ׃7ﻦ󀓀 uK`҄K`X{ՂG*hKŭwG\5WD#Pz5ɥcx%53UR#F dl0<Έ(8&.P s+uCZ,$Ibqi!{KXLA{@洘RrAڦ0ٔv_Å+K#Ck*Č9"=@([pFUGO6/v%Qi uqi.Pob w0xK[Z f6^* ˫<+VKˡM&z$yHrRn"En5kh@9YAkhO&]ǗEH|$7Y19R(@|BFWߴ̐?NB |Sq[jnFژ4*ĭEث؅-?dߜB)u< 0\kf]x]QJ]Gk-M[Đ]3B,ՠڄke@["d_Ekr&uV"@\Kud$×o2.Ȍ7W {qYKf҄l|iE%AOBۨfi!fD )½XTĊ5aS'_$oӣ:B 2N&iz\t)].:8q-f!r~Rt= ~PSepwE"=dK+abIμˤ tiiy(2ER=RJJRRwf3JJYJ}RLR695f4JAJo|>D)R2'R#aJ//bꙩ*Y׫fMp:"$ =M\\o2]ERuDks;oNCDU̯e(5DP発;||sPq5,րFE+^ dbbq;ZJJ@]mJ)7T)U@)IrKIHHRfqZT':TE5#Zߧ8XkƬx#pֺZ)u pR~`Z+wƅjMI!fy_bij*[#.g7KR'ERN~3G5"ߑB Yәy="2E2OAyÃAM XbiAG _kA[<6i"E}ź{hwsecWSi_7w?Tǔ m^39];̻l>"j`o$N`),47s.!#e` D@s` EhCqmBԲ$itGڽ4aeX6xJkXIZGRɈ@RʫnOP)_䙒f3M]Z:Ի &T"Ϫ5JZ9Jt$ 2]R3$҄*^Eoelw~ijWlYSkX隒Zk\Z#~p{91)SV4\I$rb=1$Fi1A`Ȃ p vO>F|<Ī4(R)?lӭ-[3 TEW}Ǿ7~2<=6-GNhK@R8M  2kZkxG)R̾|zI=1,Z:xBJ5T ~ۥ3"?$ĕ/]KWSKFQY6ڿǢtT.P,XT LB)".9ÜV/br^JzA$X}(G#1e߰ xH스\xXIW )aJ_VWè lvwUS&uWyVDvR9:}gi{z)|zMhkkɈ6,DNJJ<3+1[Čq%a܁nXS'#֘i4%+WIWsI̺,&ms;琊A:Fq礽EܐbZx|؆-a *zxP.v{Y,¤ woɁ$l0L6vkr03"̮AOz µmoηK$_Xo uqE.n;>xLC^t̀i\E&TJn@̦igXf\&pdz:P*"W"ަ xW-EWfsgcQk4훉d2vMK*>tb@C[ooD#ҀH1:LEw셈\A2[E ߫On5o{̛UmreDzVЯwУ˪]4f's6QRsK[,5$_el~NgWsKOXm" &Jt[4bxǦYAFrUV$I/Q |gN<5#crt6u qd86՛JO$ :;m0ǨAY)_"ly_|nUg^NJdF&Tl0/0TI5 }BaSTIz^v'g%KBߊPw[6iB-PRwrIYޠKIYUH7p6Wy8",d7R)+RzK/J+XZm"X"ڴ NX߲tWc4-kN0xPw:-#"u sU`# SGiC": ݌idjA߉9H>]3$9CCOE,HX$|dx*s~0\`gwiGK7u[*lL u;}XʗOoue8^ta;B$fjA?&?g+;!{L1#Y9dG%Nl$ɪ ,KԍHՈp4YDhiʬGD.Ȫ` |9לCws_s$]C!cEb9@܍ @ ?X6 7&ק,tlDÈr;,4Pf {\ґiyV [*s_ UH]25~X2iBثD;Fk,ʐXݔ3mg%lp(VRRSh\`J)&|:YNRh4ٯ8AkA)R)lp˦RJ%jRJe!ޓ >fɣϔRi|!p3#vRȏr'椨HB:R'գmDi^D(m tH@RWzVu7%x  E|FnH5x^2GFߚ7OiqzǴ #z"VgVh",G5`l d0t5}7udd]y?,ثejȷE.Wٽ/ACrҏ)LMҚhmmH-=j{h!g\ZZ"!,cuֺl~ ])5{!0.rWZ Zpp͜gJˀlSGMXYauBLcR;#]dm.fzK 5"C~@E,J!ֶ),^Yewc"n> ^D087d1"(#7bJ2TD`<\z!n[7 #˘iTD<ĆGloa5i.pRCSP$g;;s6ߐ5b5Ӂ/\$;8!>H".m7Sn="\G ZG]H] "!މe#20 'uhsr^.D\mEܫ_aĽ 2@bJn W#"%DuDF) 3ŎFaB&0-BUyoP62hiՔ) p|PQ$} #bVm-mA&.h^F\x@Fs̮t>{6#;H.dT$i ^A|KTGdtq)v u}!sO%n: Ͷ͹}M|ĺL{HZo7LJsܰNpd*bS= q AFbPnVM6b坉Z`҄i5tU 6OcMEUu ;_M$~F.z$>ոɢ?ӆ[,TeD\7#_lS&Ŵ?d2\tsM6kuLBF1D> o;Xv$`+6E7~2!}pOɝjƯכӮ8z䞻( ݂,O6G"|:"jX=>xq>"Aop9 v{1疖5AHX94>?%QjX(2@Χ rvBbz b)I0 ŘlD0$!֣,q:۳#"^DF,D'(?DgK*z2R֮kj*ːa'z2$-γ <\Hמef9"^6AdOM!nmil$FN!3q֛kbaYyf0$S%M#LY5`XtnKyW/C+`C%{p"6fkOјrt)^FYgFdcEHr)h.zIYxMZR[ĜU%ey G"&FqiBB"mDb$i@,> HHgdu }X"J⾋jׅo}NVė=zhڐ< 4K} 2]d<4nGO/WkC;|cu59N9׸ĵڣԵ MyyX&5Y,ەAo1ɶՈWCJd rqk}FPQWz4ZGھ[W] $|%ey]D(mwJ\HLaHe0FkvX͈7 ʑ?BD[Ƞ, @)h'R.e~d6߈8x,o&TIY,t"C+|r<疆 Z,-Vm3 ]5p%>Z@]ЩG:/""JDXJs ΓW(E|G쾈HiMU$ua ǡb0vk(m Stҋ̹:53Efr9^ G:}>Ē2#;WߚŲ} "M? kTn#S˗?KH2dN:bL]Mqۮu*sKk(w WCIm$b*^)R~2z yӜ)u7!~#4y7,J$f( 6>\['=[!Vfxp{W" [ wyVFOfi!*2"| +v6292dN$b| nĽt$Y9;g>t^;n@DK`" XXZI"9ԭOt5Ljƌ n*ٿ έ3utCFăt@y7"Vz4x4c"Rs㦾8e{a}>8xK)ۨB"J|&V_ sNX*C֖]Lc!Ldи GR(KNC,jw#FS1ZS, k"Y]6\hᒲO/$ā6qj?qO UU_|̣-VpmXIEmKmQ5R;pb,GgWhgof7K 10<q#`b:x# D0}& ,:_"Ñ]:⊛̢+[{sdB\Jk:.7vmN^-kpk4jb%RtYHGb{Ã݋9v%񥑒q4˅cDjM!.UCe":݇X"FDs,G ȇ>K՛ ,Þ=w^Yt1[Aܓ=T"!JCϟ!V>- !ZiS F Ap,MV1Sv hĝy;؛{G<l'SJ1 6o5&)+֧m#/+Z1Rᎈ/2[q0DftB:5Ȩ*)HgEF5!nESDT bn12\f߁t`155Rct-{xx`pjxJqr?hkt9VĚ+ )OBD_Ws$@Zu](k=3Hq#~tAF7sBFIط|bq3 IS8b~ `x/C(h,M(s֐| mnu祕y9v6 0ܮ6#_O|F,#I  b/n58y/s"*$e'`dt>/!7nȬS7,sm#7wW5 bDLNB\G<Bd@9"v|i@a8ĊC\⓫`-7na.7DO5%>=h::-y%eyHڌ?^C^>-A6&!ĭ\ ,1`ʛ~P Q b$vr^K:z n-ƴ1fwXYuX i5*#"~6fP7qtԫòhUB̸JBbD_N}ϐ"\ɱ1YNh&#@sT=7RD.z%9wZtj*)bDoscf#?ϞD&L<':`%2B}qފtrL3q i22 :nq܇,tiǞ5hx?b!<qaFjKWVQsD,{ lXLGGW!S6d yxBcX,끤&$=qq 8j-);xg\{yyIY޽gsg"!ʫzi~Oz:} ¯Sbz)Jp~UViJz]Vc+0sUST$?ˎdo M(W uskJܠ`ZCl0|""F "qx IYXEQ 2YtDylMh-tT/7 pdDܖV ! `0kStN=9Z,Oo=do͡Zg{Q{͉n!=S,ƈQN뿡ck* h{~S S an/ye0җ>[SiIYdW j|ܮ@k(uRH|g6Q,Rj?J)/V裵H)5 qa;!8Hb`D0܎naĢr-"Aƺ_:h$x;ΈT+ͮ @!qN5HnbfUȈu:_k,2}C5!뀈PhJ`G\"&"3X{#b2XrbB\K< ̏"zW"OՐ]]$D<RdTv94s$ ɜFn,r6 's;'C,E 0ݲ3V<=^OcWokL4vGX7+z4z0;ccv}\S$ꀎ4(ۓ.Kh`5{Ƕj@wX]%{IG%җ4"cߟ|#}'ʐxdaOQJ], < ZPJ5+Ĵ J)?pRA/Om Wk]CO!<̳ #q723250 BDA0swv;93u{YikR^@B.+svY]=rpyif5 (\EF)“ѱVaf,FniikjuȀ LW+2ZL qM n@b,;2d\e^M~L q4*\avhei-Bzu6".A3`Έ(yX# Ѽ!.ȵ膈n 0'ĝvپj զM  s+o i.>GF. ;w3{"n̹cv}a˙hrr\͹y^9g[DtNï!D| Eb|J1۸K, |7sHpB6ק/q9<șṶզs5 Xo |sXv&m_͡oږʛ GZsJIY0ԏUz+r6hWf;f/X;"[իnrUOvzǭY_4䛯}ޮ}gy< ј+1e[&rňUtYnȢ]}y d,oh͆gL.x?v7+)-ݔ[ b]˓rkbrNG2:c DLۻ#yþG1dyҹOCbF Ue8_  .44|m2?[Z}XTrxO*;u{FzJsCv&xiZ.gwnK)yO2wܟRQv^5{3 egy;ϩ6ӯhΚé/8{oEyu Vt[nӺ"ӀA8,ϊ~tsM݅&jdPj_UWIY^z ||IN㢍:OR}6r[Z,mCfoI9C$byI1dGkFFjÛH($p߇V=⾈DV wD|e!# $F֤(Jb/DDI: qW De#B s9HGw&"u(J.dD X!I%lw"e?Fb2=25Y IrWvb{ z?!]sw#r.B]WmD< fa{L5Đ)s{BߎX.N#o? " 2a=tR}7)>P9/9sl.~գK~T׫)IW4s}ثnxI3.Ϯ>V;W=rfqGV0+w?>eSw`IY.wUz1d"Ю5jpSc$hSIx=S꒑{㧻@^I { &g5r@R+a88:?Bmq(.V%/mZE KQĊt?ҩ F\bh쏈pyӸ z̼w͘\6.;y޲ﰗw\q^>pKO=B~'"dא.5nQɟ}?lסTҚ9޺ òu̵s_4E/ܫk~f%٨r5ua/,_;db߮3=zR V?!@*]cV,Q}l6"ܗ j(YHF dM|fibFqb 1nPW{#VD;"֢g#0(Sq2""b&z艈,t^"_!>渇#VjDG#"X昗 GN&d5 KASCs R"@5`-D6 '*Od5R=sQq\0A,'!2A C(1){9g@Ȅ\D4B2Un5F='>P >p dIk?>YVݲekvY\9c Q!6:_Z~M r{sK?>)/nqgNAƝ޽su!#a 1$*)@BB{9Hfnd-PJEfBXw`zD3}lަٳJZ_YV-qZ[Kye!1 #y:tbHjHH ÿ De#_#;!bhODwBFjBݾ "#'žD,x?!:D$}ʴy "Z"tr(?] fݐѷQoչOvc<\gΥ2JH,@j(h#ַ C_)b|q+aOQ7 %% !iC`2b:f "r$0?z-\`8 wnc=X!cH!}Y}z z;o7pdBѯ-8!?i~kҹh D*g}U  \?yn~3ܱwsK.-} ?FQX2e?sHj{4y-Z-([.8y>Y6C!Bf9b!'#1˿HZ #ȨoDވ`w9q;ލq泫;!bM|c fc qt⎘Abv#ld̶?ㆁ"5|gkWx I"`^ |EUDc\G VΤ D4"Hj[5HAbg"/Qy.G,D\_0WkCۚ`!?4jcS8n`K; Q~#; "! lT'>  ()kSOm\Nj҆H+KZo6WÛd{W]~}S>d Z j Cqic0?RUZk'\RM9p:BP.QJ"/Z{3sepֺf=RfH\qyNld"LijNZ YKo٤|.Ff*|ȃ7\"(2F :#֧/ӀJ ˜j@݀d_"#&!#ϷS1YGv4,5?5q C_?Α܁022)B6KS26ÿ!V}Bds~ku:qC:N<ܡ/_A֙E!qt"nYzP`?jy?^mt | B_ X@?J0x[z78sKî-eӛr5{\o LTp6pPvƲO7Tw##77G٫S'! =T<=2sIY,xvr&U)5?_k}F"sDLZR}Cd'H߼ p3Dqz}`ֻ\-Agu~E\` T>=u'#vZ)v?"Zz ֢UHP 5}]HGq}*U'#.EɨE`-bZA1۪z" <^_4{62S*E"Ϛ!9TxS E4à b`??!68ôdfs? I{1Ӝ_'f |`Z2]"$KqiNш ? 62{q ˟O#B_ ;EMv-]ؘY'~v܊BKB*-%^JO41] q)e>뫔OkOh,Uxm>VJeiZ뷔R!A{&RZ4+-R!֐1ȗĉ]U(D`,D/@ȏk>k LF2ڵlZ7$gFqQV"H'j^1DtC':*j4A:'$nj"4ra{d&}jN7Fth͸zɞA#bo$V<܉A#&=Qk|?KytGn}Wl&-פ Gk t}2Dp߅]S_|+9f1VGJ(3IK{]}l]H}:l\@kd9MZO1+f!#};M)5/F<^SJ ϥ+GR!1cԸWk]=NR=qq;XUHt$:#i!yF!|[3qzqhIE ьL\D 0p!" ɦuIU Vy\9{נ\!bis"p܈hkb[HNFFQ$~>fHpq#g[.QӯO3՟ѴvAYԜoiJbvZ(?~5 G ts( |_#y>Wma(KL.6'Hc{ ?eπ DB6G [֋++0%EXl݇ۀ%C{1QRwJ~nyJ: R| ΊA %ey!*MZSt;6&߯Mn\wgN~ ݈M+^qf|=Ja̎6LjoiF4ݶy4AM?wmfeط]ӢIz"UGD8֦ۑ툵j/S)df`bqPNht> 4 { z7)Q6# ރL ׅee5!ٻy=1~fyDĖ+t"4ADۋ(xOs<;\+;yP՗go +dT=8kĆ_s 2 KFʿ 2ָ'L/wrO0S0L8ȜS2bU4yVts>1my֖ZBF:d".T]=Lʖ@qG!ZQ>εIkL:S\(x]*-)˛d꜑[RF.H 7,ԏ=8էaomղl5+&Yu#[~E 1D\{r ;ɈEy y(7"Vos);qK_UJe,/QW=":ėFv2d+{0R1pںOO;œ z}c$2r(E,UsvB,LD,}Գ馭7 gČB)t}0c>ɈWsaJ1zFɓB7q+5yG$B:"B&2"2d^cg9,*}!/)ci. D$sϛٷwg;wXp>ryhZ??&׿? 䊮 d]א3Nfadd#nT?쿡sm$!}URF@w\qx^d+~! O|K>LFm-7w.[d-(! ֛$NRב"d-(FOݐĩtDdԘ~52#&"̫r]Q>|-"#c=qeށ ^Ӧ{;I1>fŎ:"G65eid&1D[L[@LdTy ^ ͱך㞄fY`f9un|oT.&~ lHd(oGDvLvƲ *4~]kGʚg9`+q=oO^=`ݜs]ѨwY}cWHD VI`t<+q>CDjD$!S>9gP6ǫ@bgBQ fH'/b3!뻛{_ "js]3BRxECXs !' q*3/D0s֜L|Pm9b{|ov9V r-Dr=$l6!Hڊwk#& t`;`nK𾥅c2K:j'?H,ͬ]ZR?%eSSm1WR&mHm}""/D ]g"ndva'sB\>so!f"f[7k7jBӳFDj]VU^^P.ooXٽzC,[ #sKMQJ7r|b_toR{k c]wl7Q_6~`+V5cw.朕R/ԪHk2i3d4'".jL1[}*ҩIQ: B( Xzݹ܈(rp܌? g[f!q5[S< bo8q:6/ /fS.2@_yU #nsD7L(s>6zGfjjDl6U*gcwҴq9\w'm_$eD–m'sn5uvr4m:.XX8"VTb׉T \d'Yz\DyLj`ǜ  d+; 4Nu.d⼎xaK |?6f,TֽMs2SW6z褚of7\]*㢌k?P;~]f!2`Dcd]( \HR(E,]aO{Ls&l ua~ni,?J ֎p=g|;qܰ.sLV{㲻UV h,XE)Zn/- 1K$5 ?/򭐇(䡿+@w J*$u P%<&>+Xɒ+2֔k'fڗjDt[C܏]{kO97g%LdF䓈p[U92Lt= rR{޴qp! :Cbw]zԻ<U0ק8(W a0y0TtM]s{{*TݮZ}kК|)i#"u$Pwb$'qS9IW0d ~E(>YkC£:s4KE)_)RjRT_ICJ]JsR泩M^JRRzTJ2ez\)RjɖE`cR*[wJ㶢gMSJ=euQJʼB)fzrpXHl(pD8 ^FCxz5~Z3Zk\Tmr(kADE;"4Gs=J<}*lDv6mKzM<+q#ēɷ%f#F$&ˋXE,s}^OedGϯ g" ׀ij,@bsXkڱ/X gCdy6mn⺾|;:QۣR1u:".;&͊V"i,> |s`zdB"DDG,{/?9x?k]^y=ue+IS[=֔_4~`'$?M-INLc*<\2uc˫{hZ#].\K.;TQ5cʒ~d姼r^FrSfKҖ])I5}Ȭ̜'?up^ŪA$Mii*6y߉u>Z*gQ@kRdϑS+Ab0 CBE2YJ99uyR/RMA)vC&^r[dDiS\ Zwi!B '9o9!3 9 үE◒ݭCe"n(b=^Z7=u~=餸p:%egy3}N__אqe3Q؍&{+EiD*6YT;SDAF9*s]jI]LI{sw~=c4ig}qy9$X">Gn:'Ut1"̾ s 2$ }@UL C)tn+X0@s~w8pU(Ɣ dv(|e@I9EyqYCLb@ cZ`E\ ¸"KL^b31#%méH*O_tD~.M]_"SsKהuUi+zWN);fPKZAt?#ɝk6,zӮ@E~ni𶄉y[ %eyyzݵ5+yϝȀdohD#x /e ^:J3( }ZFX$ J\{Sk]+V!3])߽LMMP8Ĵo9:P`W~2o3JWxVC-b!*u$2Z<ɔ@>z>餽gDD-Gue't2"֪ .D\(py(tH,c0bꉌHS]pIE[GsHsb暲;8$v6-K(PQH]@PAPQ" "BY W@mXK5-{&9?sz3tfN;3y^{7ɽvj͘|je_d]==T.@E;u>cq]o$-ܾ/b*4qzql,Dk~3H'm <Q>W5T<{.hql 0)H:w.Fo$R(/G-~7=jNѧhܛ0\}^G?~>a-Xw;$RP<ٟgBEnET. xw$}^Ƶq)@)ʹڃQWa3-GϺjͽƘӎYݞ}MP%`}c<|MRpnC'/!@?P!FS]+ -*\`쇨;PT22fzo@8eX)رBX*;H(_Ylޞ>ߏ:_*% SP2p"**PS(bm{eAHX <9/4Ɩ۷7E4CP!znˎ1۞&{nkb+jP4c=Ωl~;A 'VyvX1xoٸYp%j6`>EZ^EاD*] ;>Z5]A5>}v]шмz6/s4t2r:O:s:O'O1='R)@`}h-Z1ypipsa`rDPHIߛ.y' 4ԂiuNFoMnOݱtSLĽiuNVW[=jnX3g T =Սmnl)])="r8㛮e5m YINvQ4l|u "!`1q4Yl0zK'/P1Q,DK w֋%7`* .rDHQa uaKP]nY#AU:V x-Ԓ2Tv;"W=~`wE]z-Ј *bʽOQK T84o@_E-_o?okOQQ2RCa6j! CQ}r?N$xE6+ZUC>Xk%_N:-:p]"q1{^]Z$C~F]ף0j=u1ZDq=t6^0C/tWzzsNm˚VW;vZ]jO2iN2}ֺƱ ܹ.{~vƱ7◭SLz8wD.nۖ/]ԭiuΊ0}R^;~jcOO}L-:eS>}Xٞ'>Xuټӧ8 >EdzsjI~vP/:1,EjDO]lxRDf1jlh۾u|%z5miVވwBo_Ao/7*4zos..a9(Cp/@] ug8AE*lM a^D~~ʕ{Z|)'PX 7 R6D"ߊ>=?Q5ОVԥr**\BuN##Vn?XwdގUa}}m7T_Z\L`wԾo4ESӊ[8ʄ#1U5ܙH1߾_ ZXU}x9Kg iuVx#DoEUYy{  uB<|1[V ܟNwFH/_r=wu~O¡LG"75YAS 0};ۋkNpotpⴺoO2ԕt}ywZ]mswvu)Ӌ˧NZ[gk} .cmX1f)h)چuc`[u4jcn]H;uo#89Pе aNB@u=ZDH&Pls Uhl>Ev4Kq; m}6$EO}p%2*PKԺREЛ[Pm~B:AV;W l_laPH(Bc? =M&|v@b7xdMVCaMӨvhّvhTt9;9 7Ϡk`3Z VƇgD |ՠ:ޔ&] ZKvsQQ 4mN"9h?EEh,Z3&u x6+]|w[S,| gMYwN,IH2?ZrQqӏxn|C15^uK}6X8S}(2)ϞVW[Y&P]?iW>~O4u՞ZG׾/:eϴګ9SL =@EX$X\0cZϳukB, C{V7A {67xޜA㉾4qOXgZQ!p Zž6 H7OPeS6nvDJnq*L~=QN.d}p,`25rZPNji9]:}B.ȠV82Ehi S߱ÞYAEY+; _Uxqkhs K\QV8$P|rAwUՀ ɿX.DEYd~D*!*^Lm9* `cZ m$RZt2ޕkߑHevߎxy3~2uGlM/}w#gbG&wW|Hp<~E?+:Bj~vOB$NC}l>[9uiucE B.Y4̠ŋ2w-\T r.Cgq1_X_S|Th5Bfgtp%BaBdnۻ0WAE R3 )@" :Q!ӄ" TTQ3ΡX )c*0&sv$jѼ>mg{4yx {NsuCPWOQ6gqBqxء% ==º@;cojBN>} VW8=O)Ȃ<.#'VF.Yk>jE)-[NV݈fƎz*4Jy̴AE-fYعT "W-x*ȎCཌྷcKMPWYO4 tlNGf`1eQв'Pۀ<+>G@a>j:( |Шߝe zJ`5jIBՆP{zAlA3@Y=tqJaAkǎBO VABvA@AI#'ҕ1VNfC(¨YG04>F~Q+Pqu; ہ8Ѯ*"4&< kǂ=_S1n(CY.C>gk*N$HDo?A^>f_%gje+~騥]{d:)W*b6j)c& C3b=cݘu',I5p퍏z?@;ЍS7LM ^C^/ jÖVW{)?D2:e==O:Y&"UƘF֚jy x c1|fCۦTDd'B V+w!l&2w.Jsf Ոr7~8^^#D %\The‰ ZߚCQנBM1AB]}@EW$mU"r6$"7HVD2"oIK"򆈜fY8T*xZi+2% Kl]1_km[Gsuܼ\v )U0pUͨ4kݮVzs&>B %:&Ȥ[l6#Ba ++׿B]]CVW[ ?L2 Ԡ l#*zG|PPD֟z8mt> ecFHƢ0ziuv:eznsy+nRy}}luƘ/{>lyND~vD/t}B?v1S1Dʎsm회 YE8/O $9ж*|\DX,ބKK`@" XZPUNt)W5މJ`bľX?W`]`ugȎY&;LF9v]ň%4>¶{w3*d\Paٺ KW*νnGpX47{+P9ڎ݈ OJKwbj[nV Aݷ { ы L2ף1^vz!{ħ1.-Z6_qyO:_tV-,ӛbGnuE[_&ݴN2;u36Q)WbĒw(^sP6\=ƘDd"j w'~ u9c)+Ed(0]P.By ͺ-ݘD nسhE迢$!v1`|<_h+zժ S|9ci 'R#*"¨%f{TT-]\AbQ(&볾d]Wuf*EwAF!zq.uG-RF1Jsx՘)\X ,TFѴ = Θ;~!ؾ=j8ֱz3Xek *0GQeu6f}Q,{Rv[gef}]u4I5T /duNA{f-]4fa4M2$Rߧ =h o8uKKud öaZnٌiuЇ7?655j+Pm3Z*Ƴx}?'.]CY_DpTdiyx>fbdHe*H TX,D o$F<ኵ1Jm 60Y@[(U hg X1xsGlپZe!IJ؇׾>^L-c^݊>!H4VCKʗ;I4A` ZvŽ=ɞ+WM}oD ._GESIv娵`/{[nǽu|voIu6z{.IaA0xYv>7.BT[0:'R %I:2ZT<[6GIG?:eZײ͔Zt ۴ڻNl;֠ץ֮Vl)ӛQZ׳ɸXaiEPG<^Ǯ-qM)LN֢ d|$*u2r+bM6K2CܧP7( ,YEYhkѢGZWKM0mݍ. 1 \~hֶvR7f߀Ƃ 5"Uos&*&ڹO@6 (M*zqsMh+'(4;w[;維}P;4Tߣ9/bj.:&c \qMD-f#Egu^evY QWۨk4Fв}\VfҞ6겼cv?R={.M{ϷaV: f]|Q}~la$RkW-YZNVW)S=N>9bKetnwГRg 1 D>&02z ]Y=NcsZĦAo쥙g6C@ffм4\(RK݊k7 AYq2ŅC(o1?@?Co/U!TܬDEs۵sٛP*mDhl(T kv%;6оBf@T.E݀+ V?lK4 {~&^4]ݵg}= ڒg *>EUY4Pa=OBQ ӶveŜ}@XAE]ޮrs4 Syd;oָg;lCydD*SN[~^ózmO2uTfOe y[qmE2fGR_WU.cB oBk8ع#]@۶@ Z>} .s>}'] k{!(7P(;ӺGּ'R7d|~"Yl^dϦ" Nĝ~ߧN};J4@^1ul><)!~!g cDl{~]X[TrT kfB t lLnW#ʹ6^N8A22SI[A嶭n7חQ儚+uBUK j* Aar1TsKeܡM T$ey;Nú>m7hTM"p֠Hʣg;Ǣw5hV=5{F{ 8!yh_ZsЃQ{ݯsMe͒}~j6 G'RɸKa7[2|-D*S3 |P"g 1=}-mQ˳i٬Muey[N4 ;xYֺK/bR MzTlV@Esr{AJ`qc-%pǮ ` $8wrQ9v5 y 1\98;\B2*X*PfsP(;UX4hܕVPb!*ζsBP^]]Jb.d]v.sQsj͢GX Ay%vntz |D]?J'sk!TDnOR.zM"9Vq[cqq!ӀsW`| U(sZ]`)j]dzyܕw$(ns(Q\Ұ֚,0YF(p 67ږp?nPhml{1K(:8εB՗:; :hqjA%{}}&ZqTD&AEo}OjD<*>[9ǥD*SxGU=D*y`M:s%&ܛ3UՆR,;3NVǣxeYЭ`-)̳qFv1LiU)PFծbxwEXi{/6=nR/YK35KE+ P05UGo 8@|7UnN5.٨.:[%@  4 $aB\DEBjWmW1'Ė۟ v5kp.*fe#PAxJcܹl&fǖStnWMPտb˨~Z3*桢u[`n:_Za'TݍuD*s*{6T;ϟ#Z.vS}5gu^w2+;BD p1k1.7Hcvl e@1nOOgK7KhͥIb Mv]Y|-.Rc BQHqY jI`_w.p5l6óX~kpX6d\hٛ2ݼKbT8TnFTTrP-ojn~./Zsh4&/K7pzRo۱%p] mM-|fQWլ-Zܞǽ ,\켫:O.=F{ʋnX(=[};OW8_2G?GEJ潧24 HehlAKcsfŌF(e[nHĻxl.]x7u>|z]MD1hVGr"1Ƭs馘ϖfwM$R$sPrM[gt?^3ƽf\?~R7bkXm s,G\byxH|/Q V:TntFTX9Wjݹw!<0vBDQaP a-R b 㖼PP3%\5*B ;AЂ'sr!p>B ,\Fi bw$&9<]iЋMƠn *=, 9^+|15739bU}o`_Ɔ]n.*Ҟfh6q}/w@Eށ{t2tI"~FZd[bzrSͯJ-=tnZ0ƬØԣע1i |1R]uXc,bUh&yp1&6 3n1+fc,bERߕt:VɖƅA# Bl \mهW:YSugEYZHJcЉ Xޔ< C< 1 pg7o'QGh C(bv;dv~`FNiX` jZvF+m+6˪0|F*LA f:!:7d]'\2TZ9*f"k] ,=a *]9ؾ//}<< xG; 4q0&B9 -Q Gh?tͷ^gn:+rž+hӽ=OnRyVl+1H1E];/zsi\i)4Hs~ؾoa;o\{)vN+9_j=1*A[]zsW$]Gf +xnHef!ʜoo6l7\y> S#c#mkrNҎU3|hݗ]kW{⥓z+ƾH qq +m9$hZlhRnΥZQD(mXʼn9fU8rBUD0~P*Ԋ;*\ ;3jQ+M#k0aЋP+HT-us,GuUuhMVJUvvh@(7@;s/5"CV7@51wVQ-*`jIТɕp<Բ KPwQB-5iu'}46Îu}/>eR;t?N[{~v=@@Y'6> )pkL'w-iu[蕩S6Z['+13hrFb}аx*";ҵZ^mZ""5@Hw^qME4H*su (}i#bsO1\L@f:7T~ AmQP0Ёi>0 y,bϧ_ Z2$4a<-F_cETƖ}ʞo_N_4w_mz_5lD*SoD%{DTG[|>|cca"{nAK/@c C4gu[:*^bN[5Tf4D:)t, 71EkZJqnُͫ0KmlQC+idXd!^wΣb-u9i0O*TdX2~+ ht} h\b@EMoB$CT,֣]%'c5v~DoGʾvw v! }'jy"P9UZεLZVT־v5 jA~&+ 2!XF?@Z*vF's^n{YMv\-D*s&j9/7綇ϝ[k˨dB?W_ʙVW;})ӗjB=*٦CJ;WYF] Pwo=ڪ`;lD^N,c(疷LTUek+mW"{Nv!Ȫuvy nG-{nu^ hO丕x 39=Ol-E4y=חpD*C@X7WgFu"V_c -P5M0ESV1P8&_*J퍋[Bt.0_7^#A 'ZQ+xTD1VZ.DPUvW>m Q!!BPF-Xǀ<#_i7 Fg:BXVV&? BҬAPU"UYW7m0*4K-.tM~v\BW]>44\ JxӮ;ΞMT{<c>('Qo7wl9Z圉-j} 1bffUނ6_GoKi(Zj\0!-(BcשM1]:vGTLDh-sp4ިqk]*jeXԲJ 7쏳ޭcax@B ը۬ r;ĩEj#Pas_\~^l(jg} @GPsA3<]!W|u.slAeKv 3%zĈ5;q]դ+ ʹc-DEvju9.bUIkPm jc_cuFEΨkhŞv}L |lo #ޞٴiXG$R"t-hpzTXKI^<:fL RjKsVD+LsҤgsŒqĽ=P >run[ Mh(Rx]Puh TG`]@(Zk@fT|\V? h]nPČ WFPV=zeݣ?Z+_#Р{NM"ξGw{7T\sQF3}1g˦WWldyD*s*8-QCnEZȢ 2\,Z쨥@}n(j,VB -+051 &O ?/&-G-6#\Hheй8]9 WgedP=`G-3V2%^2 7;dn5|E$*;V^nevU' 20t PUmً-v~׫}s-{]Ul诔b{ *cY dHevxD*s"'xA:_xYE cЀr'PL lK,E B ZpEb(ܦNXrmo sªP Q@J1j3&\Fi!Z|z% kŹG]a['F*E9Lj)HPhLIWO}9.>8"iS׬89\86l41 MϹg^⚇`K"pjr7E%tU#k_oNj}ĨH Kgs ܁ a/-1j(GpԇZ^O21>L2u.GV'%{He 5%G|3uOo0+k!"45N64ts۱uƘ"cw;ONW'QYw>jA4X *n\!R؛eցZNJɪ,y#&;޸7WެV " B-R(: [FX"&dŰsq°ES3Z\|9kYrR% *M=v?NϚ+0V¼*VN=Kj>\q푶1)҈xk=aTVZܼ A]8glFKdpYnEE X A g¨@iႭ+fHejL~'  T3Z4}'鿺&lf1{cvCiF"1fU<JJ2V;nhrSBDػii9'bh^)f텛[fFؘ@o.ZxJ ghЊ5ۖW6g"He׺;ι]e{W0s¥a@D TFkYu1_.k jy*h61rbkW{.>o_NGƞmWiZ ,TհjM "oOlK+HH6/CMZBEF`j-NV`wZi(.C ec-~H?lfA?c-St*kPk^7ق㇬կ̓E" "Ən纒Ֆolzۗ_z ]j r^.9z^v.Fd E(< {ҹwK`D1O*[SbH٨}.5 -D$\VIc/1kؔX-EhA3JKc(yc(k^N1*\X(M()+ A_5d|-k1N:[Tf,O'z{.OGl"RoC= 71wdoJ`c2H ľfa}p1fL2agtOyUƘ]omo-ZD*s¨&hCdԪ@Peݭ_IC]J.e#汥+ -"H(ܦ ]XK] ]ZeK 3<]-3'%JKi8qYCIԣ¡vV0ljm 6jmI%hwJ^Ey9{5dbP, 7U ZƪmDTC-kjKE^ Ŏ!hxγ;!{&V&a8kk|mڣK,4v[i:Z[/D%B3=Ol!b@-bI􁰒 ~vZQƘom'1v_^c,vBT*Qp[w$G1!>uoc67hLGʹ砥GE أјG@fr<9@^bV QZ۹e4>n2̝;1p. ,g*_2 Kv-%ss,+9UrϹ]}yS#A{"Ƀ8s:7Nl!-*CJјevk$\N>݂4w.M"BXkXP$8hdܹ.B+KTZ_ۡb9T<|uo HbPKS}}[;՛@ u_fYFv ' >%d|gTDP'4a A.;׀~v,vJ`mbBfW4{#ыYhx{̿&"hkw#Kj6Xv5]6жMI")GoƗl:*NC/;жl=-*`1jBM;b"Zݹ>jqGM{ZRZp@`5_mk msәhlQKLx*>f4ZZ *VO'&hca=/w%K"lHS" sx(ߓHe"kX%R 5t2ޘHehvި@w])Q@5vy^2d=kUe% i@Y֎Tt2̧i|'DsK -n}=O?[6k iޚ{%R@[X?|(߽$R)hed.ʌ.@ׇh?=p v EBA: e)Yiߋcݜ Zl}eQV9.JvД3%=dzqxFAțhPW)ӎ24 !fEWьx}i(4"ÊҚ<\i/nąYFxQ׶kTAyMK|:JTD*sI:g5u gbs{{gZ zzt2ޒNN' ~3yɦY3u }DNg1;lɤдmV'OѶr]#:Zl^kP [DD*3nhAťy<1]T\=}xփD*36 9uɦ KihbFҠq-8yg6hʕf枞$*s&Zk֕U`Y:1OjBvᜍmqA= _ײַmy}90ޱ"r8p1e>c1ͼ-υwMz< Ɓ]֫ T AKAݥ4?]Mhϸ:Yӷ&u[Zp[4cB?dzoc.c}Wkhby: <ɉTWoG_`$H,5yѢI["Ct2@ϳV/SҺ͑N[7I2SlHlj0=ʜN_`8WQK, #"5h迉}[Ż_B-ꠖEĵ*iPW0ƘE5e9۠dPvEZgEd1f%>$ oynʮ{cJjsi"r'p1f3xA"J'/,[g=-+)6g9+Hem s=.=OTYg-Vرĵ1*+Ǎ1_OlD|`cr nu]IJ[4>.ɂcc><-"Gc3QxDž室)&[UϓNo伒E!4v$RD*104C۪599@=Ѹa0n4삺A{(Vh9QhJgF2E3޼CXG;'B@;%oݱY# ti%.Hǭ+Onl+"!=4>b}P.x:$VS;%RU/%{l@skqLoascY Cc&mUxפdzHe&O'uoДqQvsh-mQQ{<[&ЀӮz=l,FaGdzEᅘdze҄ftl%[T؃~ Cy<wMz<[(Tf ДNWtsݏЇ7bh\ZB0բ*-]" []0$aXJm;Zj9k,064ui-E"!܈5v}A+DDr׍1+DAcn_ϦŻ&=, pwڽt*8~p\[w > }Z]D;ne"9ݶꌟ.DmNpVWx>|dzHeGw@:"4 oQNI] [/t2'ɇi@k6:>~f'}+ǜ=Ƴ"p#Z=Tq_A`NDv&鄀/^ 1g )NƛК>=^ϡFۣL8ԧ5~x=s&Zl$RQׁt:9q4>p6as_H'Fl5#?EAU?)"}pCƘ"ru~kSD^ 1Heom? 5 ܡibCS^K[V>f W3%g-~ 5>HevuuTT;x HeNH'.tPX`AB /  MmJ۰uZMف*F ͺ`)W ٵifQwc{K׀Eb4/ 64I:_He@{m(S2y`Gb0Ā73gbkghM3߻}1ޟG~~>,y~ƷFW.|e1u{xBCxd{͇ S*/^_6x7Z>dwL1[K[NƟ,}-**8X%|ĺ7cM ˇG.~UA;L._sxz/`[3~z6"b.׻1z8p1摒e;cs]~!D*sK珏)hL5-+#UUc[ 7Y1֕eKu0!=/.:ea['w!, 6X_AD"Ƙ{ df-fڜ;x,߱?]NoK'gM<ι]TfFs p$Id|-8`c^4*E9cH2TdAjbW̭&h_w}f<[(i"Rk8cD"򢈼!"?uU"򦈼&"eE"2,"EO@D.DuYD.BDkQ6DVWD v)"r<,"ȯJ-"u!%'<&"ۉ火EUlW?m9n1&"gvpȃD^s("Y͎)"Ocҝ7-bޞȇhktuhj`u:;%R]d*Z>F"ϯ({mٲ,27gb&*هu;y>WMGeC!0#eT![Tj=Fs FϙXt2&<ز}'mAgajE[N/vtW*`U]3n/-c2+L>c~cDbzxLD@:CPס(hׯA31_lm}_\ sCǾ7ȣqǁWn7."c(qZ}1LϠGP+1H<*/ۣoO^@EEE[x!줓ٽ=-t2F7vFD*s#]"R7 ;#zN?llwݹ+Q 0ˊyU"1Qޒo!29k"b 1JQj櫣z.*oo--7Tc^(Nkcr\_i[+l3yچ*U_AD*\c>֬cldyxж`$nq84e:켔νXw</<c04J'Y>F:z+x֖H2.șʨdA^C .4RO1rb7xg]"+* X|˾]+Ƙ" EcE]@֒%sE4c̳"r9P lcVh\Tw}8yMDBgulomϢWK^>\~wi QD uq 6|&,XoXl K|1O&| tf:=lY8N:{D*s8t}I:_l9z[޼VziAV{͇7t2$*_کkɲwo~٪B!gb?n?b &=fłm\Z嘖i1 s+e= 8aW?߭AwuZBsQK|&޴. +γSL([< y\E߹)be hƜ=AiHe"Hr%Ҋm~'gL"*Y,;cRn"h@#IX:\ު [Qƀ0*NE]kD%{+~yhp˶;k5t綟^eš`4>[ms(*v'pϢdW-qݗ7[SL_>zk#Bx\߭c>y<l =#gb?l ћd|`mxo}%*AhԚD*S'椓KKI'Q^F9ʎ3-P$ުa@Ok-, 4kw~k>|ku#5|P9p?A~P#]ƗL,&R=P_^:_PRgĚ= )ގ!r`$a-BYU< 缲?x<]-bO4YvO->_Ƒ7m纻~"t F>̧$pѵ C 3ZGn*A9[aMJvx(/A؝C3uƯ\4a-.'qAʆESOr&,Ԫv[>hOKA{>kY~m93bd|gW#4`"jL9lmעmD 4gb>EBI2ہ J\tN" p-QXr64gbӺ9/bt2>u=1hS1ὂ5W9in['7>^I!ZZb#Bq00 ?r&:*M;ڻ'_)_✌Ϛom|Q`>/U1U P(F ` =gb]l"b K'tz»Z/0PAr>˴Q،߭fcm,>V0}#3řpuTۡa+BӜ20_Jpxٌ >B8dOD~Z9]_K珿 8.48&<3EWQ}{OBz]s&[pP)iY2/iAE 7F 21d/͙XzccB̳%#hfX۞VSW<ևt24܌9Zs?tĖT gb`lheCxu@ / Uw^la8 pzlPџZy# k\K9 ?ݏ|iGyJ6[]>~# ךx<*K 3s"PY*N쒜lxפgƖO0M{)e'Ml\LvUSТ{`~ZˆLƒBCr(cIDAT0oqkƝnoň &=2f:NALMCPM0&P7fT?g ,s6a8Z_lGUc ߵpʒ˙X<BLd~|Fաwr&No{D%{)*Wn"`̘#W_l,zj!M zϢEG8 %vZ<1+wC jͪ#`B}۽V mJ}X2A oW>/,c2/Dj c-i4a DLlbTg+ 1O | 2pmWD%p pcuU[ցt2պdD3> <Ɛ]V_@1|+5jXVՓ+gUO#ŲHM*ZW Z j35+fOԊu:CTI;ज$4 hͰ;3h7|g>_ c ZI`_Ԣ'@>\ X sy|0-f >7gb+7d$Te"PqpfoOHeF1aH'=*0p1"gb_✉[shwEV_,|v]i͑+ɯ ̩x0˩7=c 1`ḾH Uux1*:4IU4Ku 0CfBakG^ev,Z_I)]`x!?lzOpuFݞiHeЊ_'`Gxj-چ{ެuZ੨dт+QwABziU%oZ8l#Pyih*ޚ^heEvAB0ʬX\Q܈ȮQ E0dkkEEߎ 9$*q٭9u< )He>in0ܾ'R\OZ~Y2TۣE{s&R{sp׵B^+ ?9wEc?FǏfnƒZVD,}fl5T>( j z~Z iA@@^ *4 6_,*kq=.KAݦxP¿n4 ߝx:['_y͈%Œkː,LŨdOB<F⅘?ڛyD*=p pc:G Tt2 gb֥X޷I' c-uWOuNf^%D%[7FGwW´L ڔ|{ַ(Q| T1U|}(Ju7>h_r$mJQAݩoJoiWt/<}D*us]:_I 69.H=ZG]: lv-h:_W]Q&b7J z^c)qx?[娐OcaE]ZжDB۽ ܞ3뢒*37ޥr^QkxԚ\@ݛsmKƯ(O4Lle.˙X Sbh|"9  =9gb*"5u݀7M2BhF-ͣ=[d}i2F52{l X+Bӷ~>T܃v&,JB \lBS7> "ڄn;Kv *ֲ/L8jh(dzx!{j%: g¡7> He&֮4pyұǶVMʲ]ˇE@$gb^y{8ч7Dpwex#j܏V4Ks.͉JW^ZwQ@E[ G]Sr&jq&tHD%}z{x]=D*#@Y:oqˢn^1+e4kt2~]6_sĪ?vcn~N_L)!ʌ.WD%[֟Cўh_Qv)Z;%OCܷs&vOTÁə˝7ZGEK9xyֲk n5dԇI}$nqcs&voT{ԪщTT6V TD7;6+oM ϭD*3= )inw@]W ^ghQn| x-gb3̹>CvL/$ix<[5^y Wgah\x$RAh;ud/O'Rp.a:nP Tf *B asᅬtoOdSak؝<>>,DJbϢǣkepFhŀ7_DoG%{tĚq3XLl {< 1Ob;4P%AokL jC"Ǝ݁_ϴ/8uEKuxZ~Kf8j 3/J`}Q?MR M Z桖VB-Zl"p'Zdd͙{<^Ljy 6>lW`N:0XfUކV2x*NGg6ô=|P,Iq*ѶVO7{dr@TzW$*ٻKV[ֱPW推[ /L̑uNJZfP |7gbs:l>Eoa_HeFVhCKe-ZH'PdgֶlӳX hlpC  ߺZ3ЬZo-*ٿLl>*Jv|0gbۨu̠ϙXoY=O ^y5Tfy Cnd|u" V,W+aU|E{ *CcnG m=Ri M /Jm4u LKJ gbeaO&={SELt2d);d|~"@#礓qHeON ?[=Tf2 ~pklzJ\h5k]П I౜ۍclKir z.xx3CbCChmJ%6.'4`sg41+n2OQ@kl-A{El :FE^8#`(;ЊR竀=BOC?] D*s]4mo7Вqgb#lb[lTf(73'p0m,[t2^o]]dvO2M'KJv *5x j=joZr/ʙShpZgQɾ ;\LA|#*٩h9Z`%x(^y=d|y"-Kq* hlQZHD*s j5 ֟:߅.QEd7<][T|U?a0Nz:b@PAo9,0ptsx<=b-ַe)oshlٗЛcd|aɘ#Zd)#ϣ{VbəX}T_@\^1 لIm]4u_<\ZL'?dۏ5x6+~΍Ǐ;v_S;[7*P|[#ʙXmO0^yXlvhKіF-%)y2gb>ӿi̫#oŠ'pQJ&Llay<>wMzdjO |8l?է~bMrp$O#ʌE/b~=dǵs}͙ث@G*\pB̳%?ZNFcnARbDÃѲhd8O#&*-%ϏJ:` lx<~/Pْh:'Ek/F)/M]f]~B\Z<};z{"KT{G%y/l%xgKf8#4h\ 7+Ї!Cv?K'sErE:yhƳos<O 1ϖR`xV{КN{_L'h˛yΞ 5 qb{1nx<5ٓ7b,ϦK+^`k眉-/Y>0;gb]&oΙؼx<}ol A+翅ZĖAuh]]D*4(f]1ls&68xX+r&&*="M.JvϨdQL`}>BgoD| pw?)ivy7]QwaOwT,Z0*;]6cx</<[Dz oEEɣ}) `G"9/l>r&p5gbuX?P/**4sCab iUxM.L2Wqcw%B+)&&_r&6}jxtl$R06 _V8F`a;t2z3O QFLΙEA=[/Q șz{.g1W~P+0 My=y*Њd>vL,dQO?&*2)B 1yJg% q6QWd,J2T=}[Ψdw&C]sx<bs_h/W3Uh{myyѢ=E%goO?^yZt TGgg}(LJ2 Sr&D%;6*{qhn;.* jn1wћXȕ8 #ܓǣ64=}{{X|UyP}x< olդh" `2+ mcw?K' *dLDT~˟xz_³ՓHeC_fQ6OV!0Eɸe[ThL'&ߤ>Y+*SЌޫnhIs&6fx6oxyߤ/'>|muӈ< HeI2>֦He&'ECD4$ڊb"x-^yzɸnkg jmh& ˅T3 hdYKd^U'L,vV w/\ٔǁGYD-m==7dJd\<φWx<^2%s"4r}x`1hKɸ~8fEWXoQD@fLS+\T~d:ֻ[lֻ'=y Q=hc6>OvƺSPQ%lewv>Q~ъ;xӖaFaMLЗokr"ؿ* DK\zPN7þthfLhJVE%[Zq߯Exr>Z]?bE]41Q+xs" j ʙǪL}'ș|uگ9"x!NTV1VZM p*ȞJL+3ʜ]$.d|}MWldc9[f{<>b9 w1 vhͩrzI2N'Y4=5hccV '*#8ۻ +لQn&~< lSl:|ۺȹ]}Ѻbh0TVɐWHe%R3w[mgk&HؖENCn9L,؄x</Qӯ1sn5*ȪDZըHe&&RJ7N2ITQl\əc9h煝Km+x<[ǎ{<ڋIGGJv8~2` 7s&ֽMxSd'7L쭞lE>G W#Њ_ I2Bk̋9?Ta>?W.[OE g}YN{.hܚbO?gMz<$F:"nFE'DvOe=Z{4 XNʹS9]'-}G] D*QCb<䫛h|dzBY?`yO Dݕhv$@+a=}7&a^ȈܽCi[^M8a^͙ٙXq0PWrc츿ەlW~NƓaڞ$*]";TQx„yB߫y>?֫s/ک(D\mmEr[Z8̖і1+–53tX叜L$r毓)$D \?>_A\~=s>glw/>sV1{KRvfmCX &r7Ozڞ;"S8wS|W a挘G,ԋP%حޚА@{P|'=9vw&b۩e{ps ֖1%ƒ>9gsñew4 sTcT gĤ>s3WoFI!l)cS'>D>t֘ ՘Y(F?1w[K` ^$5??6ӻ:E m"p6|; 2:STcna>Xٰ Ë8U`M^I&&dL#Hk)69۫Z% |BGV3/ bSƮ} Y I&+cKYp uL 1qĖ'>{.MzL x) {Ǵ!I\/I-b#׌E@x ,:sHU1~ɤ5flf9uIv^7\ӽʘ/4ΈIRL%9  @*zp;s 8x|˘_\ֿt==9zgh2iL1Ij2[Pl'% |F`FCoʻq1_O[혱z^yf}pݑf3IR 1ߑb0#X_ʘʘw`e)^uzcbm% -Ij"$d 6`5)feBfͣx`Ԅ3O8c'6ېl1Ij2,ڀ7Ӂ'+IvGÀ?}wm0Q j qϷ&%idjߎ=IMS>yQ!&҂W'GخmN\R IMdi=14SIm Y:EZd &I, 4`5ĆbmFR HcIPVURk$Y(&}=HCR\C4XU*P|sv9"4p7#d-XF, lȶXXXXlI /.n \H,,,,,3"faDkP@!00۶G!Nf(\#3)%I$/ eVgeaaaaaWXX;q3R#?o%H5?(NGgLg+k԰߁Y@Pg~eWf[faaaIM&̗ oNqAƬߡK@~ ֭OKD֪HlcI-"!>,,,,,3lhbFK@ ;jvN~PXS],~DU  =pQw"c+٬m0ՠ0`BSPm4 s򓅀(Ezb%bL8  ; ̦ه'DBT~hr&`3 ',z|f7ke @ M64i" EGQdKQx H=Bʕ' eEϐ0l5"2d|\}^aaaaa^aŦlM3CJ1ANMDuc>2FgD[=j<8&]̶(?BmQ~ahaaa¦ؤP %({+ )EE@(?(-p6278 ]:Un6H&cфqQf$`+~-_t'"oH|fNn\jR]\0>X1Mfhm%(ԗf>"] )}RB4# (\?KGH@ؔq.n~PCp0ʗio6&x ?(|haaa1< .HȖfK(U3E$4[p"?'o$/o1p割{BB^^7&(GX3kXkDLS+(T=dm$9 GbS@ 2g{Ԁ׉ht71 ~+M(8x/O`63GD.p RTg; a:p5E&:DNGDmŦYl HB7>7uB$J/q302 N^pa~P8sѺ3 d%kBK )MPT`*J*1TO*bfP^5ʛU-Ge(Rz"R`K9F$ei|ܬ3 6CmK @Ja)Mb(C3G_BK(] )pZ4hK-,,,61X"f MF tRx-]e~ mN^:2' D {\d t_b~/G }߀&"F32GkKNB-]"5/-,,,,lhbSӹ8|V<l~x *{|^%fMB+VD)R~F*Z*J}1flDF"v5yLӁOo,,,,6moN^)nc\Z B3=DĬ8!'%CD|>`kD D+@.{"eۣ, (d eͯ@[<aYXl k|d֟]*;"^MrV᧠+Lȼ' RFl㐪RXl.FG}:GbG:zi* [(x I.D8?|4C0$DcA¢aagMZX P+#FNhFe"liȐ, 3!Bj/ȤԴRnZcĐN͑mA#gXX"fa/D͎LFN(lڢ)\P~?l6R^2ύ#tŴ3_  J\n~D3)5]@ȴ RN<F (lլKmοySVe_K,,}XSL.\ա;ѡnkSvn2HhR{Ge> ,mjj+361-,,,֬oa/C~P8)p>G}fGރ z  [ЃɃTʲCM^HMj gWפ_Jy 9G,ozo 1@접3' 2jaaaYXKQm)uV~P;)G;vno4N&יah5= !5{m!.En{ș'7ru,,,,"fa(pDIQ hjT#+A?F-тީ@`ۢ叶%ːFHwW+djaaao%bks*Z$1ef]ڢ*|K'/- h2kY{DΦS} lvIldYX&!|ucUC&/ZOW[rv)cIGv?52^Df[߀p~PXHMG ఃFάmXXXXoX"fR|+ ׭>}G:6Y7<4zۯV6 ߍF C7uގM/*L[K.رU)}Sʫv͢ ֖Heh}xc!6bH4BTQf#Wb1U4( wp8@d0. ?wk/\69ڬ.UYƪ@ߗoPF+<][kVڹV |H@&"62uF#Fiԟq l0@1U+`G͸.\q;Hx _ v=D4^R2WY4l$0A'9jye{wȭNO/#FI,URu]>iyfѢ <NC:Wc9s@`aaa`J/.[OFP% a\O*]f@Iȷw y󺵹EU6OXu |R׮s:ɞPXr R5@^E#ީ'4)YDN3e"E* Y ٖC: 4r#tB 4r҆Ec.BTJyF# ;R=FU+>z?. ǕhyoКgŢB8ơd;D*;1< KA)3O?ǽ>(z% :`ڔSm0 ˀIfYۑp/J%|͠~G <  ^zF t:5hblA#gewR9!RvR?.ǨiZ2#)@%.pzT3,g֯h4r;,vh$< 軦Ùؔk?0xyV'ڪp:8yCYs;z"vyS !^e~ChDD*f5/K~P8yCs!N77jIŦK8^F/"(lRn@1g"5@nʍK Ou=-Sv_^mz~s M٘ce!bPg¥Ɉ |V/|:-Zz83TE5]q# &8yAai莊F߭viuaAҒ^*m䝬Kuj DT8 QhL }7Q sș/orB(eǛ襣knvqن8E}X"1?Y/)|_z~%t=;AobXԲx׽sL'a 9zh$mLQϪKYH5 3 8r`W.Hڪ2wn(O; 3Bmw8nuZ!PUsKMrH JNZVu1p.Rn4^LA! 6?Y?O1SF\ JtCE7`:Cpp|rIݍu,v2Qh$RR )k(u7"eH, !"5.EDZ@4^OF«|%#kx7uǡp ~`mU#КTU[rEM;'`PUmRre]˪el>KӚA\c B5UV'U9„ EE#PX>N y\ U*bBh$\#4xx.@Ca Z{p vhf߁@42SPE\wVg` .0^u4s=7< }DOWEn1|Q2 N( "h$F'/^s`,oU<ʧƬ#[/{uKRG9 #t8.):Jy`A#g6J1NkrWXpog#b?}gB2P;%3!5h$\z /px a J3PԌn(ak! |Z+h$\z~;6uAD* Ot=? LuCHy  ϼA3^GC):p=?1/,obk.kL65?Mn|zңhjcĐN#t9oA#g-$($zsnv5 }l U*bNFyVzG5߮EA\ѠsZ$ l)l戸,Gkhf %hp2 r z=(Yz~+doTGMwA0\oelФ&HY;){QI CJ,>&s|_vs^q֓ v(s,#Q4^UU-+ˬ.MC/6?G'F5 74b9kNG%btB3r=XW ^SǪyh!烀pptME}@Ck,"Wh2O@!Ih@%r6C52'^>2}BOD#ᥦ 7$Ǚ}Cdn wD~|a-sф@޳loJ+=X`2 WJqK4hsL{nCmjG3rl3vA5ɷ4`I²[=3:yV:QbCm:ڳݢ_F};v(ӜD9@\-HKob#%b|[ː=zHԭWO-Z4-3Ai)Z!"# ]֏=+pK]j ь$<4AjکV@=25e ̌F# y(Lfu2pӆ4DZ!eu=gDW}"E#e>vwg#LiL[T^$}D#᪂7 U8Y\Qs[Rem~91[նxrzB'oÖǠYM#rǮ=rb#%b9Lh 䑺eBtIf:{Ey԰"`cҵ"FňT9h}Qhf  )WӐ? L@JSZ(z~I3"h x6ZAShF(FLqB b ̱Ckint=e4iV 8~2Gjё0_O=!yYE~QSǥ-^:;?(;.ަGMJ+14-9w?7^02*F<7\- E.@g,,,mp=3DDDpv;HҐ?ݚ =HhN\[̑SЀQ<5ˀ/L:ϐf =UK~"kij\Ϗ Ĕ40@9HxLYi"9˴C(\SGm釋lX|*+dWFulٚP .wj&93fqɒ^8)_'ߠvYkZ]5:"fh{77W\^bfiK .gaaHDl# ?C>Re# DƎCt+\Td$pPK4r3ͷhǡeH2D #8}Qx|"R1E\DnD*2R̒ь+y;Hxڴ90ޘB~בm"]~uxvw- }Sh$-331~74`'F#z~ iFk- #bHzm6+_~)wkQYrT%Nu68tjΦ*Tީi6Mґ2zmoBQ,gg"9KQHs/\chPS Ak]7j$ۦNkH뎈`L& ZU4[U;ꪝ*Z$&L+zIf8U5Nm]Rө_M:tZ߀{u3E:5?댢XS]A]j\} =:ZXX4,PzO0m5if({g]|jqDE$P8UD z'h$|!iטG#:DGL}z#r)7otf@XήSgRص&f0Mb 4FF#m<"I,a)f#%-DH&#u+ >CbHE42Ǔ^&| (tvHm !4E$%" (OX+Olx Z d"h&(6TvDaьƛL[&z`HxCN K8#80 O]v+ 4 [ަ_=oGLOD(ѹAa%&1߉ yn&7x* ـ0^K@anvX>,&@5^>!7ƩņU}4 #\ozy\~mPq,/tOC+1|ԣL7;yvF> 18ՔlPD.3BjMW%DMєȷUj,Ct2>1eES "DG "z "馾KѠg JG*"oA0 KyӔ9?HYyx* HxĪH9/#qUۭ$+3 5C: "{#b:u⿛DG.DbѼeb@ԐCB^pQ_ K:=@$ޗIl'Ha=SQXUDb!163 eژ@ |Xd}\;!TbmIÁ(G_ӦaK^q=q,]zf$2EHZCPD"!(.H{ K\? )ng\MU!4~7C5}wHr輤Xs8@dT4aW#L߆-H_gV6@ōUU`RA$9Af&R:C/g7f4B=-,,6 FYz8>ӯ?=TmAn(>XL@ћ,% (2ڏ1.@#hv3=`z.(IBm!(no4RJ@()fL[P3MoS@h$|"!_"B7)x!VݪI_3Qlh;Ca7 A5&1D/EotRy8=7ƫҪaԯ9<)flaz (,kdiWB(!BMqh01_g&hySM0J3]w Z–[MZ铿h$ ¦K+ٞ5>6 # YYf<ǥq&yPMPZtWr2Px97Krǯaq{kCˇցD:gҭ( b# u@ H4+H>)^k""!%%fWHH1ݑQ}M?E$+yyET9h%jAbb"_KRu"mPMV8z!FQ 凾0m@ҾHU\nW&"eH}@(w7uNԃJz~5V KsE)c I+ԗ)Dh̤]ZrPɬ~}Swu)v (LCQ,gxQ,jӝrw8~#D M\5=] '!0B*Q[4ބ}(E'`g_ zO0ef Czfp_D!ė yˎF3滛!4SӆB#1|­H-kRRH4&"6m{ T[ M`?oAcp"isM{ -"fO:rD̞7Q3нW Or+央XN::qTUmMC/*6XXXlܰɵIq0=\WWEoDZ#w1"--iR@~EיtFw8%i5$} #p#'(<ҎD1 r4l>{Ĭy(J1]MYHrG9ɷ+_YHj KL[L*#r)gwz>4}DWb&4竩OMƘ02H[QըC?P;F$A+sCH۶ulEE^nE=_9x QC5z(;F.So2zyhW2{;}d"Ki𪅅 KhNC!A5"P}ѠZG",ȱ- pRfvD&@kڏZ7Bmm*)CUlhzL@iH}7m-'d5m[˯\dTR) %< D`G#a4- `BLBEq5{SLyg#os<6BיB ̾1nD^iL9 Ih$|"9xtގ#?zHn0frC}Bր:< ZoffFe}97lu„&B$4/GQAB3EfDDC& 3M9jwm5u3o ԧ>d4?@js^B*^2w@k5I(-26b R gD#\? d3P5D@6wc9& &erҐѝ*{kHv # Mm zΈL@aو|^A9|x F(/&)B4HT Sx5 "=R]C) }Dj~% 纞$"j#RrƽrD>3mlf{vs=Nf)Mk[I"%[%c1 QNL4A(|9O5 ݾgtE޷7PGP.D(s=Z_%%7iHX2z^+ sr\ ƅ50%ai$^'"e9"6OP HGw@$D!vHͰ;doyvDd4"w"Ex"BH=낈\l GdS2u0@y.4݉Hxӎ6T bM\nH2dEH{R\Hm+Q(=f50ǾApg.Hfsך^TL?Eĸ)lGfF> Xρ0!(]ןf)u@b^ MV[0"BHx@g'4qD6FaT 5b]:/MoR:P֣fH=KG$ "4~nK: "ay{|+ CN4 @ p61DL/C>RLz>i~暲HdwL{:6bws]Q2=26}:8 z,j]`]JYHxh?ú;~A/ǣ>0ʝ8v(dʜfHzQHl9V*v4~,MT5G^fX!%P4O1u`de)g7W#E<>uज़icU|z FU]?KHx>iz sSoz~*"mb7t-$dt>*Hy U(Cb [tMl^陛]Z)g'?Ѥӵ,b#%b &ԶaTgv?"gKDxh  W؏Ѭafm'iyæ* դD1 ;}I8DޭD4A$ D]wDƗ+e_%#aDeF_pn|>ˬT'7۪WD~6eTȟu 2V$@~6Cn)"!?r DvBJ\?4>o XD|>z~ UvgP™ծo'4vu0t=c4^osetBCԠ599{GD9{!CK\IyXN_DP3b9o!(PXRЂ\3780ɴ?(x(E%XX"pҲɩo҂Pjmkg )mIFf4Y?@pfY QnD'!R'~F)"(G G2妠`8Sh l?M& G HeӾLo?43$9ntRfжy$NDs/6}z"us4  gGv3m^2ߏHOF"/$D2 s1}?o)Hx 1>̩G B )S;G9R> =hu=ֽې%UD#u=dhkx-Dr^-b9{"B?D.^ⶹoY_.i 93֦#D4#q7R@4=B;G`rPQMwp#QX gJ)"qo#aȫ:u+Crk"uR#"J$x [S)ԅ畋LJ T߃_Qkf;M #L,eYه04GmgnU4-M do Ifh$ϘSH%ۦBVf&3$EwJ: >`*%sG"-Q?)Ɣwrnf72As_^PJWFVXN7C$WQ,gop&8{%ӟ~"F b>JiR yH舼OW"D CZRYBKDbHzb ;jA ϗ(tM/!CL N4G~]>rX}F Z2ԟ^9ھ ɽ&L6X:=ߌz's/eS:[,0zdcb9XNfQ,砢XNUmcp;PVS仞g,isyZ؈`AgDt680LEch>ʺ k]?2G~B])k,GZN=` 3GgT!ӮQqʄ̙(F|E\^ňԡ0E9+Sb-^W( )Y_nDLhڻ+R>AbJQ{5 yʐ:9D2w0?o"52]Qh$h%a9RvnƜI!AlBQ8 k"s -5rXiֈr*| frK t݆ .Bѫ [t}ƉaS<(s_R6oy0G#7leOBuӹס K"R[`䣷_?" !b1N‡}A戜LF*C!a9H[ۢfDDaPFb¬G< Ko1ve!HA9hK e> "y韥(no|$2Ǔ$v>0WSv]˦YD0D#Em뒊TCM/G#yיjcH<9sfRD \BQbВaDdVD\"2 rB*#uoBV0Ъ(Ԯ)ޤh(6׎MAbKtMw@U(\* 0e4]E}C 5NM()H7e-,,64aN5}D>v1y@Y(eBپ)y f[ 7u\޽HdggHċ(e-(͂=D!h4r HꖘzHb)RcӈdE#[ TP|mHk~!I `!MEkoD˴2f߉His_ZަouM.ug.>2Rbz, 3s%!E]sG ?N@Gu?h$ScFmMd\F!CKʇ -yYmВX=?Y1 7LwFbB@#h4 4t}_U tV}"§-ZGk=!Blaa*b!z'5 򮤸^E BVD)ȇr4H:=r=cDr)E ܗ/A*RGD~@ẓq|!b s HM[8DBY''g u)K4,]T"6LQ#H%+ߏUz~{ 1\4G0&Le3Q3/E$xsDwB/ 1KV]5 5]Ϗ^^F•lG81# "''ڪL7hCu,Ȑ^zrnCw"}E orеٌB1,.^Yb="5-ivuU].%54ÆK/-gP^\j#pF|=Ey<%CoLk("l3k`xHaǜ>(h9;; v1~72"u<"oߘz<xDF3BN^۲+*pf8yC]S6#uv ƈJb9&OAA$iu ֖&\q߇LqJ+|5 8{^$?q$Ap&a,[M4˚~%R*WM4ş]q=nF b? FWPXDAĬED,.1 (RfDTDvFoU,pa9NHf>BcȄ~ Eb(h$|;"c#Bf-͐Tm9o:c{D.F•HZ<#~[+AM fɡ#o?-2bW?0O4kDB{."oG# $ QmBDw}c7V},"ERaݻ E)w>R#uo;Ҧ5&Ƈ1JbmʢXEX9橢Xꄗ^=(s<3Hq9|惫 NǢ{ϚIyFs9"~Kr_^[lp ;8o83q9q8?:`qvg8:>^8OmpHy88;98;qwgT:8.+iG8dw|iqwgv SƓǹx} M&Jw֢f`ŽcKeGoZ-z?eah`_9ɔq #(?N7MBfCs`R|>CiH{C="NM~l4a9:OR暟G P,f z={v!E衺cNA@h$sg_L$ Ӯu!:\C3KWD9{搡%)q:g!eEt R]•$P!H~d#[t\xj /1)9`v Ґ5հD qʫb]@DRd\D# { h$شR2 (2D2) a? ]_hj0=lw \_ϡ4"9k$ ?} @*X<뮦!0XuA'PS@on~1?H)f~葈PfI!5 /9ka3ttw=QALaNޥV\aM:7QXyzaSJE}yaݯ3jF[(s_2}Et ֢EDzs8[W˹y&wq4㴬"z[t߬jw8Dt@C/ 8o$&N-5QR %-EZVE>ƠxY#miAi} o8}s7&g0qKF Kx2R_L40oW|])!%z(FGH QR !tt^p=h$5A2Iوl45]z~R<i|)]!2=gvqsz i{DFEHa󻱖ltVmjRP١:1A!㉈4A?%Z萡% rC˫[uh0f(zj*+Gvŵ躚^ыIR@{F wŭ=g'Of?]PnvA-Al^yfIL9>zɵ(/JI6۴BC[qv28JA O_ն;,k@D#xFy$dNBaYE&ۢ7fHQH +jFbא zĂKvܑ߼&Xn8tM+끗ß 4I2;x e L2 ;g!ǪqЋF \0d0^vA T Do@A3A2@jI6f[0 Z ZR=rXub>AT^i}*?bf㝝 :(s RfO7F#BǠg=􂕉gqw[4oh RJ?_,6~ArwA kښKB ᄇ Hߵ]ߡ?yeu_Շ%bCvFcQfD@@rd: 2O!5#@;ЃR^j{I h5S%؝͐ǩ9Ra ejW:/ ׹_,4H"5h$\fu~Q2"{y.3m{e[eKG7D M"o< }&D#04aڔhx\6۩AR.Db_5yfx^BS{&}ZNhp}Qx7{-65?DomM9 G-ѵiCF$UbF3\Ͽl, xBF“bQ}wRŶ@_fϘ[5[թ7Fze(shnvaEr!uE^d}x:OXԟޯ.S9] Xt-@tdǁ=O{pZ/NDpS}IBϾH:CR?\9-'1sDz!Yxq=hׇRz)js5@Dr8")pL4:(Ioh`~cz,"]LCw=D2ߌ)"%`"' ,v篸CN%_.X:Npv)냈` VQ,gʩ&Kt_/ITYp8o 㓘6 >6ހň/Bl^&GD,xjO״F]bm$&=X \!2T\vpfŽȗTԭn0$p#0=p}r|()z{EfqLF#s"l; siCa#t Ru&V6E[U4߭&kpC[S+?⡫Q? N1Dh:`Kӏ5h?{M4.5~($Di!"B; p;SPި8!ma.E$R6G163HL9HLEl!R̶@Y|M&~z u}[lLYWA2/W'Qp/ Z0$yM]b9at^NDJ ޘ]JBP?x#zڏf7އiW5f?]΄2%NL,,,֬0^QBҳV$a\: ?fHX|V24.BhĎ(Lq(Rh8F|EHxf4EC94߇rhjH爠V(RJР 9hTK;ͬzSp`^M›*2z/BĤ7"s_!SʛYMW! MFBaIml; yж6?4Ǫ15A4/(ts orw@[D('1GSV "!M$h@~b-I-Wt9݆/v>Q[]J3 tIJ z_8rXkSQjtq[KA@ܳ}o}N<:E!E-XrB4HN9&壢XJ}3Ed4-D:#dt?S^uYXazYg* C4~8 ; !v1~D|&>Q|{zj_5 !uC$ G#%>G"Ҹ~:RgD,/湞}G H)` 靦(D9~-(G $@6{]a+Dv R>#a6> E a0ϔ5ss6RړX/~KFc<|s/̶р?Zk(t]ԹWkMq - D!cVf'D$5inMf~6=k?q6k[:c[t/άKkTuP]]]OG#Yz_;zyU Jfg Bsb9hUgIJtl&DAi%qH$v''y?f4m<* hQh$)/ U,EaOqV4^zfցyf"tDt>"sD<4D&srh3"_yHaI>ՈM6eԤ3u zh@۴#wSv% -3ȋm$4Ů@I4zbkgp t [RߥD݌( =)HJBt s | "a(:Zի#feJsLE#QxA0dhɔú蜴Co̿o wtGTIvI *j8ND#֥^Z5~@(T{/3w콴h$  /;%'UmѦٴ3Z5CPH<>xN+rgr~%%y@XWPhf&:k|%ǰHPCmaPǙ8kwf+ uU߯8sH\XEl%0!(tm}G!_f8r{4D(F'%@!t@AfDB"|]­ ͑z7TvDDasl{Rn( RҀ!fekLZ/: @d0biNK .3@4~(:D\.0,ɴkI9#_;"R w0Ӗ6p.F)\N4ob9a/e 'nѭjg#zԍư[wfӢsfsWBՋ!؃?HxiBKÐj',jhFwP~;r5RegrE1{3Zzc.+ӟC~+W@$d}]΃]Q,'(`Q,J r*场Xl8ʕ}^^-V[9NAmfۗԖ?_ \?D97HI4=\?h? "7߀/Hxo' Qb/}f_A0aAPtBD%RnF3]?$BPέô1)aelH\G/> 2hH㢑p#ÖϻU)?zߘdW3mHGD;z6>2Jڡ$RS|H;Ҵ;S6 7QXuʉ="o3H@$,ɿ3 Զ4" -YrI^׹a%I:]egwVnz͞7$B/)e-1qW/^ѽm_>}//H\w^o/HUMئgARn"/]o .z|ŶMZ-m][IC^hښG"$uYg#{/( []VBj@RQ,2k*.68=@;3 xEɈd}18q< x2]f? xO'`qAv4d8%RqnG  <,[9|tgD#3>ͻV¬i8x/ 21 ŷOAᵭJr@Db"Cg{B2ԝ"clA7 Ry6B''oӀ mPfHEE+n/Gi+E OHꌔЍHEā=/QgDRB [哩-j xؔy"xL2Db"i)"U[!kzE]FjR;d a +٦:Sn[DZCnE fAd ]hF#HLXPUݬWeBu|6eZq]>W;Utp˔"<MEC3(} _ M^n*"rZgD{t/ D.TI00w6h| qhyquCVA28g("YA)xHEpL =+DzDa1fY/"H{]XYv3* W =}bԤ$ oA7R9n)OUwCD~6ODfsT]oikkʋgįF£]Ͽ T嚂TiyI c9"['A: v7/\va-FfMQ̔d)dTi9o'#YRK)97K'H{}&"nLDH1 g#bA`HAy ``ۺPo(3/H ULM)8AKDp;\u'VZ;Uצ?P[{/{~;w ڶxzUuju]LIz%:jjCSSR 087m^]Y\־YϟݱiniW̼/7xřuez@/ EMx1 EtCf>*GD, cРH5KE#Xū4[4<i^rs'@)9EIq QV c`L VVE/5q'EP,k@D#7mhBzBh> 戌k~_ϡPhoԝHx?DϙC36'$yRnE5Ǫ %HY@!f~2C+>?);QH2eD2P!Rٴh$<$S=J,D[D67}"'aC2itF%=fb.zܴ3=$'sTӖ٦L^2o{9u(8 v=)"xi\H{HYVt|?~nyk_yйy]c$Hg|ۦIAՇc.fm̿-CVJW6?:mUyV;xk z-j:k(@Q,'٪iZ&2kr9(·\Vʶ{,ڬvrP ݋%ԛY8o5@wn~w`X[%c "텮CQwRnvkc |^@ς0 ѳA~?=_f{>DEVk .Lg?0JV02GICφjqR ^IYg9"9ެu *y]o+gTǤrg ~u' ]k3Qq@/`zN -I-/6ہcFfdjMl3 b֊|)522zwBX'v36@QL&ڇ( "lnt%6hHBxo L-Q&@ݮmp 1;ҡ"7}4@͹h 3] rQKde]\ϟn|qu݂@s\)MM~jL` r_VIs^QE# !O,gqX]X=`YhJݦvLQ"ǯ6n:怄}m B[o6{ۦ]ʮ=ᾗ_ܩIFy=:|5qN;~dA9$ |PG>3YdyCAϕe]kM'#RrG`6G eoBW Rl}7tރd74c4>vSQ U(Yb=cJ9yv d2Ee4nep=@䮋F£r7S4^e7ۍ:q^g-CVڂ}z|T^tfv㧧T<ڎwwk)9;l@w~8vΔ۵lɩS襣2$?Ëʞ#KK;pWdOB/gh1"_W{dYnvHES#:m-/ V3A8FqA\u*b {#v*fQ(DVWوO*R/z~ 4OG᳷;xͬmy2z(Ѡ\z荺RՎ7(y'z7 GL6$""> q"ܫ('z~h$!acSH-7w u"2J?7N4~ xܝn'HI!"V lH(\X zdžԄ^M5R~6eט}[xh$<' \ MJF%矂T+R9R`I4FvWՔ9m]ܬvV4bbVPz8ҁ)A+'"`矃LbbO64ҢMY.۶d'UhһqzYU.٬c ;^6mv]8) RojQ,e5YɳvѲvgoYүEּ>E{s]osЎצT&NoٹūUiQ蚜$(.^O[$Yd$m6&8zYoWǛȐkh$5RV,%lkS֡qRJY}Q ^0eԷQJHH*g!R$FٯoϢ\}E$BCE*߯'f D1m~%"SL='D*w?PI jme "RO"U w=B5jSJgU࡜|Lq=.n`Ǟ@h$vf|A/ O"%m9G|e5z~[D@(RQfw7uQXӞ2 -juY ' mtǖC#'u8ʏ޺uyt,0E t.G2p;$-,_Ӷ?Og=[onXuwiv%іiur0NjN}5VZ+e孧j6{M*^8}]S56)Ӯ$4#M\նQY+SMsg⯨j:4vAu/tD]2x(DŽ0a+. %s+"'h4x_C!M#&k9"xMݚ ?ԃHt3["9_";H4.t=AĤ1~+47 &5 ';*>rz튈L{JԿ 7FϘuL͉?{g^GߜhSww6聋`A |.0b`CbMrՠ5.-d=O$6{]+_F cg̲g'HjQTNYͣ\/ȴlc ]Di}v 3dZoF~}#fΧ!C(w'ƚ}RHy*i҅d4r{6DNnt0$rs'7uGmӪ[|7aMI2b2WjL'3V CjKWv~bUUY{á OmTn^YE:iiaeuvYs ?і 2wɼƬ ]y2%B9y@߂dQ::}Glk(@L@5=1GmA,vHgpQW]j2cn-е@lM>2IC2݅جDJ{~6ZB,V9:C1ǾD`){#2Ĉص#7 f]wybeeY"D[ӹtM܀LAO!P)WBp_d&Z"3 fYF"eȌt1RFկ !f6Z",dbmqV۩1f !G-ڒG98T}D-dd;:ݑohǩK/wa4WXUkS^sCEfvƎV5<$k)pOz(g}kl3nٿe-4JZ…ɼNhx2?p=.'%vъ=[\Y3>^5N:Flp֖r7RyyJ]/C !X;"Njy [Z^P'"t䉏Z"gkg1_kĸDlTJٖb OF`'*C$G# 9UD1A܌Fp{DegYvB .Pl"?D Drx=gKa5gvʮ Cʛc@U\ sk_(E:21Gfb,˾CX&Z! 1jϳ6Ѽxo}v3։#s{_Xަ # CG@0LdEOQ?{U'Zա2'3$Î&Բ ^3Ǣ 'ɱXV2kV7{٭-|WuR'uIbJ<2]?6fK_Ktc?I(Y}"g W~"8CТB"2O|bW'!Sa&r y@,SSR'!P6Cٖ-zO?s` bZ! ܢfJoϘhJ#}vx^p(b>E ?z HP4ޗ>olqg ^Gs*bsJ˲vbanVFỳڗKc׀Fɼs '$ڢihNo63 y AM:YĜ!euwZX-K\/hء~g-I)oGjS.|}2w3\/h'ӰcFNw]OįцzHLOԨkyws~n3s[W",H8P;g"Zu9ڡ߉u?t>ȹYRTD@d A [V#`vRP#`?bc J~V@ltlVyA LU~"]%o #0Y sٮ;1Zy/ kK$>'⟸^3: uJ~zw<#h-@s b~"~">&N~;IWyL!@|5y{+{ gl"L[v-ܵ bD~4#23˳ En EA2/ǣyVԉ>A?uIJBZw~Nfg`mm8Nb0 ?qpO,wM ~ͽY]708.ag<{c: &zX_+_\ر&H whPF>?(uW:5b^_d~KG j ~[׾/Gf`o{j@;2P#_Pj=,N@ඃ%R]c9_f ME{ vj Q t J/"S_=sƾծҖ ?VH{!2Kve@D~r'X.~͵; )ρa묟H~Mz!1; 6.B`6~CFn$sJP\fa&}h]/hމ~*y? Ր/IGJgʫ3) +\` V# ykG\شӖ9M[!bi ʬ^Jj p9 yh$0 _'Bꀘnd})7q]IO{Oyř~`4 ZP dOb vBf&Pzݑ#($Ǐ9n%b*I%<]8;g#K[[ Pn_߫H!6Gl6δv\o,GfIV3tfa@֦o0þˮ=%>g#]Y5R-=㫭>X'oZ4js"зC6wyCJr. XcB{PٔҳL'gǜrU,jVs g@UXް(Ո Oj:$3d~|ͅ}cޗֈ92/WO{deo}I};+bRA~Y'4ֵZ, hg0ήaxwixSlkV(j%YmPhӹ;r,0 836B / D>: 9]u2r} -BG@՝Rç,r'W[BjLte\~*{h¯\/k^†\<"?GHy^Nh@)tU 0x:+/UvMckwӑ281_+T9m֖-0=1Hc }fX`gd[lV" sKF"|4F1,A{O^p Z^:Wv>O we2HQQ#6s'2!CN!y+d ,<_N5K6l\qt]=j٥Wz¾7%@9o'HQ d4(5c͎(H]6l[d/o3DG'5@`T"s/JQ'sk4mYw\4V qg7C>sgd_ո88 UF;pt9ּ#08N#1? X6vǎDgM. co'u@e"4XM{E`  "%Jq:r/Dr#P2dz )vY! Vn!3 d6F)';spm2 ,E}Gq f,M6izk "V`թ/[E/G̱!56f"[>5 }'e;y{ jYĞ5b#el5rjϿ 11km@dD|^s `?jy' _$$mJFZًN6r@%ygĜp_R_)bZ eh r\6%q,䯓큧黖0 uC.W0,`,nv'OI263 0 WY{ljL} 8γ0ܴ"u@vX}]D1ZEL\,1Hc\/8~$䣗j9 #MB9|횭p'lƢpjwUt,tRe}m]|-{ſ!Qvr2S^\ ,/xnF3Τ{11fߚ r}gE5h[m[k:9߅X3ݛQJ{.7_9\loy]_⧢3I5̬Ўg)[.b:!+5bZx9z  m%0*翟v:dM74( tDA8Ow3 (<|iQc J]r e:[X:Ir> -4EH15- lcшV;݉F'+$3QR\4.#pEX']d)6X]\/hc$ĶE"H^I$@Xn9YwZ_d޼-Ȕ ):S J;bc^Bum@ +H=/Hh̫\@@bB~nzT_]̙Ma[};w͠^V w]E6-H5ܔ¯ y#$iOB5(uY|@aV)GDYsq H-0PJ7Ba;s_q!]bS_Zbr|_ 9_5Yo;gĀ6 [B|O+]O‚L9`rMqs>>h|-=j|#4ʑfb/"r߁Vo9NC/+~"^m)(tBA @'*4@GLVlE2C'⯹^p&@Pz^@J^ v]e"]H%vVo6'>OmߺSA2/'fQR>nsf/̀%/$d-!fS~nZ׾[I(k9n+Ѧ5/G0 ~:lYDuK#E Cb}r02w`R _WCH|v$[ȿ}\>b"zHU2mQ[^0Ābk"r YD]/h'?0*:N509RR\v8GaLz}K ?G(qSRgebj`&jHH]V~dŮ?@S Vף3#]%62+y|)Ab/CLNQ|zj/AyTy@KOM\/xH\!PF1DhS0Nq_zo:y9+?@hUWs+cW_`RH8pEg/Xµ -t;WWpvfORd^on)(fXyim~CWHW:У8d-ܐdYkM\'Lb?/]B#; [!6HazAd)x*a[ p4QuzAdrvKPz2)'.ȯib] h3RϬt]Kkͳ'I-3PαJ=N P*ET+" F ~ ' |ZzBh~4#j Z"@1e2f>W 1\ň%@I3Bg"j#H!DdcKʫ@n&"ư,gSz?Yy$!Oc>+AlGyq8bGFe}p<c}&e˗i?uUQKmhE̽sqWdXU6;h3&X].-\VyEe94Ys y>]KCl4Q9[{ˑijY%{{Ums iɼmIr1wآ0 @:Dnv51z<\>9׎ 1 Ȍ;Y?b3ϐt7 |u)HE>,[@S]D|ː8 Ѣid:D:eGhR"/AD[ uEs">t5R^7b)BW" WS;1I?p"2~AQp]Ĵ}@L[4Cf [ = 1{le7ưj 9ќHCN}0~\`VWm!@̔g s+u}9H~nɼFVCӛHLi9ψm[5,a,VYU0'%a|3dתب'jlʪ+UTԻ~7m8!sOn|a+UMw[22JXBXd^pM=b`7^}y;@9UU"hGsܿ:uR'kGwl :3k~V|-_}@XMN9?nxL*#FHYs]_|iդGH)b@Rr1:)NsUcO@O! E-B j:'"6wX% 63h=N:e>sZ~X#GQGXOX'[_WzvOO~,Qn,? W|N5X5Pk+v0Ӟ6E={[ F9kVQ#3kjmDӟMn]~qf擟:oϧƮr5?'5O& Ij=RS\cR9JiKl9ٓoBv0);sMl,شۜdE>Lޝ>>nڮ -\Q{Y=ྂdp@p ',_ղ-tj4)m:eQrWckOA6PǙiv!!ЛuR'ukdⲆoŪ~}@HAg#6{ ȟ5F;8ב92inӮl›#?nOrufc/4"w!-k7)5̱7"v5RM0 3%DmS YzrοߞB&Y, AZ;s{>:"sΈ{XaQ\A3;A"Rj`q9 -]̆cp9j}~bƢ!ؖ2RBG  b4g[!׈Y4(ޱ3K#0b$>P̋޵Vو]^ơ7#էQ28c23J?(_K 1bR9Kg ev&qmX"-s y"ieʴjgMgwI6`S{U-Bso#+͡~:e v|>tb@a #2RQK1H{ "$+|:!$D pJV^/b"s̉Hٽa_#6zT!eI'9vsCLٍH9!꼟] ^Ȥ5)YHBN"b6: Yَ+n~ZV^pB;vXө=}R|nE`m0M)&@g J+]d T(#/kl/6Ytǎ*As60Yn;ƴ=1]҆dUQ˷)USV?6出oդbNF!bE+6h@doU1]Nna7N#c3?kf ^Xvrd^+ 33z\1ʝ*m ј`-o}Z'ed-,_~,=saUE_OV'د r½&J MCJs'xA \3Mvb N"ߪːE",3SBs hn' mf\`iwq+gVE`{$o'@댂 7TZv@L-v<ȿmRfd.\ޣ7Պ]9mq}:ؾgs $*-*h&ʜS5wչ7.H!2Q vEӚ=So3,WIɼIHޝF8D_VoW(w_X+P:HueyCxo׽|kkq mл8/ ?s[lR/ Ƣ~WYKk8זHe֟Z@/(Q7vHIspk;7Ğ'zqCa-v6e El(c0fz;lہYQm;po %ͷr^A긤(z2)ERxD`NO}( YvZ]OQ4gfo .GVาf9*?,$J+r/ȼu-j&𜂘: 9YǑ_Xp ==W #cڿ'b F[/%[*D (gnm)&vOطˈ xy-z#CKs`J-v5dlpk-prz2I#'@ci'5;YC۹:q1L" zzBZMB@?\ )͟h<`0k'Q@wIJt-kK[Ҏh; YVZޠ8#dE 2B3}Bɼcn;VVNYS/XWVv,_R4FkjqE75z}-# yAcq  yG:Q OE?a8֑܎1 %hpL8~j`ߟ|qA~͕7૓?Vֱs؞("?c~!{"UhnfdBE [`)YHRdGgcu)n\/XH#vm0R!u{H6g쏔U)uT͎vQ5댅t-׶=rE~> utP8O`|߽ .Cha\' y|sDkoµ)9 9ЛI3eFbQKWu 3!ףq| Օ/Z,۾쉐VJ0Io[6N"  eww,26?h;l`uR'- %ZSnw}Z6GjGq2 d_ laɟ uOCk9^ 4Py"'a<bU.G.~icx"o*]/OWbO/Xk0ru"VG uRG&#y?Ұ[ A` <#,3W2#QaF9#&xDoE=ar >{O_t,4gQ.kY+ ƧyO1i3"dX&lF~wh!߶轪Ȳk'[^%3ZLoߛ?]D>E]/8܅:Uz4bz9U63>o[8C>]E"8`訠~s kcj|qhj`Iz.k͢nE9k{ޒth9mLzȬ+ y4y*3[/|_Eidw:-\UBqF5y pQ}ΨL#tx8R#gdbE׫Dm+uƴqH~"_ ]_zk+Gl)@983HEk%zq"QGh4p` bE/a5zAvPʾGYXΈY!XiϷ"9)@^EmGũZs. -!v(Jk2ɿje_g[@~֗ `ʪMĎl^?|4geU3#FZMLgQhw9qG[9QpIft9;g٤"oAhb.}%'/ =dB|@ <b8t.uUT呵?-,%u9ɼsI%z slusv-'ϊ bG>H9엡/ N-Y'us2Y-~HT&aZW8/ ʵ\W'^_֮N9aļ^p𱟈sP'ٽG}?%jUm"S4#Fs1iO>uJ`괊 7%81ֿk$b3 u1#}Syw)gȔ,?0~eUm [8 7ͷho|oZYg>Bm}8N pI䂒(;8ۇa*{MX0 ; i@Ku? !+pmA!SDs{0\6Np'K[q=2Ō7b:"%u?nC`!F5h!^Xfy5cv2C!p ڡL)ވu:*bgRq?myz M2 NAKq.{#𓉘W99bCR_yúu93@WסEc@?E`b?b[x6z"`c}8b޵\'\/L/.mz'/,>tq}ogI- 5{\/hPUU9H@kpv\| cvK,@ܰfgY]Z=nw+P'ckyS;4&u|Wsڗ]st1ouǜ[ oC8gLgN87?pپlN~n!38NyT'm1&l)6%qq! ~">澈 Zv#OYtf z"F9XEy'7lآȬ ^ ;Atfb`D6鈕kekG 7)c0ez Vg3RD"*յJШ^沯zu~a訫ENs! YBj~Z/F&(2[kkE):YX}֔OT3Sw=j+ß1QXd6+ruQz,Bk {n~pVKKz3;*JNrw(+kǖ;^oݮQja^˜juʴZ"pK1A.pyɂz?lqƹ?ɳ{i*~g4C!#?j`@>{o۝hA,+V'J;W r`` vmy1 yMs WM bLKX,?p]u?Hb 7$Yk,=sQ:4bʕ5(v'!ɭg߄cRNgP+du"̽OO;\/ȶY"02OįEv}&/ <eڱo11#+ VYݞAcQ*ƈavɭ(T[LCv^RUF n$ym@;m t33[bg p1] "sEE˳U, VxYBA"?YMKӲX,+\ۆ%2oδ/ùh`D|و~LyZ~{@qy #?\5ȷyiMlfUK춪͟ǐ_#<bdoFꪒ؄ '*ɼL7r"Ũ^甔7_gr߭_$~Z@X p'DO#iUW;-uC|_c ,He!nQiks㱴4!T0HRUQ& Cg}?uIM2bHn3DNd:bFXj?E-G,P:-q Og X9XR v@f$!2n.2O֝Ȍ&R!QBOg=L``'m]篧^k:E*Qeq;s&!Hcr-b ծ:cٔ9MDfz% 3s(F`/2맻^p32C ,xBL0K#A ̍Cggϯo}#-L;^#V}3]L]yp_,S38J^N'ڙh>6dĄ3 ͑ \^lwoSh6(=;\լI)Ү_o|1~"^nzߤg~wumȯ\^ظ'xߧTU0gc7v˥]46Q2˿{N.H r'ZzQr *bdeԧVN~n-~Ua1m`)ys {]?Z.H ,-ۘNwbiqgCBL;,QQD \}31c'#e0dmo@G3m?G`]"nCcR8 =;UVf9"@4Y1*)Vǻg#EV1͐V-boA+om}1'!fkWRVݬ]g* ^@1d^5b:=Ym]}\/xҾhcr=j v4u]G.:JW~^C,k;İ!sտ#:?UrR KH{"pD Dn^PD9OBOBK<"/a&'X:s +Q_űh%ɼ#s-֚OxPd ;39Y\]/-V< |8Z~q޶_oH=j$ǡ[6MO+qB .1gF,g vFr]dJWE 4s]/`P*g^O?FmlgRG~"*}}=bjv7`qbҐ}";#7 9XA(7ס9)뫪.^|MA\[Y )3xuV"з)dhV; %@Ի(̠sS~"^n9Vka|_XOZ9h=m[5ufzzV͍C&~C]bvNэrVl`hd_A7kH1 w9 (H -ה{HA2/ř]{ѷU:X5zARg'X vCĄw𘂘/R})Ov4bafzfDs6Zs8;s)2^cVIE+C"SAĻ9!?8R/#V=bZk ͋K~DefO>2[niN4/Cs{6C,ΖUYh>B!ìm!j4!bAOC ?_zAwk2X@ 9ET#ҳ3Nj%E`6D|L$@fs r8KukRb~8z%bA2oD51VN˦ӻ(-$GswkkK$@k=.#utP[&*ɼhm{sꃕ'ɼ@,?pjA2/LPF?DYaj8098# jq p8hHwSV<{i@ F}WRa!:#Ah|_Y:"5=?PE/פ. !-.24m 9îtvXĦm^ìˬ# ŮB.{-O!{:8 7X],t}n 0_Cukdء&?P!;NHغj W"Ez=6?ڲҞz7]@[Dig#_^HuE Y_#pqp`' @M7CR[k< δvl} F܅!<*n"C 0oea}VlRU}rV 2We#E*?Z?HXzѮ<@S51 )?ة3h]` zAk66?IzEiEr5>;J#hg6#xmR*!4UfcODs'J\\ d޵4ii`A0W(9taue4MBkOwCUE+ʪ+ʿ)Ïs 1r=cN5%I~na)pwA2A0$tƥ>zWoE ?ͬB+hCm<7Gkw)0 wG~na݆Lf)0 8Z}R[gaXd E}:0; ]]8)hڢqˣI]uU~MK8xE}u_d)G}1BnDnU!RP/"hA[3XhFfD^ÓHɜeם|t"_'D|gIOĖ@&ge<砳3_+F1J1)Ft D>RYT"ľD: @om!m%HhXMAJ*Enhc2>^*^D !г 4^:Et&>wQ/']͇ȗeM 䘣{?oLͮ:лrT"tVuֶ,.[Y7Z_bTyDhzjuS]/x8Oċ}7(*q"bo{8]}'r6\/p?j1@#W3~"!,2s ּ8?E3mQU1ʬ\̽+YY"ScU,FX I5MZ WA(([x5)3/T_.]nuzKU^(]#B;xm]кwkA2(??%6gGn_غqorq04shsye_Qq~a鑼l@ ZMM({)#"+QN[ 0s\oT{,RrQ4y:\ ,G )i0 Pэ@ʏBщB=xIGӣtyC+NNla;fp ~8#R$#-#@Ϭ~"1Mhbefc%+w!{ '[C <K' F b4# [h?e6': `G ilg>X56QSX[Z!0[kWݾk,s=+ll_$f9r62-_ֆ*#'[W deh3s?+aA2o);!)vkqjevff QPmL(HLȧ)l Lw>~+VX? $!@VЂd]EXr( rz}q0mkFht5#ia8-]a8:a8qj<ɦ .C#]/xmm뚅v}76\fЎ=/H?,Ha4 Q#R+фV3z8\!or7uRͼ-;g_@>GCbU.'Iq}ڎV^%ɲ Hic~εz싀ľ%hmΫynYbhQon}Y>T$e[+I%{=gxd"pw7RXB;Pt<4 p1'b{wM@h_\/U\%=SRP->?&PR"堻6cَ>InJ]4:g ?&(\'kG-;;ŸV}v#~M'[떾z xR}~bT PV"}cA2/zz?6~NCV2+/Kc#a~e+C!@{:)+Edl8N6 灇9 :خ' G7u$?hg]/xO]˥עdK!R,cc3ߒWG~ )fLmUHwA[#Sq_W$iHE#Y^{&X㬜\J\!giXN8 -\P NB *?m&7ZDzS)- cpm 6Hq@}C)[c\7`-Q^6#Ed={#} bδ~ke֏!F'a}vXkcZ`2[j,H0' ~ۑ6ro.Lё?, #0 D)j~6;v-RQܵ1?~_vZǽhdOgGt( _KU)qH7D|0 [G`}tr0R-mϙbB e"r35Owt`ƇZUŽ;f;e䄗 %14!0-"FXkK¶G;WGDʧ)Z\Xk\R@ދv5Q@X{X_ YzAE?OyQjYdvz/#HkY5HsHG#.yhx{2n7! c}2nm@;i7λ<Xn0X"xCuZ@^OĿw " $ v~z>kCʄs;#24b.1ҮA<$Yͪ^ q~\YFd^w#RƲZZc3Ze梾fbMg y~]Y|S̛֐ 4n@\$qm#u rN~!h~IR I'm$kM'ml2Gʌ𼟈?`uk*sw^~"]OhQBlʙHI@ŞgSsL]R]Dvu&}U֋RlWWNE.fߙi=1\!G.R '/[?\XHiO2P/{΋1 0ds홃{>샀_b+ v -xDk,bEaj%{zK؟⟴ ue6w v}w926E51z;i_}dY|LnvA|{V~F HI.0O_]v85$.B74?pu~"R)Hev|[XU^Yiil^UIq;fn@9ȱ|@ 4v#bDAkT 9I/М> m-| "]@*$X vtUɦ #l7ǹ^0)Hig"0)ȩRXb &+1X67"|21U!7ݞ V~">fCL)Hw1xKS=qv )[ 2uFwB@qZ\4]*Nd>b^ҭz5vokdmLo -M_t@;;X'@@,Bs͈FNo!P^lĀ,AqE3슀̖1 `9> x RiF"í^l/X@1b$A>?QtjYnGhsyw[YA ;k:Ȭʋwc]_\ ʋ֌_*4&~UYfō+g|vܠb"E4xe o]+ʕh-6X9@TZSGE>exbA2%ps w:"N䯒Mp`8N{q;Rt苜G,ȟ"c _#ZF@`lz)|F:"kYz_+kYĜXd]}%bg@T|C{92v `7[9" 00-Vڷ:c } pY/2el~ 6];H|A#{ߞsbFZ"ӣm JZa)ClLIߞ !WXtAgև#֩ (b ֗X 8ldm|l\^C)U9s0m\s>>;lY9o47WܮrAl?f&K@%5|DLW+4DLh-Wk_%x6n'"%ˀ(&O潽FٳKw}A2/ ߑ_I0F=+vO{oLA2/A*7ԍW%bA9R2:?E6%F 1A325H1H CYAVVcD ^C&49##VCĀ܄LE~"~iQO __\/hLtљ=Dz"pbGDdg29\wC;Ē @gMvvksD)EVW?\_V^o'[ƮC!ҘgY"^?Ϗ:ϐk;L=@p7]CJɝHkG]/8# R7.E`#69Gt@kdl_"۱qo6^fn򑕚y2=04кʮ+~F@a+0Tڠyc@ c.@0 |'W^h|;q5@\m5Jι5biE p[b~h@[)Ai;s$0\X]f {U#4vORagh\zǏZWۮ|ދ+M2ѵ>E`zא:^kG`|ɼ{М t>SM-F yIUhDk&6vJsW^g ˪6뻮NɽT?.Ha"֮\;?hNX46ClAɞlL|>|? 0臘aAlbGFgmnhm<͓H62>G8gY=кեL[waԏSb8$󜍌aEL]m ujZc^ &q0<qy + p횵;k8k0ڸeb4/Z-*e"9h7~5R)sI"E51X V&DL!鍜޻#.bG& #R6w -7Z]@lH %rA}~ȊrrCJnsdH~(gD91Bϣ-(y{?t@~@w:zd7-xڵҮ!}>#Dc ^_Rg5g pbV6k8)9y2@R]Re7G'x~uF h_k[rj{rHtC>y=QA)b*Mm_U3EsygČ,@=hΎrk)vMopfPXqZ}ӐJ4gZٻ"pgzdsԿQ{sU8 "/ Fx@~nɼSМX8Nz5&H?ebh"W{P>=#)i~"~IDATw 6RpBB+ZĚ#td9%(u)bxVWGLFf9%uDH퍘EK+qq1R KcdضH_PTh>)S+Ϫ_bHr3ߊG/PĈDk"ΛNG Rp &b@eDZ =!b.)>kA*e(kG[XIEA^kcTG`yYaUuڄҲҊ^m(X# t][aȪU3hhjϝa =hEvDdfxqa {|@v@6A,`z->߸:z ٴe ~=կ8 /cb @~n0dSl>C:x2?ּ0 gYR,qqk+㭜(0 >=ťkhgWaz~S qa8NKuΫq 8m+o0;FcmJOADrhhxrbؓh~7r~Oė^(5fJ.\r|XC]/(`;1ع|FV'XX{r8Y[;Y="Ss#LARNA HѾLJd|UTW˗--V!3d 2DJyyIRlR\4eU, r^`}w1#VT΅T:Ug.듮h?yhnD@n s P(B@Z:4jOGh޶Lj։laR'oW <5#nx2 '9w02apgv|!_ax8!7ڰ6sHQV:ppivp)mwp8pW9 mL\ t ð̥lR+" H'eGu'!\^=2bc1;MCr7Rt͐!JZzE,vz!z쎔sRbIxtBq )mL'#R蟄±VM\Pӟ( G.žDCfGcy-j" Zy"x, RLՈ)fey/-V֮dW!64qIh`]G#4 ]R+m ?_nwcd^WΝCÐj!tJx޿sI3 꿶ȄxІڨN 7G0֊_T:}Y ðq `~-ᓵ{"]0ogI4~6]adt0 H䡵z3Gq6a^QEhcIK+e->b㼃6ׯa_% #KeR; 2 )JRGw1Ff4Ja~"^zLpG`+kLF&~ @J-[e]t' V!{#R]/O'xc"5ŗ# p#@%R{ 7 Z!Ž~MC@ĕ @5C "iz@(p> +b~ wh|u껈݊"^p2bۢ^f8mˁoϯI u{gm[ˈX@쏘h \zs3{2_b1^XׅhѼ"6>Ϣҗi-k$CrmA*yi%cti3 jXrvh36=hC1-BI)cyV~gZ]6=]!-\Y;q:ihwQ4QC+#s ܮΤh\WLu N~58 ^0 7T]bh 2P8mJ~Ā0, ðr]oL1 vGlPMULi>Z.Ca)R0"E"YzEzD$ͷ);dz;gf"%l4z09 !3Jdn;Ȥx0RyM#GgjȖf)kwb#Z3wBBnR#=)ٞGw#@v3~( A d:CH|1Udv%]~">ݢQ/@@jbNfo7څ-G@s7{/b(v6@XIZqiӏ8eCkY[;"1=@W;2+h-Cfc.A;e {xĞ l4_gF%gTr#@;Ots\/oc>V("Wgk~ T^1|DzxĿIs:fWE]o)~">k8vDw;o߂6?d^ ^nvcѪ7N %O#04jy x< Ú׎opg`Uj"i ,2+Z~N>^q0 :, eldqa~lj08GrM!Ѐ \DY@ lO)ň-'Գ{!7H!LGr&&}DWg>&α `i'!&h`̞"2H-Fʪ*ña~">ծ3=!e9iDf4~-F/S#1 H*ґ~7dZjewFF}P󭎥@ A3>׳3^g߰{ٳ^p"CG]UavwFhѸWҬJkh뎍6m͕!p2m lPHqed3O{ظ>hqڑT24sg!G5 Ц$s|"4/:H*6&Zeczh+ ͑p{`]jk'XDU[#Y 1ne@q$,+_W8hnYr7~]6 -<: yɼ- hڬ^m*g*Z8W'9xq0gqhe>e vZ1s/FuǙ6pg<2OZǹ 8N̝6Y=Z|:Iq1z p=wcMGȿ)VȼS㖍'_ܫgy_N90їhDC (McD-cg Ñk9 B1 D iM*AJ4kj rV;!Ɉ 8AHN\~"zH~">؎mhy1bFb(ĶnU^a%2}|'XVYH=|*w|{w굩h+4wB;Ka}]ذխ?ӱHC%((2kE{Χ'/#yA1[ g"vi&2qB[ hfME Ħ"dށ+0k-;MM=47V%h! IX1'F<:]bwwPQ|U17D#+7[{Eȷrtz^JHA2 KN U[xGv4>+o?GNQ1cX̡#%XHQ2.E ;jn$#V7<⌯--IhD6ZokWGR E )Ї7!ː2l <'پZR;"ŶjĞd G4}y<"ț?)u \ɯ_]u0v2= 萕gnYǏvAX#'I@ZUu=VxG]/Nߏ!_d)+;5>o(^,no!))9HiIV53Djw")!Ȥz5R#-(_lf! 6p\/HgY92>T-+~">OćXE 4BƖX"02z h_3!v/"04tI !6.[mQ+i6'.GVCR-#DS86X\h}qjhB|[x#խyj힇B4ZY^XW{Eo>G#p̃'!`0>0†hnT]huWZݎDlHsٹ4_Y]O#Pe@x:RA]O&dq!k>Ʈj^:&;.][9u.)^my'`1@w+4WːYb ϑi]XFs~hőw9D]1zǪt@/e OwFާ!l)3ۘumm轿$"x=FO3T']2TY?ae?Y#v.ZxߜN5WTV""Uٻ/Z{#\ve܀"K[3[{T9gG9V!r<2l@p GPx=>^w`7kӐrDJJ3K O44R"Aݛf)*\ټ//m~Zn_+FM2Wjd8f]GY5eD|?2KwD^`6 )_ KrbV[uF*[kkw+e |DvmƼNh"[O? B@qgĦ9M쎀*R h^:T}'}u5eKcS2Ϥe-DsD# Cdn#~heCfemvctcsAj&ٳ#e3sCTN+l_ LBQ?a\zD X#_D]4!)_dY[=#͵ ,X5^Fv!`J*d")XRzX}Lx"f"H" 2KIO2{ubV@FXAlɘMWMZe*{^hKz֧Q*l@4"мq֯NmgȄ<ʞ= +I _-|ٸS}.1OnХzm*2 l#Ż) lRB۵I' &Mn,#e|7n XYh bK0"`(b 0ޞ 1;oL&RdygJ*$k,5)>YHfE>s;Fꜰ,  xeLA#E| bEv,OyZ9q'mG nkuk6f ?o^X^fcz kF[:?n~b]M"o!2d#0Lc"p!Oαf" \]@m1+sE란@wv$?qX}sywXP;{f̮kbɷ֎ֶNFê2VUVƎ;ֿ7"@ 3mMVwa c{۠ ڜ%#h4wDݐv"z?D^"dpZ2ՙԢA~N uk 蘟[Q3_e/$?_zWgQϰoBspz{WMO 7Շ~0 }qB`cN u vkvOGw}= #!pc/@]|̞9#6ÞqWڐ ' ú|x(i3f,r)7Ю@~7 C @(؆sO3?E5>v=kAV&@d&yxߞ2GQfwzV#_OClCH b}3δD^OU.duؔ)pD`12D12#5t&,~>-)R6 z6fG@cts\)C5!@Z[h^|%9wA aRfbڠt54^f,qlL@]8F(`F27Gj2gPlaأ)vn3A96&e1͋="\:\/8y_6E"}zQh^j@5#ņ(u"w!z%; )F/a%]I5W…R ~6 z~"e:NثAҌ5ӲgM3%{16Gs1Lͱ[htBjC<\8ΛR. ^u#0Î~"}9L Rli̲#`Eј6AJ>ʲ톀jRYw%e)#9eߒbV^OGI-CQ{!0OēϬol#[z5eu5O[[uUU?{:n H**cww^ݝXW(* RR" 5]3}_u33cZ{oX`;"4F/[ңՎ8B浑Na&## 6NqHAVacklrwgDFա>g?;v_yU9~7FRw=D|.RwiPEK;,?_=0`^r\"]yU*p)~iyt@q}w>d} ͑~e MlۣI 2ـG@ي5| Vu>ݭ}WH&!h b:Cի6?{!t^d o~g#:\tbжF.鈍M"$bǠVJ *q+M{3k[?kgF@}pnrkKk46±7m=k ί$Kb~e!Hg~{rZ<"o+urFU[7'CIYq̪z9Mʖ0GAQP0/ ۣy뛘+ 51WXty PsKP_-( `WzXAJ\/Ӝsϡy|Gq$8>o涁PrahAW~ewP[^\@}v 6 1 !BvW~ba"HPl%|;ܷo"d|7PVYrA);Du# 2ĵ܎Ȁ5C.iDsL6B"e3*ZD KtS1Z]!C~O.ϟB[kcn֘ vl8pԎF@0܍XYZ:V--NK;{An4A< 4|:Ւ^/NG4#9S/B\!bBvGzr1NΥt5mV#!AK! - OqHO%{ b@BD 3 Xg5҇L6wwF5@ [hl 1OF@ bBbz2j}6[Y@:?!=?J7W7^ϰՕHX9dDc;צ-=~;d¼+Yci].9pK-vkSvc¬nt̡~?ͽc0+ GYh}sW\a"hsco/?+A0'7yι ~u3MK X2s# ֤&LjrF17Ml,Edgذ:Ƞ썀 4׳QߒYe+݂:]H!Fs{ZA' {IVt>}ӕ %eɌܪ]:/؜|rYfyW #2DJЄYDCdt5qM w`9#6j:24 vLWT<+D|;#Ve۝JJb z!`;A]dZ[ iuڵZɅԲ>EWܮ1?E~-6@NխFEN( ֏!ЛMu[C/_rІs)? -PfXт!PKg%h2>H/#;["@'LgۻQ,!V&3P<5yBimY>GyI?f.qKMnh9dNxq&]k Lϯܝxtδ?T B~+Ր2m[dgC A Ъ4ҨKk :4%BaG45B IO ?X >Tb1A,Dsijxk I3-HgAFz(~74ghms\Uv_jTdnNg$+zbNCLH],נq )yU2T.˜9 -ֲT1 N[ Z3Hv]\=W7SV1ٽ@1\=XV]Q,*eI M!ra+͗#:YZ3y$ EQ4PsnBFzg,ZE@lfF@n(23̯||uRj1)i iS45B(7f##շ.2chR 5逘gb_@a=42mKPĬ|ieY ҊPdzɏv/xŴ/ϱI%K-)2 [Ve5@l#|bG3 A \Jmer>[X87տgmo@qj"+zAH?koӁ^Ϸ~@ȝȀej@ۈm(F=06C@/huG~>Xk;U\a|]% g&O?<ӭXQ' #~4"u9 ]O! ED|q O{8dF:K6tՋt- >C6AEڋծZJJg@ 8((Xs!}*BR"whNBcr]zʵFbWh!>/:}c]QPM'][TIldX*_ahX s b2NGF8E9|4FUhRCo\%Ŗpta?"c A+JĘi&%JF~UQCS@- Ir @F9{~gAמm/C J^G@?:i%d!!bDmZ&ZYS%̷z>M2g"<@GI@EeDlÛ7٩i|k+&.X%cP\c9&+Q{sk݇Td@`x!bJw&m?KM臑ot@) 8/F^\~vnWi2bb-C^X!ji}0`Vm e&fh-B݈W}CfYȰY>cu{QLec-Z Ac{Oq/gnfq B~ϐN<>=Sft=mGrUV4}[( XLtҲ%KwV%ok˘+ˎȕt[K³r~&i.bwZVюh [Wj1mRD|Dni2y(^{!#{*зN%"#Ĥ{IGd8 <%@`@XK4QYhz9bNhgUȇ|gۮ [}z_dmF"b2P5}=iv Ih2ӭ}y85O7wҩũD܎9`4?1K+锃ҵV?5O|dxzŵ(@=nh2H_mhygp 71EMFz50'~=ӣAs4Kw#bgV8 [5 ZLWif!0I%J$u¢9OyI/q?y9+ݼU#~֭Fs?d *t}b>ϮFZ]OF|$HZq-'?-ډCjRM[M⊣K"pC+/gB:vNf5iE L%OQ7oG#`\eC+1h¯cj(4]6;Tw^(/}#rxDK/Xst囇tvr?nnО/Dz;`#a@c˅(n b6݉{Zd XO@ j ڹ# ]]12ɞyPG4f(m/B<3Y1'Y][ZO%xIF`2wͬ_Z &d_'Q|W?sqVBQL8F A(@uX L~ÐzT*_ڱ!bg{&;"fa*_s|6 T4*и[ES 6džDl=h<Q/5&ӦAT|Z="V թ}\kߗ_5y+FJdh2C+*4.Fm!#D: 1fyGY>w!X MȨ]U ^H92޵Q[tO%̥T"/AFf:W+ H ߑȠvDFh\md  Dg2g#&d g{q3(.w\;g:Z䠱ɑVHf!iy]b7{X)>{ '(;-JXSc+>Wj}ΰz\۬N/%/Vާ3d~8砅jgt F%. < G[~ːNޏB`HQPp՞?;eT`RhU_N Pl\} v2^'!0QeuajwEGO^XyoJM'Y@C 0'CoZa_(Ѫ+t-A?2bQ\A+$QɗȐT^_&|G}1ѡH4D hdCpE|[i 7 --DzǷO!b$ ;?6GjUr}tFqWG s4߱2@,׵0\E7K/!h40ܯ[EFb"< "dHך+]]HK;H'|Yo Sj8OZ=wGnڥ8Rf-@r/%%QL[kwBgZ씧VVe4aoeDT|{CcYJW\i7uOA l+{Im˼CH"FҩhL66#z1a3 zITػg3Gcn?(B%]ݙU^9=}]cќ_+P=2c\zPTlo!H~7سuQiA Hg? Ú7[8r XssAA\G矘65 v.2ӑ1_ ډHV"V#BhnLؘ j:M+bwڻ D 'ζR\(+| Xl؝?Ҟ_6B.?dbORȀE2;o _[#fGZ韍VS 2d4Ȟ_X 6\H,dz>ȸ]% 1YJħ2?g#Cq r6AL_.xDN(-:Vo{JG˷؇빘* -\kx5b!&Tw0Yv]C:7!pXkx;[^DRz65̲t蘗592%_&t bՀu*\< kиi ^|ژ+* zh.n* 6Ԥ_GZp)"O36]}tZeA]t#h1_|ApzN&Xp2le(EQ*2,>:d|X2A6y<k|69[D}1UmR^@MV/^kBW=tn䎸EHbǣ3B$D:Z˾Ax!@( \)hȳZ=sgoVcCn躹Y^Zv3#>x:H[9<.eA>A,pνzιKi fibnAFX4^ v_\!MR4lMǢU\Q(d>E}[ Nu~VuV˼n2Zi";qs W༤і:UVen^r/!Њ(IUg;4CH7[t!2#P`:dbOe]h;֦D7F``}W/#v%a-&ېmkcmC% uMKvEthهrM7n,lsn^vl7mnϧ6kmi._HI*4AIkɎ(w2)^ҟ&GAJ2D3_ 4~8{mf噵&1#!2S0Nhrd*]# "WO(3@Ʈ}G#Jy+Ni~ 6oy]6ۗ輩xMu`mV{g홁M%˼=HVCugoE Hc WȠ5(](>=rQmX=n~ Ӗ,DLALjKYc )F8> q_d+LC~A֏{#<:K׵d,K'%A@3돎HBF>5]iu:!%V)GHgK{S@Gc}Ē G )VyH[X~нRy^ҿR_&b8/t Ӎ+Lt 쎴C`4Զ>v՞&*R.bF:9czWP!TPI9P+?&R|/AtjO((s@hVd((5ܦ1 .i`E 4ibhq@Gι!A;6c{sa\؅O A\-ImU3vuk-hà &yw+R*g$\={zI(@jd&! Ѥd wľK PسP"dhz;]Q|I}x瑡&<ɛp ,Myg"򌵣8%.[ +aAT"!tdAqKd P@^٧MwUh07ڹ񲵡Q1ZhvҍUZ۬/ og}}+inhEL] B@&I~vC,,qҍG+o2C@Rk_s^eի=cL"e"u;Ѣ#\G&5D|:hGֿ!0w:p>yK],|dӭ-z`β} 'T"V1b=PEvVH'ˉAl \SYa% 3b߮׵t1ƗVj_VJbͬlY2Q@/m"|ot~w񝐌Bc!\TQPP%g_d6\a`uQPk?4A0ŁuMB 6L$':bHssfoι'T-i +^ӸߝY=gnФ~q*K7 c~mxC=b:!7I92Tyhb BudNDK4h_TVwfF.gП i)"Fi+M ck?4A jXۉ8n -Dw/JĿ4!CFpէ~bBu\ma;Mxx6%#<hX5hB>716#p,֗<"<|}B@kk2J! _ G0 ?Z'##s=4b2 7ig j~-{Q_vB?(^|4q~ئHB{#;{+lo79_ienGxo퟉@1mkDgq!pd* ` 2Y@J뛥Vr4Z9!5h!UG!@Z3^ Ġ&omkauuY(uX陬Ec>npaOH1WLRy\㳑~68(( +O;ɿ}TsVA wM#vd072dԪPW zI2yh{MhŶ{3d"#N(>gw꽔Qg,s+w Q,ADgVMEJd|#_UU 2ȸ}v"@#@@ G N#"c3$u eŠJīgZ="ew\`Gs}Ni HT">K΍hڪsVi2h{!3词RdXk#sd/Cĵx:D6ۢLF^?dļMF8 fudoBgO럳h&=! ~%'AFnKM.PEro6ҡ:hXHwj[Y[5h2:h|hkwK2lۢaI[t2BUDgc.Z(] 3a1&Z;C,Uqzыф|#P}--()$2'[o?E'Ń;0J2-ػߘܻ#yXZwe)=uԷ CM9ғo/0祺5fmb^8rkS7jo]Y_!w}(F00M̰][!}~=_ii u4i܊CXdy5B`5l_">h'WkB`[`t*<Oj"=:T.'M7ʬc/E x"Z){00p]*/_b0dҟLԧT{'cC\ԤoMS@)J,Kqmtpt|j$ꢘЊ~ʳӖ(jwĄ hWMT͉.D^&Ő:ȵ23SYS {ANh\ )|cmD{! s;2f-)y H{#=~X8˧=4 K.0{;dԟ&p+k߭xC{I)=b:V|Xg['f֭vveoZ!:XSkf-ݺmhj١{x~A z2pR#&ǽ,Ytd}w}j{!ҝLMM,o9,Dd~hY߭ER4ڠH_Cg] csPagc_!~^eSK4 wx-n'L>-;31n-$5 XlZ+??타v';Z|ϯKդ1;ih|*O2TtEhR!sb!2fS'byEF92 G#`L Jd z5х“ p&kItR{w.Z d:2 4G{7Z9{Չ#$;*#&ۓ' ôM{!d{`vf? +ݙݬMmc[y{2{t;2 nգVhBoo޳~%Ӿf)wH%8T"ϑO!p 9u J^rŒve!Ow'ZYߌk<-F"^*k{U(:H!㸴ZFw~X+o?~l|aɵwF:oMw=7==W/t^ҿ͞w/d}V\ . H҈ )b179 &{fqryD)tcP+h2QdT\ ֤Tz#2YX]c7D V}9 @tcZ'/CFd:z(z;> ob|2$0О#-I~bhAaWdDsY־p7;\|G^/E~m/wGWU'rd䇛L~[yW C.NMcD`2-2y+z { r1fY%vBpɰb^F #m`(A{ X%7T; wS˿7#n66G`a4pv*뢷Yզ~qW׼# 9huV֧ìfu(f!{[t; _˃WxI{kw^o(6 ..C@p՞wֆ6 7bD|WZ^e../&ra[1OSTO<ļG@y;6:/iܮaeF3ty/7\ ((%bɷ߻SXPA /*uI~ԁ8EcGj<2&h?0{Zن1U\d!:{Gמ\ 9NNDc{XCm (~LCx0k؞i wE;Yo2P{,hף]]RDG;!YxТˑU\KSp@ׅXDݑ놎yK!p+MU&2aYLDb NAF #DtGgMtxb,5rϖ Z΢1ܵ]kޅ8D+jL6!dZr{Ck tb-Z]#;f2dYt`m*_n}Q:X.!02/AHO߳;A-XEc6AtFPXAVƕrwX^Xzq]ZL@:Y+.ɿ2Y mn*_Wߞ*l+(XPrU"=@ X+m_{EjүK\>s|T"nϟ@[lO"_A92`9M &h,Ga&2ȰϲT"Nn)Z_lb|&A#F2!u*29h?mX8a~`>ۣ UD/ߏؤY}1RTpȝÁSI^YwƉ=ňx}d&I"- c69OE@9drX˥wE,i(;@Z+K3흛B@d˯ +S 1 Hn s\g?X[CyIVZkCzؽ LS6~U QeB}OۻR}A٬T">H?G 5hE@ѽ]sa8 ]?ٹ{&ǝB .ƇM!6, s;sm֔4N5u>G9:ZK7A+࿎ͫ^nMScH/7}b'bLZLE:.\э Kuhl݊ $?X[5DЂ`kdʹ[X8QkSV﬌fZ;1O&rVLA9-V#w.G~Wak=^ƯD"OT"^)=eNsÊ s{A$|. bQ\a+/ι|ιQιqιs;v >uεsMҝsw9&:;q{39w v:5sh+[c_sYssrMP+]wߠ^ҟmcWX~>_ #Ydܺ7~hH%kM^&ɢ8gs.༤)OȽ6Su+y&UL Dtb|'dvCP>+ n91V;Y9!x(>/x(O6>t8dB#|[VtdCRxҡ 3J.B[ߦ!]M8>]##%;ZE:y02u.oI)??X]b53ٞ`mi< Hwϵoir܌(&:?M\=b>t;ܔPX+[춰<F&!V* $򗀰+dǕZ[CV~ιP~6Ϯ2|9ps:v@dh]OI.;@ v__zӥW!#0qߎ脘5hثH F#þM,Sф{" ԇ# ]>#bz!X\# ^! ݑn;FT"^K{̤ȨrVn}{/\kp ƣK%aX05M@F^_XwYB``'T"^%tMmu"'QWر|sb:]n= >P󵁽":nb'."Pգ,%'dg7XQȭs7VǗZb"쬝]#,!8io?"75,|v }I/?"0z{Kggw-$-A<43aL;h}oocwzCU@/B䝁u@T"M$\ac`PCAӲު*KkA] ![f-!-VW,5rb0[9.&z^PWgm6>M+ 0eA0eϷ!z %O\ `!s?Q'iS8\1.&()$@ ܏G,-&Wޯ :xN %܏I%Ꮋpgkl nGm/AH>.߁pmgu{1a/s`IĨRӬmאѫ4@BbNA,I 66xAu2Z1]hQ_~x伟(Î&dv/[n$_F}PtAflp&muMdR1oYB.gБ-@mh!A#[%b:_b2!x)# D6YwCUlio~foreukP|dҭFw6v[; L`P*JA<&#]hhC*8##2䮼&MyIV}7}NZ&TJB|Cd#lB4r*ˋ s*em-#^,͋rz5C Ԥ!}c \_f.N5Iͼ [@Y*weߟsIss~iI 1u>O%^M#1bC7h2o؊f!vbUdd!\ A.GDd'!2J;Z>!fi"#91X 294Fh}%)cm#2'#0tb@Ȟ(O%/'s{LAHdXOnM%Ql#c{`v2y-D~:{[r&9{Vy@L_57 z[]Z7h¶&[tdbUGw&O{7.[ J' C}:o30b:홭o#rֶPgX}ҭ=UH24Ffxy Gzv2٩D|5@p3.g]bFQV*q 'Fc$N`˳Ct%|s_n6k2Jď4*ҋGRFw逛>y|ٷ`-;H?cc.*8lڔd-&[CPd ux>Ue-jo?sdrѢr;dNJ 9w<+vAla+A{p V. (ahM"A|[oLeF^3lĄɾ&eȰ B.M@!+ jdFY3$BGwdx'2R /鿆 "dh[3ȸ#WLsd- 2:[ .DIḋ! ydץqG;^4Yf"#=h~ۣk$#C"x4YOGnCɭuXV/#ph<k8ʹȱ~I#*d\&ZG #z3dmR*ܵÐ,^k?0~|%/9VfVG{0!]֖(j(P}Ӑ^h}Xa}d7E J F[Z2:KXs/E^ҿ\~c" >+A.Hϳ< i{4;mBYߴ~ '^?+/$ tkQt6T(ەSr<3P\Į76"pn@lp ; 1Ph V':Gs=EAA+#$ X?K BY?퀘y]Jy ^E6mTÂifO^ M "Ҍ^o,R\s>FƲ@79DZcy vpHmXR6 G# Z4iD{7ͭj!F"1Y~ ߵELQ=CQxrCZ_@׬"r rQm}{WbCd_C/ 6&dܺ"PʛoU^a&g(y9ێNw xbƎC@hokjx@3 kH>M-B:Fϭ#WM#bH?G!ޛ3L1шthm}sҫthlcuα>%SRX/eO`HnO%7+l_T9DxT}Yˈn8|QPpi:7'W ]mBeDw ;((j?%Ow}U j6iFldv +NE[U@ŧq_DU^[ DI{sD|brdUD h2@;;~ b bhk|LC !M Bg1e" f!= 4+/bkqhrJ?\s!@Y!K!Wb k!#ژi]a?eȀ8VCa>1=8];߆Vj4I%7{IlkRdlZ!?$Xz!p|ys /鷴vE%nUyI?vXgL&4`:Y]: %cmawAzSe R4fA[/!\l2~It8x3!?-Z[O5YmbZgXz!d9 4N_wZKi}%l|k&h@c<}tCw+1cH`^.D,}7 ]+kFҦ~K _]c[1baڬkJhš\Iho%&f.DG mC+GͶhA,hdF`SihO%bB&]V^Ύh]"Jdj{u2YAO2ns eA,H#v+,$_S&~P/r!@z=U= 1dml& [ #e\ ٩D|{3M4>Jz[ET}Ui' I 22xbNDahWmy#Y\G@UXؤ{Ɛ,!2FPK9#b3/@*bFCrOGexU&1vu8Vuw9jTVr=fuz+~|ڴρH: f- -nI%߳ }ҺdI3>=jmF8^UfFnj 7Ti4Gݎ@$v@= g!{s[rIto1PA,fH+ #Qjҿ=m򌘗k+EJʗgNY^fLնl#(G4ȰljN9?ҞVˏ#`1eu eՙؕxȈ^bWC~-a|]/G w+:@2~hekXw,P? # !pBrg#vb2ёwXM,{Z3k&V!rj2q=w+ rͭC (Fh?/OC`caunX\HJ=]ڊwɰ٢/k%-XTY-;Oli:EߧZ@ ;Q&͐ѻt#r,l]rמ}\pߠ/[_~C^ƞ_j}_e7*ߧ~Ve_gիa}uh2"4"0%Eպh,EbĚ":tvKGU:U;7vo4?=rjektj&٭EA1WXخ^H?鳇RcϜh-h%FLd%T)muKG`Z>EAwyV۲c {"0ɤ6hB&Xخmъzѩ[ O%_"cN L4ۀS5?IȘkO G`,GPBT">1#9W큀OT"|l&ڄX(h9܀ t0BlK~R@@6 C121a= godˑڿUT+_Ej +2[ }8[W.oS>Ɏ+"29sSV믆@zf֪W7[6!g2t)HrZ 6ٜoU֖RZBzw#^^ZaʖQՖȐ\ʃMFw}X_hmjtg K J?EGsA,Y:[' -odzV֫өjXCjÍ'#k2OGc R8$b}64kpxQNUK+ *Үq}ڰ5( w98Ķ9hAh!x-cXJXt4.[!絨/Ds'PjRMI<#ESbﺡDh ? DVؔLdp!p#@ >hU~Nr+*;XkW*K^o&н="]&vDgQ=~ yI.b^~cӉVg2ch"sɩQt4HOAW{M##\G?d CF Y9وK"`q? gC 9a %~&-w8ݑ;\`k7 x5oot~ [vec y߱{cu:hih`2^\Ӑk}}`A U8OE#rg!wJ I'c/dO?kDUl@ַVp)ҙnp(6XhFN>-…NoA ,J78- v0}5;_GLb2#ehi?d`r~ ,( _\Dwv@sMQʢ`eL[PVl4X?+-,-[Ǔդ/m1b^ҿ1!A`bt*/@K1kDgv(! r:l>17Y?mxh@.@=/b/@ nr#= 1?(nD0t=ؽ^&CQ?;V{8(1#܂M{#] 1K컮>v!b 0doE+j#v b@NDKRD_>e\^Ghչ=F&nE_@{kvA:?UHx4eZ?݆mNg>mkgRHĮ#bq8>yb3سH/hr^ߣO~һcd*YWZ:![h`DzshBc!wUL ?4] aEA+<_]f#}nvVhq{gQP~nM s.-nN`]p8F~VK ;-XOFy *s"<s&<ι-9h2eVOs[A0ʾι==tAsw[#_ Lq=Oqι,d `sA9 s}<JgA09W )m@ROn !? u/Z_DTPM)2Nc"@ֈq Ðkg 40!gdV#R^ҿ=z voe^XȠ0=(~)ۅDnXm݀85D`i;׾&||}/2@gx/2[ lVۅ RތAbT"> x+TOzICƢ-b2ss=`v~eqr!ƪ=m YD`kxL!&jxIH,F5hܰ2HGx<~?lm9- RM@~}N} mk2DbĶXpCj ߗJ{I9xֱ<*A8/[9|t~w"}JC 2j4B5Jone߽YS9Ea)4+y(&n)9-W׫ i-6AssιNfn$B &:WPܩhhQkAsn9v@  s3B s30?J{v7`9Dņ0nK l6uf h`[d#:T44"cC|;\s0Wg*& #и Rc ؈^0;5KuFRw3ĊbE`yVF Rds 6OBL^k{32WY"&n 2 o \%\M鈍{; ?%/韆b>F`[yID;4aZӃ~,E,0cW !HorэsZڴLC-49](^jFzHE kV#>iT"~2Т$x}h|hg[۳Wعh=ie.3:Q#Sj?O,4#?5 9Wn͚0F-꯳C غO__8*ς -s:bh> \]= {so)Te, y=+[2bD| r%QL`DB/C`cWdlF!f@`g!Yd"Z G'E o 3 U]:bS?*[k [g#3I#jnǐVk{#Ci RvYhpov:6ft7sG(39)í&u T">K#eJmt hx%3,>jAY>Mdt5C2fVyyf%jWC d!pV*K'I-bE 1DLٶ&wxI0UM z2юk f{Y5C,_ NAw~K.h'3_!6~.';I1W!hQT*&>)ιsQ@s ^wŶ ;4֘ȫk7gιh kb\S4?ynT<%Z$1dW{ 9\897$rwsC ιȖ1bc^tdApJħڶVT"^%{>2ףU½HI/!#R/"@ JDGu(JC\kǁDl׃@T" (VvANG V1(XDWmneGi5Go!Ŝ*6'YOC@`ΰطB~WڳOEF[kmC[? 6jyI0{Q{3־99 OM?lR /@t~41oPJ;N*|n1㺹=Kc5WUNߒEAZfp Q&S7#[X$X=-}-K#j>бDbD M+//Y&җ?1c׀?'\a-Gt-R+X)q<GsW G_0kwށ\Wd4OB1VSsdm,];,@/snˎtp (sG&y:_\3u{2Ro c󘁰:(~T"^V-B4# - zbavB'~uF `8b3!ա- kC#Z[هA Z= F k<ȟ"UdCVz Ν$׫@<,C`vr.0ndJgmD(bg9$ޒJyI"`x蛋ZOG =PH,3d¬sO ئUh{cwpP4q@:3Y$B.3ƩeYtHOv&K 0g /N,|QMRFAaB􆢠ƿvs6dA8: 9J(x$<㟜֌]Rx~@FxJ* ?t MR+PLM..K%oUӳg4@{6 y~r& /18 P|Xѹ]#q02ufXL݈\ JNG.&X"ZQ 5V 3(jm<@](֬)b\#P.hBgVmgOC`7nV(|:!q1r~n&׬~?%/m5Фe^R> γ1?ڳo"`eϻfDimLPAUKӳN^wF`z[]2~6#ሱh?g ֳ c+3ꙁI^Aڎ֮-x~7Hsy %K Ec͓sт&)+-w̦?>RCnLl,^s΅^S~:no `:qտ@ŀcRI}زȀ@ρ)^o{*,CqRüPyZ7 QCRxH \ GԤ?3} 2((wjүHk XT4\fOC62_}qX }D|sDl@2ht^rvb PיhwoD@ dZ"1:EVNB @7qY#fMĨ4B)|TAw{o]Zvq#CUS U&I^,<#iJoXbrhdr0|2ޛ!`-rAVXOGb!:uG # NV"P^2_!7:hhCb#"}jb/:*%ZOA%",eg ZXqt++(fd˻-rEr0H Q6VE 0~jyؖTj_WJצ(o!28bddFnՖ:"$dHo@(oAM"hڙ B`zE z`ۮr}bܞ wuZ0AU۩buo]7`yY4vM4]hm h vDl-& iԔL>X~#k r3F*<2,E,!$;9 fgX%xI+K;+c`H%e\xO7 rGN!: Y1ks$9ϢV\;Ó]j} 1#@-OY{qL@kc;ҽN묟Ҭm[[?A $4vG1i+3d&kRMI5&m$Fy m % /%;K2T`Y- ܯE؀Ż!CrXڈxk)T^ CqP;#QSv{wP0< tI%f7hvAƼRJ+ [E&,Crv@nHJ"3rE]Xx.zsrϥ#cdr 2nM|'ش@ ]cSm }?n2k¾b&+L>y@T">1I]v xlhkU߱^үr=w9d] g{!]ٙ@]cG@u)SE#sk-O?-n bVV}1w[X ]YD6֤? \a^suI5&oS ^56[hV7 sRdPkPӑ `ƺ p-ymTY62 B,rb˺!}?.#J/黴tP˽}b2Y#W(.AX+rd@A9&7xvDE덀'c6d܉ 5;3nE~܌ci<eDKhFZsSj'mX\E8DV)͗My^~*vgֵ~ݟgz]8/xp#sVhҺj~]zE:3ol#d Xd_!Jх︚LnψjүO *6_OV6@li,  ,mwJQth[kKm+ 6*@e!f(#}dcH=bT"^e D{8p`*_ir;p)M@@db ;t Y)|w"&3k n5s<%wRx%Ւ pS/%b:x0in}V{[]DlpGVl}3-%{osFBZS"M&Aֆ8[?^E8֚u:y]cTZXnpr֤Z 89/QMӿBB'Œm^үڷU CBDcd"rb/GNP?No>7D`J#Vc}V/#<|-g C<.Y\-S م4C'$:1ĨCi,2ci=C-XGyIʽt#*}/?܊yd'>`WOg.:&k&LZ(` p61Tu0M!rEaYwD:9k M[.ZBzIiDw09e =j_%^ lob* l[0K}QMFjoL1W݅Y5:"ֈ(Cs‰A,wD@# \lGGԤ 끘D4bO4F}r◪3b>" 4:_%SQ\;"7嗏dx(inXID|7;Vb+t) ${^["0wS~֖_CdܦM 菭MFP &2dwD`P#󮵻Bah=;! b.!b(/M |D@T">KY}ئD^@(-4 Vp2Yg+%P,VC~4re:NoriX0%l{%ҡSvn =lC!K ?Kg.~1&+t23ؙb?+f?Ȼ< D>Pܝղ8/sY\m=H%K Df/ ܎\vaT">K"&<1< 1# /Kb<:##|b2 dxcC|Vg[#i+G`i .Ekd 7U_7G,Tջ)ZY @.r.Xă5ّq<;Ҧ#_6[]D XmI"`ɨ #& ̵~;WJjPcD{v ]|mzzILVӽ֧~ *і&󭯖7?B+I.!mDqp}dy[yVGlT"~^tdzGļxI@*_ q46bϪ+) \a:v4@L 1W x((Xw~Kݑ+;4((wkrsߢ <9u\=|}Zش+sA5l_W,yIs/bbRHd; ; %DƼ!(B"V)B,lW;@x˔(~*@o 4 2̳Q/鿅\Q{! Vg"U  M+n6rh`Gx* 'S$ Mس] y4ES'RX@Zb_(䯞yۚSUvQ~{AWQ&ˮ!r["uX/oOĦ=`7[\ 5_ܸgӱ7xJ#v|+˞\Cm+NކܰVZZAk*:L[c>o{?6\a6[1x( Fd0; 2wKΟ*6]T]tZ[vu]mSs91WGcQ5iƈEsYSθ|9VÚӿ!vm:5KRx^w#C[)ߌDa(`{4b?A')%sa];2Z>j wDc{+щ~2Rx 2h@/r݆hp2욢T"gF~O"fT">.H%SV̿oF:¸ v!!tq~=̺=Y@LT bgc.JUM;&͹UJkf"41NPlٺ)7hcD7>ZW]b!E S_ ێc2 A&0϶ Cg/9љl | G#g}{-bƾ@.+q֖i_iwd:c~X¾1b0s!2.ALpCĎ|s'\aooԡm Oؼ؝:m;9 hKqaGk[.+Jv,E~ DGԤ9Ay!-wݔ+s;G;Ӄ ٩פLפjlˆCخN~(>4*P@I(V%s}FeUD;!y0`cb"C$O>3i?Ow^>{-FFttVrNEG@:k׶\`!0X ȘKX1ɖ_hPLd!WU_kβeZwȵbvvDL[ؽ&-CF`ia |WeA ڍ&9ޮ^>OAv1DdKӑ@sS0]otC [`#k[CXVs$j7>@z-͕&{[y˼7NtOub𪢠`XL׵dFSGc![!C1"kds7=zI]S/|f=V-xfrZ>nz]KZ&^z i +H"<͞FcVc5i)qιhws.-ONvT GcshϫyMZ?큘mK&R/&9 vx5b3#75<totJ\3`֢ yvAF%5=1s#wD|tl^_D| ~k+{ VƁ}, ruqT* y5&)P> w9o:Cp)oK|6}6 cnHuW@erG5Ϊ[Udu.2|t`Z X>Bn׫gedD;v)9 ֖V5xܨX!FlbZ5=hHtzڋkpcxH_*vϫc:!F6{~ssLk۷Yf#ҪiĥWT- ?7UtS`wDlDWfeZ>iOC;G|0ɸ6\EAmjw,2i8gr, dMUNJi\ɟ%;#X1]%0Ѫ" \Eǖ#n[?k#c9MR"#݃GQ*!=2ʕ/An$ rnch%D[soJ+%eza59!C:ʹʊ##; (nnk! 2T!ƨկ4 Z+^L>[kULjh%͐m\%ػpY\b+3F.\1174֭`joT] .EF#r2HRq/Eta#pVh?]D.٣;vbXvFn;w42Hҭjsì.ݬLoyCK|eխ̞|W1W*WN `sL>, \)H@,C HV\@`oGC=: { 7V_6#jP +?M>@z\ttK4vYO fґ@:]RZF_sXkZFV 0I?%Λ1]* A+ _3Ѥ]( etTfh{&7896ȝJWyk<ä6|< QL`+sm֧5g59לӚS#YQQ B Hs`rsY 0k鮮pS0گ QgV<9ז:QiJng"yKݳv ۑU\\Աl|Fβm0QtC$znF/ck{0B(gx4%r~fHx4Rk~B"Qi<%s~mwmqȵxwv7rυ8eM&DhG tȑ0׿QGs<kuM5";y>ܗ-Q2/DO h$^BnݷHLF>cȁNY(v|rIA6!wLme_'<:fX3 4gȽufa2PܾvZșz9i%\o; 'Ӑ{^xԶѱ_C%&c7 lku3"Оc1y\%=/!C{lݱ#X=ɥNmGr`f0(:$zcζd\Þ7vp%@(;uV@eNIݏ-Ey֚9˒fe?P[9-ǡu ǣe)pg)t}tFawh6߰NAyN8$EoO hk]{&w"?@L1to ]t]yzEߵ\$+=)ݏ]cr8 7!69 Srnhd ĝcJ:®lae(&yD.t݆.C(#n@_#Pn覹r_G:vFbz}Ӯ^9DKHlp\q:8uXv5 "H^DhtN߿P6L| M@Tԩ46r uە( ~m䎲[xy?߫*gT֡=p=G6w!mGHvoSK_OwT<cotwka$F7]wga;{W6&E"9MȲvיT33L~9%u5?CvQpB[LSa^Ռ%M_>蜏@ݑ˺A43ld/䴵jcL2]% ䷯+,Q53H*o\8}}et]XWorm zF`z6r]5@YX[YuX{e~,qY2Xp;*+r Bv.ȒO,x@O_ģ$݀nh 7:jk T#` D"ӃB.\9OsГz8 e݄&E7[ UќrPFf ?uC?]mU !! WPh{lEMǶmYȱ %2?ܻP#hd0K)?*ߎuHrx+2AuK$6mw$0kml_6JmݑȈ|lIA8tӜrz!# ܌r}FBm/Ti7A3AUzʮ/9)q"P߾sv; rpZ]H 2ܿțH͵Hp\:3avXF2uٶ>9gwe6 9zA<Տ%Dd1rs=/ߣÐ8ȅ]d'8Lc}PxԠ|<$AD%Ñ2Ю\FcρXx4]ǿ|.hHk,{ eWO ,s$0:/˃ K|t}1'ApJr#G#Ydw$JBrI9x4 kH)4.ޕL|FNJ;$Bd$&:r?Q݀|^ {DXrɔ8~04pbǁ #72ӮQ$ţA~,`8' jnmۑ81A꿶-2DnE>rEsǑ}} HNc=ǿcF8uVC?}mo`<aF!Nk=ď%ND!V̱ft >cmzm/hÍkm^s!8-{&}P.&1HorS^zjJCSҹu?8е;W.MaAPf|S\S_9)o b0kb$R YZ L* P3;'$@߷uTuӭjjnJ 7m VRں}OY6֋@[${8WЃGF \0dA\r#}zʮȊpMbK6 PGz 7x4RA#CZTw"W'B$^@GN(\5rl*P&/4y7vjF `3$L~@9SAN@t!D7P0L26  NB)F~,]Ǣ`n;X Zq;$l>9Fǜrz#UCޥ-AZ"j6N*@xQ9Fp=G7Dx4r?;Q^Wc7۶V qSlCIh:ێIlDp28"9UnMbu.{׮9$h/p=GQV=wHFOa%p s޶K1rB~,ɆesP|$r|̓Mn4!0lb蟳v`YXt큮6djSBFբ ].7B &&+hSح3*G#煝J3(L]/  mR$hDa6K O ^e$OnK#K[ȔJXr& _A \c ѯ[!t}? pq˴zEy][ % [g>t1"N1A]= 4t/}.1Ap1=O1A`9*Aloc{Ȋ1=}_N +Pxb3򕦦_G#gsH`l9$N>DX2 %e_+Fy1#ݷ(P ~? Phg7$PG)0vnc]H G+Hy¶`{N&c&|3]Z~iJ1ǐ.E"T$ɬ 0p^8Ge۷pe<yж~,q,*p.xPb8$nDBf wMbjߴX}~f* 3٠:G`*]xxѤ~cO)@*/L}$f#g8'⦥ amcWQhDu_fйC#AY72Նm9Oc1g>#.D1Ai AP7,srڠ۴{m 8ܺlnς `JŴ `}5m~E@[2i'[c|Kd@^zD*vc7rᄘjt_֔.hd08u!J_׎(`>u59 uJ?脏@_ݑ%Aaht_M} mj[ر5$Pr+$:F"7-"bHtTܰÑReA݀\@,+3jVNC57ǣ fd#QXd9 MM5 mJ }Fn`$V$ض/FHDo<:l{.;rR(-rڒi7H)/NT-vs 힆nxo v溵lN`hzToo^NWx5zHף'j$:#oಬ.])1YLX69xWnjW=+|%zW@0jcz]# ѹ{ ]3z=N1:˻Ƙm c<I>y6>?:cG}:2SbKcL>J.I6lxyI8&1;B\JSѸJ6`ֱvi O.7G#߰d{SH7de!%C^}P]u:Ǒxٮ $HV.}Tz9& vild_# o $B#C E@ ᚜ܒbOy6G(\; e!wrAmݟ0nQ~,1& 2ކs~,1 ɪBۢw2]v A(f]l(,P/l{hd:;`aP'L_G$tG#}M Cĺ@KtG#%Eq3Гvk$F_zH(F\t鑲AhF}|7+٤߂ &HKPJV^C=v?^tSA3z$n= +pgуC%:v/9 =$eax&>]KB82APfGO>Ap9r:{ƘH}dн\F@{cLdi"d]OKp1Bm,@}Pw |=Do%˹Ƙσ 5F%1fůXa{!4X6\FaEGY\nhdKx(ܔByCaߎ:N(la=A`I$Z/H|}Pg2QRߑ{u? ܐ]g-D!(D]goVF¤={/ȕz omP(Y wlrzc鈄IG;qd^C9=glҿُ%6$}ĶȽnPbi}?{! [ Q9Wx4R=-%3hlFv#"t8ˏ%a_LcmPXȁ{ -_lH<` ^ alqˬ!G\nQskjW|]VQ]n#JkJQ T$[!Av 6u٥w4fTW=cdϿ?bsZeEalr+$Dv`;l$#3eW ~OD'hctO> ƘQ1^ k,AƘ3A|o=Na1Eσ (5<"0Y<[0B}KZ8imgiƘIUOc.BqI<f\7mZs~/ 5 V8`+ܿGfRPLD}Qzu9%9uNw"Q;(7k.r bUFO 6wG_Px4Rc݇mQ=70(OӖn.߉rn~GM. :'ٮsw2gmwE lFOp_/!H,U'8Z~n~.9;}<ϕd&h Hd1\mwY</K> B9촴eWI@NQLKO^]膜lS d;\:kkø] z~4j}yV5=e}yd@?E- 5]Z׾W5#o7TJ{lwO=<*E] |bhCTvܢ9'Ƙ`9Y X pvr%t16zirClJ47Qסߑz+Pm/ա !HF[Xb\$BF"A4 R$dHCurDaӷ(ϑpy~^{ls:=]MBp ՝nUK >bx=mm{HM¶a=m8DwHFL"m*JܵH%K$lLG#X2Ѯoa2 vCޟG#)?8W_ex& SAxv׮@c{tdJTl, փCoMzfO]#Rhs$.~6M}UU B[ykB ]XGf0}jqO{|&kk&k!isV?9,"q1{IđQ1Ț\DS,GV]h#hDB$W~z{r9f y<QY >f֫(y}mm>ܵBNbn'@=oUܙoHͶWs]'"!{r:d8l`qfvģ9KZGs3(<:/uEA9]"2 Q\kѹuZCSn#4y م Cj; 4זfo\޹*JavQ}Pzt8爭$,P"-Pҡ HǣjQꔾ^Fȁiޮ# ':Z P(Gg.dPL$X6Fv}9v{8_tEo V_53G"2t DI,;(ӑL "$~GX%P|%rR~A?hFJm;vYh J}d,$_˥Fyl[j|D0G#vnuJ:ž%D\":L5N:ĻgQZkʭcyo@Dk- —j< fNP5:k{=ܧjZS:Π@m*;_6w4u8вq,tH!%&Bǣx4;;jonDEHPrd:a$PAԙuuV(D*QZ$"֗C_*4 6ȭDam0`#'d{&u _8sx#J{}_¯'%XFkrF|]r.hj$j޲=rcE?]r/A`S䰜gg*?k۟}m"rcx66~u9_Q~(_n[P d/@ȩ]tO}aN*?~w?j^0l0ZQ4U~z(LUñ q/,aN?a=Q}F^CF@9GȝQ(,}FXb$jC>O AUFf/P2Q-5 (l܉c:W5'L՞ʏG#ШqH>a(G(oh K|`?Г"eW,!WģoX~$pضrNDܷh$h=<mP%pKܝShd݀L%eb,mȃh`+t;7B]k#.Ы۱>K@ ‰F}#GqU9jÿ{&] c 39:]iY"֩9kш"z;|ȸpU?:lpDr3(L1r^DbdKx4rKB75d u+ ?ܠ͐+VܸId&M> ubסNfHAm{||䔤; IL+ *] S(߬mǣQ8-Z9;hEnD_ٟ Xb*}/Tģ-Q: vZF(D<҅?@e$ZχF*Qsm.'{ŏHNLDp]dď%Nx4rr, ˱fej lFf.aG#o/~! Qx Q.=)|N-Q'ţt!֍LD_{\{(㑛uǶ113HG# ~,儽Fǵ /7~=۶iz=hIF5glrrNA9]l˟h?>ņXPFIe>@siդ%2#3RAx#/,ZйU oIn¿ѵ?x LlWM=`z ׺ q8kN$h$?ȳuÖg 02Q(^Ve;1K:P]rɦn:ϛXrh}BASa#'Q8)xۏ%NE^?Fz\rD[H8բNFR*xYBXiF+ա0w?B鶛`CuM=u XJX{"3$@c/CbT3$Gy=ѵ]f+YmuTG~B.baU`[&ڎX 8!,kB~,G#Qrxz}PW}*B5~,19M= 9SQ<>rXBx428ɏ%Bj r6D"b;܇{z[Rn4MD.ɵUF.o!Qu^yHP_\؏%QW2g_+B.B v?s k'zPHWom_9be)E( \%@Z.@ŋCMu  v=$C5 p%8!֌Kl ߡ)k{HX"=K;H#G)@WufE}G#o.fWE(glg2SME;䞍FX(;TFrD~x%s?Џ%ʀ;X" |Q+<9cnǫh0eK̟G#~,#Sc:W+y; v:YZ: ȱ}p;6ޘT^k$C(T86yϠ:JWkh8!ּi DEcJ 9+}ٽvA֋k}}7r <:n]M}{A =lr6FņGvg"Uz&yP*Pk`rȔvicߟF ]Wp[Į~,qe<學Xы($_ٯx4r7*W,A#h$݁=}wC A֨z~%k,>=OhPgp?8]?Ad@6ģP'Lh?,Z񿼂vHu;/Wh >UNϚ ' =5w!7@\4@!=(lx* sP(L*,O߳+8Tbgm(G]Hh8!IOcv+'U,^3nhtdT@Tt881{&yk7#"Im$o]C1N|9k[s;eNnsXE_Fz+VpSTϕe p (zΏ%_n׾/1wsOjd[3;_N5 <,2x&y2 ] sة̧Θrƣ4G[('; ȽKۣk@rgjz:C%AA ڽ3RAxӡ9EhMJ<9,[rV&_'fbno/ҟ)G[ţaPdH"" `OiQǶkK?ܖX@~A'x( Y= '06vjeP>\ ЀP׹6wkOnW*WTM 63l$OLVtF!ɗ^,~hf8Gl ҨP#3noSģv>cQ֋[l1Q"x4.mO9}ʱ>6( rwhcݠ6L2=TTIz$GċP>fV;Wm_B \hyhvٝQ*_ܰFNLr[4u TNs"Xp } nQcv9C~nU-?F^r8V;F(}*2|1z=qY$KہOSA]Zm@9m梺aϦX}ޞhYr-zX]<+H` p:ȯCW?ܽɳQx2f0p׵DS7}Z9j;"In{a*83ɗQ˹(%eV~õյ۶>J~jUS30t}< 0 _sOH?ّ^Jͯ=rRA y$Ask bZKlÏ%Iƣ侬x&*fvC=ԛ6а%iA_EI= _{]QX%Ն ^sBGrL]eh /*h_MN go}QR|)ȕ S=x %.$p5/4HIDAT#jo6\fݽ,;XW"P 3Vtc RR hhkzGRm lJ@N0Z'a]=<g\C7:ewEjjP~v u5⢬B;v*6) Ů_󺞬JS3%p8~d `$aʌ^|o&^v8.G$pYÊNS$;Bݑ.GyC-RAxO[s/1[tl աAC֗gk]ߥjfC;5:@J Z\Bۥt8ՉsMº t@P~A9GHm䞩 6,4bliª{~?-~,F'LյzM>M=QQW'p8<ѤmМ;V܆,${&rB9 {^CmD+uDMgz&aKA-J?M89a- (jiыgyIvl=w8eB31lA\0Y(DYorK•Wd5Ep7Aux&*=jd6vhd&98x;RAe|TC ,TbC BT#T<  ep,9k;Լ]R軙ԙyeP:,DRL+1p(oB"iO~+X+/BֲH& 'l-YFH`? j9bG3 qsBoRriꀗX s0ugp |`.GS霚 ~tgu `_Dm. g&RAxjSp,p4^FM|W|͹9cO 'I@EK/A4Hٓ ¥+2tBB A#A"l!rx&駂2E8,xy=+~fkT݆ !+F0$n|{ {&sUb*4&ǢD?H=M,f}8!p4RAxEpZzNh^C迅HPlx&3mR;(Qd[O 2P QzrJsQ+q>3}mҵTFNނAjh ZcMñq9bG33͐{ 9cHDGGa*HzI*hRA+@!_PX_`s(Lm~ٹAMV-*qV*|donim观pbMXq9bG3!Gz&y!p^IQ6H(g [(%JܵG/c`r$Fr M48v]+(QYQz&{*o#1v%%R4ўIn sV;#p4X#wœQ%W1D1o DiVQ:"9fy6!&/kse#'>Q:p(K3 d-TSF?hʣ$|c1yP ^9#5C($ER<vHeg0g9%eM$ իHWh(\T Lrc4jtT p$gGpI^ ܏ZMp]d}䱨BvsjQ@ a-Bh:$sV*L5~L&$$lہ7SAs{܃\HuC!PF9e!`Tp8G̱}qO<wH*߹&"_LrJ 6hROQiPSOT)_o@g/Yϡg!*T}p`O$OX]SAxg9%AAm$a !| :Grk3,?(eZYkZT^!rGycQnӾeHt FRbMDOm.f>Aah6ow-j r;x9Gㄘcm~nzm9=c~,ɭXb4~MM|$)'q7rei!8~| ːKV 6Dv4 뎪Зٟm_3ɶI>Ꙥ2p ¯d?y(T;(9 V@Lڟa(p8'?G#s;͏%cXbF PS1r.Y=JQFFL@"IQHB? h9șZ<U똏0@B.Wܱs<ñ\8!h\X"k&cpKl06W v!Ȥx4ry<9~,qK\z _P!UW4o,{ 6+ 0"! hrP1o@iyI*xU ruhڦB[t(.rL ;sgTm*.p8MbS3|Ώ%:EC~~,(?Xbzڅ적=*A9T.a;14zuEs@,/@N;U.p)̆() 7$F!17dL$$x&YsoDb%p Fa)&!FBHs[Rz&yȚañㄘFr !92_h;0 CP%J~ۑ?thB U93Ÿ;,[6ކgJG:R R'< Ӏѿ>Iv QUeAIQhÞ/P)# jρogSA"uh0[c} i@b Gs 8׽3 =3+lΙp4N9;!aKSDۢQ4;KV5(ifP}i_:𚬂ч]y϶;b/W"1Ʊy@޷}㑀;H|MFX 2nOy##bg 12dT??spE3ɳAad^¿; Q?!1 ͒UX!\1Gs3r1 %HoAw('@!W OJzWOmS{ܔZ- WnSRKlR1!/tXݟY4RA8LL@%vHVܯyH4lr(/*UQe?,4h݊YpBZ\H"+pSh>"+8"<wI;墝2,vEP}.S ߎTmhr<|%E_hBT&p8Glc^/D44o;K"g'yH܂jd@N\T'T: %W[~>MATQh$)( ͑K #t2rƪSAxgo UgYjH@VW0;As(,]?4]#hs)u_ss>"E46@.H;* 1Gď%h]?=脮HPφfT|CM](FDש <RчQHu^F}CC%>)`S$E֝I $DqBZe0h$i jwRA8]:$?9hTj+g[RA `O嵯A(`7[mx4p[jIIvg *]omp4cs4WA rvnFEBoEJWn_j 서nZ_4Yi9Fvc F}Gk'Or'6ͮhH$F i0*i{Pn= >EWT e۟$CT`wo7`3 G$d^ ];\yk(G.(`*;8.>:Ǫ9%~,AHd!Q0_3ȈBh;ݒY =4x4RG33letb#dO) ^jϣc—O"BO1=HF1haaQ.Z6ʿ #1sw6)} l}mZ|F&5e[ V`ƤI/P[sa(5#c9bLS6pgk7}3lȅiA 䈵o4G#HᅞI^O,D"!9J6O : ~Bܯb}$&:'ܲ HPSah`sj"$~Cgs686r :wn8u #h'(| -svfmPNY r|- ţvccC#f"w支8<F!\Z$C >,Dg!QA:ZظHgݚmJA[;tJX|o'dKT5i8ZbfK\ܮ4'h_,\&U7k5h$cG0̼YvˀȂOK \cWw $EwAb+?QP(d$.OCe@o"YHS EbH~n (,$avT>p8j\hќ( *P j!J.7'ov(' |]S`g{&,^H:zb<4 X7bըAnh  ]+hY~QleAh`C;:n(ly>HpeM#gl*ʛоڭ0ʦG) KSAxݑrsϟѶ&;zbfK$Q"~r[#Gf'Tb*I?!QA/T}~sɨHP,@QODȿ`d_xX!jf[Щȷ_lP<j;4|R&Am tm,`?9uHpA՗v[}IǢẃ r$?@#)GmLJMñ6*;~, ֨D  9?Ai74r"M@KQ~؇(<lmH?x !Ea*4; }PW3ь[Y1)g-*EN*#$ ȓP +&@|W!4 } TWݑH3ht7\c? :odr.F/ơM$ñVᄘYaF 뽨ϵoIfRԙ ѳ p;r—EE 1vs#7lʟz%WDrC:P^j; \db%HB <3CQ@"$$< 3)+IG< vPmB7G"pkԞ|z$[ lsTpJUDlqHv<:t9: C9^!T]<42;T H8ݍD͍\WkP $0NC"!]FisіosL<a$zE|.sX}QaMDi9r (Fsh;.{1j9G¸O.|ip8\%H,tAJ$f7N~<{[s? hFnA&@b1T]57 (v9r<)[ǼC4 \n? T uF9.ڿ bzwAa}Eئ\!1< /}{RA=rP Nk': M:%~,9*S(}H!@¶()G?rPEUBvu\rZNDnP$r < 3 E͎| LɵE"=rf)4=>( : %!l$j{ eTH{]E$sRA6 Q8Brgcp8A\h\9kk` !VBuH/Rv26Dc]:̶5/Q](Y ivId/H(?ЈH VmCܮg$b7T*6,${z&gK# KXpB\ۢp7HQ,f3ԶB{)},4Iu(|y eD'h1/u&";ʵyYa H֭hB)j'}#+`ʇ# Qȷl@-ZŮ:iѭE({!!40 $@Idkh8!hy)B›QX/}o}?hꆝm3ZehdHx؏%w_!B9Mum%kbT.K'RAxb M4r6BmcO$PG"(<( +C#(ܯ qcōt4wZ| `Q~AǁH4",~r+Py-r栰1s~,u5}7^D9M3Ш()\\ >Fy`# sP(Eyʭ{XsPB9Mt2YVo}4 µxcㄘrF!ˠEIO2c1~X4'<l&h_J\ @bwD %x4֋[?=hi?tUX/4*-NqTX6 8.Wf:f\1c~{7b0$7@#iLM{ñfpIGs9X]CvjRߑcJ 'D:ē(< W^1($w7]6 Qmg.Ə%D*i(?]B}[ܹ~IRAN2S{i+]QBT7"*P$Fas3 %7 f'6aoţH w*PFF&Yex&w&p 42DԾ 12wO; kU THSA8-z6Dw" 6 Uڸ]IZ-\{.nD S9lLk$hE"fg7; 1G&T :Q%hdr<Uӟꍍ%3e6ra҉CZĦݿ 8lbhd>rt~,q7rʑٮ < (oTDOBmA^H>f-8 te!2ߩLp38!XrtNc\rC%Fufţr2yLlY/:ui7|4"S[`5 ?ģx42Y坽 l\Ydj7|M䱞I~l vH #znĝzspsXgj4zT(%I=XX %D̫H4Bprǣz?Dס( w$#F QH.}FXmx& eiI!]D+DZ T4j:~]F`D&6E.ACn>LWá9j 1ZK샦-2hDc U oF9 ېܵܧwP.aHDHrq}-Q{Qȑ׮G#sVA34 < '!1?jGܱQX9f!?jP{֣:`j*q8Մbu? 1hfd!4bpA*Wv8Չ+Xcj$ơ2PI $NF= Y *ukFXHTDXqc%_Gzƒb卵BB,L}WTC4Gn*T:v-D5Jr7Pڵh&op8!X'c+H+QݪQزMb1!J QW;rm]( [4p?9[ɨ~O*Bn*zlT_ 05t^%p8fbu?FmܪXDbi VCߋmPד{wX) ;>EvP  4 шB:z+_XI@3)& M=Q(2@eϞZ"AjrԾE Qݰ9+ ,oM&v8zs<~,QY)H\ &?UU 4M2c { kQ}z.=[_G 5Q<Y p8'~,Ži#!H4\/Pݰi(yL<zf?$ V\OQEm3H3-#\Jp8W±^FjPjģss(94E$ rn@i. ֘_PUH`e#ĮV4 Q>(G47M~h8G̱a+oG#^d"3O4 %־oЈbT5tfx4Ėp8'?mC4(T#hd x sHj,pBX+N@ 3֓x4RwHժkp8!p,?8; |j~9ceqBXNX"F~8c 1p85+_p8ñpBp8c ᄘp8 1p85bp8k'p8N9p!s8XC8!p8ñpBp8c ᄘp8 1p85bp8k'p8N9p!s8XC8!p8ñpBp8c ᄘp8 1p85bp8k'p8N9p!s8XC?; l˥eNIENDB`openTSNE-0.6.1/docs/source/examples/02_advanced_usage/output_24_0.png000066400000000000000000003535261413546205200252630ustar00rootroot00000000000000PNG  IHDRb6;9tEXtSoftwareMatplotlib version3.3.3, https://matplotlib.org/ȗ pHYs  IDATxuxކ;ԋ(AN9$p0)vf|m[ TPwn 4I{_W#yGw#X,K Y6D~QNՑtr x[!C« hܼ,gp1jG ܳHntQ^%oX,:b h#ԅ~tb#i7l@G En5|$Ѓ֦l 1`s䮽Ya9w9vBpbi8leYeG_7uf8.-Q%R$>Cm+~,J(q>A P[Xb .=\.(qvE+q]NCVY,KbeCVu$r^(qn(Jg4VT$@W/ƣQfGQWBtLh @ܓ}JmЄKWQ=?XiX,B̲APA"jՐ%79Q-J#PQi%eG#KV5ZLB,Z"ǰKY,9b h3ԨrzQJ<҂FfQ]fh < F/C(gl.r Puұ xb:b IV! ?׍5ٲ8*`@/HMCX:\hۣ3b4 l08EgYmϊ҃wTCS[Ǜݻ).*$C  al[`hqTubX, #fِVܡo{@AS,,Jg7E#gf ?KS y:Jzͼ~0rx,R MZ6Cr?0oܯL|sucim1EQW#2'jm =xƤ> %NΨlO`BqTZQobX,:B̲Q'?wL%~NST}hOeyF8EѤ%Nx7?z8ii'X,ub ݋_D*QxԼCm->[MWMq \Cz5q4Q_W+rF-Ų lhT|ii?3Vm򳦮Wk-OKf@Tc*PI ]1()? xmD4TQzG akcX,2jbSUx-jzqc}`߽{5} Q|s8x(B=5Ǵ'r#`8iC>QibXXfS$d^FRuh+%@$^&#QvPk%NQ ]ӵ-d[')@N|`[,ecR7ShYg8Euzr܄f ӣGuv6hQS׃wY,X[66YCLb^b6*6 DaŏQVh*,䐽QBLWST ܎*"6Ϥf[,ŲBlCWQxcX`/D6Ow<ˁOSQ[u0oB_-JX,KXGbYQ]0MGJfH}VVT$̢hJBf?SPH=nbX,BbiB!'lpqTz%,)J8(G Ũ}Ep}`ȑQ3;bXX(Gl8^gϛb)aFqT: 8x{zrݚKQ-{pQߵ-Gl1P* 닣ҡ *$WC.0`7`}e{36v=1jwq*/;8*]Ub#X_R/GGGxZS 4 7,9lmD@6@ڙ&yiX,Ka0_@Ր@:DڦdWc-N˪Q/c"p'JOm 'zpi ji:84b+,T$Fu,s0UB崪i4;$ϣJ̿Sc,~DfPqo٘z?*b[5i4LE%NQ)nkma;FlxSxwIjh Xi]Ew#8L.JaY,ed}e=)U9^r^.J6܅QGL.q: 8*԰GcX,BbYo(q +G͐D%Nўh򛁁9汜<_Dz6G pX,5 1Rts9,m,k jdGKKʁ x (IŲaٽ8K+[|"}⨴%0e6i:& u=bYX!f4}fg-xDIj8[nWmar̅ v{0Ų.KǸ\,i|nyV2EfS7})q8EKQ[̀ :`PAׁhb9ՆblX!fhnST]c/kM:̨6EK\}õYQ"4 c6Ut8̡C&/nY,˺Æ&- U띅C4&*\6ǢZoj܃PdUn{q[-bqTs :?ɠ(CLqGdX,+,3J0/^ 58f'mӫ]Vǎ=z;tG׷bf8);g8E*J?nX A!G2yvcbX5VY,+&ݵQG!c_^:b,`m6*%_n*P5E-q:s:1k 0!hX,KCa皴XV@S4+6x\? ܺ/q>:?ߡif3 lWa]i4WQu|u2^:d1bi #f48U,-JrhܰEQﵖ|<U)6F݊’7-1BbY1\{Mڥq0չ{{? 8C" Tt:qM0;k;3HCLn1X,KS 1e=`M::q-_*ќhӖ?W( 苦Cc]tPAl`34yWZ,KCcRUhrX(qZ튣\kyf2P4A`$K0 5Q08:d򢕮eX, VY,uyQiShʹP aYU7QQuzfe@'`4u0߼Ā>(CLmX,M +,:0;㨃w(>E.0 hV[ЭME޴5hO\FqT5`OO-`yhX,)l eR=BGz(UPY<'^T>QtL}wmTd#686nX,eX!%N<0hB29dGMJ *# IN=?bq)_tn)<lTqy܋8^.w=M]}' .1ϡC&)u-6Eo٩,K ]м~i1Fd^ @3Wwg_2lAR^QFWnucsߡW|}߼TGEg?{gswq)p0!;5أ( 5X,&bG0hv, n_b;M @ppPS;2<oRNPmfm]7u^>rͲe-t2jfƷ>e/pQi 68d,5Eiec,R6Gl4d&ܸZ5U@Jsw~peֹ-o<'l`ߦ&tOyF s[Gj`$Pρ)@Qa~ 9bY[x h\@`uVrp 8ugc;0{No5iY(CNf\ǯ_ xkS٪̹}5=5w-&{FEx됬(#+Wl6.8vHƶD<` c } ˚lX 눭g^W?ne&do6jPwπ(72^>s^KQWˑmİF9:7:/}ϸ~L"v{wɻV]٦c{RܯQ@$p[e͞cy:yZ~xJg~Xd^8+9wP[A+9Mũ x}6?]S~EtQ#adVTMΜ#/0*/jrfyu5 5729:6®e n>e= &;'EsQreдZ~v _6ϝkn_>gnQϐCO ?UyWW,;%Yojv0f&r{Vu-⯥wKMP}f-:⿭b?Bn}IkK$8ޞӯSW7ȚG kA$t'FbiX!քpjv7'x%S^b!j`uu% }fQx)x]?| 8ÏPfsB!\j3q Zv y@ a5?2U;nc\ />g NE6xwDMu޿n5@ &5`,K &XQY>$VNvw`*GKͺUݮ9gR*95 R 5E0G t@́+fP>a4=*ڿQe'(1rf0f[f`[jKQtݣ̌˚M_p_vey~mcʮ>+^[X" KPE^$|vfHa~104sM61LWffͺp9u???~=[#wmRPs6%DHGJ/5Ά^Z' }#AhRH!!YnfgP#Ȗ"7nl'Yס9,SY~ ?WC'˛pN13N\ZiEγ8Q޶s_Y2Ļdz>H\__C# D2 ^_F[T'0װX,j &en-ѴB5MWIrELW_VV_X,M#4xUܡp6/>&D(>xeՇt=>D_ȥW]#@W,3[7BeE%B,,; S\?|)POT 7q BҜ3Q|.܏|$ !YBoH4T DԷ-B쇾ËiGAHƲhf)wdlG4_ ~PY_Ve[_iŲaXbQH f -^LMUT t MEt8rAJ7,}~9O/~w!dƑ "a5&Ϛ@.Q(_mCuΨ\$DIk>p rC4'!ieڸ/>B~lZ]|3A(?*L9xk[MZngpE|&O pzx@3soMJTU?r=#t-%U&wAB D2v5C1iX+L$?m(k(L7(0*!Hz ϑ뇓O47Ӏ/!Wa7*\:>M&qJ~?-'rȉH<4C[ܳ~8 }Ѝ j##5b>oZU‚qYLEf){{#U6MAe13N˛:bqKlyly9,Ӹ/BH&5Ut1 ԛbB0*F Ca(:r?*/~!;=Qxb+Ϣ\?L.H8:ZV\&~Ô(:̶#Wiw/G큅Q5998-fdr5 U~UBƑ{WD{u^B_ l@:Ҍm❍n^!('C\ܳ\T[ V+tzs$[NH OZR٧7w6hכP_;Q#b o6 &Ě X, :Ƅ#on6};<^MTxY-D]ˀ[t.J5|Þ(l>aPYu?lw;"pח>ɠrAE~cHl6ot7Ao$Du5ngLC P]PX٨QXYG$n1a&Igc[G3W0+nyz8c{Fݰ$6ښܥ[6I$c/ngK{+lzb4ӆn@FB4mEJ P0Z&9(Q9w/mGMOQh3ۡnsMdݳP@YUK  Pxpk?GB$Eߠ_cQbuCn֨ t:*jTρ/ T sEs?꜀.7Ioфx,av5aؽ:nKn,tUu2i8HƎEn˨إ-p]a~ٗfY`%PzE]Bk VbBl-0"xq+Yt,r4Ah l</(_NÎ&j2[^Ձ?P$vB7=1#ԫ^ nB4e5@+`!wf> PP+OCj.B]oD"f,Á/>Մd"1v9mPACmS3e/q@MJHlurp^>>P|%{fF>UͲ+7Y|=00l];HKPajϑ? /0mŘi>[~kskmv2P~XX,\;:!bUۣa69ۘ0ca(g16r^6  Q4.Sa"rFP5d+䖽n{p]\_6Ռt݀ )AbMTЌ;!"rBNݫ(޸_̺IAdFNݎ(Wnfj~xC"d$RKh>uv__fϔ6qz# jI}}܌Q 3dZ:=ptckNbXGlNMxp90G"!ܬ;i~rFm%̶)Cg$7Q2>(L 5j%/5cm*rf\=Q{#1mx7zQsxE&Za} fQ3o3zPȅ[=GS!h42&|}r7?rp(_VW87ƾ ~N$ceKɘ|/A;@D2^3J pzHZ# JV& [ǐXYH HXU.!Ѳ )7m[96}P3 30=̟PZ>}9%ܳʻM}0J !(hd&O3 Fs9ZB9n8sv6坍tp+$zG&(֌u$r4/*hp/Aڿ ]H"Ea~kioODӰ_XLL@| b%V;A=)W^W!qrC /5˖*#u"8nHEf[ѨI;wWJU릓Ƚ; Afar&!W $/>{w4?&]Iuw$.D۞H%gh]طA9wYzQsG/5ᤱT#(z9Zh$ u-u,{cRp(3/j:gQ3W䣨 {cϚ_s=v6˝ ui7:n*@ =ͷB"K$Fx/>AaW{!ld#Ht>ܦP/>rfS} ?G] Ai:& PLw> h4|ͧ7Z GMϘbX6@#1?'MHD23E ]{CU"- F}of;-+CF7u{\|kk^Uc>ekB*Je#!4ܪ{fSlD"h^ŏ5-P.׽u9lsN̚>lQYTUXkYgj^ #YBs=rN3YfǚHx?LlX,`#kaaſ3_Z3F! 5*k`Œ PT0L pAŗ~,T/T159Cmw@=3fHE9jRqcmܮ [x+j{7TU^̼ Qec]SB!mHA{W;UWPM!P~(sNcG."BJOqd|~D.CaϷLݦPg&lX,>BD|LTorG"ktD;0c7`q2I\?;!arZP8mвjOOCbݖ"1IGPcQq Qu*l?rFsf>&m\cu=:}+0n2r<ˑø5{L@/#va~Y|* ;YAנa{fX60kLcD!SP%>QHX@A"(GPHԽrn1}.A9YDQ[?T7C(\~]+#zod$>eg$̱?n9*w|P3_/Ot[c9#Z6#Xi!˕- ,U:d`od/OQ~T&5bf d~x 0~'FtE9W *gQB3vI|TmYhW~x"wsQqV(`u#! =j ?[`~^|Zک hfG0$t W\?tY/ᵁ3(<9 3X~j ڭ-NwA.ꮚ]-#EC D2ހJQx3IdltTV(ԛ%zX,0Vc0 %6H\ mzhu pAa-]?| H\uD9]}\M]?ln ۡ_>lF3c.fdzV˜ p^]ɤr{,т%Ԅ[nR/~\|l<259.))ȌqStӻ4d0+-P Gx9N@3DOpg/T0ќxԜȁK_6K؛eG.AzBqާ6+p$X;d)/sQghbYc[~ (c: C9JÑxů1W_pZ b$D.F QY:G!3 Lvm]*_n٧$,3)Ffd UK2g̜uQC8gQHXHM#$@2s w-AܾǺ~^{M̱dqjeqkj@={UHePSxZn8#/uef粁Q0`\$|kFZ~[ )0 ~eW9 4eBW zbBq0/ȁ(CUnE7~\?ty(1$r/%(l | xLџP}% %Uy3$6E!`F[lq N]`"w>?ᓩ>^|Ľ%J*bBnDQ'1߀ y< ߾\4]a"=kڜm61񦒲%b_vnt2wrs pqK'KQ˔?C[zx3M(/D2%o*?ˍ{["{0lA}oU$lY,V/aH(C%$C[/QHXx_/뇟fQ뇷(g݀*.wڔ|#of#q3 W}@NԸn^x/_2`LNrZa{P%B$RdP1 ]?ȭŒ9Up87QbNH>*6B4nC|(d׹~x-rHZ]?aK0#vE!]5WUk񹨈OY_VgҵagS`P"]Y9YWTd;ԵWAx=0l*Y+L+GPeu,+_8 k/>1}bxw]?$RBk/`k/2 _*]ym[t30ۋ*e+n6tǛ'puL0j[qhZ:H iGx ge'|Ñ s"59U#g9Nq[;՜>"O 8<>Ů^h>% :d=xWpn6t!? [Wۏr [۝0G"kD2س>\+*ƯCd"ۭ0lyJ$c}JQ00l~=mb3VfPn̹h*H|,5ZkQ^$F|m`OH|}%H|I/œvsc׌-EʥCپH^gH|cƗem A?Y()ػx`T(TUX/aYsd$An])F:μGrd*3v`$^;!aD1֚%d;:/[4vdc~Bl E0ͻZpN^RQTۨď-lw@bf??BNnHr"1VK:V߬ce[$&fR" PHY7GOF]g?Uf:cQ5Hp%7/NFt.BHB`k5? V?-9HPfmFcw9QKT3sPO0i(0K{1yPHn>TOgSoFH Puc}}>{9w/E߽sCTҴE"//KVH$c;J tCM)r!cDBl=: \o $8$-aw PϮHܝԍ mMA*Ʈf,& OPH݁J$■Ps QrAbe_̥gy2e(;ص@"T89%YpO~~k߯4DO|tәa7P(W߬Hml9IuaH<`8o޽ 7aX.br澫"*vE.pG :@]M5W>.M9?aBaa`io^H-k!'0ih ,H ("$Pv@?_0l~4,@b$0ӏH݌*XZ!ans8`ehNOpT-P.iR]u|ns? u#rP0aE9nZx,QȄ?:3q 47Ҝ]rR{ ~ iRfM6Ac1r"S0`ܢBCn\IcǎD㐻62(-|ZOh-}!d uuo"͐3v:Bl 0Y@?tEia^B5~H(6vBy~OE*B7j*`+7a^+Htފu/>Ñ~ 2@73Q+v@=͚S~x+(;rgie~8+FiW#(>xV^Px\?@ /&zMm>ǜEFO[ d/4a(A!7~x\?lȵMW$00zAqT fGneX!{X0` uM.r"LGߕAa2/BbNuV5a G9WN AbiX!f#Uc:?NŀLz* "BPGPR#+=m?4_F%BE; 6ͧя>(Te5By^ g,t}l@ə*G/kzq\?,EՒ]?,F̆|}sW?Ss[D>3y[ rG9I2r]?Ld?40Lѳ۱Ko5\9Oy;9!& PXhEaߑ>N=pdݐC iߏ !{o_0`:4@d쭵r-/8n!uzXc^[>E(L_ d59S*ʫW9hvNU5Ę,M+xWDB3 hvl\'d%+͘GGՌ^|xa#us >'VR siv1;gss$B|HLg32OY4#D(O$x ɕk5(dnqTim7d`Ԅ/@92ƥ'F皲;=PT~eR`g7Q4:80lJBXWBj#P^+^RTWwy\ 9EUqEQqq`PE8Q}̜]?|'_ͫ8U9M[<~~xA-> 1('OO$э ;ߺ~x[. BQb]?Xx񹘙\? xow/s@/&r6~^|uJBWF}'Wt$XD ~XxȍK H-AC ~ %wX4yf <{(j:8Ñ昛Q}8h8Gs=CDoʌD~O$cEB5ɵJ$c1W&THDJVS j9\BQZBd=-ǣ߫QH8NfEg4JX!VB92\?\rކ@m(rgXrmk8/Q,ܻr򐰚rNq0?pYvrmrANO:ko"vz+ $/aJ/ûM20|fT5귦`Nv84Q9,1;L'r'9wfJP(PxeINsg FmQh$vJ-X0`\吁 2wd_Qߺe$Tz.ʯ\]g CEenG¸f֛(,UXp! NAm9rfqZ}CY[RGDQ4qÐ~Ohݨ+%qtǹ F=.7ꄢ=Ͱ/ZǑa!(vggd\DQS(sVGhE ^B.E=Bȡ SxjO@a)r:D" `뇧ǹ~x0U@WL=A7OQ|'koTmx$~Z˾ܤ׮5 x)D2F[/5O-w4rA.],$,`~̱u绚^%g^MwA1X:W9 ==d`$C~*x=д.c۹CӁsLP_ydl$p [(/[& ˢD28_#K49x'_Ǚ8Q}c6BB(JM/v(r T@ܹQm 8N;QyJqnG-nB(8.cwqd{QMwXT @(]SB0 jHPDԄ>%G\R%B߸~1f5h]6f'J?8!׺~P3a`y݀“$1ha^~F'm̴Pb.86AFSIK@r 2Ttݞ,e Nɘ &\0,2B9ZCfFʪd"9A eu/ X㳬Wax*93M;8k`fw(j =E9zpŌ!D[kqZFQ>QUrȄfLV>G`qFN{+YIcXpNCҝj ݘL$/.v,jY%!flN R$5^Ǡ|j:`='ʓ*Ac]P>;_lO Umez \?(uG9m{JQHA*ԫ֘?F"n5x뇿P.(1c3.f(|6sF=\H0 f_[tcmapYw{FHX_yUG A{Ő(0n4ym(X[D2z0=P|" OaC2|L=TbuDtyZHtO "s*oDXښ!ls$D9; 1܃䋰uFMf{gƳhQe|d1i:w3qu٥8*]RG&)0W$1OʊH'ik<t_H2Hu4{UkWUczHM}fϵ0l&U! ?rSzkHZfhv'^vwYSQzqO҆#NN{=D…f\5= hCECk-g9Q*4#Mr(FԵq:&g^b~L!ɭM;^$D2kIC?#s"?r ;t$Nf1S^65 Xxï3~UM2란k*9otq^]Buߜu2;F)%FSn^҄VB-YDX_z'Ҽ>ӕ*DB[=yݹfO#5œF:Ҭ:|7g'kMqTZ$&3.{~2LF2"$h;ػYqk\' &9ہVde-F~y/W_6 z%=!-l=| */[6{k^Lel𥧊,9lwD 1Y!xj"$dzSX]8lǼ~p8уFm}4FLaTP8(Yqq6f;j]ts'M+֌覟ٲpk d~^Les(oC7ǩqR`Z9X E s(??$|.PaGܼn/;rcs$1`AV65S Rʻ9YH d}6D_Q_29w0E-W^2W@ay?]?ym=HƦt $HC/ցU79}Ϥ%5)zs xi6i/Br6PѢo{ (<ˏ^;ދ5[hNw]EQ`fh]ٱTk>F^@Zubkc 2\utxY&Y~: 3LGJPGit!@H9 *L2G[G=/~0+4Ս~x;|g2A:候JMCI(2bKPR({vYlaPx\?ː[HV`Pa '$L#NHCYY( Lwcۜ1OC=Vx7aѓH|Vs09]?<*/jʻFf ʃFUI s~I]gb_t"!_샎!{-0ӕP0`oC ,,0^r/E"y:X툥-.ٔ3፴^lfoݺ*+$/@e &%c>jgaUuZ>zG旭|Z9SWYkvNU-c``r |g,WFp|GZow#]DFt9e@+.Idc`CPQh2cZ ]*Fp*%fwJRr`~q5ye]?lDH0YXDH"6T6I$tBa<$ \?&vs4+kry[G3ȽD7Qs r<1Ǽw xG$DET =%GMY7Fq}P\gyV&qp_+0na!{YH^[_`a~Ud}ꌽ>`ǜ'=PekTI=uvh~0eƟ ^}K$c : OMs' >ZuFFQ~nC\䲯/m>i,+֞EH`zst^ X)&DyBvYHtUG`Y)t3OF7UH 7nDp ^PfKc7ݬpc~&)` Yߠ\( E9TX}9vP~H0 K?aR 9:~xòO{> @3^.)k}&9zemQ,\?k97#7϶躘h^?Ӝ(Ϩצr7kmת̓J/l. h Ql xjp1-?|{z̰zi8kOZOh6"S5+j'YŇo06cx"o^|)A7wфp.@7Ts͎9=POǑjeƸ_A kFl{ o00vH| i7Zp-8d.EJJ^~)r]{`p*]?c߶XCƜ~=|XSE'&z= ]Q˃:+sU&^"FggP_69cޢ׿<8 ;d,= E{;=]7#=X}[,+֚h I 4ƭbj1F܇ncs1ݍ w\sWZ QB^QXc:ʣlu@Σ!4Lȯ=0THQůL?n(cwwH$w nZ 3=6Ab4EזSS}l)X85Cކ_Pw%4 }. MJtSu r>Aoqk³]?^Ea& yO3XcL̺kș@A}Ң8?O~x6u&U|7*hG a+rZW9bH2W1U|B~0lJ]AdxK.˂{.H89VWUUU33Yp̦]}[, +ւD2ֺonu;i6=]:gw`uşC7Vȱڤ\&"p 6F`jtQxqT=(#$Eb bIp/AbQ%{ḩ^u^3؄vB昇cWQXCIWS@x iB#1I'#KVS w9wזP(z)hRjVj>f>B~f[}{y4rQ@.f+Cok( ><>!$⿭lU:b2y^S_VI}j1;ئ(g. whݦ -t:#Wd;YS*Ze~S+J9[+֖{} .̿pM(ӫɣ&9 ݄M[#$N xn{<~aIuFm+Ơ #w)ox)f{2}Ӟ4ck>H$lijő?v5S|@́x{8|[f0Ͳ.7f v?$|9퐳!rf~:.yBM)HdXܪUDtgh$N0$J̶H|ϥ(鿽yYk}I"NmƢ3i?jUѵ4qWS\{0nL*/X=8힝ʜm;g[dfFs.ʞ;mVVn̆jZ485Y֜J U.7{V)V8Gۭ(`8}+(ZIX.hMC4[ PQ z k>6ݰ7}h>yc(KTU? ›#Q#A5r ?#0;LQKHPtEneC3H /+i߬SevnNJ^.A?z%g:߬墙Op%7[Ž9ۋ_tyǥw{br&CZ^.eY}i}PfQ" ]k\DF~pSEϛ}\j$^h'f֏ǙE>sQ-pVT\ EQ:;I!V%JyӑjjZy _EbEIo^|뇎G/"'$$Y%X PXet{s / Gf+(73P^FHn:M+f;> w&J=>7BGe a[v"1s뇅~J9YM3T(vb`(fht;sJEQ8Nۆ8QV&QrTRut=@bi$LEbڠ' )d(ϡ+4 [^|7!kxVj̚dKPh/;pԼ_=ݺj;.~1 pFC#?x4s\?VOB_^|3⋁M1(w!H ĮFHRs3@?ju1 HdވMw#Wv*^QW;  ZWN>,?)| mܖH}\ 12{σ(-/N{}+,|cN}֖Ni9sz(YW_ٞCM;5>=; /RM1Ƚ\HF!Q90ÏCY2w܏6d9UkCܣfW`;(RQUSỢ|R9_Q2? jױ6O$EG鵤aX=&=/D]6|硋: ݴGܣ+PgE )4}P@sm+z9:)>@U= LG\?tF)juvl(đnDeB>ݐw1@AkF8}JE!(D)@$'LpW{?pf]\? q!yXvi3歑PC`#-k/IxȶfML䪦?djJLg15+eU\fR_6-n0UU/s"ٽڷth6Y X.]_ TCڳf`,_Î HD4l}:kno`D2vM%;=+9I8N{8E726wQuXi^B)0DQ4!aE1|{{Ͼk1 +Ě&vS$~Jf'r侤GEcP.lfx_mG?0Q_\?}^|BO />mTG{H<5B=&")jrnGN$nDO_ƴKcpd~WǨP0d'/>Wk9Wf{Hf"x&d}q݋B=z> 'މnǠ$P^mOF$rz#1{Z4uw/^iOtЭf AZ_j/7kQgoy }A]^3(UT~G 'Q4ON& COˇ凛N*L'i[5 E91s5vKzvw*/ǡ譎Cuf_4ן}s{zb kjn:/Ebe@o~yʦs(j0tjf684r/|54Cy:swF7L oe/.+0yolNSa)Lep0ބ.wF^|nc6fDΫi̤ٸD2v1r wbՋʍun;{3o̎zL9brR--㘪MπeUdl^~;y 9 ǩ@ԃ|y6 C%QMqgkqQfgw潴ݩٛZmG| FDYƶeQ"O"{HƊaE>uv5mxjw)zM/THs@݊zI֦U-{7zNCUr,+Ws.^xVȕnHl*qpoY6e[Pȩ9UU0$_͸Q84;ًuy98\?ۥ@ ##we䔋ܣ-(G.,f+]?bFNhevI^_v3cIͳKȭӔL 15DsPr79Fyeo7ol9w%.fUHtUW;tq2oſZZYi]킒1]/{W~xje}fGm9D26SEY;N[eeV,-;}R3Hڜs5#WZ4˯\qS;Z8YW@n"kQ_Vybt핛͌ RZEi֚EU-Ӕp{esV=Y(WW~MM_\?<=Q+Zg "a1 ݘwDJmQ|eQ^-o^I ߸c/_M>Pt,tc t;#wr}hn HmܔHuE\pÀMksk/>.AE 0 4m EanDG#7-hROv05G'W.ޯcOΞݪfsX&K\?GO*|kDN_gk뇧UWsi]wp2=jn+j2*_òwUVB!9q9 28##ڮM˩[3y$7D߃ۗ|c:E2I1?A߬bjfHS.=,cףQ72d,f¡h]٣e}6ҷm[46c";u%-/,k:mQEG66d[K/'y&UQREoH,uΦN"@NU}oF-g9Wׄr!MaBl-1I!2yo+LM ~P)Lc^ʭyYNE7 MMr=Դ1 SQr/fH}ܔ=ވTN}ܽTTY\Gȍm~3Lj^Ԉ( @'`|9Etj۵u۽o7a>O<ߎÖy]CfS9}'+PH{e- ]2]ϼ3?}V%VĨI{٩Hz ,.ãvpʛ} }a -/K%E36|e®lD2qa~f@V"{ 2e(ggIX_QPlp^.W]MgTXVy#0ya&g*%3Hx<~:5{唕ry;SyDfz.@X |߼ wwzuO{&iya~wB!f7cR2!wyl\PR(8Pih=; +9rs6&o3q21I$cɊ'(fa($߀BB;m`sWa~ FJtR,::bk_AZzWEnz܅:{(gӆaOIwBaBfziQe뀄2LxT__(~cvrF~ zU g~_MMCj$ZfpC^hZWKZ{رeY(?i9L3v-d%ahry=-̲o ֶ(O#0(y?r~ByYjصqx#Op ZՂ %&lELEǣp{8NFFd پO!78;y?}5Npp"y8-$i6%b*rVGK6|2˚cX2BvzU vxtCR: _P@,ӏ(PS3$9;-c/>' =wD+ OCV;f35W 5٬?rPrxŇ0\+ǣк{U@;g#TjDC_TŸwTn)R.Hxٮ9̘'c%PE(/ف•!R?XnLFU&qxJn^Pf9)HPnn3kgp(Gmjw>#?1ۮݯEXwC.{=W9u0Tn PLז~+PL Ϫb~rCI֟ꦟ9$榭jhBL3Zݚl1edl_=̡f6ot@h]vM$c}DFe"4 M} J+_8H҉VV5ovRjj˷BĆܫig+`VoNF$tBm96R𽉃^zGSͱx>TmDdt$rka;zHL'wCN`y`TҫdU9yŇf[3@\?|6|T6浞1}vub扬}mv/O$cCQ!Cr^ $l$-/.nY+ZckL>mQ]oEP :(zl]ig2>m]m =8ߊ}.pOE`ًEQpckGeZ]/l"nu;j K%D!{]?|%Ghw}Óu\i3-FmIMUf*m2j뇎 5>_`7kmF_/^VT`UiW9nȎ~c{h<-7}1?®=Qt'O|jKb-cY*_ߚᵊ;" J_ovAe p5f"$kyW}ݠS3]4Gs=@h4wg(Pl̊棜EW㼉~[NE]Kq>((9q{'GQEA[FQ8(v5S}8{jF1p^EAh44V5 _ROa/^eB$hʤ($RݾBǞBI_,֍ꖺwj/>qޏg v*_c”f!L]䴭wHQ`aj(O:4yzm9ɰ9T#9/b?4R'Lahx&=7~ǜf6(|uX;LᨚrA"CN{&5}4-C{O:ӯ(f^Fx%i҅8N ]P`|mf=(g8i߱O9 rEWb q^="R&gA 0@#(!/ aFXќx+7%u*9HxhM^EE-\?<#xŖN?u_)qEߣU;|J_῀#\6j5qpG#=uz kF@VAUCBAB*n6nZǣfڹjUs#۴=;pGCϨr5ma~ 0Ǹ} ̿M$c' ɘHD7"wU`i`(ܸVz{_'EQ8A\(ׄ9@E`E_bkݑhz$CWL+ Z˵q{Tո.J}/rtD= {2J50 %_- Jvd$~Am.R-3Pi VʣyRe\: MX]cr \?|Ѭ3 a^yHtUvWoK$5\?t__Ŗ=TGQ˧sxNP󿽾m}0,̤].]PjJL, 8}9֤w= ªǹFiL3"lj g`cqv5Vf8辴4LY_wC 5 s0BIL^Cʞ "TrVqL'PGCM&pS/̚d"f6E~uAw$FC6͌q` մ/R1YkkL/뇯ok#Vzٝ-[S._ʫ xTG(i}j>,#B?(\}H$cXSXJ~r1 6UW2H[-i.^ڲgUuVVP9VUgb#s^kZA-gcXS #\NJˑDQ(9qir;==߉>~}c6U|Hͩg0=*6|!XEv%rE/rrYh6/fm쉒vK`:f׎vEOL9ѓz9P}gOba!uq19dNP_6ۈ %medTL~﫢o9 65n[l>N:Y{ ]m TM0lp"*/[0H>DeN+/{ּ%waڨb r~8#~g]H *n90Fz8j#?z).Q[눭!&Y s'nJMNKf܉`nh uP{r~EHr x뇧ܓWw$̿w0˧й@B-Ϭ]K͐h]GMu%m%̨`;"wat*/j5D26 ճPGM.üόwBE|"`ߊ͞ᕦ$,0mQE zbJ݇n"cۊɗms]?_GP ,6Հ0~m3lCSiַ-.Z06{+~t2Q5c?5=ծE7 k&x&kuHq1 \QD rMFMi56Ev8OC/3T9yo?7agPl4]qx;8'8%?D,jE[U;Q4pAcQZ a$?a"k|bBhtM?^HCu xؼw.G75,.*K7C}yE22*;+"go+`[Ss/cUunvS% L7 ]a~YjP~wQX,q[KL%`׺\e9x-(}:#ۈ6,nPȑ8WsUvt:d=QPf>LOc 9kfjrk2w]iZ^W_1~zU1Ur6c;>m)H |RyC~x0p"H"$:f2V(5TicpCZ^\S<oEQ9Lsi~#X=H@R4b+[^(T2g w?1޷gO5;8=Vkȧ-ݲK_{ x(= N+* %N?L寋bkOoּqt%*ONc/;6uxhIK)ł\$;v.K3JZj6B]#99M*|@Kz{ݏǢɕT >XC[W^d/BN);) z [C7Bz"6Z@4.W2sX CP,f*.=LdA^ѪID=VsV]0^眂wl[x2czHZW#@aq~g8]ҵD&._?kIVғ-k=K럆7 (K;CXۃE%8U"01]"ꕈZX_+; $:5K$ja,A!;#G!逘EX E|ܞ}=ZÔ}eYjK>dQ\/po 6G~@ȴ{ɢFF^=2]nt vdbw($Ʋ2Ac0#=k_\/|re P6ߏ| oiG@bf?,Z< HlCshI+d&KCnIb]/ϣ2Y׭_H j Pi^֛`/&drL_܏f^p!0C+:ۭi~9_?=L@[ 㱊U;k\QAmK,w,Gb?oF.3*WY?-#kfE OV'K*xr{5Aփ5 J.?ŔhwCA~<%h),U[t/D]Ϩڤ>#v zΛ߲ܒێG 5E2E Fiz i~<^0M6ڻ S?Rƹ^P@;3aFQQ\q@yo Rv{ "P:k\/xu2]$My,zKicSrDzVG(#b.^bg2=?\ctHi i?uv}Іւkh\ƚ=d@(["c;4"NGTƹN6,_~ y jq!6~gɚ ðX:LJK9&fS7%1[ ? "SsD Q?OuEC[VK` t]'4fc"#> 1kx=&~<6Ԁ23J, ZU憮7y12|MME X䰿'bo@ "Qzw'|CTXjwI\/h e_k`RLM& 4϶*ľkm_j>hZ*\/(sBlڳasFYm]L65x7z1JGS $SBR3f\ƍwME̺=yĒ6*+YX(ХI٫v}ڦķ k6|]ሁ<^h|:ŒvuuztA5ӑ$o%g4NCEW\z~< F~<-t6g0IwG9]/,%_>(+T+,шub,Q}u؇T|p: x,Jzy3[f{ctFIѴj"ةh,7^pA] -Mf B"zr+܂Ҏ ?Q}mo/ӪDEٱD&vfK%e[Qsݺ 9Y׾mVQVi٤lM2d9N_ϱt`+_9{7mwO[Ca['aX% $Zwa8{c78) qQʜFiaNwĠ#W'ug?zhrG%2M,C&Fhz8{LB &W QCFvNCJn&7jڅ*E>5o^%)`"?QQQtc~|"$2ɦ)jk!R=Q>2׌F/:pm;AUb-Zrո>sXn[-#}nڱJC:Q5F_ ??># 2[\ݫ@|juh <4&PnFb쒏I [)]~қlZy/q o#p;55hG$yEQV6fȘ,ЮԛtmsuR'%JQvhq"n{>NC=H#0m6F9atٟ,u$u@l#IE *L9.v8*<*Hq#Pt:-3kkȽ]-b^p>0Ώ`%dvNk*lz)hzH)7Q{bة6A $Pq a浛PS_&x)b{NC{(X'S3C,3-g!r227EWWí ;#P;4̞'()iT~ԏǦ۹XKvy;(?—k?vY[]ca\T]X`yEkfwUa3's¥mN+f7Ӷ"@:a0qflPQ9- V2|$8R&pRjI-#_$@%~V83c?x 9eG^!@u b>^D4؎/ hDG PrWi@fT[,Ԝ׻><a$Q2ӓڷW =Zev2R2>Xvkv~nv׻^x: -38f()H5EY[_ c< ~<6ďǾGkh1~M*6*CﮭbWL|sk \|ecaq9z! sIX>L9}VAK>{K 4M͓W'u2 1# `T5e;D6 ?P' b5ˉ@X],s`s]/8 B_X.Qs4Fh2 sc|' .rEՋɾz;śؤvy_=/|)gzEjo~1A b=nz3#^fm?H#yn4ERh0LP%E~Y]]/H* ǹ^09z?c1ʧ|cг̘ ;X l3Čabfa1gd9=Z.],] S2Wlo V@Ua0m.|88q8cي#w$}C:|89a>8N/p8΋(<#_$c֎᷎d"׋1@0 tg0Zy#KYF"9#k-;H%zAȹ@EΨ|O EIGG |?35!y,T9_Ed8b֢عӍڕ,rMu! MvptU]/ُF$7);+~Ghʯ2n"юEfڙ+Y${"p"S  1OVmb}Z2Ѹ?] #|D{1XϾdEc0r/aSĪNt`=vB(:;)DiIZ}|%}96yEK&]-Y+#?r?|(uબ*liH\Եw Gr¬d˓6E QyEuos? xqQIh=i,#!pU3ǩaqxq<"/g9a.SN: V0ER?E}||j;ZEr, [ճiL%9W(Ғr\/ TE.v|K;Rtl)NFJ;G))^ Rb#e}~EG~x^۳LEG-Nԗw[l;(P~;_H% Tߋ@vZ%_ d^pʺc{cc{ZXTV,{U_08͡e7۫ȪKC4oQ#~9(|9|x5Jaq~pNYye L MD0tJ\IJՐ0 "F<#,UͬM(\nz9(6>^L3F'Rts\/d_Imy]{b]o`̙3(n; v么:>/GTko J1( 𸃫@#np2mTXBAPSƂ,tnx5'~ٞ1*c1S3?cYc/{k[5(~zrxmgˎmgbNhҽ^k@Qy\(\guxx h({ HsϮ,̜VVĭY+;~{ЍޙvA~d_=(攽BV_\X|{eׯUDfw`Y ;ce-nT̿NZIȚ.Z28R' ٤Rp"/둙;~r&EMA$$N²z`Bd yŶ#2"?f"9ELұ֮.C  }@g? uw@lR cw @Yv6B/;GhU: P+kov)EJq.2r7 |֤"pu\F.6T֯P 9^B-I>v\ eTZSV}Ϳ0@ѮGcٹM߹ ]QD<ԯ XC=$׻^p15o mU߯\ՠyW \ _WX9p+Pmҕ@)d$g"V9~8s3d!Q/HO/OKO<: AlQԁTRS(ߗXIh~@,`KRXEQl:!@7eخkA#[`{"&bGJ"넊!嚎(9CD/Y_L@,[p f@*]/hogoJd< #[DR} UX;m2p 6Б |z/"_iP$PMrM{,/9 f_ Q^Rbϰ;_=K(劲XnETNhhR @.] E};ʹRmKv`4C}Z|Qq橻8?EVW40Iǣ/eh* CSϫA&QΨ7h0 h%vM'-kyEkkaqh ۀ "6pPaqyE~v8{"|A^^IuI\O{Y>V(:јLCxl8eH20?+ _d.yrdLy0JM1)ƫ=CbGTX{0&)_ v`?zA?ĸ GR{22GN:#fEQK5#1v#+I~F +PE!b[QX| ܶljfb;3kr~F`8C3Fw5 "VxϨ躳_v- wS2n@5犋_|6('u& o^m>b#( yEv|?E0h|rpqX?FLCQ[ _LhKn!b &? X*Y>1vi:b^5CX: _ވI9 ;8750a 4oG>m!_@9jz;gvUJ6ǒ |+z$ضr>?+L~=dAګQZ  |.Bkm"+ƾuAClk;~\j PNJGy)1';wd'nE;#ֽ y7CON)LO8U R8 ?^+' ѺxbSwNƌr*꬟A̲ΟNMGt 2tB}l B{!_B # I"xd& !+tb|Z**16/bR4܏L01k t[s?'e72םm!J5"Q;:3QP%͐Ի vMӓ=辡_ r7Ҋ$C@MPk~<݇fo2>o*~-E"'۵H8ƪ妊ץ(P`w]SvGZ D q=EZ|T[UYְnK+}|ԟ^"m ++p+yD<6LD,%hY WXw՚$"T zܕðg kZy$0. :t Ca&R[")J' P6DŽwqnBߣ| L0~zZTP aϰsYڵoB;]/8]RÍA2ڱ!@rF@Āvd@|? ȔXW9=(U*TjLnOTD Z^c_7˲2Py{^Q~ÎF!692'@y"v1|rU dJTH`d$<5GrMqΪۖgdaHf i(J1b`8)h! (Yl$6À3\/dq4_olC^` ~jb`-JgF!:O^Ɣ =D53໲zo,_33~9X;7BL*+#j.Aߣ ?Q 翎֏lڽzAOk~-R \/ ,Ybl_WZqyEaaq1hL'{5m4_k8 7}4.OiޡQX_A^?K n.,οEo8|PNʖn@U6G>U6$@< k8 É㼊b}ENKu?09 ïu989h,"h{q^/YF8.ZM-Fʲ2  EYȱA)5^~<Pz%K(tdxl-䭏(d&tP(O"6ZzcZ/vnYsPfGl(,B왇@`5Dȳ-r4z& sIi{;dT"08ʢWO_'Y-zwXMHzp 'YA53 [4+I<m!Ey]q.b$OA&և\:'[ B VYe龕HJk ,ZD5mNJytc(d Ճ IA^Q%mP)+RX eA^(+\X1,d!oݩC$˶b *1`ev!S|i{mf:8k<[%o'KFp]}j;qrua0 ;P/ r3wH!tÀ6 yb~<$zkr0,Yx{!98G8X/.%Z#e "HUcː]Ċ {RO^`jhwSX@e\ zAQfe#oH-W9!G\`wK>!`3Q )܈E@@YN/AfJЛ6c\/x bvO#.>V`UEƹ^u SAoXPʁk[; UWxS8.C[칿G'"9юKe>@fh&GX^ЈDTp Ah+DmdY@3&t͉jQeeg6&!# vSW}/wդ[}״Ƚ deZ>/+z8%j\Ė .gyB73+ZA ]qv [8N[4:hG|,кq4s_:3HDcebIr)6O$M,R&U~7C7+A3fbRJ;.k^j#3cnr1P]bpTxzAQ 2.ף˝kܐH0 be kob79C >lmbј7P[!"&=r DL֖]hD,R4=>7f}Ϗ&p'"`w>2C`[J{%(cv1 UErUs?6D#h|?VZVΘ1v={Vl9}@=g#4wGLg2|QAwׅh ȤUzqh=̘\[XmA^υHQNGig953N8u[N mZ6(ש< 8yE&Z#_%Na5&+ y& ab,rYH9w8Ð{dg ҳ9S=O;s ]:Ԏy8 CeSb/ b5C4QH7GJ4QA`Y( 7B>,m v|rx C,N `)i q'@I.bw#*m_Bx\QUE|YO~l@ 4pr^KK:S0L yz9f 9a b(&"eq~6Q dy1NK0v"pjLLݓj8fM2O/lI.tKQeJt41PcsxlV G=ٸeS^k%$U8M{ǣq(GM= 4~#%3_$"N"1l-,opEA^QMs)YX("D|d@XW4Ͼ b6Y^!%):r0~k=/ ِ$;j:錄Oy1dߧsѲI1?5 =R>0胘6D"􃥞HbSi䐘 TaND\=wh56%I̛WQ)}\\cEk;+x] G4 ٜI \LXekX]GlO_kJYhϮuA d=9>{hVgseJrby,R>D~4F:Zlg xWÑ?Pֲɤi\/"x/iemL Lo6 q/= " 's;IGz\/86B15Ѽm>~,+q* 00SX:d !JR+jO-[۰>AuI֚A:&Ē'n5F`!vGH#jcrdJ $O~1,C>]OʮUo;14I4Rc&eXD.HBsj~ 6kshhD¾ܶQ"by#g M o+ӎUhXKIyH^&5{9䏵6۵p]?lԤ}hJ4^#דF W#ϩgX_F}<4Ds`Rjоۖ)+ZVX?Ok;>I^! &L{wT~ >0 hT#F5x+6g \K]٢:=b2wAJb8cA%B];Ȝ?7b>tUgpk^;Em7J<@M=X*+d'9g" zA?f7#kL[ќ3IN_̞۱l4 vBfn3c3- 72"`?[XUr\ge hQ.mz~<93G;" %솘IJtc^p&oilzL"d5 Z#F A"?ͽGp !ߠ]Kg_ `_@suc$ 瘝V˃v_IA7V?K L VLI>5ܕepBzzYxS3U"Π* T ~),E_II͒C6bv] pC ?G&(DA13w[u!׀Y^po* i%,CL$,&y4TL{)ZyT@&, q R('?E5,[ho1X.B VY&qLD=#:AʽdA]HڏV"sazwu2zA{ n{6Gf S~C?{ԏ ,q[;?5g-b(`&w*X/F6k"C"ZH{z=[d t wZ6@f{дcb; H;ηh}6-;Uk믿8brk44>:FZ>![bfDfL% %}j`abgnB 7cX3 2sL)vIrA}zb&*c`D"޻@쟔c!l,Pb则G +v$vrİEEd*y1U/#"'ѳ©33^09߂܆~<ԘOzI(ҳ/ xnx_cNEH{Ӎ\,gjLGug[|4USk] Uh@ dluFL[:i|c mR4C㱋ٿ -P]/ƚOZ[wճ 4vM5oqxS(Z/XZX?"܎8luR'u"13U%oEiG Q$be`4gE.rȘO :#%Rv7^ =du"8 FK?{9|u2D~2"3=Lޛt̽*H nAJ ||׻^pg,Rb2A)9$R#TO[11aS|+c?pMxl dw e_fNȔ V$>>U !%=b[t{ޅDf*^؞LQ='G7"k{d>A=p`OdzԏǒLbD`@C) UȟR)3SdZklW7Gʾ'ʺ~GW^=uCT6U &b()@cz @ڦ E-]/kgQ"027 a]^A{!0+5U&\rQ `a kWs9_Bn SYG~ipc]<{f+|>{.549ďǾsOD{R=cp!f>z?v":ts?oAs72{oL&^XmK`taq[Y'`YUIhzZZMRqNv p+]8s8# ûOj^3 %5X=8K0L-% )x_,gU.mkHz{Tfq2% #r82mP-2D ,E) yˍ-}Z/9^x47CL{#2|AK5 |NBLR!= #x8r9"6q%0Q]\>ʏf؇(FM6@;Q"SY-LdGVX!7>d>U{Dz/QvňɌ2U_JKOح<'2w6{ITbhz5~Y+2uɮ>;$§ʹ\,5jK#?{5 QC4[Fќx(~Y(-43mp zAd" fD4{ XXo4N7wʂ:q0 .H3dIZ+ebneUU?,pu9)by0ê; g#sS%2]\⺢)Hj؜5)~<+k}~<6lؔkSAY`z2kcY_6g>SY_Ms$"Ȥ RM W5IL̵;"'r$~ɾZHXic?#@7xdkG5h y Pxc2˨Ë,{G. 2 $C Bm N> p 23^X7h>E`dľa}ARN.HdEI#s:X@ g4$+4mȲͥZ+5_NˌZgae_,_;34<[#kOv-9a4}x0 ÇflKV,58! 5Vp#0Ly"P-E=OEc0Di\ vF@y%zѶ_^s4 ^ϭ ۳؟ݮݦ!6n%fֿ^]* v'پ_-e__XF ^Lqg{ L^~Yi09ֆ^i}Sll (0."곆c@*ѻ䏾=^X9U= / I[8;׳uR'OO-H[/a8gH{f ֧o:9, #Yz)>hM8-gҵ20\8N+;qaX8 d0 8¾zx tA ' Õf.F72슔h@>;g+n|]"<7Dƫzݷ9/Aw+Xq`[O'M~lN~2TCĖ\GJSg0ۏǒTnݍ$:} |zA<[㒟˘ю)\6H,;!GH?k?W|+8mjg#ЄD2>nEîڹʎҏʘa禣O"TjیDʓEͳ(bpsX|}|k݊OAEX{C@bW^p13;@}f}McߗyZi[v^tHb}2a7v`d1PgQ#_<̌ -٤^׼;Q]ǁyEp:uAaoaX#y8NWW ðq> /i8N;0 W+Ǚ5&857g7p(Mxm2xqwI-d1'' ;2M} f.2\z~< -*IR Pd~^}#Sljhr@ ȤzDήgtL78: ,209pK̾tU1*n6e[ʸXW 0A@t~y-ROo]1bnG/mIX?"d;2rLbk7@g>F@sGgflͽ3 -.w j3`/ YBY]+b,s<\su@s~"su6I^Z>Bh|=׏|}3oJ!@WND SŘ!͐iEŭ6gzfe6WuR'Ty8; T7deԌ!ND$Ɏ&T[x=$ 5 jr0Kal"{1xNB7)RH݃5XOeN[#E=zAG7Ƭ5R~?y$bc>kG,A&zĒ/˴P6CxYhCuPz[>)OʌڻZ3PCkM9}ŨG[&qQ41)EyǾCk4"+RdmVq!9Ho09RĬ-Cc.)^A7۱{5#mb˖YnC5"M۞sHxL ֞4䃆])#6b㱧FJƻ'bJC&ЎjW崎AsoȌy?bHskLE&RyQʋ-"plքQhCt ڰ9ɟ)ߠ5e9χa| ;vq昙k,FH ּ5;\qZ+p80^I:a8Q?4B%l @l+yl?FNYk)KG^pEHQV$&h<%XRY!֬pxlO#vGK{p 1GQqđYq_WkF 1$Rhl b6h7?"JEkNGxq.k#sjA"x,rwˮA@U!i2EwQg Pc^3#)(Io\eי"5_皍@d\(o zr4ΏV!{8s.F#Pg-T?< I5&mSPq47}I"Rtdg|L"&;#Υ iSz]rQj 1ƍ~_Ef1 6ꄯu򷕋ǹs_lnS lxΜ!Z׎=6%k98rK!61kGZΫ2ia.X}7&@1"uGQig&90GTSͤx SF )MǐZޣF@p\o r#0"!.%\cղ[q>Y d3l> zMM(yc\ gjzJ C/_+FB0sY^v#em!@szvsXT2c߸JcmEvCXn#e,gnhjyk.hLq?#|42㱗혹,2=?d;CbĶ㱛- yf*]L§q1ʱ5b= +b@ٍٜs6aPԞ]ȶ@: \{ALx7C? CB#dz hemY*E6/?'w&w5{zO(kaq(\d^Ƽ(,oUWTVNj\a WsZmP{#k-)L ݨAj!X z $9M}jA68B_G@IHi6)A KkR|-F%/\'2147Oo t+qA N8WIZW_hG}1bc9>Y0$me?,o==>PF+βB 8]Xim\j3vGjecI EqzPE ֖NfuI֚u`db9 9e^pL Q!3ӯ=XfC2]\/Hݩ^pR!]0T ?oRZio؍ ٨;cL?b~~J bX~DdR?(c2I8o끔 $Q].B17!#@R\/8; EBbgf&y|e꿜B氶|ؤLȏ^]YuotPIs7vE+`'?Юs1r{7܎JU \ zA`&踹ǯ*.9OEhpUslm@2qL4eW+'WWw@d\(h.A )ĎB˟D`L`VybUyox)hhNmEWz96nYLڕ6v#Ѽ2 $-ҁ=]/8ȏ>؍R84VM󳀬^+?Nn;`bEg7T䏑Eu{dbAK\gNqIS\hR܇x}.b.d2v@9߁WL!W!з7c:ZٽW Pf"9vDY36BX N#d91 ytUzbPvG-Jp%sy,=D1d~Fyr uR7zXhIb?{WeH6V:af⋭oʇhxEN&#?6f42oTؠ3XD 0cgs8YlTe! Q:hާ!@{Q#,+qQՂ6~<'#@ = s_@l=vtd|$'qҨ_j20&Z[ +M.Gs ϲPwHKةUV,CS24}wC5NN6l f)vGyt@ %Jڙ_vtUk<--;ǐHgE1h%r%}B"Sֵ5<޾eyK؎Ů^z5iLMWX{[?솔NdX{_sd-6Tt FJWWCkǥȤ̮s =&v{3j}TI"&I%GD9PsΙcFDlp*-z(:vsVzb=?#;3JZ_݁Pmx+Eft϶I-@cRDA/vhcs2g{z+v]\/8Wqhs=$뛉,sцXǧcvnRӈ; / skKP ^^ٵ7uZD&F\?_z|胪IV 򊖣ML&;̷d+wOetl=%HW 9c_veJ;nvN%RoR2@eo v>J3)m߮~i\ cSA]amd.BJhvDRdֹg9$k[;ZH9Efx;>۹^ppEmrt8ѮֿH)f}i{wk4hNF/BMO@ 1'$IcSR*4C Y41=B?{3Pa]s p! @R v?1 f(nXa Q" _s_G$LB~s^_#mA@f]HUru4w03xS!3 mއ%TPr~p2F&( w2IXBМ [64,O߈}eˤ h)b5  3U9K,uR'uO  `5̒gg#p RI#Pda(q"#x)M,d 8G4vFL\80Z{"E/dz le\/(xl=,d|eDwpd8ވؑ/"'6\)YF"ov+RfM4?ۜ#vsб}/ _0[B"*9(Ł(FCSrSa%+ kդW[nK|uc^p I9wݸzYKK3ӗˡߣ !׷Ypoݏ.pHip>Ϟ ^A *hܾRX̶kh>@`r۞h}w̌ncc. me($|,sM6h==zoQ}v4cNesJ4ɦ_&c >;#vɎާ k9Ow9nE2TY?eay?YY(JW#e~C/^ Ec;nD 䌿R~#3, Sq+2 gYġt"SGfOQʍ.H-A s 0J W]pĀLAm@&efb F۸$s@xUI)'rQ;RZ"r)jYHAY~pUz2~<6A~<2)$Q;5?dYC4gN#q{5n0s2ێ5߼]/$zH@5GGf4뇭D`|}zx4ǣ9)P"(³=w l޴͈Z:/9mITg> =/حDf!m97;HͰ ߥ(UUplgNz1"_|o맷=% 1f} ӱ"=ur*Džk=J[+XI W0<@zjH:s8# ût83" (;eC0b"6bW9D~p;Z["ukf_a$/x)^v߉va\\/vHL@)ڸ^pī#% )H`AeTo(#IzW2>֮|nDJ;򣚁OH"噁!FѦ|A+^X=M~c \/h$?H"l6b,EW0= ݃$Ӝ.calnSr.S^T)=QՃ~bU=Wg7E>qf?{gi|'UKi~"md o7#3cmtxI1DH8G5UhNmҶG p.8 Fwi b&!1h3]>2=qmAl`z9b2Cc~pou]_=wO!zA*yE/m6qJꗆ~d8N0 VLI_8JaXփk0 IB6[;E[5u}mt^9bբVBUݿg"vMHqNBA9~1TӅ\j=Qdɾ#RDvHɥHތv@hѯ@ DbGdbS!"E)h_&"_Bi$_^pXN9[^-.! k;$"j?m,ѱȿsd ]/ 4ErG?tb}zLC]/x&)6bzBUR-Cf5k;XɡiP=ލ={0Ǟ )vT33ktv1 c+9=|.Nq`8?#9շcP'qJM@6 :[ENy q(B9 Wa8N:bq:5 9!)"m}$ga8qv0{?N\zr(]'o뼡 خVvh¡6 S#Ƭq|z)DJS'!ȁ&=ClA# @u]FXBeD#E6ێE'pL.~vLhð)%HY#ڵO6~B,4L9$<^wsgh hzAS+~9͡>-20ͿG9Wu,p]=D\Ԟr2!'(U(FO!&Y?D@Scn@ $Z?Ci=dRo=7u*9eL.Jdg®4qݎm+yv7B3'[Yf?T-6B:^2h\'(9Nɞ9N9NICSOS[S-]ЦZރX4O0 G"t_ɾ]" Ê0 #_0jD^;oaX q3|uW3bM/L_7nV,nӰKy!d.zSf"5  9B0P G QĖ5DJzyFdg~/HYR8]шa9ҙ3Al pcb;ҮW!WEձOEPU\dizFV?aϓ*i(fXd#Ϗ"DndrG~f]UFHuS?r)6K5ndNG c鉔kkR `wE#Ϯ" ]3pt?#|*#J*g}]/xW`^ac5o{3F)GZ"?X?|u` 96 -\/Xn']Ќ|F8% Z?ikG>)"Ā^̮HjV)h\B*<͞ 3{;4WZ_o^ m.Gs>UuR^Vx՝OAskZDrYSrHi;N;1'O^$6@9mPFH-jv p9ﳩ_ ^D@)voʙhQ 9A/sW WQvY" pRxl"%6eh'zx+:h#7?n^C'o[#(څLBk|1sG"F` pbQ~'!E7vf2eª}lH|h}{7cxWZf!SRd603Hڳ6 9# vrY!^-NE/2þ LO~ĦS$|@a;Ģ@ (b>D5~kѻ>Ewyme%i;, Y1H9*o^kྔ@\1}` a  B2UTe 2Z8%;?s_p^@@vS0zO5k\i[c9YuSad &֞L28q;6Syk О|au-DJ, D` ^Ȝt ڱc)/N%Q6s,okgzX{WuB ̹dyѤZ|9ߌqh8)cJ@~BwgLX"nhmnz2?z\61SsGl,hW!oG&Nwo'lNSB=;mbD +s;0=EhoGeKL=;Kkso"q6CeE(-rfZf{wW4/*u %~֕Ilqރ;ԟ7*kZ ʰbq2Nu^U"b';@~9b@ mˑOqȜ< f Ԏt6+scK|3du p ɥS㜉|4#кxU3y mjF#Bc(kf:?sg Z;[ñu"s/9 oy֏*\x8WEH @D(Hm&PFlݫ|L>ҙbq*X#Jz2R0;J*!Fak窄K5 ")`>X_u@/B*۔@탈b\D-YǣlNw 0'Wr)b!GIESC/t ̿M^Od]~<̰#и|&Cx;;Zh8G%b""ԗn1o|忚=d>͙hnFA]X^ZĀ ~.kjuCK=`4]|zG,}*-Zd&#/%~A.EJ""G؇H"P3R#s ic8HiG;ːiS|ff!P- pzE~$ +ZΉ)7 b)޶@)fk'nvU">1={Vžh<٤䶄LwQ4Ez5|!Fۻ*+b{'!R4^t2śHdXi@R |cSHZzT&"rxH1p!d vB,R%Wxltii6;3} !9L˚́~pHnNu'_,\JW)vD`43Q^vCvFv MF|2}l؟Y݌zkhAy{ؑj D.1e( blZ0ϮB)Ä]a$Dfh!< ,m[p2=]x ^h}GG ~<]ekq |'-[4RC܌@G#jRqxloIb>7p2K?&yyBu#Fuz,JUkPOC h29@mHxdm(M<^'b ޅ}Ev|&(&׷wzDsk9s]nN䫌Ҏ4xz:Z! }5>1acUg͍Ki[,qJڑnpojƐwK"\929_qJ~CMԽ|mza3lO /X1#c GQE$ֲsanMLB6EW3o!VgH1LF9#[oh*? vE>^S h'}YDa|Yc_`tE~<6 )i2cT&XKSWez"~<}?}Ս$iΊ@9wA`z!`Vs"+\/8@USJv1jcLd"7tŦ\eÏǪr` G@{mok(Y @&)H"؆.h\hёw~Q$as` M< 9/g]p#goe517o0joDmLF&2&k[?ˋy4C/r c}]/L:01.!йm.}JŁ}]/8zk* *'9}0W" ^4mN9NV]d5(kta =?%z:tr"̌Bl zG ;DբN6e#v.2;ޚrȔXL"}#dd;`^AtXHinIdV)Z4*B9vc72ޣOf^7rؖ=mCw@=#%8[/c"RR{W,9hLBIiAr%$9@'宲_̋Ur3-\/H3|NC*`OG*Ҙ _㱥ٷ]F^M)mB .`}n 1]ZRnS@ 0\#| ;c9~<62鹲_b8രQg`N?zȟ]c'q}2:W.SUr Hsp/s;+=QD4ޑރmtG&ȯP4Yz>K G2RkzޯZ8&04̝j;anF>Ks;}'?OV'u7݁|/!3Vș#[ievO<@IcظkA]W}qlWWOLAظI߿ lFh') n?7#vh'g'sr")y;OPqJE#ri@so%Nƌr*YS'@WY\ V2J>/C[^P?8j?_u'"PCYO3AxWݏH*H-D$kö YLf]`5ARS36uC ?xEJw*9ElAo& n!B-E@s8Z7BEzAkXE19w:v>HqW pUz :|LI$VMdFs*[,z#25jQƾc.C,kZ@At<^@(zT5{dE`EXW"wZMz(ضHq܇|Y/Ahy_ nCѦq~2!vm0=OHIE;sX d%Jz ILйGK%@4(4:e z4/6!wJ20UJ&`A9Zدh "0|'y(أ#R'^M2dZ*rk|cQ6.3`~S; 7hi.Fݬ}ۣ J'4':!7M1(u4@~c!VC]chhz  '~ym =fEsUY䕪`= =Ѧ?7=s:z߷gJTUlb~`k= =9N8?) srfgbhWVHd}d)qa.G~S:zw ?=l0 U~' J#3;rUTd-r0Z+Ѯ12S܊K; e{R"hv+*>VEl%Oyv(/x,D`RjN -p b wDsJ#fE 2$Sع)>H=+i&> Xc-5;7W];SKZ"/B d5e,P7QX.`Wv~?BU@Δ߻5d͕m ~6L"̾1q~M g<4>̞؅,Ħn6Ð$XכМyˮ9T 䓕Z;)CsGGZ zh3xήwd4Ao46G Gd+zyy%2'izI3ob#V5Z*{ bIBMA5B.3+:oǠwL杈6!˹pbG5l+zG?P?wFrP,:)0kcdux}޿6)g0 't!+}t7daMXƤD\E)Ǽ7qէ>҃Z2:aA;9ߌ#= ҏ"bC"#f k-í$  AʥRѮ0DconALSG3~<n \?-ߢd'3xW >\[h4rotK5]/SQ~<(=K}w؇7=X{ҳ Ӑ❏zAYMv)Ґ"}&"wMPmƥv~w(z 4|e(3m?زwSFbQWfMGx@`o!{(#_>sX$?"cG Axcm;R$~B\<SZ*t3km^ W5h};˻!`tS}/BYs2٦-C֗ #hVư;s;u_Ko2m(~@7U9z8% 47ֻ^.FP/}MK^nrbCa8׎|f ð8/hDy4 wy a'83)@Pd-ۡA/DiZFþeZw` Xid0މֶ2uo!3ӎm @bu%~ֈek@[̎UOYv.ٸ|a̰xˢ1HC QI`>o9LO'}@SWNC9Y8%'uc~a8N@Gz9M|]0<0 8&)Y 8W5Zꀘ_\%p< -=6Z膚Is? X*,SS ?(`2ز(j\p n5{AH4BNл#E:-'#GƠ3HB旭"KzA{`5G SO獂jwXnwEΚ|̪sQWۅ6Eh'2LXQWPGitm/!e=>ػ(!1Z`NO܏Ǣ<^bR"6, ƚ9r H:_\H"Byھ_+("E$ށܜGsLɍXw 2" ,GkI-; R:g h)z_wG"n:UOoC {wC>["4dО^| /]3 ^9;9-\`k(DlG~vXν^6{Sxls.p8=(Aa&g"FJt? %' r{br* _)KG:- qU+JĤt k3z~ Lrxl͗'\/ ԂD'~"uGUD폀Q9 _<# @3W L_5=#3L4D+]U\U^;𸟨 rF#hdބvL޹m{Ϳ@u"z$bzȴecm1 F %ޝ큘4dZacR=XE$uz?AFW0][SrHi;4̭9/ 0@S$МqІcM)p8_aX8N7=8I]i Gk8;w3W/W"FV" \`d@M[`F~Ђ_y!0ZDz).HD;sٮv"퀨xGe&RW#]/hd#|V/&LL&Eߙb~- %bvȌsxS_!ZU?2JQ^ej)fvZ־/_2;5+nGNX zzQ[![~<\e;1[t\ -7 '~i'.GIi:w~50CѼ7Ycc ~](9+tDJ0MN;2r]; H? lxD^Pnt]  O0@ !t:ZwB@g,ATިh4C{]\xEAKg-p:?w=G6hL@_u4%i~4̝|㔤!pk4++Yubd% )}MS޸\:㠍kTqDzm^j:~8O#K *qԉ/b# I%Fx-G5`s*Nw7dm֝9(jWQ##'(2@=\/vK{t#2uzADJ"Wٖv0D϶2!F E H }mNHF/ :$EDC/֯]=8H&]/t ` [Lm"f&o&."?A>=Z.~hw}f}ŮٻdWE@{qpؠl@@=p^q?J\guřl}"ygٵo.hs4*6XD]Y]~FQwʵvB>9Ԑn(z}A*xX\hz!7 "?[ܞk%눘L,ubn.BjW"Y(MSRއ[ok9(%[.#QbɁ{4̭qJvGs$4P,X4]-NM0lTg1 8`MZeO?ץ^uZ0 ç,;Xz$ﴤo <^$dcrO=Ђ9 -ҫ\eBzw|i~4v&n91͜sGҽFg #`6~"UKĒ-@G8!6Zː2lʽHaB;vbbh'91XfqcMnD@X{{=2E'C@nsbZA;40+ۚi tUnf@ Mj4NH >NaP?{HyfAIцvf? 6!@kcr,B FStNP4;ߕ\vhcZ[u/CfQ&oC!W/*tb <\E%"|+]/8; @Z[X檤Vb?F vbI`}ҏnD&Ӟ*+E\MBlJm׿):翐L]# H\ć$5=bwv9.D, W^C@!j"Ly~@&bdgcdfH7"u H4>i(DI$|~n vL+ 880߫bWF y%]""bg̜9b8[_7AuRǡH?!Ȥ}`-0e4z-bgf9e$M\EI2(b+s{sι=ͧh<@+vIgл1S7ylnv@>ӭ'X{oEkA@0c Vqh4Gg4O@X{?Qt _Td|i[YqJN%nO)[ lT@U9Rey?b*:~@B $ƁL|ӑUm|J;)R۳97 {hKR~$Jz,'U-Phw* z R.[~1Ȭ R CPlbPU#CK#zA8'b  ܬ2^#9~<1L_3ލEHEQ!}3hf}<Ki l,h8fn`vѼqt?{.jE^ށfhuA7RW3j3fD b;OD3bNp>Ȥ|8bXx:m@I~\L voXə~<-a9N1h:w7gCNdc Ep./E pd֙yH1GJ}lD/d~KTΫv|®?Ex(B;hqos>B{Z]c$]l@IG nzAvQD ?ϱURGy]U{,vC3e! e~W,D֏gabxu 6Ub *jѳ)(ۻZi1ЏJg1(' =kkؙ|w\*}w.M>46 ߳YXl#,26#hbcr b^$ko5񭢃ݣ- Qtd܅)?-Y.b.F$IO_xB6=|03o%zW"P)zc?L>j ^{~2wBy9+ƻ^'zFAfw]/mR꜋MF$N٨|MUqp?G"^0Gwc))ĄIR7gDž)gfHߎvԻ 254&Nd-6)] h 0AbOz5F 2@,ž\c_ !CoC~XutNE$D6㱝]/-;۱K8|ӝw/=jW%O~<<=I[[e#7$*Y{gp©d\f7X_N2@&#^揔l_Q'hxJZzDl4; I}U@m$}Y,RW?V̬DG3x %+=&g" 4B`;r!y͜yh_LG!6e ߏߐ›zwaRTiݙa%hی9g۴LӚcVW-sy׵RT0aDiL" @\ 8삔 R4wk"ݺh"1Y[Cd(R/C~YD @`Jf:4*  P ["B{ 2CJ)H{C4srݡAVY~'7M2B 3l5" 2D Z֧5(5KN ""-"z ~BjU)o'[[Lק"EiE#"Z*D`QJSr(? .w骨,ϚM0~< =I /J@*( %X]OТ(Ҏ(нѳ:PU;;D["b;M_g)SVZ{/J8|q!zoF4iIHlT" qdh.lFjh s䬿=J"VOe7R"nBT4y4Q&d;x@룃07:%bο7"%zVYDJLHjE)ehr]MJ#">dCqH { O";DnA eq"Sj-TE,w{5H!ꊈ81 "g7'q`A?Hsϧ>k"?@.QgVB$3kT}J#4o.v}21O@$>T'IYHE(EG| G#@~@q]zѦp)b#[D831u)vʣ6 aٹAfOZ?w#UfH-x'1ņ~'jJv/虻)Zc=->Bs6 |ft#mzN6FNJ$Dg9w~ m0+$XHPCmk9qIdeCp" '8}7{?qWR:"I6Ghn^g"B:pSTEdi( _qX \oGFZDe/߉6yG(2&v=$y:0&MRnW&9ΗᏕh&z(0^ Mv$tO ɋUco#l$>q oGdiuSx>GAY"rL%I}Euv!;n5D"³:a{ )!s2"!w5pZ=th!ɂ"PC(tZ"&;QHHGw/;nH9$㓢|nmw'"G_{p||<٣ۃ0 (*w *6]v#ѳ-lH)L) ~>㻰Thlvk1|u` O>¯fwhM?sѢcmڵ<)Ӛag}5:Inιؽ~v;9窼C<bbA7dN ¸NGJUH Mb!E="7M#p4AsAw|_YEEgm^<ʙqu ,knF Azȿg4M@&~hҝs5ʡlTF"Gi-<‰>j+jY_ʰ_;aLgg"6Ղ0>|ф{]HћLPrx)sG)DD"VR,&+*{w51pm唚5jT=]I,Uuu72 "%G&yۂ!ꐚiƇbIEkVHJu`EcE]ekT ]e%"O?PFң|n} (A75ӱ{|>g[9px_J]-"JD$ vAD&v=-JzNBzSkOkղ˸B+\q50 w<3W"XѬm+k9x)+4Z J@ X99ws#ι=s;wtέʾt]9sdιg;rιgsWMs};9w9s%&Elo& ы4& Eh)''cDM!#Q>HXod(744GJh1k@rPs|@Aw[D"Gh+ܶ $}DMx)?E֖GѤGitTpZuK4>dH f?;qݖU.@-e&WZNCd%/R3Jh쀒UM'QJZԐC`8ui-(O|@wAzo#"CϕVEJ֞0ov >"-["?nh|v߱{1ٮcKw BjX[VkR_R>+GSζmv&({mEDOEjuA"+#bui'jt2vhOCd=5(/{Y^=7--2&qhj4n!ut;^"S=';?́->}j3p]gYmK1awE#smsoئxG:V(;?9-,ow-޿;L[Sގ~2l6/,,Djuȡ`]Ldg'NGl2xjoH·DDC/h`&I]`߇^d9MEjVHYR1w҉RVl~ 7"RV0^cF!s*ԗ6 Uh2fT6n[Q9df&r<i(q Dg[nMD'!v=WNDo~M:ֶhE&ͬ/1P .(ä({.P$\4tDdn3뛵ZT36!\C͈t^qșa|43uzt߸*duȯerĤ$iy)Z[ω'0>Mlo"bޮ_ 2>" Z_}su{M|coW6FBMaJ@|UnT_]pQ+̛zD H&笏zY&9.BRʓLvE@OSvJ%Q2cHHD|A7"Ɉ"n5߶8yٲ~_(Eވ 0ܡַg@?ED)D[:(瞲kq&9薵P-hOZ8v쇑R= هʸ9"a D[{k}RA"`Œ Eͼ l9;ze\ DZ7ɸ(xQBc~ƓՋlWhAràyx2!j Ec89BAJ ˽Y^gRgr"lgmKɏ-2)!HI{; !B%&z;w"[RZUD4B/Έ bRGv@M#EC.Ԏ"2}&P;e}O휽P" ;)_طI4ܳ8Ȏ(E])/Y|jD;"ߨpPK t ;0k"rH A_BIͼ]d8Ž3!_JP1Bl]cm;(]e}͟&_D$7ݮ[(JYێ6; C$=0& SoD*T|# >Aίo̯Oz#`SajA)e=k#5Zu322KfD֟Aorov )/|R=zB$][v-zѢ %^OeUduUzWhԇvxGe VRՐS+sPZf\!ϮF{-R,x 89w;>}bX[i{?{?973;m?9{,s̚-97?R C/9"]w#SH7#ru 2}&y)|@nGD1]JE DLOxzR &Ԗhrh>sڹAf 8MCS6CL71D4SNA}h⚂^+!sHHiTI`Sу9Za*dkmMt+5vH;!1MЄSXx4$*T;DBF#RR^߃#eofqJ@bdTR BD+TX }΂@~~[8RlA[(+hbߍFrq}# T^q(:9ZvRqǣ|6JRk۾HdDCxwfI \m ϶?:w-ѽ=+_a,{EՐVHw==iaŠfu׍-49x>8׳q()aB?J娪ų)^\P_:*Ђys/|霛CwKymm ܿ|М= x>RR`!bf: TGaiD(vGO74͉d~/!dJŰ]$R Z{)MϠzueۍ Tlۡp`\ϝ"¸dN([DTB4F**VEJ="f  v}Y`(و6AD];_Ĥ:""O tkkbǮk|Edr&`\M,"8)Y=ф1 "ugDOx5(6C#u-"]o)k߁[6rJNcA+0zh>Po,c{,z%d[|h )/!sg {$?vÍ(pJgI E܃֖(;ڽ#Zd~b ]k}|?YЂ_YP5 g [w~=8I{JqS2r=pq-6DIHI=kP?6辬~Qdb!EMBtDXTϠ4V-ќU~c^D_8RJ:`&3 XLA)ce4i.L}/# }NZiwC4BiUηDqH̥X)M6Є_mwE'g]* uvT%mׄRdd^:Y_>TG܇e~`9(r o{yBDzB;"xҹvF>hVˣI#⪴}hž" 76= Y ֎)FOLQ ¸;"ԆSrH_@Pkc?8iJ" AvFh*ބʷ"!+oFQ!٦ Z }dm][MW nh3_'\2TI,&2>(,W43n8 >(w7Tg!޽(KkdQ&ĉDPrLbah֡_Eܤ wFg{6tGdD:EH%sY@TiD g"\'{:! n;ߑL+ A3kh\=s/& Ȝ5e3DE EW/&]4mLv*+0=];E!r<+ȇqzgD݀W2Ajߚ,=m)/> [ؙh1v  FrG`AaIBCL5Vs4`D.E%D@c0"Z; bz l $I{4ɪ ʲB`"7{#KF2wd Yt0|jf:}ee>e߶qu϶jdNx?FJW4aZD9DHF#2+NM!w ݐ(EI|{= k!sqS+Br2K)+4G[E-?_;dӉa)¢5 ߚa\]q66RM6 s8ε Ja\iDre4n_1Meݫ$e'1R*{8g)eS|9rvĤSPT;fx1^E_}2NChuC$;% G0Njo ZL=T3 YG=SLD;$(:賿`)R;,TD ̡@,3HȲ3df=nfѤP-*H=eZ$&T}/eYRф s*LdUD9ijwj T /Dj82rufji׿!Uuv)"bqhgoc52&Huaɭ zy/QKȡ} kD&&Q>{]n㳳;:R7Dv.@0DXZl@BM&gF\_3O-[oiL7nR8IRF[l}>Du *'`}}&%ѺAEь|u{1Ф; {eD ~oG•ۗU+P~&hoE֚Q]7=km2R $F(NTl:^b1K_k<3:?ldAF`#P4>~CuE7 MSH1o8%\o][uh5OCN;I8MWα1":=?pK@|mVկɄN[Zd|T@űs/"\^| F&#0d 8" R9Ad(ܮ\tRDVT2Ϭ;[8am7#ʜ߻"ȟJDNʮo%4Ce'uk˾[ ^[Q=EOF$ DO@>IYUߏY@(]-s ZݟKJֽg";{!G?EDm[DoBDdJ<75=mLj`,+7`4")2iײ?ZDj`E{/_,iuUEG[;U{6E'ж賳jzJ!)"Y~mu%k 9W{Ur~lkR?qm3Q6OoW^ޯ5bQ!bsk74aBT4ɬkנ<,#x؜w@&3aw/Mb_72ک|UQ:(O 0~L9M&As7qoEAo"ot#Rvє73ti}qMBv]kH)99 xخdw 1jqmH-M 6*La a}Yb-H@*;s]f?x]WB$/Ca=4GN3 za0%z#熠18myRqS+ztB+0~9 3;7 P]R B~uvA߆N֗ɱr(#"?!-*·Y} _"',X6 \ћSh볻os'p$) o}vlN`z#Ch{l7_87ę =3I5"S Rt&*(-P3xf'yN}[-H9)_fMF HFo6䁝0~)! 5j S&̐{ w[J+q*"lY;*36AL%L!WJ KoEND +Sk/ۿm_]#!jcؙH}#8>㼙& ~?Č>2#y3֑|LD ֔d[S[)5"Jo cF?>ٯ+"saAaSq: -"b* l݅vAAGADPFֶ*BH]Q> dPJ}q:Z=|2l;?lyDz1R,7UӘ h<ιw).<7L97oSz ι zV_Dzι'z-4vN✻"gV^h~DVL5A֘ r-v͐[3hFvܱՔJ&潿wJ~{ߠʬ1UY 83㋢|3[נIo$^+m Qzqiu4@߶OE{fD+ZnPWXD?C>H'~Z丼'2-(dvIz R8 u.o*'!5he<+ɿ@'H%?ni] =اceEmk(Ea{ R ga(A(#j@I[_ J`9'_0!&cC IXg9=l{1OLn! 2zSt.ފ瑂9'h=)$呒%Wn"^(QHnd}2+A}ԧrlH?fU?-Jz)'#"9RV[#?fh|bNBh혊^g]QJz!ȷ/Ţ\%" Gwy￵b߷{w=/ۯFxwΝ{j+A#ޯ[w/p~M:Dm=W9vAÉޯˢKw;g 09w~+s9 x7r5sνΌ_q&"9ZP7:X(ф66f]|61SЍ<xۢޯw E(%KlO5Cfkm>qʴKOiruc˷arNYX56ADk0R(V2D܇ EXeA|vm}1Զoo  4!<sCF|ԬyN^>6ӽFQ>7-PzֈPHϔQ>*{ZY?ȏ)FN^vؘ\Q>W?RihQ>`\o>hev{-0Fަ 2?ёfxL)a@*Q[D8oA |l ϴ'2NW&G: 0)fn=͵laqgLɟ0!HHeE%]0H{VRH1hJ&́'ˈVw OO }9 "fs-1E{sܴs(kPrh["w:fcfq[lsGdx{?95#}bW~lmwq%R1`x9/J"fڋ-ZoqQ@&$>Gy%7?B# 5Bv xgգ6s͛z')4)5D}"}yk%`xzI[Fi F"Rm[͌r]c3"#; cCɎQws׬=/e\X}S姮*pAs#̇?<(HPg]n X (7 tBlJ|ƪPjA?d_LަQYY3ޏAT=)^mDfa̼8e% `wD&رhFYC]}Ć̒J݌ }5HGj(.Yz,.{}@JG*M}Rj* Av8Z~ 2&HCg6ѫE&*oXtm:!s^u0u4D$ui6}䈖+OI*x E}uGjd^/O+3(] dEDTo4̈?ёjO.Z;{,"F0o7]%G_ "}%1|>nԱYzC,Ytf*XSJE~H(4Ga*RCMBRB!m|ߡʨgr5hoT{&j]q-5|6>K{?9w y(:?$>O,&{W-5&csQsx2v6BfLι4=;zxq[L9/|β;[,DDe8c$w1(|@$f<Q>:}Þ_g>:Xa@&@*A f@d |n2!3eR9@aΔ/8ԇ4&v4XϽHa-R/\dwBfERȝۘ[O[M=r P p$3?wa6RɨCQ>cOI+M@Y'" |]%"SD3&Z$ߏvWDbeDy$S8@.qAgAEW$4DEdm zDH۠g7S|>, Wh;go*3X8̀"2r97B.(_{i54/_?cssl0\9w+\_4Ҁ/LOuk=b4+G1EaQ"b#"Ґ~?L_0^9`YzѶArd1]ⰙmЄ;Rֱ"StzQ)"[UB>w~1 gPCQ>7=ۈ + G#w 8ۿ()fw{guYku4VE/'"_D]P*o"Mf>q]NEc2~+t\v;\Php4TY Ţ!Z4gR,޷ݥC~Y /n?A -P޿MÊd`d|<7w@͐Ysm~DubD&}ئ |ucLy{rC#XXY AKr}4i @f|nN&~JVz7&(՟HQF͔ bԭo%̼"m#dM#lǼhM;r6L"EBukʁ e#cH8vdf[î31yEf .CiszMXlښATڻ!3[9i3-UA(ږȏ5s xjK$&мL#p#KGY{n8&5TsHUgy4 T+P=Ҏ^kvs?5v XW uZdTc4[(DdW;t ϵs S,x05%aUn؜$kr &I^D % ?6+?\̥6.X@ M:#_J-3%R/@$)Tka|]oIQ*-~`ב0tDHц ů9ݤ=ݬN x ~?TK||23>I@ ;a|B˖мo[9\݇C|ǡ/F&P"K6hL#}: I +0nCQ>?Pʻ}aS`_>8#vOL66Fc"E)R%,2XϽqϨ r|M6'qQ>eƇ#5ʕ)w"r$"EK#v ""l"y$/QƏm0~/j-j}qD*q8)%YDjp)Ys"݀ Q>wG"#w"c$(iZՐv@ezHcOU5RlEƈ T`k䄟ݚ#ߪ|@E xSrS?Z\ {G渁KA_Pj,"Y 2%5P&H<s!"HOulauaxΗDV!fjO(Fv΅-Lߏvu)&Dp0RcI)Q/ТBH"E"C`D5<0L(!zjUsLrRQ"rp<ff(0 D>"5h"??>' XH<)q; p,3)MDJKH{>>RHeꅔ18]Od^vPB݁0n^&]D2H]a׻}U)-\9H$: ih'ORC?, Լ݀3k!܄3Ε1~|}A?sD*R,7̲$!?I{_KJA8̔F\y hl@ceDX(9쯆L-nD+ -lC 3ן|sn)R,Xb<"Sf4s%%D9H[7㧐hkeܕ|t>C R2B8u-p=R6vO#72Y#d8+T @d̖Sri5T{=JŭWEdgwD((-/AoRzHuvO(*;Id^5""q"#Zg} **HYHm7RrÑߊ9GFLfF@F@2a4"<-oh<s DA 5/"r%俸V }i#RM̓041oMnW.[g f|O% G{ReT6j7ɶiHN4ѿCDQEC<0n^?HsGHgPPҽgG$::BQ"U?"'"tDfSbmϱsF*ځ('T2+!_!%hFף|v[gHʿ@D5_ТbKDDqe`36tPEjϒ+,EeB {/.ܞbss3w{?dW3ǪP Cpb)-GEwsÐ"`Y4~&eYx)Fު#%m|+ضoP/Rϰ!JsiR g9yBwRƟXgɎ-®\%be8|J/r0uںT Sqv DfjPꄙHYD$"U"T =(pWd Q>waƷ 9ڲvÈH<"FVTbZB+DDH RA J *4>) =oݬF^DAA5M0Tn *D&OUۃ0^ۮqN,.(XQZ_3_äs!JɴS,XraezԓX3/U#hbW9g4vFhG({ߎ"D؆ U')GtRߖGqhqewAvGk{kJyFIocAh5 x`(b۾Hv?kk7I",?D$oSDE(FR(WZ!=R7FPQ>wH;#__i@5ѤHY8DHAknmϝa۸6 F$_D+!3uPeL["{;" r*4ua|/*3%<"{o{m]u߸bO80IbSq ιnȅz_i7@A>ûzrέ?I@>:"Hm9㳢|n$L7lHX)D"_/rAJ+I VDבjs'2U.Eϡ0Md暁 _  'm?R{@*i%3FAD>ML뛣켭cv ~J!Ȭy?Edm="}kL?d XC):p- 19oCƷFܿPF6RXX*xD HA b|;f̘n 8+}2oL;#n3TdF;@̈́x~߯YD񏶮Fg1s=GB8.EwIιkqp1\53_N1#ah?>A+GܑAFߧ#q#ppe H#D?!Bt?/5 x-|{y Z$Yw3 j?90폔vCm|nDe"EDhq#͡]`uEvRҶ,#r%9նl̞y"^L"*|n)<'5kSۀZ2h QW}JhN#X^q1M~Jr xWˣ>ZQ/Gc6IՑbf }A2 0賣F"ԡw~QCS\^ι@}s}yR)sOj؂ŒNEʔC!!bϡz:2=Ljk b,"oK!s0|0D1DRhB2g" ?! oDr`\4߇vBj(ΛA?Ըײ֮/*! )aǠ0>?>EfrG)K@(70˜N*DܻA7T 0"(R܃TĩH%=8&璊\mYlAWќ&(W۞v:Iύ*sKF|VEmK LX/0"콶`SkVĵ5L;0sr+tD+賵WhR|X=ȸyS=k(-R,Lws5C C?"_v1ۚb(e֟&o9({)# "\w ?R%~@j[{Dƚ|aCJnfF2z*|#!h{-J'!Pgw.PP:ԸUݳr&mla\^qI7|œᰲ~ +F'F -F6ENQA}+JATQ`i7p1q==sIIr {|@>j"ј+q 7(? ~|Fc2c?Y̌"9'"Ǫ̆DwHrmq~pbVϸB+8OqJ*|+N$Wq# -2tnK1ὟGyʾ>xǢ)G;綴Jwέཿ I~ K>@l Rzu M:9enK|}{mɱRF$luD6ޭDLX+pnn7#Ez46iqdZi*Lc":z?"SV PtLDz(lPDg;޵AiQ鷀DZH}zx3"2%HD!wlJ6}av>D"> A7ADX2EVH5" 4>̓0ΣM ϕz "Y@EWG& 9}%ew5Q>P+L*niMw%g{RٖC!u}ϢϾWP֖FU-jGYgbo@q[[h֡>>„o})9QYߣwn.zOCX XM8^"]i_W; d H)')9wFAFDs EӢR2ׯ/@#fjR^D J#eqDp%WHL92@EEJz3%n[HAȗD149'N {!;E~g5"ȄȶxKX 7ASeU݃#(v_7as Td"0P2qFY3&AAkZdb4:a8RB&ԻIko-/ǢڙG*z.DLU UIq= 9FNEzgPJ#"d7g0IR6Bc- D, 'gr3J5+zDW1C@) WX _ȸш }vV5 g.:گeNP쮅|2-̧ %b'$c,1H3H،G؈ҋ'dMtGz_IDf*Fq R7fVc {?~d޺GwȈ򹇰 +ﭐm߷EŪH)bhn}; f8| XM"3G# H96 ~]5}uoU31W5u;kKo#P#,F,F# N;^OFy ߯MJMgJi1Hu0Bb "4EjRme.y1zR:BI5Ad\*JDoB* dN?"}{~|qJ-s(Kp1и賓lM>[qoј>ðUPp "^MQ< $w:O/!%pS)R,H،Hijf-9^aܫ^zsaRF# H} M{!26 8+PMyitMEr@+5os%LqH5/Rnʸ]Ts:02;}$2E岲(UQcyv*o9OgX;'Ě[!s 2lnhHqRX_LVCJ[yڀ@]gpcX IAR"a|0VhBJzC9 $ ;FA?a3xl RAH@Jeώ˸x,On 3mNGUUu:t WޯW"ke⥊RQ 8P٧j8ֶkR \J^A^zۖߏym͘+\Sև6r_4~Rg Y9|WwW\> |[?Hb34!ߜP^Q>QF]z r\Rޥ; _QTeD nEn"MmQDbdz|xs.h5(K؏k ^nRc9*"5 M`Y\ǚ7ba5CkP,U0 M6g B7Y{1l"@Q3ɸB&"b5"q? ZSve(+Lz50m1n6[fѫj?u[jR٤/ OO]c~\{ Eg2%5@(~Ϭh 4+b=7~yY4WIUWT\{6ߩwc7Mɸv}*hgf\aS쵣'J3>;D)R"qD;v|Ziw7!7"mŮbwȴ"SoRG0()P ֈK=>;r mH= xu 3R"UݳQH{Q&ڵyK>ۻ|(HbTϝgfv#K1ȸB7<6ctd?=jBnkh뭇C?De]j߱vRE/G!"AgGe\Ig';Y/PQ>u_IĚqf4eݤ PZm97}+ ӷ٣ȏ[Z9hnPogzu-h䪆P;[g**M5?Y 5MiaאGzOAODQ"2z!NdE¾HK5E*#Ci CDARAw*n ]GdŎu2"bj@jD^A&52yf*14`bM&|5:f [?OeIaRښ7$qUCVǯ hz38\[U.c~mh?VN޲yEvUq8 XrS[tv\eGe\BDFw}ekW5d`ӌ+3+3qMP>gNLV7.އ M&u#1;=_{EO%z84";R .h8 R0jx-Y6[Vι}v?z?p춝G?{lkmL'sx58{_l#%be¸2eW4+AC_D{= %^ث""&z˱Muȼ"cE\CG~E 2u. LoԠAjd*s5AeHCQp;h M!J@2%d))[IMɃ\)򹇃0!Zee BѬK#"V{ 5 :=2vFnH0 6)bZyk3. ?%V=åe`khP=FW^ݦs C~{SnS2Z |9;/խk{?B|[ _y#7 [nC#}4vKQemsz鯌LwoAof7ʾ{ &$2{](EQʍ M6ۣB֏X[!r>] Qx j5J00&}|/7=W]q&HǨB*otO*uRkz< C)[}2g^wMm'ܔȸ–~f$*RIGE&9+ 2 zFn)v@ݬHLg?FMZ2 'oWx*a ps{?T.@/h8Fuι(iE;~-L8.DM{o Td)$8皣 r9S@J3m#|:[+М{Z8luݎGy{9*8-zoUEjι컯W!|n.A郼1HX̬ԝ9[c,:UĽ""5w#Eģ(GQ&# mXohC™(DLZe*ZR&Ad[j*("m_4;#z.@q?1RnzQe|k}H[=] x MH K`zDO#)PD zA|>A_4.KiJua|~oy6FAob+T>;¢^I.Q%1rݔ|xˢm3>;oQ/9oitpΰ7!PvP\̢ gD!=82cJUL|F#&^!hV7!r8 =k3סhbJֳl_jTgsU+h|K!A_bmz.>Z=P;w. 8;UAÜY#et]\+(a] :5 q0z.w/Dt+UwӲM@1 Eru%"B*áEl<0RM2?[> z$[e2T&?Y{ɫhҝj}x){ĎS([k|Y1lEd(-j-=лyEWgv5{2pB?\-M)W30z>& cg믮1UmлUgoȸŽ̌+M RW!8x9w%g}{)~KH6z 8Ls&"M:`epPDGa9N@W$W&"E}gf p3DkV}v/ 8+FelLO$0?Ir9dsp?MMZCH4D FBm-R C$1SE|*Vr xCdf! Th-3:fדGsa({2j>_K M)f+l"wE~DߤDTTZ!? ~Cjs5MèZ*Y!V5*տ}|ݺ)}ӌ+\~_E% >hXQS*VRXB2r=?ˤ1B真hx]8wS>Rr AwWvw%pR & Vh3yXuaշ}.[WռvOZmåhނԻ$}j(VWPn/KSb1E$Yk)O1)!E\!2%|d;zirZ56;4Jəe=YeV|~D Fd5) IOr/%.0V!b6B6#S`(43hn"U#2'@} |qdV⣭_6CnOsH# ^C_gSϯe}Ad:SC tOoGfx"#zDG> ywKnW˵5Iu&K @}vJqeS4vGd&Ng'Rb1FZ(şAJfD/(#h d@&$y]Ͻq{T&gbÇ%PrجEDk""JK!5?<r Ḟ<SZ]gHU+uUϝM9CDdB})]<Jy* $rfÃHYlaB&!Z%E&[>*whG)RAD /G4}ID</٪~NQ>";"g0"bM旵"B!(לRvD.)it"{"A:t!2kۇ9ySd6],żH}쀢LEVf}RBKS{*PErA'E|79.^ ¸kԴ)qA{"5^򹛬NGs,~Ed\:D&"N7@}""Ðb"%Ϟ[;2o?_?E% )[L=ȿ$@2i՟xO7pہϢ|9<H1;~ Ԥ/4Lw pwϽ(dpȧ#D4T>H1( /, d DzP ){ elS`Hgg \q.?R͸Z9ǠLP-1E ir?{sTv#xw|U+dowy=8)εrJ$|)"GE5VXX4;Ɖ(-GdJ;Q޻>3}?Ln_Z~ (-ƊP_qQG2+3>Esiw@7'ۘ"E-&0XDXW TD܅xQJ-Q!eY) IM 8@nCNطP!2H!"9݁ĦX8"5( =jlX١Ws[+>[ȸ~5z}v|DoHb!5M.0:ws}84JoϽ"aR,ȸB6|S~̘qeEbVۤWh"<-l&#EE "T5jU~=tJ;JtF _pVD꾥hSP_ga)>X JEۚsWhLb W>{.JqȿnM)R,ZH ߠGp;RXɢQjbѓ;'f\ጢΓKخ P$f >fbgrz)R[X)2MAb#Q4Ey5r 9ĢO\賷 {}6 vIb!FH4C|nEYFP>FɨN7dX;l"b(W&WD Wh*L۟V޳v[%}v20 ˸BעN/UW %D^K"BHx1r`[T)ॢe\aED<27d\9(X^}F2((a4"Wd\Nx=mJTGuwTbE+|rW}I~Qn*g,ReEʸB#n)RX&SX$]aGN)=>[GXE@ "G)} *x>7_D^B/K 8賃2 UJݛ:H"%b)R,"0'\[mNJ>;76t.HU[e]d}8ª 싊\kD޺}g󵛊J, Q'ou`pg0)R,HM)R,:h&M߁EhΓ(M#B!u)V_!bY E ʂPkslji*Tϯx K""H賣3pBL1 IZ0͡UOke\> [M*[-Wnڵ*"ST"MELvk@./+<4/RH1&SHX! G!@`ϢGiHƨvJƙ hکfoX4zeࠢώ}Bay4['}ӌ+܍G~b)R,H)R,n3p_g}vpNZG|aF]ɖE6 KH5[ EGk 2U?R"ŢTK"b+ !SE7νR*PP݊>;z,섲-줌+C ``g?א"EHbAVCi EX`Ow(q@ TWkǴ+,-쨍6A+Nr+섈(؋*("sעϾ??+E)K"b+\Lvj>>cyC`i\q<\xu͛v߉ l̙#QZHz xÎ)3)#s`g. J8/.XWycuT?jp)pyg_NUEGq+EMH/!UROr&K|PLWh!f|UԬ!j||(R?L RSC7bsSH#%b)R_w'Mx8 S_ j L-]tğ\s1md>L H/bA7 E% ={&U6MӐ+lm&?5jߌ+lכ{&r0^)ESgu)R>RE,Ee/{Pe Qd܈Y<dEepfBXs0lW_(uDO|;8sO"EH1q5z'wd\z`rg?Ӄaܢ Km[]f=l? -6\;&uo2|wg5'V ȸZȸBPُASHb&S/x8h,{#1}6Y(0`'+]N8VDy>mfW.nN@Y˸B˹)R#HWH1q|lxND[;ii ;[oDYw+ttMqU4}c9N~q DfC}&oE\ GNrCg}| )RH1[D,E+d]PR~({fOl95SsTXβ5o>;% 6{^q=R<2*}7ʼ.5R MKHbA Kb()0֗wi? ovYyנrAyEѓI73ppD*Wsx𦝧f;l: ?5޶v""es;y 3>;6 ۢ2ASez~ 3"EH1ᑉm sô". Q>ۯ2= paݴdFjU>ѯ=m "'#>;e^^s>>[eg{)RX`HXx';' ȤV B%Aʟ2&_!Ctog#(26[]suG ݕq;>;a_@g_˸Bwx|q(=Ȥָ)R0+R(l]g/D)$<%{F@ό+t% I]/Ջ~l?XvmQvV \=.saA 0q+ <\q2d6/EK:RE,E+t ?@_@~tm[d\aTx;YgKi6]c&x qv= ˢ98۶I"E4}E WXDl$*@`עL9VH|J <`9[V,lK$e\L;'1E"[#G#pк賣vs^pML"ŒHP_)9ϦoDvCN5>*d\%r ;y۶-Hz^Cg'-pQ5Дz.e~v)RH1ϑ),>&!#X d>., r~⊈\=kN̸9Wheʢ/KX$q!n+T=N"EyY?" "'|n̂nϟ0DJ+ ) +" v썈Vp!JK|Ц!geQZfHY{_R3 th txKjJ(Jum)R,aHXE+7mɟ}H ɶ++[Wh D`xx2"Sf7݊L ?{>;9  +:K,}g"R[f+@h~1EK.R"bQ`dbdA7&Hy+GdȢO8*`r2s2png2cpni֞OǡJ46E$WhVC9SHb#M_bBƫDy|M1Q>WWȸ(oYD0&!gSD2"s{9E}{WCYo/p(؃}LM/;C>ng)?zϫk ]is/tWe\ բ\ '"aR _@ q݋>;u~\‚qQ1(WۻEq;{qȂke)$D,łFG`)T| #gAl=|N M@$({szBљ1I%= SߥE)/Be '#%E2_N͸ @~0oA TH9=xҾknnj+<l|haSHb#%b)4D_Ϡ?߇#Є t辯T1E\yu!s DÊ}}ˌ+lޖ ̐ľǙ(m56hq:ۦ)d=植:7]PaW8x 2kg@`{DERHb#ͬbAmnd"zE=ԟ%<]:.FN, uL[}}}C%8d^lj>dg]]_FH )~DYSDDɸrs؞gQ06B}_a^ɂI"E%"bAr_7E70  %1FƍRKsmK]4udb52HF|ydBоk E}' MS~(1l R'#rQq}>EɸBe+yxE-d\am:"]H"W6GUqh>4sLvD-ȸBÐW6R.l] q"0ۄ~aDr_HDg9MCEODte{eQىs >9x6$EK4R"bAb;F a<9Ll-SWD>~DQma%6Ďy+qtSpD@<:#@k|#Q.Qnա\4BY#<x= U s'RHbEJR,HY,x 8ECހ \'vGG秼L"NT >}>H > #?O# p|gJmM7B (աw2V~ %> xU4AJQ>'Ȍ-R#e#`B=@.b賯t?E3 #y92θYB mPwg̸/ F[tO& »*T^'1W@ ځ(c`.$̰+ lHX) R"bA4?sgaAw[gIFx[e AiTنۂTqlLB9E>zYo~(Zs3๢ϖ.{-yYݐ)R,HXǢ||ntP^7Z=3/@~RQOQV̀'|nKiRm55 }vhE}t JB:p6n"/C~xSo20̕ȉ͢>;.qDg'G)RH1ϐKJ6]f`8/F l (`5G=\;mNGUAg0lX jQ]IH RV,e?{g\acDqgf\a+m4{)R, HXȩ|m` E\}E}4o+AIMFF9R6}/Sۺ6thBF- Q>7Ў,k+(l+'9rymqEq^?KZke\_HkE%(y7ȜEge\aD )숲TޔNd\ὢN);cV][SHbqCJR,0Dx7:h~Q>7l` !±4"fMPd?e߰Pd]U|%;^bIH F =a~3 x"g#P=$ R#Bo~Ab5T6D7Fi,^s'X*ʝ0!HhK"E%tb!*}0"渨H);~GDmj!N2J~3RB0g6c0 l<D >{`GEo-?׆l_R}&+9)RX,*b)4DGG\ #{c#g8lǠ sf ^'BA #GMPVj3P2.WL +2PLy RF"3(XE7 S*O5,w,E)0R"bA4OQs0>iAD+"Qѽ1i~H(Qggq+QtEs/X{]Q ɽPc)yU/- i3@/Tɠ Tgca3eH"Œ4b)`!Uj EJrkCE!e sܷ#SQ>7m]_@.FџuVTrggM`hg˸B+`-bEO좌+܅2O@Cm;<6E)R4TKH  d~Dm359r8LcMPjDښ¤܇sr|_ )]Q]> :賾2Pq.E"0ɸ`%~\!Sh#䇶 MA6(q _)RH*b)0O>(sQ>7lf(Zr "W/ZU %EZ`e4d%D}{}'_s q<" d?Էk#QHA83!G&AtCOHe[]^K+t}v9E)ŘTK o;?A6@_3AvC)޴WCũꄔP@ƛ"hHߐ? (H&EqE 3 T f|)Qj[D^HEF̣b Ui>c܇Ձʢ)қ"E+R"bA^4|Gֺ(l4OF'a5r6AN꟣hQ=$p8DtA(Gɸ+(8DlG[X~!lYt)쨌+4B)F!B|'X_QPDd\ѽ)dfʋ&W+Mښ"E%D,łDS4g2!F3d0% muw%D8d2Ӄ0>nC;!Y|EaA!Fdj ? Fs>$}x' iq|g_BfIH=%`}E[R"1p 1#2HKIX)R!%b)$>C}"9v0^1}f @<5I xm)" {n|q\N"%la쉐AkFahmեպL0,K[Ń^QaMJ%-Ah2.m;HH"[Bk'.'zٗZ'qEIV\ \QVA~N%ݍяeu.@%>ٕ#ڒ1puBI/ 7cމ2fϣUM{pOT 0G~(6}j^39v Aꏚ.G+N +&FYab O4!) ^:zY}‚seuIm^  ( p*=`P (p>MEM@pʗ(yʂpF̚i[2Wk_>Pc( OhdŇ xU\ ܞqc(b`v5/B}a#^{5U/OWZ2bQtW@DiQNWO W3Us fpʞQP0MyR4*{}x Ɋ;熍ovKbt+ͳ ((aWY6Qn^CЎOkA=s#P7tZ\jg_GeЍsWeglCp fʹjV ECYAXGGQcz&({<mqٗPyߣ.^Vg6Oß*Q@`݃VA6ncPlVXj F#Qpff5S`X+/UxGs4chd%YqV(KPoU(KgwRj[ov]2B6 AOq b,QP[,03YߚecC&fh #ɊÔdva3n>.u~˟CcAGYD-]jTZKάէ>bLLf^?Eo|JTr-Y@̚&O!˵pʦJSʖC.Ɋ@EHwNb`=qA]t#p+p?*Kb~$+O$+^v3рcb+QmH%DJT;493:'3NŁ5] *>(^6iSPl|ㆬ~h>s-C-ތy8)ɊuW@}j:zY]T/7jtn2^WZ-8 #oqcu853o 7*Qd7 533Y1p|(ɊA%WQ ruƳw0:~8˥dy6t$F͸{o{!?eEHYJTx^V/,̬#o M/x#*$ɊhK/CX[ј-Ph1:*ut(04;+?m]X}^VgUhAœ0[nE ^}%*е$4-Bee(JT3=931hv@WKƍ;;< Tl'Ji @[',fh%yO?`nFxVchhQ.-)۪zB4&O)kڎj܋AZ5~ \%̯Oh̬3s f(k\D+~<%Yq Իi]J}Q0(OڽxQm0wU; S*) .^@ߛeJo6 @̺&iEF9`i@A!hߢQ )(5!(5xm*j ɊU|gdgQ_F(9y zYwzY{ -.Bp+O@=w۔&̺,cu&IVd}(5/Fn%Yq$* ʔ*m~H4~9ɊPv' y8C /碕4^ϽDQuJuQ4>q`zYYD3O⌘uUwQ($+Nb6fFi<8r&|P 43뒶odņ2jn ܛdQ\1bI;euAޏuP̬sF:Д8e@[BhE0XT\F/LF^ jRy1&bkQsBxUh/kQP 쟧}k򼻚JT2Á/&$3rF:Wע :'vE ʔ@D[P?h k?MEa%hŻP)@y?#?l "V,zdE5]G.{2̬N#EPwGt<%YqLxI4u ʪMx*ca(КJϣKϡhA@ܣ4~M¸n7+$+C%(+ 1Pbh;рK2_90enG{YV7Phmyfh{7}ǩՅ躚u{ĬS {ONk|dŞ(w(+dEt/8` *U;b}.({85ߍ^#F B"}h1\>nݲd13ѐi(M(Y2`3QcV(*p(u * V14~ x`-h<ƒa@ h+K6sfU 7PvJ8y۠W}bDPٖ]dE?*$+&YqYYf}8 Mf6Ƈ~ Z{L(0{M4O㝇J-zGžCDC]G.D%hN!;y[off3bUFL+7fGc1.GYhO9-9o%kA{R@+Gd63N%iMud(: q7O Ɋt9eǖ RW[/o­1PvESOD+L\ʙ;gGxbmuNΈY4~M_-AZA"Dsȶ]aͽ _PlT {8Oi|$X431.D7+63F&Yew%Y)ګr/ |]-CM9gR'y߶V̺7[dH1ۨxz eɆL<ۡ_f7OcC23挘uwX1[VG^x)0-Ɋ+SY@̺4u=GwBX`ffƹ4i^XI?fffk1333& 333&q ffff$̚āY83333kbffffM@̬I51333&q ffff$̚āY83333kbffffM@̬I51333&q ffff$̚āY83333kbffffM@̬I55IENDB`openTSNE-0.6.1/docs/source/examples/02_advanced_usage/output_26_0.png000066400000000000000000004010041413546205200252460ustar00rootroot00000000000000PNG  IHDRb6;9tEXtSoftwareMatplotlib version3.3.3, https://matplotlib.org/ȗ pHYs  IDATxwxކIwTĂ6\XxlcX'}V2XmĆFĶD(w|<%x)bzES$Qk+GtxS\ -K?\c988%?_ǥY,/XG)Gz ]] 8pyP.K\D p9rfÁ4J 4Gc?,Ų"#f[R9^( 7 CoPqou6j Q r~ǥsWHv3U[>U8EvX,u,Ws `c{>pN] )5Zf}R-qG 9UPMZ]݉{Pϱ@ `?ԍڛD`gTBbY X!fVA) HܫggԂ/p70)ܮL;EG*0P r&DFktY 8."bX"}y@⸴+_k=_^^щ(A-*FBi6:u1Y)qN+qNzϦqi %-g8E]q=\bT:b%qieST^z!0)j`n%SQ?x ͺ2{^8x 'P8qrStA.ڠpekܷm{Y,+,KL{PrTyDG]-u?g| d4\`kKS4p-F!Ɠ6 (~=ymOfxFNU`Z,B)j& ;lU܊PtFB>r 5 %5em_A"8[ܸ\ge0Psھ>ℝssڷzL7X,ubUÓB/pJ?9A?ǥhxKQ1ݗv`$<UO HT}aDXt|C@ raIO#t_'vw訊/VbCm(q"i>ʵ*GSGYq\V/8E=kP|PޖJ*>-KǙuwD}>Aqf?ߧ8>ݗ4bsSt1hq\:jbX[5i`Pct ]D l((qV; %dq\:M3ݟQOa2@S9wǥ/ F*@3E=X,:vַlД8E>/$h2_O f $v(KZLUtIq\:{<ۋ)Y'фݓP[P ӐQ+0g99ka(,TQpq\:bX,ձB̲AS텄XO=+AS| AUwǥ KƨznzxmfzCkMHg퀛1`lW)jwW^cbǬbXb&6G̲}PrD` r.^(KʬPr%NQ <q9-Cf00|m[ǥC \{e/K0!ZbX=g1VIh}7Z7GU3Pu%eE ]~WM@ uBaQPf'4g%NѹbX264icL9@*=O*q_:%NQ^StBSiN#)j]9:PAhӑ@)|rƢ5-X,JbRǥSǥWZIStpKaFf'k͐{pZS}$rn@p~2bYФRe|[9$y* *;g\wֺP'%Ni(l[$N Q!kzbY6VY,uCC4wSWq\:0FSt(jKr[#uPgQY ~(x9E e*2?Zs$~#X,_Bb_J=P>W[z|=:&6 NDՒGG[ddW1bE8b#ܒ 9wCQ*KeՅUSJ˜owy5@bClŲPu(qNGj|[=0]6z0qն uoa\O5p})bX,+Ć&-Q{_&Ư Qϱм-P&`YCbKr U#֧{ ܑu=bYX!f? Lzg4d%qex %Vɶr9M,8.hmҧ{{(A3 u<bYX!f?:cmz@ST%zufɜhQW9_}Q TJܼMdX,kXodvdQ^ShꢩhrKϫ_.4dXiq\麿TNS 匽TNZ *B?:buXnt0/.@SJ-qһMQHs`AS0bߡ0:DŽ$oDsO 1% " RVh&ݏJzuq\ܜC畗\5m):]iz((qZ=UnǽVX,ub|vqlcʿ%Nэ%N1qXnn#͞nļiX| Z-i+묁kO!7bVY,KE]bqc8ɸY dJ\fhc`Yף|43EsLcY}Bほ{]HbKlCWeiƚ봿ַ׺?OEyOf?_AǥP ۳8/6g滨Ek?AV=,YgdhޣxHŲαBbY1mtT0{tXܽFqǥ;ǥ!rv w[Q\(!r]Ʋӽ} ToݎbXX{DgtiMyAGQNSbmϟ{gQ;$T zqµz ˦#p:b)VY,Kit(nɩ9]>c63gl_|5q^Բ>|`>vwV#KJӋ(qÎEqrʗb=Ieiz#1e]dedI_>v7̯W܎FX B}¶ZL 쾶ا{>g?=ܻ ̵=bY_BbY8u=Gz5:s.v{~OL7}f ˮX nFZ+G@S.Y,4VY,Ks_DZRǥgȩ6z$qtES{ %N%NQ41>7;`ęb/cs,^D&;1 =Y^e̤ha3`Y.M$)'pEhrks|=Ͷӽ"^ܗblXG̲Q#u4-QOhu .GBk Rm<9 V9qiYb>wF g?nڗblHXG̲/ꅾz!ݟVrh$)^J< 0ouS9qi\C[Q4; XmTGsC5S!ZeVJD.]ڗblh8q./\ϵQCv vw7^ ZOvJ}~⸴c- >$yփ֛3 ?wu$ӽMHe_G?nZ,zu,fryA %{h^_셽 j|_Sw5+D9h/G=3PHoMCfh_Y$ڟDnJ`uwVov[a! Cnȍz{]0U(O跦/paRu, =kD }enn@cWw/C-=J&7e rB]/.q ܬ8.QX ٛءӐ98.WGǥWW}t}4@BJ'\|DuobYY "\zyAÀ^(5ͼ>8P"ήapl+EﮔsA?(}wʬ0U?`o4uO5uJ(kNeє]:t׮ِ>Fu_Oǥof'oցFCgH}J'k<`*pLaubYYlh *N;>Ͽ|EPb,)i"= }wDy.kT9.kը4Z N)K0 YZ-JnfFe^n^ǍiN 9>oBq|GQ+kX"$T<]-eB*جݷwΝ|/>z; R`qK \\߯}SS/B]6ǥJBpsfpqJ:yͻ'K6S7uk5OPS tL: M*~larE [,K]`CS Bb^C]G"Qu0#YtYVö4c+N.D^_mۍP1et.oVFg=~׮vzSN90iAF*fh\~_T=;~]?Ntn?&vYWX,U:b^ }w oB \QXyԼB`|e\qS "+ԗjw=sM{To V ޺pvͮi*sg!j0uvlfڬ/?T?xv=a|x9abXV +c Mi (FyAt@Mm#z-C PϮ4wցH A\ 5 KQR(׫pDEwW]aA\T 5UJf91NZ}EVG{u]h#X}^Qz޹0QNJ?&u_Ųb?:MPVM2vi$dym:#al)ෆgΊ+sJ7} Q+bx> a@{@Df۟WL=EwԠuDmLFlnl5fO۵;9uB#_0W,l:URd3䄵BTбH' obYI\7OFLoSLFT'1U1`&u[ǬpEwۧ, KP+QA԰blV#>E/񂨓G76~69-ݙ(pDۙ@jVvDi/:zAW̽g[;|:?gUy,˛o0e >-|u=[VT:YUDn~Bn9ںf͛X,+}j[0blTi10)/-̺ݐS58ݹf4(<7kM @I36ܸ訢2 {C#T [Q͸| F.Y{~W/3c9ۣ=&{Q|Vs7 ͸ڣ0n}wo\I_q:ErYcu08.{?ڵ]u7@-(/5]YD(m(<9d!C+*&aXU"ew1#w{/A[^ (; p(<x K^]cveÍ[ +PcI=; =>G9kS`@S,=D7(:26H̽ |>ݵ6)jǥs`IۦT]ttߧNtl$ˑKx0QrW\;cY&XX,&[8yGU. +!ƆvF :ٯ;pY'jpO b09@cwMfSC kxŽg>TQ(r{z+Ue?TPw_3߀K[/pYÉn)SQnG(tݑJ'7.L2M*l偶 <$B:wPמ^òV4U? ]b p85@t3[K qv*pD㐓v+r*M O  L1OܞQZ[QV܂ܰX3 Z }w}"\#JȨ&{VӦl}[_<$l/{Atd՘`0Q6 J'O+p2kUI=Vʅ^c~&nbX!w }trD)V9KΘdہBfэ}3rE Ch:IH@^w"-ȽjܦRe:En6H6CdžDkǢ-)^rrk-P5NZEE1'wxk_&쓷p7w-w_آs^Deo6܏(m\ʿK["nalϵ0Q=(\rŎ@uD߽u>QbYX!VxA UBcZ:L}/~A!kܒ_Vкa{[ ݼG{f\zyjyHxAI} %f3 oM_ͶP.4"5C7(,׬sÛHܯF=^݁i(/> R_B^` `YnFgt(u:w/>zM-r\t-ʛ_ҡvǥem@I9zQ(ǣgK*<0QVcDY:N3@ߑU&Ng3 eܓblxX!Vu `%HHgzb] @y\7/ JtiG9q6#F'P@7ӬsDhb UE> }^ x- ^ܡ b9}PDFfg"A9 -Jar :`'3oOYUDg Eϻ++my-NQV=Z X?)t)bDBn.^I7!A ݲK2sֈxeHBEkjwxe} 9Gj0?2#!ݤ4 q"ao/F*ֿ /FP{'ަ(DDBR%{"/ }Dn#Fș`5MByH8Fb1 ;60VOm^;漌BA+K $dP՟*)Va;$v Q~H5m_7p]:ΜKFfV F;fvC²rL' (x0Q&!d0U\DJJ'DV]kbY?+!Uz pP12sB^[HDOwC55n!y̍:>ŭh>Hëj ܄%ǣbQ5? /6B 󑀻fWRy܇hs?[[vq}>-Y6wk=?{\3w1r !:y 9Jw[Vc_ɮ!|0Qv*Cf ek3YɇPD-o醦 k\ eQZ, 눭[# jh)Fi%kByYM#t- 5Fj<OVq:X]F#2%tW; H mn( 7RNkBCsГWtGB$~@<݀OPd#s,"%wF-FsITE$Qqw+ {w>󐋊 jtZrkjDӑSf7E N۱&n\ 9~XD@aT:UwA'F7PH4e4l^ܫhlLt1zYD'k!rZEz9i]({9yB}:CSn2PXwC!6H,X6A)^mKUCK΅D[yAsO'h_/\kcgl^S;(|67էtN#C߽O DY\(]([>GЃT:٤NX37OAtQ*lJ'L;)L݃f^B?u!NvY]& w鏜Qr z>St.8B=]/$jaIb($rƠ@0׎k$ƣɕg#'n_+qVNhʧvp[T3 :勵Dfܪ%_QH Fݬj qQuk%=oƼV'v8uv![$G؝,\T:$0R46gODT9 Yn3S}921xfš~Q};,kwhc רHT퇜/ka~$'6Spc>O/@q^q sd9ʃ[eŒg"(W urܱvH9f{Ph2/B'FE0 IH毨фq',o5tGrOk0yms?9+ǮGTz®( }^1 u7e;z`_{Z/P( c,V>34~E t m}zIt}jH(D臾;۟3StDce Ѥwm{%C7Q7=O̽odˎG2arFU@\DD'䂝‰9q`軏3Dw ja!hu(_ߚ?ߠ !Q* n~  5a+F]$Q/(d <Ĵ2Daoh&_o4qUR( Ntrzϼ?z@0Q*&V&&-A@1 I.@[ 7g.4'yAMC j mE7޿M,zi$ꝕv&W{ ǫ1 =@S ѕb MGm1A뵬آծswa<`=MFYg3։# tS4_^H)^m&Qrz=3Ψ3kfO W,2z939͹f3r螩~&^5GLPvw^)Lp:/ jQ7e4< P(W4sDF gI0Q֯]Q^>HL^eEb[΀kz;m $ 64>>3N } [rHD嘩}w1[#'L33l7Cr 8kn$a#ntiy*4G!r׊B]d(Dz4NC PQ%YC=ܺN@l$w7yע\(<;G 1FYAyW!T!! ,'ڲ6QS!q|6_o^(lz9i4rOFsCVT:#C#43ӤbX`ZƄƄ'[Z#A$^#''LA'jxAtuy-0$DxADΧxAufJ(s}4ͧc[4qH5ABa, yN@\4>HL`f89wyAtj1 x!C. '݅^ Aa$A 9?9LϚqB7q(wlG+\&޺ I e"a~ښ|Ƥ[ٶP(,/e`݀B]. 3d@* ҼZ(5c¥Kq*c%(ߩtrctMVaVŲaX +CaSK3 9M'Pڗ(Wzf5Eb2|$17b\x_A&>%ƿnCPk^+rLOFX>E6t LSL3vwiBf9^i_Pdm&*L3 Gj 09`Ϙǚ)@u7E/BykOB*,ݾjlf:@+Dׄ{.fމU0QɓQQGP)L4n7=RdJ_Wa09N^\(:cX,>V>PlT8 HlP}} B؋(l\s j5(T$.G9b!|8l~=o;,r#`;>D4׌(ݏ ,WŽͱ€vfPU: ?pGbsy#[#jS?C-h< \ ҡ_#kܒn!1qy<# J{ADuDmkZ5w;=5/9Z{`s$GH4DIm ܌ J(C"h;aQuDŘICߍPۆLBFD\Ƶ v2j$9Y̼VtLGB $61cm[srD[9 j1sLpEˇkoCLbX[ lD? w q{[`q51B]ѿE*9 ݐ tjҚF+j逡Go3kF`p=ꀄJ%Pp`G/E(GwhrFyd9OFt}ͨEysQ5/grL}|f$Egg#*zmB 8EsS%8vNE T7`>LÉ(<_?1c=,t-X,}>@'эQ-m^gmo6P5c[(+&gȱ: ]1v;Ϝۮ!| %ś(TXPqY nErՌe# Sf {vBk'mQDg^儾5 ^^A(W$,;Ӊf_G i~}w r~ABEZO T!U ݽ z ڠ{l\ccDU@,ˆujwܨ M= VN372/v ;"10%5F!^M3MPMz;&=Q/<՜nydϐDeHLDBl+tFUh|K"x 㽅(왃Z |iBu6ƜS+Ṕ8ƢJ/!q;soT<gQw9ۢ\;uor>: ! $(sV6A_l1{4C!iv{uNsQj@/gQDXBl2 8eڠXB 6߃f"t 5O!`e 1%ru^@Bf7{6wh/'v<[3ۛ'&@^(k^w} ][wզPJyE-_7 ˥- nHx`"^E70G7эUȁۓ@\OF$PBᷯPH;jTr;0(wLuY??sRc!r}|Vb(rB4/FFⲟVfKB,6c[s'#qyʷ{MVyo: Q0f"l fK jQ CQBC s֨qiU;pvj1yu Zkp1j#N3Cښ |Z([J'AIw&V9o0N@߷r:*^?bk1(v= 5%˓hj}e3zAq B mxx5a}&~7-@;"B449K"A/r8 Hhe9]ѓ0w'O? =+p9r.4&X#gyo NC)TMj~ UK5, jDjBp T慾DNX)<iҿWAC4E] VlпWiHL<ڽ蕙u#N^#L2pt#Q*kJLtc lѿмs$f-Q"L(z—S%| K[tX'ys߽jipjhz޹fQB|z]C j;t1w717uXGբDe&S)u! ,tc|Qw t9YI_4 4MM /0HPZT4A?۶,3 U=S^G߻\C*<]7e4;o@hDUbYO/6GnL.}`B{W:n^eZ`dH$Q1(;!v]bD}(|m? B/h3/# m?mcr\ͩ=t$T_Y$8?1ckv^uF.ًȥ"/ Pt'6jr' }V$1LGyb (/gֱNF+TMHD- :mHHtW2u\D٬Dȕ,Ep7NJ'Wgՙ50`*0 eqa ]ïϥɯSd'h 17NR({80Q vQkx]>WR&bl@XGlɜ÷Pn M aS#GYr8nf{?7 g*3-T*H_u9:;`u3&,rȶB%]mRdF rb8+^cHxG^;T̼? }wE+<\(wAIvDé'bX!; V IeZ)Qr~&d–^E()=?Y>3yC$,Aa]T/@:^+t|D-.~D.R'$>GM'# tdEcNV(G{FHTCykP>i@[qb!1u2WPung>ȩ 6 /"gpHd|N| _hj <)3D9.ir+A)hrl߆ 9\_ZU,=GݿWz)Fc,Ew# TU0QRd{tݝ5%//r.T6c"-u&FɍP%fڲ6J'}W(0QV2&kE.w9PJ'uQqjXj\ jgk2TOF`4伇! UMw5 98Gd>gӚbiny }wtעP=WحfH=Lh)A¥(D.*H9+ɽt[789GDy3n1rzׅ/w+ |VRէ w i{>Tvl[ڄ "$JAorFQ{w4E3s:~G9m6["$]emҌ6=蘆 {?:Ag&k 0QvVaBa'32&(L4eN(,>c ߎC 7 =R~qW?wB]ղ`Uc7n6YW˫-?kr I%!w0ſԟ9KoPr6H8hI[̀qC/:%uyUMrF"?酕§z ]P.)>Ѧr:pr.46p&!EF[~DnT>ʽz͛%jv3a.CQ( |(7k`F gY8ⰅH`EB3:AX!x=3o{mAY˚҄nG\s kk}^3 ۡ ^s6}]Rdz 9Fp-DNt\ =PǸkβ*Qk"o?A kfY9=Ԟ* q|64 ;?ݾVɨ;[({\c"tC7H?`twM(g'!Cmȵin?nPHq0q,8o }=To$+6J͡^ȜtK9 QMW$0=nGH~QU>^BSCWLηvQ39g 7/"s^*T=bm\Qf5l $7zm kѽ(q8E js{BĿȯHl)Xy}ЃMYVT:ͬ1}+LerCky )zy5NޞJ'vBqȥ u}q ==g^q~8HqrqҎ rg ܑ|8|8Nvy,8Ǚ883q=8Yq`Sq:S8fw38}is83fǹ Zrjj ?Q(L'uQ()|G$}y/pJ$FFf["tVpƼ _Q[xx zfEHp}gnjw$@Y Trkr!];ݸGw{r#Bx>FaԆH@]gハ6SfCQR^=@2 U19Ap23Dbw!GSCh 1^uEUPaƔ8E{8EFl^{ݿWA=G/BeH(UtM] eTՕ!NvIdeirzȘimJUoM]QXcDJKzQq:[{;Ӯ*z,3]Pѿ738! x8WKc8wB=_9l8(:qz_o@8x AbCqw$Ԯ %.ȵ3(wt5 Q(KF(QӁ?5@ڿr.暰sff? a 7ȹy ݑ)wj8^^kT?; ߁wyA@BDW!!5QU%Ĭ8?5Wܶ.8s$ jP~`${t$Fޏ('4Yyg=!#,lbY7bniHD. ?ҿW)HhUpi˺e^뙙`l4Z(J'f)lN@ڹT: gNZD۲KqZByU+LUo$mp2: !u2mxqFP5% z8MgLq&SAU>tuaܬmm8K[9$9Y <bilrO#n.j,F; zd V^B.ϟ_C7}g@-yf |4X~w^$PGjI[c 7}Dд<7MwJ0C*n3͸{DOyAt> FrST}mʸG}Qs$pnCb2daҙep] x8@}Y:c*3DڍN@7kDCT&0ЍV$8AbI#~oGn"؈b^ -{U QK.*SQ8zzNptU ޿WniQ[&ʖNۡ' e$>*Hk%ltL8tH',+~T_5?l9}.@iqu*wa엿lɲA8N+[81bqzEf-^Jd}Wǂ8+^yǙĊ} mUs8Z88NBt_<{^`ؚq3 GOܰɡ֔hMA!(AI2`!\d09E9(,r.5+=27hBv0ko;||d}o/(DǢv^1˴F$^"}=r2: v]{AќaOc/Q5ګHd@.FFnD w05kͧCtÛ¹]QPM>Ɍt—o!ꡛ4gyEq*u|/K8EAiLÊq~ CPu'Y,2O9(pzp8}kiT:yr.c'у񅉲Rfz 5tqn4y^̾~)4$h{9]3Vb;q|AqQ24G ͽle+|F{mՖ 4Fd l8^qeB[q\C88~q_bkƛt8 1yø!߳5;};9kȕ*{e7AE"hO3l@?̯ dD%xA fk3f# \Ju(!#$BG7ow/4=Dw{At* ށW8tDשf#fBwxAt6`̌fMLCV`z/E&,z0r g'P^\wxAt3KDI> A 3f {3p~־k[ qieI 3!`Q{^ؿW$k ]߫wgeC*Hv9zƚc)LqYʄMt2yniSb`Pcu^r eK*S eɷй"+9*K\-/N4 Ln^sg:gexqu ϬmDUTj<rx9k̺W`HEa1 5W]r }w^jnJnU"7'4fT9:QUqjBSq00k37WpҤ&eUyɠn߫=G6߫Ga{ҽ:h{$  f9X #j>2VG#v597YekgUJ'K p& NT,v|vШ5P(8>ײs??z}y/㷨oEqܤ8C=!)(겼cY~ﮪ(׶g4cX-`ryDGfI]2J=wBM\oCa}zF-du6aP؎Gq:q(z3pDSƁ^GO#z6fߙ}3Hds^jr:yAt\ d<3 Bf{QUIxp) hcw{,Ygbx;% JuQFxr/@UKB(w2ۛk,1pifeʈ\."qYrsP[nBq\:̜9\5;rsstv^^H B$jQ!W36LXjV/'OBJ'!Q80Qvs*}۠7aD8FfBf>`'?/"oGۊ)ccha^ʲX!V ; P- Eɕ1 K.CR>[~CIrTiKVJ$?& ?{֞oCjׇ߅; .<;ʏr6yc; 1i:hF#ax E7QHuFaf?Ld$4g!e9V }8,iqf`=& o34\}w0!^FnGGN[sn'#oFNu%tPzg~ Ljkn}49n[ aVL-kWŲl$HFWOUa|ALV.NGXEbDR"t80QVYlDT:=`Lj9A.a6K~o/QP8<~?_(>( oY;,+jP b1AW& 6(>rК 'raA3H`܉$Q"\iv(uJ.`SOt 5ef|DŽYoBȩH~^15E<$ENF3O]Bsә?QËfUŴ@R[$2-D w> }8#.BW;%«r-$6\4K4/.1cBMg; 2_PfsQR^.כmt"S\3U{ k#ZB &ٽ]?Q_ e `inEB ۸<~BH}Y˴@盅ea]Ϩ2t]^V(icWU[, +jPVܤYnDܘ|(tHeW%Q;$j:f0TMsS :p XvhR~V #JR#2yU 듑n5%C"Ll9r$*|V>^Dա%z FܜՒۏ7do/;Td|SUn>^@P>H_BP[ UsWHD.7Q8zks, +$Ժv$ eϊlDSBlmr$PJ' e?o{A>؄w@=~C]#)tD?bB EJ݂\אKߑY?8rS]rF K=YyAȥyKc 0@c 㟨H|lQH\⩓?T5֜crKr0 P|+-2f9[]- *BE ku$S#r:(3HB}=d9ޜ˩2^EC5_stjK@-P,3r)`ȟ9[o6y~+-PˢNO/>}w֊_oWk^qS۪ٟkD٣5QF n?[韅.gH}>3I!60QM@[S)q$i>(m'jQt-k#S|a췵Ͽ=hӋKV\OZX[RDq\S;ܵq߱Yɱ\<yZ~*^y&7P8L)#/7|9G"I̿DPcM軱I>/Ps ?ruKzAp2\9zٜ̺P_oэ k툄p͹ MbDڛȹ>FQǜ˽ U&Ҙh?YkNw9R4>S[N o~?:{1Cjěf]x^$n[[?`y4 Zn`3}Wԛ̱VJ'T:] P([gat$AU_&t銖Y#Qx3z@zҌѲs2],wmM/:We`E?&di`˴;04!Q70BB'ܠj- gC߽'kB#Bw#צ hn n{:$(G{AtÐ0 2!nb/3B G=#!w)ʉ`9ݬ B}%ş }M3^fTo,SA(jmB9C˰}Z(w4}0r0'(}LNOL~ݳgg/9Mw2r6Éz$NY(]âߡ_2q'u#G7Fͩt2 wq+ȩ 4OmQoA+Q3dy+rϡմ8&7 _A_j8?߼7@ߖ8_1=Vkqjb 8S8>qCPT>AyVsǹ Eq\NbK <ݚ,JT>~POB|J\nl3yzT~F£#NCB\һ7qʷj4(~y v9}ЗDeHނrk,DՁg!a-4hkT8aMs NG@w?ȄY服*|0z9x݀MunyL {^Jݒ 9Tlg'XG߯GOEnЭ<ĖSMe7r+o.+HW6dVwdQ!48&cDa~Sy_DY"0 UJ'/j{Ӌϲ^sAǙ8naoǷ; 4sq8qF_;sm p̼vGi4qffyt:sp@S܏q<y/lq8Nuq6$Eӵ<_QkX,(~+H @_MSe m1b$vC"'z4^tkg$~(,Rn83B} ېlxP4$r ݑ8>C"1^~;A軛tFM}^E7 S~軙i@nӭm^DGQL"D  +C :׸j{1>f(Y-[F9;$ʧka>9(d6pBTyL$DF7V(P5EUs3OkGc$uPgOvݘqM:2M_&xiJ'{ DY%ފ/м;-3aVƿܤA zH=6'=EHVnĈ4ߌ#W({}%t5Aם\!t!U9guɨ0^yLf lgqJ;RD3yx7;t&+̃YFxL'qWm8}PbKV!^\mkY/uBwyA4xF 3ˣn{ݘ5cݰs#,i4zv3-1M[3F_(칫(j[f, *8P$3xy3a\a3L{ mê $(?C.uZDUYH e\i>~DUfl홨1 T8W_n!b$nGoyAt7J>lwf3r.A>fUN)$Os3>D AD ؏qk3?fe_4I EQ6;4 9bSB93wg{Atg'Dx@gP폡VF ^ :Q:a~N1X/+X4mr~`]UOR9_VT:yY',j?YAqL__7{JނzwwϏ^1;іp&Gs95 T:?Bt-oEݜym0 zn8^_j$?ǩ~Sɏ㸦ρ yI$7:bvh2E l5=D<8Ζqw1?C:vG[CBwpO7\y^}nE?ߘP5\4f{Z\@w%yAk9= xbʀƿv:jF#dSB3!s99h>aﯷ47pDgxAy:̌C~.6Z0Qbh/`D/ B%Z:^v^U :PH} _AlS~F!7-E7~覹p}GYa UbD\F"1sVdHnkɜB$}wMJkj\)xA4 q늁{)pwO"K旼sf'wzs$Utq eR60{7v:hpFGNێ6ot(tvB=iڶ:` }ؤC1Ԇ0: FmfУgV| Y,u'Q\qwtcO|l@OC8|8,BnpE0k;uƙd3 r]z uK\=SuqٸӐȹz@KMn*J Q[^ g7k5lV<4[ gzʹ}qB7cQ.ٯHt4)`~Vye7jBT= PBsv~04mMj°cQ8f(y4} W9**hѾaܟ'̢S\ːwz֦$]+GNh2Z٘QLO5k^wkET`B7ۭ|/F,k uEx5 PR(D0QVJ'O6v톪fOA?P%/tr;reza|0+ ^t0=?o=T:&2Nsl*ʁ*]?wA^GPX;l6wMMHoɻrr+Tj{8>@5ΰ}{fֻ UW;ZA8?Aթ)bBldbt 0i†-q[cB Q " SZKd حdl'rC5:"Lo*/rtnnwZ,ѴgQBQH ZsA?(=1? dDvl <7QEVXoD" rM*q3=qכtxAf!\iA!C JY9C4a{o}p2 5TϡzqH(rD}w;7zӣg,#-PfSTYҜ4 sAi1( JGʦ4' I6G5 v4iD>{A Qf(|8U*nYMnj5hz,/NCC]ckadDpڵvYǰdt/tpЍlڿkNN@"-w,Iu KW/zyWT^vϻgtbBl`€w!Qtr ݃s_v uR!:vq6Gd4Ca0 #Ԍ@ L }w[r+$&uo1R'R@](t v6)#Jg{os'D=P7ӑP Pyw]Ӗ}ty/Q$$~% r }w|'Gڳj&G:HлtrT:yL*0sAuOJvqc'`η[Bvǘ^E/(ËP&tȍO#/hy_1?dL/vTI3vޢz~?~zfut-˘"\–(f:~D[wj-FmXCWs5=r^Gdb8FJv$r$3y0 r;-k9p7dЃT:y(kT:9=4wLy+b"N^P§uaz8|2۳,+Lw?2^v]ɲcغ8S%/wea,F?܁mnM q(= H0 Aʙ: Ug~k܆%7Ek}&krAׇh̶H=/Jp9Or*c5U5Y}8Ȍ7$>BbBh/E5&zuCDO -O/xSguThCԇj6Pup =GM,>N5$* = bFh}"#Q0cJdD?lװ՟U}i=gY> ewH5~&&r#M(Se;9osj+9lP8 6n->䆣:J'@^O6c&0&C'-Y(WmLJB7`BalLY7Fo}gs̟9DZa}qx%q;+\_"96V}J, _bKau@y'/gQ(KtBbd0z>4j! }PXj8S Tgf,5ސ }QR| ?`J;\̔*aQNU[M%e. \QSLf^bbs " CP8jaHMէTxsPGcƾ"SY4S[4k~`]hkFj`׫Aw|h\U֒XO]ya=rY",N6@g'ѯ%D.Ho 90QFȱ=~_Ts膚_Y U^J'O(L-7ToY/hꪚ96GN qbOq|iBuqeCZm*B]ݯ<u0GUp $8ޠV+ƢV_ԯjv<ި걲&G˴knܟƏ%`Dw"2E4^<7'r3^k=}e6d¢G?M2DUcMB]P Dd0cQruv19_rkLU/g56C?PPͺ9(T< 6C٨%B:2{Ig '8ˆAT0`F&-gPP{Q(J'wB7^!C=a.3R(^F7pw ꍷ=mn%ICq~DpXv8.7g{qq]8&Yl|8.q/518PJȉqp<q73|Ȳq3[!`}_~ zr?̎H="fz C}Ȉ&1{th wfP5Q(q)qF4-v=sroS]܎vY2A/ִߐn=)i^<fM61`X*| pz;<{I!TQǺ֟}l^"^c{w0eEҫt$vY64s'O)ws{JEۡw+JiާH1!\p.aV2}kz0\8Xľ&+^q0be\N0˘{v@wR 2O&^LaD 8#w:ZTw@g#3&cK)_7>~U;,h8LF>,t>g[~>>n@F`1WL{SҮ1X̏^C 63#XE~f!;0+m }O Y|giT aCSxX>ǞV`^Py刦NqX?{ g0 Ci\ߥ*0}pXmq[Nv [B_hev䖳jw:ӀgӾL2Cتbr}=zz+xcngYF~32,)kULcgR3ZSob`0("1EB/@W {&$3"Y~Jb@yP^; YeJᐬmK|3?;th2P2;e'uoܖ(f_ď*MZP $x[RRF}QM>L,I9&큗WV$QhP\X>V-~ܷrs.Ih8` mQ05vόqŅJEG K}2gaLXcS0 pXr(:>dER*ka @ p|bk.'Mxl1T hgċ!\vסߍe&oᮊGJ=)d1Ei%"/K+r2-ÂXV0Nː/͇R-ȿRQ9EȏD>eR|VˏV h 2A6C!(0 KIQ\լl-i/ĺXYW]S)I9@GI!r `7G70u(0[bOY񘂘V%#ڃwtmΛUY(p+:K)J0̮[C7g(Ku:%͆¹:MY b*KYL$6F D@nAQ[O"$Qt<"ޫUqa@hGl5[mv: gUi]s n$= ؾ$QT%BܞG O?^p;XHyYve%tAJdE Ba(w#%}12G,$ƣ9[Q?׉ KDM;mNV( G+DolLEvV%iQٳGGWy (=L};;Mqa+ZOgwޮZYj_ W"m}Ϊ|cǻ^p-J5:"Oft;L3!<-RL^p?by49 ,A@;pZF21a{ 9c3WͩJκ_e2^p q?c3]/8F[vCǶ?2n͟ӦY /잝Ѽ`6C[֥rɒUNeIL- V1DlXDQ]{c+2>'9VjZ @bccUZ9@U}8U7A2sUF?ccPi\ wDek H̒ԍB 0js31#ylIhtUquDL vdt(*GCcuA(E=y(hrn^d&˳S-HigE^t`L@,h"p V@}@vzMFW@*~Zw?ȻlQkPp?{}yd Q4eIh}?ݾ7u@>#P4_&4k42紱`Qe~p ;XyIw= MmpH)"bNr KS#0FLf :8r~fڬLs&WW.^~VN)UKdZ #(wժ8nF(ӛȔl{"҇#]^Cf +؛gh* f?檘٫%~<6=n d{gQ^0Ȣp`7`ET78~0\U(Uͩ^p.^s|6i[!w20Ϗ"S[j﩮\ u 7:n@/Aʿ8/ t|I Y;D`%b(I"SҰ$QVҪwݢCۗg9USqU׫zc|YM Qh\g8ͿW Kov.z߾/IKҴ20 )+.a ZY7rq[k%s+kqhm^aw$.$@O"еJe<2c!pUK2I"<*C12?g&?,;d(X̳C;,2=Q&.zA}?[zHiC>l%遊(ofmkm8x H&<c 8Ԣ 2 &2e2ߺ.U#rrW+r}fר0f'5)(q`43'knykdlFf1>9|8nu12v'3>Ynd!%oxl噫.It`o+>d50cN–cz @$Qt]ІG3^%b7K2('Vc/ax} b?'g"/d4>n"p2֏^H9b ~k]/,(c C wT7 p`WJ'xÏ^5Sb?{?)KH&5VY,JWu/{#߫)ȁ{bALd>q0u1b6u/L?-5R1Q` }eϳgN~Ii~J5kk3 ]#mB-OAcFhL'\%@݇L, Q.oWrlM2@N\bsVi(DQ:pd[kn% _ͮ8uּ6'0nI1P(Lѻ#h,+1MBKE'9-JC>ŅcYELke%a>} Gr/r0|qPt;8) qnŢ}ZǹJ`XjZ A O`_3v/}eVd-b.bTF*sP'=rϱs!11)a.3h)\,;)yrvHY G'"k~]F, emU\X:$Q;nX~jv* eG]olu+ٔYk9ss)%JE? mR8쎽KB=%򰠲$QQs5&HD`*]B6h8Niqf0 Yy"˵@~i7>KH-A\/QW.^m 2_q戹zcϤc?^ ;\/x T&Bd*DNĕ PzA[+6"s̷yzԠEEλC3RD"t;#hI"P䪌σ(eCdKu|֮|vq!`*Irin * 3SrK,zAc |uP?! bF~l)^V<ލox2Ia[> 135YKw(0,돂 ȏ p `^a?!b :@_=a̠-vluEs?t:5m8ȣ"UI^$A KxWTi(bMFc!sZ;rx-I3P8Au4N-V+k&aXLqkC6Ha8jTvDkiW $^s}sVւz)V!Ae[#J݆|XZ,&ݎQ~<}ʵ:#euz9X>¶AlX,]糑tFAf=/D;쾗B2)yi<)ESλ#3h}2D@DF֮\/_qtBc7ڑ@; s5͑iI PME'i$bkCzy'lB~|h]E䮖XZ[k (.,,H.|rw}E;W|xǡѷkhCAkR) r}#8|p"Sb?#b"&"?+.,]6;e L( jٰZY E#k04 L5%A lJkI-Y<h+8}c"0#"-7kEޏON@;_ЯJd"( 3]ډ$wITd#Pch~kaXۏfYN]̣b.D>bcڢB=ſ7SGdXԈ܊]jGwLB@-t?"6'*Et3&5ؽj㱟-Ŷ$ُ[ {!hI12 r;/C5!< oQٮ4Ur=d& H;؏hߐF6uR?zo;/I) 4Ai4#ܝzN, Isbn^q hU,U:;_UyX4KSB433u* Qd@Ie*KukĜt,SI)L*G mn ڱD}W P42&3cC#8]&ID1o&]ԩwK6j9rDȔ3ݾ7-ޑ$}bqaT88x췴J4SP6{ÂhK]R+Iц6]cH3bebK1E澕%؊1h7[ݪz `+ʜ>1BQ;FL3HQzaHnz$oYUZYnEeq&^0š^EQ"ηDHuG0!提_O)]/(Glx^}d뀒>c! ܄&@]Ge_)0YlB~\jĊ^0ҏRM#FcF E͏+?6oEBċXFFE<@ \cp`k<13jRѰ-;Wμj;UY@u>dwW;N"l MZF K_/I̗b<,E T"+;V֮dԙ*ZrtNG@@ӈipS\/Bȗ+U#l)#aLGTع%R;"tќ*Nߪ ("߫񈑺|resܓd:UiFrmn9_z"@M1"2,d' 3YW9(J2D 9b)+=k0Fv oNv30+ˑy6b氒?uL}HXY(lĴ²ndGUQ SEn;^'4'QJ@C B_;*iw{ߥgM@XIh=D>JT  !'? fp?;YW^ |$s˦p>b,WY)OO W-W$" d>sEO;ZAvTi)j*`~B@GAWIzwGsES2gS*GkLg~qG\/xn_>GTQ`3 a 75 ͼ1”$9%iy P^>q&viTƛ9U3JEjVj埑^P'VhQ e~ϵφIA&]q3*ݲ?b)6G(jwd60Q `8!Ă-F@8ĎfX`jT3x(K\ES#PAS9M'?Z!s *Ȕ32y=LTC3E^pQ[6X{&#p7z9_>Eõ6v2v4/h\`1AL}c-b; p5?{L; >}K-:)@Oj~>ݺ-!WNo}tl.b 7VH#8ʲ 8dƍ;%(r L#WA~BL@N h 槳6V],G3/1; ! d`=%b20QUv+ sZ퇐WI";{[kd-IK^х Kaĸv + /}#wNC%#л]v K4gfuh30qAkb rI дtuM Ϲ ZY/ebfsMY+dG SQ@)h0i<]XF؋KsؤAY\}@@x"brlrKm"3K\/p| 8Z:`;P 3+=T9|&Zǖ$.+I)_(X3y+<GNa3T$Q&C(Dhm,@&CF%] K׸*Aqa<ࠒDQ}Nh[U]g.he/IT\X:s}b&T$hXK3:=eIXYOV+ɩy{D^L{`OGON)Ӥ*7n#2~j;#?:iSf[L_c]/%O=E_ĖmHIwAJ\`! 4hlcWڏir3kh ECNrV`\G=޷>hM\Lv\/)Za>A!v/" ~c}29k*2GuAH \THlϸ^rk"I[#Mٮ>LέX"֙ďf9e P<М qUR9Th}s,0z1p( WEs1@+53HJ(o_o\6@CҥleqaK'Rw4 D{'-wտDQCڣ{6rY8${ʟt@DDQ(.,peV-&/J˅+=*[)X$Tl#0 w"Wakxav]jWq`dLs~DiF}>95F%Z$#m/SQKff/PppqR&!p~*p"Ua&(=X6V (DC f5bX#b)㱯]/(E@TssS| `p$$7[D(4ClGfGg#p*= ]cW^pEN}B0̊!c#0*D0E9*~# (.,] /5_$NA7ƈQ Iqakk^JEX!(*.,z$vE>=W$Qt0te)I.Yχ'%gѻ:)b%KE26L$.I \(*y|73, p۴v] 8mu)׆dz L, RK8Y絬ܢndaXcŮ<\X=]/8GۣlTS7#3*s\/8EMAxn2o"[\/8%MEdJ~hqo ve2ݵFs2}yHld'2f_]9"eI<{ӈL>lؽv{D"{[-a:)秐ܘifl$#3|zy-Mj2W#4[yt, >+k4S]>XAbP~~ϟ\*mp ^Fx(s}qa鐚[C)Cki@t(z>.I݈֘ѻ0m".F$6Fs4vsO@{&VRqya6; z'zaX8<\8N!g0|q5|28% hny? [ЦK8΍h?0 k{cݔT' |(`[$d^ )j+-6~3??Xo z}4:FfTu;!q!Vt3R"'@{a}4Zf=E|@ej6ݞaHNA@HGf3 нɯ(RKk܋]cϝaؾ|= ]3-m,^{pfU1k^Nq`g|n `Oh,0fgZU4O"?H1ڨ[8g6 KJcmѸF1lcIuox&bO7u` oW&e݋dʒ?+H6 4C>cKEICWc˹d.Wm;Iqa3Bφ% #6KE[ @`Zk"9-M@Z'N KGX&\{R(z W8?a}! i6 0; .asy08N6ջFbj,q>DNqk0 9/u\k z1o# Dm2}Xo>Rۣ#1&7s`-Q ("o'}InK}qE0e!93?~bUWs;:KYaw? ⶤ%Y>KE}лQ1]6_6nI-.,]n Kǖ$./Yp%\c[)AkA s$A}s=TOZ d2MJ" 1㼎EaTIbw0 q4' 02kt v8砹~л8#I& 8c &ㆦ,A>e_ 3HN_^2eٳ܎7-\YƿC?"9|Dz -У DdEqg ЏF q..~NH݁Hj^p-k-FuS=̉p"bϏDXT4O6=H7!~b q~#=\4o:T roԱc*UHe:@1$2j}0"-gŞ;pﯠ +c2wHIhodou]97m7رm aޜ/)I]E|X՜\9P<6DSZ\(!0twD/ 2(IE>\I+"!Ǣ~k'T Kg$gἫ$Q%zO3u)4<ᘾ5dncqv ;4oI;19itq0q~$qbfsqav7dyduJ.n43(ʾaӁ?]r&pDw8K-`8E~'; ȏ>Mx^%*+t%H rydf!x?-*%g.A hD-ԩ-vy#L9t4wLlfC|="yĚZ!l=&@AҴVL4> ?c1[LW4!pض(cg, ^)1~< !t]/xSйv:\_\/zezc;s}Lr/v޳Gp~ 66}\NF%TPD2;W/>zFFF^LgR3}x$Q*+., K*.,}wqa+wCAwh12URha*t ״!ŅŅ6,.,}YsU3y&"_O$*Ibi0jEn"VNaR%PaFn#3pĨ>v !Ssdl|8`ޮrg Z_ug]0 gG^ebĐ٭9n߄(O͙\cTtVb~<6k($C\/MX\S{"p$iR(BOd ]R,'|Uчh3˛-z"\ə3*oR6㱾(QM ^e+`*WK n@/c>-vUVfG}̶xt5Ђs>mvzqE~q^:6aVcIXﭑlئ!7vַvzhs5ZP`CH:/~Ǯ;R =\&_ˉBơ91ǎp-Eh_ Z@6FQjnd hاt jR=Ԕ!5#_|uM Ke:~ FӬLBc=xmof\Sg%Z':9 KJtra鋕~6Li540yF8k8?-UoX:#Zt#`P"@L`$,ǎNtE?E.de_H zRv.bn6nV_g>Hy^Lǡ &PSFfv2 |d7 ~}K*?B h:̀G'd<KOD3D#8*r` ~JĎD2^=S<˶~dA:1s]_ -WDŏ^pIpcG5ƛOKU4R\jcoEM7 xbFB0Kռ: 9NauTW&*( KokRhX:lLkkC[`e@XIh+A@z+d~^_\X%HKwj\sAI;EB@yXPIJ:"H(wv*HH?\WHSV%YkSF{jeݐ:,Bb;8fUvR1hh\;b "jM d nA>R7k+i((-DFx3ɗǩ0X#)~l\ %CDGQ8B ?TjyZ3mT~@&Ku ځwN btG:9l}w/Z+ wEksŅϏ>5;{UyCT_( llqȉ)?-QMS?u^9@;m.- jV/hN@>7HI'^[,DEr(jrI)оS5!s~[]=7 +0ұk X߱GKEw| j`71#|KMP[VD  ~E6rV=Ӿ.sd=/ы^BLIGd;_x/JxzئTPdG O(sfN3C;<܏@P Aq+R1(vxl-f,@'|.@s>Jz"2VL \ G@F,Ā]{3[!Ybf(@ոC5Vq弅th?7G+U\XZV(:/$7F+(-R+3@!,L4ubR":"~8R, 9Mrf)|ڐ1"};$PLc~Fo\,ٞ!s'~<1WHqcAy){QJm|UC)]eϷ6"{>@h^7􈟓1x[^0'Le9H+-ME_E<{E^dcp#.Ϗdžv4Ā4JOE~vO"sYKg$.@6},@so\Fv=֏m.Cq{Ĥ&bюA&78m,j|1.r$!pb.͙߂wʒDҏVv F<<eKGPa&a-.,X(\CZYR+O:\)xl9S-!ߟ1v!mU "`RO!sXvOE-uph+y~<6 q ~BfokH=b~ECl=_R7"V'}]/x{!b_#_~M2W[$۸^pRJ1!b> dR( \"X"L@n1U!JM?{,{#@v?b2yfFf@yK_ʶߔdJ g"̮K6GN$H] 6q{T_V{آv&kmv 3wIq;Xcq}؅yO]]/h]ۘ\Xة#]T7"@t?rY`C׬ sѼA"F4ƹ I `# 4>I4uZ$ch'xc y22m#~J7i/QEg$.I).,]22,IuF $iտhgJS+,Gm7iqӀ0泖aa޵&mp@q±2X=80L/ôN@Q?xG曔ļLk`IMM8, smʶD!'a~<6Lr!Wvv@>Bɾ2JA u/AiD  ]kRۑ)D`X`&*ktOGk5/')HTeESַ6)5. <جPS;İ@MO~_cAQ2DZl U؇7|˹;"0d\c`Cg)ڗdΫ~<j e<<|Y?Ǒ_(dFȄ͍mP} xOKzh_@S)6 abNqV}y?{?sx1.E/IQ`ǿ]l> {Wv+1~`U}JE-Ņje=qUbaڹ,s'.)8NGTػ*[ 2ȺˆdW0 HQFX zA`܉} 0k~ yh/{']5Uy{YT?v5E29lc5FJZiF]*A^sCӏZzըcO"_%J C,ַ}Fođ9 Ϲ1IIf"-Uss3R Bcc@ˁ\EhdW\/聀cC$f s(a,*RF[t.GACf{nLmmRͷ h\2v|k, A[ĬN@fc{u>@,߿*(vm]/U{#Y=4nXD0֮1l8}eRһW(:mf!WTv㑣@Gjo`p )?EghvսbnA* ^CJyS ~L52mñ>x%=LgYjf[X[P6G}2"ݮ(dS@?;?^0a(GcC&_oRQ=@Qq # <emәd"jX=̯[z]rc (d¾)fb־v(i?` Yjmlkkϲ N1?}zA?d|!iE |+[?)1? QvL2E)RRu뀔@~ GХ6_f<'w#0*0 Ee=cO[m.b!E{^a`0\ijXTCnbՔ,`0 !"RYC_̉u|s?9G&j IL-r |a홀H+W${){F2=eTZay$m󸪣x9Rb[]/_ x@% )qD?Blzld,='(k5'sAd|eO͖QSd#0ۃdJ{jľ4@q-[݉rcfNAjd>Xz[z!J;'ׇuoG}?F 8dXk>p.AjƱ5FpP۾r/?en3(cf!7/A2 c}s;p-ܿ!e~Tşگ6Wڵ=|bFZ?]@W֏ lj Bோ= zAE ~rȯf =#+=4Bt]k ,tU$ J13")(DfJ,KïsNDf4.B`NĜ|c\/ pehaƞ? P"`vYvS|LcP@<7X8kk_xc/Z *6 1;Z_=޿د3:GGm|'3Sk6y\ p06T!2܁|GGKj7@!ј&&$Qc-x_!/8s欿֢S xޜuF1q_|78C,[C|8NZOC~ЏY;rZ~^ ^u ҫa8kE7?L5) Hv2=H4C`-by)ޭtpE#U~<6Gi%?{ЎC ^s$_%P\z9i{)29ջ^#MEgv'YY&XC)|ˢo'"2MìO_#YY?G}ڝc8YLtuj:sEކ^/кOkhE@ `YcwDs,fhEZ]ƹFg#"W_>?>%lb"(cw  _Ah>g IA}no JgCxt(7ySJ8 "hS̡Q%'bD=2a/wrQaAjU @؀=DM~ř]XMKxqaʺ+wZY?gLyOU-fqXS/!@|c P퀘Ha 2xe><_74.t`"RX)aCПBlv;f\LJ/b7Wsjayk3zֶl"ߞW #7+ B`5&St b~?9$SNiw#QK9/7m^C&ȭ)M~t:H1C<<{aBLdBn0bh6E(fsO%{v6I6n p`%roW~{}ީKxcz>h ͣI`*c2MgkEJEQ4j+rQ߃Ʋ J̻m`-}|]jfZYONSV%Yk,jke@,EǽdN'2wuS bX_ BL"py;2.BuEG!PW*5ˑ}"<A~O$U[cxzdv]+[]djh)sMvE(tFz{8@LH?EҜ58O)(rw6B)?IH7@ N*/W#6֑?P{ o fֈy f1Dt&?T1{,Z}UIsf4֋|[#@T,݋@^a+G '_#khn}7~.G@u2(ƃ\/h@6_^0y"_y| F;Ǣ͈1Q\dLeDI1cG*9Ie䲰":kShShmѽKE;c>{^^QNY(* 2)uPjʚ?Č)P8(leZP CefF )놈eCGYo=+M3cw ))+R?}vH co5g"bmB3s5Cj[(Cvqb5&MxlsoZ.R"PER&W %t]{[JkGdB|yWbz!+SlDO0, { -uQ(U4c:o9o9x` >06$ےT~ǹ^ "a[| #+\ DL=d)}: ͵Y}#yY("WGScc5\1}ǿ4k8F-mœJlp*b 0f5n}kUF {+4' J/ xofMFlx&}1jnU\XCIpAc:{Q~=`D=~ {qafU/^+R+S@lR<j+#e7dF;R\ihFʠ>B1 Uɟccт]s"J4@R,S9f~17'#bTqmL)C3tEdS=UE`1 YSĞ-)L{!SmwbA ~dm:Ow`x\/x<,#DzE]dF@:`+W @+ &}Ѵ׷<c'! x.Ɉvr! ~0bͭT71M]Fd BQϑ)o ZwwdX6 SHܸ#}U1bo.FǗrmcmol"^0‚cJ1c.0->%֞j4Σ O5"pm,D5d9/'IYe).,\(D}:h\6FoKVjVOE(pfE@g ^B0RjHk)!~<6UW: !Nsw)c]/8 8g~<6YVG)J B~wzY{ Zف5Iqa[$w 9 ;eya?f/\'gr%U+u!{ʒ֟V(}#qT@HQ|{HII-MEs]!,|4))MupbXl鋔$p_5gh@ 5@79߉XIh'_ؚ:"EȾ)߃l~<s! z.UwMl!%ˏn6 {#GJ:v칯{|hz.NpT#03U#sOGсvGje x,b_Esq;8`^fLzY[;hΛ6N]/8uE`q& .lr{&ލC>Sܾg΍m@o7Ml0<ߗOM[oQ] hqa邒Ds= {%[ K62mg. ڜXkG]ǩB&D_a!"+G0 W!^ax[+IZ73vhq)e1<$#/1H؍HI@,Q37{ sSzAc 3HWdv{1o! Aስ )tD t:cd& s)LsXFyNgftP?!GTl#)&!Pűv\vDD!p7 6.0;΁](ۆ9p.p ϓVb*UxlEq}̝lP@T8ͱXd1GYӶڤ ~@`j.GS#=A֯cY> [xl=1@ .Es4.D>b%EcXLG`4Yhs21B"}0W V7f ,2SytCȄ xlݜ✏?c|ؿXq!') Ȍ#ІۤܯQV.77 V]I`5R\XZ]\XZ/zlêJSe;eNSCSQNyaAO`麵ʧ}d%@Eyn8{ ]8q^Fz8:8C9rqq q7!u889sW}enB-P4vx)_H*hKM:25}оߔ -9H,F81xB;HcQ" p.R2!5ǞÑy)C )v|YxĎ|Gҏ,AJ.W!{"J}A{x\e88ة\F3ߙz< 1r%H`}g R^kw 0{sJ! j?,I:G9Oӛ5|Ӳ(in bY{R?)m"ؽ"} 6ޤ#rAlT:fg+4FR ?O]+}ص|lg#Rq4oCoibXjeIx"zwP4dk_'<~( y yn{kI4W㜿RELj.z9ueGeuy&wy%,d;eUa@|`4.wnAsFUi߹ZZ57Kϣn< |ᑎd# M}%qRlC20jgErF3ǩgy' Z kG ~]tƔhGEd0#&&uRp4a!`fDz#P} &bŧv#Pu *i3Ř]/8WW0zHMkL43U׏\/3r?)e)պ腉 9(3smHy"7_!}}T@UIlxJ-zq!`4RY \/(M 0SR[1ikcM@ ڣ5"|KPmx'br3t=损u#4#Vh4w:!пɚgo^pɜR7po&W3lvgi'$}'vŋiyjW"u(ZuSO`hĶcccouxjULsde\1h6D;яw?-kOj7Y̗lD߃R4Л:VcHf_bG[5F`:-]N2ÂWѼ h6 MX@l",I9>?֟U*cUWh>_Sv\yX0/a8qh a0qfoa~onOqkYx̖Yqۣ5m -ehoXЎr^Ae,#TZ0Gl62cf ("-=l^{Դ{'Im E bYs`5tV3m'!i\/+q< *HG&̓" B~  9~I& 㱇/\EވoWIlfk )Eh~>]:~t`'so4v1v;kvίuc_kiS:e׭Df?{xĮ52!G9>E@kxk;.ro}>6(ӭy,~d^:_󝲶@`DS6M!&~xSNyXwʶC{'.hc.Z"@ KMYu*zf^n1|֬Hn_!'6廄a}kkY@l8_^JawC}t` ֏`g_25i)zh߁dbp!0D)ȼ8 D Vv[aHy$Z/!:WCl;z4#I* @RsE6Fe#[Xhl. eNX>FE->hNҀhS4X\a4O]㱗\e-[^3ӿy8ήY)L[S\Z9/qZT݃|Efni:2(c/>o=XCcJ \kܰ/b}/k}򝲞mU}sW%i?~x`qS+h v1p>C{4_af]y`VC edu%!f=8>otH @XzGkee13}(& /D k1R,MH|Q?I+iFHh`9GˠG"tU;FF913\0{"y>f^C ?J1G4w: L=sr@2SRS9Q?00`' S}dh5 w1R㱟S>oVi׾qЏdž^0X&zfgnbѦd#sEJm%Gw 4,avz$`\vsG' -Jso u㱷\/܇YF,w|s|ʻ Au5@LE$4oZ^ޏ>knj}{ =`n_}>dmzM Mv߸v=xt]/k@qqY;081]/h7zd,NY6Q~v?[EfC_\oк;, }=ڤ/ *̟|l4dCS2mYhGc^0uGP{&kH/. pE-"<?KhwWa #]?%R7G") `Qg;5^3q{c3.CNCECqd*-:6y~2k} `U}t*HF^+ɮ)'\rT*ks6禴A g#ݿ}{)Uͅq~<6,Cl$Կ7Rҟ# WH.h^6ҏ>00ya$>jz"nl+WJEuqxߧnh|'!<)S͔]5eSVdl> y$N8NC νV슀hX̔IBc|(~"?WvWG_#6qas(䕝W+/k=2M@Ѯ]N㱅t3R @urmQ(lhغy "WV7o9B Hv"A@r|XhLȝH"3h7t`[W5!ˑ)h>Nb[3ǑiiXG )#`oLA36w`֖ Pީg C ̘t2|2|,!#'(D/N?1Y+?#e evƞd \jB2ZKL ؽƷ_Ccw<494֮TzT,K^pD6@̀nfؾۭ]ߢj3м@`=+7Ю?{|NCVv"?$%?¦[TqؘzGh.4ޡV l91e`V"qo9"Zމu@s3M9 ^<Bܺxff$M˞g?e} m?Tn6Th Q( Y@̏!ھ3=AJxhha-H@%jtogfdUyU7K̑#eq-C$݃L1]mg4{ 19hQX9u͜,.D &2U>Hɭj#Q)_m t3 ~UNȳϻ 0?Z^Ii'6̷kX|ɪL+HŴ4e/ ŵw[*JjPnB^p͹,hXSQ2n:#SHƐ@-([h^{_g}4;7Gs7 fKOV-8UlP}6cZ@|d8 Q~U wG'ηAI6b!V)df^slQ{m?zA7`NBl ~6^H E1ڏt` ZȻ䵨B`h~zAzŘ>_پB; =gSʅsmTi귩 -\g#Ů]#@2^ۡaR<)K(5C `Ӂ NaRVj"k "0%iZTS܀:@ϲb $CrPD퐳hNDCSHIumozm*+Y]Y5-.b"Z"sb>BHc`z r  DNK#Y[Ì,A5IUHI}ݑt7in/IJ=c}(hGxUS=YTٺY֦јUiC8ƾ_]ٟd:!]j?Č\+[S~L4mvEd!ߗ 6AT7YR*ʵu:tmW$#ac;8prL 5Ds)]'dpw`7  xG(}\/^#8,Wgz+v.7y%ٕs(ok,i8v7DH {!Piy^p䍮S1`p_S6$ qTf^. wʚ5w8Z{C>gQȅd:0ծ`SZK!6>wFc`AEN9h9$Zd Ψ"cε-"(\=SE%b5D,E6b5B/D0R*Rc\+nd瞈hȉj655pdiv6avŸoșvSg?q,z=C]%)͋/Bd`^3ߛ d#Lo @ X"PxfP %6ܞ%0o6YlXFrqSkO5 )v~4NGKŘ|\]/XcsM4֦m \vgDu'ԕ"۔轚Kz̳mb?7Y x좔PIΝ(8cz 3@Sd5 5mV6ouox1mYtDpPM1E!Lguѻ6g~JW@~xO6t6/V}| ]yXPBkR򰠢k4w^, 2|dEsg{{Â2CSu}Â/WAa*Xide&q'^ $??a+9w^ tF\tb0ʿ]%@YJBC@0u? @mȔQ)؇!R#erS=6~Y*7lĐza䪊IK@G=aIg]/cO=) Kvi$% kLk&hN"OA@dO4I&Fqu϶PSo7 ?[dਿ=s GUwFld ^{}4rX4>2s==Ȭ=)/]o{m[!srL :Vv,s`XMR#@i!Cj4(냖Ȥ>bz-?ۮYKz5w8*$I)IsRE\9"P{O `7Lzũ@W摶a8g#FrtsTY"5T_r`;;tE) P[O{')*VnTjl)dQXu z_g۱Q$dM;m~@@tX:!èwN>@\|,mL* >wNJtdyX05);`§Fu'4/ ñ.dB<0 Iцݩ睉,D, c^D̛8U1k;X|('!u6Rǡ6o&/ ZE݉hw!fR2ǙJcOdNqФ 2cX*\޲ x; _=HD E xl}}~gY;^Az]c3;6;7GAd*#yr^Gԟ03Ў=?gXCydƻ٘; K)YHQVTUh󕟬kJ@:ǏǦ2܈c֦Kc/ζ탔D:|=o)2`}k~<6[+`& ؂zA}BrGrFocɹx0W9:P_nkb6:B, 6'w͇*;/h1@$ͽKYF_ѽSꓚS74PnbQIwv!W~<7 <ۓElW[e(eO5OI7eKܾuc< xS Vy8׹hC&Vh.w(QAA sv6tzMmϜmF0"A%H,[ifXy}vwCuUuޓ|o1%E`@ y#a|N 4bb&[!:Em6 t"ZI!wX&&fojaU9gг=S>} /tyާqՀ3߲r12 W0)Fm~^#{dG`m\`7Pe d#Ds`jmsuZK@ObVlg!Ą#ZZbQ6@/E|Z4G2) $?ڕ c eݞ =>"b s5Zc^m^@ @a|,RZ? 8iS<4g195 . u2GN C Q61<0n.2$ ۼjka|/R2E $vQ ABʀGfQnBs0^!R̳0jdڎ퐢OFz#vHDEa}2b0G`F)b]rFx\'SJùH B]МY̑9[Ë͇ pS~h>il0AgcQ7P6Id֮r{rՋL͑C@xkң]Y-ڀMAJg|:[\kǞV9 5R޵nhCz"~FV>Lڰy 3.lYiwj)NZyj\"܄+h=D়bӽ=Pɥ2VιHliŵ_%$ |5ި[!sfԤDg"<#+nĆ͟xƼ$0oNG~Vjhd#3-"ErVa<-3@GZ[!f&kE~rӐ9-K}޽eȬ(]z ij|r:}b;E!0>1ZtJ&Cc4n{ !ƮXG (l,#8#R)NG?aDe ocfqKdGh4@ У 9$ 6 Xحv]; @@ ҥEl=Q63#g$HA"|=%QLAk`zB-n'͕ 1MQ4Oqm/T}]c_0.L;խaм]H1ge3oq$XވB,A_6@+"?$H`6WG507R Tz'\E4->}!b&˭U(A <_SKעk8PO~8CEIdܻι,psn9>/K;w/Z=UL1MʚF.)(5)"7#0UXGG"6ډ_|ZSYbrK'FYLb-NQ6ST8C 9X3f6)?|YP ֮)5B"lf}V()Ez(S}VlG=2,he/%>ZTHMQ>HX[oA/{=\$Թ"5} ƥmQ?7L 'Rg м(Eo4ǒ|e#0ZHߦP vͮbv:ٳCLӶhñ.pE kM~Y*>A_25X"6'EKZ! # o>}6Ȍ@s*{/M T^1i<$zv68olk-6-PpOz%4 ~\s>It !R.'>>;>'\=k6eeEט/Ʋ%QW.pXN 9$GRSp B~CsѻsΡ1{19w z&5g"윻1c:^"ˀXHFZ@1[Nj&hb]NA-Z0A͡1wfh'=x-# 6x4hJF@ckZjZSRŢxoyk d`?{y&تrceebt@Luok4/f n00oED-"B7!ֿGMEG{ao$uhPHn!4W (y)/4)9Gqnإ(!6_d} 2C@n D&{U @ͭ?C?5;4__D "vGQBjq}Wimovkem϶6u2zGG [56 Q6/P0Eh-oϲ-ژ{Π7MOWhPi`tJEC?ƕBS[1:mnV A]5!);͛#mR.wLݘi)k6 5r@ާӲLFs`({7 Kiε5jy{1{m_VtBԐ3 B{&fa-P̱h)-%S)-l ANk#xMC?Z代H >!) ̶F!a#U1I 'Y7pZ_<#fJ ¸nD;"=#xȌr&(m@\KĦ\" ;y;#p7t8jz T#p1*56(TsgzsYnYnƩ?.ݴZr2s7">c=]Kny@!Zy>bCs=bMfNã8Z7Iܺж(?^ާ_TP>vIVP>vq,rsnzb@$MJ V)2խeSWhQ? ){'ZB#&Pӓ{29c1  ?,PɗPKNZԚ F!t~GFsOF3;{0>MSoAOI@ᇈug1d6*(M8%Xh<}2^bYL+5:" HY^k;Ϫ[Y h"v>>b=H@>-Y)Y' ?Z_@{Wcy`[۾3 b-Z_w{?6+E@h~v+Y&w=֜Zg@2;y)lsG[#bF[[梀<%m: c>u+0e3XvJaMho@~^vsvXd:3_":1-fY_y@eek?VQZm.)<ӓ>Zm'\)Z#OK܁,œw~u w_Q%YRˀX)W$7d B EH|UnAΣ{62yHG!sR#=5DɆ&fv4֦1R=L^u%Yj&" z)?!EL iRDGCA""w".o[#y b-8鎀E~b P{2Ei{ۥi0 Eˇ#$B5Y{.FL8Q6]$#0 1%7ؽֶe){0Pa DnGIJ]]Q6$z'#pp?F`f}ʽ+kP@_ͼ/hdb?1 J K{X ?Ffke`7"2;lfvHا{ٝ3j~~'avƭlf\`͋#;!S >icacL-A" e]9ALfJ݄.~ܸL#P)ƹpx˺ wb Gc8 r۠w?ZD%O5mrs>=Yލy6BwӖ$ˌ1>BzX LuD(q??@4d:.ֈJ?zhXSa2 e?Pp|"VWH6[PjlonTƠ/Pe<"ea|*# %ucԾոGOlѓzѷ/b잍6&k2 -"3by>T"08F#2wHFxֳ8߾?C%Y"ē듦Q63"f~ (4g$ P@(Xб~vh1.?i3%MǝQ>Ǣl+WhR`a-l͵by1G d w?"VLR1j?@ĘIhsbC'UmfC. uRO#!hѻ  fwm&D@U4w~}z`E(P\4?/Aqw $%)I,3@Pbyu5Ec'`W%PTL$;!m>{hVhQ(2EtRAFLO;(Lg98 (1KBW@ҷn\9 ) xQ(`myw,b`hfd]e1 )2w@Jnu~xgVGJK)Wlf~1MўK!4ET:)\X~$kH`zl!v:r|WFlJ9mXI͂.{%u^wEeHLlRtÂ0;P..Alwm 4ވ!r=rv^gDۑεe-ѻ2GI 2" v7>=hr;}TD%)ɿ^Y  t-H>^: _ȄqrmºR^È}vEH|O{+PHI sf") hv٣Lĸ-1bGlE<ϴgN0;X;@% @!=: SڣA|#,I=Ǫw[>tEpq2_YnO@&'cY1!Ǔhb 1=26@lŠ4f&ӁAB Y?l`# mL7EEړ)zxZ&OS`ٳ(yю6 #Y0Gr9ܿz"uOn}/oBmލ֏J [J> 3G bD ̶l)Dn@^hSlf~4ݧN\b*NF]R`uEE6qhAsZ&!Fvc *j~Ao7 Iާ~ˣ99w_?qcQD,^i L_G[,FB#R)P$@ν 5Z@C3R"j+H;!3": 2:B'P`;ڸZ Z!P83P#o<.V.h"n {E`k}ny ~Bw[4!Ί,|䜅@v!i)"bCJ= ̃3ó b:WGbאY llf2Hb;?F+:h̨[ILe/,prZ?ӮИN"KӳqufϞ{%X/(\=Nh~@y(f@U"@ C&2G&qR)ZBq5|w2d芘;Z̭g=KAc׾M^~O!p5Ay}' .# ˀz#<0kb$s m+YH%<p> {ͯ=$K&eu#ϡfcBA xr-Z|e37_ʾhmkB2bl(J,LzGz++vsPcoC XqV9bN61 C(홋a Ծf}s6ڨLG`~,I2RסyИ4D,$ t~| sC3&2v2)8yh nAz|! d]3D!r59ߡ Lj,rzܼO\Jezq5>w%֎s+]wsε@]ιqh#Uinv5SޅA@{c밶d dbHmh|8;|GS(_.;!['" eJEh/7!e3R+ ;DQ01uEμBH 5kcxd2lbrX?جuFl{ƛ/@ RɻL)vG#徏=gыtCsE>]|5{pԜ+gg|lP @qm ,R3l/lw^D\^Bl>h><9sW1X[ߠE ͓)#\hLzĘ(  Fs9m$2ÛԱ[4{ f.a춳~x͋f: Ŏw{QzQ6KOEJ<}'Y4DY<r7ǣ9uoyYKd< 䟊7jn (Dbr)omrNvY m>e\3e0*L ?=U3* ֔˽jk gr1pI},C-$hx1{:)^\gn<caιІZm<% ^y䜻kܒ"" lr <( Sh1mBucPC Y?A`iw,4y"auQ69tOE;H@`v&@FXot=Hy lKF̝za7d"LcA@ڂ#\a G |4uH*'`9*1$"|82]e3|`}Q63>Pj2/Vx#UjNxFaARba<ubH駬Oε1|lELv1?9}^AsgUA,]L췭(djl1ۘEfLXյ>)6̳c=oW2*2D]#/oY*(ԭ,C>-g4%f݌X֟!pK)D>WhևMѻ0n)~|"mOaY뵨jũF(1RCMѻ1\n3V V){mJ dn 4ruz/4ZEʬJĊuLs<⽿_8ڌWʲzx'z´5ڽφf by=pF `ۭhZDLF-"~SQ^q; )#Q6N߾eJ!@rem&P."v)~`<&G^)J(o5 z֖^ 3.@(4(dAfgJjJn V([=,aHQA`hM)IHnvt;7S%\ -ؗ.캯!1`Ri f~ VF,Nsw wpgӎٵX# 1R"V1shmmF-9)hD@oId'Q6IEf4G@/7Sc{L@p;HN!>E_jCD:yAW1K_,r>]98 =W]hm+E.ZMњ0/ e(Xs;rYh=x8}ާg9QeWz;N{ܺ~9'Ntν&sڶCssuseͬs;e42%Y,s@ sOh1Oh!IӃ0h)iWa| uWT#Ym[l(2oPIIX.gqH/F ޭ%!%%YY怘;hϪ0ӑ28-.E~5Cb뫽W;(l #6+o9*RFvh@; o7Fu(bmHEUFV:(D>Eq/Є^ e3_b@BlBŝ03WraU["peWZꏾvl}֋H $IHz"6&a$2j@i,=T>@}Dk_} sQS3N'7vX6.j:#E.{ X6A(,{ޖV"P m46DDGe3/۳l}8ێ\E5ڶ9Re 1iyd1C1c_iudڠgYxw[lf/@}!p@ݑ,[PHT-ڠ TM|sqYT>=_Cawgl%`ڜ(vIb) x)xI0D wZgE A&Hy6{;0דAfHA@(לESRa͌ x=kz;2s^e3Q6=n^DTm{2:cxL "3{t@ g _vl-{2:"~ד^6ݧtn]w^: F<> RTm6OuU}@z>ff sDޣ @A$b']mR_(DLlܻ vZ96eRO+EcZWvl^Vg-P~[DCShE$bLCf쳟lfXns@׶oHwt{ C:_aT\S S.F hEc )hsz?ZWл7S7Yx)ϥ\n[Įނ6v— W]v;\=Kc ;BŃTP>vIVP>vq_ݖ,S@,͐R4R$XmUa|W"rqƽbXQ6ʧt7R7 @:f(,lUCY Bg(2ގA md&)hifN:(f 8xRs)5? )ӋR7RR!\CL:dЏg4pTs[Aw5hc=joY훟4vEC #?>l AG`l]njƯ"v ֖iz 3T؊v4?[{^~goL[q[R0Gh]U;>ƹݷ)ՑP ͙ zmf(S8(~LG h"p2Bs'k9U)uΞ޽W<Jsf:GX,Iy~eLIxe#>@yhnE8O-9w/[_nÿQ f9b@J£x3@i܎h9=OtCgga|b"ÐXqAe3/!aKRU)dFR6~皈΃ ŞbP(`} Ef w(/sȶ[NOوxj>]5A ^2 ^ȯ[S-X>f ϵ^ Rڙ2I$!&&!!&|6G\!+c~K32Rw18 ֧i/9G]W_s]9ʂ"y{5DޡMRZѼ q؝hc6?Sȥ&RY;~Bkc+h3͹ox=e7]jmÞeskk®Xbs "sO{6%GhOB.)[04y zuS. ӳ>=87ru@{}m6# єrߵDs>}}E=Kާ]%)II\ vO#e6>[L}Ris:2FHES)hnsJU4̗Ϡ0 s F@,֎IyQ636PF:d?s%љlWU]ϓWܩ]/lfYf2kLu"V0񏙊HI.C!`Rr71u $)1hџh"by뫍3#U\=s{P$ "Ft[jIT<>I\W jiֽhc;/9 ISe吢k? Bԧ%f `? 1 CXv[ |R  +صBf#qR_H}ȄpZGxG` Gfw!ͤ9-e66CZ(;]vy 8kN|; ylL#8e:oݣlf@8Zwq~ ?5ㆉ(^գ[Q󉙬. 8x*faEf*lU3+ۘ|l]䠿)Yt5+Uhnݏz2۽r@wq4vϑ2ѿa5Dsr hOv3F, 4G ]Q6 ¸]U ˚ +9g"-3 m*VBdFJ\gd:|kJ@߼O r}Yh#)[>=)r\.BV@c1IjGKvy. +IIDY&E#@,"r܍vKa6eF $dNqMF(}n+A7-~9َCl^""g8WW\ ͑_)iz-칏 G,{]۶R) QBJ5bڠv% U?1p{CHs5Q6n6o ;r=F_Y@Wzy3(W5p4g#5 x|;!f2PJ8P43 ֵ  gϝj|͐Ԋ|oz~"sT Zٽ<$ wDTw6A'0, %@i 3ݟtbm:+$݀a0oXmiM +;)@O6B.À|CW>] .`~tf8Q>=.rqΠ;)TMB5x_S.7BN=OK\_ K^1s:׭pc+VJyl[a9bwnD;)@[?B|#|/g6昹2C^KG'~A){W?9h)zqx Z+l9[Q{-֫#p<^Q՜@>pD dBr~o|e!) תl&qOٝϣ˨Ҟs}'fQ6S% 1 EW>h-֟s|ؾ!h߈nyJBZthD杗BX *(hBӮ(kEmm%8ݝl6 ۠Z_uAi Ev(c7vOge3Wa.͐"O|F &nBgHO3{̓jn~<Ѹߍk mC@tnE^M0ral(L!L(x2}sls4-ybUZ}z2!QE8ֽY%sFI~8|WZq[={d @c{~"l_޿sqm(C97{9{FZΤh6,žX&H ^GfG jbA>C24F&:pH17Q(q> 9DLH 6+*5oNuxwȊWׯ\/'3 P1nIcW)Gֆ(Eg׮q/ZBbOZ V|wGe:LW0nm}|?ywq'ٵ[ 7Q,dX?n6~\Ƒ ݳ@-Y- #)At@~5mlf3w h~Q630@9۾3IR;ςi)&lCq6c];мOd+nG kcs BEKd| ".͉4{#CcyxI*<919t;  a c Cs|b-[Kԇ>q6G[*~2E>ƲM\'`bާ}P_F( Qgq91IrIM_9Z^{#K?,mʲ ^ފYSQG#@0 1gcČp3})+H)@%Ip4DvkԬ̕;#bm{3kHeh4F {u:MVN.A4H9񏈩9fZ"fn7(?VHOFۋmOL P)cB;ƾ4 x9{o(ye?oem]e3w[5URÁ?=l|(Цl@\k0dF9gQdUtg[]b,kAFu_\e'L@1Xmf0o~0!]sroD%FL ¸;R?1"o-I>́ArU9cb ;#2Dh>`޵P@D:ϴAZ4d9 qN{!զ˵BkĒJ ]XBsp`$ˎ|soe@֖ ?>r΍@]{sm68fbq5Dho?5"v12E[3U t=Q FEy}C{FHmow-$߳ڮzlU2 L 2e9h? 1REġ{5dZ=A-b#g}6C}R­@Y5C"Q6sw"7wɎ^Vg]!1C`ꁘ1vĜe3a|-bG Yr&Ɉ iad_e3lg}:m|W1g! nLImG%ʭk Cܛ")'zbU NƫGVѮUSDQWFg(;Hqǡ`Tr"rT|c d}1Uߡ%hC)E%^b~R3@mֶix)I>({~ 俕`;y:Z`Fh.Ӿ[̱,?4^FEOZbs֥cu,e7_A);K՟0͟/(,F&ws"Ѽ\rY1Ą>޿,+GѽIV.vιW0eռxzιSqjP\i[DsN&y8d@8'A;R]s;pDN~-\]luCkQ%svyޗ}sM<9W9ד뎀Gι,Q!ŅZFJΡEk(2}Bl (c)ѽ@MDS' Qk=1 # o'Ge3#HנH*Uh"uHa%`@BkHiߊH/^# ;Ra|0xmF"jCR%wdia[*(( 2}$[S;q!PLe-[ ) hw$O7~[tB ǣs ?BIm*z /DL޵/ylhx9Zvblms8N@XcM[ =-Rlt ƨ}3;)ιHϫ}vιO-W#& _2 uha ^c96@hGˀ?~X)P)дϡ2?Ո.ÁlfPz x[22 \X"P!b<6kk8&- @ĂR7XG!v_6nO4vv펀ȉ}=8sr^lvEMIdDϳ1Laãl4XCiCm\אEm?J܎.$}! BD&No_ȬESV۳Q5cٶF>gM(TH4S0^~C@! /[Na#@e3'aIv'"1uvɫ #Mg9'EٌgVݿV٨l4ly%YrfzJU{} ois.آ s#&~Q^RYNXC/{su9>W۱ eذ#J:cd| V01I!dzGi#4O^3sm gFcstEV{4?l8EH>ؾ^':NuVIP0Vxg:@snsNH4oFaȿLqεEZ3bII#Z[Yosi y=szuk 9~0|Hu DcmGb 1ls0؅iQ633_$} tD ]Shwk͑?_cyUT2md!;MQgMi=>C]GEh֌R$}o7Q@Fd@Qz1G; G@ֻoYMnpc׻)H9B,arfE|/E.3zQ- fr< 9nO[ڳh,*:H47^BІDA7b $&M|ZaQ4E_a|62iZH0v!6+TAaEY9BӴϽ43爩oU9/P(P~.2bSDf PIkSX'Be3~ugϘ0Oȴ>jygXIAi>1p]Me3}eyFbx a~= _ 0LKwX0͍7k!3c.B~e4!'m}?nEj[MYx !_CA)7Y|v3jD1S|_@S>-w" 2UW#@vb0vchue3mMzB/ )+Cs&zWZ/n|cEI 7vo$!2KAk)99gax9W-Dl։kfbsKx)*#jM"p2EmE "7?"e<յՐyӢkN0V+I9~Gxs=6dfB`g kCOdL#c Vg`/j5+ XJ?ԭ7=YYk!EdK4ݐI/m?ga|a|@AZƧ1۩HOB`a7ZcËO[ ؎4@FfVW[R&(f p02EWʠyսAƍ"]@u4І!3Q63ةvqAy|i >G!h~ i@u@d>f3,IiW+uDZ %JXrfm5mU̖85bJv%dI2ү˝Q6/: Dee3'r[2 s`kUy9+#`XX[EwE80el |e3a>uZ dLMwS0l f#de3gߏXVz~ KQ6Rinfq؏W5Z9 s6D;Җ"@f?Rc 1mo!p p_1GlBe!sQ6ߚcj@GlOe3W5^DdG6>o/FT5P: }ގR\n vK|%QllCm &SH5Qeǭ=&.@&(ӜVc*j\3 m("ݻSR| $\̒,kIJReT6b  T|(R<5lY@EDe?ļ$`'kak#?2 4E~I#]}?o߅lՇrb.EIJ;#k̟X3_uyI:ʕ6-7OC~6hN@cP!|& vESܙELu "Vnퟄ]XTv4 ,z+1EP()bz`Œ-RXx_q4"P>,1Q6H"@2̏Q(u?KHEGsdCITf h53gqzK;!3ÍM7#"zX. ,rnq|V7U~[?U>]3oIJReP)@l$R4bz|)wknbVBNDGA_0~2fވGk*@dwYdZ#V/"5Gp3, /7 #3h Vؓ+WvG&C8G!]wcwٿ?!/v!2}K`@enAIkVh4Bv\!s#Ȍ89 x?;@IZ'sP2-lf=߸ 7 PIAwTU7֦(%DٕQ63?H:"aַ X^aPQdEmQ:#t!Xơ+bbC H6 ?AhG']Mj) >"MLƿȟW$U@,R.#uwkZY@YM)-Aj9}b:#h_q2lmv@YGqo#EЍCgm:Z9FL6e dvj}R(fjaT'*EMDt̡P23nBLNʐT\L=Q%u)du_./$fTbjKlXg\٣Ξ3-o -0>G#ͱU%hEc>2kr4V_C hL?"&RG7:R x=%\fEw<%a_#0BK"p&(lgo#Ml|"sk}ELc\g?_0TK+)k_{ƍQlĮCie/ʞ$=G_?Z 2j>"6,yӓ_UC+%WȬsp_ڢn$ w2])Y+)< Ya\).HYfloX1X:X9[DfHfub oB=\6O%l#P9KZ(G۵I)>El}@Q6Ymt {bvm,c(bs<%"py%bDs&Xp :(釥4xUw!Ib%.B/aQ6s!X {kr(iC~iT ы[Ev$KpLAp3ܟPA|.1IڑQDfw/ɬޮCÐl,2e7N~5oQ& [y s0\ [U" 1=sИe3 (C x[Z(g6cgY5C`s~2kh-o}1X ˵}3*+'?xz"ιr}(*Ts eNrPt[O8RJ I D%kVlOĨCQ (r#d@a Rd7<]M:k#Ż:2}^@>\_]8bҘ iejIͼ ,{XJ|} h@odDm D[ ݬ; $a  xc*6vg_ &3 >e3p"ɁR+:-l 7>ՐiiТ/>FIl8edI0x`!G" ]٧|]Bc,Eb ֓1a}x7#}r?Gk{Ղ]Jo6}zISZ[wș/ιs̀ķ9\s}ԁشs+/ذVJ@uyG>2+a<8)f@ N5S"m@f R+"rQ6Ȓl퉴AkyL :/sw"g [e3?!]#+":GA_aR#V H|c>Q58&PW|[J>呃rwe@ȧVv|*Pɡ}(5ƚ]t[DnhmbcxcQȷ7# X" V$#? 9vCCQ6CF~uk!01^ͼ1ϴS9.㛁k#\a֚$uFʮ#`qzg?S[~u)k+ SE횜rZ9&K\9ќҸ!qFj N5)+͝@3besIÁ,rsn |>S ߆~^_J3J>b V|Dj_9B/ϐ9폶p6nX@`}wbG`FAuWUh@ɑmϱFcIeXAc6ӷ}YĵFC!Q6sͷdX T+q7{)\};ؽ+j/2O`f$ӵEkvF=a"bvhSv(V=rc|]ry^Z}5]Zm4c8uH7"D"ޯQK 1ߏ\.qh toIjsk4GwS6B`tyѺysn.Z+$NJ@LJv_d 0[@ a#(+ &VBfHa@;اfAd;l SNQҾǂ0~(w|G8XhR8[!}Pʈたe3UQ6AwG_C>g):B^ $7\er{{uѮhN&7Y6k9?ZqO}ѽá9or>]+[%S.wp9O\n_KܞO/>VI~!5ߣMj9 49>mK H i%i)A/L\E8IֺEe3/ R'!v"߱/G \k" F&=}"ю{ Jb SYhLm4#9Ka~N!PӉB?WCdk(촆"%2& n7#+- е *(֎loƃ#ΗWd2MDs"_0O!lSVB&!ꢀ'lf1x hqd^S<m}jdTL׉? eĴg,"?y'`s~{W7rr: .k>NCx',rST1pk47 `rٽ*6"ӃƼON`aWVϘ77pSާ禺:"v]@d9nc^C}\rc)Ƃ?F*]deu8*$|ijCZw06/0%&ϻoV=^Z4 flu3-R/,k/ȹNZA;zH)#D(RugVIQ\x]) E`IVkX `!) 9ѽϨӬQk&@`r O1]ߚe&ȴz L!߸"8L ?Ain|a=#%;.HZ*yroQ+ Q]Q3H r$*U77l:fg\ns4G덈ܻ?\8QK;2%/JU,W5(u%o6*\l)GV6Esmߡ>X&n>0)-*o=[Y@ Ɉax , E NGqz9<`;!E=hb d7V ܞ(4hy>jf|h@͌Bbn!}# ˹ʖs~ߊ[D1E}ڹ1 qĊA(z(b})"2]D]LFJֻȬ"vBT )i s]ZG+b? g>SOHoG0?ZD.hlN^ܳh̀ԮK 6VӶjkm[ ,JoAc\/yb=dD!mh2.Hv;O޳^ymFLI0^sobYƑ]"g|e adݝ_&(9L Qidbmh]w,bO*@ - \%ӣw:k,;nÔCSLؽA+VM/Pދþ(ҴGZ#D~j& 0նM܊h.vFlhΏG` 1͟V(׿K5ڼka9Er厙eÜ[v%ze{4GNQʊ,wEWQDvp|҈%ӥ\t%)J I[ӣl lZ>ߓ0Zt 0B t!wv/ :#;#ewh[BAR oFG!ЖA b$2 @X:S>R _w}NvD;n9,ӄ%r= 4 ="j7nn{I=e33OՃ(tBbZ"sѫֆ#q22C$xbml 4g6f!m(u+gr 2}خNjL?!aXX0fޡ1tokljA_}&93庎)wQfsu[W4d9My}ר8wJŶE3>9Gf);$O@ |)r;kw(5k7lҷ׬WNZAi=~ʪQe,74r]ciьQf l-R.3z}zTy L;+J[umu>ߵWHW1fUu֯|\?OVEs/$fQOU-X2A@l R?Վ -LĊlhD RֲϢlKc ܀C'{k>ADJ,.Ϙ-P}ęyckHsΟh?g% vAJ nJ{"R?#3RBw5d>)w F&{:"%(s!|+lyEZdT?z?҉p/RCBO4\o}cbeC>jڳ֘]/1{ G&W]Ym>y-k^8vĔAo]_g NUQ=ώ}r~ \UcnfqCX|7Q6sb]$-[@R.sch.w/4o^]ƬS[m< P7f~$ O`/7ePÇR]6mbD[7hʑ4mhSfeV鮌gAk6J8i֔()D쒟ueмm&{棂$er9&`drM"/@ާ p:,r9 =>; s{?oG.Um/Ŋ%pKK:w?TjovޢR7D@p""6-If]{=[r Rb-ꬴkax~^)'Հyr8ۀon" ׿q5]3BVĶakeXoƝ5yԊhD6!EG~b$rA3>*mbŽ1yN\+޵G݊_Qh1Z'@. uѺT6$> r2/,Es~ 15whS~8F.5"vεh0k6I^׼ky݁ˊ1%n@}ɧaQ!52tI!ʋ윣q01̕Dqh"aSg? ́cshm;`ĶAlιˀ]){ ͐CH,#;$<O &,q VТ.q7 %mw-0K? NӑbR!e~+9@xvvwߡ>Pl ¸+zj[-Ӑ#wQ2>Q63-PӐ_}w%*3"P- '1>al@#xJs6druKd  ZKO6Xd[/dI"@kj(qc;} Sh6,f8ޅ_tph{nK{ާkۋEH*ȴ&bE'6r 0͝Wwy:, ϫwCJd }:H-ʲ?iA`k0bDlOkݚWVCSL!UYE^HtqnhN~i;d- K1@ֶns#Vmuctt7,rDU1tڞ@{I/TbORUT9XFh@T_e C;QTBy\?b>F_?e~=>@oɽ^I )M&Q61@;(2S R2i}v.rvFw{h@n@/mj6Ք -KY4Iu&Z\ouAf& ro"Da1b.bS)G/O1{0>Tn36"3lYMT+o)ƚvPs75 >}#0+PՂ0>0fjuWrM@={Ǥ\n")xaeE-?񝅘N);c(Jx]I5bGWW_+f:\wj;NܖX IcHL\%<cs' =Zys5 lx變)ι%b:C,V vgrΎƲ6wSs)lޏϟA~ ,L^_P0 f1_Gm6gιE[")'/!`Pz2`(`}Ae^A7Da mFAe3Ɏ(<=Flκ9M&#yKe52s؜h;?ICdhODcbb>cex칟E`噷HsQH q|ZwC|ok!e\]h"ˡ9,R $I3sAohq TXhG4dN!u>ob B4^t (yܟ AR|Vb&s "bhΡ3ѻpWڴy܇ĨR˭'\S<@ qCk:GїA v3ѻ~&K6[ <,*efz@CιĵcC$3%sm|vIn!Mщԯys>oif- dpmL#Zo&&I]|]^`mYy!XTs~C0p+--Ԣ콨^f3&?ki4מeĈP 5zyF!Eϓ\z{6[0@5*" AE (y)m0R3HC!shy%mQUxɝv%2]AmC~!f*/Sk;o ͑9+oh !%2]~Z`\⃕rTuivz0oK7);\ɼO{  ͅ[):yJ A< 69̓H~rsb@Y}@.+h3&fJ'~EO>HaC]Sk}\cp}4gOn)2M8Z"};*kh8;u |6Z[M2c>co|ZW-';uuMF`>a~SKp_-% $E3 3\Xѱ)'c%<e3}Nt&RZc݂0,fab:r꺷1jl 1E#xE-b.F$& +C E-oϲF#hC;SSx8f+S d|(?7+u-2ؠ爲W09OQAJ ]2k=؊񁒹a}$f^e\wB+9\Gplͼb瞀&(oV'R"ń-/[5);6hPl%~;#$CާY`:ͫkYie忳m,F,gX;^â=y\N\H! ,q<>=5r B"AwEsg4+|9p%}W$޿DrF?5?Zj_{1 y6h,,^h߽.yb^٦ϖ9_XS},y}Dž<ښE8-X ¸l1QXIoWG13wR?#h\}#ťiTxȗ0)gUm7A P`tq(IhXJ݉@l{:ªӵ'&= U.ENm퉘)lJvxF0>%p j̴ 2KS6CfЦʡ>-94W0"@[_ ]d]1%H a|^m%&w˃0^.f&?Dy@pGf^v!jfOIB 0g7ZRvdNl|N0~ES]k^D,H~XB1u{uM0nJ<+Q6e)*$'+XGo3f?Mŝ'\܁\nw4FhEfhQgT9R\}Q b&K\VO?ޓaXs+aѺ3w`o{y>I?3(׈ƒT$EJ@Ĕ^\[RA~[. 2C;a"q>$fa$쐙fsz$oLcNC3=~tH񥑟ŨxE܍1"e&2c^iB/F 툅{7f_HWa<`jn tY"ۘH} ê LAfGl4] #ܵ8!,#6#[ta#K6 @ahC!b>F㒘GC;nO_k׌)j#;?/D{N?Ⱥct3Ay>}fZ[F,`Bާ{3;z}{OMܗ2N$%Y 17V>AΓ (2|j WTTr>5-p\m[ر? 0VG@ Z9*+3+Vy-( /<gGL D XAhTg^ccS x{aD~eZbz4 Akc⛵|7_bBf͹,AJgjcمtrҼOߖrh Apa뇘%=+˦uc9>=\ns^>c#bF)ڬ .I\}`O^%)IIRb&f"L~Ii(}C !3E !kFH)EiG-Rbްjy^(C_uQ(I(B٦(aRPC权? 0r *fQ63&v8`!&c_ _(b %;x-J: SsyL^̱V -[o b[S.7E\O?\PήKyK!ŽPo9glƷ5rC)si);&cj; r"'>\N%)IIQ)"YXvq@X'Q6Lc \e3?/(.:1K iYY0i߻ ㇀A Xs/ y=kA? Q6N501_W?X={` sQ6S}(D*9FH{G vu [\@B/3l̶}ޔ˝{7dX1Q(5_lL^ɲQ-oԅ#N^1[ ,L{`/w|IJR?VJEb .T߉7s?)["{+:i&/8d^x3֋~bڽro@(zA: 1E̷f1`'"ƩXGqf.-2")y(8z˭ ^ /lT{XԡQ6u˙6wq-,aؔ5Ey}Ԓ$CJ_/g,Y\ClՉA?Xl6rܚͭ7v&/@(5wSߪ)J-0%1J-PGd3;BFk:p.\̲"/Au@ hK;NznKǡ\y?\;N9?K<̓4-*2#Q63Z--MI\ E*yI3.vi(IIJbY >9"cjN9ۙ3|ㆍ$BxWEwn/(~E^C=_# 21|~rY}խA/wCPr+e$23`Afݐcl>L. ]+dڸ _A)k6De4"N#X񒔤$NJ@$?Y=P|]@ާ+eoR.Ry@CٗMPIJR?S"(>㧐I5wAEN3hgԞ`t{\YD A8s)"d+_6^eq*YD O/H\m'HhrGPC)jJRu6pޢ-Jo}hi6B[ZdkPfEFGj,o˭nC, ]3$%)@J?K<2-IDo51B%V͌Zbuy0[L]% ŢFQƆY͜˭bL:^\*)_ճeKZoEI^);ڂJRAe3`?J!eȽG!2g~SEHۢ}9 1BN]L3R.wP18H[ ˰ G1)] JnVr@ڢt+;Prˁwj;r}ݤ:Wާ~6ErQ9{\Md,IIi%5.Lc}zrB9Qņ= rmQf%u`/@5Ro)@H3c>N9}.ӧ۽vNΩYD}%)II$6\B5@_ifnkyNL@,,lζXXu˭\/ynqb NˊڴTĮ~\wwI9Tm/Qpo(Gyl75BZ˭Yβo`B^@dJRMĈ$ I&Jy~/jC#WFw& 1r)0J//߁ay*%JbާutǽR1[8 E% qޙ1m;'"A0kiPbחZ&UJUmb>j̋6}_2H2HE""68H9O998yyfr}~^eK{G%zaMU e%5HuiYX ޠN\JQ^~,7nuz#LY֠ǁˑg^{MRUmXCu\R@^Z)ʠnOܵ`md3lj{̢ >8&JԷa/.\ekC];X;QgJ(7Qn\OwB$8PX0Z& JdKbE˷Dq"+@Lۅ?}w8Pcx_tb} f}/-U*-A!n'28 xRMaq r=70& $ܢ DI6 l,7=P.>~M =̩p?B"F$ľ(9/Հ~Т |eۢV5 PT+Q<Ǝ5 3 bSZg5Qyi>~Vd`J]Xcvț"0vܚ?vp6}2ѯ(__uk @_@+Y׺mWmr. 6w hXE0-m\1O}n\D@Qv?kuaZqRw[lIhп\^9||>25y& CrM6a-b SpSMfksD>j/[2n_P)`j} -vǡ<+6i"3}>w{_^kT ܊ mP0!wqQf-b 6UWG14ݘ%j1Vզ(hع)JՁIi~cOKµv/>D*ظ*$l49 D(L*> hXa42wNC^PJhఢ &|7"| l r>h*4oj hb$ ]tkA[ "zV佌wD uM2zggFSa1h$\HZH(]ZfSQŦ<{uE RB#?VE`W;}{yPp 47u6?@y`=c4p5=iEaEbQU !6eFcB0̀Ui6~&:#}/\{E\:uVV^o>jUffADI0> HT'PQiMdҟZoscw: &/KچFNfU:1-ZcGD;h\%2Phe .*=bt50 10!fM9euB|<2Rs8\]Q!4]Y[= }/)2_ E\5l{!KZkmESGwa^ߙP{d7pQ}0 11!fDI',́3x\%iܷk{a } =UB9j 6P"< 0 U#J>j37ÏϺ"?iT[{T&#/ӛ7GJZ'Ua<0U'.A=Tg芖d"ބ%h5ԓ 0*bFUX47MH$Zjۤqzd+#! G^o%PeYmVs̉T$E+E z".NFk'MGYyϷD{`~ ,ʠ|U;*L{{^Q4Ѣ1Zlm4  8G8 P%cPc'NM98ȘMp=H~Yw"+vhV& Cm+y&<t^7G"Z$.Nj !:Hg0MHϠ\ hEXkhr$;}MD(z"s-HL܏Dƍ( uŬS9ovzES%.[ 8 Ջ2{u&jn L+\Yv aP+HLGvhk- N^USt[t#jaB;KoBYnPh t]MsƆa&LPu0Jnhۨ @Ws"j_v|Q_hkjP|/H(|/oډ"5^0 -(iM )`PR\T=as=Q`-!}w~ %ĐF$d@O2¢,[tnarL/((492HA+Q}!1MFKY`KpXd-iijH~*_?,T!(j{~oD'x(Q#7Zqx7My p$鏪f_{1>,7|5Tj4};G6G?a؇#ף6bqMm]QVwH`  עtב;V{'!qd>X+{EТ( E(/߉Wo>a-bFx}݇ht4&J.j j=QZm3of񦹜 .eM!\^)@QP>ߑ(]M>{y$@9(t uNC^ep0G^~8}}܋c}/O0ր&(vfV8]A:#P!e@݄*c_(1CTQ JF`k$6@9b)MTTr>ZsTIx(}/ڴepo ɎZ(OskOąn lHx8۱tkFQQ0 ;aBhr$[ ʟ/˵2n[ ;P14gDIei\Z ZRAdq8y۰h&V\e]{H@ .`O#?VBJ龗_W7p8`@WjiWep#ݏF({}/a4&Čjp&[dgqTsQe o gOmʭi8W"1郪_{^~jQPd .sc~䗫]N{~$*-2?+ lZƏiېrjGU$[xyW2x`FÄQ ^Aj3md8|UPB[E$t@A_(F#_Hl%f"vOkv$D #jԸI P;`ס{ nU @E8н<ĎpU48#Q30([|/_ E٥bΔ6?lc aFFh7d%Ylr~^Mk",J2/JmTyi=@U$4V)%١uˁBէOqnU䓻y@s,`dQ3PE>NBhCtk[^.J 52W{{_}/5>z eaj"fT؃C QuDOi_v'hj./Kj`q+=3M QKyn떪wBIף#76)^ l{EG>o{rY2jKwG PyGhnNAIFdz f<_K_ٲ0 3ETyz"R(^cP~] ̹Sx_QƗ,4-4'Ef7T!XU M֠ܯȷO0\ >|%;^O49;廝]*?E>ϐqǾYA/ oE{yg6=DZ%7(.^#Pk.} l`=Tl; 6L+2ۏ*ऺǫ8n?$t.cQL?VGm.)sQ'Me0۵Hw 3 hXdDI6UR>Hpa+$[]Pfg ;ܫH|sP*'#($r1DbE{z(: '0 CPv$pMc|kE!auQUm`sP5Hd/q (9%aML矠VH(̋G (Za(A4 x%F^P1gzEI!-N*swр Ԥo{R0}-(|/ Q"At8oy(u UCSע p'>>퐠>j%?{Hoh|c0 5cf}Ɉ j-@՜ב׫dcC8%zHx\$:q8&JuiNY{vCP&EձNi~W^ H#2筊ڱ( $ kPƢ r7BsA]$voFS=~XD$4yx$ϯ]%Y;1iZa֚44q jMG-D$6DU*8aZǡ ˝p"J݀Ka8PF:h-,Zz6u67^-y{JH嚎Z]}/cspAPh(jb<}*p$ʦzquaF ÄqZMnvC.J_ّdPTlkWu'5=tԮ{_lƸ^0p<\&.v?QUI˯AFe{;9aHX{n}? >ySEV?*-GU|oaF/BFבh?d x*Ë=ʴZ Mp6%Y;$Fp{/4?/2?_>*F:}Z-… jݎ*`'hELqgz6k"2ϣ ټ(|/Ѽeaj"f49QuEU@IZ V;Q&jQږo{߶ȗv7J3ݹ tv7q8fK_܊E}AmQ5e{6_2XѽlSzh32Lv[78Ӫa bF58U\Gӓ#hx$ڢiNsܬgQ#Qq6jղVXZ')̀Pg_`=^m/8m7 ?7F箢 epOc"=ɢI9wI/`]|/? pL;(W|/? #Ѣ.}/_Eb<ڤabF5C"(a_?'AYsk; i އ*4}܄$HP]7Mۡ){`xeldwt$5H-̭RL~hS(TtD~M UAAo$Cc3+y9Rv8ja ljhR8\V}x͑gl+쫥 Td`yV^lO*ì+6"̽vZ Ara lm~''r4Y F-➉w##5ݾ|qC [ۢH{9U:!6JhZluvj[-39}4onXT{7 Ji.4hc50?F=f ک(-SkB(Y,eqwMTٜh' } 0Z%֚4%jGAmѿ%MDޡ#c@GQmۥuY4_5JZa(xa-Cҙ_A}.k|/?2U {m!gyhabFUH4OE J̣ 9 %w?4S~TeM[v~Pw]QEe+GgG*Яi 2GUZ4E(ExeQ= {}{־o{yf'jqn^{l:6x]<U3 KaxLUUǎ@cjuzJ4NZh `(eO+GqX"v;2Ed[DIGISd\5=?CYmۣvf{y?4:8޺PϨڹ5jE >D롡0VyČWPu=.`2> ;u_%.H4܀|O?wyhr]d /(si~"1*&p%򪭅m@8lycE|&e{U"v[94Xm~GC Pum?{^gH( Ja&LUJ'#;(Jd?UՎ@>;."c~'iz'7e0HH}Wgǚ|$ ; ǕP[[`oʂ\ h3M$:"/QKTzp<6JH _l*|$Fʷ磬{y/{(}/qOA#37`mcb0ֆ-6NdPp,` =܊EEmg~eGQ$۠z(+Tq[ 6 aIc"JM/k$xX}xoJ:XUeC숄Ah~V s"2/Pm<x?z$FIv$ù펬^ U.?-M'6E>w@Sep뻗.Rx_{3 aBXqBmOGaQEI } P#0VEIvj1&~cSк;V[\NAl"|*o?u.c/X38(o|M`VQ}/ PǨ|zQ[dѢ1!fDIv Zm0e=ۡ6Y JCMJoDI M]f8~n9>9x뫒ʭF+"!vsK3sT{>j/D(P¢ =k0ć[QK$d>%wC~MIޙ*k]$^CN} Wz=gg2S?q;^w/C"?m`BhLr_<#14 lK(`!dMRs/_38`BT:GE!cNBͿVEQ*T[{#^ j \S .) ^k6 X0!f4FyV&FI*p Z]?^cfϑ4Ǥqx!Ԭ\%ٞkqbKTm莌_he0蹢 -ࢢ ޯb4GhDIVW2㯉d֬Q?ČxT-W_ sUv@ir^)Doy%Ƶ9ro@JV5JA xW]dz\9˞Fρ:ޤ3@"v7y%/{ME^tppp pD׊wRې-㻭ATUl T>D@J"`im>+6VlϝLG#b 4SQ L!r3a1_la.rRAjQWЖ?i59w*6_,((J\GY (ȷlPH]Lu< gqpppp pD׈!$x,"_,6)HKCTj/l? 7(k"b9$rH\W%x=}e˪lK6_OsUdR2ߪy)eۆPA[XGk*xW2/nGqPAjȱ>0 $+sM6_{7a x;hcHM: E`z^ jDFWPӁ2{{bS8qH݇d=ED)@USZRKv]Vo׵(/(Jbme_'s`R>!hγU#_aWҞi^$& ]Dʁߣ>܎RVP}؄#$|9z3+yzئupppÙ&~-""3E^G @oPtz}HY"YI2 x+i.6+Yȡs-R[P.\y)uksCewa !To/;y%޾i>~G=ѶE~6"f+v= ݋F-SVl̴h#QSE^ɻ/`MW2 #b,Map"#}Q`2԰iTE'y%3|f 3AD#G0J-!2/VL Q{ 8:  RlyFbu)Jڰ:8Ӥö~HD\ "܎z RnE^Im),DID~̉~M*JGT(,G-$/e\"E#A ) EG6?sؖр' E @`?$#u$=q2SNOAmUl F U螾)}R-tٌL"伟G,:888lpDa[F-M"bey%!hy%-Ŧp9"K(H"UoUW@)<Tt"f`50z*{E[@ ֈF{Y/JqrpppE1mE^ɂbS&(r(M(6~LF=>|f!|o-6#u!RD3i/2Q^p$ʤ"`5z(2u aNl Ga[IO# DvNBӊvkCu8]A\󐩰퉙̘W!S屈hF+Qz ,嗡RVцꏈtǭtM)b:~@ʺW<0F"6!mfvOǏ#M0pgGd"Ŧ0 RY{wB$/icZDi, "x rX^5 ppppp p+~hmʢuM>ED6Z³UfŦЇ%Z]<tAQ3a[j?"'ǡb{l|.HT4=f609888lpDaG0H_2s?buq* k77+6|:R)JsvCoK@;TS\bO ʣ#KA /Q_1iaG$ o|d)RV)Cч eTd޻͉m{#߮wm;RwY'Oo=([F%th׀{5$EȜG&ʸ~$̟0uU3jG&w_aWGcDF?0(kwD#Bv/FwCf9EMfsQ@-?*"߱{6诃ï9*_rfoB1YHtLC58xrՈTJ6 9Hm!/ he$A]Ao YWrgW #A05P`&|) E^Ŧ2M> 9`L-JjMTy`rWGK=kFC/(> ڠ[L RDJP8ERCQd>LIZ3iQٲR֠ly(sT?Jd~F ]/G>d"Rcy%sPXG kIO~H( E./'0RvFeE^I#6@)<#WyOBn"[!'ɿ1?Hρ?"2{@W G 35kU݋qu&P1hM"ҵ!CD@Q(H=W\g 6"'63\ԤVbSʦ%]#y%1(-|XRNP(x=6xp-sOAaE^Ŧp'`nWzS>m "O/56HٺHJBTzD=QꋯQf݄DfM)D9mu_sp:2x+*6Qd̍+_Y/jbJ人+VȅhS8N`M\d|:k#b[~"]+l4ʼ[\|f#H m77v"aՈp}v/n:888C$rmz "aC2ֈ̊H L#3A$uG~f}spppppDa+DWRL ?ZW RPϑ 9CfB"_)e͈]Hm 1[&V Ga+EWRcrnSLav JQQԴ9ޯ@gG"伶Նc+KofܤE.玃6GrBSft|M&GeC]%͌I4u9;kr*Vrv@eȷ,d6)]6O:qLdG58Ӥ֏W2F2PV)BbS ׂȩLP(P0d2MN-JGrMyŦ>3k2w6okC^- a#b[9o^OnzAϠ%Ըw#˙sL>7fd)cduŦIEIIKduhn >)uw;蛶=8rpC"dR;p+[Rg%5|ž@)bhoϊ2i'vv`Z3LFʠh+qE^-Ca{  8zܤEl68888l 81D)L%GeکXSFR.tt0bBJ8eߖ$b,Dc#32VRjczIt#a2ia-bI/Ӛ|1ihN{"k?8W3*TLi95rSօyj8888)"(6fm-NA]ܗg]]V Gs,z9?r]HY1wed@ (E#9qL#>ߛMZ4=pD@s$Xp4m3# lj %VemNy%^)l6yx'Iwo>uX>ޒoj<?P>d}׬ʴ.B)!Q$e#֮8w_DlÖ#bC0V@$Q'E  Ȏ?n5Sc 9oQWǑ׽2pEU쳗ѿ_W3UwoIW[@fCQ9k['M\Q\(6)@+iӈA!G8888lp>bk $ PFC0[lm[=c+鱙X' PJG^ SE^+u{i.>[ǀR`'{viI-\>Oܧ_){}ЭÒX? Ŧ#Jҝ8(=69|P8r=)]^kL 7Ӗ hJᄌlޅ8^`ۣ"d%j_ʖCfѤ潁e}_ѡ'~u:|؜|@JR-}3nGDz$09)&=LҁѸN7i:a[#bBAohOZr[v7 # ̵/ ts~Q :Mi6fk0^C}ݚLER}s=JeUozi~l01/ x4љwy%MaR7뀴bS"䊶h1gh`<"apU[వ_'v@`8Z GB6sd%%#x͆/2zA%%`QWm)<db^*":osM{R_Xl 2l?" ?o\1nҢ%.: RVE05~q$[@S$XePbч6c i)}7g?5-޺M[Ŧ;gu};o3{+i*6]N('W=f6ں( 88yHZ<%qŦpfvޟ\E[՛_P" |V;7?`|0MiE0wȏ5L GwZgZ;%hvqfCSǴbiIrp[ޟHԭ}q|nF+0 zĉy%oqymSĶAXBsR?:/FByzGykl-0Krdmwҁj 46#'wc#@ZXZfE0]?g^C̍N~LER"d;@ODxvA$k ]͙"+p3s] 6aSĶ1Xv5pp"[bW:5p]#",R`%[q4ɚZz)DXlkM:#0cz 6 LΉp Gfh64v:~#Z7bSx>+6ŴYy%(pjO7'f="#GOHU+$0qL'̡݈ v?I[N61[Q_(@"#Ay4\pV#U3 G_&GB1Joo0( BQrQ ®OѧgkO$X GDv(K[^dz7M"9Ul \ܴpOx=: -8`<`A`ةx; X|Bz^t_`>H"躁c:˚+~upDlC$ P o@{"35;?90>?568gDPm"z(JTl OB&MaiW*+"-/ԘKsyKV$si|L=Q}cNF_DFEhe&ͅjv8RsMZkK|pHA^zmЖpDlD$XGIrpt2Q ^DQsh"\O"d&"tMKNBIZ!E[(x xö0p@}ضo۵̚MCP`??`y\G!P`^!"EĊMdVj2 |}Z_%"Z]ℬ;[دkaptu$F$`8'V(~w 9ܟ|gZļQ.SwgdJA~i 9)W" n,J0 ""3cI G9 Xv*2q6T`85nQy%uŦ@j)̋x%pE6,[Rݐ TիCr]u Yc0\D 97)NR{ܤEmH CϺ4[aZ G^{hf[;[BphH(>ЂuZwG#Ig/٥[K,u:W<̐HHOk G3m_ &n#58D\ SQ>>[^R*QJdsn>ؔ6/8_ӆ'u~5bu]2Lv]읒W6%톉cz!nMiXe~dܤEiz_ሀհ \tVG~yDd}l׽2'nw? ꋑ֣(ooULYq%"1#DvF5v2~doNoNZWg%bS֜y^9)ޙX/iל/UF̰Ə%vmLO/]-_PG~yX*`8t<_J n> pHGS@Y!3buŐVJ'p=L5"t%R^\̢qR4R>B&8A02C*l P`r0-kf~ob sm[cb,mY) 5e>wCukIONmI{*_s,i㲼t/W\[#6̀g49MQZ!-h>մuݭdA^[N_=DRnFB+R0"/ޓ1EU( w32.B~<#r!U-S-fg.jMM` LjE7{ sfJH"sl絛-{W=MF&P`G_=4U5ߵx/ | GweFBxX>XA_s7䡏_ݯjLY9oeߦڹOcGh T=RHm^*qYdiyp C/. !gA^y?e %vppQ8El+%NgOFB魾*GCx)!w0\w#rI"tW eD&#=3kZز޽:n_9h;$LQ&*=EJAl)vD$l42ovFo#3jڛF[Ȱq󈋗Rk>Dld̎^]sw_Ьܱgv}VXFjo;9=ΓZ5vL;.}ioqTt֥bN]mrh%=W̜w-"B'?zJ u[սݛOTG0%H((DT!BhdԦUAhzvD]y HnɫzWvŶ z Sl˻w+#Ȕy2=IF~ewdeb>K`!%)5M Ax:`8:Hϭi,ԫk>}5#eM}VCVNs*t))4ǖۥbI&4LwGQ t5vB 떂nɣ~pDl rD)hq9919='#+H(Pi#wBjAgdFQ#UD,&Y]%룙uC2Gv@3Q@r>I(m#sd7dFm?j*Ǻ!j;:|.A5Y5J7ކREDuN]`8|{; æ%`Ӈr`eS5;}O\ު=b?}@q{9jP(oں8J}$[^_&V `D"Cow=n2J;1E6fh+?#(\{,BB]1nST"75(]P`Y0]@)/.DE"yAlDf#d<׶ 9/W#EpmKyDrX{`86x+D)4sϝvW':nը>GrDiy~" uoA^KG!.+h4S 68niy~&o-MtppJp "9a;H9CĦ)h!uH(nCG!I"gr?x@N5 /k?l"aIhKq-J!`J0]@d)j}rR2ٶIHh7HAo0> 9wCMdzi>ܞ=" juMpԟu>u|ٟ)-uM_ݒ(ee{ǡ.> gӳ6"y/9A䕭E;?L ShXB lX@Q̝S: mJ؟~&")=x[B _ TY߱M|Oqn0:E_^Hޡ(ӟ׀Xyx}PNmC5.H)l"TkNl?&f0;$Bbdf<)jDL"ů0(P ` G{~ ~r!?R#m93vƫ;Pѷ2WudUsj.*:elaaE%%ɷhyͱ)Y.!@,fG Ɩ/mmxrU<\0䕭\Y7-ݗoc/H ` D:~4"E:ѧHZ|eEoncϳe dZ?Z[硈 <̟+*qrl)Ñtr7QP82"™mfHI~VwD R 5 Jv=;noHu?;7S\?ZL:Yr=%vGEBqzUdt={`rY!F6xb1--4LAE蚞QۥWXjR3~ N4_|A*H-: e%=Jf|0,s I>~xakXߠ]|yZx_Fgwo+)@'!3^; h!rUHJ" Mh9"2HP ")DBil̝72R Ti$/ aȜ2ѧz ߧ*ۆ=z4#q/Lq DZ*|/45s"5sZ P^a!?h0 Gi"b'̇!SeHY$X]@)xٗBo/%}2Rxc8 x?m𡗔*toBj|Cg ,czg12%(Oc|@$ 1d]0k$v]Ƕ *2nnpC60:{5;ǒ|q 6/GѬʁ HEyxMz"`и5?5`WZ^A^hN~1ͳ)@D-mmytF*Ky$"~E5AQ~GJrV"b="Mٿˑ -w("-"h r_[lY=ė"g- a̖R3RvBM$ym?f㑠OERA{?WTiوLz#u| >׶R"yk&"DY"M`c-A;vzxw=44/|(:E˲czc 2iǶ7!'o>S~w";jJoEs GH1~x #ەgʃ -`8.;CjSDBO sk !hLNF _M@JWg]'Dod~͈Yv3v@ۋ {D6 F]*4 Y^ f}rA^఍- f8lyzpFaQd޷x $LDz$O#c#,D.BU h1h۳?"IG"k$?; WMKO4~ER-6O"3MnβD@=5F9FXvsg D؞cq;R:am:""Ў4{FcW  , ˑ7)Syy "HiX=ӕ-iI{>LWL-9mՇ͉q\^V/#{8jb Ы l樫<> D^^)+[]2^AJ\]W6gs G6p/: lH(f0M#S_|w򙈠,BTe(7 srDNGD"FdLૂ运2"'i[d댧x)KRFc|) 8>K!m_Bm|ICH1莔l[TB")LՐ8d"n=6Dx2"=?d:-'[bݎû|h?< hCm0kSޜcGg~o=zayE݌/uZ|1 X+8^Xt |^Zϥ9ƢxSQ4w}$j|g[H 27zp=転E~."Rݑ/V'DC%-4qL'2=>HiUL"ahA !uȳeD~v"}mkDnpdR3~^@>j{!os$" -h)Z#9mcO[7xm4t"6#"9{FG*ζ}(GW"m_M]uo4M0k3$YTһl{*lvg42 < | G?.-9L~=`8jzu;n@/ĒU ?v?=lB?#s3 e7 (-s׵"dK?4&I)#Qïm*s'' ?x[LBѼ ,ӧ6G6!rNɈܤTDNGz/UZ+oJ92F)LC03SWqR^U:DD v"'Ӑ_p=HvC~\5dŒ!2Ԉ=pt#tF$XT A"1?)zO"EHOb#7_v8\8\3賂ښ@d?d`[Z;N}/'i4'{)sK_A*k ''5g v4"m2[ARZ- G6P`*g}ьh_!␎d!"'G_`Ca+"e$,@&Á:սӷ-^txԡEHq fwEdf-#$Ul} Z<)8"E>DJA0ؾ\ԅ+P @Ql8!`|$uXJ[DbvGN_MH4HHAR[(l[3juzo #Y?ߪf %ߵ(qr$4Edݺ!uܸ1mar-FDrtn]SZ$z,-7q5 }>-)EG\W敖wm䡷 d'Qv]r" Hy R:!"u(l0wv޹P`q0ŞH!Roޝ[BYIH1LGAIJLG!a< s fiYe1]dW[_R[w#(CmCx^}l_B{غjm ;v%o= G c?)/EB[:nVnG"[זB$B>kj|xn6~]=Sk92Ab v;88X:r@~aBp =Rs;Z9vF QO!2/vG{u?ZAQg`47>Ǽ=>Gz;Ѷ2ET>TC@gDnGjӶ7혴 +ĹklRa*ot;6!lOOTԍEN"sPDGQzg g)t6@հ$4tBjth|OKlģf4dY"j(&h}=_ޔ6:88rXۡ?, \ Gjf>'# 2!"=HIFB/!l;w~c @ %#ܠ+(-;sfpr?iw!m~D5!5+ݖ!KoS6XD~:|J7GBh2߃O~[n'DZ<} 2[̽#7}_:/ToUo?Ҏ]";a uD:"d} Gd}ev __vK/~6G"ͫ BA^Y3R8"v|IW4JbK̇ϢHNl4c)dp0 BJGX|*!|"=m=UhlMMw319-(2pDDȇHy "2q?{7Xԭ7Qmm;)y|(%#T.@Q <"=#lYͱ&z? ͵&ɟa|ߥF& ;lvD>>#1 GuEH ̖$ 4сZ}Hj:Jӑ)뵂b$ȼC#"(QZ 6'"Y) $G۟(2r"V^A{M 1񤨇#'7fڊ5$GH0EGRNTk4]R:z#(S^؇#`87J=lR~Gb3Ѷ"uGS5Eܫv$@_VB4o8ZPv7pZQH(91iB k9۪J͝UH13)aXb_r +Pn7h^6c~EBdw5LG~M#lDѵR4GGGL!"跟ʼnT7 RGkRH&ËQD߷}αcmi)9ji5Q(mRf! ^(GO;v %R7܉((im8 |/  h>t߈s7&sIrO3~t%aR)[}v)zI+" 9H oe;88lf8"F^0-;"FDD|HuE $n݊̎# cZN@JUrD}H,EJH .-~zs5#e4D:"5K-A{#r+ڂ?"7( cx}k}"2)|# Fa"*2t"LP0@&rh̞l{,2c|[߾HKǒ[;6+ ciĎw=Z#7 a뭴JGkQ)fH GȗD>@Mram"`8[,m LkR7q҄ܧ3f-ݞ@iy~rA^Y %l_DE|iA^Y2|ǣ2఍xK9 G{H(nHizlqX|>^"i;d<#Ahmʫh|4`*[;ϐ'!!EDV_ۦP"m^^o˟ڇ!.BkU9_|*Y)}IJHTYH>vGӷyY{L,Ho56z0ʎumӶ瀕-@[+#xE$8Bcz+js% cs{4vSPZ?w? vy:Dzں;#^s8':5gߵuRGg$fqF>k񭋎Aff7#76w^ȴ1b͓7n`D+  l͑P6c'!sLȩ8 4!1&[ Nh^Jȥ^zϫ тOKw"K0cf  'a#bټXV=SHBfR=n <2՝ԔsQ,#r-_ٿD& ~{tce(`$R{-ADj])X gX]Mee:T1J7q( l;~d1H@m*9M)oDFyш Dk_bihqԚû)\3#ml/=5DR툒#bfc[ߋ6h{βVײC펮y||}~H( WW6 xDѺs<) E]G?Gʬ#~Qy萃 Gճ-梖̾MM;m?ڪ#!^1> )COp4"v#QLc1'ֱ }z)b9R^FmSznM|'􌟙ad-F[P,NEg'[N2~*>?1"]m۹LD HDr %ž"#u&bbWذ2iY!~:NF0.Ei!z0"a(g{P`a0k_ G R K-ptߏ6F iB~Bp)"anjx҄#s42E6ޫϗ/䕵6;}@A^Y 2Gs;z)vMuppXaU[[r}lbf&7vFdh27NG A\VɈpMA8bBDʮ#?b$ XCH8Coo Ѣ 9/Agd9HNH9-"ҬAU5(LEљ9$ԥ? $T.Dӄ^AfA$P`͊2xi]vDt -9,""VKXS[#B9ӎ<d ; wdn<ۇȧ[jDFe ̾= )z^7 A, |j?mW-p|mM4!`d"? XD{?g=6)w0f9mH}|Q7o}BcSzirRcr0 l]?ƛX$92JA^M4仸 0KĞ䕝ev lZߝyZ"sΌwv>I2gmK[?a#prOG x-D"J[j"%Dd*C{xEq{]ʟPTay"&I8wDJR'D6]R#?Om]l#h/wQ} \1O!"o":6/AҟmOx5ٺ>@g=QBP͖ر9Ld&T+ iw{02)^!Ď~@_eVch?(,8kֈZ8 ͗uiFciDsǬ *_EsK4QUZMI$$,ƂB 7]Z߯ /J?D O첎N5]j2#("я蹶^i{m>j[;UIP\DzLzC^#WK<s62]:rԯCd@`HgWtz!/V?~h?Ȟw-RIl,"aˏ 9qgw!<ޖ. Gsl{Y<x) ,!aKlx́lDS)mIr4!#s>DsdlTF`k12cFV/IKj?+\D4fUJt_o ){w/+{++[UGԬɸoQ=|Gl3#vC$e >/7!AHExS1yF=oG Ug8s 4gg.31oT >Q0h[MF$i8R" ED-#/"`-]Y `8:)b Y}H1jAQ! -61ֵ"dP$M%5~Ahi"%͙{ P`8Dx56MY+Kzv G/F-M$mwÇH(8f/l5=#`8zC$.;~#m7z.G+ΑPklnѤ 3иt@9]nj`҄Nl?5fFw9p(6OXiy~K6_ v2wF> /e6W}H??){fueUlD6)%Mu)HN-mlh%͋ߠh#z0TYSd>8 q&Cu2af7;,0|> ]Xܼ]Mpmol̂5oz B"E*݉Htu۟3}hͳxՖd0{_ RZ>En"=T%GBhJ0jJoVSj榞TAmGy{"w>PoIXw(~U[cϩ4!w/DPC/>V$̇ty;5'QZ ]м5~W`yc[@yo6%LDE6#hR>HYrn@WEed{%|͠)0|ʇ"U>e"I1DZFa'lBĪB݃Ҷ_Dу=›H E3V Ɩ="]uDlo;)})2pdrm[U2=Ԯ/I2Iu2!pmh..9;U4.NE_`svCH(n0{ ͸9xrDd|jsb&vAO&:fq۞HqnF}8 bs}xCQ:>/-=3-+`Ǯﭴ!ShޏE)/RIx; \gȇsdbξ)kٲOAl4څ9rԠ$LHa)"%=ID4#r9"$G"%q?DơTDvG*X< -BHi-(~kr-H9)Ǽk""!BOlhjK >IoI۶DbP"3l7{d0MGNv?+(6MaӰ0DC+5>G*ֈ4KH&5ݛq4WhW _X<1bDvCiy)-ߡe"QHuYi]̚+ڴ)IO RzR'_cնfn53ҊfbkڱJEb)">eK2D&xZ$$f""; ]w|.g2ij?ȶq_K7Mx4M,2|@K&MȽz9q"Νº5bz@s䂼&)}Y(=?5ݳy= c' ][Jfw^DPd~&ۡ1@⥺^<+cJc̮}b*Rz.vL!2s"x;"ΈT=Tеnuwݭ7D.HE[bsC{=pƓPHczw HYMȬT;#qz G_Xؔ(/';l=+ A>Q/A͓h;)zyeuW]ݑ{2BΥ:=v9CDFt'S}~]t Fsr[ˑ| p籸2?%5e!z1_WPTZ|A^uvh;c:5bGcL\1!קXc$sƘz~,ҹKlҪRS߇jdyh!3";GJ͑ݐzpmߝuhak$){vzEL3lLDh)O+U5zDs1If~lsv|=^`vل6+"6h#`8zzބg!c G"e$Yу<2QDV|hȵ̞j"3(8\TT~&0 j)4L_ѾvuTa(`8ZQ<ѡƟnAb]D65 elg/@UH,CQ>"Za "! G;ˬI{݋ԭ~Iq7")ҖC50^R>Gf.AՐ~҄cyk҄؃S<{l) "hd?k@&<4_g O#e]$T{2,+?4)VyW[϶J:aHAo'班g0ƬF.n1|֯{zcg[T1ЋGk܅־Ƙ&NJ1A&cL=.5K7wÚFuWDV/DH FaHYRpESCdHhD mT#%f#-`} # ۡ#؜-JKEU#y{$聑HL>1a۾FwHY ː(!Z̶~v?J{#@^9HgDZ n#8vcԂY-RwC9<و0? ".e@D;2= f6"d&.FCo5)`ƌ=qHvS?m9ʳģePRWV\Z^nBs:&IB靎p;oyo!ϳʴ0xG 6GaۂSvQ?ĶAemz/pV R"eDҒC?ZvAޞș5DEm[ HUiA hdB&N?Rk.@flDAo-]{(%Ľ4,X9G65»@ ]p4F"HI5Rz!_Ѣ5~&2Fd$>bL݈݅T!9)M_EB8$ %>  G@ yH|gC<Tg;iv}XջԎQ=;dB艔,Do}oy߻"嬏L"wCiZb-Lx *4ƶ/9nFL`ߚ0x}n8I ]SWϜr GL_}aiyHzMг=BOvLJi2MIo y=0Ƒ)듬59ŷtKG6FH(Dڧт=tZ 2=A{3"di@(To{^RgPK!"4 j.ڈz"9DQ"Q9HIG hD.D@}u@f]C، ZnD~@7dD.uDJha~@sZĶl@jXvB19Ύ(`8mhC$`8%l^mʹmȞ>"IW*kׇH8F6ۣ\LuhN|j?@>d^ QMoNa[lƌs -H4"6]5cy)o01|g[lGs1$,&w!q$tțZ ]WvviyiLt߼wmuC}O~E&ٟ {MC'- 5YH ҷ#ptHX%tRM]ɈD !EOv]NASo#RhƐr-kM 2 G?F p'7L@D+Ⱦ@&Ar` ۏ:DP/Bԇmޖvlu."oؾyƎߞ"R"q~̤hԶnAz 7X GGo*4!wr87H)}?ԿY5.Yϕ:-}@d@Iٜ4Dr;"ڦn.rh1}o%;_Zuiy~cyeqk^o%|^.e>T)m? Ăhͺ_Kb{*G$$:c!Rt83Hyû&!ŪH(2{EMN H~x,c̋iHe)" y_c5"͑DwFf Icp]9́cϩ4!^4&X."YQH=l} 7_6 .C.]5_[gږ2<8,4nn,-ϿHU m;4ٽZ|%Ґ<`ϐye *fndN{G~)s.#v:E;88lpDl DJ`,giRtFUU#R_Q3"SQ*HYjiAuhaOFj HQ7O"!d۞7DVFij>K}D ^0=Hu!v5#2퀈dXAR|v&#t2̷"S$LRϛ|:*ۿP H(p^|׶?F—+~AD6Rl,:H!72I2p)hDӑғ^68]7hNDQκQ}xfrUc2[0( L[׉};O;jxTv?wO]uM7eȤ ?gن6 l>С<,HSsJۿ("1~N0 {ԪcXioej{{$09u u<1\`#7Ih~vo\O?}lym}+ XǾ0܎&"$-ig/m?X!D~hg-VP`Zǟꈙ\½cH DQ)聝Ԥ "ZlnC*_R4%@f_dN"2" iȇms&"CW DBG쾕#q,Dv\Oɾ~|^ZnOq}-{!_ouNiyAHc2_ڧ9b|lLcz=pD\Y rtVm{<"_15LK ?d`9Yb{7'7lⰡi+.FyDzDo%Jڭ1kR,T֧/H51  T!K)^B& 6rD;mHIBgh]Tf3Wm&v},@m$;)4زq olۓJbkւӐw r_H;hlFg!e89 xPsdNBr#W9vf`JD^&.@gPuEjG3wuA}>ocYODBFd<m^'V=jI?^4;ᜳo}=YSY>ֻ_k:g6vU6 ^Q 9=?է_1o^n#1-obi1~_K= $ UZkiyyeߙu *+ JcDvF߀S_|gٿDϋh^\|PZONXmCRo{#%2~xaÉtwٺ>4Z/@k&)b?4?1: 4P 2݂HO? 8# Կ5%Rms-5H=ZDtZ"STz!7H1+J1 kTwd> "6DA$dRBj])zDg h;.Eȱ <@"SPtß39 ۮL$Cs }0 )WرmJ0A'v/8)Hߎ ȼG Zm>-1Mr"%r$RvAft ކݑ2x&C.5vܲL[~9Z_j ퟛ#]`zzPo<&ͥcL/x8 *e U,0KPMi ^<yQ\}!7d>0g.U#@ 7z1Td9jc̵ex6_ [9>ƸN" G3< |3kO 2ϡ_lW"eD@&;;!%%='ٕL^#[WՀHϮ(woz3^HHCO{LotR?DnB7@A7#Hٖ_ԪEgla>"x-$.L!a> =hm|osgXEf]Xf6ߎѭHikBݽC.!E*ݖ)kw -;}owo!Byr QhvAs㷶d;|H>m*'Řskz%͹O]'$Ci&%@KAcR9 ZJ5msϟϫKZ: j z 죻9[3 fU,ѡ[Uݾ Y_2vSJcxѽ,2_696eU@.6G"tSuEd-"N "pqA#[;F6?P`B"#)H% k니X9 6c:fT}֥IN@"t)}>^cVC+;giԯ+A PJ -A%foy&y*W1]3TtoiJۏ4_a_b_[~l{#z~MvDf7~<ǿNeI)n nl}.S @ljN=83b}flڅOuQ\ׄEJ55kee[/{Ϥsw룹Y?7jΑ5tMh׹Â3ӫ^Dncs5$شv/"}ќ cJ_-+[Ӻ^KOmHBsKX2+J*+k9+kK/F#ŇCDͩK=ƘdKօ}`M:x4=_[HDC!݄hqL}9"plno*%ŇPDu' sm˧h9$EčGD!LDa,"ۡf!CfvhFb;<[1-kH zPU| : r02%e'ez${Vr}e۳Rzp"{`{+c={`El$ zu`y?]3u+_ m: aDZW 6]C*ZW!Z*m9 pM. Tqm؟kh9kU^?5ݠcQ\-'l@TD T"+wEo ’4K]G},Rɒm \cGos~\wl Uk;[F1P5^CFFD>BdHMz># Gy彄TCkRW!p ~ZO!4-̖w/)ĺhhM<-H )󑊱-|]lG6ͳ}̌5;pt`8zGBx͊`8oZЦ.xkNI)^d}GezMD'G%2rM"9ޝ̣$x/ 9YWFg[ߠ&SZp1taVg :#KUTjJ_rxC*geꍹzZ^+o&IhNEω p4vF'0qTzGmou`RN{t:2eP񥖖;f.7;=u! ͥyt1fyc2"d.}.zN:#bm2H(0 *h70/@d`%n!t92Ђ>IEl2-~#cc{)7Y,Dz?-y)WKim ")(>m۞4V+ݳr'%(4 - !x-ٟa/#߸=[ZD:F/юvgX8B:D* ;,F .{qC\{=gaP{Jթ^ͷ熞7]ɦ挎{ .oO9eϖXR݇>sS;{)c^qPfv]SCD>|gcK9AVc.]ې393b0ވ^ns*UsvmnZ<`炼2/uDj'KK1 ]SwwS1"xISyK11W6}0Z?^mUEYtKAW1& =.{pD jmӐ4kjMۏUș5ۧFnH1S0,ۗx$# ;.c7َvloCCzT۶#^d;>woi?·#BwA$X GWE?l;*"3|!կ e t_;‹G^F>pC cEhmtLJHTд! L­ڟ3x,6 GJR 6*)[{*<גuɣ?=˴şRcx:ưɯ,oY*QxMW_D?ca^>:cwP(- 0+- \ȕzĴ*On8<|tmO*a}ئ;z k9%[X,J,@>`B| )3H |HXg7gѺcd&8Z56 Vތ֛o .2 G6F=UUĝwGBZیh˙I6=YAZt?G&P^`M0u "!DH>E$Ï`8 r&EM:^W݃ݫY?"# we0І%9j?; , v֢T ;Rrr̓VY G#FB{D$:%qhdmp>H(F0ζ UȌj~ŶgGȥٿB=gٝD4HŸޞ 7"+p}v>>5N:L;+!B! A,8-RЁBB˴PE/:H@nt],`?dgwg==|_9gg;۞w;.+OF :ZݲHRknG}w7^jZ^ w`1cZi.nyJ˪.~{ݽTޅys۝Ax7'{;˫ {;k-h܉zպڒux~-^3|K8hH^_'}P>@W:![X?4O;Sk|n{W9Ɔ5="1?/Vwcmחkw$Yk G}-qe4[}q\b pp}Lmcf8h/G a:g/GO\ C Cl=дC!F%\ذO|92AS(p r^.DR"oDK,:ю RBAG-@$Y#`r{Qo/֕;UN0-?٩h88*nD -:DϷ,}FP$`}v "9zCs;yZ#?kH ]01jp"g6:(,h8XT,N;rY[Sg _6d>MrNZ~bR4뾎ؼh8>92h87[z߮XO}ҥQãE%_Co@1zwͽ{OYp|iFjnje*GE'V st!aQa>m}tjS?6ǿràwe3@q<_1W}2ji๢’wMpfڳ+*,ٮ٘05~2b; Bw3l".PP$v;24EٮQ[d{4|ϲ0Q/-!s%HAd"`z"v%JH"_B~#u]a@)Ec B{HňʾEwE hΰv@S֎}c̬Ss\QZĺ!ߘNvF۬^r = !4nZ!18K',E-b͵~wpw(;ĞGuz_3XMKmptZE}HmG&݀93$j޾Zg'\׋hfs`m4j;eNBsu7i5=}֩M$8}@"Z6+^f-e:uE%Ul}v7up}&Ƙ=4KFZbKҘH9vsEß~g*G,g" "Yg39+lzJ@J| vL K ~,fbR QbHl2_G-=$n"vC _C91R RRdʚ5ȹs~<s 3~H߈ˑ)sI8(p(rw[2bre̴9&"%̻av2|W`9 BdtlXOw=3=XǬo=C<{ h>'j@\g k KDcĆV#/X]~> NOoXOk>fG?<%p )PŹH͝!9v@޷z`8  ?mv@6艱vzl%@wd[TXKF7a8//ءP$چ[24D77mm{ *ߴPWTXP$F GQeKで’+\[U:N5{_Zl@1{tl⭪=f&>s\(QvMړލJ0p#} FRl z6y~l*H+1~me16ptLZmH:Pz!ib]D ϧH,cGմEϋx1_HsBءld 0w;bA)ߒLz":P$6rDE(+6O蔁g=(A !+z Yu{ P$v?RHWBI҂UZgEC$Ss Fl^ bV"sfWP$ Z:ZYvC6 6ؼg߷e.8W^]A+vwCf! ͥpiz@ e[BXO6K40"OX\k?)aXІf5"xg'Q;L^!EbD^ֺ=Ɯ6 '"#z}\ T^~lϽ~O_ӥ}NK^SSiWXǀÊか K^ֈM; LFlЦh?"Jw,@lĘZ8P$vE9(%wSF<6: 14sha(rU(F=i&uH%4!yL(yH`F 76ETNdXb*<ƫL%HiD<̇goL"@I'>dK!7(1شlz>^@L*3=k߉ sVf=P?72Hh8 Ebm#r!}P.ہv:3WnA<ېIh8 IJBعpp+s1X_bYs|8)MɠIK2SB p+ۦD56wWlzc3,ֳ6>]UTXR7jL,b`rNa).g+Jw떝=w9zyv gsORϗ8{~ڈkW-nq];5Me}su݌v,Y;n;`7 F 7 m Z;SrzWgd,rQ Ah"hDo75.uwH_pk:ujo&s Ebr8EP$VvH1O2X|dVwdM]?'ncfyػ"#OҝvI'Gu@$0O ׇ"VEH1E X)c:#/v sCf d 2Af 3c({o}2<ؗ].xPλݫV,^fMTҬ^Spb9A`ε{xbI ֞gZk;UmScq(2vxu,R{cI;M:"ih# ?Kbw:>ƤfXF oUu6ͭ򗑚RZQaI)4.{ޅ]^XsM}um?w$r* vYn{{vS-=:>cR7vV'MuzgbTmOEV0Kၜ>16"/d]6) 8Ǔ݈}TSP鯬YPvCrE 1\K2x.b@ &m]G>m+nڑյimLCI~>,9 - )7HPv-A GT?0Dv~jhk<~/rW+r"3*w+ތK6_&O8MK~ƒvKqgџ*{^mE%Wh>(sbe)hHK9"-mhLKMk`=l(**,YVޭȏ)Xx`&t/M>  A뺭Aߒ쁘p]UbbVn-r؉#\ EyC1dZz+cO6h(Fi"BNmR.X`VH{ i$s`y L+Xou@׎=QL: Lo^`*r_R]7 0# =RzbמF"[˞-Z!jUvdw_@MokKg*,EW#_bD RK"cdڂxwĐL!So[5 d SSh8,[)Hlo#pHnbӛѯHu;#/mC;[h>2`=PFE5=Y3E%_;?-Gs ڐyZ,;۾ےOx]}r9 y<Z+ ZrN=Cyje^L >[%y}o2z~(u]:"ۢu/j忋P&6:<q&y \-?%1~3.4Ж%mm.}9V֪n&>4Wl |iEbYb3b"1UW/z~8HlC-{F$K  LEvb!E2r+|Nc>!0Y]#R C@!{{Q&SP.}IަXt@p.zI)܍@ n2쏘)F4>ĐU"V`dRas,O@ѥsIΗn;J;ڷO7Þ'd&=-D~w6m_1KU߳Kzu>d-6Gmlr ,NGGזDg}݁_bjs//g5\8qWw@\J<}I=:==5ۍ) pnqjeMFY$ہ<6d-T:E2XR0E;ظH:ׯDl0e7iC鲗T.KwI#\ 7 FjΰCR) f4] ,"R+5x+ZGhZB{nNg79#0߸|d]d @%iubm>E`[:TX= pu>b3#StUH,%e9stX9޹ 5h.k6Z>_``78dg3OCo-0UT E8_wn=@|)z_FY95Hgd[!MMO93bnF!/[8N 93v]weVqs:ZmqFbOQ~@ `Q-z8m+:dlla管HJGMLh<7Esh&oFY*vldȮ="aH/",^ al{v s(qc{" - J{=rV6N"!u; C`U(s-tOki^>XQjkmYZKfmkO EbO}ӣ 6h88; )hYa}%%律j7z 1]!:+}_"y~c>D2L}{wbq^f[|{Xe ۡM˩846U"+׾7h,ٙ"1gԘ>>|6WIn>EH:?`վg]0OD>ZiȿjL(BJ6ƣ깢 3U?ڔޮ=!V뭟OBy#2{zHAfipG4؜P$vb^ )};ط) EbsͱgQ/١Hĺ 4]̥Խ3. 7!\vY(d{x%8t#SWzߚ_;:m~ESD>i^P%j-~hQiVUiO(D/vKFAè>glh3 |F!L0XTtǠs^P$晜\V*ɳ8hy16u:@;IjJȶTmFy)/o"ĀeK7__"@4(QHdZfou1~4E` L+CTq3kQḞ`!e@&`=v1'"j8,Į,[z5؇CG9A/h bjV 9s \Uӷf/wn~8b6#o2N2՛W2gGlR7 e=h8ث3bz.gOSPrWo`M, j%]sձzmFbO޳>~AVmlF"1ѭ.u39ڻ`#ӏhs# ߩKEm<9BwW4 IkI=Y6ggM*^JԷWt;rY%ӏv\1&HYӱrsOҁ6E漾$Y'Pd E%og#ц`Cq<0r-,<\TX8Gw,. {b6!}$kuH^6AV b9z@tYo03Sc9ZȼęJ;5'œG4$Dvpp|(K L'OHĔWغ)EhQ4kDZ(/P➱:@Xt@R4BpN9E$ҐRͱC::GfCkO}ps;QChF}0GK`](;XN)ڎG]܄'h8!Xl>ٮD P$;֏DYp#G@o#g"5twg(@GXj1.Eі;x@}gQtQ~S) f/~j8?0?\|]څ- hꀀ&<؆Ɣ6٫R>3H}ʔzynP֫rɚ\n؋pp}Q!":.+h#1~|.*2\F2Ϝ81L@b{ _ {uq<0 MX\TXZplOBHq}-H({)# "HOB ݩ@E4;t5rS@JE:"GH)FW3 ]3y/bSnRww ?gnAeRoxSk V4uF 6W@@o>ol|@yr^ rt2l}'jmcRۊ5$Dz!=4P$hX@R|ˬ&i=bc 7 U >΃J>r鎤EbQ? 8 @s__d-@c0 A`!q )]ݳ'UֶԯlޜHS[%-Ez4’:Uge=18?$&9}>n(*,U-.Bm(Rm4vծ. uJQo*@ Jr9I068j'oVi)WiЀ(kLsK!qBP$1@ p6NI"OY2_'b0pp([4ĀMB ړ9"F;X{2ѽ(5ݐ`4vGa=o-b.GL1y>5 do|{HY`aT=0 ȸ 5}9H#9s7o rqn EbӚ)C˗kxj@wƳ;(ݑ"oo~82)|ZXghX]M6d\LhN,erzs1s664$Aޡ4yvV]QI͹|gl` sKϟpڴߜ - PO/n3Hozxi]Jںi #K"g!(|G/fkǾdtÉ|dT̏_\Ms**߃X틎 gG?RbdI(}$M.,ɡ(!DwM bz&$}тS%qu#bA)7tԇ"z o  OK'6&s=.Qs #fp:Ү=չ3! L㑩s4^g +o#oMܐS׀r 2_apURRTXX#yUbj=>;$<} P}0ş8u)]PWWV^EC~Dگ26Uwj$ZU}kzvM:-";83jJwа"3.O֎n {ԻSiU@nQaɸxw(uJ&@MNVn㟿/O)*,قUZUgY1p3]חflږŒE ,!_EMbCc}{S;0->cdA S6R@ ŐǯALdr3UvF<ߤ[O6nA`,򑕽cT(; 1ڰX"8Pr4? Ebo ;9l?nuZ=?FUk\J2]^(d BحNlmwY . EbvFaco.Cl.ku5۠CO>^̎kv+sCN#90M':d/!pI&KR|~nss-^IQaI tW)G0kk*f1kKUMnWq t2&4K{%-k^ûu=0਱3K>y?qhY^y]f.9t[笌yd@ 5X{Wum_ ぉh G|~]|GWm.#jDCv})ikkZ!?+ >/޹ȧQν$|¥s*rˉYX x);"%7tC;nHA z)wP{t@&P\̎zT[{NCLͣ{HqFf$clLB$Li%yȤ7% h6R{ȼ]9lEUHWɺ~D()zQLPw=Sn\~W@f7x?K)2;as- MEVߋX]llt t<𩝡Xm@d;-`eq6,}P_@Č})Qit`Cm}v;TZTXR}a.>輷ʻX^-шDۑ֦[SQ}3+j̴]iY'eW,;mgM n\md>PE}=:j0~ߔݳӌ]~KD]2ǡ۰Faq8dYq<03%s/v>3*?lI4u:{KW]֒8W֢\םuk΂0cvT"0oyKC b;/Gr&bs򷐟I?~NF t)bC3"hf#@3qHAhGotC1!I7 <HV ܾh..Bb}w bF!:߮_a#THyDr!Psn|# EbC3־Um(ʍpnM+Mu܅Xucs^uDząoF  Wa/Yl&@%/'"?4YTkÙ՚Ӂw,"Ā}F: Ff:,8E9˨Y^:|+[]K :ZwЈ.dVq<h(*,PT O%36>;c˧\8ڵ݁5/:&geq&XsG׺)VGM]YnOz PtXVTXR%sΜϺNq||9nVPu{;͑܅6.p;s]֊πs\u E绮q &Vn}T fyHJyvTN."Kf"uBBXM4ĽĪGmSs J?q0pm4lL/5\LQ>˳G1u"E]@0Ι~bVj?xCAX827 7`0ZEc]H[e VXdʵk3t)rܞ@oJk̲Vo 8EB:7rV XC(k9 1!>ΜvԒ޸$"fs7 HCIz#TYۗ"g\e қ Dp{@SHFb[VfG\OZrr7(ӶqVbmBx1Ā7-.倛Z]~SQaIчmIt6඙0k灎PGn۹.ٛ+:~ZuJBh|UM Ц 'ehyk K&-vV'ͳhOì1|mUi{u8d[uom}ท?CCSPVq8LҤeڀ܅ҢlFf 18d#0[/!dYTX8JT֍'"u| [߬@,z  V{:WOt ch97"h89[YO L1\ޑm ؁{ E{K$1𱙱{gx :pگ{K۞1r:"йʬ5o1min;BӒc7EӞ#?%hOhΩh>Ǚ雋݁^H'31~'iF;Lukh>8{:cib[KZ336ٵwףHpP Eb }bLs= uƆ"p)P$V hl2wbe^Fd02k +HkqVɛIe]+;-YaQϋ K{rZWQ!v@ЦF4lr8s>Yl1`lCVq.u]aq qDZn@8;qTz8cQP*ߡ쁘`Y']lP$΋PY>;3A6 NѴ܎$3[tN烐icĦZtvͅu[b_~ D@]w b]@,Ne9Y^f G@`,2[YiB i{jtE dAs(n<  ~$9ehuڌ^VHf4w].sGf\Hi}dž6mB ,aux6z#`Tҩ +1!KU=6nSZ(Ӭ h' ܕWkQ4̍GswAG"@c?@XH!-3E# ?bGe3;!H,HY|42TP'.-gmLO_FJqԢ’zK{6 ]WiL~Lje{#7ׯ8堛畣͆ ’vH1+|h8MtĜOqZ~.Aև_qZb98N>ZA3uiZ1st9{awsi 1*T>U" ~<v"@`p7YYO#e64ČMA(Z"]DLle9j2'|W sힵ\ Eu@m!R@Q/Q[_tr/@fjkwW꬜ ͏ 0sc5R/}vs$X~xd-8xSy >* Y\ ,Yζ'Zg,Bk\s~^;&lW6{* obl.ȇM/0cº!H1>jU!;k}ES.E P>y>t+9d8ؠC Zypp3WyKڳEɤ#ƠsL0U,G@1 ȼ: Bg#zRLW# yy"p){x#ݛG [ WVի?!0dw̬ r<*6MKL& k&ӚL˄"2Î Eb9ڑh3ȯD(IP$W EbZ٘6Z2<[icrݼfrOmvo> s>\ynK[(P$N+rW4OMᐑU.5u 8>w/8+c̍DDCsu|4l h@q<֔,J\dFAXSY`{aE%'oV~ėvGRnV @| טBb!%1+^bJBX仓lA׀h88L1X/G9XE}de!? pR" ?C&PΣBhb/bT[{ڐ̋1[B jv); @q!7#/jD -t$sVyz!]=bzEb%6"Q@Y ·d#ƙe S0]37ʶ>z;nB:֩?p9_rQ$T`f({)FBX>=1-jEv F,Y3|w!5M0M,) ޮ\)YY>7G)1iSP{jW:S<A; Š]#E%csƷ:-.un3>WHCSr!}߿BlkP$mB؍@h8`˩m0?E˯֦rKVyV^YOWTX䛖*LZ-j#?I f&QMZxe! F~?@fQ[5-Q"ybd|S( , Vl> 5#qbC2<5Yk:)k`z %r݌HW"e\"PNBJA@*eg}/ŗLEb! v& cm,@`4dL[S)#b^RnC؏@_Rp̀Z>WH8mtfȏذ]ySǣ,{9r%oNC&PĎGV_iw>]f\#Kצ+G<*֣y/lH+M#dYe4VYuw69nДDm4ߴ!f[ehyG]F`6=t߁kwG0Lh6vf9 Ksʎ{c"’[4G"r2CQr/K;]dQaɟの)hoh8P*hGb"sy}Ջ:ljʑ{Tm}i) {v>Gw-︐2:·o0b+HGy6p7w?{#77@(uޥ3'}#rwz;"n˙)slfNfZ8 ݀Gޘ:jLtG̟?t۹2hڽ*?t*`rBʨ)a(IEb'"nU 忞hq  x#F'.dވeZ!Pked!ߛMQ酀!v־{8RHWA7 蒕irCcv12AL> &%}̼TG"kTVGy{ke۵ .^4)C*f#XB>X"vm3=k_~8k<&'[ߖ"rwFlB)AALL{1<} ؂^4?2]UXm=kV.v:2Agg/ɉpP$6$%^Gxp0yҩ'yN$R/=ˇ!%mz6w IJ2נh?/w[ #eGn9%LnS KiM>͜eؽ`vn }x/E1߾.l. ПdB}`q< zt,\a/Ç;Lvw7'.;8Cպͽ5&R> ɮS@}-p+л*򓖟*G Hq+lc-V5N".DLSb|BfIA, seHy#@7 pp}(+B 8ĴERsy׀X𗹸Ut#&!MBB^vb?F2T3aClBkgUi"ߴq= .:Y:ش>7R^vv!`c!41s tWtg}(҇'NC}!͢$CX?zE4sNC۶om q[B:Wr_d^(2NDV!pwAldv]:/~T{#t :YVXMmX`iZ&EA%'C}^dͰn<0 KjQ9驕|!y1Q vk/_zչ 2 E`HEL zYރBؓ(bDkEHlL4279H rQ(+2fZ=ڢ]>g4?!b 1lZԬKMk[%̼و^/%H[Az-Z]cBs?o$M|>5ԷzaE%5VVy{=88~gE%QJLAz4hN(ϭYqMrĦ9Ȅ;/[|n |q-x’wߚئP$v%q;G*7'ԢBҿGO=*̔;Q/*4wh|=C} 888 x٘S& ۻ:~;h uo41z8i)I]wqv.!'!?V bP$(Pe ZӑJE;Ȅ1e"6,{ S@GT#ϙY]V3Tw6R#:8; -W# ; F$ G>Gi-C]'J|7aC`= t{VٸnXuU֗}"'2Ȅ[ LZ$4{@m9׃Yps\ lnrMwn}^bw`c(4o7 QE:+J Q|0PEb eX1k[9GͿk!h3t}4gi퐯߃#]vE%ކ'bEq] so9Y(+E}ֹټ۵3ޝscB44oF ENFA-38 u{xuOu8xќD#x ;}8ؿy㼉֏]M8SQuﱿSymg繮U8`ߎbc `g~㔟<^">HE=]Yj HDE9bw.GOG q p  ҭ=vZ}$?%}^r&dCsevL m\7rĠT&#`@~.lo"y=L۠~{Lowz}7 :-^϶Vk@IC u\{V_{($ّRF򨣮Meqb4 _{t/R2gv@C/١HkȕhFl˼G"Vt+QP$mz"fR+xnlG K&:˵_xdK>Ruֿ)Q fس3˜}/̮Fcx1;w/pq)»Y3Ƀ+EWR{iz-t戾 - GADlNג :t8"b܂vUhq/b5E;`kO& f90)jKkدbItdmomW~@'D_`DM9 xMY_T+ xIG|GJhJ4s%*> ?NEbZw>xh8XkAh8H({+I nP$Ȏa1;{ χ2L-*,]tu:$pP6A<;(7n+(oۀnn#n15ʟ> h]# vEEGN~ TRd KDYyIF㺴s~ރ h B&ln1k7>\jٙ9_~aۺq 뺛yp]̝#Kȕhl.#8q=\s% Xf5-hliv_WxwG0| hF b R'&ZTFfIhYl}PXA&"_V5$CXRE n\ 7Y[hA=}" c[=`$ 2`9kDs(M7H?vh~."p5څg&lZF#q̥=m:ڳJhq߂h9U r7#[@so / s^([m.i̷/ ;GtIޅE%u~ ~4/kCf"A~T}nZB--.k]>;৴ŸCT&]A=~A[b-iR 9|^Du_\ǙNj݁ev#pBqi,u m>{mz1{֍$c}fq93U9|7kˏJ~t@=>l Eb;,f9G佐 V+-V ERЎs1 rvC ejbb(J2Z{v눁p h[ E}.2O2AP$fnBb}_H Z!k6$Ypfu dD7CpN*>N}~B8d!1G6W$lщoHC~c8Þ2n KVc؀T E P kpm[n]ǹE@͡B B(ČmˑHlA4eIjs XKhC2 "V$A㌙h^޽E% j (w?.XSp6YkFtTUF'xc6)Rv[%i/A>-GhcR?lOf)Gh=+DkZ*tBK]rEHCANw?UMY;٘}S,}\__[o䟵 -lյсTE@ק$MMWR\fmZ`7OE$>]ꈀ-@<@L:}Tk;Z$ ힶ# [B{NZU:`M=I:qi#_OjR$RDBJSgCJ)_ y]M^NSi?u hTGLNv@ ɴǡxב<"Fdd(JM_wd K^ ɁM>KA}oaYՇ?mݟQ= K7EcuL^U988hW$--3)XL.*,Y\TX8KSIDSyڰMoi3?LAq<0-A@[];C1K}o&4gFvs]h=j~~wHF [oyr6Y?k8[Uk:"M]JGцH&6[13-H"~h\B)̽ul0Z;5TsF岔]\,C ̛Vi(R R!vO$}AfMD,b,C9@uـv'6NO_{?]=ˀâƐ<jz{ϾM@@+C !y 1yKns*Z{Y[A&b|2@T)|vB,cIFZi+;7 )?ƴd3h~OE v8N`ȏƨ>R#Y*C;p5ty3;E zo\ʵ~Tg޲/yuBY~ߺa-I%)α:61'E%vy73 Kr{-FAE + $h:dn|2`q@/(*,y⻮ΊG51x#ȤƳ P7oͬ*_G~4%EGԅ"7<)WR.bSjZ3N2ih'=ϳʐڀXҬGX|LKC?8zM9 Fő0%9SW my^ +!EqyÞߣ3w2Ax|sIZZ?nu@QOB @ 6Ay Wp-b<I#zbNE<0L!G~o S' +]jrI$pq m[?#mF c,9Y@ bc"VUV6ws/8bҜ^$3/WP$6 X_{O2^-Y;FoKzy|_>H;F $&%n>6`g[u}q<"!h{ncq(k^-*,vL߲TOO K*Qۛ~V |fATx`JQaɴ;($Цl!zwףq%4' mL'UhR`#fi))/m^o/^hV?8d0s\h(@Qf+M gu61ЂK/Bʻ)h8Xcy\Ee:pԫ54Ӹ9.gfgH!K7 @y"/(}XE2gdB81E˜f0n b pp0r!b n>3^" s9 )( -F̔̋HXVOR Eb{ypkK|C@d=,-ĕ u)?gY.)/9]]oX(fIDATTo1N|ˢIgoWy8LOYQa_Jouv6p=;*i2"Fc RJ Hq०~*Mu3kϵlB Qso@L/MHj?pNGL:EgC_8E^w)a/HJlϑ|M,&Tľ+OqZt=Rgt #K: fF~2#bx|*=grCF!9E t$R& T'!jO뇓jgU" {p[psA"d$,dF=[=X;ɰ̜yHiuD #H 4o\d #8̷>:Ǐ#`J(2὇X2;>B#fhk5Yo۱Hq<0rjzG/9Kw/٭O,];vc.oⵊ}f8#EGx`W*%hnl.*,i߷Fi~6T7ދ=|QViK~4X4,Ebg#ICtX `@Ja%[]ꆨȴR#{"P61nr}WIZSY\h8%5H)p}|=IYw}2;=Gڟ߫P58|pl׮Eb{ 9@@=:koO?F`OH,L WZD |)bRL\K/k>8oA̗gۃ$Cyg*U{"6(8H|ͩqռ՞d$ڟ@cͶ>ηg݉"k؜$Ϡ yP2Gs"qknq}ژ *(Ff-Gϧ־ӭOSМ} i>o/EbBJ31?$ft߭g]|7d&ƔsÆ=|bNƫf;dr4[c#?Zi> )*,Y`.-#{^q<б%’f*m_ Kji[Vi_~4@d=AC ! VX[X)և㑩<\ C.E~z x22)L]0{?+V,h %(>+Y'9 1$#N2P$}V`[Lƿ|vA3_-#-+}b9d v 0V> ܗHݮfmPW#3@z;zM+RK2hG0އ6ﲽF,49vEh%Jgwi R?S#P9Z?ea$͵p~-$Nih< OzNx2$eFcC 9djSCしʝ588lYGRdMZd!ցV.Az`%iv7;Q uo:uh6@-睏q u&eˏ LHĪRM(}^$G H)=X.BD[,AJ{R>.a.3'IgZ:^ Bȗn\R{ƞWd~E,̧H{Jx4bnBHv=)]R)G,o8`ugw6Cb4zyPݝddկ_cg"tpJ&bp֞Dc p8ލhx9 m;u74C)FD?3<-B  <Į[**,Yx(m nt{]|꧚mQbM=QnSYUBvCWA]Q@Mqu_Ck%mЦu@UZ3C7;M =߰]rrCz )bRJV N&'!&n#B/xߣ+oA@ J"l@T<Ӂ9d=@p4Zy0m0VH߃-xozfmۈ@,y7be'|XRh1N1X[69i,4<3rG^.>*S-GqץuYN"Ko[棹Pnu`'|nGEueEq|NQڤ轝nx$/u]scz={\}ryYVRv~ڼ8N'=592~`gn &-B:}?hR'I2 $ t0RanF&Xz)r+b@SH35Av*R{F?[؂#ɘcGG+)['m"czV ̚G!bƚler?%ַ EU#݈-ۂBS/dL vj뷣~v+@^֖2hR85h7ǐ_J.nSYnrq~ OOQVP m6SnɣOJ?[_zx:l֟<_hLPZ b݆Xu& ^8XzW;Թ. .Tζ"? xsv';hu]g~F>𚁪ׁO:s!p'ncKLqG:0_9 C ,}ȋ7\les'6\u[2M:;]qvntxuݱu]wm&Yq]q6?~5G.|49E $:#QC2~{p6!(])k"Fa&RH!^)z߈8K/^&t9%ӭ>KX'Ef*ڱiLCNX.cm@i ypx4|4=IDM N v͍| 9"_WX\1vobU ߤ% C5 .{-0xJ!c vDhM=cq؈XSYa@TC6m<@@h882&y_>Z /EW[cB*F֯" t x!'ɴ2_򡲈iA=Iq<)|L%8|h^ %{VgH8o1sʖ>4`p;*Hl1ˀ}]׭iVm]?Y8T=A!'#4)z6!ӑRC݅X%됍9|I/@}vz4|3A`P$vaO`XHIu@ 4 $2a-GvҤ,{mppn>ʪG]Hc&<: (PĖ̵D&HT7Y@ |\^}1YA ƶM7 A ܍ 2+bյm#u)b}<@t:<U̞}(XVo1q. Z,Y鏩e54LAspӾ`)x,@`3g"t?kP$$`w06ʯ:@2@e/}֞-$.϶l?>:@P\BX7df~3n /أ;zz8K&C@4WxdG$m%}9>d&ބwIӓiف{ 93C?h1clHFçhf RގRڟtߗ)ČˑYbRd"h@'I Ċhb쉜ԻɛL+9+g-@KCR'/E fS^ʏ_`\(l(B,+x돇j]{ $~n9ż1?Yu{d2mu8g \kCms\U{]gK "n=OD)' _{"=X{ ѢD1@1hgγ[ e^8švG!$w("x)h^.GiV6j6)hsG nrgG~L>b?gP$v2"#ވXX19B>D b):4>G zxB==#Ƌ!ɮ] |<_q0 +|+k&JxRZݭ}giw% D ~5bb()yOHI/{nV7z+gKƞXgB+(A6fC6"fg2O h]Mv0=s|@/3ڈgCs汔YiRE/rc!K!uk5м{vAioL5y' M?AyvA@vտv4_e}"S6.3@@8>e#Pʞ]`|9ګn@@{:KB+J ό8s2{jϢh8;lܺrdJj4ؑ{;wXƫ$[ STXrݛL+ZeVccJPWZ廑YU~cbC]u|im  1+xi\pJXheE# GS{|%2h 4spXx ^A@=LB~Im&L|A>@Ȅw RD@`}D@e= @G |6oT>bX9'#ڝkI >?ˇ'ÑY ~6[YuC2U3JZ6>3KڸTk˳'[}I9q/E ԋ:xr`fb#μl6EE_mPmuEA%I րiE% @o⥿_dMsj_m͗*?&iͬ*_G~@ú,trXN\N)Ȝ[h|]s*;ݐH?z"H^_^Bҟ!IEʹ!#yPW/G -Eh2Y#` #HBv*h톔_^ #Fom !2RUVo/ED<6իu֖ `S7eU5b}{!b B3'""F'R]]+.5dvj"@8 w#yFm>6 D̩sYkzI_u#@WmL<3ˈ:.~l;eBbО@ǹֶPYծD2i6N0\Bdkmd&-߷>>'d6?1:oeX×@fOg` EbeZ?1z{VܿA,Z{`W% Eb#h8x۶6v<SQ_oyU[S#X)*Ȯ"#~mRo%>@" )#Wg'\%H SWP$6-+ITCLgz9:wU-vBֶGPCh<H1 ?F@<"F3Z"\[): cuR kXXCYgue}7d4, XLUb Cp2;5ކL>!8 hB_YW Q4`ֳ?j]ss8-AJ_q1Bb޷|xA ]C@/|6ƹht́ʶX] ׮} ]ґIw$:صG[}/&Գm@LLWZ[>#wTk;W`c9~h~{H)l|4 =#?z91ƞsH>4F`:~=~m`HܻnՆA^g3 kÁx]  99 Fp&ɃAQr;repp[{. u$Y1\oJk}UeYNFNm?U|bwr-Dq>fJLjE:P$6)o5)n>7k'UmX)ho@ kW+V# kHNڒiD,zdy)ur{" ;U"pO+HF 3H)߉d [E 6[{OB s.{[[ %XmThkm!q=St&3Br@m)Rm, pwu #?Ħ[ax #6g3KSII,E,R9(?a:r^C@< h~x'D?"vUy:9'{ *W]CtvCjk}<ƽ`eVѼn7 <4WSVk6-It_޳Zʬ=^$d}v 2Fm'"`;wOڠc`m\4>ϕ7h88>kgj4,e{:lGi&7OCI 'b y;p b&'5UZ'$? fM}h-lN87Z >s3cP$v*r4lϝJ;{)ХY)fsGvFd`mBk=+)Zz!#[#6zI/R|HArHγZfu[)(63 %?udb1D2};ppB(; PY('ps2L~D S+؈@366gÀ׭Dg6NHEwOp9߈"+ `9c ٵː4d5eHihH2@ e?Ƥ ÍfӀ ~ޙ^V_ :`me/-_$?B-_B<4?| >H,/.oroW >Μzf2%WMH;:Ӷ_+f’* X8 ARo}6k;$pSoK8se݁_2q8m꤬iq)ֻ @Sb>RJZFo,85lB k?rC[yh8xoj @>2¾?9jR^G懓ZP<Ʀk !GlH,~c>]++;'b*&G֕$|R/)s~!wbH1ku 㓭Fp23hek}Fc;r8 ĦX2[ijNa,'ۘ#GG^T gu4]|GOqk)I#}Vl\>,7ȷky:?;)=`m9a!k'ӭ(Dlg}emZŨOB3k8pģF\C55 2Qζϰg[ݻso[< }`bA`eUdz0ZI8:^9*Sϔҿv-4sO"Xu0>٬3SibќU}ZZ[:`cM/va}Q>i<_"'("g$xgYl2]F8>=s=LzfXP:d\+4pbwBcգ! CnA(=̧Ǔ:pӳ=}PUm"ѨdVe)Lɋ.i>'C6o؊k/zvW$ѸQolByS]hĀ#gľR p{dyYy__Xwum*aVXKdgt=½]#kx24#D5Uj##_f&#'q8bȱADٳs-lp!b4ϫV"ܷG@e, e"q7^%yuEc˱f.C.Jooҽa8pv+-*}/Z@r8 Sөܬ>ޝR{֭8 ̋'=LiO!jNX Xz௄V7Xcw@x ־"Kf iakz#v3 L/y-ٓ*!1q6M~ơ1=7כNB!@x]G4/EcR`J<>x*etpsAu|L0.h@nhPeuo;y{[wi(gr[yA|gJ '~D$~{)͈*H%b dd3 3!Ee[$v"K ǖޚ3ghssPXr }֏335}7b+#{~޻wKقj4eZ‰Km tuduALMXognDyw MOq7 <U֯{(8œ鳁d1J%bA{ȷ1CP"I^{'?q踙k/ 6oud2uѷWbWz3:CXl*@u+ s)hؕ|Wc7ܛJ>(v2_h珰<\{MÀاʭ[sYh S;!ܺ-snDv=T:{9oy{ i*l#@Έ̰ix2=1n@ אQ=.`>B8d"`+4ˮ_'!p) 3 Lt9: %/Egр0GaB*'5q(3 ֮,"pTP(oO@q?`Xi]aUdgDqG`p0OX>mG PB+MVod4f~J.*O "dЖ ͬ}n ̰d=VBZdrYo 1LNĖANovٞOXCȴT[}lA,Fp#YAxB;hJ-b <,E#AHRhBqSacڬV F-N#ǷJy('@Y;KA-3dT"Uv&ϿjAחW-5xͧ󭔬̞mNmq![ؕn /|T D}l9G8A},zр}My]myVbWz/~[m!4\O4>^KEv }9 Xp1~s-DV J_jpH7ؗ( ӽ)G x 9囐QxɄFUK1.ȸ4zrQj4rt'صm"VQrs=Y=jةA Qw`%\U!*M&ՂndԉӀ tb!\* UL4G ɶtZL!v22oO%bt;" 8 hڱ7T}v¬9퐣]X}Ɠ#Rd\4df6+[?Gܬ-۵gM tџ|3wESj}t{OZvDc:4x/!p;9  s 8*xĴEyV@'!yԼ_Ʌk"l9`}o5yG.D~bg&oC6¬dY)%L0gs Yu@NhR;{_i y{~ebVZ!Q*OAF}$ᆑ( <dNEF3f P4D" 6=l|9r f*h7#vc2Z׀\سuVtգ ᦞ!6rSkm X ($ < {1PHC}KJlux#Ax2k J6ē'}-n7> XJĖxizQ{;m V>HM}-=Z]KCf%rZd6 &>PP4bg",{ M|i~;Wo&+ty_Fc1rGY?e!gwɭ>du(wd.ᦶ㶃3`ގG@g= S=X@ 힎@6zB Xql#тޜfyɞI&& 3]O#氧=4 |ēyWn٧- L'_ .&ҰAOܲ(gu<-mSEL(ϊ2_RSJvҟ"`q͟DV7eŮpmTlO7Qhm܃hL@\TS~N}ZJĩ|KbFq-Fw_$\aV䄖"؃0ǣhdœT"VJ&"'GDg R)x duHd 2FS0bbu?9EI< @Gn4G nd9|ddMh^^rpO8q/ Blu7[]n') ߥѩD}E0 Uē!{"f d DT"v-ؘev pY Hm|V³[Y 9p( miأ!G'[Da-w+p{wJ[}EP.!@Zkon;~y*d@IXǢY(Y~sp2a^G)X ZMy=k(ҝ}j6 :4TڣQD9iзy(6֟kh϶m'ӽ)9'Ӈœso.ie - G\2Yk|N󆢚Y94^Ev`,~9o)vC=tFl3E m?ٵbWة%~/Ht4mgxR}0]lhq+U ꜛWIїmšx2]! rXe!gbRW/ēhԔg ^D=:|nʱʑ9ӑx0~„~ٍBlB1"TCLJBeԶw],cm?1썀T9ϾVBGـVWѽ9ʏPw BdT"6V @pu<x2! \{"^%HnÅ-nēFkLFSkc96,ҹMv ,p7, gc$!aZKg=b}Ϯ? 9iD=b7'X~t-ĊdOY`tj8m}7ދ!nK>ӫh Cݳp/MǿI{(@z[65{Mwn?A{?Ǔ~Dl3Z-kUBѤ{Y]in/P:v@w9sl ~LW_1U"?ْ!<ٔ_#g&!}D+UK6 ?M ._&.vɧq]km}Oz6ܠl@ sdw @|{°]>TfXζ"d<53Aq좪ѧWgXЭfܶ ëVE^ny9ow?fь?b=rX9Ǿ9ahv^3l1cdn1[[ \9'v-Dhԟyy@92Jb[ز(ȧxa1ۡrX!{]G]}nDlK<#jrY>O|/)k ۩Dx233v5=hTkK`mXj?HpX]Rpt.yьl gTg@GT\Q($Ӑla9n[=.Aοe9+M6!Gh Gw][B&Cg.2iX#ub K/GCk{HS' =)ism%b۞Fa⪚fOo`=?c%h\C쫈Xdnf]<>0-?T"a-wqի+^ow!}O#: mA}0هL X˥vOs4+>F _znGߚ %k՗٬5J֪>ey9uƔ5ol3~[,'Ðh9|ζ'O JA >fz[W5eTإ|c1rq.`g!CXȱ߀U'dG"֤9:3b :-*@f# 9Ј6&Iu\Arpg۳5jƒsm-PƎHvCf&GR[dz'{h£.C9j+PhdgHGƛ,9&:w KB@sOO ֢;&s+I%bt kH&&JB3(GA^X'F2D\&RF5`֢jDx2}'K"V/hr;b?OX]'ҹmr:8eUKMך[io)+.d+<3Rd"9pSLm#˳욵Wdׯ!ܬ.;>ؕЛJSQm>PO"?swusʾJ{,'}f h:jD~J1ert.r@!9Q 4+T QjD먣:4;iCλT!O|dZ a r!ng и1(-Ǚ\'wϮGs7d"'vկ Oo k¼UT!\l,E<(; 6%bϛbamo9i"c>w$̹ N: Vg(c׌A 7go2G\Wmh}=9Yjjlb.OAz2q*y<X`ہ_l2j2hl[v-vdY6'",7SQ@vL-=|6E@9<orɖV+g}w, o:r[& >|^i0pm=q(_9,TK*$rm[Vfm*dU#=+И9[&}J$fз/ar=5CrHP ~_JZK>/)4\fe`P4[]bܿCFN#8Gavcoa!22Dl^kϛbYG؍ILj ꄜӃۭ,z/'oT"VO459WQ^jsb6\7X.@@^A`(sTĺzWP1k3#'c?Y:p}јhBBy+;\Nt_j]7G+~q %*Cr4i.G@pEߛH7ZNE`x]NO{d?<:aDv#7n3%TmuD(@ZU~t#4"=a>prGOnޜר^#Sm?;ڢT~e[bȈf!ð۴r& m4MX@Fi^hAAsy&:'ԆIl,>q踯0ՠ0=%7LE~.?P( d[D<w%S>-XXs8z>qG:u3ɱ߯ƶ{W]te= _*Yw.TM1 C GNj C{"v$5㛝%=_iKs`d#C <Wdp̱m@ 9!u)avw#ȉG̻([A:FJ{օomn?eO x%bn0bL#dL '7yIȎBl. -&oAPK뗩v_%o'ӥ&Ȩ?ow2r W=ag>nuj^-Vǫ 1O'=ݐéAwC9M"'r!+ U|dygnE!3 ` =hr G9bQ(Y6ґ-bA&f f}M *I<KXs ҅=^)bu6Ex@h޹r{:B|4(8d{$ҭsP`4a-FҜ0,k0#֗Ln@84ޏ@f4^>ēP?oN:X)]3p(vkCP^d_׃{rN^l-e9D֖*3#P>k3 Y|DS?1|Q/NsN_ 2++Vsx{E}[Ev<ҋw׬-sc6l;'ӳbg4϶ n,͛-,,^S jo"`bpʑ GJ~ Co1Hw!GY]#'u> ClkG)aK M{pQtk9vȉvA=1vkO%bsV#v:^<lN%bUvfddvAn.\[1Kz?wpn:r^="ŷQ>crG2Ss6ҙ0/ExG'&Y@IKGHQ2 1D!&}?/;u~gR]d:־ooϟw0ҥ%E&דMfEz]m$dp1x퐮zMf>q7&O\WVJ_'Pla<8!ͲǣqlGxG:̃L6 MNzP@ & pZAu,ü^k,m,d:-\ :٫>̗l,vj|Ů4;p%pLs+HL-ӊ]2֟ ε3v_=0[-یleEMEWE2˾:{QbfȹGF;C)E}[3p3[@ X߉ÿP1Lc97Phl:b5 QBȈ_f/!i74AnDM 2X/榁Mk4ŞQa!c,2t;#@ 9c&"xwޏyTx2= 9șg0Dv<3ai};L>OE@#綃ɿﱝoxmFJ>'# qbGζ Ȭ'!7(+r"zoo΄!Vvչ/RRSpWђ[zٝt- @r l]Ͷ>K"&fe<~a6dGj>lG S #L/F9i~d}aIIF&wh<دMh -Fzv'PL7xNt7skB ~ ЬF0g$V=tbv$ܐ&3^erlFcdg1h|X,4?c=mO&Z~x%PmM_ē|ٸ<Ӻ-\[f~5sRKQ? Ddକo4]fOjzr{#pg2/on/ M#P%n g9rkE(#[>fCTѡnź33>; ln){ڙlrMVh}Vt4ʣ0me1 F4xZ'Xݣ ir#] K<^t!lH0-`cXHœyև#=7̊@b`mV)Ӷ誵T6smyk}^)%Ů*Կ;׮iڟA@?٫]i { hl:}#Vvνt> }r{?9 w΍vx&a먿?潿M,_umdS؇=2T' )F-d4z ێ0f{˄ {#֨ 9A@T"6֔Gn.+>, F082dr㚊h;S?r]ql r ^s'8N%b`}9Ql@Nl_x2}=f y[;Y pO;"1D vW4D+ rfLax52#rJ"V!X u" b~7k_gdVdrC0K(l_Jq~\bzK46huض.g'{ZL6د$c2 <h{b"RC?Կ9'̏6ٵvU#pWi{d>㢕ͻzii2t3Ø"\i4s1&]X97#7^X#p-09}hտ L7[El$|AjC[ĨE$P1St_''}ܱT"6;L>}A+co Cr~XKAD g2y"~C2z=kW9ي5~y@`G\otife(~=sI>Řmt]kkd;m 5*A.';#g:̀wA0΋[OG3S؊x2rxB,0=h#  ivdEUd:{LCUkAnԼbG$ peBΧ6wmjs9"Ү_@dz5(\X2:WPi4&p@-ZhIBA"|=~& 8@T Հvȸ#_m8=LnLuDm6&8_ Ϙ|+ 2!`[lf.B$juy%'Rb7x2s'"hB: ֨էz(8Z^5F@`{E )t}̲w6 ,G>J%Eйtɾ M&:.6=ou&Yq0 TAIP]cJmo6#sd7;mXerфs<i6!K0?Aަv "^zExk K_|ƓHSؖ2_ؕQ*( 4*?ؕ:4XjʥfDr3k*_K|+} 5ߣyDF*( s? ZqϽC&+JڳO_b/׏l@,Ǔx'}:"'1MXx*dF3dRH Q"TG :"1 b&#C4͐Y3E؇9У=\_4PAUYu%ߞξVytB!J"АNA`RՑ7 3|;ĮaXN'X-xsϵ 7]O@b R/yh#R{`6"DR7YKkSH>d!k71CU lWks [<.Q@k(Uh^wCp=(tQzZ&!*},kSތ s2C_+3Kx+DL"]¶uHLM%b+f'AtE@tPdܪ8-7X=ؽ! +L>GQ{h \D kH#5ǿ"ψ={bNDfjdnDzgxCHyvvXoaY,`ƵŮ=$הO=@5[r6^͝HZ2_RY)v@uSN ys+s{s}:Z!s v gtuq *{tˆIdڒ«ĹnD1G3I3)lYĎGP V @(_j89z{y(G{,kME@xsM/Z@er/!޻-إ!6)v%EF WI#N{Oz 9<:ZnJ#C{d2.IU, @+ Ox@N60,L_ؙ܏\JǓ` l_EgW1b)!P9k'`JOD'i6v X~n=ol3ԧAH?4Gyv;^@ojW"]P\@>.Br͑m'I0EfVAV 09Eɰӛ-,=E0Ja7" yC?ddԃp1uyhRvbM/C8Gi BwiO? %ޯ4:;VHW񋶊JgbLvD" x:Uo廫bB _bW;z`9涫44~X+W+̗<n.>gRS)ιh? uI9ݨ/x2o^Ϲ~sn4V" TkYqpNvu0u\?@)d@y5[%oR2G!frv!@sr#R5q֠bȱ-A*ơuH_E?93]{hʑ؂R2չ5 ClQkBRtߑH^59f.B@:d!@.bi'S%d.G+bAc>pw+vfKxVY{Z<^y'/Z;]Os>UC`x{Z;wC@xj l{n1YHw9GW4OZ;D=cHE\{NH@kG(k1SX;1iMЊ=xpbS Y߀pvH&Z_vA}03&3;-'! { LLEh|`E{5T!SC SVg)> W 7@kaeҜ19.h"Va(fK"C~xo Ů44zǁd\5}C]ii%^+g7JȯBxٛh\c[Zy w+>u'. x}?koi*[)%[ֲh)/˯I%buQ<@pv*`g ]tWdd*[OW##?zLZ Ǖ@%q;=wy.{"q2g߄o[rFd+G*lG!Ƕ 9`V~0rPH ۻwD,*܉|`@z"Gt 8Sd_F kzQK͊oz"wB|1{&wGe(A9>_`yr#1v)u)cLc.RdE'^Q"QX[`}6bښܻ"nW%OK{^}>1O!P~@E. Pu1ҵ!Vgdmŝ9- ފ“3~?byd]lu{9@ ’[}]ňOWHFz刍3shRsw?3x<٦UmW{2ɦwa׆(ǎh=3߳Bre#Gu ġ~guK+~[X5M,oAq]?Adw+S0cs4\YK(vqyved-HoKU_f(Y|wιQXSz;b'u*x29ȸOk@8 i g,,4;%m*7eû!4PHx 9 W#q4k: !hYN-rشF@-9Nd cDj-l9';;8Ov-@ Ǭ?rnHx 1'[߬_kK?ZfyLw֮ Vs 賐sjmcKx8;HGۄdRO\O_@Nesn#Pfgx v_  5ڰҕyePۗo[1țW(D1#P-uDcc6l6uELY+B ?ф5vfkg%K}vv!0-F}"ڇ*ȗ+E{H׊WX;MR59 CA@[]Enۺ-r| "'L4鮠1{m U #8tlfh>WX> MB)Bab/Y@,Ů4]o,G@n/F·c9{?-&j~ltdTo#09Z}\(2w 14o^]!2&?~UصH""̀ؽA *Fg"ݿ@J4αD3(59ǃ㛇X-9q0rD0auo[sDٌһY'<92[+۳/2[mˣ\gώp9c+D$d:ۣQj&Ky<ַϵgNC+잝zN{:ZXb9[oʱg@)V=]LN;63cR&HF@p;SøҥAݞX^E]/` (7Vޝtl'{H!9&lߴĎhG7|ңY`Qb(p.e\4w:YƳbWꊊ5ac ] )%}}`:ƟbWz6!>8ؕUK٥#~ bf[NM6lwC8xX<72{#~2ץUdzdEN'B%abw6#c19ߚ fC4\ODIDba2u~<2PoPN]Y$LF +UrPfɣ'bM5D*O\r<%VQ BlΝViȁMA3z{fӁsSx2͢[_?Q7pq4Z$r5LcMXE@¼z°|{~ "u>P# #\yX님QNhzvVۑ9%1)Dd-" ëoβEN9T"rrfMK7 m`- sdj]iAUuS#֗oSHwX2x/Al\-rҽLv˭[wFs ʫ{ȩWXNAlݻ!Z1,Fg҇`B3 뀴/x=a gk if/: b[>r+o(vm_9ao1߁ͷAl`r+`/TlS@׻@7;SSX[v3d7 `Jέ@K@F5Xzr g">9CCT.2V^$rw,Ñ V-5"εdؐz0aǿēɳ3!'_Xȑg?}ƞ+2 =Z[[X߭EUoR[{VLtsFVܻ b{>;Cl}5e-(œx9(#\L @;rdҩDla_@TB49>r(`ƒ["^)h  k=wYVߝ"|d|IE+} *%cSJ so%EW,dM6ĶR"gC-~0r8rD{nzNg@ɥy8$;"Ǜ wkbF"c'bPXf_Mq>(,x[N%id(rkW#GEL͍XL4GakVb&"9"`Xnw&"ч0ϡPafmؑ9X0Ը1kW 6,\og]@}`}t?ڞTemzΑ@Qw|,ӇڝO K"d6ڶ HWRgѤ>z!]c;>k.'k3E֟يݭ=cfA0Y܁&!ֳHn%ë{e!]`i@h1<ށN o$L)dFHO&o&>YDx2]&4ANek 5{_ʶܝ:`mZ xͨ_EF>EE;~ @"0-$<5rO\*A j%muYJ>E )e!BH9yA+ZĬPYYճ%]˭]A^]36!g5 ( \ D-2{ֆV@"fZ=6Ba\d2ZDd#h9}?>bNU! uA{]ȉ9Pzۮ]ar&Ӑ>pn&@fOH6XDV[zNy+m}U`\@ۈ|[]n hf_rĚ1][C,Do4QӽHWX& oZ`!S\muVGq.=*c/f#}\@]?2#Mm@q6iAzVJގ'ӻwǏ'=Ѥ&9G폘E9aT)%u* #vE79~rM[($kT"qCq. MHx]1r+P`oNGFX{9" LAd=Jd!grrXs왿CN(p긳q1rtc| 7sBa=DlɥԮ [QA9Ӑ֖e9 1Y}8SXx <&qm@!Rje2,LB XkFxmrT6 s{#(r{n; ScV#S@[+KP~J8n1=2=lD:9 4v[=J1F"0=sݦZF Isk#haBKSqms7'cEÐ>D}6Q_vBz{&V*;O 2Ş7zjlS}K.^W%ڻfbW-%u)4N5Ry]Xh\{sgh|YιSsY(Rpw (\ 9@sn 0{sd4iAN"|"a'Ӈ#rocgaS,9Jj8Uf?Eb4S^v mt,^ODRY(xRԀK R9eS=ϡߋл'w"~r`@K AH+@=ˁ7mCS˱gAȐWPiz!=k9jW;L L%bw[]!iy1JR0"s#߈?ʩVV&  w1O;XiwЍ]`KBD;3bܺVmlnzN"lr^B({P•{|3ܕp?0aFkF!֫+;7TL7;ڽ;J{~ڸ1XCQ?n t&m1Vyֶ\4wEkAHo_vv dg"pW&3ǓS3m3n~je=ۣ9$fAaϖ*2#"HŮ4hz]i#39Ů4EM-h\\ ?9k_g4{_kιv5N9wa#&n4)oGn&{?kIUXkd#[nro40-'caEcp:f{#Uo_'ܥ{Ƣ=r +(/BYSvE!"{F1 3QHf2bF!Hرln$G`I%bƓDN bNaĂpϝhֳ*LJJ7r9 /3ڳ$( kw54qit$5l.G!lļB{BS>҉,(XKrըuT$Wr =tvBʽmYt54AˮVvVv]\#s@q䨃m3.")c>?F:t$Z[W!d/z[&0_5}_'؀Lj}d |tSe5kǓM=;Q ǧJ<~fDls hRJPլ 5'&+A ŭH~*v!x{ԿVw!t ;ؕ٨w({PJD)3'"wsb<@{վ8Nvݏl)[w?4F?#shʽMe+ \*khN@>!O<|v2ȀC@rQrs}p ;#zƝ1[ $|{_%r\w0FɬELCڀؕê矊hVr4b_F![-q*;l;71S{6>L @6F]n9"n۩"zb}E]vvvgr= QNQX&_:!h|9]L>W##2PkkWn}Q 7!U -mP VQnb5b~dٗ h@*v(#bR'۽gg[.8@]I.g#֦}!Ol“#t(ҋ2`g}، M=!P}%![zs#ag4yA&3eK* :ؕ'ڱjs1QT)vZ'7M]lpG7ld w(,DrFz}&hbp*\NQ#^{?SϏr?TPTqh"xW|πؗ1LwDd&oFdz=>[PNDX4n@b2 9:~.rsga9HE3H1s]K3XOO?rpw! "4ڝB]G@ʷ 'ӗwĈ`ϬGRl?0@•YZ o>k-=[\Xy&{ȱB7ʵ+L r1+>荀X`,W"@iף kr~^h}9vsG#=bNG&7-K '<|psm} v읇W h!*X?LhZo:orkN5Hφ ]@ Bf/r`-k3(wqahVn8pbt-OOd9m՛z_of6&ܯ0uva Ue=k2?+HYF2iBd?``x:]~#:?K-&]H׳̗\M|+e4sϽ9~F% {#Mz{;疣Ise{y9w~5azrA%6ߨ C3Ȉ|JĖǓ둓덀DD A3D'rQ_bRyA+*!FY\Ч}&[Zi\b. CE#5pZGFr67&69a:sP8c_B v_/—@ L6/"dx b–2 C+SsdRkg.1^јu&Z! Md C:pqbw+?}Yl׼GCu( L05'dP8CkP[)H1ҧ`ۑ1h> .Ŗ&MQCbs.&}=I4\Iw]];ɒ ա_oF5/Qu &0‘Q%W"9 wI?p+F!*VNgۑs?E=IgaV(r3sou|pg\{OxBy EN~=n,@y/ D᲎h` J*n&|6zƓcCq32} `S*[O19N#<qɥ;r9p,0Y >i ܠC 8n7r3QFta`#zbYʭnбՔeZ˺A]>B+Erտ%bdz9O@w%OX?Gf?7͵vҮ2K@X?k>;9HP[`tkg}4߷Ky=oo>qH_69-BTOr(҅w~ ظ&# iut5!f}ӀI&Xtb[#kF)%+}.}6[ߐ>W),a  Q%oAV,X)#ԗhbپEʼ÷<"G<PJ#V2_(v|߄\}V>"\mТhLc!_ϧp?2O]MmF6Cڃ6"hgʑ9 C7½m` D9f!'8NDݐѭFUv+DU0tD97H@LdY=NE%r 4]oThբ \_@56e֖<{p]RXy<~?SGLݢB3V?r|[ꑁTFFm*͟h6@>;g!1GAls2 <OC<\"tZ"#ExM9(]󤵻u,@)ݼr#_Xș%{=r"s >{ nB`T$@-b5Zۦ!vn]ؿY˩CwɽɮR; }߀ع V{_a"`s7^_6i;lLkCGB{w9W.0h8m ;0Gd A9Y?Cpkk{"/!cm~ϞlgK%gpT*gB}<q/FG+d#h[~G}1K] |4^ >=edESi*MQ^1T"Vջ;z/|XY}7p! 1)kؙc׶DUsdw͐C[U6S /b6 5 9>L{>B!ňBAX ՟fPbb,Z'!|]IMqoh׀B_0f{Z"/O@㋈%krC!51 3 93 9|*#Fhֶ_!0s8YT=k[[`)oz ׀@ғ().]OELmmS @]cp@E<b,C~K Cn#Vu^G&;Ys~XOo4}]( {jg!V5!mfICU7O4CzeXHk_]QHw { Ql/͟Utd˒7ΣG~IZk0!>٧`HO(MA04j] bZrfnaTJS6 l hVܖݻXu(gj52VPD t((C=XX2XE)NF B3ȉzݮ遒|ĬfQkpV 9/a/C8s޲=~9o b:Z9AM*"4 v%\DpɠFpL5\p eANv*+L]~ 듩h<\i Dz pM*ZU#2ɸh@+gsΰ>hc߄V ѸB:>~o4Y. ˣFW^ݦ6CmuǽTΛskv$4YXq?th|dɭ- UlNo|H͐}ka('<j`2_5TM18DT'˩|xrh< ''FGxrb+ =̞IYZ{fKتf#P(r)c+d,#C: kpd:{Ed b2N-"c%ayc:PTWd m4SX`)|30-ZC "-@`k_6?ꋀ]'BxT!ِKA\]Xjêg_:ZӲaNeiHt˜hgu=8ӛt k:>tu}sTX"dz3EXa} rʪDwaDg";Z;hL">r΃Cw(wɬ9jB}?r ?t-ұZrz.h\vBzzKVۻ"d!Anh Fz.}Uh louއt~'1$vX_w%sk"u:Ů4 &';;4vE}xj󭨟G! FnhܼtonE6=F㯩|YY[0Y?'Ve:~uj*~ցXiV나T"v%Fxx2XlT"9LGF}amoGȪsQƮ^ U(,C4+lnv#7uYa1ٵ/!!'ܪ@'cA{׳V5hf<ͳfdVB0>-@X rE(.kի %pj ur;|7'!p59i͏ ߬WM_>6]rhG_ @t2&j9ի9yDlK<cgʱC+oD!^#;wK9ҷL֝K)49r#@ y=cs1e},LWb}{XDC}5lMFl74%Q\,Z`~x>þrſ/runǿ$<4}AYl@,X3k+ߍG9 hx0LMĮi\O!*ac 7=,G 원Q$L9Ʌ1|Y #2"vbzD} !\U \dQHGOlb p(rjYz &&vT*lsWiX}m_?k`.D,OG2H߮O%bt1'"rt).M蔂idu Q2Z#7e<iYVVct69a&.ayp!<˳?QNIt-2p0@vc?HKPcŞֿ6AނX[؊'-ј9ݦ}'!fߕbWZPJAr:̗ ^"Yh̗T kY}+]ebWzKC͹{[{WG4i:pTGιsEh_-[/(),MG{"Q*!F %Zx "GZ9L| r !f 'AlL @LTdD{#&I =.$Dư95uE;*,I,YLFV)F=ʝpGLK!l+3o:{xJ>O?œjȨt'm, 닱5v( TWOn(b ^ d"|v;/(2N@YEcGyW֬lD 0\:ltņ l@ҳ-ҏX?ɿXIDT"6?b g/'(`X/YSJ]+%uxٯ NkfS|5hTZKf>뉘ӐNfl9@C;}s(zq] `3)J=bt"#Gu<)YYOo@!(rrYػ6# rn!GUK|P>XtʑӺ9haO曵E0ϝHwwG<Ğ,E g#FT`Y<9v(9' d [AFC·riQ4V G^n$<Kbn1yd1˞y0bœQhK,g0kL "g_J''#{$+LGػW 310?=O[# 4 Z[,l*!3snٯ&ݻ'oGjn.x݃t3zE㙏Oj}Gz9ͬ_! s=6]ܢU>ԓ,p@$Q_,8H?G A'U8dԇ!o&ф~Ř%[*`78t={>+>ٮCkt+lzl{{?d`C;K@ea7NEE/w=}wqWyCP{s%;Uq/҉gLF"fs<2z?E`p bO"ZҖ"61mr1d a}3gS*bY(c׵7y&5Yй[D9bK1ci4*re=&]4]&Qœ+̗4qefT@8h\}~}%b6ȶ9p*;٥#UJ Ċ&_T#s$CAF\퐾 l3>B b+l~*[OOE ]N{ 9 {PUhC1(|~y )eK _E99M^F`4Er8VC4,DG7Xѱ|=h ga2<9v]5 D>x2}~eKDYx2l [@6³G7"&b cl䝀K%bdzgD֚6rM=(0E4"='M)ŮM.H? Ůt{?€' 0r%g1_~(4[ Yh"6XsM&y[UBf11>UU+HCƞEL֝&G VĔE8Wmm1SL^ (g E(,C J;(գ7)M*{bWD@+=|F ԗeMkb=(p {_m7}_99 4xsÀ;f{U@ LwC|lX<.DN~B] V?@{f#<7k7/#S9e8r+D9<Ǧ0  "#^c6ڃ*n?NG`'U2lXJ EwF7om$w@9܇*A"ET`QvmE;b7]innQhvd]10PЎv@$,7"`u"O<6h\V['7 Gt*b9~T"&3O{ so*5L?X7e !cb2l}Eȸ|H939܁-.wӼ-~vtx2=9 B5kc3T&;N7J']Ф7IsVet>ی@D/!Xbɵ(F`b!Vʛ7W]v+~,G2_v?cY9 l3/eVȞmAdr^&ba|uh5KaLu,ȑأ37Ӽ4ww~VȰ@lC[&-~OEf 5(ᆳGJEb-Cc EoȁwjnrYb?Yh:( QgG$r?[f:av}9PYSռ;=Xӭ~(@ETy"ֱ-|"`032'Y%}leGwrޙ~|>iّh>O E %|ΰVs[#P] `,Djg7ֶSXĮ-Eiէ 7DvS_נ11%}^|se'4>@z&FM,?pU5z<4Q5Cr&P{} FL%bIيe)@jL%bdz<-CHy9s%"QBol*-LwEĔ8.B@䤯%t]!\i0n1]' ',e g{P\=v3{fqs̋DC Y\Mj+Kw" >رYH,DoգMN;T_Щ!  ȥ'M>;^CΤ's›Y(GʮpYGuvF.`Tar-FZg_Pՙf1@#VˈG) !.@`&\h>TDh vF`}rc?gX—X[#= kOr4NZZq7EƵ֎%-:=<:aD9qm9}4k|F?OSi*MS"[9a{x2]J*}mTzJ%bňiXM( t8PXK&ēKNAyg5x2$rHPKVYȉr ˆiA#`vͱȩFwB`2heC5 | jr[mwEK@h;WDU,zu!~Yw4@c4yX\!(笷Bݨ_҃>g;".TJS!5JĶKC脺""rנ$〲_8 gV;޳!%v\Ė׃< ǓӺ ⲑZw@) 59`Q(ӉpKQnKc$c%SBbmyqE|r%"tanU[kO=~`%W(XݹԞ1@ ͥ(t69hoٵ{_@@': F`b(BmDa_#pu֦)(Q ߪ(ukmvk)k]Vvd伇#w#os~@HnՃGTy&wA 2E`>@UU11U=&*hyDl^kڣI߁SصK+&ZweUgK^ڴĶ[s|uO]|ھ9׀lXz7|{{be6o (#XdzJ*ٱHg"TJVZ^pwYlA$`FWU?Ā4k5[.lSȑ@IsA`(vo%9h`&#}MC "@QRĂiu92IӸǞ' ,GDwYnMQ@MVݻ 5C (b.E@:9LߓJĪd-z͝mW7I4Yا^־H?gr@b{F0 P@rWif#`7Ov*rM^WXRrT" 5d b,}f-#k*wY-;OAahї`_-܃hϽlGÈ}V';!qw*oaj"jVm@XrCCQP&&ОTƬQUUAOw5X{Ϸϧ"V+pHX]"q tէ#^mxjh1T.;?³u337uT~ZE}%2nnώB/?uʽ|d` bBȤڇLPtF^rȤ>#et̒ITI_{=a@Vb젚%=*zE1fzNtƘ@;kn1C p1@kO}ܧ!L3tOIF*k@cLrY%"^kkƘHxs5aU1jbz @x#5}[9o̒Dr桇l-Ϣ'h.Acx"Q#;2HƱY(Cd,-Ds}9-@a6k.<^BD8rSnm~!)0ZM1ZANݣ?jԱ(^̡Dde0DBDHy:!eʆZzjlc^ewhQΎϚEPL8]m"ZB܄T7ѵxsˍlD@ʸﺺF+>E.qc~yqqp,"ݜݼH p(,-8Hpq|zrɈG "kOE}N{gnK߽ E+!}ֵ 7hɌƓ%bui%}L5SufC&5 x.dROmxG`2\^,1`5Xkcc~x1iۍ1۠5Mq"5wGp0G!g|i "\3ѓ-ʂ-K)D^Ao+Ѣ8@~V٠?b܀_ksZ8F*K-"HKx;2E~gЂoHT|NC;L"8r"!w3S/iR.BJڳ읁(ׯ:7 ߵ$b_xg+bgԝQ'LlCXb'ވxބ޶E ŝ2_z>3HDK 27|#R;#0DkwjͮWz2 +tsΖs"]5)VZq`vǝTÑRY;FDTޏR,Lvc'Ts dRE8ըC{LꞴ W6ֈWL=°Gp74;ztDCi^(Iu#}v˃W}e=kkԎ+PQ89dR6f'GRi^锯IHy{4Ljzј/u|̓KcL6MOR6\Ōsc,z4lcLZz}ԤK%ѨJڣtv+]o16t܋ayd)>PR/D.F&z#2gA$MDzTZa"N-5x#a荚%־xaxxd:꿻sdñnSq_ ח_=w ]u5ލan\91r)N.Eb=ĸ&ʆm6O._MruچƙoAJ6xuLz胔eh~F(:'{~S֖EUs xGԘ_=OZ]AKݼf@ňmv8VU/ vIG!*Ev[4G]ڊ#/ܷsmg%/g#eawrQh+ݶE " ]B&5]mQ׊K:ų_n/P7ܔv)ۻqfUכ?:W!OȤ#tٟ+B/szQ^\cꅔ7c[E^~D2oxltFs6ve`jǟERx@&h<=dX2 PRTDЃz.c>w Z[H}4-ш`,Ed%"j]_GhѽV7"bu-R>H٥[lffȕ՝f"dʪx˦wF""AQ zSCLj",o(6v?t})Gtc7x/:]#B2EѢ6"S-#"$sw7z/ нOMQQۆc:u]ݹ+E)'"[.<9PH^u!*|^Fn"G@&Ź9lȤ_R*#R7- n?E.;]_n.2dBfWn:v;=ݺ_:㴇:5C2 !i[ڭn Rt Nw7`إI_1!`zR>~ "%e|#AT}w";6N :d :sؙU)móB&u;Vp!*GE3 X-BH9%̠o47qr8={&}_?6WE͞%bHX}>Ɠ#\&z,A [G@ ^1pZ:#cz@YP,Co7ɓ[y3)-ˮ;Եy%Rcs힏Hq9w5}U5"28aRWz!?J]=,#LCiTmp}{Xi('|Aym23 nێE›} "Ђ#|ۺ#EM$ѕ*vq(Zh,Ǎ~ EA#gEG{/nf-VֳcEm3/wNR1"[/tMAzw̶(R.szD ،k" YѧZ;|'bol%ﮚWxHݲ=Em֭VCf>ӭVN) []5ksx@qY*|g+}F+g5X˙ DosI! < )n6еjj|o]3"t}F.Kx m6dR{L9#z6l)Al[tf}1dR3OޏKs[:6{"6Ɠ=Cs02$OA&ĝɡ mQ\T ed.ZP*1H'm\ikLӑ* +R!Wpd s2액7 h1` DĴCzxJ~ F>ӟkߥz^a.\0}C_71\6h1y\1h:)ou$=t<"mf,Cs 9r)Z|"hYd"Sw4̫YTvl.HK#%" "^Osq"Hxþ-G]wE\@t:m n$bL4,B6og<,( {Qv_U,K k_[>t{Ձ21W)m9] ʯ{[݇DȤy.r!rwB!. H;淐 dR%:7Ov.G>b  Bfs`^V!z"ưnxM_A|Ժ1D׭G#lUD H5VXw4:\_E>O"x2J-|o!Q ,J"7z!R+%b] NCə7fbCoe^J\*Rkpu%7 .H"\1{"nLۍ+s4D0uc-D!Lm0/o.Sm^E`dn{AXː)sƼJ ?ׯ Dj0 @a:qd:| Eƞnq%Hj!ZF(="KZ='Z ϸVc-\?۠ܝn̿t>q."qd~J?CD;\E(uKƙۏDd2;-K Z7-4Sŝj/,M¶ uK ֙BGPB=(ӧ " : }Z0ڞ, iկ)7۾ vGum(mImxU3&zۃ\B?k*b@&:}-];) xxx[L\MnV!?,~.eZLv Z1ܢ?e`? G]?!Jjdte!+@5RvAfnsm/Boٔ !2%58:O.v흈L%Fģ?R۞A=z~dp) h vnjH۝0I@ s;E#b1Z*W8;tDw CSw<h~w"i|LGj!&"eH]=_3Z5aN"ӢrDJXf+,t 2;?Xg. ~n9DZ_77&b|_ZD)C:d9h|RfR]TEGZjtRY]ܩum] 5mo- vps=|6'[k?o11lfc"bkB"i`'D,>IaF:V8=r1Ѓ8 SǶhEZ@%~ RE).ADe~2zNG$Lrfc4 3=H:F=Ѓx&zoc/n0i%"Lqs?DZ!G  X׷r5Ə=z#_TcN99vieLBDGwrݎ|f#rDb+tˈde\ mI.`"QHM4TAȇp9"zp.@$xR/8#h5o sh<&Wt " w윀H`= q8?[ENT<`N^ǣd)уQ걢*U"YJ#$bϢ䨼|!י\MdIisl;svbԇ;VG4!c&}?mݿ]iY/ʗY1t1R\ssqkz1辻̍mȤOm8?C=FN []Cf2mK`t=/&|`Vsf y.rn5u;f[jܝ"b@I;7'u=*9O=zptwcLW!%dj;4O~Vn<3=OO"+=8rKJ+0EWCH"D`3ҷl := D<;6.5ڝ `3xca>.CqK`]lѣ!]}urrچ Ic!jՑytHY%z3G/0"sU!:%m_{k6eϚn*rujhJt^ǦA<%bO3= 躺 zE?6ˌ1%bc~֧e(KĞZ>G'8 v1fzo]> =kA )GP}uEꐶ֎u}b1c,lD =8JYm4 e7&vvTc Bԥ Z@zD=7{Ѣ6Riwrܟkkw>\Њ9#OGzST?#"/"%YYg}xݠ;_u j Oh1~#"Z C*n lj5֙G&HBo\'!rTKxn̨Q]Ee*"Z n]!F=#kgH,B-!"1- j~Tdh $,dRFĻ;, ˾fи~S"{gu˂b`׃*?N[S73ypt݄]Թe#`49>r%fs׍+:cL-:_Ӏ׶Tk{{%E mx90{4ی1e/1!t/i]KƘ!H"CWnV͢Ƙv3od~dԨvtm뗓do<]M7/}a-DZUD,U4f'bM|)[-EDĢ}StC܆8"3_#F!"8썈O(F[B-ddnhWPY>tJYE4EdD2!,d2d|b ;>,orYq{zEɬľ?z"uKQ2D\uU+[4|EE i!f H k~溱d"+Q{^Cd2d.v۝?] uHa"H!ڥ>9g 0)?>szƧT`j"٨x?z+Gf"t~j_V q>{#4QSQֻk+(DlƓ헌-d۹ԢE!yʦ)oUHYM~v% ٜvmmB&z!Iغ ;-,ۢ/Wo_ kmj ˜ƘVf;hgib鈞MzYеyѿoc.u k߳p_/slc<}=g rn!kEƘwuƘ=780G~C8Zc&bx _khj4=u>;#<Y@e"FKb3I٦=\z2\D,2=O>Ez:;X5AgT]u7C d!!l4z_ntD t5A@(E2zh?)sKp;hc"Y'wB7ht=oHC&(2ხqB&5Gzq?khc̗`&@eߺs>R.~b̦ZڥƘ{5Ω1z6uj'8*K1#,k >iH}Ps'8u3a[=[Aqx2E5+ ~Odm6@Wh4z ٍtz)  _l֞u)9GQlRx bUG@ȿwOpV;iӗ!DseVȠT|.JU 3bD!XL+t0Ɠ'Ű -w}bDzSHȦ>x۝^BDu7zrD.#b$kƓ>؍FɒD,RHI(uDr>HgX$돶:F;cbH l@_'DЃ3:s^[m'^lB/..,=+vr Ի [/䳏\T]nJ~xΔ4QPdu ѽ5 z Shtsc.EshQ-Z\;!RD7D"xEm2n~En'"_OAoHh<+" eK>s Wy7ޥn.3H5:-Zt_qsƹ qvtm܂MH9z|^F$7HꎢS_EE$*k|d}pYnG nkQzםݾے}6\@.vDN\ί)5m1}Ɠ{)>t]v.|t Arļ"MKUHIWòEwFg"N(dش~Ym8aj\UiP`)j8-u˃Na$ uq7:?Bj:NG_6g+mÓB&5Gn( Xќd+ִOVD,߁ˌ?)'6Ȇ=I "][?rRv%H1?#3_oDv&L#ݑ)o9Z0ݱBxxLj݀_GуSD2$$,E E@]27/Xj@E^n\hǡGz,)*i\8m@D,̑RD.Aȟm,z"ɏ"JԵ3*Jc ,T1Czz3]/pŤ;R5܆L|ќ-B2dgV+dLe/@>#6Qju¶OqXqgP](7.X~/ ȔAC;蜜yc˸X{×-Xx"h U_䋉X$}G_k7\~aH탮]C kx<"k;k7;X4֙D,!*"eCg-ZP*,(cˁ C]vdF`wmmǕ،Wi 8t}t$k談ǖc [D3j RR?wrFg'C}B"Rq82MΔ7E&)_I.2;RCҒS,RC _׮:Uu+4Cu)rdNȁsoD!d)RD!a9 vDJQD!.upiv%v󷽛*:ps@WW׋aLwnH ܓS4,I"kT~ȼuWCKGȤz 3d[: 9 ; Zt ߓoF9ݽrQ}jnU5[$:†Nwg |k{LLê2ݐI<{&<<<6cHO#i)""A"E>BJGZE 8d6[hAJ3h*E %,IY[H 9CҵRu)H0d"Զ4Rz^t? "3

rs+2a91"jdN8%8͟(no#,d<oQ A JmHļ2 x!DѪݯi>FSִa *reHID t }vrk$D@%5v "ai~S{ܨ<9ꂷP]p\lYnoŴ F/q8^G܆[!2kOx*mÍhE.j[T_rOچas O~ڡ(f%:ti%B ghAZoJ" q꾯p+M"'OC:\b"|g™ r`. B>QWՇq]VDOB$}?G"i>R "bD@楐H-*BdkGD Ej77_|F#sA PʋوdZSX$?cwEHwursG`y#eTwBdͪYtE湲C"dR|x5}eyt$519G(G't"֋/ؚp9Db2z1XppEb%iσx Fi*=FS8|O`D*y ?7Oha!W'?蔪¼z# 80O^EFC D,`L'+NC bsF!ia؞Ex{ Rک@&ވr_EjEedi(k׏QBRIE*ݟMhD}-1|ZF T,ᅫݱpa#7sd\26U  k2ol O""Ĝ0܋^>iƉX*͂+"hƓݣ䛉Xdy":Oމ_]{LIDATWJ8fo(#RԇCOb'֗ahNBXNGDp`'tsQ"C=R@C_FLEkg9Jn*Z'KTz Xd̖nyR?qcSm-Ɠϭ-ʕMN6"b5{(BJ"tmI!W6d+V`4Y4zB.¹_/ r7ygu zvyxxl8x"VExQD|6ݑWq?F+ @wAj\rD%oΈvAD8Ɠ~"o+񊐓/Rؖ";%D\4 Ќ%G96g5x\!-xbD,V)3B& J[l9ːbz :giˎU/ۮE+1u05mkFuE)VRi= X E5,J(PKxxlfD9RZc(0v5Y{"*R#Ar)% "R25Oym#sh yhq?RZ#dR5YWFEV`/ dRߓp3׶GsppUȤw T{tOA:lLj~چG֯ .^wkEs!}_nȤnCQiެRxxlD"Ɠ7!YV?E6/P"y1OL٤Q>ɸ$(AG4,Gf+O@eiI\)IdRd.ŵܵU\W:4A"/|"OA>J"{o.J[.E>\~WgPʑiLAȟ)2cd,dR }^"'ئ~~Ò~8\`MJm x2dRWLi^%>l:ʐIC(Z!zDãHz_/ƓK"#vGd6ci<3J lO̒QRȃnή0F2B~}ߢZw"O+rbωXYi\BPsip kkrN"15e!8EDE` –+ô6x-smV(^O3h(x 15RfƩ,EDF~tm#%u"dÑj "u;#dݴHD0 Ow'_t __r+A"YB6"C9{\KsrM]mLy=,x;mÍ:*ݐ*OG^A%c:(EOv՜w!:M/HշCچpicB&5u&Rʐ#/Ϣ~(!`gwٿ,O֠o;i~3dR}v2COޛH(/Zk=YmD$bj`F<=mAQ߇ sT$U O(KpyߏTV5KV_޻6e9s)nKQ^G$쭍q0l dR);D;m5o,i.B&(L7/K̪w}ljmEjZT+x9l(R>$:fd#?5OSHԭ#QלS=h>C&5uH1|?L۰/Q᱙M#GSз'w&bb|nOW4|Qmw,`- LvDgb|ٱoovPѯP,\ P'6ѱ-O ~H~IsX mש5};(#mÏAk{Q2_PT((EGB^݌|Fvڻˎ{:T*>2mÏgv%݋poRxxxlDl .\vxzϋƓx)&a ?3}co EW3)s|Ay =b/zj F`8V)1B&U5W4-%fu6Jr)r/B*^XGA6on_SbRY&̊ _>uX\L[t ǖOZ6P؞{^,Zضa/ӜEf&1[k_h<0;4;>A G]C;4U_L)hKt\Ԫ|ch2!g 9#W6ʓcfݰrFB\F|/ Ի~95tA>{fOZ0V_SȉwMh`>f߁3>"Xȿ1F$l}S[4\ucs[́#ZIupȏ tykOB&: 9-B6=4L8 T㤫E(9n.|>A&ȧqu fOĶ4X2Kj˨4g&:˥Xh-!D*vEv@f´Rm!ԫ׀y! #-^DijTyxxk!*jפ"F U|#H}\ UB(J"Kda"YN%Ǫ\QNnHI t("?6\2'O:଴ 7Y6+dRmV2IJ& sn=P(M-:$}-kq>6f'kcKAȤ:"ruܚu)IO6 B&ϕAwڴD 2O]ǖ֨R"Zel0GW>jJpiF {A#a}5%[6 ׯ8R~v"+\ؠ;N= rM[jP]fewѝEjjX_'x5ZoGI^D -f`8AI;Pc(w_"/䷓k*t#u+FnɦlɖJ H't`puHV W: ]IB&գm}@bnȤ 6 [a [V@@~`%.@ 6'@,CRv_V@AaK,/GTDhԎ=g ? O?_Jd[!j"{ODHǷr=J{+2ynK$<<<6oxfh<&O'}Ɠ=c-jv.u440_.5uuA4'Ckqv_YfvYFe}Yd*E>\PfmկCE ~|l'CNPjյ^Z{L7l'V g*5}Hp^AچǧmUJEib N6ePsPz䨟q}<8 8ydM}MA!]MXj[z4FozVKVNV gP` )~(s֫eԾ5+L4Pp]ƞ v/h00Sm^FQ|-YpcQa;DdڏUIʯIL ?$_SˆL(׷\9xxЋr%"ain3 >}&K#gT\;RnvFkf#gˑBR&&dR'}l!R 㘥w. ~Em͉X 7J8 ES1!dR(J0!߱eh>C/ek"S\G9ـJ|W ?}7ЬW<<6/.:LꜷM=p_\]i^@ 2mx O#?|ӵ*#d^aSPZ`V"yaum*dR]QZ؀RCt@DDG5ǺyȌ  9ii^S@8dRGmz߂'bi~}2?XliԚ`ImD߂<h72P.$b/QqisXPf$0 UhHY61j4柠HSj k?2cx4|E{#V~^ޱãYIMI鄑T E?GEo&g 6<{ ڇ S<iAfIE_+GQוhN@9 7Joi>H!=<<6"PԫRDzq} pQB&u%2ɝ| <V#b~VQԮ(JfY`*p*V3Q ""lWc'tDp>B~LWemh>puA$[pD  B+;;&_2ۏ #Yi3xTdqX#,RHm Ok(mƃ[+"iJQ ^EMa/ |2H-;+mҵ1 †j}2O1/mU@UȤAf|Bw9"h!+vrYTcyxlB8UL|H^lQy(GV"k"#IIm+'C 2>,dRW@ "p]_m3)vuC&u`Oj=ki T뽱9Q^؄Hp#_R*בHd^ Q}Q2dv{2j֗kcʴ.2eP>``JP}hBji{rռ‰!z(H2>(qt(P!ش ?(Ew2{xxxlx"᱉'oS8ǣFH BfQBdlzLҭ]t|H_'u1{)Wތ 7uu+2΅Tk; ]KB&8'J2u O<<6%lWbmEfUǹmZʃ(UE3R#?J,EIJ끻\SPzWzjچ[2(X~`?C*4=Pzv7`=e [=<"'ARw&b6uD,R'_xEWOZ5ߩ[c1/JOIn2Rxf"U#H;"C,oT2Vcs$Hp-{6X'`aچC&F< ͕E$ "6V[{]≘FO_'d5lVK XdE(7a"IH9)c#2/*ny "V _D.|kD򾅐IQJnM v6|z\Il4j)zyԱqH1(I3BT}%bh<3 ='?Xdqvh< )gHK@MaXүIq"r? hKɰ̒/"rUH8(FNhNLKhz3ݖM?G'@ T1IpDچl_JzÐ:v(796x>;}1D,2?x2'ˢx(B-*KsO'x<LCX1"Q 2ݍ%?A X?N ~ [HWwۡ\[C&6&a!(dR#gC3].ˍFL괐ImLBL9Q [e)@"'E{X$kZF=JtGދƓӀ֯2Bq((-$bG!Sۏ XdZtuo$L]~`!sV՗Q51+6VAhw[;.1qKA\Y0!;^~RH!LӐVچ挣7pLpEq`$:!C7(dR{"?}јjQde+r/Fڅ(mLJhmfCch TwƓzlޒw:R8 2%MvveQ;: 2-(\lR'^cx/@Ȥ꿝GMw :L7:߀d4V6\ᯁӜU)z_xSCf9ȗlD!(jFy~,Dv@fݐOkK#r5MCkqkhN:!? tzLYb4lo~zxxlI6h^/jװdk܅n)Z#s@o` psaSU>hdT+DGJ6|>ro*EFd4 ԝ/D>Rs а2C`܍̣u Si^/5(2t$R6860dR`Tcr?t]@c"r{? f|QF{آቘGC4,DJ N} |'D><@r4{wY-()>SK-.un>!9AG82Um^I٧(خh~:#Qd}ۭh^ADHR3pY-%;!",!D6ѨxȤ NG^<43KR4'u(ݑX+d]W#wQ{8iwcK'b-PWH88_#-h|qP"̲uڤИ ,Ip6UQh)D&WEb_Rl1n(Aulz."eCJ~']Tq j/^2w|yH66A !AĬ7REd]d|Ucqzᱥ1Wƨ?"W]2[pdlM9lۚnWNp5RGZ ihA?QR"Y%"sv$Hғ׬+̝,"'i+Tmچ#"{1pLȤ@Ifkg/XpL=R#) EE#?9 \|syo[!<hiԫ_"B%`^|B ǝH1:Z`i`y 9x/*g4\EΞ@Uچǡh!?.RW"s84SPd9M3lJ"oQ0**81s_Fi6Zsf@eADӖ~Y 3p{iD~\Vwx"0 )B_ԔQHD"tK#r46&@)rHEk;jqD mB&U̮ǻϳ9Ѿ@d/AĴ5RN@D-H??OW)i'"ȯn!"ĸjC&GD/ ^ "~{y\`EiFA6]"wRi^f7Hib#?׉Xd\4T9P$H-6:!2Afh<(CKBoDxF#UHtDELn4X?Ig!zܵ*2Ip]ȤDfB:Z77,FіnQ5/y21il1=<<<6<h 5h9@U4 Hъ bHtO")&O|~V-$y;2ZDJdE$ t2+grDKPH(B R}bD "]ݡ9ſi@Q"ytm'hC mK7u<<<-xrglϯEΑF#[kWwp3J;%LƓYJ";WGd;nD#E6-dRH2E|JCCf6( a1JE]{go#moDW"8h@"b d+f@}SQ!*Y#E"טǦwh R?G&ȥ[Edb0(OvB ΦhTd~|Xdz{ 9 s̍(EA iu М@PDyR"%#m=r6wߕ=9{(r=iEiHXJHȤNmbOg׎_?O<<<6xӤGKDOdB쇔#EG8HG9"gt25 G_[ :ʯ&PA>NGSrm}68dR3*rjfrYkس9Dc+]{U!+|n6 8w"|#W'(kwIIܴ ;%lOr ܷ2 PG>BDG/zxxlḌ% *Y'ۡE|8ZwDdaw-Ɠ"H5;˵0y ȾD!5|d{iPD o mp?"R",B.}HރTDJuRC&5cy'7!%"3.4܏aB72\d曨ۍ3ֻN}C6*XE;{(82? 89Ɵk{(sx):7>] R\dr72HQ;"g#-xzޅXd]HX4O)"?7!ePQ\B~]g#kn "KAQ:ݧ(-E%2Q.CJOҖAp2Pk]˦Hpmy]r̕ٱYu^hihL`YND6@\;TG42]n.܆.ȟLwv7~ H%"7?95i\H^+Ch&LjD(ČIGjTd(Rtru?Qe7dl@Ae#hA!pٝ uH.m-槇'b- # 9D*ՈPDH^T#%h)"_ ծ}ژR> B>K#h\Vwf~6mI G[!7P_D6H5D׬*>RGfyLyOچL=JA2 "n"r4/ vÚː)X gB&uJ",wH}b_ȤK*U1lnzo"ߡ2pA?)**D%HYL!²ĵ'^Cg Y#diXd6=ƻh@JնTB%/4]|-G `G),"25dR{" )!Um:Tׇ,͚q<L4C\[飷{0mC& GA4lC.9lrB; q&""s\,mHzZF{J\2}.p+Rx5Z2tEuPDed",}1+@&ϙn(W6IV "RS Det@ĩ8}~2Q EBGN"y}.JD,8" ES0R+L{5k=2]P]ӶX$E^K"vSHDY ,mÏ!Rf1.B.d;#/! buRhLHE k`kc pJȤi1֙_E]u<<<<6wx"RDˈD,$r.D&XP_LDdRiƢH :\s"ǏEYnR=x}mx '[gNB~be>>g_&c'b- &)Aq"b (2 EErD~+d}*䋶2wtWc $|^26b 2QAÐ x("LG_37y"Cvmм R7s9K@jۈ oW<><:sҰ89v|DJO"d"RAT R H'bx2|&"&Yy-"!sf7׏ RAjx$OB&etچI_D㛎R#DD?FsQs)_5GUC=ړEU,H(3zxxxlḌ9!k@D&"|#x'#v ZA_ H K#S?P&(uUH99§ÉX 3N T50#mYrRT?U@T琹r1ʦ"Fl6:Oq#H{c?gA=<<< x"0a((רJOBрtt9PRY͉Xh<C%x*h<9pkAgGx'pəC~xu T`I᥈tGs#2."etB~Vu DjX"#..G>`n/QʌHl|Ȥ ۬xxxx0x"ѢEl4lH(r)A~G/ȾB 9sxDvG a6-ːT:ӑ*DDbR~JyU"Yy6\O.rq"X5D!2e^H~³PdjGY`Tț6<(J|ՖL t}"WxD|RQ Qf] `po-_Ff3,>Ɠ:#RD$`?&!b?$ۙC~Ƹ-Rh6` DND Ek'T D "rv(:o' DG(E~P.3-y84SQlIH2֌E X ?Cjrd"`aD:#Z8OF P@fD2ds搡?48Yܴ Ou~ԱhΊ܄PQk;d2qwTY= ߁Jr`GoJ!dRC\6bچ9魇oh8 eCHCQ " 5HlJ$z趭 ^@.1l'QN:d֬uo?1,[sds&PH.yb 47EJD mG4oo"l:ۻ}W P2i T:6-2->GD4E-HBa5(n "J ߒ9*@x+yJ )>DDe="GmψƓl"SAgmS҃|hȤ^ !:%r )܍G*RǪVs[t^_Ds91dRt;du [g*EzxxxlQID,R't?+Dʙh|z"JD؂ wD:x .ȇloda"];e.c8xLv :S&\Fbo=?I*E>|t@ܶm DT:uensU=<<<ohƓy!"V#PjrDZQݸD,pǐ8E˘n4rAD<`x"YeqoFY⃈FS$bx3R^I"-֬2@iچgڔ9vw4AY2~LQCV EH,CsZ_5rÅab1 WsDx Fyb )YU A~G.JQEvg 2}rVsANdLCшoYHVs0_2m tM4Wh XY4%[D֣s{k"x[$<hA*Sgd&[#a5czEݝQ.DɡT|2j,А)};R{V 3"ei]BV'H][CE#vQp R2X`ź7a cDfzXL9E ծHm| 'b[0G k8t"b?9wG>L"2_v,/YһS'<~,@L4g(@& O"H)B-"k)n"/ϺIJ-!2!:,dRC6ʥ̠֫yO/IjE~~{7!e+2j"qmmLʩ 8lMOH"+2P䟐R)Z#]DuUu?A@*LGd>f(둉h<t}yC~ҵ5AZz ;4%HzE"s3(BR##(!k|Xc 8K~E toPh Ḍ"4d$Ef{#xRnFE4Rj,ӭ-)dS'Ը6[ g#W>+vYLp"2V끈ʏY|$ƓEɣ~<ڔ~2mYo?MGNhiw9"qhH'Ȝ<懑r.@AQI#Vnq{xxxljI x)^rZz]HpTnyr/CCD@ƣQ-2пE>w%HA:EYvDQZwM;D,rz 'G{JQבמhN eDNCs7)= ђ[<l'D'9E~=ڶmg3Jِ-s #3\R2x;n"aۧb.ƒD,2w}FȤz!R@*ֿ_@~u {!L rilãDc/Jz8JJÂy2"\ ť9+BE(CdvDNHUzd>oF. dx"Rrx"m:dR]d9/-,moGcsF[RDV!t("[iD2TD*̶=R)4+s,"[_!GHͩBXw:\]l-vEvvƓ`Z 6l6ᨢA5y is]ؤIx "\h2D,RĦ bD WkdA5,RhNsdA&n.#WD{,싔벥68eW3[چݔ}TDcG4,C~YȜ@>Z)./ 7m!A(iiGvrBck'b[d1J11w!" Jt+ +g2v9CZ d ly"i)d%|T=]_>t}} 9Xdy4 Ȱ?C-c@"AjPD>CmA! w6]G]Ϗ~ŮP"כQeD^EI_C&ʉ(p6JR*^ >+Q(;{l1H"xx',2A@)k|ˊk_ڿueU皅˶^X:#5-@PX?3;JQJ>X&ãEÛ&=XDW1(z22ߏƃnlɢ"T'̖G F/ ȿPiy)@3BDD, oؒq327.r?Dː_RDz#j{[lT"qȷ+D&cGII@u{PH1(fl1-XdX"YÑ*o=( DN.D5 2@ 1e('vȜ.NAPnXRQx OA3ТD70tbͬu$VO,).XcWX,K7cWMZ)~\D@h2i Ͼ>=?U9噝~rMtcvr$\t2=L,9qBbXP8UjO8rO|%,-p?CMRJ+d2PK8"A N3Xl:aKrr!^*XsߤʒB %ZZ \7҉5߆MY=bX,b[1}:8Vr&9Iě?#v2U!6 Q._Po8ӏV!VSVo" 6]S(8xtbgI/X3mReW(d~i5 fԤʒS߾fX,Æ&n~zb/JBbɃd! f1P?gQbI(($3 `K{+OBU\Q:Ny^GlLĚPXZL ʒ/وZ'Y,Blf)#Tr&t22| ~sQޗ!=HtD$FaZ] T՛8c@ӲQf>&;^sRWtbU/ a[l3dg߈˯NHev-ŲX!SV?V:fPdN*Bmf+GvC.2@k <`y%߀j}bwS޿yV9%UN.Np,c5ͩPx[~]1  !|8-p\vH?[1fZKJ#o!\cFR%Q"^f s+ꤩ6 =Pn;H?,sr טK.|`8UIP~Ӓ<[3^^϶?c/mh&*iѻsY:tbͫ7{^YZ,edQ;e-ρA(l5aTj@qP1 A9awWu.f%UN9@ +?IsЛfO{}noxUNyaSAV2CSyr:0^4xxP*u7AQq9S).bXցuĶR}GP YBI(8zMF #Q+'̶d_*~`?T*|?څ94bJ˚5xָz]=NB*|\oɗg!=k̿ -8]t<M{;7dK%':L^_Y0+lo;blzB+EUNT5{!whd EJVܴ4Ψ5hQ|) M@m%gZò}9e9{OQqYˋ Y#JpKtb]m%طtbsWߘ_xyGt Pbzyg}r&:T݋D4KcVꉝ9%X ǬtO6(r/2ߧ댽 |Q3g&s2T=Z&W͝~I% ~ qzekҫx,\:ZWEX,+Ķ^F\]E cS2OEZS!qj^݂` dM`πǐxF!!+ |Tj_UNfCQ8ޖjom6@b,{3/fǴI%ǕN=zE}='Nq'Uktbd$&bX *qT3% ~gd$ܣ\e#ԀQUMb~2+#r2ۿrqB!ј9X"/u8-68U%; ,<Jf q/tʀ(}tbM `~h~ N|1ab+ĶN!gˋn䇙d:UNIJgpA1*zz[=ό}1pSUN W[= Q(GH`<*;-8Һr*rǶE¬ ?c*EK:P6ߞ%g'U C!aH@`.ϫmRetb͖t-,eq-4t-Te$Ė"Z(k P7sQ,z9XsъPB8ܟ NI6- Qb浽9HEL/0ipF`z9Ӓށ9w\PٌI%,$XM{+7@󍷜qJSsιÛ<6ibX,Vm%$#aj=9]@= !HH-@aÁ(O%_5['`|=M󠕘A˴lT<sL-়Ӄ;_jӮ0lGvI&pe[}{{-eDC]̤ʒ0=ՃW?*$"g2_<sX,VmT9-(!}đ3Qϋf< VDzs$F"m$Вȩ: ~f$E!Q:|4:r? Dfǐ+׿k4 w.%)y#1wrb@s[U'3uj( 4dl[xu^c!sk4ƼͤʒL${Naǀ,vZ8& x!7"E*?X, N3}h9f jsJ#֌Ul~~x׾@D"T/lEa$~OS1Ɍ15š{#uZI9 }^shhJH0NEa]!.nuO.?cNKgvٿncƺ  Jr7 kQx $!k.C/Ĵ95{]ȶX,KXGl gWcx2Ot6DK`k6rY&/@+!1pgT9_U)AB9vp+r7U-UN"!1'Q쟏n3I׃BHl@4?\¬SyoΡ'r2޿4P\8}-y +!T\!3ǝu9jBS= 3 fmvKVyыm:9yg&Y:vDH 2_X,Bl+)? V&QȉZ*EA :* Sf#QBvk r#jr֞5C> m7s(k( sEq O^LGk"p[p9nwO99hL?0;Wn}jA|Z0Kh.ʹeӞZ=xՊD-8倬 j{LדjG2 4lԛxrqu+>홳y }' Ԯ{#_/M~bl[}  QxE'Ǡ\0/?0 QU)rf^Sk@"?/C\"4ZMY*%&~h{]6^{ZRh!A p:t!u;(EO\s̝V76sdh~z<͞sf-k9 ǎ-z}5? `8&le1[YY3kh1?6:h$~{[fX,mT9٨T! ~D8 j^{F up BaB0HŀWQp$.B`/}¦!HCa|twS;Lgc$yo.rJ%T-I6')ڔq[r3<)Fmd/[i)7',s#W>`2j_"7Z c;ñt]QE~} ?͡pk^I3u&#:b[nucS~% Y{Q9^{5 J"5B%DVrܶ1mk^? s˵Ќs6{PZ$]GI3{՛9OBc3<-B F#(m{sy׭Bb z85J+Ga~gIiތ䰒$.+fLog7GZ Mxj >l̥x fI/1F&*κ=}M9 ̟w|k6QMz_}X$wPΡX, ܺxTD(}ylB+6 #8ЍjF"nyGyYH9(9<ǢY 䎥̱A1E;%osꢢQ0k@$2SY^x2dTFxƲBvC_-8/cy}N_9ܙh+ׇWpv}S( %(̿)“P`^VQhdf77H}Z ?hVŲbWMnET9ۣDte}hCB)i"1_tCL!nː Y+r&*9q0YAU2Aݘf/w2H`a_mƿqd97ޖS7pvmLTn3]T]}Y((cuSS7&) Zj}g  Λ|A}Z 2Xfe[+v9z wHŸDcmv,e3 㑠re.`^$0(Z[!(m@=>OjaQWT)}*$0@"ݯ|I+)-W0fEr{Tp3U|]7%6GJ:IfghNyp3LCقX)  8D9TAَC0tA#S)u2#!|~|οـ}MߕBbtlVH~ˋ3 s' ^nKxkDnU 1˚※Y1xP,a~7=~x;Zy .DnerVަ%Y+ AIg%_xZsi (zŻ I)slCS M/~}NbX6/lVȃoUʻM'(tԌB{-_ PHP? V8Uz"ks+Ñ **khpSUN9jCyeufv>MNƀ'=g쟁͆!m^}9++ꟷw Un7湁 %ewmw=w?hgdonYg}q>Z0pp*{$ʧg4XoԏH?- I;-@q/zEg$ZWm#Y,-ʨrNե{P>X:\8*ð#y 1TV5nF]6E m=<$Bnգb|]iq7>MlzXVBۚ5!1v9vT 7sGC`8vzrϑb|F.2r /,R9e+=\ 81 QQgSȭ'-L ex<ko)VY,meT9 !Tvb *~V-Cޒel}($Xf|ZWDBkk|WH ES#<|]Vـ;(­}Z~;ytFfK_Y $"ܿ|jiq07 lѭ" NHgs^XIMkԖfz t+p*<.8t5B9:W o5_:ip>0}F$S7,{X!f *s)G?*ٶx$ҋV 1έpt1ڒDX3j"aV~!Bqך+Yt1e,TH3.7CtwJDL1H.=ŲbŲDCC%TӁK/X4_c=ft2< {.JԌ w"q# k3pQ>8௑^X,Ɔ&-@4r/=5 FOne֨m6ezӒvOw~< tYMH¿ *zC/BF P ,q]XMٽ.x݃QVe&p-Pw#fX,db҈s(-2?֖w/8MŹGw!/@yUvq֤AU#W;բLQըjT4xX[˔x-hա֢Ų^XGbBk6 2源raJ 4Fe=M^|W4Hʻ)赲WP>Oof][3J^6r],eXGbيY>Lo~h(a4Zbm$?jv} $]qv jB*{3X5`yɂ^# M Us<ڔ0Z ;ا blX!fl7SvwYŇyz~oBNnX UWZEHH¿w/pD/~b/ZzH¿&r\Z# l, MZ,L0!hP nc.?xW Ň:<]TdPEQq)Tnb5[ \a$'bX(#ft#pl41x[lBab9EWtq3/W#'j?iI{­O~ND_`iuig) $$@.o2_<~X`8`5P5^.O_L9(h(zc QVB{z\"x&șjwA.< $_xKKN$s]qPة2_M1yŲa1e# cñcbeTo`8wzϜeu4m^.yݐcfJA^}62 % c;GC׌C#4 hcrƾG"t3n#Dp,5~+ >GWMAaI]7.IS t~6TC[A3g-,i 8 |*[,zcŲDCp( eDS`86DC9p,=,y59kע@~0;gQUf]h(V!!6;t镓.Ќb.)WG$rnVd?`:艒Z拿C*Lb`X: A(d<1'D`_ts~hq!$]Iz*z}9ߕ1=w`f09^ L>gp+Qجf@<6 VG`c9FF;m;*\pT^NőFT2_9ߖu`e 1˖zFc9@c`86Z=b{p0$'ECYp\T*UCe&B9DSˀP o0 j Jo&=[JE0 -^x0; ߶=Mkb+# PƮfQ8HC6%Y$;LqFe/ŲX!fْ 8: \p=l>2PA`Sr.u΢(+t cO!րV2~!~K=E=P>سha {=BҺ2/Ӱ88aiƋ79ksg0  ۀk)F⸣;z-B7nhH9YϢj QLMyvxRͨL`0M&zg/GB,!X,X!fb!cm8 232mnygerO+kRE#Yx? ^>J"Ck ۵ :ČC+x(5~ P8?*WKpƤd!ǃT i C=эj|]0˧~V#j }ٯ L]$crjP2؜ c X3}k_0@x* Tn̵TDDHdD+su>w$@xn'ucjnnv^7 L1<=YMqbXG%7U} kJ#Lh`868= ھ ]73_lINPP & At9n6L!ѕgIWBkȑlZ~MȽiATT04Ps(0-=!!r:F=<"( j:]E$/xxLpo=bH޻r!}xKѹe !G e f{" _`'$K:UK;f]拷WblaX!fñsh(Mub_?*WXJ|6js`VϖW̓PYQՋ=P ߢ(`FNW pES5fہfO2ok~>֖H5(k0#( X?$67MJ\ 'ZW\.GN^m"G"4=[uQwGW>)mYa9b>$F]ZPH[G10G|sL zn6ާU`7^IA"rz/>bCiX~X!֍7ӣX| :,hyS|d*y)D]+9Ac(y' '5$Ծy ȁ3Mr! Y]{"erjH`%%CDB.<ފB;-(9QQ"}b Ǯ 8 ڹ>#J@ύv:m?IwG!U_]# Ie23$?AbwzKsK쬆(ۀAPN$_ؘRM,I-itynHG# np7pV7 4|0ou Ż a@4>= lP4/ 4e* .N~>I*\qȥ\ti3; E+"^fڈnꧬ13Bl|&Ḏ̘Ow²^3cFNge{܋)`[t]qHԾb H>fwcۑY Oç;zػ!7g$?^ÏYs#k.._PiZ{.D.c,"brB{x]Z?tU =Txڅk 5 ϗF4k[9nem]R;|i}DX0FW?yRq(Ͱº ñ'옮@ W"r cQȩ!=+ȥZn#ӄ  ]`gy=9Yh-CQ†EfGPh5~s͸'uf%EvHEѪG;ñ>Pq`8VdrEC7 ,GatC7@w$"o*p\$vH¿+r7c̹ȑ xwQe/[\H3N;阯 T"dcYVخ6OB \.~,˖b@4h cMXySXKqr5 l#x/Ea*T~9( aQm-%l rtFb~6 ?7(@͸1s; ݏ1r# W@- T<4"7+z8Zܫosiw$6njk zO6ucǣKQ8`83̼r$Z.r0+Eh(0m6HG>F$km3Pu< 5O$עkuQ/P/Y$?ߊ]>o\Io([cH9|"m2KG9N] a{4.z\ŲycX7 VkAܒQ?;;鎶1(QnP+4{1' Tñ+ᢡ\k^DCf ;Mt2J?9h(\Ac+ 4r4k 㑓 !f^B"V$Ưñ6@*-P=p%$.wc>3N-#<h(~9h` JAEBD x$20] <[拗q/H?/ʻItoMo%pG'|&$Ή$?|# #w8 *ݩA͹bH~@v0+#83 ,2ϝñc ֖ p̏n6bNgECxyy=F-yԢ؞HxP?(,]܏qMHddGPX sh(09ݍ,t#RwF"eG3^H>b;9\Qyh5]/ǨPsvD$(m†yۈ|TP.R Ԝgyh鑾pl zjm^n ѻI#EˆOsx}EchjDJׅgKk-C=Mw__ ߝ5V묠Iw]$ơEYJ=*Y2e'mŲecX7bďBm{:~7݀qP5Ve]ސyM@IN0+ $:.$0~7HTLm;{s4'ƞE"{`8=r\$>4P"K(!Atr.[ E-A7ё؍@A4h6?($Ga; rCf!G$ ˯Q]c>ۘ4סpYqE9V GW"1Ul=3%vhe9HˑDRs}WqJ/0|ؒ߯7\bm$b.Vq~Akk" >HϼKMys 9W_941?#sKXLۑ_H ރǞf \g,ˏ+:`8'HX  چ{6,ʨk4}ŢB.ZmK4W;d𻣡u9p@4, hoqh(0xK{~{S47G=L =Ѫʵ 1]l2ؘh(Em@^Bn(U3 ݅Hnv IDAT(XCkoHĝ|bo0ۍA5g"F(sӑs0c-Cbd{9'1Bn>kv Y(T:֖LfQX&F`r4J} cB#)κӝoqxj/b@$G'n@ C̹P/G҄BA}o=Kskoɪ,\ _Funܿ1eq7F_蟬崺sQey KeblX!Iñ"`8*)Y6l<0=kP`?z 9jŪ6cPb0@ ^##v2VRT_`8GF9ްuc:r 傥̱^Fyj9E+WykG\\ыP ñQ5Yp6$g"iSu99?"uPh@P6A |T$QsyԭwwطvAϓv;!et]z_>$CE¼1.J2_l8o`ҭ"M$T߳!ǶX,?^\FI"(c"4hdC6磛m4 A]Kv{_F^ qьV6(ŔNJ˽}3ߖ6cPºDL#rZv׿]2?~n^_qF-臮(WPNJt DaeHCB'f>{7ע "7AFUhA_{ ܃\PqV ܇v#ӀyW1G!sE:LVb`$@'ֹR <,K/>ɬ6-HvGu4F$В\ ot.xؤ~󓫽 TNvIĒ}fxW..T nlJ}8 }L'JHFkg.b㸮]_Lwzynʵ*FVoYg+ԜuzCSyK,by&?~%kʹ޸~P|\Q޼䲕N{䡝buZ-8I: uI-X>|7w.|>g~bQ̀'; 6^+`XP#aLcŒg"`8%g#7 _9f۷M$:a/FIi-p`8v-Jr/ZG7^ñQXl* `l5 }6x  g:¦$(sR03 cH<$P. | @ Wcfs(a;%Gnόh(wxTe?w&=! RIPXGP:ZֱֺkY:v,koQa@wU,c#؈"B'!={7u]u]f-7}:zn'ǻG]3{۵swCc_{)Ǐќx ͏4 f0L8k]KE 9ک*}TQAɅVև$w&I bPX~!(Ng8!#{4bP@hEDAnty/7 kkW`|,fY}8/N G"[7XCoD I`"p4~;p@8o &rw#F.QDY֦"KX[;C5pH,ak$#6f V+bPp7kֈd-lmՀa2A|#9{8z 1#Uy;"Wi02Ms캅t4vˑ+qf(B.r#{{VCc͵Vvdk9@v7iHiќZ_Gدչ 3Y:y wY@M^!11t/[Zfy7X>X$T:aА nZFb6937woySKDž֡ ȼt$G ]9%k~?2ĮNVQAJ W~=#% Cl_G r;U"T91(== 6s"Mŀ쁌]+8| VGd4kQ䆣NV-X"W־ Eno܏Z,@{ 0I;(/S֧Mm؝p4Up4~m@oCh<]^ ?/ 0Hh@:Y;& pu)aȡLﶚBݶ')U;VoM5ݞ3PZі5ު0 y"bNKZ}=Wא+4`2.bXМހhdzdc 1`;R/EBWxG+ZWڱe{K[@߽OL뮫Ta9ˆUqBuKE}d5)4HAրtoE̎:4/^[KٽH$w+I e>2yz>5h|Jĸ#2#rr_ i; ]"z\f;v,Vs;-X!`9[zİ>Xs1( z4?4/Cs `ti}mv2$_?1)C  ,G>(L `e~Z}8{EB|8n"أϘ|cEXS+[F\71#|_wyױEޚ8۝~||Y|HG!V_' p;x>_x.ˑGY6{DYu3ꍍEB|Ԡ-j'_Mr2׍Y~K]s7 O;eɢ>5Z48js=WjFN@ä›E\͢eť}yu9o1Qťh~ʝC]))ZTPtk$%)]p4~>pR,#H ۳ѲGP&KA *¬2fm!@m~B`arA.yhDJ}l\D1~ڷB۸Qdy) V#֮Y ".i +Ѹc1C f,Fv bp5qUv@[b!Ui.b*WoUXnkoCLc/,`Yح3}#P r B`^Ĥ9LAn})q]hĨͷEf7{KQ~7y\l1Le{XmƢ*_Qj㚬+E(WbuQA*ܬ{V6TS?5eCqxPeť'Q>ea7:O[>, f'P[KTw}Lolja@,7n7O&{AS>f 44]9 hWQAɥťW9 t](.-\)*(YGRI.2"bP{7;{[4=={ - z@{ͦgh@j/3jm&X/@8i+B6"а Ŵy. 39\^MtKM_L#b+5YHhcMG^2d"|`>vȵyC3`Qmc1 1kZ@j5]gY]O"6r!bFX-x>XY!b( M՚@BwmLX=Zرks؈#JkWrSnD{-1kfIX{ sH(?Bqk^ X$2&]ޥ{F:&<SteGui3u22ƕo>z8 טX,.OSR䭜zN;(ߋ֗;[u,z- צp4>Ү&/"@fa己v2:` G^ʉİG^ QP:cu5 kڑ@v^ncLj@z4W o nAic1~[?Zx1g%l| |{"%a3 o\+Ӈ'w!&#b1.Bև(`"ht_XK S z~>7kюrCpI۵;7_x[x Ѧl>mƔs2sk/oiYlѪ5ťT۵4e1ṷ\\`hqi=E%S~OE%Q<ضT^A1h _:KJRۓwĒn*Ci(S^4 z@s=EO=/@@`~620.@ N"%P{QV_[W/?6 \N͐o@ƹ ŭ݄V=oA@<& 1ly[SC2 x# mNCB}b2) m G WXN(:m. T4=wolٱc˃Aw3'\ӫksiթ)ƣE闗mة`܃n>d{Sh@[odmr J?j$)IIl؏mwǢ7@hdж c 2^*;{d|XkלV # ?_X|v+ltsA `]e/Z50nqm̕ 4A5ZMqAsZa8 -JVMx)I6]~26"hmOkUmh1Ar^&\E Eg碹)7 h\ҷ G㇣{jp΀lqF-Ϟ MNg605mhW]ڷ~r:7kq_\Y[VW-.-,rpЩzf]RSjךBÇu'-r{7گ0Jkrw$%)O~@@X$ԸSvCF2R>v49;^ ^.Yx68t{ߌ]q+o b6Y9^,H_kO3ev\Ф=PLQKdt" Ю=}O1:R]F'C/"7d?bp4~=-hxz/:&q&YMSf 0'X$;'᧠x =Ž݇܍1\ :=^L|1kM`XbzcѺ ƣ {#> =<~ hG@pZ۠-<CYrmJ2 IDAT@-7.~8]Q /ls4Rp4~D,)ɹ圲i9 N xՏ%p4)$MVܾ}6?t1=}]^S]r+gD[x` ,(-.?%PNNmne/sK~kKD!Vk틒|.G/`:m V'#{2D""bbqdlAl 2+-xeHq{fd#cG; GD+ejsث9 vb}. |G!wYX`7 yY8ŮD1dM-Q59 yn?uvE,O@lhnAGɰ@bˉ611Yr4rdp VG=Ze:-@䟈Ǒ['b[/!#Ĺ B (QOM#(ylB`Ӭ= pb9f֏{Q<1\{uU\R&[5n=ǫMVVرPaѢ$T5}{(lf f8!q촋wne-x6yv6޿3VӦ\\?CqИ;n%:WO:ͫݻqReVծһ[ J;<&/e]yDǖ`CK(w\ "d[l{R|#K di(Gavlhdx eZe-Ӳ'2_#!R"p-WXȸC+3A.Vȝ)ܳkgjhDF;L,*56̶ݐ 3Rn8x1SU(v<1vK ;QCn "CRGS27!҈[} yV.506v P4 p7]",X*ZͫۈNݬN]9#عo!ck pj+5yW^n@w=#޶["{hNXUNفw8&bpm{2tħ9+*(6[} ЄR\Zc.Z[hn@/{E/݀; JOu$eNP#C|5k <`-Aq$)hn@ x]Ѓ@E3dЖ"6f52jX AoKCxF` yY[ wȽ@YX$tLx{X$90'gYJ!&RF@H*1T{!X$4lYA`tϮU~ڱp4m@?3P(C S;V V@F5׊#Wq܎+aĆp!Ϯj!Lyv+'L 8x51D6SB4/R3Y{@s4ՠ^zqgw4G[3 ] !o^80"MH;@'3&A{y05)Y7s$mٳzv`|Fޢ-H rшZKێh~z1~57()IIJR)g bz% ]O GT=V,u#pW؉{َ 4E{26mbOV"G` @ .1 D=|3xk%p4>2FKhK%sK,p4>{ bNGͬgx  rRB>,d?EBl:l@W~7Z;w5=6 j ֒رϿ!8o@ zRĤF{ՑʑZX#܌".GeճtTc6iXJCsLO^ow/\6"Vq g!sp4ȽoѸ$wE.@4>{#;q8|AO^ձ kfgl tmlmmb%_"& 43ыjLlh?o$e 0:XJȤ"fe(ibeEy)zk9= {&2"xrQ|D,CLzyYW#P,d\"V+d@2[.Fa~>g}oz^iܦS,( t G'"`G:AW[]M/ 1IbF i?s;-\HAX$4 _@DĈ{  '!r xxʥo4ܜuNHK8tŀy8V Oؽz1.t4w4ܳϚNLOkB4ژb0P"X,z(H 1ߒDpջ)-js#^M[ڿ_^Npl8oS Jť!&x?OEs6To=YuqiauK'%)IZ~1bh3f:K9 (>C/28^cȀuHOm(dd̎FNd(7##:r5nBzg ϭ"C2e諐q /B[ EU _bp4,Cۣ`O!gD+`kz];s'7)s'_} 9"7!(>d+17[bXϭԈ؞F#qW @mNcvߎj?̋Vmy$7߄X\doBqba巩„qϺV[o5yt4L]МvY1#ohnOڀEn(i Oz[^F"Z[3"%j`X$r[2 JO4`=l#b ?s]iwf{0cIIJR=5H(g CgڇP JdF b?NBhWH]@XlX$6OAoo<)XzkЃ7ڲ%yO^A!pp22kP S뭟#F%XK`k>R|`k Vf_ pJRhr9.2iV{dcء|gӱ; ͑Vfv|1'/#ocY[spԻ }Qx9bu#Z! 'd͇Jkbjr-dzyȲxiRGLR4W=G_G?'w-X$]3~  x]ςʫ>p{-m㮜5wOB$%)?]hRdw@ybB?FR!P?Ė (^g QH5G`}8?1 s811AyYt|.GmZ"Fg12+r,=r-E̋ht]<,1,j~R.#HSt?RTPX\ZxO3p qKRҳ[6ہ+Z׼l\]_E*iv?t^R~1*鍟i"b; GkcPr><7} 5T$b} 7#uH1BF{_d2X$TFR* #`uVqF?hvd[? ¶cK}~?xމC'Ŏ_}\Ⱥr]w֯\Sku6{b1\XJh^x&pV, 3 c߹dQR_d5 nI%A2#d_BLU'^VU `{SG:.;V"M*ꍂ+H_Y]QV:b="` aZ K2^ns pYvS'!1k㰋:+=b̦ ?7gدImq<^XݭMBrXR"1hAb=7[>i埊 ||% ~^v|0Ӯ}gUZz ϭZoƻиSS([eG1C173F`ig+{bM"o:Z+3:rEh fu##|ݍ Ƞ qŒ|)]e}@ uSP:{;8n{^F Znvkg^lzt6^TY= kϭٳ1h=cƤ11:L >-(~e*BE1/!#Xcz[@d>z2檷eӷV7R\Zxd/QGQAɺ^|tP[{3 ]8H 89p?y]6g1GqQĉYjuqlA9sFoEXVVk\b;uݏG-y{8St];]+&m~u$5ChdtkWz:5ݷթh< x)rp^3){PVVvN ۃV-2f],+ `%\$ )D.R>BA + G6Ҭ%~dV'&uBLL4] ~6&3v_tOE zkS EF$T0iRؾ7;/Y_@Vwz[TXf# `|wΈrznB+bk߄V^km#JGqcev^b7"pvc#Wq_I(&)hΌBd{XmE |Lm,Esi5CC:bg߱{]2rz>_bwmoq^R>E@*$)۽\q;3u)[ۀ\-pBqbvw]u A]8[3nĎ7mK6W9s;zބݮ~8sG-g68qTL8uݵۧmCh-d{b1}V"#حiVq_M8> zȘLvby!pkQS&rqމ،/鏀HgFv b*\϶@.g,@ÏTI!妚mzLN3{(z#k=LЯ{Ƨqtt1?jcu$#6D-֟3l:Ch}Lj:(mRW:=zfc@`{G#PDsp%htq6/jӧ`=bug-ycVg b;gXA+z`W!6 4¦hNBa.bGlu?kīO"F _tze ?w䲵{Ɉ`D->{Pl@; K,'9M zg;/9%_dD/260/ ֆPvnrq]i޾]m68dZomB897-Efp437At$2@$ހ>y[] *xAPMALL%@E0ýq&l소NGdx #tZ 2V!+?2ߏ7@v2"z>ӑ˱r[ع+X629JniO4']0,<>q]w[׻~+ ;:n¶%Um ,G󳑷R Ȩl- Z>>Ecg;BEB'##s2DIdx]P@15w" e-&Ph*}n.YC}5־b>@F}OvF~)b^< _s_(F%28?h@nB+ Vm.bPQ Qb IDAT$ˬ_" z8Q_e1 *tPlхXr`}эֆs9b.Dn7sG-7=݅1a_eŭ҇\ϨČ26x9Gf,@~i{j{IXsP\imr xNG V4Ÿu}wm!P%f @8om iuҫ( JjqYNϡR{LK~*ZOOfoߤ,+*(9s?ηAho/) Ϻu\팞?Lӗ<zG/\?EO 88p8)vN d[;Kug+p]w 6We{qM@KyMFn:/gO#C@k+8x#AhzV, VT nc)ٍ灎#7AfAOg˿Q@hDf j+/?{r~@@ֈXn}}M:RTĔ } bЫK+#b8wyylCHhU8J 衳~0E@e>H 9rɶ"|i ( A3=L z: tGUa3=AjACz sW; "dA,3BnJFI{X_F8Jp`P#T/Hnth*›x͏#0NCNWؓqvYuXq6{ 8Lt^lO /ѳM1dԖc<㄁ɳkAxҕ87/ef}c?FhramB[!@V"6b"&Z9-pEo᧋h \ߏG 0& zۘPV۴9 =o?@D'Xe h߆9o/nh% Qb'ȡ)Y!֗휽MA1d_~OG vۦx{#=o=cPmEB~[F(3"bF >e~VxMO7nϳGx& @2vϚ.Dx >V9XE&GZr\+RX+|ͳ }pسLms5z_-腦OkVzꚺ;@c=~@u,˯$ť-+9 3~ ZUqiq)*(nr(mO8#o5l/2xtFo"C|2J#@L٩MEʲ2EBxJĚ܍7ewQG7"v2{"{Z@q\-!`G`r`S߇!W&MYrqduw!vpd߳A #6"#^) OL暈sU#f`E Z7Hp4w;Mfδk7C /RMoL;"&}fC ky9~wkbV~KVkPqnzo!`Et֝mlvM=5泥'ȰG硅ov VZh^܎ءsp.F PP^;ۣyZ 6#LiV'ced}C֞\B`ͧ[eec3aNl_7ˑ=x`}^ cUXϣl6($ۂv VWbd?BLN?)0hy,Y[`TM]̎fzNZgmiEI){"ؗ|3T^2׍Ƞ (z /W!2<7 \W-! Cc5  9*g"6b>NOҹ?QtGlJfα[YZ?ۛ_s'zמ(oȥVceW#&}B f=x3{/K[x!LĤ4)EB]lOʝX8Z C4ھw[" bg[[ 6fv2HGhX@@iުב!\>.) ؊l[7|@bĨ"ssL4lB$= kؕE N űH : U!Ub#@/19 8NJJdt OߚFZ\io[yo#z r!zgu pq7ro1ϭYؠD܈15 qBnYh3g"6* HPZ 0(&mr8OCƽ<- յT z={bO@C@ _\-CU(EsK6@yه٭Po=+dledy;xOМd/(HG/5Ep4T>'!ߨngz ۘ''HUe;+ZI꤬vT'eIo!un=z8.DoK݌X!@PZhN銌ȿcЃ!;a~~[O?VVҽ[{|0݄b\d'!@K!Hk+4p41L֦H:FK[&0= C`j g#%;UQ|[_ްvoo_X=|HXa*bhuHh Zmx1Zo^6+Z`TX,Ykǖ_=Y[sQzj.C+wO6!6~δ k+3W Ƴb5!ܭw9Үczd+\xyG-;6m,A pmG/ \ty+xHXkW7,r{9m/<<-UJ9.OLhl{C 4m8Go"q~zw-gQwR,MdS #7SVDd!oAQi,[C52Jе+1=f8Ft ^9>oaoۡ됱x Lk@i U-(!|;cfd G G8>n_avGM>Z#Fd+~W5~7y[zJ,@X1*nC& ~}]lI־S(+G1mg"9HvF,0Nƺ嚾E')WR!R^2/M/VyY]\o3wa/ETX.ذ^b؍5v1]خ5^+,( w~g"?%j<>{̜99T"v2b5/2y[y׳4׻"prb4Ǜűlr ́J04| h ZƩ -P*?W?idh?EຈLPߠqvƮ?idO'4vNH, i,e9<~ٜ(t}yHGnιтyw4nF^Ws;8F#;ι+Gx{/' W@,Lgel,nw%$,6쇌[HYOĊ@J9 ds-PGQ{Rۂ__:V?дdv._ߒ(?3srFL9VPT~sh]|GlA¾@uF,Ǔ=޵b4@Kn0֚ 6Q6uZl}Rn'c&0bR|y+)JǡT *tb*` go\yMԻ!%ogXDlq?D;!:3u,plO@cxh84D BkMc2d^~=Wh-bgxHo䡱tjKc^sι%ι-bys@uι(dpιdl/`k}s~snsnC{s `b;|`g}3s7m;be}sMA̼OJr@tS乍r ~q] l_އ]Á hүwy+KH_A7;-bW =~sn_\zň!6c!A9's* bkeYn QK8rmap.] EPx2ZDPS6stQۚ}hE+b0>=E/k83iauE@Y&>y+5b Q ɣФ+zw'; XRdގ~P2Jjd2P[}otC&?и>AýX0|<~Y`5aj_: V /_ Q2t 3!( >v1H%bmV&xXng2 J4n~>Mp :i{ oۘwydebWS؏a6n/O5~'\Q8Fnb4n~;>?i $Ȇ5_8Z9O=9wuk.8B19DrkGqC'|X˯P?Džs!6\>uι\}::ps]-ps5]j5 ِ9-"w3ι7.i,k8) Z?ѪM uhud,6C@| "lw#u7HS!nD :x5 lZL #0hܹȸvD@4dص/oo Ep>`de!#];d,|®kb X};s3HY۽ǡI?<4'J8[@mĈAR-v^Vbz*QP{x{޵u8Ӳy۟|{f^#0 pdځ iOSĘBnLUܢ5\be\<hnDƲg=V_7N_@@>M]g1}}]C|J9W}?򹍥Ahfyٚ \Zf |{5Ȏ}YsιιZv_ޏBB^X9=s-X x9W?mYoDnl;"c7 X{! xh e(6j8 SSO$ 3RIмH/{ c6`V 3W,G-2t޹r kpO2JZ!vU۽>@&,,4@M+?Cf}_CcibV X,{ g=VYs~nEҺD.}h_J>'W1t#&gPb}'ZYR,κ٭l.Pdva&;EY%{ՔM݀6T|D /CmbcBSb%;b>{nG8Pn+:h-C-N'{5jMZV_ mh,e/ |G~>hk|\_}W vָF} GW' .Dlw\s;_Ъ<XЀ2!; nE Đ &AjvAQ"dܖ U  oiDɏ7 u,2ꇢCbBީ*Q@wGJ#'y D!ɣxȝV/}X3 [-Da96{~DžK"Ѽ(ϴ w_X8nD@E][_]UOC!L}r8$b0Bl[5bBQhFœ .XܬBkgH.ņU &xdw]a-d5)XEEu Y߲~5ѱB5(kO~%ѱ[W,Ko3F+MEbCTi<јjұ8PZwhOl68ܗXx2\J\ROكhoɬ6{sIh@6c|9+)y؞[JVœÉR¼ou^ة4WqsYP$cvW и -\. !1]FKvU*ʒ36kJ,D 6$akfh,g+1{sY/X*[NM'Dlv<^ׂT"vT<Y=Vu2Jd]Bd!]ʾ蘙X7r콳h}]>5]z칏#p1xld PGb9`iraNDL9bonwtBFpb< 7,z#w'u?2An wRӹk*[OwFF3bnnpں[k^]l/XVr ui\{@}@6g"@ܧ}#0rםe?W#ikGͮ}|-r\fE,d[X;=Eؘnȍz4QPሉA&\E֗È6Omp^{vW[ Qp.`x2"]jwYk~S*FAU }nqC& ᴆ- $]M{w:F&)9 H4dw8b2F4*sPd +6 5Vœ>ѩD6ۮxbC}@/V x>xq +=k)qe徴nrܗVVGPFr_TX !. T"ׄ\qذ˴7:XtD b)GmM'[X!}W{Ccy$9(E[Dkvv֞J>8J?rzSHј9!LR?SEGlĕ 1"!j}H Aa1 vbCVtD Y(A Ud&#=zե־H!vDn־#C|sk<A~"Rx2}kX{X mإv(.G5䥚Md(YXKؖWX Ji0yv@hF /!fDʈM7Ybl @z;wY&T"'y@ݔ?uk;lCx㪎5YHf:LD e4/BlSAZіhgW;C%w6#@=9z9KИ t4b`kng5]l}<}R ?%I#Cl'N8xYJ\ٱHk|1>QH= XTK_5P<ދ&=#pPK?U,W0S_QƓ5_6$F,dz7B4X vPP!b냌x!bzZ# ܁ߐ2޹VH {#p6lZs^BLr\"V%Jd@ Qv2f!6ȭ:*W"$ؙ0۞ bgaϲ[ ŏCh#kD'&b hwp<>刱dF? yw36GHxΉ{=LcϽ:RYqB ɹ `8"_#0I徴ĕuF+ˮWva/n#)=}Ek*ι'{runsW{{»zoxZ68 Jē#Qa`u>,`4 u2m\Ñl@IWdC`> ڳ )-#:^UBHJ;Ԭ@J a & V@B#&d!Z%A4A#ZB 1ut1/,G/!ҡ,Ee)}\FJ~bMY]de@>gvlWuxNN9&B,\2->c/+g{z%,N%bœИ[Zێ |Uy-Z^Z.P_㲘@qȸ C o g^3}dlo=#6  ܌踧rӣj4fǓb,A خ )2O WDGE&f 38LߚJĞ`bq[#Y. @_@%$-Fd=[%3\h DsrC%,^K7,]8{ U~.C޴ղ1T"62op) > }t-rY݆(r\&"`%قe"zvr PJ}vDlK]/rU#cZAݍ\^aAI6!zᘤ)C#@77 J;Tve6jT"6#L$͵4 'ӳ(@kĤb@ ɸg v{Kr8eid2&-3 h3yj2ƓG`Z86#?-": 5d&4ΆY&b;V)FODc(d_n bxcf/keXݛY۶GG=eU3 *D'3 Af.uL׮DG 'H=6+qer_:ȠwkLG {]wn:皢OG -Ѽ}[q鿝8MWcs3@cq9|*$)R:mSXy< jL-2tMAJidX!RHG.ģXa^@%ݍϋltD6$̳wϰ@갏աPk1C#TAPwo.EltGwxE ]ok?yĆ0'ʼ+2d=9^wĥOuֈ|D(>p'/s?jt+ZաIݡ"Oghr`es,4Z':Qa'ALA=+8p_Mt\} ϛ߷_@ C"PUoir@V9h cy7Q^^5Asa20bv֠9vtuR!Y\6Cm1/L~3LɘܷF ckK>ԛN]X;Njc> Bg#k}t4/¨ Z!}G1h7HY?k<Ħt\\ʮDIz)KK۵ĕPK&4^ sٗ/C&;FsH4Gs5WxW:o;瞲{Cx';![q4{ysUk3o _,{hkdRp]XU=b>Z#-Ho2_5X[R4SحB`jm̮/11F`(?'JJɢJ 1eV'|07#,}xԨwMx߲, 9 Vɯ!/5d6x=+w ZgN*h]~@وP~M=8AGs>5[G cb; ^!lll[;] Z=^A`1P Q-4GSaLz<824ޅ=n@w1oH^W[h@ pSGQ5F\J\0dh*Pp4As5Lmd}g0DǷA}!>ϲ{[]8| C`v/qeذaeMιq]{Į{ǽk|>&;F-B<̵AMy@\Xt7G`n:e86'~j]CޡxعD5 @+u[Ugtg2Zpldpr9{Gmt.2teX=!꿒9wFx L&vdJ g$Jo#8ܫϡv4z#E]I%dnߗ[;LMІl^"ż (dڻ8 n72-'gQS@O>2O"q%DroAtZCd%e־׭~w!rD /F,s&U(vo*L&Ӯ4Ebɺ'29Escf!ˑThpX䲸 |1>keGĪW6s{ČNr!b΂+hE_X@5hFWXVKk7߄'NgM 3^ {"6 NO`A}:Cԧ{{&hެ./W&{3}7ZxogCLܲr_zccYx'kv]_Um齯s}EKl:wܜx0n7Ğޗwn%/uK^N͊lٹw" T1} IDATre_r) tx2,Z5vD 5a醘!d92&"# >!rbm `]r6.ALjOR!Gik~D2tK xL!V!n}JĦ[T"6!݉ƶ{ӈzC|޳ @pmCl&R| dε,%J:y>Oyן' + 霛t2RZb0~vZ.Bci2?}X5x2%2PO `G6(Hz(2۵5SV#w3ߝ(Ƭ a: &e$21`@xӼ@e&vOW-B``>ɾ_eϽ#XeuB:Dc'Jhg ,ok&Gh"x)(hj@g7t32 ]_{*?$=O"c|8Z:F #̴<1aB;QĒ!:;h tsYmJw۵n&&ஞB WXK땇QDVSJzo<>1mPtwfUE]jϭ!"墹\bܝ(=CcVfKͅb%+B{6A2^Z 4G,4w34' ]X\ZpwW{X"tի+bCZ)H`uĕX#I@hcew%LO/'!D)FM)PX<; 1hB ]ܲm0k11W8f E;vDF?bMBL(6L r vd?nCr8< {2Ceh|Jn]Og4F# s ;4(^Pg/#ʟ7?LGeוyp<GK^g#[|3ĄdG,Gr=/Csbr zq㖈[mg!`M2#D +g=$BCch>M9%s gX(cmѸj;8x]\WqG.X.}ј 4B,<hT=u s*ƎWͱMr{}?Fٰ X<>2CjȨukd(r (s`\}5]6lee}")H.B9H)B{8CJA)ۗrٖ֞hy FvORޙ! ߚ(*B+UDd0hl&KkĊu'ӑ>bUXoI\]|?25Dt2T5]@SȰT8O>d޵)Mon:kSgdPf @qVr4.Cpc@F dHB^g2O# Þ8@lN@F+u.E& P]dG;[nEO"92Z"b_D}{ jX+@}ʾ?CL6ː!k ӟfb1NZh.B>̇P|{H~X"!lNQxA>Zdp5E}=q4ooFcFso.@%x 8ĕt5> H}V-o{ C[j4?Ak%M+{dԳĕ1dk3bamO8Xjdb6R1+B+/)+g獪ICDS!Pد7l퐛sokdshA`g1R}r$gY)HfGD*{&JzœX#ܦ;Y&⹺ZA69ua=ΕXT!@0#`W@Z?{~FVĀ-@vHùqRoϪ4Y5A 4Ķemd`ˬ4?iͳ'#lO/291ND`s)2VL/qVZGcz%Z0,Gyh7YtgCKcXʯʈ?œЊbdPh^Qw4ϴ/$ZgMrVqڽ+eW9;[W }"&& ) 蜾4ZQM R캩]V")ߞ ֞!V Z!bCzē`a*Jtdx2RX]<n 6|8ͷC]\X?Llnr LORʃ i : %dLnvibnC ۘ,c:NYVvMs4֮C/M]dQVQ4i&OCb bǶC&γU 3sQ;t23x!c7V"Cޓe;dC|tHQe}_@sl 4"v,4?2H?ؼSLv+<n2[eͳ:W9YE-^rMxO*.!OW?aدøp2|f vƛ<#bi1ͫ5SLorܾப"rDs*$ߩW'щ a!N`giPج9,Cd/hh,DlphvHg=@sp=؞վD"}(NQPME@d"wL,4CNDWs VxҾd_4@FKbLh,@rw4/,'{1Tg;?AD桅@F3w[ JHtJld-l ԒGl0ĕwe7k>*oeYѻ&{8+X'zr/]mlȝ^K//qe1 ɼ;wuAca0U.Ejph++D@K|1촀HoAWvdoF@"A1踲&hэ(? *qe 2gvnL}g-hˆU6k 2xwG4 U"0~D`dЊ"XU=/EdMG r7Z5&"j{R{OĊMFLE9' lVeVld{"(r[nAq;Jx8/C}Dkێ(Gqhٵ.+M!02Rط|>'b(#囱O@WɻZX'H_ '&ϗZwUDTo Fά܉ĘǓ8zҞ1qt#$m  8%5ēшܒ9 ϫ0܁*=b*rCnn{h<Al]&-4݃k*08x2=hZ_mfߎ_`O~6!JN()C_frv緬n.6п>2%^YXW?PDb:# @($hC㸔($r_w+kף6wAHzٴة/\sWvbsoh,.XFpZA %hsp*ǟQڍ Uq~ogI+s^ i:6leCL_q9b`z#G"hTߡ(݁cWS72;)#Sx2}+/!ܖ!ֈy 5#:XpvȠ!5R_%:!OHQ ^`W[[ SWוH\C"쌀I(f9R R@ dp;"`;+ܛ;=yUAq%S?-19&ݝؽ@SOyUēY!Eǐa˲!Jp=kSr]l{GtpF<4Bc!CCgm2{WbF 63CzQ :{# .gCz^hl`Xah>vWZXmT- lt>h%\iLG!gB'@t4ΏA#Y(~%4f>=Ӂal6hCqv%lm ܗ'~x61 ~A;"O!C쐢 ;2 ԇ(io{N m@1R&7"6x2ʉ!e)MC 3*DvD+߹DF9RR}ì~͐koϘ؉zi&%qokwA~d۠:1xG!z2ˬa|j"L+N]A*{yGK\`W{!ep6 cPӮV,]Y]Sdݻ(^̞՛蜵{5$11~ 4ɻ콭]3L!f^_V-X>za[œK-2Pzi֗ ̞<Dj>ۘ{1b  :g6dӽ6s%g GR*^HI~~ΪڢcR@ p_.ҩQD`t2 8(s!bA/Ok[+݄bHKY#P?s!օXoZ6GL4^AjkX5AsW*MEma%h|FKC"b4ZDEca`L+ܗݽ?Qv9c˼9F!`1C9%bGx9NA,˖&x2o>DaX@T&!S]R!`52%uE矈5A\;4!̅D)Z==Q!;)}dFJrk_~uAp=ճ}<2=h yDx2<Y;=drC,{f>&5t-LF.&s>ZЎ/LƽZYDtuu/fmgLf#u`m EH:R?O=&|_h/Z_K˿#ZFmuZKK\Yȣ/8]]!QQDM'cИqHuhE\$i?hfh > hSI!Ѐ2?F6\^h<3eh~!=4 >ɳ%r_wF_Z윛` Btvιwᰗ~O'J5ιl@,0^mJYɅDy®JĖF] uC*W wAl9Dx2}-_Epv IDATA!*&e(k&"uѮK걈ȠhR#cN%bǓ"Z~J.6Gv&R"")PꊦH5G+XGTXG#֞-Md6# &MQcR`k=[}>AXd|ە n[ۙ\[dYΫMǢ^}?>@FmuDx2QR7Ey2>%a95&}L>b"y!ؿ9)ha#d]{D*[3Ik0r (Dll׬oU | q6 p]Wvb#9[ }.J$$@:gf `_47;8)l_e u) *EkccI4F~%]G jl# , ,;˲}~|Y I4k_;[=s?sC=/*Dr},4OEݧVm#kK47Bd{O |]SuSмsG-X =-J?A:I tr`X*+m]!6XHG/H]G)Aji~H0덄8Ow t52Vfh54RU!Hcu<> tKdus u@U4~uRs<䷬'R؊x21, ChG4y/F̏O rdhФ͛/YMVsUy-b|,4l,B@.9+QJ ȟϘF`Lk4zLGE%wi-s@U 2T d"t[=MǻtbǓS؃OwFs|"V%<ΪZLAJڮ\:4<@Z{o3ÏPd:SlēLu%À>.#y{Ae^-2{ @H^%n,P 842Lg+~89MؖH)]@:dg Ehv'2.}ܡqIJ 9pebG$͑z"2PᄇG^jmV>M=)@Ʀ6^@a*HWo\,ce>:\SS,`8o:Wc3a!6*{f}%pN*Ʉ''ē^dœA w[t@b~hYWX>_<3?>o,"o͵1G%"Y_C{ bv@U6Nēx2=ƀOirm$LoxlЏZ,Q~ 45_XI$nӾ vuebv.ԠoI8%=j47>FYBr} 2@w~_W4F0 Aˑ.Dzlw_.{h~!} Dug~|?Jn@ +F/rLuEGw0;@sܶTpεE@j&3 hir tEAQ dӶW1hOO%bc6s"g8yWG/t Jyt".`bEJ ~IS6o!chϏL`m<>0±d^d,Cun6fs ?lG<+3Z߇\/lː {,dh^Ale!Gr7 JĪ-9ЭEx?aJ)mڢEo}$gV~;׽du<0#51_ۑ@&9`j:} ˳ Zp@Zs“-=)9~1łnw]s*{vԾ̵Т~CKOW1h1 F#3 8,+4_̕8P} ;z~g#Hǭgn(G2:Bf {r+ב%(H涫F a%9 s Ƴϐ݃S־WӶ?5H>. hk >uiQW2x◔"h̆Biz{h?ߟQ?F=ƓS,߹hlY;`~ 0. s(2"o^Cʢ<( 4竑;*X;j.B>ǀ|5#eXZ`E]/C+ s5U6RX}!`3Ϭ?vCLW{Ο" _e׶~VɹHQ/8_E?g!`ш=> >j!ko[ZeuG}y~UG-Fd`~Z] gbwFj뻮Ȋ_OxTR=B#du:rvBw{_pؗP@<$ |kַ#T"63LA󡋵q"Z$G-o{w~<4Kbƴ|XX΃l__NNv㦞rq*[}%J KeM>Bsl)$EDhd@@43 OCshBrL}M>f&;[GcGx_4Fs /A:CGǮ$r2$g"@{!&Q|kdOy h^, 8l2јT"B|4IC( ]]UȸFF']&H|J.afDLPg>{O9)+s%r7MCua:HAE)~y;䲫a? );2B pеս9rMCO##06wB&*bH#vF듳m,#k({Yʹkku/fk[n};2n>? Ckf[m؍e66+ODtAgO{,dqXT"6X}\Ex<>eMA?U@f"!p<2]=͐kq=2uHُ^ ߵm(4)d:ٕDl6S{*>Sn=6~K(QWR /[|E#[Jg\$WJ+rIFv=>$"E }Ҳ> BgvZdŸњpg γ^@gc= ͳEHv hG,͛ HN7"Jl-[R]q~ 12ߖx2&&24D?o?;]B)E:}^a"xD[_ "eVc_.E zFQɃ"z Ź)V&# kĴ,D+/ڻ;%X{rMĶld/DNXߞX'B~P>"CU ϏamQ&Q0}G#+ Cg\Kv(®?9|>2$7X,bnʬK:Py^z G6&ͧL{E o÷0>XbL*udz4y8nь`{ ے0kH@r!O#=oxm?14LoSlY^sFvfeê.?lvl5vmd8wuvw:JE]IMiP<ҠSlBԕ܂d=Zdơ9m8QZМZ`4>Gb!Aw%Z'Lcqq1kfP;{{[pG9bwC2Ub֞d{'_ eA ekY-Y ]S,M!/Bm5Y ( >$kȀ  Cv*RZݐ*@FRx=>E@ BeԮYI׈ HN,?DJ)шJ[F"؀a{b{\DW9ZZ܈ؐ?uHmbFFxV߭xb[ A|{^3dC i݌} MG@c VR`nc bK3EH}mHnV Y(Gs ௹[8;';g#x"a@n; kF klacx={r-B B$ wywe!cRزx2= xÐgǩo;..S|nC.k \J޲vnēyAq˂7el{rK8r#G)t={ŞE$J|>ڨ+-G]?7Ceh* uCz4(ƞ}=Z4Bww{s(ͯFBE\C aj$ހ"&]W``4'i nx -Cs4,J) ׭l-[r"R,mjT"'W--}Y &MvT"`Q}2nbv! he>Z}m@J!c˚3:! NoC 4d WҞ1 1'> fǾ`RK#Q>(nNtG k$Ͱh?G"'d=5ڮ{ ) D,0c]3S]T7zdX{ʑtw݈@/Wٳ"ZN,w ymGof]m"vrb4C@#Z tМVnzs#ōDFx2=/gFLJ%bƓ{dc dbE.|9b'ͳ͵k}-4g{Ɠj4_!IO~߱WXb]m'YUJQWrbxiPέ|UE֑* o**$P+oa62T1p#_{ 氛=dt>Cؿب"dr@h@2/=Fg4#Pz2X71_"vVQS؝dz25dS dүtbV#v7N 6~żj՞k6~мD2t9!Y~0ӹF ͍qrq'woorc{ ]T)iu\$HvKb7a|h'#35m烣4(nZ^H~qJҠ)0ylu%"i*҅[ߧX)3X=}X4"h<V+3>-J#{G4 B ٵB Zh?/Y.si= $cY]P5Xk@ f^Sت|.2 %HA؝0N?1O%K#O")[ >^jmnta(5Vq <${} d?D6pdz8 Bڧ 1{):v=5]OAF+zn #5P6dX_YO6N@Ƴ--n:]dt6~C{_jOOFS7{X}c_-خ]JĖ[{X}h>e[ !/ۡp?ZHl VTmѢ"֔nllջk* *%#>ڨ+y ].MlMݓyVnƦ* Q1 ]u%oiP<u%ݰA2Bs4#Xc͏Su[϶"Ѽ GH49sМ00Am?d pf7 6Vs HrT;!ApFo{A0h#߽g_|!WAſE1a$iSd`VVND+湄 ;"1;=j^2XhH)l[-bDFywF#(Y M@EbGYU4ĝYdm hid#F]dgCwz*{ L?o][;_ə c8"?6Y]}WwNE@@@;k5N IDAT/'ӿto{r;nO,Dq/ 6w OO3e=1+CB$r,r?7m-KgGTf]_e<5__ 4[7'簂^5gycbǓLp 7HFHީD<Z$3]"p kZBp2ا;?, o@+!kR^GO'!YHZ4}m,x4(~z=<s% H#{vMwu9t4n5hZ'͐l. :{*!|OrS9aR7!hmlQ@ MȕKx2}K{2̓ 21I$fg҅0e6?nqoYRXw y+PPO^;ׯGH]g8)ng"&DBzk: ~{{Z5_|!t`]W:䚝m혈2R[HƬ@NZ{X̲gmgm" 0Oصc ]|γ<#8^O!O\b;J~zƓ+ T"VO9Rإd5ب]Q}HK]ha= V `VV4?@4/"|5,[8{vm H@z.b Gsx. ͳѼCΧhQ,@ɧ_m;-T9 ✫|+tE htU psP>wsI w/ oDW</9.C7fAp?˲E1KҺ̾Ǣ?(M'Ӈ.AF#F ad% hVpCq1HO$̕BĘ\q/.^WEE.?nJ%bds,"Lw!̸^Mp]1P}ʳ=ݏph2r*g~@>RkvXmڹ꜅@ڑ(H`E }V˭s3,F=a#p1AV>&x2fllG@0U'Ell3Q6X{F[=#2rViA7Le1 @ٶzed%4ʀϧ J-uDd@ߦ7t{v9Jsկ=9'=CPo-\֟YLxd0YHd#Ro[_nTFU)<#{6[o|RhW1}YYEYW)j` H;:_FFNv?}QiP4J^%+ k }Bn),`1f!4Z,m?iH_vk#7y@BL֡hף(qקX2 AϗWϰ9>K 8t#?Cs0)sBus}d `s4A9.- (sνܝ)@4\я/?EP~ Ӗb!+1+b!t;2h2 (@b^G~d#d[i|mLޑlٍB;7L'ҙUf @?wnKBCl7#XvHQC)xH+b}eߏE IbPV[E`7Q ki61@Nw%Ked V{$2!x42>uz;X;Hz'[#܊-R@)6{#@We?',h{#[Ⱦ7c~Ʈ?zTW07{VQ`cmmCx\QR_G]sK(F.$^v@X͵ߗ#aơr=Ru~5pf"&K)Jyj@qfA=f_[sY.:,^ѿ~]fd: ?@WKq t&uH.AKƨ+Ǯ ~>/UQW5HhϙCx4(9J-4 >Cx/5}cɦerssOBfRv݋(^e`A'}ig"<q5sU8X <;._E:bG"ChB{@d)E`n³ ToĔ-%L)2z5R kkSX)rqNGF(.>CFXؤm;"0pQ+v(RfgE;{;1M/YC ǓXnhEW<4"*x3HNG^#Y" Ƚy 5"snˬ2$+V{B;uhe^kO@ܽG"zQ GwH*SI%zbOkRX!9pk: OSH'[;ۘ8o$am:}L0{)@g5H3F } 9gO<O{Ɠnh#׿ *|˷׬~a<5yvh\xG%ܾhL9-=}eM߮Add|=梻\VC#Ga4wAs!C[e(/ SA*Ҡ}4 ]w0SϢͫAҠ aтl$~ة4(>w@2$Jn@; ]t{?FX6t`\s.  BsyH>vȠlqpWk>>; LwFh&}pԜKQ4 pR>'T_AdC|8~REqG/A:q'ӣf]kB|׻=3gi_ӱzi'2.B+ c_MQWSҠky6 hYdr}>̓ 0ز>h4ɇQ+Z"9 ۚŵl-+ ,}\t9M<ݜs# \{4n9A望@x:pEAas eb+vV5[&n?o"㝍쳓CFl 2ka D(!}/ Zd݄F`S RᎶ=#1&?GFz)b%ZO-b MSHZN{e$%2 ;rկ!!L%b'm_"kdPE`BCgحEbSN?f/ϖ"0lgu3R{^ `t*[OgZ.Cn_#5gXҷr+/>oލY_ 7H@n//@r,rO:AN6~ ι ; oo @)ǥv6>!@7pZ$ggx4F=kf0awiN%b'ӯg7Z; u;wX<50&-QWL. GذwMb +4#[r FցQW24(u%oyd5kU~֊yM[9G#)@>` Мi*;twh"Ji5=b-,OcEλ揦%Os" WB 9M^13ιz9®3_<-A966f' MBeGB7\,D+rrjoЊG5d@||?$x!aC}2KJh coN(Fz7si%t5MkJ;!k/0U0^dL#lYu>\xQGdHFЙ r Lg[fY%{f? `s1#G 'ŧ!ڝFs#@vbX#Flb#dl# \Xaxfm))yŘ\H"o2ZՓHqv#> Dn 8E D{6n)~֗B X4#PcOwA~v-uۣ~|I%b?\g9$[ C]ױ'/QҠxG߾WhƨCԕ7Y]۾&2^4o. +>عêg?d6d9jҖ}EhaA[P>3uvPG'KݑY!&qAp+Z45lU&=o6Gڌ-[{"%?Ldshd&",݈h5 7ԓH@BJb<{#P=P;2}D,H%b6_ c 10Sv(Y]B@݉ܗ>!Q;J\JDZ412 w8g"[qx7l!b`z Iyژ @,O$R!÷C9jBC'|A@ nY֗иOBnݐ{ފ$w"q??e7fDZ/Ֆe|mec㢮dĘ<u%gj-~MG2 eE[˿Sh f7=CĸFr]| t DVPa.6t7Zo}S&2XuEtBȲvt>'SXc*6LG\2 ߧؒ AD$ _ V6(fd,2ԈCo-2>T#;)vmbB:#C3@l\JĞ'!>(i9r :]j큌tr5GP>DT"vE<0zd\cgD#o܁2kmcγ6B`3Oֶgo}'.Vg}z;bk1;ֲAƺ1ǣ:+qսƺ%bʬ.G۸m1ymhDd#;|1:"($,?wZ[_אNNCA|BWb#V*EXi- |AR@zV7p9z&SVدfH;,bcy=c_VCZ[C{H'q &!ϰk5yc݇A*NLFRX5S1bGXn} bʪSXIj5mI@C*g"6 M>W>8 B~1$Se13 -V-[}6=\}1G]'s2k-ӾkmWb'ǠDojY&;.sTEa;"*BrWzWLn>G6!]Aih%!nG;Cp$ px37NRxG:^Nxh;hd6 Wgnʡ5 4)Aǁ?N 9l''-Ӷ_J1~;:=.Z_܄#l#tIxe;p*>/!W]jـ oM%bs9YK貟2h-F#cVo"$A78Rdz[JjX#VXȈ%1_AG#ǂ;!1htoR捍'_"xw8DI2sNAF>حMV_,~}|2fI*?L{ƥGcߧX2NgmXO{!wœig>Ӎʀ ݲW#ukS/2Py.+O1? Iv$뻽D\r!:Ǯ (}rDx㟭>>=Anmu;uFjrW-Z9 f;{=ᦓf]koز=M"vd^=xB`QL[ U&\B'w*;YDx2(b'f)C,Hd8AU,D'B9?ޏ\ڥ3srIUc9}[ٛŻhU1POD,S+A 'ӯ"2 O dźSؚx2/2 dݯ1/{|O4%b>OēP]-<ᶳp1#E8v8*S0 g rδqհ>rB]Y|Vl{Uw-6TV3m 1|{2sս.EQ_k{6NAcY߿#wE]K;YZ_][شz@`]( E@Ϸ<̲~(D .b;-j2SLnH%b_I{#M*[ `ADlqW֬f´F.f蟀_y1BB;GO]里A;`x*{9Ln}(.%[@M%b!et6b7KdT: rK A_je}$)Rrft9Crޅ@H 1]s!y[(wLԲx2=gz!:~"qT@B4֙'sow>݃#@~emypd(0.L 2e?eyL`%ќ0Q?b_#F\> ,d$>MkqK<Kٿ#ٕ|]9 9#:?j qεvq-'ܕG;vE3a썕=~+흫}2x9w6}~W] 7Ħg5-لHNFF8LX@nDv228"v}|e0MnFm4mXbCdzCcn{'$ܷMDjWNCy2) CwaVUW;3#m,("k~%FlF&Xh-v]56DTQH0wmψTBzyfswb^EK7&xt9xz ) 1|o fuO|:wYcK,C,H{bT97v 5+%RX"3H)goOA'3oF CWGC>~wL6-hy Z#@d3Lp3v)4WY[z#f[gϾ b4F~75>G7dW:~DeOiwZB;8d?-FPlgUwcRfR"p&~ϖҠ4(nGB9áh>vENMy8#5fXM]Ϛɜ.@B͍0(~)u9;ᆝWYQ9X"5?IGZA@);0`c5RfG `is(K_ h')CRB+>x@92 %:UHki^{, ]Dj%b_F@!@&:L\gW 8Ϸ:EJ5b@vMZkPn~i1ٯ|7g@՜% 6e+ϟ15Gg//uE/3AJr7onKƣDJDj -#{5RxEJھ/1>CPo;8!py!v12;_de.{#|d4&|=t`DNDo'b@ĮtA~>Go"pyڹ֠9y;ayg}ZomfÜ h>IƣSцX"ձM whz06@wY,aOzrk\Iz[3-TJE m?+!`yx$~`_XlfƵZ ] Y%/:n `sbo5\ ֘9Asn |6=L_X

Lȧƿ(zX>aYپ?; @̢!ѳV)"֩=kd/ 0l *?GT=ۛ,h8 ) 4AW"?)y](DJp?"`{[F`'X^iu N+yeGͽhJGLJ|şL,&smmZH n QHQer%c&ɺL>Np Ys3P9onb$.bzVw)[Xy;X"~lk~4맡Ȍ ͥv].нkID"u=f֧ jW(-/΋%Rۘ^1 }Jd< Hn}8$L" X_p@Gs`l ߡU:Z;~h@NV^`qa1]:O|m @=OBc1qj/{EbKL\IS/]w;a0 :OE䷥A&MWJFȣGDy!RI6+GL~|J_saIA׮v>aY[ #^?[VQ67Ӑ5 [Ch!64x a, -*>Qu.߉@uY$B ma:4-!Z!b RG"OHeXy#^퉀KО)Vt*#vHicv)kTo^hY,아qFhAq&b5/.EmB=z_NZ,-]PzM7Orb?zd<:l#'=3oDymnc6>e WX{ X-%<Asn-C 3~Ylz_rEX"Lc RhGƭCrV.^w޴#TB ]ՀX͕@7]e͎=F|qߝKěbTgH4C<38-=̠X}E l5dSK󵫕YL;4_f[[4E7b |fX"6"hܳ.)r%@fD~L5iv' TzHiPѽ=Aui?ǭih?i:o~V ͻa/\I;to,kVJ ѼNC!~;$M$?C8 !KQ2ibC$rV2}X7r )Z'׊->f4߮_d ){{)H)֞4LGLiQVYVǎh3 ;Ĥ@UֆǠ|%bcj_L;!pju8b,;#уjuH>G2*!Etaջ2xKgw#Y__`c*9Q"u"sW Cs r?nVIև"CͰxt6&xp{2}.HEڽͼHCCŝo_D#1ʞֶ67yl }h o.Z OCL.=c5gX;Z ?֗uȟ6JdDxWiPCp"W0LtFȕhE,:uwYYՊ#ug B[R:JJR[b+Qܤֈ(@@F'ܷ|jiY ]Y `0R{#0 5hN\c}QguAH )OC蛇{kJ fV(ꑃ@Q!nC[d B.C@"9,6~!\d~cuinu-3g>!%!HUY`< IDAT2Fx@ 1xGkbT>@X"$RƧ"YGD-x gLHƣIFsgͫ6l0khN/@l?M(޾x_eYgjHƣX4ʲcv1fI AF_݄ޣaFF h [#;ꯉ!X? a %q@٥~-vh3ぼ4}]WcAɳ־b썔>- O[M$MȖGlb#վ)"z'd<:mѲ_kDf2(2 1 wB ~}h@L8& R>\1-Da:Seh>ABx=.{+{ 8 K#0qۈ1igeG'#*U!HwAu:b $bd<-;8\ZPmޞйz]CLT[G0Ov3j+{"@; p)yVDA 3 H}ܨJg 6RnH}kuxQH).Cp 2=EbHi]<::5jy KMY|DxHa]gEf?g&齑#ۄ@v@~Ȕ{m<.D`;_`~4&1ĞʾB{+YГmO "=9`o|eYzޞ,1芘l¼C &C9:My=*cH9zo21 w#Lwu#s+tFnT\CR4Gn2hN1F'Z#6~=QY*r%w" Ҡ$? >I[PԵuj-_c9|-k]Ew& 4#x\n016m (_ȟ0מڜy3o, /r%'+(:i6.#` 6n>ڞq1bҭC`i.7ӑϟ1; eXbm*/10r(ؼǝ pJp+iya o.z|_1Oƣ{v>0o̴Y{e6ὮFk_24g>lܖ"O4ڳd_#3hgdzGJ) C,?]2cp2onc@ڎvm`})‘guk"OB 2bxy]6rbanD!ߡ T^O"b˶-MƣWZC#/ ̴$dQ9+7/ ;^9{Yiϻn S Koxp% dN_G&h"z9)I  mIW_KxmGE}6 Cd1`g1!\H+9 [c SmJ7 Ҡ4G ԏJ1i]ҠxliP z.F߿Al:Z{@k>ιSsws/:羰]sݝs_;6W]s88&;玵{#ι{s%ιsu\l8f8Z/9>u};s1bkiHhŶ1(q1Eb[?asNB "Gh#tb>G/썲3Kx弌NU;4vͶK9d|L펓Hɝ֐Vcm:dU~<㷇 3s:yB޷n!/ =)!H BLd&E;#!0'@ q UȜ-P>"2ji䀬†x0bd͓Bks1X 0^@ L`Eo@ -0S;DiX"uX}DG'[#2Z;%R͑9m x!  A}3aZClfcbU}z%Oϫ9k%RQP\I x&C4 Cy4C`}] EXs×퓑OAžGU:zniM)$Iιhx}Y V8 A|l 8G{0̖sm@.pyF eM@lYP׵V41*]pv@H @J^Z+Gd,l 䴫B~E>ڭC`cZ '* :LnuA~2UYm?mxOF;4BSLdz)}eyd<=hU {_2{bZ|b <{;ظi׬}vOsǧVKMX",JƣXX"h{rfD*/́3nWs#Sȏ _X"LyYvӭ9ۮZXkz%H )& Mk&_#݇채oǘ@g\]YkczA?|Bփ ]rՕy8v\=mYbl3EW+CW"F`9 yO mM- 6T֤8D~ 0_9?Ċ\I.:yiPST?ΎfhiO'#CCoR^v(b8F/HR]тvB[[|bHQ XhoOw M} =.hR6"dhF۳jyO5HI?Ͱ6"H>pߡ x&ij/@lkG>G  βDok?}V\ZqXy=](سnSw|_=^v_?4ҬϊEN!]v$ݥ;1eb}[h,:3~KCت.H_XA:dʶ<-F[oQL[#@w7-eNƣ߱q[ #Ϣ; ͑\4P_!q>jc.蝻7}dd< @,M֖FYX"5}#CUema# f.ѻq[4E^ G̀k:=YJ:+JbwSok7vEZKJѴtnٓ̚A߉5xXDFԺඳ4>4Ng5f GuɅιh]fo$/u 4`syAJo]@MlWhx6j1@ژKEd'خٙ]>b'&Y)o&%wH5Ga<:Q)ahTh!X^Dt*)^GlL&RD>գIƟEf#1>ux nFLwu=(LChq.D mHG,ֹ똕GbHD/'h`g"FCz[>'d#ЛX^Hƣ+ch`ׅv_?>6fJ">{{r8'"& 3memARNl< .=rU튴тZFؘ\@'QIľބ?Dc6 N0 ;xЏj~;!;o>7[ޏUxƨ q]bgGDfCCi$nsH'="ݫ>3&1coyD?bE5agXЮ~sJ:,|ŕ`x6Ҡ%YRJ UCDx{Ko薖ݐ5K3w C 3Ѻ6AV+QhsF"A &ju@ɦ/e68. 9 YC&w{6{5 $HZ=V<}譁cWޒL^ݙ؀ dΜؠvI ÐQOD,G7B`]e?B{O\ <[8mU͊\Zmf@݊0ȇRީ5gZ\32!y7 X"wZ#v7X|ާ#s Z *蛉L[9Zݦskr.˸ A["LTk}5r |#Кg׮B&o2Wӟ޷w]YڸNB ~ bG\m}{ug\~=g'u=6 708^Wva臘 Bp6 6Bw-Z̿GQ}2<T %R L%RyhnLƣ X\` ͇q־[UlRn/ /*r%oҠx@}\@y->54(~{Sx,.r%lm6k9j]Mh+!W Q۵%Rd}kM 8bf)G Avkr(,ӮvjFYk>~<%R7'NE6`[!P4Gc=Xאjq>~==d(4>[V/N׹Wn /=|vFf2c!:ϱrXYit0{? ڵ̰kzY]_V^RTٝ-EHwBJ9:~DL؞Qȴ=%Ě>q-)EݧHkcVe-h^7b@'yXۼQDJ4_#w)sd*D0H^G~?c}Ann. H P;ky bNfN+>kE1pd޹1CR_oK"{ U"r1zBf{Ns;_R9'kU.D  $CRjAl |A/m@ئ-4y/!x!$h܆H,*F4>5d~Yӵ'd< z: !ːv5eu@f,S/ -^C !%vZ {" ruvۺeKoV;4#>, IDAT#߮i{X@>G']S-;XONE bF ,LF,:kcv3RTX?Afk,Vϭ2z  쥫pkCBA" Zc1G~:b%<"=3+mL.~".9 ;%C%l[42=>lTG"nb7;s%:9pN,JGJϏ D*̪h>f>Gxz-@Oxd\%RC|,Lƣ3}dnO| FId?feǶ3$oֲ?B ̝Kl0`73m]LB; 4dD @}9Gq yoE#s Fhz4'>4^{Hƣ&V!@LB^k ||>y}&R~l{#ƨLEO}m9`?auz!MYsK O%N!g_Z]Fa#y$͑ۮ㭯Z6[!7k3G7 1I986֖=g fg 8(H,%RX; ^_K:i 4tpi6ԹC?~aϙ[eN=K.{[ʼT;ឹ_k4t=r3brлwDٯQ ܬhW;~f&i&YG[ygdFl;avZzEmmV~nʀS獸~e,72K.Nƣv@`]?R+[!C "F݈Ld!5i>8 b6@Lv4R<KyHٿvw1VZdɿwWxԣ]r=bs|b3Þq*Oo"S2%RYDJ0z-@&rK5w k{?K*>o]J!2ބ|E yxazĶ/geB v%Rh###amec] b/@lxgVCZk\LC=hB>yb5Wg`YME~ a.IG30̘<5nhtL_)z8 \ +q dryˌºW/\ЬWeG.2}\2+s'Wm2S ~S⋈MÓ mўxjܙy' N{n$O6;5#2zIy6>sUM^<@d, j:R3X"u.2qх($N" C;bcNG*[%bBJz%ȴtz2:H|{EXn8b!:+bYoF D^M:{=3.Cb'X"u=\|V! kqյJvvĞ<>HZsd ٧iZX(8 n>=5ۃ)? LVBsa/X[E@ oN֗%ѩumK{+db6lkxCfnNC{73 sA?5nh[4 QH44IwqZl]v}]eZ6u]k&{35e4oX'WEupD\I6zEZ4>YXpx}WﲂU8X= F(dP@n^w)wIsd'([AGz5uϬߵ@Y:FAE"e[y{6os*8o)K+xտOƣDʧJ%R/Y5#cK]Oq5_L>O"9:ЋYUU'{C;0,H-C`#I!2e@=L-Y%xFM"EZ wEdRo!4{w+^c6<):°VG,N=\Gws#׈iYncՖ!v}eu:a)2.yj)3fHm̒V/cc{dfNgMz:4?oFd6wTyD=b$J;/|ͺmaM`7أ4ewdKiK> (QUP\s|܄ ,}*d=Z|>G2 ׬zYZ=zKAr9r D/ NqΥAA\6ح<|C2*hG-^&|d?dm8c54ODp6:iy$ڝ~Ǎi `t2`kKfA/!fg2YGf72I5C l =q9gZ{(aum!dvX;^gbybT@CȇdW! ]1ss[X _A)Z#`3@|Yß/G@w"F\h #S]'[{6;wD _ TLU"Mvߋ#ǟ{.꘍Ddʹ~`D vňM팀@G;%H-=\GrbƬYc}pRGŗz4(1GEҎ=?o\סjV eK%y+5f}so2]VF^>?O)$E+YJn*r% 60}z?kׁiFXU8S>FI y1 7 ܵιK?5s.9wsnsns,|OιWC4os8ιpͣι}=cs~ds:*zl{Vw[yx V!O[F'NOƣ{$#Z#j!Fhop";8cT ( -ZX`xBniPaQJ0}D7O-G(=Tn@;QCIG/W"'юAfb{!6V]k0an,>"t7b: bC, -1?h>l' )\h8;ͭ׬՞99? o}a~ثǣtg,ʹ뵣yusC?{o@tFmG4?- +y B _Iȟ6zu;ᡠ&3qιl ?eg8|ם ќuuD:gbW79wA̡9hki9HD&/Dy|LI, T$хD9ZcԾSҜU(+ 4"cTC_!pp5R½PXT̮\46w$ڕ Y $~k\_f!6\ƎqĒ[@ DnO g6X8|xt"VVg҆#Ae;&G;"@ QKh1:+̌ʚ8LwGJ5Oƣ?Y Cf0 lakl,&00H=m [=cZi_.)n[gk[+B{*ʇ9/ ^t_ަbNfKsZ^Πȶ+eRJ.&J"W"d4q U^Y6=KJ j#3ή/OneK4os_)Ϩvdꑽg~^]S[<ϕZ:W9`Ի;6= gi<^/ /r%f'I?#AL2x1%gК Y (]= X u^ι?%uƈ]u-RmR6cT4H͌%R}OXk,w4']ѩY&RZhL ^hqLTKBX?!E Dl}U32 Z ,ACkSEx" ?^A0;X~G ãsO S^h޵qtπ1kGfD#X"K<K֗3|m8 Lu 2Why.;݂ ~#HuYz7S?/|0t%+d64HfZ~]<=6_u˝*3[4仴`Fz c;4(Ex$1Pt#Ui=GT.jE' 3ϯ!f=͝6hQ~ߕnu✴3 2X+K'KՓE7V^Az>z}},η ( FEA?~IpW㤾bt&F'5kd%R"a" r* ;0MOt{C2)X\Nk}shա ' .A떌GWHn؞B B r&!vzI[C ٭>D4U~{?, %RGo%R7[td85BJub& s_DÛbHGLٶvL ӅUdyLG!}D^Ͳغ'!tR~ZF\KF!w(l0E_αg^@[a2}8HAlͣW MO=+"p pr7 v埬]E/#ep,r:}FzPu՜=|!{ ` @2]n0/sk"w\aᘼZ[F󚼪yvrNv/`ePX A#y -mXuٷ9Ggq]RP)V?~xjP'RJ@|.}:ymY5ч_h_6 ,Gl0<ι=}>39w]2s jsYxv\!Ga "t-v5/g-!&dmو®N !p ݞspya}w>2y|~|ȅbxhe, HεvmGpEJg݄~օha@TuIdx/" w;1C;t.:80XUW:RKZݓ-ҞZ,Ԟ9yvm$2܂x3K3KKlhӱFKZx9t<` 1UgBŚHݯդ99;o_FY=P)o8x}'qCwc䮪>3;7^& A AZJ^ԡ( P)&Qd#e(!H @6d3{~%yIfWn?= E+ g;TbOd. `Iර+n[WPjFj|. lXW6y25m]4c).;:1 "%|kG7..>qq1Cs `GʮpzIP(ڵe/E6':9VuԽaoK93FV͞ޯum*Wp;Bxg18UT"0LXUR fg2Ԡ#䇠,.8Y Cҝ {!7R!e~e <eH A=L n#>2Xkx(i 4Vd\vP 䙷V\EeR_B1b;D@;zE-L!6S~O;^+A_\'X('~a}}t`xd-^7Zh.:7 yw[[?6} -[y,E\remhx(Lo3"@` ߉SMIg/4'}_.sE['뻡B6(H%bk7. W-LWԖj_=eE/Y|Jebh BsL89 Y^a[-&2Wm>݁#Rʦo8bxkO:ʽAo/#)d䘚'SXGS Dx2}.rXވ gnȵS?V*R18K"yꂔx+b!-+P{sHBgv 2}2 3M+_h? jgݏvHяBࠛ5x2}!Z W(jCUu*[OD1JK!@խQni홻"& IDAT<6=w!rk D#bps pز/+!j#`1_&}D'w=αwD;f1x}=&{CL_?d&]JĦǓkA} R]mT9-(5 MtWlWqF}Ħb;H%b3f>qbY7djsu!վj]_@>?okdn'ӯlUT`_unr_ fmC3碱&V́[EB+ afιк4wgN Iy)g ZUb?CN!s\A.;c#sT;k{q3Cؐ|z bfz!ew7b!h#=|c"Ո5G`dm%bF/[=F!3 tA=ث!+(~]^MD6L+C"{y7d: <|/=f}{c<h>F,\3Rl/!sid>Ta0b1hOG@mBLc%pX*;WR+C b䃹حzStp%~N>'XqM`Zp½r+!Q_̏}O?!YPq#K3W\{;-LY< Uu3N=|^м8(LJ>4}hCn~urLs\=-D g?w0=%mIEI[使`WuʶJ'DT1:չ?2GC c%HQ9xP+p!?ׂjj"p}n~J1J8bBNEIs遀8< +(@zs@܎yY@rg7kYD.L\ؤ Wf/bر*.dZ9_Acom* '7گȮnH%b5֞OǓB`@<>kI޾dj*c<6+(V!_#ɛyRR>[i}~ !Xo)bЖ#>m ~lD{nV4nH%RX_u^5=#MN0-ȋ:fOƓI/?'ε f1ⴚ\-vN|G]fWxuK>horw6,*,fQ>2Q;Y9k.b:jIK v-"Ϯ^SpowY&QZU+httb ?)oO!% )Vs/UV5ۻlp U pr yG jTĚuCʴ_#GDd:Lv"GܳRXviE,?W6AOHœ9ܺEDvi3'[ܼMX`S_Գm9~bXkߦ呶7|,h/1U.3 >+fȱ}cu=꫋6;3D}ՇQzW}oڙS Ķ G@vc 0YNP!Ex=yq}Y_Xԣ=|fZ|G)Ėh-x27LX')U8<4w!x2esy8x2 @F^?yO0\Pxmuð@c:݊L'#lbOXOB&>d y~'`kqc1<(bjlwdv+h+#F `͵]̙wd^F#"b=[;| 2O@,Kh>j&|5LO@x2+L +se!ۂ|R̸2c ?Ep(r7a1fl-?Ux2}Xae .̩_}-[Ρzk+42 k.g7^P]1)J {zbW:^vm .3m.Dcge2_֠ N/+SJ*kٌ韑)m!bZh&#bXlܷ"`,ZnE&6#5 )x-}"H6ؿ3.mh]メq!+x ǐ̢# K !QvN+"k)y rDŽj֣l Gg.@z_ bz ­H)A~u { _B/PDc;9:1/E?g}k*{.wo6#6!? 8h]wE$xa% 1UqVvvMP)>L[!S+Ap,bOX|e<7<]2Ͼo'{P9/[ ) 9+:odXL>0kT!Sݮ?l1JWGI8Ăn@`%0 )H}b݁:"amE5 Sf wkBl_Ğ/ ,b&#`od+ELep?W#O'ňٿ[=œz7!F`pP* 򀂀 ٤؜xo;B,d7[rqC<^j%7'G"ދ36?yf{.(2\#֕g?7w[Х}]$ r1X# 181WWhTSF]Pk IaivGwh\ Pyw` Cњğ;w.l+zrS˒_Noo>ՋK?uʶ!fd~;9RtK%b(=nN)|5#TL1*Y 7<:k?\1qeK[ZcWi LsB4w2Ah\=ZlVRX{<%4/);z[v<}{.D!0"LJĞ{ƓiGX6K#WW[a(⧶]߶n! {vbÒA'4D\S)ԆqѠãeVV6'7,]?,O[]4'@s/bIڹb1/DR`aed3[-,mB\5^\2rU>샬tߎE&/׀D h\Q6Z׆?N٤vy=+;NFʁs$1h\mFrp\ 1[qιFm>fv^z ,gܦ[kmv#RncCh76R%uJ+']~ );'@,P{<1z`Tc!kF'ZɈrqG"}< k]ȧ:ڿ[Ch˖\VRȁJkG+[h<  O߂S|g`(f5Lo/S`3߬}ҁ+FOl%JlӦd!}cԶʧ…oXpQkf _[XZ٘#}IeOuMu .ݨ_oȖi+^׮!:."5*v9+2|V0VVD]f[OG]we^U5hjez"St6Ea"24V{KBg_KJ s*<mn;e bA/ޟ✫f:炃aぱιqowuG-ι^U:z/Oy Z 6'D쪎-@hw?/=D–!4 ߞ]dEf/݄9;D,+D%2)li y2d,"omV &Hk2#B9F;ZL?dr1B8{CL"4e= _Ag&'DBQX Q2Kt+\ڢbc}t( 2J%bœ;n@/yceg nc.k#'yCjYQ;.)Zr8U=P>=ȼqscֶm_~Ϫ{wao8muu)Ņn+b$F۵Cp{ 9 Ķ,#faV{|h{+bnE[r&dErĤD;: ?:meU#i67̍S X~drmϻG78 H4"016o,Rf, %o-@ v2au-K;bڂv h̤ݭ#JI"p?ft[?~q*#l6U&ԍ|TzAGhgYjп~a΢Blsp)_"gUV lԆ+!v3IT9_K%b9,'{#ؤ,Ow߸0^]|UɅ,y|_ԼZQ^[x*;E]f+ݱ}1ē!\~gZ¥߻v)Q#0Q#p|o}Vc~D懠q}eE>o fX}UC#(qQش=P0|tANQW|(5oVz z~VQ֏MoKm!09;EVt{wRz#W~HY 4'# v{F אr)DC[" @\Ew IDATRb=i,ol龤̺`nmoa;\Oc?B \X~?[tM*ӷ362[M+6g.A&O-վ&2' 2Š[pwW ~`.~hAh}WKmaO{0ѷ?]9Eќ4gkX }4l0E]f2:qa1n-MPj@ -i'{AMRX96m1I3j6uMGmTٲMyK\MgTηݶ.tš _Gvӕh0j hmp}ИGs+6wu=Ba>4B,[Gb~ƓKNB{pvZu;h18WOO9nKc?2QmJnP̺?٭ myȷ8?}&kBߍY3 ;oh #|rXoNيtm8^J-ţ;- AX {I%bwaNd/ۜ`#&|d9Pߝͱ ڽ߲k'hgjՑtF<Bފl3=֑o LoH`f LAA{ǛHwCsZ,vˏkq}12e:{NG`;_C ]`uje‚.4ΓIkϴ=Q\\)!>ݬVYYjo c; ){,dNamt{(¼QxiEqQ j+@ʵlwF!Dl5(M-A"DlkGwBJax2}DE?(>Wsua_[֯w浭E}cKA-3%7*L. ~_+hsp5'PѺവk }d߈b~KGwkDs7AA 2cE/- Hdoh.;ydT!Ctx2}2+6]~HeC32a^̭}a>y\)x:,r̫UB@j=2#>vA2UVh)n] Y]C`#e A,, . KNwc\Etya֍QuDOq%{?e_x8>}legxMkCJh1٣gKZa}"Ո)+`|nƗe օëVt1k~du銔Tk眕1G+G6$>#L_v;;]ⱓflŠd[\AuC*c Vz6Ppp@h]{HUeQ1]P'E]thXTlâ#[ihvUc{H<5N;ʇ&WϬyܷs5j5h̬C`DdwA+ :hx|6E]Cl8łTo$TH%i}eg{?]/8w~~'E, 8F&vbMx`7!k2Q퇀Ha^`ʬ/GJ+1A~)CJ{,B#dRd!?EZ냀C N.C wZ?pT>m\Ѣ,A7yu @&VkaV,qDLP۬lX!@FV{NPɵ5`] `'2?_1DlI<~!`"[Wnl+jgK:GAy-AhTČ~-=Y }h¯ j%u#dڣڤZ;.^>Yݘ[Coi\J:c%LUOg͚adW1ؚxa!.3 [QڨS$سѨc͛kh- UL@CsYt.N^}Zj 0AH Go>B'g4WuL𡮣P+ r&8 J1hCbK})) Ķĕz G( hA!)D,8u2ӽuhZ <ƛȔwsg"P@P<{f&!vP4@A xp!aAv8| Aڧ~h< <@a Z]ΆsnY5T*;uɡ\.4W΋*k^TH/#e~%"0Ն +.@}sM7] CJ6̄s3mr8b$o|R&QYȗoaJTV_m^z~8w?bya0ST"d[bw^GW@l'7#Ƨ 88j_hܜ)88<oGn@_lj_Mv?g=;C\G*^=cuXV|>mBohA'+Q;O g}(p!MO9ZNBD Q:dwk4MnEB~^~\v;!@t Zػ׊kRةdz Z֡Eub@f36:^|g 0EJlyZuy*CLamWVh=bF#kdH9MA .FU2JgcoC#=Y8G)bKVY[zw}=W>{tjkȟ>\{U+o;..#dGH̡سCtlĒVG-tʶ SULSA u3Y-dD"dz ba}<:cdNcd>  7yQhm@WHQ$9W u|A:,Dx21[ӑҺh &eyDle!3j_tlJ̯gUQ¾t?}r`yeNB@f.r]I}|CEvXҭwj^xpAZׇt׼<,,eh&y#@V֤>v_O4ޞCxbp_;(.@K>kx͐^EzrSˢ6/@gGxg6w/p~k1ʶ8ι/O/c[tAtOsS*8`> {=5dtth/e; ^۞&HivQ{RK@|.rܳ2C&$^ dꋔH|:hqE=< c|㐖^ u?,[*H;b;q3i-H1ib*P Š쾴a'w$u}>;y_OS 55\pNbj \韬!t0T |Jfbœ铬hܵX}9ľWgsG&_Խx}e:A˜OzԞǕ hn(w9lS(ިwCġ>H>L6c%V,byrbzE] 7GvڟZǮѸ~QQi?,*1R MusJ_BmXھAuVQ|۷Ş۞7ؾ_|WulNlHq䜻 1WnсY%eh#F:#w#җL;i9mM0G %J.'SPТUڙDA;Hǡ~5t!x߱礑?1:CY.F M+۵YB{vMаݳB '5H9F@؟Ukc{"]t+bG~O{Y>B xRd i$d^b!`{)QwPb RX=3g S#:SyeQBK\;~ybSY_}ݵikU<nLTd_{œ֯YO=ʿ$fN}ΚFvw4aT[.(r$?:5-+*"Уhb#B4Wœ{L-hz2cSؓT, b|D8]LOM_ `V;@;oPO!6` B hסI~b)#I>hA@nb^G k R"O"VxK1R{"k+|xJ%bDf !+`Z}ڏ**(-_1]B bΝJĚ"[{gl4zXJ>b;Ik{l.?Pٞ-}ݶ)2^Af#]D"vȅ>@dbBcڡv]uaQ/݊T}՝hcVlִH\P74^F&Xw.3"2/F]ֲԼVz|ɀ R\>ŝN/.9jalU]4kh6ݾ!1>֏_b!zZVE #XK>kAh֢uػW="s.@os--zw/ģjx!϶R5uEv~"Sscˁ\ڜ;1he A1ιAotq'X&AN8[(x,"M[)?'e#Zgbi%Ϻ Z e{/u/y>dz],! PbƝN/CJdwDlZNC -T@_2NJ%bAиyM jÛeP?kЮqĄae*@h z!h)VVm4A`#|;b }A #ڻݮEgGMoZL'gՊX4l@՞ׅMm>;>ϱ{ T틘ELk]ha}u$\+|@uvCf֠0eē\pH86rNzĀWY8 d)Ԟ#<ӄ09_ӊX }B رԆE+Ubl=#hl7բ~ 7LB7o[A}օm'Jռ^:4sS ߡ(WvEi-:6_ XV7и|)΅>rD9ABAc2`ҖUO )qν6ji\9ڐ_ B=/r1Bws";?{h^95O1ƨ-B+:\dbg'`5]{ 09?A)y9w/S3@#? Ēnx2}(e D*KƓWP.L%bd-V69!hG s}m]:kB"tho`cn*8L_P +cCĒ4}ahgs2DfŹ1P]Т[h!o"ݼ/VԆ{ˢ-Tly uH,Cm>z_C"=Xn{O:J,gDT<JЁ% ~UO`-vK>xĴmTd"_!M]7wk߱O)ϒ?af韥"]_㜻O7S:gO}K?*H;{4B1S'|>Ђ4QrG&6K@jz ]ش WV kM *RH5mdz(%onhE l"uC ChA |.C] TX}"bam!43wq|iEK-{9ٻ܃̹ `_G cwoko#fmR#zbJ 4KgO@ c,ͱ밾=%hrC{ֶB||vh\v~x>ʵLg}!2gYY=O2)6x23jZOKV})l}ɤƶlQ]SkApѧy~e*Q4oL(C Щh위H%Q{BB49嶼223x+BB8K[?)Jj_A/nLJVV\ҬY^6>m_X< !0*baj_Ř7:C[jQh,`H/<S93}u1Sݶrh{9#+ض~ιݼ9*} 89͞Yw p;;sV6l.g >œkZv t~Rx~G-#/XG*5a#P$(~}.Ff/+HʐCd/Df'8"f0p!2TvEKEݳo ?b–ha}-0͈DD@6șy79[]A`g !^#H6; *<-Kx2d.eN$`z=L}=n#/Bb^>(@@yA6bOnIc&#I,cy LedUػr+?l]U=S| nz[c? !} 9(/ЅEK|K.t>Ehlc;`0v:t13X3,\OF]4nAsg~hc*2ɟtjh z|f޿ifo"0r9G~m=ix 2M^F?yKr]?9ܛmM~wSN/gIA#˒e*k*?e⪻hWoUA"QbRId}<#$r׼9i|?-9k4fþB힄ڣ L+F!ιvEi`yH'|ۙ9mE9hI,-bG&g=MW"bSB4YOC'xX"E_MJp5pLF"r9Sp{}m+Y!Y=5#/2^XkF%W67Zs~MȆPB֣)EY_yPTLdx@G3nQd6ka6ZH*ZvGb R ӑº!dNћ'p^d]>哩;m9G0[<pX0 }RSѮҮDlП9&F@j邔4 %\cͮ p 3ۮֆ!HLA;(2u-bҶlj?d9cv-RP#7 {Eٞ/w?Do"%)df`; @8#bJ0}-s?C&+sjptfV[ڝPxĮGL ַ{݄d<[, hϽm kzݩMg~<%pd< Yi1y":4wE4ϲ%hnvg!ۂY.zxڗFWW} mGx`uA Pڗ&딑UfdnNCSLz/F_eVY7??gś&צ r iS0]ܧ.o e&({`rc5MB,( )T"=AJ71nm!z־kA>m0)N{!2Tص!9bL#G/J2@lJǺƶ˪猸g-n-lyRGK )ۑR@'jD CыHGܮEf#-"8.C@d<,] z{HC"!Lyr2iM+>mh y[\atĻNj_N2SN`niڗ&VWWM1sb g/\\+qUJ15ȼdnZ>-zS!3վWEAN1}@j_ݮd$#?oɘ&s%Rݑl@BW҄&E܅@Gy9a"3Sք*y!1=H9uDEG~ͱDjctPE[L#sJ p8>ۀجF&K.p?,;#47 rʸZؽ6܃0\vBf=} FvgnȯyW+@OEMI6y1lcmX!; <ZBfa(X"˾1^8py Tފd3qa.%#Vf@s+ṰlA# $@L& w}K\ՖڊМGWuP/]㑨%*6,3o:Gs452ݒb5*B`+ĞDN!r` )H!D g 0:K1OӮp!l}@FOG)"+7R#kOdBv^:5gVVaK @=LY(ʲG" o!@:7!؄@R6W@]1VA58DV {KPܯp4bzA=cAxX"5rˋ~PJQoP0NV/@t1x` vx|t!eDqзb|y=rw|=|K}SW5?6'ؑK2C NU]7O/%a2kV:`W ͽbWKﶶU]XK[KHۘ+_u wsmز;ïVa?Ysbvc?`e^/$H $jo2} %2E {H|r j#1J\T4WAZǖת}t1^F|z*b#y$?"*h 贆WD p*Do|ǭ7Tsnz6vc,uDFց1st)U:j-}NeQ7F*;U^Ʈ{)=6GʢA WI;LGZ]ZW (PSs,"mɞ5- KB&FF"`(Jf8Bg!b DxwVKs cTNӈ v}X6FEA'AܣѢx)8TkOE![ا9N b_NAs>7G"DOj\mR1<7F쟳!oa*^vu:x;b|=;my9K{\H&cv2ͧ9nRRK< W⪶Dgf5Ğ(<ĊP⪶o-e߅p.߅~9wZOA`D}szuYbcy,{\MO~Q`HchZ8d {d/#~brF,ߕha~58~` tHY܃@V)[QN<2C2O@),BͽQ$f2G\f_d]@ ?>j;mQbVNFdn ́m;k#Fd bpDNu)$dX:[E\}3ھ&(b@lzd G(Ϣ>ZݟB@kE} 6kKl>:|U1|'B eKau; 'qϦ%)Ώ4:[%jSV[6j_:k[@T\KȺw_ k޿j W:BS_A@1S"sW.gE Y;0Rz"vX"5oJSi21U@a$=w_PϽ=i.GЦ! 酀BDTt~ jm=Ku2_OB œ^exI{N-N"SV94 EEw>2.h@g/j}h,X:k?g="> 9KNb=4"w;&@ϊg-yF __FVQJ\U9F$Z>FzsnGM97>?<97{Z7[kϊ%R(=|?0x'[K۹m9bv>MK.>ĘsIƣHg"0rR[!0%R8Xjf vi)RG>Vbĺ  bP=|O=RWniE6vs":}7X IDAT>|~۪Ȅ3fWd έLs"'i)GÑb_P*[&Y?>?hMA Cꐳ7LZOFϲ@@bb$|~T/ ́u ehN[VyÏ/YJ\U>-cX;>iY{sCј/{6眧>ʭk\j\jgĐ)6H^:#4.n^߹t⪼aDjLƣl"E")јjB b, u#LC@%(ǓvD.F; G`2rSvg%ftVF3Rem> :da-U!j)2 drbb~>^O@``d<%RqfvokO/w# &4ΰC6 pdĴ 6hv1ކ eƼGϋ%R_eQ"4E+lͥkG5xS ͖X-@,cc*o1ÐOa43 }rmv.{rr}~dd=%REQe X"\H[s5B +Rs l(m3!xU>hGΘ!1>:#Xh~̷zE?2+0[!`>|!$bv@`bvGV1ʽ6kmz݄F4mۿIͬt^acEA_e!ǩc*_^juJ#0#ih.6[VJGNq}ڗ>nR⪆ c}^l[Kze$#Y=s`̀I`F;|dĮil\aɗّ7i:CzX"w2:X";r$uY2q='4 !%w J9/Q$\X">󑹷b}3"7"29$+xtX,XZ-HѼe 9v#)\XmoSkVVMBffvG~vV{ 0 }BLD`c/X~xpƠkeAv2@LW'n]{!p%-E4bzZ?$9rK3RyǖU-qU[l2e=;۟A>p9ͳV*9hn)R1<2qu;k6mNwQ?, HF~lQz )b9:av==R$!M "j*w "вΒ!o9xlcx }C&09 !ȯl$bZ<2}rC`mCfa*t_pﱈLǟ @;EB Ȅ)v k:A`߈kDc$r?!5-(FsY}(yȴd<8;Z#66 ]h_39gnvŚS(F[QʏK|}=Q&c*c*ۍ @`P4bl^FfsAȜުHm+ܯ^hY~g}uB=[˶5g C)ĚUY7F|/@yѲO9WLn!UI"u |k # ?D5;0! oaT ݈xauF2]kmv@eZ}6m|[V׹9Z5]\yIǒ~YM,b&g47tWolPjAPVK?Ϗ@c{ @mPվt^g7ӲBgl4kNYebDmwR)K|zbKDt@&- ݧ7ti3!gHBJ}c]}oε3sߪصΟ*Dq59GN3bd3=NC +L _!001I(J5 A 9x܈npOs̈dqXi^GBtAJ+ymrNC잶hNLE}K6bAh ۍ B:>b"yNAc=о:b!05ꐇn^6{eElx_={O8'P<<+_LNvSı-:s^-nd ̟ J\UtX,=pѺV=}i&Jr=眿Vxi*ڝs/yk)5v,s)}rM=c? ?{ pݕ|FaιbWZ6tmB#39eN#H 61g ].yV~-Bvh9kqـ$GKƣ&$v ',zBa9m;Um\/Fkhðh[>@&7=[16$Cs#F͍)h^}e̒As"\Xkι|+sMg3T\7{{?;(ޟߺιsѦN@}%>{1]!+@QvMdm5/Hmt49wrw89/di8{s. BzoZs|`J#B8 SJʆΈn2 CQʁRs49(J R4unjs *GKƣ{3ႀE-l,^?/Q\)Rg"г7b1x{.߀88H=p{ J~  Fxe>ZFf½ gSp^2vM Rg u-R- 6&{<̼5+Z ׌kRE(=6DzDA"z߹xti2mꭌdY= Wt=PG4O>7cƖUs.NJ\/'[wJ/_~̗wwBD\;۬.#@u {"@v Js}iRJ٨<͛j_n/̀x ѡ^Eg{G;;96suB&z7GU?[`+ %r /CW8kCs}s=L֞{&4pd6۞y ZgAɋ[ιUhktDr+-: ^( u E)/%ʼ)\.ܶd3X"X"5d-'I,rD'Z #DA>@O [w8D@HYݍKZ ~^DybvBd#d|G +vHƾaD*N b\NBsk; ƢG쟳{A~mJE!?X,CZv}{ 1uxtB23RKF O=(4ζ$;~T1<m@,NuK d7}gZOρຐ>/ y2]7淋1OLro)sX qm&7CĞ,/͓͒;& M.\,;:$<m4v<{xsm$+u$sn9>@>hx{{DC.Gxy+w;#~ C.GZ]FJ!I4/(y+%ir4Z ,MFJ~1R{A; 2<_q9oLN!mbGbȱwxU1hAv "@X"Q2 HI4)mH휲ho^AQ̟]F!pH1\!9gY{b f!vVNsӄe=)ţ1wD9g! )#np6aNB@m R&<kX @X"#wY <ŀX"5%VZyD͓2_l*Ɨnvijo^Q#1u]Y钝G vTҥ%*dz4{݄9# ^iA,H܄IpCמa=9ͭ܊2,XFGvm z3FT`kBs7;Ck)o3_Glt3۵nc.W6 !ϫK[뀓Z*cCgDkyCjK;?:)}6a'?+}i"m}?b"OIGzV!%tl2}OP8ɮ@ϖȔvwة'4lj}P_v2#xb@S!:S{΋HE8BM`4 Py>RO!1|%2kފ=#* Ȏ4m;ͱ(|x3.F8מu0@?Gy!Ⱦ-=ΊNzi+7wJH lԶSM\?Y?ZJ\fhn?ږվtO{x OE<.R_0bl:/FLo8#/Eٰ-6=s@;3h~18粜sfss.ȳ k+{eNr9{+md^A#d'<~snp+3w?֌ S92?ʧ"^Yat 퀎9 ep(d57 MYo>pR,|h9O3Iƣ#?mdjAd%E މ HYnHJp^"m(˳z^-l;udv QHO"s8v v>2wK.>81u" 9>]%HiA|/oyHz0?+lУYQNWާUݩh0Nf=y/B;z0ZFn6Zf$-Ι6 XK0GFoYy2 Gl?`/%R!es R %pN2fIƣ3cyFbuV9Cxa4xt.'Pd< HaA .1|Gϴ"FL՛2ڵQAv+"2m؎譱DIG x.H}8b@hK47E&^h\4a5ʽ! F9 IDATU@d:zz}cD1eр@Y$W-+X"MxDX" ܘ}eKVtϏeTҗK\LjM ͑r`ǯ[.s_Yn͓4M sh޽6'9T֖4;@kւƼЧ0#d54RF}2aȆ !F a.HƣUvHdǾ*ND>`WwĐKF!睈q臒_(У1+!v֮{,9Pg-FNAh.V"F au7d lBi =T? MGdzƭИGkZpG@p}CA,r=ػ]}-+Ɨ1?m2+֗}"v(C PίnѤ|cS4oDsk4 uG n&b %<>W5_+<#d-ȏ %DQ`"&X")r+rj>S!t8-9A:&BG)hdמbW*u*D*8z h_h}F[6܁ FZغ,|e1{J{f>Cz1ad|wP,jvoG F?A:rW_o[X41Mm<Q }/bqr/yYy+Rp"գ[ݓDm:9`>n IϵB~@YH9Y͉V qCs-nqyx3#p;6#~Pt`~T {7:H=G7~2} o}Pii]Z}G^^|#վWu& @x24?МlF'jFBӣ Y@&7xj_HF~@]Z|zxp7HY#6@|#N/+3G"kcev{gFb@ڔu+ 忈%R %tjX#/uHA!S2f| {v_Cꋐ"ƨsP?z&p@ ؾ9  o@x>𵚄{ȗXĖJ3 ua )k#5E<w s[@/iȼ|x&H6i4g~Y)y]?~Y(%Rmv{:__>[V}ֺj_zY*A+иE rrO sum/&FzOd)r EChA;?o(kd˱DE)Rm Ow{_K\M Nƣ)K_݌Lw!H7#GDjFm<ĈuDMnedbzr)rN@;ZĴsТ[o坔G_%R8"U-O+p_%H %ѫ}{kw2B61P#xo&|[ǡ[@p r?^D @h ;) ž}2Bd ?},4 1s!ANȏn[dR= ~K$M[Ff]Pڕ/W0TkLJ\k7xitݢlg)\زo dkdm9^K"Cc*Wd"_U"V Ӎ,E>^_9Zj֙YDHFK6d )bc)SrKL#aVbɄ'&U6c ČA{خ튀p4Fg!S3l?*NoP4h?B} =$Ly;ʾ# 1T#Qڇ7ͭv5J_m uGy XbEJl}́MF! ͕/hFV8ovl~u~_9M9r tTT0m^q%07[8xIYYY-̙70R\-_˿64Ͼc+{@Q,f}=/ڗ>Z: ë \/]:>,#Ȇ+R C#H F!R _Hy6"0(O)%R;"݀(on)jXa&u)<.E~HHƣoscTVE\OOx>/C fMSxTV.7 ƨƮ@4!`t8 >G!=G/%H"4#߯#>NF~`; /b8 f^z;4LH1i+v"P&ks[W7@܉O4mT76ְz੽ҍn>}>? (vwKi~QNޣ3GL[Y 6^#F`a"VZR޻gbTy4_i`G}mվ < {EUyi,FZxG9##muÐŲSЖsz/o-vU|5_^BVgC}r.GnW uVqkͿ@9=oĒ-7xHS^)s$p;y}l8AXu9|$l@1=g uh1Z@y7%+0 J,CQH\@'#&uS ? B!ND"QQb|;b%_nMϛC@rFnOY!1U#"L"T$"m:%H\-A_ RE)F⩾ȉ–< TuL$#a$@oUa^s,rZB_9tc$:.H{uvB$azn|yxwsoo&2aŅt%ku+jWlQP4E f]Օ+0vPẔ0,;Cƺ [Їs-D@˪vXw~U{Y,VH[bKUj߄#-^>\acP>ȏ@ш8*wfן"1Oo<[-`+NF ]2-9(j)J`OBB*Y A#kJ3uE,sg$p,K)W@Bj$¯Eo_).W/|?~('hB%rM]$a{9 1eޯХ[ u1ƹ}y=#O'c7)Đ=ٌ̼ǖ:w{N ^Egq08ҦlAG.*`LC39/>8+ۭ|n+]f@"*G;Q5ciPno=aMAuENcquo~bmYuEg{GIy-C j~BH Wm5H(\j/so ~2^n{ F>Axj)vg2~kL4OI­C$(?l$_]أ(wUאp⋎,; #=`$BۍHkrZ"q]J>z E${PEl3r3בmC@t yFM:{¼NHTg#mڭ@{xP33WP\ rr\,V  4,TtnzE[ ,ϸnCn$-*q}<5;g]E8MM,ʺAB޲F9#_2鲍Z,VD[bsF^wN!e֣H(t¯; GBQ rD>S%c%xjW$hA!** k= s$ [[GPq5!ÑPDzōy:d<ku1m#0& fgXox6Ȅ(WEAs_ILP876:szt#&q=PxG&طxW66kpLKbd3"r0>G9#.Dm>pHwtgXh Skh,Zvy-E~*,F Œ 5A}j\Qv S#98k`2lF&^0|!'+~۝;Dw,6B|r" m.Cs@s\`^/P]m:O=N2Ǿ%gL-,.\zOw7uFT:c7:D{νWYn´=܏nGVL`I2>m@y\. i&N5s@yːp9}YM}]˃PQOxHgkv50),hu7.LP#/E щuІ!30u`e:bF!RȾB?(-sHř(7u+@"u9g]MKR\-vrlSsWrZ.D?K(tʤa+^9#Z$mS}e׳SPI7[t_sFskW&rFs9:{"uZE;h1xn?@AYHD H y͋gkby(? IDATsr=}kD!ן#4 >U"!%FC^-7~0 /ހ"^?"s9TW2t* δ]]Z0q;MWĎSϢ $7w$>ƣ~*퇨}PP>:s[t":{! z8J2$ ~\:нrD3iD>2cX0%t7KQ>tA#}\u3 ]Q{,uZSAjpͫ+]g}SŅEϷ!^%ȡ}3Loi" hD4nRQU |t>n]Hwt,BkH(e5ּ9SITE&Zӑ 8 -yȭXr>Ga(A~H읂Mӫ~@㺝tSXH}u2aoW!w$?E;h-DK_$zFO"3~8(9; ψS"q9rU#Qt6f#q9{sPHJ]F-VFF@S S./ C=Gjכ9 Sx#O7s]׶D+#uwyZj K{tU|h7Q´GGwS\T.W.>{oK;+DmC_vfkr nU>.ŴXvfv$!鏒ˑcqK%0;hkBхA;9&{ՍHD#Ð C\Ӑ E|~lOwJZGpf3߃Q #߃W ߠy.h9(?f-rCiHC" Nrt <q yf|Y$7u,f]W[LQFB, uGyr4/Ѭ{?_#Eȡ՝%/% bZ܇_sqkSy(9ė!QR|߀Lt.&{ u-Dh9s}$~/Pv}]÷_笣W!rR{"gH\2Eu0͌yr>FwzYuPho OG=$¼^Wq7Z#=;H<*)PgH|S};Y#d,z/H#F퇺͞CDv :0j{r2hΟ3nDB'AABIt}FaH0Z|F<@`둹9'yIdBN4^ȄNϘǝw*{g찼^[QΈK;9 @h;6,2ϩ Em/t\X,ܱC 1d,L$Zz%G>C!uh8 }j~PI{B0'h]a -"ԁ2Fͦ|ڍB *riZS; swZZs+uCmN@KuV3&z+r"8$ca/wC$5Hj 8T9KT~H$#* "]yW|%/ew#:C/C:$cExj0$:#it]8(B3X}hqoXe@]"*A/q>L}#P ףȄh}q*la{?rl"so6Ljq~vE r2tԣkA_Z<16xOX,I D.(+ {ت@᫏vb-3鍄sgѷyo'^Z\!=4 ť9:jG0͘M=^0߅D7h"0 O%8,F"h:_1s rѼhϙEt{F/}>/ "N #Oz!c"kU{. .2"Ú}rg ~`zT"o11W퇓GIws `E" )lFF9#XUgw(BWUhSlbiELBl 609yh4wC P$27b8V!8AX.h!B=ۣ]Т~ ~b4u^0؛pojk#hDT9YB rc "At   Kxs*EDy$vE.H8EBe"ya<;uE^˫OеȻ$cL} k)A݋Ϧ'k#ߌg$:9 7G) s̮hNuEzYh0=߼׉0& c^Sv;;ۭW1?g0g3L/5nqu[,;0;JEi]Pz 6DB갹xTvw+ P8los{JnB (ߨ 9AM/Թ9C%+J@RQXw1r)KhK*wk9avnM^zVG6G :{"Et< H‰ByrʞGa3Px<䄖4[Rˍs1_J!Aa(UHD'l!wqpOm_^3_K1(gDgobiC4X8B haY>kWJYG h1GH(l u~6ڼ z1aPh!v/n}[@;֠lDإ!.ՉLhf~f וZcUyyyP UdLw":X,;);ShrCr"~uJ/G oWހ+r>r-+sтY.h"!GdoZd_ߥAENTrx:>t.z&OC.ŹH2t6C6&z, ψSo ){G-v M(4]ג߾~h0ݘȄDB5|MR}Szٯhʹo?(|:WŒSvJ!G⩇sQFnh˜3;,Xx7pڃS>ؽ5tz68bDO";Z[kd,MXkN$Cvy9~9r߳|; 4 &󼛑;?ok <Os%xXHOD iGQXy|!oWQ(t]m40 u gxi9 `pUKcY<=n,Cs ]3^xw}iX,_S 1d,901 7QgC1\\G*o39Xg"g.݈výf~wO"9נH|B]ExfjKCXwCo~"$l?AayH|_D t 3~x7Stm@NQuEe2D`t?BZ73MB'hYtmž1$9+XS H+h Ћ{n%(bXrN#)L5KюuJv퉄!h޺!?3 fK#aOKcrrGxw9hoYRBeGکHkK/jy.ECȞtn@ H֣ݻ [a!y{f r(OHHy]U9iY$_/-Zݽ[Y[r.¿(m ܜ[:49H5 ,KdBw%2v-#PTj8| -P?W[" dt Dn9 Z{'Hǟd"9X_ >hN@ `0Z[h3wDqHWl!G++@wl8qђUN@+RqG:kEkaED&9^?;l<`Q"]>JnHdB'ӀνfCO|ߕqZ,b  Y0)OB;z"~OM@^. [>B{ M1Uȁ”PQ>r@N+Ēx IaO'BhweW.xA;d/D gh*Ԏ*_}kC*cV_ŋyoz0hܸC<ק&\w5ڱ@Wm br 1ot=9~mkvQ k]_׫_}Ų lhr+I PEHXA!FR(Y={>55leIDAT; )v@Em㷣屹(`zwFi7" lcCPs"TR~Pg7Z,KαBl"ɫzpNGy(U%3}jd,K`!18 ݱ7-=9XfT}jvepcv: n25!QB/ urD.eGz݀87\($ r_`~GaP ^"7eGs>Ep1.#TS }Ű~{@9blWlhrۘޙ($rZTH{߾H^ ,ۑ+6 n*X8~dp-ڽ!F)Ύן b3?H67\(|~uhd96Nh!_gn+3 ^}rɪqJ8.Iy}gMhg槡{Swo? o"G)q(7!z|ǫ9a?@}Cywf&?~uzBO"*ATW_,y9-:E=Nb񰎘eݻUbIr=#(z,ǯUlr`}(MC ÀJ'.9=8?iQ1@"|sw}йiX,9YqzVׯmnS@ST_=yą˿9ATDT~rT>GDhn4k2}B@Z!]U0f'-@nMQKᝅδ{wSOۢt5ڄDs'-C!RŒl5 7;И @MSQÐEóZ$\$<e- qG\?cލjjLS)lv*;9NdB/zXǕ/X,9~YPkta=>qaʹS?@ރ/AknRUsطQe(U<|G0N+ grn@76o~'`D!;L|Θ{gÀGh0= j$ k!KbY<[K# ǽD Q$rƼZ^#;E5 =HpFT=$]xQASr #~mRS_6nfנ9gӧgQ'2^`z) Q [bYH<՝y 󷧑jmnGY5_SZSP ~+(@"(l/Tv 3vF_jzB$z#w *i xkB\,Z_Cc?V%2{{ 5Y/Ӷ #\ƺ &2hߧy|?XG̲UTT-<`6P u7gYYL U-EK$j]j=SJh/0X2u&P(]v@@ O~q;* Gsv7jZ'{PUNx~g};rD_:=bYqz|*<窴CrHЯEd<܍riDYQÀC*D}#@[3;o~ilR@=HӲUw`"GqbK" W'27܉6 @^׭ώ1gfI"t _ic{t'w(,naGUs\Ų9#f tq#b3QاVpպ !HsQ VDs_W8FVC^!&uC9cGF|s8{Ovb5-ΛHzT׹%Ek(. k7wvb+<دVgذJ0 uܓ-e{b10#M @jąs4-& EC"T 0DŻ%GEZODarxKz5UN"!UiU\gKf,B.L2뇒{hy\[_(z'3ˊW?` ^s10iS} QJxe-J{{w.gX,b11nXϽѮp5P j "lr@_^AO7Vv7zU9u# RPX7*$xȠg*W# a"TI$ګ$?H^DJ\{'> @ss4,b VYO\3t`/@z}"PW'yޯPi?>(?Gl!7ր5Gaݳ"ԑflG{o֢E#ˀy&wyW4^X,b- Zl3)1 dnm遜c:VTDrJ?՟A&(y~AH>r#]i6hynF 7k}PҦ;;9D&txr@.G/r4biAlKaܰG"1X 8fąmbS"*Iw~sGaȎL#Ch~Qco'#1AH-F!Gd,l3% Q"S_<_OxS%$4wE[j`zVoX,&[[{O\X5nX [ok"rG\{oHq6ll 9J:&r{E9_]Z"dBtM(a$Ca 8)Ä.#T-K4 o@"E.WD&cxy5h-`1V1nX-I![w?{' (:$H^7=4@by|yH nzS]Xm66r\ƢZmwFDlZZ,NbOڪ - 2\BCZ(k(u9 %U7v&czs_pOշ55叁-`zI"vAX, 1ֲ?E0"~]:"k$ќtjPͰNǾhW)08TwJ-_׷+h<%^# Qsx]bi13nX<_r [1ww#fU(!r~rB%.械pT"q{0 }uM7eo~6Qܯw`D& bIl EQ9EIEH\U"Qш\_v=cyYvՑ'M@H޳`3 iYAd5(9@Bl:&j>PKcntk+YWS1^3=4_}o/)9,jX, +,[l>qႜ1MDd99sHhG.P9D0 Q?&?<|O9s;Π)iH y# uG54_.C0kHdBw(X,;36Y߲5t1wXQy^@BT/qO"}C=#tЎ@Q2S%cZM$22ϫ}w҅vk/f:.ƅh!ֆӄ޶#x* bX,;VYqz˱$xj7njd@_7".%cEx]$&ca/[Qy 1>]?^[\PS :{GgF4^ \Ȅ*Q},r {໅ˁa`& ݋b%lhҲf~'.ӑxj$G%* (rJEx ܹd|/o%*-+*ˣq|Lf'LAyvP6) 잌_A{e:9=jL¯dѮd,{aP=yKp4^j}PaKb|#< %r<g";&M MZgno0U`_ M$:y* t.& Gܔ.Al-kPBݨǨSCKb׵gܰQفT<"TnXâvoաyF5ĖP$lt\`nL&`h0=^bX,[u,[B]I‹mcą-՗-r%B d, NvCc_b6l(Q17 (LdBEvbX6bD +P{wQ|_ǮC9r2Rф: r+N|*bX,9 1e 1H4@\FAd,\e6#Cݐ%c4cQ</wCX,b˖SS#`=4Z6k x6GX, 6YbDD#j}PH<'g#_(t:9{yX,b1e Seh rʑ"v5J̟~h0=+  mU{bi%X!flHp#ts* 1x.`e`\bX, MZ,[@2nhc}~{|}'cڜ r$2@"qX,eX!flx8D!b~vNL­-ljL{bXX=Ku]|կV%cV{Ȅt`9 Z,aŲe|8q p.~%c9! cP%Q;P#b"lhbLmTL%cᥛ}8NOۦbXZ!2B7vi!*W)*HdBÁss-1Ll`blhb2N3v SZKdBPdw" r04bl+,- du2Enyo׀J֨ >ףaʰ9b˖qJx~lIXY,ŲlŲȄ PKC+vMbXr MZ,;]¨WvLw[fX,Xv:~<7ñX,7a1e?l= "g#X,b1e!@bX,_bˎ䄁Z0r7$b|VY,;"FbX+,GCuM9bX+,h0= JV\ WxHblAWe# t6X,7cbX,KIbX,abX,KBbX,%GX!fX,Œ#X,bVY,b+,bXrbbX,9 1bX,abX,KBbvX`0E01LD `"bD&"01LD `"be Qt}IENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_11_0.png000066400000000000000000004215041413546205200301430ustar00rootroot00000000000000PNG  IHDRc%sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxwx[߫Nd@Kl( 5?heh)łҺ^bT@C@@laH;3-\ap>ҽ{twZmc0 `\=`0 m# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# `0:# h&:{B4/&=`x:{aMGG6[_Ѥ LVF;A)!_3f0 ?gބ}:{H0T T{u acbĘ7* ܩ5W{.py4 1 cVJȗ ,,Y[Ni$\e2#`Ș1af{gOd--i!_b8`0x3n #!d $KL&. L&%!_bqgN`0:# {Sg,C?&wȦOh(K؝6S` L̘u7ԩX{fS_`${*|4i CaĘ:>U8F!c!%t~in7Ѥhoe`ĘMWh|e9ίnx@jXIb bʾu}N`0tF HdM_Fc &lJ2x?G&{X <}_v'/k0 cEO8$Oڀ ZTk &2$,̞'0|I}I!_upp@gM`0:# ͈/QM&kLT4D7],r3% B)`tք #0b`PzNh_܂Dlߡ8e*Luy%/QWsQ7d 1b` vȗEA-@]4fMGѤvP ̺N!rđn% [l/Ed4'e: 43!D\$,CB`8ʠl@`*G&BWѤtc`06c7QeU /_< g>kM\4p`ig+KL&/F~I}?'m!rcȗ MG?(쯨lƁ͉ҖЊܭ-Y!_ !_bE4x!K,qg!QՉ a=1b`>wwIJK|)iM 5rb!_b0 ,B3I`_,d6ׁPq@*b຺d{[VUL*c+(o%p9^̥}nLBD:K>j^:4ʮ@l6=*IQpz'sȗ8sدQ۬}ߐ"Q01:QeUx@}S7Ц%*y+Qv.p vuj|Kٮ&↺wf#Nl**b$pr=Tj.r -ҏJv?VS_{N}_ޞoO?oyz~CmSr],Hn0l.",jMy{=X;qugC4ߝP,`zn,Xȗ Fv^O(_#0ypCİ`0t$FEA @>[(7*q^uʠ:`JzjיjPr♇hqզxW5f 8+v;mW;;0'oo)˯.W~M}3WjjVغfE#xOys׉snX6Ww;²lgy@k•Я45H,h^WI? B&H yKCDs$Fg EMWӑ %~T#;ffYEm쏞Ei06G35UV@$zOMm) \{Q@v8p*ps쓕v*’O! Y`,,*fJvss!1 *{j[ʦ.zW6ۙU׷КIJ6Ŧ yw0`϶s9hoW  wȂ#N/c6v(^)oŰqmSoY| \|MI`qKWP4znDg|w11ٔʪp&VӴ , ]HUiWqʀ?>Kڐ'd}pڹ'1t03ً6Ů'e V@nÝ$ْ歰bXWw-Źw,#VCrDL&Ӝ꒷yPV^sϫ5ϐgd׏kk, +a mnh#m۲`]њMO 3W,sY//}Biiȗ .`0l1 Ľ@n,[SJ9\Hl,FsB Ү^VeU KȮhl$'Sԏq?9UVŭH|BHE+;JnS;"ceH[9!݆7-qY]0{39nwzF/$- Ú0blZnjս0pQŕvubc3`zmÀE@h壍υD&,q^ʪxNB.-iw~+S/:y=|Mm@9g n[WS%e wߐ}6u:386'έbS_a@YAO}+.ݻf(tu-sW(rOڄgPLԏM&ÀnϣT~ NC} rV&bQAnseX`^cs#{9ϣg`4PM_B5tM!џfAj0 kˆm*x`G:YvUVEqgHm|!CqiB9J9YH#dI#SȒ4|?6]1wцYɲ$KsojV*q}Gnh+˛ҭr_{[[ j.9>~{r74i?tڽ[i<{ϏE.f$K0ϧ->;+dtvB@^m({v EĢeC4X]څu"ͯӵd~^OK9Zݑ? }Dw)J%z`06[pj]6كTau VYGa%r?.@_r \6FTNaO$Fc)s#Qpsl~{'(6mg6mO8r=11V:)xy}7ųWw9S>r_pE!XJ53h0wZzw9tȞkwN{Y.qՎY7o~MҦiONbdId;W įׇ9<>N\K'^+ Y!&Q=BK1 /W\ۃ*W!ͩ<+jx2/4MEs1H¢+dy-_aaWN:nuϷ]VΊ=C:_+sp ĭn}֩} ]>el,^}L,X <Abw%gJuke6 Ѥ80sC+swܬ7#q}_Ử5e3%Z@aY4ސ/pƘ`0l Lim*b4}Vubwoiae# RU"T& 79 1$& r NȢU*ڕi,/}~{zϼچ5Az@5%gXcK8NZLy/Vhu]nJHQ}L,xu"A9YKYn8x6Eqhҟz<> <%Buzv!_h.R%4W`؆1mJhkËOAVf. ˠ KQUȲ՛ ttq+EX^O+*Q. e]FPίix}ӓz V*1;f8̩yNb{^yubql]]fZڵ/Ѥyv*ħ"zSB y{#lP6&"7rWSA봵A܂hZEs)B a}0blaۑivE-/V9h##wd>m5j3\Lcj@ݑP[|ܞrxY>D;wb&g8/tQAcrt|l\PJڮ*5Mi o/Kd|+~$~:KHDPF#G}Qk#[yk*v1J&(Dq{}9u`06%Fm#e$[\'mȭk"4KHkEtd#!G« 8"7vHxІ dM*GB"5̧}dy6(j;s<5>ٵhY'b`$~=G}dy+C u+vM3u^마&(nn *FیFaEA,w|Y?qjB7c`0: #ƶZK^H eW6~CdG"w"B¬Ǒpp5ńy/+ۙOql+TjB%3nB^TwÙ߿7dMbW RHϯW&ōZb؍t2pZl3*&〧BĦvU@nWQ\FMc!_bQo0  PeUJXR⿲=֕(Wj^-v.uraބ\uE.siD9H;}ef8셬\7;Ea6kvoT6eI=j4}3Q"@s/W՛!lMv!9k=CĢI]9Hd{h> s8{SBȎZByS# 5_ %> aSc,cV%$!RV![6q!,[!d1; uCdJʪx| '$ ʑ{"˜c)6]?`Ȝe_-5쿟= Όpg1 ZA6݁1h4` ڿM_/׶Fȗ(o\ x`1bl 5FUHd_PpUb#Sm5G5Ac*|RhCG;:g ceE[ל䥖۽)u,O`z]}EU %.ʼnme45 %1\)k_g|N,X^ȗM99k&;M!_+Cϐ'%&:ߏbCص)]'4 -#ƶr1]:(@,P#ף zbG#K\Z[#VB/VSKKvJ' Q9,uף0̵+v]7>egTl6m,c\kb!-//jSB$&dE!_be] ѤYVt,2 ,Ξa3 i(jdMb@1HTn(Y#+L!P_BU V mACQ<=RB+\Q\wq p YSֿ|{ ަ=(`*"՛ʺ&ߐی>xg#_-!_"zEmѤhҟ\,J3El%.^ *hw &;2 NXƶ~G֡5Ę l; Sـ,Rǣf)T@u3 $F3sP[(q;4v^T@(ge\oɸ-6w2vs vwUUsvXeUV;d%7DV `qdD0iA$܊Ppȗ~do{ln8mg%~0I +Fª'? aƈRI9 (E[+{k{E3T(6l"4ym>]6`?r;P[&Ñah^1 GtKY<7p 4|ON*BBqs]Yb7t77Bĭ>& _C}-lGCP,a-h߿(꺢~ X@~~*ߜ`0l[)UV;8ooʧ1c)ñ;K{JHT팄O#ꆬjLCц5m6pw]}#@M]=v@Qn4 wA%؋}ҕ4Þ?*ʁ= {pKHvxO8|ۑnD}id c!~^%V9%laֺSZ>˅^a 7x?dw_z#*Z: i7v=(IȘaKXƶ^z dUMxL6@7r" W+ Qr6R9z^[eUEMUY'QV_XJմec^iWϮ*^s.s O}ok2Vkɦ>WY϶&Z\3LwZ~Gɗ '5R|Y([|hҟo[='#Pɒhk:$_A_HOG+klB^E !)T8d"B"6!B"-M';}MO7 Öc[/Kri|CWgjUq"]5mp]=ʪmw#Kܑ  IDATk#' Pd zI_+8ˠ y9ZeU*D4_ipU/>UGTҮʪ8g ^.f z>iWëҮn*NF\-%yZ3# ʪΪmCt@o_ODQT;lj48%DB/!_h* ([lsԞ/3Vw< )2gPq# ߞ`06{J3^6W3c<#e.ދDUڬsF/|LGOVD'#wdA?/OASGo^+-rYe{W UCfna]?}|ok"quE-΢U%Y.`/ѐ}# s^~cBhҟv-hȗX ghkՕ{x}M.v)T7HڹD8eAӑ{Bbxz`0lRvH8窬HQYQMӑK#KoJ ՞% LE*_u?%Åo{eԂi&g:NҮmΝ8=n*@đ:&dzF^9T{~{-|!kHDʗ(^ pӒlBrdl x6g_͂)so@"dK YC'gه\#Ѷ'dUrCVeUWeU# @*bLUq\UaMH,SKXy~GȗCInDAEG݋(強(K |bq&cQ>SV,*{kڹ?R1blۥ SeUjIB"o|A fGjg  wVs?ꅂHDBυ6 Eq}74i*:uA1dd.z&|&[T?HMcGTieъ+nX=>q"q^|=rC.C$#ӐW>^3¼! 0}aȗƹdc}3!_d W57>w,δ1g|?B-=u^!_bRG`0M  쳐D%ԅY0z#*_αoU+ڍ]^dbE1xmIo$~hE.Y0ˠUՋHv4QATڕ-мOF";7|qq-ѤL#J2nY~}8g&8dEds!ї,| 4 mTYEhG~JU2([H@YN_꼶m^$}{]?jFl |]}lUa8̓hҿXe݉iJ~(KM!1Vw#<$|"w9|ȗhvJWXu_yi‰&!KjR '? A] >sSΙlE^%Tmw?EI3ۗ0 Ĉm*rTs| y9y}s6l 5 :Ǿ^9n82h\Os}Ac׾{"Ktԋrš #KQ3c"!_b^/[9iZF.Po6X)Tzcyyz=_lYpC`ȗXke0 qSnTUY}P`88Y-k2.wx99S`6Ү\eUXvu&̈́`$n!Wp+0>`$^4N&zrS,Q׵o~9!_~-z=3YpXuWr OAMU_{t1C`Zȗxɚ<F} %Z%A>N`0lV˘.k7d,d,ame,[+?0=@wJzpS-|c(SX8Y$1D~W:ejr599Z%[1@%REC1ɮ?_gGXޫܮr ;3=egeC#f-iގ_u#6%܇a|/:`9 ab,c*U*b7Yb#TWδP&TH - `$^ e~deHn5,_㩧 rGsR \o叱/ʜ CCDb Ǽ4a{xDr!jhͩEV,ţj斗h-lArLD; S0b=*f |TUSiWOEHUqҮ~ocus%b(<މd㲬vhwlM0?XdS޾C<ʟ@b{<w_0fH9{ߜ*5yTힷ`xWkK g],muʂ=_W`v?^PV(_&}u ÖcҮ^aCUa!/цovb,AC/iE,X)sὋQ-oAva0~(3~5%F!(VkMqg3vtF>A9^|%pMAZ ZaĘ#px>BAӛ#;䘝QS5 I@}w,H-t:#795-ǢC%$]%+Q_G麆ˆ]Z(뭟9r@JQ0x(),3p\cs^wTb,IK/w'`0l1f`*tUq rc+Ͳr,x1X`$ xw7ǢX8k^$#qpsԻ3~8d5\C|,`$T<%=Y. FZggH܃ֿ u%z•cґO}9|oIݿY<{(%^ ^&'" ԋ',T ܍_`𦚏`r1ٔ>sUc 5͊`$BY GPX8yЕ!Qpk,yz\TL0{`|Œ7=PcQّoPّ?tjtBYxza Ӈ 9?^Y|[V-җߠߋrܐG>cBe?y2"<ЖJ!KW37rz$ĮFKQ5sp`N,{{wi;. F%J0/FB|,i5;pʱ7=[7*.Ctg#7ۨnu;2F";Kx{N7lME3vg*0i5?֕p !#PNP=U1{_$Ơ<$fj hD3B֯zdz90S(l{$_lo Fw ީzYI.nMK/|sYk;z\ E_u)Xcи:nA 7|!/u ֋c`$e5NA]Ŋsf_n>:c;Qqp^|rw 1vk@EJ{:~b2O)D-Ҩ8˹ ȍO,he(䆝%~[ C!%>|ɵSvjpOX8 Ļb{zyUw2wN^CLD%pb.;`0*L̘ake9r+ͽ᷵]s{XFHCdѪG1`]eg9]# 3d!: 50tk*خw;p~ql;aelhyȗ`#߷`0l3lItC!h+*Pį]]twCf F^ 69.MȝY7"ٝdiy:bl(̫9n%e!w4Tc nW4 Ňefe\7,$A6 FWg.ʺL_Ǖz-v,xvM:!_bQ4׿G&ʸʙ`Ԩ5(Rq,XnWkFpumv -nWj=N//ז-x U; LJ{ndZ`06FbǡXH @(*=ip{np{9!0${ 8`< ^>'#qkU;<ܗsp,`/"R X5/ΦM-B`$3Huu|p[TVb Kv̩70oxѲeGo}1,Ú/k{# =+.Y_e3=?k틖 s1tڻjecx+jβu\?M{"qJȗh\{4 ˆ1Ödu "$ڷAꜯ#zgPHD(x&XH vY@Y0cTrݼKosoBtD>Fְv˹F!3&rڧ"K r!VZ 2?tlϫH|uBD||8'0M绬~5=Vy]'m3J m~aks]Ъۮcϋwf?Uy0P`0l֘lJ/6;8x6ҫeLjr#wӱp1zPY$k 1 W1]e~ee8ζiY:1/ =7J$#k^6CYꜹ.^q@²ATbcL7sz9tBCX8p-|r3(evNJ:~9sɶ[V4&؋ֲ qjޠӒ*e,x=V5vߧ'֔]q$'㹻 dc # 5HY>#푋X8!Ѿ[YnC1`_Ñխ?0Dʋ*$Zb (E"3eȅYFVsx ,fzx\96l[|wy=RKbx7?V2'OuB6  Zbܔ͝A('pa,j'@.dzYG֭E@ǡ Ier vA.lV<#me$pU_"g!xZH#Q7XZ\{(X٬\ S=BE5Q@!%YȍIp f,zL!^w1퐮ܥbtˇNk^3k?ܿѵW,|>UOuLJF{ʰIP%^ a0 ?c[1Nf]?ulY 86|H< {Qֽȝ7AM͘; !UV!rN8,a~A$vE'! :VV {|h@Y;sQlY#r4 ׀\s4o玅n([03c>L0\h2M}%sRB/X%MV*=%4,51xZCwvӖ%Y>Hi=Jg\ַ`]M_k`065Fmw1%/.;oeѐVo=m[HT(T9;,c 3; DdAZT z$r~ IDATڄ'5R`$ |_+6I (|&f%6mTn{F&Yuw~1 `$>Y FgvKx1mf6b <G7]?\RqJ/5pl5&#ŖE屽nw ݽ\f~/$fhmȗX~h_7%L]30bl+Yx%#4pѨ3 R;SiKǩ;v6XHP݅62dYu@Sߢ2ף2H,C.?b]cPOŹ.H8כIb8gc@Q0?eI!d@@U sˊK~,a1+}fR)BqcO;zIP`6 FW) q_r#U_>֕:Kv~5 OJtN,_/I?49ܲp\Ն%o`0L67 *@VCZ[rXA7<+67_g#q e*%T2( i\? m5Hع;V\w\da{"'#K$.A1lFnLJcnE;yβңmۈfc|Z$V:"ƖɹEٱpY<  6.ߏ)D֭(.  N?%Cdٽs8 +K,p-^U[{}ijgX3 Xƶ0޾iPOra|Cz4=./gZ툜O% `$~1HxEг{1l w!U^d +D$XnAl_?bz#(d.wG/І~*z:B>u QY+3r35/v" G66 Clgm2kݳխ} .NE"oSqmCM/EVa ~jmhzN@B7(LAL4?}'pxisno:%o`06#ƶ ޾iЫz`Xew`!K/Rd(b:%$ƨhWLhT[,1%hTĨ(\ R,9ΊQQzbܙ3眙;=9:vIEᦒ͛0y<825)ַ 0H X6I13oc"ؓ 9DS fK!݉@(䪹HZuDHp/|݈`)[+PlWm?`}ג(Zl8 :~~y@^b\j\b"4>atAF(Nv]?O :aVF}>tje&zy P\fi, q"p ѵͺ(}#UƾG6zlߩeҩײyzvHfHnME(jlǜF.ge=`or#|3hF襥Ƞ̍oc&̴:!;ve7I3(!lb<#,ފ2Dz=wc 48.S,N=bj@{Xtˑ&^?a#o5X%&9bemXv6&(BNoG(yFNWဒ+rV@g3L: W "iB +m p;%*E_ȧhBnQ4)DRujDUr#f!ʳ}#eC^h>/W8ȽApRfJNERA@0)U[Y9P;X`)4n.%NS@b>廬e-kY–Uƾ'6i\ER;G._rF.`,NřǷ.Zٳ7lNkn=#xM< ZWjvs[]~t&z!~@k& h{yhc9r#DA N!pe!XCc1nʫ]h`TP՟" EL:Wl? ?VP,d`M=$܋yy }[ʧ8ν&Zhפ(N5XNt6嫾u"4 V>p%ZsdF;!Kk3sx/~yHW×2 ?-mprKk[~Zֲ÷,} (N7O^BGV`;v{̻+خ<:.=8r>5՗ #hD.k|Ԅ 9$-GYR/mJ0C*dۧ#e4"k sW[0*Csi(oD)rAH;2{H5Tb8 w]ЬsFa4kpq XTȍ[ ܵcA݃E&ǚgҩ-oΚ WFu!A4,rk%^fߔY|nFײ=Q;ek[ƾfa;Ggb]9.  jZv\Ax.(lyOD ;w!Ij%h j Rʶ}WܠHIr$i1(K+3#|׎G UqAA;g~@ضE׺+ʇVfTzwn `JQܞXW \IatmZ 7O#EqrF?C*CmA',N5m;rX?ߑIagҩp%z{笮ˀ[V8\bhiȡaki?+x"rHK,GJ䶼"6䮼%f߈m2)o#Po}p?REj}":N< ¨3a4Mr]_ڭÌ^]|vQ6}ֲ Ʋzl`rgAC y EF/xܞ {ƃ*{۪p+K毋zatQڬћy:R}컳PpF@r = `:B(5hVnnT!w&jh9+'Q$YG@MA--dr*7.yQDֲ`- cAy<%|"@ 4"/saQiik/ڿ- [6e qNjծzuF'R^ɤS~BΧ+/CIdϵD`g(:% x+E@q QD0p1l"ˑRۧ)_!7#FL'b^Iv@G@6&(,DcPʎݍ]ݶ]aL?b;-?+sL:5î.%:>N=эv].rqrrqq/bGI|4&Dk?~u_cAֲ}/- cHaz@I*.=I*vjGyqҸ-[8\kY1CK@#0z7NeMd-kY^Zc4=J;~CP5ؿdvh? B ,~ Qhf`<dov=r_F p>i@hM+>(i-]-xJ4ͳf q!GMo"wd!~VA$OuQbY=G Xo(λ ٹE` #eiA.hK-zDphǗ#0Ūmb2 2tjEC{_8qp&cgatr333e_e-kYlqؤq")7N]ackX`#It~U  2S #F3- lXuA`+{ݿˁ;S9S1F.LSm5!):Ԣ~{ +'yRJ!0> -R^ge?L )tk4վ_5~"m4qST-@X{klOe-I6;w/)Gx0*Bn24~ 53TI^6gCfҩQ;r0z)_~aYZֲ^YV[vB1`-4i\=7dZ*6f~P4 djS@ugl$rOZ,Iow.(ZDYAߛVGQ-V=Dw QOS1;e5lTҧ l=L'孍A0o8͖|Z>EYy'X]z"R^GWo8vgV a ? `B*khy> k̡{4+7#SڡfE.)k됂 k^I}yH6ލi kwTe-kakX.V͌4bbZLE (hR@~@8zl+F0J!stζ\\@Afj II\%r3AwR{.D!Ui.cL" ~i8~%X;%ҊSAk=q+Z(ɧI_^@ш`_"EkDOE)Mļ["Zĕ *}jP.V!CV [Ne?opКPѾL:=ƻ1.~A+Έ>;XֲO2*]hz ߪ٢B| EG) I*@n|v L:!7nH}x%7?RT yOH0*AA!%f( 2V`0^i IDATa)rARbe^@&R|~Hgkk9Л. @o>Jq9Xmduڦ]V䳅_/#R (t_ n6?(.-G~]I3xsd|y,>Pv=#k>I=I>k!5a lo6ލij8kZe-kY<ƌUP)&߅=T$?)4,Dg)\o[BxڶA7ftj |. Ax|W hvϿ@n3,>c'֪:tBK=ԧnnApm/Bq%RYX^x$"8[[$6>D)E{s%r]` mG*N=f搤˜Dyۏ=5MPX1X 1,D0:{at݂m]vmvfiہd]Y&jͤS ~k^^Rnb2@qIJ߲XbA3Djwamw}6%:i\4,+SeǴ L:$IhOz/rG!YD YR^B{rGu f"hb{*7EjV[9'-Zܧݏ&\b~y~-v \m~ic>wwc!j QT# \b\԰(-@RgHv~ nЍEuA~յuQVQljo ?G3锟aӴBRFOY}73<{]Wn-.y% wpQcKQX=o|Zֲ- cMCUR' ~ O!Y 5ٿmGy[kc*k*҈( oTi$BДnob- ra(ܳӄe/;KQ_̖lAJ]eV"g?$'oHV!%kcuF3]A ],};!lZ]"hM(ًvt =}"h6Zc101Na`Ѐr#`r&AֆZVմ1Kq+wneg k k[hI5)Zs3[bL:=~}nLEe,ᙎ]|F|Cwlֲ6ht{,:#R]t]gU*]̙qէOc)ĨAP\j4wBP2I*DI"7ݔck]2v2 C2 (;RFF~y*"<To%hT>o Xܵ+l֚W qLsH݊ăSr "iHl-T@k%rb.BO2M+lϑs!57$~ѲO/ =,R隬",eK)KI֨|Ж):qgҩ0}FKwy # (Ias{ash mK3ԑ֏?uhCZVpS=MͲUCq%hYJzwiA]ShXUX2y[ֲf,Uȅ8447WP*i>a& wuĨ_xO'8%?nRP)/Es9慄%nxx)N&_#0Q܌ q_$곭=~6ag"Ev((; ;G5$=|H&AF/ArɒC \ʭ-K3Hε(K-"`GV3gz:*XQ{lǔ\Q[|哬EjvAg/Z[[[u\Qg뮴}@ j]c0Q& v6ţ E.P,2-|ҶAl`TFS>Ia$pvF2 NE̦jrWK4v9f5|{eߜ=tG",A}:Nϣ8Z۝=8kYzk$UH=(B_r3lvGg}>%pxX ZRimSz;Q!(Fj'L`?Y!%Rz#oAU4D=EP4oT E(=]ʖ Ia4\b,ֽ@\\7 !w?XoGa~Mt>$sAVk9.Ay:@;2}w1s\]iYy%־]4?|th0s005NEoʤSϷWh"E+[ZrI_e-km0z,DnЀz,:Q+Q,37}!G藣F2?>i\BE ֜<)aZyxQ{"mBphIQ >~.g8|[ԂܔuN^X< )yaGq^EjIĎF B$ ("ɑUbew7[{"txY\rc|RW EVV}n܍X{ΰ=ţ!ljm!?(IH뙶r#ܝdOݻg"e/[˦}_~8-TXj0ņ]jKr2ҍˤS"g>-:$&aSYa)GZ>4`ȤS71|vs]_[b.F)¨zx$G9jZ-NL:huk@1.̂Üs[` ųUwrovv]'[z#0 Ok][Cv*fҩLIP,3A}"PUfm>QhbFCl!6$+#'YL:gҩ3,Lx00#B\їzVxkXL:u)/kߍ9 s=^?+ܹ+;9:s =~BsO;wν眻9wsnsu\Gιs8wu:n}_sbssU`[Sιm9s6euu{mv:;ްbGX;8um}뜻x9wڧ} Z[r[^ݧOOGog!躖rIGoJWְ^GݘI (hGyA*$Q>(5BGQ?wG˓ruAy`g֣Aޫd3h!Xnk_{REV@Gqo4`6($o{lk۶%G*W>NC7 F05lhPr畵A(m:roފhץ7#P,[emۑw0ķ ou|];4q(oR>DN]⪖ s =ͬ| 8&O ߬Y_wB`g|>d}0,ճܮza!,MXgiRBK+8R9gm )SmSg*I>_m`snD!EvP>~gM[\`yǛq9zF8EJ85+8 }-zNla918Ggԅ[8f9 M\2=7[rfόZ+]uwpo">r&< {!5H1S hs4(?܊=g7 CӂFգwGp;fTSj !׳$9@RQ4]SFV~?A:"P~?Rr$XE%vR9hm ¨zGK{|VƳCN]=XYۛM型ϘCj@^܄I{|`|HL:%Hz)v kIFi%QbыQlߍ6wBP+ yY뿱}d&GFǣ +ef0zHlȤSNȝum>zYɪb?xkboM]k}8opU/q<97$uiŚ~)mq<~-虾6k$ۜk_&aʝseq׵9vw/q78s csI^fsFcǷ>mqռJW f^XG# mW6LϠ~J-=SgUM-N],04ɡ`& HhEu3Rڶhoa4eC4O?uHĨz! YRT;!?4g韇e zAA_oAijmGiva}uϜN؅d%_yPbtmIB!q#{5˻Nׅmrb@l[S؏[.ɡ9:f9;*6Rk>wYv^>8rffMqgݫ| R0:)Ks nG ;=}!}l*0Ǝ_bV?tyJW}КaT Ab e PQyF?!IЂ '}~@*եaG,)M:3H5\t}@װ]=;ȌD#؇d&gW3uoe_ŻO;'q} Ac_K(f<~l~kf#@ryԲC {3H t)ICo>nw뿞H!B߫`6+s7  -q[4aTˤS#;͈Xd!"ɧwDP8|=c*rݷ3=@5$GH_^2%qqMݬPߓCA 7[v#peaf]KO-v^dvvkS?h]sF2Gn6+W|)3Erl־95V?Z9,oo|7W5[*ݶ/X]ι(.Ҷ9 CVk1&"媭kι9ގk^M9/*^KTU@Sp4Aoн@3 Z^!> W.l23lݙAmڀR[̤S-i7*F4n?FUvܡh}ߦmS4Xo~Hy XJIT=z$EȵJ <ǐdAB48'#ODAD":b+>䢘@9PPW]m YCnVH9ybmy7P.C1Hec֖+zP"@$Iq`s/ ^sa4CnY}7v]Z\#P4y{Z#@ΑK⮭Cp܋D/Anc})mK  qH&ZjIc7nmCug,k?Lې`=;#d0x zp9MRFG֊~;4>ZFU@L:}~ T{Q>X4TUb4?`Z!h` ebދvI(ҟf[=J>+)MDmֶ'26$qR40?%W+s,r&22RA6?G IDAT_[l$2ԡ7襤zxZoWgm_fQ~9?Խ~V>VRoT&z tj+25@oB*VG!cܐo5ZfdiD=T~Ã>U,嗇D}ѡ/>T 37lֲm0nʙqՊJW=5gA=!soKԢ$>h$;3-ڛIh@_bh`/;s!*Nh >ʂ0I#}wa?`8evp R*ZF }?VTat+ɪPN5H)g} 0cFnxA\֢{r;Ǟ*čV'y$3,=c1rF־mCq_$My)zyn|+{:^mmsӵ9&I90=C|Ata40N` d2d-kYlJW]Db!h@@2k*,CKD\ *]gUvZ-=xA&[mxҁ֮~kHT~h&].r>\`Mh^f@,䡾lB:F{!͵9Wmb!@/Drme-I&s'6m܂CJ!Rk~iU f"lNhiY[6`ow ;?StC`}zկqԢYȍzQX&$dҩIA BvWV qz"5,j[?{n}w5~fjBm[|:3|6Cd{.z(Bǿ0+>`2ݗ}I&w싮A^ mČT@=xB|xR#z{>=\7$| όU-g@۔I >c"? c;ͼcY]V!txʑ<>@H)RMB^HiQO[<tTa;fs ]"O`*rgdFtiMEg Ajx{{s3r+d!z?Cg;֧]OYp!` Z~At*]H\>#|s z؋Њf-CWKIbL#b@o5ꣿb Q/ha+1)3"4܌:48墠C*ήi@h9W۬.HbvzGm>Bv(~YsMGIz=5g ck4>b@0G.RL#b )5m.bmxvgQ0z)o窵>?ՎS|^` )WEx=><Wy]j^u>9]Z_31L})a}ٵ"Av ѽP~[_n}{&%N=o  ApWAh"ݳ~FRso_Cϖ_9 ʑ]O뗵eg?8tծU^骋l>=*=zXwCjŎ$f}I砇rz5JWUm=2Tu&*ϤS4I*NԋcAW;> d|sy`E ssƖ@p1!L: qtT0* >?MH&`\T[[Ft}_w#}#X:fҩҮ$IH~!oi; ,%ׅ"yb5Wͬ~"\9>j^{#k& /F*b.HdrHٛ~H]3+mBW3Prt/zzzkKot#u&g޲zyl;I Ȝs}pCI@_5s ?x-}'DePGIָ&FqαzhSv8^dF/hrC8>jܡ[qUCrf\̸`Cj^W= @󖑤8$H19@xJ{j?D.yH~3{-'X*js0=S Bpz RɤSSd?⯮GjFHzmnK> t5EƓLփo֦vҮ{g!%4kdtm^N~ˬ:#|$\ZwZm~Av}?I!u,pNȂ0*oM 0~k_<b#h[qXqixSmle.Ǯ|HÀCMA ~q4)yfS8f%_kkk?8[ZgaCu>zXOCo[GTЀcV۱,܈~P>l.zh?3U|܃Wq=O kٓwl?y=۾fS9jA!PԞhwFٶsrYUء ",Ej?wG r9Hx Xam 0N(N̤S/(Ɩ?=F7Y#O?X`j}Wn!Cp>MM0HY'#k;Z?'q.F*=B#)Ym/j#n[!Yaʸڶ}nF'P D)FAP+ *N^p^EVJvlXXd)=>0~ gg.7@6!d i:Hb!pMQK7P/("\HhlH4HxaHdy{?e̙9y3g[ {B ?8Ώ<46lC:r`l:B$D&&b4#l9Khzۆ7n7ttg_<'%1fR+*\U`н&Ñvy?A1E*[37r|<]᪾eJCӶ=9޹3rDH{F 1uBAh&k2(F.@&< JL>[c@ޕg#X/Ͻm˶jj-rx՚hE3{xNgbwo V;"(ϙ[rmĴD]\&p5Dlֽxq5 ̟d4{"[cV1c9~E4_6B',hAa=iLȻWp{?=h '#|l />9,EG}><9saqw/̓㜻{FQi _[-eyc6TVWs;zWಹ3c ~+1W< /~&YӀs=s 4nAcs/\|C+އ#܁~Pma81PTDGH٬1+wѤ_j 4QE%mheذ&٠+ ni WUA͕WGik 6ZR\g l]BMere8D2F#Z;I P5:Z ~Hgːambu hڞ&ӯ#rعcPl볐bb=A@bMD1{ŏZŽdopMcwS.oie G|v?Z:ќ܉7o:^#PιsCp}x(nr}s!n4n/s}Ql `p"J%6Ȗ/Hx:z"c{K_8g91{Ȩ?V+,~lMYQ{eՅxrS6|?mc` =Midl^F2唇 wh 5ȸn(@ PX kW,}!}Vnm s!}o#l^g, 7!@3MJ@ Ъuq],0sV]kA.m@o}b&vtAw,зs21kyZQʌGR̶~:&F}e5v~M'ץ2:HFÐӯ LhCb{VGl{+,[qO"V8̶APXZw x{q?k~e/ldX}@d_枥]ʀc'(dcןPFn]Hg7Q4n7,cP~Uk_@}9p#,%)OF\sd",@nzP_)$nh"Z^2M킀kk7^Bײӈ}j1 sZI݀Q Ipp]/ 0 C~$kK x={khxLo\G9S;T#iCf&PD9AQ g S=ME On7!UOX1F1$).#d4FIx6o=k{x>YoG1V!p=Ů]v5[ߕ2_]@T&w LuĘKsK*3 V؞ֆ~׭^KѤ+"v"bZ#PDY"qGjoz%nd~&;t,ҥn^ IDAT=H@> ˁyMܳۛ׻+ؠ;Nx ?(_⽯齿~oEv?b;P6`m]wgn~dIEz]h?A:'Jq&K]"vX?UM'2sK ߬OQVg[iWB̫H':!ZD! :ߊ@jĺ>1u%L>G#Vv]2ca8&bty#J6.lfl:9Q:fY7D=&foFE2~M#s82R?FbF32 1"Ǖcq#5r 2s=/P @F;'}O"{Hn} ysR[;pCKkVm׼A}D{ڵdAs~1_)i>pm*Ner3 wQ63cվ Wu,b!z4vCF爭ߥ0މ&1ȸ Brė(2aS`Ȝ W5מWQI Wydl_38m8!2W CĄkd*2g %[6'ɝ@Jl:91Z6!oZ{T&[DbfrK/Awk%v}Z,{;#Joty<[ }1Xۃ=e_krtq!Z*0Wk]{4ZH@ 1iڵ3ZH\pS\uwP@= o=bE3c>M"j[ݶCcpF@erm?duFDWpG"ͻY@i!"GcۣU;ԅ8/'leļg2?NKXnDn}Z_Ccw~ tQ:fS6{0feO41?&B41n ytOLx'e E*C,J9{ҭ&&BO&;Ve}+ \)6 XވM\l2RD"p;h<1Tcy,1y:18{mxha V416jwY/6'%F `B}]#{8Gr11Gz=xzl:\*K"=.S&R I|HQ:fR0v"ZU S 2W6uUվ7h@!) B48d|ZQ@}Ū}eq٤%Ʌ#ɦm8I?d(Hd!c52>-XtF.ACW! Cd@ڳ2dwC}Jd!IEOAx\o"=ĞFnpkw3b~er+A+gϨ6bg2cm GT؛p xҧ?!}>0,v/ $}מ}31!GAM/>}, v(`n8}X?'݌ؾϞ5pEL@j(۟JP`^Ld=&~Vص$_|"9mĂF^CSs6RMhɦZh+ ]s%h E:R[3[K뽟{ϭӉGq ޏt} 8{="pSYnˈ ]!P8O0j_9`_ڵt-(z.ClD\V' C0G4d!C7UͮrW&(F8 oHdv^èitT@ƺ{%:#@RbA+J4"YIXT+KPMG}SLԇ㆜sE^sebo۰ͪl`Kl6 aK Wt&vv?+()~> JV1 dpUM!g6ZGZQ7UyO(!oh`R,ELLBjtF݅:ӳPّ7фq=>)C26rODȽv3  מ1ȆA:#~v߻"=xܳ>cĒ5{{ػ6 w\҅}.nhn=?b;&K-`Gz{+bj ZŞYEK{^= sɊ-mk[ZK[Uj2^;X$Lvg!ps;~\@ǽ#j5$HFekk"?C`Gw>0Ywh;Ahr\3Nnlu#}:I5Wm_cu ۽ijj#SqY{~M'F@uT&6^05-9W(~w+eq:`9g=9V ٦y ]C4'콿jwBm74w{=14W24G٥h-~ソ۞p_zٕUʆ W2h{"dZa<]V⛜+yM"`^dx  _&pkXDӐ<=R+@l:y- ~߰\ Rd M` G ~dPCCфq' 戶f XCڶVmk'm0 d'ڻG$3<1-0߆@y{*d`Z|`o#wĔ f_2s7_y[kI7"w4\Gqv+7]!;,(zspٕ!2ʮCQ, c=J 2[_7{WXU)ݺ?| _Frk=z\4_&O jp lAk+ ×1 ʢ17 ѩLOV&{1LG ήB`f#!o&|E-_{h wCBHS晰1{u&.mW~XQeR!# 1JُBF7ahaiJsn:~h4ꀗs =19Y $:\O%C0$K_+DQv@9kޏw/8zl@lOS6k0f%p tb9Uj$#noJݼ*wA2s>k7޵}7Ȁ pUS}&?>%l:"k dP_GD #ܒó® YÖ>u3'o34m %OvnaaG^_R y6#ld#CbĝyHCd0Kg/A-Ah"&a ⍩Lnpmz$[ oϩȘ@<&S=2O"ڲC,G.4P!&歑nC|"S/ٳg$!U'<)+uVLg"&q_4q݈}G вb5OFocG\Mro#/,wB~ֆGކ bGhrXY[#9䚏XΓVg݆>0sOa"F l1fW(ba/=s:c[HZp69PX_Mer#-{`n6[TYf{{E)sxho>, V3^ןǕy;zsg7~]/8{G>RcF,`neKcDݭhe_LpUS9 dIjdG+HQ92^+$)` ;S]M /5ģz>Rxl:XGp22v q)bAz28Y\#95":10UuZQ E2 =mTF5" 7 # A )PeF!; ݊?J=2 YkmHg:!Y z`א^aoE} eDF̗UV3Z5>v0Z- w}qSgضqevWz@Ag 2lbFV䐾صk{-Lˮz|rmVϝ#u#K!ؽ;>AS<ޕM'/Her=t w" cay&~DfY"P:7Q#;~m-yp!{eR\oļVet-peIer]䉟tmG&f'h窱 т|✫DsJ4ۗs;E 96LBGڗ.2bUfsMpsI{޶ι,4{/[FA&#슂_Aj9b@.AϮ|d(γgs()4_ $d9ve8}jo)j(dLβg"\RbAmn 2V`쁌3#~kDMȵ"t32\#ZbAle ?>_A6$2/7#p~>ҥgL&uDU߁ i8,)*үA6$i-E凈=kCMFo #{i>Ӱ>@ i`eH6sXwrށɉ,Ccg"gs@L;GPW `& ,IerݐwoʊP7Xt%& C"gٻ3,2-E'$KL~c*H+R|'KeuhNGP''|X;7K*sKer?Ȧ>鞎97q9g=Vbƕ5Zm3Z< ?c~瓼2 yBWsw7o2W[h^D0or\c<|3lr ?`Z=/ʖpPx)`v_EQJWKi:p@\S:!쏶D ZWZ,Cu>)B~Ø8ꁵվrĪ2+Ѥ~R6|#塸EtT&#$UB`w#D RDFF|48R< ` 32!ڵ ԏ#za;YW"MCQ&c-!d6 RtYdP c݈Z8+D ҿ)yCWO"=bFZ/l\^@[%"d0Фm{rpk;OtDZ~ mN<0ËdgR{64~;&=CȔ?^E<(~&b\?G,9H?gS]4Z?4{W"`|ծ Y+k6E1Q+/XE,A`w=l:YɿYR9褋s{:g+ A:# PءhЁ92!;d`Gn=bc 'P?@tĈԣ ZşX*j_!-ɋ2;%|Mm]6uGl¥\ kb.[!q1bnBA &$ Ů[Z?d!C l3dD/$Ylݮbk2_E]]<נ#vBF(Wly]@Vps=9: 2ې 즠]!F!)ljĻHhdmo7` IDAT"VoFnsMZz<)‡"R#=dF0+egˉ)1֠9o@[y?L ј:s?} P3)"B &4ޚn pE6 Q7˖জHM?@LB), #]\UhZ ;*y?D8}%|t5L*us?&my)G8R|dEn Ի,Ȣv6y/d{+w#ao NOer{!cd!#]1DGԄ}׀XK1w]\2^SvOLtnT#C]8۱ uC ,6wĥDW?|ήD"J,/F*GLqYlpUՈ]{M &=C"wb1clQLVu4B !I!)d@ldw&w`(ϹlE4,GA}~QP@;0A1}#P?])֎"kGbRB=P!ȍڌ'H:U[#;7#t7y_BL:b5 p7ZXtGQ5ʞq r$!vMV@lܥH {VH[̦3SGٽodS^hSTOsy;! 5-B^_2{5#"3Wt%^m^1t֠{ ŵc}'b$$ `<-g!=doo}µq|-c>pf%hA5 Q:f^6{7%@5hh_d྇&cфW&& m\R+x;X _8z0Z@Pd,՛Vxˑq{\.(.BFg;"m5Pm]-ݑg#^,v#/Fv  X] fG5WZE;LGqN!^]s82W"*6 _/OAq<둻ȣ-"y%Hñ 5< ґL־PcBGn1T!֎<]Eze b[Xz>t;h~c6ғ dq$ҽ<s{LAUkH'ݩ Y_Azxy Ͱ#7Λ||m.As~86ܗ=k1 Czu"1uI>DvHVb/CH↕~0+P˒䜥(|K'_]*\4/Ar2!f>r# +@4{pX&7s p4A;+|@vG)^B\8 D*}!:ifAT"#8)V]bĎ,GŰX=< :1d(rGu ?Y!Cr]A?8uʶ'xU66Ҟ1BElM:|֙vEF:1-oҴ:Q[MXA9N1>CBN!u`K#|.~dBHoBe 덀Vկꛇޠ<[^<)t>]_⦋pHv'nimۋvш9xJ /Q@aQ] _kRT&ΦכKqz{s(^lEw)PͦRn^"f(4݋P[?h 6>]G܁2EUkW!H<:)a\3C t!=Dl:ٔ~~!hxVt%? wUhkG%hc\>%վQ?[[Xo !ӼH޺ 1?TZ]6vTƆ% t92 4p9wX+1+h,AF`oG #Uэ|mP`,s k;;ϮD,ب{. ͈:2ԧK[gGlH&#C`i* *6w!X~hu:be<>+GW18dC i֎H߱{G3Cn8;dVIZ!Z{]| aSzGW`T*;-M'CB#PyH_3/ oF.ꗈsrF؝xjb(XnدE"C_m0q3N T&7=@n_[0߃]c혊 -sOE!|!uɇŎRv-93pEh /~#:HRb~#hEIdx~վrVgkB+ф:=WT8ziSN6z!? Z(nԻK6Mb`NPvyYN@}z#Pdr-r* 4^Cc_AFE-4-@L\=NԧNξDSܓM'OLercPk> !Ԇ3_`}"/C q13v}8>hl:*Usu4GȲ3jW/41)Т1@uKG-1,'wZnK=!r.pU/iXVo!c+q]-|yzS1O%UT&Uo2h2+338MdKQ`rdNgTBd,B}ĦLC{^b! v<~Y'vSKemވ1yp٨B,ho>?8޷nEdvz"su*h‹h{>0=Nnt^vGleɼJSӍՄXmun?߰v.EAևY#WP `ܾ!VdSoZ!l7km p ClM1kJSa$UjjZ`gϪCvkӈ "W7pڅ#&2z4bAz2җ)L|cι!h@ {S膿{sK(&j*p;vDll'ιџS-U*\U 16#M!)#h^Xrd>@gvM3Z E5ѕU GF$6 C W5Ś]P+g|~-"m&AܻkY 1BX7kSޟrs?wsz[\k <ꛧY! Hck#'u߮U`A`7#e&b;Gƫ;dt@^]F4wyTob~ȅxY]GƻH-_Cx!@7UhY[dbק2GZ]Ffت-k/AX12M&WdEᾦ yԖؽgXv1V"zK[t=ȭVcRdkH.˦O[}iĘZα:nB v#۹ĮޟAAb<gO -&~&ZT{̃LMsWC乚wa,&]k7mv9Yw4'=e#{:L6hxA; @moG% aHo`9}q ιU5r;Wh:7:Q6YwSTވ `=24z{*фW5x=17"#B`!\ۊ͢j_QPBt5NސM'B}A v=5t>x]FF"@F?l(Ar] zx=|A"8k b6Z(Ļ-TK<) ;*zA܇CZe혃&5bN@FUcXmmAG\~ťnM'#1&ժVA9f2w5rǾ@A1I˭=Ș].GI5 vs-[++19!P|Eq6lk"{#VT&y=]P|Bm`ۻ`$bF2p6{^uC7 P?*0]zx+⊆"R*t%. J0~gr{Tuč{!=d!i㬍MV7߉\_2Cmb!p<Ż+-~.LALf}ՅG~hkח jq\懧V}!q/am#i+@.k/ Qj ƞ#u6LNY jɳ{uRRCYG3U)^4OS.v}{#sn4r^H? @/ܮsh.0{syd(s"XlB0irAdBm2+e-ϡ-,A;)>-N62X\aC됋H%旰*V}w.[|}m>H϶BƦpZ+o$2"PVsadb~d2XD="b djqn%8KiF" bBҋIV"X{ v~|wmm QLדև/u5 ޙS/! ;M<*!&"Dn[Ky:NNwfy+o\Ӡja 19/4) G!zϋZY[G̑GtҕUh,|M=26y݃& bC-\J<j~.En uIX֐Fdv}ڛxL(" HS6T&7\s(i/~=*/[YvE3[l7Joas hKsc&\ s ëO~bl-6,VJT؎FՈu)GF4QvBkHЅB YV@h2p{NL]BΦkR\ H Pط}i뜗mRsk8h|ʀε"H F&{.Д#cbvD5o"f$ơ>8o| &"`63eɻS܁x '&FR2 %RܐM'OMerY"#Fig+ox9=Jer#uw h եhLLf91)1?X>ۣw8cyqdx1VE7[6}?7dJgcR\H OE "cXfm8Tw_ 8crt"[.&5]@!mĝ VV2 S˵xEјCbN`G@6v:4~nAl:0p K*;)M'u?W3(o"7R.ATm>~Er4ވV&hbª9 b62(ɦ/ر6 U 硅6zM'V! ɣW!5d@@k$b+>bA@`o{ Ș5 ##: .6ziydg~j6m\k;oD4D[\qHFlCWĔ]{tvgl:jlCFvOknCBDW(ۋaӆ)@K IDATh#5AC" 41_V'GlV[O 56v:ذ~ҟhjɅЁ_"4Σ= (J2)!T=dY']6G bγ g;{SF^.BT 1quFa0DiĄDsnYy׮=1X[[~;ߋQumِE+Fw7HٸFo >IΛ_A_nβvEgIw6,>ϧD|۟x2z (!$w@K^Xnn=:΃*U9yu&6F? &w6C3XIH;fUN.3.)\{!G{sP+4vgX>>Chhue4m %X2=1Ύ#$)TEiRԵHX#ɭGUX"p)"wğaxm# 4m1ScٙT! 7u{3zB7)Zvd⪴Sk ne&xB ܏2 TtI%3= w-9EH ?_hSQ CLY>32x)WVcSaT]혙 +؎}dB<{w{Fbj>#(1]ϏFw'{V?S_xNy4y1wdtU̷2V :&JoTc?@Rc@VIMM'@w9ͫ*ĠS>?ܮ}5z̳l-Hނ@?|SIL ~/客5R.!m&G;!o#؆bsR UWFC~MgeL*+NDCL*^21;~ bhEJG?)P%V7M֣112 b˵{<0nFcK)Rv3왕8E̟F<;Oq sNZ\p t2})#`2L{"` |^./EJU !QB$g@LS-27[# 񘆀fG&^J2=+' SfOt'9J{^kΌwŹyJkdbM}S ֧ͥCBk;y`mw{,K]WO+&rW1ɞg2IҼ-J,K(,\)b3dOA\{GH F?@&wkRP "`@NRxBer2)Vϟș,&(oS IG!Pf̔Ug46u"{+wO"L*E'+ӧ刹?!fh`kLu#3zyHOlw_gC{[vX9h!OOVF҂6v 03WihB)je] L_vye;מ}3׍hOEn!!OQ3Z){.iXu[%CMݎf#8o(:4>tblR~ZYHa2k ~ܒ=D vg!S x?GZ2Dy2}ͳr{q)#P{P0Vj4[nHFsx1M7Yul65we)R!@f} ykDꭍ}Ѧ'p_%;}qi2CY`uDwWJ3d=l&2G?Ը=cº V/f=`~'f-hn"V kggއ#9¾ $Y*^{h6imZ+0V mArWAZ@AL?G @!uXkA`b]~FDJkG bpv2,2R>gRd:~bvAn6R("!Xk@y~%#) {0b|,8ǐB ("PR sm013 1>|Bj}Ssu _FJp8S{H|GD79p0*#^_8`F}79W `/:j5S}C}تS>V$?[_=Eh>=ӛ8le).j|,Ob+FcM'Y܏(h0y@V]kcZdcq#u9͞{w7*bBg6ZLlib Zιfr `Eyιˁ(<Eш| paE<ENۋlW`l3RLb|>/F 1!T >wz""B A6vGT2m\xO^ݛS(j=Hq eRCЉ P O"&-RP?p$R#eG4t's@1h\㈩h}R3fϺO/NZBn}09߀ɭ>fJY}!wb"b<`C8yӥ 9)locC"@31E'#0۝ D V7# 3MhoO~NwLe,o9iuV;]!]k@f[}g`퇈{}нzn%_)!Uy%占Osqˬ"ڄ7">lEַUVvlZNC`w4/.@~נ?U @&XK/M87]sQ\ɲKXqsnBEFQ4j @-S60%]R*V%h^No5H!\G"Z}'87#ŵOH2}ܪ)h'tp01JO#χhAɝh~1R0{#;΄@;ZD>B㐃4ĚB!C"JaLGlD~";egkj=RvKCz-:4gY^dR_XߍD`FϤdshD[կ= XYLV]Fsǹ*E |Ro҈uOnIL*qw2v:NB࣐'Oijo"xg#2EEH4E}}>x7o:1fA v\8'iׯd@]MI%.N}QxN."̯:+}^E  h-6~.>H06y+"a&Mq_zrOg>$*C末&#Qή͛Q vwGQt1cϢw zwO53朻Dž@&ˬ. kPP>:53(:_6ٮhrW v}CS:?)4~|C R# BC ~5&"=diX52[ 04)s ! 0R6?E /R8'#oB$LABcu.vhFo9򣻅6!dX&hmY&knk 0Ke򯳖902f$biι1(*sczwq-C3(z9wZ65{^E\\tPK!*ܓp(9_:YqDxNF}&R*14I@Yf/"HB1tF[Kt_y&XI%F #0)J<Tz1O]p}F/ ' !s47#3 -o `}W|ڳu]R)X-!W:#pb0f߷!h5)m#7b uB2o,Ůʠ 0_1GUVv$_q"g|9hEl (UhaO}hգSbcڈvތ'( 1LoXNDR|.;xM$8Eߕ % jFc;X>~ wջyFGM[TI2Q<`&4sz`rmD@Bo a]ҰYO0736D &4Td:h2]&_ ,G澰lRj(e?b-*ĎjEQxE(FQi?`n*pMEkpB:`sXAٜ| <.mvΌyn(w' `'sxYdJu]eh#=YKH225m%gd:{ڙ@&"gȟ3P{ SrbHAAd7%zV z)Z8|rH-DJzXlڍ/x䯴='4xy 4~P iqN@.-G F=35n; vKn Ѽd|d>&X;W"gv:`|Si{[a+x~++;Z{. "m&˹ֿ]lu |-?DŸ݀z4r>D[>{N`GnXwE/jLg$8vh~_XlKCB@4/_A5Zn*{R2Almwub?2X$;x+A@M̫dFB`/>?9osm:EQ9{ &ܮH"K)p \i m3c•Q۟Nzc#'JII|:N8UQ#B\B@TVikB1HA҃}Q#JRlÐ.E:Q?@nA@gR%hQ:1i+ N"SS3) {^4F+k1.=| R?rGCdʺ.HI 9d uVh\eR_$^cdj)-^uhPGiXIֆ%T47Cd[ PRU!bjbZ_C>GFBh;Z9>v^;*2YgDkkÑs2O# gto%Đ}lc !Ggѝp2}b| [삼Җhv!<̀wsYgފhB5X_}H}de̳GVh  @kyȄ4ǿd:[Mc#ɖǖq&k:Y& *h\sZc6w`u\sm^:'w s&\;}EϠwϳι'hf@(m̘N}W*DHa|~Co0Bтw>صH{"6jI&A25R^QV`$GhW'0%wbh^D;",9.B>M۳Z*G/\@|dYq-9tm.@sr!J+saN)֍YՂ>Eh 3Zbx GS$"4>n@#FE sZjE f`#1Y4SH,>Z{ug,gr'$J&#<-hqg}j?A~xgL}}}ӒVv֦lmj#l+T_̈́z+m$:܄?}7]4ʦF&l#dg(>s ޤSz>;i? qZsn#Z&;IoawιІys]ssEUe>M P*.@4ƣt9y!hk-}`sb&v❑H5"u!' RX# )娟 #v{VV;rN-=P?X:娯oAl^b$gC'q| 1Z;VvD mdH )=ת5̍D>_!SBm GI,|fͫah'gq&BjЩ _g}j gG;XomZmȎͤ&CÈI%ޡM9iglv66fL: `|!W!e@;po G)Iu|L!E6X?!$=ʴ'Diuw@_þeNmR؂mIJ)w@)i?BiiTbs}6Rb1W!)avP"nbE:n8)O1u5<^آ%?BJx߈X 1pO+ o!` c!&7TbbN}ϟ[Ch'8mYC)D}W"p# 3Uؽ9D/F 02;y: kF')h^C;Q|X}fnZ_9{vd^nu|P$$|/Bvq;L0 VFggf6i6Ό C HMDgx!%5T0/V^*UFC}- do8ĀO` ~i\H?F}q!nRl:""I½_C: 5خ=>G`ez"b=}؆̬ED1b*rLA "-Y`m(G`<({)3DEo U7"sU(9LF7} 1|X,F4>&3va|ϵKY)vJr :;b DÇUgk ̛bmJٸq?}Ob{[ާw|V"=7B q_4Ѽ}1xgl~ z6}@pƣMڤM]3chldbV=:5>)Z8{ vb`e4{ o9gpEvE`R>i C?ZB b28zh}>:6 ' Q[ 3,tԽ i0f]g@p m{:sofPI%g}LgJ?F@VT&XkucAʻ͙+:-_[SYNծή)k!fX8z=Bʼ"k;s==!6[kg-ү~kw-2CVwmBv;!$9'=E4_"?`ϵjBֆ8E6v}pz5Dԭʹ5M0^hmxOW ;wG3 °qȻD)z߀L* 2Y?"&m&_cd:[T$wwZ '+ uX>A$B:o,1!'; ;Zh_CNؿU$κnwEKw)JĔʤLg!2uD;zj!)jȹ?C{[̩(A+1K{"v<ު2ڋo@24)O'<6MD`l(L*W2֞{N+kN&x7Ξbmk`Y?#Ռo e0SӨk]SrIN #s#Ee%bώCs56 g!fE5!;60k! `2vE#e=N f z*aR^|Z)#9F8|*{;O%#a{Ǭ ?D^>CzdF-^wni<p,FQ9?Ƣ1EmL>DsƟx:J>-J&h@YI|e{dryeoyb!~qHQ$}'҅XK!!mX}+q/Rh\|5QfR/k7XZS_6)2AplO H,F R̟ #7#s9HAUE&Z䀼 ;9>`!2Bum2Hḱ6b@@kogBpBČ! kl]z4vl=߭V\kapc} +@YA=ewj8~ :5-}s(twl!oiVf@&GZ eu1"G!Std uE!Ss=+SolRz;A':߱VŢf&E] ,O dG0B! $JeS Lg'Ch6i**`zwnܳZqs5K|"<tMλoU2'8~ -9ȏ3y]c[̪npK&5MEuZQH]\ĸEʤ CJڇw,SLӐү{")3Oő2eO#ɠ!=`W!ޜI%.F7b" 8Ξ9z?4XDVHYT9;>Hx _% kFy0Rևy9֏OOv65uV, `|zѻpbzyge dʜ$@ {sfnmgM"R]C֦su1ho\ 힟1{RkD+m q!oW[@s 9iB&j;gS@ÍֱSL*I2-Cҟ4| cH)n|$i0lLg4 ʾu l5(Eon@N"ir_^?`^bc8oA|=`E@SBo|;7 ݈Lo!*iZߠ?W_QL߳)B/Gcw `v\ݪ,]Y5NcjO>aP :zBAhX?qO pM  &'[?̬ >Gg l [C2$ًN$FP_E`^kmPsb<7]^Ҽ6Ωn97!ERGQ$4DNI*`J2-i׽[xgK>N35?*0xnEA bTXoܞ8h1;別iBUth{@͍\~o:IXb@,>D@뫃U]vdcV^ȗͭtouo96^]xn\aM~F{vc1NCd@Y~+߯oDL6IϢw`wL*ms0/s .+۽{vSVq9z~_-z=hNhEگCݛι](:9a"y3gEQsr7EQt3\h\(&8o;纠u]v~+<1lfFQt—ض%[}-6ބ2>W]xMCK-ZxEK)mhsfj~FD2z70+sx[1V~! K,:*/[]^Fi:2+~!TLgbx |#pѩA46V_aVk>(VǙ+@`-{scdd|}@?@g/ $>ol{{F?1-9cHK:)y힁VGc}ñ` 7P ,-(s 6p}#fi(F8DBiQ;m`sv2UYq`bEs)ι'973{DQ9Ѿ-pcEo8ގ+@yEι_r6)[ ]*ޝ;-ŀ˭mxcqY{o=aWlcGWFE \nP;"Kʪ.U> hIT&5_E{TuvpR&x_I%*-XuGʨ7X0V:>/`O e}CskRW" "b:ro]3b>B lL*J2SD!3h.EG"Pl%bb#q;pj| ~ Ѣ7)8r2= I%V$'IL*QL׾Sߩ•􍣬7lG!` 7"īs6b׮/Ao0{*2|ew ih6@N:r23l@ rb>@] yW "2kV#Pւ@|X B(|Bt욗m>tXhuFhn %~ ;zZ=c7׽EOhtƢ8@ȿśmm.k./#P73#LPoʤ5ϩ{`^&%may i44xFٵz 'EQ3 du?F^hm&ssvQιh~캿*{כÜ^Z2':@:k%[<Ņѭow׷{8}tiwf2=)]J!%ݘI%~u֎j΃Y_< !*@?o"&0BY_@sgGڸ?eE߃9w}jք6^sM hgO0wAMȤ3ƻҞ"̴2F4$ٱ>';ƶ=7s}F\߸ml܇n|7eD9Md4[CvԤ m*HNQ55j-.nt {4ߡK]~3E %d:BDP ]Yhm^Qy)&pwA?QW2įT|}.:א)H LmDRP@/n b@w7"f~Yy{}R>1 .hhrh2aҮ})eHq:İ,w퇔w_B&dlA`]|NO}s7SPuUNG<`tFr;4K PM8^||nrVl-O#Ī֯w"&1Pnd2-uoez4]lel1hu|i}u+k!l7?1Y A5BZ5q"ޛa=dc_gV<HѦL*ˤ&}5y{/9ܛSk`m}W`ra~ TBBS~f̗ ydZNBN%2Meu\\chApVTopE*bެ#1#͋vVrd\J'^f}ecy3fׅ)֘B)gRvǻcwAe_Z5#B)w{iK3y+*i皗rKV=iB(7'Dsx zJ'1_O\x6JT%ٮ\ p:_H+ɤ3hmR(sMnˋ+-qꆦ&|&?u]9ι~hl>f=_?Qvν霛dsnTg8^u5|fnz|ssњrsEUmRjbԕHL̤E 黙Tbdۺ$ٳ3twGlʾ vHO Ohވ {Ю‰~H!@RG3z1"#@r S92Dw!HU1556Ra!5KK)I#X)HtPK~|z!Pbu17!bovz.c]<[LsUH`];15 }."m! IDATP+ߠ5 vh}b|@ оz[Zh>=L?=!F_xnq&e@0/8ͭ&"1zbK%Qσ6 hϲt.>ͱMEEm .ʤG%>ޗ2E+Zsh@ǾB &_sW-}aߪLg;gRU|v)b#%BV:B'R"b|\ ,EsX~X!TB`MwG EfkP<ɤ lc!F7>ɞzĨTIȮ'.D,~shZwtG,B֏]%+@1_>YS1~uN~e;6[}!Ev4 2 אh+}=U=.fϴGx۸z{Ѽw]`^ыݾyvFo8:9yS4>5SD:}7oO^fyؘ>DJolttvuT܇(I%>MڤMK!YS H7j>TH~g#t=\Bpv] |$|{k!eVv{83D-`%L[4Qn9◲}aؘBB\,BG{:g sDB YMA2I%>Dا!@%nB hP>%:_ :7=s8bdV"Vbc0%Jd31"}: ;"Aކvo>ud:b~䰜w_߆@ެ%{R `׀L!Жg}<)~kg1CM4*\DUt$Îa}yF6>{?$PCHӄ>LE>r~C+!6~8OG[{A,OG}?I ?J<﷤82I@+ߢvy-h袭V6ilwfY,'u ½1ݑrhF@=X[ϠEXf#TO 4|pȷ,e_d;)cLp~_sJr:3b Zƽ/r9Q،]mDLĔC`ܮo ,.@nNm/ s{ PR|6kN_(oއTv1^C:)xC,йλ>:"{?Yk0iUNMv2gW鑥Ͽn )ޘ%LXF'>~_ԱOd,EggIZL#K]eN&h@ŗCq4 LB'!7#B~Nhn[a Lݭ~[~mVvDU-zwv=j#d61׾ U E!f=cͪum6vgod:t}ob}y.U9R&z1ڹoDʮ)B ~Oy6b.uhҮYN s59b4o#}DԂ;3D R7w~ϥHC~M8+iɞcy]ĪᒅC~ekU?+N}Huy8E=˰8ZӁH GA>-6<@CR?gW"vj1[K'@9t32Ǿ@vO?{)J]d~{x/k$G h/H`Є8sh>iیr{IL߬_CMy]G|ԡw`8zbD Z/t'T 1WlE ھB8h?~̆ӫh~oIl-i3SwXd:#X+ؐWn~XnFŇup߂P 4R$hACr3{ Etb#v|ѩh݀ضɈ]C @NW r=2U!G0"Z{IZ2ps{LUfu*v,C,Х% Z_RpmYtz!(}Dq7돫x1;y Xg#oyLEfs>A֮o#{Y _pw'"' N_u=+u#]|A`k7vC6k=gm{ 歓]_H+3bF!?˘}uV ,}믍Wq~,CR9 廢96 ҜI%d Io8-ve4>wC&&m60% S/ȂK!_@HzR&>PZp;!1!͆uH!F,Ƨy)בC#!Qƨb9LE͈M; u,oi(scs R{#y*+ۄS )W!g".3rI>WH>>d(p_o#5VbvK T∱G q59eRjd:5\cDPa{6Cml<עvf8.?DҌX8}Z.π]Dzt"U5@;{6 ʏ8mqA \4!Fѡ&l!b>_E}9DwBSͨZ\tuV-"ÿ9FQ-?;Q; E5_VοYˁ(w x-m/3K21K~no!>Vez8R{AjF!e]bAlʳgwX?/5;,_鏀cG>^vo#ND,^GJqG~/kh5ez|ι=ǷpQBdNBFp9(B#d:ǎ`Ag0@ňlkϗ ϛGl`tp2ͷnf}} H^?`ڤ'M4i$ծQLG+6YC|O8H ?k+) +eleW{9U4vFṭӳp]j뜻9sns=܍h^v{9s/us}Kι}WιLvg.77Tި|0oNƷO2[lv_. Fx΁idODR䦙U#PE,OAgF "ҁ ߑ( 0u}HeBr= `oK~rDP;}6w@d~]bh|}Tha ¸[\GE?4 ͟t+FDBD*Q:G"dݶ4 T#?:*Z~lK[ř#t{"`H} -+ "TX_zC~,DN9p _G Uv@siS|~h^ ,@`L3wwƖmϖ r(r?lc$!p~ 1'nk;G;+#,1GGmz_=uy]Ğ;.ծv3ĺV6 ;h.k#:bY|`?G N } @s a^{Ѽ Ș.AghzپҮ dW"it2= j9^6hz!mzgb<@Ok.2i"/޶ҫ:c;ֿVȣX3w8Pi Tf'xvJ'΁|>\}jپ|֢#["166σ`dAtEл+-3P3k9ϚBX{cO4ϱ>66~JFfs\u,"gMPt՝0~'Ё[XLh@ňie: LvtD*s'aI# %RgDyNEy ;(GG-c$Z{sΫ1b C 1> \Q}HϊY s7w6CbuL6+ń'D1)RnE q%&^ H'G)R4wXvF ra266=Pk!F4,΢qFa~,Eӻ9ߖvM𯺠k|T|_aEQ.h~Y\[Aŕ;v.0%l D~lKATfDqy/-dF6Ex3~sG!&&Ĩlom{-c`*]W SN{ћt> t@{z;Z8{p./_SB@eCXAHC(rw݈1PPtPzn]@/"V$i#d6#nDXlTey7E8E805 5p_iɌߝ66(hu%ͮ82{?߽6HeTxл7y."[< Ou5XT&&7jWO,ױqp|{c7_t1a[ȣqEUX|kvNph>]:?*ao1ήʷeQ k t)yM\녘GO;Ц*庴aK[2@;vvi+Q;ۃ nG ڼtCehH"%L2?71 E؞bEy䚈#y02idD*s݀ dsvw!7-1x@5^+/MqrC @)ڿoCY/~!bci}= >ot1Ju 6j+k?a\e}Q@{8kz]5D[]<1o?BijhKnP^inKM2{ 7I=쎘+7߶l"YfmnDsEֿQT{" Ge["~pMA]Rs좹`6b ᶦÐA؃Ql+nB7̪ +J9(lČ:4O!0o!eC,6!X.C ak@xP[u^|kq4oka͡1g}('pHaA5}:fGdň [|ǴzҞ?5W?$VylciEY;MV-sfD*QhLA^ :  ;O sWuX/.]`l-%T"uˑiA.s ovy!fdL'b  v\C3^@F6?alXHeѩ8mNoN2Z "Ġ{fbݣ1 2rs y1!>Y -A%p*h<ɟ'0kDMrC;vqY[nG N}pآE M+;!2]ZwGrLX֞˗6ya-^6=Ƅ5! =EO{6>o9 ֈ#݋6dx,OdD*By.vU"2MGW/,/(ϵw#'txÑy>Aۄ).GhVh81w PSKa*"”'y6v=O>O( IDAT{dyBԿ^X;#1aEcAtx[$ȹ0 σa(E-oGsκ#Wڊ zo)ZBىծ5OG6~Rj78 ^v c#6ug,` x z w~uDf'5,ٽXE t}bĻ+H[ I2mr.B%R@;S$R"k(ׯv ̥dcRx{VX{q 9Jx2L%,d+Q%(_n/i>x3eto!%Ȉ#vjtG` D!T9u"[>x?oWpvnm)b\![}Dh#m.1=E,G"P/'!bE}-8b>Ҍށ K)As؟-Bi7>vI2ud{grFqL,oX?Xosۺ|\cKѻ$-(op081{uAM֞\]Pl# sQnxA|x0Z;9Rܯ0 $l( [vtZҕ+J"&t'vbdEn42\Y 27~Jg_Cy-w!2 ȎȰ݃vN5>خJN6Ur\.\@`uw2Qkl؀AȘlN?f #o~bNDFxJA;o"SҖ"dېQnFs)\d|=(  ch.usdQScD">Tpn Db<:VO;bf#m~b<"u?1 p>W/ݴam=KoI'+}pw :}+J'̾p9X+|EoEjS@ R]t4g1dGcvLr4y,H3EoV`]/D`ArMG?CLDt2T]m{[{~ `b< ƒ8>|z`?_jEz:97;gMnt2>낚FQ||0|d{c=%]M.fK8j[»PE#1d X]8r&R^V{K:ˡT-d.bu;rBK=\I{-mJ(歟 c| bz" 4ٞЉ=~ xtnO <} E,A |JQ, %]Ei+" ºs'4X۬3 d }0b0wD^sm+(9U*\r1UZ)TfO  Ekcy"]+c卦XfvGS)rC{~w4zjш=kk4"79fӳ爵"M'SYOϙOX *Vݽ8 n0\-cBĆ7KX_6?>V\[jExc۾6 A]Ph8uuDE?5I[]%$c_@Xdhc:;4nL%?̡oB>:@21j >/^v>I@sL&J^HeFV2zgh/ s-Ah?=^JזDr8B4_54GLZbٻu tV]_ @[1qVܮv݁X]PB-K>GL C OESvn]*2c mu NdkȜ})FNwBɴ4!M;2H0Eе'R{7rݎR^ Dlȿ7Z 4VV Bx)d }O<LE u32S1boHGFmqks|Q&O;05@WhNL~hkC,VTCL[ ryUCs|=m"E ]Z-mVVjW_[\ؾ0F% +OǾZE(6[rh3{G(_/ªj/~@ﰍX|}/]aG}R|@ͺ5E s4"wX"s'|~~aa?u=xb|ʅbFxhlZ(Cz!m1 3ϟCDh3p=ln+fB"c #w2BsWkdk,oR$_^hCt7]fe&~|>O!Po? sߢ9rA$c( yrz ۡϣq-D^҇eјG ~4K\=0? `xbv@@G* ' I (gz{}pHWZU"ʰ.k Jt-V)ʿA,߻<ۿgeqA&W=?=WbB V( NuWH!9N}fEnOooP+A֡Nm [ 8 96APf `g+l)ιs n?K$T:Eד  \+xA;!Yh"]/E.L>Nh|Wn7xmqG׿QE=s>lH:He|γ־@dH|0qkesdC2(Ϡ]dC U9{ͮ )P{"T1ې[4h*(6"\r0Z{"~ aKr.;_ަ7brnGZ䊬~]me"Wn ]lC`baaXFj8D}q2l??#|[NN[Y-OőE}ʕ5T.׭@ʋ}?0bZ_fܘ~SGc^>$b6ǡː{pJJz=E4?;rzO'C1R'AŽw׿Fs|Xgu|Foowu/6lH-*ǵV/ʜ**Lwmn.@cKF\f(bcuÄ́5fVD0Ks{A6 QιKG&ιX_vjg~KU-;E3<9ĬG Cwod #p3aF,E䯑 ڶ_EUM| Զ$vJ{g[6ץsbҾ )9qO7gC v#k/k+F΋F(Hw#]^זֶAn/̵dUVKN[L?Tcҟ hٝM_m`GlX{[ m&fyQ`|=—L@k b˷{[6Onp+) +;C*{J1܀dFYmloCU)-{AFz \Vb\4kXHe5Zڂ 8~\sLΗAW^UdcIz#V( h7܆LdH@ t0hIwGdAR,Hd@GdKɻh[ `:HeFd`oB*d xMN!z财7'!֪1nz Qh\ wOu%b˱3h81Ąl1P ?5ׁCod/@L β.@@ӈi5w`0ws7 ۥP{U[hjEx/W'R dܧYivd-4qRP,=hў\hJ1ٿG/Gc}"b@`z*t ok_3n3 ,Yvd bhe/Gc bSB<~xUks5(A@~/-u?*.[m`Cv :"ќm?Blq\ֺ~ծvΗBծ44N-kN jB?>Re8c]2ہ3s"6Z }9 XVι"wAPлYri7M_H`_"g-8_횟 dT+غ-R AAA"@(AlNYb6^$o%Rvdq >@8漍(F.<,]:!LG'<ع1l_vXȐc#uE!a t`ckw#w ;~ P}¸1?He~m@>/ J"Ţ-V#[?&R#x}:_HeGu*<`b}actbWDcJ A{78g^ Yޯ6VMBтᾦ(Yhϰ6',u<{ 1!JuHe~ط*^坹Z:='Xj\e`hp$$uA͋ծvfgoff©W9^6-KX2k\ [A%[09+gxiu f5lKsws9w*z{: Phʟ3ʬ@ dAn;aݖ}8y:JVfe D*JK9)!h .!+2B{Y!#ovJۛ BlD\0b6J@((PzcW{^_+܇۳# 8.E,BgA $ Uhľ#>MӵbﯠC`~1b6GDt݀@e"0zB@ov;0}>@PRЖ%+Ƕ +MKQp.WXD`k콍{Y@$e\],l'"Vԃy*L/QkYAڽ;l >@>vdYnߡ@t25Muͪ/|.b]dwO 4 #7Lvǻϼ#aa[aeY`WkA:;8ƣF%8 욍skd:ιJ;_/;%Ōkrg*G~a?5 ſ;5D/;-J'S)l$πD^҄(PQq,t_T:'ֺLkq%ک=(.Q(/4=݁@;!71y+z|Ti$hXa;!}NG:t'\9dt+PU}8P`},B/4QĦ5&mN` IDAT~4#(',E@A@At4Xԃ(c1b<(h!3‚%ֶ};Fu'vGћQ [  >g=E x+F[vi;Tȧg1H!tbN @ԛbm*" ,ϣX<W(D={\2ڿ18 t ^}ۨ?{MYo>K p|_fq;I3A:c OS.vcm[3|aι{s(,by\yh3mT9w5ڀ~lB&TD*jL-N~&2I"p{_Ċ!VftB ;"HGx8aEѺ(d²uM^69Ǭ(@< ~ k*HeCaWd&.BL =ŗ]FF#=o=*t kCI} `m O]:do@8^ބi b `uG)}x >WOLshFnp\:XL_%;h@iF""M1J'N:?5ݿ47vlX_h鷛?/X#x Ĕ})'_"rM۳;f"rǢM@oƒ#;]oͣaڏXCG#.ܢXIm8>I'd(Tfj7~]d;*{2Z&~K>X}dA>;404Z̉hA{HeF/Msd}6#Q\ (gl wuC',BNߝ(JP)Ĝg"? #n8Uxt~_j׿G(6n= f}2eKMȽ;QTP4nacRi:FkX 66,I'W1-^:6'vmz]:OD*tUsPϻ@9m6Lo>&p74fxcͭw"llAo(r<~5lكź+7]q}-Elg dR|0H &#Kץ'aͩe99;zgPec4%^Bdz>|tR-cd(#3-y&vV`F>P""^~΀%٦:=1U4q7VmOdĊч9ӊWY2-4s 4Yɦ퍣Wqօ쬻{ j_3,So!;tRTL1K#8؆Qݺb( {r{lrsDiAO ~KsD#Qz"1KY=su[}Q3(ƭry\10qo I } fBkG_6\X@և%?ވq= r[%LEsşIZ5,Fl4)\=iberM?e3Xt2"ʌ@ %ʼ@SlE1|(qzt"“?FOGˡcc ƃ`>oߝHe~N_K2o1%K%#2)t.Pt!x0^Cx򪈐E|S#>z0"At)b^B#|#fYsYWlP{kvma4_eBS6G["9iN%RYY |k>Iku\qAe._XX(pu'=@9P |P˅>wAƤ[$J!dG1`d`~N%,(b0 tv/~sQ?llD 1_јAs߅X AՃ;ӀQ,md)D )t2^HeE}'Rs 6݁ob5I2 cӺdf!bJ}d1O6fa9[[>-JV&H~ӛ|ͣ<0w}' Vt4J'㳱ַZKKֹl`l 8>g#@6VvzAQL0\bq" /C ueCsc욣ލGMכ$R `ի{#.DZ@c\0An}L!@h1p+bݮ% vb|쏗=t0dsϲ5ػ۵rSU5 `~B{z0" 0 k"w" |IET;(~ H79= H2o"Z'R~l½ txPE<.@G!(HnY[I2jBڞ+WM6aC|y/X{/G@m5Zή"b..Ҳ!\ Zbn[i.'<CFz4rAŐ[*gLKQ$FUFa.2fň!NHחPQ:/Eht i#0PvhKcH^QlU{8|F}yw招!Vdm-2EHmN,>Cq>0'aah80=ߠ MՏ0N#dXxE3pj!-ӄ6@/\d @2o`cXrhCp "|I'ˁ f1wT&_Y%rA[Cט>Y.gL-maEh|xFOL'sU8fdrwIbZ':s.bvchqx-}*_\;_ˆNFqȘ&ݒ-EC,: 1c:(J9s}">ɻv4gÀXt:VD0Ks{A6IOWA= )_CQ]`l=ɆP=h!2}q+2,ݯPvb^B߆vh8T!6b\OE,AkG8 x %HO!R`}91OkC x@Ch׌?mOBc= l9|"T Mx%z9(F9rnj%L=҄@܏KҮ/C; 'pן;7v2G &@=@8C`t .g~n~- Y&HeD)hn(>f*0ןP1>op1zN܏\s=Zni$Įŝܺ?Dq98u/c16iVN$ծ/SPuAM[ h"HeZڷ*Y*Q 0`;;z_&`iaYg Oؕw$c zk 2Ou΍F At8.GkݍțbI_m,w 93sfo(~ u<AڮE8Anh㵇L.!fd6FFb5Cŭ̩ȭO>VQNL2O ~=2rkB3br]i_3\5 ܂0 dPCc8[Qom׀<6IYwAqbPT/v:?obì]|llT/4g6ALt dY{!cVb~~$LFؕ ԎvJ4ʤ4>o%i46Ū>$Rpnk:5JNƏO2Q*47GxewO}hDŽN|2]ߋܜ !p( ID*3NWOK2[񽭭>ks-aY+vCzٹ[~ $` bָ l #}{ x.: |NkZ74V׭~˃;sIW/%߸ڔ;spg:>Bn> &利v Tfws2*vB h12({ФmGnlJ"yCFp b |lCc]&ȀwC.vhw߀cLE6Bh DkBc3~!H!ݛXyZytͥMz MX*a{&euϙ ҫ0(aEDŨ;D:&cK4Qj41j81*vMO'{. Hef" s]{[{ysi Ug uxn4?w&`_bh>رzu;g_T"Qݿx<BLzx2}A/F#м1Z}x)}BZw ru \_E&h ?vc{7|ZpV)4h<5.@3*5zcb9tHKV䳡>W>{םU3lw B>*vmglgGM0fݔU/x2s%Oϴ5 ц="> mL#&v:~b9&J~afϰ k:1cƘkm>;o苎7Ƽ~QcWqhMfc1cƘTkmϟ G[k˳~dsd"!c*[O|G!ގ^#rȯDluz+ӈֱvѮuo Cv̇.'^Jze ROQNw%j @K!A')wcx,@X[U/F V7ԧ.B`x+в h!,b+ 3 )3W1b{Z ?!j)m钝 G#47\{Ch,Kg"?3,6"PR؟ v #k=/Cow+}OPJ!^?ݹ|j$LZU@WcW{@ 4X(Bgtrk;ԓD'$[g}5s}wp8?uqG'z_C6l}gud{&2%v_]7yYEo+.y{vSZm-A`` &" W#QS|P__ׁnWT t HFP>̿֜ 3 cgXOyx.'Lf&M\dѿG1Ykc]k`؝oZUAh3d1GZk2F[k?4li}xs zO;Zfnj|J;wG2O!,C Wk:G?Sܮ';W]^p{nCiM\_#f|!}ha!*Rĸ H:)w jj=P \!PA;)Cu%,O yHHPhdĵ=~T7R5 ^jN"kMv/e]"Pب3ӵmoȩez IDATWRZ@):pp<R|R@/㑒~  R <8*4sȳǿ9C3Sʂq;ag!x'q酀h@ 7 LIEdJ~Z3GQ{az| `&vO^כf?ʭ[mY$ f)bC1m"ݑ\+%}H\S.hfΑNťfkY~v]GZ>>+6N&oє"A4l;\3>1ZBh]1cwXҝZs 5Z[>"=Ʃz^nywn +ux2#Z X{]kȴH.F 8K%b݋g-rR9S01H|QhWۦ@fG" "?#Vyw. E?0 s hg6OClϐ?@Jl<ؤfvs{^z~@wQ0yOQF_ |̵ey}n+(YGoC?hw~Gnm$L[ 2G]oITMiu3/)vxW(>1rOY8׵bD 6ؼiX:C1/kMafC3jQ5g FOq643V턋5g,o'7FW:s}-wL 4ƠgE"3ˑ)kR#e<8Z=~)ȭœS T"O_Dk{79EM9y= EP p!bFfF V. H"S_ 01 2ԇ-( d6uX.E~Mt3)"uB n{>s]tGc?hϷn-B#Px8N!ALoR9qD)Lg W"=0G\|\u[oq};/+Sĭ1;AYKbDhĨ/!빾 5"x_B,(s}~G{؈a<~̍c:~<}{SC;< _G@S< Ȧu h C&\Zܹg 4繀 cDZv2L/Dݳ+B})- ˇ5t'9fk% d|NA|䠿'&4Gt!Eصo2;$dtצGr9yM=׵3G"1 x{w_`V!u<IRcǼsQHyG~i ZNE­h3 s-qu%gu݁a0"pVmY;lC͕:q9}=mG=NyeЎ6y\k@g<[!r++8Zs+Ȭx;a$6j)_lI_ÈfyX@l!( (nF֞GZc-"wFX n6RtzЦ j]z3:(L%bW#ˀ{\$mwbjǤDT"v!z/ƧIAL+2~̧=ѼAϻ6#D+ +sH}a}VL#W~| [򭡊a4vŨ_CH,0+ 54*RәDh] Gl5Ǭ@JqQS}KTo'܉Y9hLae~1S:e͙ vhWhY#RBl0*#(s!aH9<@Dx2},#kV ]wBdX'Z]<3cPDRĬ,B@c_nL EbXC =-GZ {wpBlHAD7-m*D7Fa!$' #@n"ր h(t׃ v$bdQd"#+:z 7OK jE}Sf.ܩ􅮍YG&>h==9ˣ nr%n, s ܸ"eb^'/q{ɍ*YT"Ka_YgMfT;EA_@u<=_ݸ͉0A XsT"=-Lv}icS6o NX%+@Ϳ3֒;_(/1O ?Xʋw~9_͒OΪrWs]T?h-h\:QΣÜNescldʼns@/F,h t?9zdN]@K*#=\Z݊Eg np 3ppzy6ٟG 2`T"2+,N%b+} Km{lh~h2\[3 Zzx;kY"=М6vj6Z[u'ڡ:S6Kٜ؏Ђ#죴f]f%RT"h<@NsPš!UwP"p; HDl9hj.L(H%b/ǓOF;)h~!sG|\a"HInplF4@1]c7ewtA db^B,"wyT"l<>w;;k-'c A%EϙZ8_J{6_V `ḂE4I |Ss8,gKf-ݲpcqz}+R%/_х7tw>4CSX^i_Vc1G8E];ǁZtœ6[L<Ӷ$cܟ~qsBB$a0ae lg4Ϛ_ %(پvRiL14/~ֳSv+|}aY{aBNMA6Fi4L\" X_\=92'HP|OpG7 | Rį7Dlv<i.ɃD=ؘ2tn&(td8ʟ}| 00bD nAzB<]Cw/ƾɵ?"~Aon!,#0MQ0W@-Pެ,@S:318+ F l왻ׯл Gmߍ'CSL`uxYsײE?]-m]?B VF+AIw%!)BH{p~σx2kx2}L*[ZGLn_Ȼo9汵=떌7jGٻ g;2_6ε"pTت83iW,v p9q6#ھ@lǢl‚Ѣm1h֎qm㻡7Zmв9 H_N`^,FXsPA)2zvT47~mB`CՍR+-}jBh]4n JO!6 a<^@ϷE,wcP/ZCN;?㾫JZ=ȕ?otVKeuИJjwq}pS*{yDcvȗD Bc{Anp\ P)k/e&[UFvB%+s휌yw$cSRT`(yJ_1J:g=Xk/E xզ}Z1DĚ # ܵnAH rZ3J5"7Zc8kNN{Zhcj=sm#Him5B`_:\'6wkƘ#2Dk<{mv@_׭5f Ɯ"@q)B@ RM0 >Kz+RF~ ؎W]FQmD/)zy!%C}2qLv _EbYe )rw/E})jd\@w>qbYtpX*+L/E:O1<|jz4qDs`x2=fXyYXεq~30 U(DA3b΍'!@{ xYY$MOC`wv"*_ nj{ɧ)X/q>fQzZ,<͇U9ȴ/_裓[Y|V=кČ|f٥dlUW~۱䰪*j`aLg2XA c~]1c(Hclڐ]j Z[Ƙs}mm]11:kVYkƘ}v_5>iƘ{gwHv1gȷ>ԍ%qW!cڭ]5f ƜL"`]!@)|!)EMLhMHὋW>꿽]!yXؤ)O+Q>LJfƓ# jD;f/<2G`\C,V_wɈMsOhu v綠qبوիB/c b@\14L߃vI}|4* 4׮6 J_,_ͳ&k> -|n<t*Kœ韠N%7O %h~A.7ڰx  oUFy榯<:0oo0/{&![U 5շ9 "Y Jy"s_k7rtlU_l3LU}RX.os?}_p[kWc>BkGƘк߹ i͸@h}3 /Znyd8Ns{ӭc(Hg|^nfi@'0d&0ԁ'Qח 98޷i̋Mv c4E<5!5Aͼ>>Hosєۡ݀"Vē?C`fTeO| 팀)4؇ vIY)?_׾"`qiek}5F9®Bcha -eh S`-Y#0K+?4Ӟꮵkzq[`Y*'@*<73TԺH;ϐ:wD`x&bl(qu\J:X*{;e9˶%{2 Uӣvʆ3|6rIZlc)EI`umr@ձh9Oq}Ǒ磗d=1)R~ր[ k3}> !|33GC9_؇8MJ +; PR lK31,# 1A'!Zn4>ȤvS[!`r)]\7[L dDOkvDٌ"硹ҕc-JJl(NA(wYhW)P,q?!polDPՆSd']hL~p;迹YtT"*2e (-|uaUO~x'cdl<ԌU SGZ_Dܶ,[bmBƘf'c11>u/y*Ƙ>h]XLE;|W<>"yS/8~*08ݽ0twe}V`YmgcLO d'CcWwdsg@!_l(bG5 |vFWm IDAT#4(MHV 1T"Vc6$B@x2=,F M#bB}?}]b&_AAՈ(896!`x*E~~^A4k=m;=\_\J FbPܵB&8b.t-J'gbZȆl(ҋ˦=Rc "!FgȄTYҧŏ rRIޞAJr+x2=1`lRpOd,JnBLP 8I ?D@vX?KJ ;js0b~9PנNowku"2K*P=C3J+W4v3\;nAw>mRcwY%:ot8œ뀷yh~ |fܽD |Dlk(QH3bgs'gs#LG/F91bIaP]U-ϣл'ZlZ;{bNNuS/8~M[wɈ<h&/ f V:{Z[ )rd'  QJ Sٱ2_&H 2k3¼+kx &G~I .!kk![QmWo䋴ZlA0VzOÝYlE/ \Jqw>vk#vF4 -ȼGӍ a!`Ek 9E,[̓y "s}1Ę|v9D׿8nޣ޹Rn|5õ։'eHYJ%F'ӧ#?\+sE|` ׆nY;S:SG >cx2(cuȉ2 Q]_;A&&<ơnHQa#6?e65q}OA `53!0Q!Tvh+>*1}TPfF@*ɸߏQ|!2@ 6!039Gխ 9B}}R4ub{vGʫ͋<;&v2gv A&[X uӋ)E-or䊎w#ld|Ɠ==C s83Q╥z`CP>j7w ZS:S:eso"'2 -G@][ ST<+9|*rmЯEGwT.EvO+]J;2ӑJĦg.Bfvg?Be[ X16R>/,wCx)bwԶ!Sw6d.MU#<> S؟Yk]/G~`g lCGc wY{ֆج#G/׶" N@?W؄ص+SذT"v62O]>v+]JCdС+J$Lw!ɹTT PՃ( a bCs>JEʿ9 zWmMtJtZɷs8b}Ncep;E d2*v;q2(^e.[.'b/+tf+2yg/FQ"   P8KP?/Cl<"?z.x2})5#%sc nwtXj֎HaLW! 灬C{Yk?['1P͵}`_y}l].C[' kS4 @; 1!Z߈J'8-C|7uK%b=v/Rú^#j#h-3_u}NN|[ƐyD g JX0sš#Oxvm,/鶄 YH}eCls6%UOn(bZW`#sfW& 5 "6`C5Zdȟx2SMPs)A$K0OPpl = 2QV"6j +>h;^,o_?2RjqE]{?D dJmy" 3 WL/nw}_{bˁx2}f3Vt+ik4wOG#ӮRwSY\H8F@'ծjZj·FMwLVmTSN%.F3{0񛌭ZS TRj .o^Tǁ[)ߝ) ɷ @)R(ќϠ; )ΑC Eh} R}"[(jw ČAh)h~Ɨh S֞dw?ļE}NGF=x.W=u@4^ Z ŋ}:\ڿ؝wYaiS}rT[=@ؿܽ~b|\6Ȗ~*s砲KW!0뿇]f"'!/v}#A>x*x2EqA 7;LF:dcʕ@>qkS!EMueQ {dO+ cLhso(o*%cFM%hNBG@>j=S6#bKZƘb#h]lL"%;)Oo&lhC, "}hdQ4*G@Xqfkb~P7 ٴ&ߴ3< e8!pAk F 3 K [ۻuAcz)E>̤ͅ71((d끔B JcX̅׺lGIp}<"y"JeCn|!=gϨg"7koFֵ]sxEl[hf}$L-Ǯ{ /j A+n~w-eoՇzb!b½ ߴGw9vcƽ?Q[bT_"㼨~SVS61.(|rѭ˖f&7.3u1fk_|Js߾7H;eʷ&x2}1RQ{jOi(2]JoZzwED+5; 7F'_G"O xw%:}RFBz) [஽]n&Hq/2ꂔ;̵yf܉--y "ydtqr4Nzsu4r' %НbC>^ r{_tjBW1=+$`js!T@DϏJ<>u^ iS:"̗:!w}Xj1ar)%\S6 t]w*蚭l_)L-yV,X dE9k뢮UfWD`$j )QŢx1Z wW+ƘpAqֻ|xW.ϵ_W`%F[k播^\K,;ks߈gAXkSƘ7 F`1=//<9 t8&u 2LP؏]Uȏ͍9Ԟb6KK"]r ACF>KOk '*FVv?|^T"P<-'#FڴLr]gJWW>ȵ?üEMu1j0\Z1)?VwlaE%~o]y*˧-G\T% $Eu)b F _IY Jݱ"6R>#D^hA NI%]k#][#x4g(́ȡt|xK7#pъv}9G!FbN@JdBAPjK|=Q/h= 9$94}%J>ANk9&ܹq92=1^G͸Nv#HѼ,0afv'#x2}IX{.%d4L߁Sx2sV-/#Ƀw*KH;vcH٠:fcVuG;bvʬi_K"\bD}͐ځ6_P?B'q2'5Ek][^F&a=d ;+[n]/zDGJN{?9k"N|>`.c흲Ȍy \ElqD3)ē@>ES1EiT"{WL9< jGQDm&dC<^E>E#Pt#{XJrd P VLnAK@;U/!r"dn}Ӯ@BdZ=O#}peqΝ 3@o!"S4?|,@e(b|'w+H[gytP|B`w\`/̌rc=kwUt Zw~;HA<s ~%-]lPwd \ds^ 5-yu%O*o'\U15Aföց%v)̷yzB2DMu)׌5E[h@~'7Wf,LnDcY҈޵ufkcL)4mXkƘq]#{hW|1/|s?27|ZēقW1bM"HqF Q: wPĐJIJ@e(I%bQ3V6-@h~>1c9-@~! EJ$ %XI OݹEhOG[̓#C@rk4/8[g1W{k羖U6OF>UM[vʀ!&`l"oZ !cGMFMO7xguW-/3}>ɯ|,ZKMȭ# j 6B[h#?6Ncʁ kS2SOq`Cyĕ٩ j s Z8Z_CA Ё7LoiwoPW!j4bCؿ3`K CuQDV:3 ;"|Db;R!@D@AoyXAN@)LydBXB dR P8prO~!2/ۈ)F,$eR[d=\5[ڢp;|(P!e[t>/wRt'l7]{=v?>.jWPqIltW[?54.\V?l@]JI[-V[,Wdr#WJEǢ FM 45o l[`UˢXXjy_u#GYZ5"L4\锯[-ƘVU43qܩ\{`}cD܇,4tv1x}6ӻc|6D:e[ME✛_D1`V"p'Ⱦz:qhAͽHQF`R*0).؃ >xSs{ eYwlY^BKG^D~4o"%w2 B̛^q׮A}b~hV Brw:DK2 s2C0̶A{10SH͏wy;#SF`1x 9@رߛcZz٦~PJOG !/T@<"z 1dYwk"Rʹ3\Or42.GwٳQ' #3<z!v'w~ R{q%F1; IDAT`!;!n;Mdס}/ ~w7!5kǍ?`4®WVkLr}8bM%bkR>xPiR1T yː's;,gt:b.S}4FMhTgl ~ηh \O jnB_ZJVAmqFuigEM?2uNNĥL'ӏ#0(^B 2})D*[A%L߄!arDP2k YE&Ds0yԚSue!ԧpF&nGHVo\:i0z9B Db>[Fגl@ծ F,9(gPl,b`C,I Uyp2 ǻ"qp4$\ofKI<.ZRX>jC(Pd/Rq[DJت& c 2&b[>-ꗱU/Vwg߁UI=]U5_~)J'3f-9,A 둒~EUo@Ʌ(Pa ||+c=@2}ۯ_CGM$xAPۗ!˵ɡ,Rd#21ʗ;77'ZQ.+FytJEjeТ}H>z)7GJz;!D{F#n@ 3PjT7و]F v7r6)w^Nw[PN.xt ݼbT9ЙG?cd< d7du#ьLȏ26򠫶ћ&6!~A=st wn[ ?MI>611w]9Ƙϭ8m߬ol\D>a`kc1Z#6&ѻGh͑#!U˄dGO$گ~]7D/cʍ1c0Ƽk9Sf1mc{Ƙݍ1u1ƘS1uIƘVr}5ƼnysvK1 c(51O׽hcnucfs1#c3hu1Mw0c.t?NA]+Ƙ-ݶƘnj1S>+_c.q??oS qƘ1f1{Ƙ^1ƬOxH| R6FC,#B4"=s胒B8 :v)x)<cE>IyMs?AwDݹI^|eA$M7&O!2?9DجF@7אa2 C)kRHMƣK%h2}4*'<BFrCl[Bx>HW!S3a:lҕOR쎻N 'Lj[Z>&Qxhv"#y;g_7Rqٻ0c#VvG cPZnbCp5QZanVmQ]KcC9ڱ{v,zYpu8Z+R/4 pˀWݹ&𚻁]= Ae(z(9=}qy[ww khZ;J[i~ 2+[O$bpZ}U]\]գnaAV';@T‹{h"D>'oPNh-TkGؾN/h} 巵se<F_n 1X"tcwC͏PmM[u.>H-@c˪t0)C_V]6Dx~<@=%"WPk 6?M6dl#_qU3le3,Q+[*澟:cL5C߻݈r^4̵N6cr?GϸwvMݻZ>+?ds1^~v@Z ܏zo^8;*AsLT1 cm1 ltwZdGbOQxM=^ D6bPBjP(U4CYRHבJ)^9fxAPqZF[K[#眂 49p ZոmEp{"A>_fPuR> +H=GW$'WMG#&//HY);@bh=Y?-_qp~|lan3R0>m=77Ƙ]RkW~"RwvPZc7k1Ϣ(Ѳ/S] `/km1n9"@vUxћ6oysbՐe{}nXK~哱í(|1}WGgA%Rۡpd?Ij0c7 ބ0JBnEaPXM 0OPNZ8ݹ.>u1v1$JSQJ<:9ې7}x.BS^JƣPbGkxt)IƣYɓasHk32 tsF[-Mz^;Ǜ26\%c#I߇b1e7JBs {5cs>z?cm1g,B:ZD}Vb[k4ԣ"!ƘݭS1^Ƙ|XX=i̍Ƙ1Xk_\t'k۫yY-p1~/?iZ){MZ?To1fgk[:\  \'kK T~ 9^LD UD ?H.DkW &~ Mv:A2)[ۢ'H =Xƴ/ 1ɾ}킅cܘu-@5-?|ˤPxtb,/HOƣh6H="%R!wd<ˡd<2,3Ֆ4,$oJDI_:+|M98?ͥ8/B!MF "_^bVy$Zn^s\>|6c!5 M6ƂNY{ սߍ1ִ۾#p1&>GϳvDo"%T]Ys1"bJ|"p1 0Y@{aٽ= Y(l#"0g ~h{*?G*U~& JTD!!RѼf= (EH^A# yDG#XcJr^GГl5_Gkݟrww+W2HmFSQml\2Rd< a]bDLݗAѮw!m8"elu7&{Dsm@sq2+C$z pBؤ<ʙkm+z8c-|Mhq)("Q+Ub(4晌GCFl2݈m艺>HE]CؤO ITV DgXuG#rI?锵ȟ=ՠEJ`D&&3 _GsXfNESN>|X#0#H,.JQ!HEX R0M#"sB&*!nhou| (JG \q jY"ܱ;H9߃])"'&QoݝwK_w" ~lX"@d`Rg<Xd=Aؤ1=eXx*"]tJH 췧YK  = hYX>kXb=H^~S!bBX 4A9.P~">| 0U"H $X"UHD!7/!B"@(dy3mqvԨ*큈%*hpݽ㑁 @Y_s BDq<|sha^Fƒ4WMz0/Ha,@_[YNYؤO->mQ`G5U>A1FdzhGʒD~""Do_Tz͸Ao)F(X/>Q_!q1¿ e[o<`@!c&} Þ@=$\uD~**=X}qw}G1+UhA6Gs"5|V|rFmDn,"`@.hɈ Eq?Rf /EC0H ep9E`8J-͊%R\ c+>M7N8GϬHB$}Т-jlR󃥋?1͙,Hk̟糴6{G36~e,{Gnv!YH==bE;g3+ܻ~HMLƣcTNbTiM틂WCU;xFf} &=%!JwN%y)?+*hC{= wUڷ)eK޼l(# zR^->saA1][~cvjӒsn5wC$Ļ&bOݵ߁4[>yJ` pcF1 _*4Qbv*:""N'E ot-">, ~o@jhDrJPl ;G |ݠ1b Zd+Q(]8{F[=GdȊD fX~Òyk+;&0Amxl; ot_]9_./.^ްw:~P'^Gzp }v(R[Z/W<{D.D[Җʶ]}oƽDj{ɨSis渆JD {6261|X|2cUrG$s6)PHI+BDg{|#0RUD󭸜"h/&rF($:6@yw{P@d+v( IT,R.D^撟-vL>aIABQwi.`iE3H<׶M<ⱖ8qikJ$̿o"5aNw$H!T'Ypo"QHs *iK? ӿEy=}kr;l t, 6{362u}N&}?{ T E&_@a%(w&>Zɘ"ZDk&8[DPtg'0XUcDeQ2T'Zot 8O!0 p6HQU^ST6ڍFh!R 4d<K'WOԿ[VPgd;-R0||5.jX#bH:|ٌ /! R"Љ|&ԏ|/ZݎD$k<3ײh u+Ep; m#nةng&я{UZ tQyN5ÑX]&&=^SjvT)bҽ|X%|ejH|-q RI"5?"8͈E0tmz|LB$uʢ*IsھM޴xhۢQ>Ֆ(5uy6}rqH^ːG3/܆t7s?y*瞈>HRuj0\[1͋أCwHGS޽nHd럽 m.-YՆ)ֹK&kI26Ҿ]Ow;#М%-0f"s"i>*1k7Q#ֈMmSNJQk)RVtNAIw F&gc#"qXxţ|ݓj,̽& %R 7~IRcVxtF,5InK^oq'rK0s&l|MzGf"D|Aa}eYZz'O'߱Sdl)l'!܁B{iYt/.ߪ mhS`iy/T>m~ZJ?-\Ryg T+d,lK;lPX|;ߝ~W26؝w޸>||5/&2lքs6V߿#tDs;"8{0 g 2Tx„']n(;v %З0DƠDP۠l. wo\48DE?cﳁC(LZ#/&<~e*j-ꎹpR<,O:PQA!~ V!QTw3=HI:  po,Kʀd<:6/̂t5}\-+*k{gd[Cыp@Z`M/"V'y,*hCsy0": |?lhBl*VÇ>`PcɀߙlGxf hC,)d3@բp~"D&-fS0Ђ!Tm0j<)mO>OgYdm9D^sư A"268HZ8"_5Ra(%HH@)p1i| dZ]Ѻht5p31SNG)+kd%O Qhz9Giy|a"Qs1Z>@֌ ~q7R E2VUL 7-UGdGM+G;ϺقT.D7)JSP(tMItn@DnP^*eʭKyI,kºZ'"i`,ܾ7l06;Es'g}p<`unܵ|lnE>n(ͭn[ =T܇b>6i I?S|F`ͻؘ4jؤUGN_3>2+W+т0 Xɽ m)S}E>5bi^^9"n8 ] NQbE;H ۈj7^/wf2]gy>ef5<ӃnPM:BH) o:cB!PqOƣ蜧6'tp׳K^/ض6 W@KwG"lzvS7E)mT hMBaahF 4 \ RwC G᪱V .Evh #鹦~Do .Ed$]-OUWo< iUrU{@,^mX̑! 6AÇ/ |Rr8RNfBl?0y5{|DQTDf"T];!"9}X!l>B>#֟|h4;? ՎBPfMր g|zX"K yvRY:߶wʲE߽s\VI?ZA~`CZXIgV@M]ij‚=a \,zsc7-Z 6`J_39rAB H8c#ͽ5MI\>+c&CxNI;|'ܚl{ Jި%RF~bA7hBz[Kj'%DHjʝcI+v*"nYZ*xX"Jƣ]HNAm}ZI P6PU3"?@ˊ]%\(Y:|u ϶fl t_Л$̃ H>tBY wƟ. T73u)eg Amh`cD sa> 5)X}ؤ-%҇_^dlb x! n{7N[5ߺd<Ħ'B2(  AUy^pD4aۧ@HA@ ׶( Ca$Q/9r՟di""y t1|ƪnDcḊMﻯ.;7dJ[_Rҿnd9$ὰI Vose;9ͽ6 4_R(ݩc$[>+=?l۾V QJt n+Çu30i|d)Jm&Ϲw-N j@9F( -H͡y/6vfl1lsÇ2 0i|(ykEYv;z4 U Iz||+cWCQDy3!R\2a68Wח(dk26P.ه(L=-uXse:f N/j#G͹Ag7H tEؤ7Ht]ns+?L1i|ե5s'L2„s6 ҹćhg,!uk* \G/q]_;ZDEaHZRs%RfP6>WyzkKy~FΔ1Z=dGD", D7rWel>lϡԐ\fٻ(~"*(Ixh n26p/ޒ;Ç(0¤U@ 'L3cӌjK@9;g#Qbu1Z8E;R:vEg! 7z} Y E lRҚI+@:~}m%R۠k&߬R8KP⳯_2pT[`~~4' yEE+R棹TDJP^ @ؤwA 9SFoLuIf36R"l%(Lmఞn2֋p.bnv0yλd`X"UJ≯5jg0jQ;AjYZC dze ]hw /\A\dݰ"_o[aHeFDjjwGIy;Aۢy Ç'cI~#(EM[?<(Lws~ɗx=H5{Y P,FЖ (7sB9Aehal QHx,Z@ .Mo₮J;5kꪛIY xp?˹h=pc,zhFyP~|<el$6gPyUR36bQ3km(W-J:n,D +QW z`7,jz&Ռw?~uoÇAdlp0o›Kd/b1z}B$%w" )^ .B9d3P$zODŠX>%x7aDjZw ٟȍ/A ^?|_^y|6Wx6@M]׀҉jW'{PULWY2 :}@/It/S362mHH%p"H?mݱJg_%7X= Xh:gldP3&]G:26\I*(366YЏXk|sZ&L3`9#D nJW)!2UMB}+)ـ2 |)f` XOG>; tE(k~_Eۅ=,A4?{h,qkG宅-U٤넟y餏ltۓ>l}՛İIOAՌK:͇b4l@Q.6aQp$papZrjdy $E36k1^y4.pW!u|Dn6Gڝ+܃fln}I6ʵ>|'c>VQnMx-djY|BxH-|}5bDJƣ&gݶN`X"X"QzRW"d*R `?Dc<.(}'g5E"?6#68k}Koʾ E3mkLTQˉh޿N炁 ˔]QPy{jI_6]{2Ђ]گ&]%(%>(2 MHA{b\갰I6*[5yMC@DnAؤ{r->|߁: Z(He𽅪FrGPj>J%-Ğwd<*' ~28j.PtNW.@M] H[rT.I&wƚ{PQ5uLW[ K?H GW0H+iCB]ť=rt.p1q\mݍỈH h{DN9\_ؿkt-}Wc7,Hgؤ#Ë9vhGkDFMS90v (o;e=_ǹ܀"v v"ׯ 'n= 6х}*do_gT;&iÇ髏U"H e Z1Ty@KY! CmbX@J/]8 5&}Rճ# ;Y| ˆzABA.q_b/0ڢό [+j3Iƣg((%zz5u?FUPlj끰IBԅ]HS?%o*lG|{}'yNFX Q>@4W[a~vN@ V8!c# .dzDbnL^ňn Vuh}aLd]M "Wdl=>|oW|I&5&Tj[E퇪 ~ -N+VJP IDAT(̹R"͋ZUPЧ<_!FեO"_N,l6h +[tS.m1޺EJFWMh2,]Pޯov6pR,5D2]6AA>eQPSW= W1U \  3FWzD,l}⌍l~q|q~6glHF:zg{p5yui@D@AFaD; F*KМTBTy$R4[Źw5F!ϷX%meԇ`j$F&By PX\D.@' Ҋ.sO&fSXd5Z**0O=+2WE;\.?@Uۡﺱ4v5ZN-xGD Dm!pQ(DZ 6)Ƚ(~Vo{77=_FaYB; 8%l҃!, o8ǩHad}1)m^C{C֦ ?Lcp=)PH1 p5{U@"o' v"kX"3yrc&WDM](z!neɇ\;P2ְ_/jƌ|zso~yt_;׶#y}f>V> :kvOƣb` SOݺ=\G_y!DDn&a6E:^n_h+'*ۉHG/<"TxjsCjCscoik4to Wzs>|W|xdХ5%fO@ L>NחU~տ>VSW@d`.^5ܧJ3"㑵~+\AFlWsbrL[J(Ў;W"c{y:"vk{)@E P(ԇ> >Fbxf-["%'=<d<: mChE!Oax%#֓UuP6+JmA5ٌU05"HT(dyf.R`Z*0HMBM$ѷ6 5u{z*Z|AcFՀF!?@pڕ[.Kc+ ;z7?9 _~pcrIӖh`+ˬ`ЂCɾ{w'}셬㝮 j6ʧzM3&8W3P%ty[s+&jj3PkSh\ףBӓ>|2Ք>zqhӊɇxX|0_,2;@2QG 8꟡0(#`l.p%#MfXKړc'ѷo^ @ǥHu.v[>tAS  ;L1"D, "WLWbYzjs1=?Ewď,t7wl1nMw_Mz[KWR %ℌܷ!Ç6|2'8d"Bvi@(U|)ьrAuKfdsӶV7a>k MYsuk#_Ǭr*!ב˱u0HVTͨP ˑj?>7q\mӪ6骊K9{·NyeHu^ؤODDXxs)c##r@ؤK@F&}R 2p+o[ÇSX-\eyh񙁒;xYGg)) 7jwA p"5ԉjU·#{QĉjW#PϟG=o6j~m-ւ1c/_#f(xRNWddP;%7\w-vWvKgW f[smva O;36RW 2c%RcQdvz2sMpI2VEb!||/ݛ8c=t=7jš#bc'RcPwaβ0cx(H?&>|u:wʼnjv;F_x5u7#UX OPMWQ?NmS=ߧxno,"GW|K"%f'"%RCt/vAXM]u NZUSW}%8v*m#8}8j5 /}py2] m쥍[L*j_1{A0@6rxLxԫ<ݎQ j|ꮀa:y}6;~{}=kyM;_=iB6l`;Mfn\ Wއ _}+X%Zi9B0nX!̞s=hU"k(k<+bȯo=]ܷ|IDd<:w/*5߀FT~FxvkV/EG> x;pM]]#d 3[hf-Z 0Hƣ(4ѐ7&݉*+CȔv`ǰI?D>|X.(1` !"%Yd\E¥`~Jh-Akvh pir^6H@kg\!qM];=j78 O}"Ç2|2cUȘO&AD<nD#I75uյm.*h)no=OuNGWO_x1d<ڣ68?{\h[lޖtGWAdz:1OWYmgzVueJӧu`0kOw)i}/zE[]׻Q]XW>R$9D*(q81wԆ@M]uןA}7pV=:#lyiJ||U1+E,{9b9{Σ8''iPZ(XE eG(T-P"^9"`E8...Tqp`VBA`;H7Zz=ygNL if>99Iyy~Iw~O}5n.[?4UAo۱"WS7抲? OyLچ!?2tﰥ-A䁝Z`peuޤ0&0c〈 ֦-M*ݾ(()KM>1 Zq--UݿW5:#PZR\lTlޟr"6 yy/߷OxSh40-ӁSi_tb }# sA[[ ܾv\\Op uN؝–bgҍA]Zpr&[4i U9?p{m;%_>l߾bÉ3z\.1Wf,N;5# ߕk ) e=[X)[VޢXǻ3eN5&T\onN77,1wf w׼SWu|[['>R.M,7{>g)8[֟PXF""ݤ0&F|r&Wb/~S!]\}..meM[V϶F]udWjؠ-#Z\w۰kH'=5Yc_]1t|䜂y~cv c/m6b GbFwnx [; K;K4ng6R*)n~wn4u>jҌ~1qVSs "+cW3&}]9 o`?Гo<׷FAaQf$Ha'} 9Ua6|T*;+vˌ ˁ onpQy19BYpr9ذm|qpp冶;hP|ۗM`ٹ70 XD٧MG?R4K?<1Ys}Fl3v!Z` ¢L[Px3;aX'qX]Pqh~0cI{91`\PlY5"3zīÆ-M=Jy4b3y{nź\ \]V#G-~<*"rPR|lSliR nzdLBXiQVBi݅Pu{|x{x[%]Ԙq\#vp8;q)U۵q?q*m/}VyOC9m DXR6@B=2}7A|77~mKX!׊n63V(uc g'6@7L)b̈́97\cE3c"{ 06Cv&5643&Mn^pOdP""ohfLNOxH""҇) A9/""GaLDDD$A3&""" 1)$HaLDDD$A c"""" RI˜HDDDD0&""" 1)$HaLDDD$A c"""" RI˜HDDDD0&""" 1)$HaLDDD$A c"""" RI˜HDDDDloIENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_13_0.png000066400000000000000000004624161413546205200301540ustar00rootroot00000000000000PNG  IHDRb6;sBIT|d pHYs  ~8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxwxO^&l lha)PAKe@2Je%(@0\v $dAx^!J;s_/7s{>aaƖ'0 0 V1!faN3 0 h'Laa& 0 0 baa 10 0v„aaF;aB0 0 0!faN3 0 h'Laa& 0 0 baa 10 0v„aaF;aB0 0 0!faN3 0 h'Laa& 0 0 baa 10 0v„aaF;0m e~{0 hLƖcc1 0:4-@ {HkϱabeMkQa be8}_aaaF bƸKs,9{8 0,Y0ڞ c Q/ p,p)PҾ#2 60G0ڞpB/K`'ꅇ"8xarx=d`a!.,~}=Dz>X_ޢ5c{??[r+7]pړo[ꗭz|(ua[& maj[5 !=1{@ C ̏z $Ī^1`80/`7-so?kuMa bц[U+.`anQfL_?Q\Rtjzv{Ss5Q/pO^M+p9abF\/{#&x'e݁@ 6* B n3Ԍˊ b^aFƒ OJxM%nO_)0xH"DX`&.g @?=jgf—aѩ1G0ڊ9@9˛~˚{8#H KW6P5{ j B7 sEatr36 K{@Sa=oT *FX{7^{[naۊyH\CEJg/#/CČY_~`_ycs|ܰjzՠ 4WbZ9{['0&a `Ξ@%$z$`P$E_ָXnt߆uvFQ/<$-HP QTFF=aUHfs$0N9bVE D ZW .B m;@/[)]x$ŦZ8u0G`6Wtj )Q_ r*QX*)r"(GB( \JTd|Cp@!T}6kSx{6H0ꅯz[^8+ k^aFńZ#*@BcVe| Ӈu[ 6^SEY{SYۀ;Qv4g$PmW(0b|#$?1zI(&$hz.Q/޳-0 a9b%^( Cr&Q2~ OpD[fׅ(\GsҕFuH| f a QNWjn1tUr|p c%Zu.#(wn6Pc:hѪXM 17D!(|O+/ȦosW \ꗭ޲nDQH -Gbk( [VBf"ՈH|Jl/>q{(<j$p}. 0y,4ileOew!7g1Z-jSje#eJFuD$F Q}ww!*S15 1uK?H  0 3NM B<-.vkc!Ǫ (G/-+˂@ }Q!( YJލ($;ww7.lh0 ܘ#f+QUZS ܰTAיhEckWרXm뀇91ch1HM@j: 8m jaai3I^ oqH CEjǺ!!f| 1镒 ~Y5*qr QE_&p>-xrJf =8 ~_֟0΋9bƶoQ/Rli{f3x[D(G+ $F+gcHl2BI󁧐ZP-a(l >Byiϝ(߬?a $/3(=ϯfeO6WYT.lD;!Q6z2OC$Ys-rvr-~[ꗽƒ;~/kj7 輘#flLC",zJGqlE ( 3(k Mf"T{4IP.p4Zi9'$JdAN*ONLeՓ60N$.atcw:m(8=jJ*{(7r@&;Grjat!PrJ\sV#1硊3Qh{mtنa[ &ČmRFӴ>pAk#{Gp:A"9|I$#}]| (ɥ;zq)p'8}Zꗥ3j󾶺h03y^+@/=q=҅M3ڪlK Tݐ`Ak=#gH|F%&Pܓ(8@%rQ(YG^/femqa[+<~J`uY+>A^xd R *]7b0Ԓ#JYE(9u䐍ab[b(Z19(vNW`vC.ꅧEZsZ)%L))aƶ 1!:sRI[븎|8zz`"qp-v?UVMA`kQɋQ݇}X6q;3p 0/Eػu5?RR|&Z-)%%=0lդaY(U’-X[}x1w_BwՌ} 6xn1* lCn:TٙTao36w8#+$3P`onyh-wEBj}c6 1+B5hR`x$4gWXY۸cMX}`r<lQXUP,z>?53/gy͓Y^֢\a9W=jvDT?4 9*/$@="vzPHT i83ja16 }&A5桕twO=cƶ& BD!J.FwD[g[!#N3KZo ݋z{0ylMۖ?l R|q: ܧhoPJ:JNӑv u(ww6疢3gQ5-͝bJI_;qyHalc#f8#Y<-JC.ީR<(hs[5g=jm?x֡Bע> &3 ]s/Ifg_Vy}5h@b $I$nB+, K$*gԲ(VLNJ*@^/8AJ+SJOAjS'N_p;0msĶRBDF(83K < % v##wvͅy#a)VV?]رߩ{ʬ|wRg@CZOgzmH#8hEQ(Ĺ U 9r֢eK6ڶ$SJ/B$'N_ 4 03"'c}Nl끸эmP,1! l`fx$8uV{OzWH Tߟ@"a4 v]\E:/ ~Q&ợS`+W&@ CZ;(w\_f^cJIq" ھ#2 c[DŽX$>ГH U;/BhHƒ m/Qt㾶 nJlI1 )ꕹ/Ah ? 8#G"6^{栤7? \>I~٪*1 ZTwS0x *z8}a:&:)߶M(XXtCJl4X"S /pb"M9@(8Go\_s.x_P{H EFU]H|'ꅏB)z" .|NJ՘f|Cm}_V< LߵKC?\+x%kh,:()6 56Fd?0GPG+w2ĉo>f̩9((*_7S9HBWJ3K6 }XROqyn\B3 G!6 fgGcn"O[m1[=8$/>d\u쵹[+;N&3} s\3//gMMKRAN]ϼA+w˳j*9@!kQ/|z2*N[V9/IE+\glk"L)).D"5]s;Z9q9SJGL))>}%maXg#=oҡ9%.E#*bP^ؽ(吝“ ֵ%^w>.KJ?b#*@( >\r0rѢ(ʬc2҅L?GosQkb6W=/kV8H,[VhNz%~f}s VTH/CI!X?/{(WU0v$F(z#SJQ8=۽ٸa|n m#WNtS" ָ>H0ڼ՚FI/#d(rJE ;^>{zkPnQՓ#BTD*]ǀ0fze7CrkPAR97@e2Pڄoːm&<\?١X"7wEw1끘U L@]qk_Bc>*pnpy_|W{# oRRZ8}I%gƶ#j~Yd( C38̭ ܗtSPEH(@7IYnKP~١覾RwλS~O#V܉<%ώGCA(Q2wKkXVΌG{~< e1*zM9|b2;׺a ohWYY\3&3|o, M砚pS0 Aɿ[J]Eu~Yc;\bܮ3I7DN(pK0vk5\? Pexgnd%$D7H hU($H\\|ntd0YA(h$ BTɊHw {#gIx$8%{SBKQ<$ξ?^Φm;*=IdG=/@S*ڣˎ>>!G]`ݵZ E$qѹH]~/]vRR}\z{C}}ui@y5fFGƄXarq"U>-@w$.G͡XmT{%29QH܊r#!*ZݠsPXE~=r2P]&mDy\dIԈUH7\:~o}9o/#i`#p}(BD7u,.ېP]uU{Iyr!=n^Sm]5%Oox/N\cKǁ\O7x,z @-r)JnoFFw&>&pcFɗse0 ;b-H(8i<*oirxt{MKI f#B $֢ܗPg(xiyHF.H@G`/v ',3CEkP,W1}%QHP,q{|WX1"'E!;ػ¯Oww,^k8I /Jp"89`Ku(/zG bW;Ǣ~Y} sה_l4}hh[N~ٌ^TR6VdJIq?d3I =PeGWY0U2~Fa൏~Xj{cqv@%)7PӃ0$2I݀H4ߏ&'(9 Hp;v%*< rvwsXYM: 9`gbKP^0 &W ϤT%D5E" b硢7k=Aˉ G)n@{e39ιK>N̬OkNϬkg.G^[-膷P>}oΏEp5Rl!&^FiUeny}|f 0}%nal,lCZC.H= +KR#&w'PI369HdP(*b5 qQW0nR$` 9X (8=x\GB jM #rf"Sݩm' >ܲ&7"H{! ȹ\&F&x-Ea SW#K!װ 9oBˤo {P]ʛL=ջۜ}W8ij*>In.OݏZWoƼ/r9S9딬n]ޚu>rWTe^d6eOqEp褭bJI7oqUvumU=.WnM%9 ưdC"l䈍E%&Rͻ@n-W!חX.$|ֆb6}C0|u$fAUgj}?@kˑ;QUqNVh.s״}P}< D{u&eucqϽ}ԍ7|NZ=ܦCܼ2x)^pH7Nkmւ$ b`** # *e[58);m,:o\}=ZRފ嵽 Xϐ̌6]dES2^"K.퍅_iDabL*$ܬI'Iz! 0*q*P7 xtrc&w$B/QA u8+f^]{_$Nz UDD;~3J~P$ >z_:d}~Gnzx$V(9[W#9/qc=uk]q!PNEN#쮌`YFSSt޹賕W-,* 0(|9!5n7 0:9l}[ɺ `ݠoEbgm; c39[]ʕdB"m;T$r|eܛ Z$vV#1=7P8eγɻ(,Y8w&]8 '%x'D/AbK$ Pdw)k3s^H9-DQVSn"yB,W6hhڡhQըƼ@fyȲϚN.[jyY{Sߴ̻.B![a|F`rj8oҸiZ0bH(Z|Qɗ/~F䫇B7e\'[Ҍɧ|(W`ty< \TC$(.AyH;" TԈ{r|Ф&G!)Y”\PYp+nH|U a$$Ƹ7@7d_ f$g!Qx1f6EHܞவ'p[_/qEg"gq $< Vb+kWu:y; xvsW%6Qvշ7\YgWgRs^ >㞇2Hph&9džalcXhp.W/?Ɂܷ.szU;"5 K2UHpZCB(dx@f"t6SQU.˝;#H Fe|w=ô 1QKݹ_EMT A^0(;p8C!iH動;&`mkh&@a戵!}lm&' =Zu'M{r 0; !kps;S. fǛcP,o,GEi?pGf(ߗZt⚍)s%2nH-CGyw#:)?˒CGj-D'E[dge-f%+ WW 5qQ{0*oX"ojIzp@`f.P/P*GAP~8rzQ(KZn0 ]1Gl# } FBgCr͊QXx$X%Fl`yH G7эB!wvr!!i]`\H =Q^Tȉwc<9H AqG*~WXM%͏qcX]T$;PנȑR5 \cBm(tueH'' #PWΗD+VcEY$`ču'$z#!u G"q4廝{΍wܓмpQ=ǐ>]{t iwבv8颳!o:"ݝuTmCąH^ȕ5}OnC} >s07#?oN(9h4n3 aBl#G$@n<l *WG2$vGXº#KtC$ -BFk˵/rF.St/^n,U ȹ끄_?_D$RMǓH@-SU@K#g) |)( J_c7+Q[o#oHC*mwI 3էpǘ\GXiH/lO!7k&{ܷKzPnX3\1n]n>s6q 70|tjXZJ&pҸimцX&GbFUF8e8W puCB)>X㑨{ps+fldWmӌ\Tې9L"q֌خH$vۖ:d}HTUEn@VHܜŐ(r%]N#U7l[[IiWq&ݹkvޞQx$X9;3uP,qp(ٕ^=kw}#zŐDz붲9?;ˑ* 3!NaȜ7 yz Czm o.Cp={LME3pwS@vocƘ#blb#6эHDH(%I_r9 ݇H;pU^͒d$r>A cuD7s/ 9ފa@aGZ};Iu$F#'BZ>/qc(q~PR$>Oqs4󹛻(:gZsCen=M;gVHkܵ(yc(8t[fHrU%&K`P,q7'K=^5,rc=#GHu: Q>xj CAs <6[n@$ۛT<M.vuј4nY1H0s6 `$>~&!<$&!A 0= ͽVEވ;(̶rlrp=,#잍|tFX Hsjl5!`Jt(p!tskqBdUrGjt"w;L68xHDH]?D-g0bc*HJqHPH@[ɏ 7-_2q7WI=Cb<:o ehFz^:|^;f$L%cͦgr`1iܴ߾as6ʕꊜʷA.T1 TYHl섊C7(ndu(7`N~*Y=UBCQH4=V\񳑳!Bz) r>@CnHRMZ5rV#iq ^DyHh" r0jt SZB)ׯ%@"ye !g7 ֣iܴS팃wO.:9|yM `r(YAj!DkҸiokFDŽwĵy9@3QȩwEaHPtR rF%r 'a [ܚ"dw $ sg< T]W s:(nCpDM3 DOB5rPh,]Cow(rP x/$\V=<jqe G!n“'܁r~ȹr߫Q8qG1O>usD*DKtjK4}+@B$z"r8DNOwL_MfܑnfȡH F9Qogt^.rR_ XQ|;cB~ynzT;ޝ`B$z!x$xn(\%$jtZ^G~(Hu8;jQnnCDWT!KnGbաX(!휾 H d\2X5!k0dG@v 7$w4nڬV:_qޟ\>>31iܴzL7-9||%ZTwk K|zolp0ܘ:crs@c9ne93 "=1r`tOPHk*KL;M 7&5Bc1GQp.r#.Gn@N֕赯# @"a$NC"'99\+yZrwF7}jJ {okϥMnEnjFI#ѐ"nPP(W}$28 _CM@d3k~nvC/nHt݅a}W!a%-8L. [n0HtPiAS \2iܴ[Z kkWt6xO7H.B͹e*/D7 iB݇ܣT?řH܂rVff6Y~je"Q~L & ?(8f$W|IwsǙO{ 'swcQ#n#Q5E.P]#IN яQn|$2&! u(seEnAL#rD ]8~[ Je4#w/ZG%O&ϸ_q6b"jȞ]RO"`v VkM|&˕hB,Y ZX)AAf (h1Be? k߁x8jLv,=-^M[ ڡGJ+Z~*b+kc|Mg=}U:qmq:j˞ݿri_^Q]y^#9H6B Ddm+J'ed!FY"E77XjϻxM'ĒӀDq NDΰ>ΫwRG#{cJG}7ͱjP&H5nC{\~8T.2~hr>:d*o6Pv|7ꚂK'Lwz/^cu(/y3 ( )Oʐ92K'oGqp#.G!j.U(3DE*`~:=AhND_A@Dsh= )7xݞE&(>@Xf8F?R[>>ӗ=C,pk.>Lz;7iE%uM^dj^aF^jʫC#6|$+k b  7 %;:Дa7̲~hXVMDFv6pm,ľLhKfn%3;Kfzƒbq†lv[VzزǖvDcy%2^D`?9y4ڠ0$I".ܓEI"RHT.Pg/@/pv"\՟FCdW`t*6:;9UMzuɾM>w!iψ!&x8~|4#DOr*6"p) x?{Œ.7-OcH߇o51FP }B#4ZȧE%RB:B3T5gd=el̵Žl?jt;"ǡHG/Cݝ!i_+Ϟ@MKkj[V3 yw6XI砹~XZ\d v>"xmCUh["'b\볉Dn+([ 7++ȼV#e KpZnćX2sӹ|adfdND}Bk `@:w?w6@}cHk:_@@Lx8r*\dkbkX23)j  mL3 ub< ic!02- Osjx!u_"m!k*^3G"%0'oEXf!#{nIDe1id+U6| d:rZH/ +yȜt2.E? V{vm1|zR Df} bHB*ԡ5 _\y!yNDǒ 2/@UddcdBW  6Qt,>A`UڸEt"z%%ز4N~"]} i MT.r>pzo %ٯlrNx8 AA~4 H, HDa5 P-+cQh3#(@ =թ씠槍A oW酏6 u?~˞]nvr,B~>A?V. ě%?9dƟ4ѻbL9v3/{e[C ~N6YQxt:߯?%A[VZ@@81O[Vڠx飺 1}C1)\d:=w>ڜ \b0?g G,ŒVK,iKfZ"^O b@]d[~#F}]EZ}BG,ObT>G˦Uo}¢!q8GrHIN[Z]O#p.OYPĚ_TR+±dfv,P]ιu^z[g %bAn*FS3om>ȳveI/Kf,8}{ pb[>ͽP~o0߄@Tp-FX:2G&Ohcshd<1O'' imAd IPͿ X>CX2$6+30eD.nb)Gc\i-ZT'b̅DX23r~F ntB5Ri~D x.o !l4RO"{MhRg#-79RzKzyD|̜%<-%3G"hO!smj<Kfέ[*NlKr@[#ckV =\؆B-Ɩi~!8 ::).w Ö 49_L%7[gыbL3־?Y].CV,2=w6N6LDaBF}{IȼAa_ 1eQ[F}wacJӱO"rnFGpu $C78Gy^(đ?sAh"1Puy1!Mi} yLd[lOfDoH5B'B‹HYA~d0vB3Pr.BLΡVfH9刹#`6>"kM6x[({X2n!s^;kS_mұ*U@X2o-~YmAawdnw"b!o6~^E;Q2nND_Bϧ9 fx8h̦+c@Y?l@֖Ydh{#`-iUWc>(~fɱe-+P"+ 'k+Q|m}4;(fvo6(,2xLGidKeNG' 2SuǒuFAU TNoQ2 Xp/Bb~%xsp0bvE@pW͐HdҪEC&&H=W![s X䗵ʞ5&}"t0Eg+ns,v\L!u9{ ۡxgS8?)U?LDX2sF lEַ=P e7YE,9ňu˷8Ƨm~5'؛δz;!g+ڀ'L˱lYi bEGL[Vz"+,A>k_EQu"@۔?M,wKѼー] IDATF?LUD۬f PFYK~a ; b1v/obaHmJz?^[%b"u@初釀@ Đu~Bq74oĒG~v`vX2s:e)Su* 3aױe3#L[VͿty7VP磨I"p6Epv)wsK*C x8g'SHX{ߛ i&A~N'#P_|yb4Q|{oX?hس}_JCċ;<-BJ13l8#s'A WL"@Ⱦ5CLC94g o0ϞMʳ:%Ow&b s NRLWgĒqδgU"xˁS̄r1g |4;8d%36mgu!p`EYLȧDL}bB'#| b:և!ɏX2SbQ@@zcX2L,نYy!Ad>kNKn9ў~ЦE/Qn ĺP)AT.r RT.M*$E 76?xtT.Rj6U.D9hi([$#ND'#5dDDˆy@r8pR"QJ!Sb=_|ÅHwAhv"&H FĞ ezb!_IVN)ܿZ շ bCQ?@F>_GLR ĨT!;XCPcb,S60\f' Y11(?6 &e!tР-cqtrw ssĂ틀2b֞rT' >&~>#k, C[bF c˹nzزңP>Y+~>D3X~≵Cqs(s{ v>nuND_bL@X2\L?sӽeA:~֒?kzl'sP튢WW699\TWw-,!zAsZfyy_>7Xm<g;oBSH ZG*p m %hE:#0_EbpvmP \8Vl6ꪺ7df1k%y_lj9{='~ 祝s =৬Ï)[i[%3<#&d2b>D֗(sܛ~:yxڭ![B|t G I&!Pv3X2`#05wF5}u A^VֶVGG yG,*l ,݁ H@$oɺ2꛴g#Tku_آKC]#v }p t4֏)aVn ޅh>Sg#YhG Ñɺ=pY,|oG@.GfT'yswg^KfأO:ULIy뀊M\]c%n%oL -[&TU?D^8 ΞgWn K< ON.csȉhn6G&{цR476h. Ax5 J"EȔ׭pc*IrSH:eqh3wOP\yAؖ [ KfT. bj;n=劯r%h#dOjQ߭D~2C9Akb߭F =&yx(gvcT%!ӣClvh݉OW"bj{յ=dl@bj."P3ZE%\ND͑}CX㑁֮}BMBϳձ}~koX2ssW!?["uRk?.g!;9W4NZd髑]<1vl3Sǖ69jě5W䅉dU{cּ߯icLMCvJ;狯w{=yZ9_FZn~Z;[p*ξgwaD*iF W>Uw(7O ι"p2:>9sYT ι8rιιmCs9>r΍rεu=`Npet%sݝss}wsnszeu={~XK;s79>mb9snEsA+cs?l@ Zy8s-YkA,pWnKk'"ݑ 1UȬ{!sat)DINbum@Q+-1< |e֨ALjy{X_AʹUݱd;R$xK@3HYܝND\L 9c%3O eW:X=X_B>{֏~1.uͱv:>B+7.#`w-"%Ҿ/DA?&T~'mt5X24|ome8x'AHΎLu9!wEkGKB3*5v|7UMX}tI'?M-g= >.L"Ñ}6+мᬵ\DO"rߥr~]B$4|! %x7Xۥw}0zzF횱@ ^,bZGr{?܃"ӑ暲͋ApKֱ?n>bQ_@М+ R99{79S:mA4h}a6؞W`C@w\n/F6A3_Y} 잉?Rf/!'n"3d8(E걗c2NDOI'i{vw_-;߬~~ZBdb\@rKţb@'@fR<7?W;-+mYV!Ys?~p  Xh.6~[̧] ^vǍۥb;(uu6Lٳ./f;҆Y/L'?Wsy+h?ublCƖ[V~r3Tl(|n_Ӫl/ή}[I"[!& =ւiȭa[Sm0{Qnu9rfvRA$ZxBLa<5 %2-tϣo#>,O' k^'^52|z@Mzy #sbA`96>!xS{#%G`d|ڮ-POcV ;^MC`uh+Kl<>BL-GEȌ؈$g#l [a^\n2A|Ē=\i2Ē.dQw;m{sAcC]ې5p7ڳuf5ڽNYצۚo\qsiιF?(ŏ-pv$SZ IaAPVn$3#MWh~{-DF6׮IM".rvQAY dqh=/ONO^9!Z}(1Ms5kʽHL=ϫB`Fc_%ιFPUX1S*Vk_y!{iQ4)Ђ2O F)HQV"Z8BWdR!bOuo PEw'1؛(2CvAi_.D`q@ DX2.Kƒ]҉vCv @ bB3 ` 2%??(z@&H<اc̕`gZ_NBJ:dQ̫ )G/sVo uS5PX[|XjH BE GK(4Ո;2!ߋJ8kav]^yrP:u.gŞGNW} ev!dX˧wݡEw1%ΞE|%\x8{~rY?*"JdBG u 㻠yӊe1eEvA烈Zl]@{b^{_:ք : ?x_[;=gebrV][w祱l -d7ȿwO\[f w>Þ+~@2X230Ft"W,EdĎ#B,HG(OrkL]y<NDŒi<̑Ew" 蟀쀔vgxB@CG`f><ӞЂ -!ktL!x nad3t" s&bA`x ʯ ͞/ B'Ϸ罋L{RxPle}jCI{{ƣݬ-o4d1m|k}_YvElt'^8s!bF2׮uUV2(QAt"Bi ~ݶٗQ ;/{%d;KAyh~|]m>.@#+h朿yʻ)кs 9um,XmHfy]𘫪(z.T?p?)y04oC4Ȗ#[,K'cȪ/H'FEspn?~  ة/S 0ZpZXbc GLb*n3goA;RC~Grg=ha/:2J@,{G̟lXYz}buu{Z۵cm* k@'V"s弱^q2f׊5Ȝdr\]5ʬ@><~|%H{Gƹ a!&N~SK G,mlO#I:]KfZ/0=j]#0}؅M6-nZ.[V = ͢?1dԤ->ڰYX*# ,>&K<]EC)E\L"yp[6 /~"/^Rn67~-rmk@֞O"^.|>K"۵Q4v'}9zhH[ "[$N@Z)N9#pR;#Pi%F i3Xʑ 9:ѻ PF&3*|AJ%RލX@ScB`~8ȼ?m0G]GRL+[ѧ 4G?D?oBSiW`EoJ߸w8W.bs}ɰ@9Ȅ6Sl͕㬯K=Ӊ览F]@%V~3gQkS5RU"H&М<5\Kfֹѕ@sbBy$ӉyDt]˺m?T p;v^v*z_REmm̹D|4GϬZ8!eJo "v,CkD}_|~`%gWĤ{#zq%P\֦r.(t=ٽUbB4HlbH%3CxfTW!)>h{hA}ؾ)V^"d[݀| !ߥkb b=#bQHa;9<][iuH &ЦHE|DB,=@(̜M"\COrz[zƒB?y hr[ x"0=w:Kf!ѶqCS{6bжA#űd(;Qob{]̏;ZB IDAT+ʫ>}k5V_q6ץLKfBt"ze0g9_2\ɞcμAB0cyO"]췬[*XkIuu4zN|?&[ƨ^јahsmL(4{x8;ڒolO-&V۩+nBd~EXھ[]q˂F>]mi#[,%3=(1dANdp#8ő;"켍@Wh$b6Q쨅Czڵ_ RT=vC)lbLEVX\yO:TP#0m\U[G+;}#Wh7~fX]_}xږ r|!Oȹ}Ax#~Ksb4/%3#v]??hyTS-zKad dW"}+b8oLgY;#c&;ÐϮqdfO@~ iy'!dv>.ߵ9=suxHia<8!^6*Oe<w*.H#zwDSѻ}eAh= 9ꊁ>{cw9 T.R֟?~\YPT2`E l'Oٚu Zd,Kfjimr RW*B&rW >A,Z'R "Blʍ>#&13_B#6!XEq=c2}w P0pP3ww-.Zg~OC='5uz4(4j -k@>Y(?01AeȄXyP"10~GnKfNES$ZND+C Rr#c~R$sdB/gl}1nl״Df<h؋GmG#eW욑\4:pS:sS҉hU:@t":8Om3u:]ND6dclVܞH.FvXLU{-]=͓]{'ڸO8ವvMB ,ҝ$j{.i+Z@x992()pssކ~ \ܟ ˑ_3#)mwMs3bZ܏"@,Lܔ#sjZX3d?=xI FDh,dZKfC`a{ޮE1$4s-R{"6E?yGUT֩gO9Cf~0C;+ $AB{ݫWuCH n"n}7hddu6@H_-GOF0#e1t":) @&ŏm N6v#P"-@f/OFhNT9[ϡөm#͏kCWhn\nG죭^@  !]bߝ1V6Yfэf5҉TgRHKr+{MjI"Sod|U}S$̔1_)ߛ\d/d.|cva\C/8)h̯^EKVe/; Z{Z"67 DŽG[[|P\T.R#Z+|<4#q b=RCm .:ewld9w8ڐI{S>d $X9!f 4B B)FN/"2ԹO9{fR!߲O8p{( TYXe!H47b~^ԇ[rFaoju;Eݑ *Aa) <RV!@2CBKX2pEUz}n%Y Sb 7 vEBTI!6cǍvOW/w͐O\ˏ bND ׈7.&H.N4>i,pvitͥdW ҉hE:`_A;Dty:{:"dҏOBc(R}(釘Cr5A:Yh皏Lz5.%3QZ"BJfPF )y?uKhgzWb]Q Z (jőySk7Cwgean{UԅjH@w}u-ѪX2G%wu^A#?m鴡hWy;b!-z#`Ȯy`;iuGsT?7vacY79 ND`ϩ):zF^x8[}Ӥxy juwtH*PM]ۍH"6Ͻ[ܱ ~;p @T.RND+w]{=#z/֔eY\`T.v\K"GbaO~s>y_靺9 @ g\Sd=8K9u}l8nGsιIι"&s̮;3.;npmWιqιsx9}sV_,M?Uȯ4N1@i`Iq?o^,Sőy4M2 vha8vCrb"*;@~;%2aA` 5ڝ@@S |ͮENDǒj=[FL\ bc={EѾ Bd6!0Vg@H&T#FO1mN7i=k_l{yԺڞcy@y695b F`ak닱ȷVԝ`}1g,iNDm{o!lF7EsĬQoKabNaWG[{)wasflxP>~wyj)ÝnrZK"mo>ME 7w/Zp`~*y\US糚SІQoȯ DhvLpv4WWƊE-J&d&IgE 7'Ħ|s `G'py߃yWy_3{?e;'[$#Lqtrݐ|evjm?C榇Ҝfȏ2uEhghN2@|b{^Cn7&Bv*DAQch:Ư=5l;Iju}ۮ-dO\[UX$X2;kI9{f oM_OB/b"0-Xvs52+dDg<} I)ldf4bQaY}رz{''SHI+G ۲[/.}M'wXи|ցb,BK=ϚRޡ"&8 vCƹ`7JjB8mNL"p]gg4և` nn/[}EXh/ιVhs7!{h#As7{~Dk2s.ڔS1 9񠥃I"{ccL>bMA !sGm.b@D;f w+zY&E4hE@i%b:.B+vD;7H߉1ϾB5`n@H[`F:"C@bOc{|B/bkgX>CfF_"Iy |R^g 03.=9?LbL1۵ϝn~s]_ E&2p Gi'C IȤV8lpɦ~ygJ۽<䯲PTTq%2O>^gM{F H(a^zd郀^'LD`tb>E8 XCCdrO  vy1IyfV>(}b={6g'Ff.D@b%/4m,oqrh%duK5m2~^N~E gL^Vdž:O.[Q6gu#ptCcƊXok*)JhƗFEVlyAIQ$-T-HE?к| ĝ4<:euGG&}˲yA`qb߽g#V޴,keY^}9o7σ7]˲Sg U/[~b[ٮ؃SHc9-#6樓AYݿCΣ^>) $٭H+:A@tt!Ps][2)&ĬX$zfL> Cp009!hbpV!/H쇀U@|2^Tӎ_tڌ@dmGN蹈1*QP`#9h;<̘:hǐς"RĦethQ; )3h>ރ7;{?Rn 4ˡi{K)sx-DQ$}!z!9C΍M5|^̤#؉$e%Bp#?bL@^ jwKbށSƤۑ< wYi鑘ڸ]tO/YVY?ilʘ颃&id1 3ob{`aix_`4Z|b@@}i88T :=2hxtkUr!gw#'C}4nA-e,7.[wVIv Jw:isfz'bm)&.+HrK2)j?Mv M2^CK$Ā?p ^ۣž9'7ľ4Z?p&e䞈@ąh3^B\C9$Y$Kf@``<:!3JgߏsLy{T zw0cЀ\W"i,2Q9=C,;%6f902rL? s[3_ v As{ڀnxqsz6Bs%f.74،Jdn7Q^$pqHGs-@x!g< 49LA~GZSH ĽC⸮/+#N;5Ʋ}+ik=neU=7X'\rٞ8/9l_o\ŚCޅռf_ynhYje>G`jPIQd#(耱 VGyčŭ* H u[5*'eY/{oׯ -'R2j) nc,lI2*vif =W#pFW!%R_ syom-9 N9(Z3쐓(uC^UimNc]tn_X*>CѦ%H4rSSM)>b7ԷK'. o9 A,nwC}AidRJŒx;Afwj5.A~Bl׉Hh>*BsFhS̆ p:R#Ÿ>l!e[y+3]6NK`(ӯ4ds(pJ8g5 D#zx# |`Xe%b2F+?b&Y{!bf\=shPB&$ԐI>.(1r CNouo9@V8XoAڌKh~ mFA+@`dW3.|y/'<֌ w)ZoG<ϲf:OA@wp0ТHixG>5u93Kr;b4UwϗE:Ӽ M[>s+7y]/Vo6` w5C%(Qk2 @}3O'vE *9-C`lb6@ֿ^#b6t)к24&ı)b^יv! 4bgm60Ӗ hc7 .Dc\3JJ"?߬Ϳo-w]_,K_֬xzn ]? Cue֧gks|V<7-EO, ]u~,ǵR ǚu}/`@ˢOzZ eht@{}XHeFNMinaViV/lKcQ6a E:) lCWʫz!USĊ܌sEyboF Tp0PLC@gX3HOHxNAy1: `TM291aI4?aSJJ;I!g:?b ~r  !W(QOH?+Qb%@I(E)3j:ʌ3Εc>y\e|07V۬|(-Ĩp0`%z ۀJHZ!Rh8Xh @!1[St0 IqQ3=ќ.x! `t;KK-->Z|չ( r[ϔj#~bֶF|6\VA Γ/|W[8ZWNrٯ묌_zΑoe[ZWw662ɰ`>?u@vG}.@Aqf>BUTYW-5H|VZuַH_pKVvnM?GČGv̜@=\J1E zǛ\8KHٞ@RR/GW:dٌL|9Yd^R^p0~i\} [zJ RfeH!C ӟ]q R_e|eN33(83cnƸ 2e><//ڱ\:~c̽Olm/p0K3yԠ^:湕Yok~g`WHB;dZHF6W RueֿE"`' *A&NyL?FQ"PCp0pF4M(ha:4z$MD@m&"F: x(~F?ӧ'{Nw#"ax(rXyNi߶m{_xFj,6/Wmma J"-?;᫭ylDSv_tzjz t(i6e.G0bdٚ˲^ߊhYf0XGG/'⹮N, pܷuݜ۟^,kObu-*&[)B@+b6d!%rIsgۗd"r (X*@{MF5HOF gFA|6'I3  ! x끔Mmrz5Wܼ)وź EoQ 213\C#Y8G+0iS`moaѤiom{Da 6@pVͲCNNokg'YAܷ5}o.-)flBkf.@ nfL:y|f^56C&YvȹRR)G&7PW6EੇiWY ~q8I3쑁k;4Zg퐓 ˪G,;c~_rFoս]x<|竕Mol{j,:¶(ӛM&oɍ S.gNMUR0)L`?BLc:41a`2ChC pgU{;X?%EIQd-hqV}Umm+(OOˌ@cƌA|W]H|h=5k$M)f|H$HX_F4]c/[v_7z.ٯ1}DzZyDM}_SgYv>(C)ا-o}11-3/U(eC3߲ɕh>wEsM{9̴PeMkmbZ~ ><lh%E!y rIQV=I,)hmO{"aul~C˲rj+]}-3A˚n :tgE߂t^u7eY{{(`]v49p0u,0F!,b֠E0 $LT,@ Ծt@&9X!x!\ݍtm6a@GE+qG'~ Nj;䤇ĩMUaV CN ϐV1R'ez )&3H.CN~8x9w GZdg]e`d| f&q t'1]<3 -*LNbc/,,wͫ6V/s[:ҰpոuXn}eM Wv s?g{Vv͛]_PzgV[[X~/9q]ޞѐrM:d洹=zK{g]VRy4Z|W"Aeѯ:}| `m9\-} :ne[[2- EhfXz1 ز]m#]׭,nY';,JC:XugXti@cLɣeM k\ TNk f ɗN[{đؖ"i; L7WkB@ʋ@b{Đ&#s/7 *:EtC') W?V:/LMG@'!kGn<6x,OD'}\7Դ\E|ءLʁ]9R~Qd*A&O/h Rf<n#YϲM " <@$0, @SsCL$L@?!g*f8hUզbd3%șz$".bF>czw̍7:;䜊xгY!}5Ȭ}%Zg `b6%dtO 7=0?]Ef!6om@HEixikw4?_IUA儵{]_ZjuYTߐ1$=n,t3kM57֕ 4䘄&R#+7݈]K7VnMИΆ` o2oEb Ώ%R2Zk*PuW=ZS߽vuvF5Sm7Uv~9Ȥhqrc^jS^xuN9]uҗaW@mK,=)[hOYQRY-mD\`Y֮e m:uMa^r]kdYV1#"lKfd uV ,3A`OnYDEu[<$n]1poRZ%f mf4$)Zg#SjdFs! TEw k4w#߳ IĒtD;ƴaİn(]x0 7Yp9S|''{P>n#R p)67㙍| ZS"Ɲ_df@3L>BwI򃑏^d"9 n`t!E9x9M읂2p0Pk?#l7$nMj9%;qf&6KtFNטϻ%oƵiY_"SA?n0k<h0C&DBoog,~G4;t:dh3URYP-Kůw/g*v)b--4w6×^ѯS}֔ ]6] vvYKHs_ڬ}x@u,:dȾX(xR۰ɟ3f+}t__eMyrܐfu@ ڬm=R^%EE_<vf7*d3x@ԝhmX-{J"7kogUя aڷ}rhu,涖Ų\V%c{aBg.5j9EIB98r#)ks9bNB\r@L/TIFS^p08 Eh= HO/A;%361XZRi:&E-Q?`1ڰ)p, \e[3l4w̋'P>`tmnZ)4F>dЋ~?2WE6 Kn;oHSytC,J̴ۂto6b|y)(C_/N=57cSzA^"/Fk!3NS?_-"-ZAb>2ل@,ڞF z'dc|B0iNsG8XL٣/WEs]-KJ" _ڲi^oW+{vlM_VnؐVVY%ETWCNzOz Fp0WXDJsP{k}9{~qO^a)0rn%E<7x!h8ό#hO8(ٌgkK"'o111 &n,k:tuD_s!맢h'w_˲{eY{!bduLE>IH|x 0zxŲhQFuF˲aYV&" eH-%2}V 79@oߜNU;|榬2eE&6Hiw'l: C- R*q8CG;^C/AyR"u/:bۓ,ռ93C=5ηCN>ژ|~)$ !Eocߖ<!!pJ2o&^$ F'G͸w@,W{sbOD,F9bF WE)L/"|5TA Ʊ 0yӉdȌ4EoY;CQ``r6Bf"\ׁS& R\)L'Yx3V{ᕷڻ1/ `G8o/p00\g歑dTy)Z+l!v}G#@vbYv+_yuXU] ("ԏfelr^ClfwA 2䛶дgn)W$͞9((RZ- \<߱hqvS客MΚ6 vy}#^VWޡiyYn\e k7zq 9aņx 7a톟 w ;ge}eV6mͽCܽo'-.ex +i!~bG# uD~_}(sf89!347}+D@8刼KpCӶSLCP WMG:Qm 9qȶCNcłq+rz5!gB8h EcgYh`W9kJ lU xgLB+'3غfoe>=.gMʀ7)wL}ڧh,6nChZ6K@yC8 )ݑi'/ 5kc{* a^Hit=)ہ&?E(dY;N%   af̳\H2n#b L{W{׳g?lӞ(h:`rqVVKMwyÌ{:b03G 8߰Gjci= )+{bo㒖@n$}+M߀,7Ac&_/2s4c`yd*[Sl6a9 Jy3]|חwK{ 1_qsw=8g|0nAc.0ʂZ2j|lk,8՛f/4@2" @~hM-7m&1}ZG]_>.T3gTn ā30tޡN o|{URy x4Zyͦ3o|ߘ'g *]1[Y"Ha63A7,hh{VI@96! yƁ]CNr:w3|&8֛Oյh;(kuxʐb]jq? ^h-Cor*L? !Py؜בH̫90%gZHz{̿@gu[N m[VmJ3G#TPvצ#:7U}W.Ng xly m﯋5A3#;%@Msq䄃p00|u!3+h0XIk-X9M@~kWdKa`ㆌO4" m _:oEYۿ/2vB k&*lDyC`/0k{hmT4Ker/b>0Ed%=;쿪yE_B̻xU^8nq]s S +*1%E_EiF}}($>WRxnKz1;}z|h|~?U@fSUoY.>+sKŃ@%EW~WۗXn%&)4 u7乮Oiow3,~ɏ\~+:? `p02?nqz+XIjfr4kXvy mȕHMÔ-B}RO!Jp0piGӀ2;rB~Gf8EC!dD ~5b#_ w<#NTN" QQDSf-" fW67| {!C>X-imf$9Evȹ?Ve&Pc bvșvȹr/(>یl`RB"݌i%&-r#$u4Y=kKTۃ$# 5t=/4Ӌd4b>?6 i~@lShr=K3*45(b&,Y3z6SZV|ذofUt8԰i{ 6]u ٙ=,B=v5b;h߮/K.&ig&~]f(,<f6B1W\8'"3x#H2g,sv kLf݊%/vCΈ8x^3V>Ѵ9`p8x+ ^q|H$bwCw ݌a'uI(Z+@h]u@V4?`8xs$7m|Wf?ki$ |3FNf ^~4֖ $)-*|_ڡ!D`r9)ٖj\ͽ\28dYզ?WF-ߜWZ:lVRRyڴ3}WŸ_Oxng \R-ޡ4ZKipt0|EJ"g_E<0G>;"f6˲<(d=ݲTĚۮFϴ})0uo +F )XXňEH pɖ#bCW&sE͍yTzȌt b_C3$Cȇp008AG`Q;A/NϵUMF _bE6".2y)c˓6BCMC|vE wѸ? #;\{ μ%SvȩBU73U"`tW3F/d>XeIh-Ecª׬(%;E[12 {i:&#f3ݬf#=5p0Pk}>9G22k3f^rQ$Pi3w#>e mɍs?0(?^|ypѠ׺tk?Hrr>]R*^轮Ik(KOY٨xˣYe"08%5Ca ^Q$FoK[R>FW8;#%mx5cךx]Q0K-b:NA.F_@hURU@}i_IQdQe,q`eYԨcjT鿉e; 5^hT29k~jᄡC-یgR@>FOvdj|-ЦC2KhDX2MEfKdڒ@||H#)RB'#HY/yq7ѷ]MR`+bDGܿ 󫋘ȼVk%dq\u2#6H&mG2։Ve~HD6*sJT;1bL4BdL.B` CΞ#2=kь r_bu0'kSY 1Ŵ \`y4yf,4?ihv6?$딊k*;tCNQ8ˏH8؄6a@gGF:1iMC.8'nʓ_|(\V:tיy3][/U=߈1=U%E#O"󰗺t Y-N%E_ċn@u,UHkn>\ KŽJ"3//_ Zih/\m}(peY\׍p]b*[0cVXv)L>CuEt Xr:E~hn]8!6t} mÑ.C j{<#g+;~Ċϫ7)%ﶦ-Ѧ%]Z>00 12!vbeHW#Ρn0}`uiS!#H5!f#0 A#-7Powvm{ԒӇe!@Ti\`@X;b *4 l_Ӗ둢Awp|7Ōd^\LO+b佁mf_&8H)S}po@i&;dD ri֜!Lzݼ;r*prݓc1r wgZ@uS,ÆXf777f~\fF?dn}O_Tͥ-0u'h]yL |6nK 0VRy4Z|9Zo;=ƇЋFk6t CkLH4ZG{yV]&˲t]A˲cj˲@Y\f_,kW;L"k= 䜑$:bcC:& kv[W]uM못9'\ØẔ]ØQr$x,O93t[T:o)*j۞罏6.[M 4[F|4re+ IDATODaReן'ELG%7'*bO]Xh<9x:OVfe:l##~|=x(\k7!7VخDʹv6Cמtuxːa;Je W !"rY'"`mՖESs! Iu鼹u4^\V ;]詶禭N_#fr^֎'b#qx*䪬ƓW"wc7D,t]w"vXEq-Z~lz/^X5\Clpآ^h|n:ͷrmLAn-+8k5x2Ekbm(B3r\'-,Px$'9fp;ȼn݂]Zӯ+ЀPko \VQ2Fb?.=܃K%45+xcCce}k2 =, =)h4Q;f)4 w7޴$?X!͞]l,4磱8y^ 末'DmH-{X]| (ͭ?ENYߗm[μ"2q.4b_ЪpP:a@ߵ `LUZMwy Vwm>a.YA{P}3a_WJG)FǷF hL U$P\˭_*GEBYlJPFyCwp-ZJe[zxVũج{m @ tPCȥ@B j_&boD,RYbZ6| Ùm&TXcEF["I+2bLC%%e?{"0KfY̨ *3s*\Dn~m&T  톘ak ~h7W>O6 4gp]9k|Ϧ;˹R䒌X] h4:<`nݢKZ?k]]ն x8,n]wK{.,w'۾oaB@j* qSȭ53_敖Kq4Vx}!8z+ >VֶpAc.*!~&`.]g[>֧[j:6`- y@Q4ޛӰ3Q$b??zï-mk!nShqmUTZ\^[VQ{f\J B^Fޫ߁ @*JF1oҩ*(9#qw"As+]pJͶgt- ;XGآ&1bgE_&M'>m'+!Mh r1A KqAj#]b3DZqh>d {܎܇ȥt;2o!d6rB֞\.` uZ>Fj]Q#sx"6`c]lmj(&llt_Z ֥t/&{\dD8jHͲvtAR^ SZuNn]+G-Ĭm97\uczX?~w p3+KQځKM1c(^oq-Ru g!v]f8H Jb!['bs(ӓv6,Sp5rfWS=o嚎gl*Jr!%2Y9o߷/g `6z"`^4rWo_EH1Gi~I -Xr`"mx|~m"р0OGj,EwWDA!\c:8G1\;21/m6Lk6Bl4v\v4\N Y[]ڎ䒗n2V#2ߞEt#?#x;b>DLR=V .G,kː^1x["[㼥jSE.v6]_jm]IG܉et"tFS#f" 4|?[FscEuCa@co"ߺ~v|kq}rjFnh̅tiqyx#lJN*-.O->{}S -;7='Z,z^QZ\T'M$M{ˬ^ƓET4c.Qg],xPۑ5 G-j 2Q떈E^riz 0R o/yȽr92" 28mbfGld<A ϳ(k2uw)bAz!&M]&Q?foX*H/N] J!tkDĒB_:R#rS^@p8r3C/"(& ^rCNGLktwNF({'=B V9AȨA#d@Nw_yf!p'nW,Ҵ:n (OY=9}-kx=-D.mh1Ѐ@jWo_CF-dϨ>9H̦s?=\b$=mg=sj>6)Wa#t vOc Aξ_SB}/ײӊ<4)h!4 5Ixݸ&7"~T)uOEk0ΝK"wDɁxrr)yvE ̍M!cs꫸yIxسnM"3CE{d g! I/nyY˷ZehUzjrƓ"V-bjVien Z̘?(4G mOMCniqPtE@~4&GLd4F1Pgp#Շ`=d,&Y]~bժ-/Y@>Oȫx!.o탬M]7߂@ =94Rvc8z clKP'FوEuCѸ<-O~e"rZO"GE|7價s\٨ωƓC$D/3\VQZq\G] Yv7e%F؏̵dg9Q;/9wtLySsL(? W,+H E.Ɠ R@?#Sخh2~D:&d4 1a4Ʀ"r v б~M~ʼᬢĊ'S֖.*A C `)14Y[ďaB![`z;l~;[נje{GD,K˵AFsFd8ڜ>ނ ((d>'5oEeE.O& {1o5+җU(-.¿ǯxe;3nS~KWUFharuiqg4:|@i$kЄKh b@lr5lK!ހ H }d3 x=c:bB43&Y7#vckda FoS3MDj.Aް(!Fg( {?%0e阼;f"8&# 7 b0! }@Nd "֖*zieN2^l}T`u@vz "Ɠ'%b8`bO2<'O#`S"fV3YףE*!}< LF~ #l}m:2Z3 ܮ}p?tt6xU=&Ŧ&M[wƓg&bj$7 ?b˳ϛ!!At%c۩ngWvɕUxαs^ʖSYժ2;z-(9so9ca6W6?Æ|RYEHW I_S(bQ?z=9w:lf'ιy[#E Iι<{ٽy%ι煁y<ώbj[y ݙ<"s:*]x2-eD,N"=|9dt%iÕBDP.2t+; Cv72xEdB? Uh&*s1>8C1;xaw!2YMUZ;=v-d\wE<ر!`Mw+mL3 R_X?D!@ҵt$Zw? AlD+g=_^;#kx bފ:aL 0i: [Z Vi+7=viգ83XkFQŜ h2~ r>Ww(A.h ;䆙Xds]n}%(,O'OƓg?<x;Kg 5-|-,iߢY_> xdچ JeVt[CKи۳n8qc*Ac3.[{\lT;Ϻ 9'8(E<[[\ȣ}fNxcu\M ءh< 'b?ih쓈Es}sd_\Hy=-dVgYnh  j|Ik@ E_hR#yD`"#Ѡ~#N~`A/׾(8׮ pwC "GlG/n F/j%+ھI@c;m*ONA@m6]* w+xYLx*O#8.C-]V\Km(E'G!F&o" Oގ@h} IDATOiq_Qe .-.dVI~1Ylĸ y="}[ rs g"?#.=C!/F`f+廬\F"?rⰜrPPD&WذM/k걹Cl[Q(5o~ <<#xL#cx"g'B.+xBvK3@wa]\%HrC_uwkh `gV߿x\xau:VW xVvVkguoeϩw %[P蝙 ,K"1`- ݗNx 4fX_R+I[|-REWth-z~)( K+. q!2b-עЂc硅E m(-(9~V2.(HY&&bK|xzyxbܱ0^'+s͏@j.'o&%b{|' ~ IaDχ`3hb1_D,r%@4bh: sDK к 9 @p5b0V 6L-KsLFZֆrE5vo[_=t2̯`@lfh{>z}W|DGl;@k5*]Y5֋MSO+W`ϟv]h<AF# -b)@ǎIъo]W߆@MgzQh,kX03p+O4@`k b٘H#XjJb"Y'Z!@5hފXlwE;-X$b5x"437r#O#(&*> -گ˲oH*JDSsIsEF_VQpAve%` |9phYEIWiqǿsG`g׽91Yٮ;Z)(A4ܵν<Ys4?y}pyg[sx#,D,g4Yק# RUB%2Ql`'8﯈iN@%HzZ bsh~8gZ[#C;2FL^]UrBzo3[nO"oDSkr2"Ypt4T}O#T D  3!FiAY X6d,3=F@Ipd<eR୴6eF9DlTKJxz  SdOXVZ\>#񯲊~)VI/ٔdQXM#k fqWJ&in4t]A8"u2F HBy*23BF4a.C@7yK/4i.GqZ=+#V|5:&m?KxF miD=O֞2("2P*OѮB`tg%A!<=M+Pʅ[# ;о t9rZSwyu";OG,Bh6D,X~]ikĎC@GCcMU=rJӯm5!Dv?P 7kr>i|hFQZp*v@gX1qc{ٕlv(g$r}`kc}1=y puExro E~Y"Xp_Bcv0q`eUuG" 0˲QlmQAY*J!>S*J/-.'4I~4[$?F~@̎: x&[}^Xd:yhx4<<LƓ{SI"3O4\ IZGG-G J4g݈X dp"H?!}2Ո ~\GAf&bV;cF`{bdrrg#P];oAp._&Sv0rmn} ցW\;^!cڲʹ #)b|n3]"-o f)?ش1[~]8^D ;-܃O[`U\7 `9Zd(ZCUܬ ܜ Err! !7kzvwGdqǣY֖O[CXbNAc'lr'Li4nqCl^u_xوFA8OD=_(/ ϣE#+QX3+\aftl_&~D;Q*JvG}0z??] #Q6I4oT~@RQ܈-2Ik4IuB@܁\rM@WUF^f׼Y\yd!ؑȠE逌Qh"݃].^4GC{ޏƓYc9 SzBn}M1k8"HpYD8^ȍʽDU'Hu#04bAL!9ͬ-icF"`bVU1ZZ-36ݤL~ {!C Blvڽ!t |3u߶V?+{1^x>ah|QlzBc6r@o%bE๚@w%8FH8jk|Uzwm>ukUȝ6tdu7 䣱cg ÛuԒ.gZز`{ _vfugqvjh:PB:ӵ敎*Jlk&oI3oo@D }ťh0OކvIމOOP[ˡ '!9y#2֛#2? "25Ib-CmXh?h(0df!#_#ȶ]_Բmwz!)~ll{VkW} \[z"/h\wh[?%hlX? &HҮ}yh܊v?F=މڀTCԏCt騗 p2*>ў{GG"׺f"j>-˶~`:@p6\cv%*E͎gRI4Ib# KĨhd@Xd/D`ipg"yx#O>x pҪ"c bGAj?d(%bP|{RF,PdA7Mv=sbFn9֜wj3yr#^^F@ M][ϡٳ Qag!WvHkg W"u][y" l5+(ۡUl.1/>Ҁ(?Gv~Z[-Xㄽ$SY(ȭX]A-뙅T'i V}h\̵ rOmC@# s)Acq >yO+GV/-mY4բX]ː9zg2+2|4* !*0RGYfף"=L #2{d9fQ֋ JYEIPoT_{i40ةejmX3P{F}J˧س@ Y95r^GcI~A{l4X?+7)O$omPZ4/I;d﵄!ļ۲0<|XxM|!@ ꇀ>OC*dhI"K)#0IKh<2d;%bvߖȽ>Zڔ;48kG 2ţC @!"8uB.SXcܺĞ9k@lkg.IdАB*ƶFaPCN}7j?MxXx`i;^M9 )"/VB{K̝޹,{@4N&A]hq]%9L4OAcɭ6ߡ">d@K#6L|[ļ|v.ߢZTBc@ڿ7osv!)WD&[w.r.B.VN-z]:*`&upq/n.,w"KUߛ}/3fEsJ#59Yչ9YQZ\JYEI'`/,()#ؔ#d3] h [[!wxG4$[((Db3!#/!HO+E 8M^hU? 5Fɖ(m%v[4b!eLMCȘMG߹x2iJkHJ>Bh[KA.z>l V(=j#k U'V-xt휬g&6&|bVZnGFTD_|)crIC= iv"3x8wz^k27>G`o6kL'P(DcwZ MpX|vEn?튿n>HgذјK1ʾ+VN;,#Pch Vl}`79`.>(}j{dsODڮ/s>;M,r'5e%Hg[ {w^S !])JgÛ\ޛ+;Wm̒ݗ\&yι~.fOM#7ˈmi]P"CM^ EvO/4C~/4I#0F މ`((?#feGdh>3o3{nh<D?C Dg{h V D>Ĩ"PIYD-$ L!(dpX8J`t52&7: ?P@MvG>{⻐ծ+24)ͳiDnPVmN筝]~z*'`my^d \nĐ5%bx$ֆ #q$~ PX':{xw5thLr<4^CD>ˏH7 36 VuBYMGmȜh}pC g:G+vEykVQ Z-"Ɠ3M'iLZD,3UFLg |/X^)-.Y|_D=YhZ<4TZ5=^,3(dc psneѯA 윻$xD.u}oN@Csty^xj@q6Z(7s?ykDf}s+mr<.(hRs$G"\s lEj>y} _"OC {AKY-[Vn݁y/[:[t}ߑi (wD/S&'ya$_c"ddoClKd<"=Xю<H7C-duГ쳎SaolƞC>@ſ!F\4]j Z]VX=F b3јX |KH|戵*Bu?C hճ/' r"P"JO@bDBpxyssMH 2 1m@{^e&vo[zA&z! p_ލϹQRVQ24.@ 2Coae%@_ER?Ϛ<YIU/ ܿ=;89w0\j}LDvG ι+=97؀^h䟑s tjZ#[9Dc-.8lgWIh<َXEGmb#Ve !F` Qh@bľLBjf^9={VTj[@F #&reB"@5 T/ELWw!<^Yw#&I$FUQM: W'8s vU#>M!^ }e:kc垉r݂ Yn5z_CpJ}Ϸڒ \wH`:[ Ɠ~P NM"G~op;{t,B@FIx:ځ K|cAZ}#Fj\"9bD b|llFaf|}i03HтgC{-&[@?4_[ ;y<\ IDAT*e%~(?>DHпYNR7v,ߗ~6Ɇ97A!(l6΀Ȉ}s.zh49Wo={ hy!e\sn)30M4|_LVLݘB;\|C%y ~\ǡGdxoψx$WƄmϹ5XވMz!wPZf<9I.|Ox5@bF|>uC&2J EUo&\5 =)XdQ4ڞ bCwZ]AFϑ+Ec-Anrd.Gơ*~RHTDkV@T@zB*fy{AuȀ@Y 5oz.ΜD㪭[mBW !q_zwGqw[!ozW֚dXL냥hj11v Gh+ [3Y9Gwh hz~O!c|p b&CmC=ύk`ʮh,G R|EVIh<Oٵ!q!B["/*#/1ZUG3['X[ꟷk/8F`s~5$؍ 9Z[И֞'4zX!UVhQTeF*ĺmDΨߞM"k~Ƚe%!#@Խ\vKܓ+*Jv:^M 9k:O~|QWܽxfǕO' --x{iqR+啨!6%6A`sW{+rSTIN4/6zoOC>Ϩw fZ4Ɠ˱4bxrr={U@v$^PGdR`MD h܀J6rf##u62sP0H@ΖXdM A,$Ĉ]VQ,O -x *˥'-Ųo"6h-0T"a{& 5px>14XuX\glОX nX %3@dszLGtP+92uT2XyfY['@瀴M/`+A nqV(#t)2z}QdǡC`DlƍlXsF& f6Z LFȀ"<ކ@ 6'LD!+g 2[#Pz=-cX\,vFOhDq-B{0g'YuDgѐE>#c 7_H #XͷqhL̵>{Mƃ,h<{s8@cXh<9>;1pG  F m1xY6[!ؾJH)D#Gc@]wokݛh5iD*@r4lzfD]Q6桅S,'; {U"9 Ƥlzm,RVQritK^6 9m߳GoUZ\^X2S/jU~~Cmߴ.I~/K7V(XM~ov=ZQ"c] gԣ]q!ktD 1 C./9 rŞq'&bM8g#lddGT!x"b"#.CXƘj{fX]{ ;hdK㬬<> j |ah@+{8Yvoڴ?EnƓט^Dd!.M!9ھwXBE稚v_A_N<7Oj)\Ԧɳ:wF8{7pH"{1F3.o#J JVK>~})WwM$ѷ+ǖ8^E.Zv(m4i&(UMG+CӱP壳)'V澺 d,&!sݛ : X_֖ȸ-B@?>%FdLEK52ٻzlo7YXڳ"YJ d;u2a9ܐ ֮m P#pF,&ϭiA!\b',lB`g 1gVweJ;Zh@l2v&яX"l_zƁK >yxvvͣH7wAjp}7'LP.Nھwǀ}t)\w"d%bħ>YePwlYX"w!t~X{[g@QAIYqia7;+KoҩF j@aKsלxg~L/UH~7\՗R~@'*܏@#軱Dj"#) Gwx-B5`NƣKbTKwGȘ!6Yا{c ޗ!MSd안\uhqqOFf&m&7X"=2+Lv  c>܁ 2ژ C -ߝ'??gԸwc='X>Ez}xW@rK&C;[ׯM#߰S85fC,GdF m O7Ć݋ ?Cýȸ>&ԇG4@KSdX,A޽ힿ"#^h9?1 {#P2kOsL 8Ľ֞rk!ߡib< L]d b/5Y}aSg'D@4A@e"< #fkvt5[:vxe%k{Zf`j}jjM1f=o/[~O 9*vuE v֟ Иfr,pKs՚XCwp-ȭBo qq~Ǣ$4n+{g]d]M5k# 3h5j]0&;Z{﷿wB -D={vi%~oֹxL}5AZ69F^}{/ iȍzEkWS[.9;*fήtH=@RH?h:#_)B;K3tmMƣ 4tr <+kֶSUMoO;fC~̈́U5GL둑HDjwk_W~Nc]a4~ђvU&W nOkvs@{pK L밵KF O'*'/2"+jJ"r3_o)oIB)hB_BQd$Bص} v>k~Sޚ91;,! bw#OAF|12Y}`؁M]C C@}&f\{^ȡ;~ 3Mqb4e<|  ވر5hcekh 1@#Dc<#gP,ؽɲ._"ybh#槳".\҅y?LXM(و+ß!6`>_4 kْ!>\`ߗElcb\n#Pse~M%jgBT/&)v"HOwVqyؚΙͫ ~ұ=zU&ҟ #u?!C+~nh ܓG|Dj8n;;ӊ J68SY]qyUNQmަ6|/X D,# 8F:?]iu#us*A#a~RA?' ڙAGA/-_ mju]\YsK^ |9>Kf#"HhMƣwؽg"+ MM\(3 |6Vyu;9c/4?=W &SRL{:ϫ(f3drV?CI i2/Gc@X]X:& d@r"RJ)img";w4.' d+]f܏{~aIG}mTmjk>`m&h2ֲeHگz|u%4\&gg_3H1ҘlPY>bڦ,2٤#_&gK6\\-?[0x Ň 0uwZ Hex_mA: ?7wN׌n/KwT6 ð ;݄0 C ,Ra0[_|Հ.Z-\Ġe'spn>NTY=&ׇGGt&b!]q`Cf!ٳ#V}gU1w4XކXMv:27 qȸ#mv@[:ٻ]>UG%rNxYh"w3G. + {;%{>tā/m7a-!)r#4wg#y!b7| ^?|q)]P#Vl[H[ G;={4 `<6hH_|R ^B)ډImM7Vqw!f2E-]\w!KcG Pt_lL6CPN֟75"`(Z{aDsc;*޶r}%h(@;t!rϷ>j} @SZ_އ6:n@yZ]jhQ-ףGrd IDAT6S\fl%R=[KJqxth[{Q-鑊YmϺ6=Rݨ&Ұmwfr+\ZpСK0?M@" aoh V^7ܲr!z&HuG.'~F]Sz.%ϡ~@[@h|V\Zx :UA#ԧ_K$(A pl⩫w/"Fz0wcѢ, q;264'~0 u+bH pUnrpsww+i[eDjX"uV,Nh;"{# 0F(mH V*A2r*&"|>\vB%DPyD^(&j,b+ :A~ &-v-Cmqeos!o2v2Óxtd<@Xx݀3Ql&4Itd?s#H~fXy91xvj\Z8nj;WKG`fAK}LdSAh}ujZ:\9 {fC[t`E*g$ h<`2d<:ڶ~h֘<^1=y -Xaz)(&QirZegq)(7c]BHBJEЄH8m;+D*KvEV)W|CeShfWof|ЪKjD"îw_u)Z{v2HGq@Z,+<KLBp_ Xkn(@bVIi̽a XGd^s yYtG`wGEx1>XVid d<H=vG%R#݁ҭX{V ]ha;'H-{#@6KZ{ /!3^BCv=-#v7>s7#=&^Ac#ߔ2íg"ƴBc)ZP{{m0٥rKюUF E2ޡ}W=6'hZU2mxy r>v8:ұ'%=|fp`hqiN-ȭDZoB}ҥS.M\tQTP20YE%?م{*aN 3O/}ϥa a]}<h-A揨ڙATHX^=+[d+)Hme ˮr+G Ѭ wFӈy&jׇ M! D7UMf"#&\ONAb~g[V" !rr.GMjg!K2 L|z;=Yב(F;bbGe%%RG޶eCmv-VVnA1EMcxANeA  dep;T|V^Ԡ;FZQ>A6d\c8?chmi}s]@Vٺn=ճ#}XQ^].? Zdau@l8}4A܅V!Zh2"roVP dtW44QObh2wYF#r"3>BƳR/C˹*d>!{X[&dDnUZ;7[[ogLE\? ф: RNxwٍx2?/ =3HïzZ6YqƲeM{AW>l@etK[R+PVPKK2SL/@=1 #P 1rchJ"nK[57Z0f (.l\딷S p 8[1T=i 5 ->ˏ,[UHƣCSvE;Z^Vhb3i t4"ؾop<K%2l#2V!xtr;WÇKƣ3*py̞y%bMgX{GY;/FƩ)2\3}!c~>x,ހVL>{,tBd~SɈ lHƣ@h^iJ i"&%m5}^Ke1PA sӞf;1ǠDlKFv@zsէ~ܵ XދQT52y4(c Ȱr'7#ҧhz1GK{}18ϙ#5-N:#C8 Ϯ󼹼\8䖉&ˬ5y w*h_\]}Sk|ZТFL&;s H'¯=۝т=16IQbT/4nwD)*(-*(_TP2 Ix9Ytk `QZ.sؽgpMqiaw{gX]Nz]vkgϝ2 (4sg2G1 @qiťK [u0 ax\v0*ihkAz)bJ/p0]ADﹶ|&8%Ra]*4w@+J1MMlCEE+<,G=-*잣yvM7kƮ9:yȈ^(gg#q14MEϞ&t1:)g}MSs ,k5l`g#LIJmjGƪR?Kt1B7xrxtg˨3htk_kp4 ŶF:\|Ja|vB?W2]KZ!CKZ]e;.qz$Z|cJ#F^+ r0dApi.J w fekq-oZ ]Bl\rI32ȭD݄nX hR1eYN]Ac2gU¥hB Y0nFEd<[Y>Gޙ@*H} DZV_b}~+2v_T,E?>rh2yB>dE B,D73 =g29UYliܰK!km VG.X[sS Ð;y%0P?K/_B!3'X8xdR@dwE?ϏF >G.]YKvDz P2hg#ʵ{]0udw'q#[T5҇PnsNEcg:b'596B!c77Kt[&`2fdd`?볣;5dzf#yۅ}>tb6۴0"`<۞3w̥ctv=1k};~2k;fVHNKѼp?O9vQ"=iǮgc6Hc}+}fdKQA‡|Vf>b**(9颂̭Y҉,1ZӉ, 5 W:%R][]k䜫T}ѤOfdd óM3r4~xԆ{QK d>Clg4ށ?*f!2.>e  { #^D߇&^֞Q+l3hG\+o{\.+b\V. 1gE֞vvErY(Dz#bJ^{GodV߯02f[h9'K^ &OX"5Hv.X"5?`}ᒴ^( 3ל2 bDfc2oBlLg{{Zg!8>?W'%1VN, B:uҹɈI+ҳnvL|`ZVW[?6.@ama(f%Ӯ c==w;i2o2Z >/"≲Pӭu P9H7AL_/xXS?G.yjY?D@7Z,EiJvǃ7-dNE);&+iE,tX^GECG<&G q;L3YFxYjZouLCsh`m-#O-t;7[۵_ nFz0U%eu1]bdǮGL&ճ#r4#  pD*H E;:FT4 wbZ;$9X۲d:uJ,3ho>z ]Dj~Eϯ\Cbzm@@ {G-2k1.4"&/7 `SkpqGM e}Xx X@ &Ɉ]Zorn@XSZMì}]s;1+xds5h10dOX<L4Ґn"&o;5/GĮX_ ?]#֦ |BW ukf}! I(%RO!s2]nI JqiB ?c"JfY[xU\ZNTxd㷾{>_NlͲd{F4nE.>䴧ɭ }Ѹ 7gXˬ!9 XZ h~e2a2Iw-E u=EWH^Aܚ;PSkFKqiac-ИGrzJ=>5,*Tw IDAT(ISFs]탐'!@c沢f]җUSOd_1ȝ7 M `(37g}2>. 2, 0PG߈%RȰDev2bWIky}ظߪI2]oI.d w-w2 HE9dryX"~> V.E ia UL"}H?:H嬨3hI,:dwWR\Z#?]Cc0ғۿFFN'ťbO+*(ygk;}E!Htp?p0 sx0}0 o j$ R~Db(ҐƥEr2>Xg W ~HEF㯈]qyfoAi#dM}6bAv2W &ma,,HFS,9l1x 8Y=\PoAFdSxj]G W&r#œEFcCd˒hh9wY]:"W{VҝV&ѯBWa>.%RYU[89pQp3G'B&F8d#RUcOi Z!6"2KYamln!][eg\)Hc*_muqww;@nɢ7P|tϝ2=rF98 ŁOK.=xuk-0VY{zk_{F_4G8;bH3&l!}(E -Ej3PT#hN@Y[o%R+Ѽ!L^Az5nk~Bꥨd!Zh뀛 J~,~AdaX H?N˯G>;u2^g}AhˇhT"#23bf/6Ԩw"gvbH7 c.ZEM#WHd1p$ȀN6uC~u-pVXFa9HŒxIGv q"FdNA$P cx0H]N.EPd@gbٹgLCtok9&C,bsvx;މ%}X޲F.|XnA1z |#Щhn4<w/\?V]pGA=|Vhݻ f~ a=+" 4wF0˥xA^t:GAm(j=І h_au@Yޅ8 MH"ŭEIiY3\>eͶ߯L?Ĭ#w=I}y_f5NoXG.C,LmwU7g#G,zw/B,z<~8՗W\y -ThNz-¾HӁ}0\]G6.= رIנq Hu(Ʋ" ñߺ~ļ% l`?%-~#eq_#h MTX&ȸQM(q{Z(% J]z +Wȳ:lwu܊XuH)"pWl 2qZ@XtF+; ٭En@` 0cKtdݑ1u9nAkXȸB \>G6aHX[C(/ P q0f} q)@,ZÓ@F&/EѣP l0bQq ҉<{޻{ t>19E]ΥJ&ppӬOF8>7 9V7M/!&hcr&KQ*J#"h9ґ9ֶLy 4?ҏֶ#U j0Ђk,iVX-E dV^=6 ]׵vX{{7>@sfd|BQSs@U}DʁR_~jy 8",x3u"> b AYa~E^{n p{sfX562Bd6 hrZ%lN@|pgg՟㐌h ݇ȭ58*='<hBC-b܎ȐC "d|N,L9R"#{yv4goK,GG'[x:sc@X؎@<%R'D*vO1/kMF,PS䪸1i # PI&1MDj}w:?H݌Lg.^7u1Z x7x^AA@y k}\ )2{}'Hv΍3Yv=fϱ"ȠE`E$7gB]v39`G,Lw6FU;D߳94"[8-h]wJmX^\Bf&.ߵj*;-sfitvk.~0z UMgQLdxZ5nͶ|3;kS_O*aN8#Ѣ݁Awp%P︧ [wQA\ƲϬluF 4F POIPO Ų,ES󢕽6]8#j52pc! '܏CGzG[hb;}M3ڂ&h]eup;Eo7d.%RcvdXVhrz  r!jm:#v?"j.+Q`d<!hgYH3{kZ}f g}b\\ǫ&x^ó~Yt/䶛=85dߍu*bjhAXd#kKL;C5&|~PbxWx}~nKF ԗZCloxNv% /ؘ0 #l ~GB"I|}4l|6L {. bxt!v0v,=?n.ZoAe,.sPs1#pkXKGȈ4.g| 8;.C l^ Ch7J{羱Dj-rD ct$n!y!bY}Yjf q($O#ɯ MEe9{7j:ү){^D O :cx9O#yv$@cѪyZnd{H?]\JhBmd Ru`{ 4:`mv;ch^!YVHzł.CLP_)K})ev}Av0̝Z;mZşƙ(;횋D7kDd{ sisA[=;h\NA%{߷ns Adi$PG4܅X~Zˬ%2QPt} r19NC( Ż#HCN$v~j #͹+--FLۑ,O}#ҋe- #=uJG X.\_ 8ۻj3vw=eVSH/@d4& =!=-0!AzO4֢VHB9kQ->l!ݾw V~ݐ#|6 0}bWA rsits!m?%n\KElxt"vy 8&huC> @{q!RάAp79)[&o\\V-0Wd#Mn dhh xF&&Yxj Jc|z,k"6aGλ2$h0CFʵ)C^@1V!c9wc) \W#|գ2=ˑW=&|\5xt7HWDsOECĸ97MX/۠E(,`g!"ԗ,a~gobaa{}0ne<Cw0n}1 M.i[dx6 qgd<8H%0{Jqt6>Dfuy1b=X,[ pΞ?#bYȠG@vM|z7Ah zm:Xg3#W䝈E^0&2 0zV!"יۮSduYs+"}v{g1X|#!f m)&a-wˬEޱW&ccGhDZNv0g4_ZwBor]V\"_RgqRנ?1vkRkvk?E hGsKgHg!>q#xX"]iʐn?f-jAuE|ql~ޮ*D?*֨\nh|M@e֏%RO+d,>C6Mj.S2T]MtDQCFфViv]Z2-hM"%Z tw\k[.xG@d 2m&̲χ!c~ N@2ޱD(z0&j&UyH@lod诰qFnx#v >%l=1<!֮ ;،b|yG/:5Y zoF@l29Ÿ]Omq-ƻ6"p)rU~3_̪Am7%%R! =;n"|Bo9'} 䴰܁f%> ˢ_awDcm<.8G&mnYܹc}mx;HE[do@|)#0-~6 ݸu޹,׮5yy_gˁxtq.ҫ󎳲{eޤɊT;jī!U{QcƮW {7z5U7TP@" ;,l߽s&_!q<̜gg>9#[h혊\j3_L]Ѻin08K̉'@.vH'i)-FcZC^c:G`Hz&-G mP`F afkUmAn3b9쀘~(2O!)+"և@`!0z/E =DkϵE`se}o2yꛌf>i:ʬF~/SxIi[>3\7R\ݐ )VydN-6FK-6yT{!)8 BD&|\Ch#r  $i2b|(O''oLHN=.4>GqK\"WǚD*SBdz<ɥ&qh^IJZt!b( % 1Gߡ;䞇Dw!"i88s= {CXETbykUڨ܂6vm zEs'ڠ`+>?XZ ?q#bduv_IZ?C@pKg`}=[g983.3h6!@s94,.ih1yRZJKBjJ'Z svC-§"CZ.>vkּD*(ywK^NR;"O#e3RFS (MbSQZmkЀ~b!sj8f]HiCk>Re:X`Jaq+WΊe'N)U;Hn@U輛NƧSL'z"E>g._쯰P] !DA=c(3׋gy;ڽD*s;Q{5N#?w9v6ޅپjk29dxE&p'aG{*_ѻG@/ {OMW9gϺ6;U)ƹֿa!лڔNƳTLĴA ~{1}nMDLV92~ͽc1+O2 v)@fxwԙ\c5\+t2,mP"y80Kn+0%ϱBFR~d5M&Rhq\ Nƛ̡D!a/Ɩ4# IDAT`_ G<&=!gD* R\)qR( ÁlSŝV%r)vwClÞD^N8<-bB*uvMOE b:#?͈)=،qd|e"is;#@{D#6mRҭ n&}&Vލ|~gΐoDuVW;ko*,̻\$J4(A "{v5R}BxdGٸ|@@-Â''91?w>N&Ro+*#&_uֆ[߰&z&>'@WX_>DICn݁Cc5M4&P:oQ8cv@x#/+ udR]PB;!aoebnGf"'$Q%֧H"3H}Lt4g~Wqw# 15C`=?oQxkCp@w2 |w7S3/$R[vAC A~>; s"@Ti}?m! X 9;L|7֖mdcۣ/IZn־A~nrMok] Kc͏,S9w"zoDl$RGt $d鄀UzW NfD*hy36C far4}So8Fq7Ht2>BJ/hC xBJs*d|M") JsRF?Cl[{H>/BƞC Yx7rwo/Qo; CJH)<ȔRFtR %yDo_kýHD | R2lO_ZAsOd"J=7)hG ;R+GY 4oHenBS>bB ѕDC1d423RԥDf;&t2tD vGcmEM\K=(lP֖pBMxgkW"Vc2-Esfbv煓YMhF1@(k3Yfs?~  >M@@v}["qĘF$z/{wuC5:Ƭaչ?o s!N)9fC-:0m'uG{{@t2aRp9;޻ooFkV.wzFrDUs_]k)߮l4G#|@SUh!߂A ) 6G gH!®=[5vh1_bnԜCeBd:s!e7or ӹK'J'MȟeJ ~XسC SMD7(,Hp:No2Db?b}ZkT&!fHeNGHi&@P3'6_>H>N'?Am:ؠ]:(f׹Nנ95 1S/ɭ^s!7d҈}".B&Nh@f ;R/ @b/E-{."i!_b2K#d`\M- lRk)&\~aV~=k)ߢl,Fls@p._2ISn/G FdjuE[3ҀU,"E]XX="ƾA_4yS=rĮG0%rd/AZ!3 DfljT-7#_k؂ǧ-b( qb~ Pyn}s?(%"D9D6!ZȼkT[4>{8]NϷ#.bTDBDD*Sk69d7VO-D"d52spϒ߸e)J'$R:e 67A`Da$<y)D l]hEQ=k.,f>FoٳBsg liWL[wb=wBac98"`6 !+pge_@ =5>[[[,;!ZB.wX}lw "P6D)n!ɪ1\g$-R~Fl};m`E{{3ι㐫B zr堵 1gyXqnΰgLyt|DfMm4D1h~8q=_ilQU\&}J=Rk#X^cU̧91 #N3rOLk`MسWyC,lZ#γk⏵_U,>ݼ?99]50{9{*Nҹysѻws~9Z?;{?̞=;{swt=DoQ6FRRf* ND@ɢ}0R}cqR_mz@fQ,Ўx<2o dznxWj!)a}TW|KbAogq`+il~ɮgQb4~.W'RnV_HSId Lbzsv]r}ΪdV֞ha9WvemܶO2݁|t*YD"z;CCXx84d}9tȤWqhq?pP:"LCsx4YClhayb{Z)c@d@-"[9zB廈򧂀˶h{5mT47r9\a@=fVUwhζr5D+E?{@`>(s19J M&𵔖Rs709w}^I6{wz5ιsw"&*=%Eh-x9w+zo_菩@"ߏ ?K 4G|?T7g1Uunĥ'XvEAjo:7 Pka5Q>8O:g.R;Y}HT?~.RjC qeW`m \(5ӽ֎C-D;h_^̽"HeG0D?cLbqس@㡼`mkB椁 D.bdN }^'-z1 ͈c7-@ ;goHeƄp_Q9S!4^!%kk t[W"`r(Vo0&L{d.1wW#<-܁'\)&\G29^"u^rymE'[_D {6]lL=C SW"54dW4AsubͪLi܂vQdH6;F5yb26֧Wd+tEhl9;o~K(i\^J_>s:4ո׻_z:oh m);{sý K܄`ޯ/r-BL\7"Mef,E{snE-[)_o`\O"1f#8Cő;c\=yq㑢CH!Vg_dQmCaQ#rB8FNg"` Ѯ~C{d^@ TW")޷eֿ?&RVH'3P`lOF&R)I&ߞչ.`cZc)َՏ,S7 {hG?o.2& QX]k2lmlWm,HeDqr+Sl/pVQ$Rí]Ht2 )} 3d͍5hŀt][Sة)2/EC"O8bzs36`D>quև ͽK6kpfumkz8Es@&#54ͫ\HvWc:fs则JY;ogoG Ht]#[h=/He3_:} 旿o,Te9RTֿ<e w)nZXeغuKwVD*]5ni-OM9w0{nˏlSP@|:bGP"-F a4b.D+8"`4(i8^|֝vSXIG#Zƞ(L32Y;.!־y)wd~#_cLaD,D?8Kz(7ZFJ|{XhGdɡ ӕ rvd#Od|9ov]s!rd#pvN2 jkblXy1dC7:|}Yi*_Sף֞ќCl/pX@XAr ؇ncEcT\69Čh521Ϥ{"{h# Uϖ`%3m>K̮D9ֆ,Xd*\]Cx;+l:q?0Ѿٞ,ͷ~^NK:7~w?s NHgcyӱ[ n.[s2(b8bpA@*$y(hct+q~{"ׂ9&Ck!Q6hm2)V+AiDstvB~cX/p㶵};n[$o&.:k!v0|Sh?j!b"@i)bX֚w1ą툏Jc :5=]6ֿ>= ߖn.]N7ocЄظwDX-!D<ĄC-O2/W#_ЯCN1KTv]爂B';ϲZBsL 1+lJ=Qڲb&bɱL3g]D*3P7rݬebJ_^NƳ[^:m}9;l]^Ѫ^%ʄE50f}eA϶ihݯa`9Ǣ D_jqo)-e㔍Y!ʜ  "x3 j:-M"Đl:!Tf4ZQ_B J-QXݑ"Xع3k&bjw0Ih>'H_ǑBO9Kb-GJ\t3@r rɞ";AK3Z!Xj`VG0{NF@i$==M' 0mX}KNeL!TT'gX{kY5&X_G &@ aľau")Al׺D*3 ].?V~昵8kt2͔#FDrymd"dqs7"6h6bB@k/Vp?dKt:2ublf78,Dj47_#:H2 1MV2=>l?\^E"]21D֡yͧ\\`fRޏ)9#4!ɭZ8yEVu, `6#@|cnEK*͇hcu.pS8(5TKi)-l4gt2T&8ϯ!:58>䦓CD*ϑ2yr)+L_t+}iQ~Ao9<Я){ ;]HjmB &R'! t" ">Df }YMY͖hG`- @ֿ&r '+ 3Z;)e`D 1\n߅Z?vMD]gU 3~`2x̮V'B~o~1?! c}ڟcu>mTz4'fS(48vC@yʰ>͜:wY~csM֗)htCs`'dƪ:(/63b%b}omHeB/d5(GEb[y? !ִku 7Km#t6 s|6 ?5|N[~}\\s{㣏9;|X=?wo̮Z>,m]Cd]X@Yw5Z#ry`x}R[Y??-ov!R6KC@ t2ޔHek F?)z"xbG 1'mr^n!RvBqȟf5F ٙ\c_"G/Т7)C9YNEo9sZ\w HqwFr+rG``[k:"?-w#^ou 뾰d_rd -CȞQX DDV@H,_s]8(: @{#%D*skXXIضyֶgkvhns.ͩhnԠ9*4[ hέv~6FěLJl~2Fd 3C G#!o'}<@m'>Xm,jhFBz2G]L/t櫾1K^-Xh 2yGXg`I!5;wmZG| ,#H9 +!/E]lih]޼nono}#X 'shq`3r +_wϿkw7(9 {ƛM4}w(_Q"?-Ϥ+u s )s?VDӐ"{1Ru{#?HvAF!R(HI;6|֧CU/[mB .-FGeL\ZPڵh=)VϳDs!=a&&fuۙ(?J|EA=u;ݠ.S-Qqh~X8>;OKT&\k߷E?bgR 0GGcrBg.<>/^o_j}x%߂V+r4'RdVeӶ#d 4g vsg-7)J"uɸƮTk( g5()"|B&'ʣZuߝ~ ׺+*Hee­X4gCuA,6_iQɬ{:MQF [w4ևϢ֕`]T*Ɩ(ZʿzYbUAưA %mJ{aIذ$Rˑ"y:_HeJ|3g/{6k7vg!Ӯ|؄L#CHyEd(QH 3PMnrD)H13ǡt}ec5-ogt Ėu{fXkyHa?XGB|9{c>o]Nìg9U(dYmmnk׵A}kO))<ȅoE'@-m 9iDM8 0 1N Gs tS?[C<sٮ<Xy1r`;QȈ{[;#/[Zִ.@̢G߭М <"M<K >ǡ pdg7: ('RkO0~@Zs.~kNw|o>mMIm>[{Muq~_*o|%us'x'߁vsasSY4B@6uC2oX{иŋBs+9h̕#*}Y0Zʿ(9-5B)iι>HuBs$_k1buιh<z+sۡu5={?X \1ryP7tιh=.K힯{v'6l_#֛*D|[㽟;h_!D&YvGzvsF{`W:CTLĐr9e$ 3h!vZ"sH{5ENU ]XDMbp/NEFʾ(HyvGgh9F"0 ,A`ci4ifmۡdҘ-HqSǚF/h!.5ټNƗ$R bAnM0hZ~ ![;uC+(м^ȃ9͉jFB,DHv2MD z~{͛p2ğkvwJ~k`Rj!Fm2|gֆ>H1Y{DǶ} -gž:U.ǯ$q~m(M+lp` F>`d1~;yLW26M`߇ރcW)Uhޭ/,.ԡiюEUr߰*؊O.qI/ƖSѻ[5d]bN@s &#kѼ(XoQ ;Q;#k)@q΅)Yιg1e}sʩFZ=97nPNV{:MA(zWY{_u އd_Ks?E..x`\9J|Mr=09ws7pD\kM[!VѓůEd]^uV# -O "0Wuwn)މvP\nH6 '䵏}SH@/G`TOPT>y=>:59v$R[b? Ў3yfD {vMeЮsbsj| PxVOgؼh2Gc>ܷ! )Cv%)z9CJtH#%8 ZkLہZ:"@xR%|\.Bs48) NGy֧D*4xad|͍$'۽1(4?mh]^⻯l7W5R疹>a2ڰ$y!M3w:ubi;-ZU*F8o297@ƔNiāִs}r y:>Üs'd3vٻuι|ٷι ~-s3Dhlx"?{?k8 5mx9~7&xpO"y)/JLDߎHE )ÈG>D#jXbDN!vHqؙB${ sWձRH15 6®-#%UAGJ~3#P&o9Zg X! g#0ӌ֖ez: H He&V&chq@aG>&"tuHɿƥ&(|] K;.V bRKwGi_3n0ZXf#]2#HL7! du>*!V\߿!:\T,~شsIt53ZTHeCٳ߳ h(+ .`H7^̅2WqChڔlրK6w hM8ۣys4cH6#)s'V @SjakþG}a{ud 3ǽ}+CjHg;ˆwy޿>,xq΍CzmbbB`*-BƹdDxST-@ bDz(L',Se۞4&/Bo*wc圛dPMT.s6KN~ݳ: =p+|xs:犐jוs?A4SdOMZ_ e5)еHH]@"Z#4)fĶ@RLe+`ET!$BĴg`[-Vc-kio#ss_тN<V0mv@@&4K"ߡ !r;9[\A)hAbzNDc't2E_mmmBj79"y#Q{@rFh7?=5#Mhgz p3Ej3|aN# J4F S!ZTc&˃bƵ zx岕w`QxyDsPoo x1He¦!NRkJAb1\ѻf@Ҿum&/[[2vC-)OMnuvz{U4A_U6YF,7&Rб\Kt'P!SLu>Z&!6l>{ E&Q`vVO=y+Wunrba!f%Qא`/gR7d =P2F,ZPxZoBt 114 `E~hE;h=-yt1 J?O 3RUh;AY'@ $ZFG73#쭉|9 MfٰD1ê;%nmC(C'o22F >gDof^3=֞-5΃mom=PjCHL@@l +6&+mnmF]#رXzVo[PB31xt6Rݰ:et<۽]r.5B,H"0!6qҭ/@,| IDATdk%6A6݈ؑ"T-Si_ /,sKوҗ`Ǻmn_2|P3P|8bW`img}{t9+bӳe'l1-Yꚅяl4 HeAk_w]HHem ݐ t#b"SM.MFʮbkβ=}sj9Rޯ E(|b#r/gd>E|A!mw#h"{C"F Ȭ8oJ"`SM>2MXTf& 0 -&Rz̮_ѡȜ5 qzpMqx"1Y0.qٗh\+&"8^Ec vD cKY/M0@sj2WسxhutY;Y{u>?>g&4ĀD*KdG #p͓wB0d?(I.AJy=/ ':X2d,Ĉoј|}dW~tNhnLĘ6s]ZtO'ӑ? W]VoCXX6vgHXOFK\[!_k`(C`pBwϷ@戵)Nn!w{!;%2==b5[ rtǽg,[Y3>E>ky yۘژ6y frbn$͇5X}֎B^g z.n}m ܻpD]o$Rcn^yTLG3b?}b"րXz"4|<Z4ObRm|!囮o+ebh8?ˣ ro\|p+sahRբ1rZKGe<ߦpGSm@R 36/FCkS?`ZJK+Yߜˁ[޷ڵFfpZ,C݁N5z"҄X&#)B5!x@J)"  p %k')Sp8`&}9ǬbCLׁvypH:8Cwg?F+F Ѷ,b v}wls9\r]8k-b; 0kϻIl\G&QH)mkm'bB<ň,2d9|&#[6&!߸xqz},p[7 _p$U*A -ˡ֧ TpN$ڐdF 5u6bV~$r1hއw4r.XPKrekjCvҗ0wcnH..D\;~4mrFOKqQrKDfnDYDB4 8җOvm;{ܷ b%RKb$V"6` PP4!(~v3Z5Dc!~,MW >9)hEJb \hmLd!$腀:G5S8-2@ٽװH2:MEUn@kl gٛ!%3qn[0!p/! f2 @bHy2b̛7 ~5bWL};0)lew]za2 6X'M4j&[bFcl; ֱEQwy}WE<;o{s7&LC40gvD2vV~pw*,{z=ku?4oV'ރhmWozBVcmD*3'T"=#=6? VmQ=TV/*g(_[3 .L"0w۾fZI5=} 3^!bW C\򣑂ot֏jnʊev:I2HXXU p2DO"4 B3f#RЛ 0 !%b9"Du!\5zNC]Knw;d!"G^O4Y~de*GP@71;cTL i?Ot2q"B};#T$ΰ)4qY̼< 1?K9@cs#mMMnG`!M5 %`/}!_Nh-:ŕ=w L-@ J4G,-B;)/CJBP_ޑljmFzV k_;gok+\3z? Q_ƒVgړ K2=C%>Xw CR\Jhj, L,DNh5Zv'c b*Vy/b~H-AE7! xLcP8 Y};/ܲڀ6/DLfN!ra<!@V d*9Z|XO܅oX*"m7߳j\AF6z ̈A9֯ 2,D <('5BL8n{Mہzmo*4 Cq Cy)}m(y3Ո ^$UhsEZ\%] RnZD}H)j6h|46/"Yy4wn &5h Nؽ^G>GE]/˶a5QY|vh!wngڛ.Ҽ:wG]Z4퉙0 nI,e颿;Q-o6U~YgD*s3JwQtxjkTNƇMYV @xO4T[z!%oLNW 9N|v +LpR#E2]@Q1paq@MQ9}6mn#R*_WE`30Fh; ME {D`R[;w@fd67G#nZ 뇻Ѥ?n 0wb2!{2jD*;$AJ,vRv!0]4!@]VntL46ƠTcIգ_Xl V\:?Ղ=S'4N.L'MD*SV ¯XwK'9LU;d as>6o-?Qa )4~.RK;pQSLD ?{"h-*Q;+Ǣwi<1gzSIy⪮NƖ{ h*jװoT6-)G(^`D``J+HH4?2ι*{s7sڎfEs(hι90 aeb4_{I:Tf_4L#d?"AfHADžhH{!Sݳ_gli<ߎ"!d5# f:?!ʼP-#LGGUA̞{G?~@jg#C:_ Iohb` Hػ+bQ@GY~3!w cCrӺAk# @ ģI/dD*:2˂`e϶6lEXDh+E xO#HD~~qչ/b6Sm64LA~41kQvVfog2nHeSwG,[ k1! HeGc8/Acc3ٝvm_5VKB -C #|}dI(_9pb0~K_a睋I@c[ +~CgG|,x7D q07GD@s$z'8|s>vݚ*gz;{^%sqsT}k^H% M[ M~7!E$]ErĆ]乧3.^Bk,a$b{ 2tF)h@֥HBW넘_>A~M`=2Avv. „LL2#HdN;?g39vY60VY#v-#ps=]kvtBHcV8A7݊U>E֏ݑe~1bzy#\'`+X ୬Ȥ9D|=.:Bo`KcOڵGX; m Ljl;|-ak-շF9JTwP[4窨˾=>c S*K /C=暂ЂĨOAfbJ Sn <>@dwM mW >'%:o9=+jZVohu@5ιߣEι\~snsn']90{sn}nb?`]״$R>$L1J l2dMV ^"żבM[&DRdz+B&r4 RPrĒe&9v2(d|`ԢI=l_{k/5ó7{"\4:Ŏ#V~V=6@>im} þ,i)tg%޶ mӥeG =#omGg`}6P:T80lJ^Nث*JѸ[`}ўyIֿ;(@f. ikJĞݒNHe3mOcw k;`͠qӃ0Dqkl%9]x9zos+hj \!O0/SףvZo^VpTNШCnhSEFsZܜ\-9+ޯ_sn5aD=r{s||}~ꖧU֌Kط}(TdMF 9b1Ew4NƏO2[!Hq]"qOd+\ waj;o"Rň pB QHlbCxt%!R;! )Lg1+Kd)iQV>#;Um_Xc!μ((LjuGJ}sĬMaCR{d<eh:lL͐`p[3kXmo׿MveH\g-tBJ# CPv#Y:;'09ζ3 wkD|;C %vPN1Q9ȱ#弞/}HswBoFc! ]u> ~10B|yG=@P.o__?cWr|YeoFad{DoF &4qzHIUy޻",OkBƣ6iDw|l"@"5\ dǣ9ou^]V~%hu?){AxIƍب3,UE*y/`?󛺐0a'/59"XQB C?@"h*R!Fg7dNV*3>2\jeb#CL@?d K vk&h[!4I2x 3Hev1z;yǶ ˋ F @/n-u[NEN @Sfeܚ0Eh#K+K R`7@ 9؝"t:hq8 Ӌ3b!_#}vRw P }eO"t?xzQ;z]Q4TxW)kB Q`#%} >V #%.qk"x.; 1l-xKV4c||]л6O@"ųV%XkxQVqOUDOݧ*ĸ0ö7$RgiN'ZsQ;LGL).m_[]-|lu7h!Fs7sִEm'Ā%)(lS" '$RxH2 ،&WdDŽSH vBnLBx)xR{#$eU4E6hR|ND '&b4G5R6ǧOޣšbuo:~E@ ),ctȞa';(2)⭰~:V־X[ED*SN[[&6v'!2KXNƯ.I2yHsHoD>gj ] ٌs֯{"@կ77qTB;07H'D*3݌0o\ئM=m/XZ`XP#|Q_mLOz'Wɍr>Vo__ج˞nǨ~o4`X4>ۢBn徨˾lm0;@r۾BSJ9 u1ȗm;c_O#;(c[˟**]~@҅D*s9m"Y&c 7GʡpLƫ}p[&FT6.2 #d"`vb6@y4gAt C2uLF6+C&.He"Sn4vB pf4VE'4Ͱ:#7nҷ+E& "OGpr 𶛵E瘟HezZld|Ri >eΞ!ʜle:?!?8~v[v(k yPĠirt21FpZat2>-<@K>㇐X@eu>` ذ?D;i?o۠ȡ§G|lEAo@*d޳f+gW+T] ^v蝩6{lӶfz}':=И|l7"ލ  u']Z[&h[@O;1/ `@ NƟME>e)2r#FL/޺fV)`.@ F`d}ф]n@IOt4{vʹ$܆]6oHeC psVAƁG v0rDby(vLmd(ovFl<+He=E\s3 R(&ܧT{^kI?His72QbD8 vt\6YE;YJտ1A_38nɌ5bO@&cI-𠱓 ǭΠZDN'XI'`V`FBj&iB5U[ 7 N7Ga+-hEI¸= ^A"c{`c`'/mSKܖ9߲hCčlQ[qц%f)-**!?e 697E;jD*sRW 2#OH>Gb8Ǘv?[Ҿ}Rx^& g0 ֣WE`%Kvb_EJD*XN"6u RAv.V G3d=R%F%{6"=^H=g))ڭ}.rK=?yV#(ۭ4H#p:?1;"]#bGVw O0:f;ڴYݻgpb޵h&w`r*An,Su!z/}[xR 4P"L'_ʞN'Iڣ1_C;v,Of-@cz" ?]ڴEcxZG]3Rؼ˞ٞ`wt.|^toL1>aJoS|pMh펔H/B2E>9:{;!2 MA:TFݞ[+N[`"鎔h);;qg^GUե^~b ص)\;Us>zO}'۽GE>5HyZ8 3#bAE= 2—/-(S;l]s9Y 胬&BJ3xK]r-#*kb~꼑:#QF]{4jjvgq \K{bD]ՠM&?!. -G쨌 04R|fnXx='P\[-oa&[eդ.9;-/ιWϷ8v6սj<.`> ?לs0{?ھޯ\oT͜sE_e|)?~1P4Y`DJ̮&Lqjߎh jnp]W@'"vZvD>]! 2GފXeH_cן2G3bI>BguD*s7 he0+_Q`:Ab\G^]v\Amj\?nZ$'uKKq ȧ:6`[kӿ3Lm"R .KX _wn+-*^@mKQ\  5CQb4.Js>vb"!lZ&o>7E[+)CCcSe.[u_S9#ckri}y`#:#ι9͟Y ~&@`\K2$RT&w8ڽR<&z]p[,@Y 2"3d{OF]9]mAJž@~q'4 N ¥Xol:Xl 46{!!p{$bZ<]{5~W|7!z._64?6/+ڂyhVqG/Iei२*߯s9sSܥιss;箵c]ss}dss%9{ιOs9朻9dMiyMsܻιssCs9>s]ss?89%ιbzsns.K{8v}sncܩ,'ιݜsCKs+Co\9w~^ι7.eI&L^J'"#vD*SVR G=ْG "0 9[ߎߧh"-C"h%AZ} >$,B`#x=bA>k*B%C&" xMԥ#SsD'٥h欝.X>[E˝՝keИ7cOJZ|&!Hw]d/9@@Ω[݀rQjW&2ܽH/,o{we~\ s.`WW6DIHpAH7D*1SG#JN SI$L|l׍F>]I2X gwh/Cw#B*Ѫ@R( 'CNZClĺlCN(1O"sn@~IѪH!ܵ {|&R F5ӆhvR^ l{O!u>#=.ʌEtxbȟ\kon!yݐ 99,OC&"ߌ4BYW;t-B[%I'W5) GUqeo:vvYl`J@WGBt4b"}MAr#+`IeO@JY>mViKEw>G.Dsh^ˠ$#]EQ}͏|5SdNG8N!aޥ: sҧyEιUb;CsY{DD@le:j,Qy{97͵a9va4,3"ל;Yl \<Ђpgeޏ2~juιQ-J0Mk Ԣ LG!(G樻H<&:4pNED-a쏑gDfǝH霏K{d[24I*& UO.D1< )hV[#0|}4& ƺ íË́ %qg,;"St|zcX#8zin8>{![V'+vbI6+b⺠qh6s4"0BI2K2+[D o.W)9&" ĞB D! !t7GϨ|ǚ~ ;r>6=cCs>֌p[c?h|=s\ .yQރLŷ~/l!O{:ʼ#sQ䲲~ 4FJMHJ0WR ιRlc~sdjy޲{g}yͬYr(w:ez.K׵K˲[|ϳ3bf; 1H="WĒ&7['dxV:?+ʔ jq/zM~HVo!Q"в#bJu\z.=)R 5`#ZGD~HG#64 knol*_GLX1 QL5v& !Iy8ح Yz+9Mh" v>i{[=}zKKu=+S9hQ7#ʏSDGx?r}Xϩ4=aL#̔rpsd4w9w۽$ιۑ{&+- >vE;pe'{gF`#? 2hnwĕ6`_pۙ0Q?H5&ƈ vx22=NƏ]5#c_kdnLmO#0qZ-;9`|@o#saw>b@|&-Wpb& )o-\e4&,DHN}knX5ѓv7˲="TA 1ڡZ4f&Y{4#SI7>UPAB'krVۭϵ{ǡwVvl;F ];G/j/8^ Ee/dZ;YHw& g-[ Fy^:oH2_o@~[t22ZG)Q  P]-}߁gs>6{/$Q= [o{=E :4&w|򯹼Uր8*{VYȏ3khb/@KɅ{BfL1Z5T#0?U"ĞA yo;,G>t2>7/Fk  /M)@~4=2]&l̍)D*s( 9yր| jw5B? <A?3mN_LrwO2@Z5e"1]LE^oNW6G{us j"y_]2i]ܜN/M2X_N61\ `8'KhZ[lbe '#)Сֆ>Z4|L%ZaELk`IHe/Bc|)xNBN\A@,ĺX(!9{0oCCL'٧B]Vi&lo@& 0a\vuF{o#%, <@Ge<5x`h4C,{^p^D/#@u#1mL^g휓K["D/@Jx.P\2gfWs 'aY}WlQ71o]ň7tS{xB:oL2;^A^b؅% d6q~ܒNƇg}s@(:ODvX=kkZ\d-(@W{TJ" IDAT |!Gom>l/"#%>-ؽ*nˏ[NFW߸R'"9=3|\|[B>i!@S8T\AXbyDF# q!WnǺ!SbU!8ьo[:M''RK<~ڥb|**C.581W#_]aR@^ |>?;Rm7oot'Y}ꬌAƂ IFv"<A zt2^He޳6G&[euXb6杜 HeF}Y[Nk˸NF~{kdQua JXDubv6rVnk@ Say>PBGwmiY|r8Ej+[9_fTjm<%RQ;!!cP̯7LEzVy3,͉TeIa2[dWeq.Y=4gaTt2\?e=A/$ʬ7J86{L-& w*&Ui xpU5~%ZD,i&78(G>]ClVi$?) e+}*bB!_)l Z7#6 cos4476톜7@J٬2JzbS<@A"D"+]8>~;{Ю:̾ p&ah@#2.D o 1oK)vskߧ}Cl[գ$@6)87Fqַgac)&bt2 ʜvAދ+P3Q착T&UfAHD*Sd}r>w;Xd{# x c6m汸T/9 *y@P1` F2SE#2.Fl`#2|+bѺ#@Lٽhh9!lխ0{!>2je#:L4|w.@ ނQ;|%:mp^X۵]mCecc INƿo;$ I'AڏJ"ډېNK5-w-NF;BWUGFkQxܴ6=z6c%#9S㮀A-V WVO;MhGs-b"A&|r>ViViϙ$R]yE )aog:C@l05!3\)`t=|lϛHe>Bh3ߴݬ"_+O1Apd:He"6 bWD*d3}Ҟ{+_b"]:U?!j\ZuDcAF"۞L]֞3e}Ѧ39;؝O;4@Q={%XY3$+L"3 ficdR|뎲!omG "߆kȌU f$Rwa3dm@nF@wbׂ9?oK'l5 ܺx>Xo-jko)kn䜿#@")E t2 R܇{%i91mht/>u\=v,Y_[^]ǢfWT/((mdxё҉th,$|{pD]vnE78L}Ů˦-|ZUZUDsWǯʹH@i88g2]Hev#tmB氡d|>2މL)U(tt2~=Hj/B&Q`i12GU0~QAM4vVnǡTr>;'e:rm0>ND hdb\/ʼHenB,9v_feĮ 9俙NmdbL2;5Z;:Omvela]J/<oD]R !=g:5䛺(_P\X@hTv v޻mSh_ef F6}BUâ1e}fWǻMl{xnaO]gzڐ]_‘w6.L`UUzOa\y|ގ99{qQѳ~n+(s4TӒo^Z-v|ݚbUZUZZo-d|T"?d.{s]LEj}A<<"RoODa`~0lb_5d<Ծ0jS;x}tͅ|M?y1i%H9?fO_A`Kbl.HC,_;*?B>j֍ |lN"7d6(\N?DG/H' +ز<_À+F",D|o:)M~Pp-/'"`<}%mDryG/)BvyQ]wu[>`C11PLhC PB I%L Sm-lvi?^}=ϳJ;#s=/9ō.B!-> )ĞE=Gț ST,lSiAqU>5̹w tB_`65x"]sN?,Cߥy~Qݒ\\>F`w))c'vu^Ƙ(Tq HK[UD*)2ʇ>/ծ~ԩP'|4gRnFwUKa O`e >KJR_X/ZvM&R;y-܊"dETfgT.٥a'QxWm2f-BRk `?{n%m^l7C 5: 5-EJ ќ/?u޷mS+F6|pA`șFt l%@eP4Rlo0|J)y5-b6Sau3uݢ~͟.@ L"YTŧyO;qgLeY=p|`6t;v+=NA bv:88|+ZvC4eS*HI~*F̅)`fϰ=]/^N&gQujg[ǝ~"}1&&RӺ[]8" ZM- ,GT l1{&N8F>vܲ?ZL΍xoկpppTaW4 q:Ѵ6?W ֛6yfp ~6)xޏR5s!  J$*ꜣҫ_oT ޼$2p}Ѣ/#?nW<.KnMÒe~݌ȇxi6j)&9ߠi^1KcAV l ˩'EDgszQFڧ=>xKz{ƿ#, M}KN]#Pf:88|KYgsA%Xk80!&+Hez׳X/ިya:okߣPK.T5GfGQsP3@yA(*#U;V.NvzYn=vUnTljw$R`i.|^D-av#Y9)%Jz4ִ8z<שKg~xe>8e'D*O: 5 С〩!A?"/;fseJ=-ABY$g2n૖gؗԴ)˦|CdW`gPCI ~5Ds._R^.[k=&977VD*5+ݤ- jI"En4TV/D*sNadx|שּ}UʑtA7 {}Z7;<ԕg`Cx "wQg#+va5֗  bz+!qQ9#KSyӒZ|qA ?4ΏDgN(\m󌧻Sk+`Y,_.Bl8zlS{`(Rtnqg7Qk ~C6cjn)E ߗw&z,*GwKR}JRpT|{O3QZ|Cf{ |Ըk I[ lZZMe-mlk;'?uXySPTkLV_Yw}J`#v!^H D?NW4rwo&`Ɵ{mmcլr v8zj;MxS}~xbũuKBj w&RUs<ߊHPnUmB)}Ps=0g-|ۂgS 3疡)>} ԾƠ va}[}! }ݏkq ]ѓPI5}9ϻAl?"r A7*j8ȷ6/A7*wB R6g^ ۹sż( 쨡Qu6= >Gٷ G|S[ϠfTpY)#a+UOAf=ypjWoWKEfS`@芝Ե|= "2-P>hl#} *h@Pڥg;yV!,/W,driVOY}օ_-v\NȪ IDATfKn))+δ ޅv (_ N|2 uN߳æGVߢƶK6O&R :zorïF_>oW[ꐿjm`3YL\vcl3˿V>t26k9 Zb`:Hw;=]CKP&#GCOnE&]nZ- ozT~9}KDƶDYb-5TSGl nm~\䅦ő=qWKE2J(Z6,lJiE9 ;׌ޟj 1.Zu;88t<"i Oξt2ʸt2:](&ax%y)1Dߢ'UUh^_x\nע2]_‚ "*3|4/u&~1~<"ZFTj]_$b=b> M(y.`!bkl[DGyes0mw}./x(p}]?mE9na{C]Gc988ldXl_S[6MG޴(DTX=Q-]c8888-sXR];6b,tFS*`%G|KW)*Jg7_|xvH{5 k?z+\|Êp#Q,ap Rr((l3. 8 I!o\iaSœf~jR7t2wTjnLŶ@;"?QY x5ڗ!3:HA98`4ˏ> 8:e _ ,!<3i]3g&BkÑbր>7ע_"pgW;̴K,pzA"IcdK''R+C7p&I747 IE6,5S]>ͰԠAܣMӺy?AeF{OR ld7 s3B (cUo888|Ǒ.V "]="5*$`UH9#AvXw{z?O#stRPة v_{ NsX_xdM'E@M"91XU۵ۗrHiJ" q.*AY*FcB`Dp͜0< b~c g۵3-6]ʚ(-? b k~8"^N/[mE/=n"CQwBtF2Tރ:E7JtCͱyuKg7w:pf}5B")M2'R+y׿" DۯUn uFsl5uMDES6yVlmSEaE?eE0@/F]oXG7ӶY|c2w ws;`8"氦FJr1#6sm*iɖݎ/K2-+/"UH_@7udݘ;#EyN:?56UNvIWdlo$Ԡ*l A17WT$ofZ| 4 "93~g|սeF k [Nx/>kaCd!TA Cch<< uJ'v}ci|RpTzQ 7l=%HkX[큽}n\E164<|Y[C!e>޿> XS RME^^H%]בɈaHV sXLH23c?3~q65VLO'-oVG&RCMm29*@ "u+2wCT ❐:vRFfMtfQa &\>â&JM{{cA6G$סAM5|RrDOB Yaa }_EH1alaӅSV ַu0-ƅE~qܜG//pLp[Y醴gA37)c15tC#Dͷ{ cP5wD%J##N'RGe"݀cezt2~S{]ۖH20̆V23ajBb4*`l65?n:~ѿ V9Tm h|}7L|_0;v_4lhE!G/| cR^F%8R&\<"CyDζ/CeHuFyt3pFamBr)R;L膛k@n +)dc9:DuweI6=n{=~ \tWn:88lpI[qSyDz6xo=J3ś8VGη%ȃCΠt+/+"_l¨ݦFJa(RǾDYO_e0 ~EIvO@>H~w;G6}0>}wc4%j;n8E̡M$R?*~` zW nM˦Ef\-c!5ya)мlZ "}hD D <P[ V ! 7l_e终0}>)xG"Yaϵ|YhT 㔮M'_%vppppppDH2%HE iI䰢N- |)%&B*%N6;-;WsU$B1,3ؼJ7,J*5'p"Io#H}(yPs6ʑvM9T~g_߄E{H?!Z58888883\C[D*1c\"&BhYD(Cf{c-|}$•' -2A6ͨ;*,r#rnH0D^uUkAv% kgߎf U}El{?~uuHevO2LD*ӵ#p1|fb xo bȣȉ5{!'"#:c5#Y X1"@;#׏3*E.D zeTf-B98,b)uԲekw)WT TT'RK">C)b! @zn]L@ԙU'|ٰ f"#s7 ͨw%2Ϸ(E1 vwOQxt2~"^Cj_t2>Ѯe 0 *O#ow^FBt2z:*?wU^B k`8RD"u"18+S];h@,ڬi0Us=:ʄ( |CDNN'd9 Tb(| 0RG%'Wg'+Bw!u,%Rd>{_ui*cJ"6\HeNoݵhݳ {VTTfu^ǭ{o8888"!C"Q(YI|9J{yR;dn&sAR@aڴ RNƧ^%D/kއ!bg"~#"(FݠDJT LPfG  Cș~5_YiF- fµ_݀kˀ 5Cx Ud:=zVA"̮/HXf u] b{=(1\Cܬ3~I$T>v0]+6T Kl3QC+u hdܓ[ t Cm6ҞghpNG%rqpppX'qȄf,ZRQ;+"B!bRbԫY=rt_(@jq}mL9x)m?kH}Pr_0f XHшf%/5Rc' QY̮6D7 omު%s}SnNWP&ڍ\<WYs l޷Pg!@D])M@G*[=υFWt =\OvE!yunx*/NO2 #vEDm3DΖ!5 =Rzߋ:-trCckyrT S}D薢2Cj"};ٵ.CJ((1=pYWx b5o7D._C !rӖmGUaPjR73lknc Axm*BeA DՆ#bp?Eo`B `z8]ΣOИMQTe $5+M:ݰ ^VE9[Kyg{t2ހn엣؈Ӑ("_UU>;"EZ4z/psAD='v=?k,G%F!"RF_Muduk%u2V 1)., BT@+4A`B6MCF3-=?ίM_)G?n^+,޻g988ljpD!D嚇ʋ3 T[AhaY(j"$@jOtV>s-("hD vը$:9v+G^\!YFntx?DJv}3yJ]hq j(l:"ѵ<|DD=S#HbQm;dcx{Dk Jn_.vep̯oQ ds}V6)ҤCќ P #)0Y7POHL@7jDDH'Kn3STn(BbRG+(=J;^u(jdd)J:bX?us4; )_ dDJk? D*s5*瞒NƿaC  bSZmS ܏) )gU$r<ㇹog؇~ bٵY6}]V2aƓU1f}fX%f؂ݏÆSF:cd>=/RJ$RSIdpTQet̳ÐZHGP=u>*K D>rD8:x]56Gq )%3듄Y3 bRFc~HA3j&Sb!2#:?BĮ]He@0:3qg/wMߣr1#{OE"{<_?G P94`gP! Sxlg[5e)"!=5Ag;: R+pg-[V9J_x3cnprH'Dt!oWe~P/M R}tBtt~)TZ3"a5,_" \RT4| ͧg " }A^[玩=jm,ED7 <~D==㏷5RxlFoAd|gK#۠.CAђ"DyOdXzdYe$TV3l#4drlL;0_óg -.o>R vgg#W7l39T'"`(ҡB dH!_W&]BaH-jgQq_8Dχ3Pd Rr9v^eI=P1a֔Lgu )O-ڰ(!ѝhPh? D$*X TܳȄߌY/EeȌpA6kG%Ě?ʎC{.A^ɨ\l{3~gTߞ[F Po bz)=yܿǙp="ǎzn NsX!D*sc[L/zo?p!Rr[n}n_4(գ]vC( D|"QDr5 SL@CP_tLC=ϊ"0Rîg"d=:߷U )(ED0cpNAjZw{?)LDgǀ(J AHYĖ qppp1U"Lٷ'RHz މT?ݜHeB3!Ei} zT~$MBT[TbQꂔC AȇZTFME*`sпn5J lDcP Z4 {#R2 J!I ?HDtP)5TaXƾ'"y/"{7{Ycajr"5H9?m(d2"Q Py4+ }.)@DM{ b_mqpppب&γ߷E3GǰtCQ8F z2燊N@$]$R NgI2C?slWlP HR 1?X5tD`EC(z==1C9b{}LE#y+GItmƂHum(m=Z{s+CھfW  ! (9(0)Eh<+2;888lpzG" yCJLE;h*m Ȉ}ed<~T{*>g+ rb臛<`l?ԨQo1RF*ЏI׆GsB p46vSDToY ١),F * Qȣf"Df hKfPdHL1%ev"S; NsHy͡|(! F)< u#06&O'Q9pC7h!z)" ]+xu?5B8888lp ֜ b vqI^S.]HO8h}#D)O_!uIy>+CjZ;#v"sP0ɏꂈSH"9ԢR(m6*?Bd cBa-L>^(a=OAn5 98tl6y3ATmT뉼R9DdF" ;~2y?UGAmHVȝJ54 }4~+"B}J6 &T F*وH}Hi>lwdR"Uܞh䇻 )na@`&98tlM of݅HLFxjD`F uQ u-aRDQ%"1tA(*#.EW"x4">?CuxU+)<[a)ic}^eqCm jݜ ba7&98t "غ'CWD=W# b fodn 7"^"A\?DRGl1"[㑢5<H}2F!EvsQdƇH Bl *Y6!R"yD߲ REAvkݛH˕$6E8"gPGaHٯ^h}=)0Alg_p~шL=HH:lGYН:F$$4l{*+F_LdԳdBDƚQI1F.D5*xRؖuD)tH ?S R.F,\뚀Hli.Qr4S2<_Q)t\6S 6*oCjRdѤ-}\Wi`=T rHP%S('<п L 6든79arG$p]Bzqc&}l{:6K60LM_" 2W;-%i`@]eͤ4ky~,fV$|G]$;qCפZ֓q+, g[pF \%]/ <%IDAT\v&!r_fϒgIu!c8N(c/%Ҕfo73]̒Au N4_H 9RBԶ?Ij7 i? L_HjWZ2GR[Ev:>Ps6 =poi1I"&M2=#o6U{?z;$iI0I>$i)1I$$I#1I$ &I4$IH b$I#1I$ &I4$IH b$I#1I$ &I4$IH b$I#1I$ &I4$IH b$I#1I$ &I4$IH b$I#1I$ &I4$IHR0y$%dIENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_15_0.png000066400000000000000000004672101413546205200301530ustar00rootroot00000000000000PNG  IHDRc%sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxwT?wf{a  b[4j%K&_:kIu5ĨDvEEA@:.l{Y<>޹sgp=aaC`a9cb0 0 11faю3 0 hGLaa#& 0 0caa툉10 0vĘaaF;bb0 0 11faю3 0 hGLaa#& 0 0caa툉10 0vĘaaF;bb0 0 11faю3 0 hGLaa#& 0 0caa툉10 0vĘa#1/r\̋0 0~\xw c'E ݟr?a_0 E0g0֍o߄aђ3uc][aa01fF{A0 ё 0M}o Sb^,u40 cF"@)pbxļH=ҒW_w>7<7żȞ~bU;70 c”F"c^t"%۲gV=NXU~,1/rj^XzH%S#Pa3f:1/%kBnӺPwCڻ--|/+`Qu! pEo]n) G\h`Θt~ $+S<C;m-YtKkﶴ o-\T]e@s˃7;h`E_6!EM_OӺ͇ ~ lr]+ө 7۱a4ڊ@%Ti$ļȹ~vj&˳[+-Ez;! pP4"y+@q̋r?QzWcycbh+]ݿ}`{X tu b^$Qp#P ; mK -2P7;|#@NPw& "X&0alL3ڊEH,C)@'zpZ4ma ?3NcOH `k`*W'905Uu?n{o3ukX+^af1Uybv䦼ܰ qE>dx{ܶfAp,$Eɗ0H!,z[Bw?ǼqI~by\af1+p8 |LE:$*vhFŐ(F(#PܯgQ>zaOܲN(Ly  ;˵9碼'ܱ@Ƕ#aga&ƌ>` p[̋C!&>8}qOQq!QVfFn^X$֮@9e;SLT|#v>uH!7 XG.WalVXi Չy[P $# VnpKFb>؃T*"SBNVXBK0EGnߓ;sg'4#; ;{5?s/)NOaF[ {EZ`j̋x}͎sH |Bb$H.B\$@l{3H}9H-A^A-&>䊕!NT]H4 06 SJ̋hП")A B_S-Ľ!ļ>șC߆a7$>qwDWQ(2oL}QX{W@90c'vhvf9vq+)s0 SJ̋PIQQ]PY 7ʶng{nGktfˆ ȵJ]sgB5|<8+^B+o7zbQh+wa&*qPXaaaJ.ȖNJE4٬yȡ\/ _ݒDW!v6;LV?PXfWP2w"Tl k"+fעabbh5b^$, @3&4mrr?ٮ< }O>E(Vr>\Ȧ $[BH Pq\C+TcY!;s W 0cFkrp'[l.dLx7]2aU(b^'*{ 6 $nD6)2%0~r}(\K$}/V :$\Hܕ٭!ao& c QɆho u5tZYO!aAbj:  ?Y,(ļhA5* 0ZMi)1/T |}G⬾Ox>MAnD{߸Xڵ;B40$*];_Ey\G!}~ DVК#Ո\Y~8<W'WkȽĶa1ghkj껔폭]}c)$@3qAwy(x;rVBn{?rlB!tw۪Pכܴ6lHo6ofʈ\/yO\!qy1 yka11f):q //XVSw0D5pl̋lOLÙ-}=\*h9]dY~w"3,L(e$F%(N.BBghl{WHG! u:oӷ؝wp c֠/>E{]aF3LK̋^gz-{1t̀x י;oO /ES3[OlC8*x? NүWGA[EHeU,AE\Hht? [34˲x9Y k]š=CDYg4{2|NH!!n<5wxX!ۏ=tҼB0uĘ8(Y\ld5 IZkcUYЂ)EB&f-OhCe,}ԼƎ,IyQSen  \z=]P}P'*;F̋f`@p)*5 o6-Jɣ'hy%ϝʵ/x.<5r?Qy+=:הTk㥮}Lcb>J=`mNEʥj-<6CR/T{eK?E, o¶,&oyN9v<4hUH8͠|f<ܨL7x"Ϫ/r栐|T| D-W ǫ WEHAVtqD3~g<5ww׮;Q݂-W ݔ DbY9D%R*Z}a-1"[P1/T'JB6avh۞#pzT+~iZ󇅬ڍh,@a{.\\ˑH[B}@v +AaҧPwnPh"SZc99(EN휹㚓Nc/''o_n,Po:9w:i^ƥ\mO= uuXkt"$bW(y閼V0 01f|?9h1H4-l(5ye_b!Ъhh,|YUZݑ8o}b^$T֑;@"$6D?o~b{o0,M"> Q+~sjFnn($FȅJG/@JeHH®x]4*$5z(4Sczף7y9>ӽ J3ل9kŨn_aj3P-*t̋Fܗx3;}@ؚIv&MpkDî1/rZ{5"H"zCyP;EOB pƼȁ(D7Ga b'Րuur?19E@JQHs(p#Ph2 |\ÑP ŀQ 9i@YԒ hAw ;A0}76ϐ&{K%+#ĝooWhmLɆ)Anb>gIFC~̋2`6x*}Z5An֍(j{$G s BR= vH^ڿЃT$F9{ǐMr=3 +<(8X"DY9A󑫙Do9v45D9c 1?`Aw<ՒWJ jDfrglBYq!N|6pal(&ƌOLy%G To13 co? ='(Ϲpud3(|W7 AG_ѯl$zzeP7 `Rrξ4HwOnbaH=ɛJBE<)D§xO m>r2i$xZd441ǤӀ\!oPǁP6|{Y@}kWC'k†a?cR'~ "?Aw[T^PxY}Q`+wZC߅F8iۛ_.quC.U(?Tr?a̋P8xw&!`? n(4;mHh%F/2mkw}(9i-ʡU!=21OG_dI5-B5 h-lmJcym4x1ݞd4tck+EvHH|c{"ǩl1 u G@X> '~j'FE(.- >8ViAuA |$&'oGa##؄ܖ8ˁD10 cz"^UNK9h˛j{w7 ߛ1/RP'Ƽ(3rBH=T܉V: _R[4 $u~JlljI?xӐx; \*uqVֵsP!--wa;bY?Qo}1nTNCq9xZg8O{wF]T+Q&,WnH\-P鋓wrOk){QgO`h/P$yHUphoPo\50 rƌk+jn,Q"%|W?~V<\[q5YXQ>n[F\*$|:mB wpY(fh##r(:~ k)+ʽ稶o`Θ5t.%k}Ϗr?./}n_7pTCl/$h@crj^mj`-B.ZTls$|$25X- SQX.{eH AK-݁k-i䖽G-qa9c|TWX=J_R\u Me>{*J02E F<"'B{#1ߋy 0SbW(N0yJ\('Ϩ>}S~Q'iɾ2 01cO|mY~5rVJeY?3LQLmP鈹k1/r r)vxH E[=]&]'&e}6r.@.OPE(00 01flļ:|vlK_}YD!lD]xi(%ϫ+B?Abr ܅\P^Y= c^䯨@9$+o6!7r?qv̋4y6 nK~?u칍3 ӿMGF>B;FFgmÁY1/KAWGam36)b^稨jfxOm__5!A6xU?--UTUX_ݫCMɳ{SaPӀߗ&L%kw7k{]Qb~gLͰ%~"zba/6$y1/R f\`; SVh~UM9T¬5HLkF%(ΩKO?*TX]#*o<_t9Mzay;! taXy;_]h{ЫOԴS{2UTTyNMy4 /ai0\]߈^-/paUAN}!n8/quY<%"A@ɪd.Fsܱ|xϽVO͹i9~a Y 10Ęr^etL_FzyW۹iÉޚ]}|NӗsաoտYm !P_F`zO7~yȈ s |0W9i|T#;FsAkBe,@U1/r*Ŵ{d`̋_'HOakĘQr ux Vv.,Zi\WkӘ}H7%G^O=w+S{(TP{~kL]Wlc~CQWՊ(?}54STb[$zAB/s3® z *Q1ة(laolT5Jg.{5ݛ< /{ۖ!E)7o_2@; 5ͯ 4TPD8ٽo{OrP[;{8>{sąОQشZh['[rMr?1{:0 L xjH'>CC01֜n(+*PHAE ~۱)+ɫnw㦜ϒ-yN.0ODxS|iE;7x\' 0obaJc Ou>[۠@ªe9+.Kov6+<>qƪ uͪʚܼAzQ])[6C5ľwd4T~|DϊG.|Wy:.e@AMϲt]^0¼:OL_6Y0i%Cjx/fa!&ƌv#Oyh9@" վZ; lh]Y[1Bo|91?mWi`%U,yŐnɮSXہe(Gڻ-a#&6a8$D`|2 ,F\n欝  x?o#/rn@m~-i>芭򚏖"eySs^vd6 0ZKD SJ{!wl2z}[nk*u]Y>tA>]6a>rH^QW IDAT.;zPu @}n2|Au!qQ"aچ~+i,)no̢Kݳ].i(mtANGT4ZDZt͆a ,L:Zxd4e{7fm jd4V8. m6m8P.; ~BU_[6WߝGͪo>Ǽt~άv<9rΚYQսsNzcn&}g艋uj t0yltmzalrP[GG]y麌VKmztٷNL;{ &=tN~2a؁懅)4,}@2<`{Tg d47U\A ș#o|/~/V.J7CA k\:b랣>Qn:W}ojϸO)#r #Oy};%7v~G}K#Z=W-Zs3 p>~vѶ3 F%5r!pw z^q@%36fvo &zВE'/ꪥxc5n*sr|Q}{9l9`0t,XwEQe 3~$!?g.fϖn9됭WK7U"yWVWv}BF;@hUO|ӻ˴7rPjm >z{CkQ`b}'^ NFC4GUT[$Ѭozd4v;޾ < ׷F `ٺmˀO.l{{`Q^U݃^WE> P5c0!q4N<6hV u&J%XNvC-;7HD/9~Ey=a&Zpm/ NL|-Мs ed4$Wkh ?#OrJ+"p闇o$rK9x -;ߦo =D'0y_ |Wn%0/3E\8awGOWfFab J7};&Ǿc{=`WgT Ҋ7;3u '%~H\cWp ӀݑXg%Z~)Ch@.4@~&mO/5 Q{nXtjO.BW!q?{Ayi}A8: 9I'5GHvD.&]?N,(*pm8  G+,OFC1:lh"4=ڿrIFCԻIH-D7fg6' MFA~oAp"`p:Zr$bA>b`Y0pBsOـqyw۠6O-d4zS_: P^ WFN*ԟP9GQ?zbc3ƏcI'z xدkƆabml^A r/0L@918(Eyh`$ގBǡxd4xQB$.nH]^ SgЌӑ#qb8*rP}2 SPh _l8&&wq75`?Y쭶maB`-6y:+? ͝A2A!f%/rь(pNHh|Oa%Pc81 S\w-hҾ|7>K_Sn'd44ùBnIyˌmD([KOmN0yj[WO ;m[UT3n2to(̶9bp]oT:b%zX?>l@BLs8s2 GO|im! } ؄01ƸsܟYNrh>BW{P_q9g!f:`5 *ާd:4ȍBfw , az:?0O$:"xj?$NNASU S!Q{xG/ӈ+`޻]á׋p<-CB$Dkzތm HplKFC^~н0ro{zɮLu3 > MtFnlxNS/ڬamV%o L]9: n[ A z4(p=BQgk9|;"$.QI$*ьp<5Xm<saQa4h_GhP,G4xN_2zm>lG%Ht~Db&?CIMc SCЌhp! }vv:+\AxM'lƢ{7cy $rʖ#^DR=da0 C9{Q.Td;!slDm)CKVg%ʠa`L>\+Phk2=(g?(2X@YgPx2 ANO W4HewA! SvށϋF>_Px0wT @u%r SB7"GuTb+5)yn֧+`[wL[gЧ(+p}MFCHUko76ljBT79 PxkHz ,ϠL:ݿۡr.ݗmg<ϸp< =$mP)0X+N@O' Q4 Pn Sパ!ހӽȊ2S}1A =4{MGk__w>MHDD F߃>AyhGc<\bbkaܬĻQ8=BۢxCXDˑh ⹿? ;FUww`V)w"h{ϐ0UhLDb+J/@Z-l<\jG9} o W7,UL4; |N  7%Y(;o{2$0Os.ٙu OV,xY:lY{4Z2 S?#yA^\Lq(.ȩjB o@}fހD\fFafY=w`%Eb 9_N6,S|83aԪd4tuuazbbɸJi$NNFC㩟g'y.y0 !%d|F%!Q2sՈȭ#'H-" נA j;j-}6%wBAzZ&ڑIAnhhwwd.wm z_?T.MFCϣܷ %jLb$KԁH +9'0 w\nI'WL-`;i/#my~׆~ȅG(zOR-XS  Ft CHBa\P }F=>ùnA>r2@[1`j6 "[A0zENϣkrbp<=s8fAr$*sYL+0țZ<]!HP\itŔcww۱jCLLUsC}2Vꐫ&UHMDNi?t @Gs4r=7}][q2uC{!gv rusa ۃ"7OFC_ތB;74,@T4XԣPK="QJcJ4|A^hۓp< :Z#kCO4֢p/w6~ƟIC5*$>D1f`#d Qmi."en'(ȕϵg?w{%gsrlbhh& ou9/Dz"&{%,NgsFNHPeB6#E~yOuH<&= FkH/BbTFy9\g A#l2rm фs54@p;V:\'%Ԯ;:x6 hq[?|DuyS8=ᗡ*SS4| "qՋl.N F.H$y%YSh$[clNcsn16wPAP[8Y4~؜~傝>s/@G&:f&tsǪE9_Ð(>ZwxC~^}&X746rڷl^׸0 1gl5&L;pˋK {> -SG<3p И=K%me(I@$$pr(h  ^Dx4Bvd"TQ$lH, 7e$`9Ai x ‹dH|wۇYhhq8:-4T>Or~Ւ@}<O6%B͜fBx*۰vrm^rGBLx. hбm:9ׯuFB'Dtfe~d. 3\sg1D}5GRhrh(Ľ-ʛE șV M{3Db3x 7rEHt/ MbH Ac0 M01֌ BDÄc=qM#hh i8:V Ih7>Oz5( *50r 0ۃ@u0gt^&]D V; Amϻs4pDn24 {d=;6^èfS]7 :ps(̙G6K;"n]SI~"̵c`7:&Rнax(w8 [-FNh%ZΙ,_,)ar۷W2oý zhhc;!:pr*ByHgQ>>}9=O&/,y {|\$nP^Hݵ=#wsǞB{_.7207YJ4xd4t c5ct[:g(p<^Vuh^!3eɺ)r$  A_ɚB{G9k =r!&0<^iBNڵ3PCw!g)r~~$VnEA3rAz~GsNV?I # ?yN ^C 3 IDATG.^[}FZ qrkUH&`'e锌ޤ'cEq3E,E/}v r2aHp}ĝjej>KFC6;~ r!3nm(Nw:wLͽ ٜ^{sgQZڮznq膜}7r?pK~ |Ga7&ƚ1~4p<|`VfMd4 rS^2ZB*r2D艽9PAڵ]:l*dzf{mف0d+BN[O6AnՀ(twۯv";wE]vqB6<ܽLQɄ6vwnvגM*p@!)hh0)OsĄ}ߡ{ԅR$*VRvC}QQr"rD/%[w]u_By[.oʳXn)."@W'rt?7CΖﮫ2 O KFCn!p<5r+/FnV_ttBAswrي\C+e5]۶zB%T y9~@B#yzwaz` A8*h֔3{f/"7 Qا(·#w(3זY JMEe(,{p|$0fy=翺 O=vp< w!lуDf2/ۖ{n_f Fl+>BQ umHցHw {_D(D6zB7!7s-ѽiTte 4r^C4 3 h[xjreDyEl];?/ӭ7+ UPPR>$dmQ9玙 qV7rj#ЄBG4kv?K]%3[ gbOD(wwm; WnHc~N! zA?rR|ډ<]P4\P2MXJ;گj+/HsOwSCx } |K޼w |N2ZTE+#k*Q8&3tp(lE᳛poEގl{xsT/Bˇ|hh Zjwm7!Q; Kݶ=}Fk/ {k͈lyH{C7'o9*r-!Ƿ uC->=}KaFY1{zz0*g~Μr>C3МfFd4vh(t eYn%#Gk[4X]jzh~9iV#Ku6P`E1ꍖAb?3 <wHPq; j}9CTBG m {~ڗ(O/〷?)+_?l˺\-9;[w9s]ؽiWƀ² qއvd4HFC S>Mr؆l / j`%A<-4/<<'!FmdMD"P(d EH$H܍@H܄pcH8771*}s=B*͊f)smX>!1 0ZR19#ҡwޡz4J^{\Ru&LJ4Bc7a4H{ѓ3hA 3D$AyQ\rBX>C(#\kraFܞFNE.N( 9u@@? [Ms۶E۾h[b$L>|BgZ&_i%x91=|+F(wU?N/;2* bcەFFCE%FlWF#z0F *Qzx~!J<̽Z{-9SA62S^ LSoO20SAY=mXKK:{ soSV6Cr4'T9 4y!>ND#JGn_Q"w"&/:`8OL'` SV>A{yJ{-|9#ڀCD`7YȜ(lLCt[맂k?Y76.Fs$"2Uq2CfNz3_6K4K|l`IȽ39G,F.vfe"3 ; +jIH<"e-y:?OGJ7RC;7O#W@O|F"?3B< )SzEUH ?S.D>]T#"rTW#j9RJ ! ikq9HwDJ®ҭ=mA;[}'Af"O]Rbm=O= [sM$*t[姂P|K_/]66$f `σмnٽ_Uo#yqsV"@y Cc])f!;uVNY{hy6GYK[kރ7wh<oV#o#ЗmZo9wӵ ܜꚓ7K4Ɩm67 F'M'w#`1)!X[dkӑRգ~ '->)+Pȇ2@nD xBa$ ))/A;bdBJRf"3bD#bs+K?ыNe!&ago 0(W!_` |n|:#m+wJkw@EQWI3H,M'~*L+b CJeޟ⧂#\~hu8%lzvl[S?#|/dJ9SNvH!мoF̶?6>aJRXÑjtڮo"rgHlVZĢdYZ)DfW]srigX]zd#}1'{V[Gtf}Sh}vT@FQ[lp=ؕہH\=EQ@q|Qqy#ָv}#2#`2CgȌL6jLcAX-h"dV=M̾Vшi*|Ch=̖t I᷇+kzOuKV|f"0 ;yTwBw<϶Ci6m2 {>6\l,,ͲQecv&EGT#2tG'&6g|%ߞLɱ{@0~~]"K <) 1I-Pթ(@[Ķ!eu/bF.rzR{ @5 1~!~qv2p,DM}TF`a8RXahk@+Mb1o(,@ D[uGle7{f,8&δG:E ~*½>Lj3zMDVI^?מQk1SV#`"G,av2qNXm| 椓^1Z'NNzU揗B@5?AgeaYc{t @z1U0E͵' e@^֞,45Ʀ0>\@2`?YY6lfʦb ?} R ڑE!ZwEېv;@h̙,ӪK2u/t\?)*R }h![k?1cb(3"3k!:8}-?CcbЖ e)ȴ"Hv!vnZ=9ʟe}_}{ڳC~R+EwbV"7>#l1Z~Nz7V=k+ys6~ұeOs{ϋh"M̍lAbX*EI"0'7j@~5Ayf1V 4Gn!߰v⠰3qh6݈AgYݒVh_voaE@- ͏)hV=ޤhCsv-ڐnH'6ӛYY$,3% ){s&Rhmev쑉53k" ^Y2P'ȩgHIAf-]68Rc7tBBbv <Ĭ@ ]0809-B`m7X 7Ů H1G'+Sı^FJiĞEw1(@ӖHiv >Ў8D19(Dt[LWI5 wΚ[2hᧂQ@y:EAsd/7k1ߺ36OS˙wbP>BcD?F!6֗~N=bE%+и.;&2p#j/ B#\Ĕ C3 P@\¢\doOkfǚYec6ŌY^(S?O!s@ `@=(D;O҇-Pu.;EX")V|W=+R%1) Lb-3C^Kb141_Q @ /HG97[#`2٢M ^F&|? C&=׭]ȼG]n!v<|6( .Fs^ vD f-~*h;ܜ@24ڡ6#}V_7wzz\XxkޣfpG:S% D"SB1Flhͥy6(Y1P{wmMclfif^$r$k5OSR2ǡ)yܪHLG@t;*Ahg݀lM`GU R\@"d^D'"d-*m"b}ʭ>kGщr[="%3q*tqS''/{sЄw!;!oޏٳ. d|؏rq2Gb[Z2NF r\ne>Kb@vu:]-{1`ĠI4Kl{f#sݙh>OW E R0 yc RBM1bWb-@h!HKSieD k:DM$!0b#ɣH!C`% ģXT 1"P{v:0hόbe۽(E9)GHaU"ڃ8XVuYGl n|~Xa^vzuo2 DvZQOC` "Y4` yiGl ˈ>T }|*VvmHȄ5VvGW zk+#1vVbL9h|1? rb{ٵ9| YA d9_"= )ZlnUH={}wScQxoژ2_B='#3:\9d^jcr:):V9qi坆Mt:}dZLy zĂʎod~s@%O ԇAsmF>_@I~H3V]Wmeg=u\N G=-7)/R{3E'{zQ s&P$YFfImBQ LN@>/턺gSWYekm#ʁ+Tp8eB@J( 3YBg~#RRw#v  ٵёI%{IvC֊/;@rj%v^:+wh22 d`+:YnY5Z~A+0WhM kQCu"ksZuG>LG|0]{FGGqF 9ϮCP۽"iBo\짂I}:y82m}nj~*9/I'7E!XqtF YчCs<!04qؒXf͙jG3Z 6=q8}лP@݋$6keɉh8BUCJ߫6-hZicRh>k%a̓z㿨Qᨏz/G4PMY7 hL9:Xm$8rFZkc]\?,3+b?]_R:$ 9+]r-z!%Q 燌*8 ;PΈofi 3%_G"eрk2RD\0 4,BU|uRg!F'[tbF](l['{T &ηXH>4쌔j SqY(}]9h͵gH'[T{tD@f!63, BHKN;Fes3hQ}Yg.@ &Bdf$oyݓ>;C-mڱIo ^GH{ƃPx֗kLҧg 5Ȕ S,g㕈qM},;2 }%#@^~ɭpVz{\NEYm["bC\4#x4EUyc=iY9جvh^D* Ժb":~A&Z~W2 G*Y_TLb?9IOw#fnfr bZOP:d7b8mTZGG > IDAT?iġ'5YY6lK>c2#f#b `6F2]ܙNzקA׃׿ҽPF3JœX-F!y9ݮ)͖ľ*ivZ{fL0#8~GB#RLO!`lE'<G~ew!?#d**Z*nl`5R)"iDn12oF4I ٻU׶8˾2v)d`\y$odΈT5o!ߤvE 6,zޅ??IMJ ?f`ɸD  r O@>,[1z>?Tgڥ_P^9;gg4%1@@5F9+4|Flq)!x Pؚ{M:Y%.R1}bA5 m֡5HdNkx=q\[̈AW4~s,[lK`,@Zd?vCrR#SN}6#Ы l?~*Ȭ4kYYBe2D/#TEE=o$#42?@LdtһH賂A +6վU 1"+y٭Fy,09nCiG#%@im*g$ge$j&S{uKzw@fӐ bk*?PG ߺyjEhEmw־I/g!R+K-͟'"YL3rͳ 7!u^5?bDw,_Cc>PE r{h_ծd6"k92^6U`\JdbxuEq-p]:U>Iol:-D++d4OFىAcb"WTZA.#!4~slfe3f;&u~*8 "A4 &:1)OT.A/M}w!F& R:d># T!&t-Dq[P41 /"dzNUX]T:K}2HwG4_ZKy|D EHAW" Vbe @AրLAi7+6v5b~v4+I?tCC.VaGGj"KԈ 4gn/Z]캽Nqzu餷ƢɏDn@ ΰFJַQ߫mL!8NCβ~+܌O?M'Oj9:E =?}\ۡ!9h@r" 9bÐJBj#Ea(qB4f'ʢ` ]hD}ܻ|?}mbid6:^6j*5KM1Zb2WF9@J!1rA $S0 &1SS %IA hq= 2\JL2+KѢSNˆ+2["@$42]gu7R!r a2 Ԣf=r=L_FfVǷceKbtҫSA9p9oF&$V[:#Wc}*2Se"sPEwֶP=Ds 07 1\'C餷>IsIkANu| Atূ4o%!-ɨ_j2tilso|zpn1Cz9x8X_\䧂tҫ62wq:mo+KAlxӥ=t3!(]rzȨү*oCeġwō]~4K|Q0㪐N! `j#Ri|wFNEPCf7#OBlO{^ٳ~HAce"`#tbt!u$R>)3.9]XsfA8_2ì7\Aleg[&#o' D i1RQĂT9u44>i}s(?A2J]D隕e4tmqed;G3# 4kَ=.f'2>_Z^4' ؁/h?7_@yo|%=;mVMFԦh2Qe=9&Vsyh 6c0U?Ekߎa~9w/pCSg0L;^. ?l`OyH߉؉Kb^LR"1w@fh b,Ўo` RG do""҈@o8K."Z0s{(0,@ZWe?C 'n6gtk.ŷٳ"EnH1BJo :!c04#t"1+ PU[{DtեXa_:ߣ H+2\CHA5}@'?H|V!е/{#6GV#pe8x#?=}yoׅBN}4?oL<6>(餷9G͟S8 #7HdƬB>p. h=䶭Tr\S:BT@hnT\lZTPᅪ,CL㏪ ~m\h5ttI( 4dT#.Qf.ι=s/ܱ0πT̙6ܰ0 '}OBēs.# QmoY(g)g"X:kE"-@;!PLvO H>EJuB晕톔ڳ؄S3ؾdLmvϪ%tzT•=58EvSAV:M[j] ܜuuZqvPUtE>˭[!P9MC#Jн\4'V 0wWTpv:-G@Gtқ駂4?*/S;́?#Ppaݨ\5mkWg";h# ]Pޯ-]4W m4狁o8'q7Fے8s/꾭ؿMq;?y9yC2\!u ƮFGл0 g;CKh]:9 %V80|9m~㜻c컛^meCs;a~9ah]' [sa+"=Q Aj0eql@|JbLw]):^J'f>k@d<k?|DDٟ )#b#vn*ZPܵ"4!"v"?OK6S(Hf N7hnUx?'l &DLh(M> ,@`dj^D#^o(2B&,h}ޑ8;WOjy{İ=m춷vnuLc@T{ȣӭKjzwt{"*X+b!|g|~(I'U~* 66)٪Ay سB;y#c,GkK|79玅YM{߆;fN\~,nn psnsn`w%rG7 Aιh+ 9w&axvIi] 09Wq h7aXmwR;Ba;Z; <: eι+|WKn-u/nE13A^޿}:UZt[꧂@9O BORhhvx駂} CʵrB6 )^(zlv$2YY˧!R!&3Xavփ&:N!Nؼ #tSW%H84GیlE`ߕH|I^CJL]ymE hDc JI3%m' i7E^ַ \d|?zh|y͇A9v6Ђ3"vS.2C:MSAt]U9uk2ΪYq~]ge}8Ákz.fj5vdgkVdFFR- Kл}OߕhZRGd-)]RN0_aF#_̣Ͳaҫπ}ѧq=G'!ڌSI䜛M|#Ĥ֚ǜsfp}~aT8㗥Yz:xMک p+ra4!]aV:6{3Цd9w+Z_&[4CNF੯)WC:H!Fy#?D>(#1`޿T0 -#؈r5" p=bM'w`Ą@VvMYn5yhZ5}|nF/qGnW_ 3ec9Sևݭ%q nJ"gⓕMn51q,W~*/E;VK#jpz\s\W0wںz7D|0 aXa7~(FIs$_0SnQYsMte59}ʔoι@" '&-( JTp j_.K#3b/bQI=JŝF濏Bw%#cx秂:B@^[xǚǀ~*52e#w 㧂xʱF'aMA"~Z! i_C/(b3ȜKحȁ3D`9bz>Fd}6xI݁ C:hdcS9b!fY&bĶC Jdz! >N="2o@V K"_ .+@!1A{7SE=sw 3R`L{=y8޻/̂ʹaߺqȓO۩ϭ]E>hݸ:e;QekcKGDLJͲ% jܰ/;f_\05si=[ޛʓc߲+'sOwtι)h#ϵ>DxoʽH?}蜫C9|\{-.ιTdYVɒE@ʭp<B/FT螉ؐ"tjO5"-؊Ð>YDA4y{ ?F4oAO"v#`D{:% =go*ڽ%=)و;)U=ز_Y{ 0q2b(Mkvg$Chg LI'Z ZJ 'n~*v}2dZ@ ^O_BlͿm\F-Gh ]@këH!v$G6o"o[6k`̠hLvdk8HIwjnf6S/t|:-SY8zYi!KK[9~P%KGspl( Uv˦a|ig'2Mn8cͲu͌}I 3VHg"\hQT " jW y:HE `+HG,OX⚅k&2e S Dv:@2 Y֖l6, )O'WT}b@dnVN[{99}kg|wg'vҹև]O-v2;"(dk={ y5{Xt@` +kַGY{o@l=?K@> ͉E< \İZb}j ;r_Xs#sow+[ZXx1)Ϭ=풷 )]tQew+ëܹ*,K0bOM66&[ 3eO!GI{;w$R"_b`#q N;"?FʳR NPFF`oIJFԎ0w#~Rc Rؑ-$mqOC86.,'zʮk$f{!] .|)(yȏC@gZ=g!@U N@tlv/c~X8dR|}S>6n+-/ǓB`6+(E3o'GҨ%k4y;KtQg 46IN;DX#cBc[PGxޏ).# A"eġ'5vĠt}4˶([ k"{ &%)ST)ֈ.>B6b@Q2c(h@lrdJ*GĞ޳rp:)Vy"[X6d2_f3TMU&dlu1>1@IoL bu &&+=iEVyhuHDc52%^ |NzOߡ1Bea4՞YнȔx]YI'?C@nk2Xlmm]X~w*4_:!sͭu. IDAT ?(뉀j==x>x(Љ־Bj+[fuD9OМ<ᧂME2*餷DU?~t٧}O|yȨaCL,ͲU(peeώGJi 0RhYDX|OCduhk3Qqh@Kd@A bP̕Co/diuz,}|ɪ- Bfڝ@c§ _:Yݭ?"ғI܂>QW"?6TpC|XwxnW.l vZ볇aL g݄@0֯"R$"P4>D`"ܟ{"pyڮKgXLX_h^jn?D)ze ĩr7k䗥ttCLpQe'$c&}8`,CVN ub&H %Rrlx<C&λP4T!/1g9PD -)\ Gqd1!4+{bDw2e"0q5ZvvzCJO"6fWZ[r78=~駂c ,,&[zo++>M)"o}"d Qw ; v@@NH';awDR")\ (hxdBԚkXB'6@EM{Dʡ3`ڢYbB'SAdEE' Cwe'k/ͥY%\V]+/k#G#֨'b:Y_D9̲p]In2b)ѼHWb:g\kPtnbcI'OȤe_J'`B>k0¦]`MSKI:5/%gs]3{TuR:ĕ.[:de(\IKhyI-]rڽYoL:dmS֥YesYsU}hy1LoO6:~ :)ӑB35S bƦv]])rΞG q\#Pi4Ůy8Y%@1|!2cm茜ok")-KYiL|L|+ 9/%uY9b:"skK#bϢZ؞"蓼v~*(A NH=vE_f Od+F 3cpYBMXϬ#-@J!y$_? ;=p}:]NzG~* 餷餷r@`m%bJqoqo|;w/n2Ϸz)*XzLumĖ3q; 0dTY8dT7oUm N2lIt%A9+vG MYM!ι<{^p9:~u~.;}MMGT܉"<Umז! .{.Z8@6H9B.ZF 6 .b&!в=bdYQ|Shx-&gCu1^XAȇ*{^>:ymOD(^5 ;+W秂ȉ}8po:}~2Q #ݧı֎2 z!3Ry9kޮ B ]!6DlNrq"l 1R[WY{/]!0vjFBv#? {~VGG_B ]h h7l셫v$e H3\s[ME7b~7dTٻK!V/ ;U6K ܧV:qȨǒ1.Bs#?jSܞYdЕY=T0EYZWG0 '}ϲCW0k[Iхaظ۾k] 0}e7P4)"i1u@ר1 "(X]9-R"g>!ezu7blSh]O!y9F@J 1IϢVB lA/C1 ^F {8Rm6}A,NQqV.(RA5~*muNzz !4N~*NzìZ5:sWLT7f!P:E;!N!! /B,oHr|͇Έ\6Z1tҫSA<!ȿ]2n& 93g,Jƨ(S餷A W痣AIEn@;pƖ1f3kIs𗛸:?8|a^~|p sCsuu@f^aICkA&r8sMF:2nֿ#s 0=0䶓#"8 pEGÁs$vJ0 ?qtj& áYx0槂)bHXt>@`@m@~ٸhG~Ig!hO{F1ZP!F/Z?gK!sewg/o"и' 2% gN 2)s>NzT|y!O!`4["e=c,V)?ʚV>}Ȫ0/ϡӛg8\|ɢ|"VQha{"6t"s)qD^`,P>T#쯖jh1ijЂG^$`0Y=*;-|*k{ ?J1\d"Mo5ܔ>fCp&w!M=[hwE4ח{6 U&c&u=*G rWǖc> =J̹,: {40. ιιhB9a890 8JЦWaǁh y9Sx04 ùιa6: םsЄsν L`d8Y?a9r۾U T蓼Iod"+Hތj&!ک!;;Y8H'"۽#lj`B 8hL邏^ D$Y98>wAxbߴ($P]oau8!r"2NB/iS\̟{ \geuO'{˱vE9 ްq <=r]̇ZAU6F7"|ͅ]w F!Pb=*B~;kb?LwG2XG>oE;avfPuȲx;WV7]YdOsݨ⧂skt'5E[(~v}tt0)Qe_ɔ UVNN ď~Hc? C7u}6A1e}-$bGgsYSaN{? -{O0 8F]0> ùaF@>>߄\u(D YB8ι[lhIM ] vBӱ!wtһ)=Zv!~G 9({W+-Rҟdz|LY3`x\>"ZWڱHA/@ =P!? r@@:+t;Έ#[@͈鈀XO;?"-`tNFfy@W?,jl<ƢXF&^A f6z}R?܆H!69E/K| sMaYVφ3C>P0, ðڐaxZv-[LD^mʹy( u`$"D]e ðka8IWoa'6v>je5S |1I :UNhQkHi_s=G!E:&p1?Z#et<ǮFCj]2)@ƾh2^^7]!C3ieC z$NSM=O5ݮ{o᧮8e򼶍\Kf" 4[{ #g@߬|)+Ty}ErN|( ֮S 1Q=xNyb~nut`"Qм)[zgë(?}EdQ*.Utj+zݪjj}n]j!ŀ *Cؓ@;?g?ɓޙwy}DO!Ȭfd<L!6Z_[:|D? ےh4~DizkDvT=n/+f_|?glawokʰcӢl9.,٬.>?h _/Zw.\3;0WwbP֪VIo~]qyE+@Â?z&$3k:59:ꂼw `8G؞i%Uvg9P;pkS8N%x q ȁf8s-)[nZfCK\ПaOr=#lԐ)Ho߶)ҿ)lĤG`;2D@j8bnA dG )صU͉!9& FJ8<= dbG9DJ0b"[Dp=kf8|-' vϯh@\,juZvIcZd9 3 ߰E`0<ʈ VbgZu@Ly q!6 EYt-CYE,c=~d}Θծǁ]cQVCl;8 ྈq WW#xq8:e ЭF#l3R3;2d,rBk6o!&$E@lwڒLSsl7VnA^K͐Qiq=-:o۴,Ufn*RXީCK47; 4?يҊfYCSAAX܅emr. Aaqh;H+,SW,/Ѧ-haqYheGi?rv!As, 5[:>#u] \һ/^ 9Uh[S.dcSiW~] ,^268!&lR"RLvЄ/G3Ziﯵ{ TS`[:]YH6Bq\oƋD3?fz{ ?}z[/c͍ /}6R3QlDljAfX (5rғ'TTX\'I٭e'd M4!#PP@7.cv'NG;3-H.+ϲgFl ;uy3b[{/:u=wX#`C֩h^|our3W"dKdVX (By+q7L 0 o[YXhrB!90 urQ!u]m#t9Kκa{b _[A@ZP 'f,뻰{q,LB^oWwM re.=W+0V̋ ZRb+nA'BuVY)2 *+9.$rx8? .+Z9sW6a~4~JG jrc] dc?Gk3펒q94U NDie!e%_X[{ Wg5|H.Dqt`R4[ &%*#~q=?dϮE`ĞMXޢݯND;t=8 8=]/{Ӊ| uVu$8-#:L ߃;M kX;{AT&R m]t9Q2Ƿ 8 F|d">.> +tF u:d5}WkO9zY)ظ1K(ecRF_26B`[dlQnxzSNIf<יY~O򤚀t*Vaꢟ$LJ+h`?^ϳp2;ڀƿP0Z fsdzzϽpUN}RX9/ eht@T)B gh,NYXG+,75糡O?- ` .q٩F=˗z#d^{)(Yܻ9~wGJ ң'brjOD`#v+ Xjh@tĜR MBD% -T@T_kĔeZ}ؽg!sX{ 4fαw@HɟEzN`PPMij̆le]dmF30x0W}9 4ح4He8T HρXk D|9zV53CϮeMxɋu5Pzv.?Cb,+\Ki Z 6=2!edx.r˸bۺ!d"~MO:}L5PD1snd"O\̿rnA[U {!w&UUJlMIh|oW/ze #R IDATL7]ѢÌ~U>:Ԓ8iѷ³oSWzs ,,o6Pլ tHN^X= yE[d%d{4OstħDYA9m_&PLħ"gEBr#Bkx؆v^Z2S )ˑi|yz"}xkwN*\d""rLgp"Ll|g#129ޞ R[UYT[[ju|iF>v%A@fM[U (׵%[1SE4MFf"^- }@^~?]bLíxhfr![B ~1'!ŘǬ=s'KG&w24?E <M ߨr=cLĿGM't4ޞ ?hX:2%m 4G{rMt,p]eU_Ec+K[mUrA^Q an|BkCd)r Cɂ -+ZSX߶8 .,οi{X=Rh^(+v4XZUӳ4,ȩ>~#D|"&"&lb.BtrgLEТ1ٞ -e!w<F;i@Ёx;C.1̋8) p쌀iHɕ"JG`{zrt1b,+žV ʟx+Q8c9"SoG 5TLu&=;E9ߕkxI&=^׳؟DIFc p`2ZL_F: qZX cHfm0vN@c%Ȭ1bZK->ǯΝU3d[ OCQ3 1B\3uS` ewa0_QW))IE<݂p7YD|0+hl=7ׯ%[pQuwsuՍv6Y+mdyUU}4G۠ . ڜShmPX,+m5[_Ghx%dNҲv][RQ~{w}!b&EE z¿!䓈z(]DlđH@~dB<b9 PrP2? ǠH Da)2}~ˑ#H;'9OGfvD]j6ک~v͑0 v6m|Awok`)Y;LCgnv=sݼ{wL+޳9-9^oi5[OhzP_L7lz:z QHC ]NZtzn,X߬A~xPam|W!F-1a #7"ҋgxß n!<=C)+/ֱHIm}k|>y>vumNA!R[_K"ފNoUͶ"b +6s C&`u }E z bP~vR&p CK9"+ZLc>M{51M+@˱ze"]@ 9WVWe8%ZCٮ_jbvMZd"4}1@p`2/{kFʾs0s& ʘqcxN :]9 ̰@6/ ^Kiqh|bQh?L&`)k%DeKT R5НZzro! [2,/!^ Ŗ9m^uwoäȇ16_ ٽ`)yE#'}UrF^>]%om~83!Lzgd?g-V0q `ܦKبH}$"  `>\%t )Kvz0zѮCR!ߐÑhb)@9n9-V=nhb:)\d-My)'ZL~@%h1"Wkgh|1nY ֔h[L`ֱvxcmV=J"Cd$G[w /@l@݈D~2R!rw[q ZKx>O&ⵅKOu͗@fPMIo+Vҁ5wk ={ rX|t*rkd[@ ^D& Cs]Ξ2USј8lasl5#'gJ* |ӑr^MQq=? I}D|3sᖰl!6t>tU" XwP[=h'w^9K6AsG˶Pֈb<88mA^'(tj}C걞Gu#T@cvb a nRǹe|q[ʐ4DKmYm־ ٮm`6-e7A!Z5}N N߃֪Ah`ne9( zA'ywth.\YT!XZ}H m@ts' ]Lcgw " 4x[[{!% ǐ ua3(jK}3RӉA&3PL繞OpXR 95a}o?;Kɬ? )VEyG;MBIDu=PXzokAӑ8A}b7Br:V]AN6-8%=S;-Hc|H݋@|?aH$ZLgC `v FupfTκﭙ>!ևhhî_L'`?C!󚙹_ea[QRѾt=?\Z\;Ǿ[̾aXbV< {{_Mڒ7O'-WWgpBA^@aaq~x L6Sb=5XNJ6245L&1D~~0lq&u p%yhS_s_h 8# 鞰*qc Ov`؅)N њx.2F;3o*. `­Xw٩(uq=Z"0^?ٚaG#Zl{ps=2uB;-a A^V27Bp,|3^kC)+"dR;RL=Y,b3DڲS:R_(XMV,р "P/L}{!$/9+~^L5Q9*׹_z~O&kJ&j?`iW2c-EXrPvBfD ý΋Q1ګ}ʞ! R_]{h<}(Vۃh_c:vW[ʱS/&񚉾#"6^\,nV {8nS VC^E}z0zP?X9?/Dln%9u}Eu} ʮi<xqu\APb׽bA`8寎Zb&݁8@qBi#6^0q"_t-;CJ-:M 0Ѐ-kZ/@ K94.Ef5C/RvhѯF<2hG!`vJ2L8AKq=`M@vxFo-TB&ˎy? 1weH 8Nu,)#)"ލC~:{2 kF~J&gY +]ϿV $Q-n PuBWrofsn1_77]+(R-FL+]o7X]Q *W*FG'46ρ}uCvo$p4w]` OBrĨ@um(E_Ǝ4*+ZZ1OD,ek:beB)bkX W-zoLNAP8lO?AZk&Hww4bюsg C>TԃxXJ&tNay%QQ#Xt"v,ئGK0bF nH>D:-!Bg\B6k4H9g#0w,2NF@}_صDS$h=r3 p 4P 4t߄ f!vs5-3CjĠUH#9&\ A xqW9$_>O&o?)7OPL33o܌!UN9*e" F(i\wzhh b0/-''#P=1ke?a{`hX2O%t+.CM:7Aˌd"R64W擗LīYf'fCPb %@j̪X][6ѡ OA!.2=vjw Ʊ:\yx.? \ϟ miBޝ(@R`]]w/qP lVN_4F' "38wѕJ[9n#D|g{S>_.[%=ўnw$b+/G:|vB2fB1Ăn*~V1G'kS1`A2ߒH]ѼڻLX֨?>zjSdzD|c풝[!jCGl&[!Xu `627 AڟH%u# TS2,+s%+Av0GPJڠι|$WJsr ΁#b;-A7>C@=Rm;(pB z6 KȜ!ձD 9b"3)p=b!r0L&5ۢd\ *WY&Sz~ ׸?^B@bسz!M8$諈MB@SkG'? J2\H߉3lgw=?QUo\_P;FKNGC{!>Oqp UjL"t,::yۻ n{N/!`t8$@d(cE4!@v |SՖ |3ȇu@CE+Ң7R93K[Zg!F/C,%|(i2uF&fh!DK\3b wM{l#8 !L8ԷR#tݓAl"' XUW:ҘKE W IDATٻ%jF1D?A }&C7#G ψ%ZzHb]Et=}|ւ~*k&#9Y] 40Ea2_Xs/jot˟u .]{:B? `'jrx?OL'ZzZRg Sxcd~r1e{d:T^84I _[{wvd!0a=v"YۻPz˭aRd"D|(F^d־KNz@:4F`*}jgW@o[/[(VEco$xhtEcp}V ]XzNit@kv%\WXߤ h}~ 8}A^я]nPUW]]Kv!XZCHilJ.G&}3 i*ʐR-#b9^B5QZz_#{@ˋHɽX\z5]g6Bl*uIt Uw퀁&cjȎḃsr3!@;-gZ9Yy[NͳnD#"t1)./ SBX诇{9i j{%b"n:lb(9HOX]YY^j!=,5b+7%cIz?d=no2=՟l]UiiUi-f߿G5bpB0 *Nt"=9ː.1+ 4|]LħYvu=?4F}}&\Yp~2ݶ_hG)ˁד =$N D) -M@^6B=@>opzMzT{s;(8tl4.&2/:X8 l{+]);*ͼv &tkxdz{%zӈEQmDS="&-lE{1N"3c+"PJ 1"Q}D Oha}\ґ2ݮ=jaͪiu:܈2!sxcV"p81v"Rx]r ZݟBn+^]KK=km~g )r^F<4ulm&e{^յڄ)XQڡ;bFk#3)/*lDz~[ 'd\e6g s7_91'qDrv]l T'2ōNM80[b6CŜfe Gj8BqMd Нneq 1eB󥱽K:bs T3Wq>E֖-Os3m;cH | -ZTmQy\(cC$d2Ij+E'9pk=0ok}> :~ .*! aطpE}97Bs";1GhhT"uqj):r9Qb5@P6*1~b$_B @@ek\{)}zA-8Maqk Fվn ɢMb$ꇻ풝\vV0vZHm "@B`2 -AtүQBU O"uh Rjd>셔BDJB"8BVȧ fKȔqt:ٜW=='+#fk;X[܍QL8:fz~d">l}ٟ#GbA#"k5RÒlYH 4H/9U{7R@jP∱s)"b}px d[Z0z~k_@@fkXDx ͭ\kh4D: #Shεh,k4N%9ɞ1ޫ!/"Ǥl]+WKkhn(7#Ç29PW8? C 4fwxtk18T߆9--|VV~/"sk(bxgB[8@"`XvV%YhYM]ZAC%QUhg_-"*ЎU+.-v.R̽N})s۱kvgbnE;6|,R"A g3ڳ.B pC.Ng6n0hxH-D w@fm]? ޱwDy%~[#%\ { o[ٻq7 F<Wt1ULM"ߴi Sl,~T7͒T6Fhoҙh e75#;!glN컖Vvl{~m}̮? 4Fubi6E`&K& ,DkvNAym@BZf7G j1b`BИ[[ c k4g|!֬8 ň+'JTU/k'OjۼlV7hڂea0y׮k^PXmƅl6| pFD;83 -#&g4;d֔ORԙnm3tAjEcs fsĂN&">h@#eq;b 1!7vl< A rRB&AAha#@t=RA;[&"&F횷bZXZd/L&]՞u:Z|\ OaKpXB!R!_HAozڪ}C0ܹl:f4Nu8v捃3S_{\ 4M!00xwYmZ3"o T%97B,P L/H܎7h<=Ww"`ں_F p 2C}Rc+D4c>C1{v@$vLgh ͉@es4-GT^ߢ KcCg9%h|WW#/13IVrb{2`ϙC]߂(H4RheyB6+7l'RXJ ly݌{Ԏ'yEs K 'b6Vxk,U͜X75%q촌yy_פmǘ?e ʊ?!s0 k}cZOn eqwFAGdA}N ǹUm/;3ֳ Nt҂'{K]EPz~ScтVSd] V"pPJ.eA$#}u2ѩ'IDd2 ;iL"Y9h*WoB)PnDo,W=#F.F,R/5y:EҾO;ZDf&??4#{d>b4t= G:pqw$c7.R+1#X 0Yro04rw4..B!V^"&2kVTj>hS34~+Ac;YD''A`2^!зHu,ztsnS ?/,o ,@w?8SA^OM ]<6-z &gbm{IKgM> 6TDkqwa#̍"T][c"\:d C'43W#R@F>KYnR||!嚆o⼄XAH1zHY۞]au|0dNέ'x}ԟGq7XF v/ҳ]^"}_d"^zY~ e$+]=eq:'v][OWNd>keswm0D-R=h/6<yxVBk]+;I1<\y-6r4^"nv3*E 1-Mt Ek1FvzgkќkzDD]noX{Id*TOhL-Fc~TE4$ȏ,fKx!QnERC8?mj+'{m("sbM$ E?7fZKνʏKνrΚ{]mA5qh. `9-wC 7[N4@:&Jjl%Xq҃>٭6ؓ>db{#13ș|!7k*Ђ(G&dZioD Z-a?f|r fi; -#S!rR_ 6*B Ğ܊hT&amQʺޱt4#pZmYEH۹ĻnG{_rRKؙx‰gN[,K˜:yD&h)(O&⫈~F0!FX1{39"v=? Yݭ=A^Ǡ1C i(ZLW"VZ=VlR`ʞ]@ssk&DrN h׳wj}_?~;ŧ݉0#v=:w?R]8BB& h4wNdN}31xmnA}4F\S!P11͛0I{{-wxǞ=IW4fCRXXCDl; x8%jŨ="F5OՕ]f̚R{)e)??LȗD,ʺW2bMwg v$K/yAewq=7oz~-zs_'HVA hR]b_@-䢝Y3dFh@J@vH%roNd gKVt*/~ 7 ]#L-z〃u&)0(7,@I&Q2K,h]SdMS4Fz"c`+}9ِ } VA ၁*OBVN8V!1hoqRT5BI؏Ds)V=k%w緵zOd6<} 1n9И;ۑirnWќ[mu[$ b)G V>뗛[ 0 ^|h緪_h@vP0hIk_%˿^9ʘ >?e NN 8AsqiǏӁ`ed8q! %)/P!r&e3cK:wx!v Q꽎F$4ο7X\28.GX!sZELT6D"ewWLǑv- )V>HC@d2"p]-a.ɇ #":Rx 2o9hM%k|{hrLU :jGa[D䈝n11t1bM&8'G_)DE;NXEa&4[zc Mؗz& Oboew"3}MGvm?d c#YQ>]qh^Ys  >~$hC0\?k{nաřgh4r гA~ 8Xl;gZ~{3и Bp1[Y: ͭ(9Oݭ{B'^lmO20 IK#b~i/+^|ܖeI >68iIk9I:)eӫ++-~(wqA٨sB\Bqzl q\>qw^ vTNvH0L׺6R#'hu߆@9Z;Ei_}7Ī|D=E"CrBԶ!%Ux%Ȕcn?Gbvb)Ys=7VȷRm*3eRd @euii8~A8 d)JkMJ{ ػXYvp{?*.@j4@lD'G;C]@Ll3\/@&cr,}5WT x@Q2\SP#:lqwZEU{7H_7!]s8p]IZim:+_8YAf&rhC#刱C_vaz]FЬiK}=g?wFL}Y,e/NYgڊ!gOg"841fi/S\8Es)dv^ztyul2;U;YNيܪfslH,MP_~b݁ f 9nCGl&[!XѸ-WOkGu6O$Ԕ.m';,8D6Z4ؓ(d`&(G,X YP(/'p"Cl1?vݬHY^*u] mʵ5Rtp\]G'"%3:L9ȔoH :B a5 g4̓i0SS@;-4"?nyQh }NEj!mj!$>%r 1Bq|d?C&D/G7kyhaq~7d^:BT֗Ah\-rP |3S,6-yG y&w`,d">Ty- NBDXZ2LI-sY-9ZC&xnvE刁_͑ ӑJ"ŷ )xID9_#J]E4V>gH!_mM۝d7XBlW _O)_vz~ϹI@a bs\" b|z,K]6.A .,zB$@J&!D[,Umd1ƽYxp&<IΜ;9g=R#o 6sdh?]ߧk1( 9ȷSL"{wҮw~>2uC(6N(ewl(HKu"ɻd9 1֙F &HU"54k/߳S5ѷX~x0 /nEHwMA`ZkGј϶w\ ]f~}cgmCw>bi d!߈Li ')§"u\Y=͆H,q&Ac?q vx V}0H7znEUlCCcu 2wG +f9L;Vob,mJmߧDb #h͜)|<#hmuFD?t3TL3{nz)Bʬ.b 9{w Qr6R^ujM]{ RN1}M[.E|@;>ni RI>G(~2d6>)pF#;wGI9%h}4HUgWR!`y/A̳QXh? gcL[Bd~d>R+cݭO49Tq eiuigԠ1\vƚvOCˑַqǭ(LctAf;-{#Fo&idRiD[ ph\$G7o$vas7܇KE@C<~+7 WWT@l{h켅Gw܌{=2_U^Zl3q%_奕 s,bu0i/N> //S({T}juݗ\vgh/  }s.7k2?’Gc"J5EO!s_"z'7L&g1v4R#ALuW&vQZɆ ~FhIdrr*}o'Y$_tĀ6K+WUT P?vF@yyi嫛\4?Gr!b}{04dMr,me[j60%z 0 177 p#65Zuh϶k*B )=&$IJE`0MGs脔Пn# RīΧ9thB4ǵhUBe<6 @ъ |Z h V@.?rM޷D?t5Έi\d1R'ʣ?\5Q1nm~']vm b^܆_[_d$bG÷'!4)B@/VG.AobHN=k7",jC.TN!vrYd^]c <uoLE1i.ޝdE l<gε}bhDߋs߈#}Ոa@*swIKEUhLOFtOyieT%ʩ/睑ʥ- 2y2UQU6-vvG}gyiЮ;%˽׿sm\:1 :xe! xrcR6PՋAua<af6"F"@FJh? OD vڅH,Q$>HeN.dRYm'M#`R#>)Hnk'"]iPz.A(PZaOBg$Xhݝ#E[!zd 1~#vkk;| U{GvpRw@ aI' 3M)cp۲ XLyw뭻s`c!>4Y@ C2OMX]_]vj"Kp܎)f̘Xݐ2LC+? 4n;߭o렿i]XbL].p oQg3" %D뭟rOXF U]h=LID=p{ dh'xˎ7yg`aLe/#奕 ѷxn5|ieĘ@J =H`mn|zO1~`Ӟ.œ^SGme[ٕ_E% ЮHj7( |{;Z?&rN͇#c(he8VHhº#`4}VHg! #ENFʣ=b>C S] rmɑX~ Q(b$R4v)R1 SX-GßՇXT#Ξ@N3]vήsPVY[09Ƃ{bmU#^]]mڗ8TS !3v+N O܏ YɈkˍVpm88 H)fB^'";~["u! Ȭ9%}wE rw~#lLݟ19 pkn܌D`8 Yf?/̓td s<'x/nz@_4?z~#bMvZ<%T{ Ak;rL{Me:nTTǠ)0~fܦs27n @rfxjnү,겪S^Z9o[=vUau ^hmrМ}?8yIwH:=ϻ}˾̹|dsG3}jQF0Mv6Ig憢ͱbhx9ohqt^kH,qAiWJ<])A-JģŖp7H,RDyGwﺏ H,q%io!Ey1:Ʈ+EJ,*.}4E FoLA& 4yRc;sVm^ (0j:%\Gƈ[5[qam;2mD~ lX"ֲ#ė ~9-Դos2{'v5 c}a5Kt&b9G!@ LvWbrڍ VJ7?=xh{ 1o{X;*d{h۵!{ƮVt,]W#@kL]SAX ehs_91X3Xu~K}tKw;-G!uf$p[\afH,q5bEc@̊m=扗L<&hN奕_TTotqyi}xБj^p K((_+A`斗VN(/lzC׽?sȗ\xJm_V>p|=^uVSW 2uww[u^'XQ奕?`A]iQBy,]Ċ{<:lrl>A oSp# }C٪ EfܿgN V%h+C "|D,){;pS{G,Vmr.Dypj$xːr)z{Gf4eǣ"D/R \D$p(({W4dE;ő">LC @`Pd*} hR;VҍQh i@V<&R#oXpkY[CH Ñc ) \`rjiS XMx40R4<֯eH Qֆ  Ѹ@3%&nO@[vvOݿj Ow_j2B\bXs O#ۂH%C鳭"KFؓ ̱!` s3&r4\dmU̽j{ #x11:1|G &lөq˗㏰>v>IM2hbvTƈ!&,<֞Jb[[f"|6]Mwi[xe"S$hc1).奕*Bf%M)n0UΚV;g 9YշW׶-olʚ5V^Z|$wo-[u XsN\C3heTT\Q^Z*+@ M3奕7ed串Ҿ~4orTWgb ]i>Gx=6s&y }y+><}4&}#B} xm92#R71 TT`;,)/U^ZE}*/199~tyiZ4}OXC6Ep6-G!v9yUeVT=A<ۀgB! ISP׾w 6wŽܹoY|b?b/_*M*{_~ s }~ͧHyާHG>y6-S=;i_,[3]TyW\4x-GmXc$8)+9< *H@0%H쉔]ڞq97/q;"81\GpR@OtHȉ@k(ޤG0<KeZq2E!x;a؊տi4Ŏ5"emZa&idǎ VFjꊗ7&i@ vm>bC # Sbx ko.)k"&f;G+o}2tg@g#֯B !v{9풭\"3Y-]ӭoz1$lB9gAƥr͆[Z!|뾰سسDGmMs]]NOѸvEf;p]c4. +z4ٳ ̦ >}W}7#" @Ffϟ7&Kܧˇv*q%Zٱ諗{fn'==3h|I3y漥fu/P~(M$>?!% <]8ׁhH,q2 )Ф;pź9&&{oA 4 ,DCc2iAdR0f; IDATQ8Hau@u5dB%A;IAo=R8KCPF7d!),dIYe2~j)b>@ &RmGg(RE&9&/[t#Y[DXn8-XZ[_ooIǂ%#u\̶>|iյdB>._="2IOs/BcsЂb4:y-2/ (?i{C+5ݞarGdܜXvܺx#cnǒw0ݦd[O7 kǠg#&݊HVWmr"f!a_7{ޘpSR vUTeV6Xڣ(3 [M88#1߈k}Ɗ)hb+J<"V34UNe.XٌXyMh}}7\pyC榦<?"|y!z!ҩl`J7= aqr?7 RWY`H,G<Ǒܦ r)}/ۯ ^HI2v-"?twk}F] r02aY%TsY/d% `F^Eʹ]웓Ué'ރo#k&;dWW`a$; _%{ 1Av+&cٹ<~A`\Dx&pd~bcL\J#|m2l~R]@6ػ=d@㣈?[mm aLJtAګ d5vo7YwH֓tؒ_9(-~?M.PR"{ɳ6aGfY<:ĺydˋ L764@~}ӬN{cc~؝[څSc{]8<_*v~/W}6gN+3s,){!}JxnYMpSy$ugEUY F3?,?ޓ(TESE3~>--ՀQա֡o}[)/xOHuQ{X׬Cc|V}$J`7&G[(S2\ 1ZnIbpMCC{'{7 AE3}xzv]b? JyXYnzrZtiDbKp\8iGbi$/E)ӥhoDd2 #Alx4عhRrf  Mpg!ހh9k" G,JkOEJ?RD_^%2[H6{G._$8Z%.@RXۑkW͎־LfX=ž390KU#?Qt WHiC|LMG༧ITR`.Y ZcX{0s&4L!r,wNh䎙)A^ѱ~p*!szcEUc<Nh :b.d;FӐO.?VPqv!bEsMs.O<<5l~p1{ئ}__Z=c*nz892R7=둫?ݫ:vۦDbRl(Guv<iH,TXܑ,_6X|Ah"B8oPB[[ƎAJ ĢL4I桕\@C9h=vx2iMCa b}޴z_;Ѥ{vnAl) ?c 0!3 1T"rukhǎkA@#pAY֮,;8%%HY=~@ h$b C p(b &wkb߶zkh)l"0䞃қ6Re1.b M;z"PSWH!{62Au583yh\kڟ }[Ḛ>apr3k<ٽhld<MΑ|]|{,(_cnA߶~;ٙghM[,Xb99oV A)sNz׶wǏ?BNOojyTVh{vG:`#bgY%Gp3h" q7}e2 \^ZZeV݉c̨7<>>beģ-nϡx4Ma"G~'.LHYIH!k#DGFD^@N>C`/c )]f4@C 0a{ Vh RːOqhG+)&Dð3Z#i+R/]IѮfa_XF]h? )3BI[?%zd1vL82r4e}h9i@ ϭPG 44Nr3Y{R~&rSq3Eq|v4ywF, d11{p3سCcj/Dcp4s$;;ۚ<@R&{SΝMXXGd#2V΁A9&CKEUK+ow%z7 f/[^j).W/kjJq9a}_Zܔ{}17Ue]/\9Cn5d,}:*,^*33hnjX_]ώS{/ &8MH= NoӺ3櫺 /JEU@rҿx[V3S7) ȿW0.^T*n XПEJ0Ӝv.6Eb)vt3!H o e7)튔+(5bE u!Rъyb nksGkBlCH[! 4{}BLlZ9:Ha]2dd!{,ĞB xջ ZĤ܊Hw~vPBÅpH~V!3!wo}!Fn'JL ^B]YJnC=Z+Ȅ+;,r!@]w$F`k!5i4Т Pl]\D&oic o." Q\a g1UZ|奕uUe'VZf\}Q=q59ƖV>kcg,kKxӋZ_ 6 B"6UĚGֈ@#}1c _wv$`vFgN.[w4m}vLfut K#vv~Aϲz{&- M݁ S[ndbC{ŊMVtkZkXOMY^D/ wK`bȺ9]qk>Ξ/-Fw>ţar9ތm[/9{W!vޝ[K[w{gAEUY38 n3қGs#DKLG6}u޷l߿m@l[Vط-X"Xxoum}m‘wdnXүؿW׵=#8=?g]MW9͗UeɓfiaЃǻ!1Xݔ^nnfY=:zhVm;:^<nsy)~Iģ7s nrG1S\)3>'!G'8McpvEPdN:M%hD* B9&}v4Q3-wA,^H>MkX*kOGrBʵ1pM>[_ r=bX 1I@[)l)`Ĥ} Mjܜ] #'FB$1{HG HBHd}>[Sa 2eXG`rmBͳg@HXFXN?GY ~eTlxwV#Fܮ9|)E;{_2QvgG;aw/D@f* ]ᘚbN4vA,_J7's4d}3јiCc~7..:4|:-zD7i[{NX"50.%ϯ+<9G65g] h󼚖-<;s3K-\xS6{w-P3_0elNsx4&犑y+or.I( G @@&Z߆&Se&GQ֌SH&4̎E,5 𑇀O)Ȕx7R;YI)4YddGfv {wջ)DžjbG yꮶk~G۵ =7Iq=kd''q?AvB^GY5y"㜊 ϙE7b Sjg@1iإIpS`j;{Mig?pVkM374ed$ӳR2²4 p_}cȌ!o<&I4_GGZ ^)/xKf7gf (̛VX;1H' 0y^zžoq60_gƐtߺģ{"Ľ[pui8V#D$MĿC'l;. ishuFHEs/cx4Bխٵ 6!6d@P"&e4:;h79HٹLݑr<9A y b:ٵon7-ֿH.޸(&-3&(2#&|Bl2v!kxR2V,˨mTWw*@侣m1C۹[Ϡcd7uf۱)(QXY{Y7"7WEbX/Hgm-D@;9)}bc2 b4]ثY[4.2X|6?YXZC?>jmXa  /~\E,]"l0#bk_2\6r: R: }; @KEUYKky)%䛅]MV.۹5޲:fQA%oW{6f-qz*%}a^SCN)}u 9cѬ?dg֞ W L%Wt<#=Ŧ7)o]r񊪲w7-|2Ue! ѭx⍋i[q;?~u=\y(f/H_ySg-}sz7O1ݎw}.۞@!\'oy퐋Jww-o磅h30Yb?_g~b[8B+pA<׮N<Ȼ#_M&Al&K\@FmzbC^ɫշ'2qU#`i~{d[Xt>UQs{Gu!R%6K թR>`rQ_EԵ$E IDAT@]GZ_?u) y""}+9&xsaA#DK> Eÿ́EsO5XTdf*؜ ;C:޿tq)C뾈>fC UT奕*.F K+mry^̿<_K?_3}߯vL+EV/q<4p4084C}*y޾10v?K53y^:QYy^[@9fh^*|_y^pzxČy(}­-R_m\& QMBf"3 Ohrq@΍G s V_%-~m@ @{3L}GX%@ۣ@QtFj*R>E&AaMn# EրK4dCEM##p$ۙ<i@\SX}dU_2Y'kx4\(v2m&B@4h@i޳L<LU=LE֮&NX?ZeӬ@g7;d] OL#`Uzd^ў'^Cli/o4u'E>q c_KM7 0L!Ngع . b]oBcW& L@ctOkSm)bzRQUV 앖"U߮s-p.u|]Cad*Kkk:C PɔV؜`۷o=xvzWBv>%;u\wȚ'BԸAʹ5k[3c֮?-Rxl-]\QUUT*9Xg~{@AfN (ȃÿ{}Aߘ+EhKKg|lxQ7}|(<.W!}ash [͕}~k=Ƹ ,qN`+ܽ? ƬLCJt0r(E r G/#m)IkJ"pq,k%P(n+㑲;@ "%=bfEc;P6 Q TV |E (f#)#jwt em Z"sZ.U .&Ce:(iFc^0FĶlgig,g:[L75;#qH@rKRdmo_LjY؞E( Jz۽#v` dƎvU[]vgaPvBe/s&H GChi&?ܳW ec'}G=u8?e:<&b}s7GB~v$AkF4y 6IoF4ǣ0p\<uT-*]0%P7_˒Yߔizz^Jigmj-ӵmgй2ӛh*+/2U^Z\yi奕/{ɡii~a M_]+upƂ]b'd!KYSh$XH@tx4H,q+[##uw!2C5#֡H, F 1SCФg"e(R;).Hd & L@8Ó{XbR.Cp ]:<jζg n!x\?lxU`;^w܁7Mvm501#Zaj}ߝ Y-A:t;'M3NgO8.m@-HvFt6/A,"kI־!hN@8A.Dj5ţ۟ A 5x _MOC2ĺ~?T78=u+lSXʅy횵5]=O{Fbɳ/kduV'VToMTήl^ܻ+:q E_,WEUY?RWT +/tJH`{f`7f43~u=-|Y5h1Sh==,=ok A`Mo*{wXx76B@7E);:/xi El.T.-ݐyk 4 4裿2lbx)58 )f5Kk69{"R@84 mDLO?N&r4Q̥V|(mus!J 5LeO,_+=X|߱ak}:|wRVi#s^'kK#,!֬ڴ5~xͮn8 b4 2l;`ScϯCahLG&{v'h^v?Ay-vD`%4jɵ5!x4R@+x  @nb~҇ b$eI]jUN-^Ue^rHH,q!A6j6Եmάi\׈eg}TQUv|yiDĦPQU?g=}Ue]wp犪Oͱ+7hEUY/4~]ze?~y#o> 2U7Ϊilݔ?U=ϻs=A_1-ȯ23F S->c?+02. 97R#6ĕhbh9F1Mm R"[=νPI_h8b/#ҕ`7{OhN.Qggvv,c12]I.s.R#fG7tAS.C4k[BJÅd2kg23RM6"pz2]GME~ lĈ0aXDvC͘auqA' eq•19L"@v [}Oyٙu[wB+럕y8;0)lҫPl .k,5F[Q51]Xb ŊbA;,˼?* J~>sϽ{s7 !/A@/tp;`t2k"gc2pE:X}: ds 3ElҎؼtdk }`6O#( >=l\dwFh#0 1{ ]Ƭޞ͋{ ?u%􉸠.Yhd|q"9%WhA,چEM~**nA: bC=m0wBc6 WTM@zVyi4ס,4]%ιޓ\8;تA܉! ͵UsÚCY .[a~._VWZApC?[6z>{'a[iUo*{/&1\o ò_J'3qh]ƣE闐[5Z\&"@E D“D Rg{ OuG;I@D^EȂ,B@-I;F7̑ȥNsFb_-L@.!*BW!Sa>}oFF7Cww_BY>B`wWd 7v6ٴuC X"{Ěl\-Mb|Y Y[EDioHg{wz+oG\nӆ6sȥ7bbu?f ֘NƃD*s{"9b .#p F2i^>1>nh᝭OSdF p}J*ӑ^AF#?m f#`X$?)6EnvAsm;H UTuB_ վOuqo孬V j NnFū@.I4Ҕ1;h(D~.7Diiɯv}:Nt&d׷GCeк;mƲly,d4FiZd|eg}NX>\rxc"里HGk~-/3 Ѣr BQGx3 d"ĎlvS1+GFo"2&dfѢs :)Ȑo@R~ Ƭ"O"Fcabح 'Jw+޼K.ϴM@ w2QV=_a28Ч@B@ "70j7#l犻K# '(vuGLT{.^>7ڞ}k\Ce!46e7D9Yu(~}d0D r!35lRNAo?3ZFҝ0?R{@QZ6N?^ ~ aLh\h909_ĕFCWJEeVHc OtZQYhS8tw./ͮ1o:hۙAeswj^T5p@cSan`*KǮmƞ \K^~vU8emj\TteGΞN3f5H\ ]^xml,b٠ He1 Qh\6C/aR);=HeE0DQ}(2:>#h^Q Q`ZDFh1M"݄<KsZb%V7!7mPgrh{_ "%lŵ"v>|˻֞A^ֺEsvU<&x wm,59@q'^3Gzme'b ;K1mK]L.o\c{֞}r?EnGƆIxxXDbF%R묍Q 4f$# }č6651ng#vgn笯νccJ'H2{ ='sO:1;wK2}l27K2'WK)ґͅrK:<ӷ~ |5b]ǁkr9bux|gOrN`7UNƃʲ:6nݒA(fʲСπbO~A$f߮,cL!\ Cھy|AEeٔzX6(`,n\肋j۝t2K2m1]4+hHep/;D*s 24C #q 2fd=M&w,A:;ĠJ vֆݺ0gP Of=h .#Ww/BO[]g=G2YGw{ T&R#!dc7C nrAOh3MxէEP r+g"v'COheec4b{홭l\sV_B7ܵܮ֟y=+p1bZ=-Zy|Mv66[! -`'jȞCi}cİg z G!`37+ݷ `uNA!Ut&gS;ɽocc3oYnV4tlY \B&o}QGʲƠ`94?NN]_K3+*mjۮʞ{TLkSqъ"& l4fh',uNfUT]t+l6Ц*% Uiʉ,۾4{ƲcTf4bj *'e #nȀ(fC}yKdD*s,E87 6A  :dU}!7ۡ׭>Wտт䪗ix/mAwkO.䀵8>w{,ڢ7}bNլFX0SM>]L1ժo#ضw2@^v@`t s; dz3%R- uA`Vl}}DQm^2cij"` ˷@_ l -Hf Ohl%q_־Q68EpΑC6+|zʲHOk,<Eh͙tfM3n\zuXy !-gW1>ncuYӱYf\;}tJln0:d|tڲ| IDAT2> }BL4w}n8&<`e2-9W P-/ 6B ]P^ JdX7 q4ڭL!C|bv"UPf b~NƯgvv!vs;m߭EՂ"rgG<wEWֿO.h@@,g}3/F] υ({O_z#>=P"D"_OGd^饈k&_Y[#eO"wlNF:<";$"]/F:)қC ԧMFϼOvG:z2Z9RώE,?mN_'R'R-KDh=?+[ݮH3389줟˿6ߵ4\u5 +[c>k~kj;ukSrOۢD1ʲT\s9;d_9wocιEιs|C.sΝc?_k}lߧE/4 w_mL yH9FDE1鋌ho5(g=@{\;gWŐ1eu rPrs'ȸ߁^E^~=ZjO݌HYhu>[r:[Eُw/>ԮȐGVgF+xAC_qf=xx;L ߧBX`ȍx b4F@l0?G`_ @@ViBd8m F:OzC@eo/H"i(ҹ]B\kQ&Y}%?6ń9G,Lf fpbf vQ|c Do G @19yk6d|.?p,֎{NJUo޿>OVT֛݅8),*Xìԣs"܋ݭ~1l.ϋjWP V?0!784kViNot'1kDq΍#6%yvjf} 5jnAsn?=;ײ.0Z^˹{ח[%DLjyŦvH2>{s hgsd  ObeEg0غjdўs a\@ĆAF ɟcri^vs1XTdؗ![&O:?ڻ=퐱3Ai*!pbwk׽M>I R*B.t2>!?Nz%RHe! r3b oCq.3pj_E,b2ػ,C7pڂ߿+nB2[W{}@39CiO(KxBaغX{?A:3JPd"psXF*y5fdB1F &!(UTrʼnT3i1?0B.@n32Gi7_oR[?eMnaͬC@1IM=SСM^=`r%bŽxYh^NBP4:F%[ f˞oXQYٚ-6> r>`}~_8ג,@: O+*˾fkњG|8-'Z;_shq9NyƮ}s&s1{osnsh9ws}ι[usns9wwKϗ8&Z=?icjcf Z}t6h]C@|T=crڇ &~2NŶl r\vt>g 00hI/y}Z>JCLuhg@dnF) q 8]Ps*z %ag s:2&۳W{W"cx. mKVXv{$틀&&d|R:Oehэ"Ѓ0q(ko lI4c[]8{ߡ/ cP<߈@z89h;5F2kJԦ#co=XR{nw]g C} Zxm M藚<3ncDx?r3+v3M,ALίZJ mTC(V?"0+Y_^hA0(S{ cE|*D֩QlqbEe04*_r|=1fh4NT^_;4mk^۔ǚonSX]~%%߹X4)# x VlgొʲW'HOf%?T9%yþtv1>t!}QFՕ/A b&' `KW#̶H!ι^(eG?CV|5 FZf"dG7oY6k(W&R3È M"28>e7ƗԬ%ȈT 'i\yV׿~fކwp Z{@ KX݇,̰Kki)txbd|G1% 2v(bΧ0kn("Z?;9 bR!7 d> 9z$_2MTȈ_O.\qބ$uA`gbFKL19>tad[(mZ('UHeh<Iͩy+6yy&r5EۄwzŰ+bB.]M>i^/+ڸzIM36^h=o3-u]į^^6$He2Y۞֦zk6V%.X/Eb{&wʼnTt2wt3!3H>ҷ]v$2zmMNӐM-]jn[}JM\tAsuC280ogl׺3L,#u(X1o&./ͮ:#@4d_f// l2mWsYZ߾m `snZG@qApu> {f$bs!rKsΧ-ߓk׻ݲ1KxV:g 槓qJ He^A?r9?@GЯC@BVdg}3i=vCꀌMk,%@.h>-P03VȌ^85쀌h^\k֯QO FFz"A.$ C`-;#com@1ȭ9MvﯭMfr'b@lO [e&Gxw?}#]WX#xػ5lciωƟ }]1ac"!@@+Ϣl7IcFصx=,r'ژ [V  yWWo«^R1rugyvV{{bk7+6蘔NhAW7: Z /]ͧ`s%!}ugx?"z&D`|YxsS{a}ucv$r98Gs_$Mût[s19kir$2O Pr7Y"Z|C;'c\e0 Ȩ->}_dDsܪ9d~I\O3AIM+zu)(n)~!PZ_ ^{PT\he,R!p SBh#ؽ{2wmj)@0-Fq҇ojoҶVň%Ix"3e`^Yĝl&LA߄ ޞV+XfGFsjE{-&˧s15&;d'TK P2r{`6;|d~͵6DL d#!.B(a\iXM^݋7t(mi6s+k557GWcrtgx^Ge^-/Q^=4ua} b~oB?} KAyi,$Z{ޞ4 "۳'^XX^]Y^=}e|,l`!fQ3-A<3)8ι&Vs]sQ4w_uurPx;犑o,kQ68f O}JX_t2OZy6!dHF#]Dz{T<#C8N2B<Z9Y7䢛NjdFb5d`{v {gp ӑnom y1܇ x4j({&LD@ ځD@%c&>{ 1>Xء? oHW_bOFvhN-~V!֤Z̰t2$gmS)= h\rlYU[l뵎;v8-\/|0JşY>SVYbbצ\rK=X^\(`0wHz9Υe>ھbc sTۇn4f;MV=u VIhAкy =sW!1+=˝s '%6(?Xj 3oaN'|ͳ!vRcB݅0A`wH~X.``^F('!K~_X6<5d$z#»FZ;NEeyd "st2-폘swTf'd|ہBLpʑ!m߹W&%}M}݄o:ʹK MpF~N?O2 YF?:^9끟Gl7U !2L"9Fb_z|2rwA4+# >ߋXꊌWEc)ҽfĚε=0fsSv#3L96~OvbOc)ϻLw#]-@*tk;'d\!F}{&]=@i8Z];x]ܑ9 9,oK"%LZt2ʲmCZrїܒ7dڜ٤ׄ6Kbjcl/Ko[]U_QY6-o޺SE:lwN k.ʇМ7O4cy=ҥ6(EG4SuCT//WQY%Ե (/^]C\b!u~+ژǀۃ xnC2c#Cu _fCR,#T|d/bDv1#} NCq'ldHXt2D*s$ZPO'$RK 3?C ૄWXSq)wFF}:b#i"CM2@p!p2wy@G B@x,b"6߈I:p+kK:.Ԑ׼(d6y4;'$R3=E,`Y0 ǹh>kD ̟bc] Lr woo"@sb|6vC@wөC[Ay92s֟{.߶Ef'َVOB^dӐ4 &›s;#v}@bdD _TTv"zb>f朋ZZUDy h` l,۠Mx`%%t /r})eyiD*38k=:}zO (oiQ e}L7F?͘3;2pObMksмqW؄bei694#vE49q!{]:́Kuho-YBB){GnCQL2.vIC쒿֥?l L݀ ?:1t2>%E< EHeC Oтςk!|dˁ<=6>UƖHٳ ?AqCjKLRֆcTƻX됾D#˿[c6Y;?GwU \%]/bgotr:_$~D* ʓ?~lݶy듏*ڢ_6ֱݼK{^Hy]6rHeJ^Z] Wn^n ZYt7uȞ{]u_mZ8z׻֦fk0ࠀk*a //A;1}8mJℹƲKm_*TD*=3kxE}v`m&k7ao&"_ g'dHgg OAТ dઑ1cBE!W4'!C`㐱{B +U@.@k _m-t"Wȅ"W:@! dh^FFsň;> ܌CxC^U:0 5!oOdVcӒ;9 z:@X{[0Vhܗ&]L P%ڍS&"w.&lc.7Q`uY(﹄lY]TdTka >n19A0B|0H,~_lm$/61T͉tV[s> uD:\e9'K=%M«nD=~Y+>,=m=vuݝ9uUN; R-Ch#7pjTr0h<Ɵ엀a^ycs}H[HF4_ucO믏ЯbAm𺘳6"|O4fWVT֌.0_ضx_oҦC`4z"(scXߕ Xut2>/LDqx@ Ac.Z`|"EU$H\dπ% v={ቓh9!)A"g#^Kt+ː=;v-ڝg]uEtAYhZHxS?\prqu3^ dʼnT__c2-0#t2>/dw2a{!2{G/´ tz#XOx_eb.q+-G41M{F;ĀuvB m̾} 5^N'R(U,| kϭh3̓C TkMv%hNA1vZ9r9&MZ"C-Q!_8E-`՚}YM 9k}:̘zbuH;U{"ym&imZ}uvδd{!!,M (bX922%V6@AVg?kl.z m. Su{jKVeO|:R!W%)eC&MEeYlk,D3{vrŒmMx4]^I?b+*ˮFx(oy%/=m;&^_^j>}K=RQY64c\x x@H~?Fiީ-sp_?ch}霫خRz\2WAps =mJ"-r܌ɭyr|\w:4"g:= dhn@_@xdwDF34)nArmg&!qghDR3.De0}O!H=A @'f #d "tg](}d|+ިN~N_\͸6OKE_;vˀhaTa~u׈kde]qg4B4ԡ1>'֓6פpz^4tMR'd}&(pĀPdu7#cPz6HSxv֮[G ,:ε~,'; LX]d哴6^cWk%LЀXl_G t2~>?BI2W@.[{IIǙfn4՜g%Fl(m<>Fslfyi6wNj{nH(~HΝq7郾_>c9;f?]~ t߿qcα?^-/~,e1&S|mBنʲA@UyivO972>I_6Y9W sH 9bA|%' c%,?3f]1 ?;I'[}=r MBdB6yܐN'R {DUϽ dhFf'#)GS y"5_-rŻE&|1@X[Q-ҽ#2"54;"@9e} C,G9~l}iAX^"N/X)_Ѿ}ƭH7|^""&)Dd14?He6A%R;5ݎ_-9ҥ&6v&&3 oI=\G ]e6Xk`ǓD߄cB[t";#?CYk vүzB76v>?Hg=Cwf7j `Evu7?s!2d6_{'uz9ZW 8mgD/휻m)࿍ ZtS HeM'wgEإ̉TfD:>Kx˭- -"/2v%RB>2"w[Wdꬎw9.E5Zd\vG`] !;Ocw `92ė!c9bβgWvfX I//#lC >r\~ԀvٻT@e"y _X59UhlDgؙP,qyXh_ؑQ"O-ԖBWho:^~գ1`ht5miq] }BhhXX~l@**zHWK7CP 0A[l2ߵ<\{ mnw('+ +^u#ÁaAh\/!uCnhlE-C^Hop W`t2ޔHeh'NUM1a<Q(UWEx3AvGghJ'L_dϷyt4ZXަ;: 34#H7OM2'!6k"2"ڢX|+ W|,38E[G,9&:`O52h'ξׄvLyH2hNE:꜉γuFOuHǞ@:w/hXr{",ܫVK(Tf8w-أhs֥42Wƭfv_ʲh"b(ː|N*3!++*O}!>'mxaEe_!.@@lʲK ~cd =ۮ}$s5XSv8hn71+KA,p=*jιкy6Vc'KAd|utnh7~ }dv8? V>}*22A6 O G/i_& %xʑA.s 2,”9krguh߫:ک |kù-#&ud#9-^ c{{l4 pA:rg7 v`1@W7G.dD*M lgݾdw'RmK6@@# uxwl=W9ɦ с#' \TxӍ_+LVWX$ wT;䳕hZ`m΢C@(0w#b!R ~ǻ>ԧHϮ%ʾ+2Ik7[;j(&?_>0&orUG` !?ɲ?6,!m;MlAPQYV4M {EeY74[W^D:wĺG+*N@.@4Z2mj;@sv7P=3~C=McܔsΧ>YJvϕ a7=g,#(2ބ ˪{rF"TLx9qOiڛ0RGƶבOpn:O%R[9=]PTW? 3t-wuGFv>2#@ЙkY\83:}7ڥX sv ?0:u#h̷DJC#B~O%RɃɸe>N"*t20^O&RVuXdD*CxA~)b{}5hw@c"NƯL^"r 1x'bM#@ ``LqS\c[?sݿ=~LB>b[V$R'5>C-%8PFkϪ!C8GOyK4w ۘN~C96͛Z9hhDwCyp2.^ܾʩ^~{4-Ob`dRmCO!Dry ߶fTTm֯O]t@kN_6 QA,С4{ŏը(fM0֮l5ہAoɗh97߯+c1ιA496GDcy8",Tsk;Cz!cVHpm~8]@X>w_ԑ@oui%^d|A"1y|^ALæ>=A2OO{V|8qbO vRr`"*~ lHezgٽ~wjvT&NƟkv1At2>-bжG L9h}vgV59_HeX~ȝX OLy b#@tbb&S6dٔt%Tn[#tGm^6>0d4w@'cM*,X~Q.;Ԟ~ӆCY_G/=μ\U'I}q-VXz6;04# uvBHE24%RwWy,kw؀#FGw~~G. ʢٖD*3K'ל v+q_ʲH/Dz}*/ \])/e#b4{(A; 3biEv{ `6!sں<뜛_M<(o;<}7{s*Z6+ιk>*ech?逌xT"֩NW -Gq9 0L@ V>2Șv;0w0ޛEQƂW"fLK㵷Sد7HX+ (E3]3! 3{>{^~k CmyHQGa)C?) j1ږc b\"H k] |tB'Jf =Y"By큜X"ܳk7)hrdybvyt]w!8ck͇u8BR,Cí[м+C,nhˮYh|\,q%bO1JE^:,3z&T6#^Yk%ҿv!? ,2PJqaɬdiQ'_L6H7oSw’vrNbBݢyιe,=| yODL"U5uAw#zLIzw9{mb<&;W#}l` ?C]Z`zF xtQ,)uYAn}D~)cވUjR :; N=j&w-@?Lp=k &UUamCb}SX"8Rq>F4kdIJg;bjc2D`rV\16.&N-6 IDAT0~|d7r QpTa xe@&ϸKM~f^bnc1MX-~זDž]}v+/ͅ:u>B4' v&m=+[4@̬eAOb7"E{p b^FYx?^mL;ZfadXۍ n&ǮA 2; T HFB?R]k Hg#v!9ȿL5G bSGk1רR4X;*gOD;bGqio_U푯';)F)Oţ_X|1UZq$&[nCsu/N{ UI&zsC Be)&cxx?겳26vm!hx[b^E@ R~C8`M/CA^7"6i[,˝ikυ6&& B}dZ=1:Y&Q=<ب,Z#)Wܖ]s 3M%K’/-)g魚-ާY__s\vkUv]忯mW | v9)}io?M*刵_'@uB>)]R Hy؇C;|9R~$2[/Clt0iG.@ 1G6o}jYѻȘ-\W^C 9,X/ vb*̻#zV"PL[R}{E>QMK!'w=4d|]72X0@`;Al>uGs%b9N]ӵ,k(8u5lE~KţBl]O&h.>xNڸ j{oD@5koO hc;vb E7&e&+~/HD6NLNUNFu,"W[uTfK"u 9kg,V yge^Wul3>>bv}xGS}#:g׶a}˹{luv]eW,?X*Axt2'gm+ӡ׸0?<,IţScH=J?)ːb@ cd:e8P;R~r_+~@]?ʞO S0 3T"P8Ş{,DfS,BdKBʻ~~K m}?bYn޿!j6 1nYGyl_7/cc}+b>[yJ*6Hw@>tm#v5"#ɣUKuxta,hut~rP^ϹjowgZ8;m6Tp^\|5r6 2-\'(@ɓ+ol>x<뀓C R#e7_hu#5 3~;ܚ!/dy{ HU =A̯+pX̀#l,nab)"'(Y\)]ƕBu}3&)&?i&0w#`3@a,toZ3 "@jإ/g(bj𝋀'x1x.BgO4?e}1~G@>T4ٴ@ %ཇYlc}MS55s,^|Zc'?yzȮޟCaPrn˖o嶨tl2>+RhӜmHHţA|oh,- VDO$HfWf宦&~qmPU=ԯ ef[qs9 RdRV䟛a^ޫE K*-(`Bqa6I;<97<.97'Zx)yޙ[Zje=^n˺B֞)*Z#4>B)tC/lrR!Ȝ716eﳷ8:k}am j=?8!xIx"D滽 A\HѾmcP|D\XJ kÂkWfu׼_&?EkzEdfN'Bff݀b3I:Gu7TEWNɟiz?%CX{3,U{\Eh] ,-: ͝hiZ@:^}>{ $j^CK3•C٭e{wБ鲞>Xlm~>'ηvD[,-O`"YZtDqaɏ}W>9Adb1іM!OgIsE~b)Z3<\ s1(Z{Fz9B3usjf\_ڢu,p΍EpmecD0\kF1 6Hy!3ߟ?#%MQ-hXGlhB>Ku+mf,~)Lߙg d#P&0)=I='o9@s ~:+ yZ+[s,r~05݀SDzpƜu-ryK0^Ъ8 )HAe+Px/HLsw!l~Ɓϡ( T#PuAۛIkc42χؘo2F -X#mdect;+0hlgcqb̑~8 6ͷvC \]~d>gcuvoeZ=t͕,`~3{zt%{}ӾpΏQHCv<<<3M..,&YZDdiQU% 5B5olݾ՜sj!G,f{>, ևC m:ggnlUHa+F;E!ۙ"'K7DNoȺzG翤߅vMR ]h)c `p\wQ?L"8Ř";o(Ӑ2Z_N{cuXbq\̡OgC,vdqZtf""._A6 V1jNNn@s'6fCSw- w!~(:{JМ~&m}U?G[;Vsv8xce~PDLbz`}ic7=Ѽ2DSCbtG#FbK0H%Ok-ʏQ:X[b=omEk+f,ZAؐ2O~ugܬ=z٦kU9#ͷpF.-|mY G>~JeupĊo@nE@zJS]ۺ̎nQݓy}'n'= }73zixWyhMmFK=xƞw/n s,|WقCX"%PLwI0YL!3 Q_4)(۞s25DҀ@J#FG"3ֳH6ClC ?D[`Ef,Rk}is3K $f.49'2Z䎍%x#1yd cY~ug!s4b|%^uGo ϱhh@Dlg ?-,mbqmמF ]VX"NC̕o=}bVZ|3nsͥhA1#C+LVy.;^$S:ۤlqg/)oy [Zy s˝s5o,oƔ |]mhKO\u&KLt %!5-C@%e=&^i))}A>UW"erq݉ Mq@mO8oR"ELq+0~#"b(Z"`w$RHhk@aȌ%R|p D}[[[Z=k< {|fG &hq?B`udbʶq<l0b>`/"@O(D ub @cوQRhc,d世 b Z~3p6#PvSlxii@e7!ȼXv |jrhvƷ j0s2Gkr bE;!h.]hm S9|<&AJh):#9g-C9Y\X2wގ:þSFyfJ4tj9oٟ^(7 ^, lVc`Hh,~ƳL4o[’m_rw^T=eҢ*ɜg̣r:Xzm2 x9wy+sf-{rlbٱX x9Wy3x79suwۂW{ ơmAGHz;D G).Hk8"}A]wz'H7GJ23 Ns^K (NESwL_4 6k z),4gjL˭mhuOC@sTtzh[JM鏐B^bt{19~uEND܍H1{XW"k$;k [ ćhaMk)&& ?ź IDAT m>B}6HҀ`'$'ދ ko <:;G#pO_Ņ%lRs8bhHKY 5u9 rlX/*\ M~CzOJ~}_gv=/n8MAkh,-X\X)QJqaɄdiѓEDk"?b/U;$IOBHpre}c~4W͂͟1s?xY6!}pshCv p=7þ9_(v'& a1bnEWPd6$;sv{彺^_AZ!/2&hA#S&%=;s:C e=9gi)A< MD o=g#wW/bלD4g_A8}G D>@0*>hD`'1 b& @~C&} ki,ZoYkrs9N'0%<5CS9͗++G: 1!:A'~ஆEt(̍v}֏YvP4WU<#u@]$j<:mqͯAM%!@^a2s[# /'mo|?hɃE{w::Yk 5k͗>,.,9tq Ӊ y{ 6oKǯ޼be^ KG"洞S#Y^_rhM7uhvEr/GUqfqa[WV?$KkC!|}eŅ%K~6\z_{m ĶGq n\M0wT#}H,naۥXLɭzon;fFbpq>2Aor?T͞:n_Uwmq}(կW!e^-nbƝɜ%wtlef8PMڡqH_\Xٖm>s^dw1rEWĬ>vxOiE 2rK cu+; {P2p6/Elb;X7ٴȥryr;xY|?Lv]=י@XvdCS’ok{h~̭t]-ng#x0YZ |S]od$Kv}Z>4]چC u7@x+YZ%KTֶol{z$RMnֆYH|,-Z^\XhϮHʱegc{"t=RBE>ۈ|6HfE@j*Po!p RRqfHфb-y'x˜lip!sSGkHNha]ehq1:,+?VO%iFtCt0yLC{#Akc6֮EUxt6)U'}? yh$L(-yg8 `nryb @1FL|5nwj 7do"8lѸSttÐIftO1o#3x1R^] 9 ha2r 78@& |ͭ~֞Q<}i"W7G@ vmMۏ35Nk<` '-U#Eہ{0GAΫi>0cE&ÒE?+.,YS篾eXfFj|_+\B`2C,-]\XVsge.g>h{**ʮ%|u60~zmG Q8Xb#(Z!%73R,D~B;)f 5xKykDRUΛiQ14dфzΤ!vwwCĆ)tR XM`B|}z\<uSBsAKRjس=Mm&:/dm!:ArkoB]s)xSW{8YWY[}0R͉uv_Q) aր]n4G33rpn>?uJMO!EHǛT<?u 4/rM&_L6ȼdiV}wÈA *P_C&v n`;!еAj7l?>iC=4.>=r:Qr /E mBF@t \\XM6} @9Հ@g<W-~o|w>{;- 9oQ80P{lָ?lpӷrplCS;^⇾%pm/ɪpȜa#0-ʮ~% fd 1hQZDL*2 @,JNBdfutY]k$ Hz)⃀=XR$¼..E-Gs1% #ihR!0P?sMJţ%#`Mp-Vs> {]b}\ :ax= ۽~jӐԗ-AiHic 9kM-$SUX  '>=McM.A&z3Lm5YvkسsD_U&w?4#}ЖaOB,S`؀av@!`3 |!buA>Xy hlU֞mLm{s6t\ۡwe VmPM"ֶ݁֯hF'h]X\X2L]M7}V{k2 7z!..|؟zY7\> ѭN,-zo HќO gq}+ԄeC$9rzܘ *.,Uve)-[)r>bD G>I/ kбr]5,%IUHZEVK'L ¸:CwC&>?=Sk ?F,C dcsUJЎsE-Dfk%HI Eh Nt;9+v vmwkt %R@X=7dZp{# 1y{!hhkuZ9dTo6~*`/XJS(Hk=FkS ~\oMScg/>X(Zy&֞Z{?GJ0TA**Hι֞ {Fo,ua &L֠WOYNpJ=b*l|Z؄Cuz7AʗD#GWC4oi#ژ]@"r { 3 XŅ%EcBc5pseF$t?~ݢ /{m[{V1ERճ=s-L#0bٷy1"Ceh4^ }*[Vv8C mHFeH1ס|B? w ,Axբճ XX46{]3f"SԑfS@pb dk>>>O@@Ze׏V  m-| )-@ 6lϼ )ﻑB)$J(k֎h!P܈; `VϩVo% }|B -bH{D;Ĝ>dn?Iţom"q&vh~vh{֥hsbF?#c |r}`\Nr#iGkiC-k&25`au=l |+GLX47٩xbmT 5搟Xhzqa]v`.45ޏ 'd=@qaK3;vWwkݦml ;rF8Shc-D 96[ & r[’[Yht>] k/PiEYŅ%ww$EoVιY <۬,<.7b[7D[s)ι nq݀9#a,G綖F,zd~A; ->y5HAB'!vRD!ԞOţHQت {{˛x7Z[9Ǒ\#bG ?aZI?`{tRxbG6w1R]29RKx).ZDLORݭoՈ)D& VZ1D0LCbUECWX]ݐ){Al(}yrQfkvV_qBnu"%|}VaM<vԠH{cD%#6n86Ԟsۭɹ hڦ5ȔC "HLţ اtR&:knHY] @;2-1ꀀh>fF+ٵߠ$OVs,h̶G> nM 4[lHţG%2C@ie΢}yE F&_r/*[zΌFn4Wj< m־1"wAt;[z? 8t\VE?-.,f`}g.ιYnb.Czf}ZVEw:xtHιG&r} VyWcxt6Q,?HdZCV%όv)n?chKHaނrtF ebiu 7{~g4aވ3u8JG3ȦYѧcJ~GX_Sm/[{B |6Gે}v# 9vEk5͚ܿY!MAnnFOnF}\T~5[_8qB zivU=q@s]wp@ꉈU*{Z"eTh:O 㓷߳ky/d d,- df;4_?DpWܦ威 gTr}rD\mu*Z^ȉ,9~Tiys!Ps]juݍ2HY]#:^+D=͋=ϛ>m6ʑެ|z}`ן 0/A&?FL:JNsA"PGXQ/H@ZmA`JAsͭ㭟Cڽۖ(.,FZLDo,adi(ywEVUb;m?#ز{/%ƭ4z :ydun"F?t=*YZQ\XC|־\YRϵ LK La֮Ekv7~]231F ̥_ȴ` Fk>ιC/:к6 c~ 09wy <[cϙyާiK6E&_<9wbOܳ:u aKfS*ٳF{79(r/]󼽜su~Mh6reXK5L3=dmj~[iX,>*R|A>h(%`.E+R8 ,iq{Cr>R_4@DY֎!C߹<0>ߋٱD5d:`U9ӱբ 0|ƥ/hb^Ai) |=c.ڳd2EW[;%(79V#tɭ# 2npZvʞɈ <NPB8l/AO-9!eF h<% 5ddmc2&`"z_L2K/YhO^*DzO_և~z[!p?9:~8Gg`u)_[ Yc[{/%GNxt,-*#8ɿƕRK헛SEHV'"uAoX+\j]rBd:W?dM?©Gluy Bzi)Ңߡx|,-mH&~JmE/?H0{HϬobpie5-,9ruF4PGoys38rWhyoP\yUzH|۳oI˝s ࿹;Pyl7`O! B7|\ȴ IDAT209 *dX"*c0DSA/~6RX'  s 9cb^Y~N j3M<&hrKbn^EJ`#'Bܾl#5{-E"@,bK|8z/%G!PǀZm] 6[A/1HP[=l β#~6pCM^a @}MƵ}g5t^D?vL٘}&SXv3lmmYƯ9h#eW]B8GamzX"}e,~f֦RyȌ2G*JKccB@Cʮ@6dtxecx~ZX"%{Z@ZYGb1`diyh?~;MfF ~$-ۣm/9VE-//a{C]E3{l,-?YZw CMlbuM?waCO󐅣jNTlDuG=>w͞ >=48zh xX7??Md9C7wyk&ZgtcHz|%̫#G[ sХy RZ2٢a`՝]HGЮn={ :(m6 ) V]Z"StL"$S>Bʸ`\kD}c}C^{}Xd":R3'!p9~ x$frzwѢfGN.ZDKd͑rQfMHu= .AJC n#xw6F+| ,4@ooVXD Pu12Yw6Ȅ}6Wg} 8! F{TT<ىHl3s%ңmLB@ԏʭ;P]5sH5JSPlUpĬelo-e^7˼~3sk\_Xݙz]ܐ]y2nNLoIЌ 6;}mPbQm~VaJXT[Olu<mι)p5ιιv)h \k֨o+3oCm6Qpε~z8?CzlKK8Ǟsw/i>p \ι6@{e0SZM}- 6H&Ѣ MˑFpj1~7zX" !&H3`":X;@^@G C}sԕ´zG`M, RbЂ j^m:{zGqtr "/Ͳ>G ^#8ѯZn~1}r#vCf.@E,> t@@]d^a9̕y5F1T<5KG#ɠ;Rޥx  z& G,=A7 {e_}ףIߦ8=bELt]ϣ+طL֯EwĭkD̞X"]XͫB@Aw?-ggc'3b;diu >iO:_xܥŅ%~coŅ%"?~^3ӿ$Kc+͆~gh?EE[U'OjfQ{5u߇hKÀ|C,'u3diѱMU;\\5_YyͯŢ:o6r󼏝s?<1\`*zsnsn:;91 &ۮ2Ws˟jD\|׳;ֺ5ֆfu0YFDO#&d&t,_{t*`lF5 ˡ&4G&& 5xX"};0ٙ "bXc!DfL-:QcQ:veTQ" s bDE{gݝoss.BS#'3o~hkkk9 6PsE(Biߜ6 mLycocNh~A@8! 1gZc?EUVVCg\2 H|H PW"f k"HSauZ{9 w!6z:܀L{_rnhm3zN[8mDW2s =zYI+;[2]g/2d hgߢ4AjgIƣX"u bgo ?DF Xk:٦v0ޱD,Bļ2ф[C,ľ@q,h!kH<_=0&6~ĺ-D]dv<-)eZ?LF'iCKki1^H wF`bs.;"3]-"C V1o]#p"k b]'f-Df2ج"3?{&VN=Kt*bXoG;P觉ƿ m ==6wH H zuE,,xt*B g3ќz,F{&4!,@t중wao=lԱF6>ؕ7#39wiikK,չ48uv'߿lN+\h%-N4oxJo+ SƏ9nӷxZ.L)YhyF᙭Pm^w0l^Oƣu"i6UsHx9bĺ,Bfw?!%Ed H#vŖ>jH ֗ ZG`lV*@ f"@Cշl"_'X" fb Xpg[o4)y|^Ez'p]d<wA|i$zY}oBK;D:Ϟ?8Otq!Ei{"3  ,.\?['.:ap3 icS-hpȤ'#$Z"md<%RۡyVV8ŜS^-e bε6~o,fbo- {1XkקAHE,gamͷ=x)c "i=kβGX?u|B(,C`yg88}XR4z#Uq 3gZ?FYheG +F k ,? `i'̝K!@irE?6dn샔pؗ"vs(2\bǀyD_;w$)%R pi2My$X"uz*a-X774>Nةߎ6ؕ= /Wm_׬Ɗ]/:Mb N޺Nh]>e(;9kV+ۄKF d<}B̘\Sh!y; Cf,Bd5R+:>~ >5d#+J^ @Smy )H)La+SI-2'fu+ݿ)#8,GJ>m}09!ah-<ϬBۓȤ8? B/:`t>C B+F+g6rXLyd%h}K~IZ 5p< t;?Ze?V#NFCK>=ikц{1:\<4Z6&9LFbijF>fbԞoP6ϣ? dt:,%9kv+uw9y>'.:gO~-TFO*e[c#r)A"fЋ~ y5RLvg' F#)J"mD,\Gu8R` y^B$L@lzss %\᧋x1#!ߨHQ@ׅLFp*p:"`#`vMC&ނ|Dlԁh!. n[X3h^X46!r2ll@`%<8dvr% 6.a b f.ۈ=*E]>4#<мF&ih~S0lXo Ȭ{j h.Ea l .C -/{87~Kvh& O)SΏQNFs/+PISmoߊ=47Fe^ɳBu[UZ,*Bb)iBdb@Yzϔ˞Ȏ)[ 9ÏCw&bjwF qݷLs2L!V7]a#ɶUr* ϗ#w'Rg ż=7 \G["~]tGlwi?ʾdALe1VY}!3'MB$05x N(Dd}_'HkLֹVJXV`Pnd qD١m_oC&}XzXxthob7!P[a};kڗO刍 y?2kttZTd6G͞l{\N^KG؎؟DX"51!&EVހ@8Om,DͳX\ZIʵ;L Extd,Ơc~G+ו-7IhSi+z?ŶʏXZ\qh \3# eߤe[IIƣMx/xBgȄ-vŮt`+ 푈%E%0F1v=X ?j#wݻ# ܗG'#IeW~r?C. k^j5SX)+=?+vg$e!6f% 9?%RG@q`ySnBuZG_ u@2V襙شHɞ{Vk_}hƨB Yd]v=Gt!>qZicq@w>E 6ƞ[*4l Csxo̡6N]EX"}2]祱D?pH,lE1H-%R_HfkG_%oV974#Cw8;|gF$ssn7,R BoVa6nj>;nݑw'Pk&CU=ܱ gyD;)HuD;R!vH)_&|.bHC3G)LP+Z< eʲbvDO"`11WB)p~ b{Rx,1+{ ,?T7X5G``j,:UB K>JƣW8>(l>D 2kCή^sc}&2>lq_Yۣa Qme@jĄ^_wP{7MG@w ѣ# |gKܕhdu5hL{#f5b8ͳ#J{?<4%у/3~{bDle>~x?\GJleqdD2_vE?_:]&=r+ιn[)D ł Yd<*,ѯuš xkHmE90h%ROƣl`:!Ŷ/bJƣ+Ogǂ~ +BJYy%A쩃)x&HEv>e]XHA|#K҈q.R;Ybk"b? 41l#&l%Rm_G/Сٝ~صЖ"Z"`<:mʬ ϰ#AМsGv&k0'ȬE,*9rA>N?e:2766"dRSyS*߭l`#i)#2*/R~kT 'ݐ;:C,BR=OR{Ǯ{<#M".#bRv]A`R?TXG i;BO>kcmnmlkeB V>-~ \ʲz gZ4gW  &kNݮ9eaȇ.G?ukb}bKii:&ѯUZ,w[]6料mfl<X{ #[|9]_֫{s/`Vd[cW#ۦN#8b^jbTa2LƣӐb"&nD"P=z8Rx4iGaD9 IDAT`ѽG#*:OBS|í Hب4bl8( e)ňQG R;K!OD>G7Hz;ӥSm0C?y"~/K:!sW뷶|X[2_NZ[#T**yZ;+_JMgvoZv|.>_n99COc1ME4̼vcKzm㕇*4 ?uM [O#w b>Be+Dk_uV%Ȍ쿏_/UZ/f6׳!Ry\.ڴlx֔bwʷ)dh/ OVSRCM>y{"74`}Zg!og>RknBw<4Z#c b:"لjn{nڕ.zamsWdϹx8>y߄Nkʚ"oza7=z1q>ɞӬ[ ]' E3d~(Cc7m"b|? Mh?~y@-C*L?E@d]<!8nXosABw 6t:BW?"`}׈榟C-~0bV {c2K 1`j߅X˟Fdckg9Wy^ C Fgк{&z9[V-2c_*bF#VX"d@n AD]8̓~dBl"Hw5d!Fh43|fusj6 cl<_$F;ao}RCP(blLX"R G1Ve중K:Vio&95n_c^y.pVriι7aD4|t=[{Z5n/ue碭QVrA1cX"UkH="ߟ|큔x62[]oF`% yv"& z e\cFEQH'~L7;߹)1)䏑p7")Y?dAb>9ٝf;GEbꅀ`6!#2:~A+1z!i[C망#@(A0߾tB:csz홯"qb]Db BKE5w!Toמ̃77k?^X=/G{мXb д'߿LGKGL]X" 4G~G׳`OAUfJRJӥ!׫asnZVSܹ-gʷ.-EogU~cvZd폘# p#Lg)+3I)瑒j"e_Z+3 ): 0E3/@*Tr@NFɟ B?碇L/ #F@>@~8W!DJ;f52=F s2uV Qև>6Pϱ:5 f,u.2>@ d<@d 1p7ݰ#+wD@Ol@s64oJƣ1C;2hIƣ+[Iܵx95ΌᲩC. **ۜ`X,*FT0ȯ!Ji($HQ`)U{djĚlg~:YRÑdd>}A [#Rt#2MJ6;tZňk.ELnt7^Xݭ+3/'cb=Ĕ@U) JkBad1SӭOD, 8}t_Pme^I#L(v3]酶?{7v9va+7Ni߉ޫ y'y%ۜɸUZU ٦!}H"$WCL3AHv/E1eK x%VާC=auO&k51FtyֆCX#h+)~L!P݌حVhZw%Hk/kxd<} 2au;! ƶ;x݃;lcA^ A0+w=c9 F+B&+2 ͳK8~Dbn á.ѯG=h]_u!c'9_Šez\PfIhPL`pїsܬ{5 m~ynQ~KS4gVWRpT:Nh4Y,_JkLƣDi$!f\LV 6"Hcs0@IGR!#MBǏULj9ae٫#:Aq+@iF&^د(uЯ<1eˬ"e75ZYyJFn k * ,H=zߑ9o8#Qkw z*OC]b~fĜd!8Jo:S ?n2"ta F>k:"xtO^Hƣw$їѩK[5:پ+BX"bTOdLYVߩ@`zy5͹ߠww zj,1HIRPd8aD~Κs}k8n=#>6ؕؕKߡ .{0ɢ;2@bWTsmIyE".ӭ~g<_\sns/;79wF]7mιιspž ,3f>TܭYι[&?XfI2;H E6V!F꧈) #R2-Dlވmcy".|xΰ7!EEHC !/Hy)82CѾC@=ʧ"cߟom!й۩̬"4H@h d[ 8H'!v4SSm\6wm.܈6i;G>{jߏ['G]wu.M]݇OlVSGTYFAf'Y9bExȄ439]FvsCmCU(+R*ZWz)RL׼2_[sx7Kؤ #s,bENOOb5lF͈@F'e[dI#%=@( t3N GL3rclkm m A4LRTK.U'0w׎!ug@|9F0 Z9 |A$2k>L"}~~$2]ewmy;_uߧ$e/Jƣ)v+N2)ī]q@SɕdN{b}L|.&#;6,7P\̥5Ůk/ ˼&Ze\N+ 3^qmi:$ps.\q|9֯"f^y޳ιJyFe=7&/ :>K:{gW/np]69ʸ)Vo hk;4ףs*#$Gʹ3OY_^ ?_n▽eO X?"=?Yz'7!TN; c}]>YwY{amDz 蜌GZ'P 1LJBL@0J]8bTd~3Q~lXyEӃLHƣ|2+Jgyɹo*dEҙvMv; 8=H_:V/XY,* 5q6YEG4+){mTSK篝JgԡM)= nh~$YJo+JfoVޤ_1]6,n껯)Y}}ks#n]yι2\y^%גs/cO 9k7x3Z\yi`sO7xLKso2>D7Q&u>ءE] ["<ϫs@fJQ1;2_mXT0Xt1YvA[}so":)Ue$ F` b>@l>k@&^HƣijM^9>ot7zG4GC UKG{ h"~&yVC;V.(H'+|XKd<:N@@ 6GAjd.vOƣ7=K˸|}mr֤tY9KCnƯra~ks+ר^Pꈿ OXPkceFbk2#pٳ/ݴ͖TTWnc\VQs:04:ɻK2K$)=q]fveiG<#ڰmL!V` ]H8͹ f|7klU_Ʋh㿁~nr z7>?WӭÜu=TYٱs(+_, mB.rՒSynKdFOͭ@svֻ &jW.v~ \\整&Ŭu&T9OY̪&!"<97~zWmG \7`y;|u@WP&; zs.66Q6Zb%`Wչ9W;'|yv=Ho8+mNCʎX" t!Ð@!b ELف6?d81ldX^rFc;9w `/Fۏ8>ӐwO>ޅ|▶۔97aH7<r1VK$HL"!Z {"b Ag*MI{*M#40 5X&\ )YieL@j+ӣhQ޻ VMrm/<5tPfs}[2}|A*!HاiHeY!KzF-;i}c^hGV# 9/ l Hq.B~ NzwA|d< H݋X1V@4~k%RoQ?O 7<2>xt+vYG0 <hw4fy%_<`vu 2$u9W׭6']d<ė3cNU\nznfaIm}*sfVMS.\iiӜxr2`(oa#+YpǓmdW`..k3WWn_LƣgoN_Z "֡ d~BC}VuȤi}}[\M*[.tF!})Vb#3F, 3xt|>vuD P}hgy?b]vxD1c} C :[tR>.Da5Z`O,]L&GguhpFY!12S %= ݁gw _!){M 4DzF:#е#nșu8^BXE88m۫'pq2K %A+IdC]Xw@C`4ݷX"C2|#Rp,H4"p2-Fބd< ~\Gߋ%R'[1*~ oA; A/24&^ P52[K Bs9,y7|jSnW̨Y5G2Nk¹އe^WJO k6KC {*v+BASgյ$sW]#8+UJP퀝\y'mn_t+ń !vͳ?Al]Y92h3tx]vaŮ.Jy%_ei,"ZWʏ !e{kZ6ה&<,E1trkѢw(h<rZI?~U}_vA+ ysC%Dld<*HdG{V~v@%hLw"8< Z'g (Fxtc9-߉qGDG`ڣ bV! beʭ>cQSconS2D2A}}o#@Z⟬A# \@2_a}ӄ!DQlG5[$C|K:44e,DC0G@k\4J;#{rudoZݥ1hp͔HQqU$N1Љ؇)bY1`m)ٶқʼ]Bc̳.Ҷ9h8ꈵ!ԥ.}VUW8m!HvW.~݇o.c<|TĜ@/bX8xP C+bjF;Qށ@ȫ m@hbUZUX2]KD yWUOxoBJJtb~cv~rTO"H*>oF, NME.)Ḫ(;'sL$Og?po@F p~hݎ fz#E2.B %R#ߘLȄ1~xQܕ7$X P a9 ֏|ɷJo24׻!ҡ חgIw zݬ61~NPH-=?\X̍! |I~%RC m%َm]#3r6yI&g{|NB`qW4!f̻co!vaPNzzUpE[8X57;L]Կ+&4t~Uf͓˼eŮ4&jduȿ ^[ ;7]W{|b`?4r-hstXbWb|G&updN_FwAkZ "HQcKpSeF7<^8, FHkVimW~T` O- >8 αD$,GJO-~$v(#t9IJL~hHI5"EXX"ULw1݊wWl=,f7 i|d.F dbݍ^-Cx'1^m: -ȄHF` z!pgAPKlX^ޯʧiʦCk/}a32\qȇnWw~bԾ=05ή9cND=PGk̷E[QYw-噝+3IhQR4VC4֡/ ~viXed@N&/&c픜a]hjY=)oկ= wYVѐ)~yU++k>h ];sN=H+O?~n\'nNI w_(vm<Q7)e^Ů{ Çz2uh.౹vBiońtm4pwoWϗhVimC~t`|t-X"[,s ?i Z]> }L{"hWxfiF o02+6 p*2FJо[ ܃YЉh=  /-ג DX"u2獰61.g"u :9y;(!SqbR":|p##fzgu]+.0jqs6f9_kww!!"+wUm, #tr_PI}@ #lZc ]H(z͑#pg|8 +ؕ梍C'4@f 1k~?oHeSRiqfo}Hofdoƚxzn\t1=@.G `1b%\Eܴ})pi~ڨ2TCH]@mb2m@$5s.U [:< o+ #p꿿#7Edw !TؕX]8 m^G(/=fio^h[m^foWKvʚP~}yvd]jC/^8/L'#]1٬;t중E8w@V5_~EW&bK_[ʼŮ.U lc]v;kC|:bҿ"_B4w)vgy%:u]wqX{/1x9P I!K /5M@  :Kl0t)6{o8BKry=vgg);s=utF C)ZDHRG Z buMKc"¶7z(HuVU¢cYT&w/Jer NNOer!lT&3N.Lerl:,툈"D DوQ΁T}MqMT& %cR,;R~aYd1Fh"m S}j/3h2ZwDR[PEGb3P&aDRL $`2uЄ>b/Q6~8g|VN%aiiS>d"KA} T`?^'ce;Վ[Fe_B e%$:穛N0k>Ѹ016u‚SW=f}J>>+ UN?)~Q=pb;זV iIz[o24އw:55,HtEl%x JA6\grЮB9@ >u 'NO ;p?*K~#B\еu8EuLCN@9K*h: Q%AjTBߧ9)ɋyu|nQNTuO m^6|fZ pQD&JDlȌD^ODԝ!H!k6)IߠTURl:yq8UEM#m!Qf}MGф;2 >HD"(/5GjA~G$܌oh~2d("E;:"9&3Z msS?sA<'/DqW ex]ޣOm\NyJþ6; Unc,ѹ9luUBY%<ƃu$5?\ؒA թ6.* 6z=¡Ѯ2cFAᵕ"\?]/OOl:9sM\z[)'g7;qAԫ6!. c!}g4,PyXwOX$f 6 Bou8UP4lBy 6w2vkQX3zJjgR\oT: ) ³|'wAI4 HwS\Ԝ)t=GՒ/utaШ}H1) by5'q0wnPb(8TrU򗣛|051B 0TH:e )Vf <QrF;c5SM;ϔc@$w 0E5\ ܏&[:9 |Z7euS2Xsb7;K&A!vh~?,8E+ĺPwG{G`">м5{=Hns?[-{yv/ z/7$  pZaRn+4:1Þ?RD-G@اӀD㒄' o"D;ѽ<_?Wvo._y('+l?0xURl䠇)iʻdj-,62lVT&7R:N>S Es`GPt{"St*. Ry{L\ }D uMcsX="rx8b>O9 &V (,DUheH0? 6Nh#US>msE*CvA乫Y_h¤"2ls x.N\w1㟐M'CUFg~teD.lb&"WqNOT Foq躋:;(vwXزEZot'̺'#3'V9]_MA x`.^Ǝ5a2VRX zOTLGF CJ"{FZg}"TQp7 ]t^G05A=:ЃʑHc=lCCF =0 BoN[laaѾܕ5Ň(AԢQ tk^; fTP*sZΈ7@^b/>ȼM>.Ca>HBKd%"2_F!&Dh>8}?|hg'77TЩ_^U^|㢱/3` )[ysQC~/fí&a(X^A*+f5뎪6#ߪ!@E{)wܱSpm L˦몲w'ID]Y㵈d߈#:5 tk"[tDv-납EdzW(BJi,)>)XD_%ڱ-Po,4žna>Vr 7) v5co+B9 S / A:# Ed.ȄkaaюdR\9PM'˦KM^(#SQVE/yo8z~M h\Hŭȶ /D7&wEI"ԌnQ;S&fy#RsH\R=`?f;a IDAT{XygI+Rl:b*[HIfC\EǵهFD6c&T]glBT&R~&~L,N~°5oG cM#v1RץKj==tE*8VD/upA [U i\'U FǠsUn^[聦 ~ )͈}!xzQ^a[PIVnNikj4:fF[•'  m@_H^_۳E(ÐF0p/F*l:9 u z:}yj7^9J1L@PPXD^G7+Ѝt)RF\8R|te>?=Ԅg"fM|Tr7.5t#"3!M>qKqBעȚ'Ő;)j?l:YoΖ!u]jb_m&O]bvhR<U0$ M'_Gw21e l:y9{N+{hnޅKr>K?,%t^Cs?$: ""֌gÃXvYzo[8TLR#2JU D#0ՌMNC>t1YK9Zhp#^̣bAp!nD gg`)>E|=/b#梌B!膻*,BWtS\%J`|"ؓ4÷=~o!Rں Kv,qi9)Y<8Rq#%|~U*THi&wѤH,4 ,Gz )& G9D2+{s,# D0FdqH*eޝ"!Cy&mDUcƶ3tɴ:,5>T&7dER'Bz΋ߋwvwiw ۱/"b2U&PJ FtF!ΈQl7"x5̭Epa*L{mavPB]Š]kb;!vzdZ+)ȸy9\Mg?憲hp0%SA;ΌVd&;j RDHF͂eɹ(&(Uk Zl: Wb_AUQ&_!q7h]Hɚm\n5(xg|B]4q5tRl,܈Ԧ(m"l,s4"]#ff'}j~ԲsPhj&珑"wRDn3m@ @T&e D:i}ZL"3(Z,|xecaS~iHxy!_=t^;2?Tmrn@綀]h"pMNo#cJ 4&NW ~Ҵ<^"DT:Ě!G-(uG&E{8RgDl)=ʪjiJUIyÜ/&ϡen,™U ׉bꀅF͂bT&4~*!RBINCJV "RXöͅ{g"4cQed "[sQG}2"D:Sozl:H;)C1}Ϭ"}(6 [QݡLke>a6 SܽHH]餟E럈  ,Ier} &]P3cCqٟS>t'ÐS)tooL߮pXyMLM) n_/e;;x#4&5"$j]<݂&[2V疅ߎ"%ݚMKJ E[[#tSsJ~ 4( \:OAMm/xp.qHa+~N{3K巙7*lXP]#w(0H6Ӵ X2Ȧ+U R\JO'Pxyi=HxzmSίzdsdǺ#p?")QMMX7xo)"?KQI=KPj)"jH1=+gBaш8^ȟF ɚT"Ba9W"5"Hu6*M'?4U5b ~<};t}@"'/QR9WNᘭϚ0՟BR@u=۪&iѐez LB3w햯5}ҰڜwOKٖYXXt,kGdT&31N.JerEag͆Ei"!a6B7^f<LΦOn ;6S˲T&w6+!M0 )!_3\՟'vhb4MC(6LV('REJX uo8"((l]1Ҩ ]SILk=*Dʖ*b!zUs?/|m RjLnܼl-Pus *=gs2ܑM'Ow\Y;{764aWT&M'{,RQ0喽T(6(svE3ۙn/h2l Pi% ^&R~bu>5[fl:yz*@a9>M'Nerjf|޶C$/x]M'+9ۢde㗣} RV!Hm \]ڣ]G΃b}Kk> CH˼ X.wܬ9oD{]o^װ8#rg)WG>e•DTQ41AӌjtzoKCT|A pvA߇ۀǂл c=5P8[T&;"fqB/XZXXl8l>cSP~<R\T&_^C!фxJfb,pC6<-NFy3f (zn7a;d)I 5 tS2Rm0ߎnDH*Cp4?@?6chBĩ(5!)|4kFzy7ǩ܌1{ kUvv^ĵ;"-l*)xFmH@׍M4c %"kA2f/ܫQMo51k{)W\+Gmz\ckkDVkYhgzW"vH5 GgW zXF4+ (Q޷)j74}G&qk8?tM=iYXlaʕakxSHn oDZD@0Jυ(GjZ*;M{weɖIEH튈DDG*^T&/B7K1rvB5I6ۊZ' D~|~t$h(N4i4]dYA-fQX.Ԗ鈘HeV3M'SI: u{\rjDzU~iBjr躈 B﷮;+]Ec# z"# sK-y8z]{T1XPl=x RYGrG1t|E2T8ƥ ǁfC\3Y)8["A5m 6L09JO"B4Lht#>Nޓ亣NpJ6^ݪˌ]FClD H=ꃊ&Pe^3"p[(q{/]`{%E3hj6g% ה&jnk~X#z(R7vͦ L.޹tk|z r-sws6\=E*H ,ɦx >"([Vڣyo/9i?F:迆7 rk=(1y` u̺(Ľڟypt-P3a zxLK{q :kbf㋈w6_`dPCN}z]1Dd7nޛ潾Boy tl:‡¢`G MLBc0 =}j*B[T&7=W[o4N^dDy( M D"ۂQfg bg$"Oˁ*7+R#@=a~7#ڋG*^h(H] &T:~ $n;IQk4?E)s0G_]?(}k +z("mdAn[hvwkzz ^ռpL:*vxbaadl a/FUh2NCMTr}"=T&78N޽햢 t% ɦ7&CQo(b6hT?2Cl7*Nn}JkBJDdoR#۾Mer!iԱT&w="uN6rYHer{ҳHMFu_Vܸ{e*HWJZ#N62k񻢐peD\ڴ8;E*4K$ ;@|ُuFz3\ ]Q.d2=)E~twnFUmlA|t ME.tLVc vmҞDiOaeȜM;!2$M(atMn_Anپ(șڊp ^jz&>c{?P*늪1w@ \)%Ln8,Fc |=|~qKeZÙ*4_ (N̏Ľ1nZԢ3(8]-Ag.EވķQ ߋW6YXXlXel M'M ygT&70bR\l%mn&h yE(-gzϣNH&;Ѥ5 M 2[u& NM'7 YC@PR"%Iϴ&a~H2O)>ezCR؞zڊ 8m&ӑ J>,S <-?^<ӌ_.fb;qp@ RXPL 埀EmicB(Zbu}Px}zN=W )Idu2Av0}?]ǏTocUW:^7 Bo'r2cQ^N@a(q|?LYp>Rq]ԈrRk~?|E,K0IA."L2٨owDNA$ /NDsY:_1X%~ĝ}kf@$T5"B&J a0hg!խSAXYާҪ.P̌7J!AmfT(8u(4Vp$F푅 Kr:bBp>ZZE]*Ū:{zޒaF˳->UQ8&'2, ^DU-dz\ʦ׭bMX g1{DBDF!u2 !Xw1EyzmlsW] 9x)_w!Bٌc K9+Q/:~uSY+'P:uA˹?t&!10K5^go 1/]G  xy۫ZE/ \ (_ _s\YX|`ÔLd4ݱ ay\Ro|E \in:"[!R Z _P'59F hM'7Q.v B m-m `dz8;\?WD #˖Qغ+"]ꇸ.=);綴HK>*~h,2\=PA:7*{!pIz"񇣼 8NB]w磇::dk{;uKPAgȺ޴-,aՔB-ge dɰ-dh̨C4+v7Ʃ!,#fTϯawqo+%D1F9R֟pP@!F.BKo!̲h|5yphLdۀyhװ*b:;hٕ[6Rqsw~v>y-Lcߞ.d2"AjQu]߱ B!"_bB]|):KбzyEDu\ǿu^gAP%kj7VHerײ.GJ-t5^4NֶZ\`^6|dMֹ{;1ުهp`Fz Kn0 $\PXkU"u'(D nYC\&|uBh2})~z =0 IDATOsh=v[hDO*Y]A(Oj lv?;0e)R?n B~ GaȶeYz=5\Jx=*.-YB7kb]&olg=O-,,dl#@* nBt[H4ӛqO좉al:u*) t-c;kjћ}sU q g1 n;(V;T[haa͂ Sn"J۵z,ANCф?UtrR6<,NTIhb[)R_2>G%b_,h Qe`3RA$'"ܑah::ƔvulKJQhe#S/\:wU@#̲)x[##Nd8){xn%ƨk S9lEH{P ;_~E_CjŗՔ.Ey_1̦5L<&jtEV?/*l:T&ʦ-Oerdp4\h Ȝ+UEl tgyoQA[z 宔bUa;HXh:x,ma]2,Aǥ`DbW`Fl:?dDףRUoT&7 (ɦkNXmT&"s+[HD<^_՚=)E ~ :7 8SAԎ㙉 e?\e+PS , BBIydnZxQ@zn&)(dz*h3 a1nB1<{"בT fbB;Em&l}ٿXXX|C`7*!W?v996vը`ʼnzU ^Mer㐗{tM 7{1*tFQ;~mv¢5,<9l:9{^T&ͬ^M$-ByE _F?ۄHv%T:~랣gKj""Bz &4zy) N{VFD)Pwf|5'bfQz{ߊ-VVZXXXl¢R\ trچφ'eS"hKkב(Ye"8!Y<|Sɝ`ZXXtlbiC*sR\,r;S:B yԢ- 4f|4E3z CnB=;-,,,YlpqaūNoZXXX;lb#1î0BesÍpu >ctaɘf񫀓HͻfZ>?/ 64)}0) 65 |V1!^-?q~r\ǏiAfPGA蝵!haaiÆ)-6TmU{=YI4OtشIQ(fKPrEޣfDU~/t;\٦㑭w7-h\Xk jċ/fVz:rie̒ǻO`0łu_qZsmx<*<6 #-^+\? ӁuπÀ˃ЛmaaÆ)-6 _FXIxx!^NqDC'ݒ 6p7z/pۊzo\XzLͦ_]p.pzzck7"U5-*0@mp@zk?,,,6}X2fqdSckv 8) -@&jm.F <rz64󩮋t ^ޜ BqDv'}0 pz?#ڌu!L+D<^} B:cmZasn1lmp?1\/~ 쏈S x):p2[~y I&tT0eD@l[1 %c%\\mxP6 . 'wV)Oۻ8ϲObs B/`45 ]JS&JNlnM{[5,uMH:~48 8uehqDݶk8gmƍ@?,ʁ]Q81EJWؙ"ak.6k@%s-=y߅>baa1%+? _ ߨB屘#c azw ~M!,gE7 G)!c'x]ІeۡI>c~ǐm q(KI^2 ]LX7*u qNAǻa r%Bo饹;p J C|H؎Hr̲MZ#'sD3L,.BU o ,(|7&gqӕư!~q/xCh+`6*cP ͶcxDCÀ#uWHMC/Q[ :ndv;`O~ˈB#ԡ.@K7ȀST @DnzAkaa͇U,6jW:c(w'@D/"Pqc!"8OT]4S#‘@Bo\ tn6dh킈H3 e=Իw-C"akAU}=!1it^ B:S͚+c:yBo&+5"^A5>D9fQZXXlNʘFo.bu@jM"e }aןAbD|d9_y1hDƦ"U(H\( RB3;QHP`\<Km:8+˼9}Z7rZÄ~y K.Yl 'cI@ST<r.!bm_Dz(5?R~!쌾+]#]ǯ4^j7V(.S̿~uQ8 +x m*W[ͼvi/}2|`e˭ A5+ڟːjmDuYXLC,E*<#^ G<#BWszd+X"ko.ATl]gaaaabX2fu|L18%!7# z %c>7Ԯeѣ~(y Ҭl3,ʔ1t{Mi,j9_`o=¬c)*Tx&~l;*C `sqz:U@tik<Ҳ؜1M SqHꋈQ""&"@ǘׯF};dy )Z}(5 }g(9@JjTQ]ZNFƢ  | HU~W7,`O_B h.Ha7( zY%0:-A]jԲ[&֦YXXXt 2fT%;(dمҕ@$  B\n!B9Y\zWc 5k:^Q;ObIصZHNf}?ա MnjGCefjf{W!IA:$ԍ Ymaaaa kBňtGr "S%(gY4wEiHzkDdnSf`|DͶ_C9\CbIP؉(ز2\gԼ<MP@+ [QɈEdD b)i.d^ lBe(Er?-oG]saD 2]s]:: 8uky-,,,,:6gbC*hj[ g"e P`R_ ~,<"/ˀ\ǯF$m*R0 bR΢QK+CDJ f2D-D`r̝q\fKP/ɛlyVd@djT`0)l5ۂЛ#naaaaёdbSD)гN~2< ( .7Qu'jT%7ٷ1jF` 2 /Tf,3Y7R>Gf]rW@!I J2/3Rכ2VeYHY uxTTIz*R0??®kط; >4f{eM$tu3QE(WHg U7H\"l"/lK112C-GoY1H1;l/-JCj"D9ƌ=JPeDE-JQs!r/7/ؐ9PHB!8ۙu:tTQwZղU,6I" 0*Ad8nH%:]!9B;"1}*k]Qrrޏ!3ά7"d#P\i(99f1[2ʀ%oB=]\V@D>&̺zBj@~9:鹱cFos_YX<Ʈ ֙FXZꭶ e AY$Epb ,`dQ"@ " Eb*,3PJK:mg.6 ]r|I{'!O{YC`V(]F2 IV@6|-Fm*kjR# g(BծSP he IDAT(tݏP EAmlC!i*:%%yV\ h.P.\ C1/F֣]ϡսh~+j~ \>^,O_k%jYTK%FQR'1J8tlۋݛ Y((-(LvEeP2ȯm\6|Nbff[mJQ kv/%]QHJ뇍啝1"Ԣ4;RR |U.B*G;;,Oנ0C))5|, [T%[6 G\9 R ց֖lrPޅBbP[>xh ugQu0$wwmbi&)),y[13-rezRRJfyz&\Ї5OtBtT9ɓY.*%#Qo:G2Eih䣨Mx ZG6Uڀj^|P";o<U x7{9@㳶PZoff=W4,Ogl澙IY(fV3 vtЀxjB>h(G~ѥ܌ZM'5 U^CkT%x!qGQ;Q-Ў)(^vJE6Z"cۨ (>o'U𨉟qO ,O;703ʘ_+(% hA (w᪟Bi]<=Ak5 U9p%MCձbl ZZ}hs(=4 41WmMQ0< ߆6} mC!KF*kՊqoQ$jK߹m1,CkZYv*gB֢ 8]PHOAv= 7M(L2/hDttJI?{=?!F-Ůʡh+ʝQ;hz܊ݚcQ4jߎ72>s72AmSsߴm1ьCs#(hD^T:E}|zUvB.ED#(Y#0*aKPhZOvZ7k2_舥Qh0Wjuw&h&M(| A!BV.gJQa43ݔC)%z4F(ۛ(trCad¿&VPkxn_4nhEhh"~1EТcP1oQ8ڕ;?B{QHk߁Bۨ5hgqT#(vi6 hYm\33cC*%}7t>ecހP1oD!k@ m 5h}0jAB BKP5CχWс݃)biGn@y(գ`Qgڧס@8.mMi;,Ob(ČFUuz4P)v נc#j'nSY8 TDY`c癨84/(kAhvl< \h [m\G֞5gmD Ql/Ty{mrGh WlWJMj4:tP+X6Cea; + ֵq+ Tèx*ȆRYՆQ?^Z]4bTPq ŴPhϢ|*C^gx0bt8z猙imX3ڭXFզnwP)vBeU ]Pkr{/_]܈Y ZWZ#vN))f݊R4fct\TY;%^hGk5ڹyP\O1kg* nhm1o t33`2fƻkȾF?h@Z#D*q[S)2Lנ1Q8Sr5 B{*Ghhsh2 Te[т*sEĵm7cr8|`×~V~o_{e Bhw;@( * A`$0ss@yng4ܵ~/_//psCݼP7Ғ%fL.ЁOq@}5Ԭ7'P(C bPL݀Tc?larSX֍vj"`C"=@:o:i ܪC>w.\_a XjXFtvf+\MU.1`8P,ҁ8xGXѸ4 =BhG}~LVh +< "thKK7lVxyV?rPgk{3j \R:K?p82#uz,1KKvQmz?cV8X+춣Xd*CC#as `F-?@u) BoU@Iy 2TնFksk@0"f;=jX+Lk%$$9gӁOwvfN߬8MBZ}EFIlC`J4ŚЛbuZ"]qmoy Yy8xI`8c8mDg8|9+j^>-&uΫP("b70EH%cEG`0o7GҗΑ7TtP]ܦ5 Drz_".tאZihǑX&".@"h? * 6%0tB,xX+tkm~b͉F]KM,nңM,]z<2=ʰv&FbNk<0!8XT( Q#hX HARk!FyfD@@ҙl4xYƇkMR,<_*] 쟧UܵS/Jק}89HXV- /^:#љZMUfX?ݠVל% ܡ'гX+\l0 xႢkY^Ly?@龹>qgi@?p|dk ґ"c@6^ns+ b(!n~"S c?"H#6+"77jW) f@ϛlCj [SCW%Y+s-rs)Oغ_W$G"d}p9,ZAV. ]AG'o/ }7}DZ,"2s3cr;@2Ͷi hBBP(ܿ ӐRC("!u`߫&D5vh᷇>m>za R6ТvcʼnC,eXmX4m51Xk/8Y"5eWy${+Z]2;s DêȸhCzc'=uCےϽA7bdkbk ̘\лVe? p5w Z BQBl?( 8Xkn2v,ŚtDNG"Qa@EF4T$|WZkc^o]?"&!"jA(L(2JG3o!1I aZuƀyHceux=>y>xtTԻҨG;[/8zLKpOO!9wmtNBPl}.+ ZVJR#۲a"pt$U0X+ \]9- )~@ *"@ҏER=3熣Ch,냈{!泧"ѭ*Z>:b0u T {g7 x&2dOж7aL5FӬQl= Om؁`!xy9 Bـ8a ZJ"5ِD8FҎi@z7\oh((9I;6Yz"n[3oO5g%u~"M'+>kH=ר}b¶H)fL.勆1|ٮ{iwci[zC lZU(vC3}fb) .Ĭ´@:-េH ԁtu,EP"P3ڮUX{]")9EEFɊZ/{K,RzH:4uӊͯu^8)qfS'qÈni\lurд>CNA{N]f$kgL.JGޗ&o"GղUHFX3<-ΩP() {#)L##وЪGRT5H DhB< +̊D2,sj$hAMˊȄ-8֜:15M q$AR 3XVh~&t k]sIGK4nHXu< ܛ7$?HCma䂡JB [5 p,-ˤPk)MA"# hh\^ӣ#YQ( E;O"`gۓ=q6\HdZDDj" qs dEfđ݁ ͟OA _- ӐUe/,# j"b0)ꀸ!v$Xg`D&מOO;{8rx @De/y/oX1DZLGhFuo|Ohe]E{ BqTjX+ " 1r"d!t/?<$i i,jeH?,nEҏ#MH59$$f5_(X+<5b;M'C{=X>o+U89W̹96&ٻ̘\A,<@V\Y4{`?q!$?-s 8U( ŖQBl}"GKR> "uZ ؤ") ];8 YzBgϘ/Ei^hc|,D3"F*1bHg˚}l^Pט1쐞yHڱCR6{꒳/LX-5S҄ mrcw _aZlD!|sـs=2q E|j` B+@Vx89kQlVx'"@Kٲ#b)K 6GD)ԋE|dL3Z$JͰ%{~tK]sDs 9kᆟxBעqn`KP{[~Zl'(1Ձb6]ochnz>6|jx$-M~oR4G`ތDCߑwo 3Vn:?" 1D\#s_BP3Jǀb-N4mQD4U5-Ψ:i;R9Ig6!^at%Qdk"rQD|t^ZCϚZ?GYG)2J%!}_D괒EqCRDN$:{FVj$M^$}6 ` s!E`,֤Q`"~fZ֦M2ZHD.%=΢H*t 'DPޅDh^p4-ֆxf43žОz֒l _YZ!#5H BLUCV0 Ũ;cr f-Z=vՏKo B(!vAOq)C>Ln+2JTr8#Q&y3[Y7!KxLz%#jc#&`#Ҝ{1<geHzW؄yN M#b *G _|,$nlwEl5Mǰi(y.>wEnCgyUuø WyDCVKX<dbxÓ+\ ܋AS( >b'T2/mNn+ H\xJ/Nx6jMJ<vwDE@R+')?Y|~<"ƺ#Bn:1YCjxQ`c"4)y+*`_> g ^(e]Mq8CpAɻ~wYگG`&sRLcﶘT^BP(jHV#'#"^D]/i~c|kیjDh*v( 8$֌x%/"#~7һ҅ԍ`9(%gv԰Y m~zguGYrmV\at)c^67*sۼMfL.po"5ՁiȽx] L7tN[OP(XdGmWGEZMuZ%mvD#TޕH26THQ"#<^C 孭Ʊ F js{677>y\sNL+Oh{ n,L/L[[}VC:(;mnP\"#`*ro0}I7tn 4qKK :/6BhTD;>Vx"8}/ oDYciY՘ͦ+oCbשվ? 0 I3 %ځhqű!u\KL;aEo| EFIx4cZcT[r)A;r1`$V}\>nb骽q!qHKz#RvmϹ) Ł'uug!V#iiHTu RdŎ‚2DUD!5iYDṟlWI~8`=W"V)`QdPa@bbsKA;>, iT[9A1nblNu=H1򹰛wn TLP(v%@,b)- :RB'6~RT7_ɚز")OHJ>aKвZn3AcO@VRqf!J=PΰnY9cJQ^s=H9~q/ɕ<`ZpmiiԚ dE}Ho,a%*bP(;09I)]UZՀ;$ՋTaUEHd)ScC>5k$j6 Ie:{k{眝%k>w- ϹaȂ8i^QoiM[ JuYܧyt]FYazޜC :yϵVfeޜS>| 1^m~]x:3P(ʾ(y=ڇ+.Z-STdl(2JCjg]Iˌ'kґt)?wBRrdUf L :+M yiyC./={dAA]'%Ol6Q?++x|vf1:f~odMK-H`G$<%&2GNNbTq{6j$"&tDx"+p_  D\o" bv[WˌNR(]BEPt} 3w=vxwhWӷN)D#mfؑEY%#cو^gn/gsP8|x EӠCF?K;KP(v ;@Wwg{޲]G Pd4wտ^M2$؈DQOGͯL$v|j]/ii%ec}^C'EZ7itaMCD_]!ѻHZu975˰OOجK|)qC|潾kwMpkVb_$MrՓۋsIA<F%`.Ncq}{lRcpb1#?904͸P(b(}y/kLEFCEF"dYmӉk5=w%H$"v^CU s H$*5kGg" Uqpײ&aQ2 ϸ 2lLgD!.IoG.2Gҗ7VмѾ2(K|i7Qk|۴xI 4,Z ;y/ ^gp+6Z*1àgj,x޷C2VXSYףtւSfV@BPWԤbAajJ.?kNb(ֿ )+쬅əz=0(Y}| ZS4[H7{)P Ѹt::*sW jlmQMzN@)5+C |4|KWrWfaҐ~Z9y@uÎX~ttHTEBP[(CW/A)Zj X+Za~9H=Q%_nYU@dbDRbUnh(?{>w|]C 疠ݮ)[N>r¸u04hƖ߯̊\3W?Y$_D\o^:s:[{/٧J#rBE&Ov< ϘVI~FWC"`)V_9#Av!OXghA喸Iv-␠m QJv=|fѲ/UV4' tX_03&l˫-8 G{DM}Z?Q D\"@ĕ}hȞtdGۈwƨ6qy6wIjV݆u@] ܜCia쨋:}YP(TjR Zi1xĒ?M^|}Lsy 2{RV^U QYHk^q RdTF#V HKAۻڏkoREGw|!U~<2nb;==I IkDJ4'c]2w? ߽#c{;A{][_P($*"؜Y+r9p9L[5pW-y*  e-"^aI1 Y%yp  lKfI9=%>{Ozo֏뻡_#mJyZmADiZVedx;WgZ${SP(vSl3wPP3&{zm#1zK&0i%s6~܈H|L(2Ji:Ii\.@HXNlv v5gKI #[Bh$CVm.@ oHw͆솷`B8PBLM<С'6HOk:)B+"NBV"LӀ': ziH Z>lDVwy*S D\U:b"oY,4s_ b_E 1Ŷ? 5;;Rcpv(47y}owwCC,91BnIC}N|+ÄVj$:|',8jgP(bm1 q!s{2*@nlېX:]CV~z>jW>`:ëۼpI :+ ҍVHg{9P( X_qN >Q 3&hOAvIUc Ty6O"Q^gܞ~D.:zN B"b>G=7mȔJ:wx^8ĥX]}>Zdn{PtO0ߑHĘ5I>yqĞs?;GX-/pWP('SP RA;u>nCn>YXѼtl@ĕD>:å[; qi@5H2R!CwD݂tRxuk"30q]l:ÿֳ7WߘSgEUQ :jG) bˀ3HK?tΚzeHx30wbN,F;Uh|Ct}C?n7Gu ijv!seorg/ }*Ty3d@3]ݵ 1=)) b tGDT?$MT+a'+j6לWS/щHҋ{`_aF۰0fyi떧li6Vm<H>kFZL1}UڹnhYieYie7">pp{ :;z B@C(C}g$b.zlԽAˮ83㼷5DY{h-T/{.#{Op4ܚc +Y( c}cdQ42 V` ՜G"c@ubYP(ԪI:x)v߀xW]]߬[!ѡcbJ(IsT57{t1A D w{VdgN&`h9^gվɈ`e`fí:Ý7_Chh8Dc[c.`-];|]yM|֔2kq_P(TDLqrX N !0zwۃ>׻q+Ro-ZɈh};]Gҥ}_>vXc?eϯlY.չm_o:"^Hsc !hs*:joCDbW^%_ o ~xg.ZP(SH .2KMSueVjX$

iXaY>ٕymN "\:]\kgMÀƜR*k/o4c?7 o) |u:NBs9uV) P( _#Q?-W2֯Yo`?yXW+M+ j]>I%J~eGY)yi9qwgk5tȧ}u?q}Fl >j9w1i)ؕ3k65;wXt CO+ة>>rCiך9tٺq\_0(:7GSs_}Ko~ 7":},FVKSl5o!i} VkgYgR( J)UEꜾξ+CLĶ!_ 'ыD.|ϽN {~-ڎ\mܒ"G@aIEo:8Di.lf]qзH]NxGi׆|Xv' +ALtc?wL}; ܫ>L& &A2Ђ>@LHo!Ygq[34+?sx¡oYw")Z[Y$56ʹsػ~xf}@ 5k٭^? ox^dU :܄+ ZvP)_bS!6:jajaz D\:0YEyT]gCoakN~2{B8s`ݓ\~iuvBPBL<<_0}އ_}g#YZJ9^gxS6I ,L&8ddzmDR:\ИY[޿φ we@3< AI|$m'@uiҙe6s:5 :ȖѡmQȊtT$ap]Xb6^iؕkS ~7=@P(bmJ?GjɕsW 6zR $tOnC6!{A{y_M1<0 ŒdװB}ۋ\yuo D\θ ߃E5̎+>WA=4c;Ų:ro hHML3IcnB 4: 9uxh]lڕWI혳̞SG6bQ@dsfk*"F :_9)%mJ瞁 ?4OXH'0sWxz3ZjU] ّՐCF"_g} s 0#Jl@ĕDG}÷p3OlA?Zwt-֘H)źnDRub"".jNU^@zM}džhau-}<(׀&˻G 5 zm1W}6 "b$%#_hUW$U#4dR669ɺ8Zs J<_/=1}d{,`p:"N^p;P_1뇪usxoaX [>9B8QBLx! bdDi%C}#֮#^#U?"u6Rs/J#?DǮyg=k٦Zfqp0Y7nAQ?/[̉ ,tOZj { >ZCHUA-k:k獶=޳;-vˎz+F :S'7-7 uz7HM/J.p#AD Di}.vHLy!K~Л3i.豭i74eV}VytJݢjk3~vV*MS O F&)nH#D6gZޝۥf}H6f#C=-HT4G Hy< $S)B3}eDVb\4/̯$7nkҺ_ǃz~dJc {לKkbj )X4s7yQ^樮 _Bz:~.hZQܠ/\񇮚:=d _CZ ;< @j D\Ñ4}+@.Hd/?[*IG~"Q,(s.k̴d'+H1EŠ}/C"(%KnCS" Z"tw -;V??>Wf*zҚjL4͒ӨO8GCi]_-؊ov̯4 Y͢UΗs3WʐO{ڬ䜮5_EV &W! &ޠ'"aSŸ /"hY,DV ]@"j"H}czb冱G,?y#ueLͻweRam1nAScrkǜo3u,fb7ΧP(@S$IE kSW6Mǯ]ÏD"%8#Q׀sڲOR쿆a]2'D\#k|L?!j{R 6*D74eO>4"K9zuᑣ/=SlS5nlR#"CwP$X C/5{&Ωx0qjT( @ }_5$܋=1@Z}cf'jSȪA{)D"i $ݶ9<7Hl$7  Mi_e&r,6Q-v:aC;^Re@RV] aM"ܻa1|i:rr pP*v~Ԫi=7l1 37}f!}_=ګv̧;tw;:/wuޝu w~ D\yH0_[iv/Fn@uahWQi"d'Zb3@myu8"'c5 7) OڐpX/kYKXFjm)!W7,D#c\z\V539O Z!,"^gxN 2T,<Ј-ZDRg"sӉA{10\D5@3`E|^BRs#5]St.g%:j:V$% 5$j%*|(nJ ZR)ACDܕǛzbDnC4ӈa6y8?in )=p?4ikO6Gz꘶"/8!Yݫϵ8چ3nCz(Al }#76u˺\ӇXzl.RsODEHd̃{{ݚnOeV,^5WX\ <,z+y}/rIO]kxTp'nfs7n& XQccё/HhBPb;$# Z "Y5}bH=|}abMBDPZ5H*D` Ο yM5:1V_ɇx"FR!~:{bX[Fb69iU#"R? j1żC.A=$=Ѽ 6 ͈x{fK7;soª#߶t̎,fk8iw견c΢oky+s:}@5dGtk0h-uN|C!>? u0 OEVl?b)Ug3[O?znZݭ IDAT[4ڽI!D\t=FCM޼߻N["R7iCw%1pw$|0ً#p)hVTox"+ ?wdC[JM'8DR3n7?I%ލσ>c fAx#"ꊑͱ& `vDu17=DNf}=P$hE KH}.>JozDZ굈F?D#b iCuXDd #A;)\AD_=R!nGM;M h^iq$ M=SuѲʞ#ѴDKx[z{1^VWe{F-}˲}o̶H"^@Z"ϐ(h[_L~[-M=#ϾN}l ܈{x_>zU霊Ͻz;ŅQ m? AF!Q<:3,hFxx t /(!1 >e{,EٕDI/-ܹ4M#^^"5OFLwhMॠ}9,$W8EV=Ԟ-G\ HT-ii )ɟc>ԙ upCvR_ր:K{VL /@.ݒ'CGl' 5/}Xq;=1h2 Fo>w!+`LK4+ռUH*у∐$JlA*6$ a%JK/DD@,*ws{GPTETJ-5z-XXb-1DWbNJ Q(low~|8fQ9׾}ssHm&$0&#JDz+sŭ9%ĸ#P[qT_w&wR>EZ4N!7߿m>ɚataL:E2ح%}`mE(@$frs1eҙecc >q :{&[ pd`!g7U?;L3K٦F$H}qi_{-bkm1m#H*L:ulݽ#Rp=0NBV&ZP0etsQ&4I-lVψ2oCH[!I]r=bCI>oDb/+\ƱHhzu%k矎ⱊ}v:EH٘TWl|C>@kӢ/_b1\s hJͱA;+?Nnh3쫬3{^ܼ/ x+S3rnȚ!EpQ<1ߎՃDq=}iD \8Vڞ<TnWV7OO %s~ op~Br3EuKe&9;X%г\q'C?uݡx#[l[ b] fҩ ȤS37tۀ38s7}n"بq=L:&u=("L:7wnNB?CXt=?\{Wr]zڮ _DnAhJ7ZA9_"Z f=Rz4;?Lՙ8"R^Bo~+ W+G/G@Ye{G~`umtj*@M}"7^ez~6LM& hjJ&si 'XWW%o̙|ϤSYKuRImlGMk]-Zv@ڪ rcN5K[]Ҍ?V_#PRؾC8ԏF7gl?f1mfFOmX\aOɖ[dҩE x`h[@uEʳ:(RF3N-v=Y) V)LKѤvr-&4Ԗ]:#[tDga6 M#y(>,@m|PfoD9)AX{Sv=uU=w="L:հ_Ԝ'iqmۮhmG^g_ϧ eRXQL^yZy6?AݿY&C=)ۡ,КWVMl5 E/ J.ܬ|cǠgoG}1銞W @]-b-ؾb/4{N.rIgF0s.7Qsc:|Ycdt _zA(gZ8wF Q!r.auU(H?9#/hrMhBJ*)RI|bEt({#ߑ+H]s -GU#Z3R~C5 ruAQQ+:%MϽ}>5z5g }eҩouv8kH&dMxGw)Dֶ;|fX I"ŵϵN18hiO~zq7 ;6M$ї K&"tCgclDz5WtSZ:qԴ';)ǽr=zn0qԴM%UlKmc";@N4F.AȞYD!Q(к8AJ5ؚ?#EAWeYjF^d\| =/?AZROV3)P7"E3XE*HG\h".<?NAiOUe}/F9*#9Rz! rgV*ޅ&|L:'_._UUad$n!9ɆRHmU~[uXv4obiGOh3HSٴw([[r5oB׀ƯK;6nHU5Kr/X3g]H!^gCuҧu %/gA@mg3?تyx%n׶C.%虿hiՓg/Fs/bm5R4RNnBi x˖`S@r=}С}Ѣ3JT/\9bJs67$lukG ATwkI&Zn%݇&Hu̮놠G*(&_kP١(Wj ec0Ś ) oBU×3Z-`ґljoq=o>}l^owD*Z&Zu|ɺws5CN^z`o[ &"8/KPTא,Z )m_56:)2!nl-I6V;wshS7NmelT:lo+7'6uقoҗbbG!+z~_4qLi,>CJӓX*Q('uu04 4ɖ"-@T ي~yvL]KfĔ@f^pg Hi$@Ak"GBJV!HV 2 faAT P[w(N`~&aW suC*Y RW!ha{"pdUVoAV*V?v#nȔUt{Q I@q@Gjwjow(Y,7.d0a͒ 3{'^?yA_e>&~]GhAm};KVު8ͧ:.8eWw.-/Y!56~ ܉}]:b-6 N1"@9xcۦ]Ъʼn^O5~Iڱ!Eq5*Pȵـ&UR$[SqA5}&pT_iF 8Uha{L )q3QיhkS%kl#; H(7 ݉h˥3y6ruBQTvkՙtͼX!N}cXl]붾Ǫg+sz{ON;|G 6gVוMM6nO8e9u[&aDJYW+,B Ͻȝ@uF@5(ֶ"L:dBwGQ؆ X&jk jM5mdƖs :vZrg4gsK8٢vE/CGVĉj]Pb-S+b,;tcr&tMP`RR l87YBD /}K#J1pCP,չv~Vf.Fi&vDn(FB#lrC.P|L:u̵(ME \Uܚ[j]D RHkDg+kЅ;"A|+R}r=72[ L:uƖ)l9@QӪﺏͼ'_}[l֖Ŋئ}s:D 悜1N}IVz׵ Ff#U[ E),:c"gmsݏ`!\DGVpFʻAC;g!#R4kQL\% #M+K"r. @@xV^1y\gKIp=v}T@ͷP½.{Y^Bm6C@}csv qnbm3XۄzZ4t*h5ZW R g+`QN\ۢNv@L:Icmv)3N\?ĦnOl۷uݙRhzOBI:IƣՈUȅVVB%^r;֬u J512 D9@J1 " X|J?!)sGVPM9\ʐ-N]^]Sܚ`bmF\O?Bg]yQAVps>FJ֡(dHm^Zm|vzdҩt{*|o|Ww7n{gD6 mcǹC: OO<`C,J9Y<[lm-V6eҩUH(s=?GQΤS﹞:R|S&C/]AQjBC.qȭ R#EAJ{+%HuDeDƚ }PZ(f>QFlupo]8JQbo̤S矉 ˬp?nHz%Ja.@0f 0عg=xz!l-Y֯zuA.N\{`e H셔AY]ݳ rsC H߮hյ&m[՜y2 zjsUq),#fbHT; [dRIfҩL:1|jBIVInHy 7g~Ǒv,+@㣽@X6HZÜg uG\֎5#5kӯg-8x8 (¦w;=V<IND01& F+PE0|%R@06:#UEFeRܮ_|d~.z+3hu ,o"_>IQ#R:,@0*M(Sl_L{?~ ߱&jb>fŇR̳"ZpZ\z~S2ݖcpMwG[@+s=/_l@CImY\6`qL:^[}Gv RLfI֠v[l`-^5veb҈lW:-9E+*'ع@'gҩLˤS\l<l ]?+MƯI~VGRꌠ AMVN]YF3s%Jڄ`#+j7UkO+ep;"4N]zhTL:%|[j3z[[YرWLCP8-9Hl`55~{CBnqykL:r?& ;kM&"kҷW]/G0?ܓRkQ 3T͆ʈ-b[߆]LSo4ѣ;1e-?~s0۹|)e߇XQ CqMUH鈶(.$C Yֹ]-+lYP]`b0v|rգ  )vN!08R4ϑTԶdXгΠzμtjp R[2[ȭdm Ė!8;w_QlvHp:o'4򺽂wwec!v,k? QBۋBME6<6ly Xkj:rߏ!,b?o"ʮq&65$jHf!wC.7uZD ɤSrOREA IDAT@*XՂ~.D2<e5Hۅh;bZzKlD5)@jTA^]45ʮ$NvVlMزz58d~`N֧VHBR#rVد<W-r9"Z\rW܀\jTTVA,DP6{Y:dȒa/TCl>G~Ke?[roeUcE ggҩ_{(foW6z6̒ȶqi@O RΤSu}l[ld]cmMv9j?_77gM-/BHMhsUt"*8lAJ4INv='(|3־[ԃs˶4?#g: bO~\PЄV(}juCPm#{// Vc8J"htrV%~1 CJk`->@ nAԇ(l{Ub F@">ӯ9<gi Aݑy `jkUVwqK0j|Et#r) b?a}@w2 s=ߏձb- n"A46 Q8N-p=o;oO4r={0zp.Euٵ ŽAHO"Eg>ߎTJ8@XNE ^ AW r-FA5h<+눔\y$X ȕvamlH1ts65 W9ڀR~<'C δ>?b>@0a֟#YK&:+ܞ>FnmKE0 ˗VQ_bwgҩjgBg-ho4ȸ ]O|=3c--MgD.Z? mm[l;aAJ ?N-u=wګM+ \zg2-翅n!zxvkj(,Z,Ǡf9nJPWv(#ŪюG*UrD vAɯ!Z0~Fy֟Bc9o 75(vm%Ul?!Dމ+T4?B ZvtFKk7!C%oaw: sv U5֗PY իJ4꬯[#k5RF"0eA̫hZ#)돶/A0u?+mr,.E0@PYv+8J2H (UӑZY`o}5r^lӬЊ:O{Z\-~;~ Q|gD]{lvFd#-b6O[@UL:6_܀]? 1l$Z6û>B*O-&dDp#X{ @XK4F cD GA(fQ؉66M&AքywY٧ [v5pAxpm]]ԣ{N6)BB4cḏ߽e{ꮵvB`[7k{ kL&zF=Z3-7ms^ |Je3~eҩiϮC&!,bm#-2Z on Ij,Ft6@#PL! Ttt=dm\˭ $k8L:Anݖo_bѦdҩ/ed䯬犟nZ[lŶAAl3Ag#f A4ZnG"(G[>}LI4œcvJ Z?)GJjg) ;h+MVm:NDGz-C63ri% FG ٰ5xLԕ_X2>)Ie XAeٗh C G1B`. -X U`j-@&Zz.N2ԽXq\_㙏.y~]nu +?+g0~UOqbm֫,MEHyy& Dp1OC00)=kR:wᾍ۠Ց܎6Ҿ Ͷ{! (rv/RϪ .@`D{>E ׁ(Ň(fd(~Rj"m*W'zH$ KVr:fw'57P'A5֟1ַp e֏V~ '=(VlfYM(r-EH8dmc@\:ؚu3 77I6M-~+b߽-:U&;#WaML1k{8{q {(#xV_4QZ#Js=jf#X"G9JbfZ{)cH zJICOB?B +ַ^ nNnv}S5JYz%5Z}/ (:)w'XjTeo'(V ް:D.D[a|5 ̎])V,o#5A6c0vvpgͻ#orOhaCIRncvB@r z̤S-P,_._2[H2(r=ZRl{)緾nsL:Uz~i;|梂m {.i^קcӂtb-ڶľC =Zo'<r4tB gҩ6ȤSYx*4?EY}&? 4M'Uh++Ѭ7іE{]I9+R{)hKqG#Q Ahf/1#@ i׭@PBKA/a:bAx1 Z8eD;Z9"Oݬ]?F"jDvA*V?+'6֞OW ~j3ԇP}R@mL@`#R !u~Rk\e&ZI?_\?K&oeg.Z_Udҩ>n F6IV& Hf?>}r?7}0qHȱ[lmZ bߡ+fK{YcY41\RBj'/E(?O3MY4G^@uLӹ5NKRhr}E9jro "Ȣgk)v.`Z a l xhE_>/I6<:AYO#Y/o?X{!rqԿHu)Q{' E4L"X Aމhg*TE2R:۽nngRu=Fj ImOA -!['G*RmkleHY -X_r{V#[iBtm2TO•mƤmB{ ~E[l}@r)*GREE/B+ A.Q\U9A #l sQ;hZE]M|/ZM!IkH%GW;7# ,EJD㬞8ƧpD!h_S7kgAn9r}_a]ȕwGKQB~6^y6f;!8; # iBE p rtסص&L:u$7!0nDVg9E&=nc )[Ծ`÷h݃5tbzfҩ_6[A_B=;.LlbEl[xp{sK4y>&N"e&YJ|>v]oa:켇 H-)Bh4 66>qǢI ?!Wf9Jq <]Z]; Hx\F"0n')v;g6>}|܄CJR{]Yuo:~ԷP]QvBmVFw݀DuKZW)K) /9()n/3tAnFl*D5?-PEƹz>Ezz[9Rsl 6z#L"E3bZ=*@ rANFJ5DID_GD2+.Єx*rر:fDՀb!UcRN•[9 D(E„! Xf"`h"ί 3yDMC@y'R>@xQ`g>ȭ6~q\o"PY\#uJ(cn{/Rz#E/tb)DiHBPHd!'H*C`U~;!}~ +m'# V%ls% A`h۫%e<,dҩQ|ء|J3, '/?lS5bEl+i0͍֭p(8 v@ȵ7MסFHal&,D_{ф>r1Qz=QPN*Ñ ՛APg,9yCo+7jg[NDA!'Vvyyƕc!; CJ)r_^Azdq=)@=< H)|+ 1(.A/b`"jфԹbݛv@Ж@'˾v9Yz XA,= p[&j4:oçֶ#r|ID xOscƆl{^k;b~: o[η6 >ړ L 8 YA|go(hZXs s{~rx1 /MJ+*See$C)XD~Z4i{)Rm#h"1ܓRNz -0tSUug}u:$2*Gp dΪ>!?>f횎F%[ĕVߝ~rg݉ M}DR"T\){]6WAhVe_[[+L=KԾҮ;R6ƻ.ߎnWR:XSӭB^GdD\vDTvg8# xˎmo OmArt#k>:qQB}`kq½d׃ H-R L෎\b/E_$ eq %~X~>GA8N8 V:s(=m3ֶ8oľ;QƱpkZ~&"x\9'u(h{M#M޹( +Mԡ6nmh@GO:oDSnRDqla0vn&EHg (kOJ @nեVVOZ<|!R@` {whZ!5i9i_ӿ0f"XoeЊNpUDGAP.!Vཿyт6f&dO$iDM26!P^a=9H\kXdT?#%vM]-hD_":q^6[;9,Dc> q&QR/}?vmObkGKϋ77 X 8Χ(^~9gJS>cP؂ D5/mYڐjQ֖-8%ATv/ 5 Ge]D[yqnDԟs7kA{L:UcG]?OF R'rOvBwȬFe'4)4tm-EX5JQ{&B|ށs5dK?>zЈTǑ;HVȥ9HɫD7h+QzWB!NI IDATQb@Jiaxzk++/ DPvtcV?LNBu6ɮ;ʜouZC_{D3*l5&4 @ ݽW8{tjmuItY"绞% %EPyҶYs= O26ܿV~߲h=Al6eҩDyŶ18N9B8aA8vJ105Xn 8WY]8DH[ԑ@F4sYA1'AqmӓQdžԳbۼR$NX˖:ɴ*܉T7kp4R[j8Ӯ+Cj0nFr)jdX،)! x)@6[ivn><$=LRc #V~V<{8 H [;DH[EW"r0H}E'[;ٸ=hoF]1YvAlOTأE5:l#Uy6E/@jF/6FxttpFuFPE ԡ\ϯE=hz(m6]/[m&?v|ߪoA|/5!k4R:DY>|7NCYK۟(gD.FM1vlb߂IۿOwh.pw'o8"b}B*W}8e-TojO#Vֈ iu8k}8Af!Ajq: A<863Zi1XIM%)6 @,*QRM"Xk"L2:F/j%GiB9[D]`Hn#JBps R.#8y@w[AHjrmRqvBB tP@!=F ZaK> -F@;R}δTR=ʳ>Idp+'r!{ hݛ~(~% č{occ66=m>.GZ?/D T]nmKsQVW>`q;86&ʽqߖCv?GowfP\ .sк~݌UD7kNvXh?y%7=҅6v[[lA;oimdpg-= 7;=m4+=;jnC39M 㴷ke=;~:[w[&0ttq)$hJMh"(?ׇ"eMX o @{GΊP D)/8\{y:yMEև}oFOm8BHZ\WXyֶ>.Mvx4᯳~mC?)Lӭ\\ h}]y#R"wFp݃ؾh6&(k>űFE d6!`D[V8s^6VW`cH@nr-aH9;D<:H'bu 21_mP |k/~5m dw"7VC0WjXظd:O'+?LcR\؟ؽϳmu YE;t͕3gw 'dҩ]zOW2 L8u}wX/7Kvm*K,ߘdyG{baXG;0K]ϯs=Uc>HZ$ \I^D4`Ԓ}qx9\.s\i=?) 0AVg!841Aq;WIs$QjQsMhBM`H٘ierRXD7/՜z}HO[s#w@'H!D\^ i[Xz-I:ZqW!.Ez3s )/uDF'X_:!`hhQO!rýzh@ m]`b) $xDy$%v :9Tvϰqܜ5vۘi}썶$R. sHj}]ΤS]] ӫP!RW% )K˳YcBUZb7k3t?#u0? Ӊl5[; zAXkmb]w:Jx0Z}MD- L৙tj'"Hi4aԴDR 6@@6HYȲ$KO;7ܝIw=?/oMuBkLo{}WN:DY}{B@_~ W E%طq [不M[{=?g><0NM s=@;!5Ot-6&; =ϭUblly߇wf[޿FT֭Q$?Ze WkEn(`:b5؞h2+9MD@P8h2&UjHYbʲDڡ AT4Y! *+SZUkX_lu8Įb@U OD*ФY#Xi&ʦWmfW!UA u!p#CArJQzPy< /PAKNEP+D*/tx``fҩE ڵ8Az#FWY;9D7,#z>B[ˤSj)qshf;φ k^䞽}#N} E+?yw){R:#:Ȯw*<.GjU)s[dUZ[!ϳ#P>eҩG} !G|MX ;ܪم;wx\?꒻%wec)Ƙb0e d -@@H` ?w64ӱ)F6rեpL @ &{G{Nsyϙs*KкrY^i|n‰jX݆,; _OK/޳hDž%EjvJ'̺6#H~{M֗lEbʯ.J7 .Glݯad@F\ޱ<-|ʼffF m'/i`dJ77Ws7?u'oAo\̵(qHI=Sĸ E1D>@C} Ġ=H!HE筈'3؆ D1#eW?Ӟ$R9mxc!b:7(}N8.u @ܦHo2말s xvn kaVǡD@d|(OK2g#ev:}:bJDu٣(G9?̇fǘ[9%/ϕ6{(rug㽝|@D{vk%bC1"OI#p 23fao_F#_?1;wg[XGYNC ig+cչiC?˲ޝ Y.\ Q-m<\ /lEeE΃EmrgrǐS8g pׇl1n)?D*3`ƭ]'uڠ>Ƽ;,9hB2XOeDn:O,a>]^Nj[}UNA˯\W8FksݹK3Q65(Tx#_f㑒y)"N[vG@*_1"s}Vt"ߣmF,>Da91mJ{CȾvCO9Xpp&2|Dd12tm3li. vmBx>~]dbKd=?G[ 볃=Gr~홅 :@[!8cc! YnmjcƺD1HzưQҎrCňՊ!p}'jC2XS" 5#?]!ݒN%Rnц@X򺦊S&zepD%ܮ @gi19<ۮ;Co%w}PqrI2L/4/# ߐg.kmD24?EL|U>(4G :Uy,۴ Z[Sm)bu+r`)ιoz%'D*Ahed|t2!R0Of Jr$ O} ؼX+~dCD bvvѿC @?BBꓐw@@YŚF.9)pYeӋvQ@팢۵2#V{O[JV7'nk9v/>!Psc![B"&*Rޏ!?刉9 3#bB795b+j ,؞FTSl@ 1Hڳ7_HpY{m6} kFZ9:ͳ{'R\ Q&ksKc*7>B"N6^C?nYClI@uO-q[|*؝_iϖúh@F :0O|[%];1,aV|O ֦}g D[TO''R9ݽVǥvu|>NƏK2E \5[_@M?دG ' s3D ̕Og#Sd!д3E|!:gh%9["0!?%OGsf6V+̞leC[;68F '뽿9z_qΝ97Gzt֤M;897> l齯wu/q-o3hι;z[󀝽uι8AD;纬Z[$֐X_Qh>R7ųmxbt08/A i=(]b#EJĮD  䥓͗#bC 0 mXG:@aKKu,چneS066,/ݿv+C\İ]p1=&`OE`ti_!`:7Lŭ .sF\@U"9.,zG9?`ϊ!evپR!b&ؽ2(L!"o A#TCtBt1 |G@ǎ1glC"!뿃lNFD*23ر܀(FgÛ.[oO 6u" @"0?#%XTGߓPRc:_|\*TňuYҊT0yz'z'PUym=b [8.q:M17[GV`;,=6`%-{/m@ld!>Mι'F('$gqd|R!}QѢC ɭhb"Q]@d鉱4<BZuG&eHQCU=/"nD*I"]ot!J4Rݑrhu0aXbdnDSַ6~ ,L'o99v߭>X!ZiI#݄q\\o}Gv1|"FsW@VtjeٳN#J`^J/3BsY)Bh1'_CcF[_uuYC"dA@$Vfo+w:OZlۛmթ2;9o(xu{~{_z nH3YpU^qoE} =p\7d}~4bc@(V\;bJM> IDATɊُv7Z逆wu9k.ې\7JhYErnuE}?B㽯Y]~S?{ߊ[f5OKHe:'R֖HeW""^0*:6 ]{6r$b]6G{ U@d2%/hJ(= ;?1"`2bTd]@LZ۬N{3R5ha;Z[oM2Knd9]Qhϼ9ʙoz4n|d}#.|#ĮE mvDڬf#{$!V(h&AB9dKb\c;с#bA m(GO Yj@T5R yV[[zvq|~do!uN4_T#QxFZy~w-f@FL}:=X6D*/q~2'C-?/>O][\Apv\:.DL7b r fu[Q+?-.6Ǟ~#6{{an$;zxA?b3X+ι<\6r!89W _?DzvԢm[-sέo~bO?)Y,QI:*z! >cȾkEd %7(Tl+5y ;U Q{";ޞ?!RaeTdV@~A}p:ʸ-?m(|G(NA,S sh;sC=н[ǹ9[V[_q:څCoOQZֿ +wաDc?1 "E>1bI`K;-x2y >vg)@`-=U!f'#sDmuXh77:uCvEf{$4BpWyp>dUDi9iB"_P,{Q %Vm!HSSnF';O Jr? f4N@%JМCspb"?Ю`c"CDTM-TUNk+>K[X~nBƩ% щ\myYA+O{R;VuN֌̿2yKWkAp`n>0G߭rhC43ιM ιf.k܅,ϵ+g~GQ=ιңιb4~-\$Rpt2l;Xm%ȌY 0 ),RP~)H,AqR4-uyE~t~)DO-ʮ^Xgy1 " O'S2OO DC&:)6AJ0[y!D k:h"⚅#7xxqSKߘ|ЁGDc3''-+Є""A:̕{!0/G bG@0k<;(Jh"*%TX=Zg#\AI$[wDx;G# `^+y"͛Btͽ~6n4m{6ZA& 6E6#Łh >LV?] iBVFKu5%d|&kT]\Q+},lB{Y4~au%[K{ļw.8_g]m+'~]5Y+~(qΝuI@t2[bX 4lr"< dhwHw$:18bŐ"{9G2SclrPa!3H9ke @JoKLUEHAf?-!`Pe߄Lj"Vp[뫬MHQOGa+ XƮOOwreCy?{?vx`֦OwB2d='2ͽ N2[!g7vD`H^@`3w=hicwRX?1 t׳z&)$r2,퉀gM̕㑒>  W+D,/:%(oCs>}N?7VqMm DmC_İ]Bdbk"M+BT4ߴrZQTdZA"4&R> Q[~I׶M好[;.oou#7k;< s1#ސmlRW*K:{翮ƮEOނr7&}sa)釮OQr؏D?т،6EJ+#Z&h|9ˑ/M󭬏]Lkߟ#`3>~ ]C.#Xnc?ʙU-bh>qa 3=YH=1# w:7:r}$Q߳t~:ٹQ''!@bn=S;[!3%C`osv m6 Vk-E'dWXLK'LOn]S-؏V6mM<ӢOnuه 66/?޷Ė>6!}Xۊ61!84vm[ lsX^9]wÖDc~F@yGD7LGgn}nO3)rP-RFtF-H;,nDy_0ݽHឃ^Hs؅(iEeHIb)fĔtA@\ta f2mrFf˝#Jf7 + 1"8̷9zb]sut2"d"Re|l,Ji}q,Ky bwP"E?/Ds!\@d/T&[dڞ["\:{NO= !$Y{2/Sh^:M sB!DyRD>H3m#CDYAl|CB] ;".Vs=RgBI}rJ7uEme~͍@?ѻՑ(G'+ !mvЧϘ.'Uu}QQ'RkNrH}I4Wwl;sz'`D*@U'"sޙ JdiBL H)C k5)`9!Z;")]_{]GoHی+QHe|DAԸ?2nǚX!R_Hpni|X? @PE}`]iu'Qνp9t]"k^GNI>x˞׊طN`ޏB QB勭\{(AL *ްuO[l~a$Yd$bkG4B 9BL;Ƽk"P8X+#SL>d0]`y:oI2#|imnD@nrAsaGkh0 %ڸʪA:4^!d2B\Lj_ P馆Q]P?9=dx xܡbyl4qlQys͛Vft-d>>e(INr%V#T#bD"|Ls>+xC E> 1G#b2mH/G2g# <@ {> /Bj" ~A VVC~5)Ya>%|Vf|b4#1tA&[t;b6A@+/DHBlpEmN2W 2.vyCgQ擁" "so YvO%8k!Fp۸]oXbh?m9ʐD*>* t\DA\Xy\yՂug Sdנw He:XTX:ob9诏mT\X~̵|Q=Vj_)Q'W\>gz>$b,B…uliYWkڂJ?ch[9INX;ܝR Y _6E^=?4,(D*2AͶH٤xd6_F ,D"v4m_oׄӒ[[p0bE멍4`bf[@ R `4۞u=59 ]5:lA2fmf}%}~KsJ"9aPNeZ^ͱ|..L'&Rݐ2$|u==X8̝m$+su5Db!JI:nNEi# "`]LPb̷vNB +bU.V&F<;ّjmyZHI}گ]( \]{FNShWE/αݏH}+)UBE)vGzĦ\>g^҂i%W7-'F-GL{NV#1W<YkM)'?~/N-oZ7wX:g$RrNW~ϝ | ɮ1HY>~GD p>8+wDKp,MS51_P R#&p^:|DBv`N/!LC rXY3#e-+c.@X@&ͭNMCs3 FX}d%R.d{~Wzr{"etbEngg0Z|g9:I2/"c#+R!Ո@.VV@DL "gHR pF}h@fC஁ ;& E/`}~7JE&byw1 !T{nH%1u+홟!S9vnږS?5mʾP38eoc="^cVF"uguZș~ST[TX_lf:?^881^6]NI2' @ڠs#P2/~>1DƢ9[/LBk*|d/:ȦY:juؤ_˛N XF&-Цr!mQ P&.m;/#7]5# y6s_D{ڋs$o/wāqmy2e>6bD*SX#TD䤽b cs)iF,Gb *#6~OCmoĂ5[.DV,Xh7Zsʼ>"bunB0I2/!~Gg ފ`'[#H2D,F֮PN0IȞn8"Z6R$j=TS['kn y!|t"{^u`ڮ{s=:RyDa?(At!>K2[[Fp33=gc{^5oޮ}JN[ݏH2ic]@<МяD,9Zl@j)2CLg;׸(ey e+^:&VlMI" tO'|ײLip翶nhg]FbB&,~wlv ?'k^:sU(tʼ38齿Ю zvZ:G{Z#;;}#Qh2ލA:RxmWh{}Hh:w勶e=X穏lpg꾭Eȼ>bĺ%`F:TfOX@NH)U} @ CCc!ж-AQ" bS6AAlCV$XC֦+9;ܞ)R򝬎m[P~>o-(-m43$x|:HeN6C61dSSz""khk5}?3hEaUQgrK[W p4mh#T!rb>&iD/Eg3W6Pn!Ϛd -hq.gGdBIoBi.b\[֛#(RV?}ի|UCs@C"]ԝ}ǎ W~rY"9,.7uK*{W~Z\K VG׿KКswgNc)qν֏|u-І{G rν}>ιrM\`Gzdwnz;:uX`~EgJ&{}L3z9|!{ZDo@'BZYb(D/W)[lg'~A:He>AƣeHVnu^dU߶9"`l[^Ct2~X"yc)& 7?"5 p!87%. r.Q6V )dNl7!`A+-"2`q 9E>ν2RHݚHe.oauȢ sJ4' g!0z"m=y͑NA~he^#IɳO Zj,BrI.bmK?(WXؽΙX{, mӆ3n>nVQ𕜋1!UdվjK:vJ3'kNڛ&nw [u}]N{a}]Ϸ#28GkE_f L6yxWX;ɴ3ZE^p<佟$"i[H㝨-*~ Gy=H2B<޾b/bD)fV"3'!Eąhqȼ)ˑlNO@ nw[ 9bH4jcgt2>He:E"bb 02U քعߘR(Wy.][*FHeN"~pw܄}DwwXǣӰ7"p]6&5=ߵ'b?+J7'СWUː ?'?x0֪bu__%Zy#-ιhހDp/}(3翈Rc~س]Sgb&վCա?mHvϣl?|#D2!`T@/V@xwDIOD9c3{r+.RƛOt*6ty}dzkKg{VoC6$:yKm?΃vBqD*s" YT!P:X;7onXj}5- fmK0?C坈rp(.]TXи6{-x ?/Aݑ6F[3ND!RnXbsBG`X^W>pu7/\h܉D#ӌXrA,[HՉ(G\8ڼhu@lt #Z"Pt:td}Vc @ Qo^(e_L2Xd%l+}gt2\6 |]`h.0F|yR:œk(g@EXкr4Y"vXYn׸;^ޮ Y9Wƴh$6M{Ms9w)}沽tmȾNs#4R= {[Efu|ΪКz#s3bEʰ)rd4hDy[.H'KXyH/۳B:[-smWӷOZgs@F&W_[Eb1;{ߦ_KC??9w}row}Bf܁ι6w" JN3E."Oݳ*bkPk `jMG$+ *BeH)7!r%bL^G KHYv@f:A"_g|)pҾͱRصH I'2}U!R"@ppK"3PW#eu_{܂"mr1p"~Tco\"W#Vm]XD*YYVXJN16^ p\dm#bB懚mZPnQy`kwȊ ^\]o|.[@0gՊ̱6#3[y '1? Y*x["kch^YOXoa?hC{fСyλ3R. a>.!Fy1.w>? ^-%2|RDW7XW: b/CDx_cnj_pf&Ygym+eA{Dy=+U?`#fu:_px)[Uε< ,~U?u5+#B,эHuM'MTYkdD*))M-G W.(xKB@LAOpಥV_"_ hni+i|3B dӵb1"p29o)A%~agoF&YH) Ppڜ o+Cʵ'#`XEO $CMH&\i/F3u(Z|bdnbiekME$wl-oxrozs-V,9]iD@) ^}YDz 0vE";[cR%cf))lBncED9,~(x9m<#&hP~MCիC`Pl_3]HO`*C̱,GN%h\hVZ E>k9 (ZM:d}n$ʔa +~~ȏѳ"IHR<W e|vG q2RB! D>=2_'h1^c )t2N2w H'Y b! E)vmbiF8"Y{& FG#%[:G~3ά)^D@,PMmԅ܂Ӑt'"`8ƍx9G BBv Q؍Ү hm,+Jd»ž"p>\邘Юڿȑ?»%V&^8KuAN.As(ІLs8q > >Biݭ7XnXF`F49DMV{ z_U9|ϒc֠1C2xT@@>H.61z5h绍@l_59Dk:2)O@sV<2oh[G9ElI8;RCJѐ.io`qxI^Fi:2w?7uL" 0߂WjW{C?0g\exTEtkbnqzt;d:E?V`͵hFb/ ў3ʓI1cʕ=?6F+f}^ c|V ?` r`[]?zߑVF ͉,F=.YN6:0m|+R;W= kW>t$'9Ar@G$jk1d'LCzQMB[|>DD*>RbZ)&8Ko!H!R/f8!?Tf?o0g>|Tphu8^hm`ؕmѺ\bhb G ؕXU#W0ӄeQ\.HG,VOv" r>(t4 {Z9"K!@LPxͅh@/#}Ĉ˭׭Z}xt>b}/~ض< ;:% pm2hljc3 ^D,JRN9z!Pc zҀ@Y_0]ށ<9A#eCXkNHC#'9_iG$Tf] )ԙHa݈eځS:9^{#b".{ ŷϼ rA~c81KCH1VZy}x]"v5*oC&Ge?59ODg!v31'=#tDGE  0YV(>Z2o#v:ˬF;FgVL<ضϐ\hOOE Q} yBY4?b[lmtB- !pq/nVO|lر89h[?XvkK#2ak7YAdI")^nh,(.nj>s}#'9HMwy~>U/K" 邮D;&tBM A5GJv{\V@-=ϠEr2f!dO ݞ5ih v5d:)V:S",6i0OE ӓ@M9:tP̲ P7CLI>::͞b܄|"9 g"4@U }*D@d;ļXm"SdbBc 9wE` )A@$ b?|-(DLj- İ팘}莀U0I^֖~vmh@Nww|nȜ|=J=Rd![CO"2~&sl ?}DB"`k]`B"%?He[Kv&<$NƃOQBؓ|G+(mmZgX~Ⴖ^kN9K}cڝV|RtO~Yr7bN;e?*҉ Vygo>)!E 鱡HMA`& ա \N/Bn<&C2%O2bMg>m9 `"Rޛ!v/9,0Y@#bXvkB;Ȥvs]9Zm+ i c2)Z[BQLֿuo"& L!P@#L_%V `3Uo}4}خ6>iBez̔X?D 1QĶ_Zf@g_kCG4q(Es ʞD,@0@VvbhwϭCs̆aTh1{܎"i z'wE*;D8JjtSOV@|Wɶ6k9 mGA9Y $ľ5fǥ߉W/C,K|1R,Ȍ92GvEl ت@J| )[1# uR=XH2#|E~+)@Ʊ@׾SV׳eѲJ `1X@luOc=;J ?NDNccܐYd  D0D q=dGJd}7 :+{?sf !EVΟ ཻ[hXA Rp`.24XY>Uo+ 6r(9E, mkclJG7zu' r4L]ݛ!6[itFv z@L74 >ӞݾK;|KUN'JbAQndmB䧚Tr>b߃9z̆} 'N⻔e!.G~C 2) @ t.bbvFL!|.Cli) c1ɮ1B3blh=_Ld=8{s#ޱsBs+09we490]uQA]M0>\F=B"]W>)섔 k@3#*fݓB l4R RmAlMGJr+ukn);t2o@[$tUN!M=e}QBEvCJd&ˈIx/ -\w"vܞy5_(Heη|~EĴ #_}wZ_대""sg}<>Ԯ]S)@P p/H r][Mtt=9:& s6[ZkXXoߴOS/>O'㍉Tf(H'$RmТbb\-ZKO;UMp?Z|̮)~"SG!pRXt2%#@fc0o'D1*k%֞R!~/!v?v=|HgZv6WX֢P0y[{gZ=Z[wC`^RnAh8c[c0@NI3V>_kclU57վ|.xۀι{x{?rT !yDa>r=FNրA KQ9P^!D,f.{-HeL'sюB #0]g"c&"E\ȟ~^AJ |;zR|סadt2Զ t2~}s2L2Mmɡ>|k.":P͗3!% E.<` OoWoR"2.:ݮ[8!Q)C]b]Y )b"U}D=ˬ.F,]!`C({pC%?` ,X"w L&"Vj"\4׶1g䬪?3== $@=(Uʠ(. RFP@@PB@`H&dsy l@B Ri@T&Wbl:yQ)~2:1#Barw#2=ϼO93ι\ 9) IDAT:Bal`FI'}%Ul:j_ߏ4'#q'Rn_$b2fLBlǣ<n;E~MwFXHi}F#b"c'" !B`uZ@_둲 7C V0ĠlmjuJdFS[C@A__̾K%TFJ?@H #kwg0tEoWqD|DPІ}8.#;Y:4'eJph\ kĢg~Ĭ]Jdbۃ# :p߯v]cRf0l:]*bMGZ>,0hM6>֌u2sC:\q!ŴpfQCIےW|]- s?B{6{{C~_䜫{r:s)b\/.D3\_-ݷ&?qIer@.~f[F/}"ߤG*ZژB \u| %8 @/bhεwM'_Oerm3k:"ƨl:ygv#,%]S K^{"La;REJ3~jks~Qx+8)cm)!2Մ fjh3;hD&o=2/Cʉn*>Uۘ>E%r"֦9@;Bpx'(z޿= P; @@ ۝=~v+h>Ԡ43߮!% n{ Q^"0dW[BSM'Rܯsjt8(I[W yt@OT&wZ1Ϲ}ү<~2vtrź[i,v{u]Ժh M_7㼳S|)>!oMvt3b<:))t2lSQF\W?@,lyC'bvDrW" 2)+3q+w[lvϦ(η6ep#)EtEJu!%Hy&Js]M+o(콽) @ J2adV>Un`K-G sW+h"VMES< Lձ|e@@ksR!tb3[Ѽlmg}:]NWt/b[a,Gl+K%hޅF V`4PYdPac4}eu*[N37 wPмV[:ob 2]kHy k1qdߝ#ߋ%)C:w=w;{_WٹLYPZW?15}D D5(]Iԧ[> >ay?G>4G"F^,B,@Уu{vRytBb"G`g0.ӑ2Iȷh)R+` o!sOW!}OZOCJİMerBJiĆLgB`j5)(\ę$vY!S/%R(8EٛDoje|) 4#15$.G`n Q9E/%JSoyL@ۣٗ93Dg'bY6V?3͵[3 :Lly<e[o}:Wl|]| wwz[̰wA h].(?u?= j]5{zmC]y.G9\|gi֎no,Wkene%j]R_w_?Z|]uǑnF"LnZB^pz_M@Ȍ5R^KKy !_H=hȧj{{&wЍX첶Ŕ_"+E@kH$t{r:Z0AmGT&7>ed`f.B \4+c#(9(ꖷ?e{6IԌG'r`D\4S3Y3WXyƓLRAv(F~ #SĠmG;2Aob@~VF\)i6Ve-'۠[ieA7 <;2WXAVMDy+11G^j߅`d.:ټb @*E߲ț/uqb~2E7(KG(.&fA@4V*4.2yA>۶MK~m}ʊtp)?b5@{ӈi?p_NshtKlb#(MH DJ%0#k{=$F Adқ¹&NTXH؇ZV9ou#2@bۍRRzQ6Xk@*˦go#Vg,R,t"RHamu"UDf]%0ܶ@Vӭ޿ް1i#H齂Q<ٌv]#YDn_CC3w[߅ PVwg!e=. ,'K|6B7_RS!x.Qg^ت'g}w2/mF_RB2ƾ 11 ҉ DfAly;J=FuWLrm^O6e_ם![:Ӥmre5xfsd$N./|R̙DLR))CJ(3 &#ЦRq8)m&{S6\g7Ǿؐ? ͦL|)r,@YED*r6| )6tr%5E, s|[_D&2N;?h;X paDLN<XC;)1H"`2#ӄ44VJvQ4(~2"H>~`mxƻc9FfkO޷ɯ@ ZW<}~?;ثmyi]ί"՗(Ch^?IE 956zu =RD]gd[RV72G%]kb2u ?AO3к ‚`Zp;b-oNFv(bEhԠy!o9 ٦nnwb#.239~M'g#p,PB N}Қ]l |3dR4- a7/ ; ۍKQ,ofφB@,>ic&.I@A`e c;J_&2/+յh[̵XF`W -{R\".|"ͣsqƝ VGO6"?Scl|!0U !:kB_J.LDT&wK߼^@ 4Wu61Y__rx瀶4#& 4FϠ9Q㧈5::p-BӆعhWn NIK,S@gŝ>O-?Dd fsbXb5GQ|TZM'6!D#ӃL7|f|4Gj\\|yctZo!3A&(n[9d֖:׾-vIM~~Rsnmw )!Wny2b?@&ip"<QH*Q^&mx܇J&^oʼCaZ˲=yH6܀bj |{Z,W#P=82o`_ˀiLnGtr%R 8T&S+kD\zt?7>WUkLO/1sС(o>g0{e+|Aw/T&w7R"';Z߰v߄@{/klMG)Dn(D2=Y{"^jqMN1h{19v+:0m62η51fbZG`#8WZZ#Pz4b ̔Y}{0NRtB}C@u6w-cؕ.G}}WaF}OWskshN< |{_tέA';CڿιѺ{8{߲@`zϲ^n ڇE<0{zϤ|T&79̳@K( &UH9Cz,zmOTΦf/} l: 4? t2@(<I: nB)b#)2L!?!xR2D?SQ+{A;+~Vַ7)tyZ[;z?wWBmQ)[,0ND .fEp;1h:مN"|1^}Q̮3O6>@Z eTB2G5}(i{ e@p':hPvG#z?RZ=E,6Z:BLȡ/\@Q;3)INo匶19aGhm^@ 7`b&2M#l:}Mn̢G. dZ3ϡ6,~WBҀ4chԣ*E'Ɓ՛a"8/AOn ΦnQHsEs:hlWO{'X;vNp΅ \73۠5sn \_'x~[?0MD(leϜ|D'!Q6t"N5h#649C )[%xy|[l2RΟX2W@ Y-؉QRТ[6g {~̘mLuc e*hAM5b%پXx g-ӭ]zfm?Aigt>'Ltӈu`{ 022 (?EMVB](Vo15H("`CF F( )Ğ/Gxʻ1W 1́=> ]hC ttH@~w<:($<0#h.}֟ ,\FNZ!:d͈VM^ Lq$}gaQȭkx F,%hx1\i/Gf&eh>kVY(Vi%%R tI?ZWC@,Y~[ι1K }vٞyn@kPK<@k|<:8F{ۺeFߐCb6TV/CKtrj*6{o{";V V( i,kHerM~JU!@9ZT9)S P [ܽ-qȔّ!EgR2N~9v]+M'Rl:2Rȿx58M'ۻ^Ȧ+p X%H _OerOWrG7>O#:: !@𺕻7$JPr?[ߴQhAU!B6d{cznS){fm Ƽ/#93s+Oysտ!i%L?6Bq4;.Bsk@,kH-+e5!&n!e4םahB ͧ2p1ٰ./ZqWyτS܌l:NM1tꞃ@ Hi>Aw'4EkB,e 2&ɞr |Z9XHeb>ӥ Vɯ{ RJer}E=|>XĄc8D!E@DvBKpXde}~ _/CZL]i}Lm@8,!7ۻ#(W 13!]@@@U)KYأq4D+wc9ν~E4[Z[~}(r| D2! z>֟|]e߇[deȧru?w?oTf IDAT}.?lfwFܿ(:R_'J8~hZR[B罂|VS{q%?aluXgsk]h,vD1v :;bC~n`.hYqM̊?J[׳ro}m'c <8>Vg ,AUx_ͧˡu5nsnGh ֞=97Ǐ8z{s=V8bpιǐš ?+ O"Rd5|i{?|EJ*lVnst&XRuXKTGdC F&86-7#ZGaMJ`$Q_ME)lWa8L. ,[+!jZ2<>8{t%5͐YM%^M-X^vƜݯ\+mWroC CP"!LAHLeD3;(e}PȟF HkSLO atHٽX<}H]M'2rk\" CQٿ2Mh=Mtt߬*(3"*2ZL#\x̿՛VΜl:e@=\mWF6C`w z 4e_X|]d`u(ʦ/Xl:hPkRhm@Gm?\jkC90nẁ3F~pT&7?aj]}d!|{W[\ܿϻބ tU_ǭׇZW?ڮGGI 3r`++Kx_hv }؆gE͏{1YGW{YșZX/9禡m@:ι0No^8z3{Al@,m69D~*!O0I.A``.Mn^.~~73M'W2o#ۊ̮5x.NXF,}Ó4B'vG`(2Q G.R4~jeMn_ akYz|owg]@`lmZn/xOA wWLoER!ԅHhf/Gp/;fMg׾s`mcm#~);6ȌUKȲ %@"Al\4DjpnwثZshFoC7kBDIķEe &Ghe^muk"NNHerB@6|I/dw2^ܾD0X@-(AkPYE`}ku֏Cќ7$"J8o̮s2b̞σ*ɵw!\C&p>Ϧ-ݙM'/˜]Lճ}?ŎeO^1.?5yh?C+  Zc<GKynGl֎U'cUWٿWόBP=?Ur;>?t`l"?˿ym=λ-)oebLB|D@$n[h+./[")hx|C*۾H삪S(ҩLt ȯm&@[0bdv!@6<;)R|硍kXОhbCj>ڄ#p.B T&WN=jrt2ύ,%%ʩx=Enh5NHerA'ۑ ):Ou&\BL;:Gb=*.B tr>>bܦ @1(t++Y xU=kֈ !`Lߴ E`#hsP{bdDXEd&4YwÌGϫheφb XZYy9@Muàյ""O Lm])s4Bfv7"1H"3 ɹK٭#d]jf YS_"]_=/= $6);㔽܃$ !Ak$0&{#؀յ>":4CM VCT,Йߪ%8fm~XI_a&/$- hRxO)i Rr[{B΃?z4R3&?f '"p*8H8{ hܞ 6l2D˧b`RiB2EiE~C2E۫}O(mHI>LS 2)-QHe]]jwT&Wb7#y$f \H6@BpMB2쏀nX,Σ \0Zmp},.!3_+ShN݌NCԟws{`{C`̽bd}w/ pbZ-3=g qD7&XB$!$l 7GNJerC0srk/V4?߿C&]  yAsh'{x$|#ѿeM}ዻV(~B6mju9ͅEv ɭlCR s{mZzU٧iÖwwbx&fgoD@z5Xـ̼hEch> cw6uGXrZBY3#`v%̅@{օ _h%c*7t-ꖏ#zGo-Εh#!r> Ԕ,^> _E$h]6kb_6g-,B+ʜx9O N!݆]YF.-A$E'h#tLE6֕= iHEiD@O"_E_ _]dIQɚ%nm9w=6?AJ)l:b pv6&HB8?eqb@$!>̻X\2M.@V6d S 2>6NߵqiE.[vIcգ`{.nW ֵ|ٻϾLuDby8S@{( T`\۝h!AHڳGwg[o.Hu||8gFZ骈'Ag 7A74 Ck)nukM&vtpx?0f:VKīs{oUgŀQwj.惥Muc}* v(Z!o{׶dGsu;yoiAs(hw"b=b̂{΍[tˆ?QJR "hRңiaMtb-R"ЯeQbgҥ=99ZJ*EW?kף[,B Q'6EV(MI)cu12^LJƵRCJ"Tj) \)rPD!Hf5 5zw}7) 4 :b:i卶>VR%> o!~}{'@f<x 3bJ4f9z8]@D]v;xd=Ƭ ޿'b>~QV cW*S/ ʏڼ z,P4OZbV 60;_7wʊ|i|Ɋj\P\\N@<[?Da3*X~ Dx[^#~ݓ߂<!%Uy<_G,tz_-W0!>4ZW+jC5bf}&WV(7x_һ}y|IV] PD0z:ϋOT>C-M~>cdSQPT)L"W R HaD6f˚'-.KTGt.uB7ƔvCI5 )3|ReiLct&Rާ"eH73X͘;дov+.FJmy6|9 G̎2J\El:T&M;=Rg,\zWs *ātT&2U෽/-T~D~)!+M wwA-G magؒƺFlB{Djwz~19(J;tw67X_c|2.GJ||oS*!PT& C(V/m ǽlEU6S䫩Ln[Ycu\;ڻND暭ηZ;PbDY F{B엘o]eM=-bSm_#{^5歏V ̿b$t6brv3Z3QĊyȊ/(nsl.vTRZW_{!r6zlZWӚES{T_Q<>En閍B6F,6iWA@p> ц=ɞ 91Fbv栥h/Lert~&Pj洏,Ln0{9 !)ǐB J/nK8LGX|x!ɝOB{H1CB{Wpq?0ln2_(MSt5byE@)fR]> HD}>޿}b! D1pg1i8at*;(c8Cdzڅ(qաR!-Ɖ[OClӞVNb Sb;c%*?'QZjogɩNۃR@Ǣ9Q@.(@b:|1LևkNvDx/Ąŀ勛[ڲI=h kn=ח(=DG m2Cຕ&D6/rkc6}I=;( @{.kb`i+'-OfOeriF ʩ k/?g]˷jlYX޿:$eUW Poۖx MfWBH1^a8lv;0_b{K+%Ӗ[Q&1|?Y T;ǡ1dJBUj ;R^9DG#cc")kuCmo}21pSQ%q6 LϦR%,*#ſVH M2)e(}v#lGRC9@ے/Y8`0Xh &Kcɜ(r4ǎ @ Ch1 b5[IHSc6'Y}!hG 1Iӑء֦ې940?E2_@sj R)_F&`[K{{{10c(HntS$>ZzuJ!6`dH@F KַjXuy۰lGd[k,m߭B~CD_)o}OdRƦ Uh/Gt4L;#?{ovB*tN|97/l:^] nѹmګ7m{cAMon(y %h|6Ժz7䥻.LLYY_[=ʁ {{0=s%{J [̼t9 OE: k/@.Y+7ιcmea +v?;X_; gJ>u fԏDhؖ~%ڨ-¬ΦYY@݉6C&_T50jMCi5 'ov~S=&oLl:^y?dP\; tXj cӌ6p=8 3m+7oOI ]$U!?Uȡyd LNeroyVC 2~ m+HxDv@Jءt㮍߄@@m1HLvH݀"v^̦+SVX"?lsdBπ?KY\,8CX;D#K9Y6\jfe<S^toL2Ʌ[_F bXTsX֖FT0i1 \׈.}۾ͯh `E@lY_`l6u* vȦ_0׀g|t0i@8F{G+wBnKzvF&>HwwPLer<׶2`b>ŖxYNJVJn)M\|]K9Bպ*Tªb([W# n'ιN#חFGycʧc(&S68( bik;Zj4AlGDm[=0:J{w*m_R<gRNJerO7l:y2yOW0+p||z w͈m[!E(5iH_ؐ1;b_M"29p?x1ßr|7sSJ}5;"05xSCOO `u4Jer@'ѭD?ϱ6 !h=cVs;\Tmi= m(ԖxgԏE&6RY3tdN*o}@&} ^Ϛݶ6g9L&4N)i"k80@)nu X,NBD}S`h-AXA!|`e6S8T&w/*\1 ͣh.I_#`x5bDx>6QoR|D]K*WV(K!Nlj]d!7ywZW_W!˟2%%H(A%U e}"_FKcs-~Z;^&b^RXdxox_6i}>{1ZgXY9?'@ݏBfs?4^*;M)^nl1y?Q ‰< L@lP};Csڠ{ p5[>y^i|PoV1u͛ ҊsLn3B)!HYC%L pS̭uI 52=k9>1EM+op^_ M'߉M'Ler"6?~mgMڳ 5X6EViȌڌLeIersOw1d#VD 8:Q3ow?N IDAT~l|X^XΘ. LW"tRɣ!h#@ "%٭zK =!qPx D }29z]BkQU/㈍9־oABÐ"R/$DXc[Hޅv+b\Dqn\XNdN~Z=#0u-R _j |FC` P ! G7_*XIY U)  )5mM2ܟ@{p/4:k 4/*%={$Q}ZJX=c}6F6k9=ynwC~\W1Ȃd: D w[Y^6X{)5]W!d{8RDc;c_Hy_6b+7=[sE5/O}KI..t(}>ap聗Q3Ղ/3 wGt1OnޯӜ w:vA ιι\v9OE_:v#1uɞVeFx1_%/7:!dcbǡbx-j enBl7L4B YDPܫR䪽gK M[? 䣲A0o l]rC b04!T&y!g #6m ZqKBe=a7sϜ0LA'~hO4R\~N5=܈|)輅@Zј"Pюuj=?wY3 Bہ(hSe0سP<܄adn;hJerti]]mTt pwEpi!2)D GEN$NG&bp/$ZS{S nscL^Hb8Yp4߂Pk{bHF'?[~;D@zytU#K8@m8Ei%Q(&GbV*4Xl:K3XV#9s!Fb?պ ӏ%G?~iEۚT&wSr xۻfli^ZlĂ_ۈŜ|fRk_2˺da 0 ^Z+n q f-w)1`vfk˾᜛Ͽl @Yp"hsچ)<Ԡۅl:X*h]H!\m)HDt}OϦ/X#Q{IE6x@mr:ۈWA{R܆#@qb6CjCt=~&bvG3򆠓L)1L/"It}uUn#Z*5O?_Ndyn|V)l4'Í݋ͭ=X?^x>8 +!v81?AfI6FtT&wB*i6oQ~Y];޹mUc P91{hhoG>:cTԣu߉cy0p\@ J(B[h9> *u(;(h4F%د~%:ĽIhStӏ##z׵4ORV4η|FWTl\R/~kڹ* _lktXKuh^mܴ=k3cN[.)\@iHg&r6)Bkg|"velNG@~QCZ{l b4w`}qbY[,g,Ym gɳ+lRkz8Oѡl:٘ +3/' 5H9fEoBD{E4?{&eu;Yv齎 JS1X8(2ł5Dc:$jXcNK:6 8(ؾ\ߕ ?쳻3or{=1HpљO #VkmRd϶γg߀dgYYj[!pOBsh%K?v3haT7rWVr⍍Q4'Lb\m5Vj?FWF4I ;w_^xsϕhBXNIoC81LߓJ }%d`M$LkwK>V:E;OEuG+O.YvB"AC{"PC; OZ9vE| JR `UG;ýM7Ga*[1Ƽ cE#h*`:3־_2 72xev'{ē }&1o[~9?C{3+`sG4\εwC؃h~BHO7~w6):N%bX$>X?g4r#PP)&GF z<O9074IbFUo.|YdLkDMy;ist١CmoœhUؿa(Vn] ,g2W`]}Ym}ut'k ɧMXo:~s to `h?4G _ckBlcd4_p -I#aڐ? 2hNqVAkoWmѷΊFiqV`_-j˚Yi[+>/|vCm6xy Oc;C2?‚I֚*"0daldT&#ȗЀ}19G"E~_VZ"C6|O|V@Alc3 r ZQ*ENC̢OzDd໑~ 1'{"b͐A9M^?FOϴ4!:#17 EϭESPCvJ/:]j;];O1[iu(g82L_ ϸT"6@o-A~ߘÑ;I"P}sg@l:hkKv{ӐoEdމ\ 6žQ$dDn8_fuۑpj1XOb+aDv[PlKx\Y%KQk/ځŁvjZn](gR+jFgO`h)@}AfpQc< P |߇=~ت^A}n}=`mv:d<.宬zx\!E]n K p>w` PDA!][E6DNv=OZ2NQ4)= R4xc@ΰ,2 ~w^]]QLI]ORLd&HUݢ.M r Q`"d2,F[L94C:w(B`%2AFT"v%!B@ea|O=dtB`<~z,CttH-le&aaOz ƭwƱC t;F+-V8{ϴkB4ob݈hG:>ݍؠKy,AJ@3qzcj\mq$d{ 0pG])X=@&zsYyK9YFo_Ca-4D ]s w[ζ2]uF`~Q< ₜ.욭m=λSOfҩD̥zjr";'̹\Nq F kQr)@}`&mq Ǐ pt_UVEH4 ܑē3fġ/oaZobBqQfِmQоC7fgSrW)ell V!#}*N,DI-r2Yd `͢Ut?dZ!92 `B' -Q.I.-shhl@:hkOG)CR} rY&*GvV!9MN퀓 \n}(Bv`T"!?>FfV74fvULɿwI%bᄙ+'A _>@3crF йW__nܬr#̤ JUv7Gۙu;oc Tg7¥-bq#֞ ѮL/\35"(o?Y*v"3?8LG6>Ǟ珑iEoz_alG.ыx}d#0nEb;>~3bEKܵc+*;= OO'S~!h,tY>U_XSگ0冗FK+SP|&dNcAඥ({oÛжGmvyF_7OVݯk"]'33.xM߼5\M}d!W(l-#Ih7|a>"d|b h{PFR!9#[=cjeUR+s_{gGTcZ@v݇βeX)*ε#F+Fpt2id}]BI!;>Nv^Ѣh4W IYjMvJ.Aa uUzK7FCd=m!̓X@g.]ل,i`;c7i&bSuzR6 4>vNW8NJϊ:`E.uu9 ܷo%3:U=UVA%"['/7:Ō6d ^y@o!pg[[{OB̟AUBl!bʆgJdP}I4߲[fIuq`茀hr?PXBIJFAl@'lܙx*{1LF#H;|#}@SXx 2PCF2- dbv;!x2Əd>p?@[(~YlBm;y؞Qz#1!P*bi}:dǙ^OAh򞊀qVo{X{yƸE^nua=L=柒Fo `\rԻ-d[~h"v&,kRdz Za߈8*|:\l?Ӑὗpld@:im*8c<7D9H'# T"c-m!? 8@rœcmpK*9L{Wv>{mO#:07쉘a@ LSЊV*ҨMcF!Pz0 xNj*zk mo<'QlFxirػ(6̵v+E1#Pql_BWGgm{b^kТw2 AF, O9|EumF-8'>@@b+Bo$8jrYm9gw2x.h#S\T[;m*6tA n254wDL"P<0}k Y+w=!xo{J9&ךڷf s*G rWLPuUAӁ% ;?G>k\ \W>\3] X Anܒ9=# ײΕy%[\O߰SB~@nם˭kk s(?9@`[5,k9ιqA<cCM@s\ ėu#4R<;sQMpE(0 THxLt-,CxJœ0pb1]9v?0 "4E Y8۠xQvj֩mDlr<Av}2 }DdT"6ޒA=CbG }x IDATթBd8Jx2 P;kYbs.qHuC`\4'=UG>>F;w(럈qx~yu7"bn! } ܙJn'ӷ#Ȩe"@ayP Öܮ7KNFe)څۡETwW_iL&_ZF!&bē݁dZk3;-krWv}4heY9Vd>B fMBf}=`G#my~Bmܕ#1H=|qg+s aD_ 2][M$Rxkcm!k As¯\ι _J868^97rC"|o@,-@@x2= VQ^ P;Vd?(βB ?<{<r nDxYO x2=,a( W00!kDx2[Dl~<^\K"i12ƣq h}0N+[ eR?s :nV:%d# 80JܿȠ1? vΥΑ[lmwCfܝMzvM!~.}4;4au"yt/2`-1:-O%bbpf nx2}e;H* tM m:g(v,bջ|.羒JĎS'>JN=|[XB`s,^ ǶBlx2}"J {& ?1x{ 6; /Q,D~>@(( .,Z_Mxyי %B[&$ed) H=1}ݬ Ey%9\4F "Ģhey<w~0{޶Xrg_! hmc+P<0OSC|4,we@m7`%r dD*weMQAN#% m"'}ucs; $|k6O3 h.9oȔl<#6PYX*vC{$EƯ+kAϱ3|fw)r'e44Mڇd~(!bQGư9ZMGeV嬼*g>V*85pX~ 'ZdF@z,Yݭ=tލpV;Z1Jk ^w.BB@({B~]bZYܻ51 7iOGh۬^k|]@~+)weLԞBUR3B@\Wd+B Y ڥ93?r/nghܕ-A~ ՟ 2AfGARʪ]ٛ쇒>/ι hړ%ves59 &͵w:>߃@B> ֬8BࣿF<0>mCx_1O;+l~6{`a*˳Xθ} Zdz|\4 s6l݉V3d>MOA'g}V[̰cJn>&/.DzRAY-r PGm>5!Qn"H9"yh4m.B h=p5IoUwrLh< H`X*6\?[3ԯF!Q 6h`Af . u40oT)QuVb~eVᄄ4,@Lx O_G* ?ƞa%p_Gm# KhO>۽[M~n6,v'H5edž@U"}ȷ9bFU|ݖGnuh9ܕ}\>}rWV\ 2w lu# ?g[nM*˝sZ7Apryow (!ZXf i[d={wMƓ!u5Ԭ5â&-Fm}UF_n"fM5VwHN |\Q=J9Ƞ _ 8Vù퇭zv bR'D̻p꼅2b݁9kf;"#)؞ZOSmg+'bvAmv?rJ |< +n6f!BD >L'#f/hbZoA1h7~ߓ crwUX]]7XwtF]7hbGĘ cހBF;{#R4VFX;=jy@;/vF N=sY%T-F`n{vGOhun[Bf3=1?E m E}./!7ѱNeթp_*{v ׬|H$zviuZb 2A{voCPbGrWn+[YMb̚; nYsnsu\v&gy9\&KD 5y 7P~w-nds0bq4~V^ bdT_:d|ìɰ303/@O!clf1h X@u2>hG?']vDccE:?I>xbFٵ{V448"vA,; 24 )AP6G@i(H}%Cs i*CĬB@b{{sqVJ^'WZ v͔1>mzqaww+읳`x D\L1ٽ[C6+~$0"({;oC sE&ru@L%r- BاnY3o!M.,-G7Yي"a!_?g8b!OF:ZCY3B A_"檿Ljꚤq6lba={'ӝ5A tݞ0_TF.wP7$sb[VNƢاYW ^ }m@] Ӏ؋.5 }9+=>榫[)]M#/rkן\A,?b<2W=1`\mEVP"u;Z:{2Ɛ|hm.YnRsgo1Sh8ڳK*-Q3_=/ȉ#Ӟ}yxZ=7ZY&4 QHY+S!е#-z32PaqVz^g/e[(NȂ|:b6t5b_=;b'Ǔ]'k"6l!!vr߽g:t= {N WioVnk:: ! 1OV/M7wi@.nOXV +ۣV}픾w-r^M߯Ej/Pyu=|Ԧa ԖEyLA9aݫ>D4 σZv~/d9Οa6)DrWz4Ρ~S:)E[l&Sشx2MH"@d¼a+hb>03-h2Di W z2ĝ^X?$|.+PBl-GGh'pG,{%M%b.L߈d AsƮ^|c&dh!Pp(jOy9>έ0Nl*p}$_ƵA|wF)I[Y#ddB0!mDkg- #kxK}6-Bhnw^X"{_g{A=TMG}߄gq"7({h]= cFYٽǟ\CO盎1Ej4wksLQL({O fFD>LwC yꧻēDlm1.Z =='Slu؃XZO}j|8܁_l܅\Uh Ix 4+weM"ٺ^bi+t P 2>Vdz͹9jll]D3+n<Q;#cm)dY `]u'!C M@boE]rG@d92mp Pœ ̵(s<J!5#=刭CHh]srU"CZXd\w"bF 'hnK0P 3lSa' 6)hC:>eeVo&YVEkK£ԞَI)b}2n RV̷{k=1V&ovdg:{D+Rm]iuBcнvJ]Ӑkl/Qt$WZB.ӽMG}'[!4{4Ό'ZAz |ʓ6m&~SV=pd} qhQRt=Gg q4Ov%eÄA.$Zp3Ǵ*t AE$6}-E~̲Yh>lJv J)s4\Vrh290o4FѤ8Ǟ2U7 Y&HFA@,DG 5N@Lů'IȘ<Oϊ'ӭ,wG$b.G'hFz?R2DF mhl@Ȩ`@b4\vjB곕%Sgk*/3=mPzh~b'_|OI C 0ު9a.6*hw V4ʰ]2~PXa6{g}^l <`*i(+Ǟk?~gZay!1 ރ ~(kǑ|o>wOUQG&1A͟HP{17ǓlzzL^9B1Kx0;%eUl7Lkh 2@`{s]%l"Zlĺc]ݏ-<uK_< 2ܛ3tg{o`UrMd#f/4$A<1"<b#!d^#Ehi+r$ߝͿsH/C&Uض@#`<'pcF =r'|C_h Z~1B>t$] : %b¶FP2A`pd}}oz+?~'Ǡ@x㸏=c0bĸ)Q_BWȕWN- z[[%<0|_FZk-$+UֆAc# A+!(gٰ~ 'ah.XR/:=eeo.XwPuVZY {~4>r'M me/j}heGo}̒ 7UyBoibe}-%dZ!Vr< Тhy.kܿrfՍ AE4Ze/@F!櫘':#a W=%2Q^ @Bϥԯ+nܷ r?Ż;B b:ڳGO#G 7}x>Mt- IDATV{"pr=e!<YR{n=bnAnUP`zab~ *}lUڷ>o@ M>ar rJfē|j܎MC̡ة,NAql%}ts>JW 7T[`\;]G!Zē鑩DlmG|iR5!z'붎UE#PdD̡h\P||$p݀\\"ԯN ypm2FP:) @}l+l}k4p=[u볳rMresls{W9Ax9omo`.V9nruK=?5ٜG5Ș|5Z~&e~W9M|{|&T"RM+Kd 6}G @@ k u H%b+tj#=y(:-Bs6fiL ",$l\Œmx& ﶼ FJt/u;œ^40d|>A SMO>ޫrޅ\6wIX  4טWR|B!v|Q{t߽@K#Nhħ6M|型mYV_ BMM1imbkVG &ib?A7u'E?aVf! f#~(Bfx2-@6J];k1-ZN$FmB㬼ũDlFzj|cFسƎ {ėfk-R s9a?EӧFA6py4xA"e1jOāx+ Y?AP䜫Fcl:( 9װ+.ι}&NÐ9UAWaV>X9 f{:CqĨm"9Z#X*'mQGD<{Mv{30h7d!C125hR=1KJ"5K"F0j 2 =l o&|V6$jc#7Hp~A4 (%bYaN5pԂ\\2=-gGPЗ3j@@>gEœv7cvai'1"7V+G+@O;ȧ#Ԉ@G>r'ءgnv(f#Zըn-lȂ}G*]fz;ֶGzXOY&4]x2O6:@>L9>x{,fj_Ԧ5o!AAȷ :hp!CnU9K?*1j"]ϣAfj:ܕ}Q(q9jAh  2/D\Dh)@sglOE#ha5FGנUꜻÎM2V `4b9wj܇T{6p%"l`ʸX * wPVM4"aitOH]8T"VJ~Gr,b#-2dbÀߙT"bAαCFۣ T7@Z, r6 ϑv@_a1i VD.2@hE]}"ԮmZ4Y&(2NO%bG  kAFhTWwoٷ~#1\OhOnyc~mp+%/vhǹ-鼗իz~u{^.ǡ~| ZL4W;AbBrE@*7^7]^[w}+,C  "͵w0{MWI%bcS"B*E}U ̜MHV!{!qu +d򑞯C=Mq # <*]o>imvA܇AfD4 2]7^*Q?A4b@cw<&qu p=;od~(  xh9#c'Ag:VaѼ6ٓxߜs;;Ql#fnj-A 9hRV#Ox2=O%bOdؿT"'ӿEFidHE Pm Bx2= 35=os,/T*{n^C;w1bF"J74w$d@JC ̿RwZV"#>%Vv}mG"̲;F@YA[ >/#יWb!V,"c##|a"zd7ݷ1#nP`عVP?GcZN1ƕ-"0uKP,A4FF`TfwBr~XQ',]مS;#0)WY]8 X"Ug]#hZ#E캍xhrת~;宆yEE_€وlGQozV[EAHMP9Ax.aUdV]A<:wWG/ι u8O( n@x9W{v:A8qgy5ld2Ca|ΚS!⃉A3[hF2dP}H튌~'e]6 k*[bS"  gЊi,r'nkWt+wP@KB_w ޹h\³ʑCm\\fCG hF4QlVcO#]@ő; EL+iZhTo"uwYf! K<g~; @O/G@ζ{>H -L'~Lt@mA=M_ hL:]AI9KqsBf}PD+H%b- 64i'<Ԡq~`]Ci}UmvQXi}hSa6,/|xN s[4v["b%Z`8!m\6hPi -88vFu ͛v(r`e4R"ݖ#7cQ Иػ@ T~{^z/weQH#}z!k897~9i{36 \*+1z6M+9zvGaۢg#R("•b;:L?XI%bϛd,2^14֡8,2|u; R1`D✿.T02LGS ē|;e92҇8F3?C8?>(e'Ĭ%òž;1RO!˧vx1("WO%bo!䏑,@La0ފضDl#?ʮG@Ht.VƮA@vI!s7dfv ",~wFD7]mO' ¶70D)r/ē!btU`/y[[.7?j=ߎOC.Vr[?]s]k"7cZܟՎ'ibm-!G6R4-G`ޣi8Su +#7ȟ-,+weזRhGӳ5a ?+oS~rpsn-A B`xmpjyvO ֵ#Vku\A v4 3@?P o)h5?I/PPf4"CžUV 6! bQJ*0'\ |X fjK@%+Jz O"vpTGn]QMz p|by~ȘMEgFf _T"ٖy09΄'lAsdXY̿;|T"66}Tb!\*{.} r'T p'^\t{E;!}بClRk|+PM0޷7ZvAmh#rȭ{ D3XբYGu.Gf3Ao+G盪3lO"1p𥋁#]? `?h}˩ȕܕ-,weur-A,plUnxc+yo'rW 1G_˂hHWʲ[Q>p9ZRM -QߘTfF b4eɉsns5|oub5\TGYrAaP8;疣`"-~3X&;< ٷAḾ)jX Vh܌0a~\t)/#X6邌eH7ǓV-j#-j}Lw݋ܾgLE@ydP!Vp0U"û F̣0뀀shgwd CqjߣOEḼ;$Ǔ{! E`g2XFv 2MbG%2--#k[#60בhαn0} g +x4ijLXӶtYxmʚ6"G)宬1d>Gy 'M~4Pƣ1 WHoGMK :W憐v\ϹKȘ\Ƭ]@}-Z`\5|b3#V齶݈6\_=aܝƈ=Xis6y߱My Cr! O;z %CMD+31wyɹͳ{#,HiBhq\!P< u 2]{J}nFI0'#`M72Ʒ!6 d ώX&Tk0d̿L%bǓ(;~bY8;þt1PJĎُ:wcelO5أikP|nI%b&s-S؂x2}6 ѪR ;#1; M} JjDο@ j0#PYY{ػ0}Bڦr-ᙏUu/LU!p={5ۚ*PߨCcQv_`:ߍo י9v9dzr@*[v#WSY'?Z)we s b?Fԉh"t-Ǡ~QB zE"p/weҧܕhip%ar^֎@~waVU;]c Wcbr-DzhFMگK,\b:2 3ǻ6gDT@ g}v;{]ko$bvwRu)dZ`dZYrs.XF`@E~7@,D*)%DiMZSSAD vL6NIi82ծƭy9IhHiEC8"mO)daef!@Fq(#l-B~=k"Зhٗ XNƇH75肋dXL2!ti=z>#ƏD ~ +kt2ڄ;0-G(Hy>8U75 N_j=(CA`-✇D"_Qa;RZ_wBNd: )Xo19 iVt2^`a16N`"6!yP6[Y8 OAc·S9!?mCa^BvoߎpS$bqUκ#f D!6l_*.A}7YV6cO4?o6 10Ud' ?HeND ^>DF6^ HB .4碘b/#Vvs)/"bzo Hz )oF.`|H{NԠhuD)E*ͽ\9:|'|~`= Y zڪho;V@U//s;Ѽ!bnAs&ĕsD =F՟z\E+}yC8fe6춠B6;%SȆ #\D*s R9DqZ!EPC/0$ {PL2=<;AJ:_BtlyI"97jo=ꗞ^E418mzlD`uZ_E TCɧ\b\B S %_` '9 )ֽrz #(C+8'ǫѩG@<"߳1nB N".DT'[_Eegx)TfP t?B'D,Z;Ȏ@B_W ~e9E^^O"+ Sஈmunx/H2ARd}vAq&!& $Mbe!ep!2 f#m徎-D̳z\R_͠X]C8'<]>/Eː4ʴr"y445W:anUնh^j'_@HU!)"fc}8;#wp5͏ވe}v)lח"|,/v[+@ \gسMخ~ 1r[=˻zm]ua#ʙ:|w:<ޅ@nR*v6ZT5-e";X'㛈td!0t3pOM%d X\(sN]Aل,($]XtbȔ=ԱEZd}H' T2dl`!UVf>(\ApM"y<Α q:!V,'bf2A .!:/w5 7{ %TfKpu~ ܚN%RD*9a""ʇkeNI2[!o*i@f,kÇ) IDATd%9߅(!( 9NƋXI\^S= 4sH=޺.Gsx&A A`'1?% 8wlUt2`McNTHK4ED*aT/tM=͉ 'w ڰau6EG{y;-ߡshX8X 1?%+:=r_'kUz ̎I|ZIhK`;1@"va"/He7C>ʹ@0 p29O>P+r>CQj> & -g"` -sHe1#bt^B z&b!@^",A R'x0ʌEEC.H2{YODw>)F xZD'̾Fv"v j"Nƽe=J2.Kκ0wk8^c~[_4 m ,'P~"ŷ;㐳鈽l@,y6v>!LjAR69br2W@HI?e}C~.kě ě fZ˚Ў \yV|Q.|)Zw!$,X;PP*u|}4Y!"J4\$r|C,.jjlXy.C zܚڔ;^inuNVLshÜ8j_7yp@U+}-z^D*GKX|hBl#*GEȬ4r>Hez#ȌE"|b,z%+Ki95G`&8{1ԗk5Nt2>W^CYd#s`zٳFn3DLcXYUH!b Z|GU|oGH)>M>̱[pELS=:ĸO'oY߇V1~)-\6v㷾nVWcss]3cȤEDJt}b>C =D JS8vze׆L۝A3/S7:r;X%Ȅhc: [Orb彇L/ySgc?{/t*#x- ܟNi\I'XC R u6-?W 넲VtGeecRp=L/y #߼>hNY,yzú9J6_/עGN7"p=޽Wus.{?|طZOH1^/͜zчŜi|Z0E vS /fZֶR4?6#41tG7ĎYfwE͛RtҴ/0+?HeA~AV:+h?Է.nA/P \|J_2u'\5={VvqeJ_u_wz?E0YB}<9`2WQTH.fff/EqB}\(©hNOG}2]q- ;MGkĥs#}P d9wewpޅ x97{shESND߰;ac!5K'_jst -,!dQh_H<~r>-ČuD@D*3-ͬ`f{v5Z?#R"gwt2>nLRr>b'R ,NB/a)b!vDB`k-CЋXLj#Rܽ:QEU/Z=#wMoϝ`?\Ē6q;ͬX;Bt2NV63Utx&u\k芺VMGX礓̻h~t1lϩAL[GD4߿1x#d WwC{ѮyȱD*sd~/z+CƶUTiUd MRi6|& Z_=G;VJQ a#Q h#Q6+Ǔ29wn-sW~q^[ yH V<җG!`o|9-7%*}J"K ι(\x?s=;> 78"6V\y͘Fh'}sV??xu-aGVS' 6-7a) d'*;܅HYw'Jr4I-!> )zxw!үFhPÈjd" ߽P*`U!)ϑBX}'|CTv\׾[m ?ȱ*b Acv}]_|[4v!@ F;H!EW?Y;!"=ͲrYBʛ)3OgEh뿞ؕ`{V;4Y_с֮;oH 7!k_#0˩])FLbpREVhӑhG?tEQW KBXN?5s6"\pniYl#5D>K5,;h.v j">Jwiʓm# zO~F8B) y'R_gD&+}5)Fds/ks3!ȷ%0S F;9xW}Ov}@9w?ps^]ɽ{ 9Fx6Ͻs7܈%Yo-+fo.n{?RfNM6"EMҀrHx)) k6bN'cWhq)u#F7 ӈبm{rbzGZ'#v16OB޳ bZoQw!$cR=̌9ϳkcb'8*_D~/"8)q"M(,FG<I/Y*C@H4[G'Bi|N*}] TqBCýW|;4~܋U Xj+?[_82mO}$RNoCT,B8*"&Ld)E) AH}xO뭤̡wTft2E|NoH2oY-m"W̳5fb녔y(Cjeo{zA/@`Dew'#)y@܄N))C2(Hl5Yh6h]R] ĴX9ìY[#?X: %؈܈|[?'ZY>^er eS}"Kly \o!P[" 7?%jD:\tLY! RC` @x ]"3%P+]M~O2̡@Es+-5)Gڼ܅gk2_t!16/_O,˙=k,_C9>lUe@ :ƞ?H_i /z3Yy9Jyp԰c3~jSn/z&!0ƵbK[dE {_d89j0Mz{;禣{69㽯GSιͬY1ǝsiEVCk ffz)w+8ctebw(aHqgm-" ωTdJq3S52f|`z@@%(^DFnKe_tFcdz Q mh=>1()<1e!ƵY"OF鷐96dZ{kUfegC?E2QnL!`C͟96A#v6sNƯ`yl*b2GY]@G5Zjb#PXؗ"ppM9m`9M<  Ѽ .1KغW{Ӟ{{PR)/.9hNt~ǥ}kwo]3-?لa.=PۿDų[]>}(m^<` Ys 3\E|uVtx XZk\hVwv9]Oմܲ)jM=O8uYtlc >FzhyDyYX,?1h79CVqh뜫G`wڵ{^=rQ@Y YzAC;RՈF/pS/Eɔ_d\kTKf2,2)?5Ru q0c[!A"&//|>A e7IJ}EHB;*Ede%DtFtT *L Z0 y:b@¡Tf7@l)R'3GJ[bU#ZU4rDw*0"Qly֏"NƯO2d:}같=# 1SNk-nZ]>ϭN:d'XXȼ didjHe66-Dzw9'R=t2cOH;naVk8#Fͷw}c:x݆h8C5]{ +}$RS>L^ue{ұU&ň5k@+(P0e5]+p{:'|VMc!$a&?ϊ3f:h{reXQ--YoS$R?$@]7kW>PgljcAg5b f۽`cvD:?>< M';'RZޙNϷ~84N,J'ϭ]:40yxI[KcO]LW p'_#&K8O}`Qj=׭PKbcQ[>IJ$MS 蓢l/`҆Y9!Z_z d> KbYG&.D/y*6d1κ )q{ZX+([LAyBh8mnĮT~s0AJ8]7)h@W%VF="墓!"X_Y#7#RA տ1= RĬ//N/1 a׵D+Chޞgu1bB j#"[< uA, $Zf[Fsc)CCu,sʩ1{տ f,N߷p65d9L[4?FSTf3>ObIOOmxiqqshU65Oj{ĜލRlB8!':$Sq=חlVQߓ܇bnݴtH8)ĺxNL<i5)irNƫO.LV\ءvDM#1#|j`Y${ 8)hrdځLnU!eۣX#R4#& @zP ] X( XN'׵D9V{zSQB5쇀lt2>B0싀Htk'䫶0ʴFӣ-z hu _AG{"dkvV߃>֖_";p'dg5l\0W ] b!S_ .Cp>=Ğ-@fOL92!Sr%@:u(e+.km+nl(^QPb{LwW`ƒ^63vR4sR2e#P?hieWܕkH@q'gyLg_z|hwVjj THWl[5 작d|1w!df%Xǐ_T;Z Ekb"~ bĖ)DN.NDu=b~çD`{R6 S_?u]!f(0(>Āv}PCQJ>irokgB5ǁ); vWZEU"`SLvg!ԄR$!sT f*|L=QΡ; 2 E,B HɆ STfS;թuNvok}k}wFd})3lܯvV#Pe`U]ې}X9!(~\CZ?O (4i[o:ÞC"1E ȧjEau9uEe3Iw|xt{ȉ(ub_XYЩϥm=D*`B}00~]b塾Ce2Q ^,hFfw'܈ IDATP ?&ZF! k&o]>ȤkdFxx;2~1-mL!+<Oːb=G# y&p%x-sz/Ba$>g^0_m%ݫ:7$RH]LhF|d!=J#砱eu>T+1|>Cc[5Rqyb["65{ pw`<:gm{1UO[n@% `>E!<ƭXABP=3:t  @:d0 !`u4,]GD*L:Ϯ'\D*bfow8䧓Kw5ս;J_\hcN:.?,j&}k}x~؞Nw"{ʭDi|C!Kh[حv ,P8Gsx ߢD`q/{۠$/'6Y?7ͪoZ[ YeHeDN…DѦA7?ۮs}>y^|܁Ao6k#&M)FX,Q#\ 9MpC3@@&R@4!]X|OHyfjbܒwF@dkx"*!bD 0,XDxO6쏔DRȝ@&Np^b5t}Fxi6Qh7ee=Lg"֠y:R'1-Zb:V[{GHd`)q fE6SH~Zׁt2 ,7 r9 oaS5&ѡ;6Uge{u9j_ L򩽦>:oިC{34B X[! R*n7ڙyy !rEx Y֩8Xu3[.?Fwr Lf_Zsޯ*FZR}s3p>ߓ(i/X}b`EF e6o&<3W=/ZGW!-|Kt2>0߉K2 u.xCaRKݓk fd'+#; -uH g!t'Q I2Ŧp'RQ5=1~C -"r^ 1}'9/DLȹ_ȟ1Hio"k+ݚ(DMV^0#næv_tĞmN#Z}kЛBP,L;KlnVDB,ۮfsO粩tevm.Az\_7$R"Pʲt2>6/M' 9D*s:NCN7a=/wMhnouW a]U( 'yˉ5Wq}`J_޴10Mq{ _X~%9U+h wcaH,|\%:使ǀI5[۲$t2nj{m{o; XגNƗvz=|He̯kD"F"{}s;E+dн)&Ă݌"ZM]YΓ 1<0ʜH,ElBG^AtMFZzZ[nAL2xdꛅLQLESC!20F\䜻9s\}sιsιBܣιϜsO:smc9sf|׳;82vιι+ K}~wȜV,j~sT],cTT`~ \y|ιpzeϾx{snO}r ̍p!D-09w~w"?_{wΕ"P\097onYiιb~ (`Ċ@'9@bgN;uƼhQEpc&%J2Z! qnݰ~.joka~-}R+DihS{](AWk?ι2 /p݇65)S4g8 P}-_/:=?9be~l>b*d G&, |d7dGJ_>aIJf^cS\ 뽈@W2w,tYwuqNwrL/sg ڇҗ/l <䅛wiJ_~xD9w(hK|WWZ5dqYDL9ι- "׆Eιh-!e6('o"k f:%ιlpOqrs߹Arҁ1G:1Ol'Ȕ-5UcNvaSvvq˗ Rkuڑ(:oЋ0X|!y] h7 ) lbS@>G_G<.nmsE]Co-x Zb#m]ؠˑ\dջ9f=/Xys8y 1`bcwlL|ۤd|sdN2n² w˃, zW"1$BC ?GhskO6fXj3@p_C.k;v{["kD{GlI4RxCm,&!ִ\i]6BfsD }fN_CsfV}z:_J$/K'J'I]S@~yWws+As 4uÃmUM~Ȃ ;1җ7 UJ{1Zߏ@`ι als"%W9>fX[f=~ ڬU#WˀP\1ώ9>A^ǯnr}:}[w#|? &pPFGlcD*5TwW-bvF&X삀WS{s:RM)>EZ1G!pF('{!tc~U`lќV5GmHe{k3 G ձ8XȜb"PHaP"W+`Nq=jɊ7NƛL7Ddw?\OV? s'f}1| s\1[O"7qJ킬9 ;x۰F6)s=P7|kĴͅ)|z^NƿUl@CCp5qk9(戟㽯1π-+h;H%+f0!i2=bx coaL"9=D>L܄Lv8}. )<n~ce>/HqvQ(dE\x#^;wi"3sbQܮوAL:(q;E :5k? ۩Eiz>C'A8`5X6oyDq^B`kK0^>N&R"6bw2OELV8pqDPXb[nNOG *큈!nS:" !y(dC"9",lZ[z -"9cc9~w Y~c-XP%|o Lrݬ]ΙZE}֑)fx9C.':O*KERv{#}1GH=(˵w9& @3 #QT#tBM|^N'SL.Q<"v^iu=R idsz$ OCɬ'7ݐYr.Z>ʺr1ss8/ʼmh{:k"y3ysd1@6~㷩dm1eo#5keFv:bR/vlӻ =i}:XӁ6'OG渾֦Nƛv;;4܉F!|b8d# "?Ay kʚ( S3b\OXCt2Y"F~N_!3}5d: FV#t2y!3H1߄F>.Bgk_E 7hϻ1_CD!Kh9/B=(zd+ONcXOsˡ9K'Z"i=|sw%cb/7e&7.T'{Mvd`" `.\l?=mϺM0[_,A&ٓ}4bAX-!Oi7Fr1JqFǙh ߻-3J2!5Z,N'MΫ#T&f~?u3^-"-"l$R\;2bcvF >FK)bAo3=ml$$U֎/0 Vd>nb6ffZN7(Vi6nlsjجVteVWuN!lEȠm!p<N"d CvR>`dϠRof:#t2U*}y "-KigD* ,"e} R~afhB,Qr_@!G}hJt2r4L#%_NƿJ2ːBB`[@R(W=b 3 :XPo&R,,# Tv꭬3;4sb럋y+2dD*g NƗ;U[\NGZۑO c kyt8b>F&\vȶ~F_.Y@Cg"V (Ĩu6 d{t&]mG`Emvy˾qR* J_[Xrf65ԭY?;ΟEV.-/9D2= !O_7F 0p4bEd Q"cPhMz-CԾĐq"%Rh۬BӈykCb˶GVƙJk IDAT%RTfg$bC.뭽!@J[_'25ϾTg pQVQt2)3A S e8و.X9+.jmj|fxXyo[jcPe5!`r[zX!WA#$^j~:q!-67IY;R* HeNƿ+-Kij8Z%X[8Bhl_Dy'gړ " TBmmӎH>Lt%HNӉTbQZ}#6 C,r_;̿4#D'F"~{F S#px0'ʴC"SBq"V=EӖX]!6fd} *۽A C,!v_(@lS{eO~ɲFi[Ƹ~ )mb}@[{NS#RoY{Ԯ$#b.:[He0]|{BD*s=t2ZJE-eҗ{;^5vd.Tإ:r 3y"?UZ~if`bDy @C'BL/r~+i0lMFʶ؞10gY' Ve{\Cyd9G꿶)U{R+򗢃7^Ð mbZ#`G \<í?!pd}>hu-XBy c3*l6 $,$!ԥ:HHQD@ 2nQ3 $YfS}wI>>;ss= 1c[u#R?;&Ys"@ @uG (gYuB?gjm !;a>[ v޿76UM>s 9E#)j" B尵]9X`d㼒]g\L*sh$ps;8^vνs97"v΍rU;ްkFlG" Yr5VAL!u9 ;)soCFw_4"]|j+L|b4hH!vyD"۬  2*Fjn!;{y@>Xd8TBgqvHeST'(#FL*d|vKVFdsm01ȱ 1S8e0sH%,RCfd_{S8G}A-mkf^l!nEkUU.θ9:tX _1ΞLgwpL*A&I%.^맓2ļdehw5&qW HX4ڴ]h3;~J`=v" ص]|fH5'֒BƢ}jC 4P؉@g z:^Bh(<XG@9B]$=bmH x.Jxvd:{f~(X,}ʡtvF(Yu(U[ȈԮ&}rjvGgbvF$#Ow R.F`H05ˤ2Dk2i};y `Fp8ZcU6*\aPw޶w]{iĦ Av0Y\uBmb./^=Nwod>œ`97bAu5I%NHyJՓ/v# bc Le:zMPڠ? Aʜso<:݂ )wu޴.3/:X H6D@ll`P25Ja-^%wCQT{!pH'!~]Ğ!&<(_Ȑ -EC*?ET`%g0\l^tڹ; 'axw G[Yku}*d址Ĭ5"uTt y9hev)HOGדl5txN< 22%XAn%ڱgaȏímBLkc@} x` PY Blb?arGz`@&#,+ws*X/vopB u> 2wֶV[3d:{RO^Lg'fR;=L*wߌ<[_ȤyYn&VeiA9t|1a/ڠn3V7M(A4:Esι} xoAPwh# sn80;sC D6D@lp&$2h~1%(*BRBqH%gDL h]#gIk#;0oP;~ZBL*T'љTmd7*>ɚgl" }1 _COтb͖Yyu Cs*ZlG`YjA DB!JR ^o Yxay[oqBr;9YH 7}YK 96+`s3St+ݼeZYc'&W`nϰ2y:G TWΤ*K_<{% 퍱>tTb W!w5ɖ'ALqνlFOn1l6:>j7xps iS~I$MA鬏5ƿI%oO [i ʮC {+C'+1YGxEĠ~B2hGHĆ5" 4;_ q=X9sfw{O#X mI#N\#s'|fBvn#bђTd:; bv@ Y Z!svbmFٻk@ [svi);#V ˫KsVKֆ]32ětpZ&CIX;$J<`uG2O6@⮦ǿtՄWTWK,ι`[|)$b6LD.܈9ln:!P#&9  V2M3tHv p?&L 371o#\Lgʤ5t5`d:0J,kg!5;J'VXDv6/qWFtaj&#`ߎ6,\Jxk%/D+6dR+*Q~12h.A%b{`Mn34 MЧ"U(]HՄ/k Z\ﰲ{ZsȮl DlFl !HMsTvF*AVMDXg+?qzؤA*ALHA@Z*kb yăϕ{% P~+:"UO;bC8X}[YY!ۭvke`}PY@l]7ʘ u3R2^Eޯ wޖʩ4JL 0b~͗Pld:[؃ B/=>whk[_(|߭08< -F jk4ZYL*1@7 'pB00cW;O#d:l39CCz " Plu^>vޓU̶Xv3݌+Uf~g^lw:'Λ A V~ވ0䓈j;ds{ENAjoZd;#@白o!U2 w[8\id?{r6ꃿM;tvB_C]O+#w@aZmPق6_QI$lD'"iH'EzZ-_E0*ήFƚG"UduB*v~-bjw(w5> Oէcwc41C77gRd:6 l 6'hazY[YxWhm! ҆ۆ!V׽Kڮ+F`1G݀g3d:yF-8w 7Kڵ`h~heX]"ufիo!Pc,vrh#sncaLZBZlkʤd:R>I%ړk4{֏Fll]C;zQ kn{:݀jf ĴGI$[D@lOg40J,v!Ukpy-sjZshŹ}o=6bYsBhW`d:{;bcV/RWod lQb>Xh1Ho= r9)> gkϱeԷ.\m&F'L?ȉ<EHmZWp5!`ҊXF >ʼnzyvC {hFa=VY]F_[nzBgdRWHľm+M0D$DJ6\xJ%f-!Q@E)}$I WK99halLr kФ~bb Z#bXneMEз(@a+!N1qm5:C#p=B%>VV4ZGuz;`5`w(>ou#N},F ޥw|Xmyu箷C 7 mzeRtʼXI%Z^$X2uyTH&3d:[B^[H6bPy^ռ{ R ӱ^cB 푝X$@6=~LgC];*_Tw?E^uA`1J>}r8-O,T{m{eYd)o Χ.o;>'BPTv1܅ GLlvVOo?U9j^ϳV0Fl٣iX8 #q9PȈ[<ƞlԜFd$߀.D6n9k/d5ٞx@+Xv)mδ:ӮʤAiΤ ٍ=ct4V_:@d:;"Js"RT9Dv::̭r!7-ι x"m ާ-Sv 2.A | t pAIq C89 pr~ƪoD@s4;hq]Ʒ1y6vFC$E<]fv_o#֎A#Ret$ ٝ :oԦ]#>+nk,$Q =!CfG cTamB35 YD^vs'A*bu# ;A@: 2ݷ[.E9ۀG}bޱ})Bh ֳ?'Ɇ&tv[XB1fRu#r=cc'0G_qAy:a$>W9b¹7!uU<# Z0.FE-Y &B{"Ԅvo!۳Zd+|@:iuhVnc>Lnٱ64 ;틀XT턘fg~gjȦm/=3pR/.FlȦj,]TgzO WTXV";y g,>by7̫]l7Ĕ=Xd:3@֒d:[LgK; 80JTL*q~&XuL*q r^2iLgwo0UO$_2ιb<眻9snsιι7sڱι8ߞιaι9wp9smsΎ?㜻9JMsV\sns(;~sιYιk9979Wt|sn=ϿsCs{"'_;pme99]_=Ûι39ϗ;.m0\sv+ιs/سTmP"FlJ2lNAlJ-B8U>D-kp*RhQvF"Cd!.:#0&AG 4wyRy@@4"C2<;N9v>!v̞E{Ɲ#iw+`FsE L+}mYnx=jmۮ7GCrܞ9WX?31vΉnhugĺ!>(EcTkt*4NV/$ْ yS JsC:/ x6̭ A r3C wb$_ ΀ɡh~?/9w^9=*nV (v~ p݅֜ r)01ӭι֣x9;)9D)v; 9w:pC_s< s! vuΕ/8$tX454lǣu>kZP6Sh]PYэH="{ozME 9oGlb=BvE3Q؊(":ch2cHAB;uQNiv^anDPJ@JvmE6N9#_wĄkx^h-`ݣv]e4=!)|{wpn܍Pr:qdk>aʫP8EhApjmP<3CUԧFXۼƗMPF/|XKhbti7B|4g"6:& )C6\n^u9f\ <瓴G@[v8h>h1<۹ :ι b;@ޏ`yR)ڀ;ιh]<R;~/"%@c]Hqⳅ6Ӿ.;+'g~B9'Aeuf8ʻI6܋@ϟX?y&tB*K:MP,B bގTqӘBJ\t" ۈLg/zmcu*GK^Eb+{f4a<IJMD"L8zB눑mHe 9+ai IDAT۽Fi=NvV˭"@w)י0U\E=bs"jJvavM/xV!GjǝTbB20kV^!Ξ2Dj>RnQI%ve7EK,V_ܪ@,qd){8j *y}Ae`NJ6:H34>w5qW 6 kj4?\ ,&ڠrWkv"rA:N@ ^wőIîA0 MEv r:uI;5U*]\)2%?lFplUn$rpNÃeǔI_\9"  =x8zd'T؝!xh73M7 }h^@t>r!bnRhF`kb# Sg@oiZhACxI%'ǐ-X &޵ѹd=T {"k!?f'Qq09L{Zs q*bx ֦}MkZk߬e\___n4nذ\gtL*&gwI2Ģu%XlC;C*0(5 F6$@} m=jGlO܂ qWs;bW]A!s~Vrh\V䄼E% ֘hͻ8:#smʿ\\ qDk(IxNum}pscv `' [*4M^DL]h-|ΎfO?|9716HQ[`7M$"nGj,Rg}~PgVv"p>x3Ăd:4`@\|@יVX,@ߣQC)GAHz>`LgCG;@Āy@4-.vDW0]C;~V xWYfΞ}{Z^{ر}E\dwu=P+wlal=k{Y4hM4+yKfRd:{9f}Y4^G9p/RZJ0MAH2w5#A_Ac&jNEq@jH5߂Ymw5YL*Ѽ(-RA ꜛ6 sќZ 4&?:羅֙As oe}D w m !ȆH霋;9ƶ7~>9w!+rΝɟy9,mlWT_X/-Rlb-F`9nBwvvu(@j:6AhK;Z_BT_„+ uޗ!6vD[@Rd7Lwh1О'kA@'*>vZSZ=VNY^(=h!zt'L4ګ?b#;Gs}/B@}Fj#0xGVN:_ 6;]g_;~^&'kw2ğ?L*'ɆIE}d*D>Xwޫ}Wqʼҫ 19+)6W{#0{}wn{[_5) cs.8bA{b$_ /"# p,BNܜ2zuZ&ľ<"Ld=M#/22h@Td:[^\Xbd_va ,0F s(HV!V)H]8[sЮ#g f#Yh'Ul/Bj]vYVQVKhfC@o aל=cel3?Ep]Ai|%W n"@21,WȺ"}bG"4^jj7)oqBnCe>RP^صv~){kЦuĮuG}1]@ZvCR{?qTiƟjT}0/RxCR ʑ|9%bQ,mL^R6DvwGu@q*bp-ikC 8,Z(ɾ+Yx->f`BdOD/5CH] fR%tJ`RohbX[~βF;He9 TLeHfkk]~Xʊ뇵EMW(vKVw+F 盄6wou Q>H3E`o(#Td:IA[#DBmN=ߋ7\ 0+o^&Qu2VA L*-QcrwԶ;J|rDE}6_@A's:1mh6 ;mhlbv,bԟKÛxi{/- V `Z6gD*#qeܛ;_nzb&7&eHUubTHyJ}#Ryvm:1h( {WE.I*hbA߰:"648xss[*j+څўHϞ{!_E,b?kuXg90#WJk[~-10vHhb*֮^stv= 2Dt;1_YH>\63Qj-@cR_ֶd})=aP~1m&o1ʟL.Z=,#aC%] oTDmICѮ|e[dՕ>ߌ&_@Gt-cdC;U6|cȓosĞdClo#_e_! S@wn/z#&€]گ#\u% %15J|?^X]{G pĔkO=^'ؿ2R{Q9 (Y+b #JH'[eR}hu>SFΚۧ X/^^lѧksQ߭@ gWv |sv41so; I$S(f+;|/qGdkd{4wG2d:k#{^H4##rFZD~`!C@5܊TҫG̶e u2;KH5x}nv4ӽyYAG9VQOХH *R/v6z\"pAd:7RgFl4bY[vG`  ;Y[kj6 @?$Z,{Y`| J3.ؖ"ǹٯmsywB0b(MG&U9V:7P6"o%4y[BYa2>dX},;@X EbxNk[˵AI$lFg6|@]hR- [8#&7v~d' WPZNDd>?w15>O-V AVz^v+A '1Mg&u [KXҖ5>> ,.Y'"ԎH{!7P*G2w !8-^T#X.`d:;l|~?Ύ̤c^X[d:[`2?Jh m'd^7<{!g1 j?'mPێиsPhG,i's5>#w=sJݐXB;KnSƽ*%da;0,E'f;LF!_ Y=j(X$lA(.F ZGzS=&QjCwahahF3,!mjnd ^w} g]3j#bUiߌoX+`ۓ0Qq!  Ђ-R XI[)J$[mimpՌ!Vwψq+DVT -ڏNgll"4^eR/Dld:*:-oV &p{S;ukX;&1Уq|G3.?ѧ lf` F8.gCС~Aцa$a7AlmQ6\VS6IO?$-J" %@j4qzO^AoE \|~^\-ELp&b^A~vpvC[b*04Ў~"b{ЎVםu"<1_+v*-H9\ʐZhaҞv2]g5zUG"/nHچ#|nȟ(ެk&!Pʤ-?$JܕLg'/ s& le.OBꤷɿ2krϊEݗ軲gA>M}6(ȧ7](hbvC Ϋ'} YcÃ/ϢPcv4qH:Gc`!78<`xl[TF7M#$/D+>$d2?&LRiԎg#½ : VA 4Q߂Tzc"pREzVr P"*Ѥ~ S刁-qNɤo!(YhA@ʡ'Q;P%'H P^QT⽼wJX]lAFC#}y|ˑ3˄q.t[ϔ;>:{aUR7]KtJ>SgB}oDBJWp3ѮyDFlSEhLCc4&%He; | mc2`aYw [MNVTiH"#I F*^u&d4p'7N0|b"SvTq4v⏢EA41T*2Đ}vh"8Uش"p$Y%(H鿬.^x&h(`uw{6iF `bGZ'h%Ξnmv ZVX]SwP1돽ߩw1iЂz-0'Jea&:ǘXs )oCL!!J􋻚mxaC_Ecb2؏0f}AxŨ?ϠA޼w,yDqB/A2Mmj΄cf%d>$&{I$|&7Ph41B@Mdp0cU&M:g{ ֧-KѢQhbXU7Cv zBRNhO>l bv s=&o{4kuh#qhBPjfɜ۷ 3.BA+DHP!0X؂Bh؂T-ND,b׊X[fd=TbU2u@A_4JH~$qr2텘(tȋU>D+M&xsjҊC~WO_5ilH" $I>xlTz}v.t5 ZrV68_#pu/cyWAh!2Ѣ]EVWPȆ]0pk(#5)`=V:uPahWAV.Eh!I%^L{$2IJd:'-JiY=nF6cMqWsZmP$sR ZrZ}Cqc%73(kGľ-㣁r7Lh0.~̪sGO ~6{ ]ca׶kWsEAkbԇC6|]TzR1YihG%ֈ=׮+uAAW]M|`Vw\)[nu@.=܊ހ{zێ <I$l FFb b"@$؁v> xsY]bd8hkfɿ7k^P PWV׳ +P'ߐJekP^6F`Ċ;G0PL`=푑dsR.4"cl#b ^KɤWz[$>|CYd/s8czɘmf91H;zF XO^E`uƮ1LУ[( W>eƚY^D}_C*W{` VX5{@}v6fc$̛:Ȏ g_D}!"Jc{ :z￲qNɊe>_wXLz {˯\(F-0M P]s{:w1GRCجy{4Qp{mvH;32A+Dޗ}H9-:$'S5w==(U.HZ qZ ->GZ^:/EļzDQZV7 EL$JԲaKP:`ߏ1T?-%›^Dazs3ٛxU5&=)*4 ۏik,8a^rb%W*{9zxFko@}~ B"tvG0x/Kwj j>bjf銆KPD#2!m4z#09 ÁVb}bEoWum_dUĢX$H`&#t+bmG„[zeU4іu >x&̧Yi!"ɟN0iO=R}z! #}6V݆Av]Td:;ٍ}-:{ +{ՈC0vy,R-6@L*1gƕd:j͠F&Ts(Hhǔ@z Nɫmޟ&-ܘ }[4aNq{ս MK\AnUV_Uj E@{0{5hB+ڳ6TǢ1p=@ڠrE\+j?;Z]Ò]hr5R2yh#ʛV!> zW=_-ڸAI2hn *}I$l>*ĒT 4?j;j&A_ IDATv]k[5 *Du57{!ے,B`-Bq\#;*d|ND:d:{sGk wq&RF?zTY,65{Z(Z7مߓyL*XI&l7 勻9Go0ATx祈a2"=Ɣ犻U[]\huyL>cذN g]k%< k *O. G3]^9pbf>7CX]3Jutٺp٫ i )?ԦE\pXʢ9 .4&Gcosrh1ݿA{Xh;3H"dI/=BքXv'RcsW uE\ODs\jEZaDK>H.2&kv7b*Z4{E^Y-@oQ2 LgGYm{lRN#i}jϴh"ς@ ?gsɄqGޤAf)_܊)彋+߃ؤ=?܉0ňq; hct bh9ʻ|aɍs$kv'0\4J ;Ŋ" 8˞gbq{.DsH vw9'^BȇYfu$HPLjŰ:M찏> իحBd#6cUwmh@ n>>l䅸y0~Q5_[y?ADŽjMB)[ bcERW-D CT-ZwRtFwͤʧ pR#aQ@ h$kɂem=s;u[Sy`k!WE݋q$k[ ǣ8z@aj(.Dwgk'$H K6Ņ*~Z_r }>2CZB&-fVE`k~xޮ-7[ٵj&LTBHu @^{7p =|HZڐvV4pOHSP홙TxL*XLgw*eRwV_K,PNDl:԰ޓKCM%s};C nN砥>A)F"زSi|<{|դi6lG]͵ێ6$ף101];!u^/mSѪY5|Y]Q""FwTԑg!&mn@V,hwpIwCmYJGtP@;w#n%sL,$HP?AVJ]M {P?q,R]h|CJkC,RBǑ*bvF;B+ЮPxތ!v@v^{#oOhJ!@UNt h *dBrt|դWM [Kkl3xt+ҸӼb+?_(UTu/CC,tR!teHeR5#Regz^X;uEF m~QW rrdK7a Qh~b` dDlQh/D@mݧbx݅ !M *wɉZFP QfNDY+o~)4C B#ܗ#;nV~5R2=CM!#Kqh7eR&P(W)&Rd|g&x4=l7BE㧢$32Ęs3ČLW@P%0nu+,zނq`qy>P^}] PA}هQh_5ZOn=FE ds#bF[J5.1tsϸOq^Ix;~ҲTI&K7gdK Jf[v,Uo6 W]:,2mC G 'Gl\dЊhA V <}tM>H"%RM~ `d:{$w,p 4 &T@3_ `ME;Nv-WIu{mޛ~d>څ`iEy4 LgA*6d:L* >tv]yUhLgyM`T/]M%ΞT]/i`9 Ģ\paOsA,F7 > c(Vy H}V@ $lR *(|,ި#41PÐO]@1?xӐN_wJW 9zkdڛE}6yh4@m62ĠC}{AwFlhv0b"WZܭ_Qo(> #"$/D@3S=LgILguP1n_3_B-MvCOZT$^ChQ/CȠ1R T)ΞTO!]dSӄ@c>8-plg:]n϶s^m Ѣr?@|,jN *?´/Ʉqc Y\u.8t%6Gz`>!.Bf|դ83Lp>"'a|M.ka>G7$fmrp1> m3 SPs՝H67FA`BmP9ׂ.nELڙȬ4¼F.4.b.Fv>c}ٝ݋g%]VWܩ^ s6bD&8- k"9zkOL;_;RPE (s/a%h2~UHEt"Tb-6M4E9dRWb}Lgǡ>,@&OW Uo!um~95S\`ٚS./-^usbtomu r\{|դBLRgfz1 1˭U>$AeG{ΊSryzS0S"vRf|ݐʭ(߃sZpKH`m#CV@ͅ;涯7"+s:;sC)e{՞}%,jWjfݷoP+NH"/&d:COu'V|2eeMN+pjWEr:-xޫ4?I%ֺg5; -8L*gx"B]I%H$ǍF*1([>hkmcٯo!&~:!#rZ 613#[^Tu*=s{2,#*NA*E4WSxU@,. @=xa>RߖZ=9{(~w^-кVt[j+uu yJh"-.AHwڜέ *oH"d }NLg@ji-{9Xy#V߰r Q( (:mK]߱XV!g>M9bA0 gQfd@=H"|%RM~~s2p><'He1qsU9ٝ^+ۻʫ;K2Òa A6h[֢Rk5ںURRkҢԥk∀#Q- ٗǹD29'dfr3@ysΞU*̼. "^/"[LscY0PcŽ`E>MY]=d8ڳ=>3+sS8Wa-(V\PlDB1HۖiXۗX3׭H3"U;<F̭j,lH}Wz.vh&XjmΗ7<4֯uuҐ+5Ʋ@#9RhwRJ} h s.p}_PRT1iaq)|k`9"pD{:_;G #[>HFdڎSLm#<<\mPT\ˁG [?A!B=H=d& A $pjB[\;B$6$c=P !ATH$`0Nm9u[׈uȶmwUy n~5vkkH!*z zWNEjZLeL[샿M=;N@NFsiRzxh# GN'vXCέ'}󲴤.׽nm@mC>%?5Z#{9Id2 ;A`ulC~]ԇͱ_l" 3z. 3coB׌fT?>菆lGvw<'nKבoHѴ#'߲;kʻO=e̫o%Uj5myi}NRarb'GmhFE7iԕF꾪`(63M ڊx 7Ѻn-p/26 NTyKc5dv#A$vu_YպRkN1'qUYc*CU2 qOl_5[H7 9۝q-m m)Imvd:i &;9H۫&?3on ON?0 w_U zN')N-d ٙX ^ߴH;}.`H-U:6?=ehA镓L<-ǧɆy#){o;\k6/XS[>\|>2wvO"EWykb5#s"W?0vUy :(GL ^7+j3rY' k!|&"onuxw=-yF˼n3[n<#2 6hxڕ8awt^o^Ԛqn޴Hc}0wUiu^#l}/-}Ⱦ z r/B8 cRiFL^ xDfq!yHMYHlү)5:0ǽTS'zIaIB{_/+=\4pAaxHv+k(l>a3M6 9v׎ڛf]Prؕx꠴-9mw')sTv2^ XLQ%Mb5|=%u->R}bdf!!5=Hbn8p>% V6@CIQY> EyHaxg vB;lDHp*7mMIfu<칁uoGk ^q_Smm}H kϪ0D Czof۴jJ)IHi-s"}n_N*,.KBfF}q/!cF6IK "$`mZɞϩ̙17yذ ]uЁ\7Rb_),.; Olj~jRioтXpaR(LntgXZuoe ߊRJ)4S/ n^ˡ' _쌊Č#faX,ޱ08~[}r)![{@ϭ\)h _|Ǒ+6d1sapĹ+()*{-\uЁuҞiFt=4b\u_=YX\f/)*"s#m_ _ ,oNhi~>AZÎ5I^X\x^3M{Ea}0lސWӅeuJ)+=50yWQ8$%NƖ!/pز59ޖ!ќz_s!rZt_ " 0lq:C ĔV#!O m:L)T֤W|Fffɍ;6ٍ-Y 5 9;no;+U쭟 HGؑ ̆L D{a3}[yr8RSJ)fT?QX\JcJcFekhb0u%HK[S5/JqU7I-^FHux$04 8z#"<ۚk|8n'5SJG1_3yIDAT[cq:-H#عɮwB._tHv>Kϻrվ':7]6эVAl2O|륵޽$aJ)D&UqypY}@bV옾a&U6 m^o|V{| e=pRJ$=5e H&ЊU9bЁ>\5ۿ|(_sî};g/Eoan+O9y/R]K&U[a`)p?-)h -?-d>n8{A:O>삵G^j}xR3³;N'blZd.ѮJ)Kh yZv$8ǕrJfh`@U݈-8mƔ12^[CLZd_\} *R~ t`o>y Љ G45פ>,ul5kQJve?l¶Sl6(%Q|ݖ0Ms RNW^ۡ~ |`6H{j'$.67sȬO֜=NW߷ (T@L ?p[kXA]}#}Oᣗ:G%WcWفt|5C? iB!ۑ^?UA$`f5uuYdW;ۤg~ʪ]zE]x.X1Fa X e)TӌV9#-7(p=LU^,,.??n Jy'Q@ir#ٳ}Ҁ6<6dwTdcH@F]uw)ۣ7(-4 {k5]yXh xl% \RJu%#[!|{4?%(tƆ췁H jV=C R7}p` 0p"D ]`۞IouwاY4JnrxZd.1K~\dj@MBaqKצR]I&Uw[ "f$P09gm= N<n`8Ɉula_GeiDTV]D@@|lLQ]uw ,6cgll쵋P9[m?i-SJ T*1k12 ؍_)gXN64_7y /g>$UiEte|jMkx]0xR2TaqKaqT`$V(1rԷ^qHvtBI%ַCjƔRkAU*Ϲz 읿2 .inVcG'9;+lC:A{}_ W:wb ot q7؀=Hf..:/,.:4mx Ɗ*̼^ {4Q,=zѳ 0!|:`p)p;Zd.iՕRЌn!ۑ wynsaȀ 3vV#9eߢu{$[w\̐0?s7q5!Wǟt lӇ ޶'Ovgz0KC&L/{)w hrd{~ sjRBUw#F"O5Ii-2|B oO~᱃.ކ"'/߳a Hg'' hK tZpoqLGchOw姯sҖ#51‘@bМa} !-(!qrEM+- Tw&f< zEY Y}'xg9Rot}M~ȶ >:$ݬ `i%E]]K[8?lf[(%hdžapXlc:\R_@LuHqz4+*絗"9?rYW-l8r'ni3UڿHPT܀ĥ"-,yxG%E7$  :ʵwm0ݍa+3r(=Hq=uT0T5b# .+{a&GiڕmșVSlk8)g p>0j7 |ftLƒ5̊l[odwD 5 <#rX*_ `A_)- TwɄtf TJs KsP5fi~Nj=ӴI^u^7i0Xъdݖ"-V"ڷTIQASIQA/2TjokRێd!= #rnRJK5V tH9'm `!*HVe9]feI׌u|3'>dXO_ v:_#o#.( h3`0zaA0P\-}ΔR>bG眎tOpÇHӭHh'?Eeٕ|O&L@2`"ο&*sBW_ ؍یB͆ cӑm"Bh}RJiFLs{lW ~8;tMi~WT~vkk:N,6J;n6rJ_z&~_71{MDG#)E_,2l^bcM@SJv1E 4?mTGu_QSRmO(ۓJ)fTeZmtQ7ͻVk<2vV&OKB裲Y 6g[4bO(~S),.a3B7mkZ6"܋ >aacRhFL 0Ow%4L}HݓCjOm\O4#TRX\6ypc&?K#<X)Kf^8.U)A@L> ?<$v2`4M6x`R ]|λ4Fi#S\ײ d xgJ)DkĔmM.aDҐlQdDTA) 'R֤R}DQnig8 &%a=(n~+T/[Ju 3rdaX5#ŚŎEA+4S<ƖP8q22 n}öMٰ>lHL•RJWZ#Tnܱ{FgJfN<l{#4\ H-Zp$kD* iJ) Ĕ&X=+Қ3ٖ !$ YluHRJ^F&'r f"?@3ۓ *{|J)H3bJ ;=@ibl=5lb#H(dzڿM$ F"ۖJ)z͈)x]|*j^5VYg"$SI@4#,6RJSb7<-mIFkŅcEi[x˂RJWS/ xB2JcYoy'қZHfs%DmF8lEJH+j}9^T)Ti|O"..Ak|O < [‹G5®`89'#̟@ŊӞSJ)h %_3 'PdV!}~mz_$qX`JԚ's_z]Әk3³ux )ڏc)^w֬R h|d$ێdlwY3YCVX\K ~ x?0A^*u^Rֈ~EN FKbV,HI.p-,. [ , 3NTJ)u4Stg3 _A=Ě;>{t_TUxA{h"aJ)ֈl1EN HX713m/^*)*{wXk| a/):S/lSjC)c  ?`+uхw>4-|5 b``J)whFL)Fddx G}h`2.()XXӋxk&lRJ}u~6dr(RGF!P2u[^j5qRJNЭI,doFDždǶ!b7x;|a_RJ =5?Gtkd+G x|OicWZwڛYRJJ1$+@I x> ue G.ywTcu{<Ro@L)/)#҇ {syj``pSSv6LNn(J)Ab}ڝ%x݊0beA}$j\J0z(ў_RJJ1]&O74Z將W@j7 xo۞)Gq#;'N>Ï/_+sԤR#5߶j%E#v"$>ƒG`R}ֈ)Ձ n3:6PUJ))SjHGR_o)RJʼnnM*RJʼnbJ)RqRJ)Th RJ)')RJʼnbJ)RqRJ)Th RJ)')RJʼnbJ)RqRJ)Th RJ)')RJʼnbJ)RqRJ)Th RJ)')RJʼnbJ)RqRJ)Th RJ)')RJʼnbJ)RqRJ)Th RJ)'呋xIENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_19_0.png000066400000000000000000004660651413546205200301660ustar00rootroot00000000000000PNG  IHDRb6;sBIT|d pHYs  ~8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxgxن]b 6fLJ0} B - %+HHhB"-„fL3` ƽ7ɲյ;ߏ,Mu%iwv쬴[ bX,˶' X,biX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BZzeǠ)G%aه-bp0l5X,R~"퓀eu-BbiX!fX6Rx t]{ 08$,k+X,֏X,J^*Js[# R X,֎beS& :gX,+,˦GM?@O`(P jRR_bnlbޔ:ŝPb 2, 3lDB}$,kX,ֈY,ES< HCj js/bB[<X,ֈbecyf/5?YusYKw{'DJNlbiX!f26폢dÀHN[ӻ}}~Hv[)[,K 1K["c|ք@2ꕻ|oMn.@$ȶ[LjugԵXrC)fn(u#b K׬&3cPWdOX_ Ejڣt]ߝ}>7t|,)u:gbXZ'65ii3eGK?P.P.!{UޘqC%<w&bY+Ȩ%Zװ[g>}{~᲌ۖ:Ńˀ,̈4F]")nNCNnE$;h^Q+0߿DIX]cMA[$X,K+ 1K[i9EꀬV-G/yn2Òl/]Pqg꺟}ө aqhvymMqN)u; ϑ:EgRDRY<~^ndW q?tgPd $, KL`V=ʰBf0#Qj1WF`UP' H}1;6]&ObXZ1VY E\qq;'mlsa"in6Tę)WGB* ˾*uOF=^BY /ثp^}y-~:UҘEȒA9(Z1j䒬aSsX,֎b ԥG# xx$,#wm]"EnXGܾ/M.u DrbK٫Bk=';ɉ]+ V:$2sM.@c{$z#<7T%sX,֊qd)u(N2hJWE )u( 5/Ѿ4! $jnґQs_5M)Z`Kw&i*HeU[Z,YQm^anHlW.H][4- /uoE) 'e+Ⱥ*E.Oh[!4d=sVY,KVYQٝ&A,ZmwUNV#NEn"It*ugQ [ף:Np0#jnx yrT00(O;ݗGP~P@!evbiGl:*0hppPIX~S|`S<`Њ!oH) Հ"[+0BB4"W_!.%a%a=c D]w$tZ=~@Lqo6>We3ۀ})X, J²JWNq[MKϐy Y9zurUS#)EKu@fȼp]Zg#sÑz) uIIA?F(B6՟@|vF}S"N}Ss'~BҦ( &6dW-GSܱH"UBx`IXvZHف(>窢>&+=gz4o4 B$BӼz494(=Kr "GsEFx톦'v4fSde1^%ϽBvBPWcE.Y16;R 5Ӗ.MF_'(;QZ '@9NiSwJ²t!bi0clک$,[k VpYVEvӴxNw7wpn}dd8urڰC(9:QL?h \Z#!."6*ڏۓ4Ec(R#FjtYy4_iSѹ}be쐔:o+=DMm!"w|QѴ}?7>4>aDJ5gU"AVIXv%?u=^eUKݻќrWDQd@" |XYl35܂D%av:X,¦&-;$f:5r^S,wz~rbIۨ>cBkn) #!̈ޚQ B:8)~f\W7wh^GSfrJ" QlHl5J]ͶuH|5"ky'egPgE"YдbI쨬:fNE]ZEQyv%as a$2ݴuU] (])ש5=7-;Œ쇺(QT0ݺgd;E*KMFiF`=i1JM{¡o 1ҦB̲Cd,2`9KVZӵ`$PSݲ5~ /s$C<<vG2Ѣ/_^1KI 󖮉DB9(J(C] JGfn~L泥cHty( uGL 4s2%( W&(L}-=wɩ {o⃍]'~6k_KfB̲.ژmwx#=0iD $>Z=z~Aft%U+:LZtd'V RQ'Rl^T̈́<ENG^b"qu[YWJ}Ps[=c?Rp8Cx8rҬphCS9ՒyB N?f,"gx0ouEoX,6쨬|Zi5x mS\Y9v/ϯI*SQ[@ /p1CkYY(*?XQODQP~JeV4rg"i3ȉEhe!JԧfC01 b e3;8~{X,m 쨔cECRVKH lO6^dN0q:MZ(2,r,ݿߔ.6DcÁ("${B#4d.]irY.JV4?K²k;RWtƝtxMEZ,6YZGIXg:R.8~y~tS]즫?,ݿ_CյaM<p;&7!7J²[G;KHIdQD8:$"Hi~2BC;{ 129D\>b:HNݽ|WnX,m#fiqJL ;!L16_x4O^i` +JvB}{(rv8cI X ȭk={dVտF#'.h}<bXvljҢ@R- ˦R]"؞5%w~Yρ +Vc@Ϗ6UH:}4} ]k[k˟Svs+ND<@u"{~=lfSWE6ҝCG$j9;3N]x~}#8C/9p>qO^s̷Nc!r@q(g}T#Sb`b.{Ck;h꒧֦bF'`V  \0P[r':TFGCAVTo+2`=֧;N$g/ЎXS"Nص%nuisgt,T%p΍E;SSqV8tMZsYŲͱB̲!vCuGow(;S;`ƨc N:{e5[0 Ȧ"ZLqQ޽_!|>=gBѨۀP*Q`b"X{ljE~) + J@MIXkaʣFtuEv1P3vd/ֹX,bbhs?ayG /U*"עfc5&26M.YRTD`Ke}eK6bG_Xd9H( ڃVgXVmx~E>EO'Zta[PѼ^@ٽvrbf7\ƳH"߮'7`[1+Z Lmu|cG8'M>h~|bX6VBUf)bSm ,7-zmfb+6޵ y'+>}bwqwp&x|RN͠zxZ .A7J:-mڒlD|NG$Hl3`3۝"5H4\`F!Y cG&zicGx49z¿ٚ#x8 =~mpb 1Gw=T*.Gٯ{퐫yݐpVz~pY"jt鴱[`;IHBnV}KdBT ^ʛ׻w V"g+{qi6:gi>X 9bi[X!x-DSQc D'0KP8`yl#p[Qe|kj-4h rw3FjV 9;p9 x4uuE57 R1t;z nľZ,ľmcx>2\n=y}5TOo1c&rU}~߭Wkq|C^FX7g͸?)S\Iz nDƱG"n*Dyc,;GήӞgɩ܀Rס-zl:" obX,-b[b_=?, E)2|8wEvHp_1%}3h,`t!9O@PJDMcgh̥ѵ#f`'' /=mQZovۃ B"~C$nwf:iqw)S~zMuG0̯7aH| C cй;EǪo[P:zZYX,KNI)[MꌏT{$29 ^Rg"QGN@t"ˡ։E]vhJ5*ЯC-DE3]9ٚEBYTW?u=?xy]|n$ܬG>mkxlט(>ojŽ\wDs"MCDW, _Ҁ3QJs*ʟfP OAS0|=4j.:p'Oi׭)z }(>=~-HbY6"=x$NIfin2w|cD>^zLPl1\n6h $B 5yBQ/]d"g!#Os_gu Ux(NU5wMo`}cqܶwo\L7؆րKy"jNN];g#;q( vpn.bXց}(/dO(J0X,-bߟ7ູtu=?D%H 'wW{~(~ \j4wuEf"AY ;P4ٻ"uC\T?mx9Fc_6H\v,w6!F{i=GQw|V}C'oHߗ|$g'nf2z±ر#{d~؆0j?`ՙ#ME%WhxoȰXV}OqMxX"c94e(r.:-?Fs;"zdfǨEN=Qo"?3^BD}7`#Qj*󘫚? c_ӑ 1xiA*OscnoF [OG |N>g.+W:K ͎[F,_h>: sMq$GChݏ-Gkac&3a5c&z5bX!1 cz:߻Td)ǐER9; N^,E>D(UR0Qt( )֩BkQ}YpelTEۦH[ ].p>H:vq.Y5/s1 R]Wv:h  Ez6=1P23 #4Ot*K>XA2>bX,+ &JF"~# 8۸DוHjQr(*@B--R;KH`uFcffߗtyH}ju1R= F[`_a-.BX!/34y|m.+Q(4$zCQ'P%H X)J'VTpC&>=h=P'Dܝ HP5MJ)\bP05|`C0{~GI $njqw9}t̺'[[Yl]N#q1\l$8sֳnI~c3ɪ3aT3GLu&1wFL4 $NݝoM'`کJ3tQEI-۞1F?`/XbX!7ʑ@m2a%x5f-@Bk$ZPjpTT Pu}U#a.>E")iI]= |,錼sn:MBD(Lԧֈ!f?Ph0Wz~ PELZsfmm!A:ޜc(w'z~pFﲀr* ˢ(Z߸DܝjFD56?]qwvc(י =;J/OO}Ecl}Y\M\IoX^@KAZ`7dLsT`ﱫQGeAfA*Yf0)dհw"@oQW@-Dil$ 1 Ҋt\d[A,ғuHTBDѶ9M?h`yls6Mj$D?F)Sd&̹05k۬iyN(Vn{sm:m6g p?IݗHqwʇ-0k<(/CY4`LXz7(y"p(v0@gj!zgC]|B4R1sMZc&BtM'Ց6o$X,m6'L;ſ]"N^vFYD(ԸAA8,PU}= Abe:x:hR@+/ףν}hJS27|YN_|9u({~pHVTd*E.Fѷt7gWT<@x~p7r?d-Ch/!$ـ.,!јa8s.{5wWϕ4huA+hsm 﨟yx޸퓑K/E>}&qwIflc(;ǐ`{ R_YTKVNI __, {Ho}Phd[4s7륯-\״^~1F}q [jeUDܽkAhJ mt]Ru:p`}O$tVx~p=3,m'$/*H#$ oQ$gluMJe 90'CM]kG#;)xQTY dHy 톢>Ck$."y+=+:HuCp$s-tpM'PLmC(u]׮ Lݏ=?XeDHiM;2_.;eƆ={:@m2gCjﯜ|]j5iD^$WIU5YsTuio0j0j07EPu'tn!ڜK:{l̅f7@Htf-ïZ;25 u8: D˙HԠhp"L7؏3Л4(Rv>Ihj$7vH@ !qxN`Iw#14y<'H(=DrԱ,5iѮPWdqgQ]BBDܽ=ܣkZ\׉LMw6#DܕHp KF@y"Ƒh._{cR{]_GLjx0m-ˊ3!;wCf1'^4FbXNͺJ/E]f7 Q~ IJo0wq^6,GBC!? Qi`sg2-KK`~S]sGeB 2j!7 B Pz~KdIQCXZ>U櫿9D}HQJPZ0c9#[Rch:^ G!Gs灿%H\"29潨89ӸcPo!1Z.h{'=lC ` 3.wۯ@9k~{y.9feC)( pji'dwi ur"{1jTx"w6 c=?x J1Uףע+pe]6^ٙ]wCx~Xje71FEd< ${,=oZ,mC\\ FYe[Lsg$l҃O" U#݀"[ϢbܟB , \tR__(ԇHXĕ]EoS:=Y iW IDATFB%A1^Uy E\t5ۧ:8h_@󃷑XLg E[}M1kjt`:"In6.5|g:\2hJ>\z~kP!`O!"&8;u+F::4HN[דH9<`Rk6i^zgl:ڴ#W]r̄QfH؆ 1e`#bka.BM 8 E.^@,R~3ѧy(D3рR|{ q(=و<NvdE_Ewp還2,Bf!1t@%HCѴ}Q5cJێ&{Heֶ9R-PhnBR)T32i%4=ff}G$H6y?ڋ!ny;L*,wKPS [a Zwj>{>3@E!f7i;ZkԬeԸpy1JMjֵOx?q5mRqcW7k.1gQ$0:wh87ՈA@8V4>{Z,[8:5Ax "#1௎Ñ8܌2?D *E: (z~'`~'=q.*]#3o99fvTkVhػsLv;k>筨SD=czC yHܗOz~0_K;ڬiG 9 wQS@Z(B6@`5e-^PM^qȗ1=v; ]LCdkۜӷH,D` 1?EjQjuCQ (”@M难WEdK=? wpXT}d/?7/?Y[wcܨD]hF})wc*&r}ƚ~f_ˇCo;pcaVfm:wjXvvH!f"G#rѓԂHfWuf@E}Pj2=7& s/\"A$d!4ΥIJoP׾i}T̬g2v-@ t>&d$GœɌihvQ$n/TaGukg !V>"asDH!CB,s0u 4QmMM5HlvQGzk9#-7zӽh5ș@FSPBR">9v2G]gY9D<r;![˝TQ ]33/۠gKD|#Я@z3W=¯@mXneVoAݯj߇'5gk8*qԖkKJF*acz ;hۮws~Zr2~7 b:ckZ[Cz#x-DC[vQX6 AӭηϦmRÉjϻ5Zzߠltvoqjp:l%e[a8l\B7cwrt%˦-ZX2oyn\uGYJOAyW=^wfPQQk ^ҿ)_6^Dfg£eAQRv6e;!ANG$mSZA Yh"^k0Hʚ!hrn!_dimu@.w7vzW_IJؘ:Fyb! a" Kf,c71yy?b~f_Hl@"I4=B[{"&vFLd#y#R8E!SXb s5svJ(BQn ysI%q[k0Z#sH-heu9p]$޽6kB~c\ 66<=4B :Y& v dmS`2_ysvɓOnn3^ =pX2~ˊ xqS%@vh H- 9jnhBxAhemF #G9SxE*?_ubzy/+vCJGT">9[`̱"5gxȗ ؚam2s 8DylSEw: 7kB~YHy< k#9;f*Zu)pm3 Y[Tn E@mȆ9XY֞6Dgeu By HQ;ڧnʯM .:mD{}v@Wf-{SWPM4kQ]E~ m(?j^kX9~ 9_bV`P^\/)-XB.v pDy7 Z}V.yuҺ.Xobg~EOhlU˪Z/{Rx"aX|ű(X%CP]H]Y%],C#ȹwDh 8 9`"9heR(6n>R3V#؀a}9($ӡ\JZZ9~^CL zA HY`hT";r ~}JSZs )$T_D em,RnFaP18{ItL*  9xV@Y !'e)r"fqEku?/@`9H;P.TG4g"99h5BpPh8b =o0 /w=Ck[UgWYu}W<0zH?P-@g! 1^vʓ |3u %[qg+;oiׂέ[?rPQ0D2e;0 ȹS}C9o#z`KQlDQrDu,R^B>!G701uȱTOY@>D0v{͉Vb੍ݛBgV(d"Me!'pn?]3€sv}Զ^ȫ6nMXk5JkeXlF'Z9KBJ Vϯ^VH \T">KHg7~"g?PSCTbh4)f|zjAE/! &AY}FԴ -E 7Mth%?";\QCIK7m0}ɳ=[}Z%\k|w*_g57{':zH_-Z]-E>@}0_ɖilK ,D 3e;1[hr 9"9ϱS4/{n>C*M !ڊ|[a{lzN֌DmV@v#ZI{w1UVv}xWf {avQW+kY1 7BЖt}JpOl#Zw.PJīQm ~_W#pMl®AT#eS Rtbvod?0P`g|GDy#\r^~y> ! &^c>ԭ-#m59v_7X#ul'4fBG_e 'l6)'({o;Y\ggm|F[5Dc>f+/ojΠ| ڪXn@}t"}k6 [k.8l R43e?7 b6{q.G@19{j,Z D|͚Ea?9(kp'ܚVm(DwRG(.r Ufe9 dFPpB3 AMS$ùAgY>VHAzK':Q-lrfG~ٛ o:! s mhu_cBQHi)R>@ʎAUuA 0"PԨ〫SCVvnADh\/?b=&CUֳB6&N2e Ztի3 a ?  a}vK"/=D!3# vVhn@EߝU5~η碜j[ 3A/P^HplJxI.uUZyFG-=x7&>0աh|f,c6m4>u6v1Sۀbe*nBq`z*?`syI"و@lS|9cD_**!p)DN*sf"xA_JCD}Boyƈf(Ě Fo& 6F Wm.:pYQAx/J~캭aOD'"P$~ x&{mHGT d (R}&a8c'{ H!-@9^!tߥNzODPZ2vg> %q<l!g焓De*46U1;G ˁvowwT"-/nJod+ZIiٵOGpK#EcmX=9xԖ,jWc˧e,cUlޠGWsאN.MYG7 X}fܛM_Y șFt9i"8r Wa> ";g%r 5.muHkݍ rڵ'=i{YO!h; ? kWK9 1G+b !@FS=duL5(+ [$PK{~-β:>h=Q.W(ul뿅: Zrz[+Nap]jGY[yH2e?l]VEeeNPΥỠPQq#8q328k_mT"Z*~ 9Vv( 4R@RGܦZ#cBDؽԜOӛf6kGΨ )@"p$SHY9PSHٛNWcdUk\k,Dó hvgv kVSxAyjq_$(lyD!ElJ' 6G RwQo&(C+ ߅flBLo8 G5F~|7 m6i ng74{mn{! 46f#8YwgXE4õh2Dskpdn5gG/WҲtIi(}%e3QQg%9胍yq9~|A8dחgo'cXƾc۬"ŇЁ C׵ IDATO9^oQsUkcٵ8t%Vhep mRvj!(!iTrr#R"y:r~u z147"X` P[ rZ]'#\Jğ(8kD S *U,qfqNE v/B0Hy@;yIAF#z`{}B#ܮ`}m`" k>oEpuu>+jס "+Rα%mmk]+ܜ},SlGc6Z5 ެ\?? VYD0X^n!Ezˉ}ƒ.FG/)_unߺgl+)-4zH v}CE6in4W!6k3|mֹe^%5!cX6mX}duuc97m/S͚ zmI 5H0)9`9j uԅ#>9eH `^E`1-Q(_%H}..B*M Rf,RV<ouV%rp͈QA=5z$0#~.[YF΃v~_F&aNF\_} @('KVH#:o<حGIk34BMէzIwSٝX׍KNFкUlQC@aWѸl[62v۩D|>]6zHq 㺯ef,cدb4$Z4`T+%Ӗ:8#6%?`ǐ2Qm}4r,AP9S3):'3_pg璋`=r#;9QHmbo '?`xIHa'r+Pvv}'rӑRZ_]"(&W3J.W_ t&+b+'׺^ hx[*_%[,ӽRgGaʵ5#IV^Q(d+RNv `@5ߪ(<=b m؞гh4& QVYVr`stN}#(]m$Q@ ]L jUbM[to߶5TK3D|v'ʗe\AMc4Ngl6--yn1_:UN'>rme'[n^ʊt;.'7bcu_Nwפ$JL8?R/Ǒr C}oF,C8KkDN5L^` Z(*@ QX_A3!ieRCc"ejz!)sPh5Zd4 9(Gh( ua EџQ^xrI*WEOk*2 )By QDXkXw>ZvKpC.+̯6H: k|bV5_L\2v& V'"{AX1R D5Nm* 9 HqA@ˠ\v@/}w(|4>I%⻰yIUpޙJ/￴R\_RZ6֌[w %eZG/{U=~=OX~sSA~_0O-)?kιۃ }x5snpIY&17p?~BP3 9n5@4DYfՅGa9 DHH 9i jJ>(1>B;%mogD ri\Bd}յ=RCB \WiЎ]DXMOE*ЉDK"pBZyIˀcٔԡ}N]5#ZΡ#OVV?YDa~HJ#造9GXbrǠ0Dиjbek6v֞v reV^G ^Lq~15DNB[{Gn  xPnK(ֶ6-\LÎVl+kT" >^\-8?Ҩ[y]< 5)_=&cvSlt9AىɦzG"Y r΅9ףAvεAϓnǙycUι"\Z. 8Fʩ;- V{bD|=OnVV)%/{I^00/p0 &@79ڳv{ah?4r-=!HȳgCb5 DN]gy;E)FN.Z %f勳ȩ_uDV.MýC`v)(enEcE}P`~*K#jem2ZݯH%oY{)D@(n RcV5Xí^Àl/ L%yI?kbe P>[gKW; `]σQzZ"PNfg6zgufFEyeMpVVօRZuWX[@'l[%܎J?!֏=B@/"ZƤc5`k ju -RH5Yf$wfTCO0~>t?!CJJ*ЋZ7k_}/ܹg ;tkS8xL_^l=; ס1Qڻ~2۵cJ}~Al| s_;~n 7)=@/p*lAѳssVX=musAܵb z˝sۣ͢.oι8v9#R}"|\h&ݥ\t4r(w (s,R&] ~[@'Xfo=+\ [%2aގDywEv⶧L*j>;ގֿ"8ڻ0nWf YޯX^{3,/ ן˲+T6h<o}vAvO_KcКr%sPw>zqYM);=&{l!G, YY'TazkrV%N>A3uNF;!`$Rڞ{[VO}.ס|u]/\XȕMԅٯdgV_ sƯAp+gSBПbZA`! 4$/ϴC pؙ(n#ʉmPmD 0rOU)QX"ڡ]T"~B_:DGf?ϱ߻`U `oK8&]ʹ > y]<?at> S;/A~{Sј kqc-Kuk˗e`m '^f _j#yE*)]˽_b"M%7[?]B[JJY9_i5@yQI9UU7cƇuFhCߕuг5݆ )a}< APAk24#t|J|'4s- q3Yιl;=;vrsݿ"ιF@,^E*bH%c7=ٲg!s{/[yK>-뛝O-~}C>H`Rc;9ӮD OkDS 4Eo܅fpaHK#Ƿ#g;9ǐ>(A7M5NF>6͖:ZO%3hmN-ZzсusϥaU ý훏n5Naظ_ssꇃ"%!cY;M}>| ˸x9=t}EcXY(+kڃ( sx s\=;NS9k < m]GK'~e൹c' Zzܯ}Z{ʆطjDKF0tv5EkR]K+BVZTB^g ȘfA*Jmy;4[7r4zc99בb&Ot)zy9L]3@M8 1oKru O6sov>O%ZwA]8L@B(6l,yivbو`T[#^=֬&{?9fl`d1;hF.v Vϴ{r++\m?H5䌛FUn|H"0zBu9D6 EJH5ػZT<4fXLֻ`ʃҶGMC u{ ףhvމ$ ügm8 9!JF9.@y`/ " ds=s?&6HEcS &}$GdFrD uGK4+r@}'Kf9tC x0>b6}@-APJYi4F2fZ='0& s2 M8CUvmt$ %nѽm)^ ފW\wvZ夻.xntwa2j4@oଷ:<`G ^ TֳvYl:];0v+B7)E@w*66?u#_o&,uoF[%z69翶3L~}O.usNgOywY! ܔv%?~ֲtukLP NSUι=ޙP԰q}Bl ܮoF}2۰mcr {ƯXcv#~9T"HGu#Rm !ģ(C3@`B.9ӵy-W"8dʉ)1T"~X7̻-ADt-GHo rChV=F ,@`d8kVpWsvYQ^~y?y ?۵EPrvZ2=h!q2omQ6^$~kWX2.kyH7/쬪-91X߿a+ K=B=7w! |.֯_b'kQ.\{q KYP8slp)9oxI p^ή2R؟ X]AY2O+ϪؘGΫ|f/񏆍{]Sky 1~!/땱t zL-mQ~mن̀mQ>eȡގ@^DśQR$ UG R.D%Er|!MYy4yV !sAJpyI?BvH]Bl 4kn4ZG~W k!J^hmWJ;˼CE9R>jlK(lOAvZ^ )G iA묽2sȾ@&LCjSljKD:N6 Ázm*߽~SV҃>ϨĂe,cmӊY5~ 0W #%rحsD?h]OQ<G3DܥSz90439AHpR"Fܽif|5Btz۽ c1b"]7H(rSDIawG6MSD 9%~wftBə>]J>XTb@BOETe}q'J? /%"T'6m)UǮ}*'P^o6h)v-K! ZCDVYLo** C:*ނp4F!o`u}D`<;sSMb"R+ߑzZ܁)7Khno'# 9%Vf2/EGJbS8/OB!P ~/4NDyl#Qm[od򒾏Z}rް:1Xv@JdhAPu߂ {R71 ʉ܀Bk snDӆh|np{5KjUE1z-n vp%?uS6kD<8:, b9Χ؟p5 pu-,oSUEC74Tƨ_b h%RXv pjVru-E*_X2۱*>(Wv9; H<.{ǽ/Ep =`|"hE8 Q `;9#Q}5pN>Z;!g6BL  z"9Qx)\}!$GjbUXC'%ODHPH!e!;vv{_u]c,MWs]\*ȼGb/"Ū֟]صYND +"gtR%RG6>9OͰk Yi}D3nAN|"+',,& `5_?r_C bFcսiʅ= Fb>I4I/M%XW"oo H/fj5@S/闦F?pܷX9u"=3tmpvX^`1w3<ʂosQؿayh~=la=Ϩͭg%No]RRLuel۳ mg2e XKK,,Gi(rz##iG 9vD0/鿂zwu!8y AK.rg |JqBN: u& "`c RŪ 9ݍ)!P8Ag-;IzkDkCC;RWZ#h;ЧCq8 vmNܷ^6`u/AzG!)B0kvF[1Aq#//8A[I f}SR/ F{Al:jpͳ?Z`OGVh vAc*frX2=\Jh!5pVY8ꇾGG%OFyvmh^^FJvk*%;G3a57cajML!zI^zW/(1.ʯnj-(翵o=ݡ1݇`PQJ%o=2[FCuQJCtnAL Ɵ{-E 2{[J-gll6 #C`99H}-BJV '=h7"h%wB]rF܏(@9Ҧ]} :9R65B= Wo9  r&b"|9UvoC=l+TiMs*.Xޭ[* ^_O HBBmsk#KhB_FPG܂ 9"ZޣhƬNJ/cgoV.;8 Rc A{+RB3Z6 ̡^cQ /oL6x ?6MG-T"Ko1$M~0gq Ug-{#ꗶA}FU  Ԏԡ9MB/eǢgұy}v%91]_^3 _o„5@:Th;X*9Hj ()YMkdYY44 Zç Rj#ubHHYj-Gͽ4= w&9 VhR@NG_H;!蛅{>R`޶4+IFr2TsǁR*IxI -DNFka+cwg))FPw3 PR6Nʽ%s"<)ZvgrQm bmzj!SD^?P`&k=2X6T\ Q(w{Iv/erW}A?Ůw78i-_T"EߏZ`X*%kIJG#_#G2(yn_?&dB9W?zV_cx'ZO197=OB/!@yyA0ܮq z;E= ;E/y(qJks7M{+K^+sLJ%~fV%ˑ" pܜ;Y"ZBs6AB4#*h]mG ȕHMX/p\+\-뼌q%|]8s:rVv~Bи7r૭YZY9E\`^m% 5EЪ{KkvD:8wFх($v%q 7"hOF!x);BE=ș,$ᄋrkvO"թ=`@ ȊT"~#82#%CțDIGa6nDPQ-@6#@ۈXWpV'Պhi(|<}gY;1w08f: n ٺew>{YWrw:17⭳=.q}wCD& yPQofl5\ɟs8`@9>@/ZAjA96B;ι#3gƣE| ;犂 X{8ιA+zA~|8 utks~McXGR Wk~{/obJėRߑ͞{ ,rRCܧ69Bc(,4) {![]cWPAAy t!RNAޓv?LACѾb_ G.h z+F}ێޝUvBRk1!չqYkBUW"E4bG pTV [Cη6b2_– ݩ-0tS4e;Aj'H xGW ~%v\?{!jdxu+"" m@&B6Yn~XԳ;1\;f#uqܣYRwZ[[?4B D0 AST"O* X_֠;'/N%os*j/OiĂ̵Ϫ{l{؁wkumj3H3uX<>9w-#5s ~J `z~"A9_A3ukdY֛ۇu7؏1%$B/9H&? MzIP>| ==/T!g=7i5>냠  PнhX=rg"Y - H\ԡ5v2\Ԉl^B`}9w3=h<.hM7%]{ruMn AY+;pH9TkH᫋t܏rʸ MeO>@*Ktu@ҷgx(Y/@jFec!;(ޙɞ$ "{X5ZR*uϺ[[תܗ(*ADD6}Os}S ₖ%R_^he(1S5@W" d X;?K"-|5X[>Ƒ,[[h}hDc,d}N}$`,'XgO'˷žG;My>7MEtu뗴r%dGcjuCSX"N2.6k.PTQUF54w"saX*F}LO\N@2 <@lL-FV;D 6)n4 ! )fߧp&C#+b"Hه-fC͹H ><Wg&̓ibiYhV1;8HeH ܭGlD hS1Tq*߂>Y6f`@I w#޼ҭmo"Re})P?5!%Rc\x֯|"5)IH삔ŘX:1&"EؒCKފs/gx6HYZb"V,""=R&a0 KP:171AJ5N0l\$:),㭌 ȹ i@j;c-?=?.}ۗ[U3 #4yVXA@ou~9䳲 zZ+\nṵt־^QL1as6G+v 0heXڳ;tև.==<~1k(F@d<)@,:ynk4Wwscsh G'[^Zyhukl&+#LKޚ{67EU;h}pW3w1Akd4//tscjd}AD/S-5X47QiPL8XÈx1,!%ի'z!%Ӄ6 wGosIdb7!?&.| 1˭!@,(EvnH)uFʯ Y^#e? ֎lFl F*kkXGK3Bf{َLuF2!))Ux_(:39 < IDATާ f62e1-iG^vZR\Ԉþ\̃V?k0bY?YƓ 2,4C o?<]js:&\ {G2K'8i%H_XYч/zM~)A,@YEUY;PA$;կ#Sцouyi2c^}<<"g[P5 ;?%?&pu2eKb{E操| RhF;ӑve(&xHd# lh1|ʠ_v'ѵ. s읥|)#badhمHA.iU6sֈNV}P)Yت[SLʺ/o#Z'ζc%b/ZY{}617X{ "Gm(R\~hMDTg cmAb q-U,& b`?@߷<Z'+ڡ1Zk+ìZ.O"@מ+Dc>ELFxmwF"#y% Xv%R&',>2uwZxGX:rpj,:/Ne ڶki ]˛Y{C><[$D+/lAs YϲECMՙM-hFsC9߭U&db?%R-^*+BH.F@bg>G  E:UYɈ!e!2Ė4 & OB jh?@cH)rؠ'dWK"F/y9,'z-be.A/Xg"]ؾ5l)|$wM*DFXvB`g[6}),k=ϺvF&bS_44!`p;bאּ=@βO[;tCqKw'+ǻи{Þ-*ݬ"*E4S"p jA`X|꬏γtz-Gh9I [d#; ce{c-!+xwGFd:[d!w$pw+Cu}<߇P#kvoUu!yKR' 畗VnQ`l([؆$HҘG{gaw-oLw r>#pqb`CHy ?B'.G,ZsxX"CtpwIWC9KvN4Dg#htGJ" Oƣ%R# 1%_̥VW c!2 )HAgZ,wYlKd }=DE赱D;JAGү>z |\aODp3`]p >ke[@LeR8!iOVvͳ6[} ؿre:Xw_]w>!GH~iYX`b2Kسxͭ{W 6֗;2wR7d1&؀:" s:l5.,,*PKFVi?̌ӥx¼eV>]^YJ奕Ue@{1nS-sK:!;d8bYD;ډ%R#=ˆ5XGE""e4E1hk[nW$%RKΉfD꘷l̽?UR"3e[\g!e7)QpKF[9C ,SH!CL1GX^"UE?D]aٹhmm2(mKAF +JǧF rĀGJte{)gjrT[NY gD`t%²c1겧e}̣a@"`*" _̈uLCʐ^WCh'Ĵ݃etiLV3 +g~ ־X1z77h mx&wHkdR!kCb?sQ&4αp#7; |XmWLj-BRdh/CQ +Cv=1YوI 0G5b,{#vկ 7pM}-lr" Z0q7$ZynFs`bZHƣ/%R9xG؜}2>Bx I,:͉[,RQU_Y ڱpr1joӱK+HZ'^ ReKbsr".5SC}L 5 P{`x2m%R""gS .UvwGRf0+2K-FJlAs)w}b\+-H`֪`T!"X4] :s{}ɅK7`:0f|{<qM5:GmހXQh]|1Yw"E2㶳<>B{)660ճֶK5Dd| Cc!Tg#vlg!PT ?B[@^|]} Ϣ12?ގƓVAAL] i9?gݓ[ieZ@[  d15od$P\xًSEUY>:pghwVƼ& }d<1*ʊ#3׬S[н<kh..K+gJ#?1ؿr:[[`2?1< lѦd<: Hw4RF7EX"U h',^R,r'f>!C@LdҪE Z;Ο') a6™" 2gnG8vGf Vsd.gQH ř !|/Ea^~MeVֆ+doq)ZQne_r$4*V^ȗj@zpGwl"63 2ӖR:mkeps|@rK?2ΰ@e]}I1p\Bzd:Fkٸ/e.FYZ%1CʾH9/"0˒-:ha[*4=Ske=)A)푂--1{1)FoX1NQ@K o b뮴 dL=>5dt@wو^ %r!@VYwE` :t.W/< i7 MfTZkוjC."|<}) 2#αkyV=F+0bk=}/^eN.EpQ-nǪ3$S79B@7ZnoeZ')hKp}Q m>ڠ E,KT, RGbԢd<:ߏӫľx_\{c3|*j@yN9Dt;th\^hlPqџ+eFs3#r8Z](})&4^ XyYfX> $m{Ȥ@,2wߛz̆az7m.o|[@?mDfѸ:N}cخ֖N(.?KVh]^~4s'o:͓C*YcTTCGT|_~ҙ{3}>_e_#v@e2]mޏv@,E{u:w9ʍ[놢1N.E w'gfԅ<%Ԝצ[-sh.k> 06" fNAs w$VTEtNxx Z3vB|Zk&;WTan:B~سdV}SdZ5"Lc!?L݄@"v>PcPG?%R=ߧ-nP`8V"α>Y4F#*ߟ &p{Hk[w|Koyc9~v6ZL{Xjv> 09ϙ=뀌pRp>wZ@Z K ͏a4K=$_o,>w~n&ߕAY(ĎF<&4AT5{UeV>הd<: Q*6춮4-9_~껐8|UQUzyiʦz@=4NݦiD~hS0'Qڡ}ZZG<ϻK 6OΞ 5d*޴CkZS~%[[hK,L~S߱D d)g ̋!B3r0)@X6dX{8K#ժ2xLp/ĆЩg2A0Rg#{6m+6=7h&H4۠+DJɝv ?,Xfdzt?V]?40}c2R}!@+N<+ӥֶ0X"bA}X_2u@qچ"FlǦjH^sG| NlfY;ho[ؗ_!vI yhCfv˷Q{ogvN "A܅ADlXMBbA&4 ,jPS:#͟#ba4/| O!nK@xX"XGRߘ@8|XwE 0hCcEUʵ||Ͽq⃫k:'~U{ފ jvc>Ǯ*xe EK/|]}۟w2)/\]QUTT=Y^Z9YiF`LyioV٢)7`BƮY! 2\? ryG$};=Áoehؘom6_gR1Y=/w Rf#3 ѢKSi}Z7Rpmx uU}FFL17"vGh] 3)Į K,HuG y$R!|"|XFʺ;"\LPE @ a0ڦDabY,D@2}ь4!)Qu$:mYc=tiVcP+6)DLbz! '/ 5Vd<΂$t~ 3|慈 vi6RrìE,V-47[Uji~he*Alb4V ,?Ai@>lYW"w0nAcBvFj}g}WkO4_!w (+K ֔GG뀹Dj}.~U0nns<|_ 'we&ǝ- @lP ݾHYhK:y~<6}8c#]ݹ}t+<1'3~aV\QU1Y(A[G.7B;oA?Ϸњ!1aF5.-Gn,@l#Rv:"5xtI,rP3pTa-A6w'"ź#R CR IDAT1?㐏hj{ ,`Dm2I`yBl#Fp=NҭA#dlAi @b/BleH {>pV! CWks??Ӟhw*tB)jpk߭A Ly2gx9_J-Rsc`^Gl]^?UoXamd 'NgCsdYՐNB!B95={{c">+N€'9ՂX6/)Z2_)Q-:˛ 4|L8vDNr7R2{!2)]Ў# ಓ=7)&!l(!> {1ځ#VrvHҟb_eEIZ0g#r{,Z>"~_h DA#8PHoG7 r2=5#I81TS/1H1:SMvg@F ({fdZf# }=b3 w \>rw]9NF#f} V `ֶ3,~.kqb~@GV/'3]ИMX.@ _plχ,h|[ 0Wmz+hͷ>rjglKNk; dHiˈb&2(rG R6yd/Jpj_@Kw$4ٳM4: :[H vBa AJ|rwԵAo"3C"VN(ޭU@δ4F--g~h6sK;Tqb_#?g}>ѸXE̷YVrKg .>/qƢRk~1F"c 2iu8ͷbD kA6#?i`X" %/t,ڮDJEՈ(AIh9}/i+Wvvm:лKJX6hSkO?zEC0"TҞL7KkۇZ[ُck!gA`[i2}TC#UP/E,aFE w[f@j4"3lOKk̫!dzJt(zf[2w2^X4#SW"`)A(xzVֿ}ի z5!s!z4eg~#0wC㹏<&$g"_Nh<9xYN%R;[ . rz+0ژqb d^Nֺ<銪cK+|K 3%dDN0x?wsp/p (8奕k6 ߮ Gc ³lTzyڣ~60a[ߛ(\m*j[V_P;7\d͔CH~voaĞLAL Tk)F$LgU*@'ۺ;"pq6R#_(莱r"1 =#P0@LC "F!żA`n\̀X 總?Z9ܩqhNWYEhގֿ/$/إdwhs:ҧnNg~X4ۻ6 Iƣ-ڊkzzm9(,ʟ{ڣ, s\ϒێ*o on}pSfdH 8pq[elH~0 K#0%RwB1GJT=b(^AfpqJtgcQE [3&hwz/br-jGe$ - 9-g={{b(~h\,Vvd- 2 wRl9R<"w1}S[|k砝P[uiopW$1.AQ:<7>yhd @l5$x]e 9)dt~V&#`׌gmFZbGZa}>Z?ui&bIC콐iἮM>i <[}Olz2;h}"]?imYG͛@i6/Y[0+h쬶GKz[e1Hƣo޽xtD|%.V>`Y6}4ڣ%|k@D~wn-XW1}[;Ma>\{ͼ3>5r\t/F,Gxo[4/泉vfcyik6}ϛdukfe-XrXK>"di+n}IYY[7d.I75t>ʴU|!12g NW8\&wu!ZD\mSj4sծ)IwqPUtYжCSt 0100Mp /n߉qG +;'7]K1R..v!P[oe]mB@;aׄн(v(B @ 0bl-anw1>Aʫ'cwBfLBM6`;'#Fb/>C. ] _~ǘRW;3ĬdREkDJݏ]pH@R|O@fY!c%$A'B`o5MG`:I#`A}mNt _] y@A@f|EX)ˮt16EZ?~:`;j; -шEYL@ }y0IlPߙAf9[8@ÈF`]1 Oƣ DD`@Ƕ3k]WnbŎBarZvէ[fo!0}+Hu<KŹ[Ggkʴ:&M^!ñ3K3NM!%.D/Bcxkm;5-W[GXK w4g|u? KyienFx0/ 'B˕#悇e&PYߣGx^KyKYYT]WeU~F,olnN*xjǙШKoZ+GP{Z0n~ێhm+&WT FQdQGl[_%Bχ#-2<]kyvȜҪzWl}}tł>ellDZDL )ФC^jc"EW1#7Rl +]v˾1!qaׄkg^^C}E">0Y;Xmd ) .b,OGZR>}%br.B NAp(aSom[kmgm; p+[DĞ̰wF v@ICq+؝̘#݂lkxp32/@ 7KϙoN%X"u pqu{oӣAJ>NHy! gggjv -ׄ2|[b04>oBjgPXABd|N<@l9hZ|Z]m æ fhQ) t,ⱖo6!gsKmf.SViuG/+ovBC>SY۬]xA9Ě1'L=)xt @,:1ݒY5nX@\k-"D>_ pG`ϺősG߫~PêEc6 Wn=]p[l?dpED"vcgYYx93971[۟tRkݰ ۣ񝲴B.>jk'31s{?^W<(/geۚkffcϺ#_=`dމ篂w$*]i~޹p²]h`tęSJ;7j͒;|UeiIM@榮m2k;vʉ h}Ȝ.M/Y~ę\v/#G#*_MZ_5a jq9AhY6d{z7̮'ͿK^7xWf|!_CKDH'-%wE)` uoڳ9$)y|o{v*W#E\Cc-B`F'v=|MA8|KX,R:ĺ~j Lh4}yUHI Fv|~+M"Dp ]j<,vt_˻dΙiu#pau_gea}t bD- R+.cv>a X"&+*\@ 5gX-|X.wYdM\hZ׺SV :c6-'3G keΤ7ֶ } h;:s~' o7axInD@ւ/'a#Hn-Wl;q!  15 :ruyiyоrb@dR]CKg|5]~2lECnv+Y=6 -ԟ.X-ElRK+S?/;wgԌ q"_xamrWVw7!P߄̞CF~K|YzEQ&0"p /|k9Z,ţOHgCu]WR^hZo:[, GbRG w j'!vl.RNo ڡBaZm o꽇 o ;xACYO=?,!Ҏ)sӗg1nr?G_v6IƣMDʁ*Af|#w/;@cG>c6V.3ܖm)Ħot-~f {?gA;'7g+E- Xw2ӿjk}\ƴaWĠ>6DC Y}C;gKc%xMX"0 _1[k0I> g|צڥKz5ǀ)奕z"Syi2OfF2Wtק:-8-ԵjHG*ڞֳ4O/+l+~Ѓxt;, 0*6Kg,鼯mh:4A<}Н#&gV@b,Xg,jNM\KcT-,o#%0)eΈr펔xxB~1cOc1B⿣#69|?-!FG`yhg5/b4R~ x&H=g9RQp"Z:[^#y}V @K'@,Q#RaĮ#3)H1:V,å .$BksomSYzz .h7 -`{n]k h01Edt(|{9@cf I,: 9W#p:R8g5kUewDct:.czO?|\umǝ[H=Ǝ[,(uA|"avB&}3ba}س{҅ kD-apx IDATvD'c<nD*Rh%RZZs0_d>|y}dm㽆u9h~K+_U>Ӓ&o^143ȳXg[; ι3={979F ]R67RTsn7>rewy2Py(h?[wO( on(tE2%R-Є*B;K6H7 FP fRPZ'!_; )")3)ю)&_a+L,vtŽg!8v1RXg8ZXR!h2MqڳbG il~'ۡ VVg97y=h?AL vLB h W8DKήw$7 os)3D@guS̴׹UB E jK%AR^BܝGX"OW1D1&h3܈b??Nm+VcO?}V1KXb}?ud6>ƥ-_XVϗRV%X"Eϣ6@~&SoX{q=ڜcm_~Hc}>^R^ZYTz>{?ZS5]F X{_W^ZY{뿹.Sq֓?"_, qƆn/dpsޫBz9A y^s.k}总< ~,@,bT'Kƣifu, >GA\A `Z?B'Aח-"A9]!ſ1(Y{?MOK[ӇHꇂ9C ;)|Bء|cw+hW2jW(' Pcmg2GwSCpb М;x Hԕ4"3 o=~#ȡ9v4w~@B뿶i%<끀Otʵ%RXoey_"|V9bTkL6q[z p:ˣ~G>i$N[Z~+67:,'q뵏.>Tp{vuMhmL~ 7fMHE۵WUӥ lF;/*Bi~h#~ǣb}l.[ʩk i+S6EEH.ED?@K#~\Ӂ@W &[~j؃lR<^ ߤX ȶ%Vzd2<_9LEJ7`?e1Y_OMDj^k>E>TT*A #R GC(GVE!SXLg!)ZCтRcm ^^gcL2%j@nM=B~hCgd[4X A tn z=ACXXѼyB GlT?4}sT.r(NPng}4Y>l>V"V8eݟ{ 3Y_wA"8?`}< 㝝G~ sIyioK"~=GܫI2_,܅l@,HIAbA@(jʄCM>sdEI#pO&奕/mg\d/>ZCuVߑ-=U}(NihB (X'߼b6-E%+O[ ઑ[@HiEǞߑY#xR \d?~V|L9 S{8YFlFA<6h᭳gX []\]_+zGvzHسG!dMƣX"՞ pn>a|C+2(@f@ ẊqD`C,`2]KEb^DL0i觾LQlt @4 &МYADWww vG3"#v=c;]Sle"лc@|AsD,v¦:29EV{~ZkokF`Y2]K 67cX"5y0HBL `6#Dp;S'}ܾá?;G݊Ϛ_z|?!%hCW.@)@ً6Siʦ2{Ϗi]pz곳P9R-.Fqeol*R~KaH\@4H B}ޅحclL|)yI81Y  E2/J/C 1p"؈|@"{b9btB`Td|!߁}fɶ)Y`` #ӞS&X914lQA"@ಅƺKɮhB}= ݁ruqؼeY]9'Lʿ?0%l r)RDw,t\-R6Ayleiזd}b~Nm},л8;懟a}pd,*AK:+h-MŅ+ygum-WھhYu/9R奕'THyOC!/RC-4)[*[eUbH u^|>kXi;Z2A" #%'uHZ#@ @N"hRbҮ#"c#7|~vHy D@ p+FDQ @.?z6RdN2gyA2lV"lӯDžh~ ֓X"'Ft0RX"1o%*e=sn>OgO3d{斊tY1X^Zm_.Ɯ}?X[~Y筲UJ %R!m2]Kz"E9]#SgH!t.LȜ)|jtDJa  im,A0MH<ӑ9(PKPXİ)] e+T~̉ :>]wR=N'ѷ,YtI?sgsp,ꑌGW!#H"2:2H<] aXb/^ R~A δk󬿷%`HƣWQbm/wB^-r8|CiPV 9lѼ8m>sȞVgN{"nN@TU@'ACYxyʿ>HA/Asp+xVV˵BZ䟙6C౅0'&ظ|NXCB~{Lƣ?)1:Ňq`C3Ff*?sa_.uhi|Κ"2 ,GlU/[Bd_Ot5!NƣS_?b!|Zk*`f eQ)TRh bLRGJk*ꓰ~1!]o)5H Aʣ*.@ lB vjU/X}zXD Cl˅1_7Y;wCsX"}"&L5$x4|G@)3R@֞ǍBa~: %~ڊo"}h" %b4W#MD`m} 5pZ,[l)1ѱJ3^xI]c+9$xS"o(W!V4BywxW_@ƹ6}YC`~:+\;d<:uxF+Y^L#S~DSk5?CX?Sݬ_oDT4X@J;L?e6.עw%&[,Νė2~qEwJE,{łv#V/k5}99p  {кs%HG<惕G\[.I\ds,\(CD'߱-#A폐G{us;Ro~RsAl!t>2ϵGP$),\h"H6؜Cjb_<2:_!p=RG#8 Gbb:cG-ZEЯGLo`U R{ )0[~"}HWtA Wi5D2~fC`8/IC#R#z*{n=2 "+EX[k;W8wOEKX"dgEm"^NpwT 7 ~̴Dj_ _l!<-#26:h!KMEFjd^K; rw>;3?aG4ue2K VJ+XH=G؎D wѯ-т1*E<K@~_S-xh r^o#05KCk}l#`0خB^!0TMgAʥB`I2%RwX'VPנ 2eks?k`w2bMG"gXAh8O 8 6R4*{1j_b}'YoeXkB~nu.X [-]gVscAx#L*;tpb ~ z떇;kۮbR.K+!pH[+9GstޏKMfN}4As}@.سC4g.b拾:6`Cl :r# {#d8ob"?3_՘/ֆ|>욃͜z#ź톯@!d)ֈ)^<mR fY?E%\$kO ſΞ}>"; 2d)@ trP/2&p>H~NfBL3Y{5`2SRO"A@!09~&d<:-HS'[?7C_s#4H)Ds;؍A+bY;Y>F`γg|,=]au/)aIxw#\yVMʞ[4~=wݞh}@cb@5b.Es X~Dt"]?]j⪑_?r);h7{ֈ;L7MVqg?s>f"o.x_DjZ nW5>/ְ6zlbtYĀU^Z9"] 'Q.; kaWkvne?P&@'q3W1z ]{EqkqmM=\y~ιW7 8s+ECaGsOɞ}c>8Cx؅"wwHavϩZڽԮp!2β|<|W7Kr 2?xt(ہ]b HuCq&[i/Ggk}2b}17b4ODs`h5R,x݄@\|G 6Aȿ!`GX"AsЮ0@ ^Z[5)y/]mInvLȞ͍%REhA;׬L NpXIĄ܏z`~,t.U}B,e}Mj)z2s ?BDd<:ur~=J1,tH-dkc[kvhxMYE찫dR.|K+7ٙtp4Fk՟z:k=VBTʖ)yι,ZR&AͣfsnGVt'ιy8A= V8ۖrҝ-SyUι3 (<9WV~Fgd<ڀAI2]$Z@(qS^H6 'ؗ dīB1]Ymj@+B{#`Z]{"7;)m0ZmϚخ|@ u"C\G9JھD`b8u"Pu]+k_|K@]jL֮ztje[K@O5!s@|GĊK dq#H 2鞆2@LjYd{ ~NT!l}?9_@@A3`m+buXAhY[mk@283 {.>jH~.~>ؖ ]V߶·%[q(y X+O_ hoIVkQX9~+dh>ggCbT8.Br z_І)E"뜧"o'V~ 54fM_[תhEuV< i *y1?x"]ubR Z.[.;"]wS+/lPZOG tk_JV &k|ʲ ұhg#ps108HFuìцaq7D44 hNOC~>3? '= |b&W_A9O}\4As9xGh} ̲~ÓوXvbX"2MԆ"d$怾"]v pa5\ռVK; 뷵/~d&hWN*/$U^Z9O,XmNmfE8"U pΕumgV:r=k4s~h9wˡ"p8psn;ٖ—H<7t| g lEh2܅LI]FoK-vZ#֫ Ю)Ai},bYV#Y!~)S|g^G3qvG@RM"% vSVXxd<:1sG%WD2΍C%N[v;ytp-B+a"@8 )v Z@g@So146=ɩ->[t#-\z1C;sr/W6FV7?b*Bcfԃ "}?d= b&!Gϲf!x=5( Fw"Ѻ (@VAU97o>dSV?#xt^2]/E/CU(L-NVC7k8c=rE>gP_^z#1&HXR."]MsE,"]-t]yK}%cel1R+q΅k;sh=}߾.-:9mq<{ ~~]NŞBn/ߩ_l1@,Jƣ9~,YΦb>2W Cd2}6ow,C}br ^lB$)ш(YF ۡŴ1WuV泈AvRئ:4!CLPknB`/ pb=>Nƣ(:_2} x/H]j9D̚W&L} a8>LOX]`y%m~Bh@g㒍|jNN!po{2Y'(МGG#gc_)N>8%R}𡵱0$ xF|͵<6;#p~+?Q~ u1K-@,b'H!83ۭ"?/ێ3m|>'Ha$50'[tKܔ?"]+bSO+L.v>_x+-}p@%U%EB44$o5n"]֢M^wHY7*4PsX"bX"cmlVtd_"r#<g_OE;qzmܧ7c^qM@E%ιqh}}97ʸ,k/I6{I,rUiS>F"ۘk¾ -TrS')?}QW!rPFAd{ -ӑBG q"y2 Y"SHqr c1gia_{C M KALX" PG碗b@Bbd<ڈ*&6!61%'cTK'{"֧1K8O!;@i& SαtGp/K5<]6y!و4Tt}77s?/[rbd" ~KEJd<گ}=HGPom/gbN=ޅH7_X[ti̶CwV'/;0pp2]KvG7!E:!%ya6k ydBap":=){hY?ʍS+*Wې{)%HL@J1V7"@{c-)'j`lmelc2ɞ&s>!SXi$R7OŻ3N@աLClR'fGy[DuAh~|bm8#v3~" zcSM墹+A"߼깷3xڞ=14(5dod< D*e{SVMVP\?[Q.Y{ú&I?T\~T^Zy˦{֧+Sa Rի/jdy!/he]%;{zw{?|id`UQ9w/\޿qW 9K\V3ʎS~zxN `m2b``rE4}g ^C[؂10ĥ("f߿@fj{~M2}2HfKD"P@|PE O1"fL+7!߫^6 ZShhF'|i섘(lke?B 9Sey PS` h>xk5b4!fcb %ѷQ\)ݑ^Nފ*Th}@Pq_ Z뷾V_9ڬHle\y *_Zݻ#Fhz}KDɏ֍ cLwC+b'C)|4g" #k2A'kнoͲ6"{kCgBݝ kht4WnGscZ yl5BBTfRBoX|L?ٱOkjS۠w ԗ?,Ct}XJz^SUY[բi^~NwsbT]/Ivi;m]C~mڵZm^o/H]Z^Z94`w<΋3q菞3HuYJSWWr$~e ʦ Ēh }-&PH1y,HM_"ꆔ: )l-) xb= .~ ` ݍvG"p?cP}*R|9i#ȏvaNy\ 8E]bzM,[TK,=6,|Vx4H}GLƣX" ?:)[kB ;V;{y.LsFkgkxy8 ZI4rllD@{KnCxfXvE _@`h 5KFx#%n;Jb[_#Sfg&Lgcu- @)ߛ^MVvԮ/Cf&{4>jγw z4lOנrȷéYY#-ڡ^<(gdu?TOvlxxx՟r6۔BJ:]˂e==,k? rca6~)/HL>h>g]WNjumyfyuqsP&6'x"]yiZ%l3 7f"huD>Ȋt;奕~Ɠow(eECV P)kbEsF.6/*[g-YCK.%RS섞Bl]W4^D$C!b;МήK/C_b8go w LYGt Ϯ_c=X(GY;Z ey)ȿ)0ekHޭCExd<:j@mS8gm@]Iƣk$zu~/sמԔus,B.5wg*e7VNE,bX+)/\69ހcڈ=ǏO<ҭdмԢjkE,\.A蜉hk?}i,r4LqKVk;bb'B]2]]?fVjްU/^h FWw4tH/D h'"TKJѢRpЋFNh+WDه IG p?-H@Hqvo.Hv Z;~?k?K| 2dBԺ)!A`.H]ܬ/@,{k>w{ZC)YĶ#0F\Zezvh:݂|!SpP о {m:T_48o{ lvd{s`-E,Eb4#aRn7Y:|hms[ysh$K|cX=_[;gm G?%v$!w?/ߏDa[˵ͻ~!uUr6PC+@s IrݺU5лBM .ׂN[V^H"]Kܲ5T.kGY \27]_j )/+/K+'#huDkP?C6 ѼzˮMm>鲣&p}H!kMmwrC"]~]b}|+`ޮrIU#4K~e_59__47] \a 3} nw\v}h{mrz0?L/i3hgdB\@ɝHET> cZ!_m b5;#V(}x1%h|;J\D?-0ꀀV׭~~05H vGbծ]v3#}Xidf8EX*d<8H@uD@q+>G8GE .9%G tXN.[{O ̢!9&p?"Xg9 8X_GdZ'&57KmU jmǝ;3;f9{ zu_֧rCo{Cft{m?tG@yڥ|֯8_?,L4|WhMLMhZcH]+/*eahzWfodg^)/>|D~7@5qi'fr EF%;&o?V6X"6!\3; )Q>u׻>'ch} )P?ܽhw81Ǡ#sf2M]{ C' B 0@[ZEaHj8)z#S==;{}H׾ HA hg1S75"Fo~q:_XЂ; gN8cc?$VӀ:)pX!vϱqYtGʫ +rPP s_'r9j+s\n׷Dfi6N?aw+c>,B>}6?d<:Y EL6C4zs!`B@d<[,1O9FjhS }m?yhqs1@Gوly "-*22\{0-@bm7wM%@Em=;yar[|޺^L$UMK6t'?)irC{C8ZihymyiͰEK+WCW# {Lՠޏޫ|4U5xH>]?^xr:gv^}ںX"UYj  {Vf-Co)N8yM?)(%Rˬmc3@lK^Gg(|1#0eL#]X"/:.CA~J!?;һ77e:";;}â}SZZ0+hncmbi l@cs J^].&Cv,@hT^ZH]Y޲`YvIqT-jNޣ9tjgZ/̭jGIm~N46E#Agq+v,(k4\qS\!@llX6+KƣӁcumE ]K"&7o,BlǻxDd g[VvD@X{8z#jgɰG 捈1)[xSC[e 8[cPKjXeuNZݞ@L,?}[7IV|3=.3 =88G惐Y6@j+iA'mz#hw%9 t{[ m(B(烒d< IDATZK@ @_듌;#"0^̉y[2wcc'nDI"`F 9֏؁7 {Bi,{8G9= | [[_,x]qu#Y= <\mag C DAӐ='hޞf}t,>N JEs;ܝ蒛ÄыB_tK?cO(/M/+L urq2K+3V^xLNk*kU:{:4B-\}ack:֮\y9UZ9zD<_55;f2S5ꥭ}빿tqDTZ>Z`}FO_?ٟhBkݙM0}fBk) '#W$y9ڣu]f 7ThL<]fĀoAJd%^clX䧲@ #4%GQE(CO|R/!%MbXEDX"u%0n.3'D򈄲%HųF Neh q_"pq?bNCuV + o$0LE_To嵱DL{?~ 9Gcp{x,6J0ʹ:MG[@oZ?[bԭ'3sm|z]l 2$ _ߢ!s-*:kYM'`בֶ_^;82ex},/Yx##]_ca-G,uUc V/s5{,4oȌo7ڳZ#YLC`d4F%>LHATgr[O+fvS$~^[ܝJ}:}<^R^ZTf =gߟxҀk=t\.[Y^Zy aHhiXwW/ ]#o@Lqs[Qju;oƦ#k[[Fx79wnuv/bE@40r>E~N!LD@?i۞Ծbm͇; eֶl ClTw\Qg-醌h~fU ֿEX;ʮ} ;a瘔js$Nc-;Erc1)&>g> #v'M`E/#)b8W и=pZ꫃g-kfh#J(;7{ Q0K!l@XH=#ڷNH1"pu?BHolwdc4{#z5?itaWqVȴ{ b7 хX 1͡<P#u"$6|}W3[oGMl~c奕k.wxU?dIBT*AѱFwUײ]{ XwAP"E :y}c~4]yShs71S뇘w Jz"3-8Yh61 HhY;E{λP6@ALgW4@`,)?$"?Ոʴ Hl2Jrq.Zi݇[-Cͅ m Gd6syŶc"bc?\iy,ۡ<~xGwh>LEBgC / fp4[0鏻wo7&/{q*Go=iw/..lC7ri} 8yTl޻{ir,zg}b4@fnEyfn 6da <^_YSaa~/P\ *K7t~b>?h,-Z/Eu8a[J8N7S|@k S_͊뺥wgZK8WhiiuvVq9Y=8g3$cAseT$ GH۔0dZ.9X#q9I|dvA)l,Ec"SR$K A-]=t.aW-C&7˰~Z 3L0 ?v<[סXnE a%-.%HLH-סEqok[O2~qb=.@p4~Ybu\XWRd[lU"P=Sb]){Ђ[@hyOߥVnlXF}b%3 x#`kN9u uW > ІxE+w[|ƵzbZ$g̈́yի_v={gZ>ϱ:N6h} KHcg4_t-3)4:w `6꣸hSfD8ozg^H8Ϩ <H ~ ͡ ݋ >-ԗHha8w3g+{wQIAb (%r5-ւ0x&ohnj=zZ&rA* ;h&u5ӘNr]w!o4n6o;.l|ئ2\ 1uAƟAiڧܸNn*5oR"ŵ-2 Nx|^\X$4ۜ= bJ7~1i($̴B}$u}LYtԟCh(EJ. _)h]̑m0ku؀XkdE;ہx2zf!D[RѩiLz;GfϥH Ŀ:d62Y=R>AV"1/ì/i|8|]^ X[s8Z*]VQD.4EB묬ße5RK:u6B QR^\vk2ߚ>S#Zk;aS6Iiyevy'#X$i8ͭZ"=vqoR(^ ݄}1pq5XzSy,@1@p41Ca@, UUNuzfS6~շg3UEBwգnShM|Ϲ9y(%XSTrszh~3mNBIMw uJxQE%c=@ߤ@CᛗY?քJ zM)V1~6E5,k1/2tSPE&W,1W]@` CfHXK)]1]t*v=@E]S%j f4 JONCËR+|s 5 4"3b*Nr^^ae8F& Wزkb:vh7r.rzE 3w!y [h -IH9ϰ|lDsSh'9xqfYt@JIGC2DD'x1B*z~3ǿgj8oam 2 &R4ʬցG(N>mi@,鷱$smjf..n GBLۈrwϰҬ,]gĿ&)Yk`uH~@Y;,h#1ljRTR(//fSZdkPlCϴOxځӀb&Mɔ˻?ihcVbЮ?*3Wyii֣~@A14Wzyi*sUdnLNt(/{uP]TR2/@6Y)VN,dڹQ}@lvS5O_LPg[HI>ſ%QbN_iO@y qQI5E%]STRСbm^_6E_|a{ܗhmN>FW3ќ0xMvʝk҇WffLnđ7.R蟔lOZcPT{3Z^x{kI4׏Eqy..^5`EYeDzxG;xeWl6뚊ޕ?nYeG_ "@^Gi[|ҍD\̉|y#h,bGZ 2:u)4sȹ;1T'-tR6 7)LX$t7&߂REBwcB0R]omЩh{-Ymm{t)% "LSm;HaAtR!Hُ@ ) ZBZXS4{I˱~m>NV!3^olwfG[Qvq?ac\[k#{QLC'R( =) kp4>Xnci:4mDLdL$ث d^?mDNFn5 ͋lkKS ch.1Xۼp#}2~cP5E`{:D4) R`lQI(1]E% 4λ3+sTdPn%h{`0cIuS϶]޸>*|o Ƣ|K4̮WԖO䕽?(/^?!t@|j J]Yܥ7ϙ᰼뀗J ۀw"r!b_1ԣ1@{j>?yw~ЁȤx9 ]϶6x祱HLcYG`&ApF݀;? ͙N ްwK@`0!:Yd6A?g_. گ/!o0`R 3&+]^MږAZ'\ꢒ^h<6 |M~L;e.GNSUf\c}L 9mq݇hKC^"n4ϕ"+1p41-OSbvMG b,^DnCK)b.>:d;̻c+?K h |:ʸĖwc ܀Lm-ݯ |F&Y+9x|8z)0R,B IDATQo@WVfĴu G!珈MwSlwBޘX$/|"Ez1tC f_{!zũ]e=߬*hg}tVМb큘NVI>]X /hح M r'"y;N|e*@]ޙ7z82.|ZN..2k7ϛu2/C˞0=uCF֘s٦G--<a)ue%ƶ<' eO=G~v7Uo%qh,__Wڅ䤺k6fDgK"bꯣ#ޕf  u):+#d':IZY75 ]Փ23hūH>;!*ZDN=R߫p6R^ӡh7W'?|)o"%ꘂηHy ;!6fջ1]; YX9>@ @?`D0Ț 0=A @f"ڢŷ>D2Dm,vF#vhw?hFȇ X#7"V&sz|ii kmmmo?hzA|FӢ_ZS[;h<jW#柬fX,vpOա") k}Z81FcX1?1VF}m`LB js<:!=k/Ȇ-F'X0:cFfOjoIѸ3[2e"6ZzVp.UY$%5p9wmX$Tym_U߮ߊڍi?*H60c$E+:g)_/VYW?|yz\:{s>뱻$n)*)89HqG^jթ n;d`Oihw7/Vڦqxuݓ ]8]׽俍uuI 7eb>tEBMw_k|^Zry).FbOS;?R1O#VbRsS@Jk$-@))qch*J:(3w ?1K ) ~ܬwRnSFF1+At2 6?octx^졓-nOoY;@R1_bn:!py߀C+nB,n佄Lx{9@",$y!#FLZn'0_CqA=Y;h`._ fkXtc,;h:nE|od@sү:a>$/XY He'vwG[V,ABor0`RP_f*}Sڭ[["xUhX$-?p7gԦOuq kEirVHO-O۾tkR)%_}oK2G/4O6ev36$mlei)%ܤ f-Cs(|1+Ek˻vgE%:$q]ֵ݈ޗ뾉 ]A?9$뺇gŗ!%'"M'j R^")h5 G㧣ȏ+b'Š);0G"߯hj$ƹ@ߊɱS2?z,Č\IA`d<>]*b<E`.+xjbкp4~bs) wG+VVUVVm0Kp@J#p*^A@+\hX$4FEϕ+娟F 0͹~ƅ(nc0ぱ, VTRy03x&Zb>G;s%qܜڬ,Of"HL-'pʒ܇7ev+*) hS_vӏ**)HU59w$Wk8-e<3jh ˀ⺢S뻠 Rx_TRh>[_$?,o8GGK]=qviϝ= 8w[\}Ѯ zm\)uc+bHludGbc.x&{-ȭP8Ɨ. G##R &G/:h/8r49 V#؏9X.~D}Zh\𑁰Vj#)h %9"?_\k"E3N%x4Cmii:\]̻Op?t{cNB }M^iWjd6);l\VE9ƧHAw1`G@rı'd=XfҲŊ\p4f]6"f TV[{F=E佬n¦ j~]+EhεG=Ch_ɋ:G >^@kw@#>̲L}h#Q//$M-М(C>xu6M?q34SC_RTRP dfV޾ג"Ǐ5ܱ?Yx:Tk6T_OJ兓 eV:nR;(.*)x7^J 7=Ia~p40Ezi}0xo}tQIhNbϕq3\ֆf:qChOa@>q]hqcbNq]sqEp7qb6mVukI|^q]96Ķ-<~ b4d\XiHyo#xgZDxF#p!s: ew?«g[{g+RvE!g2nr*/Yhـ|w#?EtiF w`z\eCtz`jo3D*tqbd:_=|cmˌqܱIյ-:WllYj}hG[}mKuS]]>z~/{"c[9Qj8^|%_X!< ͹4~w:pY5A3H_`Gle EsAĬseֆmn? Lq|VKQIAUa~m${֮̾:\ץGIIuΐz}ژ4.O8ʔ6==|P8"! &L]4uƱ (hmv^U.bK콀n=,*)HB{C1EBh/tO:)[_\iVm!f|O),Nw;ZBґȇu2qrz?+m^6 q>چ]8w ɹY";r3ڱC_4b@ʮ:K" >OE ݅rE n; ?h@BC)EΩ Stf Z s>~cER=)X$4 BsOChXHo24mQ89V!x~a Vk~]YB/y0A&2Y> ؾnc;t{>?K8qeu9l4Pt̀92zSɱHh}7~st]YwUKfSuw @V8`|ֵR4G ;?|:> +W$?B{QIA2ޚ̷d[o. :I}#Hx&{л q]N,Kl=Kk7 d+`[O}׃'↓/n٦zy_vJKDUMo{`R@Dk׻GoO1 M(/^p,o"woq|n9mr~p4>G=3A8?)c$6h09 HhYh7 Jb{ АXYvAb4`pOzj3̧'"2YE|D'r3ubÏ=?בBrߞ!t:F R\c(F dދ#nG1zF`m{vbH{F A Ĉ[zb|>F걶Q܀pg!wYHhI~Ū0 /rak8~G@Ikh.fZ@Oz:k_ﵾgbp4?ҟ.Dٷ!hNg៎̰1o4Ma6x0#WTۡ;&Qa  _VWd\$DԚ, e/X&/6)ٮKƚlg.mxc^;$4&Ɍ-w*ZbHVdzLO]D"ؽUHh=>ŝ:r P¥qg);x~]3*A꽣ZwFct(;r33Hh7/"O]@ЦCv4Tg; Y bAh ȉfF܎'#}v*S'W"VԦ[hobOg}}e~YJ U֒n[ Zح/-o"EppSL.bvE`'rIg[}iz+=?#f݈H{BEXZ4#s,znCL̡-ngS 1}/Y^a 0biʐ U4˭ ֦jMoBf*\ \w(ZXZ ;b1v:Eh<p8 AZRstCUy{V9t#?-b,pMB7Z]c [=GCV R e k*@AF|V~*O"\L7@\yƽ7wHmc} cPAn dtm9l3/k(/k|+%Y:̝յ&߃OV1gڼC )UhN7%6 8Ҽ&2@RSx.g?&%2M$MͪO:%tCgVO~Xvݽ e,RXg[bgD9XG`#28 XmCjHDe3.S{)$~Vh77͉CI7SX{[9o!nX$Tolŕm6tX HAJoB4<dJxmLD,h};噰ϬGZ{ Oǿ#c/ '- Ƴ2!bwE dﺮƇwaƲ0^G^4soz%) 7@r#OwYf7>EB-fx|#[¦!%OޥץgB⪢SFH2c :}OM2k:1w].S&9'u'+Vr]cӈDuݕmo*=뢍eML8s!=}_t=vnP!v3s1_!'_euwvx/E6{!>laLx-'!1,=Ip$R!v*a1&=TA.ZZڽ)~&?rSHp4Gw[ z٦+́:e5P8`6-(Lb=-1gYn2Dfg˳5 ziy ]NMG 8@ddwr{w)ZX$ɷI{8~nnEB3ne}ؗ ͛/މ Xމ4+wbxi}b34 M"@`.c}_EB|Fo"Q[꣭Db {P0]pmh:u )TwV<}[RQkw"E?nں=m!oV_mvF@[W^H$zgYkZ7܁YZ5.y Ȏ̈LމEBoSq4R^ŧ"t ݋ @`^@J7b@ڻo12@wSD~_Nx -Z;!5ž)| D kfeBJ3{܌X/T̏$|v 6k:YF\ qc|+pZPVTR0;-4?7 ́ {)_v3붶 (~g19'ZC,%V0?g֊+gD14Y)Al)xD,߉[ VrxDxhNл`cR]X<.3#?;!S6;"J1vo C IDAT)b^WG 8ŭJ,u~΅A]+.*n\TR9zZf.OO[xPwX~T>KfzxR]uUWQTR \8ka~򢒂)#6faΡ~vs70)fkʩmq>ǥT|C#y4Ko " h>O. GH܌l{!Ŗ@|7Aj4Զ]-{1'XhX$4|ZJOQĶ͖"S{l9 4 }!S8 #s9AK?)YQ \Pi<E WX=*e/|f<NUgA`X,#϶O=,g#P7; 'Oڪv~njkڲ,[o8]{g2b(A 3d n GhܛJwr5(H5h<&ϴքw|'96.>ľ_Z[7|јlv5|E͕"l[xr͹nir~e9-Vөp{$ul=gֳ6bm7%}A:`j[uZP_^ū˺ &նifip@,_&gޟv_KEB3'!| |d}w;4$x>{?b*OI/?𣲐17Լ9:;6X-1(/n@/&rߍ:#>?oY/'fOhMyI̜y_=~^E3jRW^ꖙni.]ǨW`9KYY~=١M!lX:k_4odb[yf"2]HQݐ1t=XN(Lc!Pr}WvチHAcϧ"VccaNNuU!&2%ރ؁(wT?d;VP#[آȬn!Ix!qp` XnwB@dl$/^S%2hh/PX c%>93_G8Vf[ ~`W^ET[N++&ߺ@ mjXAj`Ǝh|~WZ]V^XP~Ax8Zo@]k}ch'&8uBx4jK6:5! i?֥`W&wQUzJ@ݽ0 OGh;8{8:6*u;AUy-[,O g -97˯';#f£kzXD"s3% sio Okdz#d݇`tH#"AJ/xW"0p4f< BgrtVH$_Cѷ)ݑW8Y,%&& 68Șa!e<:d޻1"_?ޥ:)yr#0D |RȺʻi\Հ@wNeh8dFdl\rK@k11z![7uv,!L˫?u~ňnukzϬcӈL}#k[l}ͣd"= )d6b}F6~[oz>aLHp4^t[Ĉ}ƅ3X$&6sM@̴IgP^Q17:u= 9S g*s@>@jYe۵J= 漭 6%sd{] eq*\ma#u[JwPǹ (v]]oMv(FH8 zRH9lC#v+N{9m ZG]7`/#%⋑RD=iN ꒂG!ʜu  xQg$?Իis25^\H sdN#o֕;/pӂ,pҬO"&;ܒJ0]@T[[D'ݮG ^PRm!|Oڽj?ÚY'* ?쓱H(Eh*Yǻg {nO4g^>EB5m/͔ӿ[osbhnE|ĠDk*~JFYgXl!ߵP[@ un]]gYF8? mJ.¦4ˎ/p]w$f@d9mR@qMm. ۖ"ob#3f:ĚB.;#bnD3վ"BZ!7KV:= Rd@ 7 <bb28¾HY! ]ؐ`#f(8 }s1z)!SCgk@"֫ y1?Z_Hb\d}1u1L" 0̸Y!V0ȍEBZ!ƪXN2{ fp(b߲yYO4/ut]rpLJ(:P[߼X$tY8o􄦑mslloJQIwJk T_B4J@l40uݘ8Dsp뺣!qf\]) fW݁6ѩC>f&݌֧~iR3Phq]&VNqM0+d2MHYm^x:)c/J~/dlHQgس; !M#}_4a#E_d 73%!Rش ߠ|n{w`X$_8>L*OF 2;/z7h"˯%2i]|*gwG݀O-Fs%AtlsazZtnk5q,ڤ4\w#pw _!Y 8e'燣CQ(Ъ@p4# U!'] [= =m'-_D׻ D&d 22vyE,G; 4=iF}4å^hƌ_@Gkȷx6X4v732u; wsLtD=FoNt]&{Yn(whH ܉(EQKȱavE`\4)N߻ '2˲Ͻ@,h8P^،$KS" G BӁ]H)}L_ϙy[ˣcW2qCgwY@ 䝀tS,T{> iXX3܌6n;qOr 1QJ{gd o"l 6dž’bm1Kw#ߴ V$t>ãH/BqFs +Hp^Dq:XW +fbƣSH3Γeh,? ~o{ ofnt<98_"?)VnHzAB=tw!rRUt Dl R" {n7KkĠLG` b,߀A6euקiȔʭ]Q۟l̆Y_"C-ehtL1u-P#pob;hܻX$t04Xpbзh<Mjh]2ZUGb9ht/!3[kX[G=1U~wk8geڡ^;Zyխ'oD~!x84=-9u1;)jmIv(cc |k9FxJAsͿnn7g͛:tPI"$zK\NX h~=_D^@C,K o OAIJ pvǖckw=(uț}n9h$O]@' Z8{=\qƺ[8%q\;ؒY]ki־o yA]?itxNϏ ?;)ր=)5 Fl D,̋w%/C/bG1ޥtw"2! 9C/mgM"d:Ro |P߷C#TzP+~rMdZ9UVdĄ0gTͣXF#1ȻUK81}2ƫ;i;bP dڪGʟ( G㝐wVd eFd^}Y6$>,=Y;9~Ѯr-hl6&ߎpR0.IIK **)XƩ}g46{ xsn"iTC"•wu%h l* ShڭO qJKǹ TZ֝-_8H@@Yo ݍ|l>ja,Ƈ!~, ߟlN#z72aawAH]s4{ )Ӊa IWk@zdjlg`:=e'Iv}S]}ф/?`Mx,hE`5tT2 36 GOF/aS脧v#(((@/lmZ*|+t؝`#A CJ-[h  }g,G^ GaKp41 9&xY HgVv:95&_^mtLxZ)_T {fUu߾ wVޢWMc4bMQ1UcMT46lWEGP ]D@00}xጄ.@f=<3s9vz^kH'-5\6g_һ%;Ix hӉ m{~6ZOާ._Szd^c,D}"$YXbD>6J YiFئtZbV|mH15#?@!V~w S G +"d?܆t꫈) ۡ>4{Lƣn}p2u <Ҝ0G:?yz/JƣsbTKג=}E+_{hpx/} ׎uً7ta#xm {9|H&>b2)d -0GlwG/B}nA:{N5򙛈։s]Yed,CJuh&dabT6RB^Gc إF O":)5#@5 *x5yϞ1D] WT*X:)ƶ{ؚ bb0D`թ(K~L Zf!1!X~JC!afDȇ,ДR\ Uhh:|HeN]:D=<|HѥÂL{XcѺ,:54j:A$M6È$ѿ#tEUh@  h8 >[M9C#GF2l7zGX92>B[BJw7tf2{U$3cTk>G[VjB G!x pzjW8vEdAN`տpuObTX"u"r2%RF;pBcwp(,BeXϵ"@v!ZG.~ 3ZؿESj4&/ ƥV)26 e/w%b.4@۳Z APrw[]_]h~uCznƯaFv9!w@:9u"ȓ xjʬ׾~?ݔ4IlM1YlC/q`99'cDURj:w*n#Qx!ڮuh!& B@r(vBa{0b)f!P0)*)+3ʑNEʌ%C vϝ 870nU6u XKys ӬX# ZAoK#g \ |@~L>?0\d} 9g? 1|7~`n;-"LҮ |̔ȿ ͞Qfsw^yeEԧC U?AXֈi}lo#o"p;2C~em<+f#@i==v;-C`p/-ESq)*%i\Yr_rNi}6lL}q50^ы]:lWG)~/fB Ʀ͍<-ѥÞ߆)lt鰟N}k~&_\&#cAzp퀖M~9`Y.z6Yw1$1tFYKC)ĄT8DfH+K{5RbZr0LcGJ1=}ps5M}}Y#6%RXA@tR_=,@@p}~>G7]C +I70tV{uUNǑLޱq )\d<Ywм֞y.-!f7/[ M Gd!!+" {01$EDꑋv͍; d i>[;>G~D bmk{ R(:q䲭mu> (re1"Pn/rei> {[wה/B E#1#D`ѥv>d&ao!%c uq+32eg4z;sEu5.ֵ,/;&9\:KRM[P 97vs.{?-gedvĐaĢ\xk} V( 4N!9Z2>{fİԠH+0tDJ.W=.ZYIad>7A$:xqY{G мoG`<3A r(F}> ?HݎE4:V#WJ)2[ @@@#Zi}رst>")A\bZ?46h^O8o@k[w /;2Ns#D`lJ2b@og:Zι D@`R8@潯wεFn\;"<9FL\?䃻 t~]Ii 6tɈ-1|Hd<:섃TGx>z!H-AJj2 E+ FHt{1y1`#psoShaZvYYfJm@J`R +`ffVv}0&ѧbTq1Ac?yd|4H]̈>s:ȍ%R}k}Rԝ`սޞ73 G"80)eȔ8ߍv{-(`bG@7H-6UCHG["@  cĵ;-zBOūaímRp桹<ͯ ]x48 g.SERδ~ޕL4/8A$oEU5=NZ>}+-^>5em>v0壑GܿHu3"{Ρ99 g,:vkeYEQSfEwj3>]'tR2duYK{sfwʬ$ߡ͐ц+ hރ -Y1z8GyCJ+If Ĉގu΍s /]G98Nu=`!Es/H/n9]i w"-ӑ"??Nn]#4"%2Gf#]KDog[wa줳@e]uBRh_</{ŪS]ztޭ%@h3]:lk!Z\gK9&k|d'zgP^l m R4Reo2֜J1̅1g$vT$X{J=mh5C 1@t4Էw!6@B݀Djb@F,0D ֞P7V\YY;{|ʪmWZAX":xCxt_gqN%R;@c!bD:A2]c'9"kݻ휫hcxb݂ /=Ӻ)L6d%ѷEd ș4<:y)aq0ŻV=2G~+x0  'sC;c,qh"[# bT;4Iƣ&lEt2ۗvAnUN*#ZQ4+{,kohHg}жŜd%kŢ ֬Șo}@Ѩ_G&fhN:^T?(re-@+'aG9P6 }fvbC.A<,qޝKs[5[jb~ai+Ѻ9a ʟ`:097{s{'npsnl`/w'<Hs.{_swxY}gs3h2;ol@l=r)pc2#=%>!Ύ%RQ|/X*b\L-]0_}-@ j &#%r>pQ̂PHd"R:$WqsQUtD xV{шklza ~a9a>"|_2 LbAOcgh(4aprRA̩yHؖфfU7k>(A,^0--6Ad=G>;B_'Vl<&" '(,\bn돷ت\OpijwM& aYl/@=2+a厲r"pv[$徸ȕBDP NƝjϮ绱@s'Afȣ#NX@YyvYzqf`} 㐢byGe-\H>cOƣv"U@X(ѥnD&7)yo}Q7k1!%uhSȇ$?܀SsA٣7rh;9Wޓw5^s-0ƞW,UlgI~H٦R-1 GkZ"vkdzth5b3>BJ$@/G d&] ]LJEu%]DMvZfYHI7CJ/P !a~{FjOVa1BJ-7'vmx-7ζfup+*A.>w8%WVo식 }G;GX^{-b.AJƞЮB`bhm<ݙrWg볗er0Jk(z  'L4r}ml܂(yUU|}~bXO~})Wa1^9Ec9{7$%EAf;m_h3´FY-T)reAcJ'=a.G.nUFVR9ю\a-sWGgmIbfÇ]:8 ܷͲW\սӎyMKn+G qlX.Z 7p b|<zHƣDgq L[; 3tR\uF~PW(DJo;xHQ}'hG) .S!>{<!@Ub$ݐBʴ:i? @udd>j3g]M$MJdl[K([!ֈd<Ɓ4DEhڢn7! 24Z#3z[y 7R&%#`i 4H%R'!C78;ye{ nF 0,~Q/w/9KXس@yؗ~#2 @܉x7+!y_hcϽظ2-G>G% ϑsi`*g%7cll^FLL]7,“"bu|NBCIƣfd𩛃L?4TGj|zlW7I4lӧ&7UbT[d@xt}LCrXh=0w]?!6U3oX7G))Nș#G;9܍G9aa@21vAi:ZXt)3="h&& Jwud<#am;?ӕT`9 9?e]k&ڠMm~nYSqef!b;gr.E@*뻽ml.E !oGL hkrhceB X"wkJt:ȤzkS  ?nlQ~ܹzi;_ewGZx[b‚oJ0U {C8_9]k' oWGu$w7%Lu)rex]+IHr":ggok۴?Yl=},…ik|k$H c@bT|Ȃt=ɧ2RKs:#bE/٩2vD]5O J"PK"s;bAl(4E'!vs%'1X+PBzd; ?_힎ɫ@@$&'?+?kS%RA 6fO!DjՄfEG/>?1K?yMd<:Ҳ tJ*LX"UNx+럹%R{9m^'?ʶ:@ ̱G֎"徸l瞓gezn.u#8} %xsmnU#f1,B d⏣wp5.re{""W,w|}cr;So~[7P4-kR9B7ph3)?x v#ۃeI7(x0cm& h{nD()(:=,7 ֬Ӏ|"kJiEl35?uWs+?->ADkEJDD ~ RhH9)[1S]@!sK3~̟#@1&6ܡ+3?x lDttZq2bXB9ޤ G'Yk01cԩD*K%R ߡ65h?D(i든=V'Q?{9UdyAQ،:VM4\YKw)b.F+͑9`j=Gg2&t\V|~0Jgsȿ3b6{@]j}K%}?#̑8 XwFl^C)_Y f~\m_ 0S?BWDȤ mF"0pI,gm8D`͍B2i^sPQ~>iz.6?|iEl,S W"Fc>E'QTL&iunګ2 Ax.@fn-T?[;B ף9v!Яzifs|ۉpCx嶐Id]@r^|nAι9-i]b@h}:m> =ws{snb~M.)x[?:G:yCr!; ,J5 _k:;ѺyC\svsch%# sl˾=0b-flKG+5 p52}}ؙoR*5 r ̽N `'|49B~Av8h_OOh=9fnA"H @2H>a}r$d9QfNCNAG}Wj;{ ) ͅU93hjn>LM$ۚ9&u27}\FdyX;ؐOCz+s 7г{v}Z+ ~靵 +Hn}9k;n۝s-͒mTDG!/`Kv'"rbY*}lAڛ}n$Mľ)$RFh]R"_b^[ RÐrj?KF7+HݛGC`9 )hbO%RG"rK,"'K}2}N@osb.FL "_,أ] bQ#p%xtp1;/DZĔ!d+f}q#t%Em8&nBGlfgJԷǗZ5-lkXr_bȕj+f/EDt 4GxfA<ݥ>@r_F/n&{sNXmpIw|'T9W6˜sDk󆞽5 yFx_·ι!19wMF;# 1_FxtA,LM!w m/ 3 ȷ3hY/q=*!isݽ[HYN(uB{!P%OH0tAO]ؒ5cmR쫀g'MsBށK-_ ׏Gzqsn'rmY pu@̷7?9E ɻHge&H܄z:9i"c^9]o6\/T`sn0Ķ[d,<SRo0)} xơO#)yyQ#c#U@L{(:ukɬ^`ͮm$Z}RNS(;OG;\sKn4s6HI>Kv%RbTY/qy=R Nf 3N NjO>F)K?ET'p' 0-'VA/x;Y]s &箞>oɀ^Zme e!2D8.@`M!1*0Vv9uE o]zi Fq2V! x>=C i4^ַiGwþMC/ [o5G@20_x%ԫ> y5ފI,y`ٱ~FNBidjneOhwg4e7,5sQʟOxX"8).m f1EPd i3F ? b97N_|5趯~؅h?.nn]\Y'4f}u]Klq.`msH̶J;om[#Io,,{٬#PE~{4r r\_v΍f s`bjnG4~ D rHYKAf'x1a{rB V$?&щGޖ'QB 왟 V?ڙ;IXJ0_:C -i늭8>ب}3[wEևo!E;1L|]BSۼg],y!o6ݿ!zR5l\qߺ$&kaHy xA@f\ĎB[շͽNζw6pn)_w/Zf}!ᡄ, zo9d%M$Mi֤G_%Rn1R1dz(įjf׍C&  tFANh~ XYa܈uBs^27H݆ˈil_&X";Oa-FL?B)7 GG3nhCĺΝ~[#:e2r9miX.vF\Lz퐟h݀WV- .*9`c_أA Cxr2wD\36>DxFf]79?H͓ǒ舍}.Gm])3rwXU|}-}ۡ91"38{jP 3욶)%4Y>MsR#uD,s:)+s%;[;sОWLìO2\XJ$[YtG(8KR@^F/Y]'"Z@C+0V[A@ ,ϡxmݬf}"~U=J^q6EDj bCRIƣwH : Mѥhn]aULghd#3V/nJdA7ЯD&fĨ Bdr j0U@[ ݂@aK"'{!e+gqf*&P{"vFzHALA%Re O :z >Hi1Z/DLH Кۻ뿏@yHz>i?FHVk'D;We<_ub#+2 ^E = %%1! D,fƓ WN'ޏ[sdjRL3:q9W"-!:W,{$Wa3vp]C||R{!3A#kgmӭԎ Pd zGODcoJP݁|٥W},4Y(ZmidÈE_"D"9~^ ЙLN"[3fwen9OF a*RП3Dx>QYR|L'b9x8=O>&!siK4׹n{9rr/ED,<O>R'tn=V xL̕#@3 nw#fSnsmKLQ-Ĺs#ӧC//H9?Dx xܶ ȋ ]suno4'#}t\MrkS{S"u b<׵s#O"3C^Xh<Ko"XkNx BRTC_KqL.`·*B:mx:ϴy?{ZJ11Cüh~&sZ_\f\ -\˨1[~O\:r`mubɢ<ũZYkm̵ Đʏ""36O1(XdDɏ[ ;MlGe)hW!uĄZTE^w9 O&q#1<'EmcԲ lEv^W'@K5U: ?aʺk7I`kA&i"Xl)w޷s؄c;W /cdܳ}sa(ʟ}!wA4CY:!]ޥg.Y}PɃ0O /[(_D,jvtgR'B&5ն(n Tdd'^Z`w_5)kϰ_m-ؤy]>vN{]\3^!s֒TG>~Q5Y)cTm~XfS-ڒVnL*mz5ۊ/1Ұ9 BckghE}7RfdXsԆ|ΰAY_4 ).32{U"p5+\!nD4xv3gbƓ3];OEऽ_vy"wB褥Wt{""vzJyI3oF&אs 4f 6JAlb:\Se C1U{k(bE#`2D,R瞆I.!!x{_[: 1uEk];}UWO)>5@'e&i^e_"sG%eKU;!l՟Ɠ?Bj`uc ˙(*6/3ZЭ kZXʒ/+ܾ4.;}C&Fi\\I]ǽhꢪ߈!9dPuA9y1cvB g"ȧr(Bs(k sTwc7o%էZpw+2iek0bAbKG1[ PxL3~uxDЀzӝ]!_F#'v;zE zG"_g;׸}[¼b' >B32q^"9 O#_i/w'{#6]ԝlw<'PB~i{gG{h Owh2d%D3ѻ~gm+OY9ʯ~2.\flYh阩7M4܊mҌlTjt`Kl-X/lִ̇@4Ƃh@k `ԟEuȗmZD?_[gǪ:M4Xcߌl*T1d ocF:Źc]oH~0@l=ed^z "\˳Mrs揁μTALO)2y۸||: DD,VX&nrKwwP0)X}1P-ȿ8#QS:!p9`B;^t;bȺkx'Lslhh4x ."f糧y.6XB&?Z@T D&SVٴu;lP9b_/jGl˘T F)5|@g@Sz-cں߽j "?hP?w f5Rhu Rhai&aPd?̟tb+@ȶe: 1[MH"mJqNsVi H#%Ӈ|?A,2 ~&}@b6L,rZ啩kgGdf>]:!^h Ϫ:O^?w/pr"qIWoOD~ Ǧiw]k N{>2宯8OAB&E&{q#AiK>{ ܒgV:B&7A,'ˑ& :f}f>:w-m oRcz:oTIcLSZ?0C=g}ahp mOA6u+",u?ME̒ngh}/#_z1hɶhU=/UhBGA "X?!,w~C42Evw-@F(MBʝc,p!6k?P(U|/V! z< =/~1Q;"xmSP^j[ @(aw=k'Dzq*wO!߯4D,h4 ONkx^Eړ1g05Yc/EfL6|[0m.k7 RUcӲ[Ll-sgl@gɒ/LdN0YWcC&5Mp*dR}BK q|OC Iv;"Snkk9ំKeuG"vC>oE~w V 'G,6mxڱG!V)r>njQI.XdĥhXXq=d]8V-u-ri>rhhިq~f3KlEZI]M2XծAX"ߏ|Ir'mC~@>^bГ.hvF_y>j^Ѡoo)vyݲ@i6. ތZt}|rV&˭+1Мu:pcf"71Vhq{1-oz-/ȪRbk=Z/#`&ЪD4!A 3#>&:I-vv]Y1[7Is:>h}x&.XۣUv{] bҭMO d#9/A \-^ace8ݻt{ۻ+tD w6L8Gg~Gw_kGB'1%7y4 EAJϋ4voCM\NƓ"e՘F; p܈|@,m!siצ=3lS6"Gcb´ ?~ Iܖ{tA #5hQ?-6G){{^F2in mxUYN~/?CнLLbE-%f68dR c9S?dRW:"vK<1P Af S)Rlb6ض]Jx|FE r7`Jk.ZJ9l*Ig ='=rv3y{hE @QЌvvDɼA9Ʋf1vL79:w]ۛQ)1cnr]Ludz@SnvE5cDF EK1r1sơ#U'G)0-OUs+E},'C&qȤvF XYsսi ېIh-@ȍ`gBc [+%\͘Zwe4 ܵˑi4.nCզm8B&5[sɻ;_x:k,EZn^%dRz62kK膗|=Ɠ?K'p!,"ܶ3.i1zr)""p,R}sҩ4 do/b"eH LBoWĜ@^M~'DNshbm^fňiNnc$",ƓGHvwx1}Sk18?@t\w}}GfF)6OF_ю _Hy3=u*D,@4Ե rfF ?ifZ5Gꇃs? & B&eB&#b6'2g"&<߸;&dRQk K?C ѢOqzZ\;8Ue~=_^#G?[M[!˭@0dRgmx>Lq.r Oň1]|ŹJh?>*/MGlm`楹'0wصKh1\qz/)M~@lu'E`;XV)#&uZHr_V!3uG!ףcz΢wX?4χX{C `[ϑH9QסȤr:2m_7#1(?A`=`$ $u{4qp { IDATZri9.A&$up홁L @ޞHtvGCffYZķ3yRD Pwy;i듂!ƓnaNB)}|#@mx9_ȤA(80dR/Ic(x1mׅL'#1L7.Cyzn- O#3-SejowYD,2 SO"K[!R8x8dR#%,B4@K%W ׼`J,)w]Y )ȚV/Kt& ЂɁt4y6~1 Ӎ|\Fg%Mqh֝PB!S®ݏ xhҭEJ^!/""J/hĤD>b Vhtn7~4t_b3hEW6ڵkLB %d;BTr{6|ʀ !MKJf^p㗼_^3HG[!D ;.xfI\gPmF}/c[3L[>,!@⃈]Y':wvچ^eh >h)g^&zp1X@{M22jJZdf-@]JWrflo U7m82ؽ 񕣅3e{iO>7~߁|0@g4/T"  f|]勭~;"Ƙv!G#wq]3$2:9)#xGh?Mdtф= DfUmXd!m3 ^x Ƭ& e1ƝEGS2,G,VG}{qĒ|:{p׆BuwCObtϠ7b@_է w&W-ywGLK2n@os~ߵ~LKO&Ȁ ~݉:&Q˒x?Z(4!%bL/D>qUy"WȤofyhtZH LCJVLIY7@6<;mm5l!Fp4xK5\`3331fI.5\j11ƌ3|b1Ƹ_3jK)7gy1(Ƙckf}c4L5Ƽ7}1ymTc>x1fIƘsVs#1Ϲc.m׆>Ƙ1c4ƼezϕR`6R44| 3 )m/KQ(i(zs (l8,\Vayd) ~&+R!Yt@N^hHj}4}]G҇G)hƓ&b;ݗ "GG+T2)ir{3kמY\߈'HmRSxнH.fDɣܳZ]k Ƙm+]1WXkrmx۶bh)Ƙ!q=rݩF>t<`}sphc32:|3RDFv7cL11S;mG~g!$*G @lMJm@"ݐl3MءaРx16 GB4Y~&÷P:d[Lou Z@Z% @YRZWC ț!L;!YhB{ah<Cu;>@hҨE`׾'RI"iƓ'!6Mvmq>v,rQAosTm_X _<6kGa7/t-QP90^F|2/Gp<轖mh0ӑYkwT"׎|}LМZ 5E{N>PVa[U<~~?34vCɓ;cxZ1 3|أk8f$pd+fVVbxZ[ `kEG Z`>?H~:?S-C1Q Y6>mZk^1V[/)&7R`ODJhpRy[,솘!ON"@N9wpm&}CXDOH"5N@G12zkG{dƓ\{ rמ;>HԺܖ|nەV7'{* ) Tpy/w0A煴 ravsZY"St z-@CȤ66~;Pyr#rV ahapvچmi^@ 7_u dP}ڄ|24FʝhScfl-*[iz~-{W5#_ #Ƙ.t1CȿBƘd$Y]> [um3{2-AV7iՖy1lk)_sRbk੏Ɠ}c&EAc=@^}#g" #x%=ч f7(bp6ZF9E=١=w!`S 2{1}HC@ B\@MK"9µm,#2Np~޻x U4D/!k1S][KѠL#y(UĐtw5R5Ęv[gܳ]ȭiJr ;^?wM`D} =&jJ:q&'B&U2kHȤV}b/zyļ}][}2!LF9`[|};Sچ[5=idžbD[И:cO@sK6#Àg\-?!93-6vtAc1(/RuY!XѷɴR%~1ٺ/`*PВ?x3 -ҽ> dbͳ[k[{Y=f2.G,'Ќ[C ֶvq:?ޅN%p ǚ^4_|٫'q&uJ!Z$O$_x3m9Cɋh98}(:o.2]C`;) Xo(+F C,z b^1R$ FJw24Yv>FhbϝmV` D+!#%%@ KY$rGsʽxy.EFwC/Z" ƝexI"'=Ȫu$s'fB&5 ?A?&8:\?70c \ڍiCȤn@76<~^O,Yw]]QCɠCcȴ ^Ծe.#j{㉽Vt}%OU+smxkPEɞHٶ~P2u֣șŝ]7bƥE-%v䝸sW`52a3Y-G _)uH4 D,Ҵ}|ȉpZ"Yʶ½YѪ Z-`S98g}ˑS  Jujz8#GHek8"-6m쾧x|Ѫ.wp{#klE,4/btuCNƻvV̯0Z׎w;(v`92<[+m'E,HȤ"|^D@T ]RJ/,`Tچ'^%fuE! -&<%XTфLUh'wG},;@mV2]}l{b011'SȤJPIus-&1dے'ycIJ珶Y0Mn@sL^<3?={k$H!~A6F QT?A>PwmS^ޗ#p;ҝ{R)鷐Bx{=m5@kqi_ 푲/CyҐ \nL!*uvsE2eD{_(6r_3Im؋^UvD*+658tyeK8W?#خckj_Թ%T֭hIJi=ʳ]3>?V;L͘:&xU5+~.Ra_*HGl%OB@l)_xRt\@7AS׮B&3GyA_%=ib Z.B`d&oJŔwp{#\[yA} 1W!r:EZ3ܹҬB3\[~'#'/u@_q(_)ݳ@i(癗b<2^wZdN][(YL}]wg4b!wy6J6l6|lچi9mW/O_@h+b/FO8mQR3_?.VI_C>BcaNi~SKs״(2%b#'Zp͘7л̞YܩW+yԲ;5`Lv㗘{#pCnPdzXa)#rzEXD,R<'C[[]&\fmYV4 A8"GJhYow4I1O! !)9qMa˵,E Y8f6]w:x{lX'ϬlW֭4e&Y E @9i"=kߋ"̭t6{yz"! z|?G*׷7y_2P_mzm ^]6.)rYɜ2a/G~&z3Q?̠~(M !6htD+ݫjl_ڼ4iI&2rn&sڊEGR\ B& -kJI"C~µ^'-?Pj2Au-5NT/^vE{B̗627F&-HA )M;81"kqe#j 4ʬ6A_g!r9N"81@NW)>shXۣ[IAx5dR~6EC^|¦Jچ_ iez*-,eG=?e@ dRQtq y W; p6ꛛ}Edb6 \Qѫg3䗇Lb;"j̪'uߔ:vfɁrCyn%w6/ vA̵WR. Xߑh;{Sg HmxExEπ3\^_ Fh"Cud AJd*XK? 1Ϥ)אt!:ov?!ȋ\̟K#CqyъYai{6Җ VR͈Rv@o'"==k2ZG IDAT̴C64G'va"E-p[ͬ#; +VeGr,lL+ʢw"ϐy :V{Ve&;Um^8 o{ZwwmΌeyiI`bV'#X!  ʋt)bB@܍{1zƝghĐO/(8MG~mM(ܹB7!pw9&B b:|>@ Ͼֆ&O3{ EZD,K{0ug%6vq|m؆Lj RlYGyuLKs0Ƈ5~ےmLϢ>GIIyV2i|f4XȤ"߿Ѣ(OvS6aȤr+#uCc:E$b#m!*B;XuȤ^X%ug_vVfb;a^ѩl Y&J"iƓcy/F+TMCSb]&"v+e -mK%2>o[r盁L6ɪ%o+sCAd&y1c9RڸsdPt2 śhY3!X5/:r}[l+@,DX R),Xd.OZ8e6,BWE@d&* F~Xrl3m)b{VX">7)=rw vgs81{!3Ё1͵ 1t2F<}d3XH!=ǃzAkPB}䖆xCMA\{X@EF iom4/3kBbB&A}֫S z=0^P'Ӿ lUqL@l-y R| Ķ܈XS3 x Iˑ?8|z 4-_;W;v;EW@LmF B%-"t)Cܵ d/:"J.[~?WG ȹJ?}_qעD%Wd2>g5^3"`" 'wEA_H"?Iި;h}TO\2u=EݗkE c z_-\X[c&Cyz ԧC&uئp>E~#QQƍ@v \Q[KCVa)wL %!Ɠ \Li;!eV&E,3\;??%)U8d{F׸c#4|_#%ZN>=߻ yRv;6 e')##Bީwo@ѵ*؀ ||7vY"Z9h cXX1Y!(nP_!GrZ]IQ7B&5u,gA@dSF9 )HA7$R$_xr^XQs1FP˻G{B순g"0ly9G Et2gX{t1HsbЪ<ˑv)v܊ 7[Esw݄GwLs*ʯ6̝o 9h=Cަ"'/@WuL|2)/M~(BմKUҺ$#D>2轌;6s[B&u1LoktT+siUp Vd+H}'́djXא)P"(}Y3`ش}j(ٚXd}("S7}ɧ̨I"+^h^"_#;SW>OEpy3GLb W|2>A>|Ł־D,s;9p;GI:Ғ%!,C;2_Wύ lPpԗ;jR3Z}nD`{C4nv )ڤľY9L_ 0n3p pg"' 3{4¼}^AfıH!ClE*@Ϲ|x2 A,Z#bž,İp6?L"qQ0d~!3b (PzJUфؾ[g7]s }ܶD,2 j9|r<#,hȵ=䫵;WO!Sh; (NjDEjd32w~VHI'!p<5Gȳx/L~`z4<E..9r۲eYV[>6uY@DA̦C N{OLe E~]}B&6Ճ1B&UM-yu}c/;[])HA=Rbߠ$bϡKSC{/~";#`S /B4H>֗cTF9oV|@Hp p}E!ظ D,2řNpGe4 Ĩ]ELȿ586رY_tzw=Q)Ku'GɁ[ yʏvV4/|WUO_14FEtj9_b | pzA53Gtnxo 'VzO0YG>)64Y2cQ_9o2 rcӋLgܨ.^{uȀpP聄- ZI@B =% Zh%i`0n¸wo|;5ht9oɺѷ]8s-Lޥמ5=_"~ R-%nխ*A+hX"F~ H i,[}Q,LC;*Z?FWzEϝ`!Z3]ēi GJTU*{;Lec,0(L_69 :={,? 8r: m;"empJck&!ĜST`Uʌj&BvoGQ-os\Ebz;GAG;K) (Bux܀ck&'W l+P" Sa dO%bǓ;S$,LCJ*qdEܵvAC~#!up*@${ ZBP<"ɘ,Gd4R*pqHB+mH0d=@j%h["jkg謈9f^HbRw-xIxt[Xk"N}Ƽ셯 v"|k870ٌ¢ C<^Ww` T?kڈAdO?Dh c ^_ R>DAEuGWP޿Z_DvEˉ Ix2łi "fhC4?^nFW!gf-<" zx2 ҮM%bĀ#Eq,""t߾nlÒP-o Fj7ae6iJ O'v(6&D9ޭD~=Qv*∓C,Ǭ]g%=r ?3;=$x:1nW(׽b󆻷Nc/K"`'"NCD bC g#N%bX82iE _ B,j4G ϳnv[Rz %xpw֍=ߧܗ; n t45KS>3t, D[p(ךu-,,~x5E*j/f9dY>"rsm^/5*3{ٕ`m%՛c7CJ*hR3YH<m SI"`tmA~-Pq2_ Ź)Fkx-,,6|X"a`KD 7#Ѧ<m=FV6Цx:ڤ{!bt<-aDHY:}s!%y͖$FOG_M.H]RӞDZ+hF"Y}1 T~cQlA$fgē  .LӶYQfBbD~?io¥gT"vE<>es6Ɠ".S Q cUfkq\bW+"N#^Cs}{iJ%b|ImzGȪi`%Z> N [35}탈j#ϕgo>a؆W-?"зݑMjieQ0eHQZ*s]VʃET!r9jWlIxe <3a`dd h0F#۱\!K0Qr5h3شz׼ߋH=YDx2=\ OG5][||<㞞tI@ߖZt=zEym2c_ ,jS-t?Z^5*1Yk>Q2yaq͛~Bض{ d=>dhK܃l)}Y\= C>:pE5= rt (I 2i-.E6N5F`7pēѷ䱩D%X% pi݋ ~ymqYyUЖV'@)!"7 ;Q,ҟP^D(D*H)GSgr}jUZnjsy  D3u2RYvA* IDATޯۜkR^FEG"Ui`ׯѬ[ Zd9D5߀t{ըHA-.,t2y? ڸaAܸm{7zEV[ sp[K]jJqYy DrQ # ZAThvLɄPʢ,,~؆h#>6Yy"[o Syi6\瀚|KQeȜ)1H7"3O%bWǓK*@A֩Wa9ǥ(d3.u(@)oS{0c-1cO%bOOēmDD^ٟYDJQh`T"v7uHixuF֍.'.ypTM+l^CNȭjXZ^Эi1T3y6&ծpšlbr1Ic_IaW7[ص#upG3OQ ]WvuQ6o*؃Z+z*LoJf<oSKGQ9iic{1+4"N1xdOBg ֦-1}>0hvpgO¢`T"6kз"Eg I+s`}sQ?0oEv3G6JtTMb\z7>_bBysf t *_1+t0=L߈lQo >eVzw0=\ 7Jː-Ԯ'ӻ!r#Z?bD ݷ f #CAfܽ:1ɷ83[֫tHmQkF릈6 Eb=d}YjTdq2֟Q=6;n_9돈F [G쇉R~lWTP٫y#3@}=~q_)-$"B1 eu}O t7WS*ÜwemD*VHq:L3s;) Ea"rǠ_O@ MD$vU[K;^(4 gK)ܮyzr5ی1f>H)9?X^7V}y1@vG:kp<>g0"eYx?ᓮ1=G˓xuaX'#LēQd`M_c2E#%?<O=25EnD.Y 7oq# _D"bEf*lEf>+o^3c,LtQEZRs<.FrqH  qM/:mT"J8dGMݓU4cEvze|<@ o{l_ߣ"3RrH152IBdlƀx2}d<]ȺqY7H֍N6@'j%4S\t-,,6bX"Yn{>Dh'OU p>o.ŖMDe%H:$L_,Qn M9Hވg#R-"dQ3RU!BDmsq2Rv@e";sRBH;gm@$d&\;t\+5J7sp|*ˠPB"T_G!OP< vC Fw㠬S}7mm$@v 9K[>Ư a "e-/1lXCd}"=A!Qh6Ad̽e,T?AJD~6<Fi*B""a่oݑ=)#rB._7l C*tDP@yHls܁"ax/RC`HyhS rx16[ %R"F\5Ye0&kO^kn*߅x2}KikMP>PC}?Nz%bT"` t!*9>}WP{nW )<$H(ix>eO.ش ܥeN(cM`Q~{pGܗvlJoaa*b.EAnCi7 6P*q(E+-Ae@g{dAb"eV =6ӑ 9)M3u/"G#GJWм&읁Ǝt݄6Ñ/]qv<~u~-@͹{yx+LPx)mzơƋQuQ,P*2{ RE0yEtS/D!Ȑk ?-…Gt{KݶehIɜ0h쪋<.Ӽ<Խ|ׇZ* ՄA+=^ ΁%b(R4:䑂5̏E.]akZG6Y4},'* $)w0);dQV]H2@W K.DG^t7NJѨ:#LPh&}&N/"\lYzsJj{uؼA6wk0%c!EH鴐g9yw'(Pu#NflfȒlzdEG&hCB*2dAMT"v)x9͜v7 AK2W!2y" P Yt"zD QׇȚ|-HikD 5LHW"`^`{߲\#۵=bb͂ #9Z޽YE(x2"F%<D>2^[PRw,=iɎd-KlXw!FƀZ1yΒ0 @q25Y7ꎍ'aTKH$!{oT<3).Fg'"R}f!D Et_uCd L6g5O ;i݈h_k&R*Qˈ؍@ QR5J%b HkLC#/Z6;Pb(fn8ZZUhm"V~kx2 *C2 Q#F߈8ݑ@֍z,,,~D+~80RX×*} Mu~2м"<x ψ|Q|QX9fv1}yN\كQY$@ۏD q:"!"hz,B1p!يX0 矂p*!dzD̮'38dFL׈ Sלxo>ܸ`"N&lWlkM@u/}dR"#D (j1p-"R/b ƾ3v6Fק|RDNEh)"fH*ADa޿y,o"!Eb@3~}Ìq/D"K{ ſ\Y Dx2;Cs2ޖ#;:Tnd_û(6bˑBs(-'Dڟ;ncQm/txqhm7eo}Z==ۊaƃ'eMBdek Jē7PSXؼ["2u( /2σYH9 QY-"vH 8BXe^9 ~?pLd:ο쌀:)sa0FIhF^ݨP< RQ`:"g!i*{ƊPn!ꃬ鉍L~uޯE8"djnwOH*OAxe.oE*C7;v*R<#pd{X+A*E r+AjuhSdAjdyzcn1O2e*T"8DX 2FI!"ׂlHnźU-AkCבx2sDp4=7 D)(dhD3 ?!26)cu'3SntΦƈm<8e7/۬4=dz˦ eDX<'6 )ۡB漵Ȧ,G$"JԠD8/9|̧kv2mlx2=$L{5;d$L?.A㜏/~tDH -47!2"pQ^9d˷ٸGaq B1?Hq|#5HNAT{oQ+˷yl0¢aF![ߏ`d F]U(нd=zDZRd\DnAJD9MxCŖ'ē[yf"Bqz?C%v5{6~IM7dz'dzs\/'ۡ-q:ֿ!4D\T}X,߫F )WwRFWub8RʐFkGEz5*zR}ANE]G8haaa)-,<[NTJa!"Y!U&S~nHupi{ (R1( ٜ?lehY";t"wQYO'Edzp*Oo1=HjDl! C3sl@U}xZEDl9R0hz,Q7+֊h͊{a~Gbv"QQ$؈ֳ+5}E*wÑ"6~eyk)(<)]QB"3EqkPККNFkCթr 8B@7yvT'm$Z0]zVjR hv6g}MX/?>GAIi Y̿6ACV 6WB mU(p: BmWbG*m3ž kv=,E]ōKBG'2&D,JĮM%bsyM)c!0YȢuqz@m*?Ow'[tĿ/"N&q21)OdAZm4a9&`w=49ߧީGN苆WtXDLJ?HmITk"A=HT5\dE2bF"R"ujT_t E;~cˡRGœ3[9)^%Z+ @sʙ߃f窹u;ra|g-@dnʦJ!5 D UJ^VTCEqtrz5"1$Lۮ!J2IGeQ  톒#)ЄVw~m:s;#E1yO!Eٌ#q2ñXO D5Lo K%bóP-s R Q,S "b}Aފ." !n}lS93UW6Ǩb *fT"mu,,,,6FKPͫ# ׫h[6:D9mPŠ}*?_,l݄6"&w#*dD-`7d-o^܊bvl_evA 7?deA0?z"y9Oj}-UtrOQ8?f¢]a1 tD>K%bn<>e=l(kqR-Bd8[O8 D%2bhS8C HzD&"9wR}ND_GT"ֶvz7P3= ʎlF;)`s/Zg!-@³ܖ#Um hE Dv B~"5?]Oٝuї@.W:nvq!87/G#Č#}s'^x!FǵXXXa-@Ǔ_!BЂDjEJkU13/ID 栖3{b^@~I(1`S5  B 4 <}ēS%.h @|H2KEO.0 GtZ\ 4Ϸ"OStCɔ"nw1Oǖu D2s9/k[Q(s/"_:xP@>\7$Q_ߠ;y[Y78'8XkBH#"OHZ<~HEh@D5tmtDC-k5},>HqD$dE"R!eD*e}$~\zE<.A$P}h5RmC{"-yW9kXH] ,W#% e"m3 c qZ0ypzt߼)ƠiWlb`H֍.̺<@֍E_YJĦSL[AO#5` EKЦZHH RF"k )\ n xeB(6ٜLV;!؋Rd 5 )+!P*D4֧kDnf"r!}邔w,"ⅨP Bd:O*CrCb37Y]Z`9y߳jV8Y:y˷՚u5B ɴC?ԦPT"3ѦU0_2$75ǭD1bcJQgkEaL%boƓ}?R"r5"hKT"vrG` ̾fRm!(N)Tg.-rH5J:+_jX^JVuD'kح*t7t%Q|[ߑZv2"W^&w"TmD*a e:H=1chBXD>F~t~{5t){3\?M= vU,+wzՙrD vc 3#hm6=yZdv&ˆ>Z;m,gQ,]eJ.G V""E30 oEI {5W .XnT5&bd?&ozߍuG󝌔(+Rۀʨ)g^!RQ| swsאWa^;)i-GRdj] v޸,5G(D¢C`c, "觩Dl Ec(R8bDEF9~L(,JN%bCJM5ܫ(7j{~ԓ"ix";p>8MHx%=jη+R%Zцx=&?BAId6 Ц23 ,U?!JVƓ_ Gq c~DDe Bpyl"P(#G eDlu툤&Ǔ"9dDL?DˑwZy(~S+[zZh wPQa -Q xQ}^5ڦ۔E Q-,,6X"f:pp=lAV Gl{wT9D4سp,GXODX¼5hORX:?"LR&}CreԌم g3vFJL: g8(?ֲ7OZ1(YFA7ϋ+4sS^pY7ک-,,,~\֤w"MO%ba"a*E =W Y{;dSU!: YHWd*^O ~MlsZƳy!6 {ˡke#=#4޺q&d>8rtmJi\Oi+XJRdG"^dcb*PYCP}( "`Zхs9O~:D^h ѵH~3{[{vqI^G oBod?LD*̭hC} `.碍nw# ~bdEPUJ Ixl@D2T Gtt[ 0TGRؠē Y?`)8 \7x2} r}Uo q[VpB@M!M4$q2nQ|L.ɗQEy}fh~4䌖•HoN@dp"`3Ga*}ᩙ,]vY e!nh:bk cCm3" Q엃Wo^t~v ;ZBd땢8> \Jnd`To9(vi1(׈2۾V%He4>uD̾( `R>467PK Pj6Y l|@ tlnˣ$R9{Z螛H}3 З1eM4`[OǬgh{ź`0ڼ ԀR(flȲ1b#)f!EewAoi0$yjEseMNEg.k'" ْ#ڜޫUzwH:|<6P0Fg,CvjCY:eEkzl:dmi;n~Dl"٠w >> W1~P %b9(Ni7Ta_%yDȼހQH1b3H:?(J%b_クՌe!#HT R_V b }&H#kp:i}#df|#b8e6CHqh&glZ"8m[H<ߊ.A-PYrD*jϝMQQ]6iCKMKO3_mŏ Y5RXx2}RqJ%b "Gld)D+ц]4׻bvlښ+,1_( ڿ $@mj2iʤ| +[O )Y"զ b4D5Ͻ& ȣrn&d"NJh@JW9M&׫H xSnEy? ٛ"*XgQ2Ejž޻ʏ5`،E_VGyGLU֍VYXXX|,>?O" ̹h3"Ft C ڰZQ,Omԯ!nyW8ԧg Uh1ow(ͿZ ىǡXgȂW`[YRz4!h}[n+sܙ{޼7c]V6#{nZ8^u%"k PtsCtx[q23~ͳ"<5|n D+E."H E؊m~C8X?7ڼ,<#Nƈ9x:dh (4f,!b"LJrY @<.S< )4ۚy#"5(AG;nk]JɌE?$Tad#k+7ο8Jg;Z:yNOkF {33 +FkXۓ6?}#fn'=O*^>$X@m= T{kKd5Dٖdgz{#eT"֡L0XՙyEZBՍ=kP\(k?U(,OB2^y/R9uA kC_tʐdY."]>ٱ{JYDf 5p,"T[Qc͹1ieYcm0 6F̢=Q-^zy䙈AshTq϶CdejmT"Eje((h7,ݎ0D%<q2^BK,-v=(^bvjAuUV>^3f܉"ÐJr(8%g\sfK(9aRzP,U7dV E<>u=yR蹭+ne"Bkf| QÁ+= )_ˁq~kQ潈nTDC qDؗfV"r<[k[M¢aIvE<m}Q{m<><.sFezqgGnJCO"Jd7C!fu AJYTRP =4± E|(rXi4S!n٧s(;{7yd37 } 'U~>]B&4no.(}UBoBVzAj= i](%еw.3 UnBs¾ Ju13{kjñirtʧ^ms3 \(}-աe%h!$˗4zFZ|-:ϣPEK$=`}|Co {zHvkQ9 UwĿ]GgnT=s-㎣ -s$ɱIPH^Ctįsmaf6%\)W,pz# fTK*CKW_AT'',B;Qj(Sqb!m_c(݋vJ&GȊO>$4${QHh@sϱּx*j{nĸ-3C/QWR=QUKP\עlhv$,`u|w Ƈv{8YM pUOoGtmF3VRhFNT%A=A/JU5| Kч-=}](|}Xȯ]^TITPz1ɹ׏cs!,%A5(xE7Q0OE׫߶Ouh#"Mcu'h[4ӆՕb]FKY=k]h^Q0\37dz듣hч](tFC ՑYB- j4Xte>Q(@ hym7$5G/+.|b!?a/i0Q8gc(h4n" geMm]=#K8'ߊ鲣ChX? _Ro53\Tx]*L]>FOk⊅޾e(d޾K IDATQw)f1O b(<3S܉FKTw/&S3{xro+'ç7GUO P [?v-eB >vfQ06pu ٔrERQ,'KƣBlF@C>Q>uO|݀נЉP[R{% f0ǨrY^j_\P Px:MUǩtMNjʍ ?dx܂- {zc,&2h(%CZ;o:r+h{ec!Z\;W̬v5կvPE zyti|ǽg~M}#Fcvd9,ǝĮq4u QX@|-걻Kb.(tTs8(|"..Gar͝ a([$;3zBocfUvj7?^Ư}*8AsUG˓B4#٘pϾٱ,M~:`Nk?ݑTO eϕhu VA˔?^4i h}0rnYԺi(k?,T K6%TY4׆v.Z4v8[hA (͋cCc)Z7rΤC̦+bfpmh4hߡDV0Uh 6f݊?zM+WEyffqˁCNfh< V;QO\-%v^񯃖 3(p$/ȥιm;s;ggڍι5ιιC_ꀝ6r5p?x{76h5 0څ#Q`/~\뽿{{??/}s'P\;hCGW;^ʎ{sy#0 x()pΝ{;i8H?>9*J99p- \Ldx!ιι3sW:"ᳳsιDh{s- 1xhu9wsoBOh5.p}qv]c jd|<ףR#0876DI's9``V\\ 0v |/WFU8K*zk6i瑊auޟ "Dڼ7@!ppsM]{ _Ohd5pqpnl=M ͯ\~N'Ӏ 53 d yV`a 9Oz^ׁ@ݖhkk@Z"wp~CW={@QCxoAQSzW{"}#T^`[G{X|L/0O!ʉZ)'@M{pOȅNB/\x#")_ȉY8 LN7{UJh\wP[QtΨ*36{kxn@  +0!޿?{? ܴssOraH(À|6sT`bYXZ6ަpڔaL??{?M%s|kKav "^Go=ö yީ2[wйιy $}6jan6'g"b{&UnXNd؀"[R <FyZ+!iFq΍FQ?H\Yl?6 Q<_#<~V L.5 0crcDks<- n#p0͘/syr$/$#}Of$/L3 I c ,c9sMH+\NzKι"$_\.#|MiO9ǰI$ b=s{ t!BT0 8Xm䚆aFUDs.߅B9y|9 o=`6`(Q{:ByW˜sP^f'̙;z6'؞0XZ_pέFěK809(w!cJTw `cIf -x"=shxfŐ|aݱz Aԭ'Zc 0-{_|5¸>{?A8JwaNϕ9#܌6G ywיGF!~>F=樶( Ur1*Tm~yjN0 0 0 X0 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 Ƨ)cF3'Wuu 0C&W_dCW0>mX01ST72&]2 0O0O%2Nvʘ(7t  |_N۾aFw"bdʾƧ3  8"c 00 h/}G]bA<QZ8f0 |)֥0O12Nvʘg@#& #Oca$\, ^Օ}YCro}2]2N"X9 .@^(' D]M0 Zۥ>TME@-e(Fb,c$\l$W`_+q\ص!x @eo2(*=j@>2a;<]ڋ?}t!Oh,n+]ZsϿKpڸO6uu? 1a0ahxrh?QvT]p 2"۸aF7a79yq\ۦ\dI&\b' /ԂO~}_53E^(Q$nPpxr2fp+0U PaœH;/)cOa Z}2p: ThANFGyޟAsdmpZ2m~8}WF$\jBFG%Ŀ2p !}N6p̶FU"[l.֊OutS4ܪQdH !~D'׿xr9g0 cG/K/Zs~]JMEhJGZњ!;IKw86'| ͕_Meip 3ps)~o" \P4C y42\^I?GvQ:^(:UR;ewpZ'0 .žTNYE[ۿ|v$zX57Qh=j@sCC䁇pL+Z8=œNs+=pҜC!lal%A:"2 N9ͷ[[ZgTgm t<+њãJ$9#)!z9l_}7vaV7&be"GE}O4d9ţ$X`^Ŏ"`g3{"#m;aY(hMh{!JVӂJyOO8xr}S15(-Uf\>N/]̢vʘ*[{9TI}6 0| Gy6 ͩhw5 &"Cb&bg}rV\T^%*/]20?k.6]-}bfʹ^ A'EF>z3ñUh61hf`uoFReHE?p hsj3s sWX^ۀ7q\yu C OJEx3/\?I(7]ȫـ"Xu!' bs{싾0 c; b{F_ZvVDѐxsx"OW ;h7_.Lإh;_%r <_AʽJX{uJV{.6>n3r7WEy>.JO`:2zJdX݆CQ%K/hTo>IQߣsDFr=L JXt3)lYEv7󤽢+t[!}=3o녜()cr]W 0p R#E·QT}$k; X)ఄ}mcX+ CZ:.@#ȤEQ1Ƞz#ZP*s{%\lP0K<?[|7pC.IطuVe3sg^ʟN0d|KX<~=d-[=1 4ZP#*M4 e"4צ7zy`+,|3!c?rgx(j H2y*ZC\geh9-+G 9ѵH } JU'j-EV{n-*H4_Cޤa"(?B^h`E.DȐ,]c\,%>{ ;;WzX9k U女oQŌWMM2랐JQ9-oW6n>aK/$i'I x bO"G׀P^s:91kq{n/ǷPuD}e_[&r"x%#\௨R`.ʽ"b5G_".C=΃zw{.9꦳.vxW*oAsձdʨG}Af4e#'S]PᛅWP$,NX !h}-d g]>H=(t+C /Caȳ(.O8+u4JM SA2¿#a*7} +5.BFq|'b_~:}͈{]~oΪжvʘ,PBkE޺;'_6v[lWFtꌆa$\,U G$K&Id\|"d!ߜpA'[X/U^yd(. % ȰLKSh0, _ _\SQZ|#=CF(^=e+zft_ IX>@!ȃԂ"щ(' #ɔSQ3Pj)p2T݁B Cj*ҰykhW6pGn@/@_6=QQ:t\&ٵS\]<ϵS܌FQg1E '\Mxr;jal}rx"b}wͅ[-8xt% 6ܰu-l FNt\Ӱ ~^ ;}%bMH3C&Tb2l6YnDk=.VɺNo",HX*[ h?Ș,=h, pkYhPHqzG*#OSZ0M@,w^Fs\gW˚aoڑj9 E@?'ןX;eHxr<8e3]k0 =$\7RbU^(ŸO\to_ϰ/8.HQA 8hnK1F}%R B &2#u *bZ7Bkti\2ە }X>N},,p;l4d}'C߮h*Dx#>y< ? ;8bCC>:2PDT_(רOFL_ (D9`;#فHAnsEyVg.vԒœ@'"|Ns%POJ;{u\yuI 0H)CŤD $_^=OUT^?!>MldlE4uHZ v8*UtEږp]0v.6m;=$NXzSЀ4 }P~V2ӂ3|)bw I% ;EFdpWWrw5IEb-l]7}G6th#2넽N/5?ӎY=|pX_ qx:z=O7UTA݋4kmU֕8 kRQ >W+j.m(*/]eOc(\Z;eUHrMAkuj_}v3~UYW8\e]KjlatW>j RU^* V6tv?7 )Bsv2Y9|%#[Y9h]ф"a/?N&|T%0 $4Gc,2@ג'?03vl{-V(>](2Պz}$vF(EޣӨl{N^7&ڞ (͗ +zp1bZ9_it=!.i B IDATxc|Ef>|bnW<}fH2jdZ{|u90sJ຃ݷˢܑUmHh1yypWvUYυ;xwRQMD 0횥hB9TFoњ"H9N\"^G}򭄋/PArއ9쐳ЧG>Fgݼal CpUJBFCFUsJCFsE(RsXʧ6#é'0ch[BFнk.XWw/ ;ykסdVJTX!"#k2&rʣ#~5ZR@Cd{\O;0{ъόz͛!=i?8`WSy }/y7`JYENeP:y=sZݜfQjh ߣﰫԁaƧPϺO>r~Md  Q.(wۡ5dCӀ:dw].!-dix`( E!o8d̤#@|OT y<Ip4hfo2~-p u! xT@h⯇s.Y0a ^VϹ+"1d*7L@$hR{UWSDFѦg>V${yc^^čԷ}$jV{Ř~fNݧz B n)xIy׾3rY>~ UB*ғ(0 0\Apۢ{F##dhX{'\Gȡ1Gߢ /"e . mGњNwb}҇rD4|PhԿоH@(k1NhT8]zEAJñ ʵh] ܂KnɉTZ}A;-X #hd^VKjY-.熕O ^G~k˧qwuakA(Wkl~eXP)GZ0&*q *G)UTG}72䆆ap]OCf"SF mZMp{ș'=!ZK 6Fg4njތNw*,{M{yW{ |=<`j Gw@Q$* _C[6-&K@x<{g Pˑw+*184<,ߣk[΂s5|VqdwkΊHC~jŲV5>ݒn~[ǐ>Vϟg=,{wɁ#G2fUTwUr~pg6Yw\ TYW 0Ke]dƔ?wT 0 $\lBWwd YOy>|l:aO:d9esuR݀G hlքoEQh-o0ڇ󾫊M"OAhKo"Q J=qὴ~4M45ӹd;T}p|zOЇ(׫%F5!Wa뤐' éREyo'mME4m4D"n{#ūX9nwkPaLmjώ~tN-kǹ_=<qKH䄪ҧT֕ @&0xtRQ[pdt-j7raƧr"oq'øO $\l4*T C2/tÚ&hy!*͵0ZT+c9+BPh.pc'pahB_]anD'g%\ 1=EFdȬEYy(6 :|. E@rJKsHG3]ESnC!I* FQi@킃G-mx 8ϥKG+ф z~+XGR>z`>c}5 0{ 96R򆂥>l!+w4 9hς_L847H9'U֕ly;029] {$Ks#b;ܧadiAkHRV#h35O,72!j*;hp rF:`\~a=cV7#bZ/% 芢(.FU h {E |5E\h'A5dd!ïԊ^ywr32xΉ<"R-DGQӁ}2-CH߁W678WvVm9쬆^ V_nw W;dz,Nr~ɯΥyvk~ΠG윅hd𹲊tM|A/kLl{BjaTA[P.vk`!7NY?@NÐԌXzӰE~d3sh䄓Pzȉ[~Om4vC~C715+7.LTtռhJwAZϯD,"^hLE(Ȅ@2ѫ&=2DhUVe =3k'Nm:&\z< IԸOޓ7 Q4e~SZs_FTsy֚ޫ wR7y^0pF4aUyiC{ʺ;ѦY{?Ms=+N*4 ؑ ~>٧}7C>i@:E22Ѭq53%H%>rʾ*'Z3Cg~7|ފ!_06 `u3.F7쇣D:jAyywAs*^'*:8kQ~ȳTCOX)JX {l'weSX2llѼoɖp7G#iȔON&\,t ǕUTeK蚥z}X8І|&_ii}1{;2jQ **/}wktu%IE5u+J";bx맓j0 (qpV4VwPǢ5KIP`O#bgYd"Y g)hBʘ7}y#ޙQz'R. ܏nHe3.6961v~v t4=y(jՌ!9/T(&CA=ɷ>H :F"X{W>usqp%Hj.^Y@'[^륪tunzƭ\VIKkcM?u{hR5EVwUTw!i{#$wEU$/r*JNTTC-dETg;Oφa;*ovP{igP𔇱׺ӯRtd` *IvAs,̢H]hpS p`}rZPABGNO"[B:vE>ehEUx%+G||HʯBT)ݭBN DZ UEFG^h=NѨ<{z(4-\Jd UL}raH*Uh'v@!d6El ML~[VQ\RU^efrH Ѩ=PE;? ZYWp5΋+JnA' f_в0 q%w2I(2{VP·Rq/q?.#(o(y dAsH Vf]Qsi"SᲿ@.(prr:fAkc1UyxCtF7 h)P8ﰱ/9`ۮ}^4}LFr(4iqZkGc7߭d$x̧YG{elͦtqO._v֜#=.> qȐ<=t= PVQ}]_U^pH$^LNMNTTs@e]IE"qU9+ zRQME*J '՘09eՓp\W::5 vYѸC|znUyv6y=2" ˍڭm:{q_܌-9~W-o9F : 'A#I= hpRn%hчur"⭽2rӺX݌Opًuu\ι b _s*]*u9sc}%ȸ)Fj@KyEYEu. DƓBۡU/UT#-NCdۊ?pK>yc;6@d(Zky{ls+ nu%6;+JfdF:EEPh4tKfWIi{,8pW. ~9,܅Λ7q>Μ3P|݀6U<9#FFWC93B UM?@22*"ydT@k:2Ej>?uS?OXyWxxؗQ9bt-f`uSBNR+p3pfyharAt F Y{h[, 6c{T>Q )PUT mzKՖmY  ,@F]یOh3h~d=~悽9vWGW֕zM4fA;^*J2V U߯]y4o?Tv[ q@sNch3oSlU#YG/]քoqy-Аܓ1pPWPd dlZV[E( UFk:`!2&j$dk}r8\cEޚ]s.~e6a Oc>Ře&xWPdPԄq|yYlGQWZ.Њa>y_;^Qh@ i=*@YEuw%HPAٿ픪қ:pTt qdl5~=.p3Ij)ȹn^"M g.mDяPt;xU|2i>M](h=ЗLj@0>PEȍ6k[{a#E H臱`1Lhf_CT7uF޲Zd$R:jc俦E\KfI ͝,o V!EDE^G[~D&{*2pƌ+_~@9@sQՆ ,K FO«ө*/SVQ=s׶hwTA1 ߪ*/}$4Q\ YU^:8g[uX9 s&8Cʺ3i+)@es9u&Yd$ʺ -ԊQD(&u%ecKUeTϖeGm,*}PWg8>zLIEȘB1HrfFZRQdԶ k8 —ɥ{7GS&T~ bSִO~<\a^KS>_zeGֻQA-:+Qjh_Ƕ:2bq_[[VQ݀qzu(wd|W΋=;yQyP|ʺ&{`] }CV<\<,0N9v+]+c{ ,SGUy\~e=l˪Kӑ|TŐ60R 92IE5k+JvB%h!h"kEZ!(oM[!W֕\;ma@hPYEu1tZ{v[UymNm_ޚkUT!ghL/D~ @Kw.UT|Ym?JT䊄FdM軻R?B22ZQʣ\=8'}rfe]izw{S>Qypbl"9Ω^f`gws-ab ]hbYEU#w6bND(/nPϠ%)L}vCè aMe]oa|Z)$נ"?Kߣ}ZAsӯ#Kkqjd|yM02k.V%xHEd+^gT֕@콓jVwt2fUe]I9ڤ7O$ LƛyTםN@dJ?,DMۭ+Dя{wB(1*/`%jv| !.msH:7v]y܉I>) l*hSKıOT1n57D@u'c IDATT:oA&\]AȠSk$*D4%\֨omlW44><6K7+?-ؙl'a;bX|2мkl7Wc ,[y*LWUT<"LumNΪ }݊/v¥YhNO8+9=L}j7STko_"wϒɥx->gΤ+J~JE2U. \[U^:L__VQ]VQ},lTò hgUy6<HUT?YU^z~x`$ѺCқxvѨh?ȍc*EU奯$\t47 .(CHX6SlTIIC#yM}iOފ>0$uw-%PgZUy-{ BYEu>2ZD Q$ߡ߅0;C?#Xꃢ+pU `{ޮ*/KYE.(Z~Uy:c=}Ҧ#>:oc$\l8mdH~A>JBwP!ѽ&##*^ |-gEUCz 4>m^5d#}/} t~Uvp09]3:fle[AUy銮һ~kX@Uy/6}ƖQYWA Iʺ:9, )9FiŠꎉʺ'ռyZ B&TgݚC}~/6-8w},!S8Teu뵋D"Z05'z}E'"}w+$\Ԓ Nem5p`UMwee/o<0oi_9g^^{pc&33Maa:qfpAg%_Z;޿)cL*yd"2nCun,.@Ye]WVIIE5k4uqSUf\F郢}yt;J$"hmfΏ&wk턶 yyz]=y9{ "ZPYIžUK.ݩqVe g]?{&UyߙX`]"8VƂcAM,ªQkF5v,bhX&zQF){ue?羮vwy/3BѸent̺WlA?&8R\sD:~scP¶kW؉mH}1eȰZ ,Ez K `YW5 bd(Rj܊FAI;dEzX ;E 5S{ǰCo 9Y ;``L/A;( -0GrZ3,$Pt髫?sR.rZZ רm('ctjBxk %ߍUP_Y8<ߎ)i95x"}Q*S٣5E$HHzYӿ?ϲ @/1}u "iF.M=?a/9ݻ(ѓѤ ~QkA@ ™M1{xnaiCF^ۍ'v;u3ڪ]m!blpߦo c}PG 锿CN>]zD L$PQvd9w;"Dzmu#YI9#@* sG't2]g(-Fz{}?2pCuzAEDi6Q]aH\]|uNs6c+"GQL: <39>k(B4è#A9z"A["C{v9J7 C z7 Dp|9Z|6<(zm!JU`w^m1F&T_Q7c,*A$pS/CgH0P~`H df!bavYډMt!BE@rJj@)fup!j "^}Q9#;;8nBk\E٧9-̉Ȝy;kR## lf֑xﴒ'S)OE2JfVCQNb.KWU<ԪF&\9/ 9_[@b8JEѩ dtȺ~SoNjK9zRlvZߺ "!L#}Rszbonj0|?`<̎]E8J"c,m4p̟Z6סԛlThvZDc6q$,VpU/Qdv/aE>@ѧkP^٪C~Q8T59;v#k[z9EFFxn:J_@2]~8x,|Io|ɸE2v$ 55eWlo4k̩*ЪJ嬵hHkWvƲNYR τz=j㫲F5C$D4a@ui<* |.ssq9z@O0êo7ftY1; `S!XqRʙoDWv=GzҲ-n9|'Jm.EHvRK'E^2>#E A-@ y*RPskQQK(BUxE wCAFAfr"G;z8*_NBS7kQYrT :@'Q ?{֠(Ud[!U(9jH]wg"\ 篡T,dߍH`+8(pc2[n9i?xIm:[Ws6 %W9`^t~3TJ&ySY\9:5}}5bce B&[|@2D-SL:Q#OmM~]ίعn^SӫekDĿG&ظi5wG58R8/Cۗ 0'%k;\XsCWfLAwb5X;:2h(䒓lRWN{2*-A`erEib{nA{Q P?i;LNehv9 PzXDU DF~Rf8X/Nv#&^ǝ8[ׄفʈpd/Z~/NAdf6:;7t8柃 {v=ŋ>9~6 r_m4 2s(uCH^҃CV05ȉt9V6?EѨI>n$fnMg B$=*F&` D&IQ0"E.zlȕ>3> (7- }2xGFN4cLY y"!g_TFTɇ:vdNۧaSeaʪT}i۵t>>s|=ϪYY %sV^z@-h.6U Q'NMh1m}tjB|5Tntj-턿y=|bS=y5=y= ~ ~I8)i`֤g Dw,D:;n<<"ܜ;ԏ \c>t:ovo<x9ӐsAEu *7o~2Aua7%WO6i->ָ dѽH$ĩs[<#, ~CN1("9kPJW6R@5SMH"x^]FPʝ|MR`HYkbgtL-A2UDrE1?G̀QH[6H Hx#P#TkrJ9Sod'{wGp6#")DbM7**[iR3wz"/k+F~iCiAxIEN}i;4ַʋŋsgx_~l|+j=4[\zChRѓ/}:gI]^GO^s !S/vȹ F}Y9x Z^;͓Ñ`v/Vd$rD9RHԓHq{H{oA~hu_=v7 ok`fAF8}MN~X q, eq+JmrSF"]Hp̟}]>.0 ~!0#@Y=9r #@9Ԯ>u9g2xx<+ \qI|K1"]W#9"UjAns(SEk 5ʨ2c[f?-E)@Yt뇈걦Y5b$oCΗ5B3큕'!+#a0 vyCe+rm(Pһ7hž&.vdwfuu%rOI4ƿcQRP/9g) 282 v!SlM~NEΰ&[XTmVzݳvP)94]V$oaE)*DZ@5Z+P^}8B}YV!~_pFƀ$V"_H/x(JaCH0h9P@opjK5hv,9c|3${Xp̟:1N 4RxI#vD>GZ]e3]W\ CN5BVM5 |m;lO ԹHyW$c]gޱ'(9V-"H%[dF-"PI:=H6Ze!R?"@e癱 j3ZDHO"W9ĹαWQ'襐ȱr7!iI,@ѸZ@y$` {W" )"vOfxqz{vYYhѡU.)'DޏtL| 鐻 `C Tstc/(u8-t-2uVG5%v)Eia.~d n EmS댼ߠZ7 )\T2@ ܋vrSfz|k"!ޜ:dH4?9P{ P3݋ 64w&)T^y(9F!Z7] CE2K/ h*?/Ͽ8cS/'ޏ}s)2p"QaFe|pec@r6щH6Xźu7QǼ"9ǡkP979r0"<ioƐc> .>lb5HB ԬBr7߯Fmj{DH|HӨE!pyGNCIzsq(-|o$7׻EkG 3h3麯-7V Gµ.ЮV2ǻWex!ޥEVm>5{o&JCNP vply6m?2+a{@9\6¶%sǵ)^&˛}#uGTPq(HO2bKE;!MdB IDATyW#(ZB ݮܖnlԁ<6t0vsv0.(r7 @^ۿH0~&x|/72w[K1]?"/EWcSP#"/xeYZmY|EMH5rf^BF]Zc:bE)s콙 m;d{êN+Mwl1mZdt9L9_5vȹd7޵Eiy*ٙ"?nZDtRJÐC|%Q+6֘헠æήN,yn"9mc#fɵBN~'H9f;``.@$x֤8CNgP(63_O#x Qy&Z&YճucCIA^C}UުƲʹ{̾ۼR]צH0e`߯`@&Ep9Cх±vȹl}3uckK^xıfl2=r;6w3Rn#E{ )-R4WmD.(lJX!>C;jkK+]\&EʺG6n}*D 6(~sos` " R54 ӻȈ[^컁Pyh~Z1QY 㞆[3ÀhM8柈 *E7cI'm+$-m- v !g8PldܲWdRؽq&2 _(z?ԧ@r $ P@Zk"1PTXY Toưv#D~X͌9 C;#g9$χ#yP rtA V!9=ɂ.ffY(Sd{) YH> ' 'ق^WC/(Ce %bO$h 'lfܛJٻThS䥛ڌʪMlfW׷: zmdCdֶE@1]U?SgȳCNY$h&pp]:;_;>Jmvc ;\R! C"Z#Rr 4q"AF)< "R^uAʻEHIl(2͹܎v.2LET;\ff"i~DJCD@,GsrBH0mvvye "OnEj,2̃]Wd̬AhO8?xݐS5t?NAm{)~=ȦVB}=dhed]mdwHn svBl93f og7 TqVeCC8,9?&kfz3؁!Xۦ`Ś6{ďKemGm>(RŇ?@»+RzS17kn]HAus[B$x932l*&!4{).h7c{) )ȀjJ)nWو,-E#|"k+A-Xhm]`x9y4R!t[~x_;" \@r7D6k8y}}sMvBЇҔc}M7.E(>Hx1o!/>i/#/0&oڴo #o:e\@M98$wݜ52`+b(rjlQTmpzHv@ο]|vzO_B`h$9'4=ǐ} rMFŭMjg^MK} N:-4 )EΪy丑"=2\{p/(Zsݓͱ}\q*1-iόukH7;QsXt!d:= ormXCkoCWK}5JU2#o遽wӮ\5ȑ/ My1a/1oU+Fk1G5:$w|DBњ>`δ  ݄#f{R /KKPH45#>)0 `=鴖2N^t $v1߹@6%uf*WQ9cC^} `[fly}QMrdԣiFBc:37IdhIcyXqR/hCn+_Uma<}9361~K7lȰ:yֿEggyޝ/ O`]<E%팙 HFB2 TG_K WswH_2$JQ Ԝd0vS^6Q͜FNG 0ϊ'm-Ӿ4߃ZuSΙp_t0RGF,zw)EwZc!S 39vȹx7E(j'H0x̴!g-KK"lMZӏ6,ߘDf#~ RZ'y8yCG R,>p;gZ2O"aHYV"x" ZH,t?F-y"%)J3>|kM됲o$ݢ/F;!fi:;丩3'jpi]Cũ{Fwvq= !JE9+Rd j \q 9#7sMmH˞Pk5eo]ue-/O.).[B~JM+vYK*c4l0"OcѳP>Jh@d .Erc=_k;VhN!uKTgu rԸ]q3rHCȈz7maa"1P"8)DHR'HY?\jD$B[9~ jto!|;DT$rA iq3C9SYOV ѫ4);q=o<'9t6y\ϿOӻ}cp7 4zT>g<_12)?z|;| ZZ $pj"@rnEf|!MzZ!H0pE`l$&֋7;QM y,@b0ڶ?6S7bertq L7s!\d0ZL"KGx*R^3%Hq%݅1 =(+͵{y>6ײ.\7u9cWVeRo\3yf=H00*(E1>@x]USڴZ1yț~9bt5G8?o/5FZkӫLJ/]jqa*yo͝HMB"9-~^cwE+S=ٵ( )dU(5h&&hmH009=PDg$j:uD>df9~De$H&TǃS;HZ9OҜowF'#U6SnCF3H=4 Bd yCz9 3Hˈ@uB+5 蝯@rt3VHnՐ^[ύ0v4^sˍ\-7߁d:@qk?G;6 eq(egf8fS"JDl.{{b硦\{NfSzߙ=_dsEϗEe cnݶ!x'W B!'yz1/EB&]W/n,<]7V?u2GzQH"ͶkymlQ{]--"|]5Q6߬VIvx퐓g!i[D`uGݔvC)Z esl;9eHy|]W@J. «6p<6v^ NHwCw HqBF@ar)n. H#!\DrT+?,(hVt9H@egNk>d3<%󿇋{OC\'i!R:]+#~9zfBs91߫ (Eۗι7/zX})Es ƅcѲ5}YSz}|=;ntd*O/zA!'#7 :jH;@l_Hn} hn?9(< ҁ)^Gl3 cEF4rkcr8e܁j E; 0a!H@D*Z#"mI/:f9CQvfM!8ՇwFΙ9] MXt㡫̱Dy=w4F$9YEv Avf췡TrEIhj/S(hZoռ=vCzjHl$Js܁J+FU!X[v5֤rAfWajPHW~\04Ѿ%>3G#E`!;,h5ՌӜ%+%>Ƒש՟(]5h3̵:llC|] s \$xCUEDr9y_ hs5o>sw/L_Eȵؽ§ ź#ԞHAoh^lM o*>AgH>d<@;DM 1_esfg{p?yUDg@M3{92ZC+sVZ8K_^YfY^vuN^nM.2ZG1(lޥpg!go2<@^2Ecco!XKNVnBF[o@zAz\SO$r:]HQyɃB$_/lr ruj#0 3"TXUMƞ$m@כkqnoQ$%a3jDƙBN%kf=Z6ucLd6JȵAWpckDŹ(= y9Om^!ߓA]Hj!>`܍f.Tc>vB6PH!?(4Ir\rӑ!X[jv2 :Fevȱ1Dsfީ=F"閴{# ||h#ؘ`CNs7+O9 ܍[} 'Ƴ|) tNl=t)^GKPF](2r=j0 )DP@4eZHiJ:J)$8FF.zfMF$.BBB);Wuh:sJbβ(JWH)#@9 E q!n@$y,hF>9`'Ef}*`p_ |݁}SZ?ih}:ܜo=4ƽK0yˢx]}}5ݞlvh~s1n{F$Hc7.BJNSUӺa7mjmؠ'g"o{z/ 5 ml;9@:"㻀t] !%NH>  w$5ӐndjDn<}OMrrQ(%7 bLaƇECn'HԠ)6]۰9U)R+dH#\{${Pɽ8]r_n P4e8fY,N6Zow?~.a쉑HwW: CH&EyfyW;¿Qdn\\?׷ "u Jz-Y 幼f]5Yd{9vāږ`S"L)ۑ``   Qbj$O2Hӑ< ߘF lV㑑;`\5s[ݰCν(ZJx. H027n`\"s^=<Мi&hC?B TDHv5B #܊o,@Jm{Uat'{<=`MZ1H=R299^cHqE)(c&Jp{#o(?5\ ~&y>h:泅fݛߏ-QL!ot!2Z.Fis}5ɏlY]׊񸷤 dA-<(YCw]f%J8׆cȐ ?y=;4_kd&U=>mW.ܿ([_yjs =,C΍?"U?z| vuK1."w BAϻt{[[prꦀ}1_joN|u`Ðw4SHl#q4 גN $hsR,K *<)[R>F޵Hl͌擐A*h$®}2uC[wG7f!)+MJ?P.(vC-\% IDAT' p %F2kS-C! yi -Dm=EnCZBĪڙzk=L\2ά\7܃HF+R=/Fdr%Qm%йQNHf -2c ]oxɒIf~A@ y{ g#fnlHy3Ev9xyYџHovT~xkX=3 < n9h䮿!03$>'a;?߂bp, RIzQJ/zֺ _=2-0.Ad%b"%!#яܑ/#1 R`#M#A)qkRypr[vY`}$BQ7'QFf``SB"l}jDmm*f!pG5d"":F5oO#붶Bz#"0g1܌ H{@]R^3g"?Bҋf}dK%j5eC#VY 8%CHIEAGA%|-u_HG󺳁6`!}Ѧ:󟃞-@{~Vڹ7~y*64nth=%T ?uh[R = DݶÐbSߣCE%]_R'& 6vȹkkz=/:;J^O@-]SHg}dj{ 'jQDCDbڡ痐!YԚvn]S.zr5 2 ,$g#9=>x- _flnnH,-RL:bD~K8A\mHצH0prCaH/5f r6յɦӑvr;,{^ޟQTw5;8 \/&HGCz-̭[k f=|B[|S1ȡOO#px/:wSU. !{Nm ;!ǮZ^l 2~9,CΥ`qH@brAH0 TU덄vCЃ[ ڋBH%d#`';|m󜶉ӦwĞr+/pu=>hI{yycR̻gC8,E,w1bA#{棌X6y;ॐnjk~@ZQ7]E +JXuy'!>dܻuD2ڕI_,kC6 ɹHZH.AA :溺h׌c40f.p4cgV!]3؜=7V$wH, =z*3_S?@$m#^ˎxͷ>D %S&#2D&2=S##m!,DcQ/&>%lܵ>&EoKɠ9~+~Ms8=CnID@D̋Asl3 Ѝwt8RTY"z"3 Ì`?b}f#O7oz moH:\B 7me'3)5/D#E> E@J)jvAmRLQ Hq> 8=P#3nHf[{ɛ,6n<sYޜf7 ɛ^Ücޯ}=9Ͷ6")٘5!ȁǭ[d]Ltr!| 7ǮC:% ˌsR>ӑ0r)QL"Qևiu ZF_yD)Ǡ4#XԇC) #HFs=!R/ǯy|EwXkqN틚q7Ǻ=y̘xp[[1H(3Oٷt\{$lD)CPe o" cF<;<5^ܜ*iZ6]AnE]Ň5'ɔʚWz= ;UU&j֖uZ*쥇j7埻h1q  ٽsό6כ}BåM^zvLI?E,;.yrr堨ӫy> Fh/Do,|ɹ$n(PtaײסH}ޕ} kn*Ruv q){<Nf7ys_#Yf{w:YdH~g6@V>;s,ktX:o!/)A(3 LY̹_C$8}ʍABR~nY EfZu͈?n׍iz ABh"*'#-Rx$t|<r߶-G83.kQ<ڈpSk =/T%ޱX?K7k}܊@d<+pb+D֪<"BEϰD7pY"0n;vJYPȘ}? _X#@L OgR>Hp՝k a]?=_f)<7=HhoGhm^ĒR˘ĀfM+;aJOϭYQӨ/=S?c͆×ԌUs,9gSs> (ϳКۚ2#| ѽ_ L^ϕ@ՓPMDƏ1_|>|yL%Mdpw2^~IȂ(2<,$KoCt_<:#BB#AKQ ],D \ ho;H>" uXQ\wd Z݇#9Tc #΀9B!h/Fg;M`z, ą6PR^0/Az>؀e [;HHEԌ<]MtC"NBXx+.D*M凣{l[7.hp4q7-Bj6iS+ h\dD~YxdpH,? K{Ēam+UV+UM:Z2PG: wx`'dȯ MZ#Z4442v 2Ndo`[w]>ZMӝw?Z7 Ws)W!p w;7Kg|)!;sD!cҌβvE5(nD(#!w{63nx$T&v@]DbnT<z~x?B{fg#̧[ߏ~6[ۻ!TסP.w:w",9HaXn<|G(H6Rn"bg?מ,@sڔms0@s`z!!H!6P=yHXeutqZ<.m]KwRf!c A<C X[λqzLE*U9'^Mgh'2%t Мd޲5>cE d}>81.o{WNPc6vMs?5=6d#`eKok_b>tsejje_}>|>?+Uo(ChEЊ\{""Pw#OSJlGr)-܊@9zOBɀD8?D&dIΣ?gD;w/#Cc#)B2u qл" LE'm D^k2i ;uYmޱ6"<@fdq#ldPQXa`">0v ¬ÍHp4gdEDH8[~)g!]'d}UԂ~a[3촩JyJ{yLO uCs[5~r7&Ydd,GJ!|^wzǒh}_u0E͵Ο/Nxo+6˽VڤQ^AY>.zsg:)Ÿ20.l Hx"0#WyDBq]粗uB#իAJH_Am suH&Mv_y~i,LsY;ڳz#߈O\Db.#, 167%n8p秌G q}W=/ ,G} xfHxQV9e5 %t#k;#OR+rZ̵g96?n?cZ@mDσK}d]SҴc07v>c&RLH0X28tXܪܜLzO^T:$`AMZu=yLW|>¼!C$ŋVK7۳fAfxk,'feaz_qǨ֊7X2MejPy7Udp8";TC+H$'&$Sϐ }y A2E"{qK8=g0{œKx!y'2 yKv-Aq "AQȝvK-vo62Hfg]9llc 2r0lq<".pWk;izD@{g(pd_W 2GmZXH h$n0Uma+g5:2"?z oTbh.]&ؿlR ^3C6dpǪ Teb@@kdcD[;G,HTBB$m(~9W})Z$V"+T9d#ouDrkW>ɲ꒳#OYlDD$ȟFZE'xiWw/D3~;el /`:#5hyޅ-b%Ym_?B&C(K"soHܕI'd7?pvrȤgf鎔\|W}>[C6>uyuw.X2ع2P/?~Xy 8n, ¹E2Rwt򵊽7op4q^i!BU2H8D@vBH.dqmR.W%Ig^Iy7 IDATƢ#o\ALVvc~w<d ɔ'Z#Bh8dB2h6#G#bՊtzHE8@Q1X:+sKos8O=vwB:xFwY)U,:I."g|͕pODӈXud7 % |3_  &dv֎nVEԊK'SY!M$H]ld՚k+,)ZֶHhN8@B HSe W #ȃ2J%mu$'^⊃ĽHh& t*x$p4q^ohL3"p4SX@6t<RmiD.FEpRzo?b;!g%sl"H7㭎V ƱV2,Ga^s;^Q!R|֎>2")iu3焣#QhDBtiLeᶛ*dmɻC/]an '7e2 %ogZoZp4q64:5ŏ|ȝߜHv_z)4oD!e~T[^{Fvn[9J/Gkw;`7d;־ 4}(],rVG2u~v>$+.Ce$z R/O! -GrɟV)]\em; ՛A2­F? SM9@S|_6|>*P_#hb3e ǁPla}q8輨])R[ CBՀ*~|5̽Y F|rƼKA!qz ][# % Ry5gڑ]D6"~ˑR>zU0#y')7#l,VNw!8&3Dq;,r[D\[6;B<#9_-aa* ˘Ro۝r0dsw lo_($rՅ%˅Ӏ8HR>ǞQ<~֠=~G,$دEB=ds`MG ҁw/vo!"68Y}z.g^#X` Y+lDUe6ңa TGKnV[&=N N唾{;rr[vZ9?d0nsҽ2P4 vnKә1/+q\eğRܽ7]K.yͰ}4pAeoǎQB"?luGZ]ܮHCs$oL W"5X#\:97 uC7``c02FdcEkPXyu&!+k3& r}`r{q$:,%pԊ"D/ZM(?zsCTZb h_=.KejQ,ޛ%? (`!r20+M\^HBѺ0b\"8]"|δkw>n :?&o@#)w;=XȚ Z O"@ii+*]vֶQh_6F<:-M<(F+:ZRӿ?p4q 򌬊GBGW!&G ͅHy텄KG6Q+'`lvݣ6"@`=Jsu9v_={6ΈpbH oQ6EZ}lې^Cgۘi_CV셗p_~Gkn R]r!4L:{FZ]Π0橽> OWXu]a/dQk.juX3eMEK64zcҾf39;7oKw3Vu1.on߰瘚֙ afejL/99d^=Y?ơ'-D/~T??NaM!~)[.qE-^v8lwٳAH6975'!Gro|k"l:yR)җ⬂t}a9%Zd4|BpZ#r;<ݥRhg4jوqr %G5G%ݘd:>/sAκEs?n,GBξyخQ"R0) !r{2x]08׼G"22٢>GBYMp t-0mjD01̱ۇ}҄ ʝVK T%nO{%۠+U˿"uJ8?HHp4q.ε"t0^ @1U`?V<#"@=ϭ4g됧g8&vK00`'Gh=,gY;By5ݑ"`⹻"Շȋ4V!R3 /DZ@q)'#rB`H݁?Xzv䬳&#gjcFdv][osoQgsl t"?̾|6f"Kh׳4C``uEsP6RF >Nc+ʯ׾zmng>ȕ#Wٸ~|b] ͽ3Fd^#-T<:u>h}(f?mIeW;jl*)S"0ÐBszwAY@U5WBdB ə:U:­|!92Խdh_$vR$}~?3-+"G+"+=%c"{xY;›ӭe1;aDV#^h"\j>a?,f {Hv%CCD/ ~gk;aG">֝nT]|eSD>_C1dmNARA"B}[}0 ek& E$,GB7TME#7~uG{?'vץ񓫷H%ׂ cW?bǬ4q;2PuܤK4p43i<눐liݷc>B`"d lj).B yFVHH"_#"e]A5u5HǮypK"S΀3x^])E9YFJxgKU,,E8y&6;lQg~RXhT~vɖ%)OҚwG6!kױ2 (@ ? jDc#"[~H"p#P6t¯B־v}ƞ3ZпE"D#!a6y.;ďbw. G#b'm1i<&RHHl@;ى0K6d OvvC '"o)]vYG*ylꙈs7p+E"k.6֧֭l.\B9oE]m@ ll[iwxq>> ^֯8Rd9ZmknMurDo_D}sDD&@e<ڢ::!M#+04G@k,qn'2UYȁ^I;_nெyz_i[=2PuY,y۸ ED<Zf; ZsG(K9 hbއF!>$Ku)$N@2ɑx ԥxVDޔWDIJ;Ld(¡ 3ڬGY#YC8kB.c'S+=`+Ϋ|B|R72ڟj뫑qڟ%C^56N֟c<#o']ZsyV.B~HnErqD|bdc,ĭon#@Q+mGD^$k#cF~D}TO~bԊ=>\ 6bC4>ߨĒqh| oӷh9 8> Nf~ejn_-3+ m GO˴\bKd8HilC/"w/R(n$l!a];B{ABy.;i]]oB6 ;Wy; !"E#3(Ђ ڮ@ųхH3#_lkO)R;y"a}G [m.NA!x|oNHqv# }y.ix$4+MӞs,h]w"R[_m{8fEɈF6NilLJ*r2Ts/e2l-Z"R*ADlg ]]gju!]B{~ "N (d$/D}vo-,Cw*0HH֝g.."=}ӦVLZs'W7mj8#W|OК\:~ru-߼\%oTݢNK zf2Nќݑ}ryFk#! (TC>g[R6"D("HdϾ g˼ϣ-" xq gywq( D(BqڥhEHxgٕΏGBPrQ2UEg# {yBx#`a!$A־) v~xi xmB>=.}Z{[{_;Ş1#Y!+mN_F?K6mK88=uw*nNWzzKe~6DgrZ&U4KYFf_@-y^G(߶XtZ${?1M"zGAy!!Y/kKxg!#"uO"R<;#{,rI!y:aP ^2 wpz8G&xgNd%HOɵ4!7snA8AXœyD~?K.;w!žv/Ox;f<ݳaZ+AZlמb5F80t.eͶ:\h#2ߋp>HZM[HiW\}״?G{ۀ'W_2mjŏ_Cxmc(ą h֖- @cd0ȯ T% }a_ב^gZ+U-P=Xp4Mj?+ꁄ(+!p/ H"y>pO<z/MgI !%Md %nG  BD(2 KGcl.>f?5H_hAޤwn!"ud/Cʽ#B*V @Gdb4 ]&NEᔎl zB$MkKy|:Y#BvSvF'_BwE޶=Y!˾[_Wsm|j<FmgYׂldlܝړ@a7wUHy!x߳YHIpLx$tL8P?73M,KetD=Z[\*UX2EƑHa4OMޚro X*Uߞ\Y9yX⑜2dP鍷yNgx<{kbS3/h$\T@-2aG)wad,lBX"KE[[ Ͷ~NGJ$ó+Q"dH y"vo "YevGݎ<%;ߠ(j$+v`t˭CQ(1#|nER}6Ӑ|wi/Aޛr4Z.<Ϟu[d4٘noɵS_#!wV-'WMxbh-Ābɠ_o[6m8ߴ܈nchfKe|d/Ēcx5J,,FF@UWZŒ-ދ*Uq{`>ȣ D K[gޭw",NO#KUhDh*z]VLpH ABp41Y@lmg d~`zy]ӗ 9YQ~w6@$xx}"<|LD/ic3 S$|kβ~FD`g4X ߆zy Ε#/Y,N7=oD[}!LGBo wjobu@* Ekan_ch܅' )ms:|>Ҿ>6k>Sx ee^V4"r+C>8;xYװ.> y)I'~nx$b LRTRvr zg!E/o2/'ж֜&G]2k[Q:ʦ~SϮ[ IDATo?>$!CH!q"+eXd%P5ymXlBn/5ctB2(2ЭFFbHRdht/x1"H=VgΘW rB+$ۯFxq."0RH OkPx^ሀvC!GY;yO#bբH30Ү %s;kqm%ΎDsmi—Fke-M|w"6?FG1HOƳhԊ ZC?9 ٻLds{Y5 eū}>z- TŒAIX^ǖ\%~KN%稍%@nK/Aki6z׻m͖@Q"' CuF5m]6!aNΠd$R[p41aHBq sއ !L! Hcc %mHbi&^&AȠ wm7"KMh YdAO<:1:G;&}""`vg". ]x]E).t&c㔱qwgAl .Kx_7_~gae^Fm>jlE!2L眄ۣ} g#eCm1܃ <)g f}9tEtG G]aY λ9(\ap4(e#RT e~"kQ#1ɺлu.2Z l!2ɽ,cF!ɕ<+G{gW"RB{[_g~#.`#v _tx$ D+U`$e)i$ЄY|H-5Ob7d{)L&ƕٗuւvx$a880$ܦ@7 Y.|م)Fr]7!{<?lP;x抐 w"yKxR#tʹ: Lj+1>s殰ņ IҸ:0+7sH^Uxat3)Ư]3X&NCK>E֙GR[D "Yn&#oė#7q?ƺwp+RAk#Xz#A@}Wwh]vBJnxg]BpBqw[0ĒLj>(I6F:Z%Cej!4X2x%W \ WY,Go+UU\.=_3t͖T37dP(59ii)۸LEa|\K.$V"<ldrc= vEqfdx]aKR~$s6ZqH݁K#ayEvLBr6ڳ*l\Oˑ\^c?;j?Xl éK߽F;'؜ocp2H=mc8־#9}P[Hwy\D:##Cw`<Os"7֞h&¡tghSeӦVx4&r{e6dl&Q2QccSk⓯3mjdz]8 oEͯ[ztZTkWJ,삼Ň#/doۈ6p}e: {6Kۧ"WvuB:H#Q:7/?(HʩHS^|Lng9{ϑF/Y"K~ኗ:_[DDVBBy|8@mD"v]܄^EH!bt>v@o H0 1 H߹\2 H + CVm)G/6x2̥moE`v"\fÑ[ o/߭akӻ4>g⋐n]{E=n; 6". OH#z(to)rFlsB=q к~&Rg#ߍu ^ a]:sH8)[' e+j<#Yhb?k_boR^v>}o|TMobyBā^+d0 / toU겞o.ҳaqkE%5f>PxϐҀ_# +@XЊd+eZ,ߓL"d˕ !u"4h"YxγՈE`${#Z0xod^/$^"sVu^"5H=v@52GBh̞ GZ_Ve5.L{B2el .p#"|l腰4%q{[ 6MHX"Tˤ[t/;Z oUe ׵ן;`VMd<m£OD_{7<ڷG?w7(WyÓ4Oc|nβes7}EI,GIdYG7 o=9 뛑v]W;-Q6Q V8 igX\K /(V5B>dq1CuPkϳ9K05N]w yjv-rjeFa$f'ϝ> Hvd܀r )<)5Vo9"jVg/ vM|Yأa'zK[=/"2s}eH] 4 K9 #PnZ\vu{dl lN6%vm>r*c!˩xB` !K{Qm>V<۽#)Λ"` <۳Heiߗ MyȤdJYφ WB[6wk@ժX2ɎuYۜ(ŒAwvQ6w?{a$NUR֐Ο,$wBl+@/5 )9\U ^Sr_5=SfP`)ʌc;!YۆHbOf퍰[|y m[6g#ֹBևfeUhv2~>{oH-\v^IuPgOgs[ՎCx62ޅj.9E?dHHl/Du]e fw<*6GBwgމGB3LI7/m-k%AKW_A?/>!ǟҼtS/?o}r餩o齛+74b/+UoIʻ7ҫl0?2F$i%pT;ՄNu"e+sP䪿/ HWHI_K ;'-z@ៃܦ{? HPNɤ >J':Z;ߤ~瘕@ p3HF 4Po# !A/MGHhKďꇔtlePA<@nAm\}  EV]k?K2rw3gۜm;w'yÞDd%Xbm~A@d۽5wK2)'u״InC.ZrVGg؀wPjzX{uVo^6.,s۫%?ZZG#eh3 9a^{qnv[ߚ%tVY];ЗdgԱT]=wKK,C~xƚ%UI=:CRM[k z6|ySBwgrrd12g{ /#VAjw 8ɮBDB@rc=eaZ# )Ȩx{f#XɣhEl( '"ouuAx$4;M%Yoq"NX_ܙVux (%Xdl1``ckuhwh玘}xvd~?4/!^o^(5! G=Ne3e_f.,p̧ޕ=Rߪ3?ȝk(Ҵ?eC-Hy" /UKv166ևiI#cKB CYi;#`@ YNB@z"F'=7R)  O KܾH܋Hʶ6#•rD3LoCy"Y?H)i9Xncq "pH ~Em< [ " H ,dAG}%Z'CkɅ*Dk`>z>Gth}:oAmGټmSU2P\:2PUׇ<4 1:7*?'OCud{bmw.54iCgwD\лE3!]ɡIxy$O@ .Arv(BF.H޴!Y7ݑlB ,eǛzu ?fZ[!l aS2@!9w/ؕ9?|P>Sn,Edخ3^Zvwumsl.@8^LuCzŭ6m>:#ށ5RȀXmqơDg;m<7[^{fܻd;4yeD;W2IPrܗ+ƦOwâ mɥ,`ğ齃i^t}RWn0~f.;hGl<;4 J7%ǒs% K/%d0_~G<+;,!}򆣉~(yde?$G!ӓhp!eF /@3E UHEJro${!ӧY4ƵYs1Зx$tb Xn)lȣЀ'H`@PH%{nHH])Rkp- x-Eީ60kkYz#ehg0^GBKlZ1 'njABXˈ0 F'xYEnȃhc`?KHLG.eFő9h"/ "BmBdh޲x? h u^XM "M9ևww@-618/+ф _<ZߢdYfm|h\?{kh T}7wp4_&_V ݧm S;s-k/5C'qʱ,C2&ɊxA+@}BA>{a}ԝ4a] #EХ>Jd/kȈku0o2 ZX{oCƭxs_ːGQxʮCF6Yy6n }}GRc6(kdx=DNDX[l:X/{`".#m;/0VFgZ'#߰bX ;[׸Z?ӣ׾mdS/1b`jzfw->ˮgKo!d|̋ɳlAC TM%8Coi~D;7 B8z7ӧZ]֏{@o@V,,G{x Z u{Ē,`Ū%ukKjοWW]p&Z7ÐwuHXh^HD$t\w)w"Ͷx!b"3hs8 ؾS5H0N~&~<W <#yvJHkH@wwe=5o^՞XУmw"u֯Uxw#t $hb[RZ_#^vM {{"^9)V;).JuuC" Fo͋ s/9p'6?Fο#_6W!r;9bϹ>ٸ?c|EZ)[ uơԞUom{ BB8Fb`x$X*UKb( 7cպ^[>ZLmJk񰦪9SM# Rp[ Ʌۑ\G/"OK@$LJdR/" nom9^fUH~ T#hbQZ h{RV"e6cMH9<Þax2 Z'=ȸ3>X{/aMo$ۏ@$f۳'jgOW߿F^cmw8m~ ^/2 #>z%Iy:MnڕP<9s "_Y0  IDAT:YPvKM]&:j}eY1Ml>Խg T=KEsqh،􅱱d02PFEM`遃aCDKеE|\m.B# tvtZZ}b[*+w?S7R,] ZbK}b&X5b#VbE]eU, Kgݙq? I懑s]\;=v#!?Hcj.Z@RĻ] /H kFňD\BnwDHIf͑t.l5i%u؏@}(P Zֶ bt#A[B;\em_X,(t ghm DF#"Cy'Rʐp, =ow#a"f5Hhh澇}`ZؿkRkam3΄9sn$LF@t*K],ix~ ª&o# !_Hw6b ٻ oK"mC5a}9?Ί{!>kx鶠(R2WpUd!:) Efq5mLB!1w?=P gښi]\hZ~bM#k }o!r'$kˀ뮖I ]52)#%0$#]At^22&elX$h>S{}$ 8p{,ڢ)a|>~A/ly>qⲊ*JDVtdtrwwB}ƒ 1B$ O">x9SйeRz";藠?e"•w EBHpE\08YRv1{f=D} E ot"^?Zx b;od /!enilK~4uD&Xn$"xYTťwGgeX /J& }|}~:!C$Y@E`CZHUDjK#~R.B!$n[~cu'ՄXYCYwA $"=A͓-*k(ǝEBtGmc6PD2Y}jV2>މo^*d0l< bМp4^H Y[D~;̀c(C܋2uq=Zw.^eջڳ6Z{lxT,z? )7#%e%וo6,zf/#%[fze%J76l+? 2x܍LkD@.@#9 P4;"\1.@ؒx O4pkJX{c ,hruve@` R* +⨵@*wA}Phs2 zӐ2,~"KNGtEH`_8tnh*sڶdYw!Am-Z=jysVsmLy)/ˈkn~pudw-+qKo)ZK_=R}æ2V'hp4~q,Z߹2 hnWi&.wo"Oҧ!:W |)7"a]VQy_*Jڡu1aK<[婧>.Y d5f|O='oɥo-*JAi}К/>+-.-(Dkc{4/ǘw)d Z9h;d7yho9/fKYEX{hkmInqJ[C)XfȎEB?LNSVyDJV߆:(x]͕vHOheKG!glҗBVom@ T YDu䝙aw!}첑;Y`&!kRĻXx )"+W~c8//E!0HQ+vm}F&oAK~v:a$-EB뒅Ƞ7:/^GX6;8"vH`Zβw>mn6;❹ZkYv" )g콯 }4"Bwghfl;Vh>?!"{(s2քj"O xT3v9*b lTb?wYDs6;2!e%S䦥=Hh?R,|ڿĖK`XKdYEI.G)ֶ`2LY[Zk_Ycת۷g[ڟzD{k47@n*zR2]׫aIh?w@9@vCm)ɤkK7(()>|\jŔ 4'gwo}շmuh]HHmD m_&ۅ-8ix1B@f%:Z G;#,vS%"D\6XDADNYBAZX{#x %p{8Dy#,Ȼs)NC:̲AxutzD,zY6.^Xdes~`c`.pfc62"w9\hhmbRT"bn@ q l<4";Zk}t WY.LxZ}=m 5 kSk 6 .AՑ(RP\]<_nz}^TVF8?tgiHBFf2~ƻX#}s3WF 6Nq5H,EFNd"./agxxFO,AwZ8;l,wBkޒTh6[\bX$tS,ZQJJu25QS֖H/(yKEBmV /HkꙞ?{>])H1>*(oڡE3ekT.C=Vt}辴ԭ QV+ Ż7xakIP%,E$$.EJ)ݥy\"q/G*\f$"x7ا0ղm"4?#3 ,gpYZ:Osje"r;u:ʫH8ڒ^5)U;xY\\P_^6(bAk{AQkpD*wW|lw6pN,Z&#"`]ec.cj2m/B g*ZK)B#XR_| rRW$Z Pwk5RFoF9aŒ2hOTZf!%hmH2 EBA_8dvU;6 汼|Z-YN*J"B7d[VX$ty,"c7c'm>xpy,j;#Qw؝:) *$pC2A8pi`B8SrEyLIHw{]F\f.8D΅yD)0!G4';32y״Uߴ2Њ0ls+ؘ6ػQF<"!E%d"e,7!õu?ޱiHfE]iu`ޢw}~x{~\7s/u]>e=LN"-sOk;m0T+_r[Y_mОfտاgWQqQӫo{u/B?h= )\} SCIh ]Udw yu]K~h3R{ |R\2 ,QEAl G nlwn @RB@/OIsI(,Fq־"B "`γ>#`"19 g+a֖y~CUp/DiCBx/iF|kdgDVX}rc^EB6_ Jg!~ފ!Hhi8"eX$ gl%tȱjcpóȳr"w*0ΰ1vHzugDTL}@mLd/lvD!%'u}+s?Z 7(V{wtAwod}EBpǥd6Z߫}~\$p:b%y["w[VŌC ,ww7ތL{#l=q7tz>Dt?ĉxOXYGV0d뉢7ruD8Pfm@p2H©}D3^d?]rF3 As x>EB Gi6FֶgEy|ٙ+eW)+jjs6W!o1_hVAxY_Y}@F8vE8q q6⇏bMhHQmCܹds^h}<`m\xnë|)|>N,-.~ ޹W2Қ*K˛ỻގEB߻W*H 4>Joٸβ2dwI!ɤ5Hk|ߝ*(ArEv.5c<24{/]cl3?0y|Zs.]ҹrwT*`e"t?p|,ѳ> !{~AYw. tGJ{%RD}Bx6"WTٻAexAhaň~i5i}j)ܞAI&X;@?-~H~Ѿs@6"PE@3$CM@!!ڀסuB*4ZrǑ!5Ww1Dg##)3'!\J$l^'%K7Γ#PƇ B^"okI6"6~ ګEklMkﮰ1} 5"olw#B^BxՕ ֏lpmӗ!޷h[Piqy]YEIOD6~dP *m+V` IDATUb,90`v)?@C Z KAܵ '"~jAJEqBxQwJ»*$:oX;1Cq;⎞VHp4 L>hYÐ'tݵ)$ΐ 2x?<s4) E܄]* w"%h"*>]R M]mhr?:qKH9jD<"TRbħ5 9݅@d,2& }r.d*-.piu3њ+~ePAI6ѳɊ"_3cC|?й (d!K@KiB˅EIҧ0["b3mYogG ~>D-wxBLw.m>R.>EBp4~RHYz!aӥo=)!%oci bưK?A g?"Gл솹xD@2b6{Qf\]`Ʀژ"ÏD}mj@dk՝h'Y=gR^E7)ѼBHuGw[筭D;-=mJ_ YZ?A9xD w8ϋEB޶S|VMW"%7f9*-._{ V]L hʶK~a5H /#qG_A84;bOs0^2 wg1P.ڟ}*s#rd ƻDax+B\yoB|Y^־N߸ptw%y茔5Vw&}&ʻHM )H1mxaӝ( yFZ{Jbh|[$ f:zݳ g& |LDF{/y5ov9zY~oT\8ػ(2>6Uoiqe%3">XhT^`dxߣGтm٭)?Ÿd+Z[_UWZ\> (xeq2Fd,nv*-.WVQr$([. lBmraOmVJKHH . aFD$"h{,Eő k툰VۡL@ XJ4 ȱ A#@g.[] Gu#t%qWD/ v/ [쇬O!6@ kpw6]p\# $ E޲W  )hS8z8ZZO!J "VȟG Qwm|zڼCJvNwNj Ϙw)h=2 m ӑ5qK{,S`#nQ)-.$ Hߖb[39CHPй%h_ ~#3SEX g"@ YoD0hAXa;g.ln3\"qjVxb} ;qO#*[H"kC87Q}׊Hq8sGH^(B$ټc{8!N7ā%(D1qB_jb 3bHp4$}`d<EdszXn|ES`ݹSGTض G"K|¦)9h^U~K-2%{ i~I1ܾWms•;1'e%Kg#J- #_w_T8|f;!e*rW]ѾH+&z?S޷v7|1Ž_T*`ZcfXzrះa" ܆\z.5?? Yh 5.-&HGXEH ug.$l5Y@w"H9<m YhCd""o]PIDel6Hq,d5FHs"e6.oYVۼo.tow0w "n$»e۞ls Nvކ,- 5X$>MhwZ_e{0$x D67ZA " jwA}B!oE H8h1]=9 <]M RFVR$ԌK~ǟmLehMw5:$Z_!R)S[z79R Gm6gcPM=bgnX>>K'aSKmYx0 =w!yO g# HkD82NE8DF dpR$\GJ !+a,ıEAl| O3bPў-B:2x+k "xw*?!w.`}~,և aufoYF;y>ļhk322}x/~N/~hEȌ :SV|Д;sݩ[w|--.Edo`Jiq&il*8/#` ofA~H`H`kßþo>=3Q d7W!<W#b@`e슼05X/B>" jӥ]Vw 1-Y[u|~X~)#ox.WbRD}do4_ټD@w!Cl}?F!Y3lmR!X%~1qg$Ļ"?Rܹϑ/w9b6C8?P#6}cp{h V<Z;l0W;EBl=ݓ\76VMw?Эv:w*7D%9rᶬ VnvF`(/|][ʫ!PVQr1Pc꒚UR@r ^ikg>y}4 1_/[KQȃEf;Y|BF:eHH kF|=:A OPO 7#AC8?3LGB. 7yAP "y)Le x)һ8DE66AKG=i\"J>R6c.֜>?~4Zp1ml\No(ZSK7^q \2z N-:d%p%\26me[yKWU3hp4O,EBla <<;ox#/R.E.Pp|H񘅄t-?/SȣC F:RG#d$|p:G ,8 ȶGl\wYxiLJ!t(M3w yRnZ"LkqH>j_nӈґp/eȣ{?A|ku1慣LtKm["^,ǓsNCǵ糐B=[hܝWƠu:^XgHDFI!:.?x͖X$2 Ʋ_YEIqs,soTl:kUƌAWEm'n̘>oYcYI/I*ŷcp48/A{/ @d(!Hh zyAоAEl+)[,v22 J><܄H-P|r _!/˾>bs>R D"]M356 G =:#+WHֆm"p)l;HmfHU-zxԕֶk hm8dk/`S?-9)]m /EB_ۙHys)+2Sr$):5w"c z]ADkhr1nQwQH!kgn ѡӑb ZKyPqR4Ab_H mKXnϴը64ҙ x~ζx.R6{j)γՌ֘> 491"X$EѸiUHyX$t+e)|!3#n[V'Jz)7 ؒx7 jk$ܹ@W ɪCX0}P$Hh \h_ @X쀔cwC֗wnG qrу]6X$@x{>RМ|o䭿y!ydx"RȨa=R eva~H6yE8t;|'C <-D2Kwy&=)(@< y G㝬~X$/2 f}(6,{3@qȺnڰtݵќEop4w,ZbaWHol}kg.ˢ#۳h#`&'*JB(Эq-.v߽D"-gFd͐l-<|m["-ЫG*z]uG5-@% B߁+A$KLe[[Z>{U34V ٢ ɬ I> |d]TTejE[y*,Wb26RxW$p?jIhdjGMZG5$P4(xل]<$ƭ 9IZYQ0mdq돀$}9g p!e\HlFxyjށ,U"`m!g#rN 7\:7~" d*(@V, `}f{w#"? /{#֞۳ص"O?!+YHo "ewDˑq% m}wTdK]=z'qi2*Τ %uw82hnDBBD-6w"?O9mEf6+VoVp#b]ks/kPݱf{ 9/""B[`|ej.)yyQ|@jop4 t2G] cT;G"dx8/E{ 3KwCݕMC\a%"az&R2^x ^A/GWm(!QU, Rqi@uչ})`O.Dž*[I5(Tp 2MGy@:)7lV m z^i K2 J,ZmY~HMX$><51Ykj\/:4m\QtCޕ6~Օ+-lHk)*u<8n?-e%(ԅ0^wF>emA본d%+|ҖGscT.v]]Q"h7o>H .njOi@a3SSs*ƝZZ\I/e,ŋp&"kMm϶_ ~i H/ 5hKdjtף= Zp'oJˣe%]m\eeHrc%߁< W"^R4?xldEjBf_(7mjzGSK0uKnq hqK]@7{nO{o.J{cH)r]9H,_x~T#A5x e?Dg ݥw [Hpngg<+ssh#Fֻ $C6 zlXjm}j ){v{ӈpW!{>Pgklu!X$2|'"idcmoۡ)brx,mk{vOHfT.%R&X|H ʵzlAю6n0uk]aw~p$TtWk_e\e& i53,pj!xejw)ZRVQ2))|q.E#"Lv+SSEٻ}I7ۇhnNejpmg]#uI,zG ّB_V^Li<=hϧsK/{R(nz* C[G0 @/A"o,j­g"19r1RE?EV{[/ f'q@k8T#Zz;]հq{/֎<-xY #CXz]pX$6QHfwm9oHh~c@@2+F8~列R`)N)(yc#Yq/{)IkS&Z#5]Gȸpqiq:ny`ג\._3+p. \in{|2$@3iJ嶫ah((ݣc_anWf7%t^ 8h׭^Tkx裕[w[jX=omE)ZР^R8Ȃ;Tڄwظy:"`n܋6P&Rm8'  ~?W5s,j G}} w?"%l  G#`=yi@+0y}%^ +Z#UHkAk>> Ty{T\?" mގų E'Z?.XdINu쒻.6 -\ QG&_:J[ ڹ70-{۟2mXGK>jc1 g'ݓوv~7”Z[+.ki&ZW.~G-[]޼~.@ca3'wi}Wd S(Ґ4f(퓜Y㏞]$8zԍukWOeMZZs!~_]0SWٙE>}޲~[^ffc*sr2hN{#\paCӳwCvſѮ_lX$46ƿ@ !Bx)u~r d?"!9e͗5H8m@hQ!mNs6Ŋ/fM BLaHoh|y"߈@D6h3҂d"> [VO qB\CT 8aЮhbv\[Ѻ֦KaJo#|-@_[cB> GJkߺ~`Ӛ}q@C|Dak,뀔ws! qgr%|ߌ|K|!;҇w8)pˆ;O+Iă.nоoͮugh Ziej6_,[K-)#@8 x(G{Yڐ@eh"" X Dai~I[ /[4ԧyh#<^* w,dwgXd!KS\@$9Zmhmֶ=qHXiu4Z?ݽ^Vq#w̴BW %iх\w޺ )e77)ٻkND'H-C{W!X{kxm^z RZgv68_E¡_DctEE7ޙLr=7y:#Gh휁! K;l~ĮK߂f3%hH!ŅCBL #x!FBþOKQ$4(TbGJr^!'Ij.)s%;ZY䛽;ǟ"/n4ĺsEh} 25Q43P3pWob܆pv$0^C(Rw5+7]yD}l܈8x8Mn 9GQ ݐ[ [d7f;o=w1.Kp@ #Kߓ^ydcևG],R^D!r.G<4Șq 7=?EPF_e#?DmEQFsfNf_[2|O6PFbиX$! =a_1Sz0#?}E|o-0/9$t_mcG\5dG=RNx>g웋?]*$XVl~&y՜5"U:eaSe%-eHsź/;>>H=Π\ϕU<М[SY=zf? 47uZb'|-?Uw} jTPldF0m=o߶Ơn i髍߳{\EB5,JPWXқPDB GNCBKQcS4[]\"r#:Q'{t|S?@6^iSWS=^!Ymce- {|K 2k HHy!KMƷҖ{rn̚K89+!m U3+=o[2 )<YDgZFKyiMD>H?clL\}Osi+m,@  (g}w!"eg?/+c`ֹEJE=u'ߢ5ο4ƍZx7{CmL3A&v~R`|zo.˜Ɉ[NBVxr~S^b5 3`qM Z3jѹB5TYwu:iU +_A]wv0e}fv  2]N1RNx9S.x~ah>ӥTkc@ weʉS+-ޔdu 7(נAJ3"L)T.2f=)>k7k%/qcm '@I)V-KDs}b.TT  %\J}y{WuߙQ,r EC 5C-…$$ eH -rC2`lظ"ْե9l@)\~?t.kyy>A |euHZgkkʊYYptg0j_S;vtµy{'せ- 6-ےiceԎݱ oՍ= Z]hS[eu`$܎h%~smhi,>goX?qMA!kNeU5#v <XQV?be-D =MDBi#$-(쭯=7&k#bZ!b>; vڌ{Hxm)Z?HPl<K0}>w_Y6!ptߊ}k f# nB@u Q;gAȲq)g..1ˣzO ڇ0L_I֯Eˍdw ѥv8YsHNc/?=sIu/obmBtUk?,˻yNu;MH~R6gonzؚljֵK eFCYd)gB3zڸuqsl AHgr_[d VQVYf@siPs֧),vqH</N[W5瓊D?"q 20Wo$N`%H,ǵ5g5MקœCJfmuK3 #v>Lx[nCSYă\eH9Akv2Dr}"/:{Yw c {O#c T"V_8jɬC"~P53]3݆6(Rد--H܌pod<)[# -A8>y56Fnm6W!d=n~ڜ}V;E߳񤟯OG< e5"p@.hsYf#  EE^@BMRPCa܊ ˚c`!jd5zF%䔩*y8> 8`czG J(#& < Цn@VPB-Gm"a%Jgy )- Hq)v1&eRLAϑu/g[ρP0C=`Pl{BC¶ (]\DK=hu-,HF;߉zlF4ց"{x)Jř6g#zlOW'ӑqu*kPywߢ׷P E땏O ŴYyYx>Z.dkD ;EaX{=Ak:4 F>(7+\ڞFB9Q[H-x2}3 D82a'R6".'wPdӎ,d)F?$4Q;!; _ {wœt/yj(p(z <튄($w0REh/WEze%$ 1_s2h፰@px)xZ``kX}S7bkuK2n纜C <4έDt4Kn!jpCħF".6ڵu^wDJlI 띙d-G2n!!8\Wg܊*+%"+l?X?ln9 $;#2aHS& ;坙,{0vFߚ]XaƆ!2ayK_+ۛZ#@ճh=9m9??$і=rA=G z1LCSLCS0Y!]ԙbʲU_UYY]l{kǫ {]{6uU++˯"eUShݏTV2eU>%@Es\+s떗>QɿO\wH!LJ%bMD,ey##t#@ϣ0PRAg`_$CnZAJ|(q9er⇝K d0{FF;vR9k,~S5Yl[l!-5!ez$/@} Mur؝Fe`b=IVgVaiay;lyx8<;W+ɵ~EH)YBިtrdO " m]. _h2nm25WVjp]Aɶ~9v}H2S_PB[ӈ%Wu<y~G#>n^1{w\/jAHXssU5</uxRGk홇OTӌ\|SkƇ {>]rй8<œAMViP34qٴqAK>}v?¥n}!ݒJd!b:C­Oɍ2ΚQXhN}&  ^;;S_DƝS~c §âC|S'}  U  Q'vgVgecrCKzwG71Ҹ"L'{!^<8m DϿx׎}-?g"GbU' *s^*[o/'Q͹rsa=G"5jQ1Hoc|a|dt2ғ=vuT]ÀkWѯ#[SkMCn֖Es&>9hkVn"ͷQ` `"K *| eU:;)!,3,v \ݻr|'l7rZM&>Ze`]{G^gCS]6}%crE&-[ eUWVWV_QY]ǧwr]PY]-;6q^<]Y]7weu/2s="LklMݸ_Wd;H+B ;ӭ~LFթDlx2=~߂6SstLA5Y k]ÐSF ߳n;hS @wX?C؁B,Gf9(!ş3Ud,@`?swC #eŷCLY{4ClOOg[dُ36ɰ ^SvU@w,>(s/a6uNA}"EbhX!q v|2$A [>x%jsUkc['^YNzxtxנ:2Dr)Jzlh]ՈF'#ۊϸu:RuJn㿤5硱u z: w`=4S:vFtZPhk'nKt5ißMtC[k\2W^CA\8D |o-)wSMRZ<^D|9Y{؜\/oqO6voZ{cﲦ*{RT"Ay9 fœboںP!kId9Jv_w"9b2r{v:W0(_ER|>@<aK( W?h )'6+F;-?5Yj IDATN%boa꼽x\[D̅O)6D-7Dwfx/a*Ҟ)(jƞz+L{Ku䵎hn)D#y9-9mˀ+ʪ>2ydP9h*~1JaHB~yuHē?յ 7pDѻ}b7|!הHpWG Ⱦ4'!v$œ飑ץx ٘H%b/Ɠ!pRV#an[8nm} ܯI-8l Ebƕ/3btՑDŽ\ uZ?۳;RۣvR9mCpOy>REmZoRr!ecc([ħY %i:(ĬܹH)-Aa= YA砰x!=1HCvjۘmHvN$|wg Cݮx\Ngc̊psmμ]HViFV!)#^jG>Y9))#|q/>Dx62Lyoy.7ƕB z)>$ 흥H8b^ŗ)'ߵ{zKۣM#k%aH9YgVUejiA^uZ1D6q#Vo!e0CtN4sfS C6 F9kY)`k{G=Fh4cvd&@LcҎxg|)vM6cE0E!qDw.]b=tSMth(aēh\E9[!h wfrˑ>CEAMp\CiPs¨b+r h>2gru!v {KA3P,iiPs(ޛxL_OGBˍ[.f"Afs*k4 H}ũD S& vXE. G8s>` HiCH@!(:\|aXg־eϾcHh|׆,"|0t /C әg㜊ўbVs5^*ֱ=;otf"~4Ɛ^'V>=(xtNc6X(%$hwjDU(Xxha7 F$HqLH", (ޱﭪß9@|bFi;ihAJV7)%]ͪr!bkޟ#,!0$dd~s*NBr3R*m|7nd FE /ڜg8C)^!BS@a<"ӷrXB NBʜ+p--kZE_GJTgs7XBV [c)}Wb2ɐS8wg :#~g N Я/bns5s$EH6{ڻ´Y&׽Zl}pg ╏h D w aI[ϥx5ѣH 1HJ Bp'].7!҅Q>0)-̃Me$20AѯBJ~ ‘'Q:|5C %\8W#DUޥbv f΃U![7?^މ?S7ŠOXSػd)D_ % .E4aoݕJ~h#^2篑&|콵Dlf<s@됗m |] ;@Q{"[#wlj{[pu"ͿYr_̥d;D>L`2ag&Dw}y,2diAʦ+%’" "P8X-wR?LZY\s'`c-Gh%d 5'aWKieU*":ItLJ`w}cƖw,Z>z:0X`.U\Νu =Q񢊲zwz?!>w%'dzY.s;œd d:)0! tGjjZ92O">RrVz7#ֈ?u |''DRG)) ݭN8rE]Čnk!@a;!FrH%bGu'~ϳ(kC-W$'M HP; "`n v+oBBhOd'Ӿ5$$@ 6:䍙M6 an|qMdOG6z1Cmt#fe凙nt ?3{F!fX< A qk [Ǖ.[ш)nlt{XAx62$$H%ba*q|!܆Γ#k b"vD!,A3 !fGODPmRdhVئwB鷑62ĸ eњl@FG洢oM+.A;u1aiPs?=ߴg8%5t ēx2)L}+ͯƓ%ȠC<dY#zyaGl//Gp߉qh?N6O+cPo}a}Dd!G{*$o@t$Z/Ǘƽ-½HA?x2Jß{Z_<ص|Z?EJuqc4nݴfGF`A*ۄ䡕(KbY*{ۧ_&s>Ea.LEYUXQVՁ$4~3KH~NCgy\ŴbV1DMh࿹=dF>8gψF?oe֒6ljˊvF2۔lxv,x7C,\[ 7>s'ЙaLeU]hWxU=7ɟy.^.9~>}˾w-?oFq RJAe(V(T"VODɳІ uwV~[.M-N|bow!2=k U·<%O"՛jE u֝ Rj@|3ͳ#KNdzE*{ƘdD~Qd뉘m к!Z* .Ӆz$ygp-Hsn9y) YktF}ͥ;-@NakІ]Wڞw5œ:h'Sؒx2 5<1,<}Ek~8>Ɂ+Xos;1-m >#F7CaDWhePHmd\;`!e9 ,x[|=1Yh! fʂYq1RFxNBj(R퇯rHH fmΈᏵ{^1~)J #S37!d#xۂ}/W!YG?Qec=1y^묿vJ-R^@[a!0v&$GSع~=8_ DCi؄ l.wY>HȲ>Y1aHa˺pe4lI+ j# x;OhZ=0zq8n}iPs7ww#!7ќu  -} C-sn2'G?N!ĉ;^]0p7d<{G{ hh|Z>Y`3D qlq87_idyF_1Ɠi'`wM<>񀋁Sئx2}7O%k  #z,D I`.2\B}0KD7R]VD'C)LJDjQ&%q#쟉x㈗w$"A{rďYL%btzN˴#= bO|;mYXCdzoZ{as>Zn;yH=ӳlMṉG:?d??~ɫ*,h?Ou*wx/gK[NӶ3w-:%kmSXͿOkp{&pY ?o+ jvC{{}bSc' vf2.zء=e$LAFowTUm Jeuyo`0 LQfWqq(3c*/̓J*BH w%KHcG$ّܾhf6ĹVyH.F{}< Nb#?-/[To,RJл1iv"&~,S w6"&d{h'ml#/cd y6g#l^bW#Ƽ3b[lL"JP0|6Cmr'];%wC1?'ӿuSk7ϐ?˼/B\B%Ht@]ieZms3<1"ĝ+p+ j!h!RLSY>'!&{;Temz!AJ<^5ϼe-z ;η9X /L?dC7Ɩ͑N9,:vGX{5M?sQcdV3v9RݶSW%JĮo[ 7^a&A^=m4re&qwFR\=5.ڴe9%kƜx2}) }܄d m^$^G qJd:dwIyyx2}\9a{ϵ~s0i};|/.]EY՛lF4@ ٭O,^CMGKP4[]r6TsḗKReh9{&4(ZȐN w}e!"}$c 3^"C4+Jd<6jk3{u_6(HF7Fve%~$ЎGV;i6hC"#f3+vw4uW#c#"@<"h>! rL3rajKZ9]DBӶs"ۘ?5)4g /bL>wks<>4Ȋ&,([6>Hy %n IDATeE̠O8 aΒ8sC ;ȲW` zO-S^T !oY$bHpٳ>수fz R$]HY>bs]Z EM$tGqƓl]Cc?h0Х  1I֧BpFBCh8~m|9{glF͈&nm/R4d|2)F5eV-Yl K,9a„]Ѿ /Dٿ4 h.E1ˑaA+O" 0Dmh"<y9?hi .R­ +kkFw(D!KF\J>>mgrseȋ2LwCQ`<Kk#.@{[HwC۝HUhxR7M"%3wEHt.NV#o{hiqgۭ,}րy<¾!Zw'6>C(8ZMy9[?ۺ=yosoNzhO}ؾ|Wu0i F9L?Ydh3^/rOf*^+97:`]+yYsL O TWUm쯨v<ɞ WI%b%EI q VW}mWkd:}^vB-NE!/ڣkQdw"f!a^y! rA@} \%"A:1|aFD4mH m Klrl<" 1ڽ"e3D87> ,@GI"B$`DeGE"YEĴnC:ƾ3Ӟmkq$NF K 5xj3 ֠L)fe{љ{n@$#@ަ\+@ y y%q6.}ph[͟S0A:ǤǬTĘF56U6w"e0|B6"P{VBV\We Bm[!Y|a?kzAGN* ~U*/5{B#PK[hg.D)-6D2.@ްA?v_?o vvAՀS ༀ,7,5 Zבd'|xC!b3;YH]\?!fY-L) p3C>{|FgSd33W&C|13\$y Bž/'#Vp#ϞGF僐B7g,}QtD_l9a]'e uyH}} |l*ya00C7 ٥;Y#3+lOC{y0R*끞E0 fDq(\N{i'"%*@ U, (y$Sl^V!b  "/ӑe? W! 1g:6m~o+KC<y{by4>u7$OO@X%/?3;6KBrBF\8_> "O?"Z@v>!{ؽEL#-@&$h@πgyM"b,[Hig"Fy\h9D;GXYvGtw"q#H )-D,:e[ySAϼ^"AKm湚:GOFX])W;ć ?؞s!I4:)@.;|ld=~ ;5z R$A Z<|}3W }ϝ(|83<8A7HQ_\Ը6fB_x5;^h=W<6=O'4GQ 3R6ڞ,sG RB+t 2!O!ů!MHIsIʱPS [kPH3Ha ď.@axO&D_wCnFo#c3tgg4ϡSp?좻ǓR WnǼ,c/d/Y3WL?ᙗ YѶ0$[Aݔ `%_>%᰸!/_UUm.оlB ?]7j{56th4, 2곢hQY]XEYU+TV(W+ʪTUVVdlo_I,KP!` !}O$Џݐew q 8vGL)@֞z|Ja?c?wns{^C? .j > yH=88-xĔƣM0 cC\69z,nCVAf#{ Ɓ}#b-B33\"$rxAp6(+MK<}?\X߰dugߖ|E 9cb* $ǧ?/8E:DBjt^l4 P1~H? H1jZ?YEB^H'k(~oFlDz{o.fvm8~rl2<Y; Ҏ'R؃|ɭ4Bf'*N@w科E).CfAP.sz;ʶԈt+!Z>bGېPj2܌D{-q#M)j |Mh\,%xOh'!zğz1Yki; s>m4Cvœ=>E@49 {{! y&#rՃM9Ƈ"yQA mA:w{#y&s g!\0:;WoG]eI΢O.7ehO>;+O!D_fn[%|Y w.4=Oڼ־{[<>)L'SX)@&wNE">/†߳v ħv @Pbny”)Hd c5֟o~eSƓ9Af:Ϻov㸡3ۛ[ Y]7lo[ػ!U| 3Ld9yeS 6c$Y]TSY]~LEYէwgyR@FS=_-+f #9&_Ѭp ^,.wk*ʪ>~bvJ$_T!Y$l:$ 0?;D {.@ -q o6$@1dQc.OD g|d.RF8ݛ쾨e  c:Ş߮!΢x2}_Dx2}N KɕnZ߳j$|wwtRBde)# q,5A%dqAs[+pz8]A6Ĝ=B5G޻=>C`mlY_KUj7LS!)6ݐ%T"{<;@F(u !k?[6? xbwF'"e\1.m_cfi{KHǿ5%9>z2[<#f5"q yHAe$>ɕ(p@1|VNg=2D]l Rܙa+Yv>h9c$XC.Ex݂hdGQ́{mL-H<ѧ51`pK{{ ǬDBx>eN Zw~z'pU | _D|tkDSw 3v=iRID_UHOec"< 4hkw3lhG}>HAN%!V?̳ZicndwR-OaJVUV__ I-<̊'ӑ4%{x% [ƾљ[smAtT@?P lSY](`2E01x2j'ZEYUXY]>)9 ň&#H <YO"eU ZL./v+ʪBMJ''!g˴d!H$"V-hߝY]4 YyA#j7\xy^Dh Z; r!"6l_DK A.j|w,Tk(m~SX%x[䎟?r2R '϶y)/k#}Y\ra65}$ci#R#A ƞ^eI/@4cc<_l#>3? 1kW3*TN1 v\*yx2= 6~Yxط_V34e峫/7 ha,\7 ǟ%t Lc,x۽Dc+S<v؊U[iPhlVQCnW"!c6[+r h.eܵ Hvq\# 3֕\R |~wN%wR aeFaHrVZ;c-H h7@JvHA:1k!.ܰ8i;i*~s6N(H%b ]xB?7ϡ%i5"uhBJ/\}2R\NޘJ^'#r}?+nAؙ3s~>htk_e#<HD'"߾xuvω~`3g2$k{"Y&+zd,ymKQݲt_0Fx>+$.y- ' v/dpWik0~R Ǿnc}ae!3a@<."y#?ط;m}_+9ނR?2H||GMG3/mGw3Lo_[b ۶eUZROBkԜYۼ.wtL4#_~72dz6#XQVCeu]}o L1H%H~\hI񻊲s,DDD7;?9*Xdz$t}9F sHANz\ E#`FxB@┕ˑǥw@j8 % ;7"VH{{:ׅH0rauc1>-l+A)LW"p ۫[=!w; -r|ẖ1Y"Y i}N ulwL7 vFJK 2އKo]lEJȀ"E/2B{bd}2Ξ\{"|O!s=6TLF MnG/CJ٥HVqƢ8CH=yi(;Vh w~Ȥ)L+M|7NqqOISRUC[mi5d78jlWQV5ݽWY]>$c hj)x|_\& 4׼1}+7{ǝ->77ڼ#p! $ℎтNDʀ;MR$mY-1.w?c§מĽc">l_dٸG &ys9FB)&ՉXD,21uuwY'[`mC:\'c꜂@>ccrႅ֟wGuE~A |t6e=>mFڶG1֏\$t~gn%+:A )K]mgFɁ68 [!F}P\h1tq,SjXSaˆF X_vOqC…],6wE3l]=.%kqV=ޛj ˓_R)]% ;yIn83b)YO­݀ hʴҙh\ R: фIxH* IDAT; .O6wBs.AzD3yB(im=?uB4p$x/D]~Ɠ.S !E+"X`{KWл{ANc!rv2C9Gm~h] qsG&V!|}7-܈?{UHy|)>۳U\7<*$'Dq!Cb6ڳ#չXdR"y{R.~/Co8·|?3†9\],Td9)uH:0Dgen6vLN!fs+S##mg}x2V緂c D,XyƯϬo,.HC 7jr)CLg> :3 LܾOuƓ9|RY]^k!WD/*/ ݊tEYվh3M,A9Hž d??X~crD_IXd R@0 ?h<:<_mn)vCCᬖ֦蜃~o{)@H`y!@z^1@bC3 Vx^D`&ޥAJ@" m@BLg}F!W!hCh]tC6znAow[)E#7ÝY@Y"7-B||lͯ_l kq j\R %t(bݐZMH;1 J". t;;v|`Jeuyxa#MւWJmԔR򮒠某ޖTP}:$d%>|xo @5Gnc\ ݓE2<12h51yB>'qmwFVvw]H ? :H{_P<$Tl, jN͔ r}Ԓ?_ۛW/W'ZB BĵxrvƓ32l؝ɼɻ"tE/G"+0sjPWeu܃2w">?hRΆW 6Sx8ij\u6.Ȉ|7j=rwؼFƄݻӈP(dZ3O;χ_y!ӹp/2,6_O r] 4^0ϻw};Nl t^Ôg_֟3onTU=SY]>@wM qBh y'2>x H?)XXhc1mmoeul1C/o:Sn@sOl 6m"s3  H!k~V߳2 1wHX;^GaAxr;ĠGo6w"E6w f+B&gygXo!As8(J!z鍿H+f A|z| ]tV6hPfJ] dg5R9a1[S0N;GF4t}Dߠږhm:Ahv7wpss!NN7 ^!_ iay|uVZDY{|3H!;4 I>FzI4~wC{@7-6!=s74t%͍yA|%gJUY w'D,rL뽑푡$"E7]o3Qi YW"!RB P_`kR&bml]๔ɹ!&~/^op iD?94cZgH> غeшMG450*`7ؘ$D+Hiveڒ-,ڦ=CLi+ޘz`~IP /zx Z볳2tD,2 ·grډHx h<% 腌6Mx!$,ED/GB{C!zrp \D,RgYB޳^Ɂ#r^WFK"x ˑW`DyF! [~6!}0?0_4 D&$8cHd2Q b6&,C>_u%=+o_|TH;4o;y9{ʄ R6C2xrw[|ظ'l{"߽h,2pf^wvEF@xț?a2dԺ1>Ckn0⁏#yao&!].d$PlS".H16JEYUm]W#ZBAhi^Άz寡%ݸ|I_jxvJ-^TUU}TU [Y]~7'| ڏvk\ut֋Z>At 2dO?JnH"+>1xrKkwY܁uw 11:d,)]h_Ě)"xb];f!PFL5$퉔}"0s.Pom w֮^4೧I}(SIUgrg7{uFWp5 yg%bV b='ZUxMhC.Fk2Q@I48 PҞQ2\mmUbiCȸ6בt9ºVzب/= q{-B]rF$lD{#IXtGLpձ85H8oF ,9gHJ|܈ۈ9Ɵz1ļBD>Xa%b mEmr?bL{70Gw+)K. iE d j{ fᭌG K5)ౝ7nڵ>kn7iby(z9Z뽭m0L> !g5O%b['")R(ؘH62X.v\"Wuq ,¥ug䄪AviH"a0h߬K"X/J[ЍJ^9bdWZwA~J5,ʷ/~moLG%AͩHh 9 sRpGj܏Gv528`I hϬ[:Ha7Ʊ Ckϝ"@'>Ů~:\ |f2NCh?F2CV5 O'?̡+/Q`y.+z"zC"ؾ \c^t ;r9bC܄pz I%xfcctd.O#r;&vJ9]TE[TQO,(0[۳\MC{'RHuͩ 2(~pb;| 3:y:05fe(̻N^Z} %-'(*a[ l'As?6E.g!Zj(PQV:+w{j~Άz4&sKG+ zŒ_8^vgj~QKӑ/NwA,~ݷ`p\\YB^FB* ӝ?)!.I/$$Fe7 A%èG…ݤ j4ut7Y݃XE A ^/k!F1opB<L]P: Vy\S1b8/!^a@ ,҈vC֊ kHq|boF CH8eϭ1/zms81F֖K[ȃVn}ng!`vqP6"[QhC=0x`_y;T[ `D,R'ϵYl<D*lk&n-rrw=?wsДEFDlЏmRH:yb{"zȲm<0(FHa{2'0PH6J"c#F"y;>ƓE=VlEɬlZfJ%AKCȈ1%ؘۘ9mgqQ}l.{wi(Zjta RU3F-+}#㶶" #r +JgɄ5αjm_r(>9Xdt u)R||ݿ_qX29='E|qy&EɝȗDz^>tnYY][vnBVUU%yPQVIeuM̾ߴsS"-O^wX F0i6![<#! "i@e%{8nG ⇠7Fp#s.bTK \6#搃~9wNBu~kA o=Y6kY#om|.Z;ݍx}Y|bA Y2G9%A#da}Њ.2fĄ x4dIm}\^@,Hitwm_  @;`O6\dEJ6c?Le;kBBcx/|"]lD,R'Bg)\l뜃֧f_] {Q8lh<LGᬆ7,ڱ6x ps:_O5]B-t'- j@cIP)"7hosj36S\IPSyH-EV i*$pLi=]Y7D}L@XO$EnO]pݙf'#ZxAHk"Ɠp$+|Qy K8۳oCٛ䒅"x5HI"XY]gve?,.ۚ ST*SVwMUUM._??rs MeUVV,(X{J"Y<Dɫ]- Yc 'GQ. H(vb?lmXǐ%8!PcW!fGI(\PBJnLç KVL%R"c8a<|cg& 1*F| Ng F( 1F!mn6VWktx7jsB"{2>}t$fNAYYLǟ9nad!zN}ǙHzޞRFc5Cm."}_`!&{ՅL`ugY1g#و,E>et:2HBɩ,FYHEH?S8-xUX >Vx%O0,Xd澋Ɠ!#I"4=,O5kFo>{* Z&$ /Ka]?iu28$56Zϧ29266u aXGֿ,ZUl{Ѐ??BAy~|?(YnSx/򂸻#cU h/!![")Ɠ1?Crs6ٿG#o;xͣ,Ygοd$K\lk5!ms^/ȓlƶM/'ۜ܀x Oee#z8 {)er w& pleu "2M4*ʪ+=vo\껬ji{lCC}MY;Pi ,zѼ]^j%y9ξnErDCo+ʪ乳TU~ >u."]ޞ|Uy3\JD8Q(lyL \N}6"!%<(<ON7}@υm@3m}zh +[<4"f1l{w/kc+ )M&η[%HY8>s.m)E @Rf#E,W^kӝ17<$kcFz!E(&} Kl{iAEtq|KWٴ\w<'H#8>(R.y; 1Ȫ )QnyD<6?!Ĩ~zGhߍ [ >~K_)^|2_PWGS 6Vo8={O1oByxCt][ {5/Y;`.3\Q)]j3SJCIPsڗ`š)ig{M-[Ԕ[ahuF4#ͻcP|ѠVfԝr:7"5rY)X/+I|a" )$b䞉X ONBhR+ spvZChFk? vGٌ?  ză&"˵q kM~GәK?"lx2lǧ!E$ m<cJkg; rs재].b9z)-lO!>ጀ-61ߣ9ous?'vCa+ۼ{lw>N"_޽ a6&$;\maɟ4PvzTP.;#z)es6T&7)Gtrlndo<(oj>$Mj| ]rB|PH3Eq(rGF8>oF 1"X29!bdɋZ=GN(ֶmy]RRMAG*G aP4f Ĥ"0Y]hb |\B3">W!EAXV! iH  kZ$0"pv~ FrSz|=@|# CqV-ݐqˇwF閨h)];"F=1bko&bb;>FG(DBX96N|)[[íO+30tp񸌌m>gΞnx8@qdM` lac\Gon?1,N6{5D,@['[>2~㠎VgK ( Ú?x._Ǣje>,1 :^02 B{ PenBx͹ ~oGY# J"K3`ᐻVD.\5fJYK-?H"">ҶoeG~0>p= J{ E6dS":ByюA B* ӈ#| -oM;hdy\C|CDK6%6O)7crhetȻs 6WӣfOAFjd l^~NwAJG$p1dhEޥl[-wh4]|]QɻOm_|>$|*iml̼Ω̈́]{ R\N5fuZni@T{N$l>޴1X'!u=kD,2~2K %?JEYco;?)D#[rNzhE3i_ Xӕv>ecCǥu.RQV5t󾟢6'`}YI"oFyhbWH@^y66"Pc9 j@NN_HPp>τӧ!Սl݊U{֢8kwmHpZf gbqVqRHkAYx< 0_ѽ쳳ۥogs,<.HQ]PHbwR:.A0NE̹Pv 7\lN6H(pe fVhsubK@#SHy OkMt;v;j3[`EV7^9 2; 6SzUIP3u 3C~߉H'h&%b|Xde</0>O a€ftğ` 7_MkMH(G q .Jf2xtF_l V5غnW%*>6MwϧS[kϼoc`-IKry}6 )%%Hy)Fl;Nvq_x֧,L4<Xd*<ܓզCJf(z!hmpOo{Ȇ5-;V$-h~;lBˏR*ʪ>k:?oս:M>Br#v|nǶUV 9LKh BgU~ @n zG#p"dՀ/d?3ghSa"PH$n6įB4m#6ܹVvHx@k!b^G"Pp$߂)l Y!PK+ߌV\$Af)@@`R3wZ{"eHizÞ_xWd9o}/~lKh")?Y8m1H NYgټb;'bSd.ܷ38f!&xƥ G"ޙ,]e6E`\+n[PNƥ14REFɳb$Gў/vJ"AB{mhxak3Ov؟nƓǢBrw*>Kw*LIP/}}BN"5[ֆ#,.m45tmym/ir%EH͓SK5h Kb} =q2b^fJHU#dX!gs|S%A8Q(S:xmen~l\d%RB'byn 3"Z]~ ~=+V#>aO@Xv3ȹв0c{>V!>Pz.sF4)HE ֫ e!s=q/" ѷKP(LjEHn8 _hX~:CꖖuYCr;R 0>`;"A{iG2@>v{~#0I|"1.>4}7){ǟfcp^*W'9J](L R}p=>,m/\ec:}-!zlB;g&bIq<${f_$fEauY-ƿ37EaY?MrwF}CN͔=G:ZY]Ftaa>wh=#f+/! IqRQVQeuHVo⊲%(XXCS?\w㯈@1'E`9 ҁ wv(; R2^G`<} IRHX~ ގ­&(튄9Hvʜ3~l4Gw3$^@ֿXC%J~W \Y!+3!DnA6)T#Es{|: @Ʌ^F54ZvG z޶ubHɁaAlmEѵDəւ쬆ƛ:nSHi瘶~ALu>RA4 )5 RFjr;\{(;sIB^Gal\F T`}c? =lFFb/R~qDSB^79<6/lL[q;\?w)RbbmϿ%_Ɯ-)g:ۍƟ-F5]P&R碹͔nKgϠ}Ӹq7p}IPѰ;0$@t͔>S|h|g%܎5^H~ h<9+\&^hYvܡJ\O݌!h}G5[!l#l ;1Vf!j@rݑO9p09ȣ .i"#6.6_cvA]݌Á6ώ9 (}~$m ;wkE?paX,C J0 ¤: g7b=0 ޺=g E,w[?/§wi[m,H> ldn;cmOܺ?wtn!eraX?]IwS=ۈh? 4:HH%J tƓ#p[t&5.Ɠ+˼; ߚMOvau/=m)P!)MgƓo6h#x2\Іwt#u >`" [{#P<yCg=rd9PCPGDlX: !>ׅ{hs]'lVrrA6ƴ^a@M"y'O)6'#{ͳp YΖxDiG B׎g#& ބb>  H zY$Sd۳V$RxOO"Sd#kl b EXr,MDwR)}ӒEgVňޤO! 6SfJ +ݟRԸ0ZWvp^z{o2Ľhj"p|m} ᙯm5- jBB[2V__ypd8t*wN~c)X8El}tD,V4dW": |rBpZ4 & :/B6‡|{_ķ^FޕaD,R'gE;uu^d>ǯ"ӈe@g̣գH1Xx;O|=cxx6o#>8^?#+Dqޯ n'ħ;x@PTA\u$/A2@͛ y: |}>D2)ywօlϬ~s_?)isd Oold9s E_vx.μµ[b2d%bx&OBtm-(,q2B> EZ]!Z:9OnEGz6Ổ3Wh<;H"_xM9ɀ*/d߫A3E-^I[r=wRQVBZ(XvEcfmߣ r36{#1'fL3çnD ~ !oCbQo!šr/ h4c$~t r< AD>)Ti E ,WC͕(\/RNh? 6X=i@](d-\&UXd{BODJ˧H('A6@[Hjl,9o\ ˍ4g߀6}{-GZ"BRSЮpkSHzȊ8~a}yd*^ )QOC7PJzV^vnaAnCLb'$ c[_솄St IDATHHp t^pL~5VnDC6OޕE!O^x2*0bo.떒f?h36%ASO&fJ7w?w-EfH70$_)ڋ#i)L,0ueI03߻韽p>C쎬5' $GmcWQ!w豪ݢfJ]:g!3B=޺ XY~w^eܖnә6Yk33K_fJ?bYЦoq5(:aˆAgZ}E(,F<8$3YNC@h< K>*'h߅koD_<^X D4} iA烪ݹndLE'"{N$G\Ja dxXoN{*W`;w<˥?_%cs1v+>-־jym޲v~o{ ]&'m[_ؚlr5uǡ,vHg!O! #ڂ͝+Ʒ&t67v Ɠ a}ڌ~/W]D,drHS"l&ek 뇸ۖ#}:IѦd)o+ˏ(j(jER$7S?ek"+{CM#--99(%W~_|,:{>Sh:qK}v͛SBt2ErUHx DBlM]ExH:5Oއh<˷mw{v8nd ^[T~ŗ=TY]^VyߥoJD,jQuMjSm74 V6Hn~/#?V~2 VےEyAml|i C1w~H`u^"VX1;Xy #.ס0|4@ )fnzGΈZxE\,'  LC#`]m~6?:]p+iEr(m xMHaFng;֯)S> ޲5GHMꌼ|Z؞y-;S"SoAa"m\^w㨆 li%Oach}]4<'}9Rl>#zkÈf L#cãIk()QTkw]+c[{bEw׶k(.JBHOs'7㿾%=O"9Onm%+OnD,r4<"7$b'ːza"m!iۅmE2D݆f>@ BY ⒰V{nvo}u.ZsR >w­z|Vt6g"E<@!(i*Wv=ww(2Zi3H1HZ r,D')SW\>,e]߅G \E2F w /y)/38iXlkDh_Λo_6jy4@f4,BFO/Er#.mE҇eL^21wԔA >Kȣxrkw Ϛ4qU h78%+n&}>iJ~=p/Rb{EBj Z"ۇvkaG!pVVZЩJ27O>7E"U#`˗hQs) f"r_B r " IwDT.+R2-u()v/':$ogӐgq t!dHE$[gW >@DqݳRE@0#զy HiJ̞b0\KNzʆƠ9m,("+ߌD7X{kW<{⚉_%ۅ[CֺxJ^qv}zY5?ټAC$icm;D!l/w82$hu\-ڛmqAfqj`2O"/@Fچ}]swuvOT~~RPy?5 u{7w CmпfypvAd)0.OX;ɭ"(j, qO, ϰ}^Xdi?D!smK1R:䮈O!x ]WJ́yUOWA|eHq?f[]]̦ c쉰u$h ;`->a5'Ҁ8xP7+w/h<91ON32M+ТEE)x[A̋0 '$b*Ӆ}`y(km[{AR~9!r ;]5pgD0c!fːpePv֑ͤ[Asv$lL{ :mѾ5]Kkz< ^E&YNC޺pd;ۏԗ!FmahSS,v-"J PHx+hyDh@DžÑ wD\$bwdi4|8OvH"'b 7C 7i/oS_G璬4# ;.H3dm]֖zzI"-ߧ\bsH00TCIeL/t_?U @WG|5uHhvF!+C#xr۳wƓ|_U Asṏ oC:S~_l hqțYF8| ": U) OAKVƺg.x`n~퐐.Aa㡈FwnoSxqZّGk4$\OOARikw>,:d>7n_벸5ڼh<'Q!#\_4d8BdR^GW<2\"tJ읭x%?}XBA݊@S_yha)Q룓 k{o$4dH[nur[Cط^1f-x2wz7v%ZiM L/y7^nm }ИwzjJSV8,A[k'bsw4yG@iHnr2EJ2^̖fn2B~-T~1 Z;ù]Fɋ2Mx^W|5;"J"L2A4ȫQ& 5H_~w!Yd=/,Q/!`cy-8#o"K ["y Q}Ap4x E!f xD,RflN@cw%B֫ߧ}qgL{D,r; x) [+OA܄H8)hwCcPs9] HcպWrDL/@!#%h6zuן<Uxro){rV2'%;s446n=R9K[rkRx Rbգ? ͝Hw7 յy8G 0 'Mmr) wh鄬T C#nTEu|0pga`ڌ C^|{Fˋk?f#nyqαHVec=@f!2b >]hîF Q֗wml{pv]vRAkl R& Dst\l#% ꄔ~sҬη~x:'pJZM`~[w 9# ~t9d\{۳GYsM8h<'On'D`Ef;=)+^~NIImvg&PPf`\iq6\'^dmڣ5[46磵T&PP#kK tBuwA"Y,%.\DYHuޏB BDJ,΋Ɠ]Dv5~k{xOQV(~wr,K7L{*4"@SZohq݅@0R0+2ǪG kϻ-1roD[O/H"Wd K `VKkZk)-6>S"7M)͡{6!:Do_S:8THY"|05!~h%RnIj}d27@h0R hUM &6/^ABmߧc"l("+}w|]$d]o?h]ru@VEλlI[@FY0ӥ[,,u@ZD6V&Dci]`&:ilEP_R*h/D㞏tODj QZ\F*vΆd5h% :(OBZdE“+ޮbdTqiӑՌTKBFX-O@pk"œ_(&/mk^"h8#o-XrH(w\}Zn-H 4DJAx"\fw7,tkvAXRMY_C5͵\X=/sH dr^a}׌W޶1hN% \|(< \?q#h_?6(ʽ PZGPjb/r_ $spb@ :dlAB$?wqo3~B~dc&n#>Q~{Dc(D o ޣx_#.Ի2=O,J%uϴ4ЄnR2.K+gՁ?m/BfeԆ2j.-.G2-'+Es?xJ4<-*y_ns9vݹ]R @yCV푰30))[G!YIp(Z@e\4]>H_@d-8@#@\*c߆\#§ ~/l萝]AD@J! ~ "߶@n P4]E!V=)egGVdihRT6F21[s4BR*#\LDP#Ez*& >}6XDݐuAP./\[sP(p\\r B#f#o6VqcO>luE!\Ց֞HP*q@HOy?ϥ)SPxH "3_׳#ezcdi) %tN$ !#K>2@ji6n'p# gmp-Hmox) )}->!6 z{'($Z''pyvN6-6{!# _ vOg'hK@wMEI3l 5x {k6ٻn{HvB%y'y@aVmLս)5 တS ˀ82w9O}'kKˮ2dG4~ H,Ζk݀K.2PO7ezI4~M( [%~b>뾕Ev={:h2>,4y2|x7~.wBi"$20[2ӫeVz}cE+G D!@F)#`n"c{"a]Hb;AX")Ɠ?Pd;2= IDATV#Z[ aO|nD]w3EwH{N°XW%bHyY\D=]d}m =RzsGDF#XNDOV + ,7k0bmپwC0PceXhBs㰊/*?\ȸvv"Ɠ3ޡP2Jy'#q.C8zʡ кvx .|}5ZfujFΐp V2>P1HAx@H sh~zk#!#^#+Ƈ.U!āVE֟'!"r[g!\GmÚ9Tzn9M{|g=R"޼U {k6#uH vi]vވD"9,O)+X_^nkW} GyͣHqlK 9CGTluYG-K3L4EIL|"As'=XqGk`I"PVEı TL/D9,D}e6JVmJL/XfM^5[*_Ͼ/ML+[ȓߺH$XE*;Pp07 [Hj;_"j@A`{3"7_h4à̴pzz ݗE~Z"K$vFx>ϝt1_^ͅS>_lDm ;șxrR0%V.%(d  .eᄑ9^ict<>mEeAI'{ w@) "hyT2:vrdqhD>4lΩ3+AA$av#~{UesBs9|F4z"H$]^_CD}y l`Q*Imr/s RG[N0"(9~R*߶@s `gE) 1 )2ր1(`$ Ȃ݊b NET yhW ¼~K'x(I;ϟN}FAY=/{Z{#k ɛh>f5h]nMC-v᧋0n{{>z εػG( l@JR6 K|޳=+7gl he&#̽ 6Z.#[}5>Z_'4)9\‘sbu 1)k=#m oOdATCixB]x'lZ{f"vh>7}Xy!{N֚mӑ1 Jk2/zey:R= U!H ۳.kX0EpfcqR"9eh;.\gg"e8.N.-.{d# d*咽UK,$ m\.7 gͲ!>ABc 5z!K\8uŃN Gܻ\VnS}ӧhwvA9"Y@!,uI+>RTenz OX}Y]]XȲؿ⳱AGlwHo" #@η[cň`ryjm L/"(<@Z7d~(Ls8֧IdI?P [7khwU\Q!o?TEWWEWE3м9 _˿^nV'l,wɕ`|3R'nAJ[;"Ls Uށanϙ8K܏@nwֵܹZ]_BF|ޚ04XE yvBː! 'y(BT!;!LsF|Hem9E/Lvεq:z$4Gʌ"4Eξwcو?1)xS'lm}IXY'8Ƌ3Sh>}F" axneL>pUզMCN6"Ni{r2^y,F֚O69KGrTZ#.gg을 .SV6xI7`C2yHq}nc*s)KBHpG<Z`ߍ߈;{3`*ߞ ❬N%bh$3vi۸ >Zr47vC6"ldE#Y;FMjr4|+~" ͅV|xD hϲhuwADׄ1 z솄BW4'^Fm #hNE㰹J>Y*h +_˿^Z:}'G#Ι}7OC d9 .Hq` kv`:Dkh] Do!E'{M.rcYn\Z|fq>l|Jh]MZ;߰:}1a >&7fm By ⇋Wyd?1U[!|) )C2ac2q+15x;;;4XT!3Ԟ )9֯y}lgGZ=[.L3}ϙ.)D R.'L%OP=-ַݐ۳?d}J \ b$leh.TX61{Kzh<  %OY"M-ۡqK[{_|9eO}d?7֥wQ?c>ezV?ϗ .ٕCB}6Xʮh<뛷3o7IˑgF}8~KsSߏ ק"Elh>Z}ST4{$Dsr~c׺h~myh]RTe߭gtǟV߱cޒVv[2gtވkAsz1xr@f4| 'A&z4oHW]XEw)~|>|w oor].ۂ{NH @4쎈̅N&{N'YݟF xZ#"C!7?{) [VJ}R>[@iߵ0T^Tm֬{Q߹f!:#OqJ1ec8K.[]yAs$g!z5>M5x*~9 O;Z hT!#3vH[!〣,gC e<K*~>OV[0c7k_ a!h3MƟu -V#lyQaϪg,Cƒ'>O^EXz2E|Π58vq5kExh6;٤zܢBȷ:V!Y4㏷F8/o$bA7X %*AoW{qV6NؘG5oԜicx}͍T4Ϝq+UR>km E( f,.r4?Ds[_GˎHLćCr47tƓOhc hس}ǎ9RѶ_ +/ 8c޲z5#࿡",Y>5~}#pǨd"A-"!@; ;6"D5H_wGm)nHikmȱc$d[۟gFn0X{!]/-f8v֊p.ςte(VDZHd/ֈO{یjծB#Z1A+zCta݀mD8 ;l{&D6F)W %+HB1۟!]. p˲ʢyKwhDJzDj(<>Wv8R]۳~Oz wx^S=vX˴{"'B q_j-(P.3,Ň}GYXFBM wp.C[hV#[֊;$%,d%[;DɾXdw]2Xo[Y-HzY~R*Gmma `hy:"—; C'UEgodp6Q-E-!]3$}EfFږњ-D`s²eHYBaDCmO˃P/c&Vߊ!f{!7)'bS4w'7ϡy:a;a,>Exֈ8pY_ZYEƓނ t}]>ZGw%2͵獶fY[HJ&3߫;`kz YbGHMk}kz^>z]>VsmlHsaa-7۳F PEZ\8ypB4|͡OVn;/w= om,~?Ԟ"Ss\8amCNw{gP[Z-;*mM"exBuǟxR>Fddx6R@ߝvЦS]o\RG_|Ⲁbp _9Vd2i֖_t(X#byfD,Q+p"YgoEV;Oɚ;XL{:C!Hb*"0݆amd;Yz߹8g# au,yZ' e5^%W7Oreםk9?K/A4DԳN1X!ϠPQ(dh{30=^hkƣwwGd^ⲛmg0.W"`eKK\]+zdQ;b9R07ߏO}R\j`oVlv! U[gG֟<>A:ʐǹVg.Ki=7֯CƉ!0TSSx>$8_tt[XێEihݮA+mTE}W$Y*pg$@[Yj~Q*?"(r!=ʻ9(єS<bFVU0_f(XdoWEuxH` ,$hC84W . P > _P~U" ($w V"!y%=Zγ0:$/Es9)[ dt yv@8El$Ȼ ƓcQ)hQȘt|.piFWkFAH4s]s*Zg9}jOC8>!.?F4{f3Zew^fvhi9nE?\KX=Z!q8\a l|hwƓEwS;l47F$b;{+66Yo@DAZF~oR+."$E 'CrC䁭Gr;~f{냅N§_f܊2 J=ݩVm*VH?x"OޒE>Ǘ<> ]J&O^ʆ3dQoXWeEe6Q7!7SM ϱka3E`f6S?yv~kU؟[' AH|hK]A/)# @vֺ>D 6D `{ kF jrx>3onHlDbFH ~C|Ư;LCd K9Xz{x-kHo}ڮ#nRlWH2[ա :DrC)AF4o313. <%2yY 7VζH IDAT~G\Ce;l@J&vS*_Z5Ua|짼ؔ}Z@х.t^h?lcƮή>GW 4L}{G CgH!w^gk /r媌:sۧz/>!ӣPh͝мft$*GWJlµzgMYPf/-*٧!LJM_S ݬa?WE(X? @ YW$b͋g7w(XW%غ"e,)o ww -.XF%R~(!NJ?55Ⱥd 5OA_? ]dijߕDy""Rgv@DH,pi݆ w# E`@dY0|Xdb4|,=t{AsNt{-*rrDx]I  PmȽus6ÙiHAxhƓevd=p}4iE^DՉXh<38lSY=WXۊ02e,jWͷ"AJ]!g4wCWI"Ud;=aqSxq#{{"<ֶϑ/^nR) wGh|P'ԥ ް⾟+wO'Xmc'ƳЗoz._˿XҋК a2d֢*At8#و(n9>޺",8 oԳK.egޠJ E3Ӳ \O{ ܻEH?%s'{O CBH8Ԅ@s #L<`ofkzlwoErR؞Bg9Y{W0OfRdzZ3ϷFzm<7/z7wh}'[5bV(K82dḑS`ɪavl~!3uBNs/HKi< ~_0ezt WŸ\rWW=MN:SˊOk R_*0k_ⲅS Θ2͖( V4Enږh}E*d5}mhI/6 @O܆y\-FXpp쏬+ F0 eit.HmM8.-CzK["jDf] 9:5 U"rCԕ!b}"v?%~1"+uDeCrHZ'6 F4Υ}N!@a*.8>_/R:sº}::p1H\}]uz3P'>?޽q6"ml[&ELgdG mhg![DFg %!ӎJE6VO!BHjED~9ڹp'bxhO/aPwVE/5?67?9M(ʞԊG0 Ðr}R 0r&E8oD&D0 ߻۳F}6rD=1{ w j^K:a-[m1al pѽKE  Nfm?Lqk[;0R I w}ϭ^^b,\{@{n;{(4<aHhYHiz3)`rk VL]ѡuqQ+f\9}[!} =F 5gvO HR73v@kH昋) }kwkiM p4[G 46gQ]b]Ix2bTD,LD,~b?_Fߴ;^yz5O^r л5e=ev /G(XH) 7&?LԒ_kz3vd<bDX=}$DH }8;l)Xۡp" z b<"~|GivćfXtFƪV]75eAENOaב3 )]AݖHku־Y@/ L( m>^E^*;x{=x1ӽ(yHdW!pdlj?dծYH0䭬icw r_fjvA+^5 qhR~ #Ƕhǣ>Ca|_Xt ?܃ڊH~D IOњ'x6Rf".AѦK~;r,q+WOkՖHhE-K+xS]'BJ؆D,r@4<˟pzƷCk0F8qM.`kih ^#~df Fx7,4W e/Z/G0X ayhm;E_$l $c/eq2HIi@kS=gCƷw⹕ȓۖXdL4<aFX&g ۈ\S[o8>0n(2eu\km8~ra–lHՈNBٰ,tFT'v kio!]N[ AA B)Cj{rG)n7T^nn$ajBlf9-\h}i)@%; Ϳl#"O5-l̂SlW"qog ćq~#7wB]N[9ZY<1 ;*Ne@Ʒ6OE~VO-SO^ /-.[f BXi#v֞KBN}W|5l;w_s rm|O_W=哊 L/8l?OOQSm?}@_&b6_\G5d}a Ð'^ĝxV AȚ =yf#BWjNHp_Wd#ݑ56{ )h5=7= G[tD4.l"!|&1Vw޹.V,ߝG-p?ӮfuyYurG\oXb}U#hEB[0>,] +e_͟H9 4 `>)y]E7L:zE\ϭx{"Ҳ-C|Ea`D,rE4]@ +2 CqX_Y'"$lbtUfxyu,ƓGd'dO2m֡i[j rmr֞dmUc_*ETS>Ʋ 0XxҘmq]w+R9b34-_2MKc:)W,Q$ZhN@h<$Fk=KO#O;+Ȇ> YvJ>>gjA^BsP=+ǮKC*gɲ:LGp{@dxjDk,pϑBVh:}ֽn2Zk> ^ۑn$P7nHEtDIg7El̆X^GXx Va4'!o><yƟ۴`"?ھ~hL{ܾƬENJNvfZ'ٻD<ąxin`q(Ĝt61:ڐlvϦ/g8(R“ـvCwIkGfA8YnyqHQ]xq;/1o+^T!;:vA (θ=~@x  FgH)UH w;[S]pkh[yH ۳ws2dTSsڻ M9_ 1ނv{saƓlx`$3qUC2Zg.7f I! g x/TnqEL ֯nyiq/k`uVϢd)ܩ`]qS#Ph0Xт[8dQu5RXH4E-mP蓛j»ӂR2fD !ޞtd)LG%LF$bch3h+gqq[!+ڡH J#]aHCrd;> #e`L3|rj6Y|RA Z#|ڵ w}d5Eu%AwqLCJ47J^%cl,CHK֎Hd|sPgWcq@"y O^bm$4E+S;,~݈h?aU4 a4Y=Y=D[g<Vz|h l 3#w/Z7sGEݛ_wU^&WcܰL!L B! CbZ/$@=@X TQ17Hե3p y={̙3Sݛiy=o'|: DxGߑk7T=w#2JoCrD_[t'l.cGVg74omlh1wmjɭq#%gW~rŻ'U##S?Gt͵q]Q1y۪Ty^T2;5׺wz'|yQc[ԒR[{.UH~=>kG tPͣj (\ ;~K-EZQhFţqVx12@EicA6} #/P JF6bЀ|'(R!. )ˬS ~"‘xx }atF@Q kl U(fCF]P?ː!{2c4J 66y m@3ߚT{ ~=s7j“3.=AQ8" $ :EbG =;KBE;{~M=4כq>{̊NF6×_/KO+JqѧHR.Rd;ECsv^W-.Zњԡmh@һ$1n Bk-_) ј?{CT骒IĿK-}ph^|UTfU5fVJ/b;r݅ %\N^p10)E Gl}]V!O{aw9Z)Zo#J;*w aPH1>k )-Hk EX,\MH3s)dL;"  t1&=4>z{!b{UG#Վ( dX~?8 Y| ݑJ@P{({|A8ɸ9U;hemnn1lC{^n"HG^ፐ\7E{hDNA DnN=6lczE/F"9ķ. Xb{"srd"h/AhlcK׮n Z!>= z P%x'+6l HWrM}>wX_+m/Gw~rovE1 GjL-+_q'yCA~Fڿ¼u҅g,YDS2BT!A;3*&̂3WefTL~ɷ7K2ӡ>jChD3% ϻ|6w|30sdcL PJ_s#<gDbH܊? \F![4)!R!htV5fuomF1 ȏQH|CqvuCBiZ76v_" 8c!FԈkKᅲ32AT"N&Z$3=ˑjExnHPE6y!WK?=T.`8 d]Z(P^kfvH8{3C!]D$c/C9`o9Xf<%C PO`r<^% ;6-jF? ~  lŝHbr-3MG wq ţl @_WʍyWy p i}]:Dd%H9%E}R96/am]U/Q5ms'DUlq󢒠b֊JCR[ԸE&"D4 _D`$+"6sU;h-CG* g{cnO "F#Obк-@2%|jL|C=[z=7HDDxRr$@xg})srl E@r_w~lu>=㐢߂א2|Rw!)%#EFU/8ո潑aa(=.5m8b=9"L?aE</Kbcd(Vln0:d#*#Z.;K:NeD|)~V+e? IDATLBɫ;3vG|ҭ^ވ{\"^Q?OC ;Z_k, .(' So+]wk\I\l}~9jmƯ~ȠåDF+P@$(Ap<-܊UiɇLj؍+GxF%lr%=:/ꜞJWݵz}qdu3K~sR8eFsG_?T>~|Բ/ϞQ19]e0HoA ]7>|WdnD3N" gsR.WyΞ 1L  =ZWd23mvcsJH1E݄+yG#pidخK EG#y)2HQZ4dEFA>"+]'|Z.C!i!EJxפ>#{MX? !?qW!A#HH7|^,D@ *boAÐ9MDyeR$8h>ds jwso$JQϩDž_U|b@ix 80x~nGç|۸i6h 56(*鼘{w{B =dG+Gh}Dtئv=mVR+Y_)/ڰs{vYCREk܎;F$칪T?rW[$) ţ7#º4dP-ǧ-ֹ<]ΜhR:OB:5h\wLC#R]Zl!E䏵gp{gv.bԊW ]U]wPJshإhqF;2C C1ȉUda\vsl$>|RO@CL}ahe F&$ :+k|DސUhH,TוxGX΁tʾ[ בahPdx ɅGYe4 o>Eƞ+>iB<O]S?etiGLĻˍfȈ9 %h9E158/Gd.CQgX3}h|.Г+%]ow;_FFTo&{f2ˑ<:ӮFp"$ofl@v=7uFţ_یɝ휣P/ I2I=+TWd2{Բf{R}Ny*Rͳ+}[\.0_9|jY7;%l˔ہR7~=Xb ~Ck3R&"e GJ!Eh^&`"3Ї{5˺U)j6t`-2˲?7x7쵡EYF6WtĄhxn"bgb=k6^D#ЩB̝Ck.\go3Sd۳4LBO"C9q(KryهڻܸԸUs|4mS$Ѽ?KF2$LnG@}EQ`vѻ*> f#eʞy\ B޷\:S$)6g@ Tly cH\n)r-Xg$T#)DQ%$<]l$CT[޼&\(BhWJؖGbK_is?ꓙs^[sfzIPJ[IP)WiCX> Uk(|͵¸2VF!,^j HuC:ʮ芔P<;_E]8z-nSwŗkkw&H!uWA!6fW=AB-,=_!|k?qG껑s5ra@{$dOO?HOkͮl/v@[_/`mV [rH,1h06۠F㧌^{ٵ:î ~y(u6?ӑⷞHQ/K3҈lyHIw\}'L$C|A<dT~`ƞ=h8"gH,8%x @~ ܹ҃Yvhߌ CGoNb?`8ή3;cld N ^#ϯr`{g%?N鄯u^gVb1wm! Rˁsx?M-+_1braLm#&WB] Gku[sNX끼6!s·ަ%!E1y {[hQET'PVT(4 q{fPW#h!/he_*8۽#By^+¶OgT\90 %fwsX GFE!H@/; JWǥ4ioAsVdzpQ4w `vG!ol);m}mncy]KG@ -]vru ʧ!]f)[u RB? ƿ"甭>6!E:Gg4y6m.__#LL8/u*ACl $;H8ߊh$nr*U. *ӑ U5Wg^-_I4ckiN* Ғ-JʳI:\JCj"Aj< AE&?H.|]GwDF!Ƞ/"p:!ϩ9şU?jOC+C qZo@xx4\%w{#|JZ?ţH,1 [oĽ2 bHm?9f"&a9 ak~߈ _UWEמ?ٞy_AцhvaV}!d2.Kk6ʵw8Cy4µf~#8L|ltp΢r#tr$]Hit^>]?0i(_ v7G| |E 3go؜Lgm\h-r#`׷3:98Vw`(Bd`X.>A3W  T`:{]UHw\ettz|]?H7B헑."{=g d=luS"ot7GfTLPi6ޮ,Gk(D(uw98v뀄E>Q1U(fYr^Բ3*&_p:0_)4d0ͨmcxjjY_B~o'# ͷرUJW EPݸ1 ڶlf}\ A)MX.(G sV#E;j_ e+B`p:6,8P a#"6i09O_-# yj9J's }){N9)h?8ڑk9y0\J<5ZމNO2o;]ka X:/FˉH"UH<5g LZili&JCB Z<'&psKMڨu%MFVRV#6>szlNicW|c<{?$]'֏:WZNx[*(oi4ycnaUtn- *K2*F;C̮m;Rm2i]sIg,Ծ[Gk|{##z\^\mQ0X=u;kB jvW hGv=:E6ߔݫ6\df0-PmxK@|Qew3WFL<Ν?_M=[!c/ko{W4V#wZu?"^OZ?|mi3BO@ZVZV~=iڥ2蕇w{h~Q1y;P(v7r;oۧluرؔK[v=G"hEuYj+% MK w@PV2557hsIǣG+W0g@f)>|(OC/i)8yf֍A}Ӛ˂Y]SN1ͫ+ =)G^N1Hn=z/H =jxol2~">b mG`BH3:nDZZUoRA7G"0,r@zpB/|6dlixڟn:NA㊌sꔯ!#y>+WY!P!ީCP.HHlAKC,C БHk_ +@BpE雬?$"%BFt>طG( *wTƙB. }!kw(lndK*UZTTތӢTM; wR.Ûw__߀j퀣?_+s\A@sFA[uVeR͡|ߓWUtz_nXbktPj}?mv+7Hr#2pU"!?nߔ&|:\ϣF)v&#hx4|ɻ)-HټmFJhG|3r 0tKC9DFשH~VyӞ} Ñ $mlG!a}kFPg#em|Yh76w!{Dkt `iG2 aQHf"52E: NWU3mmH4 ~;!,ɇmgػ~#g}Wo~:;l2 J.=>[s3w-~=7!7Ɲ?Ծd#y{;uQzמPM>yGt{n|ڛ7XBO5q=Iu]2#0ɩJoB)6htUF0`x4ث2Dͨ=Bz;NGvQV = RvN4G]]~i]]/h8Kw,@<0|52WU0ӄ߻1-3;ɻi(im$@, %!e-/ !A RZHމi@ ;x S. 1vChV#̫BmEFBRv;" g УpnT:yEjJcΏKߎW?3cs8h봢vY[v֢NI /k==w 22H!dD6Wz$܋ :w+wpwͩx/m_.F0Zos“=gn"aGAU?G r䔼?ɞ YaRm7 _L{(u8p].QɋŠk$eg֖-j݆W~/>JG~~gƣX! y)IadȠr!٘n߭AƄ3JKxUy|$g.1w1mW Z+|{ l~CFO5"u91nMs~_7"mCA<~Jü[W$lI7`9ٹ;}HvϏDcENLSx F9%鮇IVmi47fTLB>둡֘[αDw}S6}n=XS{ ^G{~GTޚp7F]w[8*ҶnGmXZ$ !ZЇhg!  F kĴ7h?|ZN )%GQ~vzu J!;1+1j}ހM!`w/}!Rb#O!o@ׅȋ7ewѠ^xŠy5bvLcEյC v R6ϕkUHanBFm196#`ޅ؜7p$A\wd@>h" :FYH?g +_([?yضBBgHiG#Do;O:Q)G+;TK6훌+k}BA,]9bS;bX}+T%AW2eY9K߭GmhARUT$_mKQtzͧdh7"5$8~)Q#qAאeW?W߳YQ[P$u'KׇRevn? ųPZUox<6r;2K*g %hhdΌ>#5|єc :u +FN2$?oD]lsaHNƧm;̾^DHwAU:G6R tv}pk[!Yk<NEbňܱ.j__9SFsnis^c9y- Eb瀡XbB<^̏l_V}Q1y/t3xnDƮupɦr bGm諫z YԲ[H,Qx4} y#yRz5a+H5<\Q~E>aHqUECH/T}wVwȸx~¢ֿ = @x·S1 Hb"EbS p4Pj`$t{Fv:x<ȣy͏Gå3*&F}Բ6lNg!퍌'l+론iQ2_+}OmCx݀d!z.~"$ӑy'-_`Ak_d;HnxG"#f<e⏌6_oo7Z1h)Aƞ؜T;t$xߎ"y^W6/#htDkUk>#_gt)~Px}ğ}gbf,C,Ai 5#>8项oX<x81 WDbA0 _QE4V gw,тL-+ƂR?oÁe6HOo츟ȝZVq#XӺd݆n{uQ'|nGoDb{#KGH,"|1󏐰 RPRn@h׷_=hDZx:KdU$WqH(h}f $6"FF:1$H!Ƶ=v.2&hA)FW[G׌}8 ea->34"A3LHCH6.(!Ȁkgg]@/Fm? _]r |uF^9Fo7>_pH,q+[ ':af}vg7 [˃~-ғRi7ggT5gyϤ(޻noG͛>hy7Veml D DȎEm?Bf<|+o~=ֹ?,2Mvp;v4R>UW>ŪZRh#<av[s>Eke:Tj|ݶ䍏eܾ,W#x{!y盐K %:C&tޡ %ENy-=|2|{b{lh==Q؟cG2a6/;h~gdhU"Qa}|+K\ܶ焂1stꚒ{#[<n{{Hn T,siH.Jӊ n<.+Hdi99V!{F5ǧHKn@xg;hG6,CeP-{!YuNun[c%޴삜HWGo)Ų<V#>ю2G2 6W>R3*&t>4n-~4kzpZ<--tarCF?MC*#{p ,}Jss:T$(D[<"!ƛct>7@섄b_<y{Ix !<7SʐG yS,"#l=x':RE"=1V3$;sMl} Үx4_7!pȲ Է!C~T, G{Nωkh}L!pZ!W1(/-jW&wGgF׋mݳó!E,6ڻ:zV dڹEq~_Z_S'<#EŅ_yH"%h"bەd7߱WhdXf;jF}1GԲ֒rkTm8 =_Sm'Shi7)75 IIPBJ^[t|u>d$ 7RET%c7U)-W&Oms4sH椡5*>}jގ `Ÿ ICSeh.wGF< e!\e|7#?y~C2#L? ɯsڌ0w(Ebx4%NDjK?jnKl )S>@.SKўrZ^0.sm<7! `~!.2ͪA7?SHhFƜKWvE$ܾweƧ*x"RANhrd3N N06읃.pmYnLEm5K=YQ4XoAքkUl^C mg`Aw#e%Jwξl@xt[!H&A|]s{O+һ6{x'c RI~= |Ǎ#Cqqs$8 6toK'dFF"^=egTL_u?V{ z/jjY"cFܪ¶A[(]YEoV 2Khx&?@W '=YȘ9 |тFt:"=+BmsB瑂6Vmu d^1 ?`)݇i{<;ǯO$8 eGFb>b^h_ 93ڑ梦nχ"Tg}mkHB2XBxgSvQWh5K>y=9EA:M+<=R˪. WEbZ| Hh9I OLѺg߻/}3u$) #d{E5Q> rӐ#y#[oww2pF{߷3##_=sӐ6˒IxoG/B2o h 3[;k-r! وif#=pKlmofT*IAZAl6h="=o ɿ>,t~^;?i%kQ1HZV=/[+sjYoxԖc.Q1Q}Q iMAKy5y÷ d~ٌ9+L͌#D-ZDZBR ZH? `_.9rCGkj r!˻ѮwFmN1߉P4o 85 мE"r72FB!;ǣ*G伱.-˕tFLAѣKHD2ވ gEL7z~ v _Gs$/4vk gݏf}γwT*h)[CZmP{2[``}lYxnϫ<>_6P<n֛x4| 65茔7E@BBו9 ȚMkؽԪT%AemA׿5U0Rs"DAk]h}{SɮW^0f׽|_0dK0?]][IPY5Ds#c|ZJr ֍eOuN6! ?hlHO/~N/7"L{ eH!/N2(խuH"vS.qo@|uI>®kl"1+ߌvO$K(5vuvHċڀx?yȓ? Hx ϭovdXS勑.S96su92*э1Ԡ_cc(/hBz0$tS&k.O}Zjۢtو'?C< =jTkSڴX{G#>s.2}'7"sN1f}d6)x?v_]5?B~fi,R!fА6kzpZgo E~:8)}J,bZc!FE Ei֡= )Nuv; B@OzfNs3򹛽 ;~/MHɯCpmc: H Q΃/ M&s̰MGj}evwTST_~o$Ax%Z'| gNByGR6R97ǣxq=ujHl/w)Cq(Ukmnl}F}(>%?⁝#*dG|08ml{:<7ɦmlF{Zm.aȠrd;ǡ53hۻ=At$ķhC3 ]X|-Z/EbQO+E0 ]7[G^ !;k 2l~J.4oדp60b˟0Z^LEǎC@ [V^OVQؼN @DhAB& oO:,o"yFQ,#[Ǵ^Ht#g{~7X*,Wg:Ah8ǞfEj S5>0#ɜP֪"mD¦g/(RvG3h:RBxKW,=}n{ S5, mSTd&Geun[oMV ޜwGpʓڛvԾ1-R~<'8y?}o;Kzmؿ^A}z|VYdc>bgAh3_hqѥ^,B:r"2{lFmN~jGۏe` Bsi(Z$x 1_uA Zx4f$jJ!0zyd"M|ѢBE{=`A)hrZXhnj^v#ޗ2dXME`|7~ }d0w+o4N0=kDТH,qQ<671#{ $$Z2+SDr@'d)"m!7 yBi@)ɾ)BO"oP$ 6~\B8`>H$\k(wu $">C`^?RδGp/XgԾH,h{}KKC\vGZ>Կv5>L6=w08KdFbp|[0{uc(4{kS3lZRnym +^_۴" z-ui=z=nƿ4 0ԡ=-c6,[64jL4F'|u:q֦"GvH,1> _nh?)(w + ajx|F鋰6+cٞ58DWjj[_rv=b(0o,%GH\5SxY)Fv"F? ݭo=mo"c2zؘPNI[Y7"ɰf:뇫w;mJ"y d8#~42l3ɺ#C Z[Sz nOErU_;dk~Of!l {Gެ #n@=o|!=h*:x2۽{;v{^A;J6_Q S7-AZ݌m0B#Ļh-ns Pi`9.5w2?m u|]nxʞeY leÏY4-=w!Dxk̴Bt| ןf\IEF9ȳB@ 6/#> 1XbGODbHeDb=>; Alw  y({yrE7ݪGBbchmtye/mchiKd˾϶w=r՞Fm YH {~IϢ(mܕw|Ykw *CJQoU1㑱z$6Gg6Db.HȻM)$芍_iH$PNAFHirQ$̮޸h^]]댖m.C~#͎LG/$[C;wߡ̓ZօYշ{󺴪N'5,dק!{ K.XZaFKmzsшƳRkR-6$olhxѡ H2}rn>7 _wmjYZIPy(%(TN)%Ak9dd_o>J~}Sa- ҪAQh]MƟs*9LĎ`lX* GsBwģ"ĥwnp \Y0o6§Q4UUG+܊Ӑ܆ |6uHLEVb_dރP)} b&6^p=Wv p𸗲wGؙ\oU˻-z2=sg[8nEQ֏m~ۘ6RxCm q 2N]z!]wYȐkBba4AW8\q^߆ HN~ͱ۽%֏Xr .W"[IC#6#>4"~mFƻz9]FAh}6#w(Cg2K.`B|5!raG,٭H/DFX7}vM|Ǒ궴FsctƠntP4mc:|nF{'WUmlɦMBB$ H*JA&"Hg@@E` $ ,dCz϶lv?~|3s~O=j좷ߡ Fu16 kXt)O]H΅g ք@7X|hS9+M %h> @VRtYk>v!knDiYWðw#*,L4:dC6EFyH+B .N{ ǯYb=+ gT!}ژh}DH@pZH@das%!F,AӑnNHY;hoW")owkHQa?߱:Z,³q%H`z1l&yǮ^1"-?NFJiERduvcnsePɚ^ UgW7aϾFv/!$lpތ=OZ4SkwH{ty l&̢p_&:^);CS!y>< Lɓ0/kb"ǔN?e~`(B}2Z/% £k/bgrNE|c1݋7::K=F]0weE ן'g"Ng,-huz/A{†PC#Ðnv'Jm]݅q]eR aR,ˌ~wns|[dtt(mFO =].Ik}"BFwTpeJVU?Qc,>rbiK%bRG9mІqg 3A`Aju۳y )/{K75E3=ݦkA縜r0 "F#0ЭM4M"D!X~k(bw#c{pC#HZT;w5F1?6 +Z;!%Y&}螈|କK@1닭F ,n\ -Cֹ].kwgĨgڸKNאy%Z"7hM'J%b+V@ҷpoP-0 82L'>'KQc˨{,9OLn>D|Frv) 0͟6x4꼆ɇo'm;޾xr* Y`rh-) ÐRUݗ<p5 !P.rsa nK%bǓY ExEH%#x W-A^n! . ޒEZdGi¸s]?њ ώw"%o/ħ ´CSأd:˾o}lD)캇ܻ*$,*F:ORW\(U(P@غeLOLfv7Rv[u8!~qYߋ dBIkD{d,.9Fѵ_9Dx21E'P7{^m%H2^I sڼ$luh/i~A]Xd;ھgm.&$4 U?Pcf9Ysd:6^ՆEb4b4贽=[J6ēޓJľ`h&N!3i[u^C'P/gg=gl[R߮j6e$t?OD[>'޻Cڬ>9-~}.Lo4.X g'n6Eu[ܭ)F^vlY;u{pK)vD{֝XO:B @6ОY5H{%Z҅^ K<] Ķ;v@mE. f~!tTDn.sǻyfx_0?{Je(C^~h#;q#\An@K 62(au?lXw% d7 ?>9 :^xnh;s7!/.'8k t2cl4ă.!L{;Au{%HBVG OgDJ ٠hu9Jdiuˉי|HǓ鳭OiVAp$*ގJ4!FAVdAȠ}.JV{O#9>CY(@k3Jp>7E;TD܆g;8%R( ([Z.N\.a) Dzc6Ѧy11(vyW!KL@,Nabnē5h^- ~w+kQ(|H9Y}=SBnA$ގf!=3ڞ)q}QȺvp_*d y.GpYDlq<i320lQ{\ܾSn!vʏX%C _esژJ'53M^hpBZ݁5/ ֯;sr!}x2'R+S؛F`y8B[ǓQFߟ#R,W+{gӵk8r v߲95KjF2{D#TWxy[?%~iE!9oJo'Ӈ;ݔ{i7gW ו6`/~܆ևYޟ\׿]5 lmW2 Ƣ~{h:`ׅ95!j@F'06NO+RZNAR L P2\Ws}혋d\בV߮Ȑ cvEm­k`!/AwH1 tyAQCJr!pY*[h ֕CMGKW:RȀ 6 6H)QCAj {1· nq$Nypӝŝlx>F2‚'B;OEJ ;B5:z7dgqVrTgZBw OCJz_߳HiB2@}Pķ޳6&<@#ej&Rڐ?"Hxjun}h!e=>J'\Z\lt> LXkskvXc.O?$d1k0Zc弰;ҁ.sg}}L=gr"diu _e{a7Fa= TTl^Ώ'vL%b_e_.ty nDH(-" #8z{,+Ȅz?Յg:; <,7g-Cjdt;@YzX=vJ]g#؄6}_䁹1%(>ܺpKG'>tj?ĘF`,.:F`if!x x"{$"D)L%bM%bk"6;=,7 k}6 :@h{/'^d}ޒJ,MFC0sRʭ'ӧ!yh^?[{'\脭a6!A1=W.9H2 퀬d"]q>Zw+z>1#Y=ׄ 0?v,ZX|TI̹Ⱥs]NP;~5~\ ~E^kZRŰwbf/y Wc^Ŧ%ѪsʜѯS_6@_T꼆u^ÀA:ߗrAZ$,'q^^f>H!AxU i'v?GeWDwy#~~˰6Vx|@@܄Z m!>AI94>|E'NXif^GZ}{œ|lEjpƵLXJĮAuͯZcsz/R#hL^$OYehoX1Zm!!P(Hq\3dZܞJFR $+8vm~BMh~;>U_.[}t8Zn,9hS5qceG]0/T8Yd#رgB11<eC֍H# 1f"m8ށY:W!%>(kU/Λ.+6u],V^qP `)"$\dLA:+}RkY܁[v>,nE<=T")T"ves$Мm4չhY,n"ƸʾF< >9; 7vjv{GǹHYހbm,L%b'Ӈ+~,\:u)s?]gRDmvɥ<$1Xf̐ Ֆm99nALZRGbr'@Jܝ٩mޠ[ˑ0 2iS`~gc'Rnp#>V5[e?-8?-d&irVО^p?Hؽ ;6;BBc!cMK9sP|*~\F mܙ3VnNRW ej?V= .rzPg"7J ¡>*c~d}^`rI=K#/U#>p}8χ!>;gڜFJ 8dA2݆ Gp %9hgY/ Rkdq\ϘJœBE6"b%:v3ެZbFUk.(#m]zecJ} 'Gpm66_ۼMWTSѱ)f^.'-]qHiiu!신5A86^x wW ph<~ `#Cf-ZS3@p^nnE{0&g#%g>⯗$Xk ̳3߶qODs@ハ2ҭO<6]GE_G,5:"{gmڈ%_gP)fV(I&< CBֈ! < mK 2m m_ruޔ-)܏@h=b.d%C*[OGH$D?cx'Lh4Ȓ{ UC B |"Lb왨}>;5F ?, ϴ/GL{bo!l(Kheo}v*Sd_-,.JĞ;Y@#\)o I=]J&~K~Y!=Dw͕>H٘J>]nwq!־/Su^GgZ9$§s3*.gۘzrwٗBΏqA_ H.ib0k(Bk $~aBR"6!.H!9 @ >Ͱ al RvCt& J>'ӏZߋVvGJs h_ntv OϷ{δx^~)F<{W\A^¥ {mY HHmqw@=B&87v 2]xIj bVKt¡S8 =0= ۭHI-BB9FIvҞ IDATH-6Z_A` sa?^Z 6=m!\Cd tǓS[ݿI%bwēiG{WFw؁dkw ¨>6BU@fn2lhu>.o1ryRwGe6u6.;a輭C{yċ\˗Bx17}_nFv%R&Wm}"%pv6ζgv@q8PZh폷ǣF4_n5Zc;d#ٻ#V2xdz>$k \e? _ o #fK*k? t N%b]dkDK /r Mv`s#&T"px2bKuCb@DT"vձ/9pz*{Ƽ0SP݂..!&ڸږT3_fGKpAcm^S`Bڜ{(w4G"C8W170bR }4Ɠr b?'8يם{Ev k҇x!:8jb.5otw7H1eX7ِ!+H@w~k#{U[.|o :\; Á΅FbGW!!ibK5&N%b4)IxaV#!{h@ZR=7 @C(̞w흛SX{[}%bM6\|cy΅0jzMg>y .Eq=2,k(T꼆B2ுз;7JĚnEb9h{5d~{WMqO$pno_sh"7ŵ^ğع*z]nE6Dn|Ȳ"8{1>wz:yHcr-WzYK8g̪^"kw6OA5sn Gj;R aj#9jQ+Rp#o8xxA¼i 7 ћNҶ  )P7?;O,ed!\o~) )5Bh3 {J`:4c_nѿ~F VSؾἁ18/;2h? yIdgh/h}A#V7=s]*[W?l, Ly J# ̋&I%boƓJ!5m6Q7M3Y9w#cq<hᅢE6zH>N%bt}!e]>{&~[7h!ўDpwCNub`P#S-DQq.NB^n=#!w*$`t֩pCQy)N%bKh;b#frR`Sx(4>.3.ŧ־)a|N>u>z L;}|Eˢn=罉k^Ve+ OE{r^E].~+C qk(GtX/\'\|Iyǐ}F{HF~ 9*S]*e6Do+铽|`/[f`'iۣߞ>)u^#H(Io Kug 9I%b hq#A6fd6žiLe#ydFkq<0k #;L.@ QUCgĐ"z%EHsJˤڎg) >pL*{’'x3`~‡7"^8).{Z*;7LC< ^OG'Fe}J d"Hj {X"{ Ŀΰ>z-e+t w~MY'U18OZ&{wH$y~-tصBE$;ڞѝ0E܅`PqQk:+0e.w%܁-Fg6| ]g",GO-hߝm϶{!͉'cl.޵{WV<.FSY fl+D#LE];=mfpVPi=hÍ'ӵiEyvFl<pQg@ԂD) ?"ocz1H"eV# k} )4-!gBmo>c>|0btovԖq;=Y2T^ \Jލ' 0~ 1~}_\dK]w{%{)p{_T=}2C~kCѴ̶k @p}ho{~dOA<>.N g!1bw~ž0yIGxUABa52_!Dr)rtƨ i_Q8\$4)X}}h2hmsdml(?zɗqM{!SH{ pI Wp+@rb~l"{\bG">ch] dYr#%RPI)){ ch_ci=v0'Z>L%bUJ%b]ht/ mg(ܕW@ q$J ƛJ.{HbL62} ,'Ӄ3q _ft5D}C׭HYks;)Y}eH]Eޕ `&#&>8P(Dey=N`Q<} eE@_L( 7"fXOh9*y]W5bdy>fp·T4d͆W%T<$ZwHܲ(+[|⒍J&ft_`#k^޴( ,P5C,!Hq9u^ó~?|o_y _kWYu^à:a்~fly ޿g^;/ og83L?ҶlS?ۧ%A{toз anhM}\5 nAJIod*Fwj 6Dx{0lko%#At*7!Cѝ tI {FF;w4#amC"{O@!R~xEB7ʢC;.~?2 _AH3~O"A'6_|ēvD<m(T>|dȬ'HU?\~`H՞G_Y"4į{[W"}=2JFʩ ۈɃmkڻ: ~xg+nHks:=u0ɧ5΋QYJp&~$[ܰiEe~eSisdZsH{)`օѾ5 _j$l2h- ѷ#.xᱻwr)O!8Wec}hU`m0zDt 1S\dI?D@1(mx dJ">y K^dmo4A BQ)MCCEX>8@6J1RR"CE Z#\B=h;b< 3ֳ{򫲅-V2 wQ^^DT">LODJ,{ eg7V 0ϬA}QfR\fȳrMٹ q KO{~Ĥf}nZfE uFg @exv@dzYqb$}Px6_.ĠC 7YD&y Y~,g!Fb8ھwFOp?Xf!B|vdVSiq}F,CFT"v,%FCyǩq06[R!Vt⓭ݱvs=wed`x2]-ߔo5 f6-z+Y@pQ1?:GZ?1^4:pߦwfؿiTR"F:<=6w{T:Lt!]L`ў()$ }FIH`fFny)y UVG'R6f޵3kE6˽:萍Ǯx繅-b^w]WN$,Cax.oLJ%bx2}3pJ/Б4{FIHh%ΗowNdWG!saiFxzh;|Ի;܌kGfD#OݙV J"VO% l m睏I%bƓ9 ZNk ]f/ۖ۳qE;ϳ盬ߣ p_ w9"?^'Ӌ]}fpoWho#>4)VcS_t톌sh_50PopG,Kk;n䐒 . Q[ Γ A:{!i]R$o4SnS6<ϣӑ4| Z<|YٸlL $ush_cD&Km.1Dcx2}`vmzHȭGo?|`mI=,J%`qtXHx[H :s5t8bWghS#ïȟt{`F1g1e~/{*d?Cz{~a<\JĎ'ӿBẄ !ߏER~EDg!fЅن!b0:@?eB$A$o]ZKr5*[@v 4!vs/\*[)5F3dztKxb`4ظ>S꙲;F'9~5ſ*gU.4…B&_Y[t凔̼)jdxWf6 E 6߅bވdО`:5-Dpu4뼆$ZY Si^4y  \r @q*w"a|{8/ѷֻњ]ת7^5ețU;=C 0E,4e{gG٠YEN$,AGk0 )#A΅p+YYg"#7omS֠TH9 ?9[7!dE}ϳD h9ѯQx8$!z#¶`7x2?kί>bsT@؝%*\`ml;;{CNB$M]v= h=%NE% B!?.tWly}>n"P4xj1.2l0ڽ V+Wē k?(:aC{ԗ ptʿVRT"։Onm(b,Ɠi.oUz$͢y 2.?1ͻX@܋Rqt~C(;uHCL  Dх̪؅!}ՑB4tjuj}esg}* gu#ӶжF.'Ӈ߶ߗ!IHqY4*دgCF]/GZ.<,auށȊB"^A\1Cmϵ#dx:guL6[ڴۜrHiPĈ Ƶ 8̞w[쉚Cy|` ,KКqUTR\fOy 3Ooh{e}ɥGS؍hmV[y A-v]XH8lhA7ѪP犒(G@K,!r֏J ^ Y/[0kصѯlP9;@K\MyGVrq?E!g<(*^yȳz%~"3’(Lm>Zo#R;RȞC8p)|rUG py e vw#r!nDxR2jF Dʊ;{2huMCg RR\F&!`k7o'ӓqjH)n%3Ax!t6w.mdܜDao!%wF<;C- $uRSʁ]; #/y  钾_;_eOT}woZWtzI*/_)X)q*dz|*[6)hG5k;O'ӏ*g-RɈ@֠Ul1# deJO//!&1H B>0 8үnBUd Յ,oO#rVd9%ES6hCpDd'ӯ!em bQF T F;/!FB"ũ }\zgm'!"c\&w[(`R[ >` ^5[luyOt5F+ S=~V[.6wHiܾw%?7L)Dx2-1e48x2}|Wr4뼆jB.{PBMaȬLWmLT - uOu~I 62 ^t ƕPW9yJƷ_)/z 9듻|;K$OW쳋:@`h< 1~BӚQr9{"B~]cu^I2꼆R;?RuмeϾ )h-zAOEx ˢۉxP#(RV{[!0Q@kϝD @0*ph€ew!D)B]6Wc7ɴK~Rbx2} GYfBh/G1?.&sd{y|#}b)ZHAyKm=@}YV ]a6*-[Dē(ÑmJ6\1h?H%buz.,EEɈ9/C:mR$#KȒ R"ѳ2F7Pfya~!Kj5i`}؆#&7}Ƨ3v !|j!{$@Lm({!f$bPK : ?)V(Jd,"览d Vhyܽ^\\XğG]czaÜc pYsd:\ȳv]ˢO~غݡ:! "߾jl{H3 w_7+^꼆AHp9 䯷ǖhmvXquŹ vxk헳'/Ɠ0ڿ Ņ<;׆+wK݂hY_@k7FIk_qxDv$T\Q5\ N *EԌDzs.E!H!y hNAL#1=О@Go "Xku", ][} ɅٞKAgt(kאګDxP@B{c?F(Z/u¾{h-@aZ=v!CHI,3l6wF5[s-L"ȼ{=R@5g2gbL%b 8rF+>J:اtPUc:VO{Mk>bUK)5͆Xg߅Ƒ&!y)ENĸ-!Gdz1z ٻ./Ar5 h\@loؘ#FX@f65{+\ sAtkC5ݛU/AܶH>I{('H]@*?.HPd:c*RأeCވ> }ukÐ k9 #Mk=@~:b[sޕ4ZNA V AR`B(YByqȢ^,>v¬hMaI%bx2}yۗFsIgh[pAd"Dװ]_JeB=6p񈡇?haf]u^<뼆/>lK%=Ke Sm(3.U)}$x [D6o~u^õ%}3#?ysO%H:>lQ%L ٟ )6eh]U!y g#;\KzOt#OB'1y:r3!Le9f#DdB8d9Hܝ/ a1\K.א<Z@w({[=c. gгk]aF|c}m8D 5'F'';޸޹ GV<9H{IQHyzBٮhMɗE_&yt:y'H1籃! }s>DYpl_g!Ek𰝷y˩h5O4֕Kh1hԟ{Q @HA=3ZQNȰ."K_)uT}Xv1ˊ̆Țm:2[TʃO+D/'Hy=.}OG# |-͹;"<݅H@Nabl73!&݉a(; _5y,y$(7+d K3#^'#X%zwaRVg<3}ґ2 l{wc5% Q_j4?h45֘ĖX-ƨcEEAwTw.şAҞ)} '!.}GeZӒ[u}M7$ >^P(>As\JGƹٸY(j^M(toiܼu giu]{7ZFנu}$½fFrq4?CkI?#r2Nc9g"2s) mLs)_.Ɵڂ&򎈈^rWBJLNJ?ҙVo"0#Ȋu}ֈh.潐z'0'̒Dw)"G'jg<,|%so׾WEwg|‰RDc(ޒՈ 4V'\s7JlcY\"so"cak:ŝ]JV "~|`&"yeuu 8ƾwyHyrDpy|vڭ}6~;+<`|٫uӊwlvtfZín'8 .& yƣ[1R@36C;끸S6~[}G'>q*ڽњhD7> A<C J^"e;R\3ȋW5Eֆ"sHؼ,f(_Ŋ^0&37x3%m(0ґX=•v^\ mhÜ EARR;+Hݯ[ǽ/n/NWw$<܈eY! 2.R^EB胑=)5Ȃ? -$"KyI =tDC!=wfxn_nɦ)D),m= %k&>υ`u>Vu=&j0Ǿg}^`SUMg&]VgBkg RN񘍷оBުlPva$X[.FA(YkG2R]/9Չ @"{0r6ߌ&5# |O+n_Sg2a긊 S}^A j9ߢHPXʥnSK} ei@ _C0:Z?S#4Rx:'F~_:_2׭\At~L4?awdyƒXI7ZKmV9c Ȼ ߓǸ/am[mN{~r\?UġՉ-# Ah^ߪIvAww3wdQ4α = Ɗe={ g!|)G2ߞW=~< /VO"s{FKϞkP( Au0hmލxrt4QhD{P? aDkϢ EHHoEJ(yE6 xk =>-MNX؆˟-+k@b/<ҽowr7F7Em<8).NZVVe[J*[3 ?}4dY-ze=&h Ńs v֖*ՉvV;Dۑh1@:&&G4qKH!]n{VY Pfe#Eo4V|R&/%&Ӆ#,ItF$Ljl1~#me ra"Yk[3"j P?9E Nev$!mn"Z֮o "N֕Y,H,z\U[xjнPI'$p,ުV'\UNw. p}7vrÄN+sΨ?i.lǃT>"N +ߊ7;>Di'$LR`r0ǁxz.ym]S5hO7E'e-GZ_4 bٟlNLEzc?e}Ÿ+-"Dh20 UY$vѼۦJUMloPx_omubZUMitK\>#Ň}Ň8`gnio@7e6Q{Hv)lh~ ◕Gf& dd!$},-+ƟBq'#e =x.HwPhwo!nm !EnK~B3s홻!^`u}"Lg}#~ɕw/B:4RĕW# x/)ޑ+W eᔽ;jÀ\ j"LQUi >`;f}b C|.( ] 4?wD[zgpy%hGYEmqV'׃ nY+wUMȆͽHQu hm/AovQV|8x$_leか7KRlQfuB HAqM;#N!<܉@\4K0ތ&rCk)⟇*t #xD!#N7y B%R KTH X܈HY)Fָb1"|8eHqlDXg IDAT0)rȢ qmubEEuuJ|b/YwFBgK)Iwb.CAuHXd= 'o9T$w[Dt}?Rԛ'ʉ>jAYտA C~bdlkLK-]'9䴺oEHϻx 5ПDŠ;[JqHǦ衛ꥒb2n h6Pc=gr`}7s%K,f r;ó\tȺrmsI k4e!~#]&݁L{K%pG ; d:,h~"읆[~>B~ nش`g?1?桰:> 7F|szkWSO=Y47" r(ZOǾyR۽\WPjw.޲1)+ |6ZۣtF?Y{H[bcw huC냥xev2אe.™rQڸ c^Ƞxw^ {f<Ne,b3tƪ-h;C( C2-eE$JQ{sˁ]U_ N;7f .Csh}!HFpc ][Tc[,E'ϫuuтAgؠC+F6D@λtDb,z,~5u1Zٷҍ]Ѹա>u s~5UkߟƵua匭%e/b}eH#kN|PU<۰;"q%HaHAd'R.Y<# YDdE .W"dҺ*)i'!ao5w)_E|- pRKQ !;Od bc{B6>WYZ#Aom,(cgcs0F]C{}LcُST=9 FkO}{.I0FDk!^[HW$" @0@0f?Oym2a'?}3Zѣ'>QE 'Ho'Cز,,V !m )r rQ4ːP|" WD.;nʺYʚ,ZuToUHub$ptւh-+hs ¼Hn ŪW<><Þyu(v`{>_U Nj]=2h>C"(ݷ JmnZ{nm ^ ~)EHy;_VW<6 g#V{k= %"ß8avC/zn}ertAq/.xW"ZdQy6/f".lnc/RvւlLB\{|kԞC[n"lHiuWU%9WIJE}%L5i$Ho1nDK҄ޫIAC|.ª+Ze}\e70T7Xj}lؙV_,kq'97`qэ 8ǥyI e=^$H:0+ٹiHn@t4!h{ཧ?Ge?>i6`Y*nϙAd `i \LAx" i-]{0RE~'!`}V-yhdM"ݵмKC!Ot WAh"u hQB ]{.F@y&F =ousR`,@jDK "e˷Q|D.vVHi =Y ̶g blX Ðp>R>&kSca!d P3HX !kpȈl 6 -ﴲwNU%+/[E}vN$ƊXAfz9]Bq]|x:Aкrg@]XEkxr?4_SFc:;3Oq׽е?Cؑ[~Բ2%yAhvH}l b¼#ZWϯjY}E Du/"^$|>1V)hYrg5Z] l",eCd:ȧښOцidsYV-e(l~dm@zރ#@Ӏ,}]ՉVMwF<qßcA IȢsRfG1ǢQnic1sxjy:x4d+Csj4gDQXk8noec+:@mmubg}nɿ#u4^/օSJUMyy΄e9v{`yyV8@"cr29XI&4`z-2Ex{룾ȠtB1H8juԅ_j{ 댄ÑI磹} 9#:zUHh ͵,Z/.CKi]uzW;kNA7 H3RԜBs2J=/ z~#PK}w!Cֿ!"EHm݁YH;:>|#*au! 84+Qxn֞ۑm|!/"~oCJ˔،7- 6޳Iı 6.{VCmL~=yG/CL.<9 WV'^Ips͖;wzrbAώM}}ԿdZGYH!F4 { 3Hq{v[sg9aU+T>ZV~-m̓Պ핟W޼܈ҮI(]߫j@:<&9^@@yhb& >yytM=n55 N#Z1M Og߱!"e +֎\@F V)"PG΃v(޺ՎMH輣yYϖwAHmݙ({h$[sBԟ}6l&YQ!R>~__U\R4rqH|8N ?Ïcqӂ[9KʒwWO?C"P6.x%:+G(4#,oeNGƓY ./>@ޓ:?y& o>2wqv}5f šn<_"%hGkHnvp{d[0=p`,ì"䆪Z뻉H9Ar߭g"%u7$Cs} S]")\ s\}/gkH~> VV|f(.4gr,ݞa43hvGE볞Hi?C:|n".uae#0+^Gwe.Z+2aE1_2 wBL^>dO:7mJ27z_.V'j{jT$/Fʏ!Wb1 ȒGX$7ܡ5ȶ $YH sx=!u >#ޯ1\dxbhn?wK45,Jтu-MG8B\ܡYȊubAddd;7Y(cH#] R&U5 ];ڳ#erݿ'6CVuHؘuF$0q>)cgxcA6&g DDUiVweii<0ƞ6ƣV t-[xG݊=oçjŝ @KFL^Hؑ͐|„PJz4YV_ ß# +w0 7p@؊2Cx)> Glg2xB kR4JCX5__b~3!Ƣ̼! ?E8q9298ug^F|BSZqgEnG|L!EFy y/B" F AkYVEX?yϊ>Ǭ~ oFGv cK=H9.F0ȣF$HyGxRa Z Sx wT대k:_Ϣg׸dU6};t)8gw[_#_۽#Cku~֏Fk]?)G54!Uu־Eh> Ka\0 iAsu~ j6F߰:1=m/[\Dʵ y7%1k2; ڢَaMad.i Dm嬬mJZ:?mIN[.DQ]a"i0@]]wvB@ZyR@j-"EߌH+hqX!b~p)q` 1 ґ~ VoN Dž.{ nGR ^x &EH֝?b}Z~{w]w^4ļ6}AC d(_,ߴ8L6OW$ǏՉj#u{qN9~ |w+{UxgQolbX#[ߞϓ]/f BҍAS ;rz9 H К_7lUn!a]m\n>yB<8)D`)﫭N,Iއ8q=$K( Rg#1[Q;i#v1cS~n {ic X.!/4WW!N{@Smu*uaefAj萮hg܁O5h+w2$l~FQ ZS9?=olV}e>ZگC`YaH.D`#DBȂs8"ߛЄq#!=@\({`?IG!w y .H>"!Em:D [v(ki١%僑E$|wtw̱桸hGVm iC4Yˆ yoMHGuHhu,.E&D$%3{!YDJ :x `}mu.B0u\'c~0ou+ni-Y{hƐvdq] vGg D$^7fZbɷtF{v cG4TRc)KyŸqP ."J|Q֗!eyZ["Ef)zD 0r2y_tڐE\1)ڸiB.g ~n~zF="ޘncvi#{ǽlO5vѪ| wH@;Ac6K e3>:tJK{^W(v|tLX^@JjtMSGY=Z#tRAxnZgmmb\Zȱh]\R /U.Pj ry(o} wG}6xCCvDv"ӊl">HfZV1ox )%HHYg~AGV$#b1 sx>{yl'ؘyRHp)]ȫ7 ӑb%8GůsF&95Y[3Hi;7)EF$0DZˬQɥxWygDPH@$69C#Hbu.vjx}Hq![L:.Yzsw RI>܏Ֆ*W ah(ݚmtD! 9yHx.D9*szi˲@20C rۗu{ZkrRVPyѲqK aHi^!(l^ ևBx=Ƥ؞㒐h~iĿmxn NL>ֿ}QER ̵M>p IDAT^15/q Bsk:,3wGz> S>XtP1wḿM[Z~gdؖ_wV'.뷗-RdHL~Ik^{-ܾ.h[W䣵鲎.hi׿Wveuʁ-%9+|hޞ{Չ&AVR$58}SW# מ; MΞx+Zd{ ?F.h j5Z)U횦QU5g$K!"z-!#O\=Pxpw뿍H] =g؞u6"gARIw ).tlFwX)X߹dΚ\kWDL+k9"ʵb& } .DDl$}}|Lƞ8h 7 tq$ A[eÚYntl<g@6[Tr.\m:sxh]X97>>p_%*X+U5z߫:QϮ="T$GsNIs7 n(D^23v+@8Zjy Oު6^ bքq%qcIܾ)Җeh=x32g) Ek a ๮)MHisFh<¿.Z_~jFR%heˇg*޻Ls``R]XˬE^R}"ٸ N;ypÜp{]X>6Jdd/GsLw4_%1,b_$WD>s[!u ?MNdk"oBDti>>&iE^hb-Duwl{ Eİ=;B3-;,C%R(&"ˈ 8KMh,G7 Y:#7Rp‹ MoJ Ǭ_~[I7-CDA *@D,C^ie{HmF߀,4un?[$aT$`"o'sڍoRd| BB8`pՉúry.#x@]XZVΏeֿ[nC^N ~uV'VV'f"#X# dE2v+oN< P# ڐBBG^|NJ{Vߋ xo" a8{V@.IE-2(d8?z݃?O[="y1l!LB'iLJEǃ=Qt?Ӭ}l<ө,.|wX{r[{&w]>O!41݀F[ZeOyu1t"enG͛\{ߑ6e8vBJl=2#n̷>{0ϴ۬r< |k%A wk ha47\ͷj {a&k.sAPbx)Brk3MgUmR4NI s_HɅS@s$@{.R vC?$durH\){0h݉Bf!)B ZHHxy򀝏Ee޲n HH646 -<26r4v/XYeC] H'jϳ]L){CX޷Oz !>>oi":{d({->\)T##ʴ3&錽HPop[F!쓐rRUE(."Y}"\!s1+1H.j 3c|zC].)2sPhcvYk^K|c!>A7>o CXy8Y8y'Ȁ^lc?Iۏ˴h hNdc X %eÛ=nn 5- 'cQhns]X9ހHyZ /V'>>t&&d "Dc%7Ոt%D i. yR# \eh#-qMVسX~dy )7 OlB`Ep)xKb"נ# 腈JD]ar.6:BJV4~p{kvxEwP9#PHΈV':$Xt.A&8t-7k+E}!3r3-w^6Y>'T.YMк: {PV)F:m[IIu+ՉeHwY`O»as0pR^|4"vf>`gP{ =HH aL) aaȃCغ ^Z@xZ|8Ӡ<)#[ץhMwOݾY- >V!A:"'wˮ{߰v{h ~V2?@/!]f}5<득G;Oго4ZɌJċc'#d! V8t݄<*o=dL+>)U]ǭkƨ]d;ڐs(ymr !"90ˆtpf6+٩a-!< A֒'0 R?3)h^l2o@hḳEi/_`Šϵv-ރ&AD2D>Q0X@1:0 r,u=J16ʛX M)"tV]l!buK9>B=)DDwsUMҥ!?)}2aC'L?<ܵ{MNW}5aś_kF/R\F=)I4&O2A\.AB]h}-Z*=ќ R YXI~C#۝IԝQ'>{ Dhp8d%RvE(tR$ޘu U;WZF##Ny^.l! {) C܁<)cѺ5ȋd8CkGѱ? r [O>fc_`_rJ9|dv'!zeW!ߓ֟6>P;:!Ñ2v7Jqs4~뇵֏$6Y;eo$|8hˑrncW?B1ב{F{VѧyYr'TxcK5E^;}ʹuae{b gCBu'C|6!a9Hg9O$OB+qMgCօ֧#/HY~0K$pRn@62>w,R`EJ79VnD)b/]7}Z?e) >GVlu{:#|s zZLCBH{e_>crk(wx'?v˱1]Jk[o_dXS@h22+n֖i6nXz ibJxL;:b-]׿h.3#nENwa (A,?BPH6xge[+e±MyQ\V~>8@. +ǃ>H,BH)GƃquaʭԎ/|m=XU57|uA&=HП6:ۈ Et,nZ"ɞU@Ծ/iVD.aݍw=~Hmueq"#k'LWXeHjNNGG$B~=c҅7eсlL6HUM[K{~f%H9ov꺰2/̘? l@}ۀ>e k3ܽ|MJUMhU5~}#‰H0J \>>saR.q[ W<)Lvt %h?[hu -#B EZw\& 6QkL18"v@JK<"a.*kZ_LͯlZyj1$4.lY+)<+U[PU<)\V'NINGWR2" TuB Xe>/Ao?vB s="DW {s{9P;$J7fϵ#2kC@T$l"X~8Z+"&e:Ǯs;@] "W_ X5[}Jq谾[i7#?p^N!KA}ګ5ַc1Ȗ#( ,ߒ'L7 *x̅ع}Q#@ӓGOz{qE݃YN/,t5&̨NIH+"KT Sn6ޓrɣ']mwG x.>DYuj I.~x CX|.KKUM20ħ*U5f$}3•<$셔(2=u;.@:v)jp9fϺy \+P9wFw€ۑ1qȫߌ+< yx!|[]B+˦IgZ9Msmbwt>H)A#H;y#~ꌄvvCAqZ!Wpdmub^UMyC퇐Ws_0}#]8ksK2#=mN%bX̓=ީD•Y_Rv@6/GOK.}tTq{צK,ߴw?f AfAik2{z:7\GAC-D֮G=GPޯuko+ hOݏP,ST"OZַ-)o" *L@71Ȁx񺍃N|K%bkX=h3 Hqn+R@Wjz ~@)'e"o0 IDATVDB^]zcA;XܫcJ}ohX3}aHׄv!Aʪe_).*mgc^ldiA^7FFƩ{a[6NE6njd<>(OgѬlNWS=fBq4'#xqm+Xpk8< =;hmn _E<~z_G馗"o(W[Q dH=GPbs۠v$zFl4"j/ #x"#'"=dU#]q"G\+ZÁUkaAsL* LHϥ˪ ]qrcHI'uJUǓ]>-EXҹM J|<~`A3^ݓ]쁜<e; q@џM>9* w;kށ7 ^k)"M*us^ %(bZ7{=R>tOI*O@kbCY?ZoˑAA g Г iͅ+q>|-Eȡyq#rEA,<`r뼢PIAYP~el~vyaye]{!"K"9v]=^%ж%HCFλZs/vf" {mS?'-.#/@kj׀p"z Z'5R9]TLE=͕k6r^Wc9AƹF/ Ak2"Gނ[g s%M<Z F(WA h\s!՝{oo.얫y'ˀ( #QuwH_|>GKG}>\^Tq*Z 庇JrzhKJf#Wb)PC we֫ 6X'xCQ*taHI%bē艔P,EQqHϴ6G-q'o̎hEM-[5 (9Hɻ挮r/B`8݈Uy*DiO|PJDlA<>CDg7ߍ!oi΅ m29{|KdLlj[.9EFdDuhk~m$jŞA~UrR W<bid8TuBNck&O6:$e*:3 fvP!KBa~E!b$ZCΣ _I?_tW2es „战8#}3;]ّ(z4'ڼb3s:"%d}G"Kzm,o폰jJKL!Gܯ+쳣Q*l$FY?FktDGiw!'px2gI˜lc~>"U-t=qPm@]MxFejwXS֏6FL#\1W86!s*RսU8Ĉ8+G[Ww\3"^CZI4nqu "{ : L>fڪzKt 2CA/5vh{!*%y?I8AYۇPk{Cl1W2鑿"9x2=nrsgECN?(km/?nګG l2~ !BX25Z1#|9G2 w)!Au"7˯w~ȑw^*k'#\h1 w{1EF[<@׌O0kh2;LanxW#;vqy"qbvC6A v-blm/8)e$H?]sO$ؚ{{,FsXQ6ȶrrA'V&IJAB6\ս]Bbd,C;و \Th; `UuMCB6XCJe Ay(½>yMtVk=C0!}cwRocY+!:%@ 0ɺ C8Q{ Q]{}D߱tH/CJO! O[Qz+D>]~+z y2}۟`Sp/w&uvmi6dzR oڵlKP"y>sם ^j{q2pP%\z"YjB0.*k&uv'Bdnck&j`sޏ/+jA:6~x~>~_ň͛kIm]CmK{u&C2GgK |o@1-|Dz !9z^ ]5qכ¾{t uEGF_ah~ODќ"]ƒ6C|,[>0LTga#dNDΠ@I[C '>ll"'ȡG #|vWSR4`paFߣ(t!Z@bӬ&eM$?pݔ\gMH*ee gm([3D!;5ESW)/Z_ `Qk2\]Mw3|~qՅ}iUַs2C來â%[oy-zh~ίLr:2RD$g)2\՛+RQ~ z>@)xZnCd4g9VMh~\?I(>)WG^C+a}Eo/Y""mH vDt hW'"[½$A&ǫ7PH vBϕK R Gj@J'!m׽Fkl̈́Nj V(ӇO./_JЋF&uн};Y]jIaI[Ag[a/k]ՎɂQ]^~Wߴ Ci?`F֏nmRP^P " yd؛O #%䠹y)(%t"5yw6|[xv/b ] 9a!,!}V0c)A?"+dT_q3{ǧK:k#4x'ˑ>ő.r\T" %H|݂S9# Ga][\nBde# co{c8~*\ ~{O%b?»"r= 2/LRk l6E!C:iAw>dz7kj$1œH#r]lO\Ep&!bW{;|*"HmL~t_{uY? e~d5ލxȆq=wWV[8 wՎ9wl̈́VslwGL3X2FY?g~r|dC&X}ZdA=Y2>,)

A<"K-Jٖo'"7#]Ry΢9YKulç6Q( Kdm{7,[٣ڗuC ihgpO.do_ܐn#Dl1AS_Ox2}Z7 =U _Dzm9/uA;H89z>U6 …4({A{oF8!+"CE~!#>`}7@–7x2]hH]fSCwD 6Aicz"-@MAdjv c4+A$671 dc}nns@Eް~(dlNG{oG̍xgojsӞYGaمȀaa"wHnc篱q?QTiHT"r<O!AfTd*s dK9b쯓2:=\b)u^i֏^.$Gx~Cϥ˖Y춿 Ϗl#{RֽiȽ&I6tkCK>,]{%g[+dx2y=l<磅8gy!c)3 'z=vhz_ *O]&B7^Eaz:&n8kܸ9fN'(#TAعT׽WRa-.;g͕mHT)R$x<["; fzK !n/("AU*yb\/lꝽ7Ys{h{"euPo9$4 T"F{HG^Ay$@IXxkl̈́W;J\hQ0\7pM;ʫ?=ioYO7Gc#n]%ez5G_B@oר^QMXDU(e"chhDbk. B#2r-:JoTc|FCYsn}9֢0˳(kFѵ(@z]iBϐ<9\OBb D e4Cm =Ca6Kv*Nx~׎D~77Wj~]+Fj(jPG~RD~td8g_z`r/A6_9SǖE9!C;}/o]XS˜jBR_VRFeh>nEXv=ASH9"ڗ S"ERt{E-߶`4ncp\qtG~/C~%A2LD.6Pz2(U`SU!ۍ ڷswgyiνG|^2 HٜcC`6_%6gqmnY V j Q.ՎLxiU瘷d /.)Z^χ_QVNt[u_~ש{=N@*G}V 8EΈ̀"^fP֏!M/ZW7bWhG)pC:$KA yHo άEE\aBVl/پ-h O"=B˦@Y'"0 'R r*!holWaХ @˕,"@z4DVvE5F5eutC$b3RYq,]8enO5 ?\rnm1K2^F aWE)GZa `|6-sYBs?{܂`T~B*jq.Rx[wDޙ!y_E"m9ʳv/ +W ˦m!=ۮy@j_[ 2^CvDpLs?<!觑o*)jk>;fؚ {qēCX'w~Mӈž|ݽ{]{67FYm1}"?L%b|".7DhJ%biA:7( D`XD\F<6,WQH_|TȁVtUfddu ]y1٨ƑEt0Jitj#" @{ _Fy]#0 ѳ| mO""CDr¦e ~36 *"H<{݃اN)BrDJAmp IDAT}U\} drӌ¢HG րROpϥ{>ژh} " 3sQvܵW<*SZm6:2 rx4eN"X=àk{Y?]ͪM9zDY?.hK\SY?b~e8RDkMt.D HOF(k(Ȉ~py rO@kFz7!GaH4yH#Fq6" irmQ*łBi5&w߰wC%γG]O F=A@`D&^F;di+ 9E "rKH[GEbAZ=/zk;xU5"̻ aki D',*`Pv PAm@"7-iqخ#Alo{Dly<EiC ;.:9zB2]+ȝ0pq~fqrz~ODJU{)PH TTHr{NFs]H3`\rCB}2| %V@@PEJJ y['y=Pz=09ɎmC Tl#rۘ|%tkZ\.="@Ŀ)ãDBx2a=LЌz.#:]#("|""9j\;fF+;sؚ k2EᲞ~G(~Boۈɢgyt@kd{jLO #sk}mbKy܄ckW2ŨT"6'L琾nD$ Yr%!O G6ۘtnwWNB:y<"CuAU:a]f(N;jA;؜NFkC~v]cx rNE䳟EDt{LG8}E~yPi1kz"<^;93D '\5Dl9(3Xn: utBXa`s%eˆOG!=`{h]){wlnHEs.[z#Y?21.v3䡵!Z+ˀ#^殬ELwd]\2;oLs̍z!z7DZv[yH1 H.2e z6΍'g5XRB`m)cW0H\@LųE(8"VCC%pR>dJ%bO[A` L"؉/A[eZ}U%"Lxo\혳Q1@x\sB V vWQx,UZMrUa4ˇK2M(#^`@k ѱ=GeXeQ5S؊}Dl*ʁDk<2g!#dd#r 2D h-'PEēHǽ NLp /CXea'y v6ƑȡBQ4%UA|rZ @V!׃ U} ƒH'ƽꎈHb~5's%(s]p[*[dOGN=ADlx2}*#Pu3顟 z#矋2-CQb%_>!hrzbkUm9: B+z>GRX`OʟͳyڥajOt<`Z_$Q(%Hn[-':XCOD<aƣ6Iu{ p9py˜H ĵiC룛erCkADk~6 ̈x==g/ `mBt a^C2y}攦˵R?=Tv\RGgj0db`Q*k/x2T0rDJ%bwēCjڝl-Cx D!B0QH#@ P7NAO_(G}"sCF}S ig"%K"bavxQDv@ʼ )l BW;f5>H~-e΁P32ѺxV^H8;v !X?aF~tƾQ}OG# !"lt~"w8HoOOd s2HF{qwBZǵuv5Hg ]DѶ"QF} ROHBa>ic>9W6W{Q^`Q]\b!v{a>r GzT" be~RAzބd5}-?_-{7w9H@Τ#UcK"U5F7 =CExEW)VT'^ lRܻ[|kOкo"!Lw9_EC[P/ Zn*ns1z^fgo ZI@5ف+۰eC$X =\mc6R!_:1tFW/BP.xw BHs]H Dx2]`wtI 8)Bsv ".(L^tD&ZbdLo`uC{%@lNG C(|濿D#HQY#vH;0a>Jr'1Hh-{Q'k;StēCm!m8LFÜx2k*S^EAsjۢO6 5ח> dt9,*k?d}&u݈ kDR"^s$4ۿ⚧:@qE*>`k)z.Gȑ2WŪum.Z!<:?!r 24 c} h?]tC-5|(L(r]c0rWD[mv z{ R7V0_"=i@ x\\~P6G>۟Oycv&g{9So1\;A"Dz@Q2ld}lBH\~6﮹{!*1tL*q l@k/׾4|MAE/;ơMPe=U2r-Zy=Ak;DL?<{AV:9jAsEʀ?x_._xh}Ull]\׆."j'H)]-VdA $<`=>0#=O"r4RN e]]b]wAajύ'=lnCNl<-d:iy)ˀ?ͩ^Q։H :kL8]O!]K&FM=2b\9:OSgƓ(T"Ǔ郐n; kKq ]R=gѳESwGQ m=F#tvLrnl$"\nn}ju^s_C@D2\  [Td+BPB7rlm~\(l^t~Ɖ~?; ]2a6ɝtM#r"DZw AێT6GmHGeQf^Y|ɿnܫ`HWۏADz6t/<}g&!7upJ֏k#^<LR"V К?9& _ Lb?EFdnYϛCgBk(ɷSLGIz"za|ޞQ dXDh.B D<]4Jx]s4\\'!ͭ+Fr&a^GD{٘^Bdat?DUJ <7 Ln}kuR75*fQv>|D!H'fW"f QL9q \*{ޟߺnาJwG$}6pؚ UBٲEck&Օ=U(Q;n%U\ض4I>.z~!嚬 "FNcUl}ꉷQ4MC#龭VdƒSجx2}Z-h!=ۀ>A-[hVknލ{Cg!َ >@ɣԇg_M虩B+DPS,|k*fhQ79J9:ܾ_ Eۻu #,hFE8ӎ6=A< >v}e" "eƼ*jcGwqO6_=: 90_C|]Wgx8;Dl>x2]pyZ*; Xz[Pzh Pj$ˆ?wdu8*/cz?G"4d'@#;dC`mD?BKt/F^'!U!Rm^oAVPk*LvC@?AJz0RO_K*{< RЂHU ^yz \C.MP胈hvC`z,G#eD*Q &`t#rn|#(d!P !cbGԐZG aiat#pDؘ~Ɠ"u/_o9qck&| -4+_\d蔈 W`eeݷjZϷ1ܾIPFo5w2AT5ĥ#^f 2W[1u}<d~L+Hgζd( UҥD$ ᙏ.E6dqT"v߇T/d #jwk"\ XʞoBd v'ӳ¾兕Ν-^5.OF36ŽYIF x@ ZH@Pth+}VćHo Z>Yu?ヘ2 _iB+z"!;g67c<Y@RmtGz?2r{yhT"ЌY;>jspE<+*d.xSÆQ|[ :9m8h~gӳ~t74޵.Y?y֔\ 0?G6- `}R9{Duǎ%L"cU8<͹%H!OC ľ'K뾨z$ u`}s MGk_T-j@!m.kl"6i!VU1ZJĞJTg*ÈL{WTC_-~ªOsGZEvǡ̄{Ή4=o Ty(5} P*[BaevsY+AiQmsW+/C)sg L{ƽ3‹6О6P?8\ѱ[n틊{!܆&u(&ƼJ>!28dy/mzt03oن(/?dT"ֆ +P!pűEE~VRȀ/Gm9dh=^߈ē=x2e 4V<W)Z4Ad냟m39} )VDynAɂ*>]?U*޷ǔl^y9JY=3x9ۿ^+lAn;9ZۡuTl/D~]tAE m-I%bD/"W]#{[ Gk'TrDj~vP#BDb!22 !'Dғv΅9bo;€Dzo7#]}ñȅFYT4؎G?# E@ϭlZp%DAnm67#صMA M;Ozp߮ܬH{[ϡuNgypY4 Rxݧ92T"jsx6BG#,OPfݿgum>aO%bMqc?pc+ʲǓD<O7]x2{ J*5Gto{Uw\ ~=Zuh/XH˔ xw+K֏u92?Q䛐 -R(xH!}sOChL_DC:]؏`PB>Rēio:u)!~q+]ߴY+-O] IDATEj0 m~'OPiofYˇ;vG&D !ݐy?Uн>y8KmKm n#hLx` H!hAJR?bsGll@9M-}jZ6C14aHAPqUdYE++)]Kr{D{y ͽ ;dzT"ֹ`|Ź^?`AZ+g?ooÅ߂Ɗ[WnҸPny=ng蜵u/3XKo8<7j?<`՚ua]з~ݲA,МG`#bXO@@`] ёHBQZdW4o: E}'#Rx2=Y]OwH%b_ޗU0KJ'M6xߨD@Ǡ-[(K_k2ňHE@@vɴ#nlP )rGi7##k}D{w kDFv!=t`N)dzVvVݐv.:y)w5=A(7ړis+9T"vTϣē98 9o@7 l;0dc w3M-ctCqOvtb;X6PJb+v4&T:싲\ GǑ#폨У\pir~"ʠ8Uv ~l(7Qǃ6o 8 FUbX7bb F"~;/dh+ pDЪ*Q캾H>D_DH _d|5"Br-s-a^QP!LB /D{47={N=g&{4\|VSj>.+ 8~Z</-Ҍ/:(LwC~1_zؿ+;7: L{yDу*\t0RFG"uZIUJ RUH h_'ҥ5'5DS!r*RKе{Bw(2hJ7E'rtEyH)B=|"dMGBYCt(28B^<"F 9i4eY@A"PͣrMsT"Tu%K?/?~w./|*7%ga^,,XqBysY?2!q3Z>NC elhboQfC 26CRED= !5َ- H~ yAR3ަQBO!}).pF{}$" HOJUJ%boē +; 8?5u ,~lGFoÑ#qťhxD쎷bo1q^[MDlo4¸ ྛJy1λo}h|g3ȳ\7Ci&?I9 >BQ8C@|"{!@W}P4ed\a\G|,=geKA=qDbᛛtC&=Ѩ>LW_eylKc}%J}Nu3K|NחGit\DbuضPS :1]_({'pbil%.hlZz_ƣw#P}N0ۻ:b oe\ƣ ؉IzGz0lIh3K_Wzs"#h *bO  2|xA;"p݆Gͻ X ?eC zEbsNȗj4.T}x^(;*+HG6[@ݾoc-ٓ..ۭ{Uȧ_._"FUY'$OdWPvs/-XQͥ.ўn)v49ܐLkӊ2Z'c5pZ Z- 2byu;]KziR#ɤR<e!e4qf p<̹<%vC`DoG7wrt]!:GN<&Fkhk{2h틑q2E{̼a̮BgKJ A'ikCA7n6mE~yy_35c4Fߟ(wģ7c" dúUx#-[-(0e`|3E@m*O#02 V K\톀)@H@?C4X/DyfۣHcőX7v)l\G"ތ> f4ۄuG`5Ęld2Âyh˺_4s͋̿Cl'KihP(xmgF>B+ d0<|cQG.~lmDfS25iZ<9{ުyy 6k~SUMEpC/`OәW%f~Cг :<]\rCf}tMNS!C4_>p =;񻅝ΐf.ݩGi_k ծ w,ںdXƀLBδ ٮ~VK2Qs"&H+_"^yH ȓHq 6%T&Kؚ;GCGIE=>G"D0K G_ƣH,Si oC عAV:7R7eIKo)`U@n~?Dp)/Ur2CĜ^hXAtߣ(jـ{E v($dkx]뚑_}൓O=[jZø3qYcrCG[7yκs0ώ,ZT{SRs.m+o_i+ zyw.ţ eYJRnhnI[va mpoV%w񂻊z,<Ķp~j{ƣOBA=|ep.G &HNÛ;WH'[#Cȁ^;#' \|9r-Ad{B4(yBvx7<79KK"eA̅RHC >cN@`>,&x4zx4\oָl;;qrt-[؞SrsrfwT -%چ}}sF;3t;۝[^:ʹW8v&oXiwY~uef osv.J{Nv1񒣖Yϐ!wP.&<[bWX}Bo^OIUMń>[-]7KCt ge:}{8fyfangȶeCۖ%#;7$ɥKDjOv&棈k)M2+_O) _^ߎײ|d-Fg p?c.lG3 CuCٽ>Exv =o>H[U?EA>-B=Dх(6^D8\h)Kz͵gp^d4qr<$Kp6J<"člc߉c܃]:K}@Yꪻ_]jF69JI}1d\:v&d_OE lt{dFΐj2tsskM{~?91!s.n[ۗ,aIey{7a΢]w&w6l@1M_P콢@N7 lxs 2iJN-KM Τai]Q^8Ëwԧ4#Z^s R]umjZ.mY ^lg6ߣr)"" Z :c}"fBd<е4K܆]o>h˛dDu3]tA9;߶fZ,hE$G@Ezs_RZW{OstڢbVΫ;5}\37m#Xh{ţ] Pp^`y<hv!wY> ";bPP3nyA^NsΒa?EsO"u =On V;ʾH/62b9 0S]YG$8ko5LģsI9̶gx9_._;? Gnq7DN#m l}\Кiɝ*Q//@Oށ6:W10! 4t\:驜| F,m(m%Ghl*@;8:yLnٸւetkuɂ8/z2ߞi[ ́(p|;o>L-ʶ8[d"#u(dYj[oȰ{( 15HY6K|@X20}^I$KHft6K2Zo[=Ryn9ODk^-SkFk›72yfvdG@Y~ J'@աj =[l}'JB4Ȁ\C~C!:C+2?BJrr.CԜTQ w2 j5(>=coA`Is="TTX}dzu_0`A~>C3N+X6.1p3_'_LJ->ӶjprC[Uv9iD[iY6(+g3 %\א 9u&MۡKQϝHbj[3킂<@M t >\4n/Xyex}9e%09賀W#D6-~x7uLG6{*>5 뢓oDb"{ CWfPFbmi4{||wÊvotǬpeBL}ۏ]ڜbU 0w{:G|P E=7y춅)>_aM3np؉ +> V5tŸ6z,}3p97Q0/8_O\n&/.k^Vv溞l^VzA-r-({1oa w;N?- GO q.2l)zg#`ӌhM#c\d< GI-@&Qپ;f w"}7b$1< <%bvbR=(7Ai)ndu65}#wt:[\l.u3wL^nid XtWTT윭6 TWo [ f:M63+_O1LĔo IDAT%vӐkSߙ/Dhxi\(pTas"Dߺ.7,Z`Yţy]"Dޠ]4 w{"9˺d^hx ِyk 쉋i c!f>W$=H,q$kPhdI^ s'pz:CB+=,-w3tt6||@~͟iNmI'y}h( f|׀aߘYyέ+j:n;=Wfi}6& Gc'a<slCm[5Ղ_ |B3;` U"gy ļ |#K2#㐒|) ƣv)\nBPrdw5>fEtE;G,m6ZhAԐ2]2ghdF#C@hOFN*Z𖏜? ZT|$ݏe淋}utf;3OgO! jh~o؝9ܜ\Vur`dte|-FQBzN\6+NnN#hy\@}_ok.8\'vlYV<{ҴGYYOhwd8od27f}e3=n2Mrl"l%GIhB:+b2*+X鬮G,XxZ؂5_ڜWţg#H> XVǣ G (z Ž1Tb#m6'B.Fb]Y37UP'W]5}e#>o8s+ F@8S9>cpkȴM+YCچv}M9dgyrC-A\1kЃY DW  Px 2C,&7酔m3#wGk7x:h6cPT7jw=LBNQ1 >QaFs x  `3|dLnD;PVj HF"M2hx-m).RUSZ}?^ǫ:u\qFL>ޭ2.>r)|5O7ٙYq?pdgb?D}.FwAV1RLz;b \ţihn;n|VdA@鹏x4z<>- W!;77Xb. ^u9œEh8DS#YVx4d<! 3 /;Fb9oT!r\<|%ƚ}VdH7\Vd#H,u5=`4`p{Cco~Œ?ȴ-K|+ p9[D02N!pY%hx_ Q],x|l;wx-\oD%hFӒFwG@!iPmT\wvr!5('Nv&?ɝؚvAh+QM6kpk2I4Gvq e\"ޥ]27ːy\`gc=Za̓vmQv욍TȖ"}T<Ოr5|^~Fno/Az2~:ҍ9wt. |\?" ֢WNA |^g5W=u!0&$s6GVIRy7R9}Ir+˫WIKvsQsQmwV$KP :kSn躠'|Pk&;GE)P;=s ^[';NfeeY3Nshx*DYVOsYeVj{ģx4hHKO!)@J%ϑoP 姈z%^U.=K܋:\uNg|RX`1)74tC`kw"naȾ}YdN޿4+?O~ i['w*(ECH.HO@d3ޏ:? *ur5ߏFC*$XZ'{|rC+< rCoɷ^:I'$,M|J6զ K GA<)H$VT^c <o1tk⛄Q3([}z9~k9ܗ }n"ѻ- ݳ\g/3ѽx9LU/d{#0|SW_=2/ua{kC@Uvt, on#@ Q([8c]Z?p|5@W?u\ D+˫W}:;rwwb9?.]]o{;9"Tu<(\՝&Szv&XJLm˚=Ggw:H#x!SZ?:49 )/p rţY^{} ~kE;*wA11dV(@ug<ZR-lF,[5ݼwѵ~P&_>ۈr%ߊ:N ;-CI-;i2ӛ WhMLI^#Py|s^ PrC/u!zmNazmX{t(sI#?v DWd;&A'93Snh/!x/+Wzc+++62X`}9sE/-4E~wM{{57H#;>F74iDQ$G.u0|Ld\<ߪau@Q秗M#Ƞiy(4Nı-6uǡ!E(rt4D )9\E ~*(}>$k*7C=mC\5ͱkcѰH,\(ADG #A [#z̷͵WTRY^=N*˫ݪ \Qψa(2qϏOi'fB[{IX|ZgUT\ |QY^ =קlMU)+YWH5ɳ{co}' #sVPfΟpڑ%|%ǣx42C]3[A:<7K Z s- #,"1 ?_k"o!gG)]:  uJ)R[/K*~"Q[7n2|nG-uEd_ rC=ZURn+N݇J#GA x%+ɂK/e|\G>?b8~֒wBF>&#{1tz!h9zXu7+)&CYPNoDXd-`}H)܀$ 34=ඝl_(r@$ ȏGkj.x@m_3rEoD7eO^A a:Pp MwzDA]VE ϗ))t(ms? t*.:;~"Zsւ| _sv"zɞsK'!vWsIP.RV<(]dLk3șY^ ?QJ ,)k@Nd52@N:eM:K]D+Fux4j/H,Z?ģg|w2]@T5> q+1{kEdZkhA`'{hx E-T 0W R_"3tDjvQGsAgsugɒ;AX)ѩ>FJ~vdH>(RtZ/jv>לk{&g'طug>Rc\~3ի0oI?nm2ٙ*\kn8 =K5@{"p$wJjKoV>b:^<y9\UH,Qh+6P}C%K@=M^%${xM\Mh.fljE66lli ^KnsY t7`Њ@w]c/>, |o# m%l#:^)74~#Cd!O;Zxc?#RDqGsi+3=)VrY83XO Z(iP17m'uoLCN@^xK<[*TH,;ehXwېUא@Jsh݅iZw}:H_q#Z=mz!1Fݧ,%o R*C%,s]QVD ]oQd}#nn3f} 9?AQ\fkj4\ԅEƨ'3̝Y3Ǟ8@<Ê̮,@kq5^iVTm>vC9ez?#t\ZeZ_Nrj^$fwwQE\h2 IDjB#62GUJ<nFj%K EAk;6`4k5uL7#p&ge-rCˁ;NNcWpp/6@PԲWMe+aw" d;m׀^n!{u6* PGGs}oh7tƧ[|(0Cdݯ;E!f;oըG@/suYs-xkm`ţeXvEx´Exrh~"+ыݎSo>2Pj<ǭ= 9Ћ*\u'KV[ģrUN?Eks1RB \ӷQf6@^Eh7 5)9H3G-F^} φhH,zl9B-"j3)'pOD? ~qh,}셀MPc^Fp9ޑ ~l7KDPcu > ]l:JID߃N(`P-C`Y7Qhq,m]kRn!$OGZDݷY)efךVZN4pr1f8 3 ^G(j#&Wto! _Xem3vLQ٦?u,-BYH)^s͊'42GTbg"9ŬC:Eߎyf?5Y6sel=c3^C.0FvX53ǚdXm6d؎A `oB:|w ~pby )P62`}F|ٮC9l7pG5[sn7l?2E2%&S-w@FA\]USq#/_GtTW/A1lp9gA^>J7F < t :Ɏd,Gz|rv2tm!SX6u*l,ZeE68ۄA )DM8h=mf:[.+)<2CGbF!m3K0rz P4Cڂҥ-HaME)i vEne>B &h9|G]qis?s^13L"uA/ (ikƠBZW/Ae~ODעZ9۪"׉w"B`ig2gX|eXJUMŷP*۔Ȭ5.O }@sKQaurkߤa)=IҒc߈|ÖNu~J3t&iߛNyal/A='.p$wʬ8VV.8 IDAT<к  %N\ #[ٽ!hH,&0>N Rnhm5Jw4"'lGހYlu9M9+UK9ιk :RnhS壀4L3mw|OI" dپ~F"&,|*oZgt(?VZ@lJ<~*KXG~}w)^( EEz!DjţY(Qo~2"-jc).2Cs;.D CX®[p3 -✋U#G`(n둨D#r֡ֈZ(8<o<^ A{=mUAx4|*H,UUSqZ7"]P/}[Qi])&j*r䭌Esf?h>+s3r (XS&S1 Yʺ'n1cog 5XhxuCWiUfzeeɩ+?Þt͜X@v#CoJ`:?yR=}9xt]퇡g <LDUհ ^96 ;}"_sMA{>gQh-讚W6G~C$goFȘoLv& u8ALi3+Y˲Ͷפj$^=ivW *Y̱Rtg-!27V(hGn Gތwݣ:1CxV/tCꑙe)70krCd?Hx,WW&?+/&}iGtЃnӤ~f#1۞(e:r襼"ĭf{Pfa)lmZ 9Tt4s?rbg&VY -uln{g-8eͶD+|݇f+P[~ 2c?R4mHvtOJPWPÇ}%}(89K4ƣϻ?P;26"<̜O){,ޘA}P&d@txxn*=~mc,kNpMrtLv&ZP* 8ٙ<ɝǣ>tOfd%+YYP ҹYBb2Jj^Vs2I@*vXTw}- j/@g2VGU3ؙ+/#[~rCkYs/gmx lPE,3 >p-` :ɽQ_)7<փ `GP2NGԾCHYzsQ=&2Q'!ҕ6VyfE A2B>QNE `T;K:gM]\WtiG bޝ|]7[;;zL"R/D4?onO PC`ld%+[gNT?וAHnA =b9숋lg({CQiHݕkҰzH,2߮NfOn :ɿ _P9s> #\bZcγM+?F̝Xs-uV6lk0 P6e!m7mER>A4ԕuq2!-ș}<X,Gmu\r:*l%]C⫑X"?K\"AQVhWbތ`lї(A-=2F̾ -1kśԁ]@>(:W(/"%Fm _b:)Zhu`\$޲xCg^id#s&XVT2VY^1^R>8>صzg&S|<هOv^[l* lb>W܆=)74|)drիר6l3Я㹼ޝmmsQƬ.!n}>KwP]1 qSH5 $E2+)nKӍA^R[ANr ś3 EF~2[@9+ w6ʞh.Fx4`,&H,1EN*xg!~ ur<2<@N+1ZvX[2c'ny `49vY]F̧[mFҎ(k6Y<~_ey5~[?Ύ P{`;eTYJV :ɧPN-Pa)g ; O"w #Sox#*8!rC/e24,3نf䋼n}R -:ɗ_K׹_=)PZ7&768GNzWG:>&:"ʠ5e;uySl)zw52 e9ڝJSH,ѵhsU0n ݤGk3a}u>\q702x-F&7CE~HV/TT{Z>[oRȹ(z6B'f ZDk@YP$"~"[!U7xE '25#eni 6{ev(V尒%G":﫶yLr\8'S")*+Y6"'RnhWF":7T_9iz ˇvv Q ޸6ɠ:ʒQRn=e {=9xs@M9fjv [nзC'jE`'^6D;;  L r\ȩ+L}#zw Kr,j0 ) C߆^w&@<\S&ƴ=5y>h "bS@t1@;t=M#6̿a(8QH[3 b)Q9(8R(ʢm ^QS( ׈WU2j4yI<ym9QUҶY:N-::\pې r]n} FήF=D9nf%iVbQs=Q/>FhǡO/Ծ%4eXoB \lAZLmggu=rќNc`H,D[8]fA̅(ev@eyu&m.d2+( bKyz>=?Ѐߗ.EAPo[l1Cʿ ϐ5Mj=8~Xt }3Pǀ[NrLGLېoa/!u#vIN15EJ$EݰΦ?E|C#^QT 7艃(g_^iv lo^Y(C٫ߖ8|!%K\2N9xvBVA@Vp00C,Q@Ǣ<pQĩ.ek?)B\~@bi j_~8HyRUSq*P%SXC?qOeyy=} H,tGM|

cQY^wdeH,1hGs׺V$XYC-rVCN2uU2ȇ*AMEvvDٮN.on@ǿn\ˇ]#b1U 𚫽 ^lRee;MWd 43\ `#jE(s҉ TbZgp _m~lj@,z!PH,GÛu&8?@YAwQh@;h=/DX>G 8ev7,F2 K& 9EXYl!@VS 0h`G`fEYM!ܹRC3OߗybLey,h8t}w;x4%r̚dex4J$pl$8( ؈8XCA_#7 ѴAÅ([UvrCX*Z;d}^vgxG|`b*|-xeK`npvITE)7HV;ZPiP|M3ʜ,GVTu:j-~&rG/|)Ee@rԄ`0v10+E;{|B1 6kS2_Lhx)k5 `TD'< ԎÛE,e9N)?/ԁydNT'7qt ]v>q?' dfm .p7x{*j*vR'd%+%KEQZ1*K3'K%vD]jFv.}]SDY>)2[1mAdW\m=o'gZgt[MwYVrĴ4'!?Z<`c-+[|2X% /EtW#u(LO2;|+r^(GQrB/u%g:(QbnCPr_n}T@D KT6H9堬3UO T;5*;}- о tCHz \ls;thCK?}n2[IDArRFvWW[zMsUMſPa*Dex4ȿM.D3(vY>Z[}JV-*XbT)=O$=wPP7%G^D?ӶdWFk!AEuIQF/l7}N6-b!5{TO!shwarU-ْCJ bA(l" (Zi""#m m0 %$!!=پs|.IBfKy}vgνޙ=q '\[?CAeC!P.*Re3Bk(;Ïybܠf7F6jv2nz/X&Mnֹ}CP?X˿K f @ۤILjnuC!rW#ڋVVy6G rl\7EAa|ax?FƬ?x;0}jv謹/7%+&w{eRl:I^_] p^ZZ8u=wY>b?)A#NFs caoBB'V("/(`{dC@vt6#+Pz=ss鼆:7y o&haD՛g~$8 Q~gAO"g>U a QeϢ\p ,k$(¡F"0bkC^8Q?=Cq4B&CQEh!n~ qhh+}u.W!oF3wK:Z?@ޚhTb?$"ٟ@]Dzl߿7P1o,RՕ[ w?Y$@:"ݦUk~ ZQS;lY/u|w>F/_5&ZCp0&ץ5on2Ljr.:bƢеqh1"0'o #mڂ\_}hb\R @}F c(9ْ z &Q*G(eK9$^AUB5ký1oИkt(DӦ|7 } (g&ߨÝL ^}ss'ggA]9Ė<πHX&ah}&@׿!NAO E]J?HЏ1ܓˤ=M -ݐڸ[M.cook]rJĂA!)'0 k~6bˤLgG2l~7䥺Xˤ:xdGq(RT +t9qv.jc#$?p35*dG"<EFdt?ׁBw7! W٫!5HסVBg!7sm{62 W2t6??W2e#\&5< C\-@)vC  GTJSh"B'c(/Gb3-y.E"n$8_FہI¦b𱻷};C]9D+]D=yM -ވ ؐ$J@7ٔ /ՇPdH mhK]B9K\2(Mcj@EG9P1dG*m>$o&{q=+/ۅ*vZG|Lw50MvEyP=hhGg?QR"ONDi*,Z91*ү^`hC$ٺLM:o=4JjK_ٟg\DTT% xˤ|k$I&/4㨼(%lAqFpٛ.w+>ݑ* . ч.beaL/^#%N(tT5x`<]<ˤ~>AB E7w!/~H$-@R'|3Єֈr:[^G-vG PgQMCZV%o^eRbG .}^!!% 4T5G76^2( Z16'% IDATPsV[HQ(7w]nG5ȶ8dSn1t#PA62=m!f$ެ_ Cv ۣ~X ٝpTpwe%P_L¯Ar^QuV1p1d}.͟R/MXu(YT<5RۿV#ATGh5@8 8Y&0YMFu8o |7I+5xQ ֧/CƧ ݌,kЍDev*0"Fsp|+L:eוh^l~4JX PtdWD<(b, yC座נjx9I.paSX[u_}4 YJ§g0A$fzPv~LCyV 7%\a**{JoPzT k8v}a7-ƆF-Z!xGFVO7Ju7 }ɿϝL5D}[I0&J `= ;╿r兗\x)W%xOp`CzFů/mjh0A0B>2%f?u#Lt6PQ| NgG6Gy7/f௹Lŗj/xy?"X'd{>4U06DmkX3$6{M1H>pQhHv7%hnd!/R(oQfL`oO>ko6/s̘VΜ5w*Q#3D8^ X-j0@lT8pHP0 Hp= y(^{hU앚GNz^]=hjhlnm D*Y@d&66~圁aCt6J|&QH^{/ɷ Lg5@¿ 6n@(L dGB©#lE-° QtdKJHx-B|;t6\&u^;vW*D&xa[o W xF1H7c WMt/iϼGz˓hx)*8pZ1H;c7ʇ ,m|acϮ.ʺU8Qf% ?}ď7.|@JLd@Gwm7_^uNO@xh)zDDa[ ;QMTE7\TQ;2٭sUvoWQavŎfk'|[raNoaŹ h?ȣ|#G َgut6E[l_B6+ۄPލCmˤ~UKP (n OQ(R#d1<׏55X; X[y$_ Wtw;uJT 0v .T{NEƴo>y䮿>򱖠Mc9aqv&Kf\|Gtua I(g͌1XPRd{UWgEE]*׽˕{bJ=i+s J}}_kPтpB ;!6-O;!A4/A7uD g}пjzV|Ɛ}26pA+ ,}7|L`s)0A'͟ LG PH`-2RO c JH~.dªB q, L;Q8XF?tjrm:_,eR7yFUhjhynƱO^ncqDskc[SC#H̆+w.>ܙa9|6jsڠs+\ͤW`oܶsa7F|-ET :M4 Zx \&"!TDzR(I@"2DH$!c:1߇>6C^a(f2>׈BEHh[sԐ\qlnmLvt]mTbܨ׺[wojhYwkuͭAkͭuM2ޘ܍3 xE <5jг sqjjƗzjƗ  0f*bT(vHÁ"h x -Du#]ʬ_/PxDm%9J!#[?|.:,WmHgը#Lj~:Ln XwbkXHgg1B->Ys={ 3rԁyhE =;UU#܎B<Æp!֨bbd,+J.A; %ߏ>VZ*ǖJL~xzsk/[G76XƽzcE^(H\uijhle4Sd$TF9aR5+F jX Uuh?? ūBeU2w@"+B^GAZjn1$Cyb^UQFQϣ('wKPXz^/ XOL`oɢwrv;U {%w~ˤ^D}GEMk@bZHL"#نVwFIm@gŰ T=kd[Q?(pYp:.I J^ҋ|X(:G-`d||q J2b/?wN^uK;+E0t6?_%**z+b=@k~w(qFWjGLùL| ~(gQ!1H<, =3ceRG7m/V"1 Pr4MF^q(t6?5{x+LӦ__rm'el}6V18:式?<͟懗ʄ}>sX_̃epI\9J ł/ѭ\& @:<3 e4CBq'*!6{|z4 Z'j\\am$U"G2e~^+l9Ska${ ٜp]*Drh'@0Ae=F;5:(^L*Dav9u@swtP]ЂRlCQTvH-EW|%Pˤ, P~΅|k5#R^⻅z [ W=<=lRM{[6-ɡh Ig]Ã98cB cQ/#!ʈPrr'QqGgP.>Co(ldp;QBdD2a1Ep4*ԟ)(;zJdȟGy4n`۟NE|ﰟZƦ``懡ȉo2n$rEafͩтZ;dd^C'^KdڈPHha+(|?Kh{+TqC:(ż& H$z2ArAf۬|ҷႶ=^ryz~ٵ2=ТP#,҄zb\! I¤a,dV?ޫmHk :oˤeRȇ}|V ~ !ۑ4*O5 QX- Éhsx )ϼ e?S pWSCl5VWZjnm d0!C/0/(1c=snA`wN"o5(X} /Fss*h};5Ϙ6!* _Db+Wk[cBh^=j-g3*Ї8͏UT~XPyA{\P5w.wnLdTޟpakHNDD@,/m-IbRf ّp~5 |V% Wس$ @ ` (T%4u"̀+CfX:WH_@a"TX]xI^Ȑ"oV2 a?,weRPlL*eReRCX_؃s\u̢^_ =qXSCKzJҖ?J5%Z1矋mgjZ.inmVy0%ҳU#zCaGhn 6!ooGh9-m*B ODTRHnWW#v= .A->CQo^ۍG9N( }N'hnmtG~o7)د$iGW{ M¶Q0VF"OJ3g]pL0yp٥$nz`( @re@ޥ*jG8֠п$L}퇼W!oMO"eD޷ ũFSHGwF$$!O-̮ ߧ$g"8 yEÝ|Ul|E#nAb+z$\!Sv]GM\k^|E<@+n+\XH7Ϙ :3g}׮D:f$ʡr dW!*|FVx ' I@V\&u?źNzҽ1Ͻho\onm@a{VU ]baa;>u- P-\E7?D7EQL*6U˝n_F^-эthp4\&F}\XԾ#~ޚ81K|ZD4:-I]^k=IgE0ˤ+: Qnt`1H^ ܈Q $ Gj 巭WT#$/O±bTE IDAT'H_ oy>XY{q1H W8XT H ٯ{ 4B.@%}p21yU#r8!K@yJDRA^_n[X (k5?Z88b 0֛\&UJgh'[P4@Uyy6 ˥?720 e@[Bl$ۏCޘjW QhcԠEnu |$]}L ٶ,ۆbQ@ sA d@)[kvڢ!1v7z|/ڻ= X6y̐0Oͭ55bQ.DE.ln@?'ynh.DOȑ 6ME*渏*@?Htc*d 0ex~[o2HgةkYlNQ1fL1({଄+LDB4Z<s/ ;#19$j W_PL|'&\b|1 #ava1HTf,pTf_C"-dlpAra+ThBo2\rX+,DFk̜5O9:>DHđZ f4FRl容L쉰ͭÁjEGWuݘ ;JsxJF(?3_|nr0 Hg#P-S-GyS?DD~ӑ@,)Q7HLu٨E5Gg ͓_FBk!;('("`$=2q}Y%OUxz˞.btǪ*7~{aT? ;myA"8 d6AB1d#W!Qr1H8#7-} Z^kY m^_oCޭ0_ $hO*ɞ+:$3ף8$/ A8o0R.|-p=}dlG!W!D{T.[X$°rswaUtc>x~SCKO9Xskhewwt}b |eask ǗaۑˤOJtC_IYMgϡ2l$ xHh"APDH~<3{#Q#ꓸ ` Q2oۆD^5_cHgL= zbU:V=╮@ފU<GB#VmP]Ul_pEaqU!;x N5Deo>8|̿w=Β\BJYv<ЉBIcOXo3Ezc2(.,X҆>ȳ;}ܱ جce{w0Xo,k#D7]@Ysv6Hg3; Xvd(Cc0C|dgf«WXw[GըowZn& ݘ `0&yP'd$QQ6Th"rEs{;zLBs|yW#EqAob[m*M|(^EF"Mu#pU.—.?_yv@sn9(uhq4u"Sҏs < ٜ.$^zzy9"q6wUY%/ԽΦ7.flEA¬Hv"t:FΡ[DdGϥH1{ 8y>ߟSA X1 ^WLjcKe<eK`a"<2Ǡռ$% ͭ%i,OqR@#ъ>&  2; "0zwQ  E+3 50EqmZA޳WU8 TW/m@9Ut.V!Q Є=-1OFBu4Q#W_ Ru]TW<݋{( vѵE#}㝋>Pv$jRAd/!AU' $x_G ۍ W83 ks 㝰A# ?{ϊ>|q|&%8# 8NA ϐ:Ƴq?0d@F箁:Jc1deKg'Ի|Ώ[",T0 JM(,y]aنMѢ~Ð Z.!HUs_b\pLE9 zCPO 7Q+DqTb*o8u?u,~qoǿ?U y@'ֿQxH,uj=L$FFJ<s- g#yp/d~6'Y: IB5DzW]8ҋO+o|9&I0Z$!Co[alp2rԗrT;u< s N| GtC\x vB | U}=rv%Dx_.奔x\fG ِM9^D3t AV9 =to Q,OBʡZI$Ax;BsȫԉQX5?]0 CI{/cPc>6|S$vB!YTer?m6,$~B c1eCFQ9'I-eRϢ0.4whv( ~qQ:_`\&re1p}W`>aƆL.Z  \›' ?:  oA7ܣP%D}jP7:Z])sW|1"ywq|#,݃<4ťܷFbr+*$@ tMbE oh cK~" ]8=᏿%-!@ޫ~̳k -jvsC2cH?O\#a0$}qxgُDU+P+XkȅQk a%.C4vƔ+ф_%0[@*:qbŦrʺZaXMJU8ueR} [ "&CP`1alH܊nEX$&..eRJaX=\ˤ^23u6#H\DQIT/Hž{Ԉ~r ]%@Q^T_$zvDb $(_n!:~ Ǒp"A$D!a5Jxh 0ނ ,c#|M(n;WKwP{c2ͭa6T*XaƆD= \sHLw P(lT% U [."cOʷ FXjaWW' !CW) E„+lf!'/G$ Qt'cQRmgBOPHs~i$onu#En;Pĥ| #1rFhBDl6\Z 0 =R=( G+7gK6A0k96]=yPt>?? PzM݂~HMG1p"Hx`0 peRΑuQA/"qVɫDiHDMF!anևQP^$"O>*cPC' ˡ|'Q~\S0{Fވ@m@^]G*۽'oE6.:"O#PNד'3qe,D訬)҅,eR^<1D?2aeRw&\a4Q(l$`YJ5XISkP_m fSS" +:Qk $\;+ tՁR$ƣHF?X'j[BT+|xyzi9wla#&QOKm"hltxѤ 7LvAӁ,ZV 0 c)vF#Ar;hwT#o^a< UsH]QNd3h Kmҡ 3},z~Ba=Ʈ`Qq _U?.rc,c#ʯW qUB+b?A+zQIQ8aa ?EosA2, A $\ހ@ީ%H,C+Pp9$|6CK@fOo WF'"sMC}cӐ@yAyҷT[ /V HdՀ"1??O V/4QBS)helʰOۣ gaaS  W8 y 5C!#~xd7kPȞC((oHpVUB@^v$fDaס;5In"l؅WP"?KkQ6L`(z|`.Z< ׆i<a2 x>HB ٛ8T{"!!QDUkjۅzPT7KU~?Bc!C^>vLc[*+!m"Qv.X,c#ǀ2xM7.E!75qxaF@x$L&"Q5dz/<=z08 SՃCzȫ*Zx}yh:'<*x+ yzP. i7Fa0ͭ3P2Pt45<:c3 06>p*^B$DBT;QDNuߝHm.O7V!ZFO"T=T@`Ha VchEnaaNpS#aTDd<6$lJD yVPO/ʭ+r^DyVD>BY/p |% ,_/ɒ+c,:8pCSC0 IpB%\b| eFawSѽc˪nE|;ȋ{| #jGeT-@;ruʷ:,Da#ѕ>$mL`a\Da߬DjGT}o1j29GЈnNEy\~T G- A,LnRȻ${P3#p- ]c07Na 0/t}Ƣ𻳁Q];Q} w4LPo@#dzGOS1HޑpÐgs+05 ËAu>q(3& c"J ÛZ x 0 a+|)U W:*~/*8G "EӀǁ=(A5lr3I䊵?5(?& c *p+0 c(=[}6݉ZeT .y ?Waވ E,> ]ae c8 UZbF0 Iv=( y 0X1 ʁa{+8+nl,2 0 0 (h0 0 0 L2 0 0 (& 0 0 0ʄ ,0 0 02a0 0 0 L2 0 0 (& 0 0 0ʄ ,0 0 02a0 0 0 L2 0 0 (& 0 0 0ʄ ,0 0 02a0 0 ,jN IDAT0 L2 0 0 (& 0 0 0ʄ ,0 0 02a0 0 0 L2 0 0 (& 0 0 0ʄ ,0 0 02a0 0 0 L2 0 0 (& 0 0 0ʄ ,0 0 02a0 0 0 L2 0 0 (& 0 0 0ʄ ,c9sps<^ۢk19l9umZι9}mXl@sL cm1e ysMι89܌gApW.ǭ`;m6 qνksι+s}^?9[[蜻9wjxs.p}=AL_qA}^սkowms.Vm@)ι'ssss:v\ya&!sk`3aV|4z`7`;ι4p-` ߿|tX |z:gn>?3 XƐ978 81 XAwyGUu!A@&TX}p-kw LXv׶n\5n\][TTAiz^3̹{'9ie MRuR*.W)RQ)^)Rʲh'R-RcRYºT)uzI)A)U VJ ƾjW) - RR+{R{J)R k}f0 ۋz52iMnZ?nZGiϋc%rw6S*RO)UJ1ܽlW* 1i]sR-eoYʲZT~}>ouȰZr]涳RR>KRƟk(F ɩZw֝Z6wnE+V6)ROu9w|ܞ^'Ja]ڎRJU*߿UJ>٬r|>wRj!`_wiBɜRu]{Rj}uޡV)ؾJzoyNBs mn>yȤ Gd@tٯe 8~3 8>˪ XxHV|j}0~? X{S'P,.vWLdP awݏ} ˼˼m nFc9t{t/mMUòp mNG!2mn~.B HC(#6JQtr3Sj}뫔:c@ ?.rֺC)Un-)FsXo{ͰbaFk] #JX sd9g##x0=XX26sq m  x=SnAb_ !.h+PRj vD_/!aߧƹ;D uJ)o~6#ȟFtcc B.WJ-JA!Ev쟁%UĹ/Zldltn5n\>4"n&۴>|jAezÐ\5Ȝ{"7t`2R&Ǧ5 ZjRugnUJ5#bzKnb@}Hڎ{3D+ `0 'bsv `0<)8 |%ֺ`q2M]pyCMHK:ZXQZ3 <Ӏs$)4ګOZ -`Që:>ڿwaXݔQ;}Cˠ׀@Oeg(-lN`,i/:ۖZEiqF`0B88@DUX է<{h0l#v-J_ MT^nωmN.'D܎Pm}d$.>y`v\kb6ݞ~0wuFIYEYg\iSj)C@>&8 us\QDtRKU(2ũ$簒+J;rGIt$\Nۆs8D.AfbSg,{ 52P$7O~W g^`0iRUWYvy9)XErGDJ&`u%ZVDS,^q ;՝kZq;C&V7z"0ҧU՝K0@f<0}{󁇀$bT_ |[yvC ^o}MN 9 B# ^ʼ_,I&#:$#7"j+;æ0 0 ; pZG Q^t4wNM\6~ҐgLylߪ"P@R D$DΌ=#?# ?`*x^ޔW?) ˡCfO`4:6wi_uLi<4uƲPaոY=-]~W Yꐜ9`0vi|s>b>HLS#%Ђ943+rBa, k`Yo`hx'.]XޡPrh" F* x^z:pM8yI.W򽅸U ٯ%@ RzyHf77- ־f *)5>I}ʓU8F3c.;qϸ!sɻQRVyQIY%e g :Upmej&s~"WW BY `D\] @w<J"pF^/֎I08wc&>k0 î>9 5; d YE#!7b$ \S~<ҽ~s~h_!^'qr2#0\׏Cݫ!eW Հ"e $uu%ŝ##*~~I\DG*0GkRWRV98ɍlAsy#P a`{X!۫xZWǰA*J{Ln ;w@OyV'5O~d(P>\"Cc|!-/3.{:mHփq Xwr6C֝=,"r>EB?EĔX;cہkN* DHe#[XXq})( rYV(87RǍ|+NZ׺t+96c#vg/v ]{p"Z<2ҩ˫f=pUR~ ac$dy3~g` !(J*~ʄZz!$`.n@ByЅ_kׇ" 1`6l0PkנXe5OJ[b!!D#Ux.&!gUJ O_)ͩJ8&Π@;~Ni_YvХW}ۈC5+)uH=mJ\;)WIY)?.)THhҊf,HrXĵ݁ghjw>GH0]hG<}˅gq`0wҼ4quqhrPD2 -))L(-Л}L:-i2YDm_ۈU ǚ D^ٱa00k` bQ@TDbUt҉ I.Gv{~)h$'&E7 rTDZ ī-$eU:GG; "aζ}.iof;PLtIkNk!ܙ =9]RVvEiq]1V#x0p3bzd1 N ڟ"Q<9 q;&gXЈޥ <#D?D"8eLwGbS~S=0ۀ/A;EȆSg-{ލBIiRsBI@F^9}燒\%Hn $),X38.(X/WQZ|cEiq(-(-~xq&=B] v"PB(C+&s%gkg -X7u gdmР Y MC.EEiZR;ꪽ<9P}sS[3y.sZ;# 67&"VP*jWqa]Aěq&@>Z#Y}l_보˚C0;'ϝ| IDAT>^ϧ+4+ӽ/}>Y k7bqjF*Fw3#iLm-:4U%eoT}%e!s%!4xENf!Ո*@c.q؟jz,FWM sb ? `T;Ñۦx_ԫ~n7ɫ_ %OjZɎ@ buΧL  8&u&E`A%_ b{?>z )6-D=@(KąS Jq{+i M*# $ZpaAڻ{,I(3aؿ> ;ӂ=/5pmXceΞY!R#b /=X;jG;W1x`0 =tRa9d8 pE"$s#.'J*c{}~ #S1`f<$D\y7n4QSQ"(p[ ?j `rv_Ú /jȫzgWJZ0Ii!\yǸ9)w:ʴٗW ɋ\A)@R@i`X#* еpfĦN#CF8ړ{HEiWo*J+),)C.C0<;I[BوUm 촨ۏUЧ4O^kY}. ΅>;/b !9]=`ҀRp"v D1SCV,4jħ<ɉmȆiP$ŪjvpiNbC{xI,"u)Jmpr`f2qk%J#.`b?$3A`0~DDt@ 7̈m%e{Chz`e8<$J+%?z"c됭h$η6ѢSȪg^MY0z#vAJ*6.f-yxޫ˃{?$8AwrFvtD@ECIl '1&+}t,|iC]>m Ǿ"~{ggn+3YҸG`Vr b'Է =G%@n#6p/=uƲg)ޫ%˫"aY酳`0v G Ѱ ;=>I^WRVy 庛wXLc=>uƲ'> k:cYgO^w{)*TL-**|zz ! ﺟbMB HԦ^mm2g{!F# I77 $DЉ5].Q;O濎xZ|S4x)~O`vIhR#}us-4+DϐJlaRc5R*ĂABxl#VNbR}ޫ]-9m" }|t}-կ3#׵ >(=PZbkIY4$̍T% !j;G:cY4m9.đ 4~#BB+.*4pV)  Ddd{O=$-N,N&$8nwvJ-4CCI%=" ߽OyA&jOkl2>R$rA`Pa 8fk)O.Wa`55VegKeH__XF" 1QaL-;dbhEDVz]:tp͝SĊ]Q#֪?GH4"Sd:á^HM}}8͔lV?8c?]d<zUEH؄ʎ U -dn1^Sw/ۑ}2 W)AH493d)S2OTuNx6pdr#ީ?!QW ^dyF-H~\Ñ bUNsRh{_FZk X ^_paqCN[Wg_sglو* J\ieO9vd{hh`N!DT8z$J#l"tBP#؞pBOT|nνngyG,9i|s +y1pr$-F)+ܙ\pt#rS8N+=YMҍRK:7A~I:5Uk(H었5F%b0 ; xq4F/vmgHCu(1>x'!Fݐg!"~M^Hw\ eEàyՃރXlYI>)o`ء}vQ ιfv(_iCH)Vf(5BHQ/VFh$da"HA^Y.\#棔ܐX\겆wx?1'^ sQgRDJ?d3Pʁ1K*[U01NC5ؚY?XQZx { "*E8d@RRD56eo,⥊`jKG2~ " WkM>}d3Z5HHB)0^$_E5pYlOeӘNxTMuRF+J*J甔U#]![[4lp3G#a}{ppfamH{z=»8G8>L?kEH[><.< w8x@GcK_32Im=qIYe6R͊ۇkՂӒ>-7/j4 Oħ<aωT4Gi, R} G/&!'{ٯݘCZz[x"9Ko8Up " DUEW>0Dћjc X 3"<"]>< YB T`dSnjM;$/>))< qAk"{P]g/?OUTQZ|v<̪}]eF`d" pL/,*P"&"^˾_oӪiv;/3zs;#J:me;鳮=Mm a)Oni}?]]9o=ܑz~$ڂglܿnVTL$֦CȺ_Yk :$3|sR}}j$D!BVW4oN qz7ͯQb& 3#酳L/u1R酳"Zrew)%9+UE)zK.)k aj_ phԭY3s-8XuC#axӾ~k=b}1!կ%%4䅟 Jt~s˥mIJBE h͘:yʑ8E{C}ʣbH%Hd^H)wLr~ٙ2e`fGOg!nH8$څiD / !YH:mE"Ov<>!_^)7ȨcrJ*u9::VJk'eS$I`} 4A{uy؅ٴ (!S3kzts$'"}b%)*RI f=TNGDM٣bԨDD[ʫ؊0## #DRI koxaHlptZtoZ;(zeJKý)A$T)di9D2[ % !Bd#bj}#|&a? 3\rN~^]Cob/ r& LOVΊVFZcd?Am%H]_Dд#㐁>%(@^ۀ=/&zg=c^WhvYqՅ 2%.*l|r~Gk7tBbvQ`)l(\=xVjvb ߐܔeܫ$-=>EEKޘ{]]U>U&kŊ kKمM34XDD-G<[㐐@li!#QHv1=ULMqި[+!nQHz୥zc߯f/գ/"f.Vf:| 3/ߩ]sXA3x3@f-ﻪ%ˌΡjሥz2`0\)O L=­o9ŧTX4R 0,PR%Ol[)*r#CI"2V!Izjs\h0 b^yH邒JnvD`QyOvb;g&k~,imA}Rf*LD|ǑH%@Z5b׆˭ށ.Ь@ *ԁn<02cZ MyĊiu 5x0T-T~l5w;7e82zzJPRV9pߞ#Tn 7/`vs62pUE~d}h]GiuNVRVyppTEi ωhT0<1L&TQU Z$ g Xdi{oqe`˧%ud'ok ULE Nf~_4nd^& N i7dAηarU'H5,;k0 6T-< ھ((lCb/$`<2`}-գ: 8wG]o{))Q FtTJS$wW;$t#Zn^nҦj"a&@3«w+sDQxO:i:B2iM 8fږuiuZKuNgHG{Dm HȟHHTu+vD9|}&"VͤWDXo/գr$/AE<3xȲJZ5ܿ? w϶PRVy&RU莊{?6G,p=qU}*ʫ"U_^8 `zᬇʫ#&r jdRZNA   ƻ;B }S/N(-rqpΤăaGtUfg%ɮΛ=3-/P[鎐E==g#9U V#l XFBgcJY IDATF {2=}~g4a(K^/|ʣ.fJ1KRTiF`vF`(- Ȁ>rjEiq15{fnAxՒSDܟDyUQHM۲qpyUB gm1h) xvu򪢡HA sblrl"ՏkEivL`0}!³1^Rk~@5ԏv5wXdٚy5+Xu^çn"F#Fë׽@8p뼊+Geة)),@,Y[go9v/*J ]?;$Z62$Ff>|YDKIM5V5 nbv무$(az&;kъ;%^ڑcڐI6 [`0vVQLwmty^U!Ir\p~8w'QRV{hL)%>qFՏEKF9:)uMVWlW#3RXƒRc/DfPiHq$DdE7 pwd'KZߞd }V%԰߲' 9K``52\X'*)|85U"4]["pKvm&uO?ἛWZ냡Ě>}+J7+}[NA%dڃC™KgJK<`+-~Lgy";N[r%Y '9*"Ǝδe TY^"()TS Fly&!xV#"Ed.g㨮6ժKܻ`d fh12(%kNbjhP06EtL1"wYzxk!``#iݝٽيZECUVOwۘ3WhCȦ.C2!'ځ]E얭 SHaHuBUk:y|9p Y"v3!{}i0F>cםy7.)*=0˜9e5PZ~S%Eej`_e~Vzp\?sݳ!hZ Uy&N--/~8lagd )t8=8ٚx#pY&u69^^kȴ9Ջؕ[h[vTkGmByǂz\We;nMc|~*}7(lKgCW=/9ÊdwԈx_D2Z](p1 kXn0pMSU׏#[_vPi+7}Xp"RHaHu/;nH$Xd ]ӱxqlы[2iҶ ON,OOkHC£Jiyqd6]QRTvv9l-{4 -bα rWw c8.L!RN1]A rnjhl͉V{sE:Liv/bx%䯇W~¿#D)$ X,ծt[?n wRlJ _{Z1z 8To4ʳOnL&\Kܡj5}nZ̀mi?\9GODFU !`t/Cśҁws?9g X/b6C`q"UӕF!n\KGt4/bOw\ ɧԇ['ܿj^',/7u0=! c-".G7O!RؚޮS551XkF%,|g-x&1@wkx"kYyeƷt_{x]S'}Xѐ^xnb3WYRTvfRH!g=sWs- F,[>)6-ux`Ko#Jl,GP+X(Y}懲@{-PԸ/N;&m]c61n)o;F '"ޫY_V/la/+&Ŧ4N& zB-75>\_הpMSؒ&U`Ѻ١tTvؔ8's~-xM!E{iP )lK \\_( vl .Aed/l#wGbE;h^3?x{J}]ܸKfzT]bͭMݾU^c1G:–oJjKAQ(,\1j䢕# pqIQ>Kˋ,-/>E ]b(X/JˋH!Rp\r\?s Q[נ3m^ĞE^Ğ,=''r^VZ] 4Z]ߨ띿\ P D8$=Q ~]w{UO9/zbSZҫ 2 S4jTǐï  [W'8-5 ;B +R38M_6a(p|z֗`pd 6^ lTFd/bכ@`6􏃙aC/$r w~ܖpK .3 PĹ{?30{,)J۾y +w xq{{xoww֭7g9[.)*+jwLID%ޏ7b@WoP1݁X]cwLcm  %Ee[KB )lU4i?E-ti$wx0jȁyHpŠÆeښc@Bk?IvZ]Sܒ{ !bCӱ0pknj֌ׁmJ_plKĞ(9̹GLMlMrfLMP%E&TV %F2~v2DԚC{j)҂ ]r!*ABK?T[[KU JbſGd}#wOc>A(L 5z;p4㘛Q}ga9X ˲XCX )m#R'4y:;/"-ú)ؔU[ƹoB٫*=ȍòn> Ԍ˾E&w_ep=^Į<8Qq+|9LEy. ߨߋZ%0u\.@xŒIGJa1/bW!/bwH jP35h~ ,APRŎEz^n"+VnTQ$r&+mX@B N^J{Cn<+{M "vUiy} @~.CGѰ'=*>ٲ(@Q6g}cD\fV4dY` 2wfM@.p麺E],bXdn*iSOVRT{9jXZ^<8 Ib/-/~Àyy;֪w~eG3PtkŚ~k s2xzUmルU%Ee|(-/ I:_CW4RD8=_ʪz{{G}"A-ȖaI؎KWTiHiH.љއ |]}̵f\]q^>e _ 6|$q}!Yc#{Vnq"O)p783ދGۖC鏈]EqC1?q{hbQE̵Gr@WSK6eF+/%l5LEEu_7V2뎹[vy89k~ok:J}| ,3ݦ?pprç"\IQٗDcօź@VcY\4<`IQVURZ^\"țBFpпg;NW/ NI_ )lM{+3\[|l6i0|wq9@zg9ZEo3m6F&Ŧ=ٚ%|ۊB2 \=$6ZNQ/49ېlq*u/Sy(o-(R+͂l|1 i3\^ľה4M{6 MZɋ(& ,D2]}ю"Hx$8j禭"WmS~1jQ܀ƭȳ`IQ${H68s﹐sytbqJʖ1"`ł]rWqbsGYnLOPh o_WK >=,>; 4(h_ ڜ6q\U)SHa[DKs immiKI8T6 f.?㄁9U.Fb1.m\z9jr\GR ]ʋ76g?4c-f9~?ٽ푃q(p~:t8 EhoF~ :ϝ15T,EFLQ1c> &̨:Ͼgg֗'̨ H. ? [Z^|齅9g]oB#-^ ! 35Mꞿ89y{d¢FUjH wiZ3iķSzH/$iݦ()hi(lڭ-c# (:u,oo$~%O?^5++g,ع!`<5L1uHVI'e?VD!b bS6)g(=Gj8.PfoA{B+a>?IuI>aF'f,w[)t"X)cQ_Em4ik薷xgE<wv\,J B냑Z|dC>b4DĽ̾(H d,M}-y^n@ =jO2 dҸ<I%v2c-F1](Ϲ]î[r;~3[ݶ{Ke )yGDяz(Lsl\ִ&den~DQy/?CcSO&f h-.kuSUzNCP%%YCit*x A"#&63r#CP3#\ȶ|68R'mTo|yg4!wT-R`""$\_a!k{}Tou#*:q,ךzvAŨ3jX2*61wqu04=7'ǷC=^Cs]Bc'Z&T?ILB޶ `)"U| +Re~К!8R맃;w7DrHӼU>BR/$;~M4CޯƂ'Ո̝< q o1C`%2?GHMhQ;+2X{C!D > IDATǒ͋ءpk,6{ky#o(09ep\Gmsݛ̽bV\#p1y2/GI-(-?{<B_&}(-/> QR@ DLTO=02/ c5ťū2_V9zB^Ʋu|ȹy9kƎz) w+_ h  ;䵍=Nkl@˧JRQ~p\܆{RoO$7u7C:8m/S|ᨯbOL{{"5çUH|eh0E].v@<~ ;Ϥp\?EvvʻjFQ4oV~˯B=lE+lvN 0b5)"X?F::jxyK91&q난^t]fT8u\#!^9>f_HY )֏(z\ \a9̃<Z1CPBFM?&_VERjVC%h B$Jn=5W&|~{.8tDަaHQ LfCdO߱%\ ~'EN%A\S7v5nڐ3YV˂Ps쒳ܸ쭴x0p6^5"̳ѳДpZ`BhD(0xzukix]IQ꒢77Jˋ8F3z-ra3WQ֯%j [jd#+w<^zǬwLc[0_kNRH SI/-B,2!A w:8ICZ#ʁh~dH-;;ΙB1@`liQ8^_%}Ћ؍߸FЖqʽMg~G z]cWypvE6Hd.jmJ9-H>_q}GB& h]RI )"X?&럇 s"IcZ{= o1[ hUzmX}[F>KʽJˋ{!7tl ා Ɩzm/Gy69WzӲ/~Ώ‡9Fkz;ss8{} Rk1:zN2iͅ "CUQ݌WrD}v\Gu"(*ӊ 5F9jkQiHJbtkQD $w8Xjm 2 ?"GY(gU!bֆd@_u\6I2?C/hRu!dMؾguOإFܱW ܿdg\it'/*)*k/ ]{rڲ=So@VuC^^fKDZ%Ee3|uk/hjέonEV!3V׋S/@Ogm("1lB98]#{}"S#I^En n~d H-'mJ=Œs_#B]dd4 Ԙ ~Sz9yLĥ!Ok3Z*)*hݚ[ωƬʚnc3 rO:ynNgZh`cSIQٺ z0W67kwoòD羒wMYP<"`kugҢ#=[zG y1h+E-ARzT·W]cf[P/cyqGMxF41'Nv(գ@(<]RT>)-4XE?2-oGjR&bqr{!YMF - ق0em0皌AOIXD GU軹;;S|Dgre;)/eH&?vȈ6C%єd ۓ\H#iQ,$skƱ"k0sqf5Y/GScmpQ^Xuň~"+PY;qL ^~KX7*3}1'-[hiyq|zSOtǠ[e^=f-%Eelh샭ٷf;愛]]Y 4z1ů yf\'R ۛ[_X0o())*k5fFFH劣}.󊒢7~RHa['d[bO<k3`7ADҽ #,*hBſ, ]yh!29֠lWD.A_#)2o"S E}/b￁<Rk~Wx} x{Ə`>BZ-(wx'NQ8qyvE9+Gl`{/oHG9U}z^7X, " qLC=Uǃ)p2+·Z HR\<9+P(/+H"o)n%kk#$q/9 =*veu+ԡ]Pş?˨[Rnٙ{mor\&·A񑫎4RMI#5k Z칑.@$l2@.Kdg+lOLĩyP%trB|1 y5{ƋOxާ"}pV|ceYY=ihұhp1zH|ۢmXP:9 "W)P~:˚/HRΨ}/bb`_ (Ar4?\ 'WzHYB4s\4W )Z"o"sH q 섢Y7'|CUړ+C-u\^f[ dp\mơu(x<3EDŽMfT4HnYיY)!R9XGm"v9 v#‹7`eTXLXc1{/E"a&D0ܝDL,DVq\ "Т37׿ ^E>gCæ:iY>^^ѳj]wֿ& 5}{(XںnU҇.=O}ڂkN#HB~9,йsg b@ۤp$dH{-Iǀ{rEGrTL+)*$99G:,݌cҳ?9d@0n._^Įv\`cU( I9{Dsюߏcj[|l+Rkgڦq"5nBӸ PZZ^Rʶ}H(QKyWsŐݑA.E^af3IDd*[@IdҞYFιY}[z #YmEOg"l/D q,7BL(ȿ9ϼQ暃Iy($W׺~+"{3f?ü9o\j{u" N, dǎxBF+ki;ιO׾FG:;w,*.Yz-8xWoP[~}{!iGI4!%XQRT@ OQuD ݌T#zܤ CQ3]-uʁ=9!vGj r@,䤈R: @A4.AUh^nS{;?4șS)ˑ]K];Hnw Ͷh.7ٕ^ľs.aBESZ7ա&L8Z]=xo}l?bɯ;s70׋;${*DNE+W{ GjZ^)lu<'H,(sR!"!w2dRt d$3:‚KW4% Ez7@. EVd3}lM(vf"Ĥ1l(U$`\ׂ_d~3ct+Ek7 $zMqwZcSkjw\/b[-@Emcw?_Az}pYM\^ϯ(Q%ni_&X T±W?-X v -5bWkzc,)*VZ c1` ZܚԺӏ׿i ]w:$"OEBYy(-/FKާ6QT }NB 9uym )F`"C"_صy{%Or<6+ .G%hzXGy.L p=\ ^v'_%"9@8$CS|o1{6E##;^J?./i4ĥ`"EP!LTd 2*;ȧ(Bv0"fm( W6D>/A^%:/IDҀLѐe543}%fˋՈ!^I 'sPþ1"Tțwlͬ4j1<ɋ6:ӟNԗk()*.-/ ybRf>NO.`[f:wkm3.=` u/uoI;#P )l3p\?c3DC]#پ"r|g졧"w멖E3" !r59~׳? IDAT4"ytqhA;lH&S[̱`<뿊T#r~P"\o)rE/)h!rgQt$t\"@=fW%硅hq}.e</tݍ6Id @QVfun4ϻfC}>_^Į4/ò%;?ҋ3 +Hup+듫h-ֆ[T RkiEB!܆W0dUo\y0|@`sz63Kr"繥o ChO:=f<Ktb%EefDH"wV\qϐ#?$*_bHC/Gޝ?gkׁz+sekLAYFg9\4Ϧ4 ׂŻ!B6lohX}z}'w@(/1W 8[3OC|ߘ9٢5溃(6<."ej$d݁y>}F^^KWpBE第:!q|10oٿٝՈM"vuj׿=^- m#;p푿oyRt'xhD<:' Ma+!E-W!b^n"Cd`WˍG,dh&!(#^x탌g-s~!V9v @t'd<_FecfB+t*2hr\{2MHαZK(b!C/@"gڼl #R'9xx"AF>fnJ3"Zq.lDޑGq}I|4,)*^,-/NŘi([6A|\eMo+#Z^{Hq0q L #. !efz=w|{pI].hymhQSoKd//{gH`PqeO<_54' )9wc/br\xq}[X z3Sp"8_f6E[jE{^}Q)y{q߀iH.@_ԧ("Ԉux}<#I$.F4dȋQ@$twdDDyT 8Dz3ǦDnh60wsddzX: EҜVD!ɷͱآ()*kBsyVV=|ߞ8{{f|viljRq\֊ BR 9] SCcg5 HomoرfN;+Ҫh!f]q/\=Qh;luE'm4@sUe_\; <Cyzk`%r}`Kͩ#$هp<ʳBy!'Y Z`CH6EGxЋ_U$E4d7l)/.> "kka9?_4/"a>h>B+Oӛ,}=/< whcz #f^ܡͽ?qc/ (HKkO6 3z)}Ca$ sOL22pD|F!aJSxw }@A]~Żo<|q L%2mk8'.U"-=oAw=2!>ry`e\@=eH2Rʈf=\sNdPX p(B4BBX uZ@;/bp/x :Ƈ6,lWm~-RRs[j RHǎ%^ĞM;f'h n m;xM |Նh}\lmFsHϛjAڢYcV-X+#2V˪y5 19/">2*fh{hόhn{U&dۨѻ&kR6\hs$6^}-/b?vFѤN";]<_Tl$w-E;b08(CMEmx{Π25-Vk50aYoP^TVt\x\ZJ5RkSn_(#S1")/@:`bksXab{a$Tr} rт5$do"b4_nG{~H>4ף/ZD.BaZҐG"dHoBEV frGեҼ[W "BF33cC5e(:߼uȋ:EPLx B{}_% ܵB /bWB )`B#եhDP.(u5{ɎP4*}i_E_Eeu'eOCʈrE҂Zڲ+{/N@rD"(dQD 7`}\)d&"R#f@Yl'3qHU1.Lq\?EƓO6ϧh"vOuFbl 0ыw$mF5(*oK~d[3`ͽx*BYei๡O^q@Ey>GE{L30: J'SJˋ@A$O5I.r\[h]3ԔHE܇HBi<SM&t="K]P4hNX] 1>Yܚ{4!ք;w!i򜺛K\d 㐔Ȍ+.k@FJs#Fe r>w_sU~ϠG1d"C"3PY8azy8O7+yGL5E>$c~n̽^i_q=Pq{ /b/p\|Ƌcp ~5N&,/'1EcW>t\o):9f GY =}YjxFEsS5ǟa͵#D"vFC,hοkK[(EDF}{TDjL|siy?랃_p]!#E/KGMvQdkȆll`[{~3*]m5UBS1)/blJlT2ƙcG*׿8̋eucVO1 # ڦoPgì6Ne*-4!k7Q8hĮhlWbL4&3 {ޏEDŨTPa3]3"X,<̽>wkL NJD3xꩫ98vARdOtϷCsz6G܌lw-ZVz?33 9b鍇IAJӷAh\iHɟk@`/+PxfV*'Ls غ4\nQD@s[)GV6ey;v9)DD@C 8y_^EQnHi k.#@ig= ^QaCvȋGC޺DbmNmv V6ֿ<}DduySD<+>[yKufa*qO_Cw&-i$8g!P2wdCN'n_:h3[6fn_OBԕ6;"f[J]Y'hu(%( u0/fػbNc V\YU[x>[X|mQ[jkkݑgu_$Wɦ%-i"=B:c90y;~O,#;TPy ~ ?#GњU(BrCAև(B3 H7ODdcA:eDLG(uzr=,`27^/@Y?\Zv"<3Z Hb$b$dv1d y9N]惊,ҿgsu,w9d,Wjc=o&bc ACd"~&K!Lė!"c[`Kx͟d#'ޓx ݯ~{gB՗4^%M trQr6DEL?>`R ,a5Js{L&"UϠԶ|8(M`>Dc"جD *\^ Aw²a)#{#rnmYD[<=<E^#U[^DD+ykaqY nd,@)6gѺDFz~y!0(W}F %BE(JYIS*Tum9}aPjmLR5,W!mJ]29#3kxKKZLp^K&x~?Z[Ql@D|+ bll*D)y L@6d.C+p}+1[\eS = ѵH?CiAўDꐣ7 H;!{Dm _(T 0ŵu׃l P)~f)zn]S*zy5clvP2x;׭(ڰ mIj3݋d"^s8 9MY~ű?+<}DD{Xޑ58jObUbѯߨ<-[WkjZ=}jWz~0*}X@me2c=iM@O;W!y_D9N-E WGEīPTr;z’ v Tui^MߜxC=aȃwB ~D^c Dՠh-%]mK!lTyoXnvHwuEy2{uz+{eKQ#q,JB:]ğP?hЎ* Ed3ɺ4h+Nq}"}`sY3}67gX{ENAy?WFR?;ngD#g[;CW c}V"`fkp] h-y=?""wiQGđ:zYt"GqZG3uyYùHފJ:iMVmÅ7<$jtEF)q>>(RnD."{),0q~  իWi}!)Cgkmw}"wqNj&R\,Y[5dQp|k[dd peڋ0ECζ1vA p@yLeY۝YMqtjǷlCr3/@oe4݃< }KD|r]ja3ޮZSp^dt- 4ÐwiiIKZ~x5a4yd o5|ĕŏ^ +Ƭ'xnw~L?ARuFΕkdѺ =Ү@qK !R&|,띍ȉ* uZ'ֈKyK KIC)bm˷)sd.Aa1x̆"m]?})+.N="CPA+xW'f;2o*lUz`61Bi>gșckYLzs/A'r]7*&E Ej^AWjifYMO!E(fjs IDAT1|HߩE3ҩqz[X\ ,&)]M,N5]XU^`nZt9}wS6r.vL ”Hz-"rň9r!eVk4nԶKj^7"S[r 9mco6lvOAlAΑ[omγOG{|4oo! DNu^iIKZ~l8vc8թ9wKm:$ guU5Żvjzkt$h6!PQ o"x7ҏm$X7B:G& ]? 9EzA+޻aP% "L&l{#g^J„3˥eٱw56u465Q9//-P,#BuSW`C2_a4Yx>~i!’;C:H7nrڃq6rf5:}nraC|mm. GPU3гs;"tQ6O~Lwh6(lYtGOn:47"9݋Xc놏-n?dn֒G 썞vR;? V ٱ%5[DHQ0nɕ[߈ אM YZ',RG)Q>3v& =h`֐FԑŨ|Dz1'ELgiKzmz*x2zF؎CmLėnܴ%-[O! "3 EdQ$&A,.bWFػHϔ52m~l"XQ@v Žd_pY9QIXm}@N>Ds8&DjjX#ImP}#lC{'2onK9@2_`n(=1R8`%v4OaPL4&& Sa}") {^ZZ=aE6X?cQW 8&bs\lV셞h^\*fc­bAN͚aj/)ɣ([v#buٯG QɿSGc*֬9@bO-iI%Ck 2)bUd"~CyXf׊VreD|W"³'2W!] ϙ(:_sm a R: Xf_0e!Fo"}n;D}kDty[(g}&,eC"X|c;6y(cQe,4}XHkfwE$Gݷ~`EVށ4E9[{#ZTB>"`mGV /F|JO?JkS)s~G5acuMdE褜msy;nr2y'Y2EFdS!ǖ<ǵXF\[ۛS!ڝNIj&;;f7-|>Yuo^<E[iȅhߌM NX!`%q/G52oDy;6!)w`jw"tph\L])Aַ ֯ Ak 9"h#jf!eEr̫Rn?@D֠+?(zw a<@U$q+ bx(ןTD&L@kb666wyض@Vfsf>LğvJm% 0@-|ۋ3*%y'1YI&!ͨͰn#ם~KjOD^#ۢ1M.a*` ґg"VH~9\#.e#\ /ˮ Įu7"8 BӀt(2pnO7^ C/aF'Z[rRNFġʕhQ J{XS2ӈ ly~p!jpߞyڈ>d":ʜy~p)v'L/Ԧ2bosɁWwc=\j~lcfs؄ Djd46"Il]Diz>l~[ؼCϑ; ivd"~S3' :=CH 4ðS:#!4^n\2 cwGIPjU0mUEh,ڔ)o]b})@Dw־nZ#Xߵ$אrO!9Xu7 Iw!RqVi"O\y R7 eq+!IYX]腼2لȕ[wDU#"Wm}o;;"h@Q=@7v+R!"l!UMA 2Y&Dia}Y6 s[Z_sEd.CAhF؜tE~."qE2n`V2_@+îPjI_=#{~0zy]Ad"> mN&ͫOmI{iIKZ9g _E͊&X`+`yT#rpyIH @zXbJ{s]9yTq}JM=" #]*m@`G/DQ ? a[z2_6'PDG8q;Aƚac"`ng׹z. 5apywas|ppD|w"ry/p[_!x݃_CQd">تFB$/tU! bc =Uv/?Z#•-gm|flrX0yUy;Cc#}%"řCqjҒt[g#ްeLBi9G%͋PC0tA99EviwEeW״>'5Nw7,VgZ~&X?[|#XW56qBD=ĪF `GSj~q;!k/Děh]fa;Dy20iIKZc a&w;w7;w{d!Lkog8٫HWcqN;"'L6v>؈0b>Zӄ0?(9="vB8-ҏtYȨt3/Bj)rxfca^Ʋ(er~2_n)7e,A7fs`ARNC*v:DևVhdFل6KD?"wd+К_m/au `cjkE G Ƽ]jYa'OX D2G2S1#_pP|KiDxp }% zQ99їdzIh8W.lSھS"{/94ǖ58!URj,BRZ#DHhmZ,6H쇔p]mށSvn"+u);0p1r<YKW;5"ƦD ?hًE]dRщ6 F 9_! AZw QCE3.C+N mKnvjD(?B1FPzA6L&KKWOG˯=uU0e^]ΪIEt~jKe|ĭ#|" *#_ژF隇SPk"݅m%v6؛m~ =dA:D2 Umi마[eLBZbcH Dbeh6vWsiۙN{]qlml@4z~o:L-8a<ASd":xV\K>~jiIECd0tb[>M--y32l S0sEVTD^Xe^jY?trx]z0?/w@ўɨ #2{#u}~22OG:1d?kFh!r$-@zdg"cpkHߝȆ[g(Tca ݗL&G٘PvJ#ϲY_s*HQR'#WڻELJ;Bt+ٹv3.H D]0REP$W Rcs±*NJ'Lv@_J\kY 9$?GC 1LC}݇6 i{"'2;6) K!\hl/Jy>kd"[uMK{= `[sxoL--im g"|zزW\lWN<|lٔ՗t%J_d"~vE2H;SG9yR HSBPzA0 F`=Юsdmf!0}U,ʰJ ll6ƑrG$k&R #I J2R TE2yYb}SuEnٔRGH<"7qڵ}DMBи-0Dzk9ߑv'l>Fv[(u1B.f)DĐ6:z~`SA6}lˀAŞLX{>>YQqbw]ݻ8U65!MҲuRJ=?,q4##WYH|㿐ntߣ(E~c]HW݈"X)dH'6Q*QtvSd! zoc`ʦv[s2{y~Ey\핕tM&ژ=PÃǑnB0t"r-" { \Hg}#9,SedhԢd"P[a) sݓ.Z>YFʲ1 FZlG ^+W'mN~#tHJD"`B#Xs߳ˈDtϏ=ΠՓ8iBc[W(b2sزtnZZ=omdhnZ &?@ F)n#"y\Ŭjhm G*Hͳ>-n՛x2]Y6ረcsv7k9YC]` .ք`6%l\x=C7:,?=7-Vvw;~?cj/"22fEyI`QwBp~n \_&MD$}X2_wپ~ٵ sצg7 "ˋFD>dt }+-? 9(lkzSm (62mD)dfҝLϢuKPg zoA*+;~.†alL޸qU#7鄔w!MGۃ)t$v!aa$`r{?EQ5H]rO&_tH4X.bQɱFk8za<.Kb+G[OE +u_/TlVؘVZƅC{WH_o㚄t}2;̈Cqd"~o}=YɵBd1̸zV>FπK9,&LDX̥#ksFH#X7}0l4"); k3/ [zji@DYd$t[o)#iHDnoa2D@Q'È܀G@6bk*y\(2|emU? .S9)~2 v`Oo*F Dʱ#"m [X{LB29ȍfr,wQdnי"+B~)yG:+"⌜(O ܗeUh_.8pdJ@Ɛ֧}_9K4X B&<Ӓ*F@mV#yv/묽,U(ޡ3hMw?yG'ڽ*@q6OM[,ҭ+8Ptr׵v3#)ac]>-mX:69[ =Ʀ"#aAD "]iӷZF@|m0UDsi|Y]/„HoΒ6둎;99JDR2' (TtLjXZ?G9HL6?G$&wZtz2h:0akO> Y݃D2"C(]<^&bzюb" `ŠR1 IDATH4Q*m#Py a4uAµԮ@ @{40ߕUw@"+7]l߿"R{guB/_ >zji9R!ǮXldF!{]v'^7ؖ+"RYǖ*',iĈԳ\µ?p.Fd`F `ha?IS֦YvX&$2)䮄Ѡ; )p1S]޽hV RBMH)X{ 4dh"B"NϣPDrQDpl^ƙO!#p,W\@dҮe!r޿o V` Ƶm=2LZ+9˛oAdSE۳쬂B6qQIH\tw2Az@FQHFw ϣHB'?ʑ)FCPDr29_kXo}<eLa]R!>km$,k?CZl-2tj7eE3;MD_ŶKuG{xr=@B8ʎDe'9Jiv0of樀||ٹw8ns %Q.U~g&B""@p-CVi˧ov e'==kam4[lWs~_pi1S!b _Ϝߙ [ X6dt{rX#rrNdHRzul6u{w쑬.}_tlL/kӲ$M'3f*DG[#Z[".e0ruY ̌D8$R2"?3A>b;?ӎ(tDJZ գ4SmLȓ׆P^@u[j6Q!/gKHٹ@Wh(ې~v^]mZ"ߑa]@ F/5R1DBh C`cb h2YNK&⋬dp1a}]+Qo%Bdy,2JU( A{#|y@d"7ޞLWne2?e$-e1tHԳԭ+~4m!Cx'л^?ٚ8#H7@}a1W ʑnDΠcr3# 8E@/îZv #rq5r݈u)Fl?~&or8 ~8P@u<҅{~pBJDX_ߴv۱/#qԾK%;&,PqBJ y޺#r{!ھюIVr&""}"D޿UA9J7·hwdVrOB "oUث?t̴yjO0iHL>" l[!29BOୱ9|E2Q됷1fѢl0@`rE}2nAGH:fp)~SY;0-iʯހޫEqD|yVqӸ RBB3x!2]Y[;,zB\"3ѻ?_g=GfBЮ,ʸ2+W@IJEQv3׎}GD0DC/ki}9Dz!rR5RJ5P&9kDޞEVuF}XfUڽh2HO ]A@BmlC(eNC0~vK qi%"8C~_8.43? % (uGiDρ+cve}R)ɏD`$Z "Cq aήQhA}fqXB*2auHm;CyeEnclL=Q 6'!ג0_|21nqk>2!y;&]_lN>6"B]?PIZ{R=B)G6ev/@梳:oǭF , } yF=v_z3Ϝzp퉄kUxk/!|C.9 Ecr6'h]Utw͝bDpGƜoc,_#B%{&_9"黗!yhpk5aʡ.f.znSMȮ]}NB=VfsK^n'kG|c T&WSKKZYǖ6z벒K0 E dݗLĿIO&;wanԋ^G߇֦&X߯eHfHfM4Uܳ)V(-3`RK]F )mh.(p.R"k4uf#R)DtܺX*{ֵ3;7yr/'bcBʿzmeלXBBlϡHU#[ /݄ p`XI]~o

 -{ 5|Pv굳Nz·]/ۻҌ6^1w5ƆA8qb,Fgj L oPp38ɝ0M'"JGwAӁW) 4EY@ھ0҂'g8Cf+Q3uEt 8怦Yh:>tJԕ96{}~ԇz"Wۋ_sUm-uDqfyc1[_thgn8 ^n+sH#"14hmW}rsWK.ա>l{=-ϖ SB" Z3)Ū'5 \)HƉU{h$ݳUMݦv5"i$Z{TH5.hDbs*DE SWЦQup,*?FNv!4A#9A=rmt2}^k 1dz$YpTnw!ѶϾq>';8?+ydzMX%!TJLX0_b1]F#X"*!Ukc~nu8hq*ΞC]"Fl Z5 FUid\&Ve{cEC}x*wFSZW3kjd:mr}<m85Z֖I%H$J\Qwxn*jek1V+sGO|W-n{~쟮>o|x]N=cJoH%:e̩ ?R1*jjFS}Zh1${Lrή#lGYQh#h{Zصg(+bghzr/)ytvp8*@ h{Bl,scQr?x3fO=3}6u_^|}x^#"qb38 ;SDF6BVtcш v"_4b+c̍v&{w?c~km|WѨ[.0_ d1?/ND>~:8FD~f>׌1{-%#cCKETu ZŤ)g; sMa10gpgCh$ڭ<*єxTE}FR C/>-.f{j:ttR }yHѢ?GTb A?d:l-WԄv}wdRR=gSg&OD S>{&lڢ65GVЎ<`XK>Jn "srmGU "]~0S'"GPp1) /N{^dY%"Q`MN1kD^4Ƙݱ% ЈL Irf`htZOQzi2's&]ȭPf ,H10ӊ>m ES}Զ@UW.}ơ#t'0 J'"Tū;Z_&hJg7wT"LgFESЧyh0TݎFA>5x6--!Jy:/\|q&GZn ^Qh-SףEϠQu*&]x@?B#X}.k ,gOU&40yt{|% hSWý. "hMdcj0Dd @Eϊ8 "o װKz^uK2ě1Rv}b.!*.La_iY)Ɛ-蓅3H\FМchZt2jm=Lgkp4 FL* J\I%RT{%+x<;T[W;3Z+KZǓϬmN|<- ٌ|RD*Ddj}Ta $ "Uhz nW9~?u3VDFvK@`1ADҘAv0Z<uGdK[[tP(4h;b8EJ-wg.u)x>k%/Fd鮻ʆPc V([;a#1-Ҳ9!X@i//k=G#FZF*oFGkFTed:{}8s>I%~]z<Oٔ =:\s%{^ۯ1"2mٙc )MNxScbٳvџ1/Xk7slkhrxߗDz*Wo"r$gb6̖cnJ',YFRrRCr@힏Qhx[:؞ wd"1r" B6]= tʿ `qcj@ Qc9+$hUhdkO4]KI/h.ߠ8'j4[`$}q􇏘_u:jikbyVDvcɶFl>}3f dyx~i'oZ7EA-K_7p|gɾ;4[Zu%IQHE˦mDP *VAR+m=nA?|xii5{ os+d:+tYS$Qtvz2x,~=50x FSZlUqD+,Lc .)q ;mhD̍5u"hY%Ycz-vi*!Fg_r]4b_Wѻ sچoQ@]I%Zi ކh@k.C]w{84^ۼL*qKwצ4fRx,GsYMa1+S2L,eh/JLu%"/D^ b/ԯ{qߡc^Q]]c.ceL=h9P1nto^6Ɯ&"ѿM;`^g_3|;ֿ$PSҢum aҘ(~dh:r5D=#ggD"U xP?)q#koLrchmx-mD:A\ޘa=8ޗ$:>iǚx<&a|x#JnLwp 7lt_Yugc6 Vt+EC{cn'cNbl'Ƙ|Ԧ|g>q[aLD~c9ЊʃпC;1bc: MIO0ƘRiJ8BDcV :c̓"+0wgiJY cZDz[#cPa1tHn,ND9 W`T "ˡ F-5cv6 W91P.ZY&,vDnQ@Qx7 fUujkP3Ah0i_/՗FO2JL*4VOQfR\W@bFgH0S?2~eUV38^q&cL{z pTW4ƔfoƘ69 41"2LD0ƴ-" /Dd*lNqVs? #vyg &"`tb,J4w$' ՞}Tqdez09ij=mXbQWd0Ij#(PQ$?AW.Eyx4㨀>"h=*è(<\ p$S[5*NX 2ċox<ݓI%V%sۯkm xhoum hÖd7| ?|k>؊wЋ'DÊSyDhY'Dd?cLg|6[L*9:(g!:})ģQ,my,#0.Ϡj?F f|JTL턊!5%ѧI5{-v&>=\_'7ysȜ* ܖLg0Kdzթg{3!">LVFDMWډcLEhd- T$9*TBHbD`B;'c *ە­Sh8d:;- :0|ƳQ7{:WW,ȃ5>B>1x6#hĥJaiiz愁e}ǫ^[f}kQo&[":eO[cUR&[|cVS"2|p6Yt9xĽל\k#k0QVz[B IDAT mjp{6cr_ v_$|& R&"T< +w{0\k6MrZ5 Z\,A| \֗܋]nokd:L*ђI%.GZ 2ĊL*рF~bx<@KScLg""EEJtipR6"Gk [h13] DEASP: te.BRa6H[i+Ж[Ђ),vy|5حF04v>ZxE7Tbo2D[w7x<Ϧ/#ca2ětZvp79w*ku( hc.**Q6m[챫 ] 2>|vnNAӢ+ڵ֓}jW4-MUz<ٌ{1fyZ r]\Q4MY\ܟC5hXMA"Xlm2rJ &b r0T`ͨĻk pH:YuR@oD]4R ph&Xo[$kmx;jx3fO1{x- 1I%V_@#:7bݏ,^;YB̥ ct ^ |, 9pΥ>hιEz> Z=5s^;s4~MAEע4|I;}&*@%nZ3@MWz<Ve1O/\{<~3vN&>v:G]ХYlpW+2O9p=fA` ="4 ؆FگK,Poh{*. \Gߩbdx5sLC 5$ahdlCs5 3d~x<-CDw~x"bی1_3ƘDD`cfE z6T,Aq7- @5FxƥKHYnCE`4U/r)*,/DknGՑhYh3!h#Q$ T@0u٢H|̝<80< >+"hm3lw51+U|p1f_oql"r.F3-X}w͝ɬOsp AՉ2TנGV֌FA-BNCkη/Fh3/d:s2=ϩx<[:3?g̞zK?k!i9g!s]}pˌ1t,3ȐD$FCLjHt7 $7x؎"c!O2& oDMen`J1GY\|`-",7Q15.kE FSdF֬1D*4VI0{{zw2[s?GS+$KZаw xcLLAvǂn6rb`1f!ZyT-):>A=" T"zֈȍ4Sm+lZx,X9]0<ͦt-W7C#h`4'h߄^7*x@S }B5"d1AQj*˨I5Xmxxzy<aQvv|ơ^mIDd1Fn2ܽk{eɤtv:W[3ȷh2Z!RU@HT 9Q 89 hLҍubͨPK©@` ):]8}mj߳ %h_A[@;TOeRyhli|{،}R0w!ƳخŘÎ-EQS HmgYNrn3e94Z5uG j3Qv5VD*Caׅc.26.o AH7Z:VP? s=PȤpjZcʸ+7,b־c[v$4e)lF#ObmD]\zr>:ˣȃ}QqwhHґw`q&h)2 t5;Dd,L&ȣtGhuev`D{]C/!s`uj{]r "q1pxޞÍnjBk\t¾/udQ4?N`1M&u^X2L׆hvdzClvXW$ٽh86G0E;} כ9aՊWIGܨ$Æs {A(G]4bBc_EvonOtfΨR&hN>̤ww<t׽Xb os5|;Xйa奚 Z e+CQ6td vJ{3шX::i!:kh: F2:Tx<`{4}dlg*:"EµYa 첢mJEGq5W ^B`bkPbƲsu1YԢiНc7ۯQq{#ET k{<'Tt~#5:&"E[o."sz{-R~ۍ>2!?@ ףy44 <,Ev-X^Wб(Ebݶnf Bר0q":{ #Jd:{]d:{"p:nhMZu2}r L*}x<'6s #kDd17 $ tfCDbƘ\[vs渞+"J4dR?t^ٓ ['r"`ւݙ}󡯮ñ@YsZN~ECTdvO3l%>;9coۦXdzC""qb3'<E`\bSs*c5Lr)yȮ;d "95ƘZ|&^vc= d:{0*T@#gPCjU8."qC A+Z V .Ai-/ [8oss.h4 b2K9/-DTD_N5ΖdRBxvd2 Wj]O1Ƽl]Cd#.; B2x @DD;"]4{2b,cI&X Lg'n=*VhO` qqEPc'֣? %#saΡ9jZ6 <1gj/ {~:u$:4L*>ޏDQ]4xXҀ>v.ZݥD1ff"Ǣ^cӲcȥ=_3dR7тpzh9Qj Ȭa"a EYJHWB1*f[CwE娩lr9SUk^B AP%=L*ц F:ٞk!i9g!sfo Jw "r13E$nSDJFB< #"5v "2-y#cϰ5ekPs:bJ l\;2%L"SnxrQ37 l-bVAeHFNPkd: pj3fyuYd3Dwdzbii3XM !Ƙzs־mC2D34T}Ŗa B kVc1H-tNj4T- I%$O$J,ͤ1D3QRbR+E@#e-vYYD7 nDڵ(Z#t

)TƘը0Jt?{mc]Gc$Lg'wdRUE8z~=x<{^&XeTavNeo*=hdׁgت$y)Jxml71 dBM3lrc6gm9ƘG G #2cLnGkaceG5f-@}x0 m1'"rY=§)2ē/J9@r QgNUa~_M*&p--})jBvD_j#+~}XR[2eNʖ{ÀdRWd:[I%u|NAߝKϳic]XrObvT.7h?-ܟ)6h}tcTLgC?B{[%KV*cb?܆֏GEYM23J{X&:hG]A꩕>-37E+\V4Z?[2=x @EvP)3ԡlO/04ˮ-|-(U+ٌx1b-Z_BE *Nƭ ;wgЈXC]tL*qզ~"Uy 'Jpc|Pnzh{QĿс&ϢfдBz= x8T!^l݌Ms9Yd:;LG]e#<bum*mC-1|7J,HPK[3Mu2ƧLlsrhAQ9KL*ph 4eۀv[F2IJ}`?\ODST;x<;0>2I%GXOwCB 0X1R5L8r m-8 l_B4b֗L f^&ՠPƆVفd:@*YDhquhTe&Xݛk)#hw1h:L*fvDӻqF48 ܂:^l;WMxϤ}1x<^y\gO jzJ){ 0mZ0'J^zكT⩍$3OוƵ#O3gpI|ЧѦL&vhG˨$~^v|ޓZt&c/ߚx|`d݁F.Gӌ4hKT|킦1 hr,p%[?$@2z=C?795mAmvK&cS(ڢmjep;U!LgF ?6R쏦KKz<Dƣe< 8iÅ9s}=?)۔{\ cf3:^x1L*t'o{ t6 s)7RP%P+CЊLΜOC EQI+IV极Z[ c?L,;M3쐆tvZ[ͤmtvOx DʤԺyCDlQD)qE$i=+"re"]7q"ṟv"}?3EdT=jg=Ϸ_b-Th1hhÃ8-oB9O1{.|I%-=V֙kGc^o C?,?|%ξl{{FWnx'OS?z&)v(ZNr}z wLE}6hġ>v~FD,ɋ1eLj]\Kt1``xh\hcP_tunF<>T>cd1 t"M)_v4fAHs]Gv1L𗮮3|c2tv0K h0ˣQS_mPg&N$ٝpǒCsFGѴ)7s-(Lg?R*W7=feUV{yKu-J!"":%G끨8{2ƬЈT:F^Ƙ:vT""Ϻx7 x.س {wwUu&Iot:@YKˢVQƂ "pAѯOQ~QpY\BQ: PV)aJ[6{3~BӒYg|0a5GٱR3;xG0@9w'-NmIժ,pm<W]vb?:_O;MC@G&W):?BL{v8œ}$\ p`F߹H|ﲿuHA9l欛_>d˚2ol55mo>Y,@`h89+w/l~ri/ fvV)սWs71lfv> zpG݂}̚_9n `fETqo̬Nmj*&L?Voى'7}>g{]~\|i}>T.sBX 1 _y ?lkn̬9Wk>x9w]7Ə:vݓEJƤsF 몬 >^L%b=;K<>?1;4m=g,~_R-*JG[W{á_oůU6.|%Bz_1fT෌ u#sjSؖ'%}E_H%b&"{çC]֏p+DEsƤץ\*[JĢD?d~O?o!/ۿIAEx2n7~\NΩůhj]ED*K%bmvc/DN=/r[y=nhWZsм75@~9w]> ]MĂk]\+ꜻdOH@1)x2=Zv=RH4sП/TOvם%=+Z:Z- x2=X: d`.W\6'~ c_T"7={ʽ4^dL.=̊C]8ơWGvro?> )d*5D,O7_ U;؛GO3_DDR.H(Ov%L:[qx2}!p< 㓝 GT"vvēMafغ%`(=J "" IOq3DZ__Z|_wfhē3MU/Ap| |ګ֤ЕʁU9gGΚ>o\q(3JƤ6޾ ?lmR 5|S} &m;&Lh*ͱ7X_AlK ԼR֔<:L70gM5}޽Ŏ'im1 Z[[YX~>pC蠛}ιTw␞dLĩ&n, N8kgJm9|,o WǾoG^7W-Nojo'Sؓd`dgS p*$e }ھoQ T|3_Xɰ\] O~ ,٘D aC}5}^_0bfJJ0txdJsuo-9߼es]_a׭uεv-sËy]3&Rl|||2Jd !@& Mњ ~ͼ6?|x2= 5o?4Zμx2}~ˑ[2Z;f*{8L bo9կ^qç\O&ʁ[4}j<~7>Mp,~H9q1w߿/ܥ1O>%6j ~uAM>Rip\ }Yl?9g|afM7'"=ma 4k^onlֈ%߱Q7'je9ץij2ff%&ҿv=`fu{"{3ކ'/9gfC:3)=O1c=X=]hE'ӏ+fѵ\9%ofå;?T"L߄6~dg8POf|@E\Fhqmx2=⢱'L͡PKG)Qsϸ`yq)U=11{v~'kc}UٺcA(^o+)T́O6]~VAJ?, 7|KfEEͫϵ.uG9wUs]oEs93.ߍ4L)d(N77АJ~·Uj9[GmU0c<~H(؁tsfοs9j}x1E4ιEf,p~,94sc ^>jO$]ʘHs\۽&9q ~?6᷆N*E.I%bccVƽC(Բ80muͩDlѮ]kgl"~h}ddLg.?{p`&07#b%;z x?<{T"6+S2&Kdglfe}Y=df6f*3̞33|3ض2sw-4smךY k&3hpV3!eik˼E3{S>`f9k=ܫ4;]٥珘Q#lYy"T/6=ef:P2&"TDD *bQ˔;n8mpRpOnNs $۪t望<$s; 8hD(8GY랖U5Rm8Ap4`2pqntsY;oS{ߓ'uιiǀ[:޷7spP;xSÜJDdDl5p&b"{e.*v,0)˜Olj$sU 0 ,bfC3{sn>Y:\u"KSY<~6̬L9{!`ps.[ n#7:^ 68=[W`PcW<윫qέŻ78:DsD6I6OxWfYU|p|i7:3;tε~FSsPZsWC;vm 4N -Ou՛#mk<9w;8+vy;! vl5i{K-t2ReLDDg]fw˹c)o6 ;j ;\ ߉cM hfa`pl0oX81ofvFp?3vyfVbfcCkf? <|^ U0%Sw;T!ιF39 o]M6Ln._Dcb`к!f4Wm &hfNd%۹l >1z1 ̾:Ak{>~3 <|%~N6s ps0'~?+moUDzU<6SؿH!i9bKz ݔ*c"۪s}l#b3~uݢBk;4gLDzU*_-ҞI=9[0U28IDATC/v,"ŠdLDDz̜3 ߬YuzT<lN%b}%үiRDDzO/x^$ҧ) “]xF|K%=HaJx2}P|#uH2\.zU?pA*{Tmq!@,bwpϧt~"}1ˀժX2eCIW:lo)?Dv[X|卥Cr?[ϼJn(r"ʘ2W}ڌ_+>}3iӕ]b(RhHE,ga]|ŎG7Di6dž֦up9V/B0ʅmdzPcWNY}jw(\3H\7Јe.X [K3W.PEAsDDx2o;" XP!`AEO{E3&"'À#9 >M=|ۛ?e]te%nHdLDdv$p[<.A D,3j. .Z Զy|;oB?0,L/ M5 |_? D3n'l?'lGͺhl}/08W6Msuyٞ"}*c""{T"Gq%1'lgp\`|)` &pjvl|z;"yu d:\Jk}18Z{"2Nm5c9dL(JDDmd,4|7Փ<|ZjGjwNm|^B<> 8_1Z;u3=#76ڧ}' NE6*Ⱥ&`ц3+4_\:4ƺ'WWkc7?>/7ϺI6sSfM\w*GhΘn*LO֤׺sz]U:4fy3mdliKzh{746V3 ʯܸg@-oL%bmw#08'e*Cd]*2&"'C+ÍݒugWWMhpܩၹAvJ*r%ʱ[ՆGBaͣ&__2x&B-/yzc3>d%矵ׯϺMJdO9c""U=q1+!3C3{r 5lzmC' p4 ox\CY}bҁDlIإ"5ɬD")EDe>\?}3bp :OxpB+ʾڝG4*m3-5kɦ窎/&?(2&""{%;x)\o]9#>]ro쀡mA h oӚРf{=kLSeLDDXƀvr@(~u Y|xm\+yO?aUfӒ{*˵&d uQ@=*c""@ 0 y L,(|a~V ZmS *?5!CSo\YRRz47f鳴RDD!bP2'F,3i|_8b}r5%+-SK%bֽV6صiCj\_cfUH8RGxxc|(̉qTt)p*(uj| v՛\ZR%o=zE70?TȽ'U;d΍эJJhJZ x7T"v5otK{OV>0.KU~ m=g*\ ]vmzX} j|s˰ LKCO!ۂ՜VReLD{詪X?OGYkL3p~__[WT3BM B ˟{Ȧ_?Q2&"ϥ1>|m灟VNh`=m-_ {⦒ %'{ֆv\rp4ׄhi2\ HdLDD:.̺hS2 |~D`LEˢhywUi-m.-|eciׇʊfDO  *vD,39`壏4qK9k|?Z98i;$hT"^:dmmUuy` `9涌zkJ-b72ԍ)ED]|8?L{\N(׼R! } })H~7Z/=57O\ WY m|͌?y#@m!+4L)""]]Cbe=u,0~a壮?lwCQI[P ] htIdGqd]ta lκX@Fpx(`d]t>Bɘe7g]輆ᛜ|M@ hiaf]"*R4|ibڒq``e*j"EʘT2RM.)fl"ŠdLDD "b!I@=p0e]tj)B9 pXŨ-BɅ+Ù"{$By[ȍ9V<-2DHAd]x"bg\և-ۀC!ˇHhΘebdgYG,S?6lˇ盁}ME TTB8_m/ÎlA7V+[FJddLDD *:뢛vik&__2kyx!)EDE,Sd]2L.t2?.e]B)2&""="b1@3gO?u<,rSH_dLDD-|\[uѧs@CgR2&""=#Puu;9 4>z0pA2d]t3BgM7)&%c""mܰv$96b/5wÕ`E%c""R{7.i=>鼵Z;qMm^>|C)(%c""R(oKl)Q1*wLX搬5}^[PE G-DD`"9 Xue&L W\ ]ukH2&""usy\S2 ]DLɘSB`ySd*v""g;o7|b"{%c""RTF ܿqUs?,v<"dLDD*-bEbȔ{b#Rh3&""Eh - W[4_4jm!""EL8 W ˹Y;6BReLDD3kJqfv\-,vP"dLDDeySM5fݛRdaJ"jJ"`Ė2QŎEH'֕E,3 FoS{:.Lɘx2}4pO<> /ZpIEMOS2&""=%4-]x"[VX wp\"}&HE,S EEUD F1)ew'Á}㟉X"&RJDD׀UΪe x0q)EDOXrQ&l)Y /P;6ޤ""WyCr%.j) SH_qp[! k `՜3n3J)^OK:qc`J6o=PtH9c""+x&෩D]\ 4e]CT lj#X˜މ˄sJ(3*cSeLDDz]2Og]v?!~h򚬋i.[ZЀE H1uA{NyHWXfo/H"Eʘ.'뢪A1KH(d:Ub"R(JDD/y x؁1Kf`S)%c""җD)%c""җw^wsWDz[HOED,דκX\o㞼H_ʘt)umƓdx2-~k?[JDD?^) Qݸ!4'5}ުn\K_0tJ*[ i'}gvUu-<6JrU>n5}ߺHdLDDzD*{^.Y]v 4|= PE%c""ҫt%0x&5k@S<.Vj'jnz㣋H(6-࡬+͓3d~gæ֝A'e@4_DDzׂز!<_կ?\R/U\W1H\tfMwS),s;CE,sp"pҡoZV6.?5RYEJvk^OT"sSGL;2)@`@ 0 X]E DÔ""RӁ~"&_fԬ/0!72Opf)8%c""R=X5yQx;3=5#'~tٰ<=2yI)8 SHK%b ۋk7'־VOIU2e)b">bReLDD"LW͕"_[1~RddLDDe03+ˇN+litP}LɘBDD+ˆ.q(0-M;X٭/oxЩT婞ԤʘH_j\e4o(b#딌HJ^jnIepĈXF1٭)>bd˥:JEDωeddLDDDۆH))"%c""""EdLDDD1"R2&"""RDJDDDDHɘH))"%c""""EdLDDD1"R2&"""RDJDDDDHɘH))"%c""""EdLDDD1"R2&"""RDJDDDDHɘH'إIENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_42_0.png000066400000000000000000015362221413546205200301540ustar00rootroot00000000000000PNG  IHDRXXٟLsBIT|d pHYs  ~8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxwx\lS%Ymwf: 8d!zK(JBxC 5@z[I؀qT,Y}˼H'wٲy={w̞;gk-J)RJ)RJ)R RJ)RhRJ)RD,RJ)$`)RJ)T'K)RJ):XJm!cPc5xh]@+83|/px#tOwUC+Ƙ1uFxfcOkys1}pg-ƘQ7vOcJc c̍1gi11;yιw1W)24ctp6%?Z[c8w_QR۫Mb~\m^@*EkzjƘyXjb|ʽtg?r%ζ@s#Oo$Bkm+p&p1fp%n:;|;ǧa]klY49x=߹Yv(zb9Ƙ۝ `4koi<-kBdkFG]XjwdnF:/[k( ɥZ;ZƜmYk6 wN5Ƹ?1s˭ 5iگ9mׁCcL#p1f%r-80˹CZ< 4zE)v}v`:3o}Wjڝ-C]}lz0*7ؾ h oHr1fg%tPkmFې(rk%0:OfoṔRJu7Yڲc̨v^i=Ƙ@sR݂1:yӍ4aG:PXkWacL1ea9 sq.pV;<o \+=o:3ĈA66 s1'ҖnۺxZ#XKːW!2l!$Y#PG|H 7 v*$=ATjRkg]6HėHxrǯ"$־WJ)i_[kg;19hA^]Rjc(R=1makmfoYGdMQJ){ѾIE`) 2 ~TJ)MJmMTjc I+(R7)4EP)RJ):`)RJ)T'K)RJ):XJ)RJ)I4RJ)RJNRJ)RJu RJ)RhRJ)RD,RJ)$`)RJ)T'K)RJ):XJ)RJ)I4RJ)RJNRJ)RJu RJ)RhRJ)RD,RJ)$`)RJ)T'K)RJ):Z!S2 Ԗ-(=L f1Ԗ.mR;VǕIo>.)e_tQRJ&B(y XdN} ^jVu] 14EPg 40wCdZM)T72%&dJ@&15 WvY#ځ4R ۯMlJ~eSJ)-LI)DQHҞ+ %|!SUnG,z<` 0x()9$dJ]DRE-(ymIQLcqnZ͸}BdpȔTv yt~(ArÝm4]@ROz,2Ͼh=W9iW2ܓݦ7a-;mXC^ͅLI2r-HD <d8H0`=RJoѡ=`ߋjs|$CvZu;XIIG)xԖ=BLkw))4R=#8x(Wvu{xԖ5L3,XH*pf4oiEFҐ)gZ{>_w(:p4M:,Y7A ]} QyZ_XQ"ERAՆi 0}kqN.n\7O\婝"ArrJu n%dJ\ `p`cAGz 7w&pS׶p:;_Uz.+t_} HP7d G5,2%Oe\M,^ R+ f~ҏntAvbn)Ueyi]QMYn@8Z gLx XŮqVM;傕`n톂_ib3}X[?ȝ"3ҾߚIB ʍlHOqzTe<#6[ŷKc gٻ)\p;*-8n'5 I,-ccU_`̇@M ܕ/o!S2%LIZNyA hdnbRݩXtr.E X,`u%ER2RJ~\r@^ws쾴?q,wAl[@ Tw>ǐ| ?sFU߆꬧H{#g5EOEI+p}VKӟxr;~g\RJGTwe/қEgX$ x󻝄dKaxoyE'wQNjH^} uMgZ_JUj~Y)TzZ.~+Hܐk@ :gd"`%+:R~H`~:8ފ~>Gڲv*tKuK, L@F[N5krn׻Hţ%o$<nkd[!S2>dJX5g] G:*$Xͧ-c 8wŽ)8} CĽ_R]1ӚqE柾sΟr?d6*u/- 11ɶ ?Շ`ݧ ޴/E<黔6tKu[κN22WyC-^ሧfԬgKm]muͯʛѡVx}VO5dޝapȔNy='rW1ac ^S`` !p0*E\E*G9MHaWL*ں̬xgp>EYK$\Np%qsR# uBAU?ِEޏԑĬ$ヤJm=VjW`w]]_ȔxB$إțH )sVjn_5o-h,WWGAFV#!՞@jpuQ>5ZuLCR[vιrR]-$H}96;I#G Y5і:lф8f761'ڲWiU$7j)[ /-s+2Gi(w޹etoŚ}ǰ%HjdqHe\0HwreHH'5 ]z`LR^ƝtJ)v%~;x+Lf[Os &I= {uF[e"J%OlQj)A} NT3ivBd86 \)9ހNK;IngyWy;=~7L\Th "[tF+)~9S;t&\ \LCF+sO ^륶~J)v)SKSIMSNv=`gH[הs>w4#-1< >rWUYz҄}G=X.SNG%$]idxq׮9p.y{="A W6e׶5ΫI[ȑr ?۞2dd(V:,u<IE>Y5nuGRq2F%` 5L!5|жh}UYw9Y{Yw}k蹑p\:X;{] R! XI ;qǩ]2,?v܌o.pkIk"HkN$Um.+}=[w]ED:';$=0KR[ۉWRQ[q15\݀6jnlj+MKpb*s hH-oI, @FXRܡ׋_$o@R溵)1R[VeHYCʃ:6wM3N6׻Bh[ҖR($ |Rj7G37#A|gs?WU ߸Ċ* ȗ-LթϹ4/~W]fsܒH?w.P.:U6K$G ڵŜH"M 2%!N`-~o!XR$h|,<9@82؇ ~ i }f[Lzk2x.0ԖүRJm68OLɒR[֊ |\2z<+ 6UG-`XRײW;OԖUn)4Rs,p-{+%ᨙP}kƚ X<}H5R-ld| 1T<~RM?HHu*\ JR޽xxxѐ)||'2훐)0}w˦RCK&_ϛ{gN?K5T?#iR(Z î.,em$iWN-#.n/e B$2%;kdrTNezO!O%21{"i| s"Q"P?Hv<2u2Հtj{"#)<9X\ |Vjj~Rj/z|I!/͞yYpF/n`2%ˑYC)yw@-KuJ)q_4ϙUEE~`n"VnY_U# +%S B6=~sBLgKJmYzeJu TUj,MK+Vs] gȍU7f:BȔ DWG"B:|T\;yFiw>qB |T tro#^pkHws0_jC8dV!RZ,uXbHze_\~SL5!O~f-Ƃ W9Azm3HқQ, ڗhy,7{.4!sK_O?5H_ٝ4R"R[LW7d#@u$~ 1~鴥\t~@Fu>1 }݀.yڲWBH T@5 Ju#]-hh$$|:}JmWJ) 9eOAFE{['W5.A/Vldմն~PSD'6ȋ{tϤD1׈Fpq}_Y+~{akѢnzueFM 7>_yHпT{v* nԖjp5KoJm߀3aHVng8)-YBX H8dj>J)v ?\90d2 q2V٬k XҺcɪ~o/%Og:a7g{̆5\IKkm!s.>8mO&Wo *+[ 9^[;wˈ?GRv V7L,=Q= Wy'6zÛRAN*}%@Ȕ#}8dTid/8+?ZZIؐsR`0yҹ>kdNA((D@qѶqӝYJ)vMƛϓK/6IR= jyaoygM{ 2K1梚=,Z7o/N~:11pGk~fn\ߊ/=yc)6}nSgݭv-;/{}l@oEŇ(4EPM9_s߇KWіBVUۯfA{5?om%T sV!AU loEKѬy|TQ IDATɛ)W7p?RAq2zI,Dh@QS*sFȔ˺!} ٣1^C]:5{Ưt!酦}Hᓕ۱ +*|w?]?Q4Rj3xYSLoQw^~ƲC 9K+iF R 0ZJ$:V"~B3hA5H+Y8kٺe.xߟ AC+* UJ)wiM9ܴzM T݆XjG $dJ'fR0?w\l`n%pjHÀK嚯.;!5omkOz s*+ᑁo/Է?G7}?GzBE޳/뵸mdl]dQQHpՀ Pt-|cAF\{mEo}`dԫ[^G^CF/mu}RP)) s~~p^]WEUU )vʟKm5NoKjYRS]j˒!SAFf:<  oG'FC  iM{5/e {kWybZ4 I L8Ňˁ\ Et%qo!~NO)r;=/)Zk9gmRjK,#LmO{op*e%PL(Mffp4/>G(5!7"F݇,[\xNKuͣnJȔBĵ2}!i|/3Ipv*dO+'(w r^tRKoۍB Ɯ59VV67~Z-{Ԗ͏exqi;5VJ):0,nj& V+hVϽa~;oQ(y$;ykbBJ1 wm{hF'*^\9|y l)FOsϽj}IWL+gq/^^roOԺ6T#-<<sMG:Z@FG[)R"Ee]NGӑcټ~p^.^y[}[˩Rx0u$Ծg }zhѼ}G6-ګ޾\2%rq2UŏKjo1 ~b$ug{~kW+H2%S>.KAT8SF*f9W#U,5_mRJ)TϠ#X=X 8IȚU˧͵u;"){G|GMd'Im 4d5`ExAzUWZ_S=PE/.snsW`E # 2Z!Q!,GQHEɣE=-I} cҒʼnqߥ@R[&032]n5FjJ)Rj34O5ۇOG?-eup>ȥbYmF:t5{?0k p9pp:x s~ _X?tڲ%!S2zMwo҇'۱dn|OͨxKRJ):K0pHPQ ]юUr1KS)]h5"A@8BV"#\Hp%!#XtU:2}jqލTőSfNQ1+ڦR{qcW$ovg:`"h>Fx'􇺴aܫ9]\ _5zOm 5e" nHпXG?B֗:ٞ*BwW v۱RӧN!u;eJ)Pӟ~pHmR=X](j#A::Dg)~$ooHПwC}W\giîu=R[U L$i@ZS6pm o[7w EH QJ)6g O}$!@z 9{py˘xߗ8Cׁ7󁧐ǝ]EV#o#HmUy}Z e@V ylE@8z Rȼ(2juܟ ,zS}s`pҴxf 'RJ0Z؎R/~94?ۧz& B;U y9iU|-+uI#U.*J$<紴fS-F"zy{/N4F/LH^LulsOGDFVFGZkڏPj@8zT  đ\H|Xz#s ƼHza=ѩ0!;oRʾ?&LZRJ4 oq;xfwDFRJ : G{!%_HR  !$kHK#][ < G lmw/H4䧿v]ueN ʁ=Ey3z9CG7yٞyzi3*O-~Yf"޷wO-?@^Oz~J)zr;) 8Yh5іѣNGL I -ΚG J}どڝBSY/`$_9@= Pp;n6B,Rߊse 1hH) v <4 z18Tuc3y=}Ӑ Gބ ˀw#A@$}3mt#EҀ@8pFWn~?D'yۧWn'hisp @$o,2k)RMЇ g"[s-\2pڌ_MZ Ra2 iv)t ԲJ)T{!S;#qֿ]_Ԏ֎s3R,Gt{7!su vF!Wѫ㟏Tfo9\xoKT<@^ VliGR;XnƋΞ|iMlW@8`#AgӕH@p0,Rq(R8xY`]=m踑 ؈c&ƍkΚ>xfP}ڌڝpRHƐAJmYǩz :Y D8wJs+~^pt~2ɸ'NAFFAD`69 ݹ;H=ju'u[\//?AJ`>ѓ6di3p 7&яudʻ̅Tɇ#A͝scv@8Q9s&guB/c:g H\6IKZܜuLBKS];-HRJ)Q!SRTgAAuj6uRMNGAx Ctv?BUd=3g$<!?}^G` #A#d#Ag{r HЖsSw27`5z=ٙ=oXd=mam[. [k|_TvIX:p8a ߓ۸vl; =<y DFg w+rױ8cڌ77r~R*"d|dpg]"#i h:P~=@8z2]48 D 1aO '$[x)tNBRrw{ӧFg' B4Z$|yڌ -RjW z\qq[z]3yMp{`$Cnc2OHs5HK#A@8/$pU!(%i[m%+ζVd>TF d #izddjRڥmč@F W\^.ry$ٶN` I{,VwP~Wx"p'{Kn>ѡHW~2 כSJ)pD=2 vVޭѬv 6R97Fc6r̿Ð9Msd = Z 2GH@F|Hʟ)4F@ dZ$:Y@~%H_վqpP v d@8NY"AcS2~F[ 2ˋFHYXNxlSKYk¿:_Z=$-r:(tx$c %oCV fTBnܣj"DFSJ)k:7gʯjP;4?QV߰ײ/|]2գh <3dDi5j R"/,TQ%R=9ރb5#X!N 12z8G"iz _##&@8 kH@k )|[po5>GJj|?:,jM׿  v>lsbG65"U6jm퐋[k"dt0qf IDAT\gGg7u$?pH )T@8y}߉y^EP/Rrm}C.|C#?8RJ.G]}fVzCx1ch˜R=SX`ǃGۀhj17⽑ь^(T1%E\iK!b$e  Ao}}ȝSu$&S6G2 2(\տƂ2 6+~ )N5rMfr_t{z˾oU )U(_΋ ]_!RE?"iu0|6#B5*~+".}ƼxPI~;Dڡ09(7k-6Qe:/\?aqDvx ?`*&+V}hhفf6 x#Jc\?|W^!a4 Px{\; f`\E+f5;LcaAE O~x"lE'|'I V:wY{nB =l,À{DxP<x10 _w:͞ւ 2 4L ͊Bnrz#)+{فZ+; ۈ:DlOϼGy(n#ŝMOst~6o}GsC:OzayyaJ\?|*o^|D*w^rp6*E? әh;+MzdvSOY C;_AƌiV 2 2s!XYHYZGhmӁ9YkhLd&5('koʶ6!gySKP6_D G^2RȃY}D ѤZ5אxT5c:oHfzxٮs: x #5ozt^|:8T{*UX\ |P_9'MvGP?PEsa}س  Yak?"7v}ͦ+ ynF97ã6-PD>,TNv8:ԩ.R~V~skcvy̎9*OEϡ$v5a*(tT 틞;8b@cW5VdAd3L6ݮy~|DnCW! AՑڸ:ӑwh/.h-FޯC#.G!~\-Gfsr>AUL v7K3{mm%īP^12Bz~s.-@{uF[w5 2 +"a!4a|;6~O/ 1Mp]\?v^;RC9+u|x:y^ƑW#Y٨] /cHm:m.; /* DD=(ԭͮm}xq/Q o4莈T"UFmRVtpǺ~swpAyJ?"W#`~뇓,p 06CNdz6W!cobO4yl>d/_y^ʏke*lv3~O=)5/ۖ˥zcsx=ߖ\wjFD2QNo;!vF;E(0 2 [tGzȞC#%w?\B7Yy wpwpTEތ(dRʟCJIoLF}̾)UHPNWGTp1)<荷;kRdGe]? 6♜"`++BuX^1t-BkC:)mm;A`O-1Еtk;op=ҝZXy+[:&gO/~y8!*B$" 8é"7>Wk9% V3Q^R R& q Rr7h_!`J!"֨L{oOr? v"v{ Hl^0uCHB "nQ^0wS4IS&^<*羼GyT(xg\? <O_ =yvGu&">g .E{!ŁYy%";VF!ovn \T575NVrHϋ y<[6psKV_?ytSSFy_b_o+9X 2'CkJlˏ6n#X$÷i8x/6rzthЌ[ObhҫW^1>ygO ܏ U|Dj?ףܨ֍&o!E'1~<*1DpF#bX #Ix9Lkxծ0c7q7 #oxꑰ;imtנܸ,`ċYص/*G!>y)vPXQ#E{0]{HO uڦ ?Gu>2vC[!o+\ <6Y^1C ^xwIKorNVIgA ";6p+.%C]/# )E(|Eף0.6/>y8ʐ"D\"zD*?S7d "T"|\ߘ?r[Qa_g"OHUlOkkJ$삈l4+f:{O" FD8"rRJ66lN^tE ݄Ȩk}cx^| N':g=CQxw}=jDN}ugrݓIp&Ҹ'++6Yy,wV (d4?{lf} v;bۣ: z܅N2zCFT~>~T2 bbh{Qb/^ tdcE6 Gr Xw^|z._f^3:W'Z.co\?dR12fxd\?,L'Mi$\(dsGi'R [?zX(`fſAH8Z)[]SڪEl& ~D!INenN`_ץ2؂!XȓE*Oi];TXVҫ^ҹ{-]٭Qcͳ`JHQ¯"ż "2B5( 뉈]?l,hwuImo;Haޑ^H>|(&~|Yp}FV:DƲzkD ^#3-W ! XG5r_?Юd,GDF`o =ƥ{/Ɂ6!l{OD|!o,kރ\? (0*q6M\8{!o ;]>~TМ2rzx?`2 YQw`FSӮK v=3GCz01GE01} |X4 {^acr^m1 /]خE/,^Ճܕs脅[w}Ne3:!c+geGG\?<ӐbwY둂p:gŅK i i4}V?DlMyۢwTN7]H@J*DRE"LAJ<ދjY$EȌ>=HJDJx"P(-Vl_|U!PβQ[9Ihvo}m}*oDrQ!/p"=rv2U"6~g~"Snų.W΍j _ %166(}DRVu)I̶Zף#l[k=*={W-Op%[\RGv"HGY@z%wkwlpr?abc+^ߊH (k}<3QNr#K!wEhm Uu>\G_D`rIy^1A'mZSW="cQ\XD"+\ N oӁ?2Bl^HKD {w {VGm+jsv-HKW!r"vmi߷Đj6FU(lCm>";7"iId}Uyבq"Q>[uPxEl!?ǵDDpW-~T[#꼄.NUsJY,*F8S_[rg]vV$>32} 6V`0pe3ޞfWdGq_ә~ 4zn ɊlT7[{ ⃉wE{3+oZEk)"po /*[Ų)EQ" H܇-Z# Ap-*xYG?F^)BF| B.":Jp,k/(. ,#q)bC+2BEd#W<\AvJllB'0I[0T)+Bq lo*9jc"JDaKQ*g0?X"B2_oHU-m} < /PN.GV_!2Cd ]逼W Ҕes؞a;|٪Qgw4iSs$ki'Ƈ[t/^|m5cldGǮIq򊡇R =ϳ9>ޝ|]!TmǢ}ln^F$yEdd/d=8S\?fw'qk-Iέ;b>c"NG"cµ4Hc9pÐz,o6Ih^'Z QCɦC5Ήm[}z+fNEKb32s!@3\e}!ݗMEdm^Ⴝu|qڡܱa_c26"oN*X|FݮIC8" }(ǧRHq5t+rx,{0!"*<yd"bz4mc}ꆈATLId! IZQ.O=6`e}hϦ *FUsyacGJX; aXO"-v~wfXb #UG6"rܵx$f08ʞHNA;+A,/DNIbHW~#1gyc(dX=dDzGԇ&wͤ;__.l\Gͪl̙St흷F6vzFr( NSбu+[vH Sn_k/^x/ٴ2Ы^u:jT]Z^oxlF`O`<Z~xN_A>h~ "cQա@oqZRYyF#R/B pZ=HmkD1nH)U+HXd<OIz ֤=xmQyM!o›Hoa =zE) jg]HUT4T6-7i <yx?" g0v$HZ#o^]:ʷ(k=?gwdW,j!;UޏhJ~DuE^%6}s E%z ŻCz{D𜴹71nH~ABl@t$^Gg??뼸]W~Eau5h>F 4Ws bͣӮn✐:{ŜE_ۻ{ rՈ l1+(Z=kXxW|׺ջ"0 .[?9ic}+EȾU VQ$±+kjK[64tsp|vo<~Uּ%Ve7vcQC\|!\E8dD9S`f=33,y ̱ 63$?za7dݿ)"u8(vĜmY!eXrsF}/~UIj, D -x+Z`\HZ5"`ӭzEp퍬W#ܥ({pdWHV`cRE,B9?9H-Fvni=mm"ۿQ?/^|{{m{y_vFj?R!aq"J^ k%Yyr#B:xYB^t!Wu! }kB3* 8"a RutD,x_U3>u:置>.Sk##!Iɘm} "шEN)><$Yn={thܧ;-4O"I xk-lѿ ŇpCxU(`4d5W e ~#BOAWPJ5R|p\?|" G*_c)+,%B w.%UEn": oc!F <Ȣ5y'V#"YЯ(DD E "dzB .DFx;E6%\b[/'!r2)QAVLNjcZn25ǖ˸ "===#G(dhD.[fs4`oވڣ7QqKUF!bɞoCDf};m(#wy@$v= 1|H.؜8Z#Uޭq}M몵ff=-m~#ku6"rߧr`D$_EmaޚT8g##ooRoţj9 E $ o&k:"Y2z+7=\z:ZX[cW$[~^ x6Ek,ù>܂Lt}~x-%iXi޲&|BUUH3ٹ6*\ɖZk˒ߪӄISg;ߋ/>Cы\5=kow$}?  Mr bhɫAy2dbౙ YGJ^hT.9 -j~PQ`gDƠ\#;Rh>|/]DzJ<#H#u!r3-V P!bZG*?R,9x"W,Ƌ׃,T3Q8!Y@J׆c66g#nDv\^<Ze~z, xڛ׈D9nD:DDE޸2Dr|Jk4W rw9"F%ڣwrDᗈd!|0"ѻ؍~`_RoDo9Jcs}O畤A_Wd86i<$cn9^|}WCFV\>2~G޹QbQ=!#*3ְ 2a D^|~mB?&˾^*^oxsskR+iEg]f'/).l'pP06IdV_f7Y_6 x|[l2"*f\C{72m!b$_ vo_ ~!0;7$*<=;+QyÉH&7~ovds]?9݃)+1 /^Wc&ԡwAڱHfNz:ӋPjEa"x9W)$ޛv\W5=,2d#{iшd#eylDJI)@J4du89H- H-Eo_Db" E;m;nٟ^aDrCJHiwK0JtXwG^z9>_7? SIh3D) B4ߞ%H _Bc6w#Yl}}3ֈDZwň4(>uvBDBޫ56;Yks~ZлӃϞ66ȫV,)n_kޅ%vJ*Vvއ?SPb-}bmccHh9zdbG<2sCm  dipE.g^ ]"$e}%oy$^|FZ u F^x6(LU}8⯺~V">"`$ڽxqW̝FEFp,yF+S`fqUs[&.@2D>Aeֿ\BCr_􀏑 ;Îs(FڤՉ6G/~t/$[@ppwѕ 9um6(^_9@yЎ80G_<9|0?gMpЮ(s/honKyiVߝk|?$b?E2*?o yY;Gb?Ѣu$FD#סZ~B*TDlnCBC HY"e RQ4"@$Qd )ha-:Xhǐǥ Yn@|}WRCOR Z`[ UhA<#*7)Fd`%~T(W^|F!vSP!2yFtDvq틼o]CI = 9Ŭw1-C"ϑ)"{+"KPVxK"ǞY(kX)i-f5>'wV3#OW"bTٳ= ߚ:ٳtgCFT.2\4Wנgpck(i/X:vS.\_Gp^I֟AbxHa>f#.AY!5'oh67Q-ϒ$9l9@뇿(9TwB V==vC+2f d׮-;ɓ!HFk񈒭jnѷqhS=/'o鄢YhMg ]`LLDެ" EGH:0_^w=buCrz7ґ"RφP c3 [~뇅pnIVv 9} e֊mW X^1JdrNAMڦo'lov1zGtiaʼn?I*'-.DpɪOtnn{>HN^ͦ'-Wh~CJzIJy_"o׈?>ha3>rk5h_T"cIr{h[FH(EDD:"DV!7 $Z^|뇃|W<]=Ѣ~AYО@g׶Eo F?@ l^BeKP_\Cfk[D~뭟/w#s{d}leue{F-l%(1d,v-f~Eyc*(سw=򤽴vӲ}Nn2rF+"HXGs B9 #]pg;˭ "mz7Dp lU4{NDGwlsѻX>~IX YĞ[_C8uJ:rwu?k2# 2NUy-OM;mZNB4D"[mh'k8!׺VTwZHCC4*mF~}Ij jޕW u;չ٨SɍuҙYۺEcM죲]BYGF^v=l5]^990j17ۀS$+OD!#r42u@'_84~G\١lgHF,F$y.P/~F1u@^ſ3r18\J.`MQѭK揨)]Z9%QSub p[?ߥ k۩%y$.Z%ٺE~6U)8E/M8 yZ,nkp]A΁YɜDƲ9y\?\HUh 튼# @#Z$`guT0"c OBB!kMG n=Qq!5yI тs"#\`Omr~xG=*q9ʝy{/.EpC͢0lḢx&cȈՐʓ2z6Pf"vӈ?9Kf1~T~tV5v4u$Uk*,YOjNCrl}而EkѺOuAhMfS: +Ң=aצ1<xh g*} gN|Omܒ.U9ぞSs[5<]حqmډɀ1uW!;}!ns9ɛ0 "B$f%Ɋ-\3de"!!(I^vv՝7b&o ~Tx  Ř9vvEm!Eyy}Zp"2< "YА' >xz?Elq[ ha980E2#mcȓK*"Pb^]9|8aVPeHQ<&Ђ@dQ8M(q)r-GBl R%Yg2ў=[ٽ@H/VE9vț%ٿrQnH8OCB|9E1x7OlǚyNQ6ޙ(mG  vG<Y"_a}nHDDdu{d3(l Ow, V6nl@V"*1܈ޕ0qha7;Vc8+(l53UxXǼ։]D!VF/^e,Fº|,yneVF޿' 8U'P<eWmwQkg^^gV<<d-=(nJ;C!d]?=K\*7r/ҵ]//^ET IDAT rs5=֑H)K? O8$Y]6/It(iF"AD h< ޶>\uԇߢukDn]szC"$4ҪpAsU&9KX9/ 4D(C2(d X v<ϮMx#EBuD|;9"cŐS(ܻHVdVմ,([UsL$s/A^ɮ@w>o=׍3xU:+ 64wM^UdAq? A[6%8dg%[eg}-Qh`-2(%zw%Z"ZD[#шEt zQD$&[_ +~H~y.!y9"`!RU H(mHhF1 DGM#?ʩkG Qh= EU*rEv<ڽڹH O? K[ 5QxwpI&c(m;!d18L{6 țgxnLdhe=:|{ޱmg,yIpuzSO`~8^|wlS}Da1b'~/ jSBWGZb 9-R8煤e#TTa'r6 ZKR]fvF|kg" 'Dž12Er|o#MjvcH5y6%e򮖣.!Uv}kkR!ksPT܉rζ[Xg~M],$Z#ٵ7N2~co]Ǻ.(m%]HG?Ob%]v&ߙ̦S.ڦa!gk9{zaCz@qX 9!},䌈!Lx?Cց\뇣Q w%9*Eac' z "glTAh?J6MF )kh{-a*dQF"![^o_HycHIGD!vyj,\3mc6':>Hq'mQۅF)ުFڜxHp8DK j][Ddv y@2 EToODlf6^iT!9bO7rɸȫyS0ɯƖٰ2>g,BW"a+Aj ~"pOa׷78ܜdNM;oA[pgl8sCHuUC/Ek.r_3f`YյZM :^&UОBzߎu9"h!*Y.Jv~8CtT.izE6TXa5"P=7\(FVy!RoB8jOp~WUR At"9pG#`Ec%-l΢=3Qh(/%H0mm 2RlE6(; Y[Z"y&گ" 6i3Pyj$H}><\O:gޑg"2XGz P`ƌ>2`~'"WߏEč|{|nY4:kc\* -1n䯯%t_ضuɂjJh 53XQ3fQvDY7~A[_o{qD"'}#eV72~;"L]?4#A)[=ٽ#½yM.Bp_d! {)~o^mz[:mXYɪSJ@uDɝz2A]Զ+E^ƓFa|!f VfL e!Gݐq:ŠsƱ)v;, $#v]3"] t `Ϭ#YiDeFU'‚(¤٠)J!> B9=nzȫ&E'd Ɠ?A+7&bQjeA ks0< [?˴{eCzeVZ[IH> 8}"z]x]9RE/2/R߈Ɠ ;"" X zžK׳HvB5]h.Cڝ`" S>j/x s}-([%kYc{UHYp[? <T7oLwWGkRķXr~E.Ɠ}Hݫȳ"i)Ӑ$Ao0R 0  ^5=9 ) (Tao$k-}H#8m> +hې]EnƓ#^2$P@`ENkɈLHO"SC}ԉIV+C_j6W<#щq>mDY5WЋUm1"Ac ! !pBv?MK&م<*@h=Mhʹ:~o?4o";M6?s6Qx7Jo9v|?Z#6!r7}ىXKg`܎v_.noi{;%b;xn2yMg@۳(x2HaƼ}ww ,D4;u+khߎ)g:t $ ܨ@ݮ@2z2>MFHEޮc';: cK4SoB%d ;;l8"IWA^Yϡ0'byE@/C3q o/>6^BG|S꿋t\jInCw2: D,)#\{s+gdLw&bet^v;;7"^"Fare#à#WOʝY:ZH\ Eds =}݃۱N"K4 mӨ*%Y.0 _w|RSWu*lQGGM]w5-_<3ZjڋDFc `KV"Y';NCj& 哀亽}0Cx~)0kCkI4ux$M ;o>w ] 0P <^ɼe\?@lh]qZh'zx 9}OwXE8 #^m֖=b%-}xӵ?@Pf#k("bϝǭh/X >yduHx,$QJ >ғHHcsF(,; fw\`8cxZPhG}6NJ8_o=b]; khC 0Jw "垵Db"-B#n8ćwP]@"]km^GL DxCV{(ee\ks]h?߰)%lo.Al{*'W#b}e ߚdiءH>ߋ 8{ՙ'X}p59z9"Y#|w$wf$r6d$<)mH"K7i`4h~38P_Rʗ Lހx k~( *x= kH&#,ʣ_Qa/#-H&f!|}uTv\U]tA‹uR]sMlaC~{963ޜbYbl!"%: sfN`%O <2\ptvE]qӉ7L#SLWݮʷqiO9w.2-6 әΫY}gBl:Czj|B::dlN^gz>d5v7-/Yg{75=놴ikJk[ΑU)R5"\ixD@%. ? i١~B&nCfg!v1R4'#eXDfiA_E)Y rrݿ ']lL!.${x8kGs^F،HПGХ `7D>nG[Țن^xblDBmȂ2E8YЦ!-Hu&o٘^67m~ˆ}2) oڸXC=}l4Z8m.KeoZnT_:ZW( q|>y.*kGD@֣n6^Ak&Iےtf~N'Fu,;6Dd8m+%Ofe<ѿ h!S[B)άQա$bLM5^&tR>d{sSXvRwҙʹeɻ %ˎZ[0!1>ΨWC?EZ,dZ1,S[#?{O@xy="ҽꝂK} aaT 4AGUD )m@*Oeav-s3ŇF&5+DDwHqyV! !Z"y=`C4Y!.G)#~.wDJ+2~puoN?jq(GvG P 7J힞HB,z(n@CoX{֣51 _x*1_kϸ~.x{았k/aNٮRSW5 [+kPxhθijL3Y>ïLdpyޔ#E#~'EDksEU gدWnȣw-}y̖u{7ۻ[##߭:v~4||]^)C2c5u;: uY.mgmkBi^k^jzH`Cy*jcm(:DL"7Gɻheum:dŞ[."11u쑈EƓ!N׉,vpYy)D{4] Y|RkQ "}"A{Ȃ7"!c1q=slO|o'sydzWFXdAW=˛Hi=m{>\Oݢd~"3<׽'Qv x.ݖ%}< vΥ5?qݦG겢ӛۺ-Yyȣ:wb맛S>dh@#=쎦֒~>ioh3J: &Hե^H~Lp8~wE b?"(T>wƧÐaaB{'$ 8}ƆMc״m<X.41`#VYVۛUau@D9kYCM+me:}B yt~l1J%bwؗd,ؚtl%ۮ}G5uU Z!RU,7 }XO̟y\mMCS{Q8$+󩟞qmUZ]Y#>{꜒CB!^!0g[[5uU{\jy[Z\z $$#GECY(#眬Bt~ue'cAV3U,ظ=k*eX.Z5y""y_k"i%ONGē bއ=AD7)HCR3 #BD|$bOFKZ!8wgi [!gg8E\& +H^^,ZuӾۂbwA` dD,2#OFI"k("b"nЁ(=ƯP| d=sRb]hiB$(@d`܊Y{!X9w-"A-6#0 {ȭE{M lBv_kVI@4#ߞ@۬ixQHqyۮ+@J[pQ"jAƓx/Fٹv3rx.\=tg]"v8ycnVK/8|gc/C AXdgBM]U.Z+&^;wD,O`o}ˋܣ^i߽z{ڿ,^]3|3sK~bP$.M"spdJ96 M=Ah-"]bdC"YdcuFFzȣ}2M&;Ig!"i!c:~> Aq(X$~{#aYHEp ·֮RQlMEƯ’f$!?[q;EBl}@X!OVl|!2}2 mC1w*H wTvK~J}WdUSVgAX!D3cЧxeo/KVq[&w| e>٭u8[{ܖE^-ԟ6,1W~@c^!+Z:{?Ю~ӕU WW>kƖSF ٶi2[3CɃ5G""+Hz Ðig !ȚZr> PCJ ;6,Fes6!Eۥ= }#d?ףbx4X8#K;$"6/(L% ɡPK\.]l`Nnf$p)V噄@֧2وD@)߰v "vF!3bwlN,b)Y2GKR1.v_m` |T'Daօ$KޡT:ocG*o(j |!{ҒY/j̻o~ꗗw֧oH9h֮Yr:SٓW\^gȯхsኒp)_mGO!K-_'5~G!MQjxD9 !(\h$$&!vyv!L"!~..]2ȫtk7E H9N 6Y['! [ )%Ȃ^l NC[}  exj!hB2-CKҥYDa/n@^\ӣG+3}g_ 8^ig=?!ܛh/W#Ჟ/b9-!PPlL!PaaspJ&1?-ضܞw)R εԥWZ @\6N'16N]]"kEXm]xy&$1-HxP__-o@ܤ5uU++knM"hfAvT\xx[ vц-zu'PKdgucTLz>49ʖݲ v,Em^ es$XYH~:{ z$gBlA\)jMHQmgz{:XV ) D,KGn2HnuCCt,5 'F9ɮ.Vɵ}γ^>j wp<wnGkkA@h4՞[n' W]Y Uy[+u]Vv|}]׉`]ό\O edH(nFf sēbRF۳Qqi@  ZeT Pd?n@k4go C}߃>Q܋{Do@YkE K)RfXTڼMCVm\FnXp;Gxr?ÛZ'#>v h<zbz$ !RdV;ۢunH>лaXB^$KB5VW%z˭/>.ldջu&›56f D*m|!9e82RE6ڌ0SNJY[$8lȋ#O)ko yp7Jn## ,+׵5uU!dĜW\ $bf>Z!LI"/5(x^[{ly5#g w&Dγ3} ?O;1&Uj;(D#hnz3a2쬎O22LC29YwmkGќܦc9g>7x{9L(Ju΄\}:9%+ț53P]Y˿K km`>/Qega E#+;!!ӆ jNpд3rd!E;P=HvC5)MHG#{R[]n#HFV ?lD.^PBRfDDs 4n?Oݿ n(8Ao)?!kU;g9CDmu-F$C$]}.=J7"s"J/صZ; e_u݈g\leقHJ= {zxpA^q_ hVO'AFDoBD#.3N@<kt-^Lj2S 6h}p0 VFɣк9՞+Go\g}3HI"o4T? ׬=?>y#uV%qCE9S(;(򒕳tZ?O^.<-fRnAm\v?V"?ɀP/L[tNiChAT+;!-"O 4"FCHH"k>yH[='BC"=!ϷkDؿ3"I@!DMw="WX_"r?A+{l|$wY##}ą.'._F IDATF྇g]u5 31]Zxq!bև,>'o]i.Y R|B^Wm~9jQV'Q_ڧbD,r=* ?lHy`gJ'W5ӽ{? 2咥&8wо$I{m4'?5sq~n#: Aۯ'Y|cJC'OO޵gz?6r]ZW׏9=:gvyqܡ+߁>]uT #]I|c^y]rˊwwsf>3-# Օ'+]cI.[`^"_#8ذ)o#)R p#)vh HyGJ4 hqA$-6آ<=t>D{N6e[ͭi\&뷏m8{ C Ϸ~\f@0;;0Oح]C컑Œw!2 yj }tP3w[q\-G*;'7p"[P}'mr#/P'k4 ;_Cׁus$0"bށ@qԳ:V#&bӐlk:ٗ!9݌8"_a:Tم 2r)ID PB6v̓eL4+Gp]m ,."ǭT;=/\H&+wu9@kE5LBxӀ` w(Sw/^1Bm#y6~J4 u#ß^]߼3g z^]GݸxsYIeuY|O)y"$n[Ψ=pFR`h<'9=tL_zݩtn=4_-ͽ~_{-e!{m4177"wˆ|?ymEiG 2\rs]" t -3X%Oz7WWu)vSTFɇ-6hXBEswhWDb]zR/FY^ND;Rl Z=D,2Ӳ,;FBzqggc_,.kyġp둕r=E~&z"FD Hh<9_= F3Cl\M 66a20pD^Gő<ߝ`z؜9K{€{ה΂:x!lwJO9$=m~.EkGdye2"_BTp%yM[d^'J"_y(>3Iyy3^L'b_³*@Y5Bz[=ڹzJM]U=ZVso˻+ ?)}l~tW2md>;p-<A;„ݐ DQ_g"ڋoZz)t#|:؈Hǝ֖֖"$wGd/,2uo\_BQ'+,EEc8V;U"~ɱ FX2]t';jRbBƮ}R}ar&؋/ Ris?`cu]! a.B^PpN槼0F.;\ G{˽[Z*n :;s^T&W\Y),~^$wTV$d)Eflϯ_œ9xުg`2I6鱐~"h|݀)彼?&#WH銟Xd>%b5uU.-_}=pԆ-} 3ƖQnt|oKw3(]Fy-#!-.kuk+~Hj''{+JM׎ w [P20ٻ8[Po]M]XSN,jo?"xL9s O+O^E# wnSk*uk:"8":)9ܢr1Hg,G֩0>)֗R~߁,/7ʻ {.9l@ֶۭ qs0GVy E?ޘS~ɄV q fLBټd?vD[gdlƖ2 ~=лgPOQ,ZU-9"t-( *E %|HSFrEk ]ҵ{^1H89/n(#-BC[2k(l$ e@#e6  b{1@{(qyV=m$ X7g%ֆuRΚUfm$8)׍h/0YƓE r%s)_H;: pC@Ⱥy=oe!( w!5(û]}X9%~Jp.R iM? %Ӫ;Pb33bt;)*[l]&]ѻv q^R;+Ͷh!n11hͼ'OÀwP;%'qG#0O -'‰XK]?Lcknd|4S9:x ?/)\Oaf =􎜼.Ҟ:` wMI>R+kI -h<9#S_\M磗/T/\i¶4"H轃gUdr3iU2Hug G lL#ΟTZ"ށAJ3 ]O: *tX,F$V!ڄ-qYf#8%8ַ 2VW_imUdžGwDhcq'Hm\XG UHs!x@<lljy%)#b=#3@|"@6e7i.D / Ⱦ`@7C(. ;xWMN5B{u;Rfܞ=A"(D,vhާO-)9[~\RA!O{E[3kꪎZdzм|yF |z)#,B$8 &+jq!7D6y}taf!p{2( h$^! $_j dyy%qj #rs 2u#꿆K>@XA}DXFׁv,ž+&H@1\HE`ںFfHҖ I4H6Q+"Ka|eu%OװBaK)6cE87E횽U|9x2H붾 D,d[^~ǿn݇/Yx׎)(lWyxw#VW֦|4Oa༬pۄ{U#SgnNGvZ.-\W׏ʼbo& KvyhN7$Q l/I/CDoT^N |vu׆.Gc^,NjYQX۸)Fj1c7 +䢭E!m '!8'gi^\<8u0"- S F}: сBNAH#\7W,`<yV:!CHHC$Mk#>mL5;Мd)XhɤO{t1ƅH8)qC 8 '8t7M[ݷ'e[G'Pgz I Wl6l56Ok}_`XD]=B$wC >9w ey)҉i#ZsvA}s_ Ѭ>ZK>Z9RZbsp2XxTD䝅F᤿~޻+W.jM]К?ިA(TpMϽuuM_;WSWRG  #.)Vv]]Y Ďŋp$_BD9SHFsuע!)}v_ 6Ͽ,*q9 {!ɧD +][JG/y4"YRA'f(Fx.܈ps3A2$W!޴/=} S4D8 DI!YH`ͶyCwӎn D;5 d|lw_Oښip뢍 =k;Z"bhwޅoAEzVCX`Dih|015󎓻;3[-HJX@`i pU@2( J(+• ",})BB}lߝsLBrA~<׾vw[zlߎbOLһްO];+߳xM4. "$p]EĢĻ"(6|~~o{O/[Z"=kLxb p0 ^܊x{oݞEdajfT:ȭ(Ya諫rŹ=s_)>?cTgwiuYɚ8)̗(+]5fR5#Zzrv=zM*^3;n•{Q }okR^jdM>m{*:й<Ц64ל\暲҆"暡 5ߕ 57k@e׻z-#5#xue= 0o!Z4kaD{eHYrF{ױ6Gjx܁?)L߻lk?FJ"b4'w l{zXb8\<d?BzyL} A{{!ص.d lBdڷ"ےm]Ep!iE>.B_a.xoמLQU&Xo }t/1O_XQi(R͢xCϯnpx5Ļ% Oj12=O+e4"PU1@H IDAT@t Ⴖ[D\YDDpCp="∆݃x9ŅJ/J,_;1:s7#ep"\ Hѳ=y1BՄl8ƮxAw#"}RKH͇' Y+,Gv5ڴ9`ۈ Dԯ@ t~Y@z| "e?YZqy'1}{iޝG6d%"B؀ǐfeam@GKиYf? 1,ū϶ůŕ]o}/@@+FH^d"C"_oOA|  ׃KY]'#X,=~_8Ѭ(JF^e=˻6l,!P7/>oզ_U764$RU$v: G7:&~|,"~eˢç(@iCs͍+u%bfڀˈ^\F ae]Ak] MV$NDg4ň\/+PLIO2,b 1aP#{ū?rϒջ=p=z{8!|ؼdO;&—"yA-<ۏRc1%זzO~YaA Xf!.?@k6Ⱥ7 481sP@W.3׮KBJD'j2~{0ᖧ>ye}/]˰ud{2E #u  SRgٜG˯u%ܭ]yZ<}P*-܊21ػ\̲8\Du+W^.'x,| ׺M`kַܲ|`(T,`9@(oo3 C\ zznh홻&iR 0)璩hi]mtaanӚ)=5m3{#8l%SDDGT}uޒ}sŔxW<Dӑ[䀷nt>;l6>uUAAF5mhكw_mg+Y9kp]>-${Ov[Wj"9[+ﻣ}wo,+YP^cC~C6|} `yhc}d*}$@ $GoC݆Pc2dYB$gkXߓc7>LwG|}?[Fm(=& ;"`,!, q_e}= "LA5D88p2bl{1S_z u쇈ǭ Ff؂xγ,B}Wda.&Rx7 0PLr W#5TZ .Ҹ'XO!P-_ybE"KZ׷wQY.GedFz=#ho"P X :-GHDo)ZW{ۼy !1D8r5t )d #gg}jek{'*sUli@\X_fO{GmB6_,6g&S5n3%}c} 5vY8xkAK YKfxK"ȁ6ELaqQۋj&#r9h1(Kw[u'g%BDڀ\2"6 s?F&$8o#`r+\P>ZBT*3%ZLxu>FDv} "$C !+ɣ߳C 5+B~"r{X4E(ki6uY_DJ!n#߉'ct+!+͋o񧮉FF$|xkm۸V2Y C 8GEF[~\>y*}_{w$$tߩ!`4iMz ݏͧ}![2r+w[hڏ4mmCvԵ̞i,G%$oqRh0tfi#ڝ%\?fc~,Jr`e2Hc}r]&:oG]䊾4jJml4+qzj6gY!6}pCsMm͔{-FkGDU5ex~ h}3ԖwKR[a{) GiHYF2Ctg=00+`X [6{cjIZ i!.#.{BawMsNyQ̧1͖4!:|g"z5E'nr({C(ϑI#~6]1 r>#: {-B2ŕ{ BF]oYics^.@6Cn>BX "R;5$˛*E䭥?ksBk= Rۜ;!yf'T( eSj4H$h}fzmoFr)JkGU? {U>RBXHێ825Q[̾g"+m:؞Ru_ĸC n|{Q#aE%"@8eyqW"xU/:=qCį 1ou!B~\,wTN kqAqXƏKT-XlA;!w&r /ظ6٘.D. ZyW2~vC!O&!MWlQ{s垃 V4w'!&Ua6wend9<۾C﮳BM7!|{*ǏBB9g4 ad*Hժ: J6Le3-\K߶Ϊog2E6ayW)nz}ζځ{n]n$V+.huYU+~j']Ti݀s*V~XWt}ܖ+ y5[м琰]uӿ- LJ5+2X_X_{G2<,D7$S`5wFgihT%B х G]<$Ϫ![~< p"ra)in|-&Yei69^KP(OD|ƞwnm\9|ѬPS,yH/C6iH&0w!v*]7켊]V(Dlg7!ER}b v*GUi>m\ihi/dZXEqOu4] }= \y|Ua<, q`)=!9H$!>f>by֬m6s)|hGlke= xŲ-O-Tz?ڿf"֒\SL-x5r)[ayQhͶڇjw6-Y?3uL]ihK>ލw§nebnZ[U>hc&퍴g3U7sLG5.{DO C #GSbtB 5b,id֞B{bnG@Kғ1uw t$[f}UAː0+dZeCL~=gm7Cu,mN穼Cp0wSC֏u~+Z{ۘ(0pTnM9ls:;hH Xwo[c'ؚB)ևm6"`ڂˁ6? U;(l[#ٷ.bm?, *3Z' Zg6lMDu=\d+%Es<>"RB>^ǂb/uv"l2b1JSk=6m}YU.4w>ݱHo)Vk@JkCs uB\O!R #|9# !>U,{;(QgP3߃@=c"ԃwudz@j]^%W %Y,((xDdLxQX O~T"> Ev4 JΓ p4D;U浃-lnU3]MWCa_$'fqRlc}b-J.;[sϞ)qs| :%Eqxks{yI݅sڻۼ&SȝL$׽qm?Brf[/Y;9z}G?\1 3i?1/e˦&s=Qq ^>go2In1`E,/ekwyqAnR(oꪛ^^L4.Q]E,Av=XƜP,J_u x6PSh??"5fD{ V*C> D $8w"$1 $ G٥jp1g6#pE xJ6ڷ bJ#8 1 BZDX̳DM!01-y~gQ{ 669DoZh(.e4uV` U݌%q>`^ğ+bk*R&@c}_Hb֏$8_H{ .[! ?}nD{(~*[?X?]xW`H@.*.ؐ * Y2'Zߏu]BHQkj]fId1ښ{s \cSj6%SGywپ/uN~]x 1Gt$ V54i]֝jso}KZ_>u6 >?.ηLyϢ,~K 5xi C. >W6јom^h@/GH(1P[*gJ1D&wnCś<CDHNóປ"BPu,in G:JH1׉B=x,D󁾈LV=heKlcB|x ?=/>OWwE%Y޽"ڑ կ$d ]isNuWʅϋ,M1@<rw d~<]e {N{6+@`ȉN7< )35}$r/F zV~>A&X_"J?97JNb2#95|fኩ>bЪ9lnŅKgYuj-$؎@ENV4RA2N%X6 ]ů ~ţwMFfY!yi,!mp^Cg3ʑ=  ml!'vwƳ5etm "f8j~D!XQϋm~F%0.BZuAʞ5qbsw!ƞomClvu@ O#0p||Cmq[dqjA ])!&`1lmwBLL$p1bG$@[B\+V-,1 K</u&{ #us>?I3wǻۺ{GiKkף,54eTLjx,3Dk;ƿZS 5U7ݼW f IDATL趎e-nek'w365Y/_$'s>)Z@v^˖JȞ8ycZG.s]TTf8e@ pi꠷6UY7-e6KX_ieLu44LϮ&;Ӈ?P:!љ@ K S:mlip1:\i"28HiSO3Aޗrѭ=<񅃁eGyۡ[Wz-WD鋔Q] ^ .Z\9\k?D읻h0D )A {NW= rB+ϖ<9BXD-R.+u-puPamr9䲛߱ m{+ڧ۸b;~e 5őG7\X_Xh_ PIIhoaIo4x?&6y׆JLj?g5׮Vt}ދî|r6VIc}m{2>%XX_D޵g"}ļDv`ꏣ"}BNvBQr E}cd9!cd1K=%ws}-bAIȍГs|7CsO"t "r[=`㸽㑄/#=DmL>Y VdHȘ=z?4m9". П@u2$ScyzȮQ|uaUϠ!Xl fzR.~g{W{YU5R^`KJgmdB϶' `!33^f2tb[PlN @^{jFn Pyad*[]9pqH?/)jiZR(gU70nwjo>c~[=ubbvcY r_8ޒUKLx{gmU7>NǙkA YsVsjI9d*})!^}KZBUL[ {/q;?_wrnqj#~[gsW9`UGH E;7*DzlμzBQtD{ݧ`4˳&$٘K(<ʞɳ<)D_}>pJS$ܹr B, tKW~!g-)|BX*B@KоtHq|/V"-V(n]-_|ؔ߷d*}*PX_wsߕlO8yGسnD"TzZ-k")0rB]um;hn]Cs+eJ|!6 8u;(C3A''אNTSyY%Q\# YP'`d%y?x`{4@TsL{wa<#3b^G-"2{7mYf/@sg ^,gۜUt|ODWc=lGȪZd*eĜ)v8TjϷ8Ĩo@5׶5xM2߯ t1: D?^Bb.{PBt?b=,~\r92QDquAʜV9 EB|+3 +nqrY{-< 'Xk _'dz>(/rl>WMyW_nzc2m$,[_`W_c}#TzbZ#3gr|k#"{ie u"2OCz7DXf u#Aso A7刐b+3v >k=?9i lLN܎#֟"E՝g쇴u Z l!`Q~45Ȳxlg"&7l&0^TzVܗc+Gn="Z8#v~m * #ˑr7HUNPNW\w z/% 5];K{b֣O+UIu!:FJe'macg=9˰ :|S׌+(A4-MHB,B<H2 yNWyҤ !Dgx\I},F2?M-!v,Q:wWA!\,cYvo;gmŃzwGH]2bFa#7V<~ t7^]c}_}{>[A{I]ud*': [r}ߒMC"b ]Z\7YnAr-ClQgl4k,,F= 0[YaDDnEi n?nF"T$ϲ=v*kst=O!W{ JD=_#$pC̣ 1qh3ߋbS@qWp׉Ӑl!=#HP=Ʈ[\GDunx 1^24"i*BID@D]_i#0䲹i3y}h֨XQݾ;3Bz!!;A^“JF]{h3FDd*B9 =ʙ=/y򸿧ڽ(Xn}Ry%#Оy16cD3 ˚^,CV+J_f;kn _Q,ߦl]^X:Ke*U753aA{bi)qŒXbs۟"W a|ehL6\ĸ!dO6Y$ F@ ZJVkmEDn9! no2cȂ7"ҏ,=GD0tg"EUm&bG[JƔA g"QhycL!W%"lB iE.X2Qǭ`=HKe6A[=(]w1$ f}\"HyvW{z#+h?׹&vQAEvH`s:fTU'рTSmhV36o!zBh[Y""k\suMmZ2ⱞ i5u#ǣMyصi..(DVN36Uhѳ[)yǭ)k3몛#%S)( %j}!r][çx_/Br5kSY$- ޳eg,{]Y:J 6]3&l<Åy iǨ;|͇Uys'Aw:YՑ#$kG1>kS{LD~j}8v_D/p8~SͭOJm U]7?ޭZ؜8`ݝJ&o^ʏs͏+}};(Y2bRxqDwE=vN,I%SIc}=.z. |8=-蝊4S^ٵ(mN>mWƵ?U744לj,'!( ح((ꪛ<_Rx~S:\:82Mm #[/ZoY/s{mjg}3*Rթz}r|Lt-v oɠDօ 1H^M"HX[n{FV^ur 91+!e'DnDq$ Ɛn4_C2xZ#E#?P $IYǿCI:R<w%g#&S}oЅSKN'Src?{–y\A^z~/A \sz]uSAmH  8ph@7~'Cǩ> 1Lhm|a;nq^LRxP> yet- ^|ͯsΖqVn-h(h;vq6m2WNPy?{*Eu_4%sԵ6*[6ǟgU'!\neZV=,~b)nG|GͭǖkcCY?_H?5{Ki7&"xO,e:W|B{h=ZgnEH~zcy;/OJң?Xfp6[ԝ8> Re%~|?ЇT%L{S IDAT=TmmW77'Lך!u*u"t>]췻 CL A'C(`md"7"W# ^># l;#tJHlo@ČHP"q:buX<">? D`AksHKxkb`Y6Ol=H`ryNK=i|"dz:1~6+@ j}vtO b͈T Y&.~rxoW%}X Y{~l-.E!dz o䭱[EgG;bbwr'H`#\$< -iݻؚ>fs~:T{4Jd[2yvIc}>j{50.GR+1_;ukGE[wOIQD GA?+dƣUlb`iߦoپц>mH/cw d(k74|ѐ74ה44~\Z>s<Ϟw īr9"?/TSw;E2WB@J}ʭUlkګu^݆h+Fn`ߍCgǞxwrS5rY'˭FKfݯ?/KIE]ӫ,bSEHԴћ# l71X)ypm'(OZ?lM+LZb鸮 QaB70NCXx2> Ŧ:9 ) *!D!&D[|z3[1 %CHj#r_ob1NnId+J "Yd*{d*=pߦ8_P";f-J'SX *B>p݀{rGV{L PZmfL1$CDND7?;;Ax S܃,G_GADN@1n$t6;(_3Y8AJ G!߳ ;0y2bVd.s 2Cxo !}# 1Vw6됰 _Sl yW{ws|:65>ێRawdNGqWg.J6}nFw,()*a}|iG7"j1s"q]71H[NA)`| V[ǿRݞm0?k;/ 5ˁOvN]Y:O1slhC-~`9fV/]}vM'y]/q}5tW35uǯ#MxNnz,<[saWGDA(23:dCģ^X~;ikE{$?'_іU7%SC>n8":y:rK E-QH{TڭE.{sj_qZ_ӃO !ig;񴍈7-BwwB&s̼&&o{u *{ݚ㮉 F ;_,4`TF</dg)$()<6zD>\ T"J NW&^;oW'r9A_nX=rGn?B|N[Z޵c+g^)9k7b:MwNַkgolTD2s%v}'.h֝?_uv%S1H=}{Siا'S(~/#ϐ/{"h>oQ0UEH+D0GxD u_G.AH[H wa" \XeLAYk}n㚈̛H=ņE H,%.!w?b(g r {.e8k{1 }gZPomN=Di+ Z\,Ezwt"ETYb(Dl 7V[쳍6{}'|r勇'm9^`mqd\4SLR8;ļKpC+kYȺy~+[89hMk1~֏Y6w|esNh{/h J!fp!@. <rq\h@ (! ML||) yzu]k{b F>C &ttFtn"O?@xtξt&)$DPH}M]]-3m!!e]4ʲMwN)-6w ڷ}sCzn-{~}1]}eUq,+D݅+OZ;0E .{]ފ"1**KzR-1Ê&w.]㒢oD%+ǧ+˔ 񃏏ՃV|߶.NgP ts>UվhwyՂ۴CmDb.\nUʷ~ '>bEwsm$:kT 㽬%Hat(! k'DQ( yأSCS|VVAFq@qa%k?"05a$"/3U&ቯ;Rܲ3<sDD2* nCݟ7ne9t)[ɼ)v3m =z?m,G3kҙ엑,W{3OWMޜn6bP ?R_} =3wԔ5 M\n܈I#)-n]*N@ង0{Jm .ǵF|!!1DKAs7blwR($w%Vij:UJwW CnF &ur_cN`SfMDn к ԣWf!h/܊=Hpns3$$)fXJϱi@볶V]H߀ ^_ 1 1"{ xt&0"N{!w>`sq=@rkSC݉(:L]eVk5!b1rQqފ7 )ݖcڇ7;&+jE{hoƷ SoG!E{d珐"?_a6ǞEvgs[ZE1mf#MH8Pw*/m("Mh26+dυw6im4"xiya3f~ {Ia`P)/lʖ޾L+߼mya#/>qu-'} V <**q4d3QD\\ۿ٫?.kt3ʊ;tmhG=|zw=ҢEG,ZYiף<j/D' #iEwDrxznX#O_p6ryG+[u*v3? ە)A1$]D4ċgJ84:$({-P~Fw-$BIDAH~}212QAwac#AA&sun!uwaO!HIbyϼd0~$xz:/N>R{찯ү&3\vC8‚cJ\0aC7t&aw?`!F-;]AZ$: BиK-A"de5yktdY Ca #h|&R;'U SƭH(Gws sl_zbrcQTT6lhR~ğQ\p,Cj"mH[wb4MvctL~mL\C|= P[r[˧[n}9ڊpInBM(Mr$#@n}>;)"3كlO#!ӛ&7|Et_Cm"msWٸwrdQcaOB2Ios7d}b( l=s"v=E@|YS=߀ұ=wt&t&[2{ػ嫁v _آçTݿmʁFm#慍-Ȃ7Ɩz޲,?fĴ1ZOz+fNTop"OaAאWN_s{-+oY}]qoq"1Pgo`&7됐U lj˶a/}Yt eX%8gЌV ^FcP~dwI FH4KptJ9LH2~N{<btum X_B 3N,E0!  )*mƮAˆLxܲ䥽̶ؚNH҆%m݈/ %CbpgڜL#X%  >ǞqГHM(1# ?!X/~{EJb׬E[lsfݗ-\1{m'dJ0wLG3~kiә>v[Uj},BcASwKܱ~3Wto~O_{ScH0BT?ޗ=l! 275=p,yV~(Hi'P! ZdHg45ݓdg# әrLR|ywzD_kE fV_p[}MBlo5kإvxakЊՅ#&Z0yx3j";Yo~C8BzIDv5~8b~tlPRwoQt!Ɩډhx__jnL$(yGҙ@%|q֩.@f1^-XF,B*"p o qHo\(p)Ng]6{tH)imͷ^9r*9poK/ZG(Gmhjj3'?2w9|+n96Y_Ӝ49lF`Jk fĩg:_kingKk 7`3a(&_4Ngw"~ penBF._F6Cd:#!*LJpN&H[8(Ϟw"!ՁHV'vTR/!R-D\D4HϷ!J5w#+Ttab&m&xwW<AF U>O!tn )@4{9t)(~'q=Ak`_BM>ޑ \ or9/0!(Z8)6!g BwRFFE_]_Ӽ#Ɩ7b7GqLn4:{%'zd8'oC{`,GOc AotkE7aBT:-tW.f- %׈k:ji/#^h#@h Yr\݊4T^љՀœ |iڪCXlܫv =1F Ù!vO7.,T%`s1j?FLl. \nq_Tбl{#F~3{g>gs~n:dߊf۽&zAhn}]$N@{m.o[4ϡ}i$({SCݥ6o=$rX݈lL8͉z[g5n5ҰvX"7ÿC?kje,'׽rknRۿ4g{{OZSCr*JKWRn${,/R5nؓV֋[}M@}Ms}: 5wlM u75ݕY/E țHh@ ? Wy95H`ˁ9DGwFUg I@ =ez);D򂻞-p'=o_߱#]ܥkTwI %$\w^LXBr(bg}4 q̈%Sd̻M'^ar+vDFq{X_mlb]HȻw}ntR_>d\X[~R8G{Ķ޶h RBg}wDň~)!hjˏy/:'w3"v5yI}M5͏$0aľ\fT("*-ErucK+il`#xĤh?-eh:^E.-ڡ$u9p߉ YRzAKKl޳t-(yhp7"n7g-#9KOJ&:e=o4HisY!B?ΌE7֗Ո?dc~ܞu b3>#&"Xӣ!k݇1>SOpsܗ#m~7٘W" t7F!._~ܹRݓ|nuhgmi~o 9ڈ{n3!A\y)m Cnda=-K޹>m9e(RTS0ZB{knUZ91ߢěo\t&;P`[}Mf4Y]TWxGК?>piqD>&743m/֢).nl|y#9 YVܱp~cKo=vS2᭾yvi@IaEñ܈NM)G3]dM`DS 5˭I')FtI rw@TVJl:tǭK=QT*KE.;]pe(& OQh<R=.bb(H2=y)m/-^%fkjp=? a[BZn/?%%^O$$(ϠLqh/m; u3)/BVp]ڜ#>_igXID%ģnu'~EQmHi6ZɝHiymR [6ڮXFB ɞ,:hz ޻r!tmMry:z(?a}C1I agݿ`5Z4m7 K{; BkhCe1 #;!h\[WF(ZbP!W= 3O7Z!peDC䪐# FԽcB%]6#U$XHpCp}/itt#+oH|]TD!u6{yѾ}ev:[$@7ٞrc:ݗk#s0|ƴ~έbF7w{At:'=t&;)P> /-h?dO ꖽuoT]|ʘyc;;&XPSU^Ƀsǯ=tIZI^*ڛ[j7q,H]\\_m|^=E=S/-z'p1Շ}2_F4=WR' -TskߓM1[h^ΉD"$o8uNWoG{-v6o!X=V4HTxO=YDZߋ軑q=Hyz.?U{~!n/>TWt&7*FU2 XiZʪxr`|اYdKMሷuzƖck;;┃Semtl+*)󪕣D%e8Qm,X||5#\~ވ!K{Vnc0x!WwX@p痗lNA‹"P x0~'Pg+b#aD\< tߏBʮ]6GD$x"]Iиu}~ 4pmCִو/nlj[hsx r#"|ieH l.,NQX fӍў6+m#$rϼ G,}~!@21lN/DL]|1+ \bDZ1~/֑]|9'yִ&{jTӗB2g^˭֠#X,"N8 Dm C-ホ'' 7zBˁaLM uoHt&{:b>uLF YuA%yj1i#ǎZ_oч~\F'}Z#do_Qvɚ' |ryI{i*_;]?܅}o#fMzDIAG!j/`6D#{!R ]}H nB/!Ay]&8fScX`pc^3yr%ux9X#ztƓ@pF4=` QbDǽ1#DB{Xx7RM .;Ȭh`D { eR6.O}e%: J{=8Xm5)n  ^ś 't 裓G+).#@ep 5m8v.f"lL/|oT ?0Q7ژ`ۊ䁥5˕EH@־+翰3@Ո ^_5+nuc+6}0Xk{ը(/ikl>]C>6t cGC}jEIlF=SJB H\f{PZ?3YAM uϥ3ٯNi@@ ߱b 4G3tT!E ܂jzVlPT: * &Ĕ# Hx b^0p.$ mE\3<BP=d㜏/~ ӊS~Vٜ4)[mmys%B4!Am !|͉[v[P:$m>OPF7/qj|W X5F^-3^d)_殽]qc>z~́ξ7ݯ4w5.V4hlM:*Ɏ!0b opw{QK%m6UKŽ}u5蔳6mBc%rK*FJg0Pq/$K!t,ETPFE;e}sqF D-$XoeЯs.0?^-c񻝈x-<~Q5=o8޺_ǬBP#:n[l|"\yNv(Cw;칓g{ PRm//(dsXtc*t3 ď<O\FcU!wa9K_y񶘐$_w$؛lV~gjlA6ī75dF|m R;oj'~V4_n،C{ ˧LYo %H&>o=l߿f֛t&[O>'xezP_H헑qk6qK!<3;[|y%5L1G݃쇲A"T΁m`.XVxL[H8djji5BւXD|Dn7'"]N[.BLU, 6F#+}Y[ADÓz`F:} ;3k^\B҃&h)b^|.ADr -!M鷁u^v5A@܏Qw?8f$$?ሐzGVk] }CwxĮA`xw k(о?A| Akf3*!1i)և9Zc un'0|=Z+uyjAL.#n-_B{ͅH׍ᶾ,]ceod?jssy*= &=}u6k1zE:i Qrvm==t&{;z[.?Y1ġUTMjl]y}Mݯm[7ۛW ?eﷶ|{ᖚ/47Ԟ&hE@ {[jw߂%/gҝWU^__|.QOXm+xz'pG:*ebs =+Z!WIwr w*A@ %цX%/nͻ'o#'"Xȼn;X^'\0ouW"ZKǑg˅qB[`OX1j|{āvVBƾí_R9M<`:NC@ bn{Z' liw[o{AㅂI$s>̞ɲDQFb@GଘOHTH)A {O(DJ#{ɱMnC< x[Z 0dƲ>2|U%ۆ% 1E;[ E=8 Yo4qn@cK=A=}Gnitz򾒸u(4/ŒV5ԎF{h?ε@at^Ukjk|-|3ۮ`#0ҋ2#ïr="zswE(fj<1 DxJ{JPCEQm;H݄lj{%*{9Sz/"2`c .gj@i6T'rG哄:S F(3YwYsu\:}Z$ƟAC9g5m͏6U5o8}!߂ሁ݁vPfmc}9 1$g lCx4r{i1)w+I S鄄{߾^ŽHY*{F]w$:;CprZBOA{;pv:}ؿDdBGjPYƖ ,b`DD@b:'-S>vXEteh" ?6n/ܰϑ8qsXro8m3ԾGp:=ѧzԱH)B$$" ܏ioGl݄b9'd :KM,}yפ:K g.#QKȬ1%HH9ApAPBZԯ@kv;=Rcu !u DVzR@j$A8Yd"Xo@tt"RFp!`GʞJG IŗW `GZˬw DdZn}s!Ano̷Kn}MKV}MsǍe/*k}''x|\9 B%A7Ծ)ѹQ"A) ^3!ȃ?5ۛҙlA$ϻn=?Dq̴!r!: [B׉4B'!by"Vv08ٳ1c01uHs8"GZH#M uuL(wtB*-6c=H؍m 2>6_"a&9mH> "Y< '#V[yn6)9إ|)mOC} F!?t&{!"I 8j>[zlIoxn/!saXېRvTwSkG{~'v{ӈ Ety_(xrdCx:2yw7vN=AN'X!o ONQ]ŇSe9Obd埍@>!Y<;~\W_lF5 ^>s֯(L>e6^wԭYKmHqv=2:5H&P(H:MEwׯڒCrGWܼ[-Qc碽{3ʗ{FSC];u|E H>-yVt6Ɩq[G>JUK:4;G9~t& t&n|_bZjj{>_CDٳl ¸1 ?IȤ4 O )$ASH!m{"}H.@#}Dx?f׎Og,{#MII틘m'"'!,{^{0չt&{45Zs4@(b>_@ k^Ci7tW!S\-HOAd>wM4p9!&Kg29UU!p9OLvh(b:To>M+@֯cZv޻.XgўKpYfgstX|.%DNEʁb$|U!`f>ֳ=Z{%W< h'u@W>+mvfB2&H,"8)Kl."^'%M F S!\[8'U XT5<$>cozЋX#㵪 fSNT)I"hG9KyZunE87Dc=k}L M uIѢh=lsw{ںE}o8v,F{ īX^_Ӽ=Q߹[=[cKsJg~7˂?|Fr?\Ht2o%( ՇOꄯ{N+q{{qu& d 5>:]i!! Ĭ~gq b5t,~\Ry ލ&0|%pJHj Z{')X6i}?iޞd3Ve!}lA gMCBL['9=  QD 5 H66m@hXjl- wHwpלS>h+  &oIB=%6'Ap[hϞn}#LFFc XPކT}w=c6|% hz1EM uG#Pt&t&; Bx|_SCV9."CgsL)w =t"ʮ٧ F=9P\3 NcK}Ga7WNv]ҙ}"1.nj; ӵLd {`aW9nm.ͻKʯ- V8MqWB'o>}kۈ\/q!c v`s!6YE0&$ENU.o!=ֵmjk; )닐۾ >=Op-9쾍/"4Y!1)\sGe?R~^ނx^5 8ڐۄ8Gl}qH;ۑR#KPzZKAhC<|w75툻jj0^)Vv׹ :QAllZEH]䔿7Nol55M u=܌C{t)5\{s.k⢂dAjC00k!q!O-?rn:-gz[ǻd?X70t㪣d1o[<(5@=kAd!;Ǡ\""B3^a?# ވ BV2B|ʚ8("M `_EDR$zif"d+lMbs2ց@I`ڏ$hiٛl}_ED8Yr.% 0\jkׂ#[F:eN Ov& c;1xȂ>`USCt&'F"-bb:#S8@# 7"&kƧ}blu6S<8znuDϬ[3 -JL1RED4 B:o VxVnD5b=#R]%qQf"e|=]tGlnbD+-RȯG d5RxAbO0rep%! `w LH^h[m&x<(D߿1ˑ̐B@Eqԝ*FH(!QڸSV9ݶ5t36M*1ɍUЕJHef|y26BL{xD#ϗ6(]H8 ) 0e5ԞY눘|vrҮi=Q^=L6Wmvu)Xy䵹5OxCron ߱d‘UKXmM u׽}~-mWfvij뢁U'^Mk3NrkaXAH2 U{ƿ'9=L"bDB=`SJА "D:DGس,t"G Q\cA,yx+GM uҙ4 1}P]55ԭIg `3G[_n X ۈySHsC刉~1PTZ߶ !6e"&Y &Uh7l]G? d^. w $IѢR+4Ǎ-3"m짟=qLll&iՅ[7ҙlŇ Ap(y[g<`p:!AS TvOPb'GZ bLxG,˻.BJWa-B] A4d,d$GA=Rz%mEkxr.C4;Cl^s\(_>xDk W#xT 0Ռ[=K+0A3Rq7@ҢD\A]$d^~yUuscvv,Vۜn9lN@@IѢ6wƖi☣Zv4D?Q$dMh~1Ki^W_8.Ëҵ[ zڦVto,8:c u^T1| 2m$Hg\BZon-bP;ZA)CQʐ;d ! H>I"!:q]:zAxP/oDP V#K( B!^>|i0ED/F YNDD9t&GmLs(^&c}F̫ s/0\'L/55MggFĜW\bkّLgk2Y&lJ# Zpsr:֫gqbMhָ66w 1W"x ^(9Mמ6 Ϭ!T~٭~3Yfz> Y\FY0h Cn{˧`dீ[^UfoM ut&wVLrYb?5V#("cX}^$~ hgټxY</yx?U}bN}̣sRTޓ>r gR]禱.]qԘh\qa39.X/nLv?d%X#H} @2D:DlF9HP׀{(nD쭈-E֣[=YBڐ Z ٳC4ASCL6FZocZdϹq`۬Gr %Q(b_Ĩ!PWdߊ4e)6?!StM u&X,5hj2x:s!i6.Ah_"1P+×3G=]f b-ẅ́y]G#4۰7{e~؀[Ё9!g@Lw]7floC{wom@v[ס6HVb9z]/6w vmn@4wI[f _iW߱zn[{߷iݿ>Ղ,AOu﫦nYX~mhwUWLv#YhO._l/ZnVȪ%vYYkccKxgΦq6;嶛.;ndgW:FV->0յ3/^jПGWF箖i[H)ŷ3܍δc[ k~ܭ--_ήdp7jlm!n].de˅'H ؊[ܪ'm,3/:\j}uƛ"X<>)R>F@swmyqUSí44T"IrA; <{=w!>Wֻ"z?{'WUd齓d)DqQD.uDD, (JQd41 Cв!@BHmKsN΢ E$^yegsO| "y[[k6z?75?2_ aۇu̙~Z%8>` 1b й䙽ĬHAYKyQ5Ye1{ .iNL]?V5`Ϯο]q=ԾU 7\Ȕ jNi# ^r>Tl01A 8f𼚾t^okPim`!EdX"5N ߼(7 'rv0"0#+ec4<J.CZˑXD\,~J- 1]!B;%ce+}iF!L_"ּr} FF#6D@ifxX"K`? گ<\aVx4f7d4S,,/\2kP 2qlYGPKn&D'!Fk~h3όպl5{Ye:3c7*ډ}yebR!Xj- yS\gS;۠͸cf ש46V`=<6`|*r )F{ڌ/}x %f7YpVia@&<`構amϻ[9K5~3'G{PЀ[~ܟ;Aڹ޶8ϸHh 3X > ىOR KCSI2.(.ؚWR\Y<2 MW|dE<=;?i~w%RPc1jiDSFQtn%@b3MVgc^UZXlBl>Q6us YmD3V:6 u_.ј c-j61da @2_?pneYADB(ˈxA&°gw{qH r#@"4d.|>F#`DK-` !Z<A2J]PJA}p fp#RTrt[7}?[cB@j1!cVo6󦟃q!S-G<º[m[Ĭ_gٷ#xM6)3Ț=`g?i1;s;:ʙšB?ًh{bZּ\G:4[/ƥ}z?h~;}Y$i bv(&QNWk`ٺIkWmܭ ɫD*??l;2gN%R5mGe7vADa!"{#1ւ~ Cqfz== Ո{X/4Y&ţz/1׾ߖ^/713+JwKK˨ǯV߬9轰amIF`\AwC A8wѧ Muߩmk54խ&D: "i$,}T_.PAj IDAT<+[u7Z`@Ӌ Zem|C|oFLXG1GrY 淢w DcFV.6D+ZxE&"թ]`i.V),@SH|?Ǻ@xRf'b$wJ}6^ȂL 6"OY Z,v[pV`c\PBL#lcd31kfǯ1Ϙf]]-N!]e}{+񢜙JCit8W煺{AH#v]yۺCNG@0eƎkJȧq>lU"فbK#m|Yօ"q,*Whk,n \D*SWkv-GTP9ɼ9m Mu ?&}P׶O3~gx[+C_:pol^?EHF͇=#I~[,c,g "sp@uYf\jFD\k#B{%xADG%D7"-&NY*P\n)oweͻMf `YL!  #q}AҺװ؃pR?MMNV#X`d˶- vR_`Z,T5sbS 4+{7휬u.Լd"7kcm!0b!0Z޵"0RT֥Țˀ'cԮϖG&#C-T迯=z6z'&.;"87N੆;ꆾS? Mu8V_h-W9\ԟM`k0ݽxӍθa]0 )BnRVjhihۋncEY=-Hy6u}+ᄇ}ȋt[CSTHƣI8ucü`;s%R!$_ב-Uqk,;/틔?O#z'' JH` ުLa-Nm} }۠}ֲqmVD}F3WS̴fp>kW*Z@okxYCtvD[pɏpY}r}"¬듵7ۀ\u˹#h7  }D1kCMe{)#: KmUe+Y LET3}̺t؇HXe6V2Zf\g֖ѡ;z؊ w-Eeh;]#Z2]}E/͜ T\ٸ7pE}mco}m?kjQ6~ـ[XX"UK.͖Z9{ bG^L|יtNeխOz?VOsg_AH[ٜA~8JO%F9D8v: N1$tsx пFB ` h@i "12Ȓd KG^ĸb5!c3 ]8 Wx< %5!ڎAsxwqڦDLΌ%RExd<1@[3XLmz7j%"Vh@xAGYP>Ԙn$[ wG֝H+2bZr<[X"Uc32ެy7n<KELja_B@o!Ʋ?];2D'V=4rw4ڶ/"fV, 9s2:Vs5埻iNq^z[`U0g.%ʊ+¹Y0ԅWAUƁs׃&m!(<_"%x `nзkpl]׀;ׄ*ҭq|:-hǔ6}YE^yf=_D<>~jr$WCJ{qr[c*_1`R،sf >=7c8 )r0}] H2]ngS*_͟Ё\_kXt2 V54 ~5utjӫˣ>{~CS}Q>eVt`wQ_ʘw\| >0(ܨ`+ZQ 2mhOb4j?ǀεo콷29p?$u˖͔Y'ѣbh$p?o51͍B t6r}[UH C7}[Ei#.wzE8eZ?!w@4caE1TĀ>AMm6=|", >Eqs͠YlM^.ac(1I'bSڽKm!7;lFBx l!@;ج_1t$8dXDXk.?ˑEGlso˲ܟhock4KaC+ZTtşko>З䅁epl6CF4PA[',i~SЂK<%ˀ}%y% MuG6a?Y8 "U饦-x_V}H"bEf)":Kbϐu-Do^2"sK\e/#Ar". bvh!@3(f2lf8cn3'(*BBtYr$til-d&G#Bh-n3f"F2de??[^A[/4d.᭵|01UMpqkE w>@mV!RY7U}o}5]%O8Mߺ7cR̀ߜ>v3'WA <{󏛿:kmݑՌuzG"Ct AMn4ѝG>c}^:=E&25h5V7zY֊ԅS쇄Ո&,\eQbՈ8Ox'<+`9 eZ-3سfOD [f-<}1ABJ^(A""Eo"LJRjs{/,CO99\c>?.{Yo,(̯Iۗe[m dɢ}q_o~ޛo_ubO{w܁% 6oyb}mck ĵ[&nh8*Oێb㙞-LѰ>Z,*C[Hx ϧ"? X"u+Ƈ ^zL!ⓏS 1n @ @~^Y.8bZEH݊73[?!&}Rjh#bT8-"D{"Yy_l0mYr.pIpO,: {Ÿ5a.b)f?^@l]H up 7:"7if]!#Fx=ڌWHV8<%RMDj73Sހf|qi}!Kef}|dHXҌHpnߗ_If @(V K`Yʬ#@ٓͽO 7ƽ(`O@Z_cWĻ`;} nhʖ0mLH[OEɚU7W:=n{{<~uu/߼=^A ]{vlD[\sPY~0q<ڷň^8:MpӚf_~/|-PR u/xsYϽV7lvhwNBm<܌Vag-s . \lLgY@&6#As Syk,6$ۤe !K^<צm/[agp'[ZUnDJ5iֳ ѭntmlLl~.?ژlYyMMp̚7}IZ_ՈnDs G nAoA>g&2-Hai-KA<  gqǺA3w5݈W ebFso1R2$IնPbO'_[/pm:Spf =s.} +_ZͲ%T\`D&R6#+9ʊX,PRؼq) |emZ_x#璣zxy6~]AU;H}Az%"Zg+5bT""pe-9 "8AG!PЂևb+&Dt@E.C[pw,ے_D*>\EHW@DdaX4#+r3V?iV#+Ͱ6Ybh= pb37\Ӏ4[ߧ JW"b>|1  PbF@La/vn1@ Hc]'">L7.H3i6ϷȦg·3~,V2|6muې[|"ǡ3f-7#~__E mY-ZO~.`bi=\5 <8W>\B3;܀S&QkۦQ:}@aAkAϿ薉Ú<{V|Ϩm|x^3}uZWx)R_TMDG;Z>tƸsK=[~CS8Ӟǽ`60twYV7|[~a~WR6o.3# x=k[xv%RdR|e]Z[p{+oͲgp|#aN*ѯcH.4dy0yk}O϶.\tG&XQ5񞙦Vӏuӛ2>ry<{dR`"1kZs{E3^^d{P>HތRjxf[!HaCrn{K8GQ||@KEDKQLg'RV#ǣ7c#!:.Zڌ9ul JF!~^FI}q2]gxGI5}XN.%RҴ /*@ ~aP` ]4/]<xĶˆ3"gSw H74!e"Yt?k5i!vH2}_~/Gh"Oƣ_%Rc8"-^H{6K+ʚFl}~ Q2=[g{ r 0<$\7BDwG](SHvYKAw J8?b/ ӈpb\+enqD?\5 "nk@̘ 6. h6HcZ-ڟ h365! -~1S!}D{blOn d=*A+@қHydz{w]LlkP|MVhZq1?cֳ1MF2X j|\`] @Ri`)xpEgbv&s>a~2C;]kh+D{tv%z2_w.}dnW_۸m~ r:ڟ wc}%C>]{mw$U_x;%^\ȍA'ݽ2^6fB@k&vtg?"l4uCͻ`a_pp܎t)u;t%R# DV%9YBzkAf@uٳC %}7O%@k}i?"TKk^K^sFM$b x">j-W# mVd[2h#+ڧuO6 C  }¹4|'C>m~<Xe8SR2#ЂKg_+f_nxJ$7g8`9f]xڞNEq&+.3m5kE@(\{>=j[cvDڀJ>i;mkh+B:w8b5`|}ŁmkL=MW:2feE Yl02 tA}L $kPҚ{˚=+.4ɘnm.࿴"\z׷3CAb JZr IDAT1mnBq7" 刐!F.O08d="tKIqt@y幛,q/3e]abDିO86]p\A=ArG&qY𤙛*& x4msSu_/6{qY7̳vCf L|+A̼bk =X;kJ#,. ie" `b މ4!^C{a,ZϪ99 11j6"mو W rw=i[-ThwUY߃ηdb<F C=-Sw/R`T?n?A@r xwkouϜ1 =h,\1H{ PT/tmŃ. ԮoZ>;æLu?Wwwj&arK>X&Oi6T!]7-+,ݶ Ee p8#`T,:)43лm ~!A$xOIƣH(_H@e@0" `=zp&ūѹR\jf,JFcg\߄(_abUnmtS-Po9zFS5phQAU2jWVtOv+:{w[/\rC4ɱDjj,:ɘǁV{# s]"H%R2$~i"eppǹ[4k si"t#wdيm3oS3!n F,ll 0 W#y"Ɲe"x#mnflK?]lYaGs<{2@ ^dݱjŊ#}ЭWug_$'QlCƸC1 mfwW[!k=3S͚"q![X01!i Fp3gLdj]>k 3xSxѬ>Wⴄx&˰G2|4>شimr "Lf7l6h `fU\5'Y\\X^- ~W 6לP=mغmU7t,6"k@$<}8͹<Ѿy:ߵpu;R&st46ewbY`I (YCJ3~b2SDZX",*K1??hݺ)V[HП$%fmf^ڦHq\rP\|` <6zkp1zGƺmBtb?|vT2HYKcAD:ez9TAiٖjC>O"bnh yOC-ҌO!R*mF׺ Duﳐi~K ::#t X ʌs Dy7kyYHa/=nB Wkb^>ܮF ۚi *G("><֔Syf(k>i6sA$ҩ65g.ٞtѪxȀ]L&Uv$źJ=+MZ~l_WO3MovPiO୅rIƣx4HJ ڀ>ԙ!u?!@X3fDjH@YpEa@֟Z67&";p{)6,mb!~hYlCYfٖ܀{7ӐlpqܟGedY9HІՖ_RQK~lGGCfosY BS2o0]ϣtF/Z݄4!N3#̞^d7敯D=YR.ĴAߞ9AE"ύ98 ƾ6sũ載s'!x c0.f\pe2g_=b C.2]]}JCALL{i9{ m<1Y_ۘknZut`_>x=soێom,6 ڻy)meaD<"Zp}4*B.cbG!}%׀vlm]ͷ >.`غ橡 rYpEgfJVd݁ %KcyəklWg:߻tK< dڬ6VkeLce̵as߅h8 k)M\f!fKT Hӟ[8\MPmR~36 粹)7!PwA&$+1ϼ~!j&E|^j.-+\A.(r.d-wfgV-k vdo`,%h_X"YdF@/4I!8 CVmY賑5<Yg҈BD ¥l,H@o Rl*qE-/D?)lבJ5:Dmƒ=MĤ@Ӑ7)! G7b% wX";ո 1h_fp{{o W# Vs8ެM.^jˑ~of/ iEh3H<tfFں8bcU/fsD˼1ޒMHЃJv sm_i kɯY̼O0k # 8 Mo9dv|G#QKpyjxL_i'oi[zz~G}6H /݁CX"u=H-c{O)Uj1[zUT7~uڻ+'zF̸+mQ_Tw%uG%Sl `_ol,hZ0qmO!ہ22b+)֍.jD{gUtV"zRx0XWU zgk!mA{60.[a& -?aqXE38謹蝸y \dfW#>a33,`u6u2v!Г3ϵu 8Ww:SJw`(.ATy~[Y!<\ñיXY^B"^s"DlґfO#PTK@"8 gќjZھӽ謭B@GG̚GO:Z^l.I#yC*Tm5m΁#6=J [jzDx,k7M,^xZy=gke\1IX"e Gy/DDd6#fk>3pb`癹͜-s6p rU9f/|m=Zб:^)l7af,c\mY`%pk gy[a~Яsj'MGg sѹkG)#@P|WkY@o}mwbԁnn"Ȱ\>pYis ya5}x8sY& XtWew|51Y Mu{Dgf#0JDl_ 86SF!DώDyhZ2\KiS'}Dq3ju2F8ěf|[\f\5lֹ?ٺWfƻ̵1c"U-f,LS2؁̭xٳ eTlG SqV͚hYߠ~~^k~[gM[w߀t/FIT9KAkWށ(;ÑL1 } Lx{2cYf'= )6 0Z[լlY[kӲk; KƣMU)'mǴX"{6ो m wN)ԗ).Ng ',_(/ڐG-yK9A&=>u[{s~w?.j}άxkg#NX"%R@,ڭ;ηaGDb6! #mZl7\`+%$,PoQHƣ%ѡ B}Zl&!-A7b|1DjR/[f~5Ḇ כ~Շ*Cj- |Cf7pُ.Ή%ROuA bYgd; bVa3|ikp9AOBZcO[Mg18PSb )6탴"x5VW4DP\\H`xЬ613ߣX3[Ϥ?6f-!8,8 ehh՘ ,! .M;ў֠NGMa*sUKl)Y= 湭mC4&vXkUBD_<QO(G4f-E>O ty-?X`}6[*l|X\ LuHٸB+>ОzlgykqED|i^ | YBʨ_ ^ ğ_BJ֟VSb#anmYWZ`ы}Q+ݱ \4o[Kk _ɬ]KB_K޶v'f[2ˋ64gsZ4燷 +)Ț5Yf˔ef,{bLZS/$W>~n"5w] Muq5osſ{ȪWƮYr},jf.8F%' lҋGez#+B|g.}s}DC8Jt!g[y+]@Uuˋ׷4ZCtO]αKݿHmv8.H+GαuK>K6Hƣ/OX MH >UcpgݵoGꞅLAcE6ח.^L#(<']ezE>ƛF)-P<1 d sB]a7g"pyN#qݕ%^:_f=u[|ij0Od1c T!P]}b~\G{F^b֯uY;e7rJȣĻT*`|맙|e]~fZ%C8wa+tb#3EI{v;a؋u3#)w-sǭ4zy~mhk|y(~W  BycUwm)GwZ]c° !4V L="!~!B KU;iL6 $x4n4:?%R C79iEɈ fnQ4ҕf [A;n. ˑO{Bs&¸ bp\0ϋbT=zM^: :@%3?oF!- F"BqҐ݈,8t"x0$Ai!!&h5H eغ~K~wňv]gx$8PHEt5b o NyfUόZ'"8'8̸?ld[CS] GyGg׺кߏR\T7q~~rg~sѠpoH1a>K~W9/]p{kǁcT}޽kйǔs=sSwEo:)[V%K^悢5H]*^ T7rY2}yHK!}3.nɏ%Rc :7>Zr{B[G ^Dw&SNQb=6Qu\)QZ-eKsP.f445_Z2gP/\ 􋆧͘ӈ4}an֪e-(ݸ8Ypk .|glϡe^Y@f1k4o͖]΂t?KnofޘYFg=Fy kLmH).z5݈G Qf=3[HWE0D:>uʂ+xpO:<1Tgo +ںK瀶ޢyHogaw/0o?_ 0^Ih/JYe/y~ -ҁmkʻ#zv/-\Z_qOlNmMr7@,: Tn%RSX"?z)\ٳ1ue 09ƾ[p0)eܰL6+妟}JZ_)\_W"W6yvjAnE.BYNDoEs?<bJ < y?r˄37iZ\Ɂ%go#gֵ?]lCFaܳ 6#j?~%pf_Kk~:P3G*m]g9& S *݂ tZ.4ts5WrY<\ +|=80'Zgπ~Ac|$08Ss+# ,AxtNx py,t! CH(x 7"C%[xfvb# $QkM+i0%R:("ˑp !D>ԉl[tfn@m@\;+ַqZݐM[ʩ\43: >X"z fL?CS\4V(|ȌnFڳdצ n3E.aA,%RG!̚bƴYM[M&\Йf"zrG+Eۂ3ڌ6Z71bdQ[)E 1]qŴ iH7seo56zg!`$Hnr]2L<mfxV11)J,MwUl/ͦJH -KP *(8߯2 R2TȆH ! lnvuϹ~H%ќk_;{ssG,(i=! )ܫO=?~"%n0~`o+=#mˆZojk¹ML÷xEg wx!NA {ҋON}ލ9 )Q4 70u]:Sp-3o? kG{d02laW`:z׏@r˻n-%?-h; }U[]T])f6_}{r]ezJ-/_J Ŧ~gb:QքHo$F6+m!=ΎwDm\F";-Jv?"vkiPf ͪ݇ZFB{? ݿb*ahXFDj0RUeE d<5HU!e_2K\/_ X+? żk}"_g;x@+av'<&gR16pfJB 1אR8?ĬGy_5Uغf 3T2#5$^+ؽ/'*R𿀼A<y~,pdU6X(|Q>$5'Pڵ6nda|Dk)DUt J3)=g輯={ !LGhBq9ZSPTQ|夂6WJNrZ fbmuz}kzg_7imբ;HɫB{/ [ڪf#^i \1j'oI# g^sOp}~Bͅhmn*z? D[?ّpq,ER;8y]ykR~>2LDs ); 2T{,ڈ0}^hN|a7C{#ri"Bo@-׽.V|3΃k{Uk2EKQd+lѴP-]O 5tg n]hmA||.h} T[]uu<؇FTLBjˁew|esۙhXD?%fz|^ dwɮ@gtMƚO#ExAr7910WX(|19%D d-*b}b~ݻо 6T# f.9`'Άs߻Rcpa]+ _lDLhlGB mܣЀ~߅m.7"$P\g A@y <,dk<FH=icud{{p*' =eh^`c F&%f*& 7,wwo|i?}6'ٽm,.NY]>x+!Aۃl ?@^%l~.~򢾀|55\ȻG2M} r sL>4?Lhu&W֎ޱHȔm)k,EC]T|/Q9W.FWފ=/@y.K(&!cre?*|.Y'Ў+rDm\"^:}=ws~Ţ kV l"Ak٘_~xUSWې~By_,ɹ66M灛 z_()|>Qgn|Ct1H9ɷH{_dtc  _dZDcl," _r}f}o2{4: E;uKo* !ݨż Y{; B~"o\i&"/S1UGN(,]Bb×ew\Ą6FfmxaKj=/ǠD]Bϸg_@v\m^rHx-GJ>Ht 8 HXasykظF!C A>b5~Ϸ߃UF4v6O+-]l3x+- #Er{\՗hycßnw9KhEwH:` Q̸r67W!+`?eHXw5#%; Џ"rGDL =]NQ yŊ0M#%e"XB{(dwG&c75\NUH?j4&<D|d0f|>Wx+!\>6l_&kc̈́BжϜ`ڊ}؅@+W,El{y F+gr Uε J#qHF&6WQ|ր pBωv{Oދ 8|N4 q#O.E;khʡ(~+e$"pbWu\dW}4[Ks}$V)d<}VakR2%RXy2].D LnHƣ)D"iۅHwcpA$z/Zpu8;'vG7'ܻ|b"/Gq_3x18OޗC@eZz-UE 1[ 13m\  ނa={Vd[\ u .7u )[K6vHhnBBK]jnqJӴ>sЏ:îy Y L#й1tI?OPG;l /x)HRz)NoDaqms{LZ܊bCv'ںT!c' s[]k{72ý`kŧt{%Zyr^= 4S\9 e2b1LW8. 2sEqΣezoXW6e+ƭ{/9 #{| רA]֢X^P3ۏI8(??Qmu};8WR2%RxtMh lkD|<3rY{o <~ڋQ@}?/\q4ȃO 'rkwWEq9|U;YC~pAL/F6$LFm>㻈?uH9O; y|C1Ht#0{ܵ㭦50,DL"<<[m[Fizu"+Lx ˇݷɲ+zѺFzX>2}a2۪9EGm WN2͙%|GhALe2 C ^T|'`ۺ9ٟxG[0}ז`N.ʿɛk=ynϾױvYhoe\֝?'/ҕ͒  r}sJYa&CҵssQ[Λәȓf}|Ƣ=̖-?X"luQh{gѯHphF4 t^+eaWu}Cl-\_V'P>-xEbT Ay0zϯb9,_^l钢;&!dxd|oǽe7;/!sHlƷ>p ] 4fIצPUA `#݈ϗXJ!i뜧'mc)p||sFml5/q"6ι=tj 2Eah*܌Uz EJH),vӑfuQR'@$Ԟ?jNƽdu sSPĤP 01">DPoP$g>ی jD 0mL-v =7gc,ABJILw+Mk"@ AbP_س,FּۀJcp[m7$,q{OzhpCeX";"w!!؜DQby8yZw0.!o>1jOk mhߖn׀Gu* Bc{|5)% ;wX(\]CM)2Y%PHD]U{L~^bv!Eh>9HAEzk|dBɰ{J!S ^ BxDdP/ނPQDP&br\qOiJ1*V I?i<|썴) mEƨ0o,FHdw,03.pyX)Fy'mu8gXh_ 9ɼX}fW<*಼Hۋz-꿾iL:^=tlkD]!=g+6ƟU6|H9g̨]is|;ٞl`_gѩsJxTsc#P^wGY77 7jЫO 7{=N#cqd 4 l)lP$.;] 3 cmLd$쉘L.5WQf֡m x2+0fNGBY@1{JپA%}֞ۅ m7l@1KS|Ծ; A7az𐋐?Ν@wɪkZ:"'>><۵oyl + )"0ֱoљ:ƾ̉i$ \v(l~֢CC6<#2߯"Za|!H9%v{y܏ $ +6W.a(RȿBy]tT=V|n/ /evH.Gs3[o͘d<ya#y{xG/zjsDj(K^+o(]'G6RZ9T\Գ;ks-\fҥjA J+wўr4`oG~iRQA˵Z&O[s9#]eX-MߒE]/[2ЪH$;yznŇ~W_#I垔 Ѿ>/S4(F|8X"uA2̉%Rў&26/+ 3W'+yT<!7*o!ZF7PͲeO8u2 '*^ccYg*/l7UɏndprEʅexO]t4 #_V0|E/#paށ]=2EkB;dX$?rxvC<FwƱS&ȈB.UMΡl6Mjy:dWѭMlUَJ $*mf{Fr.J`^ƧY˕tOCFuG" ^} ͆V_I쟥`%_>d<&RTDa=M.Auqa5AnL .R&*7oš"&?b!+kvrζ߇prK  |y,:(/(bÐs,!Ɯo@YVƞWxd91KQfzDkzwqtÔ:b6/ odg@B [ H ߜjYnꀠ!6O|jo2ߍ%R{!AAެ7m"mX"F.Hͳ1p9:);qnn  ZѾi^d܎Vj/EP݆X"u'#mkp+> gl6#"nOcϟfCAs[WzBlx`G.zmԂ뷖yv/O d<o~#㼧ZttymCՈrA=#9'.R{WAOqVE6ރU@mŀ}R/-O^<1Hj2"Xf!4OXiǝȨ3_K sAc2_iaj݂x+F_0@ T0?vJwBAQPYﯭ료{KG|V .q4BsD{ # %s^&ʬ>? W b\/˕/⛟xd;G+I<i\75+=g֮wd|ɤ=Xd|^;E.%]1@M,:C%Hx``E6Ø֥\acnFrj!kX/"v-f|iEV->3=Ƶs,#e{וuy " ,E B&Nƣ%RОj{_5vΞ!x4Kj}81HHƣK?9%[WJ[^%v1&nvHZ̻[rrEhXD*TV8~䂧fU" G[X"u򜇒TCY~} C#Gб>?ܱ:oZC(8G#sUdOd<%RB"T,iqv +{BLÈҪAdgWFd' }8nt={ ,]ص@qMf!(jOxY(ٳ|Ʈ߷HmD!i774o vߴǀt[qh Wo]ȃvjkS(w`:^ +*u>WC¤(KƣUhoK.{ 6!ۅʹwkݍ[!( &g7a(["5[X"UkkH{m,oFP GǼߣ(& `iq$1V}9j@`{ẑOW^WsnՐg5[˻q*x~SߺrQ+91u 5HQPߚٲ|ZiW%7;].=zxp9Jƣ ל c=Y՟@ *ė^s͂#R/> IZ3_:2ru5zeHE2l߇+X.418/#\xvޣ^v]7J;rEta6C욍H\dZ/d+pA]el @\}Fbko 6ZۭHƽhwku֮`Ql=J!jkx8.ZZݻ5lt3u(K^kGKN>qZd~uhiXeWVН)H/w?}cIxt cԳ(df$f#F48P2R"kB[G~9!;$!yخ;ҾJvݐA s? 6ֹHm'Y!|UH)@!Fl,>X6.bcv}nBazȞkF!U m !9չ6"`C6 {O+ц220 _g=$P"'Vs (וڜ_g.hRX7shK,: 86 hzf#p4P)f}E]nc\d[R'#10/qUŒܳrRGJ?xe69_ٴu6;/]$OD)ѷ/t]m ECc 9cD=<@r-2ZxHo@#(z_{NwȑAedTg+=δJwUį@n;+-ɍߩǿ3hX@\.41K 1_7d<%R.{)T@) DEAr@bB0+;b/DW By\CH1_0$`~>_bBJ˱;/kdqő3zOA%YYXBL:@tOEHvCiAIHߋB$e{jF^Xǿw2E8;~$ldH{gyQ^W"Y? ;vלߝ}}JA.ߖ.^]s3=-ñDj>v/!y C߈#޺)'P)Ƞ6)#(Tp+-*fa0檢x yoF,:xAkj>W%rX2v mt{#!cI7:/)<9:;o .uwSNP̈́!^ A&sH=:V'2u[Fg?Y*[h=H_ kѾ 7ȎMmt=!#2#yV 42W=M,Sv!YMln]k)gāO!&6bN s;s=1v(iu Jt$H5!׮)d# ԅg1ܗDVE/5wdmox[R% ̓ֈ,Y\G&;; E:RH#"^cz{j}1b$lp# ~>˷gXooX,g9~6x][? B4:H[ރr#)]:8mF!oМGӀxt[sX"@Vuܷ"k6gPتKf* ޵p}Ӟy&{Q_[obJ sx2(]d!9BD b?VO?Rpq=yimvRR Y bՈ}y3xF$oF"Cٵ-BƼwϮ}r{k|{:\(yX6Sg5vg=+MB2;XJ[Q{G)@v}/ z#vg=,o%x8Ůd/$$?#koMɾ!HgU5H^Ey `ъG hsFNF E gRv9<_\LHv}}ˆaW zbUHx~mJ i% 1,z!qWc/d<Kr$,T[C{WV,AqHP<i@ 6Hr!YJLٌAov݋0`kFJj]sY]oF[ WwPъ;2W|D0wDjx,@O vl>#3 GcTG%R-X"&k?b4=nsb;6O"@V} 9uYX"5yW erś^3۪%N>b~2}1H. w^'X"U&xt;@D3⍠|ǐxn)eo,CD ^[Άȅ.e3 -;F_QU*\/%W4a ɢm=[6. pѕ @F^w~4#@ HϾw!o%vHѺ7S\| yƢCY k IDATlgX"u/2V^"1ZlQF[pHʮȫH_]GA|$Gf!rJYԞoGr0q.x) 25}nKﮚs\o}X !;#܊HL%R"Ms5 kj0+"7y_"!ځRRIH@xtW%hpcb^(җ'`J]6&3ْz>ug#pi,/H}ݬw̃ƎEkPC9BՎ߇ p-%\v+6ǗK螆/[[ckWH#(L`$0nአwIHiY3H䙊%RD疘D?B!G™h,G X"5)HgŠ5PS?>ǣwO+>r\ PR*؊-lByGw;-]MA$Ӿ-eH۬E|>W/7Wwᇮ^ve:ߍx}ɂ ;Ȟu 0r$\񧗑\(W"/k]e$۷ >Bwa _A !_gؚGꐱE\KnB!!MUO{_ah?GE %=߈?6? Gi/$o`JͫBvB`f,_*w]hk2H퍀Ld;֎}[jDCl= Eh6#O+aF /4]3cv<&jjA)]>}$HLI!&R9, a$~ճ0Șz>.Er.d P+G6jKp/g7UtyH}DF EfkAl y1j)3s7u^_w<ԅ=:CHyӬSSӑu …CxJŇd$ڊ b!TUp |.CW=+<^c޸/#@z1jc5h8^;Ad+u# W܍}D2mLƣ|ObTKFػItɆ W>\f#xqX"u`,}Et3.Hƣ G&2:^XWmP/E H:_v?; ͵"<17QhuHB\+V28d?+vm_@dнD$yVXO+Zuυ:T!C }C} R>Y:U}qa 0eInl9EֲF$Kv'7"oN?nRTNJƣo7(|dNB/zlΏ3ҎQ2d<6b7QX˩xtd2m0;uLH|˞ `;Wc:Pd--u\ *>}ZkNV!f GbYS6>A}~6d<UHG{ʭI ?W}+>> B@~)e/jBFUl/o>>C E-co"9Yl缍t*T\CF琑p"?lʽ_ )Ӏx9[ To]E ğďzw3t.X bbxTo)β>/ÃdPH^F@Q~ԍ(ރ@xgY*lIƣIH#ybʅI`cڊC;+1bl>×ccK ב`+:r> @[ds9 #'hObAAPCc0 KXy՞3d<ڎU%RSȢ D>\:n[uvkc^bc aH(r+ k\`琌G?KDj4 u0z/ZjkPA%ѼX#1ٿ,@u 5S:J^yL;į?kwTY{=wymYBC@ɦ0i9< %l(ҮÐtܟG!Yh!#"`]뷄[dBiWwߌw6>k1-6%@7 ) -Dw{fWSU lyqeH!zc;E.^wO#CO>:9fښNG)9;yIƣz7PW {T i 28`^pehߜ Ul֟ \>owigX5" #Pf|ES컋 ĤB#_= `lBv9@|;y"wR~HlU%HEebkHpoE ޱvB >p$DyJSMwxoZ+Ao2Eo߶=>#!;H&!+($|Xg5xKvQDdkPfvVf>d p a9헽֮Hxc6X2u%OcI#Ecк}l>njxh;rEjGJ|^Ƈ? h, 2_SSB>"%V~7 y\bH߈ޫBd-FF5)_Bk.28yb0DWp(2-]b-[}Np,0pVYzXN]c6nWEHOuzo% D `e2my^G }wy)Gנ5ihd0xd͒xWy\`Mה^]C0]S]8ՑA%;t ;Hp=>U$J z 10b/"pҎHƣ%RI"2<_[o:}֞svL)Q$"I S"ޫxe%QLpvq(d$DOBq$DDaȓTjsEtM9l@bqu /\}X"ՍQo$`H}% |e=6Gq R, 4$EB6E'q{d"4𭃊{pZyҒޛ*̊ !3B\˨?VbVBT# #c#ȸst:)jlv>SbTEbP|WY_S܌"VkQ$@xY> L͑mKsyXh뷟=`B*o2U-8rՆq鵛J"츾q Ѿ p)9c-qy9㶋v:ˤ~GC <7đB $7gdYlX"YmTޯ)Ǿt\" vѿv*eaiW7K^%R3-vkW F_y1swx\յgF^m[X0]T Hz J %%0~ .-$nBgED1rluixi54圽{ڇrFcO#UȝF ;*.-B^ z䁅˪Ǔjx+lL8t/M%bUDh3/Fj.kr/F )( E`q1ٚ" t%Z۸\#gXpnM%btݧH^R{"efOT*VH9 ڜ*Ptl7 # -UX hh64V VH`FCX3yd@YNCZ/ywSX5y&<z*sT"6`mE*!LG]!el^}8GФyKָ\<a!#7B]{m 6SC ]@*G'At2TpKN&x)isaU҂eECyC!|33 SlLs+CF@69W!90ܞYy(=sAa!AD4G6x2}2AaE<XkxRjGD`KRd&ǡN"vBJA#qRH>eYb5 N+ ̌'"%EN]k|z֗c{.A` [l(wh#; hp吥7nr(|tpxP$"YRV}7Nu6~>pgYG4>8Ҹ-x1M-ΐЍǧh jji<=j=/7:"obTXTԉnēbįay(G| s(A>iG@ OjPY,TDq(U)^A Id D6&DG\%ZR9:C.67T7hP tf{#S)dshw\>}o*4\p[ cLPhX"ZTx$ p ]Ox:=C|kyf hP+^do<>Q6y{})+9l + FZם<˥!=yb;ͯBkm6~Km 1T"f*CG֧.x3y9w p?MZG ^Sy ™( 6QKPWld< W81r$F w G,1^elA`a-&ǾU<3IQu2Q``gk=,ot")Ǣ/ QT).[-XO觹GY 44Zw@EG}OКa0"(jjihhGé~A@ ]w }r?{!pT"vj<~ kjA<-,!z\ |+'\3Y]_W){w7 QC o"PE+4g.d8yCWhlC| щπooeu%!܅HMV#nmE#[TI:o$[z wr]\2]en '#m}@|@66x#iZ6?Gш?O>E"b?a  }cpȀܓJĜθtPJĖ} zG,Ȅ#LC ˑ߆,A +ည'ӷcYtYB/.sk0<zOژs}D !Cr('ӫQժǀoz@OlmK2 ׷ 8o^}o^?G1cXхX6M\hv(~$ \ Èx͔'vdL=*zw/uMh5g]ෑ RYj{.{}=n8}-AYj\Ddb"1dslGst[ 2z'/+!Km~V kьf4r{SKҁ@j]ޜxʝ(Llo/$(ǼQߏ%3ZXpHyiM;p8P]<<*4Q\őCZ[O 7yt>èPћ?hpW#n@!g!^R QHrPq *_zyoC<& Exx"ڇL">f?r!<2D{/碈oK-}#l0IH+ r~*ZGrd hF$;@:M=(QDk7_ɎslͶFuI`LtE2ڳ LM%b;ƓwPLc2Lkނ"K\W}7ZȀ:dlsf7'z[}m~?uIo7tn/U2dVGJo)Ss߲7.yIVUQ]o+N-X]Q0vHAq~G*J1` atf?#PFDO%bƓhC"q0bmJX* F"21>Čw^DV)IS.C tPJɴwq  \J|K`uwZɱD~]2* sByylD)H !2G//H)evvv[# Ԁ1cozm0Xq l<]2HzZPȚ,T[;욡Hz*o!ݏ}h YwD I%~>ט[EK 'oMΞAɥgw!!b4*q XõJy:O$Hyjm@JʨzB_܋U&)G!4yA!ZC(0 vŅd-<)?%v:3[9  OW|8˰N7e)+ZQ0}C!Άcn8)7a)LOD}N%bOY9\G:PC=RN.A[.? @t!S?UO Y<s)˅EL\#_sQ$u*lL(2 NHf"^-hK]ks_mBr~ lMl~u(Bp ivH,oœ}lHVa@\Vt7,E~C= O@ >㽠ds'~<?K%bYskrr>:W5.TR؃Qugjz-{YOakf~zZo"_ŲLGkvܞU}+"}5œFT"*{ӯg>3r؎,x2=yJgb.mڼB)RdAbr,WA ](A֯H }1mt 1Uy6.3v2*"0-LX~Y#Neck5g.aJEDx2}]5m'ӿGup!R dur= OX8 |ݺPx1WHv7F= ^g|U1R&FHErIV!8̍zg= !/ 9܊Š|n]~˞.Nq/ʗ婪{ջ!|̈-'Yx2}hȋIP9dqCqypC g6SQk) ׊A'R~hBN*;7 y2d5,@ja$|_>G*; )+p-N%bki(o[bۺ&~Y"g4[#}+@NDa0FUZe=Ϯ αj*+jaa$kZ]S0Vv(ABoW!)=9`6_yPnC-^xԀ?lEi!y!$@cWCa+y6ŘvD{q *iޠ5|;ڱ۾o8"WwFCsB@v˱gNAr = j##;H>xQy.o:O>uG{u]g{BE*@i? ѝw^0 hBJ${Rv5񮕶6Qqeܨ1tT!16w{dUhʹ[5J~zOD`|2}ݞ-xdU=ST"-hm=0.H%b SQkyvAۆW9Sg=@}{{hw{r沾R$タdJx[6 x., Jn.~"x6OGo=ǿ,IƓ1KDlpT"׺돵>L敡x&]. .aEk12~n}"tyu 2mbp:7qw .HdGtG:#^:{!'Pׯz7!ѓz*LXksmz[JjDy'Ϝq:M:FC˰6Lx}4LT"wD7Pڥ4v$h?7@{!7 9 )qtxV({HPOˑ0C^QC6%</?6W{iD `"*0PӞI%bǓroێ]]z# !c[5(<[R^ ) U(ܡY\ks=)v'ur峌爄#y!ټJ.g=jFB<$ȋ5ln ̳ud!e*nR\#u~#-DddKXd6ޏz!P5b=#Wy,gc*(՗~ꙥ" OZ~ ÌQk \9nra\B&|E夁;R>6:LZ>)uONSk8A^E,_=aUKa 2XBprX%hr!l/_١o{ϣ"#O8AoC3wk!C`%0A17&~:SH O!d,B/o\BAydk:(%B9C+ !v 6$XsՑp]\wA!? }g*eg$8e }rzぢ6A@9`#C:]ޡy]Qâ7> @oրN$)f3kfv#Sd:W!c*)ҋ|!룩uhŢjG~`x2}+gjk`:o ނ *ģ3&n72TF:d}n1(pkx,SŎ7 ? 3mNÐgj79H0=΢W,1oN_|sL n!= ,2Eu}ށJ?n*œO;4LT"R*'g5" :G#]nCVjV@d\?CiԾ|y!r5>BW-A)9%Z+"%3^ƴ]cHF#~|/KuR}]H: F"oT+*KrvHisyW"`+/Ɠ02M%b g@mk=jXdsȸYZ1A,F= 7/dK_7^H/|>ջu1|dZ:VncoBm~ #$C`ʠ3>be| +7~X3g"7`Wd57:m[DlE<^6x" ^\1!"p"AdlߊdH}Eo?a%hd9q ɷ}a~OϨYwlՙ.K%bƓR bH0!0ᝂvv~daT\jyktIƮu?B|)C*kgQ .bT"*63, }ܵ>( WhkFl}]u0VR\w!8,^7ēϧ1l>)բG`Z,AE Q&S F?!SY! 3^Ry_[ȓy A/Q=P}k VKsƓM]RSKI(m`Z*[O]9)A%Мnji6`Kꑲem1hhGFGwE?I<'P 8{/A`&GS["9&T*Xyx"pA q/\ph5WdɌ)(LjGF6~dt!oۺDmjPL_!sDƲ yS3('7+uO6b^/2VH3 #æ 602l-yZumEVƢH#ēK7& -LB6AtG}psn\y5׿Z +zkh]{:ҝFg`u( _E=cygzB}%w&CG3zӗ9~ % φ _[,iT FG3ok]/6&Q*[v^~!_"SQTms'+QyH? s`{HXB)]Ϊւ=6Ҋ+fd nu:@}%N'UH`#\.ָу0= ;W7lYEo}q~o_㘻HӼTwe~02b|/bZg,p^< ؟lk6̞[lC,\HgSu] G۵N&bsa0L!aX9qtM,F%dT>Iz]6tO<yhmkB!^xHLkEVNY";b dk$n4~R_@,Ҹ:O/pffmռO ͫot~_q- u:kC,OsJ9EUkPr]w G"HV9PzV] .do }/C.ҟ2<\6CBVkA^P>w'sʾ?ql:{[#TX(@rWЧ).pd z$*uO; jPxY)YN IDAT sKg~owǓ鷑OOD@"ODV5d6\ ~ZOk}ͯ^6_Cb_!;U G͎Tg) am+(dao:Cǩ@XD֧H?AD|?+( dy|¿v2hW z+:O΀bG`AgSs/w]uFg`!t=7ދU8^_QG Pbn"w[&hĸb%fH)Up0s}G:KG HXޑJĖœicvhE)/-HwNuF^Hw (9CGCYD[ NGgßs'fߞ-/zKY.FV.$;I[AR$L`1)!D[,Ϟ_<9Pkct ]D̕'^K6&lΞ$1!h+lazW7@p87L_`zqpKQ5J6!bh}ˆfs o|{%>x!zXˣsJF ( ^ P 3;"663?Hi{54Vvst]ўO{ó{hh>_9-C]T"zxv>\_hM<9$G6@Չ7,}x2߽1{.lz3-axa_9"Tu:<6:p&Z^!Ѧ6J+=n缰_P2*G2;UAr{2 v}EJkvrYM|v=sJ;bf|jqǘT" xSg`P}gмZb$zP([MFL'Y]?gAŬȺ##oHmk=ec\&E$#}OӀk˻X58!e.r*M#?1wRx2[̏!OV=&B1+9dIGhrܿ޾J1/х=vr|K*F]\#O-c6mݪzE-Fœ_ښ! <&ظ^'HlCB{_[QCªƾs"g}T"'IBvmv!@BBF ?#=S5lU#EUqIqX^)B#Og{dCi;^Tt^~cni>7 ! <+Wְq![߀!nJx,]O$tNxuG#^ډ/C(=>B *U I8y~Ї$dq^KKy.He^*{/oU#۸*F97؜\m38 gp hE|\X"$BNd0acۍb_vG/|ܾ"x|uh%%^Ao^11/80ͯk9WUhđnHPd]fEjt&[Kma|^G$tF x<<1[=7}(_vS ]?dPwNz##E]Z](|Rolam,-*  /ejw[ԶoCܢx=}ֆ(L7T" R'!{rzP ; m%:C)G tpat:bȷ ЊVȻh?Cc${1шD>PE1:d v(0!wț7Ȫ#J+b:Q"CyD#p(dg[x su:8) fb_f3wIHI8ܚ}H+fS[?#C{ H9Pntwܾ RX-hv< 1׃@ {"E3QG J\O1!F?#uJrrNChX98yq$:5 ZSVOE֍Ǣ]wa|>n'mz\9:ć~g.œ (x2}$C6E* w=xqHh?M_Eg7߸bB5HUź F2-jYqwh+p܉t1~^  =ȣ5 zD>q\CVn)ʯ+O+BBH&ٙ k?d Ҙ ODrRQ``E%͔ =VYrI;qՖ\п"r#!YhMXr Aur``yRtQvl?C;W*!ߏ"tJG }~{QCHevMmCϼڞ 4d 'Q0''*{;-.[Tl=yUM[PXwtqm~p `]8Sx2} dK%b1ē} *Wy=6r-ס0^"AP)bs%nWBE'B \?'k ]p=w9).`_ )#3wV %dEǿGֶ vnd9 yG 0'd),3P;ps<;PY^N]S[YNG&{D7l~_!Bœd<෩Dx2Gg=]lmH:Q\(SlqռFWxk_45U/jk Hy<&pDGm^"GAY=X Qbqo[! /O}Q/T?`Hyn?$Wr eFC+g⑥D?L_D&! ňb.K】k:~!>xTFId$dmgnCb=>j-kbaw)@exyoƳ;Q$$0%h40^5R'".F#Ad0= LWj""L/*ٮpx!Cpo}~^1/ZSG}uzHt+KGeѿh뻣^mHG:IWlTp)\Srķoy\7|uh_ aA:r7^0E\wOI /E D\DGk)H˻)U1qzWoyk#c*ȇL-kDz,7R&z\~^6>-nFfpo0AgAA#ޙG%RZZCv\`-d5XT<Qu&\e0(2>XPKg; Q (̚"m~{YF>rCꥲ3 WF3Fԅc_x0RsKO lMDdzwTLT"9dpFqЧx2]@8v&"AN`G` /'AWCapdz #>RB:m8 AP=F[Xcy1#05UnDB~w5n'5/ bfS!E(idok> ÞY < YH#/X@B8]k*,פ'#V@u-d9'!V FPNU;D~6E ߊTeGf+\y$T"#Bw?*Q7 0v5 Y~E(m;^7Ѻc%Um~fUYp mOKd:xo/S"AЫ;q)d,^ote+!t.Ψ ?n. w} ǷG T *|"r9+. Br\y7UFhrij)A2h s=6&gh 0t9%m 2( <ħ:BrD~Y+|;ͷg J%bs'GY [ӀR]6mE S?\5 /LOͽv[`rlv: qh '0 ]# ]z+: [wsy;B`k2 o3}탍Q<{-vՋio5zIڜ; EH7p3̽f%E-Hyͯ?Ի8rw{w9ⳒYGd_~\ZJ%b"x󆽾<6Hߕ t @JHX]A?h݊<(AP(cwpFT"v]*;8^oDÐR~솊DSYH=8 1j$FtD{gdu O#aFBi$D@W"0($p~`s}(LB~$V_JĤ~gsv(ACm݇!A\q8:J9]vȞ~HK%b!Dbmϸ? k8[j[}m-]b$_@a_Iͯ,}br?չ6' aFqx1Ǩ_ϖE£Z";Kw p#H'l7Zǰ9?Τ?mrv*z}gRX[*AjĿ(:nFxF5ÁHNsW@޴Y6B},[`r]6|;k_YdEw?fc,A8A-O;=[9o5.? 26~:k v:HY]>7*7!`6|&4a_k*H=˥rHͯhWWVDy\ߒQ( [Wv֔#DY L ֬`B+bݴ{!ER *ECVd9;־"bňvpFt!+ځv-XJamo"s=TaQWp8..nbqGnd9k@c,/!q=R~6BLfo =zmHFg h{w#OYFjcE &ڕL/CBoEh60=|/{x/@r jD ށyICP6o"zƎ Bv7'p9V#fpd׉Tf=e ٽ^.Uy]Ώzcssb4"S~_gH0RlvOZ2ʱG@t АU*:ֲk9H]_#oU7 C*A'^u S~x3y#9APsߕTw9H'%Ht%]]~F|c6 yeߍ@5xs'\yxOqN5#VNPmЅ۹=}Eav#]'CP~][/EV뼺Hv(zُ!*Ԕ& w`͊s~uGGu//K?ݿ4_~Qڐ?\^ÊmmA'[+ly('\l-%R#|upj:)2{1Hv t EIdy;ZWDJFmV"X7R$Ns/BL18~ 13~Ot\Ou! ?ƭHi8me߁@8J|s܃Yh _SN폄KO3b(6.dK D {áp(썀HZ:"e#3I2W=#´ٜ߄,HۘVٽJК".'ngQ@5~Gɘ~xk鬭S8<lr cEbΏKq= "F҉t$֙Asw\Ώeϴk㷖G7H4%R2e-WxweHBJ5d|})\ZyEVPC7a皨w!>ʶek Ct pMPtdYZx]c] aK/ݣᥫx~MHhq}"qOWa~ضqaKTh,"/ 3At)QAj+I : tJ6n惟+v‡3D솵I@o@Fƨpo-U!H}޼Z(r9xnʼA/ʃen6e!杁 4s7f=u($Ln.r8"RF~y8yՏVG֙ LB z&zD_iHPϣD0D*L:9ʜ;n8*ق@,B3GvDng %tA?vo+m|96ug"&Fkr!66;nRuWi{'qvOЗ휧<0gZL''R]KꊻKաpm4= @B7V"ox~W*c~2eOF!Ί ؊͜y9?B:<{x~x𧻓uM0)@/#P~4O k+ Zo7"-Sz9?曵&6.y;A ~@PY0ML25Ȱs$?\QE/#eX HirOW!|p^z۹ȰWs]{uvÒn2SD<v۽G r-HI Zqws>W죟3 `?!=2mF\.aQIU"e>lzp{:AŒ;54ȥFB^ BI'lk@x%2:BP}Ggފd]NEZ7fH9?.eB[(e_@ޫk7xZӇ=:(Z_5D*3)}mRLAziRᓨt2-L-0, |*\AOMVu pxg#)oK BϷ/NIӏ"OMVD*s b"g#efD*35ϵsW۵~ԔK O\~-Ynyˡ+P JNƟH2{]k_LEm.BG Er% /Y!R n1* ܗx"G||)k4 ,H2{_xĨO:!dV[?/XKX=R beW/b,R'~4ʜNmL'o;)Fr~lE!; )Wo[)-P2Agh:# JjQT:BG{i22#,Y}Wgw{a[?R측<έC!vXoǸ0~).*O4BP&{!AuON+!|qE\tҹf$ܺ<:ó3q@mשCGv`a"`SE}J:"ECx1Gkx8,^ ˻^IB{i%9TSS/2RPmX ]3gk>H--񮨗= }Xc=E𿁠S__ ұi~h܋s~'Z7l>7lh һHۛu8stYM|Ý 1?#*1.h,'(p6ډb^~N_D=҄K(lmRq^baO!TkR` ivd}>r}{Tw3ce:ΉT ԠA\ʀ樗="b3{ܪBCzIܢ?Q(£/_; y^>U%Z{.$ɍ)wZLU(\?ԣaeΏ_gc1=kBvF^Q"xiq2{dD_Z2|$܉I(b1_|~m8bN곟*?BPν&K(,y.E#W9yǚ@v7(3bGC<ΏAW$ߨF#0[ TqDXf bT"e? ɫ 183N2jO~D*3 ?B)RU\WsޡNE t)uEjT-pF|hAť,%!VȮ =.e(%`_˾s~lF.GEnzǽלvs(\Y񴅦F@Ę <5K+={FN+/\R[X߻6T "k92p/^t8 6w@mCەmPݬywC:He~v&alTd ʻqg#ld=bʏPqD*spU:0lc ABza{IaGJLq/=bW!EY=,Ů%Besˆ<ًB(oAWoIH==H5^v;wc'*p%_DELƯqu $\\ ݚND&R;;[[,#!p{V'AN{:ƀ{fc"eh,H"b9oJT/x%~. U=!H.C+io Ad|A"YxV}(}H[  ώA~R$<;v%ٷ*A-D< Dt*}B SDq*CZd?A.;(6oq.$7\nEx)P;R ]h+ᔶIP vǞv5S)|_RnJ'[ݐ+\Knrw$OxU{?mH|bD=Me^pd]c6Og#e񄨗={kMw͸ޮ5ukot..iϭs~{*Q/;ًUwZ!0Z(&^zz}'~X -ص"xd9eè hN;rptk:58cG/Yځec~ |9w",^SBv)zs~Ehlt2^^ʏQiUZZ-/쟘qg3,}_&V=ŶW>ްGPߛrCQŞ^Yq@eӀ \#~Azڮ˴o(iTan A?,&_ACH)"k$Wٱﰿ U@ Y>BU~U!b}JPeAn vݱr|GFGV!ߛB:bFu|Cl^~VD W+R[~!ΰ{ټ,!с<CH: |C v3} s}Et2QUˉ6WF<@V%] m.E@OG;G:_u7GRx&^a!{u-bL_Ҏؾ[WG#WA[Y?c/(EmzSњAB 484ǶPAD*K IDATSqbc N3,Z q>0Rnp-w.+qk?d *#qp| e#`}\(#HH.AGFDA„-ozLƞF.K Xlzk CJ ? 5C =!^-1 XҐouT2Az/ 2YY OLAĭH!/N'm6HX &? wBH9:ed?$LNGJT~IPbOd0NFCPݮ+|Y~6jZ^sokeCs ZvF"2]HIYB@Rx+OJKl(JXn?hC`묠.N )].y nkOD@g^MWn߃$]&x:[b7uu;g%Rv6^QdN]m"^d6gW_>NglrQT9(NQ'E6R]\U;Z^s裣^v-Hۜ,ٕov ۓPxi0&=T1w̰k=o}9H⋏#VS:9c:wkz:2Vxk8 C'wx!LPZ=c\l>‘Qxs8h3?ܲa<>#e@#lt}Z6kGkRϲqۜ:ϗ [3 *+>4 a <_y6֟R$T!ű=Dgx{"i}⽮\[*ND8|[_BrmHnDIBk?\UU%ř_,^G𨗝G!c6jΝaSH!Vn>x?5>ػ6\ѱot-+_1*_[ }Pҽ|,Λ^v h{`1 fN"@hTor'Q,DLHhGJPc-ȃbgwyvH4Ryǝ|]ݗ"ӉTLAFTA`Fi}aY#Uh7s;yNBE!hA޷#5l^FLb,rA.~=D*C![=5xJ*5 1Hiƴ)`~+]ɫ@TsؘV#!adEb;Txn%RW29G{-|)DzQ/;Wѽ{s֌ߵr9^T6.Tٛr~ ˢp°G9pًǝ.uPZ5 Njvkc=^KD@|e1 %MoV(eG}L^0݁!G6;Һ,j $R!t2u#ȳ,’;D*s {Vt!c%„3JQXBLj]<˵rㆾQ^ba/"Lv^F5CJQIx:l oSq*l^E;By6m6Ed|{$2CrQt-s N*"R<|bco׼9"Y̺6e!Ie|c^*ӱ+ov꓀%msra5h-JCL1PvmvDݱ6e"vgr >@ y saR{VhX]_ncJAv*XdHeH:ф>y:^D*s$<@Ћ 2VE b{ (W<>@/m-'h}X?'RpJሱ7e'9brM#G(,@5A`CA~>R\%3"nY1EȒs'R(|d! ! s\ Y XO'P ɻ3gHlW E뇈1ifxf>~9 }<ήk[{H'/'R)^a:EuŒ5PRؑ⵭HeV :.)WtRMHrY3y^eCK5.-E{Ȫ:rX>Tz3@Ő:#&Rid|\3<WoaI#6ijeT sk_P(r1Ѕ!~ s "yH~Uk^^3wDIPHP4./< E;/Hg'V}HfQq፷'[z &OJP?m5/n&?vP!Ux.'ZEiq$} rWAa AxG\6vBi52ȯB,2\.B)~޻n Kʇ/@rk9/p ]1zBoA1Av`OU[U.Ӊr_49hV`Ǘozdwڐ6߂zoBU}?RڽdI{ATf$'&{Po#>'#ϕ˟*!U!\Qln*)ߋ 7"Uh vBE^"x}8N=(̿ڮaXW/?i҈ڙD{Nľ#BFCXMnG9N­&Pʾ7Wb{Hex @:߽Q<6@&rb4_z'̰G:^_vGl7Fuo"y:Z^m{6DпC݁hX?cW=\{Ƙc:@"9 KAAzhVBђ-TDP@ZH8҂#%mrL YUNc_AC C#5w@hH9t2fh#+6ql,G8"%mbVCP:.aЮq9b^"Passl"m}m(*GPÕ})jˉT1y;15#E67秓N{GٜGq߼j)Ag )?g9?8eU%{<ʄjw}9זC=z ^iav}k>nߋd=LNBkϕ*Ee00H[3d,p<1!E 7v-+گ7vʲD*3zkN{}0fSU/7;Az;1VTWKdkTfU:B]GP y^Gcu[rOKX6dϮ!w4<jzr2^vwԌ_g᠃>D*SImeh= p/&R ╣/~,DJDnTB hQegcDF+"B!kգ(vGml'#ck:܃BdA Bng&A ^ ah~ᔯ(BeoU6z"{m>Bސp cOo(p,64ǯŞ˻ y6tXvsd&|˞n6!yCCh>L+s4(/٩cuSeCP舜Fi7Gy9?}l,}/v[EP"3` { ؏E*؇T2WYPHc _w־s=!2ļG"!nG0"@1Ĕ"X@|0E (okנDWATTMFb{~$R(f'#O#uxT՞b+Ȭ" ?=ʜk=Eds#U<^:¡mܧ²akחN0䨗DEQ/{ܶ("+8. P.Kg#3H-DMF:/_0t2ޟNgr!t2e6OEħ6 +m Q i9Et&nDJXE+q,[zuGpy NBؗb;v=~GQjǹ}Zaapv(:R\^d,26 WB\(mvHޝKy2YAqB ::y%zOK,ҁ֌"HT9?vvEϜt̪ 1T^ BcCVg7 hPژ q;Aք&`H*ڝ)F#PfعG,{ͥ/up'P޵dD*GVU~Jĺa#+*~>з3I;h{!ؙT6o'Hi@k) )]3 dcuN(Y'q}+\>(dr!V+Ov棟^=/c*ĠV$R$R/ܟHejJXD*;ʷo[nK^_*kϏTyt=6eOq9\L%^Ψ= y:ңFnRDu> D k[:N#b$/gճ*_.ݿ Qq#tV:k)}}نHeR !!(Gd$Rp> u~dM IDAT_#)bF߮_\kd\nW1Q\9PXGYKK ?\! vjTWZ]l96䈥B"ftS+åpȉ+)\aU ǶDp˖#EE$TbBH1dpU8//BW#h^?m`w%OR;꒭}71{더T]zoc H*18 oݵh z+!hžЈW˫ˇE?eNug+\1w-{-d|~6y62l5 &:EZ\_"Yv+$;.7 Q ^K~S&D0#qA WێG[At}тW pxk!}?\5Ȁ5 S^ڃ:!nR!UPlGڞ gECz*| J?y[ej|u,A*0n^[5k"9]<>_Q˞cr#t~}߉p̮e;6ϫF޵G0,<\oDI0H'| nMb} <{MW;Ph019He'/]( y!UH} "!T/<yBl h@},bBG1}5 |  ؖ#6ϫsA@ro 9)>~B!w\,#lbsH/Oi:ͮC,aU[Ua}|9&!d]~dx yUq}ƎA!}~z7 mk["H$tG`~324쁄O}TLT|kՏ ݡaў[xӛÇ r1~9 3{y@9?.eDkB Pk?P{'Ck\Zڷ.Q/{F؞lQj[ ӶAzdL9P=u͊/AiOP.|"dt  6uHIy)c%8G u&z2~N#yvaa(]|+Qx.H)= yPE9L2?GEx׵=K9?"eqޜ[Hef Y; Y]۬NƋы"._4_&0!)%Q)?He!EP"߷k.A0ǩYj#; V_pO aE:Ո~yroV\jcY. (_HeZP1"IEwEʢot0=(s~m˖E)H9~ )yZC2mۜr~)|[SD¹H9ao+Fy,o%)+\Su&<,G$dlG|ӊb|ώ <0{M}:W} ]JBƍ=?4>tI-; kAA dVO"G~\h,G8x /DȾAT09 ȜxrU>Gju\gwa$6W{ _ِEscx!yQs/$I)Y (ot2> `܈8OA瑵yb2HPla7xTy0AV Wuʽ K/#!l3 q6su3JH>=y!j!Aݑ6iR*H2(BDN0m'RO=D"pgceűqdqj@E|ڜ;a`Ddrހw@V4J$X3*Yv&)QFz,Z3R> Wb\nc y.^Bh?ޅ?hy^=ߞpzo'RK=1ۡsA)Br:mRʁ|x J^;Ç @~l_^fiJ2r}ߪ- ;&h}vb~%h=w#E )B+>$dF|ud< xĜ Ty#,('s}MֆǞ jE-/T#ۑ!ry-Vg ү?6>R@蝷 9dW_o2[Ɂ &^U@#oΏ@%Q/;x4_CFMheњ\+ Wgu,D?`}`p฻bkBܰ S̏1H`*CXR6;8MI @H82CTPAA (H ti aR'o?sr7|>ݙ;{- ]7gvs#BDQQxi78&Cq9Z1sf"󒜀f E*"Br1jƱu`:ǽf< ؂_A^d V~>o+^pft,/Ūʟy=WH23ОHevM'I@e"ko+LGNټflJryƠ}6hok; P;'92hY#Q߁B5vp48n..ww3uih8k;lDj%Z0lEmwDYx3г^7A wscq:]R#XhSp#vjԲgrwi`Ql κQ'[':Z>YuL977 pCTX|6REpC MUI2'Et2>ʈ*샌ÁhX55d4Ƣҩ j4b"0s"~]3;?Я@4zFFb5J(Ew-J%͹u 91QQts^0͜AH&WP4,jýȸ-HelFD>;α pͯ<ۢki<>Q']䍵uۂkt!m@$ݦ}_7o&Q'B MѽCR')?c狖ۜ!&E.;vнEE{碀uxTG~D-'m(xq΍>|L|T-jCQ(K wQž*_qNs_[t#vV4?TV"޵}E6u}E~O-P@fϠ xZpKk1=x&:n(?y2zxo4Zϣ%xo.ZHF+3ՔC8ƧD*s:ZD? AQ]+? GHa-"ɕ+6p:As1{ ČB>< wj>h/SsVVܧ ٞ*) [md"[gSQ5hȕZ׏=}sa(Eg~x d;ٯjnGӄ1d"'Rr=)][ѺzD톲el&(L=vN>>OEy]N1|H2Wad#LArD&^G޽yxrX F}8Mi4F[z@' bv2g#b~FihQ';-^0cc.^co]-ϡE)FaDߨ>)ogHGS9j5cHњ~ 'M?GkGO wl]4g؆d)ZZKl:6}P'aҊRYXoc"ypPN?`:_ Sd#+U+JYXscPqy~"@2\+z V"2">]$2p&v K'haz Z$Ҁ?BEd4Gdkw#r؉ ;>Et~"5ß#f܁Ϳ%`ړXE\t(5[;_g)%SlsּfisTQdZj3ىd#Ԇl 2?!h ݘ7[7eY#\gUp4 ~%QTZd}l3 8ϔ%؆oW, T{"j kQS-givqʹ6DG6{Ht`F*4Yk4&2*'q- /PD'dSG޻hCPOeWd`E9,sd@d~? =Al6[KqioAmh!,T{ݐHeBhB.;rD6{쎧>hb{-fW t|jL^[ Ϟꖹ8x`6 O̭1QMRttUQ!sn,uc س9lY-Nt|#dD_"Rl1izѨ}x/ċk/l8mS(vۥɍ+*G.~fUے{ n TNyVY ]ຜr+d|F_AJ4.Dʹt2>^He2rB1W9l&Zd3Do|Ί`ϣc.w)hAp{Vw8Gad_Doalm||Q DsnJSu&uo|}'X]`JXt2Ci]= :݉ =h"'Z<:(rtTW#T E!c"7"A޺2t_FѣR(Ǽ;PV:2hw1@Ƣ/#"ED&k0ЌEvAQqTF$ͦme>D7kR(1{sG1EF>f'o4& E6Zg AL#=f{(_s;@Kڢ*Lӭ:c+j\) Xl"l"RUF]KpYA7+w;N Kȶ B=֡^ hZl^ٷ#[VdƎBdSx~a,rnP$H4?-Cv}562Wйl=pZ[i=S3iʹН(OH'1]eL "%={ Ww@l4TxJj{ ^OkJdsг#J))(sG6c"sIՠ:x_ EȎll8ea@eԲdv2|lH'+d|S!R jpGkːyc`v2^]u = _:gcK'Xc"#NӉTa$&R\ro(NJ.D ϝꆎ0V!0~)iAbDPCl4!c"$}m (Rf%Rܯ1CK v9U"vh UNZ x^μM9)AVq sJ<Ek aƫ IDATe PNg$RQf, t+\ɸKNGCglcC*dsc>7zξם;y{둷+EVa$_+l'TE¼θ77fƈo$R .m[k MI7ˀw BK8pmPi1_X|-kDCFTWO.7Vw9D*C 58HhyQSJqٮ ] m7[1?ad ȡ5aptѼ1EGo {ydPd˞U3) U˶IhƓlnj݌o rr?ڝCM%泳 9wu?qng =p8 'Ʈ9Cļ_}l5}6QkTyL'˺@QЄ{=eh@5(G$y jl{-B'ȗ%sV5imӶ dÈG dv1 4޼߄XDYD*SkdlEZp@iY 4|!5)xw@(Ɩm>Hإk3\vmفR5k}9 x9}OBˈl7gDj;/U oȩpg΍O2djb5i;-D*sAɹf3Ձ/Uஂ5hB`=z^lJy -X® orhscχlt2M]>"9Ɣ9lBרCl4jCAнFsZ0_\9nn@4sxv"c{ٲCͶ6*׎7gp4JI>N/:SwL 梧j$R~oݦ٥9m3^G>płd|͇g(S=J+&RBi" b|Eyɚ[;-K {0Zp8ۼt2m<+(&*b;͜-@Q Tw ys%ZOBVDoC=`sߠ3h7O32 !</!CE4t-.ޏ:BEǏć$w@;(;>3hݳh'RIˢ']|՘%97A%!am][׺$|&ij:3ylo鄛w٥i|㬲bK*b%P,硊;u6 TzXky^iBk=cˑNNڐ x~bSN TwAf{"biy!<' xW( lo`){8zFEV!gѼF6N9|@sj!To?OFu @sF`^"F8l7 寷πҦ٥#n><| u3>f˴oAb!zH'djy*Hu@yM(Z.E %ۣH$}\߁/( 5RPg*KcpѢs "3?DBG$Rc̟Gs>Kq_ܝܢl3TŋTJ6TSUDѶƍ(q62HR?w6ǵޡZ q]PD<!@d΍=sc{/rn>LD:;t#976}#y<ƴ G陵?Y=@W9+|x'aZiG"$M2%v ·<䚑?Xa9m¸w-'ՌlnETo\[E)&|rcsN d|eo|B87=xD)*+p0ш7mDAN| ,\HL#"GeGgl d{:Zf*r^Y'hٸ|v5r^H#칈l~He, כ̊Yq**3^C_M0\A}D*c`P0h^QO6,4QB$hCiT=PTl$J8յđ)whhc (EvDiZ 녢@=?&R ߷ƌa2peC9 ciHb!Zx.F$Ef}@Wv3Qc0"̸VTh^"%pA`8T~ "MH Gxr+v.|:{=fLDzt\*qk:G<Q'k{'=3`i\J0 sc^P?F^ۜM27n^:;\scΓЂ욜k37ds``ygU ٲ8>5$R# lhLi]:y~xZϽ[F_jrmoa/ a mo@y H3+GːXS7,qEl4 4WلU=|,>ȑ<"0bMֈG|lGȹQ'{M :Y9Y:O>4M;4:$R (opKdQP#S&x|ą(EM@vdD8h_e~Dy fcDzb<ڟ!K ZO@!fL+QHT Ȑ]7 NuPdo1^- ~(:y4^t2D"\|7/I2M(br߱$_{nD-> Q'[\uW>jىv[eAI:ٿؙ;[uF$ig xqж2D틼<mhrB>{)L(p]\^*vvZx{=ndwF6e)YXR4S1EENC/SӼ>EYK]5Ϣax mf5I'֡+cw,-K6]rnl!rhO4DU<*`CB}U8@ #фeDf*Zވ-A Z4g O6A8lCрq9gq9(b3֣Xl=W~dj"UC: ۼI~&"sHj"Qux\A=L4;"v}"Dm-zK+߬Xu9Z`pkqN}Wul[4oK}Y[{BcD&EM"duoFMF!fد=3skû ΕkCF? Hmrr`4 µ}FQ';O8g-~V]Lsc1RBEl6[puJ\٥21ܜ[@y}1>||p+pR:_ D:NSfvs5 8jTkFv` ^T /J]D"r śDZQ惋l0T[</),֘cca5umxҌB }m9?ʜHeúmJTw֧=_*3!.G{p-vL'_t2~q" JU za `R rGQqU{yzLG} 32 0ⴣԢq3PA$i/dVcÜQ}rȰ *_HeElJ_tQP>|ENs"$f&ͼft2^j:ID*Bquϴ̏D:Vw[7?ztG](!XKGhpm~z~MpW\ϟRַ ^ˢH $ H|8,D(mTnC_H%/C97:CF~scWo84NiٵwlD*oQzы}8T> LٱP­|HT)"Qnň=Gg؉l>fB¦NHR+^+v?vNw i^|ٗ>xmkpgk +L0qpzI'6@"Fck'XD*"3Pt2~l I/Djx; ԃd"<1` 8Y(71B#WHY?d\ExtT҂E"}iBD6== cpZb65u{|vCz/"Q{ٌqd|CQKJpnX9H BCu~+R [c+OP ڋ#߾10me)d+n{A΍]Rɶ q Nr~؝ae}|HeAsFNDUTdE,.D=(8C^V|lgk6d|:󐑸E"( OnƣdtG޻񾟷k (en5JDj5bBƩ:K!\~Pjxx"YmDbWQmL"WϷ} 1O+>^ "'16c"\.2Rgs~G^!򺽡R7h AiFuJtC @ ⶂAEG}ӑc97uNLy"6t(\.7E^Z>EDX>% &6x¾oҦ>ccsHeNEs4+4<4J[\"\#Gc^w3P^hYT (:Nph{ ;iaʐ3O6']KDlkyE6DMoD"vO|~IF7y!j/ekgPDf4qWr42#1E(RL;Y0HR'J,&RZkCKovC)Y9D NC)_3l`Jo|ޜ#Ŕ0b 2!sW+m^sfl%+NbNv"ɽAQ:5kSn¨ `S; bO@A"c&Iי|$NW"}(B|HJ΍-Ai>|lH2u13Fs73>XE'A4_A9.9No#;#J"Uyd3x6jeӁTy?ojA6m!Q6U@N <ݽP͗P&oe>JH'Ӏ'RGP'$wB`@_AP>$0yj9Qjŗd͕o>?l49"y!"u6Ɗ'(ܫECh|NED kxsw<6Mm '(\fyШ֘~7t Plv*A }b5"`7Jۜ`FS<"z wya 9S@gɞscs?dr*;bOF;]!|Ƈ[ynː#dC:w̅h: (3b9N^jP$.BUMa#U/Ecf{C*󶥉MC.EvH6xVY(ZedÏL' smZh'o e$Rt2K¯H2V(P&02ik3ҾH"&;I:n~Aj[AuWݐqWօWod| r_4QVH=9\cޏ#flJN{ (}0DDI'L"HpTd:?cmD*D/d@rq>xٶT)^ ^mC; 2<?ڍ$|K΍m1ug#wܞscw|gF 97ۂ?NⷷԸ|H2QF-/;ٲ4ZMe *v+ֳ{؊QhU+ϘS"kQJ_o~fd{m`^ַ]x3p] rzns$Rt2;?#X[d|R")C{0lt$,B+"a"@r$VсM"c=QfІ-:'QDa93\I'뤢L *^#(}2(gFƲJi@D H4^`p(ys>as}#v6f W"H%RߧgqrnMͨ:Z \vyF.2:P;ب!i[R D(uCsc4VuoΫ>6d|ä~1oN23b7+: a;?Od<օ>>#S2L;ؼ0:ҽp=Qn}YNƟN2pvFfE3ݏ"S,..F}ߦ_܈>"Cg@7yGA 2ˁjs_A1l>:}_Q4wތt2>7 U' Hez} ^u;5=p]Y_u[kQ'}A mS [TlX"=âNSf ] 'yʺʓǿO>|lHU MgcTQp&^;^ĩFQh Do*ivb6"DrM/;?iYeCw9Op>0v&>|l#X[&rN`r^p_ۅo!) ] AD*s>x:$׬WwEx]@J6m35ț6^B}䑻HeC]A2rUˉEBjN(F#yO"mG'R+M:6 TSz{w.})#HeEFrtW[kÕ)2^rF)Tfǡo): Ύ[c]"bmɕkB|tC{I$Z IDAT]6ڢN6%ar;F}=D>ϢNg97AE[Q'{-@KscQ'*p9s%*_tT ٰqF :٧;r*5“]Q/[[URG)QVE:Y!"Vx6uPVF-uU]3 ~t2>'Є(8艾D*D*s}FP-[hBu;Ez~?r\1"'w 2fPPB5"H-*dl}{r(BCyGaV͜CajF>V%=: yL9Rԏ%#t50b5Ҷ2x[d (^ȠM|cU"'RÀH´bsfYΟm1u!D@ /wE7$^gXOFFbsЧmehu+pRVm0rBɞloQzNasD*}uvXgIy۟+uEg9>|l|1d2^A\iIPzZ2{^1f;^eZo>( r:_H2cTo~rX&e|W!Fଜ{Z@ɸڳܑ1P+vd{TDQ'{~ZUkU@_- , ='whÇRbIT kgZ3r(EvĶp^+\X%csQ 킵 ?E6s)7:3Xf^ j%>@К0tO7Dk3E6}mƎޟԶ85XFt2 p"Mxs"yEqF'RѢo~1'ҞjDt4A}W J ~A<IQ$wh~|:_HevEh "k;>dl1?+yՠjf?ŽꁢX;"c(t2x"I/`"Z;Du>'t:e6BCHdCH$y2mg'J2m-CSt/ }Ou]^9}siG+;ϯBB-Cu\TxOs(Qz?F%V'S1ܿ|PE {e EN۾^j$2Q}hHE#b֎a{lӁѝ]{sf{!5KhDz_ȹIQ'{OY.EkVn(r rFо*:GNr~S8/|0Mͨ`]W1D 4in>WHɽh"Z/#ZTм6y'=idǯA*(Dws-![aZ0ە\V&+PEv3KE~ӻHeJyphD#:HS kA)Gn n:) lqQg;J['gw }ޯDWsnωԂ=6Dɖw<!֥o4jiUS:7VUy옫=W@@YaZq;YO#NcϹ1?‡Od_TdTZ4&?G)bP}1dCC(o)r| ZUZT@(]k,j@o`}hY({>6 97n~?<u{"tOFPG Elg 'o8EwY;B>N1dCe+7c<Hm@ D*m4Iv(zsRMЂ8ATr.Zތ&9 F8?BJPSg9"o~~~D3Pʪ>fa(J>ZݮAp]HהNƛ>nۮH'zuA8\ߌTҌ;W4E fy(UgD*sP"D t=OEؒ+kyD?mHS(@t4ģN' T7pp]#P\6s`YCF!޹2 Ni#y::h_sV :snlc0OaT&N_o(4S(z$4;-xT_ X_/ElHw_.3c _9?}0A=&!'9`H{Ft!M{@` D*sG.]HơgH-/i`}g&PMdE\{;esLBKO#>2" x]k@k$ZZU$Qdq{"("c6i07 zBXOE|-Gd1Ҕt2aШ `.cH2Ñ;D5YH? s](ʷMt D*g:R97F챨i" G¦:NCЄ zǡZ"^?ڼfM:_u ͡@.PoS/W/.ϥO󋭡X̐]$?rf c;U SCAmD*>uǡ4ҭ89.ūCdkJRӭPޒ*ZDm/Kj^6Xd_K'oы㋊#tOOBk}gF'\}iO>諘1Cɝ;;p f'XT$T24]qI27zyn&*(<2Tpk~m(j-hsgWB2^\%Ȩ~\QM%h@/""6=D[ ʳ| n1 t2bK{4_tMB")G)e)Z88"&A]S"0"qDlg}emkQDsQ'֓wBE )򹭁2}OAt ր fޚhSFbsxҧj 'Af".)z}o5(du97Uه[v~WnT2Q^ݼG6kly-pjKkC)Bx!n BUAȹݐD"9<7\>> H97uc\~_''ZOh~w6̞͋X׹)T̴Ot/ "#Qxlg,a:_NI'㧣zZ75F  yJ"Q ȋqR,F E(W# 2:O"C -mw1Ѣ^"KW#YkW"hm `=}P:cT^u["uPD 7uy5Gqv]@d/DbQ["]{NOAJdmn^ 6GFù(8 ;kb/97Q89Ozܘ; * xb.;(/5 dx.? ٵ#LJ [bP4"ܱ)K*m+eٹĦZW}ھco"{]||qscP2- "eP$ߊ֗,p !޾h@-S`J"J"̀ڶE@z#Q*;twD7h=4nP[I] pKY4(E r:"^(?Ju+7ZR/G$cDtQj@m";#ȝlc_dt3HY+=R<T'V&RHq #I3_79θ ``yy];RDȬՁ|:H2h99K (c lZ5(rNDlĪ `y!Gu[5U<J2W rfY>6 ig bum08ͰKh=XppcÕř(fm #}u"$Cn+P×8y-*}:CbiBJDz!{MC#`W/aJxҟ#cc#Pyd\F2$q3"tb5Ma_j_ ,N'#^D*yX}7`Q'`yazK9F\}=eT\dȠW!,_S)2d"qww;!s6|/bt lYud|%"㛅wu?c|SC覹oVۧrQ:ыh_ uyPDܱcJץux~T~'dfu6]m`SOH~ a!N*m߇`9 RfnEo[&pJ{/~[\;uʠܿu?E𳁷 kkPg)1FD\֣:κTZ (dPFQ!Y%zOE*1DȾ'VPz3i(8+/L'C*|יEFR)GkeDl c͹Dd;¦]z!Ɗ]x%ڗeT -`iH0'H\&ŷTרg3}7ۆ  (,?;#VEA LHod}gg$ny^}̝{Ν{磔J+*".\TqsTd&q-@=P^ 3܄wrUCoO^܁Ěj緽\d7> (}:0 /BU ^nbt}A遗4ym ևI9ơy+J:Dv%n M D XY(;ΒrgY8s^Kqm?6yГM^4VjkC~u:r>=lrSŕ=^ﶾdGǧ;ѷu& DXҗP}ϱf9)e (i2cw"GO#r Y*v"`%@W&!ԉ"^VCDNs+ 3\"fL6NGw B0S½#QJ⛤JޘBn,GĤ8(Fe8"R#Ԛ߃P1h/ g{܌z*,e][qu"}B'?!ެv@J /Q7aǞ΍7ǎHКdؾ:rpi4nbЭoKEjZXPIa sZ|nG[QF8,6"ˢŝב{jbiRo+zyh_(1&V=S-2_ABP;lGQ|~>G3"KA/z֭ u X$UUٳ*yH&K!Ul"K/!ru="=dyTUZ<4l h?)^Q+kdGR(b`9ݬAX)l\!yABay;t=rQZ0H;R++Zήݽ]ކ VʗoG~c\]!CeJ[ @b%x QjXD|#RҋR#@!9HbZ+n+e7x&"=(UoJQ}uɨQG} tntv5Z`Euv:uBjd,X=dl ql9 s,h? i&~B{c+Bh3+h)h?m(zўkZ0!FQ(A{Wh}曍t}v\Mr!(r7'2QhMy- DZfjd[o1Rୈm}CGbu#2o$n$"-ln."[5;07Yj}#/;6+-g>0Z 0k{?7' ڊH#l'N@gP&Es"X_ ֈKp/{cw9s.o~aj"_2}GL ;yAC[^VQUGz6QX2ڋ (?a´UgtJڴ`[݈humx=يzuQ+wӪTbUv>WDʄ"jg9UtLF$q[M;\Cds#Иg!{w ZpwMܫdXnAVyĻQ &G-I—母^D0N~F_@ѯBSe Z<lL6L?-kSQ{ԡm[pV?[wM:_!Ao=a7Ma FE{g{qW">F'PJxElm:$(ǺQ~ez:8@Da(N"~g^k3VNDmns߮R"CZ)aĜTL6"b p }ub.EZை lRjDYm,ڋl덚530y6*$^&9+Qf (g~iVl9TQt_K܊` '|&{ Q<8K#Т _D1FK'мۈ#a69ads˶xD;"(pFTJ>rm=]5GPasJ0jrln$rr>_*gѽm̋e0NCK.#?j=htpxkD{R=eԚ(Ok=G,d~ %';?r"J憣;Q`}<20Bˀ?P(UW(RnF@EV4zׯA#n)Z"3k0nܵbko5aEF?@uN0{Ѧw<Ȍm)4Po5'7"kÈ=D bS0v">\_刱(z.Gp`6 6 'ơ}K$7yyCP8>.$tB\9DŖ"񕃑=e<%pt!ub`Rl+ζ7Y[ڈ7 "l3( ~%`H%.kCc(5ɊQb |I~nίx>(ю6iH@gU1ӌt&#.\yDpADs.mLzܭDqhAX3<~րk> ߣRDGa(t>k Qک988l1Y&p>>cAɧ\{2ܙh+FSjFANpA[-<#p Dްi#"#1tZb1/K}bKj{/v{紾Xswxp|L e/CfJUz:.@V]Ȉ ) CD))D剋&PZ\Gj| ?u}8Acq ',ds2ޛb6ln"} e(MҦ-Bsw6|HPsrD&nDZAfC@,)[G}(t5>q%W"OGW#5J"RȣQtg(J\ |4jAѝ/(m EĮE .ddsSWLF#ͥQm|| ܄Q.s;1|T{tb!X"ӷ'K>\Vl-*E>|3iBmmn3ew(tU#L(\VCE/iT4""VjƠz EƦ ῀K2ܾi"qMmk:n(!,R|<GhT!"u."lhxh>@פx-rL6`suM^! EJOAH46ۯx>l^ B}>v)~}?N3M]Eiqޑ֠9Qe{2{Cpp0% 0~?e u UG,j%6%҅"RN" %TɲmyVa\R_vR1dc22"XDl շ+QϬ#U6uR+PH|4"#Q]Dc C:i;I&ц5& Q#ln&%;6 maADt "Lh~TBD니h=) 0𿋈>h*<ZK삸 EgǠk2=`T czcw}#Ѽh^{>6Y,T <5 y|4pBld9-Qau!/HqɊV$di±Up91k3N.a,MB 8a:͵E ;ݐӐ2 "_BP.$"5fL" 1jqܼ5+H8; x> Q*}dsיuTds(=C XWΣZHR`O!@C&y0'E ̌*,GѲh{;3\b jH~Ch#b8Biyv$^@߽"wmEgawo0ȩ:lln gndsK͵pd q_RFFO1b:d_M%E^gjCe'}NT>2 Bo^,m ldևߝNNDJnMVB74Z5(h^w"O!҉";Gy#ȉN԰( 8wW ֙~)jo@"מUװ}:c /߼k .4gV"bu?"Xo L6w;"ǘ|- ~m103躝R'FgB)Y f#nSkJh_d+ 2f"Bu" <T>O߶aG& eMA6J`RwKǯf@r/$;g>,qYKO/,ubkVO (¥ncd ԊK@7MY3Ja[ҊnkQZ\*"<>CaZH6yha\`&TsmiI_ݽZ4oXt}/q0X7mswwĂ#mDйʚѽL67;888lVߑz9l"4y͕H(|w&,ҩžh1BJoUٱ֮/fu1p窧hWڽ2޷a/GsAQġd.8ݔKQ:|d%1$@>IUn^D.Mi Tӄ 9p4: H6w(:xF\v+L@{N) #R0ՒMAD<'V"!L6Eޏ&Zw%+DrN \E5{ˉr&&-uHk4}kušHDvۈ dX2W*cPܛk{W\BQ5-ϡ}b5W4y_oOꀏ\JUu'(FIzβğWk-jx& eAcHG%M(q:zA܇ @ePXE^: mB$\"cf-I5kFiA t<&@ Ų%'G IDATHUy"}~%Ŏd5Q+6$5g^PB)[]W~͜כo&_a3ws)Th#,uTK\L0h/κVh܉كK@)zWE$۩>tHmlɫ?RY-:$2DDjSZQ1+nX\y"v:xJ{ ' @#Y!ng݈HZ(u1D(՚hEAL`W)=_oH4yIԏlwb]h!NL\l'D,FDFG߉xbEXic 8;V>J?5|ؾ`oSG?pM^ $8v{ {XyVs등(¢U mQ>JC&"X>";l,\k;B&{R4:":njtD)nm!OɎݐN[1E9:Q~8E5vd~ {ТuNךG3ѸQ >ZU-jvA,Oe}Yn5dDkJgF$045HؓH$YJekCeCsBߏq9g:S FvDÎ_׍+qÖ&y|B,9!o cjL`i-5A"\źdVl~bO}ՐW|< 6\88888lPSv 69S?"߅msl#2a{3V&CbKZHWo|]>J_3nΕQT*$NGm-n=ԍ֠M(=+B2⇣vUy*(Xl?ⅺ ui is#h#G%tD{Gky(Y(ɼMt"r0@0ɩ@j#F\f4y_WNOHT3{ Sm&% ?́|G њRLA{`bqr 4%gw;$QF ÿ?@& 4 o(0ݘ;L676ZɍȥP` GP nCl*J( BQW 4WXЀR 8" 49=h!/S_;8h.7eϧo4hlQtmFc#:A*J=Jt_0`ޣ ]50Qc3Xhϴ( ȩEZ'-v8x^vpksi"X7A[! +]EInD E! < " ;Pſbm[byh,4ߣ93ϏC9l9dds7M?mRO:Ew6t~M`࿒סJzޅɥ[H{bX>} /|jxaIŠ sFiH4W;owPaG{Nz|dԓX ^/ADbq-Xׁ9$Q5b; ڎ~_&I`RZ7&})U w: +_B/4=y?!9M+Z\m ́L6"4JnG#1(Ru#ut_RODC}$7k|Tb8&еCz0F=;mɏM+epyI4/SFX9YNکh d6_.DQR0 |c_3ȵlͭ-# 'Yw:݄9^jЮ]{W&W/QaRU{d<&kGQet$H@"y͒8%* gx[ w*+~.޳U΁([Ē(K6$>h)$hȃx{{#Xt!фV$Rah"Xstynfc9f?hQř)$d=6v& ty=hA ÐGo+D:[P:<h{yl#*2gEER"D>P!7ˌeq C["\# qQm< OԡQdt*X2nVEC1qfk{k?{1թ<&VϮ޳"U߽"rÀ+@UV]5MZ[蘟M۫5AƨJ ƃRk)G);`R*w~^zlMG_P1(|DE}>RVH?DF2{`ZF(%7hVz(GaDEqcj:8~oHzl3?cSK5kY4tFw8݄"Cck8Hs"ZG!AQ)۫BÙ^*ztؾ K+o[m{)DԭJxw>J|E +DQDuTAjhϴj٬r#WGV7t)<-qG8vu\DBiXGH%(ЂUU;ViԓÉ#8Pq P_(p u%yr݃n}B[3h{ܜjӉ($@WvUkG̹6uOD'n(Bg$"dΑ;I<%Hx!""N}~cgzd;E}"5&=DU#zSQ()"XCSL6wj@c+JFi]žbEu{אRWϠ~yHDl%d>!T3pO?H:~1|ZOD*"2x/"3Ptq1F=Sx4)֋{ JݨAb>[0YQ@c|6dP]"G;І9zNN\xGBkr.R@4k#&$wL67Gk}%w?J$nm6H䈻( a40ٺѽ& _гK /8@&Fjmkߛ:Qxd@P䪪DT=?9!^TS3ᗡfAoLTFxZKC'qM>jQdKPMŦ;T-I)<͡X&y$"cuiVEq Bvw5K#݆@@ PjgT6hoQ炪.DPM$1MfلY#͋nDl]uqJ0MlD- Тڤ=ɘ[#*v'!Jʅׄ-NEp;G&\(ŅmlEBwjċ 2mz40 2.v?z hw)Bf iAQR]߆}$9>8- b\h=Ml.Ej/ܺ)\I qZ|4hs4xѤ1]sd؂7a+:($^ދMǙ? E >cNFP:f-FY|Ϧ#G&FvZaS#b16Lm]90{y(2\~ϪU2wu)3ڱ oEG[IbxSYWm<߃J;J ;8U< DpF=2Ek^ P ‹hy|Jcd盱QD&?<@pyT.D`6G &ʀ z !?R9?z[>J3g;TܶѺsjWu$F=Fso> GѼ\ R_4B5\9lmva#yh;fhE@=^2'YHmG2\ujՊh%֩1C.(Ep$E'"b *!C{2/L67 _ (m,R[YBa|g׍v(F1 a_cX4IS e9yՃ FBs.Orh4}8JMgϼ ҫ6D88888O0?@A@WHdd"zyFNn-h?Z\q'{9 ޾лEq@mW"YWFrQQhF$qXPM/AQ֋HUb=Eh6Q$~y}{z3Jڶ"aQ:888#2ܞ+H u/Mѵ4""grDZ~T۽r`dMp<'NEm! ٙln0"K3ܻ`]Md%+Q E3PӾZ%$~"` Q^y('(۪-Bѕ*w+%Bƿ7K{l(9 3dfE\R#GFi^Dv u0"LF(wy.2U"^SQ}H󜝫^45QDGW(M?ln]pVP ҄̈"ԵM$xx w ?sEdF^/@` J \uW m&9o"GFPF[ FdH4䝬0FIu/ʿ? {$G& ~aӭ!wOqgs,AQh?}[O)(&I`"X0#0K`.(el2Tt w{<;?m?e>LsGD36hIBtMFd}TvQDx# YL%ÉD@QQMU#61nla̢D"e:T6h#D^D t]~w䣴0@ds(cc5eLELCNrNB{]/D.]h/PU 9~e m`9lj fYRnG?An#jH|Z+pw6(GSQn9AR,'N(2JiNaslGk~y 6HRNh#Ϗ8GU ;hQMAtt@@y *>Qj`DPlEڕ(e~&E Ug@5SʎmX (ALhI_ v.EaSqD< \ u+QBƾ5S(bu pz?vO"jPmHylO"IDz!{}]}gd'MӴ)Å F (h"IQAD (C [!ZR:ӑgӘBiOZ׫$gwڞ+}_uی̕؞Oc ^6`{6+>0<2"pówe'ʊ5Mi劣BbWbI؞5 a%#`<%`|;p[š[{% ` W@:ͦbI ǂ:-9<֨*UZWٺpdll>>["%XQ5Oom+VS}pNzWG'2*lqIcxN}Xg!lS\)DzTµ8 :bIßV7I ffĒc;ģ3Ɋ/NJ=K ~ҿ6<,i?.ծ{ D%""cksxV=9b"6p`cZli"$JkR_.wqȅ9隁7F؍9]]UG,) K[_[dycMeji18^+gKS-S+ĪKR]"H.Xr7*/D<!c%`8~)TD ^󩱼po5[۪ꛞ>Q}N G P}~2\J>#ZW=5$9[CieBE&^2]$9qlv䃍59T7}'9'9?+{3+$ Ҍu'\AqS:&#d* [cU:6b#a%ˁ\20IO9&&r!A X>kq2_ilMU}Sj>,F$[*{{wNr.l2u[+Za FGG7T~|DE3X2.R~nCU} W]qCIdA窱9V8D%q6]5 l,",a|VߌmVr<=x۱d|Hb`x*0<ޭXCz,qPU4z{V"~V2X sX;3\J|(+%f+iC-)47cbK//A鱮ME{njHK>!\XS~+SY…q:e8l&xִ#:?5ff<ԞaKJ`xxw` Vqpp{7S.""(lD{!V]U\ᬭ:|Xi`5T"%X2a5Z?[ھ ,? wIJu9bCunrkEgRbr6Mu7Tnk,w5X{Ӎba%vX (cc/6<6,Z5FtCX_61plL/ޞ̓ j>UN< 8 DƚʱڦEdB(7 ++ 9 <x`;/,\ð`%~5 hX㱙AE*ؖ}VQc"-b]5Ƕ> *ń$[VVd%, bU؀`71v`L%XFUTWcAak容 9D*:)H ]͹n+lԡ{҉2aΊ1X`.Y{W勈64TceG`CؠXYꪛ) L핵 %Nb!al݊eJE,y 8ۅcM"5гA(EK@4ofY՜A`6usV׍5YY2UÒ|,HbMA3ڂ+[|Eؾ "" ʿOƲtfHօ0 X k*`bnI RcM(,yx ۟cWu!Ο24 V ;¥+( c9FF00q,i:+ ~%jXe8W+V/EDd[ꛦ`*6 o#<66 ª&L8xl`N.G a5Ta|qD®%l3 J9Zg=\8/38%Ftlc;+ ec""}Jl_1ܐդګcIa .b1 89 8-##F"|/jpC~0r 63UTnJwآ^l&k>k;8Wr n? u+oxݻjogxl]Ujmm6v ̨hOmۖX"(`ccMeo%k*8CjvANKd7al#Ou,ֽ%\# +ܖEX l،}YsS6%%XXvuH] ,5-""raU}SَݘO~83cX5XbE1뀖޲N[}hq SVUߴR{UcM){`}"NSYXp2: #`q>NV֥"d,xL5pRe-q)[*f+~56y26"_q;zED䵩oJ[Yti8ua_ KggEcMy Mg wk*bD-}+8?|1o$ !lB3X"D%wYCu'W5{L߉Uup0T%UaIok [|EhYC3݀\ZoW0&*"";*Kl66To+KK \ƚU=cǟ|~ 7qJVW7} s`5zOd5X"ʎ-=窾uYy)`%|APEE ˰RZ|E8_|`28*{\|֌n@ n5`MXy'@rYmY@%6t*6x!`Wc]s=+jDDipA6;xj"+JDr\  +]s6K`ItBONDD^w> kpcMeĞ,Ukv-B""&/4JDDDDDD2DmEDDDDD2D H(%X""""""KDDDDD$C`d, Q%"""""!JDDDDDD2D H(%X""""""KDDDDD$C`d, Q%"""""!JDDDDDD2D H(%X""""""KDDDDD$C`d, Q%"""""!JDDDDDD2D H(%X2.ss9|9wE9wsusIDDvNz;H  9s99W0-xSn0x=$v99뜻9WM= ls]ιι/xιf\sbW!\۟ >1t}?6B;u9ws.13}g^ñw89Js7t}$#,` `P{_Y9p&P?.N;n8suK%cu;te"";8=sn oNg=y^0"1x4:禾ch (9"N˼k;Eι#svmq==b^Eιm?t=FܲY$osιι;ι,Sι ;t}+B|t9L F9sn\s9ws8sιgsιs9}G;8jsxssuιUf 윻999~6ާssvν\siU}p--ιj8 3p쳀y7{~`늈l|pU\ZywVi}~5] n)5;9XK78.K{bj':U np[ ~{ソx8ug+!\~x{Œ1qK^望<霛|9n +>OrW~8sn_k@hc|M͌9x~]M]2C`&/0>i@9&Lf.wM80YB@?0mIa)=k oukι[_? kO~޿{e65#QǑ vxx(r$"*5u( }'p=;Sb18_ݕט$os_2 b2iy ǦoCqx]Q%7;?۽{&<넴]_콏{cmW{ ~8m䨘s,89,K'ZG{~{?$`zy;a,%|{ >#4?HvྋFyWr~~pO95X~{Ozǒ_sWtx|'\6,!8$x5iMk늈dRjxX;rΕ32{m{ڽOx?gR-Wy3JiجWv3{GPе+QmsyM罿3`ι( }Q~9#Gp{pDhs%7z_{s89׊uA'۱y֧}߇v{#/FӮa3q8`v<*t΅3yN$e&[l; ?ÒǢO)yA)cs}apԱF9H]%( 4;Zxo*-^ ι#E@ 0C'o~Eڏ:羋%X?d'?w"%wuE2M3X-٨?Y?}?k؃@iڱS?nsνm8%gg`Wڝ 8AX"ڰi7)h$P,^7U긳 嚳n'9-H᜛e%~r A.TWnG8SuyzSWt[K:M"ܩ| x{߼&"At!~Wxlc˜sg8dKps,~szs.U&^txscbqν'!̾%8}vOޏ1xιQ˝C_ιeKDƚ,~5 ,Y% oj@ cX ,,?cAr}X# l_pAtb =FQˁs]جb3]3vsi+yY{u7a5aN X' "2޼/z?ƚ.l*)~ ĮGx`s+epЕo6Vt`LjV3Xэ F] ?j \BcgsX Val˗Z઴FSso;>֍ #28K-ݜs_:8iO݉s=/c1s. f'"""[q]DXR 8a'4g2u 6U 4֯뭵c:""k%SVs.޷L3=nJgx-*`d, Q%"""""!JDDDDDD2D H(%X""""""KDDDDD$C`d, Q%"""""!JDDDDDD2D H(%X""""""KDDDDD$C`d, L Թj X t[&DDD2Unrz x7O艉dO9չGl=<SVOYdF n.`aoX7Q&iJD&P x+QXR1`IoxvSDD䵪seib3@![&zIiπ!9g+9JD&HJb`2D[j}CDs)@Bi_b_d+SE2AM.D&N'` P%QfxS,[ɕ j}C pHl0;hv<+|[L3R%2A5@7/+p-{D44;&dEDDvoKY ! IDATϩ=Sk̋Xav.6uvo3%X". `gh;h[܋U_TDD^\|Gg6<'fO=.2 Hd a^窋&E2BkD&XpY`*#{R l sGrg"_DDlZ5TֹtΟ`aNes\$SaN+η Ev)@u: ms y]g( e݄:8(.yEɮ?aݙDDD&\^ϗ2ԩqE+ڟNFBG/Ī1|gaZ1@U \=o3K}맯j.kJD&H>k_2oSo24woQ_|0/\F#CˁK NCtxޥᢖ9}xr@ uF,V$xL/W3Pj˜P(tM";D%"UWyuNJbUk|^NFFBX6- !87l 7:Wn~̿>8oC7 |t)P%W, }$qncs$_Vٯ{^cMe8^k, 9/qpex4Kzng-c#y{-8751"""c) #mPyKZ;uO$"b?zn3X(]?kNEPdxkX+Bl3 X\c)q!pN$<1TnWo¬,չjWsXYo $B#qkkފ$c oɴM&ysfed, |ᗽCk;pց9Xy /_C@Vb T~7r~[gLG|DDdw^ 4/hif p RJ%X•j )-{,l?2W&""o'q}؞fE?P|3,a%X a3V؈%[~]ү Mr";H :&-"/fokyƒBTQؚ<,jÒX@J$!I^_`~zZ]=9zpܮNDDvky]NFKDCׇcɅLƚST8 y@VJjo$0=?9/YU=}|Td)_+Gu}  }<MqY:J*QXSY0ahzpVZ|r`8\T7-ʉf>{c6V68%~K:B>"K`%#[~X$+=~+ucMePr%%X"U?_W?켿%B&loi9Xق c 0@Ac%"""c)60c0ǍO1iEG!VW6#XZ< '{RR-X\K7d,1VUTL;y8x{qԼP7vOV_l?L'`ނ`0TDD䍣ix, D6؀bYxx?ÉTގd+%bˁ栩XǪZpX]H&ha1T窧xځn:x6|$q8y#69sC󾜌J6ȗ}c1, =P~,,6s\`zo n{niKd T7Mt~iޔg[XX%Xy؞k<tLqhFdxAE HKFʱoF\Zpx]쾂2o`cg`aP$O$bW8N&?xj0YlTO\~XWu7<<."":W}6uۗL7L86Վ 0=dyHe9 v b Wb$[F}oɞЋY [ϕ:7GȟuH~*9GW7:,mL%r0V"8=m [©fXRٯTDX sհk3F!UM \zC ?yz75l g{*X]ѮivX_T~=Y^ӎ]v_qqMkާ5ν-_sy4%s6be{[XSLU}$:.?6V8~ qpzt0aIQُfcV~+ 7̮XDD&-YNoYh׀s6wN6Gŭ#1x|O_\~y@ r|?p1ؙC_V`۪ohT(7JDvBcMXW%~Uŋr' r ldxFukC[%X'Xp|N~NnM‚PNz}]-B{sՓ+zO~:=5N.KD4bY1,^%czUTb,J?8^<յ}=۾C/_)/7 gKm˒2 `&VZ/2.B$C?gY!s@%yu} י'QSr>PզG9XX)`*(z ݿ7VT˿eK[|Źt""s9y0GH`buѫNwn3]'$lvP,. a3Oؠ`*EzCzA,ƥJbK6ܐ~͗S=HJWUToܒwHiK$s~-]/o!s޷t\9Psw`kXTmf.lm!,8 cNɻi/qd$xԇpgpCĢ'l>(ٚ@@Iެ޲¢6635*=`CS@8T? <~$%?BC_1=XSټSP Y%E_Z?~ȜYK=LD`gͷlUX @9;/J)GqR|>PRt6 $arBT:5''lP10s$/Y1-+Vb;x\4J%\`%mJȞGsr[ֽgw\_E^W4%96Tສ}DӬ [znK$QC@#., QX(Ig-lTiE0\9ӱ\+/ ""O׋O22/K9wűW';Gj܄ mPbv3P%?r}V\(B3Y׺}O?٬hZܗ{g]8%X";U >ܾdzxnt30*Z^ > k~C76,JuV 2\%,Vaxrzk( ׍I{ߵlҾ]0sID䍪i^:h܁ķN~BX|T`U8AåRUU>B281i(eӎX-ا4率-eS7oԵd,P'_:*})YЀX{vd+41lf sAr%]`[Ja#{Lk $80× 'M 'JLrֶc5Ob .`%%@upvZƂ? B b 셕hḽ /Bo:glNIIDdM}~?֒[6,qݒgc!M6674|4u=d*:rm7%_!SeחXx0vhJ^6&Mq{M@ٽ.N:'46Z>2(V&؇BaTjAqEj/ <Zsk^x =O sGƚʍTLyU7^K*V o({y *Ţ?@A{֐B"?8L[VڴSkR dF= x30}?lU}TP`*6ySU}SmcM̽"Kd-jxPGZpV:W I4c%XgbW6b3^s޴8Jnx1TO ۓDZEo޼->?rᚋcfȮ,ڱpL^ܚ;!CAgmHuu'GJ>0}_LJD+44Tno:8yQ%a|Iz|s嵾U\T窯f`f V>qV6x 6uf"X|&Yܿ >XM˱$KmԘϊa#uvBy>¾\j}CK""+o:8?VID;=ELX}ŽUL$<, Jb%@36{kG3Oܧ\ƋCna,:2x\c2+kJغ`d- [+2p`I^7qv1K[Z"y:W}WohsCXvVޗcMnvS^G( O5"pP* bA+< utϼɡ)[ּkᓽ'$""o0UMx߼mKoHp73Tv7}j2ϻd/6هp} Hn݈6Upqd013ȠRӻxvǏ,uғliwCdlkj \8+pX׼wֹÈS`ҍ.l&*JVm!_.bx1qjdVVFZϺkP2X—zKTUt☾9""zYw6?ω^3ٟWVU'W^dÛߐ,yxE{69$V.;f6lzfOt0`)+? OwYݳ'$$=X 7ȑ|'wT_OY//wE4`FSZ G_J^UG߂?y;`W[K:5cm߃u@zg’åϩuU`eckjrhpTZᢗ6kv[1Kn7GDD^6 Op~x9HI/[}wEsen k:P:zyXlUK2+|{SO+)^Pyo臑MX}roa`3z.C k4],pp!Jbk?šO<[;uޥ%ļOISh푁!Β,r?Lea ۗ5_uY{IAVg$So_;ŭc8 lJfG>꿭ozQ^s 'p(j`}T7  m:ty.Yžܬޡ';T7}-=0/taAB }87 [p`jQpjd54]:҂ =sj}cV.i{ k*Ϩo\쎾7=3Y}s_ށI+H(8 8[W}|Q>й5+uA7ޅZߠe,.l`]H0 %s %`x?sIG޼ٿDo{^*aJ!* LboJG/ҞOV`"""/o[;<XPUt 7d%rw6:{ 9R>Vޞ ~\ nXS9(~ozރs_ If5T&uE`vCrrZ>&>vrsœ7ZO{bMp:֒v 2̓ 7H%Vqۮ;ljsp ~f@3suLA_ ]CP6whJ)dzzoifQG *pہ?c3@綒q6챕s{dOJvJD^B3f;\wyz|%^4%۱R,!Z%Ve"íjC o6kZ[-jvN6T HXr 8:1D,xNzQwk48d4'Ś0j9;}$!ycP%-~gY1}puOƲ#["sXƋf/akrRT,x`VRVsnK^~V֎劈n f=&=b>7򍾵hV۝O5l۰s>XS&8W̢7gh["W'5Tn/}IniGZ7a'CP8;ʁSw΁i '4{fX86368g\U:҆K7g > u㆒[ӘTc )75T>?A .";3[yi'{C9> uYϊifY@gʒR`@û}S{#C쎾 lq,J5 Թ`IiPUt$KdV*kK/%3֮yiá˰k*:'. ȫo\Uߔ?__}vo"/zܿ>sP`"Lй,~d8t0p4V [  vwiQ^z6e}yEy6n+g}ȺԞ]DDF:( ~k\|g Kd}k~p*E#=q{_H~qCY%dvdv\0m&ph/Tqy-7TQ{+>r}>""oc+/BW٬˰nkދQDv,O2ihsNOdwSL'IMŋ'<ͪQڵǁwpnC|2; +v x"}?=z^*o٭&>l3&bCXnB vorӊujIoOiN7&ם‹UM4{E2*’'Mk*wzWmدVc{w}XGGGMyem?`g]5A2l-UX[AJ7Ӂ`͑ 4?'wR87ַ=!=qUMKs[;_*xA,_/^~jk=Pg kW7c3TƚiU7 ,K> }ѓRUߴ?et591CQIny Ir7u7  '"^>0ƚflAbs;L*kL$ C!Ȁ`vTfDLc^[9`\) 9T̡`1 "HN<9uǹM7 yxtsvcx@k/²1 DW\ߥ _W!upԬٳ[>#C֡6;p; Ap 9uQ;O#VʱMmשϖyp I1GdaR7@y>]T@K{G pbq"#yڌڌ zsW}18.z?#j*k~97֞xp¤C&1y JPcq]}*ڭ"G<"}Z?e[}\AN95K@î==coc79}K ؊;b~Ad*RR>5[osEٳWNϟ%2 t~2R?Uz\3`02q %XdL~ߐs~j W|nPMBEfx`64&O,*,ހL*ʼn}l)2fRECn(s@ւhP{C rmy~ա^[XWB L[qAEU/|"tTTXISNa"$- ,*,~eoMSr3CKչKW!;` ER©^qn)Ke]ڕlGbE/sǑx͆G ЁmS_ΆFv.4,.; Gi>?R}^Dz`/y Ayn|mFr0"X#djmarGz>M )<b䱆8kf"0fS͏ En6ZE~nrby."=$'zs2)*Wo͸bpnsx}߮~ݷSd-3#m٨79 }21D3ii >gÞr@_YTXt#xV6im[S t_vF$'w9cWMZ&2 YKI]ZʊA.p2p a9[OU 9V;XJ_u}!F3ҟރ #Qvmꕆ@"OM=|ȱ?\ u4YA\"s}g-1KK 'N\}~&E*Sfs{D"AB(-wm˚0XH 7 P\jO]XJ7 k ˹%&f\1x>FI}K5=~ U}\ xSnV?36 iDR lބkXXԃ8v=Fk#VVolSyGT}59g "nlK+i۪B;x% "y "cNA D.))*,nvK}uB`ئR<i%!XBCk "!9CcH.Qc$GiSrz@]_(!%ֿWIUJD߾uw!Wӧ(Jy1WM'_K 9/1,yG!Ϫ"bT 1@GI١65D^֐<:%7Qc_}Ϟ]RjuRU]aPQ:w^Y2Nnvu}Nm]%у*V}ǩq~~S۷@C6zs h{!ppm-.3 ){7bÐ?HYYAᇍJUthmĩˈTEH5@mj kLZ^kc.)YۣǀB9sݔK!I~g=y%[)PZrV"9VA<O@ŖO@7,ZԿTHooǗ/lUQsjT.75!: '@$g*lzDΗ]P6)+4;)3|U!7ň wpCzAHeވ$r,"T{RHquLGFcئ RУYv'y婫*r%I^U<xUa0,4N`a9'oq %!y "5,ޒȂƓzвHGn3,^8?v\wANbb 5Ja99mopmWv}ᇍJFK{KAM"߃D9 NDP5ye{6>mFz~z8ئ^bXλuO|Ѽz~d1a7apzm]uR֞tuKזw(=uUumF./)TbB@ŗdW۟٦p̦g 7Ґxχa9/ߣ&bK%ئ%女!QEHND&v]G/:Hiĸ큐 6AߊgbA$ئa9w{!wHH@Jj=gN] @'NM{cT0XՉ@mnI6CiKJ nyғs Bp/);x{}#ݱsFk#.F*5s;vK1HD$/nG.ܻd.n%@ 1׋!wN@, xU=xhQT_# د?4߁mS_1 x!}Onἵr_3,' ʳ|W1 G%"εcS>N iS rE2Xo~o nFyoK,BDw4 <oBݹX IDAT2 +)< {o ][r$D?Z1 IQ= \_A"Dk$V Avݾ[Wѡ('}iQv<ߑ}mS߈8ʚOFmS3,dCR&؄Vr|hU4{nH~ oXNmն4,g4bNHO|5Una9DTt-A-|C"OשArCQ*K"!D푊#"$bl" _ yH"[]XuEHS$gr..Iay}C;8 ӰM_S~4aqNC't6["PѦ6Pm(^TX}S{!B8IE쁏%Y;;T O 逐gc 3 `XCw*;ܘ}/%:4ɩCm|I h#Q(4oSqtn&~Dr ny'!k_<<5ϏhzJ+VVOK. O^ic:Lj_Y4p/p]$^U_°k$A{N3,,մwMRq:jX"^fI)BNE ={s˻M푚TrNh DoHDm3 i<L1,q$h$Bh~Ca 2y&#DaBrR=tDvH4Atr`rNH[=TlTEKEm Fk#R 4ґK;ZKq{wvDH{2Qc!sf{;°HG\z$Xsv'> Ez7=x!bs+gH.MD@6W"_o:ئ^3!7흐Ic4uk[ynA6#ݱ5Jڎ yKӱR@됔xrUWs;<:si3,lWRYoq9_퍊)Vs 6xw:a9g#Y+ 'ɻ!%Ұ$22'~k(:Kc"?19bXlS_|$ lƣgX@m/JDNEa7y FJb2Զ{vg%VmD8\#$UkD&:k.BlJmUE`u "p䙿O=;6dR|7?sӭ&ez~QV,؅g E.e*~ډlOD>Y]a9S,_u95!#tUVMO3,"sw#=6\'cm⏳?ڵ fI#tM͘+:$w8 IYriAIh m6ȑe 9tnG¯s.+=uuquKPJQaN =l>X10,'M786?qɏFlϫF>V$hsnVߨ,MF 47/V < ܷT1,%☫7*۪!# ԛ6,٦u&zlO(cŖszN.I:֗&q٬4LG*$[Nx\"5$+\# DӞ*duG EV=]xAW61f]O#;Er"_=Rcn ˹7_?,4@p{!϶){Hw@w Jf.Y \_TX =x8ċᰛ%'Vn20,'o -E3ijq\k/quD}AN\ Q}>|IòVLK}>VgXNmZk`m404jrr]W$V-xu'Xa/G ˙M}ɖ?mjAfᅁ̑bc/}wW,I'3mm}^y¾K\7\W鋎{hP(,AEtVʾ=j})}7Bz&  $bER!N3TV\LErN QDDHg51:?ئJMB"6b/F[0 P[ڈvI1k5y^|;*D\@ ?6c_BHSu9הîr#pAm}^[Vd"뛏&!_=%nxRV襩Y|b |.Ęso4flZ 8| pa9E1Ñv7 ˰{?#'ua٩a[1 2WwMdžp!}g_kԬHZS"6Vvt,w݈mY %'nX|p?1kXcآVTTIk#ya`]oW]toy6V#Xk>_*:ofTϞHHÇx _G>S9O>WL\c_#љT)D{h" !HžlX|1B@*2pa9oئ~Ym}ʫo:鿱iX~W]LTԍ!F`!::5Gzf}J~|cC .T1?wPP~!GL$Bڨ!k'ws|dVqb:]=¹Bn0,w$}a $kרc"l 1yQ[ڈC tAsAӢn1"7 )V2s{we }k^8Wk}WZ]TX\SxS6l/B D!XF"AmR4IWnӻĵݮm`]E>K _mXתj"&w:ى/#.r;8 mV)+mSlH)D<A"Ky<¥]r[-/%ԅqU=Bz jhmu#ݱNޕ3VY%>AMW'MċXyP(D LPjd^[[Uo'eMiSq5dZUluɍ^ԗS8AoF dDqjT4HJG"ԙxzϧKg--i]t^7=4-"On d[=uH0,`mS;¶oPQ90,u r[b$-ǼJIf{\?< YŎm H͙th=dî`m`$x"nLc3HAȤ<|m P$$B?+ `x¨"圃'@t~8Hr50QͰg5}})$q:{fka#Խ"`%)>s;Hu:}> :JY]Yγ"Y< H)"$\!C B`OF2,vÆ\ EHKL{mk=XO~<?r.GJ[p$yK27]8_g-_jK2{WQ/rv/3 \cXο)@Rە(i~4,g/,6\ 6,zA=lS?s 7VCDE@Y<`pr_s6ҼqUmvC)zFzh9wSONG9mSɯ "ъȄ6RԖ?zQ5,a9w uzdL 9I(= !Rܶ/B_#DzxIūi/zXV5G˹H* a9MHit|Dx]5g df1TLI?kR'%>HE{" oDqPlDg@"=;;HD"6HhΖ,O#ڤVXsFϚ'HTXF#-XhQWm~Ov /*Jɶ_;11 bXN/D(h,ذVScsW< B57unr yѐ&ئ@cXNa쾶*` b#W"WLe/֡~i S/CHR$buB¾GysPr.#"Y3"88a@Ć6ϰtC=퀥H:+1DŽmKlSn.VQvH֏|j(YBr z%0'ƃFdzT}~32}D[ i'tLQlS԰K25ȤN{ 2cXY r#Fd/5;lSDD޴MJr!8aXϰ# z6E4,-oXψ*#$!FxoئZ"9wZ%Ճ{/@/ H/Bx!ӐhLz=CF4;R6]-T7D!2:XA@f@M'%Ow8b}<1h߳#.-IL0P~#(ii6i moTΉ5c?@0Z$RxD+֭cY+{d4-d1Rb!IOa7ŏ Xa9G/cHo(m͝]nFp 迯8̗#Φ tgC+[*>n[k`RW\8+c\(%ʽÓko_zeUyT yUEtR("DHېHaPLrs릧.%"yީO݆DNGT ; \eZu#ej VZڸԼvUiXNT5,M հaj݁˓2%Ϯ^xZP" P߈\ozm %RxَyF§‰M \$G&󛑈G z,a9H!c OE~!?=$+XfXNH}T8Z@r΋HT@dBAz 3,q$*!յG7'#H;r ˹Nb!%}}圌xgzʳz8ƐNwH a9r2.E/GH,DId!@Uw1!XIDKGXTjqIbvmR+Wx Fx[$,Ey>tWS/8:=u>?S=4gfKE~H\Muy9%Sp"ȓ{w-Mb 3m~&+cU>@Kɠ6-#-*UD%9H^*)z aƹQ(MYQXޫmύ=o4BPZ\r]4X>xV vcK푅~N쇆@dKj"s(:Nl*?V0>\ęQ_ꘜjC bhw"ꕓ[1!#3[S=-{Ϛݗr$bsUc} 6 LJTU$zVJ n(k?lhVK])gr3Bkun}꡴eitM}}~JYGt5Hz<ɡǣrT?H'd1Ys(2a-E&HwNsa9%1N#!1y|@4>E弬ɇ00&c]Ereg ߯|GE<-c;Z]*g\ZRKZSɺyK<5kź[oroP͸ȳx.߁|DYʓ(*, ]3W,¢g Y|-B#nV ]Jx->E'm3 G :D;"6,"w)KDtd+h9rnFˑwHnk| y%"sٻwcSfv-EHJ$sǣXd4Pb k?/'mş'yL˫ mB 4(,$Fd@b ?,*,{c)E%/6i&h)M VTXwDB.*,ޞe=xh(UeːX<5,MyNDb?M}ZAjBJ77 ;lS_Pz6q8,6gSlCr_IHҭ}QmݑB)|b]!82#67.d"*Dٗsi^ퟂ8|<A儐5C4)T=׷I?GvVxK I"Q ! Dm_Lp?kB5>bAb\-0,CDkI rD+ۦ!Uό 6:ݐ*=R Rb(a9s8_EN= ˙†O@Ed"HUj ݐ DVL<Ɉ'\Q%sSZTt_ȳ>15%k଼sJ5G\AH|:)'*aW~ĕVX*\6m+fG=qsǪ5 Mv92U .qDm/n Y5! [n}\6UlLrNEdH{u2 jsb[YG.bG"=hDAD O,G\وQ@=!~Uv:bu/!rGGy󩇿 KZۦ^&I14$5ck dBJFd~'v$-˗$U\d*$ܟB4BE$~ Bz"EXT8ӉN#>Dm"Ú5ě:nm*Wj1,goԧʇDgV d5,;AJRz ư9Csm>O"F s aJĠCt^=g%$uUj[!XDKc<!!:gdԐ=Yܔz|7˫rJ!#cwmcLB"v1[#ݱkVĥ?pjtFO*gVa~No" 'opc 3g2xe ?_iD$`2vv(6t7(X.Cmχ̽ŶW|"`K` :r^9ڦ>.nd&핆圀_L&C91,'7$* c#cJw&B 餜=)8G!8$bD#N&W!6# GoDlabV#߃ !6m2UYDrBсjb,`X(ubu(hz,cl1,CJ\izEL$"3ĥ0!e(u%u+2,.$D` ˹1Xt d(ȰIMDwb$ ˹g@& >C uLB^g!ý}It"G|Du_ԳF 3Y=zo/eujP:s1`{#BF /Waeד,%Z=6GYئ^aXhV;mww(>_b] &k'_'jjFYQay[zћ>QDDPQaqiQa@Xj< u Ţ--xh4< &DE? 3Ոm}CELbIr̋wǐ2弫=bIm 9bXNmYn9.b!gBbD"re^ Jgb;C? 6AT,bfgm!;h8WלA&F !ۦa9U[ULˑIa9 vbȆ3! ݉d< Y|ހL 9HI|<_:O'D1OȄ!%H^Zw}z +6#QOf;*C4o>bw?bVDs# =VSbt0B"F.&Dخd(jH,Rc^ߏLy_!T>[m!I 9| IUm98֔1s`^=2ҤrRJ8Ioje mxwQ|)*,C!JDq|,t,O~H}y-v:"GoG] =3"4E#\h!Am=/K`HvoXwnGóz~#@0Ƞ7 5ës C"כVƣ#D 5 NY2HXB7;ei;ǏEp)"Yyv\ꨮukꪎ*鵻y 5C{o}'yCԻw+ǧ&_3 <7?{Qpa>;@)E>)a¾^\Q_"гf>g#6Y<ǜ8' obQ+"R˜+mF2܎P$FHrO2X^Dlh(}%0,%o]Ql܍#yy"jT$8a!Cնc_:q"Oњ\Zߞ }ZSW'L]<#u-"K<.2TP]YK{8F y3ar{LJ=F$ [vpeXa$#^|HֿUÓٝo-tCϢ\-sΛҀrҥ1|x9paMm슼݃EYl ;m.[EYJ9e~/"]c؅㿅`E" PkؒQIsjAD @ƋMv{ێ6<}$ho6߀kUy)B8 M0߁HT Zxs[xse漹f>2'6' C{2bO6k^bgmP=(_ieau'"ūɽQ6ך(s'sM{d|J54zģFkah!"<./;33oh՛B@  %0ߵGb7ngYDíˀ-2'ȸVQj$Fbdd<N<$ Xk?aL#puȠC_ڬ=BDf/1)5ǔU]n^JBtn=y3Hg24g701W' oBx7Aa/J|(2J]En ̏G_Rdž\?at iȸ7;j-m%Ϊc^o~Oz^5=ǐwI&ƔUo9sQ񌮚dQSWe f#R^漼[rzw凥2&Q >dU+k]zw02K`hC҈!'oZXANn;|Wa$Pdp!5H,q S9(!9D yN+̚ؼ]9"(j.MB\z]%KMm}<ޱm#<EFʙ[S'yϗ>0Ñ@Vw .z*[u {Y# Gu:u t* +ïQB`j$8 `qcf&{=ix("Z'"0O2Xf6X6WkzdC/ȻqK'">Bƫt| od' E e~ y>?d+BN7ǣ/DbNzÆkhxv$()yCzSO"Q\G($5 Xl2)/^20o}U/gqi' 9=ߠPrۖ`Z}̟~ÁL((Dv*o1@<~%K _r0xkg̻ܽ#%"L|~N"{(:-(b< ۞hcU΁cq.~r{{.>pwk@oϠ;3Erg?lNh\nua@{{>D:M> Oģir YoDa!ߑb>kH AmRp!H0ҚL#%FDBt94" HDo(o"""AUxΞA*Q ӳ1{Cw|#iKV!`.*G]׭asY<$FY.6xa+Pm <̽ɜ  )"pБu~Je})/i۹l5uU#t+گX*rYY57=d;MMs@zs:by@'dL 27w&6o 1~F5"h ?F>$W' /z%/V eNrFbv3qn$?1$ywţS\ѷtyC(Yǣ"D?o{?+yoac82m@rDD2pE*Vu!ԳT0m*Gb x4j$d=nX 5s-^yzK^.6kGp0"VC Fu\/ Rl~q%ʿadsm~Who֣4^7Sw#5DmU(` : kYSd#ėq"<# FbUixCH@!Ai{Ň|0kL(^v۫hj3ma^'˙?y9{!\e τeK/ >HOu];É'L/oG }.#o5hB) ?%G+.CC [HH@R)I$t}0e%w>͟E]bq#,s<>6[gY; %8nSqH;YHi>+x#8Y2l 3r12d文^:"@z^BwY)Hmdze#^Y.@,1Hp܏-Vn|'r Kg7<)D6*7P)xeEbrZRH,qc<OXb$Je<~~<[O9l\j/Zѻ_}`݂㿖&r(O\w ^5uUS_}[XW7!ՐI3~yᄚWsyMqMY5uU+k[=vQM]U7XU}~~=* U +kNusl)cw׿t$OaQˁQ Bj z~?z‘aZS Xb& < SglF 2d֍VPw JsN@^1Xb>pyp 5*%VzӬcXHKIj1?_7E,ˬZ@ݜdso#-t;@^#Pۛǣb \լ{…>6z^D0ѤYFX#dd;7Ux9m4C$І\Zmy"lnsC|oG+#ăY ɶx4l L}"z h<^No4ܵzownn/k ]8jr/9¼a2P]Yj ֢j^JGϑAk>`x{G*v' rׄs:U=_]Y)1| P~?9u踈zl|? /4Dgƣ<{|<Y$ȂWSJd%X&I3CҙlBSyryDk<#H˜clgA3Hy 0llQ[+@Dd;zm܏@ EBry<NF_e/3cyX+Y FTls/j\#33ϰ/-ߑ`P7"KUp5~͗vsiByO1sJkN:ʣm^z0:ϼUM7pg1wƔ9M{՚!ՕK?ΜgR]C}w}3j~Bѣ7H'׶ 2xlGIjN]}q^˸0U/T{\};r.<ʧf>cþ_hH,qAZ+Hge0 ύGMX=炂 /s7.@V7pK V=l_8["mlZ/ ap}`"0ģ-=$ĉ#Lg:~ѷ5uU"$C@:.2o`ʌ+9O靎("/ьH,$\H ~ + xabA!'yS:̱!/gBd)Hpe+Aa!ȍxso{8],vO +HsIi#!(dI2G/c )x;Wk2"W R4hF}4ϱtR&BLzSox : N??ٸoW5ADFo|KUuL 8)NE#/&N { ~q_Zv=n8Ô\SWBueqό"$lƈX"ā&?*c#*T0"$+meH&FR$g.ًHGd #ҔT9<w6E zg65f;?%P4p[ϘnD2H,';u"³.AQ!Gb w&ǣאouXb„,DD5ɁQT5 |FcVMyWshEzM>ZM!^A,k^BZ?q)xax:%h#Z 6״5SfM# bWH{2ksDBHyn#`e<?fIw1 䙅y9o/2n箈 FowX<(?/)ԕuΗgr3Jg32N`Eb~#t}nɬւM޷]PVr)7ݓi{_E +-k6m:BTeFأF]Hs-[tÑ< +af.(9 $X3@||,AbM?d{YGB~!Q|zLl{6"O6p B3(<[}-""p@3h!^lX_lߢ8t vc62(B'dַ3)s? {/Yʐ55&[[YE,Db!aYɑXbm g >͊GÝFɎGÍXe f;Ƃ5g<iNuׁ/Z}{-͒1_<*HV8qDK{Ř˧=XqmѾ?{Pg)zwt'|Mݛ{&Vmh_:b9=XՕit0Cк>]]Y!M-}E7y?=.5uU+z`aRs̹E/m_s#rp9~2 8 (w\!d9m B*N?A z.^x vskӈʖs"ku^_wu*Ei0لx!xzsyN,~vD{yF6E nc>k0k iF_K"|UuzdW'FbNQt2"]_РT Kgn< Ы 3֏x=ǾϙI:Y6;VM]U;kߦ;GAN=϶p*G]ס3;KB.]9<8q|׼v?"D+LH6]G)%C")]H!yf(lQxV!9(d \d=IKBŏ 9YG6&އ=9KAi_s} D6aJTp)j}0Uhx x%2\mcP$^mN3ǯ띉 e@8i[1"T͹(f\DHm*Knl؟-FY9~ "шl\_o^%5NܒӔ{8Hҵ_\=i*U?lcvsRfC1MyM]0vÇ{GG$ɡː%xG ]c1fkm\Ss" F54dA dٻ ÐB) ($h^Ҿ*O]6a'WxU{ʐгV'۫ɒH6szE^C.D2's%XE3Pf%.\h dюMV130Z61Bw$𯘿f.qW0"{:3׵u'!oO}y1q1#gd]Y:v}v",A!)$VbJ^Fue9y܈BdmeqR70L܂]>0[|e-nAF~oFe${$y~׽N܏d۞(" @$W~A[: ͬXWf qL>W=K;f9P= ʓkɮ{H&qSf~x9hD]QxUm{F3\auGb\G/(}Xzx48;K؜2?I>7k{|ް+%ac?{fE邜89VQ,wI0Hm7AZ~q_[hApʚw2 CUa(m ?Md;8̬! ɾ#ŇLҶ%~Ey{sFqWzs?꒻b| V7^e3?%Fn$HXWPD^2lc1a 7C?i$\f t$@6"!d=)K$mA v9bo CJ$$7c*Q'h0:[]3c𬋽=oZxQE[J @:%u݈Wn~(B=H-6b{{}wu< r/,VRk;l$bgfZD0׵ւ'2k.sl/4s9w9\ poO>}Ǭg5߳fm:ۻCVF].=}yfEn)I$x$1f3.]>,?fs|g]}vmw>iȁ9ghzwt$8^i_= X~[7\^e(\V*ox8U|SiuemnjYd%JLNʖa;s̝H~kq??B =(*bh;px4q$\֍6-< | sL%K*=`8?CUr!2D$V"z6 Y!k[?q'*}tEǽαȒx"$"XA0@au#H^6SȻ1Y=C"eP8[c-^z̵3ךr*M5x\ YFIJ"BҁHэ<6!3e,¡ʽ@ 2<+ sd%;qT-f 8Si/s0ZKx"|/9>u ;o@MN{Y.=ȢxeĮTvn+ƻM=q,]v_.;yNC8UCȈtj %~ϳc; -nCa^+54y٪rbxmTWos 3#TjczOyU Wv'd%F#H9ۼK!$W("o̿A'ᅰmD Q)xEh {-j3ͼ<<\eD0f"d{" w~BFF]d[Hq\k C6"VkӜ eHod$GGEbgYtb%`؁lYw!B6D2FK"MFNuKl̓Z/I^"EuEVgKa){¿MU伋ܶUYnu|{vO}/+HQ??RH,1 %;Eۇo5zsӖ–긣~c%>Yo8a3Toۘ^%.pUOyՆQ+oاlя;o~sQhcM]}h_\Y]Y7PPp]nɤ),虃XIcCL9ڷ?:;كeP'?K GkLυ@O)HOFBw̖Aox`W8l.޳; nĴHH#0 V ý)f6M3Y-{-8F< Ӂ b0V> E!aU! t7$ H@cNV˒dnrLZMlܜVySH,QGXΝ R4>:ng܄dts032wYWOޟO1NiH,qve#9YԖLuzv3w3ysKKKogJ~tiz,Z[Q_C`ԜPۜx4̜7!t7/zrʴkh ,ӮC{9$J+k7Uqli#ae̹@yM]ULˉ^ldkBE!W9&XX*+ 8gu6K/$${m, xP9|= t7Q6O} &$G"MZ($7県'Dz?1Q\A䢗gε<{Zm&5%4, 0]t,ڀL X=kG%+uJ % +K.z]vR+Yl+s]nv|ZF3?Ѡ(K,ڿf-9HvtR[;ٻ5^|!tȐ0KsGö*ٷA}q~)DIwME{p>ޅ|wl,ZS- ky3*v'[jީ:Jг^&3OW]Y;vB;67s{>kdsv{DؑdXu/,>4elۺ["eC7Dt 7 -7c~ xE<3 K[f-"8*ׅ ko.ٸ9~9HIhKѿmG!i5&ϯB‡ 1}ADg9Zǘ{ۄ2"=1h)a}9lmo~.F8>ۜc<A[ $sd`#2yY#ޖ$ >[畭^hMg+|Жgazb|Yk؄H"saߝfRXsLvI,|'ͪ!>7uL %S_tOܸv%>%ʑ<^ϻO:u~q֗pig–Kϩރ֟ne>?l87mƒ/U{fPeE+E{uH,l3՗۵>Pt@*؉WWh-z~\]]Y<@ue[SWMNF,ddꑁg_$Wue.Q[NǡE?p/ yT h&s7 76 8$,X`g]  S~Hs]x /p($iePPN/sލy]}F&{-((K@bc *(qč(Ad6m-MKnxuNK_<{=}}.dT:~KD04܈³h(TDo\Ft!8{V$<~X{YyXp~_\_zɔGv{QjuCm zn\okoFP7|잙6.q&m|"J\z{=#(%ǎF4;k|"}/jߛfs~EYZG^>&)>bLOnжZLwoTꚾ^ƕ}^Of{aZvC2H8 Em] Vduuyc7zbKZ)^ٶς\T6 ABӭf!5F-CWk*﫪m$]J+; ,mBG6':挖zIDMF!-m>_YA$ ~~cՆ[ٵXd`~CI#1;!6RڞK"q8"-v}Vmc7 Ǣ䘡-\ሔw; K@nk䂁CήEVu o Ez !(+'p9Lq~7n]NώN J2mMݪeʡԸkϳtr,}ar[ 4dtz$pX}Mfx%'NKs=dC3g7͟S͜w?!UeQƼDA(Uұn <)U\k(ChG:q9hC"ޓn[W]޸Khq;-+rS:QeӾTmU #|ڤBQ1=,F4і#d  Y<;Y YAtǑXV96d<@7[qPo״k =XXc#o8s?R@> _ N?C kA{gAOr)u!Xx+/ #+)OHh%xFZ_A4dRtܠwzxn`i{ʷuXO0غRFɾrOY}9u8m"LnG)6|S~2[esgY$ PKȠϚ xXr=;/>Y_Snvߵ=gXȼ-V'G {;4]-/1om7| 'iW1EhqŰk|kS9J#%~RtfZk8 B$G_~/{F#"xUw*.1#j*R!kzmA c<4m~l'y&Cv9rC΋4GDvBp=#k"EBu3 ra ^Lp$h#2ԇlR=w F׋B[ͮEt?pl޳_,ֳmBvG忍Eh̞Q3LPBݏ4Zi|1n[y;d xxAg~;7&.л<1gxnC1h gTCVj5 Hi[pThc\z"duW6 Fas7T9ee'Ʋ$wFϢlk.?̙.U}k=uh^ӃZ|t_Cyoͼ.v%@K"2@:{yl:a|&egl.olk.o|b"75,koAUbN5ǐA H V_SYUp$R AA,C$=^$G?7 t.!= 3/,Om(7 pw#O ?fl~g]"+u=iF1##ZYaV뤍u2gq,'}߶E;>6%fSbB Q|g!>ya@HW#%hCi}BvU<_k!CKȅ wFéc7>:r f$, }ܟ1MH.:E$o ~ A: yݖچD2A] >'>ϫ+vVqK3lDl-hLͽxGᜲ3ľxCK>_yz+ ^.ocL=&\EnAkw%#s6oƞk._T\k-{y>/osd$ yM 25ϫ.otW7S5%l%, PUpBUmÏ-io,E_@Sr3O#E?)?k ?.У!AvSg^1~w"נ$!xȾ bl!"4|8 YDP~)T"b}=`Ebb#1JwBlybLȺ֟P}R!-)ڝ.BtFnZ@;u4Ps!!Gdgc+ 7Qع|xd1})~Od8p@`xw3rH9@L(q#܆oڳ>qY!lWNr?l9e~du.?=mޕ*>6B2 mawڿqm#Zֈ䴣ܝzO Odުچ @gg_B8xFDj]@h+*1D{mB(?a>@6@BtHdL5<Y/!0,)x@KK$=/.fUNt!vO#hPZ$ޕL@=P[}GoQö?RG{؉V_SyW}M ҾmmeUa?62*W1US\ \?NٴU Ѳ3v9~NjqEr5$Ѵ}k>WKc&1L6e`C!;Vx:/W䈪w a9mSU۰ T6ӧD*!ymAi~Q seg4;Y|RDEDw!׍,_:Dtw /W_Y3! ٮEËnEDӈA$6"&Vѻm^6[_1萍n!"YٱyRk ǁX|v SG ZQ!ͿCk,lBta)JA"2zgB~ڐZl#%k}]qhwϟ[$=:h)Bݙfڂ0Ew]2 L5ɢPȿ\T>^UP[n甍Bfnn&~(Ng"MAmni_8Ly. <u{۪VTqB ;|7~-oXůLLh./{? ' kuMHY<Ǡy j۰dX,wo&l|r9g=z2RTq~V_Suk8XZ]戩ð}tU6| ~hߗО{ F4ѻ9/$x#iN8@D'!~;d-' &sȸ3#ۅ3U3<1Qj#q2 ֮ͦs]bF< &Zi!yDx)X|9kssmާ9(Dc"kk ~{n=wl"@BH#(7!-EQn0JX8tDq'Hnk_DVu# 5>҄Gp2GAζ% Mb>oszBLC! s*<9?1B lgә"H _",k*;jwfو? @Ij&ThND0-{=_t[Zm>mc5vm7X]xauyc됧N/_mBs}{/*5H}Tk*OuGc"DѮg YA0!;hDa X`}?+CayB=G|!T>2yCnA~ksJE >lcڊ C2xQ<,Gg,&Fe]GoZ)-)=-#ҹ(:DS:}~$q=N x>f$|z)QݛA<)Bnکw!΋Fe3dZW$+M6מ,J9K}ÕUn~XlloݹJbĿxOm" rPUp;!ܨPf>8xגI#"H!5mX8Av°#xHmCg$ێv,I8G ֟{1Z<` |.(NaG0DT)ua^=9lA^ҏ!dڿ/ߌ I[e5jFBk*f/^n*!>[o;|3~7Ϻg7iVU6+shoܙoUTok8Y.oS]SE3 N7V7f}*RTCgyzPͶS3r9r8xifÁ}Ξe5b U_Sp )W\j0vK ggh0 5 H4"m ak,Ac?@PDK7#z舥Oٽ .^A/Ն9Їx#zH67YAa5-F1@<% "vI#rBH"ZJG(C(mE>"RD_S(q,Ϸ#m]?@|q N!q{G܄Pt0{BBB{",Ks,N#G[h'xP:æ B Ng+8o@X/<Ůy )!Gޜk*mhE{Gs&;F{wPڎh8("g/?eoYTM>9=#B`X Fn,Di֖PR`m<K6t/CG~ ,.o5ƔK.;(`{3Nm nZ[<-!2`aUmc4 "Tah?< Y D^)4y'f"Cx͈8*$H_@`rAl"ROs0 1TNyD(N`h؏Z`<(SZe{ R1ĸG%wD6/g?DJ[!l&NcJ6y]^ߣPƽ^X+^R￿+At$'_qHI})/#_r;!-ٯg޷1:퇔j4L@ <~_MGS "Xg{9e3g7/N Q"d^>3**:*/k*W#O.ͬh{N6RL1.ou'W6L^Upu}M{|~S؏blXR]mu@v$iQQDQ, 4OohPT_SVU0":[7ڋBOFg݈`}#Zr(hֽ=[Mb4ge8DzkM.&kFBDFiDݕ+/uI'hmu8EPiB'=v Z[mq8!4R:~ XN(߀4H8g[?#ZbsoGeHNd,ӚW(F9!g=8PO*,0 J^;KR'\Hӧ߾_pp {]"RɢAw[-9oBs`8 7n,^m8y{~PUuM[Hw/L<}s{G a!20r̯߼f~dz3 >.AQ^?ߺSxMU&x}MBl.@UmÓ @-(c JܻAЯE1!BZ3ow<"oXiև?H`7= yJt%q&vBҰ'&0R[vޛvCތ»2֧lALBM[ݧm;]nJ>[[~+l"_CBP[_ؘ"/cPLR AptKm/"6޹1'.vXxjb\P04B/"yᤑ%vcB{/Wdst+غޭKDi}x#U5߭m[}1HHiBƍ! G{sg!?K3V(GWˤ/k*?CSu%93z* Uqg_c[ℯҢ'_y`ɼ?bӨaגL1^E5m#:s?ntV}Mc5چ1}wKTnDː/P|9NHu5ADwe,Bv*i~T60ڞ;zU@% [Dwk#}ծ}ήw#Ų"C(2IwR~_婇 1#8\6Y# i*d|B 21шy߃.G%X5Q#{R1Ҟw]#ԒW3yV,ߨk=J\ ,5m%'v\T_S7Q$Zn{GtBxDH8%pn]Sžn ]B^/\'??~Kk*d!t2:!)pA@+uA,#Q 1fE4{c -cΥv"߅-(,&"{0[!u"6{"Gy8 9<^*wy)!߷Tg /Ez:PkH؆JϯC@>_OI/C60&M޻:)N.q%36z~z%^k\y^rLz=3T7H0.EkJv.*ڿt&gRܼc{wX  ͨEPvܜMj<} P ؑYV6LD 0m4'zE@3PW`D!qJYڶ"u"qFֶ8!, &U6 B ioGij )NKrN(M}m "ZS$ʽO@ocVuOCĔ.qLB`՝1ߐyjaCs"ףP!+Ñ@o3w &6^vw.[ԫ嬽, tӏ4Pиz%JF^R{~6+!G!%Cx,-$UHta?PC~sm!a([Up1ų;ߛsZ;{Rݺ>n,͜fvoVa`}MhlƖOƞ7;݃Mk2}:eo(9~񊺦/av~_0>,،}D_9鰟_JDQ  6eWQ(O!lC_*2L-({ F#y\8M['y]CYkJD=ǔ]_ %L6aH@E{;pp݋VۜD@ b~)#ZJ.<|#c6ш:ꡟ`gZlHʼn"E6Kl+3H#ƐLF ؑPHXw#y58aPea;XpB87uHA0y$ő3>? ᷄1CJ{]!ͫ.O| N|++wxVTqczC|1йlAuŹ=}\a  xP;͇u SKKϒhO@{̢D7XdVn8_2Ǻ 卭f1{1TpۼrZoŞLd=} :gv{k7#}p}i$6svs9e?Brz.GW;vf4_{#&D7Ln1$V)-H8{{QgxP'']8cXytd9|I摃_ʮps'z G_?px`~BE ; F )-]z0(."K !w ֐kvvw䡂wƞG ޅ,^aن,94MF9H[DQH 콽o= G"~, kQNP1:\=w#撇C)B vO+R.21$t#Wg26Bnk8yϾ,7"O܁+Zhˋ[>G>mCLj]ug "`{Rܗ K#=HH Y=ADtC#+6nA*"F|=bI ݎsvˈ9h ELD{ؕΜv ݎt F:z˭Kٍ[RG f@Yuyaoo[7xm÷v = ;mjnpsosm$A_F{᫈m#=)Y2{VwR^$3HwӕnmSA#^691;Ԟy]6#e}{ṹ,D9`'x]昃~1Y: ~s~^5sۄdQ+ rLf$3Y]8yT``Y\r}7AX/@ַ С 5 x+'4!$!` %ucqXg A(; mbv=pk!ONS!{J7őJQP&YGtsH9 ɯYAC^voylW'[:P,a PUUm'w{Z o!r!Hhpc~.Gx8,ʭ5sB!\lo wcCaoݛg>Sב-bH6˫*_U'&4i3Pv\NUm@rgHi}Med`놯⣀_ %p6 #4*M]h>CrH[d!%ΦM@Վh(g=? w#ԪZ<4\؆jFqd;@E! ѣa#NBJ2s >/Ymk"QM5J# ֞si G>׆jEyi%x^{k5Vx\;^Ak[N#mnE-#컌q R}Ԯ5R^ժjDk9߷͜ݼJ<񪙳0Nx^GՐw|؃ѓN[u򬺦_bV-  `TyD;qI^A:D`{z^ yW}2md˕2喝SkG߮9whRU G'c~:gnjyH'mAj"@ʼnK a9 9TIvL$މt" ^q=kWXڐc"$(s0!~D@*a%?`ם`s2yD:PT!"@4Bh ߇,/C/D}ϑHp_{| z S 0OTmx)m)d;B(xbtr2ͽQH_i\/BJ3b1a+DDNxՋA4 O@wr)[/f񢕅6ˑ5/#G t'6svsfb])hM^Ȼ P_SR|  6,ۼeH&K*m/-Ȥ)šA+4Յ]Cpc7X]fi_;yoF^W5s<[jv Gʵ/>EQEKbѵP> ^s]}`pCj -("b ym*~MmZ˦@(J2B(Ia~y𽄐 }\DëmgٻI3_?]X!>ل8n'#>z)-6H hd;)LӐ(kPpį7!^?Ћ)O"n$HQxz$, xnBQ)'fZMBsvgo E=o ޻(,O"{l<wTn~W64^)ޫ(Т^WUp>#5}+S?C #ѽ+rSsR͝yo4j˄X"G2vCz_BgyB޿G,ˑ\~pYij 3}QzHTjP/| t0o9kMQ*sJDC -hc $^XIul҄{~P(ȵ@IUm(80G-(We$YTBx#_yW랶u}1["E$/΋z>NA|b rC(;-L._2E||l׌Y?=5H1D^hfО6 e}tV 7ޱsػtrt˕xniBE ((I/!vK=hW"7Ͼ{9Nmدe7{uHϫjJʷ3DKH^e '~Cf9w>ptގl\ķҶv;<uq]SEѶMgR_y15nsW}[T䥺"܂苈f.o\g/22 FN\x2ӂmSk*1x? eF\Uې?ȶ\*C[o{E$MB,D#9O9Da\f$ՄHZD҈ @q8R҈GDHZdrF6 2Bmg8B~Iȍ@J aț^1>B<3.FAΙGHN=9S y2y`s8)[i6Wg"3È9PiϜcg$aYv`m1j Xn힗mNJN8]̎ 8"&yw}M冪چ2DVkk*mHp͹sʊ9y@ b#[)X%(n_| w %D0V6|b}Me7(a5#,藝ݥ>T!+GQkJF"Aa߆S yo)p񣺦swo5Ya?W}vΡQӑ\R~xU|7yGC"RL}䅈vXv+nx#Q?9_,o2yF#mLw,"']-q|HFs]Q zw:: ) ~AF2EvV`$\{x^ @غB|H}@tg!>j5BX.#@w?\y_L4E{ t &VBOmn=MO?ϻB:_ps=}(}xw]w]k*?;C&0}5oV IDAT>fu5]c(fRuӏݲ*1<r --/]}5\,1t%Q X#yn#huy$m(bj RGv~)Ͽg .o줜}f)ڗۿDmK|-+ĹR! ˕ A2FP_xs re[DwYI@_*Dؓ#!XHPm<yPpy+E!}{<6uRpv N=;[lDܖ1t;&3^ m?Y~1up^dE(fy6"1A(\`cwh=C x6_?F{c:mE;R נ/akReӃ<{n!N#efD!}~D{`)چ!h>Zgck*W6a;:{t9Bٮ<:_N:-F`{͏8₭mޕ?N5adǽ5ׁ*梼o=p Gr~hkCDXY.!C! AD4mR\ Ȑ2ߋˁy%3Fu: )<Şhsktl[t?{^DDޫ6A˴8 i:yL)iP>Ľ\yԎ+v_)hDD E|p9zQAȧrp Hn:HveH.~!RlOan}M哄VX5=OnYHk^zm|^2t[K ncwq[4bvͻGc,LDQ&2ʯ4Ϡ;W7~v$~ߋL>s8e.o<;4٠ XV>}%SC&mֺ9 כvڿʃɽ77T6L^U0(|r|ƴM] PU)cU C`b:KPh$`_+6QnYyxd| *u b3 aLOBaae۪a#38QD1zP qcI8ME@7 "8kp҇R Ɂ(ks!MHY]c8i9J~o[h57{M>6C!(;wEҺ%\Xo+JDȣ>ݲX`k":gԥ:c6f7 O#~Gt]pm}bw6/RF6n7yt(#I0NѢ(j|D{'Cw!em$ym$ԫa5ss6lM?lcq@ !*29OL J1܁|{{6?nHeHq*E) gy=l+ ?oyG=wU_S\U0zfob7-g izO>3Dre<=rj 83w83T6 t@X$l{Km9xqOG Zv>߻1%xUD^/q P=gdniO5U|57a oB;Hݳ?\]f! ЧV!H{1jBʆ{kPlub&^!gmY\HsY)#6e}֍:c(6صlb%bk}N"X "zz)+u ~4:${fWY>eLf{2&ԡ]QqDP xxRl8]e$Ld2}Μsx7`(Uoɓ9}{wk犏R{}oR,yEvC"DXB:zE Pt$o.Ljf(N#В{h} Xf>г}edИEQlw42_ipoUHIGqHD R,kQreAr¾s ^t Ž3_ .4h]{7~ .GϠw S̴7@@(\@NG{vƎ#6]PuT/Y[}gOb;G+^,l-cga✵hs$:Lcz A]mqr*'d1rr"'5rB$wdrj}l3ʚ CdJ$J8$l}%cmhяG>g̢,E9GȦyG"u$MIqB,r0"}OolD@/"@ =ԑvkѳ9X`;ލj$-ȩgгmˆ3}ҙSQM u0hJVWxI~Ƌ\odt;69ֶS90߻//GɃ]P*P@O|F&c'b Y׷MYR4ƖڇF姀E) }:s@fÞҪk{a%P̗'k;TQlcKm#S2nYyŨdƖڳIjj ï2(@E#^^eY+M uSL 2f =KF/MFǕ\$'"Eq}g㫑@G#Lf!rΆxS,Z$#A\LF׋lg AƠ+}}ݱ̎btUݵo*4su!pz9{AQ:YQBDo$hm߾c/W/zZëڰGaU.(ε!bYlbEG6:2ۓr "ˏC ƒP&ʡ؀;/^=A[mB"ـR[IܞD(vȠX eNO$F0T:s&|SC?Ӏxhjjyrc]14No͏q7 33׷M5\PT:sk]g$yU n^%mscKU=e_(Ltx\G/`[tv"6ȁL,0{(cc^x?9n# i zFÃo8sKߤ>t˵§cSrRrw/JWCKf F2oCqV.߲x-|t@x(w݄h ͑{"8zOs~MkZdY԰xH3Á+hjk3HUBC-T^\9AU|aicK4Hڅ:xA$M uO!c0b+55c.+쿚WL4)LBь=Tc)CSUyFĺAMhћUBv)5P0I2 BӆA ̜[nsh(B Hjs lRGYjߛϦA軵K2BWHHy2!Y 8syrMAEn r0W }e"Apg9}<|\EIk5mD֮iG֞u܇ov3l kqEݎ ěҼ%fO#c=?嶶-:cǹ 4 =h~2ٕvt˥ҙ#x-tw'9/JEry䦂l"&q^ e{U )m-_q:UW_zg vǒ5<ƑkgN>}^9W-N'X0~!ޏ";;c|Ow|0T%Èe[n_jQr;ލYTdp$/C9v~c?w.8rH6"8bW0 v%8YHOvػ-pF$Bֳ7CmH%K,?9#gIkCI6 ĶSΤA!'W:yi8 hU2q-vAI.BΠ);9e6!$i{v⥁dZ:a~["P`f!SQgх, )s>J&%?[y̾}PFT:s SR.o3?y]掿‚< *I&;}%_e;h'μ )+4v'c+K}ignbq"S:jÖ]02GNws/LoH3C30/ ,0K`)}]#Δl9ŢmY(57oxǰN1μ. QwB~,R@E/z%Np,q)A>I@уX#wKHpyAoQ *c_` #xxBk!2a;Qm@=lb`QYv,  0BJ1ۧe> 6I&stc "b,NǞI$=XXIGR [vE2<}Hl$?R8`y 95y;w-_9[8t 3=AZS1&b!e,C@iEЏWyzs=$txxS=gƖ$35lVVU:ؾyu(gT:lguiSCq(rs' CsnǢ{%(Rl5H8z1)BxuEJrA7(Q [_=  qӏe@?5 as2K5u?Q-ߝl 琰P)GQ BO3~>nlr8\p%ޟˮRNym{ܮMAFBo- -C9n$ގ(L!z"<޿Pv3iA5cJpe:uݟ/<w,Mػ9UԷ%6K<$?c)oѨM:޺ĭK[޼eQɆ^55-7 9?^q/xIQIJx?njn4ĩoðL<ߊߏ28!xL)2[?1,$ n09( |Բ)龀vw EDhѵ#׿Gh1^YBBcw(Lj2(*mHі !nC«A6!H3" l;Ǡt*dGުQm}iu9N8 (β0RX_D8$P ҏ :׽9gQ=l奵ij>^q>&l"CɄBB; x):lË́ Tr[V#Y ===؞P 8lA Z} 8es^u@_|.T >Wwg{*Y ׍H3けM ucI~2G䌠#ʼn˜n}S/ 2AlQ_{;p IDAT!y׊dAm8Qp=%#=Ѯ; {Q@ۮmL@ץ$\]NƐ#=܉d5^ 9do- 9ɋZ X:n&Egݐ>M OHMA)6o _nvH|.U얯:/P9E g:AIF`G+,s8qR}k@D ј#WĒjt ̂^kUCҨ0IׁW"}zm]c!znz/QEt{EvX ߅E\2_w%JCghjےJgc{ߏcC??8~ !y頿ߎWe>lj˿;/YϷw|KrY.%dU*#tek,[_WoykIaX,7v%B6v lD;4[.\tDϔ:W` t a@ٖ=[H,8cm%2|.\BѩӐp"eNg;)ېfyA} cX?tDHY:|65K"=`H̰*m\F;0E*|m[ Jlm{mwQQts_۟ڞ?aH?G!$;Xn&0GGJ`Wdh ZB~݋>|KC"3xi/7پg+%!?N-t[m;9[evϷ砧>n[ѻz xt dxhdR#͜Z_0>soI0c"ov9T:s,rNP$55aszːq+祤+PL$+=Q,#F:#8ϲ #.AYai7% $.ڑތ>#'|mHO8afq۟ sq}7'؈5*sG5WV-[PUܺiZWCao>}֒Js\,Jf H|<}">z eŊMY][óeH%l~c^5=#Ȕ zb׸v?lGx .v~ua/m9B"k㜥v?P#Pt"~j׭&P Jg@ W65mҙѳwp\k7x?&>c D#6w/6n2D.C#־u}eSˊ7V_ܛz2Xޛ`>c",K3[swŃnufಈI<_ys}'zːcRI#lAFg7rd~$2ِ ~)j$4G#ᰞvi#&G>+C0f$`H mJH#|;zl/Pd(B[lCI5gR 6$W`k\`{2QV GfiSm ㌦ҙt3#67xJ׎(Q|R:!u5 GNt/`8` xiSI||^D1JPo hͶ u[Rj'l{4T:3 =75Խh9!QN+fmG6;, E-UUR26;F vzm-sǒˊ77wL8ۑu 1m[jd^mG|PXm+m.{{선e u풴5yekͰ)c,%vzQr"= =YUZd4@g P_M u+yTG>$?݋5oX@t\z%cIx;+T'~G'?BZr'M.(o`|oH3J3wūی{o=y\n֋))mzoGBĹBƬ $%ȉbshHȧQ=, A9Q40)=k`j;d܋b/L@Bk%݆#Hp8F %Z"nNz$$oH3ҙ- ݝ54:w]u4k鰊G )kYmtfV*~TT:S(4 $rwc YBm'F55g:y! F`ϼG0 {ɲ蝄УAqZ5b](օ,oE ƩHtۿvdۼ!3 瑞@ l$0xmPͭw覧+֚E8L'ֶrڧl^mpGںӵ  1੉L6rmoń{ލs_GsݗH'ۺev&rf9,FA>!fm#vsBlOF#(B}Z$ T `?wD6:kF6NDC-ߨMM}=WjI6/ҙ;vW*J6\׈<WU(4[ t樦^ȩxXǚ RR"֢l(%8P?x)dA/95ȱx 2sb$ Ð`%4ʍl3 4Al"r6:m.{((UIg19hS @ 9]"%ay )vː+U! 9NΪgz(t,Y|BrB9GG!%6e DNXdx"98byv*ODiEQ=('ܯC({89"{'X_zpl*8т645-M3gST:So=F5`hD<6@20CmŇαsᣣ}⮗Q&S)HF'ΜPz2+tJhVɋ H^ o.E2σ$ǣD.4;We@b;_,zл*st ![;]bI Tn\crdδhkbO(F9d jrF`=#1n!Ys:z"6[ϩ y5lW#\bl:yC5'm*!bۏɶ;-i{н* Fh[j> 松$?)/+=pDu,i6W#&oYWq踶-o7* @9X4DSC8ˡS̏ )8еvLg)C/m/! 簂<8 ; l2:&~ A:Q A`DO_ed󚏄G {H#t(*u|5E ًXgGsȰOBM09Վ!AurwCzdB3u;_9(j=6Gwqu\ cǛ{oGƬn7ÑsXBazJ= Z;no9ؾ fA!)BMo9H؍D=q~1ʐ")mtyx m4}P4C9=S#|rU5`!(EEG> d^|= Ɉq HI:ģE28.}!g@Ip'YDȐ*@.Q6Hލ@FBE?Q8=)$D{}SV"(}= 1/55m t=oqFww}uE{FV%J7JJg*9_qH. AF'S̟Ac)= -Tɚ}/[a$=zPgm+@0x&CO@XΎ}$$#]u #>Ӯ}ȩZ˧܇NwuA'xsr%8_n4{#[G\D'dJd2Zpr>ýtr('x%rXdJpV7eTl'ms=?>{G29!X}:?c"0_=~)AzdsS쐻:#Nla}9WJ%{ v, ۺ'۞G!!7`=RBݹg1:"@ (pp(m X/-Ga˿H3șCY5knv6UF/J3Wc/-F{A|Tղwt=9"hX F +C/.Ȱ$C{1,'c۷]Ոi%O,> 9[v-J 097Pdq+ ڽE+cHˎs HaA as0X.w͍"GzHt=DVcwdxM‰Y gJ3C;89ohlMu|F9㾓ˇֿ߲0$F 2!8ViUӀ޵=Kre3лrdʐ#y42*#dGo=HC.Er|=r"y*dG$h/d! I ;wF^\ku'5`Ǿw: Bdyߕ@:BWq ^Xe}'u'^=I"> )(nZKOG5ވ ]C̫2~(i,Ia+Eϊ ;݃n;n3INAFGx?^o=YǡT}ֺg&xi$~{uOgqMvs<ّH3_Azvj0߮Qƅ5w+F_vAC ~uuz {l7ҹ Lّ1,X~^O@DeUk'dm`}WEQpt9g#v!  b!r<' {ǡ @DDk FCt$@< )1OOE (HzAA=nPEB c Kt1*v,%0|) r{ȱَR@Eslm3lȡte3x\ODBB)1[q#C e|.#(vm{cD ٙ-:mؽ^[*sK'f"],o`wB&4Wti܇%H5̓+%y$Crś?7jb =ڼA.w?ojۆ%xƥh؈7(j7b|~i/4y0lOjl'+CQ9@t϶ ËdoZ9/^eQ:?DI$ |unB075ɟ5vdBB,&7;^ȍ6Ǖ`7 e 0d)4:R IDATbkXGx)C77 00oQ#gW=r![el{EBfΉC>;۞O/I]D:kzn_;+6/9\oTC^jÑ^8;*vF,C{= :RcD~ېS0nM*wd]>ʎ4=g![k=> ܛJgqKu)o5B܎SR_Ӽ ;vmF[ޏd98 Y1zJeB' ./MA x6}F!eAvi1R`FYKhR\n@hs;t8lw+R)3b:9Z'C1]4#X^Ϳ/5݅SЋmp^B['z+ϓ( y0= {$v{=v6NkYcczDY=+v?ζv!Er_#`;p݀w?s 5\}M+ӧu?[1tV:7J{4#lxF*)Br_EgR@!N'L"P!8BݛW Yؿd$dGHf܇$rH^@1 pJB-ڐO`0\ts匽Hvڵ;CLxLΦc??nܯd߁tPݏ$N\wZ]g֥&#DCA_!ܡSy/riQH(yk=ֶPT7k;=#Ϣlހqk/s N! :v9~d[ ҳvJANfrw'vz̎C64:9Wt-D^(_.ȓ2+ njVT:D}ljGcKo@ꛀ5_ll=__k~ox2X|lH_W9lo$h ~P*9 2D`62GwR,K[@"ބս H4 0 ]_M4dͯ ipBYJGkAГm}@7H0v"%Њ4$t";C蟧"e"z &ivf$.GGHifokZ JXvtYn9H_XKbD4=n @n$|"$"`!+GN &sj.۳=ykm?lۛyaH1~P@@ ףz^,Bϡ3\&PhB6 +^ 3uh^ g_.dd.|Y !-4]q6|QDfXAi꽗c;+MH3cP@nH* $9V} @F) # 27v³G 6ɤ-v<3&Ԗu#Ihʾ}at^oWrÝݛ)\E:ɛm H'UdHVOCw=uncϟ8b]pq?A@ٽ TtO 0 QyD<G#X雊 }S{b[4,|5+}kꭜqw>:;N=!穜3jֽf.Ip{A/4loZ/dO[kIr!pfSC )e';o/ܚQl'oE+iU oAHe!{C-U_/_όMԫ7ƾp fȌ\0r..GQ(B& CAw tayAer t<2\&^Z U$o"0ŐP^ tW ٜY `5#u{#^r:A#m8ģP@:~ ?"x"|BzqH)LWҾ׃[ ?nw'69Q6#DTu=2^&CP H=lk GП`js}Yg)VBVRH,x:gxhu=v#xqF=׽ $?cd"TM u+0[jBF@浠wgC/訯i9nOheiѦ}s ]ƖKV4?n ӱ޽_˗;yI~ƶk[Q_wopxh^ 2{| C $U}݅VsLoP$s)#w#q$ӓg m2Yݐ~sF9G!8\^]0>G^'*pRk:Oty{P $K$ۣQtw!}4FvNoDй턞6o'YcClj{QXIu a{jǶ u)Xi$ЭCm H@>ۂ(mpvNEzлk0[11dC|@P99A\hsnDNB#3Ho/@)<#"?lă39=}<`xO^:\ ?ERZ2}ZׯŢX֬j+e_\u odYUM岵SF*|z3P< h虻(xG5jZ_y0:}Ŋ+oH3Dk3r  '"TKh K:E s('!E֊x o!!{ v]ǡt(M Cn 9e)B\ 9)MW !a @yۋ;cDѬUo^ʾ\9Ce: L/GgKO3zlf^;dTm%43_BGӲ@%tEUm!>kǕ#8kAWULb.cL.QXl(N`L%0{9j_b%UnYmB.dlt {7rfT:S 75Ե죏z= 7ⴒ³__ {S ~ލ˚79ͫvm)Kk2ze-g4wolݔ,ꫨudQcˊIbuHIfc,mV˱WcX+D#Fl ~P1?QB) S6z\!}(d.G!9<}6H: dMtF~z?YNqʌx?̈́' ^NH'ADpg$qݍ8;בL" jcu`T`}-+Cp` Q/n>JZ.#x_ c9]սHFEӶIPz pNylԜ,ϗ`b#c<m_:юD.(pYJdߟjTǰ|(?@.&|Myx6x+=QLۻ65mzw>-@w|`=2tJgfҙT:~$ľPbEN&9~m/$W 'c8J߅PŽ-@F (>F>?' M!2jk/` @P#hs}Ю%Ԛ QStplۏa[MƆcl7wW!~FL3!8HEK,K1g!nYj}:bs#r q<9 M @O%i 2d& 02QJ8;BOۀC*o]Jg.E}^~|9?'`FUE]6#]-96ҙa/w/l磱vt"w23</z2誱\9Ңο"ƖZo:?ok\"es։/7:N֏?$J3\ыtSc?Ñq_f$GQzdl{}gr"A1#y}1s}Hv"C(wcI~ƭ`Vҙ)Hv])Gct.N$<3DLƿ2ZHΕD}J"4hisLA-!LWZmnΠye"G@x :8dJ{۹|ts/xcVF"e? yNzlW`#i^t&ϰ_CA(0왞vއLpjՋdi99K Ku>nm~M uϤҙl^vEeH_o뫲Ϸ"G|]ߙnWWD6db kKf^wτ. hͩ=Hω(NSC^x5\}MƖڵ @׻3 :?4JKڢ‚<#ݷml޽}EWo)<#`+:uf4d}Ȱ[9NYD'T:3)%(_CfBc`'K7A(mU+AA2v+k!e:СݣH1[L;R$x@g ۑ2~ԑ/>A~GPLQRP DcωI$WۺkCp:V"HA}K ^ʎo)79ţH|53yE2Z$;Ce.%@H$ {w"'f,݁ !E-9GޟQ`% șKh 2렦T:sͿ=gw H@SC#H3ǐttjcwIk:F|b$ 5;\CzI~EVs407GsO߼u?jz mAJ'{X |{nl?;LlMJBmia2"RFʪEG, *#GQ(.őY{=&^|'ϒB\^;+O\Gڻד-.\S&` Vw}͊Ogޮ8:\CM;W"g4bnFܥ-\VSriGs'*O'~F?@"b]h?޺S>!+ҦgDII;L < <.q]Bgs8xI<~gl>sY)GE 4S4{Cta$ ~aZ='w#l6a[ 7!/>jb\LRv"+[2ŧ-A;.큣IF8EW Fn@w7WZAnGgh'[*ZFX"ueOƣ_&Tx/h/oSQcsSW~D汵G}%]O6n,}5W݂e1"#諃>+Cn):$t,pcAk!0@vkd= 컳#coHnڞܽ ;9{~Yvkve7",.}l7"Dl~\>kߓ93@݋ws%r(q#QaikzyʎHVWu"T+ r+OJ{HdգK n X6ykC m>GS.H,FB]jc ᛑPas+"lNӼl%~(|]*%)qc䲳(Hj6re;{wR'{ТaSeO}7֥iCֲsLR]c4 A^A(GXRP]n}:sHz}վӇV. \Pˑ-( nE2`xcsY uM9-ᆺtcsU(tff$s56;56O" uM/}-d<ڊߚR~"Me:UH;iWh$^Hp.ӽʞb.BgޥnwǕ@t/K6~o)L`^2s[˻zgrq2HP*$CtgD&CBVy fH. }Źػ3ΌwH)z]Sc+'w!Aӽ?C(N28:>ƭJwM'A;[ hoԢQ`1/nD m\Nb].*.%R Y<kG۸F[NG=x}2|a>^([בgHa4֌\. 6,zkǴQyJ()AikH7eнr!`z=G.*،CLt.i1"$DL 7!ع9 ʅh S7OFŧ^i{_aޙAf i\ w1hWtk4]s-=-6Ώu14|mC9)hvz HSfG  "C&!gDg㵔st3!oDxU8; :CQI)\pr>H2m%RqD uS҈PI}KdYۑ;e`r.G x7;#"8Td>_ ;fsC69_^XG2hٺYwt>mE@}BA'P9"(,gv.S.%q )> 4? uMAlKE ߌۆ^0P(W|уOEWS_Z,[=/!fX"u աò@M6sPA=R >x, ?.yAW G>]93\nB݋F>9aƹ}H(o'"%L)]#1ŃяO>HŻ4Sh 3?h! l,=QwE(.F3h-nW"&Hw'/B哈2!%֌Ctv}֊l^ QX"u$=Was׿gNAy9pƮFJlnm.Hƣ?{#3A[HH9Gˌh^6ҽkvB g3CL+m # 9v1TfUr9ZPqH-Bv>{ӈ.Mm!HjB&I*9x Х}8S!q0"á+8 C۵HAD{Ez[ӬNwmlm߭nv~> ,Oƣy63 dE6dn/X:ͩ7}\Eoޤ|e o'cED :I$ҷPXy7PGoV~D5 $?ΥƵ{U7L8^˄EK[|"~N^t}}]xUuڍD833HC;!r)ٵ*?dVLXpD/KKvqbl?79'Xqv){8 ױ Hr8Ӆ/܋ YU]S(D,hH!=p1ϟO3XI{ 2vk srVp+m^!/!zzm''@ oqQ"1k?݌(t2¬Z[Whw4W'-ںl{]>|p+RD8ugds؍h_C^.=Rxhޟ9"t[s, 7lLNzk,jn߶?oCW^uGT*+iӇ""?b.᪲T@Q͑frء R8=PNo NoY6oE*o: e;Cm;TJ"Fd<ti1ʂEe KC5 i}f!K/i,r;HKҍjf.O!Psڻ]IP7EsvM>D#T|_D%g ruV=HXE1>GNB vb/LLwû6!𛃷tUڜd?Ox_C>!`Oz3]*s͑K >N ?ĻIqy+5S!Hl!w>+pi ޝq$ϲy\hjW\+ǟ6OO Mt{Ϲ(6<.dϺ1 'h'܅56[оq(' Kо-0K>G!!4i._+?|=5jIjy8xs'D;j,nB16y#֢3X"lhCaa%mp3 YqumS^Z2k_v(հRꚮ,GۮHmkx{zʟn2~F~甑C^1~~Ya~NYѧ&͊>U qU$ Zwyc;xG>~`A^iA+uG9M\K욍8ex\݁ ̊.9c[mOٞq2>/Ļ_q>h&o※P6b^48 kp)Z\, x1.1! ̢sw3)";ѹtD!|bSw 7;dgDg\EPn6lic&/ױNYB3 Av}ͧň O} 7c?{59$oHQ܁,җ!wC커W=u6)ŒR-N y [؜f,D[*\2galG>4|D@~G%RdKqD xie+yg?_X>b]r4u*K[K sZ;`X\?bEQ#6]qn)uы{^hO4zKkL#ę3kp+G3?}aOA7A@LڏD.I EZ{s; :N">N p.S}!"7"uYBV|]6;Ӌbj>#AՐp. %4vӉ66Ջ1,.ĥ#c#PѝR2aG#M$;As1m\Kw"@v9׾5sjwl>m{"RYvr yNcϾ뱋^ #XįzOc l] m.\[pw+m\}#i"_DjWަ}=u/!|ֹ2vޑ̈́?y; Xb#8YC\~'K.'AW^2tH>C{ƵZ;l0o*d|ܓ{nj|a";}0bkB!B!NOS7Pdn4mYwNxc>Z>uSq}9]`%I Z !Z.тKtӏ/6bnG]r`SqQ' e)ת j|J;pt.mm"@ DSjl?Yvg!afFF4aG Ղu+h'a8W=dcyO \\v{O7^x? YDEQ G#7~dqM #^( "hF{2BE[u⽌rx 5FHHQ{DO{9+^x^fT8f+;J\vL-`-CL}:TN-`?;?ADJxB0)j^ R#СmDBʣHcu8|`;^Μoz>uHAb Y 컿۳K>k{{H<a|b}_i\ȹX j#`݄4 pOEB6Y"ng3[Va-c\:s'dv5]l@0ddcRns.}4#ir+Fǹ[>lhe2⥈Ѕm;R/AHh^,^eHxsD(y"(fpgChG2Symī;VĮ} e_e::oNMil76\_/.ssp@~~woqE}HzP+ r!rồO55e_ݷd<:GUP8Ggܷ /LIJA@ n~ټ`ņ]P(=1+|앓xO4Gx =3I\#b-DY1! !,\p͹n\=#bO0a \?'J9D+&":ojl ֟v}AQ]L`ύ%g!Ap3^ C< e!vl4ַZՋsPz=+5JQm]B{MHH %;\:%*Cj0kY2'<8ϰG<խd@u^P[\7q"> ԳlN #mY;H1]J(/1lg|jc ΰ8b Z{X|Zmy{xr,d; ۜ"e[w&Y(xWg J"w%Kƺ)[O㻶c #W3C; N]2=2aPl_)lkmPVXbL2Ψzdd"l&cZ@ + -_+cLq"5 ;䍬yg} 3EYbfYp]P ڷ)CS#U1NqNr=oy%)@h$|?xɹ(c{Ew9%Ź=3 i߂Ϡ{=ww$DW\Ѧu3x/Vl AeDoFViǻV ~#4*ցebYUC!AlD%m3[;553rvwu'+͈|e)YBt#/_q/[oNg˃gMRKkIINQ[d_i_W=HD/~OyքŜ_Etw1ذ6cBz~]͔G,ƿZә [)-D8#϶n1z t E8F(m<=sOj<;}HK/.ilO8gH./觴jKeêGO_-X.8*g+fV7BhIƣW ⧈e `'قO@n0W" bV Zg#09 u e-3H[T1J@;-b<.  !AJ|w"یbrNEBa$ɛj/$:X6#c+q0>kT i"Etr[g5 O#w=PpoI 8 Ε%gk*>9:<^ YIdPQ 7@zD$h8|c>ق߃.3FD ϰ _Hƣ{$dS9iO?5?1\G~'ysjw i<]P״iKPt7p`^$}ϼɄP(s>p빇vG_m}76o0ZR 쪽nioئ̳꓾wL&\l]UvfL(y6M4چ;bKX"+;Haw"A Y#k!i›Vv_.) p nw?J1.wj>毁],9*=#6.s]kMRn)R"L/E}>«.iCt ݰ+CtͰn6_wVYڐ~щZ݈vك'*gWGn ի6sҋ.  O#`P,;'ڃiQ}ԁtolZ "yTL_wvw}Eǐn˴˴ۇ0e\-D{}&` T 2fsxK^'0DHA <>%&#kw >v. A/TL!0.;#px+G9[&| 666swi,Zlk1 {jC{gwp]j#|5hOLw}c. . 'C7l~&X8l+3ΛS2Ckc)5Dbviow<1ƣҮp)ކd))K*О.ȋ оV֠6 YОw,]y= lx%«f)Fl 3x+ہO3CJG?nAT{Cg"+T$"I>Z  c1T#K#w{;>yY2 |(Z]u Y*CG-̳FU$ GX[ɩ'qʢM5B~I<*ܟ#z>M/|=Qnc<)_#kvqDEm~#d[:WL9k;ϐ#8 N3GS0v 5Nghuxngyz|h\sY3ovsGxV46׏Ctv~C]7YꚖ46 NϨ\ HvN`R.Ǻ Fw/ޜcĆՏ`ɖZr1 IDATeHxk~ēH8##OTjP٦۶C!Y%R!3/c^l W/#w@== P—E6<ٮۈ> u^wϝ[눰 ճ@ Lk@l\(t!26ݑ?kPz񑈘8?H0m]*9MW-H# j2ϲDLa?,K*0B{PNF7X+w1ajGˬD)&f%z> jlOq`_ AXkl~aqc3}}Wo5C==êVQ娯,[5Om1`uw[c&}736"pKϔ -U;d:YId_ sg_p5LBRs׆#|Yf!+,k v v:SP ;ysdTzD͟6ڼ;L/E9`$eHpu ZM1dŧǫd<%Ri^֯aW$`}ihr'm3klE1n.Ūߏ~ua .Ѕi$u6'#"4(gDdjîO[,:`;|!&cHpGe/AGCJD$.3b(bt{U+@!Kp'۹?\l FH6_b҈}N C+J^ee3d2}%H 'Np3("2U?"DVڼqxAt=l!>{d._"N|5Ԟ}ҚWy vRj@{p`'$#f{92|\|elmL#o=J! dݾGnaZĠml_kkڡZf!%L`n-ЕD*/!e\ uӜû L.6ٵd\Pw:onJ'*Ǖx<-9)k`ڳ{g7AXbF!WP._c;-HIR-HyԈ誋;b84ٞݏpE6睈^Ul#Z]οѫ5))ROOAğb :Wi܌{#Ncўq&\b}:XK~ax9'`w Sh1_n϶.lgu{ h?αC{IYD7W8 ͼYifFl7"^D NX"u);bX"pa,5=FYXu>sjڢU{=̽g CA8(-Z=c߼PXcs+ikښ>HD5iܷ7ײucIcsL}]5=N7N}uں}۵X"?5~GF%R#axi 72D,?~$@i&]Ehw!B6A6P*C..} A?SYH8tqCy6֠iH+=~D_Yξd}es:btxmicԋȏ~#5ci%$qI(\W34"\pcV(Ҿڀ޹ ukk^G͹$ ޵!3'"he3]}~w"|2bNE=?+Gqѿ>u߶ͩեo_^[^2ѲQ5, 'vahm&xflZR;SuJ@ɉǮO]UJg#-CvbepcM;chIaٰkd16ǔnl”|Xtp9 Br6:[boЖ :7޼)qŊKkG< >.H$8Mε4R\/לe1 B х'J Zaҡo:޺!<ʛlλox Hdg):qu6!asRhsL?Mƣc".}Σy7}06x>6|YNJJ U3lN@AH}sKQ9,Uv Vᄒ]ZXi]&/kYT]8u!A<4 ͩϾ;5[N;N\!yC|NBmh?<=XHƣ5H҄']iO55 46ןQ^ܚ~Y5(%R3Ve r"ܿK\Sz:8LBCyw"P!H=is䴁j]Jx^|hP`H6DB҃eވǞC͑` v&3gJ`׭ygCZ2{F :HjϷA .TE'HkhKpvڻ\t.29{\ i{;na:ޮ&}oNn<bpE iHX1b%aAFD abD:1xw> bs9䬴1:׍KK([fD#fWo}+>B[/ o]'mb@ܴ"]kl?&icHUj#}YtήE@iއЮ gřU#J].,[˥-e }uѦU.Xi}G>[QA}e%U[;2dcs "&8& /g{MI݁{Iƣ]C_CN8J1=%8KG#׬a#Zs+l? 36O29!a1xw _/0p{_ZgE[0wgKH; o!e6 Dė_i^a?Ui ǗiE j`C6@~~pssH!;|0@Ha7h_Ceݛ7!azna8 m<#m.@tћ}uH?]g1cWd%⵪$W"K TnA| {2{=~o7[vO d~S~n>VxWȗ/}d;}~=VEHmC]vUv56ן.1;틇56 775mXX"yO}XܓGsDH"_BR#"\n#!iAg;?H-F_?/"eEl..j(ixM! t #Pu? i̜p֮[;Cr;!bu1/ A)pH:dzY~7HP g^A@̮/}4>Rm inqsrԥiٙad<%s S?EIJinws!Md|&%6?Ex-hK☢~{vi& ~v2L{΄KF#x_2Msܴ56 ͍#p}H0|ȩo;N Po6HMoߏ֖ޫ~hMx:;<:Fkk6ʷi/t?_ ^ _sYbs-,CBԆxt&}whAe,%{,O6t6p DTBHq(W`gYd4z'`4"p&"s3\ _a}{w ٹd\og/s< \n9@[d |Ye]xtvA4 ?9o$RLNE 3{bq2}@Bk]ߊd]vnA֫BoWuO>t|'] -Dk폘ݗP+z\nWbD\\jdE.E6EÈ8 m"!mR>" y.Z\ ~t@7,bL!* Fڸ Pn^*ЁD bŜx=H"kZicA?6n@~`C56?_C>6.&Yϰb:`d}^DjF/`t K8o3 ˓YevDF#&`XDlc)nq /r% m?##v>s[cVOSc=("4ܴ݁ 0۷p 7}>Ljя& YwdŊ\b"qKƣcodBc}+׏ik`x?6"4tcsm,xELs] g3{om5;i!DbZ_Ļe?aT/ӧOx1-YA)g]Yw~LB"c^U3{mѣWA DEz{ +Cn}73{İj=>LH'R@3[vBoEa}VUgS w}s >)'.mF4՚DJ #˿e_7k?.+= )z{ZDsflgEt^!y.STc!~ra#@{ֺƙy\l=Hy`%Mg<Yv 95oBBWs63~6%ڞm ζm#mC<45)yV2%RkV&?n0 XDY;/ fOEQ,uȂЃȁKİ1; Y6pc5C~2J '!;b;c;۟!Oѣсb|R ٳ,4hgrd)cmq&]@`}2' 1 -D2 ƻ]c-nCu??|ZB QX4pHݿx걾CnWf6_r6#ol_¦\f76[j^$dqسKt1kiB{YhNFixfsՆ/27b>kħ/=ޞ_|31k3 5q+F{(G#F:' g;Z08Ȫ6_{p ʝlhkjGuxP{p&P״iB`?v(䵎W56P4c>sWHK[R!hZːTtd<Kmj|,L2]>諩:z"J:et.i|~F|u9aC%J=s("fK qer۞whd/bZR Ng\$d 1؇w̿|n@ߥM-XDT?E01l 0"ANGCcWV,->./ C^1.Y(6W 0OؘDc#W==ʑg,!*6 HƣX".%R{#:dA"0 (~ ?Bİ>v$VI`?\V8g/G{DwA:d|"{YyK)>:!Xp\? hkڶ}HSzCUŵmħi޷u!ޕ5̞?NO56v_@wFs1PﻙE?3>? a\[p 5ػѭB|f2 .ìKr܌Kо-[\k\A=lKc(gEa}~δn!Z}H  [e#,+ɲHtr,zѧ R5H98>s67?ѓkCGr=CZ@ѱe#9p)y&sG45{Ol %!5x!3>>Obq7| >}h{.k+_}KsӶ56k +K7>[&)50si-0[HxX"u< Za;xkX}W7مR7"ٽFz_jkЊmX8%D&Xi rܑm#kͩ-B>›鐳RnvA55]ۣ]vNkO>NC6wTFTAawmK4v\گ'ѡmc"h>lk1IƣlñDjr#&81Tvuӑ.E֤iv_w=잳8+S?=icʐ$ct&?ZVp]kK%,BY_F"  lauQ#ӳ`##vd< X"1h7 ? uM46׏D{x4c|4$Yvu#߸~5݈+b;CÁ;{#BJ*to#C0t^8R6]n遛bJ` 1Ea|v:Ӂ-و<ܻ Ĥ^}>k= »F#w0tF'^ pPQLHA<jQ"cԗ>Gw޼خ{XG/9\0v'ѕD LˋPמˮ Hȕ:;h=6?k#kt>DD|H␭r>j_f×"c8x"H#f,l&f ;Z6OZ?vO-ƱDX"pN2IƣݰyIUsloKA@FHbQS$Nzb4%][1hcc˪J/Kg~8 b~<9yڹw??ncb{s"vYiFg{R.L (xΘ˔9Ȯ݀X[R{#׿fgAN%bYKRjpOaLw&:?kNa.V3]w^ m^e8><i[},m#;:s,\=yfv/)U+-l;2ēȊRp/&Ggg)} Eps3'Kd֦Ћ(D8 ށ73ۂeiyd[?̷?@L\^}Q]}sCk7"X\PdDEٹH@4n7lrœi٩ u~(LeY\vS͖#)[? ˔^M5 9{ )c)C\DbnD!W󔷀g*t6"A.Q|ߡHЂ tW4#b.Uc`x]!\R:G:.D8C{3 \~&^WyO2zo(]tU}fk>~lmleu5OS++! u푋#+pA~/' 0>04 r,ۘ|²L&}y=ϣ(dA ru֬ OyN`dz:b@teĔK>!f463pb@ϭڋs>2/bJODhNyd[7\m-O.DD<綸#LoFǑbDOG<B({[ZmH Ycv 0OZ7ƞ}mRD)p﬿~qޭZ7)Oۀ~d^뷎 oēB'x2= a= @*{%g#R؝is|e.ppη8&p <[7|Mj{p%}W`oLvtn=2ߑH2S7KBqPطlS`X.i_Iopssn.Z23)j8\ޫm~L63_IC ^E=o~:D]&ML~C܄1Cy?ϴɣ(a5*8,k@\:rig>Ꟑ :oJf~ B1[1p5 r>"?FBVIO[6$Q6W`ytBR屏]]v*y -ycZz bo{q6v@Y,LeȢ1.>_@o9x1Npwie{ R"ާ  ~i_GœbzYߙݷ4|}hߌUPq-w~#{͐>oTms3}=p9ehOSPؐ<67EnOD#L/@UR\ LhK>̀˲}~=T;4Ԟ)EYg99W;/Gg+µCm,}s]Hh0YcFkO|"l(F}4+M,E{ aRVm}Ux2C<_$bFO'>ႋ]xC:>n 9|$݅p^u)Aͧ-F]^]|`a݃pT"HpېUdEnD&-HYsRY]m-L?j[k{\N~ ҷ eHY(C[{Cgz'E8]=2_RUVW-AL&rAmS%@>$Nd y83b6,[>%G]y-`eu6q\]}ǿ_z3?O뷏}_OeuES+R`'N["v=88 V,|F b3Y7dsK%bZ'd{Ad1ҫ[z "a @@y`?Hx< iOn rY w;2/J%bOǓmSY0m D~r[c9(kc\ʵ"H[i (moK]u+lAI-PM%_?#EGg^i yRQ}wgi}/%D&YT"-:rʊ~{mDz5"ABk7Ӏ\S_Mv_=Njei~L@  @"gUϥK~k{cʸm9Qv 1\DF"7oD:b޿K #y0'L ٮ'(|N$d uo .S oN}G@Hy߉SDP)xhD33db$ub67wM $$} L"kQ6qKuM/ꏔ#l,"4t~̽dk:3#q%YykAJ/"oO8TZ$`73~vИ/6{h~ :]n؅^{䀃(/[^أxՐ[u6͟<-:J@neuNƻ0 \w{/_suHNsawޭs1둈|N ~6:?οt.R?Jۧ.`œ#]\dayH(qP^R+"}B0 u+CڶhSD <> ?@z+պ!roؖպsB!FY/H05\LFxRKѵfso#j9ea=k!)k l|? Ql?C/K.j2Yl~8/0֦w q͞ugrٗoBMH -.'=j%9զx2}4*Ou4⹚7VdkicQ$h} Y&\hUKf S ~aVqG - hxso9o|ƌU)udzXR! u7t֞W2i#g-\}液i݋֟sc?{Q^diA6KV4)t_{{uRvԩyl߾}GB2C׼frw{g[dD}AP HpADALh .@I #b1NCLhS]@ "4]/Ib 󲷵FÓ~Cnb%7AUȢ29+IoŽ6|=.,>BވP&pdjAwC<\oA.J%bd\OoD֮$@fͧ22\%Ud r',DғPCBHWky#C< k^`{ 51~4[*{4,H+1g{Tݟ =gavЈ-el$,~gag}0'CB 5'%yp>LF#z<>Y| j?ν%1Sw.\{띙ѝ?#+\\[We;Ȟ^6okj}ތpf1b>\: j-:RCp7$CxX mHܥ1N@`>r"z5 8)jPM"҂h Ѡ[ /̯p6>W+l ѥ_!]Ȟ7vV IDATDC QvJlMPNi)ٶ"dz8B@dk%5,޵>* Yľhtb{![a&r"L i KR؝dzb0R(o|dkK$m;Pۮm1gj}ڑ6v( '^&p dzgCqW˂rb%؀j}ں-cT0̿ @A^S <,]= ym ;iAPhIeuT+ilvw'(}IhE{W}H,PDSv8} ܭfyhucK_1]2E\oG:f-LD;{ͣcLKgA o JdECp \Eu/#&ֹk |\\\UjމLe.@]TDyWY@ב`vK1;i#-ہq+"?BZڗRض:dnڼ͂+t  A6km :OGںεk{ف,Ss\$=`w=#(JS_t]ANwv7>f>Hh H֒E$$G ܏B/K vݟ)f1wۥqoϾէ{=3𦢂-m!2krsΜ1j L*+rfLjfln+.(mmm+UoMu6ٷWZ<>ث+R~}{VÀu.D&!bĝBerE< #KkZo!!W羼aE5UE%o>}3~Dl,!hKˑpu.)b_R-yM셄 Ҙ;wߴ262[߄5/# e_ÑRH%Cֹ<e2K'D>ebl j,X;kg -F*L?w?xeAhS=rE]0K >HMuo9"\]'?`H4H~n-Etd}iiDPd9H !ak2"!B`DtCl$H,+az'A"GAirkC&Qh ޏHh}-ak<~FhkfS>H 浑^yy0Lko_ :0sM@~`RZ"˨x2.rWC*'jD ?.F36y'H݌*$m$\m^@}|eYS]2:u^GH;͞s\s[H@oy:~?{NOַ36r}4?59~q•`c(čE /D4!b[!o/"<mzp Os%ᗟ|m< Q aaR읉0+ 1ѥ.Gh7% h݊bcr>Ѯ(0g 16"sr{wִ"Z6 1Nؘ[㬟 {.d!rٻ  (Qۿ#r?aN'QTG)JĖēǐ!U,Gi9aySkO(`mS;R"H}Aٷ~} v?:/H8YH`Lh)ކzXY%謹6s ֳ 2t2b6p~fBg:Iam ?lO4ehQR3&W5b#ģz|gѰ>c>pVtNRkNbd[s}zHϸP`*+I^&fkC?"|[QrѴ{gsǶLvUiRBp3Ss.;_lq?nA@zYЂF/}>bH\,䊷b6A~cVYxO<,UH#YbC`<7 4v% dsc949ݓ  = ܞJĖC$oi \g!x2dvx~G6۶Ve@y](-Jպ.,Gmxt "ai6RԸDG>߅H"R3$ Z9Hx)L 7M±Kt VhY>A)(^՞s=-@_dp5!e׉JR}Ş ɩDoiSBŃ?wcn߫x2}u*A*4wWF!=)ʢHQ%kk_J'R]ob!vۏsg;{xE~Of67";礪qtO}k nxy? EXE[QͰn;'g! N&YZӈ)&,@,ɶCd.r{( 0Gdd3Lg+sRsmnLy쟻HcH^Ɣ>^cKAinN weu3pix',\5e }>~0}:pҁ}G{:"\ud"t/h1КJ^2PkkK^~"l&;Iktx2.KP|3AlT"vf>on>3Цw#ĵCmD'(dj;49 juTd:KijE9~J80*݂4R-PuGW#^"7qz Hk7 Q.il|)$(NF]%8fJk?KlH[JĜQi};wj`d/Dڵcj~騶g6wL>3d:m5htw?yxjH(!LS8wڙe{@C'өDT"s[0OFH\2Qj,ˋo"!9ksZ#vO솑n[Ӳ6w_XqÁ|Ox^(D\TY]q܇0crU݌U͘\`>Od%K@*{e\,Gd~)qVoCh$TN"} "\ Sܬ6 gR".}*|Hx;R}o8i;m|clfD"`],;(kȃ;H%b71ٌ~vBK)^<~jGVw?lͿakN"Z> J}DmG@C[68@ZK!YHpzSK^eymGBZx2'r zLj<[lS~y`<a}(~Hw|RHB#.C4%"8 { GV+O8 )?jX~A(BP8\޸4k)C#Snhs09r3&WD`Pyx# [2&gό+o^9킇7t>#mG//߰gg'2LY:Fx- :,>~M՛|q۵O˂U 'Ӈ]Jn'#͠# s42O$Ƞ7nW^xǠO"w. -sx^h=lW#,e!"^8=!ZMI ^%(8CAA@Fx2}[*[# r"w8aS $w ۳"Afl]bk7^y~}#!7bV~Ԩ+G2~$; 14ߴDEw i;FG #!Aݳؤ;kvpu~澷3:6EdWCcf r>><ʁTVW<R}O:v>ˆKvmT"'#:4B<B#XWF$DhjD/\BfHAWhSՅ !Fl/0ݑa">r1@YJp/-n;R2 GjhrE61 \6fsHt0=LJV# |.wݏ_2c}T'ӃSX]<>)4!\tu !ڹ6dsڕ{!Af{G=ywXreaF'=wp18y|uJmvLb6pAv$ [vma [ʁŞF>b3E_[' E!_!gl90Zy.iwn{,h.)ܐuնDHr&|-nytma$ױ>Q%bBP@x#A_F5v}W|u]{b'bDn#"2dy1Ɣkk zȧ4i4%#MFA Vd;id#pnkdmx2w*{ l4u#p=ڞ? /"ѷP†Po:v 2% b- ^xE_l}D^sD GienL2h߸dzx2}]*Fb,$h1!ZPZ'CY5Jq15= Yd" gG"w09Ԟ3f۽r%Dr}:7!+KPJP;q{/#rCնk H7?#ld`Y*Os DU^b`29W;ÝvHSHJ ϫx(R8΍['SxaҴslN%b粃 u ЮQ0T"v3۷wrr)b{v"ću"wPX+-jxYv\Cb{a|&L K {,t#yEx-#v'y~\xsQŴy+ UVWāLe:?] ňFޓxx]S+Yh`5=7AkcuO~owtN}]t)V^SRWwe ;qB{""ڈFHBV;ڴ: #xG@0 if!"00d\Qٛoә{!r<Z.Aָ=%"ds} X?R뫏낧;Z$hy}-6dT"cM'KSXfu|GLf/C?a2B $h Bݧ;I/n5u'*2?6wl+4Yl81 w":D3FD@sJ`UkberA65d7Ҩ^JĞ'Sp9L!ނe!ނH[y?F}ޗk_J^Y]Q}3 wx_4:~,v̅7hÉA=e1"]O!Z2GJVt66#:#FCXdy:fDڽw \y'Rr.[{۳!b!j(cY/7Rfբ 2\W\&t6۸&p{aE..1õ6Bs=g볚aiw5O>OB{$pA'nЮxPOcXoۘF[d+.۹h}6CXuwfc=o_w),\b[3aH1A63:c]~^E [yMW!f{UY.doHb%QF mƓ#[vGnE=^0wg/w @@<[h:8Ow{P=crdz 񛧕DFCƣBJ?c)O* xa|Bgq{^ME?Lx2ڕœDl!ҎDDrZ"[@pLx?؜"HFPl;0Gc+z.#5% )Qߧ=A>>D|G!4C_m|6nA q!Xa:>2,t""[bsl_@h`?d-z,<LĮ4[/ & ⤜<D'2K~.'Zݛ @x!ȿ7v&\X3 Fh$nn}t5.&H:+IKYKk?Xbu[g \;[p ".Z vΦC ug}~]/J#a<#)? ѐK*+Κ1odddɛ䗷Y^Q".=?"dzZDoEcx#{=z}ލDW!] dΙ7#ÿrzѾsVV䂶y]䍖ѡe[6nW~>Vw~H1)㎳1dl6 ĸoAʶ\DW\oBD,']c܇puA-_#%j}kk퇋k1Fd;H{B_LD|pn[ Q'@azBS)?}o{;5Ywz{z[=?7=ؕx-osśVnK}a~+ud^aCK!~#> ! 'v ,Ļh{>ph}Qt\5D̹tm.w!|-4zO|t<l/O A.= mMuDWd{),VĔ@2EQy~վק_V/ٍ'ӟCmf7;yTjDnumn yr9ju?f ֿQuw۾œ=]7T"63k%FS'W"fs% + 1g w7(4D?NFq?>rPޟaQDZi.Qxh\p@nL9f;bصkӈv#,) 6e.Fg>Gڸm>3z$̹xmݺ#71x2S7ܿ<"}7 Bagª͈莄vtw6[6!$V} ݰ7Yʾow(Vm[|={f1Љ]SPm1d:'pT V:L mW5L0q)"W :݊xH"m'!J>YoEJQ6w~œ鹶G1}O}jهl-cQ=Bs7NSΘ\<$|vs3'~4crOoˊ7[eVJY't~|7,F3&W}2Y Kւ#Ha>/'>؜x2}JD0Y|ϳA)O5H 7IHx$vX,G"R\~[VG.h)/) L 7DDX:W9$& \'д"u")y 9Qds%XJthODfAB-B1CBZ^!<[܊493t:A2sBυ#vfvznUX?#mkd( M"P; 1UÐYY^F@ z~ \aifۚfm8]3>n'1eod}?jottZi_D"ĄXTَjeBV=D:3 /?m_q s3b1uHa#OՈ>":* Y#E{u\Xܧєui:6 +93âřp.{ bFuKod*C4pjDU/WNe#פw{8qY9 mukW֌ߔEK2@*s'CnR݈Ɠ鋽h3Gg\! gE">jCfV$,mAEB\z" E?z5]/RrAނ\ Gƞ;QA0_pf `y"HX*@6 ~nPԜ#H?3ɷ_ia 8qR[Ig6i& Oh|M|h[Y{YcV+e/+ILH}COwo/%YXO8]؇x]^fqIIvVl_`2ԛrHtΝ3w ϴz_Z|{k|g4~u.[C~0b`.&OpBt ͱ.Cè@ plK%bYzoBF͕-k@9:kM5RE4 Dzd;vJ>9ᗳZ3YLG4QD7 9^p.ˑ@ٞw,׵vm;&Fgh[VŮlI3Ba jfv$\>-6o5>OBT6NRM}?abY/{=xXjc܄G6%v{~p%ɳaK\o,;wZlE skFc[QKq%ResAHF1I!q)Ɠ=ts*[sm]x/E ҷĊV RfweZKC-0[Ó=WNݵ9EMȫ8 gǮiZ;:z͡<0'VZT>{VVWM=RN}`1D.n~9Ņkji30ܟg eH~y!Pǜ3 9}a*{,LEP%a[E=ϸص !r vPܤ.{Yf$.L_e[.nxT*OgڵǡS/!߆2=βnP徖_Hۦ <B!+k(+?!1ѱ{fLzb&73%,3+mw{HX!\)OBCPr;FR. p? #S4!aD\9-Tf}@P=Cٵ:ٯao6*r Ѭ=)ZpD[s)J"Q˞(z] %P$t/9;ԝd/sEDlQ@t.Ջ; `vqlqkLgFb HХ ~vx}5,VD)э$X3htok5ȡvAnOTLg]D6Iٔr^۳,FKQx;i-6n{zٕK;xi6A4tF:ZHaP r^76u+lBo#fv[mV'nioYͫL*El7wFda f8 R~4JeD\q'XDuɎDx5m ;ݼAa.CָZtnW"zv8R&EXقhH$PdmI"^FX ˘!ܹ,e=ِj r ,"l=d CѮ VXCB[޵h<J{v$ /(?8&J#^l;dUmڵ0䀎6,P`.뗔έtΞ5#! d:[kPfչ2 BK^h0#loC \gpu+j ^ak#3l Px f-_,Ц߫(gʤi/M>ygdpYwŒJ;\5iH)pқO4mGi5wIzɽ_S$JmO}le7RpCZd:b]:DG9D`A`0W""UwCDås_Do8"ބX"AϹƭG _Gj""s}ȎEA1{!-r|䝩XB*+bXl~vHn \o*lEo1BVZ䯽kuw^ P#fPbnAZ"ʑfm_$F\ʁ}3tit.\v[E bUAÓqfsl(ç-uuV\`]\]E%S ŭ&Pxj ,/ <@1@k<ȭ,Em M D/DL9 ڜ޻?qYb kV/<.N*ƭ^0E+ߏ ..3y[;o~Lgg! FV2 ʘv ~Fo޿"4A vNtV"smT"aao u$>Nf(Rc;.pv0BE6OEBB swAd!€Y6޹yOD}v>/۵;DB*<>ҿ|)TO,S%t'[Ax]1x+{ ]e6׭-w՛ܜLg $|7>W*η|ѿ,.A]L*>)ň>npnIHZi֣O""?CgxNPsJ؃u9{mls/R`?ϱTv#xx-k; >҅:,MUaH)-\wyocÝϿ>߷ XoI%ڒ}8_b}VI%l"Fٜ*C SgS/G3u3ātFI# 0ƻ#x E'qOP?:tE|nD\RH+g{Al# "~s7 B5cVBD0 < cChk_RUMuodg}Y?/Btͳ uJT)Ţϯ^?n, 8}M!ǧmDL||]ǧkw`}?F]OxVY6/}Ŝo!\r]WKVرCw> )Y-APl  eG-~^=0aʤiE&-[1OG$ʈFD"܈'o$ds>Lg⮟ES#Z72$4@4 K!7 і Ѷ^̺GZ3֢w2.~Kt]cmT̝<^@zKŎvl^]Jݹ ݘK8zKld<K"peȖnE8PxE+I$ G=)E)Cx;jFN=]"LU߶yѾ!‹&]e{`?aϹk?0d=e#e1t>/w < % J<[ЦLN>G@~ʤiN>9hx[=r E.]?yPm.o6;LԿf'fJ& ;3; \G I1^#) B>i߃<eH!@( zD\RYk` Ļ<QF C'#TEnY@g- 9%n?::l}8D>nǙxeqt~Q['49FƱ 5HZ-'p:.ӥH nU~ϐi?wj묁:c! 8[ȁqֱyz:JprAL]b6nfB<3Fgsv$~mY)/Q?p''wRCsw2^K|2bNoGg~9:7D͈&^/;1'",DXƇ%Ƈ!0 /|OZ2SBʹ%MM-UL+<ǁ xCDs_b5,j lln+|ͽ G-r'p%1kCK &ZD8u{*ԯ< n66!YJx\YM8 ԭp2}8Ǣ[^感33bꀢ)OLŚc0'ylM>4K_lŮA4/(Wܮɤd:{PcK{6eҴ's'5 Z˫w2t}hZ6*k2 D""Gr.Ұ"a4$=hi;C[fߋQI K iS>*[|ij@ TD†0x":eHP&8v,됐1–w #-sCZ6$UAX0A$eq>XLp lrh8 .Pǃmٵ7|ŶwSC7!ǵ<߭P>YM-lҙT{ͬ?|g]Y1q!xV@SZ-ă\1yvv #e>?V^e$Fա1!LJ?z&Ai}!溒~֫0|l+K̏?N|:;nd:{BokteWZ^-zn&F톘p ÐNLkBHPm-A^zY.^Gb3t+H?WVUwFx<! lŻp`d) 2DcT_܅"~%g*^-DEt#wאGR`ޡ=ͿOl"\9)IKcjҵntvJ| }ٽ L>ۮa~a F+o蠯ko턡eE{Ei{N%s XI%~̖ٷ'o܍S&M67 Ѐe|v)0 Jt&H0zY_@/"ˣȪ71H@Vx"@wo=JO+_(ӓV ~"CH#^m}Ԟ9z|2l.w"^9$0g}8F.*ݖV$E$bP]flv7n(P(h"diÞNعf4: =N.y Ҹ]qf]\עR| cUa91{#)[]p[8~_YkWU&޵ Et].wBN 1AnOW[Mi#oB_b|p?S wb^/5~;`@GP {-Ӑv7|Y(w C|v{KQ? Q[)( \2nG )fOBX{.)'nq/SLmخq"xs"bcNXN+LmNHGu V& CK4X&xONgKд)ZW:})mI@Q{_?|8bfrK_q*mTbNFbbj $V iOEsҐ]ב2 Ϲ e!tFDq:?ՔsD/@RH/s_kQ`}]kKP&D>>8W_\݌\:αg͞? /"b#@m )FvhDjkbd#)| |c}_!!>yֺ9g˄~\]iFgJHm\U\Lcc%:]L*dlˋ|pxƹ:ƯO= JH|U /^R11#7.m.䥈p{J AׯsNV)vE&2Żx@g :MXm1 w"Kag?\ z`LR1,bk;{mlз1u\pnd8;T"L5nr+;#{Bt)nǗEp͹?*ʾ{aLRPMCx%YHp6isX#M n`|#v16x 9`kDw[ Hx7jЁ2؝6 6!*dq)A jG+3\mqq{"Z *BLy|)eckz&,u\,$x܁H[쁹EF{1/3&NC흈FElގ^X瘦0 coYt;0%>Tm4?[ZyK#|WϵZ&(eR\/!ؖg"Ng#cW{="("Fit HC87fհ8lpgx1ۇ"M|'}W@g"Ku{Y\ D+ǐ~] DH?ޯ@k{={Ғl-EV s<%&pn!Cl6}$tTBڀ@ ېFA[4>Ace'"f]qvu_+K˿:0y޺C{!B&ݶ ΓHnkmt!Ʃ8jra> A љ} s6l.aqqvlՐHG۔ DZS z| hM H?1#׮#"bTpHT$D<:W}:"d=GʹRtq`UE ]:Jy>^ t!&S$m`/ 0:bb>kN*!>=m9 @V"-m/FhoDԶTI%"&Ɓ];@|ƞa{n,.)D=br\ >۝6gsVL*4>dk}B&w,/hݏCg}?iCZm^@g[?~CϤ9`c*kڝZ RS˯61 Z$D+G@B0aǮ5hyxe.̻2uA;eҴNo_Sj]6t@Ke$JK'S&MPnioޒkG m2Zeq#κ2Eʁ= T{H%w;b.)wqS tgcw»:㮴KƸ?îYht=~F[Sq#1 eD_ 2j62DbAA}ytmS_RAY9ڼ'prtюO/}Uf`6Y/ٕ7D<CZHSy"8wǂi7 `? ||kg{+ϡ#? s0Y!@V[nd9ֽC'RjX^Ļ\ŃPtW#Pq[kBTH<{[acצ%lUh=:g/PVW* ?r]l/ODn/us]hme,.h*|5հ. 9A{۔IV#Ya߅AP<_by֖5s#7!: T&xc1d =,Ͻ3- Ν~;W&*t"ܨC'k9bp;).N],*; z71k~ZGڳz,y)D; ȅ-} ѱO#&V]ZS1dY]闈!s/:g@GjKчk"KurA4)v2/Z5N뺧'ȣ6[`st.wp+y:͞ELA_m[ 9dUs\C ,ov~kin">iծɤ/ٺlrKel:G2ǧ+:Cs,y.36.;VG˦p90 eR4\mSS9AK\A-_\ӳv\rͱᳪh8%-as7a3`_AqW[Lg#{F%c&kjDNF!,duY;x5:|k-v '9YB_z<1Rx?EX4ۮߚBh hkp=dR֯)>ecXk_`srC`֭aŷ>|A`ssHI=VXRV<6]se'Xu EawQ|D{RUTsssk$E:d=;N=akT.oԒlbfDcHò'Fd0tؘ@g$Eduqkf#⾵= 8.F{~9]H3B&/#bXm\O_TT]:X= :AlnBD^g} >Y?.l}"6V8 ]gjF]@{A qlfغl:l/ǧ>đL}2R]aL'4.cD'ۺ7ε5'#SϮLv]Ѧ vA|Ow֍{r3_D6WnLgcH^I%nb  ͤO%1?|ysAn FV__9W<59~g׼I%ֽ}n\On,s0Yެ}  >xE-#v(^$< #ᅇ9 ;qg;^^",'.#PՇ`iR]7SayYD0`L*qW2} u Qj2cMs@NC.H0mG *X @̼sChBQ\w:iꂀ"Wc f׮Ƶ]s;g eThk2y)rxY,!*_gH\DUxaNKk7fRx-[% 毮ͯăn~ Ca*r9r \GbT@~lE Sp[ {0=VWiz~г_yyW,_Xqs^%)1ab"E".o>RH-CxǵoݴcD#D] .tv_[<:M#ksy 2rM:r \b#m]E6u[ ߕx%h +ЈpL=7\|i,S]VGg]!:KU6=%bxKsO!vlגύekZ&X1u䫧L6k˒UEBN=ܟI%Vf r("D'٣ӆ*JJ_Fz @Vp@#h|Hs7bdwgZko@# .oA aaUY#W=d:>h[HpsȅݞS@| rD.+'!܄2 NB Hه8AOy_쐎N @ʴ^ +Ţ޾zr#b#u fBN6}KِMYT|kݞp!fƢK= \ш5.v7Y P)J!/(yOa+kRnc/CӈfL lmCkGV,8둅|qI\mHWW3odtBUȊb0=2g^e~'r;fЂyr[6n$<@mtUY]f?刨=8ъ'F,/ǃ\Vn[ux_a)f4T5v2mDgL*bFʋczqUCvu G0xaQH,CJR[uR})l]!xvx {⽑2yϚˤ+l\#Ї\A4f==s{6ŽHqa [{H$oOGr[Ӑq[ˣxu]xvb] ;c8x ;P;R]W]Ů%߷+iA1@W2B(Fk/T3>Ϥ{& -̺r΅\A /h w#rx%l0x:'r#u5CķŃ\ Yfk,zW>Yktm1X>s.CnK/"7O YQ%Ð{ e+ ݌st. 7D/؞l ra:܉i4EiCw0b6WsJEunF0ߟ>{1둆Y֡;HךI%KFBC PCꗐ{Cu"6.@], iW#F>Db@Zi~jk~K?i(o@YzGl;㓐 DLc-9mL#mj,A`x:=i?jky&rCiʽj08f!EK r͔5?Esu&8>ℯGL-Ⱥsa&i2"s k9_6`0d2vC1w™e6aBaO=2\;ьJj$9529^]S]/J0R1D{oRb6zPr ez'^h sw֦C Ca( `.#m,63"a`sEڿDZS z!RX5 +ξak~YB|"!~0`Tuٹ=XI%yk6f:0W9Ūk!Z,;Z}MKs;m4= zQ/OѢ_Ǫ6dV۟^5 ›'tw|SHxR͈Wb5[g>X4d:{b8=~@ߊM.u,ï6|\)轼їDۯGXVQ%XۋU ?#Gnw?EorF %= mtB hu-:QHp(Ft#L )/f !DDcF#aH$b !ܚa}OW#Ay7bѮs&K>lv[˚l.sUe6n :Řs ,/^ptL*h2)!fR̚) +~->fw!O4:Fi?%l~ ؇X1,Xت)JAy/e ݶyKⶹnˤ'dRO&K3ٰ 6ǒ iO|Zy|f_hڊ|Xpz~5&4$G? $:Wl܇3!hAs ҈~=Ax?V,/̹A7؆HVN`YΞ[$4U#%v}?^rZh= n9Dɤ߷gLgDL*q sk 5. 4ːLrr7"bF.G(c߫|[浼xK kܽb^'?70۷+ / h QW ڥwpIms8Ѿ nnT;uYO[޵hxc<}bf>}^+{ Pѣ|ՐbE;yCdNw"0b1Β5 7#ZYK"({9´](> W]oꞀh+V|=w|b"ecSf%0 \(fczaځZc@>ZN]ŀm{O.JI%)BhϐPK;ksXX(VKoho_*d2R)n߄|!s]ud:{¬k2ēk{هĿ<:׾-F=[_B,GKe˕YB1B.}'? <抾ǃX5RDV=:iؾ'Xte8 jT#O'cK<ڇRx ڍ-cH,&@DDXOD 8WTuՌ,P# {uGOC.f#4q ^!!q|JކOtWD@.Eo%0fHrn"pgn4}wz}dzYf >ő6VnllKX4!hI[w=?D+[`"H1{yŞq8!W.}iVÑ({JsxL*\29?_|ZΤ}tv3L^"xZ2m2f~ r_DBah20ˣ8)Оoh ׊އ4̇>UI-b ,5AŠBUC].;s~”}C 0!*9zG@9-߆" i@BF޳T"Lg]ϡrfoDJQxAo]vjB$FWl,e<2bF.wH5aK0t6S"q_@XgclGØU agHb5apD/F.ΞP;n3i9Ka`=߶fg[kةn(]l $ +"v;ہ)(UaǑ@kCv{}m>{  eq,D@BGܾ?>~4J8z: "7m/TeнCAL~!|u \ܚñ+V%k*N|IӖ إ3]77Ά{=>"_CW奺 İg?2[>L*Jgm(J9_0}j(bAB o=T۵fSVX<xk1"?} AG4@x*Z|Ez\=#@ 꾄'ma''ï*"@kA>an-?I{ nudkݍiHopՁ'?@Dk4 bxs&ۓ (t.yGtNb0#~:6 0d挍ڼ9@s2=1J*F*J'/mVU/^8w7߿Fkupm93>Ac8>ȵ1c:} ԟcG!|R@ 2D/tZWdR2J|"ͪa<ȝ|ɣk~}T ėqswk.1W[Ja,,bk{>uj#rpb/WY>[nDq3plÞXm6,x4p&_'hۈ 6$YD|F=mڿ}MB⑈GUۗ#^X-4vF/U9m ="zD]4]H?1 ,D(""Kxh2K/K*ovݥʤ[ǓMFgf.>DkHΰioA}5l0`xm];HH! 7l3MHHCI%X-*){WYG#-$d:{!:wgRk-&`Gw?$w :'N{i}mȂ̀3;Ȭ[};͛]?uFoO4ͽ۠s=. &m`Kؼ Y]&ʷ^@bXc%ԕV=V;|||@.ZugU Lnnn|BWfգni%-Jyk 8׹$YWXUH 1RlD{SH |BD8(w?^̀ބ\ wG ZW@?DgYܮq]b(zGH96Ӥr4iΔ{Ν^1,̤t B.IąֶpgA%$C]aݶzE:WUGcpFK,E4c~>}0+*Rp6)eRPpm=7X6 h؜ӃTH ݸo;0 }L2ܣ .l}vHX܊jbW̍ # :_mW.fƃ@Kj_~͒mF(-o>"\_~I%`FL<>СJȐ@ZR;*;RzEʂR f%q,b̜A"}QIobo1ͰkGz xDHG ;#al&ld 8) H9" Fsf$#ш|Y]ml}/B@i"@9nG}?4dt`\ ] vMF=,Y4YOT_H%ND׋2Ur\I%2.Dz.Ń\9ZӀ0j%\&c[KroH\׶|oLM^ފAԖ5AxM[< VŖ9dz`YFV"C]]K+ʊaXi3:{9!h4:G#HU)_76xݞqoi[͒  RۛLgFtr&޺yb# YHSf%YmD _+쾉GŒK81Ԇ؍QHICBG2r+†B8< g7 ,r۸M aaz,m*1屏^y[L*]sR2}?@t=h|vLdŸ?@vuǕh{ϝg'$Ym GغeyҬAַ3lo>ʀth]ʦ L*FǮ݄OP@2j$ IDAT1 {uД<Ė: on@ظ9.7ʺ_iRtG-AUN7.]@}7i-J,JC.dR.˒2Tb_~SHֈTזb]+#Tzbۃ6|,*҅EdӾ DH@m]h?h&|yGٗ,95X+SkvʔhBB6=8^ikv+bl9z! nm[^DюQ ʢ;u66%eR<@2+"އf&]l.x!BkF{| 7yU]kwFxfm!tM B.Nn A@j.%BLjb\^ǻ`^ǃttsnO/GbZrmC%J$|."6^0: BSQ's+CUN:s^I4Nng;Cet> ֶ0F r99+M0QI'WezO'̥ONW%Rc6C2^FqMGƔuH yxvm[R#ChpW72z0p8x!k5‘.NƗh栜fL#< F},U ?|OpwKk!0*dsփd8+͈y~@EKQd[$sX̿f~Uu?LFwг,4|A7Ԕ_Vѿ8!&p2o>hW<4ĻPӀG :<0X C梍ւma@zHf#?|!ft܅J#E',U9di r-D̾1mn?8EDE^P 3V:F 5()vYq(P+:/C=؆葈9@4oR憛S7c[@gPԂY翣wnWK"`坲[GVR֡wq?ax%O" Y:w]ՈD*d~ F}~&Xed!)2~hAGN [d%IOɦ}qMq}5z_t^t%˙'ׂ,XVrx)U?8/IuMwRuC,oFň6 u`Jc_/, Emv3s"R{Y݃?۾6 5ffNH^qV$Rv=:;1kwŒLx|* ތH3zn~ꭇt.Aͯ{+&@Z Y nY$ :_C#[~ -sQQ,^~8(d#'jN"7Q'k 4\[ܲ{ yMl}>)%R|md|-M!@;VCyNEw=E 1鳐s:u[7ꐒUOE:| 18R.B}^s]<"'ܞNOK2XZ+,~,.c7 fCa'qL|;~_YԾ@,0`y bfH!)l'5e]F HxvbjHqaE>P )yGs.N He.YA٘NgAT8`59QHѳHnݕK}폝ǝAJ7Wޗb]N,uaiz_NʹB"9 YL' ?},!$lDP9J̹o:ңRw dNDngʎrϖ P]"2ͮ(ehscuD_MJ2dC/~s>R8F#P'#5NA5 D:6DHE?.w-yekt $O Iͼ!x@[FS+J'㯘 p/q_G'~ϛ':[;lfCpYw[ 5 l1<sCH6ᑶ_W&cpʓ*NfǜX.w ^pS7B| Rmc)H-Z>|:9q΍|Vi[}4wiE pZQ/!Aȁt"!f}lF瘋Wmgmjse76BEy/ m@O>Ex'-sƜ~62Dr+PXtor7NL˹iwg\':ٳxCK#&~\OF#c ݼ~t>PU۴sg > ;?)ZjڍOlMNQv^Cyע}2D8ŎaH^Ųz 86t31Yg!^݊Brwolx@z!f{G|{;#|; E~S.G /*?ῘWTR>o4cA7z[YpAUSn@AWCFo@ņB3μgq/d}'ꌋBBf.xYx# ~ֺmF|O"?[P݁p \|\{ʱ2o}uw#9G\irК9#BHz? (7|r< {U97ȧ[PE`+ߑsc%J)XGUŬ:$U}V pH'kt bfDTmC.ĠAfUyoNژbdkb1mevD^$ }&_Bz+Y)l<j1cbrvnEʓM}y0sQj>b"[B Dz'Dcc7wc.Pά)A Z]-z /xYf;-"y[$4_V g8|Tn:5iu{:'?;v<^Xmҽ)عAL;H\ur p΍]id[(䬞-']GHT={pSauWWww85|KzscQ';%2†;kުeAkWͮfL]+:FZ4y3,vs]+V(\+.EɔxבD*ӂp..[["Y刿`YHAbO*)eW0`4P] :C \i- (lƵ3pp~n49 Pߛ}wGJ`V"y4dy fy=du0:߫𮌔ՇE0uŠ!f=?Bo#iy{!ValCit^]r#Yp$̽OTh?Adzs>03߂-Q^kF"i2xĆ ~n:٭Ծ]cn4;Zռau3za舳NvdU,kJ_-eVvۗ$st]sck< /J2U@g?2)@Qh9;tTO$R1x %SwAB% sI̽O0k@ɒ6F _ك<S#e`12p,[h61*/;FrD7ӋPd%5{֬ŧܢ?ĝtDd"m-bd_KHh YN- sc}ʱ[z o_ ѝBU +*NͻYqgT._^ ć},:)9Ł{g+ukݒ}bWo,_ HFʾrn4B\dȯ_ݵ8 ڱ=UnGx$*E;zٮxd(Gfm,̙>ìHNsO: xw8#=f@ZC+p|=Z\BJVf('f %&RI]{i :*_"kd O7}SK˿!@57%h nj` ͣ7y~l?V׌W[YO_oox?ru=Fq2P8|T6`ր<!e6XRd8l񥰹E/wFjLCa6 1"ܹ@"[tݕV:>.u|( ):Mf{5?a':ʥZ\ć@aHQڦO#㑋/@ޡ+CHxWW&Rf"Yܻyw6zs~faB& fTkn𯭬h|{@${ A@ӀQu3ZwtJМkK']T HR%.cZsn]!97j\ 7j<2FYGՅx aϋg^A׉_ͳB=d|X{Np 38|m,Z7~o:Y) Xsc 8 wړ'"abGg)vpt)z\I_621iSzH1M8wڔaʜ!c07WL.L`e:I(,:x(ReF uxUxENA蔑v$2nۭ( )7 `y(#f~^x@m񭱧*20TXtvEnOV/nvs[\snd"_ςL W+",E Cp+B^FmB52UFL\.f b/ #ksUFa E$/"FS0D>a ~Zͦ9=S\#93Ͳ}\)佈@v ^/ FHh|@5!Y"FQHy7]p^`:YF@oKцt⹶<137sQoT"I" R&^MF!c>Fݿ. 8ܩ.]ky12y =a=_BG})EO[?v^FGktϋLs}qڴ)î_3ӆcnMQ>_ꮶQ(rd#likb>kF|aO =CXgZNχ"^4ܯ\ʌYфaҠ^Ds1Dd4)A SAN3^7f=#zFKQU3R i4:憚V fsUf"Vo٬RNEE? yַGk?[3s! Ћuʗmd'Rt2auw ̝scLIq[ HY_97Q@WMpL[ͥN~AN_y˛| =K#뀋sn%dwrnѨBBFjk}uAd9w+=ޅW<[w ֖K'89d>scm!{(H2!P"iG #/b IDATx%UȪ`HƋ |OD>"yAІ^Ϯ0ו0g"4s5HA>ޕHFC#D ~W5}¦ORcsd5<3aE֡fs6d\?5 ]Y[[B3kudaWxْ+qY{ucwoSE~W$Rל^t2~y"ڼ ? j_}vx˝M-{f-ecO_9ثӌhO~c3E&7E{H15Yk %nW>xŞ<`Q9[a?02ՔF?}B6&)_罊GH|VGg!,U=aLt?;ϲ {r2} m^ 6)=r#{ hT"~%0xo@ğl񇧑Ѷs/o>s;й|n\q3_#j-Rf!v,6!NMcE%:ˌx̥MȵON4[OGw+ͼRx){~x2Y}pwEV[D*s.pQ"9?8$zҥ ۗLͬ~5]OK5dGe-j }V4Чs1[14D(l(B){"/~Q'$zg'F>3e!^Qw{G97GYAfޫ\βI7(^ W̬-Z+zg 76+XT-'RЅUFjCtk"9VC91Fs!ˬWJu &Amԡ#ֻ<1goD*Ѐ CB1cyw̳vAJTf7d+q|OC`PO&R.$la2 #FL~:]Hx6r?u;?:+݌e,fR4[i#p}\74@ks_Qʹf-wO;1ˇ̑d|ґN{ `3)mHe -=Bu!(8H1zkqaph-F{ɖ ;I_S 7:[ߞ2b7q7dG=yTl+3}JP 6{Ε*؏~ zG|t $ؗeMt_F^6/x!xOض "R ]rQBV$ dHY#l ]d~C|j+ī93D1\A85U\ !cl 0k12ӌ]sflշ865XM0s)?2|RFxdڢT}r>?p\$3f_J'#P95&_x$X+w?se:g ~ vj+t=-j``R7rrhUˌ45^0eERjFdۈΥ5/@JTU~ӊd0U ѻm:]1>{<^Zt_H ner/r97Gg&ޛf]9>sܒoW:?)?srnl%`}娯D %R-Tw>pkP8D*ӈ"|~)/ (0÷ag6Eԏ]~ĤAfF׆Zv D`pEH4^8ܲmiye:/o˝<Fe?e5=ی- hO7y77R5^\fȪg3y f gwk-GV<\ɼޱFBR F{8_ kKCKjo Β{EtӦ K'ܗscw:Ċh:r⒓.]?Y|_] 6EBʍ4iܚrn)W JV8D_ BT O1й~ aՕdHH0b)鈿MGLEp ѯ\CgFTfD*e_El}ɞut_}sDVp[:?|z'qNxbD*Sh*{+цUD^Jm%qW ^@gK҂?\Z;ݢ?p- u˥3 TϷ41,gnRo%??a?Kcߺd;_%g0 vt؊03N5c ׇنZxy97.~b!:G6w837>i^XɆ:pqИSZnS{]5oTXy1I'+6Z7Gt24}3֬ >HeA(G:T44Uhu &乱1 "M.ݫW֌,ZX߃r8d{)UǣӉs !%\3/++B JV(\fs/«d;/3E )~92 0nΡa! 0¬m7jϚkY:Ϭh.ГHe1؄]!0kadvϛ,9ۂu၅݋n)?+X_ڪ\pȅ04׼wų5y YFyQ'Y}[qu(TnuƨC^;Aywydu@:N݅X׽գz{{,d"+(a"OscُH97:Gѹ v^[=N|oT˛h7)_3z,J2_C mJ{_OtG cRv@|z(?Piu6 n@KUnftEar3m Ő->Bf#EDPT #7#jRH2a 5f đAnR Gxp <yl"l}üfY 2=0x.[U Fjk+BNFi#a4Ȍcz)~t2~Aᔕx*{ݛLNu&wW;=+C᥏?޴k5~^O C +*mPM/VHju璜[!Ͼֵ"8h[CfMjQ^d;lj쁪$W:h_VR)wRQ' $_} i})di4&[cC1|rulSE Q';w}uKݹ8?x~ƒ뎣4"#4_dӮmufN|G}٧*N\PcvCaW"TBa!= |#K$d$o++61HBdABFF:\&T.}dK!AG#ku!c"Hl7*w1R@m?z Q&l8 #YK8`ȁkͮxmcUû6MՌ핥H.)~CP]' !V;V 28|GP?ge`6&(wD*s lG V"9 '7Nd%|K~~ءkBuR-oMtank:WnU۵]`Q'5 0N:wڜ;/dg5~אiTndԴF[`†c蜝>SE@ Ǔ\oyފ)W3&3u@$l˗>mFFᨓbk x3w;:ad4珔s]gbD]˟^j뀳SײneD;}4諪`7۠@JD*crdtS 6?=#0RFafMlHi*h|?g֘9 B+6b70/c}~ӗlֲͼʬfL?F / s6daU_5sBB ӏ]ƾ˧# ܭ-qoEAf"ۀv'񺈙kTטL@A O'&?t2.4D*3Ьc?l+b[d}ek.NͲj)ukѺt44I@Np+"C >Rm$@}`ϟ )d}Cigurp~n!*R@U^t;"Wacvʆl4= lnV:/&R+P/(iad<9T7"i ?!5TZgD@-BN=R"xԈn)y (@2m0<~^Fd|i"YĢfaA2zQ>0|VB|im3S+/ Ժ6e$\;Ok_.5a^мdv!CH /ETf_H2L^ =HFlQ*Xd|{N50B6`c~A{w B{կVMU(n^wQ'{R΍͌:VoG|0@{ o1eԅ`!Nvr΍-4؆.D-EסvnΗ NuʝtQ' ?F<uXG΍-W)xȜGcܢ;Wͼ:~[w>TDzqqt2~i7iHQ:>?Helo-%i-bƃ; 18 _ T!h/p w.`*hK!W"o[Pr(p ǨʯAf,ϡЃYhiTfg k"?.]\D n)xn@ESvLի^ާ/2yjߪie͎:٩H YsnlSыMD*#2ܚNAoVo/!0"!D*s)SxZī/A +ф.ccky2hBΏJ/J_5N2y+&2|?aa;2ٵ|! );;xopK>Uu5 U_Y' ;ߐzuwX=yk%2E63[$@`^:`U= #,ec}d슗c#due6mZRB]xzt0]0JHYK;jd )X !yf@gUXp˜)s/" Gx?\Kڇ0ӈBGD?HK΍ u3pb ]X%W|PJ 5ꋢNvK7XOǼs{Q'l C—KGג b͗#Ek'Z6|Bp9{T`h9_FQ. ׽iDBE? + ؾnK WRU ܳ ?Q'ۂmZ*C[vDg$ߧ}~gԱ3oo 3);[_7н))ٹ97vW΍ff9fWڱTD jC\03j N7Ec-_ *XpB"u|"6c(yy=G;&$EG i~66 UH~i7ki ȕR V!P6tTQy2v:/6*MU3efZv#۞)V<% :,{t!ބcmg~3jR=C ;)׵`?\'D㣄,Q|$@5b\poPV1tUZOd|Q"9OX7El|sFn叔:;R$P[r 8R:@\:?\iuwWFu(:/<%R% 1}VT)sCF [ӸsVKUqmka΅Sa7} kS$Vr͸>y'7:ٟ]׌c&DAc_|JΔ>XNƟ3EiHH& gP>xThdy:sm e!iwbf"¦Jl),!K3#QHy#w{@e   } B Z Wͼ{^z]`msn1M@[cR_@d^߼s|C׽ydȒ^;hZ/U$-{.Fޟxy7ccQ'u.m/;x͹kYdAHy]5pUϪ0|+Ş0x"#5oU^Y5,?Eg hDZ2$^oܐ#c$Y25}_޺oo+^o_Nuϊ`e| p ܐNןEs)*EPV !\DS #& 1 w63 U*l3@ !,jm{!Oٕf BY:NGyF@} :#e?x^Fa3xTEJʀXӌ5kh<ܳ )v E3oC ꆵي2ms-F^ Ady 􋢨sF8g)=-9kިj٬sa)6ǡ_QǭPZR׶ Y#܅WhhJYxN~BVSz`}9z?@ubS >mxJKHHŌ*dƚB~i&e},O'[|4šϠb6"/ݜNoJ2| ?9s3qh ^c\Nx8ix"UH8aQRBZұ)2S?>vizu o5ߵ!l \^uY۟S`CN YjZVw ӆtEkAz T])_hˊ:ȭI(pC"yڬ})uQm?B۶ XE&`T^Ǐэn!~/8XNT|;.nQ&:}"EFkq-z~ks$ LB|7/Jc2s7//Ez> )xEEy=uxю#p |7'Mk||7uw"aELt>ZdrIme8; _2P^y6500JYm#o¿i((q(_gl  9%û_mwq\{pQTpˡ(;=<53=x{ ?CA ShߔE'n~քlu *"Jڃya2Oc< >e?m-D>e:^d8b#r@5]Gq#7Dg{Ta=_fY"'K`Ykwvě=1ZA; S 76۽f_.^l^1۶}\EFɛ}q} љ߳_J4A7('"fM!'U Tb-}l;M,޼"~8o'!m _II=4[Mg"zH h쪕){]|Y㳉Xdv8zH 㭼6" m$ ^Cesl6fA=Mi7ORPh<9N"t4E!G7(JVEn䷑߉^Xg`uNlwv F)ԣQ~@˲ҿwFE,U,>a7=YָmEї/#t JKzF_CfȩQ2-D 7ȼogB Q^a'xoE+EC9'2X[5 ~;:@os`)u9eZx#'UI G޶߷ʲ  cE \dAYH1/+r\ 2̲(b#~`Cmأ~R!'l~d ,H^7@}*OYh<%7[0\9S/гCFH۶6Xfu:,[ E: Qs ]G7QG9!arm8}'qmNxE8e7",άH#P̵)1ی5a|el1xP 8N}~t.θ pK[va!'5<ԆuV\۱"x+ 5هs-:eG 6dѧ] ?rRC^笋dfXoُ%O:9 =S!lpTN캠ӸYw[gہgc {m)*L*'vg%b~l_:En{d>EfOQTv ư`,{R!Jõ8mBBQNUOT*&u>prOP4QY=| t-e!fc=Cλy'r{P`ZW!Z EVw@㑂XgU]Vf("ji+B}pJddW ^('"PQ?;xD{}n|oK?[jޏ| '](:wdCny>}= BHLh0 49%6Ӆ P!"ͺTan4ǡhԱHiכ/@Q*1.p"`"<)CHhjN`MDN:g^mrf-u Q\ef[8enBQu뫑h63׵j<؏Q0@~}|\5Ȑ$C^og>(Xޘ<{{Օk=sڌa^/&[toMFV氐c [ei7$/ eV\\|ං3=C~LVُg}AFX*I#t"9ZoDq(X"y@*;Fc! ]h|FAȞs~nq#ҵ'5Nas=r$iv81'48P:}<@jkj_lpYz~vN4˘δu(X~]|*z k!|bIk&V\0 #5x 3n ׄN=n w/$_=}+߮<Y :;#v̴]]m<=䤦h)gQDz>+F23nEAͭ2K"|^[h,EH׶ލ(EKHCJOE-gpa_ǣ(-u|7"g7ȥ qf] gbD;ю%SzrA_juJbn7-FTn5E,̶p"-Z6|.D@r>,`Zy8PMANk~vʘK 䌽@w[7=[~{E,ގe%;NrR?Euc{2e|]BN값=vzQoߨ;1k;Y{i7h%EdFK>_v/Mv*q6Lk >nx"xxUI/9X*'EqH'~⛐~!ޜ6e6 6ՅXQ0n IDAT fGlph>^]ߋt A:-Em! h ʒEckFAfd ڌh"*`sy9nS6פܜg3V!bdkaK˨ (X*>q%W+gS.'+*\z;͘]BNf# Yx':Ҍ;xߎ=T:d߶u)~r֥v xooBe#X '!%DoeAt L"GF=s0sQHCk}@ Ue#gA(f)XÓH9/F[(QfZ6!\2u?}~J0]"'I}(2 D957u{EDwFKG4T@|9B9؈׊>{i:`^|Ðf9Mts 8׀?$bmwnkG )uiDz fFd|^f bsUBN(DSkM5役Nϗn{#g3ǣ: {'M9Wx4ڢ9O^筵ySmAsxbU-AFb92|9z5!'h)g\^DQ14h?Мhi/k;EV#}Ȝ (( yGp{x2\>c[Ӑ*BI9C35[KW|Enw*ONm7䤆׿^w%kzZf_y"}~`+7qrTN~f੽-i7ܾqHS,;766ȁ<OVrTRu:6 `5ZRA*Hu(ׂ\V"*wP-qf?#( kV7`scLmlՐ태QxÑR Ea_@ZOk>bc͵oEJ3\p<2,Gɭ]+;r_j|$W64Ks,0Jސr6(rRH:0-T !7u]?a?U1}"OCqr oP(Y\o%tCj\vpu8w\v4+=:/ w0xZ;H)Ap,3 N"+́ꇠwּv$үϡZɿ[x!]xjv?1Xjp昑#>D, D,0^?9W™6-ײ1 9#г5=oO0-`IοΏi r@d7`)9߲\0>xŬu 9`qdkH\u+4jOl[\A+{lߑʗi(z42B _ }" 9ALDQrJl3[ۊpJ1/ 5 } 2sDo@7:E-@v{GRu9f+3 :7O aNDXG""aλEG$^T9fo#Gr)^K6yv/ƛUb%xsbBߑc 5l2Ge:.\nakdd̺/6v#Zv[ְ r#j;"^ wAAv 93 XI"{Io˘-l=tqA3T9|sW{rl<#^8h^ <󽈗tY^tOrSȱ .8vлP ]ք+>*g7]P3_u.|ya3 gd^ :ۑ? (gk]s([ קz\hٟa=26>7ǾΜoYZjfc>@Nܟ6@ ?0EYlDI_S _.vhsf9=3}܍;M7*s8fi3\d},O4kֿlOEɀ$rRh'^Wn؍Ɨs{rSj֛{\k8`m()M`D#Rnxr8FO+R"3J/RƥȑO!ބ3?Fٱcj׉L%uE 9W6<<ݺ9rA<:|X f!9zh! e^J0ԅZmDK.]3P|+[&o[7<ac92CjiR(y^cqs6x-fI"CN{l,8WFvFYRD9O9/_xu[>i_Ev?L(zwU;״(ZO}0>;g 9DϥȾɟG'u73x`%bGɻD,:O~)(`[E 5`ᵇ]h<#jg#:Rٰv۴a OFٍ:R ݏ2R3Wͥxo?Ͷh"fGB(]|1ܬBr yrk U!:߬!sλ/3PVe=Z( 5QԑӃw׾TYnY^H׷N#%Ȑp4NȮ,6#ڶзU`t.A52ei^1*Q bkBNj/%}lu-!'KD۝oeLos7gf?Q|8UvttxԷPD4\EnzuW#=±nu8)ҹvV7ž3(6 v˶8CT]o.H \چL#vG֜srBBA*{Ē(EsP> +h!g(ҏ(pG 搽Pnه݈yș\ k^=Fy\iG7Fꓫ^6Ps:)i7'>// ~ST\<$Snf_ Ԡm8ٷR Fޙ#6uM"E8d~h1Ha=RÐGϰEJ'QDM1u(sY@~8!KVB(U"p P刎`vX ژC@?2#_o,^Q(w!xhy :Acw*흐;ِ2E:!'F@wqȷVjWsUiD \K= zsl! d`'k5/ P30P0t_~-톛CNE}{(9,s~)9sڌ4H"ͨh\10\;x<2l&PQ&sW^>2C9K[fнi+tQW9"Cսf" JxC#ZG46bֲwEXWO=|VoԙӋ"%Sj_3le%JgAٯ'ѽ`lMyW :}Yh<o~U "|#>N a'2v'"ncGoTErv2ᅬ^mY @٩Q&͇D,n *+&KC% @XY2Z#ꐓٮrRg*ow ͍鯘`v'yrl\j|Ɠ;㭉Xd;rRUv!'״vn%n߳͵0{O-78+~6]IܶBN^7fSٲIA2I~)ˡٍ5u RC0Sj5QȉMkC` TMbHY翐dlpy^FZ~"vň7p9Ccs%(r9:x템 ܢxoFӅdmفg5x (kέ\%fPF)7yt4 ~lj+Nqj6=T[;WM%T IpT#𯐓: O@wb}[D,|*{8y?[ WDΣ*݄nxjK RlYH#x3DN!P~tqrMF6嶠='>ېCS߸f HG!< ^F#CPÊ$NCvOݏ njD!3zE4fK1봎֚kXjlJG5^sPmK>00y{SKd"2GniÂ|t 9e3G`A{g6%0wnƗH BNʇSp{|YLlX_ 8Ƭ.fu=Po1@Q ?ϵnNNBƒ]u|~\bܢp.䤞8v) xrs& DɝD,xr r0H9Rd}jD[Z(A( 頯`9 Kk2"2s_'YS#]݁G#|a:9# '@ }5MCX|'0AGvX9(rN Œ쎜:Ǭs z^,rr,Z3³ TC'5ئWƓe>3lt$1pM2<[cBN32Wg$;B9=/@ dέڥ@y. ;WUCK6$m+37Pya~tʏ|^`mF pgfsV=kں'oZO>/1BxۋjmZmSx6iuʔ~;ka`w$63v|5vkcnx0f\=#^ȶr {і%9( 쩇ޒ^H֢Nz[7!'b=K[RDnm_^~/`za:3! 89#*FJ}kkQ4r o浩DZ6M0W#`Xc{ =r3xQ?[u+/۲Ed_%朎18\#^qr! JI"l }ݾf:Խxr0H_e{l _: G ©~Ag}hd{"<=af"R%(AaЬ oFU _E ]"="Wo lyAELCcƛ0D,RF_h=lFDAoۼMs˦9E2[NV'4ũW+G3^_i iuo+;;=ŻsuqUޓwP6Rt9c՟g|T9[i%O~ ѳ~E܆W$b}D,_Q(9JRE OD3ޥ)QMR R%fsQƩ =/|w"'ș)AsD`2ɼ =(Ǜ3 M<d/ g2Dl.3jAnek/}߇} ;Cf2>\ۿ"cs74?2Es+ҋ?Αf76zՋ׵@ T;tص.v-Cδzjg۱]7scP¿`JI vJᗶ R|4ƓŨj; pdam> 9P ZtƏ?7> T5`3rʐY 2.SѺ?,^>[<0šZ { }1l V7rvBػ649cUjcH6W(+NXbo9nsE Yω;`5:sڌCN6)y^٩-sE47nʹcvpMK@݋;fq}.>p47fVeskV[[ZTI ;7 b!'EDKM|9+^ oFm(8 o#G< sn+{%C3uHƂ:+] {H'PfnVvZڬxo+v0,GQGv4c|x# 6\;_.rw34CN$d ۀzAPp.a Y%AƘc>S/C87e,VtՍMfѫ0w+(k 9R^H.v%f?f5.f1'55V뱳qF٬in3j_ YƓdW~Ղwl,(uWJ߬nh {#CNT%yYǮ񲱯 g)쯑x5A%(+[HI">/A͌xw%bDɻP!T(,Em'#:9!!Phc(ͫrJ9ECS59+_@cz~fl!\D mCuʑ C}Tu(GPR@'9>=Ϲ(6we 7JV7BiA '(\E3"<aNU"'OPF- 8ѯ:Vַ?rxw`N%j?#ơ#H׷#ҧ;An[,S,3y~(h<B(TO -Fz0O VE02V\4pi1 ̺1Ap 2y?'=Q"Ɠ{-Xo#QӱϪ']ƣh4G![̾|n6 ?+N ; d>Yx{6&&yz[.A ',fc~Qh< T<h ;b͡xl9\Jhg`""M5t9<"=NDѷfߋDEer\Qv۬Ϗ 1#ED`n1>E"sH!슗ʖ-BEr!3)^.FJ ~kPR>v[g!4EVdYCM25 y8t]َ X\vÛcLBi}Rnb>oqǟ/)_i\$_L=UHyo2[Q0һH"`@5'f;cf,bP 4x cv_#@ǖE kQ&k )۽lҗv4mP /G1AކR1:3P}sa$CPă@8|5”gPC4g H9Y\D,Xυ׷d-D,2Ӽ_ַ/~F4Tt%lK Ĵ҃77[({yndF2LKшAOv|N=ܿBکf-*]llD ֧, >/7B)vdGm~ rlC@藋X9/E z8p֠|^$f~ǫϙϮBvGF$bVX(:y2.FمQ4qV"0Z2K-<9rq"u\aC8|am3pIzDؾ=gBNj@s2ހAr*Rgβi7<rRq\^h2(CWЧ ƅ> q17L,F{jn|hD"y  _PVȎ഑Z7 flH5'gCay9gkV"7o1(Ճ2D{֝qt\M0k-«mdY#U!CQ܀kۏANC889$b&8Y/jY57{{ #kAlَ'u> [xx8.$+-ɔv t3RT]3u |ig#$O vhЙvh<9+ "=(cvcoܼ؟Yq(Eːò r&2x5&Mxnº/o?g;76탂iir&ӯGYoFw/yY;x3 DF\]5_2\Yy}V"YagY{f,amI0tqj"p^s&*'X'O"n+$䤊x}Y0z-'6#})QT}7E:BD+ ~d;~ %!8L3_Dw?䣦#{N7`.J.F4~2혖kc$e8nryFtֿ- $0DՉ?'Nm{eIV"C@yohEАWeRسy^yuݔ+W,Hxsp ^eaqϱB85At/\\[Зީrbzg#׼M `˚WS}1^;Ȧ 0v/tFW?T?^,ٳ .v0ڴȘ߹7}ȘUDY0eDx(U+˽کztp }A=wAUo'rU s(킪@Vi rQP$Nss~}f6 4 *D(g~ݑeAZV7LAs|E~T:9sJg[NJg31LgO20Mn칦 H:?f'4ք?-v~c:gCߗy{%|9yzK >|.v/deVwPtw3,EA ? Gi(_u!xJuZ*Q2EVw 3ːkA[Qds7@]gkԯ2ٟ%Ovh51ϏF~f_cQBv'pt:g_PgYJ.3,MM?FpeppD:e}ձ݂b0atg[ٽ2W06RĐJ9?8M2nȴ/6Gn^l[L9XֺlgÿK_t Z Ak; bohDY5ㄗDR>yn. IDAT0z\hyע)H6'ikE #ul#YVx3~=~?\@ʘ 2e,>uS4f-2QX(f@o23P`ҋ`enCU>lh:AA$ c?H,! 5wZ?w: mޣViN1%֬RZtq[P¯Q"6 zqlr9)Te| sUg1UV;Ig/B>o7>Ll 7kiZjVÆ/AŊ#{*pd+ HċnHƑ(IvIgI߽߶҇5MoKUWO7x H Y }7m_tO7wr2siFU ƃQY>yW?>Lkfver ˼oCr='#V~?|,}kSt_[^v^ٶo7W2uG<娹)Կus.:Bw}$vq5H Ub?9ƣE 36e?`3IOɨw|r3Q5w-!^S>_YwJh7S@eP~eέSТio,壏Y;ZTX "2t\& ?nCD<x'lT}:\sl=ncJ ~@X/)}ljk^;ļ( sǣ@jZgyG:.\rd.s|G*nQuٮ\eh%}4K=`Y"12DOWbÌFpG{-4gc5hD"ؿ;\廃~{^+J.}^:HoKqCW4xYi?ʑ .WS7cFP^݈90Hg;]&y|ydXsԍߏu{rpZ#n@fg@m~)8dHW@ e3n(x24Ӑ C !k@IKAV!'jᔣPsnD9{_d9PM<yw4(|a#(nDUқ"hQM$2 7_g7LW'Bٸo稨qV)[!DmLjД>vuɿg P]2sT~"6v:iE]dQd7G-hÐXR/Poh^ۂ| QGAL$sY%k ^8ᝫLX x[7=ߠ>%s 4S\&Z[_T"b0OEUݞ!aAk8(pX:hO{:`oh<}7F?xUǒ^ AAM}'& $$δ)H4lˁo$F$أrTߴtk}A3sT{+ѽ?0 P޻6No[~a58p#bEPq62oBN!;"I@bi,O|=Zua^Si |;آ9 u'y]k= egEԌ? y]-QxW -+jF9s9ok1t79Lc^~9)*/'PvyZ9nKuKW$լ~*ۥ{e͸%@dv!B 2ߥ9Ru#NrP ]pͲG=M)jZxl^7'QTlZFZG ݽ<½ 쏪P5"qGS( ű;J6F2kP5dzQ(aC>l8>d^wO.x?eTvF~C~ ` nj{bQVG-Lb.wd81vkM)H^dwWVwbd7tK"ơ:%;ONIxKE@du5·7O-G%u>/{+(?$/0Hg@QnAĽ 4`Q뱒ͺMpVEbT7? 9Pcs(?r=a?-f(E_"gyd$W. z,Z_z2? TsEUȘv%rhO_6Qq 'X@FPs/j2seR!6 LWlZ^Q=?qVel8.QA}+цP Ȋ \/RMAbS*VCu+ \:;>-A{6톪"硅xTj A0"Dռ OAZwJ삂f$4qs1d&!?a ʽF* 3-DUa-/B[Q3_n 15fG:DU`Tв!^(p7H@tجi(H!([lq}N M`a. ^~%Z}ho oC T Hɱ < /Q/Eoyո;k7c0b.z S;Кˤ>6߷⦾9]b\< xd V B=R}(Z(` X܅2q420dz?-BY*W2AΨǼn(r^ "U(rJȱA e=!zG9 ې1#Gֆx_)y00H9XrԓoAl{!ƮU.Y?lN"qEbtEF={}JOF'zQ7X~:I=Ɛ>Km o-o/5]/jяl R#"oIP-:Sw0dkQ֣,ܩȈGsTo.I.#I-eRqR72:T"shs|{_*B/@KsH$(2U?,\Չ(o~ϑ3Xhm1N(Bs NW[>Dۀ~Q5:uQZ9נj]_D(nBCg#s6YT$to٠*fRwۑxc_x}7q5"SY?9 j<.FըJ}5J(v"[_vQ>(׉G"(i3%!ee(8#MrV/ 3I;%L|PORb!U_٫(:9ˤ\)m•+)D&h[XE ",DU~dۏoBZ7ot#彄IoQ~z2d)H$Gl$~~ [5ff)o 6&V22 ݹLj%lcC_ix) xPfG>> ([4 I.AΨX]"TGQ, >gp1 9vr/"kː3J~4\}eD4B5x!rs4 ;k7cȑc@yⵦ܇(,PP>pdQic^y m:yX`ȝ3ŗE6i;#E*6B< lȦ8G|7NHi/U;~ JUi6}ףeX*G (1R)7H1.DgPfRQݾ{X^P h8 (ys"L/[_|˿`FTD+6}|keϨg}5[^czh bI$^ $1 xwT! $LaԔ0m0? )P3@\xխZv*ՠcWnFٷ2wqY: U+bSȡ<a^ EɧshCYșp/x¹9(U""'hE..CWدE؄B @S |(FYˉslϦ <(U{bOZܷK]cwDsl|u{KYI!!rf~Jz4`Q 4SP#N6`j?}:VډLk+=W=n|3Hݎ(W"^#_ļjEsH}]^w4ύOX~yې/Q4eR _oN$WG? Q63(VkPV/B .cY@Ү)`/m|+0AP0[gG`mC0tגm_(osӉlEpO_s\f Y6[>pI.6SןGN We(9:/|/hDU;l(Nl * aQkB8r%ی2F8ZHwc* þr>44ʘU2Bܦ NEר73cZFp(\;W({"1YwQEy ǷQ1ac6|"IAt;!ː,LǽH6D 7BPvb[gj } /q^ b ! V)/c(T܅*eRvխ64"^}r8pPrRgQvY2}SEboDkw8r83+U+C5m5N<*Zdޣ~Q\F܀2"l9ҜkXyHxyOZF\nmxG[4^% ؗ M)sCA!6rTh5鬿3pD:?x2#6,!'6P(׏مV܎|f۩(jDʾo£k`p+0: yQ*DH!n=YbK]̀y0WZF6jQR%G?Q xxˆǢE[x5{f:Wmm&yNY=c^|h !Kf+"ΐC̀zeZ?+Jg"ZC'kH#&!'hiVV6GPjҩ% hamP@GT];oޫ#J؞(;GAd%Zl'XCqM99k ^1_Xtu{:[pîKeVZmttOZ[C!Bl}a.F> UZ#@ ; Z"Ҵ "ٔ"-aEzJtKk/uႫ14"fD>xĜ4kC4.Gg Fd4X$덚XݹL\緋Poس%1Sl=@JsmOCrcnmux{+AV`^B}`BِTJ!vlBd#Qi9;eC nhyeV ^ӳAGKCM'.|9c-TJaq|d@ʸo+T!W5\Hة.SPe|ȯ@=IP1#ycGU" ~: vP}y}'6ЊǸ)4%<0P8 ox4L2=P x,Hg9_.`Q2ʵTٲX l~.Zs]ڛd8%g#5L C􈅸wߍ+Ypdg"K˳A,DbEա"'U,Tߋ]9Nv-Zr70a9a辄E!QJFN9•~<>dpU.:{#׸ ) 7`ɑP ^ /?Q=tr=bV =W,>E0 (X ,4!v(@Ɔɦ~}Cȗ8.gQ(t@dѷ.=G.[B}Wړyw{(D6&f+┆{r\*Ttcx%5O1ύ1)Vpa/\&u_ѭt֟?6/ 7C]pAV.%KYvV?D.Z*LP&C͢ÁJaP(02Rv/QPԀ}v;,@}[.}oL@0_3t0=ȡ-7?nBiRAA(TmƑx%. ԭCc IDATzԤKOnB!B Fd;QbeRM%.Hg<YX)C6`T4S_(VキKڐu$M+qAQM:Zꚭ^ߟ0Ìկ(Y@I^>H -׹#E[ˤ^j ]( )%(`nx)lu/ +ĠD؃]O|4_\ˤYx:)}6I-eR㲽Hvqv~/۠WmșTn ~"u)\J7bw c=ND%|/|"'փ{qT$|ZzځcQv-0_kccxQ,~kI${vڗQs<#2;Q@W* ʦU,rK_Ӂ㕋!Bl&DƍH7-(uUPܰ8YK"z]8cE-O d%zmC~[j +"/y{#żnD2xξ| D!͖[dGׯ ]z sA[f/}v|5*Og2DA"Hgo>!KUPOZ .\VFȁACyR{fC Z##¼NA݄NZ'顬e{x&Zqk .?-e7mDY1fDj8u*ϷWi*"'=e}f[=ޫCY K{ӓNv~ޫL}!!Y2}1 "{-юv8q NC~lgދόF.@pZT=Eo(.h }n~cyK pd,&$oeRAa8`e.3M,_GbZ+Q%Iq6nt=U kZ2b@X 19ԗt.ZNAA.55@9yw*G+APιMEL ^@P5r$Vv.R'Zj^Wk=9^_\wDb  ꭰy GC omFCpg_Klsnym?E!'!_"QOG-}ϊ%u!0% #ցۡ%犸+7]?j_|ʑjGެqlu-QrӪF U6 g 6T  hvVEn(6 )=@&S1u NFu"Ġ@؃eRϣ=ad l>r P$6 >ugz423X3nQEf2DG8%8;[ MD89[ۄ? BMEDW/7 O&vove%qǢ*޽܎&hE}#=VVebGRJ"#@!B >܏4"SǷ=Rv %@ND#z{ mzx3~fd?PEj$FU8lB4cBA%Ђ|o)ƛ"TY c!, }&/qs#K. ~lBx  deR޶v_:"GNg~45X }hަT<@}9MBt~ͨBю8x[eCER9v(`sș I^'rls?7ǼUo{߬Hx=֦ pkfB |+6עL$9a>XGPEBR~zѽ;[jXqw][ײ?#!$?lD,&DF@C.z C0 Ppy( 9l tZmfոʻƴ7}f֪@>Q{} Uجj dW܋D|t  W#_ Yu6^3֌WK >hEAOQ\@q.!B֎#?()8Z{:`2h7Po<={1dD!?+ކRTzdh_@ASGX㓐]99zs5hr">!WoneRQ 9eROHxG\W1?gm͹'MAU%IxKZWP@@Q5D9>(GW w-SX j<.)H^=D YI\&UJg?A }xUnP(J`wY{<׋5浫b|M'G1W `(lP`tq:u,/+~ˤzY?*A?6t65q$~* pː(h `#( ~ 횤2D<1]?ض`x8f0 eR}=A@s@?Ju!GY+,ݯRvvcY)ZJF>=VD8:ּShDHUaD ,+ޏeEdߗ=8VU۞8 /MW0U!I=*2PiOD:W܃/k1̂ <69#q ^{lR G"K+G(rNhnZ F*Tv2tֿ hT*t;2) .fCؾOUY4Kf6U:qV6*U#'!Qh>jt7V1l6ƠϽ+ʇ}`Ox;T~"Fz\"HoTl_EE]EQI[jڮ8R+>Gi ٖF\u˾1f_QxNTiGF}A>qvAt@]xP>B$B7J56E@nE˶1hX!eRsY`D3K/ʀ#QE*3#S^d(q_6s3V"8֡J%XF1;g$dsrt֟_Bճʹ{o5ϠCѹLjMlP@l CA(QHq=q=w/ zW4)н"t/UyY(7LWK{Ŷk"\&ȵnD9}a9 _*UKU-@F2vhncq k@uH&VNJ# ЗˤNgd5HgY@O.zle)+#^4ΐx}8.;hOVEX ?wC 6VrԊtu U:W %g )R`8O=Q.@դu8UXc(s ANc=rVQ4'hȰȉu>'#!bAt]y4k7D8 &壈.A< /?*ZlU1یqHVjpm,Uw~zm~ ȁ@ħ0=O)H!9(SS:'_rԟLWB3Po%F٢ȧEv6@4e(Ы6=s\ot+Φ=z?eRy`kY?:~#e:UJ( Db\eDX;?lY?`&=h(!BlUC)h5,Mln?#b^8RLz7E l 8ڇTa;9Ernfa@ev3|SC-uX |=DUgY P>񫁾 87 )A>n X:4+CTRZ5h1SPp]oѽwLm#(tJ|}?WPUi% j PdOڐ`:EI2].1]k~A,ȶʺ" YPfKQ%v7eR[ev|U@Cu<{FN ݞˤ>e4D70 A DQ[?+$j{"v2jm)I9#~Dv;>36"Z ɭCͮ68 Qzp3:Pi~2);3i"9ZF :G[u^Qg| "1Gm j /GԐŌ)ߥm^3 } ~-\Rf_(^S,X/뚂7YbK"B 2Y(кt]쏽}&<6eRlfP$=#x/,j&,S5Z܂ Ոzhq܋c9T]*qQT; ~ \\Jjmޣfپ߮DlP? u\;B7gr/ZaH 3"Mo A )D2P VX357TyE]by'yuH%tڀ>D0rԃr& h,-L5/DZjْ!Ȯ";dg.J섂ӐZ/d ̺~Qawה!?څ8n6]g2A7L7=Dg\z_G9ȉBғ&EubKڹuke/ $ /3)H&ONIxO7eaM % QU7}=Ig`Ūx\V:$>#q 4!CE7teǬCü~Niyfl a-SsbU ߿ˋPPZn_rT1ylDtm+Q+|hA{&ףk N=b/VKU{+;UT$QMA! mo |7V'!%!Bl4f#ݏEU';ރ| `*cl ؁EESj'IB?X!ZĖέvauy`pUR |^ڽ; !ص$^fBl%V"AKԍ.Igyd -Ȫ@+ t=#}c#\{ɻeb-A 3kc/!U _z+;ƠHe'HhPd算cМZZ@UD۷A,ȪDDX>[P,@Pt{{ ,M@W=(he , B=Ӌ˒b;F.ˤ>gEY?Zb#**C9Ēk-iGvQEn\Rh&ץk_z&?%[q}ɳL{fd#V^1 [ݝI(HŊ E W I;rT?g8U7;_ R=ac'G~1d鎉0 ` }LjucFA@>/٨ ƠBc9^d`pe{)jn7B9eb{&#ޖ·QzDG^x>z}ؾ3:":(z9Ra|7鍰}Gz geQA>|.( | 4O-㪘#gZ@ |'^z3s!!L`pbY3]HU:)fqXlz3kf Slx%{!|tsoFbW8C_ZA{ݽ( QψaTDWB4E|ߚrC`k!\&U@Nt[Cj<(rNKQ%RZs* Q26d39hTQɵYF:D!G* GZQ? ^$( g ̍3b^kԭ,g .{refS6ECe@i=xs^D/$Tzؼf!-~d7 ܟ4xQ E W "v ; IDCzE%֠IJU(Wȟ6tQOlbf/@r9]1t֏-GPb<:g>z]<}n'6kv\ =YwdO#W:vu| rGW> /eRo<> \ ܔˤMg[P0gT:䄬,.H G x9 9YUhMA_.,CeH} !1 ٨^HdDNdp5*B jG [nNdNLgs5xbn&G.j Foѵ?k^!Bl+]DBQi"1jr8 j#'ԇz}o3~G%b(uU4<-ŵ(xgLotcrts苑ӻWGW +&1YJ\Z] 7աkeR .Hxo7cQ֩\p{fzV;%ClFVM\&<֌43E[s"@@W9(kp'va 4=hˑ1nB4ͶMȱl29I]o,9lN|S3DN4ӀyY`+܋b?{F˂~b_ˤz79l eRLg#wyEeQn|o@eKP'e~j޾UB pl- ̬lj}s {_ЅbӨk D Q&DQn CޛP74 W>#T&jZd..yco YMQ3]iQenձUGZldm2GT`gP%:4u`FT4oFoZ*E sYX8tNXTcQ2ԃ<0 %19[PS/2ÐfNYե^(2_4#v hv;nXR:-Im6 !ˤJgDI uOT߿G5t.;)@A5-[T^ǁDڦ uק`i"}XeoEt};r*ט>̏}e:chabr!rԧ6"ĶC :A]z?8Aq[U*bv|:TAH5._(٢Hg}ݎF Át{^!3; %PJMYzƣ mlI "-SZ?VcК R`YN#D1+@|wqrپn )Lh "UDF"WaE^ RDDQ p"(` 5Bzݰ̹7gvI y~vsvyߧ蚃5L1h8x3Z`˦ QCC8$rH9LhK;>81:eC@ِmOJerSt+tIerԳLQw,*3~AQ9#z?Սɸ: h1E?_F%#(; -%_Ö}T+cx2ma׷P@m~K_jP!FG~eÊQh~>"ţѺM'5i*GPJ{u ݆hX^ʦE?mfVa<%@8iT9 eG#Gu?Akh}<?A](aga `/*:ճl ::a$CgF6 U%Y$ Ln:ʲJм+E*Gy^?=y((8;XӁ٩L΋ :t+ DOƃ| ZwjP15) мu(ig]`RSKU"ea6m(#w rY #ЅfXџQԛ9_DsZJ{GR`T?qA!N(y#Q)tz! |yEAs ʮCq{9cϞ{:^7=Lnڛ<-0,i%gQ( =`& s~a `BXPV1@a86H*kDs6f!w#C띠.T>Ej04 jGo?S)阎4n)E׎-]R0qM'/.eGw~?-h'A?'Z{He[}ox?mޅQm :D"o :z{F3<W\,K 1B[5ail:w_5#67Q/L"żrwoJx[t;RLl:É@բMQ,G/B0"tVyw"aМEuғSL.M'j'r&|uGt f^BXGMk?5#b\%߮X18c(2T&(}!2PW(S"cSPTg({3 2@Z6r1}_>H\$;9/nnȮD)Qv VT)n&%-]PEr,0?2D?vPn(UÆ A/Rs* ab jY^2guL9ÐQ E r @dT&wD6PSn d[OF7PPwlO`(xv(}#*Jؽs{&-&m'0g. >c{9MA\5͐Uuо3' zʗVevA,'~@12`1LENO?')})N ~ D3ڔ7reh|*;hPm7Q9-GћޥҀ2[wܿELYB_މRF7H'"%gQ{GPTZdQחA!Lm9QFh(g/=WLݨ0{݀@,F1.c *o6,@ohƃ 0 4 zf_V2ǐm@r.J׹y"٭5h}ċAvWsRi(l:8ՙHZURd?|h/; .9>ҌJD=yWr8ލqo}LV䨾 ?MCʌw <#~Fc7aj[:'BE_xke Tw9l7 Jp_lx0 P$7ETy<_n${Q4 w/:}3N$sBNŧQO@5}ҨֻUtoW~W8BEZkq3}r:!YtvFt?R5Ge01/ODٳCye#aƫqʬ? >]M'oKer t *+U3}٪RoO/rD̎AyyPBkgz_/QT&W d ݩLn? :w4{iƪt ׼("`q6]9$Zl8=@"P!goJ=@)k֋]_dڪM^2փe R$ejj}- GՉH6w2Dqdǡ(|h?XZh:l:bO2Tuh?<`dT cz{ޱhK(;5̢4c -x?ͦgIA4T"ZHo&*;/jb>=wGuh#K [>BX> \K+JnZ>9 MȹisL]kYŃPw1 ܚM'/7tr0Ʀ ) .v栵j2reb%?^ UUhp=|&Wo`Cl: ]Oer' =_:5TRWGT\:Ыy#  gǨI~ϱT&~>tH/D { 7Kޡ=n)y9JeruDr8Ѡ]W316=d ?]=KJrc iF _+ x{#Tu8 ˖j٨[s]ړGt82Q0Zx兊ofz9$Hp=Hg 2$;s/TbH6<$} U /yY@_n䌝J$f)~ x'eɗK^10%ɝM'^x]hM#EV5l  GSZ_]\u6W K`=5?U7% ӛ,c`D6Ner14 I7ی QYQEODQ XԹ/CYm90MFR("Cz@t^ejPM2X?}D AxGu<%Ftf>ZpƢȱm&zPo-Hg%E)-:fhh-!S.bVŗl:Her3eM'29Fuֵ2̥Ð-[zK%} ]7,z:<2HBY :E~mss@K(w l:y{L  Y䈪U~0CZFs@=MDrES(߿;h>&_W5\2͵Mlxq훟=eg7CT&7$<4Ȑ"#C`A"7A-ˀ#*<ev@Fvw;jxZ\"UeD;sE w2Sox~~Y:6*!i#Rg#RFYI/.uaƽaBa뛽9C'<|攎-yXUq 'Cv{ y֫Ɓ?gɏ ұDkbTkPpTnƘlQ,p*2Z4?l:8DkcM{'[b6ϫ?=X8z֊y͡w1R2pyYd:'S ߌJN$PpJ?`2/!! 2(w _hCqeCv'fM@[%BSn|v!'= 6-D{Ćx]eA9A0yRNH!/S|; ӻh]#PָͦW7!G btĪQ㦲+ ;]*d{C l:`P"䇣Yj5Hh [diR- 2J{|hg}:TceW>؃[269XƐ"EѲ"Yұl80dxBPȄ7(nyy Q?oIL>a9ȹڀvTnbwEEImOwE pF9(3hͦyAXT*QI6(W"g-t T"X݊Ei0`a͚:Uұj[mQ͝mOt􆏢L/PS86_G|Sk+*7XDٜ3 A(k9(*PvD;w{t]+Qݟ#^alIf=(t)]JerQ0l:2_f6( ;v 'Q)D" dnFARW5Q;@% vӷd+?~s^ a2Ddɰto!ڀWHܢe!)UT];vtÐ3q"pk"ż)\TGdx}yMF W \-J<7ă>(kCTЍhF!դ]PfjCԴw&tm j6'N0 cKspQ/=jb{&sA6?3Jhm51"wQ/׮w!N;C(ݮuvL͘g|Q&DjȦL"}W#ppu~eA`# d+ mJOB pÔ&rtIDATr*|XʞT&;Է;YH q?܎J :.DNOFmCM9ChXs[jC=XEZ2͠Y^=w/Or]1lL@m908'0~֛ǭ!U {}B Gh p UvHdxmiծQ&D*HertU.yh>(^E_I6E9#\6S> a(5w2O2Ho ?gdd4h^usBNǐT0boxo$곫GRr,OGHd R[>w{΢"5z4\w|:aoKRܞl:LIeɿoBă|ZoFCE}?ƲaG(vPJE4j,Rk@A$JK~ @mp?7"Gm6;KNU9@ .20!Lnp Z/XNer?Cj Her#Qs0Nc ?8@PkT0NZڧdmU-j=p  Uxq=ghsdžN?;t-[1P(e8F2ZPOQGf= ΦmLx< ɅHS}bi-r >8i?8Qɗxu([6eG%^gOjx]]o#gxJ?tr@Q+AdRTFԶPՑ3ۺc܍ӪPTo$:Ճuaosڀiz5*,lⱷ+T&w~6=6 m|:>uִ^w/рzy@,@W{{mh$j;B{"G>v[5~a{g;{#_gOyLnxasUyΊ64{7ˡ.F2ף&TkCTxw/eRQ/n'2@GF~gd<y ?HB"Ѝ$ (_/:?@(Bxi!LlraFIer5h-? yoGMx3E {5,fQ(ۈwqPOW+[i)U3XF~7`q}&-2`[9=/Y8m'QX܎Va[M'Oer)*ʎ]\Cy hICeW/f;Q EASUDFxrӶ=Z2c#sEe;"nio@'($1bQKʾ|-u_0q*1 0M';07RE/ A^ݯtne(5D.6܄־`!@mFS sYr:VlNaUM*{WQ-rM?)}~śb%2mHYivduL$*'A):䰍&=(U"\㐡{ȹ)NYʱ h'vb `tyD]Bla1X2aȇ6=DbzRYiGk9r ]MGSUe"h_1hSE4ʌ$tr9yPkꑄt5;!G4A-(;dhCdLDS#z^@"̼7إfɅg;M+j[V~bGn[TT6N|Wq‡jaܽ5ԟе6=5(:gUATׁ{9`2*4ӏl{/ g>6TQ6j}eźmIͬXMqN2 o%"26T p9p.uWc뫛с]%R<XKWԃ~]VӐcU:L;W~<$2:-ȼg{۹w?׋TPCiaA"Q(`;=-@kt@܍j !(v3T~=D~L%+C#?p%)?ih~Z _27\|eVyNgo8 ($jq3JA[ҏYace؆$tGWՎaƐɋ e"MT>+4JYWHxhei%JH$3_a[ TwC[2uWb"q{P="Ch6F,L$: ~ "s/U*^"a7_ 0 ÈJ](StpP*uQv$Z;AMxk{r[j٨% XgWD. c2Lnf> dM'Q֩:UU(RՋC a(MAEdC}W׍D%sˈjKhA3 0 c(B!I*?q%tǃ!hLۅ0ŵA2Xiw4Ln8fi] <&9Eܫ#JYKCUR}kWhsBw@R܉Leaa")hQA7 z:GОarG/@Dc_CU.A{݉ĴA"ކi>N} O21Rg]P?hT EHheF '=:~Ew{Ѩw*wx<OCH aal}QHp}נ}@״gk8bp-#޶{ C+Ww끧}|;QMM!L D. c32PFe~`/2PCh` *lCF6d]Q;+PD koH+GE=t8M'WraA~ hz u(ZC2O @mc-Ooi z*֡ V@ezge}F*+6B*2XyRGu_WZ<݆ NoGpi"iu?b oDP'*.=eC6\0 0NARi`U=l׭`ZqTѕ-PYD߇?>>W502݁l:ܖ>Ϣ[pݕaOcT"d:B~.IApV?iׇDC rd@*$JF=ZM'maa <gu(sElm~?U/#FG,b >'pہ6?]{:z׋9X'ŐX[p6ҹj'iګ9VN@&,ރ,?PGB_]*ETZxY6mqmfpg 쀄6+`aƫؖ"'+V@{j@l[y>zn|ߏ /A{_f af0db*HegH0WUϫx|s'QDj* )/XQK^W[QTwH sO>`Άn3UIkUo5TQs3xbNxȅaÕ+^$74ËUt6}u#|l: 칇(~l0 0#䛀,V+rQh_ЃFkPlzT&A|LRk*i]+4NC(ځPK kې[E{ ;hPfl{BM2XJnG EnOGr_B=O(:u* >j\| P-NaalEUOLUٵv-_UcHQHbzP_7 l{N&Έe?{HB,P6^/`+-2d w84<9'~r*FF6JR~Z\>\aa[wi}e~i`?h[PCۂ`@W{ X<P p{"NT:h AD0^7c&NnV\=_2XPl:u_tuaa ;>u[36 X)⥂y*݋aO4z%xY2`wYG쵟b6t2|5?8He`oź(j4j8f0 0T&w􏭮CЄR$.Z:WEyZ`~?fFپn|O 2~鴌2aal:Мc2=Kیjܣ4 0 عkW̯lNU 脰GTX0܅@zHElFuQz[ V"h+<"l:y>$0 0@*;%3ua06Uc;D+zFJiJ WPW} \M'T&w Y}jKaae nuzšs*j^kkZ abq(bLQZ =wm/̲=#3=EQE0((JQgLQEQ%SEQE *EQEQ21EQEQ e$`O,ctP1NYTQeGŘc"߇"p%Á Eb z_7XVOS҆_dsi6P0NEQmm(H, kg`c(nxۺyz=8d}/>lȌx^x-jtpSEQ:c^{jcnl]~08ݖD/] `3/ c8ﲒku8_k5G[Yҵ}e9k[,L6LSUESPgLQrB@cu}_YpTખ=cY댷;舛 |} =ւ??$ ~8s!{tR]E_g<(h9P1NEQS_;&=f`~5W{Y@~0K^+/趀;UGW}__oP4W⨎,OiVoy9]VE٣PgLQ0BSG~m>\eRE,L|}M_rauYIVM7w~uf[sm8 hl5:W')*EQ:c {7v$Z \ Z[8wׂ|76r |8➷ve_o|hy@p"d;YE3ߑӿ׬U $9Z 酶xᲦpS1( T)ʞEp_{y]zO~/,m)T@Ogݞ<Fjjl&s`Iw50e',u,#Y"\,iXkŭh@lVV1= I HqOwbK&)w(wΘF8" hb`.ptcxõuU9NO{*T^ovӀJgk0<)bȀ5ҏ,03lK۝hrc(M(kF`=uǛ&;sm|/k}+|`AE$$ԑBB~` "(7OΒ\D%/W}F'<O(ݨS݄P$ZLBV[;A֘xW+8 ӳ48rz{"Ⱥ ty#!}[@ gOQE*e7!vGc–%'U7Wލ1K0KֵK^S8{$=7Ob)*kLj@J_glڿ{B)냴胄;-"|s07Z/M+ p)3xEQRQvCBةHU#rA—GO>A݂_ܿ`e.\L#]ȏ%/ b,@*I4W%Ӿ.f6.wu~*ߛ9sxh6([A)e$_]V`|} eu>;\GYax@KX6Vp7渙%_[ B5{ C|hN@Aޒz2\x^QEue7%`<y〟rk'Oyc{!y7t~@s R]?o -8߾ONx/gQa$"_vPEًQgLQvSᠻ(=@p Ƽ;yddsB冾 {x\+yZ&d , ϧ޽Nƃ9mOkXRSUg mMrL( ({h88qʮ>弆ד8߸<.m־yq$i-p9s{ɮV^jt+nͥO6'Ks5`§(^)e##^@oc<{ݸMyK7vn\A#nHWqʮ&K6wP ]vDtMS}W(aJEp—uxP$`.pb({*[qc9םO>]SrGB"-m|w44g,Y{tսzHx q:>kUEP1({8pΚbꬍn7k9d? }&!rEYξC,gCW]_8n#5e*hRQ2.޽m 1O(lt (Cf!WII<|$t_AaWJpY Hep$;*hes[ُ`2$88h?wQÊy~qRkX7i}3I/@uE4 |m{<`54 1EQC1E˙l.&:L=o/_iqZ t9*X`L;>B35lIGQ~W9kZ$;sVEQ c~VO|ˏoI(熖!?.͝9O7ÐoH@bcA// Mv2mYƖ>얌 EQ S*>@i8}Xyq#nk޿Omɼu x~v <96fu:x^{yѢpCzMkT|F(פ(Քo{%sĒ> O Z')&K{j:jʦぁ%ȒHe(5hRQ*Lh,}ڱBX) ӟv\EQ}c4H>l(ꇉ(NaJEQ> WF(ΣΘ((JjJEQEQ bLQEQ%SEQE *EQEQ21EQEQ bLQEQ%SEQE *EQEQ21EQEQ bLQEQ%SEQE *EQEQ21EQEQ bLQEQ%SEQE *EL6LCQeODŘ( dsA(ʞ/Pe`y]-(ʞfz"Kάxb 4w]mgz^({ TeW?QgၼdzB({ ) Eb|d[+O%1 0NIfz~(3*EeL6fx̘d臎(JTe0." $ EQvKT))JlRd 4erb(#*EՌ6&Da>o{l8{;vc\˵zrՅս2=EQ\4L(v 0am/.Z\8s4vm=pg4LpPV[_LOFQ=cG97p0csO3?}"z0w(> xn](ʞ1EQvswdh88(C|'po]j3u:0!SeBEQ>P$fEwiЃgck CkglŠQh(' JZ釞U+oޠ# >TeD1EY6zMᲣG.+Eb6;|CNVsucK>=iO7@]MU=({*e#X5Y,NR[_mzY.^}Pܵw-YN*h{LJ$̞׵|o.{}!(}Ɣ&`g4(@98 18R_FBG[`cX  8I@F!lm4Evm1r$zEQuƔ"}2] go\"^u{o"S۪~ EbWF Og?Cr6DְP$q:{$$lReY| EbC[Ǣ-(|*Ɣe#'վOt׾wŤ4iaHl0c֓YbM`귨cE0ɑ?Xh8\ Hb~r=ӎ \F;P$ m_DZT EbcN"QE-Q1N>wk*yqf9: ƀ_"<_sUF 0 iкuaV[}/D#4F"[@_M{?v3㯁 --p^S"Msk}'}]9{+WbLDI Պ#nR "2]̻x$~_n : *v;My/!-r6 EbA`ǁk!5V:c՛}o"ǀ Eb}6| (hpwn-/,y- EbG!:}޻cW7_1"H$y1ص@i|Z HZR,]23 YCh84ɇ+&E{j@iMUPEQvuƔ Q:?"҃%"!H 4Ʌr5xu8|?i[@: 5_< YHRUHlx7k+'C`Hjlg+y/ɫ?ɃS7W}T5{(SiGT|B{6 nByw =4Y.|ꤗ^CVO EbW_9Mč>oϲx"bOW^x|{߰+g nֱ68_W#Uwsc?^~09m*{V䁝c Z=-o~a3]θ~P뚆6T=K Fm}Y@vMU]'n(&+;/Q J4\A*~WvuQ~ U\TULynx偷J/!"S$+$߂8r . c$?lS5)/F `"{I9lLt#j_w]8c8xmE$BVdSkl[SU73}I6U_8/4(G1eGYVs(Fܩȅop>.w Wi$a>DDa9RpH q[HU{HX3g%zƛp0 9ns2Svpp]( 96+EGtsY:krw/Vpijn|€!eƋ\"#O"ӿ'k<2-mڈ8c&<9(o;Mm fN1Ɯh}S1ku} c{E~Xi<D"nMZdS[#Ml28nHtDu;_ID"BZ>R!zH-6Gji$b!8c5ag_ߡ]DSv`ck^sl$Od$ E¨Tgb9[+舆ppڶ}H{'Q8t+>iCeljn㩒@먊,=򖷿ZxX1<Ls]%I}\ : i4\SUwlCvKJZEuB=%CcrYgp}eɡrΟ>u=YcLkNUcBc'pĘ1 HkAfr3Lk|c̗Jrqv3H>linEZk`vܟHGp3V`gWXk_uxf'!ǷYko6ƌ'3rmkVo^1H?7;}2*LÅ \A k{oMi8kHl&Bǭpu#oڮ?ҺaR.-#& J"([luDawDu"bq)VM 垁8 xw<+!U/!Rq@gXw#neV}=A\w6܋.v򎗧N Zx1|#$6O" {{ pםW\8ƛ>}/O Ƙq^{-mNڝZs.pc̫eڷ1"7 n1rZ{1hƘac,kqJ |ZKĘN>al1ec`Yk-^vA_ƍ1;uk1_N֞oxZu\n1ۼQglXWSU @ دHB[irw'xpW8K{ރTYCs;ߗ"ye5Ȃ |:E ĥZREP Tѩ>Hw;#Ҧa]B]! szwvӋ(1y ?#нe}HC)@:E*:H(kX"bHD)P$vo4A3kſߞLrdz5˘M瑺 ?C+w!iï~۲x|^qˁwg`qEy[{bآ94cQ o.1f>_<xS|V-t?IZ@B[ !6&U[hɷ֦b;`\KbξX 3܌?1ۃ,E bײV>gc^ĭvIg-g&}.\}RhA䂹Zp <䛭G> Ej۝$H1%3G>Xۑ"e7g?3N.\!kHab׌i._Wъ [t:]p|V[_r4y6*'#](ewڐ͘nS""v=:R^cr6J<{ԵfO8nگ>ᰝmcS5fXo=|{6vj'_%hC؞Z$Y#, P=j&!#} y>X#T<~_ TzuJ?l5qܻKWD֪#ΣI9HX_[+)#'KC씮OxĠ#-Nw4݅'i<AYsӐHR@Ym}Qiy<;>ގůU~Y0uIc,Gnlwp"cTsd{)B|B#RصYr'Ggqw&\dy1-L9c9Z1־czNnsxE sjƝIͧR'"Pp\" Hy~q(Du 6rpt w/%8rҫ%HZvDsK qH(s\@7n77l_Nߤ;{qBBD Uk텄/BYHH(s DoD;s3&絗ET)-Fݟ!@MU]ie⠾;wTս_[l1ȍ[={M!2;O/Tekm1Ŀ^lŨ|ȱEysmj[85ݕw|&՘vUD> ːJl^2'Hv'#mVzu#\P[_m Vr.\TȝW#K BCkz374^Q[Al~׼Oc CB| !39%Eb~x殼"HI-IHf D$8o_4l Ebwgl#*CRwZus  _\\\穟Z _]1KJtIUTwDnXIq,6m_?/D"Ы-0kqCKg:h}#wjvGyݤD{(soR[_}઩[ӏI?!b(,I $|/k(1<5o3~P{'9 9>D~ +0wo|pm}5fSrn6h{IZU6 P9-[;f (3*vf#gP$673>S WZꈵS1 M[f"1ܵ/ gĆ 9n=E+EB)GH8% +9q ø՗ݤ3̰yHguH&2irCn.Dyӎ>gRRΙ+5Vgm ^nGHB3{BHi3r⾝/ 󞾋t=# [BXsWB䩎r@&|ܯqhyS][16]Yէ".jd4^3qCcfuT.>n_zQ˭vx o|{j^(g'U";!qVU\:"S<^('u)Vn7|00''v "~,4-mF2?r?yޭvt5ڶL֏=TP#"+Ko{3x!:׭s̮tvF6SsWlI93blwڣ Eb?p0[$ў9P,ueë2AihYh+*㓐j_Ww&g>C^ޯ> W!VV^l:9!mB7#8r E|)1f* 7G֏*+w!mrк_ N e3_S4D(BL(",{;OLn! к?]#9ay g 8ӣ`z:{ /][|݁79'/#Ղ@. o~q!aiHTT>R!冹VFgBRrS in2+<\+35Zrx a珹f;/DܺbD-AH%Џ}F(̍yڒ=&/Jß wgS4\3ӰRa/ zf-’涾vo|%0'=G_4zi6R2I flJwҲ:VhvO_nVK;-kf[S"kc|ΑYMXYvdGUYZxNr^MU][b-e1oS(ϩ+"ZDܝz!ʵHQ^7+H(6IED]6&3&Իø拥ٴm:H%⭝Tm/5`\c KIN׀$A8l@–9H; 7[S'_F*Y}SynYWʎm)C8*}]#y==%\vDE 8rnђUO[=r}#YS$ _AH"Ś+LC?+{נ$S9yEK+^Jߦzꗧ/=\lиc-}یT}g(!*v!Npp[o]LV=_T]1!V6mc;JwTY>d HB$>縝vHUi@P  '!m8cr:+ 8B'Hl%}i@P$!UI5<]8ZIuy'cIu/@$w9$7B띹8I-ǙqOU՞Ů&I/vLRKSw {Gh3䂽9v?Rʮtއ|u-_89Ak}'Ulp {6-}fqc{>1G{%=ozf2ino if_SU-r~Yhqm}ˏKf]\`@[g'p!}gl9{z|g_ϡ]tuq ۻM[I+;NZkH.ima99FYkY5k3v2Zgy}c7ik4쩘PGJMU].CZS 8E.nAUbFB!I.cVQ( ; !IG n b*8 PY."җ+rX}\㶧pCnw3?70 H[ meWiG_/HBD$h8xz܋\8/m$7:@I"kM}}-!#EHH%H 7k9ƱHqB7A6ɫlspp2+_(Hd$jl\u!2R9c}zҾܪHU*" R/mL߮Ť}I 2/f/9?w"N1 7G!Z`n4{9HhdEDY]t_Sl rVU>0DW+j-k)sFN?גKbC~пioov~9 Udt1~M?#.H˚8˚ m6 zZ~VB[awqWjU-cwۀ 쀨 ^6SI$%@ BH - 1ƽUlwwmd1|`tG{Ν[3nj2sNϷu\s kǎB 0 e@d>O``־aYc-^e eƘoXk'cF$ӭ31C{}p|ĹB5wk1}ych9xZ1>~km1kƘQAaڥƘ[x۴c6XVXϮmw5[맢Iy)FLEӵu C2 9M}фNյqg?sc6AF!۾B"]PJXםh"^˻\&6kBdǚCݰ8ch']e wwW}Xl#UϞR\Ku¹.CEX'3 rWQ&,C+K_p!N,I>1%Ev{.^J%b:|~Gq&6kDl&"6yqMǴ꺕5#] \~]nByy꒲LhYz=#@nP0;L 󁖹K~ו)W\> ȅZa4Y",b'Fõ1\e9ޣEI(sWXZ;_k*c̝H;YkXktOyNnݴqME/^)6c۴:#L?T֗>uͱOw6 J?FJyVx2}2HϪ_juF)HճZ'"`n@c,z8 :PlK9bz!̣a:q5a .^mL02@CSrcݗƌW]lu е.d"UEe/s 66Gن*dևk ъS/sR,w-'uLѧʦUm,\^W\JĞv~ "X•oX?uכZǢp!.yeTf䠗PTm/E^6AMk`Y4l2 x+[磦\/8M+ :NȎ2sFQw:=R'5孈'3E,$cLaw:YkM|Zszf!`g6ǔ @<?@ N= ȿ=eMz7\SAkш&/x=P<>(vJўE4DđFD,ONE oDlnJJ*k'(2OC`bzivB~)z`;s r};BHJ g^@ӊJ62 |$096/'P&AyIJ9 I#y= _qk vɸhg{y=Opcd\{hvm߷l d.`u*Df9 UI,diKCL λS1>b-"M[x_f_4ƞɖD6 WM;!c˗62E#m VN ޺+++iL\J<04ZOӺ¬[?wИ=_@W5#hmbh1;dq3km1Ze@$Ȧ\͐=[ n4c2(ec (ˍ1{1_q (]n2Ɯo1_QuƘܱ=f~'c`dÚJ~>;ϣ\J\wRKm_wJjI H%bx2}/A"{..;G.1a"`?T/N@.u[]Z-A)<yLrؒ>4r2{ע8dЭB`t&Y=Wx^>1 ` b5;  r?Bx2} J$fZDchտSiΜEZEk|sI5}sCjAӑ7? .8 ( .\v}ҵ-*[[,ː[0gh߸Ս#O3k 2@ _g[lWXOKby7Eί|wI5U@uԶΪSdz2l:c˿Ut'sk~}JO"yT"YAͰa Y!(6l8{'ӟEjz*Oݾipœ!h/A XI50RGGFA84MB-413j"b#*K{===َ@ukkeHk%`,)DJ#dλ Ǩ@΋E!#>qg@ &wtcuypC,$3]_"&'uʍI*O19q_?&{дSXa"0રv[@mF1vyWӚk'Q:OĂ^k6gb[%hR:\bm}kyu{p- Wz#3%ŭ ;fֵlτ OZmߗB:;*ꪲNP 6n}m} (.˞Y\>YzaZu݂ohm}))FAM5_uJm@S<~*$ fmg˳&?VXs>VǶ`nuy?OD|oǓRXl bVƓe*\U"UN3Q[_3#I|+[6ŞU~ P6̞d\oA46r=X{<Dk)Ĵ5_KF8(b'e@t$`z)7!U;AYQїU*6SЧsʻm[2qYjZ^J^'Ns3UN7n5=亢`|LxA/w~$g}vp4+焦{62kO،xm Oœ6ЊNz93m:p~WTFJJlN%bE L@+b~ v7a?K%bMhʺ Vґh}/A?wFn{'xͫDˁHir&Ci#pWv!vh0ul#(_.rF;0|1^ۦ@5Σ {rQ-㮁\W ?!.М`NC^i۝8ٳ\%cĞDPk"P" tL!'+$ 'yƮBGZ<+ bu1܆ﲥ(^oo7u>]ēDmNZdDdnsoM`'?~&tԗAuf +;ȓ/ a m jȃ}:i89sDy<J{~<+qN^n}ߜ:KwW9|A*>)Zk=G_8{L{xe*OkZ>ꥷRV݄J5#LEBKYJm}G//YNcZWV-ue()3P^M\uo@a]w 4I9DMëˊ0֯9O/H%bo!ugj?Nvdz"[W41N?BA&' 5iR>ϵAY# v(EqmNP6dTcG@upրX0x+Je,sەqq6p=UIe(XpЎg<3n(r#v=;Oct,TDW̾d-#,Esaw=(T"u M}|%O|={O0w}by u#oeb4N!tlxG Hn;0uR~JFN@׶PElM[yhVy''7z] xwPZ`ޚ5#}'}3Jqf?+[h~/ *rE=ڙ_5\WGU׽P,vQ7n>o-[H8w^GgY˃Ϟ}gy#eY{8Gʹ-ƺ ؗp$A**Wq,ہhb1SkwaՍ#ndi&z$+r?#wގkg}3A]u bNf/Ǔ_-G@e%P},%;,DƆ2FM 7/ V/"$6vaӇ}bt6[?2V[.o>{Ӫ>9Fm g=ςsBhQ{ݡ{fn8=\>z JPxzXO6g#ơ 8;uWdzM4^u(p%fa"ܒJO\&`柸kdzxrx(0y b@ʥX;!5q=GLRiq[V"XJ^b>&@ `6WꦫcEPt ֪>UZ|нW%Acf b*t)HLVa1(Q %&z])sVk1y .]pvy(ˈml]n_kqRXցnDnAci~g-a?]f^% {,[C8.(Vn-u+9x2{zX^=,ݫMt윕@UU4Eiu kknCZf3C!W4ϝ{1pwYIS'<ͥHJi1ƘZ7OZ7Xk[nm> su2N4\h=wاJg(V]'3=> H%b/L}yDlb6ֺsY!hA`t`+: "$r|Kn IDAT<܉W"W։"v#V&G`Y >U!)&L`N!?P d"E!WooB@γN^+%$lAg| MߎwyƮIe?h1VqƘ![IWU*Da5obWpq^Hx4,,o/4wci)~m/c'(nkf]ۚ}+@~}=q]1\BmFq!g&Nr}e&PcW>^.N,t2(v“8@jĢ @ebN``K1/ѻ xo63Ƙ'3 6c31vN(<1&₿`}{c1qz]|Zһ]SƦUM'MT"vkw907b.f aQG@Y<e[>A/&􊑝3V]3@| - ^I|0re~m2W"U"#\(0|0ף@tNaElgKE@Q*{ Sē FkEḍ > zV@/y;wMBLZ'r#05MdkSXG<MKb4Dg.D}&wc+0|YR T}A<ߝO%aݶB+@KfK@փb@fw^G"1lYGD~c6v!z8 ~ٝӈ>%g#hx2}meu,_=@[")L@П]; }nqmE5@ա<@^Bn[_0ҿ~A\Hy:n*Ъ>K=qFE/ NF%%ԋ@]'^4-;օPP%FP֢XFM8{QZ_m'#r;N;ۻmEEݽbdzD<a;K%b-DT"t;4<`ǖ5et.'poM;XuP_[_3~H9T|-~i̸YO|>-ڻd};4]:ܮ߰5fwA&lϽۆ==]δm(,k1E6cX1cL_yZO3Ɣc0}Dwׁ.n cL/cLSk)rsβ^YڧK%bēQh-GT":L@1bS3x}?&jcRAQIHA}1b.C]㑫qt!3@/ #j(bbGrh>\_DvP FC !.w!R-Aǵub#8bg"(9? ru>@M_%/ V7%.}lW`_6ɻDqF-=UD<}}L/JtbӍS܍*tnGS7XlbT`7zv-''3pD,̎'!#bk;3^Am:76dV:6`;LTU,)^2Xk#{/dų=a+^FR0P^^ΨsK{ :?s*.|8H wBk.B1=y6g|P1671f#ޕvcʁvq3~e`Bpsvy{[O1WLh^n +ߪt5{17S0 |*eݎ@K bF(8iدAdXJW0AN x@m߀U&1*+wZ+B,%g#pql,侻>VbY6 P6۹xՍsdc{0/1Q %" "7|p> N6^Xf`[ƙ|׫lU̔뿞N <+2uോC!ɟ:;mbZu{vؖ[h:cg{ZkaǶ}꘱,LGp5 ^1bWI*4IES]c{&t"_G d-wF= b~XfĘD=g.bƚjLF/#_Cx}Z'ŻE[e~U.NU !Z# Uŝgӏsmx=G=+A@_,7\pf @@RP/+νҝo/7ƕ_ (qϻ,}5nC0wfދ<1r,wMƺ@LV 0x20ؾgK%by|ؖJCl~<'>Ǔq1h,kxM;Gedq4} ۑ^t/x=M1ф?By4NBL ~CPd|*0A,}sPT)A9%R>M @Tug5z9GSE()@E׎,^F#p3BPgF Fiۯ>#q$7 GxqJ7F倵VcC?@Z0ՍD=ݘ.G.% [ a+Lm6Jf=ZN%(X^\ܸuv?Y|̻ |ی9muíoQᣩDl ľ_e<俾[C |9Qafz`s12D?XYOwFkClєx2˘ZثoœQ\PY3i?+hrW s,b_V0XxF%׿#y ah->/Q Qaiz#..~1pyݗXPjjP/}Y{y}@ cF7"ק/]7v QƤD|.KQ=ZE=]z؞ljC׿P @"0e)MڿCP܈#T"61Ht7eEkv=b6G@5LJ|8@Մ&7: S6$bʾЫثJu&MVA rGn6z# SAݮUhB&Nw'5C)fCoh7sэ(iAt7P2-c_w @ w'~ ۊw F$߅\[tS*s;HK \BJ>lOw\ȥ8  p~/brED,4ѮoY,@W("nLKQԋa=?_>bs َQ"?Sx2Eޡ ,;׉[=c=my15Ƚv?Az/pF_EE Ht8rM\>(N dT"ES Q;I7KфE`c_4,FZ-3bOa 11(ˑ~n? Nt~{FcwĎum+p4׏9{ JR8߻  0s%1`sH#נE5ʹkbq"YeϡPA2LѨو;}H6׵yUh~ֻno#_C.N:"0.Rz@wy۝ >)OqmhtoSc +Aqmڴp/T,][_iu>C-\O%b 5X̿}D뱏m:cɺ5,Y;/dL*ԧhr=O@RJ2;\wA_dMAܗCYat<@- Jt}'4:xQ(u~_4C6Z] |3%S#o!0b$ tqA M3=m7Ý:nU% ƻ KE,WryW]֝tK];"A")ny\ֵs: arX6s& _0o_E.˻gnGnD׻D̔}gy;_G|}뱏m͈ղdz|3L ;N9'Nn?)k?'E(vh,0E>ȍ41'w Fg5rP 1[FZuGds9օ _5"ZHA`4Ed O .m#(;2vS!tm5H{E@C#hBx둻;R2F*mn<dn\O#vi,&pBkAI h<;oΩKkqqntJ*W57dOfݹ#qrNv}Yί/AJwN@(Df䞇XZ׶1챏RXKw*]] >鏄٬ )b: E Ӫ^D#Ӫ[;=eV m1-?1f*Bz.־纬=mVg,L_d^D畩D)*E*ruѕ;tމS}A/ܶ Vf4g0gxP\Y}.1\0P,_p=b[} /a/#wHAYL۹}wnGP|*AM # 5#rAob sF #5I6.erO>Ʊ &&ɃAU FT4ƍ};R\{Ex=;9{q̺YťEEѾh-&pf 78!Akpc:;Uo>cd'T"&=Z<ߩPg/?,x2=9d2';6V=ˊ=1feK(įQA) 5=ӿ޷c E3l|QQ̟ݺn\0_q 4h!'kgM{f3UOB@jCry}&iNRZiCI(hҞ^^At7"F@hΌ$piSYOū&md<.}PU ](ɴV\I#kA,-zwx5 RT!&"`sgjOV߷{v L_B̫W"`ݏ#P6q6!4"3ύ2$p7#޵,b#Wcιcep1l`Jw+kfsFyNfl&bZ 2)=S"'vڱ2Vχ hGglz5Yߋr8x2=b$@ T"%Yz÷YC;e_;.K}4cLi(p3dTqŲOScl!Cv @ Z] \i17NZc(ɬ}v1|x^~q c[`H0X5Dly< d\h "tK膌>e˩Dd(}6͈*ؿG!WR|9AhEMTwUo]峑eYƀ-uhBo#`Rv@+Gp1 PuL^=c 71R/ IDAT<&`Y$چخWybyzS ЎDdq swe(n.~,bf 7 w9r@:}#mNXgͫF b;Fawp1#e zs]͊1tƍu5>A(G O[?OJz݁?ǣO76g_teKesE6<cyi]k]J]/FYk;1鱏>q`y]j]2 wfF_bxvCq%LW uh2<MA.D e!>3QE(g _#hm%ddds-M ^>8NyuUDQk Ğ,@huPKJwn5 #q>GZ`6{!(Q@l8?̝2_WrT}:̍jtJнp;߉@yw3 5aRHQ7^ W~/FzA5@cC10o;nNE[tcc&E =fǺ=O=}IXv~W;䙋^]x`Ks#r%oFuc)ѧH\{g3=ߠl^7ޟ7jbڹ|Yؾ_9`1 mꭵK\Mˑh:H;U0Zp4{#OC7o O M6M\ 嵛DTWnr= @@f4arEwC~ 虍&=QS-*/3d=M9x5Pg6A`qգ@ R#O"-ȅ6&n&#峜OZEFۻ4b|A|g*@`|r ׷.PNJ=ɴiuy$^ͳk<>5у;Z yNfފ=(пj19MxKkپGn[kn|_3V 4ާ1: axZ]ѭ#;hƘIVODS?M*k'{"-I(&:t]&ߝ,]O )jSфY&eUGx'{&O/*r@r0Fd j;abp(ziA #f9KPA6+ܨOces3iAn֑Cݸzg]9 ;ArCol]ݹ| 'Mw4T+G0Sn5=ӘPq-} 27t\_0n#mx2} 8~zf) Em3[tmQ̘ Z.(cs* d{BkQnww_5`vl!_}HIa.B3$|ONx2=ah鈪M*ε]w2Xjw 3,E?D7}.Ymji@&4ܱ#%61 K!*w\@@ žLr.Di$g>~%CEWعs>\@ #d=@e5ŝgCעJziƵ '1Il1)LPbƆ>K A"hEu('ŒAͅp/F!7 ,uNPu 6ܸт 6@ׇˠ³=\ :*[kZ"0ݯ߫'zJU=mGecaw^p#F\f+떼uٔ[h_c.F(%Z3a3tc Ƙ*p '#il*{n3XD3a8MԷ!bW4݉&Ф=1F9ĢE@4{p]⍖!`ҁ#wo4@@4݃(3.嶿= 9w>^],9+3}g|rD@p`Hf)p]F{ey]YTܛ ;5, r9:c`C! MEu)bƢrwX#z;ߕn Zp ތ;hف]@a?b!bNqcx䋝Dvsc?k{Kaä#@dwkd1f7v8}rq%!Wla]]f< {c5i -E}xsnd5!95nim}@fZu݂-n:cuzl۵O 3vwyiEqTG|~%Ϲ{#W@*A, A:=l Yce~\|x DSv(_r?2X-A o@([ U"4\8ELnu}?$w˻g()`61?An\�%pm~ע@ Fv(p[</0Wpi.wwĆ#wgu0A<ޞhuwX{;jw{MPq!DnQN_`T"v;{!>!bQO.MdnCY(;!2:o@b8Z&W@55!8 M@mOo;UhZW[_V]ȬlYd} RX&L'R/mE}]8R=&P@eMB/YGpMeny,}w$~F#U.2sNIW"PtkkXQ!,N%9n&+c$c<򟂀Cv${޵ vƣ>41]CE=&Ո|]QhR>RHGXw@reg1N["No"rC_f!}7];"p=(b^sBv%Lk*w-FL}EV"wW/%=䙲bT]/9h{mRX֣w݀Wॣ\V-m<т[ T">LuYW&K&[|aYIs=Hkчg|-5OX|#3LW~G=<47/}z=c== [bJN'&k+et|Y/ĹMl"0AYUCȬ@,x41,Y籝F<>oc}|gfqE/GvfC\D@CAɝ64)̻,&cQĒ!(\q-rmG v}\V+˾̵u \!֢ؾ*7.7x-_[r'`}4rA(dvtY!ovW۲e76` 0E o ,)_* ) $T"HBl# `ދ;vlZ y=;wܻwy9 5vNյtDJqGݳo"0w$m (C?CnEn7vxykq v*t1,0e + x[WaO.T]^c3􇧮xЄ4Ÿ?7|)knm ̗۷(ljhGN " л%٧=kl\>{žݍZw=ۅOzrU^CPy#_+SGw&4æxqy{0vȝ?Z۱(SeGW|Ƨc=գ<:v 7֡S9s|^y+co&ې =>ʃ&=}1]I?L4=Oe}Gf F4y-?PQKI33fF kEz)hҶ4Ie&b#8KʳaNsCѓ}~&|yYOQ\!G.nf1YwD,pGX-]7UE  jm.fkgwz ן)vˤUgטmjsw}b7'eM{Į4!Fl_]͍|lxD+7ǹk`5G0i4YM%E;mq}wA{ AmE dKyE*&nv,"^ &pu^[ˋNEPp \i^Z(jp9n@RUdvYTEf,bBGvwR?$,ҚTec693\ٶ܊j͸m{F cr[6I Ttϸ{TKa1:I1Aeq_0ywsb_DnPKx7uxtK4jp2=7b `|qͭHA$Sc¶YYZ|N+9_skXX[XsгZ~/E*CGx!Wosp&m啥DbЄW40H-- b!4ђQ24zv«G FZAj_2Gh2c"kKlWU~GakZu.k_KMݮޅ&"w]N) 7sr^ qA\x|ӍrG#0\"AX]F뛗Ȼ0}B,(vQcj+&[:zϔom1^(e?`-bY VAP=?Jе[>/^Ѝ}ϦWl/埝7@Kxs__ $ѻcuX"U̷$ѿ紐Zk>PYa256P< -ҨJ?w8|b_{SCJ F=_D / }k)>-Ob2zƘ,x; -α^l^۟Z;s'E";z6O^>ZN$T%R5嫪}h♊KƣAQ98c26G=˳Wu@n@9fD`+f_"fw56Ƒ-Q` zo~ e`I =ǫaNݫ\&0FJֺƞ|u;3;Ӎe 3s#֬Z={(| z6W?Dn7h µw7ZP׍BrrCnd<څɖGRᾩe>_~yH; =w}]M -[v8t ŶR+Cq<в__X>iz.WPxaW#hwϫhz~[U0ƔhL/IeyM^|GWeyc 4Nc.Ea/^cޱ]1'(ηHZk_<(?%x"zf2s^cVAƜ. &< x@*Wk1_sYf)wK,f;t|ffyjȍꫛBPؽ@M5Ѝ rrvnA NsrO#V/G.H.CwHA9NbʡNİ5=G!Pd|Hskc(ORb9ҹ5? t65rc8\&oI66в]_l6%>5$-Bh]s~}o~G[_9$;},# 3XFDXs] S۬dS[kƘ8o;TnJgGA/pUΆ>۝iͦiJƣ?rO%b*hEG`k(&C7dgU  3">~4^>ѻhPyكb)&4y-3kT1мLSyF"S9q6V?'"@q+AMǵvk ^eSIQfo)GE" 1%R?pxtK2) .=wܟ3ق28@Jnnm @d׌neyp@-Hj- /K+xe4Au2;#Є5_01Z|&dzDt!%ڍ@׾B7l /ю⭎qmvlFkQRR-A/}zv `#◄-L.Tu / 9@A%06~*F.n7ы*h^@ݢU@Ng8ijCַOWo:>4QlZ(Z]Կ-%CpA;ꐙ7^8剗v%Rbԗnͭc-,*=椃n5_TR6t'QF1>0ԣhkިM6_-pE'?BLݣoB ۠A9xtW#H݄Ћf_4a$(=P@9Q:>@ߛς1d^G,OXd!\ʳZHa;ίXlYRg|fp4{vwQ0Y\mslBg`: f?׮/`\JB 1WO;C~,p2u 8 Vk&r眍Qĕ~<<tt!\>q\!FPU(f5b]L^Cw<~MJQfwsw~O$(~e2ߍ|rZyMpkcLB3r{Lhe%?65GvNSCˮ njxyIwxskcpHu]B@дeT!x߿F [trӊ Y[t͓{7<5M -;-5敬nKwUc +ِA,icG2hl1f!TD٭Ƙ:D5Ɣc$xw;1!`^chZPcDD4ނn\X"+B4A}`r[C -;cpwY"^. Y}"1mmjs} 6ķBg͈Չsv5 >I `xfSu`i^C]NFf^:=,bO{X*Aj&JhBnq\Muv@,Z+ Xl9gw0k½']D"% n1Hs~er:cm&uXFD]ƚl 9 ߅=XZ kjcv`Z17h I[,>ŞCނc6<O2\s1_c<k; cA7(0Q`z,Z&i?yB[i,!%1mx˻0}nLѤ܉&-uBMv,D`{\>>./(KLu!%OXI PC׺F8uSD!HN_G]?@Gkk8xaZt]^!\Szׇ|`DY8]X\LUنݽe'79ҝͅ?y̍o4yMf}[kNoc$:cEwqܶ}}v)"vy.6şK[,o=$PW 7hre=,@VsmyQ@gv sgmPP.Vϰt1,L+;b|,ou l1-[D>۵݅NGgҨHȅ!H4,]Hcj ¤F,v.R?v#l$Aqw܀VEvGzvEOk;.qr ;zlFFk/~u kQJzsvto둋L ގ(߳D`E`dz}ijsSLPhkY29[UwK˒F3#{̫<N%R~qh3NmOU'&F 2ϔ‚I-.-IgVO~xɑYZ"Bސrf5K^^{䐽McDj$ZݝG;(9+-Sd#&^ GoAgu 3 =Y('Kr4p!E% :\uM uDƼ6/+|y*4칕 rnE/E$mt9XyMXa?D|ݵ&[0أM됀T yIcͱ1aO &/J>tE XBŋ=#hU-94h4b&ܹmG`)eThI :Ynrlu;cR ͖͒pw?:do#Kƣ ņ}G39#R8nnm#j2z֎qƶ1ev# v"rl%b+DL⹮xJw?$n\HuACjTu='f"}1D#xǷ ِ&KƣAazM8 :T~t>e d UkJ-`.rCЁ{̍sb1%EE?C6d,0v|:c,Ccuއܝ<ߍw6s]玟J7^3Xw6J8 k4lv"b.EwoGg(|#Z!7^$1ӵn> * 9ĆVdv&+xl^%RX|mOksӎ^\4oNxg{Qv2߽xNq@_Y'`SfVn0ҵRyL}cˑ5cx-E ~0ƌ!/?jjh=! &0V&x./d~ Z>.m((^ӗ ! zV3*J/E }օX L3xi|\7 *>θ z!AƦIl!  cK۝k^qy0>^/U5^L+mWJ sײb(׹>U>{J;`݀q(6|s? 1.rوwhk=+xt~,1kuFn&v?vq3Ypo oty݋NڊQn""?$En\-!dn|)zܽ,R_>wlOnw:- FG.>._˲gw{Q)Hl$_b<} lornSCː<mP((. KwI \Kp!pqۤWYUз!}C_NCPd:G M X.v|Y).ZL.KEo ",4!v1]]%(.C eE7r٤n{-b*v?O#P~'#kչnןJ7d<l>(>u|0m-3 s ؋~w#y$ v'c%@i2 JHЮGqa5@/s|%rLwܗG" wG"yAV7iں 7g CEl^J Tb~(|7x EnU=f#λkTͨmΖ55DŽŒ)ה1][y춃\AIQob˸3'__(\'}Ơ ͭu(wf)14c~Q/fgUq1WH̿fcƘ]UxK٠chMvk@dAHMӵhxv|/''y/`F[5)"p5z@.%(zlIώyE,u-=*CnN[swdnw_@ÃZLf<>5)Gvb|Fi1lc뷯ـg4oDFaDψgv\Byk ou}da'vXnS۹dg xܭ,zWlZcCՠWi90Ī?{ͨ,YaƘ^7_k߬WV#I!/l!wFhbu#XkB̩?Edvcy_@^ƥ1 aÅs41zW`Z><^8XVrH"<n`!p%/K]'2<]&H@h$[Mn\o uw![:7dAi϶+|Z2ޅ@x:ڍu%qjKߗmy{(x pm,4wIb:e>kO8Wg{ >G7 sbHXhAQa^x=cԞx=wes>7{(ϱa^-._])15u\.t!: ]3 3}iFV]gTƐ3ŅW7Z|1dW5`ݍ_XhM#cLIa>3Ȱ7eF;_y`pvSC1[k?iɗޱϺv& c.Gs$k?ٵB[QhF%kcA0 d2m%Rt;NƣcUBЗS:4YؚC>˗E]fh`ųC*^(:v;@O&q|kpl :8mD_14uc71lO5z)$Ȫ mX:uOOB^£`6 ؙX3An$~ju4 _"k5 ?Нج~#)_0QFLس܏/~ l~]%rEšQ(xr%xX"Uw%:>w9l{ٳoU»=󩮾 2yc 8֦{ y?겵'Ɩ{BѦK[DXC wё} 'Ϸ9HvԵr}=\_Ͷw//,b4 #;ോ(c>ǧ[kƘ}1''cB$a<ݟ `1'(qif^[tkm1x7uc K 6粿@ IDATLBmu9ی vi?WФK ԍ(ZVK^vb 7O^?9.0Vּx0yۇ Է].bSUc}#WxX"uB n:r36|X"u{{؏OYuo{hb6'?9㣐+f9z/rϢIy+r ODYAڕG%Rq ِ!6_Ot-*tẄ́'fE"A$5byw+<0N55mnmeaG|@8"˄Q3oW[7a~}m򞕅e;`ʂv+^z@S=3߿G GocvGuzcE{1Í1*46ƬgmM24gc7]\Q97{-Z#ߕ=W7Hөm:+H݆}XSbƾmH?x O`@12@W1]^1"&c g?@q}˵ݓ`8͵k36?Yb e}b""7֍_bM3y5$|=_ & #вčGʹksB68SE]/A T}:6oEdOB$@͢ 1GV,z1b_F[ъ!$ CD`Mrp1ʖMO9X"U s%Raĺ51VKO}p/@, -&ѡL!{,H?l*BUeZ-;=ЧPfoQQp\ Ow8ikr6[&{|).:zP%4+tѵI?7L j0ls^h;{c}ֽ v+h'?^+hg!lp g;ډz2Ec^̮vlfoAFW!0XLt^b |1ӭH\4zGj ,6qBo1=O^ ͆UHšL!ZI]aקn<͏@f bdBZh fd| M{"҃gzQ B7#n֍ܓD(w}[;Uiv<ݻk A=@sCMĞs_VW!u8pszsxG.Ko o@WJ:} |k_iͳQ\(7'!lC+[%+ l2+Ȇww֞xG'J2pZHn\ \b,z5sϤ>s܁l}5}aAWM -]ɥ=rs#GG,=3 7/xxaaocQW1oS@-kkm1lvXo1</e6 wn"n>;9YkEaO~{؛ʌ - $@cFʿCZLDp81<Sx ԋ& w/A{5^sy&&Wͻ5Kһ<O[}R^l%΄*VQClJHT>n)/uc\䶯B"B{e|.M&T@Q5cȵ]&99%֢;;xk{3zO)%]*.qcu:kl/?si,# /A@+栗X"e@"g\ r7^z̝˷E7dCz)̭]cdK "Hޚe[z{}Gۛ[uSnNƣ=#NGTWeNzZg9/lkȞeu t/8wvkcgp/1f҅b@r=H^ifdy3-vo3"ό>kdyf̃J|x""[ܶ~ټsG/ryE|p"`nB_h҇͝^bE b΍~oDnܶofk+'e{ kA V'A ! ߗa=b&"y|ئdRpc FP*\!w G%X"ՈX{l=^9wL4޷=>>dk(6RG  I7Hƣbd3K# & ِ "%Rq`rYLQ=פ9kifYÙH83CskA Zr3Ɣ$!{ۛƌ96__K>BA̎;v_tZ~I/&c}VEF<"(F=~{~Exz6ϰ\ 4.=be]E(FjA^u=E끦i+qUc!e%cJfNumE@cČ:MHlreDpړ(;t9b\ -ҽhTd<{n]{R;XqS2`cyb(i JtlN,{|kn=隫:{F\R\Եcn)s߇󄼤56V-2 zڳ&f30KfkZ7=ZW3Qђ_&QKPBRL@N|*!`A43E\B'xykWBd}g"m#@ ˟Oݶn"RGknE$x1ZwNwmU< k)_n>@֢̋ιxD(@QO|1\`b6;Hpײ70>g".?Q(1ڽ ̻hG,9j3'O_+:{%u x*̿b~u!kJv?jjhhnm||i<؅\\Ux562G45 46^@!{В{؛EhnIƣ7 p~V4)@xĜ G^&rw~!n_31I>{V-?Ļ+ ߕʎٛ@Ջ|[o85H:lRĄ؉Dn@#p"~:ח$d~ܶ5PU=p˸s 0d^ˍ];klա{WňA"ε]qq|^r{ߍxұW˜yR"#0X"SG]{[vZ}x,?G 8?pLr ٠d<ځ- 1IE#3٢1hngͭ eP;T{rcw_s]Wvا}9!pr,;_*x+"Wc" ψJPfbZl?"|g#P( o g37O"6З KѤyv2zFAr(waYZe:r5>@(~Q&$c]XS/k_>̈%R55d[s] }~CYNMY}wтφlQ?cxHKơbiZjD l"rg&h bDJ؜QdE"AY@8|@J6R|̘q/kbӲݝǟM$ ͯgįyװk7&ߋz/ x& n__ oݦ:~6G*;SPaGلUzw ]; #}Pmҵ׸{%p0yX~?dAHGl^2=i=Knv+CƟ>!Rv{GW Sf+=CC-ͭ-[PoLT:|3^X?S_Sljh_*mk!,6}& F=$G hbLrJp!piBr{;0uc +oro"G.BHa27hwy}6쒏s%O)pבFZ#YYk\[5 w7)B=>>xBѳq;.0ueFgJM9n_e59 5*bOFL_#p~}BLWp+3id<*:㻗őROƣw#WP>n{Џ[5%ї KƣOd!55K1k|z~=!qkoˀxdySC˦"sȆg eP 8w33W+.$nBAx-CPzn?_}J1]#03Q[2>@ܵA`3rYĨ!GZ}Fe!b|ZF׸snUVG?z-!(ٝNĂCty fwl1wkhwJ )el{=ۄ l9~Tw]H -s+,RO Qpcbu} *>x2mC"Mגb3霍j?x\gErk`[<؍-Hl;!1ؗz 0Nr/, |ጹZ}zf/f[iv"6mmyѻ0Xڳ4G&F&C+?@KzR. q8)ky\K1HpS}yQ{1P\ҮOg}E/HS_@XI{&@n1b^Z0mAP(~ww1ƹ*$%ıD-!6 oA6)"oCYYJ!۵G s?,0X-HM:s@2em^1Zk}viLcLΈ9k \ }Yɑ]vǸ!{6XdbT0g-y۪3"E@Q;KFeclH/_Z9LDV-X Mb c [@5w[B\@!nUϖ浱X?&WFd"1M\[@ #o{@}3qb`:ż1{&Dk\{kr%ޙ8}l8^$* l;ص]#NElRׯRC 7>[XDdDj4ObL$Xd%43dbHF^V8ZIcL J N0kCh!ikvƘ&1_AfʿͮMoͱD6z֕rW2*Z1_[,bz$?A2R2]XB0{cA/./e.K@K,Yx8p<؜r!S`o&I`mQfG_">)k7GÏK=vK^.>֊d;Hs? )7X"UKu;C7 7 &"vs&7P^GcO$|=@YP[o5}sZr'=|쎼IvXF1f2}&}Ƙb@dZ; |3;*Sp14c<wl|'vkL>?5];3M<|2gb߀;\ٙU3zـ4zfEڮEa(vȻ'!GLPb`׳g"`LE}5"8wZ7DkUԫ_gto;׎RJ(f#⋣ P^g˸OG@+]KԏO~&h)g>.V_  xu|n|iv䒬E1j}|1OEy}.DP`Ov 'yda(%R3G]X"5]Eqw;~>ܷ16ϷvgGKƣ[p]5j$v{ۥ&HGw8؍Y}ۘcuV"x$y=k & En0;0jv =;y&hIPQPD> q@W*^rUP½(* a CB:sICuxΩ0&z=O?]u9gkw\K(.'~lz{N؎6;-}a.Y[L\PR2k{CB߼ksBJ a_ֺcs磹6Hk=hqb#p*=S6erۑv~hc]1id/"usDe#GbmA]Govzk7UGf"kM6:fMj smWZzzšۘ8YY(6r՞A:i]bM :NG} *|1\,X(|v<+~ݷ= b_X}}jnGCb9/guzy%r2YdҎNmOF F}Mzg]\K-ȿj.`,+8M-\D.H8+,F84i>"Ǒ<2? IDATKoESQV%D >DAqjE Z:Vl# nP y Y[O{*(6@cjxؤdq@qߴEa:T#>v 5 1o-֮;\n=ڗ@6\mX;߉1ُۘ0[o~'Y1'!myrU} :E6FvlYʴNZhVE"ٶ݂+L^G$L/M?fЖk 5mQ: 1E,[lbis7t?IQ<%kAsю1bǮ_?B]ef?+۷:U<6ٰL0xWXiP*(^:#7ܙ-_>o|g&փO+;*=[ DW~{ŵ?7\l0b))'l5%*9o쳣L 6 z).#`]rYJHZ{y/LJG3hN]콿f \VDPͶ :P Ș2E,OP9#Xb: w2QpwH+W  M4b3d'qNח+|ɆZpM_?g-{ʱۖmϪtfymu^Is5y{L ۻ];m7e>͕.EQh\k/Gb \h Ջ(c-dop1&T;("eFpMs*?qMiRm_vhN$wDdr1[;ꁚ0i98$>ĝ% u"!&(ahC3_H/" 7>y$Tb12ߵ;2bڐ˭:` { bDևň.iygژllƹYkvb)=8*օ,ĺ=^Y@B—{-[پfl++|1ĵ϶wrCqfnl_n:vR`SO輫vɨNjZ\/<Đ%/]{[$+rY@]o{5g>dBʢvsX4k;)eŒ=]sQ1`L(ގN#V)!3^-%LCo *!T"Z Vh2Yw|<cu5CrMO/WjTo1c8 و; ^Xϊff9w1JDu',}`x|%2A~(p ʺ;S'ЎX xErbInmvH֏~p.s՚fOǂ8 %=h7ʮkTB-L1N:Qϕv}C[Ql3,KB Vלw"Ķu[Dc;pZX+b2 Q Q`gi2՜CpΨECgձ[5RgbDv=W^)'/SF[؈KIڂLTz!Y4هxĺ&.f&cb"1,k ;:]DK21I6(n󈙢j~pc0ɾ=)6g NM3pۛHJmEu'"fSQr`z Q U]A Q1ލֆ=kÎ]5>rK+zx1S'ؘb/er]P|6H&W8iB<|mML0e.yۺ=,Mgr>ܲxe`]=F46#t=WSPcf^o 2C6NזPX&W8vL?ϏnZScm&W#hZA@jr={X{*"@T"aQQ8Zh?v7ԂЮU .PeAIA4!Jd8yZ؃F}\(ː3~DgryP3H!j-bEn-d($8 I=xO#@exI(`[F vk4F(1~,s}Ceדϫ.e&ĘX(eŞQ@j'ϴX/2[l *EŇ?W0-[Rw64gK77.c}1 ؿl:5c{Lwd. uƯ_*ޣOkRV$r!X^DYc< wiFlvba1l<+ϦC)moDwEr i> c+wGzeG6 )#|;U`c[zU@rˡE2bu\.+ $hD q 12D(,Ĝ݆&ppE,ھDa4>Ev Fg+k3kv{#63$u_g7 ? coE.С+t%K3K`I>~o7`Kfس[$:!snD_ѽsޤ3hnz9޿{{ўն{|6|6}ZFLmoFzĮ,@LGMӍ"'ֻ/@x ,TDL֐ퟨ:,} z{Gই(1=v^k)fV~&Q }mD:B"J;l;.([k)V ?ثDb:"YBR  ;DȈ#&.@n+>eـF m s7wO7s\vFO~Q78}Į^gr, 6# -M?ϦϦ}ە(oD*boU>ryosVEw|bq|ĎtuxO77Oӯ`An70\}ͧn'7'8TҭI7[ǭ?y Rs_;G {Ɲsu{9w}~sN܍e|tw뜻9-lss.c_hx93܋R]~-؎ b#H4KYh!vd>wՏ `5DEI Fܒ!樅MC ȬCI7Xbxp!"H Ƣ (Ct%b|eՎͱZB8O@2aLgSmܖE<;v`>@$x:F[Clfb48C- {Uר{E r-j>=;JD j-@cpK=~zne.^;ҜϦ/ ':ϋ֜gӋ_>#ݷݟrk7wla/ӟv+S%@G<c}GֺoWCb+k'OcB.%b|['B|~/ tzvwb$i97lشfWЁ߿6#O#%hrs=.|V" `Ԁ&J" *\\vYDaŪ> uD: c<6wq?0h g>bY[BҮۮv?{$\_c7W]lw"`;o?pyU>·>~!$ބ\>?B^~M{^Co, u+hE 6(fr*mSLb,gmjGz?bɶ,&A1C۽ιž͎qΝ1e~Uَ~|6}w&W8_=3M IaLAׄ߈zK)@ S3U/KͷCۭMPWrH(6A&BVn}.w~V;h"9%+N6n S/I|UhM q-EAWisB.$\&f6u%o&Qz xoǡcXm?En}H}=18{3:5|1~혾vt5-t1nF|X&W88+|+MQy#rg/"9bR)f9 +drCq8'Ber>tZ8Kr -P(؇Bp Xu6xFEARŧJdon;F$n@1ZqkCB챤܏+&w[11dQ\ PٱC%~%j}BQE h@Dl^"aA!C{/]o$<yV= -uq'lTP6.ǠgК+< 3=h{8+Z_VALpQv눽B Fr])Wu'P}ܰSV<g뗱ǏjW,dk<N9܁h~.89wۍOs)xu5!oO9Mƪq>r\2F8(b))$zs&W! A Adӈ!C-CLIpՔ[&.,H;,†}.cM$U:GȂ ]7IN #VmD #i>bJUCTV(cml^@G.ÈClWe-%b较cPG,!v j;nH0ROLX3qSPu3 b=K9A|4|9J=?!k.~ EW< Ϧ4b/er|`׆ojJ 5LP\rEMV$uW'qGaw`15Ʒmy{՛~9w5nDMfq[+oaXws9t],~ *8;.FsZ;%{9wRx^Mm*>9Y$0-Y$]ϡ7ۣKҁPh1nQDx q\M  u " 7 -T/^ \2MP']=PuEHվ:?Ĩ 8tCA a_ E8SP)H$ bak"X;r Q;0>ێwrA7\$JvClc>ӎʰгű$!vQBxZtZꈊw6j&**za][XjY;=M]a IDAT{2[ܾƖo9x?oOE#]fhC<ߧLre#ޑ$%*C1C*'3?]LЄ{X#ZXjŜsM9{n׫ٶE++>j(H:WwtPc=N܅DJAQ'Ɓ&&֐}׀ZaA+B,r M$=JT: <tbxB'ô7{GGn,2^1wڷ7荦DC f/dP: ^k4h=@GDEwC]LtvX?:_!>\ Ђh<ź@XXD@!q'Pz{ ?͊MеnO{7oR;-%#S7!+!kcm Z"Jiag |" !6{Y[[K= u1t1V uCdSX8Z!07h?RUѣeHes}_DФk>^B@V+~W,thy;WSޯvl顮bÆ_{gn޲[Z)_[::XI`aNʋ%*+b +n<^a?)^_bo,q-؈ؖlGcb~mogr Z 達+޲WgX,E FM9t>(;?d$ʼs{'&,CEO帚(d}XȎC Š@F@;~m ID2Y Zn},$q=b.Wm6!:(`වyW2I߰mCb<.mZAsjL~^X~"r!D fA_Qgӽ1gwUR8d*i;Z3߲RGžaȃ$N!t ]})WHV6PJ84im!#6b#6bo5UY&W,tЕLx]:#? I3\qTOebgrkE!=.}mI܎܄cТ> +/bAC+hB_œ#ĘjAo,H]8dA6u#bCqP{#S߇Smߏ(o;  0G|n1>%[ލr <agWvMDJOpڸ"&1##6'#nBM(z`L01poBi"6bɌqK/N95hTqsfpL~hAs{VFWʱ^9gH.IĐƹ9_r\R<XDm؈s67My>>桘(b7_ǒX5Hu $.pWekFbLB sU `;FH]ERCUj=T\I"D ArWp4;zx hr:N`ĐJ`C{ ?Se ."H"Yl!1ݮM~[X .x;,q Za5Z,C6{"uGڵ5l݄ܧeȽGկ=`hn` |{h}LRrWFiUiJt{w[@zkiox.]3:q0ypm9[XH<вMAXUXi {gv6Â14!~hŘX~"pZ;sBM[QӕlqCRJbbڮJvYhcA4YCdmmDLK=ղ C ]4 W*=T-H_?~W?yxRrs>v/fIV`Z1cûSXkPgݷݜr[p1ٻvڻZM⽋kj쌁ՉuZĨWj(a "+ݷ=c}ڭ؈lGcKe?r8$cq/Z3dr[ČV1/: ~{ ;ǍH >ƺ3IJ5{?UC$ZrgvU[ăCX@cDLV(>Hء"v$" Z޼Ch\"k &*jV+z]JuƆʝ֮=w/P[wuv!DpIu.DgL~ Iȍ9_BfQo6FAȍ:=ZH~\ Xvs{}~|a|@ver|6]]d䒽 %ۆRxQ}Xy&Z>< 1}ۚ+=GXHqW(82d3g>wC٧| GFm# :b٪-fι2ZBӽ}/bˁyN`o^`,Mūھg> S+`i>>2+|Tصn;b9G<x(J=L;4ζc4"z=h `F\}MŤ$ћse!W>kbjJ1 njqB<f-&!* 5ÕxƧܡ+SG?sgN~n} ݈bظGl\IUoX"~5#p*ןT|mbYvsqzd;v/.;?!$^ na'vnËv֓r33{vNJP'2\Reݺn1MEaO(v߶QXRXv{;8IiLHF;{5BII{]6)K<;4^&a)_4h^\ؕQ}1 .!`⯺P RBADCwCj&Zh.GLȣDk0x>U)stDCaKF}eFV({r)(4b3 1NqXEvU(?k$JfDq(hAIDbhce9eڏx'c 2feD󆤇N7 PJPߵްgc,MoԀl Fl+@1]v+zN_RBxCy`;6,^swuF%|עGw\A> ܱ%ЗrşBOuo3b[gι$姒nݼ5֗R2\8ιӐ{_v FXw<ιѼGse_㜋3i?yιl2a1#<2c[L0M+ٍSQ>n0?n4i i;FXDq`[ Kq Atay4őUN weV6jE}Z| r:QR@?Qh4<dc# b8b=p3>[H W T]2(Ck7~{Y{6 7ߐrsz!^x~]xG.V;7uˮ#rUkh%HntueAƭ}O!=?Q^ȵ܊\/Ș{ާ=67bϦ}&W8¡#X\ \I~at97{_q>o |{sPEyI5{U1FM=lXAX|>.Lp7blތǢ- !;dW %ЂكKuDm%@ @5]JӮ4<1a kmɈB>HuVUC}BjSٴDT#4. @tiMkpQۭ]@$:ѶrenXH+$^ Z?B]MDswA҃h9 cQ)#)Oku;t㈱3›;C@nzkoΟ.xwbnF/'o@ -2{aq)W<x`[:O?cVKؽ8[,=3W9;W_vVmvɍ[\ zSCg`{@pÀ;_BR~H_l>.;N+s2A -siMtq> +؍QVr/ 4MVDE=Xjx`kA fLF#KmUGp=اP>6}{ bpXYH ۯl G(Ch'۷ !0k:GhE/Ӭv $v Čċ&b8K?\Oƍ:"Z؎w8gY[ڶk# kcwEWMG'W6l ߟtt~aJ3/xކU>X]ߣ)W|"E͹DSͻ ѻ8yd۶u+Y{~mJ{+6+N֍Ĕm=^4gmb­8Ƙ`V/ma}y oѼKssoB^laav7`s +SX>^фr=ͩOEWYNZ݀P!PwRCu!@6ig}׌J- ]`G`OvW]?L.A €*މ->NEY+l}A]b<!#D W`A`[dp]6^%M-H~Z_!6-OMX ,*=uGl^^(fͶ?([{2=\Wo\r:`@X5t_o{KƁ"7 R(?HbH NCL= ǮsK« \růXőmbJ)WL\Bc6)W,;PZQ`t!ǷU^v2V s=Bfm dsscs<>rΥ>U]$r{[|N={B:}5`6f k%P/+B]LD8$bU.BLah&-hn shހn⮺PU!(dOϻ Z؞}j,Āi(#Ǒ fVmT/&YZ:dЂ2An3n;߻m_Ozm$*+Pl<:ٝE\aoЂ8(^l*zd:;NQE7ƶ-6:kNF%hb*b7g}> lpIms cWgڼl;;IAld7\};a=/,[/L7\5\7OboE)W|.:= t/Q_b׶ wƗL0ɺ90?Q6v x̗]x^9/(JM4Fř~9w5nDMfqۺ`qW[,辄ϱYjt_b^.9> dcvޤ}0]\(7x}6b/^u`,MS mv8b_~>`E!PBLƁPwTyqpE` J CN}8$$LWoۏ'bb›|J=McQpÉܓ!p1FCb hǠz a;nmT`/rFrQ?ԂO@$pQ٩vlKػ6VMeL@1_6 a-N}&҉G;0rC=c__dr#l)b^ vd/Pa1; #h&WXǫ3ӀDo_ i\>h9] jg zkkc4%RIt^۶*ɣݷ\ClZ|rRX- X|pKFԌ{`:V>vVl1b :c!r (TY]Pm?_T=fo|{arS>}L&W8 } t>D,D2nmǮSX/y- 41e3.FSD.CLC$*OEI??{I`WKM9SPJ'ZDLO#뉮_P=xLFN#rގՑ&o [DPF¥ (mD冖 O!rncDX!Ɋ3x| ֆQ(=Dep.$>h1@Yz}1G{fz/RzrɆlz5/dr-N;:zOjms AA I"v%ĘWu#AuÝ%  O:= gi p];v:2~XuEk vvL?|V{6e^s}o)l^FaLP $t p< h/MWjyq>n,^uPeԠXئ"w(A(jb{ OALL#C`:(Cd Qf>ɇXJLE*bfws|TB(usbCVe , P5-JGnk\m]nuԣ;v=k[>vIHB]l^kDcay]؝B]OvWGDGerPϦw|6݃XM,+#p}W>f&W LbjLxrcD3W3HWo-)Wg w'>L3~ x6 Ցrţϡg9rϴbǢ}ۿ$p;o̢ IDAT i=Ǹ;=߬z.Aj/_#w;^Â14#+ϦO'"]lZ,LRm~>Oί~ E(hKM-D n(^=ޘG +l0<Ђ? #Lj46|/ؔ YzCRg6>pG0FlFe=؝- $1aG^Lfr8EO?okG{!?\M{8]%(4! -D} ME~,4Gn$Qy]8l B&;ȍu+Q-C&%,#L[t"aeBJoǢE;l 6 V#rk_#k˶bþN.\XC62Rn@M@@lĮDֿ;ҽ_@>2v-O:)p[_)jZʫе(NqwL*07HDU;uqW>|}K;X 2+}LP7[w[\F8*E?`^CICϴߡ3K%Xrş1s׃˦\qI_R)W| 0Uއ{Â|6?ϳI 57!@l,3pqp\IG h#ZEoG J!ykסPa( JĐ[`z7]Mce:w՟tv}DTwihb}CJ8n[ûb1 pa}GQLX-Q%Nv{PL#d8~(؉Xw1oBi !Vf "G.5ݾ (Nc˷}"M!Vd2}W"S2{`3gs7l=hi>thҮ]k?fV%u  $QHLp^> (w@rńK?`cJ!;нwi4JR/k%>$]T׿r=hژIȕcdL@AYF6waf5Q,=5ع=!4،Sl|7#|dclYSwD 3};D]XwEvL9h"r5a`D|~Zg+}{.%ItEAi<O#v+}COʔ+6"'}[}71{gbm6nɖP ݷ}䅎I&WG/}DZz#6bvXf2$ grLp?4+ ->?j BO4i%E a} Nc{0nNE`b>w1Tʃ&h[u._!\\ QP5#0HU .{6xϘ) "e.{(>ͶSp7m hAɷS0r)@ ۾%em>1[鏭 wl&S8Ʒdt֯;v3l3j'"Fqo"Q}2n%Y{ZhoF&Ӽ:0?MKyy@}ͨʟV5N)*S odM BlfW>bmѳ|Ě/k1&Qze# = ?1=3@gX|1w{Pll,'SV1U_@"P5d@T;G,Fy q1f#'p/F Lva4A&vz؆Xrci'cm5/EJakdr>}*>{t}g|Xn [SNk+L^'|>rdA-s{2c}d*pK><ʜO%T& QSݵ"g_qX6fGhM*{; N 'd~=ށ9h31ua!Tchӹ5Ec&k-g$SIhCwB-Ƙ}Ѳ}b/:[K~e9Z=6C2mfe2dc2XIP)==LeF Jd}?jL@9|!?,;o1#5#X%#sؽHjE|!@cM1mkѮx'02<0X~Ǻ>^ק)H @JzѝOt$SO`D栤sei K"k (JX͡N4SUOE.+Dɗe]\uyolI .lI.gɞ>Tf8z~~ 5t@h  a`Fe&敜.OM9~p)P|R2swO&ʜ'S)h\ğ}2U@!> )r߃ѥi (,OK N&0X8{Í(~D*yz5f `pC1]9/qDy#Z$=ٝq:U-a Aӣ\D݂O`=͵3W-b*+o @} qrR11E/j?hCpk-doEzEx>T_?|LڲL nC*lIVh]ڭ5%w݁sb&P:G ?ϖ7JX_9,f'v{ uK\emhl̈́ZbsYG>bKxb7Uh"DĘl@QHq_gTלk78y~Ōs6E;'IѲ F^u\@Y81"@>3N)ahj*$ guփ1_9z1<S1ƀtі6R Lލ\޴S!mIXŀT5RꧪsB]bN-ѤjEl\72U^"ѺK)Sԅc![E_5[]as/mLP lJ)"W玝N2"? ybQ @ c82Սw_#H K9]4:jX74 ).|δ=K҂:5hl#Q4Ğ8}::Gj*A w Y@f''6&0z{:ZhVZ]u^2 8ĺܕ r=Rg,~P%&7#\]f` -lf yud0zLwŴ &}y)_\5]Zr6>'gl|Y/_/EL`Lej\|#< ֯.|/U޾<:ZW|ol!y}b)"v!4 ~:N?p٧8w#Wa͕4P.YR1//lű(glZ>7Sn붢 b!qv0*9׵[7Ӿ攌c7wv yk<3F~6oO2Oez 8 @6!OmoB hj*ڛeXt{BVh㔊!:n`ZrhWrss6T ߻-b /s9͝ŏg6j_ҪG/l$Ϟ%\A Ql݇΅kTk s4fWO,!05=;/{A-bs|}۪F俍w'0֠gPخdS+#ppVsl|^o߻\`olOKᾟ3J} 8"|&]XaS/5 GI'lƺ\ELUy㜍΍l2LCCH"g1,zNc?4 ܊j3Ks6I֠u̫2-WFs.5,6c׀;1Fk%Ƙ?Yk[1LnԢu3 ‡p< .fS?%>~-ῃ_z^{1f7kT9瑭 p1!otm1b}™-c`Z7cakkw+@Ey5xA(ڜ+0cѳa2OUఘ~fkJ [m[Vʇk hHp]~z}m:ͻf|`SzWB 4mcg[1ށ7N\KƘn+cL1?W =ԛn0x 6Yen$SO"?+OOK{Ef"`q%r(T^ 03d ,T~g:ǵYvytչ: T ъK?*oB{@U<Hu&f t٢n6!AG\--t0ȔQmBXcX+Ȇ"Ԡhw/!hǿ)Z7!%Hv#_4Fj4kB`n%]㾛A#`'!Suug+Qd]O%SJu AYF74w5ZXn#PDU%0P~Y Hk?9wG@[y;Jb&/ٚ[Q`s6~l2EJ`̗Ek0' qh2gwL6v Ju![9;pz~gNٰ?pFS 27n!|$/ap PDY dbfe-^d*r~2#22F:P=5T bq,G,Y[1nNLhc-Lj1ll4 @`&eapfA۫ڔ1e5?p?N`2MwoB UjF/n=Cx`ק 7>YAίI8/Axw-(~LeJ`*nBx/ PQ>h@{oH'V'Sm};ħ ,O'\rJOQd$Q:Ca" IDAT y(iϛ.}ەlʌy?O#b7:޻G @Y|Q6[ O\֭N9grnZi3gL mc&Ido@fiZFu1_;? km;5 1FV@;v@vzy1c=)1D1c&`& t=q(eXB`oɻD?\6 >;~+osus#f?b @7 UBS3>˧_yGvTҁOPtm#(] %)d퇢C5fd*!08wϝÇ]}{09M׸^O5rPw/p2ćs*ٱݝ>wd]{)RX-ȗv7vـ(pZ i JǠ9˰kt}d*3LekBܱY1L6z;_sQ[c&':I_^†y9đ9 Z]"7EPdc&;i3ZtֽX=9@CF)<\'5Vw,_4[s 2"ۆ}RI"k9bOq? inem7a؂٫G>rHP3 (]@/T~_1%H- sI46S&SA(jt}z|71!|\]3?v~EhѺg~M@zX펝SDA6 sy-]n@ S  k7#d$r~;~%>ýwCk6 9w6= 1Vh;}z#(b^@tu 4Ʃ]ԬM7oz_' BT9?꒿7>\чe)!kga,]UC&_# v<"A~lKc}9);7$f?F:4?~3M|js폂Gi3 s+:^?(7nc/l8١`/wkcEtJ#9pӠBM[CS'M]͌H|OO9) v3~)}[gQ؛0v\t(BΎ?A;v 3y))<؀(uyȔ}jhn`-zXX&wMA;l Aå9AX}aE&G1Cx\\xA XM`z̅ϡu IY(yEw_ H dA;LD&ZzH25ϯE q ,l{2c~|.Xo.&dȧ9JLv,@!f9?1gSb&[އgpy˕|h-KO*׽P5kMk{.tFNp[6)\/2okúb+sZߑф}'+j0O2} WL\UBf~hnuF PT ЉL][#A=HtKU|3r25e :(iEEJzw_>#i?Kd")"pS} ""0Yī+ %(=G@OEM;ixg, Dcq>[3hD$0cއ:73e ζ"]Dy&+ -AJ| z9vyTҜo4lL1i8 x!&%mv5dبc&/f_+TεI.o}мbeYm~m^TZ~t6V)% cC P8aW,uBKXIj GpS[c ("ֵOZk:YtiGxy׃͉{7?}5xn8GSqt}3DU.ԡU~)WHQ_GJnRzg9wmsX9gJXOTϭ?u~)  6  +l_HOs? ٕR=RVuv"RXbITg H!v.n 4rk;2앣MoV:.i^I']XH%O=mWbw(Zt,d9-U`,,wS>$?2RSYh[wcE0fR٠ؔ5٭sv{"}9$1Gi~SdW>0LeB^pt}oHd*s3R*:!}~0bvG`^@AdX寘}cкaƲ4"ESY-.0ń,TۻWwsOй"1Tˢ rfyOg>f\ hJi 1'79o=oOQ~|\ ٱK{9$d+ON2#qnF fNClL RB scyB9P[Dm4y_JG90Do׿rh&mVy*A9 ݈1r97!q1 !0hH ap$AI3(kدZpG9a?זQTў PΨ0-LeBm˓XpuW2-bѮ1r&XVtȬ6 1_jH/ml:32TcrNwLv9zߎGj46ќѻsOψ?k|_<ߑh69Ȥ/cݼKg(y3*u;VG Ey2paG44P9lˆsHݭ?Us#!>w &' 30rʡ =)Ҙ{d31&`_ dO/cf!Ic^otVZk1u*}`e>X TƸr6uH?QA@eܿ6>eJ'~LeF lb#sc Cz+?ͮ=c6R E6 Ed0Lg }AzdysDѮ>~_$ h;5|l_4/G@'=)Dv!`m?O{).w7k.E&˯ȣl<: Y잺7Ӊxwx(gLS2G D.M7!]A@L_.AWhdl\04v`+1/9*fAA}Y_to~ގ1,x#@d'y)4я㖜@Ɩ ޲WzlGc>P1$?0Z[|k ] G>/z OsºU'ލ`lؔ޾\ݘ{yv]iy͍Wo3(㟭`1Q2c̃&]g M([2 Ol@{hMo]u'}t};ķT|0 \E3nwϧX@\$=*w^;G.Cp2X]HvG (*tbNAGLEЂr1:w%yY}Q (:msce(pZn=@3SP,B7~y;FATj2WzFo A]Aʑ(Qaͳ-I3>%KFu֋A H^LeL299%3 E)haN- 89 Q5hAO#n$0`,2ԫ׵Ƶ~&"0ՌyL ~摯8'k.C,>? wJy;߃M4C&~y\rU5E ƹ}-#PiiJ8b2j@'½ ]hBLWLd*d*s ;Dv #EK'B>*޼}At`O(d*d*sm2/Sz~r,ʷb&;:f?h4/~؛;?~h)UYl ofll!g㫀.0m^ވݽlG*!؜&gwKcA;Yn[0{7pYd^.ֶ!zWPƘ1f?[1o'﻽}>3VH>11>VaWi (Dk29 ]41#hR#ҍc֟ X7Z}c~턢ϽLU`gXy>8&rX݈):Xp_A敵ĔkLB`&{N!w3е?kg;߳E,ڋi|۟͝ ȍA%hpGaZӑ+TӑXֻk>\߿Kyq]M5b%wfy ٮHMP؆Ɠd*s4kiJz y(}zNLeFg +r6IIf.srk֬6JCPou1&d+W6gxv1b0.oIAkrsZ-/tw>oy m_DkƘ<"ҟp4x7vDdۥm$S1_t}—+!]N2"]s=_/Kqڑ| 1I dZ@/LdB1P>"ӃoFtDY HzPt8f!ys-*m׺кE B?  (m:Z>!Ϫ;fW;n=bǖcBE>\Xָ~q}?gi:BER&QZF@( ]@o]ϸ>2ͱH]~@ ELBm$L>|B)G^C w&{ hU [hU[B0kO)/74/Y2Wk}]9.|`]CG^|޻-wWbenMl|s)PTf2@Vk׷EcF5^>-bĒ~JٛiKXc['G""I'Vm,E{3Cuw݊h1 k6/=g#s];zBэ@SQوo)v&0( )בE{eƵPW"t2Ѷ_0ң>W Mx@۫v:VǬ|aXW03@R` T'km;^>yMN5] 󁿧Ϻ|eGcO2!bPnGQx-c r|,rXC;HiA2- #O8 `KjĈ<~G|:=@Fث'lBDEV#4@KukWu,ݘ>%@Ct_&MmKb E]y'_goV;<ݺ㖺qr2L'?wޘdz5y 仵͞6Cf'{/Ku>cxy'ycߓˣtۺ7t}b*3fp0ڜDڞRD鱜,fӐ鷀~]t@᱗7j 3 Mh:ï--H>t-=sz2goWyxJz;OOcV IDATBs{Q 'SM2W"dO`~2B ,V @bX 2c$PF;aB #PY9b@^b?kAtabEsJrE)H)'b&woBt;iDY#Տ Ha^?HCk]{XzS`#PSkX]e!Y?gPthĂ4 苈{ \P/qعÐ['sɞ2m&!ٿ7+G?6zʞ(8<~= (?{Y v8LJr6\3ÑboLv4C\6-  ?̲/ENO5aB55PpP'3oKr6S}'鋦=\џݳǁt94pɠHA>]#S(?2;baD˫YːWy`}ާ@xkcoG}>$_ 4QX[r?C!Ur ϵ}.r{wnO/#0ƍC?w̋z[/VFvT ~d}30!6 @X}y/Ve?ĔC@z/V7|hY,!4U8 ֠H/!҇@=;Xu0™;oL,f!s\*EH("{=r18V=g! Ws呲B'meB g/8xW 2)*:_ːL+>e?ێrCZ!w}Hbwr䬞Brdٶ[+b'#po{ӈ)2 'sg8D!eC QSLOI=#ا}Aňn]bG7""Ȝͅe/ nӶ$Z0F,k6-+t([N;q/۔ON1(mDĠnWj cCO|v0Z\{10ZƘ-spK1g*7g=m9 h־et}bR9TOaWz)وaE 3schCf^\)n2H>Ae䤾ejMztR>p f)Ԃu#[\}n׿)3JK)JPFǛf}~1Bq 8zgU*􆹶Vqԝw//$b>w?33{ݍar%])֙g'W..Rxq;p/x:zku$3P@Y9XLey p9C7?Wm#gg=mDh#pF34񩚇M`k#0sи`Ctow }OA _;w`s@F3,ƈ[WƘ㬵lcF֎J>Tb zotvG [k9cogL1Q}FwRC4i>q3H92ý~ND҈aO\EX ^(i*bڊ&bZТ~<U#XjBa:&w 7!"tj+WVyb>̨>j2^@Kw8B׸s1J1(VWTy*9h'~^NROgBnBp-4a$ zވ9!&'] /c jm6n Gbwp %_̋/1Uhߏ?ƕIY>瞛vMd~)ׄ`?bdAc&;s 8=gL 0|SY%D ~%ҽ:Z2X[|@d6+Ƙ2"Nbcf6}ƘȐc.cxە7E;1F%ڿc@9Ѻs4n=]Lk=V^>oA!Ǡђs&"˂L6 η֦1UH5-#H~ #1Z_G(v+u5Zα>Xkgcγ>k єO<]N#{9g˟[Y9&5(;WT#6?4k6AH.EpZϾhC݂x\:$P.c5xߵͣ!<-UQ<IPIlsDrwyڔ7"X̆wE.pъ' 鋶hE z+؍wDؾ=F7w&vvFI2]@o1lᮒC}eOBmipvI'M'~LCƛ?EOژ@sףp)^qͫ mWyW{ut?{X߾~W_m;~d+ -+bLaAk؟Fne8Z?bVS,kku}xz3N>g.-_3 rWOk=kC /#;86ȓ}Ǖo8Z7d_Gz YH@ϭ z훸2=fԛi{d6)l 1hvӇ&Sw+L%r_LefI!u(* rhN d2zkYyGu _26DQ@P@0Bʼ :}JĞuwr<){xwQ+P ɨȹOڊLG C0ߜO;Ovlc%J㐹d*s.AG 픒LeFzU a+BCy 1u^4BJi61ȅ/1ra26I 9(fyr6~?A1} m>X^p{BQ;t-XB[ [=7Ӌlwea6.'{cDuo_8޾J;o_(cf7 ReƘ\j U5cI1HǬAzXC[KQ.a-s1g3?;S2c̃I@ZHkW.KA:#>ث/"YkfcL"A@ϛi LebLe|v1:ՠ3v t}&Shq?څAfb刉#gD ^w"܂J? !P8kHPt{1Dpy{"&۵1w\NC N*}>B`k\}=čW< bsMD/Ƭ)tq8R1GZ= "b՟䭔5hGIkCa&O \OАG}9]($S8KӉ\5>wگ;8@;1zGl-4z@ѿ.gL1]a3/,څW&\]L`~'[ 6|v}'sc@|_ӬZ;/iMlV<^bms&<\fBzk\4dVH J yspOShҾK/&U-d*LeJ ^FdzlAeL1F(ߘlEF S?oط]-kCw݉& "kקڹHѮ!Ar_6U[FHkҩBL>(`J 2'U(!jkIF!TlG;gh;Pk(2h|%Gwt}t}VI2$S3\&OtWO1 ~ 4a,"b}0섵)7>9 .^6  5ڜ7lNg=4b=9 fSc&_ srS0C WLٰ-rp ck)]Dstc->y%sfKXGk6%wXؑ}37֦o cL?cLmcBƘ1^H?8F}{gV]IftS - a ⾠>*E^*FqA[6P֔Z>mΖ|= ]23}g̜|9 8=^-vpf &>GٔHހ}!(2ńKݫ1tr%T&Wu 0C\* 7z o7*N"ɝ|&} ]߆V"I˰U+YiXwLAe"zI7Ul,ZkFc!Uاu`HD٘ELY?8[K|ct#نEؖ`D/`5d }c|t@Gxw 1ntMd˨>'`"8SRX:x9Nzo`/nc;s}?pk&c{Pl:ˆxw^|/|ij+`+^,,e\n 8& uL=Ov{}R'J#Fk&W_wY!L uO=Ap#rY bo Zmn qkm?A9SL~v>>}HǾdOchkk~װ׆2e07?a)O_;ĽRQ71>\c=Y]7w>BHerDO})Gގ+spH`bd;RܡX2L*9֢(z\Dlb8ab?OzbeB9f*;^=Ng2\rw!Lۃbŋbz5<n׋BTf4W ܧ1}Mn 8WK-^/u7c]$C؛X'1ϗ6Mt<)c"6LH\xI-km@$ȊDs3cQi1&UeL윋b5s}Vtk #H[E|]X ֵ#TuX}Y0zuc??ٹ 'N1M}ˇh/c0ڄEZFa^Q/`wg]j@%V[J!Faurgc_q+݇7 # a S[UyznqY\.sB !M9paރ8T&זnĬXaL.Her˦˱ҏS\"ɍjbы3tŠ(vucLH>*`[6/ocB>*>Mibo8׼솉λbR-w{]+g48)ht5gKk>"uV6hOyoҟ~DT3&j&c=4Vw; >? Ml`缱bvcla9Nwe Dp&1Xw01>[ĺc !Ac]%Kn[h㧲d_>cwc]:^l:[w&0:1MLHbќ(9 koVbJqXȋXh "MɿS;ǭ}Ly/=4aѶF"h8;Xb<+V(5>߼PƢ_ 4uL\{S>_Ds98w-Ϧ`ģ7&߃=gK`Y5F2Cz V"/Pbs7UDi+{2\2Nq`a^GdT&wx*M'`Bn&uaTL< ߅->N$12Fad IDAT< K݈?QNU#V?6 DM_v֠=8׵UM ަ,l!JcQ=ߛa³1NKer;߰ݕNdy)zu j;+^?t7%pM Rȗ+H!CblbiutrE6ظ07=1F0>,s:9Nsy#M ϦxX_X(=yٝzVa>w.α)}hlo~f%~s|Hd[dz'b]XgkT&w dɝ6F;ߕZrӲwyqƏ8ya:E+ؾH\1O_î`m?^*H\XQ}r'YE>^4X!#,Z1QI K{yv/"kƄnXXgq}6 X]סW0B$ N/KDmXm*k,4bD5l4<kk2fIXzYXGl+N%N ,-o`_Mz{GWi:sf_*;[p,wWV<C 1 c l:dT&45qW+Nl!>v2cg[gX=[&PVzuD&L`}/hD$JkkĄӈknwZ9?_2Z #1&^tA^„AtR9X J9_L1,7Kk> J{1ւL{A#K޷̧.{xDkAZ7v!L-m)~Nz_lau4KXY/V1aVOgâO%X}[ O}[_Z Մ0u=z _?(#b Qabwnʦj o+xVhpߔRLg+d/pCc0nxYU'Gp ݈ol:+q,J5j\ XGe&nfoCT`pu@W)üj_uTs EFa^D0WH,5`E,X"--ؿ,k}u\ͦLkIbHQVϊ-,y'v_MB]1#x|ZFn1sٿd.RDjldP/QlL܊ðہFՄĈ 6KfNzJ; _tKz&nnjXfh3޻l)襩Ltr>XM'3[5lH6\5lB1$QaT4D̑@,(LT&w &~d=h: +;[n{?&|Īo{wy|}'&k*3ғQyKq !(`ig11b6ī}B -$İKwa^dKdW*lc/bcFLnaV~<-c0ӗk1bvoM$=~:/2]Υut6a)PoֹwNo›&s[ !HaC*+Zg/Z;늼N5Vj׀1PQb5XjG:/%(aR_Ƣd>BVKXh̻];|:cP0l:yqMO P1BTo1!r?hh`>yGw`bfbW{:1yEG`,Zv 7ln`~k<&Z,!lZ}[weָL`BOż^:*:!7Jx!vpO{g] ~M>B!v cbؐM' ݏ%LmLxU77ڴڃ|P? JǺ_' ^nxXEwLP\Ҙu[ӈC,VkPrF3-Bf231y 5ategRܽD1BT)ňurNg> 'N\:ߏ0?Ց`)`YEL?Z=/ߘ[dxRA42rP LdyqSݕA~*i&(l e01q !ĈG11"qJ-~0p*#&E$DamӱT&D|;E+~qZ ,*@^yJw;&SS\,)jZZ8!; E.~ϱIe `26cq֕9Dj̖`GEmv{қ6bb׌A !LpV;үX'懱)g  ab !خH]T&X4^2aV9Lp|El3&E|Z ,F,yQ{c#0mo0XtAXmسjHߣ~BҔbWc,XdLjҏIers`݊sObѱ}q:#kzjJK .&z٭w[.NJҬW1tS5!BH]+HT&w"PM'o۲d7n %Y?ڸ)NΗ#b 18p7VC 2Lml:+l:19ٹbߓ_Xx&v8 !f(M)v/RܷocoZj=w{/X6|&B1QR!D*aB1QRśRB!FcB -l(bAiJ!B*ȘB!DB!"cB!UDbL!H !BT1!B*"1&BQE$ƄB!ĘB!DB!"5>!؞2z?䏫}>B%BDꁺjBl .q2 Nn.ɝL~ zB!1&27gbˁabwL+vߛRͅ0v_bD1!Į@1M.ɦ=%@giKr(AA88 ڃWB`:B(BBxR\+0x/p[tdڃXaR_kP˴ڛV>|2PR#߁9 aŝ~aB"cB]7U2zeA,E-`Y޽8v(`!Lz2ЇEn/B$$Ƅ 혘c .%h  Ly `7]Mb@ wǹE|$앝|B-#l:Y]_WO/u>\L)N^R_`VӾ:jϪ]bġ1!Ĉgf)cgu_ߊы>fpYcuaxj~>s˼3^8QѾbQR1zƺqŵ+qv p$0 <cg oO7<{G0iyy5_<չ"!HBbL1Ieroh=`+mջ _ܒ^hDYW3/ş gg^߯^ܿKerSO̦+B w$Ƅ#cΚP\>w]-[ʔ){-+I-XlxKarw\߯ZVLxkpEǀq0|H !1!Đ#5DM 1N}5->{ k͏a*ݲֿ`j>_xFk~fLvY{6" +BH !"5(`_ߘ'+4tE,v*q.bb%v:F1z9V[v:̫#\j.R!@ݔB!J*ń>l:Y}ۃ|3f= vww)JJ,>lQ &n}5"CbL1Her&?Ϧa{ X-ȣx,UI,bcK]~&/E!B K寞 5۰al%XyK1qHڃaU0U5lB%$ƄÑ@ | K;6c`uxi_6Jml:0W!H ! LK dq#K]#Ѕ͖8 6,BV^VyBO된~@5cBĘb(V,x |g6g{_uIX ЉEBLj<^dM׀-~V)J!D…C'n7f;i, ٍ4zXYPxZ0vsՏ03!nR1,i3k܏%̏lw`50jI)+3bsѱ2VwSk~ abM#EƄÂT&wD*m*mz \ Lu#?(``ZRUV"n/byx!cBB _cu\0 ~Nʀu^hyVme}? !%6 }˄54bXx8-,ODDI~bÊ[hYe?DЇ[cs2 axB] EƄÅTX ^~xp05 &bD1?h5&*E¨އ5Q7[cڃ8.یĘb/vn L-҈1ȾE`5Xtk fZ֕;ݺtqvcbm _Ytx*lK!j$ƄÅc16\bƯmw;([XKv5~55L(~۝Bl3cB}Xdli{? { ;YiZV`}མ   !6~!İ=Ha~b1`Q|6l[SB~ib5cb]`5c%0q8!."cBaE{ 8 XEVZVN+0񶎁Q1yYEN>qRjB9+ܟ%zUևAs)fOsbk0;v&j~J!$ƄÄ='N:3Qs0TRv'JUN"Z'cCȧ5xQ[q2tIB(M)&b-D.X4!^*}7F5|{aM*m{]B_1i|ng!~> lܖ"$*c.o (X-%l/L݌uR5X> l:YF!1!Đ=Op"QPSÆQ1R]^< tGr_073ŧ8SLT!NnG\bCbL1i"Qm(Mo2" Sp8j",bD3,Egx$3}KTE ak_bEbL1iv.F! 1;W ,+^d}ԇ+x=> CbmŠsk &n-إP7bHS7ۃ| f1р8ΨKGV xm]X-7lfk3c~\qYMsg4V 1&xMH !A~0 A47"AV)'* 56!\^량Y_7{CoX՗-E!֣Șb7lfkbɭ`V,ܳ.M'CJˣȘbX0󦜸F Zq"nJ;*+c밨Y-0ft[뾭&^&B yڃ|г< o^lܽIw{Q-0VrSIDAT۽EbM Hi]|ƤW#I!b=k =0ܖ$rB)M)A*Sz{Ml10Q9 !ĖPb83&\Z):_C.Y|p|$ĄEƄÖ k{W04ǻRwV܄bkB {RŘ؅tR/jBaҔB@#60\!B b84B!DQR!H !BT1!B*"1&BQE$ƄB!ĘB!DB!"cB!UDbL!H !BT1!B*"1&BQE$ƄB!ĘB!DB!"cB!UDbL!H !BT1!B*"1&BQE$ƄB!ĘB!DB!"cB!U3y/IENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_46_0.png000066400000000000000000007474071413546205200301700ustar00rootroot00000000000000PNG  IHDRXXٟLsBIT|d pHYs  ~8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxyxT;3;k& *0;VZUkUZmligZRMЊcF\ H@e&387Z-; %v1߭1!]uƘW7_[k9r<kZ +^C RJ dz߮Ux39fp%bm.FBDc KNm7nyN 5_E1沝{W4!`n/ -TJ)VuW*kVkmN `p%04ikY߾^ ;zv; wfݘ1\6c.t10m풁J)/kU~HЧT|cxi󚲃;gc76$ε87L2Ƹ1}"kmc?cFc2vDc̹ƘΗ#| \kگ8)G> `99?Zk<xsދWJJU͵ ڎ6xs^ ^s.K zp1GHME?r7#o>BFѷx҆vGfoc ZkW;)#/n}cƘ0V|~񮾞RJ'z\V;ۀ'[kR_Xkw,cV)T_*7 RJ)RhR)]ɻ|kRJ,RJ)"RJ)RJ.RJ)RJu RJ)RhRJ)R]D,RJ)"`)RJ)TK)RJ)XJ)RJ)E4RJ)RJ.RJ)RJu RJ)RhRJ)R]D,RJ)"`)RJ)TK)RJ)XJ)RJ)E4RJ)RJ.(v?3!@uA^ǣR}X,8xNn(=R<}RJ%023ݴRjߤR`0}zy?j/ ?Aw ⾔RhT?pV+ω`:4zm}w0u ޝ|ޔ6 SdK?|Ȗ~ڱ}) @+Ȗ>߳GWhA@<22RJQe9$XHH6˫nSRk9,b۬Hho;pkkG""'UdKE܉mY9[ǵ!,`)OB@?X5ߦfؔR,?g4'`$X( n.:j/1?~ܮ[F޸<4GUO_<)ÕԪO7VkT̙6mPra;{x HVk JRJi@n-`q e /),? )`0) <) $ks~4|7)1n$} ¹v>g _ ZodDrU-֧eN9ajaKWZUЪ9sg41)ul6Vˀ!J)-4R+ 4ws>:ᗃ2˞A(zPY~N22,d(`ETېlπ`* pM9{Μ[B$S\VNh'&~ պ+{AufR䟗')w NtV%'nuOygXNȔւaoy h*/wA*iBΘ91y{z{LJKsrg9VS s$Aֳ;cvyFg۟!sV t$CM1@~y.1f#\ֵ?E,5 8:#qlvlPo}4x m6mڈp/TW y"y> |4CvEP)]4v/}spzQj 2SېKTKq@]2d|&UnzkK1)k,@@$ NQ5`Ldʑ@!Y'w)4"c<& ׋4xxؖ@ػC2 AA%Sx?qcåVK"<2[!ژRJLukPb 4rI ;|@k[Reflq0(]^g:{ɂvG8hE^-8A)E^{/m3!|n\=r_TRgI7RFݳh r,4E[MLI&# X:b">Ȗ4s=tiy{,a~oE}_ӽȍ&$"AܲoF3Y5Qn+DAJcH0=kߑRkߗ{fRf`.n5q(q֩ZT   eӁbߟ$!a`!D@r m7"QsuA6ːNJli_`Nl /}GYk[ƌMIFRB.,R8v_? <\sTDҴQS|iтdZ ae9U|({rdڐ ("!e~әilbvH֒.\,aÍ49x d[΋nc๐@jhB(r&L Ȗ>ݵGu77( ߆-W Gɐvg!A%@mZ `)R=iUoS.q?RZR{M,R.jC3s~dY"ȚX37?n>_;YׅMn642<|Ѕ:iI}E5#0)-GKC=D#=/vƔvČIv䔵ug$Ե&|R; }/yon7s]mKF_g5 Ϡҹw+{SޛVXJ)T/)ωAn7 :uM\!گ]^Udtf"t{B:*r>Ecc4c u7 6 8]|L %}-ަA67X%#qڲGdxZڳZ;;J5> - `PՊ.~g:&x xXlRX;GmrRJRtsmȜsʬ[$>c: Z]}Z)< ) {/ u?kC֥F/B4fpnܭH`3 15F9ySS3 Rn9 !HgLkw{]Ѱ+F"X7ҍqԞ}.`5!>3m˘K@_G~s^ _FXmzX}XY~˫cQJ)սs Hjvr!P:)H8ɸȖ/1#4$:nAPsZGҹ]sޘ3&5vG~_sAH\ƈbpy5[U4svfUIHʺy@,?'Ww-BְI)Տ.j{7GIo/̿'(25c]>ڑ!@>>EH@W(ZSXȌB5% 0YA3zXyd!+ӹHEgϼ /w]~q嫽9>"@-][b mi_j یֺ:Kb}ι/׷M3EX̩A)jlHZ.৭ى[By1Nz'mgQ+OnؐL~S@[BmݳH&,$Vɞ|n:jؒ5r\0w֮ s{V aǐ ǧ.9X +jָT2?g}9X~wDE2\T>/y58 β}9N=zj{nƼ m5c: jG2WMt)$̍ !7o#D ZZ]/.ϊg\h=+aUF2X#/ \GR>/)$@~qeۗlv$.+k,; h/-̝B\ȹm@p!XC݉AD^H?<^cQ{N,5oNH'z:BX+w/rK:jf%lmDcDZG#75#,=H Ԉd ;n%h|\݋C)lAV$ܴލ1nr^$sȖv,w&ȱ_/=Z`y~qeG $hm.돓 IDATEW˽Q׍|ޏ#߇g{jш1-9ڪ쌷yoOvwƤvXj (16RH>c³ &wRH=72 g̖sGbQ`+J2R֝,$27jlnO~Kk\K.$<Hr!7.d rfRHU:m֛KL-@c-mʝ*JB H&p䘯6/\R>/w*Rf05ʎt) Xd4A_"2/ ZxƱuWSmj\Τ# pvspƍ5wYQ:KkrSI|,D)l#DiI"P2MG"eLk \lL( pW/WJބȷ^ ̄HBC( _NM>Ob&fk2Ƙ|г(}mp<1lb #buHsB${*R^x_-:|^d!sFe#"QH#H # ;,V'ЗCg9Qy9tԫ9?COَe 4nMkNl:i>L~|^,_¬3?4pZ`5LZ cp[1ָw1봳RJu:ID9"$׶5;Ȝ&ٶpFe"ȍ ک 5MnW8gԎcHC{Et|-=st#P %Sxz)Sѥ"Y=<㔎!Y%IHiߐ'H{gv697kp U^-ui!V>rA$'?ғRGKUqݔkBl,Lz&ҩ)l;g{39y\QfxFS:L=:/کRJ-}NH)_%R:؄F=ԱO,h]TbCT3-Ʋy~ Leйw9Yˀ?#Y9yS*0 ˕)'/ʗBN&GHYHJ$hm\7v1̒ oZv 'DIG0]]cR]G3X_A;ذ9~q/;k?mxa$/|CgOYH yH60ܴB"$[A<ȹ D@pZ;g!] d y_xE킼n ӑypcmt"\@~!s~U۔sX(yWnH;`[ o3yƜR ꄯdN@2:,=? \NFI@" 害2 o\p&R^ ?'=-yDƇ# ͙w.K`'޲瓺jOww$pVRNoBJ 1r\-$+f[4G=YKOdcFBM:Y$j= 05$h9>ϒ!vUWٱʿfږ95]NN=<-kbqʷ+#?+H$8)@a=dvI4+V C]yہdacHS _3a?FcU5Zr|P1$;Rz:?u6 Ӓr5:dg:{v㱪.M.DnR?P$["wE)YեyoBSs]Bc[SrJ[Sb2y]4}H7 ;̮)Gw94{}ïSB"ܓs7U^[z£< #N~ytmi0ߍy'~Wa>oFJ,6: y?1: & W6=&M!GcWꩊQųw} s k4!AFR U_L +ΐxpyIFG dtF:}Hv$w8 pxǗ=c}mVچg\b k l?97@.rg\Hn/Z$Ck9FhYH\=3>7r|3#F!y]\Dl(1iOqЂUyAߋțG Ƕ OE2aY+F>쥃ƽ5'Ly5s MGQ^E iR} }?KFݤ:r[d6Lw?׎]]mV?,5raOSJ}VdA8_ |hႪ#5KM 'x[$GHp[ $9Zg#Gژǵ mwמvt9)LD" YAKgLaK ~à3qYQ*{?͍|&9&xo#"H~H؎ ƚe#ǚe;F ʆz{ȺbÐs}FatMb|?^yM3֜pok \ک@/W_N,V~ 9o\Җ/opf/%~ӽ+\oWƗQo(`^OMB]`b1 prWRj̙ RJL 3d& Jv7L\f,<$ Νشn1Uh(bd]EtE-]TdK+F2~"0$@8™十FEM#7*$@_\7g{&tB6No1خI*v rb_C:+"ٵQH׼GEƚeHlgywO.5x܄FЗetC\YPr=g‘y悼@_#AqO߼$A_\1Z"涄odͱó*C+ONk[r'7ywoo]+vfƽsB戄ĭM-ӊ(d"Rg!7IG}81DynYs˰ֶtk X{zbbZ6%4O⚟ ~r[ VȖգ>$w'\zcrC$U$:Og?̎/66Z{~FIlt֏gIQ鷑)#YD$V4;sw?H/U D$G!%{8]RߜMMD#nWhMJJgȱr ˞w\:  PR\A^`>WmҸbE[~3>+?iOP̙0h^0wV$F^`\#2inˈXIătlKD)U s[s׍ e%6-ւŻC)/RdQ,,ٴ,KnA ,S@]dFiI4OȺi@G( xZ:MaYTd=c/ \do5;> \o-i.<<vr)ܙw&{%"sSvŮE=wWNJ6p`7.1xE"R_*d˞uHncXJ~UȍKX0w30;+zn3(~9ϯ )KGDԏ Mo[O+J<|"цPfbR[vRS&'mj+kL4c?vf±Cޯz@J0-kiw!pk%;̟({.d*63x~30wV;=q}7ېLcNgcqL,TX0wYً]4"5u`y9be9ѭ cHIܶXV7)T)u7Ssk[sGb[$ѽvks'd7n8i/p-Hzվ 3Vo8O,p#XȖ~}Y\p~ S؜"AL3G|݂n;ې@jeFZ֮校ӂ,PN8 ,]JE(rMFJHǼX $}18q= iǵd~YX~ۺXV{!\}%cv1˄Aߍ: M5 ܁dRA߯ ;;n5hQs/Ysgr`:xWAPYiZlX{9)HLlCPJ}sg͙CksCƘ[D7$U՟i Hko  \СH`Nl'~`͉#kcͲTᕇI92gi8|"=W1.ȠxZYlp'&?F:ɾr!dž]L$@x2 /p 7GMC3+|;H׿IH[\dO+ *_X 'jzt4090=|sq_A^/}e5 hATO|p0H4.qT9p R21{. jJ6g)^[YWHtBH|McVbMע٣Ze")ތPfR(uW8[-o@nڷ5RϠ$g_xY,3 RAM l5m$ ^fO_bMyA֩:gA閷 /\ѻ`A^F m< YTAg4  v RjW;Q4f>Zi etX➘Lk ;X}L[8gA 7I_YR)_8^cnHb;OĆD2dzI{zB\Sn P˨K \>1vFWn(79 sx{3GL$Nk֬ʃ+l5.9u ^4ݾDUo$Z->յAq@dw /𬳸t/|#wp֌IO5bQ.Wt&RE2sÐv k /nwǑX;uB;z2S6KOBݍmég$[CGOlĠO҆d5ήF6fm0x''\2bmg@C{]O4%zSW~ =C{ȼA IDATM}ZsG]ʝ=3똳g? ~W_k{T3PMi7yneG << CVӐvoœ[ĶsNݿw8/w3tlc  yJɾCJ!#\-A߅Y~rE[j|qX=|vsG<7i:ѩ5 #?4}$gL] yQ^mu[ދ\ecDLYB-!d1a0 u_oZ~Y8L|Zͼ3j=<U"/o F?}0heQTȕ`,eFR~C.kpnd9LeR`@׹ IYUq^ۍ`rq8MLLZdq4]Q&*DV$66y*\P1cr5^NXE)Qǐ!s 3x""*ͤl6:KfR&|RZ_7 9u[4w=J]ojMq붊^̷noϡ@w-HzوН=](YDPDڬV W݆ ~㵭qK.zO< `Xaβ̍ꝕZN~WCsSusc=DWg!:E_3Nץ,M*e `F$M211_ZL:AcHN棳6i[,n8fi+W0ܚ l:]%򚮹69j7O Ȳ ibbr)KusoY;tnN۴0 g ~pt,)v~NHM؍Ȅ>r ??2ݚÎ~sĊu+-cwWE-%Dvl (]*s|z© .^=gGSO: 8BSB={P7l00# }ގԙ%B"I6ys[d$z0 N43[qe>))vG!׫#aoڬa-gp6K6kkڿݡ"nsD@IlroZ}M:-C`ڼc^>,H#q>N*Z/ S`ȕ*eKz8JnbH( 6b'"/;BG?'ڴ=JF=\HvUi͍|]SKMLJmNu!=L".hۑ⦟K(/Y9@/~d.Qfݢ"[~JQd &!f4$bMr .\@ 6C[ސjSI5%olZ5} ߰~F%󏋔c/[ı'A\떹(WDV=5@у%=E=q/O?#-@\ےXۑT[VQFw?KܲkW3ذB"`r]ݶ HWՄ*};P(IuTn{mʅ[ػ5q3BԱRRnys{_emjZ4\g\~`SGGT`?ZKgF=211f67[6{+MMWi?^EM][CꎚeCNPK!EUnfi+) GⶸB5E75d=QU\ڷh.۫%|-Õ5+@YкY}QLL~Ӕ5c?zDP LBh>|ckmsv'L[[Qg4v̴"9=4dlcؙo nq!׋H:^B'QqH'tpt's:>w*_41+D{L$]pO|AQ?D'675Fr*&R",X'uC]ś%ؤ=Oȕ <*]E' {Ҹ ? 3O@3eDR.;!DP ᝋD>VWG1Z;v[v:j?spG0: ^9ڝS ZI2+PyTc<9_#dC\]~R iMOuwf[Imi VyŪҊ붎 Ɯ-'l(ɊjӎDk (޾KcWpMG󭍝- ,jni>etWUtp6k^%+օ9KHKNUzeb}L_WZrS*i?i/`A쨏Ez7jq!K2$7td$M=^K? F1 Yµ񵁝:D4'zCVS?ÖϾvGڀmYmgr϶dS^„;>Q{ؽύo~MY l(˫1nXEraxxؽH(,Z5sVz1X uθ,Ukk-شzTY=51Y1YG-;Ftd۸n9QH)q9}|\RN,ɞ?: 1wDFB$GT;ŗ <\"',a:> sK(Rf"=RRP)]xA!7[;C䌔i lʪlQo%U\uƵjk(TIo[}u| rѼY>M\C.? z[ u?:)}+J7+〓q'σe sT۴J)?x&{L411kUo1 +LBOT+?ۮ>BRB%u"+g)5 $nm(m&: kEI*{_ o%ߒdot>j賗!mwrca}YQPcAn6&&?;&ʤh#)Y ğBD~^G&GT =O}1a<3}x;T#"+!~dIk+tDhe#)X#?jn<شAEl 7u>()vS.< ;:s /GJk]jS cz_\̝;Hj88aO=H-e:Wr唇\qu>q\땎򾋘$\CjbI%]Iŗ>-_`}ѭśUT\n:K16 w}Fyx}/D^ xok+8(qr3 fdx;ьVm:"rqn3x-:w9/*fl>d/W4$ؖd9dm_^W&+*TӪx(:fhwҒ+|%iz %©ʤrk22,@ J`Vƪ{Y6b7v?apS (ȤTAzd(w#" £aޖbƅulF .+N6j#QM/*)vx|qE}x0?,nݠ`%mk-1۪ ⪊NU1j @B"H4!"(==#k#d1kg“_ |ƌM{uB@r]Ы.+ķ4x@"JunjZIc=vQ']gp8Rg6*m6ёkh#;{śsz]Vh$'cI6kS{719@LebbrZCB:!W-/}oMH  puq[Wu>}W={{;@BWsHqnմ/SIR,:<;%㏣o=]))v/:7KU_ʡ[J+H;HOp ) ogFDH38R璢 B3Ta'}&&ohlݮ]H^8n6q>ߡ]9%E# 5 H3X$[f=D_ԨĆ|Y.@)HC*"숻]D T;{oT٘+)z"bqemJ.pZ.c.}fz6r4 ,͈ t7Sb~+@斴5E#tX5hAwKb"bRj+4~x\}`0OhG'/޳XHzČD*;ŋ S;}d|g.^tdQز1q:gTeҠ|@S}d}ڟv d!#3;{K3ta 0 I #-~ VCHs^6]ؚxdo8Bjjn+)vs_'ϸ6Ox·{HIrF,دİP_uhՎsG}cEdFj "H oեr`X߿F4"ў+H->CӀkvKuTہs/ti(<%:)q3;-FKݛ1-nXR'8 SRn۷>$^i!?r3JN5vJ1s߼`'=XyL#VAo[U3X9 "Bc$ղr]n>?M_eT1UC")%>Fxx}[W6n7 ]U55[óJv#%'xd1ɁbCn(~D>)/nws}yLr\hKZ5뺗_}THFb1;9ZJN!9+R5'wޒm-ظúVr0Qd2yH"K%_xu~oǼH՟=^g%u/4d$ m>7ޣÂrľ x!D}evO]W;}bd}a,IŜen8SVq5uGF@}mb&Zdk0XBJ%-[45(QߔdFʎ7FC4ex V2kۚ>ټ`k.!W2p bxxGMע}GeoMLJ:z4jk"z$db:ul~ IDATzxnY 9rkhM'g4!(ȱ 飶G<`$uK$*"m+uHS8D&<@/߉fo`Svն\6}5ѴTTSj cEc#ty+L~khjx_iv43O{wπM17F} ɝ#:E _B{Af欚_?ii(D>[%f>y331icJ8rp5Mk-td}ڡBxmj|]$C2ќLCg~C?yRu"V#& w!Qw7LX6pAzJ-Cz|JP dy[!Tl+)v놸z߳K[.z?GyvҲ >e翈N b< 6k 'G=-7mcՐQ<)NxԒ4u08pl{nB\: HT=3?|D l>VeP~/)۬-)M-)E 9%2OI%XQ:jigsޮ0q WW:G;"Խ?._`"*C+8qN=g/9?ݥz|۬i,s7Ԑm _?t|~D!#O[%λ^Sر͑9I)Iս{t-Gϴսem M211o<ྜྷ>X4FEv/ $b[ į(l&F>a'H}]iit_e31cDIMUs3W iF?2ct<6IKoAjH LocḪ?kHV+w #e{d{5[G"{00i_8w~ Q2I[_ MkREjO!i"q6zǁ"MSj6[xuJ5\~Ԑbq`/5-wKo3z"$ C~Pt\G"EHw),붍һgԷ?;u3iZD㧑4~%Z@Fz^#=5=4鈰ްkb&j[Ĉ9u'u?4pkkT tS3Ԫ "-+_t$pqlCke' d -'@/RzTߔkI-Gج ,Zܮ*  T0LS`7zgj^c0ru[.H9wNjXoFV7?Zܒ!)I2:wTS,zÏkbOGB_`eP+e{ޖT5K7lQ!bP['ȍŬՍd893TV?}/0d&'irͭ}iS>u]1{I~$Ciƞ;!ZcylvZA: ,5"(ap4~jXQBjtӑ48 T9P6^i)IpVHqfNշ$wKE߸^40}O ׮̋3RZrxYv+\51,mlڷA8h@f_V6d b2xxsjNrX`MV+&C)9igu-8g/#@[عm'=^U- QI{o_(w쌣FyL@OMTRjPŀ%)0~YG''5)qR~r kmZcuz/vyeZ !nF2cv9^[ԵC;T]QH('lbX|gHž7=_jUcf۞OdX:'tKKC8ؽ'׶_2K|;,#s^el~vxrr_n߼t"ibO㝏m\38'3<ku?41yOb+2=Ҹ^HFC"`rK$,v4GR䤪^Wo,ҫc޲jRu`em%9iZoѧ3ޓ!@r5ݺS_!׽mRC\^\s5iKaP# pWCJ#Խ6sf^R~ _Zue[eҒ+,>6k^Rѯ4#lqU2TUg,E]>>63u5Ej9I?ϾB"xukeK7= ePh p ҟ 1~u}pb`J^斴$[RD&c_kK{{\9 I,F斴C؈DFןvhUPUKWZ!Wr HIGO5jw.+)v()v/ާ];Tm2 }1\/#%dkhY#Y/Drз'2IE.uSn^eV3KkmhjOoCW- ( a<.A?5F{-:/p3"A+7+Y;T6g55rG%nsn=W Dji5?ZnD>R(6h<Ε(h?$[}炬ofvY$rx{z:X&ʤ3(6a8uncsӕufM'LhZ\WXGiEnˎ]mnu??dkzw+\xM`KE e:uies՗-u'CLeb_ 1'ݫ:"2/PX7 _Y\}&80^B,aoMT=n9wtk4f'FI"iۚjS#{Y= ~676Kt$*52WB+O [RuӐMIjmC0n YR ct#!*{UZ~- I{ ikTEDX&3?R(Ke ی1})d9 )5 H4) 6p H%+<%@KI֒hJ}Kʀ lZ8-+mˆ: oMC mCYf7'1[j 2}v r)vZu{ yb9?_菍)L)SI*|MMqsm~zc1U|nO"?7|r3njf3MLmr_`l6zA/:-vd𬮳)O'+ Æ|Cqj]GQ&&Yk,*Kۮ]}:v?mF__wn (A4iM2·%@$r mF" ,/ $JM%(b`MKݺ1J^}˰^DH G[3ҭU]o;K)ňHov6~?IC&sV]Gި ąU-lC  vfLEiP.,Zb)Rז`g EAYDczMuA I,'.rEx`.\ڣÂHe)w8 ~Wh>I#bRn\+VizZ߈7 uVDM!>eR-" u3UT|Y7ڽꗉ?V񤢷ޚ{pGڤ MU&OUxгxYEӣ1<@c1;8Mס2)/~PGpƌճcV2HjH/YcԭXdŻ|CV_+UC*iG%%i_Tc?{w#"7У%mFe]Ǵy+oen15 뛿gJ,?'iÑ]9%>'9",BM$뺒zq,뎚"˚-c? QC _ǐU"Vl{\]*DT%jk㠮[SkɊ}X%v=z&=3\s ?=3H*#uHϲYȽnO^{ (εZZB'~j .骬;_gD8N6εN]NTRactKGLeWEx*ґBo+Lr&U/C{~uTAS %^PRʺ6b@U҂:6%"VC{=goNP^MB{Ecx] |4o1@9-#kA"XnoC&F]KrϾ.k>f߱aIͩIuE_gij,8~w YpTG-Qdqc/,豹{^H?'ٚ# ܡ="U["Gū7h(+u{:Yeb=L |PH[4+h\9tAub<Ѵ3X2:֔Hp@E!ɮP.:{z]O#Ci%ufߢzwS31IrKkq$?4z jz^y.$j3 8e^<@gִ$+pNΣk3lOtM-Fz3np[_Y+:$֯QU:EcX!LSൌԲ芶c]AFz^eg6Z!W _/lnao( DOZHk(߅4-H-des m^ADW g!њCuUIh7$٢3k/0FU`[۽c8u}Y'~vqmmS^$ٷMBU5"F7#y(1]p׮&&&Te8(Ӣ8Rd TejxxP Ȩտl7zrLWe^3xܾcqM-郐!He.&|@ҿhbEMnGs<vPͻr2~|> [ <@'RS+/ (6IjaEmgӒ+w-XIߢUv) =R۴r\K~5AUM;l-esE/[]ct`&N״=%d/mfrFkHD3i{odśc`D gc?Jxxs5*) #}SOlZڭU̸Fefu_;rC+kuBUzrnբvQ ="ao-!נbw)I{b Uw\^ztI "עTƑ‘UaN:LL׽H mg m u{2id}|ceROݾ~„JFo{ӀŽDnv%n4!&&&Ͽ&43H*8 &$iGχ=Kk*dQFD`m3Hte'%؂L6 \gߚJaCs3@2(Y# mˀfk-Z?,tDX#b 1IK/44 IDATg 쨋7ewj)JPW]]8y^l/5Eo^r-72ȵ[FYWߟKyυGA4ͣ}3+c> ҦnѨg~hWHo m@m7434(4+ 65C2J"ІSU5p".U5 +j V6Y--xB~Y y{N[G1A2YLڌ|5% o5ֺO*BHCNU&&VS8 7s^zLq!+t+ uc()v_3PI{8Đ|s*t)HnJ$䡫3+'6Gw8bkK7D,nN.Jvߦ8"nN/}ݢfy2o=~;`}=+34V$ol.PA"V"وo:҇n?{FugF}{uok{M1`@&B% | !:XI I "jBPDm\ֽ޾>W6u[Zs{6X`T ZU+8.~hŅhII=>{Hau#gy]/V(AD^B\l)7YMfOttXL:e1E,.ۗIAoHwasJVz~+{ia-Bz%݄1Djf.h@8':il"ڇW~lQ h6Ӛ|N.rql7AU'6<ynW ic5O9WLXHwJGwtWQ{؝ n9w~v>r ?0풇pAs02շgU;cx'uuM%.԰p䚮>yKF52.CtW-m iKa.l.F!몺Ig/mk%"*K_\Y<$% Xڹ4 9HO篝M_9_Wg:޽ٜgstWOՈ;cg7bxHVOm7, o+.WކaiNή04DO:]-=?cZv48sviɲ2!N>7>w=уQU$|YUmaa6dDmH}H]U#r ľv0Yt4𗬍UZԅk=-g_E6Ao|!p w$!ltDT_ܹAAOW>em1qͫ;ۚFh-Cudi˺-uljOmu9W0/'NsC%ͻH]?>a%GNzpFgO9,zY&봷tTM@L!4$JAzV75/p?pYo<2S3=_;#9wOW7Ao^~,n ]70 lU|ebg&ND!r$0iތDf>Yѐ?vCX (af=x:lYcy8]D77mH燈l$}"B"k}F>Ǿϝ1ȑٜݜ@ہo5`ocǠJs9pngG{ݠ>)-Z]7mα]NU4ן(Y)_h:]όr#N9C{\E\h%RznOhaFȦgbwg XTgf;Ǐ<ܴG$#/bۥ y#\H݉;j pez4#v 9gSNJw!H:I}p1?tCs:N#m o*" & fVyS` , g_K w;;YnޙY'q Gv1#_nԏ; ӑNkoQ߹Ud*le?IyFi$|- P͉IYۢ!p,ԇ,mG&?E.G g1O4\cxOk~Hi-tS-(̯lVSkţ|qvL+rneu@7ȁjkv[R[K[MhUv]^nݩ!Hg{NGr6"kDE!YhUv湐ks ]8Y1vY!Qr ޷A#0 dy2*[תeWk$bRD&" Uc| i߰` , =bcNYP"-3 Ė1zg1=Wz@o7y|||4;߄h?fUR;ƛHc v }pi ;13_#d@8v^4ߐ>s#H]^Ȣ?sukBR f6p=r3pÞLh[]YY\'w ~@ݐ|U Hdhd_^XڼCS\{'U4 ή$d_:>ـH$m,Cx#73oŴ> a02uj0HXzAo<IARjmfq鼥t x'rXE~΀]UM|2#Lekt) k2 ?٧n8/"Qf%{?o>I!G^#݋ͅ;iI.groDT-AW֭K}6mM޸uA,j7I7uZ8eʏrePT>puӓk81#Xy'< (Q!zנ,ֳ-]69jO 2%_igITBgwN[uZa' .I7loaͣ\&"n~s6}t knR8 LV;!hH4o- \3ظrӑZIY\z^7ߌDMG& [o + ߝ8q+GH4cѨhousskgkܚ#;bpQی|>_q>d.C.Copݶ6 ȸHں]k6ۚR,?Y51ֱ [3U#a'P-T*rMNwf5r-M]ii$F_-"݄lh^c ڥz*<8Ri| >ksh㥆MVF ͖C+YVj4vn0f^{詟wF&jB$ @f=_.uZXl0p DԴzr#z6c821܂|I|ON3DȤ0ځт;v):5+yHO+Kۓ߹"z Qr ̕@eU߽#4HiښMEW'os`I|E,@|ھ}Qdw%úIA+)A&_a$|#NoH@DZXl*3U.h?3=솬DU±@fK2)6d^9퉈~ 7 -6'ˁgW/=c?XP6gN^X4F fD07!wGqHJ#w#Q8 z3 I9{ zH$|^s1DA_{a aShω5\,EO_PU23[iԷ7WT/‘@3?+lH=Ao2+EyD3}h++:+Ӑc&CܺJb%WO2[ ,7\o ڻ8ӑJia)t"#!,"k/܏!ipl0}$3@8vC [bl-jط=ѐastTߟҪm#i8Iq|yG(虑o@t:6 Js" I I/-ڸ\ p-g_aYڻ\K'8|\* pԌ?FV3o]?ٷѐx62mSR3Yٓ7t\@PHV (o>shP՜<;tsӑv ϷًI'4 f!!;-V+!,vZ䄴yA-ZlZQYg\4Q )±ݑUO9czRo9z:p{dh;!E4±sS&)$z9Ub]IIxI /7t Vg IHRDwچ{4g&|lޑv2.Mcĕ$2YL蛑h`XIlA&H, z㭑oPEC꾖9@f-߮vjh;:8G ї:cL" C׮ވԅr$}(荷| ?|1mɱ3nhR,K"HƖ!uJ;gRku|4'%6`H -63ÑEwk 6!!ǎBޏ8~I`#lۉ#.TT =!h}ЁD"rvCoFF v;Uv{f}D񔤆\n+J9=2d /U]V:fSc1D`zOD'uWuhM m+_mf:"b}$?zH${i;˲!en6oʇIJ#"qI$vȕ%,,e +jUE*-ݞ],F1a$'}n4obg R:j{ -I9_-ؽue{G/}Ǒx@V|D5n튿 Q\: ncG5PQ9p<,c-Dpde>&!r*:s#Y )WQFwz29M#fh52v%AoIe] IDATf(IAƍH_r5G"  DuQ@G ׶DF[xo\Uۓ&gjios:D?o4D,g"tHwmN2X-/*?><ӥ=CՕ\vrk ه&|zq'ѐ}ujay2>1Y-]{[=n }|27]|6g|L]b!/S\I\7_@8VL^ϱ%p@8,kbSf@8vIͅB"W;&Q1H7hkʤ[c }W 5V5j2$M^+vw)۩$|U_# _:x. A85Bq٪7 w"#Ѹ;z풠77@Σݑ|bC9J')/CR6D^e7;+eac#E[oݥax)MgA/}6gLmM֩/bL˰9 #日ME_W4I#_ڸo:V!ZT/yHyFG&T\=3" %md|Ҵ9u15 I鲰ƬϹ/U" @8VDfߢcK±fdWhߩ{^G\L""}sb5$ͱ-1Qp5hZ_ϯViRGD):2Ug)V ΂$55"WNA"g xfdTcb5c;# `ahY0f:oE9[]8Zݙ; y)?:,}^DD@J' kX y;Bd +l,=bZ+QzV-qd%-m_wB~rc\ | -q$|7>)WԤNz`O|ށp ŧ?iOAz#vvhH&[<±=߰@xT $uh~^ٷXOG^{!㊁d4oF߿\Ȅ}xκLDc#;⹈ᵁpl) W±Hܳѐ{hx2=Lt@"^JdGRK ȥ6za X$3|H׺ʉ2"Ȣ" (o`Sx+U`\Ls'#)ea7 }lrݑyN7>}uS72 2؅k0-K`YX \j-=1:GU]Ma4 U ۢ!WYI4ǎGBHDR&ґ$\<荿؟@x2V 8X~t#0Cnz^ןخ@'#Vi_gQ0~_j3@CǂHSH=pGyƏ**@"k]%?2iGM!W`f {dScD7Pv#V>$u}J!Aouw[W" =7ٮ\6[U`?z " [|K6=wM W>]_ʭ5zMu7E!ːF` ,@865akZI֡wt M+ArBV^)'f"荧1U1Ս6]'i~X@2֒Kѐ#AU_䑴[Ygb=\wXDV5~ȄIۋةX_kTÀ/! Q`Q  ,`nHk*"HZ^<$U yt "뿈U|T xC=f\ [ȩ*d Hw6ׁ<8Qgcc x}]s~kHg2טW>La'T[640xZ6ۭ؊$| )Kj]5#"7 q2 K`Y1T?{ަKjY#G=Fj_=f,nOڟlv;:F'"\:AC&/L#^lGVQ#BH:ӿ,% EM~DCpo!nHJH[H}L p?*D8ѐOHzlɁp̈/hEY 9[ \"I_q: 2d0qIQ!bl52KDH֭b ѐ5VzITJ[ܻR|ZS3[cSEW@$ruAo|f$;1xUqkG]AootV@u]gCۇ߯j4ꭴMX;5[w1;{}ip?N*T֤wȱ𪲕ʒ7)| 4ExM=F {smm?$Zt@u4_cֹ`(~b^@"򯩬cW"ѯ/G̔!H:bgz\(Q@Vqe3ۜؐLq;38{қiڏv-ADe{hcU ۑ#[Su=@GJԢ'{6OG^F6Csi4p+㰸>:!D6~wV4?;,,&= `h%ŝ=F_;O嘧0 0l60 Le8쩌MAڠ7ՌE괆 Úvzy]2 =v]ˎkTm!,QF±CaR:Nz@gQF#ѩ{!_plR'u/Ft0Ry{4nm#ԩH=37ndkY2o7ZE"]7UIdNiw1r6^Hw=Iy Iۮ _Ão|\k砋|>֦gG#ḺZhGoioa$|2D&m͇-a>X;%p,4Л-qIܑT [~G=W6EwEkvsv\2h־5ۍ\鵢{7yA6 Vu~}\,,67C0"L.D/B>vu%?T۝::: qDDjӲ@8@8h߮yp&%D鈓r$QD;:;::jWj:MFNIz-݈#b:| *mF VBTN]GԹǮznwWiOm`=Y;5 Hsy -7" I$|yh7BvGM;+ R-vVP{QڮIm6^/Nyw?ײrydI zj}6ࢩudzv?=wzc6rŎO +BRM|(H![uZU=F±0p5  Lk7sh6D&ר%GG&)$/L|CdK-f?_/UEcV}jv@Rvm щ]f؂q91|z{AftԄWUQa+ssՀQ1|`Mױ\< R!O>G77>]Z@aCDF!ڀ*i>>k˕ӷϣ!0,ea~Q4O,JG>/QmwLE..mXxP~Հݟ;JFmF^q+ps^GWekh{sUeE@yGZȏC[ k!a͚ڿF>OAh!݄h\ۣ6縩ˬmZW? ",Dw[D!ш}h^?r !)~Ñ$ `MBW+ffqӈ:ciDHy{h>EV ؏j-4jTgy<|dpQ6$j$V>zK71nq$ " /wzmΕu^r?ngjV47±Rdn4+sX`~4!p,L쎤MGN5p2ADw_IDU!"(X;q,g4I bHV9"L1CĖiCX"(].V;s+mϼcȁˍFz Dv? zK ~cLɞc{AoǁAo>* Rwrؿ/пGC{<[y lW]͉/j& I|9YcZp!ZB!p̆? BݦFC_"避CjCDUk$#EDЌF&"[֑TH0Rv)/vbsDxi > $2ejRrC\ H'h'$AU7y8;$uZnHFv"i4;xvdm)~G`Yؑ/{ [{i\*z_YjBEuRN>([hǮdҥN{vvMI"E+{JР߀?JǤEDL'!T#"a0bs~4"rgs >p= z;8A8 Y""l; ShNݮ BY5fDhwN'iw;jV(4"Lӊr+~V۫վ4.\mΨkWSC@QH@8J pGLb0q^|k7m^U1,6 XwH6Qٸ-~AOx,6HgG7I>Ao|[+yƅK'fjr}Q8k. ][X|-pKS}t X/DR,>.B0VﳐZ_>$J8/ 9X_jV,E\^CzDj -uzA>#>TÑ&W4L+BL0^nt"*Koˎ|^4B^M5l X =7҅TmFץWQCzޝ ؙGC@8v6_]c49d` ѐ1X5X,7C&V llvځAoǵ)L.6O))?n-tY| AGnCWU!5V.y{g_"(?#ע!b"Bjw{I ]7%UOGX%ѯnϩg#+^@(idQ㠷^ʣ^g7/?_;^WޥUKAR~^Uf R֡ECxZw:" 4;3֊]zTJK`YXXlDV-|b`+.'˂vYo|{Þ`_H´K I{i<I; x-O /mDb Qcŵ=+,ii4Ԗ=sU:Iϻ]"gӜ;NDf +*X) n3#c]#Q*DSW2ӐZ-q%|$5P93ǴnG [9Vm 74.%0wq3LyI҉3.i=CәNT=GzADʐhI@ >H:H3pMm߀DtDt"іSꪽzvuzgY~ " EXˑ^PGDp$rUE7KUHzƷ* IDAT5sXEѬ2] s귙g* 6ǚ"V$ Ѭ2I4(ɫ=R5EUH}*uĜ:gp@8v, 5QO]uѐ=]듿būH[m+eaaMi?|6j 'nׯ*]S[SY#m,j$ Xx @eH@d߅^-CF`'"AR?q qÛB.?@_qz sz~2A$Ep$dSki7eJ2P_V6^:FZ*]C}& ]׌Ӵ7;̅h^l8XS6EF#MuӱpgdK$@Zb&dbX$ʅkfcs6}@uMy5eKuV#U;h 3HсjsmC "-AꖊG7"B! H+%)DN|!R5t9s` ""a?ADH T҉|WqkJaF )HʴSoAqF4Djߙ}rvREqj8j@D4C3uѝlft#yjNL$3/DװV]Wc?}hU녭?h߃-A,eaaa$|#ZFg\;KC#.n#w[@8'2="L[DAul3$9ڐx1nVH \$0޺=dMc?T:Q1Oo3cc1Sj_IO^UeXT2 ]Lg^*D.P TcXD/Cҝ?c'#ω$|dӚk6zAﰠ7Ƽ#Cw~ܝyg?Xd1`qQ %,,,vhTp`.z±aOWY|ʽ|{ѐ?5eQB&ݑhE B %_ɴ@&Lonj4uiD i(ӉqMDF&f}S7Q[ĘiGbKF?6D6qԾl>Cv75-u:'&7O0Vwɧ24߯*D^ s fֺ(Mfkr/G(]p̪D,+g!)UokZ:H@8ҝ#Tq2l͖ߗ @ #%CrYeՃz:+~wB-ѐ?ۏŎHM@c?! <|mXr@|'٧'U17#b@8R_LMcZv}hLK fj$ﰂm$ ůds8F1@#;q"UEm6"F&qHjW^<Нb[5uJ ]>v$ALQBs%"`wt'ם0P_D6 0t0Ќ<֫90os'`q?{å@}>GjRWi]G3bAH~@D#ҿleŞH^3k? S;KcנŦǏxe{]|ס#9n[Q⮅ K[5,l4a8XbGEdUq |~~į~6vNIyɟoMAE@" \EAodʬK+JY;E4^2#2fS,ǁˌ<#,Ub{. a2=mtaIyA7 "q8+J͆Sm5 O7>p.G?D^=v8}*Q}H:: zLZc 6ggNhHU#;im0,k&ZD#BSW19AWh 4r|m"ȒHm|Q]kv D_׸|!C>.D./i4;괆A {p_eg䬪og Y(b El@T@lQ¨8*8\QuPP-((%Q@"Zk{sO*YB|>wVunfLʒǍA&1bĈcſ׆Ge1x+|12XԽ!H Զ!*n,hNm[ Cd0!`j1>a^u( ɣ`,̦)x\s:!VgHO,Un4ePgǵV q-HWvۯ#`;t>OXf [ǚT?"8q;ETyWQUҝ2ֆ/tǻK-bWv 勓@EޅikC%p:r[hvpw:82FdCO(if^1 'E=N.@vQ V1vYC`xɢe;zLk}/Y=Ow"N_GG8BQR1I? \v'[ ֻg@?ũQ ,6jZ=lI[EltdPaǧ;a!O[rGȌxݍsi*%~idjt!DGk|R*@H.j@"HL40\ :"HUܱuXM0+e]Í6ęq6`%tSz {7 cɢe5q "koF~7g~Rșb+$ԑϘ Ey՝7ꨧjL-nv5+F$.ͯ/#5+ysjɢe?_CZK;z `%Yנ[iPcaH! QJl$! $_9 ZuH5n{]n|"^@MVo֍ek;T"kuQ4HIw5nd*9[ HfN_UU&qLE,Ut۳ N8TG|QG"$iBB7fl*k{]pTz)rN_pXh X|H{L?u| ! y}pA0 aYHVIME'6Qˆ V1vIt=~¼wOhbn<$NkuW"4 ssȤz7!~Hp x2F :Wcl怅ۮ}:>,$J%A X'r T@u%TEjQv'j\iz)-ѻ5O-'}O#M'R ka&Lmvv( 1M#WU2wsb:f_/}clEYETٽXH/{ o>wvl>̙_a+`V  Re,ZVV,]9`*/꣦qB(؟@\D)tvƀvx9L@Ioֳ{λJ<o#j2lpB#,:܍@L uJAlV'*h@l9!B^>K>MD*L3_t;bEow=<E9S=g/^V>)側 H9 m7 xq=Dy׊x V1vY笅 {b\v_cɢeOa BmeZ3|q,(;7 L7"F$o BV:{:)e! LZ 3ԭD)ij  Ʀc]mwd;!Du/_#%ڀs3N "%L)4%xYo[=T ET!0C4Yn7hNcR-`LC&{op6B*3 q"+ | _\}vc'E9S\3Ņ9SMHZoL)3{Or][w8vۙ{q2IQB #D}wPUY[Y$N@"n1">X1bq W؉OaTv{O;v|\ŕtTh@xKM5FO_! |Q~NEk!Nԁ#5^WMUxgû4ܚb5mGIBdy6٭XCHp?Qo1C2[ǧςLD+B,f#N2i"~q6\ovFɪhfJضl3#q˘2|&ٖPZ:rҥcs23|dɢe_cшu$(Q)s둴1+fGj%=h6Ru:*]A`M=X$}l* kl#"AQ(PjCTH5Ni wۡne:$IHg H@w3q|)29p'??5f%S=";-z s~ B|/.,Z6cyM6=z]A%;z\>>ty|Kp6y>=}iy!)Cl5 RR={;rUAj´ eн 涣Ec;u6 AXM f"-Hf8ZwBX^FԴk#]WK"DmDJ.3Qѽ8/ЯlШtiJ55sQQ(-5 ѦZ:ٷ;!I+m{pBc;/9 B҈a:-g@Rm33=tnbhycb]ycr39T/v-G+x3J%;ܧ}4S?p ^0<'^+ .jm8n/;z<>fmtMww~|%;zL1X} >D m">hh"06 ɤ! A \.L;<>#DHFD}Yj` Uǘd`Dm ШJ%{%2nrŒm?|͏DAYkio:92|X[t`dYJf ^/o{DjdnQʷЁƐ3f4DS"SD s :c'A;! )Zw?*j;42b0OHmg<>m4 ,] Q"Jh ?RM "5Xj(9SIo]@D}yyT;%ڲ[f qcK-aq4#]叹[wKgH-H= QνߡmD C- Ab+C7&b!ZՆu$r P"5%H4(}O|퓣uN7q7{BpHvjڛAiꎧO5ws鶕؀xӐ`ӽ^i$5PTU 1IaBWTf$"2+ ?ekЉ%aiڛޯ7 bw Ά,r wCR?e[L%R㈂<h1MNyѨ[n_I&0O= 774u~ASxEmW}Jșb7l9kQ#=|sÏdCUqo_#M?xAFyۙTQD V9{ՃitWOK-@u)ߎ4|h)FiO\RF&U- ThbS\TiV32a`,I甗FRƑi۞i#i7 V/BldBM6:ܲk7A8B 3HlX.  rcD_76ÑD ԰W:+nMvh6 /fI  c&$ew#5) %ݗO2sl~}߄sGN^~ TW8r$Qlys"X-Ȅɰ0g!1%mڟb;5z %;z<1buW8 _F|ʍ D9ia!n{)HR(ȌTW@BötZc[`Н6/Q!J z 9B!3MF^5TUau7m_@ &#E!W >j6pAkiЯ[O_הՓ貺{zlUb)A} R?vc'"rMw#JE~xK' x k6>p!`G _l}#^8{V[J6Kq)TəTC{yS#FW+XI}]ZLS{= VB8"Fz I}x-TO}wLm`MEzX"r 3'_B9T5ۄYs/ 1Hu)nXLơi\M#"ZV#2Љ4$5ȋ_!Gɕ{64 AmsJERghݶ} \BDrB?bș>7g!ꌙ"-,!0SGWf te?/w#_o߻vȣnk#+2n_+|c;͜[sd?Ʊ˧Zgm%mu 5ɚ:SZק6S@LbкuG$F{mO_Hid  m[1 5۔ bk=:jk@y 59k+k6c]wD)`-pҿ>c{ύF2¶qknCKw{iBI nL2 x _F{ZgT}y")_g_c%O|{yz*qdRdgw$mt3һ'oِH i;5H6U{{Z+ss|S; *8v(!@ɶP$GGl?N![9y!#VbĈ#FIW j/$1vk%HJ>4`Ԉj }8 ; ig!$HSh֖5-oARիl- m.mو #Dx8 WdMSpk?C; QoQ:m!vB^?PS2SDzo17 >~Y]MU [@dI T9Y/BKFϟK8Q_7FU- =2$vc}qUHߐ<2!>I,r-ڀ+sq8)S%>ljH4YQ#SQ^%C7#F1&OW=H= N+HlRw NfRf|ډM̺퍚jTU@ꍥ$(6Q AQ >W!ioDuH zw-B zU¡v oj񖙉9!oF'"͘Ц+_nF;JCk,V ӬNlD ֒y״FU|vfKM t$HJ3mIN|6Mt-# Q"WBgO 2`{ g2 !E.F̵҃ AYp/A@otnEv73WNdm$1xB/AȕNF&8FA߆"*ߖ._|q^9̝A&ܴw%ZA'{zMb< V1bĈb$ b!LDʋX@dѬDR mv6tf`h&@aW,!F3Bp(EJZWo@4x20VՊ3)^!mꠦǃHp([g! ;BЕ5z6Չ,zox}a$[i5McJա c1Ab #j@ᝊxșb0d+]\'rO1֭D*ԎUdy/wQ#$ τ}|Iջqi27[N&j} vE672RB ZSn7vn>5똡w8j항]UD~ޜ/g_Dj&3Ԉ1 b#F1bl#k$&iρ: 6*P&?:P1F\VuJ=bFRI;ۈh ЫHqzZh}E(!HG?bꎧd# %&H Qd߸"eH$s*YdFz$22;6*٭x}vxE\ *뱨9Ȑ;J`n\Bw8 A*$0~=Ճx0)ތ9J}Hz |8 z>UgТmBȚ;w\Yd՘ gAc-$[IY0X8QVM"%w\nV"R2S}3=En?D掷^DDDh2-PTuϱ^#i58ғmT!joZ!"J:<ێ߻,SuxDjh H-3}|ι1ؐ3A!cH߫ 2f-bQBu{ onsrex@u6FTeݱLI3<I;-gBӷD5Jn[X7utsr0ט?$ݎzu-\:1bĈ #=hRp-SF~75Z bg^ cA ]L Z?B`D9DHU۞*Z[S"iL#XhF$(iT>$R$L'QmJYD`'½ Lf ɉj^j&$Ϻ117+|\^n~O% Q/$:i=6\sn;r 3ckR]y!R7z@ta8QӍHc"0&jSL-Hn!釟Dz;ޏ*/k 9͙[^yd[Zf^j?W""VbĈ#F =}$Z t<O9ɘoݮj $PTH!g*!4`X̲*-%5$pS{u=݁M`5Оqkڣwc^""!㶟E0 jaWwSpeLF9WePfCq qiBJ"g{Җ R6"/ \W8 ?W{ywBDTum :.dRAUY|ayݳŖتWgl~ g{/G!'"w1Dz17u HOֱA29Z_QQ$x*rCR+:wLD1A&\?n~>Rkys߷3S3!5\Kٰbf4jSmh>XBF`=+ɞ'^:F1b .R0DTS@t:{։)|&6)i~}mRR#^DH06L !Cn6$}DD`$ RF UPd}JQQʜ>dT@+WTk:wcU`:֒`jCuoh^ؖf׮nprD¬;'Q瀯&kc"Jm}Qt *H*Vwl<:(Dg#rAR^45yrTd p"Y4D-m(=e+?gE]n?<ǯ;w;x/p7|q/hw=}^מbșb+XWW :\ypň#FI0BdfixV>ãLXOqHi=4Ր{H'"4뽯)N X&z+$Zҵ x#QM(Q "IoZAA*McA__DCKIRi3Dϯ 8ƀ AȜAO݆Df"\VL"HN5GoE79bCO W8 i:|1]'"gFCT#n B*thmBTY$^pXǐFor؇==¼?Q=nneyX[ ={Z y|+S 0`Ǘu<3cu=tA!5ZGcƒHavZۑȓ{Iγ~ELA"ĭ Wxso^b+fG'[nH.Y^O_a!?Wۼ#F1T6" `I3Rb!+%ͤWn)_`MM iD5e" z$IkԠ#MD]Hdgm2;D\f5o(ָƞB|ܪf%Ij6Pib&؎v1h0?̎=McS[6lUkI{$YcȸWe !O?sx8 '~oUg1f_=Ǎ}ҙIlT[ZޘȳdbQs/RtxHm8bk L}>]NFx+RϥCO&r)BF%o s. +<ݲ5 *&Iw|fov;~UWMh!$?z5pvGӾ5u?Hq`=wx 8sy!k9V? DƽSbO_a܎J<]~%F1vi\>Fn:$TV? "Ge@:doO?|̚$DGt6'@~.^W?٤jW Ru7QU6LY-d2;^KJT+A:^Q-u?Y&'W U&Ksޜ>5im7nݿti&Z6r 4d pNL'V?;|;qE{9XZsh42I]ODhi mflmZVc2F>^ڒ?߿8364@G_| |yrȇo߿reIXW0ݶ=]^/dv׀{ CHZHP+.7X(1-4*R|eVH)ͯ ggִH":4Cǰ72_qeٖHnEf_ޟPL1?5N Zsf il}`{5m6 }ժNm&%m d[޿6Ҥ):$@ Tm*YNUk-:~%y5c =&DD H{ӏ9Dr1r8_ĝO-YnRJd6-ooE_6ͷi_]kChJ020bvrCGScd!hnIͽ!;ˑɉn[U`czfZސ 0oGꮦ#}>[!Wu5zҎIa3V82!#G#vgno!s_BL1U8t sRW, G +| IR;|I|HLjB@O_a&,jgz1B'Vec2ailЫ{ИG͝&[h״=ZսBKxD ][GC!n|VYR(if.ZYL%\ܔ }4ePG:==und6Q;Ar{Pko^|̺si >gڃpJ;=A9D<-g#deHУK-k<} ѽ2AMrweK2i9Y&7c7C-[{v`e}cǚO#bZa̜)~x)Px@@vcXXHt9z|]hRQFiؚbs;{= (s jsUp:HI*II ֔ v0p7QϙqT9S|/d)Zi]]sBɹyƑbˑHF< uN Lt: .:W>:1vQR=}z #_g"3Rw"\)!}!}O_U;f1bp$R ,DY|>wأ TmHhÉt$u҃\x?jFc**(Li-G_QCTkw,)`D̓UI8(HeD1 -uý7:sfVsڽWYo]f"/;Q1+[QZVwoކF5KJ囇ij߁Y/԰*$`)rbx(za"%Jk AͦWNտrBnRa>\#_u݂ӶmnWC[_u"PLqe氜SZ1v$7:r-G&E{`dD|m?w1_{Y_Q `3S/&4 ublf.(:󣛃lQȗU9S< rê`?5̙r 83DO_a7HfqIIVZTeW IDATmQ^܇ ^+Nj.%rRzȬ{ /F=z AowW8q \8"C]Z6&Qj2ڢd*:߀Lf\xn QjFl5׫3FD]EW)h6tHݖ8]lB׵qx`@Dx45&3&M5qDzEH>Lt4MݷY) 7!q+4~~xB8R1I>rB jWd ~5fD sr7pC>'H':㫲 ˼7}șAHKJ6)&2t4 JO[/nH&̦ˁ .ԱFΙGܾ;B^ę/uHv֤j3maqv+-`mk0PzFڑ+{~:u/khz w![7ޏ='"W"e/'"f_,Uζ>(?vwNYFO_RdD}ц28"iH?|cǠ08xcu=$ )5"k efl{' Jldvx/dVDql51a #5f"kWke"3~CɞmMujAHבH"~~햿O߄b̭#3Z_T#r( QU!ux*#|>ᡆ~ʤn&нCkr}Va 4'~ VňBd1Xaǚ8⭳Iv I|1.rZwe.rܜh-\NW60rO%g/]/N˜#_7L[3GKӈ6$ JڟQ7tBoC#.B҆\GVw<[?f4LpD}Ǎx{_?G`+hbӶ|tS55qKLrpvMYS㱟N eHV݈4׸!vwl9S 2O%WK/ԒEv9YSnALڸêd~-0W81 \SD`2:wN6Z痓msW8 2nMSbD 2Cx)DF`W q~gh3'u3yѴ=7Umy(4*C ?/G /qǺ-soQO6#iņbQ%R"i fZIf^ǫ蚊2-޹8q*6{Cz[0ФDi޶"JRnv՝;#n mH^&Yk\}8ÝHZ؛v9}{cF߱2Q%go/a܎\_CZ{cGeg~so^3ricC_Jz Iύ~lcͷrK6$}%^SY7ߊL37 E=s{#lCas+D0ܶGyzH ]/oN.]ْ4 s;6!BE2BV^nϙm0>p|߹dѲ-[ngDL]zֺ3 f7;0B9yfRg!JaEf2@h!UcS3kcesM6lmsٰ[:{ ?^lL;How5Pe#-k #,dQ5\!*F2 yXvj~H7ۭ? *(T7m2 B#SU -֩$@&Td&?̎7C0nJtsLS1{X CZHhX#dtZ֚jNv+Jl>r1ؑIj 5R]~0b>I)$ǿH=}abBz 2[).=I7w>"% Twz.f`1*9S:B:EK6^̨솰 {!߷]@[KǙν+?:v: b 4̧9S,]{o Q}!x?))^#Sg8.g7lAʙE~ƁV{Q.`=K+,<Ǜy CH$JV>mi/}3s{ ݓ6>SZd `1Mt*D#~a{qK=Q_'m5XF0B~ʡՉS#">30뎽0D*#!j*SsR!3cRuGB:٬5Ov'jA0΍] Dϙ֕1&8ckEL"U}H FP9v{.29 z gvCo c W8dW" 9$ J&D͹Ғ͏Q:x|߃}*}[h" ǁ[R4eܱ#FgO#)Ӎ)v i7_s|(8ÌZ~Hl9S m(H܉`ZqW-9S;X[x eH}Z6k[X^`-')nBT~f3J `tiw线/əo7Wvu2Uqc鞋Y@O_yr',*XZs`iPN#&Q({=}=ToO781hۀL'0Иu$'K|\K&RIHKȎ] @q%_yڈz6i%%n͖>H v?fx磹F6niS }J*Hld'mmKnKI^_=~b w˭B,p3b]}ҳp(QC+t[kK6pjWxr/=ޛ؄!UEi s؂#Djȵ4pS4uru'"_\w ԏ-?Ks[룉%Eu;o#"ej3n/+YəH\CW}G^sDϔMLN/¼nN{ {֏@ o.}l-IGv4(vn!9΀`= +Ol(~|!۽S1g 2КHc$,`0I!,Tg{{&#΢q%jQ0OtY_`iY%:)fm7A"B]&Ԭe0[6LI}mEf{R"kfAxNtfjeӡߌ'kz>Ө)yumh]*D8mw kV¦7!韣~D GԈEHA1 Sgp5Tvo>0?j/LyZș!j34C۷ކ|Cq|d?ʙbuN삒-a$*k|;uydjuqiܝxlA&Fo@DW:bӆdO} Qʾ?Tw#nid@x,{ьI l~'gAzڜ)drjF3!7$߀8"ό!>Gvyo≽Nzd*B" :l7;S(6RH؇/-}=}vgUMO H%2z?`Ÿ~x?2cWH#w#Eۋ@܇\"Dns>hjFメJҽ-ꝒHukvaX3iPr*3mD.~W;5b}LK>%Jֹ+T Z RϢuȨ{j H\_5ksX#C1]0zk͚ߗKĹ-\Oϴ/#>?9|Ǧޛo>gn9x )aL^ Zĥuxd[r8GܦY ddQiC[LQk38xO20":l~۷A9nseubv,2r-&gYT#YH!죈 ȤxE]o%]2BL tN }a]ɾ;G ך |$g uDR4xCtP>gG!es)ܑ ?+'K;GLlk{ЮS/,ڕe0NH+2/Mʰ̲D 0fLXσfWcUBf""dEBHQi. o$,$0SC'(f9[ Z?u飁)6OZ;ByB><)%? xB@;߈oA{q;/Z`M[ky>ڍ+]lyN&wx3+T 9 -WW|[3|N-?_gbsZIPcu謢;@R8j޲syA%u9W%c7XT f:?Av_wv@>c= ȌCD=@ȵ!T) ydeu?オNlR3,%"jch׀FQ1 1w\E` b`Db`Y8{Uwy{,=Qs5^\,(KMtC&wk{؆۳|[-Pcŧ K[Br4jla6v*U <[ԤC7`R@e0L]C~F?DV#DXU߉2IY|.7+pAlg}>펏B rȝBp% ;w^dĊU(TLH}Jަm{\l`)[DDONTަ)kp8!^mJtV:ܤ/od.]GHds^HYW]a b4!M?Dero:6=<Ǒ5[y~{ƙDWC ;9,~ 1p/G"jE"':`7^E }HLvd`[4)J_X;}gCj%g{h+@nJ,Я[UHڰB IQ<q 9ΉM>@]I9XChH(ҷχS"*zntMFWtg_F󢻏ҟO#N$ !ZV12*\xQzӀP/I.vU %O0 W#%Dɋ1IȬ0IwfB^GU 0M?pֈ֭m ssHuϋՋ!$:uXz;]U~jDq (ׯܱk#=jHLvpfr 01ur P~CppF˚b<^]5 d|JNG mz:er1.üM+ Zv$Em0_&wQdd2ίb ;HxdKP2>D:}$6Bh/\d-z.2 lR&!yϸxkɔ]8"^J]+ ID.DQFwxK8]1gz_AHk}kDnp=j)~*$:D֧D)*b,@Ĭ,s֜σa7xs`o;W?o@&a(pu98oZW٥m'o+f7MwcD M;ۋIOM5Gb~ϫ!uQn@#rHd~~O]?W (G0LEQF[3WoG nd~QТ{mo!qEbEaw>]MH0(į6CsKHƆFޢ{&w\! ~F+Htń5٢QzCT)`*XCիQB|dLFj4>Q׹aPQo9 ϱ82AOkWmO2>ig@J:d.E);݄(W#Q,Pr}Af`Sަ"Ăǒb잆+Wх]Xk箏,k:+`{c{ /C '- $s=sbZ=J$kL2Zj.&g>vvF upmum€(]QLv-\_R ÞRNOT/oX^,al  q[Ct{5|.27T7!ם;[쩐:MLvÈס ,pXցh́\1fvveh.]M*G IDATԢX2d@?,"#$PΩ|rK5#&%GӋ(^5¥ʼFtTjqe9̍S$drb7} QԸځe$1B#^N$G^hB&ݳjG+J^vh[D ~Ѧ2LEYʹuK Hݝ:$,bpϖ=" bPً;w/ev D>D}ާT\ 佾\^9qWC 77= t\79Jjc&vKy~8erF&~R[AL5y޻@NFwvǾ斪 >禶 ̧VkŝS#M j^a ȁ]k.N7I8poܱiu@lEmzNE^UȞ&p8mlիFAۮr{V'y~l+2^77Ƒ<ۖ}eʼ+{ 'W# Mg5UQg+aQ]፺'(|]!I%оeT BQϳ5V* hhb-aj3x*4Lnx^Vg\X(UZ\ߞDJ3*qhK{8 ƈrLxc?#w6-͈R콭yEަ>~)blOX3MkFG6 "s7!|9 1~\+xOHPpykR2}8(!xŒ㽦h 03Q YG%p팮 n&6d^8Y~dpR4߃xqO<s̷HyY\9ZMHG3mջpK^B!-FމheTy#}}!co|ł%ORE}%RC4DeG? j`k6Ͱ/1͊bգJzQ4Hu[VH(d!$RQP=; ե.O{B&;x-pb\iV4HRF}U#p=G]GI4rUaw΄;n Q ׵a-y9?w#ʿ(3F@c#r6pJn)`bČ;!/v穱ʯI~A"r-hd BGU´;];pRFk}?h(ވ1t81+&"cOmj4[j\RUh!F5/D#PA)梳u=R&-haoNVԺ^oe"ֱ~݊/!XW[N.rJd/Bs!UĐ92NEL xe~ lGq}[ˏw׵? z80V sdO5nUۉt6ZZ YMm8er["*sw}ԊG-M6Z2s}qW`_[m2~EEr{逿~݊!9REXdmifc'dR'N+*X9omGF=4Cd"D" {YB`X^(zgӧڒ=U^#0w}|#)+#\TrA~tn*< sk&^S%f<XGtAuQ BJ)s!o6HX!'vS&rĥULyڇG*PR+)`s&Fz.ϣI3Sr ױ#eU\U"b@tQx5^ 26N{\GHJh#T=Ӵ80?[X7QhSʝ$xp@Ƨqd99yr2&!nm^SV#)7erH/Iۇ׼~o+B*r6^oAh|G8{׉{{?bm])3)u֖Z|=4bm ,kqv2)}Tչ UGeϛyKY5cX|C`$jfpݪ{6}Ɓ4R&wu+.tFQè>PQV8pUT՟>`G+u_ x OlԹZX@lHC:@uF7ʼLD o1F=W=geGD2^+UiUrVoO:fմ[gc/=cJ=>Zzkç:\J[3OmjMmZw: 27]=g|517_ mnol`>D_h9V|6g+ЙiTkѠg#W%<'Y9XO~ GX[zfMTJ({zFeB8Nȕh".BouU&;x@_]~M,B)Â=ӆvoR|)8/­u#95>HJ4 u$">57\A@g!#K@L?nzO-NxS.ȦD{U&gzJH$yޡGWFzqDA!q$u!а6B}ݻV$/C` )Z6e~֬5'.̫V-SRۥPF0id"IHa5~G("栔E=e_:}mPZܺ^MaG"U 1UrMu3lyo|`~$t=WoÎ_ v:D Dxq!|nm\Nóމm/4.yyqO~?2o/WFLW~LeO;GƗ-^\LJ;I6P"cv 1^D~2C}/R&bЕy~SM\}Moٵ _A/@oA=XF hKs5^?iR`=>:7d򨌫=F$b޾*A2DlUCꆡ 1ĢH?rILvrcwdVzd09SK]+wK FLnv6_"d \nuryIjEg؃(^ov /F(h>ݍ$v#͛za$ QNCrf)^(AN6i(Fcαmzɶ-mBHNyvwA G ݌(\kk#1@"[KƋXkX!FZ3 (x6`䃉L_]a_fĐpT%4)/W?MHmq}#OG,Ol{G; 'LCx3?X:wvCagR}8G"zcmƟ)ĴX!#];kG෷Uy1O@@rTSZ.[gcG#m@:oӊxEeόW?ts뎆ڧh%]Ů"!t K ,뙆F{Dŋ[+a2e#MOmy82oM? V.ztoKަgzN_^g6})#_*~U7}Y6! R&O-(cW[j&&M`:l}DOaRv b˾lpz ֕ HSȆ>&%,g. G6St(ԉl&; CXOndoA9.Z^,?u˟Y߉DY@`;՘ /kxƸH 0ƛriRzg*%UkU }:LLz &_UD q@4*X!mزgD J! g=-˾ bm#$%ڔ+(vibW #L; k9 io$d&+{-c^"b̔;kØ$MUb HY%q1Uz5o^r`ֈWi^TaT~}Ɨ#BGsߎ!TAtPB{@Xwu_Ԋ'f`֩pAm22ylH4<wŀɦm\Zx8aB5:!SHvw;M6ō!ݞ.ރy!pzɂ FyȆ!hצgAW\@ 0E[jDofuc˷*݉frt0'<l5>6~_pH~{A&;x.pM&;=d]xdәek];K!7!FX]'!nt"k2w *@`y뚠(#k}3ղw] TA̖='mF"QB򈾃(f3Mf<1wL"IsdPE O Qc(A)"wn͑kqBhl! иQ#30Q c {!|EGɨ͕Ӿ*!{g'B2r?kF Qc>rR{ہ:~\K$L,h5w%AYt!.WB\+Vz~pWRBf9qR|L.2S&7GG} Pkc3O%{*>Gc?ik C8wb0<Ҝ̣b }38(0V0hZQIvW6Z<1 A.ʤw䅈E'ye&+ㄌ1D|b<:4 >He&8tn`=6IX[ Yl 0A&zX ׶-6 ̰xO㒒mIB0+<@J@Uv^BȆ< "Q²DpQ֦Ǒo@&uy\҅l> 3<qCBQd##Łu 5nRjƛ)O0Z-[#yoV F uӴR8RCjnݱ&$^_ $X<6RMnl36*5!K$=K D.[ys׬iPŵ1u {E !O:*DRsu6BxOܵm8MS.- 3=V!"F5-J>Fߡ}v[N1 aȍkd}3ZLRA-o5t33|rsQDbIyItʫ_=Yvm$WF :ihXRG HݍթɇmA q.=QUw QsE);j~#S&ב2EH4tV?xIy8vw!s6}#x$[Yo 0n < ]# ܰkkCmގDp@ߞX#wubx3[7kFuTw4Hd܎OurѲ>4ucLvSH7$zEOƇCfI> fߺXzI`25 HtjlBY$ ?ll"ЯcL:MwȆ~&]H)GBoE6 $LdA϶$O26mۯmkޚ^8H~oD i} Vș}h,t]D{B2-#&kFCOt\ &$e% 9}djkӨkzDi94#5#HR2יW#, ?KpWvwk^Sb("h훏 - Qac_^*J9e/kB7{I\ϕz"(82fƸݯ07y_~&Y礗n;0Qz=<ٴU5Ӷw;.2':F[[tG"FD_ym-oaF7)$adV}HԨ15/wk^;/C1-#e]C^ŭ *޻Oǐ}y)[Φ奞Z+lٞlDy 2W1tKȼ72A+[-ȳ%tπ]@"|ېݟ;ebDBH:_g5C>֤L4cLvG&0Q(ϱ.|9y[7O5> Zn C^jM=޽22awQvdsjB#ɠˁwdfQ&V@-(w޳kԪN*>67=V]yw.zٞ<VA~7#{ץ.3~R*r!0Jjaޏ:&n\[YG;8fDRBI0~ IDAT3#Đ6b#J7@މ9ڝ dmBr^ur&VبҪzI(vO! Sk^Ί%HPUR5Ԭm %Ӿ\\ʸkKw߃Oj(6¼aݫ=Z)FkK aU['0Q[D'GN|a1{{W~ij=sX9֜rxˏ:);6}~taPezf=B0zhauI3cGR+xK29{1)ޥx8nn 'z/}~z7xئuQ?e뛿^ؑx0oӟJ/y~ /={4л~݊` |=(mܽ1ֶ Fg\gO6-+5cAMo>>8, UIaLyM F5m<"4UR (Vs7,,5՞7oH 3-k--*Ԋi &n=}|mͻ!Ќ07y;*ԥ.b275ߗG_"vJ>5Bf9Q†SbB PV܁["H4}M#Qs'(1A<'Tu~]]HQ=VhSH Gٻ+7~0d?SZAƨ9*V? lΔ;.qbBh88B6.wƕ(Q|6D?f:4/H#H$`+Oiˏ|+s]ΉysJוG"cTMȻfP)QI=+b3D&$Rx4[STg+gXr׌|yE9hjmd/ergh6MuqR*8X7uCץL yE.WC 8F(Jz~[54 T<-K(g0 w}oR QB+߂ȱˌCibZϧò^2e#ϝNŒѵ"vTSQ}a^V}(^ ?-c\ D^NE-!V?7us>YFw.|0֜@تBy̏ـ⭵5ٚ, ǟ\1 Ν"噕r&;x2_~2y1{ {?/@rv"\c+ފ7fU1s8ʼn{Y3~<¼Q$ff|d-[DoY8f@"r㌌%%cL.džquti "H '\?R.ui/VƳڏ(U1c{7Ζ]xfJ}8x\8{Ϣy%WcBvb\-A"QAp;2KB#Ä̃UX[xE^e*mxܵHw&zu96Ns'ᝀxkoϺYu˟$UѤ(5мfҁ(K}bJRr< "pÙJ2q\~DQ;KvN/}}$bwF?pERB󜔾}^#WZ#7B2Ԡ*!κ;u'?w׈GxGx Ш|Gmb"2n'SnZVع^8'84 :p}2 _64u ^]gNަo |]ziަ{ |0?P `w]7"F o&1tWhsYKk3c #]#YMy~~u+XnQ@gަM?'&cxoww!<"LX V 18 )('g8:({v&w5EtҔ-@ }6}G铧t7V]!sgL#kb8:2@-B { Q(IPqX erOY`;cMG,:cjAݣ)fk| &H?+ќ,j~=؊MQS4A / 79Pb+kߋ _ m2LvP [#,z t*h&e-ܽ XMf."ɪ#c(G]Be&;+0$6FR2"MZYD>F6Z-FNE:lV Ab_RZdmVxa+^AQh]A@Iw#NDGp/$yQ55T4"ER7a XJ$b'C12\dFː UDŵKaBK$%kBWtH'!ʞzO>3PT]?vDU*<q7eote  &6lMtOnJ0Z߬Z"DUR5ZGʔoZ]$W.Zzp|+GC.䏣8p Rh$gS&Mi;$ZԩT ^dKe<ւ2ov"sDIYdm{>xyKx;g H.jQG8ٵs숡.g'->CS(w).TlI$YWz [5- *Uv5B,mGkG2nDyREEnE6}a1Cx@&!Lvи<" ܝYJRXg82DR(%ߌoƪު瑵d4PDZ ›VRIJ" D{io&د#k!9Q;# _7aQo!k kW3-Z31J5R4[)ҟw,dhU Q^J.r}S%w+!ٓg-IkgH[/Fg {_Y&GP&D3ݸ/)knI.Y6)Dւ qr liy1hXRc9 L~aW X[ؑ|Uz(јhՉo$gm-HS&2 b9y:o@Ƴ%)d0vx4,DgFF kНO owݵڎq_(D܀BE\_B.Dk#Fu&e-`P-$5&7%mq$fzSc010d1W܃Lñ_U4L>H[Տ\Q#]!1W.4 (m;.OdAtCFN~JV<|rM/5!Bt}v/pEP'3B ^BgAx׸sV ɭH.yrX\{.tށkj(oC kÈk25 ,+dmf AGgp+w^ w*aQe z0wI#X S䟽Н-!_TP|jˣReOsǺ]֋yФT' [AbD7a;f7 *|*q/3P2&_65LvUg&ovmñ7֦65P|~ob]ff[i{tLFNvD/焟Mxߕ}Lv0(no^?eru;yk9RB>#mn$ZI>@Ńp8\yOLq6 %Q2܈"pѿAe*2j!j7S'y;by?pD&OԣR\d?7U}8n!٣LCds1>ƕ. 'Q9y /GFͻ+#Fw! T[I,^aF|-8=^QMt'ⵃpK !&!\B .%QվkV]@f(u2X=~4J^Ԝq5DHk$o2Q"P[*׶~%?C{'ǕF |vmٌDҮ-BtZ36jn3q]skZ)ѕ9PuIBnJa.B8 !YP-2Mh X31[[ pIՊ c̅=٣ALvp. Y |'KC?&TƤ51Ƶ.Di٪l{~mEKG"sLvp6'o[ׁ#9 E9XGyسonBpx1J⩣@awd۟F* 75̲sKoFygy1AGȎmkA|esua:ՋqOs8 # ւ5}!gх:Xh {"V[ لOt8qaɮJY˗~iջm#MlOk,^w~R YJ9 g eˁ]ޯS)zaz{Zخ_V<2w_&;bdmC^D)LZ%9jDRc<qiޕ!-F"y7# ńxp1 #FoFKצz~šs 1$$9R T6(˾TR {9L%7G"ѥ@‹d-6َ jcOt=b̹{2Ņk/ھÐڳyw !/9"J @@_} 3@6eovgަ )X5[ڢiB[E1jyKu!5#{h?3f]tۉ'7]'tOfD_:mzc29'v{=~ovo# ɽ2oӏ ɵ"Ξ C#JeΗ%ªԖ ! 7#z=<;)UZ}B0{཈AJzW LrAk_k ,,p YL4BJ74aR^U6x&78o bCH$'ʳ%m ll2d$_/U&3ƏNaQ-eׯwe'd'w#ɴ3{i][, XgL"I"4ԟ˚;U -޺n'C BZ 7H*#!~7 -9B@4)#SoB /T]W^%ePHD׾3Gɖt#YPlV& I, !ElfW-^~ 4"9-9Ę:L9;!UĻ\{nPaZLՅdDr\QaIO6 ov%;dg fo/WG`6]a5&fƳ~ڢW)Tg*ګ1+coY[NvjƷG";E|onHtU9e5~qNC)kChGxWx`}ަK)[xZ 2!~(Ji+xnꊗ~"k_Fa5HcT}[迺2l<.:yfڵO;}oEFg#;Q{06!d_cJjR܈,{Zɫ%co'5?H.N!oXo\0w-d/ *yDLn =yx`bfG'kpqgl^^] ~Kަ ݖbGj˃_^tQǾ S&w1R|JD\\ܙl<p?x'^ʴik&A*ލ1d*RBu7 @Ցߊ Gޫ"4RUgTÐh5&;_ZLFY?׾&JzȄԄY:ܾqFIgiߍx E@G!>[G?M \vߧJ0adSY%bAs IDATz_x7?lz!Hݕw3w"dmT˓,Ρ%DyFz]LPE yL 2NcnlA 5&WC di"KRvD=ZDAَ(= {ܱqx/ !{{?:M!:Y"$=[;_s{5g*$/cM&X3+:i/`nq`7yHxOV^Jҋ{D TBq"{;v  J&;؊8Co@g8G"4PW f7 Xo5Sid !sy3֬)Nv";C{Ӂ>, a~0Fm%ͪ~Z$WjdUub%iE\KXY뭎#󷙹kF pAG?&y&X^DF O:X쀺K lwaU[>;3R KȮZBT@{6&DO)hDw1X** LM;LO]˽?{g``fz˹..7 a>ХD&^q'+ iλO$@Ȫ+CYOz>GVu~6-( ZA6.1@V/R4|ePDV~CnS+۳;$ER^Ӑ.6W͛uB`VGyybyu8Dkˁ˷y}y>ܹ)xJ+ֿ[|-HY p%@o*{Ka0cdN+^CakJwa{ y#}s;h;g\B4mq{skS-4 }/gz?̅Z>g d,(Uct\Bgoϸwa <7Tx:%0h -wqz7|f 0`Q!dswwh\rπ7t# lEh/C_-EBe4׿(z;R ֢q=2h9Ν3ϡp66cQ¿"VPĿ\DprOdž+[F&7.߼JEVZF|G01˵k ,(:sa@&ZgB[=<3P 2$e5Ot0~c5Hi 8K%ۚn>s73{d| FʋC(ܐ|.+tn}בw;=ˏIR^q!2z5}ɢ޵NllR4yky*B8y f1#5<󴱾 nj~mZ-7wv1љh {ۭLx ?AF6>Z7CAzCGͿeC9D0B޶<DS3V뎃 Ò5Ʌ2kӏm3k@—O+صĜ`ZנbVCg,YevKhC^ x*QBZ=܅0sU(4c rfhh% 8Md\!޷&ds9fK6Wav;>75YN{m ˽_eܐS~;?3 5 ͍"+EdL#!H_i+2\UњElA!o#БByK79L"zK+79}+qlrVC>">MCXZF@ o1BxX0݊VXcʝ3)qE gm-D@hG/DmmE yC$ ]kod^Ͼ!:hZ3b~iO|R ӕlplnͪ;>ppj}{>?}Je{r{!?DLIy;Kazʗt-߃YR0+0XrP,d[BPhNkq?; LJ|ګ{LU4޺>{/AtCh^KmS]lcF /1ȡ5PLB[ `1dU![O-i XU՟Ŧlne{`4Vj3Ku-H(9( e5YRy|AÇ-z{=3 j!bM<]t)/A`wK\,4)%gs7dsp@H6Wfs*!ϝرG&BV4S'YoLyGm!0Ehn;)`Dam@@fwn~f GE4!{ @h=2r֤3(hƾvwJ] P؃ց!J9Z?BuI*H2d0p9egdjA(1^$t5ӝSF]Vt.Arhme9uXDNh`~FcZ! V.R |"6dzQa\!lDlqM]N2/?+?mn3r{N^޶?x-cLpDdX䣈dgWkL;.sJ1#pSO9nX2yxrr_vwMqnG"hwG,k`Q4~(O{?=XBGHuSW"&,+aReZ~Z>ؤ$ C/1U"nBq~0,s8#Ѣq'*W" !d@k;D\!f ?DUO nM/A;my4ۚ>,=xs<աi!()o *Ҿo𞐰w:u 9$ߟ>2(Y?{>ֈ(^R,DP F3Rln6SՌȆ@ d2D޲ybdSPh޿ =%/H߫AAv,*qm;q`E4ͻ__<3SBwby w-gHawomDɀ21EµiX<"GhmkC@|~{fO+ DFؿ /@l -+lg~\wX?w4quM?6IȖŪ`$9kr/,~ Ay @+By|R^t 57ly|K75]U2_H0Hyŷ9g˅D+Zko:72É'yWdžk{$'&==^%DcpםG"DF)&byF &6 oSwwx wfA_MO;9mhY۸]RD)1LMyt۞*i)\a]aoG%+|f fSb9 ?^#Z 8@s=H-XqUDMc~;K_B$bcӭ!IYn hB Bn pW6WR>P-EJJ~L6Wx)gG\lKR^cֳGمmlN::j"LxH$L<{Jaznȁ.y$>7b9NB ="e nGaGfsM1tZf2p0 QMe(7j3agnpv&4wg ZƁk8)I*Dy.Dsb5N I#ŶU5J>vgԐH*#0!:,u<lF3rDOACc2f3\]y&}wcuBp˭vlӿjZQ:vz[+KaN﯐>Fٓ@)y^ub7lp'~"n^C)LoMyŏ#VPma/\?ym+KQ:?kYc375Վ>kWO߶mwߎQ\'܀u\PdY CJ6WD|-4oB!fżQtb+t  xIH$N;>p4MDhPMa2w/ޠ KIQˊneQgE|[\,B41`6Wؽp. 25!*.J`inr[em/[0lmrc.:XRs!F1~h}:IR6#E9iFkF(K(ܥ*q8rGʚQi)jxZЎot}}۾ h Fߌ"B +::IDg  V(^ }ƶhF;~ۉm^;|y>-Z}Q$ s?B64uQ|#=-ϬstKܾs <\.CaE^{PWy{Lؐią}y@@0=%OytUK/5t ctQQh= +'|e)L4vW#qN}Bw^]n@; ~cpvv/䂁 o͝4~5"2 "Åd!u;zmh>[ F><{>ÐpN*5F|!@mzx OM?SrLkC]ݧ7׷o-gА'"[RmmDG͌!{uĠ/l@ƅˑ4ऺ"įCƋs8>Gdy/֋B)ȣ}PWLk1B ,;7%50N^ceDIdxI hdpɧ\) {#qh-jЂ4H~&_DEG&6Du& ~e2=L祊_#Zӵ>ݾѶ+P_ߒ)2>ZjnI=?Iٝ7Jdhn(>L8b ww! {?#d-ZڀCHC|IyyYvtt79 %#O7R=E IDATs 9QNq#% ܙA d2>y?9M-(bM?A'Q?պ  4\Y$t[CD vGk>>6L \zPNbQoBʼf@-Hx=MTTyZSk&Y 0Y.{gѐ,@֍b!ZbA'UMh9Yh^Or@VI!8w@ xA-WUfs{PY1 &}ړj5i$LD6W RԿ @dhN~?_[мՂ/74~ )@|y*Sg!K0F-UǪys\0cE ȏut2o]{H*h^O5,^ֶyѶ0=?z#49n;É65Dpq6W8?ߟ"SZCCf3 d)Pч| +Fz6d7],p辎}: ^E ͐1)fVݿ'edTل%_~kz5.{;܍*x1Wz!ZGF}]\v\m: uhGϯ~i[EYG=PA0ixHb$$S 0W9>G]C.b_ ݼ76u}wW!X rd[DQܐIHav)<߆{am}-ob-.Ȍ\_zݐL|fYg%IDszHhCs׷ Wh>6#)?.@g7>hk!H&o^ݴyx17"&1*4AVoJ w"ĈXD]X&9Ǹg`dmZ_ ^rxE[Ku$LA 8+>S[a3 lBzX?B> vUOa5L`<6 t /E}3Lo' g4+JRV~W6>BVWv; ].&b ՈsoI)LŸޔWAE(FG_HMng2q#ߞ5CJ6Wx%uw Dƭ @-RܹD!kdDFiBޑαlD^ j,%ʬΣ|)ܘB _CH߃rf~$(J`'f[v(~0e?枠2;7mHY^ F7Id L r.ww)uȌ[;S]L#h!w?E 1ԫQK|[zn%"رq9A'y1*a>ɝ7Ș`Q@zaZ6| 蛃<z^ i JA WoNvb «?忝B|y~|W{ ]/~p}yՠFw M籛Y?Cv`Si?<y:€'&—J K诐v4iyD,ZP&#rhJ)LHkՉjʰʂ5-c]Gv[@`m㖿$?9j%sWH؄:)_9ɼ+3JU8ٌ,Gʟ ]`m<ʈRF4oS[u'fjQwIdna*a(|)#h5 aV4b/<?1DECkEF\DV.⾛~))N% ܂OUw;QDyͿ?vե0=Yā |g<{߶(&6Ec-2x pwP5 ve&^C!2]ۼU(4I, FU VCdPihr߶:~t!!\)gD(GToyܴ)cm(}RBf.›NE:Ha,hks.FSNA& ױoȐuZ髓HZAHNgCzdafH,ExDIDT3Y#T*ߴ7 դG i;B|צ1{K#o8ڽ!C.ߟ p.M.K֗7!}/@ 99P+n-4tK 6qK}&5.GEzpJ)L)h ͽh(Acvq>TP]H}' +Vi GMa?4I=o?8eG7! }(@O =o BMy4Uϴ;@T2- tjMCTۚ [~P C/< ټ|#ʰgOh_kǽ d!Kp&7Dh'"In@ 3FB+#ߟ鞎m%)@FS(>T ӻXrH6W8 B #{)EJg%ty[Tc8-r?khHjZ@@!P}mXc0#h-#` -!7f8 ڀ_s""gGƲ&繾!CIi}Լh8zG3᮷X{ݳA6H?s @6WXQ(_O?U1+F{ ^λ-&2.VpK]294T _!LR^h^!j[%r tEFMHs3[Jazɿ}#)8y>MĨiQh38dtќYBs]s[1By^?ރe<4gs0d^ˈ\4hPF1DzV}ӣh ] k79V;2xtz+YQsATƲE"ݍ橛3;=;#y/rxG+G;hbI^}P$y)!fRM'vl<# c>ů1GaAZZ !) h_5(2>?z^ܚGopVzmYx뽋ر6yFw\w<شFh!)x RϣpE[G" 9$+ EzR6|h>F \ f&UU>n5ԇx |uMzAO$d߀R<Ln!/Vd7b"; SAYH\sVE,0ͭ_F# RtOB]k)zFMTwHap.P5%JՊ^׬ +gzc[oF%7[~;Z ڬ_F#g{ ?R ǒ'E-s"÷Kax_]{BwJmȁ'\aQؚ|) ]Xi=}V? O[C އ[x.1g{3)h[ Pa >Ҡs?(jmxArIgh<9l$$*RWVr( )%f(kC^o %'8r%ytw}KQᏻsnn &KW~ܳZ:6QÀ;HFJMߣ0~V\^_#w2ACmw XRIwaFb}1Z6!Y,7k% 47 DF3[/ڎc,ot|}k\Qm坒z;+ߟ G9Ht~u6W)]];7!#?3c\˵u{W G g 䯑(|;0Ӵ3O7A~<RK6ƒ(<xQ.Vm; ߢyzh]H[e$m:pSW;d kg2۫;hE-g QEk&Zlww/cU &B9\ՇD{Bd ?4:;.urv ˃u0WRXNFތ!kvVfaq> =(>=*N>5BG"e LK23݇d<"p:TV"<0@؝3ݛ%{hhB[\?y<,䝋u"emQ0jTʯ1o*䱳]m<>{ O?cWy+߉BMF`jlO@}]<[;л3oƤ+3'YJa O+}3D IyVemE&4Ƭh}!A+ ͸Nr=DT4Pi05q~+ IDATDt1ӅAWqgpm\2B.ȼfFBao"-zjWejqy:BzZg׼mA #LM'}4۬zz&a_/o{m08 M 9%e'7P'yt.efi[;gtiQ!{|fM6W)]2"^gkc>Y~-H4_/G= kF'D^ӑ_BDk0xlD!WL Js?xw;RH@TS4|B4ކo7.FJogDLjIH1CEpKQ(fyy69 6k[[ٞϮ5 1忌Bܟ1G޲%xu}E-Z8G`ު5-,j%ˍYƛEN>&,RDI"B3 #ڲ #~VG]|滻},+A]SoZmxoHCIyŏ2WdF( Ͳ0Q}/Dk :jxR[QLOR` ٶ%#;AvC]cyGT,GL 'Sӗ{v,!޴@$^ 2NooY~?BWBi9eeUY?}t*NaIݠ֖>ѷihA )~"pzq[7O $R|OC^O4i'ǢNFˀ"E~D:D{y xZۼ:xIKOM e@9TZs[N ݁,׳B t|}2K54]'^q%m#%h-kz}{^l-jwmu! eOaoc9;˟(rNTb`$ߟp}ܯB^ Wg<`9ɗ!H֗ŎE7!{#|*j<y6!f4,FR675h: 3 *2/?4cVvJdљ&* +s~aHy%ʨosk "\M#'td" Qt}Yءc*zтmu7{z/Oܮä jr3li/rܐGRRVAR~y z_AOCQ n|Δ潐k}K#(,C(tp^ ="@<hNC[AjZny3Ny% gup' 04F -Ba(q)j} %[NJ;6D82HtEVw> wȊvw϶M"w,)UjA5֊e]&v""O`y6WH>¾/E!rǡ|!= |X( \x#oy ѷ'dž"EW;~ir"%#rH?[Z @n]Aco}x^k午yi0@Ϊ'c ?|(BK״ do@E/Qm=RN}T}\C ^ c}GD#k(gD?ѷf%y?5v.hnG\@FYD9hW wk7DE)RFFGGd_{^>Q=AtIVgp .+\[_4|φr'y۳!".US ߃nW\4y0>x0}}J9XAh14ֻ웜ze=ŋփ]aj^| YL*-~}?D SįaVٓ;kc~:'(#>B - Jz3 M@jCzgh Wbwǥb?RdߊX2H![dJazF[ps0ރihlp"INvm{s?ՑUoAJ;f~"H"<(Ƞ0۝?>|yZz%OFXG p,FF32\f_>QQ kv -waTeNvv6eYBD("%" FUUE ڈ,@^X6zῆՏ:y@[3y7ΩzqnU;Lzm?5xmiC?yO%RSک9_ڂy&BXggtͪ2dmSm(A}OU%YW!oϕ>ңBPw_WI:*{ͿV=I]x/8UHW2 P@` <_̲rVI"(i|b.#;eҝ!h_;ͪWƎ.i5I?9iP1d{ !+(H~Y^A}$M~f9; L&ѷ(oFnA^+I?ˤs@EFf>ADBC?#zD|n*ތ% %!I@0/U_jFU"qZרDlqD-^yL%'< X5J;xxeL({2& (u,uY{$F%ry-o'e^ /GvBqEOhx,|7ih8%JL e-o2 u>_?{F[:1ϼU^먎I;x~=@xq'$=¨ G|3'/A~Nr4,CIQq(ejNEr_(b%M:ֶ9d!-{=ꐑ 5#O؟ro8Ƣ+*Y7ÜL.W6w 4ݛˤg;YXҍе sN֝Up%/W_9=3bU5R &hy% 0I4T:"K}[d7!B~bWGsՓJ(cޟGoayA0T:V Oo N~a=PjbSe?WU/}Uy^\7h,[P~s([\Fꓽ{AG ށm9{Y_\XRs& $$AdoDD~1 !DfL1dݡce%' [O:Yi_[♒e"D ; ֢1KԱz yEwzzOGa5ހr-*=?CO@y-H\aj#P{yȘg"i"фB#H5#9Wj]MwE%~2IYF\\W=FKѻB9ҴvvЦoL8~!7T/64=C˭x`%JyYi[xg9!Z? 0XlؐrRt'?Zy>lu==n݋9ҵv"JoQn,xe Cl#_D<(nަڱu$y:t >d?Pb Ts/{.QH\7eH%o*z@F[d\:Ҟc+"0>x }&V[̰9Eb5ޣm'_HЯUY{y^+y: y`VSkZDl!ơ\&Zug~= ?(WPwِ)95C"pz_2*)E)vMۚq$޷}GB;M&7K40>Ok,.<^z^-F)"ҽ3>|J%0nRy\"@=F#1 2tW]j#H *=a噭H/ G2M/\&=g\&Ch@MP?%SR+NGUHnyoDuHvj߳fKm&!!@9626!&DҙS\&ɺG; I ƕм/ @EHL d[&1݀D w#7(.o%H4Ryr~lUѦ^bdt'M>!+Z챚 |ؾpȽ ֯l!K B<{螏rtɺR ;g~pyR+\pi1 Q_7uSgM,5ΪȠE^ Q( {]Ac ^j9͇PRvv >}tk^~BW*R_MUmRy^uڷW& 4 S{GbA?c(vڟo 1 5m_+",V!zN[Rx_hDb zp@_HPȢ>ϗh}+ 9I M!'BHT]ުUפP86dpbeS)#82<ˤqG7[L'릁#/<<}Fu@*D^Ǯ&s`c{Q$0ڶkCrm;d6j'uXM9!+91;z) MF (+ٺUszrɘHﯨ&r{G8Yw #QMGsN=jk<6`U)wȚĞ/Ց48~Gf{ iDc\=w`.s%^*x[[!LɺV(l҉kw C%WuIR=w0zeLg<5=6X!Z3n nLcǠ{na{rd)C/DԡǿV=5N;l4?9I|;8Y(W\&ZE9HЯ7<["GrmH!6d@ $w57}w]DbMm-F5K^-`! jF$YT?k2k IDAT2k<QxնOV0Ú#Cw"j-i*zFWe>箈lVXp&ֳmޏ܇_2B?a`9?E9Y7m۲Pj ZR]ڂgߌ ?+x)`&u'>c0!σHXz*?ɘhKL}vϫywj%V[jGQj?gB7+P(؄^Y\‹U3Y_B 29LC zXQKd&_R$MR~rKPɺκqg|eK׾Y]=j0OUyJ[NM::† ˤoD ".P_r}ls*@ԻX<:"#L$~'"*SDv=?og<_uxтj}.Vg#җ@n߄=kJ6(ƠʓR} 8 I'k Ϣ"xW*URƭkPQ\&SB\& leҫh$QoKY!TpHLDoF_ֹIm<z7˳+g+c`"e EXE.ߔO <Zj@"JMU3-5 S IM_ρy]ʇ^$Bީ8w^V^3˜ݶ1@R4ʛȃ#42!~1G~|FnhkNpAO{ɯ#k(4\&]&IiWc8.'H:uME,Z! /(_'z(n gw"#y "tx"(>A~ I{Eީ$z:_¯wW?c ʬEz!bUuj{YI eׯrt3>OLDDuz%WmsN}*IB !7k-5y4Y`%(' U[Rk[Fy @KȘMz^ze,L2* ALLL# ]'X2ˤe^.T}>W)(G&-Ӱ^T p?{'z#OsJԴޏS&Y5hꂗ5}[D'Mk4/Śƿт6NsL셼g!uN?݀~LBXˤG@Fwʯ2*6|D.^Di)$.GcL{{~g )<yZ.F/glD^E n{~Ո4.Bl&"?#3}ykW{f#^Yq+q(qf=b?Av7"qo;Yp)ML:d"ĻF8N֍zA$ZD|:T) ua0UPQ \;φ6HDE}BՓ$Ay]֏v(ApmO$MhЦU`<'C}3;Fg=zA-'>r\0ho|U4'M~0ŢO*cCCM[TMhd>b;l/Te3닓u-LE yN֝&%TnY? duhCϡHbPIfu($o]7y:|h|UH%Y: ;gDB&?(c(Wr:Qٕ{`*~>H yvm`}./}od݇!aK܎F b#$XG %*T@OQ>»T?Ur jYg}oˤOC(ce7Udž/~}~|{sQܰ^CrtW/}kIPM`PD XeACǪ}ۓnȃI4>dݿGaNNFoܬH"5"^E$n)_Zs};uCa~yDv}ሀL?E14 3HR%1?p`"6>ٟWƔTz~N~xxj(4'ҽq[X@B!a({Q[Oկ koag4Y+ v7vd^uSG89V;ͨg􋃡pcI 26NãjHiiH9/kqLˤ2r.D& Q8lHN#/qW|x4]ˤL-Ea R҂r^ bAo$/o^@auE_Ӂկ~w K.aAKFuǁ9Yw7X5|sǶ{ɺs`V.p8f!r|'l+Jx]F7mR֕7PQ Тi_6B}OOiTY:bR4xZBŚG(FX勒AP՞Nr= /(k^R5y9++yxі52R6v*ۑvl괤G*G/4mAQ_.B okM&Z~& $Hb_Yodp< ݁j+CQ~!ޟ 7!DNҶdݿ #QRVE$h PDvBDhϧKP~7F!ցdÐN%5s E6C[Ph/3(-G$IϧKL;2Y/!_5m"qmh=y}HPĞ^!W]5ډǣXAq"eɓr} _L3rtorcL{ikC7khBdl1Im.@?\9}ȕ(0D-Ѓ5@2  `^ohݳ/[ R .{격٭ƕ? ^wI^3F0 mDC=(֮ (xI_L CP(s(lp_`Rgn֓cҶ!Q`xף]YgX$rW5 Dڑx.8 Eϖ/\.{M}b?׶BuCMa ޠ1h"Ɨ7(I\O#iO8+bLI; @2\&w'dzD&"CA5D?/{;flC7LzuoDJC^ kagl@ 1DGUX/"qM|,Pe?3&JM{dvG f J |.~k bS6nbחz,PFUx⚻#q`/a/ȕuC./x(wⳛ6IByOEZ٤KMuP;QՎf/;z{+xg.o/E[w Iv=;Y >zrd" ~MhKH}uA@RL:35 x=g3#p5/Gާ:!IJ ""Y Uc]]" C+"4O䋁˫d9d<{(q=7J.FW}]>ږg|7ȠyHtCc}sb'NBBEw d y< :<!`m8Y,dHQ(#WUUu^y!;I~}D lEqe҅l"6'фh2x33U%`-^+.LGAެ ^ɍ9q9 x~k hR;QK=v2f>^;k/u˖'Ab>k|Wɺt<*{;huC䱈rBay+qz|Oys- ͻ~PIhBWFɗ^c/D^ _\+k*MCd1ǚ"Ϛ?پc!6 ,b\&Dkv<7v_HܛLKRO 3Z,%>k'*Do󗝬+[ [ GQ{ , <e݂b# 4{e:=2~!^aր%Bv>h:]7~RTCl(i/uK䛁x%3yڵE4)-s4, /֔DkCha.nrlnCDvBY(͓R%9݃{r.BkM'FK(ccֱ "Do0M L v(9>JW;&4T_Q(i pVKu$MF<ZI? T|3j1赼a^!QJ;:߫ ^-m=+[.'b鋽럱|W3 |܈ <?"bAy"zNj`v߾pƁadNw"1FcH Нu2;z m+ɏwYuчbgk (L yH0Brb(dig'1c7W?~R.I7I4T@>io|M,nog7Dr7bՕ\YK>v=vɩ;q-|~8d+dW ˤ_e '@=3I)vMl!xM16A:ydGx*jD":Q]=hC(s=whs)G8Y6ۆuq 'F8E|@Q"h+; ݏ(DCuf\p܀H8۶=?Bϭ/lvi"#|p6=Fa k]?^y?D%jۯB0&ETPoҐ\A4@ ޺(rÛN8iǢ&?Ԯ= Zɿ?iфthM(42tfh7xi ]ޞа=/H>%Q2r;< ~Csw TH V!Q)[x{v]hNPlx<:3E'\iwHȋEj!OFĮ{?Mf)>#hb0&}GƕmU<yF8Yj'~F~yu"ij`EOS.g\FL<%DPb@[gWs/E ]Qۀ;Yw9 afH^ oVn g3m[Ѷ2+oWGݑe}TF-HKh{ !vxX Oxy֔OZ_7ٱ;A2%B'M~~ M1^lAoNh_GL/x6loo`@$ 'Vx,<ƘJhR|rأR0x 𩂗˦k_.ŚX3,F-QNM!ryN.e3h~"TxD\D >Pgؐ?jwcXC@Hݭ $VPDV E oFC#2*zk^ Cc"VhQqP}-u(xV.(lDPl=*{ 3^#oߐ@ʇsw򲂼W![+} Cb?e-\C k3ɺ_GCȊV&o<<:e](E7h CQBh xYˤT"`@G ^vٮzIU X}~i+h|[XC+2WRn='@*hMyя5ͭ?o"&EijZ4妈IuPkzlgah)`]7X>fms"OmC㧅uY{u/Fa +h3[3K E=kk}!ie;YIpo %P:Jx%J&&dqv`U*b=4A 1%IdaXE2"D/~u ^Il8 ! 9I7n 18-LQ*qr97U6o1O6hyv Ku MxgkA =D&8NBl ^jw䫐Hdb%M>:U~V߱6c˝znj[`Hb|'V?]&Hɞ%{tPXग़à5 <32XUr"Ԛ=V2eN y{ bD#s>ɺ3Pglh}w6=_*C"z@5 24[׶KO>zBҋQ>urBcT7V.^d͡wOrϞwH`A/Ud|zQ:Yd EZ흍Y= '|z&s CbbC[cȅ<`"kmf|oD磏~ .;#T Ghr+7mװa&{#x@?On/`ueҭ[!;&X7URŤ/F&? YJgh@ߟ=ˤ|nH|dKOI켵curkdvi[e^vb- xňױ(a:I}e kEzⴲXoׯ ^j(Ɯ?{ȹKGGfUW#M!{o65h[ˤN GXdټm{&Z'szf}lB^AK;/h/۶7)c.IxMoV0L?6D> w㞂WKmn왺NJM-Uljye0 Ջ7oVu`o:U̗3J G6)l۸ƜCUM0: hTz5߿ػ> 5KP1<2/w8YyVjDG"D{sz (+MF!k ^(,Ć2[uw9(Tv;г"@9?6:7Uzˆ#Tdݪy[=q#cA~KMm~OByk!(Es >oPV+dޖˤ }#i d7ஂZzI?BCEػ^{}۞q9C:lko9x{ki{uC?2Km#(y"RA!k${ n-BvDދd?,D#cW!/S\tض#d{5 {Ƕ! o۵!6M:A5Mɺ靅"[DaPNy2J U㈸HIO6w26مJmX8NJӷG<{p=pu?y{Q@i svQ{%"!Bl&4\&wI"VV7>ϜD'\YHS>s[MH6{Xr5 B?M8r*B|-HBFDO&£Qr z~WoԶq䥚M3v w 2ZPU\&=ySEb!6/L:t'zv% ~ Q}(; D|~ u}Q!zE"88pAp Y+s(uG^sqrGN@!+= =#i!+>>od7ּܪ`QxIػkQ4y B '!]Foj 6Ao| y!E "LeDDڑDdphۿ]A߅;C69zy=uB|%p>eyy:x!螎! <%6$!8 UU&I?uЀhŲ1'G ыSH|YFEm]e&doԶ_چU c%G֚}4QpwKד"DQ +d|7`Q="O;ov+ʅA;#kȫ$]% .@u< klN6kьȣM'Iͧ_Ի| uD${.݁E/Fk(P{ފ] d[st!vlk I6;b`PNhku2睬{%-%&#IGlTӶ4!e fp!nt^YO5RS6D𙂗j9KPXwP$R Ǜ CHƗQ~ J%L@iLTmz|FEK5 =Su6EyDNe;dmWPuBơFTh}"\_?Xm |ɬmY?h. c;UpGJSKŰ*>zfBX! *ڛ<e7@ɺw\R!Bǁ+M)$FvҶ4ގrH6*]RmI?dŎxDt!eU*$-?KF}DϢT#k8 r EvZFGz6ͮp[QN5*̶3v]y3N1pIX~~4AưFG$b`Z:WFlbQaٟ`M,iQ=[OR!B`خa 580< )ɺ{!CH !lij@"4<o} MHP+v $܇\qHlޫvtDKE>E:DgSMF۱ޖyJ~~ބDU&(0 d69N 1dplJnDY\.V^mIpxc|'(!/xJBpnʛ0M(@-xmR"DA$q;Lz%JFkBQ41"4qiFjD;#DdGE,(}NK: Xȓp眬;YɺF9y>>P^w#P/1Iw!H_%OC;aQ!pُy&udݿ:YȭݗL >r<[ xilhe(vb,8v;v [y1p,6ܬ/..PݏW:e?r8 X0*ַƂa?džcG+;Wajcߍ*:KoE˄e fFbp$O|#stQZEr* ~yƕև&tNq/vRJW3Xغ14E6ZJUEjX~h,Pe>\ªwV8c#Ab O:C53Ȯx( 356h=O~p[/ƣ*lk~z}?6g EXKӣXP4 +2_Xv4q0Le3V.l;?sH,ؗl}4ÏqܗH~TFM{p\t`Y4\ɺN ²O!н-X fM. IDAT Ų۱t9ӱNu!YVlb/`sG`r:6ꃕO.ǣX Xbvr(ppm<~a`c<y|0 or0@ahlx?׻0Ň,؂ 'Lb2X!r,SY}wJmcٻ(}jþ;j;DSuouh!h!'`qkNL]* e.:E߇ߦX&mI+/״`>*²u >Xvp.6{X@*]MŔ >z`k$, Xs9;o<",=; |;N!,6H :}'tY zCԅ[FܒL.d=:{L8lɕ^Y?!|C@`;}Ey O^jymH?ԯ^gŪ:cI2 ),q .j* 1h< _5X ~o lNlD,(27@<~$8_&0|!k~V^z~u,(C<_wyл{ba?TӱK{7J;PdlV،e9sqa6}b߱ t 9ok9i}Xx4_c飺-ͻ]!T^nE˰_<^U;!&KzUZ#Mc^CNѡXa-V0p`Yu렷O˪[x_2tlO aXy:]ց&Kap2]ϕm} c9UaN-6Fb9XvfzsѠ?gYHi@aD,0~<~?8};!ze77aa-v+W밬(l(hv=-w`(Lg1ZF%_mm4ެRp%{-֏7T[<}GRzkW&~}oLBJWӈU*Ā@)y"sbuƂk!b1XSӊub odž|v lm8Ofx Ӟ]i*-L [!,л _G+N9"w۶i.Xx4Vjc|l噓*/7ZH,1?܃,TrގKK y'6u~eT?1OEbcmvcA ,(3KaCRkiSOsc;Ο#7]tε3N@,n8dR额ڄ^ kMdAc P.6HUwzU[cOKW!anN7غXb*tbs ,Zu|n* ;~0#]ò(A l:-{^Dܞު<zQ7`8P<2ysɦߋ'O|hKAKuXU,2IS`H7c: >}G`ӛuk9,(Oa6ܰliw-oQ=GJwz΢-"n ~v-)v۰!&ðh: 7w67v,t.p16Dl8 xf,ԂEWbٌ\;X_Yc? %WahH,"!pPzYR\F,,sXi} "XPƆ >ͧƆmf(cľ7bCccAkM{)^GySX,sNyve29^!uv9pC@6j_Nhݙ_Ij:+Hg. ??e5&2WXXہGcUXFc(sb%O7`Y!k&X&%uvB~܇UY܉m_]3X_6] e_)z8 \tεF/lH_oy*$(ab$ ,>KY6flYg `On_d5aٴq7eÚ` vzXI>Q_ e$3ݎ*S, ɍH,16Kwb%†ոP6|8[w oNKkT)ZWS"D)j*ƍ2X0u,E,POñ F-Q9@uӹvVx׌ MesB煹?V8X_ #[zH,1 ;ANȤj`AewXҢ9߰$<g&_*"=D<n:~o^GGbb%ا`Y-X vxUbT)L]uWҹqag'`5gA<~fn7F8lxH,q}_u6Vbݘ~R8<}\0ăXA",>XS8; s8lݩNNe]ͩ*s ROs@5bAG羊Eg}$lh8/ex4<.GV0X_] G 06cperg`.SԲ!Tв uEbW&?]mN+]flţؐaXh8%uϋa_a_dž݃u >/Ċau[}' n^Bl_7NR_5_bY#ϻ `SآKЌÓakX5Xq-6o 6`2:Lv ;Ve)x4=܋͙:oktVa#`0 blaW^x닼%X/n4XjQjʒ#/ͯ|Jۅ;_+y(QJCX{0t}3su:XcR,HjFgьb,x8+Xq?c!:KA| [|O}`{*1n‚X [,``obATl l?3ع3 '#D%E h",pm3Ag x9o:8 !Ľe[}>mݲP ysn]fvLxT+3y)ĆxؕL"f?7d.ǪM*ɭdP+/.oGg9lhb"_:,:W`g? hX5%u*yRWGbw`މFk *}+Du`A#XT!˰co-^Žcŵm6bߗZ!]5]"D~G`H׼}tw| εklBUyjш+Svʰ^U 6J_e"X'lsܪb@Xv$jdžc'cXF+]GLLbٕn+p7V|~3sQ~#v ~*XH,q 62`v\VcX\,;tV6V옶bFl]6sja. ªfLeNŽ옮ņN{爔(`6ى?hK(v-^sHaԶWwxI-ߋ]Ѯs^3evΕtl/Ke4dxAqظ飈3W 1{'ֹ/:X'KXX),r6|m6jx+EI ϥXYOlsXH,qO<C Gb9Îy&%C25eQ9ZO} ӱ9Vӯ1]6< βqeX@7+1우M mچ m2^`0R%_ CzLe*]ͧk|KOۖПڶ^®`|<=lڧj˲G*Kq!vNXvdRб0r3_:`iGga}-[wXc1X^ہu" 'Xe8 FbgbsH,1|r/3ϱXz,ځefA.lodžhǪM2c46k5! Ҟp> Mbu`,y vl.v ]>D='X+ _6{ZD \WQU=] ([:ǧa-ߚrփ(J9$xo=\U= "x4& ?U XD|'َkб¸!C>zuO2$NJ1d: P,$ Ha^+(.rX@mXYl@տ`t /G+efcGVuov/VlXf2?0la,WU~;'љp`")lݹ P%rҠių.\W!? fZ5YizRײ&Tn*ռ.T _HmLG   9@b٩cr l + Jm߈4gK`acsV)cl X9#XƩR#,Saln,,,IbS`ٿgd-XւZj*JF%w1Su:v,ر=:V$M i[+nchx2O4DP&c Kp2 %%hx|owTʱ#}ðN4,t0*Ū :,*YHh,{uS?U.)RH,QhھxԞ۱`mXc3pXs܃Xax4%CJFY [pV۰XĆ*~ c_? ?%1;|{cnH e7ǣa/K} 0r+`e1[Xӎ |;cVaY`7/np87}V"WX6_K ), Tf,+ӆasƲ"9{Xb"e'eڱ9PKbx4|n,Vd5 \eb`r#F8,jthĪ nEK7Q%"=RM00_bex?QUuH,q93 {| uRm4,֫z!"D16j9p4V l5a=ƆflH͙X6,X(IJeǣe_6/iPlJlx4y%.rl, b|lՏ,R 6X O[0PX?ǂsj; ;G߆:+KmY䜆H]C;/:ulOyx˾ ѰWj@JRۼTzDAy +KXJDx4%ځ_b:K9ZI3~16g"6oU8ƹX$6dv>pc$8[\w)ᚁsF`sG88/b0=Gp˰4,ԂedžMX:׀ͻ:;>x64qCl-9WيXivEWr(%%k(y.mɕJW_ϵa#Z:?V8撆3+F IDATZU}4^DzH,!aYG Ib aooC,X{ Zu/_ E:>;-K Mؾ.2Rsa!m֧ʼ,lU οe;X:˳bnv=UJ(eDzH,X#\_rϴ!V6H,184bd05dwg\\Ue*]MXGeo?t+EWș؉;W%+wZz6ZDx4X+eZJ|!w#@g}3ؼX0 [_cVlۓ}9G XXPt̓}=+lLmNg6I0B<}S<+`_l-h-)H~·zNO%>Cl\YƵ5Ԭ;bx{R-k57lY|֫p,"??X#tJhƆyX>s /c`U~eiģvgiRӰbsDZf02!Xve,ӘPel&aTlagkKN` -eiHd=9ώMKt/fZM U0~$x[m+>JD& K#D @i$6([aY}mX@A,3ֿ,=XXA,`͡e6c2bq[l! VyУsAf$_>PKˮ H_|gYq 6t  =ۣƣ"oNX&'sn%XvEWue77ODH,q0|<^W~ь<7G?ȅd)'XE/wI""&p2XyXveDWt5Oe--~t &""WU=e_ oRKDzJW [N4TDDDDzX"+UZ&mR%"""""#%"""""# DDDDDDrDH(X""""""9KDDDDD$G`,)wDDDD$3SKxL, v DDDD98( `N|7DS%""""ぱzT$[)-@!0 bMYI"d (~U/V E+>^y6H,t{[R Z۳@oQ&_-gjx| vyFu@+}KDDD\< qCEz%X""""BW].88?  8c  |xxdM^B`쉗y~'E -.Fb &7q#o9h,6jLSjo+~0Hۋ>P%H9CW'Y}? Js(/T^UضuġL>9X0E^Q%&.vgh -tw.tsDDDBW=x>QFV* lu%0  pz_os6pp N`K$t>tҏÈ"%A <X-yK`[Ɂ/o ~~Sb4`08츯/|.9tsӄ@*=$OEWQ% Eہ:xdo/t@oѶhț>sf+(^<5;I6cE+п@16* T N &ӟuitq1/Dq "}BW}h3{x$4M:ц7OJ*ֻ^ld, f-F<o/!Dz!O ]䅮>lRp90.""O \m(18QۓAC}]#ʶ{4 qBWj"H aE1ꁳ"x\]J2P4uVE4¥2]BO#jw DrjNW Jzzz%""Hp-@so QPkrt+ih- fT`ߏ!{(O E6)8/#0geTZ'""oA`WBu5$Rh^zo:N6Νl8dVUX"9[oQpʦ #D0mD VGOo;Lеh+ijb빯l;|\arPɟ[Hg:D*.[MAWmY-ynCn/lK]nKyCY9acVNڛ^OY}ޑǣᚅ/W""oʴ ]I8ĩl>qڤ۾y|KDDz~ZF],=vU D'W} CKΊGnOXH2}WO~&``XţaGH{(UX""""""9KDDDDD$G`,Q%"""""# DDDDDDrDH(X""""""9KDDDDD$G`,Q%"""""# DDDDDDrDH(X""""""9KDDDDD$G`,Q%"""""# DDDDDDrDH(X""""""9KDDDDD$G`,9sksw;s܉9~8>9r.__Dyj_S).`I+r[kv=;um휫qε8霛krmr}@C<{<ϛV9.w߼_ιCsKs9y?N{v9vr;y_}Kz`N`-6$pNR`(pk_L&'_uΝ=M.gW D4MDGy{]_xW@7Q1Xҭs9jss7ι8_z979wsyDf_9; ι=p=?Vc':wۗ+|.yyy7/t[)|P%9w$y[&%o:tvy.5`LyM?9h?s 퍜sﱫgɶ3j'k<+=VJ,e_?o%"+L 0wM{7s.y56me}uyOy~~+vx3=ϻ⽎By@ܽ4Pyޚy>%"<7SˀCXv+#_SKggܳs.]1O;m>hcdz]~n弹M]~nY?ieӆN[s8*"KD7y<[y^?ts%p6,.&8zg1{v y^˼m0lsO3ެ oy=ϛMks{/Hӆιιw9犀6*ex30ɿ y˾:v}k68mt7{r"v^9lJ:O^xz7snpy[ܿ_scm/~/̩(..n:XuRP T6ҭwѰN""HdN>wg9lN*^$3&xkOB%؜-G`3tW`Æ~[dLܵ ۓuf<oy݆V`%s*-{:{_$p'x4|K'""NCEDDc`#ZKDD$p4V]DD eDDDDDDrDHȅH,^1X`' ke!ҏ(Ɂ+?f؂ngD{iH̟x=(`^%"NH,^1w&0]`b#"y2""""iAy`2v@&pPX"}2X""""aÁذ@ZKE[)??^9 ߋOYbyitX"""". (߳WY@)6mt7OW̝ ܏Q:X[/?{+<F`_J4`n76MD,?{Y d + ]b-# ݀kɜ ځC-ksDDDD 0, 9?+p?YbU]U,?JcJ`d~[$"""ie[* =#X lY66\PD^`u m9oy<6IDDDdONn<hEw񊹧嫑"r`!KTGOǼu.=?{CWsld\ X8x& wsE$GzCipeuEym[+uΑ*p$ln芚3ϟ_] \ j=:nGd077?D'} $S;J }T|w1Y_yp~xtvlֶ} |hL$"""/^>O`CRPɛ0jӳ;!Xb+>#=:""""斡MF Sɩk7=cɗ6@Вf>W->e x̟ټ6N5E""""˞^۫<2 (^Ybnp!?{Y}^|yڶx'RQ`σ`}demÁ?{-Z/i4,4gkܟva`#sU˯%76>Nz4vs:9ܕe;jenx܉uPiI\[^vo\,X,tƮXO_-JY"""6,+變%X7˯r v `k'.sh,P9K L]m_bn9pBhK/^1ݪ2nSW=+~"6Gn ^b茢| 'U]eJ.t',=f=X 3 /7 ְNcI{^,, 3֫cqSyt ]ԝ  2+6a>Xg#J`i,鉮;%GuA|6PDDdM3 y%XaذO˱ဧasRIJcj%?Nz7济@X ӝꁗ*;`3ITaCXlf OwCS%=oQ;&0rllZҋWEDDQ 6nu@׊gv imɲƹv 1N%BmH,ץ'㩏߀ZϰI/=>??rɜyg/뀻3Hߥ!S(p`oE3gã?9>W$컀:¸ƵVŴֱt Ԫ?[[k7Hk+֥j҈Zq([Cd2̽?D"uA|g;wν=MF7{S}#ڝbU BVH5S] IDATF:@5F`szss C-% \1;T.74Ps+Ҥ @X"{=fG!#sgg'+>5ʦ]1/f # |_U_/or-m=7o wccpȟi^V\g Ly[{L B8,WYLL HXd-M7fփaoՇ̬ H~)"|>_LX)`ɡ:ፊ`)"a w)w7FVQ7:ca1w>KAoY,u,Rޙ~R( eɼ/ÐuH:2ӛ4҅!@ Tjn&_C@9/q-:. re'o PZMwȊJ`)soO6 WevS$[d4U¦aI0,ψ3ACc_(R݊H{#U( ,dHy]L_D6EHӍt#`[ٝ%m5S#遫zŕ?蝉U"_~هׁo7WF$7:0c"pG?@^2u-),n;Z}@P(LPLHkss-3XR"'K:dӚ0,Ψ β|"A(d>OV.`;4ML!p%{bM@"S(J`)*Z§ z-~[ A, @'&Pn%21/(m3=+S$"+W * n1/f @c.HQH nG#v8Vrbd~V fXFOmn?aC<){"Td9zo:.`?ՀطMKbO3>OO7JQ)J@^%:F.]s5XkԵ!5L\sw*'9ZfuPP|BhƝUb`jb}7/f_<_!‒i2<RYdt;<}Σ;:TmsqOF.I0L!o"#D3M gRqȋ<#8` ,1]Dt.Z236>`j.{ z~i6 Ļ="륲i b}c:5}-9 nyU>O;6bQ6]@&䚒Ҫ7ICRvǝ6J`)]Hg,=]D؛{2S ,j%PҮwʎ5k{jw* WS^z*']Gru tBBQ(!9t;/_ mȯX&B=I@>'ƵCyOKp\ Fթ(o؎'8cH:r-\O?+O܉:'w->)ys~#Of͊헙_p͝NjF8 9OR qRHl{R0ِ_SM'2D ot:Gfq_/fp=Pb1y܊<pN1($6W&Hc%?߬/79e|qd|fXk4Ѧp-23t]!m_@Z'#Aoa~rͧOza3 ώBQkd^%*Z2KR_[mY_r,^]f"W|ikug'mi{yk {Hl0 -)i79 WLcP(bvFW0 rB|Hs 3OH[#6. }!Tqp:[\Z|( RC=o>Str̎r69~O+Yei'^;7ε`,+svk$e`F[067Bq!M \IIXdz5j֑5. #V";=\$@hEf B.?8 r?vѝm%eyt-\! _ObUP|qV(q ߼rEm\=3d-MHkWWœƷ $)|YBN_Y3בÀnyg^p2mp12E7>wT%*,i&Epi="d)c4MNm6mQ*\T' 'GR0HsO~^5kW~1G8guL"'Jݶ욺SkV=.Fɯrc-_S`?P(fi`#'̣X|h3jH# =s/M)(AEHQ%23/fg#U ^/fkEf8>zk_= 3DI0wDɖ&&f :Sof㧙g9mvǮn =@3|dTk7 IOBq PAoA1&@Ji"n&:bY LQ3ӆe&n֞8[ 6mݱE4 >wս仐}?VdV~ouY@?u{|ߌ,v^=GiNܚ<*s${÷^tkWw"u@iL䤕$i  )C&'2}04C( j7#[5$RS Pfm,W \Zl!eRfݶ87s}Y?߬~JkkD}m~~Ҽ' sMa| \z/0Ao5pxbJnc?wXxYQ7=NaC!%4)*NќD^XyG`\`OE(~s'z,F3aߝ/ To 4[.izg{l3UuaZZH]=hpf?}mcQ5/ ;BI/f6c+6Kz"A1э:lzY"?dݍ)*bN#O,`;E\d$yK:;/owg 2]9) E?BRn@pK] l3)﨩kr}mu+F%s 6Sy}y& ]&.7iZ1 A[G&Y1+OD$jGjC*([-mQ[rl%{=KsLNk#jZɰT}t%3Ht:’yLߊusg8\/i"utBk5q>LZ2K36kw5 SC>{m+TdAjHSm>yl# Ha? Y_V<,z|}\`" GWGW>Oॏ{B ~y'i&qѴ:Q+b[dٺӖ9XϱӐć5uO֛N:Ȋ(9ckW ]Uw(),)>O daXaW\q3An3T r =ޙz'ZZ}DuqCW\WP|LمHscs::w" h:Fpq_JۂYzj#xYivu'ZiE)KdkOyҀ#͚ڔL X̰Vs#1QW8ƺ3kzvnbg%ymvm8{}@ 5Uչ@P_5m _/L.;;ϔ02K讯VeO0}`_Y2pOhP vGE)_6Xõ|46mе`x5u v-08b5O7Pʍ9̓jH$-ݖA>O zCi\=u\X'Ʉ}e9۝om`%Vѐ&n52t=|_'͋߃?)sNuٻa>fWP@C3vl[#Y̰mNI1xFƝ* 4,\t5ȨW2e&d$k4PtZlTipa Z1)P|Jj~Tjnk7MZ3攜|{{F-Ҿ? mw\ט {hɼJ턡",'h"9{TȺek zϳNDEH*`6~ԕ ,5|{`336]3%-TgwQo͘ӸrɼJ(.+sdfu(Ν^u~w/cTaONWJcQ6{=Qmʆ%k\9MmTAÑ9yx^a{w-9&Ȳ0MA(Zԅ4hB5}$s+ز{dЂՑ!(;-=1ۊ@P8HOąxN5!iRFLv'' 9B#bo@e$W;XJ2u4\ܡ:h!pGOxYd_Qg]9tI_kh_7WG]8q# (Һ'[ y>yz 8:>Es,<}|pm3Z 4>O B1PkN;ы\=51ј̫\i1,jF"쾫.oceo׈bŒB@3U>aHU|QkU~Ƶ@M]0?q8 !v7hw:39il6bh"cEܬVaFjRik!ū:eb-gsDv#|ѢmClUhpe`*\;9y}wj%y@2MسZdf)dJ '{Ҕ$+{΁QCɖDs7 g{$l(BF4|1j+JK.2=sDhfh3|DN]_|{Zd}PTCh`'#ہ_yyk7r=q1152 i,R,k>#[ l%Flݏb?Ex%_^{VB!S;Z݅sʸ#5] a4&S]DC0N=Ӻ :9*(Q=vD Z<9dc.tS`0L3++&SÑnLbvmkqF } U!0G HشMH*g2ivV *jcȨ=ӏ^-̢7b%)IsUj-mqv3{jy]c[L3c?rI5Ao2 ) :AmW5u .{ȕ=Q ^b=ND ܎vkq^u;M`>O]K/0M HR]:MX+XMɮ[̱)4#>9)Dp5E1[wEL|Hv|P(>c+63J;i8lto-eoKd[glN[{⻄ah$]Q݆n|}Y"իj.@‰/k?4 \~2E#;{J6%(3vLkV q޳(\"o`JOc#g"ihBάD:agdMxc\tKKew2),>O`<\6vl]$хIi&ǺsŬ=@P1_v"v"-{{z8XQ}ޞD pl}.Ds%dR)J`)W\tXV53s }ˑ/5lBj;oo9}/gO{dۆ;/@*X#-^)6JuhӑcN!?,d9g\A?~L@찾: zx]7]֡]'Y|K 7lV$SUyY{7,zg )y#F< \?iRTl8r~cZm@F4 %>^'sl[SS_ S|yP|>QKnk4 5|;2կo _k6_[B|r$o͝#~zQiiR ӹ.nwN]]$S[@op2p3ۏYP1l/f/ʐ^e -@$VY" aBݝGwF 5^Po?TfȺ [_>oԆ5u `{ )%=.="w߶uỏU=asf2YcA9tExX\L #)‚iW߳bOD lD2|Z*^¿lA`H;QZB1y9| U/מ] ^9`=on8w2Fx>~s9 -go^GNQ/#yeδ&5=yn&3J{oN.ޔX[Q(W~ȕ@潍VM & KYVv&K8!S w"M=߼cLsGc.c_;7UavK~s r!p["OGt-}'HAt$ppʐ_p9jRtL9k6LH4wٕ}ܪ;*nL:⡶W%~%4UVB1PI͋+1`]Yb]f~^ U/|Js> ?& m,[q{ξo9v7@M]< tV_Ϙ~L 쏛B8H-x?DH'36fE }t nk2<@wm8BȦE.hl2jq%r"<6xsCwĊMM]t\]_[?5u y@O}m{5w-9}aw?_r ]Σ5a`T)mϤY[6zȲ4pQw]R^"E2z_Xڭveɔm͓[]e5voE 붮E >O zAc ,BAb`s0]MٝQ-σi+4}2SnȈm,o/\?gݶڨk+}+O%Apre_G(-|ڛ2)JB4 M=n&"ȴIߑnSf4Adwk4x)`%ܖ6.2 ؇N\f=>O ޻}r׍ǟ[̬MДLkW7mF+7P_[?P(sA|1;Ȇ?F}^ef&@ Z4F"-G]& 5G <Ap! J'gp=,eok>pvMKi")PfjO*{"c4jP^VJ`62s(A'_6û[U|QKLIKŀHYNG*s}R3mj,AxVѓ~So9Gu^aNrs\L/nG3w纛oL\tږ4 Hu=9]Q#{?Ғ~ aIa:2zo`?[Ыu jmHk;2Q PB1I"ob7IP|1d&hV CE ( ŹkGw+aye0DŽJF tmCS|8^s{|z5٣_Ytlt7ZO >O LQmk8{ѫs;?جѿHq,pr1 @.8a;kJ¤iAK=Zzzm\C\]oܚLl|$prبB1x~P( #(MbN{p15}c]mK$̯rӶp2<8 K+yK4գɚ|,Icu;hr=>Oo_m{OrצG_W[^kxٛ^}']N-7d9;ziѥWwVe;v#4GG]mCE;R`Y3?=n_G֘m&[9s9vi&-𐋫 RiWSfkgoD"#1ei'9@UOj0`#LgvD E58GBqr$rF<Bq/fuԭk] 3\N+l%ok (O,Xy8nlj][rWn`rU=- d_Bqc.0MX/fAd`i&wr#X]֒zSKhI#TV4T_[V&f'0ad8CwVSp&e]sC2mp%>juk0p<zѩ3=Ǖf凢F 0)l@&)]K#^=muzk#춈ٺx•>* tt ~jx{HwnADX aFM]C lWs;nSc.C+?荦Zv:mbtMUq(ƢP(``|1dʮhdN2(nlpd5BVN#_ߊQif%P7CM]ip҆͂l"llw5ljO4L]wS^_շb#̉mBȍBFH%)GtIUNw$67)SF<͚Ev+>OOx>5>O_OgOK}뻁_R GOqƗGY<#vt_V >Oy`*p7p RȤ)sw[&'-\|qןtC )y' c_{dÝYmT2рIH4 1k3;4qme6*XW Ӧ}.26y.$,[[q2vt\3tBŕF;nGL1P,0ڼ>?pݯ\[̤Q"K럓| *؈n,K]Bqy F:GsB{蚞>N)yQ^}~F]wmb)Z z@BȤ~I J!@GTD66f˺]uxƘf{m|Ye4>;>YHj$u@$ZAOd"ڝƌXMDCEH=5H'`Y|]7_?ncs[_DR߁@+Ғ(yVbں - tj~S6 V:pӽ?=s?aRQ| Sǒ׏z#W7O;[Tm;''Jd@/ȗěFiYWٗm*dx\'{:3'F&71-j±,xb"Zd>~ L0nzq)pK {Qk 2(LĽ H0MLb_W'o״H վשz_4O[rww:FlV"TN5D>WTuHϾ;QF tFGǎ`| qyۺp:wM}˹2F﷡zl~l8|Ӟ'9Ƽ\uH${J`űO-Дr7w+S/**<=H[\ F;XHl6x]֏+9)/z]=׻ku߬\.Mi]7po2m+>w1~9uȺ<vq"H@=KzW"ꁭ?|t/N͗1n# "Ԧx:8Xoӏ{b<,8N5䐚j0~axQJթ?h ;ec1$~8HzwˑJcsue5O:\Ϳ T&!0W]y=b$1rޑ9P{p"okksym±hD4ɼh}O$ñp,[(;@RPj{a/)o-n<:/i9{o^k7*ٗ-zR"ԛq?\H-OnRјqHwJ<WVcI?oth08[$7Sp&"Hzyl(SjE HDF^ם,!@vlb%/jq,,6ILp4P9MAӁS&(WyckՕ7dccUXcɉ:^\rźaΖu#:n%%eyzƋ"TϠC+R!L0`nwI!@/^jWҰvw:@_o%'C@ĩ4DG{+uA>\36LaU@@xuAqex'YϪ- `yC<Κ׏b[_vm9Vu7Ύo"^J?Úa\QTys*ֿxV)PPt3rrt`u=|j?i֊g,^O]@}iEOϽ*_p67νomR}tp k$5Huo;bR, h~O۪lyB;V#}@=E⫐T:@j[S IHD$bFҕՏ]?e x(p֞=-lllv pjɱ=[fR^*Raccp -H"YC+_-c=9uz|sy"`2QdGϨl .7݋,$KVǙhJS䉣48%SzKWj\0)E<<I@/Oe>O:ߘj&ܼ8 5{O<}H3:x}留XF؊`HrICEH!uK#FC-lllv MV]*WekS@rP!f1o.8=\y7wUQw8tԸ7TotOѣs`8\=dN=ξ\ M$EM@YW\,ۢ%ޭrH ٣u=R;rsrU'd.3_,=wE+O 4j8IyY,&0pkQ\Dzmܴ뿉D@'oo#_f 8GwaF"u:𪣴0p5=ҼB"Y1թi}0lecc󑉧c4 Ežr`@,8РE-SVo紮Op{zc+%lOPDoi01:8P [M MDCf?PǾ-tU93]]U:*yId7j& -3~98bK0U1د]OSęϲ<LJx+32Kf^QڢbA3ʆu~{=VFA; XDJLÒL2>0fN=\!v|(ЯYLd@T^.@j35Z{+'ԋ8 csIK;2>ŖyOuqxǂ"UcuCֹtM,,B{ ЋTS{-lll>4tPk.S֙RH*kl_f>o}f <]mDzX5{kF!~]C=`DaS7?Tanhx:8!H]eG^rK/Yt19}AOrkn5 !i|/DRN ǒ#;nJDCsc|]}æ^[K/~Gm\ 1r7]tC;x^JnhJDLqܽp,Y|9";(+qy6"O{~պ7U/5Gf6 A5srLeqґ{,bzQ4Iz}N{-xPןwto]mll>sy`=>K:]ݙJWyw7ҧNe|y;G<,El_8w6NPeHa%`x:Rz8U?+<1r^=;Q)J_@"?Iˑ@ip,y7ss*9x:ؔɕg֖僣];0hSe+_Rŝ9%܋};Z? :{ )g!ﷀRF297# 88o{Jyїb3d`QD򠺐e 81>TF{ x=j&Nٌ(V^#Okֵ66{!?̗yZ{ǧɛ<=\Mӑ+hE#޻y?Fj^UX|]@ tpGӰ达lm58N7F؅DŞEL)n8ѐ%?*"X,w|:OOO:"˃w =ϻ]8O*$J ]'v/pviQ\iG{#"^~|SD4%7x?Wt `t;Z]݌NV!BK:uM\(xV+74Əh%Kv%$A- ǒk}t8"^U K%FN5@D_ˀPȃr[>`zobk 'R&OI*R$mqJc͇ƴ`@<,x:,w sjG.c|t/J+ ^v ~.K M8hG6uѵ wQUwmπk±䉉hh_b"TtOWՆo]){&$U DP9LC,@&_@&D4O_R[5!J"&eA˦)4$@4&:ټBѧ8O&_,HDC{4.C>_U8`K/ññVG"@2QZZU#f VYl8~@NTF|?{obcccDH H xcWm OwN~d]ñd%'{C7m8'KiO47ym}e\BVgcɓt6*Jqg;Z t#H"D*,|9Hdsx:XOk{XGĚ< Gﲛ;Ѽ;wXGp^Zd2Y/J; m;T}~(47CǒHj|'fF@>q`n7tڻ !s5k#X666ͿW׼U?+qgprWmlmlUˀUWܳ{[C<>-Hc/76uU憕G=5DcLjE+Nay/֎ZT UeXx𳵗0yd"m 8߈R7+x b"1q!B8Ϳ̿)4 !Rw7ㆽI֮qa+:b;0W]C!1kƘ?qA& Zwo,m~DLUD0a 7D:"ԳX- ?[5MVyݽeʗt40`-uuT);y882)e< .W]Y{fS@Pn I; }"i#Z,͉D7 C-llޅ༧.Õͳ{|`I<~<<|'իmñdm8OU<t4m)޲>>q*ǾSH u<c^SB'%ݗ `3Y@)E uXD/:hCRSORC xnl-.@&+eˌ6w[wP>DGn_Z]TM@!QҖ5Ţ_}/&5pnѳSkHJeraq`DH~g3g."b{|[`Mkf1zya O xE"Ԛ;^>G=๳]ORn}8 $&cƷ.k_6[+^{W+b`1? %})lC3  }2niXH1TC.iG#b,G()r5ۙs1Q,GzZhH NwDίg!.3K|znW?JaUN~r!RxZs?Jt-#6|gt9Rc ǒ-'D4dLь)@a/E1Ї9v#)u0"ߩԩ?6C.h ,+L}iEZyE&lvC/] dźQxߙ}JCd O>=ƽqM 7 Sfo'KV?JDC ]M3;Sy X%\hhYv±d)R p,y 2/:I[y;鹳0KnJDCM"*qCg9ۄ0'K=XmY98@yOaP]Ե'4kV(:] J}R(!_[}N8(E2lEocc |sпs4\DȶK04ͼ=85q פ%0?`>cfwtME۸+ƶؼP_8& );k/\[ڴ^))iWݧkѽ㍣Ӱf| D4%h.u{gɗR(\;\8O#YKñ9u%Kk%o!{w V!ۅXόnXW3 ñ,t܊+ںQ^3kE\ES6w5WPi.(Cuڦ[YS̿)"u@<H5c?i VTBF3rT3q#wr]b>f}VthVi }Hm x:8j傎ׂq֭X"y"ۜ;H[9|]+NZ1p\{cD",G&AEnsdHOJ&?E&o利f$8#Mjˑ気mwwccɉZt$XhU'1zޚNsˇu?t$Ќ>}xwM˪j1%2aoFjz܈>i@K IDAT8H^@W 4Q  eмD4Ak>_G>>ΉZX6 bN1=e?v0> +66OCΓJɅˑhv1fmՃ`CQ)[{/sՕKn_{ //tt=*f66C U-^Xf ލp,! hh4q_4\SD&j둆vdD4Ԡc\?4n"Z{v'ޢh8hOuF͝e6kRWcE< (]8PrفD,D&HH o_4)ΚII܌;"'jgԩ/!eÐF˜;ks!r=iqE;OVHw:pӽ78v}R#wVz6U/?S{QPfW`FUꁕh(;4v0;KD4UpcI[`i"g!GW n+&6t`$S]SZJdCA D,7#)zޕ\ezc8,KDC{%cx`#0%)a7ͨ}fXDJnF&8qJ>~s E`N$zs8{@Hwk~u.dǐtiH zǍ#ul(_pB@Q0ʦ=#Fˋek #MKX21gG5mřgܶO®*gBX ={dJڧ9QLr S'] K 0م5l6ca=zц088K%PI7'#)_3s LX_LsN$zIe 2_s(PV̪|'oF3 ǒeX2K~?KN翗1 I7b**ۼI#&#Y/ǒp,yA8=OHdMP+;zm$zW2v?}w^zgJ$2BFڭ]*S #wsܭ&666{olkY_+v E'@ӲEԮz±Hg=q@Y5:#)X.G&Yw#! **`[W2H`uXV-V"0/7a'`o'P~>*Yd<9ѶfZD44ǒ.מ4t N4M0*"TǎbPJ(luYתBR}̱7^7F~_s8LLe*+K;gZߟznW [S %Ohp,9 I zI"j!n%Kw!°hAr$eͲ"d"bJ&p 5R-Kiɀ̯TA'ē5Xra"7#cFNZ#͇^j"0 H IJH lHPʑڳkHZmi @2Ѽ=4ȢEI˜GY0 [`Fu~EcxՖq[@:8 9c)#}~,ҀE`",(Xrp,hR҄22D=\cLYy4 bN ++ouZ!j`{~]$К0v?B&#]R<$`I5-"wo٧X;'<\'`T5t͌435WDHMVOZT~u7bb~mGmll nCDƝ#Y^JDC ֊&eߕ^/ I;:W &2lW(˧Xenۇ.ةe{)@*|)$ōK;L㦃yutp,yήG<>M8<-Kй|Fu]т\?~9 HXZk>* êf:5@r>Wgש! &,]N<l\J/m(_}D4ԄDǒ_ARu#YWd;x; }>K%!͏CHd-@^jaY2e\>϶V++::hϻZ7bq"ql۽ Ip+t@Z^U{/[I5͸HJޏ!f,RS Hj_%^NuS9\UЉӍDHsK~Gf]žnӊ.[׵8]t=-lllv)tJ^,ҺZOŕ;)jW"~K')H}L-˜C&[ĪdLGL'4@7 ` ,]F<0tq<6~m7XLlhh@8@&>@lp \8D^D=cXpՠZs,z0de|bTG^o I40tY7t4$6іz²v ފfY{v! |3F#f/"*+: x:x'p&>#5uK>ʱ'_V L.۠AT`NH ,vG[? 0-+~MAzV3 !3qH۬ ٰ6cwg(0 \0RDSlecc+W-|Sojlqecoc]"ǒ#+ñh`vW ؑG"KWFdr)K ̇L,m_D QUYH}5@]U(a`( .3 +sweߪϲR] ph*񝊤$V ǒKO8p8"ɶy䳝 ~CΞ340PJA}7VTd" sѨ_\~!eq<ΊB}͡[ӥ[ =:@oytMOG[Y11I8Hk[G]mllv%Ӝ}%"@ʶdه ǒfMVI͹|jv1eHd);!mu"ҁ#Гs'R;eQD.F&No39hR>LrJ+eP8ep'W4Jv:VD v*w4٠4sM/ۤ"p5vYһfwu.}KHuE)=G[=ۻGLhQlT4@/[/9D<| 2H5cg׉J(xeUHjZl] 8N5h?NKͻ%#C ˩!Z 0289D {H!q1F6F4n?z=KZ=S±diDC Y)cяLZm+p[=\l;Rj{;S` ŠǭN0DŽy"@>C¨4h8&ezg7'2zZ+F$Uaľu<x06Luzݽ*nuOT4&I-u4͸Kq9ds~y Ѭ8^GQRj u@# ZPN$b1%dNj Hw? טUDjleccccccW1 òD~;`q#t INDC۸z[+gR X g7##EVOFD,0ǰI+#){V vھ``48#9eXMs{4O)~C70_?$z X @tiwԨeͮF%@Ȥ<$bՎBj7~q#+k>co|q%d>Vǀzߠs)mW}8cۖA-K-R% 3ڃ±䛉h(1o^3Lpys˚g+.R"Ty<,.2 /xql8ނ{ә_a ?Uc3M)U X*olkOP@z!Q1u`8TI FG#0hHWW# t3PK% -y1DAMYC5YWa8"⎨od oIi!V#gH8 ǒX^TŰ\W0Lw-NTu<+F#zLJ;w?gwUثGB 9SR4+y(T;g>(Նpz-3ᶛE0@=hkvl.կ/侽d$)\$;18)L5'Hԛ~n ; 3󊛁_vwˁ"ID;Q@ DhbOz uМ9x=6l*vT7O+ri#[f;w,T7) 62h<!?M؋{.K$SUݝOEZĽHNl|?Z~T~O|U&LUuM0XϿo`;GD2'3df-뙻>D-$2:^<@r݌k' x5.䆽uDy7-{]P ``p-̓c.T-k4kՇz_-J:x!C!a|oF5?(w?Xԛ\#{`S"q[6^$ y8xwhBk:P}Kit ꐝ5WJ,!m%|2׏S<g"nWp2&]=mMƑ}7uM{.~UG͎תpT{y5Uԯ;zY=Ot=ԬDҮ:Z4gvexi?C?rջW^X>|6>;oFLxI!w"}o[?+SZcj97l)VK +@V}(a#gM6nt֟q9ݝ'/g,t42Y RW`1|":OA8 T #_'x* aՌ(FcRTy\DilܤBQT廂6__XkVZɓ٦;7?;: ilmlTIFWO;w.{$L@yuۣ>={w_YپPi.T_7Xk65p棃ȤŽH"S.;(HZ7,}O Mlwy 瑿|FFs/#W#״!F&T]رB+Sͳkdմt{٭\v!0xzQ` @{ תY8 Nyۏ>fWBɔ hSwdf>$!k+;u"- j D2Ո5~m4RFރ# !TȵiDȀk8bT%0E6cnX-]qe4)w}pDytml`g٠d!ay>HT`t1+B>D󽃳/X:wo> T-)5ݟ8{;8szp(w2pR \ޗ|Kr;Btt^8QxDWKyǿo~~G<<|yJ 3Yeo;܄W EsZ @$(2X~}s{2c9>!&nwg{.L5 JVBBbd4Su/ _`׺k<<,f ېз Bf#$l !d`5;BLvNmb[;?(kgcnJZH8d%k|"]KpfGѰXn!bf'Ub7mha"Iw&\-GWO[t.+$oVnə~Ul_| =zZ3g}9gh!Q@nc!͚dwC}*2dPdݝ텘JHHd e >y~ %6⾌N @x ŀˀ?#`&_/aݝL$Sg_!dHh$HhᛜѸ$7E?Bގ!O UTdd;{s\ *DBނM8o_Cr 8gW{rg@'IКSpUusA *O49*!N(r# "ɔu1 X}ł&:h]6m9$﬎YܺMo寚ټr -|uZNMt8 H!+* Hߏ3b[  NxX31Ruŷ^E" @LVEH$S'K;Hd3}w n_#vG dO`s*ŏ<&иfA\dy1XjEU!atcV eGZ!BC 됱M/Ҝ*L2۾ʋ}h]ʄ ?訫*( W4Lh3-ۊ{j{cκsZPM}?틏}_(ӦWF>k\KKߎn>2\SPQO9]=ܹcgہ׻iʦN.׵Wcrd{4DLLdLcjfA1gk1yyfa(X $˷ LYG׾#3̫Ϗ_>w52!Fnt_}OTVW(܋W0Qv Ȍ} Nzeȶ 3||4Q̿b ,rB'G\a*Gs,wx/WD~1z|N 9[ gZlzE42%!+\#?<8'/ʷ"9X؀m1zq^ʧ:s/ YSV0òE@oxwE{@wg~UP;۷S2:\L/>JO~g43' @L>Q( xEksOs_[OYl=e $/FW[!iӐE)z Yx Q\ 85#>5 #+eFdP'˭+(NaJIUolaQj|nE#ʼndR{ݝ;d6۳b)GԖx$nwpvna6|CPc?PS=r Wq:rtUsϟj'\/{w쵷6 U< SiS!NAEr"hUf"*`@¯'E`'$V;^Ĝs6rQCE <j՚~'eGJ1kz"·#N` !hjvAYl9_+?S?{[*[nlDy筯貸`- ghkTy'~'J醛XCdVCZܱlKR`)p {4lx .ejnX&2q@uلje"vw/1Ei2F$wR!pJ2N3fԌ<𾌎?z?1 @%<3L-:ތ(0ӀJQ {( (QYp*2?#p>Cz] Q,aa?+G?JFlT&]?2t,F jGD D6S$l($&؄+PPYfQ!!3f0bi/j|CcJ1ҝl>0E8iZkR]N<< <_yBWO[0IwDR/5ծ;JtNmX*:2l/H7CV2뎱4@G¹hx`Kq:q|s$<Z[7|[?8}DjЧٽWFFv8?|)8cǙEo¡jfF8z0AAY3k澮?)pEFG*oo   @%H$S!="QWl0Bz T!i#w>.}ࣈze  Ľ6g"dkø(ꓧVAPʪфX8lzQLUDk" ʹme6yʫ.¯v/]=m-3,n,[uϖE /N)>xЎڦXD 3.wQd׺um]=m}2vꌎ T|{1>xi߷և|ʂ+rҺh!, ly-D,wG|hC" ?rT㺿48\N` H}N$S O27EF* ;!&l+Ԇ‘v-GBr^{P!$9s Ql1`krnZN "Ö9V9*Q mLU-H~Z{P-+71mx["F`aW\Utg,a?)}0Z7e%7f:gk>; ]|, ,,D~@+|݌Eɉ-{{N|oA/)Gs6sq\+VXcU6o0Pnkd q4T! 0%dֆk S1nؽH$Sqdo@!IiDE iB1x[";Qshl9~Iv&>UHS5Euǿn.ĩ@<ɬ/w3 eۿ A)858/k2mv"Z"s^Sn#,ǭh[+8 XƦbEr}FɈHsR-+CÀ8D-jjD8q1"WTziv]vYM|u Ξ2:7 gW<~Љ~솁_s߳HZ2bp]UvCstyG?*E@ ǨHK E4:?qI_'"ܸ̀ mkBFAţ5w~/x^?]Ύ"s5(3B2:o޻g3 @X|vcG/LJar`w o> 8{( (Vcg4FHSxA/9'dzD6Q߯NoTZ#Lih Z;J4۴C~hUدH:@`$_m9"")!Nw"E {${i dBS]=mˎ |4v.Ej%wN<=}FpcmP(#gF_!? 1JF*bR` @qx]9 PS": +^;23߂$<Ϩ@- C,mFj|m 3naN"()Zx۸ڡguAHh\\(2^ȷ<_CҤgէ͕aPZU ጇ2]Y8v2`wgo+;ߐIP̡*P L媻鮞6歹B@8SZy^6#feF+v.M7)t8x2}HV-Nh+νHUne=&陧; VU?o?L} ,q.EKLϛ~}H$SY{;p~cԫQdWl"Oi]:QWh6Z競 jo?uv82Qbn!D!!!@iR(;4XN\8 P3v~@./+<9mx-]$'!};7j{!t.)8dW$E \Ü "΃ i.R#{l)|x>3-W.ym`x6qf њf<׊zNf1#ID#JVjV'Wd)6; 'kvގ$/AB^ ,@.lJc+q#Ru}vPS-j\*޴!$yP5ϮRXB)kgǣH-iw&*!  @S0Gz{)Gb*}PFǷOZGKH$S@f΍YD!'gc><(*Uop1v#X`M;6}X$ۯj$=keT! *ٶfV۹k_5` 6((>-;b\j9H u@D)<q ي"y@g `,V`{?QyiSkhh]𐯙s$H.\~Nj@Fǀ-`6сÑe黏z{ x.pɍm`C k!ھ` @p(oƶZ~|^W݃SRcSt'O IDATbN 1u Stb.TķO3(&bmn[l`LW #H9^[Y$0ɛ<Ͽ1KniH +tL[a9@0'Nkhu-iecyx5(0E\/|W^&zB-Eh\T9XOu.m}OFbF9h]vKWOa!Zh]]=mM #XHͼDhV#5~?G/wZ7gھH X`b$JV)vToۋ} 2H, RaBDg+: i3$fbm7[CLTr|G{hM5`)5N $ z eg dYmc(@-۷|.5Uʎuls bn#@vlRJ~3H%sNxף]=maĥoj=jT^ Dz.nvy3PCe[̊e Yn%W1V/؜hZ'_w 9kٌSUǀO!!'"Wvyo]CU#PZ/@L2b*=XS\R]ہ}u61@){<c]n)y) &0|gCCwGBÇFCGkXM ]P09`Z(nTI^( kiEw>Mĕa*Jh\ n 79DxEZ_aQ$6b ~~wgWv*jjPuH y[k:Z]!ѺTp p_wg{vFL[A&.6WO ?3.lTzr)dp$n#Gǎ~}<݉@  @WH>H4q2t(@4~O/ך1J;8*ĽHXCZ3S7Fƚej#Fsj@ Q̘E*. CU[ TEPU(%6LR̵(*X Hh\e 2v/BFXU;, Qwyŕ@sWO;Zۯ|w~7"!}"$0J_^L6w]|L[A$t "NLjZ26\}C @vBL8o"@%&Qy I$H4*oQ~ Ƞ\vR{"Q628.0mԬ)=aFQ͎z*W)zB2u{H+ae1 X;o&1J7*\|ǰٙ>ojo9޲/d x!b})z7 GB[̚2:*2:Ɂ}5(I~# Jwk[TP`mFo}1 ?tYV$0|1P$'q'A+}d'@q"n *?<wXH}Du BT,g5 #3Գ1j1*bÎ#ry0^&  mB(2۟}텑^Pt"Rb M)ၢGCEA[b-?*ZFXTF"pr}jl":}xX/$-XCԓ oh]vD wwon݈ x9Fy>6TzBvo~ } _舎NqqLEL,.DTɔQ@yF |zz @{QBLֻ#VC *wk`=Yi&L(VjT) Pf(҈w<J)l>bޖEQ%3_ !u&`qy*C"0to۩SA]qDZsZ9!JC/ *#*}"ӈLoz?_N$Svw?Y/߱xغz&;OՇVj ڲu~K+Tzp>B߳cWK!9)B"[ ɩ*ngF:FEΊӁftӁ'^ټ _~$?Fra7L[8 `15vKSr%g:`H$SQD-C9yJUZ*[l6QQJIQ-{mj3vb3 :gS$~^qRe¿_}ͺ!̍_p!"A_%4uYSm? :Hhրw xΐ^&zږ"8$u髋 *+UguE*6D= Zٵyjfn_-mƣSE^ہpJ?vϯa id]$ |rabf?A0`vw?<  @=j@qƥQ@3!]E7q!R| VZk;.RƈԀVę9q$:Ї,y7A sӕQIy?jVw*k7 -Mm`":)vE\CJ$okT-,f%9,º0hCixX&?G~L:*MS\\uAB ZpJv%}+!?lKAGC#F18YӠ,cPrC Uпm(mhbHk(b暙c(9hu)x6f7L\E2%u]=m#0&X!k$Q%zGd qQ7UE\ǣ?RdèN+PژiΏN9vl#(c`/+!O ,6mFTI2ŚO[KV޶|$mGewwoBTn@WO۹@ {.bqG-߈Ȱq^B&>Mȕ5JW#4`EF {<& n ֹH_)Vk@n~(oWgN'F\ˌצ;L]TپSqϰv:T 5-3z,b*}W$1U jp% 8713 h.+3`څ'"4-);\o~'$an [&WpZd!XqT-R[h_N@A5ls~ݜ92+Y)owN$SWtwMihbe7w x֞FJa#7t!BL CoyOBhL/_tx7 {D'UĿ2p67vIEwƪ<)x^ LkT Dwg?+)nj0> o_Q#|Z}d8 Q@SJ~D`>09ArU(crW=d6=cFQql_jkO>խ,lϋP[a@{Va(uTOJ2_!iYx~Ru|"zmwg{ L-EI_پx+HUh$%p:*dLC(NGT[k`x ʹS) Y>V rTs[p'A*'#J4=/EG ;?Ӥ&EEm"38H$Sǽ:}0n.V8&A>D2BE)ԅ"Yr(*rg)/뢲۬lUQ>ip'R UqL\/B#D+음'jܣ^d 9<jg>?_])N4r QW*ΈHȖLEStp[ Է;M~]j䦺>8r]V>W!oq^FuLX}ȑEoiݟw˲tRxb=8P8KߢZ1NV16+Ϸn8mS#8]Xg] ASNj/߁z3F;j(|ǂ%29s(W:Cl)JvAp\%* *Ȇg>~n(>h$_i; dG!īAki3nBȕQr˨MƕR\%r'^fc5֑$JR`L>3 _?]@m}2|IHb]ߋ !rsைRxh]6O5l[3OeDH~D25%L]H42:snsVF߁Bj=ZX6c*]SXS-?D2un?=`N^#2"Uwʫc̉R*B!?3ZZ&#_j`2پ쩯μdlSdK-\?D%|=iyDwg6$"1VFA! ~a:SU ^FAbM*KqSo(fhe2hڋE{4[eCorN3 X(honG,RhANBd'=ݝkP>8 }"ѺlrOfgoK=7mMh]7=|٨BćeҮvd~=娑F"F?NFr .iw^4/UVHHZtk.E߾ |·~`%Fe 6<0$MWy/ (Bk0MRhqEk>pW@/#1>k/D nBLEq/ER?G"Arƀo]djU\ |u=_}#'c/ݝTF]r/LnH!H#ro !ElӆT̼!oq4p~Fosnߟ|jH*~twMJ$S*#"4R^U_E78#[+ѵj|?4*oсjQ`wiSա) ύ48*" slH56xqlN$S_~FY,L61!@]I؜qUS4D#iՔE(lQ;LU ;ZPc(U1?9D 3f[sMW^ uȤ KYJYu #(=LU}ݝ+w0mѺlbRSF`Ы#5!;'b*= 1,/~ꛮ}p͹SݡV7k59k%pd8#Gƒ+Tv[Y ! t!Zyg fp_:QqW+LMGGΖe(|(.1"Fs/F~U,*H>8m`BLoDvO oPV톈Q3;Oõ7] B?&{+ MHQrSNy ՔX0ǠDvϣ*T2$p&&TMK"02 [V\6B5lgT~> jER @[hhKEiR B}yHٚPJ1F1Ja+AQ&fKhDY msS8LL؃#X=3L˟f-F+SRhꪷUQsvceWƀ"G&E4pRKŌC޷P}_|Qg;Aˆ$`\} z ʭE[wwi@Umr{)k0x=p'Rb*Bv 47g} <xpvkNlTb*(J^{@oLzj/ XݝD2]'7`ٍ1DobQhi~TC'R{mY; . pT"x`2zzLȅ2Vn<|ldEǙx!f |I8CZGZ 7]y!r_ԣTSN]9PJ |L:*qGL~?cΏy2Re,t*Ukm$'!o6EUk( >8pQ3!KF Qx 7Wr;6`UFǷT.QR1^F1 8SBԧ1ΤaUzH$SSn}ѩyy2&ΉRJڌ<-;5h'*  "nL*2:hzԕet< Wߐ0v sSZc ?% `ά3Ƽ;l@r۳(]2[~uɐuqFhyst-! gITlW j){-?+g"I|[vɏΜz~b_[<606|{ߟ,yHg aFB雖~nD2U2:tmL_Svz$j+2Q(ҳc*J_s0pEGI^q|WS'GHD䮊%Pwg0ęE+I][b`e?^~ 9 UvY|}wg{ @^6Au=ap;i-heMf v5WRx #3d6"[ 2J_r˄FF8q0q+ &'B*]+EgZ>6痙mM{l?!+w]܌ad]fHdhlb*Je.x٭xk'21~3?-:ɿ/p[6gFzc=tHjm7"y" :]<FdX{E1q D;vJKld/fǙC Ta ȕG"4lref}^51d@  @/*b92h\uؗnY)"<J#Q߲o"3J㯣7/8?{'IUxm=mGJ#+'0`~V$8=vQ3x\Q=Wk7"W ӃHᆻ[C~8e1gJxS_Z}eM;c|)C=t\z-;ɸay;ŐV)cm.GuR$p~&K^"7#Q:d.wh0ۓޒ>4;tb(;)pcFȳb:5C^@E3@/-~gDM4Ŀ1^ |>{1I'#L5cVAG4fLO]:RZDM$̜UƜm>3T F@U 7V1H?a֙zi<8'}Tv =dl$nibFAJIz(:E\;DgM=nڈGjx݃Qy[x'սQ}]-G.僋㓑zPy-?!tj1"#_ѕWϬl>2bL=qڰZqG>^\~EfۖMw$|pb3+7|w bWE;Oь Ahp_mԚ2m5B.jM"1he(5>:hhuutX6U!#HQ.ܗM4DM411 cLf1p43SX?t YCшFi1sP֬`"5v:._&@1@Ԙ0E`xRee;K_FIu?7yj-jf[ ww T&^#ȳ5*'Q *g͝7D;u:$WKM}q_OUUIyav9/l#ꕟN>W/YЩ/tƓN|(ӓ*S[o;+/)o -Au/u>bYcZ3NsNJP|fN(nT^DS. '(*[BP" yrCf6O4cײuFyUY$ʵ~sChC,P[1:~Њk&hޞr>fy:LY'H3:Y#mspjcP#^\Mqbk# p. hak3x-1%<>L< 3fRMOŧ,sHU=3xCH $@m,ܿζǀ ,*o ,\jA>4=yW*#f B/5Uȟ"V^7 $U4n$VNm}R^ԁR}X?u:*:,]6[h@+nYaWGuMw'Ua|ISUz{ҷoosW h5a,D> uEɭ8Q[u9vnUb{z2/tGM4DM[r@U @(hd*H dU\ǔ4y& 1bll6U%bݮOwFPڌVAǟԇ̐Ր B'ܨ053|z`;fй4GuQ+ ck,a47k3u ֐LIۓh&,dlqN YN ?DD]NAҚۀgGP!%)Y#Hz ^Fqԃ/f 5vB܍-eL6w}D'lo(=8J!E%DT5j;(A{ޞܚh&h"} A#;M k;H% סV-%ʴzY~'Vp2A IZ\#-(H4f&ba=TjD"T:溂*L;Y#-RKJ_c[0p*hb ʐ EϷlDv^ʿ pGzAH@7 4` y>u;m_M{m{~֡Etʛ2U*'U>drb\ ';FjE+_ юEDƊ6uQݹǓЎjd6BNȊ; Y`VA85(~S/Z.G0sŸ,#7D $^wl#zWj\t|-2m% 22hkQF9v;7DM4D{e΀_Qc1C~6M8rڲ E[݇:8 [g;6u![ę{M}VJqCe2W˵2b6Nc.m#hj3oOhWzlg\.(E#\)o?틎8%Uu[4KEKm]o%3YLWllMo0zdnֱJ=602#ZV:tUb.!sc\<:] G"08Ȣ;Вe$Jy :l$Ŀz{Һ'LcM]AV,YZ1vF"tB[&=T,!8yDI~HJsÅhxwl{,v%HPFF5hSCIBa|1biTQcj`Qɶ *F5N$ei!ҬV $ƞ ^ϷinN$: Ql .Z)P -M ]DDT`6-ɞCzۓ.=yF(yIw5&^ds3T( :A0Fm#`k>8ƣƟ/&,\; Rct~o*w.3lµCc sgQ>۷QT RubuFX::f#kH5;2V_e3[a1TNIĿ% :!ͽ 3_3Ih/ԪL6@,IGIHp?ߊ-umo!=JHlޚ!과 ޛgO8vYßA Fc${gٶh}v'%Tё-nb+VV gdYFŏvUe_Vs<(Az׎w{ 2܍lDbsHi po&;xh I?m{es|tuT ,J:ܷ!QD/H90GkI5ܰ^j8S1E~'fo@ڳ "NU4uK8o;ō5Ĺ2K*8Z }^\c-dnXFrڐ] %ꨘ^bw[P ߎ|4oޞZ}ig#|(:ONQЩo!}_)]ЩuH-rOBM7" {NIsRq Be3yOA4@A/Q!%Z-à G+p.#uḇVN1~G;vxdsj=4|]dP#^/hYawCJZȖXKŦ[e  i|~D`ffw~ McYI/=ln&Kh4QeMHh5^H pp.h]e~ )NWIWNE&-DKD;L6׉@]?c26#PcQh2NAc6(Pç UC ?1$e! 4>\DN=21E-4mubRY09pf &|' ߱ 3HU ~:]"V50-}#룈suOS1 _5J&RK_^}SYQ|ɨ1\_9:K;*]Ӈ(ieT9Qn2q/%vuk =?*2ٜ&#d sWTiD#EȠen"P 7[c9>eϫҮx7P/W&OpMH|,ijl]!nwn/R$m pm0p"{icuȺ)ͥ X9*ܔT5M"c 1[3x4'lk9i 6֍!F\[~W;UE&7b4郥qk0f5K!N](CwVtӫQ+ mbPcwɆ7Sg?kc- %n`5={=+8 0yjKQH}2VZ ω",vh=fe>x*Ƞ+#|tS:[woCĞӁ[ycg^r]\v<:L IDATw"-C8"|,C-H}̑7.\)rB-ο< ޾o{RfXڨM} 2;~ߋksCT VeN{%ŮC& Gq0@%$؅x&,>5f4US#6؍x)x+2#a(@BW#A_q-tD1 2"E{ 䝗n@]ۓ^9jb_1گGi/sDUR3jq#u^B"ds {{7gĎb VmĦV/m0Q{:Z+ŵW{6Jt\xl]`NEZ:hQp+9ԍ6Ą8]h: N eVV|bTm,5L'Hu ã8)ƓJqe-kBD}>#u-&&h֫ΛHhK^O֢koB ʴu] ͹oB2-$h{|`Bö!@lcqM*xOKo`ݍHF1BL6uN$NET.I=}4KL6/)ޞͨ**g]kCEֳ,,-)I*2q@("I6+:>ɚ)}New+ :,p{RG:,pRZRv_m>n/XWaEx"y׎C2e}wx;ezd:Ml#<MlmL Mƴ`!ih#pmDMÐqv`f&v2YKPP'.CxQЩ5Q+oO̮ݦ])/P*\6kŢX X"p^R/JUHӿpg۝~03N:z}6bkWDFxrխ Zs6 *p @& "Cn;{{U RZCQai\ ^(e2bI{E kju6ZA(p>H.xi`ha7!W;J\{抲Y>4|CoOzkM8+KK _;-ǎ"2!} 2 L}XݱSE6? 1*P^@7i6G"`-0>%@hm`1Qrx9(=&]tߢHC*ƙo 2a@kl5keӇ5LLPSkFU2 L3w%#ߦol=[+S[]baC l+T'1|7_K2oɆ1=z6C&UЩӆ8Wy(qz?S[g :uRO :uo`3m PG b~avDd?"=lDe.$%WX$ށ/gep*M!cu&>̪6t%g~VA&|Q Z߅hu'[GРBziStp&l~NX[اÈ\EF9vpxoOz,ĞL6Iʠֿ(FGD}>ޞ Qd?7㋠ g >2:7KWbY=6D{ V~_"&ML6??`Ѡ1-uA q#v]9)dxv*sW.-t۱譟o1)qB8\B{@5z`2~=6ӗ ϪAT8NXYhfLs]\,S`|'H"z{_ ^|& "\ۓ^NTmc y :6?DugYnw"މd.GT=N>[cj}?{u*r-ԭI"Bp8p 8=Z)5Q#هS{]nJwRT>[GtyKwE0!ij݂ߊ($՚z]_hB(Vw`k)FO{@v;Uϻ4&~[ Aqگ` Xx0͝v"J/>z{_."zuyq6C2gfY/Ź6mWSn5Vy`(-Ncҵcn[jj!bSk <º\M窉 d}b3H37fT Xfht^fHYjNQyRD( _9|SYE'a[074h 'bg@<~PՈ i8Jr^Cgj=mQ yDBl1xS:"2\{Srv ?C,gf7.,Eˑtx+v1|#GAZR$O|JQ Sd$u,<G1$1b=-58X]5|mD.bu њʹDd3ZLMKx߹^nAfV!_v 90!tINXpå)  }7>+z1mU}RVA{z{ҽlnF?kE܈hƜcV6p;\dsޞ 7q%2]ZfUԆ#N9)ԮZo7y }Hg硣 ~ P:wgPx#YH` }Qo*Y1`}T]E(=gLh2F$: ;věn5z`)&sKcYzOs}gm f455nz~iPY1꿹g+5,{'{ys)>ōyytD0 $+ڀP 2$x9b0iO@B<ޞ0MiՎ#cIldSHfJa ?{yGfe~ԊCEBaɢIBLZލ<%^#AU+~kC:E&o@\AJA+m٫?v`J2D*HcH:sA*r.u4m ߓ7 jM{Jw 2ѽ=񮸉=Wc*O;y(Y֪:c=Ս/9D{_u:7D#hTUcʮp,P+[6TW;*p}S"U- ˁK*Nqhb;5܁Pkn)OimP8)ayl3M#YHPD6"~lmH{qz]й1Wcnd\עVPLLYfq3~}CuX-KpXCni2k;ـsәlޞ?w#i;ˆQk )͝(#{#i;c{=#}Z/%Skt8W6J;PO6<3#ϺcZ{FMGvZ`yt;l _^`_ӓtxX01sq4/kb̺AC !X`?2C7MmO !)6UwRkh 37smQr*`y(c8!@45FB&*hUۚiEkpu yY6G"Bb 5Q@#liV*{ia;i&2Hd!#Y?"ԫ3dEdjb֣["'U@YS u&ǦՖ? Iy/o@ޯH>Du"#[2ݎ;g;9nEY:vWNRcol3 ȳW)Ͻ#a :xbt?@=Ԃʊo-MDw /Qċ^$5g4vxcIKp<6g: I- m{ A{_| `#EՁ Kr&MSD mN]k!pˬؖKGbؔIHaI? UZŏiG}kɣ;4(}) ~_wZ/C G1&-j 4ܷ# Fո%t*ϭN׀E _aFa!` }in8z1tGeZ) *5-2X$kΤKK4j76u^<ވ_H٢"鐷]wY OkAi7L]N}1pg&; 2W2[lᆳEGͽ=cpoF41,<sދ:|ٜr~tGAuRxb#+Jה)yot]T@[Gt=AwJ(ttͻr`w`w~Tgƶ6P*8k:fEkUW:ҩLEJ*rTM$P8rml F^u:FRfRDx?dsoQUL6`'}MI陵ln9e<EKta%>5X01DM3/픕kGkUb"b9?DA(b:-2yP8. o@,_sFPİm<̡`mF)k}5]2M'#9Fq2,SBl@((-C(_e{(o8}yH:D>o!A_y 22k7z{ERa΁HM݇Gį-y}.|4C'HDoo3s* 9 PwÿTnMvQ?y_'#}FvD) <; :$_^.5]^q}[];b+P:ݪ[G+qۋqOȌ 6xŮR(ׅHV8-ӱP)\lnHi`҄&Rk2FR6 pՕ^ z\8u!vL67 ;O&}'T)M}7g /α_|~T񃈼qgR!tJ'U~2q K<{qKևs͟:hfǧW+0~ :#مlG!D'&k쌵aߪD#P _E)]Dъ]K&=1ֺxbD=3x"GIg o!zCr9:/&jmUa2XAJdjweq,&9=:v"#Y8f=1ew1\^:;A^GfW>v+a$w/IoO:|$-@$Ajq{b4dy3s1 oq8.׬ڙ籛Lwg2&=cc4ՙWǩ TD>/>DC6"Qo9_U:{PЩ_Bzs&;mBkY͠W!}@..ָ^_qDb; tadmTٓ?r^To1"ոX#XD)E(*_bژjRPu & ğV (Q! /.hBcݩ[lU>Rj`M28-dsޞte;5@Rju#€j;̤ʛeqwe;@"s2em֤F]FW^rkG=śٺb,$&/POڟQ6΀m.zA%˓W"ShqD (z#D<tF. z7qP4YMV:2Y %7f{ƞ^jĀ#PG{ei9.̱*`k@s>5B|G bprW6sj,do/]o,ޞrOxlB%%Ĉ?l\8oCg.8 gceU:I q"}&3dHR>y :UC$U)$NsBA1ܝTiZx Xso]Z5cVuZI`ezs|v钁[{Mez ^ ,Ϲڒ0V3AkԶjk>&3JO |Yn_!Gg)B$Y o =L6u&v+xۍH?]xlۀG I|wĢ!:L"# F=.[ʖ֛Ъ( n?<߈L67wT*:lT#MO":z!Qo ۓWoO"$%:h֩ qTy!Cs3m n-jc>8L?E 9c$v$?K9gD 6ɬ-\ш<Szmgxۦ]$D/X3{*QkoqA~eƞ28 l*`L `H;o qP 997͙69 IDAT 'ۓ^ۓioOQ~g8gԟF$oʟ doi:;1'nA%s *x#B;yQЩ'9#{' n[bn&Q3K)O%ߞ9cW^zW?Қȁ@WAtRl}6oE%Ezlg;W 3XK{yY?^Tnw ;Tc Yj%p8dzukR FՑJUNEYvT秇 rioO;rR8Ml=/g|: 1;`ߤLMDBm8Ě\+΀o+nj? |7 Y@,p.oٷ*^)nU$"X{kO>|didrGNOZM(FjތAKBD8h[VQ?Gh$Z 3+!k .ΞqP4H}pjx™vFId4L#lxQoŨ|k5b hy6w! )'0D`]9\f~:z>0ϣB2$28dϮCOܛ4B IwWlu lEVgmE"ٺ$X@ z{7UȻ;L1@֫ T6Zv+麄s03]F*i_ "Bݝ ᶸ[ ܚT}H]dv^ra~QA Td6bֶdUHRnjͿg!ƽ D'*ҩ#yqԳ02P^eSPm@OD$ M Wd~7"H"YNpg-]YS[8s\QBe^|#PƷW2{oFl%lA(g̨m-{ LFm֏P?g AKS r;2>G߽2ٜ P?l#~v2;>ѪmcnϦmmrER'*0,Jm-Y&00Xڐg6:RuZ#60ߋE %ϙو?n@س]\a L>zDo/컷'(->Y9Z :59Uā[뜾ּs<1~_C ^ ZS.1%sږE8[К#PԵ,mhשe8gAQĈ^p2E:)ź }_Y$3^}LOGד{e;.!P.~ҬSC>~Ag@1_8"V ~1,@5"§g>׾O)1$Zj"jʥ~iX{22\ܽyf^֠],nTp# *kɯ1# gtˆcREMH}/Eˀ7j٥bj(Eq&#Lo?ԋrB v|*^_ªP!U+tW= ߑ҈R7{6% 8Ԑ~{?0 ×@l!G78޻7r /@h-[uqT`tj5/>kO~hlSO3֮J2 5[Lw@R\e$ub'y:ܹV]`< HO hH8=lAYaWH2L'ʳԩZѸ}Z|] S}䡅s6{5`-v-(S0AQL4d\W(3'Y1!@s:,cC2WO!FeQGz+bXE& yWhJAi?$"17F!4&1k- Z{@yW"!F"~ǧV:1ů_ tjBmL 1E ʋSҀԓ)\X) <{xMڋ"cG/℘98G_Wtjw$@/%;iYw"-L81|z] i:d ?IMkCːlވ8?b~1}ͻy2иwnE#اz2␴zx+26<坯POo/aj˺m#Q˟k] }.͠m_Ao8Hvb@?\p"1! D,jtjtW8WWKTxU-k_0KvXa%wyFC=U#߳~#' j3: 2AA7 , LD=mf'{Q}ְ[XNϹ #[4̾\RTLRyLj#{ͤ1X.>yqZDWf_G:qΖmg&v#xʾL6C O.&&Լ=gjB+W[-v<8%TN*ߏDD F/F$"A}4a\8R_-SI?_D'?g8Ϋ0aV }KC7Hxo^{"bHb%!/9(> 6o*3&LbƖǧz( \lWE~hھyF,Zcw6p}qCaxA#e5.=݉E)25Sgh}{=~e\LY/]2.J}XqrHPfw?=J&AƭpXMtBo8·ݕ_JAJ!ZQB}1w^8 yZ ⴶͭ-d5+ÏkBqp $⨟Yv-zP#AtGVʥu-ܱ݇`yE70FJC׆q+&4<od-b*~Gl[nI ㎺Uzs{ +2aLx3 ;1zL~c 6W:yGE31ZFM_A'qN ~-a٢f9 Y 5G$urOԙϮ[1݋L&?m!P)^/3@݈ɒDys|Vx㺈?AhW. F3RbbGtRt8}CȞ5s UlRro|[>Ϧgg`R[v#@>[XQ7xv3% _B%Cۥ42dq0 ^_t8B"$$ԍ\t^Ők5rl|oItR#IUTSjW.GFw#%V& xjxV'ݲ3mɍO%IUo ^&G5%UAl50i;g!v7#AJj2`[ bZǡWk9Yh}Z]|-;F& r3=lvI>ЖϦ'4*9!IUl:ksDE#kI%ʘ-Ԉ&1F7xX^V^Xz6(Vס":VwZMSf[hH't9㹺X+5ޣ^Wx喺r<[=lLp62hG\AɊM'2'N6 *dgS  `#CNOWet-Yzgɪ=p#F R9 $bks@ʪC6 d"26Z?Kgkl&j129Uȳ!DԠJb1ɰ h&umTzb>a6djHw}>֙\mRK*0reݾUZoH/JL8RqԆqqI2 a\4 (?yc}@D{ĵG_@i8"  ?M2¯T!Y`>VOP=/ nD*l^wb Okc_[Z\wA4D#KJ: iڒg1oBW⩏?NӻdԜ@g>@&W!?G0#nֆCۑ\R@ sEiEvѮGRBw6QMAr LGݶxwyyeʺj`#.! `QRB"p{|A+J::'!o'VCm+$ 2,w`ʸx=tygi[TٻE&p#8AkfjwFw,@rb7 j_. b琫H!Ap˼~3#1$xYI|60ҋL0l MlLЈd^6QHsك$ V}٭㿐_yO1F䞺ΚF<EʄzKS.e+*#ƗtjYRCƫRk|6;"6)T,Z@2ePA{wZC&+ ~&l q-K &͈lâmJٽO7{f/ Cp[jd<­EuޛD Sbir1BH: )rD* _ 3+F> M&A H -+oku6<]$pWpb ~PmDj2oI!]H@,0F4zpO,O$Uw57>Y`ua8'T 娯N8ai* !֙ lldٞmD˺i۫"'[S9R+BV}'2sڐo[Da Y׍dꑌ:>њ7{̘m,Vo{kـt?k![[7s#ozh1e'6flo2Mf[z2/2 drs|6}1k{o_k_|>q36-:5sa#l?.@RURbB] |!@?uDgI}~<gIbݿ!rny@RxX]}1 Ugmx_Il#IUTH r++>sĮ&X')3R}摴5eXJYhN#3w6 N FqՈfkn 9z耇\! ?nD"~G{<k IDAT䱎rA-u`Η B[fklV*=^`:0í'hvVT&W8t!*{G?!HNE2yaj6ckNݑ1Ʊ@@5mX^CLD$Gtj2;h ~\%?x:Zl#NE"dHũsK3߷^-^Ek}?Wҩ`rq|OI^26%UuA)(:jXV0RϷw67ljHw?DGR#c\Lpl>~t[L|6&+\ӌ8Ķa-P<〧xUvD6-m,"}F"u `:^TP9CGWz>=s)~;tjmR?ˑW !^ A(yX1gk`;2Q)$@%kBfjt lz?}E&O9Έ_!揹wz|?]O!6:?>7Ĩ l~5p 4֭Jo\vl LJ'g \a:|6] ?B&կ 2!_0%JqEUg'ث̹LƹȳjCŽ}@!5gi]R?wB|X,*?8@A2O!v^<wG{ʆ( -#J'U’Nhzb2T*% %Od-&j]߸#+K*AM9F6Y`[^)܍ 77hةȃHLpR>;r= ac/N<01roݭwWl\hGO:ޢm&fLXҩ[훙ܲCH6ST[F>vfrIt$cbX썋\PY5,or.3$ g$J)j뚖!$+qL#8B|3dDZ~jC[Yv*C- ecL@m?-0 Z:߅&[)X@|^%8+>6f4אlo'2'|!m1Utʼnc\!#hB]F#6G6[?b雈{v)g3NEJՋmkz#j'emXZ!aFw[r*H߫ !ZHd$ĶH!؆c b1 M[;^E<pp+0=[3D'7?+s튭IJ:U}fʫb!B:CEHGb cHD6oCfDq0T,TC&b {?=O\ED$xx9 f͹%c5|L27DFEWwHEw]Y~=WZ)4p5h+%a4<ၥk?C;o@72B >ϦA$&  HKU$dbYr(dWE >Zҩ[8 AHEol:ۯ}E m9}6gηmmPDD!+ 2 SzNJk;_hQZ눭d,E8 ޥEUG&U,6>  Ў\|`=G cH&dfDƪ.Kf{ EH6Dzɼ>Zc)Dm{xFƎ6;!~|O[fP5i6| 1VMs؏-Р r/+wt1MDpH@%j >CwT\ "+2~3 b9O7,|j^!_'Mc Լ7 _]ǸqRr}٤*>d v`c?VgxU쳪C0&"/6S߹ _nG>|1AZ >\G4 "(Z췥,h<੝}RКfؑa=|P^EdyLGf{p$R׉h". 'VU܍yD6'X~ѲMS[ bOdo .2&@L.ȱ+`Ï!\ -l}f{?D&)dbgG j$#DhoFZwDh 3ԪE,\Y DfjHnNURcwqji\GK++\a\&W&}3"#*UX퇘=JA|`5Z&Wh̪)snx$<0_/ok#¡wiϠBw"5O?DKk-K&VRA'wΧͺGIUPI֏H%T؀>d8 (-!2T.oTg7L#DF9d\p"GZF9h$!o{C nzۭ wuNBspbN~QsoW+*BTkE\PwJ:H* W!+($zĕxdRՈ_oՃ_dD.dBrV ģd.F&o _ׇ(0UH둉㟀wׯ g0ۏ'y~ݼ]Β&+'uH3#qG(|d{jb&WO|62 +te GGn,PF2wMB'Hm3 k2ߺ +;/~ݏz*W è1l jqŦ*} {oHxJ%tP9Zľ}SC?I~&U*N ɜ 7I?B}BH{lF21V> oۃ 2ٞYo4fmf?7 vs2>֛DrB$J'_߃߶q%p춧UP*cXk>a< X'~Fx99z,Vˎ$̢{? 1 R>.R?eܔK:5za CZh௝|l \GAfsF P7y@xw(?в7҇ܟN>u UƓRA$UH|$B c#XA?t37=Wm[XJB^2و.IԨY m5֌Rq")E+]]HO}!B]X۶Ӌz c?{Qzhv.bZC@vf${URmQ}7!ғp]RxbRtmBtq<=2ql؋Ebd YA}qmof+܂w uDkaЧ=իްɄ폐MQ9Bmh>EdP ?BV#Y+iGa+AU;NEr"d>l>FoF mSlzmLNNBWb^\ h^Dog#9#b&9D!M?JZI=ȵA{V14nrk=ú {@1\AW~N ?paR#%ZQҩ{͘y0c_^"AM"j{ɸTހ/~8+Ms+lۛ,PάGjhUǕ8+1&"UЁ ԆcLI`٤:թvE Kj=wxU?y5y*VNXdGw{|ދtj{vK XjO57Z6+ +&3"#,F.[&xޖ79Fy-GkPс #Uh=-t$ȀӎDBW{52§[fL9ⷙ)H>%[ŷ5Xz<%2{Tf D9H؇ ~f"$q t|hثfGblT ^2alFHF$ :jmٮ%Nf#}Y"Ձ"+ZHOrcfGœ2;\b$/!Yq;\L0wWk;H61VՍ-3]E{+ډ ѳwn?x R_/lzM+ g01 Lll7~@~o't?\ܫ}vCuUI&U1Tź*~>On)AK'}x^Y z" ̈́#[]j Z_l}XA* [v#moe\n1,8h${ "+3lz$!Dںjb$b~d&Wx :2AV?~:WϦon *2"}B0DL2z(x;VF5s!iHgR؈XlH4$:ߋ󃐊ɲ]O4A"sYce=2N3^VR|9.R4;6h{ޢe8,כ}A&ey x40 죻bۈ @AKxВuP񾪊ij?TRWBU\a\S7.ZsO! &N*VnYˈ:x҉[Se7"}5Tɩ\x&XNŰCuܖM*zDDvi1(M#Yr7Q(!Lfٕ3cݲ5Dmh^ &(D׎S$\7J!)drE$AډȘq [mN0݇Hf cڕܯBG<)ޡuO^ۑ%dbhɓ &1jG!s+yI|re̶5B@7̰'y1$S  f#lag[#x-Ϧ˙\῁nS!g[^{a^X[=)Zܘ+^~pO\s?/v43L I/GGNl#^ߴ7`'I6ˬ)shbm0۵r [ xg&WV}A=IUkui#M* /p|p~TE㿶Ɇy ^6 xqL bC>֙\|#l{"ӆ;}4)!$ALy0/G2[F".:ildd^/{ WJ"§ t`d2hPv,;B:QB m a Î1\okAH&lL MNt>^FlӁEY; \xȰhJ"}մɴڙZ4}h嵱u05Xu ]SJJ^U^^whgV5כU48Toknv=0rYvsA] hښTC5%N>cEp4dr-z ]w+غґ1=ŰF-I/3{÷18>N`dr pym=B!J[.Ïk󂽴F2`#x5ͤc>RVkw~D%{B`L0̕SXf';_Ps鋟Uҩ? bρz/$K3 ݉dF+>ъAOrr4 d|D&s݈m H{?yQDɌKizh I8Π3uD R .k1FFz|w4`.nbˀftLbw| =tYl/ ng{WkY0BT'&?۹ߑyZl+zH DG$ryO>̫g;IUlEwp?k!B\!ND$ u!QǙ-4R0މ?nUl{~<"'4G}!wۑv _BZ>xz1c!5RbdQ-dCos2| M|5c+8GѠAE⃟Us1Ʊ@S-8i2ɕbegf[R|pN[k_5$Uq>mI^;j=M@S1ˍ>L ZMU vZ9%/><^=ȽyEIdE2gto}뱅nr1ٴ "nA#ILu 5 R5j>gA' vak#f#N m+-+$h[W Hx !BRt1"2yN.*Nb/DOG2JxMm3Bd܈#I!ہ_ :!vH[3+병I3BzZ6.whkQqs4᏿u8vh"?_vZm}\y~H˖USM*N Zm4#ic` 5Ԃ?Xa^TœNމ]\zᐦY%ݶY}.ԹW+oG$iƟkؠ+ι|*^ttjxF{1b f0QO[4>rR L=F(1i26`)e꯼`f]ͿۖR7GvEk9EpG&W\;%ZTҩ IԒN!͈č H!Yvkە y X: v/Bj&!"NM\DWP$-mGWEB&LN'HM!u2R;!}Sڴǧ7S4ڂ&z*7 =侅HR;3x ?l۫*Ѥ*~Ǘ }]1f3X#qQTJO@bCҷ30g3DFn3Ԗ鉽hL-ma)h-НhC0a3!v '"F_ xdl~d!cVdr)!$Hҧӊ hh#CjZRCdFz=d.F`32fDE?:lϾgeA`,҈ڈԯD(~f+jW0&|o!{.::c@d| _Y 㸶oY%1ZsQ&}o{rɒ,Ouu**$ke߃\}<{ckk ug~򂛻&l퇯iblPm!`[q-Pc Wk kyߒϦ "Dc\ 1x0XۻʾVHB˚mY8>$3eNVU6Ef~9A9q?Ҽ!qg!"e$S5!RDܼqcf?;þO׸a߃I5b $NB)Qb5;oQmI,PV yegδʼnKҶFjxB>nVEϗtIUtNeo?1|g F{װ,9e{7t؋HT9R,$_3 NٌثMZTqmj(v`/xԯUSk<&d'Mwla!B!+dGd{bZac'IQ=G2Y8z5&mg}\6!@&~5* MHC䳑Ia~z/ UͱvNCȚc'f!kOO;,j]1=Μ6h 8\{uufI:nBdْN"$%U1VLI~eZ!HŞ$  _c n$Yho-c#-O wX T-?GwWx%M﹬=D!B4*p$2;٬Oeu B6:++ٝboAH,~eB1l/+dJf[mD.a%|NUyC/YH_۟J# ?B&]՛k(xk_fz~2Ro9gzn|bHBYY8o:Dۆ\%VOY$@?9dmL\gOMf8M\ae:sLAː:]ڧZĶjW&L؈=q~ ))@gň]]dN/W#H!]fefݫH@HB"v )oAjʿ"u!M~o@ƻ./#٪q GͶ\1 n{@;ͱAka <:AuiB$'#cLs6[EO'2tǟ5 !cQ f @++sLVޗ!rgIUL s%5iLB? [+! 6 ;InQ{DpGHÓb횈c nBm÷Ӭ7_~؃< ,J VdPx;pƇhL}I&i`-E_1*=R@Ѕ$}hm5M2"D!v62QȘYBs!D 2ME2;7#:-1şLSPL3H/5ƋH6>lZXא2Cܺ="R$ȩƗf۽I^d"FApK!v/M?>>sQnO4VD%Uq6_ҩeFޗt:l[!ʰX>^cWlZ'U2$P2ߐU̒N=>G#+LD,dgyd`kDyE(ȅS޵f7y$"HͻϦ_!,Q!BېcHC$h9ɼW4_Bap|2/3l@HH'ĘY' ٨ Ht2~S~@"b3TYRCHt|3 V qUJ5sBYQf2.7׏;2`a{,]A]C4*82¥Hb$P52&M$UtXtWöU tw֯z?^턳dDtv זt} < S|6ݿՅm{j{P&Wi!Bc 1hC&iemE$LA~(?#DLDCHm܋#k >nM6[ ؔ?tEykkrғOJi 2x*Bj !|Kz! YHJ@SHF.7MoܑC \Ϧfr63s&U|u%nJK.*6Z?} ]^}^W"sE#)];ێ=+D!B[a,5p4e@sBH_ܱgT͝wOyRIlp"@[&#d1ZH"x!BHVh~+ jWN)>lbam5߉d"5?SU3=}9l>eG}5>'1K'ι w$U\StnC$7"^mF$E0D!Bb#Mۺ{_!Y,ҪǗ#U˓[юdx>dǯ}";Z#?L Ǘb~"Oxj'WuH[I50ԧOϫ[p41Xϧ]}+!ufdtj 1:2Ϧܾ=^@u lo7"}qIF3 d\\3̏mS:+$X!B"D1Su!Ixb8"klG*OBT!B!_BA"R  d&!I#;1DLB:P:Z"Kx5ff$[̾u+J)(pG~ d޶1#XaHXnFj>3uNɵ7ևN}tK4'UvO#uULUr["D!B؅Q9*౞6-;Cu"DG 5LϛuF&zyDZXHHފ2֎>$Sn;u\I+~8\yjYqKR"t7Ls{]ٟImedr#wث`$U1TŋxrzW{HBHTRQ 1ĸ=9Nh5qg|=! "D!B 8qLC2AːTuW H,RW5.Mm/#S2Y+jMD R{5s1?vQMF_I)DEH๰:G{}DD9ovl|6WH}HMlxSy@Z"*x($܂:fwyp_^A(\b!; 'lB"D#H&# +"A_5M2S , !Yf+)[m"Bj#)e5elX`4#If]K*HE eG)D[䬮k^Dyn~n^1* H*2.v[l;lŠ$Uh>3rüAd&1#v4a05:E,_?H֣ "D!M|"Y#:W`} 8Ԟ]+S,M/3bo$j,X6w}k6<>C#Ӏb(t D$`3Wá3_&UqNkt\LԚ˵f?y&WIV!Bk ]u<|XO3 M_ԊLAHm$qB|~H|/7Iq6R9JhDZxBڑL8  ue໳m*J@ϊG:| }}IUT&UL_&WހLIV"2MɁ@W:#q F*/'rO}WAh$01B5 \!B`s9!Bbſ r"Y'~g9T-C] ftKEm/d"9tDҨxb2D$|/=DA&TO s96+Gcx,Zh`umCR_o_!f˶묆؛ 8##/'ڪo~TŹ#m_OG?pt*55@[c2u1:qȼfmU5!ڳYW7D |Ic="3bGIztL '!4CJ9W9 bZ:n!z݂u 9_% {ks2zE` V  :`00N߁J Z*A݊{Pk* ^M{Pй-E0ET+gZUDS oFj,jIǻd |ϭ E?-|UfeqJ}5go:sV 7}B^I K;sׁo ժyfff}ȆX& IDATY#.:%G|UB?~i?^w4T*QF*BRZ",t%6||=&eOaEkjP2@2HĆB.I<\l;مcQo!o/PE7tV~xx; mONދU>a3ZjΝWJTU9|:1zTGE4~5 r(jh|UjGZE5*& Pk1D;rE`je#kW!7{:s]X>P{H*Z>4l*l{efffۓRNW}kCBQ=aPuk@4Mt;nt4M4VDf4yP~yZ=d߯^>啃"P(R7b#oA-f[$NمB\QUu?Qbz.pIgu9m[lшh*DT(}7jmjBej gQ[PYFBQw ~ EؿT Uf݋7{N#\R `VA(Ӹf.,(/{Y{G~{SQP*k:޶sտP`4 e#]372333C( J3PzTyZ܅[ڬ(dU<UrZP\7Z?ZQSvu۳MjBmcbF Go]XpM~ kwTz 0ucK0ns9܉%ai!Ƒ>zp2333{h-V G1_BhnmBj, z5T) W3Zkv t0]::|okyg{BBt':o)+zgi&nC1/@y{tQ8%-lD0NߏAmo6` jkNFme4UezQߗQXBW5{V|o␯ݲooB QFU;Oܟ5m[%4 rTzګ4^i+pp3/:fp+h矍m W6m4\>P ۽h >а P>z/ZsuZ5RʫZ+GP\9{!6^6 `K B27*W5mBռ}3PKZT{ Tk9Q!5I#|.f[C.G6rH*|TU9UnE hV X *VISMdU@knAU|D%{k=~$ 8xU^gqs޷-BfZ5jB׬: t]5lM঑;;,333ǑDA5(.|m; UlƢv)Sj? qN(@޲'{~770=UE=ǹ?E NPZ4<}H^֣˾bj#C1,߀ybxͶyXffffOAC? s04 %jB{iMD g@ф: SCeVVLfP3Z@-7dwNLx}z/.o,U&1}(`D봎V7 vɃ{NonhU~ N69U Iul˸efff$QHCWjyh߳۫u >x Nk,C/P!{~>]UnDUֹ~4}  8f=5O,0 mP*֕ڤy(,=eXffff[ Ppx-Z4 ȇ8њP \3jB  5^HUwơ/Ca\{[&9@dsR妱 ƣߔ}宨uPŰtHxƈr2333BDw}^6^m*NPPPڔ}m'&jc=ƠmBM@ZA.Ћ47Z(סs}1GjU6f/؆+w,333-,=sPpAUڀ m$d jϜ6l %Q 8mM {ЇPD4mQU+ Ge?7eo:* ck Ii o+^+(L}x3 Uj"wWLIf}! S5 g"&Qŭ8fr2333z<ElKQ;&T{|n|چPJmyhgedyom8BT2a7j꺹M*L08RD}ߵnhm#:G[ulV:HZUE~/CթMhrIwQ+BլnT0N_l3YKк4xvE!a0TRg\7Xihǒ(͈S0(|l|Rc 673cyQ]tM:Q64ԣk9X߁Bhp!'pbm1m(qaނ2PAC-:PP Cgg`aB&:}}{ͼ~UYz 8x '#.f-kM?EǢ3?~Z/[_gߢU~*VGhFT雊l){4hT25n4333B3$% >V&bvBm Vб{M>|Tqݚ P.t:˛~տx-0(سui/\ߏ`}>FE>OAC`tPH?$ nLY6W(0| 8UƢuBDcFAFoBUCрB@Kޛ` {RB_us.[uG&ɇUlM&WPh P5UNGkV+L[ז^& ֟W[ Yrf[k,Ch8E3f(XQhhGk!xAv_7j~_(R fՃ^4|8 m58]&t^ZQaʾUwlbi>Ǭ3 XQk!$ Im,333:K$ >*YQեۮ ܈|^5jFՊSV)o :wqgu(HՆwEmh6ֳ#& V~g܈TVՂ {݈ 333 B: ZQh o *y nOF߂\5uj(\mEaP(p>+Q][0ֻW̆II| >Z^7^&݇>#!U`ZPRAa䞑:?Cc[4hrT^;=jDU6den6j̆hMQ M+*UdSjUG}lDA:]/?D/GEB6e%؎[7hXHNcRt1}oV ٘=,333w9p+:啩(lM@oyE*o\ Q>X-W{W0mOH$ DA/pp-ZGSnjksoB56צMOBᵄBZ+ڴl8` $ x%pjc[/ס(T݆11@Pڿ涶ώ< ayaN&C+VMp :vT[%U5{ a6Щ,333QEcQS(XL.EAUfR86n:xpEM3xDh>M>+6kDv{M F{AxK'q< bF@]%.U6EP&(؊Zd 7=y퐫G4K'Q(쫌έA#}2ZՂ& ?VP+>7(i73339?@SFa`sW PZE hVkAʶ IT0N hT؎εB9Za=P)ιZ(D!k Ƹ uHPj2333t7/m^ |8*MU*(pGD>%3_ 282HiQ  R(戜pJa @;кPk[p}nZ_sTymC_?]8(ӣF|DLT{ W(0q`mq]ʆ:jf{q[88bgd,333zp" |Q; ae&(MnA. tƐnDAe$Nd | ZˁnthD"4ON>g (PCd?wj8!A333QtT, ؋BB4#,Ee ^G$ eWL6$/A퍗Kvp5p qC-tU W a/EC0nL`ȜY+Xffffo onF՘1ngTAcjkCF WU64|zT: :O /hP{.Wg 3i>وA333wu`>4]ae4o48?g;]չF8n$ paq񗳟 j-V\19}-<(<(of/Wf';xfO{^5uGP%?Px0N]@g}l{,j+=3Ge]VkT:cV m>бG>Qj`D!m,6Ν;TyGFA?g?9Їt4ewe2EOW+[MCޫ ?'gT8`mvS3(VCNBj#p p +QC]Ip8*LEkJ((ԍZBa ~ ^A-` ke1f?b>65Xffff۱0NgAyQp%Qp0A$ hJ.B"Ѐf7 UƠPFƣ0 {8-c(XR35{j̞8m>-Ac;nJ:g3p4wmv8૨*,4(,u)Pkx1 P|E|oqC޲%; f#$4AXT9 ȸUbT]ڰ5a$5Yb~g T|~֕QPhDah]NF.zPᣨjw=)ffff I ~P8Y׉( }xj /a^z+&`B7InF/P(ݕ<סJVV؆Y;gp b%ƮޞD-bf $ ^E`pF t䶯WAM`2H2@KIc{Q5oiZ?o?[Q`j7|N'< bVgn4333%QpGo8ZܛD0NN:Q8J`Z/E-1ma^eoވ"Յ. 4O.Qx. ZEvoiP0fIaIלu/~̞8}6pjz?x-:isrGk: (&1hq\¾Fm!}y RsB@:(P @Da8f[Ih"1:xZ_|-a _N~\c(`}x r6eﵘD C1rV84"Ml+9`ٿ9pJgOwJ{it^ڛ53glXqhThD xhطe-DAkmb| #빨ՌT51{Zo$*fǶwf?-_m-23uռoP->5eff+cگ8m\VM6nA/ގ6BOPX6> x.Z+Ո5VyT" cyݷ fq'g|{}zֲaV.$ =kv3{\g>ՆZHI8pןiff DAQ; \E]'Vւ US+JՃ*ZP@z>ϡ( U0z5UvIՉa d :pJ,d} 2X)N4O.-2׳+}h,{ lDU UՖT-f.ҍXfV,xZu~eT̆] hD|}TN[BR|hF @)Jh`iU~*zUR..nO`ȜY}8`QekrcPoƯg |G]U?:cff#+&@TxP``'Q3‡l6l,Gsm ?oglqz2ppQ~ \Jے(cly Y 8зrӦGFxFߠ#(p/j%<ʞ\233333W̬N̬N̬N̬N̬N̬N̬N̬N̬N̬N̬NdrIENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_47_0.png000066400000000000000000003716521413546205200301640ustar00rootroot00000000000000PNG  IHDRc%sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxw|W3v7HB R*Te:[Jhe:(B)+b tR*( b C #;"{y/Iǹ&8ۉ2˖sK( =EQ)Ygz457(r1EQWi޸51鞓(1EQ%L$yǂ[{^(Tdײq[=EQ cMEQEQZ4M((FT)(c((iDŘCeJϼ%N<EQ1EI#edd)k)9 B[~>sSEQ*%d@6@jmsc[6#EQefLԖBwO`23SEQ*EQEQ҈)EQEQ҈1EQEQ4bLQEQ%SEQEI#*EQEQ҈1EQEQ4bLQEQ%SEQEI#*EQEQ҈1EQEQ4bLQEQ%SEQEI#*EQEQ҈1EQEQ4bLQEQ%SEQEI#*EQEQ҈1EQEQ4bLQEQ%SEQEI#*EQEQ҈1EQEQ4bLQEQ%SEQEI#*EQEQ҈1EQEQ4bLQEQ%SEQEI#*EQEQ҈1EQEQ4bLQEQ%=EQmLxl9`p(@-(RE9P0tAQ}2Sn.jfx,嗤qz(Te[LppD2S@(r@bLQm1X +Lɘ4MQ@kE2Sb@}p" 3 3n{(*e?'up+ @mm=@cvช(-TP$vݱ\e=MI~6-9Ht-x̔WE9XȘ;C)%.(`"ZOj'y-}r,03p9P \Uj?n=?] 7|x <ԇZ$"Ԍ5Q؂&` R[(1E9@Ebd'+kylx'54 >!ބ^sV82C̵u=>)n$L<ڣR$uګ'(rbLQY96ft[ _uXq58 xԖO1{ˁK|McOˁ6Dd$ dg㳚=-Hy( *e?$e=/㉵v˨ZPll~[x/k,eEZ1_s|0YM9W(ʮbLQ#BX6wD4.噇F9hN"cSCO|"GVAƑ׷B/xyl@c[h'HA8y`p++f}VI (rbLQ/E[HĪ[Wl2־lӔ@MqD}hDJdlL"+)S^a qHMM tLIN-o'(r bLQ1LHZR[bHG_s3>Y,3% Dʘ5Q@Jڱ Y{`D5 6H,b DAl5PE9Qk EهEb>1>ӗƓ \RjWuoǬ:Wdo0bl3ppy}m^klۮ'PAY"Rn9"®E\TEQv)>@),'I&D܌GVw_(;=)c4xN`]":c?-oF"iyف\ksj<ċ'b};A;z"k݁$aXcߜHh8ԕGQ@FŘ2SrN*^9%mrJ`zhS(떻x}gXW&xr'$Cx5lXV"BS665#Ѷ/#HFDN@"d|>  x6CҚ˻")S4=7pϒkN!tg-oFjG]|;v`O "l(Ol'H %þ$%/@y8f,D`"7+SE T)J<_}+p^~̔`iǫ̔x>k[y,4fJ8o@^?r swSI F^ ayHa9{X4\8i݅(lzPe/&lOOxYR[F V[!xfM &-wDL5"_ v 3Tbenlw;|E(ʶQ1(.]Xdmto|geAxXFo;{YsR`2:=IHy1$UDrc_w!kRߕq?|}7kO@zMHCREEgLQ2He(sk 4rk$O"kM(DR/$Ȋ%sduc 2:Pmޓg ѮL$faf7h>b(]֌)(j=>$ !'ךUIOJD2b>p.:r*"j*$ $n xJJJo1A"pqeZmIb(]1E^ΛѾxǒMja_ՊԀ"H Y=`ZsuC یzhJdIu"nDD DRg )_&3WDQFӔń"p{4\e-27dŮk0oe(X6Pxi9=Ykj'y{#M5>F -2G'p8]naCRUYhmf!g"^cUF.Pze*(+J3t}Nm~yY-=&my8I^|[{o[p^Z8EQQ1(!] f G%>89G⭙z[mˬm!j ;f#$kd>,XVVF湿3h_I$*OL7^+"R +̶s>:geU;yEQ@Řt!>->i0-o*4|_b_8y4Wg#]̔@+Y7R6Txi=":zQ$mٌ8LEz.@<+DW_ݥȋG"TcŅ5靕(Ρ5c"eu%cZW,jl6"ڜ}kl=?XFD \ul--9LNmDS5cH+U0YuYԗTk,1ͫk?ڎ]Woר*I.* +y),SՄ%u@0%o.+۔}«&8;e\P[,[m0Eb: pzٹ@2s]7g |5@DS=ҏr"m1Tj22p3UH DjB͇2w * +D{1i(.1EABDR+iE j7_0?\AD.w Mڭ'<-}r!c'"†"+-[4eṅ8|2X|v는%FE\OO޳=PfJzOl=7Μ_R[.;KMA;-.EQ]T)r FCz9Ȫ; -Pdaj=98U;&\iKyDNz'GEmYxZ!%IqkJd}d^bB|Rq -}ロRm^xQ44-xȦ%R[>K/PQUdC>/,pXqa圽=/EQAӔBX6<A0(E|[^ EbYinYXگ|R{6H2X4mmpĀ)R[40̔k!g~C~&>ӻ Q/bSJՙ$▪7KIW8=kÁD~HΚZrz QS(~1E:HT+"")хK(< ԏ .]&Y y5x(pcŀp9x |uz\oPj"CZ[ -4kѯ$R?]DY#?pQ<ջ{5ļ^3VU-K[(Չ R7+LqaEoP1([!Ebpe;4ўfյ{ @Kl[7' Bɀ7vcvc xlo3-D2 Q!xLZ > m,BoEL%P@Ϻ˫=7;gޠiU@{$,5x>4mSEET)6X(?+qCDj@[<7cxYZH=5q'ך\'iY!?o}'3@Dv`N[v`'?h~۳,瓚vs]BqaƊPTwL} H$EQvcC(xTDDV f!`63D!f|z]2=gcδf ~|I}RHZ @Mɬ@7Ӟ;y~]@'9Xu?.ER\XQUtDqa4OQeQ1HtC ۰@BH4Dm W쉃n>Wç_EIb {hEA-K{]ڇ@E Eb`|OPQU4 i> xuC})R):*Ɣѽ!B)pj1¹t[W}KmJD$fLI6@-o dsih<Ezh"c!aӞH\ţ(ʎbL9`p)n7giRpDSoŎu(~pJq-pI xi h}b-AvنKW?zw'8_\68 ϩ:wdgm.ڪ?[EQ]CŘr vRmr(KEnGZwlQu;U_E)dz3׷n*埋 7s{"\U۱pp +܄,Tn/_S'[~_qI!+DEQ A( Fݸ (v`@R6Z4|eMqc7{;K(0&؜XtR}M@sX[6 5wW1v*@QeBŘp#pH(06tmO\چ>-^}ԩvͮ 7 ;dHSj>ʧ5A.\yUP`Eg>OdI&}´WQeP1uBX;b[~k7 'Ww3R{.]}h8&DDD(Hkj&`^(ˏ'2|=n#('P1U`Yufx܋%vlGښak̾~ovS?ܗ8cx?w˺,>M~g4ĺ"+nl3(XT\XiDu_ȼywgdԝ5iC<|H@S@&_/.|9}3VeO)p ?&W1VQUMXQ=FƔNp3j;Ӵ^[;̽ؒ"`@gλ?μʹ7'RmTkFFA{jC%;)~j%~@+w7cD<R1ξhiŅ*7Pk %- 7s}Ct]+*peW Eb& -X}y2Muex EfGZ%"?:rcjq2gढh$p^[-X9dۿ{w˩i4?Ƞ@fqa_3sEQ$S҅6B}?kHhxh8شc5Kjx6RwHh8hCX6+"K;#t^H  +/KM+.|cmh #UEHׁldQHoNEQ@T)iڎMP$6٧p?O=~r:hemqKCH m! IDATEѢ/01vU\F6^ww2 ++KVVa%4MtHl;ppn 􎆃b^?:&CqH?ßEw~Ͻ}݃dh8u0dHo_Nr0!4x;k]pLh8(hdLJE_Fjvh8L\׸ kBpdZdeHa#$g{1OV=炛xqYuK{7NZt9?CXpy4vx700t}@ dR`F()*Ɣ7@rW"H"]CzF^ EbӁ77bOE6Rg#6嬫"<6'/]4 ,b:'"5<%|g&s 5vR}*([bL2He@sH}W+h}yZl0yHJ0UlK[|MHKHm6".f_7w7@c(ˬq}݂ -NψA];9^G!)ʣ˅7{#3.$O{\=7EQ}cFlcp;PP$4CX/Gʋ %4q$8+̛U@""_(; |I~QS ^6, ؜a?f"b AصhL"]]!f@sExd(ObLQF vcE"+4JdeaOOnfP$vI4܈ޏ# >vwU 1J8̲h88)}iDz9< >_nm+ʷG:8HlADYڨ*Vv.@ϙ+mye`mmv2-d8c)(Svm(t 1"zIZBرHň)EbSP$v8<Q{4n)dz~׭OI$y @'v)HDstgӄ\\wLI/ VRM >2yP"$D"K +wyE(Π6 Eb@e4|w{ogu͹A/>U1ÚzXo#uFDZ"qc@ҁaD [  < pzD쪑?' 0;ӧ7Gھ&}b&y<<tW-ؓ"L_1=iۂl(.|{oSQ)$F/k9k|֘u H,?p-MSy ^ot~ւɫ=]G6m(hW(.|%]4acn^sM̭ͭոTTI@ ؟WT}e7NE0|2vbC~c-c'=4ň]0cZk/B`AӔJl$Rj;v#p[(;Y2΍K "qHk@䎶:￟ujpp]({̥(3#v!Š7l QB؀.{LI5 f/ R+"mM-̞͌VԘus2 Eb#)?"<C 27n?!V5|CppAgjyPϱl߲]8@~ZHzxf#tytxkV>.@Wwz`pm)"ÑHc;7{e_-8Ğsߝo1fFȌ1ȗœ#7OB أHlpv1lF k7}H6Xk3#a3܆JE:4纱z"Yk<NGޗo3"oXk;-pcÜd\}MS*2N@fBS뎍K\1rCkZUpW _0A̘x^l$pX`(22ea9 m$ I9AR1pۜ|B,e1a:~x3=e;f-:aɤlǙFAnC"f'7^r0m 搆k;eDD^A "*Ik 6^Eqz..rD *7:(OD#u^ߧHr Hr>э7_&"Up7P$DޑՈUEG!=8o*.|{wq$MeDWO#iXvFr44u+άqq1rd[霜9fb~Ng3ofb$x1>N*dYԽDNve鏼UOA7nr&zCXԓ1IvGN+ARչ}u{p wUDMv۶\A0ڣa9I͡H9d!HENsu3v)|cM{]ԙ]Ė")H]٫|)1H&E'|n)Y~{ H헐;]1f}Sf-:أ<^x,8b,7I}yjp}tw= R^cR;';NYR|;m&XmO%: X|d#J[;y}-?@q}c;Zt_QUd*,:Llm˳ĿL|u=ϊ)?"~#~=FG7"lA 9ݏ_:H=/䎕ZKou8bZD1@~Y5iz<= 1@1"[;^P$'݅bGV_!ui7 u WvrMGVpP$6fL@CTfJ Q+Lp9㻬4G`É0y/EʁԷ;mg|wԧڸ !oڡa ;1F>T:>cR7\:sWyґkb'Āc|nHYGocwsbx!upu5"1CUTԝpIե6}rˣ]x/v Rl2r@J% g0bچ7!7&DYDe"i̡}d'ĦCXnW-be1W'|pSCo~xiR]Df !ukÑow"oLD\rrZ\XGz k=7yHdj6'AEwՀ^Yor)pnqaS Ƙi Lϙ7zN}%yn[\D;Ge~ d woy®kXJ>lcLR_n 1}A:t@ƘTP'8}-? Eb5h8zG*: wV.݉CN+b=x:?IIMF9j䅙*lEDJ~:z^mx)DxzvF$uyx <^"nn Ѧ1C*7{i׌hpuE>t!B"79~H"2?}]h8bzSfJ2iOMrw[R[YK}P$6i]5+ǭ*,Tyy'Vf$rMۃ<).'R݊i߃nY 4]fYHҮ0HW5,|K%w6B ȇMHDhİTڽ^FSQ7/ѭ:$ 7|Lϸ19[Pd" 4ˍ q#?"p瓋nY$RIK tN@|s!h8bL)IĪCtnGyTTxŶx (*3B%Ht)G[ C逿l#c~Ϛ-1bފOFR둺 $z(!Ѫw0Uw{ QD!bH16؍ r$ v?Ht{|,"R>bI7KnzDp?[ED}3@w.5s:2 P$ElDݕ;C)1uRK xa;]Rq ԚА@KZŅwtINQG4 ;$+ ܜ#N}.)A{H3@C^ CDhsXڣWIEEE&R?xA|M;C `:I-E "il3 KSL\JiݏEw{WxtXmJ(K3S fdLjgۏapWHG4 EbS3RȚA,R-z"--f" B'b܆CJ"헉H*^DL="{!g "1sss/ 5j:~gǸC.lB"c7:h8M͍##ukKQMܣ"lсzX"B'bאJLǥ|61fh8O)Bؑ +[޶P%]puYZSD{EbGf֝I{DSBr~yVL(udf|˝CPD$yCةp@"\ӐԻ񈈸 7#)Qbqc]I TY*R)?ǃ4g0,$44@:$q!4#麰W' ͠=jzFjpm3 "E/ُ:]Hq#g:AG~lؒV?CsvTEUQ>r]^).XQU).ע)cٯVVQUdt%웨p󃁅;:'ܶgMq&@'d{p5x^ ʟ"ND! "!7HTl_&aɦ{5%D;:rI"f'mvB"\i(J CH]i;ñv;eHX7x)_C 987kC5;-S 'C7]Hpp%"w$HYpO5~X ^ 'blMzUEsffqaux'TƒGcl2 UE3VT=V\X=87T]:X[D sR:Z/<Ƙ]柷̷~'s0b,8[pp[L_C>Kp?%ܶ)Y <4Dk"h`d`  M{X7C=F{=Z3VnlE"\ Ua"!iɡHTw}0%ZѲ'f5B,t3I/܊_*=}}dyA9 R5UyMdܴeb9ڏAN5Vsvwj.FނT-f[䞇OMT\z$w6{2˷7`4cBj[mgqCXw_>8' Eb `$ZWD܆y@K@1f&>Sm${18}Zkq:":, :Z9G#h))|Z;UD8&kmѮ4o`;6.%XD[_|(]̏#*<<ۀUdțƑH]U3O"^D\DR^De# ~"L{V7)oEњM9C7γIƬ_"롇;F &՗q$ ZaFYηߩڷ~tߓMI=yVh8Wh8p({ƍ3#E="vYwF)CR|+O޼昡oD&<i +7"+[O9tG=kXu]DjŃPVV.I}/`<#Do_5=vs筵11s4xlkƘkDc0R2ktcdlEr|iYWd6&1GdAͲN3<!x@^$RׄDVrر2$zúLAHd-I{mRGg1[;8 YRW7 7:9Í/.6"/ws+dNZ9utɪۼfvŹG <,4W4VZ"Lm ayUTe"{_qaeD#.e9v/ +/F2'3Rkm*~4v<<3KM4]B 8D' (&EP6ZB 4촅BKl3s~|Mc m)Ȓy$9s=wc]h!j (Z4_xqj_~8 7gZPz_D-e矷ޙt?H| y)[OOD/rJkW2 1@oAZ"2՛D҈"D=C/2P[S6 D@)fE5 Gb,}&~z  dy>UTL|6Jۂ(s beœT"6#|]K>BwLa(Ԛԧ)36 RX#*5uU{"t*vEө2JuemzGuBr uAׯZVYf #s5![ojaGyKQkvG*$n1Lc,XT!ƘZknZkccF wrXXٸ嘲#1㬵J"4cCZyѦh[&B\6T"vTm.h lpoCT"[hW/z#L? ܍خ!4!'o@Q|H` X 0xȿ!V#psp)ѐY׷"zõ'z>* :l r p oPw !_V<8?L\JV%L Jl}0p((Qj"hktmnOQ D>J;YB7Ӧ%e{=3Q!&5uU׸L@iQ@fڅ5uU+tI v5䣓↙̧r&Y ŁZk7Ƙj\ڌ1Qkmg,mp1ZL~ohgmhWkI1 1'Xkv־fnL7| }X;*9+:HۏyWdz"J)Rs F`q*OEQ Q1KG` VC=5c20sG+wipkrgPB؈@u˸^qB%ֲ A)6JQ5>i 4Ϟ-sc"bk9D7rSXt -dzYe*o'?L.Ah;VZGhU6|b3~!0 1 at1skT"vm>"B!|:q~7wD 3;~pqaw8 ){Ghʕ62MiyZvEn">z8evLsOs/!ZsBҧ0|ZX̡=qbk1!5.t>*z~fv/`;`<̗>  SڛȬ&D4J:I#*^܃VE㈡z1,e9ZDTԻXy,JYp.Eww^ q׺W[ĵD@LQd趻魹%+[5+wdY+C6zh@ !65fO ٟ$~ЇwSU{Ǔ莊:hrsv^9gw4F3gqܻ#j9Sڗm)A` ]?09GWGy}z=l;ldۥ]a!yWb\c=ݗϢt\qv+gwr *0Ǣ|b_Sz6zAdzt E ,۵Z gxǓSȑDx2}2r&+GS`mO ȜCK`ٵ,2B>=)+PʉՈ)<ӵӈE)'@q {vO@l5CD~:9- `Z RZVZdT~HqӈLV~ x(2LE5@M%bj\~"|Tׯ'Q I.fH^}o^S.j-_`g̟d6 gŞ4#-% 5uUɱ͍/g.b,Cۃr4G )}ڣמߪE~b:mme#Ӻ;c;%]%t1c-n8%SHaz6<'#5FēT"U7gow3e՟~wOHA[DAވS1r.w?|ÖD-G`1X"|m3bnFY@H1C@ӈ] 8x.:)b҅ /۴3"e왴&dYKY<.!6q^tXjxGt n|'ӥ6*c4Oo ہL.c!&ͣoੴeMѳ=xA9^Eue!Wl4| NueڝFK Eqݜ0d~7 [cPp~ ..x }zcڲLKk!H>q>c}#b l2uSؖAVBwF,'y(f^<~j*{}1\J%bw_Cu8r\?3#z5@-}r+!{/d:=g,Cn-n>‹yGf{!o|NwN3t,Cɱl;y#Oڰ{~l~elD zed`@x/sW*- 쨶3"&y /iۖl~%]!h0~{hϪY]Y!Օ-5uU 40 h-e]> p*T3±SާEhtItgT̔"q~AqK%b+;畒_A̅>C) (/3S؝#|+ǓB 8%P!b?SbK;o%͝gPn(خc6ԣd sPr5)n*q7˛z#ŚA`)̵'Z~1/%#&bsKּZD}T;schQd%HтFE 8.m!'^)!dnjE[&Z˙p8C$"A{(PwP`7>Q(bc98Z{'Y̔ēP#8iN%bv"9TlMtȹ{O}Z|˝ uK !je< ~]͇#6'tOp!ib~&015} wwlvC#J\[y^@`eE@+@F˷ ~7wOi%rnD썏> 0c\?r~~1,QĺMD)LF }Q\>8sԾW,FQ{C7o+W> .E>Z~DLt`cڦц T;ZXV?<iʙR٪ 4_<~#Oc6LcLqͼ1۝IS߇! Łw#YXk?,Mx2"e? }֦[s3M찹)d@vF,X3 Lo@fB_xHekuP_E+쎘=PWύ C OBZ]"@ ָs!} sՏx)J Xn3b|WoX} }woeHuǮE.qc~Ȕyb<>%>6'_&>6с޲c*o+kn`ⵍ7*3pi;61PO{_َ̎ +k[n= 4'e͍֣vg[‘ L6Ʊr֚U-FiU~,FM]׀;!\m?缭`@a1P07Wq\'XS1vP1>ёm:}oϝ_~7of1>$Xe(c;P\;V]*EB#T"Eg`WfTծ_whzyvmBм@/I+>Yܙ(ozsp_ Bxst9sb>ڐ| SEy<0b Y|;mEYO/ c䋋[M@uɳj#po@syex#l=2-!6؍U/wLbzޕD$Pz>=2+̭X[ѺrmMw[1c*_a@O.M,VM]Ճm ĸU4SK`0\drZ]YU\_D+k_ꪷUP֥slٽxhq(nmټ 5G-5];Rлو̗x{m+B7Q@dRG#E0OT"~K}\JfǓͭ=ț_ξdyIYE :^o/<ҭh~rGWۧ-w/jzo}Peħx1O?eC7 tւ)OG65¡nKɿFnohSSWݚ+lG{m7S aqikjw}h1g,^ p1 }¦o墎@.Ro~֮w?ͧһ7 1._Zht(Dli<RYT"H c*WCΜ /kNw\E%:<0L'A+dF׏kYR_s \+p`'b 4W#:\; ~52GJY8 d@ԺkCׇ*"3  d\}eD{k5~B|5" 4C7A NyvoߡnB9//ւ[ۿ>gFE>~ݶ >X"Si(Qhl#k oX90̙Obc>!0)rϤb>Zſt_u%qՕ//>åWYv;pqO?_kpwM?0Kg.T"Dk6- sytꚺ.= NZL:Ey.jj)!i4Eh"6J>.Vfް'ho23|?cFj4KSǢoKk:YmUHJGd‘Ba$\nN%bV ݖRZ̘{|y7Z3;j}c)ڬ7:d_0_p]`Zj9KфnC.s%[̡@Ucsq?2w/HD;lFmbY+C,>'yOq=@h9Am6>_rd*ymk^VoL^ưǃ.ܵ uc=ͫnFv6zXt} K][À/Z9qy܃`h<O8P|U| 9>#JZx-6i CvƖ`]w0R\{gs֘h$f(oQض% C߱gl+=o=nKvt1c[!!81Q)D2瓅LDOȺM3Rv A$V s+hq15)B6Ռ7;uG.|^q gNB/œ{ǓLvz}h@ d~9ӑ)# 1v^@D i!2q}((-`FOw yQQ.nluL1ƂnEkh O7aF jo .|EC>`awYdb<)-<13e0d6bZW"`sFG"Vvoē3 +L5Wr+l->H@n1>9 wgZx-_?8㥷q3}QXj:KB  G0 s6wR "#&?jK>gY~40=mm $әB"lN O!SXC1ff6tF"$YJ%boƓ/"EGߧT"6/L) \OӮG"PuQkx #|,F [6"1)<5Ճr=Z Y.@%Y~@w/M} ²(ԫ"}l6>Xb}5ߵ|ԣ=Եc\>8 +CR'\M`-E`qwJn'cg?K*[[j'ٗچXrw/n|b9+[cC(Aѻ;B+USج߸dU7]? }6pcewlo G] ~̏T")}I<,h76vǸIcyFY#FCl30O{"i^߉g3{=zv Gs=s""dr_[[YQPWjah L z =׶KOQ5uU[]YI%]I.0&7Cov/#7yf<>Q<0갯t|!H9x?.ˣ0B_x2ء&d'ېpZX(K$Fs)41Li;E[z%HplB>X{ǹYQo}y܂)X(QF@חRگwU#1̍M)bxvwc=ǍL/V&Lq͕bd[*z2D?8n/.ȲP&{NxBߧ3C-Ĭn^<{Еs9`@wcBohIhέ!}fnzm\Ws4j]u 阥 bsp=sϖk?~2p4K(_VSWUot^Z!2&_ o܋ީwuۿvy;aWcV]Y{]%cc\: )/JĚt%>dt߈":sRtd6$mp_@3ۙOē4) u6Rc4)5@ze%/Q!(,%(,~-I#e{ۻ,DJbDMdpՄ8ĊE&JnZLJD_=}]yG($bcGV;OPļ8g0;կzH} JvwVvݲ^WW. ziC-?pi+7Bal^a)-0j&X8ƪ?t QZjJѼ(A MCWUqOoV4~5uU?%]%[L4e<>X1jpki޶ϿկuϽ߱v`j*>F!p $P)x2} r`an_R!ARE/JOPIȹ(I%b5H| *v! 2'h? V;b"6"^MҢs7 mA,_9704,p>y'6|LeKӁCmor ^׌'Ӆ ./nrL#fx2sq¡֯>o٘aO-0fM/2݌񮩫 !FwZxA#zeÛ7 ;cAa~[6 xӜ5uUoeՕOvvYzĠ3.{ڼH Ȍ}ڀb{t`Z3EZ-~ӺK䣓q"ƶY鬺i3O*oi+ޚ, %ٿCēj@NL%b]*#+kO;Fj.D O)чK(A;ߤ(~bA ش@̳xؤ(|ȗd܈- xMƐ9aN[Tjf%kd]"1c1j79@_pE("tgrX_*W}Dlz<ƭ'\ w/wcK)ykb jdi&L3Y)dDlw}7~TœS3coL`XC23}KY9ecs>%k6h<4"d"͝LtUU!'ah~.C2/imj.zy(mt4oOmtHS]Y;T7YRnN$mHsSKk M=;;%!ƘѼ)ewSzsnp%~Kz?kܶ=ަJ]ɓ0fdQdDlg E(bqGQ@V) t2Zt )u;;r y RcpƮvB,>OF>%p!،L~ R-LQ En$]ԗshD"O+ v@pw ALCwE,rcLX@Hu9b*H@!#i V~IO[ׇAg,\` ƐG @hpmD nNn%4{>eJQ;{?29,fdJ<;S'=/XuU(- x Lky#(]kΖEEv dk#y>4wr-0g]_EՕ~$RSWUnY\i5mP4ne~ԻfKNdSMEe77Yb1fv&Ykod>[2kƘc73YEJSK1uxÃ.3>"G&% f^>]j+g :F#@:OwF/xx2.mȄ$ Y$"q bFfˣ kw82L7! 7^ |v7]"$RZ?G,D`]߷K?ѝ(=_t9bJqm<]rڍ<7Nܘ7>h (J4dT"[>Tܘnpya|͇^/aB '[ֳk nD ߃6G~?W螉kF`\OǹmAWRXc<>޾w5D{mQNLĵ/X8.Lv|3K[QΒ /qȗNc8|_D&mx2r4QM]g{Pÿ9_nyd41M=z;1T Wk]jW5uUHU;li9r8l;{66h%g"|+yfϑW6xh; 1Q6hȪ3gz@QGߠ1Ɠ{o{hQ1f:=Zҕ2-/*-Ƙt&{0|+m(=?|olN1tK%b:\~gBXRCɋvhCWqR䣐mgd!F/Έ9۝_ImO0t#.z#ŝBא }< ḱK8/t#cvO{_ظi L}?ZDϥ0Anu=%Dy}h y 4Y"߻!PXA؝@s^oALZ Ӛxgkwl^{7%E5x2}*c[XȇbNNzJ2 =J6L!;lw;p·9 ߾V3-ۆv/ē3gm<[/^nϋ+C.ku w7\4y̓nI<{=c=ck;8? s_ z"ekF=w}mQKv;%=+wgU ftLae"p6 ,7<:kWr(Ζ&p~zOcsf/K|yR?] 3bw, ;ҿΉ'Jm  酜ѿ^yJ;_a*[Oo#)CRK V!> G#u Z!j(1k-bq*y)VE_N?@`[#& E8K% N}I/GP˓ %ŚC Nτ,ErsY(p;obt"p~d_3敺{,Aab#34M7".{#| 0\X6_Ri-A^4H=q pKDzg<> .O IDAT7mWh `.ywCCηI7~s|栁:&LOՙlo^bV55놲nRgq${`SVKsv6b /dC}1qD MՕ+k8/FH47 ?^-,X-E|vv˼ f6MzM2 "MYY};9d9 1f7k2OF>W` KŸ OG2gѯ!?5=ס R>zF(FbdBRس2AZc/d| ;RC 0BGhu3R| :rOD}/"(=1j"8 / dOdݏi R NŮ?A p&bu>DoSܽD:s\_sV"3 |wϻpy?".z_ d>@rmAek"0Bf8\X3mt3v5*W,cosx2}AZIhVǓRoF:yb"vT^0͏Ɖ,|aU7lvR*=P1ߖ$M.]LrrR@i1}лwvbΩ-Pl53PTl5q몮]USWuv֊YSWϰ6l3Ek)}J8~ޛ,M|%s2*= c[1ÉƘoYp9@oc̾1Q [k4LE ?.ƪ2~% !3ԃh_85dY rgƓSݎm>X讵`eb 73P /Quy R@;x@;Ă"0i52? 1s oʸ@@jg54#^r@; a5Cw%|D>Gh^ĝkr|)$u|@sddсhv olO"yK*۪b1J+{&}?l9隺^O4/lO?>U큾M!s>0*?/tz֜{2ktk Ƙ8^ HG?)d=W{Q&vK>̀1Wl9.L%bϻ\T"fw];Rvͮ,2ĄT"@^ ݊ͭH ]]&L#Ž?>]AoH08E5cF:kF][>m7~` PPt@O5qXhOOwKPAhuvB`1HCO1F]ϛ=}_R_E/rNo ]6 )Fx2}!F"pCl(9:AHD>q7("RIZc V#|^rQ)&;9KwZX<V-boAŅnrMk.2.B- ,ZBs@myڍAF\"`ȍ7VqtG+r7.%6$GdtHrǗ9ͅ64 ;*=ӄҨ}.bc}T"6u24" 7f8+ݓ'8?紮n-FU}EՕ[d'R%ݒD-77oPcr[PҒ+ld"Օ^tI|ƮDN"g;o*:Y?nN<{uȚWZ>^:n/y3'?Cf> RGg_ ћOA5Rg#rRgRJt| 29h1&eW!JĮ'ՈWlUHd"|=LoD\"g RĖکGin|A5{ n07"06>vrDب ~׫5! nB,OJc` !<!|+b3}A ¸gpvh6ۑB^wkoE 'ӣBC B);ߧ9Y9zΘxs?Dc;}'4ږu;ٱ79=ikw{[f>yʙ}/5тV_A@7Q́]%]B֭pEky/ZT̻0x2M%bmΧT"VOD˧ ^.uk_ 'X5 WFW]{ZL7 u=A$AE76f RL# mp>4 д3I,"N߆R7x{2]cwF [d.z{w  `b,ͅjbћ,?G)9D,wl R0]N@ /GX<_H<0@g1;֍S1~+H7#&=ۅ,>@O Ɔ›|ܖ~-YĶ!;nz4^2uo9B~3wL`)ۦ<>x4-1Ƶ=j3-6$iY:x2};rtJĶ/Zyj®UzSc+k73"X.ϟ|&X;!vGU#SoR8g*?yޑݤnRh(PBÐ!`@(2Y8RY**AE?Y JiAҽ>? -R}]9y9|#+oMeʊ~= YH?-im'\|kxvT2#e@!jwfoOBTt WnЄakBw>yB^x=Q;2ad݄LEfF[ڜ,q4#SXo~fs/۴%Fh;hx/~} rD%<m?[C,E(ĘA!Sַ`~] r㼀T>b`mozu+brس۷/TTvZHF;A?rp g3PQ o6fc2T,G`v Fzf O<~#SAsr;Z8gXcԣ5iW "cD͆ OtI|:ecU;ߒ{"Ex(R۱0:)ћBC`~,/vQy\KIu#RLo!s%\;;8*uz9@&ɉDňr9R,s1v헬  g$}wų9(}CT&|h XJXFBt#'x0b>\dךict'@@]js_lO6LmwCc42[[WZ@y="`#Ҁ\M8]jm,*A +DI_Cvdw+b%CuΑ&N'3ch=7hoso;5`|ΙSΝ?w/1>YM9w~;_}P^UMϞF;~{ ^Z66,ȧߝ?JEt\o ySxUuCҭݘVmg{hWז^.Bl9n|I{?7?,D;d]XtI'<2c.bƭi{אHIމw+  P*;-+RKו"Z"̀cN?˭3R bwf9sB g ׂ@WbBv9E Mcc[! Kᚠ*"=DGe(b[vqJƛ Gl?H?>x1:m/i6C^X? KUX6w!]H @m? ~w^YXIin^m;s+2kwXIzG9ygXmCgj*;Ҷ|[SYw&%6~нZ?)ˡKlw/,YvYw(Ojjn(Fvoi~ens.^5*`@h<d/ڲrمĿ6En%ssQSYAZ}wL|=1;kmj틛{-Ğ$ҭ+\2`̂.i/hK>✛$@$?KS4{?a{wsK]`3E@n܅(z;1Y8D,:d Rr E?$JoA Q )(zr2rd}݋ȹ)"3Hq 9)"~} mǁ6x_~ibZ `mh@Ğ)4X{l܉6-D?VŲtָ ~X9t[B?޲ىyV3m.#@uhg:fՐdNӘc'RƋ99[^}Hr &ncˠ} z9E"v~? 1AOĞ@t/yF9L Z-*U(@;S[cɡiȅo0"G:Yc\l˲M2W\pҘ>әc[>5+R_G0vh@ j2 F*:"ڲ3rG e[ؘDl;QʅEK19iEN>D |'hcn&*,Ƽ1Cm\}m67}dEεGflsy3Lwem~kc@WMy_jsa#ʅv f|k EM6|Bf6!3'#vC lMR^~c2 6~TexOF/#lDa@l]n=9^ZB2k{6α_+w=O0x._jz?=l+}I>n򫪩,o񥺶,=bνw_}ߪkAy>TTBZj*ocp;1Gѓ:^y1}>罟s  9w3zB\xjd.FftSw}sdp] ޷:y6 vsuGT/п!2أui]+w1HulňAaCJD"`;{t(`|$)t|>qh}Ƞd/ЉDf,DA,CV>^F@lo"ebk @i!2ܶX-{6ND|D;mq6 bO$k? \{'st7.>b&hE`0 ,H[uD;h{ F^=gw=-$6VBp8bshon#PzzMe{lSEU*@[^Glg`h-(7u>ιa(Y$Zܭ@9W^+P}O9Y}.t }*ech$'*Rr:)I]*N+ݞguv-& m=H#Ey'RyuHy)(y!o2@wFv͋ՌL||&ug{0QYKn3ȮLa^Cm;AiP%H'2v/nqQZT`?btjp;s(d!>S-E{pq1\q0!9e?.$  =TX+l8[: o6gm) H}G^c@{g[d|;*R7Tw4STMe߉@mEM|zEeʹ4:w7#3ڗԳ]ÿ7=67ꖔanv>2=>o`sށΨ-} W>gUDڲ*2v0nD\S}#tum8`Ѵuzg_XĊkf2Φʙe+;֔/9~t>ec y|2/o vܠ9gι {/SDs=꜋9f \g =[7J@Fr"6R!'2,s~u][:b+F`k&c҆@h ϓ21G`loC5FR-֟H _bss OUP* 'عЏ= Jt2n&bnm='%Uܑg& 腀X`rDf4!q0omàH!壇]eETuӜ`x ~6ѝ)ie JloAc5C}Ѻ!Iv60O^CTId>=" ^Qs;s(=ėeN0q6|÷t㛐an@K_Yף|!h=\V:}51bOw[ͻ`চ%"2tz{um٣_<樶VZ{{-z}sjRw"\N[yQ˼Ksc %7z_so,Ws/gBwv΅UE+pslnr1W{?@ >C吂TTNB@5\<TRtNg9TvtUK[$RH'ٓ(yL l0;|V ey8bBB 3Hqvh㈽X(o~Վ !rJاUػcc(D "6;]o}; |o"xÅ75-J{J/'>v̻9-1ucw>`svL[ eTn(wT0;=ʬ#V0=*nWgI^=GT#zh׼0D.18|>oIW_uW2;sڶ,y~uߊ9Zum6qsޝ^ڳd|msjk~~}qWOdq}3% G/;X|v3*R8r?;u RF"B&l4bPӐY VB"V J;=toa œ#ime,B-As~<Z7|~XHA d۵Qάo!k&NnEw( O#spk{Z;!.wwQQ˯J ݌kC{*oԑr.F-ɐN[.gg )@*R#;q4Jry&GDCm #ۆrpppHtv\uM`KFNu %5$_ Ś@V;z)!vA~ Eء}8Zj흉Bk8RjOsDj2b` {uyhȷB:CAclq$i"'%D cn7Y٫Fަ ~D @58⹾55}ls#DkEVz^ Zl{}'嶶IV:}^][6X8tzH>?[1douDx 2>Jƛ~!ϥ`q^=,z~l$ˡV@@RFgٌ^tI|͔U^(;i r:z;˰)kb c1d# D,ڛly{QAD*l њ֗*}7gBo}2]~D%ꐙ/:B -zڸA!|"?gMscK{R=hsJfp8q߰ k+?[c<:/K7u#X?y-B8xbFiok r/;U`x t@GT+y)r.Q0H;r5OiALlhoi5SB~WՃsni|/ת->jeum%(fZckp Iq%/ 48t/F/8-|]=tr}Z9ՊnjnrltO 9siӗqvItgL;-5-HQTcG i'Jڂdvy1\!p>8źܾE“DI;{1Iv~nkyl--D)&ښуtw.zk_'XmcO ,T%f}?G"Z #߷3֏FwwXV?>jG 4MBڱ]:ߎشd2ut|? Sj[ˈj !tO#z'2}'􇶾{;k#C ;œD׌2ߋU 2g@8ؘh/5,|_qOyoN\~ߙ*յe! |G fʧmN~ [#4 (b?~vA;7RpbC`W#?s;C-!eQ֟9Ȥ{`Ľ'!e2MGBkr]new\c}^e׹glLDDf[d7d7 { VO+b]iފT?0~ETf'1q`.n!z`[{'#g62>emGT|9Q2B"1VT\*kmXD >+Bi 1#GTp(zqjx(J3zYhWdsI8غWVTNQs(s}Vj=Z돔ܬ[ۓm9Y+k.B>C(q<ޘ/{͢@62s̼s0,y(yOAW2==tv5y&-h]%]_6j*tgX̎ EbRd*&J(CU"ŘOz $=|콄5ڐޮ5"fOĎbcȏi"둯쳿!6گ=n}>((38,yXNm2NZ>׮7jpN#s oܼ>CTgfm6TdFo la/MT}.;_FF4`P:퓉V jD i`F{*(}E7#hP*!_hOc%𛊪-Ȭ|чJ\N,_l7J-8KxVhȴKrnO IAM;>ŭy=>pޓdXX y#a!w/ARoԣ{ڲp͑N-F Fcp5x :~s0`/L_Ǿz'acv] 4x7,״6*RI$M(-DDFoMH ` 1j`QC ԓ(f(1N"F`՚ sK/3(oi^_~=rE9~rx_]@}rĨd' 9*JQv$z(ArιыdH2{63 ιC|?9/b')=8C~:;z:H_ lo_yg8#?`L۔l3`h$x9dw@P (/-Ѳ2y@NxːDŜ"D mAHsG,%菔s(7! rYCχ#n#DS"\Jz!uAi mD?<,~%Yx2xT!r+Dhk )w "{ #7!]C$\CKވ\hk\d5܁*[7kQɩmFa=oݜgecI&FA oZ+J/ u' vG涀C %$]>J:El U|;e28+&= &7bKZL+>_xxżZ=T;gvuýw^ )6k[۠ء0+c_PD6;t߽s1}sn {sshuέ@hs =q fʮνx! IDATH(b䛒׀?;B6%Lj {;?1XwF9'`(@:=<@ ]Jnc Q_3IS"63聗AoL<|\jl;Y 2qHG@?)z@N\ 1fЦm^p &T\;k7x)_":K켵֗n̾|o2[HT!ԑu82->p2k`=8rk*@G̚(1EQֿa@gn]ۜFe'"s[ 7[f f pW[(ZHo(ce7D%v@d~xp'9 雸ﷄώwK?Xw>p;29I.Tӳo}%=8Ƚ.QSY0oN9%{ª}.p?sn~捣S_ml>d;6ZtV}{#z~Ҳ8{9_6C 8ŀr"_Fbdo Dycf3M{r>)4G#%7 \wgZl)({Rxlݬ~>õ\~?~x IEU*5(Vs?dƒtǮĒN,Py?24  JJMJem[z}SUuC& `~is*.#&|5ۏ\og"K>Y톶gW--ցX!O:=>Lnnw>I;>*~t(HX/nJrk=zcOȹ0{*WD_rm 9;o,?*#2!aH䛵8s3ŽhpB 0V>)$5wk QאQ0;GJ6 t rD4ygvo\=wmX}lRR߲1|(rn0*CD7E(.([ 6 Q~*(ugɳ>^ зW }|9J 5AT |*!p7~>e iGX O'[VNx/Q2y7$Ͷߝ}ӈ1VQ`c̸ߪ,>--<~I9018+gZ0"zzݖdi{t⼦UdYX)%k uhrȌ%[f菈ViO.ol1EFtMιoԷa>bsy6彿=S6EM6O]`ߗ#p2D,c?.cٚgl./bG " 3흯!ÑrNyl!]~ eTT ? !y-q2xCf,aR|Ȍ%c-x~m?V:SԢˀgMKKA2cU|)=OCf/XCRkyf"SJ<&ˁBAm =%xDLR>Ds1eaEU!r=)Jdk% Y#KfGE͝fc~8)Auk#RA#m fPJXdr*}#ߡ@"Bʈ9d7溄Lm=w@/#Ɯ@9 ]O"+( U(FQyDi:kvܙhm7RUl\""6//Q!;t\l,+y݂?ZwX$Ar'|'~;$[hil?@>9dƒ)-sͩśUDQTTe'JGfEXXxQvqiCynAFE M6s\i: rcpdokOF 鋈us!b_B@fgrT} Ȍ y z݀L_g|;PC;X[ĪőZ b.5[{ 4AT3!"3@-"y4;U#+REe t4>#O8dXnzLBTT,-%]%ۨ|Z72 6@lD92G䷑YfP< E#eRN͈fǴ!fH$~оuY!*obJQʅ9!+0U!:UR][VCwZ{bYkumcM3WeԏKK>c5U6zB6&7+RSk*T/J1!<k@R"ՙ(="!D=b!Bڣ[Q})2lDo#X!v1"Bkm bS-z+RDCĠ@6U5"6*$g> @tE('2G DY߱yy܍ET '*D{(=c_hd:DLhk;CeD;\wOAL]1_Ec ; ;\p3W7Ϲ^mɦgFω3Wľ;:H.._FgC8ٷ?3G1?")ofQHp#'_w"0 bnG9d>=l"e܈D>D4Ry4A&.#Yj1Nq O@׮D6CN}Lc%d}ێ i|;(fR!b:"*A5e筰9C@% $>GP ny6D~+lȇ*nc8rg46U !XI sr12uCfՐ-j bNCnDi.^P0kc{O(v?nf5o!o;f1Q*]6D;5w#2G1b{Zs J:-qwկ/%〯}`k=F|* K˧g Ĵ象KEUj8RUc2F11C;)u>;α/uMݚȧ=o)`BDY8Rd쳡HD#nB`!FK*K\ws,HN+CsnRăFy,m\Ċv:?-lovy/rW̪sb{%/JNrb'^B |Dm#Psb4.C `닔h!V"@Aȗ)<`ۈg S|iz!BֿLm13r@`k/CĂ̘+0`#V"D~I"&ijܝR(ַ,$ \Xk8ͮ3:1)#kJ`m߉D jq(z Y =(P!8%CA߄:K SlZj*߭-["Ѓ7qx z RM+'9ߒ28^E ,ҝ{/y@/(u7qHumYެw:OyX%zܐsOk~{=}k*?u9$4ʋ1<|\0Tz9W1 ^pYι:tL 7sn;T|kd Þ6}EYxNfS& 0VQm5+5峐2,brf )LTaNkm ޟb`ɧq)+.!!}DќAa$ʎ ~Awhb?B?V[5ؒ~Hi QLW#e1\k̐!8/FKD>XqJ:A'A-Ll:LG@ok`C_ivzX} =+.G ruH'h_rcYcB[-iހ v:e%Z%EX)7#PxzEU*L21h;mgYm _Gw"`kx~ HCsCi@ ݭڲ^Fls4x͠yxz 6'UXo~qdz|K'53YKvWF|}: yv܇ڲ৕NSy\ٿǬup~ 3MgyCW7{ =U4vo[p1>N@ q g Xz$W"3MG½6r#sqQgs%Ds-sp4?BϕȚιCޏ1ϕ|*QsjS2Fڻ*5 )#pP+2h`UBǔF,X[(r )|EAɮӎ,Xa}n ="@Jx2$o[E˵b@a"j zF۹l-DZjsaE g2Ds"l"'q ya^GocFD)Q5!-f%ϫh8k$^=E&vYA%w ,5&-V4~A0"m@ן}0cm͏ 0qY(o?Ć"y*PPQ|QEU*Et>Ok*˛?{Wq쮺lrXa&uCK&/eC M%BC`i ) \޻$l'y=vo;w;ia{>?nKCs?nlGe&Ni\_k Sg-:Wc5{>9UY۷aE ZZy0.{ǯ؁{Iv5ahս|xl~\_=l1Eَx8wȯL6q->5C*ZG$cjR/d9zkmx9Fuor1{;!1ι[5h|399%Wm*`AdN\}НsdFd) d %fB~D,AH;@F@Qt&pW,L|UX#Y(mZҎvLd&MDcY{DWY@d@2B*Ht]vo,Zfs IDATyte4;kP"V _rb?x54hvP R\Z x@z;\б>N븹Ln,J]QϦKS,X?g5C-Я'*t,FsnʻUL ҿj8\ѧP^3[ pҀ!EH.Fsbz!&qF4k\c5 yݐasce8w9xǤSA5$*2BkK*VYXEuڢ1-Cr~f)lN+Qq \Dk^ ?LūjZpu#s%:+8YXa VZkE)zw9,mAfbS޲ Y;B[^ԂC&҅Wd̦]trV6ܖ#yH9v#RL"#t!?b RFO#)>y#Ey)$̶$ l/.N-bGβ6N_E&Drdv[d}!#bF)R/x9N>o '5d"  Mw6)bꊭA1 dszNص6 vQ@#֖Ή`9u=v{~vP0Z!{p̯紎_:Þ<3|Str=}P>m2f!qr4#ťv^zG+Qvn" }۵˦A k_a'Ik]0ɕ2R܀mmym^yXtul"C]IQӼ5F܄' = C^6^>g5A};8lUCg XY]RZ=zsEV8z9sn[E_U G6'ʝX9c 5,bew*7޿Yċs.1ιf<HEfdPՐ^mP(D&b]btBY.4v!E`h߽KK9=@"]D1}V!`X(f `քQKLBR؟D6/#p@6=[Ћz q"0@!DOE  h1}|Z6Mv&*3îs+bEom->?1 alO"ST(qUW^| eceϦ22q4fhx1q ,3 EE X= f DC&ھl:]Ͱl4ZOTtKk菔ˣo2C1v^aaݓcgS'c $ڨMML'Mf~?b^ecndzā]1Wˑ/ZV.z|b}6\tOL:xϻk>0r+u^}yxkY\6JR)U(`3(}s΀DMˢ)qb`"fY戟Gkf̶~=ιr Zp ޯsh yKHerD̦/Kl:-ϫq ? u^72xĠ @ ‰DduuEZډ||q`s?,g\^"4!֨R7!6Ʈ= c!A?.8v]!1$|(uF(|/bZ[1}bBj=Q R̎U ة F kׁ@p8ټ"W_AHڳb#55="6:-$#@z-T0aܨG xeЋfMӈuJWfɅ el:S_CY)8тG;rkYvm?W=.3hޘM'I7bS5=}Pn1am4w7"f7u|lS%D6|Wr5z?G-/}m-nX4/lV5??i([':bcM/=wM %>&׹h'ޭ]ce-D&;cS&MmN\ZF ;[✫73vp]0cDi+Gl @ vk'Ul{ ^Y8߁܏lV؏9DM@+#^ Bu%ŮYj;vuWpe%<3( C }`_D9k}) )"!@)y@KF u뵄lîTx)oV@7ՈZ(1IlR#f,}hc,w%EkGN*gɵL.*Ier'3cDY> ͋;מHkGWBsDeXl|CPn6&Ӭ!ugH\jmar_wVzgHk(B~.Àj\ָq=w12ϗ䪞X;z{|+c?wg8ra.V9܈xYv>e@*qkx@^{}ښHetRQIGHɓJ;]eYS9ʻ_z*(ނY|m>U6wՆ5h#(<=<+1}+,j򽖫-k)"E~_-3 +N>j@) T&)݀/gS\ RngvbTȕ)ވ֭Ihacv͵4uRbQ~(iJ!(`!}g;;ԆHB;Ոn\|E}9!Z+bnJD q=0#SNoDSF&P<`zM0V#`$pq!Oϳ!^B.F1킛QT&;+l,[]HB2;/-?̦O2#%Q6Z?@2W9x}Q.XL!+*n\OVmDO#ƫ>T|ks`*2i 0  @q4:66-Du[l{ʦ;.ek(&_FWQېi fd#zG!LZ]F_pguC/wkgӯD'49G/)HX?2߷r󟯡= 8uUXϥIcn}|orw[^F }ij>|3$z\_jm*=9Gmb>?i%oZ}5,܆w0V-0[\m~;i=#*XHͦS܁(@2ŐM'W2˰|  ;ZǠ)qHNCQ Q" vu#2Yٽe3ZXz b)FQ ,YDmݫڑD I=RIDŶ lˑ# }S*Dfk*ZWX|yTnclLer@,Re35A``>Ndmc}dq߮;V'ڏ ?z͵54oxwgw!!rq wd˞@/謮5ѿ OG#å*;Xj\CP9=^|glo?F4TEMu~b޲:ؿpps&ݱD9)KI,F ^}t©3?6d( ^#:|}~3D (.jm9p{!S6Z[NAs.اzت\ڃZ3.&\_ l4q F1%.C"]y^rqLl:ټ=ec@S*;1 Тb|a]+P$g'E!SZ]P1By=W==ΎJCk ֖L7"eqDs\f"3ZĒИ@lKD1Ulو :[#JpbKҞk6!X }A)\vd +]gv>:F{VG۵>`G`r@bcx6a*;טn^1QV"߄܋RWbiޣ}WG0 EѦynm@T2c"֮UJA@2וߣbѺĆD6|# ӹRBS*kQ=Q%x6׸#}c;llUG^jB>7ѕ=s11T܃%2$_'=91Oٛn?.bF_\Dc}yHervT?=*yn"f"| ACa؉K@Iuu-b1[HX٥@7 s Br&ؐKh!`?W鏔1G /b@hQPj( XdFf;S*~:ֆ'-2M`)r ??p0dj,(z@F 2 D6ڹUȔ=qa)cu3s8^L!Cϭa[9bX@kS P8y܄2Kk>D wa@ϰ XYCI!֖TkAfŇQh/ٽ"IV1}zȹ(ݏ@`C3oKmBehm^ȦS܏Ѧ`"I"? Tq kN:blT&`yw .7"S}\6W!9.KHmf7E;;M|1-UAկQ{^55s._(␽!b%|A?c.kOan(q|Q(d?2+\O8sr}@wyrn BS^fٷ)&NY>v}|{!b[T ׹>N]=#k`씯>2&V$fɍ8%YOu>^vzD|1LAB;)=؅2`h\+5-VÐeߔwȟTUږֶIv ;)2w(ALD/k2@\deR !?aI[#[DKmo1`݌rHqQTwHRۉ|FYA6FlgBb]h>"hh) p\48 2T&dSEt~~͕_3Bq3QD`{(<wRC;yƂI7(0VD<+l,#@]*PbHj\C" &NvQ%L[BT!G}tqk8}x7d - !xWA4 @|؀W` ?ufGE>8C̶Y)bX؈UX~Qv]>:O!W Pd[O pR}:M4YomYd[?R- M}lB=i}(N刽)B,U !R{)VNE (LkoE bnGO[j_Go'#мSCN׍)7H'4Î/Bhǡ%d _*;6Y6Ǧ2#~}, \)%:z}߮sQTo=nLJ r R Jk@$DQzٽٽ7 9ۖT&W9,GֿZ!3IgikCTv;AY7Мۭم%涁s}cwۀʲ_fM;GzؚhnLyn_D@l:~`myq"ȆE-gs]r܅O׿ZVXhگlxiM3KK}>$oHg=(&ڄ=\kwxɗ)14snS.zdФSް`k0vً$]n+m*M~kH̯m |FWA w#e˂);X%}]iI_vBT\3%z[s'~cι~rvcs|DKx#䯓b='Nv2ÑQSفs<D14<^W#SospATh)JgO kn21T@\^P?G} Zh%YZmv` h[nC`u7PRˉa(EL.Θ]'*{d})~vM)J*  >qzΞ$$&{! ^`Ww'.q}O)up)+5hwڱ/\&[Iзː%>뜻 mPޟ%rNH?D֊9B=hG^vg`nds@2_ǻ(oeger}]8o)39(7=^ ?e]G؎6/!!%y;0)ÐiDkeȏRALF2o} 7ˈe!e[|*l{#JKCR]@d2Ir NDJ? )Rw F/(}LΟ e ؈Lw[eT^kǷ!3hi%bކ#[kiȬU@VH|ʭ< ^# a!*9)qyW,7 'ڳЮo@l,CfqZ$Dy3ļeZ1(z~bG:kyD8_A&@{=w,5{6-~<^,G(ࢦ%U@suw;-Ka-[|( 89p6bCލhv/Jer}vD_k(;Nl-6ֻh=)FX4D~Ve4^T5lXV>玲sLf/Z>>}b^kGEz;[L;o`_33;h3 8މ cŹ'Ezh \}w٭U}Hn5)r`c#d\_W)$t>+H;u{n!wd5XB)*Q5n}ߞ+˻|2NB5~vB 0\~jcLSR\)2%~͗ݫon㲻 vͧT&_1=0lqތ>orT.,6]"߼iZB!p3}nη߯ ڭN6䆄d;B _e_Q5x7(|;2/.DqG X{S麵w]OaK6zisN؋2k맑x#0sߑII܁v;2݅^!@X[fD/@Đ'<Rt؄. t [CTl=DjcڀXUv#چD2X?; {6+Rܱ( ]e7 6m ~h]$ LnZXw;@\g--^T\lW``vY ̓=׾UtiicSܹqZۖ_ѷi#?]f?kX'DqQGH {}V!Ilx-ɦKNOdjzȍ_owCJ}69MQ^l,D%BKG̕N] b#m`lFP) hB,ͩ@L>߭[8|1G5-ynG@g5d{jP^Ky_6s*iZU߾1:VYw{Ğ}_h?]PDkrö|Evsgl+ :tNAa&@h޼<|99ZL~tYhM|#ٔ9Wtpv3hQR[ t򩙃A "#E%YGJ,#_-!qi8-LEe%/>5ӊZ~ؘňGJȠ݆||vG0o\օ@L)0/-׳+Xkz~kZ\U֯ v2QPh#3"߱`Bi>f)oy,^!gRX^NTH"Ñ)m>>N<˓(-EC#QMl-"J4'BIUh=N'&)mPʹ>fzfqKtOŘ&fg;Uj_=V~C[XdklY{O & ȕs!܈ޝm#$;73|rw2i┭(kNzþW9cq *бw ?l:n:x۔=G<^ۻb5ÉM1A94qvEbr{ݎm9wdr g@"\eє8{n)w;^A[9\حD7bN[or:Z =єf|-W tT&7.N.cGsR| 9jߍGlH Ñ*(R:)G}ha폜ƛ9ONDmKsÛsև[i^]xy8nADF/VX<.8W_F d bF@u-c EՐx؞ݙT&A %xEAWƨ{ޮs;o-AlgJ%Z_EJNC }Q {iحE"߬mt)o=|B*GU [Ϻ7VMj%{ag6»%zdu=k eƊG(B7|~R0\5ܳ=fT&(-p]͐į&M]kHS6$i"*/XIŨ|Kl}?9z[Nud!p"{?T6|8خ"ιJ}9 {{ݮdyϙ73?mz@*3i@`)~N"2xOG r ^/U7)fĞ DVv^_2Her4Ty(J}?ہNX?`xbds}X6<]`mKKnEWz)2kנ9Veu 295)ʍi]U\1zȮΌ} %<=NjEim~trIkg۶bV %k~X; X ]]EpH"(U,!0v 2'ʈ߁@1,!&e R\_R\bL s֮m+ITl:l*r1cQI[ߩ28+d5!F_j\^c7!zB: k37گHHbʀqLTW"ŸH(~_y[@S ߡ| ɸ)Ґ{r N &UxK옐|RՁX"p0ZLˑowZ/T2#^̯N14H [{F#J^8 lF{|u!7OSt.jnQN $_M'Merߴ ۀ%trQ~_29mbP`<=H>uJlH+{sQ6ADeq9z-'2=>|Fyx]k$2wA>aZLer-){[i_-NJ/ ff$:N>o fb>q q3bٚҁ]6Nɷ5H-~;u&[h(-]]_zuw/ q%p:Y~Tŷz鑷*:":)kpR7xC q%)Fx5 \ fm5F>`"{1Jrv߀[ շ &# T0("b*֎512hBZc!gס {a^ XsOG o4^vxBL_2!n%DC|E:`›A_Gq4pf*l6Oer ˣ=Hm@@ ͛av8% 9vS[@(ruY!6lqyM'KerGDq>P&>F+.=}jDR\ ͎u<32}Iޚ »K^'}uo; Jy+5c [uLZ5V=~Oz(9 1+^HNYr+тl:9uC~# %[}E o֟wBs!BlA9Gz (lO%DJ{ roFfS OH"@elǑ#Ž}ɮy<7!-DXmmm۽KC xzPڮb?evl52IƉ̾},C&PWi1=MerH6L2D?]c; O垉T&EEQ]`0Hg~H﹫oؽXyP-"ʞ/k]?pG S(hnd z/v5kLe4N.8bKJ9?F,qvI]R>; mfޒVk6np>)h[ZTn8 ¯.mFrHHyO}RHdZP&VD}?z"eGʮ)Zڵѝ,O*AG՝|m; ^`. PP<2=hCI}"lLK_(Z2LU it}8s/Xjxs#/_~v}h tODLk)07hlcYfIچ@귀R_N6n%([d<$ͭjRhC>78 xiޒԸ*Ĥ߳ևмu݅Qvy34)lH{͌)JX+Za eFYěl:"BF !#>GPHsefP4b#{)SYDh豐cl7l4eD.nF[Qn.@L9 &*'BڇjdzkҽO#RBTU2~RK/ &Qg@b9ʬoӁ s/6;L 6˧^ 7$k!nmmg ,@+#buID`b30#l:7_#Pz(s6&T&Wۂ;X[w)q #sCֵV]G*ے32~HQ]M'2ahQ}aĐ?)_t}̵,Bf"Ėmhg܌鈡*Cl ;>Oj Rjr)ؤ DLTdY̩N \3)'b۬_KP>pd6;UY{AD<@vnodBfQPV!!&j#e 2 @e?bCYJs}WCJkjZ)}f.{?]ĺ}NCdžd4mB2c+%eS.5k'bN.1>S6rm@ dt6F!q?c6_FѬr~* iMnF-De/4n'Jײ59geo!"SzTsbgj\C1~ DY)h6_FX?uU{.}헃i޺2Qo.| }#8."pEI_W\;{Nz{܂֣ng}V]c;IS0M'_B ʁXX+t~*돜7"kWUZ#IP+PDZ'CJBf bG! !Pd J H_ئ&KHTn\|76#.mZ@w(t.2`m2Y4ɐknTr:X `1ވX.W#'ַ l[15(ڙ@렩c]uùu6>bkv!v2<Bݿ ;?W8Mer#ꏨw9QY a~NGIq]xjuecvϯwrLH6r 8yϙ-bA1O^XߋҋRF 3͍Z IDAT{?;OHLQeaS׮\leۆWچ$ׯz6+[W%͉&Ѹ,{#E++"$}C\&{97t%}[sAֆb_{w5O; |{sR$~J\ 4_#s_' 5F(>~O{͌m2rTަ1_C"#HQ^Uh\Dx) R,$8CYv A103d/kNTȺ^P1xm0#u4Qn>H~b:5F)%&#dڛ_h4~C-?kMȏZ;G܆?_b|ZU5cC `1[ik%%C$ tMx q}!Ĺe{!ZGKVh5 |e-6;~ߏ̃_; Z#vp>ݱCy~˜7U#P9v untT&wx,= ٺ_^9:?M}0!0%|뽒Z4g1a4Z_NpŅkEخx5ҿPps?ͳ6l.U}}e6䛊 <my#rp"w<΀D#K&'t,Y}Ц&>Oy(z{蜫s7۹?G:ecιzveYιCtL zd;dcLn ]Bj pR6lȦL.|sC426?wQуRh!ݷl@9&9"6 w[c?LNB0FH 0~Qnm%<˅L{]/k&)%E]aMz@,>P/R!` D`: D)"~ockO|ڬ?[[{#ߝCrz G5:OqC^נ_'2 >@zFc|WTd18f=2~ͭ<uظF@,@JQ3 c+8ېPkc%@at1RV~l@gͷ LֹZcSMuQW8n` ׾vzcm|̂;~jq /tNDk%QC;8mE11+ \f2[2K㈑*Cs6ݱoDe6+~;7:ncDضyO z!0)ϐ¡T8Itl:S@?@=ZX`}7 -k0HA׺y,e?omm,G[C Q]c>2^n}K jw4xߙ߂,G›)p+|5Oq 5pԗw )#6gChͱ*4/ T #==2bۓ捤{\@>{Ͽ6B`snom#u~;3x9o+Vdf,ݒ.!%n AAFNG"cH!ц^h1l%t,lr.L!2;d JvL;Rm{,RDOu2_>`ۗe<":jG,X;/9VH RT9C/C>nz2mظd\A{[{p"ae>-*t֢n|X၊>kGyhk[G3/?3]K%m\wȔ @`z29=؋V~bdw7*^[ox!a6 8bYw%9˯lKw!Q}ƯiaMVNu,2Dz=*4WYrtG&<Pd=fr"V\;t7U`.XŎgq{9W ޿Ys5̔_7 ׄz78sg{ow&x_v΍?<;7M`#]6e ?x)N.qgMnGVR!xG|^&܀ M)HO@lϷR-cnEf1#Qڌikg QT` r|̓¬EJc%RB#NMer%)(oՕt=Ѿ{(j>Ba;09d{$ )#mEpTD;?,ȈR2K lɐBzMve'fMBH&/{_g[T=ыD5vVi!R1 `{'e_Xm|'!bvB`)Phf-h c ߢu_%bf֗mG'NFm6/;H8l:y5T&w]P6< gJj ׯY\rIͰsM']=6&q|d1on.J>筬> Ҷt>\iH/h[Y[W<9v`uͶA EdU'ռ5@-amx mES-)ѾEєιU|y+/$.?{BsO9c"}Ү &z}Y KEw}*n9ZFe[dGa"e!ɿ?3ՖPv$2ŁtzzKZ'#֦cjVHH2#~ΞGJs2QPQHj%2XXeR"`Q*C`1l@ RV &d2ݻU-QTlv ?j{#U ȮĎbu9A`=` C7"P96nU6C~Cc#2}2M {ޡzdS/R\IɆ{֮/?NT䮲OC>J'> mM.KҞ5z;_y঒S}-!h!x}R&+3GLNELd6X/;d+[@FXr_g?lG9b=;q#ȶ^C@ލ]M'S7`2=J)")_ Err(!`659*mwdhFho%9 <(fnEg,F2o ſ7 bf! /VdЩ>R*X8c#* \`SDʥârL1m힭֏P2ǃ /ظ qb%1(!gz(PdQՃ}l><2g"7ƫ Eڞ;p+-1;SPd*^Ȧ TM'W[@6N{UEyςރʡmWՏy?~1,ۍ(z9Zլ6ֹo޻7 -{hϵE; ܅޽d .@lmHlaZs/qmL9b kݹ m#p]6"݈m7_F lCȑDQ+k<.sR`]SEb6|e!4Bnq|yY! :Ek.2MqxھE՝?2uCXw@xId8 C:*"kPon fd^]3Ie6'ٸB ޞ3:eW@ (Y~(.YfoDZϞqRasoϦHerؼ7N^L^cb|s57]8> [q#TȆQ;=wY]=KJky^rr&\prw]X-m W'L{4 8iq l:%]%۾lє*) ~Wfɛx(hw8}12YpFWLL_FP DԄ~1C#r @!yy%ņe[W=kw1V-m@Rg < ;#Jm{vM%Z[=E8iDg EX1J1ZfqP|183gdƣC`}fCbV4J!v߅֗֗5DIF5DYC!B5!V '!w!}]Qkb+c-h.$Y:׫?Pͪ7V*?33Y94uhFખX3uelvItv,ۢr&"dɎD>^ 8 l*@RRu :^<2Dy.DcfA}Ѧr`%*{{U e} :Gl7^F%b[֍%(:oR-Ȏߎ_ p6 3V+2IT&@ ߅Hgx >\k޷k `coϱamR Dk%,Ge ^wCL "SaҞ"(C5P=>cc{ݯ7r/Ez1#J݃|Gaa}W"tB`j6dm֟O5ac[^ȦWb9ֹ!KY+>Y8i6-[u/$\Nh[z = (h?.]-\u(Mt8Z$IzS~G{5g폚~iy_ײuItɶ- 3w8AtmT&6bLBo!e0 )*@>kiFedfKXMT39$JqrN5QZZC{w% @0 o!6ֿ3l+Z7DݏLԟ$bŚ 9[6_AlHȭv6ÁtrI*{ ޞvh-y6?21صD Sx HT&w:}%lAIer5vʦOlQ%T_+}{y_ &'\J `=G1?k/>?0GT-BfIk"`"FORRg|_.0%]6 ,ﳈ*!8ܐBVbo#H!úWYא_ PIeqUYL#UX[x Qv[+:IW!&j?~{!SŻH (>f4֦++9LA M7G uh,FU)1̾p3"UDA1.@=m>os툀rk}bBy}9b9w[<=y͈=o>ڻ`+$KdktخDM7&2YdɭrIn.ܞdhۊNW*L8bB>h BD_7E@ں=$ :+GlKy 85֛dKK&X*D{ 1@rk]TC Cm}bюD&P;4Li!ik]3) (G 'wGƾoG'#xR#_wo cdfwG\GTuW& x#@Q%Dv )ĵEL֟G8 P1{dg(*FT'*BܷpiK @Wn~~NGı]!gU֗VO#kca\ D&V(qOi'&fG~$piEǝ`f6gʒl@֟tMFLf;ߖ/U1aZ T$\}y_ @_<80OuUl}m#ˆSal$\n(Glx`mDkt.!>Q\NǷj ]b✫Dk: oqj \K\8{A5:TtS9q]_1* 7c5:XlєLnGdFʶR=цT6TPW3G:8 /=smY *"1oA?FZ!ի~DৈN+;Zs}}Tڽ0ngpQ>eބ}Ykydbaiy%0g܌"Q/1 ~dEÐ 7Io2Ui=xi4m/B  ~{^+ J#Zkr+]W屈[DdϏ89"mU-қ[ۺ=mjʽoϩw{m+ӲdH?}ͶI6}u Wit97֖MKerk-=D_XD%"z)2kȬO[Ira~8b XX;OG,֗h "Q@;}ˆOs }E,NYhMl3)%>p"}}}/~ђu[T|}Kꋞ&c܊h݉W(]U5Vʒ/N_4K6AsOsqs6q_< D qΝtJ9pbdaes{?9wI!]^C}%{mGc!H1ι(H⽟>Y@V {Nϻwl}s=Hs,ڗ?1'޹:v#8{?}#M{VecVT&"_#ͦuI=: /@khᥳd!ɥPd$‹7Z =Цb]# buG D&|3Q.9֞$2>n U)h!AhFd8/Џ("D j`@ >ݿRݷ6d[[[6^D/t*~B*Vw+Fd6: IDATRp}B`p#pJ`{T zKMUDd֮?ٳZ# ))16n־컛# 1 8:ɽh|5%_B]ݮӌTMض$KerR9v%weu6N9]ugY*A3v$:0R=r#lTYmqϼ{.v䄺!enHYɛdA ah/ԽιPxnD[M@{` 09w {̞sng E`u.;z`}sPd̆+|  CGOw 8dK:羁7t!L0A] _l~`p`4ecA@Lk(ȮtG 亥2"bG@  F>F+ -|" Ut""e)R͟P ~ntGi3 ; cp,"@u&zYbf!g-5[ТoeGH̷15hGLCx1B$t"[deYa0=Z;">\jBd~6צw f@tČ4ԅiϏoE~D4í!i'{Chk[3M'?JgO/5!_qhJerl:G-ϣ, 껡#9o`}>j9wLJ+@O``̕)ƞ.->Euɶ%vNWFWo6Q*?fx5QJ[0s.薁TX/C'P%6@[s;Y6>hsؐ[~X(*HtC@#kXN5Љ#! 40OD@|h٦Xl:b*;u>ő)Nh퀨tT&)Ũi`,2ǯb% pDOEy~۱mFm XakA/D. ,C7? P4m}="b~3վ(XذާmA`rQThx?[ob@gDXٳ_C $NIer"߱f_DY^%Hi~D"p}bnP[ED$~abjc(m`fW"4"$eDm6΁e+Eh'gɹL.v6o5it#sOerИ\ ZheeT%m|o2և$20i IA=׭k̓}7o3,(&N6nYk{ha/zʁB_X/Ih_ڤ]ȌIDDZV=~s{?9WsС?ޯSb=LXg8E{L&Oǽ'X[س7sLW6FבxC,eofbH_Cy,kAsQg!: BJj*?)Ѧ4kiPv`m!n@M'?HyLGJ/dѩ%*C2N̷C#P]#sގv":5=`rdc_w홋ll^A߻>(jD $`T&w 4̦dBлI6{P}(!:?B>6 ;Ʋ3ێeoo'QWn.#1*,fYۿe ,[1) *up= :\M'dߨXeߠ1lY8>eI|eс ׮' ! ڸ ҽfv"s; W~՟c ڦSn[Є?jW ढ़V}k^/Ԍh즱Yt*]AGֶ6SNlx{vΕ `vs.8vC >9eؘLC;|WC O9zZ>OF8BSJ8˞sݳsno97nC7pb;ѰxZ.  +wW=v]{*{Ѷ5!1DB.@ xZQ`Ho32M޹55m Lm vÑsM 1a72+}k@U9|f yR(iSO((a>r-,Em3[`uOCֿk֋Pl~aw<?ژ8'ɽM' f_$L fT}HQ „h&p=o"pI޽dacs[l} 1Glbܕp^7ViQ.Ln$5Of/| 0Way%{;Q2 "8bNk;n1 W2Z!ǾhC}WǸbiIµ;mihXβٖU]uDnDõ%Wcwo?ެ>z]%/5ι/뒇ꆔ4z{-綶[4Va;&_97xLk/yg8uMAo7o:^'*\l>Y:V}3LyQe}eh^fmX8{>9׆K8j;k7OtrMIercl:-בN/E|"SA),_b1Z_v#SR/Zѩb.ZD7 dR N~G[{"}aQDi+X=!~viBkжj!D3!6  ^nnc*2%^i>?Z˾M'Ner7"CfԪ!܄،wy~y)!@6ݮ1pSC_!%mP7"3'rֈYcuHQV%uo(ڲO#B.Xгl~k# W?wRx-NnRT&w) =Aj*7A,iFO$\nBe(c2b!_.KV(KBk{7y_wهhC,w?3EH/ a,!W4cWSѯaѓwjа+b6JB{ )iD)&b4[" zzB` & 3 D&ï"ֱ%3S362ѵ!F[)#p>i}8+ONLM.@@8"aϦ2llP1 Q>EƪƩ("u:R|u8b't;=Ǯo7Sb!Nk@߄?q+lW!_?(њ%\y+b<cZfwrF*{T>M'P;T,WEP{Z;+к9 v|y|lo%`e:@#WмfwZ7z!b2_kf)o~PumP_n 0'T&:̼BXiv "6< @?CGkKp7V^ Ppk0X7zg!ay_g}tɿ`ń^d1S&\$َٰ2#3qRgɫ>I j<FX4!@ wC֡|9Q٤ D_Rv#n# 3GI X$z U\_tR:bz"j'.F c:AfRԥD`,mV"s)Cscl:yu14Sf2Ln(l:Pφ۽!%#kwP9bF`jľ}@ʹ8oαkLIu:>vj:"0ZKd| bE?obb t2x}XM'J6t1١ԯʊ3ĥֵ}>!!%GY ѡ?i"sy`q|.:"N~ ~!W]`ȃs6?u.Ek~7.=}^Xyhߙݮ[1p֍g|8z+素mKd; }m)-nAƓ9v6qHy~(MCCnD%HZ¬ j2_:P8EQAJ*[w">EtȴҊL'D[Bb(o.b@l@>A`vm#-wX?"hb~M'盃 ^={xkat/@fmM'Ier#e#Q_χB g<1!2qbvN@ rq/mB>4? F ^ Cf VCXc4V*,:N>k6;l:YLer~Q^[{LDk(;ʷ,.9ꪪU"ubo2HLEK)ìo *HZk"3gưŞ;@+g,yqT|<C~/pyxݪōeKN7 ?28l S={}ǀ3? q:1CLYH3~oCUףH~ϷJ`9=|[v jO!#etO۳$*>iE@&~P \ٯppeYwվ;Ռlq(,X[;-^{ʀLs]tm̕_E0KфjGP%d6[8\C;Ԁh~$@]Yp%ff".T@aߍv(|a٪~'"v+D@%bv@ yf֧H}:Q%OaĎjϸ)M>LXGU@[BoyCDŽYM'_1y5V̍O"0sȑ+>/]CecCC2so/V+ףYdxHT2`T*;#Nn0|ÈwZ JV"?C[-PGkȧW[1ąՅ W\m(#u#ġ;\ o|ᝊ/ѻZ -|XK6̄Xu3;}>B'%\7%uO`i~8a ڱ͟N~=^d T+߻נ7~wa PoFvIlͶm?ɦaPptNy?=`[J^|ږfp!{)ȯP^ L` 慎?j E!X8&άܡUzHl1H1^+-DIbNk4 1!`1<Eksޞw,bڜX)vQ< 8y8*NN7 ɝ1Ϡ bl:&ɝ T+'W1,}򴲞kEKVB=f[?I0kBCV _nU+$\Nm >=,Y]>]l=}ymzi'*͵^4#=^pIZ>Қ1`y'ZCѪ 8 K4 W&ZX?ljGxj~ >H |eV=/-K⭫eonk]XlsM6{b:|lqj \p(A) fKctre*9ul@ԳHME7+WNϚxzv!rbo@&0 @HăZp<ء|>CHygNAf^ ũǻȌYLp"SCA냀R)ݐF,]4eև#EhؘΦ2#05 LҎu-j0eam%bn %2i~×G,u^MKZk`x DϹ6G?,A! xE%6mˑF@@(Io=qb5Lhc~PT&W"_D )`l"Z#l:9Y S 8[ Aȿ*DZph<":(txѻr1.#@*UmEI5A0bdѐp_AIk_QW #=ۢTDT>+$חk~wbX}ݿ?`OV׭I}'=fcϳўx,:<Ԋ&`8UCZղ,>ye*&"JB+ x͊o?߮zd(7j_dYi?XW 9=t&]+P"R IDATϲ`h@/DH"iP p~vn1sL.% CG*A5~{0bb6g#uv"%  !:E& B,^!ENۼڼ̋EBUhFLjD|t"0*ɍ@ :ocFdtaĝSb;HRk+LsOК4κ Y fab*/A[KF'w]mc@>8?PE6Gu9z0G@~ח(.aϷ}鬇ʦ @6ph<_yw#* &-zX_h>\z|/mQ me{޷NV͵NKqUK4>ݣfޔx/=>,Hx;k5ns#6xRb[S`L@y1<% % v(Dz;B VGOkG"E81*O"Pt )&I.Em꼧غ2ƷXiuStT&w*bF"@W6׎8X{9)֗cv ֶ9 POH톢TCNyT tr'h2!Ȅ5<8} *t Hz#^*" 5!e&k5vYݞ F8)ؘstT&DH{Zm,簉b>Y} ._CQ+bSC.D0!e)G{kEjתo]F)hvd{]g45u=hM.Fd$ZK$\u?]S0:=X'>ח<$\}[vBسBk?_s}9ލ %s_Ne35޵rA~䄞Cǔvb =)Y|"[ 9GѡXfvx9Ǡ5179zu[ιtU>V`lCb&HZMyڰ"І=$Qٕ2JdbX?;2G mmO;GeENJer0v瑩!@8~ ): U]3 H!`;N.4?Adrɺ)&ʦϘ HOD@T1tGLn 23p4gY?#&[_hqovӎ@G&E1l$g<@Iv [,pPSAD,i-b3Gl:UTl @Y15=^6Pߡ}SC} m(c.bdBcNdաu^j]u?Hx4Qnv2~bMiKJޥo}{vħt& _Q:bɱC0w,Sw"{VmUڇ|AfT imz)[]FN^㪗'\w(ι7[(/ sOs=fׯDF(+@%|,X" dN:N1tZQ$Tף/Щ$/h@2"(86PJ`j,F[^oh*@mhs#M#Uv=~q˼ٿNmq)AR%RĵD湰9DtMS*><ÑD Dl\y 13' u b"gT& b"P9ȾL}UsU۰m8>Y.E_1Ƽ4"ha8)ZMe+]|e)l !߲cE㇑T&WЇSʋ[h-A nG9hXz!ƯdBP hAhmIerXo}Ey_7y[[@ivً:h{ԡ_mxM%ϙXE@mOlގB8@y-M> WEX4XT] W0GBdU،vc6t8ˌ)M=4?u5ړ6WX !޷#EV=uɇ.fLr!R@6Z*y}JFL`Ġ!'F/;sH񾊔b *n7rHJT{^ NU4Δ5b{Ch(7R̭֧F^VZQ~bR f1X*Ker#e5%Ed>osI"@CJ'"Z" s b*{m.,=cDF(䝺6#`݆wAё1jmNpse_?8^ֲGIUWt*Gh,`Z6n[#'W4ϴ.)}+V'ls2Bf~܆:j\Cl΂8f*8zz|כ*f@;"֨Kj +c=~Ťn0+B?ڄ_UK(ҳ&|3$\}7`JuǿIÂ?ӬL"S W_zL!y"Ѽ|y`uy֡^{kUo{KO6ۋci79:*Myswk{6ps\eN!\@s9,[d+vkը Ps7bBFukzCQ} L$Eb@̞(\ F r0s@=) N)ltB#h۽z :Gqڵ4/MbGN鷡hofɅVzjToB[ډ6ЏC @TuHяD͑6ާYhڠ^k9O%DK!o}0h0&S瞈ie {>trF*a6Q')l:Y ,%&ձ^ qiřm|_C뻏h~6#xZcuD.;J` <$hM'nN6G BɞZ]5xnH.O@n0rKޯG4py׵[D`R5n> A ݘú&1γ 1̟F1d똢 xU 8oU(4ŧvb_Ut'd󐢾6{"HA@Jpu<U70a횃^췿EL]sLfw!`(:%ףip1B ?sPJ UٽCe0 "NAnHy޴ hZƶp6'$(M &a"%r]SD[@j4΍Bjb+@ {ruO{':h ZٿKޏ使þozxY|Dv}/L9 m0u>? Q.Jer6UHY5"d4n'ɽ̗Ո6SO2ې~.Q! Vͷ{seUֽG'/M]힏"ل@h#Є&["EIkuH1wgރ[H v2 0d(AL+DA ;2tT&wjk6#2mC`D@z(a9bKq) D{ tH҄Wc:zw1\ XXHYhDdwL tr@T&w\^֍2t(&bw}VG`XwXkG7$]v(XN14_oSult4 pu̳O>i:K]5xnIoW$\h\9 ՓQ@s[/?{4VRx9@]hrc*ZuЧk-iǮ8 \u{Qlad||cŌebU42ʬ%^H#>"trRG s%ڼgR; (1iCfF E6h^l;7I{|A W"=bDAG< e xtcS0)z.\3=~#)+o1ob޷gwrv 1_"Vgc+vdy3{,E>R_A'#d092`׮Fx&bnA@β5[_BURl:ٞM'v2;{v~$N)4к f؄hRB aֶx楈Z+T~hvLKcyEF&3w6&\ڭ" g~M+k~3\^އt8Ӡwa|~`Q;5u~a_tIl|ܘ1B?T&w(3?̦XݼRts!X7 k֒fDb)!+D-B&̌#ka'p臔J޽i`IB Y[{BA) &#_r(E^yA H~1h=X6b:#z-bCʏ-[L\ NͶ"{/־i_LaC`CH0l?2Dt_nc* χ8F)#n"buvw`"vq,=jfNCÜYy%Q:l[ێJerϠHk,O^ B7!s4!CX+C@ !aoUؔ5h욘;]N$\xQAmWcPZZRHer'Upν=)4ldݥp4r2/FC:HtWD!,4eh/"Q,!$Ԥ.);0I!6Y\ۆM'B~-܋ǯtr^*Qa"; 9>tRT8: $Fʸ6Ƙ=UjZ= |R("Q>*AdU;E0G).V!Q"|F%CDZz?wjC _өL8ܡ鎔̹R"3f^ DVFk!T"H@Ȥw\U?g$3 !K-T,((bD'SE0vX")*eDPCX0!m; Aygݹs{}z^-C|!MϳC:-a$O aV~n?Ih~UJV2J˦oh&0ds1m|W_./v^D?2: k .6D ~M+Xv={9ϟojwEY?k9ǢU8HEHUn|oۺ9 ~56'\z#&ە3D*n5䃦2+KYPo}y*-N~5N^o"~>E`z$"D LKow)ě~e'f옵j3pPtQ!z_ 8.E`_6dLO؎WXƠ{AhCgv̯xv}&dQGh赱zYi= *|)5֗ f@i[sz Bl,㬏:17Z+^ 38N 87 ?(@`cKX3@6mm_|0b~Y#pE9%4ݎXy}aORVkwS$ZܒȦ-.w<#*QDL`R>hZbnΒSN_;գ:G ][1x/B@ArXOEp(ZտBg݄i齞΢,eyKfƬDϢT&wUMer ƢrT&WO@zb0V!6˃RXU7u]*-5e[ ||>h lL_//є$ͦ[dMZ.ܽƹX]RQٹ84PQX]3jx-s@XiW -,6&핋6sܺOP.S]uyOy E=X`zさ'>9trN*aΞ׈&h!eۄسx3-}}OBtPut2mA,G*_E~^>m11 &9#H |%-CFؐ+HN:#UCG~2#lFi2RG,?e ExbJA6 *ll7##;oPZguS9ƳQC 2)(_țqb6# Fk0g[u&nkaw.5pM6|>Fb=l&5}6A@sQnYdj榑0X䣄A>}{޷G/ZדZ>iZJt/{A6rPGcǢh\Ph%DVsqa:b wm^-\zvTWW"w8窑$tV ;~%Ap[{G+y^Au9 4^N8ǧtΝtA/#u3%'▙#_iɷ5ND&ˁ%L;mΝKmLnXXl:Y̦ϡ(uJYd:/!"%rB?)k(HR^7Faʊ:H:TFK0%̴})^>g7\#4$@R/0`mFer+_@(x(P!ncsmHi7 (a}/! sj%,|MG ,3WgcjE>LJ옛ɭz/" jY¼M!p2/Ў#_- IDATo6]#|e+2SqLn,LnۍٕhB2bхr _6=@/#,]2k|j*+-E*]6CB tB5ovC݋[; atݤSKWQ%\ ׼5OȦkjZ_.EU`=Rݽ"Z{/+(ڣ}Co wmVHnܞqbv?/K;| ^|Kc%V-K@  =ekF$N.`iY5ޖhU#j}/y> D]a9?@?t+rV^ظkd~ӑ; ~#w,xH _\|&;5 1Epi32} D^u 2)1Y|F0}F~rXCa,imzJdr7r|e}z<ĿG H!dͫw_H V-\kBqa3 &F ;fg9s"̅׋[T&AtfȌ: }w#SfbFSA<:Gs}">[[Um9~|S烦yc?Eɷrנ'avα'\iY6,ɭ$`mvGtҳ";@ &K\ՌԌ]p_M2o1bqbwHrLf2W97j ,ps""x7=7 9W^"Ǡg,3#3d㻉A\jKay@1PL\xAd7;=c|:qs+ 8>ptGA97qYA<Aps`f;A04_bYN# *)y0}:}7D_KtAsg p pU A<ֈ͕/cι XA1A09bD&E)<=蜫BáA蜻8=;\bJs?FɎG gA<^{/9R^ `Wϐ~6cW}#vs"Ǿ~yt2dE,:ې͇B mg|rh?Y8n}bWPĂ ֗Y n+[ڛC:ꐶ8Eh{x0ؽ.F`V}q;7!ϋ7 \`b:8 )^E"|n@7z߮/ky-#,YӍx%:x0zđ?߬CV؆X[VR# 7}~u(k蚍Í!u>`-gJٱ"#rF6O|]I:4:d-@m͸9 IerMty Dg tCz +JA"s <,Rv.̭|{t 6_&%Yͳd~LJNstFϱgsG~܎_)Q%3sǢ*laÝK9*Kv)"098\AXm /H#nc3 }%rw@snz}MZyσ~oGd%neEVBËdK*%f.Eg\z T|$ZUtYQ/E@:ڿNU͘#:Gb'PY:ǏnB_΍H^|vE7gXI}y_d=$Iv\bs69p=0x գ]? 욭A@qRk냏4Y[->G %l9:+ _|#jH9ƽ![%N>AX& O$ZP<⊇gæ`=jc<͝!Aӥ!y1Z;h}^LXOF&\󇑩y01pjY#k,1߱AӖ&}Kbļ>L'&\-Mx2 R@arw:N!/YĶھsn7&Yw zأ ιƤ_;} 0߯ӆAF~˟ohoeW{CSypNJ^,O{ށdl<'ES.d$B; e+M'*Rdثb/"y;+ռE詨/{-uwïEܾ߶1#;:dXSX[ASg>h:ef>hZ@Ynttr2?;ywJ&Lwn~d>h)'z|b]+7.dy 0l^f!+ЋIm#;;*B3>9W]sXDl(5ĚH;玳9:ιȂ}s)$r=ߨ?yE9)ead{j3")3co L$tb62gl:!MG&; CD hA7M'cH\^w!v1Y>3v_DLK=b-JXWcv:X?{HjCMB?Y!0S/@hp8~سQ^"e1)^_<† ӵHd1#dZ޴Xmkȷ% 0(Nm)KC x[E>it" Sg[lc>/EJ)0 KLV{*K2x!NW}ǹ`4nBb0J24|'RcD뫗T@ XMAS[K5%\0,GepC&'\I3$4=p>Ф.46G"/5^ sﻚc3dBV-3psYM;?qv9}u݃qz>n {7Eιo{Z)+:A&O݀.nw-os rzmJ.wΝ[E\eC">LReBrY0%Nv׍|N@P|c^ 15 2Ao)vBXXKqPucP*ͳ?Rʓl~s,uRmgR]d}#0$ig&KB,L=C@g :|!ߺoo(%?"*Ig d^\crE{[5o_f dCE CuXy޹>:ϏA%FխXt5s k5^g'N.hn(slΰ9쏔 '!Z)>d5jc k2}]#\N MDf_C7[:4>pOQp7 2u`?W=XDϋ.yT -,{S`u؅}6(l:JYT&7M'WY _O0B1֘_ۢΡ>?[y;H)~gu¬C5~_鵈L#}hH`co!0GB(]cS,zpC6`\>w_V2}G4~#X_=(mGl=@M*Oy^tF\X7\w)sy 0lX2_"xT&7}#Z \~M'_&y;_qa avNg̋GfXN8.ضiA.C)nܫD\\z#\kWǢњE#(Z@@sE>hkk,e)˻CoA,'T&'T&[nH]xXW5nRXXG,Nu{1sQާ ZcR>8o}_H)>mG!dk #V.>RY]F'd니5 +lh\ibjp.7 -FjP]J`;~8"0;Z_vہ׈ec;Z}Br/[CR\M7/e L@1%C0͟:랯f\Pp1&9xJ,e)J{bƽXΏ"gl":_'dX@X'žCW"3"ׁ5 ׀(]Dk߳AJǧؙE뛖bHtFҎug9bɦ2KQ$Z'2_]su];ӈa6HcEjP/~ 2ilr)*tb!6jס8Ƙ5 .P7kloҶDmn+]6;OT&"0v=mlӆLH wkpj@QU +xSϲ0"a _-bx6\wį~{>q}[#jqٽc;U>^l;Ĉ.N|M;EEGsAсnƲ,eyC)sM)"Kertr%YSv(Oإt2hg#mE}HaA`Ȏ!4b^-IS,Lk~|B, dZƧ/cH3 柶>-eX"P"m%bv jir(0'olJCAw SHjעtr]/im)I Pg 2'VbF5WYluݨ[GV ,$1X+#X~.̵10`*Y?2dB洫+ު#6|!݊0BOU Z;B,Ck,#̛9|MFw6>Nv%f61x-(`,A*>Eto\RAωsYRw, X<;M'_Dj#fM ̗_w_BdRL>ȄҎL#RL~ ?{@yV5 )=+p_sE]MD e]3xgf& c{VHeq!bF%g#̏s 28)ع.B`܃9=2-tGD}u*~ɦ /i41yќ7ǧ)䷥ 0i-@|H>hZ,H?SFs[V#d=sEk t}+ ܄^r{ǖ,eߗ2+T&DX_0%2y @{!abN|ameo}]6LĂfIT&ׂT#_j;6Y )HA&R_׎[:V *>t"e[kxOgMlq#mgQ8z\o"+L^^س.>u]>tpWb,=XD?C?`Mϵ1xbh2lQos[~߾Owu=S8ݭE }2!n\>$Uv6̗ĂN_1d}5済mJN jޡVo$\szE?K6^id"pa>hZpq9 =E?VH=䃦gYR7/e0ƯM'od?l,C)Oy˦wo> )H{ZQzO!dz=t=3D)l{sw<5g!7R5(龨s ٳs<hF _Z q|)'Zqu_Yd:1XEaE^X^529]nDlX|b!y;v!`5ky)tDqkg%;1~d27#PډM5oY^F>DX GG[H2{ JDgɭL f/7 RƤ@8Q{,~K"`SsvO"FzYR7/hɦ7䪑OK3C?nwfəVl"?SNv޽"F%R A\Q2<<,Ao=HٟovQN:[̌m g3) ²jom*,a ,5TkW!l:Oer 1 MBlD32-Cg',vBȬF` ?Eo`9سEH٬BsO:7H>t  bT * 8u !0r? v\\Dĺ̖#I8#Mǡ=5*pQ7Ű :ƚ**!3S]f9攔#@ I^E/V uG'Rpޥlfբ,3be);\`ld.boюVKT&T/ "/3tҗW:6@WݏؘtdB!b*V#0Rǡl3; B۝-z)٣Sk,B2@Y\?GY=_jiwf!hD`j`*&cO J0F P l0jX)c of㞶v2#/W<Y2ìˮ˓ȗ^*#Go}dogmCd$y>Jhd2=[H? ``A:nl@9b=g@d*혆l:(9cvvmW|3>Go 2{,gpOR |?X[+iAzMգ{Xhm:uuj:n΄kʥtRwgo\NRaX]*[M'e違c 99C;B@:E`Or+R=D#.womaY'I+2͸K9:^:Ӊtr;PL!!vOHQwd,Ah}_jc&Ͽ9U!K<  @q l; 4S HqENbat#6`?)48 ElM/ZO>\|Tsd!҇M=aM512Y?W‚1ې(Rl,w"06^; bEl:y|<1,tº6 v(9ZĐ )_!rk%te*JOYZ8qN"3p¶U!ST]5|.im5?|5w:>rK~cf!k mSG<3DŠBO[=c>XM7Tw/iEL/f~,KY. LΥ2R\~7EuDtX[ÐU#6#N^ N5%d 6V <[ [|c>lrCR@SЛ}X2 W&sQEԳX si<1Bfs+c82|]#j G\K5i?T& ČlG#1|خS3s>}LDX#sB#\K+ dgE_T&wr*]*ۜ`R2n$ - D`a ?6Zo Dq@DC`2+Ma+TEX?=|д|tu>hJ>h,< M@p90\m~S;Wܵ}Cxde)KY%Rf.њȯl:մ ҵL~RE1.@$tļ16K3b$4ׁ@Im"E#Gع8f6Rö)Nk"EiH֍{i{"2 M￵I.~*js\iqͰWY?!&܉~=^"^qo=^;/y6wE_rEhP;HȦrYFOolߍթL-D_!,yas[@၈uNXa/~PI-]l:Y~ gF_Xރ.nfIܳޟ; Ij/t.umϴέk= 1>jLǒ/a!rA>h'#@T#Շ"!e70?T( ŤCJ;XG 0dعmK㠒 B@ b"5AfʻYv>Y l:2γ6OqΰYEfJ(pwPHerB]Zb5N֗ݳ+02cz-q̦dT6f,Gy22 m }|1ds E޾)Fx+9hmNF/ 턩,?Bf4UByI ל"VY;8WQ H}߿vMy%.7sq핊Țj"H,e)˻H`m%6 NwDXwy6n9@JTd" l:Y@~ej!`p-2} 9Euا({>W^QzΊcsR8jA~y9~=}2} "t~fT,x _Ft 1rkmF˲)˳OmHyblZa:rB>Tm1t㮝ڱl*Z*BB:y"ɦ!w/z-'!8$և(abzG'~Ȥzu_MR\uŐ=x !b_Gt݇l: ];!4fF"TE .B2[bklC>4ki@Y=bW~gYRw͔}Yg7!N.Ao2ylDT&18>1kzE 1 0daz̖#f_B[)"sR-H);E Q?g^|vgu/+Ǡ4K '#s&ͩ0!|#PSlKm0kzu*^x%MϦ2SsSܵk>ߊLT U81D]E 6I}:j|дȚz ˽ Y{"X„bWd6e>,tVZtM <x\ò,eyH%NB U1St`e6cldvNer?Φ-Lne*S~,> %yR m"D9y&jRއ !ȬꝹyScؘZHeGA@ WR o|x}Yi FPXu#Evm-bQT 6GC޴]|_F` jA$@  G7x~Oh1M6.DC3CRܳtro Z<)~'Čw@kgb6dƣhx]Wqk {&Ki9J+Z-*x]5c m&b/A/R/{v:fk[] e0cA`j~櫴3R@ޢ=ȼyR4yODcԡHerl:~6|~d6=BCi*d!ϦEn ,OE>pt15"A-9_>hzT*Pr'pпXΆ/'"df5]H"j-KY.e"~d&)<_)=wA j[n3SHMD(ːIGv2U ׆yCEv8;˭O_!L0(`6R@q>He~5 mF قXO!_G`6y[Ynspby>o>#nt=3 2@]a"֏Pޱ(mca̛D[)U֗ȴ܎:oSlE1x<]}g<=egR"GuϾmʯ-eNg8"8N`?rBiulySQf=ELv%0(51-KYRwwd^C&Z_Bj:-]f[/N db^BIȩz#@Kj3p7bFq -_+b=FZbvl&t0_`xGI@vJ|9>wSN'bO`8eM"doL5A >%JyͧAтUMIޱc{^(艌\{8hZ%rZ/k=NLer!i1<_G>g KX|iB jn6a*t5 E8jpOXU#Oζ}f n%Ǭ`c} Lozu6#+xdS>l3ÎHjj@ 1FRoCZb @۶vT($w]RiHjrL#u+$tT "?ݢ#jr2 HoupQM4XWjnb%9p4= `5볋2 |2+KYRc`z;ow[p\6lKerioDk`|sf2R/#/RLE k8G Gع'Λ<(b"Qe?@C$zi ):nE U.FR*j Jh3S{nտQMf.Bߠl}?ˎy%@rkEORdpkJa0Ca"O6Q@ EtrVMȢb;u )Gez5Mi)"ZqtOl_6Q,)1N'"E}#zMVl:مr`tCb2ٗP9MΦwY Ĵ#@41Fȯ0.HGijn^x^A`)J|ى\܀ױU 6g񽄹fhOSʊ! UL2 Ӌ|'b߉R.8 +aJ=tfϬlo+ݘȗWXd;+ŦӲ,)=tv@&-L>m! ӊS {oqGрCћU(9Y'^mE9ϮA>^҉@ LtCm-}}J/3y?~>>2;plClRWka\"z  rc݊">g}'vojrV;Q=v+b*(j [g|"Wt$%5 ڎltwEss14Z{14n!5 "PA/aCާ'LчRchƗl;|BGJ G F۾P YdC@ѳ;~67e9% ΀ AEKM[' !|it6t.| 77xC>hzGoYR7/ڔcM'V3vLX|a*22O3}I yBaJYJ#qR7sdS~|In!R(*!p>"}iF"3LkpM<ڹXe2>Im x1Bhk2_ϲ>y^j"bo7lukb[v,dWe IerPIۦ:dղ>]ao|o䃦" XR9oFOBkӄ ]Goz,eygK9\;#KNvk"Du0;Nm"l:mkzd[ɱl:,MC ҆Nw IDAT8|R4I#cs a(#KGn`~Z*yºu[|<`De)NԘE(2au ) "dvF (d" b!c(Oj u"ԉ7Cv!Aa68O7l 2f_|Cq aro!|.N~"mJerym6W*uVQ袧%V=t=.EP𮒄k>zdό޶h}_k.ZStB@$N[o,e)KIL?,'NiE~M'[Sɬ=sJ[cKM]oeb5zK수]Pᄾ[]tBPĺ#ֆ5!ChA31}gY62{QDW&2vN@/@Nĸ "LQ6[md}/o# a96G U#8w#G@_n1cS܎tM۲2d&LY;zF["6#놲~e6)ɼ%˪G|s1k+gU84_Zbe)˻L`].VFR\ԛkј^~qȬx|ͮOG ~̈́9@5HGhCX43/vXُE P}Cf @E-@Bv4לj\8u Xu ;c_]42KU(G.;o#!0_T+2 _ m ӭugecCi?&dɕ%l}4"t`b qXw*J͕{(I #t>hڢH!۹+c VVheX0M"LgM]E+^*> $K]DBr0jz :)h[8?ʝ/pqh45­; ۶ؖd:Eԙ؂]%snI%'^sciAQ. jvD5?BiCHyCP$*HqQzw R Bﰠ6U$ұD)6B1kk&3A=Tv(Jőx j׾Hh Dsĭ}[k;Q`$}dCdR$@c2\V=:ż\ >LgC? &>BdR€LgEgmέBT=Mpkx2 H"Z"?G QS _n' F~#ev$ptWE AAj1]/(Y]W\A85 ' ^5@tg0eha=y(L*W!桚Fckjy9ϥ=7Y0 ab-]LL* .u9v~UA,ͤRN}~9-]<Ee3a|41B&̊p}+"Q<"Npv9.B&c럢YO#!TTpHҙ(U{A'$ƒ(x+*2Ĭ8 !scL*HZa{bƀLg߳9_D˱HpΤ>磴wY+s5칺kۗT>Yͷt& K EQDn5˺c{}'">d q3 H$-qڭ"JMp(]ڎҐ({(7r?I.tkAE>aZ3u;hߐ}clufr5(wS(ݣaNLkLg/nΤgۢЁH<܃GʤG"?![oļ$`iޏ/se_@_!PG n$A0UXdmK*H蜍\W#HP=~ҭ?+ې @ A]X}4Cj1K(Ѭ1,6} u7L*qU3s\ź.zdȋ3w?y}yKP#pP֧~we<01k '{?`s M\+f2^LgOE.vOMy?FP}+p L*q1JRyG"q3׭Y 먁ba$Avȫm,n8 &܆z 7WHdFr aaGg+P_0dݭ̷ (B4Z~LgGo{q=rz_Glyu u1td:;t{Eb^nt[td:W_։y9,Dq<6k5 -&ƌ"&mjRd:D2{#݂@BHԵxD&xҽ 9oB饕%盈"S# J-@67nEC)+Q+ E/=~a=ۗk@# (MXR 0V5arwΕh$ĦH\[7u y m:g֛:gA}ϸ{"#iCsL]nE]x-{e2%+fNy/8Ѹ[B؝{ya+7"ۜc?QoogoN;JLgOFL*L*kzdRnaz0f+Φ/ͨN I:?| #CW>ǂ7EFPj/b و磮΃X*v@ aX$^Gh9tkqE*ݽ!ڌ"pQo$@vMko|Ǝӑk=ۆ!ԑNo"7vB"s81~-Nuuv̭DSiFn aʼny?ޱ 036.J,Ύ{tXo@T(M-Τ/K|`$[DBj4JQ$xJmeϢHT 䎿EcvC*t8V"݁p"6HX(ցҙQ tt*VI%gz8j=dR7w>pm&VV_7PHp alcVG2No+P7GH\,; +Xd]PBOF)[/nѱc]$*Q ӋH8.D5h !#Q案H-B`,j&ѝsU&hųvx Er [{vv&hNwEX]]:ǿ׉νoI˞t_~ ܷ 0 KS[#"ru?3v1T߄Rq'nQd6* s1Ou:mX9A[ͭ,m!A8EFXҀcFҥx\D40FQf'~3ĵ:̲ܳM5W׌ 0ĘQn `j"k+6&\=TLgMEa'GNCi.TC {($ HLݕEo_,>ukqT>(ZVj l  "U~e^$AϪꚭFߟ}|⚻̓?q_wo E'wyy?])_zP[h?0 cFyɤBN3J<$Oˤ<0K%cQ瑸z uݝ,,o$E)"3~}="|rxꑈ{ ՕHx-& _ hY`;$ك$Jl!hX<}!E[ҹQq=d{Qt*"3Fg=cĕOzon{MT0Ę{mt6jҨh18oRTX}E[I%N_PA>aըC#(:!zBfudkm'gp1oFgQDm{Wm{e| s>H?wHp6lnA E8W%9Qr)@ (]3A 0o|p`7yl`G~Ҥo s>2x]LgA5e'ÐD#v<:`8AH`cZ`hD&#cJSNG +E^vX.eHkEy\(<4b >Du{3`$ }fPkn̓O}`,7eq]L&0(3>dRkHQiVI%nOF"UrtEk@G8W}]ޯuF-ă~hDns [PX dq$*Pyn?ٮ5^y6r:h=}K?Ev YMh +E>nc~cf{= AoT퐖ŵCZ,;` 0ʍE MCx/ɤI469L*љI%nd1@eKgKv "_g i͠,Y3Ptk |umF+as-EPti4hEnGkr1/W L~N$P*w5=(J%ߊRó G50~ݦn7ןZ r=gna}&Ύ~ܖI%zB&8oڑ *G1I%d:5$FE! ŠH$td;JX#kQ;:MG]A-Jh4!u4ѽdO;/=qk8vr(u#6N"U^B( hYx39ZlH_kaF90WϸT/3}=A2K"b=OEY!T" *@82)B8EXC6 e_Fgl(J5 PT='Ď_hϜ{:z@A?x5v=VZ^D / e,D_;j*&QvL(cHqOk%`RS $QsJjmn@Dbn;4(["ZPtE*N$b^u8'ofRi^{KWhuqtʊqHh%H= yTvU~-5m۶W~e`0 c4/dRMLg.Ly}x7Jܲ 5\(JNTV]h?Lg^.AZu?7 .z Z.n{ܗ(zÿs41Bit.F¬ɭ_p8*WW POBNd+z߱Ya+7D¨ P qCɓ|"+ͤǢZgP8ҝ##kD)ŽY?h$^#a(E `*3Tb=L2LgoI~|Mޏ?5܇Վr݄ >@<[*h_[c{R̗uLx/CaF4al X\ؗYi.Jt$٠0$؆Dsȓ8!+H>⻑ thFB Ev%t,7$3p %Js{}7Bgn`Nݸ{l~lpWaM4K>(ن,- E_/Y)$7x.Cfiy?x aƦȘalL*Ѷ9s3]d^z,J|Gh[Q깦#a6=HT 3JDiːZP@߁ؾ ZAQ±Ս!c: u{ 8ŽE4Z3B'p&y@> 10ʉ1 gJ vI% pwW1CQ_QD_-ሥr6'@ >uA^b,6s++kP'丒k*NARA虵y3ޏoaҔHQ_2:9H_ eͥ $zXk,F<pQ&1/׀R#u/!SKHlFQDP3N(̕hT3Eusll 06uSF/pcDQ_l*J,D5Kǟ,ȤwBc> dИhBbnb SIDAT֎xd[{Ff@yP("v'52E)wڅPhrhc`G1pL8 5/գY7zhű4aqwlM>*ӽR?ʤeRBo6?^_z [".NG5`C{ EtԼ0},5ڀSb^=nQf0t8d0zjƪP4kOAz G cnsQz$LFX( s=2/1/7uQb^+HNpo):m52&hu# C'pAL4 YW7= 06 Ɩ@߹?Fi$$PM(3V'@RYL*Z++ I5Z & m&]oӏld"vC+HR (J$JhRœ~g0^c1$ّ12۪Q({v6&(PI%J}öZb^n0KϢawc^lTǵCm5[`?$Җ*$`Rסd cj $<]Uoa cfbvwϘu$L:nʴ|ÒW7u"x>퉢WmH0աRy- ]h6h\R5.e{rA¬ۂ2"tޏ4mX02 {?n ܑI%fg*T\q_KgmAZE<,V#q kQDkrsu綵Iyv+nm{ܝo{a0`u3y_LqS,PNEQqi(5]_WAGnƞG5dQ*{Z--,ܠ2SȻmؼz`0 cȘalLNDќrܔiL*x]`H,uTdUyhTR;2~ gq"GςNnB7N4TfU`5cat2 Pjj{ݶHX ‚ݶ%Gl#/ϻ~܂*a+q#ڀ[3X [g0 cCX0d_d:;%є;PXY微 YF$MBi%N8p$o/!Ы#rI$^Cɓ|e2=}@aҔasiqPcR{se{tJkP1-9,x= ZPԫGo5vB[v$cD |Zݣ Mg 0ʊ) 7b^jb UTaa}$.~\n}9(i$F6HAPehg(x3&_`&PI%`Y3 OļܡbX5YOxHt«Ԃ#ىҒVgcrNw&N8/}Ciy$֞D?@"uێΤa[KSW#U/ļdE"A-W Շu#ֽhV4%/ Av[w)c݄a8@hwGo1JU& (73 Oʼto@bHpp7s'GQ3rs'C7!. ?ݱG"p*0 3 Tcc^n/rX+tUqHm[,>r;N@ORѬNG j裿ļ`yޏa ca3 ԀDjš1XWҕN,ڱ3cnNdoj؍0ҽWE(n6fxa}| + QXaRBT@6 ֡([g[s%j h<(F)DŊHw!7~0 3 Oļ\8 6N´a<:>Q!.;y;+vs(6 x!Ly]<C%(%pMޏ/ 0:,2fFya3y?~;,t1G4ot,dC1 ՍoBJ4 U!$Fک^BSn4 [& ڱ10jrۊ f]>o(Smߐ'Q>(]f60EHDZM'5\,2ݹo MML*aalU3 /|u:~5RW '*{dZn%_E߀(5d7c+"ft@g 3b^n<*Ǘ0 4 /DG zd,@GûtNFF$EAYĭAQ ~osH\~Prkǐp$冠Hڹ$ 0zkb^@rdz ^]~ y(rՎ$Cih`wTsv%p u5#=uB¯ sAs~|zaF01fFy ?v$p^~DQl,-FQ JC.B](}MhZDd-} _yy?rn0 _01fF4 *vFnEѭC -E)(j@wx+E-`l-vE޵xxT: j~L^ ت~0zM29];7V<ۉ _SZ*F(:1$|$@|Y"GB~eڱ߸φa[5& tvB 푡=_wN;Տ]"ftH|-^qktβ"u!c];r6JY6K~ց}a) %O sےk*nGWq#Pތ;hwW#;eHMt l-b&d{9ci;"Un쭁}aE ;GBϣanRqTDH}Yb<܀v& DBT_pz٣{G(Vl=9un0 c@Șa&t5'ꬬD"i(J=<#)PYMք#LU-ѬɳK( fUNᎧ-.J 03}5 W~ AHLFaA@ܽ7uf5x+Lp☗" Ƞ ahҔac^u?dR?7("V@­uTzkyqȥHpچx[a|(Șa}"(x0*n Fg1HHpչcYT;*_txKޏAc $ݡΫ3D޲aFbb0iTx Zt1hxk%v"EѮ* xw92xQX>{ǔL*~30Ęa}&ǻ ><$PT[*"V>fK+~t@5j[*kةL*`o0 ߱nJ0KA",0jPqD*T-Tgֆ_0 ccaǃҔ^ہW̊zcyhH pG)Yc~-~aeĘaB}Җ|U`5:Wy7WAVU{6$JGvy?<2y?nz04a®^T뜊)F]G׆"DݞG_V7yo 00~uvd'||FHx0m9a[cb0gsB$Wڱò X 5 ؚ01fFkL5B -q9=%3 j Wb^DFX)zJR.sa3 ջ|ViXМɡe:0 $2G]2ֽXby0 cĘaF221 & 0 0ʈՌaacaaeĘaaF11faQFLaacaaeĘaaF11faQFLaacaaeĘaaF11faQFLaacaaeĘaaF11faQFLaacaaeĘaaF11faQFLaacaaeĘaaF11faQF?kN`IENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_55_0.png000066400000000000000000001365141413546205200301570ustar00rootroot00000000000000PNG  IHDRc%sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxyTՕߩhvYdEňkI4)':YQ)f11qdIJbT;4 l@KUGM7;T7W^jS{80c11cgc1cc19d1cLY0f1Cc1cc19d1cLY0f1Cc1cc19d1cLY0f1Cc1cc19d1cLY0f1Cc1cc19d1cLY0f1Cc1cc19d1cLr=cg7ʕ o0`x1}#?+ Ȅ@hŵ\c'ι\{h tȆ)ut] i;x;6wX͘1{~*H[{.RG5pL7ˍE@1,O3ݬ܎cb1{e?lpעՔš&BPbL7h<RX"WJ:?tVāG7,G.0Y͘1{/Ï3n@G5\qM385O \u>C{睊#c _ō1Sf̘o붊⯷학/'ck yNDׄ[-_P]ƓW @HD,Һ1Ƙ=2c&x$O~@ҡ逤+kRSṵ@/ 0)OfU¦pSS1C,3f7Ɠ!uzJ Hֵ/<ؖy,9eg{w{ky7K>^qpȪ̌1iVo.Ɠ[/]ZG^}܈&=TZ nVM={;y={؆J7m<)c1{eƌ⟁Owj ?ٸ&\HiC@Dߗ$b޵'uxk^a1Ƙ Ir[ :IT z.y82wqgLW{ы)1#^$|\OF/yntqNo|BEl!FVqK;\ҍk~ǟ1Ƙ`̘0{E-j7Lr~~EnE-兣ۊ.occfykZvVgM")ࣽ\1tVovƓ"mW^yUW~ɻN&XEÀktA3cMSR4-0$,x1l5/DɰWhѐ11(X[,|?Nmwh)ڸ91c5cf0 ]UhK^:os_Ic1fO`66߻ⴱ_c1,3.:'6 x61c61O qwط2cf->WX䏹1=,3f%*toc1G̘D1@/i1t3nƓE1-͌5z c̶41cL41cLY0f1Cc1n-OLH"͹1YftwG's=c1fO`twE:1OƓ1c10=Z4  |!z<c̎1 r=c1fgXfc1&fc1&,3c! ƌ1crȂ1c1Ƙ`c1&,3c! ƌ1crȂ1c1Ƙ`c1&,3c! ƌ1crȂ1c1Ƙ`c1&,3c! ƌ1crȂ1c1Ƙ`c1&,3c! ƌ1crȂ1c1Ƙ`c1&,3cɡP`r/Os\cٟX0f_`1Y0fz cFs1c~ 1crȂ1c1Ƙ`c1&,3c! ƌ1crȂ1c1Ƙ`c1&,3c! ƌ1crȂ1c1Ƙ`l'Ɠs=c1t͂}T4,~\c1f+,G%b:\c1]\`1 zfD!YHuc1Ƙæ){# qxw4 vHcUh<9p03O 5bplcͬXES @:x9 1Xf,Nh@/Ks6:c1Qˡh<9h D,'HMNh1Ƙ=΂_F:SgD,Tb1t6Mx@ $W:6E|`PD,!gݍd P X0f1x,3E />s0`U"in?ʻEz?R4{KgϟV4͘2Gc̮`l7Ɠy@ڎ K2.1)?RFʽ1-71?=pAΙ1e޲\c% vo $ V'F@10xm>B%O^TɆ⫒΄~iQ`-re`4o5:U;u] zƔyγ0c v݌v?/%| F l@WťwY'5 ,=fICkS+O73oy@hAw`itl?`<-cc9r'Dтz$?)}FyR h[ ܉f䵙CE6l=)OJVۍ dK֕6 (uCzK"jcLOzht^t578 8h.ƓxBOu|o<+jK"R("3D:ςtݳEĉȸq|D<"+"G1iojr3a"p8~JD8gp=+_qUȇhI h$!Dd |z4`ci ϿW&"%ιS_;RՈDt&(7TuR"rϬ#Y0A4 Kؔb)%OlxM-)uRΙ|_»(e޶H vo`4,f-p4w0Ƙonl@6 dN-\NDNi:= w?wݛ696^z}ѩXu=';֋$`:7;݆MSfƓCW{1! -Wev0(@?|iOg;i~ڪJ7ҍҍ1t?-]X3՛|j*֧vF;# 9wsnsn(F9YǓhM"{'E:9' |]DB9}Z~"rw,,"z supG˜s2c)V&y[~16:Ly Z-\'CVUX$ 1lsYDEsF/ T^ S-;[NI؃?m5~ED֣ua4~BDY[޵Fcyh*]miC gy+nrᄋv8lZ"o`)=x 44(9Ϗ;B:෉X;crBD sYC}c@4,V4M!¥{Qv :tuRz}{h;1ӍY͘)- +QκW:t;w=;c~0-PH3~º 8?Om}c)`,O.x@{bc1=~E@x]Lm(;'"dNf ~ 66lf&AZ<|p=Yc1tKmX4t7L A|FV{')Gca[kzftwHS4,EK"5dD,b=Œ1ƘdΌ}- 7  b6+ώd3#-8BZ*kټTixRSĻp,ԝ1cvv2{YY 3dZIF|=[;k$`%cf:]{N[xc"R("3D:N\٩-"NDmێg_NSzSzn`iG?!y5@nK{S0sdp vBat2;Öx)t>hgvw%Oy ]1F"rTМ!`Pq%U +XFjs]r]s[l$Ms]@oXq%vZ.Ɠ#gLn'XUu^XNA7] W _r\JzΆFZgGdhк{.xݯ_ƭ3D0LDzO}O,e6mlW)A : sn2=h5vshI:=Ƨzuι^{{L#5"9%蜛3__MSF_C2OX WeO?yA&fyfFIU`~&,pd/z{G-סi3@0 o hդcv9C&2! µ?<{ `s0`s@s0}GtM@7GySн?e9x9 ڈq>sn2}~L]xfohKb{{߸աEG> idNV%vA:Sq5jkB 7-\7p00Ƙ0fo9Ks"2}69Cj_M6学ܩ}4`ñ{iCw.n?my-c|xȥC^t:6|SDo7hﱞ,jh<A-L&/lH 2T'=駂a˕ف xV#۞BW¬AZo{ǢӒ:Cz. _(5NgxЀ-ӖsȒcL3{j'Θ2܎s"R/څ@,DHV˾;zwI4<?42ˀe28\{sMQZ&d84Hd9$ IDAT!3[Ϻ A6MUBtIq0 L5H钻+ 3'V4}[S` hyZӥyXt ޱ@/1ejw;KOYskr=8皜ss?ɁwnX4~B,-8oInD_Y\I#Y?ȴԒ+iܬ ҥO2M-eTAZhʁ 4pjE +NZ^~ϖ6-Tx-@Sh"ha^h&2]cF"y}1&Q_OEm[0Թ)v1/֌}Nᵍp`ꔱ  .$<7+ݬVhLZOkͫ{rKc@V ,6.@ЬV3;(4U)R:ZO`,4: C{՜.hEۼ}wm4n}czӫk_TZ8rƖ0vwW#SX4 ٠pԪeAֺ`ޮ"]N:,SF F(YX0ph 0E#td!)!)CUUh3)@s hwPycV3Hmt4G*"K,Ɠk~^ t۳1xR`"ٞ^)Bqaݗ{UmQk5cAxu`h[ 4 lZ1ڧh@SE{T~ y9BH D)yhV>|;4-H&h֊֕)>@k~0p+Ҳ@oDzy lKtIl1؞;XK+>ey{ˎm`Lcx ƠY "ϯA A$Ay3A6GRȀ,C$Ed/s"4g]-`^\p*@Р*fFӾjFw5h o-g﹝'6Z ܇ִc޲f'8{O;fW>:vq_d`)VG3x/d4ij;\^m?h3VA80 w̟nlA5wbhs#[I"wDˡ_Gy͐} y [Ӿ߄o;3~n9U`1yFkJ4<}}v"Y}>mn--C_#ÉXd;IY-Frk Is+s/`v\t'[bǀ=AD8&ȉLܙ{㱻}f,fFh F^$.?TnIJA0 zA~0i>ЀFѻog?_ Bb#wChh{_󀳽kІסƞA{}_'nG,ocçל-DHs7 Qz|{ הWspK']HOf߮z<z7c䅉X[9yL 0ZY0KD;^مK7 tts+Ѧ{hExݤcxr :Wv/D _FђE<ڳL~R ׹ L~@C0)ѪATwo<t JЬۘ1/()kfp84nUq0p0͙x&tCo̔1fPp[N-e8;O~jsԶ>\MhޡBnشk>cʼ_-6) E.iI2s M=Mr41 yn psc/Aw6wso2'?ʺQh/btЦ/sɈ oiι`=>C{s}`_4*&9LHjC.Gh}V]m*F 觕|k#\H)4P ̭BSc-#^G@hjrYAMOF2B%#-O^*h<O"+`o}en'G-wlӀ;8 ԐTI J 6Z`hMYM{*B*MG[m! Ojj4rh?hU.bAtjRѕe]Gc_@>{np8e_=ʁ@? Zh?hgqoxD,V3h-Gxn?*ケh9{W.79&"h1х|97g:ϼ hmsn>f1P4:݄; A ^Ǖ]3\`[iˋ0h V@{nmhʷͬzO74^[!o D"hQOSCO `,> m{1͈աxo17h:){c4Ɠ@M,Wqc0pm7m3W6S2cY5C]fw%p`AyP^iMGdRɧòAVFӻL&0:P+ f5$SMmzKYhD#p D D_n E~Wo觔FC ףh*﹌@iס-71f$b4Z˺{U /áƒkd2LhyCW>:xϺu2%rY>*XbBE`k~N+NZtI0kk9'(nVwx߯}920- "z~wWَ`9*"_y4KwI׽k+W{`M\<ޗ`36=LgkJ48 " h K2t`ȥ  L iX:txe`.8-B|LGT*Nm觍@m}ZJ4:\Zc_OuckS}K6sD' C{/S؇h=TcwDߣӖl1t'ǡ%b U?zLKd">}Le ێMj]V&[zwɪEpsE^t&IpBê5PtW«x5bK }Ɯs6u-CgB|+s[>ι=/;/\ҙjB[R}ʶѿsn.^s]]1o/64@:0w2E/a!?ѵmSSQ4PQ&e,х Ь߀5[р,dmXx<4[Z :xU e`s*~x X c彀\tc,@qhی5u{\7 Z?v):{1Ƙ@_ë9p%=)jj-ȫ}~C;ɵ&e-epj_/~r=+uc9lTeV8Uf?1>J4h/L@`*Z@v`EȤӴIӚ*ˮ-rA$ 4գ(?x A"tʯ1{MO +ε7/N~EhׄThݮh-pf4`~f&3'|{ ?}p\W֢3I"xS1l0(W_y ׏v1'Eˊ~fpxŪuMuER,hfE>d~9w/poavPj'~pI:ST Ų@lK$@0H(@ ,q-}Kq!]W⯤CF(Hڧ; G^ր6tpt;B4nKnI٘)W:~A0=]l}r4%`Y4 Č1;hX ]hekIgJdiӃ`,O{[_M*hGqaju,6 4nQOGѩ`2h6j274 +%rA48 ]W4;:tKhqhw{zS Ƽ68{g;䠭ScLg+7ue=T}t&Z k"G4<XVwʏ'[ڊ 64olnȧV|/V,k8SP@^>e+)/]Z_kL)h&UZ+'2iiXNڻV=ߡ Av@[Mhm֣h:ڧ.KʢGPD,I4,AWь$4KvwКFtYa^3\6]Psdgӭ @1c:EgfDtǟzfޱ T'hmw.g&%z/ac;߫+&l$׷hIIa>hܸo7VgP"Vu>acvn%hhAx@&/% ]Wl` F-צx_k JJG>t |4MhTMλ4u?b4 CWA Y+N͞_ G֢[Gǁ5XbD,[o+YJ1塁\t{Vcz{ձ/Θ2/u7|Y 50e7laիC)p]:h>oJj6llPtqP>jM=˃yRޯ)BSmoSSc%q{GɣЖ)4[Ҟ8-4;oM}-ٴ7ȸ4;&\B: /!h dҴ}iĝ.3> ͤ5c忼*Т0(N{-g_D{+BÁzU1?v៌ֶdhۍ*D[f!fϟ6)x=i[ڊʧ{xӻ5 xcdD&jƋ}a3U:0،){px=A{|nhm!"vIG=#WW;b:3'G+6:t/06 gBMk̘_{ioTH(#2)2-O!i%y\~^Iq) 8I!Fk&@;ӯ5_yc8ӻ[hA4 jZ*ts,\=N?yh{m1Sɛ>mJ"ucLN.\갵xr=0"9nϘ2o9\¥zgϿk//|kk7%=#/%*JM:bdrІ5eC ^9#"Gysz1rAgy[+tzM~wl~ft` 6<{ez0z9DR^:) U6j$@*G~2+ޙ%2 p] . ]qln1NA3\ C)@I4JͮhMhpu$h<. _]ٌBN~ӕW^K׭%̘2%c-r'wxltQuSkyEc]|KysumD0Λ3:ii .b~EV)K!NF밞Gkњ$'i/~ Hգ|7ڑ&b_?~'ZcW] @Wr=ЌX/1'hۓ]1S3̫1e[ ~{d;3̻-]Ԋ~ MEGWO߸;Lz~T]DkzNcY'N!xgUG碳=Z7_O}8a`8 wQd� ; ^-1ntzl/,6s$HTԟ}h9\ `̏jO{fdX Z&_oAӵh@u@#] tx,>5>fEys=+/Fahn*/y۝ ƓatA@xrʸuk7/~=OgmO8uC_oQ#&wvCň ~ s.[rw| \}6,$}..  $K4e4n\)hdQ hq  @WNX歠?,eGhW=So!4)nCbwy_PA?|UmI̸ Rh *@2i2)*KpM7yODk^D7@m>c7fs ;g޸dkh1-\'FD,v-~ N"Kׅ>׌{BO7лݩ;rA{3Օ6;]5{Gd}=s=(?Ϝ\+"8,( cfl?F~0e9'8t ҝZ6ϼ{?PIJ?4TfDk hӊfZкQhVC|GEkR%5pڻ_=T0tZi^8 IDAT/z˺`mʰ"p5h z" / i觨N~vI4<<O'p;|k{EZˁ䥷ȕW}hםwEQ78 \@nƔy/'kZ|qkXbq xh'/"DP} "~\g1OD ":oP=[eƼ chpDNZ% I@_ O#nmftRd-Db6og6^ E485hZ= ^h B=E4`奫y``%ӭʴqW&%׆\~Ԛ`{| c9tdsy56wВ/oM"jMxM1^ 0`"iƓס+2.9> J3b{̓7hچ1ksYDϿ9 UP\]nS;+?MDw-C?t%/sn:"nr}6tEc5mh<9gFlyl% ,W OTߗ?{'WY=enI6=@#5B( VA^^AĆQF5B Ivmf9;ݴM?糟99efιuu #ɝ*|ՠ&%ǣaFԹ)\ _K yhbB{hA bh0/Ng+GefBhx V޷F\\j$Zyt#D"N=eTGyϻKWp!~Gh/k+Z\_S_n41/Ȱs^?{Gэ-3,DES1Oi)ׯ Z1ےa*KOi bt!kIjUњ*}4ߧzqYC杅ǁ*aI7ڮ*uՐ1݂V6{?;Ɠh.[ g:"/-1-K-5{XsTؗPs 1+OZh;-x4Ev+hm"lzQWIM_(pS{~_r4o?Q>1-xvP^O*L= 7~1JFi-I]k&2oydVV%_n9~B%4wk[b_v[P+;Ѫp(r ] .%aQKФ 6_Fռ˲:"D1i6u9.t[$TˀB죋7X:r,^5uLmUwZ"Eؙ]1 $M B¼Ҫepd)F1iknj@ɘ ׮԰v MDs*Ĝ+ќuPP%(oA(4B M2G0%x s>$CCFUCϢ[ήWiU}]VbN2@:NA72~ ΍r"DحqtC9 0#`ف39 ^ƿhuƴF>n.*w,q="BLbU,Q+DeQl"/sÆjT1’zatīQbs]5.[*cQyJFdɅ@E(\akPuZlkl:^ݧ2R4|M=5 KͦS ϯA "D{DE.XvKC>NhCS58{>ݸM[qw6ee&nʬGL2j+OEٱ]ɘ z۟U!FY\V/ mf-<*V($BAII+CI :2; l鈦swnȕhK4W({UC/eFIPn4Y3ZUӮ{]~]nI6ZeKc ;brGGZe^88ծ3-6ph4-LBhw\//L6\vzxy)N5O?e j_ e˲ܢ砩/y~vTMݠOkb+۟[7K,ك|]7Bۍy?\2ζD~GzQ!hH[W}ѱFVW4AȉtR-\O{2,Gy5Z6%fhq@UCUԕ՘Q({hW$g~D%_DåQ3|@7/Bh Qu l:e Ѣw_ͦS+T?CWcA=~,%λ!QoF4 3wAxxkY7`uTW7«nJ (8t#❗[`"DV؞q(49_l<_z0U$\IT`E,,QҎC yD ;*'5mգ*U;$wB*(Ir`}:yW&?&EÒ׌l_~hoi44z]4c QrWqz ļ'ϠmFc]jvsd)eUlcQx-X{xl:*4{QUSs=?I{z+iN?lh̘ !9)Bv<~D8M-M/X[Hp)cWD:/czDE)*c&1QW.d̪4wku59| ړ|d{MEHƐH`$/@IN9)u67ywpݿà:Trh#hZ%yL|]flC8zlD޺cXf4|= J+ie(q{ uFchre6j2*msq4 ti~GTawɨMΛt6\Q0r 3ϹU?fdө'Mp?㨚dmQlZï'A; D$ɐ9,j^;DTc̋[0t1搢isل>"-B "22vY+^uR?,(ʵii"2V"M`1MDny=ZI ڣ[A}[X?7>FztQ:%;wn>ʵGrD%E9e˳|l:DMVb_M:p1_d>ce\~|_;VOCIÎsXL`k ;}kt'T oH*Hc*0Dd<3c'#hk3EmöCb1/CYt n4JeSFg Ra#XP `rX5(rJ|r CčE2נ*22 h<,tTR~ ?:W"\U9%MyzTq#+^oM)"7Q yx zCD6up B#,OH Peh6d!Dl&#efg1JkT T`OϣI5h/ PN9zRl:=.1jHCӧ0j>{<ċhR{͏\~W|\~n1"r$iHՔrNn0 n0,.dUI6Ƽb-dŦ1u_Wvy jE\~%"_AoD]vcnO`7 2*p"^0yxYO,x^X E /f !\%@#Bl q ~t[H.!sBk4.ͿogV> 6UЪ2vhhv6r2%#5%uFCS?DCF>a [ P]#eIh5q*mnQ mU#¢o@?(iB?EG@ta?]Mx5~Zt3m2>]dEO#x5ߞOJ[u dS'^xԔ!36 Ug,®myuz_T2A\>ECFBknsZw dөՏÀvF,^ߪk%2XlD |24p/tj\IS'O4ΦS2oB+?@s\I;֟buK+;zU|Yk9kZ[k*V5 7B߳Y[8cZ2e)\jJ%62=sб|IVuQ jsraXAܕeKƗL'OٹyuеPx,sfſɾoCB6}ZތjC舆ieS G+1[А_>MxhIv{pdl:des*Ou. k|+t[8N_4|>%ho{o+GzE=Jf 桨ə%OeP5x{ƣ"[ NqxSC5׍h x~oyYs[\NơRYRO{Gngz 2gl9bEDl}x&ac(BX*lș#E@ewvD_S\s8 gjzQBT %3gyІ Lz^Pˊ潂(x )ގ*oף!lh>x9ᗡgۛzp/ΦSa[]gߕ-"cy39 Q(*W;u ݊u}P _@U>t3/#xl:-8?2pz)~  LF s۫-EwCS7y}Bh)1`cb "B$L#DЇn8~_ޅçB|!E>k=Zn𸺪{Q+ GG%+dv*l0'{)A{U1Z@)ZJ=%4z]F׆*d#Q&4Z󷌑Xs{HlE7[9S@TY8|VLT(ѩBׁcvb;6G\:D| zCvʣ24C6 hʏOcJfөy%җgө7(ݩo-8x-Nu'tj}p#JnG?{Q"zFъeh%ԇB})ܿ/t!9[7;iꄡ<4Oi2~>t샗6-鷪c8GMyY 19(La6S.jx۪X ŤKVKv^:6F.azQtS  e"/0UƠ` Umȃ)rXׁQۂQXTq>t'ZЛ(8./WJL ܀:e_Kb e2"NB5gt/-'ԟU6l:8HY_=K &M 8 B tN |cw"zF6&-&} û4Z;fP݊e ѣ_ih IDAT&v ly{h^C%_Wl藙+iG#6tD̵7rmaj%dlSPSuhv%sZ&9K9B;nT{!}YK`93gwACo}%KEÜT { ~n> 4l[/1/aw?x<(>%y{OB v8'𭌗-`ÿgөdpѧUԼ(;-?[hMsbUz0 >vcy/-QjMG^Ox/8~^gF{$eLD*Ed|Z"r&1W "}Elӆ,"׉U[:掀m ;}{{!!%dZ;C )1DO7 VŒ(yrj KVZY!mKfA֡9h Z#SU[Av.DDQ $h|Cvたm AMDO+BU4"4\zGcr_^/NEC2T J`6/unoY}l[\5נD|齲U2͎ݎ>-!ޘ2WP۔&Nc= &MPQHiU]Slۤj$x4.g^0 ȑe1&)KWWV=}M{;DTcL)m_ cChJJo/Z^s6Sߺ$Y˻XiU&?9U*|'Xt| Pվ"kNZl5AbVm` $ 9r5C@W#YX^Ds heSah~4]yv!hKhPRu()rap"gWQT #P&'Bc8c0ylEo h(*̢iU(ٚk]6 ϡjK*{E VzxEgQ˒'^ _3Pdөrvkx?Pl:Ԇ$ p J_F(F^r-Z ~n"^ΛB Z <<%zмVhI pMXⓨ=Awr=b hO?# /W ДR3bl:HQ;?s_f؅' g)Kտ+:ckoi?7pC?'=Ƙ{DvgyÁc1yحl9@D@>lb9KDNFC*Z-9ڙX lcԯ7$"2_ -NnӅ?RRl30 WD>Ta6+;j(OЖ Ș:UR=QSu^q@e//PQu&ѻ]{] #V-[!9+'=ћVYhL7+4菒§f(me.3kh~q'?ߢ?&4$4l(D-(h]ey͡rziZߎ#bþ0c^u~Pv p)Wѣ>bZ=\Tg[U!kFby -x|L4ty/JP\~g!a :IldžO:6,4u§|rHnl+Z^o2| Uφj{uU#p?$$:ٌӉtDؽ0i*}P)3/F]}^{۴9 ;v ;});*{`VSϯh%|~E+sukߨ1QBFs[cLxg7ȉrrnhġЇW1fؿ[DcLjt;c̓",zjPŝۜeө/ߍ&r].Pib}q=)YuO¯  nGN5i?'hH[sFCQ5*|;MAZ,8@='"o׍}gSDn<_4GdB8vcNCĀm_߄sϷC]X(N Yn9J BNPz\>X)`h,q{4QP܇Ս/. Mk%H_=QhkPuQ(TAU꾁n뽔M2(9H Դtj-ܸ,Nu9F2LTM3nʶ޾OǏgөۭ=\o2r^;&MpCb Oؿ-T"c1/ƪ([.rMB)AȎN<^M ZrRBsCZqqnsPhFƷnrb-zk<\SrJ4x1ANUnG[OIoO9V 4)0^w̟#eD49fvɣdө}NC$TtuF[jENH5Oi M;pS  T6zע¿+!4\{9ituȎ\ 1ekaa%194$֊8L]bLW;PDIԗ̅Gc(IAh~دPEw]“hX (1;Z: Ui_{fdөh_Da>hw04=qh[h5pKtM Z1ErwYF%YIS'|n)i0~툰arZ ;ќ U5~xSe+$!Ł6Z޵8UH5AISf'pD U0eUh;[̑A" ;(|6gkۿɨjew^.GZ087+q6=)mk ݜC'ۅA ,ph3{?)ݎm܃?ئDЛ>L?eCC [2 /8(޵F4 SB͋Q;:_2^B)riz!!hR|xu(1(zMvVYht4mBDJЦ;IЂ=[SPثѐo(/㗡pf /ס b_AעI_ʦSwn]!B];N'r/kV#cʷc$b#5pAZ*V5aMj<yb>q/1`.4Ԭr; ] |u8:&4[3UYk8f!˾=*-Qm ~6>c! `Q w XFվ{`ԟztmvIx诳fuxag;&?e+ћ|(pu6չ'`$ APP2`'B@~jLVyS6OAɗ#X М2br,.rj]p8qRn/D82J$6Uv/AKVUgi yX6x{WHL}~M^F}ߝ-#b5(y{PJ/*jweөRi"12WX ;  hp[YǑ*GlbtMwMסǹDJzP@r`(T][*UZUqܶ]҄i&PĎ[EP 荶\Wfq֡{bFB+]۞>^MfAI5Zp6VWf {XXQnʨD}BӖEh~Mi^;%MS^ƿ ֒QDiq&/7[h4{1 w1"1`?cl{܇&cE{cLɎ 6Ջ h'P_;J, )95lT1'Jp3ՋdqQӄMЅkQRJ~VKhvK8vXhڟЛ#p J*PTiyoPߜPB1ԑ=Z5\z5E49|O`"͐Mgө]56L >vdl:587܆&N| QVIM.l: Gv]P@>*cm+!/הx8ޗ-$Hd~$C$PD¡,tcX"&-Ƙk#";arƺEÏ%R(}QiY<݌Y+s',\ȍks䈚7޴Qݎ[ZC #:(A+ m $HUxư+Q妯! T.̱jT%&# h澨t>v'6z5Dg{(9ˡ0'<Ԇ#,ɦS=e|K;OWuJn~]9c~%7eөwIʵ?ȦSL v[ߍ=͎{2v7ݗk+hkϛ`@ᔱbdөCChE oN&e(8V\N/dhL tdʑrțƘN%GVg!$Ιe!Y m/J|c(!D >PhnhPu̓iT5E8^0 -+I/?2XxjW:АqtV^Eewl?E9uq;^_"D=qp&D F1!eKppTln6u&ccG?@Dd(8 ۈs1^:;bzȦSˀe^'ke+ ;pJgc@ 8&/_C=4# eX3JszEh<9_1G QVw,S85}Gì'hV]~Ԏz8% 0\}H6s$;j{^ƿ-~Nm.Cfg J}B$uD|F6Ze)!#R]jHTaѨR6Ƙi"=}VH=1y̑SƘe"r/0{6FDG=$:zꇒ7v_d!N9ﶇ8f?aY㞵3:YU2(1HVC`eQp3GԱjSSX"TlPÖ"eBvȑq 1ĉuiJ~xp2**hdYze-hKΣg''UV~c|fY/ ܌V]>@-U> i; @а"&_`ymw]E[UWc.m<}mJ34D:߆2+z^+B;g̛4o賠uƼU|A Yr1Xm"01$b 7nX*ƈ-uUTBH1U̜m-\cp&6;[Ѱ#Uf:ͅahOъPiViPt@.0eZVl:/_t %X'zF6M~e\{|Ut*佌#+Xt UA#Duؼ=7wR7x✱,;wƘEnyMD Tv>\Z׮WF2Υ.x-(i{c#cO:ێg(*֠.|'#ZHt.iBzb ;^P` F]rbݚ#^TjC(YrORQ2Uܖ)lgV1l@+V*:޲<5g]%lPū/憝3*h}6c,2B6=!FP ݩ#l&߈ZM J IDATp ?0L5(ch»su&Z99g=៍öb󐼌.aZnl:r4ٝ܏ "}n jV4|076N<<>uƘEdmsޥ|ƌ1[ƘPK1w}1,A@q@Ds@1s2+:a.šנbSe$H߆~X`Tn[ W^М -/uqvNr W urh=W3%4-<"ýw=)rϣšh˃(1(Zp]N.2~Z1)zAnDt.67n~>q``6 !NROo^kq;- "?DS*WDV[2tػ F ܞMeQ2#7a1OW& 'QXMN rQ9~'5؈¹naڹv\oΖ . h$.Z`!Q2Z0Y?б.Gs*Q/:seMI.guӶjydy]1^N}~fBPH9ۊf2/ZDxqIeTt~SmcU{v'Rd,l:ՊIԊ|4C'W(FAX(/~u.@V [ϛLn[Nmra2da]ppm8WFի0JRRQlA }QU&'PUoA jmevkk#¦Qál&A Ľi/ϦS7{^ƟV/dөRG!B]V 6(]H}Z#J $\ϙd2]**yt OgZ!4V tD5"Nғ'0r FB1^uI.GW;6S+t gVnxyA،, O9deh|܏Z.ԡ9ktgeтt/ǭMCȦS^Z]a~6*q5NxQgө5݌~g[>!B;0vKelMo9%5(qpXqrdY]du 9~A^X>4$"pbXł ˅=ͪeS}FjFC /Jcj¦-v\JWvѼEʙ \o砆gөJt꺍YǮ@sff)cJXC[\MZY|^YQD!·R+k2P"08~5<%f ޮ'iJRnHFܺZˡhAh%KHW-OFDHFn#YyTJ3/fvsю˾## ='h~5u?dө[YrCeѰaq`3?ٔu6 4ͫkDGw, !B̀T>b-kоO/cz)"MƘJCWQD{cc]2eGoWq(8u̇ ls\1[igv< y #f za0uYx[%Cbt5@R|/u~\pZT!sa(IC/(ITQg#Z{gk{ۘ%y>N`NmWx<3Ww@묿j`e6{hF`#D "G&ILd`Ún!4GcZDt@g"c"0lT0ۘ#!BWdөYtl:OtjT65kFsU„( v0jXtm$%o%;v/ φTY,'{ %LE4^etTJ腥g14j0I94'Yj8yB]l.ԎD,~xL/f"lgHe䉤5s|k&Ol8n >ދ: [E:* K׸""/4~<#".EdJD("m!"wlBQr3P2Qu編1%(ȄmT ˻i|FU]ۤn;҅Wk" tD{9n5b3IP4G$r|4w$0Pce-}mM#hht^=e28,DYe44W]{wҒMYgqܾ ?6!"lW=p_JpsXt0i ƿVDA݅VMo.Vcr9wp1fNx> |<-wcg=jf.ThQ+mhu)hadɠBYM>lQ K PĀ(J/(9h#5ƀ-_)@ A 86u ͡hcݯ= VXd2?o04Yc D񗽌5JR.kGcKì{]W41<7B;.FdHu6d9Լ1fUGU-)ANBSJƢ9SxicJ#8ID>`C[Ed,l2i^pmKێfo(Y1q {ގuVkl Qh\9Jj T6.uEd\]޾oOg$+vy:xmDTľiV x,3(| v(i; Da`5S~%CԼ<7G D1^6,3c|W]ۊ-o޿G?r><sִ 69;30*9y!opSva%Bo9Ş}{G ;hQ˨L]n7c}W \/O̦SQdX;]¡7x,ˡ[Fc89ECaa2pRIG,A:Ñm([4+sH0v=jZ=%cC_F/ϣEg7%ea(A;wx-+v30" @@:n).'~s_|! mGiw89`㑩u4|Է`v܉NJ*uڏueoS#~j|ve"{}#u+ :}G.vnt;KAQ[>j"{kD36?5l1uPvkŗ~ںd}鯈;wWɖP0>wںe#:]8kqFB4+G#:zsW $\hN>!›j;*k뒿5{"{w3{j뒟YN3:/.I* )>MSn"מO:W ~{[`-,@?j:sE{* |rpq0:UpuAIKc=^Z[?2x1Mt(XVRJBzN R_Fc *p0j׵ w .91X/1~:x'*ZAcEjU}Z]C #R\}U7r;Y>(`k+Np53(|P_/DDqZ3&[$oD9M;|_' ~Dj,r9Jm㽗틌DG98:lCʲ 5iDRw;Zǰq_y| GRi#[O?t8 =S0&奶KR[Y a?Sι9>R{}xgKͧ1٪aԩeYc%i39a'ld (_|zW:sC5scsۨKs〖D.9->+Ǘ8 Q(bpj<^󬾳EK×@9ۆ ۍ]6̬jڗCV2c;B.;;̾ ps.kfG~{>\5Xm23 wصmAwmKK?y푦)Ic+5;?"XO5?x> 5gQ۬,!W =.jwM}W#3TD]щ`rBG6S|~ 8 تįi3isi`+ }mv` 0ѷ d7mYmw[uVpI_.suPWeT'3c+KP0]d{67͒%  wH97%@PVy\ߴ%Ĝs C3; 8?J^Xݾg#efTܖT0nmN6tJVlS 5Sx"ۡ{v1c!2~1FP3y<af{@n~:ι@:Xywqν)~7yFD>?ޓ`75oz"ېںq@I"{}8;HLjݔ2n蠽9fv>HX.Yffg,/_1lNH~bwDDDzX^j]y{̲ "a [CιkW:jƻsOρzܥf |9G{,B EDD ̦ڢ}"ןzԠ __^jlZ蜛✛'wzι O{;UڌM^sY;T6}xMy0506Kf]^5ߘx3{#^3{5K;3{{VW|l]if0l=kf7;fhfWw-fV|xf3+7 \甘٥fF}Vp|= 3Ży#h绛~uDHWVz^6:x^+ '3+y0|)T3;07M~ dfm$ |&wї z8T`Dpsn_|ߋj;F ~CӡGھ897~K;~#Ssͬ fL:&}CvJH29R^щǗW#uW |bÎc?vlSsέ ֲ ^ gψs; F.UO:V:Z7@9Nyۊ] \8VϜ$.0`fW's;:97֌ܮι6yy@"QE;K`ۧ=#mw%7ͼιc:9 x8}w:`LDDpM-li}͆>f-6ts8Y|̪l3Ə >)``Q6Ӣ5{}~3of_ <ĬafJl`fÁs(Z(ϘH8 X>#:,7O-l4c{"Yfps63x/;ٿilsg,z6q} f}o_`LDDD-H@~׃˞w|15@pt+[1™"NH_sSkƜs | x}P0&""R8Zv77e·3=m x j? 0mIDATdf%f6?""Tqsޔ"Қ1q5ٱ/~ьq"{UH~·3֖c x,3{ 8~}l8>0k_0x*#l+DT(\DDMOM8Ϙl֌1"R0&"""RD DDDDHH))034f>q3;ݱzj{ t-fVkcf6̮>ifuݣ"""dfJ ,YWٱι;/3} A;\`fVr'qMu=ΖȘHYe)Ӊ՜ q ӉՔуI3+ 6j3{^15Uf73mfoIiflpE3?2\Ww47쌼fVgf{̢[ʠ/F̻6Gy f3+7sfx޵ 0{ϡfy>OH#kf ]c"""t8FNbF'1q'vιz|&~uff'ι}s`/sG[Е8ٰxB3Vp߹yF _; '9圻X ;2?o˝sӀ߂wooi@sn_|٩-T0&""R8D]щmJ >pQPQ`,0 xٯp|m99ق>~=8(J~p \9}_N\9gp|"vν|}kpmGZ3磁< ;9k_koh͘HMd=zl?`snfp`09LU8 ==ɳ3l<@`h`\=w]S'BjܔNmokmeCj_'$v({\90ȘHܻo` Ysl , #l ~Oof6-f3 َta0*ĢA@̾gf&n{ 8Jl4~ꕠlS'뀁/̬ |`j=1q5ٱw1 )eZ~Dďn})_ue V`!UfV_/v4/ ^퇁3-|`fwfv>P G}7~ Vtgx hx~Muc9& 3R`ߺ~ x"""RAs~<bR0&"""RDj;xlnuO-j%Rck6YD DHDzM{9]ԞvOkDDQK)k/o]KK.KȘH/K2eW*!4vrJ@t+KDhdLD%ˀ8Yxíf0ҵ+p(pGET_獙qn`kOD42&"E,X3/ <_eKhK -UT7}cP0&"j>=pW^l X|&} NMS􂈥vV]t\_[$wںd)p0s;ŧv%}1^T~崋m~֬{ȗWܥpun{m(ZRUO7ωӀG%mk_F'1+.x?>>ؓw- ""c"c"" HJm݂9MK^uXջ4ΘqW*7tPvw׹|3|9b1}cۃvcl YА0=FEu֗D<ք_#.vXj|׆¾l1^B3k뒧#Ԛ}jM8qieS]>ܷF|e] L |뷣iYQˈ䲻X#u/v;kHA"/SP,^={koWOhV6,E!{40/51ZAh6`p=~/"*_0Rיnc"E4w xG~XJVZXew6/ +ӾHidLDK-્3Gἑ̺PL' g!S8zEn܈W7]^{!FDDںu]$f-fV6TʒN_q=_3!*~[ܺo$"CH :iǁ׽[q5 |1/yzM^A0iЁ6]`1>.9D<@3ͩ.8rڕLȰaIKu3>bم3&"7\ckENqN } Hg0eeVrFp@6K|t3DFDDiC&5?wew'PB̒5K= LJfMI#G9d>QCshЮ 2?F7Dd.Ny&\w><+]Znj^k@e[S ! cqkmAl٬ ApuFDD6Mu ζG @R5(h$[iav΀K@(Tk(Ų!F>DשHm]rZ] ^edCﻖGfEM3dF*F܋L{W@D:1wxl9@m] 8 xyUuwkԸq|0vPo3(ɐkaaǪTInϬZ0p`RqDD>B#c""}TRC#::% Ply |(ędH+C^O.ȑ%̅Ey!FDDco5 W9*Ӏ+{aЩB_ TYZO.5@c YB}.^IDȘH0kΥ;<r\;$><\h*ڕ|^1We*rdc1^DD:`LDZԶ@ k 3!׊=~^98EL8e%wk7*ӷH ""}XRLE:8?p 7݂S`2Lڊuk-5c""}X0 ֙p_`,XZFڵoW+"* ևk b{6ϼ:{YEl1~$#8>R(ioC.S_[>AkDDG.+A@h[N~}ޔweL3&"?|8 +Hh _62OB"sPm \\dGZ,Ѻ&|8˴_ȘH?vi}8:Pwz]"`Ϭ g1)"[N ED ʴ6H5mٿ5- #뀮ƊH`LDIO/US_+"})EDJsUj@E #A9 <`J,wc_Zǀr`%-b7Ed (_>vjk8y;׍0W]PD1mA`W Ky-͡4#3&"9Hv ׀^XC]~ ઈ@R.܄/jׯ.;D1:+"=iJ~ Ԗa MK]  /Ȩ}f(̿W!MScKOMD3K<% &[gynҚ1g"p6pӲ:O;$"ݧ13@WR{qOMQSg.~ ,YS)j/aK?HI$|α'"# DD2`o5L`NE1) c""}XE#:hXd`c DD  wxJ*"OD,UvlRٴ,vD紛RDXjwྈqE1_r0XPHhRDo+G;̴) c""Q=3lHvt>4ћnJ^R[ Ǻ>b!fRcӐ>,pDHfLDā_%+y+^{7x1iZCd1s0$5nMK|x0k+MDDkDDʁ/Ki}BȘHT` u_>Hi͘HߕpAoy?Y;&"1+JdRiTYոS;&"1>*][ח>[G/sqK .O;"R` DDzQm]8?"R|]びOE,sRuvaR~Nl㴀_Dj뒧5Ud8 F,uREt:`ZR?M["R4 DD @`p-~7LȹTp `Ld`LD~$LW]t0_;0bi]WnH_`LDXC*,T}[tVl@m]rpX"ωXjO4j__-HgEO.kHsK+Vլ=H`LDjd>Kʚ׽L?੦wRD$MSH"iuK˄P˄f{"G)يj#ӎ'͹g'WVR953쭧H v(ٺ> | X wKe̿sS["R\Z3&"u=^[Y[.~X,[CO~(D lExlE"{ںdEgק]?-9/<03]o"))"ڽӥNӻxAs_Ӫ%^+h۴fLDp?^rpnw๎nX(w{}Ͽ"Q0&"R8?Zk;G%w.~?- i).c""vQѹl짫0/N 40O_cZ+&P0&"R@u#Ux9=Y ;_ךwȗ_VssUyM7F=ٲ2|O,[]k6=DhHa _?9S/]+-Ers,u䚣فƴTt]DA#c""[D<ֶnl&?>JūߨnYz;9麈9JR;)ͺUU׉IM\>,_k)̴,vCE있17c[4 +OJ1,툂17fj?jm(_"R DDzYEݸWVizVw0pb@}ާݔ""EHhɓX@r(siW~HȘH, &[_k*ebIDCH5VΈHQ)HD(/\.*)vD8)EDDDHH))"c""""E`LDDD1"R0&"""RD DDDDHH))"c""""E`LDDD1"R0&"""RD DDDDHH))"c""""E`LDDD1"R0&"""RD DDDDsSivIENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_57_0.png000066400000000000000000003621441413546205200301610ustar00rootroot00000000000000PNG  IHDRc%sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxwe߳J+C @1 +QGAA # { `"DEAgCI.r3&]}߯׾nwgd?y(((=C((қQ1((҃SEQEAT)( *EQEQzc((=1EQEQDŘ((JbLQEQQ1((҃SEQEAT)( *EQEQzc((=1EQEQDŘ((JbLQEQQ1((҃SEQEAT)( *EQEQzc((=1EQEQDŘ((JbLQz9峬{z(PO/@QOwہ,px7{[O.NQ僎ZEy | F,k[(cvŐI@P| Ӑ%HdԜַ3b@X6,*ulmTj/6 6rxlNEQރZ7C"H{I,+*L:vk-6/hOQ> F9f*""]bym3iU/rFibyȴSG?tL O.Q〳ms"_u({ƌ)J7c;n5087m蠹KmJ+uids V"(p R?ehm`i6+.HFd& MiP2ADJ_#qg ,~y5.D@Y7ZouL` 1EQZ0oEc}Vֈ_Hd-@.+ T_4,EcfL=ln9!ֈ4,lHW f} oBYkǶ(қИ1Eflǽƙ%J+^Z*[ԂY /BJNx7m'҅L mCjYJ[y{w/EQ^)e`JW|"ʀKقद1VǗx%,/ ]-bHvdTns/(2U!tr}'Np-#q/0?e9Z4ش›[(kQ˘t1GxӑpVOytj4{ՙ6(ܑzؐ`aC;}4xd3RV܎f[6﹇X>Fo1DY2X|aV@*0?Ɵ׷›VW3EQތ1E lc]N@܃۶V.k l}*<5zmɢM-Vц掠HKGAgy6fWyp`أU /s%]2[!aWշ>;Am{Kh9 ,|KQE*e:,EfmjHۦ m[7[~E.J'jl,6'CYMV3 /C DsM!q_ X-dD=&Is2[lw殸o(Q1({N 3g Y jIE7pgf ַ nDAK9A鋬|eN$&m" H-zD l (4_Qc-&ⱴy}юD}Ҳ-Зl3UϬD,W_E̬7˚y0% zDXd]{{#, KAe xdFM] n(.S.vܾ@g"k0l)Vƶh}ˏWhϷ{Ƀ'` pE"{v>K^l\DPelr$hYER臸=aB>Q׿??2MMn؜^e/.Y(hpElǍ %,~p ~ᡎ<6Z {a}sd~棇zC X_~$\g;x`f /_\_j H_l)9c~ Fb.͙~#/dB:~h`ƎVwR\n(ƌ)J ؎;p>gF[^`PȡV˰[ F!h)&"C?Q NGHd~} 뵀Z e*C[F/?U}&fGOzlc_ƥ.J Rl+ϐ <\ᱩkUf^O+nk,4-{(lZk őNF6 y'k0fI&$3s8vԷL8n8{v"lhcYYђ9 WC23F2&_5癊;ub%+ĀR:9(cT)Ja;nlD#`^Mcf@$-7 ċC V xV$+[JWmm4%_Dj0`b-{FR_!B/̘ +58 x<}q,EQmT)X~nݜV{yP6y ou5Cʿ$emǽmXߊ\sLˑ°ÀK̼G5o/66':3YM9UEQ SF,Y "?sHݱX,k x ,yk'^5?g?z_6 k;v>Cݱ~EQތZe/0.ɓ+X^X&vC^f'gY35yM EQ}1E;&E܂C@VݝT?x%J Zovx$Fdf@" |O.LQbLQ?'W E[|IcK_K*72+jzצ(~P1({d`^K#P4Ģ,3\Qcwl.JcU2B:%me6o뷥ע(~Q1({!%)nS!3>01~,Ԫ^(EŘ9@=[͖˛n(<*eHIEQM(H/JEQEyߨeLQ{X{O/DQE``yޣEQEQ})EQEQzuS*qu מ3w\ (*Ɣnv0xIc`[gQ6F-`__{zM({nJ'8pi=ـ56xGzx9(n1'xc93jmǵMxl~}89tf[›+u^aneLSRHh;n!pX"[n@]2@Ҭ q?Hcs`%G'XkQE@bLS&D< q|8x]G pٮ)gq ܜ2xnaa(;#)(uS*{D"KuڻR@v1,/W":vq==VQEQ)kB \d;Cxc'cHF `;n10xmyNn@~dX \ EQe_6.7{\"vĊvb̸c7*w"_7$ⱙ;;8(\/]g)(*Ɣw0I"˘g"1ZnE~Ӕ(Lc-;-~ VmZVg qjN"K%pk"kwpO"ۼ7sv$=Ĝ)(JbLg0#{)9p@#f'ⱅyT7ab̶bϲ2 6##1g (ɹSPKَ!'bFcfv9&P]QEQz cJ>D<yHX[G6-g؎D<7xl_C,PTL{]Z^~iEz]&տdqE\ ؓ~ < v"{`"bqjr[H" 'TEQϥh;n5pvしBQ/.|kا[(/^mE麺 sSf[l-C^L!Wx$8bEݿ|O?~N[R$0=Pb5=1`;{EQbLJ2QcmǭؑCM)xT Ǽl6xw(.Mg[ Ջ`$l0:\< 4!U7*%e-U%\7[@ 9^ xl6w ?e;3e4n@bQf+0m)(uSREf4֏Kc-L?fD}k)#qWwǑ:i8l"E[ÈK4 8$L} ~\EqQހuqK#J6u SH1ɻ{֘m##Y=&e!@-d(cV֡կO.Q ,3) Xݠ{ADşێAܝD?DUO VDPe^7#ֶjĚUTa^ /::Re{_q{(Cm>?E zDQ2-:(5gHوאwOm7# D<َ; UkDji"V#0wH9}Wtf"[g;n%R6h37̗ڎCvu[mh َk^ܛ 枝uTT̙?5Z4nNOGQށ^ ·w~TBJJ,Ajw%>ٻxV ]N0"dA#VO!+Cboٯ IDATn&FiK6LOp">dzSVe9rLY$fIhw"vHmXUkZ"yqu"\,FVbjAd'9+UmRvYS[9R(vY۽毋4t_l qbqX X6e!%=XΦǧK#떟E6|CHH6HsD6{7Dz.YtYKb7lzF=sOW".h!Atp.+-*\6"arޕHEjdZ؎؎B'#d- bz;hٸ1w ۊ-ՂO @A@Ў͙@"`D2Ѭ)X ~f4x4""bL5"!ֶ!Vjެap!N3OY[0՛>X.Bܧ&؎;#LXI#{%OmiR7XA NԆXyK۳歿;)?jQVWl!nނ䬚(]p;Y|aQPqKE7t-!b*e#V&0Ñ1Hcح %! CHfd+"@` Jb<5j0M04_@k4*G^^!A".gxZZKY\ _#2۰D<[@,y/uPװCCG H"/l=5 R,xcG;RDXQebLc"#E} dDr}!m;} 3!V~Hx2Qm7"i}7qABP~nЀX ̺So+C,[̚'9_ED]_D`%T=R v.RFf$DBˍӿ۱h)צE+*g=yv?n<^(ʎ;07#emY;;eo^=h/tVePQ؞&=w[ƮEQ -m3xEqG?>.{~yy <j^T_4N?Ar6 >2?h=#f'UzqfV,!eHtX}ՊHiIf ֶ $kYO/"mYlAH#f"ef}sHZV $^y+ ֋K(ٶ55oLTTPc͸AJѶ> %ͨ{;(]9©g|WcMQ'.; ݂8'#*CD)HK"?| )rUAˌ-6c7!B)B.l)"Rfl{e+@by,s'րG?6c6#"/XEbѮFZAg!1ofYDxADMH͸L{}:h3Cd]j~ C\z~֙C/Z4S;3oYz d+TFk_t˴˚6!w9O ֨9WkY3P3Hͳ $nDjaМGD-D-L[4(\ĺ0b;#lf"/NW]zV$)z#ߝswym)/?(JCݔ VlǝnRNk|`g1\ǧ$3W)sQXˆ.ETſD͈Z*AKz_k+׷̸2Dt%ĭK1b֐EDo2!SV5fAB1lED`eذQ'rr(3!oڛL508Zغ*%݉O2Ǖk\zs{ثLYw0KWv.0|֣$ⱅ%=EQz'j;w"%56$5:/Cy5][o;_U'*GˑJ'~),R,u-KDRڜ;d*Bw;V#֧C>+D`5s0VUYUZ;* Vϣ5Fز(d #BsaYh7 4rY͈K!Ě"3$L-M3*(H(q7Vَ{"D<6noea;n) {}PkgK(݊ƌ ؎BZ]`gؽf_6PG˫7 yߺ:mH\Y+bxm0<49ɴ< yjbFMhEDS'"Gb|FP ^txGYKzs7;ݝkTE魨el?vqW_%;+F\":҈ظ z nBBmXDP$g _FDR"||] ůD=C`+&'l,$jYOߗ\}1Z2V3iֺ\rfl'L?S~M\ Bsf[?hA:5,r$3|$⿉xt3FSMJ2Y:j2 z5*F21\X"kE☶vܿ Vc'6q됺X_AaiGS"," q+FAD)`ۈr n\6Dx=jƟA.o BdȵLo]aDX֜#*3 q2+np6 hc&oL99"FO?}SW?ʼ'W"/Cdzp"Vn'#;|~ӈίv5x7/QQWblI=818$\H#kt"6z`b! j^+]QNdufND\xǐsGZH bɂ룕Mܤ"yǖ#3RU!r{YRA+T\m:b>j%[X&!ֻ f]X#b̵4qck$aZU خE "Q%{q,kfa7G3@EQz*ƺqK voxvq|Z5^Eш(h@|DZ f0WEF#6"b!z Rn+MTIES"!gXޏ43 q~[R,d=@S H:VKX/fC2&d frɦlBDy~{q9"^޿_"5>ExJje|ŬH|Ͳf[o*|P1X*w$OHﷱ7$GW҈Ⱦ<&mY$j Y!qXfQM"'UV48|pYA.ąS+D\e5NiA^ 2܇o,k$g\s?5 G9#|[ąn ^c lY=fsFqx,kJb|q'g=&:Wcmsp,kj!SEw~ClW#B`dM$7 XX|N$D`Bb Λ{qSnwD2l6((IJLk"^D4k)&WbjU,iG Gok$BokT` -DZEYˁ,Bu;bdZH YmcUHYAoAy5!V4 $b RhXk;n ЗOd uR;a.,~~Vh\CW[b~WSQEcL"˲E5mzOcoJc=g`y?sD 80Ms čۊ6#U<$>|!~~d#ٖd l-m[0Z緛kF!% N X@ge^5˚Zo]L(%*Cld%FbyX^ DȭBX3p z^NlQkXudޱ$ƱOof-- 8sOʩ64-_B,GADE\A! |w[D-<y q+V+[|aC:cUO}3\Ey']`_H6X шpl'g4kN"j""!oYwO#YӀ#C潫DD)&㾼i@o:v,~jKWf_ s_x3± wEH)}nHqweFMjIe;p`c"3'>~{4}m,;C+$0?Ě} [7#Bqď#%5\  WJ/HUh[ˆ\x˜~䴪rt$`~}rʊ7qAP0-ii(L~19hR$ν=nZ8tčln$HVlnEQ*1V!qDې{3ǡ3m7˚iU Xؤ $&iv] ֜Df XZ IDATkCg2q32Kз#O&ܾqaW & [|d%"r*s*ذ r=+FDЯ1fs\r`C\ucε\;fM-Y}؁B+ SvwՈ;n"܋xv;S%Zxшǰw"NcKzr{ʜS =]QU嫊#d %ѭۍ,Z &=,G>S{-l->鰅_1PϟEŸ<*YXw~@ь{{^EQcmy\~ W*S+У*qˁFcE;1W=xM7wYIl@Yߍ7\F8  {Zbٙ R ?`Ƶ6?C do"" DpxFrmwc,D,EDR bݘp+?\ͤ9/ DQ#"6 Y}m9ESE-v" w ' \bsǷw2PǞ`Zl=KNX x1L yh 1wb@L 5"ttRH̟QS䞞<H2#'J"ᖏyͨ.0jNE^TcLˢY1l:b}لwV*qn@bs@&XvDDDɕH!Ɍ|\ !4 )eQMAą􋯦g) H\Ps(.6k)0kl#Wo6B,RBXJ/}/0#ԷkU}~CYӅfYo7i;n)➫%j/f&jr_!Ar,rd-"B(願E9͜UHT CdňhDacLA 4אFDfmHVuby;痚͚e̚KF e`|Qfݪ W;-j]p>"@"َ{8s%Z猚=zmJxB8旀B5usOx(ni i ?|JX%|f C{ z{olҢA+sO b]TϨ[aw\U?gfg{I6@IH`1,uA8,(R2 ("MTAHΊ0E`d BI6mS7e7˜ssuSG}}vs=~mo w'shzFൡFTÚ3Ɯ`)? = 1]=tA\k)sl1 ;=^ݏ>X "7 ގ&1(}r\"ſ7kݾllq= u(~Bk[3WADy.64ki<Ȕje^f=^]{5~l}Wmfw̾gmGL^o7~n\]r7=4v6Gq}}L+GHz)]m%_O?~u @)z>蝜̸\ /*g%KhxSYE ,kzX~=-.9D4>l3} w36G{x_.0Q9kw*y廗t˝xMskϐ}Gy8_pЇ/ &|drxi~~Ptb-3}w|h`"SWlAޘԤ eo/ĝtk9Ԙm0CnA0'є!sp 1HeָcrXxG3}kML(@B7#گvrJG>>vsED};e(M_sDQ?q閾`/Ɇ4^h\E<5X3&z2;?׻2o1,(`KCr2jn|v}X1N jkC#"zvKw cYcvM9hoO^{sC)@ Cs~X{rM9xZ3Z;||7j+Xk1?D:i*%k5\Y^jc1]9ڨ۞ڗ=kuƘQ]_ [kTc̾Y2ֶc 1!On0|3]ߺ(J|2ӷQlm`[?紜[,lK}Lq07 ?%@hr폀׉ȏ9|3^148\ro!іL}w|Ɛ"4c\c}kgR.^6hC~^9{trZwٮW xj+UwL-ep__~Ux)M! lK91X6eNj^<"kޫ(P?ʟ!1)B* FbC[ľW! :W gJk}л61z`J;O֗U'pC$}[0-TZ\\VQC. w.-chi=`k,(*-.f/(IGsO^R-iWroa#*lȱ9 ?پ]3<qƣ?c 9`'Xg/7yA ]K{iH&Db&;ngmpj.Uֆnm~ XUZ\rYEIF??l̐ =WL6n- _1m3r#Qէ6)-.Ύ6`Sp E ֿ⩌PCδZSU_`FRMLH6䟻'^m0LAS*$sk{ ;:pmdLc̔(R/ckr.b 8߶ZVY ;,|RAΪO\rVGG"K EG `3 E1Z E~N C|rhug d eyz"Hqߔy>i"6+I)X(hO{LzYk$xV]q/#M>zɂ= >A5?{nJ[UJCKiѾ#pY;sY\0ۢ,[276H,jè`c~{{0 w|RNKOzonܝkH*J#TSe$anl'v;U[{(ykm1_ڐsF-Y8r~][KsO,*/6n7lFւ!n/1sz{HHGޚmiEc"=Ƙw]Hu&nj1޻=eDbۭekݘ۳O%߻쟼瞁T$PkG,L>6u9ད2pYF# %کu7#e2u:~)6k/szDЯЪn4xϛi/״ͮ| =T7qס Uk@7ޞ7!||ɼj\;Ďؾ8KbX" #D)ʬfSw=ܶvl]}NG>%^+H,  %֐ چ'A%e%AGA[C%'z.uO7+vE\CNgSބL rZhOǚ@Vζ*dܖUՖ{Q]$%P4~&yƺ)]nH$,ގGF<X& +C+^}x/Mn8d\f Z.FJ}]V?~\TI2`#嗇CRjJCdfEȤ8Fe38@̚;W!ř "ҌlBRO̟^$b7W#@z >RLuh嘆LA׏7N^l^i&/a5RQW"< #(H#K"汳 _t9E[Eq+hf߽m6waīNc0yiGNx7;\ywWe`Hk27;0I`:GL&n[#H'h8+ 4V/\0gLc˫8V˞QvWyYEM'zob&U&bd@ 0!3erYEˠK:=쒽LT#|AGx4bbeS;bwV7𶤱XbJ0ldt J=1,G C;!-F-e4is-G4Ķ HEPDeV*@JGk@`35cSpG&Pd 2c.Fهɝ; 닗v}/!刻zҽᴾDo[Ÿ1,+;(# bjl鍍S6˻"a2`Z2jk]ߌL[Xd |# EbC}o\RuX;_U2K[`}󰵴Cƣw:o vP]Z\j mMg((D&UTouug,(iF>KI KSHr7%KLǣ-;8T&n]S$"SBr ^VvgΚ4ţ!{FKRrX䎫swC@ċ@2r$߂@x5 ]3 XHB(  لP|xyyu#YϘy ٝ1 P㶷[z&g_ C'"'w/[,K18kƸvG'VѳL:΄֓B+[nV0ǚڂk=hxvuJQkǭ=6YPfsKNKCCUGƤ[G&{j{=0o'=g8+׏YrÁx4|~Ƥ[Vog}iq'Uxe[K; )(ԧX*J4 ^>']twItnH3>@~Y7ƣᛷwP$ggţ;2xhebJOk떻6Էp~;z[$4m> ]wCE=}e%החyUtC47go. %]H|B(%hx4|qG#?#یQ_hzs"yx4\Xiwm~1rz_{+]^Ȅr(>PլK~ bS9Dd~15nDi'2S">!`4&;ƍK7$x9ˬinxC١_w"N {{&Ef{:=L[y^eFĮyL5n C O;n.d/LzY;|4ﶠg݊m1p@!W/2KdOlC}?LfH5cN4V'amwx4\"A@鲊[~ZԒ;?-¼fm8l?%7{ 4ɖ֌@csV^~1i+l꛺d`{ʨ|AO܇TC<Nz[u=Tq EAV ~ژVYEə;z8_]ߺKK$ =Og"Ó2,G_r DsQbik tV/"f:M <̲h"//YԈ2<&IkP>}2<\C*1mqbջ[0r{5"3j}9Zgkh"3V;dY IDAT%z&R ;sq^᮱ ,7S79tg{gA>9">tG IpіmtEW/k#%I;w.M°6ҳ2|x(i$ۋ(Hxtc:D;w^U@#Ğ@, `Y5߹=6mWo9 Wo[6h4ed#rߟ>PZ˅яgŎW:;Vڢ!fhdNUE߷WL: }ܩwݢEj\>..ld{ ]JS7ƣx4A<~%-<< L17̋7ģ 5w݊~g8艘J6"f]L|(29@ Z;Le[m@@n+2؊#p%N+b(Z\C~"?cľ!Wy8AW or͈عn<3P\hQ`(CLO "ğ"'ޣU37[5xTߨ}>bbJFtyg@SJozsKAMCN9 ?J/-.Χx4d0\~~Y Y3%WXk Ugc}ѳ(IdZ[VQ=YyH-̃q^WVQr Jy>ZUa 0I1YƘRcwgik)q?, }y_go.f? hC:v$ƣu;\h}Ȕ0ܶ,d>@Gh%Ȝ8r[Fg:L͛m=`=E#>I:^y$D Q Z?Z7!v׏gNEb@ nsE{Tioc̡!^HT~3Ɯ`v(g!3YggS7> 4Tb$< } qюb;;~'2EdĜ#G#\ĖBjǔ] !ZSXnjAS(E1U=Y.wP{V]Ig) 8jGK82z;#jb"(YD`]\d:LCllr߀L̵ȏ B{~MNW^*Ow5/0K^tnlDfe(crsf"-!dړjڞU(P<)p_8Z'[慽[Z\XVQr72)>ޯCamٍ-JG`[Am 87O*Jn*m!fA䰯MgK7E]]"蝽]WF$Ƙi>m5s粔2ciWcL.2KR~ -Тd !_Cz}4?d96Z{ƉF ."*p1gh\ak]`:@dO,]`8TwĈ2-Eb[  źk|P~v/uA|}/bpm_g;~>KSV"p/~D73TyeNDYk; 6f+B@,WiC@U-b1 +@>Ff ' ͈y I IAc_Fn^G#+hbX,#s9R&mnU3{H-E'ꇘӐoߋÍ\hgǞ@`7 Lxԍg f"𙃟 Lhwc{?3ODRGG×Fb\1dn75.T"D?DGSwv|YEI߽GvFMx%rO yߠWXGJ<(qvjt~ChxCgh⾃>KdlH,q$Ϙtk;znX1m2VĐ/p$Q2}]!U"bw|5M[Z}ۓxϘt⬺:"((A؜5Q}~S{W\T}D;cL!bcR"Fsi}&ؕZs Za`u8{);noo-M4rC#*mETڢX" "ox4*OAQ{i(͎"Н[ا=84&} ͸l&9(*@ϫH"`MW!E 6!,I&oo~Vb -5*=Gޮ#F9i, 楧CLV;~$W#sR;ڄ F/`}x΍uWOw|bQ_Eb]x4DŀikZ|kC[ 6<|ݘ>k6??`HsDfƣhq8@iqkb\u;m̾h!UY;{?-\tR(0Ͳ\Y%@E#ĉVJ&7wA}_H\&b}b.KYņ6OYkYk[kA%:k(8k;ձeILg=3@OAzykWKGtaX0kڟȻ$E!uǦo.=REbKtșOwvřoDȮtܓh=jc`<?g֏?d^>Ȉ#nڞSU(Eʡ1LU#sb_>u5 \ק@k{+[*CZX=1m}!iDIHꮽNCHnL<2;Iڵ¹hmq?ksr\"סI$%C F4BMۂ6 ח =IEK-1ň_2lІ棕Wݘx"Ģvc>Znkw\k89c{z Gbpd#lyH~[[z+j eZֶy>MaFE: 3M-9#\Ԙl,gVovKk͉̹wP9#yAf~@*JBlTۿ)?w]k$ɢ}sSaU.Z;35;=tmɶ{pN;w x2?xt-gy@.RN<.tbLneݯ lGm9Kh:K\L"DNJjp#Ļ*F,8:R6<X .CpCz?AJ"bވA ,͵3Eъg#2!ɮ^&";? 3y#z/k|u%#m@lR2U^_Q+2eֵ[ZBK%2ݓpnb![x_4PǑ7{ND@mbBB&WO<%K>3G "ı[exoMxC:PVQbblybݝ{zY@j_Hkjɶy^5- ٴ@Ҹsn EFOBi-#PM->zo?=h~n+G9sT^pſ.ٙXk)1\w;@|:%>Ǽ0㟺x4|n<>~7Jd"z$ZyEbH,qR(B&],XR%t$=&,TUڋȯRg^y\{ƻ59Z?umF~Wih>s+ E蘆byZ\\hڑ{ Pj*w_"ԊU{> kg [jL;ϫ9=ݵ-~arX~!3ݟW~̜'ʭ3 HK>cGmkZr'pʝ[VQw'nFu;9Q4WՂ *XnaN(5fNem֯|iW蛻"zBc4!~8|%dkNdϤgSJ$=`őXh%;i Uh<~mM!veRJ((!Wʥ 6ţᑑX.j&ލ+hxT#QV"y!qRȴ7G,_~}f*EIC ;bG w1y;#Rvc0]wתr} ԚWح 5VF<T6Eu 3]{V!w>RA tmx;Tw!3'ǣn?Xb& IK>)(Y RZ\z7-b"; pMCӍIfB-cƺsCL]KY\\Z\ ]%].0)eE8McQM-Xb"""Ґ_z`Y%2$"( ?F ~9m8̏XJ1{hb :'$2~S<ǏP< nCf#Wۋ*\#84"R,3Q3=߀̃s=s Jh{bz.Rd+ lF@ ]H"Ղk?yf]އU}Oq㚃_hKێ=|ZXBx4U,.\x4^u6p}otItX̔).cWM JřW!H,H,5.zb4kX+t1>: M^~tHE`~G(%ƉȄ7#zK<~tz!PX?! a\!/!`XX ZJػ .EK PخӀoţ) z b~i%~Nd~zi|=[fзmE[QZ78PkE .!6=PWmߖM]/Db}*JF7wa=u -]35G|䈲])]v-k-lnȈ4zo7LiqF>'ksr`y9rץ!\!ZT `ʵ_~. %]ߓ.s&H,x4  o[#q]|0^vm&F5 99еKaİ-Eep_uÈY9ec_%Cg5ZO7w|~,ڛ5<XDBP-Sm_H,1 / c&#ʝrU"%q 5^0/Р{|4tGs"dw?t< q 7y@}`܌Q=d{V8uMuCkA<9{ M7uh#A^A~-𓼬gO8?L[Cסqra{>vx"~sve%_-^ɓѻZBw]/ Ln @[n߮\ۣ..U+KZ; 9ȧg~ Of#{2p~$۞X\d1'*9-dBx w;4.R+!fOh%KXb e ~݃} 0q}wXĒ\`ڈXh s@Hk/~K$Ƭs9nW\H,w*틀s9n"SskgwݳAڈ}&d"M8,D@j!bsfhܾpz%1k1{^.3&ݲEM>-}!gÑA$3>~blgrCtܼͬ5zE #^ĔUU,u'|)bO,1,_Ȏs$!W-Rx\1Y*kzKt&F:kU <6Q IDATv1(*kW_1o#|H'pOH< Gb F)ǵ{ >i@BV oLd*qm A 9 pm$x>b)h󐉯;'8nm%y3bgFR*YpR]ۯ!0v%"7\?skd$qxJMтoz2_;oRJP6ոk%uc0U`= ?ߘ"x>>Bw {kqc>Qpw2(!Ghx]_!L/jl0$PUZ\Ku`ciqwHaYpK݋-b۾4 \ڵ$Sgr 'l\ViZn G=ƌ PO_ %ƘC 3:&׷0>c'}uҘRHkjdŨVB.ٱu` ))hҙx-Jo"$~1 z<~-K"?>kU~Y|/E-׿;p|kyϩڍ(Ď0BKbo`C7C_bPވF -zù+J˭7uyT>S*wf"%u\3Yn{*mQ 5/_3c~|-ĈW+m.+H,a‡*7sS K Ƙ,CkMl665cLOː96}1rH%ĸmBw\/@#>h_ؼvAe飧g_A`jB%L=qSy*2z_zO"fpZ7w~1Q |8A|`E0кhДJ@VkX/n|^Hij7GGd@ ] .bk~w6]s[ιSǣ!쎲[ā~^5g[ϐgvk4顶$wpzbMC"GCdZLS/((h.-._|ۤߡ}u f} ~3*JơK{)[vz:C)@ l%i|p{ 6!WTiƘ!s? 1fvfyMZcLO=cKnp\k'cґ;7\|qPc=ky[G Z{Ujģe;9Ex RS]hx!0K\LtO< %H>;\YģR#4]@Wq&bD[G 3R&iϙ(bX~hOy1Ow W#%R>byz~BQ_-)_ #]jx$Jwubou׮tL>\@; #6NjOfVÍ{`3?Eup=d{A ^vvҜm&b1>*?%a>B]EyCѵeziqvS__VQrtYE uK6<WZVQM;7Xy'K۩Utw^}sKμPC:. ?N(().-.ݶ˰Lt#q9|0Sj63OM WMzZc,pm6:o!x,`mA@Cw];\OYq|?@/Kp H"9ōѾyYw#/jT[Є\|E]z5.=ݙh?FL/J$=U當hg@,=>~J-KأeO)L ]:Oz 3 9ycBsi9-gO7d7Y]L:TKdI;NbnlNߞ@:4/ CkAgl5"V-].H$8 E^f.ułN =¦#1<KLGj^#_#twh[)@&Yzэ4V hwfw^-2Cc ][osC~Vs@&" {1;ڍ#Qem,>ym݌_ػ W4oNԺ\GOGbݘnAH7dۊث<3W&z!e[h9EBd}@ []ߏCč4zbnB`w Pi:bH=nLwa(p󔢴l{J#vI;z:HIҩ$+*{?\ ((y4ɜÉ=]08^o>VNT6^Oio);Q.tˎx0eO+O"u25ޠm5x?8DƘB4瞆$1(T)[k[1Gd!s3S|ko )3Z;|Њ\~!ecX%_LU?ގGڅ&GHnWV;):C&:M`h;D& :!E~]g#?*ϴOsO֛iv,)q,!>F eo# u0bC yDѰu s. DLd59bZ:{ Dy !y^Y}DG&{>]ķk8EMu"k]ޞBۃ%,BE n&bBb?7#SL@`wP@o: rhJ~nSL"źصMyk./o"9{1ywH:x4ܐO/q۾检9KYZte%ڦ+7Gw_#K Ge>&{*J&[K25!0=#:6cNU3:-q9M̨oanOչv8~B*"p r8~p#@n/c ݵS0dm1|ץhD#H_p1SzIήER$prp(ţ1?+h a?=l+gmϠ*1f'3\޳Ɉ@Jݾ Rj:)V~bZQ#|WivC+_s۶jbqZS(+2EfGEQx4h$q~(Bp?!끱VwhiillQ cnLٷ]&C= ;M:ƍWk7!jwϓ~`\cä*}faU@DQqvOQ3ĸQͦY4f16jL4$IMADd_mAqqN0lq; Oܐ)cVIM `|8cA|mG߫C|*14 >QXC3elt߀@==yھ[C7UJ$kouݬsﻐ2BsF[6n/ahWw=ZHeE@VksdǘX]d>; -WO5^^>i@jd!:>S~ۙ9h8q{#`5|,z qhD[dܟ.6l"saQBgkO)ik$Fd|f"OEx6ʜD* CJf"ldnGlվN}91'.ʬBLM2_@` uRH16یؚQzF|؊OۍPY"әC`bb.G&>dmD ֩Vv̼!`66?'C+n_AiR$cmNg91/y0D?îƍ6 Ư#  x p2N1QxB h@OONTH&H"@ނ@A]6%z!cwVQH=ؘՠMeNκtHw '%R\Bal:_ LK2O&RdA.xyoE)3BϾ?^Hen@,Ry<U GQ@7w d| _D #D{5eCoTfo,rG,VwgdzyG`)@Uj{~9+@C{"TČg4}>wo 5_ףy 16HL?AB_ z2|Ey/?"l)o7~K'NƟhoYc/Fc=w07^ͫEsHtc*g?y1Oz,܆|#X¯yt2b+C:C:#-x0@J9|<H3H?F#UHeGʷ1;}Pg!%e/#T:e1 LC k8kr+]Q$ > `؄@L-zZ}>#wйKF}w;wI2 簬D`n&bNT# C1ck혀AҢ5%EMk6ڵ B߳NC@.B&+1O =~mGbɮ9a|I]Wt2@j=2emcc k% {$R1W4Z TS'{lElj@=Jnd7/VvZ#OJήnEXst8-9۩2WaHD*sdCn"tc!;I>v`,tB!j]c>_j%bA$R#%\xn}qy-]+xc=~QEh=}BcX7JbUȜ ~ `K#ֈ߷)˽7khn)}ND*Ӆ?v:\4;EO*J/ɫo=xOOFQ'6lݘ 6vH|c"|ڑR@t2d"Vj@t2~Uxbl#s$~-HajD `$4]@5OnE&!`)CfnHjW)иq:bՊт2.|^1 EeD'm@pab)5<f[92٭vI'D*}z" $L6;8em$~nъ͓"1 7\Ft2dZO'?p}Q<*7t2BݞIt2[3Y~ʿI'JHej1l3(Sd?[cG 4He~9[e3UvS݁ AAl$J}OKz_F9]oOΟ X%!*"@fU$R,e&svpb' s2BoNMWkS^qڐQ=XЯwG] IDATD )ιEŏZuЈs5ϟysqth ΰF k)_}hdSo2erH-@ېyj]9пfuec%|oZ}kb#Պ٫/+`5#&ב(*B`pwG>^=WA4G u_RqMK>ǡ"}n&g6ƫ)mKhdV<1uw y'GMkE` p >LU66 W6%YMec>|@ ){. ?rdܧzIoSHefB1I2@_ϥ{|>ǞhksY77,(:ղ^@y[N`q$xBP@tF#<0ODxj8˦q=gM:ݮ=mb[^p̓~1E@pO~;ELX\=~N<0ua2XFz)ͩZ\Y1g^Y{AEӞg_s62dGAl <Nr=:Y? n d zbsn4`VgA9w)buȎ%Nףu$Ap/R]ф/F~aD//"ȬyĴLF++MgkZ!KC"0+ʌYD*S6ju79Ѧ{߈{9C)VR̜`v=0~؁h3}s8а I>_\)51z8Cc+9-T5I9Q"d89" tR-я.60(fK퐶SLNߏ"sO'o'RWK[tFgZdAfCEH 62'BXh#9ľA |ӂXo!pG[exŵ~ Cj8B Ȍv + Mn`kWFWH!E? ۯru7bB wEy TA`v=R"knZlNzY^̬t212q:3y6̅?B湿W[s`m:nH2gPzi(Ȥٵ͛t2n_gz#V|EY]HE{.W%r,t1z Ak+zK C 6vm9RBKМ Mΐӑ"M[jAKW^=hhX(aCݘ \6 ڛ l`FS[; !+t4/ڣbŜo-94Z[ X`9-pQCFS:MU>5` $'R mT$\+b&"%3xw RWb baf".tv1jbDlL p>@΋V!B~}1- 9vPĠ=CeH&"sa b f6yQ0}~%ƿ Pa&6;E-CxBkXdN,vP1cT^K'TZz@ceP"ږ.ٌъ 0[26g{0: TJ\qFtXE}\V,=G<0 A8Oe P,'~d |9z!7QAa)͍%Ȓlʒſ#6~XWf5WuFRXb+wxͅ"A"6m<#<,ڌYB"~RYoܴhםχǔNFt2n ,D+i3H[&"LUm$7 <ՄL/g^ZO h#Uidb=U6jmc|ZZ#1wɒ}77F#wsHeNIQ h鐭{l]4M>\4y"f}[ 91sn/F:j.jsZEι=s_O"Kg;d.E{łkC tF;Zԧ"#xwse/~3D-vBш=[S)b+E`F2ȃe#E#gB~g)T"&第}J`kVxʬ%U{~gERLwLd<#8_E2_kW"cCQdeem<+ZG&~)d|<ݹ\]LWQ T.J`w۸/bJ"q?2DcQ 汘0Cw0t+Bм|T͍rB!pعut2^|%oZaa ]F6V;pV.ϭMANKeME_cFº1(;$)uG9Z[4ΣѺ;^@WAp]{ Rw8F> Og+붵uI6S;A "@A:nE,O'X|^@W:v]妃{D@+G!?=]D;[ԙuwk<PBxR!D>=nYggТ[Ul^%\e} B`aIlmEsSOpt=vqgǮ 1,?NƗ[ Χ5z _D`ƨ9ޟnϷg:c_̪!\5=6~HezK رKWؾ]#"D*.*ةK2J2N'["=mn aֺu  }=b}H 60zձ/D'(',s,Jx 4NI2'$R 7 ƵoM~cw =Ρ*:ߺ1vzʦA09gŜN1qƂ X,:7짰S$kO!<](|uȶ'L9ȽQ5ȽᐩHy`>@GфYN4H")J'56wcCSq D?O(4RG} &e vz̦0YXLHq d2m텔7Vf”5E %4)Rg p!4M F( VwM?[#E@oUz"p 9b{s-S욗PH"ҟN#2.߅>JAWŞ[G/ku|ݾkFAfg )g8|ٞG_?^׷mt2񿧓n""( "v,YYb";P];=zrBI`׷bkLqr.:=Z=, 3ܣy1w;G As|$}p 1A~ !lD)7vC`OsahE\F#pprY K Ud ep$Y*e Kz%ȹi(~#}*Z=s#2z͈FN>7x͜~yJb\> !v]ĸ[bfآYc%HxsT3bn@5(F3Ej=:My>W2 -k}HnO'́À]=Wt(Xm ;7+sabaʣD*d] fNƟ;S)o{]'R˂F|DI4+8PebɂG*/ݰ86 cA,.6 @׌@~Nx ?AkJ6Y4#l?Or˻ͅFtd]:pı>1vcvM'{m8և\P7fŒ\:C>\)WG3#%(FŷHLC$Rhxsb^B&b;: T v.H,@,ϑXK51F{cݻg̛|jLhlGoW;[hIHG \soG:ѥo.8?9kWɷC ZL:AhE&%.v.M D*=[ӵ9qH/D*sW)dbNO2?݂i-G҆cXeIoM2ǶlcAsARnJqOÜ U4shiPrqks+FssbowA,Bhr3lͻf<+%Lο]ۡ0L#j}w+_֣%fkгm IT' alE-[LxAp4-ːI/RRؓN*F ~Wtv>(H7X;eCo Z{#_Dꃉ'tL>-M)ՀȢDjJ܅i oԱ=3 lE|D#s;a@֮j${"&B.vAuy9 zR&#4=fgϣ}dfD*VסT$h6hKӁE?HmO@j qOiD(ηjGmljVKE5R9(zs%Csa>1m@,؟"dBARCP֡S,yqvhwt,kHT7f|ݪܙ!,(T8Xw)"V*@̵hqz @AYmh៍zڮ"J9|3NMvB tyj?<1JXXS?0A@l0AGXؔAd<% PC,vP㑂VKdFt/l~\LkbD~u>ѮNTjWļEG"% )?9=nP1trx U|iuki:ȤZݟyI'$R&Sz+R tUifk%*36e V5!($KGc$J:Q{dz<|Hbj>so9MMhkRA?`~ݘ {i脲GHݘ :Nc&Uh>'`R:l.d|5C;Odخ_#1M" 22u,G" Pe ͷglAؙ pCfNgȌ00qЂZFwedj\@b C5(w?q>@ս}_CT) 1sG b\V-Aj'Zo2" G'fl:ٳc&<޸dTvx2h,B8d\Hߌ|>HenGf{? L :Y_s3aM m&vj VW 6'ץT[Q+ nDuDY@Wл ;{ brAy gک]hܫνs+C#Heg ֡43%R!fĜ4 Ht5^"ߡC-h1>Q#sȗPHQ]QH9 ;0||jtBʻrz̕٦vE H ؜C6 ?X ӉZF@g)2̶>EnVo RE~[9Yk?#)CoB!wO =*{K'oCN>(쓽P.qڋ{Ej9yw 3wQ@G'RkLD* @/cSD8=PEܢ$ROoCF~lC^B!Q ܛN[x2$OEs}Fދh>~ ]G гۀAͫF847]zP)5֮;-䎒/P.?؁&3a- ݖO8ʜsuι|9sM)9Kjι6vSι}Γs߿[>̘d}}W4k@фi`#6h}T"W c!dj g/&!$L_Db :~?)[} Kn,WGk9FUbSZˆY81YY{fXz}}H1v"<)BG!Vjii> O3bx@p#S߬66mk7MV[!fև8{šk5}<9Gߎ@^J'S'\+ͫW m>:aLBrOF}3 |5ʌJ'㯴:/FjV^x:oM2Y;y4D*s%{ǾoF zdE X^Ix@*kWSЂp1b0w)󱽻tZ0s)bzK'?@?ok;vYߪ%dMI2w&R8[/'",loT}c@ʭ`X`؜9&- b=H``?Z*]!_#࿞0708?Ec2i%TN܁\nJ'ߚ[+d(;\YG\)qS*{t{0dptν;3ι0P䜻97~ ƜsupΥsbsqν䜛s;:npA IDAT,(s҂2& f ~=RL$#"LuЄ}Z#C.G IIwE'ARuLh5[+mH}( =e`c/  X^%Qܧk"!c>Õ'<]D,׌L{Z{g]Qt0D}&=PFtH $i ;a]w #Ml.4|z!sCd<[j(ʔvVI4Zw=`ID*S@7{F7 ޣ\NƷ6iD*s(2rsfwC!K #0cJj\`RϡP"eι) Ni^RFA097Y Gڋι087s7lv X國[̅GtXs: 60(f\6'N>`̔ߝm|)`F>ڳɕZZ>ʿ",ag$݇p=>)rĖ}>wG"#D }jV!CHyFq YrB~X]2"A io֕Yߺ @R|Ą+ ]_^E p~lf~0MwkG"'YouFl?{T{ǟȗ!6pZl㔶{|kW׽4ġ,#}t"985\f:+%Au z#x%l9Eᱮ>i;1w7HHeEs 7#eAQ|?1ĴDni]voGXi} A'&c#uc&շo#O[W7fBNv]ѫ/d9)m)7A4:Dzf5019V_A48EVA-w!`4KXCHG6SmTι#wc;HehQ7>ǯ7Z*ֺ72]w.G2|ȉރB0sY#R.>P+Ux0C >&Ix6d@NBh<̧"Mdbf|iMEj·8Qe]IhکA&ìDĀFYtR>fkcه0}7„1{Nrg!Ph=K]^O{>֦q1Ϻ5fmL+MElޅCW|C)KW+L[6 !gśHe*Ѽ2rUt 5+Q=÷LBJM7>W/dD*3NS$%T'R{cjl){(8866~u=6>Ў CL؂D*3-/ԣ Cή]]u1y= 9 )2"EE4X|Tql:A]g5EMgcTcrB凑c\EN@TJ0޺Y#b!@'eK1k36& YG Gwȴv=f1V,'ڹaݣv%@ggYg-:=Xkf[@:)%dM=<<{察9{Ivb( Oc-<ͅs ̓vyv1zFǢE܃2&Ά#1bELGѻ08 ʬA@&#ׁ>(! 1huvesXG'=ݗO{RLhW.r2)4UNm:*{jBEQG[=t zY585[=e}|#Rmы6Zd< FKx k۟0Z,AlkSU)r~10]}qݮ@#vW"YQOB |otAq_Rֻu e0 ;OѼe}6Y2i%ǖ`(Gc~0 c CVwh>*AabOki)D*SN]!LvE}W֯_=l 1g.xcJZ#C> 9wx26\m%{̇0|*vo!`A0]^ptETOǛ !ERKB.b|)h1_DTY[wwK#v 2?@Gdƹ@<)8z [ы`,<ងDaYSՕ,<iZN=ގQfGD*3t$ QP"bF,h!:l}'w:fcNK&&χNC&R',iT4C`TdPBn':1q4J,hzQ爢gR,*ĨdQ0_؎EZRm.xL"ֿ2+5sR8bFAQU")A}N* `sb>bo~P Ae=z[ncnD O[u^[h?08cl^C>`N_Ji(D-@t2J2[m_\[L !\O"?e)-vʬ\wwNM2>NRv}"t}{ͅE}=nPS S&>G}GLT3cd}9)[@om @T$lr"l! sC"o"vlF#`[fk@dcҀ_D~=6$tG ٘ńrD%R~t2ޞ;C"=XB}Y hY)Z8(m * -͕w Heơ~ȜʲUt\vf$wAN.b-:爵dcͭeݪEˑ=3CtsS)$C9J2YH/Dz8B?AT)H2oM%zZ{^8kE~v ɿЄRXb}72izpPX#s@4=,N >)*ra=cmXg} Њ@"[F"+UVf7<r+:dlE m%jYom hUNawkB.xpnF !Tn:_g4͙A<\YW #A[kEy6hNA@'Rt>ʤt2~%ʋѼ{Mw] Q%"Vt#Hen]?z!F`7v$/r'z_"k.Py\:kݐ{$o"'S&ޣ#"r- ghlJv3R>\`۝kmlrO+Ⱥo- !* hARpSUA|HI~WBߨeHET\t.Hy,bJP+L{0;#^ȩ{_ ޅd2Y؍S Y?bj=r6<,D"kRZ'2U/ Vh2ᡀx^@YV=_& BE8ɺ(FG_OeB61kxYn/NB`U7PsAΕγ~lc::g}|x!ln:oJ2Ct2~s3;ƇD*=&64 I'_h~۟ lmom>4$RC> t2HhHe&=R;kf}h =]\N?t6ʷ=?qiתy.Yꚕovyw{ׇE β\ӏa\>RxhAo-鐏|$癟TNHeV!|o:EU_'{jNLA@ΟX,C3Ă,%_iLBH$Z:>\샺@=)=n{;\#eAW}׬QG>N>Ћ(6l #`5#pWdq]O?^G|25V `EdBݝ0 O Gmu RO|ZfϪmc1[_WyA+m|ߵuu3I*5MCrt ̘֗cd|F"9 gkwМM2o7:]) ;|8 :o(w $RQnD?NOw#/d|v"|^MRk6'={mqxe_&><{O7z˟GU֬_ښ-ג-s#~zwa~K7G"n;C>^xs"yt2)!_/"&&j?=/4j$4?f 5 B@HD`Gg_!GHd.e3m@2 @dbʑy A7p-9o\h} ST rk*k0d*)hA@`mN,c~.#OyPi,FnQڻسXTʆ?LqG]s'Xj @bdmA`0gk& |xӈy$R_"paTfWޚ++?b1 ԟHeAs߻h k3)GL|& mNFFCބ`yd~X։'rݘ AH2g=|n0̓{M.u. ݀\4T9䪓nj+(h>mtH|="f9yD*s ׺ 1!#E!4;F 7A"}Rn$gsntHl^ș}Z@J'K-I^HYXX䯵й{o$ cŬwN_q+1 ed>h^P:, Y&RXwYe36?ap$cB-ahA ;UNȌXоP!G#7My+_ t #QJaϣ۷8I[vߌ5ؘ OD.@٬x!L]}s=V_V0A@,_{5!(7q_E骞GOl!3藁q"tb*I2ӼJ\a ̋)e!p`[9%?*bnG'f{%iE.?,'")h# t) #j;muc&6D} i:}9W.h-s [8 k[QgA\ARP_5P([y>;Ay|T!-AJr$ 6zG"߱H} |X;,6hAbJS-@v}gU-͵{,B=נSah[Dc'Lp}޼f L-TjmhD3&Ffg1BY9d#dkWdbuՄ94_>!ɶs[Ax!BSk6c/Y FXgc=)N#Tn.Td1/]m/J'N'X;F"[OX V`ICS]SYגD*{_lH.TD@PTH6fs=;D*< 4_EyP[i+Vbտ ܘHevl/vԍ3aιgss78sͳlqs9s0[8n;{~~6{{%He&7?Tkp`w%R=POtwƊ ȂkBiRH>3}o#fFn L:)6&rGB+7=dV`k/a%, ͿЎͳbq#3 =[W.a٦N($iPkkc-D&bhEoWv#Ld ,/2s~mϷGY-~MȷmRZ> IDATQ轈Df5ٚb@s;4"'9vfۺ[ y3$ %LRYW7Iz$>m|+F\Xo&R M.ԅ' C1Vw!7ck}^=?jD*3k|dZMꗕu}o{vevzE{7q}߅:Cr_`>sFƢ{j*B{zSȑss6ڣo>sc3:'-pν>ιo` @s")dz)#s#$;e z,8Fa\bA488l'91Wuι>+ xҀ`_(L*٢zíBJ"@}BED%ޗ,C`+,1eAي|d ×!B$ l3f.Fv Qc}V"3Hb6*GcȯX#t"(/C9?smN>r2M"B"(aO"VkkBP D`{5!Ve! YW2IV;w"NƵODBdHe(B}z[w)6(D*s=<CKy8h=xX6揍XI'㝉T>Ҍt2H%R^@qkC{,D*sj:_9氩dZMeWGˏ᷇>~~4{ͷOU=uj*{wX>eV蟟ӌ TZ3쪊M,wJ.Ɩ9E3:F(@?ey32ιHwL 9r Gs}sޔ_@sn9$ps=_| -o㫳>Wc{PV|C*ps.R- ]cg{'RoD*FWwum]k-{Q֎$Q)?0G=6D><ΎoC x.XE<^+\ta큍s{Ow R$"6$ OC)V) (]mCoAŃF[m2MZؓlN衸- D#rG`.f7z  M'툔B H9v}"R "P6=l^kv.@r<>+bn"m("eQgF%:dBCml@I$RTeW6AtOvˁ #4Y}{tZMC͂LUٓ텫;}3UZzCg3 l^߇n|;"sz+κrzjn>z&~X{b2p 97>-r]٢X:CC܊=>hS4.x`hGs %3yD_/cF#4 )v| aF}۵lmX_!R֛Ƣ8!4ZbѶ-{cӗ7"iWڜ EiTM`o@k0xHzO+7웶Y?GfJЛI?Dݐl> ϛ mGӂd̾/5Z|:zVp7C`틈:+!o#0 fs:0֯յ|͎XGl<_֥N,Qjmt?@TI}{b ׯ b3Qd}M\; ]-D(Vݎ53 W-Ż^p9bpx[:_I 徲+rǵLAmMcC]8 Sj;d^C׸._ ak=xU?9`i5}]HXQ*#wq3+i|feo6utg!O!?[ nn OtM両6 _l@ϸĦbVCt]J\cιIAs`h:Dk4*dc둇%Pxm >٫Gf!)zV Ջ0/)7Ă\ rQꋇwM桷Iώ! 0 gmN0ڍ>W)tlwEC:~m Cؠs,Xn|78m SW(FŶv(rfvvFjG̛Mu`mʹws}={lNĪ@M>c}~<=ν~Dcr=R{®}X2cvHD S[t22ʜH7!Eb׼Y& P{GM hst]zCgM]Y!ntΝt؉usn8|@&s{V 1597%9~g:~ qKih& Y S` 3Ų10A iY"Ab Ѓfxor!L _d{`rS'x v6V!0]V k#kg1d38X}_ml\sHh>]L< F 8]k@})B$Xq`'bӅ|:.GkbNDEHIe״0b3Q8.;\ljD&)^fc8agoDW@dW bN5f |͵kF@O:_++~"%2/8mH,6핽l,1^(\L N1t2'0DT}q0az*M,x䥓;H^}ǯ&)9r'ಶY~WV/GIzb]DcA;PQUQc w osgë]@-EA #gm@mz#p_NN2N>Ë}oB,m%|!p:Y6dYuzy>_Kln؜&'mcci?_n^DDXlNƛ7v+)`BFvI:O_[V1G|iA'%=8p;_׀X|贪z}qל;1>TNqHkc!nf W7tv}+r b' yuKXIm9;Ƣm/OzM"2rL'o1mj 5U>gED*3)D*K:_c~,9Hq@Js M1z`5fݰ K @gRdxۭ6|ZpA]) $bu^a1mY.}lځ'3R[56V#6"6#Fƿb.Pb2lٺx&bvG۸AJ^/v,aS9KV~2´'CE/o#aon>Nƻ4k]D*3Z 1t2L"yOG>%woϣvpK:isѕ7dbIAMт }!&uG uKUEb̟Ξ:z YWPsD+7EαD*38h!{-?pJ^wWgqtiuok=hvV{6/NʭVS^E''WHe~N?TUD٢ I2^Ip r))yn} ,Д);6huw&r]y壈YL1g):v^)I L-J3z/'d})IL| 2[?_!A#4 s 2ALvBfiMvao`F`ї,:刱y Rz\ u6s췯8 X" ϘNydo”# vmE`W_$Rerl󐩺a*4Mt2xf. |vc*D*g& e&z#tq#Oɒ Eu#ghiJj1N9e_Wl޴ʛxwualOli`zFeJjedq ,G[>CC` F eH9v  욙w5˝:䰆 }{_nd9 13Sv2LrMguH19ncvY?vO37!Rk{_ 3 PY| [@L(hs>b()u!9 8AS &i%-d @fȅvP׭f@=+8 9X?l#{o!׆Թ(j7 D_jӮ]?f}H\̿F&2\zx._He/4'R*7Td+[YT6O6=pEǠ~Woof)z?~xl|}[{a-uWuDp{zn;ʲ(*htGNE"٢^uMu-WɚVdEt2p+D""[q! )O9XO/Auw].{ c;}τR%1:o$?Xo} #?u"!3:D`![0ayej NX ?56 AfD*st:_D*s$2E+DT[)~?gtg1P~D*m="u7kt[hL&ڜHyK'GSnH6ZX7MC.Z[e|eL2%`\CKé" UbG,D1>xIJdWjvrц]pxA8P$Olޤ勑Xen v*@f?oD,́viR ǛvB|ڇ>65|@ulM& Y Sk/ʔ"~{!]boR;}P:2Ć#֣1ބv7_p7~vGRB" hcVέC,# X6^v\L\Ñ\Z|]?m?P׹>4ȱ;a` 8iYc ;+;aׁ>XG;nLP_ )D_]tWʖL.<قUNC(re g^鶎p2G{Xvb W}w%{J''Ri *Lt +J.p: : @SOhl62e씗~{P6$R/u6D{A_ x{ ͯGKYt_ٹNY> =#&ez=?ݘu8GjQYѩ-sp{1ly6Csc xFc8h 2܅@u}\}^ds)CH{"94?H2' E+uab+XK!R>=va^.9z%\ ڻ["yѢhλ9~&![H@vaz-B{ |mZ;0$Ah%|`'ReP4#q6Db-}٣:s*G}څfkێ12O,E~M>'cP:&R,arȈ= D*Ӌ0"!>`vhi36~kyO\h֬0xG|VV%x8rgm= 7)FLC() ;}@/>yo0??X_][^_iQ(pD*t2@['w~-sC*w\gG3ϫ_{H$_&R\z쉹E{ ^tv-@,pPAgGK / =iOkn{~|!T-\霛C! )gۢZ4/dUι xn#n9 ,uF=[߄o2y G=w-?dsF{W(I2S"1(=_AJq6jDHu#Hx켡AJ9/`<X}r$j}y_,2MaXM;{/}c_o_6c= a=Q^i.c?5AS-Q]a^]{P_E2A }=B;{ 6ʩv*z=nFRLT@?=2#7kHZlmDfv"xtKhr>lk~9ѮA/wȄz}])^&S0}]6vx@/3=kTn9R˧vǹ ~t7уt;*3'=%E Au" Q+!`ާsdÝΑU{{9Q`;=+|t]et{~r΍pq݂^ :uν望᜻G['9p8]g79ϝsY;׺UW6K'#4[tĦ<I2 IDATr!бfMHA#2Jڊ/C s9XsrYa>EBzݵ0%Wҥ)ݿ!>n;w'E-ne=W `}(!+CTL0_o@@;0E|0kK-™ή"t!mb}"02p4z( \>U;OB֛8C s L-AmdZ\e&!嚐ɹ[v}zP}*x{Hb" [|#ظg𢡄V˴ʊ-RWU*gVUToRS3'|'^s܌ы~*M2't2QVnNO'Wx#q'_<6tm0>LH2UUT_O}9̻yfc;~|9rr1r1!!ލ_3P7pO`" ?AZKk$`s @ǘ0 8CHfL'㏱ld|!ar+Rmd|~"Y"@1 bV#֛vE7g:5x9Ύp%l%/  Q@NH 6;NAӧ{>1B1tsW@Z|"d Jk'`{0v_.H'RT N( &d#7}gm vb"~sg`H"YNƻnsB`ׄ[e`_wkAs)z!VQ`0 6汄~' [#0:#w v7dX1[elþ{ꔻNƫ_Ǘj|ВUc^^,w'µ dIW2n䗴 +wwKnoXHeJ'Q?M,B.G!ks t-q qZAnSӯx3OdT9ۮ,G*AXѺA7P {ǂ X*B^29wam [Tju{}^C<>9 -|,fS E#LۍY!!e> w3br Rf߯cN\+l!4cK)Ո̣"r7>~isT)rRʢ}ut~He.i,rD*smW60riĦ["=BR%74=y;#}Vp[WD[kk80ANWцSf"㹈Qbs;®C2LvS[$`\|@/_@q OテހAf`mmOT.FoϜIdCHeJ xeQ<`4/j*# +{ɪ1/s_߁An]P}Vw9di9U']hAƚ''W*sTsw{ca54ߛ[XPZ4gZb}@MA#)G{tv `9oIl/8K2Q&Ԙ)sTO \Lw,CqLA maϕvM`1bˑr܃0/jG)vB $ճ+/~0@ngy)$aHL9vn[H*xvqm]^s+voW^Zt2^cŗB`5_:.ŖԠ\ ^U+?ec.ނ"pr;ݎ,J< jAԉ! &Ad B[T}ygT&RE %?4ʜ@~gat})oC;Ta?y񤯉Tf"R2QB=nBD* C2L\̕ |,G (@Owz+x|]7R>v3tH 50*線d/@``*^);g%!vj-K W_;\Ol +m-%u9_ N_K2O"@nY] ry +]M^v_ȃ¼_Fr75خ6/VNîƯZvͳ @DIOH}sk1xq2a*e,_6U8(kt2`yb.Ȳ>2GLl˗X< ڸJ")Fdn՗n22k/yw 3j}bU-;2w]om9[_㊟_6~o>o^lֱq VЕ +)\3[kSz^7:\qMN }L1x9 ŀ#H2{0)XA ͗(Eǧ~XMZ ZIe)R;ϧ0/O]ggV#и;bY #=gRt 4 E'#,m.312g>vbsk:p(w<d*I$=c8ukN@oXyq45ߝ/^Dȇt2~:ǝL&{?']fأ93gcu2l.mo(=?<\Vԏ<ܟi5}U>c]q'vudČxvoZG@N[G!K'' ga$u^,5zk-Ps܋>%)~m"Tf9 +xN%R Bx0{EoDobEu-1`!F -iA]x撏ogb||P Y2h |/ E 1V]j>(4Rf"sX06HeNCJe'L!0&z?ɮ}6;l= _ϧyy#LȐv!X/9{&vnsRd z[7ϔk)3غ#gck@F;Dm9dj;Ȯݩ]-nKZNI5(%S+;eed|XI2ӽ#uc|\T\= z^tcl UBUhۗW+_yoW6_E_ _0ޥ74v΋\seI6Cewt__|%Ss:aæ0SUQ== N'5ny-!  3/!2휄HbEJvQnT %{9&S{m_ ^c6!W03V>O: `TN!/f}j;2#zfHhni3}؂= Kv=K=/Id]lkG4?8A$sل_>Y[U5ȉL2M't%7h{ǭ-XLrQVr7{aKvU֒t2>(\Z߼myEO}ҝapm:oDl(9۟3}*fT Xm 3cTf2qMK'׿ϱN;!p2OU ed&kE k [,A&" Rr$ sPB晳&B]k^+|8 rdh=bQ+~`LfOS.T₼5_]`βc+G׷#ڛsJWXagg,r$xª~=/;ѽxl;6~OV)4) |߀{:'Rtn;=!c':Y.H)wf_P̝ $)4_&-ݨ=у'uфEg3R(4\s}:Bph%lK@eqn R;xD c(jm x|1bm-10't8E ߡ({+b"R;a6!֫]3mmOCdSԠ'َ9B{o^^l $4'c,:3DJXt6C$z1z`Ћ)Qzh7C "SRd~ 9ʬ _a[Jr,pY:z.!;nZQڦ/Bl_3E, YvBViݜ,ob^i%O.Zz-ѐO]إF sg "A4919GaPJ!;ٳ;UO6gt2>o?ȼ9KeRףo<ћX. D~7E,G2!vK]c:>+XB3`7$CX/sb_7v'Б6ډFfh\ PW1EJ܃B@{(D*#6jAsL~"HeHéT]6Ͼ5:%(|IWۑ1Lwٱ{Z4#plN"Ŏ EĤ @ p_ Q4ޫeZm[{a5>esyk]he.2S. LW[hly $"N,a'nm:*qL4/[jZc律@ܸ̿u/>[#ȴAHuHo,iŚa59ϺgOGA>`z1{8޹`Z:Ĝsι*y{3;m=9V86ι; qs,BG?E}֗uěI2L'B#E/d~Hh<)v _c :0D oM֏П%:Ȍ.@ Ol}W4+8^̷w@#p*Ef&3owC,1:f8clS}ٹEWND16!9w~Yl2 iu_ 8_7b={ FSílwF$OڎUȬbkY*ϊzSٽikώiB'.5]ZuwpͩK +=̛l>yo?؊IYKOFWgj]hϼOAC _cr7l R;dh$-9=ceu4崖;>XwI¹w$=sXVϴ8&{pr¤˯r$Dm9 hEA?眍\,?|Vc!D=O7eSRэ?D@0u"m!RH`@@`adaYĶx-_/҃oBJ32x[nR}o1R]1bӶ1,ߞM,ap.݄,pӮ߯jcg6^ϒl@w|J8C`®эM:iFkЃf)˕XF -̮e=HBpbcY| pͥ>RvQdS`}\ذe("sgD*6ʓvw:?%YB2$GY? n]=_5'&Tfݽ-D,b,.+&lPG98.2g,w]m0=A@c WٕXh$vqAn\KڒHeL>%;ǁ1t IDAT(ul;䪇3g6s ؃U{(TIk[n# PCЦ^iG p})3w1Y!=}z!%dݼB@M,E`u:)>a9!c s5!#o,F,X{m7ģcllmf"s! s92Cj,5+czۺyӚEAo!A@l/[bk6RFc.œf+l!W<M>p1#gѬ߲9ghC$Ļll[Inrc'a+ 4![(Rd,B,=sR6mR˟?7Uz>-G{ 010{F:ޏ>b16Z:퐹?t6hG}7@Eyo9@э|q9ٳg^>`X٘ܲnZt\=~?VA[h?\fWc:P#Jwl%oCLj==/4.vZ6<N )c FYjdzrJ 5+t5WdV6yCϺj3瓐Ql^L# ^u΍@{i Op%^YTA 9Oa#SsgUD p"gCl#d[ߎ9~k߁M $@ɶ6&L߿@˧$mlckC>\ˑRKl-v-D{P&*@-zP6gm,}Ay>PNK蠖ї(!v3!G\N@ K弑0Y9bf+ 'U^/sd2ûMyDr.Ⱦɬ]G}ЀXj15qm0f6aBnhdn^s޺o[7]ﲟͻ=0df;#Oql$p/lY Zz=|Ds ,tUYx-ik_bg}麇6t>)[˒}ao#~A^<όwV,pO@"((i>kYt2^%R@HeF7d>;<9 +(U2 rb Y\ H\Y#V#&KHI!L5kQ[!x~[=)q_jQĚDb!;Yc /+?L}{z܅0ARFVX_sl; r[0^B}ͷ?@ $;3%v[ hwW8o}-Mlkd䪗 )<>iv"?hϞ`o䇶xWd|%#DQ6Sy=)߲*o`ٽ>\: aAfکG&zArh@/7c{ DOODyaGCdTApG{`B8gxmoD*SRP֒݊ 6uvƢݿVSy!ZW*j*O@/ -{A!F"; w~ؐ;̣rXȊ.z!7k xG&@sg{ɃwsA۞xs7z,Asٙ~>>w86]" 3~=kGl*w#UecV+_({h];=HeDf)o Lq+ej$t~"ׄd' @̽Uح}R~;q!*1 kC3uVt/G&D->XRbN6b!@vH!/Bi^cm`'bl>emDALF ICA0b:!4!PbcLM)i+"~y"8 5*,W;VRfk"M}bI=|m, 6'Gms0Ou6+Ѓd4wTMHa'Ȝv~X*8A{Ck62K@ЁĄwnbKH[dsbsUޗؕ/dkF>|Xk0"PvUD@"kg?.lޡ\&\9?gblCpӓh6!r9#|j\]ЫW Ou9Z:We֤Kd`7-k[-~@o?E,]w~xqDs-ڐ >̙5 sv +^8 DܺbAsMι$rWwu^R[$ls_=O":"ohِI{w ՌXe!_lJ|CF4Y]yI,1DvJ! C w22 G (\aM'yph3Hc Q_jw:Pl8j:#{A܂R-l"%f D0ۀ, jc*[anY  Cm`!@W[#u~5YC45HFdicw+ @ؕ(6\?C*1r{zH C0KOpw8]ClΟ}(TPmcFtt'DդvV:ِ\W`JztY2Q^JpHp s.3kނg>w̚ur&{\a ]ː5^z)d@S. aQpի@鏹Lt6~:2譺)ĒI5#n׭D w ;9DlAc pZw||X(G";]h9{u+911):%V e-Q)Pb}LmصZw؜[D?مMH? lf!}}[ 8{ ~ؑRb:/xAVODKdmn/C{a[lc))k rdo pyS!Si;c69Gh6hH.ڈFi@!~zg#PCTyxa+/p0[&Ѻit;Z+yeIGgCQ 6HDew[IJDbozWɸ.Gaϴ#{Vs>XbOq_Za_C}l?14#dorzhJLz*bj:߆h-bT6 B!会> L cʉ#Z#,kw G#@dr %D'KL*)slUDFC_G}>~]!`bE4w xbjX@aLJl&m2 ?[mf!>~Wm~l"f"db=kky=v;w֯71"5XPDMkC4eVzٺ!Z`J)GNd&{/ZgisHɂq-p(jt 2AJ$kj\ݓȗѾC{WW |aH.*gu[o }K1[h)21HVvW*M Sk\݇:|>LF/9歟;7)A7XB^^I򚃱2Auk.z>'?eR[l&b"nNLY1R>ߎL# R)ш(B@n Sa F~&m A[ћo̝кȴ4h.~\V řh c,Jܼul> ڈ"`qՍ8r9R#;X2*o.;"0;~`%h>kl,asQG%G1"&]̒t:B4Aߍ@m鱀h")ַavxyX.C~PkyyқrT:g#0ϲ{\&"+Em vQ} )"l1DRrCDg+"!S:*'S BZ!e6#@_@ZEvrG u}NE)>&[铗&y4$|*IZ঑<6=ɟ[[VrΎ [ Ŏ *=s,PzX)~M;я.C*R`J2ވ*ؖ"jBVe׽1L sC0ƱX3;T b!3 Uqk#b&ʐЉHI:aw澧p>2$|1:".9F&[sp˨;J(Yc})Qσ@]nc܊b]NQйmWH[d}_LZ >}K Z [ !>g=xs8#qWgV).^-quǢ=z_$5QЇnt>+7O bW3]'睈LPף̝?{TkcdEՐ< Vm:K5#<yάy׿F>yKobzʃ TxOY e=.^ujHFrފաH1.A+. k|9KODL_0R*ûB,^݃*Qgs01*S%1;x=bc{{AL3-"0e >[O!Vb6?5O4@ *U͓I& y%2)S;ùXDxm>_׉ؼ ]Lv2F rz?w[B0fĀэ|f#VV"8e#p|BAv{3e=bZNy~H ؊m cHGۉQ# {bl>@\&ݿAxH=9@@($inEޕ / =š/Uh>CJt^!]E떌o2d ٚ$ 9*n6pY?N[GjI9S[8g=޷Oo@>84m@ jE&:"Is)BrP|1vCS,0TXi$<+8˾~yLL$ ֧',Dog!ݕ\i@~g$]r(7ȷ9zkgQ}i{\g"l̵B m4ƐtGlеD62oRW F:Ӄv6O=3"0]3I |Cؠסno':oC |#݅^"-G{d4R6ЌAZd%ՀQhnςWhKg# O%tEʷ֬ hmÆC-|R.R&AP?Pꎯ/k*"׺_0le?))n=_c;H$Er?]QAss ÊcMuν{rz6[/OEv@>0*6K+~) `3'-|jH9MD ەwOLo3 ހXW!_ЫW5nzq?rs9z /gԸdάyg'o.7"~ɨ%-mmUkZores s:Nv-p-tK8: ܳι{M$sQ}ιZ;,Svy{tYz%3[rTrV5x:o@Qy_2-5Z'oHA@!dU(]at#6*"Bc)TOiG&<.DķjPBCPR#/k:g9W!@u5CIZIVnE}܁FT#t_ؕDPg3Da@@@,8ޓ/P)DMEKeZL6  _h4DbުfG9݊QSvbYިC{(@ F F?_qwv~0b㫀sfc?|+I,EL+6,Dfd{ק!a'ZƢ?v+[y9;Z.]ֲ;&u4ܓibAH՝}ի2_H_3k ᦲƓx9r;-ϞGNxmK1 +Y`:d=LkO✛ce, ιj}9K;\s9;Rf+4Mz%Kgg!&\&Յ?؃<2t6c\;)*\!Hyunz0GJi35Z) k~C" q1b麉୍XD =Cb5PԺв0hf[!!DYk?0QZ_:ߐK˟Go'#?oNC7\cs:MDm6E| L*xٌeh4I-=Unb6!r =CwCQΨ__9]o=Ltc/G _\st 8'Yu*}sϖ!uy-ޗ__@Lj/OBAO!01~w+P1bOgD٥un@ s"S15n d`ׅ Ex'̜_.A3c#>cZyڑ'4+I=ոb² P [~O{%?8ڒc; 6f>c2{L݋r}̇zVx=T H`{>) w?ъ} 3oxe HcH"ž D{Lt6'2O9arNOx& >D! F3BIP &"U-Rt#|W LDJظq徃 ǝ/-+މXԥ eJ\ޮ\\g9bټ܆^ nmo~D."}NyzWV6/V6 E7t{0z;ޭ{j.k[\=y=!(dkKb|A} IȜY3k'{e w|9 V&{8{߽(ιι3]{8,ؽvƔ<;{Ŷ,g=|@||ɪsԶ!Q!.p܌ؓg8i#> `dMBogg1}>1g!X@cGpĄtX[HQϴzdsGή2ZHRMz~ml'RBYнvXi<:̸wV!@ 8H#wE C c@!l~HrÃ9۫rT5">cs5 ԉHY2FU齵&lG ẃGlWDdFcwH~ d푉h Eʼnٗ Q{'ȱ}#wOw.*Q] mI't6_B"h%qO?Wd!`ft6܎ُrG"5*QR[py?֎jyϥ25L=#zdn<s7ZO!p8bkg /Toݭ1_p@뵓߿ep.j9myzM94)7wu)sMaܜs5!v?9w/p)}#lާb;fycо bGgϯ_q݊Vgj|^IgC 2t $IHgOqY_C^}Xq6!F+' d7=|m8k!pЎ{V@LkbhGPr`B:?g)OgvKgW2e{;c#V>X_}SIqrh_ Ad.cGJt2f#xu_0(|Q#QZ!vkD{n O2ٛdtgA?]3Gl9@=1fdxo`w998sz{h>yy]1d: )kR[ lIg3cֵW5cT؉ؓ reoYK F&4?bۂw DsChcf BDd9XăzP)e5ȱ~ 9#&8ڱ# q+8[!n"J[0IL8V!P{mN"@r<2 Ea_)6%8J_SlL*oǴCom"u$2.ϒ6"zmD+Lm(wظCv?'#f@\@72~u"Aq5Ӑu-2ak`돱@oeRHgm6Ĺ1v^dW@q\ӹ3QC_ٺ5M*@281=|X'͎Bx葡)K@0:F5nz_U(5npIt'@|)V^fZsGϝ=ɫ(gReRH \&&YBWh:Ȝ-A~75?Y bvMA)!v9J)Pr)!bߝQy,4 U+Ud.NV( ?1W#Fhj0{UCm,xc6Do# ?/Iv~%o=se :2d\`_X2^Qcwu=[O Lw0sV:Ĩ}6!gi.R_՟*ϵ']m M6'~kQٙ1)NIg! 6;rLWwýTHV*uQ:l5w euF4sQ7lGχK2K>|kch.G/>紮+itu];ϖ}}; oZɥ;׼L}\ޏH}U^X.Z@ⴹL)K~z6*ʡn)ɈmjDb J'~C>0)#ހ@9HV!E.Nzyf} ʯIt,DVp0@A[;vB ._N%b!}6[{[@N xOS@v IQح=dk ڜBDxqq\XۜD,Hd"M"oy#1uuȴ|Sv}3'D&P6/'"0 ȗٺ6"Dcg5^FbOEhIbηVX3Ae$i} b{io2eGݭXɡ^,ʖ 7|vMŝo}K_S^tA.#74?W mw=+tn/Gk: Jўb_?!?㤇**i/OU%3l~% Rn?(PND`rJ) 1 HڵD9E0(oV(@dP,y"RߊGJ)޹`& .)}R@{*D'b*퓐YU 6Vb6G:o]s1hIz䏃G8!v7 edf&Ϳ+I\&UeRrꉲRsZ|)b8;v]w4NB )eֿAHGĸ_n`~HHЅ2O@@w;Qq7ՠnO܇<> eR:LF O 0J.ڎغbl!ԾqE4/>'Nqk]Pk^kWLU+}Rz_TC/=eh{%Y9n!m; L'}JgڭiH|h6,ba#r&K#ˤJg"8"? bbnS%bkJrmFPסIf" )šs~ bG,/ EtMF)僶]α1Ro< IMLI:~6lőF,#Nףf lxF!Bۈ?#!@L&*FOLqP D z_LI!Ř f csρCՂm1d?rl瘎+;@e9kRXa1m0d wE|Fb]P?XΥL9&,֩C `2ؘBYGOւ̸8& 'EkXC0'!v*O1dѾ 7^*FYZ%E/ߕŢhBbM4"=ʒjeR@eiuqh\QZV:露K}cK:޽P[Y RkyE ;6ww%s5@n<Ѵ6}rGꟁ jw>>y5W3*J]QgrDrԳXt6>*d\&ݯˤCl *oA E! t"JZ*;.aw;M#@laH&uHe섢.b|b 뚀̪_FL@~1$J(g&vU'~3܊@8ZCC6q6!;贾 2϶g"̮5Ƶ Eo6oY`HbW!F+֯1:^*d OZޅ0D>m} k] \"1!֏ӃvOuk?g7XY)n!H ѿ0v!Vx:27 0 eRW7NzߖSzoI"u\& \jVqUS.e˗hXϤ`v;gнsZƽ]5?\9'!S[8ѳ+bՇ? {vR.ܗˤ֦ v"[F3 ZEB6 ,^s(5 쳐V!k;R4}̕]p0BG+` V_9sn#7PDl) $z1˧F2we;?W5-T_vG+5|1 &-mO>7߷W7[}_?Gu*^Tkvrf혒@ cUP;$dND IHBs3jz6mn:gs:j۬{s3{4;Ћl}sz|]rν Ecom3YAv)u_9 ?9wJ.aO>V[9뀷y7[+.ؓ O> [O^`l7&#ІU.jñL!ߋ߉IgsHu'" _HV͑ 1xRtgג9 (``'ڐvH )v)Y);_PՎ@6KӚǠiRN!a#[Qa:ƙww2b~2T/>u~1m!~5?3El.,ޒ7+u;6NL$yɑP\ ^KzO@n@̞XSm-~H2s 谵 "&*zeXˤǫ+"&Wh Xkո3 ]^RxgBHAɾ+fԔV'V.o2]n,h IUr]}sitv}=F=7Z`>X9蛜s!P==~}~ y{)@~L"׀M]@#]wA=7YL40yfwecL3[eR{9Mlhף4m$Rw!p׍7w#`y)ML$C#C,*UOvoC (Q t8Faؑ6=W Zo H$H?n_c}mF7TbڌaFnʵ"BCfbMЄH+uH:il MDoq]s)bNkCD7kgagy>| n 훟珠nD,QDֶ5+KNWƢ2O!j5$q Edžէx<UDQd> ݞDt6w.Z oG2M|~ڕ5l^dhmqu `}ĿJr n~h}o˲'ۛCN'W7Ki윫@ϠJ&=4wI\ bo7`ٞN~Uo3/>_/ƿs},b9: %ho,^eckLuDN@ݎ@7p2Xh>C,s@=lͣ?$=2M$Pћ("|2ط"&[@Q͙j?4GLY̧Xx}ށ(`?Ι/8ug8RK҅nE7(|A>+ћK mٜNFozt[;(dA`n1)l7!̽.#lN P9'jbk_6W*z$ o,bՁ5V!:^G\\W PAL|zG![=hH^g_:*_Xۜ,:z_o'65n 9Jt/^O^1nMw[BOS}Z[Edk^x[sns}=97FJ mp G>34{m@=E0psn;ڞY^͠䁏8̔v͡ιr=]979۲πt6_t䳳u8Ē-z$<)"R ^U?bާ }hu4܎-? ׊;Di AVI 15vD3-yh 6oG`!Ȧ_Pf!xZ+X%Ĩ&֯qA3MGLՏsg/T;Gz$pTj@Z /OG ClG!ݬEKۄ@y0&8[N",tCMa/ ' 6*yfځji܎ Z/!sZ>}UK{b5.-(*\&Ց߁(yDYG/jCjSv-WXk_2{߈ׯXu>EosνsY;$y'W7׭jo~?{8{ TA35/s={ =_e/t=yVs]SBfː ^zf}ؓQ89>\p Cn8:wsiZ\pp zIF7:!ss7Xt6_\&h߈@/eR1 Kេ(<ZX)YH C?$L эh܄O"P5sXe]iĞ@fm'ـؙH)TyېGQLr!-B'S!K F=J>O"PS@G=b&|M5@>{+Ex0e]rZ{Cu%Y;'&:}~[xڽm!Y@wR dkӊN3Yu܀^Ǭ[HZ?N`̕M>fwMD_/IgAr @Hzt3[99җQ۵O{%earԶt62MцGrF1J&:174L4$ 1ehH$/Ai9Q!$~nC PT;d) ZQ!+~ut6lY_\ցJJ"q.*<u=dA& f4]dJ&#&/Dj^g큜Л_mJ>S5| N4n@(m"l(uڎBybVw1$^D[o@4du݈=6ыC1/RN Xnת!HB'5+A[l#6+iB%Z 6 YN p%Y͈WDWuZaD}+C;k>އd !yY|rE^!^}B0ˤv e܍tR E'4z!3X hKP^RͤDq!,!2<%@4u 75O)V!I* ӁFb|,$G` ʡ9HvE]SzɑD&!&[̸U wZ#,f I;ux.?*0؉^ ` ؇`Ȭb` ֛Q4Š Pb̥h?|%FkRkj\ݧ} گ {88j¹[ meRϛňE>5b,Kbpץ˕} M,`P. dM{9|#b혯!%6.*ynނh0R#Dpyh" 1$w яhbԎc!DjkT4~H~ڞ,~_Z؞H3"%]l88Ouv0 pۄ6Bal8$XMdjC{4܂wX~XʏbQw{RG AWB|%VՍsN>c\qDMȬ0 MhA@ա )qů" #F3Ha .n"FE(jE E0Vص/>yPK]i@L2+jC *]~֏B@mD bCԂ[ nXQe]--+ Fl/>]R}@;v 4cbPaz`6j7f1*sQhy@#)Z0$V,mbmnE>fLj{: RrPK?^F6r ACX?[h}g9 ǿ8_]]k>^ָs}^k I3/E`|܊|zسt6b"jE \2W!%)[,i(4)Rcc9͈}zGm9bKm] ) #پ+C~bgKRmL uc5֖[|m6^bRB'1"d2 rSv $PcX!ȯ{doek"2x;wf|Ur: UA*KLoѐ[a{v+2mDjӱByÁh4Bmhτ`VmBsuޅHЇl -A@lbLj||?[sT瞎ץ՝׾kcf=f%?{&gUϙfӓ &,jM# ":{C2R MȨHQ %$lɤIμ?,I繮v-=t2v**^eʻK21џ"Ĕ!93-0c]L<ۗ"92OGlNeKoF `}4 Sٹ/w"D 2@&;Y߽)q9BG"E" HѮD`m(!s䑏OX[tOu bZhP)aܲHg|UIP1|ضغ8"3Sԝ4vQ`Ap +˽n] ٗ /6?lCGͧ y֮xtvݻG{qs~L(4h d7 =6ظˬ/Q;&jxgA (D>n%6KгQH2@I/G Sh&I2'!t2w4_<`bk 7e/Fr!pP"T:߰lK*JQs%CbY oRH>j+| <3HQ gt2 6vjS}}mĘ| )CP33وZku#!yHB;rYAgY9-$_BLٟ0!za6~_xȴy"6ª5ўl#ӁO%Eт{2*Gy1ǹȐ 0vj0 $&ݍlFiDi X}uP)d7l[jcG O}0%XȧlL3w;\j}[i F+opOŴޮYȄ}XipM#\4ܕ+-#Fh{eod2z>\stwf/l3{9s7Z9{ˡU[<׉2߁V$|? N7p$R X@S~@A`jRc ڧ/QH"@v8bYZ̛yL;Ed>;4QP|.d Yz*ڂXټk)b^V: ;yB B۟ O.AY>ߡ\:,j\x|'bj4V!4cEfLJl/s2vq@?\K<3b<5Ϩz.&fIkzvNkr̜gx ?E YiF@<"ֽ>|͎=pB@CwnG@1K҅@á/O?1x;0vI|1W#|[6CF*@@00B`SGHn`~̿" 0E IDATI\fv8z^} L>:oGlL2#Udcv$RKZ%wמA6jt RD %Uvo}/7fƧHTt=9/\EtAg|kШ! '矮uwι /{Ap%[z7AfлzSiܥhR" f,M~b|Z(qb]u 2ɽ('߆ R]j%KQ\:1#TFf?)'pBSY $ s:)v53lǔ ɽ\=;w'b;v꠴t*2A+z^D)u<uQ-)Z/#9k+f̏@L[)sPI~L=:VLX[Q5gb%7;| GC@&jl;خ/c{#ඛOYZm;jl[aIy= ٳҎ`\w~U~nF}v~BEkMy}]k?`cA̠O<:I'㯠ggK,ȹ1MLvh Ak@}A b7~ lpM/M+FAc&1QcPSx`sQLhNA }Т#hQ= }K#qsn0r[h x΢xyW7)9z9dFZb b,f!3;:ۺ+ʑgOMHA{ 4 13_2SEʷ .C(F* ֬ 'Ym 28{˯V[1ݭ֮$2eks0=$!uHdcm9XX;s(lm| jаq .2 `ԉا<)5=/^R68w>bx΍vOAld[yV Z 0;n.! ^HKl.vjlD|U!"^jsX~T&)v ;as9/w!^, /A!`+Cm^F|ot2nrËw(d˴Yn|=?3\^zĤ_ >64T6hו`TwL0Z1M]{-^An r gKsZʍιb(ok|7ބ,BA;t2>wD + ,;W}[Gwt4Z NgR;E#O7P?Ā}q#0J0m]WZU pq]7V# 5}\9v3ӦhO<74EI0Xzn.omwu@lC7rEU}7bྀ'ʇlM,((cħ$谟2=j2۶@JnE[y-#k{91K=Z1LG:4'Ù 45 ״GQv]} k3[Bo{E z)FoOEۙb)3DBR}/ȌK 1iC9~;uSڮ0 :8/GۿF"ĐRH@/!ti,!3˯B ~4Os9zPބbAlܞvF]x4RNa? ꥕W]7r1'*뻗!jׂ|!ew? _Bi)X,V&`)QoA68QD%V@H^j"4su##HE^i6[Zn@ؘ} BcXoc\#_*?cʺgy()dklx]>._\ytgl?|A}kF c}bF2ι`^oER4SnExO"hK'O%R$ahM'L  G"ž1 nj_+?iz瘅?g'RڠTF nb0DRAlY72[~/ Vѝk"A:"Ȥ #)Z2мUa#PBE}I]|Wtp h;'$ ZX 0މ@yiP,@O"0փȏlB;5 @k6h\n4^ZE>Ey $ө'7Y&;u=qs/!kʟзE\r?rZd?-݆CآRc&TfLht2>g-u;ljzճl Y[|jmrؖdj|!(!RBh"sV~Kj&u!vb}ڌY_|ЧBvR6}/ˇ,qed 0 !y agbjw'2Um;XTY^EfO!@Ԅu1H#pS?v/@-@`=򙛂@QU6t2D*3rlvވW1xg 8hE1"ͭO\kRYٵ/fBsg `c?!ԐHef{wW"Fn[nt* j c iH!VqFJsKr];Lw$Ee@= y-5z>vVOY52>̝\ӷϼwO.h\SY6h6b;O('A<:;d1ϊRO"%eJmC }P:_/La \)ի(y L=Lz ('d+H!܅vň9k^G5bP @0ivVR7Fq"i)'zp;_@R=_̵#% hD_q{Rj d6Z_B,$kXj܈;^!@kyF,`uM!t~AE| v_~fml<#~^@krwԞ͊؜~A0LX !d EqmN <|9(vw L6dx |#BK}#ɳ|eSgVႲj4l7G˂ӵ;=ߜM{|Dk/jD&kJX^W\)\ӟlи3IɨɢHg$R*3k00[vv:_hj q;RηJ'KWh{)? F.dj͋dD*㙡j;.|G0:(hJ[CXD>)B }JV[A\"mmϲ ,H~p!PC,dA #hzFInys#Kћ f`)ti&,,ʮY<W[;=D]&R1F8?F/cʐ845{KMƌ@ޅnt9RY3:3(47SA4E7};B< K+Fuhye^t& j91Z]\kE)JQvKD*@KH#/*hc #9asV"ϲ$g"t}tg"9)!̗욳}i-E&R^Y!,4ֶ!vﰾ{F Fy~ ;cI$F,\W 5mHَk1ȧ湅0*)bF~v%=pUyVG6VSXe3&rA1 `91ͱ}>c>l;0l)a޲iP|N5oKKu% @@\!pd?0OxHedE[+=g+) i/`M6h\|͎1ckl]> Eѻރ'&mS٠q p~k-ttz^߾T(iP=lncOP7t)}ЫoQRX`rbhpMcbРۑ9- /%RA't!E:%\d;)Ȓt2"5  ȟC@O֟!vQdzľ~k,Ct b&#HT݄%k< îsJձmm,8>{p-ҞҁArtw6w u.bcc<;1gAHRdF`}aޭb6"V§uGXb }6sor!-)/_LOS %L<ڪ!4#VtDo pu!ÛI JB9ʴZw7%Tt2~Ęۿ%ipMܭu=u~(;0K{!0s .g!BY"});lpMg"7WYC,c+E *t?1]cѹ)m.e.'#Ȝ133t2 D+3)>XV?#l?Cl ~`Q[Ⱥ Sα0gmʑ= _/ۜGXv5#Nΰ>5bL ?RPX#PrbJ Os~\YIKϯZ?$h;"أ),M[޴A8AȴH)18=o>yiAbiL#%kN@ %oߏj"»l!-6wkHE.GLuOz|GN!艠{@%f@.Awkuf,whB=s ܒ܃MʤŮecqlyʦseXkcxA +J,vLkj.(o)fߊd;A[;تC`(z){+Ձx2@>L)R=pY"n"I$Ro b>@ύhބWPIH!?wd  CUǹv.R"%!5*0Rgk"1JhV!>ʧe(tt\Wb8"!stol} FQ3sD@: L|p(ODdZ Z/\}ܬF`Aƻ,A,ˈC={dloo ;߿ O5ZgǗh~}7X?<#A7c@>UEF $Rш|E Ϳg*(9A-yz7M|<,ѝUKـcvy -ȟ0c7-l:ckJ37`6(!ι ι7ރι{m;97m|ܐ}-\ιV99WY, ?9wܧ'Efl9_DJ$R/4 Po#'sS6" b>.Oc@fR# ݆|onvIXza:B[ȫv:(=)o"@}Ja2:6/RYl|둯)%cs(s++GwwW&{њܕh,PĤiߊ'=V@M@2aCXe6V,!~B1ڻ1J_5 ^=e)!A Ѻ=(eԳ^r|'@msvdƠ5}~ Rs:_)šR ιKMgXI#,k]ksA[SnEV>N:\a7|4o|wޘA/05ϳhىT4W),l+:߁%1_@,etU|`'tm*zBBhF"ՊVޏ˄ M|6"E,6}^ F!a}{jbՔ=M3a<2N"d<A$1+C~'IXMMrk,U0`@CgvEiK~턵5ڂy*(2_^jhc{a+g pSN->$_=2KٽkG`f=ﳿoAޜP0`K6h\31 6竟Hn~*h=?Rnܒ--0":=g ܋x64"z,Zp򸠽gb@5nb$|܋;Ryb#m]W'/HqUz:A7U`eq ι-~+ 97}uU#_AYNtU"Y$ۜs"V }C  n 5;vK IDAT_u4}k]!vS=®7~Ee>RZݛQ֗AMAnJ݈)0:o'dlXZm>@ s;f%5" b:Wת'VcDmL6O=xƂ/Dv Ss|@toT>llW$R39wGɭ7Ǟ.\;Ulٔ8 ۿ핚9ym~शkX:m_DsLn¶Yvʮ[uA䶠}V;pc.>z`嘮5Q Κz8cEyrr=J O=Ccb}ZĀXZ G?)4AO{s: A t3Axskx;e$bOD K^eBg{cA셾G_ j,1S6GkG_\n2P[u 9/`4k/2Ô!wt2ޞHe~Psɛ+0w zPHeNK'ͯo~S \N[шո)6c{516.Q l,akЃ~R܃ |1GKFʷ jemEXWOV#61pJmd"{öL'̛ 듭_XnFxF,O;щ̤%[4Zڜ@؍/B3pX9B8.z?<_3}0YK =(ASx)mg`$d|5$D䇉Ԣa[C5Ti|X.avՍ^  NE 5dr ~̆KkrsƝl<:GtUF|fCrQ#зBkĎl^k>"Ā;]&40f׾moQsΗo]ιe\ams'cBp ^pC ryQf\W' ͈~v#|l GA_ZG `#9 y4Tc)l̹֯~HcðZ^rm/̾H ڕ>_:w"`K'oH2CFv~VM]ȿ+3C )4b3>5Jmb&lWă3:oF-AuIUo\`h6h<|x+HeR̍X 9W*FKb/`ȵGep_l_@u.G{Ђt2272n&}ޛhι gdJι@yڱ@&oR_A8F` ̄O,4Ao?,D?lta!މ6oK9){RcoN~(΅}L2 /TuT|] t2zo{ fu"g,He&"@ՍV %Hހةi#v#4ΛYmDN@))!=`jB>h57؏/s[ymG{GտaUp4Vi259lvZ-A sHcJ2켓K@e Sx@: :2Oޟ5X-D"M*{ڻ(|EyG:%ژ@G ^77m>Id dzl!+;kgWK_\`s٠1L\ xfN[K1 lи44kMHe&`L#ӵf'dK/Omy&% G Κ VϿu)ر244O׼< M/3Ŭ##A8DXi@,97 Xs(ѥH܁Vܗ^뜋e_ku1ys.;-|w/nGss7o1D skHo/^g%EsAil|y3RcoB_Ye}3ۖm&=P kӷij~ECt2~U"D˳H''Rq0)H1hYaǗ#lG̝wXb#@j}}8)g̞%H#'ˣx3 XXB#~ ؄:b܀u/Vڽ6AmA|~-ǏHO_4gH->e;$񷐙ZXMyPnE 97}l^ϝsyќ?9WE,"|z׾s>9 '@$l۳;Opr-t2p"9|吉_u?nl,Ekj@\/#=5b*2#F:~ضG~vhsbkJ[ub~ڒ 4" enB #0c9zaː8aAxD9a69d-bQk ixHk:l-b .Р}Fuھ 7UUvLX\~{mk_'Hi:UL8vOL.Y?& p%3KFb^Wŀ號}K"a}YG˨P#pV:36\>1QxU[ȟ*`D1g &Ծi$2 ~С) D&,bஷdVt^"2ˑh\|"#Lnv9Wxȴ39js=Vkm|It aRm$d\5`+ }ayBDW؜tĦfMYZ۾ !PхQփ{?u,HU$F;z^Dk[byJ11)/"z/vvFoϻ[]094QAsᯎ;W%ee~虨,JQbjNNasޒC u +@jj3HejPY'YĦ݋M-jO %<)(±1!H[#w#wW  |/CtȄ؃@e֮'dnojMPW W+=BȷC~lkFu,S/3W@ &fYjט <4TN>V?(9<_b=@|2kk,b-y@" EL2xpL6^Ԁ5ȫAp4,0!};?vb2ֿ\Cyo%{A >mf5zu6h E Vs3 tkoV 6eώ=}?*=f'x%;>v{x69B/_&R#q)b`nD(UƾT@M)6jܣX Ĕt"!t0?mvEU( FB3ϕ5)`2`n5G,98=M EwS 6u 05FaF``#(a5CȵBd ߕ#LWGk_iwRv&-+h刑1at #~G+VKtG=XV@᣼ͤ5~K־P1kऎkb˗%Z\K]bs"9CEch">xUqe-(EyHL6ˢ'0Ӄ6VN_Lj%R2]Ncǐjv<dB+(dF;:|%B^ k,~)(:E_Bh={ƮU4hދB`(!  <*t2Nq#/aUrBsKbk'\}H?|+@zFyb#I?҇8ZJl>e h A@w]7Khbs: ф,b aB6ƖCXĀ^r>ZoTav"Frp93^7kmɟA[U ?GVT= }_ )˪O".@٠O~(EI{˧s9Oѓ1KC ś:J{t2~U/V@-@ ؔ +1H][f#~%|x(ʜ7#7 79T{o"&l-bZu@]g!fOBzi*x/j0[TW#1vX"{Ml@(n7bEUַ"Fk(-u"Di 5/#`dzaՂ}/tu;kzdz*#0ly]^j[7!fm#٠ -6 e!aڟóA\Y(EyJb,QS_^FNefK{ d0;\d |΍(u1ET:Y?@o!W(w"2a B>AMƦ̃9dVwE %v <}'pg"9EqV# G c}WY>S pFͥd܅|bSl  =1Rٱ_EL3wZO"<K" z/b;#vʬ!Jb8b@j b}i&S _߃vі6BdR@_:1^CSm>?"ZK0\c:ͥ5Ug/\S wJ̾$4)Rw[/CQ8ȌNƟEbKC@HXu'R}DF+̕21+M(yނt2;p8~m7 E*@T/( d9p1Hw!0 \LuY'RX`}虽 167usTvLD`V b!3ݗmK♯VPॶetKFwÄHMkA9"pg_̗3QBG]C,KXe9.8/hD)t[|"6eQ߳y= u2̮w}h:_ܞHeރ"4гrbND~!SlE:ߔ|7zAB hn"lиd4&  *}V]*E0*ll#{m^0 @"_ )ɥV/NL'㛲[IP㑉T2YoYŖ f`K0D*3G}e"/'"S\ Oq`^:HeJtoSxl"F%``N@ x6^-[i\;29g[ WO'RQnuȼw Vfv(9M2Q{<1vm(HֿH'+Jdj\NX+r b0. 'Ixe:7|1]S u]@z5 xoQy(<):I"y^u0D`%Tf2Ac_E=SN;BHO2Twc}2xì{/|^`Δ"7%Q: 6oGsi3AlޙNgp=ߗNƷhJ2!6gdD*3=v1pyq_"fipMÑlи2d}[ʐѿ(>)2c`eQŧȶ: 7+dBL\MA3:s(]dRKB,JC* L0ſ2~ EOB~OQEiOt2ޓHe"4 ̈́A̗DfM;Cg̥ȿ, + &iM"hiH-zy0' x"~iވZHeJ'Ϧ%klӉTf?i$O|hG&R#Q6_{vb5ld%pTr-(EyJC%D֞_?d'V%?LAcOY#? њCGP$\Z" B:_|ʼ\He.DH#\̔UȴYYރt2^X+չ8w4vBP4ha&_,sy; UT擨,UJc-c2dyCQ幉T6 `{&ۢ[ MlpMfƙoQ_!SлV],E0ΕFmpłzQ3rE_xP"d|P+@e:H'5v H:_hfzT6@KtfLp?J'BTa&۱#D=* C~{HL20tih<]:/dg.Di7ݏUIxs"-oק:@)t.?*"ϳ zgrdIgRR{%p$M'a `b.o#G x`,Λ&xL-He}z Pt3d+*8'̆_zL~:OV̊"Ft2I( (qk+o-vGwXڊlиrCvY%{;)lқϦ_(I{K:01k2-*4ѫ\񈍪B`*8/~̗Dwt2 i y%X>Xv(ȏ/ fu",?P }%1xiK,#{FP+ sMV_Y7;{.pb6h7EI2%#+jwsb#㳼5YRh,D*s4bNƟsNG]/Rt^"6'*C|B~u(zr$pn {^;w\K}PR٠?YےvC7 g&RY^Y1n-ʫP~[zpI6hl͢(RdƊFe9r#ԑtٟEEG&(u)( g` !'쿢(I:O#b):ThKS=4O 2/ǀowD~<|_{֒lXJVY[\DGnEϑCU J\R{GdXh5m?J9r2ЏsY[XQv$R[QZߦrCu G[9'NXWl+Nˉ[H=rJ@];ZG!6]Q=` Z4HeҏPn>ih;cuօo[\m)̎A>`;~vB ucA㣄Q4pD.Feh}is~3l4J|zse-s+h[EfeR>cEHe>oΣ<oU޲//P\ؤqQZ.G86(Ȩ ((GE0#K"[HEȞ饺/Uie'9}SKUuNr~ysk0n)kی1]I\Z&c+]K7z#sl[3> pj"3c܍w6V]?1%ΓJہ"rpQeLY[KDZ{G3>|.p/aޣxy) c;*ὁK7bKEַ7,gx׀-q&l6X8,T`0(Dd \;Dli=K;$ vX2`>6.mv{|o),mjce5Kaղb`ED!"2\dxlFX*.Uc%~|;`m֌9rmŖ;JTI|VHKt7.E~&%W| DdD \T%Y]Xq•k? VT{l9cn`eƇg~ouꀙ1IrչȆ*[+R9g$ ^iKbƇ;&"Z,liuG}iv[E}~56z/`ճ{xܻoYaTPs;Z7cɛ7%w6/@1nv?SގGNU)~wV]%;`0z7+u'cTY~ڬk",:lY[}޽u9hΘh2k:|0A^g|\@+t.Y]Ŋ?ئ ʱl츯л*09d^oYD0Ȉt vbc%S ܄mnkie|8Mn5೛+|߿ZDH/pbZ[u*Χp+FͭWcKZVŖ1719La;%{@6$ےY`S? \zj^4BD7 >wKwz|،1kXMDlDdkkiͭ˓վ1U@Wo/ m}5??pW%"RV c"r@hki Z`Μߞҳr4dgngnkŨ"7J19 .] 0[$"[ c"r \zГ~-""/FaLDDD3&"""RF c""""e0&"""RF c""""e0&"""RF c""""e0&"""RF c""""e0&"""RF c""""e0&"""RFry[gF?Zt@1 8\ \*pi7\9(xX/ξ, \zH˔"#/?hki m00r205Kjnm \i~""#˜tp0^u&*82>,yc o c""{W^T'3>_\ Lg|&=SfюwҴܯ.= x3>0p,0+e{""#)El,w\ \z*xp<0 87pOQ~m=KW_y` 5y;n" Ujw2>˜0inmOɶ~`w!h>Qr)@GƇL7pj|8 4 Ko~:)fl|*+jہ/M~hoއgLd4Ur>W/>?pؘ'X8[n\ S&b ~ lNN~-Eh7{YH*c"gp6pu 3gh˲O1~@p`7zb}\i _WM 8zT -AxEUDCƇw.}!4p|,""7M> :l .OaK@bpƇ[l*TOa[FcXh.kwجJg`lyj=uXۯ2><˚ǁO5nڦ c""{IaLdK3B[V$6,XSKߎwUa!ildE R3cy Vc=cqX(o/`fџ;%mXYMt~w杺iO6q04I˔"`Mڶvg`S%}ca팹j;cza tVZ5G;+خ 9VG_qPE׹3p$V_˅8dީSPo[۫]0-֍c3摆̼o {d{@[Itaˈ ,`ŕ= *V~],--,9⤧οs;%/\ED)EU$  ~jV<ά]4UX:v Oq$T͙սgzFnl)sp?X=F!A-!lj\tX#3 KWaspiX?xa5DDd/iRd?\H5&y'69-~|t_/|џw`A YqboX'X]p5Xe?o^6&cUƇ;^!""/AaLd? \:? a]<,\ӏmXjܺ*X-)VG UcA k#=>ox[/u_""D㰦Qex$ZWlsXx*]̱1qe,G1vbU%-,8Kγc[KֽDDD=c"K[n;X 'Kq8n UbK%6b,K ]=XYUɵJߖ+=?_Kh<{]WɈ.t6~aUlNץX(DqX0a(#6]D,! [F:'(6dz~:7 |zl7O|-Y}Oa}q""L)2N`[U p!( +mR\J`U;/7cbCΒG%.Yn8 j;@] Zz1kݒ Xi3pT nUџwD_KM߈Uw`n E;J&W9dCѵGG"Y[Xߙ6ݍ1wXjĪ`Rl ( 2q8ca,Xa_:xjłX"]q*`V‘.DZvܞwW4 ܶOB=c"C(pGp6ǂM P@Xx2zl6bz1ߣfeGVGqi2łuvRQn:[no޿91`퟿KOLD`ʘzUj}Y^,<ձk,VG/x֨?rm\Y a,fGׯΑÂ-EωGm1!kIT3QY+u#_|f'jdzWnf>PKKWf| \lY! X*%+V9Y*ac#0&gb)e9AO)EBүΙHa"{Sc!'nh%sI.Kىx])z&g:z67wafEO^$Ě׻ba0CȋRyv$ +():%<PMXUk Z96Wwy[;wW7rϗ,P\a->rQRXЫ8x!GS~N͛9 G&""/A˔"Дsv!QU8"? `A. ?1D;QZY~aamž> \p`H?@-UD^/䦯D+M O#F3 T=h簊P&6X,mq=_2*g,(_yvx=T)+yd|إ &"2|D^nOtS'ޙ>M[Ԧ6-Aq|DU٩w9m/eB)ΈWJYlq ?S9+z}lv ᢡDDҜ1=\z*pòcś|ͤDi8Hz)>D `=XX=/Y0/)nPIgDxPx%L).{>*łcEgc;*RHgLd> tI9P4CR)Q%=N%16ۓmVd<?XG_bikԿX`˓=b>&KAƇK|MN>Ԃs؎+cXV7dC9S nP i%NѯzB=E؈gKQ`=b P5[cDDT KW@\WjͮSzl1>/җ5z 3CEDd(\\zšox#!g`p9y:Yc*<=&܊_݌o\P`rƇEպ->߽+1!vL|Vzzr%k.l_s+߁X MFY <^967?`}u \Ҵ|8ߣ =5 =ՁKi2>|0\M_gM܊›MȐ2~> $3q_u3ؠXUl02 `kj`:/w!""˜0 \z-ݘ6b֌]SV{X x(Û~0&RFQ@uƇܞVd|['""˜H.9)vS˜H)˜H)˜H)˜H)˜H)˜H)˜H)˜H)˜H)˜H)˜H)˜H? 1(ŝIENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_60_0.png000066400000000000000000014200531413546205200301460ustar00rootroot00000000000000PNG  IHDRXXٟLsBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxy|ϝ%a_eD *J6ږVZǟˤZ:cFZX֩KQQQDv!{2s'&EMdg3,s5ZRJ)RJ}~L@)RJ)`)RJ)TK)RJ)XJ)RJ)E4RJ)RJ.RJ)RJu EƘ1wy3CRJ)ӹIG,իcVcZ1Ƙjc}ƘLkg/c3w6kNحV)g/M,=kV$>[ Zk >(1`;i| x}> 'V;= \ kW.ZۆJ|3k~"e(uSoWR{sH_?>cQs; :sJ`kpk i86cۭoY qÁ;{+R~7ZDRD;秹a1GY\>mwhаcQJ)svv$> ɪN?oRXjoh\Z۹`G`wh7 _%\c`^ jq1Xk_ $+|(]DqR{޾:7Y$&?aGϻ뼥z ^Zx@1c(c̱~31でvpe틖q7¿F&>Ƙ.>TIkнK1FL.syHדm ?O@)T&ƘNFJ t~8s1&^}-ܹmwJ`@bٝy ߌ,.ZklqfacL=8}֝c=~bYOۇLdqp{/ s\$>J)Ͼ07] |}-km p{HkЮEڵ[Jfo{c..NcE5j]( }4Tt)N`J)zypJƘUb32<R йIݡ%J)RJ)EDP)RJ)XJ)RJ)E4RJ)RJ.RJ)RJu RJ)RhRJ)R]D,RJ)"`)RJ)TK)RJ)XJ)RJ)E4RJ)RJ.RJ)RJu RJ)RhRJ)R]D,RJ)"`)RJ)TK)RJ)XJ)RJ)E|R=YĄF#+le2QJ)2'oLGJ3XJ@ĄFL16bBdxXJ)T&_P{CL.R'KpwnP߈ K)ʘG??+5m(O_y[W>s~̐nRR;N?[)R bZm~ 4Ktwg֛jok"&4pK 45{~J)TpK[:OwiLu Pյ#SgK)&DLh >I dڀeRJ)Ջ53^R[Z7dzRjYa+LGݥkT\e9-1^C,VZbqSby1X m߮RJuEL؈ RB#h\Y o@M:0-ǀH<ޝH״>Vq<&R F(jwV61!$[uoLELhxa,-up=hĄk+l%J}vRBW·9߫aXɦ~6QkAz Ti#[a+5RJ)Q;z2}3 5[ǖ+{% 4 #w"&4`]RfTpӲS;6T|e5oT2n'riu#ʲWnidGƥRJdh|R.pT0C x9kєmVӖ_z}ު b0;lS&?Zދ [u*iR OnsGgzJ)T&]|CG4/[ |iykO e67T7;e&s%o<Γt~xP1oRο1siZ{ʸ5}W7p%c>vۀ_UJm.z-Tꓭ |3-v'JsW:Yx0@RJL Ffmjt8ɭߛtsns.Wj`G|{2u€VxfI iOn #*h'kb/b* [|vKzB~uS]nuN~4&\)DL$RNtLnmKK~~s77M7%Jro:)}*?9X,`T&e7&%rH)8hUFȾ~!{bEZ"n``䫑$\\;PQJ)馻mjŬ4p>{ Y[kkMmZa@rS 4!sTZRE@۩CGG䇶 6/`*,߼ar;dBY^k:> *˨>DўOv ،t*| x[R!H{1yN)To483Y3yhSnj:q%x,_ұ&$,F!R gxIcuȥ#J!z`lNA,i z@#Le@?`wK#&RJ}`4>Ϊ6qr67mNW+k)cdc.G,`$ld?iZ9 YX9g5d^R]C`)ՋEL`ɨUa+8RJLh8;AV['ZI x}m)iSunmz4@z"뮽rZ8NVVuO;iÈT~V5ہ[*leZZ"T/1!:RXDG@6sshx1x>bBwtcFڷqnI8ޓܢGn_c?g[+EK7ڬOX;\][inv^U}۸"{ZmF8/}|Ʌ_1 Vn:pJ\膏HLK]& wK /(2k<%9Xy,*RJ}򽧀4i5Dʗ1躃[.1}߮"4A;X5 -*:v[inͿ-t/}؛#AړȜH0ƯF9Y)[4:*9cGy|m);K}G&晶\RyY邪d{`Gq?p=Rx7oݶϼ[؟20O]n3ly )iN%~Siz[(p1c?g#MڀI`o <?cʜVNĄ\v,)qW*c4*i-έm_KO7hE1m^*ok*eGe$ Ӛ 'EJ ehJ)zr}ii-Ϸ\8Ƥ7:jREia'+,O{`chQA6tCG *~y;{K1!OĄG vv7X88mr*4 *LT5m9ݸ)rv"&d˥H޼=o#5 R#i:?Jk*65 LBk͛'߀ˑU>~7"w'+CR$wҺGO×RX%? Mo 7~׳j:"a|k+a0 $?jOް:]B*-J|pΚ2ueNoÓyv8pyUwhZp {ix^?=!m=Ǿ1sM83^hɇ B&.X Df:`F:2/~ rݺdHR ~$xOGG> #?Y;dð 8TWr/ҩN`x! rj,v2o<=K<X̮:`)yHdv|?fp}4ŀC_!{Xe5/,4 H;@  KI1iz|c=xJ)zX8itA0!kx>x=6/Q;Ϣ`Y \;}M{&2Zy7܅L<'-}Bvv]ʧ]Wʗ#&@2z$"To3o֬寯3&#e0^oSuٵ-f׵L8"lAj#OYed}xȪ*x.Ωi,H=%s`̏VnR\ k4 #}"wx岤dnQo̖eN?7o \RXJhurZ ^K=i`1OB[ _@!""A<.d?HCohߖ~V.3WRjoGZ1+ |pQ*_4Η6gy҇og֕, n!ںcx68-dWom:  2vJ}`)a+Fy{ej| g8`=Pk%HTp 5H`20uHUH+F6X+le͞vR{`4~1>|cjsrgյN:T|Rdf1NP u],}p RjiZgL:X,^ZL,id ջZW!@jAp]>^/5R&N>?jQ_^} n<ؿRj4K F7ˍc8ӓJ C/Ҋ`rZ`4^An^\ ﱋQ3&͜ev4= >]B2L_`0 8H@}}2y9OaF -aV6S[7/ZA,[rpow_RJҗVe_ִ&łli~&fxktYvvX8<ƛ0эVhKu99O-^aӭ'fzL>?NO%#48邬)kI"msgБrROHՈLDEF/}SҎ[xF%Vxd[ʕRJF%}ÀWqmRV2i[E!I VFL'CJ܇SHy*9Z06/ rA㾦)-\4ȧ R1)RJuOM,~y-p n?mc~|\4hnB)HzX$zx$C8R2kɰRJ)F`nHs[ۆS8,L [WMѸ FE5Sft`#1,U9ppX5 ڊ¥HY׀? gr^)RiRh|8aLk䮮u,+=/6?35HHpX80ggС@ x3p#vj6p05r؈4O#.H[HroV_6PJ)R@v)0p5vIںMz6sڅkIrj#hX8@Ą 'ҊɾdmdH@$P iLQd s)ti>\He@=.r0VaĄr{[&Q)R'A~8 VqrCw}jb`%|o6Ԗons2=Ȯ  tjF<ޖ<\!NYNBϞ dnj @Z%;l>HlZ 6|4ӸYuE} J)R0`. 6^Wp hT^دV l[n+ҹ[?p|Xʐ.͸ze IDATdvKvr)%lO$MGg@Mh!w&RxtϿ|"J)R6]z ¬~ 5|u?hԷ= Vu);=rfĄ Y'`4)}$ E#`yXq*8I#?U#eARJ,ի<%7L¦aG|b{hg=g0sZ*5p )WHY.~ "&Hi wnLĄp߯)%l=B~xS^i /{$*c+/0 !24PJ)R@,իx鹩D oyp.mǤݸ2mh$4)[l|0ppH  :#ʲ:d!S@vR6i͞`HQ觽&㛷\m[cdAo2|tmfO4 ҉]`cSvMoI43}nՒA)RKFxדH{ݍ>/tj[Ѹ&Uo12 FM!Dzr:RX8Zr# 컪ȁd Ҋ ,UTr5iKUUeMIϕHGd_lmm\wvG# sM7 |5RJ)TM.Tt KÇלuX87O>iV7nXNuX87>ő_`Ѫ+ YuicGF=?eM95 li}-3/p ( J@+} 6 A .E6w1T1uo7dM>gfOA+OX8ۂ`xs7_^XRJ)K4z#_vg]1A299niH&%m5*b+ SյZH*7(B8'x[R"&KK>7.kMEW#k[&T9t8t`H5|7Hjh/"m--OWg"=5Z 8@QsM֡i?m>非L)RЍU lu'YHvHgw\Tb͓o->c6$Sc=m/6 )}YZEi,ژ5Ad+ L+pYÙHC@ȏFHMHX%H~H!9}g"ԣW YwR&xrkan.f0RJ)z`^5j F!M%:Y~)[#6`}DN/ao@ZYcl`Ƶ'jZ-pY%o!ѥH BHY_{f*%tuU䎱,$eݯՇH`x-4K~Ҏ{i7?.7{ڠ,u|'n}~rh gλ._xOc<X+RJ(`]@,x1DRf{Y`4~4&"uc4e\寬L }o25m^l5~/p(1H*wRzاP3˅Hl|IER PcRH6 7V6f^/]Odl>5*$ώ|/mMy}'V3f〉"]RJ"& +#`$iHk!V]0r:$ =^ FڈƯrE%e?~i>8+HYȚ5''nvA>F$J 8܆Q~=$ .߯F̒cǂc+o" ,a;mǛMHYwsi˦cMtECRD3<vX`4~c@N}8/U-/.w>bsŊPǼ8Xg`^HmH@T2xυ}i[H)_~VٝO#klc@`$"@l~& ˖,vo0ap剟UvAtd432}n&y`@MQ^fmb^WC)TϷ+{Tg]ǢԮk ONOp"ylۿB?Ec˿ss7'>~4غۼ)YomKi{;9/d Ҩ‡eQh"k!mH9q/W.PҎ[w %3Z4ݰUw <}pj_W汐62R`4>)ÞI{MVvT.j|Ah0`4H0[ [Gw-\y$gݩ/9 Fޏ^|hc8ܛ(x, ൒MZsulDZwo4ᶢ,DJW/ij_WO#e߯b Ng|,OV$ \w> ]iǭX{ڄ/sI_|8p>3 HGB>>*僫 D=5}n[]9R=Z34q^<2O.YR;,p7X8ᗐ1 Mxݮ0Y%sOqiM:_ ªX8kXr[ӖOjCR~OEL@i|:xn?`sѲ ~W[Cs0{5i b:ڭG zioaQ%Hiq 9O;"˚ zd^=mP{ϭqܪO)Ԟ 6 4Fq?#&x*ln@T;kjݺRx93ysC4o%ַx~ל~$?Siރ;\K .Z~E7֌NK{`4~p X8}̇kލy3b@]0<.>p%RN8k|@ DZgLxcΚ1eN=&xN-wuֽ\$;ex3oվ@!~f {\ݏC5dt~48 G+hX8} K?g?j\HvV"hxx2wt۶nRhdaNl݉c|gR&+B 1!}ڻzyi$(j=ͩBOu`@j{Qk =r&shu$Pl^I#{yRJ}G0ϱuJ. FDâry0/5qmrߘ6D0Yn#S]VcЙhp QmDOzdv~F<7O%vKqoQ0s/A΍{Wvp}Ż~ ,Bh|nnpWԑ!m4o5 &`aLFLWH@|z)BrRC:RH`䞫D*leǸԙqeL*ϭ 7RJe@0d-%uG{VZ l Fgs> :C)`2nRp`4> N |o ipq0.Gʷ>$s/|p(X86~*6(+b/ Ffp{3~6됺 'DLؗh j$X "~7 .r_XqOWwaܪez0J)2. ,1^tCh/RS^둽ۺuJ} "K2qA>Qo0)BPh,殜7}M=yn,x}n?P}d" #NC,|)4@r̎6 :oW` pjw>]Zc]qΛ}Ϥn #E= [߈ #}>$PCFy)$!AU=2qd#傗E:l]zRJ}{luJNˏp^ki?]H@ԞU:`4~a,x 6k՝C.";Yo`4X8Ј9ȟ^EH@w7l;ɦ5!H)\6Hpd '>n<9~o_o:Yt+9#H`;-yۆd>܉d[L\ЕעRJMڙR*JYHX8S;6#iqb߃ g_̹w筫9j$*G2#M`4 (#dSkwRac]Q5CJ;wtpt /"Nyqv`4n /<߫)Zy [QcLGLhPWa+_GL91Wϸ#͒!͢RJ)z-%Ѹ`0p4Ν:JHĂSU^Ӓ(kl)?ɈEҾq[bΙWԄdڐ KVÀ?`Q5sb۝dd-ыpOna@X8P2)_|~sEL|XWa+1#͒a?穀RJ̙9Ð5g1eNz> z`4~<Ҭ᎝-t?3/Iﮩ2v`R0z x)GdnGVƇ >56{x~**\d=߀[c@m;x.l eyidN Gmf{ZĄrߏO:nYbVq=ZRJ)f?JdkgL{/V/nE$HڂZ ېLSi18-쾶iP1X04(Eڤw| b.GWm@FHVX8Tq{5G32:eđX>pL,xsuu>9H8X8{]?"&hRJ)O0?,tQ%Θ2LM}vw\<`4~p1q.` Ddz&"ÿ4cH H6:_zٗTif~i%w'1$h{ e ~z ɠ΄GnwFHb_GJ^C{Hӈ5fVZ+R7ߕm)_u)561]ro0V{w~(𾢼``!A܇dw~}b@{Éא zdv td B^Aa@2E3΄)$M "A:c${u8RVUg#%{_AJ H'維n -bF;źY pO,X ٥ː=HRԶ/toA>HB"{T=Ip{ލH C w"M21} Z-H;H7s,xpۼ%9'? B %Lh4ы ɖ.v?{=1`My}ݽ3ss{j*YfW7sҩD{E IDAT^E!}bTo Ax乚|*>l쟒.}~7nj7R HzU6ZM3sD`} D8U1| G`pʭB4{\vAy`srJIٸVUI<|/y{-3-J>хRH!M!c =`\3SEO̱"#d^!2UӴrPV5aʫ&1JOTC)`mmʚ}&]/ǼCH&]o'"t k`9OA?AF`f6z?FyKg}ʺ [Ơb>U1=G^j| CP"|3CYJlŨ)d1om$pt-@" &z(4JJϚcA!/WVH!RH_: DÊ}i"BdHvxYLN5XNw9oH0,,!?~Q]SYd*JOF8!P. C$@@:hHSQ5>ҩ dIkE[F3Vs܁ț@kR Gx.kjl•G"Ó1Aޟc5Afnȳ55$.7@a/qoSK:9曹 qT+uÑ0 &J\N%](l+ C )єt.Ui-'|QQ~ΌX3z$'hSPj2ʷa VH[tpSuM0Qף!PXEp+S!ps:8|P]ҩ/=-:ylGF9PEpQe%ZUCXl9(q)x3QY! c6]hs5k@(Cy\fQp5 ko~B"ZP1!)b<>],zP|69믨Aq 8٬oPI{*cBt*q/$.aֻ4z}6B )BzZ$>V;z7V 4!18>Vw:}rM>@iT5a!zSʔt,-^~,ioh%_B Eqkg垺钥ۜr$0灅p7*Ԋ@WJ:xz zH!֧1ZK!0r.&(6<;Ym}w|ˉ@$()ݺ2ֿ!rI9B7 !#n f-"1[fCfGf7 @QwkC;ͬyԤx. F!tS,rq| %]o(r 80zקSS )B%]/٘띄>N%pf8H殘QնȨwy?8?X"S3:(7 ok.r/Hw$S3?P"$J SC%SJPөD{>!}( tO.J,G^r,=<0 T.B.@%+ e;O'w!p4 xyIeO?ײB:[Fqns~m y<Hi<+kGq38*8b}oW9܄,}nfz+zݑEZS\Y[7:/גFtdN,3Ƨ]v~GҜr9;ۛ'Jt!(aw$oGr1"..U5wg}S~ :)$ ?O( . YƠ.bZ@]=7#pA`%}CQўߍ@ *E\27.A-m{+AaBRXbyH> J#4澖9*ok=M6Gu(1\bH A9HH A=J6!EݼA Q!t8K\g$$~bƶ K-}.TXwAy"N72kfABsYSRN;: <噁݇I]&+a-u}^DB p_'hAYTb[@;IiD82U)1uryˏ:| zS'/:t?߿kCC )M!m*@TM|J{(Lﲤ|VϬ}N{Cn1%0gtWNc$;<oYO}׸mK}23ۑ]d;&0>>tÐ"vf:x w1"g"cbʆRH_@&1/"G؂9g 6c"g+};z=͵Ws5f(54y(ḏ)s9n@ލ3ZT Hݶk5DA| ,F`Ԯcii-'TPyƚϬ"? |%H(1Ja^hC\F; .֘uG@3Hu(0Ze4C |O0fsڬ׶cm=_ZGOqa %==P%h-3kF j gCn䖚vXfY9 -m -G{C_t>c|ǘԓfgwtq)=qlRH/YZyN,T.vʺ1L#FlK@߇S[fl-+͵|((3>ؾH>Ui; jֆ R^XVn2# Yd7c9_ ZN9[/'"4A Uj;ޜg-*Ao( 7ڬC+@bQ(OI׫@9tIy+P~Ѭt*gd;(\rAyX(蹮!YPhe͘Bjz5JZfiY M|[qA+ PE/ x5-9(7ʶy}~֞xH!q2нп׳cF5kfFx3Iy\QqL`` -*-Xzo֏S.*1 <=Qj _=p8 k|td^; mfLz*zPѡS5 yaSݯ%]7PN%>!r"Dk*ګ&L39'-^n=Y[e_s A(' 2t*%]GthTVVHqoZUydQU EpbY잆\:wĄ5ڦYİs Kyyv%QH0C Ă:D F)06p*f3^Af}|%50z5q(%)nj#9Qtތ9oM*~e MzW Fcs ޵=zQT(z3A@5֮c(,+hAgt*qf-¦S3)JYf/'I|]G^tџjC )$KI>:!EKgHeǷ.^O͌q 9u2 @d8"!Y{\m/G2oBrd@C\5SΤ4CrnZoH@޹*4t2&+z#;HntW֕I;-J!ҥI;ƴڐTQ*k|y44y81N,>k3 lD CNڠELv?柁­z &plyǐT`?smB&q17cwDHw ƽ Rtϑg搏'',4N$F?!> G=IPPaGs..75N6lq"fL$dm^{كHTZ ޅʩGEPln-*ҳqAuY;5u"!>ݑjgQנNAdw_=|׳޾L+|48cw]3:Iͫظ-I5awSՄ)aQt  ɀۑ޼t*"z c-D_P94Oe9owCݐlY* _iw.F|h%_k݁{þ9a,o'kkp*j?|߲ wvxKnuͯr&L@w_R]SS5aJ;@{1C_TT]kimS6˿P6L"&L:aʼ^ TbiƢR>RBCJDy81,"w \|$#)[bA? 1 $"Ɛ98o"YA:s6ܰOjA^*WՆ@0pjk>Rh_FM;̘HY? ľfM/̵ (oB{kB+A^cۀBGBR>J`· lIu}/k[`t*1M ҩx$]ow$l 3HP+Q+-[yhMo3ǎ%R"xJv z<9VĴ[@*G>2|/~uv}AVq3ʟx@YG.O\? )y[k*LԟZT3T̈#/ӛs3>iGn0{Qx dy݉r+Mg(? x(!O1܏<:m([} )pfnײ󰓩w"/E9X#;)oSw'ֆMw)0!olsUAۄ y3~kUDPqRJ({ \b4),`>3ߐ`(ok#NR#B䵻 ~(Ndw1k Eotq#T(dKw> $Y-D`]xh]{eע=c={Fs|։hߎCv~+w7R$d/AkфZk:V3hE:<֭U -F}AS']Ku.si~ u}ؤm M5F٫ˀ&L s*B@OȀtBw1$]t*qC|LĻK2->8g&]o 26#8'F-L<:k0c!QbƐ S)2("cT?|,d;"qNE~ď;Q\grJԼ Lހ>gMiۇ5&z6]tX܋R<YĶ'ȷ 1&l/N4[lƾrw f QAbw4R}R,l 9_)ARdY#n=s{ )Ps+jÐq G0H0ǎ"hY\8ʏ/Ea~&Jj*@p3(L=z 9TS 3Ubpxֶ%]/.dt{=ӫ59%Y?mst*sB{AdM @Պ<5EϡR0p AeJ1ot Hކ*d\C!H d-sBjȭ=}G 5aB JiFaqIWꅌfgSڤ]mN/Ce C~'#lq%6Ȑd+FL >=P5c>Hic?Dzy>K5Z8 [`v!}?~kd,\ɛ02=n6F."TH_?rpnvF$d,3w ٽ!mE ٵun6B>]BP+>cmݣ7Fx*qh 9:b##cyyLZbd˜c]G3s}ZE3R*;c~yk̴#A== y*@臼PG^ѶH0٪L|l8T`W$*Z5H{M!(A6PF?򾚀'ҩ @t*N(ttqNhd|LAqhFh=Fp#n~0tޟtsCh=(zvOBa.7=uz>%mh2о->?l,G kwӑrheZEN@Ӥ=ڣc u1W#}6}jBre ' I/J<P(E~G *݇Y]u?Ow{a)6o5HH~-@<漝({00=km/ZVڞtaXxT t*,zňg|+zsҩz?L5t*xSƇߡt*vҚ5q:Ht5Ǽl'1'z?> _;|oΜtO}kZ3eYtI.qԏV+ڄr @u( g덼#[ =[Ip()d3yf m ,~.F X/WYHcsH-Cf[41톼%} \=n6Ь9g"rNAWۡ*zgy@9VtYɦ`7Fs:cMt{#h".(=1{_Zغ@= Xԁurȭ8݃ͼ (o2P]SVvH!}Yt Zu,ٲ('QnuCrH`-˟-ɮ$3 Pe1A߿vw9Auz.f @V~]Y4xDDr(qGf9܀,$z"=a9ޘǙK&tۑ00FJ$ w=2mt0LwyxOV ;!d8+n,fW !ncp Pڀҩܤ띆5JFV<5/,K-Q>,5 %{!f )Ț֢0'@ ƑzPL?8A7#}.A58 |؟"-^oj~h==|#}GS}݅1BS><+Z!P' ̚5s}31(\re r\tU5JdLMvY? $0ћxQvEMߡtdyO9hO@kفw XkSfQѕ,}:b+EA>Xe:  tʷJҩ/F@>;',XZ׏N7]Czxu?S's5pWOh0 Cϡ=sDFB @TU!M3cj+WVhH[#2v( >ng'#>@?ݹC#ܛ8A(|LA`tJrŎɘs rڶ+lmcV.)zo$7܂d-."OoA<|FVi!ȹ?c(z;kښh"'(z=Qg7V&JC+ҫ_=K.~8\{9L~t}Z$h>B mbhޝH>瑥4s3#Z=jHHx4RV)7keCEk*}G y", pCP49g̸?a>B"ơ|ˬS7ɬX e? qXG<$pYhEϰ{b( +(-#ۛy3fہ@t*qVB`e4zV >\YSbՇ-z92ZYsݑHDFHGFKmhmXC]QqH][hy~6:`=nC,Й ^i:Q$>D}GE*j0UE" Is6nMT.~Mt*孇N%Zx>֭ץG]v9w|9~N(%@Q,݋?ҽxt 3oF` SZk*ciώ3j/Q5aJX4i P>%]?Kvm֖gf;+:I$H\2㻡x6r`-H៉|4c3y j>IPbFJ6s ]U%Ɯ)h]@֭bCE)<9U8M~ןhY++3᷸^'4IT(Tv\D rZX{׳se0t>ay:>XjľKjG.3rWڼk[-s6>t2 UMo(MDyA̿yV"sRw@ |B;(A(9ȫSwF?|mXȊ}/Nv'pC`!Q\Ԝ:s瑠{̵7dwFc ZWFm߯c|$my8J=cO@ &Xl3©\_3r}f=uv>QYֵo8\f݆|**^,bdn)\{AUTh1*\ ȷ򇞰8'B̉Єl֡|u e=Zz o}h]wG"boS$]Hdy C ^z-2H$j@*(U#/h/N@yq[&]49=&TZ{DOmR/"Hh ެ0eYuM[ȭfH!2l^s< A~wpsv_9 ٮ军/Dhlbl.B9գ=r 06zIf* I +YFm[s * ڽi~Zd,kGm ϬsktJ>ѹuk&gT.uw{YݿE{"t~t_S7mܲth:4LN:  IDAT/sPڒ.ZҌ+!0O5OG@–Z2Zm%BOZEoAW*Y/TWT'ki-m8 ]/31f6؆@3mBoE!6x6{t*1l /]h&2v=m"CHM6:؂BkW#>XQҢT i3 >Χ8ҩěKZnS<[QyMȂUvk|@Lp$2HżvA EJ~!=bdj@s~X7"(j-k3sgaz!-sܱȊ8ǷZ"(vq7 )!Tl{H8܇/6cE"~9(#kB R;! >f X7P"Cn8)> ǴR`)SD2V|OP}.O_9OM7V^ة?:[%'\ $w|UTƑ'0yC: R"G?j”q+!24ȃOxB'⛳'7hII\tkPC^rNJWZrvUSbL"6E2׳y!5 \30HtA !~혮VقP0k(ݰxqfɦXTW-?2ē㈧.CU?@J*$?_G!ۃQnI˦STZdL K E&oUM_Ǒ>T^H<^E[(lj--l˟{nڼ=_Z'oQMׯlvΡ>ݞYF8u_n%g{?n1ƺv7R#7yx~nM/t+^@K1<DX Rvan.r#S(}${U6؄@qxs_uD`Gx?BV"$V!M%] >%_IPě#өĩ ,nӔ5n}!m\dֱݬ0F@ $"P"r9$gTD6> #9DZS퉤n1V>FgIT5aJGuM? N#h&z 1p@uMU!2&Rt*1 Y7l+82&]@"CZ;RZ/ؓ|dU"?tTD܍ЩH.0 ܚN%&BbiAT<5wͷ ,Xj4װ<Ɇ1KцZ`d+E2(=6R$Nՠ/E (s[1NPΣ7mgěSH^l;$Ȅ띄6@nI dt*.J<)DŽT]SA6zj”95G r onAՄ)OTTZ#v`^E_f.5{3oVu+=iʀ+ۂ`}% f^Ulө*sh[LPk@UH?F*@]ͫXBS'eGn-EJڳTO^Rz^wBg^䨱guj”k*m]~?4YgUTT5a+5)'WT>R5aʲ͝CH_o2aW!y52w꡻1~t* Jt*diF{߬88!zH&I++汶Lѐ>gXB_$_w8 㓮w-2E<(F?eÍm]V"dFhB-YfY~tѰa 9byo#]ͱ6y x*EZ$^7gs?W#zU^2>Y,Gr5M Hj”>J/QzTp-jQb1k(}`^uM!x+voNO=l⯎hlyE~n a\k۹z?ߦG1+|o1]G5tI魰TXTl:;eK? iF`bC{t#c$ɳO# m8؁k3J-Z@P YVE?#S Dl: ;P.Fs0lGqGOd-s U1=Y?1WఉpƳBLrA F" +e tPK5<\rPAR#qH6cfHAᚗEJ#AL5^s~Fw6|+h<9 Q\AMEGImu{dtE92~hG!ZspR]Cɵ[9S<tULyqKPÿzɤǣd9F4}B u޾PFzga(xr pkO*gP[0oc`FHoODv)1G< y@:"腋׷j[]Ѳٶ fW(A_6Zfar"dlI-xȑ[VGvk5<:nrnWɤ^-fL!黊ix \~9q[:B9n}ҽ1M#'qZ79)eo7g:C+]{g'JB @CEj理`oթty[~gkߗ0|+%Kqq,Y;ါ_ibUh2_wˣ>RVo-f)20uHkm5 e"Ob򄵘k9mvU&#%-27 =^5`X(18-"=sHEqPG`TTDsAsxx7ͽ.tsW" EFϛXՉZ3S8.J"G!xt7_iy# \ѓ%*}1E%Y4;m r +v,w"g(oY:-7G6;hэid߫;Bazn+Ȭ>=~o2)7'ɀܜ%E ƏEi5?d8q;^Sۻs2pW 9 ~W[]AlgD(tNv΢(_qjv+>P&#oȿvb,ӑ=EY HW4,CŒ(]_3g^p]x( hs dFc>Y vۂMtE ɤI;>26x٩xѹha#f+6܃#7qs]owd!?̶r̓:wʗs}sGeƾUZ$hNAY4ϝ[[]=F͞ -emUG3gLKcp7SĮw`'۱M2WOk U^MrZUy6uw]<}c>ڻ];eDbxƮֶ%g?܈ixIEs~DCzw)3szvWG-ÒgQk=’)C%ͅ%xۮ-}fScK 6,2(Smu}{4tN?bʯtWO~֊_t mmdH/;a| mo{~#?=s(k/.Ɋdp%rExh<Ɠ{ ={^u6"o4O! ܎ǣxk$ʶ|:={?)< fGlT֗wu8.8>Ȯ"G+#=YPs+5c>ra7e:_ϭL݇lx"hˇ u 5C(&%OEf[;9c&?/ '_k9\3 f~:\f~oLC,{q5\VoBk(+u 58t(;~dRT1h8)vʫ"0V/]+2~"5-< U3\KUpQaՂ#4Zoo+j&0dvEad(EB]={x'&-kHʼnXdrwZ*0YbSۆR*0 'bExH/5 d>oO=㫐[xrI"yٶ;м{Z~s9YX /HǴ#ahcP>Z[b }kAK W´.ujc)~cX*Ph dS-&[P ;'Il9x4H);9ξ,gpb#}}G4VLJ=y >)~_ a=gv1PAV)\P֒ߪk9EfOKLkt&^Y v8]HdUu>SqN_N)KkwM(\ k! Pq, ]s\. |\9 BmC܋ׯ( #\e]c=\*mM1}#J-S݌`Dpnh"^Eʐ@%B i+2R@n4ExԽfe>gk ܲa[Ip"d'$O{eQ|lg#0%f6jBo_1cjДuuAѳyXEhlEKz9k7õKOgנAx9h>R6a( 6|aSw/HO7O7ptƓN&l'Kq| 7tם^|eͦINQs;{lj`iwoYm] {2K']=0T :~Lb'afCܘGs٦ ~Gιl7 ȍ^9 =ݎt4: 颇^;|_3lp܊ElE.pdB%ޭ m%כ="KC̆A~V8;{ABW0\S~|Bx#ߛ? E#u} .BϠ^_D ⺆i9 wW5NXUZ?[+M3em/_*\7T)/gk]^5MK&x棅ޔeo*8dnGOyl}UW~ʝ~ΦeY_mWQ1Y_Y^C>t ?ZJthYED,}AHhEg"0'%bE8JNs"'w{/S@B["dm0bx}Ova BJ1=oHid햡^d ҙ4q\eаQ`6 3c+6ulp`<ׄLM1/Zh<t3\diyN@QМ|I]C׮;\n?wA=ߗv|>@p3Z;JSwß0S=4^KEFAIi# jv,69!GƣY3o;rtEq}H58R[]`Ɠc12~Xdc4-دk@}жrE^d"lUy@!t3)gm03\E@H9>.uOj0 v?px ^_C'@@gZ#@27/ @y`a;(*M(6UsOYWE-(ތq<3Y LY=XhV{Q}_b^6>K1Ɠ`j IDAT~rѽX=d.DdtˉMM(\K'z/z<} N_}8?--)uq|la˜'8g9~"y5]?BчX}/$bv=3 xИE'(u[D,!OP,tEbl2i+?ƓGW,|ۑޚ)SH,Crz[RmuSc \fpF9!ǃi0m ^VeΎ^ؾ 8N|N)-h^ o5D[4Jz[f38Һ \RnB6 ߡ~gOȑD,})V׷g GzDPxu 55M^[yA-N4Zo[!`ӱ} ;BCywWo.֡NA&BR)%Լ7WTWVgj]Vt] =Ytrw\Ƈf>⊾XM#݅]5?4ySR0lsf` (swAxz4_3Gq O7Uo)5E CԁAfb)hnYy@rZ{c3=w|zޕ8cox!I](+9lP㭳 ň7g}/ ;=8oWY~iz$b09zhd3f\4+\ AKlF@)ԫ f{7[H߾SWF-F6e[$'ur&9ѹ]G}-m&lj+^nEha\ 7ݧ\{of)@.dKPnwq]^vXD<)۾Lu,9KrCCô\-豋`Fvdp^q%x["1n)]EdžG-\9#^~mEN`P=.s5cdr>h 焺r|Nכ ;.?3b{qDL49"jLQyͶy@WVg@n`kn=ݎ|- B o;7ٮk6kx42xZlnDoJzlۃ@a%R@/9N8BFG~ߌ)(z 7%ڪNMlMA@{ yy6CQdx@֊(?/M#q1zx'pSup'Et3mOjmXh<7z3}蝰s E~z1w(jx.,obx8-Q\7/ֶ}u!f 2gXgWnЏD{J4 ܤXq'' =\A B?#ҹ$ߏh<9 .A1h'bxD?6#Qab}܄.ly9sTN Fkjʀw dA)t^O6Jg28lzٹTb-;\7=^V`%:u-7>Ĝx F<>e?*`Ӈ_ˎ*-|{}Xxq͜x/\WvGQ죮ft?, کgX5{ ov1?}INmKߜ;Fǡ}ֲ58f rmZKFyE+њ!`^mu}g]C`|{5ԄS|Nmumޓt5ܣq J+KLiD,y{ ళgHk?x& 9Eo`)2D[ TX@ ۜr"# rݯ\ @љeM2vA\Ts9ш:3y9~3Ztq!@:Dcn3OZ (Tne9sΡxb )׌xPm7C <6 (G*/#J47(*7 ;Pīh<=3[<;^eA(AG"g;2i :]H7jK>̈YG":+(|k4UL3)G`h=zs+A-t bmGNQ~4OGGȳx HWnA bu7rLN[ >U!u ?QEapXfەȡW=;_pס:6lt:8bC+: rBX'-ttmZt][Z'y)\UB^vbSU:dr>98D۵m'ZC}ACa~Ҥ`1ѩ@w4Eo(?l[ڕ_6{Y?}+Nx+Rꧡp bveH PS~Bc*>Q > ^RK7*+ыs0"k]AJMKPDg2`EAg3c9]L}! xj3(5ڜs%vQģ ϰdޛ֍0iNDm+=^Tzymo?s1 ;S .ezXC F;꼾#PE$b4V~n@ Pj{K xVi4\xzbhO4EYCA}{-a)Ƒ~9H3g#;AFQRH?J !@.s%Rl X_V2/o$`{>5 *:Y.щ˲4.Կ%Њr@9Yɪ 'G!W9kEx{|.I"Ͱm 7sy׵}rZssO fq.rҹt{=NMRM/?>(7NR[]?8f,Pǽ╿ji+88(mvRi< m֫3q[z^hI#F4_ZpOȽ㦝"]|{Nk9 ֚;l7i#w2WZQϬ,Ok &{ny忹uѠgt3h )CpL4 O6-ˮ 3l-=1>'ltY{3?oّ ͽW2Akߡ ka΀.ǧQfv%{om/@]C}J/}S+75-1pNk]CMЯhgXCzʑ&bh]@آ"9A6ͥK|~闏M`?zW"yj\Q6d3/ۿQۣI&T =v j'Gɯ!]k#5@״=[RC;OYP U6xxtHW\yAr9o+eAp :zz$dAvfmav*lec/Je=ObTwS0}iv{8^7+}[[>'?ۄd.ۙW1pn{}^̐1O8ݦ\'pЁO>f?"ʃ/̛~pUפq^01}q @"agGEj{g^h 'Ok/J݋TdpW7gBߊ.XV><Ȭ{刁sTZvʥVs/nUh=3 r 9n@0} pCmu?ʋ kkjvr]N n3uP> e\Enkj?+H"0OwhE^0 #C(р8"PrRP"ѤGk9PM@JtM(w_M@`"2oIxV! <(oƴSyu(^3G[qS둇U\Anūd9 ̽|Čڌɂ3yٹ T߃fS4DqaDG O0GQ=M5h]ڜ64c6ZgZ+sͿ jғ+A%tkv x`zs#>21COr~y"61_#}i?QclIFɓQh}ƓCu)YZZܢhpkp6oDź:K\<2=D]p\X'w6ҙ*^uI4fλ|/xb6 3ċeid7!:{^ֶݵ6'tjfdY dW9lJ?>lS@v8qآ]Em ?cS4}EȾxr0nB:"X$ mmR]8ӺUč'fn uaIouwHOgjsg;G3}|M5/;D];;ƺ*4GTD,'سQg<6 V,\˴?qo{cҡ:?_rOyy+6Lņ}os.;wpKo|pl7P)LQeK*7AQ ݓw+lp&U4hwp\ ~}`eI"i݉NG%4ȭF0[?@Xdi4\!O HpFބ}!ڈ^@Nw/C>?_u1cEГȈb MsenVױ7:xG a=kFԖ"tK|3B?e#r)`?sބ VdBk`֏ lցh63HdBs\#Cz!RͱJrh^J"4]p ޼_>>"zz{^kN4;-3S>5oꆦBatln#F yˆ܎tdd2f,H.{./٭̵E{g&'!ʺ-ZBs;Gχ5 FH6/ 릙h r^ZX+59 hw3p3 9귕L&mAnU/&Wr%=HIGaEo5EԹ6ݹu9b/nx\\^do5b[ml%_zqoD8^<6*Mh`#i!6 Ө& D,<^4?/Aw `ٲ6iAh<6!/Mo)h<9p \9oh5E۷`K{ŋ]|>1w'砨؏r|⊩S}Ays>95HO'k8-^+?#(*d+Am>h~ EJ̸CHwŜZg0@vnʔ:ymHe\_wk`eU˓o48[@xlks 9Yd[4#ܭNfs~E#ldA"iH"Mx򫼕1NH&oW Fϧ l\`?Zw#ptD,=]7۴ueha퇘SO~fzH~8;ǩ]={3LelIgB[P邂MPq*?YU/׊_`IqʋV 7|>u)klLo*% :J|-}>X?[+ 5WߍSAR"(/"a9)h~٦%D݁WjT xR]h.X.Kv;}`mnU%@AءGm fl?7>\Bg{| jc8/@cd ZBȸD,h<1ǷK^)Ji2,FQ._h\JNDiRJ2ؖ^k\PUxrq"Yf@7D,}9Uy҈hn|oQ?U+'/Y9<@4< g>EfrHNu ޫY8>oQC6muҁPq KN#.굹Ȇ|9vEr>Z#т9TZ<-zmB@h3qro9v[/cvvP8@wPqfXןm-@|sAmO)[:cŰm4-hu?*껙wd>|BRrQړȾ^dlŨz)Ȇ='{PO[ކrAQ"3.Z")\7&s@/񥺿Xl(ؿ uxS''o^Ρn\ǡJY zO#ه{u7oPj8^CR溆@u!_⮶t@9oW~/ӎP8/WC?|Apm޼*y% o=mr7]+Țh< * 1&7,oGRP)0RHM@LI"kybٮ*FتQyEn9W-#(T}^wܸAT3Ms >_Ȟر?sТFdl DE$; )67nlC(z/ͳ D!Љ͚@`gs4Rh o'AFl"@ =BD "`܂_WAV|Zf8Zm)Vƾ'#@D vx%E|< Ptb#[g"z(?A6&!M? "h&.H }0MH'!>Č96ns,:ͶaF̽8hX~}3PEx&[n~UvTv1 "d5M޾|Ydguě3GE"IuE^ODR9qъYߵݛZy{d%8%4Na&!4OvŘ^Rm_ni/=ùPorm8Umuh<$ܕ׵ÀCj#ZC{ʼn-E 䧋1&tM9R9E}q_~v_x}s ܦe9o Xdv_"NLh<) JC|+Bu"v2|_19h?F3WfRaF/\EvE/&d@zo~/@ۘ$ l'd^A`v:G#c5zlF'Q8mK͵-AѬHQL~-ps ZDti?7Xz5ݍ"X)s}Ns ̦vdPPt͘FUb;9>ONކB*|D,8 &bF1Om[v,k;\˧[=91(vʒ|[]Q#xxaG`HnN"Kߐ=)ETreN_C`+ӐnB HOBƹ#݊ [eAd{F6u9HwoDNdwYh! `gC]x mkMdc07m' ,8 NF,X.:RVGfs]+e ͽjK}ZTEڝ\ _ݕ)uNaނ*XU4m=xkxugdo<=,5]4L*桢? :{"'PmEw͸XZgIhKܬ[{V|[~~uM;8NxW~nޏ5נϽ|.93@O-e¿m`l&ݽHI /f䍋 E桨TGxEdlhUcМh_2UH݌ydl5B׫~d/AFxCxf\THD[ m›6?FQH'!s"CL"oD({!faHfsp6iyf|G#k}] 3x@H7tE`_ (Bӆ|^@"yo>Twӌḩ==N;&y әemU'>ɲ8pz&͌e-Irj@- µ}[P =_[]sb2i9cEj75ԜTzs>X#Ex)ov?{~r ۗFU ԋ^6D3"-EQ펨e{xQ\ Y- -B /{ŻFtEa[c!ޓH;ynh0\St!b*L)^%~$ON !D,LB60>O":Ml~HSnQ4g@ϰDn9h48,ON__ RPC{ _Vn| ~ ˥xD,bkCQ*~9~+Cwqc,pl4\t|zt؎>vY3uyN4<9hO!+rT=K̴"nwV5ҿH?i!e; Au7P4eT!GͅڈX)dǷHܣf@ٜ(KMNh-Φ \s3A6˶Hx<^1)fᏜRȦ C a!usx+L_:1+/0#pkfᦿlqGoJEɱ(P/8 l. Oܰq͢ſ5TP~iuũ[70mrq,~,Yn1`藷`S)ߏÁn83E3D}o,Ga xHA"=EP~ CiX}p# 4ǷP'OLFѶs2+P w&}kQs?@m%^nS $i wOu9Ϗ1924I"=xrPY'T>23q+ z2FHifP!SxQfB6srQG<'/G P8y^*Ad 1XBod 6lpmODr4~Kߵ7}Ex^WB^9E O5a[躆w:Q[]];{7Idi~ ȶĐ^Hgx|v5N#s.rLW.B/Mq0{U1EL0msȏ0"rnbyڌnhHGڈw/lHDOŒlg?mOk2M6F,c"]FƇlSWa#Mnƣ ɎgmdqوS :low4 0daSȌ8dzâPK̷m+@S p|E+ɟ u6ȝۂVkvi}_'sve9-=l/|fONww1C_283kXn`>bJkq]nN˨x~y[&YyiS.X^YqԋoE нhT?0erqjEՖ~EAYH>vY%@ dT!@ߐ0|hQj9DFG.@J6Bk¯AUDL#bi|ZQ9jģ!\ʪ2d,_C˨پqǠhQ>c YrHG"98 3X&架!t9:sipnu9w.k j>h!l"ˑ! bI=h`iEƓ*lƣE +.2>`u&kƓWED4<`7-b{/t/[.gE&&b\tlߟh1<7(R>]|'E `wO"/$bSMJ>9Hd'o$<Ӻ4܊pϣEy9'byt-Hp9(2??"=,җkn;-F`ٰRx%̗";  Ȯl>ߋ{Z6H/Gm/[tEE347ˎO{"* [Eu|v{xT$lˁ6T@4Lzsi"-;3`pkx 7!y /Kw\e?wfd77 HN8."')` *0TK -v}7wD!yݙ{=U$|H8q}6 {Pp3مUBi&m7ZdHۅ 6!I\54?Ng]y2H>L@r7A3P5&(u O @J s]Q ds1{l9+C߁*6*E_~3eR27mone _1rݶMdiχH/>/'^狋Od<؇}! ٗ1ˋ_QQ$4cၗOw'rsM>_ɋR5hdykcEA4GBk^zv4-Q@ïcyf:lSbUN<U{(HM;p{_4A $HMGHX'l?@mpܯM#"9^B`!^ףv+E^e#' 8G[Ҟ䝘ifu \ߡ0ͧ 4y]; E-\d< R"-?Zd{ReBMg'UBi<־Ph(dL 8QVb!>a m!BWoC(jz"{<;*1\vqkh+eǸP9W t_\[Qi~S8 3d g4KK3 g yFXCx\1;lk$ @ۣEv2Tj&[_p[~dt[mMq%^xfƒ B"^CߙslXXt{.2ˁPmuW&O?o1|{2|dUpϭpWjgl>L?|_#lPt$\Fo%_D w1V#t*bEE1YV AEH܎GGQ6HH5 l.v"$ C6UdL(OHu )鉄ɨjɬyX`hY#CRd|Y{(X"5? v_csH݅ w{Ϳ V:݀/2t:ZA1j\4>>~HPH;9jWWۜFFJfi "{f㕥[xm?sisJd<%HǹE/G[$;>tyJ_%R#Km[ׅ+~= 򙯍9݁%]GTU7g-|n[gG:PMLֶA$tޗaE#!yALH@Tbף8 ͡y~B%be6^zعNYv wzכs(ຢU S19G\Fͷqkry+`k<]hjѿ4ly~+t.mUºҮ;*MN]]x||W"Q l IDAT6E!"6F!A^`Z u<_K#T=r6;yo\ AK?fo`Aq#H_! 8Vx92P{;H]AYxT).wBF T &ǻ#^ɠ1Uȃv]p|$CN|v3(G;aeym9;[H 8ɿ*R{T^l\Bk҅u|*U%}?Cs¦9S*FZ j%]3Wc1_p0ˡo=T|vbBAU1e\ȰYhk6H}&^:zOP_@o瀕wL%RaS+ vmP2 7 x84HͶ t?QZη\+o1_ P 1##Lt W(Ga$GX9ޕ@G5]J .A 1]o^dgd2AEk1+w1N`ދk ^hGm<@22$<ftQ2'kQO(WU *Dc4dzTr[Pt?(BqMAӑ̵6~oײX"UGJƣbԕ<~ͦ [ k>r>Fat\k7}8G5\K`shg: gBSJ?'=ѐ\Gg"p*vs >@3 bIou=I#ڌ p{ (J˶M0!}a="# 엡]+)ޟ.{4>'HM@X߻,k;gv +2H$5(H >z8oC@8P<9g7kx J~bv~Ĩ%݌G-G@ *T/{WӑP> H0uG |#6pgP%!WHzq !2kx^Vgm*n~ WC(`KhV "&M4݉8 Rʑ°?zr׮$({n~z'8P#nCY'vE RHQ~bT[]VPsbô/ =UE^ny'=T) PbT7KD)Ŀ^KƣѻdLto-A'Z[GB^|O뾛vQryS %R!elZ|HxMŁ矀x_UķH1xydz `!>QSmnta#YǦ$Wbvo#Yv0ʿu!t.o72^H7.Y5r k y]L ]$K?"('In ]GM]j6X"od*FDƉ!ned{$b?C<<<" bdn2x!H= QX Kp) /y\Q_,LDmDꋶsmB̲W J%RA ;'Aw9:(9LbPS9z#}wrGPHP~o]qꭔy_a1Ȣ|o =Yrٺ M$LǨ +?HuC^gPHǥX ^ }ba=J VN,GojmHH^APyڐYw[>G'>mAK|Rs_(f}֩k\2< H,%Rg0h}gtj^GnW K2 rY{Î [PTF2n!HfCo` ~<"v".X"u,zA P[}YNuӡ=Z7wiZny]>FBuļ"Rdc\x;H ځH |?NP͋Qle[rovCd<.C}lsBsJUm~6.&;'|'=Oj)%H*,e+ ѻAhAk%=pYmu}z+Q,ATbd<@,:iONAp^,6j[bPK@¼{!!\ B]B~H(_Hcef͛U>@5*G@Qvck鋔q"A/ߡb$`#%ڬy#evF tN!w҈,# ,h]s%RGie8ʑ8Я!H.ҭskW•-%+*;ӑ鱪C j?4izjΊ%RyC{|qw-.\bz:)vYD ;@a]CEVi3I+x0dׁxyA)̥@ b,#~"] U' Zt">1v"Ҏh сOCυZl"!茘i[+"cۘi{%2hQ5ٳPH\sۃ[3~d!yͿ]^ik@I@cqw&g RFt}|i${Q\;ށ&}$z=&[-6M5G:ޛlE2mDğ>9#^.HH5_rQ }mЪw.M #;) Br E5ȣpC2}ő%lgހa?]?v7&[k@e_"Pbi RF"u$_JX[S0=\]{W[ k6p/jΨ?I ^_Pdrk:_ Hwt*7~tQ_z* Cbא$o.@<T d9(U\%8CP|e#rg /M= v#mGR?&Zoc!< ^df#~ͥ蝲~v^YmE<b:Gty/m t]y~|mW!otWpZص\^Ej} Fsh ExɄBɕ\m΀X`cMP1"f7e3O 8H*bӐl#^׈(| a#)ǰ441(tFш~Rv+b'Z !+_ݛ𜄄]J,@d"e$L`5CS * Re72Pͷ܏`5T#Cϐ@)__P}Rޏ.E&y1{u}CƍSkw9_ I孽1<"mNi@|3Gp:]?N R~/GY"0/aÐ_"%;lc'ບkЎx W!>{!%yrͅe#9Йы&&Di!&6N;c{W=&: /.+4Ww:VWaspDžȸN>64'WB_wD\b>Bjd{?܃dKͭZ4!"<^D|$[=\nF dsώk:܏C=SbU(l%R_KƣKM;M&T yg):p L`~ZzؽX֙i >6vCOZۋ)ōÙ*q7?Ovp+پ&y#떣;(89>X ʷGz'sxl|i@LV6αH%S ef[`#KOih"1p@B;1EVҞ(YKCoۋaK#H@"3 $ѵD@ۿX"5}2M K#=K9V6Ulz,|%nԙmP>Ah`޺{=؞<5D.~ʠ0*챑vU4>/6sζ@vj վǀj@᠗8㤂ⶢL,$(LB?H*CF ;W)Ȇ֢ y_^YD9;ٴWL#hD궡wd&AD%! => ?V\݉6o!ٖRzy]vla$+_'"Cdv##Z_䒋 T,}bk9 y&v|w[i/.ZlG@ y8vsi!y rXP6\+0Ak6R],[gu`E< d;]Xa7Yu⫀{9j߬k9~ ~0=: ZJ u./^Fn' |CN>!&j9 kxtɒ IDATxd~ZiG!-_GJ+y޺ں Y DHnu/L&He'x7&n"H dlZ B@7}rN#~@iCBdu@BڻmceH!:U*KQ#r H[lFmm Ȓ{D2}z'a}o oC<ps]C u 5#jrXy  L@f"כɕ8wЅ+:~l vv;"yC 4(5m ^8id#l7pw7l=+mOmct 8" 9< @xuڼ]AHA9gpA y\.W>zPc}z.HƣIƣgWMmv\mkekC!2NVޡVk)]5=^]CȺx]CMi]CMȺn´zvZ)}bOHƣ}cԏS;1>Gʑ'Y];y&"0yV"A9#桘kX"ud<y2,T0vX!sb?FM0EEqHB(p~i_g; ]9pt(/s|ޭ =UH4N9HMƣ[8B)t EP~k鼒KƥK V#.ASf{ݎRF޻(oo[>!`%jK3lnW|u6Y)7v=FX$% = eơ7zv!"̯kojK jmu}S]CU3n(^4pܣGkS[]6ڄM!38eBLE?kêgI?;#L5}~k9MY 4,Q/r׺r{c1WePRdrDgADz([ۋYD\WڅHYߗ 5_Ty/mU\Yr!^VJ z]uZ{jCuS̐PF"(?J`|īKMM%O]9og12:f@,G׵Ƞ{Qv_ /j gjL  v%/u {r\)w\^k=2މVak7=zpS_ZrG}d<:?H}D {AW rHo{c'r$,{aܹ EEk;hߗ\4j|эJ#o<|wFZyEuN%i7bԡ(,FB؎CGZyCt>wlWAbjWTRqF^VĘkNƣ/7: $G.qY֚]AWz :0e~Х#P򴜅%5kz A׋c Lx]F E@ДGgX!;2 Hhn9'K~sk(d$DEDB{3)KQh[ ad}nD!# BEz ax,:1^yeg9~*x|a_|<+M(U{씏i!eb]CH9+y^:KEP=5N~IvcUY6+{|r>Yƒ0-[p[OS2`-/г< `Y#yTWʘB^ʌAz*r>xd#05+DB^:wpK[I5C7݈Br *ɺʨg?wv#$[uy?." Ać;CvPX{%Սϯy\N"cmOBb2eWen{ [;֖^`ڵ#34IF<Ms>ε}y L=8FϐLHFw.,БDnΏ PDOgwc;fEj{衺~a*Dl@pKK>{6Z{/l.Wu_Ėu kգcǽmmёH^ш%n2W9$/U?wy(d' =֦Nhi=&ޙ*`ѮT3"h4 !ӌ#L;1ے(ft$ \.LBo! jv\fO!,Ol~s\~n z,JJ,#.{?h2^|Ym}mqW0'߶ i[sZ;˨Rt/R,f$іd<:ѼSUoc79#HL{[~ Rc)SsrW,<(y=7+w|gp>2H8a@@AQ'Ad8Hy0ݭD4"g~3:{o.#H?kkg&[P3kr#iQw w )Bcd<~ĀU94dO,: Я!};vj1z#>,LnYyN|;BAL2s2X#oL?sܣHڀ+ 4x<2- h~\+5ϟE"d*]5Hqwa+ uvD򑌾E5#A`وmxo'.Ɠx㷐e{UYU˝|kVcApv ]rۧYkM=?>jڣ|Z/vū\^+`V}n:fٽvLWbkQR|cԾDX"X"g}'AaG$|±3& S|h-Lу{}r(ubGۑ|zG2mNƣMƣotse}?>a:jq2(t -oHc%x t齆PB/}jrj5LOr,˻ipZ%U X5lSB!cX"u*\w$B7 "q '`= 0ݐ Q虫d#f!oJԎ&g.:ŷwC`yJǻbA?+D1I h; #&^;oX"5-6K+BK#Vr Cmo2]'9-U]!BS wT5Iƣcz;v k(w HEqr߁D X%}篿 _إt#gmJ4Ҁ2=^C *1 Ǐݲ҂^ TI >jr^5ۃtwM(," \6U!\8dJi̻|՟#yz` a1EHwui-q&AWNW{Ww@$"(HGeUm<$ 5u/2|rrN@rM7>kCd結c"kB`+}ґ+//^iؕQjkr]|_ 9'斏?Od?ɖ{iCzWq,GgnO5-9j:as+4wr?fNy}jrN>!JSwzWٵϳiTtż1g&?9'#> PX*G\^AzыP5H}rgi]ʃeUV At8Nƣ$iIbxt5B_E z>IU'bQӣ 9ynEk> =uCၮ+xX+@3zl׹ l~_@ p`ݎ1*33oTK}!|s}o/j'$>X"u={zH#ߨw7ll,26#y<Ӻ"3njZ83o]pY[nc ۾e{wmՈ۶B_ͶV1r`s.rp`8`e^MD!"M%R%D*KbHеq-9d<ږG{#/܊g2 Nƣ;BbTX"6;[ PPUO@oZ!g~qj;oz+ J67fޥVׯBz|9R )K y$QXP~V6.^It(0$h?JЈ!P5H@#fwΎk%" Ϻ,X5Db߲6ЛV:y0@2:OƣD0TYn*m%a U?̭? Y2RkyۑPWa^d<d6!%e*b3b+Su*u9sֵ y y{c塲C|uwv/C`ĀJGP)ns] [cI{#G;'/p2bW04,K.^o,s (H+cԂ9B;3۾s6tg갟2 u.w"cZdy|/'oמIFنgjtu=;%ޏGrÑ/?4H>-tK{gx?CKh]XУ#ZhAg:UU<t|r_B!XG *<<9W+E*m̐}ʦ=۲>:X5|r D?@}%V#ڞq tﯝ_ģ?_!_ziI_ZRvKK>KjPZuu 5Cgj.k|J X3dU(A#9CFmuVϷ|'i$Fhs0YH Xg^!bp< y&ߘv{>E(oe5)OP߷o>kP:!\v_,;Ge֠Bz NA^~ ]osj}z孄XGkخ{]y@9ߵ9MƣYJƣ;jesw]40y)]%b߳= j!(Ywtu{vI7B܇B j{x>bnv󠆐@G3 NBd<k-Ejo[D?!׆w6鴮D$@] 9q b'%\+ґй`eN[g:W&e y탫|^]g~*5Ը_EB+/ unsN_iG_W-\Y EDlnUjn/~iCevR>;E1< J 7Y(>A}ʔSH4>m⇑1m}3 JUs7>c菓 d߃OImhD@vJ^e{'f7!Rs|:1׺aoA~gW 6u'qKKnmOMsx{KG dXAnmHƼ_!`6 r{x}VL$BrU6΍.7\1׬_֞F<1o/"3G,#8 x6WZ=l]TH~o}Ea?LƣdKH},Xt7}Dd2`)7>q>W;6k%vmuoSNl=S[]?-s|?zvNȎ[Ho 8hI*@)75.6!**݌bC%!k2e !Љd<Kcyҙ 3po߁BFB,gQ-Sj鉔cȚ suGL4S^DjlMw oj$=k'(hQ u^B֌w2*Oƣk6A5}݇*%R%Rg$}%RG-toZEڕLY(϶C,l.EmUfe\]v~o͡.\ ^o`͉%R@EK>X"AKoGjr.;"jʺ/ޜB/EjdEi4b#H;AeB[k֜l~vv;T֬|m p8TT맨%|W޷ĿYHBGL2}h;.S4 t&GZ<G zDyHħ42ȋ0xT%ѹD27EfKt 6:PlLp e%"΅F\! gN{t!w, 3  7YJpz)SX"Ob m  )XK**bPB&#J_HX"(P9]/X"x2w]CMM=%(~dPHejb `q2FaXQ,_%RP6[K5Am 9)&E{ *C8d<*@,ـS׬CHy4"!9zdKl<,ސS9XQw6P\=HqJi'ҙ!/  _'̚~ݧ^([ݭk/7L=9 CKў\kν[qMR.c rP(H:z/HUoyYV׷5z7d^wB03 VAȵrx~d¿Pdp.հ" =6-EyH~ςh])Њi}ͻoM=l#hXY+z7zw$fS[zg*ʙ%0*u sD!<` B0H#ԉ) PhbPT@Pc2|۞t /ҘtauDE#E*d݌~9E4JdDxHט0G7"Zt۳layɑAsBB\1{]8mT%RXUwp7}2}}{Oo'_xk"[kmBzJ-WOEC%}\,ܳ_{ =E蜆x`SvnWĹ,7@{,@;Cw"&z"R7 вCPwjsM$̦"p>1Px*gvTTn B@%.uFÒ_ƒ22H`wC=5cls[r#w]J$Eȫv"VpT"9ƻyGPR؋2ʣy~>̖IBޥNgZ[H5vG+s9ꎎ}j}{U,ӈQ-Ψ,[[^?U~-ҧzgϸzUgN߆zٿcKKnȖV*KW|/t)Zit J,vE)/![m}J0D+35-XϛW-rn݄{ Cח)Y!1 ˑ"_W=B5SyJnޮip5'z|Xc+2jU#yE\6 壹/7V]H;>S9fޝ%CmklvHNDȈzV#pԄ`QM6OG9R7O %.#0V:!Θf"U6H9ӊonO? r&sr6Co]fw;Ō;&syaO9-4Li>wtt2zn$v 0w)N4> 0B ? 1<@<ܑNƏH2[hu!o~ rbz391h]R$A\܁ҾgkT".$dC/ r3zYԫ*$jPrM(n,"Α8Cv|3̀^⡶>X2$#KR>. D I^"EbrTh jߓ*vmi15Zr'm.ipfDbawcܫȢ[@w'|H8]zLIϽoKNJ_L;f&J''R:+h &u,XR|q _/m =wlڿ\\F|wOٗPWs'[xڍb5=5+s?9'9غA;K'_xw\voiͳ[-ds`/?TYWw}e%k>]R3_~ MuFd<;ݑ+O}$7]<)E"YGȆRZ2/?<-}^ʿq"?ڈ-' à 0Ӆ4vMA ʺ#9Cޝ)6ިHv 6vގ^&nhR1[gm]!).d jh+=p:^mr;R69Ylgw=d歏oh+~1u#H9ϼ#8ROc˒$RH"9LֽNg%Rɋ鴜0_(]s/dy11vܕv~$toD +, W,Dc6>\rͣvZq}1 Bb{/ g  Dxd<9؜F!NYcs\DW(c,@7u.D_ b1&6(:~`t2D*1_X7_J'ht.@~-lc< M |cWhi^C+ )5egĀvz[8R.G[l=y,ixt2>8r"ׁkn_wGn\]j{yEw%]]bQ}m~B%βʲ_fcHIAguWsE`1K*ˋSpRW!":՝%<ʁ,{{fԫr;KςHm똆]44]uQ}mHA,D!Ţ/ |xI>HvH.ȅu{`^rqyP>x5yd\7- ^w9sMk#x9psHw6/#a]ɕUɚ޶|9*lͯfc;Pߠw7$OwE/' >{ U"$?\~mk!qHOEbvʁ/1c.˻6~=,d6l.ɯXiǺԭőx/\Utxi騦pSxV޵AupŜGƙnϟd`r$8\tꢂHe ~He5ydWS{nPE\oUYt2 8!fCA‹ެ:H|S:_92S<9_x_Yx $7(Ϫ %d\R$4'.D _!D*"R]1vE7 k&[*T'HBB 3M YQ!"_lϾȪv 1nA/$خ{| 4c:wB/u]$|#Px3*1ʬH'&RvQ$ A׊!`y2Hgseq ĮGi#55tB ۧ[Qn{> +;x@i"i+wް;%WtxyڠOs׀1#-(E+Ǻ MuA=k!_e9wߋ\)y_-ihY VrCGWhWU_ۘkh9U}zVI?}9X'1sP!@D*s ~Aג\;Gh8ʸw[;8 t7m[EX8}z:Nj.x] g,{\ > _A/@@1ğwA;jDɇOqC~0MA g%<;V3 0#def3^ˑ]wo Y`@Kx,o"7y%.`A"qkmKAQ , 6nM]"5KlE^ך/T1?G8Geose_~ucOמgg{#A[7z^I.?jE~źѽ[+*]=W%%=:鶲Ʒwۋ>Q:w" |^PӇBu®&Ӂ{Z+o]l-1Y=,#s{!~!e(bc)r}\,LCl=w j#,B`I,Dn56^h㜄8%5@lkQ_}Pum"$h(F Օﻐ=) 0Wi#PL\d%^'RԹTfݽH~ 7ϥfcwwXlo@";:fbwm&;w'׿D`>$wxy@H~y@ Nֿ}m/ǯ_X9ّ]FgnvǺ{Ym|76S}mcTh .ܞHd8kpp>A_rCӥ6ߔk{?rwЇK_(.l>4Ε 7ߔ~'ޖ[9<ƥxO#Ȝ+Dbd#`́,^yoț 9laU99[59^ *_V#b]{{@]v<0!YRm3ӛr˯uPH\+P[sSi+{yF2jZ=zMv1Hf听DQ+,{a{tlPҹH6.\2.(~CG N54)nNqi[!,9U92ΰO'g}l(Ǣ{hbUfI+*FQ;΃|YΉTf~:_?2'xsA>փ>;-}|ֺ&OCx]w`tߪ׮W򆚲EKr5=ْxAAgIwwGf*#A?(?Bv}>˨ y6'iV4Y {:_݀oC<7܉Q1 X+@>Cو@uRpbUhX"F""QecGu4HgibKZY@CھΪ~ *P O^zf#Hh` #Xd{Dp<@ fj20bUۇ3aT~ PȺG֣~$X{K H~d溷3\!+nx*}SvO ag"_OXߋwn `3}6`ƊslEu9T#T.b;6fC9/lCӥG"d`A <:u}Ǵ M's/RįEvЏF+`B;.0"U C-8yc^"o.!<莯FJF|ARdx:2Aau#<77?An|yJ2*Do[F"/l_w?v w(~8ΕoW@}m"#>= 5=:9Цcj[hpU⠻ ŅF_%k(Y۽$terapq} +k1RՁ[z>ᄧ&$.2$W`p9scugc)"ʤ 'rZϓ6{X9J~ "CDsYH0Wm^1']^}\ %tE|]O'3 lCU\a\w!d9\p>Uj5/<-z!x;ɧ]!n3Y]^X鴼{JOGexsMlϾh\1Q9Op,N7۟E ZB:C;O虸RsiAQ!54u.Lu˻r5(.8f ٢hq:;cI[C/˥.P8WA#XTekuY+He>WSdS`mH>ZpLUϲgܡm lﮎ}tTK =%WyICq$ ) Cvw_XO!evTY^y&[э15;==HeO'L2KP|H("$#T~Ñ'wUNrl3U;1F© 1qHT#`y)pY|ùp%mO@(b,cK}Unyd!H Ddv_.~~`YNۀ+42^h%ˏɯD |y!L'7Y=F9ԏ@˃ۑG`(9Z,=dD*s1Ql=(ʌ*[~|GYbD#pkCSwvFT_4Ov\0:yɪquHU}`TБAۼqv~T!Г`%me{Q4!YI+[ʯև-) IDAT;'sAM,˶T0V|Ho[Vtah!#.7hq؏kRdˋ!hRwǗwM*drh $S{pP_.oOؼK^ yrޜk4 r7Cˆu?ᥒ+ba3Jd.xH,O˯|u;l $s7 =SE2e&!o3vm*Uo :鼨!t*uv5oA?dnseᄻ(Wi023=?ض !zl͘Ͻ\@.븻?ORR) (B!C EXǂx- baA@4⡣uވ04xڼSMȕb8kUpm:_He.ü'd|/; 2lCh@5H,w^6"+{ #/H1#pEz lDUڄDZdlc;:Vm9!36ö~H"RnFgw@6g}Kt})ZDy:) .>v5Ը_]ݕb 6[ ($$^Ү=/\l{M"{"O3꒨ 2PduD*AƄu54U#ƿq)e?u=6l]ԯd^PR1,,e tO|5>jtMS-`qLiH욊g# UzT7|~W!Aw'k 鍞!oe}mX5&E p" p\xՏcÐ3][?qa ~*o.@!;s*\j T!ee 1S]n|vWr$\exvyWs;:劵qɔE xkE|91Q2 )E +U 5 (9/\E4ۜk1يđxdmEFy Sz\\S"kP d)pOq}\58>uU *;]} !RO{{I2/b͙,ė D*3y&Ru%a*EDx61瑢CĠ!=cA@=|X]lY hǗ],R6tG"9%Gz 1X>v"1Y+"9e~GA>"Q@D*uV)+ ``: YGi{7y{]Eַo=s#D$$FS]! dN!"pd"iFp_}cQHI,TNQ*#0!'RVU_}Y >â-EZ4͂Ka,h T;/k}Nhhyl#VYPuj Mu'90T7 [Can~ A?PX>($?~D*E!g=HeGdyyTNζW_YD Y)ίdax.o C"a΄tGyc8`9Eޥ\kppc9蔫=75mCE/|(TWRTRR]O=2&\ahAl\l$Bp{g93K61qC"o}ǎDE:ߔN/BCiD gvF\HexW:?)7 \Tkq7NF|9PaW,r\z5z(&"~#' /J$P s PvȻ܄D/]#"H΅wޝ^ g-F@d,㾈;CU]XbO޸My;(DF3ܘ.<Z^"` .|+݆k4 [>xZeʀS1oF í_b'_ + xO}o59=dPq.ğA|}]"$NI'!x3nxɟ3Q>_'i#p ͏?L؀dYǡ6n20T}-^/Fgӎ;hN;P@a8cydA5}W~eœGM{Wrٍl~'&ݹni{*kbJڋ"eE3hw#UU"H/^A\[5텬K]NƗXՈA_B^ jk{.;,1+hІu5bG!O cQbdB 3=oK?YU#w=ޒw4R BUي &@$@صى8$,7=!9)v:|s5 7evGW5p(h)6ɚ;cl| )0'ۇZ.@O $# "w6~sPzcG@g! yPiH(v]"亃Y}{#Ϩ*R\NB<Zݗ*FTfHޚ =4hjR@2v?JT CxjpHP)3B\e:ʦym[ih;iX) ֕+*蘾uᆦ#R_۸]ICS38@0ڀ~\)ާ?"ioyrMLJU"^$ܑ; !Hd3(q!;ꃮ7Vo4EqŅJ ň䗫\lkrʼnlkϻ=Glp+UcO>J6%G36MgEQ,B2Z~C 1~.@<.$ǾH'oZBYE=y=.ʌGa ~ɥ w"2m^t3},AqW`7K:xcj};>'O.G듳[&w}n'JnT_~+Waft z8&wE֛R:N'+L] |;ʔ KwGHQ^?L? 1_?΢""1CG>0`z N|r8}X#[U)#g1~O!`?ZCYY)D@t->F;}XB&;Spc}|#6V_d-< Y"80,i+!p4ε{p 㐐z}lhː0uDCI_m^+xع.rh+ua t2~]glg٧%wV-~Fs6y%{Qn\%RO%RW\~$G!D* >Fd||IaA)4|.0҆]h}ω7?6ioY}/]!EA˦~]6p wb gat~N%pe4zo=O\BA|:F|!Wy\J|r=#撳ڃ\$ I,'p{J+‡{vR|c qBքʈ\i ~E2e'rUrm *!/ 0;{otZ.L;[sy="یoqzn|ɦQ9W`y2~ qeMPsޟj+1B#نc- Q ʑ+ǮBLp  oJY^9]]Jc9Hy)Q($q}Q{ FZլX7h.A $p.Ƥyv& a~Iay 50-E`z.~o9ރlj$nE_H!_97ymO=1k!zNr/k%R₪ܹAAG5ml"9 9r]Zq R m9V?Vyf-i?]<̪e`k ʌg )܄;mpU"qa  ~DDPCQ^ړN@J+Kp)4=bߑhp'zU.T{2@ٷ+UܥeȦ\Ut5RhCڵuE՛˾2{=:"0PjkiFV"KطV OL3b1:6RΫ]T!v-VA!JdB \ϮR?-;C?!;߻%"=KL.=OL/#/ )tȢ S `{†}W"!)|ބ/[;n׼{9LgQik_bY;T&gQuRT xrԸ sjku^N'IB"z..vvMX U!~ ^c8 kXs_m=_@J]!A{Heޯvmp!㦎Qg2tsۣUh&H'O2fJ4uSpUQAWmvG1r9~T9berLgbsF~xԥ? ݈I%%Ro8/+{bMtjA57 3JJɹ"+d$*ZcAXvߢgSw7Xv.pqCJW5\b`bcvlҎ{ =``ϼ) =f!;sGȒt2~D*s0<;!R,kίD0ʐk9y25H|ơpS tϧ"+omcz~Qc=hg%Tfݶ#lG@kAJ/sG0^C}PHe~K$RbevD*D*#$h]~뿒oŮD sTt2G~/7zPAIg Zsq&E`opw%Z˛ }JJ6skmhs-V.;lN}xy@7t9;|RH!;[QUR2+`7dgWlu݀/"t8o3@d SϣmJ ΆѢ0Qv+(?fFJld!WhwQm`mC-PYS2N_̶1 B?癕ڞ="0;:5r$]$$3\ 9ɀݩvgmys4_M'$RVZmލ*Z!^}A'3x! _7Gm6t2FsF䃾vݗG;,k74A5)t4UΓk tg㢞Oo5e{,X1񇉟\ T*Dž f Kzp#>7nM=Xd9vh$R騊ߵD%ŋɲq@SJs"9 81r8> g@^e|3+`x u%q{ Gjv%bą츢s2J(T y !0!x̘;);#a7tIZm7G>*}dݯ@ɺE?Wa :ͮ]P䯰{k,`-HNFϞ˱@ m32Yfv^JS,$|9/ 8z~BFJKTf}Y_ٺ65g΃&.ާjk=4r7#' Mu_mP_xrCSݞ4< Mu_ %.{5> J'+u%2hYPs:%9{PgUلxQ).f'k:z :r:Ç+.]+WЅOq gǭƒPXx?|aBdx&^_[X*۷H+_-BWgvF7{2EO!#"dH=bX8easW|y8O@aw{"g!oS% ׫B k 46`MGb|+ЄY(gҌcQ(f{<XQW+ohۉB"cV_QSn֎l(?n 0]V]N>w󞞘k\=6jO_1T۹~|ЫFxbUYď~طzA0ַjA맹c EVt d|a"h)Q+ WHO,[G`"0 _ 81vh|@`@T~( u ROF•wB%PloF2C;Tel.Vs/۞8!9 Żm!$RE)|#!"  HYm/Ear7C5h{3j7c"^!mHtW##+RL(!A8|~z֞1EVα=S0? )g}lB ڜ{ JqE}@#NvOj>`ڰ3]whmQ}н6yI2?`z㫑Adq8FZe~Ȩ$;64=ocƐ"vAv{|EW"_x W ־_< ċXMg~e\Eߍ/ؐsϾDž9E _ɻlY&>AV>sa.$iķbywcwWbBZǫŽ"Q7-n, `s0 GCh+bY޳=Z2Zd+q CdT1躧s]r]=5E[Gr`He~N16!HK'$R!H= `;oy+*u=:5՝2$*X"ۃ(iɅg_Q(+q% l"㧶G!/z^c}k4z=paDs$l]F$H'&R]nޚڽ]ha_ۯ}.d9M3{e~W")io+нv/D6Wt "՜HeEh;fz774݃뭩Twkߖ%+ʸ'ehm\o{'g;k M8-8k ݜ v׻^Oj^*194]x(\*mγl[Vej!\=y"˕3wE$T-F=m'cUWVͼsȩѱ|ݨkJW}iơ0#z+v~oꊁB%x"ⳋgat!rCL)}|Lz)Y3H>7"ˡz1`.A $Ei2)m#pr?횻<@ ; tvFۗCo/$P C)A{Y7+.ߨr+uNv2Rpir'fͷ.{v&l>l#aCyj=MW qa/m %R10JBMV<xz)(0@m]G2ɶx |_NƯJ2pz~G%R(tf甌D*3=~*O' dd(އx.ѳ>x+pwuό.Q|=!𗆦eoQ14a{ R^xc;my-NiE|l`D*s zCd@ȸ_Pp*Dryg\g{&lY]oWݧhCq{gDꅯe,@M'>/l#}lr%az_r0B@GMB">]3ZW]~ ͠pO.ӶeP#@L/,uE\P0/+w܃GC0vm?$RSuUșAJ+{ !YbCkL!oAs kÃ>L [;zY=sOOt^Wk>B] Mu_@փe_N .{ (@֠~(_Bτ!Lg"/P72}Gx@ s0#(D$DoC lԫlArĄ9u {y6QiYW1Bۯ6|Fޠ/r9ʂ 6UTF+P7F! p PH9N)q+Pdju!BX_|S߷ gsqat/Ǟ-PPF 6fi=(RR ݻ@dHK{_ yZo}sW.I}Flα1*zCJZq:ll";ǀYפl-jh]ppf /_7OAgkźqؚ>U+)sƷ 4/D̤kbJ}m Mu?@^E6GJ2}rW{+#^|#zD/|=,z ѕ4WKv8c 3[^hc0Afr\HjVu\V;2ystydas“[U6G,vW嵶QVr'?@OD!w2,u\KnFu= > #.2Lto|  "Yx;)EH+vTR|cU:?7D*3vЇA Mul6Ϝ{}q )ʜ|xG [a Mu#UVrFl=`r"9ف,> 1?Hٍ[\q_*̠f#3ŐY 0u-b `vKHaΫFLOTfLFE7ok44 8$; c%\y?bhFrS+p5b} HI,@ᆪ6.hh;%OmjCSH ܖGj)-Bn%[md8x )C{;)_]~P*<`px{)ғlB mwk$@cg$dֵB!)vRd5g:{~wݛxd9vO{3MҤ)(EjXEdQ T7dQD2x"P@G (m}I̼yLK]Y9%c+kBibTy4.U6.aHH FڸS;WQt"bDZ͑+6ՕHЪp h]=0Uol>\l2iIo6;ѥO9D*sR{_gi]ݟ!>nޏO|ZC͕rV.0k-QX=y_J'7Ԣh~'tAk~SK_0f87)-@F(حmf:79 L'ghV#vZﻣ|( E\><=pYI+<ٝsh<(qzтk2.z;Rnfs )ܻ p*ه>$ɕz.%p;Yn(+s[<8#lH#F9euV^{g%0kUiqO9;, ݾ޽XXG5 `^:زe}\ғI@S:qTrm|)ިk7!y`ћ\h6yx] Z!ڽ.D%SVߞuՌΙ64v"qmew@uNƛېd|1Ə!ZvHZ2P{EDv!8|?,1ZdNC(`D*ӌ0r9\f۵yϲ]tSbW:)v~rKEH;wFn2}"݈pߍsKo<>_>O2@1:*ↅE5]K}_Yn&,VWuƪi !Gb$6"אv| ^1]/Pbs,A A~b,ybYs O7#$[6!>M~)Zn*Fb%{l70z\(-o;f_0`mK]͂a]0_բsVtZc*D:^ FUwMvݿA4Ys\6.Nj؇xX :B<_NW$RR9$7|)̮~Noml\Y_/Ekcp:?ֿ̺Z]ZtH Z]: (j _ƹ2-=QnXQ$mDhAd{:f/F. :1 ;iۃcL> ?G``*`8M(Y˨vB;~b yu1g}!b?E>فHÑ||z{]CFv{$$Lç%b#1!HX,5xNBq5 䎖J'Wnc0bhl6 AdpV4RW.N/LRP~ӋwORׄv!!f2Jl4Kz,:w߁@b ߿1lD&Nquw\.P׶BLpz@*g$@h%h=.ZWDJ`^,i߳/{К)̎Ҷn!Jt2o/hBl8k]įk7%ND[㗡!YWyR`c纪~]wWfӈٯZ;KgA]ykpSpi碒lU}_oaHHw%@wZBTtp@@* IDATZ'(KN(qI4\rab flY*9H"#%.^j֧;wa {̎;:tk6{@J]hM)Hi1 RNgE{o]؁KG& "청ϮB<-HecѾoesCdm:j|nH'oO23&z?6]?J'om춰~?ڿt3ps4m6+X4l{GwOH2UHKbBsmTf2x '!\:<` 2+>#V7>w#&v}CGq$`5]>o;q 8W|OWPމv~)ևc.Ɂd Y29!.aasZgٱ~lunx*Cׁ QsAF|ᅀ|Fj) 65/ZWNc8GncϠ5փW2,B}&?+ la .*0mB{%w1ֱ*.E;rqw!%oojis`Fԃ_>3F+.iji欆frSK]H9)0HQ6?cg$h>!$ XE\Q г"`< ހ=ϕH Am .} 3Y㠜Y~\L9뙫|M`*3&A5kƮeb (LxO ^~'C{|,MYOw~]PſF?swѲ}@JS`Љ؁oG*He"[Ǟ&R/.h^!F_?r/FG@Vۃhn;k [i?ZmuAkYS@J?nJw:Z_G2ԎoxUbSTf<Һ_ܛAqMOoY4bD¢k/!y .NFĶ Υ;tYAbʟG?!a*da i?p' #k@/\ &ɣ lҢlC-vo؆Q1'!&3{H\jDl]Hw,CV6t2~l"%ʜz#ac#m"xc.{Kۇu;ٸu!僝)zjCa40@Fz'1uZs@L`;/Y\R#A1GkͥuZ'9/EZdׇc,Q\;rN6ΣL9- Ӟ]6ѨJd\e\–I)6y{<.̎>|lƸ.Nչ΍Ǡ!_`^BTa}<oR~oe*AtwDCP,s 6V`ՠ8kG"#p eNYEx?n3[nF4W[Әٿjc =ʤTڧ#%a'oI2uA@{[X1f-ݻ?UbO23 r6Z[X ol"WO"'$odd۳.h Rv@@lGU+$3돐`4 kCxOBH8>ȪІ܇Ewi\nEDf: {| qξ^>Hp>abHv_bsp,sFDA̶1/ 3o|־/k>1nvkEg#ʺb(q? \Ni1x@w)PFZwYWrM)Fj}%Eg|BֿR$$1AY܈f\hC." XSl.:l{{5x  `72w;^!0WmsU`3 gQ45vBkӊ%A..0~I6g_q?&k 3#s7ovL}ъ'y9_UUY ?z-%X4DCwnW Eeݫn\޿gWmͪڍmw>;**7VUzhEY$@CFnaȮM-jh IûH FaҌnTf8rN$9R크w۹h?;uDߞ@3x%xEF)>(hKQ˖C|T߭nxS؏+<ҮMK=ŠHs&T1r}.fd1!P2{=| )'#Z/)}UD_ ρR[ ׂtsSh9D/qk h7?';3V=!v?##dٲP@tD*>%](PֻI`e;\ÛY+Y3]ht9ahʐ D|0t=R\. ZOl /id'ho{{W,+8vDw[Zک'^}ëHhMCk{.uRD-M~Z;a>F"k׍D([z_2許o8_i6ٸ N_ҵ#ZSK$wĉԦƟ8mh . zY 64 *1SVV܂ͭH:uO64XM-e%| rɦۼ w44_uN\RI$hH0N'RPh3.q!R|3ƻ/xP Yˠq,t#֋kgtYmq):+]'@B`dIG02خ;!`@f^#|>'Bth]WpD\= x׮v6nG=+.x6 3e(wV!p0bdŜVK$@|r;v/ \N_gt_+&$,{n_Ct.ULUBnQXLE.h=-C1smލm&RHeΪB/_7ry ;WAn~ M%u~PT௶3ZD5Qw@{$DG}{D:]HenE7Zȏ-?|݊\E ժ {iv^<vFd*6k1j32 ܫhH8|5L%l߹d2b1YH̴1{ЁUVΟ9x D@$g!$>6?1Ðdq)=!E ɩO';%R(~kw|:[l 7 *^l/Cb%S/V+ۜ#_^.+&_n뿫s:9_FBA!|Qɋ'w2\X``Hx9eʤ-otsVR|=zC" P{`~Q5=tE"Ċ)._ x孂+M8~7\ţОXFޤ2}cP_w_wdy XbgpVyrJ޽ҿ=h"D/T $#tY=$\ Ȣ`qaeTa<UY D\i> uc^rEkf85gӢ>|RN|}&MtYM|r@YFo@4ѕ"u)W~;ag{ `SQ5pP.H9R(x eğg̿%eEk; QHڎIk/\#;v3V"~.wgq|y?"kv}sfߓN$R8RTl?$XPt2.$ ^-GRAd3tcs#7y覍b&R֤ޭ5g_ Z'OK bnfs`Գ׮J-ʍE4;[[%wwq{,3EBޭH;~rA*J2?nL'㏤eTi&A^0v#Q5r$޲b>tPT `P^݇J:ֹ{˄{=rA9l5mD4|FwcK`2b GCsVȖBUA{(;_`ϿC~o_w%O'z T!z'?44_мuSIQw'c;T(Vlf5M-%h (eZ?@nGDTf:(E .DB!,OgA%U +fZb#Gtlx4()`#糩gxn5]Kf8 ڏc= V+[f0u#1~?D[uvk Q:``FɹE"mHx="PQ.ѕG,&}|8h7s)@S2 v߯!ODɑ_o x+pI""8D*S0}RrCti}kA!s;lFNnD Yb]Tvc,ymaj̩ Zˁ_=;.n+A/WYj !;_ϲbl>_籰[/yZ" L4'Ra|1xR}!WZpa11!܁(ޭoOD@ehB8^Ku 4"& ᝈP=4IV0<l]!k31G@LD 1$CZ56F R;a7b I's+Vn̖~|洳ߍUHXw! 7X-}6Vl7@.^뷲`J ط/@Sw=K iXEh} A2|PX/C0p&}S_#`v-i.%S0!Pg C-['[:Ջ .cZwqRSKZk֟BHjh|c"q!cк}ӌM-cZ0bnb0[!E=l n9YlwCJ!"Ք f^z^osN;m? lyѻzV+1O*  z֪޲Tym#=#6Ȏ[s!p//ݙ&}>җϱknV9+V)κU7ګ.S€,w[I]f7bQ/: ;>!'J+}./[\mjNZhlB4 txEu.8NF`k}{5RY!ZWDSǡu, El҉d yW߹fZ:A2Ә B^R̚G*YUATfȶ~wD*S9.27 Brj9ݯ@#i_t۰Wo Z'}9f:?OlNѾ?"@KuHK:I:_He>q$w_8 9; ֽ8"|bpрs4S[N[0V;1xK _޿5T`R=!Ki*-2d]43}'>hsy<$%Rk+W|;G Y[mNxhGiIW @3rWD*3l#s%ɯ-ȭ$j#4 #atzvo^;_i9{r)l#pD=; \,l,OQW9|f9Y[ettobr-GEh .Rkr>m9;yz꺎5ӏ(Zux1,ZZ#xJ`Y:ߦqOnVC^y4lM-l{GWY,_D6' yџ#ހhb7+.5%Szi{Y:< Ҋ,"%pVBѣ.)X֤s[Ei@0B8sݥ( sd`3mr}hG/BZ,.;;lGӜeDK&kUmD@ú %D>x}h\UEc8r.yS8CY?7#Py2#lL]mWp o툆kʻȜx͖DuYF oHlh.* 9bRqmV1q) ~ݭ421ˣ3xX(`%-w [aš#6R֊d =UiwP{GYS QHe4 !UiFix`UI4hg? /~[ F (Z GXj@4[MH~'R,Els!r(A`g4b#AJcg }1f$(v:{zwg7w͚SV{''RlNz?{m*z# S IDATp)d@֏js8Sx7C0_fW:!&wQ)b $X\ɍōX(sԉxp5/ "Mx5ѫ"6`2h21h!кocHi{CZr5h@V.| .%w7O"ڛƲ9>t҉sK=e=ny;Fl.`lSw$P!7Ƣu nx맣-~[3/+#٘g}"$0yf54o@ bzh;ocda¼&sFdKs DEJ{ _,{xǑ&Dl9!x3 eK;>0e])t'bplrځ0Bc0wޒ4hAq+U9>Ni}KQxJ\F@gWc"[rPRѡvRVOGa$ 3, 8a7\Hv#u'!' ui?F7[WzAE/B28BcVuAkHeK2''Rq`ۀg+CJA| ovwt28n`8*)*ɆiA(Zgv p+9&wȃ-mf.];_W l@ ev02g P%@&/IÈ!V7:h~b79Dm(~]y 1;f"i;MӂME q2rWYȎ*`#HH}Yf"P-818^ ɾle">@݈\CIGHeL'mL3xD*Ӂ/ w> 1xRhI# W֮oĻ Asߏ2MAB1|h}@W!`+4.qSf [ J7{C0n8P?\4p Ue!wC%MqL4k>h 3l44"a.Y\ _lYi08sUoxgm5He|4͏ygVCD*s52X?XN{ȍcl$_=u?m]c[S}5v@kvy\HxOVS>t@zVCsaп1ձb!!Ԋ!$WZ:>-Y;}gD*EXO2KHegEB"dRy߁cH0/D㈐A.AiO.BegDHkA@wPP|ڼOrG!sX_ GHp'">1!úi~oK`[d}BL$d:/!eD*bc:1wTÉT f݇WMz<$~ER;"+, @X:6~+m@a67Ov.rlz\_ C73G.|rVCTf"p |X,w$ - )5j?jhyC|@xleAFD;{jX,WQi>ojji|ٙWѻI{-+М?}-,L>rfG͈~zgdᘈѝh8eK$sMӰoK{7"h82y:-KPޕK^ŖEiKdՆ]+Y.CsbK.ʤHaSdq`;7v.}]g eE@!:味b<3[s>`Gʬ/ duJu?n[X޷Oؿ!J'_Q@<\W ZL ZG"eUK[X>у۟/Dx^)pD[X.h6l Wu_.%{]Z嶰'xj4 6[:fz 8  gmsrATd->HB@!ag6oq(D(zfh z V݄C̥9(&X?.EE16S8F t C]ZfH8kTw Cl]< es< 3Y fr96esb}gEV3o[Gv<_roژNo4bdӾm1>v̴y?ag C+9l,7 wLP B0'# QbqYNonA ǎdqi3b TF}R$Dk{vF|\Z]2 1*$nP-B赏Ww-t27l]53Ux4SNƷr<;~*> g 9W:W<4w--0>]8jHImI퍏x0]ߺkU-f̰a.l{ëV ZښZcHκwK6kQQO >)spA|OXt=mOZ~ϵt24ʜhI'ӐB&D@4Dtq޵y(DBnp'p| b5-%y \rU)W]ލeKP˗v]8tB.s9|"O >DNW ŚY V x8?\zKjCyQ$A<)>F:LlVm3y :yh >9+ ZE2h>1Hr9?- #kCC?s' X -vn2b7x:@w p|J]?!S,C<ߍb޷"GE;bևϢcmH'/ P}2lC6&{ 0\c*l#ؾi]第9gWd} >z@uBHah(Il{4< } )2e)g&`}LW4y|Y>$ ꗐ5 I}fãzkwYjfdI\6bѾϣ9}Lg54yk`h.<᫷_aZCxu#{=y$o- ׾b+4ߛ-/7E`?>Ȯh!oW>aD"^rZD@#Hrlow ,fh#{eD\aX ,g.qflַWK{W5YnA}=UisIlC`eguG ܃z]]1!#ǬOC`ЮҟFu"3Ѹx"ʜN!WD@  #y'=O}Y~=n=޳וH91lFp㒍,[w"7u1яs#^(݄i9-L\1cWC (>0%Ra)h}GO"`evֆӠƦ}\cU5F;WWiU)/>e}| %,$T5l/hCmSwyE(*. :GFV.k{7qٟ݆Y3ݛr{rp0[oiX٦ƢoD Cf FOC'aƄ!_E=o{rƻ4!^0we n=bXY~Y>D^xvK\\L؎-ټHexd%f Ws7~Yl#07s!`>]":<0 A ೶*s슛kUրskGah{xKRcW=m^nF#ƚo8KT΂ 3s@E P# o)=*vgR@t RR+l/nMp \lsA\>D#^_\yMw?gjVm ov\tc;wuIrCmM{ݬTGk>yxMeu5\g=47 _{Bit9R,@tgt{y(8XQ{Dg,Rݯr(hy>,·`QC|'[E>%#/TfF:+v NHeZ玛 `c:_,qEb|>`I]Z Zh"ڵ4R- 8k4W7-(yW&934dMokdpѶ-Xdt2>#իqHe>HeJTx]h",ǣ}O" Y.#.:dYHzs:ƞ9/DTw^P g"kP; i:HD1[ũϞScNi V A޹kλ=H_ʖŎnI'?[\o)GL3;Q1(EȿtKh$]1|\$Zh}1fYm}{ >X˥RBL m޿\I$_A`S\T3\IHA LϢ{]k홃n|Ⱥ+F٘7bEb_L!b8]P\Ǿ\(Jl>rj]۷X+G8}gњXcgf4hZX-X~~KmXڲ ȖGݚڃfe|Xh`N?|ލ]#6+]w49WjhC|4'/מrUW6l;9gshly o!:dT >xI6k=􅳯y߻]Ӽm IDATޜ kB'(sr. z8=_:EIѢM}u܄|l6-A$?DEd#Q6"wi#~pW΅0Wl*8q0|Cv_h⑊!R(B KxR )6  룋OFAv AzG@BA`Ξ/k;f퍔O#0 +]Y1!Wߥ.^}v!pU"))8,[+wqk ״C.InAЁْ9 ?>(ʟ}UqM3v(.a Z\ Z>pG`m>-\3/#-/0Cn V/>MiO{9_ê6 # &{F"{ExX?w ]و_YLHY*q>ӪoBV~Qt!"v<ڻ^F@He@#M9:&N99߾}L@yD'AK~=faX]`؎xh=ߵy_}*7~gѲsJխ[yۂ&l\Q `ES*ߔJS$DmzߣvP،hxbO%:!u3DGOf٢jzY&3:ۆn }}Hj" y?64:wC ͰJYCtYj]g>u$K8Ok Ѓhᓈ8W.Ɠxx}S1}yw'/A [Itfd:sj4nKV9ķ$wE`'hEncnsZoՃnG NNkim5V{n{{?w=֔JdXfdn G5񽈁@7 1! +npآlQ[0AhG"!}<_w#F?ҾA~dۈƐKDĝ+E3$0+E7?+w-sAlAYstg ! 25}؞gSyyOhЅ,. 1Zv@ qR9$\MƮg81ֲe܌h Ό@ֶ%N`Xo9iQQ hm]6 g$w$`2d-D`i3ZV#\ 'KR,K^r^{ (3aH7p$p?_WZ?@I$Z5T90Unk=pncsJ3X1у\}{E fҟT?v}+? )u#}O)Ge\GNa7hy$t;5*@%Ż: Ż͕ RŠN's<=ةu>x W,AI}û,gu-b|"_A{7lnBJCcD'ݞn7;g Λp;P6lB<\$-lhZG x;䢢1lFˊ =ˢ.r-V-nj VٻR+FQ kJ%SKkg;L PєJKͅ7ҚRg @@a2>j$` s !t`{dMKr,_1+eĹ)3#{U#@}G&$AL^,~9 ַiMvDHp>d:S,s|-|;Nˠ$C] @qd:3TWf|&1=r;AT.SV Y~NF Hyw_ĸAA35,@%>wm[e׸T+#lNDLץ_v8TQͩs$|ƾ;Ƽ֤6tqBbl.="{ wZ]LIݞfE m/ަTՔ,66o]~ T9j|ͽV-lx>hdGĢa-t"7|lrYIEs7ܬ44ය᭗ZqAf66?6eK$:z @}On98bzD5·#aYT |tV$'}Νם!l'p@e rA#r Fd{J<F /6]~:'ы}reџkt׹P+1G{f|͹҅>.acQuMٵ0 H2c4[Wb!ɁjZh9pH}КsXյ:"|)xQm{{;eڀ@ܔJYvG?$ݔ6#八?g`}x[~wƹ!zھ=oNZ5M/ۧx,7f](ǗV6LW}"'X CBږ ?+wV⟮X~/\0}ncotf2єJl/jod:SL/_ྦTd:s.Qۥ"ZRDP`(J9"`9o #й_;g"Ea(䣥<"2Υn}>|ݹx~3yLv]x/ 0\VDXW ^ϹXMC>_HѵZ{Zďg9c;Rh@XS wa#B؅!=%r=n#6) OC#xɈN YSwƝR۶mxEձ3|X3t晁EEq E;kޕ1B$Z2PÀ\W -ptFu!Fz_5CnooS`rl!Ra;h$,!rVKz ~OnN@킊#pl,+/}*DqL; ޥ-At1,b`u" ( tJy'"p]@Q@jizϾHn>xf+_ԔJ $ә@/=L&4$ә[l{ok5G e*d*B7v5YG܇ҥބ>x0Yh븣sҖas=YV\r[Y6ǻ!Ps˾[TKt&[읎x63í9ec"UiEB'Dn=.i'?\ܞAZ.+>vπ% B`_'"x9Z77ۼ#v*gax{T/6w>޳k_>QEuߏX[ q 9)1vko̕R$}{[G{~@S*eG>>cuMW|5H8<:.QL/ݏa.1: щ.s,#yjTU].k]ιLl;@d/>iN\~ٽkjʞ=)~h6BAN; AcbKrx[p _i =ɑFA10ۋFXg#&b:Q xNX>lV"דR, jq`҂%yA{*"*]9yvӹm pߐw[o{TK<C侮jY,o/(<  k(D}kc ƕpHbg+և,R@8e!{ |y/+i﹧gPq5Ո8{s Jg=ѬJڢֿo:@f<;_et[ /lp ^'Fy/$g 3tfCS*"{d2Tb{=5L g ‹G̎Yk'RٴtB_$B(Rvc0ckiPB.B+ss4V ɱ-loTn X-1Gᑈȕ ع/lDU? .ŢF ¶ $.YD\FD]r:a\.}lZ>ODBA:QmߏDi?]̯ߎ-~->XdiHF}wjJ%9d,0ʴgg̕JK3&ә8Yؼ.i&6w!Zm~gnSU7:r,ZbD!k3>2\5gq!xӎ=s2[KJa5Z PkႭ0_ g}C7mx6)Xێ\Ng!-'N]+}2yS*я|2y4&9: ]mJ%6ml~MMmɺ;)k,kZ`0V.s!a0 tVݙLuf2)5ZS*kJ%~gJmКRA.Gʽ<Kg|sZ_kYWE~|.'^ (< &t4F9:I%]r xuy.a:qxUa`uŋ9k <}v@}7[ǃX2+} #iCl3k^Z?X= 1d=tO}ֆg*߹oo۸+wI]0{Tε87wsn bwj6d?!(v-0>hF}XZyF"sYRۥ{i+()A9"޳1lGDJ$Sez x G~ bP_G7"6= !#~|͍6$s'4S!w]Èo3I3"A1{8$;WO5N3?Bn*NFx4 1M+(E%HLg~`tK:Iy=^~@Zcr$ϲ5{1ȥs͡ӼV16ZE:1ģg7:p1@-y(G8Yh-NFփ Ny=_h)Ǧ 71d;mg]l6s}S?!;hxAAco$ z;z=۝Юy^$l￁1W!_ߔJmlsйیޫ6th+ )Vۃ'<{87ev0R{aHvhdpAiwA4h "<- GJO-Fʏ Jm~ RX(L "1Wm5`ɺ]sg_O4ᚣN+\\YnzN_d}uv7r?[Nt4rOX9[jl|VA>?~Lg@$Ĩv9Pl$bVm(j-9plv|(Zl<W9^#>Nx7КA%lxyk]d`wG[Κk]|ϼk%? ꩍzjTsguR7\єJ,2ed>i#DS1P,#:G{nxժؒulo'˨gu@y%p :}D˜wD X$J)b= `Υ{v=YnG;'tzkol!aq$W;ѣn<8legTesnϢY] ֟ G=l5ܸytɌt{gQt1 :n4R`^`Vih5}e©J>E{hI38e(oz_U=]omaÆɧjqS'ZzWkCہiغ͖hH^*A;@+d:sPٔJ6 m.? ĸVRC|ft'bKͦX"#ێK r %F@r Go=D z<"Κq$[#?"! YB/;40!L;Qp0\$ƧB_W"|c7ܔJѡ8Yd:_| !9݋,HHiK'M8@A[d$~$>/|\gmPD0ȥG:`٢[Efg|~)Vx&8~\#A.$$wgp!bέ ؜.+\1C}Z\ABߑflq!R S.~0evјDS['`iw}#?ʛfft~+~Ρ.hR9Z D{'/\r0}Ȓjwd5F-Ǯs1#U|ni7:ﷱ OEm^tłGZp\6 D]R^ﳱfwUDIFAJv]xERe?rn'|^v!egIFt"]d/{r.Y\wºM=/ZBa [[ENyy˱r"ow#C[[P;^x l;3>L˘c!2 tXugMNq pu}:)Z_P_jst/M{`7tSv?ޭXM}nO []>}X׎WH1򈸺n2Bl4 7"q$  A uA tDAcYxvv6 &}!: s'e @yd:13s5>FVQvOmxPLgB>LgՔJl͋6|U!Eskw Yiv1lza8Z*b>XgM.~K~ET~Ξ5gɫDїqh#y%ҨA"RCyi)"Ͳuq IwL. M"|~;I'e㳋GXy_q6p%RG ݑbz?_RvF;(\(a>h= )RW=V pUComa+*8PnE_c{`U^0›0]5v]uBgrD4{Gpcȝ]?lf߈h 𙜆;㫵\ z=,$ua,^W6L?̮[˶T@`/IH8}g2o(B)F*b 6N3_AQt1SWF!\Ѽ!-Hc6"fg_\wjz:DA g7.C`z-u =ܸhmcӁ6}<1o[_?הJ|/D`k3)\buz'!Hh%X t׾v2K>4 X$HbNg]/%әb4}j'J=?h;b.iM pp _,F,:$!3exWhg9wؕpqMV ys}O~xswEM~ ֟*/~l1d68w?6>w! e:RdxDl.B`;g&|d:;a{w9T6Kghk޼{Wgg)E;30#jYFxZgV.M5K-l:EZh VwS ΌFD1f{RGgw.#Ц ]זM ӑKF*̭=jS)BͲF\۷fs̈́:#" N7# B$ c@:jo#mxvBkk 8}#hp>ѦT=| Ea:3m1/!PlϺ .9KTOAC޷! 31ckCZ~[SPnkwYV߀{J &T/Ed|P#eZe 6gh/⧎N3_ͱwuؚ O"&+rt}$,M6 |?ڌΈӄ;]%@{ir:^_&ә;Rm]wFIqϤ%kv6ҋW{̎<{1p_>.D"QR \!խ=9X?QY \qU4d—VY˗ ?c.{ε^|vňHGLL3 6h+ Uam"D@P y;$|&VB%j >hx?0/e7-Ż.ŃAoj+sWだs  "N7G u]7;]Ah}\gX;jDDo@D:V.~̼;ۑf`CgɊ̄:G ")$/9c?r P=`ړڴ tS*1Ĕǡù#"So}v~įB"QLT gF{g.QuxAqjGBNx4"7_ Y$ C;mHsbFqZ_t* -\A mbsb3pΞףD -6ɨ|w\4\K2}1FXD~'HsND_]Q>יHb.8$P-EL7,CmmnF sPPj j^`^y-b`Ǫ #H83 =e䑹 '⏼PӔJ "b~*4/66񇯝EάFh>;!Vs}Y^6⻵HMeuw/+SNZ߲ng!<6lşjdy8 Q Y9A{hnd<0+%mH8B\z "lI|ݣѺ1}Pݻ ' fp("_@?ݝ5\p_rsy$!FmlG\ DHк Wc |iF oc >Fl 44{xݷCS*14

h`:1m\@߇о ^{= #Y,EjdcF2|v6Jo"E.ut`YV vgS*17M:{o%ә:-C̎mlL3q z#_uG(ˌWr>WՈސ+~6V[U)Xy?=D*䐖WD>Q.9-xގ>Zf0+>#wػ5_\BMEggskdwk *\9?ſm .29Ѹ_YE E, ݄hCP?f8:RxhpDA)+d\\w5s ݴȁ :^A{ ws gK;WD@J(>P7_эhNN[ r0 ߃xKP0_[@4VfS3c}1Qњڡ1yH)΂GؔJ\Lg*rF_߯m2RV#jmaC@QQMA^?q,GNF _ h?g1el L! zu2wY'"ƍc.i8@l!q6,i~oEUY5z9!,0&VNxx7"8ms}}&6ȲQ\> J?ge(T75# J@͈Xf ";Y5!FTlzmȪW5؞{{bqrև#!4ʞF/ocB y$8?b{^_㶎IOemnF"tr}_GkŁb |8lA NuL\F'&`=h}r$x}Ǟs/ '#vwEkFdu,{-pz$$@:Y%wnudLΎS+K7+){wP^Hf0_0dml.7sZbE\NFC+ -(DE< \6\Ɏr/"ݽ6ݭ_<|tx0H֍tF? D̜5`٨Rt)B[/2Yė]rG.~.K+y~hДLg~fޣ.Y*~oΗLg"H> ,E}-l`Gj0?w<_RCIsOry< MmFG@}:(j > D#Jj/xY^D|od<7xoEH A` w:wf1Hx~>$"2oh㟆^"UhD@7HwZLFD~ļ#x E%B5)}=å}aߍ܉B҃w܄bbJK2Hf} _({xIV:P5 W^?v@  SF΍6̖۳OE{mϱwM #j4zjl.&]Uᶦ]ǧpZ Ae9(\ݔlt=c>*< ={=v/7(hה vO=c(E/W9f}Ѓl6e}U7OyC9o9Pe%̎ml1H/"p|ƶml~ `ncsiEwu !*<6_V3 n.d)hK9bv9jzG'85|ק*sCEґY Z8]݈P:w ڰ(+uo^*s|jtOr 7߅OBQy:_CBUr2k28:ہhXtf{>qS,E onO>; 2Dc]~?eĮ'xp|ߌhs1hO]bUx*lޭj>nkvHT53eKlZ|2˘lNG{a~[{%ә\wUs*_Zi .Ad:[47JZ[g`_&#|X2b0!>vfyk68KaAo˧|4',hqq r].XkzQs2MF.z7h a$$n>K|T$h7DE k"g5m45"ȚGDB!H]ؚxgg+: $әx.DDGn#<kpC&fg:@> bIû/ >av.ErDL ޿ᴀ0$(Y$$"1*gV#ayF P߮D`k:bv"ͣm]cDbllH~nFa IDATY^? EZ?d)7"w1߿߯"hǃǧPb{f`G`nx,~--"|X4;msoTS3$%t vh\m^QO<\Zf>v߼٩HP(caH[7h!%y}_yݶ}?ό^Ok;~uԱc?Ehg7P_<=6̭x1l0_q7~<<) ~@0#h x 9xbDwAQn7t]‹Dswjc 3}gwU(OGtf1;Ιm<_mϫ1,@tiqRޝd-6'ysEWu]"_n͘{W<1ʭΓiD6> []jA|l=~6J~Q+:RtPt`$lwϷ+u}CkTU˖Lg~lJ%xVڳ?>ȶr!hcҜU+@i~}ZWG%ݥK/|o'|&duX?NCgc[E^Z5!;a]ܰ˄~~{jIlѢwZf |rnc ͭ66Gsג 4^sR A@`@M?2{7 G?ruVʊ\Qu#}μ̣As_a0|X Ҭm.쏀MD*|& c7s-x)NAg]jMpYuAz0 >Kz]fKZlawi3g<kCg?k _0|U'} dCtD|JMp*S0-w3\l.YĻ:0ѺsþͿX?[hÈq]M׻Ѭi埛)i{I溢bX)f)47-/8p0?sB, #.хb؞ mPBrs[jA+ 1>h(n 6H>j)R6~t>h-Zmav+*94g!ӏڽH~>"}Uc<+v&DA1px<"gaUF"l8mmkfg&XF#0f-A`f4"ҹ:_1}gguI3iu5".9K#]^H V,@V ! s# ߎ@gs֏~6_ bx!@"F9H]ckG|Ƨ Q!"m_~h1 ,C`m",j%Hx Fص@nӑ+O,btw! d!f݂,ibq|Pti]Vc}QZVXB5uM (,lMcxmK"gQ""C3Z=;{Ǣԁ -uncsUz⨧+.++gŢcѺib^YCd|xی;߱rb@aX`MպB! njy<6@kV2Y^_O?aWǢǫ]hMؚRUt _뺋}Tb}|:|OCt|][H3;<љ @G"Sљm~u#u5rݮv}9R6< DԤăAo"={]< Ktы8x7IG!~ف s}$x7>F`s4rcvssNƻ;+Tl.QAƃ[1мYoǞwW@w yS87˱tf K?,ʻڬcr"nRދdcUHDC"raat %[S*;7wX޻}k/D|?l]lPB=ҏ2?}{-lXaV[Y/j At6_!(0Xya9xKR+yd:'RȶXfɯR%ә ߋwg} eWQD"PsD\F (Їue "fӐ[/8[B!D#K*J٪[ml#EyK|8mWѩEL | "!nE Y|Rt EL;k;.YRvf6!~:19g=.LqpnB#x9D $hlFywžf}>]q 2 >ъKTE {yk}#b+O=|Ej|K28d+!+4$Ĺb.3RtV3>v8|agh)VO3kS*+ަfEx+~soM|8?vDkd\mkIXߠ9 kg̞sXuvT^qcsM@X4hW*y-'"s׼ᕫK;637/s!yc~kJ%^>ǯvCt&Lg,+AMDeGsшu6 tD]#.~r[I9΢=˻/TZ7U,+oZd\`)"4KS܁ŀs\S_IK{ӢѴOA|i=Ѯ=osZyk;f|37 Oqc`c b]b6  ^gsv:[Ɩf뽒4h;oJ3HwNg[R>qp.0epsdhI?T96x?w yU\4XE>ZOϷW: %B!9nuɈ\n#ֆ"w OA|ү_k?ʞ;ؽ>hx[ذ n}Z/3g\wkk v. .Djt,FJXH0= &әD a9DTCkwD xM0|Pm@g?6Q & "@F-\rKqn_I8. 'mǣCrgN[׍wKw.>,At^ _8{s=39:Lv._aCgIaԄ!K#QCnD]HmsЎv<ލ/c֞ ;_]x/i9"gO# 9CpRQuH^ ;܃Ȟx a%r<>?h,fNn>E=̶A/Ȫav lL=h"; 7 i3^ٻ4>nk0.$ʹytU.;tCBO LgNAZntfۨA R&LZ1[˾\_E3lbN { .rx H+qʱZ._Ɏg{ק#N#msk]66;cy-?66|!WPDЙ.B޶y37bmN836tN>BRFj8{}!g3\.-z=>c76"}Z|(P+ˇ]jME>8!yk2|О}$>Daoq.|lt; Cĩ$ %6}Ȳ9D] 8RMaw>WTWS'ے4цl>wM ;!>|ei .^ѡ+'"c ~`X :=;LRyYVlH/X@@]DA" v^:Ae***j /` !Mɶlܹ7!Oi澮vw)w=|O] `rĞEZ敮-?"ﬡlt_~DbKfZ+t?ޮ#w;#ţϠ=nAph۫Gj$^F?Z?=_ AcKSɪ@N9B}X hO >1"󾦰q."xKﴝ`eRns6ޫ3HkQd@!-$$>3zDMІ4ޥB3D+ ڌDDD@qM}(G!+8m)DHp:] b{ȅQ6xFtx Qeuxlϕ޺eFB"QJ^\bC;bL6G^O!Մ 8W"?KAD6l͟Af1]}'pJ' jq|uPY/Vξ'qj>1йqɇ#A6h~(rlCIet1mĨ]vTHC|mM_NY N=h?Vֿ"|n$DugRltt5k(ď#7WƢyA߄8Kf`.,PkS4jcϐ_m|aU LzN=܇Z]dEHk5wƗ̲6ђF|35ak!U+ʾsϜc|~hi+7N:|GLݍ|Kc6Ek (GzZk$N+ֱmV~Mh7"Vxcsl@\QhqoH=oUHQY){#}Ń H(ӈMŻ>ls@Cx}$رg3oΊ (P.+Qq'~XGokdpѭj;q8f#uREic; 3H!G.Zxz/wI9C$'lg)6L*0~)#'wަ_Oa3[ ɹAkt?E_vmf-vP4G`ys7Y!`"[k6| Ih/e'G`^4 H wcki\4mZD:cwնS,L* P#LgDBHh"bۃGrGB1\εhd2 bthZ(hFtŧSuE{|㕈!r02೼9p!<@x$pt!݈n "xBXT1=wy'GLGZzO #a bB ĐB$§H1ŶN56 .\N d hsټu&XmekBi1|= gc8ZZX]TQxD/]"weRq1I66oHhz=[5v:qM.Oш->D O낥Йy$:g"FS5uA-V2ڜ+T :#: U͚;c_+3ba1@_wq_T=Wꎹ+鳷τV@}~ߚʀHh|?0uǣuY(oGmڋ\q_XE mOX`N iZLg/rtsbn<+F M@2T .k^2$(ZC%Z| ayh8:߰wh*ND֝SPC*!䃾J㽧\;VQ(E+ DHlĻ#u :6fn=k#q~NA@k*@IqmaBB,B-?qw=J\zk`@"͹s}AHGSf!ֹ:WuZL&x֞b;ْl @|1#vDX29t?l >c5Nʤo z[SЇ$j%>hce`{%\ Y6lŐ{:ڳkٙ!B;ҁ0'q&;>V46}~sSذ8ΎEOMnAޔfsW"Mxs"B d0rtp|\$P܈^= ]rv3M?"~m&$O\ tQJ!>qDW# 03Y\r OS`r1\|"`#*Hd""¿DLx6b srht@VŝmF ČZ;1q.l9@ٚ"MZdmJDL"av럋ik/DZסx+ߋd׏s"h5觅`˞m &5QHa»ʹ!!x|=^~%^+ (%KtvЀu> -D7=[qDW!e|V^(!AGWt[cmnWK&pi߲vß/;wņ}gsVށ4+'`wgN_yo:zFawo(V2-T ncu l9c~dx:YsgT"ZX~U*@4ŕ(gۤ6Fܺuu4W}nzO^Y|Q(}6 i#86J8\uML*q a?D4o{\23oW EdQv RE2P  ,MvwALHMZ'@ʕRqHxS|{⦰៮]68h nRa;V(HA1yv 6lLΈ;ՊM+/5 O7 2?4Z _eR~Lg6_Eth#HԏىhC B&{G=1Ef>/fn$ ;׏(ڿ#XuoufrBBsi?}ַ 2u$V+6 /ҋ ae|SԾ#<:pNY2l9z/rOXK\/E̽i(#P7 bq T~ Ύ@^B (*y>y(Ҫ|aEQLcHpEn mnܳ'@B+فq`s5)φ.B8Ҟ7X{$n$ c IDAT>B$"qq}AךHGŘ3!R i-H ҭ鑝%I6VC_zuH8 zʢXH%3sW W~5@C ޮ/?=( w͚; %<:?H7u~qSFzBVl/,Q=sS+|:.1cUeRYwz-+x hI ~BE1=ut`$1 \ޏwF _J?jMq`YѬ{nso%pm.w-l.NHsjWK^z77|Ap7lƿL,h~AgHa|ĊsfځcO C뙮gsb37ޔ/vEX7pM}_6 ;zVSЎ;lMaC}~N@{y|,[`u+\[XU~R+2#:K>>h 6uȯoTӪ?\_4~)L_GaȊB/e<)7"EG劂DHЗbjn9.+R[P0Jl}^SGl3~y_ƁpKwmS=dqc*#@c)H3,3"K+m,.+| nrI&ޡvJ Y0\sqxnتSX%DBXؗ+K~q/eR׶ުA.D5Haу\64:{#DDރn5#l݈^OOr\Tgr'WW tn7k4O#z0ŃjlH.u|YE!o/ Wn}볹XܼGX_wc_ 6mB4_j_݆p."1y=&$8ҳ6?bk9:=^(<J"&=/#_"Ry*G̯l-bfl~ȝ@JlEY~lRvʂ kb> Q wӶx[S:b`S08YC{ |uZJF;оs9l]_|nJ>Gs0^ŠUTopޞxi?7s79}RYsgT68;6Ol1TooR{3oLQP[PZރiˊ;B}O~< _mʒmj96 J'C-tDCf>1NAte"  'G'W|w6HKRNhF+vr7-v _ l %-ASeTr;h8^~}Cʋ:I-F | ]/q݌wmT9Z,go/) ǀv|>[aܱc!0D|X|! Hq)],W}mn9 )Z?j={=#aP J&權1v JA‹Jw,.~.go ^>h%. tEؤTǢ ;6UMMh ?t1Xtv?dN&XdI.f#~2"$hE@N$p܆@| gH-G[4gȲPJt/G A`k3ڔ5֗uHCv! BG<|`sB}<HpX>gA m1ϝ AY#1 fZ?ZYlڐS6g9Uh@ 6"zDU1[` Сvm׼'gRx|=mD 1#E(Fz_f$L*ўLg4F A"[c){> e@u=#p3Q͇^~]b~W2#XD#F\ |9 X#mg CuVXo[^&?r ʻ:83-Lt"`gm쁏" Q_?l5 ѫ7,D-~&S3D޾OIeTOy?/!Q*g :]!0hG: DXlk~| c9%EY(# VcQgMG| v.F`dH{cqɰ!OBtV ߁Rޝ}:Bû:[j}q<`u^'K_ume/9S\*ޯQ% sHYkE+R;>$LAS4 { ꑦZH KăߴVvSghص*EƦ72"䂵"_Fˈ]I%OU!>ܻfpe0"6Cq՞5^Q "?Ξnה\ 4v2->.{koBZJD(&#lD`'Dwrb֗1 `x6Ո܀D/ :[bHYҜ&6Vs~96Vкl"@pi=R;p5U"W¨||Nh/\@{Иr\k\̜>itVH)uaHC\v]+EunpӴ[ʮ40o I%F^E~5} fEOHYee@Qchlr~" Ӑ݈VDcM%C 5K)$(F)Ct%4w-A `@5{;~ ZvD;;`%G6~FXyxīVE>>.w]7P9a9娫Yb?)طgߕޮ;?[ͤ[`[$dGʚSb-V :֗Jk{7E8_q@hAX_B` ?p#0wd:[20N*[ݞzRB. g]V!9XM,NAgrt.n 4 D}x"jBk?c1| n;phK` 7#xc1\$>gL*ћLg]Z(kz H/#"ՋH8VD(6g}w߇41D8" K/;6 9W#+0=P3[[ȹ:JQч :Gظ9YtݿY z@\D@b$( J@L${>D<HZcJ-HP?Qk:>D!y YbVg5/Ff<:X_؄!DwᓈGڝKtUZ.-U2@t;Bֈ4 E@O2u2"0¼ip'ݼw*N>>x`c~3ϾH&h}F5w]h-G{t?oi]7k' cl[=;4K}?\pВlh-Bh?AXdϪrAs2w} #޶4Р2IJnihjw;X k)ڳlTw$ -*%J)m7侚:v̖Atk! 8 n *?͞x1NkAbvqW8/ ;ݜen.DE+R)"cl.)T7zp3ySh T=i"%JWFD~BxSێ0r# x0Վeu^N89b/Fa2;}k :>h"a;]}8)h6,y݇}l1XnZy+P]TSph3*'X[4^ҳ)lp}:D;%` ^뀋^y Nok{ch' t\f Ȓ?\y/"Ni >soI^TLy'#֋6A>-Gۥ/4N+ Ԍ p\y6!!\@k9g}X O@Ymd=ӥ=?{3g#i(QrE F{ZD\m)wnEFwN#5TL}`&Y'ȤWڜLga-  IJAa);6g= ErxMvZ?Klmxg!҆ 6n,oG7*uF9zZF &ydǠ5۸g#bx6>SWYVQ{F{U3cd9.׽m^/MuVba$`1> 9l,h3. 85.A4Yv#5 E#XZwtlfj =)@l],qʞacg <i^t8Aňhwisd@ιGv#kb"Y>zO!޺)&6V==%$gt|7>y'ۘZćo=va6#l=XUD( C%ucj5$AUGuʠ=EHIy r-1J|՚†=%ڿM/=Lg (hhI.b2` ʴ1_ZQ>}?# $W̳Y5~mM w йv5Vwߺ'C KTksWm/<;zM3K|wζ6V$^U5O"bu1 K(**T' "t7faq6/Gu6;]=b{Op ѹX83vr܊/^Mz6./ b=3}-bcrb̓=E`eXlsY0ԌɹU|U B+VֿhAP :CHhEz=rr}ū}*>yJG{!йz&.H=< ΥֲzG,݆ #T[C/HZX]nyr6*I%Z2 2EL*ˤ_Τx2Dƾ[fɚÎZuv>\돜r1z}ٚ%5w' }gu/A4RAnk%$n;G}"A/$L* H:c:GeRې9 ю!of{AHs?΍x9or |vTV!DNBPbXO4}1DzǗtY 8[-(Al}oqM?E H|/om!%b'p(޽&@hľ[ox%BZ!˨T;fd`>\@_ɤ}tYĜϥ4NNu])~zqKF*KBLzfi#yu_3]s3 :s-nwj#m0G$LDE|VRjT!Dې)}ӎh get#֠ѓz+2|qW}&Z7tYŵQI%lUc.vepx4K h-,LG Y{ i09 H8}/"@{kSapnXpWnA"|.~2 g۹g-/O Y}? @L`$dn@g(j/{#voCv9!5L>g"NXVĄl\o5XV|X/_Q<H8s̤ˬq5nA)(j+ڗ[[vG]/L|׳J u^ڶ'xP 5x**ֹ.S+lF!Kv}>N̥ok8W<ڛ#m^07#Ҋ .unW=w 1zc@nђ([/t4 +DZW.[LGW&qn?6;J6D@Շh8D'XYq?8uy qٵPc!b!::glC2֏x7^䎹i#N\@΁Gs`cqE0\sM|퉲#I,gUK!v$O77}$Cs9$ Tb.qbx''s3p쾓\7 {Sd:ZSt@6|u MaoRvKɰޣ\0<ޮ}W5//e;5Н6ұ=˚ a!WT_d5 {mo;+n2֖I%^gk릷_ KFb ^+U,k ե :ȹ7P4:mD\]'g!bN;"UѾ|Q. R"F]L*d:{ 0e9-/"ZHs"r|v@([R]ks_ wnߗNcu\v-Jt#7u _(.FK¶*'LgvߙoY\-J<&bg[˵9s䥅OϢD✀ֲw{d:{ЛI%35+5kޣѺm4qO#8 z#Ԋz|-~fׅ U#ZJD.|.C  !P0u+#[Ɲ+c94Gؽ#re}[_;5#0)}\2o"<\ax<~1Rn!oh06 L;Eoo$v[ko%6"ք~JMJo X4~cM|3RԵ"6V^[9mr{UлaGbGm>ǵK~1d. eq(caɊ1gBLdD!;7 q #ˇ=rѦ| HC28!"z##ej~sC0܍,LAD>CLh ^= in@]6СqkH b0qq?Kl"Hl~,Y2 !1!f\\6~W)Āf؜1Z!w~l9;o߷^;l٧wpwtv#J 2)d:{=.W]/[,>X瘀?}ې@3 Y<cX~cc8Żl}[q_#3eL"˶W67^۟ץ&_@pv-o,d="~hYjq\9y#O͜>JZG "0yr;So8ѢN|=|:栐=E51x7(^wLA+)09VoU)y"؛ 1Q^H {6-S{(b_Vc\-D ŀN lJnDMR+n^ 4!"xHd?@H@/q$Jw?I0OE!"B]-5R,ՈU=6S.K6"yB|tAsoAcJ]ۦFL)l?/FʇMa;}e-\WMteRp/>_s@h{_[-gf  jrwdxHiÎْ\:]\"wt6Ik\U{ 8r`P&XLgB2\a / X%HED%mЍD 1EDDuסr.bZ?V| ً@P\Gb?]_ScGĈ;HYG[ OlQ1Qk?9tG@ATFhRch܁?}o'Es7`g[)6l,@ ]T\{8F|"0F6 P3iQO!+7b4G֗{c=o:EH.w"Aag vvq_=1d_{բ~HCfݽO~"|c(.ǥJ 1| KG|iÈ@`u2 hbkYI%}ntK@D;2sųuTtPdHAː1߹jW EB~yP B[3]윕j `@yJZ|Xt"a{ ޵ mV+]3l1\z [P1|ɗP Տ/چwm<- 6ĝRy"/ﰾ/3L*6ΞdOeRRf0ED;݁x^(uϮلeRK%HykOSbϋ㓄 sKS0ү8l vBۈK{){b}? 2?= "A$Zz7~a>8#hAӀ+3-olvS[^%2O+CDt"D<]\DkƑŻB|4"l[Y#2L]H|uSo̙ ?6i>MGC|៌w"evcd $F1E!V-| =sXw.l"6NCͩKw{}׊glmGXCtVd"?21L><.U#p:Y Gι6"f]3 ȍfcGB{h{$h#AՕqV큣PbڊLK0'+c_3/nN~ )J0 Ѭ.&xKI%☓ė:z/gw#A=ǕxnD. 4.zrC2F4^dzgڈ[GřhHDKI늻xV}ؼ2{|ޝY>O@뗫8վ[ {sYXOfU&=PZ~";S!v C\H]XVչ`G7 йݴL*\8__}A㗑lymm9KhIRUUBaǵTwxUo{7ETLW s] iNg%PU};elt]|eqs֭4"5 FE{Ԕ7/ꦢ*$D_HҞmퟐ^\xߑ| Ehndr]7|+^܏u/;!b>#c>;zuG[MsPqb,eH\/CvzkkC1Hc[b[/CV t xx p>5)›{=Po#dZ a[ Lx.d!sy:ـfT2mL*`G%(Ndwv7ײϋFa-iBB ( z ԉb_?\C` W{+b;\EV<=wqsѻ>v@}:>0tɧ\up0&V7>ը8mc] s%׺Ue!bn|,Ӗ/]]gY]vtsZ]-W'_uj&x2 xr EZ({"h#Ձȭ鷬oAKPHH(ɼdLH5G:7q \Xw'~i t@+򯴦iP$ rH~&>(+֝Rav>~!*y:JW3]`C YW69 G4E G.yD:P,cGm`"iAd bvAtXӶe RgmL(wucNgE &bMDĨ޾K8X@ bv|b DLVWqi"=(c?2&M ܎dְm*[i62811c:];1RçCXl/ߵ6#"b?P9ȺZ4ϫ2 f1/]ԢՍc^<@/6 tmw{Bd:{i&X8o" R|x9Jm驈ؽ9zVxM;)],ssϵ#6)Af xG RE{o%OgJ-^ځ($z7 eKK_h×=3 b=&9Үa}ޓmػ!vȴmJwdRrrh`MIe + O{#5[+7G;E̽EK <54G#~"ڛ%=d:{xLGul>lUT^2mS;oNr 豫9X6O(}uQ;0/Xuȉ}?N~ 8B馰!y<_ɤ#]L%=Hs0hB,(D1f4W#Ƶl~p)\Ad51GifNCl$>:́~GI tģ7!z*.QVFزqNEٖEEk]++8w3L~lh-rnd F-٦Y!B,fw!z=s^=F`QEL4<dz02PPoS*w\Bdl} 1b gӈhiK.AUk{/MGٜ$jwғp &mE 7"Xccf?1kE܏w d;,"AFs;b7#kO3iA12xF}DQZd6#=w[2fj ,6wSʌo3EkS2sگB{߷C*dl\mӂp0RX~K4s<~sىM vS^q-t{q-Q<>/\GeuNG D\_Dl)q=r~31H )GZAy@U7N@ο3 |,@?mk2VgscyH&ۊArUެb+v,t% 0/}E ׳H\;- Pfq?rVϭ1k&1` E\`Gq \Q9HB8WX;-.#P<g {](8' mbkr?C{؆e<.v;zCTNi=t-  =A )T@:ϐN=dr* 9j},՟ zʋU~S:+%sՀE#G:j#Ûƍ j7*}^}-NzDl1t v r6)זA^7둲MpPאb$VEC5E< 1cͨH"(^Z38!x\<'*)o1guj6r)bs y, A3fB^Uk@}T;HXv"g-ӹ EGFQجT,WNGFqFĜ& a_1(ib~V>TYn[k,y0K^,s1lNy !GQޅRXݾ߁)ipi<[n`LXYp託o|/G{uꒁh?y>t(R\O/ENW*a|\cA|XOW-"X" Q/7CH gzCO澵O)+LW!HD$f#[ bːqS*܋lS1ĘZ\a됁(݆2!EWs9(2~y1[l_b,@SC/C\{b:GpgG@Sf=!@_nύ6e8:I. yA9'ropx:snČk^Nuۢ;67 mEQIfwE, |fr}"*E󿐷$ĘH!wgq/]Tzj/h$FМçqYy<p ͶlŽuo~?lG8;w|NHe -=}N@;gڃnQo4*G j$%O5O}S4=OOEΧ.ěgː0fߊ %kHX_%y\$ظRh^} ~9 .7?qPk^jr9\*dSrZidytÍȁA;._) BFi*<>H.\,p~Sf-n2߭BN_!ZfE`*q5b0kː)sj-kQ9,_"Yo"GJ9@L9 dSXk<~a?a{XȓM¸^:?O$X!&A\ S1{Ux߲Aټ E߷֡$*C`C.AH9f8pT (I{3yfL̚ *#eM0C?ڶ HY|^ pu`Gwݐ|4bK0뵴~{kGfL{CU qɽT9='7w_@ ?8Oy}aҼQ/p`ǭ=ul9Qmo221,O%bǓre|G6ǥ ǐwTF+2f#Є ߶8$/ e*\cNcyq\ ^gV7#~l#ymְzs 8@w}2Ȑ #Qon󿬜=ul4 u ef?XĬO){Ax9G\j3pBJp >..WSo0S?};)<J#Mi:ғZpѾ%h>XhM>+NibaY6iɖ K3L`a߸:e^E)W}T2ek욐^uu* 7K{5=cQ/S2L߸wo/ک ,K6GQl)ʂ62D9Kslx(22|+ĨA/Q("4 ؿ%^ U0M-2sK^lƷ)CqeȨr`oI._ z B-b6kwQL٠\m|lm!3m[:_.,k.<On=k?RA\;l5?D` 7.ކS>^TPsQT"0Y&_}"-'#3?;9oE Q.($ƓD B)ddd"E|5p'c᪶F*!yQ 5H=߯#}!y@'nnA9|Fp*ҫi <567M2FE7A쐭4JQ/YMқa c9L ȸ{mMϨ D &'Q/D{&( Mz߆`Kj #>ۼעε9T -f`݃TFQ'-9 aXA/e{9H'"cA.ByPRMAvļ0|E@d:+}&bxe( fnfOqhGѷPY|XdL4K9 ֽQ8b}&rXP(F6xG-R 3+RkƠզYYבTk+˸&9i5tbG1cx\g=-NrP3變~ZZ[\-3 Nl9nt ̳kHu@9y4c y#kѾ`oo2M?^0MԽg~Ҫ /V<">R{SPE122oOYOAkv |ƴ0s1ޙ\6~}Պi}9}AGVѓ}߹|Z6c(''x\7Q-bJ0m.Cp G϶yɱE1Ck267zpmԨ9oG 7Ȳ2\$dWpüw⯵H؞Wkv7cj5VtJ;rd{/FɣvO{Yff|GbV1> V]g֤E^22ЎClR{{)^lu_$l =l #6YPi3(_&|s?Pk *B=2r 8st^Z<h ˇ{;CUc VÃ~ Lҋ6G;=h2Lu%L1hs@f Dzhϊ'5B> z \shc.G1HoCpdE/ks8b ny:1eYQW:Ly3p!هƜ擈ڲRX""Fׂ`-6^L_# 2x2 y~m= HhyK:f{[ @*W5rY5fV6_0^O Ew?ɬm|h d?Ȍayaa-Po K@Fly40T۹ϊ!ѱZc1Jl/d,n/}w֏Z|$s a$3a~4s;\5sW…KVM쨯Z9k3=GRfڬ\7z:BM܍e$K"5ɡE(5d>?2z7!>]a8BA8w}qpd7⓶r\5|y04?_6d4 yT~ Ѓv ns]sGV;(cdۊj5iBy$k>mqA`~n[)d]9S!HEG$ gt-r\,n䨼!\kZ1=v?+|f9 r{#z?ے)&QޡNv_5(A{pdֵx6|N흺9B7.@}GL{1œ`P5!^?!v $1&k(kt1R$A nyz]Rˆ|ӐPElnČ&^Q? \r3?Y+z=28z!9gqQ9ylRmd܄` A2A'{Cʕj0|u10^nA>@qXkėGGa lhQ0L Xm20R:Bvjm9WPqPԮ1 ig)gj?V S.^ T"wѿ *W>X+,_@xo};R ^=DlaMj8RMa~fi!jo[Qm=x@l^[DlIqg_L%bOϪ dh%PѤ1ȳ}I*˚~Gس/w"9|v0W!S Պ P]6@>L"$3_-+*_Qٯ@Cf Y蝋"'\5gaa։5Ьmq^UIKѻ>7Y$,tn/GQۑ3xӵ+svK$~J^'t<-Ð)#_@S,YYo1Piș{WL;:V9fHO=93AUk9G#gu4./e^>z^v#6DŽv֦O_JĶzJēQ x ۖ=%泧Ѧ}1]1ӌ":2NDVB WTcz1# sKccx zBoNꑐYz3O47 ZoU*[OȨ _GF!dL4/5&P5 qYcyS؂3(/nYOFBz=p,/DQ=ĄKA# UȠlyf>2#f_5?DB`u>z2{>Swc|GpY*;k v 4~CG|yP R7-@UucE]Kïu.-yh՘@=~ݟ;_x\&q{ >DP}plۂ%'y몖 ,M_GMJ%bq=AE_]OZRA*=; ʟ'0 _Et{/#90#ϣNFGmJOAP-2vA*ak:X'q\N\6\56m8Eijr$ϻEmZZw `pؑH.r:Ð^v̺@"mt>g0d oē1 yu!}(iZp6S_V\嗢^&,Ogz{!dezک ,dМ@/5^3m-> $٤q8C{XjR|#H삢29dgH܅+F$U!%ƛ_|s~!"gI"ւ"!_p f`zR` io? 17Pt,$˶ynCdLW HYH| 1F$x^6s̚yC/~= qYfmqo5%ĬO̷\wOČ, ݌`d՛kef!!eHhO%Z =o-)V Ȑ\d9ٌ&JՆ!d:O4JĶFwXGQ2 1[c3mBW@{gk%çoTgMhq]R?\G б8wFGV!X"5ȊUO%zkmOl饻SsN9F2g-ct0+mZ];G;%-@ Y "pWQ ܪ@2T"b<~ 9kFwP1(:̓Pdl RAJ\ϝKo^*~gYC;t"^l X,q^?ߍ'A #odw !HCN1iyBLD!ef#9:kƵ 2c{BYh$#.FfLQQK#2re9g7}vi IDAT('adFki(Å|>VH\L>BT++LCrZB8Si[ ު&Yȡ}*RY6G7ѳ]hw?0  /{VXR:݋)׵4rOz6 H;(74> ;e-X12T"zc<b4yH@ףh$O6"s'\2綛5m2m<4eƢhBODVۊc{u~ox~&7GQ/3 B?YzO-/eKsxuKzTx* ^9-_o5Hlki'UR  l{b6*G OEng)?G; œ$O%b}u?t?'ӏ|Sc7 ֞ȫ~6A Q!Eq0ѯ J+C6_$xj :H Ӌ #q"޳HOE|8 |A<\|20!'!q| s-\q8b4l r~|~pņ }ӈfƶF)["uyq7WP9͜߉ vV' s2>"'M~z ch=2ҠMfG(d\543^bXJQ O6G-䁽Ƈ|i6W/ş psHѵ9XBdDla<zDހj+b[;E%%/P);41:)/1_^{Qd+֛smE x3nz.|p=##7Z"(ב_Ȼ8h fL_6sYD#ߛJIJ@6L߀߈`5* /g#9ﳛ_\Ay 3d̵? h4Yy^V1E.¸\rsq[6v$-[l?&gD'Ԕdz2bh sl=`fַJ~~?'go܃^1p`˔W[YJy-uZ3pxsZ‰轪F@s lA|+g^G4&[ijUh $*Zb5osJ1>㪏[jDJ_PސJdY-2 p *r)|1L9E"e3Eq#>9vE}.zm%H#xlS|?Sew"{w"VE2=ɔæwp%ul&6VBfn#cpq_dp Y@x8 QPG%- w7L{^ ȱ<]==WL~tx Dx2c$<= [A!(⻶ߋ{mRZu݋}lNCf`ѥ!=|(26/@kf< ͱ"Qdu3K=tr,j??Kj+ګkexUW2~5/[9}8(o.R?iG^f_j+BgsMޑtU'O"CT"q|%J )ޗI%b6I2> ~1Gt_'n zٗC}n||>3͈!Ed?wqBS~u(gM<8"seEo{Y~g#U0̣E_ @I9c@QgH[#!M3N!P }̸<3:"7E 5~:2|L>:N1GhY,0s] usͱcP"OW[a< '@϶f HQ(^VEdT~sO51zz 2)1E݊wl-\6_77fe9kXě҈؜P P7ppC;f;vU{׀cS0h[R< ɴ+ORX89qvCp#'7Rؓ\Hd" (bu*Cm0ZxS)$wm?AۤPt.Ȩۯb޶Ʉcx~ՙf\n3? \|E22=\Q 2c@I*n f$f|I-Ao}zŇѫܣޢ'uBy<%3߄7wyGw+(z-g]Jmy8!>ӹ4ACxԧH/@a\il$zj%w?n~h`!cc0Rߓ x2= )&1Ɉ%q^Bq =6)S T ,m(t{XX _O\JO aBU_F"f,#BBi'_1sVoX\2f]FFȸH 8N%fDFٛZ"0[nh1 1*s#JU>ͱgU d h eڊM11)HEލ8?5Z eF|P}SU8< 'DȐl@d4߅[p<R6Ƕgx(r7╖_FwN eSWDqFtd AQۨǜa2U#q?lK|ނޕHxH yD$o=Ph<0]O=U y~ohq@5AP27G:ڧ,۬edj&/.oz%vJʮyM7[ɼ>ִXDp!zRmupM*;9LC:JJ|~yR!cb95 ~xH!2n@/7n=GאGLFp[:99 (q5򔟂Qp)w@A[g8bܧ#ZU+ c"sZ|6 x9vGE~#|n9gbc0s^d~q3疚wV0W݌C 51kR`/ʣ(vވUAX2h+o׬E>C{q/dз}(e"h} ^G2`v}۫[81"mn\@މ"6b9r5q}|muc7^%GDcT p/ ǑuU˽}&r4;@q~iCDk $T"8≗Ⱦ50ѭ:4D|zw% OlJK#=3+֎ugkmx[}F 8'•z58 gfOoW6!mr4Za9w{rJ3QBh©D7L [{N2t6dZV윕 TN;~IASТ]~+b^6NdtƓ鋐wZ큔y|8ݥqr&X1kE=sI zݍ0)cCA^oz7הzA@KP! md5MfMڑmYS =Ӽn$ Oyl4Dl-? #|(ejW:k_`!]KCU~o8i+[h[ 6?VFڞ9(zU<f 4Yj f;Dꐑ\U \ ܄uR=l:[娀rZYˮ}..6G3=ֆ83gMrmTw e5)OlQ(Ħ0d_EQ+ZsQQgQY@#j Hފɿc'tw/ ᷑'f=H)"٘#ȷ5r0[>aa!H5M˴?t'nGV&騗yTKp2\q^OEI!qm#\œC)j{6ֻosgn=션:\C`k?BLz6RDL0no1!,y#D&`>ߢPHL`2Vܡmb#mCLĠo0} ^3 -dX\ل5B%kp09Y'ۖV#j_(`x(BG`+>vr{ž\ALofKP6hY\rs_/7F 9&1{y=tm5i/V7kƐrNÎ[7q]u޵Ƞ?wFvM;Vֽ\^).z~y0 ^=el;Y-ZmT 6scܳ˜W1sn9*rп͜5:*۪MœsCDuy9*~x*[9@WbO!r.r2ɕ=ln2r!9cZlK# rFX>o)ؘYƖ7/):~gs\ϡȱd "{ҘC$暱5lrOpSX<`6*<*.@ržm0JZ>A9Y#g_ٸf*ےqx2=ɿT*~á\ yFJ刱NZi B 6̮)YE\^ ߇ o#`M3dgs1qdj&h>x}HV] cJ%b@FOF9E׆~TDlN<~9^CgQ5EɆgXe~& $lL?$ċp9uH VFAr1I6ި6C mZdr3w |ݬf>S #i1 ;9~O%b[FdٲW i+rem*goE?tBXF3 x|S0*tݰc9y:b6*q]a>?1sL)[3@zq(pWG1wGFBuO#/9I/D~3nkGF-4.FB*Z #TzB IDAT62JG#Od 2cL5$"&m6c%lYRnD笠ʃy\Rf]BBĖj3&xVY#un3לdk .Og *tyoELE Ss7HOM%bť0C-D~Cl7RlK0^8WZ9h]Ǣ}bslВ%[K@|H@z`m2M~Q/׬җ-+o;S 0sp&+3v .Gm%rQmR؆f}MRx2}KQuµHn 9C\m{3W'Gfǰ\s=Uf [gur ]ƕ2OF`N&Y9}^ɯU﫺GnD6S Ku&cHI`#EhJdXH9 * js ?Ch2V<уPE(l%2;QYV0߈H@\n s^YQq?g;2(W` & CDFlgz V*&z' 0.@G+Ps&>R#H^ -}'ujB"Fi@m#<#rgS.Ow1vJ%bD앝Ǒ>r9XtK%bYӔmF8AQW2e{Wp.?2V\?+GxD@ i2큘pދdd||y p0v!Ny܏BFs|1rf~ףrF{ `٨~9R7Vaɠ5)t+ Ƿ$hOǢ"S"3HBFI$(b3r$rHnߜ7\?!m8~1g̵l"8<ܴ 5l//p`GU#݂ c_C:G2pD۵RO%bom)O36:bQC󑳬(9L 9'[eAQ/SVhG"~(um`obpdJ[r`TorPb?G NlnAC!f:܎ {?@іPȣgEaᒏU!AQ(2QeE3nsQ)SPW9QBqE& H@ǧ*bp"Rd(X|/\BHu&RPs~&AsA8P3H 6tcojU`(,se0J$~(o']u,IK[V[Xn+05Μ[m2is,a\C* ,èzy2-|'ޱZ鼖ҹpm-0?Ezֻղ#]^@FWD{ꚫ>wE|ö{Q+-E==<O!+)WEok"`ۂHC z%rOl臋wsӝ|ȶ(oOG4k$ZC6-2d p%f\c\,}_\[Mk[nKz Wdz"n?QP2'~{ ݐ*S?>ڶ4/M;a'[KBo –!⺪۵<; 'Q7PTl(b_FQ5)!dpUDLdq VmJC %渵((= )rb1d*M0c29֚ ޖ`E{"/bW ;B(B6U#C12ɺ{"ct8q3$z\?:2s|EV!hf7k(l JĶ[Kw]px2}x/]{=+ C(&_DAJhO>0 P/n K 1]vCoL芵Y7?;>J-z䔀G2_DsD|$#q,۰Q٪ࠁVa^e[y\w-v<>&ٌx2=)nkP*}Vt5K$7[==뽏.Uu)G;)}, , ^T"`gQ4Ԭ6+0 1fub c9-򸿅FcE {b5H?oq ҹ8Fy6יӳ-|e| {yAE){-~IhcwԿ&9hj :^EW0TQMW#(Ό'Ӟq{ Md6`X.]]ǫA{d_Jd x_}׫/I (iAs 9!hkRYګ9 M#V>8+m)E ԇb|5ĭ@ % EѿoYc˦–*@BtTԎjdrx(ޅ燣Ѓh6lB0y =7K q ވv(_{SplڇP dsyk6Evw6J9hB9Ԉ/tM8G!Q澹7]q϶ o{ES&]p4Oq/poB֎~jhjE)"ay( 9D3Le>p5Ɠ烕RR'_$mG W嚧:$27t\7^a X]iPw :c=NQ﹎WQ5Uf?Wjeܐ!QuퟖoUέmobDe|ᆳ;yfIuCG7h~SfF46tRLFѢd 6(H3]Y( =_f 3ѼϜGASO+(d5AJФ$ DlI-H%b&XecbKXA%Nx2]OƖ o#q pg*O(t d#q7;? ?}ۧmU m\G!.H r!1whd 4(?|f#A5g}QZ)"'O\?s#!(Tf#z:Z9g`oy_漏C7 {PGMb (bD*kɱ8Z[0}/^ƏvYldhxZRƏf]ǻȭ`Mft.L(sf5sr﹧-JG}im-o(|bl|=]$^5y&Ҕj4@sOZ㻑*T*aHsmAXOAs/qi*\xfPj#R+sE:7zox_~aːP[GO׷f54dF,#HrZec4Qaea7Q4ؿ?pŅhp"Y,*O%bb=|U(u-c:vGso$,y*Ay'(NuQݝ{ b \ 繠ƫ͙8 D^+"kt\G'>=XVhy(R!-eRV0 ]VYFē/#S푐zdp<2Ǹ.L߄>(j*(L둋_ ^ÐPLEUEWhN&pN[D6wJU AW/E߉he V8 S,Z<^zgE5l?DhlЀ}':4&  ǟ:)6X>oE9ia7"QM$?'RtsxLj",'t?M3Ku)\Ƕ_jfE9q~ys盻Pɽи7 E969dҟmŲaH%bdQ)h}/("fBbfē'5ʽd(63[)ai!Pml "^o;j(39T^AmUˊVYsvͷ"8Aq/`VHiXϖ*G:WN%bmdSXW<>/Dh lǓ+h`m (=a$ #} zY57 'BB)fգ(³E mbu(;dO#t2?ǐ@| ס0 sD0swФ]t&rtܓE澌5 \ YYT"6 LOFѴyd:&cjf sލ޽JxCw$Tl ]G5.]~?6 wo'G*'.׼$㝐Mx{X, D5ܿ|,4wE@ѳPHhRߋ';ē7 oQ;"k(4߽GΣ8stXɲ3W*T.4_֛׃m|3 @Br~**i膙l8Z,[[2f}T"6{=y(jSJ sm =xd:X*WEm~znE T9$blw<,.D{h%H8U}y=xkQno(5n4fV$r&#a%p>x(RJˡhq9H3? 1Bm)Fƙl>hxĤ.J%b H0aM.}vI%bƓcz58lNt-+U:2UǺ׀5vpk?'ӣ\O-yxXn6q:^/]>d,L*OO⩃L!7m@⫨>iT\}]'e0WVԋbs@HE]β(WlS󪻃`Ĝ[.+ٍ#ascvѢ2ѫx2pټWk$qƏ~#PfX--R`Rw9!L7%VP{]Ǔ鿘4~JJo œtuk ӿnsM_fiSPz.?!8/:o{N=\ ΙȂ.@o kz=kQMq?X,I«'z gGMmַ>cr UD /<M3n`rD^[Xl$+T/pa 0ߡ(F矻9/_cu2~=L5llk5x=HH-JtG0 C.dPrH$OJ-]ٗ 5b2PDY w=9gj IDAT]^+`",!FAB[wJ%b5S9"NzN#<݀d%jp{(h?@i$>Db.['';D^5 "T"6ɣ(^i:u>~8^5W-x/ $1 @BLN&__wfTdsb<Pi"ZO\ŲaןJ^jD#+1 9T/z>E۠vjy-Y (S(fƘt>H=tͿQHT\Q~;Lٵn٤ܽ.Y,6|P8D=\OE( @t  mz!w)@4Df?VLՠA7jέEÊ=@;In;ۛ'H7鐵kow*^fXkH*e@iiՅΩ(bTڰ9h~<>BBg HcO00mg.DZN5bAkGQ,E }9vԳ:=OG FdT"6lS&SEBMۘN-oV9?grLʾt-܎DHxB ]mKwf&P]PV1g7٘oL"=E5)ݫ `Eԕ\El(,|tųet=WdhZ"bYu:PdT"Y(bqY4' Pyhļc\NbJf/EԄo2 pڊ,%G1-"H%b=1OB鷺m JľJZR% D^"(4 @`Uj4h[ЃxH@z%*-Fpc?>4V.DR5yDd@ ϣQjCf@\œ -1(u#jd|Hgum'Ɲzj¡NC̽e*Jʒvdgxѻ5~>w* Gt+W"DFUH$!XFPb"&ٵ(Z C; {8~b̠h{zB炰 iffx2vP^l]^b|Dl6XJ<Oo#mSuj0_"މhEh\ h=8H͋U] H\0 x܏bب6q@LάPJ /^'*}!4 q4@,r ,CjuM%b/o6t}Q$+jϼ= EVw{ptn yBO5#F("5x2=ܷwis8EzJ{xcmySTulն5!-sצF,3o7q3m} ]ηPhgYA+ wmT.,}6ˋHIKF apB[(z;ZF"5U,9b)B5dh0ٿu X,T"^|ٺwE4ͻOhBSZ)EAuig}_,^GYa?8|b=<\r=]ŲEc#Xk lVǐ(RSBd]ЈA߮y H}T"3RC ъ׌kyԜ |1uȶ](MQMWA'D}$5O{k~4^Yݻ E&jN0?6ƓKcD),5BW q::ޡ ^m?뿽W\ar+t 8EET69׸Gjn} ~lj*\[~aҊ  ]OW!n6UR"C )8N۴ w~>w.=YƸ]c^bBf*˛yB`x;r+nB sףL::h>GV3Zd9FwC/ݑJĆ~@<}c5]X669A+Dg:+7}>'w;\0 ^q$ZrұALy˧d2Hߊ'T"4Eʀ;iDlix

x??L"aD@z]rگ OonV5EK^܃R'C5`rѷVG"R,MBxÐKBRE HLLFw5+#ՅP/t9PyaZfD~G;E=d}#;B!$f0QK_!dE|B -ehN8OA ǣqol1ۍAMxGTg<ݳUrmT~:NC!y'owsOYȼ'cX+VZ7Ut/35HOϥ3hb*}v>c-Z-m@/"ՉD ٿ2fڜţ/nޜ-'ӷ Sثd*DPo@<>=cx@9((Nt#T"U·(6BE+vcP,V+];YVx(1u}3.AѨY~5g9@bzԌ2>jRj^N+T |_?4s}_ҟ:a9TZZ KQDH6<ڀn|(,vnmCzw{K:BbYKx2}+Ѽ&095#h1 O%b?4svG)6|'^_Sؕdz$3~4"e;G?L_ibsŮIѹg)nx7)W]V@Q1 عfXכKfui@BjmM| z/r8zP~R}Z<~EFRL)8 xۢt9d4$H%b'l"UWW{88w J=\1&+qu7fɌ9x!Q}oT+4 vƏvtN( ~~뇻{33~s=]ޚpCA[Q k8]p;ϪOU>+]j`nj>K -e ]{5/w|PO7J`?g1źԅwt#Pmc 95eK G7x|5zWt TѠw  ꘧]O6QT"O_B){q|5}Ky/+@|r`;PaJ; FnA1 vwgpb&BS.D\Ja{]UII Yolgݭ5 7("q)b=x4)|xfhЈm.4QpeHma7o]2Mp+fqf׫9YP(͑6& mPHT}"Q HԒ].Lh̝-ߦk"Ų=_LDct*r`\*{to@<Zܲ^B^?Tb[œM[,}ΦG<JVTDZlU:썚C}F R #JJœr4`K%bop~CC}=vϯAѻDlk>l„NAbdy9pQm~@u'd:ޡH݊]⃿ܴqwCB4GWxըaؠ7x7Gē]55#H \mBk4֠AVKZrƜdhX,LZCR؛ (b!09L;=؍1œ 7Q3~t D(۠HT=x*5Àb4-sa|$j2~tix(Gw\cX, 94zNZ,`g|Ӽ cWOoAkVÑ`T࿼x2]"Ư͹-<#0cy-s)yMmq/(}302>s(M0h}X,k+6 DNJL$Ѐ,>|,+}DFH|W{xXd#MX+ZũD3ߛ"?zxnB{0|$F CչYwoo]0 Pex"LGc*z] /z[I-%U gxfk@ƏN46M[$EatY3~g?#v&5f*,eUA'blXqq<5h"E7Ezx_.&z|7L_3R؂`g/c:h'Ӄ7PNƏf %vC<Si6%?Wr6P(mՙcœHE_Ils$ʥ~W"e8(*xEKe\ǻF$ HP4vλ-s1UbYXJ^[i?WOE&EqqHX-ž+a%bX\j*":?:B"F,p0.g82+yOƏsBsBעlsd:v-t&[@ohО[_s3~t>Wi:1BAY gh{绎wQbX,ǚ\l#۝Dx2hH%b=EJn4|QՀ2L%b_cކ"9 u7kF'18T,@Q\LGcVd>O.iPq;Nѥ)@E!?XH .Butگ(}i5nbX,X[ F` #PML`T"9#'5e9ē lKh{< Flc^Y/"WFEhtm+,onk?:)m0XP/kjjX(DƏ9UeX,Ų9aS@RX!L .2_蹇VЃe(+,_BM"v9\Yw8>8AǀԸV^Fi"aG]ӁzzTZ'Ub 3wp10ua@YƏ~wbX,e3F`tzT"=-"}(moȎ9[\U ^=5@'.B5N_ћxh9\N`SlD:P "(Mڌx{T5ŗ{GudAXo甁 L|{8)N]`AuZM+2X,bTZfX6=ʁ9ݜϱ$!qU.DϘmL0"$BRjb"[]+Ÿq|{4 &GEu?b]x[CbX,M+,M}{v{o܂U)F8YW U :o |꩞.1s. r=o`9ȕp?w&z5k^bX,enē]PT"vن>ˆu(6rz%O碅3~tx{z# lsSTUM㝁/ uC}robX,F6GfƓoo蓱l82~}¥/(|`r,P4"WzC@}[>oAfۑy@/`ppjƏ6i^?b-W5br-/XqeX, E<'=-NmXf AwoӱlHEٖޯ0

'17Rѹ0LAj F+)FJi/t:v̋9c70tÌmFpi~c\kwu-:!7Z ˯e3 ̈́T"6 ,Z$L_R辖JV7Ն uwWA IDAT_AWkY$~u}ru Q'$:BƏz"h")q>JweRC)?W(T"v{< /B d(bx>#`8*P>r?)bYYIm:*Q;](zdEؼ[vZ4mý-zkISA/{Gyh3æZ6{\M)/8N-NylW|'$txeɮ>$V+Glonhp=c?1ڢf!1 \P$\W3 \tBdSFT\Ņ ]n_' (yydꜧbX6/ͳ\s/ 8sKlAve3-+,U+JrJp}Ə^#wP/K(:R&+TYmal*: $X= dO{vwo,8cnxHm_c55ni_9 ) Ws#~%Pf&Ќ}{nbX6 Q˥&c'4ŐXmQ}|dB_/Zo,fxP\{ QumӀ?g,~X+Ld 4=_E=@z> ь}_dh >J?"9@55E q7pעvS{7k{9;gϭKBKvJQ(t+*bσ{mQ(!}i'l7bX,V po1*+:r X *WV\n㠺`44?Y6SlNC#>:Pd50x*G'c80u 9&B` T"Q6k]*ڧ+koUO7@܏۷ [ZV2bNJ挭m)?'{A+A{8/(U QDk!4ZAv% ;,aB v blēT"S x?'4ole(mcͿh0IJ yh~@*_w^l2`0 CobbX, qD{)}R~,j | Яd`.YYҹ(FȘX!˲I:޷4=?ANp~GOT@}R"{z5p `$>E"~TGez8crLȎCoWgZ$x'G[ϿƟ6N[o=sаꑝ--^]Pa'R9gP*Oݶ~ԊB9҂jpElHUaz5<x6G/[bX,x23PJz4ghݓ)6RQ MEK:(Uw`vЅR\>8hFi,zHoEuV ( S܋Vz#OTo4iݚA~zu/yh}o(JEY*k1ǯs W]\6ʽq){zh1MVCe{Nk񪿗mqR)|]]{*GK@UjqȉcZsw%xgd`6jX`om$NY&zu0L0Z<:9?:eO"GQ[U9r08[W E(GsHp<:8`Jj\ OvX,˖A%px'z~ R޼>Gȣgf47Z.Ųaeb#n煜sTQW+x-GGI ?/Z,( k |ݏbj w:s3snxJ4}Pً%0|MFu?CQzXPgU@}:PZ Ã&`gfzP@@*4(>"&Qּ^c7g>j+ŨFk*r$< 8u:/t-#L~(?<oxQmH) 1Uӑѕ`q7^IqZl+Qa`eBNGޟ:ȅn3T&;\7ݳ헞ܷu?4(bXV`/Q+r|ۀ,Bkp~ 2HBQ<kyWa~"TSQ#PPH6Ewǣ%:^usOrQzT+9/m 6iFvrƏ挅Ol 8%bX,jp6&~?$|ZtlUysI6P4 c{a<>9LLb&XBƏNE] #` ( 5|tӾ)4(uR)F(R 99HԚc݇"+xۚb @a0F)FPTSL9 V͹ 1ws7_~9FwnՂ=>WL0 .u_">t$$HVb~`\:y>8G;?LMH[JX+;ȪbeQQ\o%*6A@@W(( ]螴i3siiI#9gS8g>~wH'U.'""V=w3GBVثDo qUDq5g¿NFcd*xg%a͡z9"""CH4ģ0gr"d2d kπ9K> 5lT9Yv d4xxaID <f,MNī/ ??1s>uGm_4o#[]ѶzY>_Lqb/u=16ꛣsSH?Mz[eMb͡UeM{s|?fS-w6'"")<#f{h[seuW?ƿ ʽ7gh_ ;}GCu 6YP0:Qz3]9GOn{@i?jf)KՊO ޳t1 ^\xO6Pz(T4~ьA#{N8&i{7ὂ)qZD2zoc޻ \PQeMJ&Moh ~.[+W^¿S|ޓOOb9 4>qAd'KÓl cob\x &[)鿁歎_/ޯipkTv<JFjD NǁC7sSm}Uu.;ls粶qX4gV>Qݹ q%cO= 6Ym"""oi߻Qv\ݳ&־Czk *d }8L=sfw'%oym}c@ >X2hCugv*ٸw0.϶ +0ݝV8-z>ZhW*c/}x:@+>O yzq8xYSC͡͝DA-U}-X[8 uQdدsڸcڧϝ <`hc {~fVY7h@Ƞ7Y 08ZCx9x: d3[Z_"q4͡DNIcdP5sw ޏ@6>< 0_ u!xzɦI@-^7x(^ Ϸ{:^4pp&/>Xd=lE1*klz~j9xVJE>i}tʆŅczZSG\! /_,񊄻KV汝"""[d3%; Nw':F\\*Y*%2\+ʄUT=ܽw@RʴːPeM^x~v`N@\2sogWiOZݢ',e6ˢ_o6\x^Ie۞+/ҁE(;KOĿ|^/e-5RUdUtEdۧ% tja~<=͡:>iȠk=9J7_zƿX ݔ.JN燬vp?أi^C'нrKIҵx032<(Dl{^/_YUٳp~SğB݆Wl%;3ziV/p(>j1жn^]]+ N|0W/To־7>VQ_SxP9~ͥ_7߉I^Nj~u--ZV"cq\7t O)JjFⅯJV/B< ^@}3<~< =m̑v9dP\-$C,J>?iƿp<=&En|U!uk!x'$;!q, K> n2Ё/4m󾾶RTO)]dgq5I~Te_-4;k[UY`ñ <<ۢ9Th0k~,#y;p Qs ݃h7փgT-6\N܉g6.ƕ;Ӵ 3weُW6`ɐQeMc;cqUIN+ks^"<q1?Y<86?m0zqxИPĶ߅dE9 mn"""1ۖ*OYdtੂ=xГ";OV U;4)Ur\݉o>'OsZzȦ 4-OO(w xs[:i9KUu ׃>7^!& (\pҒec 5[DD5jm~, '2l|_ː}Z<ωPY>`dv]PW|`H"B5jS~q+%j+B |d g)vF=VWG=YqxsG%sk{loʻƣWWz۲+}f9^OXޫ9T VEDD6q^S{\6k~o/ƿ7k{ϣO>u%ܛ6 ܖ28ԃ%;(e$[_̒li$~Ax3l`؎˩?%.b+gnp/_p^tO֟e䁝K˦t#[)0 ~sGEDDx{C?+VlLn6L~=e6o2~/`ʚKs^ )|qoV\.wS~ᵣn ه|ZO  œDEH'/ԆlA5!UdbX4ҊgTkX١Kv͡:͡9TZ| < -߳k_' J_h;\*e4CE-[/TE=ēOS}W=t!"""g>mm-݋񇧻ZA>ЎJ_],;XêrϏE{OTC%퉺/B1l^u&xCWYpuE&z!^h~1.ڒ5xoH[ǤwIH CJC]ͳx[rEkQ__O;/:lț5ܖhBM 0[OvZE@zWZbDXcۀeCu_x`/տyտ級T]+-gmƃgYc> ]JdK!$'ٿ!ypGaInmho*Xv[e(Г2p] Re"S89[xI. xQFˎ{oOSEEۭ˕/Yq}L^Շ[Yߙ돼ʚCZ5/#vEUw5b""";\4a]E{R COPAn@`B5W SsTYD<9TYLU1/u7^}>=@9T{ WN,WYeM〢PxADx͡zJDD䭩$G;}e-uz>d- u5p#"9`V5~9TyeSɄoh<:> /nqp`8yG.}+f)((r |׍~Wg,>PWCDDdoN=R yj՝x*k.v h';N|,.""2DUYS!ps3Jvtɓ*k0hC@ij?AHUY5jO%""7,kf7y蚹[frmC}қ\ϏY]ϛf֝7Y~-2 fG3l!Z B9pp݀T*C%9_  Vl햇Y!p+}O3Oj?l_t3z~wβO f6f+=+g{o8 zB? xw~CKF_7Gr(pm I||tCf)<jt-z帷1G{J3q4l]tXWxf6XBWfۺmB7\nffVBG`LdS%'OWt` w աBiws{  |% ?g__S/BO1gBS6BGgd&~A2=sV=Mn9V9ÁI'و,CtoF]nf{Eۀ߬+5SM <2{5ezalrs1&Eo,JyTzO[t(؜gfyoBymmVl(~oj!_?]'of? ! 7D"~|d\fK: 3вY,{ C9n5ѶﳍǤmt/!X͈~C/l~EFgf`! !UeM%,?!BW*'/pw3 B?(#y` zsՔ݀?oa^ vਜUoGW#gBGp~^!&:?O7{wB&4\fXB2&㢛E!ޜ'xǻ_-lwM]qym9E7{_> \dfqM&tۛ.osJ!ŪL7Q̃gw~ݗZBO›=8PBX݋g{> |%ZF7_T/r2C^omدH^)S!EҮ@Cr<7ڬ&hf'YX}laυQOhq&tۛ.tl""x1/{e3{_ +/D?)3=O& dBtz3ff 3ffo6 xZ_7/E)|F o9pf6oZ`.mN!~q^ 8 30΍E36m6C@pD7G#fv8^qM~[~BS2af# |w?l s),: ʁwi"3Ϝ~&D.w""'w6lfٓxхR>:YmcfcB`]vO>0o꾲 m ]ϣ1PO4b3; `vsf*s)3;8{uXޣ`-Q<)l@S%#xhRk!MW !*{3[< . 4Mn1s-^A Akџֈ}Ӿ O>ځ6@ |<ګ&g7vߣxKx= բ6Cxv<`4J ЋaExjo+ۢm? %zODd>ŗ+T붰Ľ?nf̬OP|::!|ʗeSy^@ax>޻":瀒hFL$_KŞB8r%Bxq3X)9! ~DDdefW71mHVvVyWfv0TA;8BkKDDvR!x-,7;d<1 XH<|QHȅH(X""""""yKDDDDD$O`,{N(+nvCm}cqK) oϱY>*;`:tL\Itft^2= 1 XQ[x7v96k Aֻ)rKd5 /8梆eq7&T u5ɏe@%!\oGũq3->zBn݁[j~&KjU$f?k(\ݱkn4[]:ṇKW~~ϳmpyoy]sliƙslVŸnSOCw}oϘ)#_7Bb D88av8FA& P[8 سd]_uGʾT'|d"0נ IDATde5E-7lUMteG"|}E,m4f^v07sIHX vDDDޢŭ/< Hg> <ve{2d2` p)pv:6Amp-oLP\ܓ)xxݔn[@aRe=ǯ{DDslVŐ { ?xJ`(A–޲f^ʽyv۵Od(vˁ盾t{vDDDޢ{3ɄGӌ }v€KDDDmdͪ8`C{}Eɯt.X i{ŭB!^tBëƽY)}@;txa|D\ |g}G~%qLR@E:X<(8mvbAO2Ƣ]% .>X^UL6lQRWqUg*hԃ%"""o0~޼д(E}E!J >O(|ba穪]H`">ު {`J_.:vޑ).HWzY6VmyAghzv/HJ #&\Ł/r&J񪃕xOVPeM,sWTw'G^OvKDDDdQQ veGMw$_;\b6*`@PiEs+J&d܉--hN031ofrb$ 5fm4 ԣtljQWQwEOOW`jwZjG7[V8kLm1/I"ML@HxI$`J*Lq%Wh$U1g/ndK ;c>3Oq֒:] 5o8gJdP ibi0ndo鏁dvXatH~r9|D8AoI!{R]$+EvlU2mx_ުܔ ɜu i?އLhjtWKs.َ` aXxzޡ %&9*[;ehd_qqwWI t.(H@ogt)rܫ(~E, 'j8exVB6-0EvN4zq D|OȐKDDDdo~]³o+k}`}!]Ry^o٪ +yrلL&SW^(ib;&D厨L{]`ŽPq@}qsK@,nᩇx W05Y~_yǿ`|DX""""CW/^}&E:O`yO5!\3:38R@e%m]{]SL:]:\>:Qd⠉ߛͭB }K}4jaA7&~jj~ڷD!S-ɺD_R='Lsw{d06A9< fzMdE*ŽZ~Sc$$3[vc[x!f.,,Hm}c;>~i4pUI BҖ5\HgM{많 F?|dC]%e毼i= `MK)Fi|\U9JO ܃*]'`KL荖J'KNh[^^x_}%A˻۞o>f#ںǮK$|{ӈ%kOl1!!1q\jj5M{/:{o9c#;`7@D}d-;{~ /P?m)>=lOew6S%axJ@)\ehP(""oZm}c9^Iࢆ^GbYo{ڂ-S.cnWE2<*y v,^ u5n<{ee9Kd7oqmU [^xF%@SK4slVr[+""qL{Cy` oh*cvD_fϲW xPPU| %""CS-_KJ q(EPdp=W[NzXt[Zg/ͱY%xS'^.oۯ|RϠ(L`*HuI?(=|`>\W"""",AP[XL}+9epu3C&޳pxEjp9i75?:h#2i vT[X\  d wϬ9hʧ_Z=ច)z{.2P=/. ⢎0 OK)/?8 iN{DDjj v{vDS_tp?nPKd: 8-z,X9* Ɠu: RV j`PO@>pVTiw\Z5+"2|Anro }ePЛ\"[K$jSS<$cP}v/_o O?l0R@Pʀ} Hu&x>k,^}X^m` u5;չQ%GslVrꁓG 41BB7XF>+xuh7;Wv$[=`7(2D(Ƀ96kT3 ҃2=K:<3]eJeӪnv+aϯ([`>TeN:BA2ev`=S 'gΝx4|dTwW ! l u5Kj>q-ZZ:ミ^g%Wt$@Q|mSd(P%se2`H)&Q[XYa/.OېTyjZW[Xoeas"M`l^ծGX2]x007w>,_P5Lɕ6E <@U}`$LݏUW!Z7[cR!|g㨮6ZfI ؀lL@քBBOB DPBg(F`7p\dmqxrrydiwܹ3  S`8pAB7̑a~籈zM/¡:$µH9<׿Х/(/+rͮ 'v@7̌k/si9qhZxQ598i6Sikj'“Hnԃ] C3iDre9V%W [)WO:= ڋ2oANKqwȁ _nz|kQlHX`[OU 醹2WCAGy E 3#W8ppoSO=u7+NEh}ЊUb.ۺ)CQE3zdOW??HcQB"]H`u )kRO}aƳM=PЩe=aBGەnpP8D}#[/ovcHRy\¡8>{H~Xpr8 spUSOwrYM/^-h}͑hdD2)J.p 6FPBƫS ,fZ"aoDX SiUSiU`~+쁨F8!0"d#)7ƽ$5a-aCgmq8MH8~e+  Ե-ݝ\hCo: ۛu- ")SiHm@]&]x4M6K,)L+BN~ Dz(Y# tn0'"s-Pu+< ؋FaSoE}LJCƔˀt0BT0%WftUV^vs8ˁ.Fw9J~s#zc.D "s#h{46:ޣ\*uM3PFW^ȝbM4Ɓv7hX`J1[8ayiK_[t"BMk)q  A,$ Uda[hGl!yWOc8pyf8FP wV0}i|E]*d{ۯy{|ۯ(wٹp표kGHեӈs@oVa.pnppBX20oG {  IDAT"j*$gv\8pÃC5 7pnOCg~W$v/k. de}yi o+ՄWz +fru,r™Ȣkmek#dY`Lz$yfeq˙3LW`8p`w37HY?DnB  'Arh_*s[zШ^r`hYcF$}Չ _ 9`i+nW?ƅlbfU8y'"7\e sWA [nl s Rwp(u,{,D:ߚ*1n52QIo箶t(9 "| Ym )R_}SGd&^uKkK,wΈЦa핁[{|z5<=4C?y' UWaވ̍/Ca^,9|Y+aPe!_@l#OR݂}H4kU]r%C`~8~Ƨ!VK0$J_C$;!#`9kWl!c1656,gZW۲}iW4݇j3[Ѹ(8c}PgN~ [&)}}BТiJjޮ;8pA7CvG奕+62B6rS fe&q&@7@Wnj PX;p`‰`9p032k~3)>Y5DFԱm˽<м6 wG?0O{Jޘw/0>u%<4o{"M4XUđcȢŶVL# @r>^m޷]T]QU@ )?\tTT (/Ku l0# Ȇqy5gO6/)9 0AHtJVdCjn9u@9{HsYc\sͳ_"$n?,CCHv_`DX !um.$qp.0¡G~lTT"K奕 `9k0 e 8е`cgYc,. 4g-w6?,?ZqE+v=zMe-^גMm6BŐQۦY}ހ<4#tՎ7 ~DsXD'[ .d̺ xm5mJ͠lRXӿKK+8p"2.EۡK¡}f^?A*$r4y98!W2); SArAևH]BӁM?"D^@#7: y "=|=s:WКZ^7MPjE\t-t OrK++w 8,{ ner#"]q1hns܌X NzBv6W͟r:(786!>_.9 4,Ȏ]Xdqt_EΞ1(mM?\9Md'6 )} ԿD^TY$'=rҐ¡jk+X}V6F<8N W?FBRYM쌌aѹ-Z F}x;"k(x+"DnSPQUA 袉8pt]! 6 ˁE5<8d^l@"Dz`% ;2 FcPpnみ"sd>3gü%b~4nGM!Y1d>~NBsԱ/յeA!rAzƾ@7̃g?Xq7$e'"K{%+j[lh^ h=-V֞v}bqO;zEUY1B`u΋ā=r!PKj8>y6}M/ΒSF[չqp(K7!D{"i'Dqmw)ִnYH-Il-ԇ W$#tX`s/װ0mN[i?ٯzuO`z+v~=EU٨;}d8(("~qK32GF>$(6C;bZnw#ua-@./gֿ 0"RD k~JykB.@$i2!R5\mĵ )>B֎TߣֱHtki8lR$Rd3Mtu_C=afi> g}=jIKR=UKIi/ޯ9N{3I]8s7.J_749΅8؃,{¡`%maf D&Y0ξiɣ~PGcշT}ހZS;1n0p[PMKm9$7IS Hʕ0`Q2: 2hK}w֜^fOD](6(B8pFyﲈ@mq[ƹ71\G RDnP#BB\8 C•HԠXţk@/µ|U $jֈjV .Sck@RAjlCkxG?B~Js|yP[xulF]tq_Pr8ufOKs_պ&ݺfkQ99he9(uX٧_2$N//ŤFp>vy _#\p4mIaU]Gv K+*~ 9yȃ P'af#p(zMa jr{ݺwPzIU8Њ#Z|L/F6hZ1hF!#[: nLŐS6bX i0Ԇl!%R_>W} [/!Br $6]}lBXuC a;B^D䖿E {!tì@>^q8\EB~=pهGow46C#c6Yۉß\OqmK 8rlp W#:(`-Er&#rB߆HB5]-j|}.w{4d<4d >&(^5W?j:^s` K(Y;_lXkqAX8[,D;XZ0\jwT8l qw8|S^\/&;đShxj y;KE!UvΫƋMItk.J"]oCW?BGH$i:wG!lCtÜA|CY ?;{ܬg:?$g tt}o|z}QlY:iDC{`w8sG$ . 0d; YACPp2kbO\c:s:TmrkZt69I؝CRҲ xU[0=֠7 6AUw|u<"٘l- [;>|/}I/*F$b bB2M<`bpzmKQjG$.dk2F4 %bDe!Q2թ1#T_uIumfYu\uG֗H|u|7 OŪm'W NkCZg9%hX.NqHQ[GV mIOҘ=yЬqőlsmuL|jt <Ix>.Rp(xniO7]ZCX:?P{Fm* bp>  uiyiÛǁ*|y'奕_uN7L7:¡(jӎ6_42)D/"[c(}+5+$!0lXHݮ%بRA"IHHNAfE"g1aZj,iB¾/~Ⱥt3\|~;pn5ވ8}K,xz&׃8;7XOO7\v́QӘl>V^Z3'=ehyn3\^>~ ԅC6jG@ 3OHbr? ( 1ƿ;訶5CZ5{Ue! |4紵_唨t>]1Vl3 ,EW'ydԠHfBL2bRx?|y(}#b1!ZOIdUB}Ic%SMHAّu\I)>~TEmLԂv`uZj $k.St[8 R0I?}pnjLud#*/>onڧ8g'o}34u ,ۏp(@7̓aGp`Mc}6rҶő./[5ad[+|c.it 7;\끩l6υDWC1+л`!9>yeea4OK+wԁ݃Ue#2' ?C?膹OK3'y#;sK!40d#`07BlEȋ);(8uumq$c:- ߴQ,FV!W3gZ$bEbP@/H=;IeT dA1Ա"FH.>oG ߩo:mi*_ZO䕴<ˏlݨ`n (af^Czeƚ `FS奕%wÄràlGH4bE8ٵ'"۸CƝxWakɾnތDGgVt#;"yolrIKG{M9#]qq I0Dv+ 3Y^gƇ#u|\Yi)!y>eϫYAՏZ$o!W}Bsb?FNFQ6 xDF]4I\  M#\"d}""9\=my5HQ5΁fwa\kKn#Iɤ}q]$QPvJPi2KπK,\K+#[l`CHlf[T3OʺQ¡`P>؆CDv @Ov'rڄ5?yH 쳬vܛ VL/ay賖}z^o ֶ" ~4PpnÁPqggab?S|DxG#Z{CУWTi2VQU [_o:nUf 8l{za7< U)ҵA7Loښs{jɼ [F*0:p-PHXG$]b$Rrd?b"zFH^7M s/u$&i$Ol ; E~6ħXӬ"hMr`W`M2tsgh,vZxg!H?aC,́DtnC]{9X]q6v F"BZUӚ5eY:IY\80 :-p(4 F8!'CIE]nNjGr )ճ%z&_m$"rM86ocesZ[mTUv$0$Xuq#gRs՟mފ!9/Bd}jtHbZq|ۦ'z8I0oQi(:ցuz.MM&__M&@#8c:V  fjā< Ut_wPcN{|<(*+G@0_B4LJC{y@, :Ue^ mw.x+U}ĩkGb$I6t$C?S%" p0pB<Y'{ O1tU)!¡]"j5ׯHDi)ִ˺.prt7t'ӊbeӁ!6[*,]ne'~(:B]u!B@t 0ɟ:Yt /#;[n"gH MO^y 8鐔6/VT! H#rZUC{[ 0LM)F§LmZلN^D!nxiAhE,K_iuB,pdc:*P(D5oU 3jf%dNW@]FB4)6H|ORƘ ; y 7]LYG6zƨ{ bk.A,EjUڴ\^!vT71P6 G"u[ k\;UQ2~&M&:p!X<( ~V`n CA۪Զ] IQnU=r{6p֮'2?,"[sqTfv!Gog?vS(ǷoK+c[jng8v4>1P)hBH5X乣D(1j!!DJ$ A^ xjVÚ0dL$ڴ6e dH4*+ٍGP6ə'V^ۆ v.-'STZ5uOlR؀䞍F\ A>4<R}HNB"]Vg4}HYw(5tȀ{~d+HQˑț{ `k3 wK?.bd0ڳTCuG"`/}ͳ׎yG?bI0uhd!I` " ^IڮMy,=/[P[Iq`wG8psm2D0͑3kn~gy!Z"2BjHFH^ElXIRI=Dzěz]-5Zx2pkseITe]):6Hu#U|o}H$T61Y3T.$hK:_ k[s<7&LgClB^/@p(N7LOzx^0z3='H4PƎ1V*dOgH^SiNQ^{ `k0b2y$B&!OsJI]pa76wF-^wgYT23;fZ^;8ǺUx&Y$,F6"unW-C"^Kj" s8pCԮcB@F Ov<ݾ¿o"Cg< B\Ad9exv^RB(Pn B,k!ki-?&(aG$Hv~MvdؑnGfD,&(Bdv$oD1ǣ\n2_Uǭ#iFYmM.܈B;U򦴉"I&mRr[jIC6 Qahj\v!ԈXd nO(Z!!5S38HXF+ S V8l  O vy5m%=k׶n&%.kκa3jc]'s^p]_o~N_WaYUeVvYmCy% 48x.J^d!Z\^PpщU^,:1ɨYT?]s\ZW ˓{PdF+.BHV3"E-ηM=mwGEծnˑHW Rx^A{td{bG\mSc A8~3Mm8x8\DZuX%_biK/#b GpJ#ih1cCYMP߫ضx_lD:yɉ)#s{Jvmtdg5"$HKuVϝWI5@ֻdF 24dn9\̥EIMݣuЍDbȳj~ !RﷳP_ۊ5Mn+EVG0ꇬ#-cک>pN[z_wiY :Ztt^٘Ϫ*rƮ!}; uìEs@7I@n? 2uK0rrub(;)؇#4y`Dnqp ,lU?!ZG,wDս="<bll0)2H#u4$ay@7L["OUTE奕KP0æ>_|VPl%wwKTTRG6٬e8|[7R% lq>EW[k{a}>nbЊXt_\HyB 윤U|.Lo۞Ǒ(P$h:֏9$ G\Dm۽w"ă%JFlؑt9U~u]ë"|u|j7$Atѩߗdr;eЩiH*~R%!u.b,uΥyBunxwFM}rsg#{/Dtޞo}qW4-Yh_‰5-+^Wx_YmyiaCJ|0dp(-!X{9īk6,hO dҟ~gk.q!Y#GknYzAOGj}USOb![7^6U7?# +1bd@7ːElpSoDPG_CEJh^Рx)0٭)N&kn|{0(3)~5!ylnncue3 D)NvГ3b #zɥD"KyM}ώFy'ila@t5ǖ f#04d&Q$#SFlwjެޏ5$[i9V j{)gCKjшDRkuRA[Aivd6yP?#kZu/j}3I qŝKD rQwǭ 3q}rn=qdw23('~-+a}œЀt12 JV DTTV>яf4_?d]R]; C9=Rp(QH6d@ %ƾw5FFV<ň~dĴ)$ED|=Yӑ O#z5 YTKdY7d,J%3f6Z85dGcDW׬~n$ Sk ]-mXif"9N7L~a@OVda&6`+rWK+^`Xif3uZ mC綥ZQyLS<=^j;&sPG|V{3 HNhz9B,FsbSHMrB4;ٝ/@v# cj"r V] -EZ.E,ہ쭱OZ Ӎu ׶jD:;8cO~nMn & "hAg-^kYY+3o5 88l2pԣ*ӐBԯsK+t59p٬!V0ăw3B*x~̭zD r!ģHB6q{#_ɜ|אBB2UN6&!q4ܯNd8 |T-s|D}$eZlڊz)B&EhT~$jx$jO]7 uu8Lw¡t٨Rۀ-lҩ kP0`ʛX Jn -#=##[xmbe<|Iuǿ8]lHF|-]-{@7̑@z8~\IP&oD$ۓȚ>Hn.^-Ǽw#$70Bn򐅷YL"*bde=8> 8!pw aH gCn3U?EdzpS8L]#c06@&)Ȅms~y\\k W|so>5#kPdKz5I }3qZ$vٕ?BC;RWT+FEK+Wl&B퇕;*f!{Vn6?Ł"a\N}Slh}ʑIiڣIa VEͮ /CHBd.D&? !8lBC2Ώ̽$%9'bjTd%hH)ryqׂARb1jnu-E$17G6 M8T#H{oQזE2"fZ~T7#TuP0UHr?dMKUcI)v:rq镥W nDes^*ۢX}dlDV!F8\{LN_}c\WTلS&Cʼd!Ř? Y4İbQ8ڹZ pca cdWoɅ^MQz u" X0b?BnLodWn#[\\H!rFyw^:_1Fyiee=rf`]VKGiJf9奕[ ]Y^{7DO:p+RM" ~D`c؉l: T:q5\o>DZC\eF6m"v)qzgv$-7 Vշ<$eu|Y nC%PuDF@#1:}}~9ۯx Wc|qX?v[OK0[Y2)i-GF6>R傃5ylg^:󮗗MÊgtXU4r5B(8k@ uq[BXhXX%waYI~,SVqO2K䴪|KE㋷9YhPK4D;qD",~$fɅ^SI糀X;qDc]n>%?e#~V'C0F7A*wlD}PHX#0~Dkav0fG{ԢcQE<8Mn奕lag2^QUv1*ߡfoh1̮{twE.iEUE{lWm}H2!Oy!Ѡ#{)bt bBw#C~hDIVdh +j͉%9"!y 4YqM[e2<|(\%܁Ȇm6ORAE =lọ!%fiى >_#re;JsyfHֺM`Z4dS Y&{6^H2knn|`O⒕oPr8匉,3OOoH(71Fb޴8 އ-:"Ys9px$;m'|Xm-4ZJ{#Ld`4eq[_mj=`sw+վڼLmv8k/A8ܜ\p(ѐ?κҊbϖ!@gSJb/ӊ=Rd0UO"ʈ6%~7EDw[$epAnmrvY$ hD.?V xdqy!R5\E&$&^ͺaSazRpLmNyi6==Tumi9hn*W3;bM{;0X9p IDAT:Qe訉%j&5hQCꨱDMDcԘآMXcK\(*DH[`{r|ߗ;b gwo}o;=|VޚsE2Y}[C ||/GME3oӆ:=LՓ[J42 0 [ zkI~ ѶEs]M"鄵*W~Ҽ/Ks#ĒP5Y;~x{+gH\J4!}h<9ehIX5?FVI9iFU(2Oa5KuS1oL%zp{}*|&T-=軃y~e`}%O yK\'!o @3t]o~P]9:`ᄏ5"Ph lHi\ (Flkdd\Q"-;,8s9Np|7Mc?MR:Մl1foIo5s8gٵ2H߷?8=_Y8of}٤`}I%OV!:h2ǭp+3w&xxCyxe{G6r7k{^[5ҙ"UºOD?6>~ӏnoGiR  D,뚐uCG WPTeFf ^N'18yUoGU@6rFFZq Or"˵/ >ŖdCwyr:<'Z:nh+D^V[EV"v_`A)]K=r[*>X蠧g/AcDz,4~Dڀ?{>>5jNS{*Qg}m<[|KW[5W hrFA RE:܊A"=^(AԽ?\Av/9yoUm ǝm;-fCbW0{;aA3o k*d*U{t7gse(3H"IGɩ.!0oK3Zk̒]SB23X\8{.*{Pcñ?Qʦ>.Eu yPԧ;|>a ] Zjyo/Bk ߟ CI*HJ4<pWmS҅ۛkj\hJO ǚ-aY ĮM*m4}hu rpZީgSv4o{ st;5I V _ώbd"t^u]BhMWW`"P {};qڝdOF47 /o~wC26 ܬ&  VsL?^paME.CQo<]'vfqXh<);俐r'Q2hP#G"zg}Q14|~ Xޜqk|:7+ WD>[R⯈_xzn?Lްs?͹Ź @&*X;- 3قg+֕{y2ltimZ?>mQP= ݾ}ӏTkѷr cAwsーiÏ"~h<9ܧ,*?J dۙc DɅ eEȕԃu z>O}eݣ}FxpzG>ʧ͛4yל;Xs̗:4ڹߒEDD"ɯNEzww-i#]][Uo C6OOw!A+!89p\<ʼ1ǚ}l 먳 mq M-kz4ʽ]c۾U!TNdZd?,w̏6" d C 9B=Uu(_ʨQ\>|] {k |:xX减gt7ҳ1_w^yo67U;!NƓii[VlɆ._>9Uf}\a/ MgnGW% D:bՍ<4`aJr?QW\}*<̯mx> ͫ4T`eHEu m^}iqݭɱ#xHm˨+ 5 .מiL߻ނtyPF*B!D,>ONF@Y#Lvdn@I  [%)HQY E9eRX]h~AAs5#b>m;EENC^^x eUxl(4~i? )gP$x:v!8d#ZlIwƌ͂ 9t9.(:؎l/shr94[žFV91 񀙭DXgl4j<Պr~mcBZB4ӈl[٪YuUmCQ&dGX+̹HˡP?KyG%_CC*7wúAJ||&3=ב)wo5ؿlťo^E.6,|﹩Ń.ͫ]W9otՈζ}2{u|>Zәn]š@ S鿸p_KwVu9צ~q\jL6D}u *yCsTx ~i(( h26?}IOk4 ވG^|9x/<ƙ9`YOs"} H $bWQHD5xqP[z&YPE썼vE|1 ?F 3rd'xem d0F#@Ԁ M|Ubn9%d sH`UϘ=K4M6&q~\XrvxǮ`n{Kr="vk\<{[Dg9~iD'#h |%/?GzǠYxu"9+O@P"i9w ƓXddg6wGIOFMHOCζE{,K9BEEjm! v|Cg >!s2eݐn4,Gy(bf (!>/+SFߕxN9;ޠ9 t\aI?\7-9lxeUN;BsOm+4" o%Sxen(c(z. 'x.p gGvqг#m=dŌ% قU;v^t+4>8R萝=zVuM&tY e}t(p[3ŹY#~ɱvYVP0oH.-\=/s?ȴ;?ᄐn]-?*jO_br ݃x_YX[$b7ʣ3Zr}ݡtAd : 'bwx ){nGo/d`_Akd||x%j}!4 y G !ؽ~R#;xî|L[ٷϢe~@U|%-niVp]s[N6һxpm~u Zخۧd;~SǑSF*=~Tccr1PVW`u 3cS>@چ-_&X[s٬7Wx#݁Kлo2{h<ˋg ӂmOWxI\zၔ.q|`O FKF%K(#£쇼s>s]3Q)]̱sxhã]}ӈ> M ι}c֢gJ",5'mnJr9pyS=?Zxn+WVLmL S:>K@Gt|=dBF"?pjX~CQ<\L;m^ŏPp{ U7rGT:\fh>PjWju33 e`0^nR?*!kV:4bfmiuv!-ݵxNٖ$˚}ƢgE,R]4! F63h%!zy~ ZLAQ7g Z+q/s~sσg}tQŒǁ EÊKME9Mg-*x-ou+ZҫZvO  l;ß/:aPHO䠱gA"yƙy9>:0vE)?'*UmX [ _yX_8T`h<94O^E^V"c+*u_bٿrC'=m"MQ=Ad_A; ko=AH*W2"8 U9NJ*whGVeՍhaAxѨhZZ`3FKGѯc g|Akgkl5#o⏀Wv\E/Tbi;EG]y'fnpܵxh-F񒙈mzzYV]{p}.jM]m%?er:,!IBr =D,rb4ƨxr*EڣrZtiX$'+h&h<ي#4U/ ׉t!͋tieY+h҈9,FWZˠBQh#y6ģ1> IDATzk [iv&kvAA?P"J!"wxlDm?ɘu:# aAZ/w5܌fQa(?h?Fw|M+~㞫|t]d9iB%=;oiI;%EDɓYI'D,X'>{zIaK}Q琳OVhU(/OOWYw٪5ߛ4}_>)7r3sW3`䀻2i͂Kc'mvB,fqsiydyoX*[aR 4Ϩk*KnjYJ~ agQ)r>nWV碟Ho?N+GSQ~tQ){?Eςs놞O.y6SWu䍣{6;߈ܤ:8}٪J:ʊJv2+Ҏ}Yt9s~S* ^؆މ?o1"ӫ{"TRs60bzo`M^Xķ=UjЄRg/iyM͡3Nc!ȨЇORx|c bsqxJ%c2 ϡ\`E2WM"ǘ{~^iT?gRܡ}pEUTj\\]P[\^ZpgqSL ۏ%B /ee0x<' aQ.{Gr&01O~/U3sOy;P"XKC:E4F]׸Ii:D6*ρTU( rݏ@oDRm"B k!{T@MAz‚n3"d6mБf&BT[  - 5 sQ9ddZa~q/C~xv] 89ܘEk%MN;YV mK[W=3Nm>ҚE,=O~/F}*~5K#^WېO+7y'rG ~Pt4;C[}+'}k}xTY9KAN:qS@?9'/ɯiI4.M"[~; 1 W#{ =! XuDzV>\6A엯1(26QmPm QVrlb8&gyf]DfKqm.[xEsf oMp6!~Ƞe=ݖjH\w8pi[>gŜwܵ{ Xjh_}/ ӿv`)vޥ]Q+ѳ^(w&=#_*Ɠ"=y6@%b,@3Ӷh;fsgpJNnG%e"i0X)Oވ"ZgA(q66 9V'X6nhv wz؉lxT W`a jyuC2*:Єz*+o .־)@190dS)EQd6ǼLlƜg/̏a>ym':(ӿ%=F7u::mlTQ9iUZUu msBWx=rϠa@i 6~uVV'~4qmأ>"d\YtWզ'K:2phZir^E؆ e?D,2O"6^U}W}Q^+@s1R泑 ; 5<LJJ|,w jRW 2>.yWu#Wx͖.хWyA2[i[&O`"u#`xƻQD4vƓo|F-OS4 ;}G=a!";ﳤ0ԧK$mo:i״V4h!7LGt(z:MM&O\W۰^o3s,t\wkx=#@nFY+z>OƓH\wLW3lɛD,2ϴhGѡ  m4^kP.+Q~O|>sk5㱠kCztV3(iHm, `O4+%Cq(@9G!rT">،GKX[_dn5v"@ٷ\C9HHD/aE暖"'iȾY_^)|oc}'wVEw"WR _WW_ZSaYs~W3]~=+&ݙ~E8;lh}":MzoCLEhYo4kaEr(MW\.[4mg$R} mCAEFe!+p R8Ux`xY BUeDa$o`e352i={9ﺢWB8 5چx8/?qpͷ9}}(Gk#= đD,Ɋ++^w5+7MJwAxr%h<kA!|i4HYc@l3H싌ax/mqO"p iAFyHͳjBܟA\go;p_5dc& `wgJ/ΨFmuچ'S Pw$^H3'{Tu:>uǼ[ GAM||>vV}q\elyD,rs4Qe>rt=c;ͿhƜ Wo X4EXDdoo"yJOm[H.!-#v{6VW&y?j[q'~h"1u>ԧ»rzbJgoۊb=%^ܯ2p]EmecrV!ZqŦ%(GkqݍWD,4GyN"38Ds_T'woA.ҍ,!mrn(w%bdM5dl$rY}ځ}!o!^sQ_2slYWH2 lg5 !qʅ*1܌tK,1g͸mG dlm{d!e#]Mși+qg!i8yW>|McΕC$0?ܛE>G>DI_q~(^Z֙.`esa[] IuߢpV~j[Eehw]klҢU CY-Fú0XMguȅ\a.ͅ{_?um~ &ﮍm'Cxrb4 Vm/52[v.OAR >SDq[X!\%bD,r 0>>|,T߂韣4+.lrUOw|lG|BElWm3蝴Iԧ'!~tП)svm(8~'R"EɱnDA^^ٴx67% d4<#a\݌x'b3H)[.[!)ȓx>x(Q4\ y;CJ  L,@Fe ufYЌi2]xMCf+J.5?o¤qXOf2#͏J@sp@^%fE)GC\"~4'04 %kBPYO"Bnw7~'?T8tvn Z1?t˘4a~uR נx]m&mLjf+SSΝچ%_ѳ>u 1T86̹䆓?]m% clഘ}GULq>7~pKGzdSLlhz-T8+!<؞N(eݖ?Cx̀[*v0@"p0DG*f#E:U]#Ttx(Bε"D~ 2KDLިuH/rz#бX[T#l-TQlA1.ҵ!d"4B̡f[}:e|gfD!Gb/sxw5:+S5t ?rJĤNnzex KC\Mz{yXVyVlىmY_'Hi6};PTldwa.f6&?i^m/Bu קC~s&Ņug C(T[ۻP"dS}:r.xWMz([(/Nǯn來DvQlQ!8]xr y/{ b8sXd/|d\+z<CP_6TM{E> #CIȐt"oT Yg3ڬ]W#c|;neuuqX^̎9 ߌ1kw'(|dLG&T]eUyjEƓD[:?O~ O,D,2Pjr%';` /ψ GKw;S95[zFm|𨠏D[T6\[ &8G5D>u L1=97Pyj<ΗќT̜]%/RW.Ɠ!x'E6Mߔ/OX);_^l~OS' َțކJ!`#C!B "mW;|8:O0"] ܉Wo9)^ٹV=|}&vLYB1O- 5HL#ngN6Afy;[ւ)?:9ۇ ۊ><&/Z#I&߮G>gƓ>ǾU}_f察c;X׈ټWTYt(U3h{xm[Х 9kF4U.0c#=J2`tKNK6= yCxnL!: 8/XTu)Ɠxç/Ax`h<96O< )7Pl4Ӗ.+`+wWsa0E <6,^ WcCI-!3_ڌpf8.v_YzXY=類CfA}K^l֟yuayOu"ř\QJgK\T yȞ@J0O>b%b|叏F'["TOF`D,wsܩ(62փީ!HQ5qdhBVsQ\q'KuߖS? /)N7y;n5mćd' ʡUB3DΏ-%5U܏ͽ nh"K3z܍ y5?{V8M"lR ODrqre+9e@h9Wfnb.ʙ(y6lcglٮDs>{Z OogV]mC#? GzdSGɓS[`䦼Ajn(m#9{XOxxM՛P/x'({}'ko3HV!:򾁜TްEzx%ҭAvk ҹx:twY.OSr~QVn!:hE#_k˚k2Ve-E)lA$Ge)ܷ%m+K M#h[YɁ9fV=R tnW󗎟M5EV.{DwvZsTpa=Ȃ 7Uǥ(Gqr}JgOOg^%?LL^'2Vem=`Y4< yf#Pp7kXaU,@QؔuDg12Z 4Q,0 /d^C h?x|tBQe)Em(21ajfzeyn3YϜՅ,Uū\/x [驠ߞ> x< ̱(ɷQp2t(2yd,><'mg@._┇nByл (bV?`=Yj%9z9ܬ6& 뻀S˾.rT3oߣ{.M/1=vLexr `chC;A; ޝNT4ClS TaY¾CwNqg~#>x77 oU"|*]:yަof845Xn4#p\4EuPvD X&Pj4#:$J\@[3GQ+@Uv2rK}Q'V({h{RK&f~)1mQҶ ZE'^U&Kѳ<5mBe(:h.gfӞKZh[dO@vW IEZh<9 ?_JkGŅͻIL uP5i> Q!84 ^!]65Tx(ZW0>d[ ^ ЫC6=b tݖ>L_oCϹ t͠gLlFLnșv?'-̯&~|L49/7?U}3^U'D,rC +҇xHF)`=(I5?k_a~pRnG8DC<o"r ٖ [#ݽr+ $Mx0c`&l]HO!0;d4e,AFz3mruxg<#A%hbYTBbw9ExvdJ̵UDDKe/Wˁ!4{h<p["+k*FP5uUC;_D :'<˅:лَCne6 MJcoŝ}uI(bgG}bJ5@E>S`m?c:PLEL} OtL$R/!ar^:_dD߅$R <uRZHeF?\2 9,@LmrG<(_ v36Bl#XH80klp-C+׾r!yr~()>qI +.BB#,?Y<$\#IPHFAQ ̹ʂS#+DCCsk7ЌC@ \u7j]WVU\vÞ,jhޙ){5uUӐvkNzr5uU5iuemh>sY~64yC^}޿ኁ] !u4fhv[Rie =?46?Ԏv1ٮF;7ߏvM'hH2%0E|TWYMFEXz̴"ӎz'~OdQ >.4 ^FGv?͕ /FqfPmr!#>*,xhdXׯ9dķۑB:r mH&ȨX@Y7ں@@#ɾ T!`q$^?b C9x@⍾]x 8]Rd+Eŷ'ϸ\=y`c؞HVc1J(Fޜ0zFKc~!oWͯN:=(l}N@ZywO{c{VFy@7\ګ }ƻ|`[շ:xلǦOZ d+k32#XOؼ`cWgn/@di#? <1].H2_Ct2D*ڴ"G"гLI')l} ƝxW(ݎPGUZ)oʝ:e`0;pr@")mez/"prAE҂JEvO݃+'_L$BkX)r^ƙi!{YA .s+K2QN̅d|ᖎ7[ziWOYQ=Kľn慪m:m=3txW?WУ(.ƣgTA8ʧwl{+kιxq#v<?ޣOYHUB^.TQ{V]SWuR3ƣD\SWU,PvY7 xӊ,X:"Cp5"p2Aϫmc/)JɘӐ1 (Ovo_P )c <̇˽\ #RC+F\zo?=63xz;D;T-@0@WU}ɻȓgsY:ppk{ A7傺h9ED?^ö HGd˞\{F'bV!q{Cxp|oK7#lyZ;5C|8?FueWWֶU} e)[Ow=~m"ߗye̽Qm3!cƼ-W߾ʭ`E_ښ|uEU} Y} Hez$RC m,$l:5k-t>YoT&ѥYf{%R :e\#E]T"bĿF ' :' <% hʿF uP¼Zԣh}]R$(:yD{m _}倚˿ !.̰~|:A/ABuIڻ!U"e4jmUp[UWޜ{s{u;b~w(%n8q>Ȳ#ұՕKqo->pj.EN]z~x>|s'@J6j]Heʷ4fۚC??nLL+0mgu!z46a./2!Ț)]xCQ6YvN% cG:*R-_f&\d }q @ؗQD^{ (Jz޼~()>pIc}Ń|9DQ{!~-o ~ڟ 敏u,JqP؆F$ rEA2r`WsSqd !D|ku6F^>#HhZ\A2w{nKĮS@`x|jky榓l·imG{qݒ~qfHaޝmD*b;_9\u6< wܲv69_bN.eޟC 7.y{זB1ߧgďẟ(`md}{?Zk]]wzFi6R")D够A1*FD*s@(d)`v\,,`~A wGc_lC5w"s<>ᄃF`mO$#+hp#OP` Aسc o!AYSC u~)dELa}=Fb@Ź,dx 'zTfzO#eZ$옵($,.O ];lZQH޲"\V' EhB '@V&K9ʼgBDsCQ;9 ީHcjdwAyL Frq)#b"9Px@eQD zg4kDr뤩{QAdicEQ PAW9lN@" Eqvm# 6?HeF[ǽ@"ՃZBd}9oH2[$ƌ[ax @)(OD*SJB!}mF{~s%̅.o9ʔ5eH.ֹ He.x4EyhU6}o_oSyCXZeQě#_YH yαsښq,ED>v"lo$lz!yڝWE\DrBb|rBNd*gv @")|ں\cVum7kvO_ (nTbs(fgLte'AP'z }(z+ʜK Q;zJWWέo?f{eڒ.siIeV=dݏ%[8ECL} [śp1R]YtcrCoeȓ 9$,Cd%ΏQd9|>܃O0ܛsR\NI;,]xw!2xN2O!k;Yȳ=O~HeJQ1!:/;yG' o&GxٯQH/@vf[v$EȎ/ ӈ{g;5kj\nk3i~W+ou/@yrH~,<؞ac>RJOB||WEY\bp %VjFs(mCs"r#/H2LyEڞ_uXȖu-Ed,M .oG>ڃ%><"c8Zxpl+ٶ;m5ߏ\aUJ|`!H2L'UI2s&0K'rMl(,/|;ߋ,Hl0aPR}Q>p 5ts0R y^xdpd;`%S{Y|ZPǫ6r7"c#&v:a#CBm=e}'dGA_>sM c!xo79; wrC\6xő|;R (DyY,7&P8ACfA, H8gx!>|zN NƿnB682\JQP޸dEvе+ay梊;e%{ZhVQ:oJ2ֹt2U}V6$\A=RVlMs~K=',;_S<#{QxD,%C?h9ɗSߦ̡y琬y\bsZޝ5H9mD^h6!^[Qlpͣ(Eઐ_Ƣ Ku֒IyնN}P8rpx<v2(Fp[b+ Cٞ-uYIģOc_c}:_k@|֭ïںX#ʵ7#u9YE`6TYnsy񺵰uL {QxtٶԶn}ƶ 2"[y40oߧ}R(1GM銏d Zt'W+h} $Us/vT.y(Uy]jD:V%x'O'8YMл*dqPﶉ{"نH''#.cfZыs"p=w>@@I sMt@ r+ pǐr4g$b UdMCrnvZͯ)lc&Bu*DGA6YnH8>>Hσ0ju@m2/z2rAեH-GmQ)B 6H;o`gz]!oϥ.C ǙTA[{X1d}1mehWHN|DQ󪒃'ԯb!Qcӧ ;e3:at}(%RIzO#R04/D6ǭoV-;*i ol*hhAG,y%RNN2l+J,>qo!EPXY?Ax){A aH( }B WWn)$$\ؕ#oݟ_Toy6 C('m'} (āt29qBC˖X^D{kMgqeS㐁)$Ơ H]_v_J/FQň׹5!>#bˑz$z;!áueūx흣B y@g#/Z+ b75]E/Uk5oin+^Gk(2>x|:v*Q00{9((r]6wZ^k*e(J$ؚ?Hze t2% m}b# m#}w/wc7cE!{++&u6كN V}5ܷ9-ι< !m^(a01N9#WЉ?L}GyHeηJ(扟3duE+_C~%';S $PmjFV||CKϾqJ^ek*/DƆ6e%`Fm(<;[>EEIkߨ?xP߲wwH~5uU3~[,8,7~;s#r9FiAH2? ;_@/t2& Fa+b.p1Ƹ sP?#t%T5"$6"++q'g!)W ߀wJCy*G͉?Rv oQ{"`?%/k]b1S^6gXicskHeu"B]V[=z΅3g6ОE'Bg6YevFud]G<ߧ6%1ࣸz썬%H0E% Kv{W&]V>eرa|^>Z<\m+M\kݷ=d8Đl.B[{AGkEZ~]9U]Y=w1ZP͗yuz\_۱5O޿N| *9?Օ7w)ATSWu#bߋE: Gw.{/>43:~NkBCK{,5{!%tx%Rפ$qD\N7ZB Qm܃x;Qa^P$6!?5+P~Czo>QF^uLBBuͫaTeya˅GѺtL;ȣ}9cTns̮ i<QWU7E3Ξ񳻀P~'O1Vpo zB=w԰3J74X|yM3 1洵G1//T}WWѧ`D*-ĶPK{yvܒy%Rdu|21Qx/ He.B@g]A!>{-e$tr#b, %" _ ב(`wdY YBU6Ȓ+lU X@ЃCI.)!9F.xp#b\e ʷ;ׅlޫH`HP c#q@!Bv,\nyJ ٟ3nL2']V=hulp ~سGAh%4GD+-gAa!Rb Kj$m+k]SWA,o-y \ᥗۆ_ە>FWۣ2ŠvZ{?#籓>>J'G.ݭ釜Z !s \ yHm+˭5蒁HdJ[jTNjK'$R=Q+\B\x z@}ְ4o '2~}FBMꗧ\"):ɮ ̣=Jý9 mIkG x=?GgYmʟv<=PtމBGe@Kbs]{a 6͉Tf 3oJ:sGP"E5Z̋tlM]ծL^l7؅^E[^\c߾o  z#A8OHO-MՕ#d?A#ωXS]'YA/~(YǪuo &R vbη# g}> !v_dm\.#}͈w"!ـsSt'be!?1ϑt$\1 p^DڼftXwYocsk݅a5[q1 ۮ=˞Z~\X[ Qc>Õo N[AuhG $\.kD"4ANߝ9i++kk~?ϛ d~kwٲQ\z;GWQlQ"&RQDjA _|$Ȓ:Hur'U|bXi0Eg!f; y@y$c`|1I@ B^"C Vh@H\@HAJ:-FV"AFi IDAT @[l~HV Fy&1$Tg5Fmb$#: f H5g"_@Hs PlߏrlLDϮz}d|Sȶ)jOӼKQZ wM@pvHeD*SeamCS N]?7ѽzXrP ּ;@k0Rs)ɋݭ׶6}e :7o#u͆A~{GNp̫y^O'G!x1p^F`ODƪ_#pZߧ!75n/D +"woC*ķ_d+ CEٞWظ/wu499^CZy @xzB|?l9 .n]9| 9 8r-J UDPpAęΏ!t2~' < E17㟉T&0H>\xg:=gZQŶ'ui̮Tfضgr:F0ugVTycD;G>ϣ}A3\v=T`G C6FtzgcZ]OOK ޗrO??ɽnMy~,쐾UVA2d/<Avbs%x$|޵c]^H# Cߥ6!$$" 0;0b oQ=F$} L.kh5NX #U6 Q4w"AwYt}&Ău>ܞǫ6gPD* hGO2YGV3?T OIկC{`ퟧ]6л6}lŵ"f* {DOr]@A|/w)(9W Ee@M]U/R?GJUs zBvۃlNOćUŐ ^-72m3V_8洓BʿN/'/k_A)EH<#NE{l9'.$RG>R:y9"ͽygTGJ|EeC$^z"߇ .u<2l z7H>D+zE|ԁ(=ܮB2^ݱƼ1:[/cɻȫu .>'"\w+yY;nl2::ɭ@PId1L$Iyv0蹺&ڳCuN;ClݩgeH2ZdUzDR>P/7ޚˑ A0x | " ׷u-WEg`!p3.|t Թ0Z`=Fs&\ 4w䄘>H@t-%  $y=H(=#֬< T{kW" ~Ya1ڧ a5 ȋSAΉuv'iAĮ(RvZ9?TW~^^B1 vW]Y[ߎ-ACkTwrԍMGXpuem>;MFAJ VnUsyJNv{ΌG]Z;ZL'uzNCԕvw/+m3t jOt: @͡]D ࡜~Cy#N_$'kGVk + is$ Wd0RFX!Eg0v`RR􌝇˳1V 3vie\P8!߁=O1YUẹ[ӧ )vǂԽɬOqV_^,-V7?He Aor|zW<߮VSW}gka{ɚ|/9XˀXyeQڟ'0O7%RH7}U|`z:0ʌM2?6v^#37Oz']N>"pKEGM-.Br [Z E q&J `ߵf$#!#c8xp^vkLP!& ^ΰȫq߇x-&#~x)ns}JCAm-z 9+Ei}B=1wl" M^Pkȧ ۼX,Aw?Ki^EE/scOƎʙdUD*F!< apz"f1QOmo|O#`M6o"AQfjA/)HXFuGTfcE5G"_U9/ۻ+ҾsIx{O;];@PGBf# &H ѽ0jF k9HOv Hew$R_ݐܹY!),XOXCK6}`#B{\ m(o<[)Ȉz"Ⱥȍ3Qd%Li=ӽd:&8ʩwZI/n%4{/+E>,*(ec9ZC6{ce>kC lg>Ҿw2qՒ} 0AA1g|uN;gSa\Od僎Kn$RܕHeNkv\zXswb]@PE >+XV޴8?)ԦH't2 |_;bеRW6F$OO23y:,r}z7j,A(D!O b6w\#a0 m!z >"P8 'QH)җ(9{'}Fi7n).Od-G(&ܖHe!Uu#E Ej= Gޑ^h=N`y>^,6۷wt-ޓEV?W!$?ls ?otko"vCEM=ٲpoas! y@ v1 y }C*#.5.쇔^HeA޻]\Ek((p%?[z`O+@KpD*}VsD rHeGrޏGfb"`{*}:ٞpz z%ұ t>r%F[o?blg6Qm^a/>Mc;:c4+,EBKYr[/vUW־ZSWŝyUt˗̙C I2!>s;2| Xj:H2.<䝙FuW^ ϚK'_A9mg<"o'j#_\f(B"Lu6M' u,oc B|+!c\_Fptfw y9//ڸ;H.U۵\)e(L4Aԟ:CknkoDzӑ|#q?T`_/DPt2rS&d+5`Bw-($ gև sP.q|Hm% t2J.yG1z Qϓ!A^] [ (PHeN28i𺰺2?'X3?UE!KT"8iO;YBA *Ǒ~- ?}7oK'N2!e0@ž̇ OVRC#~>ZІl. 2俁]I.7ry&/{ 0ܐN/5 qOEBC/+E/ $xV"01 m|gC Qݑ7d󑕭1%6d1q'KkBDz ˆl-@aN]dsre۽>w!f(F{+\w6=ʚhQy嬰o@S{uEaG"Sv1[Kg dA>% !&~9ueg^Bd~K'Rcҭ}b'~Hm}ݏ; R[GѴY׷Z29/VBͅTW~}{vNp?N6Wg#fRAf:RE|(#nȀ%ꪪI%?0s!U-Bxz" G\$/D 8rU g͎ S:O}ź3t:<؀bAD섳-^*8c( " -B  4aj!;=wWx$x:g}gZk)a鋑QhӋNt\xkW#۹OBM&H tdPu H'03ʬFا Aiq~쒩̗tmbe2Xק9Ht-dpŎ,$oNEP7AM&I @$B$TL@?-}2\/k]TW@Hhs.,"gG{=HBZ A5E*zmE% Sn\C_"2Cl >~kَ/q=6/qp7ƍ`M 9h1cAHXGj3aCH!+]p o5m3M!y\%ئ][d*s(2]hBG78c"AoQ.4g(tm"?ٛO<+ⅹ ik9vvMV .폼S2 6ӶWĤ WeMCѻz8HwNWO݃x+ʵBQvl52xHfmeȻ1A]YuW)tmbvW8 <2x\%" r/Ecnc1v<,=H0CFR9!4|NHp< 1!x"ЎyK\vmd1{u;1R_Dy1Gt!ZP?dob2#FG7@ IDATo'dHgE )$]v{&:v{>nϝؚuq4UGd@ۺ ꂯ"JdG|Z2X۷L3.[s՘ bȣz-+ %~{F>/]hs՘/"7T CvN6FyѧU4k؇bmŎGt. Ơ 1Tfe6F~Ut="x(kGav{}-Bk/18{_zEAɈRo$?I(br q_9^f!{M`1zח!ʪY(,/z૵5hoq\ϨGm>sXmlc/G|x0noFFvH? dO~ATf ʗ8 Ơ[(]h0YDaÑL}#9)a;m3GAAʝnEwpީ]VNZS>lgD#q6#-9ŽǗq)v+"@X<&"h_ls, !ohLy뻕@Tzgh#`vM}h4Ldҗ ڊsxz˾rj׫+ʔ{$U!%F V後wM/$ B}96!yU_空|TWF"[ >"7)1]d#l >vT;nvrؒo4$GJ#nGCӮ C5`} 9NCߕIϡħ67 x>]p qTf2S~9ߌѮdк[8fe8cnAya ~P: AF,$3H^O1J\wІ)T|`9Pop.] Ӑd*)o> VdWEH>+~DT+ , #c]!ĞzHtk]TU+Ai1 $Tu/CQ9S"4H<8(C 3i7 P_\7~ 8lqw5Ki C6Z%û`5"ːmk J|ZR͖HP_٪ћ] # dT!&p2mKm}2vl=Нm} vg{ab & { uhQ=R\O*? Us8Mh_lj?.=<5Ȁu^ۤx4ʔkmǨvf\wUcGr-{]3- vա#-aƆ<(:%yϭ\MeM7 z56b/pԾ<^8ld߬ Xضc7 :aO>GҵW(U)(^JU7ϡW.D|b62>"Y WXfZxȑx#*dmEReA x_x[Ǯ'| 8y$qYȎ+<}zg<>ǫ ,!jCQlR$r|2XA 9ANpcsOӁCsHXM=w"G<t!# "q [";mkt-ɼ3tIjvvq9L~~H2r< ,l$W4# Ǒa:2Z?-r>W2u*`s*jAʰ79]5}[ K:jx,:Rߏ01nÇ#)r"Q/=jc@/fb&Sx)Ĵ 1MH` ҉G{irYGەth,r$ܱ 6ڷҎ#׍xTT]p1 7u[p%F+PY<2$!UK,u臄HH5{| "x}\u9WWW}(4ydEmonCDTmg8SkYGݧSǭ͓h8([I=H~gEXjlBB~ie\( ӵl ߂r6?2|108#?zE8GE7C _~}S2+AssEɆh$jν]x{]8Rjiy{]VG9d;w3Glӷ&@<QG~'܂?P_xP~F#{0D9!^K;~2a}=iī ^$*ltچGSD[CPtps1#H˴S]N2SD" x/ar Hn|TY'x^>`*2\甭FH=:hSyJ܋Zl 6Gӵ6>Q!#N\9ɞ뷁Zhp9:#؜wA< 쭜h;~Eтd*.ڰe]Mؒ[4U5hFGry~uTw_>G™H';;Fܶ>ڃi4J 7[vd._0_Im-:foB/H8mBnDP sȓ6 R8qc(?mz; 1uHEL81nAsx FBHu2PPPƧmn}x)] K1u#* m*_ld{?ydžaGl\Rse71p3z[$,ރat E5|+l#")-]YmdcL_$W^9#Ev/OE F],qϾ__.qy~Ύ.d*3ɿs՘gpȒ=>|cDžD TF֕pNX_xk|,2 A>tf׌9!E'guRrC<!k Ck)^6#p5+6 $'cv/;b^|2ۚoD e(G9:m~SϙxgDr3rعN>E(q7&|cE!ݍ%dnz~~m7{;U2dʦK2h͟B:HFт~cZ(ˑ ^O1zftҾr\J߷ܓ=ׯ펱5E=4 0*݀=  i|2?&(C`E-hg_z7b΃ 4Мυy6 q3m!6j$&HkO|>sr$ c;[9BѸEvoZmy<_2X)H| ^$"ב7y@ n8^( _+Q=:5oB0>/DL,}˹JB!RF㍟Gm~E !]Ʊv.$,ŕw R:mNKl}})GwLe&=4\]E~_ud,21y6Ҷ,]]8wBhM!%ԶHAXN]S̎";CVY/>K)]'S6Q׻x/ A{?k^^q^x U]یoz SopP*ė^@r.A9p]aq-0NPý9yeZ] D7?n 2$KCr!PrCT E Fݍo`!W!G"yo ]{#>2yܫH>}Dd*9]p=.yf3b=ó?ekzj1@2ϞFJ!vLw6A2~ mC~ec4%ppgz6>^DQbh?>no/}dbFNol}c+ _TYZ(RHX;7JU S=[yN[esSH(C ~\OٍCT5qd۸HEMK~k |wUcX=,h/G.6?yi\oWa÷(u 3_r}ݽEG=xIfԿQwJŅ-)]:kysV&LX}%%rkږM,6$qԟfP,Tn$G^ݟWC1v( sDNoB|e-;g<9{ /;{E2ZO~~.)oc%Jdbd"Y}ֆ rdz9<= ԉmguz8yd!EݕʩvPg`#(>0/ilEPFXJ2w^/ ɶٽotm$|&vPҝ~{4EBrwZ6qK2'QtvL!=a8C Y,WEěQtNc;l;}W N m`{ %S F9LBїQbpQ>G?8asA>$qйotH^ v$X^s]$׼5V )UH߫Wo!Urї2m*X Aܽyd#з`!³?Bo[’H[w] +g0>2rZ c(*tlii/laM0_r=UyrXm9RGYN;>"d b0C3yGQz|}E]aڑWUti"sf\tmbc2ygkas}|Oۑ}+41CM7`WۏGGO]؈ߧkk5v/ t, 'S{յ'73Q]a k&5?T->G^!nsM{hoO,oܺ`=O'$\d*rOyP6) ÷#;ϿA|l}7 h[k7">ri!x+t%ʕ9H9(Uě@Pd5xdA;غ-vQ?kCyd@vVO";u wEvNMH! ?\|}cBGwmmdN׼tmð%/D?UB" Vx9Zjk QLeHGx.]Evv?A&鹒_}8zv`g?q<[7GҵVճ:߭w29Aҧ:L?x] h0AF!q骣O̮߶P>zOHEENG ~PxTsE1JgP6V!^1w92I ,8žu7)!GShF/~qH;޷y9t]fqЅ6d`cNA)QbIjCC p,01S6cs҂_ שּCm!2BOjj-YSS ( u<Uۚ) .yd\n/NQm2:~E+DzudўG!1tW##qD>A*[/'S_"(ٽ{|֌˖qx ^ ?.$Vu´!AKvF‚x+KZdPzG;) r;wxx>Y]\GNE<@S*l]"ٔ<>J29?򻞱/Ew蚸 -7WGJƠ'sv: AtWsM'^k:HtԌ$$D%6sH9w59@r|7/m!Lrl!Cmmuv~ASFўh|)\TfCmd*3Gu?\d*Sg kyirJ2Sm8lC+^Y}"IG%SEYӃXozw:B'kJRڃ}YQM ^|v#=8(yu#45P.>z ``=jq*z.ʾ:!ӷ(#y7PRjmYoP<1dw^#݁^LQANj mCSEu sQ(iw\ˑGCH8 pu1(ӄ*]NC7>i2B.V ?@љH@^HK?R>brgdQy)7#%H<$@# ۚ"GHx~žz]{m} {T&0 F|o $Q!(I~y4.DEHxjؕs/@FVI&`RT h/-PXPy~yqgX{/~u7Ju 3 6~{}<<4g@*)e3g )gz~cʪڛy=?|dɌH≎Lddu#AH B2cUG:BSvYޮqNBćD|! Mq3H-v6ߩzFd9rZu﹖HkFFd5qy!3,rOGF-9-Cr5A$~HGg[ IDATcEՇZIIҎغK@S-AtK7d*Sn۶P~EZw(]XLe> zBįvMh]U :ÐM,xa_Gv>G+wkw$SO]ϒ#}O#~Q?EMDŽ!(cYr*6;!1X/:(]  ѻ..r ^]]×"!q4[C 7" Ȼ0z_7a`Uӯ+oT^""W &WE=ޖXIh'_Dt;yrv}О4|9"t$)Ez);eRǮKބ|1&lXLeb;Xߝ18ZNVߞx:z㖆wiQ5̼xvM}k.b=]ݥKZV6<>v!7_zmU->kxu~pӧ?]y(nnĐt:J JRP5b呀Xang{죷@ 96GQl\%!\o/>- ]fЎ8aC C|Y" #m[w*BCQ|NS}_ۭIY$CF<d9[;\d$c\Y920x(9ecst L~+}s~ȑ #'kؽK2!ٰqϢl6֟m~۵h/BF:$uh"q~A-I2AM2 tmt?as164)ʌ{.bz'ѳt|ꖗ Xi>y۵p }C+]xߜzSh7ZtX¢pK @< ! [dQu{3E=nF)~>@! sqGE4B)GV QC.X8-xa]l?y0,9xz|%ȸ |+2om=Hht;Xcٺ`]0$z76,a^^=K8dXcr!A/(d?6H;k|gP&[m ޣ=s02zRS}{c`< 'ROsXxjTy@>Rite5OtuaH)ZU~E[òhC^^\ؖ?d>9l׍n r΂5b{" z>^hXq|+ " ړ-$FCc=T\]Acگh'h`PAMt'S/ )bllpy:0 #.W"f+GBXcP1(@}\ϏBy$fq]6 ? i+ЛE:cpۥu WnvM]3]XLe>;ae>bhlostwR#ehoE (Brrl݀z/l 4uګ *nDVwC|(g?k 1Ɋ;)vͅ/aN~xbǚ#/qcs)y|2|żxz)W{@[j|F#2rt6+CƘkz8|_b{&,.NM `lP+|sm}'ZA Nl^Kkӵd*B2tmFTfd*Snc FڝlMo۞XNG*ft?> l]5RTtm9^iL2mỉ R: tEKaSٖH6|sGp=:_ ZtaS+V 8<9gTW.c'c1u7.;j,Y}Xl]ĊwJBcQH6 LeBF\dA̦_UqpΘ A$R]saSݸi+ ֺy Sj# 9RlGܥ1(9dJw/5FB:>oquؐ\ HȎA+ߋHOUz' p RǢkcA0"a3maGQN@G~8MGf: =qh p>O&'LQ| ]6q(T )HoFF2#Eg=HɉD75||Wz_Ebփ_]tgR㪮af2t]Sӹ}>T}pH4h" ͕h8Y]DF58z̮{tmbQ]Õ_d>n-J[xO@Iy2p ҵNd*3'?',%E5/hr|yd  F{N"a+2~?.jr>+t|#m%ו#Z;996]*9nAd U۵]$)Uu 䀒0 0oH4u\+~Km-+ɮHMSտAэ Hv+Dm29b8|q$SV- ɲsl}[mMG-d%ڿΓ_/ӵf^STW;򁧿k۔F&~w4p3Hu W.-+iEgZOMMI2EC+v 2KOT:$!ž).2EK,G|cOy=q=rjcW :Adx66מBr)1~AԱ9G]C'*a1# iC%$Ķ睨H59:bkA "!J#Z˂)w4R;4OU <2[C67v01ʼhks< Ƀ/#d*;ڿG_tKinI2zFҵ / X|$!v7KF_%ZS-e߈,=:Уhv!DkX NO2ӵw:ͮKc?i|,۾tp* rAҢc?> 5G[^ Y A@Q gl#aH3*Kl1>a y aɧ!F1+l^.RV %6vh}12$h| yNj_@ MqUNr=nG޽vϟ9Xb@SQA\>@o\|8=ٵ]ngӵ[GP$y>yfuw'ٚ呇)hJ@ Z# <_B'7r X.j܊@Sp>+K֯{SzUkLeGQ[;RG^"[VoTbgunYtmP2)ӵ]Zya+Z WR}hĢlFN P[}3,]Ǵq W! `rJ7-ɺ(g5Wm3(m^u 3OE]S[}sd<}y+6LexBd<"r{iWs7S ;9փH|^U"~ͺMAHWH3APi7xA]q8)k?>w<@ƚ,5ydl9B^" [r#^)ϭ~l۫Hw#cyߊdmB(nh Qv=֫9E{ҿV~ H-CH'SY:nc/]{X2|v%Sk9oYk+F㘑d#=-tmb;d*s ?Ch/-D|\<©/5B${cgEbks4pr o2o4^]L+nu'i`YhYOW jՇ"~fX@y)Gvx86+ ~6 EH\kx^r/|m9 *nsϏ@c9lH(Wuw!#خrj Zo!d*3 O H ,$h!J.k2D~tbOC;<_MG_h;y #|~ݽ!Z泶6bk wv2y{#}5fsZh/̰|/2Dtm><]0D;j{wۢ{f ⽳{~9ķHVVx]S۬~Ӥ)e%k\%B: b yn@YA@7F ճk꿶˸ueݭ_v0v>zTہg̗k2]UStq"BȎH1e6 C {eWQY fwev x|4$ė\ZrJ޺9c*+ g 1z8cλA 0nz4ރ~MBSu{P%;I+R{K<R8ׅc^x$]0J#lߤkd*A'4|dʽX~7d*3ƽnيIA]bECkN2K&t>%SBchB2w4/u-~}8GR4G; 䃀";콈sͮ͠w{#6g)'h"Le"T+y>;E)>bK'+kH\| 禣v1{vMekM|ۦ?t`V6#SK7 JP{*@!;g2eSõx3PW$[G| rdt2eit$/\"Etr`9Y"WQ(|QN|vu#cp=+"Yd t9? Ere|u,)W[ec>*idv{nF>6[+ fm.@r8;g}~F́OCil.1L2 )olpl._p]bď?LeB#fz]wy:*P Q £h\ݹ!o\Bzۑn1nBN: &S^l=,"SrA@K.ɷU!9tu 3fO]!E_#Ǐ񽿡wiS2Z=VV,E-?%.FL 4XPx(E=,Vfc"İC"vd"xUfzN8:ClSg|둢"_tw zqCA(2gw/Us> xq! vN0> /nl;=3V$ڵ_E{ 1A;=[hsFs;| 8ӑ3{ #O5 f驜+ m);Aox:j$ٵ?gL&Q'v~u9P76]X m4Ҿigưb~/"׈c%gưS~\goGf^\z:z{<Á?wMwN2ӑҵے=AFq?7Ƃ>?" dT03o[L쫧>6[f"~1ZFl?xٱ]it P>/]w p#~/2.N@EBzJ|%|AWf&.d*3);Ua7"I~o+X#hm/F=3S~m\wOo\3<|_D0$V"t?KmBavMv#b T ʼ/]XLe6k9~ɎIۗttOs$\3PRE2oTuE|Խ J%wm.{l|d>W֋CwoRi<_n.T +m "֊xEu!Q['' -Ӑp=GQm`d܉}\I=ۻCV;oB ͜WP\grMȘ,ET<`!Hq Yn">7v$ʴk{zCg"#JqF+rz.se KGˑ(oy:Xvkr7#~^v2OL}#YѤg> wjY9i/8b-:a{Eү(]VVH6@hX]̣w|_Cz]k"wt$ 1r\P\$얣/V//E7Ða)\~Qs PQv!{ېEvw=zZ#B;B8+{f KfgK{l1!`1F~sDprϢǧ]Qv/17c?=}h=׸hH՟QE0uo N ѽ3f\#~ǓˇAz4gS:uqQ y y o\k:\ 췃lZ::OG#Ͷ"]HN8o8f뮑r _Uo rh&ţZl-6f (Ə{ODU7F9l=_A=;8?w&~})t$\aW u]iw7y9R#N!Z8H K2cLu29wo6HDs66*t$oDcJ*|zMS\Qtm"M\aD<(!Z^||/("Ȭ#o{b-":k9a|Նj?۫>d++~4@0zˣ]C3lU*yNF.tmwtmb3"Xːwonoī1`Gh J\E*A@D?(WswF7FGsbRCr76-E6Ĩ]ţ6<% a—uo8|DQ-> %i3UyU}ml6B-}#%"oTT UT.RDABIޓuvf99@ "ܹs<|Y8$ mȰj5p9IfN7hOrR}dX\(Q\TǮ*ށ6hgC3y!CF YiVO%R"0PlxEQVihӃiCej'f˙fNְYǠ$7 E x.Au8N1m.H5(^1d6yB#lUg 8uշxya+khh H= 6]#H퉔}EBOu =z>fK㼖"DY|þ@e, |ДkoMX"Ջ)@K%0y!3,/D\tHZfׂ"#$ua6F6Vp@XQaޟ 9^cq^-'C-{xd.5bbsٞjf_0*ܵf1w3oa5Z4"'͠qD1t뽇|\ ƒ?<8_  []z)58r O#7{[xfgg5N_PE{}Ɂ0_G_XƯ 3矿K~mO757^5hJz5'vx-tbȃd#GO$oL 3!/`q"; ,D+*PTcWr|+Q2(W d"eVV p*͜mCQZ{d;P)+W،@]H`Aޖq7#2$'/ߓf^Ҏkdi,MnY$$\G<{ 0wD-. ,4#bكxtA,!XK?lǕxr +"X7@t2> ^䐀hUtuVfW!ʂ [E#}E8`'wKQv y1BD 'Lg@/Gm+=?FN^"op!evR;9ʊ[̧e{LxlkynWG})]i q?X" WyttŅѳ$>e\SK9~=xtd \J$mվ8ea3g ;q|Ǥ`fݫpWdHm-%RGe eSPF\}0ןk;a8\4ְ\}_)7( ᴼu>*T4ah?CMͼIH/X0c:l|K%rrʷvBxt#oboR%t~ JƣϘsV]}A_i |}~~&uUDg(wQmoga;(!\e3yy^^8꿌d?X}ҡVK>KQ@ѱyl=hM(z0ߊ%RAtΙg.#rG)Tȍ)=ڛF6-[ܳ"c+T6"?aS1شR`Z,ě,^0KHvϣxx}?s'%I[nFX'870.mjn|(?P ^]yج Ex/m?0Vkjx:c/ͤC7,ȘuvQXGhP8go{457ɆB:" YD괓`|ojnA\foAڿ2]ZP8_9.8@dϘy}V 03TQ?ry#E_8d0N40\ ߼^fv o#Q 5L,06O%n_WuzD1!|f@s/UlyaKp\xKG}dل=|9ZDGm)M^kGoÕ; .s\=/M^CcM/V6."Nxb];&믇  ĺhNF vၯ3nՇ n"R Rؼ*Kﵔ=Pt KAs1@m![h Rr7Q#O:ÜʯPki̱eJ9ƘDF(3Ӹ\d[q=;RéB\>\MQQ_8[kma|-hPTofEfO_4 +d2H7 at 2ʑSp21W=a, (*e.Oݷ=c~~-Z"1gԭcVxES󕿹KLJ c2L HH; "OULApw=ټo泣̏@zf ]$3`9>+Gv %kRJp=l9jD#sq>=@_C@2*X?i] $Rm^-_Dr}dnB5Ԭ*k6 i+@MaK12nX!:1 RjCb#;S'5לq'4b+I)$=u.9va7dօ'_)zѳX"u\87m<^$ޱayμGr)2 uF͂UsW܊S q_ّE^ED#$#Ek5ͧ( V!R=o3poK 8+,_pݜ\6rQƻ~Cfq>82`#lC,rFnBDZ浍-б4t>l_ ty6ZԹfuT\K1V@Y5Ƞ|mÇ,,k~qԙ5m^iYMEHYaoT,FJ A2=o8vdb,:aO/[!J؉tX">b氟l5sLwCi}%*C4nV.ٿc&ܶfn`ˏeK# yKiYq/W76LĜu}e(9C\< J~KNX,:(}Lx֗e)[5a#@Q@I!eV䝮F¨=@Pg"R\!SHP/@*Ur6RW0{.R ,5%xURf ⨈kqGVs(j༁T!TB q"s-f=(j5GKy~[͞^+/}YJߡd" hE_sl\Tda c)#;s dmRj*q`/%A 3aX"uc$3:&ȭ0ߞD*Q(r?C }Wqu ZglٞՊd&s܁?mJHٶP Ո6fitGF5l"e $wڑraIayadu[Rm~3rk<쓍\r?Cn\qykA{P8<3(TkCͣ?f5Ȟ)t7P[p) UEŝ!y/flSpP3}tm^+Y&y%R#~d<|']6HzN\y7sƒl~C}Zt;w&19F$LJƣ5TqHxp"Sg/"X_+1yױIʐB\^QǗFX*E3̾MF4?^3[VBPvm5?lՁdtImY IDATY&v[({gE2"pew7=~]Tv=ʟlb} p\/fo\Lh`~2M:g KNƣ;*};;2\.tՒ!&yq0-a1G5/-9>5m&x&cl* m`l|>)2Eݙl~|?YM&b5 ^"!~L0#aEV!eֆ-Y=< s8ɰv$"!m{*ƕce Fs >׆hjsp\'2<rFHQ\Oi(ABFG&#p O@B=  67͜R)@¾MpFe=uMCB#%3*ʑ\ =G5Hz ZEQGr5FF ~Ȩk` <":@,D8ܬR3} yQ'҂f>7 nx޼Iƣw$sh;Cgk9iM&"/Qqz@]agҢ((kjn,|ݓ~0>3Lߟ.]߀ $S-!(FbNvٞQ^k0ӻ!ΐ H$,. ٷj@Z7#ۈV)&\;' Zc=*ANE\{|Xىu#o16dy?b 7 !fKƣ[h\7E4oރSkf1 & + N8KKHg5ϭ<2gMc^D2ݶP(S`~W}A7W#K=;u!{q[ibZgP>Hƣ' BEm֗?ry=2vC+K7>֟8[g'4o^ޗ8omk^V `d fsUg&^E4 〴 R"DGGۂCs׏[jvlW5DӋqtY\\Rp/2V!ap,3gs[ͤĬv\qo?K 22'"0v?ތMȫRksa bk0V%Rӑ\f"$\I[5֖ ArE|y6Caj\ ٶl i/b$,}7, 6F, W: Uchm,EX܅dSH[zF.0Mo^@ƪ_wKXYJb,{3s-3^su0s:j+|N׭+^3qGۭ\Ygj୮?Ed<GgU<ۗQ$"M!9iuO@XAFpܳ<sY3ކP?!GmaPV\)Ѷ$PRiL{s]s:{p:8&F srލNUbHF֙ZudZFDº#yQs 2F)sF䘼9HEL1o}!gf?ֿig K\kpn$׃v2e-9p,hs^ኄL1[cNWI ?+΅9.xIgm1(R7a0e/?5{>VocO~S|>n[^.x;yV+_Us'\?z%K\V3ab0CT@8wQ٨ፁs 嶨>Ͼg)JO:pymW/657x+F:v9ˌї ys&&ߚvӃ$ AfPa(qƔ HkPeۓ!y;m_ @BJ5>pesmΒD#̺@mBos9fN6yʔQ;92Q0Ÿ6PE&V g}6Sbs kt-@ `E& 5@'3΅g΄l1JZ%J f_Gψ}!O:=H}\s+*1cѳhK@, "hV\0@YU1=WV2]g󘆞ׯh; W <+މ Ugsk+7/*2>΢;=ӍW.Z{pՕGɾ9; Q`z1^*KjzRך]y ?i-l%4QV,y#]duj"vmDԸ.\l^,  W ֈmGᨒkMBF@1r^֩g^ lAQ?bf(bxPfB=57 K59ʿ{؀ NiD X9+wGȷa\0jA n?Uó%9\"TO!ވ:/+ѳZ32F&;}'?(0MDmy#}뇨afإGw^G>9>x5oܭOն ;|~"}yo3iGxι?HEɺp0]2:>`}v0֍dM z-cp޳w= >mV,E;fmxʼcl{!Bчۑ,4-wT֘:q=r/!q-0펊5LB% Y5) !oH•_ J*άt/[?edpՆl-+WkؾHGc'pz2i; [j0`jKɯG rs>ƭ V"GSohFR<2<5tG)?~6?SZRO*WBdKsG@4̥5kfn h4f/lѸ#W&ѝٕGSscE{v1}7߬y\_.L9ީgdN*[~a ^(0#zk?jLEI S^`ۍ 3av10wWKO9dH)}y~:uUW65_9hF2brS~JYsD*(vRnEQ H C=[h&l߾J\e~`am\#VGrUm^UL|]gEyD2)#97׀s- (m>gwERaYhi!3Rvj20Շg fҞG't溟GysY"gE8":z/ ^g)ĘGx?cF@mCπ ]?“qݿV(M"s, Du]ۖ\E(rه Y2.='74U["X[fct{SgErg _Kld<+W'GLm}ۭf.H|蕻!*9l0Rd Em˧#NiV,EY~Go;ïP{R~-jEpmk`٤.z,X\eH8}z\‡y,@B X[v)}p*qC^}v!*49琰Z+:g @7f $.s.K52{qD6m쁼6Jf#^JދP 67ZC(r'M͍g"e~_(v4a3#;;9«oZ2C]uКk?:GM͍u]_zz(N~?zmw=~i)pTqapwa/?S3|hKoQdLƋ |d߾o]=tؽMR3dW0LQjd7 C~6/)ʼl6}'OJpؓn7D$7, WFrciKk ls+ qX,EƔej|f<86=l5*$b 5;['fmQX"U>!L{RpmQ{rrξdr>¦~|XO[%R^^]߂ -k;/4O"gUG}ai#HϨDv bԡH$dp= 'ޏ7ߚ5^pk9}n2|~2F* ܛO`7l.xՇeUl6y{Wܾ|>O8e rrݼܟ|8;SY8KŒ7M8f&`mU8>w1tř7&=XRlћ;ѝ"8)K Q7Rf!2 sp=p=K!2oeK̵""pcދ~m^k#Xę`m<|ܜ ڞEFX"\-NȰ"p=D4X#[-*h`<)@>ܟ_ zwsQ/}7ܬw4Jފ{3m|Q C#;kK:Ng3,"8zV4EsjG.woƏ(*$ڨu x`Vm~g588fx mQSvܴq~g6qs`KǨ#*sʫWn޽cjLA;[/-j/k}~y( vzөݯk+K7g+K7zOG2m11K~k%Ctqe yek=!\W$6݋l2ڞX)KQ]+B"[:ȩՂdm"߇ȱd[M2.Lq(,>\d{-#q[;GdH kjCUd daJc}z95.1{2/F ٣qfv3%܇j.ZBɖjq^K-'rq&ѷ<+Ku׶SP=M]^`Pr<ݻz VC}%[vxO&W!{ ڰgܠ;P'M`!KՔ쭾#|o$[n#dH/SkXّ z fy3uY2}rʑEEMPDd3w$=XN5"6 {zenh[p2:v},v="fM5lB˶}\HiQCp<GGk D ұ4}?Hoc Uem˼{^CFx73#'X h F~Vzit u d)";lz~ٓ\-[)V❇"7"a~MED}9->R/G'^|07G,QZm<_#ƹff#/նϩ,po&{8 n< :9#457:{"H5cTC2m1 Ga&w^ ^on82 N+kA%i+VhCka\i#2t);$,] BWnYpkkCywE[~(#w[hY c5{!>Wo(P) @Cu4ǖ-E`E_"*J[pE-Qd,2HDϊڊ;`h`\:xj*V[92=s2.8fԷhsT,O5|Kz-I@? Y?+(NO޳獂 {n`!d=\?}UxHT:g$-%ЮF,= B<%%󩄶R1 $"!݉BHp~m)Pp#\^QQt.ϧ I[ajه>(gHY*3sm1ױ*B[k>!O  ۊgu4_4o#4}ڻ3Dq8|%28:,n 72BDCO"M͍6d20pO [Džc;Sѳy-y#5\KnNƣOKdI(:snns =Zkv==ۿq ?C|dOHm"쾙{M|d3zS$놢Ӊf-ya+L-r!GDTH>ǘѼDjE ȗQ>S/6$pVCb}?o@/3D~5(sY*>Xx́_8ٲ [/|r1Օwq}+1>30X66?!gOĂ w{j"Wڼ@ƒŽ[Y U+cſZsE2)dtg <aOgTq!6B~="l|d|;_-s#pK}"{s0s|M=\8CI9>dtal֜cٟ%ȡ6Q[My] {u}xӹ,|DHw’M{͹5s[pn?~sl7k%_\x[{aIH7U 9][7yk:I/?KǧwcfW# Hh~?5"%RQqqmnCM僚Uxd8~9=XUES3` L/Od"?#UMk E(61J=~[~&ЁH1s_.ȌqXUQwfsc}?ʥeemtNj<QO¡'*8d ~UY$@,k_VYe)rjQo5 FN;12> 4lN]c;tcV!,C7Z\Vo?gYdmFrs~L;g!LnDCSM|~xX"Μ]~e'"{OW:IN> #9a-ލ0e}-Eiff8O̓et@sljkׄ6]!m>JF5nñn1]*>zeۤ P٫^DKU_^pcH,ZS8"C0)ٽ,rWn}pũFX55Fz/}QPچ%B'zjn7h5};{ez [Ȗ[~0gG.ܷoȎ?^z{~="'(bu|vƮue~w{g-穯ePFjˎ<K_unߌaxtQ,)lYH-Y#epڶj8ϝZO_}c7Rkp^Ef?'>HZYP˱y ef5[՜{*ZqQRMf-h|_O"qǰYqM @J 20mR.`In:c$>@h=j{9 '|9(Dxg ,%oO5׷U}@ƿsR c*#| )+Qg#`_@0ҵ?]6Elޝx7&E ~|XʪM. YLo]){ S,ⵆ)Yo0|},`m+[FؾcmX[fɥ+hcU%Vlq\U7g;īj075T.r`,ǾҎOu%Xrbո`(\.#M'zT$׎F)ŧ}uI^6Dg5[X}4akY#wF4uWxE#K|ԣw$&!l9)͛,dB!Xc$3l %[F#ElXd?"FB)."5~d=]5k<}E5=oºz9^D}sӮy3|UHތF͞ 'jKi"]2xX"5U;)t&6v+Yߎ0R<}2ׅkmGQF͚,}0%yl1{Јpכ܁3"+͚{pt,´އk_jq8e]2ÐEU{!Bϒex)(^Ӗ2gM Q;zp8cWՊth<^'.֍Y<ӷ=?c/S?XP$chyW~HHE̵w39ˬ7"RN4ohäHwfs6LBJBG{ x[͵ʑ 0ƘY@`B//vI8 $f"ИrY閲c+tZ摒` b?y;55L6zq0qjް%tWܥ0kۏ[Vn: u޺6|i'pr_=әsk֜{KBh>Yxt}SsȱҎhZ˿,bQg5ξqo8u=R.iI|{@sKּ+G,YG%b"OR]_,"g(Њ"g >!FhOcپ56 b7`~lfT!jl1!B X,{"ye†Mf 1"<)3nC FH"?G܃[q_3H_F_GFњ42᳽wb#㜙#:3dp"B3gn;s-ոa[.4{z z1sk3766 W;guVVDU+絔{*BNcK^"1ރeWX"u hk\~s؏J  ՋH`Gc+O{U˱uΚ D`imU嵟Fg,ygvf7T64jE"(9J=wE%,( /$a{-[gg3 H'zxkݝyS~!$*y\C^SK QOa-"'"","t5.=T A®Di J,>t毲k!,]~g݋ :vnW}i5bӢDI>Ml?1\zVOAۇ`/Aa"rwwG$0=PhJyY@ק3:B=٘zsCJj ޚ5*j{.]=1$b@b.P zF)#oV싁ҙoehc=Z I \M"gºus/(q, Z.hz}~3{|Y+N&1c"N2t[66~%]Ng C[R$:n=>ȥ!q=oh|@n^hW$xz Ι5/;{,j^oHv %*J)_pAsScx,ᓑ Za<dFc!čôr$ 5 lx,>sgQ/A(ឮ!7@=*b]DHDX@|c=g KBmHM!O'B675zh-ўhH0`?ޝ5ڻhxEѨBg;x; )LhK\}󥇟rצ3/+ݫs8R*]?a~=hAjwƓ~ ӍxdUd8gvSHh> 1I T$ٿÒ5xcAyZ2t7@(Zk3PWy w|dCU<]wK ^%mq-.x "Cָ7YɈ!^2e)wQJePǛExhgHqϝ?{s0+폢T1^k;0oPv:_ݭܻ}/k9k{k~2pssSc[#nOgA( =4PQ@c!mOpEa ;!dB !qzӇh$ Iw E;af!^KnCjtFB!#0>V92~yiY^.@ ػ&"x!ʇ;.{#(J ~<p&(͎Z\O, k  זٳ  .dF Arg {1$+MG|Nħ#IAn,D/1(yڰ=z#!$a:@%A$ Z]h~63SBLm=ne IDAT#ڊ3FN*7lqh膺{7A7xc+*XsY^RZNO ӗ]sY־\?2oUWnDΞyÆsf{›!(V~-tڿ Q~W=)]tmrph|μF:G؋<"OΛ#zxv"Av]JXqF%9wa(:y:{-e}Z߁6R0u"eu})H߻~Pl@H%(c]56!܋<w"Y#`*!Α`tCzlNO([zR] Hb}3͍C_675XeEaÕ~$CT5#NC &D\w)6Hj1!'J 3F֧x sR ~^d2ZW wcWms.Yaze1eëƏ|gƚ!Qa0.7XAO TcPFv%yWioI;d%͒$3ٓDEgd;8_#s31Il`߆gU!UEV8 y[&P P;rpHKQ)zF<){.{B:!<Id.GIˮ ,nwm,bl|#}7h 2Ĕւ*Ƴ?!ֻ18ۘFHi[J@ҪN{wLb59<`}Z,WZWw475v3ٟp!A{-ZYjT<؜~+AO(bH!pڋCI{OsSWHgW{dž?% *\8bߞD[ I/c !15H}~!J>jykm} mnjΟ]޸_Gk!ĵY̌#E~ΜYY+g_meS/yȇ.گ叭q/u`􃅘1P'G)sάy?;v:Ë^Ys?\475h%('˽Q#CWxHv;ηPֻ@P\Z@=܍imy3OƢKrdjê=Jr:D\s.)yGhd4xͣ`MB E+LwqzO!#0BdAC콮0C|a2^^2(bd[?V"4$ۍSh>1X\iw%ژyk'u*pv:݄B/B 7P)'#%#k=HA-A lxeN x67]tuӗ6ю@0(]Y[z_cmsɟ&,9lzoߌ ^Z~܈O ZoY?^C]sf{2ɞLZ=[QXt^fCW;N93C{aΟ=h3k әlydv]IJ=G9!VG([.1/"s컎~݄JD\ALe#B;)*PqjWf>/GQ8ބPw z]F 71zvBPNB%bO=͈!x6мrMC$`8!-P1*~aP~/g8pٺDqRc?!Q)<\\dJۛgpJB ^Wﲾ|ޕuFu.bJwsF!lۓ(H ;ܛ>j1lfE=ɪӃ2zW^(% uupy`Q>>aMkܰ>jyhnZr609ɾ o9n=a-bD !c7R^9s[Uۺ ZY?ۥj( ! Ν?;](*Z:O`msSnnjܮ}՞$ ߅O 3|AC00 D?֠}Bg4[O5vH.۞:A F9e~"B : 9) *m|][ gIX(%1DH$6`wBe/#T`{[+ `6"BZߓ( 3B$YGJR: g$,{-gi{$gh=Į~>k}V!VЊ(m"&!g!4@[:= )G^?C4hX˚ҙ@L:ƽ-Dvbq\ M3-^\SUŅ8 qD%f^T7N7L6 ZϷd7575z0'T@+ڗH HlN3\ A,ԯrGz_>wnMοvGD?x稡"`Y6ZΝ?rtޔ6wqHzάy/kWjLvo%'ɞv[ә Z=]h}抒j 툀v+?фHKB<:gz(Fнp} #z`6N ԿrE^U$ -*OK1΁p6' !^]%( E'Z7A<)8g іATG?r=s= ,sdow0I6WE=m^ (ٽml;PN?Ni{W̖9\e}Hqj (b퉌3HPuȸz np󵈁/re򺍂[aDIr Q ˾g V?EGFr]&?&*q}zSC&?犘?<ɞd^o̚W;w`!Sw8_yﮟcהzHgP#B@MkәGP2\G|m !g͍ g>YǞBȻt&'J[kG-N!rd9%|Bs|<=Gw{ #܁HfXTDd)~yuQ/XE؏6-pSg(E1Dz*(~,A2֭%O$ ӁQ~#^\؂~Ïf[9naVysr;мxt#d?׭@LB%NVw#XJ!ܛ| (D&DB ӈPN(A\:bHa(Mn b d"h%>G𮍍’ȶ8J.B0D&@)N؂+/GCDD "ΐGqȪ91l|XvF"̙HͲg7[dXMyS|p~D`wեmk( w mz ls݁Z೭qC P"I2qÍqZQH|gaD=z 4̨MG{;Zm3:{F2vQ0Edr`kΟ=̚7gּ_ϝ?7 sϞwO[ 3 ũ6757QLa)~ RAv^FhPbw#4~t(V-?{pHJ:iyPxOLހ[@(85 ɰy`7%ŋ{u U,.wG&*twGO6&#ICwA{n3c$bsgjQI )x bVaL '1<"PÇp'p :MKәHT?x$Rܾ<`SqsۓOnx=M&je8znGיoWKkgSF?^^$nk 9gּ/ﴷ5qAy #fsSo룖P5ϔ߹wwT] [Cn7D"мǮBf$A0_CH;"Ʈg!R!p ֞'ܽNI+ssClBFG{yTu!=PRA'RǸ/(y7!x=r(R&@`5!2#X~a׮DƮ֗;QTo@yUsCJ޵ȰOOO"s/ė1# |j m}Fynyu)O[e(hukA{ߐy -lLvysS7H1܍Ճd8,Gָ`Ϫgݱ7G-!P(*Q[|ԋ 3/nJ/ҙw(SxX"9=,,&3jbvyCͣC>p ݑALxB{%A^OU2#L{֏bCs<:'yn:q 8G,P'i} Y4՞u1)֧q.D kݿRL;pFdl "&"vHul:=fuomjvT::W`qj4H$HPGkU|Ţָ*׏}d}=wmWMdL'1nFdELNZ"0;Eelwܼ^Z!\Dgfdp^$:R5"`xcs5 =JYEC u=ͣJŁKxn~yEځgҍ=97#r~ل"⎴g`5P(C̪ ծ(.!ԧj调.xDD Tf۳|13%x[O  o^=h #(x+v%u}G!kl{N9 (vDB特r8 HrhفOs}6!%%mR нet&4uH=LB):Kr`SsUȺp̩.;[̚w32*~+9HB8OTcob=Ae|2 mD͒ Y{Y&^_m#,/1bm<0gּo}ߜ*X_En)Hg7𴓛ydW?}Y,'180ryn'degΖaupuC= 8sGx8(""&&ڌ4 ~Dv#=DDsN6bX_=c]oзʍ;}gjw;zH:5M?3o[jRE:E$"J&>o9)bz.-ؚeR됒w64k YB:[ݑnBgh0P8hcp vރ{oHp73٣O鸤qC}Ro}+(ozZ㆕H|Mw|X IDAT"gHV?bMT,sn!L*קVID-xڄG"϶uRsScj:[G R8J-r@)']HlF(ٳv@.hD'g kSr-( idw7z.'@(*r=>&pDQoF(?PgXNםz 8\L$ #gNFU;7' ֫#i.zbpńek+Gʕ3v}{H}P w"% ELAPl:B[3RzAg*՚׵v$M8./^X[,! AtH'C"u%_WrF6ƥ!CjV6"<"b$oC1_J*H6',7Cļ `K?>T"Iy2O/hVH GV(4AH.wEW{aFW"Tm\"({pt H! 1v6_(VnuށeMw575Lv {x!z|?EˏCy$pU9s̰ۻChC}vٸ'؜{b{uP{Ppyw.@ dlBLg475ތ]]8VT3N{)1x)R88ïjnjtI#gu H675Atd$DѰut DG;qn]wX_r}b+~gmc乫#t2 XϋN%V_}$@(jċ=i a lģ~xڸ!Ym8Aȏp^x"(B9R|oY@ah?nU}!|$t!r7MB *ķ&yDR5 ]ɬFj9>j9M]|%q$(oDjQ+Йm۫뽈| Xd (Tg(t뤆sV2[)JƩ8&с:= ҺW"]Ҵ SBUCbc4"#BiGv͹hėG v#H"Qc9 wd=FL`6!әt 8_!E\c7B<ǹAZ u(n}}ENKg!ieqHt&b!e9 W1 T5ktAhy 1]w *nE"nt&{ҫx^ek1 n7YZd<P;D ~I>~ǑICSQTCAN!% M#aDS#C/ByUPJDs=@H茶n//횽m#=cjs@%:EHp?B^_v?/)rͱ;4)RbLycjS" ~kkRG۽ 9hHZBkyM2rUq":!-(4t+k&N_zjO@:%yܻ[N"B'F"BD>T!VJE_"F{>F ݾ!DP]k2E'wZ Vf~&'İ"475d.t&!dy )@5`.O 9],ygs^eKgg#K[/Rqw775޷{J2TsSW75@H*]M}yqUGָߖHӶfH}sSE nc}vyM~HwZ#d=" H וʕbǢqL.#B{>/ !oI{ h9Va}Xx*r s!yBtw^Dל^ܮ~%͡PCKx Ss)f|_e:)3l܋p U\ WM)X=zv!QVR210o3hG|qn{ȣ67 @aW4/Z`< (ЁVV G{Y=16Z:MG-{3zikܰx{! 7؃9H:)Duo; Ν?;Yp>YZ9b`5emr(oL NN3kYsho(;Br+n7 YD3^nIg#ka?"*Pl@f &A‹ADVjcj,u90 uGޕ~$@ #È!EJM?"ȕȋv=Cȳ2 Y{i=9CCEHah@1Ip߻Q8qY,ә(G֗#6:݈Пc2RH/ @ =qycwk<7`guO=uPl<ٻ)6Ǐ"fWnR˒eq(ZH|-t&^Ըm?Do oG$oGyDyC<">O t3Q; YȞW# J5 z ιl96M<Oؽ vO]Uɓ()3'") Hy "^qH&@b b{YI$X?J'tT8{ vXuش= *^ńpӋs=Q!qb;ubG"MM?KgH(_EB+Rfݟ!0'!+jK=r`. җH1E=eLp2+ɖo+*1S⥔7G-eq9*GJ\k!*-DczWƃ[uwE{@ kΝN/(L(d#5Ѯ}7^K;y7'3ٙ(Tt&xxш~L&잃hz h@9G(?þkǕ$1"Y Գڟщh{W {oDuDk7HaBJQ?11eQqQ{U@v8Þ#nBIw 婃t$6[}xȸe6pR6((DY'ű@\6[oG!vm6CI֏] 6b:baKlme;} % u# 2A[yZV#ipnsS(jLvXB bσHd})/Oricf]fޱ>ٟ(kmOz~jv)F}_z@ۼ{ !0 ֤3ٛl Y9H)nsw 9@~.# ~ZsSҙI1~ݤʇ0@O8yR9}KYqPF5d}G-S|5nxg~s)8uߣMLJLFQHx*5t{iȵD;ӊeu{Bzk_Jә3zM:y{{WT08k!1!)lϽ^W~!DfGtx!ѵ k3@^xבA yJJJ6ظ<.$KYpŸ EoYsЊ*QZ}m_mcXB;!,d;n8C|kWc0#v!>eD~jcP4vʹIlnWǓR {Խl."Z~΍ sYSBpgsSiE CO#Euke VMu_|eɑUut[{ܔfq{bE}{_ >xz.ذ仿dq~25X!7J\v"hH} h}.9\޷#KGQZU'#@ !{%"CHIDt7<6"LBW  [`f{ژwDC$W H_1cjЪ53{`E*1*+g@ZB" p@:6P<Yh@M_KG#&bH "]$4,9IQY!ds,о vG`/*ڿxp|Az#xO:EŲ ~(2*y)@:٩7Y6:ׇni ۣ*vT刾5[]~}3Y~5Om܃lΏCGsh; N{S[:=ъ ^IȀ4Eށ675yܓGi8kݓ^B_@@݁F ZQl8'qǫPHaD=[B(R@u\|$+}J@t"Й Zh3xroD<;F #^6 H(xזtGo\hBlHh)RK/cxJrxO H[f'h+ ֹ s^{@d[= ws*u#fC5H/B2q6gClb]_'+,LҞ9ǔ=gkb;Rޖخ@BNz}mpSbZ{^@[[ /Q3_G-|Bؠ8E>EHvd?u$Z1x2g;i[^nۥ,P"Hh#UC әXD@hCf0/MGYHX$[C`-a##Ս^Wxtx(垭E4cҞ?hc8"Eׯ՝tƮqn?tPZCD+w܃!e=w>P8Ǒ&N&9ER3^Eys?Oص%3 K{iը`o9q]:mƧl|ldDD@ruC(%Dt C@u`F >^{Aяa@{ie]{Pa_xL0-QӉ#  +dt IDAT/~yXy~/#Qo@B{wصF2{gƈ܁oaA u '= }!ހ (]񯅶cof㪰/q$I_r_>csW7U9y?ZF-ɨn-x'` e ABQdx~w}ԲX7l+N{@.JQ h~޾~w%,Ct~o, oc7H%-RgvAhwDG4tJ-M]e# ɪxxɐdDs`ߏ%]!Id: тHhZpFYϻp_N\h:ă}Rl6"cfRvku#"Ge7#gNmr=!@:=P$W6C@y(7ZBFW#0%إj`)]1ֽ@}_n톔= #E ]\#HFqmNwΏ6GڼxE/!'XOb;WÚMgsw575v2- nnj|#j:i>j8zj5$\y4?![*Gn$wG-.3XNQ#Ӎ~9_;x5n, =L PO(W6TPmL3+:a|!=)X QqB,d:RTQC=I +bdu:`9[LGLD֗*BAndN>bW#ҏXrPxP8vb,5A۵bp{.1Y f)G! brRH@ptGP%K욓%sf_LHg gfsSc>.󎓫nzoCJ TA VQ|DE)Xy8bAEzu! f7[g9Nb s^so~9{{cW- aC㑡E SȀSD2ɻ{CJo ̆3N^JԊ %o yLn)z O?T3s͓󒽯GZސ:ek _)D5Y6FG;97W'^7wrX9mDfܯf~ۺh^_|"o잭hjۏ (b$_ғs[3l{Iuxh` At=lBW yv^Y=H1 z_;)j ȳ?B 6TwA/لN #=EkqD^nGB4f1ؑQFfܫA#"!Q}>+Fڞ e6jߜP,<`}1ӑQb! i2"r3Cxߍ;wڞgؘ,|\On~ `k!#!D Kl5W(hck9ӛ=_:/)j#f؀)ɜW/_-Oo!q=@:15Jo`=pQJԋ<ߊ+G m9ܞ4:;/7Ĺ'lEJ3<ɜQB ;__}w^\8鴽/^rJh]?:ٕs B!zBT {*v(/ x|u֛BxDhrkcy7~ע$yɌHFb>LP"x:ۍ+j$ql\ZbY六a0x_PD|f,r |vz$;3sulw*I$(u4A'o=5^^G40Fy=}=G"g)Ao"\K.Nk+eSCFnfǡ=]F#9 j+WJM࿓mw\ 0ᰶ6M\ܱ:O^偖l\.cÔ?}cwG(+KO#'ma&r3=MɋF{W95s;ӌ/:Poo"މ ^=ӹVo1>1CjM,L(n?;#A2C`#{SHTrZet4A^1C`|SM5 vO?u}ȳx(2"BFG~^ w}(Rf.ۘB9Q2 kAQ$pH,义mֈ掀a 1T}@l 2"m>tvAf*!*y$Mu;H ]緢h( sP#W(yNh/k~헟@-@f b>b>;Ͼo+u[zY2\TuRFrwo}8YC;C^u9;N5*8+1>B@/{#o션p 2CO<9ybއ«$7^3eg: 5lZBރ6{4w~޵滾uC ˑ8)!T=Ea}G$ wqٶ3 m#_wXfk՞+NU(v %W(b>{q1UgoNSO6ݣPqʺx=-Z)c~(O/5I ooZwGc-|@:OG{-5+*Ǚrt9άBD%uOGmB|G~{nW,]C|va@VZφVE#Dn׺KHu'ğW#8=D5A6BOIw"A(jȾULI\sClձ#ٺ}6` ^fB=o oٳ"djkG2c-R5||c׭ QՏ!y4nLm@ *5k 8'#t!AjuZt}[3Cme{.ח[.t”7tƭ3TSe,}23(z>q@ۣ]=gmOXU>jy5][ΦFJZrYS3 !)#6l˱?m3"36b A,J(Њ )gPD++yd퇢<=!F:e޷JGV$צFzifH26'R삼Q}(jToGS쎹BɓQ}s$ p&1<" yzWa >G:u({UAeF֬]8MM ձ UZ1Gp&Bbe? xF,R~֡gDFQS HД\T7z>ܽġDK߲ ݀O[FqGwq .BJ AFЅoqM[}oGun!k^>F|((b2 w 00Fp=s3]F(t8Փ#:@\B^tNH6 w7B !PObRWD>$? 7Џ)/^9FHI.V/`T$)RgR6>މl']į.*O s[|+ vϓye{/NdX~}!oGFCpMHN(`Lύ>B]EUdq!@jm= ^vՌ(kיoCzx  q|vU1J_unj[]Rֵ7-=g3H9,$CQZ!V'ZFh;k_GȨ}zyR9g.+Ǚ8Ċ=uL\3O/M`gc,27B!d5l+}ힶj%h8 ,7R7*|\g? yALĴgw#p_GZzښϦ x*JV'y`QJ΄KBiVb]|E3n㦮nZˑjCϽ;{7|7g(Ǚ?qfIGttԱdptQ:)Ѩb]}'^Yt/>?7sv0'x]s& ;X9"&ģ+1t~tN@N0ԓo!;/aAfȞFUOLG| LFJ2ٻhxܠ#oy&ʮYcyMPgb>{[1J l~kqZw~}&#kHؼf(ȹIHN'+ 5#3ޝv c' S+!z7OcHtw~y ѷ2X>'r! ۟_^Em4 ,_ݶN"}uĬ'Wmx՞Tc}x҈9ޚh %uc0gK7(Ǚ^6]kH_^ 9<&A!q1\ j27_֎8n}!g+9ENG ;<M7#VWiSs騴垈I"E{iIh7Fu>61b^{h#҃"Ez{x1σq}dL_'bO|:Q2 !(,F2K<) ;;;sw$2BLօ\$9jO%&@AH!P{/;eL(7 B"5^P a$DvAT_[g{QGK]Gq&! |vkm+rP>SJ}^`a9Μ:֡(fz:cJ9<njF)ucyiOcEW6FciH1@R[+ܳ )F{g[j>S5r(ٹ$#^oi:+W(Gi  IDAT@J "?"HoJ5ivv42HQ~E#KFlMͱɲ ʏse{ ]&!.\y9lr2QT $[*e/zoG2ԍq:?5- Qh 1_M#~ꏬ{ץ&g;8Wx^m5 #%#ݹhա5u)U`0F6Ml NY2Fk[cB<`CPڷPU J#H!+~8*l0 yVPdP&4ؼ)CF Z-3Bxw#2#$]#w[%("#N"诐uRRBB`0:clߊtG1#/,zv1]ՋBֻ=S8R^6赉!]) Zgлx ͚Rucm{=v4|d9ܱ礣rQG )̈́Q>xt)_(ǙwC; ANS|y'|Gq8]2$l&Ot|uG2o㾅oX3Սд4^Rl$7 &YLڰ]Ya+JGQˌ%8cmGR-tr[ɔ8 \< ݧj,qP.VAw;mCEz`!>Ȃ,GkJ|"DMpxT(CՄZC Qh*_li&D& A !磑A /i^ͬGW"&jG P _#8B3ϣT J(h 2\J'ˮ& <}?-Ƭ($Q^Z!s x12ؿރ{@9 |pW;w!U*Ǚ[EGPڪmNG> "I6RoW}ӆ֖u6}# 6=x\#E9nL@g,{!~_[)lZb*sٛL =7~O$Ljy%UH&]dL?G]zrvNCy1膢q$FN7!S$;TB^mޯ'6>3m'1B^ܘ+DP\t92vCnVqUOhmHxlc[K(\5 o N9ةH q/T =::D:%8A-<(w;M f?sh";ZS hu&,.^)W(%О-,/*Gh1{$Mm冺 C|UHjr&:A_= u%]'MUQ?:˧ 6hd@~溑H,B<@C `{"4 ͮCȕ Q< !yn|zY%SkR; D1ț(hD0B{+F__8 < 4Zk Q0yRO,D:2AP*l왍-H۵\Ih|Pyu%{ zo/{$vzaOﰹj <HmkRxrv)^1c~3nfz]:9Huh?Qב2p{5Mxnq]_k:q[^>JE>D|)_3 GOs"ER|ߋ3!Ih;dBʫ>nϮGDʻ Cv HNFRyd4o%R "/["?,{&:K|9̯JG' =Hs=ꄗ^d;inNZcS}-b '))BQD2݊%Ka A܈hQ%E\ODbBaw?yAF|%`߽HlcW8Z8jc'{!dL"L!!;n2lN yH `MȋZ/2 ?$[ O 2X|Po ny12v=H*+H: qb#(Jh18'l.^禚}%؊v "[ԃ Lk#"lj Ԗz5g@K9xfod߉ft#AD0;O^0Կs};%/xyԋq՛|9>j[Z{ _9owB8HY&dMFhpi1D[avR$WccH4!둂[ RHNGؕ"Eۣ7jq5ʡ uuv?#1v0)B-tWl?5g"rjNx S!&l@Rzx?7+Hs};5y;g d6 H9G[m{|ݡ$H˂IT^$yٺ"\G(2}=n(>u\ay|+BA{W*P7Lo:8|@w (W(|vmz>*rҗ.Ji #u<%H^%DXJ8@}L" TB$WߍPo.Aw垅bB"R$=qxbz K7ؙK?H#bb03~t2'O|n$l}j{5C&~#2$Nq!ùB#('i 2""@ZFY[Qv((o2@ #=hTc !x5|#l/QDeZ16!)浶^7!fdȽ1ӀѹBsW Yry*W(b b@mNtŕM# '[$b>{rP9l:FuAB]QzzwTQjGgs1dK`x۷3ݓQǡӀθE6F b9}'f-c|?W(Cs|vыv2+ E@~BǛ<uq@շ2.Xi)铀trm QE8B>B|s>_A<ޮPc95'ȠݑЋQWI(*6I xcc$Ŀ"9X W }oD }AG#hQOn1CڜF-9}m_599?|!eͯW r&C}ědc8ў1cBUڜnFhݳ917z'^7ٿFFB(Tosݞ#t2 \4Ϣ3v%.D-yhu33z'u t:g!/9;؆z;ok`Y!"-@Wy tgo !y%j#/̮cXbr |~>*bb6Exھ\{CUjy, Whr{ 䍶.dSH9)oGIP4<[B$Dܫ:ydDB~M(",!z%\[v6y5{p5$W( cӋg$>) O0CV!2d5:->*bZEBJ抿T*ދŧTB9W0Dz{>WL<J 'b>{VwQPp磍sa^%%߅yEUȊ %cfz #/2u@>uϫc`"T{Rg&X~(ozoߎ"E?De=`~n[ PgJcGn'mp\%QƍzRлP0{$$=f7B/&D̶yaPa{ HVy%H&w& o Tcyߗs>KcedYjsGl8wB2qr#VyBA#TkJo"A[5xc:-A)$1D &~\GdD(;\+1YApm@Ș%O#~N+]'"Neds[mwgt($YʱGCΌ~yn]uj$g?hX+ݸg9G#jtFGPݿ.P|Ni6QGU9lRVsXp3)Ƞ:7BI "'{:SX<ϴGi.b1VHwu,` Xj/[c×4V=Jb }hSQDfͧPV@:so knBϋ^\Jxoo]Xյ\t8bφ/ km>ӐzžuedϏPdr"[lkW@masPУ1RB  Ћ6A2쇠3t:+O!~谹zP}8H8!u|.TiՍ c;𚍸vrhG[_/*pOBP}3rҫ[Dc7<[7Chww#( >ҘBJS/p Gː~Y#wS IDATѯZ5XY_ySm yo') Z f|yrl$$(uP:%{= Aזz$=yAD{{C  z֣>mWgp BiR4A|v/vĉXy_aF:m!֓5;y^ۋO^_ _>6忩$.?ҋ˗H܃cՋQ(Br΋y9pݼ TgK\YɘxЃU[)o/Ǚ ,[gx1_N)W(dhшw(s{#6Bt'z?<'"lEmSp30;x-*t=0R]6.!!ފ`s0A($WiHBW|&El!!nrdy+#"(`5vͫ “Ql}fϋ+?NS#6=HnEFˮ61| A9GdNG`{o-W M#"Qo{2'5 }<~BW嗋S|j?#EQP+J\gHV!$g̸J!%]r \1^QGϥ͍\Ԑ+fX"ŝGCȀi%[NDʲJ !v.!}v^eAFA(cVBӐ\:D[o{TﰴA$N@LA4B1!v%2)+qu0adXy/HdtPd̋8_#[h{t77pr8E(~*rv4Ex$GN] lk5`AVj{Fׇb$#4|cEj| ؘGkeuClxBoeMCqf-E<Q.fSx^kpֽ\^𢊘7F7k #~b Fi3gϓS8 h(R#wr_GL{ B/d01+GFg[goc[~lv[r̮HDBw O=tvEGUba%(Ճ> ͋{ۼxfCg9^sR$Hzk6HoP!Ub@FꟐ~~= uv{!8!HW/rarO+B*WؾX:y %R׷q%gq39eum3bkXAʓ[J4v†(@_DHuW\y`>^g_wW c~V:Dpbqm*c3Y߶ֶca |dn1ce`~\9m|ۆշWʃk?!UIhƴ1Q͚QnQG%(c: 6tQtZ1g toG:s2jNFNG7-C`,D\^t2sN|Gf<*r݌ ]`d A| >G(01H%"Rh7@K Bзao9!ٟC2ݫ!rGdty}mmO톑Չ6>rA2ǫcUacnZ1]+NRZغՍd#m~L(ܴvH^MG<Ms AS'=vH{f[7/5 l-Hl5  s=o|o/㿢\3B}6 Pg7s%Ͼ:G W3?B`kz>JS~b>N/m֋4b !t^2O ȘxfLkODd2f.A [ T$rko =˩BL!W Fgg bMvƸɮ_B۳O߉0 !j? %-џ&)B_b>;+"ok%<~G^ԣv;; 71ːL$ %x~H \û; V {G]8ϲW!r928o٦|5J\ty1-Y!_gPم|v(W(:О VP%Te z"JML5_O'x]O1fwU0e,Zt7~e*)^3Qi('s myExdEv۞8i2QdG(Avd 1C#vvۇvjȜ!^%9 nPh)Dqf=[1x(W(v7ʈMv"B-s=b+_szsϮFHJ1:d\9B/T\Y(C$Lq1$'dĨk߯CBˎ C*J6Z^~ܽW1!hBܐ\🈢`,ߵ1OJ#3M$.>m(r%F\~T7wOgQ5 5X"E }5bH 6&S#Ӑ)oL,E7Yim~WN]wC/(zӋ QRKۿbCQC*Z=isro&TD灺+mnoՈj34W(%-I黎GvuuDou0Q 55g>^FњFQ2Jg-WFoCǐv>p9$qg._WT+QHh\zgl2#K(ǙB:rY:NJ rN|E8B>cTIGt/L^&c Y4=U7rxGwM\@Wruv4~1!}NACóARb>{p_Pz @r䳈݆b>:W(ݏ󥈧6*#>XF2!Wwm>d:|aw֡u(J#B& "%67  F# 6MD%bvD.yy{G<؝>8&+DQ( ,#0kڟXyʚwk<32{m~ )2[!ف Q#M㇧׍N(gcG9h}i?'!a[ٶ#XCig >Ӏ"k+==]۴Tg:9gZ4<9gX #[>}  o)w 7{? șbdHhRj7C˗dM}d+׽J}l"kӿeR(-BLʽeoF'TݐxPQD.5MktC ׄ?JQ*AqTA^zAޜ?2. *oA^]3f [ a7'oviĂˏD}& u Z*䲚rhT[ZbHX)zLa$˛];D!lA~&rGC͗iu ?~V?f/Eo$|-:"EiMx \x+!#֡uМBI]e(/Q3.^|~ZysF!`/$(bGW4'e{us#>; 2$ط27o"zv8_nwZ?@|k!s8HКWZ#؁h| ,—.Ggck͒nu6TaKrGw”+ ]VS׻]=X)1cam8~0nJb:M#]%ƜDiH'47׻4ק_">epƒeT~]hxn}'쫧CS73Ll:iyظK{Wnۿ+m GC2{Be8496~qcd[VжZ*kCJ[ۻ߮mmXtr?Z\.`sOkeD݈P #4/`YF!!|gmZvհ-Fh˺MCr Ŵ|c77vo811=]s?b;#fA"4hˑ[;5kP1 д~ZOEp?0Og Hk:/y͙ 25mty#bKܼ1 W=b#HiubzoW'gnOJ[qpuBHX-,du3zsKldzkY ;3A-y >8bA~4+J/FOItމO4}8Ȳ_&3xG|tqm6Eݨ͂/cpkss9N.zs:Ӌmͦw^UtvΈE~P:8[3]YOD|f_|#ۋϊp)/"auj T<]{t њM֚c!ttz Z|)hYX0qaB[p1(kǠ]hg/s`M<5b݄Z7_t5؈VL*caD!#n-Cqnٯwzѧqxwݑhy$-Y}D npr`RND?/j\\gmqsqokkNw7C% _&ǹ1٢97nlc y 煚+x$)< 폟3 ~j$\dR"V_lU :?cWlnw- AnҌss?9.nzPkl?yDoF=~{5n\A̽R%,Rч] k[#fH>֭_ Z W "q bkËV/F*%hmOyX11V aݽi^no/훹Pe@k[$7f+D&҅8Z umx.,m:}ڌm3ښ;?e>9C|@BHh_ U*Bba,2\9jC< ȺۍP@#9Ϡ3=vXWZhwM˜aHzb/Clu(GԍK;)dD܅]> LE4/H1j@'x^=z}(QC~@R[)A >S~uஙt֏y6'FrT$'E4c ^3 H)-srZ!`^-(|hIGB]2g15d}l?5L)--~2n IDAT7e\\ڻ*&x~=->wbxq!1`7ĴD/k3|ĭt7@ d:WIJerT&w0G-˶[U"1DL s6OdaҾH*ff홊l$|aV+BGȢǐP]Y2]m bKh_/1DZwyxklve+߽:x[&HDl"/@{ h}5g7PvSo)aaZwyA?-Eş~)nuV LfitM'+ɵ/%IHU gϭxb& b)m>oHo&E7ϝϝz%^z{[>o^  >k [qh؃b.EԴ+$@~DDELDwcjDħY V@ADZ&`ѡ4wgG50-6 ~1{|1 i #]gS( (5'4-> XWX@`*Ĭf ݟ].C̮a]bk"Fwk?sHb$p!"6 lz=nؗ4&T3ɦY>ֲt^SqֹH]\n}`ʛښټhH1$>]??[)E,Kj`T"ȯw-2UD l?C'U_Ȋ{">xd."n: (Oa]o G//5_ތY\p65DSw,Cn^Rl:b_ 02S=X~aMb$M1,ȃ`Di&:_ƻ/Iuł(!|zJ,kkU`@+>ѭ9xn ,.mk">-s#'C%7Gw}:Խ?)!NQ3 [byH!UhYшW-@b>} >+c4< Й @YHrn}ͭzQٱkD_8Oy>=Y@A>[#˦Jk7t_m3S CёH{N0Hn@ Hؖ}݈Fy+xbLy?"~8b^@.V_>N@Z-2#0.tغw!B }UϜŖA[YC10 t/.{#'i!Bf+ Lf*͹RdB<z Be b'pc;%}o ğBBYw1dbKuubT@Q$~NFÖxsVE!R7flAZ nYAIK,.oⱦARTa[Fۥ9M~h[ˉ ?l{ ? Hi\*tfvQM~-vª35/N:"qi>r}w1ߧ29s+Q〹%h?AtH.N~ @xܳ$,eEf Ѽ;ٹ7nDCZuR3wn t=Z ش7 wK"Zގ؄NGtG ކJ; WƗh'|P7)?F|x~^vspqb=>/=/1"}]OBgy{މcq^|KH;pc:w%!-G\ hÈEES0@5kSHڌ!&?B$67b6lJ]m})7N΁n|{ JHc5;&GJ0;eq+пC^}~w_bM!lJ]EBmW/_?杍Ӌ'ggɅ/v;ڋku \tX"Lni$R bqK6Ћ6l:T&7&\ckF5" 6lОM'tpu"m_q5NBQDD 7!q4BZǣ8i wv[is43%,FD1T:|;OCDj4"h7.$+SZrK^p "'=yڀG ȍ\79ןBrbW|9@{{-u7 PMd5lĻJTH˘$_ lG6_ F`7X\鮔8X#Q"ah\|hO%#I +:| >|,LAiO˦VlB^AoH@!/cqMet+3K"BpA|tF}~fzEGbcgeޙ6"@{Zh3yT9ވخ}<εQ,MY?,@Ƕhߞ\dnpђT&w'"FQ7|qG4f-H v♏!AѲ;4koI,@J<]sj1(>JNrcAm"[Mֶö=FFU•o=ٿRzp>_rp$׻YBh.̚CcNXz͘9Zd㢆&妃\{%HdG|'zU.,g'<-#1dAܬ4DpټMG@"&ZY]o.#xd)]70A!_Fk9Kn>FD~e|_ц7=x|ǡ=kQ?9`8=6oͭxk9Z7w6|&"(S?aڰrWݿylȒYDh~,O w:KoAg@kZ ˶{ v<2"UÔ}. )BZo+ %ؔT&|3hhԕhː9 @Fd7-Jk}w"Sv"1G;/} y7ɦ̽pgn+A4:D ) ׹Fg嗻7#W@>7d^6hb6Cq1Oo17>Y(ď nތq}YPq2k/yϭY-ukvݼZE㟠/gX{ |2^ډnnyE*^?KjL8`) O|ҞC7A${l\Y#1AĬ]i_$/2ib~K?75\#ǽxCD_ܬ-݁x6ز {ӿGwi-]{ٍ3bZq6א[SXni4vYֳdVۇaID"^7_(D]}OろMA1kw{Eɿ#oZجO"%hK R܋xD\[LT&CʭgSHX,ڽL℟G²H9ew'ƿot!Սh>SRh=\ #Eʜ!_]X " OEa21h[zgl@ 'q$;ѿoEZO$a:Gw@4d2y;C3 w{i-V"Fz|Y0h 9 41Yr>E\֍A.6_>`6D6|МLZ!l@њ_ڑl^gEPh_rdpGoL%O$훁A~{ 4_ǣ3akp0:GӶwHh} n__! /sfx{ghu .p͙Dl:T&$ܝM'c(i^5,t QJYֵyHsц1+⳹]' G#s4>q Pf}tߔ1w#Bwo C1> Gb(k~(g#< CbRUHJtp߂pb`wH؀H<qݸtPYD$h8kZN7KegYv17w@_#.Nk;trR\`}6܀pYp'UY=.DB| 7=DBPG͝W a N|A #{[eo~ hoMJ^O߷l?~b+'}$e@x&p[$^i"Vfk[B8nh3Zƅ%nUJFKZ3ϵh<>Bo68vG:DNlBgVzE Dzp'JY@4s_$?j:&8[ݥxa>\>m(m5WҚ?43\*s%]ݑ<ڢ݆թL1ؖT&WFv,dوQ=d<^]#dӑS$<_E&w"S#ufaQ DHhzDz6#>h-">A+4y]_!9\ D`Y Wa?͏澀xmd:Mq70[{7S=ުc"n+lCkԃg!f5ʍ1&0%T&7 H{}w%uO3)(ujh/8֍&=oEde wy5;@k!lAm]wSK  ;X=ƳaW8w^nVǑrF!l?>LD?'tydݟ[l&RqGq*Vz+ÑAp;ieT7Xk+Iǎmtځl:ٓƚ&οhguu:g !tOeɓ 9 P6+@f%pG6}N׏#`TF k@gbeϵtᭈns73O5 CH\ }g{̪uϋ{q7b77 Ȃv7'xE_73ltϻHCx> :1K*d#f[V|R q&ZÙV\<_jp>μggomC2G 28 4l*>[5(\`Ib HViE`YԻ~Yktrw[kΚVXCRoolJ޺WҒZl<WD6Z{Et /C o ˔am[%>&>i(>SvBqL]91\7о;VrJer PiT& ny==L(dMO[ۑR%>YcRHCfC+ `e@A,:4n L׵h@D1CZψ{2Ԛ.cl\ލ#h *wu6| ^CUۈUwa4~Q"&x-H%p(bFt0eUsE9WE B"ȟ \tU:FupuxCk[ќM' ۱3F"\hsYqoJi} BqO @X?e(ȨbOåޘiF͚xұ]VZ6ûf7 7-W)#zpj6< ɍrˁV*^8!\*iЏ#~7s,oA/8%1!}R!ڒOY>hd4eC=XB+e3@_C Ue}v4X{GnDk.l ͍qbp'!~c19fB7nUS5>]'85Hn!P~eϫ-|~ 4 IDATn}!,TO5tk>_:vŁ1d u!^ GYgn a֒/ 9;tϫLZ~o2~Ak" Am q`<TFF:cCO׮^uK[ySm5}[ {ڻt>5=\"bEaD$Dj𚜲{hR6>8\v Pbs׮utyxPCDƲ`0 @[ 541v2!7֍?KY^}/G?]7O|i%5]]182wN׷j_F 5A$؜4=D,׿ Udf\x9r3O?ʱn܇E݄\3w.ueI[D b$A ={[׽~DQa/ZWL5LaȻnmݸa)%Ri-[ z˚0z~- { En1h~_B^ܓM'/|r3^nՔrHhuއho ]]7݀b{pj Sݳ[MXmf &[R"p+6ե:)5+/͜O d-J߇E!1hdڸ溍])>huYd^z!.GO/n֑H4B1ʑ?=v/g!,}Y# :(gB6˖e!Z/ Z;.H1}6]gkoc=a:tbB2֏&eh<7[jnJG ģi9s׏%-HՇKG6ƿ/tA~:VZYxѬm|6Th=X_VwsXH_e8N?j&zϽCL!~%Ƈ>1j)Zo˦uLg&o6,2"m{ !m݅ȿ9!< CaqMDoxb3 $gZ {&6sKck%Dt[#Z&ĸ"wo@1DOGaܛCǬI7AdE\,1 ҐNڨwYirsi2GYmda<i~wUDH1\X=^ =<ɵͦ&!~ HDA+3fF7WKОk3=R,le/݁k})F^~L2'[;mx0,F" ߎ a{/!Ůؘa)B8D%tƢ#9Qm2怞`~5kxwb]?dSEUo9$ND.W7 zq5PY͍^D'×x#F ."ZuRQebl} xu}9V ω:%?k+ŚF|46#H,\.״VXR|R߼7q1̥lgdxGBf( 0V2-A9R?vSH8]{ȅ(_7:;\nBhCeIx xY]s9"Q֦D@~ 4K&`HC9]:T ,Gn.<PaaR{`ECHi{9ȋ<-պߵϽ6O5pnYB@ӬUyEXcZV|}=z> y*<2!z8:VwrFX6 {"pB|FV"M؛P Egi>4$\|3)ɝ폣dV_q)GJt>FOe{_7в[n<_Ax?DA ݶ pLKn7q<"5GFgJ>a!>6w2>)w8ć{w31GX(xMAi0,ƻy1Yx !C<- }WwbLS+~u'Wͱ-x97^r͉2㛃O%k[фC "u La1KۃH} 1Ի,o XG15h=#''fL2du]20/,b"+s}Gϯf a{sJA{`:?Hy%2 0D؝N[~  ,I%NZ}_rR׀sRutm\3 F4a; ѵDzp*[EtrV;Lk݈\#x rxWx+K}f+jtߛҧݿӁ{Qj70$MՙT׸uxooۍՎg}oЍ՚,9o.S9x xg1˒3}[@]l89ƍ`"/k*LYfz3 {^1j$XFAKVaqcQSc[ZtWͫ$Bm϶r.CO"~; 8c;7omo\Ƨ ]T ]{gKXYǏ>P۟MS\OŅEwPc-5<8yp݆'l-+3z6KQ7XN6>Z70b:sY YP @(f0ꀑ m~{>kBBFv@Nv㌸qߚ׃KGø^] w6;_-xThKZ'5>sqMpDJTshwbOtڳHnGѐC.j 9}M< UCɁRn2V,c &ӑ5ƒD!~2D`o ["mSg3텈V]⿎xex< @DzIzu.Mj o' 6Ǯn,扱]n=,f8wUȪg25{n.M1:Dzݲ7=qx4 SnL-NI˔h6ŰStU+𼭺m͚erXwK˦en t̜cpboWn\AqӪniet6~\s_ٷ5~p#aȾkV9 0gx}'cLC ̵l"Ty{ረ@׿\F#k)Q-Ͽn<5!D5Ìs47app4ӟgsZa{U8aHi{y/Pw"]4fI}l:ssXw"F>掦坏6:+=IS_,w,:O%]e̦zVD@( phKWvg#>>K}0Y, t,iuĿ6 Zg jnEܽr~y^[Vij,K+d$ LǻB⋼޲Zڨ7w:"DM[r60_XcExh[76| ɰj,ӣqzRB-[*D6gklIfȦ/l3a1Bؾ2"B>u(`a!lQaAyO( ygֳfx}5PmHaH䍔#_ޖe Y- h/Y^aKC'>,u2*'"QĜ#CTG7L^QOw[g MBjDwƕ@1\B@e:a`AsG ŷ܌,J\< DFLe J C;>ĈB@s@i @|RD4Cw}d x<(EZClGs-1 ͳ?2wGZ+^{Ye.Ĵ$} iWdW#Z!lq"_ĜK;?| K|%N 87? J") ǿp5^:Q|K7^04[]buѶ\B.Tw2]݋W.Cز5 xe* Kyzy e1CtyDEguDW+\VtS#^0|>4}܋W [A hD45+xS -7kUh2wOeQxP}?P)[inNT>XG|Hxėc3²ݍxyX=qn#*^~c)gH.0X7- @U?}>94}_}>y,(V]zΣ}x$`:iAIG+ohk.vZ Ѱ2me5,(bm׺9CmkhHglP^zZ*;e`{0N2|NzNDd:971gzBA|ו 4\FZ+ۀ?7O|1EG1DS-d9g}0P!J}AB \CLr^ޗ3W/J^/BLeL5tmF%ee$y$@ؗ8N1I=(Sѯvkdǹ}0#G /M'd-w~?qZSCk#lŨM`_ьۡ/9aAi\3KA e#J> '?Q۷Ve+^F|mGj˦ 鎜߶Ђq~۾ѺǁXH1u(03Nޙq+ףA׆5:#zt B!:mxŢ%iG3@tmO[Z{=1&[Y=Ӓ(㳺n|LWލh| \`7 Ka,`oCHuzYEs 4W\M[ m\66fy1;ňOCoCH;8o`y]_5ʖBJ_ |Q!lZ,/D@%( qu?!dJ9, Za;VU)oo>}A6\rX[w nDnzHeFq<_9NnBtI$pu_7itVol+^6i{ami-":I]gZa+1˸d.臈-GLsW3oDk #G"AHR oމ8A@j]չp=DBi'oDVYnC#z_ƻ1:7׹ߌA\lg|snfZ?:OˮvV7&MCo㢆0B3:7^ԎfКϗȗ.ɝvu{ Y%nmJer͈ܖM'ȦRI< ͦ_JeroC{Y~M?Lf>7"~:;`{=߲Y6a!6\eTܒe=}#?t]߂w}(ŖrLׇ ъx)BL.G<}umճ#@%<,_dkBx_Eў[^T}f8*~UE]յV@@clJq/{woˑ,'n"o/_Tb͘SGG?AhC ȩt_knuƺk:a`UM))F@F,^Y6o@ߐjsl@2 ]Lnb2 ]zʱ3u%/Wj ? EڢFDoC5f|`*)2򛑶 (>ÈABDT-A o#"v_jbkBoV Bi)/F@rt@OCnGk@W~+0yMߵ3؞[vi!-^o) s:ȕ4vfunGXܺF|eSme" [+F#r! inFLů# Gg?vuֈvSߤ2,S:vӍQĸws6Íˍ{OE$Zժ%%a'(# IDATA[]! }#Z#j& 5{+{[\'WHCkh;ڿS_7k.nlPu+ccm emB9Ѱ?WӸ %s!lF=ɋ@4ni8=pD5̛貕&I{!N@|YC1ѐKm{^{%>&:ʂMB@w#<)>n|97DjJ !F ֣uBwp넙<gQEo >~c|aST(w?YSj1:Aݐ%Tzߦ6S!lD&ʬXB67"ix9w)P;2^Z xRr))t:WR5zB+*goh;VUs`UGpZT&7Rd*;i 7#"8 "qMGQ`~gwϵ8Ee!5!KYA{ >e%JjV(;Xjuߐn"~7"-F]=3z1k1pt"% LZ"HmDH7 m,D%дWϗ]C." 5=Y Gb3+a֙00"à\$< ivET&wS ,~{,>թLStޖ~^OçŁ]ehSjywhZ*4ЛM'wT&7ٚɵ>L-  VtoE ^Ij5TF1nLS"mg>54<_ ؔ]cw̸]Z0cU?]x{8<ќ?=?սw\U?wf$ miP"HvAŎbAqAEPPX@?Q,4irA!"=޳^fxɝ, Irgwg=sg9﷼!Pن 2>aٳPH ƬFޒ(lJX}| Z0d \9 Z ԘBHv Kަyő EZB`&24hu 3@X>`L+s^ZwGE3~}hFҢ9%6F`=Iu}A(t&&Ĝz[b^Q4`%U$p$\w% WZ9 }VߍY8O>ճlGD 񯒗~B@j'`1gz#(4ɬ" E XIy0yO8ЇM۷=gk-i>-tL:jO6 2rw:=܌>wx}<= ;}j `Zlnd"= OڝL:4L:7u6nIJN9}.@-yϠysUy}{b&D,' G h3]ֿ!C"4WLspEh~͍ ,qiǮ3!̻֬ W#2 XGΊuU7% fCmn%VFEY`ע^D(5nzBbYȓ-|P~)Ė(7Dl>Y*x4;kŐlh]7O#l*ОeBFeWv]6{&PI::5 d)lf%;upFD C瀥f.`,oh?ĝ_]҆ylQV5D"s> 4GMt-'4|0& o-~-hҶ"6 j`NPd3Ar$>MhY&9h1kB^ `\PXKPK{3+kIf_o6cYso_oq>yF#9sM[h~Z>ʡl[6Ҍ 1 ~d8oj/?{\,{#QaL: QL6]J?G6Z,lܵMz:C=xUh:ӿL~ܘI3cS6CU*.^$+$ղHPӧzd!Oh1.8& ex'Z3XȼSSMM#Ѣ`w'Ty4Ud]Fh*[6.5Y|&*kUƽ0}Ar¼;[T*<4[+Ƣ0j7%x~.I'"֓:Z|%_cɳUѼ ) \ ;$Nv<#Dx7z(*~}!cW3]ϏL&* }͉Qh `H1;ݎdlAdf4SQȰ-Z8tiw=2 HӏOE>eOcAsibPE'Aơ2}DP; %z݌sLL:i7ǏAkϿIi' -A0fU]XN[(hw@$֌gZ3^"':ZbQ֙I(ZEasоk?r2RIm:0[4_~-d-T$vBnmy g" gpFVWD*_2N(`ql2-_0j|4[IT' ku81_r+ /72϶ހٚvTƍZv1 סh"|ƌf6%]cO-J.`󄦛ͱx| 鷕]E7k4a-dV4hA k!TOzypIOBﻘ6mLX&+rv I;0^Gkcw+.q(:mnF!4R. WsZC,6| }vhFG&Е yE a5ȤS"`-<&$GmۚHzϯ'kHC #7/BJ{}qZ.{=Ϳ{#CEh6A\`4>ֻo&oCcgu/hs3qz+<~ +zTl`n+ ߳sJ(jhZg9ɉf2ECo7VN&dG]3ɴyGl&DXkg(ZE4BPh(4H'xarMhg N6lD8Dz&ߏmtչqBM8b|[k.dҩ,[\&{oSQOQru z~J4C} |z -F h ܷ7d7ⶶFJQȞ-Y&J DD*H9(Fȶh]6wEy mgsnj-Y,߇y,aw-z~>l9Ӓ&GVbd~YϪBΎM1 W [N%*Wm=+{)OQ[.@ssr&K3WE %N=dF3[ "" G?ˤSϛ.sP{0Va*2E f޿{F "*;nE끃M݈lBY/ L: zx Iz r)2~i@w܊anmm}ꛓO_zr z? -~εflj2d*Fk'L-N=j潑f\ўjL:vVLBk>{1-ཏ=ЯB@ż߂pI44>a3}M2ՔM L:v +Ba'136 ]dQg -2%Lr*kd-P4h1)Cֻɰ)nVm[ 5;ޟQ]4QAg"z$ZlhlaE$PS{h\Hd1﷛1 +9 $N2c Y/qd=f4 @%%(㈔CsȸaP`^&z&nD( xգI \.h\VY ̿&XD%\Fg@ 7t=M܏e);qBa/dҩ? mf?YZ7f -Bޒ:1@kMI4EI#ю<`=6467N/uGXD!BţJ4A6yDo!RQi%p.ZlBPЌυ(m8l>DlA&36҆KZ"Z4[=k[ 3Nϙ+SZ.Dޢ#9{ƴ5 y~7 Xjw`]gws]j[=D_+j8[?KC^MӥV 9Xi65Nv=:Zz[e닳 [8z1]NG/4>"y|xDGʲMV?xSM齢ge/dob.hz"Dz~=n8vxyT=6{#`%9IZ\?Ҝw5a.(7ڀ[Kc#ЕIYz~=Z(L5C*g5< C>(*p=IL:GcT W蛢D^3*WZ ڶ%k4c!CFO4\|[3N6ӹqjK7aIoYDmI\?'cjPG{`y 8Q\^GVND4IP7hҽ);RHUm/C#dTU{Չ+ q{ ΌCP9w{d%`z0mO3>mm?'T_#&"7!iP 7-4b=jMgk;!a-;tyRW#R"NPܬ} ՙ|KҪk߈|SqhYS{MΘ׮hϬTsF| k?A0 bl7:ѳhiz#Zֿة +%`1oq,OX8`H^ hd}/苍YUV1 k-}phkq꾶x- 9}"Dx_q~>gf"17|+仅]aD\z;\ϿI۹o Z EY< Ld!Cƶ]ft6 0Z/Bshg2/"/w"ݜ~ϡ(KL:GL:v0nj f\[^]ϯ̤S`qޖI[l.ujof|}f+\ɞ9eׁO!# o-` [[p=PA bq!r  ۗf49Bmh?,p"ۛ/%,4hٻdڍ6*z* [UVҶ`ޟbm v9A_bŰ 7ESQ<9w G"kPPDB@r$[ՏjA 7wc:Ŵ`5mq6 PgLt\cN1\ehi,p%`ĸۖ6PÆaVv}Ru[s2f- O:O-w}9Swe׊spL)jrAI')=G!\ؒt>pL_CP,nӵ8QCὴή^Cwcmhx6ND-3yd>`~U 4iOF^o"Fǐ)g4 hQ$'9467PUmP].B6ټሔFm) a$$rsmޯ@ MCIl>؃(sGa @VyRsZP:A{y4ccX-D 4 !Ks9f"O]h{*DŽ(Ϫ2ZU渳2锕:_ f/`I2F3goenk4g-.E=[ͽIBɎFXCU$ 4߇Hy-M:g\$l [0mԁmK;&);hwYԦqؐb1 cX9ˊeqi.h!<\ _osh%0tmHA=21uчrBsMBƴfҩG*f=I>PIV{}89\ƽh3z>dҩ9f.P!s_ќXbe IDAT>భsz( xU1Fxx=lf" N =Ɓ^7*?^ͤSp.f^W,hypq}ˣe=Cy#oD#>o4c#Ft/+w8 tAP۵b?,ɴE. V|ӽeqΕ3!B͇2 {=2v-2̑xPrf QB!քNwʤS]Ͽ˯Wq E[z\3) <4Ը;1퀣;Pwwh~:;#3tPJ(Z\]6Vq~c<0fd+GnG<A#P9["l,"I^Z[=s֢`&oÚCFIssk v=ƛk4m|0! )>oFUm,͹CM_zfI\g(p.X>TKPDq 'x5++: ])#LQ( y'zQ պJ!O#Q]bQxr3ԁact,B$sJovګ~`b?n.w2TOh#1 gaH(Db&DCH!$33/틮_\ϟXb1dp= "t aK1I ,'ym mSq ѳԪq/,; b*oiD^f I:ى@kP! k+51〪Cz5(vS4w2qg"x20NprAF$v-_^>rT{x2VY^9"\T7ljgY9ժb_}:4ݸCD2(4X#`0yϤS}f^+ȋ3Y(fńRR"'h!zCdS+`ڐcEg!mޅԒ4׭O:Ha{pW/B$OX$yaezXυb^UPR@ $ i+Zr10\ٕfW"كHV;nWck܇Hî?I'D@ᘛ "@9Z!W*+9{ai#&GawB|Gl =]"Ne$ !X 4;H'njϲ8Q=s( _D4^O}!Q;Zv[&^=wI:Xc3Ԓ~2[H%&qּ9kEh<Ѫ36 msn\k%"Y{3TB4B+M[1+kل0b*g/`'gl^˾v9J nixou|4S^ @<?N^s񮤓w+>#ft0o.dhFףދ!I y C-}pȄ7 7`AYFĸup!*s-pSJ\גyC °J3E %WI'(Yнbw~=Nokfy9Jp"szwЇ5w#o dҩKmvy\p C墴"p8k?<{#6ҪDI Df^ m0!RbX 4׽0kuP'f-vyy#տ96M}ZI(q-"k;IJBiyPkm=juS"ob/H_ T?șg:nPW Q`<ˤSb3H(_F5nzͷг=yA&/kdsX[W= UFN<-O'0i3ޒ4>Ƽ-ػvbXq.mMB 6+۷݁0"D}.Ǣr .L&A&ZJs?lusht!2Hbק(f8iFƯ$l-;.LWG_ZT1j_FF}Dmr{ypy5d+sA㠽,". N=B09T&,}`D&p=mOek{/3?!>[OfYzN("%V$sWԉT*wp˯A5ȃeɇ- 9mXqExAHJ^At(R\aYRBsͤS \!RQ7G8cޡYyp` o*ѭTW=C)3X2mYpбMOut_z ˤSKbAݢ{5Pz *F{Fa KK`*BF6BFW#! .@Ӂ4Xṱw̭M/p o{6mF !672Utu=q= @w&:y|E®&y:< "R}@ۻI`%"_ ~ׇį{`޳y`y46ï"ˋ==s~#&,~9Ά:f+I8di+@HD$xBыVduD_ pP nm|u-"E󳳹-H~iMk \`;šdLZJ~dR4 &WFueC0 +P9#o? O/ff7aBՊW*+{c>} ~@"Dx$Qġhto%W4ړELEKo"NUI;Bjʃjv=2]TNBs2 D!(?g[z&nOcbIOIDJ穀Ь4!Bq)A>4l<>aۘia>Ʉ9a=_!2WDN꿈u[M[s yl!aK"An3;TN%$lVijoDZ5egXI8D^[ИNe@;I?,t[NI oc ;~i5zg3" %ٜ I'{ږʖ~6@ iH!B#H: d^Ԏ@B! _awds&-C%(VwV5OCVdu^.v1}2ÿ7\sܼX[Jm~url]X>{EK =[5N|ˬGd ""Dx/b&"@ ]'+2* 5CJX1sZ/StEVgmo!"m CQ'TV`mO8_:m }/#]ϟR?]/ M7"5CͤS+]?y#B_f+p.W _Fa<"]=eڌBE-{rσCԍB/&64ۄУgho+(,f\s79wwDNG$1'2v" rDZis\"9C^M{G׸?XZZ|x "qϷFCUi)9f vDq9gEגU3k߬ P"=p"Dx9N:{VݞXŞغD*Quߍ $/9]D0<^^H;gaS;΋%g5|dawotú`m8uڌ_!?DX}?jL:ՊHu=p$f1/4i6״5DDfc[& }P>L LغJ1wEGmVaz,;hA^{1ȈqKs =8ʉs % 4 MI]~:1^;{Ոx~Jӟ(:F/"jpQl$T6 zA(/F>ȤS};Ynv\8q1=H Xת'k>a=f'J.h\n1B"l8L~TU.hlM:Icc=YHAZ ĒN&4v05vh*F--8#kmXr =R.h\t ,NPtAl&bDGu#"Xq8#tp`K~x L7B/©PM'+,"+@!5/ :v"w"+kS,>f2G}͋ Z^8/ "6MyRՄ!1DM:}@(NHŠ(|pDrGy wȄȩH$set 7 IDAT 09\6~d-{ NtNY}#{:G9Jfcs <I7˿:o*;"Z"Da[tShQVvP)CbdO_}p T ϼ{{)T(˄cV;]{HLi+ETOt= D2RdҩL:5 N0,Ej7{(KP֏>-4ZbH0c"䍳E^JC!Nugҩ31xmn=^3X=.w(dNvD&})*^ۜȭ|&(vš'|F!B&f9]7*`W(D,;H\ER:sA#^M_S2Hυ؞-@oSCyx"l#MO1t'|ɾ\6k}7!Bw=I'W՘N$T8о_ H]cI 8ў6&$ `Şx+ioCʁW3.9uڌGFDGp=ƽIڄNB5& O%L:5m2ލ+tp˳}w>a+l&H(D!Dw%Q^w'.5GzPcƜ"N/оiܻUL ؓCMa,7Hyjz84Nu00$N%C?zg?zVq^ H:Y)7}3_ #D!BSZfU~?_~E0v{‰-Nv2 54.yaATIw 5C յE w=O$Azi&zUn{^pA.h-~ [Y9~jI}i XCGQz~j#R;_hIt "z(_ʜy?aڴdʒ+Pt$QJ~meǴׁc`{I=gܯНBU\uNv-A^>4n g,B"D؜H ݵ),'p-2 bm]o=,礓a"D^ V绞k>Dl8b@ 8ƄMBd@&Z*?ڢ\Cwipi<Q^W HĝaU">(DhCl \j`YIH2}m{Za/C";!~y0V*l G!b7L:O?W=dIU:pI';u67E ]ϯ~\ϯ۩]NĤM 1tgFyc"D!{[~#К]$4p*ɠȜyi LjD.GȤSj!z@^r=.kF.ܬ.Z{tjg!m(|DdS#4}{ zZ inxQ( e|CPxD"0mTk9݈YU av(s1(ͱ =]6a8jXJ iYMEZI`뀛k&T6'YE5A[}o.h|p3=B"D N?rd_C<A pTU gX7#"ͤSU8躼gfdҩVNțW'$%~x&*7J,k`XX Uu"Q*2;%j%mQNjD'̴gΉsUi2 6ǿdD.X eҩ/_ }wS2; d\.h NvzU(%q"D3j{B[<#>݉'7*JE`ݏG2lܼ |p<EPVPĨvwd}J7e(dwdZ<;]=>[hkMosP&;X\ ]{H=-Gلj 6חDTc þ%DYEѯNx,ABGqbGp̅Mc~P_޴Ȳx"C{Qǁ{ aGKzԕ^e󨮥+]3B!'ca$qBݎ>c"Xp0EME"Z"R,q2s D/~1K@ap*JE"!׎* 4 9"k>bD鈕|a|G֣Tϣ:P&2s&,,ۧ5iŞ شitm!J՜,d? ;?3j~:jP{Bd{-0 ] &b/ ,c8ᴿ.El9}5E3w8!E9ڝĸ$A$d@门GR?D䎿 H25aK{laQuedsumy/4W@fD A:AQᐿ8hy6D.HX#­׽wc_"qX\֚p&do~7vjڸr$j܁c~mu"; 2$r^P{Go:|է.Yx1”ݬ 0 bʽWF?=ȝo*eJKZfT3v3ur;¨zTu۟`i%źYLj0m Qa/[\n@zha[zZUKq+o{ ֠GѸPJߙH\u TAfE/\Č.[2l0aRM(% ^$txaGW_=~g |!X0 0K4^L6dKa6Ynd9N5Ml7u`Ӑ{ ,Bץ H#q>][ QCID-@|E ~$K/~ Gu~-j7Fs']et\Į2HOX |ꤷk": X\Sls6 0 x2^L6W^*=ogEfZӡkbb%MDuO=ꪼų2 m}(կGVQPX Տ|5=lF |X}yD9zMd+S)Pn֐s xX=f17@) QZy058\iaQMv/c֚ŕ߀_d}ߡ/F;r +`f44z1U)Ȩ4wQTVC$|n[PD7j>*Kg&R_c_@loTօ;YDN5xmxJA[4 Gסdf2[ݨaa;5&ݙ'J4mFfWTBB(z3LDhlycQDlhdRd뀇P YG^xy\P05XSÅ05pNנпi)ŃXγ?J!4!Vc{ѿQE.&?jaaFA ld~֚^붏E-ȸ 6ٰ߉jځ7 q[e"j< QKO 'F& v8s 4 j 0] L\jzLGTSwV 0 0E c+CpFd: m_õ(J3d91"vxaJa Y(=q~*>C5^+#.wJYڽO(MWnLZ^ݻkwI ͭU 4& 0L6_&g& F0^P(^'ȹRds3PDD6 V<HAMx+ P_L6*WtbyA 7Hl>Wꋽg<sXxTA>C<<mr,0 c8eT\R؇do> c{b)ƈ'M*,2(qlP5}o&{3j#1lYTI/{mG,F0|(5-hT;UdOסĹ(*ՎRGqIktksi'=ΧmEd[߉&gu05tak'-}ei ݹGKxXX aj| xr];\f`jA7G^j`[kz]瀱lL`%8rECʃ=P[qh<|ߧGӱDuM{ )SƁ(zp;pj@hNs8xGYdGAHyrg>n7} 0s>okMw@~Rqj{yX _撶a>>x՘2 51l+umb&; PT'lz/&MYUKW}{y)3XxWfd@Ԩw04%?_7{7,~L4rѶ*qp;s;(ց"L 7*NŐޟ^1=cnI +) a m=O%/Hہ0UHף ?*%(%C cGc) ds{՟Z/0[dsIx7^ E@Y8{Z?s.#?Fs[<&T05-[&Xm qMy?bպ=| Z`)ZɬEEF(aEaHIDAT#pA '-~\SI/0tX0^9{dsǵwdsgu;J Q >L6w SEQdj{Z 7{׆׉"N5D2JՋQc (]p:s0 "kP^R`+0mdxVf 2KT|(O2hA`0^9 =1 8}5Eh8MBD֚^y݂kƳq$v_I(uM@b*D]#Q*o̱}qnPnDiSR }:D>|dL+n?a+ZUW Z@\!Lݶ:`dppi!L |ENi >$f4~y1XaTL6w ֚^>d{ 3-8(uYGS=ޡ~ 3 $|.F3W{( *8M7 ZE9F-Q `/X/f-r_\SWa~d=SY'AP(Z$z:/72@ʆ r=n(dt'ށ/DX7`FhkM?"o}hds l!^@&k~>;2\Kz3b߀j| $P PZU͍E{U ~A{\VIa|x}&;omb|4Bz%HGN1/}#h1-ރz'NAbl%Q[z3e :x/S_CpjFfO `;X1Q/#V6g^1)M14UQ΍E slջq\$قVI`xY!Luۙata#6~`]/ B)q .}K^v-_"NFrPohbm;O#U \tԗ~E c;b0q|(UHS.e "u(p/p(2DRHf G 1`Qhޚ5 0^32oZT_YQ{ס(׷?hq"o"g{b[ \%>[@#-9Sˋo? $.߾]A2s@< Տ݄DөݩU :hR@(h~cF#QīDH Mq40+)_x7!^֚`֚^aTy*GFi?RŏFQJP8d%j`W[.nk_QWOR@vl怯t݂s[.L`{UM<^!kAI(E$@g ^n $NG T=sǹQ.9I@vBq,1VZ0,0 c7"ͽ! cD/@C2ȿݯW ao _ Ӑ( 9x7#a'`h@g$,me̾|r uA/Wu_G{'W capW9)RzFieqgKa剡ֿbAFT|Z,ȔO,$QQ J3o_8>5{"a>\7ʱdS^07A Ӊ^ |ߏ̎"'$Z<+,нtkd/}k 79鋆K`0F( 4 R?ѱD+^LU|OE*Om(~JD׵yn)K(jVj`̪?= 2 xI7_{ۘY7v&-A&_D&S9"E!f&ERL"J-ߌ㷡ix`<)u]Bi{-0v$bn[ aj#p|R$@+(mCb9Yʹ*z>h54VEJJkbTuϘW*T 0v 2\pYw->oM9.hSuHlB5( [ a~BZFT?0 |7##D "JD)݇c k+&V qQǛd ioG^*& c18EzQ*5Ҏ_A>vMu_fj gv5prD/(㛌gK#OԲ\0]`u:ÎCd__=:$~PsH4ϖ_HգŶ)`j1XYy#3]K4k,|ܮ@S~=ʏRָYy.-?>&7 )n!⊶t5)mjB?y 0voGoEMl֋D|x/~S(ԁ6EG JRTF*SKnej@b=#:=F+a;fra#d?x^&.4E<$Fir}E~@-w 0Yg;XS}/rB*Va;gk~aG65tAԊv0$'}(ZS6|7-kkO4ߐxytߏ]Q(~J0;& c 򉠦p0 /Q9NAѧ h $j&J;^"uBn,T!L}qO0 c!'ůǮE)^x> Q Xұ^=Gd~!jX|O{W^\wlbwlp˾mK[sG7'N)]1~X a02\L띍S }IOQt9ԒA+H5'~u[dTNThmaldo(^  r[JnҺgE/=gWǛf#Zȏp:KﮛPč&/j٧oF==~~~sW5a0F'ujf^O=}{{}D $KhBl֋ne(p*QOC)yW5QN=Z<$K a{چa& d#.j.< yg>ZBDuRO~)X"3(Q-V0 ނD]MU^\4??7]ToԓO4BwG\im;]K4OXp¦ W n=V%o*dbq{݄&g+D¬_<1w3G@>'m g#sAʞWo?M5-DQ^y#x} (qQ=U'r@ Ϻ}1,bY}zWe#~q!Ltrح0e#drE) V5CSѤz3Z&[PX+o[cjR (NnQs a0̧l˲o !Y>aGj"Q_^~[6 O_7~U+>}eBh^LJȁpQ$g8,1`}=]$ɰAaOd=?UnX"ZDkU>]So-jzBnkeY{Ɔa& _۸Wߥeo@&V?bw @Sia+6e*Q HZVE叡V?OJha퉊1yrǷ7.l{ײ_Ӆ130,@Y4&> AvSh½?\Њe?JcD= ? ෡hYAP*S'D 0vE2ܾ6Wnܻ I )c&\*ʨO#J;EomMom$bKX)W=Qdk{X_HZ8*0vbL`d)e7pBM (]gnۛDP᳧mCZ0uokp $%p:{ 0vm7jnkF7׆AlftR -|-EϢ~ozt_.%4SÊp^PCW9W0F& cޚ Ȓ'@q/c8*P6Y A8if_=O yaj׎ܻ5m\ xA46H`ՠUw{$⨮*A6#p b.`~VN;AOQK&4Cn# X1 vVISԡl91'V/hs/AB.@i#';hWma;30]8h`cb37Y= {!Ǒ +Ɨ_D+K\NHe9-5} osy {-GkBKQ6æ*]e0A$|*S~-ʕhE C&QAs\,\LPS}>a _Dy#JBw CtOn r~i}@9 bS4nuQcp+j2|?W@uP~90ܶaX0F6'A8YO8D@͸w 5 Kr dWt.).ĕa+d <Ӂ >dh0u-o"oZgUGEHHmD=~TSdo@FG"#!D!L ninE c -J+(Ơ4\N;@ƅ 7V%o@+"SaO2!V-1a`LqmM Do }Ϭ!ʨסikbȘG(Sa6p$ZX+ocjZ+0e#dK[dcԔʱ (F!T!LmqGl1r9SM4߷cVt.B"诨j-r|-!n[}TQ"QUXEdpq.jE>,*_. ,$| ʛ h2$Dx70޿0 ؒdR#3(\~rpWϊÐk`Y?>JGѣ9aƙo]>5m k ^~a+0Ug! \HW|ma@ ajC!L.ͅ0iA]i_?:wzrop)j="cjs২5J Q(0map ahRyq#V vggk0 c'!&s e}-W>+PO¯JjTikt,nHjyk[k\ cA0dv4pZy`pc aL} ve!L}fabd?} ZNGb B*Bo 0u?}0eE@GmK_*!~USgal;NL}xBބ+ ajю:V0ed%?鏚ݮaA2T7T>ح1eaaQ%0 0 0 J2 0 0 è& 0 0 0 ,0 0 0*a0 0 0 J2 0 0 è& 0 0 0 ,0 0 0*a0 0 0 J2 0 0 è& 0 0 0 ,0 0 0*a0 0 0 J2 0 0 è& 0 0 0 ,0 0 0*a0 0 0 J2 0 0 è& 0 0 0 ,0 0 0*a0 0 0 J2 0 0 è& 0 0 0nF;IENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_64_0.png000066400000000000000000007322451413546205200301620ustar00rootroot00000000000000PNG  IHDRXXٟLsBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxy|T3[-2nq\QccuMk}bkmjmSݩZM]QD–=<N00Yɇdso{d^s1f11f{1]cLsRJ zo\UkjGzJ:y@ng!OƘ#ǁ;')+[RsU/ُsg/R)c9sվ5ƘJcL1f5p~1 c^44;syB瓺On_g}Z c[k`}hهPJ~KU\TiK1XQfkms_5PXkgK5X`>0Z۸=ǀs/1ak틻yL x=sm;g>xkmc+1cE{p0yʹR?4Ƹcp7SiR4:wIiw;@! ADco|BocLkfcK fw'Zkۍ1SIO Fq@vPa]u-ً*@\u5`[1S}VA 7 v>akm kmZ (5Ki;vq;{mx,@1~k[E1ڏhE>Y.ZRzѯUڗvkv$`VKMXc=;iknײο}^=Z`Z[ gvӍ3vDzscvz^0ѹ_)3=W*}J}?Z@N||1cI܀nz832?^6;?A׏7+ƘƘ|=s1fs 9a$ۛ ZkXf[ktHc16Z˚vcy 81_)*=W7*'h;s`(<J5 TJ8uӁ1wӿ?|& u-b عO#wB kƘVdB-`#'ŭ[ˁ '+?79;fo_O)=WG:W;ۈ[k{R{b(c;V)T_*7 RJ)RhRSɻg+R=RJ)RJ^,RJ)%`)RJ)T/K)RJ)zXJ)RJ)K4RJ)RJ^RJ)RJ RJ)RhRJ)RD,RJ)%`)RJ)T/K)RJ)zXJ)RJ)K4RJ)RJ^RJ)RJ RJ)RhRJ)RD,RJ)%T@)B8XR=RK(p+TGH3XJ/6"RJ# \cBC)R֦z Jrȇ#paORjB0} ;+ˁ)p,UTj RHn:9I*Rx9wÀ?W9@p PJ}<`), ; ,OxRJ 'G $G@b(K4R!%"RJ>oЁdbHF' \+驖PJ# ꇜyWj,JU]Zl>*iYo4ec+M/Ot͛sg;nF` ,wn RA,?YZkRjK KեYեş.-Y]ZYqT4e镦lWe^܅Ew_|]h}\ُ,!YmW/HFd]`I*@ Rե.ϬKrj>̬7Hp`G%5FN|9#?yJ);|)2F)wҔeC2%Vۏ]SػenC҉en+XR`"pAZv>mӕ,8xprה۪Au5 <U.9>Ð^2?.^?*4ĪK&! @77]}ܟܮ`;~KlxA{F=ЧR K'"ٔiΏ:V.$JFϬ[}|@U2p7pMԻO_EvsIDv]19 Ըu\[nv[Xd@۪7.iH`,Ҫ0=ґj5R>l^ԌVKK4K97ZsO1IQۇ_mx& gJ.-GC)Hv iLs67xde&.`SiNCK@]&-w/s+8}[g^{WLKsskm{S>?te(̚[m368Ҕ]ZnqiȵEzuKS2J9 5u1ȉ<hK&%z S:PQ54~bǣ`T]ZdMA AU n6nKuiqPM~G#:U3ke[)d#no]X8εnu&9fUmfr&eܽuh;^29SߑRjiv̚. Iy'OoKX~̳+RVU_Ӂ YSK=Gg@.r7zZf;Ս:89M,Gy.uzu!YuZϓnamGt尣Ktq\_r"rəo5=YwR&[ɬlrq^}>|HO౭ĭŚJSr[C?s[4 Ƿ'sVR( EW[{4&2x-ñ X~N,΂\)^YSgKEJ[Ht d57ά{6TElm˝պj'i8; =l^Yv9PF|ȅ%1s1ڻψJKo}`Hy[.~!H ȱ.@}iCdbvT`g!/q/FJ}%J)s X&dëKFf5BZiGWqiKߋHCEősF2HG"唙Hг)W t{0|x;Hۛ@I8s ֕|+Ϋ4eG E^4e缋'!!?nF*fRJwT?@yHbgԭy;ե##E\o6֌¶rK {fM3iF$2 zZRn@'sdx7$Ȝx[FWG;,@.N7k`K߾n<Vd4v ފ̹ڂd#+P$`f1iRJ5u Hy7K _KȺXHL`&|3.yk䂸XӐ}KwnAV+ϹA-r&H ~G: B.1R70i~Wi&j[B@;rlioC[lF]iHaҀ?ˀ$ d:_MLKRJ# ľTU.-N6X4fx "6 ^Ϻz tkʑ@(\Ǒ i8u\ŝΝnIMvWN0s%p%S@)L6 q#uAqYݦv:u|,; "Aw%,7##YȿKB@&HH`hݧ"HDµ;s_qE,R YS5n̚bEBJ۔?{S5 d\<6Z>Ҕ#%ӥn$cBw# ?T,ΚdZJ E6)sm4n8̑/l܁wYZnv.gpBXR㉕O+{"h,v'|6yלYSԬHkHNO`ފZL) s?TߠPQ@S L)Sې  x @I5w~.2{_JxvҔe% ?wY/cRj\#R=]/IEp  k+W%|̫snL 5l{nKOB2ە̈yH[q>d6#W= $+)= 7rq  T_o6tZ; Y# V0,]CYsg?6e۵8'@_"ЎG֨b&eW1@d~VXjy&&䒶"'? Yc+I\sjxP\@kȚMKXv! aw`\3ɥ%u.+VtmbX,[QS;NFd>HF*4!q3RNxuV]4 Gڷ'44!e'#]qGu<|}u|p]c;ڰXyG?rKC'pOw{xl+RZIHtQ qڅ/yAB`iHq0 ܅{\,j$}]S6XuPa儑'3[ S=&JS !H"T_M60)9q{;HDm,LU$c찐e%)nb IDATъ5f3 Vz:f#Z>*@nd"?,Uw#̚;hIClsؼ{c(?OġH` ^~7>c\BOlfzuO wc ,8\[7P670~g^(,UKcsIKݶëpWJ}<嶪9O[h5Da@,]x2 !NDU"':22,ci5qX26V^|{ Wy ȋ{]W62/3hD­@P<*M٨xy/7{3fOܵf͝?7_Z8x" }:6x?~b ]iKng^_TXj>!x5;%3S0)dDRW׼T[xfO/}_\ŪTEV-bòft$$0J6CrV{ѓ:8O_| |ߊ,T&e[s˪kqYc|}-9G5{?͚; dl-ΛٺMRB؍d l=d~w2d8ؼ,F+vC@z(p dR/D)6l9{?,d^9y} 0 ԀRSQr[Ls9=q8j3Z09Ͱ'V_ۖNs'#9l,Rڳ΅RJ859i1c%/ɤ\7R2Pbsg#b/\ |i>thsNk[7r!:f 9k^{&a=@WF֬mlomEHՌ\ !H`z86fO_fK_W /"6CPt[zGk6rW"J)*MYPzDv`)FZVdr?9Ӏ?E#XGa'7#e}""S3Ȝ'&!mΘ~&Kkv=9^,5w͞n/ 7OVHgPjNӀY{͇7?Ѵ06ufWKl=>qׅ#XϦbj͓ҩq~}14'J)^!zE.0":d6d.HdݠR$zYdK3J<&)Xtt"1Rj6+g<5J3X)<$[ lF szi{W]Ǒ + r`w ,FG[1Ȝ[B/cSXjؘ>%ݓGfqΙΩ}tNmG`_(!tNm36ϛ=f_JǼsj5RƅdD2{)JnGʦ6  kpHC,2>tRe'ii;~w9Wnb;ܗl$ht! @ߎˁ?"5H֊d[80lB:0~6H l$pmFA]0r'}2/ %g @R-TƧ*5Ʒ׹6quDFӽ]LXɣ;ȺT#̍-)Ug͝͞O:R.x>R PnV#j*J[4eɹT/# Zl/flm JrY>681碴oܖ.F,?RN6dokOeym)ɞdZj֪:$3w&P4V(HG6Gt!e"O y{vہ bya(( 2ЅRJ)*MYIKUB# okeMXI@mG5 e/q˅ CZoP$01??) RJ)W*MY6u9iX 2p+nN Wf]CV[ n3<{E^\u%d-\ F둲w ȯ|;vL,?0> / @tH7/T\P:vӁzqgQ؁tc_NƏ;aEwׄd 􇻝14 BH`\"y՛Ӯrr׎B3LvƎplDd4RJ)#Ht@BaE0ĀxIcYi42Wܞ<2j7X̷ ix0MPdaT$HMA =M4ؓ_yHǍ=VSQ91"B$oon;mTgamôK֞0Ab  ~ Í2,z1M4&qtvg!セ RdQm5iRJeG kR%4mH{t^[ظZxuWGֶ8rKc_#Lg#L`?PE5Hv5HVMEeQĶ|\qdžb+#o Eg3px%dq8zZȺj9o@ E |;ʱC,RJ;hVyFM8@v H[sᑮ ,.:;cKWWȧrS'ueDFR1O/kf:oUEr[Օ} mIH@1)m lME!sjWT;ZKI|Wf'Hyoe?,|{;HEUY;kYYzH_\k{{?Crm/C,RJ$ېd te$[buԟ}舭SG<ɥ7[A]E|y9X⤦ÆTY6w4pOjA!j*J2OV# HUbTT BJ%&3>Za)F2oHfnSz`4kdž$AP$FDf ͔ o k)`)Rjo E+nz2/%-K4ZtCgQI۱w_^]iޜ)٦{,Ҋܕk6Xruk;hymȚ5嶪XRiʮn#AJ$ ; n3EH@# AxkAn)8j'!-Q `)P$P4>RYRWבuY\عd }ve|n:f}=ciwNtm4eOˀtͺJSE$ 4HIp T$w)s9уt\ڗ`R( R؂tDW" k;'H=NjdAYK^?AJ`_.@F ߃pːR E%B + _ JF^mx :N4 "Z0.2_?ȼDѫݾ/DeH Ut ۪ܝr\$$H+Yf)"~,!J+3z_x8 d" /?> !u8,H]l EGoyb(p!e7 q[Hw(aEH9W'1 RQ֒zˑ zPJ 6O3\y2zlO4D.@0ߺ0YY;֑!cMh< YG$X9ӑs!M. EMExE2kE^pS$[s9_#>iB@}s_n9CwD:~|  /Ci;]X, E㑒Xe" Aw׵d$@ $^tXq+}J- R#hh̔1ϛC@Ş:R)zOM{)Zjœ'=p}g-91"H"$+ϭm hwr__C |`)P$0aU?Ǯh%PSQi~uME]5%+S;EFJKo8J;<|fnթK^v/WH]<]C~kGFy5"2loO{YX*4|]b 2:dP$0?ŘUbR}W(87Xr]1DRѬyM^j/8|FzckNzco LސaضƸ}-]u+YifO_ 5%y!2C.*NB[m@>Y lD g{)U_`)HGoΩeZ(D}$'stxƦQFsȚW>dR pkbH/)B]4[$Ӵ ÿ6!+ mbMvguvwmۊli/[qd3@ Mڐ.أ/Ԏd`"u5%SKԾtp F#gMvب՗=>eҕZ(F2kod!Ot4zȨ7-\u}ٿJùtG:=;}FZ'L zڸ#Ƿ 6 MFvsw)8٨]` @g.pS[G{Mq2wtƹ9`)P$׽5}T';wD7=0- $O(U+M2eREox[>~aiX|Ƴ>ky|q܃h>r1v| :΂F֏#B$u? HeGwvuMMEgYs_2k)A; ЯCܕHF@Dld7d̴:lz$;[RƘNeP$0 Y9!%tG}h+C?)kYSA#.ٝ>3EN<v9igQە '}3$.C¡p c RO5wŅӯz67u]9Kv |C EuG:xf͝oR;Z5SGYO)!m[k4u+aLDEFME 9@6)i>|$:tN#k*J1>[l ,ϴ 5_\:6,harA${X๑c0p܈i<1l4P$P+.C+v }|ɔY0,MyG۝Ѭw|]ќl z7&l$py(w EHks50Φß]s<펠?7^[`) w n ij3|d WJ0E.?xbIw37nw'Zhb5u9` AJ&j*JB2 3v3Ԭ->I @MEHڍL\wS>#Ii/|䬖5v|f Ѹv*MΧa[$ca|)qq"CJy[HQAW,d.7q[ C%H`;+gjǴOku: *M!]ZnH@Piʦ!'G˚0zZq7,Xu)2.dw==x 6iONΩD,% IDATxV|O&70y/5w @ڼR:Ttk7(S5% HI.ry\|}ocX\SW-0;M3|3O; qW'N%ѕ9e@FڶYs?!ehض??h[gNu+oѳo'"b$[eyQkL`Ȱƭǜʍgּ=)ҟ=+}h ˦kCAxp:2+ i r Ҽ]rUïdC@Q:mgf{sQӐb`D^\CP$4x1U٣gHx,ޕG}6߈6bqO,;+fk]CN=/E@ `^WiʌO! Ѳ;̩*sӧg*H' Jڍ-kYY݌?Ju5kڱU AަI耀Ƞ[νyfs}3t;Zu 1]CqtzZ^͊P R!X7+"{wHQY?Ofx]1YlB>(S:::U\ hsꮓKFFȉ4ki3(dTas7L[t\ bh5-/, 8~MlCFZ=h^M<&#0rC總\r_L-۶&=Fd-2Up}F+%ݮh$ggogWm-V<-V!g*1XrM/ aEaLD͙MUjd{F;DFƢȚ1H%)GXn_0kO-l}`/lX[XM-4eˎG[3׽˧߯M yAEIffOӐDNF*d-N}JV,8BKn@X:2mvazA*Jk2=D&cMfS,f?G+Ȃg 12m.a"uzYI|G{,{ Y;iR)a4jzaÆN~wFm1篮|,/8X6 \>s!g"' d$i\CF}gʻ?6mt ipӝ?RPO|[\xom Le~sMŰbr*ԞlnlpM3V 4ijʹ z%u5A;{"#W|!L 4 ץ!|Kkn+|*?g5OG:6I׀q6QՐBa!fO-5tK+RdgSH/<38;+3' F4Ęo& )yOJr(G%:qVKؿ;!4i&h&UBMS!)K~YAI~ "S?iV95ŜuxP WR,5mLBA>,zj] ,*2{ 1]iw_4%DQ8>&Tjʷ^K=sf׍VG1CL?9a~(iIA BbƁZ}TB E^GOIOiXߙ3@o:7Ӭiu9P!;k9ǏIJ1l<4S4*hT#]!qeV"V뵒瞏 n6O?ʔ0<6xM]Ȯh,jvj; ;?B66.r#,wȨHɔM3ۇ( 2i%>%U 뭦tpJSAl RDb`TW"#\5Nq`[\^k?fL#Ҧ"}]Xܾ!4J1b6(rO1;kWq6#]# :;Gf^ƈqXyB@R)cjPj= 7ůa@t h:G.CLkzŰrb&;M졗F1՘nzm9fkp 1'eڬ.I1pyy;.t(A̢K7O$ ]de=;8Bu<ΑU%*\.dH#?"g,.=oeyvچaEtpZ}:CL/Rz *3Ȩߤ%ؚ(NLDbK'Xxڂ19m Nw~VtQ) 8s1ј1TɆ_rc˶iT`3iWj{1nRX'!Ẍ)2ZlϪ 2T7gsCwʂ&`;ͬ237MmEYAB8w"m$RT!N)V6z!vvGF>^ozqȹګ([il쎲g1~ȎV1()+0iQ5/yGvގvA,+ d ݖIhfLƘ <{s=>> ,o2mS|Q͊'f0S!z-kkaINU  ɬ.\st,8w%j澙 L:kanZ2e,s4iakAxM,ەuk˴YEґB_~3лfl-u[C; 0[sFhZ9f3j7э=\GUi9L'|j?#~^ A:Z}!Hv`BپymxWE֚Ȟ]i_sCQk9mOבuwd䣐"#Q[8mUIdcȵët[}Vt6(0)2!q##VN{zJٚlMtDH#t|7ɜ斊C3j'ZsLY<`+9K=6ySY4ͪtIPmB`2_<\EX::]#"t6ׅ76E: +:^FYJ]jD2L[锌\S5} wG,Cnf9 ,8eڬbuUA﮳eUTzǶv kD6 'mUY>m/=92鬝7Ю_Os8CL/]t(bvc0%w& V*Oݾ+.xD~̹ X~؝o{6 fOR_ɂʀ$.(,H9F%]2DLd!很5mVSD!f0ZAXzLHz‡?4(Bk]zhKW45v~ LBE5xL{9Z;oBF>SȔ+FP HaTno{9{][oZ(l[&6#ԘNWlܑ9/gO)o*t"s @Q!nUU/hܪ#tӵ*" p%|HH.x&$4:8Bݵ 3ض4vo-fPbHµ-aLo IΛ!O.f-Mz >WqT s;\bl:GZ940>hN,L%b$9ϸs]2X7qİtÙClYUyy8~G Ǐ)齐Z wJ0RbE@ShR պ&.^xĦF,ȕ 1G6/hG.p#_V!'phe@m-pc `kXh@1E ک}| ܴm{/cΜh,Ub=2k>w;Qry Fe 1}7cLDq=u.}Rܾ`p[:1jxC:h[Nje3mYLo/(] N-p.Cu5/ڣ8Uk?@yM뽊V-9j6d[sqm_ܾ4dd֪j9f5˚Z{38xAQo ːjξfOE;F7#?f"|d%#NiZG{p@1n7]TӧL \O !ƁJ~NE*ۧ誡?r f`Җqޓ^-RN#v>6R 2FRȌmZ5 ٕϛ {1y ۬sNײKŀYVJJ^$-]9kċ/L\Fd~j6p('r?tᅣ@񇝏GqsY'&@B>RrG+Cuo+W1;/}]ckov 9RhΡp92mQ>p^f6-a3jh cB 7G+%W>ΐ45ٞ2*|en_ek:Ӛ/839k&C(Xͬ5y{{,؝[Bs͍Zc >K|)f,!E{3t_]vmktjzpp0}A3;4ZQQӷۆd˨V+ l`p?֌blb8 7i< ނL9 ^8Bk#q R!#KI*ӍU@ izO!ѿ +'x*8B3Mwo>h-F%YKX},k}swT K}sq펩}c H4Gw m6%֏VdhQ2k'?<vNN~ںlH2IdqT#!2uph@g^sLL$-.ة@dJh^5/ٶOsFX::]OҙfhOZڅLu`AlQ6-w#rq؝+OdjΑ~I[?4ըD$ԖhYٴ{Ҕ*d6X͑E'bT U&ؙum8B/ J]\qڀK 7,4-Fv<)"I/M9 ֮Dy <14=SG!Yc8FElk5mkva``j}3۫ۈ zjnk  5`$d5ԓn{8B;#κ漩w!Ez!5}7@A,ak2r,F%!I[)y9Biطr 2ĀL'\j8Ba#aG4[v 4Ҏ^ !o5kR-"8BGKG}یux֨1fTE꫎_Zze.9js[E>W+#vZY33 q6xV̎e R8xNM%aMUBQߚ4ks# U tuDtt/,qvOKuBel,F`i Z)`<)׶l@_ہcVې:]`}nZSt+XĮ^W+a{Ao%qKڻn_h6/x[Z HǾ ̣湅& `3# qɔ%'~>XyENLl?F. ;_D.]dYLY$%YEZ (o珐mLȨ4QπAH'k?8B^~-$xy,P ۄQd-#t@;>Ҍ ;.#mЮ3aȔ ةߗaD 3 IDATX::]Lfyӕiht:CDPK7c/*,$uG+j:jr^6z;/LGIdM@;쉬3+B]M}*R PYHQjLAɿ4Py)X7;Ӎͤkag&2U~|)/q=8BscHSa̹dnA_!/sl%ۮ~{AVCItlh笿VQoHi/8Vqd]o{{;vڞEM 6u=~~KI[9C}T´$#0Ԍ!ӧxRלpCgK7utʴY[ܾCJ(Ͷ՟.TTp;Od$MZTZ6p8i-%Ry4d`?8B_vocXW:`$p^Mn5(;#KnF (Ѻ1TY?n_Zt-BfJSbeDrPd : Dfy7rX ,#xnD'jIڨ(òl\;o8B5|bi}#-$Rk݉G{|e;H+J#t?4fX[VfX[z*4B^@6dO K = JB 7bkpz?yU+ߛɟ?n9+!B7Z v6AdorWcngaV Z?Y]ͪEfs`7o !c6sIs{A}-&c{ѐT^'a Exwd]lvm}*ng4{Οu)BZm/@N ZiLiS_/Xtp?t؇BGk:tү?TWfVϰ5T k-2 UlGZA.9a){ˑ _Ns˙g= G%%#!Nyo S#V6iAqڑ3#s#xҼXLtO7Ƽ3cJ O7f{5_iԻxϭi(J6P_t7<tFG6w=?ur d-n_p>\tؙu1ʨ}r]hōx )r@uRy^\]m/w{GCF/g/iP6!gsu9\\'75`N^[4tz{ÎUi7fqdސM!58B?<飪j5%?Μ-0 ^^WtThn2E͐~*˳mTad}ز}mqʁ+:,{OGN[VQ6pA6i6Av#GTGH_l ,.KX%-0WIWԆQݼ[qwI\L_ >jnnϰ4O5RjYUU6GIY4@kw8MmJ6 66h W_oFGs12Ddzozo#uKG avlLJK rY 1 /ENQHvQI=>ݞ{uCk'@6 lύnGee#(/G: ѐw tC>u ~3 9f[{ /R%%iI1űv6Y/͹d5$s`jsːsU%Xcm<dh=xҦXL.m׵G-޴_3S:HAfV}yF<ТڮX"Hߜls2ppq|KGH@-!U(PT E%f;yXִ5ld|B*e2To漩w<݉,є,89g4$ fZ3ytÊ?4gU! ?4yxt@w8 S/r n@4FNȚ|Et=dz)n_p%p6Q4d"={!#%>A chmAFKv)kv\3Vq_~$-6 x]Q9/h@Eܾ^W-gAh Nv_8 !:>ur/.k:>]?hVsA.ƺڡm&c~v"#`ګ](= W)Bh=m\!9R= a(%!b ڳ؊M޳p)y?0(j=b}ҳľ{R8ؑQ4Һfi=$Kݾ) E*2r׽˿1Ip*M&2z <aw ]`t!ˎ{"|1oF aT7{YbٚfTIwg~juߗSt6sތLe+VGNތB6{f::pVv\I=)_ܵۏ9#fPTUvתڇ UүdQttCNt:wbu|nNU?)C"n_0LE,AWOZ;)9887Z+ϲ5۷j;xOQ-sMxNU7n[VsK>!2MJQyS);lVshh-B+[6DcS|fRƧ 6ѥgޥ!AGvnEvX=*xn_PoqZa-ȺBBbyUC<R)4MiMmOٳ PDﲸ^mœ^׫_ ߌ\H\R䕢~Us<#{XWXD=@TCzA -.!9RLk$5q+o2h2&D{,; <׶9}E}ȔRqːfs2r6Y[x]s]+r_ '}K:uqJVbj[Z7,-'[L5q:7%p.[OFMæhɔX&JLdв3j{VfSOlZfر8 X7wŕ#%~)W|E2cl5i엊 1|+_3]΋>}:bGNgKۊLi\^8BVU|WNY[ M (BP=?){V6Ѐ^ױ="ۏn27Z'2M7ȲG^*jVdMbD,}{;?=ub x]k; HAw>nGd ͪ<9{ &۫?h1s'(IT(܌\uƫ-EN"['ѥ?#lnF|dZ'|>3`_.jH^ח} Y B."ηbX~&pҮ|pq!?A|M'ܼĝ#mYʉ {v%JH9i*d(vTq_P)Kckeͧnl~Ԡ'usWrQ[Z2p1 ׽Gg|ؚYuǥCX::]HIv,k՚ sS!%Vw9ʖAQ)mWy`AH+;@#H(N-3qRۑį3^'fzi =>uǯ˚[4٫n%7Q IDATb"yYس:&::G 'ȹB3y`*^n_hx]C+q(_mQQXo?윆T)Mq)&m9p4+Ⱦ מU=im2f;Û#EQtOLMO]ՊHwJs OxJIGz,;&Ég/hxe-fh['KU ag6`8BߪvF*#ꀆS͔;z&Ngv  bjnchBkZ"0>x]P;m@K"!lFLTցag&oȴ Gϗ5 F+ْLQ^ԨלOANC:eqKh~;CqRS4&vՎ0 z3d#tic n_Rَ< )^ x]Hv )l>ut:E~(HÚEk׺ ӎ4*E^+HG#4ж΢vdzvI I׽'+)bXݷMz>o_wY#^ a6crѬUqny7$r./\U.n_pcm8_^/߄t.p(gt,%aDwA$URfP\WFd)dEDF}6 kyVS{i֒cmYتƾIcv2dEq]gx]o~پi_ ӗB[R^W\zSV[8dI`n_ '>9bp'#~* t ٵ]G_n_?$_9$O0u=L98܀9w?3ۋu ]Y#J(@@H@zB $ B1DbXz{SqXŸ`5ϳJ3WwF#y{{\ M7HY) }o^~Y;eGĖW a3}gskP&Q#īK&X6lt/˸]U%hicp7Y]Бi<%"MneOi*+λs⦛h_ _d)+֍64URr/fYuVX^Tgz81.=29gc7HMm`$>   Ɓ*P]joR &Wcc}gݼjAy?LEWlBjEtTG4FR<CSJ\[>Μq7jE)s[pk]^7߸L=Nh%g^2 Q!$ qѓSw"O?T/ 6,YvM5F-LlI(C9٬s?2XzI{3Q~sgMl&t4:]YG?JԗE e~rq|RѤtP Ԛ&ڀK\Y Dmj^&=.FkH}sT6sMA; s}5]b"1H 6 4/ x{!5? por,66 2EU%.cVg3FH+v0NAiѤ*D|s5j85X _GRڽ7K,.[J Kii/BLdAl9ZMIgsZ {\Vun[ۄ|3^pqklq,4$J]:Q ,pJBĴNϹy=^R-Ukm?  Ѥr?̴=ˆπ |в8lٚFp8lKWk5@bsDצR=n_ r}f=kLIWZk3T; ht!Fw6<@NѲacwHH熫wp`r'"p`Y"5;Vը`$*c핈&G|ҊM+9 -^wsˑ:c%Q^V!dR׵Rw0eG_GI/BDAH ahNJߋ&6{+ouV#doC36Uכ#+ltasF}#F wq鼳>s؄`mnS }g Bftũ!쫌3sKdU^kC߁Uy{/VmW,j\ lsMl>y`zG]g=ohG$9k2dtUBV6}''xd7א/G٬#O<3Ծ &W6v#*/}j=@7W z,Ҹ`$X8ھ.x$VX &\`i͓2D7 K{/,VϙHkcKO}O;td̩2^h(Si +hATrdqȣxnAd#cs @4 i} bx]SWwҠÊkHoXlNuߺQGR!H-$70뛞$Fdj/gpGWEՋ1`j5?^iꂃ("C3ʖ/x#D'Os:`'.h%Bz I )n$E Ttd,Ű Ns!8Og+Y6a}.0>; Y!6ѭI6_#r[4d[DX8&NsNCKO;z~F}8P Lhү׭*M^Q뒋ڄk|6v'\ G7'[WW@Ҽ~L Fw+@!F۫0 jkF3\1Y3R߷\GIzH ik=)7b#$ DjAj~P IIfk-r] g%n B;x7gFCR s`Y{g;xPc_:?),istq)M>r(0<>yꭵp S\?VV]ʺ}^I^'оqZ=_}|-;'O  sALD@iNgj]76 "ZDHȗ`,6 /1p}qBhs$0mi{^5e^ڞ_V>t99|zߡN[zX_Fe}֮2d'@oxӮl͖׿>+?T 6v'Bc@K0/@RlnX8#A4HhDۘMK$ 92dI* ]7 ַm– .,>K"J2$v< h3BVm!}f쏨f|hyVse=_PZاB!'X8z Y3~%6?iӑ5|?:̆}{Ԗ*ZL\U;K}bijc'O ->0;ǬdZ;J>~&E"EҺ:?HHqWy+ W4#$3|k`p:PMϱkwleFyvĝ&!_Vټ mgGz\9s^ ۑ1I)ȍAŋh?Ԇ3oN[[?-NkjF!+戍F/kHST,#Ж*vE _eZ0`$~\,X}H1*A03jaG HXNaH#fH*Z3Ls~^]awm4$hd _\h?Q3t:KH6lQs?sZZ; m&qeQ:y,^uఊ%zXp!q4ԯk 'fi4Tڧ̪ʒ5ySfnWʊOtZ6#z,eޞūDxu[#MoD~H'3Gpt{xzU]`d@ Rg9I[pgS9ˆGN#+N{\P Bm0jFxEGwT9 %#˕iэ4ފ+ާ\s[z_uˀVk5'/6G:/]o{gj8plT ;!d T4ECp˾G ~rGw3~{Xy"OiH r M AH<#(edB9tK%E|M#5F A(p`=p8W3c8`KV00[[vrЏf=e :zqNg39L3lohz 3y}z_[WGoTq }OE:,r5snW~sI!Wug@K{`i5dں T-Dx!ȩEhޮmMl^GYr(in+i=7~'-6jbGOL(f;,ӿ<ƛ'>yoޛͣ` ɌdxwHѸhٛo෱G_zAOt$# FW'Qr>E@ш{pt0?/F.݃,:,BVkH1Ѐ;BS&\IJHq#FF ;-".A0F"k@40@ÄԁW1ٗ/ϗ5K=_?=v٧:E!bhrR.5sbyߧCDs44y@p=6Q S1;\u-|omG N>IkiD"uc'(\/aߛv2leFb94L3MJ&m߭PX%!_bVw BD)ch&ND sݻ!pУ k7'#qOf8˝`NL$ki&k&@>G *wE@BFG"uC{5}^T>M!̈́ۑ!j!r "%,K}3~LP&&!fFFmZIDrT gj4/aF%&cg&Y 4<@mn:-hrR1w{3 U:h! ! .BѤ?UL]C*NBx. 5Ѥ!FsCYY6`u^]sF&J{\cCcuTy M!Vzõ I l]`ٰѽYeZ x08S5a)X #K[AZW9czվXpq{G|EKg46 Q,E/#qYUG9r:bR@a,ؤ%.b ZQ+s!𷶎7W]6yj3S7r{3YK'>>qEԝ{Us>L@dU"&'nD܀~MlFȗܞ4Mj tcd>\r5#;BĖVC\ Z{|Z{Ҟ*_^hwg:6H f āH|5B`$~[,K:qۀ(Wsk `$~/GNGun^9x&l\#>@e,ؖGC_#)l{!fl\4D>cSy\-(`u4y# jB%oeS܍>4 :rc> 1#LwCӁ^_K^4"Jz5oܸ+6agI):w+I'N5I:l4tT;}ш47pb:~'#u :q/K&V}¿ֿe pxghB|&O?=~7kyaj.]3SȢ}fSE [pwg_RVnC^{@@wdA>l! ffyt^`ٰacWUwH{hXv1mG~Ǎd(R=jW}e2赆Ɩ,Bܹlˀp`$!Klb> F?B:]^bX>$3Fk?T`6,,!t3T`$^!9Dr"d50'D$؜۵ V3jl^z8tsR54qYT5!]-)[[i !#i݂hGթF~r\ҴlyYᚫf;ɱʲ/RŅk_[g66R0yHIg  6#H`&<+C X8{ d8xϩHX8I׉#aP^%H/ pzGG!Arb;_6`$>h FH\ 9|O9cy[u 7M:= xM5 }a:n 5M_֕#od48Kl40 +IUȵل\[sET?^(6-uG?u] M3(۰esS0>@\%p4q;p~it41,.l\He|D*Jx0R{i5Rcɰ 6vcT戻L|O3}}YO=y%tX8L4;^׌TckO!_臻#ٴg"W#+ڗ MV+`},KFEau0??Wk5e?.PMXQ cRT>"]4xiyU: ~/EZ/UxQq"9yU5jQ[(H܃a, IDATڦdc'`]a 5 uӥ?fiԴK՝x !0F::h-H*]/r5W%:7 ?E3X^ĿŠV߈gd hK(Cj4Qխ|"IѤzhbr9r; ә3YŧVMH`$~^.6m*A< YNxpZ{:?^x[ÿcCĔ=QՌρc@6k[zL_SR ]a+GR|5-|v|z =j%r$0Zeac[p9pV.-SWwF>~쨁y=jMC&.~OYV۰5< Y݈yHoT2jhpGs&Rs~W %#Rky@վ?̺TCM Z F9=l7 V`iY=6 Y7@E]'*^LYjIEL/". !`$>Q jc&HϫBRq ̻o}*JW쇤Uf$mN ܈Gw4LDLMT'%r}F5]l !lp:ry4p0pgnkGY=JK{I3yjK?Yh':VM3n(~~m[رY|(BQVrm le; ^]nyvJۃ"SwGx>:z]o|w^q3 Ip%R:iparӚFnh3Ŭc`$><!ۡޚ$(e#<$ X8I3؊Ú Vca,6Mzk3be}P,8W FgOvg-85ɅJ$-F!bHS9qh@ Fm40uʑP+PWv >>R#L,P&3bp.BEG41~ҵHf,KXd{QF/SOhA3WG9Ӊ//rݼ,r,rM#uw^{L z.W@R~S[W>\_&Z}a zgO|wŜ}r,Y-[_~ۛ7#Y\oeRȂg; lecOH1-Tjc{_Ro4/;0V>pXGAN:TK4:74MMA<_"qf#i &ex22Ѥ??Kt|m!F#c 2Lڈ|&GFT"`$> IkA`$>qSw1B|QCDI![6/u/\hN۞.Ѵo)D-}]Տ",FH0Hzk}]fQl׵#dAhj,0xv7BM?EDžѤ%O(Yv4D<ЛH)ϢIbDWSD!MϜwZr$R@2.l񄣑Կ(+x`e{Y:{Q <*rRݮTQQ>:Xn yxԱݎB6F0/@SrU46n䦚FV˿1?O܍LӤ4tuk5 p:x}k|wID@00XAo]MGoow`FN$Jѩ ?# cY}]XirFW\bHG0>#qsکH*Q\H*V-TvRSUf!Fjί#AԱQo],<a;Ԙir^!9yB~!QcEH͟Ѥ8x'K|E geDapJUxF:^j-!j0DBx !UF㓧UZ8^mCěƧ! h?gpZb_ ?!_k-eÆej)nMoP}6A4H~lvćy5㶺--=siGa=>t:Ӛ%$XzFYӲRәMp81@:5bJ 8YD}s *E..x-$VJTˍ/Z{9b4TIՋWMl'DT^X"^k" YC;nĸN4o/,!.E0o Ri0L;$9ٰGlZRYE]kEKqrS\}n;}ϒe;si ?Q6K~|Ot$Ru;2%vOY},!`$ /F㆙p`N00c`$~6B6n@ҥ!8 !+Q$h{Iu,RBBUؿ5,BԂBYi՜|])t 5:'&b #AsJ0j>e756v+#|oCΘ~}#`fd־ijfw7:;Er!H$#B`^ GZ!$c媙9X8P^g꟱p`$q((O7T *U7"H"P ry $E ՜e!\: G4:-dFj؆s@&kem:z5$@DUxwt6Mw&g|~U ymѤ7nv#UgY:֊!_bhڜbMzA|4wⱌ@'ŇU>yhwWt.C0,SndƮ@05r15[*V^῿h㻋`$~Ҽ7(?:4 >D*@T 7D2&7 uϒe׮96FjI^EݻH8n,kH$(QgXVR!$KFU!.D>R35!irAGe*UT8qi/d 5&Di@U_RER [VNNT1˶;Y2$X*@4?0/w({N[J\\s}ViR`h1\֞zSm9Kk hOkH]JQFj~7MlR##|c@Cwdž]heգd/NX֞.<%=?7HУ+4$9bbSD!O?>@ !zY=wDzJoVoA۫aB&m.IUBRC~LEj'#eBBW!)AH觫yr6Qt5f0]`ceV`9q #G)V:?'S,[w7l@ D;=xwE4 ,xYŒם& - Yyoۀlڛ<;/mԵ `Βcjwww|ROqO^;|4 `/aZob7C0?i@Ml S*fuc`gz,8Rbp#b\|FR#$pڮ!N)5oC` Rw5DIW7q!(x䈓G^W[ z=%gP#qZ},f@HZc!nr`ZJS/CTe5oGnc/@4wG53xO&Z4I~=>DT,rv4]G/2̱3J!ѤTs$bqU7{ &Wl TduՆ=Ek79tlP+  ZER& YrJPRD9ꋐˀ{~HouQP]=}󁃐`s-T#|ˆYE B8W=)F|$UЃԲFb/uX\K#"7՗X}:P! JE__?}ނUe3jٮ ]m.gg2 W)Al5U#d1#GIp`{.GzӀKI!_b=-!KѤD$Lra(^B*Z"lbm K|e5bs 5؍w+ƮHJ{EބiIeܿ&kB*xh 9f$M| BX;ˍ(44RtBTGm!){Imyą>H!.zFҪ.FizQ.FbTk;!j $MpBXJu|܃l?CN}lm=T*˸q .,̪;Aj `W-LЬʶ!"icؓ GYU0n7Wْ׍,SѤuvq 04q&m$Zb,K\2LX8Y7L3Z͊D7F5X6lذM1MG1IٺWmPU%{$"D$s!d迈*rz1mHxBjAKmEIHh%9MU5<#dˉEt *RW\Y+Ʋ2j&:?o-["W VXdYD=RHVPu:,G2X8P9|MO1M TB{,;<>Dȉ[=#16L$p #"H-VPlV;W!};kNEH>HS=Z%'BfDW"7IWD ǪBHɅQ#8 ʥ^gzm/9TG׮~شJC tܥjLyBo3j3K*!gnu<=՜ةYmc)fjaj&lԷ<.dz4 gQTF=֬BԓёMsQ|GFXB]mr{bwPlذac) kkoF6  FH|p>VN΍7 wz!Eiھ?BpF깿!^FR#Nd' ʩO#b B+GAHVfA+ܞ^k #"{ ͽC'cѤ]z!=B>w 9g7?_QKZV/:6oej[ڨ1=^YlF{[囈5ON6v 6l\NG2jjbFɐ#m]+ZqMBMщZm40MҚq?Gj:H ݬ^+[rNcW{B \тB*GH{&6q JW[7Bȗh  Aj t,G|vwȗ &F_3% }Tk5E]6~ i;=w b?~ S.j7`ٰaƮ+#6pd+c}#28G"D DgY2DY M8á4_5?8\Z:H-E$&w(.@4qmK£8 hhre\*k>˦Z$UMMCs,.뎍fsdz2Qjҗk߁{.]du++H0cOQCĆD0M-4vgȗh'PDR>Piy 6 y8b>Rga1𸦙Wԥ\:FM{8Acj``ٰa.SʺnA'ʻ{N6vTz`O,]8HZ꧈cP2YDә4t-Z)Z%SmѪ~M=tN^ umE+u g0(oMSEYLIQ"B~!׃4ټLkm9<ԪЈJH!$mVͪF:O{0 ZLpmQ7Wf Y%u&Q8'mRMGҼچ|M=HJjmnNF4w">\mJZ|!7nWX#!O[42!_by0/Zbွ`Vlذac1N0(&v?=H|$MHjXV9܅(/Rs8JRBư,uDJ!5U-w ~z}Bln55 :2ah2u!?kk RǗ[i8( הD(&WsuȣY,Ni W)lÃpc8 ׇ!SQF7B m/L7nF~mcq FI``0/|^/W.m!FW|:=~{Vw35)ѤQzlF=iؙSmذac#k^2W\mO,~d`$!*E%92:ba!)bY;UǽJY]Xj|~h,/[wH_ZpZ)KΨ2ϰTO:)\+!?`J{ɥՙ]G\ iIv_i{ s(wNAY7#jۻ^!JqԜg#N^0M\FfMQV< <  ۀj0e9Mlذa[=>l9sM;8*[{/tEvDP[Ql7Dgt\uȸ3H;.れZ5a lޗZ㼧[&A|>ֽ> /G_Z{BU!-g>! k$mځ'$!4r`MmtcJ HRR?6YBlRPt;pN !D5h~J^!6աM6)B"%MQƎ)qw~GeHתscܹd=B Q*B |+n{ o(laqn ?۴Lj#~e*3|CwoK݅(k: R<ލL!)f[+S]S Of! A8+ٙ R֦FMjPS[ IWJ"j\!4W!$tU2z'~Zo !ӫ:T)JMnL:`kuJk+^C$Q~W2JeiCvꙉ`ň#~^Ukt%Ɠ 6ίADihZt B~Ti.P kNt!B m$#e!#e5m.XE֩m>鈫8rӈms!SY 3DF AzrՈȞ%$VqMQO| @?mP 5 r} H ر|IxB?CI9Eoi8$1KXQPf[-OD6UqC!〆sRYQXӥNl-}, liE1bĈ18Q! 5H#*n(䎰2?^d cǂM2ى6H+ZWUl <)58R+-3,6wn[ ɵۇ@x[G'uCHSv: JTR$ȕ֦)yrJTřo$IZLX &޷*3A 42F Jɠr5>HCZcP5\8_tP QoDWӚu_qrZH뇩)?nS•7Á/t &X1bĈ#ck@BF"UE0NCB  Axnwn|Cc @I"f9VX[Ø$T(iR2lMTAU^z㮂͂QC %AnD#2}P )AH5S2֦єɖ\zxDyVSCMlJzH%Hf GjWkꬨc L¨j&֐Ty@O֘h<. k֭"*͍9ҳzzOtUq' ySL[lپOe-+ 7dRf4Mޑ7" 7wY;o5X1bĈ#ƣ TDf`FvH-$kL"$t>وOgڳŽM` ꇩ' )5`԰V @Ɍn0m gHArj 00)@1U¨Io|!p5BR"RwWXqO]L: a1d,&$֣q:׀À',%"Y-cqkc_O5? i&|unͺ՟Zn^-H<]\/Y:f֬[pbu4$kyЇڪ!ȳ3(ԛ{ySE?Lb o=YT"&X1bĈ#ƣGG!l΄" y0 )e|rMQd!Yaz=0>a6l=Tkي[W7w?vL j%ND 2(#9nhkJHv!Q2y5o*WU$ND3*)YHN ꋕ4՝Rl]{.-kH1/<*grEee[5.ƈ"`u5|h]??}DRpaM6Fx r-X8w|h[W3\dglSvHJn?ڗG"#F1b<{_@RF[~6kni?v3rVC T0gZ [ޖCJuL둠4 )Y")k?G,r`j0nuW!XoƜ@ۼ1X/D_#˽ Ku,0 MW5TJߖxBc$?f'BNUz*R5? tb8]ek֭{O!ݎBGv;cn}=]?CL xߕ3BZTi|QyFZNԡ.mWBԺ*r]n{)ELbĈ#FGƛ@ N É=wf5.#RۤxI%:IDi`ݸm:-{$A%"("6]`"3ǭ=u;qScDQ n"KTk!9c((uDn; ADд~IS }-jcAblH0Vn5`:.݆'ktTES2qsɮ"1{7(*WQy ]ݽBU=4vr5bdb"FDnvygen'.9MȄ%[(>Xky1bĈu># mc<9W u55QH 12؎}=N + /)"EJ.nLLMKk DҀGD6ח asԦZ?A%)y:)~B}S€D[[^{)~j<*7ɆmQ߁gsh)_ 51_@HM}=] Y]-}3;mH9HÀ[ yS\[G#[4SRfHE;L57=%[);'`ň#ƣĚuӈnvGX%UHݛ676M.`:u9E$/mCM#u }6D5L@V8@^_$4uq˘]Q@J+Klfmoq~?5hV6/ #*1S?vKy:íUTh\m3nΟF>,~U5oJ'$Ib6n6es!xv V1bk֭7ռA_6,@7(gL"T }FHPt?WpFp}:zӚhݐ5coiZ5lXa<ڪ]nl`CKj.11 !AnGkX&tkR2^n4HE!pHp 6ǝ3_?f/GX>&0;,De(a쯒@&/@EK)ʍ:o-.:|⾞4\_ g_O׎Y(s5m0;ow"VzE&NCoG&"Ja-DUC 7Llj3&X1bĈ0Xnj#!/ql']jAm^kx4or:&wٹa` XnYHڪ͵!\[XFG!jJ0&N{PfjdbrGZB=:*5W3F5)N "= Bg2oLǖv\B: \iݘ_׉5WB"5JpkLHt˴϶+yN $\$]*u یFM f,P{*-ܔ7sQ>3fxXJ&2H]Q9?ќ n|?RCTP@GԪ:w!}f' E;2p 2i{:7&OPީ.n=_uRdʝ=O,9ܒ7wlac<7Ob#Fo۾iI5LּG\#Ɓeȗ<$pR8bagA͸ƷIĚ AξjZ!ֆ %؊t0kCB̴c TM2LKpW>PoE^5@BR@CHOUjiM SmAIn^ӥx{ӥ4 ܐ<㸉07۸]k-ߏ*$J :^o 4=Kv"5Oqs꾴AC,cݽh̛⏀ߖla oP!殼)^ 9ǿ{ hxqdtTZLm<{+~^Yٳhoc.zh&i.9٠rkì6]=7%| o!F/ֽVԧcr_mF jmIU r_g"Ɠfର9#g{/=ާ{L1b<^|G{~r L<Obwt3\CXe IDAT jj0SJ@Ti%]k!63ʄlDBB}MhcRuƈЭWFS?[h i;zRWå4K4, Įrz,? vrgelSfADZ 󘪴!~:GⷘTV sFDø*qj0΃궨k𨩎u{1ύz<zM݈rr*rO ֕Sd0cmk[XNX-$o!˵>Yr|'JqqͱJu>ƉTAw")xg!O<Ⱥ&Mќo4w(ʛ ͒-3w }{ug?甑`ɣG7fjj-e7ktbGaǐ{1MuŸsSAF@ds7,$kfzLj۟'iSӞOwS?t3*1b<,޿S1:hU)j x¯/f6Ô1Xc|ح 'M R3Ż,W5I3 BRt-%rS iB6ym=LŽ6-{.27 .ܰ󤉱 %t|%4AtލK'$z~ߖԹR`<0p[C+%>D$JϿ菒IuW yuC%6\="~M#6oH4 M6h^Vc OFMڰW x'> 4W_Sv GB^Q>>P29W_ ~ۯn|e{l#"' ΄wmLH"D<4G^8!;\t!j³]񶝭͇L=}C^:" %~*pڞrAbV?țsq>O {|07ob!}}=]COcx,tcMAbCj6N>RP aa"(N%' j! kW.6A!CDtyMٻ 6~$`]p\:7UD4Z57P>M'ZK% 0$z.{`4elfs[5ڽ+ siO6VɌ$A WC [>̀)v )fBZTT `lyyRv4>{j i\WOAԔ@>lಒ-<8ōkZ`O~j8)nZ9-3~*Hd"%9uum2hڎ84G^O;TG o //@%[z"jiMSCwe-3i#RxhgojUƽ ѷY[oY}F䈶xӭdC' Q|yn[!3kƈۿxO[{RrŠDCpeȗߛ\_y׊9mֲSOf2phl" {/U͘]?.{5RZCcҗ4Y3xI@f-ZW1(Nk@cUg6+SCL+f~1dT9m4fh|- S5MTo}u;k""cÀ)D6H@++'@ yS7$߂(! _P34@,T JaӟoG Vgvtz$ bF{{NQ՛7^ڼµM$~2b"q[gnA7sէ遼),MU~}{+Fȳ6=Db9Dz9PCҔٶ7(  _OD?P_Oן}7/濿|q_M$6l_E/T~Uk `݀O)cBgi/ _z$%l3Ի*cD]Dm #AdM"4?n*ӁM5͙m屩>Qjsc_ t<3 wCÒI5!?DW& mN@b o7V ЀCHNnDz`2yS8"YeV2Cw1JB_IQD=Oo9/wto19r4m oAd[1ND|Vaj6;nӳKv_9?lvGTOG;\i_#*Kv)>{uܖn4%[MF& wBx:7n_#~!N{!Q XAAT+=_{ [>Kp۾B`x/ T^"7Տ/7{_X{8Ĉ• L9Ӷ;Ukyi"ϴAZal_kakD#)$z6MoɍA(^g5Wmg P#WH4ctcS ^BdCm* 11S-X% |4 Ah1LjL缑NFLyT'[ju$_e 7A4!"!D؉ȹ_^/0 NϚȴ&TujwʿޕઁWԧjm2$.Y4:cQ]0IªuG#Mo6#V滑_kjd|Su2FHI QmJ*k}n r~NC&sySWiK).BHK0v Q{H߬t:cۏ0Sm,g=ȳ+C6 U(qg!$M1;b`q!2 *pwŜ#L#Ɵ9V&z挝xה1A(|hwo77"3DEljBJ$qn&"쭱Nv .vr/CI@&$D wKh )I35k!ԫrf 6 E63Mꈢo|$\lՑY%J(BL)b fc jTA45Igr2 缌} YM 9w!)du ݝMNꫡ`$c3\;b?VcE3prm,]ۉRqϼ!7!_&jrO |/*CgKK00 J` Bvc4}؍H']HH|WBb:z߶_;m #fΩTnNdTv^%;xGWm#`f!Q ;M_:X\<- V'kWEhwo;2s2X^ӵ61tPHg6{G|k !AicCISBgk;F2z,`D?cJ)Wh#M")"u/pۜ& jW3Q!YM̔^Vȓ. 9J݆Cj u{楳{s>7X $>!{Ɲ˃%[MQ^@XO쩍'2yV2ZkgZ'칥yku4A sg)d 7=!LcU*rMt߈i |DYz3qsUg'08XHX&xҨZ7w!)4lJDNDwo|~§MZz%Dm2>E.P.C?y՜UZC.{ӭs7D]Cb%Hitnvw{i=X{1bĈLR$+<ފ<(9;U]| -)BZN1@t2ڏ3<73D~ԤáDajl7)t,~|P+c+mDҔEfO$ 03D44lAҎT}@iҍٝ#U,x5C y0.7ֆ UZ3A"gCx?JE\MEM*H2$M,`bܚؓ7_فVDuZNDl_ BLX6mD3y! l?Еȅn`jL#otd /|.m H-BooG&n6 oTx_'ka!W _|'>\iiAySLlÍ4w.# E֘lֆA)vIܑGlq;tWn^mqb !U.[곫!ϛ+va"cݽ @RQ~'gd1bĈ_dtH༑HY&@M! ehz+MT߄^ޝ'J]zZ >҈FujAɎJuNv>DD&`̥c*@eN Wj:y Qk;tRa&˯ud>WS[SmN3'P**)j A tSHQ}#xDXX) )$]U!Ǒk,ذ<6-}V-՛:N}(h ?􍻦ڟ5*$n+)je+iym6]ǥ5WID!DEOv`1&<$텶uD[%[lvۿts)NdRFI`b78[-r] zW:OK=}_{WyɄǎ϶c$}d _̛b Rtd Ʒxݒ-l DyroAD' 18!uˁCC5CHCd'~AvoBj۫?8۾KVIoL/'~ n;I ߀xս_{}SהlQMM0Dꤋ7 x޽lY"5 vӄgz+JYCnEfY&jUDsM;\'؟)qAUD3Ytkr+Lj#ƁI`B> ϯAHu)8a6f 0t3cR= fW<u&Z՘§i~uWԑLE꺌S3 @'(Ldڎܴ.~B{W#ź)",X{-Mٕcnj0Q uw'"3N% ?"ܛ ÁgxsyS8Lg\m +)}yS'cuF!Џ+yS|'Kicj~yrޯz߬@ U =2BHlyiu}"W=c{ H!iMcay؃E 01bxҥ_(νE_Q_cMґMH8mя>"hw |?R>1ОseyI F$up2=6wA6k/-/snmlH6ۑf7#5%[4+dg DzT(309sөeNZ5i/~̷#_BQaż+;2jgj*9Tx0{{sMGÊyV2g]>ս`3"ŽOnO f/2VBӂ<+.D>_\7!4݈*&D:;8jm,i &&z`kZ1l G)2==~eᏈ5? $eǐƮ !_!U[!?CifiLuzN?AO9bu;fŪ7`zwo [Ն} 7eLbĈq VN\ٌDAT!oW5lx5QK-;J <4ؿ 4Bz+:FU't{0<u ־}2绐#,떔`)!+Sߘ8S[ޤٻ $ ;nDMM^J}MYCh6m?T\cƐͣ tU7sOAx8qySS0j=3JP#`8mtf q*Iۛ=gxxgI.|nmjڙ1kooϹ$o6Oja9X1@9L ̦}?a;SIZꬖ+ؠNFVDEثyS\(F͒-ؼ)bB6D56޼,42 ^2|ORz֜VlVLv^r[MpH-٘cP{>|>ڔiev=GR:{!#݂8?nE#gI+t ;w"ou/  bg($" a͈\Ez,G?lc8 5%J"rRQr˩U!rS  iQ8ZԮCYw?c8Z1LjO&_I!LIbp6"<%v7~jBnڈzii!n]] IDATYHc핉ȥn{;[cwzȀkў\V֝?ZsIzcPD>'/s8ZNpǵǝ;UeZGdD#FBlձmռk/2U+EHo#f.TjV' rĶ_Ϻ/H$ڋ?vNGi\k \KK%[x일)>(ol;O:Ϯ&,0xȫw?yٙ7y|gE;kv5Ly _Z{.s虉5pC֔kAjvEx% 2/"j8r=}̍a s'ݩ$Utv- cF" FwoyERElv<93{\2|`SG^ckܙI5hˉpǾ$vj}=]|<zBCk&K`6ߺbэéwQމ|>_G&/t;EH\7sRw`m';0 Io઒-wؓ`=ylm6j/;| 2y72kv)aӅ eיra5l}qO$t>̖"R_ja)lfx_@~ڙSХ˙ ?]Q )50ED\)'J}kDjT!mԓP)IM1mE&}5OTX"+yV܆sϵvLLM%W jz.Ǻ[ߧ&*B@'o8pS-[Iu ?ٵbn ,ཹ3?[Tɬdޢh{+&\ۂ ȄAd 4#cV031ڴ#,gtE[7g 7]dT*+߶me'Cu4y=!TE& DNy|!!inSֲbH"3#Q^A3)ei2Gc(]P>ܽIث;O[vE3f2xґ7Es§7<5C5gkkUPF>yjr-C:vy@4n"9\7ۗBd#N|f#nUG|xGaə 9*+ݽozl `mU*dU- +}[ 񾞮#ԓD99L!Z["hf#N ;u)>J? Oǫ "-Du^w#A;ݯMc[f#Q3UoJQQZEC1qF]HcҫB8nt}O8ރ4P}zHQ"A;-Ȅ7붑@>ru-p PuCM 4kmC3[\Avde(gwf?9#/;wby(q9y3r?b[76jYn9D:iH:J'hɴk&ϛ?W]NUN7'DJXIn֜ #mnj/ߜ9+:kMՉ`B#$~Q k'RgÍlܵ6@&%Ioi$f1y{!SK6 I#g+ 5@-(dV9e55+?[?L$GdAg_8y~vqD "6tCܶ!)ɾo?4b':C#|%_\̿B{{z7!(|-O6tIX⽼i-칥f斃%Ju"_oIlO1\P2P&"觻HԘ g:h-DK)M"D!;MՐ PQ0 9*Hd[F#DThݒ+| DORʓy=uD͆XY*6Ƣiz\j @TcWFc$*ƚO5O:H݉;Aҏ Jśl9#S}=]p o/ 'g  ҳ+wyȩc3sN>Yۘy _:tw7TNu!U#/EupLDI*^ 4KJeoE_laQ\ܕI7]TgT.PJ^x+︠6$m}zn-lΛCd68qljls25ԪVL'L:,m~?,8C5ŕڎ2l)Bʛn6wHOTXH#%H(BƖRlNFjζlᰧ+XlSm;#TmǑ `Eߋ5N<"?̗Ӎea"Ƒ$$h&vTSG<ݽ?޴1Vt8|k_Q[]a7qȱ71ɅO-R6"Fr&t&n! &HtAMiAZkMWjZͬ!p5IoFb9 D9ACkN^NJvT`ڜon _ϹC1ԧgbIo͙Ϟ`s@7orϻWRc{Bd-ބɈx dd `<<ߴ]NG ;`xɹkCS9Pnqy?kBRGۑ{ X5MytHA Befdž|&.#n|l/qM׶vֶϛ gI} oid)~xb{(rHdfv5;сM}{v[S 7 S%Ś`ΦcOyS~ Y|NKx;FBԧiJ_cDjF f ڨ4,zHzoKD ~5Vqs\2}Mۑ{- S̐T )8j0~gT7rUtbNϕ*c =5oKyV8{NpH][ mc=oM;~l2Kri㓉(Դ s~.2Ak{_O͎L݀Ww^'q d$ Kݳ67+Hl5IO'Ͷ3=VO|d _>o,mQo !;^kBbˋSHοlF3-YM!;Rz$c鹃JXҡu,#%2Z -h<∗A> goD \>;1j؞*@jϺiWT[ҡރ/?dž-lB K;BƐ{:  &g9nST3ye-c#jʝ6զ07Sg(t^oD_/_%5B}?{h;@_#_ UU \?dD=1qk#4$=&AZkg$JkNJe61>ۿq5F#$T䙶z2dkZϡOj=%JfRm J}әMH>J#S [J2-U8|@&ԣv>5MGS TjUlL|4J5&D`ùY!OtZt[.L͡^26޾jboss39c8QpLbd1Tu%_uC]HM5})1Crf`f_:Wr^(^>CȪm^~_o#w5 cx]yOcJ-To_+}wtmΛb'\kzL: T{M%o?EԦ#=NE^( N59xks,|pfۑTG27&8Bæ{ڨΝ" @l+00kL6gULvNP2Y[߰pq|ż6$؂(:? qo 3{bð.2k")iF/hno yx]@$w _SΖѻ$wܣX6#9Q LUl×[kpQƠc&0Ljt*#ۗku@) 5N}htTKd _gMh+Lmq,Nm&"!0jU淚׳uP#a $!~u Lo=OjU).%@O|}W2RsD*+Wn~5yjOuI=LomHϑKLs,zv"Y7 "o|@d #']']y_z>tKp۲su4~qnaN;[57Wf<0-6 .G;?{ofYUע*ʹ~5jtqTA/dFʾ0a:|M+OrٳI[":ZKv2ﴉ>q뾴>=W?wfۏZC ג e }g8WIX]i!ֆu'`;OC6/dz#}|6X3>)/)gEn/_{=-}h1?b1:%!w ѐAB_f=$zez>\S/VZo䏋د! 9(.hCuH\$xeY^#!HgmH}?#HjFk$nLqsڐCk qEKvqMKԜ#i 3kaɲ{٨ y60O6u̹t1ﳳfiG>[ECسluОwM| (sql݁_leMZ݃^<;=96r/.@Le>D~|cS: }!flkn+jcnmE%δژUGluvfK[L' !As(H4#WQ8A&vl~}Pʶg;b[ev/-R.!rJخan;ز$=^ly_n/{nm8r2M*7v.Zp>6=LWEұlif.YxhM;OZ::>Wܐ MF:}l8̬01;@شY 1Qf\5 ʘ>v 8a9 nI AI¶gߛ#g(+XNX4gN=: c獐}D]șnJW}!̌zjp LCouCpmAeh6ZК8Qp^#$%3~-׵-<%Q?6<̻@Ȼҭ$Y^|bȻҽ?˾0J[:4/ Ǹ]]yWkDpp:˾9mkyߊM:;24^G8/Aѻ~~֦S;s G#T_b^<˲/|"JfOcb; 8>Jg87h֟w̯Vt81miVhxCu8dHX$$:fسAF#Tgc4&XhjWâg}BeӋ\2AYZqY!B#!ftж( '/ ϭq\Բއ;osG_cKyA|s=u1{Pl>ՈAUն-p8nYTۃ}xM}v/! 9 )^9e IDAT7HفtYߛ>V'ylG:x2Fq:Qdq~U]HW7ƿK %ɘm _I]eb6dHשB5-0p'p{~ ʋɄ1k fcf=j =>ΎžN25Y<u^Ĝs6oBskUt #Р[߀)}'2!dA˾k?_1/|6QϢ͓SP+Bٕh߇Ǟ0-/|,JS*A(D3 cⴹ/xww@Agk ꣪Xؾzr*;\/] oeW;A؉H1ӷ5FȕWű Zd$.ec>WU@˹$wCE5|(rg6{QI2X "SבEQmQ{8@_ $p9#y𼑵,8kCu`Aa> tq47l{ݑ[⊋Pj>‹7N2ؗ"#ۢ& ?;3F#+|7iǮ#'e=\{P$\L`UH\] ;H2t ʨm&08 Sαgl,}v1_qr7KIGzsyuƔvp£:{*{X#oIUse&gkܪWe[p(3pc5typu6CGێTP quz]]i!p>pu[ { yZ!Pƫ%J.^ppvE<ބZP+Ht=ʰ<l ZqD-AK?2^}a 9Z8AAyzYCjڶc%_CY&dSֵL3&X4CbӮ$igo40@<HYs@֗L3,|ʄ˶sYB/ kѢ-~M=4ZCJ3vDOús_NRoy d׊?2/vsF߀ 1_ڶ:{9sɒbGئ! 92X>#lӴ"*Į sI9HYIȓ 951 DM!p}L}̐<9hHm"Ӊ`56&QL~70 5b! Þ}O=iԃf՝YZ(9Jߛ\ۋ9m"|9?i٥|>gD s>-=_ݍ2_ϻR.J e_l]-yWz](ҎC V_4ygO~x_yuLmp"^ j//s#ry; x :%u,F:k 4#Ssgҋ\G+i^J41Ig3sٶzN Eu2a߿#iޘ/&]ׁ[jHaךҶ4xOf~\MAx*ì̧X+ޟ8hԐ [PH4'[j v?cM[4 )KGI B[Mo -G!Gp1I+$Mm%rqd 'QonNqYsa8f2ލqH8w<0L3 xYz 4r!j:cIZOk1 ٵvm"11r|M#2ib3tA 8kzbQ)JdAZi+__*"@sB>5+},JtQޕpA5;AmNPfLUʴߋ5ރwG`)(vP9'+B81(ssho%̭)]}N#itEm̺aurVzD1`<]Bw^uq%Z2ymہs[$>˾#!Jm怷}awޕEͿk.t}-+Ȼҥ1>pK8J&d-?o? SyWjFܖZUXG,fw-V/A5PZޕel:`s~m8Z%>&Gĕ(q(s][<$6pMj1$uSȖ*kxz̨-}4Ͼ\wo}0$G#ڀG+6ϲ+(1]c~ERVczۣ,Ghzqƣ?BJ [DpLWNѐ'LHe aFl/~X6׻! 90))] ?1M`ps:tg:V\pMol;v9㭡"0i^~qsnxZvgrN1ԅ2[wwv_f*C;#J6 ˢY 4wN:zOCcgLd H51;ERS&vNqGga)策n^+gL4/[왒ouX$h ແu}s/`\POuϗ}akzڏ| eEۀ6 +H"e߈˧k-UAz]~w39 ?2+q@k־w"e_˻R߈u;6""ε1s=#ENƒe_ؙ/]4(J@0WioZxLki E7ɍo1]1< Ht lOkŞ  xpX`wU [&tve_FȔw蝫Y4,&}0u :#\S2ӬJ"FbzLLgnERkp=OG(R<K/.\\|@_{ 9z]U* 40]gQCă TnA +f i%뷡zT:Mg2&"Eьv\=t\p(qc9:ҋP{լ:o6IEޕ?m%1M^H|yWBX;ok{(nλQ& 0 Y!JF_'f0ٲռ+B6ͱ 5+zW{\\9eZ*D&[ƀKf9W+ѻ{kޕv!/]pmbV5-ƻn8&w?[;?>@kz zWwQ֓ {p 5zbI?"RVGzA۟jm4#LǢ^,"zx6se PrI+;si^lZr;Dxh[d'vr1MjךSu]- þ`=2{gp r>FK }V=(zprCg!:sr.bx(i%TH ӵ>.·'k6v#,ښk~ ddlo޸ 4&=?&q%0Ȱ?gkurV'TշTp3bZLBѲ{t~NQ~43-5(3.(c͓Ke}!é 49a 4lr i{ YKdrә9 m>?#r;cy 3k~/$8]ӆ}p)0>; ۾~ϽQ^dn#Ԏ btڶpZг݁zwOe@s~Rw^x{#ȉ*5QsjmV9eAeamF[3Z]3DRq'rںiȹ>e` m{QRdѼ,/|ݲ=Ȁ߂˻3rG$k>}Y+!j 1>p i's}S?$<>ϣx0՜̖5! *ޅ _fx!h!dI_l39\tASD7եa_=d'ya,-Ehy$ !_Ejel!At8L13@zf1հVe=8EXMz5 40C6Dqm{ڞ!컔I-4/ +,Í$kDj?wq=Ok܄(:} f?,B q-'nk?fOʾ xޖk3c[k#m*c_+^vZW4[T}9cٟ\eݧ?7lS޳/| %u_u_?of]ANFl:?õ}ވϏ (sw^Q?F ʼmA ˾_\pq@ev'2HYt{DRmi#$zoAEYC" #LZA/j;XsZ-Le D4>b]|58s<;)c]pEse##!`@/%dq}@_G縮")>[tϛc}{@iHC).BoBȸYEBdiYcP [fj ?֯ʈ,>:& u52LWXV"_4d9 };ckī$9ζYkEvMap॑#|$ gOQsOk)S 2x=-=c%Ģp=$t`j鞎ù{JA!АJޕ-\o>m]TjׯB5hc[hmʭ+Ni[ZsO"A8~AB>r\˭,3- }Bddȿ"w(:l;k'Z[Pc 1 C>Qq}KE`s~',dr@Ҟ++~Rk7 jv3LNԑ.%( ;5"G[(*6N`y!1VG e]_5e.}e7ӄv8٬81Mqi(hm'Y:ن33ǘs|AX ٯX-bRiC;4䏀w D3֝H513eTĿ9#sYk=yafqru\w]nFXmIWچp(rĦwѻ} ʎ gМuv]"k(y< L75'o@M_;W5Ijfc;hB9 11Jg = ~(IpHjw^9SE* a/d%hAF&H;pIߙ1{&a@F.C{\EYQ݊26akΏj"Y["2: ĻJ8ռc4[sg1cg_:~ a?QҹD2gtcCT;DOS?)\s'[ ɡ_%|1\|)y* {&<(OCDIj,0k{u6pk@n&hY2"H?]]鬼+TޕP HH[,jAq:B\ҊCsj3ʾPN5YSDZQI`gGz.T" y|?CJ,x+I`ìW 9l%PLlBi22k9q+rFP XMvI-E2z%IVg Bɍa?|]C#'q;2eaߠ:x9E| 9 DŽm Q!  ~\vtJN3 5d,S#gr'IR۴"(N%9tP5qilj%ɔ- 6Cc@lFNY{fb\ vR!D hmBWQP젴ondHMD6M7#p\ԉ O&`%Bz}Qr>Sj[~j[.8Bޫ-œ$_ǁ//lCF~rES*D9C}Dـ7 Qt_GRri@`)WZkOnѓR|6նcd?5NA7͆c<58d8 aiZf&z{-Ɠ${yf9NjWm4%>dbbԣwWCrXIm5m(jގ)`jFhnv#td94h#!/?CCW4pCQאCOcuT)UI8[ȠYڨ165j9> X+3c\.Շmip63VL^{c"\}r|ݫiؽ_XN c3#_$pe m,\`Zmȁ{~96^Fx@[gpރj~"zhmLg5*e$ELG ` w%c|0+m7Z?>EsL4yW:p7 )GEϲ"Zݓ]B6ъt1Ho]&S$AC[=yh18&:DRrt.tj.jEH`YOZĐ fݢۑ2F:4u=EFrhd'nsef !(:ߐC ~rd6 }$ K; 9)iB Dfigz2:6!LleAd2 ,"ҁ+58 ~3#.W H^B6m9̙k95̀v!{\M$SZd,gp㑃371;5m2bo 珝c*R9=p0 ~^o 1Zlk8WYs.hGA-h4#ǧ eM6Qwӱp AWP ז}xARCB λRkY\ּ>{'i]o˻b.G0o; -hhjgNEHz. w '~;fּK#/"ޫTw>?jQ 8v\,lh\5E~@T4P&Y1,]-F>u2T`z\4`LU<]Ȑ{tC;†4119/G7,s:!iI-Qh$r"8? BP$ &?h} [P2T aTb ԭwBHӼm,[ke>9;F+33U9{Hwd}BF]QY GxHZl(\BfeitF2p`bu{v86F*߽ \ӻ Wc+32yr%7 7"w H֒4fya-~hs;n4ZHv9rn@vAF:,ҕȦ1:Ɯ{3UZZS&hF+?tE#VtmxvI{;Z2z&CF4v%eK#b5n -S"6U?n_2Z8;c݇_jGQoAoB8+Q),zZI:Ku;R@=-P"深LgC'}Y 8!޳'e7v H sXukѻTEp3p(ZGp|#rt%+_ 7>eY]̼+GޕM t?5Y#'#+5&#YF}tڐ./NfBmO'G}-Lq@_.4&r~dͲsl^CSX\LǞ$3nI)z=এC] c{J)r֠ )H tmN\g(SV%ySqH*w-Yѵ=OҐ"Ȩނ y. >H$^EPVGp x9h}e(b~ jz5b/leV%!omo{ȁ:ʺ,coj81-n/s W}#cP:h&4 =-ޚBd$5H2ySi)9XtѹN&5s;Rxy4%J |~6X64SlX&vh"[O[p##V6+]|3JƱ 3'4Apݯ!'̡9{>%o47EUOP_*•!6Ӽ+.@G^3rGzBT |w4_C5yEYOp'Da D1݃\$蜭9W~ G= Lg|<^ZψhZ݁I+Z6~ _Mºeux4W3$A˲8"+ZNjީC!N~IiѼՐ#IH0>oIy>1;L†sP0ʚVko2ŽE1#h"(T"TK4fB ŲLkvٺq]WvgV?!˲-?QV-ffaU=0@V_: kZfpjYXivwaY8OIH{SWr9-alf5Tz1T̲e8WCL.AoEPdL*ݪ&]FP!y/ǩfOme#_}0HsCt+zOҍ[|?RnCQ3#T ~jIl2z\Oͻ\LGGLgwBDuMy3z's/w,"Dz_ 4 gSQKy`1QvZFs8>F$+gk!>&HX~Ex5iY+½iȁwP0^}6Ǣ҄ IC.UJILk bݗN̝\ ˾p>OQar[S mGvyGhnF 0 ]Zv{Ig*%ٍ'lVB;+] 4}ӏr6zf9ُ;6D>x  r3O88rXKym]wt.(q@lWƨXF#o͆s֌H(,[v-r"hى"H͘rs~,j+T"x$Y4;gω\)Af@_'pH icbȴQv>̑`Q@tcB7|Dsg݄lo=Nc.w=m !rrZ8C;?e_Q\hεr5KBs[Pxm!;%J#s+<YfO]32(·9 :3ȇ%@{1~(ɁvRH/*J%7fhJ>H1܏ghQ@݃AAIFʣF@\28pdm *܁̖H6ȹ^'v8 icK`bVdQZtN7I˓@*$ztWT$Q1=Dpg H&Gd$.lCFm@NY(2' INi" ,wLxeqDϑLe+k*T Dь*%$Q&߮5rcĖpۊewiȸpmV\GFsK89{ZKnEVi&!Ġ?!u4oEOҘ](K?-|mwku(sr/2dg}!۲'?j_ONg#vy3ڕ|_ a/Nxc>s A"~X'G?C}?˾+-.F=$}Ƒ~51֑vdC쉒udf`C DH Xi(}6?1~aȉreI YᚕwJ$,nr};c5%6!&KQA(; 仑cK.O.5#;2(@?ASQwҴ#'Ǝ1/\#%3P 2Ag:w av?m^ѫW,X6n4ɲHB2޷AM(K؂u|2pmͳunN} vKWIf$hh(Zئ-<7s:SfQ`P!gd%['w /gL;]+K`qޕ w=˩!AdOlF9[pًw?4h.mE٦}Ʌ㛳o+%5ifмx}ڲS]SgʻCǐT-W.zGzhN͠l>"iG(ZKeBoF{@p^X6BuĬ0g"`a8N.#爇=\~|v. Y d'Qv䔝dD|&: 7A Mv+M=Fvgx5wfzNښmHjB'ZV3 1E917#ֻ瘖k?,| 9 )Vrt}Zl /DuԜ͛M$5[:\Zd&?CGsvѵ+c_ֆ3um~yWj.¾ ^# c.3'4'yWlhbǁxwOllͿo8Do 6`޵4#Ki$3ၳMiIawTϥj .}EnZҩC0Ďh}=^m$* 7.ddiP=vtv׹v%>gωnHCtUN?ܛ>=-;2`L˴~TɆ2;T@e᪠lS+[=A5m;G+.e9I~P>xPvmrzϯRAFn 9(R߅]Y+.ƍߟ7\2C[P('jE韄c.»-M-H?p,99JIHo(I4KHw IDAT2 Aϕq4ݮg<\yl%[FR 3]l=G nJ9#hQo?큾ozgbӿ c7dҐG%'іYPв&v_λR[ nC ^\_|* _E$Jx:M EGI;#iv&=sb{7ICQ$ig822rݙ^ebߴktbajEŠf<F' \9ԩ"-!5l4H0$$!;½ 9]qn ϞR<i\epMxL~E( o_lL$tK5tw:"(zB_y`/NˁcxΞ'n}@kޱ7\0rޏ\D`,5';oiF[PyoVoc/ Էw8->w աY(I/C*9?o؆tV/XNxԲX~bB6G"h8X\'!';[Vdngb ߅_j&q{c{I%r5}IT-ҙE-:N/MbbQwqrUe[Uw'$Ž(#6.]QJA Y͖ N{wU9'z˂Y'{=w;F*n4XQ@a+I|Բ_vY%{ ^Şi:MX46FVtZi[|X~H4NE!E>ȇy5~!t"/JAkZ"o_zwEA\@d!c֔ú ob>jum"*Cq{f׌,M~g 5</Cf9zp8{iD #`#{O"Ol"&?'VBl}XWNqordaO|ǶmL6nw5'R4y95Xz'zW |nGq=67d"CW,a/dp2zWMwXy9z^DW+] O]־?p BX,7 fqxW;l|$V6\ ;EsDH°ȐW~ ,ŲogX1Np1B}~,}& iܤhZ?9Lk7Weܦo67Bpޝ:#+\&uw.z0!H؆hf1~TQŘb|v#&>6nVWF/PtX4&0TL =ȣ q8IjrKD(jA\ˤd3"D\Y |D׏i{淧sMEd}=Ru}wRd 1C>/{w>OwbhQAᆸzdq}[B 5=纍C`kr \?Cw)8#XHg>-m_#Jǝ:cu6M@)bqSo`OxT^S_zoxٟZ6v!"г3IP@?h:h:%z6"55?CUޔ!ǚ BWeTKWwnnqQ=}ƓVԞ\L ֢+N:u k-dP%X-!˹"X4RI8 'g#}ہUЪlC9& u,Q29+VUG3,6"I-]+mD M7L<._R̀%&@ &$\^\&5@Vh8:tE:7 G1=旹nA14^HD`$. ^-f1}9"h(di6pyf0K@op ~wnRT_}>DYI~ק.@$u[(+p $CZ{-QIZFdCľ p<[6O-6qOt7>ܷrT'DOeM@SOﺨm>ib[^{;}O[6 <^ɯs:BގeRӀSe9m c*v*%Ͱχo'{iX.$Z0qKL#eDNwHU#Q@eѿIǪt6:T;8$Ƙ-ugPR.1S(I3Z"h1A1!I:W\USZ}9݈""> $TFvZw? =,Ϣ1ȸ6@+,t5Oa,0ףH|c-"`>gx{; 5KDZ 7,z볏@!Z9׼r0D#ewn>ry*{R9Lyˤ>ܟܒOk*v ȷѻѵk?R܁٥}nӒ:k-ɛn\p==yK}1ߢwnH s=~`L4,!/Ռ/?e%u#JnA&_zb&< Q nF0:փzvrc Yk-Q.Nc1e&ȫZ3ewJ߯oC&*Xmk`ЌqAֆX Pc + Ѣ(hw,h![ ~w)N2챸fiw㻻/GO<Ս6<в3bƮNʽ1V[hN3tm*t2ul(t^E$wsr}(|Nd=z4yQh8=m'Өqw9}6i>T"UO( VLt֌<D s}%a"S^ _(lr{ ՏȎAy/ ϒl@dD"u"Cŕ*"07Ah:8ȥ3\aý߸]V-dϦHWL >oKX$3>~gϒb-tnf Pѣ0ʹOJU%pFw!Og>J|rZXMG!t-/I|D`uG@B?A/ИFzTѸ:h>'؏(bi Ժ>@:c"bZPÛO~2wd^-,Dzv%2 >ItNF)=!5"{++Kܗnw=|VzwW2sBݗm#I\&\&5fڃ6U5'oX[_jXuw=|mkɳ 3Sѳ܎z@_xJpQD\yO ^ Gn3{kc&Waν\AՃ 6v l.T /\m^"%0&ٕT ߶O l`b&}1$V(?Nx^8js ":K[QvYoB <`֚[?-0ymgv0pqW ,̌wu-?F2be MNM% hmĸ_3$pJ5OҊhRmͪL@8 :>Jr#`-eX:arBVu֧PvK C%( z5O[ 0޸g缒\BĜhKh2!b,C !ɧl*a8 x Ra{,;Eha}%"U%3qZ1 [,JP8vt pиsC@Ri-}~DIQ#"T"D|x2D`OڍM3Fh8^ؓkE#{0{0CL0 2#_7 1^b2'/owslrUiB\Ē߹q,/Vs.=YH$^@u>+Re,4s 'MXbAk26vMlkadpBW7 {I},*cA! Qb}4&R65,\&++]z0Z00?@H=ܭ=(7 Ae҇}JW(Ĩ\A^hQ \..ͷ!CrW oVJ?pD?pr$bk56|Fl4OzrP13!D|A*Gc@ "5"1wZ䫓I(԰yڗ`D "N!OS}>E! >T C#x Fўy?w.Ep4C "Ϡ{4#bm-V饆iu Q!jE7oOa͕6v<8`wDػDfDlfڏBKWXڦrsXY714za-v~H;h1MxwMRZHY[]mGi{ L`S& 6Yj3?J|`~x] փ~G!QDJ(a ` @ j%2\y9qO:ƣE4@\rơ񭗨˃h- F sDT i(1Q]I~#KID/Agf<" PAV"2;mHl#FXkgyoÓP2XAr8յ_$'R."z!eRl׮ :=ˤHgSOc4{v^-C رHgoLO2Lߌ#܊ cU`:OtkKHȢ;4uˤnN`ټE <ˤ*v]3nmrXl ǘ̿{^Dm}O-q'ZLRbC.lh&Z\MKgDgwi4&䑘¡"~Z8-B ȿyގji9jpmxGeZ]\k,\߁HTGT>D<ރDdqv9/! umۃ/[^ 鮫/ l~}Mw2t6"t6\&D$NKFQhxdXǻqk5xA[4mcT, IDATt&2܃ꫭoCgQ M>?;b‘}?rٌлt?|ݏ3l3n]jT'N Fgq#zGݱWmA`]:, ͷw N7i[2)?UZ*=-og-_}ocl0ڲ5֎;ul[pk Zt2cFYĜ )5r+嵧.82"tlBre\ 竜U2 l~;-;}7hQṵeQ$7 T>bm]/L@8 P跷?_cD??6 &U0]<8XML.Lgף\zwMsS7c-}ݱJ\wm;ˤ/+w|;V_=5but|xӆ/}sNL);|`k\K^Q4AHj;_=kwգl]RkgH՝q}2k/A:ݯdQ*`4QCIh D*Ih%8V3<'l,l_߶b6߻+ɕ-fRraa2K'c5\;͛~Q p5=>W#+Jl.V{FCT4>"_G:y@?oumLFDAyXw!x)*E]QNR `xQ _޵yuF>4!"Y -I6눌D0o苹kTOkq5uYK3p19Aݝ6_^!78 -_c;R@.-Ϳˤz)#Ϳ]:w/_BL8(7K@cu߱xӖ{Z8_L]hfTv۞˫Ԯr}nV|?"D7y];C\>$,kwbG@fTs >Fvo]k7ʘ ILgGWېv' w* \&(UHgu(2G.N9 <0c{Êƭ[YM\,U}@DrL-G$*P'X3iɕU52M8Eunwy<2 kb-LUy5fl+,V&,V¿!k!NNhv(Cḧvj dd6B\ϟlUbkWFw2/lȲu < WsTq6~!L&SŞt6Ufn^"Jj 8VԈT4~ r-6(XIXQ}2" !u]YpF!'ޓ sSz__ɓGwXzH[Es\0H[h4qj;l;f,x쉐Hy|ݯ0|K~> < a.gX3h}l]H ZHw]*mئr2Sok{NЯQdF#FQ5ZbK$<@dS*>W<[*` C-BmV,EeD2;'JũVʹ7;f ŪZT1L-14 "B/Pչngw/Z- yX#t="^޼DD }(ٌ+E^ȈIUQV͍>7<|+lABi^DF>U 2&7 L~xU.ŵс<7F!|Ye_+b1)nAoeR\&gV?_;.xd棞Ԯ{N| t[1\>(B)wг{(R4PM8a⺦W`̳6%׵N]zߍr1- 6y?.d.D5Dp/C:x6$>Ëo+-C ~FRd`Af-*DB -`ʃPږ-l9dPU #w%_LjѨM`W!կ:Kz=drԝ;Ul -ؤU3@2т-[ UHg@r諐(&$Rq4,Fg%7w 2|[ E}߫ЂY޾(˓'T| 0<$02}>dE7h>ꁕD2QHn;F>7jGsW)!x+/hYu1[Awun ur+zQGWHgM(-hpf*"oeROX?F|d|{;"p= \:ixK3Ul|oa}*8@iM{ݦpz7.lhBF>D~\;۱^Y'5r0|fKj[1ɱ~-wo,T@d}y^%jV ѸEq>oclVvKo%8dsUxMچm Ca/ |)w2V1sZ %4b<Os7Z `)UQ8-W (zJ(lp8 հZ7" &~}4Z@!u֡ ?eR=l|x2 ,F!OEdt(BqZR~J)O6sjݾ1"4BMADj;ko"uK}BM-(1D䰙(WX M~D^pM|vgrT@.Ng~woouD:G7ˉnw\&ug|uԽMRŖ?ݿ6S_ڂM.fgZߏ?֣m\ԆS)86MZgm3ī`[mqSQ_Gib&Aבy=RRW@DC|}qa}؀,c+*LxݖH0Xd=A$ 5Hwv7aq8ð $,Yo1R򯻘\ cߴ<>c>}NwlWHg߳3p=Z o6Ӟ@"[ۯ u=~=K#R Z?B^ѿwcu2MCD {/ q po:C.}^D雮 oJ yRDKNj[!BQغ1ęD&dw:"?r[DQt{5Pd]f|ӊoϾ3 n)vN,xeh-& F៏$Ձt6?X-']֯}if˜ 4,C ㉼e#8=S+8=Ojzu;GbDTTSKw-8 z8DsϹLjyNAp"Hhw2|"~o:z6A eR%n0U5ّpG.AXBFƋWbXsw$%c1HS<ʹ 6ʹW 6;F#2b`c{`kw5bJ2Ѐ?"a]L}M6|/^;cTrm чVZmEQ o_ߍ(L Ddl"GMOo[}:ۏsx-_˟]Eό!_=Ʌ=E|PAs; %ʔUYHgSQ^ xk.fof:YDތkXy@!r_+%|1Qn (>$qtZweRK*>oBpoTȏ*ߧ 5rT}8ʋoX*Eyc>YGi?)J֣,,.G#Zi7qxµ_`߯8Db_+ؤW OLk_*v9X{:+YLj6_CDM.Sֶ/=>v~K(ëI _5q!V~&gyCUF ;݈hmiEFa]WWl8.XXQX Ѣl_y?ѳTv`gf <ӿV@fbiNlXh-5˝Kk{L{`|V-B?#Dun>yhajDCPNyx06XCKAkw8rV!Ot4ts髝X3;7Ʊ:D|('D 7qZDy#bVγ]K_ʇZO5(w$<;:<)%^w CCt6ap7(oƣxphPk9wgвw#.Ddf+ $yz2cL9wcۛxʹ_ޏ@]&i3lr7TmDRXG9GDdO+ kPpRQBs2'>yp \m 1&*0Uoew5FtO~=<}~ Fmh}^aؤ+ч,;1z_#?D-rzӋ Z0 B*^ApLeE1< eȓՋ 1b䙚 ܌rNnC!=Ꟁ΍Oq/'m|a4Bwk{_ds?zEj-ˈ0M@dμ!KC$Ag"" Dს}2"WW|JX"Ke6zG?B wͮPNyy;  hGBC#x(3Q |ߵy Г[,tw[U8K,/ 69l6DBqzg gs/ 69fLĊulTw"ʷ( MObg0YE!}N!`ݚnDӈ?a,h7#rZqV4zEJ!hcFlr>[`1vחsk'{C[= bqZ+o a5:|FCɔM >UAh!s]*LyG:?WE8*De:H0p$3}Bm(ƙ7M:\ > \oDJFYoAzbdE& 9wǏs^>G>hrCDThۍXL  6]%Ss~!2Phi8].z΅k= ut6ʫz]MPD#2x'"v}o[q\&uT16 6t _8MlC\;&Q,K{_:~u#mQ{BiO&;8F2h ;MLkܶ}>Iq(ߝ["2 ᾯw `%1I][#WNCT/%T ǣk4k!*^hY݀*vk2mr !Ɩolk)nebQr6ɲ4:Wm\lfi`K$klStc8,oEۑˤ*Tkl?P@GLxd&[r<\bN#Px 6~q&꾞տHqԅ_RjDcDS"6d0*"b%yuW]Ҹm98 _rǘ\fگ`ep|>Ȋ-A6Uq)!l7bQ;?e|qƇZ"C@0r菘4D6 9 v&gе81Iv#7C^>ˤVW|~*~YPx;Y??YɅmwhx-*v-9pgu|rT*v 7ھg/>hqw"?Bc?G އ\Dndu G[)W{М!Sл2?3sZBހ=܃@?D::PW@$aѮ(,׿kkg%*EO  ImF +rmިm{-"eR+O辽rھ2_:͟ ֆyfAcoeR[tb h3D׿0ԡ g0ރjNעaMkegϵՀKlr_Şk4dX>jw#($y>V:V۸R4_nq(W|na_k!0e[Cmb(6 .o&WN\,?7"͂MXۂt6]ˤ~?|\&uZtTQɰʹ,`i $y^A~BW|z E&3_g^:>:_HQh'dr>r^ R3Y߅O}#IDTи[ Q/*O)XH!}hCſ4>ƉZ}>/еu0Z F0IT09@gg?彎J|+0x"IYWVr~ꮟ;ޯL""{/>|\Șk78ن4{80^)&5l\&UU2N$"/" ɃN"2+49!>o*z֖SXwX-ѳYY"A\0 Dn[1aA@h57 @{ ]0TEځx[1A̵h|Y[CA}4;HjOWЅGs/]ct!@&W+PWP*vyMpW.GJweR;C:ߗseRW)ͫ{y^J-G+kPh mFahWqLJB^h"Zp^@Dm 鮽J%fTɇqq_Dn[/1>i]Dvz} FzB4>~?(}_~oml tV"2୹LjD292Dn/eR;ځ\.~ujI&3- Ot$sv0"6E?g^?O [GDЈ2LĐqF(G=UއфTDa (<`-3ՎRe+}^6yK8w}eNK=!ډM]n{cnE.:?2vw^Xqmm ʇ+ZY`/<*^yRvU>g uUHf=iEĢE(܍J}^p2?EG*yuuO?F9b.Zp+1"/;Yx/{Qޒ7n R4R4< B DC x}l fYS(roF^"V4ȓ(y"e&4gpO2$xw.z}\ĩVlgPN\o:_ -IrGnKgCn#RbmMg oś'яBGE2;Ċas N"zGAf{}hLſn&;e$QXAk:Dܪ؃Q%X{ \H@.ڐߏ[' hr1$05"p," z*$>dəcv4Eӝh eC_3z|i3o*dq;ϡp =)dU70ߊdɯ>mr%0 Ym:g V_: p3GBZH&?yJDF#k~o% 4#DR'Zt>b [OLF[3;8l&d(J(  ^XU e+ * h.RQi#/+W LB`}}w}xL6ɦ&Iv--<jAiḍܭV<" QƜn&KdڐEѫ;YO"}2J|xe= $ͯm`Lbw}ڴi\Fn@;+؎ʗzj=50w['ŲQ+$=h"܏\?KZ񣰾Tȡq_X?i/Y>q+QqcE\%fQdL2DӼ߂](׺л<(;[ڝY|ߍ_9ˬ!̅E*40[4Y|; nPӊ7+ L|ˁ+r#LQȿžl5Gлo"\GP^LDj~W.nCF kzey"#OH zVuE4)^#aAFf9+\ YIByɇY WwuM4m#y vU4?y_kRLn)Z/RZJzoQ+r%(RYh[aeU4-{lGLDb4!ur KD$;nz)_2<x="?Ldΰ a?w-AB"=pȍO|H%%` {Ydע'(rA5fR c~1 "Uϣf̰ ceCD*= "}w̾A!q2նv/s !-qwlgDeTl}MXd"(?E7i/6?H{F~1"G *Lb+^l)~=C5i/<.O{TlyGR_>:LQQp)Y^s< C򥴴`dVӈ?Squ} *J\E.H3ԩ| ,.C3d" َ; uoּ9 >h*AXP3@Gğȯ,dMG3xc ??<}s%^Hn&XPRpW"u"qOBD۶㞆o_A]3o@M퉤QCɠYfvH#7~erFFe֝Laq!"`3^%( Oț=]}z5V+hWt'Y;7(.A[gK!P9q%+O&wn^,ZZ+!zVb_c% EQX]%ض`;#E У^˙N7h[h&я%KY7ud"~ƝG]L~G#Cpb?M "j#`ڋ Sw"LU P~z@B Tdx7$GF{|R ɶ]ج{!4[?_1-E/ |`+!nEѶTx("֌i9Chm|P|}$OulwG}o3pᛊt~ۋxz ?N&u֫Bе|#wtv?H;`U[ D0z(Y1NX˞-K4Z 4*AY!L^Wg[ubNΥ`E^b;:Je/XH'P_;qO>o)ݛO(QHKF #5+Hj ɒ|E@sE( 1VaH&hˡ| X"de r䗢\(w:'#喢(r8l=]Hԏ-9~W,cdPFA?|vqk}QU2oB5!rʴ7q/=+q+L>d"%E !ێ?&]VxxyTti'+u ?jUljᣫ  x uQGYB%l` ? :{+gh:;Şa;@$?ۘnM`1c5˺ɐׁt ߦg[Y>*qÀejaYHjK(s=lH{(/$1-w)i;paƵ""Utd[^C$ rUS y[߷#r=g|zdtFѨF~z·..;:s (u#ם߹j,:0kȪ.YmsJ|dl"X}Ck>,Q*ʟxQ:7jIb^ݾ/E@ԁ-B3#9G+\+Q^Q:[Q+5>,,}|&kɛPJugJv܁cȜG?DeHvcm=PK&$eMG$d"z]LĿj[ȡmǍnhz Awg'uc2N2?~{E&xl+( DP~HNB SiBC!(Ud_0Ea} )(5)*UD򢨮r.T۾$? D1sdїi˰v_CQkm=x(їRq1m 0vnَ2N?-oQ+ ݙb߶ qQ4|vہL?ٺ-  X}-%~tcT1vASև5kVD˺sQ8u ޛuro [ZpMDye7KdMfھeH(]{w'&qL7V }CŊ}|=tNȹDQ~_[D>n>kQM"oԴ.W=b-reE >uvD [yD|I^f{f,@f"% _~t9h+f_AJcMRch˷ٷ`FJ*sx 4no߽υG|G0gZE$LȜ7YVto^ckR b;ȅ!$iIH]p ZD|R-HN؈8EQzMT,D{ H!#__l5X#VS$c# 7 r[euPQ1vܻl}A#_g;_9|0VI㹮kZN {U?k3N+,/oE2E^YKޑL2<̀%мe,Gڋ͋ZjԉO{1a>p/6R2B:k,x)ȭ{7jZeua2-k~j3mc X5"6DRc9P^iGG'QD#w;"1@B$hCedL`oqCwEhorY>ؗ"r'H@؄Þ1f,$l ԠI}׼*E3 { 8dTLğ+ Y߯6f#g"y&pO)['+ZmHwW7"Uˉ#D#XczQ58uD|ܜggXȕeuJ=g|7k~/꬧Y1sY3#|H f"뵮U;>YX߄]D$gVI)ծ]6uyࠨ8Be^釗  }z-ᱭKH)KnwnM?ڝe6.g;n|P(8;LdgE2&9?!u?:/%񗓉Hv"g@AH&?ǎA(+])`=DtJᄉ"XDhOH(kͳ!yJ*֘u5m 3zDΐw􆮉}vw˺a*Th5|ʜ'DI> `k!`m?:e&֖ةOtw\k4EWYywVcO};@My朲h@6 jJ^,$EWfg+sߝA핣Zɓ+K42!^9@JJxk!2!:ۮ/qLe-ol%ɞ2WkvZrV1|\lqtmnOrDo[[ F18 -,^@~ ܀1{H& EP҉(?635<&"HpS+"^~U ʋ,CѩY<"?Q͚}Q D%2FhD! "1"^tq9E(~HgbS͹D_t׵IH&mhOØTmf`[(@_{"B2UITw"L=6y? %478cpKYѣ\2&& lǝ%WM!jsGU 4!н:^ڋ=Rةkor!dks6XV7ֳt/"^ }}5}&Z0En2fPv2)Jo@rCPdd`t6ʍy-xK!}%z KQE)Z͈( `E Q?ױ[-((  c}Bsa89w.#v|ogu_f"߻ﻓ GrgߘXʒ ])zIZ:DnC2몈c2l ,:LϫQbC(d"Y[roɘWP1$I9>N몎ET e-ru%aŀU4.@ Z@JU_6ߜ8^K;1?鼣TTY-WXx=GJ}[=d"vȸa> ,~ah[b^F[x$voQFmSY~j)8f!hEGho[\f }e#(W?mM4vܓ͹7%rNgX jE W._؀7Qt՘X@lע5HvÓd`0GALl\kcHVʰgeUh-'W]I)n꓉< C^@2ڎ{5pQȣ;jt 8bD۱[<R?CGVDװIjɶbnJ,WY1H_?źuo#8 Us|}fœ8]Շ0ݚZCQŗ+hhG/kkPǞ(d"j;n2_wDOEpP D&oq[2 W&޲#Vt uBh@Dƚ'̲(5E|7C򒆥[Fiο2D*fk‡$mǝhEd$6tk2ٲK^hL IlǭAQAD<3ѵD2 UcV=ؾk>{m63_na lA؎{=#kϣ`maIeYX#N]ӢOT- ɱ|Ν^呔d"~Ϭo `fYR"ˎ5<1\R=GU媭A4Vpu6Ae(TcVXj?Tc^lWV*'ɠ[FT_tk^%ָl<;-{m@K-CO錹[q Rz(NEF#D9"FD&!҄b$/x7yeZjD!\yEd+щ(dV6Wֺa}jG3n0uHXO8$Z\Yޯ&fY_ l-J__0zAy8 ~;?- W=ьL@ QVX8 ra?gu\WP4ssٗ!=<~{+j`Gc*r~VS3;3ɫP>*SZveIW>,cṴ*64{jR#PNg_|s{u=_U ՔLneechByӼ~jlgo#XTx%"FEqurFч^daՠ{'Ko}z1ʣ/ rs{QτQDl@9z{ ÷HC5o@r?v!cތHZ"L&`^CnE~+ڛ3a^W"yDNV(`tRڑM^v2 [=hw('PYRͶ"HcKt!:^kͨw?/=$(.Z 6K{_X#TU685+mD3||>ۿϚ ^,.>m| ͢ O{wv=v45DTs}?wZXmTQơ+z V)4o[?Rg\Pn@ѝg=?"TzDJ u--rhIu(zEPH(p9"aaE~i93U\-B$| ^ \rP4VV9pk2_IoC}Àk;9`wq/"3˃lxs]TsVSqP强 [SOnM}(lǝ̻O!Pfit?]=,:zAa; ofȷ8'ueEʷ%hN7:; ^bJ}'LZR䔶;0*T{G6֛Vj6`;y4s/T#j"i/Z3lET p] N H#M:<ÎXmE`?oY'}\`#LWَ{.{Br3фDv W*ee ηnCѷ:Ck(2I"p$7hzf@(ϫlE@ȼoSw$tCdpY~.R|8Ef#RY=m9fοǢӼ+[SoAm5寍(|tkӼWbv"CNDnh\!IZ_=௶㦁D2_`A@v$7l}%HHF b}ыBܷgc%zQDz Q+58ɛß|fׅ~g#FN/GCsغ4R^neyݻ-iCң V-h@.RJ;(PV*y=WdwJ>̪=ua5MTik OxzmD|~f d>E¨/ޱ/ >?`܉r i].E P4) ,vGQP\B2mHq \SC96o/c*p4"\ f?woێ{#\292w2o6؎{)FWT2ȆEKh΢'!ɇMP_r嫝jc،ɡ3F9Da#\P0dc=^`v܃ЀK8yS/@XFd30!mqzV867I Q+u0*{!OZ^%J~aFw _J4C} 0i u7 ܧ9#R44> py;t|{{݃5-qhi\:Tgx|W?jSaEaՏ#mTEl/F2#)h. Qh&MEPGD?~ܵPG&(T*PkAR)%d@Ys8KMM&+6uL~ M\5͛1uF94TTe NlT";! cKՎ"3/={@tPD$(B z?&K&m[\L.v<(N \{|ӱo'˺K 2 [i4YwOc0љmK~2T HÜ{f$5}uCsO#lGϝ IDAT=}y򨕲^մcm_z֪+/8v毛}R i 7b GWR0 y4xidaEA~u%-HpLL@хhEg #2#vX$"Y֌YaH!j5 E5NBӦj4)CG)D1|թ Y?qD |sҮVXdqlmcȱ}d"gT*#_3d4q{Eh|lKlǽ/ `Ow<%{9ꏢRkA'(HGPiiHڊ:a#YFQim 9D@ND}wQ 8E0͛tkӼٮ֙nM#\Iq)=azBvߚ@6L둁VADzD~Ͷ*gѵ_a;o^lT]>>EJ>4&_CdhNڋETWdCP"y%P̶X,Vj$݁S/zJ?9>jEC^4\f=D Z窴I7`&䍁nH*GzנgBT0SHR>^@J%>EBR("Kuh2ФB ǘ}g)L߾{'ѽw9Pλ\ai˛mڧy3MVv r_@$~r*5M.Q^lD$נw{ K/@$ 7xP!&!`5cG-^ݶCKYՄ^PdX>?yZC{v"OE[)3W)~om}E~d$N6؆b(Zh08E7ڀ;VjF3E6DNDglB2qCh Q,`ĄO]p{Li-'{вku9 ΐGk;xv\v d")ed">v#NDWøꓚFB,HDnp5*vf?f$DEGyCѠ;aWO&YpG%Ӽ&4e\vܽ1LBWP4}AG##ȕ()`0Ҟ؎[޼8lS~p,m![]?[<$ئLė؎kqo1BH}:U09PhU=psU`4z${N/!bJX<g2dAtZh"(`*px͈e5UVi-VZ1Fg Z`&V*֬6%."(o2NbE.}s՜W̆K6t^A0 oVv:eeai9?Ʌi/hS`@2َed"??D&B240?4HtX5(cɁGQizWҞh<E>vC4/??M$-kBэO$Nǀ ȮD|L;y04/rn*Pjѷw$׌έ+4[AVNaRE/l| +0M&nn؎[ ,^BaP5=!?5`@JAp4,0ujl側BQ+h4DB?^k[i(jdU"֩!OnO{:[!@v2=7S@x1g݈(uS\9H wYlF2Oz1TMh>8¬8(*n&[K1ݚ:hͨ~t3`f2WyYFrO@"C~ "[zB(6Yd"~[Os %lD4`;)F%_nvn)Ws| QjYkF7Q+u 2`qkWv3-` l+rhYTBAA8;<lwYT맽/l5eI#y2$@84yT)"Y}B!r5oEQE|y/m0] yzո4W㕘3~Lă(nU\$Y E/CFwks2mC7h[$ @t]~gӔfD=-`.a:{̟H՛[]=d"1 q%ȉj8 "j"gS(?Zu{3eՏZ2SMʇ &^ȞX#^l#e ZԢɻgǢAn%s^I&wڎ{1L?@D+?p#4E,\ ` ] A'֦ B`U$C9 e"ʿBHy>r<I]D[ N"u(قKx8JDb;.D;-V`^^wL߾=j`B(r^lѵ/0+dE?\[\Hِ,#zַkFvJ{m:2@߃Ԅd"~#LTF(-LDk,gq+`*_yYC(ADD,gI*<@v!ߋitx@9VEw8DAQ[m;iG`B3dv<eCCC'\tG#[H{Q4Xet)2^Um~Mbdzv4(bz숨FӷQ-TƫhksTXDERQY9h!"\CkHN~I: u2t qOt6ף'y'=M=jb9"NQt/-2] $cQCdʹ,#SRjK{u @W!*a dY# i* {QjWŪE'QR4hn2jZy +AO2({ߏMP֡r+{Ȟ߿Oѽ҆jg]9 f@QX2ߦ$]9X1. DU;KXyH]ڊ(9>@d"IJYdPR*PlS <소/ʑiƣ Р  qGn~sQ$q<##y HM]>"^!5,B\DO~D@)BH/F2H/| ~ C*OImf(ۋhe%f|a;nU|& |vَ{2X& +D/G6h|p+2)iDC?dZ4,kGJDDˏ`"ex B֋ ef8%C8x$#ڣV< b Q+5)jÕpV'Np+ d's)TMLFjdfB`^EGhb}8!H!-$ 2x YE/ ʉH*b DuیCC |2lp5Z}je"T̿v~.['H'"\(G6!z} GdC!B)FT "}\4z֊;q;Xݾ2<00GOE#3lp2HYlׯD6Ӯ[H"*D>KAnkBD @lIDTyo!Zs/؜:n- + ɬ?{o(X^q+75@ƭh[ry@цsPTd"IDP#&қ( d?Eid  !b 9LΎV|vQ(!tV'َ[}.*5j)YHߔLdUd"َ[Q,$-Eˡ(0N%1ٻ8r{,M6&BEF Bq(#(=p(T@FYJ %@t6{2\ϐ%$z,3\u׍QOcܯ [06o\&,n ׁ5`9Wkbʛ,z+'ӡx2m=Y+UC>;dB }OC1䐶#?/w p+{X쾌ebeOa5X{V/q,[qXUϱB7;`Ӱh쫱9@ZpJ֥|w'Ӈa$f`Bߍ8M)~}ˮ;8gQSؑ==ƲSyXQ؜kmX.+) | [ 3m nbMۂZ`QXf|pf,ؾ+y+`KD? <$ ծ|lr{[tp[GtiuNrn OS`%Gē9ZuY,aZTX9l~g+k,Pv%]WG!XV kP<~KpT"v~{ $L;,FUX5="ɺ~i˿KN|r sSѲ ƽCXT"9*4aٲ)X&r, at8`{Y,h~% ЕX>H<(,AWjvr}S&2X"on?VQn''Ɠyd4m858bS>[;"Gʫ;x"Zc?o2ZŲw`1XF\Xv)=kWe=`,ZOѕ˰}@~N'v A!LOWxk޾A7_EX W5Y._,:H٤G~lՅǞ϶3lݪx2=:Lœ鋂TaX;AXq(95`瀫 jqh[+s8{.j ׉luXv$;,j ~z՞2)y/g <م3𜌕 -6?溒&?x)J ^D+Z9 c7cXT`وG( &aeiUIp}~f(1SsT`m@qEqeCE)-n$F=ȵo WjObYcV&|NGCpK%ã47./>fŭï~|zl~2lXasX3kjNl]J,K?gx,0ۈYc?; czA6x V5d[H%bē l+}S8 +&p'pN.ڷy'j@'L%bZV5;v0]ͳM,(hJ.>a?{VV/ N`a϶rmaN4 IDAT&Bb__}*ÿyZ`]P.8־-|S*۟0՝U4,S"XM+0xX.\yt&gWyP`%{͡smgW}擰5K%b_'ef`AGvGbU޺A E}7qpk}en2ZƂqOƂ,c<ԉ%X"!5yJ_J-T"/?pyÿѺþko_V/8R)z].PDDDl.T lurY<;.\uba]ñX"_ +o+cYhG3å ugMp쀜x2-yKA*[Ow!J^''c"[<<{⸳wv v2z` J:FU8Gt;Jb#YyرaǶ^- |Cl~] ֍K|8t۱ ַ1GN͕ T=(l܏`OY}X=51QMXd~ *(hj-eIJ_?SXS<> ֋zat ʎޏapmd@4Ko[Q^?+'cg[t88s[]n;GJ2;C{wɵ;!7wঀsdCCbg8N"6\)A, G7a݅e!O|X%Nˈ܊5ȫłz,xw`ݛ"wł?X1+o񶦺 a aپ-{Ś J6 =۱`|#mǂ`#T,c˃ce`sqG,ux2]ͻ}JcsXct(-T"T"v }ƲMHwo"mY2 c_V?>obdԬ#߼aиLK*~jW;+=Y |9.U;Pnߊ>lOw^x2ny9V6$6'/tSXp߇еm|b9~{̅J%b^yOc&,_e ?C|b&vhee}ds$X\ Tl+堳hX< ˎ} JAߎu+\ IIh9ξ=I~`٩'َ5ӱ=}-[Js+f9D{{c/7ַmRw{edl۶ΚUmYK0Uu~u~ڧ/szp WK|;Zymg,@#YծDd/k$'u ] nX~; n_Zo> 0VvC<>kڈ#1=܂/9h˰9J_Nt33mbaK5Piv#Q{>,~ ~kXYh0$(\DłW"X&,_ـs1,;}*/\|P no ^nSIHɕfmw.z8[*ڽp3Ps.T6 lo}z)w ^w6QڽqIFEX,N`%iUXY_1lYXQɗ\ۃRcO:oGą0m'3T*{iH%b:o909nJ:f6=/b-;ǔґ~'ּb2nZ5 t:i2qt(uŚ捝 {wΓ,o\Ųh  X#[L׀)ї H?p_QԞ-dr_qQOp ]w86]'rVF`O 86]F7 H?1/&S[hZUtyek{OnCfbeAؘ!W<}v8ʣ[:67$k=.E}O X O b/a]a c2!`kFwK<ױ` X,(= {ϾO7cž#lN{Sd ,`[a-Xf! ڂ6c_+{Ăf,+5 ~<5(2kyq9`HSjG* ANpxve< n h_W++. RW=S1Etl)[QLDx22O9,qi)Dz[b ~2tO.X h3|]*ծjkU1O'g}Vl~Z,`ZWaMآ'ƂK߱ @s$,8}*;*~;y / 0 ~k#Wfv[9,kv7T"("}QppBZ0?RjKu~ڶ:?gu~Uu~w|s9ۤ盖ұLK~HuRX֦}y?ƓرVX07kqnhJ LӼ.(jK4XKG7 z^c Ll Nmdz#q asnƺ^.J1CX f PM˭X[X T}S `[3e@;f#m~ ,[),kfcgG<בiMu9pOs4YJGtVnքH?˜4aَL6l~aׇX<nA~;`oK%b߯w?NO`凱%VE087 lއ/b;w`ٯ?`*Y]ԠMqM+*m`ϻ ˚ ? k^_`ypZ+{HRjku[LivGoo[>[~|O?#:}Ԩ7J稻m8P:?7_V<aecG` rvb+рo‚{uyA|=VV a;5Dlx2}cAէ`2|VeN[| :cX; R'l:ܗc%P3eW+j :E}Sخm`H_?ທa;fjW;[dv6Mki{'߅8p!S:\hr/i:$2x2A,?nƂwDl2V |CeqXZ=ִ!_Zɷ'ӟ|[R,ikF ,a,~cmVEb Oa}lk@29n):)x6>KmMWZIOQKDjW{֨{{ܕcUgo!wcZFg[ݝKYw[ ld:dWbm/kǂR,[yDp2OXW‘X k~|>KXSzVf'l VX͹jnBDEX"/T籝l0O{C LkpY*PudtlNԅխXRb ` -N, k+S}Vҹw5k*+FBD:?jW[ lӚ =0Ų,[ = O˰& S@2,CkN+o_yP]X[p]>kw5++|76-xhM+)eDDDDd'_ǖظ[ k&6j<-֤zȷM 8E;]Xkppy9sV8 H+( /N%bt#ֶ",p*ڲ k8RJrXpÎ]cۿ\J2s-$^O/:4Ă+aܫ4lWk?+T">pݠKDDDDz]< $c+b͟-? [`@^wL>X""""7 ~O#X翱X9X9RXM}v]/7` ܃ +xvG<zF+BDDDd`)NEJĶ`EP """"^",e+ȁHFKDDDd`Y 4 ȁH(_-`D4"("""2,psˀRqh`x  \uIj뀛u@50x<ȁJȁk8p֖q/ps'o BɅȁ `,P |8t1{_1HZ憁XpMm=@'p<<{'}4;w#@<T"ۯA t@M`%pp4w[>)+ 14w/DPDDDs{ !a:/}hxs+;n]V.үDPDDDͭš[ r핥/o=e:`M``S h#ˀalnVo,;+!$x\ `&ۀ\ȳe ml<WU x[#+a9 5˂nR lm/>:rФϝ>)BDDz_aAYآÿKoc%u هCFxeʋ"Wb"l=aן=}´?~[Rȁ/7U4?ڲ_.CSG v;5. y",l_lOt}Ø6c77 |C ؁-@.n8oV=Ekv, fΘPH/G"ŕpf-ps zl""r#~u l8nj+=ܷϔY}M.b)$""_%h(njjgqdp-0lo|H󄡹 gN(p/o)%,#ucpͭX澫b"""{g@c'l%vێ20 (N8 +~vbgy`[Kɴ+DDv҆;GV\cΒveD^mqG[' =5?x2}ϑ IDATt_\`X""""""=DHQ%"""""C`X""""""=DHQ%"""""C`X""""""=DHQ%"""""C`X""""""=DHQ%"""""C`X""""""=DHQ%"""""C`X""""""=DHQ%"""""C`X""""""=DHQ%"""""C`X9V9*8zs9=_9\>9bOn_Dd~jH7 s;~[kt=;uys9{97u\snsKkVs]߽y=ysG8{|""S~_O]:皺7+`ICow:s#WÀd +ιw+ |w&"'i?ջfޯG~1Kzs0\s}{~>_ w9x{[}{snZp5z /vsr蜻'8snsz\}p6n$8;wssιw9u=<7@INwν:}18CnJ`Okf/_sIOX)BP%96ۃ;vg.38_f`09W ~{py Blxx"=M>`('`=sp+v\}.`^Z7&3}z间:`S:+e9ws.fQd_Lz˩X%S{\ 6*u>뽿%7cWz{+x(p),P _f[<O_""S~j p ~tk`Io4}{v΅3fvg ~mmkS˭o{9-Μ;otGrlGx59wsnܛl{w%"i?u+uA2?>AM)i`sWv{Fx36ptSzz`Cۃ}u`|z&ED_ 뜻MKDh?{~-%`Ioi rWJٯ{۟޷nwG8.pΕVq M\ ş~sλӲQtsιs|`9wsL\1І7ox?&u7}^7E%xDDvOỎ#U%Xk;ps}B>lv6Cv`l'춉ocW 8&b;ez6ukFb%6ws=ƛ_>r2̟9lKO~zs;{|"{ĽVd`q]L-,""˴`ieApHwOeDDDDDDz\X""""""=DHQ%"""""C`X""""""=DHQ%"""""C`X""""""=DHQ%"""""C`X""""""=DHQ%"""""C`H/7xhtBGDDlgsg[f/]IDD&/9Ѓǀ_A:tS@> /9.S/ -@DDo@65x渎BGDD_ʛt}dqYoc45s\^~K&"" 4G\u8 @$!E \Ry@%""t؆Wh^.EDrB:sʨb2w33-88/PDD3}T5 6sg/]BSDD)Ň J =w&GC]s=z&""g 'X@ ~B@#p`3M ck{\Ȯ(1[ut  ~,V+@XHKDD\u ;;?,OT"(""^b8ʯwՎt}`~A,8Cܫ| 5ha&"",?yXy] @%"",F=yW`eX@1p㢻چ(aÆ}pgEp0ݚ WQHKDDT"aH/b,5rBCCŃvfgfM0a'pQb׶ry X"""f"m*V k+Kk-,%X"""f|PTR9q >BDDd%""2@,9. jc߻b\1jWozl""E =>&WL:̈u ! =(yd: lK%bz<""?(مx2]dSXK"̚ln3cI-""KDD-ē"R&=]jf]܀sXŽJQA}t~ĥ-8Ddāwz "IXpEZK DUZ.-xDdH%b~Z豈G71H." C&j ׅDlE 5Y 8eYG`쥥ێ{ :Wz\"""{i4d$"2X"{`j?vedQ<+m}uWŽRDDd:Zjcw3ưc:apnK!ypAfRDDdM8#Ay`)P_C DL[B8SvCxt٢_㯧L(8_cQͬE5.ZT3KYkٕJV,5JݣKd<! LqA); v0m9?g 8Wwjll)#yQͬBIDD93.Bё,u0p n{r"}DW y[}-7aÔqԱ 'qOMʊMj=xgΌ%hcŰko;aк"̳{E5~ܵfod93HKdϬ6[(!.`*OfG݈쯽7Ö́J#,`WYVKX""ku홲?<]4b[[ǀˀwbdsjfX1 D/\:j2b^n8u c'*',җ(C3*O|~|Y=TvŴ_R: DX{sjf(-nrF:hxQͬX;ÝgvG!xn?pQͬX`u1VX"xg+7(;M+sGvTF"tb;~ T"֫U`Rg6zJG47r7rN`'C*?gM{Ss99!G8ӗ;=7bC]EB*>5轣֕NdHѶodC%/9x2=*LWkZ'^MKx{sL""\ jR]{ ۧ>@CP*?/5'-(5,psG/ g/Y9`6x2>c<Xrx;VlQm} {w |P>o13bM.w灋jf9Y6B6̗4&O.3cI~z"N6 ;\}8s_.> *m|3K>gʶܔ::"PG([qHJĖK\Su}~~=9|iq3kO醀aXƞHWc YF>ad"9_nRFO/3 (6nDms\+oEK[ehI^9gƒX̚'XkΙD.䀢kZ~!aG4㽇Mog_,|Zۅ?h×N󏍭Zs!ٙeL4"6?,pED'-_Z:jXpRKW]t~x?|ը}xڊǯpf+hk.vI/5 `QjfEvJy~a7a 4b,v:/$lKm\Ap7hq3cI'@\K>O_IDD󫊰5>ׅE|'MXx Sm'ߵ/h4Qߙ޿e䑙΢&X uQ.Ĕ93[V9ՇO?V<̿]x2]JVuY:'/cw@QNCŴnjfh)r*Wl3cͻ) 8jeUj*\`<3q NgQͬKXy/̪.(*̈́s9uCxl*`˞nWd_(S#0C VdT|Lۺ-|X˅­WD#|]v-fMGwNe8?WVOq,[g6&M ņ)6 T^b\ZQ!d;0νӷw,]@8XpeM4dJ1MԐM4LL!X, Җ]٩gFVĂV~gs=w=~_GMUGx=/ae8MQ_=*k27~7! LyfGl7/K{GBZtiOꆗ,,,, 쬮c;*${\%󇹀_z<(C"B(хx85.)l ;6/{9,k?J'EG'&PXF0VV_=wZNc3i] #USsy\}풔E$ɧm70 9ô؏C6^ږڔ%ow8" `ȸ: ٤pem*7c3]qYZh8F(br}/𠻹I"lq*8kQf{Rd%rN;w.pk *$Pre>?u icsQ^?=xc+ @eaq0XA/n{Og:%yo݄M$Qik- lʮ&~K1W嶘h K!͑ ؖD *bBvgέ_=j6d}wR7o3`=/_v鉗ؗ0:z@7 WOb)KBچr7h@ tBwB[ K`zb%L\277QI Aݔ5Ua&K)lxZ^VCoA*`kޛ]̭Ϝ::eSa採8wV*8_ffX<` GqڌŻhw& +@Er REwԿuQ;'H%'&*1`9Uoa^'_\:3K[xb ή{rյ3Ny`6 n>e`jPb'ʠSO.A $i7P"U`rrhx*Z6x8 eG\WNc ? iz=[8;cZ}sFMI7YeOeumԠ3ˊr9:)^ZqÕ=b5Ult9푗%-QC7ѫ՜ф'qw IDATvǷ5串UBٜNh{9BdaaqxX fg9raۏ_漆O?OTV׎(YC#C; iWvT-H}_QQ"ԋe3r>+D/:c H27X6 4aÝs {#uA3A4P'֣ޟWV]ۑHkIyJ=<c<ԂX'{sW6,yAD{}"uf̌nEFt~;S7'Ŷ5Lwx[''w@o,k{Av8`񰁫r7qTЉ@ȗ,~=Ͻ_R} ]w\3#Xu S.6pva}c~`f}եvL[^xpn{MU:9ry +ζ%YɆ3FΗ߃:D(E[mrQќ4h :E3#)LT%LgOU{!߄+N'qۯ<': .NwbNl} M`t?F3dO)֧}poP}6ըၐojt9{۲8)ȯm__=,l4Eъ$m{r߽Ԡ U4G.x7[|J.'iHI3sy؉ßݜ4cH/; zRb6 mW kbqW!4!45U{C +imm;̤8)Ɣ&NGfGbGnڹWn#ZyGS>#rU4򀖚M+m|ֵ7ΎrSVf&exm>ܮ" ؉'=ћg@3`b ! S'= |FJ֚fdad# 2\$DgkaaqX fP$ȅ1Ub$#ԂWs{0N[#QPePM_ZD󑽲CKMUE+=C FA ,` `kaAQ)&˗ !p?k&f#)اwΝ9CXV<ί=u߾Vg"ⷯn=u£sb3_:xQX9TjؿJ}<`@q!B-aYVp~ ېJ,)@7U{TBl^8wg؁ Q 3:1I,'cwC_gZ*Dj~g*Ӵ5a.G^ Åv EW E-_x^FZ% |? |0~o0Z):LΞNOkgGl zxWjYwf* E^^bߊDA}yJ!6Rlinjҙr{X@̾_GfjAl-*2E) E$B.{K$ Èe]#ŀ6&&Ǘqz%w*%ں}  wShY)qv+nDݟ(~o}5 m&F!@ fBz(>XJD qy"5;-,>+u0f $6'fȓ݉S -O_G0PL o5Mz1ݙ\60ݦ7ݱpFh]>DM7QvQQEG7MbT=iW& bð?0}ÿ4,ϳ+SVi' '(֋ru&kLfM,y9|-Jf.p=5U{,sNE+PiC}fmaEgk?lDėڻD^^09 ƤQ%vmndl9}~5e,5UAT{h4(;dnRH ƮK,IiB*m@=7ʎ I&ľF2eqܾ`8uD`{ oAkdqȰqsWۣg$^ &1zIm{q;$TV/G`nGWyY3Q']\fGH E-GE\g.ЃXEj{>,,:;/jy6>+&Y*,qϤR~,{wܽ{[!hlQ{]qR/9wn,wpz ЄEN]USUaHnr*klϺIgMB>:11ۉYxS ;37;Q AdM@̊Lv4h6R%,lPW USU!+k;FK@ nTJgw5R&T<;cZLe>!@=`|\bDӻfDv6 =ٲio|=q,';Ɵ`dڵmZZzh`h*wDe^ʄ\F5iQ i%R2`C=7Bþ}өV+Á%#2c]+E$]QeKS#K+7Qy@jUr4l)^{[e~(H,k{.ĝB^{t^}CӍ{ h]xރo4OvqƄ%,#FSuOrkEZ)]4z$1]x[AF e i`J`%YΦ6;J\EQt+](hxd߂P0Q4I[Bv1B!<[gfΩ n/v3ܦv_ݥLq׺mC>ƸNFs60s:IyڔO,W6*F~op7)@A5J~ջ%Rڣ~o`,_r.{.~V+4fV[Pt|c\GX |tS±4F=Y1SUHI i nu{%ftxZ_}X#3z{>2#ܜ3‘zcy?il*HĢŵQ{.Pb2L7J8ift9~#h͖D"ij{(ue{U`p̞LD{h ^ԝ)2@Eu(F*W6>3bg~'s/6~s⫄ߪkGޯXeu 쮩=*MGv6eZ ntMHi ދP[eHїSQBuf"TUte[.s'GCՇmF-T&QQ`q,,>::^k/Ɗ2r4f~GB@zsmG>w o&_TUezm?paSpػɱCTUDS ~o|7gDb2*f' |A,,~.r 6yߺdީic@ I2ijPfʘ#T-e7jn ;BcMMFdBưN(Pbn0*> X̹K `ܸmbUu 4Th<'i͘?j@DO$ARTdԍ{o JD m#V.Nڿ9}y**Ub0liw&QQS;rks4\*ʭ'>ZZЀǓ0NG *JS7;fmPםR@ BDte Cy|^JTUxu$ pVP4m5U-]9S>;둭Nzlxk*dzd.ﶤl4SKGC=(Kݿ@,,0Sj3GS nHPn3-KIۭSOR_K_QrT=k!ߗȂ4!יxve-{tٸ=bVpE{ZLOg H3a7F¢O8Qξ(),גn#]:PuJ 9R߻SIFT!)1\㙩סϞ#~XݧE4*})kHYARC |T[@ߐ;'wkn&{{7>uˉ{)n޻?Wc"Lzg-/O/Ό%ܷm/6h1C^͘!@6=zc3LT.'ϦikWʭ-P7$@O ڏCksM_J\X| kZ2囜NLy!lw׾'z`!>g?;8f9IARHtT䩀}5.-;•DMJsc;=eFɫu=nd-GE&@Esce񘣲vڟOT$z:,vVd ԂJA]qU׹9bveu$~uQbvy[j sC9t9h]'RQBo|[נl}Ĥ X#F]K"{lm /O="yc.ؗ29[J5̑ ;暈šXEZTU +"ˁ/->l !n3eFc]2k]vY}pTt&6mj4Z pҶmz*Hlz40eKm'jISDU9rk-y@Y d0t{N֑[\[MdSJQ)z!USյNB`s p ,j(ƳBr2Nmv; Hu'/۞qiEBȯ  8o f_l uv~kXx@oL X͇E` a. _xޡyY">Iue?jpw֥P㰰8rf_J$ m21< OL$J91˶MДtSΩooA YTUZY]mbwpOwƥK5=Ou9<)/Ϛ.zuq!}0Gz4>j@ sv8fWgd1TzON2j\lu-As2ի*66Z|޻ٓjQp|l2͘ 4D乨|*Pv;F[ɛmaqX1X5Ӵ}SoDGQis } (e0T]PaT}ˤԱD/kDg:?lwPNs܀K^jVL/c_͗QBMUEoeu3 5}E*5`qcW:F\w$ -t=յWTUBK@W[d׮̿.L5 6uw$0(wgѫ<tŨ' L] E?pd/p]|%;{ZgBtŇP;\:T=1Hnqtb , Or4)1u~o,,,bEMog!EZT ԪutB#T]Qk+zv"5ho_ʵPCEڜȅkm恳%W< o8vvO+2z}+m-I6**J; )c}5wlu.4`gxn.ީ )a HH02mPu}/'$! nq՗8 N-.vĘ4&$L^U^p(! ʉiT]m(@Z 8o\9߫k_@ʭ e6^H'ݦ'N6LiҸeudvcv>i<{lN[$ .4dc{ v"B>;PhF4ڛ>{kaűUaYX%,QEؗ(zY:/nO-!+&F59P84t-R)6C~x9J=J s&(G66P@)PY]jJ^+t9G ߢ%^=6L͝e7r7Քo07YnɍIpأuO,%#ݦ'Rv5g55l8!X{HePѪiRۅ_wBiru7g_l[= 1+őXdaq\kq{E;Q㴘rþX%2 );{ _ۖ@nk0I3dP @*ju20 -j-9t_2TSUSSVi/Fb9?][?k}]wƓ.yrw&rw${ }Z/wM7B(HMOnOؙk6oX<횟}%7QuP07̟~ 8caJ4AՔώ4E, #j`aq4SY];{"N9Bچ4 @.?ܾMRߥԺQMMӇtG6 jǷ- .QeHڥ(3U4з8V kk,Н:Lcw$3{J^؝sOziXj;ʶ};@w-ʎ Tv9pL|jaq"XG?Nуܓ/B><IGxqj8<*ks*kK8,,V@T遪=U %F\vmjq,۩9ʪ@M(IkdӇ}k5f,O1Q ?k [.TS/@eu(͝uIyK,`ޥwꝮ{Qً\*DT4RT[L$)Z^pʵg*\Z| Ր(S` PAB2T6Ǚ)i{p%,$8ʩX7yTS3m[< 7ng'=/JH= NَwĖP+[ $k0޷]st^Wr޺ƿB5mJ-~X~A [_[ -KO x+~Vذp]%zP*5eeu07(`{ 3ޢP|k^=s:hRYѴ?\ö5+}#6|o b7P2´k8ۣhjjHepGwil~)!O!T ؍2 9rG,>ByTE睉ӧƪUrb T|T-vjA|=J[DWK+}rU7WkޯaKڱ֮F? g;[ƩG~8ޞH$\hI#k^{i{wQ J8p:0Dc Op{W3'|[ ۋ+8vP(c+* X훾0=+R@J686zL4ͨVTco?bWeIbΉ ֠ RXH9'-&`*T:*zڊNm|o=;~˯PX?z돌hY8,r2D<BAK1mr_$_AYߏrʼ yH |3!ߍ?/#KϘy,^A cC=L-u?eвX$8=#a ~@0t% |WZSz{@ȷ PT>Ԣ4pa)TZ,=:]`Eu:1&:HB"oQTu7xazKI]O!"ۚsl(ߺ,%DA+E8Ɗ`YXX* r<{Қe+1CV-,,>9raTTzrL}$![#pKϰWndTCRɫL ҍםwȢ |67؂rٻkk*ZPѩR@\~b0k٤IZ:n`0Pj; &8xe8q2*,Tl7YJ;ȒR|ږ՞p$DGO4hе w{7ȝtݼhlW)&R[Ԟj.{T?-c!^+_ xo,,JBcikfpv:4ZDJj:QtnbЮǍ7t2cnTܲSD#P/vʆsoqSMtY @|<ib}q^]CNFty sN2n96oɜ. yH4)h [ AȊ2>IӮDŽX/LP} |vT7u.G7GX8V⸤V BSjO}tqf tV7Tu@̞BeޒLghY!n3oeps垳G !iEZ?DEƺ,oD$ 9݃Al(;յgpYqn_9C\C{+k=Kj*6߿: c' N p102[yXS[+1*|H}93y5{ʧNy ՍUsǮpџ0Op;;^jzM5Wl:RF\wƮU&~go[kF~OT7Lm';;c B`Uu|cٴ[TV^1hF z!/#7X&d8&sl,= nYX ,5SڥHa R@d9z2_o'zdf{840Ҷ ;G.ly3ZnR}^GE?>i+ 2XW{,Zjд1Ol._x`}5U4Z (\̆=很ir!m78 ̏QH( |7L,2P7gMC*QuT=6^*wUu+n2f3^n,ޜ4Ӷ1K8յCW];eWzr.qmd|pZ)LT`]mw<`oq`E,K/:-n3u(` CDtz2CDKYXX|sBs-]3q2H"V$OC&30=3Op5443&-E{f e*Z5Jh@.< UKtL*I l{Ek! x|UwM_Ɵ5c'/~@J6' hwCE`1((4 JPV|'|fԠ_EELDՐ7%+)q.ഇ/4JsA,{be/c.y~6&^&hpJ2b&1mq a ,@'ISFEvVW Y:q;{3 H~ Ĭ _P2gBmCj_=uM\)u-4jE6UX ϶'et9%́o\:=z~#bCG15ل:`Ě spF֥h!BбiqKk*|)K2:.)ءB%Bo] n UG6wyl(݋/! +?9q>u5'6T #!T@{2xmǺg-Y/+?5{j$J4H] P}sYX"$6[˜~7V,=o:Rk$mh%X(`:$+@YN=L15J|fHriӞMus5U'7C}XZRuVם;j)wPSU!=}ꍝ5U^%FޗwmiDSJwZ{c9__LSU;/&*cx)F7ۃןz~ߓ״#~ҦOG<@wه-TU *8?J2. `-"ZUeq૾ͦLSa&^4ۛѵzK;TYXX1&BTWNjs$i;"cEYYM'7=^ZϺ.MV(ˢ |?E5൧6%Pb@,D&>}[/y8XUSUWY]{)K*Ξ,O[F:dHgef^47x@wlwjhߓmnNN|Œ&| %ݻLQ}U/{B k28߶R &~ X"8Ú`ZB2 NՑ .~368yxoo6>1YX ̑ ^ f6#%aHì1&2M ͳ`ܐ7w%jE D({I#JݷtnU#Ї x(wW[/i<"kni:l)%ncQf^C$R\ ĻDlߑ83l9wܙ0jOv y(w)Q L\lQ.ɱ̅a=5.JT驟E9t#VsXXL&!h;5x8dX!yZ?3\ ~nW,`aȃ 1,_)=!=^܌4#fCqv1{[0қ/7ϑ >< H! ](S]RR B m 3J4}TT ~JSSU`i|x?տoK+zpgGQ29Ƅ/?G&=_.6{2eZZ8Td-"`yW3~ok` ,7{!m;)kkWMU DGMEFC'b&Ep^OS@!=)dⓅMJDKB0ia) GsdKgY\w1CoS_@ȗI]Bcgqyc>p #Q 吸϶F[P=%~ocX'!3e([uUgeaXp8KWU-vK+g Z~}W4ҔWHg(E;"A].Ysxxg7EI`/1bR1[}H2 y`R{XDž'&_uE^>D&G,5FcDU]=f,ġTO]ɪխ[CFLrgf%"teҀ_`zK"Z N#eX0l۰-ÇF)` "ht//QD&T 9Uo3$o.C_D&t>4ѩjdD -4F14FOm[ lP[[!iLAJ4^^zV":/s:Z6ee)Cj֞Dg~ ܽo`'8ck* o'JPE4o_$`2' AB2 9`o3: #\]%k5XF ^RUK7c{Y,n(, [ s0t}~;eJ6&2ύMOwu= UW6DUvi ߶+  |1gǀes~MB|!ϳ}Njq;{/%v;0hCh4f2S]tVt <9,& 9≷t?uzΚC"2ΞCގkZ'z [_KGahE2LhbRbH3Kh4ALuهq7rF`]{gP#=;g+|E딦iX{=$2SFgz>D&th[אi2bl+s?w#LF39\h4 *և[6s'fF#[PUtW2j"AH'd+'% lM<ћnz 4Ln\2Ͽẁ̻ȡM IDAT(C[QO*T K;qVtl_JS ԳwŋDKLWv2N-4n# 5h0]_L^E@˟k_CΣVnfu90cͰ*|F;cdc'?rp UĘKL߉saCwp8ҌyqȲqvWf>U.]Xf4f|X| O)d,ܱswV|g2_;O}Zpi4p}k>O:ﯞ x5ߕ푩^nn\rf`忈P<9 ]YUmx\h* D&h0ߍD&ԄXED'{j*6Owg㖱gMit)8fO]5'xz+peZ|uټf_FiD&cY$?YȄC^Pmi k Rvy7f i4Xf7v}ۺy9p0t٪LkӶ}afYGg4Vo^*jS[y/`& ?o{[*שR ǎiGk4{HvCz6 _tST"s@{@vTK' '5' yЁf];/ۦӭ{4~KѼ+"T57`2~%_8vpƫd,lG)]ಞ ?x}/z5ѻv\h@2 E{j7: 7 RzTt>iS Wlxjq23h$~`N4+D&48}}`"p.ЀD~mG(L/|&p o2+Wnz-Ky}GnPڦ]Ѽ[DfF ?b$5OmTfܛocw/]bWL 3zRU؂e4T-^3fJ"ۓp_h*x;"ۮ,!Lﮞ{}qݵ-Hah0Ӆwp*״x7vƎZi˘L}Lhn/mэ5ͻ"OyK'XӝeOd,SI_ۯ.ydtwX |?G|ѽG^>o371G]SުF3`D&Nd,|s}T/X7jw!kY׻b,)Kׄ8ds3_kY>hB>~[~=\o x `ttk7/=6+N\eX8[]O7@F?h4 ߀2 v|mSe3eM>O|p܄_iׇԬ>_vxm\90ϊ֋ r`=]VaH]-4ezೈYL_NY/ڧ4BOij4q0}ۦ`)Pk-11S(ҷmpS}yp7ܩZ|N|HMe-jҨǴ<@5XSg.KG"}! MJdBNdBG/o潇Xa5Gl^v@ǁIAn߼h1ޖQ~ulEnc۸95 sF[>gxà2LN'k4%c~vW=~z \\2^W'lbnr=v6^g1B5"pl$* a#zgZ<'[VHs`l\lU:'†ctpS9!a3ow LAF?;~}ݰ7bVsm%k`ȗ7o^3grȔ j|Xіw4wI$ ,oH4mHdB6A@)j[qиz `( }D4nKdB7gq@}4^k|ڲ5hnKyflېp*UVоl⌅f]; ޼1LuΧEH4,z {-FyW$2!Ȅn]tv]ʪ@YWJCW0W[yA+u6XaE>JDWz"q</(%QEVY,:i6(|H_Х;Ƒ({re+cm\^Y [[+~ G=}d=~Lh:@4H|Ĺp)RO] 5oih4l;r:Jes^5yK7NhhxO>Jw{=n}Vz~,ʡ,<~NSKꁞF0m`s(N!RacYy4C9LOdB ͇.5m쓔e 溳wN @a}Ld8ix:h֕s6"X xYs'2bʊ{h"h1L(<lMϮtƚe<>8Ѳ)ϧmæƺ5`:;Sh+"T]vUҶՕՕ R'j4{ onWPJ)'wMHGuH&PB[ #. qnW%R&8 x8ncG/=0ϼyjFeqAc׽pȗ۷t[ ndථ!ꍏf[^xzR.eJ[`8>h0. lY?m [x';jOdBh*do*MI V`=@ ;c6nO/'Pˤ|eubhC ,FXfm}_- &Rl^\ [ ݼvFB՗OYJ/ڶM02OMIVfȄn/ξz,Dx)jx p2gD= Mt Jna/Х7xLQQg" l "pBwՌ0V8iKv@݈s[j4}KLo+ȲiG5L'}/Fi7i"d-4=Ɲ-]5qu`*A M[k,e`5nB{0pn@2Ly~ ܖȄuQMu˕esӪo>spOrqp?` "" (8?m+'` `zn|4Z`i4ȄZg{(佘^48ɕ] K#Ff%fgvM9lg̝E"d1> CRDv5Id < \LGޒzt-"޾Z8Pʮ?W!ѫo!i;s,b `(Ei7 x|E]"\ڣF6h FSy>H))/~f/VF=GH$"Ou6@GHlŤl ̏gF)`~Yv5^F6Y1P1{XNpH$b/-B"Ve:dmڐhD`s'MS_PﭴXt?A/f?@פ0or׀ j~9~ uΡ}S5@u2^'nHa 4FѼ{KUK7[cw_͑xH_ӑ:Rl~jwhKtKh4>TUwY0?3 +?8m9Mx 8 X')ވ81ՄI,IQ6/"iՁ=S#"]rᷗ-\oi>?`^ D+ar7L?nHdB=cH=xD&toCGtV[St Fh4}. V| _Wb˿o]>sH| F2~vw߷f`IdBlcJFMH4bD LIo4޸Gjg_ӳhCtKh4>\eߺzhFjŖ_TlʾuPDG"(D<4_mh>7 ރS,Fks@ď^0h?rH-8YG-T- ,1 !ιBuGˁ%XXG[r'2!䩉Lmx*-K¥ӈ:nEFh4@[#zKacrkYKf>y}恞 cC"r*a=|?!&E׀zq~Q*/E{mc w+;EękQ@HjahBҾf:?t7ݳf m.6 Nyp#4WJ` EnZiڧ{F7{6h4*ml`9k2r๹܍86ׂ,(p~ޣ8uN6%4dmJqa#Q.DuU8?w Q)0/k;?_"*sy ιK=;?5E$D⩡}6Vm^7#n_<0 Oٓ75._|F4=h4fҨq8u?9ٻP?9]5HSDO_YDH`?p$m*0ȃD6!Bt7@W+P4pa܎0HXxn~4{H<|XxId,gXs7\t;mF4=XF4Wl>sVhUz KD\}{CE⩣A}- (zI=uz( Ĭq/*8-D+W:֍eU <8vL;rDH'c-0~Л7$2Mm#,~vՊu[l1O}a #֜ \ lA7;\vu}Hjt됚e]R;\yDp qBU6E6$"$0DrH9@f& ^S'c ;fOޚk y%2܍H+z@{-4FM`M]uG65\Ɂc~Dl Xx3l$0I{˹xה/ /p؇!V~DĘHl(3`.\(J{Z9?OhbvF\6;Ǹ_ޒ}9Īg#g+ Mڈ %HmF۹cXxZ%2wz̞*W_qā {twoըחonG{r&X.0S]v[;w6^\xH=zQ% N@v[&c]9a$,ZA\@3S#BX{a0ahW"ݪgHWi΂.^`3Q`ʃT[2*DMr X?LƒfxNB>? b$Ȅ XlHѦ 9Q<#,`I$22>XkOt F \g\re(Yk d5͠#O1$c\v/RUٮSg#IE?nKFiH่QZ$bփې& #BGU?GIZD('\KwwE1eu2l5m[)< )zu5; ?f #OvBy PT#3_]Ν%weΒV.X P0 co2Z4 ctKG^ M`L5o~|shxʏX,C,o/I[PHkʡ 18,=<8>݉D]D4 ">7@> oX.`^6!)"}_m}(]c DuFy\?/$29HM'EJnt6:;[i7ծT0 ۦ4-6_Xvjϼ X.3?Q2';khd,._?cvA>GY@3_ WWg@m5FهS& LDjf?f&c#@6 lWHHmU3T8IqD"Dc)1(mm[(8ImQ2#KvTȹrxR (Z8={ىh1"D`?q7.6E9!VR [b˗|_|'gq7ޡ]䍕^1Im;(ѭ#ȿ[j]>==L?̀S5FGqȓGVZWҨ1m ?E5lĮo*Q#i`08tsۈ`7Oٶ|QRIKF9E0M/0AN"kPM"FU/#N0;E  D&tY":\酥 ʥ~O[,  JW!.G Y0"y6B"'2MyW\q'UazւEAFh]}< "ԏoI:Ӄ}rL.nS3Mu/u Pn:.l#/"QoS4pyH3c8)ee+v(ODmR[-Q M\1]Ar/v뮪[U'[u#p90k7Mw4^Ԣ ~x&h0w>l\+h睊V<82pd"(L$2@O4ŇOQbT=p9#> n󵟢Fh4.֧Nk" +䶅݆…H&$%>q7!)HԪY4L1UrS ~UQOZս.iZ`i4F:8m^+Wxj8p>d,q;048rf`b`р<5L_G&$NCD Dg3EsV55"ȭk.?~kn} Z9cNx 4w6"h8_ÐF'"Z$ca&8YDaO>{y%aczr_~讞y u֬zrsCH'ף;reK6S@>#wb!sỴV^zck˺'~n-6agUG7G?|4{ -4F8B`.!}v!UD`1{?/x/#U<.u2p PN@'qVC"Xk2b9ζAmp/+K2KNS4*giXi֪a ȁGOMo۹?vx|yjUe];#*EW`ռ<4P~.z -tpY{[]Mh4fc"".D⩏ LOEXW" tt~bC WRr:p H<59^$ύRmA"=n+U갉0oqԟ*Pp&nl"o۔9_n`p+=oE1uP. qs[ ~B={Py9!uO6U0vQ,PI)SnwTb=w͈eS!v2͞Boj 䜥"u`qnD\B! $rҠ9fPȄ#fb#رH5i,VSh79`88u#0}O[+:XajNw>n^jOw*KpȍS>PPg-_[Fh4Ո!@ -nsESsf lC6;/?0+x$4Ep>ڄĺ}1bzQ*`([gexY|h)kCuYt{h*vֆŅF#b`r(0=[fwRe#H<0 .&BRS?1ڬH`H>^(Ƕ1(X5souԥ]=m5^O`D&UQ&t:9o7|cY|ϊywG3`hh4>F2^.ES uYHHRH_]5T0 ǥ,~'s9)}?xEj b[ -w`": iWpea(r#H4,\  Ȅ^nh2Tz?@4W2uwҾH_򻛟}jmcFh5"nAR(lXx3fR՝: n$0ш#duڳ)bnntSnzg6<=q}6%.eVjsr?|0 ZAD"_(aH5X < B"7g:lklւe.HƇqxkyܷԞӚmKh4}H>T-C4Q)y_,6ۑgyy8bjk$џyŎx'ca[F>cJֱE]` I, uUfGi*8HHdBc G4; {F`D&T qlD굼,(՝.0 jٺC&y6&༥s%w5t+St{"h4>J$~q ."ԱHm(F"£)_ gHURmaݍ`9mJ#YQ*3p<ۛc %&7<@1*X 뤷Vse{T^3)6R]@3`nߖeJ/7EEjaxpR{ƥHD=1Xk7 =`i4FA?AR"9d,i{Εa'w)ߌXm@1L+H$be*[m`6PefI Paf?:Z62ÍVw}9)`\%UA|!M+X~G Nwr=L"đ ^o*CQG$2Jg`D5` >RDL`z9b1tZYjO!sJ1w-/fZ;`i4Foq>p9h8D<Rɶ + =og!B$Չ,*#faл戆#6 d#JN FrR厵|^iYne(п(sY)jA1fɆe"N݈4~"H5 Lښ/ Ȅ#&%Dډy$ݯ9o %PBZI?ZD`h0] Ӽ,Fh"T-NϋHXF b"T"H3Ӏ#Oٳ+Po۴u7*uetFD@CD6"$%OсbQLsq]܆nBTRQ9ٶ X*hjG͍Y7 /}FQȿz%w YxJGKh0?Q|a;FG4F$cHJ~mmу _5l3J^aZ` aӾ}-2}m#~w$ZߓF)8}# ?wFNKh4AJ$R1SUb%DY4"TuWW8[arHTl$bQ; Ak7og̓1g .;_/"Z\mTRo:/bfPrNرr-gN^w XѻElLo ƶm ((՞T=vp}|@VNlW8<Lo#t70o\4ٞ潁ih4fk7~dm'd(X|ZN1d3bއ4mѸϯzTS*|A1-tSDL͇;fw/m,FT%`[#[nozJضb25W-*`PO V-n1R௶1R ӹ'w#X?.qSDY v+c]LMSB"XNTjWYoXCG4Ft[6<[c8H UŇCFDU#;-vEs~9߽Ȩf9vS\:(ۦj]SncۨU*ܣRnت3d[0<[M+/^U!ɐ](6=o&=Pyp D3 Dl"z,':jUKwT??{'IU4yg6/,Y@DmbhP&/zj9\U*^3ZA{D ^βqL:?:]{z6lNuUss}ތG(!"X"D!lHqg*;zBr^D@)UkgH;O"9]1M~BЌ4GZ!`& I)5u4* YP~ŃxO*IB8) m>SkH7F'aϼg$g`Rk Lm;Gz9u}'ׁӁyO:gnd~"`"Dp!sY=ѳқp\{Y)zB5!Zr$.bd1$ b9Bɠ!>Q-Br"4hEs8(eB9NHDczzcnVxk0f"">kJoDw(ܞa'ݰ^o<1qq"E"D!Bl $ ̰ɗx*b@aduj/t IDAT/Yh&_㻴&.ÞMlV7vADժmjP!)Zkb5X'6SA 4¨tЎDЦFإs],/^ |ˌy>M}ivĵ.£obg%0B}V"D?X=x_F&^ { 1D1ڝz2qgd ^M8k=NG,Gkjfq:]gҮ89].kr螋}!^>"^ )2;v|B(9,n'gado&bFl^ !0Fg$8[$ #O.S oD/O&P2B. 40T,V±\,{(nWm62qichKHB.s>{!:p^,DN^4W>醭| _}nx?ܱǵD0( dKaH೅\fz52B"yd.xt;plkR1\kO>$ulx>B#  +@3!]?H8Ȕk$Ðճ"DQE`5d#PjKT|BBdGpbJs}+eO+NC107qʙa"1AC?S$id܍Fz6ԝ8*S3'EL~|={8B;șpb^>@6_<!fv~Yg B < 42uX\D{<ϾCKy`y_QKE:; "Da.QQrb!=&b[RJ^\(}K,:r)4.kϹ!=r0kY C:i>Y^ M<+M/N\O::)[lݳV3 ;#.Nwc}1ZC`,Ž!|qnĨdH!iD ^D :W"EJπv!{<ٿzj`q]ȪU\f1"D0GFLNB|ϊiG:5z6fNZբjsL,D|z[ 3fm*A<r7f#/RufBvr!y`!!u4VdjRSPs>[n$ ܪ2>esD<pN6_ b2!B|mH=~_Ծ2 y5$p6QC,~Ϻ3N cQnۧ+P(" f:񙟭M]$Ɇ!,*4GXuc Z%69Ce}1w!UcH!Ht,&#Q"a<]Hg\>EoVkmcAU@D@V_Qe~(?Al؏đ‡/ 8r"hˀE1m N1XA8p%ɦ} [s\i$g 2&3{ 56BH lzk7Wbqʅ Jf% ɇbTcrS6_;HNۥ8$ru,S?"$D GaaNK\X6IW.ݮ%̉r/fsu Dj.Ϡ O-26Fq=pY327hAbZ Ij "V1U1 HBDN\Xmk۸ i%n 6_$snyltaKl{.`?ɉN7;:񞦿 }}G!Lo !$c\Hz!I] [e6^Da`l8Ye#f?h_e j:;^up.[UHbB^ޕ:o-2n!D@B`ZH^SB^ÏuIhF> ݏDs4Gش!N4?mEa}(ۉli$쪙vYY\Ya:JWTy&fZkَf?~LkM\qXۯ\/2_$:$UÅ\׳܃"!)`e<2@ n Inc qϣȠ~{q pdŸ\jԪ^iĐ̟??nHx; ӜoA9rĜZ)B|cMϑ_2Pk߀5B}%jN,9{|oHb3wع'hSI;g4d^S)ԉG˹Ln$B as9t;Tz}b N#̽Rq'tw܎^_#d"Ĭ$BGiU:|HFŬ̵B;:X]'?x2/!4ڳp YL6_Ln9LɨٟK'ᶊ$xc"ȸB"!>'\$j8h*^C&Fr৴Ʊ*;gDwHցꁎ (=ͩܙ_w&9w`asƙS4B`*4_ϴFt[!ɸ&(Θ8F#$ؐ35,}8˼\q !u>k]e6rhQAZVUD_8}fE0V[:oj+jD紉Rx¡Gz~?!h'gv9"U)}grSM;6@拥l׉-lxf(`=p$э R0O /6~ a׬׃W@7S9[9 Г:͎X_lCF,- 6ܠ}ϰIV#0qZΧ}bIo:)Dn i "@'^oDZUSiUJy߀iUkss*+/~bJZEQS2o`sۤ583p! {~5U ښy8Ϗwj?̽^yFʏ S׼׭7|k.҃sϐW"]̡Y!PW$x z>GR PJ:5nF:}2ǘƮ`:v붹Fk~A+].LK;Z] ۝l䈹NYyG2cx:a b-F-_ DAl #BG*=?i mʈc|Czs(+/'$I9*}IlwH˱m Tm)%F`m]<ˍѠ!,I{L2v>2G-{;ف&Nh\EA&ަ!5xȤR_ e?AnPܖ +_tj@7Bh9BƧBgQwvd~RCͤԖZ-MsF0BdGOstWYD&J Ǖ~̙{~T pJiI$C` w 9XDjӁŽao0I{u:W ?d0pGb?]e߃`rH "kD6_Tѵ).b)T*dKFQB ݟ;y{M)Nߍp D"<)Bv4Y:E|',kGhtmaF".`QCH9$w+ޘMM:ޔ2}ǩJ :ckiUZ֎^tJx0OҲGiUO*VU)lLz}t"O $WN 0vz ̲kzDBkw&!f=/w'!iY+v\RXnpqF8NgfGrON.US  >ۋ6l%rͤ;B|% 1H!WTv~U|x"D{tbsӪcivVKDL# <A=Gvݱ:mD3i[M)Lkwjcvx{07*2WݧE8D#YpFeOƒ$K#I xTG^uGԅo}C߱ӿ8 ;rSA?ʎXdR'^7Lщ0k$M#;W|{H23ÃUrh>-`Dzqo!O5u~s6%2Ojju)W̹B\ӜW:5Jms}5[&~Ɂs GnAr> kL:1 +2#B=DZ>YRP41p?qY_ps{ѐ|~֋yE\^=o|T/l㠕A}x &UQ D.ɯ;rht؎v wO^ (2 ̗lrmM4Tz{#ܤ c%.}א:ୱn'N}8{xr_> lq+O:lऩ>,'8*َn3] m$*wk#Y.q,ڋ Dp,39QmY?VњH $ϼwd=Cde :l9^&._sk1Vv6 U!:+ʖ1ӆ*M[ɕ\~]]LH?lth'FlU&lS.*hp6G3|{ko…{OiU:c<੠'pU!w-NglدqjFxj7$kF.D!*2H\`IlPq"*/Y)]hk|ho xؽq/MViUg?!ȝZY#Q_Qǖ{eN[ǥBg ͯvQ:릔S)"УEB.lF"HLCn{;ik9lʎ$1-]zVڸb$g)4~pvT)hC,&U&*M$ ~_#M_x~05o[=m`k}=#" |<X  B@`uRC{ U A]|F%gHvP 8?JQ~^&Xko;6+jPqPlu8ZCm [e!E  L-Y拫]+Ҫ*]V%/J*GZ28#8G;*=<үS&Y_"YO@Ti|PRp<16ܥiUzma78'\FǺr"/VSU( hzWU^Y;K.8ʢUM36h)DlxL6_3ş#4 oFLNfBS3~{h\A q.p%p!=8~{iUj[M^Q+;ܭSCzp h,z(J|"pXؚUH]*^O9^pc{ XH|~w 3tN}Mj*UBZ\pP,:xN1Uu m&Ƴm7 IDATw _=nˬ8O &"m\,@6_jXo_C͡wU'AK1iUz_~]Zv=_:/>'ZP5=~x6Xf`]W17x_t,PqQC6_LoG_AƉԑ9;n頺a=MnzMA cX^dG\@ٴ u n}@]+,BRA/HݦivԈ$/,ݱx_蕁q!_k\Nwp[2ێݜ%{ft7ř߻~$AriD J=ї~=0qWPBLMFP~m8q{e[Ӫ4vqjI˞;r.dN=R_xO݇WAK!9L1@ylwAGlkzJ7 ]2Q(/vͲ_c]3WA~6ji)sV;CĀ qA;L.@&k3w~ߓO?Xyا\[*;ܚ_U(g!8nbrC;:]r1&צ4Z}1R^uؽ=O$J<]8h:WYLqثsGh&x1TLu/Czo5%]d_k173=Yns Dx*HbgXcbCLd-Ӊя(eIxL:Dct1a";kĐN3TvE?8W0/̴1*2W"J${UH_\bOζÐ!= ]^# 6g#%Hl9Ud[+|&:QK5ore tژ%.y8]6N}$܄|{;> z[K-ϴ pu,_.+_m]1lM6h/dŋB.FXru|1 t>#ˌxUt !#L=Z Vؽ?$5|:бyMz+wt~ʽm??pE ~_8$u{LOzRPoD 'K\1rgU#wv:(ޜ8ۛxO$;WCj:}6^5%%۷&W=S&Xؼ77 ~4!^GJ>:.a۱?>vwv?b"B6Oaާ1dBf۴LZDf14"DL^fޤmnӄѭ}]G~﷜ǾۛDOnG&mWC&\FgK@IW)3Ng᳕3eȢ(p=]tGm$ƊkڸƟ lHxޓKp?.8k O.'p4kՏ7>e\*]tV$_G dNqH$:;X.x_Lmba/rV`nBtp4kO*fL8 j]hlhy'20w[DHֶ)$zkŎ[c>A,h[^UrP|o9VEi.6rcI6_"\\PeiUo@"Oǐ!=X͹NFG/ELV#.uCɉ/Fd˙#p֐iU:~q7xnJ*Bd.$!7V6_\tr{pI!.v5!512^w#,ai{,wZidv !ʭw[mWev  pˌfo"DeX19'T0ϟtypL=;+6h\jlBЪY9.;GF|z,%9 VV:Vmf DbVh$~ ׆+?>_Np>=w\&VmrN-R4}w>ɮ_t !=㱻9 5sgU&gDZF_쨐qƌ#DM>9|,4D`{R9oHmanDV $EfLureZ8ViV (4w8)9XjnabHoE d[wvJ5#!U^<G7!n^W|eka]S*(yұ<'/>-X埁B.x'a Nzьi泮.A˞* #Y K[U5pI8 Hd3X07qP>:4\>mC?霋U(o @ |\ l4gB&F+]77%*&uK-:O@iZVY/;ەRnm7zWN.mL->UsF?o%.<|_vp{@]ڛpM.6Nߜ|0>^dҷ Õɻ+[y/p"ļPedCz ?~< 5׾cIU~g.LVd\H`Mĭ1x%8m8l5ڍ=C&42^ZC !XݽY.|bcER9 g!4U$9VmЅ$/@VcM܎Le sB)w ;[駘 2Yـ|LȤF8-GW7Gjb+s0Ā!"X"QH_58utMS&\EAs3$J~:x 8ə218)T}GXWܷ!9X[` Nc$5IC]38or<$YjIY=jlk][U}mYu^Qc =HQ][1 ~Qf`+! fkϨV`@hQӷOoJ&܋aE8 n^V;#s`] g#ˮrIi_&_=(27e便>jc-¯uxB&gAs->u!_i4@>"Df[/)\M"dU4'Zt`@08=NS=y!=8HH_?!\|"U"9.6-:o҃Ӫ!=^G,A2 +J9mIUaoe!XGDgS4`úR۫F\Cu Ѳ`]=etvBMD3Iis+v]{+h2!>s_" ŀg(E܊Z^E$[F" HF]>|c 2A*-B +NB %&2%fcGƫ{Ӫsur> !=XN V_KWlz/NZrZQjYW,O 8s@T@?ےjc4L<,ا6\|1*lu&OʇDW!YHa]Dg6reH튀i5$8:X>7}0)x";imppU6_Iv | R6_܆<;W׆8xi=W:O݀Z Y adDӐ+!=8$ VQ|gm9o]Lv2s2xMpΈ\E|,Y=p,228hеF`ڍ]&j~ب| EQ19Qk楺S;cEZ/8v A Dy?ؐJ6$6ǀ!jwu\-8Ep=k%=m5 =` su-1tƑ/<=Ae2xP❈4^Dv !'Jgpp::1>Cwg߾c _肏A6 >' eGCe:սc2J?PlSJ|d+ꜸqyY`r?/#@F:n;r;bc Y]~IFy}>"R 4#l'\!P ZDCV aB YJCX5=h$vAgq;o*2^Te p:T,i?}u11(I(~Od]f p8i3/)6Ս#wv=)2YHR ?B.sy4NBrmâx>&"c~`RmɟCh`̛uP 81jN8!q+Pz]޺O':2~,*diPㄋ6Yq5ڛ$pXj@,i-zC:qC-)KCe]=fL6]Ѣ㦐jBA[  C6_L"[&J\88'tɌ!F9B^g2NRW1=`|ۘVW!F1da'ZJ: _tz,k?t"X+z5|_'KcislFj'.(2˜JQ :#޸Hoծ!@Ӏ:RSKl3vC'9Z9ۺR5a _g3p5\!Ʌ>2u ۥc;'y _;)j.7wQenBV-|q~6_<ɟr*._-ƛt@i:/O!ߓv9j7"Q!K ?./ 8vӌ/\6WQ.;Bdoix_WeL!4d~GpA-B&ЬBmj¨HN )YPޱ Gsosm>n[%*."6NJ+S~9ol 0_ʮ>,|\ő?_1 [fT6_lxa6_i6_ܞ$A!=xy&WFQ~<FfǐgdQ޸26I}ʹ/oM*f @`]-w-͌wړ78_ZbfO3~Ʃc,͌,|=>#X6 3C鐟5l~}6_ْ yDzJ ʬ:M;i5s̨1bHޅ<]HMSp8H "ʼn!.|8`-ԲyuGB!_D(26 xSʹŕήn$ߍ7*nW'Otm'=m17UH-4BnB,AET$^T*"ZkGZ7Iiv5yuky=~][NX'$!9&OCƪVDba*NxhMM :GSpu*2 glُ $<XoDB4p\RbJRJ"Dحڎ\{iDpx325bCiLe_|&.oOEF8Խz˪ã]<]H(BqěѻcFRQ V@W\7!߃:s\+olpzY:\ }ݻ9]]+ɃHp;xmαw ]ԧ\*$++ .de 8!54/B. AF6_ї`iM#_[ˬڹY 4-VA:ÐN]N(;鴷 uiA(\ӸmOLkbl^oxԒrQO?uP!cW M!v' ^\,pZDT $ p;Wtۯ& ]"\o|g`y uL}何TѪV)B iL> IDAT|$qp2ysBe(x$c3һChQ$H RhWn–*w͡LNm7k$ +{R.17j){$ yXCBiD!!ˑ>cqH H[ru e@~ojC^ϯ: δDSwyn"Mƴ*MFd*}1O?᝛xbRKoKg|'wysw~?zo Q˯/0;n*V?`)M?L$5uU;UY6/A\وB.3Uej\f-`‡@smBb5*gc~ U`0[cm:vrCN۟"Wk}LxIJ6?h,䫧+}/׽a_aܗab]j\o4SJ:z:7hnZTZ^VoM*E8 DlA&sKCӪ<8G2JOҪ{⇳I͈0p"5Jٓ| a=">¢JkꩅIʡ M4#F'*(ljخ[rdWId9:DpuNݱ>Ь e]K\H; zQQ a^b]Q?oH}4aTp-ES Ejyn+2Fo ?5F$]7ۄ8ngOiUz2brZj|"N]?ZZt =<[B )?'"yN' ұ׸-"ߓI/=kKԶ_N_>cߵ]wNM Ԏ$T\T\뮕5r*T܄<JVq` /.ڒ R^d'AXWf4P=KzTTHivHayp72PU(iNشWU[rY٘Fd)$i2{ Go^Xev^{+Wu s#{ҪtxqUwb=#oC9!ujDv8Mm[M{g嫆1ƞ7! "x2YwpC_2|ۧJ`E0']p0#/.Fc mfv]kպix91(elw\*5#uITw^3Öܚ\nu/`<6&gJڮEP!Ueb]mh=3}B0#h*@o%5K)Ed@e{!r\rl8Ȯ:`6\fm6_|ҏ܂LZeG/E,2%*]ފH잌 Dٮ˓}'ck ^@J܋yݑ+?._}|M/90vO/Z4tIi&ڨ{ݮS[w|;0IrNUasl& HPA fԫUًU.s-%/ 73xϙ]vv{}}©SMKr:Ys/CZM|+}`A|:ZcȂ6ߩawѝfs^ =/_ZXgՙ+ ?"Q8ZkHdڱ j=HfTv&:9֤&/٬\qH)V!CK)>8}!Q_#UQ ۣGɟom c $ku9q?a!8yO1\}ѩgu3g5?*tA$UM9MQ}.KD68rB!"J1d ,8<14TN],~<FZC<+#7U2H=Z)䀯}l&l l-ⱺg FJLHVa ܑJgޚJg:{4JB$8t$!P¯PnV;!`FN'OCލCѡ5on Mj tqb\$ƌ#W":o62g"Ns:N~';r:~ˢ?//+YZtQR N@ڈvQwڄVž'zmG+*km= *{^X2j*İLAҡeZiL҄Jyp/Qϻ(eӐıHF{L$4 [_;0&PH͜MGQ|@yBeKv155-=>4%:MA =:D:*ۅ`OFޥ a[V(^ƶ #|5PY~ ?X8znk wMGoxJM^ u յV:/ֱ(ʁmr*!s{Ki !R@Xhhz;zJEU v<9D߷ 0zR8J%Ro>Z%H]ld*ɤYHm c u༷y~Z@{ډ,DGǂEf݋H@yw؝$TvARر잣m W#006Z9 s^3r.floDއ6"I^\.pMN'-賖ʞ \PKs:]&ߌD|^8[E-L?hrΕzpTp[kG$JGrF+4hϋb&p uV#]/7q""٬}(w~ \*WTdܭ[6EE_u;*{NDކDG,]Wr:8P P7ȎhDp/2Bҋ#(N\c# H:Ktd?o.QDU}oKQ #% ~6 : O R3iN'#;ێ*)0bY$!㜍'\σ"$!{!>E q8TF1P0KřΏF 8J>XH^W$L wGߗ-#n!?#ىO k]ҙ(jdChK|NL/]rЩ0Z dIBeAV|_BؔRt+w>?^@o?M~O702[ؘ!#DW3/^?뜾ni9͵Ȼ Zsm=NԻэVt9{s7l[$Қ^N橚9=dW$Tfg-{!dtmȄ=bdA8jE=4R99{<]8F&ȼ"8.J9hf(Ca}d-N[f*#qHKӎKYg"/Hbo{H>Ψ*dI*9Go~>'TCfNBe{uNH=ScX:Cj#N5c u8J<Ձ0fwZNVѩ%iRԛܲY3#s:Ycɯgv2fMɰZ`#{Ղ衃+sr&T`uW ^ejm!*": iZ=P`[7ɔ 59*y#A*ļHݲY<ٰB˯'(4 O>Faҿ4 oԗێ/g^x A'LǺ:D|*ΜJg.N3/A"`P*5* QU Uխ.ƥ Bk2 w&UFბl ԣN؜c-2DB)4O;@}d*# 59#¥\b`kp*IZ*;NƄʶt V\(%97h6źeQtY\ { N>᧛/6bU)бX<"$Tv2j[dB !_-Z`/p)*m\BEC)ĘRzdj f/6&VΔj> +eLJR*[Po'T|Wgkޒ3_(WOE 6slׁY2NiЩCq>y3BH&RL/2cE07 K^=dwR_d?;8Lx`?[[]kT ОJQ3iB?ɖNJ:\Xz L]0W^dK*qdP|XrisWAWuK$T"ѹ3y ur:Ҝro x#831Umh6`Be"a`V()lx}GAB"3 W:M3c*ۧ B N{+u˶7 |i/ߩcb[AbDG:Ր9H[H(|Ge1"N򝥪@ *SkӬr({~kZ0~},>8s[gP3GtVyqC7C6 Wϧ17 iW,8ǜN>jmN'k^WP7geiSN.8H^$t^ [ צo@;@ېj#T6*!tuN'?PyaNlzЯBQn ~!P{@3n7P#m($T6,;}ZAfоoм6k<Fo?87JZ ~<;1=z}O=P;|\BeGz}ʤ`U(RcE&Փ-y-@iw۟oQqr+`ٮ2wbJ*z>L`j02ٶ#)aOCR1)\Jg&7!,7yK3[jGR錓uJxpCtyLB|Ы~:t -B pom}@ @썚eQ!Si֌ޏDEyeZ)и` 'jny:ݭ M q'uM! EƊSkĠىw HZ(!!r{UfNF '@yT:쑥iebbxl{>a ֙TfwP3*Yzu9"1y:"NوG_|NdJBe#iuVW9[ִlC+9&k$?Ghx ?BjI+"l`yā:id.@ ?dz6-~،΁#9\f!;;{RL0%9C)GZ#NeWf_`5ɇPBtԂņ ꭔ3:LDy*ML[|D'DS=Bp2xw[^0KeF3}VOϦeuY,-\}5D֚ a|?!?zhuta^ᖦy[Fu PtRtr[Be?N!c}n֦줃Z-7ksN'lȦPYP&'v#s9f ҜN&T6las:ɂY7}\rOKg5J$h1/gl0(*'\})wPkR:ҙ?"+Sp{?V|Qq*#O3~0B&s}TlDtV 65ָr;*s,cke$@$t^_#Fs^rRi7Oь!zQE|2uթS̟XH r/bނy>\g~*Y0.`eZEe^`F3ϛ*Eԣ&K.9^M uNyq贃/i0SHi%9=}CmHf+c v[ ek.w|BAwt mY@1}H֫~3u 0c  yzo;[֔&Ta`9ϥ75ѷ@úBQpTL~[:Z1|s Y/L8/$ˤI`!07ҙtwunN3Bdvq| z[U?k){q TM3΢a<6DYPB:("J.66h#xu? @7>gy?&5_kVD\ {c.5>H Ş1ZĿr=LCtƍ9o۹! KT:35>k n{ӂ z}OF(vs\sAB]RBӷ&51YUvꅠ 'IUVKq~3c؉ݍ0BF#'܎S"S[e IDATG.z_4Bd7Lud}!ATb=>fLG LC5v֊OZUw{浃SJS4L7u(ƹO˒A[>Q }ɩۗEYtܐ_%T7^?|8QoNqY> 92̓ mFL_&Tnr]ZO!RĖ#OlZ>gaSO{e ۰ ɶc.Z`)?y'n.G"À5(D6x? AkVw^:ܨYu~]iGEʔFTg]l&}Yڵ9B&LFk bLM3s-z>d]Ea/tbcMuB* N*QYv82ˈ" ~%db!smwWg ӟ&5l" yrc*ySBfόO{WwWg9\~Hk^l@*ꎓU҃[B;C+ZNL|(cl`)|1,S5ނE&9A\#ΒdH+PTnx }냩t&8R#w#u8Z8NeΧR|ĸW_:eZǬ. 1rHO'G> IlVuz;^e˝Mv>+Ce:sybk@V6a)bߒ''XI t=3Lދz4΢9uV8;GF)TF'1yRk֘K:R$-RTN⮜N1ҙiYcC+Tse51<>3&G_T3q+o  ^ p}j؍:'N'~Aa#W$ #]H4,$i#HqCT0@>/fq$bgax[hi1S! 51৭ khPNdcEGtՈA|'iyML_"w}wWgO*y'B`ig^D-5gᔴrpQ+Mqxv_twu>t9ҙ(׭m4|-'(}E69=9Ey(AsyMx?+֎d^Ϗw1/C7Drŝ37 kC4?d~d5,Ħ)O9}Yw>֖z_8loEc͹Mz87i鴥4/))*m^gh1?jis:I$1["r`n\g&Cea@{Ds^lKa'5شh5GͶ(W*F(b9JgH!]jR,9K܄$UH<9썮EtO`twu Jg~{/* pS{ie}w+ cM&" B{#' fڬg{D_-:-t~8|o &]wv 7NGQh[ce}XVFcS_2:HғHz|1gf ͫz~}n1:ߋnQaT@2-yVsg!y:RL?4bPGXw^Jg{QV@{?ZJ#v:|D┓FVl1ST6&vkۗ NBĹ^Ymq⸜"gع ̍F^vVq̭.3ir(>8zG P!N`cBeRN'@skL7H} (\8+|N 7/ed}ol)V8V]ɲL{ɸ.@H;&L 7"?=v'{D-P#Z 3X>$qn#0s*%lsg05ہ`^^&5P(Z>(ҞߒPكz<3Pt=cIm5Jg~DgmXXClV%ȇAZQs#(ji{dBVhFTf,4}+pQwWtf!_N2p >˟2Cf E l+Vٷhiۦkcj*͠)s[ceiw(Uu3aiYXUZ=m-^~{wW{T:sy_7紟fƻ8\kҶx𭹟Ms]g|H*$p6ޓߴ؄+=olj [Mxb}c;N:ҍWWyO>La\ #}~v8k ˜xEny}9oEUt2k@^)#x;Ru8$|4SAg'eȼ<!1Ry>?PpdVWHe|g7}gGc6Ҧ.Q* qCr϶Ҷ}RFC'kJ&]D<ނ'2Zqho8Qiwj3UF`6c'ZhmKP9R)$0Aét|GPLu hg<JN'W'Tv6J 691I3H1|F0(cΑT33Y@+9ъDʺMG+O FH)̭@<3}۸I꽓 m } FϽlޟAj=xoI{V߅g4f i,(G!g9.j<uiXP.+%::>([vB[#gEE|~n"56Z=tO:7wwu>Jg\0`b( *$аx0poJoöL: I"e8ky 9m_ 5c}hW/.pTElgP Fx ~Į:2Rzx#/B ?F0#EL&/2o't1BkR=ɱHD£6!i+h;Q(]F.: CԒFR(2qҤCtu"p>іMn,0~f9}HT*T6F D :{Gv8L!' *:mg@yxMxx s߼S!|:6_ <geѽҙe8hO3E2K*mCNA )}1G5?eKwW:`( 7=?BCiU@8^:Cyy't9i+u,TžKzǶi/:o . L)DJCn7 xPpT-EX?̉z:Ƣ?~]oj+ޔP9Ko?~ 7ڠߝHJgRHؗ: ͗dEzf#Ao!C?sDoQb属P+()emE.okk0xDle7|VRLda#.|PY가#HA5/N3uv{6!pUhQ DZ9H^lk`bH\d@^!ы>e"t5]f 5IMjRW~@FYd !\  $pTQ P( ,l ѽ6fy[l$>`D0W Շ}' waSA@2xN5}&Fw!8gTb٬C Pih[Ik=΂ѧq3>+f,A*f$0\o:TF߃dv"H'D|^twu.M3o&A^( onvh06L?beqBiGtX>2.rlcBMv5Twl\sMG[~{R/?[Z!s8 {<ΆKBe߆@n7 ǃPԎPM/>!8d}<}miC?voR9ك2w'G"R KN' H摄ʾ7)'̹fX5LdhЪa~hW,je!Nm!: @ X 4awrS@0_y+*(ׅ<f%z.] g ]5o~X}tD"ȢE"!T:sQ*ynGRԤ&5y^BF9ށ3lUeM\v%pmv%sNJ Zu 1Ġ9_C0l4aS_ہd~dq(xYht_/4~8taqxq15QqPJçmڷdS8fzp,߾\~3n|wwWJg^Դ77w@jU@z,D"Oo@ȬN6hRc3eZԸD-R7n!Ջ_h%H3] um6閃ߵ:pzJ4ԟCnZV䷇V#9 Ў[7DXBeP Jd ihY}I~(OO/%/|d !p"YWƞ dN !bg icebC~FTTm^f]\|fQ). OY&E+|Y[E|.3m>ƳU8B ([bkSITJ >#ŞDd" ~YHA9{QHw}!8Ro5IMjR'S'.mPvծЉt yFg2$V7Y}$8 o̯FS!p^NkiK|k|͖cs[N|+0 X@׭#ٮ:QXK2LeڭC`NRY_M_~-xfOkE)\9wR[xΖ=J|0T:sT@F?~i#œCerݮP|"Сd.YAy4".u@[L-yᗬᴼ+Rv^ƶDCF xi/4:s@Y87aq󏻐@Ep\ÆɌyfD:穨y|s$ ǔCf$gQ"kc4؍ 2Gq+8 @o^ cH}C>~NeR8XȃJWm ۏ`oUeR{tpU1c2 t}+XvxZ*xEC;yq_G RWԤ&52 2.G"7vw%*A{(SauM uH[KA?ÈAkkzJHk~VD| ǜhES{g~#_0Naq: 8~ HkqaVmGU|k؝):5/RQA;WtfK8ocSˎfCOcRktJq(9hAޡ$h}:0 ptr@O4N`1*;q&Tve}M!VBe/EJo>rɜ ݈.JΜNl? ɤ_[`Y61:\;,s I8?|8 y}Yd^,Z{,z3|"V5?4#:c:W{I,}&DY'd{W?p{*pSԤ&5 U "Yr`VSUjܹ.[(a=tZ=O?D=E YwaZ zW,3pmhP-rc \JgUJgv]ۑ:oLpܤ*)<m߅donZb}MUҙsSRLKwχF( H(/^7+?%Q<ҠEiݟF$=.VtsS9W" ]PY[TCQ{%·{1]x ÀtrqIR̫^;yt­ lCHȦWpdYu">]AMKK9}R@ (KYE9>CN@ #D=$JbxS]Ԥ&5yu@PvmR٢1|cFCGco8s7kFqQO"!2z:a> &3Nz9  $;gA a6'6|l>\tHuZ` X]d|> y1о">I 渚~8S+dZt¨$cAM!CW!UH*C`wWgOϵ ~HK)z s C+~0͇Fv>Ќz/Yt]߮G6D5(4ܘi/n 7jGn#9ܴc3C'{㎌VD x|[[..$ }18.\⠻~ls9X+V ŝCqZ CĨTDY"R̭lVMjR7Q huŵ`v ;Tm;J=7;>PAG aĘuX8^HfO\|[QM~@`ǚmG P6Q^_17̬euu(3FsT|ѓ1PB[ۑdC>.]y3#t˻:޹s&rޏO9'Qy ynq5pp:G:X~M߇{Gr\bX A-G%&ׯYzTMрh߮y6`2߷fB#s~(xyld`DBڃ-7{L}P`g*k~Bg3b0}?RCiNV\=3\rr U~AFaOz#>"YQtYԤ&5%ΨNJgNDp-]ˋCH&B4eމ_3-G&Q~9RgUBރȯ5.h1[(Oq5؞GY)BJLm$FtXYz5H,4Kk} " +N3o:9T:1$q!(D-: av ;0w?uEKl|{qF-+=ƔRN(m[s>6T_?'|"~WpLsnI[;B=? t{{3D`ď"MTې8 76Ced۬~ΛLūB+{ n88g6d#w 6NKFJcҙ,׺:+զgE!k*̬M& iy;@7RWHQzDLXLuc cGm ;)L pvC}OOFڿ?2; @\ЦT;hϙ{m3y? Wr:ycBeGnVd٭sMo"5>Fg.XbPyvQĹpW#c] =a1*#4=?8 a3 ˶nkBN'w;Xojf5tKwǹٝdIz=ZbtԓUuwܝe !SyMjR9T:s Նi, u>oniA"*C7u\ie$ C B #Ј#`ol)0Ƞqv:YBGAjQ3lP;D"^=sD*84vwu.g?H*9 ɔ݋@к}Ѽҥ_X__EoCtߑb}Ց{T:s2~鲳8+~X + >vݜ*{[~ZXʹ)z1 |"so V9aM5>~矷G.sSN'w'蟊:ZRxxƏYHyGGְV, P&.kd @93dc=Gi6|\,SAA0b&;i<-7M~Uao${2wt#R_@=tfwŎ5IMjR}s`(kU/bH^Xpp[Ge+_ak&:k AoYD#,ۇXGp`6FVC]#T$(ϲ̘e{G1˛h،ǐ̖Jg,/ҧ/=twW?:K޻*35sOw.8f9^CW=dN|Hy=KԢ];YVGmwm}7\̃;@>(Tn n.Xw\*L5 =6Hđ8%繂Efa|>* fhF * +*[Mf(b֞1c'I3 o3,4y,p*:"Y(] ؆&XB#uÈ32dl\|0R1|!L|F nJh!g𡈖2;6h3K.#?XVfkY2#FHq]1{zG!kw- K֋XG= :K߽//Nb}՟ 0; ? r;B}҇_޾S dN\o܍Ap5*#J)my${{2_wKl85Y>Ӟ%:~`؈ey8NLJ\BGr:Yd)c߭U+btwu~h m PeBeH#JgCZeYh="hSޯhaMǑ,أ>,[w ݂8W!M(E@?C(`DƝ,KexRG pݥ&q vWv2~U峑=|DAߎD-6פ&5IMtwuޚJgV!4aLQ~5BB-T:; X^Ȝg t"H1J#4薻uG>B,L1N3,cYUPYcS/ҽj:>F^#0K-Scm[#f+G=_㭓~7_[0%o>4iMřA]>E)+y9b6$^pcN'*C/tr(#u_U~A!yy q }mN'o \z6`ZN'P7$!4o^.2UcC Bës:yɈ]Pa"E$T6'"#9\ji:ncCH-v#RĹ8p=7c/EgN?s<":-T Gldqlc½r&NLi¡+6֩xGUO&DL:U٫ |o ~od1\ע]ϯQCk:g#/Dƶ!_Ԥ&5_$ː:0b45#6S~F+l ̱]FH}F9n9CHķ' Ύ2×g&qf.[*xͯqBYʼ: XϬfc}ϤOԙ8?pj݉t[$ZCF-WmE~__oSpt؊ج|N\glZԅۀ%U>@֎UKشiM?cљ E<,>igr|`߀w81ﰃYو>`trCBe!tKHC+r:y]BeK_*Be ]nu7Dw\eϙl6=- U4Ep2vؽ׊^̵(:UbPR6ə $lxޯ׾vwisyksu {c&Em ˝Dg_+N+Eif GD!yד@-H<}^4[dU0)ckSk{kYJm8 $:}&<ܽ&r&E` 3PzlOn@zd'l9-߆s/jޙ"I^0Nfw>rEQDE_ք(̽|x<Ϟ\E Èg]qi遠FZ׀8W#3x{&g 6(" $lHi}ݟ*q {Piwuu8GLwCGЀ kuߥjՐ \,'Ķ-(W02_ c@ݨV͘ |yfOᶖѶc`ͿgK{jےw\cŧZ#1hFѵvi~?pu)Jx8B.}0nq "\-&&(j䢱' A%B#^,|j=Kj{聽ni;zB)Jta)JG]#hb jVd TЌ-mov>cǮ쬏ԏϊ](gnQe~mckYzuK{5f+- vP[ևO뷵=W[{Ҿ1F\+⾅*H-B-ș} w9OS\a΍⴫M.1n0Qa؛E漩z&_uCLÇ]Ѻr[Ćd{e_em:.AV^q4Z +oxV#W^[: x`QNTXL9̟Qs]T;Ս]^_"Nl}+qrə'gUw9]{_4C(EtOț j(U7$#Ab豆( }Ԁcwkokp[?buĥaNK "a0<}Sk( &9v=.ۧ`"/OQ.g?>UƂ |,N}i^3S(O irE(/EVL"t$CZqDEHX7rmA&,%z1:5H1 Dg]ip}ts5ic(B͸tgfpEFcTи8}} :ai'nZ,./2}lV9 ۻL=xc Ɛ@9mPze gBm lmԕֹQ]Z e*EL(rg\MđjE鑨ғx|tMCD-Rtξ$.#!K&WY=ڰunuT cI< [ҷ474&^:DP&JI!2iA0kE=(}4CKtFQ}B؝ЌOkqx:v[>4Bƀ*r_W' Y6[8l)ȶKO%\Mf~1'2+w<\߂RކnF "1 XFEc,gA ,hNr +`J9E棙DFf4c>Uߴщh8b?ssb !GCQ T+Qwǜs[3w]f*[FeGa 'dG,."sG Xf?AS1f<4`xSO`&Q[M22հo7L-轷?Y}"vP6ne>"]? "Z珕Gפ'jV0n# r3'[G3##{U}$k0ch}Z>GOpy([ݭ<L#aٴ'>ڭ($xW!3޺xB.ӗ?Rct}UagW6j]~9;,E퇠VȮo ݟcNA{{ouM8ׅ YH6`}*qJTK}rCj.J#E."`($Vɿ>ndǰ8؝$|  4["4#/Z1hQ a%8:VgnSemZҷtעzϡkP6kV{^m ,,då(}?_tN`t 5xWSXquǴȡp?)Ea@9ΏF}kDblc2 xɞ `gV ߴq]>F\e(E^m[`ӳ[O4Rmz)B(8:ʋ({[)JM\l. GKQ- xs)J/kmT+$=М}*Of22un7ZXݸ9OHҁ>G2;)Mc<5x.CThtC21&eR@!X}# koK]#=KZ5maNg`9~oeuH9(&2NDvE!V%,HOK-klI2=iG 0[Yӥ&`^O'*jUoFQ*U\f/x<DPeVfŏ"Qsh#Rά #\߈m}/$|hV*ԍfH\Mڇai9cрwE.AN_lLB|}GyFc=hevߝs|/l@YOwv YSO~w<EoZpξG:07)?ޓln8ff׆+AV_csKgR]JQ待N9sus5W P [<]_}x@cCgu!u7hX)Je߇];>|lC*5vc 710AYlc6:.<^z 2sXe3cx-3]Mf=/!DUYZ IDATgvYue!@!"/ 2I4[&0!̓I4m}G-$PXIF4 ο]){loېjTjP.@!lC>&r*,/5p ="6U0T@,DQq4A!ru)駐ߝ{P$\avMH QEqPv+7g q_qZPk!KT-׀x Xދ ŕl@9'[bzvb҈D|-S6tӾ|@;$ҟr%5-sߘh ϱ\:SHLUI# =s[~V7GF>CPF1w4zS Vb%皾}6IlMRX=M;jĽGZ;v>VA3췠Y)(b0B﯀3ML^hU밯O?B3^5L:ĦYjG <hn {-N][;>el7](3 :oRelxQTX4iwjqtώf.= #É;tnMn2t-2וt{܏*"Bs}O`stF.} E%= ÙPtw!p;݆+FL(ݶ :-7?}mZ DD n[ҮONȿ M\4a4~mlV799 }hr?`gKNrlx (3V@)6=2d/|[9[6QCIѰFZL%\ۻ^dPjUh2Qѡ⠾Ioŷ,ZWԴt^|T xtXjp](Jj=/hS=jn nϥMA)Y{0w\ά"Ӊ#QH8Eu[Ľ'Rvah֩Ox6Qe0jxq\.R Z:EaA8'?iG({~;jg{{_2݃J})J? \^tN@w"$B4ÈmCo>j߇^m1CZ u:'C܉@p #/I^rۿكXЍhfw0~ߏ]SLnD5D[Ь;PfDzQf;Q P=v}!{SM\Z\A>' Q$i*T/"pE9@\B)WS)\-g+P$\`Z=/[e&R,\d3|y+:!ÔW&k3ygn {cY26tdHDg4L\ T3q2Q9x%py)VLnk~v-}]_u+ ?(a4^t7hؼRNzR:3LOm_N)J_eBʗ/SMFR#$(>}?lZtNM5Bbr': 9W*߶BE٤0xTܺ9fCKKq 7.R*3565ͳ.2_0I>8Y Y}܂kfMJn۟k2l}:L'rSa4䁰1YA}Ç_sO3ρӐ虋Rn]HKl╨uxlev?FFt/֐H64`*huVPbhPR63 e QQҀI7t^Ft/2mq&u>|_N:qGG>c fgRa{-0pX1  h 6ޱ)C/^e,E2;,-9 m.H:8gRs=taWc])2K ' xXV;4Ay,ز冻7*2@&(-l'F޹HtrMgx1L< ULT0DQT}q(q)v16{!hd_Ebs kv+H688Z]kkTj5`M'RFQK8T#LA.˸ "oۅ\Re.-2AyComKX53k.zQ7-;v+\|'xcvCS+q;cOd)EX'f #ljUZāG] d1x*Ayo!V ۴)"\߷#w 7;J~[P/.Et/E7-[ix<%/Bi7!E#3"ѱ lDc E(Ů E{VAνH탾[FO!4x0Bѫ?@JF^dƄYpV/Z $\GQ{{(6'bc6'$ڿ'M@E$,,.~l"tEaAϝof-cU9 c_ZPͥ(=~\Yiȸ⋥(-s4J\>;ݷɋֶMCkd6 QPC௥(=!.]o v:x>>XyN$@bcRΙr]L`{SH,5Q40jڈFa(nrsL?~p%M au,q&}R~QA`uNCrc͌Ai|B.j[,.^/2}vtӉ䏨@]6͛oj"rۚ؈ڟ|]oGPW-~ 2s޲v{ H=ݻ&I@u@>lY0;[]nXy"(E%]ܴW¦k0D"]&0HMҗ,N?'@!UK~'vtyf$|%6RcH5Pэ0=.[Pj0Pر>f]j 4naTD;P_ Ehh}fKQ(vǮo):ɨ˙ KH("Ro&CJaR8Ȥ9-C]o&N LscÐ!)(7D!aBKgM&n섘> ]ҿ~e_G-K} J)JZ j4\Au&8pLwjee6&۫Q@M_.vF)J~ gS|Pz<N6_MGW3|6Pz \ wHb2+hC`c(J[@MhOv0rTр*b.h>Ewn[cĶ틐ru4"_ʃ3:v*{6];& jHwا{mj7&SJQzP(w^I+u/ׂ"sKaz_{PH$!`ޙiw FFNͿw;8rlAXr7Fej/u{|@5sL+'ODs&EpgQ_Lz}4tNTٸ]3sGvß ~ўf㎱ޅ5u@ڠA~h?ִ~XuRk@SpNgw Zz"z_HGj}:~{FmR2Is7g+AWu-  v"$6Mmʭ*ޠɑ]FT3 [jO ȕY&nf#q5!`pKgŝOx<dzU)Ģg2:͂ cQ}~vpC[6&]+Զd=rB 1HM'r BPO #gt(Up5W+ ʨ8B8ibՅC휅Kev2M"h >B."G6~i>,E]{Ί2t&3=i˯ P8LVGٵي| Er(É='e8 N} "18}Klxg)J9Q#XxJ!|,!g#1&$Zz<7`}#Ibj@ѮQT~?p98yqhd ꃨj3p¨EWfپ4 gjj=FHXmk亾(:0 d?̳c2g`I)Jul+6t;lxH6_xAtom}ΓDE)4h} vPtj)q6w4H(aw}ބU}.bsb] >c)?.͜@s Ͳ(J!x29 E Jxv6Y=\>1sn1O'17EINMfl}rX :q(hQ5.JTGJmkhJQ]41"gL҇7]y(=|Ӆ}082 e7x<gga#P\eE#n2$z A!y z TG*wv#j2:$uRF}U4xr.~$P}v5׾X mW{]n;n;7$PBaj3>pk-M JE`B"jվdл5|9^5ouzQҍ qn20tF)Us&|b9@_4<!q6$DzD)<S|~W'=}40|F!ѴJ a5d4i܆fGQ-U!1؂D*$ZPޱ0 z zBh7ˮ'$i~Pi9NdztT?.E -k~dek}3օgIku-m6TE)JQ-,ck@l@)Gf X QdXNWn@m'g"՚4q!zdrpfm,5B.sP!mU=ٙX PJiM }cGk~}ǪQ߲hF&$|g":P1љ(Z vTՂZèv1V%] \*}Ej6, U;ih쬝L8 `~q"QtJ շk+4Bry\^Rv*%llTkj_>X>mEv O|U>o;>l +Z!'x&B.sy6_8~ps|Ȃ}*]@B}Wt;bZGYTf!r$Po~|̾E@iv}`U?.j@_{HPME)b5MAoٵ,GY2QkKQz=r 0. mQp̌J*w77{Fle`xX٘L}ZK7%Zk=0Q䵁x<C6_lBH-~ڴ}qNܻ\i{J|' |y2PĽmgrzQGajRՁ3j?>OMhdRkDF P*KG6 LBQyrSvix3dųPƋO4Fecx_MO4#J0`'B̻m.çz<ّR;1CZPTCu/%{R5dPQϽ`\d@"d_; YDnEMp"Ť`rPd@5$7v6r+>H9;wꖯ"khdlش+cfhϴfӑZP`ﱍix`? ǂ;'I|d2hC,#hKx<ώVQkBuX_A5Q.:u}(OucrQ}\ٓ2]҃zG<hrh(jt8n݇rB.s3l.B.}8X곾 n-H<="^7U,Ep4G#Ԁ\ ]ͥHLu mǂ]n B.;m;B.aPd|q2 9 Aّʆ,x<; x \e:ۜ;`n xr rlxMOE}RWW{cҾ*JEq]=$z'L1$NF+@iJkEQTO~\Ye3@㮨/xvwx<dzېsB.su)< ^,Bi~ (uo S M܌U+E5(Ո"Qe${.jן _OC"oxӈ?Ye.9gi拧 Q=<ࣨG\f65%Å\桝Ϟx<g<^e#3J;h!5pr;^ <ҶI:)ǐPjEu`U EŦ^:h4WCk6n̰WVa6_ܝY7!ŅA18*/نCuhdzg#Xxv[(RuˬQ V] SoeGkQJ܉(blE@PQ+B)3ziHW.4B)ReQbGCѶQ`!yk6_L~Pe.Ux x<3yj$~(5 ԂQu9B:(**T72D\x0`.2݋R^PerYwlBomeny]jޏ+mتx(vеx<ϖ w}2$F/T2 Pq$ހ٘]= S2HW,PdlDv8ǚ|Lw-c[>_ 'r< в+t_P*feO[ 3^`y<m)2C;P-m旅xDh ED:SCѯ" סqQ.f$ކ7SDu=\ weħXy5WnF..z`Y=&EVcH!C5gg|x<ݚB.3`j6_| v 40{HLVHB#H݄Rϵۙ"?3 dPd }̍HD9i\f)E-[eEW5J~izuMTMQSD8 $:9;/<IY z 6/C6{#{IMb׈"^DQ $D\}LUڐXlUQZzd};,l^fmFzQ]K&߾s~~>2D0q秳=v^_Xx$ g a ;E밺PeJ qW Sv]ͨ+ o#Ԁ>5AdֹۮHL> s˟{dSOɓQsȥ<6 סoHxx<g=|q1B0Y߉UQ O}~}})PŮcbsn# O8< u@5c0v!^}_ѱ1r'rS1㄁_7b4v3xoGz/y"_x<g$/NFD{ D){Q(J)i / fS!gPV+v |]fٴ7r/CVdFh_;>&j=Z$x1:M(0@ѺQ t\e}<z<c)2}(j`6_p Pc^rY<0riqkZ(e0;ѕi;6 Hn$8/E ɴC44=8x=5v?:1$nDy^S=f\d;"s^zW<r Df h J QDV$h0kPf yWEr(։j9dlx2[P" '{Cj~G :/_>!'Wh`0uWlM| hAYi9Wda S]RbK2\Ȃ=@iS.Aef0B.e$ ˁ<v;;mY;3z~]ΑmP@K6_,X+^`y1-!N4XhM{>Z+"G*r<yee}>nziifA!qtQ.fp,#hnL P Q4`hz{bNWUw=jU9g:Y:O~}%jUFe4 Ek,j:Dh)VM]^߹('Og f{ 4lfffBh jE-o='ϣ=[kPh6X99o,\ۉ*=uTAn#pjK| d~\w6]vw32sXoQi>\ XB|7ZWgМZ?{}[Ɨ ރefff{Zݪ3?_MQun }~pݖ7v?ͪ h3h^%6%4.44-~xӵhl0w7tۗQh_&M$/s nxt.Sn 3ltn ݯC¥ho2zQk@#>l|~o i 64b)`:IV,Dh/5Ψ18XQ `amPe(4vkMhXhOX͖IP<,Qf6+8`٬dJԾ(\IV\ $hN-D՛h6Kq}ĢG6M桼<!Tnxhm($xEcQۜr5AkfeT#&^$| F˒GQDj,QȪZyOͦ[lVA6 ~F/E{GG ?Bթܧl={QU 7& IV,F/5 1ƒFkg=Zkl'#}:lt.ZwU<MRPT[s^?}>hbmp2338<^Z>*3WnBߍ %~$>H5|8\Ӄּi ָcNs4]t"hfffFKЇk4 .F!2|vU&jI<`bLd24czP`8x9 lhNOG!j+ 0U?]@<7E26.՚٬r$+nD#٫u{g?C{C^@-_@:\UUa3VDh1N5hДnھwCOsE5M~Ef-fff6+i6O㳀Uc:P_?p%j[0c9IDATDJ(Kjel.+${#P{((C^ޣq`=ZN9?<(zfa3Om~9ff/MI[QFj VlZZ٠16xc$F+OaTŻ *ZPsUPej!Z tW;LM[\ \ ~MJ)Y4,3333 Ɋh?ލQ=:UpbܧY06ĽCO?C:|_f[J"B-6NEPGi;:TD7zx1QU4|c6mLDAn4a=Fpߓ(h ׷DQ{7< 323-%YqZ'PH 8UJ\/j\ރnZX_=y Y'YpxClA3333^3ոq(xQC:*9okwn|Lp<2:X[t=Xo {z?JH~]&iefffiӁP*V{XuǴ?Ɋy+-IVt=fm(0u5#l,K6yZsGP% Bwi-lffffS-OP` $1jx/RN4UV$Y$+^2IVԁBa]<Q: m;6Կ\s*]ՔqVS4iefffߝ Cv^ Q n3ОdEIV$YŔdrQP/Dk ,uջo^2?j\@JC- ҩZtq2333%yCޏ^| +nG:p&:j:7k$+:4G{]r:@;UZ'FwG4EhGkC2QGKbYv333_!O$+BnFړ5\ʇр^tx<ʵLk9Q(ǡjBy,z栃Be@Df{$,333_#OG!ÈEPx.Y(TW>wy^\aJ lVHd *Kj VvRx;& l,jwT-l:De3&#Ɋし*m(8v1*en!߃[lAռW/@큣h/|6Ɋdef3[&*44wIU%B)ݺ\uT<$Yh B*Qh<R/" xKۀGM @A4Pu֭lfr2333ƕM^RR%Yipp/ F.:f}j@m+PKh]4[A!o+s]L=Xffff-dŃh_VDMڣ4P?&ɊQ`MJ<:94 q1j-Ag}UBPxZZQ+WD~#Oӧb]f,333CtU; R/FAcڛl_ myIVD'WcUExC ?9cA5u`>M:{,>~P B{7.@m|_DehD*4r}-xwɎ*3hǍ[ Yi|ooC4Aa+BC! 1ꓕd IVdyKw#:PxS8ӫ@aT:f0|[} U V55h~,G<|lefffByL(:h$+btߠ0R`듬hQpMbݕP88{>!>v~|0j8}vGk {@m@UMW#gG 333)dEzR`# 4)U7M=B/@-G-}Bᒛ뫣hn5َ:9HA$+}C0>K xF5t@.s T%\>:uC7vTa:UDU#hOwN-F*F{6P2t)p(8Ƅ9ε4V7pe[f#ѐy74^Z @o`eb&4|\Gt+} 8 N,.Ԋ8{U h*?U-vE;6_.O_7l&s2e4.Ij zlNCc;/ _'Q+Q{VTztjzQ T:{@S(hU Wa(ݗq5lV 333H"\CSBWB( :lhQHGsf:i ~Mwzf,333LGU: Running optimization with exaggeration=12.00, lr=108843.92 for 250 iterations... Iteration 50, KL divergence 8.9036, 50 iterations in 43.6444 sec Iteration 100, KL divergence 8.1739, 50 iterations in 45.7009 sec Iteration 150, KL divergence 7.9832, 50 iterations in 45.4050 sec Iteration 200, KL divergence 7.8977, 50 iterations in 43.9690 sec Iteration 250, KL divergence 7.8511, 50 iterations in 44.4052 sec --> Time elapsed: 223.13 seconds ===> Running optimization with exaggeration=1.00, lr=108843.92 for 500 iterations... Iteration 50, KL divergence 6.4946, 50 iterations in 43.9199 sec Iteration 100, KL divergence 5.9617, 50 iterations in 43.6204 sec Iteration 150, KL divergence 5.6756, 50 iterations in 44.2530 sec Iteration 200, KL divergence 5.4932, 50 iterations in 45.1531 sec Iteration 250, KL divergence 5.3658, 50 iterations in 47.1845 sec Iteration 300, KL divergence 5.2714, 50 iterations in 47.4659 sec Iteration 350, KL divergence 5.1981, 50 iterations in 49.2679 sec Iteration 400, KL divergence 5.1394, 50 iterations in 49.6450 sec Iteration 450, KL divergence 5.0913, 50 iterations in 51.5995 sec Iteration 500, KL divergence 5.0511, 50 iterations in 53.0170 sec --> Time elapsed: 475.13 seconds CPU times: user 3h 21min 43s, sys: 5min 53s, total: 3h 27min 37s Wall time: 11min 41s .. code:: ipython3 plot(embedding_standard, y) .. image:: output_14_0.png This doesn’t look too great. The cluster separation is quite poor and the visualization is visually not very appealing. Using larger exaggeration ------------------------- Exaggeration can be used in order to get better separation between clusters. Let’s see if that helps. .. code:: ipython3 %%time embedding_exag = openTSNE.TSNE( exaggeration=4, n_jobs=32, verbose=True, ).fit(affinities=aff50, initialization=init) .. parsed-literal:: -------------------------------------------------------------------------------- TSNE(exaggeration=4, n_jobs=32, verbose=True) -------------------------------------------------------------------------------- ===> Running optimization with exaggeration=12.00, lr=108843.92 for 250 iterations... Iteration 50, KL divergence 8.9036, 50 iterations in 41.3583 sec Iteration 100, KL divergence 8.1739, 50 iterations in 44.0357 sec Iteration 150, KL divergence 7.9831, 50 iterations in 44.8030 sec Iteration 200, KL divergence 7.8978, 50 iterations in 44.5963 sec Iteration 250, KL divergence 7.8511, 50 iterations in 44.0719 sec --> Time elapsed: 218.87 seconds ===> Running optimization with exaggeration=4.00, lr=108843.92 for 500 iterations... Iteration 50, KL divergence 7.0117, 50 iterations in 44.1787 sec Iteration 100, KL divergence 6.8478, 50 iterations in 44.2544 sec Iteration 150, KL divergence 6.7850, 50 iterations in 43.0467 sec Iteration 200, KL divergence 6.7506, 50 iterations in 43.1292 sec Iteration 250, KL divergence 6.7289, 50 iterations in 42.3653 sec Iteration 300, KL divergence 6.7142, 50 iterations in 43.3017 sec Iteration 350, KL divergence 6.7036, 50 iterations in 43.1021 sec Iteration 400, KL divergence 6.6955, 50 iterations in 42.4524 sec Iteration 450, KL divergence 6.6884, 50 iterations in 42.3116 sec Iteration 500, KL divergence 6.6812, 50 iterations in 42.7694 sec --> Time elapsed: 430.92 seconds CPU times: user 3h 23min 13s, sys: 5min 47s, total: 3h 29min Wall time: 10min 53s .. code:: ipython3 plot(embedding_exag, y) .. image:: output_18_0.png The separation has improved quite a bit, but many clusters are still intertwined with others. Using a larger perplexity ------------------------- .. code:: ipython3 %%time embedding_aff500 = openTSNE.TSNE( n_jobs=32, verbose=True, ).fit(affinities=aff500, initialization=init) .. parsed-literal:: -------------------------------------------------------------------------------- TSNE(n_jobs=32, verbose=True) -------------------------------------------------------------------------------- ===> Running optimization with exaggeration=12.00, lr=108843.92 for 250 iterations... Iteration 50, KL divergence 6.6121, 50 iterations in 155.4301 sec Iteration 100, KL divergence 6.0752, 50 iterations in 155.6532 sec Iteration 150, KL divergence 5.9787, 50 iterations in 155.2036 sec Iteration 200, KL divergence 5.9415, 50 iterations in 158.4592 sec Iteration 250, KL divergence 5.9224, 50 iterations in 164.1987 sec --> Time elapsed: 788.95 seconds ===> Running optimization with exaggeration=1.00, lr=108843.92 for 500 iterations... Iteration 50, KL divergence 4.4697, 50 iterations in 156.9712 sec Iteration 100, KL divergence 4.0495, 50 iterations in 157.9296 sec Iteration 150, KL divergence 3.8464, 50 iterations in 168.0550 sec Iteration 200, KL divergence 3.7248, 50 iterations in 166.4940 sec Iteration 250, KL divergence 3.6438, 50 iterations in 166.7832 sec Iteration 300, KL divergence 3.5860, 50 iterations in 174.2202 sec Iteration 350, KL divergence 3.5434, 50 iterations in 172.8181 sec Iteration 400, KL divergence 3.5106, 50 iterations in 167.7604 sec Iteration 450, KL divergence 3.4848, 50 iterations in 163.7755 sec Iteration 500, KL divergence 3.4639, 50 iterations in 169.2613 sec --> Time elapsed: 1664.07 seconds CPU times: user 19h 11min 10s, sys: 6min 32s, total: 19h 17min 43s Wall time: 41min 18s .. code:: ipython3 plot(embedding_aff500, y) .. image:: output_22_0.png … with higher exaggeration -------------------------- .. code:: ipython3 %%time embedding_aff500_exag4 = openTSNE.TSNE( exaggeration=4, n_jobs=32, verbose=True, ).fit(affinities=aff500, initialization=init) .. parsed-literal:: -------------------------------------------------------------------------------- TSNE(exaggeration=4, n_jobs=32, verbose=True) -------------------------------------------------------------------------------- ===> Running optimization with exaggeration=12.00, lr=108843.92 for 250 iterations... Iteration 50, KL divergence 6.6121, 50 iterations in 165.1051 sec Iteration 100, KL divergence 6.0752, 50 iterations in 170.2804 sec Iteration 150, KL divergence 5.9787, 50 iterations in 167.2433 sec Iteration 200, KL divergence 5.9415, 50 iterations in 167.1109 sec Iteration 250, KL divergence 5.9224, 50 iterations in 166.6234 sec --> Time elapsed: 836.37 seconds ===> Running optimization with exaggeration=4.00, lr=108843.92 for 500 iterations... Iteration 50, KL divergence 5.0955, 50 iterations in 165.1969 sec Iteration 100, KL divergence 4.9934, 50 iterations in 167.7396 sec Iteration 150, KL divergence 4.9625, 50 iterations in 166.0314 sec Iteration 200, KL divergence 4.9504, 50 iterations in 165.1204 sec Iteration 250, KL divergence 4.9438, 50 iterations in 164.4031 sec Iteration 300, KL divergence 4.9396, 50 iterations in 165.8241 sec Iteration 350, KL divergence 4.9365, 50 iterations in 164.0402 sec Iteration 400, KL divergence 4.9342, 50 iterations in 163.1385 sec Iteration 450, KL divergence 4.9322, 50 iterations in 162.8973 sec Iteration 500, KL divergence 4.9307, 50 iterations in 163.9869 sec --> Time elapsed: 1648.38 seconds CPU times: user 19h 55min 34s, sys: 6min 25s, total: 20h 1min 59s Wall time: 41min 57s .. code:: ipython3 plot(embedding_aff500_exag4, y) .. image:: output_25_0.png Initialize via downsampling --------------------------- We now perform the sample-transform trick we described above. Create train/test split ~~~~~~~~~~~~~~~~~~~~~~~ .. code:: ipython3 np.random.seed(0) .. code:: ipython3 indices = np.random.permutation(list(range(x.shape[0]))) reverse = np.argsort(indices) x_sample, x_rest = x[indices[:25000]], x[indices[25000:]] y_sample, y_rest = y[indices[:25000]], y[indices[25000:]] Create sample embedding ~~~~~~~~~~~~~~~~~~~~~~~ .. code:: ipython3 %%time sample_affinities = openTSNE.affinity.PerplexityBasedNN( x_sample, perplexity=500, n_jobs=32, random_state=0, verbose=True, ) .. parsed-literal:: ===> Finding 1500 nearest neighbors using Annoy approximate search using euclidean distance... --> Time elapsed: 14.00 seconds ===> Calculating affinity matrix... --> Time elapsed: 5.66 seconds CPU times: user 4min 17s, sys: 3.09 s, total: 4min 20s Wall time: 19.7 s .. code:: ipython3 %time sample_init = openTSNE.initialization.pca(x_sample, random_state=42) .. parsed-literal:: CPU times: user 1.66 s, sys: 96 ms, total: 1.76 s Wall time: 86.1 ms .. code:: ipython3 %time sample_embedding = openTSNE.TSNE(n_jobs=32, verbose=True).fit(affinities=sample_affinities, initialization=sample_init) .. parsed-literal:: -------------------------------------------------------------------------------- TSNE(n_jobs=32, verbose=True) -------------------------------------------------------------------------------- ===> Running optimization with exaggeration=12.00, lr=2083.33 for 250 iterations... Iteration 50, KL divergence 3.2514, 50 iterations in 2.8158 sec Iteration 100, KL divergence 3.0818, 50 iterations in 2.8074 sec Iteration 150, KL divergence 3.0695, 50 iterations in 2.8865 sec Iteration 200, KL divergence 3.0668, 50 iterations in 2.7726 sec Iteration 250, KL divergence 3.0662, 50 iterations in 2.6979 sec --> Time elapsed: 13.98 seconds ===> Running optimization with exaggeration=1.00, lr=2083.33 for 500 iterations... Iteration 50, KL divergence 1.4430, 50 iterations in 2.8882 sec Iteration 100, KL divergence 1.2700, 50 iterations in 2.7344 sec Iteration 150, KL divergence 1.2087, 50 iterations in 2.7087 sec Iteration 200, KL divergence 1.1795, 50 iterations in 2.8707 sec Iteration 250, KL divergence 1.1639, 50 iterations in 2.9316 sec Iteration 300, KL divergence 1.1553, 50 iterations in 3.0808 sec Iteration 350, KL divergence 1.1490, 50 iterations in 3.0691 sec Iteration 400, KL divergence 1.1456, 50 iterations in 3.2036 sec Iteration 450, KL divergence 1.1433, 50 iterations in 3.2834 sec Iteration 500, KL divergence 1.1413, 50 iterations in 3.2604 sec --> Time elapsed: 30.04 seconds CPU times: user 22min 52s, sys: 35.3 s, total: 23min 27s Wall time: 44.5 s .. code:: ipython3 plot(sample_embedding, y[indices[:25000]], alpha=0.5) .. image:: output_34_0.png Learn the full embedding ~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: ipython3 %time rest_init = sample_embedding.prepare_partial(x_rest, k=1, perplexity=1/3) .. parsed-literal:: ===> Finding 1 nearest neighbors in existing embedding using Annoy approximate search... --> Time elapsed: 253.76 seconds ===> Calculating affinity matrix... --> Time elapsed: 0.93 seconds CPU times: user 6min 35s, sys: 41.5 s, total: 7min 16s Wall time: 4min 14s .. code:: ipython3 init_full = np.vstack((sample_embedding, rest_init))[reverse] .. code:: ipython3 plot(init_full, y) .. image:: output_38_0.png .. code:: ipython3 init_full = init_full / (np.std(init_full[:, 0]) * 10000) np.std(init_full, axis=0) .. parsed-literal:: array([1.00000000e-04, 1.15557926e-04]) .. code:: ipython3 embedding = openTSNE.TSNEEmbedding( init_full, aff50, n_jobs=32, verbose=True, random_state=42, ) .. code:: ipython3 %time embedding1 = embedding.optimize(n_iter=500, exaggeration=12, momentum=0.5) .. parsed-literal:: ===> Running optimization with exaggeration=12.00, lr=108843.92 for 500 iterations... Iteration 50, KL divergence 8.6100, 50 iterations in 43.9514 sec Iteration 100, KL divergence 8.0667, 50 iterations in 46.7819 sec Iteration 150, KL divergence 7.9223, 50 iterations in 45.6121 sec Iteration 200, KL divergence 7.8557, 50 iterations in 45.4719 sec Iteration 250, KL divergence 7.8177, 50 iterations in 45.1488 sec Iteration 300, KL divergence 7.7932, 50 iterations in 45.0411 sec Iteration 350, KL divergence 7.7764, 50 iterations in 44.9336 sec Iteration 400, KL divergence 7.7640, 50 iterations in 44.5941 sec Iteration 450, KL divergence 7.7548, 50 iterations in 44.5967 sec Iteration 500, KL divergence 7.7478, 50 iterations in 44.8961 sec --> Time elapsed: 451.03 seconds CPU times: user 2h 21min 31s, sys: 3min 51s, total: 2h 25min 23s Wall time: 7min 33s .. code:: ipython3 plot(embedding1, y) .. image:: output_42_0.png .. code:: ipython3 %time embedding2 = embedding1.optimize(n_iter=250, exaggeration=4, momentum=0.8) .. parsed-literal:: ===> Running optimization with exaggeration=4.00, lr=108843.92 for 250 iterations... Iteration 50, KL divergence 6.9774, 50 iterations in 45.0601 sec Iteration 100, KL divergence 6.8239, 50 iterations in 44.5858 sec Iteration 150, KL divergence 6.7657, 50 iterations in 43.4053 sec Iteration 200, KL divergence 6.7341, 50 iterations in 43.8213 sec Iteration 250, KL divergence 6.7131, 50 iterations in 43.4168 sec --> Time elapsed: 220.29 seconds CPU times: user 1h 10min 33s, sys: 1min 56s, total: 1h 12min 30s Wall time: 3min 42s .. code:: ipython3 plot(embedding2, y) .. image:: output_44_0.png .. code:: ipython3 %time embedding3 = embedding2.optimize(n_iter=250, exaggeration=4, momentum=0.8) .. parsed-literal:: ===> Running optimization with exaggeration=4.00, lr=108843.92 for 250 iterations... Iteration 50, KL divergence 6.6988, 50 iterations in 42.4042 sec Iteration 100, KL divergence 6.6849, 50 iterations in 41.8349 sec Iteration 150, KL divergence 6.6753, 50 iterations in 42.8223 sec Iteration 200, KL divergence 6.6687, 50 iterations in 41.5115 sec Iteration 250, KL divergence 6.6634, 50 iterations in 41.6096 sec --> Time elapsed: 210.19 seconds CPU times: user 1h 6min 54s, sys: 1min 54s, total: 1h 8min 49s Wall time: 3min 32s .. code:: ipython3 plot(embedding3, y) .. image:: output_46_0.png .. code:: ipython3 %time embedding4 = embedding3.optimize(n_iter=250, exaggeration=4, momentum=0.8) .. parsed-literal:: ===> Running optimization with exaggeration=4.00, lr=108843.92 for 250 iterations... Iteration 50, KL divergence 6.6576, 50 iterations in 41.6053 sec Iteration 100, KL divergence 6.6519, 50 iterations in 41.5500 sec Iteration 150, KL divergence 6.6474, 50 iterations in 41.7626 sec Iteration 200, KL divergence 6.6439, 50 iterations in 42.2903 sec Iteration 250, KL divergence 6.6410, 50 iterations in 41.6484 sec --> Time elapsed: 208.86 seconds CPU times: user 1h 7min 27s, sys: 1min 55s, total: 1h 9min 23s Wall time: 3min 30s .. code:: ipython3 plot(embedding4, y) .. image:: output_48_0.png Comparison to UMAP ------------------ .. code:: ipython3 from umap import UMAP .. code:: ipython3 umap = UMAP(n_neighbors=15, min_dist=0.1, random_state=1) .. code:: ipython3 %time embedding_umap = umap.fit_transform(x) .. parsed-literal:: /home/ppolicar/local/miniconda3/envs/tsne/lib/python3.7/site-packages/numba/typed_passes.py:293: NumbaPerformanceWarning: The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible. To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help. File "../../../local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/rp_tree.py", line 135: @numba.njit(fastmath=True, nogil=True, parallel=True) def euclidean_random_projection_split(data, indices, rng_state): ^ state.func_ir.loc)) /home/ppolicar/local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/nndescent.py:92: NumbaPerformanceWarning: The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible. To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help. File "../../../local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/utils.py", line 409: @numba.njit(parallel=True) def build_candidates(current_graph, n_vertices, n_neighbors, max_candidates, rng_state): ^ current_graph, n_vertices, n_neighbors, max_candidates, rng_state /home/ppolicar/local/miniconda3/envs/tsne/lib/python3.7/site-packages/numba/typed_passes.py:293: NumbaPerformanceWarning: The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible. To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help. File "../../../local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/nndescent.py", line 47: @numba.njit(parallel=True) def nn_descent( ^ state.func_ir.loc)) .. parsed-literal:: CPU times: user 6h 30min 53s, sys: 9min 34s, total: 6h 40min 27s Wall time: 1h 6min 49s .. code:: ipython3 plot(embedding_umap, y) .. image:: output_53_0.png openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_14_0.png000066400000000000000000011360501413546205200254440ustar00rootroot00000000000000PNG  IHDRO.@sBIT|d pHYs  ~8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxw\Wy疩wŖ,0c/apS_I!/BPCB!&~Q\e\pl*+iWۧ{sfvvHgw޹syZcX,bX,K-3=bX,b<ƢbX,bX&aEbX,bLbX,b5-bX,2 k,Z,bX,eXX,bX,$hX,bX,IXcbX,bX,ƢbX,bX&aEbX,bLbX,b5-bX,2 k,Z,bX,eXX,bX,$hX,bX,IXcbX,bX,ƢbX,bX&aEbX,bLbX,b5-bX,2 k,Z,bX,eXX,bX,$hX,bX,IXcby(2J)=X,Yɪ˓Lb9\QJeֺm P@[CgF:hZ[,rz{m{@uP06(F(QZ^ ^0ĉt ɴ$cQ=Ycr`=g=q6|:ƆsG",&0d\3UJmZ?Et >gX,lY,sO[w_8tGq޸}w>?ifMX'tBrhV-ƢYIJ%U46"drQf՜B1g~T)uֺ2q,D?1HƯR-TS}*ԜK~N2-p<^k2bx-QJz;$ B+.nZaY,e&4}.L*L*5A0)TʝJeLi:;fӁ뺩0 SdݫM=A׮ W]Y#}љ9lٻ}w!SU(1Ls〗 | V)maby)Fۛ'J%NrT ]J)UHe Q(*LEңY-LnzGunbIuknoJTB7Qr$:)NukK*.ltfݱR1j+QJ\3O=oσ7ϹxtXzTzh|4ʇQzF{{@y;?>A6p^j0i,&i GgjëT;37 Uъ= Pד~\ʦN*5VBےHl!ek_ 8 )?nvhwNPMk}mrĨ I'QacZOtLbyr}[faQ* <םp] +?+??ޙ?S=-_|hڮ׭=f"^HIu9 OCyT[,XOq1W{"F#Zk_7u`w.@,>/~bpD k Ğ|z(.sߏq<ůyZף""Zoa_b9lX9 ? |5QoJWV4S*th^M~x17"ʥ2~":KEtQJTjBt[T;yハ~袳^y;_:¯tÆǔIU#KBB:>P9d𸓙I?o<@ ,~ĴZm;׬{؟}gj+~QHzmA< Z!uF[?Q{{6 WS#F/E@,}SQh[gra#T+p?2LcX Vk}s2(ziWWJ)~Qdr{CS]7#+Q!z9>VYhfV}BQ)&F_':+wZ| yOΏRE|)L\*Tv?"z&3>^$w JC8b<lG?B5䁘6+."…b7::kWܻ{^Js aYhkxbb|miqu.WHeb*X84C eknhso,b[Ce6! bLS܅ԆFT/F3pay9G&I$\Ҡ/R_E#IJ$3#@QQj Quk>R*c9d]!B`>J5qTE눏YD*r| xtndЍTaKZR_~|Y\ !^Bh؊W*ŏ-Għ+ ȏLǛB 511Jsܔnԯ䞙tbb/J֜__qMߞy~wsV? IeJ?^vsUkSב 6P)DTພ'|zE 4v NޞSۼ+>l6pG:fԌ7ARr)=>v]Ï:<}ŝE?֣}Q jY@NTMM0Y ~nkG,Jfv %Mb-DNÕV UЍ6,*j'{:1:9&͗ޞk꽞#k,,d 7W ^,⑪ L +(<aDB!.%x̴*PABivy`Rj\տCB()wNj5NZ0&*#c{連JUj3)k?o +{8bp˥A#6j}]x+Ϫjd5hFD$S3J9gu>5-Iഅ߬,ήh8sCiR{q_k78jMy4]ˎRVQvP(D<夼TE\qÐBs7{oя_|RM_bhsoBU> ~uc+"+rh4f+ZFJr^G[Qܱ>4xs3|jW͵굹[_ӚJ/ۙ*Dˈx;ss"rϲ(~6! lJmNԌ B3zk,aġ1LɪL)Jiġ6"mNu*N!2{H`?0MG  l!+n|)"o&ZonTZ&t& ~\*Dk{=C|Qk}O')8q[Rv$vn<p"j&kPjr'6`rs汩zLr8w,Y S%R8| +d Zt2s@k78jM)~ a<ꢬ.Q^@7 5rI;_~xdrCGSfH>Ύ ߯J#W n3RpNh!JB9:2 $( tr-[;ҙv:5\k jB$r~[5P=Nvޞp8@{ :6׿O6穾7O6TV0#TRL~_3tTTA$Ô6G~$<+ȄhV #;ւ$Q4 6NA}QLsLf2H.)0 $5D*}_ύw#pRwHȤ sZRr!rv ydŲ!5gsRR3BRm=,~ͭd19(m6 u RP$oG >IC2UmN `Bb箶 ժEy0>g| _nU8/I͕/͗J z9eg߄ܪ6T57tVwۜ\3sAE/*|z{nڲbh<^z{H}8Ҧhs1746?f]0jn"=?@)mPē=HGZ}H^akݦ&$W6 YPJ%R/Fr:"&>$,:*4^~ZfުN$`;X4Mb0A81Lfs`SMng8 zW)uҤAj[7 Qm¢fYh\(t[X,˳{wm8UbL̔V ssQAxYӗ?_A3wzCNDl+?46j=_sNx],^~97{X/'B RO67ZsGg3;FxET+6n#ߎ. 7-5'ұԶDrڑ8?ʎ߼+=Qr*]Wg٤[ YΣ|qyI٢͟P=VPlaR*wEuM3-bQ=rU'TR V"=ϥjǵ1qhA ȊO8 H\}59G)5ԨֺQM/> TblRT8;C T !2f^q?ⱬc EB@J` b?3|[5 HL"9r}}hOۑs1J_z[\T|o%zs$Nj>&TޓcMΈb<܇T \v/A>7w'zUmvff3si"(`dyHո9$4}󀅅 wb{KriEo]矞hd\=0.ze9,V\WRT>>6]1x _FݮqOTٵ[Hhs='>e*:Ϗm7dWk߹yk.ȧS!8YVWU@qaJmvZt"C1MW Y~n8 )Ȁ9ޑ;ЦxD$8Nz\| #y$y䢖pԏ#U^j^#TR ?GQDg@UXJ^|%LU̩.ڕRZbLvӁm p #݉,'˺z<5rgJEG?ܖLؽ緻ݢz{2Ѻ?ܶ_T,[xN-LԌ{S 8Ǚf.Rf چZmkiё8D"I!X=&'¡3{o55(#yE%t̉4nv#. =e=mZ,{L,06_tޞOurbÏ61rcvRVTK27J]EZx@&"n񹚱GgB1`!>MWѴhZĥ~<\CV;Ǒo?BZF|ģgVrTW5 lABLM~&$4'Z< | Ku>ߦxRG$ׄLu>a*!npy!ͨ*YZ,3r&%udu0D%\'546jf<(*  9>+-2I7(5L=US;bCm|<׎@9p~I#*)ab9 ;ȃ7=֑/.\sI͘:NK{^ySA6]uטxF}/>§ƯiƝEz&pWq*an"!j~31|ikX,a Rې+GB۔ãAD'illˀDb4ӊ]k78͚Z(t2MkcW]gmN{iR1$fqH%]՜ɨs渺\=S_~/~7wggݖFC>eT%6{/!{PRfڎ,hL?ܑI$o=i^17<{):Ig&_/:A7?6@WY J[kWozJIϫn*yVؔ)ѳl=U߿Z{P*\L[9(;͝[ Wg^v%wrq@Y$q16Og)nKOϗGOO__k gZm>XUwRw}#hˍ&땮?;3t:VbE[*Oh }ڬ`;>6w@ŽC9a\\Ԥja a5?"@j>03ky4  $&@L\T3mD ԍ39WR̼j0h"oԎx۽Gxr|6Ў;L{(eu ]3Rv9yG!+E9"Fڶxi-Hj-#F&TZk߄|R /S[ubXi \ͦ{鴹vji)5F I舿P7!4b[jzmn4?KHU?fW}ݔ][ҙJI 3vؽwnٺs蠺w6vϕ˕fڜn,7 9%i+a0)gtAXiG49 `?]T_#ek0Ǽ:t!ߜpF9&8Cq=|c0 O>X'NlOwrP@&@lȀ(Hqqfg0$wg P] Ԉ0Lf?SQ-mmȤYKNpiܫp U)/f[#om3oOl Zf1ۑB! ZS5H܃ȄY5\}c;rsj.#WJkВ1=<=V_-:sE PY5A*گM#7į[{v+>vUJ}.~>Ab<1~*IC%'j-󗒙VMF\ګ&z4F[(p8(UK;rhؼk/^6!lx4fZBhq4ʫ5=9QQN&(^roompz;qG%AkTFjy#dᾨvAf55QDڅ($PWL8uv㰩϶Ǡ\Ȯ.yx{J6gS3RwxYO1Nˎ3pzEO%MuBt*N׈fXFQN\M2Q\ .JjtAq+3nxcP/.FV`{"j#s-r^fzy.CHhj&9`<Q(\6P.1,@mt,ⵜ!Os b۹٦/D V[&bDQUtFus8Mrx67,jgiwW\nz=[;g^[k~ғO|9;2QWjuTJ H-_Uy\RG&Sdq؆)srNS< W`2YJxTjQN &¦T)kwտ?ڬգ ~9 JBfUlyY/k=sm/=g)AGʣ,%l0FgW/FT=k,^\|LuI/D&ډx".Ӆa4Q-m711~dm'j⿍ȨpJ%atoS*Sϻ3z5x V!Y~ &ZifiS+KtJ$~PNJoqsK~Q2 x)ga[]㣿G)P&TU*]T_|lkՍDm^.c& \i޷F/O_uW|S*M'^й8y+Ozdh(Mx$ZIf=ě覡q1r!?a C==^@Q kCFm˵MS / s1eS 7y 7s{!.j-z4İ"QȍLN]1(W;1̮@ "-J6#O&7c>K\ |D~,?B#H?nJI7b3+" Պ1lg6#/U4vnrR͈1e$ߡ¬okdϬj\~D)ub1oD&3^8b$c5Ŕ]7e}kz3ƃ1uU3t#kk"Om+ 0".sOMyM5IqT( TyO|c2N`ORv='%^S!h5!cSRy aur&_@P(LIM1(hM_{_u=C%ÏTK0 7#YPH@I\ՒLv/8Y(ʕJ  I]^͟W^3ϝU(W: 撽qqGge2J)iQxsMGgG(, dYTP1AFF{o`1f 8G%f}Mau?ʗgߞZe^N,6'9tmNVcxBrjC=2HomHU6kW;$O%`2j:PJ2T~ցpMST#ޯ#痧oΖZk pRj9؞s316I冽& IDATk?\4վf^$RꨨCoa1FRB  s.%E2zBʴR4}bh9J Q07[M K;)ùA秝Jg+pT=7ZJYypK*BIw5}+VsIG/_۳JQʁ_+'SL1 >D`!}n)u=МJ9NQO6+%Vൈ6oS=ŭOU3Qj1/:ֱ8)o` [*u6hSi#/@K"޾;K՗Qmτ1"FBvOF|E#lcndbX6HƖb9ݲQ]$9gT QFQ6`Z@Bh')mfhwߟ֚\qqt"3W};8%]̍#T˘ 07*Uj1f mn`(ŠxxQU*׾CJ/d:5岾aV7Gv)p0P"ځ){9ќT : BͲ}h+Cͮwb.ʻ;>?Pև##]ܾh^k;Z۶ 5v\Ȧ߯.ο/ٔ <QY| ݔ:ڶe΃Kɩ$$\EEB+TTrd]x{wSb兠rz|mP-2YGm>)|&jHAZ@>OD|cur% ԧ W"DFEw9dU1= K8z~c-a3Se Rku)|OTPA=‹(K Or{y Q HDb=ug(!x 97&@dQH#B2=Ι2P<9\D ™Oz>INes^s}䜳b Bdu*j# A*ɸֶlV]cGB[wpD>sۇn%57\^`d/ZϯhFN͑&0; Dc<3 =MB8i؇ "@isX Dd+Y``F#^}2@[CςanN2tt'3Mom`6TM:w UabMa*dXRp%X|~䲸&(I$D-wtAb1/HONAby̡ Se8Vh~(o4V^ J2 H&qVKc[,+ EF B])/C{ֲſz \[c?ˠ;|$gHY2T(}:/. *O4hӑ`"nf ` NyKe`Ee5EQ{ρk M~[ %͈Ȥ OdY*\uA;%:TU媢DE+Yl(;60 Kʥǝ|nD<ӯG :a`sWa`Y0<G0Y8H1Ed4Bؘ ۬yt ۑjzdق3m}"BGbK\]0a{fW_2ޭ^}]3KWNv%fGm9Pz\є"ptw{G۸& UuYWnI.2< GV5c" zy>#;KA%4!k[!4V~mگ/k|xҰm~ ޫ np"n *g=ՊJXߤA*Q1Iǃ Tha82k^@n#짽 QbM0AX@QЂ ֽ$TkJ&Q mg(J1~` >#1&q O \W8*~Ov(y,w;JgAvr$|D6HmBF$NcG7UQl,{˃,1.60H(T/lv3Ƃk؆cr}#Ho {8h PϿ@.%j8TPA2ܷAj𐐇H EęNINvMU. 1 +OւrJܶrӳIYTVc"7Km߸>-ӈDY]ZF.(:<2?|Lŋͮ*)[fV=i:iz i0ID٢}]Ypq\L(0bZ925VònՔpw/j%'xG #ٜ4l + \)'8.?\3;|KU=Egr%1I*z.Z"J2}-](9ø s8IFM̹1M?&^ɛou}榛+>gG[Z& W|(ְdx{( 6Fmp#^1%C1MѰe.08/9?+OvB0@J2#c7ovW%NSYZRI=4MFy6"…XnRy)d0>2(,TN94U/yy+3EE,/@.@ P5Wpr@)9N\4}SA$HTpA!qP=ؿĒ ˘  eb&@I ʎh<> Km?ZT~4==s/h~%Ke$rcy1 $&?16q~=KD\s> J ]\3_RKhs .3&v/-Nv+Ab5_[(qJcb#߆<6!_ *U[ނvevyѸr'áÅ牐qb#?Cn<3+}MpgkNK[dL evnj}gyHRcOoGޱ]g;ir[d&D&1qaoVbm.d@deH>7۶ KCT&͡+TCJpGj9Tf*9u<;xh󼢑w\DQ̸-K06ޚ Kn<2<)*rӲ$:EqD5kׯ>벋ڒ/#apr֮_Eߺj-؎vGAkU+;B)!T36x:ʼn6 SX3iv%1=׿WEDKAk! n{>79G3s>TD|ݽyϚEm`>g,\d5"G)q%X[ߚQqtӾ][ *x"߄`& A/ Q$`Xہi]Ф=k#2`*IP\?/2? $֢3hab?/r- ,͠`q TH0$$c 2 ! Z켲FlҸn KWq c ι@")SP?r;?O\SG}aSgKzu AlDJA@$A@߯fP,JX+  .ſcPOAõOeTP"hVx|Ka &veմ5%Cxkyr)A5"8^h\Χp2a镭Ⱥyԯ_L|QDGuDQp]+ =XR׾\q _'ּYkƌLSRe<)k@

0ckeٚD¬C}#es=fGj!E6EA`sU=Mx0-!hZ,/f=3l+׮_c4&Q] n`dŌY/ӏll?1?]}I͛W"o#o$X[ OE(x_Qg: o}K.O4\Ke؊^<˸9<+>7;1aUU7|}~dt 2s$ŢQ s ("D"|vMҘL4eRCݶd<MA)+ pk7ʶ+(" .ׂfЗ>sAMD2v&H<ZĂA d ^ $txv%<. Q?1>g;(Ύ\'*mYb pY ќP3t h( wxAD{f( AvW2Xr "yutko-9(;n!(iTGr/}zf (k7G|  = JeSCF)c!g16EX? aE,VP,gPB}åLmNIgE7_xmi'܎ư$zTtӾg$k>%q|y ȋmE,P\VA׀ h,NPT=c=(qc.kә? $D(/č Pd)8 |oCF) rD\+J~yEf eaj W(^'ձA)@HG1)׺츈߾ZW2ƾgKHˠZsv1r :iyT?hӓ@)}$_>vHX7%mq;Us7fTǪ- *S/ APU}xBY9Z5M(M4f@uY6Bv[/zE ќzw,}\|14)kĺ7vJ"ݨT*`f>ؗbMdut'g{ԀiY=!T-Ba"p=^p]cH;|n(`.(rULGv󗎞5'Ѻ -kj[U׏E}|(9;py. DUTOP掋46֢i}_G{[|?Ks@nA"`JDI8N 3*,iA!p/4Sf3L;׷L@3PhO/+PY^65RwjNd-Y&JwHl?~6dPʦZRV$@Pbe,]49'#VδMz?v\wGJr v4w5}o.% Mש},D:г;<@e6xMSq9QUx!3ƾ9qڙ"{*?PfV^3ǖ5x̫]+dw[}rn9H%vq i;OK AcЛ>W/8ũ\scڞ+6X\jQje|K^ꏺ #3>b9ØWLb,{VO*Բ3m> *Q. XIvㅻ_]/1*w>T!Ao,\BαapvYP@`gvS̻8.a$o:`ę@MĒ6#RS$v[K8yG]ҁdyml9%Ibt< nnle╵! 籴,&#42z,ơ5x*QVrrM`c_od@Ew>G6LU.$ADF,XkuUX<9A2bmuX.Mq[>K؝aѕ)ރbvpqG?YAE,yjJ6< &P|X(w,GST]b(@NBm%0Cr{zupx5D6%7y\#"-87p`“TUe$hLE,v=e/wMq@xG=HX*+{vְO3,^sPr32"AX`? %c3e "Jtk(f@sAjSjMJ gn[g*c#-Icx^MȰ{@6SUA q ;!'# %K{܈y_Rƃ}$C%0 o;(;.Sss oՆ]'7vbXUI`rm4]2{>.j(J1X(H5"I)s뺧a:\P9h0$کu"O"Ou():@bTA3^cc,Ф; FX>@A ~A5dmVLDW #X3JBY%W;P" fd]=N$7Qc`ABQ-\ TP~!(>1ȆچXqsi!h'R1h5HHJHh2cK Pك1= `?&R 0[.vکPUwT3àD;1w[;h\ml,W@.{8|*sZ $zOhYP*VPdGNwvA%[2;bǒzjz2Fy[jPwl޺ 031%i>7*lA!-p@8!7\>Zzf=ppm9uၑQ)j3YZ  [g{qU/o<+~>;lV (?oů>Ph*PH B'Ĵ&XZ@׎;ÉToh. Qy8JgZSUV?˶ S&MsfcCh` ib,UC(NdFCBX ,Ĭ+@c;]^}6R ^=S39!ήhj\H&K]UUQu#G*ɉӒ$5]wb_UQ 09_{?߃ց/DA ~$n^Xނn WcAuŞ%@m(-K:SGA G',C).(AJO9Pd|TsAe qJh]+ H47z qw$z/ v,$Q9.~ ӡ\4)} Y~A.X@ Z5&@Vvos+i>=Ens`%A@# \/ U&_kAT q (mT䌮TP zܮg@e%m/s6@-H(!3AuuwГ@G!2cOZFUu3/eG~ ù:d,*h4ƥfYJş6LUk7P*E Y$B:vtw{pJ)ɒtCc7opoKmF?l׿.gyA/CFbΪH`ߘP59(& ĥl *L-ܶJE ͍ &HaEǜM`&>7; Q8ق$u'pcIWy`p 7OjsAk(^C?SK̚7{6q.F8GG9Ͽ9\6<*pP*2&H,8HW?EJ;{Yc~rs\ +{ / Xi փ1c N.orX 09Jqq zu/H#\<K@D:P=t{ D:Ly|'4A"IuLNP_p'hg*@ܽP] *?hR;AonKH3T)#!Xnq 12x}cP;o×j^zOpݱ:srn[  IB{}t!Ҽȿkh:Zx!VxncSƔPS$Op125o]p46R(qs h#nPSC#q "g0'nDfKH(fV,iy2A1Bi߶mp|TܜBd͜ZxcmKw^{Mğ"O|A56aA:c`*A"s ,"(\@֝@p<>dh = "c`} U99?TϜ r=$@/2q8Ay? LUȼ!Ű KoEsk˃W4f*WJ@H fH?H $Z1) zϰ 7',8c,HTm+`7R'faP IDATxV6P!F)O9 ʥȜ |0VBU(y-+3 ܭ)L Ddu }E<#s^d}oe=5 ]ކcm t%h1pQ" KkR@w@|I *xyv6 "8␬)Y9(B=[x,L(ŀ;cU=qx`ǿË/{ZpJV>x+> v@isq;&\`kFA~ `Vx+x/Sg(L0yj\8kVR;wnItrxΐ*һX e{Ew_ZkJur]=Czy[p1 a'/ upA0 ϕMQ=5RҴE\z&D7 CcGCR 7fňU+EiJܼ֮_wn>f\'>qW̙!~vp:z 0p0@cjQ| {ʹy%cL!*{p"yyǬ9qsWTE#烸V`ө:qPy,sj2FprT餹]M&BTgs>z_ 8clۤJF5rЂ}L A q%,؃v,dlz}gߞª(ɧ.&* o׿u"kFkGɽf.Jh j'I dPs0HIm=@A|k/;E}R0>S}.rABx 2(6u t:&f PJ\JYW e`I,gZ4ܺ/u >it~?Nq#g/g>k&~y$I *3 u]];ij}qJGxVݪDnd=gvH͆Egx R^]Lx_>~*nh^YAlbjnDn.}+ ɗO;7$u};n]OSOv\Rt|A$&vt𠸤-hPwiu'W_7t7~j hmQy1g; `9&/{nYU֮s{sP@Q`Pb5Q%hW$ JL4"jDbTTp33 zs<<}9?CN G .MB,hLKUݥ;)h皏TjlYcSzsJ{I #f]OJ4;"y% ]/kSsԿ|_;519mf+uG0@ʎLӭD9"b}X%>yhɯ=m?O*}>X~pJK2>U.36<1eI!HVͺbJ1oAOrp@~I_ҁVW];N3t}?--X%v),gI7?GnA<\_I)o=ʡ6)—F*(V:،65ҥ숎مS[?@GXP\J !n~rtDC쵫Q/ZF-F,!ĿI) ɈfE[Dž ~||:)ަ]P҃a,憠$ A㍃ųEK'WB/ġ RCP@1.B-"ےE(xUtQ ) ^%~/*(C.GyAo\:m(pvGBoB\%Y%(v:{fܾnQj>lHqCI&ER&=dŔ@ ;c=eZ;[V _䭇_ʦlK2*usnvrnɂͽGeH{+xW Kڷ[gݼeq,Qyki+=$'#NLuf jg)3iWͭV2}OO-Z&AƲM2v]E )8tf0pNШ 'ڡьeiM3c~r>-jӚzDV(h0CIaZYٰ2OOA<ݬ9H7Gl%pF0z{K_nX7|Wy?Xo%gg/>{`V_e`-6p Y%YD!݀v(6R/b*B'/W6jMl2{gtMp=I[Nt/?IaܸB`@j>Ύ?ic(0{ro?jOEYcI H#sjLs,od7zq7Rӝg(WQX,]kC1)e ‘%EQF7m\#hFYOC˷QyCy.3$(Ё2 Q^"39X(pX&D^ہ*M^%YgYʯKgZ{`~ȍ(6-|RX8M@]&d6#>g;n;&mwqJ٤1Rݨϩh͎o{"Y:_5o1[B?q%swl_\TPߋn>xFyn6,S3UF+.;ɉAc3 ! -ke Ž_"eo-sZ<<3['%T6m0jZjY{5}ˋn{c,=H@3B?P`Nݨ܁QԆzF~?YQX| (n,v޸05^ARzYɭ(r"s$%M'JMTQ1m?Bc?N| jV3::!_,ƴUoV84A=5f:y 4'*̱ G-\uMGJHu-nAd:ͣt@Y cI]M1jdr>*lXrʣ8Z'<'u$QO !l! Q h,Φ^ DsBY(T jwGc0TzΗQg-RW=g(f[c dIyoC26PKi[y[^kW%;I&|IYCwώ,W]'ĵJ~kѼ&ǧ94Hڋf];0r {nyK]zv]^u:r)1]4f1oI>nR~*UǭUo7PGf?|Ѓեx(H4dBS4 LO8MC0toP2,jAi%1eSٴ*يS!JH4ͅ S`Аb*Vmf`rG2 WCs&\kB0F/fr\pDl'aH>:Cjn8@cڳ\E⨺y_s@f괝R]yKF2vSF<2R'Ώ]+_œ~`ǝcg<}㲎Cs#]0- hOX|TesV猴X{n %έz}D:p}GGe>ɻnȕ_ί읚Rs%K`9!ī[JE~BRFPyZנ6@?AYJŨi9&j;Zq pj !R(/h\2!(o TGg"sQ?Z;/C]_Fm iсDߢQdQU=L! |%JY| ; <x5*.D.&1m3QߞBoPޱ vEbXP]1?őQ$*XQ !@-<_Bis $P2Thh˂z958?2H{}GoCԖZ4XDm>q;.ɒ,pCy)tkz58Gv15!|YBr!sy#16|^(}4 0@n/+ ' iqӍ(ESZQGc[TP+`%JeuggؿEɫug]XKU'O'첿E}.M}k>"5DݺO#;ۗ9̸u:?{g6iۡW^ P?3@(s}kz'o=7s^oUkdˆfjmV%]' zY۔4t~V;V6*2Syrᄃj3Za8)$ M0ʁ_-oXmvr勷w;G@_gIs#hsQ^Sw 0)VQ1˨3sŨJ^ 4Ӆ@68RBXIG P^C( ǸZJ[%jOEX8=+Tu!DoD}} J&jAs-xPJfs?~~q^Bõ,ɒ,ѤwE:x`j雷3S~%`F\Th,#B|v{U[EgS\X$t@Dk#1J]BBDu8M-ۮ6wn[2ζeٛo,v/.\[6j.ɤ0&]|&yJ\} M6-̨c[ns9=kZ~]??/03ZRw=(٤8ܜ/:M횘8OieْfՌ|;.ž:^KVO$^25r)_e? W;gز=ޯ}{p6#s3\ Ť%f#,FVPs}VBwkBhiiV+ 6TQQ1qO)Vš欑npCrL: mZP=@틎CwH޻slxr;SE( A %R L8.՞H8|7U7_}ŕt1ϻO<їmzu`V8S_EE>HDqT꩗=$-*kfSr ;tgʕ?Ӥ5%ZVKV6՞⚗"X|d ,>7I1'Qy|'Q*(EͿ|[JYBy ; pv |j=y(ZkbezĽA1VB5+Rg2?ӧǹ(l@LcQYFuѵǞp&ZɠN(_l,hA Ήx㱍#iS|,@ !_$_5)1̅DP!Ĵۜ -m7 ףAX¢eP(0跘'8-j$Y GXbE]%%{fkcs]{y.>zVQVYs27m-o g3!ɴϾp(,HJb ZUń8(3X2>†%OmAT;ͣ+͓f|oOl0W;h[/!|iFmlfl+t:A*5Vsy {>}s3ۤYJSnm]mzil CպQiaYi\K7CS#hKMHB!dX`q6w}}Rձ?}i]xuuvN;5~iH] 4òl+f+ef=n|"]x%i ֬nsEp0؉pکJ@ںiUGʨ 5Meź x=\sۯEu]c}JIŭ!L ԑ=mon{8U-o~ӛ^[ƛ`ݼl(8ɲYޕ ni8m<>z?|7֖?1pu)(\*eZk3Eg:/,C3&K%4]T2;$T.7C_GܽW=ĊX|nV7B9jZ(/E(pGQB0*NPߣ IDAT $ޅ0y(3/!~<2 Ǯ8OY`*}EEyZSBl 4|Axg נN ?<_qr*c#CFc/RZtQ ?Qa+cD}/TbL>Up; Hy[Y;{HgD-9͙輧eQ׀B('t{{{p T迡B']4xP=y⢞;PL/z3+|/6!f"^D׍C5V"P^&y)K$KD~w[P1\U؀DԀWmeN׋!ިBuX<_0 xM2p=C6jՌ/|&$уh@I7ërvlct !^#ljRuDv|Hy&@ch3XӣcklX! Խ@շ Mp%T™Tjȴf#DѶJeʨ8::8遯C/'7 l~}pԌ%{,S)߇й33N&a`zOƴNzn-B:72 BdP:u57\-rWӣ7xS7aϴw.R"0'T ?罛+{urDuo6bY4-4'63[ZqxsN$.KNe=44{(&{y/ adjRu5=3NMkɤ{?;5*ܕo^0\"yRS2{dyX|e ,>7e*?( }nGm@6E渊QP啺@3v4@1>fB)V09B>"܅ iTͽKORCTBJQ?js ʓyOQi/JZ(k-fcbh1P%ޭ=#Of$O<36X]X#gam4at6{Ma>գ^ < ]qP\==-&q)ߡ@tGĞ{ⱨF-ۨ1fJf=zw*tU%G:stP AWojhH5B|(%Y%("d. >Ov9(cԓ|X0o.%Z@0ɽ0 F7A"T CQֵ<hTX~CkaޯK}h zb0S?]\ғD&o LݨE-KӠ5!,NEWj}< &F=gi4#m4\jj\T9)mA7xW+z̸%jc;sPhT,' Ո: L&YO& =m!gUP]ۀf: MSE'ٴG pJ`ҢYlkT^ 5,]mѝa /^ƆGGNHZ=jgRҝh-L2T+x2^K|~`kWo vggtYK %kϞTT7B:ҭ,l7ny(Ȯw}}{ &ķ_;aN0kBܶ~pͻ7NtJ酣%oJw2G\{B)_~\W2F)ιڦ0DCra2Y.fZ3i*~({wzo[b.e ,>yFV^uBGQS|^E7:c:g8҉8'P[XN^!_Ȅ)A>q*zc8 UaRQng (`643@ϝA%*L!j󘿡?'>PO(Ec+!Ȣ]i3(_^SFèYMV.D|%PFLX>Wȉ֠,.0:I @|ujHy,in>*Nbe,+hA/f>Cv~ã'T.Ck' ׄANU4AID@ SYI5P1!OwpCn,E?컾uVKZb#;5~_>myyWvǟ:JZ̏nvKOtw`iٌZLW]QR><44c:gF | uMuc3p/ & ZtY!C!塊aNJsMZYsZ) f^t {2:OJ_Ά̇O]yw,-D-ĺH.@D\xQ; PZOoqW Jvr@7;5Sz[26>99R [S7MY+M, @:`k7f&Z#ΣP"4|ёWIU=st؆372>~:8o}_YiBeVKyH dryKdav=\ޅ2@a7 LػƆN|Q&yfuSL&(5͚&RW iޖђ&nfUN_6Ov<f3O1ajǍсeL"%K:G.B-zy^ZgQ/b V6(m jBr?ǔ̍pUV !r(`!Tdm*O5E}ꦞ{d/ q/PTgw4&h,qaޣX΢rcO}W{(G9.:Fy2P\!ĕFAQ@8OF6C(ŝD-QvvN?1UX(~;PƾQuQ-q @q6D}FiG(/Mn 85'Gm]XQuZCw0떼K${$7CMidQQփG$ZNf tS9f)f"LМpzW,#t"6,%"^_<+_ygOᇋeNL9~*%-gl;??/qp[wWBy,7A2 V$ЩSC#<<<T:qBZ/(@GES"X3Nخv n5L8+Sz3&Tn:ăڊIۙ?< E(][A!_.7Y©>‰{@ 4RQ: 39k֡;d`qz{߻k kES]~sgR:gTl݉PR&zX!0 g tm0u2C ĹwN?+H ;)!u7x( X7ˉM"}OB2Rߧ-(n.a3˖ӒJٙ=palBtM#D^ CEFlpЄ%Б&.x'c2Y36WjB5tq9̓VXgUs#Pͳ8=A=wܹ(`hSϿkGX(X:N^ol1(`bTI_R6(ڊPqhkXŵxlh*LEpP820eꬫ(( 9( SUiQSPs TPֻG`t ʪG#7E1{S ̠8P1SӨKYN4guQ5G(F\Lj,9܎jc|򗨒%[QsP74(ޏyX*c線Խ {"%Y%.[VIv(bB{&^H9, ]XAi7WnPgCp8S2ͮTBH2Z%nt*w?G?גW BeE^ԗ2Cotrg_cݗm/ۉXqq ^1MW<JdɡӵjZ* 0wRqaK<[CCOHZFRVd:y?Ĝ5R&ouFu0Pt cgִ vף#CS569&ûtMWjeZ,^]Y^(:>>[=n4Tٻgé l5>*QI\j,^y`OLx@NɓmL'K)Ϯ:No&lSΩ٩7 ˪5Yrk̄SmuI.Ͳ&aC2Z͓RRJ=wl*ihBhw/Fff|>j"lIgBЊo)Bv}aٕ%,IT24Y>qn q>(. VP@%Bqm,NBC 6F j Ȣ (P4?,މ g*辽EJ9y暈D*jni/ ǡj,{b,luqme1 c rԶ3OG޳$K$o$IY%tLQ9oO'uKU0ts2W(2C%ѐiQ}X$fEdA/efK;d=RקTjaqgL=xvgƗ}~hȕt&m>mm˳7dE$/GGfi@id&lKؖޑJ& 90ك~ \ p Ot4VοTwh@ӛPԼTJŮ0ˆt *L4",$|rjit$4S۔Bl[4, )%5fAJႰ@K4DJǁ;Fx~M]/_U2ǿzwz _׳چIrExiƉ%wts4Ob(nRYh_Ӓթ'[co v>N\\GYT7gbׁmE'szrgRRb uӒɤsfhUq˞WNiZfO_26~)e[8 KLeSI4RJ+ PaP.8hNkSs2 'qݾnXU.=a0{!Ȫ%,X|C}R~QBǥT(4ߒ#"GJDQTFxQɥ[(0EyPG8omcP1K@rFSRWQDBQ1 HGYE1⺧r(C&"7·Ir@}(dx)A3P'~.%sv,"r7qJ]Q^gI^;.kEԳ&P(S u8ԩ+|~tz3P}Pb e5Q&Qr[#Y%YX>s*}F{~ݗĵ~*Zyէǯ*,;> A) "tbƳs`ADUJk/eP3xq-|F֪Ə mߘݳ}5HKk'3cmj©mZ̫1wak|Z~5s:?3Z~?q⟜q P&#t0ۡf&{^L!=t -A30o.0N/}7k 7a"mB(֭y(&Ɏ)jr: m>.!aRMh"@}RZMkӝ48.#0:8'a à/!JdG-ozH2Bj2O(_'YP=>+t%\(L x50n`6Լ@%8"x(c휼09?v|[7xzmvC/4SC_g;tMC:2[ae'whB'Ʃf k>53dRжm-a[ S7, ]H$p\W-˖5ќGn.4J2f&7=L*3њ~/mCSZ=( KX|f6䳑w(/"x=JA^sԮ8|.~Izc՘/ʓXR?΍:(oQ<( p^֗Q/fEdc{{{bهڠKR6ʫxpF9;w+_cvb@q8q8kM8g<.{Imˬ/:/uҸ]yqeBK)ge!ă'FF`orB2ʳlEȎcBrܥ~c~4qd.mco:I$5_ yY%_L8Y"'P(aU[g\K0Hs^-7m4 %v/(dkXa-hhh423JݟuכUcG+7o ˗n}iyVaW3;ׯ}G6`dkK+D`ϋGۚ; Cɜ͡Tek:BJYs ]2SDrhkx5hnӑa~5.BA654M JTCV`QO^껮YZ Ȫ1R)՚mq:h0}Dnh2Zf#vUoiY;\|l"98ͭ&^*~da?S0) +'ϓahIEW9g_tٞSXd +5O~{c>s>~(=۴M>_i4GBMVKwK]K4Pm閐`$)ydXfLd[90nmndk];2Яx4JNHwg{w0s]Sπ0zM=k SL5d濟r9/J}+xiRG+[rEa5=#W-+0?/,B fC)aaa躞BNҲ6Lu{fjAiZl}LI<'}gf/M 5y5\*0RȷN:g9On~d ,>CRNG߆صp y*p1J<5虽(F(P!Fs,RQ^ܼ@cy;(0Is4idvQ,h3jƀh9i]7q85f'TmC$(ŧ?$8 1P^8O@L>Լ<}؊ۅ{ߡZ_~ ȣB`[f{^ǔgD (٨ Ի3-"ukB{1]z0ػ{ Dj<;(Fk'I)RN,s#]~D >sH]Ol4P|59W":)z^%GqZ 2P ؤ՘037O{+Il"b?.eQm-Rlb,>^|)SkGm[b|ߨ|m}[]ُ4O_ޓg-#4A҂lvErͧRu]ï—OZ왭9APiډSSfW uRZOd/T*Ez;kc?{k~Բ:Z>Խ[/?%uQe,='ȷ͔MMmeA0#`Uvg%@H$DRnw}5!XBRPJa0R -!*Bu#moy{n\uMGcYi:3Z)ah:- ](V{h3(zkn^\}ŕKXDߣ6+88>᧱G>F=qܽ?Gyb@ym~tKD'Z"| ʛ%,uM{u$ B`Œ)Yg+(+DZ\55HCY+*xҋ桼%uQgބF) !^\)]tIdIb}PQ荎-u[ vcFw u'eVSdMyqΊt~d*Lt¦ m2dGZ>BKK~0Z5\6#rɪXRގd°n-\ȧKV)hBGwbVkPUy)VS> 2|`_SDմ9yA8g %Y.ZYϱJtJzgeVCDE9D!85pNJM@BMuy@.0TeQ=ptX ZڡXi2.lg@sLU)?uyuUQl06% ,H!8Hr 7 6qp '& !2B06nk,iԧۺ|{93& yi>z:Q-/,m;wenDU ZCִm҆LoaAt+,.W_H۲H >TjOZE+XkRnKb[֔(&%j*g | °FbmdV@DQAEq8GBp5Hf,sA.)U+*6d`M˺ʕ]Vׯw\ 6]AdLC,8kJ k ،L~)#mto,HT(iL$#U G{Gm#s;zvT#8#-6ڐH2 I Bzee֎)>Zw$}Hߊ38Lgqo"ġ}|xH5Kn8guNby-rO{8mLRt=H:pRN73ݢ-ڢ=y|RTG|VC2@LMNe)9irAx!$5Nkc0xa4K??2[^CH_+yŁJ#_ 7_qCU<uQnʏoR裏֌q:=qc%^LiT{cii3>/n>G2>=<ػZZR9<J 0>_l g\P_:FwkmmMNOґ|bB2Kdž,W #n(h$5Y+Yq%Kur;N]%8ا|㒀0İz$Ͳ 4G𼎙(ZX}}h~"9)ښN K,l<_kO}~ʾO# ^K6o<`[{_x` q=[FZAU+[kqG:-{XJHTQ^3ln6ʕ*$hiTlxPB.ʐQi hJy9}gLҖ/TmM?Eև ށZXx۳v:p3BFv.M *HqOD^,Jg:CH*⋐~F@9%_uɥȤrM7?RM'7SS)"r,kԕAw# m9Ȅو)G7WAɢy@g5:+ ʄzy8OHf0%g1u5 7࠵ R*\8[HR$/#mx$Ӛ^Ӷtf$55Lows>=%?ȿFHZ}jc?S\E[ d>,"1Vw+|t ʢLixn!PNNB>3s*KJnW)x Qs-| }C][G'oDz@pVlG#zsY=2lwkqr&J?E]^ְ?<=b"Yb}jj ~EAw7܇,8)~ReԨHC)zij`hөKl~)جjQsYXf: #}dlJ,ӘjUrp":|mؠ/u `lꜗNv\0V?}։pI{uǝ#\ݓ3C/|љfc{fVt~EDַ*qCSspAXu,F_Gp=9 jJetipZ*jzg_ [e8Kb]_}hyqs;] —I݃52:΢qnpr]7fkQڇ~W7'ɸѺVV I7,}9Jټcs yJ,c N%=6!e;pź`lZ:}qZ"T8VH_'S_F,,J; { fQHbVy!ğ!Ds E_K79ߏ2tBO[*MY?~Dg!Pf!=#[]U)ՁD!i2OfsE5^YLe{Xx,r}'"d&y.B'}əgJ!kЂ8=&H}J vib"7+C<}H亳xBa=GfؙRmc,,>T 4‰O=f!Dq*| PJs*hh`k5 oN|EGasT&E`sF$ \5uR!X㠧 }"})6{?ˏμMTÕВk?1Q4,aԯ}՗7'og\Y;K~4~ꓶ}?'fr_vo߮98^8:Do鱭4)xf0Mm:6aʝRlfㆇ/rsWѡU &i'+ *D01:(qLˇ!_ mi)u7JVܻk60'y4V$Av|]2{u+?T{mo?ܽ-T+Z ~gW9Ͼ;0r]? ״_a`p"%R00kw?T kfX(ݗ:/O DDքEJ5Դ֙&L؜P$V)kk=kyc@k0 a-*iobjA@lj(r W YI(uAzRuJ9kقyȋ8dZ0;!nO%M# #Dn܅#k":Rj+U)֧9:qD&TυDAR!:" yS-(-Hrov"_t\̷&l8n+ZUk3Kg~=ȯBחw!TO?@:Rj!r>Ϗ!%Zd5Yp:YPBܲh_Lg(u李vc gszM =ȞՅ8vU9GZRJZhOuJuFTyol_N|!X܂d܏V$=Vl{>H+Y> iǝ2g}).ſ7ԣ} 6o_s~'Y *; 듦ʒWùI4[湠Ll_ۯqm2\&Ƽ8:Ac-"j3`t Aםm˼3Yn^hA]g/K:'IgԈmL]ab+[}쐝d ٤ؐ D~ޖj%:S]N%Jc8m;v5:JdM%?ؿc_;3= #_mzIbSN[PVheܱemgtʀu_~Hz Aik|m 7:aQiMRTVkB[Y(Q+PX C.q">5I'q'U U |C5I$__;u}j{VŴ ȋ5DrDp;BКJMO{ZN5[-@"F"/אy B?nNF RY4 >,/0BRVLH}ߙHr,gc!ROS i8=voa MdJ3φ}V)kh=L5Z;|}#*o9 I# YBjoGB; jYHZe2bM5iN5D?9HT;=5:|\XE[c0'j9h|EQ9jN=է}d=yuGc"OR% C\kb8$6:0|=u\"6ʓt݂<̣Z*L#RJ Y w5쮇j]$`>bfQW#dd RJ/4xR ww_5JjMELיgb7mW)X)5 9zL뙋4NW噰/BG(H͝!xOQ},Eo"wz틑 "㭐 8G"gm.2igtT3'@ֿP k HFqR8?"e͛/lOkA77""8|G-Y|"mtYru> QwfENlmR+=]ʡ15F.'pYuZX|/\L-T k kc|@}!as8ӌl/^3fMV q@$%IBVCGGTƐ dRςN8IH!6#΅eX:Vk|vVߚC纮2i9^F6@8'I`-y?g$ҘXb _9xc|d~% z3 ]KL(8{H/m: wv?):PihXk-3 i2 x)RJu!$NDtFvLʚgVF~ sn'$6lC FSmױpUg²H8 #fm&)Ws̐ϝBZwö66|>=xW HX "ct8t8zk3g.Jq,ڢ=I8m 'F0e~K~Hvgn P[`˦;Où^vIO=g&g`7Q:NgQS/R&VX[\N}U84r.MMu-;n+ng{̊[ܶf_!6aR+#c@[L00P& o04cf}kdlla㼱g|XP-GTsADZ14e0ZAdR F#ˢft6WS.a+7~y#;?JeZ1M5m,3'!ۿ=b]6fŴD,6 AT1kA'4{*Gas$jHȋGDTڑz/nC!.(`".YIA]̟_^xxJ'#į1Rtv0mޭMs6!/D"A]w'UEp>ͷ[?Lޡ޴}?O /m9}ބ1zGjCt92o4mu,%?D"+gg]M'7%L3r]FYt9}>}Kay! admўU.~LqGn<2!U๫95+oɯ}ne4ۈXB $saY_5c]~ukT~Ӎ$j}\ qlyVl>vVOE* {#V__G~Ԕ$wNOZWyA~|kL5(gã{0~8\pɃ㮣UV:6ԪUjUF])<=Ehŀnhq=#,}6N?n+0]m'iZ!JsnE6!Qh $>J= SI3iExCcF)ȋF5J8!V[ܫ.9I.mSzioTJ!eoH!"B9d<#>HcPOWKx DJ=Dq2= Vѭ?yƲۛΐJKkHq{ HVNDґ;ܭMi oiy3!L~*-f~M)u񧸿E[_|UŅQtN<3 \޵?baxF;O!رPE䟧e#H2}|8ZyZPW3O΢0:.ŪvrfA| [o;^?{Zs?-]_a|&o\'i٧K\kUl\.eeETurl 1ME녝sΗL# :HYjݧY]0'ybrh<(OgA2֙t'Lt?zt 5ob::7-_B_\pn`YWc`pZ+Cedݵ Y`l+6]/>G<_R1<;$8n&-άHMO;&^YkQJ%ýݝ-M\.7Ddv`]PI%'*Q;gDPYw\m%#S.j1 ه720x³~""qH2`ؙA"tRiEd 2G-wduu AN6!#sw ) ҟW"*g#Q"!O0GwJ@۔R+ZkGR!e,#7 _g_~1GH!G5}-˨pK1؃D_/JJeYDS>dLCƺ3m1XZ7IoJ g%<㘤# {$πBх\d8k,|mўq۾r¯eMݳz64p +Мݛ߰㗲xDDh|:FY1s3eY}#n;.ۘyq{ÛM5{!Ns!ISpqZr-wSvmg?tVlZ-v)N%[na5 R% {sNъ-nu-v 7}+ןqUX;Q֠ZrZ%3  x X1&]7.d 'fWHHH7ݏ'r`$>Ajm ?kqI8hJq_B41`MM(.Ru!Wdbܨ8!‰(,*WqQl.Wq775c) 5<k"iVCiC؜5I:1f>J88$6t։qJX$ ˑr'XٯY"4 m# 2`R'"Q݈r!/$m.۲rHLm1):V"@6 ĺ!A&.=*FV"^_x]} "C&Ο"$>|/A"K[gDרߛ>Ud솀cHN"ٱrvv$OӼ6@l2: |ўӻxc [BCc&S@W+*+ݸ8ƉXZbsZ|>8ֱWgqAa-X mlgnͰ6ח;tY#6vSr87Gw#ƖG'bmio|='͉ԪURrI!SPZddu*҂kc/ JZ^{( jcIB$ +AŎ+NR*qu â'A8YsEE6RETR@א_#8~=>2YQ_Hʳ%tg! K#GOwo=S_=d%kE^Nw9qF&RJ}$NZkkJk܄x#* 2,~L"@>@NwoA"ېt>V6{KH=wh|c,zPSv -گ߳To;ӫwJ](| am'Phn?N,W&øKsJE9~DUrVb cscb~/y( ,?oeG~ѡj*Nz۾{ ~u~M37\Ebw1^}N|ᑲkmZ\+?'0*.ՃSyתOt?o}'r?wml뺎׷V׭T(>Z-8q ꢞ[5 6@5ة5+tt~N$^6ьKܘb&Xv!DQ$Tj5rB#q5ڦ")j5$)Q(853ͬ鵮O/iOj#L2/sCb^x .>^3X/EDvN~"Nn-eoR(:9oj?0ZH)խs"qt$IRՈ$)n \כT@qQ!Ta:T#6j0h*b`zsS`km# {v'ˌIلbP\kMDy~D)5;67Xj$RȚt1gKI,=$)3rEH]v#*˩ue}ԝVZ) +ޢP6 Hdv)2#K cE]j3FȘH!QdK@Q',LCjUC$ze$yNP!7BΡEKqd&PSJ}~5[!M 1D>6|V| bۋ#3Au.2 jt+s̬x-B%{O"AkuQz*4l>"{]wm~lp=7GFBG͙cIxQϱ,H$`G<(\:wJfr5*[͖N!z\qMGq2}o#O;|{zWz+9WNIqgC= \[٣ka˯i~}SB[T\/;qRqT-j{4l;qC2-GZb5ziErp^7vAȞޱh JPTe۩e OPec\W^FzxŘ,|5uw55T ]â`8@ٺ -^skJҜs`{sm`٫wꨙMPH雬?=<ݗN\ޞmMq8$Il$ZkfMD:N=(' Pz1ݣ]%F8iB0s {82ubQ&m6ҥݦ9W  _MIlm-$6A)46q]q$I(l{ op- hjOGYod 2GAhFfdR>b19fz8k1[5dA"rfDy$=,5=8Ը3k6_U* `i6V!u_B] IDATFW"`: ʢ>P'?DCnMz-˨_DAsC8qX| qLl@N$2}m5 ѱXR|ކx@RRΠz'}2rkۡT,4S FGwXkC%[02'! Jm[l_e?vo,.ڳ.]#͝+OjPi¢p `h `=4S!LRbI) hp~*V p =*7awzCkƕ?(R\|Կi+t;o/G 5vK?_a!'rϻ[?,Ÿp0N Ezށ]+>_F4`ybiJ$cp|W#+pu`M_q9 5W'$#'QsY|P[3mլV)29eV#q'Ts'3Tӓl%2Qt#õ'قRxZ x-؏DC+5!܌<Sl/2B~qO"@V#i+U ?\`Vҳv"}WYF?8({2St&òl*SCMkd7"Y02;Oݱdՙf;̢-گmM0L 11 M|GfqЀEYM<6v4å픮nE"U :Lo.%azZ׾G͎7oia]ל!jāW ]k|O' >J&K!]  a͇'ظEb p"K<-lkT^X>Ccae^PMjwFG1. AI%hB" H]e̥6"&s䭄DsJ@$@.6$xhĚ0:cm5U9oˋvK:)ܸA03+8ֵdlDڀm\j)֬U}ϾN;dPX)ج%7/6kh- Q Ag.2ptH$akex~`Us#6C".> F;w IDx1y11:,B Mbs>'N3DdpATKHRg#}<4e9-JNwPeǥ<* J@ ii1dѸqaA*O)47HkVakiߙ#BLA@V^֓1TJ= Jv*B+6F,]EKyUٙOW1v #=E"".SJ]e_?>J+5%H(4}3]jٻE럪jt#ksI@3aEqE{6KI\*݁>IΈEIC)6'6+2\ӵ^L& RU9;5r|mMU{ѹVU=҇?N4 l~yn-O7?ݯ,<{~}S.f$zh `TqK~ W-jq.۴6CP-$LR7ZA03FkB]_.[G^/TIU)dr''=5 5'.֌Z)W&Vܼ9 byI;ǭGw3~O4: ~ig++Ȕ{`c<+Wll \GMͭ5tO lQıŘٲPǮk05а}u==닫qY.@MJEw(MEL]&]Vi,eΈ6ӯ-uQfKG6 SWbAY=Zstp[RA'mqTrT4!I5[6õDԾ@"-hX+4}y)mG@&ʳ…Hr㳐y21{U,n6~:b5=D¶!Ǒ3Ke_*8!՗+VOSR[=Vkb,x~!ݠB>kE<k -oVPW]_ОtL}Wbjך']sn7o.lş|l(@NZe;2 ؼB:~//OK?/MJW|:5| _Sl$]:=$L Nb-Ηԅ-[nЍ Jqz6&q9@Ѓ+W(($ |sBOyܺ6:Qx jJycДGQxXLCa2'`$67 U'"[sP =1[ފ]# ME2Hc̎YG3(3Όe9+0!"*TM'uN[U$Zu}{wwE7[=Stgx`Efe)>$S>IS2v,$%2\c&xzzlXzNeJ驨9ʣ,Gȋu#*$M iLqsx0i\CkK<<@ɣE{yugVm4[/g濖bAR[~?#"(&f&$-D gb+M0QAG HHoG"#HZƱqxy<xr>RGL\D|sbÝA>{i[g"2N[ XDhdo#5k@ֺyErIݼ jٰwFa`p Gwlywc HZ.r=1Lg"%r ˯}=M|&~ݿ~{Qaʣ3FGUǍ헟Is9]q iT˷vure1Y+j'\?z[4ʛ\Xlٙ[ֿłv 3I4ԡҵ~Sŷ7a%b..'vg}KS,()<%P= h7*˟t2PYf_7lEQ~~i ?Rڻ4uXEHWE~H^{_2[ha=WƒV,VMAoJ`vU;AK#eANc6 0]_6$]g7SU!$"0$V0m|?"08a>=F@MH4k h\p; ldՀ::zދDiI!&LO^;0B#C}oߏ󜖟0Z3i}z *$q|\ s <l,A#}N>mzʜW3֑+.-4Rֹs+=5:K1VaXm6@Pi!,=zH8UjXChԁvuZC8RːA1֑k]xGƱcf`*@4# lo뺊&+sHZ~&-qRiL&zli*2 d-ƞ n2y@Hi߁C[ο}3/򨖋2yUJyrmS\lD$-Z0WB8n~7&K֌Jh4n.gr JUvUZQTΠɪc;߶>rt׳t=ՃnemeS&Gg peykǎOgydeϒ {;O4uЪƪf!A.KҦh4e/"^qͽ+Ϻ᷆O Ç78-nh;vmcýڽ,ԍ{*ySd./x3ZIё=q{݉'P"=`2PF3,AC>'µRN{@7{b9R#(9˾ud"k^Vl {IZ:cZHXm7hy lK7͛~78IȢĘݻQTRFJFz_5mA K }oe41cL : aQ*p/,t-E}![Keu<y(},P%iiJ:MbIqS.OZ ?U6l]ŘԲm\6n`ovn>5Cby 1.FlzW5y=WJYӢ/AZԀ/#ygMށDi76fE5?\<[D*?ۯҬT=B|iT} ~OA Ի#zi)2N80v9\DV=˭@q^yhPS4]d|US{'vaHs.lB;wZ^x!g]lV jͽb9SP%쵺l|gvx1w]|Ք5'UKT;pݮ- f}.Y =ߥ̋{ǟG;)KQhj=]'޹yבan|$KA*(Ʒ,;ksؘ޽5JvKӑmw?|ϸK *;JӃKG=keZה}ς[ja\[MZvZSo\:ذ5C#XХQGL7U8h28*3s\P k,[\{7JbȓmX[?{B˓DSJ:|әw/{(V:_JH̦!f >gjV.4nA@i>Q{Vtm<@:2Q(kSts$dב`˶q "m [;ڀ: {A2(/Lzg49{#vAR4,c^I>|8 vȳk Mȳ\ǢGc+C~$jV^OGL x#2GEM;cZ ]| ,ރ8]cF[g^;Bp@$\)d (K£iגlY>N˥ >$iE[f:d^)]7D"GNkA^˼ˣR[ڑe(NPdm)՝ iK&St>7f(&L},ts2ҷ9`ձ`]Z۰ĕ#_Bt2$#Ldɋ[7 \woܨ-o83';*gqn;z3*v- nYtڎsBUZuJRZtb\Ĥt{lҘ-i]b{qvVidtntyݺ>KukUiZme^!8S`u,;7tK'R ć" (d]bP څ0 IjBa]iZ}߯nyc(Ɖj_~ˎcG;&[In?o~ʯWy?5%.uڸuIo"VWמ~oŧv+P΍l2 XAoxx.:7+>ߩ|:ze6f.fj-D`EEH-T@~(*h Aybޑ|Ā/8\)cJ%ss)<Mb{e.9 ^d/E"G

?_Z3<>7&v"S$ `R5ziC$IBCF 0K7UWFdŢWH*/KȦ -TjFIM5fR%U:ޝEiDE?p8=!Əqѳ-V\۳/:̐trWvSD#~WE/9 116kg6Ҋ½jM੽$ ;J:2MIcD,9.IP1ض674Xo=1-{tZ]Ppgٱc=Wyvc %$GƐR'R.2 mBb@h*Ib $4PiBl% anDט:<;L._(]!Q rT֤1opNJ)eYrCT?/~?wMu~.];ݗ\lT%ckTǶiFs.Nݳ.͙:LhL٭0rީ4i*3Hdt&*TUi(Rq \#Uܜ gѹyzA:8q{> XC ^ƷG\OBBt#}BW;,B]8cJ^_#_!߾5 <iAGQNhD4ʑDj<!ژ!}9˭z!d$*;Lǁg+m}4`^15 ,,.;$qk44uVԺI$_d%B_DH{=K^))B]x\)NWxhx5:_Ey}ZZ' ;&~*+ŧy+  I.4"ӋRlet_@.Y%LJs|/z.!ORjE{T6~W+]áõԭnq/{տN6;X=uzǎd6;]uߝ}ov׶,'cu/__}ɍҗPah8,w7>NE͑p<< MS1 BÈ`aa<1\TiFУ=Lcm[SǗxda/ybyO:2U1>k=\n+l;2] $冡j;\IM"VMa kdެ٫ƋaɌGLÚK`4ta0kI#t%aR@*t煜KM6^.`[ͫ뺟;v^-T,ѣl Jh 02alb3o?Zy#bA!Iԑᒨ{ T92d^|8V9IV8Ń;8ēO'*qHcߙ4bD8 e򴱓2H2oBn.;hp~|hq?Fih>?U)T⹹ P إOJZ8γ3) [w!DK)&zs;:°$uIi{5o%dLvˈ\#(K?ͶjmHx[0f)9dQ4Yx58lj "=`36p8t}i℅4ffy?wyN8 jB=ZEM$rU^9﹐Ϡ*:@L^,VG\ 8?Y\D묾q\EK .;O`<3/86wc$%E7yF'lPgl.U9pb 'Wm_k&ך]^hS* 7ʹ6/~y¼Gzh˿5.{Q&vƍcz._A%.Uݤox;彥T5e==K8 YRJo(1 )= g$tiPVC(xMuEHT1|/2@Ĺztg;޶RX#GUoV6P3捎YkjzPO m$'CKڱ˰5rgJ.m\( h(^̑ )JJuN G6i?uZ@a׆Aidqb& .!(jOtY@FH# 8W-1 #R+KneBN?Z"qR K(4 ߬VͨLWt"=ӠI bZJ)[eZfѐ,qcvVΌS)z]g+>fls m7sn 5K j 0b!a6'%K^ tڃIZi*rm>t"'=ô酫8.abHA RJp+/\6Ѓ{"^&#j" Ӹ `dpĀnɶߪmz6n1˄^2yig!3HX[[kDz\M^F}BtR[EYh>< #k[vw IDAT }@GNZdu C}cg*h Je.l+SIN}Xu+R~KWMO<gc7/~:D哟8 -*C Y\GOZhF߼{ j7~msJ=U%'ݍ;Vk7Ku7,F6*8:k='('}2y4:߳+ [M1UTq0'ȫ! ,b| 0 mRDbbʅ(0T$AqUꅮ]uQ$T֧"qİd!]߈'7HU͔ťb. N,*ug hǖĢ݀}bTJȶYfr4S%{{Ԓ쵦32:ko'zsbMtTϲ![>mw'0m}.WN~?P~ڭ#i͠ ͭ wb=1^ oPR NƔ&0),Iܒ$dE[H WCs< S ~@q׃ ϳEfmS՞7S/>/-YpV)uZCz_.hBS4i?FmzA:TC0~M ~tJNImI\.]/$J R>E}3BUDE/6dL'E?LY>iuϧzAn R0`=DOn袏-JܨR4Bx:Zz@~ }?GJB n!7RoC<!D4Rߺ|^(?}^EO~"͋|趈̣m>tE0^#7~?lTK>~j[wt~c78keuqמ}jXnbg~㦇~PUt; [LO_=s-~T{yJ$;of}'Nk ZzsGNcS}P;oE|@EʶYM>p#W>v w:aDƭGn''ӌgl_=2qV+i3NQJƒ^+JHiGةm:(RR A.ViO$N1S<%8R(Ŗ8YZF2EebX7獶 z{nsb܄YڰICA ðHK6*늣QZUmQ0 <=pc|#VX +x1E6 -!|4p s.(M K0ߐN,68W ov5zU˦=kMpk31gb52fv` #suX=uUv(|H}B7!n Q\+91Gb (cP'Ʌ mFݝ C]cDBgyG]f9χL2`gaV $͖|i- ԑӪL>?V+_mw$)y9Un.RDĖ@i^I VBdk8`HP;c_>:qЋx6 "ɢR$߳Ttv:r4DD}/7k^G?EG&-soюwD;1h2utPŀPe 0x32@OC=1K5!+0v ҼRҐE9YVխǾHʋa5&s=4YFVdp-jz?_;{G}Ko⡡V~+ٳS1,31,^fiHw'3´0Ms/Zҷ3жyfSFC @\[0 ԼECKfjjMtemمCNk2HmWFФ1[z ?|lQ[g)&:g>kQL\;93vnˌl[{Cn=. K3bE <: ڌdM࿢Ηc-8g~Y BFMWl=Ы&[@_KKx^5ۼo yÌW&da͗˟b\in6*puQJ* ;숙λnmm_~_صߚm6jͷG>)gthX=Vm?̾DI4d7ǏL3DyLj!MLF"l:.MZUƲt.'r @ycuIUӠb4=A7$ategHkрUŶ3E%#)<ɪM(,6:mDbL;CXUb |$e&R2OZئN'&6Ymȶ3fHyJ5%3T&P)8s-Q#K}n=]m!+HÄN)% V=wmbռ UIH[S #6y&uQH$N%nvyk^A a=9ʷsdvW7Pƶ7Z{W~ΫO pz/>sA@ ;n%mDn?m>R`_w炎 pIekgN>͸}RҋЃjZ 9k/LEx=^/,N}{OO<׭z_1^dB@)x怷^)>hO{ZYrxzE?vO[ꠣ69t\?NAڲ"L%ID; "qY2@&wc&f: ,5@/D)4z|+Bڒb=~:mW)h|)5Jz"Ґ xfPxE'*Nt1D~7S..Ỉ~E (@z3,rsHKK͝ 9iU7M/Fnzcj;V`|بOѡSc_?[]{F]?ݪW,>H+Hv(#OM1b@R&yՑJ䔙 cVjr-?J:jhj.+uEI=Po 8f8HV$ 9ᱳ[{D9P88"b\AYtMDÈ& ;GzH-eu2gUL9N-6Tc%J+=f$AE=B["0vĒ*"6L: I)hMk%vg/@ Ccdt}tãP^G曤]eFԎNJyJJ[%"h"H %UT`ix!a?4"l}Ʃܱ[6r{_Oqqqǽ'՝*~5 n >Lտӷ61LRzrn6'xx2L;*n=0$) +q B42RnFm u6_;Oo'pO[܇h6RE|8J8,PWEa5=6%ck짶fi'r/:3`D[/.A :f}trH5kF0;cA]µqgKaSziŃӅɤ$8;lyX.ݹFޥ6xAcIae_=Uu^`&æls=a ۴SQK$I2NWhhl ^x,qɌYSLݵ|ٓ r I ? I%0Ñ:F ggRm"%93ϐ EX0*\iTl8NjYlfrT(xK2_4djN2UB aZ#ډWNn#fc(G*"eq+qJM0)S6PA@f4A:VLwr?Iˤyl!H1HQQ|!3 Eň kAm4z#''1C[54HB(pAHJ;YKT*6"RHa7.=C`'.VN\5U~s*Q G,zY|_>) s|p3h6~bL:XD$Qxn2y m0w5."ܽ"`C)n=#-Iۍ0FM3SkCɨ#6NK 1 gX g 8b}q8$HG= 3j#@Vbn#zOFa]$1F%6%I^w0*.aNM\g{L"_eT'Eh; <.*-洡R€i.{>IJK?\؁-" >B2w*6&q}S#JBCWL^ t\s-kU\mg0Z:>1صꖏvwg@muG/mlgo%/1=՗͍>ZtUo ),F*1!vP }۝m`4.5)Q,xAjhu:g۝.:gGaKP~t*MhPBl^} 1/cP/%wd6VqydD1O]Cߛ뀻"3ףq*kG!!q=D \B!DV 8 &[h3DzU%B\nGGjOKFЋLȕŦkމ W ˧Oֆbyf8n/)),tm^l^gSge7C*k̷m6͚tOxoVЮsXXMi?\z͟g8hko9.yu,TNﭤBAQ!MtFDrFi! xQBh9Qa"oBbB&l풥H3A r; LA$Iy@ Io"OU:@J v JD`u5 GnAL&֧2%[NcxJCB"!+NAIz ÄCmhqL^lCk 6Fc4g7j TWJbl)3r>޳vYzw?<1} JfY{Opj6 Դ3{z?Yͬ/.V{IY iAìYܐHJjbgkzF" mQTD3tk 6sv 4ZD-@bTD>OS:0p.3LɃbki@m3X]nMt6_kZJHB܍f?u墄OT}ԏ^. !j ufz^ &_>髿^%\«{-'V?rOe)xڧu8^f~T[ɹӺ P`I)RUM 6QI{."0=۫^|ï=`ebpwyW_dXܪ W+S;ΦHdm}S:Ndp*9{XXS[rǽaTVwIJDli0r4ziQqmeb`BD:v?MP؆A"~Bէw61I?婇YhSl Cҳ|s815r]hs0SRl>;6- 3IBQu3_E;jc"a VU|AN#J ޛGIru{c̪ڗ#`- 1{Xd xa=0<80x301` BX,Kڷ޻-232{7ee׷Oʈq/[C`C֠!Q\c," !\Bd)۹MS7;kdi{결H-f$ʶ /-]XPvxuR_qւԏf_moo1Yr+ę\ݕO,zͥ7/ 4!7Jw[Db;. I#\A܎8uV|ORifnEb)m|5F |iuF ~Ht:H)%\Mt]9󄎌 sZN&"=K)Dc4'PCA\f;*8]~W읏Nm K:5MEZXn !-#A :/ۘՠl E&T.Qgge]bvXa Qh@썺TZ5,v ƩQ fGzM G>S_uqͧO>DNׯm>5u;fx'kOi 4&~c{cFitUWg4ݕq3Tk3pQ90ۖCI۶-!B.d8?dyo(c1W0T\BnP`W߇sg -C56̢*?El8&؇Q1QSF|:X ].@/"r zMz0NILd>ীeOkByfC:%3Ϗy)&BDxQƼM4h!ĩ "Ћ]Cod=CZPkp>' k?zH70 Lnʸ ŗѣb[w=nݸ n}Hhk(bnkke:kE8^ww8ci:1L>08Jm1yU$˔s,@KNVT k xUϧ]el7sB5HMV=yK@_mGtL'IDFEJm;)(h l, ?%4G2![*10b ,ML#fQavwʄkjCM ,h ]T[$izQ)܍F#MUAc O.33.;l;٦GCs[v\)ʱȄY$ >$"T]%p-@qJ3 :h'oBx6+&S#,p>T(gHWG9N7S"ʈ 4]n- 'HAظ rd -z~"% ޘAjCڅXTZ-6d m)G+-N9FNm25ZU.?N;kڤ,u:6N}lz+Cُ[Ӄ)S>4&f+{\M}Ҏ}nOS?+\;ptK';@ܯ21!V7>|}ƙ4[?š U"!!HE*Bts}:ujufoCc34i5K޿HcQQB|wpFƤ 7al[~-r#oKMG.?֧VNJxxx(+Ԟf/G:G#pSO)W$Q^6Y`j4[)場d3NXAmY%X.o~Og+XKzu[z4 0}*\\I?QzJKi`^P C:.aK~cʿQBJ`1D^sGa yF16w.ȅCd83CKgb *ƀkc+95Ni2 \,8>/Z>׍^DBlf-HqFcC,<[7#zO;cWɸǀ~}).7)] v*; ^|yEk"[bECrx3"# Pϗ.f dYB#E#̬ZwGd0XG- ɫk3]pە_J|N]qd4Sve8H!%LgH)٬vZkB۝ReH(dT&?Ն E h7|&N#vPF l9q jX- DRHԂ,׃N Q[Xa'D-{C,E dinl:Wi҅1Xګ^}|L$-[{nݏKkMQ}|[88Ws4466%~*#opFs=?/;{0Qh}1IPgZ8MfeT:.EXAIƮY33>p'}sOgwW|.l lK[rƅ")yg$"vF {3#u9v:gox8QDOh6Q}L L LnS7n2e LMVоlj`g Kp-[x8Kv("Ka޵3?_Z߳Ev>|K"N 0iٯ&x>5z_^֩hQ8?ܨ|=Q #8~ S玷].0}߲}<ƻCJK/uG }JqVƵHVNig"U2[H*́x4G?aME3@A"V(R7~ lϾR\uʗl;ؗIсxdIM8Z#G& (/PY?Y6?m3ͦNd FE]RYжΤ1gy#a .TXr^8d(\$#Y${8Vx+\ <{ U&%#&AJ*4\C'ڋm.Bzd]I&PQ`(@%&vJh%6ʚGkLML045 P$ dUvOY"9fWp&wA͝A_H{߉rvyK_ỾgLm=eQ{=W^\^u v_TI꟨leLs!Wdߩ?)/[aJ/^6~j=Ǻ[uP7({Qo9יɯ0$3zӍu/ k|l~la [xArٳ#ؾ"ahnr{h5~>đj:8;[M\dIVb[Pu.c*5OfLRӴH2TW@ ¬nCp,:*oc .&"e$$q= rx}Zf.;xk}_fnدOƶfc2/[h ckYԳRiv6ӻPZ?B|\ILҡ:;8c- BRLɰ SaRGvK pL40n;m'v19Х8ص 8w6`sP8ֺKb`JPbb :+a+ؓPi]Vhҹ'əV=vǒvbTul͝t}o$~_xȠq'IF2˔9Xw_n;=[6fO6flٮ#CҞ@ ;?5KBۢdU ٜ^wb -![:+ o{K6gxyˋcj.B C,X(!(>=*Z*/I1}IϷD/}w{14-zk1J uaߌ:EeAj /c"8 |,0󨋙;׋QzMe~nA/ic\;|"PziUA &j nԙq>5^t|h.c5Z)=}-l3_mW\]H粟۾|HjP9qA8iVڳ1.*eUA+o4]y >=˦7bUcV?[h\S͚qjtii?+[?2=}]|ݻkی#x;;V}֙'j'2)z=&]VHM_Hߖj2CGn2dzUVY6eriN\K.@zr.1[(B@%=c1e5ߕ B"IpJ>KS8n}K_p\ׂ\6k7p"UBƱv~ޞ9E A{/A}N"nw5>/٬ڛRI2TTwpwҲ[Y<ۉZۑ2v^)Gն-ꊀG! Q62e褁tw}f1S2c(dv/~_kz._B`޹!uyܛ0qAT("p ;ž=1]0Fy#y_-la _K|⿺w|da=-K(0y8f%WST^]"ʜ4ȼT8s#2mr#Q*cb *—CEL$5Yؽ`z BcS)o{DYq$kk׶.Y}AkWOI߬ΥWvO0pa%J2Vom.S>ZYʃ݌8Z&E"QZJiF]2 bWIOEYD[4MCC:фNH@{ lӭ"*9NxI۰0l1s- i5'p \5H"CqmũĔk C}؊68O=:aN+ǦUtt CSNWCxxMfۘ?өNy^"N|d;f?ƚalFhw!nq]6`eWV XG]G^4kodV.\/X~px>ď^TkxWw<# ud^~`Rt&e [,X)ue=E6ǘjOx7LGo2d /?^1 0/̵<=RcmIy &RxD|[sL(JWG0k~)FAϹ'?#PO`r XuP|yP̥"}bCN~H0p\E_D!0&j?K/06;11X2`?],ҰaRAߊW^&_^{\2&2ߤ$< |tP>w [C-殉HMӆ/Lm,,&#:6JI{5J~7M+֫6+5$AYyZ[tL%,%%;с!&Zoxr\W؅FBTvzϠ kKp:~lj ˣ!엝"#'ڱ=2F,Ktj)v,8Ve+] RSx,e9˰tI2CV%[X8_m=S ),;F. ѤJ?=Fѩ`F)o;u!# ӯ0{t [V㢴 e8> h!+5֍Yf(cU|˻>񻞤1tw,<~1b[ѭZ^U+\쾺-_agw>YIڗ :{K U۲d 4fe#_Ed4[<[m,"Ŝ6~LZ6ʼmNE~/>9A`ƣ!O1/c2sA~aQD Gdqnà0_s~3G57:"O|3va NvRmAU̳&b޼{Dxo<&kz:a>"Mǀ1Qk#,'Jn<^q|Nk׎nna [xq7T7_F] zKRE.'9KH-uhdrh ł}/ʰ`)<%dtq7ZV^ uZIvm_wBou+wN?}G&!mӍRuYr~N2^*ݦW6G*r ckofJTY٪Jng|ovuVSPi˧5m'eF(+J5"8g'zޟ z*WV4.N1K 8AFSBȐ6T PXNL6? DkPiXMlUB:˜mFfQ.h}p!M{ /쨛۞ L6n!jhnGtFSNϢR~a ZSqg׉mjNf7G10ϒͬ|p6%SոoO, Tv4ޠimB 6 QuvA c@ { S_U6ֿ kzq+s?VyDt3UjWR}{֚nv܇ڑ˂m߮l!O)I5u Mw- }fe ׽$-| 01= 3eLlN[x$H^ (?O(0/Ĥ aXX0N1 yDa0}Eélr.o6Mi^9IAXOҫyze#oK) cyi'Eo;Eǟysl:R&:^OSx'c a"16&ʷ=7o̿F@[k}$;ܧ3'} xǦX"Й,7[sߓj!-'umwa@DDY2&E oyXnYv-VNeQL 򏧻TbQrQ/aQq8tc: M~L ^q]X$s||d@f6 )R^j{2g>.ʱYL'[> ;,"CazKl |FfPb24HxsM tkk8^̊Х.L.@Z0ʎy(-SEe 5,VPK3 NcKऽKs\ZeDu t} %#3,!VVҾ)Fʢ?:T8C#Ew:~h(&&)Ŀ|S!i*s'?&Wh Ȏnk S6DZ`'u\#u5Z~\ Ǿ4V%V^+n}AH_L=ܺsVn{vh>|>/e˗|W~?}-ߩ;}f`2f8V>!u6 ,W0]7\ʆlWk\'W"N |·Շ<ȷ[nciЮsyÊgw_\16&1/×^:e<_,E{̑te&8 0T䃼8O3Ә?Ǿ39)l9V1sf !> ,U7O=-la /[NȪV?m:ncEM+"qGSPe†FS n)ːCR!i J25{4sBgxUn5w_=)/δSt:Ruq&n{b6dmWs?|Ġ{ .끖驁3Wڠj$)Ul=bc~#VHK%Q,gW$dxg{}Fҕ-bXlRJTu_NcB$Yy:! ]"ܝ &4{N vێaȒa*+̬2F̏@ ~s~{jc^ *bx4.ys3b d)!xбi(d20wt܎vlwr}6EeG]O|~>\ч?[6~![d7ȿ[hLkхض=-pmZzĂ@b1(uֽ?Cu·?w摥3se[)[-?t3⷏?K6~yu{g3F?t3$qpM ϷY _йQtyj*깢aEˊ O[ю&n!}0Ñcwc&E1SL[a,KPQ2E #[AL}s|&EVE"/ŷG)A"5qϛ۟(XϞ3x"K {[87˦D@ Tگ˄`#;']ˮoK

0fv$$Y^v2SH!޵'mm]T^Zl{e`8Vz`XfwK3%P^=A%O_rʪL˿b_ev&wT|7]uV'~֖J 5Cf*t(㖬)Oz:=ʽbσL $$EDl^$YBV\KII)P*Bv*/ڦ#y HobNF2 mJЪøpɒ2xK&Xt.ӓ*P@} 8+0řYJ^b?C ,F͵SĒ aiD")@z?SMVO2 :g>q,JS3r߉)9,T85O==,۹Q8[;ڴx)H,;_Te B.uuIL?^cTJu"p'!Ly>aCef IDAT,$p2H[fH%C~D`=]zCsAPi ephM qeR< R􅸝;`h#?G-!ha~|60>'ȂA ۹fb^W]altS$#8a7U*kXe-ݗu*L;Q5_9څMoh-_zL~#gϒq? +? La`C:;<,ie$i c5F+Fei,9sṋ~RvD^ܚatgB P|pu{]6 Wmo@b6 pBQbJwxD~V+Z#4W_UDuĴD*e9a2)e9?HB#';X$JֺڡcNB 4hHc/u IQet(0|tc Lbj2t/Jŋq^ccz0<u0EvNu& `rUw1L=s13|y!Oe ,DüKk [ re?~XwjZs> HmMONbk2AD.F6 ;Em6wN*vdiŷA+M/cUD-$RIwf@v!7K3zQw]5z#'r_urmkzPs'=vj0RT\JMȒ tL䙺"$XΕBh -t@Ơ!EE1S ޥKyd(JRSUMdE?CM,p[e N!Bq>D݀XkJc%s0'"1>+$e$}d)ĉi)Z*]ͥ3{oOKၶw]^\<]}4w)!'ۓsL( Jj,^{u;AqX!"xUmCY^!P[y$&uὴ|Fiɧݣs$J [02ϸ_YF> x+U1da~ %\fϝ* pDui!ܨZhZ.x] Ԗ ׂz>Z6ڒ^׾c{ }_'0)4I!]snLn}Ԝ_5R w~l|_3mwʏt[/՟u|Y3nk GE I _fȢBȪKy:(WuC^7LWx9lVۣ4Πt$N/3Mo5 , ! %dc幧xaR|##VY x !{7ZxfƍLPp1 blz7Pa_&P3nЅ˛-d X8%>Ć2s:i8nB5Ja!4^zH=fúKW"S&61B|[VCN.n=6;8}N OQdR %%"DGZ:6kNluز qsNY+pS B:#=TB4`T-^-иG40[7{{_q\uMvhre3gGMN5uȰ!A"ȭW>ZԶ  9gB\#&i8뉠)T"9.X )k[3n< *>z`yhf$ zPH;[gUcl댊`P&"* xJUBpx&q#(t֭~FOO4[Hrӹ9X'R!!FP׮_rD ,ЪSX7Ũ;?|- %K9}+~~[/?v`6* '!k#4 UHnLR(pg[YF~=Gn?/]woǮ˟8un(.Ix*1`a=.Ockm}7Bqծ{g.?4bW0z}꬇ύK,tXRJXrQc= ;j™BD{BBfP^Œ63塉]T؊7ڎ yۆ"/|0!J0嬮 `bE-b!|TLƘ׾6MB|H`ǝX 6.k:ONgͩ2.~m\%l蝆*^RwRD6fKxbgg%341~Y"InΉe.xb/kZ\K05*P6/hw'ǻZ:f__nK:rx?Z;rUKbꦏ(2AC"$@7t}:cDriLbUN` ؾк[AZ98CPB=.2&&!qL B-ZR^>xDڢE>&1!G`T6WB,{8^*҅cA8x'.DʒT (=7IG>Z*PkPmPʧN(^ވl[n״J[ETV޸kwokû`my>#O-TVaCMc qAJMcҩCibf4+O}򆁝~={Nv|7]wE_6G3غi!!Ky6͈D_6"NXkbJs5n-mYlXM&=lx]lY!,Yb ƀ 0+$Xs~{!"3uv>$ׁ=DXg򱐶Ƙ6b+2Ԅ6} c7c5||I@οgVsJ`rjoJEPƎqr=adLQ6y @ڏZT (㡔!rǝWMPoiN݄ӈT" d=ѱU.sˑ <&NC-Dqy2ŗpr,CP4\UծJJfiN51~#>5A1f.؈E)\Q(AkJ :Q5D1õ"`LKt"(2N2HhwIӋL3uŤ1ǣcngdB=fMsbdNyh`JT[BVpfCG/ǠS$&BbQ 0? & 7A TK!Q:QL+UT%d;f4'Å?ίwױ?$Zcl';jjh7 ڠxNNn61zko  w~G:qE}_,<<=,:SzuL>Kbװ vjw0Hnpc).kal8`ےttcXx?PBRM/F{rvk&N3ϰ? [H#I Kj])I~E b}Zd@h|jX/?CƅDt| &z}`i~S^r n6)5r^\5JHB8]-UGK;הE\ G bRD77<`&D^=t)8Jc1H0-'θ)p=vϡ QB#[cQ@ͦx6B"bOBbzDCuX <䚘~BL$ENJ4¬JhțV<ZoC9Hw A=ށnF gy_mG`xn7öls[3Jl1xG[Ğf0I 5% vymP J??iudtm_kg8¼#}`V|( @'V`U DRuq;ꇘrMyGڤ q tӭ^XO$ , gWYF% `56֡sgVI,z3ٲgC:3NkzUDzYȯ\sJuqc>!l(b+sus>ĴI۵Gdڦg3ƅcLF>7-%"hc6 &^70|'n0OQ\kJm^.~W$dcuۺK[x`,.τWBA!bz*3PF`R.%ɠTn~RԮvVLm,n6z*Sbr$pK(84&+PL)Xi{~XAzCDXEBpM<$z,Px] mTN#O0c[+QEcВJU8jCCiGĤt5y*(T'H[5Ch@S44#NE扌KbKWeu aQ8͓7 G&tNA("]nC,NBӰ1Wjb7b1U.Zn`'ęA"Ab jk,4lN[%v!ōBBYH顪^Vk$؋ Z/TXz߃;7n9=^+mT@L3j(`+G54ocnMN(\T`JQe/,CIOj lፂ(\4 LQT螗wO'K[p0[{TGHO ADAc0x8CJz4#)kI{Ϛ9+|Omq(AqSJ1A $" Ғ$CqhS&}KWwT2v:m8ӗ6$ew"MzEPCmVr T Ap82fw1dZ (^|T7`w]uaŭbUZ;=J |]zW$kuڽ{%gf^tnt]CHrk[!Q {9'>DomIt%Oyn< ӿl'Xm~u;6 K/t`QM54VA/zxOy~J-l<̛PIF.+oc=z9WƖ8'?~bIjiZVf-!0Wuxfv1v XI=bH+ȂS$ϳ"fBYX~-&^0|pMx8~8Y{nT'&UX{ɬ-'}0T縞X$j7TNN<#wgי"$aDz!9HơZz^{Ri̵G~ÿP1flqhw~gy tp/0Z$9&=@]d=C;?mca7WYS H&LH),iNߝ>ĎPU=΍^LXo:9"@F᫭Fd%ֳ  >}[!` cVs>mXy ޽܋U9l !B 9K6c5 K8tXB`~K[eHTÒ{ GWaIsK/7ۜHlN'BZM`n`L@'n\)m=PJP IDAT蠌cm@" DJ7uI\ZrVȵiyZT5M|5vj3D"Cͥٚgk92O72G,c3ubF'v.h1e"00h*&ǐӒ0z"&@K8Ihi)Ѡ5 qM܄@A"NzxT9J0$"$0%S~gj~¬P.i'UtfWHbGħLyC%u#[B0qkڴLsrjE^@ZkRYwV{`-T(jn=S3{ _vONٟ/}% z$# |9t:ZG)W$hL^]h{Gpb_7SECMSM틛oxoWr)Z3[7#nxAcƀ~ kW,oF;'H9;I"95ѯfO,>2tr%B4x4XpmMmW1C^XVB,A֣y&rN"鹱QSIQk ae(P sN.&0ws_ YC4yID+xX".I*HbzbkT߹2D i7#~~E"L!Sbޘ,M>,tq < ЊtH3MѫMSsBF0$Tam&@caC>@.D@䐅>UA O.NsV*Ӻ׎ry[UGM[C[9ݐ!u˜goo+>ˏ:1IBgdDp?>NߧRz /+>SWXxJǮ3^y+|×ty.piO|ٞW_{m۾yygջ!Rqm|Ł;)|X7`m vr(>}0|O/]xMxBQ§^Oc۰upvD/#:D/5TԎޏ`ޫ0-mZ%҄0˰ e -)&hcVYXTXԂw[Ƥy3r5c'{]! B=X`EJo,`kζ4F~ }r#M` a0.žXf!/p>U<ҿBOd6Wl\9o1r.e_4E 8Bx6A-sh0WiccY|t%1q(a*-lׅ8-őzR"ڮv; 399a|\''b{Q'A"Eڒ:" \P"#Gf" HiƬW%TDIL{_Z+έA !k}#ʁfR\éJڽ %t^w5ϯ?ύsþS_ot)B.׵9xy؝IG0) ,_8Gw)Gԋ L^C|C_zǾ̋7|\@߿RIVQ;Ro<W ~O|?6\Ml,"/;'ү4k@Yr%$bhmr짎Mۜing&^R'I9Ӫ֙fk\h9Ȭ%csX>ӶrI!" ȼ!mۓXg;YbK.M8b=Զ ; t?=I46-!^>Sbˉu/?Uϱu&8ywnb-zu<\srK9g4h01u( K{mJ0iiD)eU-j6c\#hG";cܨ#$`Tlv"]GLǁ; yIB#LlEL#awJ8b ;5'M:G5nE #Qp}D=B<13%r)){1斡FԖ& _Z( +PX &%B50I dF{7Cke€ZJkJT|g2X?}^<8˟ %){xm{G̼#ݭ餆0g/QsWhg\O^u+\_7( ӭîSh.-yˏ_/oןl Oxja99q'GIў7 ;GCh+eG{|?I`?y)B?q>wjk,nD:zBa'W`'/|YMPN.4DXTk1]am ͍ꥩ?FVozhH?_*>=#o` 4w'E!D+q7 xXc 6tz?lb$-@]xG5<_9W!-aE[*ab#!crP\zQaL.Msbx+ӆAbWQy`0JđQvP&qPT,#%B%<\5(pӠدHÞDe*W: Ar!1P﷩(+!,Sa Dcg|#!!@#)C*# 09S&^ux͒ z*\'1E킗3B^,"+=F6d@v81|!`.>5v7wb Ӷ$`}+ VsvTXUDZr9eؐ3BIl4G2ف%a=g7|R7 Fڪ~U-=m;Vv\qʃgM5" F%t` ֋!(AHY8PŒA 8jD$z'ުsa{`ԑ&*SZnȐg'-b5GР/H(Ua9܋0f5^fP Q}\:/-Az@C$Ib}&l @<*"|p,}`ҿtɫkb˛/zh{~)C驄՟ 602ۼ;_ +@/B=Y1Lh赎/cxd"z?li `}R{h}\<,^3 վcXű3ƒN[qOzv p^eX旁o [#^~x){7`=į}+tMl{ܣ[OLSl]!:HɺyLk*Kn !}J haSR\r"~I!:N` vlx*{$4EUv"sIM1!d-+:U.ttW'+/qM{Ypb_GR .6$ }j#P*[7*3Ⱥ+AVf#DpP f?PTH?@ -bS, ߋ=A N`F!-ҧ<ӍY,6YF̡#"n㔷aT"?b1 n#ʵɛ$@ߧ1kUqb$zǣ!40+:9@W(ua`{d[O _xPrtKŝUAMq6}X洍_~ {? s{6:,)&v%(ali֓?'{Fl ιo!X[dW.;&6Ʋw|U%*揼?fbȖ_DMU$Ouh 02(n B6GI F@X(E93S$ܘDHZ͞.Ta=q2a%s PbО+T;5Ar#ᢈH(N DhI[]Ry& )I'Vwi@ I.N2quJbbrh$4]RED&8I஠ *I! m jw|Vb8FL\b J/b(hh]N>#+c@$Cʕ:f$y:*RuqL,|ѝARc`0To+\4VkE-ar#=oϑ~Epj(xVK>77mq m<%y+nv_({mߌ˘>I9,/{t$ e!\|z\NYvŖBj"{VS5ԣkBHwd^w-/ .d%}9 xKzuA1uN9/l$M!vbgI6{q`9}g"[dmNNbMsZ7xmMX#q /><B{;WbCEce헱dva>!/~K6c's`}=~FIscߌ0a,)rƱm0^ɼ:>7B[?_ilLFm9!e (uvޯu; ٺYD[bZ԰gSm -3_)ąjm5bWv\P &b4fk싾Q%JxILN$ %-"$f6n%`9LZŐw{I5WX/c}uj*[1( )uNi3|G3Զ+D!H6CS9$4"Iv,ˈI}bxk,xˬ(EU1Qヘzp 6R"]A{v̠É;>D쬲8_!pQG^:Mo te}m-0On %;E'7+ք<-ֽ\;~_wN>L_9[&j~ډG> /եGnXuO/O=6,G߽j.Zζ޻;ovm[8]j r>qwIdgz߽)OK(;07;G/0<9,O: Q{kY ^ׇ/]R~Su7&n7.-ՠ&q B } MrIzuA3cB!7bYWY͊Je|fꛙxs2] }=g^8=7+|%&ק i ?Uޢ%A ۱9/1-!D'mNjbY42r077d>sL]bhGW a83|+~xyo46q3,0RfhFlՋ+Ɍd.?f⊙d]zUUWv[4oȡOx*㕶c_J59(`0##/0?iȯvbs! ƕzcVNuHO/ᷙj&d#ӿGpMث1:Bҏ'ۿwHZS _G#Jsr7030\G+6S`Y@.JHh!l eڪ,m4uF.C&`TAnP` 䨳=PUxulAit%f/b!_f[GG@}p1@0Iܨ{hɮtsϓ-`6 c?g8@VY%{Ɓ8y?D7nSu-m UޚwsoD uǎW/~ z$OPfNX,Ֆթ|'}<%e'DWn]K])IIP}yrtKuG1Hz<|Z MFBt!+I?"0;-.B+fQG}3Dc|fN%bsdIi8Q+)JWwq)W*8壣SJ]ǫXŋooUk X89a#N&K$%2KzWŇ VhF?Fb Ti},0}j߳6-7ÌZ@"& iBNH1*V/(_*~-GEgw+o|ͩ\Fi3*.N{~RIԛצWIm_kKs60ruH S6Hp}6 ൄTo6"\#C9Z.1.6wgɹt!e1Eo6nccfS1r0M/QJkrx΃ $wB҄'; ,A8 ; !~_a*͸x'BdpyT,%)IDxRF,/0Ni]tԎ} hs"_A KmQVB<::XE:-؂V5t}^]>NU݌&@y&Z5"s ;b K) Bj#lvnֽCTZ3O% c [i,W mT$] mE,-##̤+18Vi cYd"bM,w4Jl$RqVLsU$>2˱dPDxW c7E%H)|Ǭdfx$4-%P`HBdb a(썂$[) GȚyvю#,WDYj-ˤpCH*)G7`Ezpfo#Y?,!e1j=?7F зMeY~B k#iy3"3c~?"L0 4+d݈10a]!i6qAiLl~D!+tTu_< 77F]q79ޟ>n?Tv󷶾g+ʎ/_~t|lw oN2N QɌpB b2)@Jiqv" bzYGZYT@ ݍNѰ(1"YT U=# ᇮm}mD^fqQ tшC%.u(?nqݑ:]B8 D15YJ]$2Є{2Ѧ^EFdY\],DZ(&DWFz@Z C AGG^ tm^5e YRuXZlSW\=N?>Z} $GmѪB }lq c:Mץ"Dd|8tϫXŋ}o]8I=Y2!1C(CqZe, ǐ#r>=Y'a(Hβ/̢0PTG)9AQCHfw7Oj"4-P&5@DT_ϭ񬖯8[mKP3~`AP2HE21{/4E0dH$1H'u=Z}zs5EыeqS'Mp0 "T#F]ԂV'tVw6{)x ZH88AL r!xuhW ^h\}cz32d-i Xa\מ.rp}xm{_O6lj߸V9ӏ&+w6[u'|c#s,uX6feq]-$2iEC[( h{[xgl3\3%7#Tđ!xXw  ׺ҽ\ۜF/lj7\Rq4ڀK)!I\ˑl'DZ$DsK.Dgz1BTeMM4IЋO+ gOhZb6VH8׊͒[57=9.H?+5MPJB8'ai9:/+u\Y*^  c#z=}=yu1/mJoI'#MS66v4{lAu Pn`(#N=>=|m Y" =8'k9%2PǷ>7p,ͤ>\Fus4\dcNlЊՆ},Dd0g1IR+L!I^ i; ŢPTBXǏclk╔ 'Q!v} NKrޘm Nx[rȖ Ǟ[@a{7Oc/`FeT_ٵ>ce(q 䚄(y 8OV8NlB5fQkLl)iƛ6Mn?j }gw~jW_˸B/~'|G<-ROnUw Vrcs ~K̚11$ZHSŖ$I:R`)x1+E(U1BgK]sR?g߱iFJmM:ڽ,vj"!aw_>s:ViG4$U3MU:Ӕ6i ^8\9礿\S%O.2笿۹0z!Ϟ3V 4ңhgN4A-xZNEMqoȵNAPJ5:-FCv"=Djirn#r}G)uEGC_< O1UiiNq,ᆠL(Cc]", U|&r>vVK6l$-3(dlӷwʬT97[6}d݊tM/c 7 Q̻^&PV[Oepy|°ES䆸_b!;@Bm0kld0#ESUDi扲5Dz}DgQ46?`r{'YkHmaףM0HnLiRD#,GLH͡4DA+wzm3IQa d.D}'ngfdw޲5Noㅱ}=Vf壿6y>(J~k.02P@4;C>0-6;vXq3J稦k_~>5-wwfG(b'6Kv^Bwf66R1]w.bt\ 6BieJv+F,.6z=A[s,GgYB#"l%<'e 3ch=.ESO|D@εzX\qec'ہja#̃6Yy$6Ǝ@LP"V1Qe 1D΢6>J<4M24 pzU$[~#G/!LJqGh!9q` UC{`sP(M`dz;p`ŘqH9#n sEo$ԇ <Ϣ!osS{^!*ʛ@`E02Iz<ʒž`Iaӓ׬x`WZW_4? { aa|R@_qulh+Oms=6I fxSST <#,k `;KmG/?ӎUBtnn~Q+ٸwEϟDKޕs.g8%@K&鹘@׽G8L!ćJwB5BJ)O|4E(ύ`:Q@EhR -~5s\frFљ he~*V5/mޅ0t6\>u.RۜD$*1|TwBRœ '@-* "S D1^ͳ'XAN:sny07mxsY{R3[JbFM&"M́Zd h%d2$ +L2"PH8NY *A$سP-("  ep"D&݇rllB%[kYFU3.@ClxB< (6!rE/#BTB&S^Jmr&Qa⁝T{z-zLV H=8 nIXM65xztGGNp!sWwB0}JW6?:>6|-_gPkmA@h)NBLwk Iֈv}D}%.m?Њo|m,K4gmnse |2zFnJ Л;ў43M..M|][t<I~keyn+C{1Ņo&h'= SefBʫ!6[4m{K:eF켮$Re٧YVI }?PBNH+ wn󍻻"F},{_Y*Bx}˽`YR$vY(Vئ4|Y)H:}5QEH0$RBDA(ariDґKt.f6!0LhGd m< V4h@η3~(9L )rz~6DsyL~Nm'D"Ĉh Ӯ8]'HR1MKX L$b`=8Ce "B>ҝ5, 8=vg} 0zjWJ>DiBi6в=! M(&!IHi0i-ӔEQ"ٸVƢ5")#Ȅ6 6'?UWz7m:6sO١TӳOZ>)9U77.{*H‰L˽m_*qS) t8V)0V 9(N8DaJ{EnSMWѲ*Z=LqU:u=R*Z-8vz]sw*+ vnL=yByJ9` :׻QJ5-t Ow9s$M@'Nv{XsHfy{ɧ݅h׳0VԢȪ0D2dpxaa0(k xx&Ìb"g) $36+ f { DF$r&A{~Dn>CTЌG81̅ $J{_1ghc1qȂB7z;  w 1*gqĜ 0GB5[3P'`qЈH[?2'N}:K1gOr͌;7֒w kYLA잿תh*#܇<P~(ïտmYRǚ)AWqI8!I+Ǒ& %h;x`s?Wjz/*b4{/-GoYe%A;MJl8<)Edq -tt /f;z3:Ȣɀވw+$;S⚶?&zORtIaIKM&Ѥ&&0& ;[Vd>PJ)!DكN4/zMz_NkIIpuƨ &偷u=EG_Qy-g>TڛU׈ڒZfve1mK'-HdWb(X<V'-pζ7f#%GٱdqL8MxA.B;&IXb #TK0Vhb{Mۏ0cNLۉY cb%!*y23i2qܨP0{l5I~h:x[H+ daMy/,\$D%s!KvV]LH\smY'_PdB+)ca%;:ElIq1eٕRНC(s|C򦧂|cwWNn^ @|O'պC6?+lKPXA eXd|]J8v%%ޙWH-K_rٖG `_;C;oInuRy2cLAӊF~w7ъW6~6_O3VbJKӝ4Eoϣ# "}Zis%M US%=PX{.D,8,՝p4Wg:ڶ\N|ZeMl_7{N-Rϥ{U Ѕ.:e;VUzH[ bJ0m d"Ff$pr(z +6s*:>F2V ר f[bF4&IA3ϳVPm\sqY|3Tn&?dљb3-ZjbB;5L q2RU!tk;B+_UU\6Cj9 >~_wpwADXX2Ij&}'ЋJa 0-:ȴqL)wLPg;%px QCABDhp F֭f"f&0~L?a𝜬T1Q˖e˪m#ʈ&\fgNe1 m7 0lIfK) pާ^i }M~>*sIۥG T%1$L Bda 0N:JkW 4j0k!v!mJF`jr2aCD\L mY%0ְMkSM3ufְ^XrJ'XABӍ]2O̽k#kK6MNMmrMc- Ma_Iׯ*=(LހDqk LtG 'mKDX%A'k[йؓ(h@_`haq?WZ%K'jӢ1ô?&[*R&]5v6-QJ3$ZʸSb)J4\\ 't=DŽ·$ߡSS |JU\!xsNp3>8q\ tH<IGtD`o(!]=;TudG:9< c,tJ)H"OHX1R{D9Sf)f-ߚj 3U{zEMS`u'6bF ?:{suBZ=$pxpGHNfK$`&ֽq J0g^97q0#i  [~2EE΢1 }sp ?3e @ZGbtrYpɝ&1EO5潂RL)*]gǸ`p9n?~hW:O<'V9HlfݻX̼ msqڦ%?[(l{emsI`={~1 ~K@x`ׯ^ saϸ =1%F [W;FtUd"؋N;>&oG_Xi=V3-+p_{S%[Td,K?g IIac2 nY.E3*!?*WdBNJf(#@R[WCN.)v$kk.k˒DVEg\ɺYVU\.>x?{z5=yK^f,0Uf͢jm2=Ii) B&J`,Xe:udn3UvGTܣvJP0@ muHĊ ڦGHfeASHƦP}ӆP[\+{1!~,45`SPMHi"ڡ~2M$ C¾y5dh*OlgmL80u|'Q蟁Ą|$IJ Ⱦ9|̮q(v .SI- IJI;ҧ\s¨24 C6;m(9ohLf괋Jm}:>HrsG7ǿ^.}c`"2EΣ5?X+kFBZ aJJS9gD@/ZIJ,ͩ/m~|?88V_[ӒZ3p_Eg X%ԑe! 5Ig IEG\~qĹ޹i]_Hi 4FcIY^HёSh*5uyV+Y6&8&?@;w{]NIMbm<%4Z!# NFGsYGmh{x~RjzV<~o[f _*$gXBuM͐kDdB" ΛWIY(G9q :~xIҔW@::"!"Bbrچ#؉E=-ɒUfŊv'*cqQS kl/Ug%=dMSq07O1O6e}'OFbgmH8-yك:tR̽s"w$S7`9|+S X3BCm T*Ds1:ƎV!֖ 6 L(nrY&Ns[RsBTGsI"f7& "![mGt/k{+1:u[ tI*^?a#`O,hfmFmS$20ފ]#G\@貋s!>GxMms3j}ir&ډ?JJ%+?s5ӟϫ-c,^":&'VqAM_CyMIbm}>gWwX*ԫ-O4ۄZ `?Ƿ}?D >Q zutF jCJAn^$2W' "Ҍ0%o0վE[P*/M'm\~1xۿ[#>tn_ 貐̔݁mw_ͯ<aX" e$N( Ka$Z(m\yS4Yjv. 4QֶX~u{ɣK֢jKͳo|4. V8Z-KPD^zH8s/+nA,c$jDGA{$b5Jb:u'6B\1ZBb@j-ªB'7B̳ z1hCJlVVǫsgՇ+:Љc{;9:RxV jZu[,> Zf7DITFWy3gHYShA9sp`AaU!ɳ?Q?3Y&F鰵'"SPTՍQBiϯ1lRjq'"gSPCkxˋ0_[zHAF7iЋ> ·?yDGOuw:fVJ63"z#t3nHRGBUD އ➸9/Mηܞ֦0WU k=/>! YF# ζag잣^XoCP2I(Y 0-MLT `m+T]-XpK6y_L^SYSfS/ۢ1A3b3a[#ZUHld2yMuF$6G+&yz?X(/CcŸ*̝cc'`(|p6(V<ي8YGUOnXoAĝuuV{ljÉuGoi`=%łզHfniF[3v#nkF۟|bqQcU=9O_//fR[;=怍csy#=CXɥ Km&QιI6${nmGr6˺7}mLgt) ދ6˱v?xI./6S߉lA?~O<R֑u՜/nizEǕ|{t:栣e7OcH?y|tE{/mD^Nm~!~jJ `R*4~xҎsF.TfqvRMz҅rDG^|Tu]mrEЉp3hG[хq]UrH{c=:pw->W+F;tTϱyO*VP}Y`=@a mPҔTC Y\%5Ym*>RCӊÏiDŦ!!R9! JB{F*4F\[Gg?lRxfx†E.3τ_%)Ҫ.AmX[4\cdF捄)oC}Hyʄ Iyܞ9M` !@ĘX[@496?vsF&.'hc *HAeHv7.8Xi#%̖k@@00L_nl88|ƺu*h# ^1ߓqyۼ哓?ؼycmV >'Jy}i:YAL}ɾyYD= gGv^`,^:ieai7{Ћ.hq1.ch\\0֫]oDΎ"%%4A|1#tuZAvVN$?s_ Up5Ku7fJѩWlh eDhl?RJy+9UbKP6H|ix0# w4m2m7"b3Rmta$JhmN6 ū}ҧ"Ew2YgH5ROD2#i<'+uCыo,0DTTrc:'eZ=M“7 "{`;ptJ0=O‹`U zZXsCPSп^,jA N1#_5/@hC ϾfւYP^H,É\ZA0L uaІq%JCZ?ķ|=GS+nJ}D Z}8mV@l,??$ VAd ezb?UQex>= yǾ RIU\9UKJ~X%Wmt=k[)w9d;>5XW:U=υ AЩi *RF~8N@,ثZq%y9{'Z`J D$z}/kq;ߣ5rii],D&huVqxs+D!Z/ȿ.9,'b32((60JCp:4;R`ۈH@؊rwV3tƳYոH˴Z۬y98L9W/ؼhBf{MR?E}%ke١r{]d@7(Vs *w㷲d,zQ CIqņya2OdZfon$q5a|ټ HZ 飚dx[P$B6P"eO6ao:ޚmO > o~m߬C]wpBY8&m\NoeKj:\L=&Zy,f,#C;jc\EthTr-X*.DG&zB]mQЋhQ7p|@U|^эqߎnr5 IѪOAG!$Og:)c'7i.֋wb#: xq?`/o:Q.mw-Z&F^mVhXB(źvI;&$K$h]",ڍlp~UlP<ǓLL2b2s)Ϗi2RIP5F^ãY/0=a# 7JY`$H*K0-ka Ǝh 蛀M[n *.~K! I2#@ S]0|PcTn:ws-k|H8>'>ys<9Յx`;L}OUu9#L+ɒ)NJ-5w)':Jtzmі%ֵ%[^=@YZQ"H@DNɩ't:ǩb@H3 }~f㩪wگls;r^6CaN"5M1JЩًU5> r ,Wlm>>zvE'v8iKBzvs;lL"$ʞ:^ QTlF:& Vѣ٤3 -T5tX`"tW˂YԓbEBB9ZdE$Rp| IDATzKW<nͧh 3 {) raƧHIzIcۨFhB jl$z@Px&;i8z-̴B [ HP agC:lHs0BN5a6Ew<2zxu0"1li4?{:Go? Fds߄gO_3#گi}ͤ jI9m^.ت\8Ɓ"/|jIfW6|ŷͯ_,^ ܞx'P ϣHhY^|8>/N[Btjݼ<lЛA(©ND@ 4s?~0Y^!, !!\ŹM~.eThKU5ڡ="Y|Bgk ܿ!y`8n`){ACǪ˫fSn[PC$XbU KjˊB@Hȟ]VC5:+pKPdZz~+ bVK1:XcՉڇ90*B",=NNv"7Maekw BeGN a:Ck$砷j)0#xOC5&`.vB4rOBrf롔L3d{ 3tL 638le. :12٭Tte m}f PKM9DT+,߈Mc:Qi#tҽ_,^@ܜ"'BdzʚVS.LԮի%FBxſ'-Y^"V-.$vYE,j? |%^RʝmQ|t-^Ouj} q] >yrV[%ۼ}pmKg?}jʉ-ȎmWURM ,J̰!6Gt%pԚ!4>T`V<X-jkcJH=HLL*-wTTMsڴݢb_#ZgldjV+] nbA9@h26Ohr$\4G8z Al6NwRtD$C(A4M#@5 Mi8JY$LwQIoVqFza 4NA,b3-uiMfc]y->sJސ6h'8C͊Qlytf%om~օN*dpŁklN$,v>|%//0 }BD0|IP܌2Jգ/J͹x4jPPK6QEZB\'oD7R+zo.腜. h{yJJ J4I)/OC>ʫQ;-'ÜAQ+bBS(ϮSsAۿ"P>ן:"&_,⽨>nPAق0jΊs946`hH@jZO.ZYe#c8]l-Hڣ#˷5FC#6t#mqm!qȜHL])U R4W#lב v˜,[ҴFp:h!a(d5, 4 #7@8:TTW #z@(%ZzD)pT^(&q Ҍ0Y h9=]ધUKh*AIjL ` Y֮{\Rg$®kGfOo\km=[sv}ۢ9T.)/EnܨA2VUʖ!*4ͪF#IOMOcNm~ŋK"ml~!ʓu5*@Fa:4'3 WcGyN }}3%ڧ.QkQ5ԲP!E YByg@CU[;@]2|(2PN WC|>Ho[[_GLF*'Qէ(۹ڦi2' Fè/G(ɘY)42r]۲Y+E{6|oɵ&7e:y1tn`ln.`x5caeEĉ"!!4GTBll8k2wtP4i$e'Y9d4 g RˎےƱ ]D!ɶ2)qRW!2at-؏Hd83Tގjc=Ph2hو*nT hX =#!uЅԋcU6oáp4޿<GsyW GBYsbQ+:6eˆ7J)C ]Έѯ>"X0N3*QDY!Tv1 nC%r3<(FYn~iݑT'O-thaKu.*(Q<(~u&'/Vۆ3D9Wlj5=.@] PυFqou!3=]iLTk[?,n>ĩeV$WăYaQ`&o23&MXqx~uUbHtZG*FueAg=2S#VqfQ}ږ,OM)jPFPp , z'[L[Sll g?AUZ^ ~b!T b@Q^!8v%#6.\a[0k\zx=+Q?MHwr|! "U:.Mi gO8UBND4uԶѶe <{í ?p*Ҁz6N_xMUjГQEx߰%BlN>Xm[_,.ӨWb%&v2kȡ<"dفL,l Dr.ڃ-&m4ili 8RWī\e3ݘIoʹ;CZD`cf4%FC֊dOz;ЫaF^5V29#+KCuHp`y4LJ]Ghi'+o$W bnyJ=46Q;d9*y'J ؉"fQDթcm6JZ6GHPU8 JlѨD{q*Phުh±\yxC^ۡPx̌w{kH{ulWy4b}qPWxZ v8 W0vs։EEd 7%n;@qZAʓh'O䩅AV뀷? G%:FP} {oLvI: /P幇^h]4K\fQ"=F-_؀:^v&kS3F84Ԍ8j䷁?z||||.8CE_|=yΜL|m8rnb"[= oF8^)GcP 3[3_1(b8]Cy'ϯօ`N9ں??Ӱ؏[bWg*щ9ԇFzmJĺ33Pښ$"z6OrzȔDE6u昛oF:ZF9< m7s^.I{p z%뿖 l VZ:2'ihDY33MRIЉw#L]lD.;,FECz( xv C|68$FYUvT m~\-0tVwf:c7[թzhJ z`"z[_xVL;fPx5FȒ TaFX\DAT} 5Lěb-*OհP3%WA~y A(/g%Ĕ!6zqS&%ĨMȞĢ@PNQju>pWH)g.a/DUYZ>>oE'~tbt&w57S6סSrL[hc.<|3s$BxoqXj *Xv=}&@Ƕ͖7Y3ONm6ۏX?43zc)BIc"fx4GC8xpYb~:W-Cgd1eؼx9&Iu^ e 9 -1uXW lgC+{ǰ'p6 epEmTflM!RmJ%!2Ӎ TdJ;W2KcźV7Zh~jܼW2e+Q+Fׯ[^^x^8B9WY..!Tۃ'wlFjHO+S1'`%+$wc =D") t;h I4eCFزj?=+2M$N@ @ Wd:<'CJƧѕ)]H9ՃS7k_HAA! ӨtMg jȬ~8٩P.R<ԍam}"yP"iTxx%BBQwl13 }aW9xddMuG;r抆y퍇zlacHY;e5 H9.XA*$Mֈi!lŠ%8N.(D;*Bc>XN̶FN/ Zr8U'dѪmӐqIZM +ԝC8e-ieBh?7rkŞ&+PnfoOgIZmޚVS4 6{^2o?D@49UuuxFQiƳK 6J/`1׫56if׷~Pϡ[3^bŲbP{g#2ҮزvuǦGG{wշEk7Rӎv늞lh:tXmhCz*OE-k`3KmhP4j9& 2Pj3ra.~QKSAa & ۮԋm-GJqJM^)+ѐ vpЪ8fh LĠ}DCác g'aw9FWΤ֦ۖ4U3Sݷ$ sbRӟi=Oۺm6sŵm[hQ9Ϣ҉6ti -i'JpXhڹf)v/T Nf j5{xkNnjD&kNwΙUiA٢;՘aU;"cH#u:Ruv+l̈́fc26+Sۑj<+{J|Эp֌ۙd0PݤM4Q7%r ' tF0UŞt\vL ;3Ρ-mz\}̊3=|Ö"(n6YΣlޢ#y\4*?%{Qye{Q=[~_ٙ <ٟ M m4@+J0%$oBm:;6 | yK0 D'PǫURek@P8='t^! !v TygQޞ:TxDi0QF 4*hQ.D-'ry(uTm * i<=8 Vb.l!ZhTcx{LQ!F>Ԛ»ŏ&o˟5&qOSk{+z3Zjb{,,%"@sQ#=6}>ⴆoyP8ޱ&MM+ɑ"6 DDP5Nv[#X Q4sV +1#5kOsβm٠N7#L{D,kƙojWٷUNÛ8)l l 'ek'ߐ3+-K9= lo=Rl+7]>Tgn%fEIIȨ]ak> LISύ%_v5Gk4eL@DBѳv͡Bm(QK*j7'KMI\Pl唊TRʲb%R9T^'8tR@A$X{ٌZ T8` 5]| %fPbP=9慯jGIQ G Lʞ^5Ѹpw^k-j^ *L$<|} Exɳ}'t%ƿU;V8W<ѥB ݏ:U1 < YJ@Vl$;a0z2UpzȻB5ŏN-tdPV{.g3\!>(eڰY=OܼkŦOV["l,O["N5.]FbPx*t>ٞ7W97hebK%hMK EMA|RwZ&LU %l#@鎓OhvbTE!Z ZE1Aר2`څ8'tH3gMy-eΛʏE7m<95&P_w+ 9jȱg:75V4+ߟwjGN2@IOve֪?j;)yjK)WGnڗ@H)!Drq0RWh 6O4Ąc$3Xpd2%#FW0z`t³wYLR4U  %Zjݴ`4b!'+ }xo`3==VҰ_h+)U_ks߶XU[ZѺǬ jc1tOYTz'> 5?~Oh7‡ʷC/._/FRm.s/`B6>>>oB jk/ M;jHY-g>ٱ+޸uYOn:>h6^WOZWL:t_ћ{͙cBsYz[ϵo۴{;o T$R]]Ȱvh48186D!-^m*2Ji\}CCGכ%Եμ z`"Q,3HYVbd`!PHLݴ҉' BdG;?֑Jx [qH(fy#XW{in#Zt\D3v14?d!N%ۼx7w;/Fϣt>oM"uRz!/(0,(`2֡ xK( %漄4jGtĽ-Gr9T!#Txqc7 nGwZrĢ7 &T8,ʣTz5B4˧ij-C/Ek4 > jBdP?K.rl.Tۿ-f9,#mg>:h:o7OƜpl&\aرz2=׮ D+.H',+NYrO?&rbeÉs 2ľrI}}hO>5.t1nrcEVR$bH2)dd lV @8h#-l#ZpFmI۠G@)2 YH n츑Ȯ->@f_O~g;pMLJY|,Ij ܧz+׆7!Tӑ4{M޷-#y|\RyBLDP᝞W {=tCi>RP_@ /0 A}x y܄oϟ@ O]VTz'V/A QPB%܄KƳ^G[q^9͠6QG ؈QQf`چp8W/Ç$شzh]/[t?>]+}[;o|2Wv2#NަrɄ]kŌ lKuaRu-=\EmP G^}.qBRʊ+:zPS$NPa!DUr͝| ؊Zz;=|5wsPQGBMJZb7y۴*Q7]r TgރAl^)u^RTڦTPD;|)JWnUk{~V@ه/;.*$(bW qC^D'C]9㶦_gZiGW[w։J\rMŦrɶC#ĨQYwY]T-jȰLC=L 'eWE,ڰ},$3/7d:s~cvddek*uI04,z絰t`dU~r+Vm0(;CN>)Lmh]u28|i{ϋm<[:»W?sKso޴Z̖uf!#kbXo0N X O8eZO/'BZd`Ԯ Ņ2O@VGSSfDg*JG4|$`qŷ<CWuLacudwu j>7,xWUj}+ۀ1y_;ۋ}B7;Ksip+*?w.̹Jb6(X6P}I&TXy +Hz:JzLN6!!pϛ_,3R >Wy<S(q UYu3J8VPcY#ԊTQjQgBcrNT(AdpERJ4DyWP$//Sg1 e9<283&qTnP߽#D4Wz^(9u{&2+C+vl]8_d sǷnddcO1_+L =C˘c6VDYlTVf3p`3r;X4T vuu߳Owo r_#m^ Qnٳ˞>m^<Ėr>;8G8"Ү,y?K[慶ně? 缐R9<*3H**|c5&qZAP޾J狗WR,CğB v+N|c`(BzhRʒ+gQZYܽW{ʧ~*0t>zmMJE~?j=Sډ{=MOccȆihyӍt"XihP)$ PYd< T Xvg;Fnb{W៾|0.=7/qqD[jু[xe쉩ŗYlЏRpiAf4Zr%He?X\`|sR(/sRy! (PijAM@Pao͹!ʐr){~WK.yt5֫PLRpj8q5D` zkG异P/P$KO]vt?r+/Mq\pL4yPLՖ1p"jo$ ?{KJh=1m cmxRZ Yl%җ=@RHRv4$<:Q)ijn<^䴆^$t傃j R Ո< y mCjV5MoCm ŢEJwnђ*d/J (q0B/7Y0`|!P k-{PU8O&P*^J+ ky/E*Fy% ҚDyb3Ǣ7)|hm3`ՏEf?nn x>lXueudWiP.eAv;L[0Xƒ:ykv 2lY=` u{k[N|❿m5rZ8v8cyڨ^?Ul{ߙl8{ZP#/ũR,ͅ/})%B 8jb&m4pp>&u?gxa)8!j9 {)9(/sjrU-P=@ئ2R _/G?R;\jcK6iwןL};W.3 *U+H]B*QPogDDF`|G4BݑkIT/p,}|6 hR%%U7,uT/VڴE[+OTD/|_,,nj%QXT:"*oq9*d1ĩu^mXIGKA(HTǯ?%F Bɓ@4QxI|swWXK3z_PؗoW\ <4ڑtZ@TJ) /ckqL+=ǹ{]u) Z7sZ9GÉ!3Vߎ.S7 ^պ3!>>>>K2pßõ,[oZwhiM- D*:$Lv7iFfLn8~%8-@ j?\HG<.1seLRC]W9k͆mϧkڨ5&q߶MYYt\@MTIC-jwa0AUOQ9!j ~%inTKq˨JKx͹p.%ډ b<"Uq-x]ӣvڽc]2䟿nGur=hftY5 & e$Ԡ9}:c_&3r)KyV/4↫-mws6^CW_H *W1A;YŢR! bq}OoP9*uS op0B6rj3:YL" REn/rwQh7~\olM꫷Ev2"`p$d'>>>>8mfeGĆ]RO_fнa |MLWPӗ@|9vs\1R%ΤpAx5$IUFi1x߉ll>;§珛*_܃l^Id5bQZN\RιGS6$IZN]R7z{YGJ$I JW~$I$iX$I$X,J$I ,%I$I$IEI$IRŢ$I$bQ$IT`(I$I*X$I$X,J$I ,%I$I$IEI$IRŢ$I$bQ$IT`(I$I*X$I$X,J$I ,%I$I$IEI$IRŢ$I$bQ$IT`(I$I*/=` -DIENDB`openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_25_0.png000066400000000000000000003113221413546205200254420ustar00rootroot00000000000000PNG  IHDRO.@sBIT|d pHYs  ~8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxye]9w=U$$TR0 lrm C0FHjv3~s^eUյu-y߽'3_9w!B!b7} B!#ɢB!BKH(B!, !B!$B!B!.!ɢB!BKH(B!, !B!$B!B!.!ɢB!BKH(B!, !B!$B!B!.!ɢB!BKH(B!, !B!$B!B!.!ɢB!BKH(B!, !B!$B!B!.!ɢB!BKH(B!, !B!$QJ5RVC!G`>SBWIōtJ!(c>AB%ɢ~-mB!&[ !ī$FJ-mB!&f!˒dQH{W)Җ!vbcoiK=IōOw=[ !BL֮B ɢaμ5mB!G~?K-B۟$F$R7oiKBb}oiK5Iōz[ !BL-k'ɢ]BqŻWnY+=Iō뱫kB!ĭcnYK5Iō=[ !BL틞-i'ɢOSu ܌~B!^E 868:vTcGcG%6 *!ɢ̮R΍u|?u!č!=f]u)F^TJ| Fu8Bq]1&`}cG~ \8ƑEq3f筵o2JĦx+9BqYSg#?bĦKqcIϢ~@\RuC^a EB!J]|ѵP;Pǎ?E׫cGgxBOz񋞷5;QJ-{0Μ^ioBq&O1?_NԱKp)ʋ;$"gT<}$6 qdQ GR-6/A/<"n+Okj!әx h#?Z\mʋǀo:9t_"QxPB,r.vJ)oR*P @9իhv?^]B;N1!u'ɢ!RZ))4\.1]1ͻ]\Ӌ̸ҪBqWPǎwTѰ6l)rKQ<.T/: Q<-[`SJBۄ C'Q>n}ѫdС&Ma)e)(\\Z "|'B0srN/տ?6[^`HR8GǟtCK;\#s_^V/t`=GDV/\+!ɘ2 u7^@BKEq9+(~Чs=/')~X~rhIiB!nql?Ϝ[7{_ZUY/|;}h7No GqX[pLsm3_ael1h \9;8T\a~١E2ʓrJboi?EILP&_|X,HQ&]Hҥe<< ee=ZoUJ9KB!n3!@a;ί3wgiӧ=_i/omO]{zu[XzzQ84F~K<LSAs뫌^2"WqR^@Q\Eyb1.]] cGIl6 ɢ@( mʀ5m2YlQ&eRQF2$\+J%SJn+B kPclm+~SQ˷Sh^|>w?8ޫoaq|+Ow6֊U/([J{Y9_LL}Ppq}{U1x~^3ѽ1u,-&ny4!V.=-1QL6$V?%B|<#Oq|>W 0F=+~_efMs3a@}Y?N>uvpʤn ZHT+6aTgЮT5ݨk 2 f pl50ڂӌ\2ᔱYQ HϢحIyE$0sՈ cC9_qx 8qm=L{ק9B!uZ ٻ[>UwɋgY=KȍĦ''674PSNejQ?h-5OUȼ ,\M5\ 1eobrm-b+W"~R)H!#Ohrd&t8.j|u:+{wO\ DHY0bk~ 懽p_ŅVYE-=jvOS蓫gJ7YECÈK' |zҠƧDP|`du@#Xl`s?RF[GŻLZQ$}bb}LYfZ(}bĴ(NsYv !7dn&2OsuyM~gz'̌O!zmܗ^?{JYuri9nB2嚑m(B<}1|_aA s=țE=i*F Y:[Mu_}rIOXk4fh:ujsk+A0ws~/-[{_ŵggba[$:JTxt5;U!) |\ P1T"vg -p} 򽋵&_f<11'?ȑ%B,ޡRGP&vW6Wp2W9I=dC9ʤp)*N]ix"Ah}h5e 2VVJ^#-zͰqt՟ʷS}w~?x!5O+ ڕ2\Ə_jjz=ʥ3\ʘR&~".z;R3bBq+bs[izi/fmr',yaѩf0x"Ū kwy0c}q!jO8ձO,Mt#ֽ^QN56ٞIos(BΏQM}5 QGYG!v?]_N[vLY2.]p$ԣVfx_<Y̓h^*aeZpfZ4fuYhxpv\c5^=i9tMg,T=.ipQ{v).@7e!:[z@{[_}'~vM?u!5dWsN3.v}Ǚ (̲3/ri|ߦWV]9NTe\H!>I=ggՓl1yBZ^sby*isY:*3郬u:?4[gM 6[XtJfi¹rA * \/Q<9T&crEYYhf7:_Et!"AbMKK,M"P\f2ervse3~;]_6$VpR<kB!^R-#g(lAPрP56ynRHcBC7s\/'*c~n녽}F=Ylil!UqmL yBn-Y`Ze{S9 hj{ڮ5#c!pӄZ}t ZAZޠߚg beԣQDO_8x}w=b~a?Ȓf!n0I\'xo`gᤢ8Iʞ;S.L vҫAx eQ#B!^/S cxnR`}^R.4M7X6Xڤy= 9;l!ac 0*rt(hB4=~ ?u9[l!x`cЩo}O,!ī]\g^fla \Iأ\:cR +VmPJJ)"=6slɐ(xyɇdnuZ= 9oĐe::ʂV.5/Dy&ey|n/IGT=_L.;6¡gDKainK42r OvД Cz(z>Q3&ԊY c ;˔WNbu {rߞCy{ClOMl<g_zOFÕ,6靜egIjcQy JZkpb!Fyn>h_4Iazy=5|z5ZU^gH9X}S>ZMmo0]gˡzr|"\!8:'p=Nt6u>SƠHȳJ8M|r|Sd>5/+ КMSаz3175́FK3h79v5dYʹNf;_?_oUk}#Klddq^Mq4e$9,ǣs_}\^ TJ}Bk~AkEiEZhp4NTRT^1Tf>- A SӌFhGsvsZJszvǸ(sMArج"/V=0Y"u ձU#k,K7Yt=k8An-Ҩ;(jJae\Lyx=3sdYW<|E\_&c&A'N3羹$GNv"uG5ˑ%B\G,>FIod~>v7eLחS^Ə'?Rs5Q!mXp]@y5ic R$hnP8i`7FCUw]jG?MiUkO'$]xq6 P}XlMqb009-s\QDZiNVeE ܺ磌!# L9'MH4xr2d=痪U峧U7ܳ{|]ok8ƀ'o~%6 q$Y3sv IDAT_דd AOŋq%diadʮ3~\VcrQ.Y-J)<|t\V!ɘDͲ(&j `h}ĥ4ʃȩU,Z^6#)HRd((,4/PlV:[~y̑6)ӘZ6ᐚ&EVЊ"B6FXofҔzFLW6-Lj| ͱb+kۣ[>97d5Ԝág7-9+OoJ;Yk8c@1l&""uF1C(UU4 .͠%BW3StvlnrmQfa ̤e FSP**UFy9-ߥh1U [> Sl z=)45uG#X]+yxqo$SadfQԘNsrޖJ9^sNc j_X}j}y=V;sGyݷ8j*!B8IL*/.4as8]$:ᰰżXO5e1C\icB>]CL[z|JŝhA>E6ıu{8p\̳ݯzun\(Bfs=anN [Q-5"78P:~I._:{n-`{W)/)u70"4 "?B$鸻}ɛxk'zT8}})p#2~_cxQZ,,<V4a/=/w Q([-uüCܿBpdvQkvLj <560 ÀZFP!qlސB{v`\4u kN5N/o2ٯ~fEQF k 4gj}&USt9SYMj [f_Y,IleHx_7vQ~.x'=jkKV[pL!2k/t*LzlPFq#Neu4̴|Y( !kfiDU؋<_߷4s $0 kU0NJj |p5&qlNϨ=DU}څhE|uFO _S afqvf&9Z}S쟞w\`8h(I+3t:(Oc,X;ތߩv,: p2ѺQ\ aL)\f׽Ҥp҃;y|e;n xI5BGx;<|~OӥY^2LQ:eq"5ʠC7O)0h |ihǡhͩm;̔ԭ3,jzJfjV#j˞z6 p\,/X18<jEPZh]nCaP@Q(co߫V2g.fKq+JTTANgr3ѰpPe'!\OY<B*50XjxQ|m&xŌg5Uk鎶91"3j5g/Z0H N=_ u1ʲVdT>$^Dž4%#T] )5iag*hm:^>Zv͡:grzOP_؃{|VcfZS^ DN]XYFFf(h(LAn lou#Bzo}~꿩peN5 m5>ֶ^[0c=_#ha1dk+9<硅=vLJ\'T3tH|P;qF丅I#`;:ۮVZhlwܓZp( FlڻFVzFf 6=F~`Ĕ[:ZnlЁO)x >bQVdy8Da4>Α%B\I|]3_LBӔ\j&ONNfy|svNɋެ yB!U~ag>GLv.7̲_N ^<Ͻx:$ .s *kU\ɋB{k7eE3t(԰4硝^WdYRzk .ZYuE:n{fug0Jq,on̙V+[bYgqndsns{޷wr|IӔ,/.&YEA(\Ԭub,#uBcpPB\ۡEb_\AC4śsmsɐl^y){k9I('I[nS !Wt|9Ա=?٨8Ioc55oTmUZ&jºG?RoZu7Z٨˛lCVe}WEsWYfBivQZ$QQOLTդ֨en"[Sov2LY/2=TF?b}u/9Fùu}8&/(hL5IAqrg;.(EVEk'#,%Kq$zkBzH(֮ց^j9IdyJ&PΧrz*NeᜄtNV]}%ցXkof1!wwWŧ"U]y_wh~~I ALQaD?OGf=| nCVT+V;VÈZwegQ^`m0՞`͈&e8c@ad0MTwzRi MNg0ƃAU͸7C3qσ?::&g}{`Ht^̶"L^`AR (:,Ar"albZ5_Ȓf! Cm!g(Cm3I&IV4)(N=NLo)BnM- "ɢ8ZkRL)l3ukr m.2amk34g'y©?z~ri!?w?_{r&n1UU(cQJ7E7S[a4:3>@YQϙd{^/6-NT840 R X- 0["rf0l5jzz2?,.sԲָjRSga~I<Q lnt mhoXoS5pH &8HAbe1Ma@HDrLCDNc،hCg*xK$4P9J\R )b H)!<#>;H@@%RJ0j(^b (Ob}|];opջ1/w*Iw]RRRR?(ĿZn~x>߰pkL^hPM. Ϗi <&nX.I_H)Fb  $yH- k@d!zLb{IiZ0WV!M/moj5lY?Dc|V,(}9iI(tMj4/L,h ۱p8MB-ʸf{d8SGSMSؖ#V:X^\@e)!RdY,ʐ)!K2!i6`<1h ̲  8\撒WA)Kn;+(2r;aԅp'`U`&N|Y̟,*R(DB}^{9=m())))yk3^Oo._10,bLY>b#LS@cI$34뭊sV{t]+ Cn G2)G>UF,$2 ݿ|ݸ71X, BH*L<afe+bBFRF1E狡Bbx_{zw+M]ڃ([6$;?͝OiԠ.R8a#Q![HfHuJ}2 w\;UN1qxn"ɊX?Ju ,A (8tMB L򄧎SBm&Ic9̂, 0Yۢsr IDAT\Gή`0QsP$\X^XeVt 0-c @թ atMYZǪv1ŒWM)K*PJg fXP|pB !>fYޞ|EDEd5)evQRRRRքA Fmkqs$Fow7;[z'u2EeAb9oňlgH Ө!y)ѫ  \CJ]RJ)DZ)$5M3R9 <} ˆH>K3Dzpϙ{\ t0LQTFz>La021^ZEAFUu-jHS!eV\ N/?ڒ[QŒRv !=(GR<&f`h!:1fLف%T&T;_w>?n=?VwǫREv֒Үm^؃$:X5ΒFc.ǃ(T܌"&1ݕmp!Dј76R6$C3Muq02RhFD`Y$膝0eu8TN톖 Tnm1Fzͅ(Daqj(,ai 1Lc4 84K!t(aS,: b:Bl1J->IIIɫ%} !ס~hӡĚ_`& °giPu>fE;_(oau4 v( RIIIIm`I%7݊mQ Cc b= .IA8$ )dՍpis8\撒WĔp *iA|9N*̼I]!& A1+A͙ȏE}yӴ%%%%%oS\pB"eH_i'QrȂM e!uA іdj2D[1vlT$ 4)2IHni17ص~Ck#:mHхhkn&-:L vW`G>>> GP8nB(Vaj&4渰M;>^ڼ.^z /_tMvo=o,`1-dDB{ KXR)=%_J Hc)1Yr1fu)sXml,s'2S(#ǬdF!4_IY P=;Жv~,1-o Ǔ#J'l+L, GE8i<ɵz]{pCulfL,"Xc+D: ɘY&Xw/ng1g.bOpQq"(0,Dk{DpzȢlpE׼[:׶i6/~>ݼ%%oJX򵰠Yr%1XdD'Eh(2p:f5#(o|V~g0 ;qX͗@ nFA%(i-(ogJ<_o<;оVUγJ1 JּH';6[!sw0t5(5&$e+*yQkC" ~6*]acEtcNʘxYiWګ j/.vR~T◟F[udIvsgOCR`8K))01M IO)Sߖȭy۪c?Co'V9B?:6ܝ(ܒ\A (WQ01xb"U}(QA]/YVUY< Pb҃9ގgV'e{ky WEe̲ȅGɛBiBB,ǀ$qBGOV{KJJ>,׍0Jq$aHƹ6;)Ldiլi!ŐB 6|m/~;[ӧNVٜD.ugГAtɄIb`q̝v-s)"]5$a4IF).].K\dK[w1 <mcD"iJõNb}tkX)E{L")ާwU7 +;Vνl*OĭlO#9woJbף(KPnY:P^JeP_`/_a)%D%ET *xJ]z1Ay]Pm'|KOu? !䣥.߂~7."#:AgMuM^zfneڃHmp(tؾoW!mDZǖ^0,kshX,4͒Rd|16e#i5Qx$nJAL)v8@u${]"tAr-L&(J{73R 4˝Z{c%ViIs;j}5wO~f4}Ͻs!.m}[aKI|7qsWAY,H)lPi jHXSf"X c %=J-(O 4ʤP_jȲz'(o0oQU(ɠ ?< Thsɫ.֩8,spIIIm`)ێ; D ꇐ0-ostrrY] -"4 &F_iE*4+/Qi> x6ׂgY:mIuJ3PeQ[ѫ:Ysm_z6 WV{++q=CÿDH@ʅ(ޯkx?_c}nm!#!">XQVy{%„i3k@GL~ԗ(q3(EēfJL6ayEVV3ō*n Sy~"xm~OJB~YP"ס7yOBR~uuByS(.=na6ȡw/))9"z~q1v,Tڅue4f R'Rlg`R͉nK ,YjKAd]rǎV{Yr2Aڼ;ZVҚ-M' _sˮk;B͜IkZVÒ\ajV8q4[.zC$ !b@R@nLOL;wA{<=u;-cco 8/`??1Ӭ/PBuSa[{ݯ)gop7o۝sn@uGZPˏS?NpϢ>#pk(0 w-Dk E-Y(^Zrj&p/cVr,! o[Pؾ!dJP`ŷum mlIII 3pbMSj)21L(+gl-fc2IXLLK9d!%Uq Htw}߭!c looj%;).Xf#JdQBb( a2,0O H ?( ܽ/ӈ$!i:@(Rm&A@0 "%bd(a 3W}B*B-Ъ;va2fABMe6pzyڙ[;}*> /ݝd>۬bRttԀ}1 m7oJXr(Rʐrʛp& 9߀ץP^#( (1)TˏQdN@ UNa/^QRCP!6Ԉ*m_r{wn JH;P BE)6ARq!dʻ] 1fC=;)TA"a/a-)))(򣋜5d\ qĈ#Z6 kۛߌIwa21a&jzWu{ 3?'gܳ80w uH݅$aŸNInETHf:`1tBy\h RQhIx5kEBS#zcTf&%$Ytu\n.<6}O>w}r?Ň~n'i̛sm.zmsjG 61Gfm%7ϽӤ>!%n zjGOfHw1o 1PA} faԋAl{yog"V+*TP%Ox J!tv`)=!! 5 ̟2v s 8Y ju?u{|( JJJJ`$?,kumz tnKdUK^K+:3k]-1ɩþp8DNt"CFki:TWS" $jEu}Hk68Z*Pk!5'V .XZp]L]p1C#Lt0P!!! ઎b&(Ɖ-Qdh0c+~5n^X[54%??]|rga^.C`Q7a8+g#w6a%R綦#o}p O IDATr舰 &$В6:լ:N %:|6вP'Hhۮ?66/m%s 3J(̃偔~B(؀z֊|s(ASdS-=0c E(۹%ߙRq(R~/ CPUl.ɗ )~B'. J.f<5 slW?x߻G鍘Fqtpa^֬t.-,-ssVC{ds<)_C=ێLıyjکIz{Z )ڧN@Q )K׮R,$4lMÒq@J(J@7nL4xEk. uƗHFZJLvl[.KGxQ GرV~ /Rr =?=r'Z*<+McBQ?s.]`[G;m6a:A4nf{1UA8:\V B%񝼎:X,9.gQBb %&P?'_Ρa%H2A0 U1 9i~\=alǭA ?exP/>A1gPWv9(]|]fBՠ@yG;ڂb2{݉Q)f%>_Koj!mk{@, C.xo:WCvm7<>XRϿATRRRr.J$v>z4s*\i 3ɰ3l5*V$\j _>! #Zw,$H]0Qu[a5"uɉH$jvY'l7Ay~;vժ6Wq`~b`Dp^aHALbBJ $"mtmBfkt CmK".k~H 깣zLO?szK#btن=`\}VY}`^eݰ([tfYx;p7s9R#@~ ߅ b&[m6ڙ^PtZfW>C 7o.wL?l`6Ƹm"w#soeDHѩ3iBͫVA~Td_p'Po%e!k@5 ' V!_r6NAPm*3YVz@y/#/H)KÇ@ ,BP =*/Y~Yi1va6'aGqs[E( _"R\J {zyR ̄BlJ)#Rf>h8ueU(Ϡ.ߖ!MPBqP/1~žW``P>#u `{M.)))yU._za&\WBsM,K:w+S\qR3YiL0<64&s'}Fڵ|^O`lĘK&f]h:+Z KTF {ih1FS`ak[//ZOPY5:Jׯ/rv8k~]v=B˶Q>_/'Gxjx8{$O~S'>}ʘC;%-WPfVz: ƒĘȶmFηR?I4BgoE8xW?te#(^]LA}bg@FĈm~=cשnNC5}߁#u1JXr)"L1sb`6אAu{PY&9f^BV0M(h{z_6ʛF!Fކ2ۄ4qBHQV PCċP"M.S$chy'Wjz%Ү E&|.ܤXfBy0fh%j`\1ȏS4,fs6ɯȷiSw 2P/Ȣ pk ̠Bj!:E_1j@۝n]xO~YcWr@u P*e0ب>򾕔-̥zqӘ8AhsLohەH4(5DD3P[('IǚDfeb׏H+QMqXpDjC8Heh )꣭m&nC cHjW'>>?Ȉ?4[@:9Λxg]43[&'ă'g0bfrkKn#KӨ#'$_|8{d%s@,s@bq)ol39.3R,.d 9yىjnZC@8xi(JXr+FP©5A {C#91nLlS"0*p11*P(Oa$qHǍ=dKBH*j*\~>PSP:yBB˂{- aYε] ֡g0GE-NhdE_@$?73dqcONR1d>\Tbg̎?)ӂfȀ1B'mS;pѧ0o)rExbncH2dn Q~;"a˺9hRk勝a~.f03l0 GsC :ÿP f _?46I$W t+mF̭'eir͜Ϸ'F[d0C"`!p-<1Ng__ETc" qn2xhqF,`8px{ I˕St{nn=W|VaL̻=)V%VheٜOHWc/sԔєm^YpUx5B0p/'Ncww :;Ϡ͎E;;੿pxy6 xڜ˃d3a,.P КYt Oŋx;(.94JgX|ETiHm|C &|{ԉD"x;yp|byYE6 u\-΍3.RSF\foj#{trLKv1mvT H][/\d9Jc$mݳt5ݳ- q"r"$a CMhyjϩ&EZ$_Q,O;R#19~NUzb_"H$^VXL8:ywq~~Nى°lwvd{_Slw1+2Se5bP6bzN)%by+\kz]gx3.ic;;6?|3kDiHf17o]\{t-jtOo(*c W!vm}JTx~D";9//M+ͼ{{u}ܣM{|E'+lopddV2ٌh:FFmCʶr:FD20Āz ]4DDM;UԼy]/aՔg;;{=f=&W-,&x Cm:I }ˑFf( WZ>9:ǔH$%Bי]gwBly?օ5F֣i?A@t1jj[WhE 0b(B60Wpf_M;n470ʚCno~ujKqHˁda0d3|'rO3 bhbyiA7lDo2D,1Z\b"H$/0D'0cXoij%6#Ҙ]6g~tO{1gVfOǦ0F,*!:2cuk"G N0&'3jDEQw}غE Sb"x~$x071|0 퀳 moFݥ!dγy,5!h+*`^z-'g5#ZC,9WC8s6'W ,^,,. IDAT7[fAdz3h/5at$D"x)0xG7>;| ٗ;ngyu{<&N~Z"]{*sIlU*)mRgmf>Ra!p+j3[~͉UE2W!A72 c  ko8H/u ڱ(힩 ,pFUGD"H$X.Ǡ{[Un/_Mo .*^SZ!*Eg|6J0pb u>!JMRQDTr@8Ж}s[v$mN$6Y: *k70DWE`.(QAND"H\MDr_7*6o/+9g\ GJ.a#Whud7w ZZt=β J;IH{NL"|$xuo|5ӧ.x1D3|'7VWD"ZȻqU6&nCqx{Vn3:+CA=|"cpzUM+D"*λƤ1zy_"͍-vh̋}j "Qphd^E7ZݞbkmmplϺ X۶1GKJm2#XJZ>D"bx[Q^{roy;s(Yg XaٵH$YP&J㳼ojm]n<&QhͦQ'MEh,DjD35Ĉ6ڎ)N0@ӃĬ"avӗ%+d_r= -o!pyji/(e9kˆbn<~\UÌFUómH$3qy'oc׽=##x?ZLYS qC7XGsh|tU7WUd+n赳; &q}G7@Ģb@Lçh/9|[߱l'C2/aDd$~ [,# H{(WFTnPsMD"x}]vc&jo ;cE'(BDl Y`KCɈwZ86o}q}YʲF. Cs_!!A5^rO#w=O%mN$.!,^bD;m k[{8 |!r7^_0 4Cw`‹! iaaQHab^1ݺ4 I`~9*H$ ~k| Ol?sxl 7 Gh!n- 8|gmccΚ B[˩ YdwRm)kE:Qj@V]Fw'lrc|ۮZ'yf͉%&K| ]K` c@PwX3P`"ׁc "{4v nq5v-Ui.}y5 iz`D"}] |4( [ +gӬOl1cΠD6uub=bAFfMNsd!"2ϠUFRiZւ7F5Bg yUF(Q Cam ZذnK!<o;LUxߨj3|MڜH\"2*xfg.Q\V\P\F1ǻZzދ*~BUg/rGI=^|e2D"ݾmDUȽ -&H{|h%(9!J$:hDˎAQ'zHf6v0MGo~8k4* 8h 9BbGu*JiCauDnWYyC=y'".G>s8Y8r<|? }__dKK21vi8\1M\-vOᇏh?G"'1ԭ("ߺh"H$wy8@ȀW5jUW*5F\c,U2\W3D"q}]8f:B[,g8L (s۞ЮCGF4CFdN-JDUqZ Q ]C)ZLʐCڪ:eCdBpj :m6/s3v }#kq "eMdkVEwb;O*];Q*ԅ鹛.gt|#?{_h=?J3OڜH\"R,fXٛ3|7j) noGg ٿS=-C%WO$ t`7'`fnBnB{:k @FJŕC_89i7Ha#"-JV+H|^%\BntMql_!PY\E&1RbѲ CCKkO~+Ω&(m_˰\ǒH\m$xY0~),+` @ x%? " _~N"H$yKܰZV ) b ]63viieU2G08lp݃Qق8#hkZ5H$d0%55@`%Lykb\Fܰ"'j}nɕbjϣ9%ڀ w@@2Ν6;wE_?5䶟>>Dj!ŋ0]龁!j'ſ87Tuu1 _V1-\y™H$wUϼōˊˆs(PyQN#f/sh(bA؞dsc,Bf#F#PDK=E .8(r ,U2sh<]ꡛC&Qb0e\3+*4<׳\5\8%"BFuр1`3XQ.'mО &{_oM\n9}#COl9d$xMy}e\.kA<*}G#GUEd X_CJ$pwmoe)KbBt=Dm=& mɻ ("'اf'8;"Ga];\ @ayBZ#AA QZ n ڝ-V 3o\=SUYmz܆ j ӸO{ks8&0@,hO-gٛNYd%n{r}6c~@:bLD"Q""x3wz@r5GϐZr4i CUH$s;B߽)=x_cGk]O Qr>zFo2STg0scauuZ>,.Qaig¨g1g<%T*ۃY 3h`QNԹf&pߚ{7PUropjZi`%s5+Aٳ9XX*xV_Q"BI#b11DzqJgܦ`b+ѾD"xF1΃ˮo泯j 0z4FG"B Hh NTdx|NyM84Lf۶ g#6M~يBJgwg6,y^9FYۈ`xV[h9{z1Y!D+#f1%+dc+.<+ː:"K9q|0&D"q)8}}_p_5:D3a 73$ز98 R\?E| 2& 붣y3J&И-*U%fAc\ YSh;UYs: W;t?1< U8i]9 ySdܰ ;{FX*B(2D#Ef4D0_2ND %#@DF #&|0jpJ%5i{N$Ef1G:Oֳwoinr[\_cQ{&uurg)$- mgeUA2r }pٶz7%wq4 3 Jg#ΰV Uz*iO儘)ъqFtmajMjb`C$DcQXQ(+{3*7 bD~~(is"xJ.W<"2na{!xS׋9{i O!tE=Nc+M"H$^VcEEiM}S Ym(qV#{3B(r)AKGXs#V\y 4XȬ k+,& uMK2<:qdN BY2>NUQa"J1lnǽ8UA:Bq+r$}ϭ}5*[ Q mQ1"ǜIڜH$Y|,'Z+/ nҔBznpňR. 3ï}pvZK$KwIy{[OohghǣLK ]GEEwٝ#&II^x\hQK1i8ǩ1dSŌ HǨ *c@پG92E(WBi3"!@ dٔv]|Qx mD1Ӳ̱Ģ'n'm)mc (E]Io[d%,0N1t9́7^PW.y.FkiԖ/yKxY{QGU%D"(g}y+j3m^W&KNP݆ؕG ̎]OWlcbFhUkCqDcBVrlnZLX=)-0qaFEe0/'0D"dkJU=/YWNAP+Tԣl_1A4+r0}!)+9Y.|CIӒDD29[#Hd->6Kx)jˌ * YQD"xKLǾ?N&ǏrE=9= Ky12먴uH)4g^aBtS{C\D < xm@BZJ:$S4D4XZuo}$QG+qj, C;ۇS[iڞ ̂V Դl;ɄЉU*$zRD",b _`&j`u,W.L!];,..K7xvi=>'"GD"H\KJwӟk^vF߽ފU5Or:nFncMnMvF:cw.yq Tf!3@X ) eV!e#* ݨA+]tU $2ʇs5Zb0z|[E) ̪/pkU`vEX NE8'AT_:E:Ĩ0{eO2ρ E77q[ q1\n90ʡpqO.]pԯӋ[?t.D"xKʥ?}{| n$@c^ތ7&M6Z[sNIE*:x?* ][T w msQ:v%=ycMU9QRFECHxe tرWsb Ŕ؄xljl0s.iA Se&MF|t P cZQRSkz''y`W=WbD"qP9PhD$Wmdr,s wX6ir+xa{Ga5H$WRbwI<4ivz7$f=Xk:֯_5cu.NVƽzk:{ruNTɛ"j|̑8Edmb&su e\Y)A2q YGQ;*a00 #4Xp5cY e eoџۣ܏9Lk+2P42ҴHQ;Zi04ditF"xJYCN )3mq(e.o/O $KLSxMǁɼFB.<$&H$ͻ|6KgܖQN0ښVp✱3{%Y]e+^獞Zqk='׍!Y̚1Lۙ uYqTH[2x#0;%ǚHE`^ g5ج%y#.7L{Ψ!gIVih`F7Ȯq`%s}`-w |w?is"xFjhV-"~prM &oy*._-ń6Kz_< &~fbs(.(]XUgGD"HX.uii\ˠo= eN1E!GN`w;{bW}h|^);8/?Ybm5IԖ[H,ѝ6 IDATb`螦txC 9gwеygܡMfJLb Mtf\9u737kɋ̹bS_Jq=JqǰN:tTJ >x c2P~  <6wB˼aPN!1k,u SP ݇VΏL>3V{n y%ʌ0D``=*CC Y;g="^aW{.]S"x7"?|#p O\z r 4N~`+K(2%u.MYƁ)\~,_G]_ʢ~wM$ bU@1 T͎AG=,G{O16RmV̬KG;Oպ=V+'kBT)Ӑ:xz`GUw/%D2h>s Id$#7ךNUCf0$Eizz|(k)|'PR nӦˊrekfgBWfQ_wbU%+6Z6| j`F1Eq2g) (pvr k+Cbʼ^i&[9՜h:a㱚;ix}ᖸMW[x|Ƶ}nkTE4`Ni*a"`< B |ã<&is"<^rfqI)\ڨSq8`|aAT >/h)%sH"H$.~:]8a%@(: XR#La]ڧ-vn'9G*؉IhPoLiB;F۶sk"dybFt\55>kZTY X3KP +aTu԰ǻ]T`Q_/H$E4fe(8ĮY>QgJk^CK52˩+έRF29uEN3Z^v7ڇ`3gȎ^h:cRt]QIzBhPƊPż,z 7 FaA0  XW%D֧ 3 M'|<Ci\]"ƶZ)T1!X8!D'B"0њ!ڡ3W} E 7/7V bA;l;_\sw{IhjXX<C^K$A %@Hd#DI(%۱DQD}o]ө3q.6^Rt߮=ܪ:}w&~6򅲇+$p.z(b&vEMK͎Z,rI!ZE*< )?MVd'ϝ 8[F@iâ6ӥJVG呟QzI~hZ9x( Ӭ#b`<ЦD<KN d 4I#($*,)Ӥ.gd"݌.u`7W_tN(nsk;Nj.͌{EXQԑ^հ0I¹@R>q8)R5x.ZXD@hf E1P4, uY$KMI)ոvҥ)Vhnsl[bX;vL4_԰ E$6--b<,YB\Dlӥ gNi.=-ps™cMΚ~T Z:~JjZ [=8mq &Na>FKB0{Vj=' @V=@#QhU;dx́$r.+VX Ɇѐ4boI/p F5Nn ƾ 4UB]*4JҦQUL#/$Pj_*4JA(O@i#\ xC_xEޓBiMRJ&d%<'`0dSS٩H3ȏ gAK; ˨C0Nx=*K%Mѫ%W (k5b z0<*w&RN!HB,M9!JP5wvғhh5N*!DE@hpyɄ phMAV4#t"V*krn並YyUrG]T +z^`#7&^ql ~)0DA5@Ah$HNp*Qu_=Zևhأ=8IOXq8yR y&%s<j>:_,P!0@yb'_WTY0eƐ-$H4Br @SW"~NW+/hK>Tjg3TT B9R &HkU WZO$" j}")b J3DqMJ*qtv*y eߐL4hNdJ"٘eݡ_M8J7b*WS+M+)vJܪ&t6&*h R jG,@A-MZ_EOȹ8>Jpl!GGǼ:mjZc5P"4JJHg48R =b,KFDČك]P4lIR'xi|p+"p:dJBP M@v |.e @h!XZ I{P#D+BQxa2p14Hr`TăۣO2 aAv D1'&=\(Ia-iͮ%OXy`*1F*N |A7966Z,{Orӯ?y/-I:alZ zS:L>5Ԁ85Ns[V]@ДT4J"eU; ^0+ʱ'YR88&*"o')ή0UK2T;" Lf0Z^-D*lBdh@`׆àeF3#bAA )'K^A2` ?̑qMdcMoTmrDhˣ*T5T/}-s!!cDltK*U)\yX*PKTwϕx1Q>16,' "]ɡQN: 1w=`_(֐87&TYs{p2ջُ L;bj:f*);BQ@TV\bwi ɏ&߳ǹ L{Vt|4@O c]RCźHA#L'I5i%Ji8B+[ KP1[B̛/|/(YxG @BUJt$iqAلP$Jrb=`+Pɜn0ʑuav؃tn# uw7]NȌ͓C^+rF~1]n.`$I[[{-۷6LJD!ז( IƆHrn66ZEOO}Oi"hBw6N~TD'Iȉ\Vj}*iPLKLE()lҰDc C*VX Tm Qqq vQpOEP;Tp+#ĞxgzR\2%IG uBpo2@O ̑!\ea&v!j`"PO<"b= W Тފ>Irmui:2p+} g%j! fqq2Ob#^u3 ˃76!4Ѭ߀xWp(c1vմZgճ,xد|C܇%~J,; &3KYD+8u;d5%D@xRIEqK4M<5Q8**NHHp3&f p4/d54N`8!DJLe/: a^-~d]QHnd4ݬB P4dB}cp·UE@9V&d Dy!0"lY0bT,N\&hۊM2E57.1cG 4ܸItXc,2YԄ>/`Yc6\ &$Xj A)eC/^eC֯:6ĵYHyI~Ќᣳ1I{޳c6ֳI_Sa|GֺHt\%o!E]VӇ%8 H49rO :`MR'#M[)JW $"&`~ӓVzSc.Yba1]r8@ ٖqZx6Kp[*AYWbĖ2~n")VȊjJDZ:fW&+ewd98ܸe3J.-v$k)"|`>,w;$e^~P0s%qy;yOw6[b(Tg?'"j._m%"?V8EOOn_t.M}\K(O+g{VB1>j##9'AaPѥBCCV bD81KpCPsmEQ4'iϓ"xHF=vN vb!"Q:םoUt5+ R{ݎu1#Z7>iѶZ;oyOtdqY4n=84R`b{)풽;o Nʧ[xpY.1)RswtɄYMZ(*$?w]^x=ǻ g<;Båj%ꗤsZ.{ml؀.vQ4xN <$F3~9ΫzljLZϴ- ?|?JC`<պXTmS'1$tXRGIR!=tj qA 8 6" Ȋ26ıVJ+ @0 s`M (!7J[OYL( $ā ,I|R*zVM!CݜPttքT,!t|MKasɭ Crd̔oc$9Kq/c/xy{!cNoq8@ 1 ޺vP U}&;/B&0ců}k\K/H3jGY+icszl"KDٜG&B*SOO3+(Y8!ѽg_uӹ[֓q,w/0נZ|MtVUη47e1(<z ELE#N$@!, `r١cQr FL` @UN pXD$ D50_YZCpFJoBmD0AM8*,D-o[CWlw!^694-- }o˿(dJ$w≧ר?& +xb꾡sϴ#![Oo\ToI:%|&:C2Q]s.T'*Yt/V$VOiTrJ?_duGi9AZ^!<طYπBup cU,s.=6xpT4iB w"_!a>N!6l%C!Ѧ+q UCxmIv䝈9RxTptʳ  qԮFx}&-cDhE 9MP}\HU r4A;l9?0 !v5"G:X*qxlQSϬW* :{B\{eTs&l 4圻eWX;&Y-lj2N:D.Q4 IDAT9#:6!wz|D q-{छ1 [8$r YuXOvN3sxxvnQK/l_;AZ'ܷoXZDW4^h @X'a7Xolg/!Oloz6Yh۴Uҝϥ`] )+#lR'4'rNT&$N8ĦYPH{$!vrۏ0!..{C0Ql Y9e jاscGVQ "]Y-LNg O V,l$ 66VuhuPE| #[QSRh}&P[\PK3PYPc7pƒ)ʣ_KRVI' #p0X! iXeu~?<7j>2y4OzB%WygUC~V iュJ(#&@Dg^H]3Fm@hj(6K4` A dx {ꃀYV2'Aqf.Xޜ,`;+ P=eऌYEdžyez`9^. ˪5ݪQV(rJflG9n"ݚ{:f@S9#~ng`T$vE@jPC4K[UHcTl}}66ZϟTC\d@뱾DH|nmo=[RiSeܡ1Fi}rTI+Biח%hE(QL%rTpuH{SY +Adkzd6F5Ü]*%Չg{x7 |M,( =S 󉚓׿W1Zolv z'Q*&W\m0ے|Ϩ?C78P_ː@ )"dnL K(K<m @U_vSXcqawQJG=Ջj=-÷TW= {{1vX6iZϑ{=ឃϜ&9`*BSuuϠr,UCH!,@60RŰ=b&,9(&KXWC}u'ˬ6Qyuu/~VjeT`w\4t.˟.u6 z 2۔y<(4+A"l]XR4]W촤oor[_I:YT3A+{'}6)Ռk8N?e6UTNAE{]4%t|]cA\ ٿpxR){,Mz9SP X[ANz~QncDUmR΍icsz.z職?e}㋀kgW@;wgq NX?{[ zֱvI.oz;OgZV 5`,lq쬷T'.Qjn*Ɓy;=!񫀛FD tumafKz7tlojo@` ;|Ӓ5:B*>#1EถS("-:a=%ؘn0q2e>ܼ"$ I݆ͥ.9Y]aYy66"ĨOwXympAݿJ-5>l@$ {'HHZ_T *\+~O&Q\w$vzF\dg̓$Mwf]GsH-i4 d#`0ƬYﲱA+@plb {1> hWX t13Is}wWUWו>Ǔ533Ӛj~*|2\nr5o{Q)E׏%;0 8<+8<&?9-_K[o~f߉8'Sa5'3DQqmR14IIS0&LL5 K<"jʰAWql[4*~:UQ^Qk1)kj )$j8C*DIkgt:nYLrtA EfK&掤 .:s셌ƑV1ݒ[&AϦ:9Bʐ2G!3@K `pr"E8L@c]YMl]{Q<SPPcbu3(x 5ԅ8[kOJ}zu !lT8D}-`sAբ V4Drj?o{ˮwLWH沈Jy9CPn)%9#\v3$&'BpBL4K9Y,`w)5!xde%V$pHI,&5&{.h <#e$~ޘWYYq^1W`pj.=UvpT0EN fA )'6Xj[ fdE4BBV3G8T%\Tcbq,J/~CF BEƯy PBԤx&p!R5YuAAAAkgdgԏΗK?<Ь}d'I"?rٴVJߜLx7ףLo-πhl*i%䉃eNF 4y9|B3t8 &m%!KtFv_4z+˧J K2R7 zo#[P^0ɄM2 ;0?O:_,Ĵ"!49]V3lXHS _&DVlxF9 ]b7 G :Ghl5fMn*He a"N'$汀Y"j`,ڲ[Gc)FMq *{h5Fz1BHta+LqT!dDOSro2ĨՖ@,6Q1 }T\` \PPb؜! IDAT$/QTp}$.&J߁:Egӂ\v_מL$ݓv9DH@= $I"Llb b[iLBgs=r5`a^j"WڮAi:b+TKgkp3[ $X]5|Mi j@^e\Ƶ2 'E4\CP;0f,~IiI*0ô2]4C32ae%62,Sb  .*hmh$BK?U>*,T)țP \PPt8N^`f|+kʠ܆5|Y]O.Jlp1;O{KSkJi S酃X ,yA&tzO4LQA 3ϣf sHIje\'%3"\@d{fkGcە۳,r\ mA:\DP_>*mޅ<S悂W`NJE!F/ p7*-B {)DwCR/B8(j4BPbhSPPPPCgF/ƻ'o·ԏ[ucJTk(FHlJnj *zł)Ǒ}(䧀QQ)`7rO jaDP#2J((XPPp 0P-)exSs?|C_F9ॏhyYi듦J:R2SR2nwc:( m5|R|a-0ȧm2\ܠ7ԱNL]&܄2zJrtG"DzJ4$ߙҨd ;"fh14u7ts2Y GVN8$4LDD'lAԴMyB@D@9BW%LjDeW0~eTʱ 6bf'LTxG?~C@HJuT7ݨ9.{Ϊa:Qu5t,z/J ]ΣJGJ uPN 宕7. ў\K/jnuGIO&k:9DD ؤӂQE ܐRz r19M((:mcSuf]%Fzri]TX2:fE'46p=jϡ'ZB6g=%CV6[TL[W#czBttm n"z=rkH Ad"2߬&)F"IugvX2bp;36S ^qyec<~ϵk x1N7kR^DFVZ]qp8sdvoT^5k47 E1N߁4"G`f "ly ¤haJ_V#/mPة~0I+` S0=6a NR.>#*%{_N*k N䳂SB.1L7 ɗ``C7O9;ѝ6}%quz :@ A %2ǖH =ϑY\T!$Ols +({\G f'X̰oxg)*~(i hw9ovWTТrH0ɺ:"$$u<1 $60t d@TL7XLj 1$dHCG&y9c%LD(&+ؗA:R~O١9 oQu3k8%xrHe}X8N (ѧF@&"ݲЅ[.@x" у[=("fg^xN Lmۼj^B|BJr ۼ8 1pomޑb3!DuP V.˼$ϢKE!v [}?`x 8p؈ޒ|*ςe>ܰukw9FglAm`vXiLZڬHOQ20J<2F6=P#N n=!Dv0*ގF |#Y{ 3+'z?hc׬^-tr͌LjFCeSK=FtC {O bHJt$: oraTC8'he6,gümQ&xƈدD-`OI8ȽCZ+r` W`g.2ȘJYtl @dvB@0 RqHWBN5T~y}Ey,ʉkj8z~% no U<ͯX aI)T3%TTEuCB2")Pg1GՑD(Csz&U0J(jimvs<m=H`im].ıwG}ֿs9UO4Alhr/=AeMϥ@*)IHbθ>Pa*mYb*s`0tj&" 樦qO c^h}QCsͷ>m;>( ۼ(2%WIl,=j;PJ|sF.ЯePBjߕ%ַSTcze. ^<{VW8+n$˂Y M/zxOnvؓqbEI6fI05?IlZm+=\cX8$57XG$:LRCmzYJNJ.ed!q>Gu^@ FD7i. FfdVR$_ˈe%̘Ԫ:릃~<#4qhҋ+I2E~N hXd6",@n @Xp2'+AiF۫͑mF5CGao9f~_NGۗzĕ9DiΎc 'F\ԆhpstϡSqm\Bl">څ2HPJYV5 ~bw#nHx8G ^#t"kD6#/4SKIҘ1*ӱGݛd-EZ=5dI4/̐:k!)i5]TR_V8/5 K(ܶͧQ%;mY_HƶYG|~׿_(9 eWfEQ9!JuP\]sЏj3kQkPR븶_z T !6QzaQ5~[9O?צ8Mk)XdюgUѕ ҏ p#NjؓԄCqQЬ'7'LX|e-ԫ`)B͹Emp0lh*ct\JMls4Xm]b3]I>BH9Mvx+pͲgA!NO3H+$T0Rp D곙IFC%9#MKuBh; ^Zۘ=ھgu?zZF6 <P\1M *}P"-Bν uzcs'2[FyȷKP5ێ[Fr"ۅ*z_/^x}Vp3 xP6Uu!ąB0Tk:|#C>ysՠQ>Ii"zdjjy꒧IFe"@Uj"\sbjҧ9RrNfF3a GZ,戳>یYr$cdɅ}I۔ܩpG7xR~שizdk}K-ڷ׈: 4@75D5RZIb40[S719Sd*ZJfL-Jh/dIKOg+NMX&VJ.6zDLf;9u&;\Xx**gX <2$؄,չ]}W]!Sy ewp{ .flֿS"sqjzܝS<6c x;4d51caLᓧ6d:,28Cd :/ؒi0uK,whe2'?Z pek䨶.RU'RߺKo&>>~7#Qk@hY"FEΡOx\Zmj-@{Qu YBJy;\2T߃4~Aq~<ƢƜl?.(n0Qy6tɮfzrtzd@{օv^P1؛LRnL5KzT場LpbyM@^Qk`bdrݦ!ay3ÿ}AoI*)9+5Lƀe.-Ӿ`\,#=DT'1uZ "@[yze[._b˞ 9ڠUh:20%iUӃJ=dF튏54Q<r$z.YaJn]F0ί6qG@ym4I zC= z??*7deLjE)e"8jrUojy2Q 46jvMun"|T/l7yAoJ0C`7P^خ[]j4e7!dE TԻyR<<ŗu@io:.N5Ci99hHR)ROoNuR0{L4 wWŹK пn&+ơ-S_#RF %RHqTZ';gqy]dbao?aەnj^ĎBl%B5qQojl!g(zAD[Qc72]nP"B*j2u_YΓyVry2ZP*n㡃D9x>*Y8>}~RpO_ifY,qXy뜻9 Hh{w\c3OB;&eWMd)ٜB\FkcyBј7`t>=l _Ck9 *,Ԉ Ib֏=0dE1C%tmّ̒'&xOڲ;a43b.[XrULaPǰCFK vƜw _˛'p7/ʱZg>=<I,]yjots >@?'s⇘> 6!9H 51LR kCjaӱQSjc~xb?NL."o8?{r5vhGD~^6!TjƏ_o6/՘{ׁ!.8 agPNxRrQGLQ3Kx5:*/P礃jN!3o_Kr)/%tl}kO y. #nAji+F~>F}t4ս2ʆcڍԷw @D2GhAb'%h^ŦPOymeS!҉Z05]bv"@vGumI,UI5 /%^5BZ4..Pm붱'|' f' IDATs158+\vU9jv 9Iλx`~.M0$Mc, $­0끴3oHao0'oqec'[ 9|@5=ݟa{4rHcE~:!> K`J*Xр$asQ|`dô'ltxoӅ0"X2,)͵ sU:s!:5Wk vDfgl8_TuLX RRcΊ]I=$~㸇OS,W {%ttw=Fo[%2 7H@3qM#LVD+ᢹLl |)>$^#0?Oc_64 0*\qL?pSԊQeʐ=^$72x  hk#1#6(l \κz;v;\`Mc(s%(_B UVW| (h]*:+.mCD Ƶgʫo!\ͣshb)'QQv8;'\ ZN&1/pY?WӢe>wFSGf>@|3i4!tZ!3W1x&mp$!x˼,Fma^":yAS63.$ZNx[@lE wb~D\Xg#ZzSӤtN'!ik6~)ڵ2GE{`n]C2g*Fw(o )ٺM=fzΙ$-ؐiV֠E`DaY}`e,k98}6la_k?G(w['U2{ξb>&L =ę^>̱\?Ɂ "rZJrĵd bKmlab !w ?Sv)bj\L߱JwCuaQ:JPn;M6/&DS<%k:\Rʇ=D|C !/Hay%.ܙGށ:OQŻQ |z-P:R_ wވRts)CuR䉓6Ϯܺ: ;=V9W; [&kebQ"#b33%S%2–Xf"݅54 ܈s]QIF&m|Ś0 Nf-,WǒNE\i]4mCm0pL6/T8aFfx̅f<0;y EM^$۔k.qaѬT9T 鰿$4F,L0}S2ѶCa:(8P?̑W3{튉{5;½_}48G!<[QW뢅r¿*| XߙM'X3.9JjC2sBq~j0D|lD9/*[)x] $#"FLܷE"28JCQl'(De0?AVXOt7rڻƝ]3oԘ@>h1C(P.ϋ}ʷbTcXxsρ1{7{'\`#яo^M/QwV t P)tCT#5JiW}]Ri&*t Q)HT7Q_DED)5J7l{a:/6Q%*TGHPN=_$J|~RuW+ u7T B>p%[o nE cBpx~wabn<qm/Q0!tdDò2Fi-ՙ6G>إ*Vya_VmIgjb*["A Pb22'`ꈆ׫o0ehQ-iH q)ވJ(=3Iz휉7-ck[uJsAc> nO}աϔr: )1`谖mY^Z <?J 4Qms!__rxh 0_3"_{ {О]Pv6bNK8z$VkwilK8̟9N34o+'<7=<n ^pyUeCLa >:$͇?}l;&~<?2>ø@TTnJ'N/PgDmgQ}&yu.,MɇP56 JY(G'$G9Jƛjԅu')T UjKoAW׏qM !CW3|[a-JڋF]MؤRmJ,GChǫNm_cևE"JB\9KF9 lblLc:8Hwe}9旻_0yLD&$*PEy2ֵ-r.K6W^y5hKPRf8 `Lt~9|  s?Uwȫ"K"FlK&孔ceyǤ,$l'.EY/ {1aG =gԌ(ie.-z{;xr~T"`VJU@ X*-{*P'XHLIIV dJآS{] a!M.k:jthstya;p)+A {־06?bɱ7joN}D=XgQ0[u3=%iaTe\q9?(&؅|n.pP~XzT}h;1<=~RϪun60ݟжh|E>kwW΍ %Rh;`kW~?)::ejO.t~ x/| -+۰kh hQt;3y1:{ xo q TJ%/2 !7'g>$za᭼x~mƛɁȊP*%N]l3ȃU*bn$ޡjzFG)'D_"1>g޻Rmt1Ϡ ")&JⰏ]A > t6M* "@@DEˊ24-v8>q˜iCHD\-GVULG]g)̜K+?ϥhPsmF$(az\Lmx8Ia\qզlA#g Ϧ HjK""j)T}NX!U\ cBBg88mʂD8 O8&OG <٘"mLQcm~/ӤsR; Ib#u//n;9O_ۣrbC/=F 9 =%'e7,zG6f=m;R*BԁE{wEB/)[êB[HUG ZPhA_Fߤ\Y~s;.d ׎Ao{ΐ>ۋggw^xG.CXT]:4 vOP:g[FCt1-H(+GSUitcNw$ɶPMGǹP蓓}FɹvPZd+R+16 I&E2SlM5g0,]1޼.O #}ua"w]F.c>ؔr b;Ghx+HɾG9ȥ2Igwy:S%lnO2a\"-NS\ =gw.;]R(*xG?s!~ѣ4yͣ }A6K_}3|u[#甎|'M( J[ 3ed4sORw̑%\q"w.ER B-S[i*A`؜Iw%t6_+?eFhp>һACP ,3"/ k= +f`^gFhx =H  tE]BBl(  mN&%x, I*hw - o=>oC-tex\ WTU7Aew gmv2y^pȸZv1DT`yy}TQm9Oʀ0{eM֟!#O0mvbX $hL[k bY7d=7bѧ/܈Q#`ѡuwcgZż?]-R%}+c昭刄%˺ܪƒi;Z'h>F=˘>2㔛]"uD7i"tp8cPjQ.wNˬ][9 'E$ysNr VoZn츹 w|9<}QGl`ѣl|m:/VmQމr_9'z#BRV/q]{]]ظ8;㔌rkL\)^*sZ㭉I6֦勵U dv@t@8*cA@53F=>Z Md%2!Mw:q 5L"7`L=/=4MBOQB _CRbB`Br$wz"%ۭ a/Z8h/vQUzVs5xo}>l8۽R{0l͐qM*F輓C艻V|…3 OwO!%V VGSҵlZ];RYݱStEH =EeO~$@tmэxFTLh1Zgoe c$159 +Nf)0&j5IBJ1%el,ۜl߼CYFf)l,B$$!G&ӄ0%H'"V cb'+: SσUhWU)t< UɶLo3+[v3NJ[و./ɠB 7qb;?. :&wcGIȋ#aq| << R*B2I]!=si$ B ×"mwy9ׅc?秽} IDAToʯs_|Co>U}ݐ*S|l(  )w7P@D,nE:jCDw~"hC/^}bG:v˅U<@iʎ !<zN)=I,|`[^hސ2n j;ߝÙ' &XRROIS!/uDՑ `,J@Y ZM7@PctYakb%r)JdwTm..ïYc>sF2r] $}IQ$\kU&S>KAB=T#<|kdQOMQJlz-cl2$A lC!ʥ <-&Hᓨ`X2ے)|ߢt\5oٜw./ܑC{[CdWGHrQ4a 0('BiJa`ۙl;ba六Ŧ &+*rzq}3y%o? fߨbQ/6%7n`_کd۳(_= ǔR=!ė=2U2}TE"3L5)zE_=ZM3aeQ.EWqb_ub(P4C b%ֿsڳB&J٠NBqJU( ŋܝ"+FZUŢbl0W4טx\R'`ğgWjr?Uie[3b3>^j9O^얍{ie6&^dv:r+?MrQ Fũ'ݏ0hH!q'81F} m2\d3};gݞy齧4xӶ6^s=z/z8W/>+DGxF{K肠x+# kYl|KOHD)DEI\`z ݈V/h_<:QV;yP=hOƷ+_;Oax=,Ύ}^L{,lmluq ! G>RM0@20ŕzL $(эRjXf*`}'2n`G>mb[o(PG1mD@8̴M#VBwvG\3K؊+ WCgC2+N![8 CfYLap%_ ,WqRfo(}|hG S}_ǣzYbqPE"ѕvЇǶ 'z- ܌St)t]ܨ$*)teԽ0O >|+76Jq!ķ+W̝oΡ_wJc->+񎕯{ը=(NLnjZ6|9#d~;a~bSk=.:͇/jLlAG̰5{>4L$Nݝ jKRBC'l ͱq'~zq f4,S "};_ζm5n68LER"kJ2^ &h_ߓ@)b/ȧ쑃Juep SwJ1#ͥo]09YYCqI RRA?4/RH}-)FT#R])6Gsg|ąu+jOu[yeOLT3aEPNVL# jQWḦQ@.A$n9J,CdPD.Vno^1Nmr:i)ۘ҆ӷ:s7 A:tS=]Xe(&\,rKX*T0o^/W͍_&qD)F3-ZU k"UVڔ|DR iBR Bɥi&,.RJnC_JhtNNBte~;,1\Yf-w}KZf{̸1؃w}- 3^<]BF͉🖗ܹl<w.=mF7+ L'\fط"#_M"b9\!dg$bcfqGRR5i"MTzBLZb:EBN0&=Bi$MI2'&f̰i]gDav6pV'=AoF3F"FQmg#TXsLFץ=}O˧;?~A+aO;7('/t)b$/7Y1QJ)Ҁn ^.k,VBɩ?ge۟|,׎.4ytF _5ط7bfۇ.TUO!gAebVO:T;> "#Y>XXFDkbwR 5[4IE-/jO)tM(&6e'{,PI.skZ 8_Zq.~9ϸrՋ7&Ϝ:nVj.icEQW^r݂pt!WWI5+,b-Ds?[Ĵ.C6}4Y/Bj+T72>qM)lN$xc dJT4Jʺ:11Fod/E>Q)Np,ƵF.IH{+, 90Aph3q͸^6!DA"c?p;phs?/sOݹ2yߴq( 0d@ >y.RZ?E{+ȸp w$yb̹'/9w#U6?S%OW=!(2 <:(`6 5$ 7'4]\ !ޘNFOXx=H"oU+\խ,G&aK+ZX+{a4}So{y}}A]jn$S?qOd}=mWݳ(6͑ JDFmc ^CRWGx c*kcqM7 ~th> zewc!{;PcP<:t:6}9WA=![hO.^]]fBf132n>ÜTJ=&챺zZAA7Ȳgz <)U'IS'4HAGЏ^+Ik4$fFnpfۈ a-PӥIGlkOBkqeP)v ICI,l钴'+Cp$HUrK>i٤Z4I>զz؝W&SVJ=vXc$"N=2` ǗCgw*%-f&ґID2n ʺ݉O.o$Ej`@Bz9tLsc d@G@b艫nw 'lsƵ㺈ŁG+EsLA_Nx;'A aqϡ=,WAe\=JTM~pj, ܼ7% \Í(^\q$+V!/L͜TxYս6M˨Y`ۊpF^o) EK0*𺉲aP+z"E@(4) {=tCA2*̄9+lor9(UrD:jB.9eB 4t_0z  v}RB$4s U/=:~g,þp+-Յ2,$(]_%61& {r>ёcYnLԡL'\,vi5vAU~zP|OAU` 'U4|dPf~a%t(I݋gz|3e3#\, !H?d 6KUIYHQQ &:w])5qsh/@f32n>$\}y[߽w|:~}CE `S_&71 Xp4Y[JP(6X$ҡ9HBY `3M\A )2mZnji"R GW! HHRTy0"HRLO5g \ĹYヘu(z1̓4/N:!UDl.AT7Gژ0M'IwgV:k9+5ی'}rq Tpk?_>iK3҂ H RP@lA3Q F@m_P\_1Zs=<1:'灿^80n7jjUu]u1TJeU.olr腀?CpF{v_~=fzPȸק#aז|mt[va4%UOYCe#CJ% v.%Bb"DDL!xLnq?MAv(6pRI)0UJ1D,P)!* )$B 7FA#QR*6qj֠> G:~Kz'"#%љ|ʥ[|&]G\2I?&iD"#Us$Ɓ3POѹgß[3:nf=YN ܨ»㻿[M?Ӕ޽JDQ,"6kZ6X; 'BW.L(f\XTJ%B2~`}{k>r 7PT62ytP%Π/>MXa+!BT)+.fGqOF~wԏvpcT |HeA6LHHBHA eH G@+dN{E)HeJIUL߀V[5C'a4ʳ$BpQKXzRft$*2Q` AG`( a&bȌHH"I%"@mIDB"<̞~s.@b}#C߁x*JPm#y8l- }1 0jcF^.-wYV+D_-('Ng?>L?9t[O2`MIFHhMK ,Ѷk'lszp/9|-DŎ;=f B )t)`̸i,V /WpȪfdܤ|z,wi@*0Q,FH:ֆ(Cπ] F+PNQR3Idsi'.@|,zdPfۤ<8k i g4# -\$ Pg+.! &z*# 6"!ǦjV(T7);C_ZM"{Ω{aq @snhH!Rd*U)(0͔nb:2u'i2"$eAm!PsF]U)[Pl;TYdĮbXMBR&*!y,F"PB_v_d8JD b-i$zn;ڌ0@q쀴6 a 1n6-X?'\9@u-VLAzp1c(mMe+ZOosX$ 76&Pw^k @:)rpaO 1(W/3>AwL 蕎5BtHd& XF Ӆ m)ҿ7gТp><My;QWJ/oFFƛb-al)rL Ò`,Ă&8@DP`iF41a`$v cZ Pi8qJقEuHQ£k*LxD`)$Z(Āa"6lD. z=0M C](mʇ=G>5}5A)0B8y#KTO"\Bҁdn<%SeǟgMz*NnjP8u"c[/nQX!m@Xf3/XB<k2-VnNEC Bd! o s#b"/"sBRG0񌌌O銐2/ \)mo)7̔ tjP؀@ C046 L $ s-B#0=d?ªsTdӊ"m\`u>"*BBBLS}P r: F:- @ L%R2V'!ISF(b']R '@G:ɹn]n(M IDAT]?EڜKw'rV^20"Vf<,AAOBscQMQR l]3ۜq}aof@6DUJˀ"f;\@ PT6?zZ?ȸ9@,GRl;is L]fB! LaT$*PFj  5?'|0 HCTѢ2S/N3+`w<GVjLIyay$7&K7ΰZTԨ;p;?KJf i(D~ݨEZj!NUϭH\ MIe 0Ǟxa^1Uhޗ.g.eftՉQ'%[ "2ZwYd^:N=%9R/)CqoUŗ.R@[y=7P?H4s@~b!誨E'ǯw bhRJ5Yk|B|?W3u5#\(M2wM;Tc5/$$:!_e3% I%f6r)D690J8# &ApA,\3o$7bXTH1P2.pQihc,P( ic09C6+eQxR'1k!>>T8u/9E2AL_ Y rܮ788lBƛ焈J?7gl-\I 1c,v窛qLMпC)\RiIxM꓏/zldd\IhR~^X+^[btu̽hl739`-mò6;hYT,fd\__bXVmp/$ue˘o 0&EjeLcڰ)<1&\`^+-8f!w{ !o>SeqMvA+JQZl&eCum2 &MK2UEp]tlLv{\p$*ws fyJ,m茫8m79qyñ\|\5,Z(R"v`g֣s$6:gGE]FfQ=Ӹve޾oq Z,gi;#S eKb]!"΢ F 4Ȏ\R 8 VY 6 :aPO4ی1~Aa4e?i^w &vsAeuYZZO8O#j^{C\rqSQ_+pgbs!Mqs!ĊpY\⏁-t~2jAg )f^d?,f0 ՘40 70 3M\CB`؂(ln1i3-V 2,T&MR-7e@B4B8Z,E{m 2EC*S8na'HnErEЫ<`vZ..ĊQdG{kExg!LjIfvyHmO܌I Ha w*W..@+ }07O'V'oxW>%9bhw{QVjTwVVV=}jf6wzf-]4Vv45ODE=YZko`&7^ݹ9'T`.$b}+R*ݘ[:V)ecS̚:wS=ųIZ&B{;0|Nl@ւ"Ch/XnC{2{I#Hmm,fQ)p|tX`٠3آ`4-ÌC|S8K83;h#8[8Iӆee[?*2ֹ̇r#pm *dVkyJc0pv.b.?ˈͭS{ .i jdžbAq9h6hZgR؃Kz&/`l~e'-k[ods7à[E^a.kSPإuY6N8l_Jl+Ʋ$gZ91Rk7NiC40IC؇(3Weu:SNvYjb 6َI|.L` k; Sn2q|os}MZ\)^`#שp4xmP(;mrJ֠i6ᆳ'iзӴ+esG to l!}{_O21yM7#}/QQ}g턤jEJӫO41 ͗fKW͹cۭjݳvUJ‘osvu:+^9SxN׬"E:?ݕKl+rWJ)$F̚~z*duh1 !m)`VWLBw>Ŵژ 5܌2(I@B}a.D8fԼ( Rmje6[khHM6$3IR&Z9>3fEڲ̀ ZT5S/my4 ѕKbk& KAR]`ڝci[):?s?}:܅F-z/!,24Ց̫Zah`jhW.f , B4JΉ9v-6 ̼ʖ'ugh#V~uN607ӸRǏd53;fُ_ٮO)Vn@LfzAT+˲%O|D7fq#,5Y `bi뢸t|Ŕ`uq> vtnaN%߾x%tiB=c{kLM ʴڮKj4i:6UC"̓cCއXA4fJ( aS#aO8G%L)rqSq(hw,TUBqY ;[GLCV@ S8)AW< 8܁cB dI u?ʭxmm[3wc+AbjkO}٨>㴷h_q]7V'-i٧~Z鴙Mtc(Ve(~,2&J0mGc+M:7(bJ=-3/&Y<^6ex}" B_(/w߫a:;/*p.hƚ(V $˃ ,P |SCCk 4@cBE;|/fRtc1 ! FD6yV9[r[:jQ;&]([]joV&s5螂#pgp2 \ Q@8|BZM6f :Gu||V|mͫ>8zJ/ QzN(sC?1_xS*GPܢU(ox 3NcٓŎ/"-oD1IAp90%..ݘ5Z$nZ^6a!xNEt撦L6lph6(ļXI&[`Y VJ3ׅ0pq?Ǝ:_tbg>r"qbjQu[$MO&e AկQۤ-:ݢ57D˯sUn۱w>(ֱ5Ա=`ll}bΎn@@tD;;N\IvݎТYg8]y̅bf:Am[p?w'ܿokdž!zR=l\qK]"R˽fqQ>Kb|s1W[VqQJ@=*_Ik̉z}ec*ޛBK݉~iBpDm>#;[odUf M@fZY JWӴ{rke;K ݉T#j5 4NMiѦ I!ۊ4`Z5n԰RELh9˜amS'l4vuinm IWе2:WC- Tv)s9\PhkNp|}k|+4z h*`8d؜`=j;ըG&[n(#y`mõr:kAth(n桉rC2זjmkbYQ-nn:(~'L]|Vs(9$~ 3g1Sf1U<|~ìYZk FB7L7|~ilz *p%>֥,1HJyN|+*˥ҌTmA9I= W  -q7 ; ,&>3M~b#jh8`8 Cf`MRJJio@oOV>t+ž㐯I`zad3G̣R- Hl^#C/f}d4 [W=kS9By}?Qw)]5||K[:"}t=(L:C>RsSqۈٷqU;S1m;Iה>Lv}=6L]_`T1\ʿp1\u&N]?S5 dKA+sW-mC4_&QABd34{I"("K [7a&Mr-T?vi(P3~ AZ=q[}pn\vca6,4}IהݿTP]q68Q+z]yǟA'سOw>nNBPT6| |h/"|֩zӘ? L}9MĴ/ላi+A̚C00O`ڔǼ6cɍ/{ X#5vKژ0pӆZ~C9E2X&һ]Ԯ?o>'v1CrxvBWXR"iW= 0\!Ojݨ-L?UKn8HV=I~tRR;L֍oQXifgg 8vrhxpaP#S,m9H5?459,L'ؼ캇ڟ#P`Uiא]JDZlYrM֞W?/TϏK4fp ; Wo1 "dq)uiLSv~!EY8v=?yeus=5U vLoΖsNVvРq/X3ޓ{ΠyZ 't(«]0rB;JpJ3#hey/@ef{79,4m(ERԋρG*G6 ;qRꌕXv25cVXi 恲Tgw݃fdq f-W5ٜolg8bXb-5PJ_f|ygU׍W]#.ab:{ӆx]|3&|z0oq5E)b~uA)Ċbb3gtmC e]5#@|ò&a}8v[.r&U (]`uDT@RF;$/.6 ݈8$X<'/Ó+GP_wǝ4zE禚n7aM>i j!̭|X,g,-L07F)a~7cnUoT dQc;=rUI,&/[1nK6&~+_ާ"J,[i!. /-Q'm]60まiz4qrCIr-̻EgYLM!+!b"\2>IB~܂jn`ҠmtNɁ 'gx=n-I4 Nj'OJxIm괪1(~s~3f峘8nnyTP?foC@Ek=}1_\:R10'90cN^4f0?C0ɢ TJ=n(lYZR| u`bs/&6wcb͘ gپ}V_>̃@|m-j)Jſ*|qgfE>ΟM[6?_z]H̩4ݍ:-xE+7s}|~+H3l({<)ŋ_7 :'ֺ 0UGkٗI~J<&LIŴMކiUMPtlz}SS^cc]"&0U[0{nDQ!7`&cݏpp홫{%l[VjH+VYD*&n Y;k;Y]_QIJ¢B)kDG~r&7.Q~bx!ERcT`AL$66;Jܛv^_>ॣˇSJpxB!.;/G޳!9my3,H8Zi6$DKµ`96:.Y~fB~=alj,BŌxp7$B\&93=5Yr:ζ0kֺiCLŵ[$ IԅB폩߽x[s0u[77l6;pr{Uc*<MB([u`99on9r=<',eO=ٚz5~yp7Yxӏ0x? f u>~Yi|%iuΞB!րݏxzg{_MfŞvLyuWo&]Vewwͽn# ǫ'?Ps{pOlIbD, qZוR)&!\\X\e[|U웭_W`Z0-38Bl9 Y1;å~{SI&˶Y;or{Tk;K`-mpx< yͯtMrj'E!VނNc&y`^LC; FgB!M;ΜU-VJAZ8V֦x Jh# zV4dSzqɚE!V8fXq̞O L )̔~2*BLrۆ{?뿐srׅ[ lNħj:HjJ9eΖ6?{5=N,IX=ڝ.piLU1mlvv0I iwB!sVoy_+OGQpnl6͒(ktvwr tj>e+e՛r|@E+_{`v$6e ɢ{#b&:_:#B`Pm W>B-Kp2*ӥJ5%jǞҮ7ahM3زT?=&Cn'kXE:_${Ool`ZMsu+-0IGkdB!ވ{4o3هG}ru'מ[[_ߡuAH1wAOG+H-ISu䫥^"Y\, (lf0s8 <l;1IBozNf{cjq-O-Z΍*Sחܮ1qXSf6$bdQUJ)5T1!s{T_|S!*B|v?h3ݓ?TZۢ9[v S=<6YӠ,V?34Ebx>B^m̐cu1[iOa\Y8ZgJ>f GE!ivaj,[ΦK~oWkj>Z:3m,0ݏ} z-+t%Q$B^sfZSAf=l1jS`Rj;?l!b՚G7t׿x)VNcl~s7ZG:uKl+(qQabTB!V:0Li[{TB!ڥh167`J~f!, qPspB5/&i, E!. @;ǰ0fCk/A !kz<=W^(u7y>61b͐dQ KcVa'RyPZ'KA)ΒV<8ieB!ķOvXo/{*–O:o辰Ll>ߪaZYX$Y²m@R{;^zH6@s_!Xml`G3 8im({+M;4pX=>ciL%h'\ͲQ), q(l,QLUi`&<TAL29i9M)p_!Xm﷽_~A,jOw)7bj'Z1s,8<&YI2F gf՘v ¬_4R ( !p_>=\ i,np35;hcRYKoObX(ą3km6;;ws$Y֢R+uLB!ķe4cKr0kQt9sc*nbG,V=, qh=$}aC aH:bb3=U!/_`:jlרcW&iy*SBM6@ݣ+'|eU捘8Nr)%O^B\ 5a0_ aW)-I`a3J'(,^;RەR}RB!ĥBe7L0=nJ7|aN=t9rf16G8#ݴ|el~NK=peONJl,iC±0_buqxbb6TO)UnŬ{|8ӹo/aۀ`rzI/!k y-ms` ,q¹ڌ 8<ަy`3.Pܹ-Ikl9m*ܻ'O#IlIp2!L*_bO &|8I˘$*N79T`s SLo3B!.M)Xˍ6:5ӅN7q4_˝)ׁOUF-bm,UUy;!zD7ڳ-I%KE!.uppǫšo&;ckvL`zy`gMvj0{3ZBK%?Nbn͖#~Mhpd_^";Iԏ& r 疔d+d•.SE՚z:ӇB qIdQ Hk)ՓE0CnlLK:SS(`&0I0k*4f@NI"{#uw{g{#)R(JTB ܤh(4-WҗAn EPVDQ7IM\ 2ʑe[%%r3\N_Y.ɑL+r`<9H]~Hq0&I(>r_zkXa}2]1 <|tXkSL0 S4bkjWk<|] ] Jv֊& O0 Yu]2,J'IrF5||i ?O9<6|Q`$kpRA{+`1x%SIr] rô:PWų1OU` &fM:ĭ ^Y۸=a8?-漎-l=3yp'>ިfX6qkMR@#)uIOF"5 ɋDvw~T+$8 iĘӞ>GSaWϷYY[>DYOCq[zm1'VAٙE]PP'xq7>F ;7.]*g"- uI9vKbyAQk.ld1˲VЀCfe$s[䭽}ڃ>b-˥6l|uz̖ON~̿_6(zeX6XY"AoE H)P$H[k4xm9" %RItȡs`@5ɢE93rYVܗz|ߦ;IY7|O H~rU'M8 UϑZk_n4i*瀛H[ftfxx#7*Fax $ITku $a]T5sY>={ɚMuzIDAT,m8F_ǀY5â4IEb 5@AmI ӤioFM =$I7swiu@QBك"#j8{?1g㠠:wd:U]@@H"iȚ*ͺ9 Uc{+EJI&H7_b qή$I7ȡO#*R"!kSM+-bY~/Yx`e%A自e k F+vMz?Bt]rdQU*R\oN_w{~},I +%! )06;BX< uL[Vuͺk0]zkl^6eXƧEjfs[3]hNFf?F#$iMY?Eyy7RH+zbݦwXY Zts5%@!@;QnT_Oէ譤!am,dЀeI1ˠۅMXMzkPG/6mr']w Ҙ#M7X, 'ȩkU-.ݏQ$_pwh'X^dp~>t'iK/K&/$&66_vD \EjZ'] x}!MIN ʵ~3`NSmMeu][,MC3"'ʉl цv8Tſ%-3t0,Jڰ5R0Iq .ȫ$9+s0X~,?OQ**[y36P@9A UE*8E %Zg Ion!WI]#m#5;Ha.d$ oP=ݏ0~q2VVkz=ѯ).l tUk,Ju7i?JeT =C=U1&CU7Оn_oA1Z&4'SN.}mu9}ik$MG9GY N9[^4U$]Y$8@џfe~:BQ;ӎ\(:hnr hURp 0KxEiQOO/FW }ҚHItC8<"_3{v@7T"(f B!5(yi t]3,J7MM28&(ؘKt5cgy\S:#Iv2v/bgR,u.6i DR㛺f˺,N=pzmӛYqdQii:?izۼ CJ+k풺*};, ddp԰J{*Q@ZبS kBy~]珶v [ bXƯO6EZx+ kǍdS(It6's6νsf^SQI .zTWq5 oa7T Ҙ#0Bh!]ͼ45W-%I C6.͉|P.cj43m4P80 %*/O[uCp͢yNHnf߿3@#p:#Ik ۠fjsTZ:t 4xe3;s飭ojX!-(mvn$I~Xm\MtA :am)b)oӪJ-S;#Iɰ(m j]uKm)>rhٗ{;*֦/MnP!o0Y4c6طCâIk4 uN! 8$IJϵYZ9`P: tkq=|,`ꚉ`po˳ _ Yqy&6X{{KqW\ NKVQ۬Â) dkyw/-L!Rmkn,JNZy'i~p\IS݉{=Huu1>d9*_@Nn6aQ<f68}R7c,H$]pL]'qIM,Yن؀:7ubUY~zV}imgs>4>NC6χ68pyKooб%INp"!Ve&ii+V:.Y{;Y=GMBhoۀCդ{26ؒ$mS.<°ّO0ybX*[.ksL<C'Gֿ]"π |+Iv(HbhS b E1PK.9t޹p /6HcaX6FώHd|x-$I&2Jp u+Waxw8؏jI 6zgͺ1Uවcg6x$mg_NLc{)ꜽeVUJHPphѤUoknPeݙY7$H`8Uk|{|"$I#>T$Ig94r贡 S^w$3T&4Zo}yndEik`Ml > Bsc;+IhZfzy[,+ɚf?Ɛ}4>7L-!mBIa K F z@///AͽSfm Ei {N0Mv$Ik$6f6?YێaQ$I45$IEI$Iâ$I$iDcO@nN1V$I݁4;Gcm,J p6t'$I6CO׭WnB*`P$I\r}&ȗG9w0֞[#1?qx 8p{!lYI}-HV3(zX~E6_aQ6@qu+o%ITGjs%@+~k9 U6X!NSç"O,8%Uu yS!+&3YO?;O: 4&!}c$Iھ,A}3^U/[Z{afފ 4F!Fcc(IAf0~&]圧+Icc|x4BhnIM9̃ul/Po_,e 4f1G߅ !ug$Iv0[O~8V[P%i Ϙcm/[xJ$mk10??ZT#6%inlmH8Hΐ.Y0w0f %Bå㞭<I6/9[y>oY-c n1B8#Ivw0{3ÇS^m%i^"mQmH91Rm.7t^4+)ep-,WQ9$﷦iZOSRh< >! RQOc\gzIV4g ,%d|%YMӴ>P_$*3) kOahkzIV4()P t{e$>q~^iZ1=P`k)e M05MzR+y@ D>]yiڧ0 fV lG0oj) sOLMӴOG2p.ERl**+!iT Mvv\=LS;iڧIo^=@:p!p 0Om S4$*)Ey@jRFe:Q$Ziڻ tVa}? X?x?Tx}Cҝ~4MӎSRJ>KKZ~~২قJxk=aj4Kx@b|܉f`|crQdQ$CQY*8 耩i=Y\0XL%߾\9j^;tԴ׀JgŶ,0w d2Q=FݏQ?[P{+e~`6Z.q2`T=_>x /{恠TTu}s/Z[`$f OJ8TܗsDlʘ;' %FTiwjI)_D})nEan^8YFY6RQٹcaƥ}t:`jZt$5ykݨ@ T5ӻ^G)$N1Y*x 4;{sf @^"#`F *0u頻:/ڗȋuE=b@P'.7%xֳ耩i=S.@C5,k َɨzj `[_P_v:*CF.sPٴfoe3Q9އC}?znO"t<; 'H1hD Y2LᗦH"uyi0xsQq |oy,` ԽWQ3?+tԴ)U!NRȸ+뒇 M pm)Bu zB_6 y'j)G@Ho[~uOjEqbri\̜ 7m|{ nt;i{22B #JJ<#CLk 2(7d?K?Z->DMt.*w=fwrqE׼>/q пV}d퓧LWbxoF`22 TNTp,D}$b owp\vMlS.fNBu#qSd\h\;ŊKZ;U;Dugz<'DM!ëϙ}K `,5@!2c._kYZ"#x˨52dI)ֻgeX%LzGiKwTG+>3BLMOw!<- K62GGk~}rb$1N,5rNԌ/e,|jmg^whBU]׌*'PU.f-vGeƗ/kY8qa.oxkS6GHs_5.LHDLnB"vISP`R_˖Y;jO2oQaI ըecu^'{l{NnTIjo@_`O<6InFN1Y&@(\B>*YYbXfF'Fm#o&vwM(ĂQ3֥c P73Q56#Zvy$>NS@!B6P&F&aBN$@2-*a5؃3z(05?wr y; UQ?‡} ow5 SzSy{I_2!lmtD9j`5CU65| ;p> P~7Nqty\]|"3\!k;vIl~s.QYrK$[dPeъ:Ye_7PAejDJpYG9bqд֢vcf1S66XԘ)Sj7H%Sx擨=:Xjbd?fԲcaLNrHےսf!TKbY %J}~Lߦ.+%?3/} e׽س|\/*A!7:S)]oj(Ԓr%3LC |ِ҃Dm8܆ uzw$K18pdp l8CEk?i;tԴ"Þ@CS{[5y))t|3'pH@M,r s`w e2)w$QgJQ7afڼE8.\Lq\] dP4 #'aNWx3 aACXFFf)-mѬxr(մџ(⳼$%ꨑa OVunfo{thiI&_;#xx UQZ\D%xJ!?\r}^x6*7ybA[ c .#JmԔ@uһd[L;׺ܛ8,~5EB'LpVq йJDԔԒhu GH]XeeOʸYv0 *2fM$evtY u^;-;3%ؗG e5'p[ˉ?r)T'yS>R޳eD?h/05?82.<3au%&dqA?ڳ^Q;f{id k7~Gήo~۰f9qwv#g}{ p\<-Vԡ[f˥Ok܎EٖF [XD0v=Í']#d!M_v9#R.bfW/͘4ϖKbU ׽>`߳$#1P˟3Q݅}rbLTRP'*p> trb&L+4Kz OntE^u&W]"]ahLsvVNe˺<m30=t|%xVQA]7G}lTGD<:v#8qMfsS7._p4q#F+#Y #?|:կD1)d?T@8?ų;Rq_/RSOH%ꨪ()3ݨ 9o\;3 `Fq_W9ك;gdv3yrtshPo0BF`';vjWtīre̎ZDʦӒ!Q{1T@+g Q}mK>j:^Wg >}q ~|̟[$ xy %_86fCl8MC{Ua|ESYb/3:iB7756KY۵>pg$P:L׻\gL=OD6dqbfjl^;%toەHu>|3-)3-)ܰ2'o9Q HG~jz4;=\ߺg:Mwr S%k>q|ޏ2jKJ?UzD}>耩iͨLӧNeW _$H [G/1ɫs[wfvYc곆/N+m7_ DQGC5+Lj@ڧS")C{P{\OPYy=M׆eCc>Gmo&;$g.>IyN6]9f$zEիjX8gK5hʠ;LyΚůCsQKM3?hUQK?&y>ORPovI )}дOX؍ CN|B3L`< iOLӈg~3a<}#vdy}j[.N2+x{F=Ye=d΋7 yE.2ԇ7Qc\ j8dH^Om)dpq?N;W\nfXiK]쪑*>=weo<]þ`8vd["#'YSR E[QF˧=M05c!V/v9<'J)'J.`wZvWv#Υ>Läd`Ѷi0}1}]qdc̟\a>i~Ԁ/ )yx2+E[j{, UQB*:Q=ZYLPe3X a_  ;2'˃ϓzguu{oWfoאH8JRZ`{P4, myi,b j&cS> {Xx;9jW0ewcxeZ[;Y-~-IV/|Z 5kQV-]?rZzu{:We;=jLζM[~Q<N~*j̾ 2j0( l!G_~>BA&8ecOLQ+BFc -4#u2n 272#/. IDAT3~ xjkߪ?$ze; 2KxmKdݪφ2=r{%iYQYOX2UU_FW375CB <~W9:4nLۖ>YHm%[t1wVFi\LGei[g S5,:sG]YYI_qSƴ>܁"<`,M?L4 0E[Äf*#]Pvk b@ZWflI7%`;i_">-{joMMY)> τ^Fw1u~XdjFĞ]Ki8EވeoDqalz<>|:pHt9nQ" Nr Wc{ڇި \J\?k7V'c22gޯ-}KJK% \ ?_N"is>\. &(^i2L{zfL.lcH&supZp_9pkU<ɴgϹ*=_#} 6MRQ)rlu8IA?{g9t29HX.9` (xHq o|e\ڞ<ӝ"(O?#/xݷˋƕqJ-]`t:^h?>ȶVr1ִBajg@o?Vl>5->#hWAݛ岣ݐ#r#f꼌ʣ ld\XwmÃޢ;?:(q ;.@oEVD ~PY9Q6e2!Pf'G{jK0h0V&Ck}Hsq3#taj$#(%e{l.j{YUutRO쑕ɑ~}ғwTkˆ󌐕Ҿ>%T1:L- d7^)fvduHNs缼/K{@, K̫\Sż!yRrSΝsQss[yeXl_MD耩i=O`xq+Z3K?kF57Μb _%7Qwk?j8L^{XSSQ͕-I#oZW{/4?j-<" \OG+C3BҶ_cM#׻,gUu뻲=DӎHp py-q]$DmD.:{>`eE't_tԴF.Bf=/pif;6IQ&0\k4zFoG49i䐕ru˾?յ?7ǽFIѮW”ͭ/O.B,.'ŶIA{2Q's\F9D̄< ##L4w896oQp¹&2ȉ ? v%_tDoKwSS23]Usԟ*P?$B}Ǣ} tԴM|U`lkDĨ98=}@Wa)u$%++i-/.˵FJ{C)}WJGr{Gi@=L4|6Dq:5?D׺ c܅ N{Rθ &p.~`D;Us%S=ӔvW|]ٕ!a9Z"#ͳg9P]-|6>ZϦD T眿ϟ޵bKNut:`G= 8;;_o]rIo{;xv'tE>ϙ̍ޤǍ޹\qeظ 'RNu ~BrXl)n5HF8=p[p2[f[@qZ8#8(L5YYa6 -Řɟ{wZu4L~7Kd s}Νϴ7%%B[?()?TQFۇSQF oSN!F&8>c qu#Su_镛 3q>uH}IYxX<Fw΍rV|jK}&8Y\n$9A}/?_-5>V ,%[$}y*GYwg;e'"֓I7U':igg/nm]pnڸj&˗FWe¤/8[ vLXї^rrZa+ %d|Ӈ\wb2EH1??[-V/Web ` {[N>{h .kŹ]Nd+|:C IK1k+se񦐑66ruMW"t$^,9Ec{MAm-ݲx}yY hzܲfyib[Ln.IgtԴht[ Wug~+| `iR]Wvķ/Ji'c?h,ml`5!/V|/3\֍K&:ڧŽP$sLW^s}'U!_pˠe3f;>)f"k/Iao=ܑc67xöPvoDYȏHs 0Hbv7 d8f-_8׈#+ 皇 d? :k8?r;|]}:`jڧlsCR5_9`FapGNMZȭe#>FW5m.`G,|+ŐX;;PaV9f!;*4詆 @ZINt+&ayz'[C}`FR5Yukծfa98{t=3-85}[ݿj=1Qgp׀~VKtWyg.}{|~9Oquk|ٓuS4tԴO\0/sAG/rv;h]p0/ėq8.Z;rQ[FDkoÕ^Ӓ'rFlvqxlڳn:F&uKn8,Zۈ(o+~|itp 7w%w[k_aOo$2NmO r;̖KCuϑo| xmh>]1@ ȱ޳I3LMVDT:)Vq$^Rgm!G8)ʑvFv?EyT1T#HES$ƁGrٔ&]})Shr֚e΂|!m7DNp")fnSל|=1ٻ5ٹ2W{FDr|GQ]7r61O3mPU")uWa &Fq貟_p,ԑhOi¹Tm@oeT'WQuSzK QCZzb;o v8=7#v؃5VI6um>dwtu&ƶڗ$UK3 SvW"9)~!_G:Ռaq8s&rPun:C;~u:7H+| }֍O7.H`W3dרq q% n%_s?zBKƾ]d_pwLjl$<{ܐ߳t]>rэ3.k[ c(g`0t¹]@zuҖDe[戈\ix}+|pʚ`'s!KeS'!xN,W 5|2dηNj,7ʪ[>y'm>l|7rjDqkɠ;xdídnpC sj[B~ry37[p(ULSiGn':2:w(W^6mޢsKCl51*Bo+^vqtGm算w{:F/A}t">mޢso;8ˁt))%xCLM; <_wsC\?tiopQ-/ƞDVJe]b s_4 3++sʐI*`طV"I1g=ˮݙl8 u犡?Aun ~dOMh[x,_87hl#F_LlwxSeǿNgGR6M(Cn FjeZw t{'mvIw>u''Ͻ7đ@^4M/10*ydGwaMsb|񆵲)v@jFԋc4P+cxMĀ Ldv0=ٽ@O>S2@/Ҙ'%E & 4U@i?䟈؆@kkA1JX}cpCȊU$;RGɤny="Q'H?h 6M /S" rijޒ'q$ U'e p[p0ll{i8}0|4aDjZc^-H!jO2ѡ"|œ}+Xh]ӷ/E&@U}Pyeu,BLd? 4[ߎE*sK~<^}>QDV:[pebu=1bV)XOn@jFFqRqx(X9kD & ?ʦ(;^'4X ]cPٶ%^0bZq IDAT~@b-D j09RIH&G7z/8v<ЯNr4 T[owKw3!ٜ<}_ԥfd3wy[W.$>$ac#uN) p0(IO$ScN}f+_rmXYPX "c3!x_=R_Cw]޷^ԻLe8i`:m2ؐԌ,XG#s'z665#<ˍ8%+ p% &3"(_ J]}i۩X@SH݉(v yV5%᪭ ףI5$e7<"JOcڑfoA> &-Fjۦݪ"^q6*Qe1#s 0mw^bf72˗80;a3, Iuؖ|綾]pR(L70n}e/!^H/$) p呀y584j֝o 8ycU6OXXDT "x[P&Դf/56ϵ¿{r>bu"W{y+a`C׸в\uH]P G]Cc ~gzۦʱbN#i kg{ BXmuEv"d_AGߑͮtYtY3OY xN`1X`,LO-392s@eh>Jvik?rCx).[=s/I#XHn* viu21׾Gug~)x$9|>]>ںK5lPcfuQT #c*b7mZN0 y m]`m<;@["R8EK&e9,cUm&5# & O%UE0 `a\z3O;8c6*,.GKk_]M/oG7J#%.kL1((}6d>:F2%Wܻ<^U *tɂf?X^%^ARHrEID &Rj;* pY 9 @g4%4}i㯑V߹Z7!p]csX7 uP{GBq/c j aWxOȾ=MR)MTjvYܹ_^|M%A7om .hǥx;ȊڛTn SM52c׸2FJ8+8EKBx/` 91S@xXWǦM~T*;bKFVOX~zo|{pR C\RuCWIC>FZbF$6lQmݲ; ?T9:@]&Ã#qeK5ƫ@60=79 떺齭?8q|ag}{;PA!BN)T k` iu 0ih=l9KaWprLS@21.~,6퐹D&9-{wJ &{d;QȨԹM~S}_y;&I6+eLyA@JhPeR ..^>eM۽e rqn/.rQ~9M˚@ӎu@W@yž}ǒ'>.+H*/I'`a )` d p~B|5E7I;y5ض8hUbBG0e7РVc;wvN[^O6EO>/5%WV-\) iuD~"냴/?|Gvd 'w߂yjږENAs?]a`;m3,`l⦅L B^Q* LğTÕ9xyz.>bK^~ oQ<6FeQ6,G۠?ƶ%طԷ>FZ]Pn, s$JyOaռGXcwҒߚ澁?ﰵ;uicG[Ə>fݣ<3qc'B::-VZO 7UNX^q`Ԍv)Aw \_:K$3A`#6MOkW< ]?qC{*@% 6L=/F?0V01.ձ-W5)COoR!q`z*N`9O>y/ܨA~}d/TV쉤sk$;I~= }Sͮpc)IڅEE ;4]-nzǾNȔjo~'H_YCԫ쁨[gHU!/kBNBO'7fH YU͘tpEUӭMkwߔImiA(W6}Kq˯͝!A{C;W` \vAre+$1ɨ1eR)5ؼ@ ] ~J2WþՏvnrQ3`܍9[Tc=DuZoC*f=.5z3ܦi7}UߗFrrWْU3)0aIR:Ēݏqy,(7J$2ݝ)aX!BfEP(kAу#OSk3F=pLi4u o}`YmO)%8E0$4%_v_ c`( /qW_cȼێОqy } ~'i7@n:?%M2la ;%D{r5Q{xy2k{Zݫ߈TB`de ͺ x#xԌϋdy}x oA]NT Y8ܟvP 7 dǑnzՈ j{1_-87.i{g>3UȧÃq-t >bk'Efd=^":_orb.7FO‹RSjs(c=p;y%'E!>hmBm#l"N0Cz @@7 @D# @fO"o I> 6o<,,@U@0StKl_aS C;$juǭùo<@b`_Z<>V37̲ޟݖu~'EO- <_ ""X"&< ;eK}ZycKC@OPnF6UdiJo?bPjuHԌ?}zՃ wovKi AqnZ,a`D(N; ,'6 ΥV9p<ͫ7}wWRki3,'3cO#RNӹi7R<7Xo1m"K9uZ!Wשǫ 8N6R[X fLŵF706+Fy{K64`FuyrjwZCy`90WtTkB@ `$5Fm2hy 52V~pOl?9@4= .q4PeB inv6cw}\.@2_p ʘ b0:t+A;:Ql ^q49c$bTZ/~{gͺuUYYoձm~+szmة@:v톺V뽯u5rS3ҿJb+~Âw!fs Swڧsofܬ/Κ?V^ʖl0q6 ד /FmPk ,]`ZC^ZC:U77`E`9 L׀=%/!P#p@rGfӔvo$7Дt~)_Of{"~cζ1XqCUyNŠ$q, >Ǻhb L ifyG3*j y c@/YKFm/ŀ]Eea \KԀ=[JrxfLS7U8Hnv.MI/ognM&*?OD`&%<3_(0ΎvدE텝㻼v FQ^"DuS#{r!$4ߌK֍%u_#ѩ7{>ʋly%2^`_F|v(>5#jW}Cvo TӒis@@Wc+[vɥU;srVW`9E!7U~}^xC_op'`U\gmcX8gNPkHȮDՂyu E0 4%}7*lhI`mԑ`U|VNO`)e@ hamA#`Fu 82= ?m[)64F"4;Qn %5 gw IDATl4n $UtmG$rn%iT]頿[uڏEt URλ兡VNA"RuNCuNshN vNvjFVǑAh~_q0QyZ3@z^N&q>^c D<U`rI^U! >PkF=qR N_ SzMєcm/x]. ^3 $xBWe@vCu$0g7{6_q q@-f4~^;<졤G$!Vw;Xї\;ܑjlWn\UzHnvO0-}{Ul6px)}j8g3Gx4zYg3s`vd,hGw\983-!=wƆ{'J>twu*d6̿-ˊ.K}$оCbEu/:n޻?tV?՛y3Wo@'u3PBEջcP} ׺u۽@Ӫ-fdm<^0Ԍ^H"0X^w˴3+ܮ9^H֐^`L67֐Bg%i4P9kK )5AdiS+X A(jcZ_3}dʁg-3he[(E#T1wBjM6yC/|tQ&6 lo7K0[afEk>xg($$"(7¦ݶ)ɊXxqɶY.fd: <JCiCgk5MgiR׉MVX^3R3Lzs`^bHlp%7ye0jFp`yh1Nqb4籙L7MIo!^`;hJaq`R[:BaP$)S\fǀu~NSo O^lM8UvRyO7KD$ *y#u^Pjp "DQ!Ksûz5:]=xg}+LFzy=Ϗ(@ozb_JWBPO\0Vz2j05LզnS sQ* 3wSX  "2ҊU7_ {#jJ/7F55)"k` ) +āi*.á7pJ%`iJiIhj,t`eϑLm/LO gۣ'6dіa}n=,~sR!E1$27Bkmc8M>=8jِ -Yq~HU\ZPȍCnccB ~M`6v+Z8kd ޞe]R'4QSML渴i 35#N>Vro+PkH$8UFG <}/E s8W(Da \sl=|%cMI[ɠ$"hXK8MC&IS}C["mE^͚&<1xVtn#[n݅BIZFJ[PUX?_#piDGZ{vw{Ln[=l+[)Pz+2:>q,[qM __tF_ZrԌx b@CSkrYKCIͭN`m(M6M;c^A)yL6d_]p2j ,d6ol].}#HUuͮLtISR2ܪp0<ϝ)uA\lSp* Cj a4У)p=H2sধg9[1B J2 %BK/fgXά-:+U0 Btę 7LhP8+^7:yOMZUşk#ݬXIzxa76a_|Zȏ 7cF\?&v{Zz3XowT'T*](YO;#my3(8WmHq}Mz\>fd[8k\4l?,xZ$N\]'Ukh` ٝ&~6hJz=.pGdiPO,zQB% YnPܲ6Mx(@oq (D^o3ɳ[[Ds\Q_ 3^x99lsh[֎q|05GB%K{=X(UZQ1JKУwuw1PƃR;`3R ~VAN\N{D R\9ƅ6m bw`4P^!{( L͎L͋# ZQCG5@v & o.X@c{pieH+`i-^M8EӶE瘊pN49i -\ZJn`<S+ZhyRƪ!\*1Ey(nSn꠬+Sd|hF0*?uͮ Ŀv~U$Ѹ]{,\=ؠz6^%Y44cJݣ473CZ@2K7/ͧ-Uc^kzhb4so%p5 bؤB>FДt'Lre!giNOx3BrC)?|%9 @AI ;56lZ ϵ;X)]qz[a\S}8w{8])I @Vůf#^C|a}ߕ;7ښ?i|[b*^m6$Ӱ_=Fkɂ:yAJ:4VE.eEgLjngUuڅMӾe tJ#.XC8I%f.wo(+W8gAdX3ڊgcV6,6X³^{2YGr f֩ՠ4% LOC#ΡNOʽ@4\&* @yl r7$|,G*Ł>%-0C3Q qq3EbyG nߧkFd9' E0OKjHˋr:5 m ^nVk^=5#LӆP54ݒFt1R. MTfdm^8kGn^R [ :Z]`ϴ3ضp|3Jp 5)p%QUf4%BN FiX{q S `a`՞^`. E)h, lv\Xqf9_7|sˊLQ}ރ[R>J2["D'%:Jn2/*{XĘƟ9P74:w+Wqfd>4@"NBAmokg\f'-X9.lK>1Q5W8I|r#`aw+ï` L`0$ 11$L0Ax)D$7;CF7G/Sw֞z'g<yВ2s,Lg"#/x05Vh%w_V]"upB}XSC-$A栻VYE~^/.w9oc*Ə&y;qd{'|ؐJ5{Fp[lV%w8Ԍ?,5?zLe), Ҽ6uw9.nm8u(t 7 թg397 ãs>a2#1VjҔx0ѯhJz4fv:q'E-y`FYŹ׏@fR8+/}#$7{gP9]ʈlMisb%^;1+'ylYlj2GB a 8LL^"iԱtsWd%A$I;]Dǧ>NGsKޚ7*qݞDi){^>n4$k-z Щ@~X+~XU;g|Yd2^hIqΛ9wBOwPfWZjΚ/6wHǍ8)V[|5\SCSҝq.+9*xfE>SC*ޫs\.SV{ 땦 ,SR@Vh2n\Nx}r{^.z|R/DG|\*'pUᨳQkLPYβ=FQO=τ>Ө\$pU`aTRr`0zbolVʛNԌ:봒ҊxŴ3:X?| LgN۞'?]c ˙ 2#L ?G!@{қ^US*5E]ٟ,XZo0@Z~<Xi H.kn8dz0# bqJ@̋ /+촂R^>-|M%`юڑD0W^whJyiJ!2p>d @4TgCEcP/6-G&~X-6~E%dy,q{=Gp(DUG ܜvAA@ 򑒎ZTPk٤Cbӝ2']6@U_]yk#5/<ʯ3l A56xx+rs"U3|5m 5P)ph^Pf| >=4]aP~lLy  >1`+G/Y۶rnIb%&vJJjvj 2UiqK9#A%FjyK:;Lv߸_~**gXGeA`"39`=XӮ ⸯ+`^`4EG`!,gGo`Y)No k-ٽZG.5`44^ MI/)siJ'ne :3x/:Xlm<(BU0n%wK ?`]yHnN~y+-"DɁz/({C+&RXlp?(Fid]K9">"O_.⣘y-;kYw/Vfd;S3xNP#`;oyn0^`^iɴ3JO)tݡɎi>8^sלk?Ap,7orUd\3u>p^ p3G"r3_ 膛2rhG4q2[HspX ^\~˘_VF IDAT&W}uBL{Iտݧ]ui|>Vq+#=' YGL!k{|w7fbc%'O4{ڌ;*f06vtw GfOS;XS z@槁Bca~p:-{/h v;9F@2%^%=n%~iN2@7ru>ѥI4 <낚kd庄2C4nƈح 7L"P4~k#sT dRT sy6%KNN`EP7uԍV.\?D?G<7MEG?x3`t$tTi+6{DBuD랲w|Y/GJ_1>P+@sr͖'(^z_/XTR%ͫؾ{jop'_=7ynk駾pqp" m@nSg&fuVQf4.ѥxkϟp^yU|/r  嚱 pG|sICA'̾8 %& iq̋ #Dž.8`m :֐ ϼ[K|S,osEj+" F lU [Wn jቺiTj͆ Yn.oZɉ̙;/;pk=mFfĎu.*{-Pj*" /(ٱ6NT/YHiGզd377λp**[4Aλ+acQh_@7FRs2:I&w7 OZn1 1?kz?o#!e/`ЕSQ76q.Ci5N=|)gϚ_ me&O]v ykWOӜ?k^O`h~M"vi >fTj=0oEyd^^el)0yPL[s*Zr6E2KlD;~azN-=_;JӼNt zʈ3jZI6U$}f_F \9볳뛌aE_q o/,#*PݻovO<xD!SY\O ̢͓O0I_ܤS2q@8%Xa0*`'G3('˰yp3#_QĪ`ew,*-/M:HMrRHVA]k4tU,ѣ7?[G_g$sۺYU .F\܍1ꓦOʘq9߸l=KiE>I{f{Ϛ8exy^0a)0{G8_tFՌ^EՌ,@^(Uz( K c/-P~),Qg<<Z j]~#{Gx}]߰ߍQg9K[wS][7V2KkϜKfnNoßO9ɜWu{ڌIӧV&L'aD8)(XV =~;+QfXG7}9;цu*o]eX ^CGJno4wm̙>Wnb X8]?][.-PxcLz-}YWm&+z׫D4wtPiM Ԁ,Qz8\&we)e]453or{bAt!}ЀJ%LN%%JEb!#p:  b0[,,y jt,lsg uQM:K0t%fzbe2Uiߛ~l|蟋ޑwX?k@u5}^{=z_p_$%[+#|N?yP"SRsI h:3.jm\Lm/`ؚ\6DJ4Tӏ z{5RU-.;: & 3 6/lB @cef/k1A;(Nt%mCxGtB5C}Ej{bݓ,@|'aNFuKcLuL#HRHuO([^<۫zY^;]}w5K6.2,@Mۭ.zD^3 PgM,uGCaI^u7 !^ߔPjh2r5I4!} ܃\m + H ]zV"#r$`V}4 ('7<֩GiM[~Lr˭SFUOW^;=o HB@oJZNۊ"%O]M# ?{+RGs.<^"d57w#g*\oTKSa<9E?[p؄͢u?jqWl5vi,ɭg4Y4F@ɂ1z&ti7F9n@vk]CFWCJDΤSɟ5\]ΠnИKNGYK,ʀO4qN8%{"nz5ھϺ+@ym@t7!Q^9y@N6լh`i(M>:ӡ M,-=;ڻǝuaޱ_"X]{|Kx6A4ף4.-sWD3*k 7PUTj껪:i`JGyjnO,`i3M>Y'DkI&ORO~0=cZ0QF8`Ì?_+mVGfliiU?:lbZfP?C8Q7D喲*EJE KUxrg;3@T \u\ (&_>ӅػV:-}Htj="Mza_a ɚl<1ZL=L K˷Z|*YoVo֫/Y'(hKj}ꗌ$D =gjh2|KnhJ~4s_H)kZ0^MD}I Wl7WT,-S aR $0}2Qn"lM8I}yU{򐽗u5ZTMZx eNN6uyh)u`gŏmKU)ٔH2ڮ};0-K\FS[&'j.*eXpU Whj)NՇ5~wAД"4 dNN?]A?Y^-ii_YwDOI?yγ&@R~M؀ᇬno267yVJ-^]nv{WK KSȢEw}ﻝpH.unslATQ?yNKEE[F/SO>(1oklKB?i'n}M3Dv"e9GPuDԘN#ʇsG'6n7k0pQp:Lm_tdH>,/E,+Pw=Sǥ^|R`CP_Va LSP9muaĨ>z^& /VkffЪê7V\k6un;7lLTEoF|rck-ɫj AibeI'?Y̩r[X>UTgd7nشP7Q {bG4V=~ CQ8=1wHՉ8H6[, sUYnygVa e0 Zú ;vaz4F/1HL`0 M^vtqU9eВk7)4_UR'='9iЛg .^HqTGxS&v@YC?kH87!/p{ŠL'1R{?GN?җrN=쏀D53 ? mY~Mf:bP_ Eil,/h^n1YDRjvdUNNˇ9{W+z fŴ%r>>9 \vJFKP7)&N8E|f]7n .OSuBP_À? 0Mivx&*tD)LS(NʛQܜ"PW QCU:b w^4d>E wG^줬ukv884tO׻ln(7N\(㘭֤ ՜X?s[ŽM\en6W74~~` M׵c{Khqiߪ^O9\$j>d7`Y/Iӧ3#Aa +cP+eXӊ@_TGr^-4Fr29-;2b@ WW,<覉 퀔6{34/Js #~Iimqzzi n?t[?OZj$1:65&|v&Jh*Ϛ ,BZMŪY/__9 %VզN+*{z1Z&́!lm#(ꃭ=2Y`)D JEy ?#֓M@o[9-V|BB8gOcP]od"za2ǝMOϚI XƗ*A-RDlu[%пo YZe[>AͶt t3lxq0OQj;t]<7I!c{&["̞)y-O\2t@*L!0B:76`P*mvWxM6p:S[6cؕ/C+@y`N6a 5a?,lK!!mv)ӡF"QF0t>YJNߏHfhqQG]ӻ]HeE.uXOn~˺_ ǀ>󯟒?kb,jot9Yp^4rwHfTuc"dt]S?w]/_W|uf5S,F Nl;\ep1p:E95w_6{Ch[p:N\g[[^<7%p%(5> c[~{*UH]o-類_)f=$dgq7"P.#]{}ㇼ!c=zt' D%+JjOl?44۲цxSug7"e~O<x}$b8TSUv+XD>G~䎭uSy7{j0 /,1Pa/+#p:swQmІx esVtxO;r/(Q<4T-ɦE V È#dkLNd0F8`Y P P8n :l{/NSF9ټu'79,(Vw9|ǒl\T՜4 ģ!%jjF0  E&R mDO ~ԩBz_9TecgM8=2?-+ʚSt|Ҙlus!QbC`x; 47=mƚ₰?j'0 f@4Tד6.TbgS*۝fɦ!7Z:!(#(wcu!-X#IQ2P: wrKDLܲw^ȨhZT)>bMiŮґgQ7S}C[RDII͗OO_4̧,5E[?O7 `! |A  n܊v%#fk]vwځ*i|X.lB-BAXEJ(9 "'`Y`YGY_#-(-_`! IDAT|%›6u ddF>)"'2..x@s:c”ֿ7* UvQ Bmqhm9"&*`LM9>Q]b2h컞$?` 4'MfO i_10a:D8`&B-fƀ/Hlp:F>6^ӑa.LXܲ.'FcF5~mRFqmZEW]A=/1hj4E6ͬ+0p¸޷JʀuOl .Dk*00?w''hv=:ksE[Ƹ[&G"ʅgMafnaIflFy(0a$0&~ c4&侬NT t 1$> XQI2Ὠ9r`o:SF ,.+]e0YN &&2b "%5k!iΊqѱbp˔JO]VO(@^zWe .1~wgM4[{c4"`27F7&?wwu"0!mvp:Ġ86{q((k' f':KkT={;C8ӱn|T4-Q] xG-0IӧV{R'=KN,-׬k9d@DSe.{Y7Qc%RIMW'4.RZ,_uj]Be՛酕kO#gM|>٭+XN~%Z,4?\v|s]C*O7؆TGayNG*vLpK=j[4ab3j~4ԝLT |܆ mR'f/F5̞6#'|QZ `6`dd'Mr;s)"Rnd@HDKObn/?ӯѥKQ]ײ +oAwWgƎ{:3Aޟ(eW41!ٷ$^: ʴ&Xz~K~R=Oӈ Aթ'>{ڌt%U,4 [}s`Z/b@OOTCSr#݌4iԭG+Y"Y@8 ̎e؟'J !1`ؼx1%J]NѣC, b8^$i O8W =ݺ 4\_֨#^xS?|lhB24.?Ke;z,~Y0zPޒ5)Aa~|8~H?dʟ5/@ϟgqCS30+QQ5f:ithTf 0acRŻ;膚Mf*?YB*>CQ">ҚX8y0-KJyiq}>6=oX)1+x)HAc`Uee ?zHEޱR MKmᇸ{ ׉Kon$V؊>@~.&Fcs)ͱAs4 ̇j- VlNcr^mboµ_~fl2: B$Wo;IY #O"wTEg[ln.@"gM%hZ#WץeUYeATѳ"1igOutTȎ>9 '(h!+(Ǥp:6PP8*rR[(Txy,p ́?ddygreIZ]Yܧ%7|} )B $TnvZ8q_fFX4x/3m564|Ip7h=9zT!(4U=jSS Sc{ACAÄ9$ՙZ-;ψj-*. p:PPv`GPuBQQǓ&Tr81&%D_mlzPJA5)o0&1f|J<^nf#  fz|0J s-meXb'\Xw^ˑB8(EfYJ5-wXE/@r˝{ SmQ`LXj緝q=wp伿ˏm/J>d3P_?8fշ]o#Ѱx^&ȴ̚ ¿#n-3V Π4}F/prPUi,oVզGgeh`#@ns6a&1ߍע:`NNG?tĆ~NNGĞMʓ‛ˤ6} ryĴ<##7+rst P+v( Zܳ wVٙT` k'oBU bܨjl͛X5$^?c1DY[3?+]qaeO=8e@5,5ۿ0xź3=f+# KbQjտQEcّb$jz/$"dZ`V3B iw" 5\=_8|ZQA`>Ӷ/e$=~CS/z߮HA[x_ڢƢVj8_V(5jG3ll A5mSEœ'zeMo ]EYr `@z'.gY&| ܀ k yV]5&yv&*:ɧ܉cQA(gy_ywmfC1Um}Qw~ BA J_Ѷ*Oǘ9fP)ߗlp:cڪc)7}(˦QDQ%߷*,@%xhH`][vmYhƳsVxyE-9-w2GKoH&/76"3ꤱ}SkWrEy|SZf ь7seNݾ^<Ν?kb Ѐmz&d gߔ/L]epRTU'AoO>*ޜ4}w)QgsqA4= aG 8p:+foRJW{!(Yj~ ߽^1(_eS9_<aly.#eehdnW7P0|U|U Yn)ޡ@ {vlG)wIoަ+Z w3ӻc7@p>#$0`0(Ӫ+՘KS #~)8>7wzǕuSYSi9WU`eg&eB 5 !ZXWդ99i#=Vr1p)JS2Dwq0Q"Qvfaah$mvnZ] ?/o#>Y4?2S#F1 hsz( P`wQVqW߉Vt%B~sۣ .w^5zasSvDH0@t] %Rx̖]#wv1kՙE x*hpGa ԣ6˻-3n=Dțu]+DpF}܈E9 ?n54[\ntJ-k@p:H[ӱ559e.fYȾ^ؓljA@30d VQ\ Hcijg幥玽XFGG@ $!hRoDâg4Zbhmj &Z ߼ȋ,ws|* xu$^0XI2* G[/nu"mf[7\ G8I!AB"{9יèa*]ܑtT<1M[rt2ov/WKf)qɉV_Yۇ]~N`4͋ sbM}ƈNpa>25uJ~ 7z?\`co0b(Aߚ9e` 8CӰpdnd>Dtha ߢF S-5qqQA,%ٟ!IDRh:D xQ]ݶ-=}ܽv'W.;u]uqYW{v6m]"!-漊ڴ|:e2rKeMz`jn"⁇gOqSf*L (՛&t KP+ڐ\=*P%m}@+hk'mR&TokH~7yXPgYN6{-DGVq%fe#x``2cé-hT5s{ͽ!%϶ 1IįN `K6ɩ\:n񆓶5 xN\Ğ&D$ FdE~Le 4{Q7TF(Twm0_]6{p:FF-  QJ ?cGսDŽ=TN6(}r2\#'[摂Zm/=f5r˟<0Ag= Uk>7D.'c< :"bK5Qf|Cq۟MXTUb?lF0:`@NZ7\+v]`,[bj«Q34-&rq..x#MFOw*Q&`p&e,O0YvyŎ_HU'l5jr 05l>taփ&7s>x&#JRLzl<'G'_jXyVqV֠mP >['7(sނ\ֹww7b\g:f y}pW/￰ω%Þ$o_i+ۦ*B4!N[avkq.,Od!TU55śOa[ίRĹQ7~@a%U7]QY0a yw+V]Db昉r6W ŎDT/٣P <%߷a3PdS`N6_fCLHH2԰7eW( xgr)>ۧ迭$ieق}{Ɍ-2=&iյi  SQMK*^kɦ4L ğ}4j~|iј1H@/aE60/!'i~Ԇ8A%?WB:d<-<:м{޾=PfrRve^έi~-3)buȑ/2jG.y]1/;6Ԫ@G g8lՆC{ Z J=i0!6j3fPWFx@~yGtfWBmF}Fs5W|\)$dԢ+֎NG6f9M{5NtPEf{~fSg;{lKYnYԁ㜄{.-kj(Ym6u ϷG֭x.yVe5?+fؚ!F_}vf9ܯ6okmYE}/? 4#5Af跀@oCIc1~@S 3#K\\ 2{ڌ'Mz?y [an"m͡_!-3`lweS ^ǚO<5:f/>)|2")݌ZflNOc!yDfN7]CN= Bru(VH[\]Res[ wqV6]3מk𘒓nr凢 \cnhqb#=MWzzTGX- ެYfǪpucS C@ƛhZH Vt(ٕ.ҳP (#0 df_7G} }PM(Tͥ=NTYѲ23 v*67o&Kjbmv 964hm]ܖ?sw{U^{cZN6#u($/@[|`@Dc/Ґ CGĐmK6qEh/@5lmZF]L~'.cW_ [V5Y4D'~҂!zlnFUטּ9:Nxt' acA!{==`Шs~ u)%G-ЅFG(v3]>$g,’a3].0ʊeCr{IJy3I#N %p*꼏:Z1;^lX)Y@+ WW}uÿ nzDŽ/-CdO]K| p'YM cc)Iv{Z,t-`Nn&|y[Y4,H B(6 E(-(L !h 2*#"xO(1;`Qwe+jhaQ~DŽ:XPĠ.Jn-@ `cnBrQYA[,wܷyhX6.9v72Ȑܱ=&TTFo!w;D~K=0!to{CC?t}}0NT? 3HVe?{yR>aަES~KoaC= cĺzה应MZYc2d0XAbCЫbO,uGΤ{ʫS_+hՖ==vX<(t,o{c`p,|潀P Rc .xZO" ߊZ1#.Br0*E  ]䔒 @bx!PC8G$7Br@N<*)AX@5Sz|iA!wuǑUR#<:TvaHߺ:y_>bi 5WtɃ%L\%fE6iBeۖ ʫGRO639wZn/;OVIe>b<;bgw%4>X6cfŘ][]iv#emKUx !8-pƻ劣@u)~{sBu;mG Rz zB><> X7}AQPrEтBP:C=íJ߶vsth2!"wKP7P=ĢN3]r=Jӕ6}Z#83Q'tQ2tɎO;Bxbټt L K~* 09"v--av; X5uP҆-Jk+)f-Vq"#Z᪨GU640]PѨ }L0?O ^ק\t}q޺hZ7T_]ORT>)_ո d{n`DWVkaM~j!YN)q32GO/[0:)j;_J%.L@}Y37Y*BeTm-v,CeI/j [)vh.܊bX*w.6J3P`i4D~tsyue|`bہؾ#>`5`Ĉe~{, +mT<0x ]vk&<@xg/99GM򝋀 :wc7"{̩'jh< {w]KnbvFxMjp ТQJ RCʚ4P^*3[!|ubCPn+*($ iA!ۀe9D꽡^L;mVS;35,84T)vw(}k /K3Da~N.Z~_Ltu ,4q|l&sVj|=w^QR va]e-_fTi~__,5ӷ`> g2V;mVgN]~:m]7 N'oL}9Q#O0<{ @ ąEk[w99?E RrB4H SJuuCrT$jY6C9!=IA@Dnז}ҍ}(/ w%j2c{Q |M(GSjHsoP;K;F5:}(sVnGeok>Mx<8m̷7{L0OC}>@̶1]b6w,'mzMbSQf1ɪlppd;aAۀQ@EHlZ;`9J)ݽ׀.@>^Bt\ aQfͽ͂B2 ,aGh5RS"rg̼]mr y$t~|Ze90yYŒūns[*0m?[@bp?fNuYita?lڟ 4k$e2*ֽ;ݼl{vig_H[{b^^'+K+ě306yԎ:'oAB=21;#<\>P,ow,梮[K~ 3B]^3d[9함 =:0Q;I;PCeVy,ppJl`Kn H)#%~ =mojX=Q )61yYbZ~A'$# W6(qX#`1XsJ ^3'{P+6k[/uFAtb')R7me+= ![ooZ'ktXosͺU!zU1zp: TnS>`Ziuv1;uއZH@TʟxW:n/{s-J{jOH_ *moCg 5e#35)k =}` Lz17G;ss(o=hh' %De;ȶC"jr1j2+f62Az3]rWCG'ȱrξpDZGzLXXu|9߷R" kRc̃ ^ҷ>?̝6˃,ꏚ fb!}iRz2]mSJt_Tu6+$r]cxno XMgPrnNaTȃr)<$j1jTssx(/%࿹94sg\ _;9_Z #jc%z^gjqMQe]9yFqؐ钝f),\xי'<4(8;jV%ݍW .<-= ãϺ_> !=ߢi`мKbzb6 [%;xipZsFL9DMCN))3+{\^zգr))%zZ |goT=aiwzjv>Jջ =e+m'rsx'7G|;>V @GX2]R_)z/ yY"o_z_3 *:&4K0W}qg㊼QIE;c~rL\k =ܩxvnL~)M{Mc"Q}]f4}ozgNuϝ6+-C [l~|`d霾s{(u}zT/1`]4y;*#|!dP.P%f ڮ|SkJ!i?T륔`K,k2j*T٨aH"%k'ݨm!j+i?Hf uP}BK6;ĝp7w 2g.E-?*Ee%`9,Bs|?T9$M,v-Gss:[9`xU Քj}yYBtZdqk(!ݝug".yB6: R|.jߊ=!uabZoWD6uQvQ~sCuӃ5>F|&̥Xl߽iXC*Yft:6=y ne+)%P764-=khą"PefrIZߴl֖ޠ-!4,"*zP"?Be&xUw NTwƣ3tG,3]rjj GR!$v$'ikhݳɋ9­s5) {}8?#=z~Ǣ|>&yi/tFZX;>0'npJr̴nAҺuFga!4@WHs֎N7!;=ݬ"lQ <6Ti[m;*:QbCQeodFҔ%VP퉅 $W&4z~tsdOI*z ;aiy5M4U^GK;ה6閭{I:տׂJ+8"EPqM6%tcdOZj f', pIkJ|7._T}c*r{uM0 o0`n FfXS}n~,¤;)5_D{Zl>ahAؤH9?8Kug ]A6ofm`ЩFPӯQILB:MlUPB B]ޏOx²<$Laj5c,' `^hJ ߿1:lH=Ausaֿϕ -q[^NCY/JW:v%8.ǯ"xHZ3@o}jcLw" `K<1B[=*: %B:A=Ru.uoP j-\`)J!UV2>?Pr~C IDATDct{u[ӑ=usg).;DzG!D(~[b: N:H խ,|ck5gրچmJ U辍䟝E6'e1JJ]MY*4?7⢴f]<߁ 37 N8Fy շhO>O0P/Mff]&Ղ*G M 3CK^*൮#7~]6tOW2m9v7\P-i)vLl QSl N~?ziC r4j7Eki4=}N{e%J+tqu3ϱ MҮ7[MJ]w札Aϥo>?ʷF<~~҃u4_/Cɚ), 3Y#bP>w+2ߌТfJ?܂Bʱuo[7T\D]%vetd;.뾪(^*.}^ y\l J0m1BO=! % ^${IK;hug/(+rR&AyQU/\V4#/wf\-"d"|u֊_1z0Pw :ף$AJ_u݂ۤyfr}$ֵf"1/kǎU>LnJ^lʜ%"Q_$P;(n$t٪x L\C$g '*+>6WL|6%mAʍKl.9af3qg0wIhVaCimAec_rޡX_ Fs^x{=+E6#~Ђ*u]18!ͻW*s=#y_4mk#sPzLo#AjAm7LA+MZ}tEs͊DYC5( NFf^BNǐJrsЧ߅˗7}t˲@W6kH=_,*>sE;WЂDLocb[R?Vy$I5wB>AKnhycl%8ުbBIi%tłBGzdO uMzJ6m)kآnys.Zu a]b5K.]4V03֥*c>w,->Ybb\"s۸[h\|uC^kMj7ʻ*͙2կ;ɝyb3 [n}bID/ﻟY>单;V`Yak +hVw)~KeK,Q/;@!jjqw<Ɍ%}&c^Yӱ ~ U=AR"^q0Lٟj͟Ue?ego$s? }%@8%ҥWbOS| utE Tϗ k`#`$Ȁ?>Oeu#펛b]ؠjVEZsSn*/K\=U5XrׄkNX=ʤXHp`ٓBzsXCVGP," %{3N;b jɎ:Z> Ճoo= էA@3!@fǒ^Y_;˿ﯵR哢r9`:ТKuf.h5ĴYPK[6G uw$a3Ѳcb` 袷sʒtuD٢/5Z.+/Ș3h8e-[䎼,Qe 3?tiS-M:A J{r!/v?g䩠s b Ra;K6.EMn:xϵ܂aoJxԀ5`Ƙs|ƍQi_hf] 4g$Y( tu {lV.3]rЎU'}׮7%$gj pހ!]- GԤ[~a]Y"U%+c+IbA ,>]wL;Mq\YiL1w@8͘ Y~s RPXHy"}_m={kf39.jLJ@K6 t Z4c=Nqޫ#W~} [l{II{3œea$BN)䗇umLϜWycc{{W^iRBTqx\jݙ%rH俨zT'p0ބ3쌂(|uuXmC!JM!$ewT2]rK~(n0gS.=):kQ߿>zg ȹsݢ[bc|!@Mʾ0eS*founzOmR^ͽ޵z@nq_3LM`$oQ $ NSy_$Gfgl.bvv5W[;ND z> dvl!כM+fKE-8m~JPcbbO/j7P9Ln6P+@j W@ /б〳򋤿!bO.98S9+oeC (UJJ{5:[LSHM̃XQ; &|_դѤ/n{@\4SMt2Fyr=ә%Gɨ KliI9 AD`@#&Rlο2/SD8FL <% N5'ġ Yh!1)sɭyY" 3/K~A3jVCk?vV )Ϟ1hFl|'LaegaLɞe( 4/$P # QaH鳝E")BCj;k (鍨_MMqV}'q9ҲUg[KMWgO & f?8ؽprZə"θu1n)L~,/K\l72mT#س/C uu.D Uh1L4< PB-g&0EhՖ͞0ܯi}X/ˋ=C>oNylYGA6үtZ:ܬ!t>S^ ?26-G8Erw^xQV5GddAd-BU5)5$Uh]iW}6Z~S0Pr=!%B/CXȞ,\TT:ۻ_UWUZE4 U H4na.!ZD=(WVPklPV,Ƿ !AUyYITyx; jXBT mERf.BVM!$J!%b @80`y钲!ukQbzԲxxc]q˲>oǎ)3ʜƈ/F4hEDiB5DSDʖ>(ɼ;P{-\5{508T)JO6%n 9DMߢK^CMePe*@؜_$C|rwatF钯vӇ(Ah/sHj/#LOMNW|SM7YwvWZî=6^c fȘx7 e5B@]FUPӔ,a.C #Q ud Z*,A3kQ}.(/BX_${򋤡-;@~KF,7(ګߓ/߾۶c`-P`2rDBу"vk˾ MF xm&v~eo_e&('wnz.Nf5lF PϺŗAiJ~e>t~-C[+@],X_$}()cD%ӑO?L-(zP' IE"y%>4쬔1vAR @z_ #>-V[c7 ELx7jh ,N\+<^rH)a/0 xC:_1,qkQrcge0a TN6j~Ҁ,*H~S_g<炙Zҙ> xKWouϤ8Ls=<O:57V}Mi$+NP"2jYz49llyyj+wT1PF:F uyYb-J\=u%>%^)n݂OSٻ 5PxH Tr@E hDQP>0[6 (H=Zp^DViE{_|]%4fӃ1(9Qq|:pJt`XyrgS?qGX734 ' ؆R_YMA KLA2ER#P.)^C$yY'?i[C,64aOs.E 8j:׮.1i>S@?z8aՅ+2N|lMpxVnxJG~XAӀ"Y2%/K\֌+ KVyY" #)([\yYB#PS\BH8ER1A8{>Zq\j.*3k7,6/MW?NWxnYRRout ya~mn-1t灟<ŰvM7AbLéUXm/ePVbzIK;$԰D:jo˒բ;%ZߴGT2OmF "wwǹC5I* dOx X|Eo{w)s| ~۶Aoͱ _O/RКMfgn×!pTea3(1u30 /H\δno;5b=܃Үv3Hqy@̯JYV\?TQ#0SԡO=1ι~kʫc^%YϞpu#R(Buhueι9|Vֲn;02L5j^M}:[&&SEy4K_Ȳ?>~ v0F> @&Ơ_I%2$+9CusPo6r[ 3a|)tԧGwb ϻ V^j,3y5oVF@UWUԐh_[ hG2lS\i5 m}CHaPxY_%`J~NCnJfOQ @L9cX4VOauy, xm7>rJ_ LܘqLzb FWWhbHOFzJ"s IG!0%?6:X /ڭZ36.8-) ܟeђg`P1u}5!!3F3S''S-/l4ECյ]Q=wyͦޡz͙*@N{[5K>䷏%9 l[jC3]I͡*}Z "HץW٭l%/@e߿yySN<%P!%Έe*C*wWM!:5p/«|a_ªbk_9%`J~6VXrwC50ekj@ݪyb~+HD ~ǐ>oyo6ެjs_#U:!pѩCQ7zcZ8is鬢{[}_x5~avХެdSbs p bOU1{rRϿo"z0#4U`@_F?M9 1p~^A|/ S'>TtGmO[mr"_Olv7>| K<n"{P~{/䄐Sk%IU;.%(n'حZݪ}lj.C F42>=E̛=E{}^?L_ _D'T(XSuӀD0 榐*o R<^2yQF[vxC!f3?"FQ_xt{v*Հ$DaJ~P/@hj=E|r\<{GPcXU[c3'djÜW Ě4g  EMdL*f*zX8瀊Ǧ2k)@bLC@d|*f52([fkl'9aHz&Zqc?͡W!2u'"/|? d](@*Pyٽ.BA$tᅷCIN  qHȘȐ􌙛Css'+#-N CswVuZרvih]ExU NBfU@aPڡDrLV%%~GNr;(C} 7P@gg>eƟ FѓKϹ{[@aEWVy+Q`_-Pv Fž6`ub ns'bxaaђև-B=}sط6ۻƃh?fǧ%,~@m^. ͗䄑t" PajOpl"inJ8Frs'{/ Ǹ. Et71,zHMxH-qghCWFrAC"CXt0'լ#kUK٣9L^R*Qߨ{O$-`JN'[[|E͡N@/<%,Ոc E]8i)WltNޮsEц:ܳ)r]!gkQ`jD/TPr0(3`HUPB5&eؚcդ$g2$+9-M6٭lU;c9SEr"KS{`T 4mپ(q~9ʢ|>3:Ou߭e6.]7 OFj6i&./`WwSґWTT8,9]}x9T͡^H??a+9T?KDij͡V5Gf^,PgV;-kB,Dd$g0ÔC|/p܄5a3=v7ajEMAC G-F:̹DU!/hm LJ.k:K˞(?ռ၃ծ%*?|}}O9zЧ| Fk`2}ބVm{gޫ ~EBSaP;$FzӁ{ytyGM xh3ZU{xOvڭZݪul5u4\Ҕ'0g/j9—KE +0@A ˾EQ]R."k{"KܙeaN[&vv~CZjXb"V3:D#"-͌0Bq"=LoUk{sÑ!l5PgmN+)rO2^qcs{Vc] 3ɞ?5hCxkWԜ{//:nYZVFfƕEDеhHFhf2TAA5u"pOQ?xcV5(]Ao˼W%^%ؿϸ#K\C10dK~H)9 @ݪ-[C'I:"j/ǀe'@.R*XVlw8!I>}KwD~کƃhEL|6u`QHghݖu[wqA[9.𯍝t ZZȲ6/@ȉ{lj\:NeVɯq vVnsA:ǍȞ&;V}cV22!7:NkCt0iol_>,=p1:UeMjlcCkLsSpyUhXdaTtH@XXѽ^ݱh"Ͱ!ghͽˋG{\PT]ϻ X_5o!ۄAH)9LA$[c{1cOz"!bZ2Zv O1Va.$s~]/Z (5!k?5)[ ׀|?Pം&% *g~zg0'Ś癈435=c]xas7ʑ9nvV=7'䣬L'"QλWU67cnob9qGHдޗN)2$}3>6^ퟻ?7zyHĕZnFjպpwshU޾q˿G}FY?IK+\Ӈכu9'bu^׬䷔h7[[G%Q/4sZC++O#ҦD+WhXJ+e)swo%^kxy9[%߃䷇,+H$PjOy% MQv+ώhn;wi5}ZT jK˥  `ђSڀ'^m3ό{Un2oմjeYfרfW[ShE@c@WQi Q;sʃbu rZrȐDrj?]-1 \_͡V{*nҵs\uږ+XZX#`nNnxk빪z=塡Y+n 2{nN'(IмW"c}GF60pO޵σ Hm|͗ B}CQïR"9<`J$GgB`ȯCMEh; ps`ssVTHy: ‚կ&Y\ތU= gh\fss6}B4YVHgqtջZ?Yc`'JZ+j '"Y(`J`c 94O2حZwFVͭmupwUs$D*CHuj hw\/!@TADqP4d8VrH)9SiJO\*XTdvPg=BDDVD {:w$!< ܭW? #C `s ,2PO^@ݪm{[E`Taƨ+S詓3C<=zp7G}~gXM}UDx^եfD)Qxz4EƙwEi箌v!1q*nIiK܉̔ [DrbXC9^~ " CM8: FDP dsJ~ H_g,.ii7eM1aM9{؈m]aW[qyM#PWW7ңx<.|uwM-MK [m_kY3- (ȏ#Mgi5yr8*(O~͗DrBHS"91wt9ԑ,Vd.HFڌȾ=Ґn՚mٸ7l5Q >Ѝ}ݩDU=tP ) IDAT.* eyѓ=:nWAqEҊJO]g4E7šM˟tKVxAj8(:׳;75VEv׾EP\b좨(1PKNi0%_aN:z[g}7C)h9PK:۳PC,rXVPwMg  ,׎h@3pd =GIgC'{nΤN1MrA[|"3 4&&aQͤ'6gvyOQE  i&,sξTĤ,S$g`J$U[ksku~c|FC J<>:q ̀ +gj9tsœF_޸~/'Zm#M=gqDH@\QYusqy+ 埲!:P,l(,ʧiqM kKm %֤LR~knIՊ}@8p МyOٗ əÔHNGHz FfB)[5Gjzۣ#B/r߬[(X5/v"GGN@OKNϘ8FGa ika篍QBD{wp;0+gӾCi0%_9&?3bCestEc4+|hn>Q{ڊ(Yр}@~^Q?}is@ mرiiۈïϰ6Sy#"AQ]WXs"/C"i4ɯ~978f񄂥\㣀 :3&9377Czfila@`C{?. ujrg9I Qʜ%f5tP+18HH~j")VSz5'dJIE͸ekz=:ο OIAhnzs P L,u<,^{"^]5m>ohICC@A[Ӽ:(=PK7,Z3_aJN i0%_96Gݪ /'#{n΀C{=*Jכv/a.ws NP '|mcH\@u{^0z؎^6y|J^=*K]!Kέܢ N[GH$~aU]`s]FU>]UN'^}*״BWeed@os]@?b#z6%zF\y{#Rb7ݫ[Z[4mVЮ1+6.ɼWI.>ə.M# D+n͡"PƉ9ԥvV{*=xnNX^ǩ]F HrѰgOs&5hAdƖ n@h"tfk{#MF[@[i461d ƐQhhU3 /d3BW"9nH~4!ϒv^!S }w/E$:epY_ް͡?;`.!3gAcK l޳H>47's|ThLD -OV5 :&!!4ym9Zx[ʓ9q}ԔW^q䙋By]sڌFcϦώ~hH):az# J{k~I`!];ߒdcyu}m}CyhrVֵ|d]}T:d*A$A4E z&Sꝥ1M@ZXhH%>VUd6tM8 ~{K=g)~ni4ɯ=Cy*hUfedzٮ<lFlpڰ͓ݥslJb|!A_~~3p#B'QR*?FxnRck>j? LFYNOm4_CSrBH) [DqcsQĕ 7=xn՚u}fحv}ݪ5"B/{ɞIhpPw8.FaFd^0Q#<8 Gl!NSϽ<͑{ٲ.y!?s a@8w%9I?C:p9GN4&`N*'@^w^;]fQ+*+tշYZVFf zW[ڈFhaٝZǫJN7G^3lxx׾g&w^z I0[s\$Ur" DrIQas ɑEve8-A9x}gW4fg <>_zmKqO v y?L֤R=k׮mgԺjOudW"#"GpMO^rHkD}I3H~jOD믹vej>P#E@Dx0uA2}b(BU 욲a*w+k[ +%g7=1󶻞}ƆJksb<1Syx}:Xپ dr>M޼Cx`BJq1Ɗ[7VW$iO8*hX̻yH) Fh5hs@&nվ=ƜvV=?x͡@9sygorz[Fb;H>&sH`nqmb%'t_Xv3_yh'BP]St͚BeSȿnڶ/>YSR*˽roj&y/>uK$?@L7ݪm9ԛVm^aݪ)z9@{c"g=0!pPpy2}uP#붾p.uۓbbˀaF!S WoΝ7fH9lq{}}˫xGZZs 7]TU:S,54$$"=596q_G>WZD)X׶).9|V-nz#XC_\ݰߦeg:PWkWyY "C ]{ءн$"< 3@]lXh礸ꢼ*5<_4~~5s~yY =70!`Ridzv=ÔHNCVesO#zI9TTnՖ;>PV-+s@3TŰݫhTuO0 8,]o](\iuД0pƴ>'g_zu6W߃0GJ WWV{}]d0B]uh0]U5umQUҝgO u `fkkYqWI.)?W*9ÑS"99d]P` ݪ]@uW/` ͡y|4V`1rrx5=1;xiim'5iAX9aRc̡5}mY.ͭ3LJ׷뺦z2*BBү.8cv Y 1 /KzaqK$GBL4A `j6z'"I[zX:D9͡Opp PDHݪ=~nFPYEۄ M- =ePsy\sV}{0*D3Y:^5R-'gOo+߭_yՋWE%7B?a ~շWܛH)>nՎG|?+~hNJUms7٭o0uQq"B"BK *u]߾E .׍:軜D*wF^2f9Coly{>MfyښUMP AVS@%!Y7K,bǹ.]a%뾮tm"T,7`J$ ~ YM  ]%L#龼ZYU֯A]r56l%=<v6 ʲ}S%3`J$g06ح}9PW C@AC7cjmC \xt.o -^Q)nw@M[,[sOݗL7,,,ܾe> %^=R.=J|rEoNՂ9 7|:dDJVw||\@lطm˟TK J$g(6:x}(_0"yxXDnJ0C}P'VO7l OpETXyPZ˜>64(輽%sytҤu-553|}22}R,*~ߎuded͝_G: @-B3 "^olJ vգ"5n2GD0%C[!EۭP" Vʈϥgڴ;Yw~ֳ1#D엎3 S55'-۴Ʀ,`C`uZ+u]=7gm؜,fŶ]{Ns?ߥ{z(欌ohÊdim==֔u=XRbt$$=*9cDr3=vnn[VnVZğN?pScjwح-¨ .˾ru qղ y7ے1э/pOzȞ3м).gwAHr7UWzwV}mڦBB7KGņiM[j0$ާ Ez@."9D#P{NyѼcshGy5|Vڸp^S \gƋF ]iv΍glܗW̗n1k}Lܳ/2C`uS MyiiK͞ t`B -3N8fĠLFUOKS6ަ- Hy'y/i!HNsVl=yCۈm7mՉ+\4op淾\s׀Ԕ5~yʴA{oڗᘕT_@I/UM_oQ-6kԘO"B!{@JHV]bz]m^PloOUJMYq! D"Pz[lup͡>hjNUos)6jݾ,<.^ꇀ (v{^ͧ m'D44 o`&-#UϾK\t~OQnX{sk-f]a{&N F\Ĕ<8Q㽟a:{w|pj,r;5!朶1ܻK]k&ֶkbI?>ztj]][ XW@\ &DsoWѱ6{6W(M~)He>/sZ- %ssj.H~y!`ٺf坷Z[鍦ì&@IQ2j)3V~vicͨzn]:*bF^4p <D"gs-9Q`BZ%880ѻ f' ~ZpR1DĜ b :u,& NeVqAG&-?%wdZZ qxledpn9wdr̤ ]ɹ4ւ:[R55y$MMked h2 F'9Ӄ.&VVAd m%fi'?_LӛxeKT!Ag}W2`䂬lO"DZMe,X$sW=a`쎷;In~9YR2 JG>x?2Za6ٝéviU,qGyL0 !K=@\q>szW9dY1aų !1)PKC'}]{SxmGyC VWs5ĥ"Hj2eYްMxݛ&Zyԡxp F%YV2W wV&r.Y3_ 9rڢ$|&أMb셸؇wK3oڧHt"Κ:%ꁣG\v[Sʞ9]Uxs>Иj6Fswwg̗b.MMɣ{ҳ*,=c]`b/\o-]5swM2x2 &kIj쏾tz&üܿɿ }Q\ Q-jL?pĹlX[qFrMq RƘltH\殇! O eeX]b&j{.aX]Xr/? V`jbl&2ǗULUU=* qc^¡SVx-@8dQ| IfX+;^szp,#9pYR ҊU,/ے\6uF19ut +b_iC;;XM}wo4:SS绾bJ8l=S.*p E)ekV.nGC.ndc}UY]M7ۃv*bm򙲊|ű͊d])J{g7Ɓwuʯք&9-PB?ZiVqٛdڭw??O珆-qc_hh,9ϵLF BA3 .EKC=]]P7f:ffFgf9jw5z"/?Cۺw^ 9 CIN$KRgDlDwKԙm4GםU02;Y%PbyS!p2SXz `ꏼ~@#nLe]Qf?ggF`ɩ,=8S6؆R.?;{!?2أ44=|xg2v95!FIgs a!f~ֱlW^F~-J&[-Oo׿x fXugѭ6u{\|3sk.L|7Q&]%s|E!.EVAIJ}ջls#Dl)Ek3Jr-r0t㯽Yǝv(8qwfy4*ItͶ7FHץ QipRb D—WCі×؜bUwu'TFyC!&!cR_V4-0b`6<0Eb8O`y>k,998enigw]jf@,.XQ1켂_K xq^4M`3?7--`zѴ@7Z;ۖ^rfi vq\o8"^2d0\PMȟQ/YBfn)AI^K2,\ɗ{.I '8 Gjbwo -ܤ.ATqe?8΀&2Unl>.D$= Ϯȟ1ot-ټce3߳/9PF3LBi6DV@6rSp8U+KL9\6+,+{nh:[6M݄Bt:s8xrA2Gmv,r ^8 cGNqӟ }: +k5)sKXJW1s=?fg8W+ۚ[ Nk#b6y O"F!nEqOxCcWy MIgsC#9Ji6HSqoYu箫G~97(9vUzs D#ԛ [2~5Սu?xF !j=Y_֣=``Qb'іNhad3"!6Sz'0Rd,gͣ 6kD!Q5gu5 #?}Ҟ2K-UU ZmBVCž|$ss};_ubNq{NPy ]j4Jg1ߠOĕ뇮,\L d@_=m̕gg-9<ۜˌs#? sPӞe啩t,r9@Tf*0(e,gzh0֪Fuֱ̾E $3sިצ8/s{w_`IV#} I/*)73e:$ :,~rjǤ 9%ђ,! 0U-yKK}ғ<d7YZxb$r23̖R!qz(V!p͸aUӳj5d܊š)J$c~%+O_m>rfFe5ELAOLE`d 9uQ`B>QR-W p(rKc#~lIV= -E9Gu)&7~K$33c.U׋A*5Msn3i3 KǷ8ۗNĤs Ms)9«˧21d38dE{O\}f=Li mJDk-U+*|Z @h\p=(e43`@S= =Z%I6D~SZN|{vVt[mR_(X4 +6Z:` &EBkd 3 ~^sd~)u+ea-3)[z9܈#;ʳ<5ok9VsfH#e5[?|zG8+|r(0 !m`Ł_b6΂ꐣ7Qq<0̑! gkBUX<WIΓ{9Td)m7{5p"'`2~tiYeeYw(* pNCшSkm?qFac”(>]IpzЛ#xZOq| },H;7` cg:^KD`CXdo< ǢP@4r7xCFaI?IQ_? W}!yӝJBM>r$̓>b}I-{XS_c7ETc P`B&M}LG$s^S>[MsL%3ɩk,U!\%~Ffɱ?upppHFI.,ʞSS|7܋)yI4FITzkb]MU媾+ב,W@3iVkޞ%IQ00MR$00ppPFcڳι@.Oڇ%5*!R_Xsdj.Eы $ bYLF`CH)A!+͉\GI9尺2'2 viO&tmt K?3}74^?|Q`BNY`p)DjK f$0r}z74OO! LB)0p6 BsA$>x ơ{ZiFI9m|}MbiA6Ƨ&Aɧ&!26`3ƾ ~4)GI9-9_2!k. oMSO3:I9-Qå{),'fڊ^k[\1C>(0 !q%YB!d(0 !q$BƁB LB!d(0 !q$BƁB LB!d(0 !q$BƁB LB!d(0 !q$BƁB LB!d(0 !q$BƁB LB!d(0 !q$BƁB LB!dtn-!XIENDB`openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_33_0.png000066400000000000000000004420451413546205200254500ustar00rootroot00000000000000PNG  IHDREXsBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxwxTeڇwZ&T&ETŎؕ%UW]]uQ,  ")JMJ(!:3~<AEeu-99 yUZk `00_ `-``0 `0 GL`0#`0F0 `8 ` p4 (0i0 Q``0 `0 GL`0#`0F0 `8 ` p4 (0i0 Q``0 `0 GL`0#`0F0 `8 ` p4 (0i05QC.N`0{4 r>ob`/0kt ||K`PZ_ ``08*(0,'s`00i0<rr>OhX@E~v x'~`Li0<;w*E,|,9?0'`QLӏ3u4ߙlE: S^4 "B@_ QxO`DϐFK]c}sTj' GwNWuHrLdiH HT:S_=#a ?*Mcš@=]` 5R!6_ )/^@2hǝMQ`y0i0Q޾@0 IbI< ;!B1"{fe r֑};|LXjs>fޛP:I ?kaG0zJn@"|r]2`+M܉< ]1MM/*w}0g XFRSz(];XIV`8Ɲ^#O$. 6P`":D0>C:]!541nJ_ZpR=9"үDQ4k^ pNrmiyE"(ƅ0~瘔Phq$r\ n lGf*hAĔu{)@=2_8l9}^ =`~8 a GZC#1 b`q" =Tc$*,F|"d$x̆)q ")etZAe4 %#dYnsv7GUD>3W`c`8zb9])!K)vZEXES.Ÿʠ:dVFԆm!CƱv'tnFҷOt'|'Twbz]S4ӐƝUDf :舼"AEHP=r{8ŸQ;] tH{V-SUd0n1i0|*ۛ[g?}V^TP"'"uDCH5q6 "ɦmmb@ IVuJH[ϨZ$Itnep"ѝw+'We,Volv$)9{i0|?@?R'=hT4jMEː5D5+|Fm򙉍 2Br-p [%A|fִ]}cєFE>s.EWK~|n^>!cglJڝgE ÏajCxjC5D^ZPw/~ H"Ug^7R6Sl餭FHT,Am۾q3I%+.4w_o#EӀg'ι 1<g x"p mq2z^f: 텟XS}i0#OEꂏDGE6 DIޅV d~r1b@EHWs|/툿*${!J׬{~[NC[F+ŽoNn.kh3UE;z,:`֞[&(坈I7iz|]ٽ_ F0 !ݾ#gQB4JFj{Hj6>_\Tunnl<}at) io2a䒴zd5=:`TS `5SB?U;LkO 228x xsҌwq11z7 GLCY@  _xɰd?l[ڗg sj26 H:`ݓVܫ/Fjӂta ̉}648NE_](8 i&:T8`<hE1;xM $zE \ׁv};3hA91T`=b`&ȜWllF \["e_u$Ьֲ>Ua$&-% wʢ*j$Ĕ 2DSiιq ]l"Polqvtfﱋ JyG 6i?|- K`&Mw:SV_ЅZNGLml19ゾHYDKvPnE Ua(Q32Cv,ƍTcg <+g#R*V!ؖI᫢R2*ګoۛ_m5 Ga ?&!ak [AYitX׾/v[ iن0F$MZ\t-Fu RFH}0rL!x.Psl~QQC.WL WI ?F9Ca{akk,sB?;"uHCD-dqh K^,&ɪ$n\: zg'84KsI@e!NE"HJ+" *3fI֞aF;&l]6?y[T%K| F{)S/pڑf1`B"W(\,sdz6[/sF0 ɬg_{AίzXDb #84{=o^F:h.!myzxmy Bz\f gfNy ȷyztה',.8}fw:tDwE^G 9cF_%jT𭅽 O:qg"ˠms@);Wۣ _UGiSL(,U \j;ݲa=D#ݦ!eڮ?2G $z۹skQ^RkK,5hkIM/N'톙tȂޤQ{>WGWM8 t& +# Z{ to1 \ ,y GEe{#K,j݌EmDj%q2]]%Hl;dTe"Т>Ul*K_R'={c3<G+A_Ң[+&0"}t^aʼ5rnߺ83Hli}\k |`˿pmzۿwH$ Xs&oӿ=W|{\yU~|IhCj.<+0'$}gԐ@Ԭ%ӿc^ Wm KE4xKm;gxkn9sliM;t#=+KZ;җ .hv6H|4lo<#F!ֻd=!Hx/?rA 8&4Ce{["ADحBhB\_ &$ 4Ġ/HH,)4;WM(ޘv+GwDTê>a$5|A#ur`RZ{v|8aԐdz5+֑U!ofڗfϜ㓀78WX۝Sڦ,p`V^^XC'.t^2w =@BF3i@kqX^jM/w^dD w)jxgV܏D F0 <ޏ4@Wg$Z Cvֈ@V#Qb`%JEOi]!+(D$D>'y!m[Fꙏ$WrF `↷ˢr#A6uM:1V!)HQ!Cv<i|dOGx;rӀ}[ |ހ~["MCÀywԆt'̙<3fU; j+mhԘo\;V$N\:"5Y(/YIbBm\m6Va'SoBxY1ǗkAr-ҥئs?XyI8M;i6>=")ȃ1 x"rO@:UzI1i5dƳ@ℬfea IqYj~+ ;-(ش,ؑ2Xxlj|YP>hF-R"1fcs$\wX>c;V;x|#ҥl`?c@fk go?< c }d$[dS9Ԥ?2h"H4cY=ziٌ5X4.VJ81;_8G03qþRKF 'F9>OD^*ۻVx97Ⱳ:#}>c4sU(5_D?:g͏m7'rȸ{=3uL۳LoFb")׮UZkO֪n\^>pƞsNuͯ״\<(8ʜbZL/tBn媭mxJɘ 3ߛ5=ev; _)oz%OWC/&WeϿ7w`5Kmf K}7„47\,yëUs&aʰtn۫i]y_`UI\ѺS2o.ƦCyY\{:a|-ko+[p}2Qm킝يF1_LnE_{fb9 IDAT6g(72Ʊ`!ڀ.Y:wζs}[ѹ*sw0=RdlJ;$BY}c*}_N=he8X-v>;E ׽r^Kyjq<Αt֓vy+R\?lI;K) Zt;n!H3]Ǐz R` !7|W:6%(}ZTާu\e{"#.? ږpOVCZwyrEq~ޮ\9O)V]~gI--kUzB}Y ^TkkpgF5aV qziM2Z5\T6='IӸ.4VȜnW+}HkO6S&=Ze{[ῶpDՈCq9h`pE|;9uHwqpP\{j)O>pt\܉U*]nȰ' }jJG'1ͬzhrU!iˇ$mJ~41p̧gxk;Tw'r3/FnEZ&Ĺ甤nQ1ӢwU<#ͫHn測6&)%x]m\!j~ 5*[4$%9#lEnp]>|cއDyիvǫկ^QJb$`csZĄZ:"5Lݑ7:p 6eC&4v/ Ut^8vh U;ߩ&e.rW?:%@EoOCt ]kQjo#HSL4hKtKp넘O߶,Y &m^>櫳>d*$@6U'izrxjDl A`~nZkgmns<;׼rւMO^jRsط?@ 3rg CkZȽu "_ P!& F0 DPjFӖUwРs$x/wzK$,Mok/ӚUEnEn?tͶI݇t6"b؀X5GҍW"N42.UYVVuSܹ;Yչ+|oEՉ[W'^0e,pymfΘMqWO,/ '3  0O>Kt*LEAs&@Ds&?X4JBجV5LuÝ% b=th]q+m@+*uz7)(We:sUf+$ GR'Lgk28t"߃zu@o~f]֖X˖xG+)ʘbwcx0A'.r"@6#1@3ֱ :ըCC}i6d}nqC :"cEk*׳Ӵ ,F0 ")=]S"#q>z$ʭ }rͪNǧeH}$ںiY;5hmKǑ&C6]$D74]978w<8glQYBmey d[U\ Zw"K}bw1$+ ޤYwj ԆGx~ru$C'T:U;wvK7bP~Z-L,<8PBܝv/R65ȯYvn">.dW#Eٹ* lHGȞV 0WwL5󒂕͊b>/ !6̆-y;mؑ k3~|u!WEB #Bd ץ<$ЙȚH]5y80F0i}&޻ iwtJo_wɶ %ۊGpYg90s"rsSgL-?8BlT !ߊ4v q!^꾹+*s<9ݹ*6?WS_63H36%tNʛaS{ԧs҅`Mp:9oeTe4Cꭇ7t{i7N앦sU1C(|P=Vm+&J3X EkOhB_F7t'̦6q3"}k5qy-m5AFrƛŸ!Q+r|uBB*.Khm۬ϹD8E>ɁWUݠHʹ>HD݉s[ujIŶ)%haXX:j[G?9֓f"c4IkQ0M * `X^1M11i}2;po4PFyf iwqLJ;pF2-lK GϹⴺ؁v=?`Ȟ 7nGCB͕%:L); fV6ƙ\` ^XyL?o,e{;[X .㊶D]I0Tev:I $NOgiog ]#5կJyb8'5g;m̄j`ݩӝWe{ #ox2U?ۮ:->{.u7*uV#$: nDU9>W!mHiHg8?[չ**bIkSwx&!"ڣZ$I'W[XS9h*-܂V 'f.K8,ky̖PvE2 @ua/OuBH 4#dE N`~(aEŶH4qdrYhN,?;9")Cnk?O,$,8k=[%xp7e(W6HZnQuӂQeYiol_lON3z{oto0wݼ꾬M52Ϊ@W}`[S=v6WzM&iWk\"JySKR?CnKqŞybg+۹br4Rxq;a[vMgDX#t 4$ۇ}@wUWۑۑ}mi#ީP~n?{>$RZPnlo8hV :@@G7@*s#W'"b071im̊8dts&owJ"Q^G?ѥ'=uA?6HYWht@sQˠ qGϒJs*&uovv^݋ٰӝљ3vG$*돌|h%N+T}b~t. hrcZ!%Q5kBN~c(oVHZHS"9b y Иo#:kd8_AQEs}g/ݓF1HR' ?pn6?@f c^9KզtCTgܼ74BV`iwqJ' D9InG1YMcsl 51Q Z$tO S֮][!Rs8U1v]vqkwjPt羉DVR@+wiAq)!N _z/K?,=IS@^]_ְ4˧u:i4pYI-,ݧ!VM/({Yjl!b^XٷV蚨HjT3UH7w˖'UuʙVys$bӷw-sB>7+lTI?eBuȬHJZ'rLOn7wxT.|k3$ Ijh"-tll7V^bA, Hh"MzjRHe2}fg8sOWJLf֬CE IDAT<<@=?@iuq0PIwPNV%cg2[]C,ElU^dvu,+eswkEURPv-jAlQ\jWzD󙰌J}7!|Xmqڷ]-XJ{[mgkz4\<&Lzܗ@l:ʇ_kd{/fp„9׾OG#@ӌ>|#w}{ Y|*A P}Z@@ 3"&!ѫfl-*P{XEl΀sLQ.苠s,(`PimP8 71  9S?i<~Mf#6} ;^T0~xitc-go?|->DĴ!\sF=&\ko dcN|ymr52U(YסS_ Z(M58C W P@6KU ШXr|HCij~8^>,,9stR֖3. qO8igO)[ݫ=*p\NKQg\=7fg؝j!E3L1N.Gk[ TC=y@`*+ 6! DP,Qxgu}"vPӆPӃzX+A cI`CvrB>!v'S1nY»7AM<1Hp«Q'::ڤV!񠒝Fe ~nHkyoYԙFI B3:n M@\Kݡ18N+­ v "Y 2ȂXtWHPu@Ojcs}Okkx^uBֲ_p%> ٪gUep{<ŠEvd+Y;|#@,+Bzuz ތp:` 0LyޏYC Tsv尭97ӬM}ׅ@!a6ɂ溴cՠԨ^!=2^ b)PX{ )+Յq@j s\zs\cMm0d1%Cs[f ^PT**|ZUy+ ^fABkE FM6kl5oy0͓;C@a]91mP4i'4Ŷ+CM0 Gmr FW6XC"h.UP7$%0y]1j3ń@48Eoʅ~@dGΔi]M7JnNjoW>#!]cGX_/g?<sZaviSJ^UjlpȲ/rJu⁀5g8Xy\ ,,_@" BR:"k5HM-g[Ř6 -Ы>+Ύ;%#9Qܤ-;Nx\M~ehDNƛm}~JgT/vl*~4^3vE&<`" (k[Ͼ#j}R ([RQ 4f/r?ȸC5=O>abK'AG BZӚfP2&}TrxMNF)]2K8|A]GZQ2FaP鬡3?y7 3i'pJ}V'ppvn.S mƁ+ D0'AZ1s[}%[-AUPWhtKBkj;Tq^HS=G69hnDZwF男A! 9uzo?oduoދ=객~(X޷Kٔ-2фme{_ {i(J=A*Shڸ;ncA]0ԈgPC7@JD$P}q@a<t ՒjD:cAr?<='ܵ4<-)WƨgGf,5EPPgxeĆ ]"YwcEbً@ Zz> ۭ@e T ?Oǁo~ 8 QF/j|Qӎ`}9P dNM?0$LVa@5_ A$˭8)V)|(&HwuҴXJW8]*t==.<; Cй:׈°q qH,3x:mjh3pC (C>ԅR0ֲkzϜ^NiJ)1/@QOa"AJDt|2DXڐ ŤTs] W l}@܁ 7kz=aȷ_'{9¶_ tQʛNAarvhS6ZYa-IY:f5SW{`smYmA AJ0Ӱ P+b\DG! rԨz@]jWfJ.* "L~Ŭ(t:݁hmevl_uho xk=]]|Y9q$0C+I7$Ԡ Bd!K b$Oi M@z/ s G~d=Gi!Ap=ƃלCMd:0 ;O!p;mNG -M jAYtޙ]s(\U "4Mnw*כOv=#%f9.Vq()v"@aAjJ hA ^ Ajn8-A#>g}#kg(y@[ GFl V; fFަ G6BUXu?ūoor/# \f4_BQ&D 267R?hmkpgmKZ`gEd33.)hWr|/$reR+8%(``}s}*M3Ϟ*9w s` fmOS3j9KK9Qo dފJiKHAz#N?KYA]¼I@18;eF*= @4-hRoV R2OAjh5**%Hzc11 WQӷ i~)_? j(/;4=Pyl/'g<3!}=,#TvMizPzHD^ ;@fHt0$H%i,)k@7A s .A&{m84-_kbu &ZFZ]SZjծc Q#0Pikn5뎘\}!M|{NtX{_ y,5i η< L҉17nHN=fp}pm<\#UUJsB5H^ryu"Wք پBibKm{Zڮs:`6#A(nMi:[݇*I%JѧW[`E Mu0ը 6Xyd4֡*V;}5A. nZV'"eP" {P7) I"_caSs/}I#؍v;U w>R9=ȾK{6ǵtw;4U&d<5*rVP@I<[WnQrDjAyA2:cEd#RvOkQg m`yU A!㌓E;;k YχȊX-+RAY TGUj~xyz竑{NJU=+k Eq NqN.|]sYCs[lWrpds@ff <"qklA2o0hy&o!/mh4w~.˽sUe}3FMեH uvptWb2@[7r4:A&/@J9RT]݈&S2c|o/|م糣P`2.W{sL3N[{ߚpc PQ|89&ZIR(R ՕȊhK<<%^>!Rs]Z2cWx>PF4+HX-:44sDa |-ٺ"P=Q6OSI6@8i|R>VM;pn i~䞾Nm=pr5Oؚ^6u ]b[H9)`& ǥ&F标[*3Qϸ FqLэܠc/UltT|)w',Qq Er/hR ju%D!{[cm݆4mP.)Au\Q5j3U9:T DBӬrW&>f8*;ZNXƏ!. H2"J3D ֘ ՗i-PʜQu9#$%ܮ[O\:(8LÙkK} j(6 J^͏1(cy-!U -3jTs0DL w r3?{=@*}!H_* %?/D3Ac9p{ Y J>ё/p'lC+"YbX&2pǛ|7sFjld633JE˱fvCPT+F1E5 '5!|4NRa~ڹY]nynD_?31ڐT܂kN$x9| SqQkڪ7yn]2\YqX]&l-k݅ϝN{Dgby[lx \b\s&XeըP` 0t75Cf XqzX6 dz;:*yqg>M_|D25Ř={gV/*Y7XmITRO<{ц+CO{bV@ַ(&^SWyS8pPl)-F_F ոDn E(@9PM7 b=ƁDAjgVBwrw>} 8sIEc%MՒ>Ըx@KPfa](}SKS2ظpvUɏ+gO+]5U!>9u@o(*x,4rkIFkb UƌM͸x Vgd)U1sKM(:'9u>E,QFjS扒huk@IDjQT]L-uIywoZCU%H/N %}POeըE -tj bF `v)'&n [? JyWqz N*f]el@#6ZkЦ=S?U,[N`Y4E ~*'|Mf{W82Rn, 5%mAϫyh>(|9DxШ *gP,Da3DTVAӸQfo2d0' s[j>l@*.7lmsTdw8tix]9`z@<,DrBԪ.m\(b)Zu&zSYWm*X-X+w@ jml<fo;P4] ~^6=M ̋baF5ߜDi΀\- BBpp"v͑rKW'y%2$} ;,5;lY'i}'LPC5GЍ?ٍ݄?-h1oxd:ADL w@e}?~<>Pyugg[[x!>hpT }6/MIRmdQ/b 7k:hncjjD$13(K*dQP3غA&2 Hi^u/lU@~+:P PsJP^mPƮ/*pՊ"H/^ @sD|@ FUu jʽ"`P-գL1+㢖0Mu3Tv"+cQx5ih0QEs>f&ō#ҏ8RT0[~#F \{2_C\2$L8#"wn^L0"U Rl僸ڮ/QUVGrn>c^BAyV"|1Fk6f+IΘnfg"#6ʢ@*gps&r{HVB,(N!MiI7cfTݸ9MݸlwpឦW?O6͂Sf躲 w.Z ʃzPӁLD Lu^|MA 2 y t?)AYzU۠|k8>-m_<|o_ 13 c)7%!k8quT V:駬$HA_? \XP8XE.np~5r͇Oԓ,w)_S6V@>gc@yM mܯXLg^m|OEZc>jV?{7˦Z[M $mw\- IDAT܌W5s2.>J;l[S @_+^>nv|۠ 2N[ohògRr3@Fπ'vRnJn0nmsN|d=mbLk|6TqF a0JݏE\2p54U8<,mwO(J0( wg>M'bZEO'ݸЭ0fS=ڿ#~yc/>"drΌ Rlu*_ Tn8$c؃8jo@4綇E@DUaOo D  D@Ne) SOH oZ@ S ;=P>7EEӁק@,}@ `;QoEBLBVQr2#qMIg(+(vD'd<J֒6(nԳgϺ֭Kl<@fDKwM~[ܲyNU=A{.p{vͭ-ߚn>R9M;_>vgQ齼ǢڝU;GẀO`@J&yZ\mzNTUHBP[rjADxuZA"2P6PP>q(3jwgktV)OOy90o3̿O-{K^)[?މPj6 {#ߵWnu u7Vz"߭)pF9T~@>$6p Ll™'L<U A0+DgvjcMxs@V|/qz!i2neVժum دq<~mV.eU^Z>Z\|zڠfTx׋(?=:Vy_5 0mhÇ1뫫< &Ϝon!&n@RIQ(@m|E"OupaC9 ;0ԠMưG('ȽJ8Ć5oi>[±ݛ滦ӹ32j}c ^FmzoӴΖȴ3=Ǧes\.K%44SAqfGS66.`#kU/Mݸyte$6P+F##ΟտYZ؋qqT:w_sӡצ|$#vt8 wgŗO<9W+''&}s}s-{{O\(GtcPV`P,yB$ }*  茻d`݀΢\ W@aYH)$HVTI,v3lESIIl)?(oZbhs'xl J;~xG+>zBSyF%a.8-\f=4Ady!Ni~1^S6G".fe8E,V|U.6|*^UpPpfةG:wJIPD1tfo$rF> cdҗRnl$_;FY,'{_ - 1v@9".^-pF?(5=o|[+=g>>|wTߐM` ȵmE%mlZXpLPdZ`ۅlPJ7alwOn͌g2,GD0!صOQ#_sMC0'*{Lɸn .Aܬo>u \f<9'O"!얫h|z WV"}i8/D*A, S(J%H!0DBt@9 < r@d %%B< @!2u¨:ĔM_[Rv,w')^y:g_;_jm W=Zfb R[ lƔ+CFk}UΪM{3s|ռ ݫݮ }#%>.H]8 O(z,pUx~lDSVOSe*J'BV, rYucjM . !8F>Lu&*5iZfPDjDK Xg}V¥ [;'_0V]?0q3c7"K g#x^jE5`yAqnI(oxFPd%;taw^:oҽ!,~+ΏJ. {Ȫ$6Ofq]^K5i>UG$1-j(/٥r=~m.2"s@ K =~ZCU+]9t/xϚ fY I,$ H1$M{BF jK:HAKIXJl~d"h<OlMEz}6umG玕| Rզ̝ 4k"]1^(صݫ K3\;}}W*+6?8od,7s7iR _gau\NpJø-u{EQbcꘊ}cZN/UB#s}Xn~+Ӱ#*ugOʾwD wZEl^h=swhkLÇ6(7 7/ V'o|ئEa+EQRu83WSy}ApR+~03 1|woRFз8_0tOm{XYy;#=Wh[l펬52J c}q}BK ܧwV݇w RG @eIm'~ QCZ0[Y0Qح8ꣶw֞ic 1zR{0󒶱LN_&ȼ;T~zQROMm0`U猼76DVtк'fd o|P`>2dևvdj*r\VfP.qO(\ *u0ltܩot{\%"K iuI۹>N>?z,ka`yFO>} @rlQm6b/3"2P=?[q,qvKzt]QMɶߺXS0Ӿp Q.uI&sX] W bHQh$>uZG>r>|kJi͡D*ȓ(xnQns=}%T7SCTInX@}gą̆wyŽ_H8 Iąv'YE"&ǩ>pd[{PyZ݋8?osW * B=>0qSciA ՠc@jhr ^y%Dj= D=tڜ&f +_Q+~;.P@I=aW'9o[A5d a tP 3- >wB$ʞ\Ml heO#Fd jiE5l-] 2g5OF%x{q״ħ( usA ;˲ʧft~~CF@̱gxI.*'Sp̌EgOyCN"5ʅK73<oLlm0@ WOߝDeXRKSM1wjmyi(_$"w]C3~L+= *~*T| u׫:i L=~G؀J~a>x^= I9՟׬?SMn(w_v}Lx}|0O 50qm~ i_7![+QÔs/DO['} mI|j\0a}NOxN=} +l%45*֋w -MZ6mvWȵҲփI A$6T3ʫǪA @,)Ȍy wf6ං"+ߧX ]u|ʦFѽ]4K퉈?ozXN韺|5_nҷ!' ;1^WAuwg?Zˉ׭8uY >>7U^܀^̡)mFu[mݤtJ}ٗo3=0*g\kܽlsF{3r4w3-ձWݻX*Pco 0i'|D&S+PKTN쓢Z">^9Ұ+wM=rT38Py jq6EW-:ƹRU8 @x nmnT_q0qScSQ@&AoALPXT/ 0#dN8NlZjZ]! פǿlZ nAy‰V1*ۗjXԵ!+t9N[A1 yStݲܽ*Ng*ǁ@-1"P(r:(Dox (;A"#(<{chzx0Y8s5r{RсS+[zOorO[p}% j;{δyU6Op^g.ݰ1|O`j).bu]t>5)4שh=:.P`4H& wwɾY澍RF1|²֔ܽj|vDX-?^rEbڟEdbP~WX/ e3B`}MN`)hQ * *@ *{A VvZtԗׯ1^ru˘=2gqWr7tf7T`l"E}?`Q H<\4 6xh{J>#\nGh0xt H!:23N`Ummۺ'ڐLgeNyjhNx;y}DӟŎۗc )QaLVzԣ'h$V>46 *|>C%8 N}˘m-!pϨ݉zׄAJrŸ8x6<{H0P|{+:ͷbȾ){jꄕ3kàeڬ6.A@ZI يNeVH?b*ÛH/[_'UuAlt{yqtgO'+^ĵLswP)"~Z|jπ;ߴ> ãkHnqPKPW-?ije"Ƚ}g@훙JMɞsmDZga&ny~b@D[ŧ(W`Ph0c/N$kI.FeeUƽ"q 8-a PaB#\?w oň.=5ʦ/^;Bjg- 'Db6@p ,][Aʲx|ji@@#@%2ŠES@d RK)c tA=lCwZNM km+;R|@)W D^#^*A9M^g9os:&4ehSŝ}8*ZHV0I=~Ԕ8WJzL.{^?ecU"C_1C84_&R8h#U3ij厢kBmm>xcE dݒw~o*Ou= ux|DZOɞq0q14JcZ%߇A QR٠p)@; yyNp@L۹ 9+.qpǥNryj1$Mgrn:_vT݁kmC`=6{WZvߒTg!iOc5Ӏp ~1\EG I*ТZ8@P9 #BPphuТ,`&cy$ Laf{c_>ܚ?j Fk=\Iϧ~O%rNf#sby0^10gQ׳luJCg,_ydx-KSp@Q8PݏL1{n=`^g ਠ4JB>ӉOy}ЭqLI&KsN_ΦřʫudI(xݫeiYmM20iS{"{"ū)R@97#*@<%{wG&n0:\5A/,u_Aj@ZσP"MAI {ʮf2I&Z!PB tH삽 AC"E "j%t{/qM)w?=qL3{:ֹ52+2IߡO唃G @@kĉ155W'JFuM:enTbhCAH.CABD.M""Jļ~ ö"6{fQŽ<Pxfġ~9!+>H }űEEG|6wKLH`7rm@GR]g+~sS6O[?xUZuϘ j4iq wHzGoUq` sF ]7n)o7OZSW2X{jJ 9C/?\Snm٬2L` 0I-Gl*JUP[^L3_uFÜ{%pYmZ|)z`zN/16ێAN#/g[Ӆ ȕU UF tdAQ+V{Qz#vy-$J/yIؕ羚vRmPcqpӬ .)x2}_,6jp{[[VY |}%mbWg= (i9!m\/sߔ߲iv*CJF2YJL GX)OVmr^#@ټ֢*O0ػ`=`- )45Ӵ-]QE) v7<bjn~`jnո[Lg.(,'Hj>S1;V8WvPKQ` {ޮnJ5- 6WuR+z !ؖM b$ 78\$xŎQp)>BXy%+zo!F[jDDf/+MJ\&F uVR êt3T=7.7C__Y79@}`y"b]V84)kR͈7o()h#B#Zf􈔲A0Z|>’>t5sx~jp l7Gxy b @r|\6aD@'VG5@֫GW [n+Gu_#nRrfyaL?"cT99(w[!NI-E9L|t"0 +_d*Z7ՖQJyв aC/&U!Қ)5]!HПiY-m` X"k[S q8߻.UlːR )9]hը9E>vsXvb6CJRgI{<4 4gN*)TߧMu7_ʭWk q[ceWN:9,#R6 ވħgܚvpk'u^B"M"zᘱnEGY>1\o؟ VC*+ȊP{wbLM罽BdzW},_wr(Χ2?K-T/s^݇wsH0yuf}[Cwj|\Gw_U5ukYĦ4z yl:2/ވ|tt~9¿\ CE}'#!\\'ܼlmlϬ6?ޅ˜6ʣ*jP6M"f4y3_QoƔ VGhi>fM}'>̎Yǟs6.S`~HG.l66̵+- jQIedJ|XJY1\cnbA[Zi "z-<FYm(UX#ȟPײ v z^i־^zoHgfT*g~!iW6/Ҧ+TlMF #\ t!ק3@i*D K=CQ/W8Ghe ƺ&ʂNs ^6Bm\YkY|xc9Ez_NŇ7w#R?z!fy2Gl^)%P`o[Ifܓ2=*gFlf}e"3 K\M"LnLNm95!f]jyC-]"IdPWCC8}s5G>,sERD{ zObTSԻMZO[ }FV{6L,%Yȧ:ěpks_ӗ 0fx{ }@ơt8Z+XJ)PFa@AfbOxG zmն#56r$ dke}u~#!ݶT-zp2(ׄ}l)-gTn-]"5ξva@UKOC-TZ"5X1XdzUD )E< S=8"DlFv\z||{`K=b}ֿoȔq^yN[7 WZ[UQXCCE``PYZwcUx̥Ј@K;N^:֘#g^괗VX^GS2oʒLPky X3T9/ )E1$#ew\Ho|w:W.tYJD_e V)@Uy4`ь}L5\3m}&! PȰłF.!A3D]0ZXʭnމ=gS ɦ\\IMP(]OHS-WM.jmol?qMSuFeKqYtN{%Zf2kFR[]:r]b'U{ۍQ5sPN3hGdD+ĜL#`zi?O( 87 -n:!nlMuKCb'xT+`X ?ⳳrnY[i?.'7]F7ƁkzLD oCe`Cz[l;Y8շcj7]QY~cyFM^+}j%+4afZh/T BΧ3sB6*JLs*Fi+|wT^I3ʐR򛿪"U|AW_w}z5r)SxA$iL~VYN>:N8m hV1m`^6Y,aAgpʙr,@4_UnMX>sЅWӅrq R("BS4j@9u_MN x&}3ڮuܸ(+gz=p i}{ۣ)`qCA"/|w?aNI) E֢6ЦoZO̚ExaFiCBR" O@D>JDfR5]ڋ.J5: Ա c[7)_ugs/ZwXqB@ihK@g#Tė1()tա9Le!=0&CJѮėۓ*h%T9w c0I:&d46z45y:nK!io*Rb``' jKi6?ڏbb=q%b\^ !ђBgX};iњʿ]w0]@FkOlrF=!}~3gDԈzSC -puw AU.]fyQgR"Һ>fP.xһ;NU7!C !k֡Vn_U)xa!Ig Fk'L Frj%=HrЎӚ@Tdrud ȶ%rtak"{v6;`^MZm쉕 )edaa1Dzԫ  aw@3¸ K@\ bVweJ_PZBklPx1w<ڠP*AnE23.}fUFC{< f _ji=jcm/1RcQ^\84ܬD ؍ -m"2Ac ̔P31Uμ5m~X7-٭SDB{tws[EK̴IR̋םN$eHRvYNxfJ-IY:f,g6 ٫Ӗ!zWD{$-ZSдʲ(c~!ڱ"Z.]w8g]^l.;ӅSWI0Avn6GK4Ui`K@rU,\SRcǧF\Ҵؔ wR,kiTqmQk086J>9%j<bqAV~h%nis5NRoSsi6<ڑ- E8D`ߦuʙH!|*ni( [Uu;"25#ku!9TӖ,)t&(ܸѷiDAJD2"p!ўD)kV\=Le q*/0HhB?M9va" D}"pa!+*|, d9U=ͯ#ϮS=v;_/b=:-eJ<^ݗGWk@; Hk<ߨe#Glk]FW{6 v!RO!6""nT/gxdtf͐R<}Ǎh{*ߙv4ÿ~_v'8KxpXk'ի:RߤYtm=covzk*xƢ~}RޑhM[Bc˔0]!!=M!|U; "c;u:%RރW@-l*wDX bv+Mׅʈ*3wwJ䳛njPi,6$S['gIS mVZ۳@?lqcoV ЍΦG {CA\Z?2S/) DJK_BԺwj ="{OlXI4EXꯌL_AD* v~ 5"Cs;D&!FT(6DHB6~ m=B6~6R+aW0@q4E{+SMTrC,';8 } Xp6*,b`(*ok j ^E寖RO£IIlF\p"Ln8Hf0 n;%/X$dvO4ޢ.pۭ#0 5\zvZ2 IDATna%JG p6YL^כΈ-"X1qKQCFEYÊVST 9Rm><D.$ ;fE+h#>Qw1YPj~wlXt^b֢Ȧ`ZdN tRHI֌(e'Z/,ȴ!i3.ː8? .tC#\D|Dj$bU--D^7LJK(iE@sPݩč3mDFCtqsCc!2o=Ge;uRQz@ŬM8 %MC#j' UyJ[:Iص#D\w+P"+bӴ .! mzOlU={k6O%}37Mr'2e"Pm({c9#cP?BПko94<3D~DD߈HA;ll8<͙ONo$sIO?{7T"ذ)f/i#vD{B|_l9x}kIu{]!q>jDoapB@-}ɂW;Qc{ a=JRWcHMJ޾ٗX!У[C1*3* Q#7ЏBߏ>\ې#MK&x2dĞuћcfB2r&u(WwjAߤ³fNdrGn%ţbGi#rlAD #rgw-mSQEh숽$)dFD/^y! (OKHseDDED& A¤ʙmR"m[nX қ<ᘍHذ}h/"MdҢ52"sP|>7*wGR#ɿĻC|$D p.q>D&pYGq u5%GS=7:\$ 㣇q|E6f^XL`gp_慗(z'9SqtX#v< q?""Z A~nz|:Lw~;燬y~ԵAXk+b:Rm")VWͱ=hSq Sj zB!/ݨc(z#¶֨$K BeNe[c|E{͸@ B{;] P5!"}tf,d!&wG:(%zGD'IH6l_4m@)f/][G@%iњiR7bV[k_$㞔3[i…8lA "ܫqܞCd{(a?"@!&=o֗i< H(?ؽֽF޸>naPΉayN-|s_85ajm8k4sW]!N'޴{d, i42lk!%@=7 &[Mc1י.Z52 9ӊ !wG,b#*Dŋ!Fo=7lB][dCgw#jxR:v:;L:3wq)""9<'Ҳ%ٰ2Eˆ9 ~@I 7b`'D\.zDdK HJ_,W˜M(aF}:Zwd bjElaq(eHM[9d1B <7g|er.&D(gvfv  ܌\ǬkH1\<#Du!kK-Gٹ_帀ğcbr mzkΞh92'V㿌LĠ.XD vq %B6JS}lSUhc&"լFEoŸBaC7*eyq@a4EkZn:lݻRwIR3rf a ZS+j4O!)]8‘iA 4q>9(YǕ$ˮ]n$HDr,mC=tt#XXهT%bmDYC0:{UᐶbcҝDVƧf'ũ~ТO)==Ձx4DI2~j"j-6 r)Vu{sxeK[6y2!_Yd-Pz/&}wR3.lHe\7|@~|^Tj`88ؕX=#i>ۦ9ZM.ER8EldDETK}@9?k36"jlk$Kb .ذRd# #o}1lXo;(iO#H!RJwD {Sgwx7~ hC.?kٕg^ $-Zs?} }gu8ވ ;X|oVD$لeB:N& p.3$"BFe4*bO FBFePrnBF*m^nUM +=J.Pvm]O(;.j *ǧҎ6퍨} =8?W_Cڭš(Gmbn3K ";hlݛzsOG0zN =CmcJw9PqeJ^`ݡ:vA> ֽEPa$1F_ c6yʐRJWL@~l>~VMDDm7uε+ᗻ~} EDV!FP |%R9؉>뢟>b?^i?^20wYGm;Y:рNoXBdcCHGlHVpd]Q \e ## D|ᬉ/L} RoC-t><ꓙ>'+ݼyLhTaTP4'LTwB{" bh=g\%9:\h`{O携8Dsffѽ *2VGdB)_¢*G}cΞ]ÿ}[tɳ)gGxmwUEݢߡZփfy1KFD|u>Dsf"tĤɚ |NlQ䩓 {.]|† 8`,O୹8%ɫT9ӈ(W}SV?Rtg]\bL.1m7L_zU+*Y =ňlEĵ22C؀j j.t| %Ds wA ~b~ b9ȥ n3jzܻƢ=O:$ TbMyx[<w%qNDZ؆unnpU?6k[cKOC €GD]uH:3m ٨PO{[ak,'p0{)sEzqf)ͷǮ9ο9/R:*#[Mʏ9I$eV/3Qi@d'*!QoiV6+EDu7 6ֿx ap#!x\'B/Ԉ0 m"6Z< |P,D-s(6h 2M<]vq9#.\>e?)}#b4l.9Q h2*:TtFĜ*C77ېȸk=] :u eJ7 TCވ7-6 ,Ԙ | F `7b9V)')0"}b{a@^ޚu7"#88j"\vw[z=~[z6Jf/1IRf2=n: &K6gx.l)A̓\rR1A;d9=R'HR*e9 IY!#_d9ճ)A h8=( "tG^c*+gO\K@cs bc&x+!D\KP狰{@b2Vuv7}fĻ`Qqq&潤XnƝS qy5Ox$@.CO5p3ù| b eQ0z ,MmwYr >̓ ޸;NuC?0#v8g=jGرcO>|juժ%K$0&8w=1PZa%DՈv5¦DD5#z$Atfʒ7&sWv궳aeމ|Pm6fM}ݠ Dk < 弴1s+*w,$euB;@lJm..obFj^:2Q=ibpl{[%vV+"Ln@ȧ@ZpF"-䎘׺vUqGn9y-Lz|1yBH7S7چcΥO W -T[ÁVbCѝf#!K5bJOgDz} VX%{Ϩohijgل! ۚCXQDd< TV{.FȥS\"`SS/ZӥzF}ظ:<5ur3]ù@UvG.]W~\1Z-GM-(lPɁH6 ۈ$5ƿ?\ҥm!G! Jf#ba'!>2(ڐ1?thgM%mÀ;LIR#e91USu Buy>q&9'U ]M< HF,mF_YG/0]A!;*NZ=ƤEkz׫~/kR\ $~KۖTV|CB<׆0/~w6OFo qĈG}=~^!-jĹ'iDa|NCjBB̲݉H^EPmڬ`Co@@YI{jpsA_So=ȰkiqrosWIBuc5b2i014_MD#EIه| )a9] p9*8#7ouQel @F:drpi @#5݁_޴ 2A0@ur6$ l}Kr nwa:!,78܈2z:Zvsltc͕?i^,j\x36e~$XsmfٴS-ƿ>͜-Ftj3kNyIST"lCL:mU>^@d=B8E!*>9*M¹ q6u~Q_尡kXy}ډFg!Φeu~/ÆنE-^/=\jOrh/P!@H}pc1p* sr_/6 #+nCgp[b?HwM%åޞ&GOdTD#BݗM8I-Ee'%,Dwy!\|=T1DT4NNSh6 2fA[$?H`D]" ï: hjj46 [*F+(w>]Fv:q:린Y'S;FNA,s~S]b'yt#.02}N.j[]Ud2Љe3N.Ce8iU$U'!R`>5,!n Ij)VkW$kL8_,y׷QSV}[*EIr_UF>)f5nMBFX D협 B 9HֈsG9mHHِ{_+rm4FdA=k{t~.U!6vĻ}49Yi\)u1[ .cdO۷F_ rma| S$@ yX30QO#mB_##\(Ȥ*SܥɟycSoϯ0!0 i]9Ŀ v\wmv2-Epŷv0"=sg:gM[F1eQC"Pg Y}Kg:R٠z"Zt…}3?s PpՊn3!J+ɍv.26!+bDH'V4qڀJv nŅ<F^g&2U2gb#ݿA]Մ!}=sp]sua@`CfJ]8s"έ?.P?fX[pt:IG^8C7L |1_Bүݥ;sHS*;%97!u )Fĭ-B@4_s/^unnQFrH)D>RWgPq7忂, m^M[6 LR=R֫% !$&rmPۖA+[PEjDָD,OB*[T}2_L?|(2쳞Nm LWqfP4H)C BTE‰F>k@ @)4/Dg+b@žגd?}.22mU/=N;׀d7#M<-кitSOZ\=朷޺ȑ}L``GQ hM~4ªi [3Opl{7 g!mxRZLN] nQ^h獢hȊei(aT 5{)BSpdBCjEwOV$$fd%T5٢()/;݁n i!;h!H8ӁnW=%uLEì_kYDOc Zʱ6PFAipxג|waĨϾ]NXh1}fGg˿t`\39\$ "~dae+$co f U.`_!ρJSn!0o͎=3Ŗ:b¯MӀ(p |yBguw兖b]tSܱ s϶obiPv)F6>qyDl]E֯ ,ɜ,P ,l>ŬNIR]s lls!&rs]LH .FlM!{pÀgYQRvjr%3 Posߊ-[W+8Ӂ'z9|Ktz߲% X6F-锴ustlՊgz̸4xSB͵~`c٠)$2[ 쳑22H6 9%{Е#)P|RwlhWym!ɉD^ߪx}@ %'m. 6~˥d|Jr¾N#Qi`k^yrl"?,ç%@$ZLA fUkluvevʝH Mظ{-[ضjuw*fi0ǂ/*uid&$Yƨ+7VyyGR@ ='m.7"z19i)p)_3Pk9fOdJnT ܓSA=b!ƿy&e]Ԍ>Fsl98=!e)íF>,EoE,̧@Nm6!x4æE(&44@X" [H.3'?ȐS8)Y㰬QZNr_# wyQM'7cty';E}#ЎFMo$AqzE[d*cFq*W_fjb6t#L^b!>b*2茐Uy ^*f=W%ty{'!L>Uv}Z$_[tAJIɄQހUMByZ٪@N=\%`&:p LGPv vZ:J <ŕs1#BEs&| #=*PKt6>{eK$C~,IBH0ɓ#9tdf?} =D X5zlF G$dܙV<*T'"?t(ao ZO |ZQpgơWs(4X'r۝ђS}cd؃YW|V{+:-ggn _,Pw$ಢP($ofDqrnFrpX4)įp;! G 8SޖJ@Q(:K߼ GHg܂467ߛg/@G޼ynVKKח"C l#y)|3| v֡7Bz)u EK.ihVXTk9pRU%YQRѤ#uu\,t)zƅY:^*jw$߹EDGgW[2KHش1Vo,@a>ɗA()oT5~gUM ɹsKfsH) {[' c|(S'i|ӒؠV>xOIɡ_7V0{?4=ZML\L$xif@KcItbFoKJp_+G-_{ 6`"o>eM >NV2>Zj]#ͱ쟟,Ռ&FmN:UTU-Gj. 9?ۧw3Qo*Q#lL*k|ҷ%^bNmzx}TtNllkUiys(ȄLjg*6g5 پ k4JT5SDF$R/ LGPWís-}R[*HXoP 8N>n G{ <IEo&#f5!׈MoHduȀ]kÐ,,٠2gyk & Ǿ4#D:0=J B]0*i!!\v[a{9%Gz].jg}H%ڽϑcgkyź@rp2eG@q,V@ CU?W'^! e&5kcb?߯q$yZ ю}U-//sgѬgy';{4x-A_% QO(R& `=R0!ɛҬA=A`GB,ŖaQ >FfHhpFT?ZQT!1Xպ?]}v( P;)/xj1Y^EyߗCԂ{9Q|وz *_E†xg~,}')-G<:K,~G{9L=8b*ajZ6 UށGeҕUK4gT~H8x0м|7..uC4XP>8a4zbӝ,cRuOZC*V@E8T 6YZ^Kd/ULrWd gSQf!$a!'m*0&$,W"9p8 ԭ;43-(>Xh14g;q cjGGÐ g"~~0j%r~LY⣂mk<*?g(2 Hmq$ .~P Q@S|A` f(" DU~4&8f~!lZmo^$Sj?w:?BfvUZ4]7o~z6#8=V|UMŐXOCQR uߨjr}H!.ԎYn L(\#a?_%RtH(Ӊ.TyyʉnnNu#,3 A怲 ÚEf$ُ J6#yJ#DyA/v!]ol@ 6G/1::$ |wLWO6[ tCQS;~Hw0) "/`_r0osH ux =Ov ʙRH6V3 xɷU#D抨I)FȀ~ݑ!dw;۟_yfNXzVyipޒikP{j=O Mkyyͽx=6flr谧yIw-s~}^<Ʀr+N)+:m,h^W{Qkrd NH 6D׳EBA wl>2a:9]QjB\Ո"qs%:UMy̏nD{#+#_^n8Ӂ?v && F,0a[ߋr#(z$ K1E 2ë\nN_Fc&Tk/#\_q 3fT%LF;a H(~aj&T2([}.J%WM] BvjʚI\/d5[cCͥm_w>Gэ*9?յ!nӢ tbgf*} r^;VDsM.[} ୩GP×"AWHx5w(I=GB[c=9a;HbEڷ"R$ ~FU()Zd](ƶ9 h>Bw}U t=ѣ.V $x!K' >Ldq()CDLÈUV87_$ VDrAؐz萘0|(26.5Vcj& J-6 6Hp.}BS󾻇?%$}dŅ=e)| 0リ+uׇyh*?EH+Bt?BCX]ks[csɻjY\jU9mEԌDJ=_ovxkbDnK2(G^sɽ_._ Vn|)wg={LJ~ &/}u̪ܵ:eOkߨ̪rlz BKrkNRkvu*A{S_١)RiB\\6jNkLeqH8~-^: !ϞmnB @@DWZIݱk7T|*%#UJ(ڵ [[Ӂt?RUEj #Is]rQl}|D e[XHWmS6-UOW\Ѩ|i^f`jFzߏ5+;i(jnP|ͪP\BimQ] :eav2H<45{9Yp'|1JhmR}cuƎS.x%w4j𘋶?7mĂ>}lժ2u{m:6o+,9Ƀ"Q$8iџixؘP)*2mZp),K[ͰcuWg,Q[B«TX3NDꮌWz&= V_+%h7#KLh|G˦xqٺchrjwP9Koɼyѩ!>wڪ2l686ܺCrA^l]pp}zVn-;4!a֫Ɵ?R=@;QHAI 'V>7I]}'`һWRQbޯn5bkV-!wDGr/!7 ĽĜxAHI?pHL R] n|ЍpH Q 497%zp7p2maֲA~Kk*7-߫R "ln)]]7l˗k#K5 ->HQO)zL!=74 mGzz٢jnhEգ YCuϜ2MM,>VgkeZnI^n ovYz1w\oMUEy؞ 7mm㐜λW4ne4:U+k( hMm/y_ 1;-> jwϒL SuYr蔮SFr7j)놦#moDm( PF[7]+vT%| Qza:DT狪\HƆk8?#Y~ T6-v LbpA`i5lͭrs4ps'>ǁx7a3~/g6􌍥t:}0.hJ((&'5jp!{@dY3C3iߘJ7n-ͻ[GwwړngLVGUghU̱C_.ҕ!oϪK7UiS9xW[sT6E Z_{ ^+AQrj)]x\ZRbFҾN~,Lhw`tsq<?Vc\rؒB\'F4xNN%o/{Z DʌN+WTpu"4︳UŬM8]4 A dV,Cu k۾-_Ga%%~(y׽uq(t%*~]h3B4V[?\&>͆tw_MŁ~ sAO9O7eM}ٙ ?#}?3n,qr(nL# DUR5{~ / |J(T}4N#'Rǁ#Ui*T)ܜ `i煱Kk0+@-8~[^S4E#VSJWMpV6cpW{};<.첪VQ|5%rQO~ұB#;M(ըkHQuqWUks6Ě\kT;ej oJD' p|uZtXTZm:seg1XUSZX~zo~cǝZUiUܔBY6!m.~m84 ӻ9f. ֗B*GUMHh=7KHqkHmgί^;~^vz9{98i>n?”bZh|OvEeno-oe|@/~MtI'+H[-Sq7^>fܞcy9Vҭeč(rV/1YZ5 Q Tg `MqB~dF5{܈æ~ޚ2ux,!׾6NB~ c-prؒua#L hFGtUEW;Teqv^"_\@MՆwhQqfԌp?'P/lת+î_iݾdͩAܠ9:#5D;ث?yHC $N^Zܴhx)'/$7(-H?WFύs V>oU@vk;6<JP.;kZ^$h2)xRKSsx[PhILx]{ n@ @{1}}I9ϔcS>aD\V{6:Űc7(1\U]6\4}Rl7V1GȌ<^ؖaJN6U%qm113k5Ekq?N.J33-H0tAG]$8Q]:q?Qgo/Uzv-{͂A-ͨj6%W\-E>?RfSF 1 6믤Te[NZcJ1k^m.66EY^Ef#!בH^r> B4OW2EIQnQ8Mci˩شhiۗ>p_4OߴhBC8pMGRWzæE4:ps|p698#̓':ee8aGoD[ꊐ0M{ĢlF]>]PC'_sඃ=:\7cU3ёKx=m%$Ț-laN3s >n*󼚅-Sd ^RCNFF zuyE199P܁&JF+ҤEkӼJgm#%Ժ7ORW߭yreƞqqLSҗM^[43yT[臣殟Wv¤9^kԍa,S=eq►MUw3 UwG-}~V`.ʭi7ozmAL3>zK=uu$:^jWoƩO.' kXz&% q@N$̾oRwh()SR UMG?0lnkٴhiPWxoZ4aK6- H_ 1Y,[VjWcp-Ox22w4sBr%K׋@ q.OYH9~w@6bi̍Fٿ-{.'Mx$'v` I#tg,fͻ :S)T\[("ˀGĈ)}=ǺY} ?`_J^ٮ_`Gp|U.K'/XEr}1@rg˗X{{믩hw^Fc&>x{ kvTr7Ъn#|[Sz&"w\~ѺըQ}nϿl3 Ugk.~dA"8^8'Rrc^ wǔҕͫU^m-vwVO=gdu ƊSz>RwY6m ,ƻnr%냣~!%RւKwUE>o499=.wָ=U"ˀ|١)7οR~yEۥꛌa- vrfd;b!i 1 VG%%Q7ݺ1)7E@zD[^5rsƛM,@}~< vӢ Ix䪑 X`קeoشhK_q ){CQտD@[W#Wgg#dF ۷"<$ |>_Gj?T,FȚUA;$Ӷ1-ZQN^j7(At'&x3_\|eKF5p z=]貾n b缲l%OW|C Km^F%yCiqaZ^v/jO|_sov2(5h+[o[|w]P!y&ҾT aQHH1L_3W{Vd7FF?ǀ`P+>W-E[jui{?Oq^mo;ڿ yQANKi=t;sW}^у~-! DUd e"2,:='ٙKu`!fE>-[YK똌Edg߀khM?A ys1gvh IDATw#f̙Fr#@hП|:~2<|~?.Qwytpr@r0G4灾+bAmFB'@4ְmx8 ʼ+'['׿ËKQ%Lc4֟Cx{NM s;#@p`ݘ~~e,{ 985w︍uxE]#7n%mڪ`jWi,i}_S{rFjݭLDC~/*y!%20 Skh3dEl׷jwP& bHLLIG؀33;?xu`]k8/Q|kV}lc?3>Ud8marq ю&O9_]Q]QhH h oQ_ `91`V(xY>,v6 ]*leqPSoRvf񐓲3w CEhLoD8(` P[e7 k3gkC6Wv#-\ 4̘3g}^_~׀BT"fN?X{R=c@/"Rhaa 2 d,R,`@m\FQS?qmء+,\G.K:I&Ƈ@CfJä|麆%%8O^XmHyt@Uݸ?{tpNۣqLC'n? ו0?$} qun'V+(U@]MwZ08T vA2i#?>; ۾:ۅt( g*J%M I݆6CSW]ktΗ PTo(b@ ڍsE3i?Y\WS/ҿOHκ|xyE+,;trR#J݋dSq'֩WtDQ 5%߾tcQ5o_}E&pw꽜<Vo'{n7u2i\|sc|agOz>TX oࡦ{o?̼|o6S#V9'j}f51@װ‹X 8 4  .$T_itG ç7~}.win}w3u%gE1pΕR:9`a9RÃFTի} N{T/}q6m7{ۼ&Ca#cRDE :"i+?2l$;3=}o{oxÁ=c2-īu!FjqrǍz5>D;CBv:4 H!Z71 vG kK2˪l|#yEqyDͫ[9orC7nazƭ<'MZy1Zgl ?WTΛ#c~Ð8>D.5<ں-A#.N*pK7ֿV B&U%@"tmp;'K{-9|jot8](ZƾGڤn N_\yoH߃`>,L #")EBW!yc2LrkdӢwf̙U񹁠n 65~8\=U#]L @]Ћ-lm:s}3<6G p4 -&$a@6ʼ7 K-C2/Rv䡣jO+^w(` +$}xd-@rnds CL2! !u)i^%'hh"E_o\cNV*dGxo)'‘о-Ҳ>? WqO4j =GqA /8SϮcZcמyjhRg#;3=xâxsm/y!)F$#Vk ֤WGT͹"q!0;>US;jtO6 pK#܌jJ$cT a Gt ι')9YsOA ܕ^󆚒V"}GVP;1i@C'Zejz(M7:VX#0{Oaj=©ff/"<[#0KD"?İ"O6m_x؅DÙ#]1ېB_ B5( N/4 T& άs^S{n]ށxCnD&*]my.7glUfy뺭hW1'!D;Ց(8=i!/΅L4i/< .n<*%H\UR}yD@/9 NfhEeʶMR=|e09b"sK3,Lg|u]\c8"w9"T7 `O|xYf`Eoޜ65L3nԳ!.s+^]UXGcJSMS-<,A" m̋_B`*GrJH: :ja#E*U@"HӷF@ >vېO{/Mo't:s#4\n`$Pd]݃S5 !36Sr"E_"NӀJNփ]MIٽg>![~+!ϓcNΛ¸7mfe.PZ#эʘ+u>啰r$B1uc5ggW76<^}s&}E7ٙ6kLitD'GYB닎>!!լ=Û<>wڌ9~HS5=VHr Cy%P¨m0AiFxĊBv&+C*!yDmVdehnAu~T{- -D3~ޯ}}6ky΋`3hHOJx]"KgᝐS8nMߍJ޽6¦w oXJwUT[[o|z@s-Zx]tn[OM8u3TCܾδ)c{9HtOacɜ^a :LJ"6 9MH D2b189ĸ-=Aq0e > ٹc"oǁ-4!hrW]&"\>so9w j'^f̓Gl إ-t{JF:R9*J2iGq*UkM0'Wv}ŽUf?Z! y}{"G[Ǿ~ >N:ղAJiD`z^UU2,WUsLJWzM <]P_8^`؟|=cάdڋү SGr<l$AChoFrԉbnFAD 2܆($(vqPL;ڹR+2B?U7bwۂIOWPMI"˽vM_ :X"V"]xW!iz8! ggf$GT(H<)U{B웴VnYfYeTLna*Wn芀]1M5s>ZO;%ZA (tyX=Ù[hיv~vN툰Μs;>־0=OR~ؘyycsJG6K7")t@H(Rt1z X$/ 4唏sծ^UBZ^+m cX%1ij'Ü!.p_夊._ K<:JKmYq4hg *C4\v׼p3~Jv"~=aQ"ki9f>@Rd,z8Z2۝,:=UC:ku:p!Ҙ>X*>p-3/Zrnw;>!?xX=Vuffx/~=LWRN#!xM2o:$%6 @>QATwoF4cLjX"$8 wDhceDWKk E@mH<)تC➴ e鿱 )YQ*|XX]u#ɞKgήy t5Vhu*Ox3Q\64su]qWJU@`YKo:u+;Gnj1j\$'~1! wM xl$$ph]@d1\\~tF!!`'b@jbK7um;RVdE,~98[N-8M[D&bڸ*&֮ܨݙkd,"c^_ 5}Rqܟ:騝s,LUXݗ ?!-zg].ӌfT5c2|JkQԒFDPHx=$wr lUSҾ"Fh$׷$\h{:dQv ٗPKlۙ%y{6!ѻdL7v#gki:UVsԹX@ ˴* ϑ}m5E95}5)cGvfz |etȑ /=}-cy2+/[vjW5ƅG\}kW} < =c7 ѯ# nvޯXjiZt-&$ >!ϫE{Yd,4wԮ:;cά?I ˋ֪R)!UFR;֪xKJ6:#bp دDQX0v+DiiA)@" 鵪A^ByGcph6P:íijJN-d;Q6!;-"k"5%߃v~ @ Ah4PB.'0 nz{T&!Uz cr׏؃[Qzc#[D_z֥"^Ew[q^qoS'#XM7.$z>1n]p{wTUm;FӠuűZ0r-;t)z*Hd睁:h th_@7, v#FI$DPc-E*"#GU}wLƢ&h3 IDATܜhreoHW٫y:`WDY҅L3^+t>3,3gzU֚6; gS\CI_6CĐCX4!FD>BrJmJvRk+H@[x^yy|qٞn^ͶںVK͎SOٿ/ pnk|Xsf kJ-Ev ݅D F/J TBK+7YoÕXhAgHH|eyF|AOx0l2Yg9 B$duմ%GrbM?T(E,Jr5%_胒tjJ=9]0.vp^ȨyHN6yf5vu~DE{q o 0Lm0g>6б~:}NF͋U 2Qs1A q[QU;$C@<E$L.޽}q}ZI="'*zE?bVkOn)UX^K&WSo}onYզٙǐN40#}&߽A6jDG.AV[c2-Lmu.;3]Q>^ùFegt,m/8N5u&5嶌rMvPSSU(2#H8;nCBA0#zqTe,{_i~0yGKa0Yg`@kD[ [hjoA£.ĪY.CBĆv\׵09yX+ՔB%'oHxă(޴}k/;JN֬ F]_ ,=TH;q'8/jŁOf긐I~pƂ}eLc!ӿh`[O"+ Yx-7yGwvO**Y J+} ["M=  +\d'SWixwPHBQb blF!23s"n6vX6tݑ%w]ǩ13i)ؠ]q׿|u5jӪ.^qZؐs`Uo){6\WUU83ӷU;_;i0]w䳿U_ ?L)^^N }w$ĈϏ݉DF}/LAc2]\z.';|)ߖ-*z.:(b\]y:\Cvgn>ccUU>ƫz"9|.CtIwD=DTV=3cάUEkUϦT~M_# T0"ՆY1H(%$xNґFK.T ^2Ԇk"ּG3Й3;a>d8s@zTՋԎMAMXɀLMIBr_wģSjEBae]lh7!=ΐ@$M.;꣐['&)^oHs{]`woQhwL~@S`;/ Nl$ e ΄nRg>_dlα!ec «kC^wWB2~m겱p(N+ dg]殿]xuaA,m\n7IUlp00dPQs"~U^] L/F@oOpޟēv) zo2:J `ɉG@@ f÷d$A<϶HU-YI Y|1X1 3^DV,P uwfGB{+JNPݢع//@ Ԅ|,/VMI+nƽO\m_Ȥ2>\5T:1u@owM9sy;.yuڻ7~Vo2sDn Rؾq.Ng/ UY#Vu[pˁE6_hLV]m_e`{ik18 `gΰ^3ی.EjJC~PHN$TcSjsf̙G cZ" ^o߈0ԙ닔y#;T!RXIHk!9 G x" =aDiĒNeq,$nEzs6'(S؞ 3חAy#`v{\MIɾ bw@X߮VWl"\!:_k@"~z>poB2Y#'h?az AE(׊RbLG4֪ ?ze]FV̖!B<:|&p:aT| lX}Ο4 )ZboP_ߑV=8P,#::xͥ]D9-G(ƌQSJNV" bYFZS %Xd$Ahyɡ'ڋ nu$+uB5:.Sk8k3K7Xk]k./HKھ>)Y5Va;Э/D;:D(F@{>2ۉ#FnSzq)g)kS9qo>6Ԑyl+>w>L=}!˻N:qˏƍt,\_z8p<(!gXDvf<W7#էS{DP"^m S0si 1Ģ.}Wv\_byPZA9BQskϫމ?z#5#>1!UىUp!&*V6Nt|y>w"S0tB0b29@EʼjauՇOӄ r «hG!ֈ<gƵ"#E z i s9~QU$_XMI۬dY5%{56m\ؠ],G#%O?3y?hS HҶI릱,P2|n:?UP[,XUԫ:|+pZ'O޼.X%B_#Mz[]av^ p15y*[>(:ܳ- n@- 5UvEE ||'# ܈.DA"ћmH6Ko1 ŧCz9Gc#qwꅤ&q~˺չCsyMvqm{sNG_ TrE;sG]7]?3lԟ#)_|\UٙJ=u_tԫΚe;*,b"{.d3^>p#zxUMu^Ǔ3hovlti[Ѵ{͍ͺE#WTT?#i:#`:-,G7H>bmǭlhz MoE#?׬h70y#L$X:fjՈ0 PvncFыb -HDŽ( ģx:AL5-^;W[@X™!V픟:޺@Yǒ/OqAɉ=41}8eH9%"+/ ?N;N{'PڤNc@?elW'ӰQX{U Sos{Ŭkj;8@ptV}Y}-}x=K#Mzf]Nm0ˇqY*i0\-׺G H41 -3%fA/Hk@Qs'eUVO}HTo:ee{B>HUsns=ú?'Q /6tk Mʾ5|cTCi@AA/_#dXU_%=j"FpP?&cQqvf}Φ,׳$u$b4DERHQ`2 pij뼯>|g_wu + iW!#Ƅ' ~@#;4!>= m o!953?,'sH~i."nĢSkۊXg0X#-a Poxh>B˰|= /EE$v9}A^͏b &S9MgPt) N+WSRE5hQ@ߥWvGϸw?"\=.2gODTӲW\_L K6*/.@{uD݇q* ]b䚁KS7uhfƱitfm3[#46DO3ǛuM#A}OBv5Yx9]!rMBZ-ňgzAItcBv M+?b8Ɛ EqMNd!^nGcԀ)㖭i][` mΫ)UVnP-Mٰ`g#c21֗,}pEX<-E<카9i @Y2`mm?n81O Kjߤ$W#-c$WՀ%Ъ6=~}C/$~鿓^FDqo%C'DXwRv9- EY<]Vp_~y G,& tdq#VYvl9ېQZ¯!@x%p SVY+am}i5 fRK7iE{7#v@ӏUU5@QT*ꦗ&̌j_RPV56#XhsDϹZwW5QhF=1Ƥ^:0~BYv;d,*L2T6T]YS~L>C7.HE]_x(N+:?|-U@,jѡ^ j6UUѥ46.+=(!hmn|cṎa]v!,?k*EѴ XASly#Ct9L{[S$laFDx2b EπX>D _;*!@B"i:7|_"`2o`KCӿ4 "/нx)VM \$M edlظb cFS{k Y;ZᵆU˻koAESىx6Ɗw bKm:Enm;m 5UD4W@}Oxoj~-r$rb"rAxQ׸9/AՎٌB֭uD u:ׁ3qs݌J^[q}-(ľqO(Na2bEc\Q7?L MKÁ}7ԤJ[|ˁKoR15Yk#t{8*55k3e5"סHxv,p70X.,m3T!|lQh;`ԺVu T4f 'b!ĭ­7N՞e3 O)!UH_֪ 3} VDGOA"E*K/A~GˆS EkE[E҄GB= @n@\v-o 8CƫOܟhDgj\@˦B~K\~|?Sc9kcԔ4wDCWMR_=R,>s IDAToMcq@)y~A^BXR$ Q|a:X1B&9.{=yݮi\yZח\Rf]ǧ2.\R%@^t+*|;ꊄcREbʹsQH.HjaՐBbDR)G5NW ;kxҫ;ܮv@>nFO _r0 0mС|Wpyq@83?O̝{'}Oy9kZj$|B|g d?}ɁF3trxuZ^l8+SKߧd& =_mT- Ьf|ľzc@FxrݡHRXv%H(fr~fw!)э~L2.#Q}Ϭj /UCY+?3!sc6G =B8݁Il&E&E S N%g&"hjfa<x1?tSp^G7< n *1͞foX9(Ԇ[?ǛB6#MĂllhVē܄ uIeEZAv!As j]Z63 ߣ&#UAz5#4#Q]5a uGCganZ;tx(K E=# dM_lE]c:8&6w7GΟWi[]vEC@EH3UG/P.yFhv}M-3~ Ca 6{Ӯ¼ р!?r%Hۈvdo[R7Z#XBO"?YA:׬{r%3"6" klFE/cأ?n^ӂx^zM?>Ct@6=W{0>מ GK&tX?MqF)oY-ϚaK&ݐ3zFjXČSɹ ـ!d@sy }c# 7Ġz?,< jo;8^ -\#xMM@C(kqLmuj_?`|g gdނHud03Z;O]Cu@_ giDr|m2$*b# Hhi#rG0KҀl^¯FӍO㍴BuE6fd%~Z&Z#G ZZJf4 c $oC'Zf#H%g r"I:zmP9䒮+s C{=;*ng5v>8Rtb-)٤ΓBLޮA{X ۨ*4kWVfhB<[5E.qM[w#n LFHuӬ3Xw€cQj#-ecA G;Έgvٸ } />$m^C=pBM5,ň ̀v)f̝?u Tz8!V|:v =FW_Z>&!{JѕޫԄ6gL6[@v5}(  J0D}wD.CBߞd 0siˮ~8+sB͵6%|SmqZZ~ a+HK/ɪ3aX,r _Bk=B(:9\GCVJzxٴE63)فy؇hGGX\o7jie1/L1Ź˲Iq.(^X/Tl&EƔ%oڷsgE;{PSuy;<Ṇ\ MXb3nDR@Q=^" H% Y[#JNW|uns#%]7[Ew yHp`DjUĴsr{ZWQ @c|)x[l24n{W4ٚߪ6QMe&d3}MhR`žv}Pe%6Οzp!wT!o&~8}6~fHY;j/O*;}S3޷@D^4ye-Tϟ<^s#{s"1"N˘4 ?W1AEA!Sڿ=pVϐ6J{',"}b r2$oYpA{{h"Ãl 5 "bj$j?OQ2jiZZKûC-֫_K~!,RG6 Iqp! }Vj䧑MV :3$ۺF!{X+/4( 6ȕT`5 UR %iFwRBR#Z\$q-GA CE'vrHv)b, C_W`oīK*hog$*i/bh/^'J+g޽/``?wʷېk úC )# Wo7|![|E=G#vSAȰ K%G|~k2hhVoi `Qk  Р .92ԨvUuxPG QF0+m85,HmQh;+p6}Bm)uREȉFуY"(|Nd#XA4*;܌ 93CT87F>drˋ]/Cd?BPh2X ͗Y -udܑ~8e49\"y?p,G>W"C6!iEzZ҂xw )71]>uQd#:z 1g<}wă?wĞx$=m 8rhU4E]n)x_Οz>puOUߴ!.xL}},uFwmJ%nj4: jeAkR7ef/E=i}hPk% !a#a6^|JxPyq~z"ķ v X90lH^9$(6 FKX)lAASW}ٔ5V#P_x D N --C*9٤sG"r_kTrw+6&tC@WSbp;HKNw])g \uPזC]1sO.],AH3-G±HbKe#шAiF<巐pb!![YKow3b ߊz$꿿 vnxCǏDotj;o\ o>b.q uvfc]bhTi<-c;,Xl k@ `gCSȓ2t(JiF|m6kwǤM--u;#(_n :px^3Kk(g !@$ 8w,́f!<IW"adCx T.|5X?d }ki}b~֏Ն%HDDQ+-(!cLe٤C=:XsQLJM*9TRʚ+l٤4UUn¾~eq9V:>OV5(\%Fn^i Ҧş6(?)IQHS#‘57Y+~z!U0gӏ IO d1hPÜ^ ,!瓫w:YgG|@ǽxO|CC5Oy]qy=f.jw×^1#l\}8nuͶ74-fm4B$;] Ɩ0 2raڴ/+}2f6UPO/Bdܸ6e$o_DžOq0Bv.@."yb2A r_X݅!I !7b iod ܇ii"^Jfb.VLTıJfb'= Iqdi6)Ge}5DQywH%ǐJ`c|kZ٤4 MHDc}>[KCI90hjSceݑ~\xR˷U#u@mZZ>gxj$r/CDpW k&1#}bxV!}!%!HNߎ4~8Vp3 O d=W ?bxHy$rY#N~une@~;~jw~v_\?[ͳ'i!zfd!cZl_xmCY7ZTa mwPnsr{,NbXaQcK$` k!Q=ZE6yï O_N`c ;l0KF3\J]! >!!b{ F"=+#d\FMpڊrm̕]9e w!Ͽɪn`ŃvO˘斠&?Kp@W}&Ӻ@:(]ie'[ӯ{(Ejic{}$t@٤_,[pm7 Ӡ?]{[ť zi=rV P!bvܦ8āHL EH`"$8!>wv`#S0bArH@ Gj!! hsz׹y{ZZiW t׫7*"^Vea~=+lG59ǏSxt#S:LÇĖTvH 2UMk|#Xz|ݚy[3_W{1,:wQg]3/o:W‚",(z]]~V98iD/iHxԊDqhp!pbɁnԍ_ZwtsH:h+8nf3bv?Wbwǻ.E(5P['̓(O[oVM,˧p}iqoxW[G#=0k j45 8TVet(晨 A1W?P(hwt}H`}VKqo܏ATï}}?b[C-ytbcٷJ} X2*3ѷɸ^Ð 8 Q7hi{=pnȂ샐Sr,Q'"l]<,p͵ ޚxHKABUϚo5hi'*5[IhTrڥӌfy٤8SlD xv*BB8f/)čy؍"S{ U zQp7fUr;MM:zXp0/Ud,WY-uUU G$̷pk![ےbry)}3<(:߉ėbGoWE&>t"D:#%$hB֍@Qӏ+FJ#əZ1>W!nD޻:W@4c_]7޾r-wԋa󯏫,̽p>)=A弑 IDAT?j&{:/rȘ׊˺Ļ RUmذˊ5=R!>H>xbv4m_n~(qh;7 ޸lvNԂkhUL&ɢ>eYcֲ푳"L--_S2$/"Y %8 CDQDH,)>ԋ BHn<"EG}RThN̚Fg[DS 5:euZ=qձu@6tU44:vē!L䞻hW_N}H~Qwߏ(c_/%!/?>?mHpB1@ːv ! ԯrd1\LG5S ,&!侚r1W*D =Fmfr1TԚ~[,6&/VY@>"`;SޙWj6$Sc8qk0ۋ/̈́ .GB}uy~ʢ!Hh/" D+ w0'uBf(#sz I!:^[:՘+ 4/sUOV59^]a[0yp,;\ZU2\YNu*yVӴfYtx -n< 5B{ڀXP؂gqM˘SCӢ.Yi =ԙ[n/_&^#햋 5](z"^QnCu,h+iB,㺨nBr4_Ԫ<s܈(N C_sڃpa#^xyHb JMMJa*9drrdb8G8#KYuZƒ*8H QFBrSZ,ŞWg |whb`0Tx-ukTݏFLEFW@k&VB|MΫүׄ$3тнeQ@B9E1/ _M̟BZ:ow1,c/_/*-W#O˘JxDK;u>|[2htA WtYCk;1+}k%1 %nN˘bVHǠ(~Ѵ劲xqZ9s0= hd# +SdѢd&^X!MsuMmR/5!'ji V*{ x.FdyEQ%3q--6*M^`o*9G;a'e`A xjϴ%) G"'k!b bȈ/%ý[Cq|)+\,g^WݻͻVkW TVԕ_jZE?!$:uAW!yNmH(ҋr|Dxwf|~]£6#r@-.g_bⷷx۬2K<`zty/9GY􂖕>10|CVsb7TL- qT7EKH4)lj@!YsArҟHxiDu h ];8scHwCl{ė<--IL<1?O4ZV2@,@d& LGP(^mD[R8$t1m0&:qcT=Y+jtp|=ma5"]=ېe Rڡ!d肢ֻ/EC2!GB3}#ɋVq} }I@U'#&$LyUFMB zǢMH䣪w.x$(+oï>|TUwEUo lt% -fcΘ(w~=7sQ}]Tey+rO5"@Wۜr"D-!&8ݱ.s}YewrDpĘe~ wP; wm =”EbiZZ~2H$ԺQꡥW2w#^gX괘5Ao?Գ?s+@66F1w)Q@Ӭ ~cu&x#-38 VM<ɩ_@k7qk(l}PLxpEsxDCDFBϾn$RA{\ΈzWXKc޾nסU8v ep0qӓn~ {wv>p 4ӛ̾ܦy춯 JݏC[zghMdYJMaܡ@ǣ_v(;c\ +}ѴC޲*HLTT5-c9~QL.K|бqR*gqI`X.0.( >a3sO* #b5=cІܥ!M&K%'!n7&`+;~!@uQ Ɨi9ju4*+$x%b8FTWA¶.p&shG\vWۚC&Zڪ5Jvi2#σiXZ07{~΁@5dwrqŸv^{GMqE%;?;ZKUT,|o-q.Xt:D=__[qK"/1/j6OABwCP:2 1,GTArf'cq??qOj#}l@Ko:j38,T!EߗYS0. _a\>&ܥ(sh/0dtb$WjvE>@/w.>f|))xA7!:d#QAj4꩟N\R `Qbg Րb5AiFcy}PH%[n^]^t+nXPQj¬Sڰz6p|[xxCUqVg,/ *]Iy->4x|OyTcv=6$#1^k PwzPt"C럕J*@1)h$,߁*>F 6#My۠ Vt=3@;.3P=k¿k/%+Pٰ1IxbRnEf< Ѧxu_dǧ W??2lݺe>| @1 9-dM5$7 !! Eԟ![M!t _VT_3jq[qWmՏ7".B;~/ĭ84a4y~խu_EV}g TQ ~ 'dMh 6D^_djy,t$x7$'k=鼨.O "kcc&cCFې2kZYs:0X`7VtʿDV<633P ?#!ɺ(V"܇(h5#%3jd|FK˷6QOߕNwW M?p+؁brf[n(&){ $̺M:i@J yHRAJ>B7kؕU:lQ-0\BGW>Ȼ&wT_R|ݺF^ihxW>[U~k>oPpt^)76BB#s߇xj!<*ݍ WdJS:`@$W\tS5>'\}8Bz%%1Ig]ϼ 3sY>׌a48g`$'˕o#mWiNS^;w),tVO78̚L,)^r;!%A}jY JN/MAdd׷@C]~#o1" \Hq$|xF#9ʋX_OpFeK~C-K~.;_M8˨_:!^w-|} 賻-;UMJՎ*2fi=9꟝9~qiE0.{{R)'(R<݁mߢ0ZZ?m(kTBd*:wx-ܷ񰲐p ė _({#Q$xXdfw@F½Nqo;#l{7~kyD;u+. oi tu<0nwWQlGl„E,lF>LrGbu|}&}}ljosY^5|~FWw霖1[C><!G1s $݆_m3Ac?vs6Y/szSld/lR?H{'u򦒳 *݃v 2֍h ڣ͍$#{H=yw{!IH#=fap ݺc]嘷,G4cyW!e17F:kGAk 4%!lסeA8ȍkxXyo|&2 Gw1޾$cvi6{oC +R, >pvÅ*ãsK;꘺:jh=u<;Yz=RZyCOv;[ЀxC6h#̟F)_dd<]:姒Dr>z6C3zrOP-yAA5n&Z3u 4% `yA]ާV列 ܣhZbhR)!{쎄P#+Pi)[+dg8cۺFz.*a;g0\w߮uLV${H֥WcQۣj'j*zUU{ҫz<}=gт@*˿28.e;7wr)IHpGd(Tr"&G[:־3g,)!6:8Fih RWYs<1;_‰zҷ\a\0V^¸1INu|.χJ4qmxCΚڥSrjKA |5w)3$kԝQ<4N逄-G T &Yc&_JOc-lZ ]?@HtcLQO?ɷ+V{OmyqFDHu4~т(oK'ϣy]/aڥ=6MIJ2t"Pg:-c@ Ƹ'WT!0.<=Mu0&hi_2o )VNwلKQAGܥlArIpÜ_HNg!3iP~Fjtƒ2KI)礮W&GHE(Rd$8m_K-wcx(RC7HKD)5{/5__x-!&|δHւԻlW2t>~PD`?]8ƒ˂-"ZF% MAkƂMtԻF-)뫲X8H3~B<'ג]݂/\^ qO:t!e; gKGrGO~rK)@'[`gMCIsXU~ iBdws ?WDj X2ڹ5Ӹ?\pɜ7g͖Jo}%"`!B $D:cɇt~wl-#iPWup~ҵ_gN'yq IDATeHDkZ;|av AHDŽd3y߰,Fr+}ꝛNXq~Niey$'݂0,+/tYQ0."H. 0Tg ' n96<2\3kMs2 =1o]J5 {ޓd^r&p#)!^!Ϣ3|6ݢ9p>6 QsA,¬:4rukڤ:ͽr6G厂;ψ D&tGI"95ɡ )IJ.W]|㎭ij?<Ex% bv%TV~׭;4j in_CHb$'FKVF`-G9RInz`HN{퐱c!N+aGaHiIJcE {LdRǑd8Vr ^H!{P䐨ީg<{1I': Cp0eP2#V2--DYߓM!^=i5zd #!Y&M^_BP- ~9)cQ {KWҿ#6^2732ٍ'!5ܪf0D_u.8My;zB3}]c`AoqDƺ;Cm͖`[sz*|b"] HX֫r? F¡GZш71^lvc}5/B]tc/͠51@S~y"C7pCk.[x2f{= xPQ^|6ojoݐN_Wk.mm=w)]Bud#]J;}! ~DE/Ŋx/ BE|)Rŗbջe&}jn܄s хiHd'"{ށ[1]%LNW\wkwP2oth=2۞z@ *Z.=~FDtҥh 2=% ޏL `z- Gj>ZӤ wS_3qOBDXoEQFwo?.+}ߏ]f<\ <7A%ǎkك6ׇG젹K Hm^0^F9ň{sf7+z#پK˺$'|ԻkNjQx׼Z|QRIp"cyZN ˮԉ|.@ ܐ G-vO3REb7ЦmRk]]=s?M:i6BT!Ď>>SsZ7!a$ vy,,z @g8YA^o3V644[__gk}U7t0";R\5ꂌEFT7u1nlț[aDscD!Y01}#c1J<3P m@.a HEV&U˛>" ~\G^g%HK 9ZY^>a\Bvo#m-3-<']}_IEs'uW޴Vǵu19n-,[W޺_ҍ{kB=XyPcHLr/^A+P7#/r>Z>;j:>+QN1oDPMu59VwxK"#R.&*b=j"F+OSgG>1 囏 cԗkk?b_ө:fXukAnoW9#^D$D }fV&o3mQHIkGVb›fBq9CF>2l(; H'Қa16Yo8'!4[>91}M;ۮ8>?ZDci+ s]F ;_zO}=G<%!LG`hWQnɹ?fSz\ui9xs.j{onnj|?ژZ.s+6/.BQ梦-Z"Ϡ+țGя ȶ^WL@]^>B aDlZJ{ ~dlGCy#ray+@r;!+(;q]=^yɍH7EEլgH1%(r:<Qp2lD^3/߈K SP.6h[Vžs뫭{PͧG}Sn|eºG 9Y#Ww6H1<)߶@p#"ݛf{9~NDm#p +پ:syڼW:s@ZzO)pH(C^>F^>(̔ <߮GINm&FsPH672OPCkQ?ќ44'X.cMo"=i_O-|9WO!uu7APrG#sv8 D%N%@k٤fG~)抦4r8{Ҷ/>]'\0VkQĺ(|pܣS H:OsSv#oFi9v4nnS A^03Y%Hz#3&r\8߈= (Bn.-@^6cы6g"*$?+|sj˘0eԧ7?N.xJ (qo\m3RvC^=1Oi̛n}sMTU;2K? vm>]:F;v$|I˶=w^ZO[s¢؈f~Pt8 >hDoaQau~Zc?gpBھ5w Lj_ Dwju^  '-'C< T"ꯨ1Q"̣_39ie8RB6O$RW~ԉG]h?O' "](ZzLe#PyHh—(Fd`=%hD*h'NGk3xwqcCv[XH_Ruk֦~ x*)5%jʴp\>Imݞ2:f Fìwuֵ6 嬁Fn^׈+xA^s'sS#*Y,ݣGO:鯇s8&Q<#rF!?u6/H*L L540 de}sGgm dYǢE#+Pʟ/[Z`t^>ogex0a)"o3>݆K.ڔױϨq#Ea:&}'~/n(v֗ePZTFpeќ.;x106l[Z^犧˪G틮mz.hmﶎ?r_г.F`.j0i$;N|s@g8#C߈`|Z!Q<xHuF{OyCalj ªʤ%/K`sQ~2YɈ@aQHDn#zM,s7lEs \K>3cP<)摏P\6͈A(\hòVV`דJ ڨ԰{.ӆ G܃D;ZϷco^>}_Wv+^1W  "3j\>d[zg%7  {yv+Q. ~޾8`納ax Nh>OtO0 9j.MYzF@[ȩw =5碎YFzmNDaYl:ʤ X'g}p -3j\^!7w&t8.otj\_r(pP)G%;pΈe5;^Nٸ7Nź{FD;\v<`Yx:օ(o9(ԺSm@_dLDS$NNDUNEB%9yZ?a=Vc9c9 緭O O3 yI)A)_7D0|A|( ¬DxN4O tТzG5#˧!+Y,ȼ X 恆\8 #sӈe?RUPLE+F8ݝq=6pI܌D.]ԉ֟Բlm{IW#@ =Eg#*yϾ4f$>{BQtOskcV1ڠOBYu:umt^?gr a+M㽬L6O~hc;t]3sHTrͽ{ՄETZo8<QYO|*~/jb^J6Xg3ڛTYWߩ%jTQy9v([<((w%]o~c䮀ӉǫM m?C"ߵ gx^lGmWbC"Aц!h= jT#M`HJ'8\5 G3HQg'XBm0vfBn7#~yy"qGu`a As]F8*/YG~6Q4H&҅(wD^hv -53v0 j8[mD$ KPfMwy-]]rbl}6{޸ڋPb Ni:]=6t1 PU!l_QCע]lf MT5 0jw]g[oڿz?h^֜ԀoǴkn ;|BZN- AM# Rwx;?pi=`)%Ӄe0^?yڬQ恶`+Z-N1vMֱGr/*(c-0?0_7i9%]'3($Vnu溌!ˈ42/dRofkOFx.22W}弞QRP )!@>ǣ-E!+FeX\e_~pe'n|fa;sIk &{ ? vBi}䛅 '8 ޢGWUo^RpÖHS ˯{~ lvz\qŵ4uې%mu& ׇ3C z}8 0s mPhKD ̝3]ƹLEP Y'a:=1o;BN0#Y*KG,QN_,#pV,c,}ի_z:v?hw\@a oj<7q&oGk/yԽ?w>ɜ_߂a|q IDATHKPf^p/DBh$ >`e~N'VI%Ua8Xm' {5᰿_|m)ifS䲔='O+*5->鄟.m2Nڿ"JhprpL6=>ۑ\BmN B&ڼVp(`+t&"x `w2"QnY(45{^%'kGUg66 <T~0s3gNBhSȂHr82r.C=Z<#/":y>N.{e3Ж3N['pHÉQY_0Ձ*9 !- Ls<_U_TuO◔.Î #0a ɂLnAb"%~JEjyQ^$hGTXe(Z\uChghѺwQϺon}Ӳ3NL *hf!o& RϤ75s]F\q\uY,2G:h0F.<4)񘔌3)1&ϨqӇ]7JNG@;ϨqSx uh(ju"+ƽ W-j?Y g=[#J ʑкV@a~Ƞ# oCy_(vڷ{޾}5ѷ*+chgNz qI(-o@F[8Z0deRFl D( kt9"; S.z])Yi\_T[wGUz߽uiOul%9y\[*T|s]ƭ /G^sk>~t6m dy49߽d_͎v\hm x3j oQr`w)hvN? O-l}+>9xy Wg;Xn/D@ܵ(/ C&9>kQ~@c݂*r8uEaLgo{XCu"jĄ8jg`W}붧7uʏråL=tlAे5\r.`wskľ:_xeۿ,5&wVKX"j㸿ț oMe;~gOOyU]}'3w?"n% ZTda|^G^`ϬQzQ 1"ˆOm{פֿ^銎ޜl3`ф!@ZN22€cQLZ2"ZDˑDnDg"H}-GV4MN[D@"Q*nyز2?{}B{ Ks~ L0\5P^(uQFJ)7 T>8!5diCj?kJW|#tp޷C'$.~AfYoƉeg_zHV6d7lmzNA(4ɱ P_ْc,!a DDDO>dHԨ3;LBQODFg"{cF?Yƣ2`vÅo4#~Yfg~{LGň\߰T_@va'uFzC۱ވ7"" fK TZ2e޿^O鋣dN~+zxx 2?%;Z7 ]EZjZfQH4:*PZ_͖ō}br3]n$.};deRabU3aaCg8iaycfPc9TM#?<f Ud=?qi6TRi#*y6V=UZ?"k;Rh?2/eIBs4ENxŰMvhfĤNugnŒS{81>;*c*rZh@鄩<3#!Q+ O_ tg"؀جLyLFBqSw\D"_׼sFuZiVMُ\h]=|u(.;6}m6YAQ=b8tDԯ#AOxܩkx 㞉oo>ٺW{9"P)B]3!1L|Ao#k~z| L''5|@V$ m:n}5;؊j+~4'XB`XR 溌 xdgې[`ك/6`3#OooAP JqݑQ-z6-8pQ#'~+V&5MP|wEɧr O8G~A4!5L_?nu7rPO1dFb7Ls|*`e)Z%Z?oӐx 7cf~>%z܏3o x8Al鞄&7 GMgenU9Xυ;²T9jg%u-aM [#M:鍙E|L`ӂ"uP 32 =Ks Fy!;ÈBj췡b;:x-K~kE ݹ/7;pҀ〮Frn wuL밼Яeva6aH$qLD!M X~4Oɑnkڽg5 1ÁޢMo|SS" #F5["1u%nLq?j'3} 0uR-p0^v+dlD Gu@[-#y|Da3h-PeсvM af ̄f6U{;Iy{J5v&gg-0|s ̍u0BmXĦrW}WLK-@O?D4ϻ끔nJ g"`555JUGZ;63O{b._ S'Q9LB9N:J j~ĈǑa<YKFg0 Ƒ8,&YWgؖ[`dgd EsM;{{u>`Fvs Kr ̽h>1jׂ>i3j\ fC/4xf7a)MJ+.;?ϒvrmPhu {J'!Z;8g *!oŴ6?wGi=39n@yI<4SygLE=*K``AOMHaD?yUbi^[[pogDùis Kǒ>WD >[X˕!BְuI8°oQ9L_>7x͚AFk(rWUT%wܰ'b4iKL D-̥=RD*Qx̴]#"ʱ+.&՛J>w{wn]G -ba7itOCh"2zzj(rHf X>\6Y4e?x"%ףpVh=8"Hxu_mx uOtt+|iy3GV?ޢ٫II,2d"}>߫6-fG9!"|>A&nm~uy n=:w6pȸd$иyu{Z{1h6~k,n@Z{-x +l<N>Rk%[]NKD$WSQGV$"W-Eflw88tsvݳp{s2Q~c8˿ ftJB V> ѥ2_'dS<;r%3ӗa sDBm sBs 5(G>"nHm2Ps#i>wknZR{hIvO@9tӠ縷|M |qf3}oiaӚ @%Ï51C|-@s+& 52̫g.FEROJӶɦk s _wqx y-,Ex;煵!b殀=FDfWzz@Fޥ k܃֧X\w+ukG֔FO{ `i.݄V"wu]?a1)+O9 ~ M}Fb`/ۣJH;BBްܼ*AkBFY 3$5̶ !൞y"UXw $]?SK Px1cv:x6 FtD`PqӈM9d0)0o3He=6k6޿G2i)YfO푼0H9Ĥ$ۣ&oF _~mn{P:uӬ@uzv'x&uz4P(u}- IDAT7 woevS+&"AHֱO:&1vÅB+fc6Op寐R9H{LAA {R~aD"e \JIjh2b։zLNq6)$+Qؾ7>Fa7mw~?JǤÄg9Ap+7l ')7~d n#iAuznzvuާ^3Ϥ?}Z>d9;5d=~;B!NqA*h +Ԥd< m>`u ]®m i\Ӝw:Ox9bf Q<0tNɧ rcހRP5` bcmDs>KLޒt1ʱGa`ڦt-h=kn3=.ߣ1u "^1hF7.43mLD̏د(NFe5vfMY?sacӮr0lm |뀘Q…TQZg ojϨq70/g+ w6y-DpQ;O586tN7"v m:HXf%[ݱKG c)EFd*3di;EN:Cp6T̸4r! b>Rn-0+3?X&vTK9xB]ApmR Cy$$-=MIt[.g% lHk ξP-y_0_e*Tޝc)o,_T󼫯nF`um=%:oj|cf"*ϊ좚EnԸّц//]71꘻Eϭ3 h BkB:;Xf62ZF \p5 ,۽wIOuϧ6=ԅFm|aF$(/h@0C^{0 7*1ERi<7Qީ? Ơלī&Gm/z%?Sy/jY䂏lCPq}k#~wir ̝pPT_m6;MJťYH\B߯m%mrBٽ=k(T{&"r=P58$z: CeJIe4NϖvH܂@#*悏l7<ӻ(tuޫk}pܓc) 7\.zNqG؆Vۄi oaDå{w_ NZfCOp/PlT9&W@Ьxܫ(Qe8^CRb$UbU10"ܹ0Ӑ0uRӮwrm^B\ԐgWU"͑< c5 }9Q]Vбf5L3645"2m@Ї-|x,?Ueą"8@~EMڷ!|?mR^qBRփ|Kl<7U:$CJ`29y7,}GN &a qfR#EX>/AD8=QUlٹnoЫӿ]XZs kHlc1a`;-0!2 zppk\T9sO"Ov% [r쉊vƵ4#iko. bV<|GT >gl Wpt~0HiZg p2)oa ZD07vM7j\Bo=yڣjp^T Z: RƄZG(MG3:Q:`=ڒQn5%〻<7sVM3(;p3T۔awQ;%'F I(D5f ź;X7*&JQ2HBzr;$"(.{ܹs̝~{yE|;=_{Ҷ:dވTԾ4nF)C 1C8},S$BGm Չ6G֐Ț)c;HAd_23V'j53kg5&+il% i1vǦgG+ܽ `U\m*zG/zR[t!yэ5XL:2F]+W?k:Κ:`zȵ~:4p2FȤ# R8R@&R<$͢Bm6$A$:"Ei?rڑKy-#C}ZL$b_f,aO9)1JAcX27!(Ku^( n8F_4Z8QTeJ2F)mx>,v" O-hei?8mI`ףjT#2o35uF?8k`DN S(D::=_D,fe4[C2ig['S #=+t_|9Y??4cT[m| hZӼ!w'ͣ>& de71n82oDTHj$,a;#)HMts ̚:c2/ `NʝJ#=9|mS(HhVSYȶ>'ӂI, t@ yV#,V $=;=_ۖ=ID[Zs'Nh呒|DH4b(oͧaBl;aG'=_x ^ѹsG 1Sw\D%eHdBA#yQ^Д͈l~{aMw8,C?*>v39g8Mi1eb|z݈#HDhv;!"~f'\17" -(k>?:tOHU$4`43pmx `*1#w6A;1#=MN$22i;7,J֍0]{Y3q7kꌻ'N\q#F-PsoO6G50iA'jBT;瓣@{$zi YysIUȍ;RܳcFnh?: Y2=_;4Pi)gg+Qv3@NVv -QO1*xvYEte3S/RNV>6Fd @MHAkiP$2\ȱI&-YSg(>&{fM42lO)'AN>,kMHERź!߷1#$y.-?,Ok|st}YI~6]u!2Eġ 4qʺh#}wRӥx©=0i[ !T!fEc23uPmiOW"^M6yQ!ߕm1ph+1pZeXРϿ#)OFd݀H\H^D b 5 C"{QS(1g {`s{!5,NA@/wh>_$KS7Rӆ=\)NWVk%rk 9sg[%oϑB!I0kDC w#4kY1 M~DdZ!M I<` ꨕ7N)GLf0eEdnwZWvAX~Mdz(߽cmEM.:wwމ&!@?¬3vi)nO?&@Q%AI/nlA~ G|m1J"Am@n~o#Ě5STpr69V4حHCH${ mzn4@ކ̗l7O`sù|lhNV ~j#exy-DR{m{mv3$*^ 4tLOUXR$1ifs%R RokDWhD}QOs($JK4&Iiഡ˥{NP}$!SKEX v\l%'+ۍTh9 R`>_iܘ߫P..'+[]2ڊ9=JWgT{BH;FTۼHuFZG*yw?M?ku%3 mhA~SL״J kqu)8)FYߌ_EEBZO4=)"P fdOlգ(wYCJz`s"Dx/o%߮+LͤzIh_,;|Mþ2'+ m6 ܿ~%5EIC5p71|ۊիg.+;jRub-Ek!!<Έ'N||R")$D?o 0 B쪈a$6R @/d2hoGͲ_y>8SQAwqͱKÚ-Q6'+{NwB_avukbbLXd̔Um];9de!L{'a/(1q3y9GڞvM1>8mrmFzfb;F^3kG*UKf9so+)l LS(@:Zrv5I k}  = xiD" D#FNupMW\yin1MoUc)ccSPɍ׾PU$'ob2R$֚:{u~ncss(QI +YzkۃItL0#*G,TU$2"Rڱ5ݞ+79 9q -$YD 4RFzԅ hv,r7PJRmFSPK0Vg+l{ 2fMk;<=|hsCķdel  /k܋"aBE̷9!ACJc'=;޸l{(gt JZR\.G-ܛ1,@,>X`&T(sos9Y٧##=ӊ7YBvWl/^VnO_;ٯwe-Hu RC#IMlɡ ,!5GSI23UZ`+*<4Ǐi@%Kba8̊2x9M<Ӕ?\XF3\SRod-7F'PE@EU؆ hŚ?S<!5HuZ_8ܬG>5HF2'h1DOBÓNntYiG IDAT]MwqMKѮ XzpCQ/$&`^)NH?(NF$'k,O,84cqqmCbL SU3ǑErM 0"LED?qMݴoסh +>["P24Ԡ4R8!lA*o}/<=aq^ZR@"A o"~ !deDmc3r- CFm#DQ%6$]>բ~@j(3W+y&gۭ;rDNgNWw$:bK9,7f&5q;NW("vɸSn`ZukwTfnvxorHۉqK:E&i XOu.i;+FWzG30ō2 UU@)Y"YpC_NuFkEѩP "Hs&{$oFK.r2[ҕ6= IF~T鈘 RZ =QinfkR[ ^Yb*~GoQ}r ';#ttG}l2 ϥŔ 5(H{K<͚}„Bz:R7 4Gg6% 7zF_``O+wpg j$8Iܙ8IBWr#f]}z U]Bw۽irb/o>S\,re!ɴl2䓚7eg!ծyGjmzڕ} 0b^*>ʃ;!l nJzJ:S1a8Gn?eH?R6YX ,.1弑Pm,mK,&"2w"}t !QP5|?Fk>3xg%Kx4W"՞HNT!=HfEHca!Q着!'rN Dm-[1s͉|^]l꽫CDyEjTdIm AjDr A ~HM"U\-)QIj\) 3wйఠ`\Xc^^=Gd>fŮi9^w >;iw L Q.4G"mH0&c}7qŁ$P,4㋪dUq'V# >0_X LtnOl5ǐ\5P:?g^ ]bsدN,9KH. d!j;B$CB@!(Q#33ͪI=ӱqoEZBv@܅>BHMlMH!~1Bf&F~mBl}Y{Oìk X#f߾qUƄ 7;'ë {`_Ĉzdq!_`rcS 4EӑmA B43F2(H~KE@0"7їVd "n*ЉȒ`a?ęi*R8,!"Çs7G$g<=qtO MmM~]$gy9N&)\D&"dq JjNbP[P4C̪iroR,>36^~ISHU0b LGȫ? HS\>+HG,"F!לf"68U􉴬PoGkS(!2Y_գԇn- fs#J,+g*#KN+ߌ߳Gz뼅gA m$UZDn`I7Fǡ:D-+wӸauvm-,vdxHҊUVkfxph"$2܃,'5Pڷ~ض!cBw5IjbHT,'+>zd&"KwBz7[݄|?n͝sV}0"L'Z`e{7ɫ$WZE< ;68ЀڌmAs 7&C<e1cϯt4L[xt@rc!ka@47QJ./B~ BN?9Yu5$rRȉ } 53L&ϑaͤ&587$(}{ztH) r-YvA#8%@۵GEMUln* nR+*("9m+*yb۝?ǬٱK$J 5M44UU+1_7 >ffANZ`(r𷀜ȍ D Eapc \ꖌAdlc\ϣ(onmtcσEAʓ=i|YC6KG<߇w|!7N~%E"LADv|n_ێD^ۋ[mS/Y^T5ǷAf(9~ K-9Rf{3oݹ1ɷ'%8v&>$(؍+Ebo"j k"Ă?| V? y4x6s|IQ/u{=TG2Gki͊+ 9i!vDj2<{_F2F.F@|s DBȍ\3e|m1^sktPSg(y@!d&@"HVU4[*%~|C ]CCH^Dt#aiQ1vˑjRqPik?xp(`^?" =}pv#7Hap?!Rm_use"r97wNӄ 7=\ﳢi]Kw 6-5dp$E.WVLJ* s,"JEg8i0bD]<7#n.c0鄜c?Db ݂D!+[*'Yma<ȫl/'5?ͷv|: Dq롈 ;D W >StURQi 9x6-6=1%O@ *@݈܀cC[HrRz@H롂59p`W}sS"ٯB\'dns{uKmjB" tAjb#7! 7w$`FzuǼ}߸{{RTVZMEKR8K`׈#}OGk'lG2=_2FL|j)" މ8L5c6~$VP}R -޻xe_X`_t|,`ݙ\s~oa8i^ksښ,m{0)YTGZݐ֑ª-"SF ,נq@]NV?j9Ode 5R'R|C;5`ʵqV}QGۼ{>ї&v+WUՇDqBσ!lqs"9]b"k4վ)9G5ɼE w" i{sLpM$1+A~#2$ boLBknd5'Mc8)\Y?x SZm)zhj읽xmE=3j\pxL7$,'+{FI/=u܂.mJDGO]}O'|;!ג8RUZT,i1 g vbG2(ઌ̅P_&_y7<wDJHM ~EC%]g1" xDJCVl뜮@8wT?Iwp !|)5ߋ,D5,A `|isw}o.Jޔ%y|0,G!'7Gn*z{-MeӊPGt„HbK}8K`g9"jR}bKk)|%Pܛ|7[!UH`F*%6Hy vӻ_r}/U0k[ 2H.k;DDz`O@Ȫ@$g]MKOe/DP6kVzں >dsسy>|9iA r23" j.29e 2z}/qa8]I>|Z.5ë">dVMX>pa {GZ`*>z]fH4lHIlȐ^VnY_wD>;g3u9b"DڄAg 4`e|=%č n|FIבc%a!!AԧɲU++|1sV*pߺׯ v?NH1刜z '+{+GQ@1c-HUluNVv<ք @NVva>[T(Rںr3Rez3Bi'1jÖWO^|ׁ^+-^#/_V/am&R(:WwK^ssӵ^Aܢ9S8]tߓ ?)нC ܧ^Ιcc6p L~8&i>m|O/r|>ڸJ2a}5uU1Fx3-%}Gۻˀʸڂ%5SNVaî@&t>Cn6= $!965-q/w3'+msckůf! I.ZToLr]xaLjV$rFyVKֽ͊)shRjGm{u;钌uH߇|`oijmSŋ,&w+[Rxpx.oږW tLHk  ѼH|fWUդK 4`GRHl_F/T_ߴ*3!w7 e<\}4J~( <{wM-enw'}5JHأ~2n\m˗?d.o[:6=RK 9콑qTV$z7#C 虈یX}D#Mހ-*wssw 3jYi5o kn"YL?BL-KeMPm^y{s{uowN+lcb۸ X/iR4M[(J0[΋4q܍{>^J``]^7oj,]R}h9Gss@Z侒u;gY0 80P[!δ'& d#i|i$0 <җܷF#-MƩ-dn7l7 8{ >q1aC]W?y\`1׾jm9H!HefK BKe'HNXaH0ݎTh~MhxTtpHM,GLʑjDBUQod;RM[jK+u7L4~DNןk'֠qHSsc1]?|\}ݓl;t|A,Zo~#27wvS>ߡ;2gs؃yx_6pv L>LEzG"P$SgHm)RS:Pt~xG$󒷻T6 h-(Q@S૏v+MyC{ǥhIhP1w{]o?RiUonlA"W"lJAܝ"aא9Yٓm{a!gZ9YٻQA$z, ܛ6|W,&+)#=DYh H;3ڻ{!}s>+.~,C_ Y R58>mHO~I@+.>9+FlD5[Ѵ?mOAiCn:e<7W\c`gTD!bA֫"w}?!{ IDATkʅ+.>P?][sjLH}RTO>KGuEA%pnoiy!TAb # CZQ '=ذuѾktLNLAr[rx`Ʉ^=- 2n NnaR|Bm|;%;Ǽx5(?4VW=B*pRxg3 L>8I#rC<4{i='S$ӴCY% 0Dhٮ>9p?,2m@?jhrO:sR`IU1!Ǿ޵-eI@#[^vׁf<"0?.Hk!+|f<'+{6a߉8Amʮ9sHB&}d9֨uJ.No :daёorLpw->6fF_zwLNQF2Y"#!y5`*׏5|&7wNKhZImUs`>i1Ӏ!a|鿇#QC1p-rcD"I) O-(H:k%v͒ [f~4."!u-UQo{m۟~Y/-A$C^6~Bs}A5MK)4)^B"Ay{ѡꪚҜNv]HT>!m&h!*I vy4{ܳ}ᥓe ngΩXd]<0sM?#4`߇kJ=Jxƅbx=[ 3G(teaRG]eQ]8ԥSvu]դ|_^x}{}"szqCw uZө/>:PkxP5wd@ݣ-XRwWt 9x< *"Wn~$÷E%MI~][o^6ٿ]Hd4ؓ蜬9Yٵ룈$*YM~46[mScҪRfI5D.%#=bxK{u|aDxXչ/޹rֺƚ}7XSSs_`ĺ-;#eOȠ8h!2A U.G<`'jİa.Ӵ?I=:(0 ,oQ=i}>=){:$Y2#5!l=o#4`߄i'[B9|=CH'CS,Q4os!O\;>8+˪VKkp{`X|Æv TE?;,-U6zM& 55s:'mt|)nNV6V. 6=.'+x}NVva,8w>aD#-ᓡ!`^3{4UU?4ZOoec?vݶePNu&|ߋR5Zk!`N[&Vp#nIߎ\`yRԿH1#ȟ 0Ӏ NEVĠdGRyy!:19}Ԑa A}NWiŃg}mՐ],fS8r>}MMoTH*GS~9B#~8= SVdF%DvbDTB `HQ%DrT}E,86צ LUWb"mD^6ZI?}~8vo0ܳxok]: &*]fd\rO *:p𓈈GMi1l?G!JRc*"UˠjE)Xf=o,>IM(+1܅ӕIj-ĕk0swyfD"\ň2, 4`߈j[ dWIKh _~xv7?;vtl:r+MfPaA[ S!\tN^ܧǑ=jR@bny>e>ᢅW7 W7|2a;BɈ[)\DcDr|7tXUIUo|o|OJ '+[9ϯ!iFyVÞryy>St\ԫn7Ү]|nf{lMB)~ I*Eߴol6gߗ.:nڵM/b}_~7bhqXUf4 k_EN2rPKdya^$eqճ"lRn犎_Tq0rf\cK+Q6F") m}=qthhjTUX,G 3glF݈az[``$epMԷ-Xr2Á|a;w5^FrYNVݾ>ӋзAM{[8gUH*R9ѣAK;f)k n?I:dm^;89].[cs! N;O5$(Cb|G]w|UsgAVd mkbqWbVmUZ7^Q ^ 7M s~OQ[ڟx!9syz}3Xd'쪯ygw޻3pnB\δ?͈=eCEeN`m]DcxbB<=Rr!P@ϻw]giی4DbH: fS+M8=f-lR,)ځʱx$%g^^PmSoq9 ?qUm:5މDv/[`T}HTe>,s}&A"jMcH{ߟ~ei=d\]@NEV*wڔ+ġ]Pv֧׾`k]ݵKe+f'SUKVX~IϽ֓)d!&gf?dǖI;=M*#Ñg_ɈA2D \reHd@ @5ջk>Y{cgef_g7}$=L([u\͡CG!dfi;K0NFъI5gl=[Ic}b#uD.sv?$\:c5+NRﯽb}//5!ݵ*+ię  9L1Nv9 ?z_^/zշ2$\t $@;5%-b ^@:#tr ]BD<[#8 9ם=㓲RD}3zUL=wzedf,??ficuɩI7RʪӈMr?]8^x?>mR1 != 'EifLkF5i8nljɍiiFJy[kKhbOs2^?G 5 rEXx+Koe2ͯޝ:H*.SĆp׸c׻?bÆۦ76w<&wt7fs;]# =(k\/,}bnQcW})Ǐ;nDa Ȣ>El)oysg<<4]!D Fs(0i%̙f~y~݈TkVdL#U#6y&{ӳ;Q;HA[Am*3jbܲ;#)Gκu^/{哳?ZdLd ZHu1InZ`em7{dd8]H}/b7ɓ'YȰYvqˤ]A&V h*Iĵ&沎ϙ&ܰo}R}G(>ۭEjN׬xukS0r\Ayd&$HHH?7t-XmZ 6jsG\0ٴ#QGK]:=#MQ'-jXS)jW9 MOf& GM3rD/r k3j,ljh7+wVn۵Imbyْ?E ]`-{x`)HXsтf; Y c;|-RٽD=ԭ lw(jmAc}#|4Ovr(IK@_(e\IrOru 67ܽj벺_!u܇S8DLv 41F ՆρK)_\][khߴucِ\Y҈vS2sV 0ss1dbҚNj4#%jT Z(& X9__v]aOU^TݳþHIug@`ay\A[׏fΚ󏮽S9x$#m;4 _#cRDo  oSθLqeܟ#PJaZ15Z07}US ןyG(E+O/e\O 훗o>1S~V Z`X6 y}oK>.5v~^9EC!Z K(ijfX\Դ)S#F]t3~y4z3'+N5 #uj.\pX^C^Vsg%*1J)GrRbsxá:î/w,\reYR#dQ C=pdH-: (ۃu$ӦLGFv8mWOZ3ZsS9xT"s|"c!H:qrci#$Mֲ;jfԼօHmXwEdNR)PMh(EEEt$xہ #p5 rj{$hFg!F{" Er~)Nѕ׆> ".]z<+.d݅#w+˾N˒t!'H3͍NH؊(v5%߶MOn6[;o1#4L2CqqGGۿ#EFct/aWu0ZgY(5iJg_Hm6qPZFe(U >w;V_!Ș IDATfe>ދ|F05bs򎏬/ھڄD#ȯo7"]i=F?}aUsbQdI꣑4`0΢!#?b뎿e\J{Rv' 7obG|j$eD DlrUQ&!"h_HJyC"OEaλل%*o]נ|Ƨ3{~Pʎ&O`>k3ٺoJ)·iRlc%""!VWxᮟv!MX2-Iw(z{WKkۆ F-CjHTY P~_6#&ZTؼSHn >^73XWd欳nhF ʇY``QӦLm)^v+:¶!NSȢυH4ҮJBf$MtwHm#H8Z=aN$ v zN.8s;;o^? ӦLMUżg 4À d7HTRܼȬ1S1S1P'"PݑH$B(Ϯ>uWDc[jYj8x nj"[͈WV+ܨLdQb$,wcF! {@p}Y-cv9R׫yr ;g[6m`naC z#.$페C"#H/5 g4Uk(dŻݖ0,Tbe*uq#b<ښ.[p%pȤ/BG!ˆ:gVIQ ܂`F/[X^ .0Ylv&ׇv,lOAa8"Ͼz_t;,Hdېh DްnBi]3?]69r.rgdۉ1t@|+\Hu=J! GM~D6}qmN,HN pIDYȴH4ђ܉\(sL?D3쯋kxqoVmA2kXwS9ȔTG" crs펤ӑoWχDaW-â: /Pʍ4g/ 1C؝v:Ѩ>jeH ҃̉#[kFP'c;V@0b7e6FD<杔V̜Ӑ/y0DC%] CHyV*I%WN/a zl>_Q_Ee{CEq݆Kn0-KiF1sr, `q bwwخ?ÁiSnGj.KV9"=2،e;loU`bAW+n }:fdيI E}ȒSWly>GX`kPn[}"" 7ہs1vXTl/"iӾNCvq븤Z$:g|Q^>!~g4w~<8iS"B8.)SM2u#x}G;W, ck1V/=I& ݲ;\NWanٌ&@45X3M3Φ};\Cϱ@H$[DMNI{Ų֋o;͡AGAffYURa܁4BjiJ@ĩ+eO^CJ* `7K0|x.ṳOFcE}s~ZzykU0Dl"ݮ Kymh\ ]}c}"qхYA`iYb+g771yb"bMw VJnv 56eO2u@e]z&]r9R9ocFÝ70pxۊsiz\|^Ƚuiڔ[!c$N2uX:@jy 9xS9,6g/!7s% !o2_]Ra NX46𶹋sE O-v=n=];J})H0C7" ?x+G"+xw{lmH愼3@ҐӲ[B9ʼnIeHChaYmׇLs22 GRWYlo'mu.yuHH䂨$z.5l&L,#&yˆ`F([U:Y:l^%_tkH7-K!QqykS9,6c%FrslAnٛ]F2)n 〚] G C7vXWZT'_~]"Nddu>$ XD:[w_1.0uxGd8]a$ZiL4Pj$j\s\AHCR6˶WWvE[[-H]s4:"J=5Æ6tN/P|T(+9ΚwRр/V~wf|b9G; NARZZSS3e: ڑ.kGQZ> tz]v:itj玤bPA(_sS9l@e!QKDw Q$䨫{=r 'k{ɖ]CD(jgh?YP glDC{4 Icf|7NxJ17{<R-Jay,XƠg 2z޻#dH8NtmߘQ]S}Ty?7f_yN8oK"ཁOUm}>;1[ 8+33}EBRˑH-u9E؟gu,ӦLaUm[o,:С05À Í^{/uRF?'wga竑(p "W*6{oѳ(P&e}淼8/#^Dsjjt#c{t{%&_~nw{ڑ@ΎO[=j˪pٻj@"Ȍp$# [(w"ca05Ã^6" ND.SJ9mbf,=XJF4^CJn[Twٶm~4[`uxG(51c?1&a\hƅpϢCR&n Ţx Gi9IDRW<hR~_"mkMG]Uȝ1VγkH L$(B\~^BFF*,b(nnZ5}~'>V etF#f8ˮim pR*v8]eփc({w˩i!E -YHOv7{〆TW"C 踀ĘjbK mi9'\z'""<9\} LҼzy[:6TӲ>CsjսE[7)Lsaaas3-mJqy|Fy?D+-Hʈ82ҁ~o?$}@}sh@jncvDj(/hUV i$Nߞ~te[?2oC`/Cb"  @e`Dn4.Y!u" `=сwvlǐ smHjg>a{Vjyk7 E"^8o]K/0ھb;LkE5tb8niG Qf0\g!eSrPgҼ&'b2HcRDz y92?8{p uE,iVCg!! 6]DdCJ`] EtDRg a_߫_zp(wMe:oMx>=EDxlAJ1͍;":D$F#5c "Kps/wqI)e%CSVY6-lk}:ގ4,AF/z#q-Z=I>4"d$M&EHneuHD[t9]ԮcWFО{gӲu_}F}}Fn^Zhf&NIS?xmr| ]7eyq=fg hNzZ;:.g-gƑ욯֠F, 틈Hw0E#'nmG B"ǐ:&`|~>iu~Ef_{y ] cDF`F5* Ȱ:y!b+׎DנHeEnvZ"Q+[("V hb o` >DFMނ,nc#y<ľ_^DsS9̘Yl6ZTg#?[Er) S)HNw Z?˲C(˾urי\mK[XV$R#+,An1$e~dD*Cdd b+ gl}˯.L)$:| I^/45tD"Q/>cTA <#&YsY[."qHĶIz݄D#󄇭@Xw]vJaj4!:ߤ}ڳ_hԴ,G6sqm[ڒhȵC,ܤ#YHӁ4]HZv˵1j>PTva7!d<#Ht 7bTV9Q [$wgт|fii%tXTS~ǭZMgߗh5;iLYtֱ׎o}=>xYعyR 2c"b7ge)˲IFr9huV Xk7 Ne3k4-a"ϴHV}mQ=؟{͠?t5=s]@RYI̟npOIAho\&{ה4Bv$ Kx#2{X`o4 Dk4VXFw*Aw雊4%gp{2>)ɓ'>^pX.Wr15`j4(jR}zێ4ۮ?ZE^nQ4{[K4m0bdvZ#H:-A"B" v?BB?F:°Bv6n}8)AugfĽj6c[z{RE|}hNozfc/wyF+h4&ڸ@/ mlkK˵ء "ؚf) x ?pHCGA W"5K욁 "CkBD6d :6[DO Lym-SNHNY \3v*ϥ|%w]~ݘSL]yvI{~j1zd5#(o<2_/Fa+Bh5>LD,~  11H񞝍;^^! {?k0 & }cqΨ:fuUGw.;-3S}!Ǐ^8kw832|92w-8쟑F \=D;i$r\8!kd Q ygjjz x dKx/7g8:%q:+b$U]՟.ٱ K^FL?AҲ7#eCf%_@GNC\y8TKo;-y"G!(1vf$= I^Ф+nMOIN}z@ncFsFJd-is0Z폤VWޠ/h3iTbde^fmb\mH85*MAw 7"} 4E܂X,WZF#8|tR*'QYu} N9(hhǰ H]B7 _w =({SH^{ "O 385d^޶8*6D4Q 'mޅD ]HDj>UF2rwV ̈́kkA|/r5'2ie`qB<E FTnIDATFH=V'V U~$=$bo$ vE-:Kn1 Df?e]-NfX`?;9eĺ!34F>/s]nDPf}\~Aa?w,Ə9u&2x9ӁNι2J`Kpf F/yvaˍd%w6Jڊ(\n=z8hr#&##X 8.8 k2 +C5$(3hskvHXV"S`1+)J4d { !hd<yEfa{$r 4isn-F1= ^.#m 4G{2 3|^U{ϢalA ]U1g!MJ;_|`4m`j POD2QijsnPaalE`o k5@4X-`!p908 *&i.72F` cH<  K3d K̀Lm< 85>E8Enn19bW\aaD D7BZA4M6mmNG&p ({xX ! U1|.BEhs]5F?J@XcC5Q$VQFa$P̀OPT `.u r}/݁ia{ nhtVCΩ$GL;#&! ;&h @Ml׳ݸӧM߰r,L U_wSe; c7̢a|Xe\g$.]PJPۄ"h7@_Qdd>3QYxm97x `kM}aaLZ<^p.HkA5X7>_"->|[.aGFr@Ϟ WGCr}ұ4m6~Xa|5(6װ=p$fW!ǡLbU d2WuܴAe1G#s  PtDEh}y!d0 حYȷ*|P[ nkok/QI >WW=9yTr 0X_Uֻ!4fƎjrՆ`P *iA"jEU04@([X^ uXDm Nv@})h+ yN ̢als.脦^"4E'W~}42hcWQIk!_齯h:<wg0 ؝&vG"3\4y0PTUV{P]mkTSy9+ hRh.6f|^NY,r#<i_v}0]e oF\4Q (.@) 3" pA_B:$p(ypxԿ8x*xSw}0 0݅ag.-xs&fWՎFHs \1(VݣRע464R;VU r&[[s%lwqyp7|U]rbpcb0$$7PP/c*g9B.E+KLG㺓PS~*iW"XfxM?CB E6Dشtaq0p;;mϸ7m|U܂N+Z>٭zլ.K*Aڜ9P&VӳkR*]YP H{ )^OEc9/Hu JvF(ih z #>e3ecHtnAư2s@-De%{mpka3M*+}rNx9Ӿcm6GSP>F:is:ʸJf0<|Z\A-OȨYSSJAM[B).Dfm]j(̼K(@ (y9F.@-)3w]2]Eco-*%M kQ9 ιHtJp2QC@ \&^ |A&qcH^jC aFm\5?% ]D#m4?6e]y \LarYy[w>nSÇ&ػj 7}^N6lc=^I0&{_㉨9TFY6 $$!_XbuX6E='Pq<<(2JeC%5ޗ7Zg,foa70 0~"C6FP@ab'Ԅ:"MFzmIG\R_-iɨ/фXu{&jA$<Us"m.zmFo>šs~djx@9;\F?Ae-eȨEm.(B~h[eDZ7*y=x:Ơ leHڠg+gR ι~z4uq眻9slw0 0v sr#sn~kSU!-PEu!]|6 6Nob1%'Mڟڶm:!mD:GnbɁ~9979 v7J6~.7\n$Fq_܈iGaf؛iJM;7z =(e ghdB,M\܄J`Vd7Tv8nFD?T-POT4EI{!3 p-p2Cs [])}(ia{M?oו$׸^hx&0*5-Pue'7$TCqq|'Ol. wGOc'?*rYr'EWge2[ MӇm!sU;]n$6|m{ ֳhx:Beo~sȰuFRQJdm/!ypupNF"Qx攠ɨ5AT!|>Xhd>Gzs`P?aa1.7{ԆQϲ]truS=ҼbI@byUTDI$p72ˑ6_@ti5guϪœ[Ң[|ҺΕ>#287N̠SQŲ |^Nup=F4uأEhDPy4 >LC3,_^jVL,4M*TZLzd '_fQp<}s3`?$ha%݂eDf_Z0 c/F\\"dž¤'W<ŅR GpZЖUHg퀥S7Yu Ն UN\5) ڜSr#ﱂI OrL ^w9,ߘa([M}_j@o6/spLi4n?&89.j_&#cy <ƑW0  TW0y_3q5un}W%'#cArU"mN*}sFY謁 )Ƽ[UC-QH,pˍ|³|Hg }$K yTէVs$%9+ c̢W;}{ dрb$0!5}@%p4ƣ~irn2W{Ks(xqpPtys\, A<.X4̢aGhwp fһ: ;y^%U)m J аb9zHA\_4Xt a-Y=j)!*O b{46[Hh671hxTi\"#WDaL@2 tv ^3"^Tya59x}qN>kp4ঝs\{ 0 c1D[t[%lf}ULTfZ͛n@\9iq:!"9UyWd]$ל{՘ˁJ3irs=u\M:CJC/E\\j.~z%2KPRhxB^([9{?=xGFp0M@= ,Bs(y{Cakisy{ۡ͹jcƒ~T^L٨^g6#ŀ$r+~RhMu_rfIc$Ԫ; ΅X!=[%H= ;Ʈ2^+FιfιPְ=vDpF+P{yCe}PqTLGk+AYP[k2` Лhp/X_9笼0 YQt,\cM%TvBO mG /2^f~Lni_:}Ex6?1>nZY8U|^N pC5ē ħS%LMMe+k?FQeVģytpsniGbq\{ՏҀhGQLE ,V"J_&la2hwz5(CEs xd{Qꟃ% ?aO@/a6# ,9~0m¥"mAkp)ڧ_i%]&OoH#{btj>굥}!~݅T] '?G'Xjbo&[H ΋~4eeK0h썜 蜻$tB,E$'x?p=,5 V"% lGպߧ-P_amh&" _\ >b`2D>׋У;6a.c&< %ˍ,A}Q5gڼI+ 9@{+cǷ ^;97NgʄZ_wgd  (p kJsG<aT,{ιzq @Ck^CdA[eLa2+h1~䕠l4 yT X4qu 0Dr2ǣeEp:4{1JO?EsP\oWT~eaSq(AO?e H6ibY _&ڃښ- ? ̿̃׎|s«.8NX3iy+%)\K&h Tyw^tGfIsg;3p)m1e@##^M(ĩ oz6Y& -@Ciu2IY+3cq2ͅh2j@= YL[SFT*(uMv6BHCo\d! O, G}Nsnx'=}}n3^~ nTz2A=o?sQ -@&3 ܥ E1AF瞇VBp2Y1R$e^JZ=r ps. /otMi(:Q֌ ֲiaSq}gdX0nSŹH/ ^fÔU:fNMvcycDڋ集*z.DV.7#ɓhވOBb\0ʝf.m~ UA0v"f TzLҖC ܋L9aM;OCql,4S (8F%HD>ht`QLe*@1&gqh \0z6Ae 0ݑVqE jγ㪆$!=@?ԿX01iB(c8UAڼ4xۻ|J6lp}P*+fzƮ"zD`cGdo)c1m6vS,{4ι' ι5(R91xH P?i%5H@G•H^`vz0s1}Fܾ Q:28wc ;ʀjO4 0vN:i?rvk3.#N a[TnZP C IDATYVhe$6E[ JTbAq.zMϸ5,E})h?.7W.Kp6C;m-\m7,h[UT1l'=f=(73'>0 Tjz'i;!\RQzd>ghIE#saTG/n{?9W"DrFR@mha;41?E@e5ǒ$4'9EF U8dbއIG\Gcʻgm)jiҲʡQ<ԒrVp|WTz93etM\]iViQ+u,bZ4m6v[w4dsq@L޽<>D-JY|T$zPILql ZE*?ajR$LX&Cu%pB ԋ QYNFW 8!=x{r 0 I@+oڜ"Ț3ÊZa)WK6OA<HkȬ +8TjiC7nipڙ9sQ01ULO$ѕ\H61Nc %p82sAEK{ DŰdO1x﫽ι>ιqι3ι p.i>*;킲yh5q{Ũu 2yha'4&n&BY"^P2{*s} e#9ĮhƋQ%AF7e-;ia;+Uqa_/K+rw=F]n$ w֔2ZlըTBZ}Pw)IHB=8GW.|MJ*z#7,.H+25\WHXAOӔ3`<])rJiB`o`=mQ nEB/$*AGιϡ^?0<2qoH(x*[84lI 9kPYlScs^JV@Be=4MSCu#Bǽe#S([9{;9ws 0N\}2[͜Z0r#.7ˍ$g#m>fr?6~r !]AgܫERZ&UTՆbb±!x|meaI૓&Ozǯw87ym̒j`M! yRӠ͏y;nÊ`}MgP?F`쨣;*OE+=|*#eιt }M?$TY2+Q:ԳV߭gKQNܒTҶ*6ˍbzxp/q_/h;4c ғ?.D]6 ~9|[Ǣx90vl&yEAd,_.-!svNfLa< T"P/A!dfdk ^wd[ksȴvV{s#u D&(/jt}[k Td@O {<;} 0 a &o{ uk7r^-|4aQ츰sW_1[PMBHKv6e[m}~ӒYoӬ%7ĄH Bmuu)3tVzS.\Rb_V֛;p[\-,ǎ܌Ji"mbm/ 9%xh@ϝ Ya0,hUC߿ퟔ2lFC`棉H )L@o&ҙuh@CQn-*퍲s1 ebsei9ȜιF߂cʔsxksY _2G]s/7 0Hy\q';w~Ꮶ;x'Q7ώh?Ò̴MY\j2J%$օV&FC쇪e MZ>NFfr= w2旿hY/|Y&R%\2pa>^95{7nĎ#}ld(d"Y3[t?4r;* zmDl(+cGD6[k[bf9囁Lb`?$ оH>E| GYLA9V2ozsрd  dFS~3SNpx˶ai\˝2x;H,iɻ㢚Q'@lIZV§-.m^-?j=RTS3lKZ5?3iK?~&q3W/\Y0I@BbnTV6/M4;m3CZҼSY32T֍#E9(xkdy8z8a#+v迀a,{<ιuh:Zi6g MAcR | 1^JMk&FFpd,(rJekQ/=hk|{s΍G}]GςFDf@ֿz?@;%o-̯:kG.uUܿ,?Ճ6aw^CL5{W䫀cHQQQuR96dͨb ] cbfLF9/r=LGCm^AYH&r~먔@Ilz%*sT=Pd0M=ιȌ~;~as~hG uι˜s2ܽHh3LDCqq)2]Mιy%?aƶFi練6lqǞveBGMtIU~$'ҿ4cƟkj\V]m|p^a'ln߮*yӣ_,xFK}cϖ[W[Z~sw?2|ܙ\>=ڶs*62fhuښ+ʦ1&'b!ͫHbrƎ8VǡʢxS-#݀hh>> cW`fح72?rҷ#"}|x1xRF!q9la݈)9q1(quD4ϐkrΝ/pՋHkTZ QNkԼ~>0?^=& ŴDea6<;0pC.!ڜrj\nfiOwڢ&Y7i+ )hPҘbl<+{OJMܼ9Goi;kɈSoAŵ)ފ@CgctIn.G1ݿ[?v3W0\ H 1>6r.0Qp^j7AQ HM3FG&mq/ma>D;}sdD[Xl>#cW5hsQ&d?y"h܁MnFY @p*=8~ %;PF!Txks!9L}ĮڞM8867"~sa{]P<`| 64~D]nĹH{ _\]F|L;7Sq!p+}uHޟT'a,vQ >#[JU5eL2eWbkz|v@i;)ޒ^>r䧝o ulMRb )M(-GD.3Z:eNC|sj\jF4|s ޮ/m0P9iƧ>ˬOŴI1hn,"{.sP$}5otCi!7} t@YPf|B}Q}"Ed4[( ` NB欒)/x:b;' 2h0r=9,@{/ mٷ4GqE?yC3Jg?·@h먙(& |?WW7C'MkzkխxZOTcK;(5'l W2';괤//[?}Ru?ocS %\qnu&-i_t@2c/O.|-?(lC{8i=ϹOz?f^f2>&ؼԼ<z?SiDU[(f݊OG#ck y7fJd) 鏢y"3w.nT]sB%Ӂ=|\:2Qi*HDY5(cX; Me9ι dB8$>(5v0Xk%(x6hkj;iaIPv:b{U@E\Ҿ!G Pd0 ᣮ|d'MEs~ַHӞj+CYOyb}W5K3Ŧ$gh.;u|ν @qhQ)q7Gw<`-Ag0vԍ,^2#a쨫Q9fc7̢l!q0{?:x,uBSDH CY Ӹ/*IEW!Al3_{Tx@T dHOF(?xP9:4Cq֪di8~ <⽿'xZ4U,(5zh۵(jx3DB%4* mz p$5e看"Qd  ɩBf$8Z`6pap1q42ױm>AvdG:&8hPWkc0 ȥ(K({SrfE#=k=:ij[|e|y~We6+7nQ=)u>/gUmȍnKit/+oEEٚpfjyB;sPGg#m\=kՈ /wT*\J~tvы~x̊^sf?>`C++[@+h ;H 7s6~ $s#|Sͧ ʡ[ь a&Xfy/Q)gK&bT1AStFYTTnGq7<Z4V=FD?|tQ2ȏ k7x80 Cc[^$V~7OYMGS 7&EP-rvO͜[քC1vޟ 039S)|#,/Ou)۽G߉Gw56cYބhQH g&muػߞ\5rޭe-^7sK&/զ/&]X߳O=uJe$r2~{& >)uɝsf&SM{ <:Hy af#{4Y 2w_:0kдE°OPoz"6k7SӦ~( tiW5*H]E_X2վ^\l߷7ՂaCzMRQ0 8S3μ3nsXs >>cG9T Uu;HGT15fcbfSBfV=S:{;BI{PR}l WСih_ǃ1SQ_G$%jsfDe*{IF}SPDu}-97 e!gGT: 9ewMV{ܱ9^@Y}̱a2)Cڼ,=Ӟ~/$S=oݹcnB5R$'//me]ԑf$(lʲMv-XtEW,lYմcj>,mc&}<)23 IDAT\0lօ,I/HEu)$&3oM*.-<3-q~o{꩷L.WjoS1ܾv/Pǘ?v0y5Fz4Ǝ: w0^Z\ caf#/v]8P3QO( :Eb { <4x 2h&$ P/ao4Mjq¨4v2WQI vFy;.v΅hupI(>q k=sWlAιNwϿgQ}80 y7 OSĒԳ/O{z Y+O)KK3{]z]TĂumƂKbLcL,vED1XPQDwX`ay^}̽sgy>t229,mY{c1}<6nNmU7ߪ ,S-jW/\mpn Jv23IQy;s3OF|n0B1}uv nMΘ9^ ܏R.EAQIy Ԕe:>R_y"N=מ7G_Dk'bux~ĿZ$F[GX.IHOC/ uCQ4T"؇>:zQ֨ xG4 "k*{Q n(n;dE *!!&Z֝' k^ke 1&s1ADA.k=yw#i38n:'櫌S8ug2-fEZt韮eUp;<Ϧnjq~anэޛF 7hY.m-;/G~w gƄmF?kwiרde66" &r0n "zJ4DD|'u9ŷqup[Y1Ϝf.ipd\pj` VsX;mfP3uYfq!Y XB*jR%s#,.3ؾ(&?ܷ5bYDĭ(vwrn7l:'}. }!bLr&AEX퍸9&nj/CEעx$$'DzWOo?x9<橧 '2ᕳ|6fg}U[o[;ɏl/9͍3rƣ#/ QFV (I"PZ?VmBHA+DW\=jNN}E:^~D/|OH_4zHFuf:Iho-(zJ5ĨE8(g4Ja=ޝwp vNA{1~ ܊"ܱ!?&hkW` 0AO2H f/J " 0v<h_g _nKqx>wҝB_]ybKzfm6KzϨ'/}Ʀ8*OSeYppH[^1'oM 7喸%CRj0p4֡[?J7#`ڞ:d# ~rDę7^cpmWnSacP'Ԍ 7Z%Mz"dFY673P'D:'5is\Tx Bb?5@c)EnDEH< DD Q~ :4 sJ]ysH] p(e$ D&oG'hM( C2*F3 E"O TXPNh~nÐεE۬pn\c ~p~6DA?o=˱2Ľo/?;^ݴlTBcg֊T'bAyR\U%!۶ӃJxmŸv;ǽ=yŖo0oJ-ګ}s-Mm{i$ _FCGmݎx~TqMȩ:OcOғ(h p^[yZdcvz\j;1p_~Jm2gٜÁɨqM.nx?>fm 9Y/L\ T2 GP,Sc7"71f`pXk8dc_Pc8/!B!HC;QND*Ps E-J!D;@lBE?s>%`ҴXvbGDRÐӏ"_}%[4r$/@aZl6/gylDQE1)(d9bvsv[aDo 1Z/1e3DAĿ]>.OLfˢvm&$"!E랻'"aCj]N}gׄ=nh۵gٟma#wb^O*DzEܼqD^ }v4"ol؏8p ; /^ -1I@N87fV q$\a1?c9[s*z}B}y4,Ծ&J!dǚglv (IaK3.AiG8n .cL qtJ"AtZp ACH ~ZGw\JWi }{<K"W lEuXRJkP'hT9yu7Z@%` b6VB hV1{$t[ʿp&bA_CsG˼ wON2Wƕ*ڵZĽ_?v܈Xj='NX9գ>zH*5kɩ;ueÁ΋F "%8Gi=lpcLoԼ`DAt7ω_F:r4Ȯ9,?رiafQej"B% 74x'ԕ&0G-,c]F׬8ѰҼ9mqZxe݄b-EV߇L!ʐ遄EHԬ@Y=hc$q(Ms;rיʲ9d,mLD2sߡsm2Áǵ>/o48n 4H}6hvy5dY6I{89gՃGB;s9}=Nt#Y<"EG21(ZugG= `<"^DAuw b>G<(+fᨣI h|) |(bw4m<3}t֡hםH(Ɲ?QE("kCs@ngFE0sy$1fkǍC"kV#Aձ( im0DUnʀ1 1Sf}:fߛ;q{4F3|b62{޲&չg J-6S\ }ŏ,.̲9MfKAF;w|A;tb(෋_o@Ցm[^ɴHq)ۣ9iÞ IⱅӁLc#-тG۠2(cȋvz19kרf`--F]Bz$L"Q8ވ@>EtHS#hL@r /xEf"Mo tC[-wDK;la$GGMW1Dʏi x3yU*MQERB[xHmM @)/wviƘb"2s*C5QDxDFͱ5]tusCj:_tƘPpkac. )0 HpBmHD$ :͉j3ҴC @v£;aЉH/~}1sw3ONZ BBbckڒ"|^vkF]W[ri,i"۳gce??YꭏGy+*xVI(s G[jl Ng!y;Hl~lijLw>/MѸY k=ŵ/u-5m(C_??DL0ƴEa/2ė#u=hV${-G3NDF$ck3UGsQg1٨#k^c̯f@EH݂TXC$_(2)=5o[D@дa{j *PZעt@Jv$v3Pj)HuCy$*Xo rc|kU:V {{ L1& P`1&ZhF&olY}$D0P|HM$#PϮrZ`=hk{HHHl.?{QQ} ڒ&r] jIhpQғADA+CYiQTf?)K^C Hs ZS#G<ьgM;t[p~.8 gkVorc=5!Y]]}?zk҈W;mu:vZPx %&J~Ue&1Ǎ'ZMqڙ9ԉ4{ k@C;"e'gͷ}&s<9'4FNHtMؕB߾=ćOA _U y5ȩɘ9!Nj̲9[8N>go%1;RsvΈ,ؿv-;}(iot+ZV!Ungٜ} V=(=|BRҽӋwn{4I~ μl'IUc}6r]ďX<-V$.GC1ሬ"тp)G!BBnZH^km1QZ1 Ty>qeh2ay[7@ DCDFc郚ԌBr56kmkƒ=4>R"ZĐqHQ3=@%(rKy7;;_)mtcf!B}f.Bi"ZkK]=}ڛ1,scz7Ɯ DAD >`n}{9Ax' q܅XW>'Ctk'mn۸1-k?3s"{f+[ \:+ٞz{̀f.9|Ϝ2tBQriۄВ-8?u)Hͳ{롅m9#zCb؏xhyC_ԩ|{ ,AEB2 ]qȮzqsgꀞuǭ r>ZZQ^ޕ$7FB.6^PPhw-pP-ץ!zDDeRqUBWx1sfھPӞk_b,2.[0}r krS}WQzLÿ/c,| qȲ9Mf<ܗܮn_r O2 Ց),,Ӝ;yY6ǗsȈꌁ6$>Ԏ GDP,#ph1 "zGZGȚyg ݖ E3Ha5>݄3RS IDAT{ⷡlI[&~U߼[FSx0 cp5x{8g?H3CֺUjy*.o}uջ }iosBg nK ú;L@<e=8nkqGZ'G/6!E+c|?p'4on?'zE^{#7툛c_fn?G]iKks^ L|B}!]O(OI/}7]ۅ]`Eɜ 4FQ'W,dd (71-)5h2:N QuC`iTy%vMzХ`E<$TAg(`hNS?Z.tYRU{,A/@CE‘/yOоO*FoOdHAi>"x$ȅ'ŮJQ\A_O?W)IxxNTEj‰C 4qs_$$W#W!hD\a3_.Q2Hbb/ˈylY_X o[m9M:,h9s uՃwLf p-|96ϑ [|k6&p9o_xiJwG#+&=˛o1!>5V_f!m |U=V߾63w`w` l j ps%}Rⱃr$<,N@B" WAZ8?EB8/PḮcPӕGQ1p5>; E~{h܇^{E]g (*{+3N Hy#[E{(FE]ԍl,b/"MmўmgAϾ1$>tP<$X7; Cbu;_9uȃ΍=1>$ƸowXk =|.pp+6iCrD/h c̣;E;U(p1fa " 8𰈃v#nAk[[$R>Y6#MZv./#?<ƢnTw8k'M +W!Qk~0]4/&n.>kag-uoc#7d䙼iU/Ĵ(َa>9}^@'Yײ+[֧$&Gm-PkIy9WlInU}hulcQ|ģ(ȴA:<saaOߗP!#2>9CEnMAŦ|v=[ux:vv.xFCkk̜u mWs"Gv}  و?]!r3YUо>,L\W ڼ[gQ-\u[IHsaHH#IWUx{&sx Cn.=:Unwss@93E3rmT'dBχ!C#JQEQ62^B-JG=h "#xP6q޻+HK9GV۝ -]TDn'y;Ăo)z?Uhr 0xo홱{F3۷+.J)1Knfs/어wck/)rotz׍[NupňC!F X3&n9ss@dM%&q~=g# s8u!x<dC"Z2:-^2oWmݣe5?6,|p | &:Y~à'lNgeߥ\4^]m(Zkc&2[A{o'PRY[ȜvWC6ˁMqsd%wsy2pi#CLY3fL`dYogvºn}mf}0dAiOk4NFCCaUN("#r##(Xk7c.EE BDdܥ>#aݎ\MƘ(Z5Z8kH:-d3PD5H$#QPTXC=4yHU$"$\\HVT5:DyԠ:_ a/wn{\Ddގ)RٶDDUWoe a0 c̋kZ[g O<&X[kƘƘk`0{EGODB@1סn =`-6湝{fS1G}T;~>V<ȩpo-DA%]h׵iB*rs(Z2qƒ9wml3#aCwd5yBhژv1{8ҏ,D^7n(t$FZw[Cn;O;hڽucjQFwn 0Ƽ`]qHfE'qs P6; ũHT_fc:{~Ǎ=Jx--bxcX1[P䢟|"31洛וּ4c̫l 3Ȑ[t.6 ⟁K}uFMAYb3sڽ0<8 =#&-4wۻoiîT͈wb:xCYǐ^Rr~M}x2ŭiK;/}ܞu>W7:zk""m|ѵm+||{Ŝ+!܋<`l>|R[KmC2I()=@r,d;uER^_VYx褆hoh{\W?t՞!YN^7{/ rveO'+_=1"$8EwdY6gUswxyJ%rDܹSU!;ͫI9̍s[,3; r.|ȖqM,cI&3/|?fxwFFx&,}*6dD zUYY6>d ,L4wBh0?Fٶ6?b}Ƙ0\a=2\DȻG3"QTHyC m~ mAQ@z$l%.HTuw"q) AMh!G($ [鼅HwtTy'"(-բbH^[~$Ev^WK@d~yOGJ "hJ$:5Eygmb Z74V},@d{1f9,Bu {f+@d˟#hm4PMzAL|KB$m~ EM% ߀}mF]9 Kی4SPɻ mua53h"cUohěQ7 V }Z/?ѣ-a!ui>KIH 1W}a\ѕO&nN^U4cǜO *jF$4I8nnqU)x3Z$[ѓwndEysmjRR{IoB"3a|UE#DacBd來wri'u%ZYPX9\ L~86pܜesss/dԠ}^?Uꫪ *]{/6+PZz?N,S m2ܜ"7 ȶx7n @=ޝ軰Z|1.Ƙϛ/1ݜ Jv8C0_vMl w5>r}~ |lIr)Ma_$r1ynPzAC 軰qxrDGthm#j꾚xPQTj)r<f$<={ʼ3sJߌF GY.+#aWlQdقm{G> Y3^*TtⰆںe-"~wjN4glx zwTn޻bmf\j[& '<$4RϾBn"lI@bckHwS#bϹe^a'#үnx=;xI7wJ"FȨk>"$:^&3oWפld.ESl@r_وfؔm2#M5遚E nNrhf%Y6C''7lU4?Y 7IrzkmLQiÍ{lZ\r]DEQ=3ʃqHEr\%&_iRuOnoi#!(aXkcQ6$vг?c OV$K@xejYSXSA0-Z /ؕ,y!Or$[kZ|qGb+2EB/0$h:E-ҁHfG$=z(Zt[s[`%YsshQTOxw]jZ;s\ j(n]gcynvrT2o8c̃NRÀzMzks~p,svk#PrADzIUza.Wԇx\ϩ0ɓ#1Sf6f9"lݽeB"MY6kTA۾(([Vifc6l jzWm '-[EVUg_Z+>>(`0ΰ% ޟmkDy6=N+MNol6A^ǥerp1$`l@(mph8@QXb@/ѽN@ yl[!qB">Ed$얻iZD6#Q~!J!pJDѹ2DR7sa@޺$D,!m{ͬ@_PɛEPjn1׃FcL 6s(׸6H0=ȵm9nA/)ԺЍs%ףTsoj!"D g3;=b$@OG0=Q( 'yd'cSZ̶燤DA}& <mt7 xHspv£t$ ,kbiE-J[Ɛ+p a 9>k隿'e)e~qO^1*޸1}~:TEH^=IrH5DhyWW}:T$ -J_^~o)]0}iيlQj!k9ͳ=5'(hLoˇԯڅ"O<Ę w:xgL-z7}vPW]b"jn]澧ly5z.?h>7P/_1h3tB];l-#1f)סz^ ǣG7S`!UH$^~$p>7]35)Au(ux:hZwe8$zHAi+7пu-} x5i^rC@pR:1DM|km0cr<7tv^趦:6|=wjw0E [%nRP5H}i;td؃W#An,B}qGw&k6#<p\}CEB," 8*Z_+0*Mg|Hli5S+fGƂx{16[_o"DwϘ\n؉!WTkw.K 6i 큲}5-1Ҵ|?qsD],ĕH$l)F wHBL5o\ mŇ^qnŬQɑ@ IDATKZĚ[]x^9JyJ73tdSl6]ܼ/̲96ḋ8Qd?Ԅ7W%VV_i.ַVPѪaaB&8$BK/{CT-p[<+S[\_{'LzaGԹ!iCwCEKxS}f&'^W 7{o_D-{鶥$~ p}(j?+?@P,3>Pz1(uW(`1fR`='bqi!?BDgV|2j61f'"*~8z$.@uЂmYA+mG׀R7s+Eڳ+Qq;HD>$^ P,y@ n~G{ٍh5u!-ua#[$usf<ƘXc-3Ƅ1'ny(`;?֢t0wݐ`g ;fnEm=n댅ڕi4uMv  nbkT7KhTzc֠ݯqϬr>31" m%5kHlCP#]mFs26#M *waSM!"Yqπ`\y-/#dL,9=ӐV8V`=^ns!'{}0nWYקv|W좐^Q1{IVv(z}۳lxQ]Pod=J<u{Mhqo}~*w@V'-Eѵ'[nu-5{ևmo< oYZ,ou|狺+j;DMxɌL)6akȲ9f!Ȗ4G֜Oq~=Z]zj8<{v|3Ѭiay^$2tw0GWʲ~8mɼ |ӲVa{Ev?&?[˳_qY@-z[xZZ;ز6%]%ٺfi7|!0J*4Fg\b""kS" XW"{2̓?1 D6 AFM` 1EcOv!1QNFiN56yʶ 4 Pj }2-[Q iT/!QPo'$j@A Hd6V cn%<m$ơ z_tcG"+" HGV7DBWو`CPσݼr"od$b"=7SYwܳƘqn |im|pט<My;dwlPZi_$ K/QN:@~-?yT:ӝ?y/)z]3 n}0D ttu1Ƥf" nEFm ZoDNWi^h3ҌB\3Hܹ?xH?5(9];jGd7]|L25%Z"]<x}jKcH?}bH)׺\82.&0oLgd9mw;wS9"ob=GtK8.)_ &z>^_9"p9 2|!zrv{C(2ܾþtW74T%5xNx )Zq ܌4zyh_nCYnj_L<C7wD63ȡ]esf"nNpc#d&ḹkߦ/uO[Wl3 wQvTǺ'5A9BH`6!&c-ƘdKH"H4Y#hr'w?j޻fI3wSjWe}z4ѶxnÞmڤ摧=9MNK>p-:IV⑽ZFxt)c<)%YTLܣZqH@eq~d!K{/2?" qJ)5! V 6DX~lDY5뾧!V{!Ea[Mk~N'd{ىrmE6"$HInH Ēje A iCĢgstb }4e%:&śȺ'*MVJ FVn' "D߃du"T?^.=gԟw>BbMS2[w!Jle*BΌ53IϿ!rT$diWf!߁*u݉sr΄?=D%*Qj?5wQ#3 aEtκ1xF̆U^zigOb?Xg.lL0MP ]ޜ[Δ-vwɮś$ZȜI}KD bu$)+Nx 0ɻbN^SZǝ |WkdA5;\-#ZR.-tg"xܩ]%;;VDm3oA`^W'㙈[LO`l ^;u͔!]#+=DoVL7.繣C6Wa01Cg2(Q{ BϸUka{>@ vW߳ר޵Sx<~UNkۖsѶ,98N UK|k£T8=Z0@ 1#q&ո8\lL-`s͍Z퇈G"ExMob S4ԗϝДP%@VK)9Ez&LJJls?S($|?bGiky3G $Ͷ.HOx 4 Gd!PpWZ?.ſ |3#9,*ns;\zXc'fؘ9Q=({]ƃ;I DW#wؠ"ؔD [oL伩vko.M}oBpEQ+65k[s3!-tŘ lϯz< Oߜ8XZ>`gicE3=+C0. =kƖݝPRrƣORӵ9 !x92|R>YO2d>6|eA|ØԭʓZn 3tGƪFy&ۇug"D%.ejȾ8 sAhy{*"7~|J?zm?D}sW;&5z]$/*%]IB6ӐCMR/D_-iߔRV92znӗ8Sd>(sZl쭳!D#JȦۉ1gnoBBe|YxIm) WmEcZt ZD$ `;^D6>$x Z6 !/jB=lT"uOcY=+>cz%sRRaX}o# v\d3j{nw$*ۉ""`byFCz e@ID}QFzj.JC,kЮ"YXLdSq^>Ԫd݊Eo'*Q$!{ bxkbj}(oh|{k[7b66+gsRvVpx!'賰X-1Gmdm!u/ܕoLg[7-⫭\X8 ?V8Oz33P~[0HBɈ=Q LOB0ojl).uݏ7UΜ/vxܞ rszZ-}i/3kM6ox"~PnF0wFmՕn}Yl8L-z( 6_xˢIv1I@N.!T/s,@7a6#PKQKf(+a= G]DMRnU(ņ[Çj.#{3/)?fKޘ{mt \ G9O$3}q*#o}O"͗!+/a(*DPQ,߸Ά(Vܿh/CyBDH L@y!\)"j^D ՅX]שT Bn4B-bM7MF)udh-#2y5R~"ͪԇJ$uw=ؤx"5Je}9B2PBH3EYݣHXk,ݻQO's )DaA@ISc6`"u"RI񈞁AknPz|\[^ݧX=KBѭ 鵹!e;Յb4v}KQJTe=W#z/.MUb3(rj\K̃WaT~ -!4u;168\aR/mvt%΁Ͽx\}pzʡM }]튯w糙Zs#o8ٛK2j XANFp[6wId|{' <:nQs~2wn.\7ukY 虘Z0b:;T3>uU+5Gz5`v/խG2$$uB,l~ #<.|1'{{ U(1F;^ L֕aKR=MohlDzWF ~[)6[}@ P?>aI+kR>Ç`x/]aRdTQ=wkYtz QV.ңCt=zvR#SY!DZiɁ=BB6Y #+?DH}deмK)  Q ^J3YBt!t>B tq$#!!$`<(ZWzR Vy|"L *~߇@1r`2}p7Hj뉈CbnDq*C]GWx zPїOmTL1X|y~sQ],=$,=B̷!ȖD,ʅ"}YiY1D%DwMl #:]Lw j$}2)t~~W)sy՜JK`[&6_``.l M_x4#irTkcKֽ5۹}wjE[` _KnrϠ8f~xV!ݣ_!u)\&Pͅ-+Wo87! x`s,!8p =-J=Mu=?+\_zxv%<<zᔉ) ;Zl0CUo>Zsc=45޸'a= ]2,$)Vn )$dSLqcΛ":= ¾Nn6C-1T4rxkoHMZ1-h{xyʈ?nO )ac<8+Rᛒ$t60bB^.d'/C<" r"؜_B2NG ~`Tý;t/L/I)c;Bo>\z/eԢ9;2|eɬ*6rYADEN"acc*[&5l^`s:uBSŤrﯽq̩^viկ?%˻R/.-+ nLFt”%'Ż2> 7[teIؙ7+Ck]5>z. -@ #7Bffal3ԄWfE"2𪖽=%z^$5C?0pӖKr:p=qH4ex'r\ d/굏hŨKD)PJUh2 ɊyAh+b?+ fDQ!$*iP&!{B !V{EZA(*% J3a IDAT!73Ӈ|xu!j1qAg ٨m?ȄjjEC9]y~Np!Dn[n?!yVx34zK=|ěQA{+BCW$#,b"J7UQ3)' #S3z[q$(} Fގl Y*F*ʉJT﨨W:+++H :Z_r}`^Cl {vT f-qyKbg6\Sn_^Ҹ*=a센#׌KݽөbTѡ][l?~rg=ћr0lW96[L[UlO :hۆH7ЯKJLO.Lb\'!! "&lW̥cRIR'}2im׫ϼ7#<Ƀeգ*uuD9@Vs{G&MaRc21mG7T3`y%00~"<)6K(/gH{_c ĸmİ#?旁mjKJ{)O{iZRW!*ߒg1**xBrB+ P]lOGVgBr\lD_pgQjE@j%r8=si)~W StBЪuP~5"dLT9;K|D=ݦs44tz+ЭW^xe߱BE#:'{ץ1O `L9j-s=HŎ{mSSzG߻պ~˿'+zR3#7G P ۹t^9#N^uvDr $"xp+BĆ"RfŒlji\jEzoLG'#0%Yn^!EpnLJSgtߞDSK<%\?&|l韛&cź TdOr *"Q)1q%/B6?ED2.Fu`:W(Qv~6X.Q݌(anw\ncmF<:S?+:0ֽk ^ PJMEչ^ٯxMr(E,mu#^'{q,@$%z0 `}ORj{^K)UqCW D ;B Hrݮ۶<&VM |y5>cT"V=t MwHD"I?mͺD;+,]_Yzi 8+z!~vܐ{Ɯri疷0m4myqvƐԹ2O!\F[[C2HkoJ%a[E@S";Y+! Oy|ګ}L^mxTF_wĘN;%#)#k#1v#Xw2p R7@!8Bo9xx&I tv*l~Ѐ}$3( "ͅsW7=QСlc\ C*I7s; I$.Sk;G0+ٞa &]rRW+ë=ӜuFͭf A6DpR_a$D=p1! n6l|ndzq ~|)R= nui;$8 _ =A*716)Sf&bo  qTDbTUҏxA7> @GBuRX9?DB$⺃U=KoU[ɆDXZRi 1U4>4 $ 6QvYy()ן4!KyYq"',B?EzR1:PD:SzrrգCIa!tJR0o=Ez&&^ٺ6"&#g D@oB3{ 1nP/EVR/BG"Vk3/5ߋrע)Ku:bx)ɒQ#$^1 ӭ¦iv)^}.$JT(mG b%}076HpwS\7>&|23sڲz;=|7,A$ٺ3l 1SWHoIT~lWNٳ=חL3qEֱ۰fGO]o¡vuDήOgNHn<9-6 >@{ٯ~?5V `]IWoZt˧1ڷ%^\usԖ= !Eٝ< G-6XnCCu,G-YboU|냗vۿ_|4M,F2CT&1v<1Fݡ sb|PBs R7>sOVL4}:>Ao{Cz醟9aHϻsSƹ*o=0ȟ仴HyPj`, CL6́#-PI-ᱽtu!ßbs re)ʓHp/DbTU2 $IF6*QJT2k/O #6/&mLM\v { 0&U'C :u;>0hऒuq#{݀2S(9oN̆yowv~:o7=E?0vO ]=9p:? q+qfPOb5f֍sQ=fac7 zi:R@'ݍW^pR%!b*׿+Kڳc5m?(^y<freP?m%mlǽ"x*<]&d?0Og؈`[qE|Ngykw̅`CĦ_mnwLv3;GByfzۑd0\-q:*t&ڒ\큭 T|#H/+Gj!d5!!= fs}H=T @")m6]89WبkZ&YuMlRJ#y ]xFzks J>]zMzՐ)Uw8*zC^m}ݜ:.os3pw_N]VcSK{}&F ѓ d!xՋDT 1Lsn]]59f???VhWGa^WItMwc=i4Œݼ&q蝡SlERoڙfm*;DBtbcCZIIq#,Ǯq}k]Sfܺ\gب ?jh'S\׀Mvg) Z`)7&fbvO"d7kEt5lnFo]s]> (Y?]Re!ztx2rU>ڜxlȹ+gbaڅy?%ۍ*}Y+ !X(X}/> !uGn)IJD0G!P!* 9>M֭d![XIz.s֩;Xk OSJ=(ρ]2șŃH8l~E9I?^CDBNcdo}\Hz&,P_P?!UHXHUWt^5?uyLF$$DNUG2KB!%QJTUf7_ln ܬbDZ8L`E%MCa'k^9򻹖R;r0`{F){UuYyUVLX3? E&a?qmM:HhP q)o>/ejѷp+dv7t֛=;C̝!!3_Pza퇇2G'zr_k1BVv>o?;'%CBF(wĸ&9 [%(xdX٘m!, 0w b86fkdp֑W{XoLnN4oߜG|b!%*!DhKߚ>\=g;&RcϚr6юR`Vo8uN^n%%QRʎ(.4oVJNLJBiM /xn9>D9#^;M|sGLxx6 o"B.jkUHҘL$Cimz CKrd Y∄C&!$sRYul)B-R<!st{]aݪܯC quy7\<CJ豾M$\z}ar= ǕRw@oCzz-obB/Կuz~OCʁ\Z:|NϙM#ݯ>6y=HT+G:YMn7m#rVA/>2MO) ,UJcD%*QwJ!G l ؠrOS<׳?n[L=!pJ G=cm ,`$t2Y2I9 IDAT١m;hpĤih~5_.?n/s; D|ʙ:\&6W8ޝwq>jcimwҝ#!4l>:ϯ"3!=e!d6cd]4u$s%boE@h'|x-W!Rtc|"N=_>4(@wyQ?RJmsWGBF!F=S>B-&}04[c=_x Og+b4EBo흂$٥[?v#S]PU^?y;/y'D%*QwS.A wDmGx#.% )\ #t7go5(څǀGHj)ۿoMro,{3ӶsbVҪ2=۝䎉o1k/l=l'_kt [:uє =}u}Ido-L`7*Ȱ4ݒV#wuչlm]9:yo'[wWCGZ3zH < 3t!HLL ӾejUOM37>?}GԢ+h| PgOcst=i-ϯ]!>N_rNY=wCCC aI%C$73:B݋sQ][ٱg8.ǔ8쎻tYI1sJ"۶=Ձlrۂhp(T?JꩵCǦ k;F}DbTi~Lȗ{i~U<!QL*  (fd3v!ÐM}2blE9V )YB2v\@Vm/Ҙ57n^oBHbs t\?kݯQYbk,;b4QOs?ro"趼z5i噅"o+Ɓ$~Z'1؁9v#rקݎsl0B>@thxVoBkEz" X5OB[u:!t_4C2A(N@G3E%*QnLfAlHX.oeA@t]ЍxaUv[G Z0T64ӑ壱uiLd(o]UShA;̰oKlCH1Q{𛇟)8rk3@>b{67g-`٘}דTt9ج+lUI90nbPݓ*eceSr&oHXĒ[2sVoZ#Иךql܆M <^r#puq]Sl?f9ՙsբH<긷ۢʩM8Ǝmi7m|9u'4n꒯$TfZ[}ۗ= )ԫ osTKC>[=k4l1: n4k]ڈCs2B}17fsZwaS;=Mq]1 l2BS#v>K4vs+ R}R/ݏpe7R7~}48lLX pcot-묐hCd1*L)@,no}> M)][0!mkGM?%RԾ[jDxZqcUW].$^a8%!̻k'(<~aeU6у`s/[ymTLNGG܆1L-~sVLROv9!'4wlK3ak\{RH~-匬tO}+{R<0"QRͬe {-g{7x.Ƥh9ޒx[]U{Hȫ:4oj\%;sƎa߆ dsН3kgl*20ں $2*)!鲷?wUE;n:AsH2 Ā#QǙ1:& fEQ@9ӄn]w.ϼo]zݽV}=T9]콫~!;S[qחS-KLCD`,FW3C*h'H JːE^CC:kAUK[U^m~4ME$mV#БScEo@|^Ȣ, C|"@; xLkRjffA*o.S_@R^')He`ZW*EL/7e5W y؆D^F:D#kz8\HzP6vѦl6$:Y5\'MSgM86Ք# 81! E`'~݀inJR[g:bXWٳ_SVvg|#{ #}=p#,wjxn5!͡VwR?)N6}X) `Ϡ1u51Mӱ9 ,2 |Q|~D6pR9s?}0}e/#gbLLCRyC:8H@&~CM|[CUW=L!k2sV9':,Tzg1JW\ kw~ˇc\@czKaz V`j#t&1A db)Q}㎰ȃ;ŇotE:,v!mJhʏ8bk t^';h?mXدiW ѤǑa[@kTJ5!Q5soE:$xy$d"PbEV{|ވKH{n2IQ sBD.Q/.danKD:a[ HDzz!TD8uu⩜J!ڨ|mF H2`6-H7{;H`6+~}==V#xGEPI]te3X.QJE7! R7>Y(Ӏd,šwAvA "^xs u"<v2?m0vEHb>X"ɆyAt ڌ'mBM{> blʛw_+!/kYdFDRVrp)ɵaNl=/?Nc^YX⧜Ld%139 IsJVl*]6LJSڻѸXh6ю SZTOt9ùu}ޡI9MOxO^8.(Ϳߌbҹ|v&!3kƢ'2,q vW{1d,BA` #d?i;$&T'֭9?qB nͽ/Rr#K2蠭Zaw$4TPA]McJrz>GN(&HU]Üqoj7P5.ѭ!m/}.c[98[yHxqأ=;l읃7kу3y =j:d"Y>3D,c@B:ʻMJB4D.V+>90جWoJ(mBԜ٥>ϮK20pŀ[*ܸQs).* i'NdfZXqM]Ω/G=zvMSJ{#o΁As| ,wA@(؉=_x2P~2`NԨ?%\youZc- u7?$n)Ωjqu"A{[b\8-pL%"cmC[#8J Qk]~ۍ !dFEHD 4"f!$MHyDHgm!c~"!ٓ HG@:V' J'<_=@|Gy  ƕH|;H0tSZWu0\)5I㜌"B5!:>;ܔkw=Yi۞B:g#D MRMLXD Ur""#Q[b$"lyHP$ʻєw!֑P#.8{I Nrr?6Ɖ$AJ|06/]NhmA9:rMNد^a[)}G;m a[I>DG,={mةB]|;o 6# Et{!BU"Vͱtf"wb$^PtP.dZq5R{OG ,ɘT5rڏkk{Yo2dtJ[lO\r_%oZBklB.5Pw:=V6=f]gZw$U;<%4PߥFW̎}7I0(#峍Jـm58}.}_O [$"~>T&7!gҡlQ cwhX al"ptd`v I9$2C:ŗ8#y̔3D!8KНI%oֺU)D&# mDxnAY͵"C#xB;SYv$(ƴc(I3K#޻kͽ4N[HhhR7ُv HR2j \td ܌j;5k ̸L[Թ!JӐ~^s'L^2K}B)uXk}E,b7S}6TtĒtYx#'("rn^(}9.'jX? ,lR%.Kv/^S1 9^?Y9mKv,&vM ނLI "m9-ߘ ssϸRG0`x=KBm:1m] x{y%s32:[V&TMtmzo|.mQ}yԠǚW_n1-we 0~ 2]d"rIqN(unJ IDAT-?mx׆GĪq2ǔ5&}ˀ%Ə~(mk\_Z6 hv&=6;:Y~탉GȰwepWDE=FlKݯ[ )OЮۃluݱѿTñKlS]ָ냧+k]jK|=b&:P6=)owcGL vki(/>o։ew82=A^1>A ۈIn&kNfqr?U,Ұ)щ4\|C8?3ޛ T?Cൟ)99%Ppb $D,o!28/Op^M=q<1ʦ j,Fgl,+. s٭g2X&5E (ݧA: \ЄjD_E!dh[ݐG_IYϻ`Gsg]j3椥.wv^|c=v##tJ~\^Rj@Q5JڠrbqH }5娅vo Tϵ/>=CT{/u5T%Q4?J{ckٙWޥoi*6V{A{cAPfV*}L*N]R:REnG@;W!\o,AH`|©'-5Ukn39@Rm HK+ұ@B!s'e5#Y4LF:h!ѹQϐ%zjSH䪝 ho$4muH /0O3ex/Z# L@""vS1lηF9W(WkO_cHg2ײ30(s6[9z.5'x;MxbG+#ih[Pl2F.eC |\OMARi!Qz VJ=>s+E,bsl?ho݋&Ux~NT .|VXJɱ x;[7Y';hI {OOW͵}w_U6\yk]:%/@X|i%dOBG>Gbh-'^niv4,s?4'Nv16Z<{򱫜f;|n殳;s{ݖ gTm-='Ňd\` HT,i.gjsv׋Cܷ(}B+Šf]ot4z W:XԞ@s&_lgwRaM[&hS틋ODdk2^XA%X+[ /U躆ʨ#z9J^$ښy\B@SH?J:N5UZJ7jGFs|/dW,Iہ@Rh>I$7մuҧ܉#!`G)kNizDT-0u|<#!B"u7/`!2@7ݓ3*:Hɳڀûk%\ tq֛9N[FNKJ >\ToqQ/[xʴis D+ 9 lD1GhTK/ݔan_ہ+-]\LDϼW%hҝN Ӗ1`Uߎqo?i#\_3m}@Uy!l;nW>7㏩NS2ku̘oZ=ZhWDt3`~E0~[zWtMMtkUI:iWY*fk-*ޣ5O&E6wPq݁um1eΘv+΄φ^]3"h:YkvWFj{h߬r6`IS14:+qxeI^ ucnWKYC!W⫢:6&t:rZzۿңsjM%zT PęoeDN %l  ʄaWܻ hbڢh)ͿE`1bc>akHjch{Z/m0!_'2D4BKM@@阛h\;=@!uGm~And߿ u DvC\')Trr@ {9lS&8#K+M=#Q㟑^l&Qgi <}zSzܴif4B2*FRl7oA$4\)C"Ӝٔi8sڇDw-l5mkX"K;> !xi̡ͫQ1bk6,_ h JIvT)k>/~\}w}c=xoI~0@9ڈ&ВvPm 4Z\ǞۭufE)q]nb 2XXr+}-kq{&'ꠚٺG6)G;闵nڷ#/t`K4զN>8(+`ƞg)WZ{o ;c6vmZ EMxJR{?ӿ*->:mzb^t5P]k m[m N{0.-AYtl g/X} xמS6lklXi#P-,QJk ߞqvԍ"6$u2 ^,YH,e漵{?! م *DގDDn+3: 60|4Gvf/3`>$ʚcy vBRh?As3mgܿc=yk *'_W[3q;.-i@}@?tT~{tsV}wHSpІ;u?yacUw[1myjNcU$cF|sؿx[Wt%:2ڵ/^l6-,g`5M-uME[c)`󺶼Q*8JY!.5[_{1S{-wsst:Z氝{=M[bSo^zU=O1 8arvotsÏ-4E'-W["ҩGG uwṡd&VdnHU$qyC"@K8=r27/͜HZ^d zf"zy 2<!@)k%MVS^WH uPeR6OPE822ZJC} fC;JN)fgJ);ҴOwfUg$bXS, HLۏľqא]\GWm^z}qM8]-~5v5_NB"KsvgJ2!C6#:饇wݴ/qBL4shL>cQ rN<>Aj◷9;be i.o{z'}c"#+l#l+c6+oX0=C '` χh}wd}ѽKX1pgh Fj2QfbePTJÞO뾂;?IӒ̛_2nj2λ}s͐6ѭdmɴ=c\'vtY;_f10$WZy+suͩ.{{Ł^܇EXOeglړ柶gi{` $k5 mfzdֳo\5tkmCNy65F͑5_$Sr wԶ2ֵx۲nxzPqmXh'h&hb6 cňKMk]?#E8"i#m+K$Yۍ,@9I,@ǎx($ղe =TdND \LDoQ#Qv$G04)'RT)t2X 7u1w2mkGV uǛY SǕR#i Zan0D)]y9n#qB }hyHq(b $}R5HaSƳMEIdSeڦ)D"EH*)-svdF4E,b2]@Xz[ h9ޡ7lڇs!=XX[1W9}8hu3tU[ZMW1z+} }ߝD2KH?6`8~ՃYΰ- kP|7k=LuKInaq q?tvX{^O||[&!: u ӞUfMHo 9|ǐ̦x$өlR}StmlM IDATMhJ\S~4uCەs;ۭ6٣$[~3 :iJ&zm>xQ^k%ښBnɖ4C.kaO??^f~uo/_ͽ7un`jjw{ꂷ654wGod~)ധkBݚ45=MF'$`-`Z"_ߠ*,F5^']< dߢ<$#|rd7'f jB{#sü{:#^%Nk)dzyVq;lKF&׻\c}6D_30Fж#A9{A݁iئ*DƊ:I1J;4'#ˑh]?$a{HD! v/jKR=}ɴZ-H0CkjvI@"ZJ=lڨJ3K< tvϯ@6ܟv֛X"l H' #NVrѱ/eM; ouEwh㯮r{^im`rǻ9,'8' lC sDϻ!kw κõT1'^ƴ^9eƷ|EݏY4vuޘ?m=i6yPK*f>V}Ǐ^44\*=U@Ϲ e=͘K.H8DO!Y8; g&~v^llR돋ܞ%}We T4=Pw~S̚V$Ew>qH>m5ۻ8C'njt-.$Gβy-u/GǷ%!&!!{>?4צ~on[b(q_z+'.{ uث-ѶȸE^o?f}-Q麨a餼y}#ڱ0y҉_㉍L][q<;>j@w}\51G 9~yM$w"")ZO@ZR bj1vALC <+TmYlA a_D#JCq$"ю@gW.B}EhZTN5Hz v"$W ZR'1"Ҋ@Ux3uϏL/GچZ&}h+ D8KZ|T""hnb+nfȾ MۥjO)i3/2 @VM3 ">Ta|͎&EEDAxuձ; ;M=彦Fv*@p KkRj#^dE,b(7 x-$n{2Og:;FT *@8Km^dz?e\%Z=[&V"_FrMH- CH? ҧ#32o/p?Yt;YEcu7L)OT EyMTٲ><@ZBzQ:mCڂw|uzk osEM#}ʒzv1K/ut= ^[2ڬEѾ5ȘC k #Z'.|fpFh`ܶzp5Obܛmy ʫܭsT#u)+=>v1:M7դ4Tۆ m=J:ʏR{G!ˏ^~w2&ó7\Gco1sPصfiu睵`fm D읙3f@>6]/ʠa[nJ F(D+yw?EW,F_e9sHf"J/#012I'\D @+Kh3l D. ~^ؔ!܈t-HH#?څH:^'<6|$ \JS̈́#c6=mw26ݤŤ0mE{v7ƔO@:KLy0#e-^T܇W@iik7>dZd2ux5m ф#툇nW70Vk9ô֟!BRj2`E,b)3 ]Qa1ס}b^zfʤ7g.9s͒>5ްYfvC9|4'0DhJ$͡>LmזĶrVN>Ju*X=$.x4NO:l^j>w_>IݧQLpr z5}>{t/s.; ^e4Ҩv^; 2kIYƵ3>huiۈ=B|T#)ۊlun^ªGw\%;-x'Y܁͟1۟nvx J_FL;ݱ5VWL[sfo-Vb6jsǜeeM @vDz7<+WA%,qt 8^֚ηp9ZdVe@{j7p O*X?/w4Lmm8mwq,۽س3gL3|yGYmwfR&bҫ"RXPAB vQQAE*X(ҋ % IH̜<ו+[9yO;K:۸Ғ;_M(*\uhK~m4dgɸ y)|EXw0Y JCJJmBD e~'`uˍD3"NhDNAa kYɨZgGwG(1kWc**s".wr."#X|<"T f4"J;Ѻ~ڱy–#ȍ]Uy2Orҍ-ȫj17uqyAqMw)Ep\Cjnv}&r'<Dc̣>q6~~GRWwn(w"DTf<9h<,In y"Tx"YkQtƒsw~gf|ڿ`zMr¢I9 #铚Yr>q_j>ڧ=7 :]m>〈Lת]Iw,]cq=wI)1UʉNv)GXqVq87;MNqQHE l z͋xf'uh6kK-Wk\|)o/ܚ=_o3+j80JLz~6}ظ[s;njޡjbKm _>=Y~n򘭝ym/)Kt}c<Y: E6v7D_s->3HuD-оҶm\izVс@o<neafq5zhW춣{i壎kZp$~7ٻ6kI 8KNxp[0Y JA^&k. L%.! ˀzƘ(=ڎm=>CëP[w2D{"Dċ9h;"`m.t"yF{Ts}⾏Aayԝ{"k&=c)(L'38"BU|ksv1f YF "׏ZknYB[ J\5aH@V<YgG) 5xsӐ"0=HI{K䅼~LEUֹ܍w,2""څƘ Y4\ZZ)a KXJڼiwWG6oZ711Y_~-^δ; mY5hH5D Mpr}<scLZj6{ ߨ?Z{ Esޅ9GOHɌg{ 3n .c-v$g:*RwC hkɅ#ފn~ʁ}~{E4y|Uneۍö2x{ &hpa`$≮GG6_`3`3-n3MDI칹hhˆ23ϮlJLDR>ߡޔ%)U%\1SqpPYn20s-Y0izK:''|4*m~k[d1,8-@\kJi<>ߺ=(INy?UCkLZSZK$^@%{^&f#X;4DbQєb^coy,`Q^dM@mQ QwPOcRQ0f^.H9 g[9nf!`<^dׇ,ch7桪]Ƙ%pm "(f{F6<͍G+7XkwR!KҀD]1wXk%,b1{Q!ʸMALkEc5[^o@1b6  ccCd-$.gO|sO_`DWt[!y@Q#cR{-%~EyT6Y}rwd-<OLÚ%;c3{0cHG\DgܱfsvXݞ{߂\q$WHr e D"L>k1<}ϻ gAXA Y@~h~gx./nU465X݉f`N;sO. Tt+}Qihv%JANVĚ)-=#} K%fo E6K!.}ظgr<2얡F(\d0>X3qlb~Ȑ: X}iFhu!w{*M%%%QG*c`@m-Ud1,E R:gi"PTla-/ s(Lf F7 9mp-,p<#D͏h" +-z=;*6SֈlGd0ݵ;w-nE_Evrמ뗂w5wVng= |e[;Ƞ_g rcIPo@(7uk9][;1\?FAo ւƘG$|'ZkcfPԿ#2l\TgYϥ͉׬dž.tLJKq%,aoVo($fp`٩rj͡_1OyRV6$M#^t+o\,<e/fruw={KVԃD"?_r<`X0"Y}~Ky`,A>"&n"h m xV>[Vܗ{~5/{}4_yYYL^Cկ{hޘ~'ȸ'i0:eyG'ӴۮRb9 ޶ Qu4 QH79'`,}dlAXE {7ό(䬴k(y15um_)h ހ0?;߻EZvkEo+g|삋o/<)pKew̽`70UBk‫c_kUD]p؜>lcDzćnny=Phͼ}rvk:і,؜GXP Űw-EE j=#7Gv,E^DB޵оgRQ`e="*DE-Q9:ݱ]q>Dтw--Fdw "r5>zhKOA[ko4B!5M[{2+խkhq|Uw ckBcsZ@c6A.Dd1 ^cNDy <>Do*&R]t{1nc&sϭ&EB i=͗k|׷H7vG@ms1i=`c>CnYZ%,aӋv/c O&+p FQ/7%{=ggrܮP5opYunɵk֌8=ـg#ѶTH("N_u@ }lz&ov#V?yi[|ER" 2X _{06ņKVf];өq\xނo_(bݷm~'llb.Vo.;UE:z{ՋZa.xƲ MJV]~ehsx)o@इu"L]4 N`AvRedkʴxesgGO{;Lm5askrm2qĨ9ښiMx<ޜ5dTor8hvJ[ q梍[5vg> 噵^ٞRh̵=&E3>g?8Γf˸ߛj)KaUYd_>lBc!m.@qg>>l\6vVYMr"#lΩADj^FnDs-)}cJuCDИcꠜEUC̹hy_.1}?C!ǽ'ADVoF <j _yk kJcLw{PX@*)!ֵZƘkmZ{FϏe f!Z[tAƘ@6(7?V@¨x BJ r2Yʧ`{iO>x#@i<-{n{#܏C[;2 G'{6'([!/EF_SqZf__#jI5jPCNXE|>zg%͖\L|~a;Il1[Ib:t:9W:r]^lukG{t=p8 pٌSւ]NĹW7woD$k. 4~ 3q[J2Jߕrkk15&0٘uzFuT=si_nlx0>+v~Qxn/"cv #lUvDguDmϘar_)iq鏵J>$64*H6і1?%A>k ˿&͞{FƘǭ21ƤߥSC<䙛B73P~KBDZꗑzb%!";d^l=f[k!S%Ͽ\m~q"a@TD"`"@WܨgU*%}CǸٮ0PXK(GEq4 9a KXx KӍ eZ-l,}u{uw"ugZoLl6d*OmItۓp״wϏ;ac;ch]:3€B V0j[ꀰ1hTf/$e}Cye\b,YݩoQ}}:Oݝz_Z7Ϛl6̊^e`gGW)W{_^딯,asqWv*kLF";/(*-Y1݀stD\ʇ\k!l ȸ1CjFumqhԽe1TORQnJ̡yM:̸OښMOxc23$UlϾ& ̾pdtǴߘlY[LoRDmCʂ~ߦ뀜c,|}Ϡ~̾YS/;|rٟpLTZXo&2TYX~c"yl*uДkQ{5ZhPƾQXY@nAz|E!OmhOx"W-6'2Y~E!nCxԝ#Jk KX(JR= XkG:1 ]y("6FF[!p%W.%^|y'>՜<-ڤQ>̹MVuꑶ3~Iݷ^qw(`(2C/ \e"#.BؼKE1ۉ-N`ʊVOBd` '>oj;[y]6O|ΝFvk/="g(s๏Klkյ9i-Y̮3w(&T1GPSSPEcoX?"ĕ%o )BZ uߺLD9E*1&"ŝW(38y2YXd7?pӀ@1Cjx/'֛GF)![I)/?T@kS}7іhK@㛁_3fELQc/s忙,~rbO⊠<t+vڬ$B[2 phJXUB]<UHG`Wc9V6D!xmF j]D(G0TrC!8Ir@{ Ztn"2?6BĻyo" (cVF@u?]oN6"LYƘs6sܵ/wcHZcLGċn,CNM߸-Sz6`y)"c7) ɍ(Di 3!"V̇KX nuǕinĂd?N#fD=#cA͙ vp4默W-ˋ9D5ev*|>p-ʩ2"ngQKQ79IOD"Q had8~$=Ο"ࢬ=5Zig^5>OOݜcb"~QDX^⛐>kGVLᑢA?>|d2Z a✰F$nQh">;/z ݰ@$W%[kH5tqm{Å#֡b_LD",ڍj}"b:WyOhPePd 䕬*ڴ"9Js1}|ke/q((/Gj;,Tf]>C'ETlwqW{wlׇќ8nGS1D Dwi:!rӍk "cL`+4υH1y3Ƙݶ!Z\^|/=mY*Y?3'60Guf17 o?•_QS-.oà==Lb a~*9 l_AR/SPPDFaso*k87 N\D Θw.EQ\f랎'2vȭHlkT)ia&o]}\a ؿߓS ݧ9O\$Y S;-sqk4vݭv!lGl֟>6aE?3IW_<OvwSz9(%,FӐEcmZ;ݐ:Ui\${1&)%|J]a7nXQchQ|ZR9&=1!Z8>G}ڹȨp7 XC,܉[)BD;"[P@O"G y !$o"@]އuFȻXL6pnG,BDkc4"9u,m탬n>4޳ݵa7ՍQ{!og%(Զ|d Nr}y#e R6,~ (%ZTEָgbu,+[FIaH1`lJO1툹>Kgil*},#QsifTh-{ڌS5ݮ(Bk#/,9 OqSP%7`/BO}؂$tn| z̡gkIEϖu}(Tdu> "T yBojv>W?}Q5 ¹C x8?87`=cDDRP'內J=~2cofM`Wu기)=o^G5::Nˎ/Ȥ׾*Wcnz֑jnTgͻ[Fuv>>5 -\T|J= 7aύXrVz]&Ϭ?q8-wA.~S 4>c`֫G^l%uOE:3:xzՒ}RJ{1gD3$?rg%26+_"CF͑k_I_ULKOCыӐ}]<1둷"7ƼԶH~y$!Ƙ*_)ֆh S-"sPOW$.D@ f9-mQ>@_ EFD*k=g<5UC)ƺv|(ڡ3>X{$Aa3U^yuCd[qoA8BWr+"p&cPqP(O?ЮƘԕXk7cƢSnBw뭵Ƙ9n|C!#وuoKK4/PQG#1,Rٍ2,a6 XۧQIkcʊǦ֟0vT7&Eƴi p3Sv3o|Gv"B^Xr ŸHd8It 튛{C67ZL5]TnǎGF4PS asw"#j\| VGݐadbju9檀ۋG啕߶J=n$sOkz6u||:?w}.>#Yt$*+(jEz6sdh}Ϙ\ko}6#C1k[1wjshX#?)._zcr=ꏼqeO6~y|AW2 =$3FwTq3W4ϝJ\c!!5MѺu%0PFClVі:w>ho9\'RIZP?P蟟R6jG&\Mz~Y fpl/"XI̩9|:xg|Йq8£$6hC4"[D "=m\ND9U6Re*$Rb{{/mDE$ <6ꛋ^}ȷ;9'w,KZbe|` ù@L1ó@hpL0␁x ; ZF~bđo14\Us16Xy<kmIz e9\]kP~L"h25E/HtB!s@cLT>Af4^LF`Ox}KzJ!uG%6ZMwGFX!+($L$" \tGsq:5Gd!ZTɜi(Yomʭ(\u5/RJfWmz6"+Qa3ZՍ L>RлB!$"dI>]klO&GkvceȂ<ϵ?U폈 7viMA}HXڞlܶ3Sr/&Vߎr#^µ3 Fؼ#<=ϑ C>eŰjlN| V cXl8 zd^\() ;S_-HI܂&f."*iHyByLnF:`}Ƙa7֏~JPnL\~f # yVZF ]+4nG!"_;\:);PxJB^]*DlK @r"QDм܋yƘű6݄Q"،7!+}16TU;Iw>wA^"DjcEo]@EoCuHas> IDATօ{ldu}r[4ƌDᇁ1q@kOU-{ 1}g-1Ƽ7WBY+wXk"/BdDHv{ Ű=ͣv l*\ԁ؜FYa,{W%mPjhv}PGWGA}rSr~[{nh3[/|f3/M`ELm2rm#xpG!,~3ߍ坈>i{]~ny e#zos{jw-XEUpD&8_ ;)'ӷ_65.&_21줲 fsd' كEqȈEcCM?0Qs~Sو^Bʨ=y? lx5&f!"REVhq(G/dqڍ@2+?p'тȳέA֮8P2P$DBa! IEeQ8L d܊\p%!څPi%Rn)펀ºcCDu. 9"yY#;g "g=\ۓ;ދYR%]iukAwHQ<{'18ލϺJ3筵{ >Z3ϷzU~1TA!S{Bc kmPZ[ KXgddT5y=Z>QY޿g:eК'%I26a<~<ޓDP`5`17vixB ȾweCf v,>An1W->2Ox|Yjm{4`Bv?ʭgD!lJRK3*S}Cu6ykYRbv65xh01iXZ]O% m 58f?(|Ejs?S>S*nˆw;/vچųg?#n?/y tnFI22ޏϐ26AAVGY?)/E C6|3j%%`܈[I?zfEؼ=}ظ_1Y҇і-d EOwy ԑoXza*8DUD 3]SKt&.xb,3I|ģ"D2d:d.,Ђ<"ls+}o1'[1 0ͩPV#bT(e:o!ܥTA@Ha*EVh^^FTFP;t:r?Gob~ EݕjTT;v!2W? \1~y}:ƹU8EΛ7㧮e@^-=x^fY:r4߅6,a  m+3zwAx\~y2|;HiЊ.!/Ca;bO7e)sxѾvfYjk~MO[Z5 氵,J!cj4- g87Wo6E6nw,6:,T4t5q|W/bW:IiLo_Eȝ(jW~[zZ` ;Xa9SgrYCs[z}ܹ^{Vݾu]۳0竬,.3g"lDUcCuHp}(AI<ֹF# Cj%k/uU@xy{8:S˿bZB; DoqAvRqq~Xu{`ysed(:iܟriل6Nx6mC% ;~$VZ(3*cC3/\'3KbisziELP†Ȁ}WClkd<Q^!8LE@ *cT8Em35ӹPgK9@U aIW6ksfTTp$!vr~:|q[!kyU-Mdr+o{Dsd䔳,=/qO(= afDn @݄Hʶ{@65 |HضIE'G}`kz#i'E/{g}ktnPӻv4ގ 66McOWG&e.`teRJ.,)oXd}qnB_|] U]wi3|c2R"rlbp\7i6 X,%]#lw5"H܏@Q3O={(wwگ2t;c_:>,G<ΣP~B{,DVx/!" MpZd@hs1T(\ȵ?+r*!=&G*,A\i(mۯ8>^t3ƬiyRN:c | k k"&c1D$OG*"7^[uG;*s^̀ ك줟u|~+PW~7̘? ɰ&oL' lcYR Ym֣jPR}Y_V /.*B{)пηҜ߇ݏB2x΅-$D{ҁƘm|^y^CFi iRPC9-ZLzʺU%"|{Er* d5w9a4kOY{`L$Sa 7]5tӽsȻEߖw f@ٱXovd'I+IGfyf]??i fP?ߙ(3O#O˟ew T"GӘ,AT,G/\] t Uq,G8e1Kc>)&!+."/0:h?ŪhED=4gjPau`"vh>=YG ATx' -.Bd>&PYg4S; E-Rr}N61]B&(5ןT?I>ǡw.Fd͏EƯXk3l3d0lBylBKx`+̼k*}qaa0 {qPEۃT`ơu%%UA5bΨ~>F6 U`;!rOPί2,m*wƪw7oud@ 1jE @{N5>W^tMπm}7 5H(QLߥDŽyUR8bGDWm3'+yW7~7uQi)~YmqC^CA"v֊ܿ飏YXzF|4%x߲s,_Mu=/ܻW7%4,//s{c5}+~f;mor7خ .qƓsVᱩw]XeVh:Ϝt^Tl(jT4:E(vcAa],ذ`DTЦdz_Έ$&1;暙ssbs&ZCh=ؼD^p.O0fY_wY 9׏"%!SK=EM3JG2>))abFpHIsyR?yVGJO<$d]*"RQÀ9v٣E}St7Gi"ys#"" z=a[Qs\^ɷhmBN(J_ROc :B)1!]ů]b!9#RX; Br!r06W!yr?vEQHdVҌَ1'3`se^O܎ѿn !?p-rZ)'o>G/_4۵^xm8v#/ i^ [> 1edc>Ny?Ir35YI~zMSz *|R3 g8+ ߥiO+l$Łwd5T5jPeN*i`◙پ¤J^ZmK!>SطdX껫˪Dj;t 7]ǜ0xٵ# /iz'6ӷreY͞FJ dD,k͖oopkи9X|[槁~۵]nP H߹k2-#قy Zy[U7^FȹCasZ"ۤT2Sw1_}tƁ}!PMPGG3Fr΃*k > 5pEƺVx_wMZ!L  Bq{  7PK׬DQațc $yx!Jmn3m;[(5v*N#_Z}w3 #G[LM7R G dО t@Fzd ܉Fxh79H~[~cz"N(F̸6bz߈{ex\^d.6o!:n1?\2sod;_+wE=Las[p7/ ˻{ SfOB.ˇM=L6^'>v1c/7Su;+t }&HJw.lS/16:Cњ@7)bمn d ):mph ՙr{Q0xj-sCO|C4jN[W,#iCdhyuⷠ;Ny @: 0 3E@~v^mW[f=G1xmѺHE~!6S{n~ =W?=F^ig-4\8_!c;]wM?s]T1؃\"{Q4y#=jk c7cT(F:#9wH^> ~ɴEu9ɠHk&]Ӊ ar^vǡanT ChtC& 1{/p{%.嵭~h5C0;RDF #zP˒ N"B":,qG;m>~^\q%s}ukj/.INٻϻ {2rTXҪ#lGܶOvuv7n=kOQ5v䱽o`vcۖ2ZEl%k0:61sum8kp`מ*2( 9nuߗ hQfˎ}q8k tr@N|a6?c^bXfm#6d'&69fVNHOX< DwUDQv7-qzhQ-%EfYض ot:1H_xp$Fv2( Vn/dHv gGGsE #~9 y[TxC1Ju93Q@~@<4D^u(_B[3Tsy7#q;&H~E$ >kv7R톖C<15EH w5c o{ǻ=`銼 u~PQfƘ!:|zp2F1?E#D[K)#չH~ uBǺؼ+ ś,,"AƝ׺ol92ޮG^E1a$!Kxh+L"z"09E:vVSEi}MEi/wwȓY"p)Ck% y5@QAx$I}E;^C{qƘ#g`1&ESWXke1"נ]G1+kzVzZ(FZzɜAYBru rĵ#Z]E-L|.J7txhrrM~Ș[M_ms=';58˟x'N&y64^*硸'v1][F1jv:<0t9=NO~=0S`ot`{6HxkyWxlzI6dƇd z}<:]fLĸ#`JF8dFpCd,LDc@I)?6ND:%"1ꘊBKPl~9Pg"nxd|i'dx5ji<{\b}hը# y-kCFL(b:C{LAEs qPj\s 2:!)RCUh,DmcZ 5j)hbm{$2{o>+ km0a({Ms 2zs=wt3Xg QTRGO}Lɫ\$벐G%^T$dJ?  ǀzNCY'[h0AܯƧ##nd+ ʞ&,;pxɟy>rꂰ%ukJá|d ltª~VB9)}eiK~=};5JzG8cNޒӲآ % h=#K[ٍz&m)G 7k:/ɌXoaxos|~|-*KCÙsJ6_)vu-<Ƌ[ԭUBmK{}{pϣ6vܕy9'|X:~Fz.aijMnRwƢ!Fn RapcV?I&X^A.#l6/d̤Gڏ5G ed#峣Cˬͳa8qnߦMa#I ;p^$O֤7ЂZgNcEzQJ!7O΋jĹ %~E-^m@Ώnu9TYk?4|F !*pS^1-Cʟ 4y @12ps?w͛QhdyÝ+zu[x')NSQz4zd|U(^1 ʻPz鈏#E (Ck-`!Jd=7 n ݂"'!p4Lf<_ 61^F]G`7ZkcVc`Ppodh~S'%u>Z9ƘKܺ[cG뭵5!V|l(F>2'!D[>#&͍8KQz[+w90q پdZ3WH~}Q8&ɟ5rZ])A$bi7čC{=22;̞\ 9R- LSv'e5O7g3\wxIK~*6.03_f˕WؽamùE'O8oĖP v)GvoQ/W]_0nϓ%]6#43&b#![ 9)±eInWRsd+h6;?7%7kuDƢ5[*-\;vu:ܞ[V'7HxS7kz!ԗ3Sėw)Hx W?]! 9_/}qUO2ͻfAk\\2yUB6okjHM{cDw~mmA6I/1pK܊MAW=#CpT3 .f(oӯ )#Q-B2B Z(~b=R"*q("P-Ai6[qZk]X`˿rdYPZP'cLssnFF`{92ꪐc;v1E.{Tj,w@^^A*nwVZk<號CQcڽƘ8 Z[~1Q~r#3@8Z~ͧm'5(4 !$M՛'-PNxYI,;mY/^͜S?yݱ8~+oH<*9}W `+ ~9i$F itE&ԛZ}*wIU˂}ttPWM_1tw|pMw=1(L_Qp$ $T 4$Ϣ^yu?5m~QPS{[%މWE[L ٭,%;rє|- VQ w7d~o ǎq(lNCN뉌VPv$[װ9b*ʖXyt8(F05'5:y)/[hlߝ<^,a2֋׊;v8>Cwv ]t}gW_|cjүHyl؆PǔOTyַCj'X)gDZ洊[=$~r)/ojLE%!n)}ݲ|I~SsJZK,%^Ge%W4@{e_pILڟFSmϷg 2 3W|-A3_bX`Ȩ iG4CQ7NCH\"G!eՏT*#Ad{{H>hXO]qƘ\MU ch^@e 2.c4ƘEHz޽BBx1-A/FY~\R7#Z[Q w>=(AFi? x{iQ" BaSoiRD]s/>y1㒋!O6H}4_JN lm \GWqb%ݸ%EC*:=rE|_lݝvmj@zi#EKo_C .+jYQ* oG\Wទ7y*ʾ녌֭ٻ 9kePW|?y?5 &%}wbv<(_bϥpk3msC5Fӯj,Zkk1Mû)H-AKkD+Тڅ^i "j'ZxH e5Ƥ1HFW 1HE y<2vD#iڵ&7d 0N xzq=<$"KQTEF#^ڍay (x%s)7oaf ՏxޫR4fKZk7(x:2J=BO)0.E"ԁ3R "u.Ak~u*Rܵph}(zѕ9h}zz/17y(FUfO<ڍf\ eh $+)*!~!P5gwځk|ɻNHע9ܵWfa}YmQ%FuLc*KF00A:ңhB= 8|lQ;(C"kyS/qO꺓N_?_G21ڜsƮˋ<#?#靾 H+~nc#N\K$ؤ/n:p ذcnѵW{C/T wtܽ2'="(5y?/A8?[yK^ˊ/,3St<{OYyJXM+!x}.וUw=lޏ^ o]l>h]}=9^w;+2f'ȴ# ҁRʧ1!&ߕeݐy*қ6r t6~-CFMrIvjc$w 9yv"T!AVb $H6튚xY D]Uˁ`\$: )17T~8yZ##1صx"*[1G1+81:4DQPGތ8cL/TA7u* E |=ёKPu'"/\T 2rQ#'HACqxŶG΅x …Hj,Bu; s'4 ä d>]Q/'ZH|t"};*7 (V7%:8nهhk<*D(Wב1b|(F̸M.mA73feDmf:xB6fQ&!6};0kILSYXN֫Aru0Oas_$ x|*Qlb' <j>ơSc$O#ǬGbsy};zgbjuF,afL3\S̢=2꣭5m9,Z`{Z/DFhUy5L*Fؼ9;+椩[ZV~ы"0modwE,ͥˁiG ڂ-$'VUgs:և_i7=F%kn2܊.($ R@QgHEnё;m_E ۺܾw|:)KBy ?1)c[BfkDTYkWW[{"2#(mܵ/qx͝4w}ܵ?j6+UR}4\ҸFŸX'b)>C"{]ZPj\wMi0"/ihRc!;z3n#c2 E̊GX _8s6!rÐscBE ,63&HޏLG̼ebCΪہ]zיu1PaaߦJ! %=Ǐy:Mq/oCܼ3ϦOl? q!!џ~~X棶k&Y`Qމr.[,P1QުWvbķM/fGo8[IoߺunM^$B~TĹG?9ZæzgG:=w ~_6:ep!3ARc+ ,,A#y#ǧN!!nZJ8yLu/0m_!&T "哔z'%E~ nGjمÿm}?bJ6|5LdS$Keu ϖ-&d#KB?瀎',A Ƞۏ?zy["#kʑgd4zg;N'Q$c۶xۖ"w3ZyH-h/_(*wgBn]~#jdEo\Úh`E,zm?lFv(e0zǃ "c7 ,D)E|9hJˈ=^GnL_? ka 2X \?힑wOȼ ]g$܃(k ZO0g1ׇPDjE Y4\w-1k]+Z[ 0Ƭvpռ r386xK7c1gf4(F?K^ӊAx!r6)E@$`gpnEX|^Hh3V-'Sd4N/[;$!gZw}Ϻ{ 0o]y͵Ks@`ˀgͩ5kY@Cz]WmشieìI'H]ʍ5#]^[ז|}wIm0k'#ފ~mlkU*u7&ZW!=끄p˪RZ.6y`p㏷Z>cH\^һ_jl\[=0 ܤk#^\m߲->YZ Q'oiJ2fR-yaq9@.^35\qk4 ,ݩew8$)%rEuntF`ӐQʀƏ2jšiG\|l .gy;R7\~Ϙ0@;lI>w/1/_XtH-UHah$R"<TkV!$-Zpn_oшYSP  砈Zjo cq4@k-J9y-MWR EvhzŻ-\EZ[lEb-J m9څƘQ^& :TK}hFs1[ķy>0E@)B+o 1Qi)0Ɉt,w.1mP4av@1|WoT'djw]8OLEbs5yZ,%%Ql>'`lˀvɟx;g6rU#9u "ufLdE28g:ƨ-c/Xz_摼h;;oK ?t9̞c5s`'qz#6&gYmpKv\ Gy<0(603 gJ(j-sLǸcmʚ&ӧjeb:(sEܢCㆌP7:aq6~!&lGuhTu9W!l9>Nr5 $h}HFFޜI@BcюQX=gQ1S<⚡':N }cK}A y+BD }}隽\L.BV=Z[ k`B48mFlF6d86G)"\x2yߦ#oBC'#m⽕yk@Gt)ZQs9(1nvwOQPȝK.A% )\ !cL1{k1 ET,=Q4t/2j_FQ`fEw!@.us[kc."}s>PR'(J$̸P%2.GqiHF'=y7r%^x6Br,>ɍ|́y``{V'} 9rQaDKW6 .C</v +F>DFNOXRdЧU3@DC:fBR9;gk 0-[Щ w[6[ٷmxծnI%ƩeIӷ _Zuܦ6o=W'l|2{" 6L7;Q[IhTBWonN\RsDkmm+C]5iխym'f: {6{͠C ^6/d̤c&] ̵6TԤoz1}*rX'w{ 1CFMIs!&as ~mq瞾*>ɞ+Fd&jS2dԄUƏks=crHlΫuUTߘrM 4k)#~<*ǡX !oFCD! Ѣ_y2w"z*Rqd0fEQZֽ%oPrOjFrϷǵ@v ?oY02&Zkc oە(JuWPGB|#rBlD7ykI7v{M#܉1B `㮱 e;mAkc1JSM@<; )C"c.Y-Aw~wO+>R]fuZy:3\Bt,Nv*}tmw3mǁvƏhȨ E&h61CƼH* pM{vbv兾vϛ&֌~cH~Qco~ާ.:G֡HAB>1mEml Jh1~?J4-C܂7"73]VdkY cL1X4<韠:GMPoci) ^=c2+;KX`1 d= ~\"%#J,CJH6D4"2/HY:EA0dx6v@!E^jEFnd$ތG S HtceISޅscw_{Fä> O{sp{W55=Pk>q 18wwwߣ r^sQa{1yZ~9Hj{mf࣐,2ff"ٱY)[67E(@.4H>KEnBZaW \gGϟ4B|20Z 9>~sɟXb'lz d̤^8ew]yݭa5zJ/1lot(_\~LJc}g==6ቦN!l+IgIf9ϭoNgH{w :E"$`gm|ʘiNiz~K Js7,U[p;߶JYWhtXy[ mKݱwuqkזu+s'rtN:i[ܜqc6y![<XrÜmmOoηyDCfZ7טIg6o11kn mrd4/ }mnpMYߊ*l@Q A\2 3zghp&/kqϗxն]m({G@BǤz;梌2 n"H_JVd&1f40Z{17!y1c2}z1Hyo(MF !w4@Kp-4ƴ%ۛ u2cQ1bl䩜?~cL˿@o#/#~>+5cʐaP@Z;)zӐӅnC34AF7rbJKyAZ<2l/A|Rb|x2lAh]!E.: z;Ļˋs=M8wϤZ5 agnDt&E2r<>(.EQTx*Sݽ!:~d@kڸpӖwLW}mխl-b]5nSP鋎ߑrw9ic 3j{Skض)/UPRεX[ }n33"^:xoIϭ9&;׷OY=bpk)8p 1o-?7Bt l^> Uc6qGOɈ/ya볶#Gd-ۚp[}x{幻ҎP㵫,yj2eo(<6![16ѷ=zC?slըr sciG$#y!Z[65|5Z{k6ںt&<ÆW{X(avVi5p`W%w\]T녀)uѿX4HA&Zl#:88yEF"CcTE "eފU3Y@ۦ-02fE0ވ1gȠHFc`{E9SP@1nkkm1>914M7ೱH F;Vlk{Q]ECw*2l!Zs6-DRlZ;gw|Te$tI @(J  b_ņ "}ukC+ T^lHG%!Oy~|DֲzdΝ{{;9pnФԦ1䝄 8w-??<}aHwDrt' vz<;6_vcL:[-m9!Wڻ7}v r{AT;X(xt 0Z[m8s e/BbÁXk ș>@ ors*C%CP0;|;.l ]ɻ!6e6 :dtx#dz9d=v`,үW q0 6E:),q4z4,q 2ўj~;9Z4_VYmMoRN֩Ȓ;nL=;ͷe.O7x?r;ǿ9aM4ЃmwO >\T|ʁͶSݕ{Lh֊R/K:aOx]^M{yJ f'  Biq%5|Qˤ7:բe%[65W"af1U~Uv3 bֵI[mw@&uuU}:Y}Qhdme:!;yӫ\7'̝:2ܩc.>~zz@fFe5uCKʛojO`Εo ][.OښGW_"'V/2?Oa#&}x^D~Ȱo7$A柑X Ȉ/EσQ_o$(G{Pf`K7\ν;W=XtGhcC&(S כ0 UBmFp3)K0Z& wгR'ă h3({),FQ&(Z )sQ?D]C>AoP@+wL;$'^-ZL'6Xg=ҶwMk2[Fnb sl4 IDAT?)en͂6Bry6goRo7+y^Pfj>22{ cL3 Z m @!lAonXol~HW<ÍoMs?>oTS3vIsw =k1aKu%:>a|⹢S>(ޞPr|uwQ]VVqg{+Gȩș#>̍rbSv7\j v]>G2m s{Pc 9@-=3ޑ(Mx5E-+_nri;WErihihsŔ_6w7%.,뢲ڲ^kS 厑i@ն͹d l.֭kAϽ}CnA Y܂@E(@=1|¹S 9M>MI,6y=,cG4gMw{x '"l|}-̥}.o_~zS 'Ľѯ!}܁"(<ҏr$>w^kcߩ4BSxcL7{n9 Ƙp=z%jP6c[krT7r ʦ>#> E0*=JTeo@ zWQit{wm LCc(8Fd]H4&w7#Gkx3Zԧy3Pv,#g081ip{jw?"y\Ftsz!$j+N8s0Zv{BſӳX{ @ yF #_N@ϟθoKhBD2d{)2:}8ʐ^ kTCrxKo\FY0#`W ͂c9vo.ݷ(aG\wDys”Pkh1vE@4F cS_m^\_P7ۺwOxGMz_4a@+2 W݄P4x;¿ pt@ J !'(I) 7̷;~`gm;3??sChƁ75^"=5O_樜9?k۝WnE쓋fhꗷewthq(߽G@}Pnj|{a-KLʩ_INr3ٽ~PTBCa3m{^3$@Fp`v7Fך#T*vA= Y0H\6]*e!*aU־dn'6vQ=l6fj"5c<{,\tx۱iMW$mB\Xl.{eܗ@ߤvۮ2{hpJb-g_C#l^=?%xu'z9ck_-;km֬L|Y GwDZ=ZOEDx H7Dڧ.,kG6H9˺T8ZzƤƅ#ŵ+eu55o)ڲ]KtwyJmy|=2k 7xޅW!лi0ozO{W~_ R~3=1aKhXNKz?S:d|qs sZ /Dq`"'2?Iu+6uR4I wauB4VJ2~cQYQY^"އ?޹ !) R$Yȩ|ݿ_km1ôED{Hַ}%ھ)r,7w^(MƘC31Xk׷־U+w!5ח^B?3te(]^*0.κ?ܹNEJ?zNγ%F)ștǝ !<!LBc\S"P'"F|/h`[H^־l9*ٷ6Ƽ@Rs!0Ux\$#guP7x~wo׺}fk$GLw]o;Qw16 wr%izCdj+L6C2U t.@x}61޹w#y@ W)g kw^MJv_9ꓨs\#c3}Gb}go.$ڑsvX @<7/B?@baP9'K[G5vb\K_M͢+7vTCGUޥ?g?qrPm]e{V _똾?7r剡y64xp0T9U#x6w]'}ò⷟_>3*hIs6];kXY|":9Z\[F>K .qɇH6!8xڑ/s6`duZUVxm9>ˑ}X?ibԚKQm9UO6߈͝:<(]I  u:?q ?K#Q:uEߓfF?[&m_oN'JGRm$~mgȡz1d2" ( D`Pg+Ydt#}<22#hW{{ӕ~ה}~a!QT6C͵Q>ym6#.,9%Hq&4>; CNoR tk腂%T$g7r>!x!E@C[[>3](ȱE9 1.95HF ڭ'feSk@c^+yƘn}glkj *wpy+2Qk?i өڢz2[GPiet滨Z` rr662p' H-a3wa82^l}MV"l^]i/kb:__I0ʖe|,Zz?3SSO-|BRhxFvN|ǟw􈮵WL*ѴHm%2G$WG kkIvuYej]W a+<@X7:fmYFDuѲЀHEђ>66Kjxy-eNGFN^E*7x(f";/;kk6Ji;:m;us`UZϓCN9IulV<̝:f͝:f(r?A{!bd5TrL\\x.2IB0㐎kgV][   188Gŵ gZk;{= eG) 2~(p$nWd&!CQ|(Yo펋 E"J!ɩC zӤQtM~<ea*C3Oڏe m}4ψfH|b>U|˻%N~#C?svVeeM@̹`#K֕eLX9qڬi[6z%wytd^29m `|IHzg3Q4P?Qba;7H3½,&_* 7^ܪf Φ޷+f7m|ZSǼv575nؖ/->܏ 9yKW1hJ )[:^3K's($[$;x _Z"ہ%H`BcH\x_s 7mzUCKwl?oo'߄ TA5İ9֧Ī'm;{a]O89cg,s|hı٧e߀' kxJ6_(ipGˬ! vw{lrr¶.ؖR[f1}u]/ nN P;ـ6'͈ İb\jCؖ(x@|&K{'OVkEM_Em،)-ˬ>EWFXyW%Oa16iN@ܩcҐ A@o|zw_k>n:͹{;z\G s9UF-}UdL Y[-,9:KDŕ?|[,tr;1S4pH%FlSZw.EkmM." P6Č5A-xOC9IJ눕E= n݆Զܹ_D$wڰ/DƘ@vOv7KQm"⥣⾔De QEhb2p^#g%L?ᄃΝx+];MMl4R7I "Rc_Gz Lr=b}ŭi[WSjo?іn8ƘZ1 u8Ukn&;=9#c e1^IB=Pt\eHs<[I\P9mO,"r=lk.c{okrϘ}5ʅOGF"v@O!2쳭 N1^t'H6BۅA|b"=7<3z6{6("6C- $5_l  ZBg'̙=~obV֖k t>츉C%wAI;%Zʺ|a#~yNmBvᓮ$K2'FXVrp ה2:|᪌=w6 -I P~mG<_ AX}lވs *NDsin>Pt2+#m9qPVUĘ׌߷`΅{-1"̊C7[{'Zs<›~7yeVstqצlџj#9󒇧l k{aܩcpk69qQh1/Gy36ׅ>D?o]YxAfĸ@mq5jkk#ѸI_<5nn-{ۿ}58p`>X8g" ad;>R`Q\ p&6eXkf#F⟁ \/;ؔ6Osԛҝ8 ָ{^YeHEߺ.Ƙ=4@c'Rr QdvGv *!P^H^@X.Eȹ !)bQPbCTě/ O,/n{'e#l@;[2QBƘ?"2oU k}ƘkX\hH^wPF_"ި\Thna"4hԢ7f(-TKү,~^AF!C-(kBFi22(:6d@B]<);"kcƘWzt6syjQtFn`oƬ}l ۡ^wdr Po2k3)+EٮqgQTrҊQk "f(2YtijC#E{62'R^Y9sףgoQ9mO;eNC_^oy@eh Y6<c) z7:zƃE"9u_ynѽ+IA6Z(H)f~{[[oQ~}Z ׃wD@~$umؾb47oJ3^(֣q-7ߧ{9#li{t6eN`,s F˜^ a#<[;2dd+4k_I鿹CQIM`vJ"j o-U_DoЀ'uiច‡Amas% >pJⳬ*huxDϺ./kG=ٴ_Hoꌙup0|ZyoT簓o8/_#u 9xMW7D0 oNCb1J.&y(2PE!Gq™_'!lsODsQ܂쮍ߚXd"}2Cd yJ?h /rFq{E?DTDZGq&rZ~PcLe 8 Ϡg Nt?AŃ2|Z89Q "GE #|T:dw\Z#EB%mzh%6d}u(# 5vk ;yI[1[k25x]k'd@!PS >9NFpkTJz1f8(nypr3.ڸ{@FDr|#]Otӏݺ>GU 3|}+_@{cr!^K ǝA䧐"ҩ &o&WZ)1#5H}1$tGY(Fy şζOig=\};7˜DX*nvo=0q)jvn&ޱrYcG)_TG $tL]gŇnNՂm&OY{7SaAm<^F8S#)#}HODHR.pi)Ż͂m+ʋL#79r.Nhݿ?+yFosgX?:Zn>TC^O[|e->ݵ,;u0$EmܧƼueͰ’µ+ځ?"FMS&{ >3>$Fnw}?Cؼo()MrQD,/=k3뗺ߚ8es`ω(m"~Er6"PF/3)o㡨UTod}EXף݈~L. UH*w*szEv#BJPcFF( şQTx ^D XKb֢HnDaO=JA@P]zEvs/Z{1WΝ`UkC((3ԝ9]Ƙ<9Cwc^GșNuk6r Wd˺Ee@n>D9Ȑ,E ⇞wt7nunElN(*o0ZWM@)OJmwG"g0Y{3P)x;OӓQ\ ?DRhEPv3yA2wT r^}͛m /P IVKmDxPZ6V2g G7ivvs)_ƄR+zf,*k%%qm$xhխNBw{\A 됍assϥĂ~2a3z㐌a#>Ȗߖ m-*g]I/X8oyiVc AgѾO?zM0oՖm5 ?J^ϮulqQjKOa`۟7ス׎v5)Uڠ2g49ŏk?͹S<DG6op\qi~_dO|\m& ߄pl A@m`뢜<6죎" g2pG l@TcPd;p;@AY6[dE"9yFMGew!Cr{/(u.ʂ܋z[ky1h^k_O!c 95?FA(+Awbw|-CĿmOnDFpҐݩ`^AJwנzMs$Z~dmGN塨gb;R,Gr}oEa/n=oa(խˍ[(G=/: q򈻦uD;9>UwT+nm/GYUm.rhBǹiݺePV @8.Tή%*~g׳) _ |X 36%PrCu8ʲ^Xބ_##aۈ?-"/qYİPbӁ 0( eL#b;9>T7S C ֎ZScj˻\ߗgWޔr\=i;SwGz`ƛ'_#׽Qk߸4-\[դc.>~;g!k;2}Oˁ܏AU(x[w6 9Y[q zw5#Ⱦ(u=ÀnO̻ɧ!Dd'"6HGcwan/.TS[gcZuzHd`OG *=!J@+0W#J\JmC@C FO;fx˔"[ƘcqƘo!R뭵(CCSQ܂2<( 93?>pE(CwOUad}qd4=2uPdA")kn쮻݇!rA~_51FcLcZrI} D?Ǒr' 0܈3nmscz?&lC ݄2(މn}?u;i! W?C+y1zA[Q0,ģ&@;&DD'l$.]۲- y㕨rEzoJܱ!Dz!a wQ|6 jGPU <$w(7{sV/ z̓^OC|[nf}VIZ8rw#] Z؜was aEW+Cuݐk:7r<:MMz +=5j]J?);QNd"7FG!ο=n4EJJ^D?un+=eeOwC*DA+uAٶ"w&EA($= PEC7%K-vH ] Pt5yax)-)Nv>e+1"Nz#W[k7 Z2< s9ymy ؁J2F+e닔d_e/FFi?J,! c1&('ZkgcPd#kB+"[l @uD؜ok;a~<C 99SQJ5oٴO|E3$!`ťn:_2wxۛ ™<3p ?9=(G #M:cs0Pj]BŹh3"@% s2(["a<)o0pJDBwJ"<[挱 y?}IƘHf!pk^EMʏF2$و_& 1:Z[ey9 Eܫacg[Qsʝs5ʸy#+R9]QT>؞N(Pކ³ֹܹse5HGrq%gBJ;PYz[ G:pr`d bƘsPnMpr8[ uENfUHN!% TX bQ^AFj*Eާ"^]A, ,ZoW0wB*==(b˿<^$sQu?Xw[s= a`0Q3yh2 dbOGl8l<42:K5h*fCNyoے1+-T^[?pVSﱻC'4/WRx8k[c.С6P6⊒TT7)d4i̫/)>d%b{/W"l^CjaEj Px7D}/$ b~E,{tpse:}~DwK%~+nyˁ)q<8SN+)O[J-rwL]۹SǜpNdg$ y旑S8=a $}>L>mگp/y9&aބ'<2 Ed엥H^ɫٳc\KjEgx=g}dH'Sྊ<.HocXkm"[1 8"$ yCc.G9;zcơg*ݹ>-GQs$aTN E}P7!^BV܏z~W#Y&p vEp8 h97 &Bnx6݌1 2ܚB։a>9eg"s*w7;IrTvo3vkh3@z|s|_ YkwӁGRj6xmc.6T]FKlBMM;aft#9N7Dͯb $ wML>Hדּd&=K{ӭ QBö/ "lzw9#Pvcͬ SJkuY"薛OQ9|d'ksO]q3Iq56_m45p# >Az!½bU6 Olc,r$!<9Uu#Vc#8]9n 䗼yZ#g?$VHpؼY=I?y?n=#@9=2d~+.-%01fE*CؼxL8DFXS*d̟6 k־U2c̅iGgE9"c9e^sDr 5c.GFjdf9 (~A(@[o<" `Wdd|b+k9EAZ97^_HDAbeySwtvwG٢`ţl{k@vJw* wF2D%aA$w}ȼњX]}2mEmGJcz]Bh ^A dX䮭7r|䷽{Fכs ݳ>HuuƘ,7 AF֋X~{L鸆-;a.5G:bE(uui< 9 P+() v|"G13 NOkCR݃E>h*rw+cmb]B|0:y7sD]?`jHLy|]u9y=/YQj6g/5o_]k3*w k]G ۴M|֗ؼp&_ EQ(^g߬X\yd}xe2x.E"^Z[c 9.a39?ބc IDATzij. e^EYǡHwseENrb`E3Q}ޣ##AQέYHu4kkƘ~(;[ P Fl`!r!VVv1&ȍ㑃@~z~2tПn) DLp 7\A:bu({~Kn2EKHvE{=  n^Z~IQn,Qc'a8(n'-(m}{5 l_SvƫK-x3_[򠯲8WOpMҗe}{'+2>KAz>B塤O 8M*!l~.d#܂ w]0"=G6@c.D GvSrI7(OW$nRzeؖ[ ? %=I/iѻ,+!]]Qæsv%dԔ /^4m-S <<]3| hl!m^3Z=j=xg㦡Z Q%JagH.sF5U%7O_\=tzaYI'Uv6w|߉ѧKU9Onʕzt`ҁّ"^1ܖ_`"<#ϭ]I<قVB;H>|ccLo4~b|šcL֮odhYk 1HKPvcE$ ͏J!>فCad`P4P>Mt GrkbXHE P$n V{1mQ#4cw6EpK""Yj4q rt+*5%Ǖm8&IErv;[H܃(Q;o  9ph$I6mI*^|4 Gca@l߼\T?5~^J$wqRU)˲K؂]EPD@DM$FcL"PQ1Ei*]zم]ve{9lmg?wܹ}=.qq9w2rV4=zg )sWZ#n~юps9}PG'_x4^o7UvܯkW {FDw,c۶IZ3k }$m޵⨷9~K3B}($_*QH[!<e{ֈ{ +[]]no,{ ::vDtwIy}JZ^,R/\:cݶdG,fs=Coϖqr-*Ya챣~iVʎ[o==­)?͔|ztC(nE |6y@p}͹ ]=++~%l=k@fq] :OȚ$=MCPl4QDVJսP;`Z!nABq4}?@ËP)T nuś5'US#<ҡ(ɛtB\l.s5iUޛS׷.ԠK#ڐ0e08pO3ݝ؈Jh 5uGp"$w#n ?9%MQcr*!8>`9VwPh"7׼(m'%eٔyn^\.|sl=*n\!S<øGsȺM{xߏEvߢy_ r&h^lIF­5Zd~}o9 pz{쓈2c ^6X{Xz!B @4x HԼDXwD.7-Gۢ$ʶ]3 | Alɨ.r EQW| T5xZ;s2ڿf=Hd"Aܾ+?p"Ehr7^{ˢU.Vݻ4Bwoг#a&B1h<}Ec1Mv mL6&4=y 7gp=w/ͻNʸ.onϹcU,3_*(GcƗŽ[ `{]1 _2UH$[s)ނLKB[1vA[N?aE lhL(yKU^̜E;F@ $@?H>v*r&rԺD)g. UGd'6R^ݻůoTƌ-P#E* 7 .9?Nm9qX3U޹K=٣гX<7gN 9χk"n.FBBau-  7z+=&3cU֚ͻ["NGG&eh|dM=+6$mfߖMb"g(q/GW 1{A!;b|"6˝}41oLh1 `\nݻH5]\"<`vb]H(E1"Z[g t@): 0"4CѳPKуe !qs E#ٺ , PFJl@o{yCjw܀{m3ԍ5#2; e^B!M"cJP&+( Qt5]os`= mW~<| Q6Ddc":r,b"Jwc>ո쯫1؃KecD"\wono]S{߸OY` Z3{(gZ4pˌ]__}Ɇ~j4ƕ|ӱ~cDs(lQnzMÀ^yܚK$go:c$g׮SQGIKSM|6IۘU8$qPKpNH-&ڻ)7GItQ+ љĿʃ5)0}##[2_f͞Z"- >.𖲬?*x@u/z2?{AyڍֽGN.nP[io,n^^ 0!{gp&̉ Z5{<6qg*͟9UW4O4`p֝ܬ`$wF4 [1hTL?AڣȝkK`]U5|UBd ]5h>s^qsJP|8(pYMfƘ_]`cYph^؉XC1wH<MeA6Mvsyem& bH ARaLuۦIX;~$QVo{`3Unoq'kxU"/[k׽]_Z$yD`'gL(ݝOb$WH(2y*g=>˗0_"kQ$rv}!\&2k[ESn=7dyF 5qq|c8 0Kkpjδ$9ԆRW&?@r,;-x[4Ӣ[Y9uց@6z y١#Ѻ#nN! ([OlBG5$V$78S}GgF4ήz%n:kڊfu&9-qyEoZؘ󀔴XgZ<A4 ?hy\ݗTOBAs9Ƭr[8>R1,YѾ{eg]}qNO3Wd  LKHs%#3KZ, AU |E5)9-<Ľ? ^Esͫ2sE #Q_?AXd[ RയQ Z[nIBxBDTZ[cBlv#@^]YDEu "Y" (vP"HHELAB/ý~sT";vy'sEG]$~O<ĻEsp>uJ} : 9DEqq} 6-0E#ތ(jǸ_H*hܵr4ȞyJ=xE77s٩RђAS0l{">U?i=sy"N;-I@:Ri̔ ڥYs8RN˞vC+dkmťe=NLU8nU8/e {']'ucm\at;Փ"! n"N4{Bɧ"nn?g36~9 µp^?O/mȮ  jh'86\\uoﶩ@L$P]xS\ 9qXݛˮ#JEݕVgV\_Fs$kͮN4 qs\um /ㆀ'NJ&並-KuO*D߹d~>/!# ƘtiԽm@Xk[ l$_ kmH@3-m.юlQ $~nj$ @$d E[i ʠAy(ss?#s#u}>@~HmAVG !AeHqr2"™n??F.dhG41WjcH@Ot }F[2 jDH|Hl'{ "D8Qwuwיr(f(GyREZg÷I?%cSOsݶfg̢{1| q3q-DL5 $ {CܽՈ"6yV,icءEθ]mC[Zy|Qk!6#ȚU8dOZU'[r?#ܼ 9b"> >rs ֻE\,O{OZ:qDžPr{]u}8oBqCfHs^8b#\ ljP ;/_l9aw'XF\޺2](/ZO=&.;)v 9faWQۆ8c(ΝOa{rR@3nm~uVўst ;J*Ϋ+''[} q)aKA;7OtD`eW/,e}gݙ* D{G;ԫZ=Ģwkу^L1Ɣ Tȃy 񤧆\Of3 ֝nBRLޫ;A\8, ͥX 7[ !qO^G[ZL븢ZOqi˚ʻږ^ȋG5N'6.lsbA՟.ֻ K*ȫrV@pΜ8}GN9qX|Fieo_0Ǥ] E%m?z|c+?KNL);.cۢsWݫ(u89vٲk{Py)QdCci>@]H/p1|91>]i@qg3QJ(4j݄y-YD(D,1ް1y즟!3M6.#@r(Õ,21k"T]@D w7"QVq,;{[יH@#5vi2~y7X>  #PlDd#q Y>BH`EB/j3 -n'HVC1_=7#|hq 2;n䮭[>(< lt;!~xd!n&֍LףәG"ĸ9&1$8ipټkl#Ek2iq'e%ԿW<l=\ ?w x n}e!͑Qmhs&q&" Q@dz4qκoP?^}{ZRaB~SBvժ㻜RY[~KfNvQnDWH4ObpyyŁ#'q%'V/腂/+} p\sCœp/;&₊ AY0ƴʬx-ܺZ8wrPKT0Ɯ$[kg8waQ(XDT2jt=Lnt\D`nNCqj' E:d"V #@ "5."\qd܁$ чsUomH9Xw!\it{ nA;tǃ->wq?쾑Ba`O,C f|?q 7gФ؎a/ 973cAGm_Zݙ9i ޜz8W|@H IDAT/(9]J>*8rruO޶-\ sGaf]4iVd=Ny)WW9"|2!eÀϳ#)Zvt33|XӺ"+ a 'TlEh.q`GZEE_7X>Nc7"~u@ճƻeXBgyZ`@ѱHAD ƘI(6 ]>(h+ ݥ-Cy]['m[a#ơLSDb}6AHBVdwܟcf늴$/B ss"8Zq(rg(Ylos>q+Yl;OC v/G T4qVT/þh|$$F9C"C$̜Đ씪䶜3/j.yg.u sWӜs[2Z^a.ňӽ(Q7G(TvqH`@q|dDNN`F j&"y"{q=_?x%2gw=/ ^>?]ramVuQx9xSFU@c\*vy蜶60GN̜8rMEiicLDŽ }~|]W3E;=0b예0 }f`i|/Xkgk[{\ _ƘhI7lSc$~Ԟ] rdMA4g1Gz셈$*ԣZHuDHTnA"~$ I="ވty6"a ƏQE%LAV(WtUDo"jZ?Ptuv˚5yZ{ƣh^v1 E={(:Yy"aշ]wcxjsD__j[cYb~t:V5wf|@=7W ̘UU/I27!qc+Ҁs}}l[?r@1 -ݓY;eR.+|QTC.zH˾v ?AԁhPI(N4[J="0u U#Ov2ם?GӴWbЩCPftˌScܼqgTˎ}}7etl ,E> e1d܆"u(3-t.iE&Du 3@9 5$FDA p_a "zP nH "$6 b*C3FM6s6)HE"7b^w4a<e'm.G\[Hqp5"E` rpז>u"^ڇLZkD}!?T>=w<98y=Q'L^(TU?ώ-a߂- X=lԡ:=73=h6$/Q)8qeD~H!n@iLMt~ Ƶ@ZC8G?xneűk#yeCV{۳%۱>.rFbü}u[/ X\uw}[! (8]w4 2cmXY/Iͪjx|hh[gZZW*9Fܼe17 ([4a?n]qKdF;$2ܹmCQPu׳ հ%CH(ʙ u: L[kW4OÀO4~!w]=".(z7ѥ9.GBE/DW.oC 109N / ?8/* ?!Ogq++{먌E2Hk^WOpYO[[sq9}'޴qMnGus/.lɠ'l~_uGJRUrc06GWxj= 7Ahum@ܜ' wic|^C65p݋ 1W!q`錬PBZ5gÝDOGFDq/rmPD7 } x u=;kܿqn{>#W ފ,.H@ C'(#Ue:"O4_綕#Yd.tl#QƯ mdW""lYm+q =Ƙ+YΚ;]Cտ?+ =ĭ}~!ohÙGe|% $o`ڪ==7w [|+ˏSLoC \WT:)J\ uɒ~<@ޓF9"n,LUՇx6 F| #^F"ͨNHAU{k^WyܳprSϭ]ewO/^8ʹ_3' z.uKa9qRg66rߘטrIGV'' 퐫cT"s-gDm蹝 ;?AA%(f,. ?̿cK_82T/u0vgFvO{( ׈p_O%gC>tEYQF ֮1P1fL"qt5YFDF;Q/bC٪6y W݋@$VZp?,$zs޳TgT8EpAD*yDAkwoF1o 8u(" $  Iv GwF<%!? 7Odࢁ}zc Lb潷uN][eq@g4[=?¼B 鉮7[8˂͛w]y mtߵkۈCf;i{;yɈ["}D[" #KBUq!Ny(9gs_׭+>l}[]Wl)5=R66'߈]Wp[ii =J}s|ƪAI:>Â5:zڰg~jݑ]:cgڭ[2R O5?f'P^`ZEUs\$ _1v`jS4c7W"n5:q؊ohߴ`[XkAK;1qVoh9^kW,vtC0rP E$;7Am ;NsT+N",D~ס2|5P_#HvC}3DF'_rkDVz]ƘQ}e;cc6jfSk9PCtmy"/E54FHD(k(b8D`# Ӯ^Σobc8Pp$δ[RNBܜWHĴBi/EHl`Lв_[ r @B-=r$Eُcov 5rp8'WeV?1{?Z:…8Ey+[d3'8?>hɄ=DŽ E+Ce3TOx3x=%#YPɉ 74~׵y7{ ^Oc7.hFӔ@PgmQC`ׄ ߏ&k17-@eB1ڻǑrY㑝e#YGT  tkd.BD>P/@*Yvw"`լC:۝BTEOPÛDTcLMkdǍOGU;!;ꥈjݵLC5HH>l!Ư[kˌ1.Ez"ᘌ,t&C@!}f?n]}fb"82psw ?7/lL3ȥGP:CA$V w%Qnq\9W?@yBVDhްG(Y~j1jPӼ;7\8:Wtk?>go}<(eUJCU_8x?X$Hۧon&6 ;sG֧$`qqWym;ϗ?}lw/l} vxG/8Ģ1aǗ uɌ}&E"$棈v_|W{`2&FD3N"oDg!aM~F֚gy$#rYH*bɜuF/8D`/6 #Aw>O>",Dۑ5T۝s>2Q1fqݧ=G-ǻ#; MP@ܙ@I9#q&{ZC )~P ;z65 rࣘP<<0!a$䢛;C@V4ٙg:]>@ŒS 8QlGΗPC_H]HF gE«ͽ㑈쀂Q8qݿh18d-rĮK=9VlhNN\2sqwm:;v^Ra%6ŕ*KJtD"64b3?c}|@?>6&|!e1pb7MZH S:/ Ƙ r@c=uƘ-h>)BlZZv@P]c&*z| 5B "DѠ*e#'_cmVkzTUy/n$Pzwxϲ nzc0X h:ʷO5}?0^Gu[[jǃƘ8cL<ݧ_|7g{~sXkCڿ[k q"P0"ȷR(y2";Q-+( M JFdR{ǐ C3O7dKD9H]Ƙˈ cnBuHނ!$^)Mv(J sPn u(S;;YwGgcczc5ƴ:_ 1p@54nVl.'~s9-1$/9-6<-N>fCB$i']eIڨc֩3- qh)!(([$@B4w<%f`o2@u(7'"޾3U`mE&rAZͳ3+~;urwǷ/\^WȥDy}썥-%U>o^z6kZA}5Y3^@㈱cSk+7ZoX&qQu2 >v I`j;GYUӬ_QI랾s\1Sz3c|yi UfZ[& 1Yhrd6[e*LLT?x~q`9w18#J'$~~fO(W6P{( nY]kz.zPw~nu0Ad,9rYq6Z* q%k(C߽E;h%1fɓeMgdA;߳(3z?3[gd®j˔Á;zn }'xt:-ػd`9mnjo] `ñH}{p#R0?DrKl68ԼWw<YJ'&ği77rs* @B`M@(ap:)WNd4 ݷ_P[Q`$8mmxwKuR̍w}/sǸ!Y4\n&AMG6X73_ߗZ֎NoLA$Lp~YV#VHDaDϢ5 ({}-~|9DkwIN!S IDATfb,Ui,]]i(3Y%H@&$m]Hs"9/Aٿב}$nCd}0M>ֺk{]kT{սBwjPc@kBDV(l9`o13f|? DaGs5?E\Mпֆ#c re#ܼ e'ObH1>O- k)xId@֌a!|yAp Z$7C s;nPW4:Wy? 6Y/ e"7nwӓkP-pǝq+] M8\|kN> ܱ G޼azK^>vm/ T|/"n~kMVa ^'ll8I#_{#6&|Пm}`y!5%Ng.st5MˍG͜8k/_ZMu7/{H}/e+ơY4>Ib6#D̖k uj'd eczAkw1yG5o;\ 3Ƙ(Cv݃He'y(`kƘr4jDv"Av%Dz. öh투"Qe Q3"h2NKwAT4G "t$sQ.{De9sQ&Dϻ{]O E$OGB$}Z0ƬGD޳Mݽkƚ_EgLT=5YLQ`,wS_F9I'_k wQ1!!`pGRW<әٯ͊/߼x]wOo2$. <" CqTMG|\9֗rjB7+oKhZx >}- W=hkؓ\_^v_4~d]#oVR8=S#_87S+IOxm놲?gƬ5s\-ISu°*C̺j7oYܺ ^[44&$vGTB qqK#Ǝ Μ8l452V$Mu{}TٌqCV,Zk376YkvzR50In2QFcӝoֽ64{<}[$MDJCHn1'z aXZ3 @cZTw"D|Tp>^<^n[I=%Ƈ2ABQ5ہk'dY>p{mHnA3@1f("iƘ (?XwjGAW/"ng-nێ3Iǀ,kdխKgC 1_DyXg;=``F (yt:ۀQM(:} AЦ |=nd}ވcga99 a `Xs?9( k1WbZ dt'k.k7zoNgAu_磨1k9Y n~o/}w((hfc(]kTj"H @)M.:RnC6 Dry(8zwţ[[ ~"C5wɽׯ\kDkL~d" qƘu r%,s:Z2li+RԴ[oiiI.C 1ڽӣs3_﫸\d~? nӨXC4Dq_ckmMґ{ylq?T~0[$2PnP?e[`e[ecP\$ &ܜ)um6n e$Ym?O!LǤK_~򶂻^`Bx1=7Nnv(a@ͯ__rߟgzue[SP[_-?ĹǷ٢<HaƬe'3f'3J+W'g=ƿonS{7 ZɃVK`]]}18d3{3Z; gcXkw~~Թ1&ujkL.eIM#1ƋovїV?\sZqe]]뷁1P.|M'&Fd;r7sy2%޸2ӛҤ)O;>#MfH_3; xER+VxUmsIܘ@+,T?oYl'dl̏pr Ȳ6qOO\|zuBULLgue[޶*|yWLe^u]d}1q;=~c߹{+ʃVZظCkw3ߠh&dg~CsSvTenji% c}&$ 6&T7qUmqUjv}¦)6ocq꭮ƍ|8n@@Ƅ\7lOIмh89}k|݈$Mg`I<ЗHY_!>_܎¢E7tǾB^]hs;DU<DKc̣tkm0X1isDx2bksoJc1yQC kni(Dr3@ RnBnA!EzQ8Uk !Kw.EGGy'cD%/BⷑT`e]a~U4Ƙh=Hߟѭ@^πcbAƘG%un&wJ4# #8wx؀] 摡/!ke06 l^f)9-ňtjيcp[IG/ iY;ΫcHS߁Ǎ<Kң ,oB^3ټyuTcƗa͆3|}35ϧ*"FQ@EHg@R601g_􇞻u[R/?XY9<ԣ8.!p/&iөu]WQz`.knU'/iP]U~- &$WW}ऴLvը [^?Ҏ=|nzy~ʱCf-쾤^Rb-Q4Z]3"5ܡ׎V\=IHI/o|<;tU0WxʺzF'uq;"G,"*%?(1*僄e"^3[<ˏ1ݐe>Af> 8U R?D[}CӐw9(?m짢Ƙ۬?)+1Ƴ$\yy>}Vc(Go4w2Q?gc zN?GdwLD#vK$U#+i7D,prD*rih -r1o#݉5B렮%7݌$-ƘtoD$"JWT-AyO)'+~ܴW `>؆"+CF#]2%DF{_ l,נ`O{J+k[1kʎW&ͱ,*2' |cě2F)cf7G܌06!nqw[z|?l=l82Ύ4ky-1:*?;獄_ 6x&~1r1@lG:<:Nf?5A܊pd$}|}`use~FYUt7 0ыL˚'ʢcjoC?iKc6է.[|iǞ?)Mvnk d\SdYB2ccEЍ*tUQաJ ,?yj]L-ıC+Oz{mbTƙIgʡW~MQz67(}<4,ܷfɁR `Q.?ל(ĭvy@d,Ƙ1ӈTBF,c.WH/UJFJ 'oB$#GOD:@k:y' 2RtB(2HOB1QˡBrlum8ͱqZPyG-hY(ohTnz+ʝr\#ef "g<׃1ڍ%ǬD oFƊW[ꤿA4)|"%ax }.(DlciۭwV@̞1a9D$"?,E{M-"xZ㽑! yq#hȇbs=w%=%<''rr}aecS)uThGޏ36$͍P움#3ZiB;ֽt63fvDEWp+67^`߮>qr =&>/9*9jS:qIؾ@V|uՅ2tנ]o j4ܽ5ՓN^3wI\WOo#yk_n-Z;Qm7ՙ t|` z厤ol!2ڕflEktUm3BQlEijwD5I%5u~o CG c.iyeZRǍ|8 Nz쓭QU3x+ ͘x#ӐgZ[l v5 I)qRvDup&QcL0&F$"ʰr)alB/[P׸av[g#cEx;l䒈tISM* ? ^pK? eJY{ {a;(d=EWB(Ѕ0 oTSwPh1yalb?;@ؼ&(7#m]l^UX5ojkx;{آO ?p*qʢ|ljty|| |ы"_f/1l$Zy_n/%6$^cLi 5ܳ8T+,$Vף\ܹˍ1w<[dE{:p1fj[d Fx MҬ2Ƥ"e~v1D}\xbt40 ʏ%Ώ" RKD;(Tɠ\ۆ=޳움F#oQ53ghS^ xy³`U|hGޝw$"exrFƣZ0 +0pq:JZ;;%T c:9EfoDk9"a\_J" TG3_@QrrB>6{- 4';9̞kA} az ]U䣽܇ȣWЦjsؼzJIGٹv+ڋyb ̞o^GEo~*g^lmrXgV3bGԲ6_/l6@thgeu]vkϩk,:nMn (WK ẂIɟ}3IL474ћ%ϚU9yYe'RVtdH:'UU CWh_^~DTA97A$ͯFWTo{ C,sνUzF>|¸^:j>=\49 o19zgKH68+,"GDIG \t~QhЃZy z槣91 y놠e8vDYs;k5dvv| +uQgO8w?y1Mzں9MEn݌1HD"ӕGsrͅF FP(hD N#]);@xGp_,BVWNKD$AH?>̞c#‰ D;D13(y}Rj1u}? Lm~umLVa0gIM,d|-pI ..?vE۶S3oIOOIOm';.+\Y?<6uY"  L͹'C(t6>z3S#J8קPJvp&Zc^gԍ N;Z~=8v{LA%{Q?wsqz7^Hr49V5*CX q Ac̿QH7 k<оε-AEަ"7[63PnPK ,$`1%vu!"!DV>A~coPƘHo>!%$ilZ;בsgw-2JE$ЭFVhEt9m'nѱͧXRPtgw$\;hlBVt#fW$kjcL" yzu!HlrX6.jogil^[.ƊV}&~0eMߔ;? iz%QQ[kV!nQ9^4[J6vsM&| Bܪ͒!=*ӑG3"d3߉EFǮiF/Y%]k/Y5qz(]ƇUr0wyM|xSyỰ;0m9h9n7 #"9gO]E=z#hQX9bT"d&zu8"Z{]DJWY9D.^ݴͥ=z!DzKP]ݣƘci%Ջ I(h1& J¹Xk_vϺ4]F`t)H^g)+"ry2.\S3wz ָq?) _}wLS"DLyq; Ph-:Us:K{ۭk~y^~^W^Gkb1HD"wNޭ~B5Aڄ"hF$R_eKco|2zi Skjě$TX% yU8۾j_lao)$nn G딽`漙5(}[~}ōw77"o6;VnWqf]W,]=v'uݺ&;|ҾkB~Dv[~zߒA_LMbiaK{νO7UŴO3kw3Lη] /{L{bHR sgC1 l !=-"ec 2yb1I@5.jxZH݁YkWc9ʕ1y$Uܳ )AZ2[dU,F9f2N yR7#%'$ԋavHyF'?[WP[0y*A]ʘ_>{asN. h)I0jJy3tjeȜa:.YuR cso]֎MH=npIJ&Vd)UD46V;6Ynw˯,+ě2 G6b3oc64?!,>>wORSm˒ ;}4rNM "| eqwo%%_ͪhT@864qzޑ(uZIITREt;f[~E߯gw dkzU,p=~[ڪqoX7ߵ.u >}vzuzES.w3rovVG`'yVLh"jdf:t`ߚo]`Q,[c()C.w\({ThQGayK~ZF|n8)罻(p\mQ(8yVk(>%HBuc7Hiwvco*~{0cL=tG^N; <"#AtD֠p6d\Hrdt8}57^hh&3܉D\ /oSe$p'=F[kxD$"]ɥªhymPqN.gd(C2")9tB(d|+F ث~y.^,ۦěD.EƼሴ|>{z+e g:k6o,N7 ay^!'g"Sv#fʱc{d HBѭSv}o/OԤ|;cCnc?]3㲏/J-)wMU ~ko𚊘 w\<Ċ1q3v,BC V;m߈['%TW58a_.0$1jka[V}o8v]> _oSɛΪ<,%oҟet!0S+`<.og7"YT?6(s+0Z1v@T8m܏5ՈMvyIy=nv;'5| z |"!CwK#qyq_`Y췡>. rvbzcVFD$"'뉖ތ[d 3"k7YhlKף]3Qi_+s5s-G}McT}޹za콙v{`V9i{:i1_r,Ņ8yJ9k_Wr'P}3`xr+PJ!֠{!xIDD9N`s|NIĔ5beM0.w7\yoou`k՘/S6ݹ)KhE S=Dc44N. a?6=UՈ`y5-W?.lw3qZӾj; l=oٲ'ocȇ:jx %# eU<@^+Q_Rg1݁JzRL[ +_\|8ZB(5"߲ww])vC!AW%&,wǻ99(\BYE@ cNA-6" Xk?F ~$ =$'GQѨ8MZctyoCآ b?§"g="jҽ}}jk-wE푵я _qKXkc"0oY`VkGZQpDml˚D$"?[ZF &I\fv~iQ bc3 ӫ~-b%1nE$e_/IV֋Wtí"lVQ'重?o7AM7~ \K8׮:d%o MvB&?;o`Nm`/Q⌍}k TK""򓓣,!:-c M Jg1m:ݐkvC;R7`qcm8k!l6x~pjZJ%} %oG G#(tRcVi݂s4FcLgC'jcԩzkqzKPg3yr ߆#/D3#@%_;B܏rJʐ' \FǣP/Xʍ1׹sݹBH7"i>4W?AE$"Għ/+x/8h־|Ga]6srybD[ `&> ,6r+_13mx3,K7Oĕ"L6=m2xv^ni3t< b7wK}{C5T%斄 v oDhSMszjfvxkP pV]Qڥˢɛ ,-X˗:ԊպqsCs)dԲ+.VU-zԳ/lrڊͦ Kvj|yBI娢Ըn-Ex@F+{|" kn*qC"sκz7۸"-A3ewl~aLXPو2OʋJ?ncK3/ }!"?u9"Rz Rv߉BnD0!ew3BWf=9(qA!;~!u#Y<|=o < 2q R6hBVh#e;#=Q>r\;J;~Q!͑pۆHIZ_ZMA| j{݈dkm1{)u5 E#rF7䵌Cs{#h&<2cL.2m֩zH~k]mf9XqD$"?|PmQ^bIrTlwM_ ۴D^O]l>] !Zk @ 2nŇp͏p/wPeC溹EV6iՊhi8Ѿx>ְ_ejV9SAzX뢂lo'CQ|ӉNHy-K5=L~8a?aiϛ9>{⌷> ԲB$6`RRZ_wkfM*kw~V'ko)"{A7v6@ :IFaN1yI=F^o\Ug3mN[F7NX^W|\\0v#uykjjkg}p~#"Gdyfr%OBqkݿh]TTH% 4H9^% /e&RR~1Rˡ1& nF!10)<*di Px]PWxD ݆A1̓MBKF\>k{> |=L|(,9U2zN>&_Z߅OAGұY⾛,ǺsCy1nEnkc{y,O+DJˁ[\%84g/#"O\ kQ6jΉ rlL̈́mZ{!j asȆm\2C{"DAb؀ݱyD(=AE^UF߅[v>=!08?>Lo]|*ĩ8lN7 w%h5,.%squ,+-YnJ W[aFfDPoƏp@lC^]lKl+hloZ݄9vg>91]zWvVC2@hs&}yPQ\~aQ}zc35ٹ+kHvVO 5Xr* 7mKƸc@&oDX lSVS N;$gxˇmpQW=%Lj7L'ph!T\"h2!AfD*/G4 )"+D!jT+tsZ#x ZkPZK@P1QDj!]"F ɧ\Cg&]{}Tۇr) :}ꗡ,d ȽZ[r "xo;/st=|=IDkc(di?%!kc;8#2Y(p"vi(I>莙BcBR[ Wck7<+o?kBch&#L`Np!(O­ e֭+1C0F$"9 YH2>"\e0l6{-hی `B6T^Z[/nO%Pz,2|>LɕN3l5#x>7l5d߿CE@S36"&GzTF[W};ODx" w#TGqS FtV1pY{|>܈%!s/G| O,;`s;u<7͓-OKԋOY+MZLv֨RnXv|F+ q/BzF4Lܽԥ=嘻./BUZ04AؼLG: 4Hoۉ/lҋ^CzI7O ;~`&6F9zm.=xM7]4-,]|M^L5ﳺ2t6Qgs *9J>m3@HR 򪤠 `{}jѦ$ER!|3p]xb➹vj'w% y[o~w!hj6FW(6)=Hs驇_3Z޽! yc/D>$zMFs1HeR#f;9+ <//zk8./~9zk!h-\/x@m02" JGGfD"CrrE|Ϲ(/kWDB | 2k=P+vav[wEw `xD_Suƈf 31!Cm%-1|=z}qH0BA<2&`y0̥7i#*t\BМ |55;qLM *WŒa5p pD$;+H!oW5kt5mok,{ ={Eͷo XTf6-1SjPʔD)@|:6X3yVÓjcr.EFF T$xKpH_+/Aؼas5 ^CzlaU)e1se! 񗋆!]ƼL͈,cPֻ4YQ訷@Jl rAUsLA$ 35D+UZdwkGug~@6dyrXD1" <#(yk>Dn@ɈCft<ڐ&sx"C IDATeu^OUY7DSg(aTyt G'!#@dH)Qwg[d} 2"|tD #=i_ c{YcHD$@*^GJ]pd_E$"9:$'zhz5cQ?=.!,dmz,{csT@5.F<E4܋0z/l6 EH93=)9wt;oD#ƌoN7I82#EklabO%X_M*j+諡1dDŕ/oMDG2b@ mogvA~5qq%wint5Y~y1al>x/˭IZ;*YȠ$Q=+̧]ݪ͖V3Eٯ/hw-CXGdˑqv926z%2$fo͏<%D#9d9j=ƘeǏ_@`t R ,GJvsB,Zߕ݇OcD!s>mk*< '1ƴAŽEUhCΆߢ5њKɛ$DvV,$o½>HD!Gg)7" VX<(,O{M=0>7w R'd`1U~tȍ5nZB 9?)E5xH"::!Rwֿ#/b;!" 3ݫBa~.5 g= 1*>v֮wUCrqʕ5.AL=m=7cD"r5 jJi(,2lE-m `gwh=ģySEڧ S{_S\q_̢M/ |p* W"V[ud)hQ6GI'BhwW 兛]5H>)1oXkcPE&ȲHGa 3cD^19}< oC)4DG#QpO/D&soWrd396~rag*Uz6_!%bRbE1S\hF>A^ʹ'(!DڻsXc,wPM!RF+4- /\%0XKt`Z Egg lxbC*|>fk!ynA\^6OF{_6$QS_L6T_d읁.',L7}S; ;>yG!j"\Bef;ap9;.pGԏ}cs-;h[̛5{FkP*J8(6g aDp7 hmUlʸ` E J &`81Q&3^lu:sKӔ6vY!H?hv6Gy6" Sb8&IcRoI5*>}n_ߩjre^rfx#r@9WŢb<pڴ-ZT3煵xR«@VNGdc:Pa( ysAkm#K`v*6:"yيHjƉGh W)*Eչ_^j*"W/8HC}Z; !<׆.dYF1kC pg!C)ޯ@J\BYOCᭉhdu1 Pw~XYkˌ1sݱ3clZ[દa,F$"?_9/A m#yQ͞qoVQ._ˉ Jg7_|5ۿJ mFV`kG7>͛FOE2,Ϡ_|9ClFW5x`t`%'6Gw|Dwkܵ{'l%_(ҋ!jݥfwߙVo4cOޤ Y&5j}v=kBзgGlٙ1iOi*aDݔ0}Ñ! _9Az#]-H"+PCޤgqM*#;k^`mNl6 -Չ@?9Բ6"oҶgz *@hLD"rTXD~+H9=-" ^2g4+I<6*G &B> ,@-,@n;42w9o`ODh}a .Qڅo-4LGHY5%>Y"ۑdz@F %Y||"ܽBn_j>"iÀ"rhl;AΐGZ{;ͥ * Qא\w\foq*NxjczE R̝]bx8S[cp]m-qA^/FRO^޴F[Q%hozF8Uhc+hvmy3Jvh4l< QM>9T˽@N@ /`[5L_&q>Aޜ=l^96bjҋ`c#}b l* 2%dZ0Xև,H'Ce([j^nK{ckʯ"\> =j4uF'Qn:6}LcWi1!6]3Ȓ7iY}^~ȣfSdx_*1YFʪb? ݪR[L~g_ړf`sD*G#YL5:EEdf6.0DxoJE_sE؜D8ՋiKVl˓{jK`CN.=l2I>E{X߄M!lB`go'l! asO~E%Q>:쀼R1X?ETEfiٽl6@*oZsl]埵60ʛqj鷋U"<~͟ W^pZ?}~7IecfoS9z%%Y됎B#A HQ⾰66)~qI̶I|kudky_['kBW0:)!Dnj Ft;IxnvanyQeZfA!;QD/>b*ZI(t' =8ō˹Lrܿ愭us" Q _ӈAq0`h\12GK0Z/##," z~^ ؙ |cAQ2%{B- 1 q6=ך-l.GUȎ=7A6T1܈$³! 69ȉ8G^ $^ɈH^@hܷ&&K?kIx^%sO;`nm9Ǟ>9C ?>7W{nbxK _Thl氩HsBr#;ّ8``_HbLA}LSw툵>HBnkwuт})2Cbak TR7pVbrmͥ^e0K=n"'ˑ\ވxh;ϡh|N7Fh6DŽm:T`n&ZMYK큜lCNv;~wxӕE];nIMx'?;hkqs WM69qr/{5r2CO?7ΆFafZ<ۏ=/>v*褝-~ۡ/eU^-h+ȀX7y2>DT5z4*J1 JcLmn4\4|oh;V<"ou5"Hp쎑" hKvkZG$~jq ép1=Yk''(Ed1ZWv~kUXU1E4o.c>F4FQ$8l}G5cx(E,bߟ&!iv ZgckN<厱ۖ!>|80rJ+ 9<W@q(*W3 ⿵ĸ\d,Gܜxmӑh9_c/DhA}\nb< b % U ͏y6KuAj3-m[YkYr{n& B(<;;xxg;79SC硇#J[EnF J5%w9v&k];tMxor'}r@nwu`-9٣zf6k>$: fW0jɜYs1u@1ա`iHtLz~%X1k@ߛ/qsWr#'JN2XEn0OHEkpPڨk) {R/'/+b-_^HѢbC>Crjo2#P1!cu1hdEg;.Ab${7x8cۿ;ވ4q#ANuEDuzϷFAcL# k}W%I1Qkm.6]>W24^:tk$cT ]Иgyn.0 IM{ϹLBb by7{ǑhA?1oi;*cu㑑QWiN{'|B$krͯ/ovǙ"0@&21'" rc5\d&!)j:/Z10DMmm;g&^T3~Cwi%~Ch#Or21ӓwsީBP~]"!Q"v*&EdE[59J#3~*^d.^A(򰿏|ؑbGCqw\Ɏqq B4x|rط &^P|lr~ȾM( "Y3e@AqB|%"ZVp2ĸy;Qͽ5E =n> E#= w_Ǣ5F< `0d'39?hFTOqLnE\/Nӫ60j_tf%fݛ;itUNw*އۏHFkR`:|t]upV4c2Q(4ZwWuS[l++D_[[ZqW~YQnEr`qnТQY3+nM czѫ]@0aN4yh,zm BĊxy-4I9"Jb6wơI Mk>Ab?D]dkk9/eȠ\"^ФM I ˄=4q,E^!M"e"jo|w ddG_k!D^a(2Xe䌁^rkdADD ah2jYZ,4Bt"kg[ȬE>v{B4KSx9SM5O1>r?wc<9q> y# (D yH3kچ""^sD4 {刘1mdӠ1Ç-5Cw^.b-GE@OV`=nG\4gm EN/@ !c2Vhq}~ EPPnLFXe}q |Ah,Zk6Ƽ7 kfїr4F9ƘGw;VG4u )Ƹ o3t0$kNbIumF5,tGf~kFD 'P'욢QeP>ʟyxF&1gښ޵>z7-EUh=*x~)f&(1ȱ-E sV"GK(zQn'nyZ>|80/Gc,Z^ .4\xmgn>`_n9GPT-;-z%o;ǘ}Q^yֲSjsƫjZ nwڂ~b^u⡇>Zb-c=KX<gr&]t5fQ60%51Õi<6=,NgS^1gE#WUQN`EwCD3ohM*NW#CГvE\c}!r 7&o"n>"nWZˣI~k:#Z?p.+ '7 $ȾC@_+;}%mES]d:ֆ1$V 1dFH#dLDFZ#/?Mї&x9+9M}7Bwyng=yZk>EircL`27ǐ Dk6vcLnmCot.vY`g!Gts2OA2U<෠1Uh";AH"Zgym[)Ƙ%>>|80/a4L9;!Hąkt6)< )zT~8DFH${y>5q4wGyf'<%C#nFl)˗#c *rvR|kpλy{@B`;cjbc Z|czMAQZkFDnyZ[SE#oNg$2`^8#mDQgdQ>AƘ8TdW޻8]L1$띿Tt(^^/?!-*h]`fWoƘ(R ?Ha.4?dߍ@ZJ [lܩ{q݇Ȥ-TAҠ񖃈$\tҵ9ӇIҏXԇؗ‘xXrqeqEe8k8!z` iaxӫ]1ϳ5+3CѬeHu +hZfzq*dRz$5Iqs@=λA}ͻ0jA~Мj~{Z"γ|",78$XYKs͟GN5?/7w4Ӷe],XCCdGn"W'LnN|o5G.m Q< 5)*.xSǍ[7hI7.>$[07̝ - ^eÊId D{scc^}>9"%kt+Uc9}!ӭuǮdZAr.8ƘWPyBWjfĢ@Ad@˗Yk+-G]T7~oGuȀoHss303}ޥ5d71ȨKGc- -dxAd@A zF j JK,9X>qӨhLY>|> ˓#a n`qBsW| tбرd`cfhs<7c"7CBkѵ;n^mڡ(lZsbyz֍O6]fwhi0gߔ`4By?R0kH~/ǣ|f vm5+C 'pwt Bn0F<]O !'{ _DN ĸ9qiK-7nz禔z-1&">qvMnjt|㎱. mvn$.ء-@+]1Orzd3^^X\WCr3cxk ڹ=k:UJ%Fh5%E"d!bQEDx3@5Yk h8 xZ؏C̮IըX&A-6Z[xpad]ILCi1]"(IQ$GdED0bg&;^k-BhEUhqu,]G]3kƘ:XÇZ%IWyPg7z[i yC(DBQ\x5l'n$&1\e[ ߞ䱳;m%c!Xxȱa=s&A7RP؍8R嫿 7frsAĕvk#4,x!1뀤O nnIq< -4fKVae鉛--,.~ޫǜأ u檿'[gu F7>l2{ %>B~|;5C17Ƙy1!e[d)PKTE,)yA4ID۩Dњ8zJ\c!5]">4uw֖[k',C:x,Qa9E BE_Ƙ {C IFRQ(cPn ɑv1;_GxTDin")zLvZb}cl(#{8N)$ nyEhmx!jB]qs49ODY!@ܜb[sl 3Q8wq\ 4W ;w>xB7$3]RR0;L2_9/$&,6W=CR:qK?lyn8wךo+;!1M@FaS:ĵ_+h7ٸ×VWȨ^Qv]y\|%)`ÿeM5xıxewI "0"$OFAy!{% @@ hi&pX<3PAv?9ZIKQz=ZġڡA\1&/!ע5ECc)"uaGUE0$w]G4.wוNÇ[uOQ]BsW̓a\)w=n7ZK2lDm淀yiVLÚ']fO dgn.`ص8&' {DVŕGpR5yܬN÷%'Pu9աĴ$䠸q@lZWku^b[\ͧ[2*OvشS㒭[lsI6ۼcfKHH DO!-C904@CjC\'!LF8Th2@ÈC MYHX/ g,OMƘ1(#$z].jR~mO` i-;>E `ZBr'I1)4Zk (Do(ʱ1Ȱ)h+A*p!bE=oGF `#O2>|E9@x`߽tE{' ^ T@hq;BFh ,2t1nZۘKZ3m؇cC#&7.X+a)i[vzAUo.T0>Mn~l56;nNgXkrYǣ`īeq].O _  w2;U|YˋR$%'^zM56o=DTߟ4f{?~I~[ j(-(y!9ED2DXSs.*B(Cc1Akkf ʿ;ej>-x}G(<'e6܌O"N?M S}z BgVg 9(!UbHGl:4.B MA1obb`_VƠ> 'ۚyۋ1IG\9Es6&hK@9kO*`UDhA}L >&1<+04%7vCxwܜ)i)adEEfb7U?Cn~I>l? qig KޛR_!9t'į[zd+;-J༤pUq˾5&݇ X> [ w3ǾBFv,n{-6y2 W"ߩ(b t@t&{SA,9MJPt` GX[NFo$[S`Zkc.B c}vBrKqh\4n"=PUrwF4'lF(j}1&徖He{Vf,j\3Ykv6ぺƘ^G{>|!Z*QxF_Hh$?설g#%@*SIK`fFhNdP;q~ELf ٕ!Vah5ϿqE<]r^Q^mom=#)q(-Gr37 ';aEZ^xD.m Eh8,|cq_y/h8$E=.kJ|7h _*E2k\D dp.EZcr-H%٫@Ƙ5AkmI|fS$QOsECcڙ(/>T(?l&4U< b) jw;YmAR 4۸s/vxU ]>Çk؉] bܜ @N:K\kL6H y5gS 3-Rn[zMn"hBEXMnH1kAiG,7q|\`*JHAb+?ݻL[>`IRuPb(փ^E377r?^>dz[#e;{pk3/]~%7Zh3`'! 1OIajn,ccKR0E@2 G޸M(c6} JLP^kϖcZwy]d$#2=00Z>ބs,$ Dˑ7r@zO#HkZw.I F]de^E9͐AE 0~낌ܵV%{9/Š> 2 Hy[@Ih{!cqQvn~-.-6d~3gvxbzkd ?-=wz JBܜ5ĸ^?^ ;d[IwJeDgט Ev@M3{/غ׾qrln[gN7$UF؂$qyS'`;t'R_+g)xb-On0d~?`'MkG%|#qÏ,{"E?y[h]C`\}+$IA w6)\ ϫ}&H+G.`o}d]o1C+]t2֖H3o3m섞}ȹpa@rMHB:#$5y~㐱]?PaOCd,|˱qs!;"n^0p0jj8 9.x` d6dnƍ=y_E 6uK[$7Du5}t {Y 04e.nm՞c^BFZr.CMlt&Ԅ{褊@\Xgrvn O7qGė(},W7'"-MB˷c|r~veSSX9W+Wl2"Mc`z16@I < 4+oo8Xz]poC,B/CwMwo46W-,$e.kץ/ngwgx=2EDQ(bS&׎nX IDAT5(t0(2djZÑt4𞵶j?CĭucL_쉞_/p`꽹\W-DDQX#yG_ u2P $7+^&du hAHM(K\$ԉ&F"с l85V7q &PA}LTA4vgM()~vDgH+V`h<9ᅬ9 #jĿ7#'jÌ DJs]\Ú05cv HcۑJT[ZVxyQwؼqR*Ht*󦽘 ~|# Zs;Lz! J$!\" &"^nR1SQfKZG s67P-I`QNq함(Zb2denANچ|Y yaV7Z1a 9n~œuK'!rڭ\|2WФZ;=P1$Q EH‘ wccg Ҥ/0\"xD/IjkY5o"}QnZT 5E##/ ! i*"0PZd6C+Q~lNE^@f+>|095"2k]z0ܜP؉B[i%dL]I*C$ϳL4Ml4m6XAyv!9op5#r'LNv&JXFn~۷NQvٷ+JoLNO׭(=PA>[;Ձh\4hf 5szM}Un_'3=e&W\`Ȭ, 䒛?z??)!3|5 yhF wPz6kRwP̿ Mk\km˙y$1{JQCƘdQT\`12ENBE0l惧AmDӀ;O*됼 U=-DRիҝc3/>]؏A}Lzb}OD#DV,™s)I[{qE( =:gtd+${2|>jPg3kHm> iHrYya{Y}˜Tf6ŘEc!ko\u+0 €x12x*H PtC*W%/k 1"nͯ,TT19yPkwj)pO+Wc̖ps$~o,>AQ'YG5,^u5^k'1Ds"+(u#2|?+ZkWVBQcȸ9q0UZ{EBUPd0  H@gH9'BHf4ߥ(x2} c*x>|? ۢ.(0n҈жip ix-hskмyhT-9;?O&f[Ύ|3f v+GtP9oגvQOL7z_9?bӺXYlL}ڴ4RƵmP41Q0$D0u_ҸRڔVSJZ&p+O*Een$QX`i=9hROvm-ُ0#H.2ίC>M΅uĩkd/ϋuQ5޺hk *lBu4l~b 0^*!L@K{gW""[BTׇ>:h7â DI$"n>3B1()@ߔ@]qc{Fkr.&NDك_xiXmSBv#d^EnZ 5hCbGoF++X^\XZQq#QYxU@q`Bj$ [l|pz]Y?.%0>s3*rM^Fܼxq>|cv/##h*OvAyk(R4s1Peu{k"cG0QݑA@ug HVjww| U CAѿx$O܉^ϧ]IنX.(z}2"xTHd1|28}`EpqnxCEI! U t۝i\̯)g>P7qa֐pՂmIH*?nҬ`\r{"^<e_fScZ=P<`= o,xQZk;ZM$mkm5xI?j"Hk<DckH T} Q$=^,04rxZ[ %} v E,qw;u%Vf-@ z]֮ć>!␄?slc{F7X*MGu%V؞<Y-s 0eJ)=b9QG oGjc̽h!;tCHr v1&dvF_ ЄZӭ^n l%[Qw{m-Lp& `C$UmQq,WHM8mr' -###04$?U닼(0TT`"Ts`A{ׄ2{ׄr؞bԘ @7%КkY_Š@755 <`gh>jp%kNBq(9ƶC]Mf&V'}͉Kqs51nҍ=5u_0Ȋ@pa%[hHToɀa\7bG 󀿣 a nKS>~رrkm?``ʰwccҐ{~?g}IGyKPp>!&qi"{(=Ԇc*J/ 1'k{}ч ϡ C11n 2~A%FTqS/An%77wE|p59 텿K/յu2?F,p>;sbb Do{ը]Hϔʊ.o5Nm巛SK܊yè#N&ͷ L>fif|6G2Ʈ_APh\O|JVb}%A鈈(boT\j&;{;2RsREҫ;>|?@ܼG6SqC}>mw)EVi_3+[C 3fU]ufQǴ vW\0uuJP, W5n<ӫMn>7'Ǐ,ӥ . (za-:*g]\jiAy'܇.(ZI&jWDRtq "Oxx%Ƙ[~IÇSe?nNNC|8Gŵx+7V(^nj>)p.]TMUZ7P^Nnq7o.>6U4!nKP]PD1 \j1 Rkml oCUJ(]/Z[kmP#InvIsE$S~EBFT|g4˴p? t@qXIq>|bO5\w?O {L |Xk p@P kp(lgyJ/F2@yF"UNAc3 ot E$[!yXkˍ1 Rk|ÇZ!ݸr׆@D~`w nvЋοVvMː[Y[LG "C)^M$X[ \3*~xۧ0b{*F q95%>|Ƣ!yJc̫@d \I<Er % Y(WF`E"e}Tqu#p3T%_hFRjy0Wcn@7}ǡ;i-+Wٱ9w@qW玙خѭ=g͈{nbcb'P{Pd0kMj#^_" 2>gcRWjxH0P=2T>c[Q{S?@4vrˁmy/^jdL>}*^z.6mAIoXkÇ9N?<WR}WgSVl(SyO񹪎]E8^NߎB[Ul]ZV/l^aQ - knWCcg_57ƴA+N-*F"oSq1kfa,bdTʢ܈Q4s&8p%* \U(J}Ç?K}X}9C\| @Q`M(˜[>#;>ޛ9r7[ʪ!m (oq6W۱Th$g]\q,GT}Q2T>=@9 iNι{˽ 1TkƘƘQı2k3 Xl;g}M?wξy#ʅH-=n1 Zboܿ>| 2 ρ LsE=n^齟;i94/c) ^Ic&sn CN? %5Div@aq"r_]d7@ܼ2fKg!eČ-w|f'0HY*(AjυFN*J\X ,VQTD*(2԰!aBȾkFV_/_{q'|=VcFĻ/f8v!킵6[MX7GGi+[9ND1(3!y,Y%Hc%݀(x<ǀ (_H^Rm \;nfN\Dmy;{XDӷ+oQis$&(|~1G80˜αŢ&-bNN1X;A%pYuCZdz;Xx3n\E1&|t7lXk؊{l 0Eke-p1fVz=ȨhB駽O< ?Fi/_BF(:_,7~.F*8|667Y@6ez!Pөy3'ySV>`tP뮑@2*Qχwnkmي6&#jvirYsc!mDNFH^E;;fƢâa*ԉ16ZKv%pu>ꪶQ3js< J[p$GC͛sQmTo@vz<dzH PދgT_XYcF.g|Fwo_JAӁ\cK#%! -A?j`2~ۣXȘx3rznp;F0/8N'Se*jn5ID]OZ;}*/CbyנAo!Gs֠qCMh(m2$ d@D'Qb`1&a/x<_%gT_X|F{*ji +kg}&_7m$fY ,z޷lWz`>e<}?;R׬ Vuc6oV=6ϡOCx"1/J4b.K IDATfڧs!wPSP玱0ƬGmWӐ(Z&E4=9ț9q}z! glvitV%'O'o%7=>p]w;M$}EHS'/]22"5I 4)))C7e!p-@䐫7'%7 8`1#c'#qy<{N`f Z[Tg04B nQ,Tp,J]\Mk`֢-Xkc\ VxeJP ,+?6Y!4FE$?<2rYyT,O$ XKZٵ]X3 jנVG1zx< Ƙfh6fd=om3Ј.(9  zs}aZJM|?T-j y/G[k?x<AOf__V<7sbl UŨ?g<#O5A0F `-mu<0^>'ySi cFo9hEU4h>b1uiZ;ֶ"#3B4ؠ6&9Ӏuh6km1&7&:)Nc~NxZs9W޸ײ웮.%`R#z%y3'~^kwv.JԘ&2 azcF7zljj$WFEUV#7=cLfYkCƘ42\f"iQ b38TdJwYD \R[7dc:km0.km1Q4@8E(/[kwx<aÆ& 6eYܜHe\RZi*Q7^ިAKv.^&"TIkM?8-ނ 7Y8 mغmۜv2n𺆏s豏#K'3t{<8Xx1gw󠺡.h,@'|/AEM@ס:*T$?(tKjGk6Pr+ }t <UsD:=":`eEԖct>򦄀‸-@ʬ)bda#yk|?ܹ23ͣ$+#s*9Go}BRtm"[Rن9Gְx6|ȍ9达:|ȂKY0> ~`{g9$Vc@F^ 1~n':AqǨ&6T8kv7p,B0f hb ,B)a0 5~Z[X(u:lhλv_V_pr5ٹ9 +#sjsvnN `ʊ&inm۰Uѹ6_ĐZj9r;oX?=!1_(|m ssRXe viŗBH 7%o[CǍ()uGUF'߶f-ϻ9Xn9Wt7c+>BuQ5Z[鶿 xSMMi?Qw.xGc.6ױȘ=ZZjn!-udDZeomiTҭT옵vܕOTu'}7)ap! 깟>]&k@̛0yfUQ CDMd骶\D~A\kSN>fnjAУThYP Tl^jϳ ^c]o}Zg׉>2km,ԡ)0,nFPC@Crp2pj?w3'evӾ\fܵUyuVF ^ /<4a ے.YՎ.,\[~U@Og<~eU/,Z5}{byC MSbȸ>*"205q ٗq 3LK_R{FT4TgioNcL:pi.-+3?KEwg<ጳ-n@vkGMj@,6hhuάƘzFQhhp^x<{Idą9;;7g {ܜT~8+#{G22˳ssnE DLBMKZt`nmܼe M:=RD_|ߟ+9{p[~sIi9X9Gyb7N|F;vq H׸>f;n]'=&XcPڙ D^˭" S)c̓Szg8nD{Mktwv+gg;\^f9-DK$ d_}9JӅN1yݢ~9iyIDک}ܜXkQL%5Ïg2'qñujsz!h?1ݳƜ1ڶ!]W\\`Æ߷LM}^7z@ws<$s>D-CQ|ds 2`؆ zQМB E>x2.z$pZ;٥Ai{S۷f}5W5߹ɟ~`߿>Bݺ㻨<$,P]pQ 3 +wK>Sux<ϡ,j;ѫ*iͭNp=s05+#3tNfedY?o篰F8gxdNNj<=ZL{42&s8T\/!M"#P[HUVUUstԊbܼRǴNI}1#=6ݓ3I7kmZcibi3εxEV;mQMF # @3>&`37' Pϋkh9;7c1 JXg@Fh~c J Ψ?wtmA':ix<SmhT;e=ƛS^227"g>dPFQlc9kP 7~# 5뿗KZgy i`mBn-Kjs^vnٹ9&Q%1i߷yO?z_Td*R.CkʛiK_^{q>ɥnږaYr D=^^G= $0EHZg &Yd@^ÁՃHxԴ> F)bvGq?:1=Qr>7"cowz W#cUiƘHhB7PmGuZkˌ1#~!O%'M3<Lx<6Uۿ ypqWQÐsFkA4*7Nΰ6!]C9e,s%iH7wc-t{Wʺ}_08(j;͐.Ҵ{ѹJޚ?EAo6͜XH6]nj,7z쭨][#L@e3/Y=XjD^cs]O!Y?Ƙ!HC)#O﹈s(d(Hv#0:M BcoQm՝ >j7dlC#| ͐1*J/F"RRy(x'dld-vzuX<_z<$^B(CUi2298i3q@vn `i2| ,uQgP(suVFf5cP)R`CQS~Of][8Cc s@Z OE=5HJtIӀKOi3fwrLzF4|-nj0nWxp2b8z'kϡ7=xZ1ܺ f kݎ_m<#2.~FZF-u?lRJjij_mw=2nC5 `-F^UMȃ-& (:2l7skpTYS-PH֭?u{<RhC.hI#v1hY"+#ڍ8qƢc˲22gf$!{?+#㿉>RҶ=H`$Jqmbޚkwm^R;2"n;xH cl̢mm~qks&nj.9 cZP M.D/PzfS`ߚecdkַ ` Q7Q}r55i"O#Q[~'W!Cj"u#ܚc5.Ry1$qkw=Qx<6W,R9+#(U5Qkpڜٹ9YimfqcSWTQ U@YΊ"&L-)bNFޭ(6`0;4`vvna*1Y&-pը[;{!9y3'Q}6=/;9pu}3=Gc2Hܟ"п$6L; 8s?how?gƘڼ:v]ןV[g!/_1٨&0d\EƘ݌TwqcL o]{<ǜ{ڱ(Q洺2gP&(KurP~;Ǒܜ(itܜ[\IKc~̉ո//:XV.CxC\( N(ذKι2빿]t1wVV'Dl$ TL/(7zl*;|HU$|7TρHC[ח7؈11&?'(Eڕno01'cn6ĹG~`+QMCc`;42DǢ$$Ѹ1G yPc:wcb1Ix<ICd!uԅc6K c#܈}NVFnsoFx<=`}sZY' mF/Υ Ӗ‚P~>[wB]H m>3;7簬oQmt0zO8 uAn&̆4E3>Dڼm;ԾNۦ࢕vĬ:] Rj;. |Jg5-\{6dg̹8G džlx}~=^7;9<]}OڦijR]Ga_bddGZH~.i}CQTYUjuDH 6l* ^bvd_Ks:X:ޏkQ?A@v~XӝȃAuF @"2싺JcL tQmch9EZ2}FվF)ÐѺQle+Ƙ#H.v7F=QںQy<dw -uS~AW\n/FKPReIZNH77saÆ6IJebK.ܼm\̉<XT8c% ġ۹R>>~R3P/ ##mk}u^w#d7D(Yj6,D^VPӛؽ,lvRiOAnNE(M@ЋZ?>xA;)3Ǧ}eeV_ڵC`!eidvnN,ʞDs6@mRVFy3^ /iҸ IN?k= wZYU_ZtRam. tq m\Lf`GCbP?Sol#%%flش~/2FNRG:ijX-/ _pb}A IDATz!܄%7zlcRnxæ^Gvx#:*r]dDW%߹4l~x=|3ҐAxW;O,u;zv*Pey6̴{Eqmb]iy+Q*HRy,'8wC=qڍ7u=Hd9ŭFt"<by E ˑn''lW]{o[: )?4pƭ3ODGE> c"# - )ov nRЏjsVFܜQ;hX-2'#C5kmν)>_pӏ . @Ҳĸ-1Iv7tD>%`"b z<7sbh] <ѱJ*K]+˂QӿÁ>Æ 5a ^=NxT$%l*mC:Ek;;ƘpbxPm8M}1'P\m]Uk.2@Xk G1`:?l־}(·4ƌC]D#Ez%5:ӭ/csPQ)O쎿sPr(݃ax<. gjEVq\ϓ"6~X4`,ǔV-jeP(>]pV }*(=梲_9p@vnN4l.]6oGZA6fedN1n={  TfN闏 GfTRC SJK}r.&=cpi_0͜=?.E7.*:Tpiu^dD`E(Zyimm~f5aZeICm];cnT?5)CE+ݶɨn 7TzcQ|,U.[T(?>:Š1!gyOk^ʶ1*ZĢNi@KcL 5::݈ f $SXS˸=8 &^fJnD"zLjAƫu1"x<@q04>twd|B(6.#ov6vQ+/+*ѧK)g*Y٠# Иhɳ&;7 W+p&pogf6?22v[r; ;.HCi{"BbͶ@UGDu9?OH ksTqhso/Yeu iء'g/Nz#oYX.\H"ȉlQs%EZ$q/xo,z0;1Cˌ1Hzdh 0cL}x=K4>6ƌES#4u fU!)`19-8 u#p.JmE(2@FԋƘ8Vaj 1{>hQȘ̤fF](}Nچ# OOw=%EƘy1c2H#y[ܺHuBM*nĜ97c.Ч{}yPȌO3ټOŽ? )UH{mb]ǛZ DDÀ> [4,֤oi4YEvhܜT_P(ɃQm} ] nBٹ9 @q&G%;K]t'5 Ĉo5Z[ulbSem[0sZTȹ%cPzQR_pڊ5z?޺΋]1oѯܞ`C<:U aHeO?<;2*j~UewsEρdl<}V;p E֖>  y&#Q}|e5;yjDs tߟ{6Z"cm2nOAQm&ڭ1]gۑA cEE!tZZ P*?ݟȰj˧G\b^c܂Po11 (s&9n_BiOp/j[l 4Ds}3y3'j@qy3'QOmwS- &i d?\sθs\4dYiY`0XX/a)%H/xX:y+.-+>2 l֛ιE FMfkC'UUVÆ 5~_=.!7^dEB:=o- orN:2 u(2A)g"tTGԝc:[C J HDNB8Xal8\SPn-"o)c QDu[9Zki n(:v1.d,.C͍1K_0̨u#k w3 yJtx<YKcRߐ7<+G`촍/]гiiMbZk )i!i. $XݏO8g[~QȘ]NXTi+?#9S zjS0iU#Ta22eD!i0H7&p ȡ82Ni1+#jd@+!t i`~[MBXS R%- *}챎[BE@ ]c(by0Ե7T-KW}6!lgNRwn :G{!meWmnp!b04@PK1; yǺ&,u.: CfJ1͢ h;_(!\ދ~nqkF HthlEA(AאG.$`=WXon  ? ݚ! !m3hnnkE1mElBwM-ܱk{'#ܚ60 <+P[o1ou}hC(m m(տ}ЫJ&<}Ү/>Qt˂$n:eq:E\6޳_˜(~EprL& Ge}3ً@zU_S덴w?l{Purs}>R`˫׳ "/a7 C`6w2{#=f-;ǝŨ7wfIYꝁ]R{nWwȢ72oZ3?w1ᙆ㭵+y80Z[꣇b*JFux~8J(F) ȳS1[Poۧ}09! A+uէZewtc=J@BIGsk@ ܹBDΛQs ΄Zk@1<,jxu<}R75U##9ՎՁx<^.@M>BQ_LZPv|K-Β%6A8C*4DY,7Rh[m&Pd%Q. bMnN:rko-F"ykssлr!m.Sw} 7DZE+ss|b l/NdnqV9m¥ 1N?Mwm [{8Qmm~k{D*\Zib͛Qd6H6{_%ƘpDn6Ƽf}vIȳ`+QdsKȌ㬵1=d[#ȸEBX)zDA~44~8]yóF n95SD kjcȃy"jpu%_tj FDr9@d-J4OαQH[f5xWֺ{Bm; x<T$揁 2oZ5rlwږUiT)җDKacGάxo@#̹{C%,s?N,E'[$ۤ0ƼLESi( ϐn"m^ V^3Ǯ }t"mn4lM# m<ENE?l7~͒}MYӟ~8{xsKʷ&Ehs28fZƂ,&͑$"ڶJYhs2QZ ^So,z~5c@6zE(1f+01mQ{CIk'#oRch 8Z;lJܭ'ZS"i\v[sm ki5ٹ9!mn hL8w-7Aڼ|W" 3;7v<ݫ+l@Iy3'EI?>U(br 7nj[ t1{^ڴv]튓=Hc cnsk .! hŚ#|q-l[tx;zB?8îŸwspEϯ =+P}<ʹhk?<= :Ƶ(zW SQJ=c7G6 =jl0$ >~fD^*ڥȰc̟P=`Q5Zmk.&Zj$\q\"!#k]GA:&(h;B뎽 2 n?"1w9j{x<_Q9q'ߣ̕/.o}WᜢRoc?z *CJRxQir7 Zac:|x-!:析 m@Ѯ ꪈD5-/y-2,;7N4bgVFfqvnNKx"rzrFged.EY?Q]tvnNPYsoEVFf@ cKs\gCb#]iQQ|U}GɷO=5O7PH crzi i,)M6;]m@ﴝ}z /7=bϠ̅(6jpQ@V c=5g' !F&@,Fc3C_>lZf߂q1SΝ&d M@WXk/2 AAKLVȃ ƘUHh 1S- U!Oi$a!Bx.Dz NC5& #|5zjĺQj"E~*1`@j;[D-I^" 9 ACͬݛ6:7zvfnyP@co'}8em3c5DM?i馄M:4ңy%" \T ܜZ[#m#(y^B"Tw40 ċX=!k LJP:_؎oPjixHl}E2ODwM iH<9C1 n EG!mމOLYh""`,z@S8롚dT7g,J'A ֕5x<6ڠaGMO#޴Beg"moLl)4с/h~W\i{|٦I).-TVUs\rkqP(T>n+YYѳ#)#i'g}ԹiR[OFoH`NGCϳ22un(!}YLVFܜk22 mNG)E3W3{77^>9bp^{̀1CAڼjҨaE-YM4nx15\96''O.0pL'`jLL3xV `νǫ[}Ii:E6 ,/Cyq^F?WsEO5KCFb,Z Eѩ[Pݩ3V@DWG1'!a2֞F(hk `<][] Fn+))߰/8g͟~{^+׶QV G/[ej( Eme(;ޜ{@'eʖef@-"#61!m;JdedVe$g^߈߰9oD XإYZ6hܠgYKؕLx騮h9k> Q#K[8qB?Tg8>_,Z&TvGO բlA!؃(t d 4X>@[/$4otzyC^drBU{` ӷDž\~wÍw.q(\aÑF^ͨ}~KBZ+PqnȂԃXNaFcw qrS+PÇ]2n^&a3 3?*ʥuɊ+й/'=Ѹ䄄@0Xkj{HA0 em {+Tnѷ}+!'_rE+74?S} 7س/)~MzvEd8x:a0 2֔O*B;2¾S>"C=9U=sS!\3n)a?nE[Wاoo{5['~ϛ%%&]TX u&G97gгI.w{tv㐟~cw^yݎw^;c]0q8Y E X/'A$~NG& y gsgЍZ3Q^;"Ad,E"]э' LI V`vk+1xZi6ƌYkWXkفkcn%p1X7 (T|$f@ D$tk˭]n.vX,{Phj_,R cD(Gh˝x^jTT!ti߅mku|?/RQHoE%#xq斝ޫ~>shKR@mzrp$ܱqmm5J ݁`Km7yW:[3ڼ/O>n7:pHpާw"3rR3J?*TkʴqIeU@ՔiSsLz|R*)Ӧ#IeVpnF"nB*F^^N֯2:,Y?_  [zŪ9G1pꊍme݇r0/B\MWz͏ 3@|q*ɺ6?{(|x!P8g{UnD, 濍1zޚ$>o>UЊB)50^2U4"A!kCj"> hіPAq#G0uH#ᓍ6&JPf{"OYNG9ocn@2+G0 Bh@5"* 11Ok!*AyMrA4 "!ygs#UA!J<6Z;%}Sy;>|sQHSrȹ ʼng/qHM`>o9<}|̎p{͝/Vʮ}I=5(3-vb}WY @itK/nlX|2'/ʢ|2OxAn^2mHL^u7OFaܘ q,xm[(.TT`A I /=iL[cSsj{G}cFX^CSGM۫~wzG^pW] ݲꚍ?雯l(zi&ͩyzD PkpHJrR㎩:x#;3t [O1`܅q}e; 7g/_X<HdLuknDVAM= < wк+y!"𴻗\ٳ($wn2[\8mV]czoW={Q͆Uf|8);T?yOIgyww+ĸ>z=<UmA9Pq%ϣҡ@ 7`6Uo.eVvL>dK =7:׉sP節3](V$X⑕-<ϣ|>,b{"Y)G,cC aw <>Z^lCAFa?c~eyss<~y5(>OL~y )  Ƙ3_n}Qj == WXw#WhDPint pW+N7en'!mÇ<έߓ@0ro(c^tF$!۱me =6즺R{R86{Iyzm]=l㎚7lY ꖒխagnhMٙwҒߙo|RYSPε@|0o̬ xޤ筚2m}SMiSu"q9IIe8z,TV[>y NsK\=QcC^yX9SM2ы_>wq6Я1wSWfg loimƭ_;;k~KHOlælWQ=-۪v&'%޻= w޵S佇yVZA(ϱq9y;;*~K7OޞqaE%o2o]1=qq)W$ ̙J$ ߘy7;굁y]9dCFs2'u hEtʀ1ak?q 5gtmGg8c#/L7%)?7m{ G"de J?葇:^ؓ;-嬷?e+ϻ :(#f%&_ Q 4cL\( GmbܜJ"ׅ;O|/#/JXt 2v6 iwvC @Xw[=$tgv )7=4d^S&=%ivuKB'Q[Bpgas$}]kK2Dݩ>2vE&9.Zo=帛9j\#32஛2m7+%V|IejUQwIgl#)Ӧ~ܳfOʑ{pxQMv֜yszjJ~`/>qw;5w^Wp)'TUA^a|Q*LcLu(U^^Df>drM~MN>f׵  69?XSN"XF$, BaO|^!$ī/Enz=8tёnXqLp B/k/HN xx'pGٙ-xa80^Ի V#n6ȫw.oYs>{+{^Y'^p0|RY۔iSWwogo%$+3{~'2mQw0Iq\}Z|ŽM O*tsnUT&`9o?)iwU=%f{3*/-gWv&mCi &$tg"{͹p_JIIlwqǧl\:o6Oޓz09|.>~|Z~N0|+g\mH<SvכXܥ[s&"+p@a/9h@ Sfz?mF!aD'fE̅HL-C$EsKUS]~"qkQ+~Ƙۑ [kgdC$*NFQ[i(8 Q(OzG" ݚWr- &z00Z;ύq<ƣPr\oF>|b/7oxdDl#}qDͫ/ZR:24m|$^E8 kFw 9;1ٿ`1_O*k@>L~cfZS{ۏ]z)-y3f0NKJ1fߔiS CÈ ^=s羿iͲjO*yqj-2LJy}F4T=W_,õQv _YL!\<ƴ FdmY:p:ڐOϫpy`JDc1&d@ b9a$|#Tn[1/n\aBc̏ўXk\ۋ"$s?oEdn{`@}$G3\\kUn}sPTTf; B°9 & -FyܘZz4"ފ,:(otz؏ uncD['Ç/#qD⬵ȋ8qU8HmlCv\a 6PO隝_zH5%!;kN4}U 9 jA܀Y@)-y=;| [&NmκΞ7撬=pK;kN=)- = >IݼPB\z|rBoăy}+; 31enĸ1|#)3e} n3{789)E  I얜ޑDlJAv(7@;ۀ g9>PSMg ^;`Yg? b[g*|?wy'"k(DT7zOS&!8u3jAU!9a)E+#XV(pPhM@kc0Eq׻Ru> ƥn#" FQr+OA$?]c4jӱ !&96"^ dy[B (D:7 Ƙ_ȣ˭XTnG|?( ~&È/CoFgqs4>;( v <-!ԌR#nBakOVȳ}iu՛v3v?Ϥb; Qad8k~x>=? `2 ;C /.)MƑ"8 'McL92b*R1Du < *zz\?9~w0<G0|km% !-G!uzՈ6|$hґ15H(nDޫhzƭ{n2"Ƙ#s[]"] Y>|ё Dr$7|佊R3x/quimO]cD"뫶wϋ~:oEʴnJCok3>1/ed ~u)gB?!\$\>}Ǝ]W~q[o8qs6%]yݾ=wɮmѱx& ~,=0'9%+/%V7G]c{k%пǡ}H 7~kL;O2m)sP}qPy7hF70T_=z!6 ZwlԀ|w|M?hߓX?L$n!=zͰͽ7 f*k 1~Zq`]mm ]&03U4]D0j$Pk#wn,[kÇ>Xa_ K@ш'ϮvvO ''~UA/4Kzbz1nZgNX0 IDATs!&7qsYs6ۨF[mEɄo.]PSZ2Κ ,^[|;ԳPJpkC ]Wn~iOMIm# qIɧSM[~ytD"u+n۷gmUwf] "ΚYݮGܼ"h I lݘupu(Zd\ "*Eqa4R0tȨn|}Op_,pmFh+ڈ|(!!Rd{qn~eaw@y֠ʨ/͵S#Rbюr>Z\?d_}pD蟺|ƒ luTt9o2kmyAO$6 Y*v +1sv똎-ZȺg 2L@ǻk!s羧ڙ$#Ⱥm-"dwX\˺\9s:jer%WÇ>"B"^D8FQn$;LˆG:oM"~EnHxg ^oZW_y97>>cLZNkO$O^  :D>x^K_Li(9ps%ߘ?wt_V>N6'13eG͏rs)` h ^XT4ƱECg$=5c:TTEY{'&Ƨų.$&&eg~I`0 qq" p[jCkkgMSC().n@{8<'/93P(LOIg&GQZK;N -U+"neyas Qu 2_8|>E_#akm1f"G@"'yv"4Y69Y6E{"=6hF^cL#0y#1@uvck"𔵶kyp cLRx`ve(w qKGQnNݖgY!_|&9"`l?2$m߾#{`pcOOFF lAܜ @ !T> SZR<|Ys6!.zΚӕ=kor/EFZM-4{ՔiSM]p];G9Z/  :ۃ;jF>_֨a/;{ZBbwM@^vfFĄpk[[#֯5)̻zGջv얒uMx&şӣs> ƕl<&...=o$(.|$'SQŅu^܌~[C=v"n^8bWH5/kPN1bᩕĄd_DV#R m H 4q׫F.b-6E5N |]{(hy$cnp5']y6co`j|c~D$2PBDc'F^\7HĄuתs9y[:ou%p$w,;! ]BL?ҭƘ+Xpu㋖ϱ~r>|㟂~&)x7lW"pܼQP7m?G! t|g]g3|Tfk{q(NBB('~rs32ҶޟTI<ŠCVj5#>fϋ:^ܻ={ p".%f<6vԄu,mg͙En8qBRr 0Mǡg=v֜SMʬ }{qV?6֣^Ź(@\$ nCޞhȡ[WE[Z U ^0e3w^sSw-\=1VkXGEg>ozwb#"qLj"|(ʇk83X<}s Jh]\e@!"H݄rRPfTH7!1X (nSǢlz 8׽ ]_Ũl!>ںG"Z{4܂Һc"nўqWـDhw j:pxu^rӬ/8.<a:1>|㟏 <3stĎ_y*o'ٌ"F#> 3GG,n.M ecZP^[7-z۔ڲCȹgl[w,22nG hDY"NG"vd>qZp5` NXP3_Y?})-7Ϛ97O6CIeSMG$FʨO1'"+'W|C޳&gcLZ>7Ri Fb;%jc̹.C^݂p4L$x Z>BK <ݐ#&(g#FBy位1v $'cA'|k{n^Y.}ׄ <`Kkb_q1̽xm圓H!. EmO붳pà qv֜[oY4KvN{=<ɻ7AFJՊr3v֜5SKE9Y?̨S)-9q@|cSӗL>JKN@e gx;{g5躕O*5eRcPBºj> G"UÝJMLM 7B̌G(DtʙhU!uުK{PaHNA^0ܽס>EFbNFMy;9LAIizk7BD#Ώag |_Qn mĸ αxُw۹E|d扗H*>zdOMٿ|}{Dm6|d܃ZJV@ $ y<399K/,湅=rGz0`~8?3gg6N8a(ĉC, ]84R>sn|Ϣ ZcT~x46!LC«xK-ļjz#kN$@9 O߿0G!D"mGD{kmmkf.Ӊ1,&uZXB7$h7#Fdzމ4 -Fq Mo{27.c&#+1ļQd[$3F{!cGw0v[]@wׇ>|`Hddp$Vt$VmX̞9:]<1kƚ""x>[=;P(7_G^^9!{ΚkED~i.:%FXU9qSi`qH|n| 2R康Κs0n3rϠZıQnEAܜ  ,`DjR6&^QnBܼAqa=@/Ņ{Lz!>|RYyY'{rޟw Ǽ=">|/}|pUAo2\TiȂY,gދg84DZmsQZC>ZGwvDZHT#Y^ r2!o'.73Ƅnn }H5v.k&䕋tnDsdpM1W'pqZus4?3TBXm$߲ 5Ƥ/Ykg0n n1c"7X LJ>||9:R 4~7yR"n #c7 CI%ԼgҀ#}h#Wo{Xq }QwVͻ7oGܜčd\^ꪗE|ߔX pvG8j;GewVW9Ds|ǢTqE`9_"1ɔiSm[=}H~ѻn5˗ }[::") ՖǟH|筧u^">|| |e0qBqqvk? !Gi@ G}f֜Sza^:o6! 1̨֜}zrSQvw"#͋g2_gҒ9Qd4% wOMϘvg@ "Nzk8]ҒDΚsjSM@%g;aѻ'-kZhoYE L{ggRB()äx/3y[hxV(,74oSQ5XNq"w^U%꾟l>|DE_p0ڸv\gPHC!+ ߡh7Qb1xw D#QCFN z.!Cd(ڶtӭ'n@C/t'"!qsOශҒ,/pHl$&C|9yuHkm^[$|j͇#.sƢ"?C[~IeUSM}_\Y tJÒ#}͞1IZgĈk܉9XP3 }qSVŷ_l)TY/k3_݆>?E_ \"t7oБ dYHX BY _p*cquHf d [=h@UFdqV5\!<%,uNBn0"Hv?A"3kd [1c>FcOq($<~}$*u b p/D$^iSÇw1sta-*l6ASR0%}WnN?*;-e=n`M[F:j0z F+PQV:qY> d-2217"Vy:&)uPozVAV_W5IeSM}8oʴ\q C6Yyd=G0X+nvÑ` _f:EKQ IE =y_Z|#Lzn4Oч |DOD"r8;Gb!d;\S0w#n[k(RӐLCʛx׍Y$?vt7 $n~YZFkmKt` /[k~=/[al: ֱ3otkG,,Y=T؝ Q>.LELqa` NLO>)#v5omhC)knÝ1=95c mFS'c7BS-흝YO~ekOkm~qP/}|)u.r%VaQ1 |E^jQxi%cSڏUt6BOF,2 e{A%ģ! Df ڴ#Dos?mѕ1G`&o_ cL⪣kb9z-B7Eě&$聠؝{ִy0<%U[}]/D WLڀT,'̔\@/SZNC|8pu DPm* W2ٓǎ?8uY݃= ƅ[74$6ff#HSPoGk# 6 _{L6|R>iŢ1;U[ۂrkFb+i(Im(̍z5 aB4<&-GE/ (4m~dƽ? YbӑW}Dנ\%H}y2GJ԰>B8DfDDhuƘ)nLJۑ݅%nmѥG$, ŝh_hKUkm%1&5,^jp܄Ç>1st W=756G~uU-6rI][5=kBY6b=#'k ~HQ#Wlݞro-m {q34`Nz|byH -_eohk; E\'j&D"sk;ڋB$/zX"zW'=m)zXD9;PU,g"ECEv֜zSZ2q7Wݫz. xg{~PܔVyO8nڻ:s7i6]Zڦ@ / QeEG"T@PZPd괎ʨS@SdT,{II,7=?KB~txA{oi9m)M^0u>;:}Z (rm4CQ Gc Y1Eho j*Qm\Q0T~о hhgmhzTt|L!(J1tn {!CάE#p͞)y'j霗|>f`Aa1uMm*|q!cCgeh5w֠u\ls1PcgM!C8$p֊I3353!Y tQL,zpյlQ^*?E_Siiߢ hQoNJYhYIaul<esu #6u,C9uί[ʞ9!3(7oEi^;O|ZZRR2,7o-ʻQ._@W62u\>%'6PLPUyi*'P.N;ұCUbuPUy?yA{\>AmLOscm.%Ͻ-Yg)u}6iVN9oo.&{SO hPpӧ]bl{\,΢2}\4;Q1X|}Ьەt$lfkM~ **ѺfE?DhG&2'B(E#h d DE<4Fk EA1%cf EKkcK/6Ӻu\ `R1B-%at`(T'0smk662:i~ڈ/ffk[MD*朜eFL>Xb쓗ÁFЯOG&3xՆW T-iLG7쉲s4v_𓨈| 8-Cmڔ. ߝ:쩲ܼ'68hAKQ4olnEh#8 yET~hL&hsbuM:@3:g^YV7{s*Gkh5]\~'*E\⨭涶 ss'RӘrh;dWM-buVv}]6l)g=Tœ4Z|\/63($O>ue)MUEG籣!iqP>/߶ֹX:1|^fȊz4Sxg_DYi^oB FKQzSa> v4{6do ~m$ǠE$SюkW ~ ERB [ܶ۟3>t||Ӓ-H[ځQq`33}EBW6A3L&֖L7u^E?ʂu܉EsVk/ ߈5+BUwQK= 6~-(3E 5_xi};sdC3u:kLJS%KF\U{`fv[]dwS{6O6~fNҼ^unv>M?h/֮Uczph;AAae Ј3s?J*'~?9~CkKaǍw?͗Pі>mw,C͛=߼qmexeatpkA)(- ub&P+j:y!絨hZwyfm;PSvqڨRܱWegmgwX**&c(tHs(LNG&u4*ZP7Z312~ P؜ܿ~/%5\(2P MڡGHx4:=Ƹ#B܃x-ffxX<0<3! XYM#O?嵿F⑨(54(Ơ5{cI[ < bfͯ"'Q}6/uuwo#}t*wfmҘ;Q6ށbgmls=-B1)|HPu 0NQ-ER4x*!,@#Gߠ9(m*s8c6T,~;Ƹik Ѯq/W03Gd0TT>>=TUǡ?)עnhρ HtdDZ׿f ;jw=u(D_Grve+,;l_Us͢3K !w0 ؔ VP*lNlQhB3QQYHDh=ދLhhO͎m\hwdclCgWBUTg4K>5CUe˿:ؒs9K[PIW& 7ЌÁ@!h wFr?Z)(,J}奖qĝw3o͛i;tGQq6NŢc}SDG`EvϡPiFQax aCmM(AGe|(yZt0KhlT ~?Ƹ޺0>9b.G1?{o33L?ֹ_? } esfxZ-(\PҜ3Pؘ9#PPC~CKHgP6Qa ZV2aI:}}WYs]G[뵅i;31^1g_?}]PmB4$Ч:bb( fF4SP&jC]7T\@F1OKp(IvF5}quqvfR\] U%LZ{[[;_GkG %h fqj)ZT;^2-9J@E( eKO]p겢[5:k *{v)hyO%lgmcB(1>l|8E-訊'G)hD:(E#h'$mnlsZ{1 -H:`x FrTMZ@(F嘙RburOPUX]Df_P7t}~>(oFy(˟'l&vt*[P6F@\܄6;ȺJN?Ʀ 0hv6Gޫlgbv']-PE!!oF?נ.h-0/F!Q hk̨?b07h&r jY\/6c'pD433ۅtlNʁ(5ʡhH!VtEߨ8[*Eq@ܼY1mv jx8&S:8/jKqG3q]kz,^x]gF.xIqkDGsьyh/ͨe0*W"X P@#SьfzQ0f7g$h{mbuX]sIy 6C(௨0<Mp6ZшS wvҕͯ]V`EО{%/1gbб|A.EE!3#Ƹ"9040OK6@NDcE-+hhGԑlDaZdZWh(x F-sPQHB3)6N(^G2w~/_ٮjωm^ 8z嬭b*ᭃе\eE6‚OkLG`Pn51󘙙VΊG`l[?C?hh~sƓG@\ys Џ;-)s͵>;*jUsbfX^%ƸmO'p1pZ'M-p9Zx80 ._p|*B*ۚL{zvvK+gos@z\Gq/uu뵯._T]f%߾pdG7&@5I(㯋5rgmt0:ݞEn' !쇂D$`Nq0)0TD~}Zt=)` ̬wzaʝOg:r٬ܣ:=WoX;5'buPU9e Kggͯu{"o' F//lg n̶!i=KMbsoV\ p!b`zϾw?}_k뼖W[[ڀ7TU`6Vlۉ5o|3O8lEH 3PxpMqΎ*33+_98͛[[52X4{3/oLap`q}fffM,BG_kںP2L'v莸>݁,1VԊ5)ttFwEfff[ia\E@!]NPԉ;.̬Ԏ333333X433333\,Y.̬ffffffփE333333Ţbzph,IDATffffff=X433333\,Y.̬ffffffփE333333Ţbzphffffff=X433333\,Y.̬ffffffփE333333Ţbz!ȑIENDB`openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_37_0.png000066400000000000000000007536071413546205200254650ustar00rootroot00000000000000PNG  IHDR<. sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxwU)t{Gc,bw5XQW7,,"*k4ƒ{lwo;echl1y39+6"׷ֽغkEc֗hh=?k <R( t*ЦgbE\azYZa|g:'KբI&! MU*NV!1thhZ@7~¾ ;)BA7f 077EK  "[3tc̝fCzZհNUĥW44tc]#AІE^ۊZ䜋b z ̴t0yF(N}󭠸sʳy =]GCJn]y28p NG1@>Ab; Du(|=ewf0QOU@ @P,FT)9e9\Oz\ԍVôn eWʊlyhcI3t蒫 jR7hg1y";s9_xsO`'{QWa2]ZaP7i`&]״WNWkΒ[(.n PPX(P:veHLۯnEj =SZ/%j*51bhT=?=8 )^,Kή-r2 KLcFMDi*&3'Cv}p/Z\D,,NO`thhZkR.*T.pɿrOB/:nX~ٝ~Ázb]w692I%k8ji{B{i6 KLa&hWx9i_|` I $j銶0E.,A`7TEH!*k[#s]VTНPS|̧xjh JD?%vDC%s~IG0q> 0a< a NFXZ]XAO<:ts$&CGjEQ <֛#@b://pR,xԑéH`Z!Ʃ>֙JY?xEeHa:Vk0_aʉwzb ( Sxd3: !uhEEɤ$_V|UȞ14ZrˆW%UҲRUP^, a%5!jMW6Y>{o%6:T;/^s+QQ_@0~LcQbNosԟ|`w  Q& ڕk<ՠE?@*b:؎h|XhF;`SOsFb[}|{qQI<A,`O4L:_G{Uw1r]'Mcm<2ezASB˹pVTuF,*W~o0Aˆ3t@8 i h * B+{[X8* %B![DqYDe J I'u9 @xjaYYPV"G`1R,Ox: bܴ|["6-˜Q}nR44FC.""& E8zy=H$%%y6,yFs ClP_>_.5^f0_Ưhh: r5n_:eʜgl%$=ca~4hV]a呻`ֈeCCrH? !R%3@W5-9FN/J8Z'o>N͛fޢCh~hهu!ős=zƏPaB0~e]0;vl5>X41pui>&d\pK"EBЈ kt1E󈦟GAXCǁX`;@JrM$'6d׷*]'kiӪV}G:\ma™1<+gԵ+˂r(UHwhgJ)k{\w/ 逌asź\,B:."cId;; 8wO8zd|<.RDAHքj"^.z˟ّ&!nJ-+5bG[ؠf3JMlaVa6_!F/i{M. zvxvxݻUE'd:qP\'-N%|x( zG5 @hR,vB)^Enn<&6ꊱ7GWnjr`J`OB!%}=mVΥ7}+PG6Պ}O$9QF4 i卙ˈͮcF/7U`#E;0\*{VrO:k=OAs8aעc3T91$ =A`5q B!dX.BzPNzGxb/ƃ6϶|D;ގJ)&Gօ@uTŠ%S$͛)^CQM7͑&*Fb,) jl3a}>;zOL\ ;O00zO?5)*J?3-|YĂ+$ӱkSqqIұQsʳfvwgAa],A$X(Z TĶDbM@N[ 3x}k"lIlG;hr}l 6JB2gß8ZlyR9YW_W3z1<+* OQU>[.ɑW+Ο c-F/PpS]~[AikJ/t$Z:A_/9x`)&сG1t(܄v\i^'\IY3(c+NQ2~uKXo{/D|{VR $lX8|YD~M?$.vhZJ8-Nկ#n o[g}]Y?yZ:vxoy3F5o/ x !H5t:ߖvEpKsTuu;ёPAvB, ,GW<#Ê9hOPk&(lJAm AoޢWfurɤtR9eq.7SLq倹ݮgɪkr٠"jޑrDtQʐVrzA$m/2@7,V M+':~ VWQUŲ0YW$X"iD;}!V,v<ٟ .-SO[^x%9R@҆ y^>8LRۊ t@\3(i:X r CDzX߰|^ksN "2ub\)J"MhIcfDtN!>j]5(K=n*7 ϋWxlHk,K&g)LEO鞈a LcB;֩]T.I_:ZhZ].Kd6DEd]KaA _@Ly $™KERh+mh ZtAZR_y\wҿFק²hw3V7\ߪi>}ーrHL'ˁԟA\B@]+㰇Bި rH>TyՠOY5E@B'y>i5#=oV#wFC_{l=yp: M9pϜEV&1o#`BVj=jq.Xx\%=RIG*(KZ]k#6IJ*ס;PJ.VNzSXp'}WuD.],7NH_[nL ʧ 8G՛lA_ֻiN}ȿ<.-ƎFkuM@8 T'CPh]@ei#2P\8z?IOgT{h豉);;@tgDˁWM=Y%ux ӍMNHqC>waGhyoqgjV**XG2׹7Y tL*\Qvni;B3}czZv6D<<ő mP|;:YCхz-a$I%#?O9  AGg %DP\AuX=t^'М8nCx׏+W #ߣ;l_ 30~DԣRmU*qzѢaomȀpMv)2 twx %z^P;"xͣQ44LBECwj\ g{[1v׻}˪:?N aAt,%Ndn@']6Q/Z{]sqUߖn7E'NYزC>hڮ+v^m+(yϏr`_ut30~eDCV!?䳕*э6q[yPw_Ht_ M9{8/4^w-w̋G'@Gݡ_qUwLE7>nZ=%?^KCΘ#Wxvz@kcw-M.>R]t0d.vH6c#fY+$ۂxx=?捺o],rb51wاps"*ֹ*gb MoܒΡkKTIY'5CVo(dR<,ZV']sȓ W) _}єeK՞R{4e ^򏀿@x&xHĿW*/M+I/ ?/[SvDğų7?Ԭ8Y&\2/?{dDw2-EsC>*C #̚ߺa-t "~'/Kf&nxE7ʬk\P~c%jӥNQaM&=u$m `C i"YEl֍cG5CNTΟC^M,l. _.S +=hJ{,ӵ+Ydhh^.c2/_YD 8 I`g?At23pP7QVttzpي͔w ~S?eCw9Gv>oe ı:{M+z߁byYCEȲ'ĞKI5?^^Jh1y 2e'T^8CZNk]pFOfJ9ukxg6_(YЌbNۓ/CZw޽w9xyyMrMqN9KjX: }} Qx0A81ҡ%DϬh?v~_F/{{Ư2_]k.~My?n=T <>+/U㦥eFU IDAT ||~wU"ԖDBBB Kkj'R7vA1kO}A61lՅA=zE!r_dI|ũL٨cIS{$wO^)ǹJ<60f&МHԻe&Ab]AA*#?ճo:o539Gi UYhս\J#͗~p [="> PXX:$P9]W ]ٸu۷jDyF9{jLaŒ=]`[ |mjKD٫lug6zex c:!2pAxR KhE5Lf(i R!@"\tĬ(.ạ*ۓHG,kL[")&v-ZȠxԢ\%ū3(Kh}[){6]bX6uek.VqrA&*J3?M斃kVI!|y?£FG+3.ѓ˗Wt*Muㆶ >>Mn$Fmc!TѳS)$۸EAڬ_I>@+m՗[ɶ[M?k7INEɹU.εeSn1a\scOܸ &)E׊ŞX_6zaG^w٥ip%DS#~jvʮܞ|5)=lǵjHZkl?,CI+M:Ē@D`CsAFmsi{qh\#p\SL(w;o|~h= 7\=TSˆtd&?o2o =>!$V]#j~@dh7z34KX!ƎNܔ'pluSdpqojGvX~'COR ˽/\sXE iyFzHy>K͝KXieΞM~a1ZDv5ܳn7$J?':(]9ER';RVߗ*,G?ݥ퉍Y= b_%\!y?nZ=q.Fi!dK*ՋCu1| ڋwQ)DS?yE{fwKB{~~_U'Dwy9G҆nDym©F?OeC;$ӉQe8(Dk.>\E[>I1Qycku5E3]A"=߬O?^G~^}锆8|WN| 1< v9RmIp?#0%zh:P6_ZwU3XZ YpB&EP wYC;wj[ץ ZD+W$rIw["}K ++|)hnr+X>/WLNbՂm,V]~@$<q_ҏv]]jOJ˲kO{ 3.O;.0_z-D&:MLj#׭eq?:]/vO,,.+>=-O^z9y]k&mbf[vk)8;(̒PB+Q/%m/s.~KQ&ܢ;7\h]Xxd.V=ҕ eU|?I_*\Os2po[{Fogfi//#T4VTmq 嗎pCwEvϭ-mE]xןXWMW=Ҕ9msڒ{b!6$ֽ,}La>ΙğZ/`[I"DZZz)^>?Oyfͳx?;ogtf,P1*u|tøOcmJot;`#ti/k!Wr+X]"|_]r ;⛈)RvN,ҲƓak=dp xaߪh9Db%χ8E xt1螐vBaMot7mW;ǿZ;vI{'n g|%Oh:;,>Ujx{[r`-?Mqp@$0`Y_01k{KaWx<>F&4ߺiV XUFƧ˳7;BV\o>;C7gɕ{Ft"j|m."o=-<񝙀0~?엀}=o=%^uJavJ ::5'~֒C] B7֗muW?Ac@{X{.ש;aR*bM$ns!2H-4cG &kEOvRT7u^7 PMIFD =+$'Tق}g~g㟫t[C#r;/{]yM΋ՍjrE}u$}x*qQg4GͥT=|@"wR ^uu7)-{m mD-E[S+W/oHk;, 1:u ~}hRK/~ʂ*ZG,[cE&Q"J'=JĚ?R-(hW[\oT|H̳降h?2^Ew,]pnYp/<\7!&hhj}X6D;R +j#]ATƪKN&ݕŤ"B`yض8JvI3K)n+&^SCZ 7{XMbFu0Ǝ_-Ɉ+(~#^y‰kSDQT˿M)c*b&o-KcEs=[twdZ Gm,T\re 4O =?.orת :gP 5#fs=TZ6xZnWDdlU},2+i Ll.zsݝWAq5p03:zay|oŸ")Dڈ}߿T9 /zf<] ߏU]sٗј0zF߻܎Aɻʷ~}ֲaKʊ0=hÔCw @<[,t쐝 =p,*,N'#|Bhoo>@p+o\B{f+pw#} AW8IbS&-h::etNžcյnEF=W5evR:s -FŋX Li41Sgh^SVg4j}FcGy8+Dn&>3i7JpWw'_`nXZEYWvcX/.Mlx)W5O>c'50sg/"*"v(Sf, 8ZD%Ot: gj5k_f jI?yJW0"Aw%l50<#O` e@1BMM@\XIaN<,T V]zvAWj;8(4}XjU7}u uv>B\>w_=ߔڣd{J%v/wuv8>ykZ/vҾSNʬS)wT|TV/5-9-.Ț.:H;#s +P ;:q\SE4Iπe -h Ȟ9 RcDLEQ! }i<1~*zCg+deO /yLk Z{z t(nI2AP/o5d 1 Ql1ounTT䴈k ^>7~Z[ƂuU j:Š.re^=r=vx> R%\"ϴ 9qU㎐mQ]JHO:/ -9+(3}Z}oNgѿk&=9'fmiVq71rK~qO, Es M;꒪ /&cZݠ[G1u3~(עfHGqiIEr]:U;$=hY[}^#@f?I oސ~lu߷)h,a-4o8FI .t.+u0nTd5Teh ]X2K1nRzWD 3ā?kf a")%ҳ΃׺ҀUUy:;/KJJO $"CH*PTف;ۃerŪBTrX.jkf r FQ۝L766>oXN`mXUN$Ƕ Mk(Mja@ת}ke BhDzk3h㖀[9(JKꇃqIJl0QCNd/fKnZxKx,Y1cQBU'u=vO"+>[ 39%[+Nole K] 99W>}e|{ۺ':.3`/!Ϟ~:g# UJ(jT P`'1reZV%a~_nj(&oXOl o@.z´ 5P?ø ЪX@,)'Bw"9A 1lU]k[U$%i$I_=ӟb^dkg^O/ŧ |ұF͢rK9 aDI8ĖAqXİI V b!P,Rʭ IYn4CD=rP`|mr}1E)mgcnʄɟ %UR kv;ҲԑN ¥g!+D#ֳ11'ˈO-Xr[;#^%{C'1Ti,߹}ߍ9N.CyZokd'n:پ<M?7'F/ =kro%ʤƨa[ޫdbfq'擒kx4?Y_ddx205fjÓIžט NHYuk 7VPEtȲaVOM9 $0*&0LkEtr!9,D5ǂ)@k: ' -YZw"-W4OW2>KFIni%I_²o0T^JwFm?uvջ ^Df6-յ;<h\0pF (ȀetK@7 IX( k(a'bj!U^R@mgD}~LXSm4(⋿RYLKHsF@@C<:G։"ƹ<__.paߎH3 ^UtϬ_iܥO m]p?gG oOtaf,!.;嶻mUʮ~#\xM-5}_(8BeO9e+$ qrV%f[a[J< B !,-"=ll .}()n4 : 9ԙQp\^o< *(| @N@'[KAap̖EGmaJ;>9O$&i$I_EͭҚb]2;4sn"i1KWk.9,՛OSo?=O#6R̪)|ɏk)lt6;9%n肂U望2Lˣ[g6BClᏱ&o#%tDWI Xlm*Qq$b\Vp1CW4vstΙDc(a1mCC:b6X㍁zCgzY2 k|ō'FKDTZ:XgwG,_&3vb0,vq> IDAT˛k"H0֭(Ϗ>lxaW2ŲvNuتTe !{.6rDoˢ1c‹n\mbka;*XTMvW:ŗyr a$VL P'R^z@zJ~w+kkC [H>Vk(QwI ˦mZMCz R]Q3V &/5|MArK+I2sN!pߡ &_ 7ɤCMA5'Jqۗ:*^mIWo KQ9XԀMdR:Ka"6m _w͕V\( ԦpO3HHIđefC8MbE%7vK4 ,JM~ΡVQoҢ2hXy]Gs^=nC8!HB+3]PLy^ݜ6#>.fz"DMqB٭mKϐkY?=KM=h#}fyY2Qw|쵾Ǣro-X{DQ$-a}"EfA%k2_s5CLU-Ze;s_2w>NSrxhoK0.m>Ð~ͩXbƄe,V/쇫`J1.dV0f|q9O b1?wL )DIOAmI"D Z29/ hUl@Fm-34,?4_ԁ/zbMHSjc(! e}ⳢzL"[AGCxVn{}(Kl*.UU=槯jp|ח?8~fsH9ZZohCsc'۰\[G'; Nx,0ÄF?==)K=5>Y7Q?F;ؙOJ'% 擒Sm:̓gru` AN~LS̬JKc5\M{apBVjL #n\5,hUxRɹ;0(q4S%69]*Q1{E"(|`9Q7`W-!m3ׂ<^qQ`00N՚-_ pB # ,Yp{ K'|Oި&/>~~5Ǿpϊ|nc·9]7Wnߩ+E4,֌CYM5y;z=r+X4ZLO-4QSJOM<8sJʸS,?=-2>~˶LҤ)޿ ԄePIJ۷9\}OtJ:cKiD؃.T@kt!,Y%_FN`\$C^eMx_HcЙ/ VLL ,@Z # ۡjwQ!%3BNٰf$t;`(Q Z po4x$OiVdA #Ǐ=vc*I_^z쉫VggQb=;cFU}$IR,L/KWQ"f|rvc3ba8*gY4'%=A@{nM_|)qcvx7U4RQ8-iezjm#XuؑlfxL& 0Lbmsdk9nj$uZ9hi=p^ޛ' j\Rɷ2piE4exqʴk}9I̺w_튘xY#1{O~ιv !>C/pJza8f&&@cO׿19f{û;[6)Yr "#C\un ]ҰS?x@W2x)RefLI2yBL ,)Drp`>i\!6<st q\ 2 * *v/,E;XK @Dt +eq 0@9D lW@ 1>I]*vh)Z4xd O$e  o߃_HҘ4@aG/3 /W8Yx'^MQuiNA :2?@ #ڒ Rc&B̏ID8H`bBcU&nG :x{&3'jX]Ym2̺\GZW^'DQ7;wyՐ'׏sVe81ۈ%?ߴ4}*c{08B>q%s .]JŠ䡚o{/6t~6N+ru?ľwt|)N8M,)iN*>1k_n (Gڿf~ɼFa92+"C,VFᙅ~E7{6cOJQLb 2dӲܺEt)u`m~uPqĮxU~ʛ@DZ%}P/4>u, ̢P)# u$&;]6u /7)ޜ,T$n "XcQ ǡ d؄W n I@*C NM z,mI \NZ8n;(Q7;[sJT7Zb3$$Iy @iEzѱH։zNS~Fc!RW[VXqܭնXjN"&۝*iBwܚpEZFeꛎ{7˦a]I}ͤLҊ룵LaKSKaK!e7y$|06xaހʩ~z`~˝W{:}x |=Z~`>_sv3v6PmQiz/k 趛9lѹc/Q*4.Є]/xV>XDj`bDWE.]_q&Nmeǯ]5/QB!*ZHIL4OJ:0A! e|ٹ'XC(qR .EmP<NP;zs"f2Og 2koÿ;XkNw$D@ d"YKGutp(@\ΙJ J./cZJU3&F}-`G/`m$I[ ?2&f~ބ>~_XnY2C#qWґlencfFmtҮ*wSB$IC]ByqY{8]l~invjk2㲋 J;x'뾭>pK;1ΉrdcME3Owp/5 K*45Mh.*щp]doeyU! {|ߞ;؏ ] By]\9zB6ѮKu{DxܸQaX q 45Q%t>*j>)!;qu˲6$mxen,3"IgnJ}ne_N{%=(\cxITf0VC8!c “inӥ¬,)`YM=rަe.;fP4. ]so'Djq瞴6ٱʤ^)Pҝ4k:) xSe>YA ,]="%p  IüE ZSbd  V l^q7h16XMђ S,@ADq>?v(Zz0V&I6cn-98bmHPC:K l+t$Dt|3 ?v]XeJ* p% w?x Xh0pe]&$ P{YSX>j0龓UZQydE)Ӽy'1˭:I :HK4uOf׶.;ґ9L9aUCqr}۰i``^T by$^Ɋt1M`d56zMg8N}m>i8 > wC䱮{sveheFtܸY姚AKRoO~FL' {dD5`%d$!VS6 qo:(jVMOn"-`Dq#R7c*8,Ϳf-3v~8;ED@DgڙJK- pCATB}enJlk8d6a+1 e hRM34Dfx^-XaFFzZdT1Xf8DX&fQ͞E dY~'/@:(Dm2NHT&ZJ=-]ՋDvHc>`Pa$$=e5W.}K L1ƯZ*kMTnN,VϦebw _Ƕe(Oߍ Z>Aŷ.ޝ;MvC?1ᅢ qFˆfVc.KvWJ=ߌug#v /VVԜl=][w%`|Rbk{f.]/ew(ze@F4 C1;$Qk UGv|I#ݗy|绾#[?9 Y(.sobϬDMsYJug=f)@p }E{JL{ͷ] -q,lR2`з~%7Z\NR;oԽ{7?y:}~R|CRu Ư(U&*`0v`]:TGJ.7aKX^Fh= ^|SZ{zer qJAl0Cl4NTl1PҎ ۥӶX Hb! '2ZʖZ3B@D6Gz&eʼnQkвEhK, @&Z 0Q#P^E97=Dmm6N6 K$;pȆS&f)E "("V Ç#Z%7SL=43 & !@O Ű9nP zK7[ <@ w\ZxbM)4Ic$=}mRFy>He_0qKÕ>A8gGϊ4Nwr6rvwrg9f50Wt.g @=\0(#y~ UAjtnm|ܤ ӾBd#Mc) &v[J - \U;8 yi|R% `F8nvF|%QU Ԕp9hO]U-ΪuOMPG%؜#axK (@Qq2VgT=8d5#Q9-m$%) X`ch Ц&@`[P9n`BIHzx$x }b1׿h껽wpث[M Ɗ;TAepnϖe=庳4 {% $,ݥSY\XhߖEB%.0`w1322#<}ov IDAT]v[~ڴKRRɛ3k#ja56P7ZD}݋Wi1ߺzbg:ؗ][dVxa $B]\I&荓? ğ^2C8"Ldt UOmQ~g&ūxS} 5ƌ.t]G4M1QMR)swώw]jh8phYm7|ݵҜ#ei,+bm1^/UUfpǕAO)5bj[cq¯#m9#u; :פS>$kz{6x{6#w9q^K2 |Kwg>ۮ>~-ShDBtgPտgSgP[ÛF%SC.Mq/6}t)PAY-*V"IuMY*C}׈.lzxf8޲j4qq!Q r8R:J{T94r̳/.9VÇ.RLw sγ9hY=k瓒^#rj%Z2 JAE;/-Yk R&4DS;l*y^1%rЮvG,(_[Zokb4d] Q[P_&A|s )PApX2)hAX T4rQp0UሁHQ:$G$$71cKAYeͶ}x{.9?]>C]ߗb|jڭEW\xiBzS[\t┣urV`4i@}"HYnOɆn ˞Qk߆ꇺ&\T!hD_N6].։9] @L]ߒi2)gQ=rȧ+pOǡVuȺ w߷vn Oc(:spg|kM'Ikپp /V5$$S[̍g=uktmvu ݗ#\0mYr,!=jv_dgK9$8gt!݁jʖ\} #pgMî5wQ +Nh3nQan1[JrJAIv?K{>*LN{ʣ?0T2c?\ ^A FOXO";_=խ˫;9-)WCUӧ RâQ؝6Ѣ몊*#ٴ1hW{,\n=MAwYk̊gn(E}%>S z[ @tMDG! 6ַ::ضw+{GCY n;]>5PJ3ާ_߼8*8$zEw[z Ân*ۢA;Mw9I]e~+7\=>okWv;R} [ b h&kJʍ btT G/DNf8{pd0n7\(E BPntX'XHgo]_]_3Xp -m2OW2))KcFON|h^$,&}8f7;&4R$.) LαXDjiD qAQVՄˀ*PU )0A\)K,nΫj8M)B&@McYkޡDe껕'I05C}GFS'^A0z On%,<2~a.p(Wn@ֶ :8ep9"\t<<ji֝|!3 ]6y$\uUw8_Ͷr|A}wQ:ylJ})5h& , h͉]7 kVv<ҊְM֢+ >2 -ݏsiC{ u8K߻T'g^@. (^;9M7B P!zs%%-,pNcE]gvt@":Q07[t8H94&^M??We6iMћjUWGk1w0qh⥾DGC $[5$1ӡh"&vXh qC=>u;_wozp] -4Zg>Dzo/u)7,I(`Y&̨oT1FuD"ߪ ܝXDbXT}7*QKArƒ" (B,8 =ޗВ(5M$"B#2-^r˲UŻ5s(QL'I0N=>'b9ck`D/?%G6=BKٿ5A/f9$V̾35.o?*8dNEyֆY:q~Sa a3rAv)ZhyYA2iqWB?;W~Q5 mτ `6j]ᷭYqYr? [7Ch7SI|99@ܲ|e=W۫`ua˿]guRcM.nIftFߘ,Q;^ߝ}|g>tvw3[[t=/7_d[(;־/D~!h Ȏu=cx] 9OT_YnS7&)\ED[>9vU9{YSeNO3hI]Hj.wžk`77948I 0A_K}~_~ңmRҘ"}՘*1(pσG!zJvR5]EF! ]8E)6iCTx1@7߁۲H Q)( `Ho_YL>I˽zOpAxϿ}"/|8G/toVK ^ں|>ke)w!t_L`~*p64^%jt+bB[\潥QFyN(r܋MnU~6@ZY\5Ј 2*IAٚT*GL5ggg }Ǧ]SFMwzS)k4ՠޱ5re{OiIe|b¸ o{eS|p͟u%G{O恬1sKWI/?=g(}Af"UFc֓6Z37F:h\_~`xF>cĒRͻBPZp)K3l9 4d}PlX ` 7xLw; pb*%Cr~J]ۑ>- _-ZDC9pF,אx/nx)wś-ɍgKϞ:0&cR3G5mSЌ ؕDG !D:Ʃv Cft@ h@o~pTxpڱQ=88º1*{}իZxipeُ9ws'qLɷWv |y^O,%nSxYx9iZ(Ą(MfvU>WL\2;9e]Gj$G}/O=i"ZY{6]R7I"҅ vra!\dOB&p=+%3cK`eAAHaj>8K~f1^^7\h?e'c"R>&X&-(#%BJՓ?'{/cځS]2'̝yb[ѕgEoTX|?^>vj]~dE #N;-\>1RP7wN*N"lE$P˂11ȿH (:>D*Z[ @J*_ *~ d @#^f/x(@LBVA!sԽ||'a*6&=X)O|᜿r_{þSD,Yç9m (}Yݎj,(xg8&Bij>] A+~^7]@x n8%epM|dŏ5a֬WR08 w] <:|=aG \Fa$dphpN= /,H:>y CӃkJ )@n@1AU5'N:|YOs^ ВmUSS9b9M'9J.sd( MOo~?4N2@eԈkL$I`g$s[Apx;8[GsygFPڤ@CJр-2hU *"WТްgDwqG~(8uĊ_J4T O^#dOp̿ǢS\ 0hR*VB6/z@x_f/k4@H:ܿЯp/knd Kj2uBFĚhdhO;S^ؙocE +s+rMJ|<OK0Il'J`RW.Z6딡<!%rLdY; įzUA `#S浱Tn+tD%YI>q,<%HnC +e%SR n>g>޹-5Jwc!,\܁۳K.컟vǍ;_FNVQM)ux"c]/ IrvȪʉ]X0ҰfS={?ͮ1W!/O02|WϜ0/M+PeE8i0D>, %/Xqwx t0""N0g$P7$C:`'y2o:p˾dPp5u I))lK6/tO3jpt[+ԲGX6oM:jPY{]AJpq% |z˘:4crlܞ!¢4{YqZMdZk*ҌˮM-♾1$Ha*r&~w.g9d#qǓ1hѷO0y̤,S&mrxS~|>Gj3np%q,XAܒjmp3?oҷmηYǺPK}gs.(BQ"-~AC8V~/VqIE|\ە5Lbr{7vr%"i_ϓ97xGVӟ?U;BhYen2liϜIP7ʻ2zo|MיG4%(]7~?<4ɥ ^1. v"A뭄r9<5WVj%N<İO6 Kg5媹b{~CCՀ/ߚmJ 9X)FR IDAT :[Yލ- > !A*CwC SZIa}Cn*hZNl%cg9eo{6C/ !/(  kSxAIF FSs>T~0}1`v !`&{ۇ_Q{w^z{oscI/eROdw[/7ʟ6t1Bl|?{2ѯTEu~(Z)mA$ o6K5n$N䠖u57l Pv'-4Ta]}S iG& z?2#3)0: zh=50a QT؅THŔz';nR%;jox @`t!03&*Jjc'5(֦g:1j1kjxCCɡnsX36J# glx^^~J-P4gWBWuZՃpt Z9C8OԕB#]1v~ ۱[暯X-}&kj+Ն-_Ba!R$/Ǣo /+~C^? G`ٕt!p³kor@t’8R,JLƻOŧ+c>hkZ&ZYO@;3B2H;P6oˣ}ᄁ[3G w5^%R!h?Jf^$&3P+P}Z# ]ڦg=SJe\|'TjlU"N@@cp%&<N|Zw+Q~DiXrȩd mGAUn#IFQPՌ7/J O^ hMw6mG<@οϿ1 g> 'eU gӛEzyN=nn4;fx>葺 ֱR\9|?dAI\DYt+Ë 6pSPhG5#0w)MGA(wdQ=%MyD]xkҏiR2jndzUW$(IsOd]Ub!jzws+>U6%P"MT"[fB v )f4x>="jۺZ(JOsپk; 5b()V^?氢f޺7|"Նm & r e̖8XSKQώtp\d\b~#I.1pFRp<qOP4<8<>WҏpVVOf%g8@γjv"xhP]? L22k׀jPV ! iWP. Do%xi<3cuόen,nߧxh9k$F}u ]g3QuDZ6vv(#[qƔw4oAk)Lb#"5~ x!Z║0 ̍<6KĨ,aJ锺P]KD F\nYǵKeC.OLL^ʊN˺RIgAytΊ_Οw[/`s + op#`HiTi?[?O,/`朴r8:|ˤƴX{^,07+:}M0o' kWBWwBSjto9vsFOC/>^[[0cXRBr(i4bȱBj!/(pFPפ6=M?HZc; Y'*>?|lp;qnǯ!$QʀΏˇ#?+~{}KWY=GR{+O^䛗 $v"JX!o jΤ8 QUQWL_bɄFL^g' ;H]u7-!3#k %VC::8@bH”.gtT7'Xw-QzCU1avV EX `) SH냏5JiEɁFdbQ˞3mA uH7k1A0@ δTqn՘!ZXiiҬTmؼT"v\l+y=SOKvX}澦7ǩeN=Cu̞Dh5(?*~ 1rS ìo8Y ?$paߨbes42BDe@ݝ4tn=$P_\{lIedR< tw8;-C cκ; eM $*ɔ3cLP _fxM(J'WR@$N@UV^zgxz+dIl%b-YTOl_U2_/,f`n W\d ck='Ws핇M%ffWL<]>9uK+gD*?}6Jx_>rb0m1FE;1{ 7hqmCAd.zw6A~dLIr ]gP ?28u`iHvE~Pڥƒ;܃$=8a%Z:p=[fKlJ!P0IzTu zuA(~퀽^MQH"?~eĒşf/ݽc>q 7=#SzNdV7w~_Rt粎},~چ& ]׼zW(  AIǦg+V+Z.;elɝ19OnMa{+O3kw|R Tif28BonG^@lmA詌S{zf@*CFIyEݙs6as)DX-K˭K?J^?DS!&)>u" VUQ߶ q|-ܒ\#)WY%1MսSmc{E\udq/ ʴ:iE"A0_# xS~Hv"D`KJoX<왚ɒuq =D%\t[ٿy7nԵ@f ;֎|3K lQeH{ޤ^3ܶq2\yDL {=BO!6ǾLo;qna,.Έ\]ZEإR'a ZG]Qx&f)|SK_C QVNڬUa8MafUsSgo]z+;f=LI_GȎw.1ɶ:b Tr! xqw (;gI:h( e!%)8v?r/!!)x#m7G~K0@vc$"m:F@ EqnBif6HSZl;rЄ9}d2f;h./rӦӈΡ 4l b QLI$̕&>y8^yQSnOLǚuVn]D2Z\ S#S)ezEnyzv2*)S!hlڢeJGFD}fQm4:. s dz|-[/E*u̩tugrL{RO6]T,soTUnʑ_<2#R|X{%m]]qٽހڣ))ȒyiɑDpWv FE?98}Gi˨A^xX;j$ZN(q]iM j V i^Q¨{YjRmZY Ġ nԪ`+)ϽV wLFzRu'oNr+C70c>[Rv+-ӤL5G7''E,3$&QByCۻP=x188#b1L+Ӵ7YS&+/;})=Y?+v"A՛ӫ(SQqHmYkv2Ā~(Os8[ Ķ>OJW%A2SN;=7ÆRKJ^DXV,1+I趀(WGjj,Տ!P'WE$wm 0!E]F܀9+h:5)wٹ;'tI~|S20OTɲ9MX=N8VgaM2Zi9C {P`0ŒrN>c7Cg* v}|_i2mvݴ .m@P ILDu<3Cb͍_UZe-?y}PxBm鑘% $*OGIɱ']/$䇇`}ۋ]("k>W7}8 oCJ.Q0(;9YS9S7}3gOj*-V.:{_|xUy.s^!svA9u T!,!_ˆr6ɁV[禿:Wh3]t$"d 3h $/!8nҗU]QYmF6+?(e] kbK.>&Ƨ3<ݔt!=1Q32^t$B.IC]&}{ XM41g W&=iX:Sd%LA;MfBA_;Gú?^8fɲĝ=Jbp6EMu{VMqG)mH{y#i,Hr;wJnPX9D2pw:`J@Ŷo*ʧip5`v\6#IȦH܃&t k1n`WM^z! ;\\D1Whc3 r9wBkBaleYHu~T>:9(Caslc 2˿j 1% 2uf&}uSg= R鲏$Iqk*PUS<"pY0}֞ywxQ[a 9T!刺n Sîx5K+ _^6QX_XBsҒu]4ZqX~2κ9쫝S#/>Ͽ+. QH6w,"sr8p{TpAe H%v.\i(l/H=qiXY(/}sWgW$\eN'G胸zB@u1U NfƁ=͉Hl((BၙF7v$/c_ cp&cni d|HUuom97/-gIt ~Xϊ2N?A6*s|xz"9-UZ_>u3XKw) ?*Q a6: ls7>`n!FW"V?_vYaS$+EHdAulgtBnX1j1u핻~Dը4dw)hO o0$< I!PTra[7ˍ;o[Zx`E\B!-I_7vmg8Sg ">4~ nM5WP@ȊVp=Y\<__+o2[Jk'u[$ \ >I_r/Kvon(F YSYMKt4eNc^80vRȗNF|P1WH>å6yK( IDAT!*0ǣmG:J1=ܓa#cӭLggW:e+͡׽ySF1o7]E2J\u;oth9k0{_!`m@ӫª=QЊ?̿zd'|bE\kz7zӏVkW۷R=]1LWc&#f-=-j^J=VsTyGEuG`K+)/s 􈌞\ `[/G/C>n~֍}ݙOG澄{c/9zi`̧3@P/m} Dh h$Inz"GsiɐY+(QE !34X8N9v3(q=b/uKySOiϟ~~Q 1=HS n=^~HL- klxiM׾3v s5EdE"2LKtX$}* 9-?iSث:8F 2Cl_Ҏ('lpF}ôo=;CroW0IV@J:ĞM`HHJaE}OyVzU+N!%en;L2_;-n>vmQ<#Pw剈u\N+O%F& u-"֒r=_z7 |߿#k;W5YxdE긷S~)mړK7Y}HO քO]8_ ~Լ8t$ C)HރQPN!*e J hX݀,*O.LDuĂl U$]\.Gލ $08Vrn/"² g9ͥ's)T al)?(؊ V]yf,T4MtZ}v|%X&9,T:$hoHp'. @e?6WzSWm "JؘH8|E!k6)(QY5`,X4_H5f>^QcϢߨ?Z>=^9K;aGgG6lVw2N}OVfpCi&\ Y*6LAtaw:xxmʳ%J5=C/lᳳh>$3zUIר46yzkdsZ,8qůi/|ݼAuH"0.SS9W,OQR;l2?{VձC"y=&O΀-OxUX0^:1ۑȆD%K^m^Ozgxze^C#E9\#3!@H_-E礫I ;_ы?>lۆ^ .ݶY:G!~-Ar3_ͦb!RgdMk7O̓m;U|kvS=0#ȃFQN d+QPɃhsP]X+5p*$wW+WBTO." RM˂eE"<J9.eHItߩOt KA x"N"MesCG B[dcyfkqI0GJBcQE L|ΙU/4Țq w/iۗ9-t_nݗ암lf5K"Mz9'r Tgf>8eԶ !SsXԭ2͝l9_.۰GD(PUCUk\]~./;;M_Dv!{)ԋFNዱJ2=+fLǢJEknlyLY'U;OXd)^88 g#888HG"G\+ ϙøߡ^?gUE7;ι D@Q1 QGQƜ1!3* sli9T_gu{>:.# [o/}@qkxfvɸq kh⎪چ^PZf7o觭0Y%i:#A @[SI) It2Q-ZQW7~1hob@DA =EhL3yyS |g5Lb o$M-yÐr\kv,}VgޅnG=2vX-)vwE0\(s]q9Tg31!ӛ)RܑQf/J`j7\i"b|\Fd'p KS7$=VycU!HT3&as,{NMʟϽOSht A#kӦ0yEp\%V,Ù"={[` q!%?qqp]-~sI}Ul{!&QGk7Xq _I~ޱHӯbxr."]~.ӥ?9mWbͩAd26r0"?>;BZ&Ԝ9>i1`c칙uO'W[VDVd)lb^hBܱ6}R*ӹS*iJL8, )PcZ~c{EF)jVjԟ$95NKa9BTrR Ba0O`#jdž@(cIݳ<4rLA͔M:x)xښ M80oRi&ti8HMb3'.muo>q)᪖ :nVoKq20I~&l^4$uK0mWNjE-o..?onV(qh05"9]yG!%ڧS#4fL3&9/-}wv,4 D2A2>|=O^<8y9:r$O%$,amz˹N [&91CVϘ/v?<5Ij1{xx7(;V 7eMA ƛHRÜ=6QQUWA !qs #LWS1Ck ~oc4?_3+g<֖h;N7q93dY]0L.:<]>p:c3LF9HiSQ/*#OћG[Z?3߬&x˵c@;rXF՟ի7m\yf[*pe}wHMlA}pKI[5N'm5[O?SL 9,RIS+AWM.z݂)&5;sW˖lf%XDT@Sri۲| ԛaFOTt9Po;V1nD/&< 0wO좠jLB-+/AQ"v׊Ge(g#A@G8!6GR)bޅR 5怃V%_Np|5v7hH![/_Ό^fbi(3z7Y`=jg,hn4vQGXutQ}p3C >hjoZʳtě6o=]yC`-ʣ<'8#[Uzsj8gB3EJ(_h bUmو52 tmv_FݸuJLʎ7jƑ(Y;\T hAX"żvF)MS2.<|*?yyފjٹLʁ7Tg\K~peZNo-r-^svV*%8|p\QQdJ{j8޼eǂ=N'@jS˛4oBTd2d 9g˿9<]_ܳZ*Jw놥yg9A3I̫;^ZվTW؞~1j.l'ϛp͜DW7n|8&% ksS1@ފmY?*0Ih' Is;qX"|+di=xB9`7p gƥXe y^)x JPlP+8_ŕL#βJJMC&6>f˖F3xLؑ>9{^_ Z>i"e{{CYGd B]ށ eˆ( +JO>pLJkRdCQ ecyҸ|Tlos{3 M_FeG|t?w8xHj 0>$0^q_֏'3b5K -sm6x%{CEi?ʃ8rMhښ5u5F'"CB4(#=x%U_?h&!=uHoQhװE*.]/n}L:5~yK{3ȟf=ێqks4kUvuu=soZjĔrRЈJ06;3M?\zs[ǩBS#|RP=Fu1MH!ɘ'T us ) /<2-,Rؒ%|Oe(@Idw (_!"ClW-\~=Y9!9el?M0Rƶ"m3EZY$mDd|4l#ɭVJ}'<񤵽@gsٞҋ Niv--'G6{y={w4GdWLn<ա}ѦSu s& Jg=I USO?Ҧ%.t'+s?+J3CX`O7S~GyȞR߼:K3MpSͅó؛'{l]K5F7tjv2O.ٔgW|S<_b}W Kyw="ڬU =u"S/{%>j0]:ϑZk-wr8cD}W..kZx?$Tu{zSh.5"÷zoUBPSy;V!ͦvSe)U"ѷH.ݠI.uJsd^l;jeGNP@HHq")20 -ȵZOy/ j\Xyd縦#-}*h~Kcaz 4ccy9k_C]-VMmLwjCG[Í;{hۃg5?lYcʡpvpkiV~nm.?l:1x6}ǩZ%RPHgS%vەF iOv4qX (0I`;^L| ?(C2zi?Î[f;/|>{:Ґjj23,gfD"X5 Qg`'mJͮ[$u/Qwd4r:w% [/{;t;pv`YH06uMܭL#YܼlʁC{ʇݹ`Iǫ7j:_}T2WPɓ=`uVSV!'c; W9v#8URwBb} t{9젯ox%ũ4Oy:-l͊M8 rRVDiiY`^c+SPPфiĵ8$SM$G<@Zx$gw؄x4<7GZ:泤r)- ]Kڹ9Xr)F@w:q)W9-=vg0Ut_x϶_6l9znT*/(9#a Ur[)Z+G)Z.uLfK NZ9Zkױӆ؏OAQ>\HUi9ԝ*`20:j5x̟׶7q`vGM$$ #B݂M:!+=&7l( IDAT'.]5[-+8uF|tX~3i'G vl %SJAcqNʣ,s~m=t4!/c%9̥] h˞[<3w_d`PKx4es{ٸ=)릜к u7hC{p34}^I?qacon{]t?m 'pȹ:5_ac>H/<CMvw5Ks G dU ڟ}~T7pЦ25JnXnvUrf&yWQfUv9@SZ>d~ԝ:bƲ՝ѓy8 `ڋ؏nMNM rDZZQ1>?tF׵YnyM2T]ےUJ>@KXS5$0d$]$DICCD?>e:v"g/<;-Oz˩ u􁰝f㜺xj=w݌sm r&jkJNW2~A:;WttnjWd]JڔtucѬbeeǮ]1v}CBBCg{OOݻ&?1_,+6&Q&*Bֹ7-xt3YDOXSgU,̿4At.]~I=Aqb sba"}N{uf V)q5ssf]W5~|Ws{ J ߕd o-vڲ,yB,? jߊqMG&c6m̗W;  EYʇ;\Kʪ!Q&8;۳TD),F|B\zȾ(*<1BLfP@MIAӱtܯr0FFЧf`Zp$}tSp*f3dt|7*e+;\6YiӁL⑊"ld%v-Rj;ꦋ-y_NoJ'<2:9!O.8p2%ٲOޑ%ƌ=]uUefp2cy낝yW$iU4}7~^$:d7N/G-jͬrjۚ„Qˏ~oR2|茬s߃SyLBlΤ|w2yOn4>&HA{TK"]&=GJZG>FfiݮԈ;F;)4>0cisg&$srY^-M qkg嵰ZӒ?\0z͑{o%ˆwL{vդd|5?]væՋWY_X˔[ɓ惇hh8 4cԡ!ˈ<=M/^ӥ^v댱k-e ܌edwaϠ[m5s 1mWOD-;?yaOsZEmsfV]ׯ&Q4%if+}\L* 8q駳{8Eq%XQ5D8h (]M;2<2bo×eqDNsi!93f ͷXiINPN.)x Wcm\ sRRmԅ"r-)rq \OԁBV%/+I;[O.FP5A_5cI7EQ?2 v./k?i[O?Ay@N@ :<]~ѵJKg3a6Ͼ,]pem\Y ^l9s}Î*|hI}Kxm V{–Z}dnrv+ƼD`~觨wPYI<~s( z2䀥َ`6յZfn^c F$' T*L*ߊHn>ƥ{*wՔ} 9 Fiy\F  tP!~7 dSYaR3+'o1$^ަI$nDҐN~.ERijGҶ1B\Z"HZ( `cY/[T8Yb^ެXty#k/* ˏ}ng5u něƒk7-{_3^:Һ|$ݼU |,y-}>?^y%9& '?9繄xr=3+展&ͰtI mc)懲`r P^'tg SA6)@Ff*]v$m.=2[_M0i+L.ty40q)3w\7QMvdm!T5'ЯA90qMI?t悁:z7wK[33:$BHfuS^+H,odOZ MIA1&su_kK1J`0}gS~:fPۻ5/Nx͸t2 WܩL|n #pẕ'O(1j>Yw{IF?Ԡ^]=$n1?n$U=AYGtjMwo_QTҴ]Du.יMYa=eap팏<9ٟ~ /WQu̱]&Fp; z# 13pS' 쎇0)͢s"auR{|i]mwn! V_+y$;ᆏU9M/]HɘLݟIuӧ/ڔ1L?Rݼjʂ3nn7'8~旒#, 8ak^~D߬(kdO3Ist|Y\_ɱ׽7roz}1Ӫ9̴E_n^r.uMZ_U[|,aİ9%HYs2^1=Mxcuca^|wM,hF 56ϙM ?kKm_Z1 -~}?\ܭi^wA4ӬS<ɀb}꠭w('n BH|2 3UL\\#7<a89bTLYO:/{.oӣCSRHxTUz2&LwfTgS1nxPAWitLN{?9CV9Wk&vsxס:hgs!잠+o=O@ϹaS_{lIůDFkc[l :fFbuw~3sɸU`[qc{#? Mv=xs{4 XjMb̋v3+~:'߰$y"ԅn ⯥\( :)嚹/LH5#KjnI=.zbO6NOYr]L]4CSOMk'(oHy DujikA+}G͢qoxݚ5P[X&P^xO_xpxuܷ>u?9&;Ǯx2m:=˖]&׼~}=g‘p3=gma/zեe|틟W'=mQ{kNԿ"qygZ]i "FA K=Sq_r`iҨ˚[06#o$2$$>n4%w '* ݣUn2kQٯW{ƫ^#aq41t{N!uwYّ_E霖yr]o 4Vg*~/4wh;蛭j*U, tcA(M#dEd3%+ IU Cר fjI`ۭ}-Fn s[*oՄ"13XМ3y X+_'9=`mk/ hTd\Z.jh 9i}±_sqŲkg egYWW4kK fiۉN&83WMz$jaJsЄ2"eqj9<@rf;KK[t툿v5OU:;՗?fц.~iu a u+ewB_cO;xƸ?f*l\!v;Kn1 (o o4cnaIed Ʋ^wƇ!9+ͪr%A~lAֳq".[O)~l9qav>6h7 ES{_=8%o)2\;wu,evl_,V!ɰbX4# 1(4ܛRHq.Lt$4btH@l-RH @@^@x~( V]ݳf[Bf?u###xi5 Y5~-g]yӑsh5#jiM\cPQO}_ ݹE X|oL9.1jK*#&2;_{ I牱fI}3Ȍt7Ipa 2kA4ﶲ+@"yhǻFg&qW}wؖi_( p]2V!7!8 &hmO`l"{Zlb(Gx OV`K^'Y UA057/ˢON]ۤoխp_3yŐ!Z ZzG_R/yi4l "B KX]{Amu]o_xN?o?f?X @N]#<]N7xu'4Sǘ_g3hg)[jkοx.D2pD"D+FbT ;>њwX\dѿ,(qFg;}@$dn/4prS_6>E"2 ى\-=3?e#Ibư@\{G}qatoN&& S(@)A 4 H4hpĮj)JB *"8Ka.LƑ4)"+iC Y)_!:xA|PMP ^0)#̮:wMap>2W/ 2SRu/ GCmz9)[*pnļY|/< 3zHE 7Ε[վܼzǗWo.5ӥ~Sg7Q  Nma`0Z&YiK A'n C6P$)M%3.B k&0f椒#}vMS83MYL aVLw5C]+T+O P^ցQ-N[7pΖD4g#޴ M6QjʑJ1^FwHa| ޅ;Zl3p~E1!ӞjPͨ2`i'cBbKQCVUrR Z˭S2[.=(&yloxOl} 󦅃-=nַ-Ok(3TrUwsJT3 HVЙd LBu;W=#Avȑt|px֫#^a\ 1 +Yyf{spCv3;Zc<4=e0޺.Yrz^Iuh$.W4:]6k^Զbᅪ9N1C'^}3@H;$̹wy/\`324VyQ uZ8C r䧗Yq߬81kP6wPF,?yw󌧖=D{}p3a(;3(3l40,`| ^ `${}yuD.&-wϻ*co I Iq(ػ ךּyR~Z|1m=ˇmjN$D8ʒYrXEO[ʪ+VAμe v]h7rbo,ç=?8JU>VjԞSވu*m.Sjd5EYy5Z6'`n'!^s<2-}lωrfh̓d :M4i (t,Δa!iwǗp&"g #QPAR;nڣB↞j?~JnS_i{w{so}`+,[nVHenC|4wƿ>}`,|ɇp*7-}[uCH]0bxyMw`Ezaev#iIFwJi};8|Φ焛IӞ}P'fV{l(V\9jΑ4Yؒxb6CSx̺@N5WI,7n[범A„ij"|6avz>y[ss)wf)"Hm1%I4'W5]4E$ēL_(4 IDATmWnx>Jzɻ|t'{:E%ձ^툼vϜO9]ttϳQUwX+ח/Sj=͔WTa*CS=D[A"iwU%lkZFoFآwR E.;imdžv#KqdAӏB"k?>3B](e)8h66͖B'_$u7p`!vؿ5df\q 1#cx,40  8w^ĭNN]wwGC[dEv)iֶ6fzxF3̬b)jT)d݆F i`pjφ#fu ݑha |x}f, X50Gdp  s{/:w'+4U-#qVmp^se9^,l_Ib1v 9A\C^I4i˄mR⎋#8//6:FsXe|QHɰs 9mi \l3jbd29=dlM 2lzw ŸV7?W?~/s'$7fx]nMrUqXE)*3Ie槹cdQD) /\rO @[՜ (xY6YS3|Sox|b1Nu5ӥqFDW{cs_e[R)Qux az NV:!Iahx#&0SxHԒ9(&_PM3|?w½vV?Y1+W7#)Z[@:q1Uũ9&1`gbi2$%m;fd.&d'+/1k.| =g/ @wE'5!.+0"wU=+nDb6/TM9?TC0W vAʍfK-ij*~L#'o4FG>곴9 X8-x9Z:8 9o~,M8~*cbe6\HMUXA8R=Vkjߏ4p{l m;`yoQWIRNѪbTx(F#<{ֽP{{>߽'e+j^[>bРxRoX߻pGg/\4*Oj/J4 R8G>A]s D %g2Ws4i r+O>zSD̍V4Īgbsk`IڨKllK>S_6v{h @ \0>oEjD0Ӆiy=䢴l9`fEɿnk*Y0fE޷/欳vVcWQ"6 :bl4jR#JsQSGDqa呲 'N\A=ϋEThjw{q`(\qk -!ŬaBѾO:IDfʹd>I@QDBv$,K$8z&w8 * YnI)8{lK,)wBDKM?U7 MkkץDygEv'f_+5"atYsޚjiۡ%;/؆T)H qy@(!h5-¢֟),9NX5n8!Rdه>ΝDew֨`#?ITH{቙^`m̏vz7EiZSMf׶x^1|]k"bތ∺q?_tTEt][ծmQH8+5\aV ;;>wxq͠3X{}߬nw ?9\%N躰.>d7IIWxgͲ0lnlwJFWn`屏n[ w<?pq+Jk}GH[23RZy4(r -ec] Nahdz_ҙҨLtHa+׷nW]KN)jaa~0ktУcԔ_Dc{6HƧmACׯ9/-d3;j_H `;r4|%`+{YuΌ95 K=X5G>O{kҫ^d.v ms)V~aZ5LZ->'wjwFt44Dvgv4nY[ꪟݰpç[>; %f5?gf [{bi\Xo)_u mCF(w.aiS{wZ8硫g??s$%WK>GP!?wmj6crP=IJ|y™8[ֻ>Ԓl˪=xJ] $䃳> On/%Gv_~plwmhB8+%Tikkj2omJ@r XwVd[TazrK+h"Rɷ8X8a!Deĵ-T SArO8N%.(m>63]ra&mLR,H 5QAڼlȎ0:۶D5'ɺH8*bǣxL[bScm}'&*9n(ȟR O|L(4Fz4p&q[{eҺiU,NVBBF7Wd'WHo jDv ao% #k-kL[|KF8NAhb?du:VDsN6R*%d - [\rQ)dz[Hw9mZZ|xP@;( 0wv.dhN{uHӼ_H#e_QC\>yOިkF`7۞xk4ݰp~'%Wx=v+?m#ՉT**ՄCsAyg଀&`\?̥sY4jb#K՚o}ն[H#/^QR+G{Ѝx:xdΌ1w%%̒]$=DEk/RLIҔn".(-NH)i,τO ;h쒴#S2׏ݖﺴ%Va8POo؛䈥5 aU5F} sK *28 ;Akzux+Cc'Γ4NǷl{/-ȇp&*ڷNuJ<dg=yl%tqyΑ~>pW}#(iL:{m\~Hۼ$ %R@T|Fm/wH|,rD~G#zr̛.̻7vZ?x\Ts-7 ^/Tzo`9<~z[>o#?@~.,@Bpv :k|7W#cNdԢӧHJ3$?AbOi%V.بsϰu@CepjRY-%Zyt1;b i׾tdCy:k'fRU" βeAJp1jRrO ˿T@8h$5(F$y'u>O> ^ΈGx>adI4qGpI)Ot-XXwEFnnTK"EyM |o#MߙA2zla- 7ũ"ƂUeUPFiQE `UTg:X;wyZEd[8/"Nc`Ñ23Lx f*T396Щt 9g)cWfM{kSr\}uq yO^|;/͗MI3|`1L1+@ /XŷvEס$aknZ6/ˈ, ;&mxfeGɸA}*reʣ*M9ޔ{Y1VV%eNM|Ki<! @Cjd2]\6Wk@ 798>AeKV \N7,g00oL5xno07Nѝ!}OLG9H$;ӷUɨlʦYBMhV9}4^iAtY*P0 w03{2&؉ۼ^0־ 4[˓HÓ'<=a?ɪAE{J !R,%E=gڲQGc"ښ.+hMbFs*E^?琀J$`OSJ|x [rt[ S}d"= IDATGWg`B]I^Lg(ez>SQ!fvT4s%3޳v z-4]LıE\lHwY+mC,9I14is~#W6R EUFpqޓ>cZS}2"'rA~R]@K)!6N6_Йn(#wr|Vy~v(~˄7r;2e;yL‡}ŏOfNJFӹUEVNkȯTiyO[mqVͷ0Ti7̺*W u׺h}&uE¹gT3涺ÁN.L󒙛"g"߬X]_-};:;tm[b1<]; ;̭穹cr̢kS]4(WS :ş 7L{fjxWelnPX`ü M(Q,4kMH~q$“'hkBjHvHˢV _RD@'.a>8 ;DudDħ˚YQ,h:r>yYQHaB"Bt@e mCDwdW/~j]S;֧A .S|`rH=8|6ך)4 jnή:HG_ G7 w2չl<ʄ#u+eU3-U$ް$kƚ/=(lWB%iV:*Æ5+gyo*)8Ֆڙ}`IBA9Ĺ"&-'z}mJ4̾f>i}9e =7a|0ۅS`Ϗd79R0h?kI7E'ro: |BXۖ,d@Y I1dm4E" #A ^|_ue= lϧ=ա#p5ny.YFTc^~'-1\PI[⿬_An',`ϧJQwӏǯKO;v!lQ1͠[Y+_ C͂nEpEW]tJ=a{|kFN,{؄KQ̺1ѥkݨAimuZZ yviW/'5J=7CUJ~(W֖~v^;FӬo$!3)$rxHgWJ;|Y|ca+Rb>[1ud3yQA|8R6} Sɢ+%D#a7c: 漒w);5 wn-W&T}jxA=`x»Й3WOڌD bwgwՏNufZQŧxJhf98'{ϑٷS_X<ʱEÌy,幼ݜUk[o^|#QsAho$ d'Ԓ+$mdF[0MNSG`K}g\m@Hh[ITwɟuC͉XZaW&nW#C0g\=wE5[F36AW1_ZMDD8׭-jO}v 6P}qDݳE7ly7p>Fry┴YK=(6[VMShuEL0"4,i l\ގo]IZn31NdE!5QƮd*&w(2+9;6VM[T GĦI8ؼNv"5(]rFzzAl0~S⠄8AW5cIt+ :[ҽ -(qCv]YhL/P[ J 7GZ-vXFmL$i3h೮_Q[)OE i~w%[מ[3}%WU}J=x| Zg[w[;}k[v~tB'ݓC'FMVٖe|i&$“O2L~`{ټoA+۷|}QdޖKxU댬WU ?!cDW#95S}t n{#q`1AAo, \"Xš@4`7cA OuPjXOnR!krQ*1M䀙УgLݿ\κ;DC+m2tOu6M1O ɪFL#5D26@9OeZ6)=':!?~be#ڧᴩ=c<50.T`4<d;/t[mUrmIV,z1ih0l8OK7qRxՓSx=碮Cv<‡Gk;X`20q 1ꦕ֕5}6 ԇ{y&eφ^c"K?1y/:|"mKy'R̚3럛}` t OR?i^v `Y7q(TsCSR3=@` uF+{[Tז\8¢|E7"Uahwջ"%Z)Velf~&Kl2,DLMThT!)qxsvTr]MS7 N09ΩP֊Bi4ԤVGαN ׷  n>5hYL:a,4bRZ->}^/iJsQ~Pg87dմgUd ?MJVkS)LeōlsN#(=$Xoobk"rtCPn[), NiDP7d, (J1 ϼc{e<٬g~h[f |yuћ@YIk\4c̓{KlNS9WLf߀;VOpc?/?ykP4oQjdeSp/F)=2xz=)d“O* {i%X rvi0&3Y 9 {&64*}hog[jQY>o90z5DjW?ZV\F4sLORP׍A‚x7L{V8Rqxb ©f3aoe-%"{`^̘ID\З8͇} x;YWlrFʎÙ|sz͞oGMOPi}Zsa}I;oJS(hU4kP:dV>CebH>^uwO`\rgs>Ӓs/|~ڌo9o1FM:*<[?Z={Gv9=^v.֩xY F) 8E"0`*cĵMuCХB`vds?詙w7qkup] cEDWB 8zRj 1-#,~p{WnuŰpHMuSLJ,>(Y5bgnQAS}%o]ئ;9ӧ$0dr$GR'g%tmX;=2dƣ톟("! >jNi4[ב6"2:93',&\Q v̨2x/_Dl5K";~.gORhm<` WI0H0nwGEc]ZYVzS*Mm>|7gc) |j+\Lͬ0j5,ֲqyᗆK|M&НnǣkR&Hy8$,bbzL9^Z._"#jzmwwJ\Xޢ)Z{{Z{.D+^Bޕ'hYS(,ki|~yt9I[7E}_>ʖD4;z:0<- `DZIڇo!jPhf%܂b́jBÁۂ2` 8ʖd O"hC˼2QE`Rt bUof_NSrJkXO͓:# ۆz*Ly+M m+{$'r[95H#qK: tY0Yio>JC6uY !&sh}߂2|CMNIi,g\;eMW`fZ,mL2"J5?&36uofDqb޲]ߨ E gaIvi%%+, .*Sn\)!4݂s`c&% ûX7<UK|;G;("pu98 +ΪU>ߋx'+}GVKd#['I]7SsC$vfOCPM1WNso,t> Lrjy\`ݫEvMBуhn]RdHa+;eAq#ջ~,Emvȭ7#ޣuee;:P?XK-&m|p?J*<6"uO^~Y/jM:Nubs︣ /Xv.sVRw']^u ۡu\Qo^K!z,{+yo̮d{bvCK;m.i p6k6)wB"Y"`m59]?I071ͳ!Gޟ? ~0YGØ9ޱeoGR,~ntv4FNߪv9/f> c.1(/E%CJ4LӲցq`ؿ : -ݲ3LUJmY]艉4ӢoV^$%$w8#$$0,˄K\%v@#8IÍhKE' _7kw]'b<$ P4(r]n{׎_k4ʦgeyxx4 63sԜuՐ_|J@T ft[= 1,)`QSrl;[sSPoӡeq%x°0}qovYQjuPI-6aYe`Yʇ&9ۗDaiefA| ؓ)u:maxZ(OOm}9E cԋ46Mb" sn3NovSG/2`[_:YrxpYp){<Hɒt^8t͛vۆgw^tUѶ pdubFvFb)a$LlFzhpwd|4ejWYc)"Cn2_s_ f],Z#.b3@^!鞧$#G}5 ?zz/Ɍv &dUO8>I7!t]L!P60roE<S0f_RiI&;@K+)_s'g8>]Ż!:PuJz{HUy-9o]~ =6)؈\H x W 6i z(.&G;8kSsbD=^%kGsVPtb"yS>!zG] %~9󦝶:c|b.{ڪ. '8tx+ 6!ç?;*m}~rNz#$zP "E{wFuEDT Q)k$$n#0μw]ZkgYϳV:2' \Zhp?VѢFv0s<+::؉ ngnVt;Yfa ~Cpq bT.+"ؖX4!,d4bo/X;)x*4$[%~ݩ3:y_q4ޓؕdwvAk& IDAT8shVr^@cL%x.#/K'y$D ks]dI&z-.֙eН, $;sʓ6U9BY17S2Z7\&8g}a3C=i K /Z#zt6̺Bx[CocH0e-YZ촾3:=ˑ>ic=P`3@+- dչS}wΓ!(d;`Y=: aR>nEC*a*R^SJI:rfgXa˄Ű6j a8E \F, &2u&tYEMEҵ5m~j>5aT&p.8L-ҵ<>_f=g0wr= E3Scr~^;^eWcpvr/ψ;~(:ڽ?"& e'xu?{Ft iuo!a (f%2[,W#O=MRƁ}]gՠ{!=Ҕʃt(S3' :.=lӦӸ`78ϛr0AOt>4<]੫pU=}#:ID%e,n5[Ӳ>*ؤ!ϮLgɤoȜГ]t)Q#Z4xZ5ݔkt,#fX-TcWSZv0!Wh˴bUqھQ}[us &nɞ^qL˗`L"{I6t`PP d+ 33ݗK5vN.IƲ {``s Z"X,HrVY`}rf~߯8D(`2B%rf%<[P)aLSA9?;rgOVar&3 㾟3,Ge5% x\ CgYuDUT\en{=O,s#2/yۇ){ѢZ<] ?/8r  E:Ǜ i9h G-oi[ekKoگz^Axᒺ?VVLFb2~ֻvb *)cv#;@0:A{3b2ULFcH~k—4,*71();N'l{7ɝPcUg2VHq.(8^ʎg^Za9/!?Qȁnjx3cvˡ]$M҃»o}mX|U ^q,ij4IrE_ۦr9EUHH!/V4("3>S0{K!\pJ9?s|'Pm0biEG"$~*l}0|C#M@[6'8>Hԛu [=oGF߽]~ūazV!*{ o|ރi3z|9|]Yv<+Os wĴdNbC i92vx~²M):ۯʹ4; #zHiՂ`OJ K|I˘Ń A B,gG洸quT_qpXRHL;=!؟s(OqR(` ĸ"5I)UӪϟrDRPA&sncE&t߳>;]ṳ;E*$ư2=1q3S܊Aқ%\7jKX O/G{ @AP~ƷaxlP@ lccH~%T?)KԗC.Ii<^zZ x^Pɱ(OӾUސĸ֐%YJN~L8g*7ԛUH;C7=(ƲoĘV5mU3QzU[8&[*LZV(#P_ң%Z,n < ~qJ%1 0}XT=]BZTH`s^$X j6tERIAi>|sKQ嬆jWmL"=*7`ʬ{h-h;n(5RJrY 5'@/>FJHa?wC[^Cc<{<D[YJ #ݜp )oeҔ#TSjf:<!RMu`6"6S5(ߚ H% TTuqօ^v^}ƣ-S|lz;x_gKeOڢFrys⦠u*efbw_6R>2kTӛ(f}p/]`cVϨG\*r>jPh*!NG~};:k=qZhBa]A_vRt-w 5r{%ٻ%;8!jH cfqr:;}C)!p[h/e|U}Ĭ)Fs/<>Ŗznf pg^í]Sܭi/ju;c[1u]<ZK<ǖH[P(潦EV9lI{H4%/A>)Y.}YغJÈN[JxS5+e٘tqNKcۃ)[c?w-g2iɗZ 5k=[>N~o}$8Eo3&HwN;%mƦmG.tK-{A=q٨ϿUpUWh1i%hT\dTOynvqX(cPт+H*IeD ,ڃwa43tvV95cѴƅO΃tʟ<ljG2Ҥ=4 O{^b"Qǘś%\5Y;G#$JM9%hȶpzNS}e/A*&&E†Z#&4y~;0>)Z\.]hcH~Ͷ_QRu$g/FFh.|a|"P֗@P*aRzB岶mOx ҍ# -3 DfF1 ;WҍAѶh)07&f)G;xg㲄G Y56-$B@EB"ʩ 5It-`7DzNw{<|siy0ʑsۊi]VLSNXĹ" GqX3(-T[sPPXCh%x}jFQ>ɸfܽC/i+soOcM+GoxRM7;R'pqߌ y h``Q@`RjASpCZ_3A7jſ@-)@{~dMS ڿ|п u /,Ů0.A{8\2ܘ #D0ӳ/SM~لG,sg*yY^K/xz xytOWzc^aok 1Ŋ /PbDgemyYQw{`j_Igɧm"*Xݸ5M被=uԲ4YgJܶix1veQ2R|25M$T}#T{ٵO\{2fD}N:Qo^j6٦=ҷ-&g&.sgLyL>$'5`Ն YvS.F͒?Ryz/tkZs&|{EkETfö8F W_^S~ֶH~Nѳ`rٜ>y]d"b SZkF<` ];GpԛqyvLԲYS7ۇ(l7E9j,€ bgYk;D頃ߎǭ%(܆x hbW @c9Ô|p7u/ '}Kr5ppaBܸ)d/P60dK⃶_}U ed5KYQiv5]yXݐw٭A_ 1B&e?# F',_+A1MoKM뾱0H&ΞY`s87}VX{/T!yґh!QVYʩ!Rk3f5o}],v;nM&[|^\ml >o@Jp.HC}{}A+Q3PU!qjKO4-UwY!QψaN?{aü\X?a~3{_>;ڞDO/ȘްoSL[;ݝ%Զ@!9JK%.Ӿ*jX7^L;DJ9&J-bFXARJ4\D>P˰CX2UVޜ]-,g~ȝp["\ŝS҉bPڭSVӥ8sXkGüyY;ڳp4W*xf rOx/ਫ਼w_Ԉk֨$=z:eS>w3{ u}lD{ ټklάC%CBI!-3jl 0d*Kj>mE9e]+g}C`Y*~?\;aU67r*^A Aњ[DF^W\8lp,cbD#4"ov]:4Haa`Y`,,ri^dc}5@GBB,Vltvvs>=PjvrBrݴyedv/J)8`'3aKx,f{{_:s:; ĩ%䧹o6>S>>](?XJ ᡲ]zg9`XLmdD7څ6!>ǃ`r?i䝻4ƯIa}M(v~4l,-tH.e}&qțQQd4[1.`|UM|@?͔V6c[Li垫Oe1ÝkK^ks2 aO[7R5TLFYUi{v;@)hB98H txx:[~O(iҖdWNo; e4 y/\ bXjwbu߷_jQ4zƌk4ly P*~%'[Iڻ s]3Q<F&j%An$ &@aHY(LBM{M>< C2jkE6}nJ4.<|Ky0[Q-dnoTd1V];e;QV8ٕkX%/7SN|+g~OocN[=+N6 ˒/euۯE',/u2 Uÿ} qLGiI<`#N9Op!9O>0(P^aW]:g7A- AWqs$ mV~l7Y?$k]_1!!x:T3t+va dySl' k(f? ^ĖЦyxվT݄z;o3P)dژn͍=V$qnslebܷF7Xj _ U%N<6 _ G3RUKi&GMp\QT~'dDxVDaN\D08򭭇 |qv'Zū|fڲ@RvZ-3'O8#wv$Sh;̨wNyoC7PM'{e:_u5 [328tKm~\a<=n&LQ'6|b%TQuS"̧ .pqC"&n]S='Q] oՄBL?U=![|o$=-&f$jPɅ$PX |'̿:=ZGȬ))+ŗϏB"T ѨIIfjqۓ#|޶ջK>._ yc%@ڎE*.dɢb2&v^qTGU/YA '>7`S}̠{yF _FNaurv+G 8்ȇyoIz~+7n龂G}d熪tG޷H J{Xlq|[&2 u1/X7 daOp6 ). !ecAbm.L]'*m.A;%;59K=$ɢjo|^kso_ch>(S*&̺hΌ;{{-3=m1<xn% /'d&c633}fna]CO b_W 2ϋ; .{.|EQWFS.d<O].fH<!gDzCL#Q3+('z3 cC=U>lcY_+glIҚz:.aΚxQ:L@7B #}yCHȜ y;ڢ,?P@Xph,oݺSnGqs VrC! xNasQuKI)Dd}:+ae. B3 `_wtc+j"maz\^U}Ps`#n/oV;3@A|j|Gc:b v׾w>UkF1i!{ 0RVOW%uR 3o}M &+t%djY~xg ߵ-/^?UP-(bK# 孄́-əqXѲd0ϜEuJi&vWcEn5V8xrkV|s%l5.ٮ|SgYz8VV{d-SZ.|Vm4~NL[N=u]2dQҤ#̆N룷 - l'A3^R7^{ʢ{׋g]i7łK GLʾ#tJ(؏LDjL xLt}b6Sur69fNz.]˜YFUab[O|*!2S"VXihtxk23Z'>^tCs5s0֛Դl=ǕZh4jAͻL&@-`0!W 'բ}"3 M)`weH;_(fˊjP+xt:W`rP0tcG9~ipsN,9{HSH/p+]`gk8wn^B~k"NnM輹Z/2q#mtJbV8Mx `~AJ] i g(XwSGH3[KʹWb>ՙҸ[8t,l9U/.oE7kf`3?=VQ!^s:ݴW^BKq8'4U=ӧ_Չz{=f{ v>rM#FTy"d _ h^3vtV4CmQKOzzQ5^)/*$M>{]*3f:%<5>XTƉʫ_ ; YVzݦZLf=įg%Jp.cA)m+or!m^5T wNm[H72j)@"dV ^ز0 HQKOJXɴ<$, `n2!,Gfy+a^֯1Y>?p?Ҿv?b2.[;{܊ iAsԳ_IGVND+Bo"/9u,4@zd(]b~w{޶}SQ5(i5[d0o"nO7f2w{?UtCt$ F?Th+DK.&IV5=YIyUG\@[P<"6U9%I vسl/FȞPAŁi&p#)eR6gcq{/QDmEPEKzѧ܅g%5ް,*rk~Kk6ż_βiύRZڛ[`}Ysv}=sàIe\yW#IMWYn2>Ns^ux}qF=1MZ՚L4H)@m[DiʤoH* VGUO퀐qklz#sKȼ$ZaEV؁05%(27R"bv! RZ9j@@\Ԇ56KgY/vT{Zkm7|[=,?[zŬwO,xzbYO翌M%vWZu[Q04C'Ng #ןcVCU+8%n)ݕ+)ъHǣFsI9fS4tL Z~=#Wf|EgRɦ\D S|ϰR)45ky8yrS%d40BiACeQB4AQ)2tkiCL+k S%6YDYLdMjY*PZl;,þSWM}֦5^rM}߭Q=iWh_w9Іl1oweNA߈2d%,IZCt͌&H-(%Ip ",ѓb0`MV|(9L[5S EdgbTeϘ0O,:R/gIK2͹`좯K0@J+)-gy1=(ElPKbENSc o<.Ⲳk~; ߅Av,O/\׺@pN4ل@H83V#c XK됽᧝N MYE&gLlO ,̰𩾒ZV˙뒡ɇ?`KzykcsԱ < C͹FMC5)\{n+j+WfX-VU6p"R3#1l(a+\ou Od:Du "cJVW-7e^r3FJ>r7q4rm5[^Ԕ{ɛW|͡7,b!g &VO_ys4eh= fo(svN˦>NGՌ&w1t֒2(169瞯{[!ao6n-+2eDi峧2#=z_n] Qyw => 8!ǀ2 #̏<#g,(cwa1QiXH&] VV2֣7Wv4̜,? ?pWI ;穘,}:O r.v\N֞OGs=i'qLS5rn>l=s[GtR"y~꛻W~/=p%ߎȾ7>)rۺN~k1=÷~Yuʸ^ pՅӝ͂ͷ91/9)"W1T CE=Ȑж$9qr.9y'JkṶ̈̌jer9 3 f'A;N:<*؀`|s4x`d~WG`ޭl _jW12]rg6nG]&\.zr}mЯ6PmIH[^M`[^>=ugl,'VAg-o^P$O+3-b`Q!2"?OI2 n5/jeY82  XLQa K񸝯6C0H-J:5V~\O۵_FB?`ȉ{^KZț A䪂Y!+_ػ8os;}fg{ߥ * Dlk4XF`TPQQD{oN;c/1$3k_eޙ{@g+pzCf}S6KIG5@ޥ>Y]()Y6_&?괜3o2"w0OYOL4q^x&ʶW{0s^?o*تLwk En3=1k{^mϓXx+[s={q3T^8&[ uےoj?{+?Uo晪@2h Vfѣؼ9'*,d @@8tPNãȀC<- +]E$WB ;Jo~ә%ǫ-~"yxx0%gV4,{~̺9y}:bLv'1HRM^p(n3e5!؉B{B=jU&Th7 I{[n__Ȝx٣סKNEp^r]NIԲ'xcA~Z;D T 1h۷pb_v6g=٧9/tK#ˇѕEe k7,NZDa?N{ןsӶO̐9>8kox2j:SGi1mˠ*/}S|l=wؓRB {zbpT(-EEo̼p4GaPP[yK(@E 2.,FW ^;34Hpդ=`D^ʡ?>y$Z,, S֦[/ IDAT|!W%YCQj ,i a{h/.(9^1mm>q1MjΏ>Q@ѓ/>w=mݙ0kܬɶw_o-"&-ہS-ݔ` q$@rgh*I$'O <nj$:5{rO 5K9xvB 0X𜶨c":j_p8sgK6,3f9>M' LREKfMT^{(9}^NeH`p^]'5{f vJzf:k  1@ xP(`$ s/I=DaE) dQ FțSz݆%JJI,9VEnHr_]ګf)@(04?3<#ϹݲB!T[ђބ?nn>,w8wq(s~z hk<#UR{^-<ĩ~Q i𕀚 gafr8#3-Zꬱ3or`ylvlU 0'ʉl|2R#?_{仢aD2mݯ1β*nNxt^˓D :l,Mϝw|eO\Ы#lae}=?{vKSb6붿-eV8Ns.GÓ۾}i埿swi@j85i0BLI#*zf eYƽLC4>mU [6l?nwg/u"1>~ۖtqld }j)A)*XDR3< 8֖]d!oh(L ӬSP[uiDȢr:dH2xLo'%k{L~ԌӋgtcɳJisqX!.|I*  ^&) AS*~7h-ϱt՟Fv+T 3ULA]j2fH(!B& \$Cq șI8ɰʄkF%! ~^<&kO4c%Gz`yy}3Pݗ/ K)w̬apdѸQQ#Yhp嫆جw&RlJ&~O]+xna4Vi?_C?;[k7k '~x'aÆx|ut픡W'1wħɜlEi[>lQpl ۊJl-zs.zPȽD[fKh`[ԚIf>!(ǒ & 331ʑ#w`gG)s\I֊[βΠ@}~phpU(]o-X`(0] ]1Y@m^`R uP%[bxw6i>Ŀ|_r+[3㰡 cH@lv3 fF%8$IUܺo0w K̉pTPHͰpkLMf|J?$ĒY699X۷0'&'1!kY ea} =fh]>\*4vN7i4,-S(u]\-ҜgP\퓗_R[Is ^ecSlpb3Ztjϫ.'&G.Veh#U(v4v(l9 ! t,9( J^:u엞>⑄eKTI.Tۏ,svt~` XCtgW#(,` DB^pUБ hڜWJ_Dӈ_cЫ[W4r9uLS=(͢\>gvLR-<)) $e ӛ8\@_CNPvڈ9ٰ$ 5s4Opljηt2$̰nZmMɪa 1Y뭸jKD2#!0,`Q&blRq0vvHD)p44Z#1#F]d@(@;k?P0N]hI+Û}|s9vJJnhM~;.{w>!8PNけ&'ɇШ`y|Rk6Nq.&Ƣ n(9.cO=hHa?;7ӷ48}J&*c,N%?/|IWVrPӺv\/ /}-YǡR^Oj z]&=K2ehnG1YݙA x5\*7""J_9 40 v?k#WsBe*N8ARQ yV^J3{o4fCIJ$7b/VaΆ/u̸fk NjKWVOޟ {3jp0pYTх /a;:CLG#y~)M>10')6{mE-ϯ0 FmeTi0 %3}mPgU}Й]=Rԡ3-p"49] ӲkvVDTcl2z5f)01 k@xAT|Cz+g.{_gbb%>N[E xz]5un2NLOםל5+KS*Vg,IlLj- iiv' *4F2-խmZR?PPv$z`3Ӓu)0\NODlۈ\իb˶xmk(]#>ÔIl. DMҰݑ0٘lsnqnK~ݪ*^hVbT ;.Fd9;2o[tf 댡\CHV1+&kIQu B#Cͨ5E(@Pq㣔cOJMa]{-:$:KL֍*9ÛyQѽfWVw^|DS/*fpkl?;X%hoo[g {ڗV /°,W8g|/2&dγZ,caׯn%N~|MC[l/WA>4'er]gJ1df?2lpѶIk,Lz7ӝkܻwo^E DqϿJڣ=#%19mޮo`j FHztFS,EȢ9v~ ͺ>ç'»]SRZxR+7w>Hs @"?^hڑ¸;|%d#+] Ŧ CIֻӣ-7=^BU(}K Cޠ0xbc>^P* A]Ǖp #)qտK,0hao5+:Zec3C2((8.7@ 1)셐#%X[>}Xq[8ЭԟaΔM6y40g;mU~ zȦ[QიgPy'ύ`z$[7"u\}6+a?_5GsR.6zm(Q]c ј=lkOLv2 cMG)*aYEM#{w\H Qi5jz wu^M?O?3wD]k,8A*wQ2y9}[x9`XRn"M!HbYb(W'ɋG_ yBOF;yěֹϏ?oB3~VA3v}Uvc6jSR~I'%F D2+"y+o>v)O,܊[^ sa -mŸoȂ+;E gwt?߄J0]yECLpr Cϭ˨-ԑ R|Pl( 4+ iNi,dinӒQ4}+ A_`,xj* nϰ6w=~Ӌ_=(窂iR8 kډOyI4F=Q%B\9*)`fIkDmJh釖j!ɝc$8X{3*^?s6"o_9- }8jX+?{SSOl6oO-޼bb@d4V ,`Wl Y V "p쮝^j]`a&{z]!aVQWHqHJJVJʿN?#`}/F_ɑ=fLۧ,2h19p|B*a! G&Դ;dow:_T9 &қWd(HHH,5@OHumuCX!(|)HZsT8lݳ -,Xpn:#{$#zZ{F9qAP.5NN\wY[o=_sKR?k=/;=@9hk(rV (:!咭_/k9lZ\֊ҽ60Ή;,B@ +Y>%$“0wB؄;m>tAgKY;yϖ:v>6wJʗY|w\iH:c*D#c')wyc "EgclK8sL60>RHҐx=|ܢ&SKq+54f%;wcoI*Y)VѤX1=+CU/Ne[,q!)\:0LlX0[#,ԑ$GH갃ŏŚ㣽-,^=o^!:V+N4kÔ?W(SXUpIem,l'}7g H%-D紷@5<0T̷uX#a*#Izrb]CrU VbM}+*rHcq%jSPW<&+ғ/ er1N 䃕@lxU>OIn}x+} 5e?u)) '%EU~a;m{Cg>N/gj Lz' Rc uyv|?:\ϳw',OD@13 bR~ɼEsZfieׁ{i$}i-_Ҫ?zPiaXGLBHup1!Jz>DQSlZeGc*l)nքNc *Ġ2"ɰ)V& vp'pmΠyDQ IDATv0m~|}o2AYbokK 41X}]̽l_?o 1/~.C h:FUΏPm%}Jyp# AuJ&b9&?h Mdϕht+m smmYSլF~damVGyŲl0bH4 @($‸0,]DШv cZO#ew XBU*r(c-(: ϸϯ][UϙJHIW[1䜟g߼nO#|ɽ3`_\؜XR,ӓ~zm!{F\s3nx {lޜbkzH>PQZ]ؽī꣤ #n}3ݥ+`r;MEop?Bbί϶gkYS:C&zHedX~0i IٸTЂA$IcsuI{@[G:--k p /7cu1 MsB ;Ҵ|_{XGJ,ڣQeOr+ y3gmO7=? 6dKvI_{4`*[{~4P\HSee4bLֱyu[Cx"G:s BU$'}m@Pwp8ȳ{)>$Лb-\~&V -R7 /h1jrJJz8Z  X5{,xq;ˆ#|yaMp#M6 g=ar X8N_𔚷kèNt, ::[jghpkt~ev=wntHb/,Bh` hg eIOz^3w'FZS>?vX#D"wHC֢=lQ %j?^aamm1I+ۼ = Nj,~<-;j=hM>j#܆i3βHOQJ*=rV31̠O~gಯ{٩eERiE/С矰qOQ`O]3KEzS~me~Geo|Fuu ɰW/ͅlKB0狇K /CҒSs.mlt>Gy ؞ \][R; u+Q{> [3L54I6x~w _&"swgg:I& %D^D5~;`\3q_7co]@O_Y'6;C6#˃,jd;@OōAF0\=]opWL,e9vڿ,"2$([e?Hy')߷HIniVlZrL>]͗ ;ٗc XiD84_4uu9hMW->h?qM4Ƙi@Ӗ࡬n1f5H xgD9ЬƭK+Ko>̻ _D- \75zEߨ$VeV= \ZY}‹7јO$ *>9#INi<=m9k^&L'J]<4[ԧa~da{Qn4V#.8JRS #X&L\+0QU{(oH[.n=}p{W޻7X2G2z-7]Yg~rHApGjTU-D`Sckݗ[FtlXp{ؠWf5cz9=X wmzzK`ą[bI._2"n;LȡCn6ZJ4YחGM F+D zFT>1t:9ɱOb;_ chVrgxϡ@gC%0H纋3a$e:7eL}קMHd]H;k}G//> =wWUI)ZK^ݺk[Ņ_)Y菓߂NI (96]8d#X;v0,P $ t˝6d#@67Ph,a̤wXPP)Z%%)>,v0zWb}NN}߻Fͱ$wACKD*II/s'c& x6ἦjvraۆr^GyWŸFWBPvވ45>}̖{dy'C4?\^鏄1d_r`g(2g}N?1oʕuZ2 ]퍑ޙ6K\\^0[]J"H҂t9&.^<}˘t1JJJlK2~go&_wi{bATlzw"v׌\))MOJ5$gXʻӦӏ_@óD}v2{^^ jѭ~xuJS=0znSo(.wsĺח?z .VPt^]J@6l}Oy “U>{zqϢ 5c0bB c!PkcYKጒMc>p.z~h]bW8n9'?˰ Ys!NĘɸtc$guU5LhٸD՝Er֍;i{VwSq"yb2}=TZ:y7 G5wfX>Bn(ǏmO{6ruYdOcUs'ѐb-ڦOW 4ni|@1M."[m[9mvaeB:}Q3{|MCB˔_8Hl]~ÉHwNޥ ."D1(EA,+HDaEADT@H^CH7#"ʖn>̜)+|`o^fIA.  A@>=R6NBYJ@#D\[V3?}tk'BQcm\`pTo ?-M{˔T߫c).Ҩ:_8g7})܅P^Ȁ<ݩ&DL^U"  |c5ZR幾ᰵьn ̀&\ yKc3FwIN /c¡gn!dzuF-s>.Fga;j?W,+˜!l饈^0fq"?*8؋8%T;]9js!&Zdwb5d#G.^z(hnmc~Yٲ|^08q9-8ӭs`Ogm׼g;227R4fiq^hgŏ֮'\Cȱ qGLEsBY]IwG\u85fS5`!iw0Xg`0AU -L|gw(-x@Tbf4F%I76-Yvn!k|*|wbqM^7?U9WQ=7'v[S|1C*<{{I3C 8iN~[AIs0Lxg~᜙O2qI;9W}#(Ųj^Scu.kӥr&!Fd$ŝ͹psJ>\C,oH8Ii0ǃˤ%0lkڛ:;˅ C6?7?5v+5g)N2܅PD2$]A2DDPάC/2PQ8NWQ468γiB/"$#J[yO ;.=1xHl7LY5XREy)4S"sqsw|]}l~9< m 1ֿR'7EF o.]^*ܥK.5Ȕ>=d'NrOLô'!E]"4Uj0h$ +7Pc7Է@z`rZ`X@p_yhA[LPSlx[P{Fq%R-@Q!r'cDe1C忪}T0L:d.р Tq-GGR$@ǣϷRϼ7bTBzeXp3_}t_cvjm}v1T`K=Cv9~ g?>Ԍ2Go ||M`qB9TۑV`Lbї6Oyfo'%e8GDǎ*5*"nܸn h74ti)OΧ]:DHš؎jkL\>%xz)%N®l,8;6, };i"94uܹ iRGU &1b QEhvY2\a{N|MҦ;F#~Q.[,68)kš6 }*f ~&4mf!ʆ[6H%.#Ȝ/hW}EׄdВ`a ;SOC +a\9FN)`͈Rx%BX_ }'^k܇>k* Orj͟3 h?PHݢHτ8&U!}J_^SfxճLyz92W?Cl!s{5O蔜7k}3G\2gKT!s@_AP]A%N4ʦke}}$P4U% 8N5w>Ju\88}L=?:~6ֿzjx/k 1[8}ww-ӦR^P|ʽ*8)ܤ5D}i`\Ԁ.13JEYJ|"+*τVܶi?ы_~ $S=<t IDAT"B!h͗Rbૢ$gB 1J SBlʊtGVvQSGg R hRa(zlRbOk,r2'ƲU^8u٥@0[ㆾ|zgL%ltU,&k@ThP?}}Ph+BԮɽۅFs6Q}2꓀PVZP:i)^ %_ Nxz,GA(`e‰dY8廬B3T״_Eɍѿ2yRZٗ9pzJ&˿wXAMC5=p¢޷RF,x~2iTϹ/ֿf%?R}u7Tl$s{m @F!s{pN9[u;p,A st4ybɭT)lVO8ܮ\wA x,_%--|_qR^Y:"h_|06~mI!3eߤqϸ%l0>P'RJqp'= w?gE񥅡 iL>C1hxQܔ6W&udVoHmf1MH`F,f %MIӕͪJ[<@?w?$'6mBB.9]6 "٫e0ghS]_gO iĘ ]ػukq͙G%RHDMxCk b̻8|lh8,Vv/405ےA!l Hd(tlrtڨM!`|ɥnf|`K`WO9WiдtРa  kT!#^ 6 SeF(rIeacDdΪײ"ǟYXV7BpӲ/޾K@t"f7Rvs,pcmf^;$<a̓+w+֝kK/ 9ܗ zo?jju]{ͤ >AH* ٟdFK`:W#!oei3_73.{8, \\B5%,¯(~t9:ⴒ|(QkےqvUߑ㗬wM蘞R7N0u7QU`t0l𺎛$zo<9S_@yPJ"\Ş%-ZB#z)AOyb'},߶A#f\`}tZU_ XͶ3:$`ZҶ҆ ali6kJطL^B謡_+ DXǽA | /DTD\Vv{_W]c% }fX,DlE I>g~ zcAF7# G)mu;s,/ˌ6紳nkq4F* >p);S.sƼ);nf-u> i`.2wq67;dxTs#V_Q}Y0$7pm4=oH INKI1tO6_/bϻ Ξ^٩ΗLj( [ylo{DW<2PDzA_`3S᳎~5ڐ4FܑmoPSTP.}=I/" >WjZ}WSk!sB?7ܥY]+]6R}K/獜y,ٮ`ģ!۝'HP\w˙$Ajis4ECQ(KЀR(`Tò ) PlM*Gxa M$TAM5==p(ĻkI>Ru[VJ q}顕*^BVs[ҭ/ėꁸ \V() ߮+g/;TVf /SVUpک|)` aAW_u(0&xAۿ{@7yQAx]]TXdM69M'EubQ'!@%UoRcSvf;Ol)UݹwLe#F }x8. uH8Np;  u7@9GA3 @E1?y>F^cV!7T&|@߯pF>uihkSͯ魣9UbBlZqy%мQ^Mr]#fb97$ɪIP5.1: i/m"qeTD r&ddݽnLlf0!]r+-JQ|jg U2QmU`8hC&IL`dM q5C(u{xsk,g mTMQƾwpSbr35P*>Yu9d"|_oV`f-.~@+Dhg#T_VK f-Z'Zs  #,H_&j4ґ3GZ{ݼ̖pSY?Y/D^U@\yl<<qΓ"Q:lM鉗_ 'A`@ aq35oj},@6Pg֔Nݬu Oj~5(XFGkqa|}) V׵G^rfȩ}VLBzxp5>G ,RQ.ގ6TFĄ ~I*z~3Q/uVVᜪ  \n4i4cQIl54 w )T |,o`w]_CAv5@1B=FɅ.{NPQ8<#ׄ`Vkkz$\I)-.%2|@km]aT&{>P+=e3}[~>Z*?G@x緺zb遳}"j@=zijP.JmT kT]E'xQ6HabU;$hڹ FfAaOS&Ckzgt5xehܯ>/g[@%)!CS}mјDIc16.$TdG !i( 0,',\8- udls~ .j:8W[xv&g@RKr(А +`RQ;N^]RN!ר=bPg\Hgݽl~z~\ߧn);뽻 {ErP`f9>{='018 4|X+?d©OGsOuoAw2hQl<Y`%$hc=T=_pU%A-Yb."=a-9/'2(~0B_ ٠P\6L&%^$5y4{)Mh`4 ݻXPViUpqw7ϼ꛵jҰ=|h9ZfxAtpͷoC+0k"c4 ؓ?V*!İP93O*/.uBk@ HF4*ZPujo=B/z|tlwwx_ն?$BܯFB f-ʁJgx,7kT}\tG8);ͪu>˵1(9Od3f gbe_BZ^+|Z6z wGm_m5R/Mx&CCwh P -L(hV̚ U,Q0%p >AEeCVWDi`XN1: {E/ t1O aD%߼x#{vXW l.[O7EPOKvUgyDt[4񯟜;ik]JH?jz"229$2v]Nk}Z<|o֩6zq"c?MV3S\hm6Ke_޵.w5B'ѝ,iԐj M8#p^MW,uE5a4@VC jushP@V!-h7 Z+>CǙ½}{V}m°#qD^)5}w0{^~Y̺;ur(.gć lb.au0,"C(tYͱ+j3Gyv {4YŴer'8znHh@CTէSDәX-UTe~#)IV ѓ;E﷞lw~)¯ 'B9A< Fe-3H߬EWoΜw`4>0_W&zv*S$#*CmPVs2' ޔe (NxxU%/[zc˂Ig\u;3?[zXku1agڸK7Y657X0"Pn jNp:E!j9xː:-irHo(äA< RGr2]LߊJf2=;9i{(Z}:WUϾ FusR|x˚g.$oSƇiӺ;"RY_N%|BbI˺a鬌DXb_8E=\_3Rjh5Ra:g3z , DvhϦXz(cHCy@'ު"U J݌ޯM++K$;}c,gJaN;T/< @zazFx4dIǛڱffuOEj[TZBz-?xrJ扆%lT%T\fabqƑ&t }Vo=ѯƛ 4s^ۯecmN62z ­iPo^u]`\`)gE XLr :U֯`zBmEkW'c#Xa52ie1>k*T)[[U2B},\. +εy2}dg/HO9wׁzz?>yLJ=3Gcki}Nڋ)w ].nJtU4a 2~B&Ep]8&8Zx1\UZwqodbc!-"IN̢&i959s\kIJS*>ȥR*LBYgڡQ`"cC__S7W}y][ 4OEXf18K㊳/骬P++]wnv䣢-o|OQKIs[u(eH+ʬ+*0C9θ ?ղ*v&E>Q!Qןss6 @4M%1!.LPʼn/uQR g9vʦ3f->p6><NrxQljs.K'P:G]ͷ|lz\-0]&%NsZ GY/>6Mix‡Xʒegg>Pqԑ=Z?[Z}ByBtcmzr2: 6Pk@9qcE,DQÌ @V Ap6p6WX<Ny_5Vۨ734z;>6N.t稗$-Gh/k ߴeKZ YqKy˂)A͈E4wtI) ;b'JS3MRWvVmcψ O6æ=8;E*ev(_ODS5Me]PL_i/*`ZjNU60W.t,V'cu~EsV/+gSN0i>H%EyYb (%YqZ9 ;&(hdž{`QV2{R3)q'[uG/oQTMy${{+t÷5C.wV dyGE >/7ޮcK/2eUWN99U-0La<3&y'aq_@?Y7fPTW/@3,k#z<~7/3HJpC3w7.$^?<,ay2 30A'Ny/o,HDTE- ]>yjUϮo>ٸ>yV[ӛ Q6FQ*SO4wG[xP4G/Ԇ@qukJ#P@HpmT(0VN ^MXPMQ ! vR IDATP0jdۂU77 ;h )'1it_'$`HjlqX{ %E3mɇoN,.[Qv Q_~hkd^kݨԘL87lsSЋ&*H(b D:߳pG;\L("vKu:w+@eo)=]#G\H*E|rF[Ƀ<0HV+rM;&2l+zAF3;T:Nbo^d:"} oz@l1,Dkw?A!T@T wifC2HR" l=-N@^?ӇaNu Wu)Z,!Cr:b nR%h:ع3sMO/şY׹=uW͎܄xq~s>p(űk{aH{=b)AƝ_jGfp^PMq/B S ,A 3+?[2-:67a~}ۙE#8B68;oЅ> 3W2F(Ǣ$o)ct;YSp Rj8T:y~t*YVx蔜^ӯw|&+ QQk*"C2TZ7e `jHM/ pݻLԍ8{Z (f,ycoLffM~O&7Wy>CͅX|,'WQi9cS9~s(TSv#i&L45#R@NQ(r@S5a"XMR+>ְ̲97")ia/\7T/VE6p>|;wۖU9s{~JwߥW&=wgK luzMyFs.|cLu/?K"MԾ I`@bެeuCW >/nv@;>YDc4|?6:9w1PaŠ_K@FQڌRm9Vw8zV첄! T+'yPM'j j@`,?I T7P̱~2b6U)T8QFWšuL͒?o{#[YPiT3| 9n /([W(a':>iFJMu)JG$R"\D* !!j)`xFFOhNqNn蹎U :\q*ߪmm0*mRtdueRw#i! g)I慤R0V@EQj] G_fLoΝEo/H፪QYُ/t2+ep&F7,xtIYHgyC_vlC? 6P=#>Xݔ5E&zZ}s8+IjS1:0YYVQ=Rey[t() LG?ۇZ.d6^&c4_-8jBXo؀2XhZ5D+!Qg3@3 |t|tJNk 0|QWޔ%((;/bgF]v|FhG(Y(d4TZfNn.(]6jٰd 4G_zרҿczA~Box(@&qI#zqconu}{Cbyl?AP!~ɼ1[6,6Ir" xAhBAll~wP f7-W/8Vo+hpF}չ7iӬAe:x_AlGzŧ􎄤e&G*[٢I^t!ɒF '7_ Cv)c.g[S!&kwLXhBnxG}/xለ(Ty:w< 2'%NSdgJ|2uVhYCoE!.AqkGC{$r^+ƁYUM4J>+~C'\kfzaeڐl[`go͖(\sF,ø%}Mú.*^3dF !uχA@EjXmf$a ,o"+|'gTݕV\QY#LZuq;g&aUoQL:uX믬 h;_7x -su:nx܉ړ[c~Zy<Ph @+TF/TA"0`U`@ F_G5 @'/QЁ?b$H/aTBIyT=v@y7,Mw$̄zijm|nl兰9"I?=y oF]Ue;!$E̽1Q_W# :kv&̟4_ GW @m+3wp[p,ESdBtmuzjjQ5nk Ty WH Im/z8@f# /.2)I39Or-ګs3 ѯ;'[V=2?Ϻwo[wH JM2 .cﻣ(ҷ7ΝCQP (&5+@QAEń`A%DArfo vϺ9{nwuOy H(i@ԃJN8p_#Evdƀ+lQ =;~wz/ /P3O^w~]!'倈"F,x@)Rs4OȚ* Bw ljR/-Lg\+_&߼"hՂ.DnX><G{5/|FO*}op83i<6?`1'u=b0_QoO[cjrpa ]HYN[ڂ^Z+]޳wmH5Aס*YTyzLT~USΡyb_y脝FA2#?Q1$"iiPeYX  NC *23 ᪝^*w:`K9/&W..uDmu5y: bPuw;ر9\v,!9V4"ّCfHf&sVy5|?MG ooǹ?MyR^0>O$8,ZNf DHRNHYHs:.6y@bʪdƀaQ9|N^fYb,!? @l aYCQp8# B2A%6{Ͳ[d\C 4L-e,Po GwXAT:ܬ '!%*&>vPndMgkM5&}a5VTX8ĜKq,gWv*_5Ғa5}xL%!RYGCCd׎=+w#7r 4k+X ht2c@R$" 6Fo EbH X0-"3c}y`sW1Ҭ_9>Jf xp?1$I&q/m\$ut MBbDnwR p Ț,wݜY|~=[? ,S}77 yB^sɅ#vG3HbRDx<`*<$>[/nwn[4jw vEiQ5{hUH`Cmw 3$58ۆ%{^3^]C:Kr.֣uQ3ݒLb/L?߷~yM~ObSИIW$QYWHeY敛qhONHHZd~BAA1=!5"N `Q9cϳr j&'L \Q%EG tRjİL݌}s}50[յuюZwTyc9!Ki4SxdMW:e0w7#EWPtIPb]'fotRO }hpSHi:G};/NI+0vTj.{k-zJyH'"Xc0~Q4<yO2̛Qq`#'OwJh։s4mJ(w .DBqxܴ9AX63`qs!+) rV^'wVVۑ$_;BT/:yXxb1xŸ#4 eCUX' y&$RdO@QZPD!2 IbtzA ~̚..8_0]'/t tvtp1%ݧHK_˹Ď^3W|sy̾)ai*ip^kJ0~/6r9"m:tUl#Pի=p̳_^ܾ@ٚe$͏%/q0aܲ:WnԺ3:`0:PIo@jGe9^m}EKn5.|%JQubLzjCPUܧ@]4)Ty9?m - O$U}C^c3 gnJE壎\3s6M a<97$Rޱ.TmߙUn{2%h,0(8&,v kZtiDErbᣊ]ᴆGMM;˱g<-] kOER+vu龩?rO0>U|vέG-a:UubW.ФP`S6>  pE-},z4,3(u 7O>F2w,QZa)9bI"{wf ]~pE72sj?T4"j58xT%e5ݐR>eopUI\.u 2nڽgo7QKqCU݇Hef-P a0l@"| iYdr2p!J\J<ĔUs;eބK~9 0dƀc2~\N>1|N2)@$e" bD hZxa71U5.xjZ*`[)="ViWf^yΪA6TsdfAh!Id9]ZN[eL 3EAh{V@Q5hzg'B6 IDATQu-݃:d(‚]b"%|#'H4~mg75s'C~M_=W,ܿ:HωjӮeޟ$Ǔ0ڴRUJ -5"S:@2$+n\CC\g<d+]𧓖R1wr@ѣ >pꇢǶy;f=O`!kD|ng02`#5P*A^.ILnQ+2(w~4%H=_ÑkCg`{yY5ff`6$-"Vv+fztdr:+ x>=Rx;*^| ʼxJLzkIV<Ա܉h"vTE6\7m3çCޜuau iDwۄ`vPǪw֟sM8#AiDE,ބw V)`;%5$H^1K&iV`lYea2WģH9Q H(LisJ0t!64H]UA-őG۟6o\1Py3aV›~$]n:=뢖t Ydt<,:Ɵe#'̧#]mڿ؃Rz#"[U@tQ#Sd*>w=ݯ_2\9z̐箘KU=uo{h212sU~֥hZMm);sd8J h}aaL"`V.E<i\~/ͅO\b]z=[ +["POOF.P<7gF9e𥥽lkT†$ ujTyS֢>?&,JP_HHXH]cP °3Q8nڽvgi Tj,i\ټ%ME Ir AT Ըm!"Ep8$P 8tݍuGOC0|X$]7EO L#30,E#5h[Rȑ[2\Ii2qRC&ѓ8==d͂{D1ϊ~ |d Ddd :0Ш"4!ʂReY3Kd1igcEϿ{N7j`+(=J̰|ךOL!_ƭ4yEW~﫳~huYu{ :տz7|Ľ#ǺBitvWU75nie]rjU~ q\U\࡝a3Y$>w%u`tG~*PN1S:2='m6Da+8%/vև-#oNԦ;G 1}z&U ^&j}թ/Ώ'tӾIIB(;w(H<גg`MӏM2DD&wq($iǞցFJuǾ]O`c#e? 2]2?{{/~\v%-b8sDF qmm2dqn . jwϻ;o{puJgVBfGЭHTtv{ߜqÕĒ`pesJAcT֕nBuJ4^WS}7l|NgE>1"}nXUYg1-OrT}fwaϳ+a@}l{6|Cq@v V_zgAmZ`KrQ8e#+Dn+O`1R$g RGZt$@s=hm@dX$Rm2XLH1Uz& d1T P/8q ybHO#v`p0#{.NV\;[aw=Hw.u sc^@0虂| UU4M\HPl b#Lut O@'A@ƟB鿴ً$ǶSÄ@$Gĝ(]xNQ]!m琤t''uzQ6t=7cZdA}0#rnؑPeLf $['e6)²$2dP"W%3Lk$/߰M-';7h5YnHM "Pd~tB"it"{$KԹvhv~ٖCoEWH兛, Ɵ~1=V|S*U";geݻmbjTXd]5{ᩒY@֠#jYN{d˄}y7d0+f:]]Qz]<\< 7,ѥGxg,SD.)`ٜRjttűRj>x]p5rOTyh1AL00V<1|^^sсs!_Y ) կ$Q( أHDJ0 b *ו~K|-g@/hKqLz-] <)yEvS8xqE" ?yWN & g}k*GSwڷ,&@CS uϢN5>&2kY:kʓ"E&M΂Dq;dC'_lUơ=.gq7?f Y8ᾆֻO*/r9aɤUN )7Qr6Jn ay`ChB{l[ev+6M4UgP=R?ھ_;ln,W$EK+ɱ1,Yu 4a2s(Nͣa$a,Avg &MD rϨi.ux'fWe5+>`< L_|poЙU*MxY4!(vfƫG=u/tFh̭]'Gk%\\:{/FRc&Nx:=W{%w77g]5dO/\,8}ur=DR C̮7lNGVu2",%MvwULIK\֞⠚yRӆJTUI:DdSӳ@ <H$QszGfx5[w"H X!o"h r[(@lA@s /M(,$@! A:! *=a(A1*C:,S2  @&̄LU@\䑾S#}BjN4K[ y/W/_h<!A3Adc0pR N!Tԗ*Kd|8I>i;jߘH?_!;;}08|#7z7m۞lLع|vno'Qz<W_츧p##k0-Qe ڕ1Jۧ>FIP?1,m-u.242;e{U@D5C'ޯPe놪DɛfLTB$u=KQFh'qR'q7YngŁIQ䛂 n^ {o4<3=^͡S^um%whtu>]2 K/E5[?ƙ nDEsJ v){dZض@k>7XicjOhXGl#HVV 8XcFmYZ|eOף(v%iȬTm9=;G۶,;a&&؋K0lWu[7||oEj谬9~ySFr0J w:tuۏutW̊6mӌ@䃦D6A24CHVSŌ9 0,.q9 )Ϳ&FlnLPVaBTF`np ݄#qk/&j!$Н*XupZ~P{󊠑$ 39pϋ\@L+.- s59Da =z&MS:p`C Fd)֤uwdߌ!#Sv9EJ,~ 6T͠H,=|TΗ)$08fnMLjxGimtHy32Oo[I͹K :2nc܌Ƈny7 ;L]kI_03KO %ڪ/OÖ[k2+㒱ElNK_,-:@*!28#r1] .=m{:ӻ*L{d+$q`p0Nn8b}$Cw*IS.,KT<=ށ@+?Ql߄Ad/zf[?RM2cd-"rML& .%=fgTpf 8א:908D}'@0<@BB5~h9hH*{6H$ŋf܆eQ8Iph"Z/{nNfRhk[rb("GmmS Y~+dـdC3Er$s邥ٻf~8`ROUrd$X46;nu OSڔl}+:,9!D! 7ㅽ7/|KnN"aqVOVakX[_]ծDo{wۛaNYza`O“.b3Law*'9c. +[iEK3~hEy(*;,UÂ7wyj܂XB"hqu9OȄUIb-爺& w& S~VB;A.(7$SP4縩?ۉ ^|<(ks96Y&MOcD7ygyqvgٷ;!\Ǚ3uf]lG٫kņ:u=rҎOSgT5h={SƲS+kP#IsGnMlOQ jB/A .x1@D8c9qCW!mKCn/waU3e=ЧmZꭂ *ޅ^7󡊝Q y晳9-O{B4I>i  F1pp!+dwIb6-pAWwM7vhvev1E{?~1O s甜1f^ВX#NَA]tc+pyv;alZxtg 93/x;7fF慲h]ӗ97OLKJu픚ʔJb6vY ?\ܔuבtY>`WXrTu֨&\| 9b 0Bd> hh[ʨya3 T643 3EY˦TiOl|"J(y&`[dprp5ذ)VF@Q5\H˝uL.&2ETILL;~xz*Pvl#r %˳VTgGw&/Liۻ?K^yOC^ >#V%ﺫO%ڽ#y#`fiS)|x=^~xfOqG]9(؀p=^ɮL#ECc9JO-x<'ۣH`4<`.147Ԧ}V}xkdteK'GgG$Hِ$Th<̠0(]Hu[@^]hc(D֊܁j\< `m ?o u+R 4w$!@R65Tt~GdipNjqHRS-Lұ%h^. 8p%$[DfXOCU$kp2P0QLY5<1t dj!+H, :z aNV!ٝ@/!)/" '[;s'(gq!TpU0iVAYGϸ|?FjuD,Yf,×u/4g7WIl_|ݖ(gGP<;%Z> =;xԬ%y`7;2r!g2=r43/2%+_6Pe̓dK.I(q=$$RM+炗9]EqBNC|w+! ܞ#yId2 8h̫F)kT }op4UEcfhgm~jZ"Y0 H]X֛y[V쳆\i(,](ipX^?sy DۣC{, IDATZuBIlṬgԭ}]w}pU2CHlQu=kJ9Н1#{1&d4= $$ Hi`M*oNz3SWi*B?;\֧mq2bqu+/mʥ/hXZݽdb}2.#3 bi4, /w_<-8_?q)@6pG9w4%FÁrpA2M@nPd[T™4Sfw75+dz YB@ 2q0*V7:6ReH!SH&bE Ż#rS$e@(q%* ~3NH"pq[Q 4%-b@O$GĭA L%/@Y/K!kC, 3o|S͓ŅC&oY%޺G P Q? ~>S S6S 8\By ?dkmOVMFIt'q\ٷ?}kꕭ?td) sc~TPnTndPJU)ezK+x ZPd)zUCU[2}ɂv YճҼw(w mer[imMeAxQ`2rI9L,'~:s_"$@4$+ E$hNR"K !feFR$I$&m J%ĭ2ZUeB"sܱK?TT%HѾHhkms$94̕ia$-33a8k7J#!I72oY#P[j{pL)~:=P0DBjRsA,5- upaC"aw>oW6cܲ/,[@i4dF,lM%SÀD0vJM HG *DɢA"cپκVS/;عgEUMbHeX!T^tR^=HHq h.(PU"# 6l.nٕ" Uq_p.i)G0l Y v)z6c%Ԍ'`!B<?"F1B?<}Z+/j.A< o+Y/.ɚ8 @w k8<^&h<6U-`fBϭS AguJ/n/ Ө^~g? n!w%6FsϾ䉕R/Uı,r )NoZۙ9{o}ɂ n-ozO5|M["zk'AV5.\>jLNʙZw[j8Qw?{rcפKzUOϺ<H\YuIӮnkSFf9< Ӭ,7j*cpf ֘3CWc'!Hj+B j(qiB WG`ªi0Usx\d_Í' ځΉIqݡ9F͇#z,6tԼ%$7~tA_mJ譵]YgvVet <oۂO~+^UXr34 -ytl] Y@a`sKH6Ԡ4 nCaGuz{8[&X9?λ78D].>nWmD L2DBph͢T6$:tam㱅*!dRB/Xx 8$xb(2ukDPowAg;HU#@E qҿp_!CtV- >JTOhF"IaX}H ~-Q3-i/#2bELLz*hL#D|%V])Ck5h  wG׏y-]o2^֓]In_i{|KU4o#<;v+MRa !VX[zj- ϮؘG ڲvN8"1c^"9(UPoAFD{89'kM!8Z rU|_v3 wЌEӊb0 8v8ب4q)H5m[6#0_ vim7˕T;I\J((' #*;np$Q0_z|=KEٰhS/V=S>~tyEǿoz}: E]ƫeab|e G [-5ꪋ;˜NIMu2iJD22?<md^ ZUo J)2 gsiB' a \=v9cx D|lj=Hy5ѹADǴ_:ұACBհI_鲎JmG}{#SN5tڮ\Y\ę~1*]~d͸S_aJ{Wץf,ܚ3@jWmOx~扑@q~.K©m ,qӴOgRq#E3nƮ_lGQp嗯wz !Xn200 ,SE.B iYx"׵it7(e$E3|*/ Q3 r{h$h"V C;P  Ry{*ۅ,3.t- kT?=B HyJ| H$4U+ajaɿu@1kX00BhD<<*.Ty x|hз#"Gn_cSHK-(esY^&C7b;mAg<@֞ 4‹nNW ^(*0 $Oodw_Eϩ}u4[u:À A c~L\8?&~;?&BǷWaf{ N v!+O*D;? *ak[@qR!O>:;~zK;q}wuh*Qu]Yj^63taMk(l3ޑAل\UizmCG `k5e0 eyZ I"E]2 B  |D8岄Y?La'mbQviA/m:^b%Ƌb.y/=r**vf2jDʏmd<2̺֛J>lĢjeH>wlo]kI9W*YrA"[m:K-1xCWnSνV?ӤF /YsUO|{rzS-VdN䳦cmׯ/ml솏uX?+;u|K9^'BHSbX5Q6L)YؾBY{sY4?]\!ʐַmH&bGid܌Z=k b GPrMo&mJ)fG7\|9Vv^@( z(TҞr=/!dNj+Ҿ@C߶-(v$RD0CN"5 f!?_ +m~@X; =7p4;&HtsD+Ð֪ P- ҟ%^'$B0& JP0 $*A!<@n{dtl~ fhu`EQ3ϣr!b=竫}NMi,[̚UXًNb^Ts\Y6Mm-n?tL^HD^ 5>MosݷYi/]6>c屮ɇ~Fg, w_ ; WOwD]n'M,Iْ͗5t'1i;Um(x2wSw JI({mver@2t[m )Hah>׍ ^%*\ـ 0^YSq)@@&biKnѴ6Mkk*hlZ:L*+4 ZK(ˊ^QU*Ƚ֑w m]PuPfک kGZN616mN%=Fַ'vHײ<m,rMWX]0<;grr=SϜ3ѷ9Q˶M$L7X-)4kn6 141%ky϶ SJfEpO~o9c?:xĵ sq)z=_[A`ԁ)/) a NpDSƄ@AlyjnƄWw)bC>mpu/F{K}qQ @+le!DlȠ0XJIO֞︥wQ6'̩kBh$  -Esdkʍ}?ρyYww2%’!WRmY;+&s|OCwH|:w :bO+Es**%k;xiTvvTouns-S@>̹W\5UresF, D~v |GbG^o5{ȼ2̹'>;gr܄9wS̙,ry27cXO $ lAkd݂Ss=WsQ%sf>kDdi^cgc - SDm2SKr_oAX0_v"|a>vȰ%α/5ׇF+D װXJc@Q$]p'Jh#WZ<M&n" c/Tp15Pշ9`0AAҸca"\AeBU״! uM/6_)=m[[ .M^O]-_n U}鑆m|Ww6cq8tw鷺Xѡ7@=LsO}2/ŗ?E/,fƮE󈊒%TH%%TjaAu@ctV tLeYU9jFb/k*|T_MPP`!E>F4ZD,9sl$Ggn]ST=FB[AZ;-Cf˱60ꪆGN#ƉaYz)hT4۔751F_w]io఺׫vz>tټ!9̹a_̅A) Qw{'I&'B@ _ UgXs ϝ7grW LO'EC&oju(tg wܙ7 Ce T'aLZU.;FF"qWAM5YF4H3iI:z¶ H@rY !HʛbB޵đ@BIttB;w0T塒I"kjq [I+!2].2(&ި-&.'㹺l@H\984bS:*4Q}Qۮ0["_"p@Y?\m/6'Ĭ  m@ĺf32i&4 i+-vwt?pov>=bG*$_>޶}"skh53UznYء|6lYgהXַӵɗvԷ_8`oB/v̉Yb"{KيHf$ ;*_אTٲmX 1z]W^F]QbQ .¼}k0uM^(Q Hm8$۝x.ec1.r)uN:Q[ima|+BV'WVצW?zjȢOhَ2pڅTT\8?~7u~xX7kk>7v‥; ۤݏ{w<7>j#{+[9Vz|~#7hC+wJo>3ܲ/};̹?t䞇o6X [2 0@ZUȾsItg^۾߯RbR=:I Q ɦDrs:z<"7iץG`K E9՛ɱ;o ГA~k j'= I[}5珯$!xh4 , l@6׺v(B8i;uƵLZŬ{"jqtzX$`G~LtM aAw;N# la64#SG IDATP~)+A"&fM6(!٫9i DyuO"oqQ%>|?Vav;T|߾[=KWϹ0P.q>徏%z%:6^v!%)_otyWX=>On?^51ئn`||¿r٨=??oIҎKJ7*ɖw.fЭf{D嫓N%"d51 shqJMTڌQ]te kTЬt瓁gF9n6k@-!"OĜokDݐ\n72QB ~7T~`\J݀snE'Nt j7勌h3kl[UʚaloO {CT.Z$sJ/IgyEF=}[&-"ys_MOj0w>a^ӏ^wߺ,޽?\:R)=S}{+O?[&7Y  xnGb/W;x9J64rU]:W NAQv'xG/LΆGW%Dz2{XLݓX  JR{O\[ZL)A4ZN˛|{>e!g§+6GH[ }_]|}h~vC9$!m(ߡf$%0_j}CZ2+1)=m;@=au6$vt4~n3#yk6=`#AU닏Cd^p\Yd輟 U'#ueuFdo"|UH^dij8Y!*)lHcdu5 Jk T. | %Y;#'âEч e;r]PR U.wBP״P+$EeĎ gC'*?tcKL!{5]ox۴(aن +\z:zou{01*wG(sO}iEٝ^iw~5_:8P_6Rz#lwsR&60R=A7JAT2N{Jc[~!ۊ{8ڹ%zi 45wyCIyXA gCHcI%Lk5x֖'$ʛ~x?ao}i?3|负]ﻳ$$^(p/lvGmxM7nᄉl&,oDi߇sۜY|u?<|۹CŬi F2Ưt֝&=Nn L_N?鷕 p1'.c+f ,i¸ͶKQq"'49ku8[P Ybfq{tTɹ/a\px4ߟ5͋qe01[ R ]- )μә7t.IZBgyr +\89Qu5*)8|Ƕ^澒c_&X.H۫3ůᨳb:5+k- >qS?>gE K^ۛy0Pąd\<."aJ71,RWnjc=7;܍Gq,ô HDĭw7DUH)Q`"Uf쎙mGM6T${c{yq|` o8aL4N4 "JPˆX}5E"||.GuM Yeڮf>k./o.^gO:o[+~d``|T\q@KL^dBLɲaP$Е^܋C.<Mr~ൂ-F#tC}lOR+˟X_QO-"O~Cg$OF^ϵ}*=NN߃t֖&HHzHO[W^1kI/ˀk\ZBh]؈r<K4 53dr6qeOTn\.׶uA@_vGXŃDF9GnUg]8_ҭ=G>37;%t79j<>;ҥjWj0NM`f[WlsƵG)Ze5T?ͻ2lqM~x[=x?0`W:UhvNqX֚:#/_B6:MٵWz9rFy N4 zDݪ1ċvk_٫%cHW(׿ӂHNĦY'1/G,ðhpZojL]ϠThoo1E7Kj{.{n7fP]T UyH*צ2,ئInȌjlPt:LW7q ,598_o.-{k^ZyX捣Ta$4/ƉkeQIdko5)U@,tL`p=$@,S*|Q캏{ض եNiJKa;HD1 +#qiA(_~4r&N'TdŊI||+}S_ow%s8V:kƒSؠ+%sn~|-l_Xnڑh4]iQS"Xa?6eSuwNk0#R|{;,:HK{w"9dɢ6<쿷ێ0b=iD>b]y\wBq|qo2ߴz!~Miee:ȝSsې2n5J)]T 8'Mrً -O?C$1a٠WʓPx>V̚!Ev&*= |HX w' {zb(0E De(֧6ewn@|flBAK%@j) ߉fF$Sgc!Q' zڂIbz(2BҋҪ〷18:U.55ץ8cf/=s' s5;dP}e}pudm 74.=Ҍ{^]}hGݏu\yK8=/*bv3x6lU~둔%NVL8rGvu a@l68x`CʄڧW*(*x7J5H|ĈG [b&%Eљc!LKмƎZh_21Upb|<ӎٞ]Zpbo~mXPeF^z 5^2ځ?^O&+oM&WϭD~.͕FJv;ͪ򷞝3쏤3YZ;4ydCYWCb//ܬ|4O>oo|Js]!lber0k ̊rADG71sA--woʢ{S"{8덝':;_%c?ǻuR̚X NhxBc>1 A1}!)Il&DШ #u 0ٯ.u0@ʀ+m^ d X)Joi/.9X ,J)g6{Eْ1=UW5N~<ψAz&[ӑgˋ?bу2!D+>M ߟ7wR ^R^i9mo1i7XL鬸#cz^k ¯N$N 5^P@EyGƜ;URL{uU`& ˆ4H{@bĆJ&] f (PZZWQ]@GA۴umeIqTkS k{Z[VwJJw8Ç0 KS(4 n7_kb@ר orAsEҚT@'cuol?`0ҺWkZFnW.顼\alSӾ@/\u7B41ω^|jYt*WPx୽o&.trni Ĭ =? ]?NO[0 Qr}v\aEϒ@XFH5} uH(V}[ \/4sVM.s E."$%%TޤXvqs9!qN*#iF >}nW<\\VrWxAYƽ߿xc7䌶Z3^됬~{z{yg{1ޅw֮UgCIOXCVt-՗E-[)a\%vs ɴvtyz$I1(2*ً|sH!ˠ0݋T!S qГ4d.M)K6[̦4Ba蠷 Wݽ45^IRj2H:n_Zh+O.R:0h7Q,V7tjK位Ïsywu==c?n3΢XyIT7G_`}V4nUQCxOIԶ;wV]pxvM'nuS^}?w}@J;La]:4 K:)o?Sy+ϝۯo /E>01!Q~(R0hBQ>R/`Ǔď:7zśM)}vXU0jz?yϫ~6-tQ\Jy4z;lonci@g7̼'y]{7=~'Eoȳ AiBHb<[R͢n%j_G1đǝϔ뚴joCW%4' glSlt8"SThnW4i YzrAU}PB``%$Z]z)~Đ?ΊMM/kJOWK]״Fu!R Rh nD A 0 SH!E*!|X&[QJ^iw#%٧E⧮yHXc&| 9XZe;vMc~=PnŃϯ[uZ{H;:H}[)7HqqQm&?<ֿ,~p(pω?Y{I^ Pm:hh(4E}=(h!l|EʾZ7D)5^V#),+k{:ZechCFN ,b [J>=L2v^>|'63׫t?S 8jk?wnLum[ϘM5HΔ&̑Vzd M"0t6x*-ElgE6[{躮M@Nyc4d~/N0dobÚ`B$&9Dć\X|GXD1TJ8P},?y38q,XqtW*cTDo)oz'g|kŀuZD 6Y(v {X:p:Q![ϗ鳋y;LiM9Ό#7jkaNl%(]е9kQC IDATv.2G SH[ yhVLJ3W&Փ;fΙѕs{דW5<ﯘ|4+OJA"|]'YĬBF,^״׎I) R)_z1ws >ԭz9|Y|H''x^KO&>@`ZX"$IaMw6lߋ ô iLk_'"N t.@bb2 |S^d؋P4n-+*= ,XD6*"/o[@E~i++17hx6_bY ; Yo\|ƅh[_)K^嬨 _ pġ`Á6F0O6A;yfL/L@$6$!nEBwHPFxTJ-uF~Hgx 92"%*),"#U `ހ 2RoB)=2dncm33"L e8}TAM!6I; L*SuA 1$qDŎ7;)8X+a\ ?3w_uWjXb1PKUZudd`Gc!p2Ns?|m; _5dDuSnoP}9]='ϰx/.9hSꩄ5dQV*?;[7["o:}cX նȺ9sNn%pb403n[.26n;M҄ S"!l @5LIUb@, {;F&S(eq)PR%I @Ri4d!4 L|q_)$Ѵ.A6*~eHm>퐝ij3@{ Q,Äؒ$9a0'{塉Gm#UV)6VsDI^CťtC9@O_7^g <0,G>ϱ?iP*dAv>N|](rFF`).n'k x6`dC( ;?Mlh~4۸wj#) EIk1Ӏu ,gHe9puMv_qq1`k:'&c ~\ n'+|IVZж !^ۧ6K.4PTHOuTڻ :& d޸}J>kZr@²7:ڱ8S:˿娋f}!b'bind䁽!|d :V9]4 z$aJ$Y@B!܊-1芊8h\TGflSӐIjk}*Tm; \_Q0%zeEٚZI.d &qp=mG~ؽi$[4΅!IZuZkH @ޣ~-{ٽŠq>RUk#Hlo;兇n29aA}-#?ʨz6i A} U(H\̶o2B3݈04?0窰'Y8!,arX\@l@L('U VHRhGrA?Vo俄?ul*LJ8`q 5i/:RAJ>ǩFʃDOTy+R LGQ?N0yISlj0P4! -p"@`ҭ^Sg_6i R C "|sKGֺзl={#/<}|y刯똍4) Mg1f`J;nj+8xj]mR2>QONjrlY7XA+wD`@{omfc/lETv}M;GG(˭eEj+PǓ7k,-=dN% Ml; @?#f\ NnzJd!\lzY™RۑnMV @Gjs.ɉcEEPLdX8pDu'G"bO-I7pĢ,x\@IA4/-gW]rRl O|亊ǏdxO(Qn/ٟm+:V̸K8ؔ yXKѬHZjy6K[d^ WZZqM]:x1ϑ^t X5cs]xTMX4Uf+X(Tn:%d܌ y Bh/l ]Q͌PYgR2IEET+?lXjA5_X{o!?I?b7x̲K-!HUאV2BJh6R^) EDq^Dةr ,?~`Kpbxq@C9?XaJ0&@Up3t)7]@tB#UA>`qKi<&B%WH:CچҘd$ <@*0̿w҆ܩ bʕl?2Wrutn?9uFv$ j=+2teyȸU50tn+-D#I:bH H#C,,k;|/RJL$jD/E4:NHgQ @ !(< Xôl'1d7Dwu NdCы -RP m;B:ayf%*-xp 7>wgmsZ~\Xw oF8YTNآ$zع";W몟>2*Qh(ڴ@͙SLƱOZ\+@mͅDYQYYZ32ι7cR!jpdgL Tf-pάӲ?smͬaB=k){&R2oҷ%E}s8U~11bǹk'??RFN[oWLiK?xpqoS6Tz4~1őf=X[ "q=uQWJr\fe[ZN:}baaL9f\w yhN4.RݹǩN+Hz+ 7Ym3{Fth^P4=x e-1 (~S$Z6$d [9Ā4 Xf6#.8 ρ8%dM! ~p; EY!IOZ1{dETI٠Evд*8iR郅I9yu_|ڿܬ33cf] SC#[~q y':#HA.v$) DCxGO_؍ ۽,9G'q("ا,x>;p(0ze*O]|g4ʙHrZkļE(oֵT|B3|vl23]0ɠ H 8t0aQo{ gghO%]c 8},^`ooۃ0 \̻~ZW6xDqiRKM/>$KG8@Jť;껜(Up{pv6'0Z8\-lp$2BC *P0(IsQ6={%igd.胿]'?rߩ{竑F]~W oXIXcK~~Իrw! sTTW/38`ӒdYb_fCX8~Oy۴YZ9n(Fo] 9Ǣo)(mpϧj:k}HN!fsD,:g*;`V4jZfB3/ b<P4mfpQJ)XK iC iR|ƲYeM6DoKRֈضk%T̨ nPO$ô5$3= ,^=ZI1r䪡CZK+oV7mB'qy}e dݯZsl;@W̲^ٔlOڣvC`xsh3QY:17tZnqwUV9#&CUpkS)%e Ӵ)|Bާ[@"6 J Lp#cS[V^TYgi0g z=|dSL"hzU>ר7z~)/>K!@K+HIf f !=9 lAe CdZʠ&mj%GxਗU^v;){i#1 .\C̰>Xr ]rB6ޛO8=za;@s3 ])ЊpUcQ$A4C{ o!^xs@чVy5@J>zu[Fu-Q_d];K8EtOnLhu?rw~霯Ό Y_#|='d&bWE -Kem|.!e<#0py¥iV(LM؉h'(+x_ Bf&g TQd$LO(1!coy-W0 :;\ mpK;z[4_4|֝@eUUR=f4`'pq""+d9SrT~SgdTGB!,ypBMIc d_f5'p >)8ea$m. %aǮB"vwnrnn_=RRJ*K|AJ!}=RVa:l0 XRsi]Ozj 2(b5M+3gO\k@hi"S JCW)oѣuD2:4 fj&1 d2"cvRO!lŴن'd 3Hkh Ro ť~Ҧ?XZt5 $dY!(a1kEqi3RwR1@s13O.Zf ?/ )DqAJ ,K?s^ztޚb_5b+=j3= -^>f7Omo ;fW읍e)\8JXxΝ4MՒ@24m|Wkuۄ 9Ք ںV&8o9{_d5=@(18Wn+z,B}uOpR@(@ö~2m1cRҷ);)q5[DԂvH {mR/.Nݼ}5< o#Joq s2׈=FL<#Ue%+d͜w]~6)heR1ߎc;T+as3VA+TTYa ]m[\SN\n#nOb;*(`T? b[Tm+z,NL2T\-Y~?/E<A4`kO>A##O^Jۛ ۶@,68᠜I tF<ishɨmq#6Ze>UB{\_rJ_ƀ3"ȣVW?1q~vT^đ'*6b^ x:,{Zcf8^C(`i^\ ͑wDpCԎ~ᨊp}pbQayifϣfӯ -9{ƈɋ9Z!52Bupܱqiy-j9pв+"-N"+A_xȣp(. )aуfh-2vB*,Dچ|xmWZ*jp )nHc9BbЖ¦.,]єrQ\ťG~tEn>[|qYh-\I,?Wrk Q3dx;xl z69v'CZg+@FI~0pMl*9`wѮb!Q0O x /%F%NH`1:a[\(Ut!b[w@n8<ojP]px w9R+;˚4{a J<a#W$tɄmQPĎoS$ .P@OXhq>Pdαח#4C_uWFOg촜 B|Y;-,DuZEkmݡ! -0IrPGG\_^>ook`S\yoOM׵vL ̮bq5i]rsp7m۱9<4gD|xTE̜fM&PAb؆⑘ECY7|qC/pѺ.3'R}wf.LS&^ Y^<Η'O<`Ggֺ-no/ÓX\t~{ $4(5HmD R17xNHcuR@:R^Ra k,aWB9U/69^NJ7-(ȏUH~'_h̝,joFv#(9w R<ܙ g ǓQbg::IWS%g HUUfaHn2XKܯIoNWZ LwŠ uA?]8!σA*ӿ޳^j55_\g:2M├ظ#Z"l#G"‰ x&2ҪIc%"`SU>xn1|e5HnEʓe{BWIlճ. !A.[qcHN7ڶQ4\4w t1uIIpB~͚p7s8?a?eT$dեVOpZ4YА$-夸Sڂ'IKI ;0McaJBQԎ',rٺ7{Mvn=/٘&f?AqE#ˑni7޽H$;"./R2`vMr_u8=J$q(#ۮ;Q}ЬM} !,O^Ĵć$)t=)qf?x ;Ouýf jGr<>t˓~nd*8ZoegMxPT BzxmisNO$ C$=Qp=kB;! OZḱTf(.YMO ;lX^a&DqiZ8DvYڗk}}-YO%fqid4f FRK!#yFd"nޮG *7 +/z?n{CrȈGF dA.8QasO'RE LQ HNjp^x腽InPU0th 3|UᘱI-Ɇa#Gy BqgƩf$hL_2ObҶe+oؿW"ݙV'lxoy%eҮ3!5;jƝޥIljbwgfwae+[{ rL\2 ;ywh_n8Xﱄ6h4C1%Uq 9I!€Ə,ƆS!>:j̘M[OyfMv?<2^-=y" d0[7W`4?kYCwZD%n>|bWx0وW[9cȐկ-XĐOUG] (FlLPq]؏Slx8˥eBgך$EҜT} +W}s M]q[i]6nV=P & dEL$5cJ3y3)aB8+>6ISGcKcwaWI!r^1DKYFi4x~#\<;LBM6vd3k?I{Tx Tx}W$; `O+U/ۑ "}CHH/ՍmH\uI+^yh!?[G|LN g7OO겹Ek)dI y?,HY2(Bo&D 0h*%M*Jl'b1 wQ]ͭ۳ɦ$ދR". b!*XQQhP,Hom-3^E|}?r{̙s*R83'Ԕu `~E-ܗ2{j"8Ů9ݨ\NY T{tQu=%Ve5 N`Ψ"9ӧ^K%EǹR*@ G Z9m޼'um"H 7QVy`ShgǙU_2Uj۞Pcކ}⚵/|r܈'b#\f~rdȊ7v>}؟IWb7%"6O*#>γ㤋m2#6G$Uq3Z Y9ٖpON1 G0r?\PcI;YtCj_|wвĸf-3gunss{E#+ok2Ƨ~O|-3A'Ák Lx},Fcy"^i]~NM޷|vu3iŕ&= ^w^4}x| 4]LR,~oSܼ,~hk1rƼ D{HѧU\`oDƪQPT/,t3BE h^ƢSIN<]% E Bb̏ÖfG+fFquIYjt=~#>Ye=c3TحmآgLBrL%r)+&͓yy% 6cMӄ~~⊧ R{ぉcr>8'1rmt=9nSz T\XrDw۬^C{`֟?=Iנp,Qf^ L\?} !MK ڟ~w)y;FǧVvɞ']2_ٓ WcBޖ YAX3v86$YF NJtOw'`_?: oK;3 plU^:izge`ţyNvH,$[֣M'k):v@ihxD?R89Lǔ&`KTӢc>WZ%(a7ƌ{%.G( 'Gbq%1(R毝'xd{ uZ]QU1""noֻӶlM+TƼvgu|Ţ=u,+d`" rˊ0-(UYµCuPZc? Az*#"6]e񑷽Kֈk9f?$e5(-7Z5uV =,/ۧ@)`j>BsQ???9>qdƟ[9IaL闕0uļQ|Oȋe݌-]M]1ryMhT2,@j;?{l}DQZY|*L6epoUs(!J)mb 1–VM%(*X=FOxSpWw17wzSQw7=v4VmF͕qe)(.6+K2MZ #~%Pjl\Pҡcښl klV96N\5J]ܼx_8O<ykް<8]5I~P#'\展^EIoxRd32a4 ㍲~4NUN4F$킲Ɍب==ݺ`~Eq?nqJ\CgXYTrzk[LHh(=0vw~ȳ'%PrJDk|5ݕ}rw4(8Nib՜&FK W J9ͮFT5=`7(xUSu|gkN?]-c ֩{eU}ش @(¥I۠{q-c{xېoqHT簻u9y9T$|߯K>  9E,eCG %'jwuѢro%q7*)~ Y=qBXwN00@ *7-17sg9讹R^W=;` z7uY#v! <`f~!{4qdjq~ X>gWνj7eO4 (fn38Lo UbK_a}pˁK?C5+#0@ W͜Q_H3aIw)d{ѶVW-`>2[r:&PPCoK50TABwm]y Kr>IJ\|4N㟨ϨϠDDo{I*"0jH-=e/|f:z}ݖt^:qpjľQ)<b *$T$H*79 ` qbF+2\bx88׬_FxͶFߕ]ʙԞ l 3&d4ZOCjkĔXʂgUcsԇ y4#%>x $-_utd4GUSJ{{1sܐZޡLR $f&v3=-d\'zt&={+a87|rw/$7{9krnyy߹{Q'˟7*QhlU^} ^]S^_]Qd7ڽV <ܢ4>F_$I|bM^E͊?DҩV\~:UJG~O=>[É3R] *հ'sL3P&٥<,S5JSA6rjOKoZqpp8N2>TQf7\TH=[7.ێcM %BR{j+XR[s3R>gӇH:"ĕ"Cdbof^9Ǜbne Wq,-As7u7ڤ`~>-.}lv<>2v~dg&2z=w1"p~%DhZSTiɪ 6DG~ {k裇<)\V{@:i*qUFt K 5ߝZF 1yt 9_drxH_~W5K1;"' ڸd4:aP46/#,6ZipPb=5 5l8HxDY e|cT1s wn֪rVXCRwZ)>!Zs^/,ӗd޵IOP ᓯ P/[/ؙӻQLan šA:ȀõO!~/\VZhw_L:|` );[X][_ygBÐE{ng/Wɓ6AapA֗kkϙvWv9<{ay h01V%{2{|n}|qm֬Ǽ7-9ڮNEL EJ奓"k*Qahg24jԀwkQfNӜP7E_"3T;ғG:Laq3CfWIOR؋쵂5 RC%(%$]zx/8N"ů8U_`u:Ύ†w?8#gKX?[W?k7}ݻ,~k>SaI  3y=|j0u h|: _ [EMN`iޣNR>Yzo|\6-cKATaK$Lp3)G3+5^TUWT '̳j6gTk ^Mvclo:6¢AX?6ˊ>],꜐[ NZLi*b+pTC8IHgk/3Ibv3wKri/4= cv髷_vN0@&.]X^5jute oWӜM)\y%; gQTm ˸n !'%ߵD5{])q)ΑzѴjt!PdidIZ[3$^!/.4 xe!Cg .p<>0.@?@<΁ w`c| ƋZu hI\I/;?4n39z# ]?I`ק))ƉP-݆RZ3!?qu/fz駙.1&,%G$+Hw&Աzܖ䭐j( #Ҁw xI)skȸj%*}׭@z H&ʲ梮uxeUGCԩo[!OgOI_C( "+fPg# Q^7k7v Al f!W-S]4EƠ?P 3D4\0# bvhg3{ "z0O~QFؾpo5kn۞_ZtZ%8rVqmbLGlEVE)+ UY{%6uEڤ)\/V߸ke.ヌ9RzJ9w+8IFYT]ĢfWhx`?EF e9\2M*7Ͽb x*Mn+㫌Nres p~mL歊\k?=KqC+[d"njOuM;`F=B02*u5/5ű^X:7Bg6nG@m OG O ƊG qӝ#gY]p&&5{!= \fyP<(q˦t 3ɜ3xlB[WOywx|`Dm``6 ټ}YS'7 ?mijD{mA;{<%fmI@AxDS(5l4F _591O(O婍ԣG:$avߴww43Z}VFV.X^QLH6 Bӳj~HeL[YrU F4 -sE51n&:SFm?P5JijSK"{mlzI#ycSJ?3N*k_/_ŋ\tZpޮMeƴRJݕL "qIi<* % W,Spq Q7~Y`9t/~pWMu-nXk,=R f-m@j]~W" X9k-٠GdȠvzT(z 0{lLh; P็$3vR{:R{ 2dϬ^r> س9ɛJ:'v|mjAEY;օX!|4uirK W;1422^m(7%d0]̵%=Ft{PDu[gW8Ѹ,#Pq2v5(Du«:#A;%@{qB3h@j.]s 1鷀~Wzv3NpIP+_KrZ/ry >7:E@\@?x:s͝?*5uI4>~6wף&!pP⡇'-5G&kMT[yl㓎EYiO9 KUm x`¸^ oBW1f0AwM<=IdlKw)hu@W"u#kK~u y˹Ea)ƣ$V+-پ!̨MD)8?4O ?O9ԮH֎uqBJj9fݟy#'ⲘʲpH2hm1\зa1Qhnݧ 0D[zqV8 ?` ]Q%L"0{B\N=hTm*5tҬO@}k¿EJ [X(LMD)l檜iz-Dge'>h6qG(};r=6Ң\4x8]-$H19HtU S\H1,p)s5CJ -g6:@ NaފP x!HŊu1GgKtn4/)ظdD:0 <̀nY[?n|˜z[_Gj+ZZzxj*󺮞|[Ϙ4{u~Q$4 :+H@S:'+&gXxE3,_xۤ=,޹^礖q1m=ƅ>x]EP+E|c]΅ޙˍP^}[M|{pI xV&#tTckcni}ij3{ng/=]]]JHOVi;iF&f?&0?C= 2RI 2*A*hQ:[nzGeq_@՘+pM:pV^'XxcT$ְbq.=F] ر.Cƞ:Oz4P2?t'lu -\Zb5Sh9EN*&8T+[68WtE'ir:ұw/}3$?"W9 ]ŪY@ް߲ǡ5 0T4<XP' 20Xn>"$VFO9[ x fe(ws\X:"7*V~s0 UǾVuQK`㑁6EwWBOE>]ջ'qLk4ZA_@wؙ\ άkn99y`@ F .DrJ/Ÿq$&VnN!ce-x ń,~.KߝL "WsĀ֬xdpU2 %%9MAbʪu|0UD[?]Tu<82ZqpbGu k1DU5nr H wqFY?rTIE* iao܊ `! 'lse/}cOL_K7'*p` M?{y#`짯7BO_u&*ԻD̷8C ] 4{dqdF&^07xT]ȁԅܰ3;4hh詤vId +\UfˆaAp3AMj Z'u>pws1Vm핹|2?&䯾Ei$“=cҕV]9Q`g))oܾ@ߥ(G5o3r`AD64pfQⳋxɸ?L;:*MrⲶz#qI#y:,)f¥xLtJT GzjCj´N £t@Ax2 BGY[ ]x%.&F;\īT47+ +\ų[?88|{#MVǯqZҌD^Sy0=)Ϧ@?ā4H|K쟝rsQPo#X[̜}Fd2B~hH,偕#FOɔ]JؿBZdj+ 1HU}‰/qy O ˩Vf Kr0YDԠX'1hyh1cb|֠@vZu :*h\٣E< NҬ\W1(Z-T/3RUA9~j&u<}BUHkb '3DO|3DpL64\k1ycj6#e2XT_cz3R=o2ʒQd38iQ6U*΂|/7YN[?IlvNOŮi9WZrѥ<΀k7Xwy69~hLudTU%])Zͮ;C e @guZFY9Q&AxO[4#1Sa"ǶXwuܪkxɺouN `\r&JE2(aȎ M% qS4pp"g yY;$u\Ȉ<8؟ґy 8擶C֖܌Ldp\%:@^6S<6sr9 Z( g]TSC>CwnI 559$2o\ `45L\A/|flQ 6uܧzE9ԂVƘIeGĸ)խÚju!U. J>C̒}ozdYa U%dD-K-3SVuō &}}l!cu+aF>70D.ȋ H]}}`.fpg_. L&!_u<J(Zԩ(@}M+w"D.뭬>ÉfIH%LFK ]tIOòL܌DIf#D==.QDWM["@NY" oK`|Vڌ WZufLs>:ʑCѩA3Gʤv!4>g&} ZzӱԞsuy1*pV T2%) Sɠ:p&=Ft}뚝|Hd*-=|fUmb5lsX 0Tԉ ȣ>g@kZ!+ep; Y~l\m:Mny띝Eq>6/f2]$SZ\gfv`;WU ;y D-kVغuǺ ӳ8Wx>Hg@m\ŕSA k* ?/䨩 p,HӂH(3$YwEU&l$HA= gƬ (b Iwp=FV=#&LȒͳ;T} ™;<<;UouOBf[x+2n^I9/ A3%o:͂ai S$ݤFרZyRJf(BO:]?lnsA7=0 3 0b0ouE-w,cJ[W&LjKV,7_{?R]髓lK1 ޘ[ 4WXÏYF8,{:9>^4d7QW2{:8LeAzSBBNAc)7i>^o7.y>l[bGIݦ4^~1p,0ʪT;+]z[V˃MOe#1h,NjIAp%KOmN*;$wFw}"vB-=w\>=1=iFiSM\sB;^P: \{\l%(T߸CY䓻Z.e>8|BniZM8egtVuyA4VF \Liݕ/K ߭suj@a / ]1@ 1եT(ϓR6: fd yO:]_/[w C C:e6l\ސ^sYC~ NnsU'"W@.<-zrH>ER]* ٭qm@UfY#`pdNY\.j>  CF+Cqo`⊝y0&ru w&BMtŃ@ ʪ5TVl-_%(q1 r:T a3)1^6^0Rݨ *gQߋp"(|sю$^|f{LO鯃VW/VIUO}ͩ%g}qX0&붞w)u!C:-B "FpY$*eYȌE ̌Fv+kRSnZh0Z ӎee,p@:,4 5#ʔڭ^x޴jCC06#Vxmw,9.wy7e.#1n=11B GrR!g}@OC=YeLɉlZ>F7-2&T\pє>JB;Sƥ+X𔲔W[ܱTħkn7~J'N\1=pОʪTDHU1úP_)_Ϙh 2ie~|Z,)]ɧM~/ rr ҲG[ƎV)>J缕KXf&vx e0P).`!Feu\, O+BF*SAw[F}=!;l ı24p(l:`h 뀖RJvuNmm@;jjk袠KQ~ΏnrN5HP͇wnM+9Bw?z >yuO?+дs.O6nc m >R}[,n&A G馀! ص BgYMEr^/0@s a.Hʪ3LKh_~|!^<+Q&,#ə IDATN/m,whuZymV{}l5E ,!8 h-rH)F! 25&  gk_:Re[y!3e{a0d0jkkzK.x#^2xl nwK|;c aታ^9봴ud: xʇəD*gg0DuS}$z\QbG}&KšsqǥR4MV&X`c ]gyu21u;ܸ݈ٷ$0MkpKܻ?/N*ޱ1Oj* XB554: ڊmkB>xi&?30\hSLʪ.-SaRIf'xEw_sRݔ*f:h:b5_~}_Uφ\ [J~ 5o맕{F>sNv<#~RZxr44. lĞnAyZu%oh)6(F!c$hNW! ,Ҕ,|j( SRZQ #=wj7⓳\~g}TCz=7M>HIw`J=FcN0>\Is@_UփeFw~ɿ#Ak yZ)+NKOhM^$_Dmbm~FDE揻QNhi$+K3Umjwu8mNe_Sī }5b"КPQf@Ag@ H?PhC6Z2,p\i*-K)ZJvTN[:3 A8t :, 3fJ>د{k]/vl)P_lŻk|ݶ8;H<|b1+-g]qx={Ea'XbGX3bC+D^Us:14";MJutB^s_~5=ٺ>a3#W#L3+ lw;#לӇ f77' 2¸S =nnmBޒC3}k*s 0C{m~& r V;o8en̜0no,mɞ%2]˶U|R'߆xȭ-#ޯKVΔY@+M.W`x. 0s@:`ظ_o%Y%x(#8˷ "9d]z~NDd}" `64C%gIF-ͮo+*>~qe_is3-7b}@νXqMl&9P> aZMnT{MC땢9;M Wfe3K*NYE; ˸XH3Vf ;yO?܌ ǁk'Ц^..uEGEWpagIr4:}vh%عW܆ @_ q w@'z?`ߩLqn/{hS-8~SO|uȆ\e0hG~jaq&;O.Btzu6Hu#NϾ?$5\3`0sֽwi6JV+⋀u]Nji}W+W{l),3V $5h{!} l|vy@mE^ZEOEa?/H<7(pYXva\4(SZZӃR.ֱ>Z{:[tSr1zp`&p_&8|#'AفVgk_'mff/c9 .PΕC}1ɿR)e "PF^ 2~Fwbk4¸qj39D0㏼O뎻ǭ#EOk=rS|2djg_a6>}5f9˵ .VȢPXv|ZQ6T,ԎB4"lzk~-p`"L? :~;n%psm/c}efOG@oj:k2.n΄]7[o ]@~YhožnWU 9oe/YW<"sJރ/,rFJTsE|RNa@QeG+4A\bf|jEt)-L}" i LnIߞ'mB\r2 )AZQl.TmLzBE6h`1L̜v =MpÇztpܙoo8p'=|~M6p+ G>iڊ1b`p n&`l5xYȫF_v|WrYS;p@;^&cy>NsuY Jl Y@(2=y'ҤYQ[tɺCVdyj:pHbK4=wfsob`XMq("{>k]`+P+Tl;PGB(af5d~a^;m7ES-:OW<=gx) 8pIsj UPI7kYBX}o׵';ikN@!^2W0敾)1/^D(]r-^; <\WK[(a!>ze2 ~-3A>1MO iVr>>prDt}Ww?<֮O/IqpY0&~{Yi]jiNpv+V\* yŠԴ N׹}!z*Gu񝨔 AI7emaGA?3Tn-.H6_C*nV[V_ چnR _M&T t x$'jfm31J V7AK0ʡLެ=qn iT{#@`w&Lme|ќk_(+p1Լ ~SGT>6$CwCuiŋ{ N;/K${3U-Pvf{OžDٳ54@1<|X1*y{B{c46>ˀ.뗱4$Ф2 چ-s :(Ӷ\4AaZJ@>ó|V}0p[\=0fInFny񊣦GKJEB]oUۙwQ~iy WE<6wNZo*<|.ՙ4d^ҳhǂW\2n< !>-y`.6 98'co5msݎ|D벫Fv4D'9+tFR'dzŽ@$%]Lv|׼/;n=<3icrM󅯷 f;::x*XV __㸔{_LӼ5 ~uS+WRx_}yiQhS`f 0B:|>}<+4!=, n!B̓GhOLad*޹3,#2\1nHః{9ڛ8Txj*_TQtaf ܪ=jWS7LKJ 'qMCKSt*o]Je-"ucĔ5;u:#n*VY}u$eaYȝrP4`2O2;l<:8O$orcEj+-/$_fl%Iq}w|fVhiyR$nn]$Iӹ{\:=:Φ>3:iC TbMiZ[e7^Jg^( w2`,o&o=1-r>azA&eđǕi¸9NNܨiOլ8+,:޻MpF32-e(.% 7.i(66j^#tuIFNK3u[ήvҤ[Zw{w|>?s;bYs?)WYЫFEe.ysdb芸p+G'{eX#NT]a-iJ_&L|,z|(DhUl7S35BeErw^d:x;-6D` .1,GN92EůoO~)Q-tͅ n9v> \t19~t`.<);| OD?UpۘcvE T--X"$B|FСw4@ڵW:X|jPIT?|QPYj!0E =Ѭ| ]?!0Yryǽ~ESZa>^8eTS}.Ց0a+qVz!׬a@(7ěi8_oPYSYp箻] Gw엹r[!ƕQycr[ZKZ|Q 4xRSi~d糶j,aX+|K 5/9Rh))4iyLH5a処aw"[or{H9D JGKT C=$4jsW,NBF J3bLֻ~w 0'"IlnR;\[ؕU;S\'i68z@V>v% J#*I oXY*,pBGhHɨX(,%`(eLN@Aa d~C @&{{ ~.o:=ꙏ-'ԦK`ք IDATuÙ5isnz£xcz3~½woyԪlְV7Sf3p0y`lb 跏AWkBqjF GeU7p ;_*L4Jl'*4׀mNZC ^>bObWcr ~wRe$|iX)ÍȬAayX!ρr*ʨw^_+H^"C' a[+јH=?`^?_ɢh<ɔkic6\~5-Nj~@7}(V O+ߛO88q,# mBò9 p^Ξτup`]vnB{{^}> #uoQ%%bM^0hv 'n<5}勇R箕IgX!BvHq{ |O y?՗^Ȭ[A?:f}P?XV n*|X6+,N2_D+|=~ŀq N$l3Mk17Dc3wE]dC }_qC" Mެ*[kO ^klHѭmR}!N朤(`kgf;s-5hL&}Wѓf^Ӯ7٨W+*<$G௦錗X|<`,:5xnc!_Gj_K!cDT[}H&g *w>] kF^Db47hTZ+1A{ \dEGm`2J,?=B|WW9}|r6FxŃbn =(.DeWb-9y@K@=Io[[#U8ou>} =K6p48:<6WUT~_VML#o5yt.'~?QwqlowO྄9k֐t|Ugl0.Y X@eՄ>w[}:\=ƺΊ"?g}myxCmk ==3 NWu"4]RVXA0m-vE /` ڇ `QRs #$@e%'S?X)sGFc :nn.7\4/L)L'вY7٨Cz_ AfSJ+ߦƈ<lAJ"J[Jem߫b[v,й&S\Ng=P@nT saew"SZV~vZV~ydt)ͷ\9oʓ?ucG,d"VP p˞|+zovPfҺcaqٰysĬE( 8SG~̿5|5B Krm3tіJKZFaT5[waP\EZP~`2@C`r2HkAL~k{1 \fYկu;vEx4)3-CY=W}y/I 0lT:gct!\A0Vv zM%:i6$\ӲnpǦ9maiw7bX㨤LkeR] |q OJp؍=?%khUxZ⌛vWFnd#A)"!lZyXvTr͍]Ɏ$&7l5i{p klPMdw[{f $^8^2Ҏd7Ek?]]v=)Yzit|&RAW1ByB(&Ag>^62 )0 gn:gχǾ-UYY1$24m{cӡ=< I/nEWJL9rjTxx5R68 a7Bt$Q(8TFOWx\V|zk H#{ ) mgn=4/Xo9"ZҮx{ʏՇYQ)\IoMDrc-9DJHKxݽ~ӉN=I'XaG0\8[LW MBc.Np2 ftĈkv/,; W(=Sn{~Żd||#ڴ/J2J_*%2o7:yTZ{CjܡIE<"ْmQ͋=酥alB)E\U#B)V󊂪+0>2g|"k) !4d A s AЁls(yaj)ںٷ|*Ƃ"-mq9cysd"{=>Ɇ!=5a#vji7c;W|WkF^ɧgikq 7V 3ekxç#ϬT}ći/m9Hv @ 3矊F,"S4%h|aA[@߯_IwrW=~?a|zHK7s"ìFS8ӚG^77}rhNZ5EXd+ [bu2 c'<P=`/44ߚ@eU30hPBzrtim'F L{ :CRwU 6[!/p 04dt"Y M[}) m&W#ѻ#m?ԃ@lj[f6:^u4xx8e| 71)y8` 8燙[Nz2f&mi&_ -߅,pHJزM+Hi]}] cͲ7[Ϋ`gaQc=|h ~壞Ҵ*;k_D ڂeC9N֟g z AЏk7:WKD ҭKɦ ~_<\u{Sm%e+aV4U8JiO MBFF{s6⋊xN~/)1ss ~pu)M]3ugã*9e{*IHUbw1εwETD$ " ^P^%{kZcD=Og3gf{fwym۰fH.VP $}B{>j =TP7rH<dC@DB!3u09iAP+dTp-R$Rxz'(zSSSָ6917/wa |Ja(5@>fҞƍ|L>,Pkc>#A[Wߛ“ߏ!J-6[ ˴Ӊ]{eߒA vrGG++Y`BNqSmi?HĪgKC?ZnW[YѰWk'[9ʰj]eZp뗠Kk+;qJ +~/}c<<]QT$!C };wlaD|ҁ!G4 r EHt^VwV^u]^5}> dN-"Q~зe6M[g-5̷Nw m*j]I&Eqq2XoA8Yy t j7% d$:̚]GŅ㉹J[FbXӇ:RA ÷<=⪠ڱJu>@|\5[(;O>g%UxlUTVhw8RjԳUjY!4(?Uz%nW+8.V;C^ W_~/$Ʈ.~f"golje `wwF|u: !xb1p~6fF8Qמ*C՗-_ɍUEi(L0R'=w/!5G⪳C7 75kXb ,?)^L eBIK 4WR $ ,]J V^{J wu VUo:%7,='9<שlu/8vo@w=oj{hIS&lt[ZY*F,/hObUӞ~Bt$p%KE @ *X^e=gթBfU*BzqxAUZwk m:6^ޛl qH10Z,u|ͱ\K7<:h5!]$(ߴ~uT 2Һm-\*%' d_"f>ì]۸ } [.dݍ;Gӵ v.l1KSe7=!oV卵xu#N5j%y iƛ۔䞊?Kc_O.^kjUU)ȝ^ %@}el/Q{շ|n+&D+Ik!ߺ!@g>5etPW)m:>CQI#-6 HhL#=DxkZGfh0h|ٔ\b][ť_wu  g5]D{2NnW kᆻr>b-B0'ѯ_vLh!Dt^Npۈp|RaN H3H:.jP¬#1H[-^ 0Lkso[2m%oy!_*48fNqucoʽ:TضN'o pd"u2j`M ' X-?Z6*YO)mFJ!8:wψ4)Eԑ"`RR$Xωu䘲6L ̃޶<2{z%(Hgh?v[ S&LnќThiVS@HV* *v *:dp1Ґg ž<6[q@2֍[/>wg]aI^1"QJ:FN>fR1~U&2S&)ڍmG y DOs"kɲUQU_}/PK 99VN&Jj6NLxGJ`TQ H_]b4rN&a2q3{xl1 = ʦIe5ܵ,uYIqq2 Ԭ\Fk֭LWcʅg?^TգwUuN]J@<3k10a,(Wn8+VLv|k7-/o4 o$A8UnՑ (/ ^u0ׅA&Ǡ&T| C.2U6kvaKXzCOrru1PBzSD$Soc&uI;yhs[~|l;l9k)4P % LP{/$d٪"+,^{ Z$ ^8Yv jQ)R9)9E梡atP[HJEi~^a9.@}i;-C;B߭uĐ)0hj`Q/y|L_599]<'irBA]25lUJ4rLJZ+f|so7ЬT Rx`UY:ڡtE;R@M š.c[~ Dsؐp!ئZԷCU+ꕇ=u~?XQ]AmP7L3LRJٞFO0{;egCSG/Xœ嗐ʲ3~lzwW|tRW$}Eӫm^@=iHx)'6ǪOJ(-h\JڐJc9!3Doi #V>߬6N=GԭuJ9zX$St~gLJLhP,WP ?;Mca)"_}Sq~˻c`"Ya)2V~7}q'#?Yk[C󈋘5ծO;4AC,((Ф @'61 e9puI1ś6˳p0}eKhhXT8 S!,cW'M3s!S&_w&x0ͰG#S '=ɚK+ ToW=諾9I-0 ́H-GFdb5@Cm44)VD7 fh ~ iH4`HcBq"b3x֕b/̺yk 0؊KkCZXAa4Y9?~)jJqSiP7MMBuu^OqHF5ޚy&[> ]3҅7ᢚ ~} e;R  )^N*%~XXe ][_/@DvOo .!Z  sƔKI]\^Zm)2-{N8AVsI8(;-& -'@xͩMALbMM8SqAw \kB hZ'IBH$&ҌuPm?,ϱW]sϽ{ׇq%/g$~3`>SjzTR[ޜϿLM~AN?R@EBQbԐc>b.(.7A@mʑz0aiNawʉD&+ݘE[- H!'2Ii:9{ Jc"JjBӔ&tɓ$I%t"FJ9B!MWnˋwQ.j?oL>*;O-`݇3@/@xV!(XApL'P4} r*Hx@hVGo ´sX#,6'"SAHc>m0TBlS(stlsZuƕ>W8[)T m/y_/+m]g(`+koU=s:}?U5P[nEXJ# q {7}:!rRN!q#I.|~0V=FZjyfdl ǣ'&x4d_ i&3ID!P(0@Sƙ̣PL I]h|Xz$}C ѣ2^v؅uD ~S5tk5M8)Nx4S-b yd mu#>dqtm4d=zuk\}?Ozsݿ"?WYxǜwe`+S4c7Uc>8 ;KHɧJ'kza}s[و8 $4J#Eȶpin>h2aXo[)/jrӟxa,WW:#%nPLx]C'<$W̏Pb|vmWL“eFrG+Xܯf D%MV Aĕ*2Q<&kS#v`@Я49&f i1FtowyNYRo& ?z;OWbICS6sǭ9s_yR׵Ns>|5Fk3# 9PFHqїl(P~!mBp?//Wq%nyjٹ׸ υU]/SaG(|V$;-7[^߹۵u ܹ5eVGVɲPQ!{ 뒱Lk^d X7YTdܓMpA8;%.>4\R2yxAcH' `CS7gA @9IIt'}3tSիO=h4Дݬ٣;8B= A!DC6-}5}Yy(*8w]¸ER&\bya\n[>-iȫ}?}ˋ [Ó̚=Xz#I3d]9\v S.X"Rվ0irl>Փt+1¤V)5%KVɲ D3m]hf@e;ք˕;y ((c)K')@"c2n>L>>DGIE,)#~gO*b\n)#(N<[|.こk6l3k7|ge'T'yŝ]7L]nmRWSccYqYY~m(oPω!N[?>$~]P*b[^%'瘁mo,m@{3m(0,IqZV ƒ>9G)fW=h{±Kbk#7:wM?iJO2A*8/Y,YZ*g)[*EKccH4Aiިz]W(:o`Ir16+E$0s4uS_pN"VJcD]WAEZH;)ux=c,bGB2cղ?j lo((0ݥȕ7vТ*R3"!Y[{|CV횕DmzP|O&0zm}yO{),RfZ@%c$:Jc-?[A@IxKk$@Z]q :{6 2QN4`Ç1pL.I-( 6 Sy@p&!2T.}dJ@@}͗5|G*'kmOrDwfW富[.Zo;?jDK):H3}a˯꿩1#mLtdَ ]|x~+S'S vi^Q?csʎ46luZUzֲ*yy3j{VΦſmّ"%xhG3n;=ʌ–6ڼ:f‘״CZEf. GJSB? 8&Eif:8#(C_k>-j8DY~[ZY(*+S@'RU>cTV 9WS("Gs t@l<4Qdd%4@*2uiY43NEj#HIWIԹ͢0x1DA'~gRmvlnje2xjT9c/YjnA{/{pkW t M:Eynm-rpZ”~ơ[d~l;z^2x\pr74N]]O!$SW5u6 7R%FON1Q<”uvfYlHVɲjZݾNV)5@`3޶q dX6fMwdҜVWZVZ~J'N2-w/-N\^ݰmmu`M)b.1XiV|YֽE=w\ٍ;HA*]ؼ2|ϵ(wֵht~^7?@‚\S&})e,;Y'EU:ys*+ĉqReFQ1 @Y48~%_d+2ix LhLC.AUӜvUVV :^=IO{9SxP*f8Xs+ |풣5Misg+vg2%WCs$VСWM_u1Lq)#<иdN) D71mf?I| eU!I.6a2[X |#Ve8(VV=g А E^̶J7[60G?ẓ^MO^vCLUFwB)}-{iSݍ8T(1O m߲X]JJd;OMcP nT -O"3[n۩8?g Kygm {h@^EcCZ0 GV?r-lzfԊmVU?“d٢yy 5r& ^ue.5oqr2ѓ() AƲOd dZ<ҶMせ[dI1 Wf;&O7 i' 9٢Ss}|dΗ- CJy':yE?ɓwq^Pk^8|_ NwL`K袃CSr\O !q:M  e>oFCxI{jo unY+j\]GDM^$铓V2]'3ZUxlQTV@nUj `' Q~| _)$`a#񡁠9\C| %}" ,% X _Jw ) /Yʦ[&~M+;O0w&l" IDAT:K7OTE$;NC@06= jcO^ZwM٠o@ɬG'N9?ؚspLV!V\l֬yuV. ݠkrٱkZ60<\)\?UA~O`F~ 0r\W@6`_LVɲqжB|TU->Up$~ R+ Z-h| Rh!I[}J9V`wܪoܶ)3*USoRG`{xq?YT|Lsݨ~z*9G k}=69Rɹ mt鉔=ܪm|2N\jsfjkH&-9hdҸܜc{h6 uY!4ϲGqpq$"ͱ L0"FB7`%0Of˭Vk{~Ӈ<VD¥Ju]a@w% `+;B U?aIl(!rR^|T`۵ mܖӟ7w? sZI44D z vf׀5pܨbJ50X2lYdfK #ќŇ֬ 6y6wi'415m{s*KgQG%Z݃cu%B+ի6om7?bJu!bP] Wǧ%,%77R:/xx&^?kn8c&&|}~O5Gϐ!˯#봜er/`\<& h@ s@8q-l_)^r}Dy>⍶}ĞmX>Ng_rB\Ml'u̫޺ɢ>x4ېJ.5_7AS;,;vEVߦ"6X65+WBqoN^Hj1~WٽMvJ q?ڬRM“er!:jE!0e7w1h%4iXKFّLh5UժiG$a4s.3ߌ';A)㏿mȐ^vtmVz;=XTa۽2#9%}njùVv,lMrŢrm+YM?`ԛRKV_:u'> ӴdCQV#Ε15[y]z{b;+隚=ܽ|M>|<\uB|vƠsק>ar5p԰M]).ve!gUxl_(I}Y_#tO4" Gz@2'B@2[\0L8Gj}/I=_,UjI/>]zCѶ{kj/ᨮ5yӔ>&WrS?\a0_ug#I[S0#IM*k)Mp|T>3m_;juu^~*^RI%^gԓ^"餕pg /{*bSiNfvѮ=:=|g/L<}TL\`k/>Y,[+-,[ Qym V@+d% MP%ߠX $ `M.I/'NߓƯRe_#G&oDZy9y{yӔu%N{Eώhxauݦ zrn,+y諮e)YN;mHi{lhS%i_ I7i=nϚsmNB53.mڬ. w`̔r7_.<ǔukBzg5k;*Ιrk::F. E,(**F"b5D."e]{{cQzBKuq vEyxHyܙw'mI2^a O&" 8nқCDAx=]d'څu=D)P"¿fO}#uIH2@i47Ӡyj&VW}]eH;nmgRU]ML7z #P}u߳֬=,?v/towXjB:׏ӵnwS0xfCK$>^yĻZ9?^[Sj6JRе#} :fF^ 嵚樳Cxy/fGZ?TQ[;vdfY 6?)wA|,j3-8(k]Lia\n_qH\VOpQ|2+tI *`G,~L@YEU-?ힻF){[|@tkוM-H(Ի5ZHsN5qׁ ]J ~eyѫ뭏Ù^g+ZCfVU㮞0vןu#G'qa}u 'gxt8j ڽ:LVٱ  Zz~Ƥ5{& Pg5Yg)`[oltL]K=pGk|XuӤi'N Z )2m![o-Wt%q׽!T@b}N8I.- n%CXgyAMe%OBA5K5C9s̨ ; .Lk)]/?&\6X~}|=9i4٩YUI̹u} 4O)ۡ;aB1hkLو%A&8ՁBf(aq9s'qrp0Yi_}]m/,C[ޯjQë fa>g4ߎ d҄~sf~?ݵ(P}&oiVxij s]Uk CG$Jf)|xB. ⡡jPdO( &S?}]ire/)C8=\6S?JiqҲ@%^j?Uǚ~lYgA|4oKKK%GI[zDF=={CjO));PvէDg]Z|vyɌvYn>6vλ?Mpy.]vT]fۙ36|>爤f= 5>Oi1GS4iZVxyhdݧY\`9Yϴ׷j ĕmHtt"Vڐɮ`5ZIAkrQUhM(6O}]UV֟e4;eIUٍ5 ?Z\ShmYxJiI\wRJM^-(oh *r;Xi/ /W΄p?xMoŬ=Q`hu5o܅OwT|mj;٧ۛ/v~^335k[~R_p&mq+W['mUvk>{^)Ιy;ukO18%z.l}7zˠמwi'a]PQRYRe<gAk6M]6JxAS::ZF*uADR% hh*2~hlǁǟKK@YEA:M} e SsZ5( F{=)4Tnrp3pHn,IYASOmcUdiuun6p%ϕ7K&+#P|*n\b4+d:T,vЖ+fZ.LC;` ~! ;dy~'siKnJ)O݆ilC:h9NgM ys|+e;`Oa2}dekY-7*2Mp( Ge@Vl pfC< nѕq'uw>)7lհkΎ'n§~qLm浏Qx`@?z ;1<y(O|f>تtK>㷮O??Jiv Ι=:s@G/SQ2{칊}I0p{4c3k I \<&eG  mx-(1濕TPIJف5J~,[7(V6ʦ?M"&+|g#׏\[.S=z=]Pʍ_-'mk=]e-p/K[X^2})M/F#цJO@Aa_˺49AO?65 |ug-ix/mvs>uJsk(=Ɲ?:iۡǟ~n z-Ӥ O?5j8,XW Kҥu#7ѫ<\$RM&,Q\\﹠hH@8~6V|pU!~snX4Td"иhIsi;o_pMq]Oy+[۵!SwAgߓ>*'3p̍޳ˎpd*).96WVkܪ]YS['_K+͟Ň:tSmf0D8?rzU$]}ql@#q. DM&`11C 1[M)= 4 !ӱ7ߕ\_UVQ{$w=MJ@y !BN,-.YsǾ񃻵% PJg樑 x}G;neæϾ5VU{ o.)W%%M2pӵz;]uǩM*uɈkͻԴNn~;y)s ?UORz#}{Ig 5©݅n]F"6 B!RTϭO9gz|LA yoRGf^xg]6{RIϵLCO&Z"a{8HʭXձ׺cO9(Mۙ])O:-=NA7wqֆK`wZ!.|3C =o%x?(ȅvi`A[BVgB ֫mŇxP(bRbt&"b8e3)V}xvQ -9ЌJ NǤt)]!^b~J(xpvS[" {2>zڱgˏyܕ7ٵ)A1凚ʩU20,$E\iێH&%\. ]iBްn}cCMc?yuONiVx E0ypL Ip, L,L&U8:T!]Dg'i"یseFCp儁"T%(Hh^5Ѣ\?/4cB^EUdkFkxM\Dח${{ؙpǧ7ƞs63vBT< 5V߸&~#vʈ-T⡄0BJwF`0ot OWT.\jA+'SPD! p/kpdw%9Pwh7#$6hXHʫѱρcI);iWUMYEy;R]ȇGL,O!}W~Yz g5q]ZH3pɽP1㷹O,AtNk{ u] 4=N =Ģ5*Ijצ fYOTX94EwT檷?<՝ۯ z(/_X}O>I y[ڥRju70̡Q iIKI/. SB3!W-! $ӧysJ$J"|bp n H?nIx\Sy$u+l@a:: āAMm+s\RnY]Y_?~55‘g?d㝭I!ysO(  Z2.00p\R~7~PF?+*Ҳ;wϬՙsy'S=mWum-]T'z蹙(oT?oVd3Ba]GuTڜ6vcODrzu p?##D",­GU >|Ų/ {oz?7Nrj'8qKy9\"OӌI׮Mu MRG:a궀% !/~ڦٹI[xa{{/^85?K4eR8\z„!pQ(RijHJ) 2&NZbrfX3G^5 oS74; ~Ba]IKtR7+;ek3< wM}ptR' !:msc~?!X1/*<.|jrqч;{9I}H)uDU oZGM0s]{an1۴raF"' I9oӘ_X^Ujv`/xCnVg>@vFTw.\yzG>li>"溦RvBjz؞p%RtrQj0쬤4h.Z5>a (%*TFmEiD"  IH#H pn99uGԸƶL&{,#^^R9( ϰN 7 }nڪ.jLޛaf>둜MUq"7&:\_VQ~40<ꢱM *ʧ5%W7}hݗsgΘ̳ϋ\w; 7f 5 ) |CO:z ܬjjj6*]RЮJ5F7}^(ZLSqEMt˲TWXz+%u9sL ߿ K^w)4G ]?{>3ՠh&6i*6:^e&;CqO6"Xg*t L5ڑoU1=͟tV?<>hޅ p5pP3M[ڪyjA'H'2x0IW=xl3iR7' ܶaifx]>9澺2n{߾EO pb(f %;yV{ lq<3l졉7?^z|&V!R`Gm֌'u-,yڐSVU `pvi}iGoiy RܣiN#P>{d}g٣c^=>gڼ _=»=w<3uMMc.x2)uMT\7͛c ] FԤh閦O{)uZ"m;-z$bRu(D PpA D%nr-JqJ$kq9CiNȻR.X]۠C.,kp4n9̬ m67{RTg_7uj FxRM'yKێ?s^x?K,XIoT zf)MgOa׶n~f35}`ѳI;RwRJ fY҃ 9qR7. L=rŮgR' o!9ɕ5T{ƻX'|˿0PNIRդ^4O_=4[[ |;vj͚˫6sԬd2z_TzN|O{g,:oH%ȷItB-Уu)Mt]S)(4w-kzǶRJض:V !Mբb3sk]RV|`!vec^.oJE%mCD ;@VbMe'nՠCΛY^.6nXwg}of$ÓfgYRo/]>=(rHlSOw8k)~ҫ߄RsLӎI ʦ]T7jqlMn3;eK\M@iAI!#9BK4q֫x[J8W1yuiŘM&`Apw*(y{$^{0{>4mˁ~%j;J!ARxR(!d )OT|.FR֞l3K 75־smqɱegR3 pY* g5}ŘmNyѲˊwuؾVjj65oRv>38kμN^y]vރ_kˤH:(Ḷlq}*zx1`'lurܰ,Y;:2}+P0l%6DһM`$a}}U,o<[CG ,>}VxU ܖ~gڄ9iMnԛ+}c24x@U޼ג]zIa# OI5!ξd9!zx.|mEe`sIŐ2S ɿ`C}^Z\yLϓ'OKn6 )INr:0 n `}Qz]դ\:`h0ސ˳\hz*4 cCYeA0OQ)̔?nѻjc菇ACHAs=E:Й7[m .$MQ! &ʵDqJyqmuўoQ+N0(lѰ߈+7TӦjtBɸ!GwL}`(sjF6SiV4iv C@V?& ͹O9oc2ϳڞ':~7{dhW?{˧_,X/ ]4Fc9L9o_x^M7ȐxjM-{Kn9* Sz+' PVQ^4WK8læy÷?|8/^rū-x˃SoBF3 |[Zx|WM"u]#J-1 N̗DI拀)Ɠ]TᓥU޻gI'{d&}{dɄm T2U R$(*`=jXq睓,g]Ps@4x Fq˭%%7)ujסU+bk7U ! H퐐?\ MZ嵺Ŕ+Kviv.AiҤٙYZF _lWjĈwb});Ӟ]bNDdģ!]6U%V&o<.Ѷu뺘zs7j 2hkksu nKGv"fJ][rldiqIZugv~ync[񼹶0_OB򄨺;TXh鈆/nՃkP!TN9ML <"`TvFY=J[x#bO)e/k*K>DAk%IJFq]̓+MhߪoʣjL:ުѬ :^J)n6e,Gޮ %AraC(IꆵV}U&^n)K|d(#3D.رGzcȋ-J]>{79>@Sm.|jA}|t!{buW$C>ez{ilq5~l_lpɞ:zG_~3S2SХ[JAkk1+3#Idq]7!"2CaiZwtp흲ۜܫWLv.W_!#x>+nt<ǽmyMVTV&i'M4{4$Z)܁"G7:x;Yž@Qa(=}sm[]hWᵏ/ܟ$U yM2ѳR'-6iVf|Ǐ)dSTZ\2Y[n~̈Ws;._`KNdMڽt)7]rͲswxtaNC!:KYU[S } еQ\g_w7jM<'=T'jqdC$-f-KSWչSO ~u;g_陝~gN@ 䑪'"!!Y+}+Wܰq#R=vޥWtiݶZf<* F"x_LSI¤7oVdJ; \yw|ޱsa7E_!_u%"'qOF(soMV$-땖o9nnԍE4i'M40!b+/rY!j,{O tmWu=uo$=b!`۶ڳ#n\26xba[nFӸl+H!6ρ&U~{2dHgvt2WM&{= j<*lj|f\_:-=8 9$s xOE5wRL-2℃<J'_yBW*|If) ﳦSt^B /Uo xį3rWhݸaVA^3MysGݤB*דҐmۆa}i!a-̺:(:+ evjձ{Y!WPwӬV9f^ \l3#7[k!-ůSPwpvf [H-I@sqePj u¦׉ ?l_ߌNN K;Gvqg|:o”K^[~@8WN8[ ]vi߹ڌ-Uܭ2xğLv]^lޞkتo36U-鸙i߃,nh~ÄS49_+M P{fiqIIL3bF5Mr*{nB6yLC`'cnݭYb/Mʞ@;擮[oy9sd8ӕkZ]ꮐ4y兹++=mۮ W٬U>[9"Sx7 "v'=hg~9wSs>ol$Gu}l!xV&0yH)-JdruEuݵl#/{q衇߶uDԎV/{s}TҖJ+;uGAA(._'M435hhd MͫFvMԑ$V -1Edڠv#OV~X>7c/x}4&+[*;O^z^]7& xMra}O 'pU S5יVm^|wT)ݱ*o$/(uMd$ pң8Zi󭗎͟0.V=JK>z|;(+?Tv@ Q$5 PAEQ@AGLHPAl8$96tV8Ҽ8|3o^ݷԹuvz}L>B5#d$V⬏ (Xփ~~4s7愢)߽W.OœF >oY$XD @0z  GSη ]&lRHN"_ڮOm5u#sD" -:GnBH''un`u QAع곣+uS̉#=)0Y7oԔs>VRfmhwzs0Cf)`@Աf8]O8qe B%70?ʣ6b;Dh/>z^u8٠ :/ XzC$lEIڛXh&6mHLlNq1i,ӸaNR8P9ډa 5ޤ$izC[L Pg˪pQdh.fJ֙CvMҾY39Tuԑ) |-8"F J PY*G~8E״9f/ <` :u-}ɧ>E,ETQp$Mn+IThdNOk__bˊ6|aSiP`OnrGF lOxη,ؽ{9ianXa$)%٧ .$Mv2!BB0W8:?8nC7>s\nqp黾jA(ԕ 1THz{2Ʒ(xJۭמ /wԱ_R֋FsspzqUSq 8qXىU]MF% B$5l݋4> O ksãqyㆃo]Ϸ:=X##l7-s0gW7.hWZR= }_rsϼ^{҃ ˟kyWI2Zm]gR :KB_?&ܓG"Ru cMzR?94K90~T'bJSpA,cm$3_ +ocu_p}ηL0f<qĪw%A5b*lٵu(pE-\X%E AQkר5Un2 Y5-Dzmg0%uݐ}ZdWWoxeR!Rag%T:m-|\@Ԯm<WӬ`jߟ'ο!pBEn@p @wPH[nuԽ789fW[drEf6[C'Ue`KYÏh˶[6w` ;~ѭC>xǠ 5捴*zج[w37>¬QPo413,D R6DAHJr?VbMC6%cW˲SνÆyߑ?T)pGt͟L;83 a3&H} {Tf/w9$':̌t{@"|p,x: zt)!aPJ͙/a6=GNa Cdgg BI{~l[I:3Pϛti}$kq -ya^^\ `qҊ')w^mi.>Sn$3fC@ 8:cIka:JwbN`ƪGY5K~:b^ӣ9l(uŘakL.1eyŨ~vkoeL&PV@ճEaZZ@UՁJX {ϜHrBh 7-|bv3U꣧Z@^2G>m#nEvQn##G $fmTX埊ۯǚ͟ԙGM0+, s[7h]/KNJ .wNMy<N8.˯W``'N6%+ukN5P8 I L"sU]æ/ ^u' )9f lF4% @]Ҥ|uq'kq6>^LoO/zq0 =ߓ O8qO&S-Ԣ/A!m%Jlj22`)\au&h+ <=$)#笟󪼼'C@$",ڸ2-j՞>V~O/Y[j k.Ξvhײo{),x}}UE{jw8] 53o' 7ovc YQZ=u҃[ #$>/<r֋W lnxoOJmfƻwd7JF4nH) @Ҷ 1wS=gÆ*#gkWm.W\>֫}<#֬Ymzn2`Zv@pMVaY9o,BsCH`i%I?Jjd1^U Of{&Ϙݲ]IYY9T۾n_,=W LItP(Uk!QSf@N83ݵ{P^[Φ@KD!E|ӚIn>P?Rz10&f#9ݺnHn\WN?Զi$afV `%psILM~yPrrs!)N;phn <7_3?Ӧ>:lq 5nt.ģ?= g(zM~U.:0kD(`Dܿe~]Xq%~AkW%!d^Nn@fcЩ5ǹ/j*T:mby_dy˦<=gOMvZ\VY>v(՝jtY5l*n`0XF{w4LUa6n4 4A"#j`2k[׽ֱ);@B:Ҋz7uppF -r7ο q'NЦIn@%= 03uia;":nQfmK*hͮYb&艄͇Zv8-"pR3M>~)[bs m(o8nÁCU~;J5Μt+\~qv&L<a]_Zஂƍ !tڙnO>F ]ksn&0{n3.s2cs-u;$!!)j+z1Hȵ4=!~4s~f5?Z`s)u ^8 PrhMӷ_.;tSJܵ .<$~Tæ(N=yά-\wuo<2y3A`8z" t% @8`QguI?8EQ3+iLkdJt`q!& IOQVWj"ĈVeܣYz;`9Su~安dx3v͟)??ru"Ң:ce ?@[kkͩJ%:W64%7 z)=%J$XeIap*.X7g~#%kᐤ(RJM4NdfMN][8 s&K6 {Q\d*GJC~][E< Kzs֋8 u&LmQBd)g'N8J!T; =~ޙ{?ZU {̤or]!Mr~6]ڦtTts^OޭxRSw6L˵y;PQ/X@ˆ.!9ٗo$I^iT@^k kC?M׶fs3{_E +h =g I>/ͬ n;vvc[SIZvVr`QU*e%]uC_An\o^߾MT7-- vn21kdgգu㉒AMۤJRPD%=RWH=̩8R(L;LB)%A򉢘  @sU5H%9R[D%QƑ=D~KwTH;38!:dg:|ۚL}WmEBI8R9\{zTGL$Ph0L 齿>ο1q'N WQ T#|u*E5n-^@~a~8 `1),`v?9* 'D5y%64X7$&h0 -ד&u"b& =R :xa2n-kXl*),x̀ȀTkiTB@88@iy J@(Egsm+|ñ%9.G<;gKxW8g@GɇFy}i< !E ̮ԡ^W_G;O&i{f-KK`ӥhĆKGO{)7_LhѴaűF./C,مsrPDe>0 WWE)_LDpQJgۙeAqr7ss4p>??xAn0 fc"Vٰ̫B5FSx Ksp8>οq'N -| 4D0 @3b"1 rd>R 7wEPb$МVUc\% І ENb 8DIRDIf0& (j{3AFC*h- ,_;=/lf~ܜsֺ2;9=z><%!/Ÿ ?;!Ξ;ɗ)v9}6`1lBb{7̅,JSM{nb`Ꭲ@\ۼy#VaJF[7|Y6ei.` QRݢldSDPJWirgvIOӛc(I ߛ&rX XjBRrgLX޹5qNWMϯS/_WזAEN0˄֒ gǍ۸vd,S ڠ!F(8g, ɁHDѢYՊ !pX1"8Xv⦤nMkՋ9),sTUS3NntqYI5nUs߲q㞛9jcO]^,z*a=Js= oղoTW ܻX\MsRA~}CXR$!-)A%b̓PM0jO|d_y`-im7"nA4m$%{yA&TPmYDREAbW0B#Y<O.Rr@fP(]ss㌞)3~cvvɄ K}>go$|, !tɱIdǟ{ cm$L:91Qn_ <% 9W?֡c g;euGJ`]յ] Z{/%b׮rj/\Y2ǂ?{C\ĉ>of`4Ytx!o8` G+%Fl"TТ0XA4,N|8` 6ZBfia-8:~[2R2 GNMy'&g'7 N%Jmo,vۥʋ/)gVrˈ RXp~)|o9 aRX肘Y"+zo;ZOsx`>Xl(4!P:iB1mW펞 uykdKGx0kM Kp8g8)|}>B~2煡iݝUFHh"lRdv#KF  8WU©(&Ϩ'۸ww`8|&zQf8研2>ݻ +7 L7?d|{=vɹ"Șyyyrss[|XZkk3rJZԄ$_o}fe v6JϕoyG'O ӘR fپ߲/w[ Ԁ;^-Go&.xĉc2  HWLm|^ޠ ;{dٓLY"F@@RI$Dͻ4@t3dj릉,?A܇Č'#LOUN9֫:{n-ЬvbQ #I >@0-/-7!ib=儤dg胷)E[xQ/*^ɋp-,xR% w7w%MΊҍ_S%ZSQ=RU&=_:cs{flJlpD!eu](6r؊!ÆT׊?[Wtq Իz/\¦;7Hwń|2uU媌Ll8GJFx&{WFG_Km;y~6qdݎXt̕.8qܙ+m&md0|(DD3\'"P#܄%I4 !vEAA3_ToB͈[aon4"G&&v=O^/%U_(2[ iّ7V,x"X {8.+:heo\v /*ތStvRX9/*E>o.1BFi%:5i?#9/A8q!Q\U"~.ʢbǫdÏMۛ,ZQޙ9+8金.]7P4$6Dz$b8)['gdP EAε6I7MW ,B(zϏ5W7n `xnnNͲ>A2`Zvx)y1Ak>|i9M~deY&>ek {H?7W=3Wn[2q'Nl‰Jn X&:NvE ha-ܙ **Vz|wϝ5?z TiEy&9Kd&86HU 0 ڷғYNU H&s饸|=&8&My;X+>:PO6q ty?{TL!Rb1iZg>tԾhPX?gNɅo)=+kҥQJ"fnGm"(xymBU `uדvJ2Wd0i$0ä{t[%^+ hQ߲slS5M04I2NnݤmyI<8}vGe~5Kܥ'N[0%DxTl (A EM!4BI0A\=wNG',6%\. 0vt[yaß~&}mU'x-G?h_̶tsEL.UB*˗m$/6,2|wNcE_,>d-o%y[)>YB/ywiKO{E64VwT4eZ=2SI $喩-g4#lPKdQ|~^ƉvgYZNիз߆sTh4-Y%Y3 SPAk!v~T3 = B7,!39I`WySR,!+n,Ƹ0`%YFu6}߬c6anq~A|'N8;p<&pF4Z|m0 aA@rB8b* `BgVrcC*aKf (\l<3SNֽ5gJnl܍&n񕢾_y),x0Ia8`J1Lz/=8o0,iE xa%/۝uO?͋g׵d=/< ܃H1;IU= %%yobtx5z rSkY0}LSǎ tJ꣈RW,+aJhx]& tF 1)HN;pjx\ü鉉6~K,u^}ZRS$RCas.3XDlws.sCMɫ߮ &Q)%dfgIl)6z6 p'(B%`T$1!Izuߥ^5;.v\ O8q)+!Lp@ 6p1בuz ]L ݙ)pU7E5+8GaZRB8 @?mh5U/~tv),(ŋ` P(84=yɩYJjA H0 z+OZ/QE?U3` 2?B8$I;m+pG ڇvKG'/BY/uRouoFhcF&s&洯ic'pLci3?K>\$'`h9]$cǥ|xٙFi GNۖޮ+=i$BEBPQYmFMC%tbMc'󯼵6 =Cu98! WQ΃v G`$,NaCeID* vDN/fs]69eͺe# cwn:gcމsr=%xͩ7ū'[mx )BaA9Eŏ\j-]SwFBbw.v\х _ڶV\`jX}LMG +CH!@fSS2]6Z IDAT'niΡȒp6Mt[S*jj$xF1˂IڔGv%|>pY!us20pU;Go8Wը@ ka\ߠ% ![ DP" " p/8! BK]&ڮiqil&m!H9H՝mzTQqaĜ9l%*:[`y3 &yGU}iYg}ϯ;207E4:]>XmȰa6_8^uל Kzs:N>u`2鳍LZLAdһl~DW9ߜ{%9M͘Y<Kny((q̷ H%SU:h,'q+nѨbҐ^ bXTJH_?X[(: ]y7Uu[BR*qI2\j[+0 >#0挡;_4_J A׬3 V;4K5up#y.0 &ݑ@׼I.z c~SgG_]S(vX*~fჯzն-/>‹?X"~Xx *">pA#kLcu]M@w\6%7;EsN#յtzwμ}沟Noݽ[Eomg{Y`M/6m V0Dy<;ow7L>}ڣw3ˤ6qi_TXvO]>]rIKgtsunutΎG.Z;GJ Kng*oatuwaimI~Wy1OJ+*0LCVwVZv4LR*Q'R nl`ٺ_6-~{-]]o}sڱ_h{c!{ۻfpNzVOhe > 8mH X?olSny=?,z;gR̸ r BW[kϢn}=6vټѿ`3n;FD##VyXGxҷO",BήgQr`l%)mgz_s8b =)y7.8o{;D>%73g-S̹e\ߔV f;;wϝIS[ĬZ3fX natci7#mgGG`Df@gUU*IRRJnTmhj\iM9<}a>/ļ/rrW{v4'M(cy~C mcIޙ8g%UVZ Ϭ=MGΘsk^ZT*p!AUW*O'v֭ۅ=UIT~V[>C){^ZS٦ݷ|Ҁ1 HPg=m |dΜ[O6@!,X~~$nS@l L.qL+덏niotOڦwgp%+kfjRTzl\hݸei;v=bho4M]*36QiږRJ)Dqh/;‹L$=۶,mY* ); +cbtrٺݸ@1sF']#<}!>$5.lab~H PX.I`g 6*-S4t;8jn ?VT&S<כຮwIDl{PjS͖U]&x"Cd9I卜@;z,&+! 8xV殦P&=@d=!2^"TM,1sܲ #"12XpBRX9S"H%Hta6fх3f[ҲEKmOrضJ1ݷM2ek;Ze 0¾h0 3Cd5pN,0LQ@!t_iBzL*4ՍUمX[܎t}k_jb>wl 23 0ss"o͙2GK޷bJ&&!pȤ{1#j;Ja )h3gOCiFJ%*"I&}Ȥ][s֩ܥ'% 5c9\3{Ԭ3|{@100)]!b1_(|W]rT>mm]]mD-BO aڦa%u= Pp]Vmh6b;Έd2id.w%>O1aޓI˚}a.0&'?6oW Ǵ]ݸQ5g$XgYߟ/ii_yۄ+chRb12 JEU%@B+bl0/ yړ3)k[h8syҶWEJ{Օ O+1\z5UxX| {HÃu>A\/2QK00`㧪hcHݛ 2~)xCevq´^}[LZMRFRtOE|c]TiȤWlӗlzѢE7Fm nG0$_b8vZ^Y2tIK׭-*i2@ '8,oo36dQ4\Ua%[<^F9X6v1gk5}%6 Cg͘ hyqxt5c?i;9<}9v/Lp$ˏHuIrrwm#5^O^VLpXkF9f޸̈y\.q{}UW[[: ~6\),HO(ƃ߈Vl%,/<7U_MALk0}ޢ]\,z ߰ڏ;})]KJi.{b66;s?wzɯ-gƘ/Mdw|oSY? V0vq`үת>{ Qa|#~;ٌnX A}ſn^t5x{]yǾ㢊Zq;" >dd ڵqaY2LDbv҅m bz?eeu\f͘]rgg͘9/^ª7{|G#<}NUi,A`b$2h'-JP}FjE:BNu7gH’ɫrmS ͙ࡎ&,ː;:Pm}̍Ug7VGM;wΛyԝi>}Zo&Ux4 (JT (xKz۲[ζ >WrV7W/5o^KMMO(H}u<g}#D&Ȥ?a#SO7S: oy`'w3KfTY]Anj^ZuoU1 C8C4#OѸeۖr:?cٺS l]GNHVYWjvn:iYӴ/UO'[z(00+޵1VEI>xZ;mSH<87Q50±X._3~L|vW)JDS *\Ȥ8n1!L\~הQ :XDtkmxksoz_[R%G p$ ())bgww8e~GDBPIʨEq>?ԨlSߔ"~Nef@8hόu[MPd9@̚r4)a=rw37?xP+xQ300v]d/LNe"gF2Nc~߽^=g}O<'&nEO/85ջ7wǓ.Z[EQJ|_jN{pћy5'l(w1zh_ãiڗju`$PR%\ ޒyLDg+ AXzF5qkS<랺bJ"Uo>Z7X okV\ǘ/{K|:w^"NCb\=mƞko\KUGVTjI;ovy`qyDuͪ{WJ eY-"n嫑LZ$T,azn!m g(dݏz hwICpdpjnȤN3& k^ P~gW]6mZ/ }I/M'Bwg'T wnbEv '݊=㉵}i6cxg ӴF<}FRβl@a % ǻwʑ8A> `ygagY[;\|q ?jCse Y!u{칊=F3 •:bc¶vŅ蘊xLzPK$mN|~Tl偁az+l!)$ s,?lORՁAR0n#BZ?nw#2.񢩄im \QɟLumڭ<7Vl +}Ki<,jۂ8u9@w188])cv~fXm#n|ŕ]񴒢{L:z\Bx;m^!*` lӴ)-MӾluC0Ho&L_{x@ɦo( ii aVplTFUoW+6~p1{qˆGn>u]o[wcID-+n;I˴TX)Jˮ ^OoWPKA9GnEqȾ]u%/枤^sQHtܜgPݦc"^VXx}qR @m\RTs4ReNHKk^A>b*HY?Ƅl~T\>CW HV&k b4X7/+mu;mesY:ѾhzGӴ/ռI}`F_s{͇:HN9ձU;PmJps噻jmۛ=/4vϻ=krZ# >دb wۿy9/,V.QCDq&Tma 0Rd@$eR"wDN(eԞgM[K5捊pJ) X[ȫu6!,#2(6TGFﬥ+>z{,s"+C#2黀٦)"(ܘynPEUmöGӴyuyz+k|63wi;l x͎0*1a)deQRH*܈FP-͠K?m/mEٳ l}3" | 06kᴚr-}ۉODc@E#"t~ߦ:5©9sni8섣'DŽ*ØI_gN$jgJeޥnW;;SlӺrzoj*2v K|'w3p'{mkE&}/_b uM㎸\!^rKN&v=2E×uG<P F S5k"k\FHHaC`bx9(6<0 1 kago ;>`ƍܿe7jiUw:OmkZu?zi}坈)#]GӴsFl+֍,m^9b4EG w0MWX֝S}we${B?+2w+5l)D:7b4ӗK0}$qi 4FyGqxmݻ7d/_Ul/qekkRcw [8o)آQȌ?"6pL0**p8^P NuGsMAQXMS>UcvO;s'N6˾~?}=a0{MeMm]ĕw a/-|w"2SnYmkۻ Zzzם}̷{Nd 3!km@ x`B83x$@&"U(+0Y;B1dlD0ؗ\p0Eԉ="ǀ[[vS_@̹;?5>}v<}ִ4֌;.y^M+8 av`@ Q()d,rh iT].+Y^'0*ΗƢĊܯ oĐr;^N͞lB% TiȤFx|R 5oͩ'{on)Q]ahmqKݜ;q?8{:4Z_ xl Ʈ^C9ξ%۱}b?qŽm y.=ZOeL*3\?8,w(Ĥh`>L4>r(+ZV=&&CtsTwD/SСM]Ǽ͂#>RdxbK%vа"55yBWdN6մ4DĹs r + 1 g=#*2 ɫM65Oa$[AX5HD {vގ;B)4~́{ W~= Kn ,Wφ/j\Q7lؙZo٦IO?AsZssހ^jz9wbϗq5?x4Ma0_#']]"nb$cYA=Ż.,?M`6 /?( (B/ux7&3;)xcZ.XW|'ލn $'PD8:b> 5BU?^Lj.b+UZGzS8ﻧ?eGK.R2R=3mEd; vʪEfU"s~Jl~ tCK;B]a`-[tdQ4]w2p,>Ux`BW|9wgD+!խ M_=I) |"bm6jzjq:GӴhjjțx| IG"s٦6.uqE?xчB"1#rX N8e t# QBID+=eE& Zl[Wy !dg0x&DǶ ĀI)R@fBzdېuWoჵηb o '|_=ִ@OiiOpV`" nyoS< @;S!7̻Tom/2靁{ `'\yK_L/@ MxWEo1.Kfl!츞r&jP<;(U_ڙCNm`0gV8ΙU.ȤQf2M+wZ ;Q-tY1t;0d`ceWTSQHk"tG1K&>5mGx4MD@o#g Xro| A* )2:xsX|G_̓wf ߯  b?DEL _% DP@XrۭV _>h!h UkԄUئ2q[ nG;|w; (0ƫgRoi[Q_?V̽"MQk*۴Jd҇=O| mLRDVR Z$Q @L D l‘Hc -HǴ0!Z!Iӱ'm3{vdD4c²uAn)tiVi+fLz23O*t?Wdy6S5@5P 8݀ nv36`S`7cE0/Jx)a=ۢ+{:KFo6-y*=le_7:4MlnY`ixZ^:))Q$ЍFtYg9- VĆY֮ݪ\TMjcC9)-MӶjCӋp mgJlΖTgkDNհյ${rPpD2q͋ˏY=WLv`yxM#U9m<)w3zkf͞m+?;-_KdbК}6:4Mwr0OBt?[ɍpVؑt=IӲbqwnmĿ5mkMӴiy'O%'VHhڿJK4sB+̹eYi9=£itpTm7׺WbTNMӶZziLeJ%?գ֝Bb_tVuSZiǩ bsѴ4M4#=7c_z?IT%nPZqI|yx ܪ*BZmhtig'P3x؟r2m-`ЖE{*[_MiGT%dx=#SqBlh(@)~,,ZVR9U MZB)A),X/G}OJ޶y5/-D=؎PY|t8C4K,>$, _PJ xppff3V'k'} ztB!GU +VSoOT=mhqr+oP'D[D^LxmzB_8mք9w3WR&`>K6;  XLBhUJ=*@#h@nJ!؈ϛL3mW="٨6qm;D Q3fXP᎖b;P@.HeQw! GZ&Ō7\Kz*O~^M ɶv%-p$Ln#B/L-p3UZP)p~Tk!Ig| g Am(:*b1$g[tۋkvt K /閅7B I`? [LR]gzx|S%l Öd["s+vB^iگ5*jLjk|,h# v#R_ƶyLkUxj /YMbERMƶ!e6;L0mc\4u6O&BeZ #]ti]NYl$, чRD_l.h7Z$06bBe]p'S2jcՌJ_83t'[ I ń<&Fa-­c+Q`CB}tV*@69-|Lp>Cv鿭i囶׻ !6RxsÁO%+U3 wq_/yW2*d8O'{cc̸FOkZy+|9OB!6YQ|֫RrN9{tm\ױhbQ:`e2VxX&V yQ@Et)c+6msG|TV_>vig;v8z9!=BOB`&yw}+A&~.}xI0֤1U8OVC[13ۥU!HR3fM!GnRʣ72s f=*bj "8@hVԴ L4NV溣& a7VDϰR1*i8\]K5T0WuGXOv`|zc+%T0~\`a*JW'gg $aQlRTJ=lx Sj?,M$r !"C̸ÀY֏EUa&iKqclS=Iw{?_! uInay4Խ9ٳK5;V;P&bg M8E웶9 q;cVd+ĴEҎݰqA;UHuPEu?nXJ/oySBl$).t1=Zk}Ǜ<ߍ &5aa7;i_},ÄmWN3 m@noR!؈ ȇ':S+к>sfwUv7:kXCfalDhnR-"5Up,pC nH(erom /5G|> b^ζ,0xcc%/;鍵_/GoeQd-ֳ\~\~߽Bv۟G/=oh'@*>޹7IJu !kl܂B@5tRGhφP { y,] Qf6G*\t)E E E.UU[?zݕ JGںgm &K)|_k=;vf=íW2fV+1̸"mLcS7IjF_ 5z3v_c&J &1Y+J_[ Bl8<+jk׷~B^t],ȡXq,5iRNŴi{gkZvRBGǸsfu_ gf,ڭZN4$wRԊdk ֋ֹZ"B|Nqc=\w5k4++ATeA5UZ)Sȁ|Uh#ˠ _ƫ]Զv-Q,qP V~ )4=xqvHRbq3[grjMx/N{Cz/{bm[9AfQ(:R'a*yXT֝.ƌML|idޠY Zk*[n8p,BM]ВOxZ:: QM]E偝'R>oXЀNj CA+|QTVL/:\;_U2V-آR7(r?Mf.BlRTcb=z0H0Df ϹVaɾmI/Y59XR{$3B U34=e-vZ.jnlKFJ\;v(eOAWAHunixLR6X rR6SN!6̋Y2R:밓,(tNטsM*oH{!mȐ1bRjf w&`Im?C?bbYOL&36ƄvLR  _wldWLLϽB!6@{b =po6kn+ݞq/dC^Uq6GڱllCVṐEж2n }Zm%,4? YLQL}jWܾslu*'Я%O5Nk[uknkRbg"m)ŦbЈ [oe`wl 4Tʘc|p`G:Ac'?$k8 +^SY<3Q'cn:RR7wC!i@nflre9f,>8U,tmQGvm B׍pmZE\jj,ҶT}tAcؖMX+-l)Q&¸9~niV7+׮ؓbgւ*8.K{jbYDAhf,/[Su?Yl$,M2Sq* 6Yzp:y-fa7&9e{Y(p1p:3INk\jYw끝K;!BlM~f~͸Bʗ;\<gjTZqm|b?7T2Iıi+ij_Zwznv-p>wRJS\{.밶'}Ǵ@j?ls6o ᝷٣yI m(I7TQJ}$p*8 ַ%MxBf Kig/`ƌ RPAk}hz;Gcfcp0 3񎋩Xz9n>G pMB!>@C:8+쭽擦a 1sqP3fN =. {XX)+s`V4x"me&,?9^ay]N9wn<5k>7x^e~G7=|zfRʵJ5i?sVtAq-ߤ,uSΐYltd )z &[c Rj[OV> S9$u$Ǎwœ0y&8Rńʩs{aI%~&HNZs!{ȗʾy_?:pڛrfj=QT-p*H*4K!zrUjkb'aQlRR„7~mTLTcT}+d[ɶtn2mJ`bdLI~HFc"o`1po&ZW;%B|0q֯?uIcel]ˆ5VPَ?-]:}R8_"1ՠfNg &)bprT+1ըJcoLjjYܼM7;n[Z'WX_yZj)vϗ壹K-ԕPaQ66r'T|8TtlUe*˕% >Tmg,U\"FeugA` \K˜,_J16Cҵ涩cY^Ux/߷FjCFI<]f$aQljFhQJe1!016һbmUDEoX w&4YwQ%0Ō1|3LpJô׿ZQJݯRk9Bm#rΩmzaW MpXUncw04Al{e*:J180RҒQT+KgCKqQA3=/`NⲲ=.PHrte/lԁZdT)wn_iU~byPjQ&@&z*fޯ`Xu=w\&mؠHXߕRc*}i`0&݇ oXoO~X5L8yja0 } :y̧>x[rn!Cu|mO`_Φm/Z953 2C;se+bu%GE1Vd2\)[)Uj T\+jضML"@`: dBUx|/%3nMYc%.:bJǾ{EЏx/g݀YY ,!6IJ+[1ނ 39^I@yF`df|a]WxֺEk݂b:ɹ\`FterRJOZKPBA~ݑW~=|SXMjT^Qyچ8j>;Dd"pz|,6[`$1nE-h<}-RX5tluG SΘ/L;}+e6{@_K%__lY5`aJR36?}%So޶mbks㯗IP,M Lp> bFb}/\jc} 9w#f2jPJP?zlLlrW1s-R{l3 IDATa֌\k}RߋMJPBQ+XtZi#Taը1O/Mƀ%ЎP>=ضC!LD6e˄QP /NFvDU^k)e,VJll+q@yIj<`zvoMYe|S~Cۜt=Yl(6jJR_|iT%083V3~> qpqr.z,;1!p&.4PJKىYt!Y?ޔo &`J)K!!_=z=Ϳgs;vhcJnjСi|G*?TT_KR~+i2R^?JYSSAE&]hɔ8+Ow*K~ ݾp.X􋳿#ϘeWtB!a 6-}LvE<[{O[㛦 +#[=vʮzon8ōK,6 n<0^տ{o`+<o1ռmXiuO$ٿ(q`fAG]'׻X\;tk83[Y~gG!O'YӦ>:t:跶RƴYȖnȆXTO{bY-Eu" ZTUTpAتnWǡCW_1.;|pA~m?*)S *%t6_qJOّ8XVAAg۵0}~ߛ߇E#xH4`);WfSE8%!Z&6LnafC1]TV >5]UF%٧EL<lo`.95Lڎ=00hT;K7[31XnKo:L=0axz{yB̄DmP]ln)6̿-JR@֜5MJWUHqlRUjmSΗ:1x<] qdJ^6?ĴL+x:C9 W/|ÏbkJ&J m< !믙E?~7ō9xtwk/(WNlKg[=[/A߃RDuUM1!vJ{D:%5_-ۉ+*c?hKGؗ49YfunnI'Gz.o!,4*UT,(ֲj,aY% |Ы_f\[+XXN6_wt x| ׿zOoXfA(>*><{fӁ k]O?V3y\wRÁ#ֿ~7R!x{4iwQj__Ljᐁ_^R?YX_{f_s77گ>|2VMugRA!]k~@{_T/NVֳ=˺U*_䧂Vgu[Q5[^= GVU?l}ZVsٺW,y[BEm]Sqmۋ8k?(BGRY=TF)w:Z?h_{0N$0{`fC'9 qhE p} cL`01WM^SMw7>36q7JZIZx<-Խ1c,oQWC`y2II\!3˜}ohr.>ꙿ\<òbϿ㬚wS gMxt9r?ޝn_&drF錕ϩXQPaΖ϶9 {F ^;+L&žk^8mz>TU&<}67v畗9geN.ΜvܨA!bjJ]92=z/=#{ZY xǯv/~C̍O2Ѻ!u6 1[Xi5'XfCOg-7˿u/?^fPFO)&ݯ_hԗ130mfM f=1CN$۪ 9DŽ@0U,Q߅ *1ĩ?scfR%ٷ3.R9VJm(Yc4ffu9v?f3 !.3T w-/w[Qw5jVVTƭΟ}Վc:؅,]D&))*ݞxE?pZwJV/ '[k bڅJf9.t[b&y~yzg;UUSA,$?9LS] RCZP6`R^3LmIp\s%Kb\ wld_vr%AQ!R*_NCל0v w[\z_嚟j^*5S79v_a7uu~<|EWS{dMyFǂcjMw ހ\~+_z!Dzn%[/OԘ F3ß~ȳV*a\ ?W.R+\[v}q W?Խx;;Gֈ54[>  "*+=sΩ͞/Wf'δD5MND)Ƕr7Ukh=y+AQld̢i;Rf}קN< Z 3L =u&ƘυY#T򲘐XH`—<a`HƘ_3 SJc=nKsk3J1~ yzzZ\Zd[Ix4pRoQZo !}NmsIOm?F{>ٵɆyCZCͯ*2LV uSgǣEgZf+JN*ܕ.G;*K’jt~Uj [t~6L[C ﴲkckŗvĆNu;nͷ~cv>q񴯟|;T%q6p_v0uGMj*)0f y𷎨 O<;m~ʶRm68sm^Q\ךr%?ol{Զ@LJ/)G?.=1Eo5|έFԸA^'t%.s )4dttD]v-f+,xqUGR㚰V0Orɮ喛rL_"W "C3-,=:n'DgZG-pwwEUS cAzfk)ꁕek6V;),eР{:t])EDׯ]zg^c|=k sӷ3]?"m lbzSy6Zf bcpvc.i`Yhӻb5J'x3Ҹ/cUTr=Z?TTKpYsݘ.13\#ZJ)UV)xO1KmEZ?!{.nqqGN9-'bv-q~w̝o{?XeBk!:}n>'Qga^Q 0j~n wTmx44+D ^[cJǭzjNwEzcճr|K%Ok7+ګ+go+֫>^ L7v?q[=qsCR-۟yQdSc=v Uvzuهi0A)Pչ? ꣎U΢U*5+Z{!Pb aQlR+1P쫵~@Uk'՗XһT1aѥwF2<`5Kspdí1nۅ5wv3s`nƬx:5snr]#֝J}ӽֻ7\!x[v\Ôg=ןp"vpo/;NwW^0<us?mrtEԚF LA-n׭^vWP˷^p5S:},{N:hQwgBT/[.]d' uxqv˭)4$e>V\?N5<4ЩZ[E;n@'n>kpKfuɩpVc_Gqȷ;/VUzs݃c4+- S;~ʔ㟝oFYkdLr}9'An2c& t`a=S؈`8BkݚJ0᱀ .pOr=`BE``g#N^Z3R`T/5OR?Wker]]: !x_墎F_7~):+\\)CPN02;}|AߩvZ*D|iUk< kW\dVe)kp"?TЕ]x|rg̙3gns03Oq;Cf >O 9#."*Pf1,gQއSЉ˃:Jc{FW;]R !$ aІ6E'ы/Kl(эoTst\B⪾FG`zs'C 5]ke_ \:wbQ!9( Df6@;t#3& fS@@@7]j ]H߇!| Ĺpcp8# \GPuCLn>af[ Db"] 9Dgsf^C9?޾.WQљNEk혝GA\+x Ofag u?kfmX.6?:,JepnGJ ӇDakK˨_p TR-I~0j/,,iҠΦΣ kG6V-{_8x'Hf6XDŰ0Osz؊H.*{۾|N4=lb6Kr9 rb".<ِX, IDAT!2VQzDivRݦ?W t+BfF"np r su $ ^g{ j6!T΃!Aε W@xӹv0s;}Q;q$DacOk9C9k8uN,}k4N9ztRQ RU/RXr 2gڌwjțOeÃֵ6,A Gz[[_lM%=++{0e{S9sf/jx^d-Gw|>/VEOȉb#AY1}hz"9: ~ +RSWͿv4o;?mKp&4͢R1ej3!A,KE`33n< |`"?߃,3,P,0].gpCY A$ d, d9XˀBdDkQ\f7u\G:2.%D۸0>v-f~@D̿W!r$ PF Y*@E<\ּs:/frIfTGq|y Z=g^`R~dCEo-lmOj/J*`jW?vnetjhQvB-y>({Ͼtsi+cm`olPAMLL4[%v<Ƿ ι-HϽOW'x9nH~I9,P AE33kolZcU.M9ϞO&t끃7V$|9n46S׼R~-k)"Vo]  TLR:ꈋ(վPkVmB֍EXz&lYS)zؖ ~3`L5|rE3-nv z捵א#JoLlwL^AQTRaBl+$@g.l>=3mK>v;Ff`kVmfׄ9sF9ۜ9e1]Nd/Mu1."v7s =GDC]̇ RA[/w+=-EQ$A `][ca 8iK1ɨzDW,rB.EVBlh3m3a¹9C9‡Ǟ۹K| Y i~u ޽fY;.?+_@l-.a<|w?0BHq8ͷ'6ҥ}Q\2 k ^&mIƈQf*уu"~^ġjn7_6*`=+9rjr:+:t訢[]XP' %!=6YT;7Bڽpg5 4GюdaAidd `חЕeҪ|dz}b'mPB_/n1+cwϚj;(︕djdG|"YƨK* HyRR_:m3^!Ko=t\67}:~CNYaW;IhnH.0|5Dr>O i'C(w !JmlA-FW]+D"ujU1/w~_iT}Q6ٞg\ fN}7O<r!~LNlVL5jNZ1Nɟ67e ,kp=w/V'MDhMDUڂԪT؊|瑪[VUְƼYIʖ˴U#ljն7 Zmed3*ZSv{hS0YZ T f IK2E{M0up?;g,=l _=U9W5׽N., O7#7y,J`_RMg?Շ$!ȩ?'/?Xr"O"3혹B] p*)gBuCwD Vgt~Bd2e"BwJݚjӿw庯 b*-"P ` 3x 0d;gӂH3̼LDnLd7(N%z<3!|߾V9C;^)یgv—hݼva*OT(uWY+芥I4O+hc#-,M(纲+.`/+<ׂ! {{V OEc'SFkQ =Si<Φ鄣aKL}v35̫gmP_ȬGȹ𓂈PbwRw \ACJtP Y?Me44E'E+abCQ2W V$8j F dn=G` SC_8JK0D: q,~|U@twt=G@-Oh&3صs!r>Na_*A峏|kgKż ZZ̖gcf} uto{? s9(rd1xDt,3߾~"?CfĮعO]檇O8YT㒳Q]uv"f--R*aELH~g!H崛Z;! fD1v~'X9x\0"x?UBék f.;8w/mKC9c}Ə`Rtg/aEtNһB;%Y7;?_"5MtTޫZ빻u8UMuh;r}RvFzJtԄ+Fw2[[(Ugi!~(MkBS] ѾK2U"#&0aTo EL22KۛuZ'%E*l=[]k_2Eg{)I-n*?З'ޔ=]d[Yf>SxmS7RsƚSgn>)!Yy!ʙY;-L=*;²q?ЅL ~䟷?ͻ0r19p> "N$f~m]B=ӺYgJ U/ХΠ`55ԃ.W7A( 'xB!雝>7B }; a%3oL x9JND, \eP$KO`p!rg/mmN៰ WpC-{ȫR<ʴ_z[Ƈzii#aK }NZ6gv\j=Ff 4 Vgf*JP )PU7>l.\b?Gj@D>" DLDA 3g=dDM kpD En&TfX ѳfcŒ. 3^U0D5fxa{̃HD4ftM̜%rf Um)kKGڸ6ŝq\HD\:KD l9C?K3m䇚M~,;kIsԯ~m{?v}lZaKnyHngxosE m_#655ʃ[IKR~ y@Fi&hW$f0+o!JD! aU ,v:#Z$T!fvBi&M|={n+y{Wׄ=ne ]midm_S!lϲn"s 1֏a9mO8—$g)(wNp+;s`o~C0@d !HN5Cwc^+ A~Ώut.P]%7dv~P ]To+1 wYN:B]C(x^w2s !b.'1""SrLD:3ͱ|"$C_Q5D<Qrj@"j /#!"ݙ;mB(BpzC!b-8lHϸ;C9#N(s/OI?Zmƥ6vۑ-,w_SEA-Hglx5x^ٯA5Lʌ@eeME:_KV$OXzȃRW*3%W)/@c?f7{Rv?em„$B Z~#%G=&5_gmlIJY۶[-e^o萒)3\$y` 6yuIW5e#5t'%wj8o|W.,y>z^Vng]xm3"]u7m "A'3fW??$TVnzH]b?H'yp>7, f38 pHQ#-m "L+׿veSԼ?^Re#k]f3.dػe`_=pi6;med&e4rf̨ĽSz{ r." b":{Cۜ7QQZ.Cd}½sBw"'A4E ]*OUCnSٟ͜JnDWtsADi'AQ~ g2.QrO3=uBDWQ3oGv|"j YSwC:q39uwBKЕ^[us2ӝ}t8wWμv7N&"@΂̖ly^Zlw%jL-JT< w ~G ^FZMk˟ l4ms@Uv8B TSr64<{@@FƲ%H)k&y%է0R $YڇǖQK>5bj¢ - v8Q[f|7s}ڞW0r٬1OxÄ[~w76ܴmGO9#:ls3qהp9szf XaKŲ`τ # GnzK6Y~U`n9o#ۂM$KdX_:U}^tha3 ^p.]3=8uQ" `s] AD=t< D15uZ39ӈ(AG@D*ċ|ƽ|6dhxVgAq6D̥Dt3Dʍpb" PE5r!~ztrdJC6_'o/ԉ ((NXܜ_c[Um+CW=!G߻*l]gE5 ƵDPy? mje#GO!.mċcr%=b,{?B4@>ٰL kJMR:$%OcVynZ^ 竪OPu(5SI}.P _B4tN!)3*+č!tWy A{Q/p}Ϭ*AhCϘ}W>]"9B!T9{!\; 3;u#D<\:YUy-cm3Mr f䴍@Q=puu \@%If8gcp>г&c3|zn˜q AO2 "J:֗ߕr!ö ;۵ɴ.vP.|}:wɩmm\yZ0 uoי =s>?bM~ڪ;! :ÞuRcoE֧;pb+(tj8 038E/7dMSmh _A<[P) / +0a,CӠt8Va4J~%c,#X.VH, Sfa3FeBM҈w;umߝ$ is*h XY>ƦMw26bH 輋&<|wی78T O[>)x"4֡Ē4@dU/T´1۷ DĽokE<3~,i>Q o` ՞R msV`wI@8^J=?HTLʚJ঱⊏=x0OsfL=xz3s Y  YuBOD])rq G_*AtpQċ{a "  M.PFBE3z!b )nWREe3&DƽtZmq-3E@ ]$r8vGن <5&nJTA< 5BY_{)~1{̹9m݄9c (=fAȽ!_o1| Np8n|igqDSZE)D Ȣd :uw=NDr!n@?alodLWֻկ:15>v$et%}3 GO߾wn^۬^6_G??CӷKgKrHF:n5tTWߜ&{V>N~:qݱ\b\ԎCxIL'w39̐.wTn) DmA<g"Zt'!̙ѹsB]rW$`k,3 ЕMCW,1' Zu@,Ikpm@Wzl vf-]׭ ] \8 T#:2/݋:Й/x\ʤ} ,=V!Ⱦ /Svn^ "b?r " A]">ɽ*bfט;װ!V,Ag. d<2{i*uLpA!vs l炙<'Hno 1(SkwwLNS n*F"@橥]Դ0>(hkU-'bZw4&)jVFJ5?rIQQufY|1ӑ9 hݡun /TipqdAdO |gVu6RKUE<Ќ 3 &! dLK"e d+Xl]JPgRORcu'֡gt5[YJcWYZ7Wk4|% U>#vm6>=lu@iI<|ڙIӎ@S}˿݁nHm\\[=V;74 f=Fʋɍ)>HCbaJA5 -!6UzEaa mMӉb@xgr Y#c(G.x*/B 6`H*ImI 2Sv dG˛W-Gzrko[j{/ K٭ 94IP:lO\]|5DK B% "x5{ضA3jg^EH!)X:P6 I^Tѧȣ(!yhѽ<G ٤ ۀC$3h6 vdJliXF1jE(y+8/L !MHII6Ԉϧ?IV=mdCsqngOYi3m1hxHVCWʯ xm67.=C,yDn+o %ֽY<$- =cO3oT%bbx%Ul6Xdzl"}VڃA}![m󫾫uحi6f$Illٻ:^/} Gg 2Ƴ1N-AK˥7jZѺ;Y &=d'C + qD^<29r nvQ8jSzh'$7@D +C|̼&A4D^.6Db%[ bݒU; T!H LmK!j *De3[ҭls纭\2&q:7>o׶UJ[朷{8ߧn&AۺB93 b>`C%ծp73C|mHu1)v`{T⁻XBPf1=E)6@9ÿ$AKLS U>SExvg溣t,{[<=~^xъj&{M?-$9؎H?;EMk>)KuòXT=4~[t7ҊQDZ9n}}iV$S%7f վ }"ZWj* vFo#_M+2ybUAc f# ҥ*|ȐRoֵGJ)^E kL kHTVY)= Y+-糫LuI[ZS`l^۴{RDL=ܰLR/b"diu?E"ElZ¼;c??Rz=޶vc^xO4&eHxlNer2>6 ՂηrO5Ws8[|ydFaThzMU=.sm~{lW>: r~el^ QQQ Óm)UNa{kFd7c>fHxZ! ;zZtҼ.žg8?r. eC6N&׈hb;A/04l! wĆ(Ap'qL\C-F+NN@幄/\uWk>w]<> )r pyY}B19WBD3":}(Wq}.!Dt4DnD^85nކ( ]37I56ߔ3AȭYF&C(~\pK]#9ÿlO9tGVUN*'ɻm$&[<@I5rTs8j6aS}*} mQ(摋J)I$dxԪWQ|"ɜ9?=6bpQQDB:W6z۞`Z9?z}:IPC 8\"Z{a/{3fsk4GNoڻؓeqġџ6úK۞=Н\_}oxPeA4mTV; a#UbDID⼄F,8IH^m8aRM+Rc&@6 H"k$xHʦKE)|`5ƘBHV! 4"y jP,/m; T5*XAѾ1&a3RRb>4ɄYR]e[@ ?ƲvLBI RdSi6hj̋muƵ];yAN&5"3k_|i$EJ}wS' ms a?R2;N_M9zWK||Y+8@ xX;6߹PcS[wcecÆ2r@ }l7s{,3#2uՈb&T IDATOX@`h =(_2nmrVءY[@A:5u6Wq;v)Dm}Ys%#Ԕŏ08r`2* %?r 2̓{kĬA* q x*ỂP̯kTMO4@Eu'E|URdRW!_/ 6x:O0Q@PE|n+\ ߸B0*5"BE -uCLGr" !zqbBEfۗJi>v' 4BHJel{sfxoVC.P\@N%*9?{t?o j[#2XSl X|tRnAtRiEct NxUp4;ߛݑ\S(m*:òv 5 ѣ̄c;!m;0$(6TJCB2v95Q{tFI̍#Ust\ɧgҌ&†AXa&RXIK@% ‹(v-.TM% cyyJhP#jѰEݫU ]=o#z{N nA*&sc&ߑr&{Th$u_vwQO< k$.X bk aBDNh贍R|w!ŧpYo[p/|3xg%cO o'iq[VC$HA zEsg7r̦ p+Za}QRp`f8j!@  V>dF3/5s2'ī#8 mk }m!xw͔,\ɔ^(%ŃS(lY}=bRɕJ * oQ]oP6lKʐ踖gdiT J9F1Z,Ve#Vok[o~YPh{$TfHOxrlN.SOI9dQzOX57-apͦa{$쮭c7CY!ˍI))q_Ga'~MӇ~熖ɩw^?!p( rΟoeٽJ*^:U_XozR`vea )czdKT"8-[%=8 PxCg)v Y|EF޴yכ\a]6"2EݛR,kthcǻpL嶞 }e8]ƕr6QsC'!(\U!-!$  V|Z1軫Vy ȂGu=᠒Ϫ zz'~ jTY"Q!Q?pz;%2@G?FLv>Ҽ9)@G5tD]G~=Ry Dҡz FO+xcD: ARd& ^{= spUc{nF%Ɏ_bć:玣[wm bzs79Or/%9!F9$j]t,6w,%[! p .+S8l3W d"Pg_<}1w\v]5Oo3は5k7wh(=X9x;+iwl(&TǣVSzc,-Nc w`+$&4\ 0^΁,}Hp7"U.AsͨY9ՙ\pn+(VF4bF$PJUΕAU8e%. 1ul֡638)dGl.vj#3'3R:j~7(]%6Ȇ3jk4CvZ,yصnLLWv41rAD??SϜKA4rD🵞vN8qS>иt 簋-G$'RAbl,k]y;u@79_m{(|_ϓTԝ]Cѻ6O ]~m[yȀOys>Y/5087ߵͷ?9ϋelUjS]3 sJҀj'8T";d"v*zyХr-c_ ֚jkoٹ]R<֦#?N!zu7ћݘYֲW-K?5P 9s򉢇%ؽDl7rKv_}:snCUߋa;Ia]$5C(B?hUÏ**Pq9u!D…(c++7Q xм>ǫ*$~?A~y(D읯.:PI^^ED!ٛ.Cr8{xA2Fh?*>Q6D5Dz8 \jjq7G@j|; !$Kyg~z`PڙNjB. YsonG%N!p 5P{99D`QmvK+ Jms iAoom ?{Teg6 ':~ Wk/ɵ5YOl[V(0hS{F|gxq$9 ޽ܖR]mՠ!ygDwJXb5&ĤBCo6c څ;M3>1y\i_cf2$\0ׄ a.'eSt- s [ ìn&#o~=j23dvo_v.Gv`HY#mtbQiV.'wp{%k?z)^>S/D}Ͽw;^lg{(R#ѧʲ|lmck99^ʃ8 DwmǏf突Ht@,7m7B*ri!mK̃R9?ro/_g5~6URT^Yv3H X80[Flid"TdDc~j#m"SHA SN|"/ [^׏LR|b) 1u%TJ)ĀTM d39=Xp|sBAĒx$ T!vM}HU(*sZ{=!SGW],o!^'09& E,B0Ve=ei ! x<U fsq>Dҙ4D [LD !6ܳX5{Fs#B@oΆxp2 xyݏ ݥ̼WR5_8jt,l 1P [r=۬!aG58fp'MM!48jH᪙&g]?]۩%czz֖&#ܴ Zh{XZ N(qܝ&~ɭi۹߽T Hܳ81^9 n&"1M*fid}l\Z䙱IZksguāG T )wU(!ԡCQ,Go─ivdYl8&EUuٳ S^R4;bm^4CQ;pU܋li^(r}g(NoK'ť [u==zο"Z4 <<sΰҒwggAkM&9B%%T|VfmRlO"s3e[N4HauM*Q(x-HuV6ۥrDwAQS 0w~FON@0. h5Lne%c*cڌߏtX(K08@pM^;XaxqR$P2 WC80j t7WL?TbYW޹WxVu]&A>o,!j?)8@('^D=eD9'kPvG9- [ lLPހ!qnů=V#AĉEOTH}'O(D}6BS]W+}}A@ bؿ)޲#93!\1Th!\F;| -0x8?xB|;wP2mTT3!%urUU@מ k] ܌lETvr}_ "{Zu7%Ceƺ=6l};XeLwb+:scY Fj@C5z \ȇTcW%8E_i,I v# H9!0$# vN3A&\u; Pyj9_HTsΧ{}.x{%ycPGszrB!jyfz * ^RTB q#OhT[g;US7JOpQe4 'AvWMv7@} AD2G|_k#RB9'A A$o9A!*?(hu10z~-H Ѩ+6[ y v~ !UJj}yDٙCnh=I.#BBȻ޾VSk_yWN#k/msrpO6!Q()àK zwmf.kǍ/>uns̿C>vĿ]~gncwu |}t/Ȝ;U3&]&(f*Bj9tY.u5R{ͬEf(4%IW[*i;P{xCcM4s2g=K\pJMʌ"`{SHtIanSP$Fy5Ҡ(Q2By{p SvM"(X%R7nhߥo 'iv3.g?`$}o-9V`_ܘrx+s9<GzS{(Vv$a\Bk -L8)5/P1:;bE;|~Wѵ{B}p0uW(qV٩r9c_.EnY֔v}.WFѸW ")S  AqzFOCIG\d['f:+)(,ӄ<V`h"r3{,-ppb겨&$Y!4y["}zm-zz2Βcm_92~-tԿ=1C/ꨱ7YBVwOY^p6дؽd"!nԿq}2Ueɏ5yTjwoC{j:gu=k/Py:&qN>>U5}}2s9rU ,F !'rλ! TؿCeA7!6ﳯ> @l/!":w;!R[=_׏Vg= AdW@Vmw׎B<*p}4MO!"?Qci!h;@ _H%+Apgm_fg,f.F4B[Q/V]G.ZO+SIBup2q-rcٰxDHj4 ]VP&Z/\K뉵~.r~!mIeɔ8ulUߜXԦ8@sd( ,$:F("2Hh1\L4F/"2/rЄWa(s\ H$ivi*ə z CV8cU|k@D.# rKթm 7 ؽQﺅH!-,y-:t6}nTnr̎_|v6q۾ܿCqWL sM*sDs.(W8!Q_*NQra@iIbRouva`3s|`t _e9L$eөK$'?>B"V@Cu5dwh. iCr6Bn;V%0B qVIuJ@hl~\u f躶ᮮ%a݀ie;DH:WX eÆ2md D ơJ֢XN!wq3G-.%jn]*!ču{5Q99zW`D\^+lᜟ5B2{eWMA6c*nJa{WJ V]$~XZm`C~[۷Wj\#v'ۼm~QRJ"9^N!Ao}7MYaZ?)^1AtX#.} !Cjgz"oG>eDq.A|_n k¿K+zqBMNx݇).TP&Ar0YjYH)cCQ?Z}kwKҖ-MX6mk;圔3V~4̎)G5- s-b.$R8"ͻE'O@%HR(\e1m5 #*hVc$VLW[sq IDATb*:Fjwb*U0ӝ5`$2G#2.8$'8-.mܼ I7ޜyGp֪%} Kn~$f?7>u-;:x8,%`/Zc]RxpP# ԯݿZ4Kw AHUm/JV?8Xr 6|}X=Z*Nudcw@,b#AF RiȬ Siqia9Xj6 $( \8EJiNvTeJ,\:tB2g24ƓDyPPhɊidG&A_X6/Q`0TKԔŏ.Ոs"<}ر⸦AL$ !JB"Yj`B ~\T<t8[~|WDޏ!T. '8'~PQ}7ʐcKl'@G \S Үo?yLTzŪ\JbErERV1Q!|r]]^]qW+Ț7|WŽPC 5˃l3軥>'J4{pgY[&/Lh7pUTK{%YD o=ISPlVDbj qk!]ȚE/s0S_;&LZ̯-Ӻ>Fea6 R+V/ڿzFO؊T.q9T.hei;Ɍ3#]Jܨ B,]x̡Rbx n1EEudL!N7u\8T~]W^/ލ _RA:'=E6sGǃ}_ڸ8woVdx*Q0dL4Vx碝-ښK%;\ [II1m!В JI&N9(}TATde؄x@uqiR@,Njض[`%M_:i$p>@V}(4, erԻ z !ԛw?DEQHJ~ ⓶e!T\::S #e{JORUDKd/K+PHKԈL|s0nN / $]t/ӛץHpO  *W!܃Q!i>T6o8C5_"Q!\hCդ[CRTTf{>o_]@=~B:5PC >[9mmNQ6G/TTC `ano{}VD+І]pZ@ Y#3) BW67M%?{/Ӂ@ u 5L`M<֙^O[f|>29Ѳwɶ095INu s1{Euƒ:h6E5)b* 14"TPA EH]'T B4bQS.3YDl="r}q̳<:sqho=l;#hTLu α#=c}4'RRiتA2i\b'z6| uȮ w*4@Q%"r701GzpGz-2QG8a.&ՠ h͹T"BTP5EeC,@75EG>]_GIja MX"`P8K`"so1nsQmAr$gَ$L" 缍!@hEpoE5{9+^BMԟ6ڊ˩xgGkjUT^7Ϸ c?өO`U iw9&6C}~BQC 5kOeSȺl'ihG,RMzT ܹrh"lo~uy 6t' "|u*Ι9İsαbGJUfR{HcBsZtiH 策m zBW}DƁ\AAZRBr,Z) o.T@*Y ؟u]A" $I!Q*I\2$7OL0B0&͐\P;Lu;b\Km#;˟i`kqZbWrzjԁE7}f/~z1}c5 ^ΙmI(ǥD#/C qOtQTρPܪE^>ӽ~ꚋ6* fXP|WN)TE7*<,;ק|  HI}+o*D b.  x|Z8h~ *{ǵ"N/ 6;,*vWUQvWP6I |8קp̟ͨE@8D @-! 訡ja qtZI\Tps HJuK#!^q i*v5 dtJFfP"c#%6f@5ʃ28Tj~Ccp+ U+!mf1pI '$I~̋ _fD~"/.l3'R?b @8. S8@78G{˾AB@t7~#{l]u\}>`| 2 ը ߧ4h(Ad*}op!bG%nTTO$ Eqw;qy$5Y$;Yk t  BJy 1%ToYh; HcDL8 [J1TMaD(y_MJ !T7QC 5TxB;s;rV]QFY^^fWjOƃ͋ DfM[ݗ-4)A#CT}G sͧĊ\3x8.^Y#F&12։e[~J}\U-%ýe ,SZ(ˆgLL.-]"H^Dʡ(D1HEk,$pʄzUF[Qryekq#K2|50ectcQ+Ҕm4ٴ(;7h}mHBN$hHpmTrƷkGfQU޵͊pPa*Ʀ65MsgSӪZ]=($ֱI; |6% Ⰰ\|<4ApB4O*k4kG{$qga:e8g@)d[@UIS6i(OiΥ\R0 *bԂ[v( X&P&(0bX(YDY fGNA=$@o<#mn9\fQFWϏYჂOjU- }E,Bu#޺y+*7?~O`Gu}C`3QQbB| ~?$LUB d&Q?N✯PԪ^}?,LVP!?  _A3Q!]wA(yspeC@A TOW` M61>cK7?y`:QT27{*  v[ %@|EYwMss8s#ęPC 5|-A\xXg!ǎֵUHwg}EkkFy}v nAe)5AƼvxN/gLDN) >F}Pxۆۥhf]u$T`B\ʸ~a?:``w;d ѠƋۆ6H1 ډB L Rj,BTG7%XTd2!D*ION:]mZ&ǰpF|ۼ]t[4GxLJa R(]`8CdyRe1Ů]}J''3q慰LŊ P{4oZ[rl6:ԭ=GvuWxۼ}u|t~:i|ձ|!ȟÇeU.>QIֽ ;;Gr% SG`#m/!QAewMTWeTV'Aձ%T\QJYs/;R^!@Ǽy4ߩyZC 5ťNHC5u! *2(d.qB*}AZL9ywqUkr*'?s ] aE+JW V/Q%y\7`B? Z%NGuU_(};6n"G _$g¢(I#fa!$രå1&ī݈3 P(9mJ:>LMR`inH7*Ͻe^/t7444tC,"*2(8#6**.《Î*"ڬBkuWUמ?ndV?O>ˍffĉ-PTR{?mec7=Y 1 /\11l?w~70pcz5EsCJxqg{\ִ `Pek֍R ?9jS2c[pew}1:SmM؃, 33~z奅hs4Sq{S#s /!/gjEqګP>2Q${NE&7ر\4U1<?asYua`,cIa‚}׉dfJKyHlه>]HaG<:u0)HX-@˲UK[u4lKZwGن] @_(*#]K'diX ljDMRӷGD=.RQ/X$UCI6 W{EzV@(:JɴbW;ipɚΔQ<UB=d"y(VsF(=.bTZMBĊzs~+R N.I~-tӃiT[8)񈄒]3ic`Bk"YNP1 j4cߧo#% N!T!rNBQvx¤&Y2pg%PEM+B.԰t!Ha"4 98QBv(a ~;S=@ =r^8Y"I7@̷vhxcbü8~+#YiY. MmS)u=^Xh90wRt$3ywx{?5sz\6wtɧ*-]vR<JsmKm6;AL!4.`#!\A+:0q :P0H,*UJexljoA~n~w@ޤz UnPVA/B܄ ]PS,B)yI@?"čA0W(ySANmbL!&>X&D4$.(! c29L7xmE%ߗ8} Au@%A[ A*?Bc< %9?~gD>yE) 6/ăbc|0frn zy<"rd7@ mB܅-_S@N-H=Sq|VP{2mH;V&ĎSR~]pqImU_g&c;{1q#v)blH ߠdɓpUrI/x8ǭ!`t5кphGDr\MFmC.ظ,0G5ib1?9d3[D2"%d=47aN{?v|1 %$g#+dysH4?9Ion7FPysoW./991O'Yn}j .Y ScXdȄI OXuD-o}F`غL'[.dwNhmNL]#x_h'!8>r㪨LdY^$=-']Oc8gW:WJT!5$} ؉|y:YG?P@=3kn 텘~{y|NpQ},"T.Y?VGo[lD9!D {9G:6@ ƍ3ȦRܽ9F$#]]3Rӯ%Zl`I(:a { ʳ X&CBhvsHڮ~}CyS ?F b~,Yqg\UBz& R^FPF3 >ţF/R[UO=O-ͥZbz H+ 0ݣN,ztbf>(e,;Ծ4ߔIH` u=UܥP|W_W8/:v{of3S?3/;ʸSe=$UQK?!ʬRT-Hʜ{ly9ʢJqw¿w s:c}ܵc˦{ [(-B{5 PPä !W`"EK2$jIBkt 'I zc{]UC'Bv'6߮?a ]7ޝPeC'D9gj^eʠfH֭/C)EPԵ3>vV ECj!B>U6 d+f.es- 7wcιNP7@c!Q0)9!ԷVy$@Ż) %1?ZXi0O۶~9 ƿa}(r0Oxss, m\hr{ ZS݊; ko!Ynޘ6c9 zGs-ce-\w;ht7g GSM}k)wƂ͏[pWˋ_'y@t%lD hvI% x: rhEL:IWk:o=%O*s~J>H_h-p!0%r$, !y .I L,)2kV[ifT&XWz|a:mkS6wg>Xu\k>- 4sx:K2uGdqlYUtxZmKJBIHBZ[+F?=;|T8f4rMB"7|Z{%6ٱё/5BDыjT}JU:ɨSdS"%ˇY&aIޒ{"qB3߉{p+q4㵃SjUVHLˀ*;\qd$J+`+NOQp+3]c,4($lǔ'U޷OKMp.w`Tp mh " ($ɀ*x<$7;^BVm[Bm>E_q|Bm*96!d* Bu;ocf>ԗH.ZFMŔ!NރSj`84>T6cd0T>Ѫύ ܕ7p ʀtB8Ǟ>ԶaO$se}?WqΏ>bJ~ۛ $!d?&0 >0y "0xnzKugKԯ$! lԜOmcvg3!"__4P+?um$p ҟCi hx8.N^vqHuyX<8Hɡ!an6,=_Kv<בfj"tl@{7aJM$dy.N=lm-$b&v#F;SJ>R&ZjZK&up<'|8!m IVNDB~"uEASQ\lrpTnHʡNG=MWv.fFiW2fŖ-JBZT{J֍,oͼڃg.^RĊ+DžgΙL8kB:^4Y--,d%0{gg\7. x7qplXijĪO\?JV7RsJ :Nٸe'ť&ЮHpJhwx,Ĉ R8NA䬾B 00{ymswzby^e~n֠iУJ ZL*:.M6Th'\i^5#99P . V4xQ P 7Dkxoݸ~ͥp[߃FJ qs9__q?G_ꀸIp 4M\5LA-IfD-O1Tẁ~蓼 &8Ac?Q#7>P΂܊nǜ8De@|eO)A?2 !$ 7!ʐOȗ<pf4QSTXY AԻ2(ّf㲥n Œg3Iڀ`#8as_o)XNy'9.m4@ @*J-Ǻ?zte@~*y4$Bj8o = !U% H!*JP')lNSy@=P*Ȓ]z3W;\.r\Ϥx3 i]A԰5?:ofweuWyt/&<[Xޮˤ?Mgln$$hJY6+lP*9TL{7C`#c{w'7|>fe2)i3}_ͳg?kxw\{@LdpEN'4?&BL-CYV0hBj#PS1ݒ9£|W*mwތ6~Uk_oơ :!FR7䕸2!4u9cAA7i[by H򉜹w#x8urR%‚HJWEYgqן\Uc}_7oһ+;H:iVy7;s xho0a±lhp D5 H"̊ .kufd6C^/'k__ \9/TGl@<99b'|2p?j%"8ē`HBz5AMA<*a030OWA: _?sa1K@zg!rcs0S BG j?B CdFp@50,F a13s|B Jjd0E\e5WJZ_~/- >oB Џ86@ =z68]|~^.Čk3˟wMnQ|Z!ѾDzY!h%88Ҝ?ONWs2Q'"V>YYOGmƥ_|6붙*a@⇏E)E2=fSIV?pǫNLdiNt+ fE@;zzzu'h-kXu%IHH;ã'*kN)Qm;Kjd'8H6kf_kXzoS]jMm)p\{wa -QTb(&pC!.X" c2{δӉD^<((9 sOk"K}&:(T[$LCw~e&a^&3.WCD0pb{/?$V2mxi]ZNhtNd~3w;x8{HɳB\<.gTipj:ɧ¡*YOwQ8SpD$VQc' :A{j7"1,D 6L8 1A;--/L͇~ e&p5! یA|s7j!A_B0(kB|~ e0\#zpD F-ﯞ 5U)kj&+A/k6ZkH^E8$ A(ysQ9 `o ԊZ Vxg`&QcWOK9z;sB ۴?ޣu!=y&9qϣ3Jz7. IU>/- !!f 4Mzoڼ?m)_q{HQK-Rsy/ݧK+`iKw*~Ƞ[Oz0Hr5]|f.$J4BÖiyq,i6^1 I 3cHK>F ܲp6ʒ[", Ȕys"C2 NRY6„e\pD9pĵWَu3hk@Dn5W@#g&(Ml |5ߎ\+Ε",/\ D06[ IDAT&Z( ,R&y :)j9 5>e7L^ l@$E {!sK .{<A& +s~!_ۉ@(pA9x; :" 4P l xBO@Auc$ !fjj!juˀxumd!2 r,!L€'P2(j&zxdb= E [%@hGR{Hh MŦEOx?Z˪,I`5XL.ٔ$zUbO݅I"]ɶV*a!9NVU?뮳=Gn(k[s)R,-<7~,.wCB'A(،ks7 +[B,nYD~!G!ӵ{| >( ̛}"/neUà|}RAU?߅{PT2w3!HbPۑCק B7_✿r1 uWZ!\\> rA/̜V'@Ýo{Q'Z98|_B7 j%E>. T'8Cul_}D @8F?(!4@ ͱʭ:oώa!ā|]y7;Geյx4ʳ OQ PXfhoOڶq9"?jV; F\eK6*\x~oZJ1HsQQmI.UH!M\ڹ:=mqtJFՓfSi<> XԹ ЕͻIq_н~Q]&:Kbkwofw?1MǢolK.H71q`G%6ultj*<]an$lJ*COH:V^׷vDz/U%E| {Dݡ,D9#j?M>1ז_NCȳ ܡUwix o/zGoJw 09{mwE_,!pHd"O"R ;.9 bKܑ9 !Oܳ4 {rqmk_ 0x!ps#: HC7!n7B{"|aԜ'2Qt!~,QPL]lK붭/!Jv(5"a3Y_gCu߀P63^ז$8!0W=q="2 wANGF;mT!e,nE[ Je6P tvvo0Cn5GC/?`j߽"Yy0&6j3;ȑT<9kh 8s_Ӊm7?sxnCޮ ҡVm)ݙt5v)7+ؗ$`-`8UZ: zO=¾d)+|~ظȣ];1Hk%v9ϛ;^T IuNJo RW?C Q=pU.k1bONsijjna?N/ܤe{.Q9!`_<4oxKF[BkVäqHT=5VNdRߞ֥9:ݙ2(*c|i4n\U͢ (:TAlNyJ{ƃKf@i w&aN+ɂvH:979@ Trmc1Y:E9ߘyh*N<3e#(%y~m^p1)s!(Ć)+Z,"c \C)D@7xK)R#QK1 P!@-ou>_PY Bz>P C~4K BՔ ~λj!-j7/8s~*n AĮPkd1iPQO zT4RZ}dŅmx#9_Jso^cfr^u#C3yXLkjzX, ɿō|pM[vъ#%O;AyoQU&]ynFCd-ro9-u'{?#SW̡S52d|.DCGHxXvန(r(T%ܢoC_ޜ:A %zYӡ0]d;R*-6沎l# y:a]$?mQc?>AA(gPBר/VqzB{ "\Dc~AF rP3w) ~^&8wBbG!/CsaDk;EDԈSeQ 7 S ÈzB^ 1֬ny; 4@ok,Cw_1]qkn^=B;ގ`^Ղ|;-m¤kzoj p{MxugS93 zCD3@٩tRZUnhIᨗ1'gF=Jeyes݃Bk*rYjYG<)Ü1[gm>tTWփwHFUv-Å^ui[>`߻tWƶ'p؎ҰVҕCtT,o2'/M!ēix2 D'%p9'q!y+I)K&-s+o_Sc!Ͳ7y U3wX$4_'wʎ˦Kc/E9ڨض꩒rD(LȆ39LBԜj`nȕ =y juuh5n_u+vm3ML\o}g!9F"H9~IxPŝHҾ٣"I?N|=sPmwNmczrRTeJU MqZ8{s>S12_Ty(H)^HR=WެbZU=;7$oڇ~'˳SiMB;X4YJYQ'֬zu{hͪ^S>0gr U\l+iY*"\. sYKd*\&.T0aЭ)++îyOuӇ6󽡁wDK'3ęL:;n섐y=$J;UHF;7dJ\Y_(G`˙xǡkddcryJ}ն*)wpNڝ_>u& ,ؾ5_+>bbs?<7sLhb]Q9&sy4;LmJ)Z„s%2=]S(b+bREIO-%Km=/Ti3o.q_}bi>ń}rrP_~](Q2m'':$0~AZ]y%OF@3sѾ'v=Z҉@J(4Ω$=pigqd۞>[ўJmۍ8xns, B-[U I+[r9FvgrhTrYWB *89u>9p@="aQ2#!Y3GdJ CxBۍ@(d Y1Xݫ?8nP!/CapTB;uqmAYW!lgA*B~5ՍW@zTqQPfC\&P+s 4@oC|w+/t`D7ԯ@>dVKF!A"+bS^[| {:9뮾hb1"7󍋾&Y[;.h5ZZ^t՛"݉9 S{B"/rz#9)r`ÓTs{&Eߧb瞞`f!GI""#&\׸W]5E E dggSUW:?N=owJ9Usg wt/[I;S-$4@Tᒦy #\gf "9 %y.#;x5n1 W,mm57>՛'$0iksveśFLJس,5$Ʊ//˒x:yqh)yc89VOk7tWaZ4 )Ź n dz?ug"ݚi6lF&`v~lr!wz~h\Q :qmk.͟tCotݶyPNWTh2xײ%%֫LEL;$p`iHiB>)N#eFt?2OC$Es8%vj2`e!H%k''FH0>*50"D%`d|Ji3Ҏ#,} {sLO(|0bqB}>|<Hp]U`S}},HW!MМr"m܃>BOGZ8yח=C%mGtPGmt%g; 2?W}7;@9ח4[˼V%jZZ-X!UߐTpl- Gp Ѻ^9XYꪱxAa['J0JA "B9(1ħ Qe]b -FaY^ۇS=ccQ)\m%yFSn;J]J5P,(6')v V0/&wvAN cR9Ӣ'vY[.x|:Y;Fd^뻖ܧla>HE&^lQw"ACL1b*.eFe'ɐ-G7Yn7Ww?0i* Zlz%R6jXt|ÜK~s?2/~iQ=*my,71l̖b"b@v" p DPGFݫE(ޘ5IPR)8e7W_"2 F_R,ǴD-5$/+w~CqSh:UՑh8aeG 4E}@<8J ߗ!zEExo@mfmQ3MA ,OR:y \A:.!F IDAT#iY}`RI02з 1!`鄙:9l~{/Zq`a`ev}ᄐ<0s&< s!p}w$&7Nxf0r VzYu5K`j5`K/P(yJi't}/'u`Gc҆-}UF`:c:g7 `!XK B;}n6,Pj{L]^d nꨍ0=C?@g+ҡ`jp:ҮH+ 0}6Q$`l'_HAdMʇ M4nG-^nds!<=]T0]y^|_jV޾`0dųD[#e IxwŁna IqX2%*$ju~-=k?{i7M=-.Ti4DŽ*f w}}TcXX=ZYtz>s>foBvZr_˫ U'l7^zNj%U-&[wEW[TӀqW]mzSP<|av dL:i` ^K"lQzUEYTSu XX Tŧ>vroȆ*WyRɎ""Zm&pOTe3KeKLQf㪙ZG.h4L-Uwk DPPkLޕlZ"F$rRQ pE&Pmy .h&gR=ԺEW.P}r|t,*9NS;LrG "uSS4мԀ:wD?&+DaΐTƐ2LP'd_ e1#,R,0 `-K/XF@@E;4Sĸԭ-оWIr}b} i500wPx|;S)EE`Y.pB0;O?"L .+hS𾠔OqV6gns!9(9Ϝ>Fv?b |$KAFv[u`$j002:4=g߂Mσ _n#Gq0dPJipdGt.?H^E8)Ȫ(鯞fr׼8]U>^A-\)(x2ٍ0isgמ?_kZf}g dG4 e`xa.8لP !v’pd j,$kA?. #VjhAޔwwG$z>&?w<ګ-6ǵ)Mh^?A#{%.[y.Î薇=?LQ`L$Rst |_x"w؆М@x7Xx3~48.>/W 9 .[n3T*D S9U~Oψ!>.R ԼJ{z9#Q=l)łj*D;Ş~7s19I(u5Y -i&K)5\5zb\M]KG':pyqAvK_pCa{._JcMГ QpfN rA&BWM6'Y4h`X+T- 䕸<a @GL5a冮3tE;^hQ3A)&ET+B.#Tbw?JCⲑ&ojkPhH5H+I`F/Sǯ ,R#+QRFՈJ!3!'Bv#9}v\/BZ9ȦcRci߯/u@0\ᐱ$X(hy2%(K=[>x:$R[.)#HL 9ep=Nyލ4a`9ikq3g4C3 # A*hso>+Q/)HY#6.9Eh+-6okvnɓ-r יe[`/l:JȽfã&`1:T|<3u`05@Sr)4 M[ܫgK{;Bo*dr 7n^~'ve%T$G69cqͩ*%|;߸F\$.Vm+ԖjoUqX:@#{F]D&}aS|s! Q8G㝦Ѥ9ܣbs 14yM*˼sABj5&9hjG"\(IT|nMY I'(QޜZ]*0IdnF`GG{bGiRJ8FP ֭%~֢ׄ2ƫmdQ 4??eJ=xJȺ&)͓x|PvdCwTS:RDN[;zv.U`,@cbRXdAW)Eo@_;<=1Nya-y'{: ǃ[é?f +fyݹnT)Ud9<<[A:TgEqZ"4{ # zeE[W#5ɥ~~,R$Ғ sqSjm.n~gUwuqe%HqC,Û~  jԡG}>XJDyN,C^\ߴ"N+OZ}/J겸p'xme|vjlTԛ3H5Sm;u;7UTYm1LQ TWA hX5iy_u^k6D$ (Ͽw,>9iI*L&dNӖΌG\7f ؏~iVЂ05("I"ֲ]_-][1h-W6/ʮU:r(*t !my`K^g[e!1{nr L{fB SJbls6xn!;)0^Lyw ?-{v_\`J^Gq\o&̱, dA l[@~ym+o 2mJ[}7ܱRZt//4[0Gs{O2ލ񩎮[U'辀K;hx@<'Psb1de/vu ,n?D%[@@8zxg+ b>UBrbmX7!+wk?h^y?dzz8"`}gP0|W8PnvFgM]d L0>]S=$.p?l@gIʹfV:M.)ljS,$ IM߫tI(7%脸$N$4MMF\x+ ]"6n,[/εJuk5gCV)B9샓ܻo'#7}¼ks}]8zPov^]27>p]{sx5{IV?\ye}< $>X2-4UOxWæ4Ԥj3B:%P ۱Foץ!1Km?n;pW{7i[vpw fAayù׭rw coOF/m=j26*i6m}&q8B$6J(&I݄q}BB`#!B嶹`ᅵ`$yrd0B8> r}6Pq.>!:aDz#>H":lBc:0WQ'黃\{[8L9sȦ h(Yn"  y]Fl}\iրv} g =fj ֿve׃P 0UObco_dM;qV82g_{MoN* 2m wo[^)%lh M߆ 5w.rYS;)tRP5ږtkPo&}ٞV#<V:j]$ A92~2rEn8`X^׋Y6(Va!;a[M@X{z3H本]t9Ze O7 H9?!TT7)o)א@ҫqn1QX==4PE%\">2᛭}tA^rUóv)3)ʕ{P(eY9\ % ^;8u!,)TY|;ƶiCO1ze<{_uÝ_2U^y",KMIгnEyȀxw瀱&~#tQr2!|0CBQ.Hěl7ۥ-,zKO#'%08b} 3qܐ5#5G=4oJ[*qSuӣn1Ͻβl eſRv\!Sl/8k4fOBlIS> SZP]֌ S "[,j[OD,^XXp @MX)o+^ӯk*vHK\݈@0C(qgpi+G(A=+vTu7G!1[{绾ȢϾX,У7r;5_2#?y~팓{~3lJ[.۰qM s|AIVuw% W]?pZ=ifY \ }3 "S@O;t#.F5Dk] \Aד%b!yhw߫~XfLIֲWt{<2%cuwK)y}DKċ`eE}nuv ˁ5$Ǎ`z Xx 0B5GE `,Ҥk94r #J`cC0(fB:x1@?Y,,<7 Iy޹vt|YQHTyKBB}_2{.^ ] 2=b%8]OX!?`i|G ťB( ׾dy|cl׆VKz\ =YS0F J I06ěhN. T~h:g l%B\˩+hlCi嶐ԋѢE+#f|QV19eAcvKj|ؔɋ =zKc8iovub_R{5#J~,?E:u[4-n-﨧HBd2g<.*ڑդJRGeIjTPiȋ|rzjG.j=Wo[OD0}05Ph,K5sRʳ Zc2psOY9Rؐ.ooŽ6etma${i)UwkN(ٲU@'_%ޝHWrl|cF=[SuMC S /|xJA]qBí[ֿbG('o;Z~ M}'d4iiЖ-AEI IDAT[,ӉhY|I+r6?У(ĵ"oBt~Jǿplz)/Bn#cF80t.x,X\Fv)}MB:0%ɫs>~,2FR`E]Ҋɑs;)Jir*)o#N ƾm3x1R"!a E)WۥIjv}B9Ji7!$`%'惑}FPGPH< 0":πBfg Mܧg޾v<{WNdS;!`?")+] `0]E)H(N 2 %- 3FD??5阼4RDZ \yڅ(nbxI|vTODșqIgu6Vo ͭg'~H$UMQ?񙂫#ZqL[H3`bt* *16v"5|qMtϗ :ybT#jLM&%85ė@(gZ1p 6|s02- ʺ宊K.:sGQW(%$pQY ,I{pǘy+Ρ_ */ܧ(|͉)O@ H -^YeUfg=X|{y8hCG$IxzԽ&`ƺzϏ2Ȟ 7 Ί(K?Rx܅qQ縁ytq%ù,޳i\`.єS޻K?H_{PkxXCAoCr=g_  `%0#MR'Du ҡI}9CI0l ԃ)n{_+?CAI)ɛ}<`D` "\f`8˞ӗȓ` !Z0v6&Dm5LBH )lOsվn"Ji)uT.'n u `q!#xn)c훇xsWå`_,hHĕ" 2_Խ\qdzz*(ܿrScqwlƚnreߎu%?"O.u%)=2]apV́P+gE0'̧*"h.-YERn tlq"6ݐK\u%',Dز#vgJ\>EYļ&TAN|p'coeTuD⵷%{^C_dY.!%Gw\|6I[|Tu˰ֶк-!B%rh{9yoc@\s+BjN& ]xAp϶ʢWmjeHִ֨ e̱|K!7eS[o.lԓ p!#,Ȅ=^=h 2U)2!ܐLHD)'y X݋]u >ymCm,wGijԢ1P?MȬfdr3o&r #l:y߁\0hE`%=40u.0c 1=VE,P{>Xfݖz!M`"0s R'|,Gn-)iwl${RݧA)=e\T9?!_ r$` ߓv%902jByx'WT t7ݤS`=Y?@f͟գSb9Uk!y)z\n-t(R\Dh#A,- EHr[ /ϓ^}fzm>O#xkʂg in}Ofq-7m%_sR&@yJ c3\k/3/6(?,jX;orQbQU&nˋܸίއ<{PYn.*Q6k5 BZ9ge åy!3L Z "~(P Yo{7"ߠɧ`I4|7LG+?foD,f߆8'kF\b} ,'xk0w؇{x)dm`Xg,RC:lTO8-0b48SNHRK0`tI`D foJ"!n0#BX~O9laJY VG)nIB ?H יAdٲW(EOqm^ɾmr'O=Qc>QuKzw`AInU]ۨ ۟y=rr!QdN#-UXC|pDerBbCM>^҉b_жQN)ps &n-Wu7f枹3YZ`Z6tQҶ&ݝF{QE@%jgcq>%% `S*|qǃW-LS_-Q#_s?p(v>h쎷mTJ8ogb.u; "ЩD,<5,*OM8WިVmPGRd>{?*3? ޻I-~ԢY25Dl[GUzsFJ>9!D  ,vdL)]Oy F3:hIU?{)`93Seiԃ{NG=,3R߲BHuRJ)msXO)}Ad~ (.3V޴% n('{&i羉&_-:/ թ|졯Ó|#䮬FEn7  bKPi;8`(Eo&F7 m^lkltDz8UYV`穑ZW*dw]\9ՑТiҭ ѿk{gl#sGN+CM,>Z=8^rnN{~gABP'٥lMTr-#X~|F3ySNC N*6btުâ3 J8wPxs4w oZ|ƅ]AWog/h god!cO*&$vM0oǝ3}֨;N\2/j灖>5[L4ou/w^~͸Dz<?XW3"__\?@FQ OYJ/bs}963y뗭'6h~~dtXX:{nJ܆Fx;HuPSLOn] EtV{#u b;C,[eߗS-҄˞ESJBbE}P?܎>$Emsk:PTg)[(%`8 -{}̲F6/BҀRJ0i5`!/p/PJk)Ky=((wRdߐY8@ڼ/ty/})%ko_p#`= ƽ&`xhNtoDtU4byI(jR;tQsOUiZJZ' ޽Hy[|Fʫ߭m1T_$⎀Ǘ _Kd >[,/^"D&Iu^EeCRO7F}E #/yPd@I׵c:4t*ռC pan|&;x 󝛼?EN[Nam%7̥Bש:2!"dqeS("+旜F'()fD|j [u-\_vfAx6s0*/_ Wl*F DEnO;AI y]('Y;xk˷ѽ\&l  V{_̛uu^}ؙ$w?Y/ۗM=E ~(N{$ ́;{oq'OC} @)!VJ鷿ug2!3(EPJ;)T9`|yH;pi,q.XXi%XFf|NΛQ:^K6%)V LTS<R`ȱWX)`r0rE<z!)*{nmع0,k^@5syVS# 21x|͸b҆yvvnI hrH}>4U~QEC%f~9Kz{9eAKaȕ?;Ic.GQ[ZMK@X?괛ϸb@ǧ ,w]zDO Vp"wsU?:rw8m*  Qk]Satf~/a cX.8R_ cLwy.s$c s,Pza"ϫny/w]5F AiW>H+{_ =VݑlN}ak kshym^.M .?d@ ;')_B~~W-oC6F:`d@S<`diX ^05,YH}q4Yj@ڰeXxONϾǟsj@:*`0t+`Dt=XCq!d6JS/r50 ,4KY{#`m`*Qln+'VƤ@]]:#ty`y 6롔 (!B{a`[VVBS !Jb,U~o{6|K:? 28>uJ z݊+|2k$|<˃&oX%y T81b2 wƅ$sYUCxX\&GKZowd! ?]+n#lxPXRhRػ0){^ C`D%Ps€ VW!ú愢*50#"Q@rN009tp*puw]uw||=]*{٠&zZ$WJؚicu#[F1/ꩭ?Q+͚~TڝL3چכ9m_Zf8K5ٓ斗3f,:Zѓ;zmdwl7ɗ|yW@\ > nj#A ɧ뺻ײl׮))7LUffay{&Z[Z}]w (۽ 6~:fI,dA}{F1 U;C0M(I9Bn,H\U2XL$@o4Gw. ħp9 Fz!bY, ^O|gl[SwkDh/K'~۬:6*=h篙`L{dp#x8ԟ3DZא,CV\Ԁ{xyIR;ϐ p \et'xX([P&R>N\$k?߀t]0jl'k b8YWG N"LƘa{1洮8y;< N.. { Yu81%oBQ3 ri!V_ [8ѻ:Iۭ' 1ۭ1Q*Q-DDWXjXBpo"G __RL#5lX6MDi䄿Kķs)yKh;òohFBD]Q Z½{RXN~amg-,:z[^'jfl"7:Aw!=.WEZRލ zQ$C'1= 8B&LE6M'Ħ[-Ћ19a0Dk, JѰႽC/}WgN3#ODJY<|xT̲(K4mZc,1 j۵&℣q)^*xM ^UOtTFw0G5 Y_hhIFe=$\uTIӟ]5-/-xԋzm;z\u5!,yT5گ+B.rJ1fq]y)Hfx=^ .ɐsVō v;}ޕ;*DQ[7] .Y{>,7!)?޷𚻦7Jf2 | ƏHK9@ IDAT\GE:'| uDT<,h W +{>p 8!jD܆f IbIĮ N0p $Ő$hgz IRcu{5/ p/xH:joXIXךqa'셉g@Xɑe1/}f}< 0_Zץ Zᵽ1^U[Ǽ cFu `No\FR7{q4HEuDSFZPyLضylۼ}V0$Gi䄟m7m$[)ޘ'Ѥ<6XV}7v\_d}YN^v}O#\U8PaDbssI2?2Y;2 BsH VاZsJ<~AYuQͰ+x\#5`MG*u)w},7_}$l*뫋73s%.{=u{|D156ׁ9BhkZ'* ®}^.kY+mi8`S2%^!%̭!( iD"҂n@T]%K7A3D= L< _#©Wύ:Sw9͇{OΖ_}ȗiMG}0+J#ҿTp9d+w~x?TG3W1|c}n~p-ޙN&OuZk+U?`.P=Y.\smƳs@= OЉhYn 1V`=c.q*xHO zyC\j|_HXC;#8!j/gk]'(PXmW[lMCD1*@L{RN:ǁ D\'pRg+N5$oL_ j0"fڹg0xnC7X|<|N[P=jݣ">2xhKxSk]^`+c pֽ:܃x$ ~/(Dtc6?;f_7ڇo^ I>$n}1c//|])Muψag)o!qۜDZwH?h|߃rLmsVB﨏`NCn0<=mI}W6g8yф[~ww|MOWz"C2J3KV:ɿDjFJ 1b. ZnfnFy"m71QA6rƞ(.*mfM5#SҲhC Re1gVݗPn2c0d5ȭwBv_|K_׼8?i0X0kN<4iQW6X.9H=3bCׯ,P)™[^xAk O'z?g+B͡8!@zP&#a@8h%U'1`2i]ߣٰ7QgVgs^LG`7e^۶s#P<ؾUW۲?Ik>.d:95N8<зvGup$ M]Zӟgx_6gYdcRc,># жH {V`}w'+EEo%8U!Q1`5 pvCDNYyJ\1V>Q'*1$pOؙ ]!0W$m_qqyv5f#b0䚭_ƋHN{ʞg:ANڇ4޸[5U[uBRѩo4C(%ITm@V}[qn(Gz)rpg@P wcbSWU/vD6==fJjs49./ *=λ#j%W'j7/tc@&X}S^[6~k['n-esc@ozVAC T@"pFׇ3 4@r fwޥc~5f'<=L4hB9I`eS*}mc;1Lqq|ܸgO]ɪX4MYs\u|SMG)pWzzLLs mj1^yq۱7,lsQw. 节uO?"iV~ab z@ F]#8܃k@5C^WߊH=W2Nw166zJo8<x q _FzONuXXmo_xlbCtő t Y1( x8Z.@}4'Kn9Hu} }n.p2 k[{J9ۺV;VCfj&w>AD@X폶+6eDtOʽX B(?ojp+gPAR 6Qcl(xʹ>e; :eqomۥ*2Ec%wLrm>7rKK.ê '()=!5DrBnjpȦ,95}$bo ֦'nc%(^7nZi8r0׾6sԶ`č3M򚕚&{蹯n3#4<:ԇxok/NR41aӄ2ҡ9k _8x:O>δ67|63)IJ?:YdJ9`+X;LRكX{k+ANy\CO9zX`]vo{G/wNWnmFA oW{܋j\cR[ez~KInx9{lw"4|cJ"z:a߱Mic_@Iީ~=ˈN~(Y `%4uc n>U?aN;}y;&I\;D2kh6p_> xz;5lԄQ#?K C:1dȂ3C71HL97 ƌڛ*r217νm0doۮ̒)٬ - ݨD]S^Ԓ@FD,ϝ^Κ~ޤRA.hqKet^j]nǩs<|um`,&L@ڙ0 ڿ鴸ftGڄpi3OYFRm0K>Hm(Had<[{8tpCkiuoY\d>x෴O򗑬kp)xpYDbܣjӑ 13S̼(zQ]՝ţ^RܶLR  J]toπmgwnn>!] b}1gÀ\:bbtκ[, 54}o(;qW_/Hod;A{mO=Q;KtTS√:ۍM--}`ή1w'L@ЁL=(6C3\]aq3Jtz=r͕r[~h:h@ŲpL)tH杁;TaרS[ sg#DoyvEp76oߗ+hLwmP?sۼpkj<0EpOm7`_/ G HtI'D3h ༔s |:c9yZn$ axQI BֿNN 6 ' -Hho0x=ƃ$2<Ŏ6=s`-^ʡjb v{3sg>w{nK[؞T3 N(e[DڟܫqH^%[Ǭۜ*>%q )pCMWJYfPOWp[+8yIwҮ#mp;dݺ^pO;:O t)H"R6v6M{.' :>ynݳ9D4ZC\^nXKDo)V;ZS<2"z>~i`J9 L71~KR0 0lsvD2-V IDATBACxՏ_<>Mqbe}Wa"x9 <N Igƅ$Q gTs%Yڃ@[kwY161XNDT N&m1amU~ccv,hJ= 'tz"MPwF)7+ moI "!F$D@^.zx%lKTib!cGQ\,FvCcbN?PvM r|;\`Jp07,slfq0Ng;C=?(n; 䯍3o9F_P Iq=Wfvx7/~}%-gugD]T<􉏞zAP>AࡠMݏd9'8 PBh{.M*$=6am*fH65!]YyjMb3B)ۮ\%+cl8ceR!pB lVZ |c io"ǂE( CO0 fB@-B<vc@$AU8"A }HN"mi2ךdiwe"f!/2 4Dԙ#6׼iӡݻGB}7Qyۻeg*=µkZ]bMц#fӜ;Zrʈẗ9qJJ> \=ƳyBWTQ(a$6EB؛չ]ގZ*30!n +w\aZQ(3ׯZSي]\,wѺ7-DHL)j[pv{9?2LSa ,;L ϪYA>-R,)Ү#[j1@Ol;6g9lJZ]DWs݉`ש/ @`35D\}szJFC%$j0o@9EMhޥ-WyXX,J~Y:Xoc15fth:[ǡB[H|1=XBh 8Dl¢ V33`p3I2"V!G'*$ !Yî{hOZɣMl\ogob3̉[}%xl{ʹ|$CL;_$N80i]MQ+d]ϑ#I}yZ˿'mH,p[ ~pdݏ.4vÈ3mA%2KfICL$^S\<ąo4# ST! źN:]jb6peeN,^"O0W~+3z?3G:s}#q:o~ 5]xO<ۃcԦWS=9ץ<.|/PL/qwei` bn ~rUt+րch>c7m+meyk%jơy0١v Ø5uk1ɢMRp/j 1iMIgڤ64$I}0/[wK 0[L~.n#~Q\3N#Q Zd0A(q E0e( i$` $Ŷqp{m3Ej( ),ڙ:o jni_8 1 {偱b&֗Ap<̣USL41UĬ>k&}uaO( ]κm[&6$FঢRgY\}Ƚs #^.!;q9׳D!En5oWzv6(_FYwzu52E)z7lkS15ݶy/9;,Cu)E:!Պk",QDV&'U:b'e $f-ٞ{պkyI ն[+*"IϷ:g'i;k_DtcfXasZӃ‹{8DDs;B!x{Fik8q R< `m Al'4 3A8t֨A:YpTdu; Y舁6wkyoYYWvaϔL<1>mSR㱋KӇČ&Y6h%ԈP9FJS]ٻ@iYMRN*™ryXhxĀLG7[03X'Op&U5zNۦ&gL<ۺ(0qp1;(Rk 7rnsjl651yܖe>_lՄPZ x :y "q^L T40U&s:CXp:O0k=\=YTt[H GF t)e"浇eWOw'~]|e9َqG ?nl_y[w/dyYƀ[M IҶ?%YYHv p@ccHY~kfxyࡂإ;|\R$( +0&"d} ,YgeQ;j~ ԁ  moM 8: 7g/ N C򥙜yOD(ӞxBU NZXJlepjV?A3n::N#cl=8y xsp28pNg[:FƁ 1  !'NmumY0 @.(C~!H*jD3Qm%$uyE˪]Կm󏈁ףk0ی+h:q0sRS0L?Dv5f >`^Ŕz3T3fɽ4WutU}oQ'r抉|7J8ш\eXKKl@X"FW V ۻW i/,rUiS_ Sѽ:E}yY*R`CP{^g,si;7pgWbSnRsO4./f `2cFW"(E63pq 8eً7I*75:H+ OpQ kme8OPxNKߣ:ZNGq6+yyEŮl'zԫ٭H3GoO B7{ci_2 \M$&mNGэTsR:x $vu [Y >vCD5KKe=߶L9ty1Hcз <9|'R xsUϥ ɢ6.[s6$/k-d{Kp"^*ㆵM~t NHWs'w-<'x(-c{14@rRgK&81>Ko6(B/>%c 1v=?sI֜b˹qW+-(]zI[^_Tݍ}\s_ MhɐW3#9]⭵Ƣ:jٻtx_UM3_>\:2fŏ~C̋UF9>N|;/wJ?m-;.~S޿ب@U~W~Tח_9)m񓩡b.{;i?=.d؟|+x%5&$)l31&ly"H-DMuxF$0)Gxʹ84_ϞxK˷!/K vk{RsAMԺupC{TUbVC=<ɚ6Dw`6>Ai^T^E"~zCH$y 8y9=򵿗]d NBD]::<i+n/<'\t#7[}elY+X>'|Kg@f*:xwa5^, p-`) xgApuAvOq@ @=<9gͺ`ևW(AzoMO/zA߾ m]d-޿L-8.yY vp۴N|^P7oBA݆EC[c+ 1 &n*{Ą xEfBD!D^S D-"0pjMݕ8M2 IDAT6S <vV b"̵*A.,Zv :c1xv:]b:"i Q`Fc*7cFuy:DO 3Ci|΄&DzGh u&1gyѱr"(a}-X w*|;`vJ{|?[$ ჴo!T 0M0N-Vs7L} ņ '@#|T_GO7WD7ZjjZoxF7YlHp7盯擮3>~IRܸM|O.^ZrHwVnHPdQw$; nk:R2=ƧVp_wVŢ-%6 Ү,ɽ =vmL+Gy[_7).U!$"e23#Z;Z#ܰt@#ߦ);TR5mϜ4MyéZhbqBz`o^^TX CNJ-^+lʉli7{,$"YC/t+`'ж-Ҋ]ٶ'\=g@d^o)|HT~9vS =2 Ƿ=wɋFQ_ Q%D.p&wcIフd.U>ǰȅ(dokoa$α>v7" "W˵7 (3ݾDtĊk(U+GN-qqx b|1! ]v](vsj;^{,s~wBE/:j r26c}]vnNu-DXhhuru݀\H^Ǝm{M<<<%[`l3!dfy V?0="$nY$&a{rZ@Bv\ 9%1dll-@At L̓zڕH~eGgG>;yܱi(qo>]H'?~'%֥(XVviZ>|eצSN} [i〴(4~#8cda#Nlljަnue/JEh`~Qƀ]j^/x]z8:&Fh&m㟈!@+|Y9b˫b@ VP/HBo6M 2TaYݾ9m?n[dd|e( :䓱oMk3 ܕ[ GB"-z6 Y s?C@bHCGHvS?ޯHi3M'y)RK%!IyUfL0vgNw}f̿κҭ3n{afa¹%u:n +6v{Nw| "716=o1"ZCDG8-!䴽ȕXBý^fœ.f>7":^W̄~FDwNDgAeBLvQ{"Oq@3Q} 3-LjL#WIw8vλokY1F?eRf3y!H sA9@O74D'. y?pE2̜Xp"@D8|Q7b4#!X@?y@ѱ!U~0_b_߬(:U$uln-|nVw 0 x=!ˌlFYO*8kK)Z^p};DYuz^-+=iA+bMhO@9(:Tmvu3y@]pSθ~N-o777/]z^"{:WʎO5(Zw]7r&a,v]u෦@#AU5|G|mppZ'|Rf1uѶ0Q`B\5JʴwIE3i{Q%n%2RD2 x'Nˡb%˲Uk@2"mRW:bVtu;6=q}KeHe00J*T 24+[2)L> 2`4 xP)V.Re,uՆxRg}9#oYM}2N6_9gU]mX$mlDj`¶o )Q" Nӿ:wfw",u )+˸ D6D&Qjĺu0Vi5 |˻%!h=Z& { Ve5;aVA8 `1e 1i1venn G.w1e݄-CN3\I45= +CnN{` ݰmUu~A]yU*>v.pc< z@,+[C0 Bmsm^͓8o@++q>$`މovEj|ee? ",@xM7qLl@޳&(T-LK1NEѕ)htoS d^мHu.!T*|ʰ!AP5-Dc/@ߚԜCL%\o~w8]Zl䛋GuQCʽT{^~W77+?k)\p_dR)@Ťһ6Z}g&IayMXyJpJ 7]o~Y?auVcs_l/?1w!ֽg+$_w06&5V|!#ވVg 3{ށ/q>kȓr ƺ[p{:Gw~!Ĥn"y\JDαwA} 9oq k!@LܬE@DBENH`l Й}B!3Nr@F}z_`}`hDD_ pG0q PO<@-_& RRxvWmEpT717 Df ؀ϕȕqD8iN!ntCԭtP5f~n0nBZC,tQKq/llC#`k!JJ+ raCRt6,'+9}3A|dm$ҡ(l߬h:Qi"bmߖЛu²$)5D!H EnBD@HvDa‹@ē+|a5v1@[/K45xmciWOxw74:eðx@o`qR]{ګCw֮8qզ(t_;?6xmDI&?0nvUUToۜ:2Ua3jUiERUv%FIJVѵΪ ϾTwXBYSv% jaX(*FSH@y~4g2%iFw񥀙`v#3DYaqHF|ܥ7tw" ;e3nsN]pb3n![bu- @D@,6V>ŀ!E9yD<XS!S]A|A|w8eXCwbu1&=47^j!akXyr!Qj'}7~\\"AD{TIɌDTKD.vAusB{1393ow}l,7Y`]2n= o%`x%K؉}oG xFŢP3ӊXH*RU3B\ZHۻ!)/ѻ|3{ dR%*=3%7AB$XH @yl'Kk#[>KG_ةa;,XVI[ MOl@>8wnzww&N& o9osZŸ|[nѭ ?/yI@6 >{-xn%K.,ٕ {L1e$Of)[$ދz7*e Ӄͤ2Z; ,yY3 _XXfd8 u=@XQ>&o}R+HTsy=\"4vZ{;[L,f8VAd7dl8֙mzg{}~Uoh8S]V^3ѡp @`泙WtD7xh_l ˟sgƙ/UPE$ RcJo?xsr/@7cɲ"GR7M'(\$ҤYOoe ᱑~oP?b7a?8B 2Z~پeVlt9l7gˆ;}:!Ga<}k@DC -(2(z(fI@DC F@0n^‛_*1ϙyͅT@,hΏ `.;Fp]ѕwI8ĸ:"nQRpk Bhξ?rqUQC;ʯ°ѿ9BDb BD=A!fҹ!D~f>K&yN23)욳_婬(~-X J)o,DjT 0 C$"bofɚ6TU"),I Mޠ-69Dv*V>K)ӸH?1LR |ZpGM]6f_LU$.􎔥}ڦ$w$#@+W4H>ߖ֬ԃ$kiE`Ï I ǥHyr7:(4 f[(mm%M~# 1Pf>2CLV,ݼ%͋N`lO. W23~6|s!* Gt%R_|e˵{|sdmyxD3:NY" B}CI]|r+t~Z Q\-w8K &WAȇπ`coey˴hbژRt?vNa+yb> p"Նhn &b8"D.˝A3yv?t! DeY, 9개E$$9I2!YI%3G"$B-Ci()p z ӛw-$ NgdU2!eAe@߲mvZGRSΰ Cẑo-Kc#0+o7?xPw輟i 'Lox`/ҏF5{;f5VH<&kVֽ}y)JN)zBSea0J9hQdSO]^-JM^xLĿ}2 J,%Ԁi>UeFZtVاzd˓CHi@6 Kl{@y:4m,B~A j͐7,ذ P`W%#՞R2%W^`Zp`z|v#dWuWEή:{L 7UUfcU$tF-1>j˶o{7lwLX|5GQՐ첬 IDAT8:WwdXƔk 'fucF$" pH5m{xA{D4bc9D" r&A*"6ln-aC}'1rIĪ-I:"jb;E7$f &Lh9Q#h,1f؇9 !v_@ȱx x4%*p uScv߀c6*o.[1)!PT 2继1wPNgJ"zBf Dk@8"9\{+XUӄog &񊢗y͵?x$@M^ 5ܫw7LTB`mـ*|GLP$@o&#- ,lίƈQIhF򙷙׳ df7L<+2`gm=-s\~X"}J;-iVƯu%$}m^uXhPu\X&M#T$a"- @2 j)dSRxW-]iSGGYR͚\6;:2cx Yz8|~(]sC~+[yZPxb)v.mpy@x0 XrսJX79o.êy'TӉ*[ #" EwQ /1?^|`/_k8:m7nj+65잁ۚ Cvy$S 1I6s'&D׆x|MNI͆A|C^k! DtWb*?w&=|OFD!Bw=AvD#k"!.Zc_A"6wAF6CYBb!AFʽ- 8`3m8n'kx9D^ޕ vs j"isn7g2y|eVp&3/rJز:}y3+ @^ Ds:x@0K m{ݏh"zہN;v9RgYf[df~'NDo9w<.9<Dʧa*1`j5/G`dbgDp(a^T2{RI&a#)Phz0H )HgX?JL٘#O< $Ssy齵H_2mH2 ۆEIioRSttM~2~B7Oug3{ʳz~Ռ]&/,u!pKZ:۞%a ;S֎2TajPºዿlX 䍭 Kux]{[2Jj,VYMخlIo꺮k?7.c@%+ @S)S^E"@3 }BDqػEč}wk1D>$Fn|k D! c672R.'7g'!t7w'T̛"f8= yrup0OB\P@+)*(T Z{C BQυv31 Ơ zY؛@nl8 BԖ C΅xB{ \1m2ܟp(3/r"p!̼ Bq"UBes}2sZ/} m:?Ĥ!ymF8wnq"$} DwDk;AC*X r?ՀP|f=8wӡ 0!P|3" Do 6#jb>~:t / q S73y&9.mLom,K pwϖv5ɎYF0033}ՠ|-_}-gm~7\tgg`KvIX3DzaN(_l6B?)恽:Z/\gy,+. .dUcgVDW:yQq4 ?Zr[e@Q'(hmL u=rؕ;n[5:#`IjdpdK$g57WZ%*!)Tl6{w$*ͥ]{koWzЫe!Ҏ0[a6}Q$+ r[I| !z`e+n+/ZUs0~;9w搌mreǺ/vple_JA,ᛛbg|mɶL {iH4C'D{|-G7|A[3S }izc &7ӪZ wm C0SC# 1g"\&~ݿU6ɳ+v1Du)Nf~J!Qs j7B~s8H"*aeu"9; X rB3ށ<$"z9ֹ"-q9ٮ9q% b,Hې|2l#A I uKMBؐe*X;I !"AOX\B/(|䛟w?u@&¥W#4 ̯؋h=DBH+ DIlO@ŗOa2^ PIe"J[GCg!}kH˻Fiͣ3=䑞'}}vTVQ[=wY-Ƈ5[7hyw+5lO~:pǛiklRZgfP5͓hOfEVgGz -w-[.B th?fH l@kQiuPǕُؗjܝb RfL^QCY䩉ɇs \}Ί6ռ:k$CiϠ겝/l6&</7q   `ӊDZo&ӼJW MywPc6i視! ?8bCIŇd`$WlG놴I"ɒ`ٖ!Պn1蕑Z5k\mjʝw^+IyLu@oIRh[Jfa۫OiJ@c|,Q9rI6Mp"* &my.V-wvrA1t'n=;TQ@\"z@\Z%(M[bpv̜`Z ,#7 "B{9"8ۑk0{ +e笼]A&Wl@ۦ91 r,b9cȹƑaCK㻋(!]}9M9״7S] AD_D+_561eޖB٭yLAB0o3Nߌڵε$ څDtӖ ԔE.ք 6 82{̼`Q NH޾y8lk$@"u* K;$x`0 Ȱ-.+LؓNylH f]r~hOD@vD~-s!6ZɐHf'x&h!0 N! V>7 ZF LsqTc"=}6_kɄkDn4J>Gɚ^|~L1'RƆv> -YA8TӦlGJ6+ P=1[5o8RֽYVeHVګRJA1K<FX.>ɋT'mWu4E4- RްZeQ.,EcG%$\iG%m!XZdԔ$w74>o`>v>lm4zB"W}7uIJT #i>Ў nNiHtc..mu~ιjv 9wt2ًS~őg\\3ml6z a#䋁'OS b)WĴ(=:ke{d#\3:/_d ӄa-'w?-gdCrTenKH݈ImtOQ$lCy3=řo7YA8Zn=|E%IGZ\~}8w{7?5WM2{dSgWS=bYa79b:DFCƧyJD?$X˜9 d77 y,uL' Jm"ow}\4r9A? p-Z;$! ܿ]NW]qlk TCK @V9 Jr L{`AvaBJw_W؝L`dQ̇#XXܮND2sYjq6`S$YuU[ݚ /8Վ+"K4w'"*l[*1@%w$x'iT<=d@UU08{MSS`**wbmCdǃòzCj@2D|%>c=.CVHi@iϕ|ԛh?IxC;@U7'`b>UWZ:h|?i,0xpa1DVA2@Rj=r?\: Etx&h`CiT_¢c^֢3K&$z5Vv&6g;+Ƅ<0^im[$47PfRʒzjoX1++5%ETʀ$'A,+k:WYвrf Ma--qBVxׯ _\q__#NpLQCV[w+ns?QyHhMUVؐWe=dK;vJS:#kiys QŇw0ߴIClbpcs7[Ju'W۲⋶a|),7[HnAqמG#N'hPd )o>04Hͦ$) GzYkn =0Ufgԭ5mI%SkL6/鰍n+k(gaSs/y `[3gbHDT~(٦,"BL\ȯk+&"9? 1Q.Νl1&"N0Bq8B_1;~b(XQu˖PW_qq`+D[H!!!h039v,//C^ȅF.WP:V:r sȅ |i.X2bV 0>ܞn,;i˫lzG6W1s#Ia= NJ!jD{G>nN#iQ97zD1] cW];@9.Xm#70l_8z^Vf%@ IM}`TldC~IK_ۆ7T"CX%=@QuGp@F~l=NAq!GOEA/O|"[w P.o˵.0>mkz@H( BB H`T4 Q(" * CIo777;c>]s{=gٳY]:Ծ(Ơ)n@uZF)?|JzwyGO)KӸWwZ3V8'ln=[[[y2QY+“Oۺu)Z6N9xi^F8j^$ jғ5񱂗5"d[=j\ x;"Uhnmn5Í6 S1ƉpA5 ?8FBw boh}귔վ{ \(世ˮUh~~amnjyFִl+K)8=qD[~wu^yENi6>75Z6+M^RqvdVaQ 7%𦕲Iʚ;oj\#+'7FE!4/9VP7?=wv߽sgilwMhIts&?{nZ;Y<}r;iߞbSmGLC$~ ?>SO/t s" z,A 鐋Fb#DZ҅SV IDATV(ˍ !){7xfAYm d222O1IqM!^ۊ. -Ɩ|Yl_e>7ks"$@wr{ QzBѐQ\HC;!'@򍊀NKȼ`ɡkCF@yX5 ౐`ZMg d8] #|9b+?rd^fA.Uy]:6/"=3/Up5"ÿNgi= >lh!" `X`CE/UR @,arss ظȩ8DpQgm֍@`A gk{:K$|^2V6P'=^DprW7C†IlӾ7 Λָ*Hg?[vٶ]a

\?9G`g (!aIA^,| O|!#3-?ڈ\A>E{6} YM\=+8' ޅm@5^ NoCF\' x2@^*H(hBn@_rns/ lyۍ}ЄڧϘ {̻kQHI<rCe}r!طTCFI5L-WǑ1뇤{FI0ʂpd/\h#d6u "`eRf5$ c'(=Rn|!!_] <HC%9U><,Y>C(@@l2\ve7E dp0AQo@OK6>ɾ{7>͏7]X>kٰ)qmn|moyӶ蛟 K|`0D; o~$NueSX"Wτ<#6ne57^[=S =,+ⰻ{c'ZQ;=LuwIrhpʬ9Psh8dUzT)4TfFWG;gtuDaf@mh EBZUr"g^cJqMZ&ŦfQ1*Hl.m*EOXVEaCN#~׼D%a)E{HɤdWnD.ZU9bbQ??]jjGjrȞQ.GlU׮x"m/]v&+^ {ꎛUm7ai,4^`zOۧu(.,.`)Y|"<;emmF)@fc>Ĉg=xZO "zwzFEGv\՝삣ZP4nK &t..o/:M\@PlP

0Uo3~fo|<9L5h W%" &T5P!t 2(Ua]3'{jmXA \ 06r}%WL\'oW~;"vGKnPգ0mRy7Pж?R VꞃZR,uPeYF\Bol#os5 CǑ¡2D!"Tiԫ\f`;h(3S%ώ=q1%w\Ltڶ8.43L9A;QԠU mߺ$I~Pۊ5\Ë*]Pb`ߨjAMy uܫQ /N;ܶ˟FP&4*?/ 7rdBfb#PY&+D$R$£n[IU#M)+n124G!X lWP^Ʋ6߶/ʭyRg?yݍ(p"Vp(&OlзR);y$TQ@\BO&9jaܦFbsr ٻi?HAw-|9Ne"4IʓBeJ(*ވYK6KvώGo>u?〼u? )bI_TԵ. |^C 5pP#ӨTJEA<oAAZMRqQee#¡ x<69UrVILqwl!{9ے(u6y %ALX`rP!q#9GG+Gj qe1:Jfr˃,X+S48E z\S&HF'G# H$T) 6 0- MeJ|۰"o~68qn#–{۶,'~w. #\V]ߏ;sl>x+ݶl~7 A[5YV@) =I9G\;J!f{ئ".g+[bϾIB*?rIXX \dݕ-uuGPy>5P#< ZPDd(b 2'{ja->rs._IQf$ rD}xcW6ҌdF4[AT%͓zMXTDh,U喅K K$4vR.F=JDɗ^f2QE)NF֑kn&ah^[koCÎݻ'[~3R"Kç=Aw .u/` }U 4F5o,ڕ {*BknͶ2y\܋X\.k2'*v-GAԮ#SmSÏp!Av@E]ּWYFُ<^.a4дW30>i+RdL(ZT$8ӟY΃hZZ9uY |oz{c#]~Wlmo驥L|j4O9[I;0ۖg*wwr>+} .ggs I2 _4|ir kkjGP ,oHG\+Xf:F|SҢFg}:De"C=ϻQP@)IԳ\@J IDATMtĥp&!G`Q` E:E)M&]=}jB 5{_A}A%njG(CvTRچ%:fTwǓ(fL|l'*Wة . ;J2z 꼺QE;kxu']yo>9u4tF.M_Hwr6 ,*3s<%mnz .F|~xcggjpPDmukckO '7N@Ɣ=ohYXuT? 0Cނ^z7^>EX3}P#< ނGP@}Ć`c8u."BwP9"`YjvT l'!G^NT;]fTuhr%&-@I`KԣK]Y'PD8U6x:-sS2ǣΖz7ػoN\+߹JڮM74#7?'Ww-^j x~~XmKt- aRf,z;0\fPՎ5(ɩU+?( *22u.݇"aQ0FjrGބl`[(!0*Lf?af +"c1Hϙ>ifB=VV9VgA:JcUNjhz.?+LAͨmQSL$0Q]W11pk[XUb'2JO❢DSX!&|eMʯ,;* q$XZr%@fJ!lS':m0>syˀvUzBKN3eÊ{S)h,(p2Ư7f"W&om(5e&ExGPџ!8Lly.daYfAvL"~ >qwyExP٣_ƄGQ}OD_@'>tu1V.Ę׎7U`Y8ќE .Tb@Tmi#8*~T_U5 5@໓B5; ]èQc(W nf=>S Ns2/ԟXcJPm!ٞ.-K-FN')ZA6U_娌-gD2,3Ⱥ2v|i%F!hho.EroZWm-OU\DM:ȴkoľ|hjGbɖވ.K5CRx8; x/~{՜E6~Ц A ف^x£de鮻hYy+dx7dB] NJ_22ůFKZWJ/<x33WF x_ ?`#p'I-@s.DT-ipIp[QQIžPPL'J(mo>"mhVH,Žs̭ic?M繎g*$%"Ez1T3*.}SR߸BtBDj|BnJG#68↨/'2#PKhd9;ҾCD7Hh䰎Z*RBRWd-֨EFG'ΐXpu6 R{zV78Z5j&kjRys>(0[P}(v6ضvHQ,A)8OA TЋ+Q~JR΢:0s]~7|= cWPKrb&4Jm/Ws#1 M/|;GdǒtM2>"=ݶH[Q1Aԉ'b_L}K~ at}ONtGԡ":wrmS'@c4ME#hdU:Vvz +aT7A9a^;";.]|ju·nf\lDS?S5.9{'ޑtw(}kٯ{o/LrƣKQ&WrG39)$s CD4|!5vtUjKI3^lIFg<q!b" 662U|b똨nU?S~6u8,Q#<i^.JJ}EzDD^Y7}xUanL@ETTEz'QPHvŒנ2j_"@ 49Z ՄohhU{v>]H*dU̢K"2UWErw?~=7ٽҝ,>3?|DVnǿdlUwy%[.y .mП`?u?tya@r;֏&kY4Qr MKqROqP; S^h^![ ħ`"%IXPpQzgD=2| XpHEUMC] R 岧'L2A pܨW,v(?Aʧ]%sc'kWXMV&᠓ (W azfL  z+IbaQ2H(t/p;PK0^'"O%ډ%kXH*UmVGM FDGFqaX]-z&O3Y{C;ö8~/OĀSCe׾ïȇ끕 @@\ݶtQytnA)#YRmaqaP {Hi)ddO_k]DxK̈F{z{(?#(R@)lգ#oBA=Sk &Q/BK {w?Ե/;ص.;Ruݯ$S'Knov8v>?ǯ{K;>wn/|W׼'>c6>JQUO:* ɍI?6ǶY:ݽTiy*<j<eoAX??Y@,CG1:( QW0F\ V,P5*B\oL__h/Rr0}Aګt4~* #OJzȥiMe쁖eˤE܉d4v݂Ok~8RF<7ΎHME0 @FUyz UTD9*u$i2#˺R-?SHut3lj#n0ZhRb #Wp+Pjl9ߊI!"PLlbXI@iܔw~gjyPDEJt'^=`7\cQ,MTRcd&9/"U^.PjZyj*Wc˽9$ZwJǥ㎧.^=ĞO(rtQ#<ύVS?:{0 5(;fLjvNa^/̜g5?& Ȫ VIϪ2 չuGPPZ)*wF-fM ) #8" M(]G2NjBU9EEnCrkHfeQ U.|B/ʌ|z$XAU~f@v/.qkV''l5#ـj(BǗIZ!bfGbF!RI<-x˕urz79xnD~W{{emN󯭨$Q@k K𡷾D Şuu .S(u'3"L.&H9ۖG~|H6Ec[}s滧6|gkfs}&jinFVCg@ B 6ks5:EzFSy.d(׍"c*#K?dL8\\JDbcS-<w\ePsOe sY yOY1T_BD#m`S1/i ]/8Mחlt"Fr}D݃%JD>#x;GqFǮ8b'F d÷TU Qy~[jf0Y¯X1ѽjD+yFLYEK-錧ѭLMYgt~"ź\°unm/VSS:Ӥky% 4ي ^TԜAP& +D)\6&ETyARZ(C W^JȢP(< :C+-RH< l|L "^dɐ;.q"g?wWTǚ ?oKQ=w{697j[_n6WK춥 p .3M7nSMdۖϚ7 En[>LG*?3^77B:JE{8i/&hBp}"ʁ} 4Ogpz/w(+yQHL,?`O]-F^5:LG[6ƎQ)!rA^u\lcLpd 2ߞn~#Xl4fl JzGvpoR-w 6l Wq#;+Goy GOQMd?-XƏTAh<7M~ [!E 1߄Q7N&eza s|t CārQz aUAo!+p R fi.aa(b4MaϠt~eYD QZdy6RE_?}*z0|tb5k1ӳT';q =WF}XeꗿtLӡFxuJm1]Iy9G=*y $UD(JC|FX6x.wrFڰl,iM;]Gc/NE4r7B,Ь@Zi}7e+%3RY*oZ7e>" $ 8|l?}w;XURJ[9i"Jg|(G(۴Oª4oDPUUF{R'7~aKwG#b| / Xш)3ə‰=s]jOmJL9&?|s(_,Ք1WOtCݭUc߿G(3^C΂H[(]w KMx\|}QF7R28 l7{83]0׎fJٲ:Z-`xn?_\7D01 0r14Cl;."߄wD'_pP?<7G>c0 :4&VjؘK} AOb;3LI|xJl'S"^d+8>  #$2 D(Z) O2g=qudC󬞲L֍.ңdR]75\8!#vÓgNo{Ew+o(22N=m_j$ &K3ݯDOg/FgYu־<8F3yG)Gc(X(3Yp28VB~<ʹv+1m7v8G(Q:g&}B&fh@&я3 y9Ff7c#냵j?DPϞ"BZ@UncR,MnJEk#J {G[~]u7 RK*2ƶ, t&^khMO!M``[F#ےe=˒*X//~u* ^VUp߳wgTSE  H9KD' !w g7l"1f!j`K#X#m5,Do=[%WA[88נlLbax<>K{&04恳آY֊72aDQn=,-vQ{7X=Lotm0}²vg1 lg'=]`n'c ׫[SKG8wgG_Skǵk&86y]tJ@I4@zT%甧2D:v!#ҧGX xt __[u>Njs-[<.1j۬Qb?zgAѮjT=Xۡ pi&BTeT$6 k<IJ d`{Gk`E0\jT.XG["\%_<|M"7> l*S4shBLMK;[ʭغ?PJȯP%TT0e0G߅/8zT́ "'T*c-)ΈyTD+չ t83:\3" X?P[NM?lq]E#ʌQv#NV;p{*4rYn 0I0x#C袥Tzz=sKy&:H;߀37 pkqX&Llt>6hu#=ж !`mUt5M0 vӝV=>V7Bd"8:yk\h\okq D),].ֹnanu9ogvlFҠ-aIXv54?Ot7SN$ڨ4߉r4 /BAc%5gǵl7U*_ySE[?~3=}/_?]$Jlz;G AZ@uܨ;u.ů?& oWS>P%+3@j鿗|ew}*k\-X5^ J v.XXIz9OtȬ/~{Gujދ؜GԸk"*TCq|vPCX\Dc sSuV(5LM7z15=\!PRR'ĉ \7>_`P KN7~=T9d6 Jp } ]kDfX{J\%cuMf! m,F;\\,Ә9@ |YL: 4>Bfyh&/B3ٍxv8h]uĭ_qAIj0=0͂fA_lCÂLՔtd xU~r=)uSe92~(enhOj0X=*3> y%ܲy&[]Ywl[`IokiP 15dr;_0|٫ )3%v3L-W0knȅ+o2_߫k?.}>26kM܅rЈbN'X6[;m0ۮﶄ ¡$KNplIp%]Nˮ,J 9WntB,'уr  |嬻QWd~={#QNI}'l~|yF #@|:9:|{L\s>Lzpi=˨~k9:vU1:K jZ-:߹3P6QޏggP@ҩ܌DztQGVƛD1_3WvG I|>ɼu^#؛m8R[S?Ɓh d]dp~HWb;~ﻡdlz<7h&iةre&І3DdEXMyVca$>*QAg;~^<t "FCfje6ofnUts7߁=`kB4ېq8k@$ p& A3`&F+M25~ vJX?nNNӽ3@X^keV˺rPhI7讔m-,!5{*os) Dtz~FvEK*V(#\ 2ڶD~ѶV5LjMŶf ?xpU z`Jף'5}&krv %PNAA;&9G9 ?ŕpzv󃒶06 Sl<, ш^`,ք1LjiK+;inZIoi@9Q]8wgǪEPl`qYpx ~em/ w/u[WQd$`%^OwVT<˥g;s){~^/kZ(͑N*Gץɸs(F9ԘQcё+0wE*9z;z:I?d8 ̸A+1 ;O'2N J@&D1+muv KHq935݃3^ZNOة?"ҷ =zMu.CXw!N++h֝zj~pGqn)ޚњ\C0P5G(^Ӆ[Y +gۢ+I-'vd=Ȧ)v˔d7P{ 7Ku݅m pm 3$]͗P|VMԽuho}95+_g| 7 gie!5<"qKV*+\tRA ($+_ 3b !Y\>;WU,)߿;E|n5j?ݻFx>zq8"~=5JpiNdZruS˳G9Q\#i,k5vLTt(isR}NeBb8m05e00NMGW~HIX~;pmZt+ɇ|^YbnyN -=֤LOc{b0CG 5w~ϭӇ*Og:60킧o(.6e [ZQy[ͭ1<8=sFhenCk䨥/bXl NCnLpO}tm/*%4A7%`A/rwbu!4coU{Q|H" %1/I}MMGLMl  Ie&rc>j5߬ƷkQL]2 j"p9[CEgƟzI#>x|ysXSG7z_~?"?fsә`pnf0y{?w1J @"%.RV|b>܊Oc8;#;S8I=וwx9S;+bWޖ&sUFf^|`4m^ʬ|'MD7W0Z}= ˵i`6hW--jϰL8"{ m~TaXtᧈVai"HH691h A%[$t˂ CX< } 4]aӃP" 5.CʇRm!)jK=C!MD_6)8i&ϒ?w;#\y`sc=K#BAEhYc%沊*|*?p5[E9P)4j7{崓IԡN% bMҩR|)=cQD 8M%և WejtO^ @p&P+(%t?ęOQ5 ;W|w ڨަ&&Cx26y) ~|(ǹ=j'~u3O&? IDATy)=3d>>vÞgkw3qKWmq*ke]~tU"RtqQq;3JtP6#Ik5&1PF݃jmbǾ۩QdUӌ̠ԚCPEGj5_wg~y$XWhNה4qzPOR{NQ(X+[2~MSʹTV{XxlJ宦. g6L](ojIzzk7=_Dm:k@wUJMwM{7=gZodE1r(THE让H =Kj$V?I3b,nZP%;3{!0 g(OBA%iB,n7!iW$MV(M@ `h6mhZ@a^ o|AH"5)46 Znb֔XA2CKS_GbM Hͣ-2@6s&Od6Ɇ&憭<V}K%,$Yi"d Mp{@K+M-1bؤ x̣n@Gk51'F- (Muheyq%j':Kה.؊##)\CvJl"JK ȸRږy'?"? eMiغN4luϧKXx [̺LS֓_$Џ_A77Z脨Hg̰+7_~d&LJ}lB爌Iů䲇L6?gV0jSN)Ti_~uSNZ첷|3߮f`ş$UN/aKmܖZo<s{.FP h?S/<0qmkFXXF1;ZA͇2W_>ש*&s(`=(2p712Ǐn߻o|^I rG'=FwtbrޖY>dU E7~{r=NNK13]dsߙ5쒁g_ߜD#n'?wt{}oo[g kLgq ׯK?u鱮'?٨=nNeEyWv2HՑ8o6!䦖IIA 6tuGG95TMKtRvtk֞7tۛ. xO)|4C xONSϿw觎5O?^쟁\vw?|<8q)IO%̵zr)|֎?Kp7°& Çw>£'2@jzl-8M"t&ׂOm" L(aKU2_A=(Sn| 5mt>"Tkǥ05BGG" '֮*G/nlגX%KƂJ(M fѐ}. &**=>/&7[D&kJ(x ~{v֟*zu^m]a^On|X_zuj_FȡO84#w+Ӟ bx~` \<tH溋=XAn_zr}<ֻ_r~rUFc/)_pG݁Oe{s-ݳV̌-D3[?Կվ|1MöNd+"mmI_w`; ZYRN C R3Cz߂g+*z# -zln|`SHa{yxUy5p^ 3Hk#`W ЦH FNI-" Ye4<4*&~Y5D\m1 "ik +jcqޡV4AVѴΤ/ᛂ#lX.r,Z.]rΡ Slxy X!eWm4L]TD^F{\>p+'fd"{PLȖdEcK(I(6Հ XN}\ێwHVr@k2P~8#l\-ZO76҉ ćߦɟbr?Iҡkt4IZJj͠x)4n0)uDsJRH"NEَϫ2T% 5/5c`3tǽ}Xϣ H)@~5 Pf']uoe=ӝ_N=SNo2͇(Vv8rs^Ayg7f[{4?2)?m0(=rgu ×h=%%셹ACj: .NZB*9RÐC,Qd==ue%#;p8}AE\ VګU询ɢ*1]@<{+ H M@ iAFY\/)~QD"]l+!.:Y G fic !EJ SIYHP}lMh ϥkbs&HgIora Mê?dTN< h/hORhM@]kHǵ4j}*b(hT܃N+Mb.::;.1 _@P;_4]I4M#J-\Aa.0-k\D4Kl"@P 62)W4M)@G2 u]ZoGNOF=tg IԊϿw[.n袊.H4ntQas9^ *@oIʰw0jm%:\EJ%Ç3b[v4@Mkmw+e~ ->щї:~S65H4M-nK7Q"5tVlxz;DZK(`UAm/OsLwIHyeDu8wE,u7~տzk<щFРݞvnʰy)d=8p45*Jmz3B܌{Y r(8n^xv|؁v6FgKsP^k*bc.vf6WCmA#0ws`X*~^]Kiq$}/J(FB9 j&[%֏Z\Q!d֥' ik(gTHD:R!"Qz'e*6)HZ6 6"n=~~,>k9 ̗V,gB?rZ>*xQ!xqɌ32V+ߨ[InzMD߻yTQU^>rI 0'}/ ͇xwߓ<M0-YI1wi?ݥ9)o$+) L)Bo?H<)/Z|y->:@neub]@ i.Z;EG7娌 ZIM%Psj* G,z\lRYֹ'e ?me;o>xz?WW/zs=(K[7/]׎w?ZOL۷8xb@"݈~KȌ)"U3s -bסUі:hMkP!]2`yz.݄\Ev!lA\<+]az.&A~p a2cLc:ȃ,MjM:U@h %6 ej)%_4 4#qdDt0 ORX""L IDHyD|F=Ncj85y沜rYO;|[%= >?E[y:J6IwddYN : *o6* 2 UQRt| M|W׮]", T"nvJIʱ\ꭾ.?;W4mPFJ] u?^ZGE\ C&B2,urnc"jG3ވ(z#'Wڳu iԥPl4vYvj4;i- ZrsND,]*QS5zo,xs5OOE{|fUɊ-YɊw.Y |T?eO+~`LfL.;3, ?6 aVQg酫XQUas0n, ,g;NRU @ϝtsuX7MUM+:ytGHR)~8v>pRvqu@Ÿ~cK^?wxyN7bN ~Yo9"B^VFVDE91ڛ}؇ R=g| 9yWa iCC]VDEϯd["4&./H5d 6&K5@>Vɔm !˓XfDTz7ɺq:6a{2!7躄'*5rWFbc@k b,iP(7Rۄ+{qEBc\fAPHLaaP>f)$9ע k/$#24NǠ %})?w-v tQN`e(t_O+D2E-@Rt[-o9)Tb9iz]QJ7pQabԴ77SņK_*jPa vbiPa [Da`jVMZD^ }9.ֳz=I:lC'=vT;xf{!jFvnCg PNj5z?Gwȇn~o[Q_x=@/+Ei[7뼊*8_kc~Uϩsn ]g~O|h 23쉳AI$.-{J{pFXDW5ZPeT%}Aeŕ0:WDqԸY]ɣV$T7v3: xjzp3P`N .ٰrKEdV|~*5/͇R'߃$rGpt橝Koҩ'7{eBylgBdr$bX8+2"HKH`D:U&ae)6UQe,-Bm2$w+#&řdB)3_4|q_9q|oU { xb"]54lݚfkg|;b|Zy c\D܃-1ʤ+b,J=g Qѡ\(=mùdFp=V9wL>J1gL/"}>n蹒,V@>j`8ӑT1Yߎh:?Y7Gȯw"p/vn|wdVZռѷ( {zn`dOU#Q+}dfiíF>yc3+S)&o2]R5j=y٘Yە.vkmI.^JzwuR\=((B'I_5uOlOoLʈz?51OkGDtS?gm/j:w '\k;7wg;jim'[Øs Z(4Ґw Y%The/^&2|UIVpJYȶؚ&Q\пhPѕ )DBH eRt)6K84 Sh4 =slk!2g50INjtE6wlw1sCɊ]mDGŌWť[6:Rא/tJ[0 EYd'wr<ؓ;_~̏mITP߉oszrZzmEmG g}֔D=TJ-C˖aj)OYT'=jqJɼKݳttQ$6A-Ћ*t|@˴؇d.04n:Qߡu X W@ L( T k{?qMr[/*W-K7mzUXӨnmPK `Z^3< |{7fc2[KLGj .Ύ^&ܻAߤ23)> [Zл m ֳ XE "Gև{GvuO>/KzO(N4f ЬaFnX̢z1 i  jl [eo{^e9ah[뮷֩S^U=7D>~SEwKpY0GRjabs1k.溺Lw[ pN@XŊ-QFQWTRdĨJ()ۍ3jRxCK23GJka42fe  ,o>(`H`Sh A[(!w%(+̘ʀ+vIfI cKȱ[.#T0 ݜ]W?*6̉Q6#nYʄL /йjŕ4gL[,Pu@Q_6y|'7O"f}= Za;UOm[xSG3Y/޻6̇Ss;^ȵۓ^8?|d}uW|316܌frNޗM@2ٴ/,n.n9[?jnGKfG&?b3 ;dGfyوr%,q̺ЋN}TkP?x:VRȦFۑM;/~ecy1b[hߵﺵ},*W=M݋F(čݟMl_1n6vMgyrNVy( 9M,^Tp}Did k˔P)C6n 1BTT7AoГp|Ԁד={/V\;8<~޼!e/8WuYypn:^zÃ\׸ZO޽'[+7;ut柑Qu3TvPN^khpK8e %xy.J2ngrTB98`d[fp6 A0^b|m%M;4T{6# "qqSC̭"rw}s&/LQ*ifC\F!Idmx2Ŵd>L1m)՘q4ըD m1@Q^ aƌ$4:F<" Yp K8V m"CvL#)ђ6SKnAΰȡKJ#舄 S ʑE#5XcʢD:!ȔtdJN0 Lz?>JV.\yo!|]-^Jk<%;|I Skãٜxӟl: ;zҘqn4|̥;ʦ6n(Q >B1l'0:K鳋@ejڒP;v%pT*űގ'+XޥL+n>gLvVBI@kk!82t(M7tݞW.^[Rf_cOC(Yu!}`~;Zy֟c5Zx|=9O,P[vFq"\-)_Mi/ZF:\Sy~>F靋LkRXGIV|vӤ5IE?Kk/f;T53 MB!|Wcuwԭ7~k M0SPqշ 0SP1p~JmjȰ* 7ѱ>LUTe9X$&еm|}PvdIॄs%r=m/$H;JڔEdTWP@C49*Wh#qzi \cYDN>z9e4%) ,TYKI_(61@ҐT'dU;id<嶸iL/dAթg4U:(ߧ zJM)F M{2?l),:mfa)d}3/]ftu8Eѽ9\*@{"<+~8&`\}E=ӺlDJcL@ ˢRtE?kFIXFvYA6ta܌$e/v)1a"x06XfS^ÛA: F?gvn7.Gόy||r{q-B1r+ !+&B37'^=0~N[YߚdJ=>d|r;ԋ /oK;3v YS ($kul~ZҡxX}/l==KCX:Bӿ2Ejz}ry0!S&+#Տy 98Я.…+dW˝۸ n ;@#xξg(W$bPqG,KxfJgH446~Ch^=&$WILÜ{zLu%jҽ͔t>'Qo^[. ﻥɓfd޶q7>\ֻ.ï{ >F^V)q <8P/Aě-N|`~I)C Cþ'|jY6 ,.Xy~k¥8nl7~D';%#]:epӗó ޽{/ȱMZTx2z% SLb?<)eOf8$ $Tvxd K^^k_(Iv(:|^ |&"bҙoe曚o' uST1Δ\o~hۣ;idF3mLէk=N&q<\X\FHi[߰^{me;<'TX(#97~ HۣsClIkWK KIL榏1,5Npv\UnQIU;Į|v4q? U;-:jz4Hw/;̻Ǧwƞ|*fs*+#ޡTG;!CxҷWzn׺72{R`"ZqO=~*{Ro3CG}ŕ>Ÿʧ {H ԗ*};OW:@Ca˼g[7VвƦWƏ>+~w+[7~uZzk:PJp:<zDdTg:\p21qc)|qf>[uk/> 4m>_<}:s*scG}Z }vS_X Z[}ZS;qgwLYwj<)a+ IDAT7]viRyx|P21uI!ǠfP9O_)1!S ]Jt p݊U{1uļ/\ǜspu$yD.G Xio u 7`ulj"T!ٌ];u@:`!㮅ܘ QT:F#ʾDtc t^iD<aStMnzFMF![h3gܶP;褂C9ᢕ@ޓmj2Q oL*ddHô""bGeFykk1!u &_hLr|Xg<7=zML!] *ɋ+gs S<0X !i^ WO5^_pʧTt\2 DݘٴUu\̝,ni[ StòTԵ"&t'\ܻc`kC>+&GSe7UƒFR:g*,}99;]t6U/2s QL*PMeufz|H%VnѻDŌKEB`dɯ|de<(_72E<]G|!Cm/MoV\Mxo|bSك;O_d [oD!WBnޕזMQM\ľV<J[Qo=x wI^,~zQ`Ja1<.nP !##}/ X寀[6:[{;u̴-4m)uM{CcM18ΤETӑT ܴCo4EiP?2#BN鳑 %(3 5{)c.Cc8D4VbtPYm0f2;q7ylr0ڎa|c_Y$L<܅dW(BHFG[ }o|+riBWbs%h Etv8@ Mt[Kr2m+PqAfZ5 ,#964ɍ(lCjbXqaC!!lqq!BǐMCzf`Hۚm3Ddq$JXb;rN:l)N5fc+N[pDtZ\k't<9~J/ |pV>Zvj~ML_+<u&N(l@BVUG ǣ4Njfbb*_X޸v+#2wU㱽'k%.eO7̤V֣Yڤ3މLPEJE$}U\v{~ ŷQ9?X{P- oi_ fh6WȌb>hϣ9R'etTξo^nv1d3d־l~iA5 -kʺŧ[c}*5+rJoZ}?!SO٣1B\QZD@ `]ukgHҟ|׭(Fa/W݂v ąa27?2^>^roԚՒ4/X&;Ξ[:!Tx nH ٝa=eFt Iǩqn䰼p8]V*#FGG0Z a3;cn\sQZ9ʤKPhYLo#(/Of3crK*r\#6ɦnak!0n1G=¹|D,j(_ZDC? ILmbq,YB/dV|l1׍x!\ktR*lgRon0+֦ҜaF%#+j&U#Ph~Ͽş~N1`ẽýw(xۛr߇F)E }ul'qգgo;l~G[~_o>^zKotƜIHsɧlNP\~=}M,/,_U?f}tjq&+\qg>KWosul1SҴ,3-$&?œ?nMcԠNlD9D3}Ґ4`DH[JZgU~oa آH[{KNI\)rJM)0s^D@!G&mI?QxfLL@X2vmiVnp90,L)H ; A;j p99ɫ"? .yѯvœ"qS`dZZ<{%Q!4(!P %1 ) qfYĔQǘSK*Gn_љSOh׹sUFhϨۥ4|(V_"j8c?C7q؛ѬgMBWNN{k*\w9GMsIӐ>`w_)xRꕫuk]}2/``62w}N2rqnnKpv;.lbg ?6,.}SDYfDCS6y*.QV!%0#lcDWY z!ODoċF,<_[C6?I`)t3?c>C]fn<;vޮM2#9jFD!<١>;Rj% ;&m[i*G!WٰjUnh+MLO1;ekZ"#drad{d2E4GQ2e\ovХRU4B$65E>0T&u[+SJ2OST ̘5wH3LJ&]/%R1v' {FYC֍.UiHC:#U<[ В$STI g#:Il{4#\2#{|`&PeQqQ\#fKI3̢k#)] _\K۱^9KnP\\41/0{]S z@Y^}lCw3Z%CjFnƓ%fSeQ:dCf+:ujVͽ{z_VR w@EN0GPC,TΚ%5(5m[Za\fT$ kןjꣾ۾jm䭳:acWMy~OJU[ΕTٿb~Vș+Mi_vsY'U?4rm&k5,]ov !m$!rr:(&R8yIs2,c]HaF.ɤI)$U}m< {P]lRph{o_6Kş7ϿTV~f93/DW|)kʰ ED({fkk@AܤpP)sgRF'jY`j[-ì(H1m0Pz [dvC *8g @|[cO~hqۺy\FF*Ui8{jN'z򣎿sS:ЃP+[/NgsyO:knlx*օ`٪?xzirF[6?h:_>|n{m[Ω[O o'G*L,qR91 Qؙ!S' Uפ:S"#5)123TncT:\3>鴁SrN }0~rGY͘\̨< WGRXy.x~y&A% e:><% <ᵃ-yݙh_v?|3ncO?wH L;&Jٟmfwv҆jN b _^> RXf}v쇏Fԧl}6[յƨ?@anvK~鿘mZƑ5tҦU6Z sDACߘDR챀iF C8'7cJwtu|Y=VyH8+{kGZ|LPf3}Z)_{F`?-d֌G15%[}뿺Oxo8jJ [66_\x޺=,Up@25ef(\Xv Wiyl]gsj{ `YBdv'Dč358Ф;Jdu7It ; ⠀M c,dɲlOoݡuk:ٻ8udY@o7TթSvݿeAdꉟTΉ$/[ɜHͱR#$͆_7?9,R`XG"t}^3@M=m26X|?#En\|ow=Ưm&/mE< <[o蛌E{^WV9lr&0<=ޭT#Q܏;bFW{I5:+^8>Vw:tLglpĽ֪oe>''?>w~S)z `2. |הKۣqnMI,s;Bߏ\9PBԨ=A>\o!@h\1!nWHM}4EɾY3W/LV+} nEăE IAX~v@Q@ڕzaHӺ@=GaHJJQBm'h׸r8]ݯ"DL3Xg̢K8̄;1wrie'oRI:ef#RORm9Ub&JAX*lJHS1 3.L*C GCJA1>ic D(2GD&c4!1d50 vfT&pcƄS)-Fi9dI Ru+Jq:tfW̞En1]lY4-NfH t@Cbے(zeUʜ'~)l;/|L2p L I_z$W?̦@0lrb'{{k*s7 x1&O%Na7J^_$FWRf BR=Q5dZPr Wt&]Τ ,٤Z(A&Mn:DC!t'XWRM_` ūhx Ϗ.Fy{cɏ ng1aPR3-=.tzRea`CoTM꠵oZ.n^ 'WK#SKb)u-?c zI9c(+TIP8InMG]sk(]Nv>SE)+#Y,.~_ nQ^yOhx&VyhQ%6'`! ]~||[L7!i7>7R,g*ed6_<*.V<˟zOCOuȿor54y w~~}<F?|˟;'rSr֦숯$1 Jحڨq>M(:딊.!HcXanQٹqNU19t X!؛26*n4.XG?1")+Os3-H]c;XZzi$:O1Aأ`V9_#6NLCA[ ) 0*>(f  N3a܏A mCўriH^Ibp%В!UaA1=Z}"sҨaiqS՚(ѦGIJUhab_q䛜,%Q$ =+T#?3AM Pҳ &J餘$h愱0MjF}XiZ=> ;#?m~ݯu3q4{hm ^pyha~APz\b_}w´Q\*4W gיּ/u3^#'>rWRRNYK/n˫#o]nbm.\^r$ F󭊌oݎs{}0ͳz7nLI)9MC(k]v3%dB<'lx!OС,.1 ߁әP˞0 \VN퐞]..bX_|QrAH6 7Ĕ63![WH-I fvq.hڄQQ,Of„eP[.j  \Z~F71rTcZGYOo4§]T WdFI&j:fl#VfY+!'DT}8fJUY$!)^g葍%.P KcECh)پ :Oz}:>AYkaɾ^z#̌dXED)ϨNQj7̔Y$3=SESI' c-k#ȲArdAЦzy>UՠzK?S?8_ӑqBEΩy}OX3ɑZEAE^8|٤ж(dcԊ "fsuB2Qy=UY,Ie&-6Ȩal/qN2N(V R/`O ;=TsqT+2ZbH.H1NRnU"b!].N07pұG#v^jՎ+~t4!cڊڕ#uhecE-9eYh&O*[3`ũL)t}b˶=),sc("m]G%(9J2 5B1b%qV'^';+r6Ca6p0O?Q$S9#>q3 44e?*Wz#4΢~s{kZUq'd ovӣBj4ypϔ!Lmw?-֌Le*qP3:a3N4[E4MFz+#hJÏ3 b2NxŻoz<2,Yٍ78YG }s|w8o~G;igմ\\sRg띘KcԏTjwfsr ]!Y0reD̠`*%ڙٕ^֮YJCCO2ד,627B[ѓBlj)opyu{쏟iq!id♵u\ݶ~Uky`~1Q}Pwe&՝?O$]Cޗo*C ̈~ᒺ߰}GZ%{4󓐃|Vk ,S|G,_mz?}G4]kR"ɥn,A -p#W=Sx6rAóR*nE'4JoH4JoKLL, aH4Lt9ybʥ"xD!Qޝt[&t h7%mv\_bcwQ{e[Н379hdXc_t=t-6:fČ֣vȃ;)>u , 6oq˓ZmC+|S74as&] K2b(9]/5%]բ5|P-RFq!guR"|A%1x! AݩL.FC} čuFR^Ga,6}NVQZ7UˎBXLPN Y)sTQz=uĮ3;3+%f0aG BCr`!e&1,KPGSi1~m!$c#+Ͽq?702>$n8ʯC da <7:d|3q0椒O٤fRŃ}tFe^<>8_R&W=juo^T$e !Hf-GZfV&Yv S4Ua8v]J&$9 $J#,ec[R4uJfGdsЛ =4f7=ˑUc.ML>Jmh?,/={:uJ˺_- bIl};p7F0#mLVNQZdhGEif\C؅"p[N88q).Y庛-R]0Ռf`SMTJ*p3:hGȝU.)qyFA9-ZC//_;44[|ӯ*R}R|zJo&sc>^UD.}~}<[ާ][>s~T/̀<3/{AW-gS>VTf>OYvy?g5[@Ga|׽Y=>gUf+w0ǧغ9k.HIcGOIfXMJB9#) s#<0oHʽ.@O.PQJgf ,G &l\:%QQfaDu;T+ 4IiJOG1nQEV4x #x湓 һ8RD܌ElP*x*ajQZQ D( eœĬ8Ȉc{<;'m⹂Is/mRűZrB6ZXŝPĨYS1|\9 wT 'aĊrX6lPE&$Gml-e94HaCv'/:qLb{i+_^)jƟpRv)UV9ɒ>p; YHxI$#ik"af:#+DsTjTZ5b2a)`0VMGa%7<` $$i)ܑPH7P&90֛Of*ɏ* /=\9k3<_7L?|osXgMU/E0&szEh^{dd^w Jmi  6躢Q_$Q&MCd(A3/^EWadcTPƘ_`ZHd< ØY iWp˰Jub !t$=&jBQ<5K*]X{&ly狗;K%Rb[ 0ts >,y(Dh / Ul-]{Љ5F9tg5fT2-S53Be6RQNt}.}"+%/2\}^phi<2}Y2:?sKbGI*|QScBEA*!TjCO_Az.TZg{sw߱~k z|sv2>يG>ė]O窼B"LV0-gur>buOo6rr\o-L@m.4yQ{zŏ$ni-Y_ov ~Thi ! ڳfT :5}׼䔂$|S-V gX3T'RDz(eC´Gb+BBiZKf>d;['̨MI--._wZ;C?eeǘnзtE܅FyKbZS곸b@ob{"]qe9ꉘSoF>Ćm2+ bte`agW!bq2 -E`hw=FʶXHz:Ql]#acp{\JqmRK,ebg+~.ZtӶcm b>nTU9h'sP3!TWfʜ;Ҏj&0j~jY eMGqI븱qѽ4 ny̙Z.E* E 02+-GG B*=Mq9XDӚj&\BvOyzЪRXӄ!GdRq=0s#WZ%+[z:8@VMk\.Fq/;?|*u0mIUTԢYxjWM?Hh']G"q~,2ZSE#$vDÌVK(=a2Ru"nA4:&WBjG2gZR(-6*zTAޱŽo՗ӝxڟt( ;~o]ܦ0C܂;%]GO⸛lGzv*߽=f'+[v0a-Lˣ4nԲD d2"5i1gͽxB+M>'_shLq nM 30Z LC(Z `vCv_aKhـ&Xx^Y_rk3XY_ͼ8zǙڇS|R|_ε_+lxc?=>5?k%LxyXf:w֪cr㓗xM ]zj6=i#_twZ.뻞a̿`jnz)yaوCJxOKh+i)nժm\+R*Sq|RI/ ]_Є!]oYbiO(O^S]Sȹ$2,&vbذRQ2GրZ=Clm?Nj-<3%/`+jMg:$]q>3@\ԹDupCoxQxˤ#6gek+EU9A2Xgѻ8ȭC&:M{]ZbJwmm658? 7w=6 $.hGB Ҕan-粤jĶt):]%칛xA8H$ } &L:ŎqYŶ|VZ?`+ەdk(+l%*(8b-+=.(Z#:X-uUh ,/LI$c)2Dh ui뺖&!?6* |9nbc#IlS)ʞ*_`JhBd\o }8^K^HkC\;x Kt%g/o+i]W{o?vQ}pFIYg´S38†n7ޞ_GF1:#e˜FV+讑!M[;uP]z3Tf<N7^gef*]0IL0IRch bLT_%R*~`,ydRFM9 T DzH%kTjġ3?SfAg4* 'S.rdn9Bq7m_ڜkc;"3mKO1#*7,32#t̏5 פE 0UحH_ dtoͷiuɼH,G!PbQWooxL[|gFOJ;{S`)Z@J<&9X>tzBik ښWˣLo{'n,Z7UOν-蟰o>e4? OrwG\^g@^_|ۀPc7y瑺?z4}gzL2~ VW(u=h,X^ Wy\ 88l. [y==yu-5\ {wo{w/F1ۿmdQ'`xzITKBnMEM_6#)]ZBMrcTTب9c0|ӝ FRLֺv:/4o~saFE3Z0`ƹ./0қ2Y8:w &Ǖ9* !ْ_};j; ^"^-F"3b΂NSP:CHz[$c'6W 'g(Ԇ>.Q N7 >`;ީTZ#4B=Grabc8eM'b/];2sELa[eצ/5;2JXZ-J*qDjB]刑3/8HnrˆkxIKOG8sYTd[ >DgMlv6dlP驝R*Yh&]rxY6A ˅Q0LQ:n31LCl4DiUB݋Ċhp)@H""`'C3Mو) RB1 f @Shߍ%Ʃ@% Z:Lc IDAT);ղN/-̢ͬ^;~i%]gX` TV{xvơ:tg6xPܬgit 0BԫVU6Knh: ah<,be ;1 ~ٗO2Vfjej8XdYH0| fZ $',ͤm7$2ǜ>bRnf呹631#"cv횚ǷDn:cqXx\ B22iiы!(b$#m*½`cI[xu E+X>ft~m E|{ gk@EQO,}Տ=9X8t{78ܭ ԜO'Z|I1 aLꔳqݔܐԻz01N a~d-(彲1(!V1 G>J8R.n1oj$Dd6W⋂pymښ`TqϤ_ ]$6#1 #5v(p>Kl\8FkĢr`9]D[\Rm-"G2{Qaꨅ[oZx}._يC VRqЈq>с$-vR++bFŴXq%r]8CtmBtNW1E z$q1&Cc:IaIJVIژw)JzmV'{z0AJ.Zr%tsŎq^A٩c,$((mױM%ISݴWXzţ84/CM2tӁQ4e" iɩ$M4Cq@ LГJi jrK[I%TzsC官/ *A~chPyXzMwUnc{% 8gOۃѥ/_b,6jUƥ6Mdo/۷nzuCK[n!U'Qq3I]&[{^qʣ([avjH?w4hw6iwBNC*MlJ$5`ZZT!E'W"&10`mARA\tje^8!YOݼ|LD+X(6ウ 6c*%ٶX޽vF6JW'_SifíZZ{ L,Ds|ʼn,ZK)?AKfzD'K/t~}Ϭg]v@r4: w֧Jmy)ïUkg]oX7kg|'fìVH+nQʀ,yڊ ,~ST_sY\8H?]!<>{gd# VOiv  =C^֒/>{ /./ᠳ&/gbu\cs Fi WGn6xظiˇ;xf˹ߎK-7cdܮj_  Aegzf0 !H iV\RNk$Sg)pE#X{Jq5~~7N]grʫ LIwOrzw;Nio~KI)֏NsÁA/6 mg?Gg1w}ކWt6?A¹Y Fyק~|9wA1/`&;v)|FnY`<>s#J1ͥ ,`_I]y,1$@C1գc6J8tD&,6,2~Ugs=pjE~Nq8# lX'vݡ$1F$CүpUER}1)Ba\E =m&yfA1j ˳UovtN i]4"e7և3Kmxf<#}ei*)4U\0WF& n%R,a.* ':ZFj`L9:WDNp7-O{uu-YkUqHIϓWysK'KF/om'n /7+%wڸ˽ȵN,Dr=cj*ȸK;BotKc%WGC>싛w8NM޾ \e.U׳ru0 46L'/옖kw8n F[Lo_M^jaiZE#u>AP)8f=?`o0NМ[d8 }Ybz+kȡTJi{!i0R܊kG¢7C#.Do6D 7QNT0S&nꢡ}(A2_x_Xĭ88?~FrVu n|gTOt~w4;'Ɇr?u#?{:GŏB'/e ՐiX*'+v7=:y yk?}Ϧ[ssv^O]{W/%uŴM8l=;W o\P xyzm(Zn~/k?%oџ95dlj]M*Òވ89j*#\7y#Nj> |X`$@ 5]xMijxX)*T6ͪ.r9Czzn55z5J fĻRrYw;tԧ7Zc̓,3,w5^Y{/ $ E@ȃ4i#3=v=v3v# tc۸G`&!^Zjͪ3߾s換* p;Ew#߽o=|~w9Nx򱅌SW@f0c%o4qp-%0o#Y{b\0E  kQ6NL0 K ]xk ޕRaEhvҼDbyXZߋ#k^_<2 8߭niB k#btM&8bX&n.aT=s@y&ړ꧱EMTv(E^Va:boZ^ P^ "bPe0O 0Y(/P A/T5`a8D qhGN;dz 3l[3u h&i)\D}p.L DwȤ)_nAvlMg]1"i,B}{ paزCMg(RgpT/bZA׽/ c Z61{| _HTDP(E& `BH2`,RnZ (cd 9ؑ;:r-\l@q](\jO'DBDRI{г r|x?V=9<;=]%r9=ߟig TOб~[vJH) X@prY&V+ӰlROtsٞUzu8,\#A%LqhG.}Su*b$JtL]Ð]s\maQ1xdg๜A&Y/(RXna!R*h:%QkFMer:0"3I`1Clʱ^͎T+r8'%}f7 }$f7 wy†:@2pe7RRbM,|9o,s=|W}b`/~۵t}zNG7rhά\w!Ϙg+/~H}/[[_WMJ,Ν^^3?D0yqO|sz|+eO=C{Y%_T`0+9'-oK؛^|[1 :FwWO#aLڂ>,J<]w৪АOd}>OE^GB.yC ;,eNэCW/gO<;JOMҧ1C$^ӛHto-{%rkBVD˗V _ ɲ N9 ` *̀Q* 3D (-UaTD1/D 2r',Cb TFIbnq gaQcm`e(֠ʰ҃0l#ؿV،_pW5`3j]kSx:ȸtHBVQ/bJ/cbgK "\L PgG 6E8șABRdEQU2jSY&2xPUt&F!2= M3I =9x"C1P[JWS2V'V `mY m4e'cRDLJG=5uƮ8Lh<[ôu1>Vցj[bqMMJf4EOh}(&rT hAP HRxЁD>$,bPSAw DcbA 'if0M qh&@I \$tsܕ]IwC_Ș:{cԐoM;%4͝1Ogŏ=y~ry&l\hȟ|Om颴N=no%SF5'ge<ܫАִdA¨>5wue#_x h~Ƌ5/l61@)~|y֏ 0uPFQȞ]]!7qncc 4d2r3IJ4tc-tPأ5`!IcZ\݃m0UP+arF~{#fSZO CHDEJ%R8J jn`ŽvXlP}Žb񩈲L&R'4K+ɈaZ\m?V:]1j__*>qt-9gn"_+'_ğn=PMřB+^>]}Ed'ϫ+乳%̹+pW?6.Ϙ>[x{>va_F!EeXZ8>]_66э+c®_׼.s߃F/oRAdIJt)qw>euXV,=Uރ]]NF!wC9 ~pv 8on]7:r{1~ѮZK?gs>JϜX˗][d8hڮfk1# g-hu i y]aΏE Txދ<@8MDA  1RH N"k!1tX~T‘r35l71p`<1[XO԰=13>xt Ypm_J~O Cl\B1y夅x}gߖt[Uא8՗8y& Dq΁b"f XBpÀ|I]f4hа9-BuP-9?D^Duޅ.av˓>i3H+s^FڔQ UЕMDV`9ZTCe +JHZXЈ^$OP=AOԬnT^LDYh*3et|p"B'fvfV%qL#f9:5*1$"B0l@̆rh}PPLF! 0fi"#$S%G[KtYJ 9Ⱦl쪸o,N1o,/A o__G>0w]?mc`?'׿1_@I}[Ϻ_PJ_/m-\'g^ȚWTآs"9}^>&x5zw0Z~NgX1VDᮏ־ }/]^Wm+m65WȉW?bm,OLa6o;3wcn (ݓo{@v%1 IDAT[;{l7^H6#“z4dm#Xt(3KUQkQbcr諊E@QuT@CN S@&M!2$ QD#t-B7B2cd;n(TQJ8UA](4r"To)V2܁~kp4Fa"ĩvv+=H.&^X:фO[*u[ćD*ErQaXQ@B܃~5.¸L7P sN>=Vadq0]XobP2 )6 jJA ׂ1D[#$WQ>]"  n2\نߦC,Mr[Di+7Iu:TC$ $`G4^ifm6[OPV4m$fIvjZ+ڑD*N bza 4iJC "d43FXUWt%#)*+@RO$DR˻5Qyy U 2pˍ/#Mwa_xoZ?ߔ7`by5\Q]=u'[^qjË\llҜW-H(-Nlc :'k_#`U{D2L2m: ǙlG"(z—6ST4#Tt9 EGPq;zږz1}y`FFg 5{+3u*yJ2鍋Z y3"ܠ=e` < -+B*(kL$ЈD,ĵX@eLϡ9y3m+CӔ83wuyMZ]?سOwWdǣ;_ט͓޽ ַap|=@tH z"}:~,_ [PkGx_\$V fTh}?uf,0-Q|g_A*!dKww,TL?ȍp@t&VauthA))p|aGy}- +<%6]WPŵWW͔4v-)  m_> _h[Jk⧯u׍;kQ*U2 xa|W C&, ASE?wRC80AX C + B3hi$r4$*Ñ1 m f0|.CA(3);@ke~¹RNh251r,E67%\氫"SADH[$ٴ{2|"Abi$AfmsdB*Q1\U$Ӆ-S*`xmߗ$⤛a0Ala"m=8 TJ*anF5@M :LDrqQPQ  */'KⅻNm I4.y&<<;܀ޢ:ϔcEa;=;S5ј^]iGrQbquቹ8G @F5͔ v_=vqQ8Aٳ|Ԥ _j.ٕ{j'6-0$R1 }W!DZ[ݬyT Qgy807(+MQ-j3-PF%L16ZjCGfAF8-&IJ֥Σ@5ۣ"Dp߃^N%kG?w~#57cDJ8~G-xn@`@ EOs~r00|錃>{8_?;.x=?'dODH[H_3]no|?-廴;<]KڹcwQ|_wKfx9sWawcpLpx?YВ!C>yN#ߙ~V_2Ӵe8QzciZg< #ڗmDى]Sen!A,qPJ c nRI(pP0Hw8F+0/ %v 0;La.NAD"5 sn O% I`TCn,^f<ݕv[!:4Ves 6+, \: 94q\v.6BXWGH@G $IS#z4m*DSn & @) qZ)!<` DG&Ee*k:i2@ҩ.J=D|x"F2 M-}W2.׌1#Md֜-YE IJg&U> R:Y95fVCK2ye6"j4\hh갳2`TFDOxۀReR\2J8T3 !#J!,Xd:nDWxYM.%v,Z5iڟC߾taWFER>~H9qWfm?pp4o|! D )&=m )B`!cdV6nz QPCD U&Yv7(IP:&} LX,7]4 ET W`"=3[昸(TC 3-llB9.m|a׆6%G`URhZZLUI(9Firȓ RS!!QB2U¼bk*!0{4H*0tD3liA%<;Iׯ2dplxia" ٳd0iD`8D,b-iHD줶 muH{d,iԘ4=Y".fDS hbUQtq2YB&aLEjɤEc- I“41Ɋ5/4 VJ%Ce&$M2@JUvFR 4@ #@fW!9@tp91!_Ownkbj˛?j~?ր-+!r{N%ron$Te4aOM8vbtMD#`,-7[J6ؙF+ɤJv0Q<.E((?JmNi,tk$YZےàf&XHh>{n4iJ NVjYSYHzw+fŁ vyJ􂾹 {nmCI,ZtKˇWӏ&{Vw/ؕu+W>?t#G;_6_|{~PotG~wNyd&= u_qt[+b?vcZ)|7?"Y;s@j"X^y,g<G_.N+++a!+EI2d<2 IH6mgvs0Dy=|: _ zaG@co3ijdP|o)oQկ^ds;3Kʽ6\slD?tHI3# Ӽ@BhȲ/%$yYh V//mv~r,wb .=?i ĠbLh a AA !<=+D\ 6B⚨Xf0KmlS6% z ތAXrneE p{^TMxXU@tj}m,ሂ)]A-&7uKlWCF-l`- !9fՈiAW3 $v )ZlFJl;54C{O@`#SzJxl+Y0ë&tJ hcf E]3iP p J,s6\6iѺ{Ӟ*ks#ʩ4Fjerɐ:R[CTH$aqPbA8m#3$FS')aqr HzqCUQj4i2IDhWJiZ0˔8䴦9̑j,MiDH!aI W rehH)H 9 wFA^ʒYA7SPVE gp.UNͣs`?K-u{Q~lؙDx,)%8`G{lMC7 JD( }zq&ARώo>~r>M[%FW}7Qgg0O.is%oxp~pB%YLh  퓧NH Mf'qkTifRLט:LkB EٰZ2 *agpV eJں Go2ʮe{Q8Ta[xE52&kVp 0̥%=i[‘i1 e9LCB:R)"S!+d6af'R-P9Da%䔅x4èR:u@+z`[hò#I2tz4s$]Cץ8r` \u7e7[beo97yMoKE|OW|H~W{sPEÞ 7t>2WËm8Ocw=TA}zYm\2Iڮhbi~b3*ֻzȧ7Nsm CsR]TpB+^=Wܲ ;\^^|9~>X8>œ:tc+U<w8+ݤoغL'ϥ֞HZ!R (V]쿂1X &X@HDpU5 D b紉y֥0>G#*@gaqG)'=$ }oOuέdVFCdY/%M wࡅW!r+I8> 15-T D".{p~`b錀eTKB5YP,]Q&XFBZ96\r$ob{ȨF|䲤l(HdR5*q&JW=04f-i'V2 ,*6)&ѥX tv"j=)^DIЗJ̰1 oʔB]ŖP^31WCƞ2u$5QT@FKdCxIdEaBjH Kwxg.xB pƩ`A~991{1 cϫѸyy}BAw)V?k4*"ɚVn.~c~io=J鵩!)!W-7 t-O~?xChˍt(? @2.Ku~uQM8dZ-uw%f-vθ{O\?|yտ>yþ~6ZX>Gmn>͓ӳ`u3r*^uK-[/sOM)gyTO;v٢N>K>|9P\W&_zo#?x|4gN\.,ԣhyCW;?~|o\s6*Z\*Ot_;3{9,{or}^$rҡrN aY<\M?{K$n[\$4<;0d?\:"1GT @ځ"r @!l ܴD : ƈ輏+QAYS@bhm%pdbwq 1^1y\b(f`11il!J4ZWQ ~0Dm'qr)J+"a0 E8Z$n" h.s#8d̀r 2 1`cf Kgtlg p4l{D IDATf 4h&: bq0()aAF L;iXS%7G"vumFDd LkEd 5PelQ<>c2:EΫ%А aIR)J4D0F z=WjDH=N:~%#3ƧX0=7@)X#]aV ʬ݆|{ ^Ə%T`Tؖ }@jru@M!τ hkZf0zQ$DAL.MJD7 YдRGCoNg?U24 QJɥ0bi6,$m;ゞ]QtGAͅRY^=,HȔ#?qao3Mgb4Qp›+V 0iɵQuL Pp[P+y)(蚩T6kux0vVeN˖N0h`")& ܕ%{Q[ UNʼ˸4 $ J(Eh) ``yeMoJbel?|`Nұ-&{J w?{YA~s]GJi& Jg{{V9d›MսESfyA`MI4o<#:F֮#_x$υr "~o4/+ϝ_tyH!ow?˟<\GO^zJunaGelX5) qm!}x`'r0tyj7Cir}YC+ stR9yV:ͯ e@HS"-Q| BfZTv#T A UDA1C 2@S=nÍRD + 3u$Bn! a‰CEQ NMT"uh%35,Ñ4!m\lѿbnp#ZO}𰂪{ VFFWlz#X}ZA&"O5NOw&s! M@ "y19( a`-V8&Ŗ*AW8(>!`Iu) C14~DM[Fl,cMiLZ9 UA]&{>YDz`@U%hJdw8r4*.]00B5%ij6FQD >ƀAjRDX<<,C,@KJsXH8b ^r;xثoo=^K|>KX=۰-|X\P.#I(k6R)nTZ[%eMf C(Ilg +=/jmiYsT,,󼨒Ǔ3-"ќre xeQFb>lsN'Z[miG^O^]E.^j-Y֝ݾ{gt˝eWgdѹ[gQ;{Ss'?鯎t'?,v~iL5Jn-}w*O?[_0/ww.ڗw&S?/hq9#n 3'2ˏv:#N>D~㕟[{^%>??~WZ_XX%Xlw'~to?zuW~way~㡡+cKOO:ٿCٱ b;cyktNE*>|gew.< Q 9XX p`B^-!.n "yQ[ C,E`  y u lġ5p#uu2q@o1aEOkTg F9 bk}( F j1[.pH0)‹ D_=tÿ0&j?Y<#\bzC{%N#d]䤂(F`~UBRJ]Ptt};őݕ`q=I1%2Ѥ@Kd' c%>xk#6%0Hfȁ2 Ee `l ejۨF 6"SW83!1?hNj2$GtB0cG$CDP{ kME:5\ز. MԊr0np[Bb6W5v%i{Y'j\imU>iLS0fz59vf rw {V2qD@"D2[0AXR$HD⩠y/@N@J@874oK_,W&.-y>xbEM dHKIZRi̲ ́Y^=Mž\4v1$)66Mk,xJa&3.gho<&!*OǙ- 4CP,5vڍ"|܄WZ9˳ETd:FU:OTBXpz-j,ypJ =Q/\ocQcsʡOT{e++/ޑ?=bN 8p@QVYPA1R<[ `{</fk{=z7z[+ϒul&w<~kgߖָVzjͦs/x#]t:^GtVVmՄyCiRѲZ X3HsbEV,STE=y+'i %4P SK@K7/4 3kp \^ $7p 1 P<>NQm q:=!?IPCj 3U2`D%̠L C$@a3AαrY$`q*M0"xE ͰGr"(50[x}U#l349ңgCN \iD  Y*#OР/ p0,̾8 %3 aqdaA=+%0Jx.U(d%FU;+nZJS& ^A9}P). M]qgTۃͤ7rMQ\uuFgn@uxʹ$\ixdrFTTԡt4ԽwL ^n)o"Uz"EmmĔ{ :6.svjPKr3g5p_ i!5;5;Ph9[j%u,1 sֱ> U$Ƴ7M"eJ<MIgƲw{ ]EEO+^_\4@VY~:=I[__pѸm_ʹ"R߽GW_zV>v~Yփނ=GWb2[LISX! Y2}}"k*ݻsxw+l.>u!ɕǢWۣOFX,4ћѧڋͯ}_Ϳ_jk[~oN|e>a(ίV~]AIЎ%o+vz:W;>/w.Q\cJmOZ+N/E*I1*,u3.u78I0=ep%8zo,'ѲUsyh-` Xa@X[[juA (BJL+0b{H'9ZWצXG0J;F_u0;ٞ,[m;Xx TJyQ`mtϠne\lʱ;faJ,(G&y-a7 b$r+r"UC1|j=GdSJT%qA3c=Ɣ $t$HS(P6A؆2;TVIU&aSmdUsaحKC͹O /`s]*]pׂ J7!4*󌒐nv @u:B;`;h]G,>B΄d5Ok-eLC>e1]\ʤE( ?y<ŲDOec4Wo~s^y],ҿ}Wh]>%ϛ%!X>)ha iG"Z]HZVP\Ups}ςuSN"PICP{>ڬѮ2R<0;n;gt4_8E`5jcfin(FokL Eqsgc~pzJfglmDyaN'37)%T)@GֵT|9Z2E&O|uԍ|{uU3fcb+iT Bs>@yܶ^MDu:F)e-ctT՚! I")#GµݠUp3:'9+a;>'Mw08$"fwemߺ;;'yHUV { p~qNOWo~}9.u?;%g/g~rḁLB_9/ ~'9ǺP֌@[Vz ]}#\^-k2_?6?{o_ s JVZ<.7K0=ߣc&0g0*@[ rXaSt J0Y4.+3%)Ԥ>R y zCMp[eZ.y@"郵8yERŝ ,)S@=!`U`  rI+&G=p1p4A  bcfRX[a>8yE#3-9ǭ15YO];1ubNq"hh6CA/\AMxq!|X)MMC9|"]3PFbxF1A@CRyM{!ߖ(bq<)fh% # {DQS2ښDYnX>۰M-Y+媀ikb7d%=MDž;0>ՖbjrJ ̆z$TF$'ť.ʦGCZ"9t:$\k-k]դgvĮmV zq EܶV +a YY[%yb&Ha46OO`-7'%Kb ^B,9rs~t֫oO8_M,[yhsNP,?~ɦ{ʪ?>bIPp8{B- jPhc]$5FQNe'p:3qN#ԗVW\ڝgmL3=/*~x4mvk02g;LVU\ /f/FI.vOZΔ{Wu(h][ DpT%cD HdZBg>oI9 *XXV!h~!77  XPÁ f}. Me!\gvc8ʑ+СttKEb\z ],@_BD5G)g IDAT?]MTkKq0+LpׯJtLa/I` :)|4k//Y,9Ѹ. C\CUG`hEfagr>t`B>L݂/B 5LU- , yaNV·8YĆ:BDЕjwᑪaCfW"1beA>WZnPv֫#O9PD.!M+cؿcdZnmS϶(Hzw M1"(ީ c ͈)DUTy ;hXA7}s⢃@PcA㒲IM-0Nh wKdOM,5p‚b'a4#,t\vX>O& %[s3?~C~s '<& ,55\aitn__0d @xb~`H̹Mɥv1$ xe:v mD>Rshr\m-j`"aɕ!0R2!+Qge`Z:`|ղI]ǣ-<:$X7k͕ɽ驒n6Z/GFW7g6VݕvCghM"׵É3[,ZT|H$A0DAtܐn;oON\z_!R ?Xbv쒌­I*׾ao? JiZ>U̥$a'+Z!F Kp~t # >-#"dj2-FPeҊxR€vEl0K&Vn%04N9'h~YssƸAa}^# r\G3zj)L/Fc&r4v w E"Cx-@֐B%gIFLX'SЁѨɞZ)k;[l< U~B وѩw G% R#2R"8k \92w)F' g҃]u^AJ¡ 7)Ax 見pǢp|õ\ma\>pʮޓIjeH(u($i s \:l,IA"᳼t;Mjfʕp|*b%ui¶\8. # QQB*PxǮNL5  3k}%mX5҄~ktؕ6Hq ݢ|ᣞ8]H:-6N4|k*ʂ/KqEсTB׭κFzRKX:qr ȿril8T_q:k+|1֦v]/Rs't&Bxd1rOJCAuWvb½3*{t:-W?d|g_/ 9DB7O/&5W}B~d{?3鬹AT1˝"0_#armAOT]l^`(/r[=]|vdޖټhnzck myc6N(\eliF[Yd #W-`(SLT@;#T##"SPfEn@$972oS SkۥMZx[ruX6P>Պ{ªv3$Zisi0q1hpp"t\ҶR3N`g#zA*GŇW+v:j:Ez;]׻F?3TfajOP'WV@s[[-[rV: IjBW+R0UAƌ,ڻY05p I%Ƴ9 )-,48Yc,S5`e~N{wr{qw{biQ{/~񫟿]ſ~Ki`1+]i&X~婔=Zi; bNEI"e%iD6,eu\(0;K(Kk+V+ǣ1(ɨңIIp0P4pɸX\e+kݣKۗܤwm>OB|ڪN&B|֊Łcd( ۆ4k~c7ɩl/׍+}GcҽrAw..w'S?;[+R~ep8L >mDd`TQݭݏQk@}#&Z>h ^ "rc2؝q] NktFí=Ū0R"i ?djcE砅xβY'e>B8[W<2pN ,e=KV"a@ ),A< $  eIPq@fhys]X\2xGgO%Zc,R܍aΰ@4A8EhőhKx,0vnZl~T=_!HauK6p>8E7JũG%W/ CT'Ɗl_e0_Sp:ax S8|QmϚ ue=z+74׈. \5Kk $&Z*k<ʀG^k 594DZHlit@%Uni6ިB?]ln8tNNrg/$'- QS:'w{[Lє *r4ٓX.,`h(s[[̾Jhu8%J˚v5x3mulFQTƥ4K+gsjADɢdwpP/ OؖJjR]J =]b vJyR%Մ'cOJ`X O/oc{Rf!,˜e`IE쒫?oC- %FSe4TOA~-! %R[Zi,묮K:vC!FL,-J rMNJ`8M8ZgI=LhʳGGg`:g\7=]5x׎Ju&)hVd";V2IWЂr{2Ͱdef~ Ƅ-TpҐ:Gy0ؾ`X[_y7 $𜃝X;/baa?X=iW@3{ۧ pF)ͪZY @ T {n튥e+%0Z$/6 kGXI`^DA@MNIF©uEZK]*/\~%+I3 Ù?M26Y +}hW!/ruӓJ=cE^p892dW,/7꺖e^ڮhiPxe)ԆB0AUۊFM{sh?E3O&4MT9oȉVZx]n/wD"&Սpt'󻇵 ˢߨ d,һF6;wntՒnSҟuX 5{*-z'bz:Jn /TsTZФǮq.TqҡL5赶JKz.=Ŭw|XUo2`^v:ھH]ҥxp B6!.E9 mO:X, h!\b601_ 0`]/TW_$F,=x##7WCWWjDiaąmÀWڛ+KBl,4DlU5׫7rg"uedRD7{|_ tŸ'Yd9=1dQ },h}daEZÿ6`|zuz5 <ѳ%|q fTi`݀%1ȅC55)*oz PsZ]OVVhGWǐ+ev47@CX0Ƣjx)i`XpoA?UCo wʹ@x/mIQ)@P(L'G<֘(o$z);n@ PLiנQ+ Ws CX:ZCfWUPXa9-Ԃ]-A.~B\આ퀛pV`LF' 9" p8Psm0_35>|·cJPzɹsTF+dyn1+`XiPL q<(YKJɃ2w. eqlZS ³w@ڃpA H=I ~OOa$$+_Vˆ[#q'jaJGOۂk8V0,@A[1`\o-VDu ^mMȿhyʅ &w?λi G؆5lf svz-S悡t ,[m~Ҽeklkk絑FDʶOBx/Bp`C\ee*ͺ:d9FIZ^@25`ϫZ'Nn.%M{rke˳k&;#jv AB(I4ugYR1!X n3x1x,}Qn-fi rʤr֮"г*.KpYkj(xxLUU(vGٲRlz˳;W/uLwFw'~r8vBEX^g?t<ƃYd&.|Ef0:z|8VYP  e?(6 y'O57Oٽ<>WclzZ}yy2K蘼L󦽇!_šם,&(_puZ!ɍv=k־wtk/-AJ99T<4!>h P8'f=}+vwdKg~ȶ6=(;bFAz7wsF!`@ HN ,# M(A2o؋C$b!R144Q>@3>Kj:ǭz.mس5$i>/Ф!xij Ђ6SD qN15Vj 8o W@ or3‰#aYL@i.5AJ*]:Ce IDAT̰!AaaԚΚ:X.0#8 XW@9No@r@0Ƌ57<~;@OKJrۇ,OəȪ&a۸!ҌMsGc* AƄNgntCȧ`OF*i j2&}r^7O_0㵶Ĩ?eǨcʓ+SÚK'91Ex8>v Ό!}d@\[& ؄횴ncl/G\~;_u;~mn--]ۛ+hTĭ#vӽQ)03H#O\u^B r୮()ȃٔ,WuͨqqZ^YO'!$uT}g22ղNGTii/_q)yN2ɭt$zbKĬ߿^U.*9YVʹ5N%{"+xd8\Jo'u6{>Nr4,=OQ͆qket΍^ZSgkʭR`6R~VZ,T^,ۛNU].o AD5bD\nXh0 {]Aڮo6/vK.%jҕ`;M#ywߋX\EM0,Utq>p=׿ڼ^'UIV.lٗi&H*}VUƜ#b<e]G*=iB;H}$k *:.i;McJDžce7T gv=3| x֭]Y0J`bR\(qT x"v֖|LC: :ݒwjwakm]a[ʺ| 8 ?`il)5t+A(j)IHA=V..kd\Biڮz}^y6'ѣ5x3,^u}u tg] Xk~*rEZևvq3l>%(3U{q~y|Z}O`vvO'o"M@KcZ捷pཱྀq,eAf^Gdc@"koeR[w/."f\'Ku[? "遗>`?fQ ăw3o͌S D#$a:@Sg@Cuvî eV LaAG`h)aeC(PVrƶe-Ntk.#\=b%pC`5R P(oJ8JPa$V%@I!) X,,>h3 ?pp8 J89E:}CB\Yf7X3J'OW0wc<ƸPj9y$)ⴆ"2%HQ6 O0Y sO{c (V՛=<®9X!k9>^Z e(QXg1A #FwǤ*q5S`:*s# & 5BPsO06+6``M{M0*#8bc6+pZ்$Gs? k5?=Ȧ^\xأ%%؛Њaey7iMC 3b!%%TH:2 Ƃbe8g^kH:푃4bxqԐW=z;F\}ֻ+.~!o-!oٷT%́ [A Əi w|6 wUMn.:zvgʁ$o!]/ Y"9d`|-iUiޯ_XU4!+c-U ukDK@-o)Ö؊9zDIhk _M282r ok @:z@ĠNYC5FP{hbaѶ-g[ooܙxi?:L~o9<;\r㧯ʨ~4,W|unҩ>یggGnK\;ヂaxTT%P7"x6TeI7Hsd&ng)E-WQ ]w 5ŔxhIpM<%ezcL*+"(؀))A䠠cQ  [uk` H6%[ײ׭ `\pu1D(UWAM|@p;7g94ρ!K+r [)^U n9K(<,qzs (`Q%s0qPAܵ@'i*l%6=x_@Xy2Z-^'0T9BvHe hใ +8v qUf0O0J!T8cPB~ ˵!*9q~P`c\QP&ky9'xүP-SrYFiNa68LטX_#:X܀!NH7# L@tWLmὂ|u`F94l`P1xy0_H!€a-= đJ"w*Ǫf8t-Zh,-i Ypt|$v+?`psG(8$ChpI G ,o[ ^PjA@Y (!48 C(ԏ(_T/"XJ}C)}XS_<ϗae 'boowe|='Gd\t fNa%/޸Iv Ou8w?P:qV:ֽFx6M=QQY 'Ba; r oi0ov8{_[yFc8M#A׹*Hsex* ,8"@nXH_cp,SBPD#gVUܕ-߽[Jh%D 'SIf ʸeZ@&)cRtiIB 8 4] vA7il ou҆WaW%-[vY6M'<{ ;mʱ\iJ(`Q~vh|f~ݫ~ߘ(_6~fo?$؂o+:8-ekI/_ܛNŦ*ԷvuFE&GҨھz AXo6]ރ<xhv pU):e`9sIH'<Ī$I\N)=<$"%mK3;(7֐4ik.))[u]ԍe1gӋD)fI]pzl?_<21JV R0D [ Jq|y$ìM9.4ޙѮjV?L|< j)_8:xi-YD7hۄRu˧~_UuȞ^4O?zhŹ?n ӫ72{bBͦ^lZˋ'zSף,]wǕR;WO~/Fb-9TFd?.8YwCh:$)'1ESxb'#hsQ*=8]  v#@gzO(Xg<H)C`$ytݏY>>>b\OC˕"*{;:Ϻ?}?$W׼n~>֫1 ɔa@J*lKAИYdBcڧaE7x&cYSU1:#hcl  `@c <6>@1Qh.x1LXC]mGzAbs8XJ8t*w69N,̑QP <ġ8SIJ?HsiA9eEїe28(82ɡ'YB  dA8>K#zV{JtP=q`\fln4wcwv_Wc_XmOmҼo^~am2Q^8wGf|?Om8\6у?;?u}?"oI(Z=PWկտ?A٣և&Wؖ[?]3F"g(}b}' JRWλVde$-g-x;x-3~7 IDAT5qM;=m Lr v@t9휬RO|,m\GlCRt4i4McZrx )jm, $ GTp"!qUkԴ(b@p6IQYk$SJB "!, tY"ڻ^"ma 1w #sV)B:cD4!8F"u΀34}|@Hƒ4>U|1Bomo"!ʍ/f;#ǵnlg2 =ۗ{~t8G7y:&G$ڣ$78ТǏ`K)Y㎮>MGiTq!_McXܡ'rYG&v*9 <4'c$cGc|1=9@8_((血{4%%Xpf֌ 2 c O)ȣk,C@D ={xXa0 Q2`z$rߖ3:|e@ #I28!"~]f =zt&(#du 9NGSJa^!he$FB* CכBc$෋c.栩 $H 4`Y 9,) 9MK 3ІxQ)!O`h;$yiM THY 00_VPArKB4P+&lC/*EpV3+;$bB|x#I KEA@sfΑPLz %(2H'p6-¼*zG,>( wQ|%Yj|OrSqԮz-1k{*D "CdSGIpeKZZltFS(O%AfŸqI_cL`tލ 9  { `@_)` |Lg-*vܣ Ƒ{$ƉdܓKcՏPf'ý'(Jqy8uAUYt*1$H~}K-y?`2=Cl=lScL}Vwq"D 6؂yt@b @!'ȣx;<[-(bx+:Z+RDG:d!" ]tfQ7]ČY׭_-!,M9&E`|J)pm$ƙ,lyʹoM!jZF ~ @P-!ʺ Bm:ޓ-6Eqz*WϩQomՍ3⹌ӽL-öj#=_9w>f }zs݃IDyy6ۯ s~Q/9,_}4z/70:?;Y}O߈EM+,x0Lu}MpU L&r/R寽O*eիh%F)2`Wg"^<)X. 9}C8D  q \ Z7QO@腀 Mr ,hH`)dF(AP K0F@ lN%o+H֫@cd`Xsa5‹;?t8.1OsuA}Ѷ0"#o@5¤ی+W03P R0*y;uK5 Tp t@u3 m@=Y]w;L$u i>JZ^n ~/?`}ˣ~E֌eNF;?Xݜ`tQ[~O_}O5{'ĝfd+9 gv<~Z>x+}o7'⍽zNwyTGYrkp ?7_o~P/|u9~ϛ_.º֬ٝXSs]%υ/^&「G8eUVdEy+\].y.; v H Y` X{\ fqRIbR5bUp. eUUƜn8#dgyOi5I(a: ^i$t 'p!q#`Tغ=g2'"9"#rQЍK*#> dQ6fsv0$fEOehMͭtgGPkeJa73maFd*`jqxUs =ߙN'^'h`W>uIJւzlLtr26HBA|2N =L8H5g-,ƒġ*~` 09r^Ə8/A) h{F(sdQe)j@`Y1?FoY>.QSy`qeavLal|nbE'i ,\hu/ Q5Ѩa05ƍI@%ZF P. ִ̭NJy)NdO'o1A xdd(g?WT<[`5gAnFYC-M-k S}M~fh AH8sbvQ/$D_Zǜ0 l B3ws xfX?p"N OE+[«v\Do?CoYhWҝ~o~N `;* x~~l C5c .aE"iﵺ#]wsEyY^hdtHr}i+ @ bHAc DJjӲntc,xQ\-^H"M|LDA+goۦֆ6U;NSxVZK&dl,l}*vG&o^حcYOw0 |1)Eqx]pO:Z5>{>Y;]))S5`LQDjw\R5qύiubh'F{/P^O<rjKrv 6"bUlk !E$  @Vƶm{yH{8;h lHG|:Gl P/17~dWcA\4L'-/\Qg(D}P{~Vĺ/sgY}1$9 ~+@TSQ8%uF{llv0L+rXRSAtYC͈c30mI z|FjKGfp9qLy#0YP# Ռ)&&=ШU=^ V0Z21b@+T CUh !:6$D`e#03D61)²*pD ȕq3<Vi \#B+`40+ܰmtNN]#d2DPL̶.u77y\ό/R'j8IJvohJk=;-nΛ|6' -zV9+C0+Sƕu_۬ΝwN{7n7r{Řs#_|0:Np$8$tӫM:ǷT ?9Mڱou^* *@O6wVVj.^,_ kw:+Vߜn02//+o *קo~^o76,>Cx.Lk#BsgkCY>֢! s/8֥(2 Yi2Pbm6k0q¯nŤwP wΖ9 ׻Ø &(kAa-ϗK}2_pͨ2*oN7MM}ҺcQ)-S)V;M84ֆ6bΉsFr{in"XM$"q(GYh1!#) ʹ 3 g5v OTՔ:t"rPt{V쭪O\#wR5dؘNH5|ջڹ$.fw$/nh5l##oUE"]Rzvdʨ.f߳C/6<»іv(RǢ @!A[1&xzVd# Vi^RF^I9BF^}E؏4h"<$;@#kf8X2> pF=0a2h,ZYbshϜ#8LrhA%3{ Gi f4> %fAgCe)N(D1P`s)S8ֆQE3Zn`ATxD DZq.2,F-< #n,z)#4 1Ъsۖ squ&36`{>E[(BΟw@U't?U L뀌(jIDP"`43T%rm0F =".*Lf!E@UJs^  dcV15xIaK4pxIdAoq$ܪv|lOwo/ݍfY۵tk2Mnlv?/}̲Lq}VE0pƵox<^?,>:kMdή{)J\M8Ko[T7iH˓(io $ }U.-qcr'y1lBraà>Vʰ 4s<R! !Z^S3z&%D{oaG#xX:u84s^]{Xq612W%)[/i6޿z gsUO(ı f pB n|@d= H +$4GMgugCFi<]ӕL8M'%偾K6 M2cZNXHW{exN) _w9E,Ui#1PO(U`;~!VP*zo=RHrAp c"r5pZ0kmS V8h٩ gE#}<`tXL>(h8YCa,i" E-m 6`]1EH]B틵P s,KFdq)3I݀uB_{ d¸N1~*֭S]fpjQҝ̈́ k7[)65Ѧt)fst{^4as]:a?`D!*[v8;tE' 7؞ZN2+.RNG{z^oXl^ڝfǕOlgxydOWTvVkau?޶߻^̯hYkomnv3v?/{~1:zyQmy~[b =@\U|-ɻL||߾~@}t[N_b̺b<]Y/`Xx!.@Ebݵu9YsCGҁA̯gpY(B8|<[;`߹{wVvtyߛT]t[o guEqc7'OɣӯmP}7^ y{g݃fyۇrȫb Ȁ8)V4[z]@Yfhص}"BXfੌkt-: 〽p)a ֋s1@ $ sHZ{!ԬcB>7/<|]yל[- _8P]w~a$GSx!+  cN fQ $j 0<`Dأ'X.=/>z(1%tȻM) h`α?yN> ɒ\$4F$ʺ8Оή$X>x}0:2aMǝ!Vg%~ħʝlx(> AElYy7mʞLp0au.ݎSs}"\G*<@Ľց{yg^z#MD\ fCP ;09H KtljV1F%b|z~Ic z==W :[BĜG> +/Ѽ؋&`n*l "*X'pMՍ;&3i^`V3et&ף[`赻PU<@f|,)kYj5 ,kH),5Id\$'CEU xXDs#[7_AP\*:wG/n'.G^-O?*7խļ^Y 'h5Onۂh}b{GhjE^Gy^gx?O1gO}c@55\JAB?%-o~76 k`&kĻ_a75H @#~$4S2㢜*ap7dzo0ϚUFV(ksB]E*W7[QteY}mm<+c-M.t3M1{0!J{O CAA!B@J !H 0)!9H)]S7$":k B1%ka^J9&Ę"gٔoN-JudUSNkJǼʷ>z΃ l|;%o JďGL;{)=p4sv^|cN<|6whamNo ׊iUSA29jĤt qD" C#R|Wyqbv MtA:F3:L):ĞpTB 38 G6,kT[vOSx$ry k;w`6 A pa qNinT \7[oPk$dk<"ڑpʀI(SC 7f'Jcdâc8"@p V%`eǷ_+ ϦH zJV0TI f oKR9RgZŘ̮Vd<0sA3g(yx88RtrD l2Hc+I[^ g@YP)l3cKද[֜M#Eʬaw2c]yi)(F9ڪ† & rh"j9l9!įr5 ) -_҇i|)MF QstG5BO¸\JI.j0 t"}j#*q%Ym#2ȣPJA:س"VZw(ކ;D󱢀 V$0 P \{-ھQ)_Zϖy B|>VUp^}z]iH+^#.>wP_?~xQ|Ox-;zS>rw$_oQO]*kOljhI,eE𳯵?ʰUvz㍗ f߽71+?wb:ǧe(眤:ڛ}v㑎}7tzq`9s x.ocIAeKxt9ĚX%ˋ.Zs|QV>ʥkA~f.e)`nKh-DF4+Nv)5@̙G eTu]vbsle^/2+54F65s72gigYg@+Y9gMtRZ @ 咔!D<0]X͙1:\*\ B-|ilcHpz61!fq7jt!~$tʗnF.I,<9,Dy3,E ܸ*N?\/_]'J]9v^ڜo=>K|hf=ښnS¯a-WNQҹKp<7Hf:#}eL+ h1*Z.f%7+W)ؐ^~ߜ/1zKw [|뿨`[ | 2%Cs`Qa7Xٚ1!' 'E|>"!0t˔ s"81ƚ(| 6ͻ6Uƅ: f?oP44pe!܄$5Z1Znx$:fB; x}b g\~K81y0kBz0d. jq JHGi?ƔI>NQf0VuuC$$6/l>i1|L{<ֵi6}n_w`׍jfwd~r#']LR˼ ='|o;,kjP<;3G_?FߞZ☥䟬@LJAT"" i]42N&nq}y%92TNP ,v„i4 vgZ+[N?>g*%0!wUM$+r?ѯAONv`6lDΙ\~=r޿P02fTFI5Yg Z[t!e6 tcME)kZCcd(#S&CS4‚M;jd<$6#+'SWҳD(Pz9듏u#:Tv5S}mY^c߾Us*~dq[!n_bH/?ӬpX=a/_팮u_? 孧}U\?5{3o/w`Z@6SJ7# = 7}Eo]Ei \kdS{x~@5Ȩ.~m<Ϸvn]|Kf,c |Cַj`e.kŻsF*K^(M-QO,Njk-륯'o9mk7A<[|ݨ|R}yҖR8yκ OU;goOq^-Vl!2;~ZKl!vjD c"O\/1d^4OX+'"bq[m-cWjD+o$o3lY Sk,[9ea-Hya5 Bp8yba| |aS3O' r@lpX<5<꘣HCPk 8$NE=JNiaw:Cj88bT0[ 4@[tQ%c81ÚřQY׊nhT`6A;=[;9f׎Y'R¸XqqH3Cf:LCibM%^x9lгaXpwt)UT Y@Rݜy ilwd}YG'M`rƻÈՕܽ- 2KG9:CgEl= T 17:ƹjMijf﯑$'>f›ki%wznIvF[l/r}WaFz5Qϥm)|h4̆nw%˩frm'`*j*#¥b<3wM<;R¶NRnEQjczj ׮qkH ]WaGzˆI<%+23&+|gwj|y}GQ8RS h?}UqӽC{'I=_)P~?~Jz#hǧ's`qOب' Mbl? xҲm}wGOso'-1o1:\vփeäY'Ujdd֙ו弬YX(Թln|1]n=G$d"ӵ|x]{vsNyn�qr5H~lT<h|nK+kjNYhNz~r1{?o[O{l/ϫvQ>э%,g=;[⾵뷾+{=O7ގTםaqͯ'?Kl2ZZ?Fx a }?[LG@^K"rN HzT( I֭ Z7 iK\6LH[Oc iy#+k`{FUW0ZzAQW2珖/u;yܧ7&VYr1eq3;v0aeU㗓zg:ִ[>"hZ QQ C9'Q2:52eBI@H  lcMWBXlxEH$7. #VidȘ>u7Z֌@hӚ9l E 5[ӷĕct }.a;f3K1eoC"dm(9_QÎ>)ln11ڽs'K'󠃝aN/4"IQ)LHQ`6 #޲#KSRSq;fEڞYԖ7[ Nw}PhXl5T0 *N[R9McV4ިشj̴AOnS,S,܌yFI@7̺HS[|mQŒy:bqC.+ݪs_{إ{_}д#|dQTZlA yV`S܇7j=I2q,Ō^{8[5.Z::SuW # ;y*`pB.3xg}ȕk/gR!4ڻvh 13ktX#+F. 4v(T>8<ềmҡ{=aRΉ*畭kBwׇU_Yr|Άhik;:g^[LU=/ug+{+XLǦ?>xog;ޏʛ, w3M_t_E=W ;bI^/׹۾WUӻ#q«_>//??3S |v?^DUy=: '$.d`6[BCf٦ sj9g ݾ Wpmmksd3 eSimG<_{huޖ}u]SıgDq[yg8v~W nMQ6BYk˨ [=}xV3g4t^ޝ˫c&{Y'yhZ:P [8E/_-GZQdɍ=ƊI tfZvmJz{<8|tуce<ŽyGnv3[<:qVYʗ 2{EYP֎+?诒.GƐ׊D!\Գ e)`[H&IND+%}C.8 U u@97zy#x b>m@pY.e06HA U 0p|1\"B=tc` Ѵ"J$Okpv"!F?@0e@dj$X0j4֣IQ+8 8Jd+]$FH`Pz\M5 C> f$_l~o&ێ{#4~y_#™]u<;7k4+υ*r}P2[&3 x۩:6N܆̱un+\&[7Kur#5{}U#}*>v6/D]oߏ;֍`0~iܜ"33 ?ȓx.mlұO|5FW|%Y%|<" 2o/@YiJƔX6-`0;㔝##JPiuk0Q{\K %Ji o,Eyļo\Z-ff-H{;†пFigą>R32 gGyi|z]t׫p vlb^5 mt$7Q@,E5ȉ3 (Qa JXX6<4ܮ+(wq8}r~gto v{zwV{:gjn&ˬ<ݿs[> QbRecϭןO\QZv ᝝Aݫ՛}m6?ɔ+Z黷=Bz7`ɫjNRX1/ "JW`/z6x| ^IB @_;00b 8q6z,tf*6.<pxC ~JȀ$XG-߄ xXÔVl N0QZn7txîM}}v}/|a8NZQe1,XK N^zrYZ52ڐycQƫuӈ^zHnhF„I/Ι12 Γ_MI74|mg^ʗ V.5]Xk .NVʈZ7()ŃIg?iֻe &[\2*uq]$Rn}*XGX {3~(.?+ƃw&ܗ/Ӎbޡ*}"Ψh9 wGm-쬱˨:JG~DR|w?ϢjD*9͍2xwf!nsLAs?`^}n\Xz)#<8rnNޣ' ?յG_;/ֳW@Bg:bsvv[TVBj;[.U\|Quqޕ;Z<`}$! OaS,Ο?19{Y|O&6-6?q]=ayb`FIK 4L~?H8뽨1]x zIx,CJ{4[0""Zw`zoMeX$1;'G: `2r9nsSxGz/wAMGV!.Z^Œi_u۪1([뢺GB Nzp=rX_RZ5[:*k-wʇ_1q{Π{˳ӋxGm$/Wntd ?-|0rX] Q/.F[QuIOkOp^|YSFGZQ[# w}QI =|qPMϨmڥwk0r7x(񘵔'N)3-Ņ6U:ƛm?hmFzNE wȺv]')[?ޯӏ^@|՘&3w#F~3c]Z|lywZ |7Dzhkuεqh2R9y-r>-juIuܘnwT`)2i?:GiA!^173X~~ 8TLރa\mLL:`q1#vi(=Ljm rqiW Bm9cPiǵe\UQsxFSXەWfe"Ͻi *ל6 !V=Hcd+QȞ0kQQ)+T%a*8<`v.quѲbb{9`.a*>l}4I@!݅}w3(: )438MYiN* [h@~L$ifҍQ~ɬ >yw%8q/(گC~N?C˟zD;sd/cq)7~?{7?z3:pi[G{ύ8FJypA7wHZM{ =1Jb3 y֏ٞ3rW<תU?8| >hu~)႐8PGOu iiƉ*iHM߼Swww w<ƕhy*]7qtbYqjӸ"&zl{;(! d`++=SPf=I=N*E>s>DyyE(\mxfs.8%A+ &ddw`eդ1'4PPu3}8΃ LN' )A r7|E ^ jl{!,K1ʤ|8]a[+\z,.=touHU<8_qÓoNq%8vO(w+?^ қKZ~^ziz۬X~- _?Fx)bxd/|N}'z)K3E/|/bc=V]3=}m= |(¡M^0h|7cF7;aʯi oIz]yyQ8҃8*34 NVuE$ a֍zݴ:W-_T%e!p!g\;%kyM~:)@,"q&,_(8"ƧP2dر qN5֫S׮Lt݈ժMЧl}y=[|}t:K"slyWqv^^tW{{߻FuGУ~tiПO w;ҥcG޳NIս~P NOۤ;;{6bx>Gw4t7BTQ2^Z\:m3F9'"MmȰxnjza`+oV086ة׍"෰٨Rlr@eCFpWj!Yj`.!4A $=nU \Y +e66qHi<5{f1`9S ,ƊLg>09]<oI֧|ŷbd N:̖v6ȼ9m7zQ =wm`pzԺ^9rYIHMVrT~&|{ d&+6V͉GFI=|bgX1i]2|϶bU^-s^վ !EF΃DۧkX<' IDATE%D$s%7_MJ=S'5J=YFׇz:v0hV4Wr~w[PSm{19Nҕ?COj^5,&P7Rfd`̮MGC4?6m X`{XDs%"nn/ ."qĵu[LYZ{`. Vd)UZ$`oF0D0*",#$6f'݋rzcCaz:|X N c8[7>6&BׂkM5Ūsz椙7"l˷)$?*w恕?u*o盿ڗɋg7?R GW_<we?.w5g~O?i}m ?Hue y?O2)zP'-XB!LyOl{r- "y"tq ?Í(ak'8^t@_A[:<flM qBpcfgЗV;(*M,|e\xEM^TqRR4-#Yv/'o(öyfAu>= #X8(Ϫje2X|rluOsٲzY;n0my[4<:~:7VY+7n=xz#- H^3A_+U?8_o=,W :ٚq,5ūF¸tZ:(5}E6[3iʰeic /$Hw Y' r]" 8"c96۠*6NZx+q@& i|߁\pэ) SCT@i(\ԌX'6s ض>3EڧP(mE6sVxa7m&W'2J;ԍҕ di$)- uX񶷅:-XUqWO9[BEܲL '#nX6<^hDY 'SpiLcʓkRK~,= < x/*ь7K ȩp3 `A9cZ\v>o˜$V ZŢ n5ȇ|ە:{_i#>HN&/)F7z[F )w[+Yk;7N+"o,7Y_6t$5]B|XږM_/>DkGuM {?=~ߺ|uw99Iܟ|?[l1<@79  p1A+yp@=vqc&m%j޶FI/㞴V4*nL'Zg呔y[UYIP,yo1aZ dlEq "nh6ymG`;|:e\wIBW&rՏÎ3.l-(0ǬHm+ȭq9 $Gss)YTwit\⼛ݽKqdG#U+.Om~W (nEm_K|܍@Pw&lwT$-dvvڂB돼CH,dwjR6'z>qJ{r뵣]Ʊwk3YHhvd{z7͇]D"X9 Xy3myU:"mwu. m=Xg0J O ܹA@X#@M7d@NvKPPƃ;$DH >Sdw3ێC;cd:lHK )n#X]GaYlAN0""cDE=Bzzt9wEe+yllSKY4$yBCa]7q-#xx 8瀭7lKYDk,&ÀҞ)ql2E!l! H< 7jZ8 w>ȼj=oE)n_'s??OrH5۟E>^estP}|z^kk7 //hn1ol PHB "Oc)2`H6D0$ ,fOxԔ<f`& a@i7! pmdE2|(l#@Jz4kבHj) qt"(Ci-z$zhXRqN"Fue~' R:Ki2).D"뷺(!qAsuBrU-˪,jkGXL)sO5A2IVe t2-%;`w=ckI7[d8;oGN)yt3q:Y[G\'~/\5lE٠=_]}x}߻]~kx/O'v;荲<| z?a@t!LMpQ-bpWs-+D-C%! A<1G+Xڟ\86^b(vzwpEP\mG}DQF$u6W朘nSt&G4ĝHP@ a*f ~@7)A40-hTxyh IW2DF  Q$ n]t&ZA'On`#g MZG}RPʣh躉p8CǪh"TrUQ%8?Iz#SZd3q_Qz$4kiXB HBF֕=U֖tL kK^HWv+1Fɳй\_2#fM Fh{p_ĘgEGSB3_=~ςrMԴu `,yD2[$@W*iNx}HHJpK#B|DA PAvGF)}<{YBM}9oo,zEmP񵣽x_{_y`?wd:нluw&_ oBysR~w\\n}>7\{kJ@<|[_&w>3,pi <н/=l"Tl GɄ@#&xrd<6W][،a3@xPBh7P& $Sҽ$}܅"1a=U2u)'H"S|Vfti:' c"MOK{29lY{gMknVy0{E#dSVUy:G RΜ!dN$GK.ۮ'*%[H$bD+{@GN3czm$̫~x1Fxe 9u{X-/\Z^ $p U';%?Lãpd]ߙx=o^!'׮i.dp-nίڶ3+(4V瑶zkZWA|&\^h7; Ga/'*;q} t(D]gE;h2H;eHNy®rޏ]lJE FB>2"uY! HRJ: ӆ9Mq@MmФ{j0đBd! BJ:I)4q#Ґ魜\ӣ>W!zϨ uTS~JUNQuf8OsQk=la>6½=w]}_Ŵli{}Tf0("r l^ *zg N/Pn5'c^a `@M ΢ zuُd1T*Ԅ~k^e M ii^>8ۋb'mʛfl>VNqJE7!f者wpT&}Z!\E;BKM# M {LXC8ˆNJ[k,!!Hٜ4h cDPNi߁~mnǿmoin~cgO?~2OD֙O9:{5O8!3|b߼s/|P|Nw^)?yKAdMPW{䠊%> W?4=w>|Ad  (nQKV! C^)ct^ tl !!tm$1!3I$euO=`Y}AoJ!v>tl଍Z`k#2=ŭ(֖s&$Z;v+54QZ~F_LCx(<}f r-ې}cwodi {wgh[&9^FuwkpZer" ęC$`.P(ҧPKˆ Q7Mù'4\7Q2 ##!{cݕ%pҬ D2$*5u(ɤA[nAIi$i$2h%Y⩤2m&nss PFUAC+inwA9ҝk,Kɛ_$ S?Kq Iub٢ }G.+{^s{ξt-}Wد_Ygd̻;nW}kMomWV_{W ?*~ /}mgs?`>$)L~dGLaᩋr|g0.#9d*ȄkE9Yk' -eO-@cNsͻe kX t1PgA1ED583"+ )9+go'V<]V"E7;_.s\+ޅ>4ѻ-Ke y8[Cv_Y)wVgUp/>V=jx9 fj5ї>8;J{]}m܏ -AY廯OK<7;,քtE>^HmdT$_pQĘ gdP!/Um]BvX ]A bdDE X юKgw2$dhd,'P3n@9fY1-3Ȉă`SDW!" i=>P0Wi"uw? .z֫-גꠈA"{G lټVJLH4MLI?;{"ʕ,#9LFqmMx9Qղ2`}L rj6O`C^vjI޻}jOսYwO n'[Q+kCB(K3 {wWA-zuP3deU߰׹cG(Mome@u"ܢ/cisu(|p\:]葎` \%>De՞- !'YÚ U,Prn"CHB-Y#眤#@n :94GX#rBh$I(SΒD6Pr'ODg, IDAT<%1{!ķ a·u`>ng~^Ѧuލ}{޾;G_yG~'us-3}<^BUnݕPуٳW5krڏ:{gb~0<^\-`:̸ v`YCGxͤ;}鋿ৱzنml@ k.`\x#l1b֡ĆB %@C(F$DT[J{N Lh=W@:K)az['NrFUYݬ6:z04Bh} ҃U߆މu\IԭAhMfii2bgZD$0$'4uDcXe0`MwZ73 F臭 {!]B)AVUm j`4_-WusGeE"lFϪn}9{1[?fTxz{#]]=ٝ({Pe6Xvm{Tyk޺saed7瓏<{j5qsȦ+MwIzGw?19Ń"y>|㜯b ( ku [k7Bޱ]n\,ΫˇO^Lyg{smd}=k}) G;m_g>>ފjPY,"m"0@)4n3HeqsOkh K=¢KL55=Z&xBƘp(Kvrc.+}U=VA1xo"GtTRXY"o]OO;hь|rGU(Dȫ Ym|lHbDon kBB7g]b+#dcʰO$!t[f Dդm;Y$%ݸ6Ɔvw;(vC):3fE*4uގIܻC RGJ&n{T<0v HP2Fjk .ɺ%s^:2-ZG[$]\ъ>ψ{6˪{G]^}zT3]%d4.ϐ?\e .TƍC[]rpqr&i Gć/$$1ƯՎ.sij\4/.$W!='K!$Ftz ѦK?ĥ>ivd,aT@2H/&⃟D$O[Ӂ6+J 8m\,I]< W>I>N*|ϟ-˟nb+Pʾ=*?/߾> .Vo\>wtUoW&w|thSO4?ouo,}뭿`d/|:0> >oū?_P? ~( xK_l| _ ~#PCApA@4jDh|/u 6@A8:ʾwUDd5Bx#}JEwWκ9 P'uYq>T TkP'"By=]BL0o* hBcCc]kSfm+cC210j8LOs)$˔)sK"9̱͹_F'<; %ŀ6`/s+k]H`xy"-㷏[] ӋUήJT<[ߘS~{Yw[bk9\$_L8|(yV_N*lwY:"0X[W|U=N]GjMn U,DT+/e|m!"A Y* 8$>Mz ǀXxv(fNN# #͙  V FE8 @k$ ΙfMr0ks4n AV&LJ鲼 PI(i;Wn$Y&!Y]Y ^~]qFOIAIJ&ҥF[ +\)0]oIGbG^N ՘d2ȽHo &EI1 \1>23m~mްcXs.Sċb;GYz>zjC&,2qQ,t\~!+V+Nֵ8iBLKmu eRǕQn*/ʝUm_y4;}tek.oo_&GoO~zx6,ٍgX%ǿ%oV$?8f?la u HPBPa3ƈ1m X̙:ڦ-c*)Й ux*xzӴƚX#Jɨg]vaݷr JNTΖZ$2HL-Ю˒UԈńYunK3Y鶩WI~So66N)gawWdN(AځnOwUދsON'{떿ߝG=P:tu NyNiÉ;5bGƹ;^AS{zv#=ZN<k,ER=s>V3ӟ~p}1?JdzI j%;IAG/74*l2a/FNoså_cL9I }}IVG}fy6~6¡VV$"'xFhOa eIx$IbHD A "re9Yͥe`J ~t*?_}_<J@j P/bnAgϫ;~'/xO^rO2щ7ܾq?BJ-'; {O/?9^=MaاG!14FTS41P X۾3rb *@4ˮIu-"<֫NJ0gsQJP !b=mD-l*[Te+Ty䍵>'Xj0(L]:=*0y}WdJ"0Iɶm4.?beB0]Q)Q1)2q1J{,/wqc%oTڲI"J}z1wbڪrڽjl|n7yȞQ)!b9 q-!NQg;|vLt9niW'NLx{0chqB>kǴxy Jb ;SwG}[RyׇQ⭜ ɤy@wn|1>;,O>v'̆3uT hb*"$8`1DZQ 6AbvyB`7VtL0ĖZblYl{jIW ,Y~9rqUaCfTC.!zuX7W%͜$}QС[򨨋,-$.JF%WKUjMĭH֏P *ჰ]))MC۱h*r: AsF܏<er2o"隓R \`:W/ϦQdUiڞҵ npjP0Ʃ`*SZ]M}bv}\KQg*d5K0uhqeUކ,4VE7uZ:FdkInD}_d]kFD9"{8Eg>$vAk  ki4#0-p cn Z#O=%߷W$@}TFNKG{OL>5sn^<7ٍǍ⮓#|*!ƣ=[)^9߽u½c ǀ~3ݟ_ڣ9sa5v/MlJ?}K P_i}W?ڗW<;Mr"1R^ BMV! PhB "q.`3.aR6:H8&=?5 ? .h} !1:cZέ1HE'1t`s,UYx!*)(&Il.(1ۣ&&ňM] keY i8T mSE5obkӪ(|=V;?i ]}]_-iJgr{1:L3:K^H 0:?օ}ke`'o,vM>EDW6f-!䛬τܮZ$g@De:/۾|&9DhWcAX/g.$,We sn>aˮ'!(λIƓEq'Ltϒm8WE:͂ "@ W,*P2A.{ #P29 ,T')7y-zJ@"@B6BN!\t$Èf>:YBAv"-X r.,X{ z[ aZ6rjAi*oťuZgÊ " $>s[G JcF綖\Jj|fMBN= 2mee#t!C4tUhNccme܂miQO)RI OF3{4SùՍK u`EWV 2wyF;~{.hs1OxJ}+JDMzBX˾u.*\ޫ.g,da B흐jdڝ;SJF(>+,u^4MaHC)a P9:Lmib1\=" 9?8uL--yCih1AanlvaH,?q)$0LI?tTs:n`U C CA.` RD 3( ơrn2ajMK|"$( sp$>6Wą@mvShp86}.Τ{RYgڧ|߿pwcJe./{ťw_OoOGO< ˋjگ~_b㧡տ]Ubl[~ ׾ůc#`qٰ 5">Լv UX iF 2Ʌ60ضo%T*hi[!9"<2ITMzg1&ƈDsk4Mv3mmz[:OU!1NQΛ<чiQ9Zny"+7~{kkXX稁/ߦj[dl6ݜTTsw;vЃvj4}^W+:veveiSinwlaVrmctY˞]J*2Z4I:WB9 6rA.]Cq`,q+v  %h8EK@Drvk!J AG6Nx2rt4?OLk{R@聽;cGU"ȗD,rXZ(~=`-[ [?(MBp59!k f5ׇ6gt+^ ge?6B^߸;ٕ9_Og͝WxtSnq~%wkoބ?uW_;؊`KE~+W?HןBw}g v@;t>7BOhb6JQ̻H01>PIX$Q7niTr;}!Ew#̈́˦ \'Iۊ45+'i)V6&<CO 6!Y!6i9 "-b;>Rv[;b Co{'׬"Ȅ35kMyֻLQt|2v]7w^L6vf='{@a` IDATYv^3v(ίw0*I_ZMZUBҳg%l>=I7~yM;{"{ϒMv@v =NnLmRA|5N?6},՘$FYBnh2drQGE$Ѷ!YճOԪ'Gﰤ {s3q*]U;]<_[BY4̸̔ G F1n筒!b q8"c͐1dm@$0E;ѤG1" Ѻ[b"]q2ֳahR\(3^5 r|trR^{|n3 ^/[epӴ#:yzۛnڊ 1GHzC|F8\% + .w}-D;I7qXGYu-LqMqPMwZ~SbsoO-7hƼ}v*SrܱQ2~4vbRڻ SbբC.nFΏ_;U1|VSRm'"Ƅ@K?)dz:<.,7Li E-Z!樿~-` 0%l]_V%"0]]z҄4dk9!ff4eDO+* :+&QWKɃ?bM~ƏY׮_|V|?ǟ;}̂wNG (DZZLAWϋxӳm_-^8Wū['B|vh@ 6Fyh3 xX3.0D @,$ڝ̝ !VM,mxZq^s1!KҸW W]Ǧ:CwmWөC?r"c;VkdvÇŎ3fv+Yuj0}P!&`JwDZm_7wdH˱qck/3ϭ)>OuF*s>rp 34eK~52nDž]r3NF1+Sqv׾̊ikt#8 ]Bِ=yLq1+&1"F6& ы[' [Ƿy'!&B! m1 ybv>6#f%1DDӔqU9NPlؘs.e)+nZN<ד~tqe$JZlj_xO ^Ͻ YϖB`<' PG'<(#0,PftP^p77l1gtH[_Z%Ձ7 pC$HtU2|.'wfTW\,|Ҿd lX\-?Ki^Qr?9Y:mᒇڑ٤>}}xv|0ۙA~yMY3=]3Eчz mjȝYMf~ӟDѝ ccz=0oW]\A '7!=\;G>9qό=:&|YtW~\,t_o }a;tR*F(Tv'Q;[g򤈜V=cW5+u]$%o7OBK)j "7 /&__]P'r<;/Ol2}vAۃ ,ͨl uKb>R-9K'&,:dR^br>Mct#%3,Y P-!y4n`"h\# @=NBt _/ -#@;$[p/d"tژD:.M!Wqq;z9ڹXO[@U1Oe|_Ueqs\~:]t>_<[ʰ^lϼ]~i-0Āh@f"1J/齛UީdO$r«TpSB\D,dZSD{v8v楁4s2Q=o4)6>~gQ&2SD#Egʪᥜa4 k@#HP)C 0¤EMRFp=R^^ ȭCi5ABĀ8 Z {mxdOV)oo/qa2!?^#%gc4 Y0A(\3g!.EI$}<5W2t kUi9zoxpM§2+e,\6UM5H'<-) >HA$Xh†LV꠪Ή.qޛ2ӆ́γ$ 'D 3F#amԒ(9٪!AkkC|}TAdMgW`M/퟾۲M&vaN]ii83XuqDvђIuP!ôlpUIL)f>0;9(0)fyf.'Ob1$ (A^:/;p <$CFb6$G93w+m>t@D=Yӣ E' csnلxoLtlg/O=}xssgzz8e/|W_w /oٯW_|2o klY\/ _h?$VyS [ArkOS%Z`*S1vQ>|6*pYzL18ivCFt轩62ɟ?UtEs)o_0Gg SL%iY99Ó>z:b e:.ߚ֜Z t" x<^h/c܊|5|ױ @yCԛRsv%)yy^w$@!x.k[H/eO"U=PZ^C,Ċ&z;j K F#2lI>M^2~q&Cmz[^mh),Jvn48sT FL9򶵽X-|E!i40ˋQtvьs{26pL&,\pG:TΫ,A>y)aܓ%R76Nڟ.4'J,uÇ{04j]3(O^({l^;cZ4 l߹-X[~-Jn<*xC#/:pDDFNNX7F9|x27*l눐d<,vgsz|׆&o64mϙ708p=$O1fy}%} RP1awB9bNCBscu/%M`OA`RQ.`Z `.(+,"IDd3?/jU]m -F)X!ь8MY2EDB>8Ȁ>,?-Y4pWw7w>Ni9A/!Od<[?\zk"C#gv t_%G[V@KTʘg;JҢn`B1rȞJI+LcED]̄륹PM!}A.cB|<^5.{C+"ת| Ae+:Z蛀٘S10M1 ˙ N{1>jNIQ;)PK}?!9NNsu?U!DI x ` tHrυІŠ@: ƗC !T]GO#5{,C̹?jχ`LBt"Bԇ(ZXKu>q\_6ANe[H ɮ]ZnL:fPL&꒫kنr1C{{iIsT3tMe[#(Nk|fkZrui-b4ݒwE63p$S,%BGѨXp퓮Ĕg狵oZ86$^f -:X LK '4|s),Bj'~6xydOzwI"=*ߺ_sCLgMnhA$7E뮝]2b^mU!& saun+qMqN;>elH=7a-R5BFth bڸPQv&lֲ<\=ϝ^,@AzOha]\%0.cm)4.IJ7h iAϐvW ؆ b8r B-%.^P70(q!ZJl]){HnTb%ߜ)O/nN?I_.u={[N#:lM@ٿۯՂv㶏7 =÷ǿBHr|7~aϟsȃ>Z:>=T=hIvo?wk/pN;RfqSnGBL1-.nWMk ׫g;ꩳnj@ɞ)4+d#oT:lD E T oP@`Ö@N>p& h? X uRn7wF(< ݽ`1M$-ԌIz44t l "SїilQ?RwՉOufv/壔KnKN>:P92S!ef$72Lɳ"I9To!z"+ _JytWqҳV _ƼQE:s l<҈g%@Ir{f0HE$r<`q$f#bq * .Dpy M v bq(`t GPb>fhZ5 b_m.손 ,=7x*IT D "Ϸ珒dQwph@`d06Lk<7-/e̳ IDATM/c H w3|7>棂{Lʿ I;G߼M^ґZ['SAp{G%1_{;忾ע"o{nE:Kl ?DZqL@bxkAөXm{}4qT];clԵb<XEeer362!Q'``:~7yMTxQ&T];H!<zMߌ_=Lz6抻n%(W'K)ԛ}x|\ua̒J1rWt?~WwzprUQv;BtWwۜmWG}GTR؟Y5oގ#x d_ae`BZk-#190UT{Nk`9 WAux@FJEJe&J'1ܙ-#)uڧ 2m2K ȇ#"'U2̃R +T  qwLDJzSaEβZT-raqW)8eLUYpmפ\Τ:^!5}0$/iCԯRv㱴,Sbi]Ab&uKS$qcn#ڢq .^+Ow~>T);fcOm7{kղSR"SzeICD9<D^r4N0  U%lӂJ $ ᲃBj+6۫ЦC*{@RbS1h9[c*y|X[R'B&ܗ?D<.oeq%?%TW{\`仲\_e[(/Qr7m_|yoڻGϯ3^Q(HTI'Ԝ  4fSပP0;\$2ig٨,!b s0F\B;@B X 1`pڮR&7^lj%?LeW(ңpƹ'މvEtYb}}Skև+m:|6M׭}U;h>tzY"pgvl-+1~;;i'ZNq܍ΊlGźN,80\ ? >게E $a? H4YI3&Yv$j7jC뉞\q`t|GNT} 4߃ԞVO9NdmZ\ycwU7F䲗ecE+6p09'$A 89c!EE$Miq{Z xIS\bP $.xnF{y2l.,Rrg\x *%b{TL׫kx2ZVUo輦z@L^9)A`DKǨ"BAf)l67Q! D~;'^8#ak…B"G;aDEӍg!N^qy5h}w~]>x{77/s֮3r_!>C؊ ^~<<_e lFgJ c[@&}h.C_-*|_uֵŨ6c %3IDRls!#ciY}4CP*e6B5)%њzK%?^,bȲr&OΫڑ'W+f'$#*Gyw>e!}(g)w^*jKw/ǏOTin*yLGشvEog]gx9 ٺvLvX7] 14vzy'w{*-fOfeDdړ_g}@Plg= W-KDŽwG؀RCB| D-<:OSk=,xtY"ڬ.˒f.A \2v}ւB,Ck@7i=S(/2Q" fQiLuY LZ&RU9otJ|nTWJ+kX\G^[|lRgUq1-l{Y6RPo$o|T꦳yVuU7Y7ҙd^Oqj٨W]o$l`W}[Faf%sBΉ3 غh6Qẗ́UFM\mR>yjƣݔ`3 +C\&BGM^[`#{);Pc)7ݕӐ`tYB-4H2 b>M8L}011p^MQD&%O0P"@&3],Z<&M)(u<ԉd$;-F\ԸDϧl&fٝo}in{/^DFFFdg XR0@d1cTĈ #$ U&"3}"m3bkv5gYHIVq]ZOLo6qY]%Qڕh梉ۣMhwHݝS ʂj&:YT}`SfP9LR 05AХRHVұ\MB: нFI JlMD}ao 2Ӝ$= 7)j#)P!; %Oaf>Ιs0㣎q^ʠr:TLո{i=/T_^B?T?sz?OgGx$([_O<@.ٲnsKJu~R>_=RThCSNXb[]R[ij0r.E<4Fc ΍4Hds?*}X Of-ec/+l/ڥz;>vCuYV5?99GIg:i?̵kzާ.e7vqλ<6nweuwH\^7w-xyhwuHP3Y_ӟՁ Gef]l6[?/^5_=vҦ1Lc:t:sN7iiT VEJWgbXOمu7!.hCdS:t;ieF]Tf~r63S] ތsK4orJ׷OM \P P `(~LDV`` B~ e_x{Vs-{ch\S rDȞaZ/7kԍbFvlx}$ASˌgln٢O4Di>E!3iN ' g;k[ղt#d?$uI8hWvc)>7g@ Њ1L"k@&+`A#uHhXo\-q?e"5ҊI$foQb@%&M9S3E(y̶͉“`,\oH93&hxf.$LMrʠ"C #VFk 0k 1ŝ輙ewfHLM0mrvoX%bkGմ5fp%0s6,yk3b2U;eem];3m80tspN9#*R/~$=Lf8x-d}8u7*Eb0"M+N/4? L >;U{ɍN* $v3J T2-zf5`O {6cu%QC\p3u1Zhg- 1b Zx-ә@ \f̒TLRcpsE:A),o?"y}/ߘ迎G):ᑊq{t!c /_؟AhQxK*vnqZX1!cN:%0y3Bj)15UsK5:mWˢx(kcƘfgOUԼPeQadd SN惋rYg?>5~<>;vӳgm]NFtWٕ/]4 Ui^޽پX>?=^UwHWY|.lú~vq|b|nyra մl=תߍn]v2em?lCΏUvY0\LA'o:D~ULem)nlLp"P)$¨OZa|0EכbTie)OO+|ߏ|:Z߷wT$dm|QHb$QDYCTZ@= &V uP(P <[+C-ɠ)L@9Y Q4j3jrsl F~*g3mi1LJhmTZCε5yyv8!ݫꦡ[:b쇔nhph<5IR=|Ɂ*hXa+ϸ:jCv˫B9]&]D,kܣq-YC  @M!(ZC6-*U@O`j@1%}AzE,#)40Ude"i!O۴{}{rOnߪYx:|5}g?9~tGe WxxGffz4)'/UGa|iX]˯<ùf3ڪBwhf`kgei Uy^mkƙQQz1Ew9vvB(d:[OGx}7D )S0wNViWnGt?M%⇳g'w3W}wW_6Ż3,d'K|nUb>|fS8gVgSgW7cQZ5Annӗoۻic_>9xY5-S}fwl'E><ȓ]vN~(h9XA].Vv,q@S_R]'U4?l~n5mK2ubBEȺNKƱb4p13bSI՘ Hc_y 7P(dܳh4pq< ahmJd LQ: y؍.겠)%EϲƠT]Qu҄mYuPi'ia<:8yav[=]QmQ8 %>[:}eSiu)X(/e(HYU)֦ dW0# E+HLȒL[=E*1$ < Vdc[ PHlgڄE6̜GMd SL+ JG@4t!HoM63מ:\*%@.e!ДU8Ԓ6 sx‹˹bA7A &+ĩN1ٔ 4LQRKcbz׷r콣} jZ18N}^^QԴx@[5gzf*p]a(/#ZiE|>prQ.o%GYRte{ie9IQ'Ht")PuWgb%z|uTAeF!y&.  Ex)FiD:e<&,1a@kE {LP^4f%,]_4^ZG.?ƹxHv쐢B}wԪ~VzIvo~~Y؇RA~_^ҭ^]]~ٿ(>~M~0qk _[W?Sz%dӹb}oq4׾Qdbf"0H_l+ցYz̨F 9g[~܇?KدK MYtz؅poNކP|y~7偻8>7ͼQzHm]Yֳe J)갶5 x#ROR IDAT=^ݝ4xSvb. ,ֈɞ}z"qmL # 0hR8RdG{5PN[Vq,ȈsC*{&qY:A+C,uA:~ㅥmh  SB% <#kzʫzTe]B<1`ЪZL;~zMTP[#:ONKR1t#_$\?.HnȹT f]ލŘOJ7Cavon=] 7fYU/'1wYVv6e [r{QЁufnd|.AxZl6|TWE0kfٓݮzb?۫n.;F_sx\΋Jơ7I{y 9a5 @L gPbIz(w~=B <99;p6z:NLrO[O~Q:54Ur&$zuߎNWY1KX9.)Yu}sa1,ZYHvΉ %ۻ¦ (< :uJӢ- khVDQ޳ʆ9[.Ҕ| :SmBP (5M ꘽h QxkY d"|Qu$m$h m3cE%,btLI2D'6*tBrrϿ(F4 PIح'QlyJu֗scr^/Z:a}>V/159ˆ'["zʫn[H>sK倍x!FF5:Vi0e.=>(/ SV: mdF& N;T_BPq~ H&pU~v+Ov_0T/vkIAGLvR69Ij> eRvE⌡(e~]ǶHeS {=-ŢpaEZ`Pk AfYU,\ʞ,9!UM(A8xSgGիv0ӮxΟNC^5YTֱ)ǿ)lY=;秴wDpX7J(ke 6HT;,ʞ|29 ( isVY+TIٗV _aE=Q9a|w_+/rɨ-7"+KxiQ$mJcl"C=/xH5FF]hʣQB1 &$w뚗uJ %rT &ə:Ԧʅ82I%5L6@ȊC@48GR^4nn5hlT0Gvv*aGdꕙtv$J2gF~6kS\!)]7e>L_ё:rFo6JòT3p%d$;*LATab=MMVTݗX̛~ݹy빞^Gnzc{)፽1w^^{l'`,z6J0q#9v`{UDgMF 2u}_Xpfr@K,jw;>*BmS !sTf SFBkJ4ɈBEfz G P!јJ+uq#f VeA8ǀI& +$60aȭ~z:ۻO>5_j_3{Ohm  }YFH](Ri[kC=GU6w8ng!_ަ.6Mg/J^3ի+UQlt6|XQʸo1IW>/&ݭ=pۋϢn"g}lptp؛82۴z}in JkT֏m.B4gu[U}ݶ-.h>y9=s$EL@C@cJnjw4'x~, $qXdHNA D_y@%S9$

{djtwsKً8ɮК4RPtYdFӔZfA|UA&E XT!Ap<{hXȌY]*EuWEybc݁~[i}U>'?Nul}^@XEOOKq ]:OEgXrV.JNzb)}yr\()w'ͩmwsz:?eaLf®8.ZhIZmpH]HؕEYOcOL^9O?{–jjei~~vf~hwѩ ׷,\DҸmQ0 սog/UBe: G B`Q1K%Δїꯠ…S[8 R&Xcsaϡ/tS$d!+u'ZwA7>Ufe:-cG0e8gyYL1I#+RHB~<Ҩ" T&AoT"PsBRJJ) G 3+K/vdM$!I|J k^ ҂)O  ,R`LX50A`DĈ,hVFo%TTX̑GvaC_˷;NcΆE~.ϛ1V8?Sηڽ "?êɇ~=|(o>}|\qiҢ,Sai6Ϣ꽃u[67C?{pR ~=>RV}H8a^Vu{y}""sR)׸'ãU}=ye~:^kVӔ<ҭno͛'(kcJz媝SH'aTn!_1߭vmJG!D@GZat914%q::?64Qut|H!HG"%t!M1:nN79s{fmHmRKV QJ9ILj6D; 3G o߯xveCJ'$Ж&&wE+=%01D3Cޑm-3GN14k&/Xٳ:k}ԅʆZb\VW:a77NΐL!DƜC V qSD2*岵\e_pi6X9yK*F(Z`6a0*^?3B) ";eYDccV"Pjs "# H("-%A5DȉR9JPI68F'c0+PbcfcYg㊩ zIn?T, UQI8Ęo"/6]|a=v;C0B$q Zc Gp[))+g8׬dM^{WTgD{m4\V3F!C^O^އʯ!gRcuŹ[E>T"U"եdA2XLŦԻ,U^+.ʪW6NbBz ?>ݏ0E_ >[5n j]5lzUSR2a5$ ۉp,`Gh7L&S t3%~AJB8T0$ l% 13Dp!p(B|%j@y:|.*St;|9Wwn!?;{VK.sbOzxȯo:F<;qvVq^r׷q!G +WA ԉMz/ϱ\%ѹœ'2-(tdI].6F$*o59<+9'cV0M=v:7[X8KUh@GPȈ#cH|溍ai]=ӥJ"sdNTNԨ5iHm^<'cqokJ#B EY\X YDFTz&yeEL}`$% LyJEQo G1vaښM h|etmnIҹUl:0s>!Vzg@\#CHUS~MBX5.C"9sDF+{ޯQ% J!}Q|W$ޫyNr;x3O&Kg~ڍ2IrV 4$4\$?W>Lu140{`~ᓸg?)avlWhsAxy&RmP<==׵Ta;Z%^ )`:ͤ(`Mq[ws=22~oWo&_loxrj 88yBlnDK Z#FO{˻7>G~ZQ2T5MW$5~d41W qL:ӈ1"=Y-)]5 !1B @y(k1Y /tȬ~CcJP`Q6vj%:CUU1*H5CUVk*Y ʘn{Ev'/Zd3%S 8^IdQ**u,7 yqteu]3o0`yq5pdD菓SS:HX1ȍգ Ţ"ǤCJ(Z Q=%PPINZ&!&"S@G2$@ 낡X'bom|DkB_vེUN}5}4L΋iOzoE{LNBm?y[5YHZi>ZQV~yyef筍JF gPLOuT$CQT5Y2ӐgKȆͫ^<;;.`POL+L o?^Eլt>X9l?>JgRЗړK C'ce+,dL蒍yrXO%c2vAbL$7yF^@S@qb` d0G 7" P"XhL@`((b=e T@5hÓ>&Ӏ!DaЬG;RXqtñTB x;5gOa鿾vpӋLgU^/nnGG)serUvңz)/k8JuW ` GD$7F1v,%DYHp1X $ubU&E FdJP4N2A ~ ɬzӼՔ;;o2f0]gC$)2 tLÈ,RΚugIP}q3y%mLӸ>_|Ge"/Q6@4%di:WDMbꍎɰ`Y|鶨O"`[<~8]ꅾϯnnO%obnm>/tYa$<{smJ ӴJ=qaR] t^eYJmzIj֧ؕ.^)]e;26Z$y52:  ?5!rH?n?כח>:;[fFCR!x4퀋3<~YY.__~!"+>]>czTrmnܢ/_-]UUɲ-,kW'봘kfBJBaR$Ie[6paF:=G=0#$&c88[z2]6tV+,%fo" ˺Kwq;O`l0f`^X[aX-=+xqea\QGy F1AadyBywfmw)ؔgHMX8/Tq| HX2| 3[[x#QJϬуREc%%HOe +fp=$-(ix2!F@Ĝ) 4yoc(U: *@<`7_9'`qT9IXkJ) @#z19WOSd5DO@lg"10/ IFdVQ1'D&w!ӝBύmRb * 1K#}/jSuba' geiiaro3hi<tPd7E!`c,ŝZr6чOT yt~q0)ߓbW?|1[qJ%uvǁiʴ]2Y|rN7}N~ǃN>묺?{ /MS=ЩYKU6O~'k*̓Mh?[Fؚy\P~Lp4]`.g_Xś|&dhQN*Atv-T栫DiCsݡGDr}!Bh6Ú&ic^ %$D1 $VDeDD#"$z?nަCc{9ƿwdOzc#IŷoG|Gxcw>]._:MNjtzjCWZ7_NIRz:/4`6^ڡy}})';̲ JcS~prUY۳0xy6ƓYO|,ssgt~rҦkn( 0F1$8"@~0: bVF5DxT;1RF|:/UYC?yXt')R]w~{wf^ӇUz=!=9(+:BN1(= NĚ8\v{[ eVG?v77azq:j\[B:J[e^.]Q}Ȕʖ3dX@&+ABD)Șlg0hP^\XYDZegFt)I^+*#1%LL˲4IJYGaf)DGMP+;@8.1̙׃&7ЏyMylMDiQTm>W/2Cnx<}8\}W'2ىǗzqwQ7'TLwӒ>[զ9wTɡ;bWt{tU^nкP/5Sl<0 ׺yݷٝ܆46_j~U{gZ B޾gN)(D&8 RFP7#fid-1%: 4!!G1LHH q K(ؔ01CV,,bB@2#FxL+Y"CDR) C:eG%8`z!tTf1;?C_Do=wX&lFrd\}ZBĿzgڎIb7<.<ˮҤa}{u Y/KRO V)L!Gn"? OfPSTwhهgyC\e.I-j۽ -0EYmW~^{ ߵʫ{8aYX*lgZc9_^-( "ZgӇ̲^I,e^LY1Dݵ nQtvF])1@!Ƙ\ci"œ,zޕͽ6?KQmYž[Ctk{XR'Nq9EUzX:OJ<70 ^)6bC2(_*E/ZIR=Xf.BL!,B;bM6bEXbFAq$8]R 8p$;) ;I V@@ofY!$m D EL0}ʃJ1 DDkGftJ`((ҿɀ`BD%R\0+m&I*3ŨۑMl$!G4X:ǦvIN;RЖ$(E%mp)Mm'Fx :]:W)R|*OBdnv*LB dV竵nJܪ/wvVVp8]*=g ĥDx:_YQQ )*kI"ʻQ|*YCd2 N%$h1Al^ͤ{|FG}]#|x|@UVDLƔR٩.hbtq'g.J6a&fe-sRFQ=+al>*t‰R7)뺫~ٴ}K6{?UfI_emj35غU 3uwhnazS,GZ/֮(34{0zm.*m$欍5ZK) @!dq>ĭ;zVXD'!M3hb(2xXp*m)1Z]d4n0D]n@Z7&7Z@ac(!yA)f9LbL/V/YUTrD f6o&%a>IQ+Z) (I+ *PLǖhP d (D$|$:NG@5=9 Vh,JD!R%R+$Cp 8hr!\"utg … r7r+PК[r3 ]"KN!fM vIb8&+|!Ȼ|,S QlLHzʲx]Rʝ Tg<]*jb,Ё}en^q0hI^1˶ui9w2?{m//,We[~IOמu^>h۫6pz|U0t!i$E YYuBT鳍'@lihty;}C9 '͌D\0_5& "#"(@9,2L1ȩQ $00 "s#-HҢ;"0&&CVy08vLD@_Hxx x|5|GxON<)jw^m֋Rglyߵ|nM $/n?K:-g,~W`wv(fD JIIxi}Ofip\yFah9hnR(m4Av? zVL8am4_;"Ĉy]ϰW Irv"Ck#sg醚tI-fU=V%f E2o2g*ݿݞPp,Mk`$䇗Lel>X۵(N6*6=ԍ0.*Ģl5p3وwh| =m āi (YàE"jX)CAFBB B8PcB(&L; 8 : $'2>#<_?kxly7gwi[,>]֏u{V}<ַ>t+@?(Ƴ)`xUxa詮*9_y5_mSb5dtw"4_zFʲ IDAT.|)t흟W,6=Qz5NN+8S,(+uam5sՏc=.3Z@ą~1(Zk6YkE(j6 ,V@1 fK[?@dF߀d+J™V%ob5|SGT]xgtHS$ÆXcѪ#)Ƹ0ZJz"ZZc#"FXhLn>TQ(б5GSX!3L1k"8@!yN5 )H1hX.)1tdPP$@~ly!e%E*81影",1nR ͒NJ$d`IHe:*/Tg5Ph%Rb;>wMپ:⼤̆a$6guu:hd)A/4%T1 *&L&Lc tɂXHG8B4Dc@=:p * GzpR8&4~ /"c` -'i/~b>S"WRN_EO"X@r2leWWYY)18X/@m f8p$AALH'^~ <=8ǼP5{,yy/ȫKBG'&S@3 YY`۷ {8q}n !q )fK}G>}QXOatWO8έ)r,1S;_3DC3 (w,6)ͅsN7q"ѓ힙8$Ȕ)X ik[!0G£pfJ"Jk5dHbZ NJKZh| Nk, ppI56`1M)Qg? s$( x1f) }GwR#͈i0G2FMg9vWqYS,èB!ȧ>z `@(F 0bN,q. *lD*DBiJ SG WAyr,aSHoi@ON.~9H=Vo/#r:[O 7 [aYV`h.Bmr&(zVDhHJNdYDp*ȗa'RL%B= *GAe!ر{܃Dw,Gw{85KI.vlkKXpȥpW:Ndyїg/YrA Z-O#On4aW2tRGj{K0Ṇq3Own\YHgk<֫Yhϭu<|0|pGefӬZJ)/zYOAIQ# .,l2B7Ʒj)N[]WhLy8%φC*V5NB/6 _aa7%%"bܠ5Z +̐@hsH88/D ,<b:G h@%-A h #vXpH(-w, 9gY^3зفxf=xRcGV 7~}Qm'ݑWF;lW $ytB{isS)])Џb:xOW,"M[\ы">7("ǚ֠ij#H0`=0$ 0 fEQq|v}]E$a\=>ax^F}@MCuB0dF|t4NE1b8sFY.uScc]6lVUD)G:dZ0mI}I9^kt*@D@IDk"ڠ[ @c3n;?sx8p0Ŏ7Vt51.pcBv;vO; c,2QHiDTRN(ig.&>3p1cc2p>%#2F̔,,ۖ .0l 8|ѱR1=9@*0I|Zp pD>1jM=RR}ϫ>#0 $`B]$6xQʚ߾pY˛eMhLc{=%}%-LzS#qKʀ=TZTSx4kq%/ U{3T N Y!@8Z(#E tg1%HP 6` CQIHt^~ M$6uOTȶ~Ȃ6Z)`Ł]9W~Xs@[A,0{PSIK*Q]H9x4D=l꒛Fcg8fB%$XKwOӻEf(ƕn` QP5b-ˠM\bYG=@/No?Ϝ׍v#luXJ!;aDQ|2-j뼬<;=umsM[TUV72~Dr1i\80 mwםY?5Q-tu1:E- 8Bm=t B'"Fn t*#tF^:&z[P3Bl.I,xV=OwtggGp", p&&1\ھn.t5l!02aLg{9cI*+7eE뿁=gzl!%t羓1Tq@M$X>eh]Pkl IbkX={[強2Y[D&d|_DS0ӊ9EW`ܻX0yԣkL7^h {QVʚ ; 5`TUVVI36x̝5MW_7CG1ɃX(֚M+0W{_noʲF&S O)}j==ޜ.S)6^7ItU񻧧Ï׽ Md)|H uˤɦi@V3mUIҘyҫC'ơQ%QA.Et= VgL UY# ˠi>cw6OS܍@ZzԋKW|4,ҾpxU%:iG"Y#w#H~ =&(])2)dRp3ƀr ݚ0|k-Q\y6Uj .kWpm͒@ɄNGsM&1$#bWH 5uQ((uӔEXlaq}GI!)JxrEe^-6c1l}kb9h:edF 8YgaHph: ihȃCh\h! 3Q '黭M8Ѩm\ X6GCBz9LJx.^nzUM]ڕ&=!'o)W,N\e&b}?9],uZ0ԜDHFeO$h0嬤˜ W!?_X8+bG>*Ǜd(Di Y8m)b50z -1iL3Q"K"< ྖ4\#CF-4y`$F #AV2mS_rOԔ' g¿ len.%xvi0}]8 t~٫7p}m lOBWHDiF MQC,,^ctz" ike0C,79Hbo5gPJA4t2iBYpbw2m6R&C5fc; `|*qrE&|-$X/g4R&X[|4j ]/VKX*!Ć GSօ!#8X/M_;`،.Q"9%u45:aNieHe@xV`-`A`\Ỳ/`D¿' 8(9#hx}m FPpg;<Q#X9@~æQ+)wlA+olH gá{oj{V&cXw@:01Bfm% RD? X>2B5H%8a_#j[%\s%륶j]? 5OyLaIb쉲mTn `>T,[#2P^GC|q$z3˦Lnf?j;+x׋ٛ:F]x:ɃLk6+9&oa4RujF}Zc]})p^Vɪhy݄z.Lέw?gIV%PBCc1T r[ h!֠@R-kx88 d>Ffk'"@^łC3`РA 12((x8`xlP`|8R6t 3ۜXğA+7;]"w~כtd8<ّYj&qB,ݬ/O<0 a`?@$$ Џ"(!9TDĈshc 1lE]choiq`m@@@'h  =X|90-F#MT+LjӾbqIj-E<8NSӭo>r/ \ )~vgZ^;zy4WՇ~TRlVŦXUy*Rid4^0~[{I\ D2J5#tΕ6RӾ9fu6;%3tst%O+[=40d#=dی( =D+Kwa)|hӮo~t^;Ss}7Cv&~BF,Ƭ^D>Ttߔ;cYl}j(c7:`{I֨@ yE`lMd;0XWذUifD  QA[E`D!l];nwM <@!f H0̲ᘌ|$OA+ֲ) j8\x"vV,*U˨TQ45Aj@<-m(b)`4H#%VRBj!Ddb!(]Zs|Wu5HIj~jD*ŪPC$V.5&l\[&Eeo=8Nw`)ϒd\[~<8tʆI".v.eUnrf^Mz#E4<3 IDATѳF^ٗ  oAj(28I*q#|mԣp>5wVPeY7du y8lH!!x%[L!"C5*QEDH,!ٲ4 ZԨEoTCF 60hb+65k[?+-FOO+?.T[3G}+kţߊ|pǻ|& j0llHe y*$S+\яc4N#"6dZHU4G jc^#RJIPk k5x8 \pN@3˰3Y8TDӔ/W8=jp)$=t%L`w᪩.GݾhlcVͽG'ٰE6xWZԭq"]%ͦ$XY>Zk):ϛH1=cə ޻fMc:P]`@ [( <Z0 8A3fJTpbK2M;IGiV\<^A ^gyM7yR25J bSX+T*HmݮE)d`lUĸYUU*u;۰jj8mJ@PHCvۯChPAaͶ8l5Bw=HpXX 0x(Kk"IHѠٖaihU+tC =tSYб_МKOS<)iQ?WNq2 w.N9юv&dR!QOeSE>P2?5&߻?|p,^0NKx!h 0خ`Ck D"lJp,6 4FXpHb8#"$juY˜Ym '4ZAa CO.mMNy\S'Yʎ]b8~=ISW8lR9tԿŢ>_nոqWu9egmRTշ?jj8cDD1:ƣs 嶺2@ MCL\GלU3}_QO;{ %__t}t;]Z+OTѷ< `W__{n {W>뿌W -W_=8,إb~ᅗnh5;7G = !ؐRviw|,}uB.623>wF)׳wWh+@xnKֆZ(r 7 m}$ͲIpMV+2jr`ږSΰG_?*NmfV.7oVS8#r`X˔lWjZkE"jD*赑M>3kzcm$H^$M>E3{6,*֞@@ C%PH}tO@C$P`  -,4rX4p05,각' = c E:l ])knHa{Mt]sO?.&}EE\\;/蓉[gҏYnE ]g&3ķfѦ)٥.? 4p1HTD"$n $Jݠmk 6eVx=.D ȢRȋ 9xgX6XkqHJDR@G[>t̏)ĥ~Xӷ,÷T_Qo8MofQ.\2ƃei,g{3;`: Ir붩Y(*M4FOcxB sx7r>& [GJMo?%to>pŃd<Eli_{EƛS' xDBAY3=xڕwH9׌^/۝]̆Gڶ=hLhnCU"5ݗRJȌuqYc̡Ct8xW/}7 pz.! yf?/bz #འy7%ډ9ҥQTP<[8p7{~Ю~b|vh˯b_x7ެT/~gfߝW7X8.7|O4}ճ럾1&W{=fŬDrW/k?h!_ڻE=-B5,Vo8o`oH7.ԕUWUɝŽy1=f7|YpFeTۨ.I1󎓤'<*:.~<MI({J/):: 8tZ Jm$k8tU4Ct exb[z{ѡ@tZLg'%?!??{]̞?Y/6_NL0w3y^ƣ(e|מAև!0"HƾsF![yQ6 baY!#DJAJ0TF(F6fu(6%4ؽsrUKWd^Tud t|꧁&o~7^HSZVUJD㘇u>i/$Nbգ`FVR 7՟>-cq*). mӶ5q;C*$Seͯs3>}z\_ؗ_/m%ů]ojtK?w}T*/XòLndV8bO_{eW_C:sV7]Wk%%pק/K|y<ک1O7$(5En`K#ҏҏֿ7;}okӃ5:*>kOc-?+<,_{~Wb#l˃^{Z &?zY!3V#aY{>1o'Ϲⵀ%R0tct:B fDlh-6`pD 8^Q#pc sAQҐlMXj41gp1ca#5"f* ȢȗB:cȺcx @QmS͵EeYu}^B"`j&%,jω$ cy-)wu(y?wqgUc=O6_7wazn ^qo8f |ALR)"WYn"/~H1WsŕWBSS}D1 ]2)8]"ϛ$"$k:XM{CuqkOM^~?m'3)Ft^Y^}}P}Yb쯝M$U_{<.W_[Yl}t]iWO{ۿ^{E@1M'B|%w֭DW>|!>ϞK5Q?Qxpo߼G?Ϳ~8_xF_x|,yك///r8˾]\n׽߸vVS~1i/xJ30RqEDpp P-F\ԏDWc>BZ4xgͦQpAܷ ,B;-m5TW$ >n@:vB M )zbqb {oeu։p;ߚGfYey;1!b&ܢxF!a~ :h^B!eH1ˉ39X-PA5Wݺgg>WU%$ҷV{~~_%Q+# 55΢q[@g#ʻ&k5w ;|Y㫧_ʜZlJVfo`MS&|*arZ❩$˴*r( ɜ+K  d$LÀipP DD[| n@fmg2 N1&$-CD]Zs0 MJ) j4r qwPvd\zj~|h3 }4]qlVբ鹂Jnq4PpۖAT&cmF(<><#¹muލߍΎ?6굂>GL8R5,J i~}*TϙM2x7,v ug_*K\Oq-!͍iopD/D vA $ EDjT) (`@=P##0bUSD4l$c5y"BHn4XfHe r6: d$)FR*tP! )L; ȤӜ8v@M*0f䁤, uFB4P&nӥ?Ӑ5o<35_tZDH07(+cB4lF:lE=My~gf eU|A3E&ԠB kpA-BL A+{%H(ss2!\7t+&o&48G;ኲDAAр;f^ʜ+* QЛ3:ABѱ#ʫ_ko!kKkgOYa6E78`}u|2ѽt'(%YQiӡ(ϵ#:`H)G5EG2 1PJ}0FG$ue#(j^*R4^e3b @2 Ӵ$(<+D&KEV@|KՖiaQV瓯  EYD*-T(ר{e0ƍe[ ! nd5e}Еc0 {.R+l2|=w6l$w]Ͼ~wІ{FmI)vՠ-İ}2{ٟz} \λT?#?l? ˗^nzڄ)Bx|G';Ů|LS1o_[bB% u4󓛊?y>uS?(|ϷX.\" IDATRw|v'v~ \)vUXջ`=dn"@v~qM“u(1@65mXԳLV B eBWNyaiQQ}#3c&duhu,)^gǽ y3 _np-Po9 b#]0RXRfTV)U}=jKHT*"Rp,ϴI* N ET( (2!fm[~{6e)5UJv (PІiMp%DӤz-)="D_^hLrް: !<}bQ\)7b9jLY.5ibwZ>T|&F&K4hetpNb&ɞ)o<[nf͠8!р %iq APa bOS`c$ab572 bP?|*(g8jyo5&׿@ZG[?; ͙u@P~kID卣S y4wl*j-&ˈX$\ø쬒Qo6AQ ks JIX9"UqR i~@#БkCthoF.Y>P"o64 _(e~^3&g0$Ÿ~Wz&)ӶX2zJr/&MK%{>#1v9dЊ~7mc4 R Jftj o|ѽUbbWJm>‡NA ] ͂ЄcWH 1[C挵JuEɨ;0J+{T R=DWЄ+xtht8nN>‡ccD90Fš H˼jޤtTwl#Z*άfTJ]nhZ?GftW˰ .f^l+^!}iMx$onFԸmyq/1Kh)2dҙ̠dzv^kT[ )[a5qѵ;F } "m¤.x‰:hY, F[&ք'@x3 )I*("j,0MqCe82!%B!xRenOl-D'QĉRJ "̦H$PQeH RQFjXITA07I:f3={Yg%q2nDyTuӶliyLif[2%sj rʼnRC4N{z՝;wzyt2(T nה=TUiFr B@"f H\$LEp]ƈ&hU^t5;+0yI 2 |8nހ/ r0yDy߄r \M͘V|Xt3Fm !n G2R"Iױm$-^30zyI[\wR@h*LP RX>T!B86A"fLZ6h̄Bӳ)ҳඍPtav\v۔4JI!?`` M"JDSERȦ$WUQzoGK!Ej\)QglA.q}_yVZJdžzp56񏍎}b}ԯ0‡=LbW`>@.R{=+4JknZ.VmTN@qS0tܗ^8=I)7; qm. ťbV˂Eq֕Zuc".YZ.jo@SP1܀N m\iAoZ[@(=ͫY%KB,C$$! h; ڃ3wm2Z]QN+w]*a2A,^y`"nqسzgta9ՠJ"FB!=?YhxXT`9%+S z'; ^6$$j|'-mmBB$"hµFϴ$ZnkG,߄u@Gp}syTm֭u Hԍ a":&_Aime~}csL)i,$brJ2 оmbcA RipeGT0O#I"j16t 4 YELSB JBA  78R 1bY ߠbۈ_`&2e> ֵg=Q9<Դ]1L6Zne^扳FO'NGM11M?"BUo雵ɓk:I%r)Y8>Yk#Du;j?SFk,$Rn-OSIf+3-0xcۡyjn6/&4e ( :\ࠕc%άi]G\-> ~Λ[Ɇ^Ie= 2%׹:<ԣqDBst7ut+A3 !Dk"m;W;`pH)B E[:\$@$.W*L m@ ( Rǰp]  HʄD Y;¦#l#AӗA!0DFH Aq #4"SJ@M0R,d2v7%xSxH)>>V9ZhAB/Yx0V )v5YKΏv)\pd{.$4ƀc&hbо2s;ŮUFFppص<‡8sײUܶ>;A[>|*veY6J"ׄAX{<(C|)v8`z~X=1[?mC獵rAJٺksF R-L;e67UF[ b5-Τ"}7_˚b7"ao?H77 /hu|oTDG斌|~)vEرuzo*b)5#PGL$)˚x&[rg&jgF+fh'QLw"y)U~B ۾a~+^"8/zPpؖ';avdJI##O-E =C%` BK5'Y rԘU& F hû+M=pEasS,l_v/Kc'x־a=dڟ}?gڣ'p~um~:3kewďͣo ktL䧂}`2\ 酥.{m3v^{cu,Du7饵b-tזj!trB]vҴZ":38GP `$,Ե2hVu>QW0KO#ʻ*5ys5?DW2o}|IttzHr{wnJ6wӶd a^dLNX6h̚L  H(Udde*FBr?a6-x~7ГϣBJ/`rbzy #4Bm)=a2o/3-`Q% TD/#Jm! @)@ PfP)k.ТF$<$.UA~0ꋛc#={v>r5K3K/J TGJ% @o1#|x:(u=t<ح|ښ"BH0T y;tNK ç;z6DܫA>덯)yCoPV Ehrp3=8 hֶ/l1y.myP$>x> ˛Y.d<|)K/UNo->'MU6lQk0{Tmjq]j;Ɨ/L^,[u6e.P,GDsQx~u4Д}}+)e\=RQЦz5g5Cאp`wy)\*1}uwMte[qBuϬr q/91B0 ENDJ" **ꍆStjiX :Dᥒ)\Xe "!Tt1Ӏ5}J%UG/H8SN|B \N WCgpMkqGhkEkjs$ȣO>Ҋ/\._.!V#|[:uu~%?‡[dTb$w޴KXۖf)wz|`|L{ԑ_B)v-^0is,'c7Y+Ka6}| k8r7AYMV{+#|x A/-@PJ%>! ԧ~j"y1f$>v]w7~:.ggSqߏCCjrr?W\ 2M1=Xc;3ᅍGDeV"gx3x1@?!$ '6VVd?L@f`@? #!U r912 )cW8cG\n'aƄy]fm,d-ݸ #pۮ}^TϽs^uV܉;/q+nm9<8.m7S;X~GH?M6;+d2t81^{(ƸNRȳ҇^R`ZS8q_~ (-.`KczMT Z'5w D]%87c?|sv%[6w{} iaՎD0@H8." C$m (PBz;ʍD$4@B+2v8Z[TjrN `j]V3ʍ VK/JgALks3cA @X[l-9eɆ&ۡL߅ H<.rckkx:~U&0ak%Pj7:%eN<)|U{q鬽c7_slKd~Z =Լ{MPw!yٹY豒&V$KqC+nQm>R?n?'Uw܇d3/Um;2wrVЖ+-ৎCAJ""0 FpVKeheh G ~].ixphA3~V9ՒxtwNS_NtFvDyOnbwB#ʻ5t(o5? ^ >0FFL認Dz%NYNMo7UvVN[ F }@LmiMlZq@d{+t\VnVEa&C>"VTcw5~a@Es\%vn;F9fRWFر@c]JΦ]_Avo*t=$ϓq;]u\+ BhW_ŽGq VZ4W: о/2+s߃6 5<L))`MOc>|h~z zL,ef.uhjft筕A厜,ln$>MǏ 0KTٱ\}۳/;~2Y#tO`C[ B[+"]7`9LWh = =;XDgn`:+U.`|}{Dy?*x&f?/*tnT0;Ri4<Gw&r<A(D\֝0 c;)qzfPw?\;B3h@r DJ8T+/2mZPw2-IoZqƉaF5(r.U0le_W(w0E./!aNT(r 叞}dl#2~b(R?KX;N>#>ߺpYcؚaAmUhoZ \ & q hz!nHZ9q<ɫI5$tqhmB[pT^҈-# {%NW^yl)VBI@ڠV\y݈ M1q}c@#Xm[CO./_B.bպ+.GZ@ *^7JP vggs`CNIbm8 16X3ʣ/yӅ shh~uղ@yng ~EgDm X(BTkMX4W9NbB; @=ˎ̕"П]\ B  :ll+7A"d}vA/E[`y7VH}^vЖ%ڂOB+wt__V)@(~fS ;zH}]%W4/cE7t9Ar5 s34,r =_pf?~3Sn-Ə-3mVB K_ܘ[cޙZ 6l;%^,^gpU]Z_}HxPwS3Jl8%/S"ۙR]7eӞFRf㓈]6ÿ aJf^sSq`z7* bcCzvBϳD-!n]H2>1>1RǮ]z߷\ Z~-97ssO ԱhnQ Cu15NI5\U%RfQHe~ RJ .C $`iJܬH$(Σ\C (.ʺڳg @>|gnVG.ih O>sA ^O[\ xm =P$&s;B+J| x$y :%?_&Vy8F`:NKS۾=O|7% V?v]O[j~],#3Quhk ݎ)yy(D=}/6P:-e&Y>~/z\}оm ')m79TxGE M[]! 0nWZttk<OԿyÝ_ͤӪs.0$Vy@7Щ-r!>n7ޅZ_DB>\n6l؆JnXhsS©/DE߲( Efn^L"E.0$2M"vL]EB08٫/SFAϜ7 pٳ^|G{f}h NBv~[V2ן7b]:vZHh H)v>v 7 h'v]Qb@V&W?V2vȂs]"Lc4mN h^^PO#~q,AѤ| +!FYgu 8Nu j:/Nnzeg~z{poгs) .)_CرKd#,fAyhu 0 W[Oű.7E TNy|d f5 Ѓ,cdB"ߢ4n\>ߒpk۶ֶmq9P ⓟow0=|[UR2I#8@lK;{<ظOxx5a0~j>ț^뿗LM=H|U%#Uh)㝰CgmheQZߒmk`Ox.mл`@*uQZbX&?@ BDJ!ajrt:*"!m hEJ8c`!aJc# 2@E/K ?s@uӮ2DbeRbqY.@F&Q~z \y+i@@R$Q뜦M>?;g ={vC+4+)ٳ(g$={v_8|蓏XI7V'ןJ|ӭ9b}mn,?| p1[W/x-ΏVBKAA! f}7Cҿ Z-BEy]ieA[)fT kdxHz}_iS-א]h2.=΂~Y6ns.i1^ _O')QqP?3Mz#UW)U^Ru' '}ܰֆn++eIZqG- Y䫂x2hC{3.KK@7<pӄ\u]P3jC%о'x08`6>;nzY6]Den1~=clՊk@EAϫkrM.k4( ^ b~`yQ?׷=޺,\B\꿗۲+Zy`*ب*8 %8]<߿ fnY-vWYVKE~hV:ywgr OMp챯?lXEH2+z\Ё5s%97u^V !:DqV" 0 (JL@UH@)J@ FnAߤdTx(RBEDIELf()z7H6sF9 0X߹ޛGqg@cp(hKVPvLG4ƣD7ur'enLÉdxbg8#Gp&)gLk؄Jo /U+6H$K{睮Ώ|bxCp*;Ye뽦'm1xX&ɸpXN|dMJI`%hXGΑ&gMwelr ZLjΝY ;6&%ԛEأ0"^PI*ՄS(UjDBPd"y8 K[=|SLU!b۔1d\Ebam.zQџ|$g諪&^hk' _}uYx 1kV3IvK;{mVR:XQXFpyĨ{a? ع@Sr+7ڊĖ>E9d;ktɍ7}u]^?WhڷXg jnFꐼjkZCԀ?Q4 cZ_,r縰!BDUG(yas+bsbS}N 5n8}jOwv4hϘ>Ik)h.{4m4^ث!ʄ|kozQbL♩: z-ϮL̠&]ve4prU1\t:xsd\|d)W[Lh_\| }/ %E nJ}OtܺCkoP_M|CV.,PHu/=u GMJ$J ц}~x=ؕ,^xj+avxsK&lIM>Bz<`KGWz&7NOt !$5T@ AW^´ٙ\! %f+/,&ULțmaT"7k$+;YZf9+W1[=u,4ӭO9LyMN˺iV7ZJ1Q;worPG[C(,ʙعsٰJ[Ԛ-hwq)g&\,󙔑-䫢D!$R C7իodU}BBVu^uJݮ {G꿪' !P0@[*29~PFW ht3"!Ա3՟\>E.GɴM0ȣVcZ]FWO5"8Tm] $QH4߭jQ➶ کVTc1ΫPF96*jp8<ѕ_G~>m/*!6ܭ{a U\/ց?pG& i]op}ԏw_>vtcPŐF~GZQ*@ͪ2O\~gZgS=}K o*d3#o_Q:GZ$b#JDx("d^X LӜDU^7xŮX:V;l7Y Rj ܦiO&'R +3GG󣗷.=n \zHd83?Lƣza]s^T:ݵ5<b8}pMvS/}/Cù_:Uu-߹s9VtW7kP]JS$D`.knrCS1Q}Գ$8]駟= ft{Em"ޅ:T՘QGT#$✠DV^Ϊc \wPm@ ]Ic+nW6f|czn 5Rn7,7!EUv nVM](shM#>9|\'(n,'+?|ᓹ "ꊬ͝ _t OrOD7]A{3p&"6wkZTMyll:t}$ZHPۯC1ԘTVdi;/s?bG,;J }>;]2c鏻O^J768*4kC- W{dV\ IDAT#%ERӭZK:׭p'^39{a8܂<f{]X]|-[q]v]݊Krg3N dh;)csC Mj%m<c_8۬3? !0B<\#R!բrQgm@<Z1Q @Ta#@jzۡiLdn/D++>xd])ܦ5ƣXAf7gs\ B;Tpv JgHɥ`P8j 4l(487?9t غ=KSFz-4j`١+CWf /w(Gf(lB[^/[y^ a(Wꍡ_^Lu'?3ɧoz\5wVϹ2.gaҳ!ʄ|tX.w.^ آ [a.ښ5vXUw"~Ȯ\CT}\zÑSc(3]ҹ~~&_ÇF] u]6d`8HD/r3 3f?Wsj9Ztm`b^7u .Gy?|ű0÷#sM[cz#Llroj%!0venrϙe:|R+8#vy)}M}w!uhbCk+ڡDk&csf{pw;(5=c7ܣPYrXM'n`=cz#҇X0G0ْw$R_%S ssqT8EzsM>9o=:`ᾛ/ 766Hҥ˖z1>M<]=)u ;qX r@_G^t{}* _מּ΅N>;V7rS]/:ZJ{Ҭlr{ve"/Eڔ>L"KEz7kQ.F&0 84Yb v˸n2ym .@]wRzp ?*\NW'si:ԄhA^LࣔzL!?NCsNd3\8t%kMGK'#ՈtL,93v4~GOmiiN'L{v)Pl h/:_}H/m'|q!}g&7^wpuGuT+E韭^\okH `zE%O/arY>˝rhErfJHЁxsL5~X!އ*$$D19&csj;3,,BjۉTMu:~+WfOY2BfVMX],n26w#އZVBĺdso0jո,\C&)-G[[|OStM>y 'a.4-3vsE^+2v98AJ6[o1PjXǟx9^QBFb[ JEAGo?Dgɏ3 yh)2}jU?NN.`UsUhU#HJړƧbٴC~lMorOx2hS )q xL }]̻&Z*G355SL [{zMO^Ѡu8J[ru̪;w*o اw}w7JYy`˖{r@ƧiISdla68זLʝr*VמDRKB瓈Υ!>Dnk`_W9Z eD}G!gW)Vċ=n<"mt(sŞKK.'[(PN9OԵ>gwG>|ۍ>qX(%hr2W <@V<}휵$2u 2߲i~DY}9xd}e@=jZy=Tѷ=9.%X-DAO !sH(8Lʭ!~?0(Y(5= xn2ܭC[_gW L; ۃB>&/׋ FՅ<#z,7HG=|Jzm~g|@B(ZWics ;w| B&8lgw&Ϭڴ uru[*fg]Ҁ7ȝ9r -lHUg>MӚMY5۲n>nGC$D(/Jn>> = ʔ5V 6(*Zmf4""h{+mD1dl#EQq^%*_YWB( oTMYx bY;f/uhhHsaif3賧Mwzڡ D^֑| %\[j6 =m/r3l>GL%^˷MVeERUI)`C _xroSfNתpTe7-E~?3*eSyzf#K*DC 27y+:חkJe! ۋ+CK[W䊩p{cԸ]Z]œ-sq̪K)Gth lD;5i&;&5Hx)HZ W!Ĭ!@OQ Z!8B>)ёQ8  Q/ וQƻe`6npVv% @]~ĸƀ~S˘Y3zhIs#Ebi@˵Z:-v˫'_~1?c;9翵5_O `(9CNyƓ dИwX:Z5{ri-4c|G>nKտ+=q7W[Oe4+T5_i4 `WTx(0C#E\#J\56H8zt"A=n/֞^3L6OWVǿvtӢmamݦ2UjxP6U|_v#dG`Wߛ~_xmr.q]Z"3_LlbuXh kU>܄wv$sM"Va9j 2?\} wk//]27 ,[!1GɩDͥ(,ʄ睁q -CrBRpE ܶ=^UC}2X;4+W4E Χk~čA\[9\+"TĦl9++zd]ibSkLˮwƇxO hjqw[s#iڇ+;4Bm5~5>!D:x(Ul|3[0|B,Rlv Bse}> E*}› ;aeyD$<]CGտ70tk>-?TM~ci_߹sY ʏD3YL23\:J*gtܱc ڵ]ONP9[iU?o9K?hSjBr|JdC ͩQ$_#Nr1sEBEDh&;{ؼ1ێ7A* d&}EiHiݖa!.$ʡ>BE5Bx*gUb^w&;r=wxO,S wk^Ń_o[;x5;d[FQ!$AF>Mj<26 I](FDr ]3C^+d7 B+xvD2Ae! ϻ^dU?oͪmu"B!^D}?>r8L,k*k5g2b1O5]ח5-z(3sd=P[̟MϴD“~W75TX*J[b\85;wJydm& _~ubw7^ | ZBEJv>K;B+j܍xD"7:ϳӬ{G mAekQ !C"v7 !1=]6d 9CHjA?O?tꁛ_OBlՈ ⭙DGDBp`k|EڱRSTi$}@w۱;յ./ñ%-O5uf#ܭ }m-2y{o/S)㽈2yw@y0G)c:[7f|6 awĹeN75EYG]qgcMƗ)ZW%*RMǻN=zfZ⁸72~]&z+"Ou?Wg:ɻH(^Ep"d~ޚ GulTQy0M? RJİH{C g9WgջL}wˁ۴#elݦy71 pz7 wkE2& `(#}кzfsg<)N(zqa=۹>a~=z?-Ϟ#dTRnVN]_e % 8鵬p6&> 6E-67eӝ\ֿc yoDO<|6I]rceѼ.Y1ʖ=>e&ʄ虤\GrA\vv -g@94NFX퀋V S:tu y1m=;wjaGJϨD&"'()%䲄Ubj&c9$Mwsp].!yil츥7.U0n-v?Uƹ]x{>}=E.6`ab>R9\q,/Ǝu@I\gzT4gԹj=ME+[d-~ ;==j?쨽S?s.̟yh9JRDz\[Ec]wkKhaw D[˲F`rKqMD}qٷ!?SȃN#"bh,e*bx*EZ4>u8*뢵?a5_=_wձeNo{u(ZѩZSQP2i@ޗ R|۞|6mu(u\B:? sPB  o#8@e\muiYuVy/ Ӭ^qP<#,yh]_E-U& 3dBZ h˃@+ymچy^%{Fo:ݚއ}؃eHVs[kFm;24$Q검N+AV?vr:^; ]]H5e k{ `3,mF3amo첖B!ow}.+[+({xl1F%% BP2@ 9:[U $sҫE͡rE#}B h|n:ie8nWN֎UOkB5s!~T F $T=n7!Xcڈ*AHޏQF}fQO En-;4Bq=5 -{.I~m[PGM!9{=;HyLO r~ F\CWփv}'0~vr:'FxgLsZL7v9;F;]{{jSƯeϻ-j<ey(앬ۡpUz=l26m26t"gRq4fz%j o32-les]2uj~!%BWR"lH8Y]Rum31L]O?B@XAZVC|vArDP-Y9s>:r6.\kwZ>I}oSِ`/$[KSW"| |3E~#I݅۾ 5ߴA^k!+p3əɩWp IDATΦK>d TcT@1YJ)Ǒye)2o( ܭU"i-ZJD홏,./탷=G~wהNeϻ>-?;ʇ(*!ޠYox<]0,s=yh+jLVG }^OmڣeDT2 Y2qMT1geGGߝUԄSZaLێI.D\: &bv UFF!BHDrh iC®"eWp/"ssoy dǑWx#!O D|- ~'{G󉛍W rC)SK_3h첲ܧ2CR<fN-b b-J9Im7lfa9sQj<wsbC`Tw!SQֶuլXRHio/9.Gq8=_C3-YeZՁFCvbj Z#F2Ĩ!$}1@.@vA?އcoR|tUKHMgշmg|17Ouwr}b :0r\y rnCLQj%E !Q5B{֝N4G-HוobIn~ 8^́WF kZCf:kC>~(AӕJ6#q8[];6zs4M9I_ eހb90!=woz1<"ˑf'zڡ_1?4d^GJ0jfcUp&kzT( .1(I%H.$g6#U 腸'u\>CBtN <|uR]=CǛŖ--߹sRAu6T>O;!@pa=ѷ#FuE4>0*Wv;<y ϾWRD~VT|8ukmg~߁~U9 ]> @rNKyop椤Bkp@+2m+Q.uMvZC!dfL)EVb$pt*>ҲGSBR:_z8zǠ(#00y^'%H- =)\ׁ(tQF Cr19JU:2 H8VP¶r-byhǠ*U5xƐE!k3zJ`4XF=@'"6OEΛ(yl򬅻ZģS vh{]mk>d>pC(Gy]Q}h[Grl**CUǼSwQJ6 ,BiDODʔiWkL9W]&uuj *X$bTgEc'{|ّjyBӎ9oc2K}K)uJN\e"|e2 sx;#gJ`Ζ;iWb- c%`^U:*\ S87h(j#oY%@Rc H1i!C6aӑP[:igBrv^3vc]Y$>Ѹ ^v q8t 7hZfJ"FW?댦{WQ fSye>ݍ>^k6kEf#G2)B^jCV*a.|i: C3K% +lM+fc5n3Xb`4h@,jmYuHM!6WSJs`Id# -#Gb+s箷"DN@~x`6MD 7!p${Tf cw\-5׭9z1ty+mf(,[Kk, LpwZjr}UMtW=ZMI@9-u,t s8AiB) d>vQQĻf'x|*( mm?i5Ӝ@ jKڍ%m-,}TzL ^턯cg*4ۉ ^91wdWY) +7~㷝8 a2xS(r-;LrnLܹeD.@t[mY(Q^R&5zdRO;BT+\P2^uQ^14iQ Ƒ< P ۀfy\ FrcW>ox Fqׯ $Iwsѧ}I! Yi?3׉ ?9|1,V v(:HYN[Z~(lEHsf|s1LoՠYˮd:JSX271Z,LuG9w-2Bc:4uUo+B|4W㈗RTy}o?o~GwN/ !eKLxp#zxB -ōpx> fuܴt>ñlP_ u mވC,Rvl"B1쪚w)UWMO׻7_أ=QGu,bBрHھpC.pFb@DZu]#?#{lLx"ܫ }Őt8J> ?^(ףϸRH5֒j?[P0{y7|Q@egZ;0 92Lu^XǗu>rsiB~O4B@nGBYnݛcӀF79T9} fN`Jq_YS痂~֣ӻƚ[[+ kB7 $^^6Ќ)p=G!=\ܣDLPCZ/"列ƢGICdn^כb˫1YŸ,Dgʫcx݃SL;_j+ dn/?e;P&< I!5b )aG Tu=4 e#21޶ Vl^od%_/dg˖{Νlm$[o4b B(^?_lo;b~ Ņ V55ù^4#$kFG | יeWm[s+$xZ03=wLe\wM-3xO3܈.8W!aqd^#c JdpgZ {4S_LgHc1ۊ<ƀEZaZݺx C3']իզ2y͜u'>ձG">RND~co۰6|gx{> ){xP&<tdUV5bX{wamΖ7V;v耵Pܣ[ NtXNIdrJI>*z~_ zR#~3ز A+ LnݦiW/՜ɤ&5}C?A+Qbi\[[k7vk;' H9tppNO+i[S7ƓZ.`UygO^5B2#+LMH.pֆ$(bV9AH<k>|\OTf@ ᜊJ멹,jԠ202 5 nh:6f Wp#De`'^.mpywT 9ʕZe(5~d%?MeE::;!"):[. 9u\7`]Oc#%%yK0H! RR+V@=HuAQdl軽Hwn%-<!J5k:?уQj>>)w,Yf N?eoܒt!d$tJ6 m&?;6֨oy%hEw˲. ⫪@c@-,LV*i tjn@EzV]j_Y{mvymj{;|ȮRKK99L Ye_mjs@BUn\\=>2g%DBXqML, ,L=~ڙw%!;e$<n@f ek+u[իbOe&n IDATPk+@=S/uZ˃[wl:weJH58BJ"mW\~w>_eMv$8qYڷ+%i[ȵFB1PW Ĩ` ,]}2 >kQn C<I(@CT)Z1](xUj\1&sIwitdL,"=Zl6Go(Xlظe}٨E?׿uA+0&XPԟRozruEU7\9m VfNGi9~PTptnuܞu6at g 2H7lr%†(s94Qhju8&+&;]!cRhMqZ]'Uw ^a)@|c"KwǾ5gaf$Z0ߣ?~slpXK^=74z&׽eǝ1o6(r"$d+񍤳 0FwxW)$Li?ҫC(&*NDC!DEUQCTƟ# DEVHF7UsY3a$<bX!1V,To(O S󾳶2;u)tFQž B|L:3uǧX/'"mrfkߺV'\zjL|<-ͦk \Vߋ]=>O ;|X!h z͏}cM1~0*2B+WwM}<ȽҦi"y3 t$/+oSgafȖ[k8RAHF(zߓtMLE o4=ܷ﷯X./?d!"ElH$nFi%E6rW D%p*=hEBW^|P5վcnB1zU"pEzwi/_gX֌ȭvvåK&YX| \jV+{5M p6-e٦_HZWQ]+Lv&Ύ<fk5ԜV]j`7RmV b`S4hmء!Da6 l$g0杪cMH'!.y1\ͩG9e![Sh _[r=]q?1:pY2~UJk%yy)8q ё'V웹Of{Ο<[uܤ߉RG2<CyW PI{$@dFɄ$BH5`lɎV z1q$<Co $giE]bL5LHm 'Q0Eǹ.=fk"gKlOz>kq꙲%;NIGPv"7gW0aﰕ. sC\s:cvil%i_a[#Њza%v¾ׂ=Sen?QkmLkj%z}(饫OK܊rmƢ$Vs }M#gP\3'Uხ'RX4v $䩟aY_vȷ„φrT!gEb9WǴtf;颇#jg)ՎtkR^u_YĂ$<X,zs2ݞgOU,BF&q12Wq QI֝ XO)q<$d3԰CsS%'x{VKI:݅[rW_דN۽^L_p*gmBKZ-Zx֫+˶Gϒxkl6&2Ǟ{k^طQ\g"G_pijN̢urgL ;f-,kSHV!#*` ȽL=@TӬ7N$ ҅Տxt6*ֳc90 ,< ?\~ҕ/6 Ix7J¡uH$651_rB&idR+lfdB#A 'C`kNO*RA9Ӭ;F`\-@ԝS򼧢 r{a弨^?{e_qO:ۨOpu}ebb$G${]#wb TxkQDޛ_oEOXH)f͉5pThbsm3uY'FFSBl!'v}[ЙB(3f:KeC_qBQ#m^"LÞHţ[Rz*ƓY;kz&HW9zvTPa"s+SyfG ,cb:ݩ5k7fZiziX'i`.=fk :ZH&D2Ye(WR@b$;'EWcDq@4)d!Ç*=P9WFֿ~fn $;D-gsF:H۾jT쓤֤nǢiASY{d{'#^kʓR]9uT9:ܨ-ГR6$$BT }+`Um.*~lq4숯ɒ I4ڊBT! hl:+Qw "$P 1 BtڐPeH(kΫEÛ3lhqmIx1F4`(8w}U#:j -v%EF0nI 6p"M(b2-:/^Ewo\X/:P"a'Ik{j,-ʛ%Sze{'CV~mfսf챚[_xX?}6?47][aȑ}HY@KNLj|hDVرOE˰1NjC@u _g*9c]~/kZ>sZZXnު5+|0RM;0#N!+ #x=,-/'RFUuiW/sDmwC`t:_B&;Z.L=w#jJE;s3{Ă$<CYX{x Yh!`Օ $Ǝd!  *O&!Oyjl_rɅ4刵Гxsd=" ,y'LN-{n|=`v_$@0詈Z܁ ֡ /m?#s[9zt-1wor5ln|ReE-cٟ"[" 乒nmCLIZFUZWm/NͤV3}F]jůo\^|YrxP0Sx g8T|m(<[8uH"CH?5ŅW?U_ ׍F8r=YbQuj ܳF39.Pw&mSbۺ~c1q$<c7-7˓up//iaQȃdBj`2E|5l?<8 5t[Ew% J`x'Wuif⒇Ix)^xjKRO K~/yȢ*$mVp R#~rb8+L~2%_* 6[k_S_v 6FkҧϗŊl WWO鋟/'X(4aWx$9#s=N'{lcqdL(f-r|_s/~в4זݺO6WXe\'iW]CzW c "D'9.oWL ' !:du/ddo8:W{\Wn~߼2[#xnBzx|(@AK68b ~ "!/ภ0Ny0|_N_@${kݱ\#wg5.Ix!vx/XY9YO#ӥPGBc"0RCSҟvE'KDEZcŷn]d)-ł4{ X-G\mr:xHwYV&ҩ$ W}M O:u5t_@=?^iyx(5̇-ugɻJW+sZɛmsSxpٻSVhޏ zhdcyr3?ڲ5+Wm|e|f  X\`Š҇܉Td"TƀR 4!Hrƹp#$8OT#E<)-a9:5ץ!nvD\Z5qq$<ťffF#CYy 0L%U*dRt"US2]2Y2'tǾ?{8>=|S۟p%[/tO?|/ɨB\B,4'+'A=ϩ"Ab֊C/J VwY4tߧ/>U}HDqR^GTt+ ]HY&Zfd!{8 |*w\2ao&YfoW"7)˺{<% n.~RZLVo\jd'kcmީdj9ZqGO_ c DGQ|ķ޷&~Pxwޭz=ɊnιbQl`_mfL\d0 ý߬UUo Mvl6Ӛ;3ү4<t[hء7#?vN2GWcV~ȑ]eVdb7jʬ<5j*#!;1c9,gEWi~+ <č(x 妐=9[D \ M [ӬW:D$22:n D) "DǮֽ}2d'޿Qfj;j}rz/}x3fuTL,L³p߶56{ޑfk3A[<19qER-¶coy2đqh59]1|MGܦ#|5CHF 1g*QDqk*j|y+:M6c1BW ƄV{tNgr4!m ȆAx1 =>6v<%)=Q(O)Z+F!\4/8re:F WB^QtwE@aGXr<Ϊ됞&10 ‡Y }_jZۓPfkKl?1S" z5H(bQ*@+w kj}kO[_MC&l ᄀWwg0yh`(< OU8L_S`q_0iAbDiy!~ D-7 2`fQLd5&Ȕ i!N3H'쩏 5yQ~9٥]9+AlNg"]7![j,nNeRItBXp GgY?r>u1Pa&2rGNZ6[2i9Kl_l(_dFUBC/`K96?n`:vg]85͒nQD VGc֍g/BjlVձj3h]kZw8 = ɶ;np"dω8lؿf5uoe{ Ypd.tdiəhD/3o'x j!hsbC#L%sVYrʼnL, v)uRT0@&D]b{.}v) ê.gBexyjءYL>2~ټS?uؿ+lz IDATQ3Bzp,p "0Я<8N!7HX0/Aƃ -NvDaX4jUz\ړINLo!G;"XMLf ;ʲ/'#{6c bňd#b(F8+<O Dor2"P5lխz5znmtoqW?Bfn# "ᰨ[0]1>? Du=,8Է{4EbJ-?qi+ER xMg;fl.BvB7qNUdIJIx~<['ًu.Z_NiءYwꩆ)lB@5iD{5gyr6z Yt8x-F8(Pfg q.:VRjH]bTGн]"V:>Dn!3ɱN+yl!e$i[q "ft!0 ݩNJ"-.:/eԓ\c@eq!׈K7qE ,ܷ?c2Qҵ_hءyoMr.luOh"`ҢYl_tWrUcSQ95<~KN%g*Y6[fkx !DkߐQdEף Tw.)*μno4 HD6q'8iv;'Æ(uڗ$b{&L,9L³N]&T$,u'B #$O~ GÃۇ)Mp؜&S4:FVZZwZG& Q4mVIj}gCzT( v,b@M,,De[jhad)fZz| i%)G 2Β"\_ܒKE%C.B"r3[Wc ܪFHGhF& ;%'NY}{H{!V/#ĭGGÞL$CɰǢ[*ٙq \"{gA:<|H!::{k5V5TA}\d>Rs#(tqH&5Lg R? 0X_8)o#dIhO)=u5L2XSckb9$ڐHO﹁2BY˧\S pv_w)b!!Q01X "zw=b_"j {NH\P7Em@= k:=߮!lQ!`bDY^Nq @B^a h3rt!'Z?&ET""םU}:Tzcbqa%}v _-pо{{ù7WF4<\:"&+rUS'Y_ħbɞuDIyץt 0@]%~*uUqH6ϵO$c)=VUU%yUTŀ{3WH.|Ii[K4mkm=ӽm0&$VY,|]y{:B:c&fHki yJyf.Ӯ'rXt`y~|k QKB. xɾҀ~EȄ!0=Pf)ZHh)u~d뺷V{!$2f4t]QAT*,BD*E~2  sȀJQB%ÜfBUa0N 5PqⱩǃ͛ssv8L#^ٚz[GB: 3a40E}^dX^Y@F& 2?wk!CI)@wdSH2Z@..v}ISEKPU/Yl y2HdY /ZvP}ϝ04Nhj@;uDN̼CƂNHp r֥tH…a6/*2M}f_0q0"B kOvhHx蔵nDu Pݻ5jgAWcj1L՘@J'[ߍrT.hƞ <]{kdҼ!o*FcXXO~VHXV4U_yD$:玁 R+F~OC9"J 7+58rˑHtW:&L,)Lgq߶܂<z hء9 xYaհC4M .4"Dpva&!:IU_d"D0bPBi`v} )"U`BӹYEy+w#a,5|r!xzՁ)jv0PApX T<ZD1?%g 9IH&82aba( m['vApjd:bY vh;TN}a+߹3.7@j!E%[!NDXyރt/_r3 $LuS#^$gٟJa:BJu~&. jɶix}(9#Y&&oAB Ӛ0(0 qdr~cY Fѕ/pa ,{2d-.$3w89Dՙ5YU)ڍAo,hy!Ks2>`#%V# }{0a0 ςmw߁%/*OLnb_D4܈`yݚJyr&j v$dp#5w@B:\>[l8MB|LoQqG{Quj)=V! ?zi \L\@GV<"땈wlX܇4BARJR$< eTG8޿K K-H4#ǽW<#D)OI/ޭO!;yZ@T ҋ4 5,?L>KH-=T8Z{h/]xkn$}ރK!kyKHk #l1Xkx "j(Hy癊&L\p mwo>ΥjT>UZG}1Ce\ݰCnء9ߋ4ԗ˹p<Й~jGj쌒\v_ .B<ȓ.ĸ[i)`ɎC8o[0yHT O !y@a"\ū !&5ԳQ܀3D4#ہ:Igj>\q$a],Cnw=Rfa=tp27ԓ/FB(7#jے__Sn1)FI͗ŠlEԏstvW*]y^2X-_Hӆ2#zDE0Aϫ}\#aXn3^2q~rW'QX($bqBpFBӍ YőkQxߍAfHkp߶[PhީwV$}<hީ/ȁIށ?x7md9ricةRc,;19.2Qd?"$I&O"FŚ Ԧbv>d]PiyثOϧ^g& [Zr Df [U#i5RkCHJԾ fe&Yܡ~#o-H!TT 0P ;BM?y"c1$Cq2GHQ ].C< -b\L8<܍(6R7DVn/ SQ+f 6h5%fÉvExgg_rl]ܽSýW]yܙU0skXEr#FM#q̮& L³0*]|rLaA M%E0]wXN B44TQv^]8H*Srr)|Mx![rZ"4M=K\oZm!: =+c jXcA.r5oדY+HH舩$s5$:7hAeb'??ۙ3sEūCHHW 恎4Q+J4q,OMsjRZdX]zvhQ8ą~~p ý[OYVtDn9s^tiv})\OfH{zϭ9TĪy|7?mMZV*&&;Zgn zrV^TUF5FJz 9 |tvτy0K ,"yǛw1OJњwX$ 9R䘟.5y%PpVB BB!U0j,Sz#3d #*43 @߮xk5[=ɪR8;pÑͽ1L>DEXq@N>rNEO6&?eHr$^ɴ/LLc&gz^ e U!(E ;4YfQUkTAYeOvN C0*ެ^Vd(1pd'rAdz2'0IV$TRރ(W p%\2E 17 %3D@BHI*%i'Ջ1$Q# *\:Gn(r]4En,ee2 < 31)AGW &~SR$,!=jeijV^1D !dgu$f-{ Yv!jHw3լ'5؏F{,gvZ Bo)QN1B@&.GBZelDDU e6xTx.D bt=e o/BV7JNWPud79DC (59O((gŎlJ#!Uj{{&:ZE D[jFQ198p#C VSpykT1,VSfM=%FcY;HTADF!@h1;ta*$Zx*t?cWy݄ńIx.գxMU:x'HxcɄ@0I{|ֲ(4Ui^R=XHpM/>%V O`2זjaddYwFM|xgsYGB UL!xS+7] e1o!1E;k4L=E2rlޣ?t/P$j߮)5I/[6jv5?ZQ#B\GKuR=Jلt5՘nF+jj#؊z^W*Xݚz)ҡν(EPM=G)x!j5IZt,Xϩ `T믩't3KFM\!sy$NrfR@ӗMh iȍ^|L**G&AUQ6'˨dE&kH?`=nNW=> e\ߞS> q(Fk ~IDATdd"Eȍ iꀷLvH-T8LCeiE0r{+SSq!*2BNʇO!63܌c #1j@B{o_ %. %i\@tMNy<=.RqX&'"OYToE&#B )d.&T!鴵z &ŀ[)ܜVAUf|OGԽbآ>;jHq-I_'"[Qd#UyȎB-L+Rp.?+ 7Y=)RXmk]Pg&ĠԜt4kI'x?+lSAdrD|Ȥ`t o{ DZW3O U L 9&aN, Fv|M]V EycJ 9IDr e\M= %d!f y.Pk*y ;ȵn 6Ys{}x ϕ%+dK-Tr5ȉQ$uLY'8L0m;h3qz4iPmTǙ$tjIcWUDo"HK A⵷  )`/{w{3Jn=ڹx,==<Q ,Ap]c46X.S$`g1,5-g/٩i> ?b|v\ iR E< 'lC 6E=~~7`ѕb_Na8%ıfw9@mF,Wd%"A/aҊtZi/`3¼?<Ĺ -syϵlNBJ(-8GN,ra+!j ]:`_bG =2Q  yZ&Eؼ 39U tV`bvI&仁:1_{'eYyiVE}^"wb@-`ѡoa jX]]3t:@; 8ĕU[w3f+`=>1q|ӥ;oL=LTfL [)*P5޿zkuRZ<եʭؙV ưu,\wވEl)cx?7ڏXC$m\h[1flTH ¢>AYG&g?4šjӮ+z[uw0^??}mF[gp,/#5 < |ۧ1pw{tWג hЊ>I `.=^On; _oMXHM"Ag 4V(͕yyfYl QH4OL*f=-~ocҟ빳cb?6`^EppNxWuz l!aMK71A0bXS0 fQ2g}qmTn27N^j=/DґWHi >7zu }lI,}[HLZ$ JH?_a >L c9w}G .ED=*m;-> ؞ 4%?R5a> PT*-L^lyxkR 728 4:c" IN&rD\ł?z1I ~)^ƪ^"l>s+#-`z;Q0΢k8<>;1A{>Sc68(C9ގE ;^ZB\ gYZ[0kaJM,m:2A;-}&ox^Xy2,ZTBۂy1eڊ R@i|1[f,sGv;FlϣX lcWƁ_-|30O,zhĎ&TIS;QL` lp&NU1C+L/bF`ߎE&#U\Q@{z9Ei !L$!ya33owߔjۺ W챁+q>k21uEqWƾHo I5.w{| &*^=놛$Pn ;Wff͝ŋ#E(Q#Yvxs':6ܿ@&PűE{[Tp_~xňk:4h:%2+m_2Wx 9Dn<@:ubTϙ1w] ON,u]~%0.E\.4Ja۰30wc?}Ku@*I8Xo$xΘ|~ ;Bu;-$q.jFhxrxg xf^þŢ>ѨomYډm*X$oj~{wSFfT$ɪʫ"Ze K³˟Hgy#Yh&´4͹łۘ(Hkc:XP5\;q\tU]),~a!c֭;~tUs^[$0 CɥnŢAW q $x}TF&؄|r$K'Gu}LL0z+nbb]_fI}$eWe<'r}%"1p8~9_ʧqЄL)مퟖc1 /NIł?zPm*>J8v3ܮ y`9 ]"v|X:]ޝE]r{Owb"&>NN(a2ޏMawq N`0_kRXzF,҃}Ʊ/Ku)vz%wbdm؂PΤE G 2^0AIgѴv2'vWEޫ`hbyAXL}v=cvw `)95t~!VB'hp$#$EyjQ{//bi49vڍ9S@G;iNxtMdg&lq??O&?-۱׸{Q>cb6"bXiIgVEދXWnl?F˨#i.T*ޠ /ÙL԰-y HK1>IW ^hBHLӉ5!yXXZBꏁb`mL؋&H]+FPѪ{r;/ݥ>Ch8>rx;yZN`e[HPmbjR% ;GO0oJDO ptkÄkP84&xҚL׎sX$PixX S84wGj FocY! $\;-vUP3_ŀIR%i>ifd)d{iGah _NX/xY%0'E2,DRaߦk0&yאz{z7˧D2x&Ǯէ?L22 T"]=VJ̾rybć ;^s=Ÿ(Dt;Dza4g=PƟID5ݶk|:ӋwۅX $xeXrMA+XOE{X͡ڃ-t*´X$k.O&~7.,Ƚ},WK{58S,YD8|n{@^:ŗ%VTH^^Ҹ7`B?5 SbG7<32A Ep0sxIt:?gU<&dnCˢqF+,P:nb^,"Qz?S&O]Ep}yn {.m'm?? |} 7U,O$vr"] %WKe۱N6[t۲Mi\ѝYwALT <ʘ^͹62Q^2Gx3}w?c}J?/S.|%Ž]9<5J=<RXW$x;ƕǚq[1JdooT;feQfsKfiƚ=xց,D!'ރ? L=\,ba6OYw9hb=~ޅ5~vPkJuAG\5~PfPU&EĢ#-`[VWV"'߈.x8E&E&ߎ SXM{I gx漢tkW,.F8,s{7W+ŕ0tJO/wb^+ѡ^/*V\߿ ]^LD#}{%'|z(xN@&>v,2 | 3m~ 5z%u3*oNb8O^15ybB\,a%饈}>3tZ/_!Vh\vTXY}~_6> ^t"ҘIcFIM7:n{?p64*vەb)N4Yo8MLq츉黬F:@hRZbUgr}X>L Vn>r}&L<ry@rpw7c$ `-RX啕=a~iBL ܴpk#&lRgP]5ddR,O&[sKg#$xjZ@)-금=aJǁ31‰Gh|ywb"06 WS ө/'wM棇LJ0?!_OcO<Tm!DXr}^KCD_Oxo}oQX{k8!a\̇7>8}~Goj^h^q%՘نErX4&h&CX C,?1;z=big&@G n&Y/%4kD|x 5o7&uL4c{nĮmOn8s;6&uOI\=Zk{Xшa7BkůG.elF<mK}s)`1<{wi\{ &J&syk^tϗRX 0ܮQ_G1a0 Ԃy: 'xPʚpqMpEgY/<9`p`:mB+g KܐQ! !D]˳ڊa !o$xm+5ۖ!j !DR,?"`ZBqnfB4b5OK$ Bqդfl !EH!QJK! B! !B4<9ˎR'eY,Ȕ;)nI Uq_w ~i_w=o?cE)RJ)ZY(RJ)Z4YTJ)RJ)uMRJ)RJGERJ)RdQ)RJ)y4YTJ)RJ)uMRJ)RJGERJ)RdQ)RJ)y4YTJ)RJ)uMRJ)RJGERJ)RdQ)RJ)y4YTJ)RJ)uMRJ)RJGERJ)RdQ)RJ)y4YTJ)RJ)uMRJ)RJGERJ)RdQ)RJ)y4YTJ)RJ)uMRJ)RJGERJ)Rdu01_v(RJFoFYv(Xk׺ 1>ǀFk$RV5_Absq|k$,k\`~ۢRJ)4Rd5Xk&7SƘlRJ)uҨ^(JNƘq?9mݺQJ)tѳbݱQJˍ,NPOnc\(RWbHiTcRJ]H,1Cbz1QJ)C@ͥQlRJ]Ntd0@RJ5S57Mn@fztd ־<5lRJ)uO$7}`v R Y|1s@.hY(RWҨ͹dMRJ7IG߼:{1OecRJ)E?QZ6F).u,I:/!46I)%Nl4j~m R4c%7#MPJ)T5/)PجRo,E=@9kR7>f*MJ)&ixXkVr3mZ(RW1;Ȋ\جRo&EEcKkRJq +bs2=U)b_^q&ckRJ7>f'oT5_Y(ԥF \`Ƙ#(#HoY&)RWҨ9 %7c1{| RM/0cl VK>!퟿o گ_&+Ԛ(͙Гlc0G|S?@~cJ*M/cL䮆w .3*p׊ 80j@70\(FN`/tzZ-RjFMpNlپ5l̘|dw70\coQ%.I-\^KGH`Z6~RJ]5?ٱQ֨I+uzǞ3Hl ,!qyZW rRcq\GsixYk琹.|2EUEz%k<]7pSGF+TgZKRj}Ҩɯ]ԥ?>S'Bch5봚QP&^E [41O^F78TT u73q<Yh$U1a{ V& =t&c5k$$SOtkI! bY1/$;1jc}?T- ڝhҨ_&K`+g'B&UZ@@ &_#HÆ!hA.t `FCn|V&a/?}uڭNP6n~Ҩ)&keuH֎/u\9@~2b )),y"C甒&1֬qJ }!^fclex AakyۦRkj|1|~`0Q5CkFuq$d^yEHcHg"c 6DF`,kh>I߾ D.ВdO78\~[n?F_(O z^jtTVx,1Y++cEaq1`m Me\Asiw{^$ӳmTJfj pduP{t޷eM[.(cYx!p}߹ 9<%fѦ$6왧IdTf)"*RtǛu5n͞@c4Y\'12% 93P?a<cn?j 2"A40,"]MRJ3>f#$6.R}^lƾÿ#d4C8+^g &Y66"}gSl^RȀɀsR`3pE<ck.vfm=@@c4Y\_Bdto~>Icvc~f$Md}jj%ݡy/yjz)jS3v JC)u _!Q/7S;Gn1o|2y϶wd]& Ŗl$`'z] րc b|Ǵ Z4[[ ژ HnQ9tεcpB]TVC]g z)sތZs/I]H۫YN_-D*v.&_␵+W+ԕ4jȹ/1I s]?? յmzi$&)ϩceM!pxxȶM3rR?.f =`;!˕ʃkNt#q{@@+!g`ZqKkն,K 8X0&Lbs3"[oAAD`Zf ]3!rw 7l<֑OklV,SƘ>9&=_dZg1א^˵ e I<$Ht&QlAnCV*hIԧsbmH_=N.U׍5b3)I?}) 30lII& ?v~%CN7 ᅖKdH VZ MZqߛJ+N(!مʍҶMǞ̴j߸6ttdqNw9{t~1Unz I1%ܗ覂$hbOndedm13Ƥ11j"J),KwzcWS6ʝdP5/% U=3Y!W^/NH?ߓ}]Rez%\j5CBD>,f"i NƏFf9 lQ:z ߄"e 7Nsɸf`@c4Y\BN{?O:u"7#p2:^ H8l6&C^5>jT [͌jlIa56FuZlH2!0E~ؖL{Tk(V y$ʭۿ!%/>%rSAkن ߔODFVr>nJƘ pju51rMGwrgw51瀿=čA],~Yd>Rfj)F1uH%#krHhqg +i;<EwN7{/?_ؼ>=7=|d_E P'$F@gK* pCY 6$X,U2vn0@@8Rȇ‰g^d-Y;~uďG^m6 _gS='N//N/z-~G5yOnqpk72kp>Ye:%N#_"/w(1Ƥ^"0bGm\D: _ )\2ه$Y`Q,oޅ@!SQ_Da)J+wȹnW-hl^5J\XNM]0-ԣF/GSԻc]:f46LG/ ̝)n8)\Zc5ʑT=G&^ߔе#)$Q ܊;Aq12EFF~ZB:eKqEG#]Izo_-M+Q{27\8}`{Vfr|BZZ-pBp/ ~)up#Cg΍t|s G|',GDm=pcIF<>v8Qgk]XX:8Yl/^3ڜe739. o{u(쮏/ս]Ku?یo&sx1|Ywg*=xӎ&dٷgQh6q#SiH{͔K!!gLB&]!ߘڌe60d1r0~O̙=z}~k)ھź9.+9}+ܲ;\ OTn~W#o36 GR#3nd]}Yn,^:<$k'B L馳eB[ pK?Ez1"?1joJ*^!SzY{) zVNI~Ce9=WR5L"=$^t@t'>RG).WQvl~ 9>9;?Ш7j5{Vla7ېD4*Jl^Fi؜ThBoݱXgn=:?bFX~l_c &R!:ȑs8ISKhCp<i2B x%C+48v#g`uD9#&aBŞi2t[/w\?PIu]5nWffgwvףf:f'_ܑ,L 'mv3]WN:q6vzqv ^&8LH*!Av:\:0{8yl<24T11y H\1>ek:'^K|h`R"}؞<r]MN{Mi$Q-$G(*.w1Ņ8ޗA1zr!(^4=[rJC]f=3qdc:ec{:KLn:duGz(7DKG )rsrC.܋/Џ"m"1}A6`MS)u94jN RQ:$mM$>c ȹtbb 9ٜ9Y Љ)|8yE`~|QF:pu;/k6؍J_HkqVj0maM!eI>8ĆfrؤU6\aTo:^|6W uqLSirqL5xq=_u \L߅4Bsҝk ;,W LV} y2٘Ro \7Ƿ T`83_kNm\s,Ǿ_IEmѫO9)o3 b*=;RdMr뚮}} Ȓ}Y&M/IHYB֕6"EHA.i3t.}b>\؟BFɇ<)d!{N mi!I5i7vcLuadc1y1I?-GZU{}6>vJz-2}t9N!$rN!3!Q$Iw#uIn"wIf14qDz K2.Nscs/2-O# ?&SNlNG@NidkYx&6_?xC' ͛g(i[!=A+XWD."q]kۢFEǞF>ȱ#ǐ)UFF.Rv%ϝMok3t#RRr;ic k\koTJu̥,2t 5 H |s3]m4 Q C9N jr{ Tހ-[>|?al<؜Cf9Yؓw]f0]ÝKU>lI4/̃; Ser>yGZ,Y67n$sZ.peZe։Sк`Y*=ySi^{ޣdr^t9N-Zn[{\,5 ؉Ů*hm-Wǿ6<=F|mީ>>|MQ剗&B!c)엷m. jLU;hgwxy|v쑙nSinJjfw3}qEF7GKvs )l4N"5$@DEzhH4L8w!em ^?1d !dzL_Ƙd[v;"UUm0Je4'5ET =߃;r{*dTl %"٦gkO$QD;/nyX;a9TRJc\5GX!s^=$v)$. t5> c0 `BF 6Dyά[NdhJ3nz:؜)^d[vl~nc3HI04Ƈjӕ7$`hmRBDdR5>Dlle 4{#]^:*I6kV,z`U%W 3:.f0\|#<{,)!A/a|ְ{8&CfM?׊М;ʤ&fփ)r9ܿO]m4k٩tP{w.M574zͽ]ɾBvq;q_>?PgRxKj.z$6k2'}YGKD2}EH @.B"2,a ՑMI^-k1 GvRIsD7H;r>oOgC9c2c6!ip7/I7 I'ߧzI'.y E: 5 7"}?Rpݫ^D 4ۓ؂$53ƘZȦ%7_ľ>$628nd3g0r!hzrOve3;ޒ陈 ?qqyxfR޿S7{lnM77oDkd&&?s=s{:j\\q;}646hxiDg򏑵d5b l!'|&q2;d `HB CI(RHB3崅B:Jp#=&yIU[?ӯ'mN%RcߨwY/ D.:#Cw"7#6!=紋䐋H$7%n+F`/5!j\AΏ9\ێ.f]vl>9Ő`e[t{wIq$iCqk*{2c >nҍn㑯4.)fNHcgh0@RTRK? >)@Ţ}٧{9E;`jH6?l D}G KA:i6arֲz75>Q3`b> &٩V8n1\.z<=66r!dW[W1/}^9pn6Z߃/][gi9W!A56 +^}ExKglޅL|2ysȺC! fZH^k}@ܤ a1rk/Ywq 2H2 $~?y=<1I4+U;KtVg蹃Hp5ێ7fsȨ0d&rsrs{ ت#Ss(r%y3=~\<9>Cō@aTJg?F:Dgo 2]Yty 䜇ėIΎ tb g$8d Ǒ2 b1Iap&!Ē;-*QY3.m|ZCK\pnZ}rT^g6G=Ҋ<[iԡR]PY;X+4ɺo6lsomښm޽4l|/C&bOY{oz7fGY|uofO:..Xz 5=tP;ߨx:ilڈ\S5g,>䵧k!$C*Y #= !#}@"]Ƒ<:I"~32$2tOM#:KڣE:{BFn: ٗpI:k"Ss:csdJh Zxc#*;leqx#; #tvU]9@>䳛A>edp,+EH;yPr{Pq#oJ)Z^+6Щ@urEZlW.a$E:RעkCf~vұF`cb!paLFأ2a&TeKυev P?77ӬJ9F:B`4j+sCidㅌ̑om6&[,U2룉%;<>r[?!6iF##2qRbi֩&CKʺ4|v5פL8|8g{ȐO;bD թ]̍XηxPJ%L/fY\6l34L3[+ہfw5rjGZM GKNTqה{qg 6=(WfnTwo'ۗp<}l2Wk,%NuZ[MqҞ77!4!҃J:'^MIjH&5di9߉'WS%[#K}m'i|3iᤍ;##A/BF#U\OYwq!L$mAGf{u{3ml't#knA: tzħ0<de/2UE6~7)Їk1E)ᮔRō9|g_56U 7D7E'8#&]hb)<&ٰI ۓb8u妩`c8yMd?ǽȹ0ÿ}mh襙fe)$:Hl~N\^ csq. b19Ol,P,Ry~Jb)TYj:AO-ǚ'Ե}!֡Go}1x.jv"}O/֞qM9=;zwO^]o63Gk[yջzVUo\~OLPp$6gy^#JfzJ1G.Bcgw {^ ;y7]/#r^Aҋ[ S@Ȩs{F HoI&k L\<h*yl?,p&aFƘFg6A헒7R&B.N*Bݳ=B]۽߽ȱ,2Z=y؇$9;7#qTtRd%Id˕ǭ7+JbiԴϋO`X٠cHv9GG5܊!e~ܻ]1b P0: GΙ[vxWx(h Љ/GlX5 HgcI4oo|1Lf>$0C@.H> c\tr@oEO^O&=Kr]çpG 5H){!U/g̓,ɮK]wς A hRvt) a2` lLblZƤ hAh4Hb3sϼWӳb/̗/~<4Ʉ+s2\\ | Z.)~B7أ5sتXT-.)W}ݔD63xP﨤UWOA&3y6~&V6xiݮ$^vmo_:sun7T>A%WlNrN]=FU1޸:zgg~t-/MQOBfy??''exnwVP *&O}E.p2(*b{z}TH;Z)-W S G{(<="QPQ{ֶigw)1s ExnE]QYT4uA-08摒zN>d^ۺ߫kkDK(QN*j<6CqGo\Prsy lR9 U?~ PfqxƮs IDAT֤9% b2 '*G(-% jbNk\Gט%Z[7;ͿYƲir@"tG6Zy$~FcP?LDtP,%%U<$"j}v=^SaLJΌ\I mںZ>;\[ېںTd0ZxڽWr3k(72}Tso.ݏ[b%,TpI%" !f2fSLY"! E*\Zؤ XJ[XEL|jaJzA6R\V1jDx‰!:rxa3Dn]g`rq44nxzaۭtܼ`h뚫a6&~e?r9^V}ᯄt`tN$S] E~֬zQ5L*N[Dy߱S+V|O/)BlR,QŻ1xI ^GMϠ` , !BE.魭 jǝN1JvPb>>Njyz>(2 !{JLQk?-)c-([o XB\:XEQ="]=(LLo5a8ϼtysVO2O]mPB1aZl1n%,$J(q'#Dc `nvP 06Q'.7ˁaFiZ|kA"~[ں88 'R!. F q*S<L*Q #EJߖGI8 d9W!BDOib,:93Rmwyvjy5+w[eLcEs+M+z-L,/w{䏍 Wk6VX@A@߭WI(4&G/w_e^sHQvEODBos%Jx[.::Cym]C 47/}_m%}K?juc@"KmȬ޻l]xj1.j{Q\k{%?ORjPQĂ/r37'mȾ>W{?nc ܚH2}3}C6.#P!;+pgMQH1&H[`Đ&!3&PO]byez.4!y?bt挷}ͬ1E>Y=:l&= lK 3qxrqaINͅjF"ʹvwVheCvr_4ⲿ;4wH+V}ȯgv5$MLY a7[k;lobaCO,3F Qg}BxƓ2,]R,k5TsPjB\)j@?9ցhM|n^G2tJhZjZ9| ϩ~*tOsQc% Μ,̣Rgk_RZD4)i k)JP^Mɼ żZ`nGowyLl oy˒USB|0(QNN<Y\@SM ZCɹ+Ͼ_ti|:Y{7k@>ںfmGœ3W<~1|vP' y~ţEI PEǘmgm]|!Cz;n.^o@_-㻨m2ԙ. V{cLC^:굓;X6BҡaXxXFL.ciY fFMDt:f q9?a<&'c#8N8,W3“>١`Oh,MX8J¹3ϐ`#L[1E͏}aI@l7367 <2ӈ҅~8K.yQh&iԭW fppk"Ibvq@$1J00-]R=(❍jp."}Qt53XB0ɴ;.V܈*Vفh"k_tբ?~ !F5%`!Q)6/sSq̀OH샨tgus($s1Qw%Bk;눌e}8ysEq 7jVLަpPmctxe|μ}6XCAp&'`,Qj; ԢiE?gSE΢~7kroCc͟o/ ū=CioCf@.*Kgx7d2\\x/q/2wt4*ZԘ Vs&*jX\[Mx>.SN_6?9T; RpEAI;37hOꈬI>Z!ajE 2S.R &'spLPX")%1 vhm1;2\aq"uk?OD,[(F./3Xn4O$ـo&?y2% at`#U,ӚZ]6~V0fГfI#A z .N; Syo'CH7u;ѧ&2.ʌ*FwcEˌAM,<j5zu-}>F ԳGP M~!į%JxǠ?EEѭ,Xd-|=nJn.5^ކKT!%dGPcC1+8~ 5^XBZ@&*JI}u7][mȽuce(>5WxM#9ae9˹,2uȾ8>rT`]}?޴c HcRR,a 000#<!]QqyDnf(g3$(imN-ڶhguCj@&S55vaBӜl1Ks,COA"ƱDe8,mw0vdeh͒CX5 / v4僳~ngHoYdVR؎%Ui6&>Un,3E7=1Bq8r  KnQ;:bjDJ_%t.޲0RZ&+x#j'm]JE%;lGPE6j5>?:ZXE9fzߎ>"еp5|թ"2ΨbBuyoyQETVezCn1&(Pu4B|c,Q;y]Z* ggL47ϸIm!uBa$ېϭ{c}mH XԞ[Z0ha7W, WPcy(LoCkNS-Dwq- w~?a1mc%.<*S:gF>Xİ2pB0BpR"0U71 2IJ{`?T!͠A]xIk1w5icma1TeHmШBteYřOCK)GW-&H )0q4r`Fxn*cjFaU+4@̦F+f_ֲ. a-TYfKS"L3!6IJ,"acDh<}bg 1rmaԼh97wP cSO鲎E)\X(P&1DQ6zePr[}}[v_+6Q1~@GѿE @U_Dz>&e!sRLJx~yN_fk8( ¹QxQ_q5[Ezjq 7uT?>D%̝Qh\av~eμ}Mm67Ӣǡʾٹ}ǼeMd>2_ ks nmԯ PEk03P~ z2m\D),(KC췖>RNQdEN@%k2%ajY:΢h$Cbba''$ <Q3L%͊ ,iWx )"MoԮbRVX%wf,9֊`Oxy!^ciajCgV]wgd3'% w$~L÷-a3@nZ2UTS*x6=R`fӡAf Ai $a ^=k;pXf)5F HISgH$ٌiԴmYs jnJxB =hj^r5mB|n!@+!L/0OqqQ)!jMm<-0P+LPm"a_̼<B эy(ؽ`-8Ko]xƎ} ՚!~^J%J(?{r? #%TLo[dF\&4~Gېںx0jbXKIa2_MPs P\ں8 .}VPU}mCOKd||!m#RlG5KG H`0v [ǡ=}5I.1مNHx3RrbKH+,t&ӖubCpZ$ >%Jօ)ܭ\D{7s~0m̓o:Wps/~eӿ΅O2(lg!ں6sn^Z(f^")j bm7{*q~zL{,%swWa҆܅%؃ J1˰c25ɑH9S $N[H`QnkBԁV!?Hm<"w"R-ZuTÔdR_tbyyK>PZV՞݉}UDotOin֎ F"uu\DC/,sEwYq|sކ޲y|qѯm6dYyi^GBSqyCo "5b|pc]x;.ǥNL%yG53w/=8gy7zϾ8]S~sXc{MoCN^[MP?Ë2ũO _qM{sn^[k(X(dQF;&ͿS<߶[xhF`[*rFj _z2N2:ApK IDAT݇Ik9TBiL O!QuB A$C~=[Dras}iǨ,:>;.p9tF qEVu73ײ1c?wqMr%v37{Χ7OPn_/8JB=Xπ쥓8 =lbzy ! ~{Z7:+!uM Eg.LMlHFD>\61}!<#K&7![wN?V73t”.NfFSNc(SbˉͲc%8ۙu>E I#D&)F v`($gG 'Fz9p1RFFC#$$=)[ӳK܅(#w@[]*37 mA2Eo=S D BC߯jP?\Abn !Uዔb^=(QE|*V}Ν("?I>e7Z4~s {2)׌ں{4&Cˍ*8Qu+0Zj%䢉߀a0F șD|{ IQ/c4O]Usj@.VA-穝-bKD%p\8 -,>sli36!+ő{ܰ9t/aoש IT0pdNDXlCBgIķQ:d) ա䌟* bAB..vnze=vHW&@ >K%<; ڑC:RK}m!nz;RmmpS.8QEۨ%FI)I(Qޅy(;_ Hv1W7s 7 ۽ .VQ! QO~/=+?b*]@@2`gv¨ Ow`܅\%36~ ? H)&\?TH܌HD$ǒKc:mr)0C,vMԻ37id!!KWyʷ.M-ZSc:8I̡poI*Xӫ)WiA2elY~U@n3[' qas %3 fgDa`sv.W }RA*qG,!ЂZl2⮼ gң%K]+<~=[do\Ǩ.R !j4PˉϡL lJ0wD% MK6oԙ(]f-欢pt^=6dž\7  %8D4q;?dz RܜA[9D+m蝅2t kVRJUŽ09>E6b&' cHsy̳rBc.G\"L[В)j>Slll   $g絋R3a XOzRwd[7R,90Qe;G haëTk|w_U7,B@| m pXE5K$vQc2y b^ԧ%Jxav[anphaz6-7zè i'PsKc/z06o3O㇤/&O?Ld&bh崹WNHCP2'3 SdDJy4l{m0!Fdӌؔ5ٖƠE;˩R΋ߜb]MthGO<`bL"q1r)LUqӍpa`K B0^bR>C@H6& JfiF6]#%$ǤJ 8LbcabᓐQxH!H7HyCFR,(❃BQtL~zoԵ`r@3)^P>*Vv]%=+'#%^|'Z%Jxw`L,׋ Ͻ67s{Uz7*7zƷ~3rm&n/\b\&C &:6;U )x rF>C.tC$aU9bai%DWz$L5<'rJv={ʋ>3-jFxF9`ɺ)= L[M65t9[bKݨr2uCHxa}XYL 57J:Iv=[]p#Ċdh`+AfXYF d$Lbb!88(TIz@y7UL䀀O\yýFKܹ(Y;ƼYn߽Щo$SE^#5:z="bwe,RDgl^EBZ%J(f[>g^p{!rYQ*kp[4+?_s@ې*6=R"#a C`t8$l-k{qcZ6*8bް]]|ŋ8q8)xJFudl^Bg^Ep_^~Z'9#v=Ĺb2 #[3*omB3Ac`%cFx#cMA#UZA'q`G fU ##rd(=錈 9rrL,\6 M̌MwPEJDS.(Yz \#n@775F]BehPw &KxcN9D%z2_[C "&s-54pqZ%p#Z)eqJ6 .` åsa&.r)BtLCT LyֻN,r̩1VηLz̑G\湀m+]CI' يB^ݦr$fո 9#BR!ٳ<1L_"ssv-ֈHV1Rc"38ita1?ٞT6UuS C #E*7T}#)1H42;HlDFz 0nLczbV wt[J.*8"Z42>3Q=G(A))BӼmK(Qo1(zHȬ><3F2")379~ e3RHZ $&(U86?a_fl^m֧N1lցUa@NTv8I8 ub:#"=`=5wk ̮F!r[0Mڊ 7 [1͌ժ.6r!WX53 dS6uoI4jUd7  [4I-Suu$۩BL 8$X8$F9Q1Ąd}O= LL'A︊_c?:ҟw)JX^2wWBUJEXvxUnw ~%J TWKsں8$ܼ9R%/S.B\DhvY|t)c!مkJK>8B`a˪b b͐|·ƋQƙ0\}:01#NutƶaEL^l3&ei u \^. hX4!A,h4T1G)`2PMʈN4jĩH*'̰1qA'!mM|p& )n @W@@(fU9yS\ ˿fj^K܁(b{3nz%E]ړw9ޡ%J(q`o;> f R A,wg&8!&,][saU^)Aށ cl8?A" ݀i/t*3,e#Bd{Zf`Mgtm˸Ks`a V,\[6&d iN$Fr6+A}^a*9+l)#8lIJML]P-r4ԃ)YƁ*G 2,@)A!l)tZU]`_ UtLY|)K(vbm]pwks6̹rgkjAG%U0f{vRK88!L LpHbr"0md!I5Ƌ$A~2{ YH7ſhbՠ&c /brōaOL6WDDw< f= {6:. 697ptLv%*oXMsV<c0EJhsBJ J>%&DZ$~Ub@b̂d.FBn DbФ C&9ieL' $ #IbNVp0j'3),'$4aV*S,xV@,?{+Iv}Ωwo{IJ)Eyp(J$B$ Ol&M;0 $DDc-%%a˴H+rܹG}N{9WK.I-[UtWu{~/<5勷 .\o;8)Vn+V 6~rkStעmg,7c:mTݦR_eA<uPť3 nիݗb"ڜgOc7YDd ;aSlye.E 9 #,mo^b`UX$T`;wi řmZ$-M"W-k![9\}L2'nl3RsOH-SWM.¤ *^qz kX08*#vd͚2,iΛc&|h&8"Ad A'#p-bM٣%_Q'%, QƻZу)>L(ގ%FY䇁g3_?豜f~_~u>8E{KMp Fc簂y:|߂5P2\o x iHHlhQEe@lLA+OB&V$(FNcʿ7B)aتYcί #;=xOYB'ݎ䂛qԼZ h\C03U NEUb9{9b3!%kOLQdZ  5QH/ACt>%Gc8Y6pJH"I16Mh3GQ\+>Z@Yǂ2pYXp JJJgX_}';}Dj.쀑`$Q؄.5tw[ z8w*}BU0ID1z"Y`y; ;<{Y '?FLw4qNlޘRY^\y 7sHvRDuB Ɣr^(MnwLU0k|d\3{`a/D8&r oF8!2- !|$m"Q]sY̖7bp̂& #"A! uTI3|+w)QŒS:B Ѣ,Uc,Ǐ/īF笨wC+41B8u jCBDZ/%e@=k?x-d lVTӿbcylqk%vwda=iuV3$V(wFLoQBe)&'=:f\pnWP$7 V IDATHkIaW!؂J2lt%g/ T3(H<3.H&ꢂplg.UHd횤SA)TG5QH2JOT\o޸J·Ѹu(oȠվ1l, .lN:4 YL *4 h*gL>ڐ{Kc]ۂ`){} 9UҔbT !~x 8lҬ.xQ+Wwx%4+ULu@Mp"Dz@ ߄美nc'Vz"Ysz6#\#W<ǙqWժˍ;%ƊS7V k6I7Z- 6=`o30Snj7x-Ξ;w|0ÜsW c: p=sM*KS`'IC,kO^ح;1 )99uHb4C3'TpB0fiW_qiaJXrB83aOJ7z %%%%%?z^γ&eg`f1"F`/Ù [؟jTQsCIg; Y5 `gC4ɮ :)}Ϡ8썛,r!QڳkgdP[dm\oԱ٧+q%aCD^K +&s~ɍy*1儸[Uڱ2ھ@gëc>̠--oH726n[`ڮmePh <#? IH(`H %Q5ԙ1;:>lM`ΒR,v,fb9f^ֺxЃ)))))@/᙮;ZUna?I(¾ 琝eٷ[I5X453DGtԇ.I9d/lɢ1ƎR4LJ)*2=rc:3|曊ҘɐBWBd2&}R'!O؝Ҷ5sڨKO;,9XӨ:4kU"GStS. 4%K5JE$IODLъs|ԏimR}̀d$yB1xf^œAt_yQǥm~)bɩFk!6#fDq",O+],]RRRrJlF"gD%†W43pZ0},3흧v%V[6᠊ݟYT5s]سr67VW$fkM gkrYܹ-CS]OaAhIʈi& = 7 H,^`+n0lY^P͕U[e̡1ysb5 v`rg.LFxP5Kzut1$&766EPx'^[CfK| (bI LU5J2f⽧Vk咒Gko``1x~lRsV=w̗pD_E7^`zBC+Xu;LFf@}h+(!-'F$fc+ גK:bhsAӜ'0$RjEΠih.ۑJsaNE]Kbd9*>Ooc]i@1 !<YjUnAXL:'PjQ *lCRIA-/Zp:"(»1/긴ͧR,+fR-ќ,?˘`\%%%%%')0K#֌0`@*[: %cν i8>opۓYR 1w13YUIܨ2l{\%]C4`f&vDC~`3yͷ:u[:{8w<;22fTc!HsRۡ4*kUG jx3('_TDAvp)h2D yBݡ'ܤE'(AM= mB0Z@a2q!PƈmN=!.)Q;zox#zx/Axgjߜc0na4CՄu=-t=E5vN}.-BQD..  vd**i76im~t^Os-֯ ;h$S̥q!ƌ{)GL&y0UN7NxΗ Z<^@%#7~zJi7Lc-3'ealI&:Č5Q"A z=WkIjf&f1.[+Bw :ScM|CRX.*14~2TQŒSXk !zyKS0 ?=֣rIIIIɣ} :bC`B+0 S+By:4 )0 ^ۄgHnXR0c2z "8Uv(׋)7o6y򣻘r{ [9{ 6FY!am IcWe,TLތluIQC[UyQ=㢩t4o-DO"&ڡ35MvsCF'žH.JĮ`(ÔaJpV4Dە")PseӮ s=SuA)̕HĬ8c[^ %0XBjZQycfYV%qSG؛~b\gl[Rr.|sm.6mRn)%AKu @ ۬_kP4\I^9Dͳ_dyN\Xll^gu ᱩKzP$nz,R'G5[5"%mj'pغѠj$fLv+sMjhV1{F; eɐ2%K ZJ-FžJaJ12P5&r5G- dcrEXihxPBA5;#P DY4L1m9:.%GB|Yw3jG9"CxO{#= f^ޒGke'<O&] ^NS^ ׊›:*6d;XfU7)Q,Lȗ1}[8\z6:?BF0YewIpsѲ>Gebq!G*M22 O*2ƹ cppl w0 H)8䞽H\`-Xx&H$,^1Wi!)I15fT`%BxJxʅ\Պ+л;Z;'0T:.NI^?T@\싦9e Q@E#3: S(?ʩkRE0rq^55q۠?2aB^m/y""]/hbT0g<覍4wꆒFJޏ(LD^;DS?QL'y&6cIKD)Zfe_m,5^q%GV,'^]RPi(&NC&F1 sH7?z.))y4b:X?4.~m΁9T .8LheZD{180~6/rztοbeKFZFX[=3&04D{bhK<B<1rHuXeJ@v1HbC^X#=ӐSKAk%8~;0cg`y0DSM t*HDDqo۞ j\f>jt yӂX56 !6 r)=JXZ`V( |臱_!+x=OB._&3xo!nvny,]}יw2闙U<$? ܵJ(qfG|^b ޟV55-4xM|7?!쯡kct#"%6}`ѧA"Ch7m*OxCx|n[M8korA>ԣ%rƽ9!\nWýѬݖ RL&=>d섧*!M ē!c1w>f;0l#GRڒd#9EE$2;N) A BS]v=rC319Prx6JixĿŬ/ 4H$c*Bf) 1;Y@x&3q,$&Dw^h9*IGa%%%.?8; E]{AXlsg텙m^{Ak/f) >o77 4Lkjr٠{^o{ɧ%*c,6 .2tV&_s浛cGɗnY70̜:н8E˺¤~k`q2!w#抠x5Me)B]fAc8[NZKkWBdؗصŕc$s[ǹo&₍ iZss[3#Iֆv./E"rI0TGS JCM l:. RBy0 c>3aqsG([-**{q\??JIIȳ^又~'9 B.m~بp6o.wF۶[moDUݥwag)&;OgQHML/'{3Ԓ#;xW.rwk>`'čjcUs UT>"/Kjr|)dd1ܸYg}Pe %# FE{`ƢΘUQHG#`ȩ`de:$!Y\`vQp])h]{BeQj|5yEmZK5msVHcs8e:(ra}X~9#J8X=J{'[c{x`-ˎ=(/2 b#O Z?URRpq1³wߪ4rfݥdכƝگϮ% `_C-oa7T쎟sc ӗ5*MGgP,3/[EeLR$]L?Bꛌlt4ec~gI,1TJt IsVȜSzLKq`1s)q(U,:Ϝ=ʔ}%BiI BQ ,3*;D!e&4(p QfzaZJ,3pgV7ݔa&Ba 9,Ʀp+p6̊Dy<NSmf._f{}ګ\Zk-H{,dXw''N#/v-w \VRruE_"=J\\F_ڕ99&7'ϭ,Nsfa!$b;ZӼr}SBjpk/`C 0Υ84QWm0ޫީqZiw\3|tx8!)-v*[WUE2X҂3c6P"/HRmg6^+ݪ["ȵ'~<-TnK14kb+hJvUV@Z 3Ɖr%T7ü(W[2VY`'~RjZJ&'lլ=O-F, 13IB|DCpMY-~ϙG'E2lB$(88fv=1 Ę!w֙5d.))9#_jqoj-Sd˓ȲP-AM0YT%DҡXs8*7rq MZVȠ/Yt5jDoh&eH)A|f%{", I&2bGVC`+J`:j)`b2F3_1^j\=ؚe41:rU人1I(@j_z$niO)%g,,wq-3'4 !Bd%j)3,GQݿOx}bٱWd.d~?,xr'MR̬uX;?E}.OYmE`ڧ, IDATq6L2m짚ⳟv%/˨e 5#aKS|I_W9gmgeTH8H{Tekhγcw=o6N۠0$ҡ籢 ^$Xm`Xn o7CaFaւAm€B2n\$nұr,#( Y jd!"gf_TށeŖYd-3й-˜oRQ%YT;]UW FF=%ykѭ#-m6 0u:.m)xօ"`vdVq+c֌Ȁ[ GymfخX?2KNh}-'@RCl+7>/,PD&K&fRK_ű:s4:KM1a,v""KhS+:V 6^򘟋9Ʃ*;3&Fgt&E& A8QILl"7V`m1(qBIalw<*T+mZ˦Ta*eؙ:Ij1݇\RJ{8SqSl7WV+M_qiO!J,1dC23 Q!oT*ZcJ!Y05_aV!ţ!K(; kS*s$U SlZXj4i‚2&yF45t40j]M!@Z2gېh(9j'YCe"NBȢP@nՔDt43QzrYuf͞/y7Q|wV-/ 3WOO1w$eϱgj΁߫?c7Źumq *9,gHos-S5E$#k7T[:WfHس A 4jt4rqd*s&ilojv C9+C'~;CWΙZi a(E`U!J50r!e*H-j) *>7zj[7}t Ra4:*MfWIt^5P!Ӵcf=WTͦD8:'5#ԍ9PeRJj{maRHZ)m9/xmޛXv9w}[U]$E,96mj/8 N2d0vb 1d y(L`!$`f82 =X[H"k3{YjT/Pw]߽[8[Ç6dOvfD|8シQjM}w-4wMSWuq-p{ݶdf7#@$_B~Tݝ.X÷2Ὸܒ*/F6ψ⭀+|ݥl|]6糣lTQHNx:{7ԽD|@zxKo+s\d9>xD>YzGsԘ^ s1j<;J+G ;L& m G" &*n_SY!9{GU_2ie0U$AQ)Y =-mF:D*PAD>L8lI: G *褅KȈ v@K'8K4d3(_d-[ҳ;#"%/n')|lv8·8眳1s ßs.gciOb]lGIcOx7M_E' [m)rnI~  h(O-~gíEH .v1pdϏ-suSI[ gZB0y%ޤC?+ ڴ ƛGv 'L(5ieS=a(#\B8++ҸvD'9EpJ W. ,/y_:^\&%6+脕c=Uf0k* U] tdTp꫻<Z27$ڼJt00`%+P}7^TʇYd"3|V,<{Ͳ!m2^ͩF=gx!"D-?={e|4ӟM{`c.6Yl<],sz]$&*ptnikш2>biK-8 hcHMIC69)}%'s7Zc(1h9ﴴ 7 *X[lU`I4Ab83WKTXIB0q *8jgDPWʘMHat8χxvܰZ;f,HT ^sXt: i9)JSW]6Ud-[y2!|4b9/ƫOρ;RD;f%\7R“5x/i<Ӹ6z^8?l6Oo P6c5!w/X1+5 6'P80QB*sĽ?Ϲ~ʧ_{ brg%W5/^*h9ޝS]ɖ[cקs.x6c)X E=2kq?B'QFơ/]("Ǯ'*B&}퐸v Õ9o9d3ytGZKK~=T=hMr˲PJ%"r,ZI;RH Zzqnlڱ|9i!U%; *ױrnCbkKF>)l\JUtLQx;)o(s"2OtHW^ׁ5y 6x37Rs :~ 3:> MZrt/2/[OVS?dUQ8ZItSHk7@ljN>PZs`ǗH{ AHrr밝$prg@<4d<')Qnlg*Ǖ!-mIvJV[=+j({9XFU m \T䨵RKF,R.N T;iĢx~+fP++Q w]%YIʼn]Q!mrux߮:%yevV 7\҉m!FOXҹU`F]_Hb>8q%5_\~Q~ n9DDs. kvz(0Wsտ?9WS_H7CKI^s5(6^pj146mIsx<1+5 <W^ofÉl2*Yy?u[}NN=| O>e?p'_ͯNP=r09[:_=T/OSp$aP:pF!a=3(5aR2NI圐GQ*tvɓ{@whA+#ƒ":7`}*F7nG͘ZN 那"UzZ!㍌m!.uk ЊRl.]*J'J%D *TUE:/jaAW;]5°ۮ\L.a&sN;kr Ivϭ4{k}Xȱ;'|[,:F"$ߧeŸYj.tPs Ҏv'H |*"߬(j&^p xtod8ۯMBڨaMn!4N9c:(8觘qF(0 ݗO w`99ٜn??uy{_%Ζs}|ij;e󩇥&@KN=;ץWa|Ț(Aolu˛V߫7Y?qC"a*Zv 2aBYcPBXZFbzP U0ˇldt_٧k4J'q 1*ThW$AVV ԩ]ZF7$ Ҽ3w9ЃQv'Ýs5;^rp[6?-}|K^[,ָ?YI{^ůƇU@ODJ6>RUϯcW4x8""^ʰ2 3:)GABcT7rFktVdY9+͏z(7jokA7$}/r֜v1wj#Z SR #ByI$0A]sEeD[s%lX6#Ɋ"~cP6,f9e A-T:/vw$eRn[;*(ŭ;ױ~fVJQ0l*+aGʨ]* 5m(-ӸY[WTp҃L6ߢɢsΈ_l6ߋW~h'JVxYF'"Wz}N<1Fws%x$ޣ k\Dko7/W\oFz/ ڍׁJlLm0s٧F6}Vm*UOC<)-9~ (=01aʧ= ?4q Cg?D]2>G,ˤG)|d"ۍ}4Vɩ/r*B|7# R*<Hzqw򁷓l/eǚ\njp~e~,J?GiB|!N( vA$Ce *$i(&F&TOeO{IE^foߡ.Q vD:  c)mg9zǸ Q?E0cYdnbڲduUtf#B$uRڵdQ8 qҮۖ&UM{iJ.VK;UJLYƥ2_a{š,-oq-Ok,v-|!>H}Wz9O-1's_[4a<Or |^g->o}]%<N{Yԡ-jBo5$sڣ&ӄ@?nqmԦESnZ% & @F>Mq]s} 3&O=J_W0tQ-Jh{FG$%PAV)siu9AǚHL:`@*1C!?'G9sd1%!uk9;~^=r!ֻȩ^$?rJ6_g|2(HzCJԲ"M`(Th4X9'MN!T:#4.I; Ɛ&^=GD䊡?`ZgsI3Y"g(b}+]$Xinnq0l VڢGE[prkTe:ڤai&`[E,de*8Q63ViX' qפ Z@kdOyfEUKofȸ6X ƌ,z~eiR ب pq4wL"b(/p7YfLE<OFdu~crd3s.0"  ;8BgFdV` AUX]e@B]{D 92S\19sls V spW uβPWeJIb+\0Zt͡D(c֢"N 8gt ] HN=.v5ԲC#sv9F 1`g \׏agN)tښDncֈ<-r'ekWWrx{gRF8= ՟Sg_,f[ὠW~Ax'P᭶dd,"f +^ڐKxzKrp@/z\tiCk ff#=X#s8~;(hyaᱫ IDATf g!L *T:V:m; :tCQqǁG ED' ,?esGߙ][ee,˱ν3R)YÓ k.?r!VW>}%_:v SϤ/g !Ji>@v*(&h0`C0"e{@q@(UCjҐQQr/`_rW F{U cUacґۦeU&lkvV B$\ i .eD-G1ITQTiqm#2S2!.^TΚxq!tY(lU;Ql5Utf&oYȢ>DQdyb7ע8|hFk5̛f倃F&7)"OJ;'^w׸yrj/mΣϾR 309qoM~("荼㌟L&ۮpLUE h &: SeZ6cDǻ{)د?{hw^8yzi$yx^5rjVzWUn'D!lqeMEkMg*HtZpe"Z#y/"KS9MYF8@g''AA7Qd o@s]]\1PX[`I@t hPv͆BesQXsJĵcqJq$@X,Y oTUi9Bi&b2SWD6䗷Tؓܢ R&+ڦ#G*XgŒ,+~vi<&WQO /{)"z2 ϧ+k~ ~>7IBr ;Zި 9|nߜ'\G)PI 7 ͘jjGlpQCQ)xJ6$rzl;o?үa}3U|_IUn7/??T5^궑}P'(qHKMi'!DmG>*TBT3  F 01DC0$, (4K;dgVwe2"&:/q4|SOtocVsvA 8uPm9`9.'Imm2],Z QTz,Q0;̑E. h_\&8;fe.Ghg3gMJt(`Yd¡CLO~f 1*trT66dJ-:QE%R.af4ReT,M7Iey^pR+*UhЕQ"bY3:gegu^UY 9wQvg'.$nCڦܷ2&xO]/ 7l^h*T6l}>|0>,S_稭"rS6wYq/HÓ寧ZM ϒOLSF^B^6hds 6Yblw&{5 hDu_cTܟ^ʗUϔ ThΏ bd<~n s@.)纏|к_'e8ͻ}drڤPɂ1PdF}1 &0PiJbIӈx=!DGvEX:9ˑr̡w Kpو6XsiK DIijGZVP#)c,6@^%!b &NU'UӶ(Uq+ Sq7VZ{i7">{8  \< Ӧ%L[[3xO *UOW'g] ^{S<8QM>œU|V.D"օfn:9Ws׭%"s"2^ⷠ]|>3MXh=WXn 9ؼ':o3hp7ݘa=s.9>,Qgs|Xkɰyh3{3$p &Oj]X(*Mj 4tJm36MԲy^ qw|Ͻ[N=ʩoJG1rK͏|m9#:>ڪ1Fe(Y!fqיUmp|[ )qY} :@ZmTIw$0[˖39pJ'g&#$"jLъi M Y>C7}ʸ^vz~"sfaӿL@HlP.6 rk֋=׏Bʬ%vJ PNד`c^*ZG1NI(Ę"hʉ;[ W8~3x3xCr*ir0>Vqm[dJtUF.!9EEn~tC^ 2aJ:]ɓmBKdj;7bDsO׿?wU✛Y1ga i`dҹ/]D T APS|,.!p- EgqFN1:JA҂%8 :!^Xp~}A6Xdwию,|z;"DoWO|C{DS]ߐ|p~:m,~HPN{%ܸL B>I?UNDycT;Ӄk |iikslvv *5*6v \#VXG+q }IQJGtSZ#Q"j\I @0"-%βIPb-ajŠ3-!!1LK2%$': C?l1#5DW~ރr| o |94y#Dq07=oi">2zM|uBsnu*pl4jh|񜛯FC,k7P8.h/xě2{ @i^˵CfaoW| {gOy(*zLmuchNԄrw0^D#sZQ2Ĺya̡dTkG%&Dt Er5 `Q:N&e9OwfG޿'9olsյf΃|S En֔K(U;kq& TpZO0*{j҅vJ}dÈbUfBT,7,iĒ/qQVH( 4 iXIm6nEg¨R:^\*xWXk'DķINӀZD3az8+5i/p3'ڿ!=Vo-MW{ 4!CSGd1W|-r{x!Z-]kVW:=7 u =̀ʣM[&f}W1޼ |-afb#7;G=Tm~9Bm q(( $0ˀuF*wdgA?2™޻wbמ|7%LX2::3Ӽ D9R*Nz3.279~o+|~奾_2YhchP-"71 $#qDV8I1:â: }\b!a+h. CcmaE=芰=+/*r$ާSUJŸ Dv\@GqV99BYcK⊼ 43P"K2[m0mBlE,eQ oڷ1*ҘEF~k+WEj1b<9<'j.w Pms>n88`:/ɕxoo'g3&k{*5jM.0?qP+p:F0 'rflޭob~_ڸl\!Rgw v3wEwiCw;MG&5^uoWhD'}Asw܍wc::j)T?cHy\GLZ֝Ul.3 VgDORO3|jbs?Ty!ERcys DdkmهFAAT.-kk쮥윟fF7ױ. (NETNQ/$4(*QN X2euaH i1˜qLoqBzlBg)c&Qw9E*#76@w!ʰ.H;bEk>$160ȓJ6 qZH["E$P h aRk3'qH(rEa ~WfČ, +/oI+O36o{ nﮯ n^7#ὡW˷ڭ:[Gw+.^tc,KÙ3㵩L-+kOa}Q!꺣7n^Xlg2q݅{@B=F=AT?8"Zhwy+fp0ǿ{9:KWSSe:~WU娀  %h#DZ-1 +G\J+ʀ ֌5JF7/~Rc}= 1AF]Ǟ܁8vwmʍDJ !$fFEJv[N BGcLj^T*ciQ=EVB@e(2Z\ԉ$(C`R 46mmh*c-QhRmQpkL.($jAi+bC rj.#_7IpxS] 3xŹ'YnJڴKR?o0i'm<;^=~Jr>giZ_s-k7{xzYpC8[7Z^ /E[fL\Wٿs9ޠфP5q:Ϋ/;E=6 3E^tv'G;]:d@ʂױniN1`4A5n{C\'3nkvcMyOO`:Oh[YZX/,gc*/X78T`>yQv4!l>,ͅr38mEk!S iq bo{ vD<`W"px{-Laї$bȐ{NX ]uE!v0 f CX ]cc•*7-q[+BPPU' *d)U$d Rĭ.-)MB^L ν`CC=}3|aY!" OuxsQ| Ox޳Y">p"/>Vo4*op>zt6 ~;1_ Dux4a O&n$^ʛ`:z5UӽcC;8FT2>qf/6sO}..SFPvT5~l7G+by[]Vև tD. ȇ+QaKL{yJJu,y=t8!S` c$#+w]>N>f?oZaş ]S(&K0]!nyDN(d3!I ;{}N]U 73Zn0 et DmXGcХvAs i$"aʶ(շ-#VRk8'':'ΔRq%dXk]h[F ErBS"  >p`FP'³Ti2"+sPczo&0va1^n+g6q^Q;oBoaq_rOY xЋqQtp}^shFb+a}Fȁm6Ff[3|ZA۪ 3𦠑Lm Ou^ }bxMb-U}grlj~;4-򍄧–e IDATr"Ngy"Ɣd:Pw"`1"di\_!&r1EP:n^[Qt⒊~n?9p3WȩϺd+%!qeA|k A 9%*qnqA%0T¸oQ!z{ՄesTrOjKhX`()~@9vxrZa*Z$+!q8cQYQ='ЌrFeIZI0̂0LqYRJj*SF(1.pՆIW$튋bH6`+pB\rO|l705)|.DS/q{{[ƓYs SJ^I6Ѝ!NVyti7c"{壃*Iu)r~h 3/(}_0D'p(ы7\O~TYɈRRʯũO0¦0\K5̯=jn'nS \Oy2nR8 &[U1D)qCJlP^XB`sfOOLݡ@Ljz (JK, @Sr)+m0qH*Zh]Gx%?I t 1F:D.cP\uR˂bɾ`A*yʣ |eÒ' S9#ydqY7YxQga Ϟ4ڀc[bgH>kpr^-EldcfEo$g_Qmɉ`~.lbSsrr~ӏm^<3.^o؋R4t))IC_h%ߩ?bˎ<~:6|9` E ׮ "`8Uwc;xڙ1R %Ģ`з&/_}fGQ g؛6܍=P?IAE%ZOu P.a`7R]00dGp,ʋؼLԥI1t{,NR֊=ZMa{bxHz!̷(UJSO)6*w|jmp<ԑc@:i(\5g|j=ݞxWB9`҄.>^X,출yd%d-ٰJ۟w k߭Ef8nw#ƘPf!{ۀ밋cX\/Q<= lqkW-!k:5m}cp.̆t7Qgv?y6MFQsrr~M#/wqV) Cc$UjmRլ;pmbs>[}1[mV!bRIᬭ,T+݄㸍8\0{̍crvz#_~(. @!NJ6HC"$:"ừHN͌;NW;ouhϵѳmوR{QȒ$cdZX%e[xZT+!:)v*%K$Ba8#pHD 2 03,E:P*ZIR p 6Ov]=r#OC== dP_1ش+_5f5!P[B\]P{XwH|ب{ix6VsE(y+RjX:*elk+ s/MKMowF}gO}~%6ZEz~>kƘ.awu 1wVx'gFFw#py>E T߱En>8C۷___e ?U2Nix=&f8PLVo/l>(BY ) bX lb9z2ͤۏ$ ~N&^_Dq^0II 68;j 5=f D.[hD@vXGutexӍ] #DHm\%DxDZ%) Cu6.bi%T@&^dQHDQ|:Nk:X< H|Pl lľ_xx6`S@c~`vbEXc U}lcb! VmÊ#پaGjl΢.V4^w >pa~_:=վ>lG8syy ^yX9Z d1yW x{|mvsF۬<vqQd0`t%Xn. U4aK&o/ؽnΐu&)ilYGhX*ck0zXѶ͓[ !<`>$pe`l[bt;N5ZC& 7( "/_,zgTuFckz9999/':`#=8Рv{4ՋwtƉ*_6}8uo=yyBVXftjk2{w41hpli]}{/N5-LF.HSRXwۀƿ֩ՃL$mRsZɫw7n7֧5 p$k.(8t!}8ia=il3 ^ efM{kA4֣k(B3Qx q}, ܐ 1S7LbzS;~$ƨbF~P [17[;XJ1Rq;-Ny[pIAc5 %B}kgq0/+bѡ+nY67z;vP~޺2WίO7p"ݞvv;^ny mQU9y~{<^G!{GL\urGRk D̔[$됌`4f; iJ"S )|7!( \hZs%fjq A#;4CTS6.aP^[đQ&Ja(b@ Jb X!M" ^O8.HR/Ȃ[xu?89o:1&:ӶƘ!ğb>6%6`̀ FZ`f;k"]XcJ.6Ea=VPn U_J8絑"ل5]~)30WxFq~D8}qcÛW" iD999ӏ63V> nlLE0#<teVtK`9lA:mhcGZdWUݹ 'ZYtKV[hݽxht?ӽO{K- wwc?84ֶ-Mna g=+9/uXoQF~ 2+v௱~v$n`F8e;q[b6V<6q,fvRcB1ԫrH&7B  ۈ[(^M1Zٍ yl '''b=ufGy+;K/鞸˪KD9Әu?;ʗd[7sVnu`﹟#{/gu|2F$O;SO|s2wyq\VCE0^ql,cm'n^^[g$m>Kb}jm/gkwrT C9FT4pomt%]?B*KLbpAc+DdDu.&at-h]ӛ3^>0]vjHQU Aq/-12&I%2#RufI6Z^Rn1:Ы섹myX\A.cE!4KVc [t؅$V Il܏PuN`S(칛}Hlj-d}γ>a(=!A0Dv X`-Qw?=t)FH ۫_Is&l3_ʶ}Z|߼_(9˯?5 E:$6i]\^ζٴ%[~Hi"s}jR26Z7?&$Ml !@5ljUϞ󱂭_5`ENcR!D =yl%uW,\կR9&#ФdZ;;?Zi~h;>"xN; { 0wLz< m@Q!+wd4t/L6F.,#{ wyayۣYH#X^yw]M6IMHs=s;S/W BHdE*"tBh28OIV(++q*>!jR\X^ IDAT$ 1)hXV=|M(H(&)q*DOcbZz a,Q]c(Dx`8PCmC06M%F_~v =29oJrh~Y*.,7!~=X qv;9{=KѩJꦂ_ %Suseձw,̙C Mn5֟.~nb :wWW9}X0]4b?trt<6]=v@$'+rR3Wn>ƃzo7 ,fLl7QsMvCLo0:7IWB|6|bG}~i<80K1(qrxQFsgXk JŴ1&B,b݄.bs Ck{5X1+]U X;ݟsXqBDSxqZlD+=l"? ŜWɳyġ֣N3iZlZiP5wϷ~u{WNwBV_3{ᡖ\'U@fo/`n5~V[!kJm U֫nho5sv[NKrqƉrAGpEh̰,[x0gF8g0F|X~!uAL p\jx̩j`2#ƊmX"leg_snhG7ԽkLWX狍Lu(9` |TKNNΫ6q3d.vw|H牺&ﺩ[-W ۹2%{smc:Di7uFۜ z}OW6\xܫ{MLO;uWZn+EZ)?,47WʺP*5hȮY+O㺏.*NsvN[,PuX5YSQBam0՘rӻN\Ҝ,eNƬm[SK -ǢU/(ջZS:kAULP!mŴMS)r"4Xtj@BeXOCv3 MaSG]mwߕ3sG6^N %l籑2p!~ pBC̀-v+z[ lr36j56f+ka#\{_;+ÜN&76ab!GI?<i?9999_cgXZQ\YTK\O=򄳳؎kݣϗdvmK~}m~N|ƭ{xeR7q=&d270P\^ihQ1r,85tzHVV${"Zj" SFݦl^KWPs~(ahND{2Ƥ?y9999hg2`stK[z;šf,zR/L^z/?ۺ΅G`瓻GQ]O\ KcFMZʨ(%vq.7J|\=$&j %QZ$QG]4^շEs㭹m>Z|I^8uE+38  n(:LWJn%^$(j+3)3E4f,uM/S#9a|FaF)UA[% 5ǣ'mAF fh)I<BC0b$ =HBP#1\U!Hmwkmsi⹣j7/aSA3JT4;H`+X1џo 6cX1Zkkl:VL6v|k+eu|R؟yzHuJ|XB\8By:%k[ڈ2΀D,¶rQ:\^ć@ټ5u{3GyQKѯnث8.vƶ̿cq:M HrT:UGҐS%Uj /8i~L? 9ow_w^w_,\fĕ*.(bapvY ;Xstt5%8¶ځcJ0d\RJk:!ݑRT6R#6Q.j0D+lA8 &$BmBHq7RwkO3{ġ&R9~9"V᳞7yKs{1>QuGּшrĺQqE T62\eH-fNlֱw {e:s#|8=ոtex]+QweYK`usIEcuG@s=8k倨 ;NMPiD 6%Ƌ W HGhE%2Ri вq]\4HXsUo(xZ$@TOF!4c2gD5JFJo",>w |-u{g9.nFwaO ;+jٍ}`;;@Ecb!E!*v(d{'B C)uKX"pζOfxckSbh<ϻ58]shs b{ 2U !c9ƽŜ7f(.wAGzi ѡBiWFnP+j?QoV'Mdܗs?vx4͗pI&D&NRU6NiHbAjNk[ӛwos_/mW`~~s?75皭w>㧾7g~+Jk'bQM̞ԃՒϕGQ*5Mgmё23RsBfѨv$`H.H; r\HFӉbJ^BW5BER '&^!AJ$D L8͢1>cTKI'.wu|x~W1.Žз`߱߈7sH֌XV֦]A~*ތ}b#011`$Q=X̣)ѭkƊ1`X1,QsJ[cB=~z.zGYcLu?tNbS׋ˇC*(cN1'7cI*Y6]YtwR)vL=ZܢTiUN^S|.G3Ӟ,ć:q7︙{oװ%&;yDn5q  ٵѠЮLTMєF jxkގg; s?sKvS GG˗LUZ[~氃h% ' tw{^`I%%E' '1en*rHXJ Iڂ$RK2VIQb(@+)2UJG5<Ƥ` )nFJYo[!킔H?=MOG~뿮0?XY)|9R彷P;,"(vx>*QPʶ釢X?e9:Tb]N A,GVؓX@4} f^̳ &asX/8V'fD1'^朾ׯ]<_si?69cL*:6a ? yd7~DlGhd quT",G')vcd砷Sr.5:zx4_#:T`'N<葒Q%WWeBB%i"Y-\ӎbKΎ- ;!َ(uBo۹m> n v48=DT52$ZugU|WqrhRasTF>]Q(XXNg.N"6 $$FHO$ֈ*^*=bDc]$(H h .峞Xm. b|V''_x|2v=snam؃ 6pxGBr|mOEs/ms.-c4cg2$BE FlGkV1c TP}cfNOPӏ6q3=suwag%2 SSUw$!HÿK])qo`*:}6o:q/:kf/R>B =! m)E8(k%׋2F"pC(vF>7ڽ膣6^ӓl^xƧ> QW&]ø+$;5EU ']␉.*:skweHh-̄6"V1ᔒhzi& tT%H0 bGY@+ HE$c6<&-RD^_{ e Y-1Ŷy+v]Ӥ{G{;݁%&6rR6[ut?:.Fםׅ7Od)24iqmsA;ֆO1rx/´{ܢl] "CI!w4iJ_:ku _& Az>˫( ) i'LTщo3%X/q}t5~MWWm﹟ǁ7~hF8b {N;׶~OZ~xmGjɂ&y cbi@C) 6&4Gw)S vą+VJ BTuNhMu%RDbBH,<HNDW> .-ֆyc^(f|!RLN䖩]'9]!QkϜM-5[f6m]>w|^"XŋsD v1aP$ nϞagϿY}l`V'+p7g^{k@1=8Ng}'C8s/9* hQȤL.\ p^cw`T8aѷ$.WV]L,B'mGy~/.:Y)~S8_qdqp.]L9n< G}#Hp^P)sED_il3ֳupFQܽ|xG9X><޿?}pI ܷm%Dc~GV\{жM<( &HyU "՜;1 J( G^-FsHyNNs6QЇ#C 5Iw,_5?٧:w+Uo=?onc5pjÏ#͍G>ޙ;p\'3Wsk` M+(Z04$eqUu 1V jh1!81ˈM$p, |C6MT ٌd hjwu6B<m4( -V4]Gu4H!OW_~sC>xƗӐ>a|MXdvGlӽv'}BxLDpe󌘷 IDAT\ G>Ӹw&NpiKk +9|<'.:2텋*z À =_8/2\Luxߊȩ}ΉHvp=-pSZ<.f" rx\ u x W"Dz\8%" Ţ7܀_G*uE(KvY_2~/x ge:1s_Ǖ^5L\ܕ a@ +A:IV]*^ 2{*JTL<3F{*:2͵EUʼn`2aGl0ev<+[-'уeyط)nƩ-wׁ?4y o(*w*͗/fWii6Pl2ųBYHڑ W2MLCqBc!kB]*s\Jդɬ+:`DyX̪JUr*SJeE+M*/o@KCˇzB Zf8iV]>z.yyC[Y>aMr M.;pdtldx$ܵ^ $1CC2bx^M H^f;!baG"eű#;*.e7q{]'p)x /AMŶ,p.vyrky~qx'D$/Ewݟy3zc\r[eq*ƍ[ppc8}p&YEA'}^Oso7'<"k'oh_>^eE'害 `["ߺo~~)}"!YIڠTXL+iw;?{/XcxՀ0Z ޤsLr( ~ )GjCs5V< p}`nD/kGoxO[~Bcmd͛j=ܞ7YH;F@y"tEf ofBq^<:1At;!oIJ҈_[t[uZ0vZM۪&<=C=̩!iR-oDq%SR +g163ٌ7#\}W^Yoe;|Q/xWV~Wvjtd5rڂ̲<*+g#[-?e~gڋpN}wA{.?Nh-.(z1[q,. mU(/Tq<'667GtPsGL~;|,~֟l/g.OƦT6#L-/Y4JpIu>FG>QšŭnΛIW~mC/']51tC4d‹},XC",iFL@bSXUh؅ ,[k,h+0Uk2mCtfEK*lȢ%5d bcgHV(kcS! bd<6 #wdt>= ]udg?w\en=e[o[ ܬw=kxAe17?w%- ^R~WD֪ow7878s]dg76Mq^]ȫp7@,;<8x׃@f=Tpfg~{EfE\!oYu+t"4dPfý~_)zxVqy(-8ΓQFG^+ޖUwd@w(}/XUV@4 Hޔ]$Ǖ])*l_JdS V`\Fv<&ͪ5nN Pz&Llzyuwh6n_+F^|2K$"46":趯Nd&|ؼn懷?thqo_énwө[ݘx5A!e5Xܢ+@zCT#EMl@R7RRCCtȓkUxi&5Z}hZs6* I@GMyx*" B4Xry*;%ͯ7݋ƭ#9͛e_1=?p#t}C:ܭM_K?02;ɿKZ,.4ilg)xh\' ћpBy WMq bp]'ꊭ3'wLRb,yssh9Mpٞ8^8{gVks|K?HAQvڀeIaiUpu?N7\ l1DݴˢO^,"$YŵmQ ct;b:6mfwy?{[s{8@sמ ؙw՟݁e~EˇU_,&>]NqZ7ִʄ e3 =k߀k9wf~ݼҠ Mw; jyWl26E9du6 --,$0]qM 6xYi<%8}e#a7z`C}@HBj(b!|l!"{5X,*2[I=xO;{^|%Žjnベ>~o$wd6ϻ%-iQQbTi\зRK6TxpvGpjzbv`n"x@/e8tq\ |nџ+ XB{Ws)Z; -vvBAY=8r7J+J+b9?tpq p"P`Ņ2sX&.k%ۿb˩#\>:zx\Ei߃P\q*&7k 7<3M6^pn޺ %>$ϔte-*Vy&;ydzUFH{ӝw&3!ȎNԇ3 pT=d>XOz[}/d7~0.?8eްbu6"36t:m8j*/n#';w_7qmo'8n7ḹNNuzeqz -h q׈H;he\u83 o>J 5Y+grMc˳M# ('mUk+ tA=MOFZtH'49OuS{{aC@mD-&}H8l\C7zˀo[Ϊ8m?՗[GwɵkOL5㗖u ڻ*SvC/L.;z^j ֏ZQ6\%n,0e~\pϧJnpm>5k6{ӟ7g V_thp~>p "E>A܂{'. N$\Oݫ 82{;έj'N X 7˼E58/¾p=O(>WǵTq (If"U,y͊15yhpabI,{,qxo|{A绍3nP,xuW<ŕo]F>.ྍ͗gOqO (83`&Ohr!DLmTuwɃsoO\u]l8w*VfN^>y+ݫg]NZOXfИ|/n?쇼CʸOBCP+YB`i 腽岚kwp 1320b!ytxhqIҠG4d|0uۙ#8pƽTAx|ɤyf<ڱS_96zGo:v{珧F;} aٱ=~h{>:"IgBQԩM_Xh=G*_3V+jvY#j=u٣+_Ɏ֛O5V|XX'oʖ@#ʫ dˆ!"B}eMVX YTCD Zk0Dy&>Euy⚁#,F4MOkb=~n\_n]n|zhǧ\ ymh{s;QiجU=窫?fVyxۂjcø3;/읿+ppkYG|dįQҼQ9qsE2e1ֺ'M>v %D!u-y NVc)vSd#W|J%U5W䞩 rE ևZ"/=8wf8'@5N(8YV=pƉ:N帉8oJg/s_l_/ ;-r@,Dܵ+)n-)^ /i,X8 !6Y=qFw"54wf*|>i㧷]iuu+']wӭԔ^16mUwܹsؼ gV\pgo|S3r-؎ igY *K Cby(9A3q@dr$9"Do[el%M8AǓKDU%M:O2;PV ?|e=Lx6j{r`m5uoW^N5W)M/q ]<#"Rr|^$Dd\D m-Rvvz' =_9I_f*n8p4$vi(kwe15{s ٽb}qu۸bv]| nr%7r%7F 1D?牝ϒYa4֘D?e;v5f|t䉟w=zG-СCϩG?N"^N:7[C3K']{n>ĪJc.?[~7\yS{ЩO}c ~붳ZJn_&MwMwoth2<|nCbbv>y2)w-/ t=*NL Tr:kłgmm ԭiqA)ψh M}%aiӡvI6$ $],TͲ3<ރß-7p MC8k:N(JPqHɓ~oy1Ņ0y0C`HDy5Z8{!@[kSi⼾8 w2܄ث Q%5h],sܽ72X; cޏ]"bKÕ}G'6K^ol6p)^X})$ɲS~;2 ƝaUS3a>&[x|;;!`V_xAn߳q/7Le.< /b8[ Da|1Y=urdJVv{_F6ʢ՗;4j^g+p67o̖ȋKݼw6grkc"o ذHS8ц+-)"O\ Z<"Zlء0:,ЁցE+ 0aDujҎes]ZM`=3X-FDH:2Ͷ"Fu,䊺ԛFPy5^sT(p}# =gyӽnfl wXbz5_tʦӸI8uzD_%s6׉ȁayE^!b7qar༌ND ҳH)>.`e~"}{頋Dk!Ɖ2oq&pBw?DnA":{p׹ . .,mQXgUc6edih,Z ɂ .P,{p8kk)>M2|p'F8kHTq\e O9:lFDpD\ =_Yt@E;(=WRB//<#7q!7ƕIfSO} Nܷq\fۘq6Tݿ"<+s["ߺ["vѾ^?짒-oME7@~1p o^:Aٮ(n":9jE/&~} 89_Bn Z{UX#KF#/WFQ'U/w_s˻>Z/q梠JÏ5 ڔ~İIh#\+r%mZIL `+mK BĒw1 p >R9xn.8}Yy2>\mݑ3\ }%%q QLAQkmWvDEcj-sNUίunEBCw|l5nBǑ\hR(nNgm|Un澍nνGr ܜ}W3Jh1_cf^k2hy ݚO zmV_6~7 \jb$+[BEk:noع/IF+Jc?VD>܁+nS;]< G'pd;[7)BDk8zaGGq&NDbCž/)B*Y(]^Q!LWp.Wէm[*KqWo'I,ϳ*.0EKAˊ_q+߃^N,6U\!I}G coEk:nj}߱5B^p-wa Ow9n@[;7GmuCl-9jUc r"extY :?/y~ ڙc<3}x~γ`AyM&x$kmz^eiww[p#8O8"npB Wy:+px i[^(έ&{~Ox\ s7)}"^'Gqq+ -ZJVJ-=e4UoX&v1n,[4y_ #+&em>.uÃ^^U|u,SD2|㝓*`ə_pW+FUgyks7R #a:<~n>.`8qsxcE`jjKfڬڽmRmr;u{%g E4Qq#Xae"J hc}mj A UVʗ4ƒFb$KSR4j*h,T4REɜŠLdZ D.d$Tg n$D5fV̆lE_U4{ pރ)Rm@/dbO Oo9jی1npo}jZ?Gy|`ſ_A7b#s}@2nV͐u R4ETT!Q}M#_kĉGktqp6ǬHJvj(+-P)c8J\_FrJ@Q0H tX*W>3֚%́gtOgՋ뎗Y= o EV":3.AuJ~da|\]M/Hل#{BRwk[Dk I+F΁jtɶ9m3FjFdof)) D{fft+߇nċw rN/+֣ޢӀ cDG]L{bwkv99]EG1(ZyDO+cCoeUH)%qst-NsY Ex-!Q3 _|vfo'J_ brą ^pb&7˕ܑe34QIY3^5J3Qyvj3bE-.V'ݟVG8.sOo,7g=l_=nqG?of՚|Ad\tJG q錑*qi*M0EbM(U4UiFIXRәUN0H[AAl$u]8Z; ]?VK $ZMҌ$xuQoQKsL|^?W ?YB=nxKE46 M5Nri5S)@>~s !rkl#kbq"nEhLCQ*b(ߍ R8myߎԝɺɞ lwN&lQTJ bYSt;5I㩭lDva3{}kۀ{>Xgkli~fBg,OǙvvH6Ōccsgw졇޺#dE⥤E=9]snnNҶf;M֒t4B0;zĺxs}ArbN9|l*Iہg AO3?aF;>W 'N'>a{/fS.[=í%Stq*oDUCqKԋv͕71Z9*VaÄIAԄEMkEp]pB s*ֺMjεREFRVr08$Zԓ IDAT%i. t \ʿk/H唕ѳӂ|l$gFS8}@{*bC"dGʥD2wjƘYܕeѨ!$vB$:x}9td1UdrdNܹ`}6ȪlmD,#^Ȏ!]exH)S͚\IYD*ϟBjߔ=GaNjirl!6{l2|m:ƶt7ID5'&J wK5"@Ov z膏8ю7zsl6uso}s @>8$C@I]fMlսJ258 ʣM VY&fNJ+rZ-,/t(,[>ӎ}!_7rUjܼ'\ 9;rnUJFs{nyv'> }KfSWV}9Qcp OKVR JE @0Z#sJ从4IŤ$LV[qWI 7;x94@AMq"5ȅ*x j+AΐTG:CI*5ؤ}P~( B6W/|m{Y߂Gޞ@VZ,_[E Rz'ՑDX! އng V3#Jۗ9 }h!=8" w9*ԹM&/q%#K^_SOWd?CGzk|ΤNs4{'#,:H~Bnc;@6۱۷΃Ilkzm{ܻ[e]}"zx롂،刭b|OAkgig<]\;|{_h|^wjOGrA7C`.7\jeJ[?s/<\@'ԹLׯ.7+qٟEyCmwa6qV_ޱ=7̫aW95&^۠ѱBEЌ4!QQ4i&n;lyJL;c8QX;DmBW= A@-ZDhjSuix!i*NQ Q+R(7ۨx” ^v n mt3A[y!|LZMTc2?04XCjoLF=pnHs:NǮ!ع O}o}C-|=*/6WZI|H570lp2j-`i-=` פMEZnsdCDڟH",]g.'.J8;͋\'+~yNE" uи -/?kco~piUlBJJP|kuu-!E~*Y,] -NR)GZQJGsbHe;@Gt$rd+(D,;8#D"QY$´+N]/37yϜ}tɮZ<{}! HnC"rE۔ȎBr)g6醍(Yd) I$b! ASH@sWJ=EF[:eV|&T Q~9m q+"P >АW7YD~Z(|c1OܜϷ3s EDDAD衇P@2Q as"2K@*{T6ݝ5bt>L.L["p*ڠ"Nw'~bRDGikHZ]PN'ۣԆCkR 9`6) PӳeP=y/K=n? kޘb)7w@Ry>*lZz 6q;8kJ")kNЛa#("ȴ2K[$!̶q)Qp{-liws\k/ ]N.egjd͗O=[Ms⮽bwn!|+!`$&rw_y)ݿ\ &JY~MrP-`K6.EiͰЂ\#B5bIY7ӆ8-y[ '8AW^=)Kn6rioW gp Fhѐ7IV~`z>+ܬXr5QbCp*ŝ3<D9BPNFUur3 7i- SbҶ˜28pD)u\*< ?F{ŸO< ̠p. oȢ0qVw෍F" !E2B|kRicw-M:ygw"b|ԖYXs f D-H* B![CRPSMNJ2`Q)5}fQ~7Gل1~K( @"9kah"t|a:XHٸt;$T훀5yD3[q~XܓWSo\7_C?kΈJVgg&o G AA/:cmF *\B1"4D:Riz/(5s%&)Ʀ>h\|*:pgTN(RCI!MQ;%M y\Ln.#! fYoƛ$ ,lzؽ ^bƛ>GjV"װ9,!yƘN̼ H}bxSRJg6Dj u)lN2<ޖ)`Aޤ@!Bܚ^$\|RE47([\{fz.˞p G N5G±*x[nn (ɫՅߝ{Jɫ_y0/yFэq[獺jީёmUGf6kN:^Do7]7ͲPMSZ&`M%DQJRHLi4Z&m4S2i19Džep!va74<u0*&01Nj Bip4cQ_`o4z {^=:M S\Ûob!d"Ԛ%^$~:sW1fc]ODv$8"O VX 1B2^#F/)1T' M[.3iڈ pM"S Nq<* 4xuܶJUROLPCK \ڃ s c |B;2+$@l\'zEZ6$J1iJTIsS3'ϓOMoomV;_NG"yfG=|M-8rcH$ ƭ!,9)rHj).O\6Ryd ~DTL,)lyEnzCtTDl)Ek&6u!xuH@<<0Ei^Q%Į^Q2ʢ˳]][O NjD?PVJ;vY#ʬ@e۰)W+ '!Y茯V#b7gUiW(BTe$v6cwn]]̚Cmkjz衇&,7{-!J_Ăr(9 kxM8[(G7{FQ2Z,+u!nEyxޓn>c\ŎSx|\E8 øfw+xb@w:}/%Qk A֍ho)?^KTga~(򎃃h"--8L_-Z4 9"窊D8)7qCq Z+{Y7n"49Q(،q(Ui6G*RjZG@~3?0R k W3|Fl x>G+uI\xӊ,uScR5$hǑE+RY#"thV|ƕYMC4Kmm$ZtcQyI, Gut?c1S5 sD_9<[@jB "vW*f)SI;`x9r3Hjy+H xp *tQ%m:)stj0DDqͥwS\bff Nn21..RH'iX)ujvK!&ٵ8M6s8I۵c綳y9+6'<:?5c:%wShږet~`ofɍ=tC2f< 3ח|6np`4ܼie֣{皵jp744j/UV#c+&qOON/{RI49Ez4<0m4Q,oV:;*Y6-NWA$z޼&_Y,D[=ެ%"z‚2ź ڞ[!kr]4 EnZIu=\l~bpdI]s&' ӔvkMȼ0^Nj7޾F9n~M[mu_B:W#]?E(~*ke?7p޴bYRJcJcRH8 1OKWmD9w ̙(cJCdC؍1 |Yp/w&vtc!v/CRcwپBWt ISmez0\;gD,sXo&*t|=g;K'f#REUȌE,%N'e]:,B]!i9E:-ky z:Cfv,SlQ։IgZ}^z%؇!)Aj7ǀ;Rm7D9C=Xî;g^Ů;ͲkD0}@ ylN;(8Ee?Èo|̅v2}(du3\W-#YB!:-A*\݀ ttCDiP\S7jNY{#W={zN;mc E,"B(tCNv^1d=[HJh{ٵ0bK>,hgc綋nin^DF❓b焮d&sEjdU~]abXl+~6b=LF'nӥ´Kɡb#DZy潩nMtY'0ft!:sM6LruXn^y?VNW|#Omg/t>M'iܼ'/|3\9^3-e#fs:h8wGniR7p^)bڈQ: F?E?!ً5zC@A)ud5oQ_gdk_:+5z9|gJDE!cFG{AD#t|6{!ƘD)*{̊tj*r5@N)u62.jiŬu%NJ<*kл1e:.#"DLێAB+0B䶾trmTȎݸ\ db.iԧ.a_V;?D@=2ꡇ*$u3bKpEIssEo!trݣ޿ۤp}Y]"ldW>g=f7X4Cvq:w;  ʃBtG6P^l?4||f|hm7-'7Q_yZ._I;dO^S/C} E`Ş:n^ˡ]b1 YCܜvovn??x'ed]ǁo_y+.;xj<췋c oL+G˛G9Kk^+RC4Iݠ$2DEr@fjϰ""1Q6TSl#k*BZLkM.il1[zm|q0ihDZi݆:oLeq͔$]Wm^*>pr1!}仗+6[.7X`-C jdND~NL]biv#bl6eB#-Nk %҈qķ Ct`" OЩo"fJ:M_@F.tϑt&>% Ҍ6\Dji$jLe ".EK2v-udrOں rmmR}n,g: lR-w|E:2D4ƘSstވx1Go9=åd3<߮7ޚ N-CwN|x;b綋!sW)AEG~zDiVL}ȺM@llE~'ߺr@JMh%P|ZN֓7#xOWJRm߳,`=0Z6&=5^,+x޸:ڿ,Öm[i5c-}0񓭮V:ѩɪM(R$C5Jr3\ ŀ(qg=@ r8NnZHBw 2:݊Mkk >iL;NHMjjVZ"_f`! 7ڄ߄o^3N{,)bx1uĐ | x/(AD}ѼfgHUFǣMu$+@ID"hiz!FDAͶ#H}[@sID22QY.82xIR:dB/jS6 ΂}ȽcSId1ۆM!)aĀFYF DΏxtإ~-oY{*̎m^Ԑ3^|o͎>ĉ`[vב7z`ؿt__7o.U(mbȢErsw$*@#vw?NO,V0kQe+>ijbcg'v6ͺHN2M)éz4P,.TUV̷O ̿gC=4[_K歳Cqs:8O}X)Gs6۴W@"|.Ȩ/ v--6޳cC?ko84\~oF&ӂqƹfmM]ԳnEuoS Eujz衇7'Yxqk/|{ߵN8rekH~āY;FX f?Q^r8^WJ@8ւ\06=c|&ۃrZ{jMQVisB&fOlGwPTxԪc~}]鶛6;]De~OO5|$D  V;+kXmk.bL䅛G#CE2cfr:4˛/Bd'7rU4;iH(t[ZV¯g06A3Ⱥ:vb7*ibNk _ցsWLPy C]]1/؀|N㕂,H @:@c Jb!="a}M8Tۢ0,)xͺ+#zrDmK˙3mcjȶ~d!<+4c6yiNѰurno)!tOd wd4T \DߎG{+]˟zM]{ⓛ?4'Oirb#;Wf<|'Ό=l}__|nS=3Z]c&CjYȶJo ^-5%omxI?I&EY:|P9'+^uO0n? ?wҪ.8?ģ8Z$yW}9P erٸsJǢ[efznHy6jn6JF8Eɲ6ZϿ׮h*]V-PʥVf5:n5WvtFR$mPQPODq\r85&$nDzDAj/|N)rA(E P<#n&7-7;^mIdxt#.>F#qsěF,f瑅o#D|1hm""kD0Ɯ "-&R/ i/!zFDoʼQ\È"͞ۇB<&"J<{n1h k,#iXRNWŬ1%4uf#6F<+NײөX;;-!.:VmD5vmW'!:e5+Ƚ`o8y.{᭄ . w^BVgؕ5؏Ǿm^31?PZ?Ou$\8KwSOM#r h{!ݓWG>X? vjq0Pb`#Mѳ~M9??;5ʹs۷oz-x+ O;9Um˥﯌g]364hLFm?,f%_=6ՌIw4WLV6bHU;Y4F1i51ܡ *hfYZSM|倯0*K r|Lk]WNtjAZZ92V<5r.FkkYcEXTJHb/WĐHa> EBdL8 BvF] 9&"Ў#ހ]H$KtD@o"p8+"(bf‹mk3G@DT!ȵtRz> "8vFF Z?z۲ΞUDUN:m~i%>xm*FMVtm{Ȏw0;g=5$2zr/uD7?VmW9`S?1-?7v4S~9 9QM:3lswFw=j`oU2jO Is#/]"v_A/<0'  rnn.:1L$_[E<~# %#_&(Bip݆Oy.1>?_󉇴W.Y9:oy_\!/8Q-6knu3e̬na<"sCN>@b$s<'t(,~lB‡?r`7|lj7|?rR&N`jXãj3u*֯Q&)rF0 M/>>^ +&Q'g˧nܴvkV3 QFC Ja˩ji(E qU\V4Z9\u\PfF% qLT7YrFu] dwٓ5;VHCǛ,CqsX";cA_Cj|$yd-?{ofUw<\]]=f{8wֻ%JyʻO Ewu:7 2OL]}_r52dcqۅZ=L hB# Q<;Ǿ%\؁ÏL2CG' 1nv^l3Eۤ,IqD:hrx5%ozg2=/r2q6\s_rg7\Cśr'Cޓ{V}gʯ;{h8#Yuw1v\4vj7<z" PAF0JI($yDq;Ble$'tX ZDx#t?+20BQFgFx%)##]߃ތٰZRBX.eIn~՝{RHM!/Qy8}k[P:NⶃZ>RLP)^X(YLp>b<&!0يr[FY6#EzTRc$VI2"N\ ܁U1v)u Yu.<2tY=rD9p5;9܋G=6 4P7"fHXb&y](`=d6П7IbX$F18B}] 2#&8eM4oۍ_\oi2$9C Q>E> Q!Dvߐ ِ2MGû_YӨy.,J].h\[$[xnݬSJ(x< Q0t$![ϧq}Kۤ$.;h&q$gXu 44ql*e1J7 <r&49l0h\ߤr9avI?1 5OK3na{v\uSAٿZPNp>MèՍ(|; y>yفE!D$>$d)_BA|eXE-v[@]J-I?PaeyeIc(o2c* C]vEbr?*.w>[^b:nIoAXFZtEft=fQABGhĀO7Fšfa!g1ۮr7@Ϸru t?Ż:YXO;IX_{Y'4&Y"} j=vmj-إ5C.1Mx5V-ƒM;=i麏yZCm&lBm>̼@6dC6e()쭖a[EFWPu56:{YTӷ>MUK,b,CrQ wxJu#8!arQrmh%`DA*%Wp0/UnSzJf$F_HG;-ޗ3̶u<j<7&82vCEI,Z%oTTnW He>^XgvPwKjV<*ajG2wJ~90ǏܞolȆlKL/ym[/pGLXnV ((?;z6qͷ"{Y-O`=C7gD^k1f.9-! ȁӇTOVăkt{s4 2y3hYۦғgWI͎+Fj@bD.-d}m08iFN_O`7j^w c̞;'=5iԚl+#:f~@Y*fZT NjfD&ϝ$$Kݟj1>-~! >qQ 1IJ>ud<)q\^e(V1&9y'C(kb O7uFv^DZ#vvH\#}9tc) Ln"Ź#w-fvŮj\>کۡm2I8(=i;I"N[,;wk 5F޺k$1V "ɹ (P v x\JLwΒ `=oȆlW&2ȝi; p;J=/ ݜ'I(ґ&ͳ[ڌ 0Fc7뺄L`ه(0Դq0ĵ{AO,W 4i{QB ˨}nM\ ˄3 2 6EEF&V`B ~"-6SdFng59yR`n+pݻv98/Av}OSF:7?7s yW?pW$~'}N|&`sw<0!ǫ[\;y\zx$ϤGOq7wqٹoz^ݱ\#F=IŪ"}]cFf9mJ̔Kfaq%̑S[ad"KZ[_N܀lZܲW̍f7﻾皦+sHdk%D` du +2vh0dC|K" W-4ADc(}<߿<)m4+m3?(6&uIF0\:E5C)*?ԋ%\BH{_JyV3ft(b&m/y{>P9y. , CM#(@^C)GI(K҄@)KQ !NᓸfP񽺜i}s! !(,aLBmh~7}(*fo/jFu ;1iM욚kn{ qg`=.M;(0|@>;5^Xպ>ĮȖmACQ[a_dlC6dC^2yHxǿ~]_m0[ >!j^]}!8b$z\us#u]yTb5ж`[5;l> !D糯ۜǃZaeʼn9Dh9>5C* <̘e1{LJp|!ɥy0nvɤg#e*yCr?H3E ٬,\z3y\pMG~ +f?wV꧌BW,e2~3X$ ! D{/]8f,>nq?N,3~7wƻwD7[?SY`?C#EpX QMbh|dМp%;t٬lήi9uS>rh XxD_lҢ!h U?EBj'z ʢwʕf'j=ŵPJa_|}v&U}jqbR&p p@jbeܬ\U@ -i? * 8Jt9b{IXR#Ҷ8f"Wu C~P_[bB(,Ioh6z/@zQmBYGu}P ۪}RFYϑ-mȆlT&RfB ?e }mn[8ѮvweETʏ닫\%&?uJ7]CF\ހxEO=Z !G- 8KTNP=\ S-ԗeV7G"?ǹqhd1xm:d v1qB&`'_Ǘgm\4**YUaGǷ܃%'2ͮjkʷ5d}~.S?in@DX7@LlNouVNښ>扃Uӫݠir|B>hlܳ[]#==R[Tډ$Dv@+mzYm-7OG&k 5\G"~'O RaS]Z$˩Y63l.r-1l(VL zT52F슌ȑj)cH-<~wl "L4P!+l_H͹'qdGP K+S"wǥQɃ]N6R6䟁3> }l]B~w"zhZ< ( lv4Ϫk(_XmDY\}Kl-`@;Qyy@Y]"+:nŘˀ   8]븠#t.BK!]5P)<ķ28^]VSi1Ruu }]6\7R6dC6%m(ϿJo¶-2vfܲ/X|-e`~w":-Zob_?;0;HٶBj3f1"B Ԅ,LFwG^#O-px8'NUut+µDS)N2 t/e9<jedi2 %&ܙ G'bstc:x$({h˧&'7=~]x+[g$xeO5JzKNZbo=Z:r0lk3J/8z7k_& r^7 CY2;8m_x$Y3_\n,~+[N_납/y0N|G?|#gs+Kw^*#C!cȀcܾ {jq12k^hIĦWyRYZ@ȱ ?4P 쒑waچ'd3nvҶI,0g s:d ݎ(4mMB.dy E6D@ABfPk@:ra 5ݾ%5yHS%z J7_EˆRH]!/AyɂE!Deڭ 3fuI^N2Q@)ʩΕQm#$]esQLGPDl}; sJI}+Hy윱+zI:$qx]@tb Idžίʵ]? +g ȠvbKee)HOg*Axg4Qהt=&Q-v55g[!gxf5Dz?$ɱ)usz̶H3_H"8/c"*ЌӀH!N#`k$5(oK#\ψ!!/#^;gwYdAzyQ`x{o?~dz,ByڜC}xҪx$X}#-%ߢk 9T眈Xp1kw-Vr;d%ҵnv\ t9ꛬ,Sln0Xng9+7C.$"J D*1YC95N2?\FK͙`ml^c;5|>+]yiLtrL_яf;jsq`e?f% qջ VTG(Oo.g33Koyxc]\cRW_䕭N?֏Mf􂒗[qLi 4-˶¨Etl_Les( )VSk刱,ҖK+,炁RXkAXXzv‘GVRlɷVOɖN|hDCRhEwDߑIK;i^6aie:z -~LBVMw ߿ZSPEzF6+(on_ܐ$ IJkRzjQ$ 5Pk lΠ jRZnF)~^Gb+hL+u( rv@=2ZeB͂n[8gtBLV] PIN,u>L,$. L.҄l6΃/tu`6P CE}ku[=宣Xy$1kP݈ؒ]% X=Z,($wC6dC^$9_?-a!D7~[.0^0&}}ŋd 8ٴ'^9˗v,4,;=coUY]^NeaR\0[9v%"h+{vpm mNJ~ALvUTSlumB"qBhl iai|> @4&wGF̖B~8a5w?;E".H(̏s㪼ve ]ʡϴiEma7][.-2ೀ&lL澒vk5Ts#/+ei쫌t3ddqR2OҬtپ~ߪpZt``-:BK#klѧc.D0 `a9Ga`cp`냩L9c]} q 7Fx+HK7q?,7\\jb|~-oHc<ԡS}wkpWr+b:_e̅-fsoc ^qSf}ӔLmuS'tw ௥>ȠzzP఩:fc$@ !3XE0P$2E6$"RQ-l !bVG)BRʗaC6䟏v@;-"c)yjPo=?z6Oc*GL7~OX>70& T)*lk)i2}f.r f0rf[ kCՓNjc5 `s Bc͝#C28 c^&`gGZ" +%WE,r&|>]mn%`sWI¹l悓Fcܓ[1 hROzhmdmOݯ_b/ok# ѫye_םbu?8a|}rd4i|ׯ'f*H< wewv5X̽~--V)g*Fӕ\IP̆:V OV_|;W*:&DAkYVOsB6f\2͵Q:+hbX6Q+ n;0DFˏԻ׻x#kTz=),)z[ATaCib5a]JG4(BH! !HB`)bx*;s(,; $kc" h`}wZO\{C7oyyIE!^!<8I4f!Ĝry(PBY&Pֻ0&Gy|yqlX,Ni̢\9ckaLmA K S(xJs3 dQ uFFar$(v@IƂn(7'} !}%9 !iX6sW_3B3mc$,gQyv ~uY (P e1P I@~ru4>sBKDX #Mu׬G|.$c j@ Afࠔױݍ `ɥ4clBa( b!!/E(b؆(p[oåO䥷}k279e^uRܗ:6H0H'aR7ܾh,V&7p=t3iQyl1}ktjMmΰW0s V7yWa*q'Bn .~VȘ>^`Q2.KpR[@&i1@sڞ&y^-Ó)CӐ9{|I&m (ͼ17 6wwLwVϻ7}8[M~׌)]_Z@sTChݼ);pzwӥ?1돕ӊ-{0*% 0Lս@t\׋atδD+0łUkn a9fQ.2kO[VCP vl'ڎѬF~OyN`3s"_Z[fa7@bHs[{ESa0 !eޜƿdwn.5`><Ȩ33ֵgD+mI^t(;TY8Tb0t|Sb%'Gw.P@hdO("@BY2(>jhB &QE(@Ay$@oe%ڣBGN:eUHc2z$q_$0$ac]0{s]u PJN BebGQc8*'B*t#~C6"IݚK%˜ce.yS;)7M-S&"fݷ9Yl1V(9%`2GziNx+T6?jO;;Fn|f &,].!Ȧ3~DE,8* ߰N!m2+Lk}ZfoOtrKGFD8yl<\hKD~L8;tf6ALk Җ`ωVb~պuE񳒅ǝk''Y07Kvi鉴u9+Z_w=Q>;ҹ|~stS-ć~}8%ytdŝtpSɱb%lv6aQP[m5N^ʙJT 9e!U7VqgݻsRyC`NovfǝrłX[]wݴ\s޴[=3#v~(sm92^^R:]=19"C#leBU}F[ ~*J[IENe4 Jߺ*~1+? \^4NI3Ch@M9DYQi1:{Bn-Aހiϗ//v]; $IJ>7BbEځ(bT bQ +Ne{ k14Q ˌ-i> ktкY~`IXEYH߁rY}ZQ>Bjࣀ]jmνUxnӺ]]bXcOdtFH}[Q} j0eC},&YJbbÿh;~%{B;wN~5! ِ y1E>C7_ȧF R߮?bW[R CJ~9VrJ7ߕgPs~ H\z,Cˎҁ85Bt>~{I1Vä3},P"bѫR~6>WwX_iF_6R)SL++#]špm2BH>fV d'U`&f6džC#eG~zn0;lO՜IAʰ^0g~hMXa`Dg֑Pm@{82}~E $-l2mQW}ߔaৄaA~(Җ2mĠZp}NkzݜB KM`/^=1|0];=+ڜT>.81Cˋ|h( z?X/D='ڽomGYs8"*eKS$.cP+P7b.jDeഔrM8Aޠy]2.f>]EMV}_]rQc; H?Hku3(pvl7@IP$kFϐXEgIߛQp,1Kr(p;_h.(`0tlXLj*H8vEu`\I)ۨiAorĮM{C)e~H(A*Vl!O,uSs-+xR~F6?q{]y{$}uu]ݕ#кo 9y?{<|ZOgmpDgou^? >J{h~FN>oױVўf$,>9,Q)BL*ސ#,Eu& _~W~i eѣJg){>9ĄW²0=_ ml#" l $6=R 9`GGP;7 GZN䱷_.ۇ֎[?s?=1x)`.X ʧ.ޟ/G;i¢{ڙ#7}޶a'7;cRoQTHgsCH</$M٧˥ajwJyĞ sKbjH iKM墙ٝ#펛Z(dg{V0< "dٜ%dޖYѳHE'!Yg̑ C2"]Xo+W].' '0HFvh i`"2MC._YvPZ f[?z;)xt*}n8OAH|!? X`Qqy Q ،>ZOB~+>Q,@ڱr𗨅I ~k_罊sP'Q+w1L]_{\ @, Il ]G (ŷe;۵>F<7qI`v|&pځ+(PNBG XOhmkw0&AB׹AM(u]4&)=Xlf "!!ϱ=wlugK `>m߳Z t3Fg(W?8cТLܳ?iR.%G ﷅ qGMAzcembbEP-^;.fd2f1Hci[1 :ZkcF|s#P/SL>%1ʧssp_;GS?yO]桟|Yf49S@TF*~ëg^XgxR("-,GI oA|FWN Qp)%s|{Kob|wL`Fgģä'7]3]e>#UjG + T&Cf 6897 6ɳk:Yۋn^$uV;@ow^n$}|>-ͧ'N[4X/֝˾WKoW=0o|}]o~[nO3y7w:[5}Gz8=8cb2I#ϑ;$Zȅ֬SiT?ū2ڐI]KYZIAWw DŵԅGp^[Ytӊ $rի`D2|D հ2qwH+ubBMYD,,gfWu{l7i@xif){<+:Sғ'wtʹ ς\gpݾ}+npq-L5 7OͿWGwRMXB0SW(}䮔yYJYp z%>u)0"h2CB+/s1b$o)\Ck1;/cHPZ}*j'0e'tF~_X9 W΍BOzSϷ)JJkbY|BjZ`~E0vV`@Ir [ň"kaqaeu@wYڔbX.2ҦHfb&`lQF٢.4t 0cK|g#o<1;y*L1^ctsob2eb=d;5n>/?f?. \tD@o IDATo0~{{c3mXR>pR'OK&U%g)6![xcsu?Os~nqb%ЧWoY/3 RIdS4ye;˨[1C qA4" KiDRyl vmVc{~Qǫ-r6.oK Nw?n߾z-7y*H,˒T=7U+b Wj9oK"zÈhlov 5qGj0 @LF8̔Ϥ~Erwf+Qhy9Wtw+˲Ҵ> #bH 4vqеqv8X tFOiUk7]Ai"3*ҷ E(~"՟G8^sCt3eI,]ew2W<^p(6w('}̤v.L\ϔu<"On6ycmoRD66&Н> lHR />3xs3ǎKBZr͢L$?Ӕ>0gX^)S A_d(c [ok/=sl.oXvRh[/c,8+w?-DqPB)k(Aic~')ela#nݝy W_{kߵ¢F;'m0bqCBx|anaK^y+ !,L i)xOU1ܼͿgq'> n2Ki!B~ԱkȂց]rNx'O\~Ó qwO vN+AȅG'Ӳ  -o{5\?VgYR׸i;yZyn?yZ/?NDrUi9;9Ωk[TY}x%TcǽN5Z(BPXT"+B>7bɡƥNz-<vޱ1ə[;1J_k3ՑA_Y'ylhJR!]N|%vwvh -t;T3lEki*wJ N׾Q= ber 6w:nikL^ͫ8]bU6N# Љ7ukA:x zۭQ[ef0 6V$Rk'%cڶW ؅w?q>^䣰ɹ>}*{Oa /rVlݍ0?蔛_aZXRO}+Z|7k_n.IL DŐ7ZGB3׾fi!&f?# f(GFY'qsX&ja ;OaWbf*(k.ERҚ_yz܍cDEAͼ2?|mlR/V }m*_;g$8Xƾsdp<#|= 0/ux 3NLYLWqȟQr`6nLP[犚SL1Uq3S˾}/OϕwiU?U^yZn-űk!CzN_ܼOgd>pJNtp|lo=ƌM\p790>U*bƎx \+l@Hj-$B\klb"BP# 'mac>qܡDLqƏ.f s#l|kf޴nd__3[~ɪwbG?ji&LiT&(,(cW: viu/#5;Sdnĭ,-2^VX6|G_(MjEaɤ_h:r)nFW֘6qx4;za|`=< 4[u;|ve)܌1"./og &1!d7_|ttn"%Gqµ}'ၻՀ_)xnW=Bm9b\cyLvEB+\nI'<[;8,9`c2hP!x %f< Df[u!ЮF1Y` 6Dn*5RJn5 Kr>ڙx{Ú~N2E6Z>f_:BioXvx٢VeRmQ~8IBgDF(5zy[[2痬u@dL *-iBP^l`Ekx>9ȟFқ(eޝ'S4񕃯I_,Oպֽ1~fʀ\E h8"XLS$+gZ:GB#X^Xb}#fί# q~lfEaWWt*,p}pr[y-bPQatۚl̛)JSʘ"p'RB) >[ {%9"hJ Ca|YQD62q2vs6o7 J֧;0B(Y1LT,u#ҧ5τ~2U?=Jdb!N!0.5ob`#Mi\K`®-z+WۉRs"Nm,MeBJla\|"!x\ƫ8Bvfsv5in D4:r*w݁Ok͈--7k+\zM  8ld#@QB%2-]KLF#-t;lU8/d5~]d:+]~,A"o9fa`q\Gg}2E2ko:FL(3! 7N1.A~!Q)zF}HZ/QEea ~Rh}إ3],e gB}2c+-p#>,(?o<3(&vv!ė)?=sSLbC?WD 2&)  灔?j3߼؇+B`8H:ɇ+:~{Kn~ 7ß6ixX|q-m'ObH67g[%&e)rlE2ڜ,!iQA WK%ƩpdȨ.1"C p.97;#&՗ǫIg gNc49.g߲9 +PVU55ifI9;MaO?c)XMrkI; 3Y\Hl kb˲deަmִ*5<{AXRXJi6ib2 pLch2}qm'Ta-Hf[agojN"Ū5{y:N9. ;aM"["+ص:Zi:g3-jj8S&Y?L0R#b!-Xn)P\ؙwcMB'эQ+@7ضk3an.n>urwfVF9⥉X|#G0" {5oy)\hrTF.$~G1c),yQֺ+$b:Oi,lpYx2A HcyѡWd<%߾8q7a!LB@>X(,^EŸƅ f`eme]A0}0ߛV~C{:u c~-&F(/k^e)kW \7w0=?_XxXWZ:?7upgi ]Ża#=Lpqs~}usȘbcpJ16)|FMJFZ'SKHR|Y݆RKNV6[JK̶Mni 5>ެ1xv{N(+m4|Y ,yMnxhޭUdUXH JĚ󗯀%f>غyLh鈮|pAmwѽQqBJ,Dm*Ӄ4 䡾pFZFg w_Z]hkF3 ; !>V4O1S|03?yZD?7V}W潴)nİܼM>7?+@s0j/FmI{_$xZ!蘿"b )&ɶt[ARXMdZI9~yK[vtcQVᵂ3"ZX%iiʚݴ.6ƛ ]G[]f~an èJw*P+ Jf ք8,q'tڭVЛ,#˖Nf-̖uN&'d?䱊łCtV/ _d柛ْZs=.Zps xݓw?T,8AʚE\bBc]r`iV'VSvoVVZ xtVggqjMLB2V87%BgFZ\5϶4JKHMHةokչ'[|nWy?n,O`_"͟</rLw3UNiz_O(i`J`Z.@^CoCPG0.ɏ] ` \Rj{pX@%>-{Rпk6)g0sBSLu]*]"VYi}{|w +[N7ga9L8RSBMiS(~ť;lf$2L7,:ҖF0Ľ[Ԏtߩ٨V;F&:Kfk՟TjCkz=p銈;UZ^\T+=]3?f8jMG]jmo+aٲSYVQV.|#{>|Oߟ.fi֗v|ee^#iֵO͘oa(:z!? MqເKB˟)b >\J61cUܚu~H'pVw"Q~f8yZ9%g'bh ̡ `|3VZ,#϶KnhMMfaJݶ HxI&-ĒPU"VlTYK[^_L-DZGRQ *F(zldd1VtHҪHf^2 i7(fH[Xb |6M y Rt$JryN:WËsȲhd"3q$֢*TWwjӗY2b2 Ѳ QfX^ccfnFtcdl)vhG" 8ZI>wXB~E~ IDATu-]hQٓMn3m^IoX[w n>f~tǀn҃_'oͯLZk[粼d@n:Y#yh{2T,?Qk=s@%\64;y<4\ 0 5Mb򒨪=̊iXYȘLj e2q6B* 7YjP|Ga- xje- Wd?Kl+óOtS|Yi. ֣<~na"M0fA`v CP̪O>kb)x^~n{~dDg~%ͅKaxpm2\YiwK3"f I S:=YxBe)BgY*# 5(YkRV۟F7!-D-BQ$Ǥh/ȕD܄@ܠzz1a L[hi{g; ZRkM8ADI䜹qY33i:LZ:m5:ՂZu?kfzksw3:W\OX_H!Q@9.&8q|y^leV_bEU қ/'ot +{;Nk񮿼c_|;IPp?ϟ'1;0C̼:1ņx5Ke1 q=!JKaE~LY!@1"(QG1hO_<g&$90ts\qWySL1&h(7d.my.P/>p[5& JAps,zԶh`ɫUf5Zx[ג8YF5<)Rα$SʒIJ6͍^wн] 9nt{I&%cvC,Q`\q[+g+UJ+hg^duaf‘4*Bc{$I Laj(-oK&1Y:k2a!" 2ב`{)V%Mn,N\pߗ5;'a7 ꋇo^Y ϗ$qffZÐhLǤi^^h?q邺&NUBFrB\~uxW/[]>r5 f7qZegiI+$_ebceZؗ3O]<>S1,81¬d}ZgWbInam@5]p!Fi}S<50^Vr[PnQ+g,yP|_aU )9-[鑼mE⠹Oja~wܢS338Ƽ]g0sdƟR D7?V 8ȇ1t$>YA!ĿZo?kb)A 27^r(piYpsE&]̘^pm 3A`L`% Ȁ9isq K? ƕV4Fػ=yہ+OV H!=ာbyLU8߅J#*^Fuu 'pP: *E5N0FQDrȢae)-!puCn깮aF4IDX0n֪$,F}0 &ڃGq~9^SL1ssxYsӲ.Jy1@{e||±]\=aD\0oIB\ \FNʓz̷I?'FF,푄6Œ;s8z kW:d٘p:Q^Y7ö]Zc^I QR B4$I8`d:KfY9hȴJةNũ'ڎZ:almN)'9!31n# ݋ɶۘ#a"(6crCZI~-sC^Aq%qy[_u j,~ESica9U0#V)ӇWٖgO 8yO`!Iŗ0/$mJ㊔:os ҶQLoлe ogfKGޡѫkc3w{+:8ճ-[všY6 Kiq2^KW'Tk#Qq]H) @i% Iǂ̬ mtPd$ҖDY(QqI[iDκ{rfHzvWh=oow/pLT 2K8N՚R4zؒ(THN nw[HE2I &qFaXUΖJ*y3w=o;칃/6_ufgǹnUF'?h̴N{.O@:͜T,NBkRX0qowC>wc1WXKSZ*n 5e㘺%KBJ%W.5"*f1BZ'<{P<”_Ҙ)^pVR!D_bŬjn`h﯍"aHʣt˔g.EB"9M_80[32o2mEYQ W" sUXY $)5t=wǬ@۵InBbLY>cEqL)3n CL7?$8vs>)bW[ N7O(9̸:Ox$V)b( m)ub"6(%O|!²X|{{?7k*JL?[|'1jzsy407b'0GySL1+ܭ&iތ׋Ub/` o@L5 6D c:#$,cMic3%8:a)gPqa׷jQYZ@(!J3Lqu8nuN5;Qd_jgZ}=b{]lzcu"UK"M~"˔I=N؎--ţ8[A3eKƭ e#YSJ?qya~8ap`yfAonFFQGJ0ˈ3Womn qHG6zjadi\I }h 0LZ;+\-[$|aBp4N3WN,m` l= p%Ե\|+fqL%XEFk?!@)]aF,T0#׆9̀aE # R ~}[ol2 \EUQXB ]EbWaW,Kauվs=k/Z S WR.XF@`²8AO^^T!2Dsqᄆ2t,&M.lZ5խo\91> xbQ=qt^AЩ&DDU]R4:tG8 zlU*#H+8ĭa%%qIJ-ȹ9#Bf*bד[;}(# H ۓPS[lb<߮Ѭ%Ѭ؏\TXkqJ8]shMc4:ͣQ(TM BG!$Bi%%TeT"wp*F97,}# n.)@bfZXĽh(IT<'|V` E,,4F۝xNj tI{NӮX]Ԭ3&XBTgMi8,я FS4!u`' }#FM2L$ciTA4Q*CsOT`{n*-GkVxOSeګ]]KU?*T\8'߭VBuX6-RY]^bu ʱ,J:˭&n^+Y=}#H-ѦoQ,|\9K1/g0w-+1u OC >Ƥp9ׅ>W1qx> ,sz-c09)9Eϼ18foY9uylgcgk|yo_ׇBa{塋R. gu`a窳Eϓ~9?g?,PZh'YFP"x> "{Qxma":FVfYD꦳ 1.3^w"]8Li6wLZ3Ŭ"`E*j Cޓoz&Tϳ;ߢ䷀ǜGߍY&0DΎ ')w<6dA E!lbXGansxČ3n|[^̣wZL<:X^* G hͶ+j`.߬X1Q"r$&ǁb^ô8'NZ|H`0`ňG-h #X`^JO=/0L=2f-p61-\6UYv1Zn _ipΜ|o:={r> I[{mZ K25?>-=Jsqo˫,U2c&cnzۓj5xX ~;K.w3#ET$v-2y9q~kzS JȜNW8jjxoyIS5r GRo2qONrmY\YDz$~4d1Fe (kҩ"Ul 7;tx>̝عЯ h[W^l+Jÿ~Ϸszi[¸me0(:s ?}r^XRR+0n#nuݼGbBDw8* X ECQ~74 (G2ZaqgZ -?Q3**>IC}GL#`\C1ttձ8o 3۽ ,{cuS=c4خ@%Y-Dp<$%*[J*Ӵzr Z8K{OY`}Ídx)ʡgKL|Ĝ 7OۺXDx+7v-O0M7&r|i쳋Vy-q0iݠUGzhY=sg1ېy_b_ fWhtI/Mg0 oa]Lq z#$0 eLikOE=sC<,-5X`/ ac8Z`8f )8@+cMQf k*3*4Y6'ǩC0 MW4ӳk,Wks0_U-I0hk22O*S뵆T:g vz#l,F)$n^Ke<7Bm۹pQw:RѵȪ[5*Ǯ׃-a:x[:YeYh<\R4O;Q! $,Xja4艨FZ6}n\XŷBBL7YBMcĻ&?zbqNT0⥊Y=G~X"w497*<95 {SW s{QgL>l5cՖ1)C26x>iD?)#˘KW.[mWX`l?yV꧞'0Y!ϓNtrNBB@4D{/+Rhl߭AdYR]U;)UКLZlǟ 2w/_ccZBh|uFtJp m: JhJ~ҳ$"jTSa -,ih಄@X6' DLT0vP>.ߕ-'A4LꙒL2#OuSV vlm]T[#p-MHqHp&L 47jx'_H-,ĩ:e2A"X͕MHF8 cn߿~|ʚJ;j\Jy=>\Ѭzpff~s}qB,(c[E3zfQSBpGkGRLjN"n:CF գ3g>n0/8/jj 0CYLss f4F~}1D쳮cLrӣ(ܜäX ,pg(c8~ 7: |PM QFcyF+: 3]q|'eyikl=\O~HūNT{B.T)E<!$cRI0!ҩWkLTT椹+N7x+^Iw8tDiljUS>Aɋ/p&BJiD7budj6(A_V.iM{4Q4-"t.ڞTG;A}~s4 N? kL?*reeeU/ﶔ|3dT4JȑC7z>3nXB x![kyya5{cv>"!D7~Z -O059fbp7:ɷ [6x<[Gu^"ňF988:!E>s->I ncE?{#.5?`>>{' ,pxcGyoz3VS`f5}LiZ[q3-&Z #2xji3%۳I=hHZf9^ٻ*JpyG!`^%R+*S֗[+_ci+ehp`0+Uʵʠ$yTKud8`ŔK!I tJ(Dȑ-̳(uaV=bR7;}OFF]+8I+rc!\Ta<s5&)-lKRKh4e`80t]#Vg^B1w4!HD۝ؓJV͑xݥN+Xk|˽)|U= =DѢLx*XRrwb`ma&a=yoc,I`Seu,0+=bUŬp+B1͍dzm&"v<!oyD~379zEc LU YƸ0-RIU45L-c9}(i-1Dqш;Bb;ɔJeKt;]tF}DVљ֮ĦK8h&ZhoT4UI;, }>c 9kTu?=Sm<ȕc/vZ𗘗l`"fr xmfX{Y?A%bDZ+o鎽Bf`1?!f`*_:0q9m¡9~})&.G#g?5\^{%瀟~+@O1yj2&Q01\K3S`H &Z<^3D u O=S,cƉooKġrx*1C34K(rA%J c1T4VӵUr_),Ђ3+썙İy"LkFR5^/si !s2R8qB Ӕj'dq tg2a%ضd GXxeRj ՚\kZL4ˮ-mKO\ǃ9W[eTՓΊ v9AS2֖- I4) z$KDSc`cضe̟mF?sL?N(ʟy2}Fr8y`y;nvGEξL jiY9ݽK#-aׂ #dz0Xi,? 퍅Xw +{1m7ńw{om[QB8YU梣/Soav5SWq!ێunMC-vs"]ЖmϬVv }PÈU梾!2E`:ssE/Xvp1pc1D@^0\;'^(3GdBXI##/^vk!],|RUf^n>·|r rMNsqK\Y.VeCv|wkߵ&\ Y<}ʮυ=%jBJ1 yq*+i!J12|'=Gҋ&:'rR썆 l%J-"p,C?yIӉxqʓ =͵%, NTB8w{6~UN8p@ IިOU!-!Ҳ+a%c+'dYb4 :֜jrڻW?;o,h=/{_sL9Xw/7[c1b1L*ދ/0n`},c)EH~e\~-RjϞS! ;Q{zx[xQfދ^gd}Y1 LD_I+~#k)ag\Ɛ\c)-`bYm!?ܣbzXxɊ<1HjrHs$YircIadSUB]umᤉږkiGC-Ql1O*b哑Q.&X9e K  GcJac.n)hӜ8N/Sp̂\s'`wW^&_uaFbT[@ORԻ6W][a/81/gdӭ6ߧߗGcp6LD6XtǙַqׁ_D\ BfzjaF!#ѽ ^uo$=˙/,$=56QA` /1DnḶŢG8{ F jYOΛ0-Ux涻s ,}`b.7P|{PUo|; #>h v7db{Ysu<%xtvv6Vx%m4hDZs-q?qŽ=z㉕ TyyBw8.4KtReIT}8O%WڇԂ_Zr;"Jqo?t=eyLh_懠!yIv%D4e 9gBR-u|Zm[&#UobN ,ja8&R'|TB9?xouJ%{\͑8}hR<oL\Ox;|r3:|5<-?}z?&X{B|x&5+Sos|wB)CRŶo n&8﷊﷟E0]}9̤께FߝۿI'0scz4K9MΦ , O+z˘b}ڷ?hk~{ n~cw9PJa{ nd DY.|nUDndј\d4+U.m+ iCF)Ew<"i("CFˇ1qѨp%z),(-TZY茇A9J/S!cM(Q RW8x? g7B,;y~W2~o1?DWbͳ)CmABR?}-'ohڿEmqe-9߿Y],&j$&8DgbDƴyz=h^GL 7/gd}czmuD3k~?&8~4ЍXkPNVKIBH!NSƚ0ѹ=[ӊ8U:x(NH4kel2q^wZuwoc#HKbgY^KI3,!p=ڃX, IDAT0JR-[,5T5AI:Rc%Z^pԚi Aųq\B2NbS2/i Y6aGA+feZo|'`9:_;0mrIN@|՟:BiCsW n^x{b! K<4b>^p#r عM; u0/DZ!^o 30MWۣ(Zl%ڏ 3`LMP&LQϞ}z;ymĝ Ĉ-L4sX`/[|-w8W)?Œٶ7J%>1cά;tCͬ n,K̰4R%Z-SJzHÜ-aƥ-8Be>[`ƸLփXɗ_~]ej\ϗFgQpQ;V忆5\ieHr*5B#%#΅PQOMʦ3>$BeuJ__HcF\XkG,cw\gZ0S ,Z׳~ga;OC-<+tHwlV4 Ŋ\7ŹW\Vs0bK81zj0oQL1!uOqEkz%b,7\`0 Xkƞ/5j4qFg>n>Yо(!nQeJ<m9 qZxX+;S"C8{YaLiZ3xA=ʫ׮ӞDT| ԳDXM%qIJ}\am#DiL)X <-L{ө-(BXib#Mɴf2JRОL8 XQBә c 8 {'=F\=XKpgX%KdYJNIҔAA4N)F8˵:N:+M,W\Kۓtsj, <x1 n|zW49^o߾ ٍ7ͳ n~c! V0ۘ 71~blgG|;!0RA1 1|;a'w~XnRYXL(ӘFB:{aV˘b__Q\O}փtb 3_Ǥ`~)az|-yQn.R%Z66V47D,2"IHI619jeדr\>TZ3}';qh2?p5Tpi_XO锘p2RVZ!K jY'i.ڣrv CD cD )1QO9Y?g7B,X1o0`>6{)gΝRqG~=z 7?}(HH=+ly_CxqW=ڸ 4֋ ,AQ'0'1M/=l!#/e)y-7+m<@ޠ\48u22ɧQȉuKldG, >vzdyrySgx);7/_ߡU** =)L 3Ð9v96JUzt{(YҙNNҝE,-*%eSg+9PXXwU幵? A+y?rêkDn-y- 9eaeD>2>LUTflGKGk~T%\_K߇%2BQs.w^ʕC:!Mk!PuٵΰE8H3*A2LlJ+OFILx֧Ul˝ɐS52pE%h a&c6[-|iJP&SX) KXiv˅׏hOtIByqL8BRH%-#,A5w=<٨QKk[jeCjvZz5,`}?݃L*3Whq~[B(?bL[ W0__2GkH_V()鋷yuoE轂!*~2oarELW/a"u!D}n}3{nvffSY ,pgQ \4oL DX{,P|JyK9n sةH]]fWu)%^~z6Jkq&W 7R&Q/yq){퉖Zq߼!b07ٜ۴d R3zfSJHEg0 U %e0>KJBd!^L iw8h,l!,o\!.mp[&:ˉ ELfP`Ö_w]1dJfKUaKH$% B,&Ir'"/9,geYVpdY?Ŕ9ٍG \\|ǂ-^x~6x5k͑ϹC^ *-(k?ѹc[3b2qٺ>fc| ۑzZVJ6m:E0#SO#%-2GnDi҂$b?82vdJDn BHJ!R,V.9V[ea8*Sg9\A6g$x,Za[64Q.9.BH|%S q\YRJ4ö-|#mAC=3?7_؜ bmٍTcig O5So9qay> `3!lԍ`7g>Ĭf9[A;4tk[7Čyတ7.{$SmC+V)fR5_a faYʘUЯh_y+f:&*?sG]`7/bƃYUAYO=/n0f.\pZ6G~*2v(gc?ŵB_bFTͧtNg"/\p:3J4} ]f) m҄ MRe[$y4MoXEHNך` j`aKsq<v9J5eq>e֠+¦Yj.yduu]z!W9lN8Kw:ds)%:G<i@k,}ߏήƒq0  \MˣcFj;ޙtO@Yd7QxaYb#pFsׇw//`}!Dn;v,M B?k6 MyM0V=Z8mcaR(45m(2}jL=,Xw 6 tKb;LM220Lm0,/k lG^`UĶخ a س+_Ɔr\ͮV^-4gZ)m]šQ2zAI /5ܺ*>@O MhJũO'˜ծ#RwPLPtY*dCT}-ǀqat=baxV3ēqڎ#_.VeI&4fpx_08w5-tun :d@C*eRJVeHRA0 a)ˌ F yRqC-6a\il@ǐZlx[\׆ <K7A3ovݿt}m?㟭ؼh)YD~^#:bW+R vrW<ܿa^2VȈQkK2.j8=FuTfgK Xpny++6;q˳ yc)7~M5k0rՓĎ{Zl:7`"N4ɭЎU;_z9[ x :l{)8죿 !pdJ7e^Vf: }H:^G:^s!S844@>_KRdzXBQK e& S֏.mÓ( (2&&g@.rs1a]=<AlD,g4#}ADX!tk`"'=plDR(q]-T !xВɜRHŇciҩ$D0 #H}Xl܊Uu`>l ,6؜bآX̽ ֎#kƂNؼYS-YyR%[ x +Gy# alz{~4.='j}b8< ]bE;68HߪLdRX 3 XljsO:N]:Ú&&EaXb kOؼz O 0mMMlh!"W,0ϲ:D2⴯^98͙zbQhH4Ad!OgΚG\9Na_?α|Oq/qzx5_~KE)?#b{n>-!J/\ Q|n,}[: ޫ7bs3~nUM ٨&Pѯ\}&bI;z"ʐPއ3η?jvRKOc;Z_Yzbcc }J"t߾k, ۽̸([ 6cO`e͜A BR$㒉okniy)A˧H#AuuײQ}_!-XZ+={`SDG]JʟeT5+J`/04*4$v\ձ*UGWX߲?pWY1l 1>9p>Kg YIc2MPiOi9 CZS/finh$H9s?N;S<yѱخ0ztdBlq2_}6d.v3滶ܑwܭؼ Ҩ|&(ؤӯ#9zR/X/X [%wXYDCI`:Z>,g=h/M`+ǰYEWx~x&RgwJ9,8%5Ȣ׈='(X){56+#}qLo'Gob) kzMҜhXd9C13rq, Im/t&\X.bMAc&E a|9$ W}.2IPmVCKurd,F2Y.BXӜw2O# C+)s1^,YcP +kzCQů,#Q9*/\V^ؔIf?%;lw2O?uΏ^\ na=( 29}>ؤjTu2:`r.XYĺz /><,[QLpۿ yوQ0nvs @]q Fa>~|s S\W+GȌm>6Y%*W*~CUC#e2 C g՟:GkCcIJ쾽.)cT" >rPo%T2I)+ֲLV*AWZYmh]鏟OfO?v(ɇ!WwpCJV2.1LR! ȗ 'HR*hnlk}4֫XOmѧO}#wĜ͇a}Tt$y*T>9˔|݊͋9%{o[>5ԧDffo`[X"yH q_ IDATŰ X?M6{[}a/ZUX9Rq:PcFaI:| 8ڀnTiʻ>?&"az=o8,&iȥo/Do'|olCJ/Ζ{jl3 xs͛m7K8ɖ&8?H&@rpD9qRb|r~򾃏]pP,NxX}}Cُ+%٤\Y,W8pCY3} sa!b@X#焯p-Ax|rU[{,IbkH程K|t45ҘgUc X+[(GkW'<I&$8LLf’WmAG?>tݿ muז;~-w(6/Js. 16 l'>jԞr()DF'utƜpX}~W@{0;IOUy>bS]&aq86y{5I 8{Fː ,_>`ONP=>XQJIzBgf}ygALﻯnu Fd %O+ uv6V&ֆAulCJP:˩x߱RT@M5-uIRTNOKԆt4TԒ/'HI1l%8R># q9Q.r0I_vD,\:7$ַR,91>cyr$͍i*a8q?}1 3Tz xe \@&T2|8w޹wބ *x?p-w(i\xp3>mX`<#a9ζ9T_ȯV=Uz:GhOw [6&%)]̿/Ӫ;D[xl~ga op p~E.]=).x }JW b?LV8v0F<}1&'-q8S͔ e9+_NyJTl|rԕ2~|T>|b~$D`X ՒoxN^Q;PV;q&{2E[aD"XVbL9X_q'ιЄb%'{#qJL |'I2{qvt:M S)U b%+ԕ\~je5>t~(v7F N;ϹKŲ4{.g9aJIcE_d^Ec><ݓy{Xsĝ㺶9wbbTd|l'ܲn#Keln㉓}<<H&<$b#;vJuMJ8.'ǧ0pŘ(N%][xVwvFkہ?W8T̽+q `4]K? |5wv{ǼTzzI12_UXX92|d",,z3Cy~}fh+b?*H I_qB Ύѣd"A}υE>U,R]=#3c,6;>m ,U=ΤA*H°~pmd0zO{oX*K1({;WUwxڮ❳OYx->A[q~5eӉbfBKch< C a M44%3P0Vӟˑ- qWLp8<>ı!'FIW]lήTĉj,DlSwm#|k1jݵǀۢ%hE3gaoj_%%p.HA6Íؙjal9.C8e ]BU]h\}3wYܢ9n [xp_(rf KolcuyXlfz ,6P#-A?urەdSN:s2ӏ]B޹nlM-wl{n1,rJs,aSbcr~8qV.@W9XN0Ő,z!l󢷳Xtt4X>ۤe/ι^"}Q<v`?QSZ P3J [y _LCG}lƪ=ƾ@sn, CAmlp1?h"]my)Vt0˱mOIS'RP*r`ជ?cSuUM M_|}G|Ҟʤ׷w rb}Jܲ~P $Oy ;yޯu kwurE{_8}sVv?Wߒ,TN8:26,D~:Sgt,lX W{!|]2ۢMb]Z2깡qv% } "Foyc3]=uXi9IzGܯZ!K/ꧦ;o_C-6;Zj+ J|Bbd&K5Vf,R\LJ wW\>::Roid`\96~+7~cO![0kmk{17?RPrNmtw⫚[cWuku.Qw}B_L,8:Dq #=@& ;-Dp|KRc:h*Xt٧jL$qrDBkXʊt_߾]պܐI+u 4ǿy񉑀ŗlnM];=zC4}x 77u?d'sd2O$7 &,8L/ZERy8~ (`Tm{ιF`~baTC0xvVjo 31H5_g]Xx:t9~8;2=dG_ÒF}!ǰîo>q=R"rIu)$ǰ?;OWO#:8zN;oa/bnu \LlP+]+d: 9^xz\nɯnjxءsݗW66?Nxpl<\g=<=tuO›>O׶ )?#9 6wy,(Y|ι^Ǚl29׌%E%U?+V),N-AKClJش9eHZ{8ssX""g<`L$HqUCzsݗ-97wqqMXm 9{@|c\ +@Op3ok6ot=z3oĦwęF6wj\0sQa !KJ }91UAkNUW++_1:{?>[?T r ]=d2P&Np< #˂ߵ3QXl^YSqsf'F ]qvSbTPxh1&8:2cޏ9B/c٪+yElL6Q5ťrs  ?s>ι\|<,#6uL [03݊ːߵgy숎KX Qbs=34ȓ:OBjG[Mu {|xߎs1o><·q岣2)afN=VX CK |ECޅ-$$mQ[+[$}9LI%'J?]l.Vo}{O+w+`H-6՜9&R݄GF.xwfKxv]{~fYr櫇jxsV&2ޏKCfG8^}>#ܱ!6&lox+Pl^=Xl~,bsc@'C''>ޟkQl%IegډVXQlYAߌSyNYYDy3 e-<\zϪBl$P$Q>ߨ _y |mY=Yww`_Ύ!%c6ȜY*C=sVls$"2897*e9'ES[|&'oaClώ\z S}rԯLۭˈuX"FjmD)Ql>))SRRgG. *Cn[\5 9sŀιsp.K)`q^ܔyPObX,>6 O`X=6]=1`#]=-sɢwbBKLӯfGqD$~מG(DY9lFrRUUq!}>ղ_f;z}e;=+ %yy,4GZ`wb=\txߵߵGY.;*CsSrHY2s/rq< xG,?]=a/t/aϗv竓%y{K ߵGY.[Y;a8DQf{?VrA JEdۜ*Q%2~מjl:u%rS8gxWu^/J4(v`,0Xok⮈,{6l3'[,2+~מao ?v5qW.{Jq4kejCpDfؔ= t^JDfzZl"O?>6ov]{eYPx gߌ #2k<)ljy Xsf+TvL$r=ywJe9Q^wqݫ؋K9t5cɥOsGԟ("rNwv+A]DWϼٺͥnSl]{=]nR,GJFtVrpΘV`s.1%tӳ=<'ځv?™GZdÆטV`m$ߵ?ԳSKY,e / t_kl[ѿ=pxEDfe`ʿ2,6_ l}[?K#;{| j"xxNe99fb,~>#1?0W8s. qo圫5D""2]=3]h#c7}\K(Q@vSn ;[7fVj)Y=kϠEdqĦ`UŁ^/aKG;X"""Ǧ~xIص7}dЎuKoݦ,"f{-^lƗ:si\sn'h+s.o^k4z7o6bnWO=]=izVճa& uKQl%@=9׌*n:Cl_0J 3Xysq@ZDDDUWO+=`mtXQ,Qǣy%i-6Ί;+6⦝ nnZ8<60DqwWoSޏ(QZo0~ xqv9}<k q=6G. cQicPIDAT("Kŋ $yVaf1X9lóSX?D K_D;"""2ֳ'رW/~'0'^WO 9ͻnJ1,6(Jy6fY,^$o x0(v.O,>E+{:v,q6DGi\z=۱؜^o,673il._J:UmغmED.B_Rs8E*ota ιe/Fz)a.s,}ݟg+K؁f[bWW#?1K@AYD,ǁzι ? "pxx5XKY^z MYq͙ ۳T`(6؛#*$VU?")Y#Oa9,\s]ae\smcn NOx"""DZ 0]=7~)?}Q[%[KCc,vfYttHx؄-Pxr ~UUalr{?r1/""uİ7Wcf>|**;`рu;O[׬) _t0R{;l۱ݏ^$"28Ǽ'~9weU <X۟,}EDd ypǑN,69tr'6\w3#Vcs"[Dd(I;UA8.v s3 $7%k؀~{{?t+""rI Βg),K[^3 g;f8<+UXd~vݱܱ]YD?%oǀ/r5_H1q5u6 =zRGDDD ^ wc/>tigֱyv0 xmc~\ADd(Yļnۀse#ش S?c% lp9[oiRL?Q\,6FWυQ[=c6~+UaTOarDD 'ι x5vX<֛z;Z=m  ?(cQ/Tuay3Czgn+;\;܂b#\FwlWlMcy"Ԕ-<圻=yh 8599QJN+%.kv/!HtkvxXb7O6 f&Z56cKyқk1`{?>c؀%k>wRaeޗ/"""r77OijT=~M*?<ִ&tXw!S"^XlEdi?M6M`p p9 H#_D.a?T(""rz=XhE觪a/FDB(YxQGbh/GDDd/}G56/刈\4Y\fs(""xlt.,FJ`}"""nsW.M2prEDDD`vGQlEH=""""""2vEDDDDDd%""""""2Es. """5[)6R(8Uιƅ\j6f?Je&e`(.􅈈%Edis. Nz }="""݁Ħr }="ry΢̖2Edsy }="""݁ytSJZEi\(lr@r0zh ,Х, Q"dj B<]K%BerR,룿n< H/5,'i,6Q#Tcs3=],!Jb偃xEbpp#0 snSDDdXl&,6?(6/52T(ޚ]'ܔJS TOb?\ZjlF4\DIɢ}蜫s?"""gS ƪ<(a"2#\; ѿ@y.KDDd: 1xy,6NDdFJe """  :ZPlsR(s{_uO?.,oZ=Sn2v99S"2s?n,x[zDDD#<loju!GD7%rI9熈xŅؙZzDdR\jW`DDDbv D䜴(""""""hgQDDDDDDQ(""""""(Yi,4JEDDDDDd%""""""2EFɢLdQDDDDDDQ(""""""(Yi,4JEDDDDDd%""""""2EFɢLdQDDDDDDQ(""""""(Yi,4JEDDDDDd%""""""2EFɢLdQDDDDDDx+q4IENDB`openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_43_0.png000066400000000000000000010425421413546205200254500ustar00rootroot00000000000000PNG  IHDR<. sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxwt]yv= @U*)9[8N;Js̬s'Xs+3MdnI2qO|nI8(U)v|`Dʬ[ q>g?x}_!Dh4f9c\h4Fhh4f٣Fh4e<Fh=Zh4FYhh4f٣Fh4e<Fh=Zh4FYhh4f٣Fh4e<Fh=Zh4FYhh4f٣Fh4e<Fh=Zh4FYhh4f٣Fh4e<Fh=Zh4FYhh4f٣Fh4e<Fh=Zh4FYhh4f٣Fh4e<Fh=Zh4FYhhnZDOǠhBJyAhː2: lZi`ewFhh c؆8@6ǷMQYhhnhDO@f,X@0Z)K(|<۬h4W-x4 K,vB`%B(vH`X<|hew *F!=].~?.]գ_@Qwq J$G/}h4W-x4uO8@hv[ۀf`-Ѐ*[$) P%T5$0*h4-x4uKCs7\ >T.UJ(p\A;ʣo^dw{FsЂG\w.崬Aeh,.yԄN3Jԉ|g7Q(!W5eD Fs]!zLT vB\UւEJ Q D, _ǡhGh4D.]JVb`qu._~|UYjeQvWx4%FW(.T>C(1S*W(Sst$WO\`!ீdwvWh4-x4UGt%Qbv `QnXZ:*IB}\"ƞGЂG\UDOWxG5$@{Ģعp^*!U=_ ?}]~5զR~;jv` JP'Բ:&o~fm[.:v3q`Ju@,bWa5Y:kP|A(ֻ3xQrc~vh@4$n1O<܁猣DϽ(צVjI͓ߣ_<0fG FsY,P'_F^N%v%zʨ̿ hѣ\;tIK\ PstA:QM+jx`*\|Uh ;Va@ +h4h4JsE2Uө99KA8W0>C ?qmI9Flf>Q`3P?vB[/qMHIFs߳eB ܤP DOWQ,:9e+?SojaF5f\ jܷ{;vAT VZxQPG+~TWVj/ x1rd .a%AmK-j4wh4@o9'D'EC ߳LV%^y5 @ݷA_ۀ>nPm慩TT)uHz\W ;26?PR;UFgx4K}K~O9;޾?"C?OvWdwELNVEԡZ8qibFAFPjI`Rx4vx4KceAF? ;gQ;{dw^Յ[;er伻^F `lC=f#W4h.>3H a+yU&: \gV'\WVn-SkA?o;oDupm~%zĞ͕@ʋ3|g4vx4K狸'! (AD}֡ 6ZDOʜEԬQ%:TňKwzU,D!8gUKDeҵ8=9/ۇI=yVKAtـ~B2ʥq@8P ^хse4si0 \cBLS"01F"$jn) 7HKYN,ʱg79M?aCY&TW tC D.뙽^Ұs5>rh(1rj6T^ D,Ju usU>*uqc$k3!QKi 00 =h4ӟ>mp+SCAԾ=ԜP✛=w|z0߆պjTn3/wN4fc ;\%k4eAyu~m8p>rn6ZhG(Ԍ ,ϵ4'4F:\-܅~m|Qg\Mx(3LsY<ī&FG_;h.]hKD9# 0Ĺp]f欒SoIZC} M}dP w~oc2'M54gN_D~.U2VkͺaH*Qܼ(תZmŷZJ5ķG;WgX^z Bho(oc5Un"@G8Nͭ)DϢ,2y@.M!ݝ\;]zV*kg@k ۨo$PrϣY:N gZ+OrߗϾ=?ˮ-ȯ &_ȼC-yh呿L0n^F|la1f<D$Jvѕ4\gֽVkSJz2P:>T|%c:2X:ZoW_ZF;3s#{uM^6n0*t-_])6hfm8S#'hxrtZ?Yڔ84R=9fHYz`6Tr+Gn(r)WZpyޭm*,{5_&-". PMmy UL/%jMWQar,rƾ{hZh4=g Vz(1SkDC]a ulFH(rj@%da35bRHu&]#9v:}p 뛶߻5cWVoJ5{rzׅ֓#;l#!NWx}SN`LET)eVf2‹Xh1Zh4oAmOb.fT(}sVF [e˰mԩ|߳̀Vg(f_˃07/WszTz R&e˔F H lb1-Wۻ(d*ޟjV TAu/V3 ".]PA3\YP,kxEQ2`aBuŏDz&^0khKsFªgcn*}x$$I^A"4Pn])@F 07LW8EiOB.)o$ف_.R;BTɥ+(f}qwrOrz<N mΚU)%dZ|.~ю`wu? hJy{ aJHK7A +TKCxIOqDdL*=>%W1WsNt p눢V$pxe~qgEuk.ٰTC?ol_LsC2f.񓨿F'K!.g%'Q+<`Eoe)A% BI-ەt;M𠝫%/kSkqV a}gjfmv[Yu^ie(90(, 5gjs,o|y򏦀6+oUjӑv{6C-#|% +5]>@rt@}#"5Z`ᩉ0T.Ґrg]ܭV- Due"gd&i^YkH|ӜFnRcRەys7&4^-[Jb; uB ͟!ʏh.x=(Zf;~+u n-`s\Q0!P"%J+Rk6f&ͦ!GP6ʹvTKP'3VxSnϾs.VG',BwԸSgflaa&ATX40DԆהҩ8,O c=\P%w.JlcS_١uա (ʒKˈqi;?_?t4N-r;l؟u4U`]=@@TS05KB MD}(n<(wd8;VW:tH2ZƶXTe\7 4R^g˶Di+)Ycw&7!T ZN*Q-a<9bGӵ5}8`<}k"(Ш˜F[#onkkc:loH9SGGըRPn@u,N:Tƥr2sc۱tLŶ kbf,ŏ &m&Ѭ5[iP+} qK|4=]ޯsn} =nҴf|/ٓz> qvU!җ759wڼxSPAMJ|hVa:4lvYNT5]Mxt:0Ffm _7G>LgݎM;ۣfc,-dhl4;k4ڇLR(s!s|KMIq# UITG=B'ԴWLTmSJȊ[[ub Pj=]K=y~w|Ah`6v%!{d>FRd-O_ùapQ\8`URIN:թo~-^C2yN] D1̗fv%J:D^tt`C4M3椙.ey.N1n&['jK?#eesyT0H~0lT9gasEjvztؐ4>J![X4·w6a; Ŏ3Hc(C4[5+jm5SSB[^V@>,TUy315ỵ4otDOr"\ȝymh|<˛?6^cQ qiƍӄU>!v:`$ .y%2*D5mfX opG~c$5Z6yC_zOÇ n9Z|i[3wnwYLNg7KnON ?=]sij _sPd%VUбu)Qmer 00Uΐ!JDM^r;dru`ZG=o, ,j~rF?y:L3'~uΟt3wf, .i-o|ݺwKBM܋ܶ閏3]2h~*+G:ǿbCNt dpW!\r;2{?PʚF]3!`jZMC#Tb= Ubg)G[ߙ}5)x%fP"@ &1?yړ׫_QK3(ea5a(0jjN-61 BYg8Q^@-*jh`[dw=]͢ku.Sj8ZZkf Q1 -U>5ly; (Ұ_^+C#wN'5HceԎ7La=Jh(Du]e?|DeHDF1/Y[=CkO)Ԗ@WDxj^LGv?T6!0 !Yp(/5M J0 &I ;Ls6 OTBep9:}3ew$8˝քy嶳J\f٠e%r#BuZҏPo㹦+7 S:m呤m&#D_GRtu0"mÝ`cW^+|Չ-ՠTuB3CSd~ubJ=]bHո;it^\nGou2h Xn IDATFe1q,TK4ˢ!ri%i9GՈT 6#(Cwa1@hф{%Yfj+_ΈgGvewmmRvU7;PPwB0 2fYeÃ^{{[,v?J#}8Ag1 3ӵ*z2_"=^{BFh7<v&.&OtF"QAurlp:k~(g =f#g!2qw4R wM ;>Tv4if+Ć*a:M/n9Uzf鮋sqQ'$vV0_= ' 0F-agKzGZ1f7 ¶( \K8.*&XQ9XMj$jӒ(7gx82G~|F~/2>5Q4z瀂x3P/=>`j47'Z,36jH:=wXrjur߀*m-'甇8;roA6`mRRDEˬb,[G+U JAX/weݖOvmpdC8C3ޘޅn@lNC QʌDOyVt7LgֽiZJY N5WBa]pݵf.ۖAbc`Di࿡xC P.TgqBں,4.+&E GiT`YD=vK E/rvT,3 VȰW1EyRJ[̔ >L'%lv@z pIZtdqrp!wFo/N:*F c[{,Q=Y~s)*ks^y.Q[t:۳S{v#Y++:>[=] 5`fkCVtY 0JebBn&a'D1,VmKƜj1hqQB-jlT PBH/%^Iq{vSհ |tHHxPL(Rwpkke&~˚H.-:B/&`8yz)58ϡ:PmЅ0Q;P9U{EwGhY.%Lශ;De/NO%>ROO]``Mm JT0#RBQ<0},HirDe0 eC&Dde'm !҉mwW8C[QlcjBոe*GϹ7!y%3h'di.q]#7裄7ʓ6nl픩M Y{qbK RI&rl"1mxX0pS"Ywf^@ oRב C*ʕ.j6ʅfi4 -xu`Gou 0@].fenBdQPe, ʑT9*\WPn%l,@I"$۝ʄ7Rg7YF\rssɱcŗpo|`YDhے1"vhvY%{h {vS>aB-k[Yl1L{hSS0i& 2 m8izqKe%NpY 66pǞ$0sq%r*('=JU˂#3%#?`FuU&dKKd)ͺ&<n];N%^B9oߏyTӈ~k4YBcc4-.$ocL(#i=(! R\ VxOxM9h%k5j œPMf̀q:HUl#lsTjyubrR*};iA ٍc*l|YxBnc`s}֡ͨuº5 ?^c=` )@3ƙ9ŏJWQ[I75 ^,u(nC|s秀+8h4-xn`-<r4~:H}OٻK ?)3=q7SM*ŤQz-am 2;Qy@"Dm;H lbJJ0),E{,8E1Z6$o Ž s; ]zO݇K+ܵDX]Cԭܤ3'A˴?jG^B3i*J 嗄•C>Ck(盙<F [TE9qmZ@͋`%ΈԙOY`'Sal֦2z.q&( oLVG\BZ]({g$F>gTAOpijBtiZәRYu"K]WlW+Yl7p3AQEݟ^Ư=khTvQo |5/SF l,F<:)` aCGHd;A$GT)/D.D6GH.%,&f=ݐ{DVDlܻK &)0:\-lNFo6 w7F DW-aLJXM#S ed| N"E"CdSҥ̱ fhHtiJe43Fu}bKm{q]zw~g @br&SGj#@v x%+ :[kTEƁ3=CVl 5^)LVڶ~*t5>ȫvZQ{gQNFIR۽!x:%܊˦S'-ѩ~QJv`hc'ۈJ@s xO~Sa .7%l[Qf#[ f9^G9ȷ%t VI# PXp 2 >!IۦfDRR}!eȉ2K:Ya1 ^0 F\;2Aܙ>2R9wԽ?ԟ@VD\yojdnx%5dƑ" 7!M Q\*t~\v^;h t:g.$l`q9mnK%K zuɡHZ ,m˭pV ijR遫|7j( sZQ#ѴjX LXθF!E#IXR=2a"#o`FKddL& Jtِ<3/D@VJW%ѬeG*z>:zRvasfij7eOhT+& @ʑЂ\$-v4.i؀]MQԏF4[ۜaKSWnFr 0(Q=Gj^b2F v-GfAb.>,hE.<ޏ-fr_F=zͶ BF!,m@ $˹!&^T%<2Fl5WZ{MΪ#4}q T0wi 范Šfɴ4i1Z6m?p0x”CPBtH#)Tf'BOZݫ@x{2Uts_,Ksrj.1 $cOHWJBVDT` 0ɟP^|>L{XD  <=/_c5:y,w_XO GHVmf$:/,ѐYKX!x `%*e I3-I T6 H e^82[7;~-RvTcآEW<~\yYT'!z 2FJ7mm[¨zMJ{34!jXh# ofh4-xn 6ɣZp;Xk{x/_q`W`iCm(rL.4~އ7vMXY/]{ym懳ai̫_؝Y{p d+3MGVP.FF>ӇP.*AFE)p01TC3$"$ BE^Jhb*mGpaJ>835ѻxJt=%7FsOt5i {2m ؙ,/lx Ba03pZj +<5OaC 3Ɗ"k(yߡ CڦN 7[jBZPyf(gS.aI|xcuA2wy Y].Y#y5˂_6`N}>?#ykŃ"jwGP[WQ2LlBdX! TfKmB"Ec j%k7Xf$ǝa5[Qh  ѻJrV>Zp xT3<.V_F^$s(+hk}ĭLd[ J鸥e+lFL<3Rw>z9Wܠhspx+F6O.~.4CRSl[:2LwKXJLs*%KPLҙ9dDD>0.R ?ɗ,5ضw@M{n.R@+gVQP21+~hͮi5Y~CAUs6/""Cb *8Vl 3>aP˸HbMGaXsa˭%L)^6l̦YɫwO,Z=/xēQTG ;|ͷ:|!ei:B 6u+ fH' vY3+h\cOR^XKq2[݁yWh4hs0ށ;A )[EšA['`SmM͵/&IJg&* 31 H$Όi7Aɢۀ7{k ꂫL;U >b֭˅ő:c+3X'PZ&6v7ʸW L 0DK!J y@t C(cDLP& "D_n2"3lipXɓ۫I9M}ʹgK hcdnpNT(Ŵ4LJgdweͿ456 +%VVX4JYO$Zi]_g-S Cm%I%XِŪ#85͍<OD`򃥳%P''׵Z[I 3Hّl4;ҙ9$[P&n *RSLmL[,'P")Ԅuo(kʯw{b qDf"(F? ? ƞ}$}kD(:gb$]3 edUC`oa==+H@qkͬioy}'GoU)|>pQbfŤD:G8ֱCiTN2iJh PMoqtjD,(A2O5H.F/ (8F!aABk 7%<`'d ?>Wo~!2KkRD}<}m4[lvFQ2s$ie!QPA!fJ! ӞJSi#\`Q JGq.ќdڸ~LlrwF~9wzEg7>ʨ!TWOrLu!_=]'LORŭ?ՒCO"0:eƓc^NJgY"㪝EP3R )"!Oǻ-Hx5m|-}N-t~LK]Kq  dZ&YpkIWgg!D] 7`mƛL=@}>~FsWK\N wg::+]d[;&8qIY%~O12g6614L!  1%dGbK~?SWcCB#Uݺsz}_c9|/hmwݫ9ToZP;1Ɇ)4<"ߣ%ܓ| lF"-9 !)WMUD.d/Ag-8W0US] pZq1 Vwb,l6{7T*.H}R,lyJWuLUH_^ڙU£Z۴RE*]=⸥ֲ]DBH?Zx%U]ŹOݙɏ=0C^Ŵf1&+}Tj IDAT٨TzG0T99g\e)+mR߉1oq`׽[>r8 >)֎+'&g*buWI8fɵrʿxZ;[Gh?/XS`JGWe &OT~U1nU'lcq兂]6\@1o@TT}2'(=:´;X>B׿q|x2 G8N#q1mEXn&1炏 ]d;be*kd#MH[+ h!Qcľ$M3.]rȔ4Qdy9bP:蠃o]ќ$E^] `x$վ*w2*O`k6fWaT$!+5MED@[]t flJ:`V&1I1d)|l"ɠldZ#p[`y %{ ~a!(aʚ=W*aZXE[Э5po mQj ԧJNq-YͫǮ9AM!Dk*#[/\آꋭp% 3ǽ /?;rkkmal1͘cxЊҖng֖a`A 7=ӵ WϬX8{Wo섞3@ywG(`fm=NɪsRۙY$lm` ]!^Σ?Ls 4[I0s~ p]VXŲ8t eb%}߻\t!pϭIǦDtAJKT"GOmړ(AIbͻ ( #+%dm68 B Y`7qPq,Ns 0PЃċ"!*l(ۜ56ƅ9Jp5;.9{yW#=1F作j|ub˳a8;GD!Wd#  ^}keiP_\qߛz6FSTЇߕ`-``-7#'rY r8&anv# XrR-3,9+'d -S''6A=S:EoGsS#,@^ۆ`cs19qx$dE,\EFeA',[ŐO4"V@Kx+ɘhz. &\߉N6f#..0gM`Ko0S0"1Ռ ~Tz{7?س=5/ڋ]˼C†m]؁ĜKY71u-^=@"VZ:JzDj+-Q~K@6ֹEYQz>SpKΗ =y֏8^L w wjiRJFJ @ɳ+=S7n) p+WtB1#-Kj{֍(k_1S^Lw>$g!Wcw70&Oqh|oq28+8#DKNJ/YXò JAߚct/㵘`ȉ!1ؼLCD_l rF^ i~-\"D8 WCN@ 3.\P \+PÄRC;Vv֙Y̺;3RGjBaZ S#e#F2 *_1}%GW0ڕ~_v; FXnC)k[ ʞdxTLv`*iN-y'^EeJa-z7Zi]SZ e\^Y֝?3]lL~Ē\[SŇ>N)mk?R;|x7OL nђ8[7XV~d+;zL+A ].4>; 0st>+cwga>kCVw&ll4]Tʔp29T2@f@ E 41(Z eפ(A-w>}3|;p [p3(iP;h kX L̊ھ~L^ ,Z 6-btbE1nI, K!p"R 4Hh_=(F,S_:{hS))lAIEgx}-d1*Ɛ>c].~emTYo`+AVb2*ezƙ'}w1{ͯG貺vz:jtu+Y=<&Injia&Vzܞ7T, r@d.ǂ8=L@Ɗ.Abi+$1aW (rzsVjWJ$FBu`Q7P_tKI-ğ Z_-W\8cOj$/ʲmLv_187IU0ŕOFʑ?mG_Y#F~쳿r}])cqkDAFl=3T^FJX*BW3}^5[2$M`ͩMAqy{"qr6Rl aZ6C\";Y4bST`Y֛tA!<| ~ fM?K{r{Sk <1sE' %\mz RnjzfNҥ@ }r FQ#[PU*kز=w5$J1v#K*62XXia"@h!_-j\U aeYe-böp͊b "YfLMp"psP!tld:QqNg^ JT-ݐKGT'ksL܃h98Y! $d%JygGT,ZiM^^:\l.X>x'5Bb.!/x  Oh`Fi !bMݥ[َC#)"QɥڌK/'"@b!>DtA/4M͉ /3Xu{S+7q0É%^W(ݘ(1' C|cVm*vL!P]0Ϫ?YAGnFkvlC+@f@;Dy$Gg, Ғ k]CM"dN{Ҽ} em$/v"`dkH"cyWW`Զcs,U? yR؃HKYijg=|FG4Yt`>6>#yW/@( w3g]m婌)aћ{jrldTq$ʐ# $u\z~ vs=ސL[/=??ݕ;]a][f>Y|p@[怑y_8\W|K5!4݄X!.8DhrzmQUN1K_a7:nO]~7jrXޞi%o8;#i(KfJM"C$@1[Q*Yb<_N@6 (.xɔEf] |_qF}lqlwGW̩Ÿͺzq4ݶ߻x'W|9?In(Ŗ :TEv` TT`ϋAR +]Ƀ=@V|][ōQIg͛lrjח\E6kĘ(X b$RK+ "'H$^{A@h} ukνu$-zjh O0퐺0A)&4wѺǴn7A[Fc'0ˋjB+u maQb)!iD`&6CxEeh%涋)z*P;qԃHY1! n%0+h C4TVMƱ)y,}U|vi͹v$U\\/:0uj~L cAx׽ORA 9x'+a|}/~-׳ǒ]ܱ;ZR#4~FZ QE=lt:ͅ,#s=dfbr5kck޸-62( av׀K. 0tA :#hN<]m># CO An(͐;L~VX!aScb ӮH dVC)q)%8wj#KzGQThwLƖiBe(l !|LN\^˲xO`1c $6TTҏ]yTJ:Mu-b{eqpPaje{,Vy[bOhXǧk0ޛ{Lg^~'э&kveM&!t`ږIW.9~:$a]Tt<;I|'ZEL@^ۊ_* =X6XfbHBbH[`=MV˳ ؃u̇頃)ti.ӧƴ3NXrU#uB:U,c镍% I/-p2BX\C[n-N mSr NB`(`Z]IH@(\ec'r/ܸ;^nEv+ @zضr m+yȭp^cȆUCGMFb-Ng nᙏq.bCg/wݫ@?}_;2I3pc ڜwdRiETJY!aH*qd L, CCI>|D`P{J;mq!u^!\;cwWK3K*i85^J, ("b|D!!ߝvI*<%[=yatA:7y4'>KK z' hY#.d hjfV,!mwк_&Ѱc7Ŏ IDATY)C,X'"W)ǫ:3L1#$d|,,"$uLeŘD)Db!h 0P_M1rrӢCh#".隳3̀FJ6F(IS`jg#|aBNڹi]&qkIosw,>tx}#xeSX=^5񞮰o<ۿlP ZRdu"ƚHǖ8P{#[6WesdQVZ;{tםzo/5]w%B3K~O“¹&CB/&?ueŴZ$6o1ghЖ&EA(i}% k?j<렃:!tXWP?aӓ|{ޤ-}Yͻ^Uڶ5PU)$}eHx&03i/}@Wͽ[#Cِ|`MQ&z_Yeo wjAQuHF%l l Ht ҥ$v.Axba,@Pz֎6R{X;ieuA$t=, MI< tCyݜ5#M^Hہ`}@  qL4 脒[]mm(`a`4ĵLd[vigRa R_ 8hVuU[F$i 2~? 2y!BwIl*JM>>:|4Z !qPhlS쥗19I);bj7gfon>BCn&ruTx׎w#z]XF8M'&2 O7_}>q8b](bg *;A]Yݰw~tvۜ<ܞh;M/kWHw!n~U k<=ܷbC8;X=b'XA43^Y#*['q&#tf8SGLbEX+*|Gd3;?2t߅Y@s+%(Kί,7!-/ ǣ~ZNz~/qn |"/\mۣ$2$+rYK& kW 8,W+mWt?gnV0eX>s_GVM a7]EKˎXX.@9%ha9Iy7h5 4 z-^L5xo 쮩hTKsܞ]1d"  QlRg=3jn]^쯸_E"2@I‰LaMSG[hN79x'tИVYtu}N}Y1gn%^X\lxͶC &3S7*#)K-E"h[٧V新*5wKsm]ׯI\>GtV*+_rK:}O$/Yk/Ɯ/)ffAVbףXv ۉ `1uZdZBDv6jׯW,8O\sj" qy|kvͽtA/ fQR;% \O֏~rf*~QoLl>܆!NN mx\Edzq-sz+VDzUqV\oEܳA$HG7EEE ;wvg J޿Uo#\37tWus-A+w }q juqEHRX?_}t}5Hqj(r1WG!qG*\;Xr6)uꠃ:!8sp'qt@;ruwUu9{pdhlX,\IFщd\"#$ߔ_/l3?w>,3L Ä:!GsbK# i ik 4ybB'6yjj֥.6żR}5pۨӷ1;8;ݘ8% |Yy3336\z&< afb?-”LI(JC" n"W_O.k3c .1<:Mq4ǎmdzkw ykѻĈ?1>?>DKY>jWb(+zk뫦K*w⩾oQ!i@<\hm $͛!uB,^%/8vL ~f|бo _SèY.xwAcḽ/o[$\rp4'#0USkEAoWzȭ՛:OgWQPQӕ`a͉8f<|A&9-務zi{ny.3cHml8C> 4]X@ Ma~ 3lY}J獛섥 *jjM prZwO5|#ڝDFeKoұc;KRgZo3ǕPΟy0>x[2ϯ{pM E% )C;]TBLGM SeWeOw>“}O})duBEh@K(qb0GAy[P2A[7+V:蠍8NG/~a/bFCQ=7h0s~>\o} ˾)Z]֖c;-Rdxב_rd5ɗ1 Sid~uԾ ɋ_BkxEI?Q1QWk1Ƭ `Rf\mG⤲3͉M];r\Oߟ~Z>'(jaܹs٦vrȹx:qskW;c_oԧ}T ]Kߟ/o93?^_ښJ75:뭫W|w\'\DJ~Ce?Mx\/.{zN80xg:;{ګEkke+e00 RbdBf&dƞIf$L.W;$f`@ 6ظqۖ-[RƄXv}:ԩ>gc;%dt}+bŤC?~iӦB[9A 9%ԁ2@0^JI#"1>6s;;|G9%4kDTK^snϡ54|K=N|&K,%|Ǵ ^$YɲcH98lL)&\m7]`W"L=\bt3y(57@sEP]/`|D.eӿաt#;# 3d)N7ѹlǘ={zߨ_piݦboRLj4ODop~ӟI9]uZy^jIszy{l^?^7UC_?trqp얱=OD9~`8+3/zsߺ'C8?קwqa6mXh 6rS}[\I|~Ph[u ղ?9Q֜n T$GQaeX1g.49y_ Z4МWtj d|{ 3_i#B5 &]Ε] #_g8u]be|fևf#ճ\4#iڮ&/5`ִhݹشX􃧣Ukzv.DT]?s;J~Md"УYK]5p^ ,(תr^fY xץE< yiC?*m\G%iӦOiӘIQ<ja8pXzEB%64@c'Lkj/[ݴf{l[}`ΣzQ5}(c&JNRE+ZJX&b%9b`jNba5ѐ0{t)] *%ϠtNj4mn6\]rogЇ.CROJVFT9IP -{ݫz(+ vkYD'Gf:ˁD[O&\#v ,DH@ٓ ֓@J2wOl?*l֦M6?*ڂUΞ cD}K݌7sq4QxF k7&H|m3ѽʉjD(~)K꒖ع<2ʙHUc[CS`['H}Vz&LΪCBD:d]8Xq̉N,DM^|s#Goc+lח'k `G^,gzg^fJNCK*9*QWS}NxڴiE[?,l sa9XtUNffST |T~H3N *Վg.\L{J1"V*/7p.RO+D1cBE'L)5}s1QD,}=ulw98?70H`& ABWv8#2}C#x*}!4mJZT_H_j})-\[ z>_QyDg:ىٞڐVrhU$Q%SwDlM6m^yuc28, 2p+/91YTx2qe^" @CV)ovP]=}&xZYϰ\v;)"0@*i2w9MXݴrqD'-_3 ]@>?-g*.9$Kz{|O '@ܦM6?mڂMh8,/$P?,qI5jwa.P'Y ; }: 9ΧC;7XƎ;ۃ\JJE٥2z LXwhd_Kzk1>Dnk|_8|ϼ/.\n=? 4E.N6mڼh WF4%8FCmRoDˠK@%œ@95Ӏ:O:`5VsCM9s]S-7\*'-Pɗɭ<1(0i4Yi Kv%CEc׳nka9J-Hv8wmP;|gWfs0cGe U={._~eQݻImڴ"a%\RrzAh:N巟N|@ uF?PdCY EqGֿ?|7DkDr_/9/8_2!0SJI+tnmaҌUXvRXhGx-ɹ3OX0 l{)cf:7J,۬5jɉ=uye97ߏ:*xwX6mڴyi 9;r$g_J,B\C *?pX^AfEȤPJTbkaalOt4/P=(> j#֒34Pɭ)8Q9*Б/oDV-㋄yu@A M^Kb =ǕKW7 Ww_vQB>5{MAY}|njW+!X)4Q +נ0 "//@%P,čC6m~ϫ>`Dv 8whw\C#Mb%d}pPӺQ1hHra?,lT<*ꩣIZ XJyY\<,=hTO_Jg.,Geʵߠir~C+#I!_`Ce)bdrOBgݚW~/:uRfuB(ۖU v,/=uS?@ӟOnB:MgE";>\#-z=Jz׾=quDp62mj 24*o䃀0ZY&LH)~M+B\>j߈CgŸዞr߈2shY<йX4QiX$Q?נ>wc~<u L*YiC4[˝LYpv|WrI X{$)7כX],&wms\/?H ~=nizk|jmygQWY`|fյ_LCjWjr]}=ϺVxy5lB95oE EVy(nH)  ? @ [IMCD񶶣׀8pd;'͏vW0E0X\3:<[oQ ߏ}#rdzcqdJ-__"v߶%\R"3v#x u`$aM! E(`ɔ< ZHGO̊4R.eAylaaH3FWV=XxޜB05^МoD[r^`jdlw8q@aoDȊx(K߈ 4ʉ$ "ftתgιH|@U9Gh൙iUxnW8`rp׸{.zp>n8Q/prg* LPl+~w迪'eDEThjJ褁>iAw6 l/0T(Z$@!ЬM{LZ:C}:PohcE68ڂ́òX47ʪ>šNй/UUm@淤jkI BaV zN^BZK(+ jTX+:]iBA`t{>C5b_ Ѭ8~peT,8,y.6{>GBç UǞ> ՁNIG(צzOsK=u*%t { [LFi񍉯U[J2 3'C+I[Y1" cqMDD\\ZSJkN4I#SKCVDLtLtԅ;P3Z<  ,T`6?m &ΛY{J6K;rx.܅ro8u³!~I="rk4.2Z4o9g:t`Q˥,֔QYjYC%yG$Pcyy72]u<vey $e%l>曽 s,LG4>2ݽ5?=ڋ)yhwzP].I[7o30؝Ig*bl6 smEg({!JS\cFwCkhQTf{ܗ4nTX%%98n跹{׋:gOh^+3չ{+/J@9t&V=q&v􆚞"鄻ɍCiױ3M fPMYOia%'tpu $)ATtpl\7>`+٨D@FH":g ܊ F-8.io-x^aĽor@D bK4kzYoD[<;*˭E9:Q1ƾ%~ j^T ?نBQ:v%}!H6H^hѥib|*J{GDw<."L8,}#@4[2'P* P.GBes=+M˴d*u F㻷f̅3s35d"9aa5W#p̊h% PTc3/%vB\D pk)pjqK^4qXB p1TwY/lӦE[8pXb%PLTM< ~8*# ( 9@% jqXu2sr;赋Qj0" ,eRdDdNSi[8o tP@z&9ZC#oDce ў7~HRXe<95w]w)~ n.$,\:aLc $ pCQX~^ꞽ\gX]eS?^ !5;-~g =Nb[p3J;~D߱:ɥ4 ,v'BgT뒫N`MYr >k\\4|:DY7 u'SR7 ](ңFHNujdU!]F0RiDv_MA_Ї&$W$A 4"d4u0h.rP9=^tcs6/M{Z+8|ۨ+ĖQ<)i9$q>Ok.jV[3@չ(3< G( `x[$B 7z&C#!2IqϾy^G(1Չ:] o[ l&[(W8**gIT+܄r؎rܽK9`sO.w"NVP/>QHk@Lrm~7:W@@Owumsɒ/=?/M U8,?ay ska:)/=KΑ$):PnV+a6:[Pɝ&aAOOʴݨS k{J]R|EWj+%MG3/N =,Ue53N-3.:ު %OffvȲ<-*>sȘ]o, nh$KzХ^: 2Oj1]Cro 7'z֟I\K1d48Y+pji}QGw)=KD[P7.-Dؑi VNT)2%5Z{Pii$. rZP,=ϢCD8hwoc%/f%|DORx?gcrg%n- Z&FEEu@G!QOBi#J/RA5|tMvGK 8pXF֞ ؞\a_3.V=u9.뗉 +"g਎:ԉYo.;q'uܞ[>ĿP/ޖdܾvw ‰O7"kyK~kɚ66ϾdX9 M{eDeϚYy0Zƒ-}Årǣ]@B%Zs,/JYʔk Nzږ'Bvaެ7j5@'DQ 7ФQKXeY& Wf=\Z+j0 A 2鎉 IDAT"sࡌJʿ j?mڴWh7uFVN7 T7X!SFgQ! MpS"רn*MSKMZt>L9'#jZDWX5 5#V $KO@TOcVDžm~K>$yɽXj 5xmz0[mM* M?:XF(Qtu4⿫0bf&F-YbE$uY3GQ{wq#VzOY!+ r;._~t/ΫܽE9q5uX rV/wP.l^3/ܵ={F%SLߖ:ߒ^q㑐 ,|UHHa%:%쳠y&5@Mլ.µڎ =\TeeCD_3 g,xTzmaa{ =$HHgӘrkϮrY=J.4Rnu=C$MkjN0nWV1|R3aG.mЃJnxl4m?t #'T$?WNA mse֢7'zP9Oƥo@ŽPݐgPfue@Tuw=9uхS%Q">)5!6ja'NnҭpVl 2,t"re\Rd'\Ue'Jox2|_)Go +2 rypt%~PGAyE(C#DYЍe:P٨♚Z Vx4Q8VjJ8GG4e*f򪸶7}6?Zx|By$=x>r9"8%;(vɻ(ti, =ޔP%rG#mPĦLJ/kA#\^J:NYCbb"ՆAf%\5.4S5M!j9ﯟ]pzl(v Hs|[4 4<]')=az.g{\c]߬7m brʗI3HڳAmso`:"ۜuckWM:;21|_-]¦+qhm#$NN* 5T出҈KŇPnM:;k7.oDkXIDu:NA Te %d6$gj٣ 5_ "q *4)&-4KptIHN:q%,.aW'J-螬$ z^K=$Ќ{ shwVFrG79?,z/rփ3(QTȲYa[5OYt 5knc76B YBt&iw |dvp/]oN.=ܽxDkb}ڷ"dXI$OGwvQ\/G̠eJg{4峿y &gOݰ.6N< 1h:f֠!85l}B#dSCwsanuDCwHtx>@:1PHf`,iW[BXzT#]G7]>mi<(M4-m:~/.6;Ԁ֓6\9#=B=wu۹w!GQav᫘v}#Zxp#jhfei%ߍ79q%tX0B%eQWgKxx'!20G YQ⫊uFHDRd%ijhp{%ހǸ9Ӭ],Y>H÷#|cvrmgj"KRxx}Oܦ[-<]t~a[?8YfSKܽq֠HF=nmt4KKoQyQQ/|B|f٩;ܒ 5h0UzWF@n]]nvavnG3N=wˡj$ݻ^53^mbu#ojw!w޾O6^`:)TG -z}ȃw XmOC]NHۄm9Zv8N---*fƉ,%Yw-YS6'@҅^9G8/ g_ [> 'fڋLYW'j{;©c{i)G GzlfG2E4;$3C-ߕN3%:bÚPe|]3 u\s le{OH:䌗T&(<18Խtvk|7Jv>PUueGJP,K}-@ԗ0å鿗@$/E99sK1)oB M2!:PG"DŴkn@]-v7d%tB9/ʼRcF#r"&8AM[f(x`WHZ-!EXQӔ25[;ZLXzĹy[&jyMhs8_PKDA]י=2kjt7pE0HkQȖ,<k$ei,K=6-MQ6IP$$6^kʪ}{݂ HȪ/_~;4)UgW?$>$f*hzk/G:(e'Q$HMh 6Q\HB6^Gw/z]٣S?Wѓ6c+dŤh8%kW?\->? 6]Z=y~OٻX^PۥY:ή-{nh&C0S|m՟ڸoⅿqz:J<v"%E~(WIS{F>a5akArؙ0Hizt)JL{LЪY>3T3`[Me%yv!."kcHro٣.I;Oh1L_A:o5cZJªl`v6R ,|.%SJ8{k욾Ql0Yi7[2&wD02Dvxzn{'U˃>$:&>d3آ}NCI/s)@!jsڟ|CxSA?@un]߻ZE\>Qc0qnQ9G P_guyQ(083N&uz͛/%~GE#c>{k2 ˡ==OޮO%f~g˄ <}Sc 9M36?35虨Ăm y ڜ~\ЩӾ 'y_>NXOE/3E ][^q0:G4SL!&D s}ܐ¥c:0yYh;ILU4+ N Kb X735+'ϭWvM EgM,?;;={r%o65݈|rp6&㏁^j@UmB>՞:/4fc1"@rYaeTXlEvJ>#6^~T?LuHA޾5u b4{F) 009F%ʖc}nd$q~ K>:1o|W'w oh8m" |N< ө}޸ڐD>9~CVHjll(W-!NeMg=k{ 0w+(%h(rB>- }/Y>uIxd7ډ r(%cl϶ 4mLnErX[[2gbIE6tqxFPgάwΜYׁ/_4߮Oݝ7g"!oQc8Oi'X:\=eyF&u&b2{1D0i!=^*^? pȫw5 rхIxF:Vi4 !'ۄM2lj h'l7ԑ@ܚƈmad =,<>RI}fobt ;cgxafiqm/{a7׼x{ԻV;F7bf'7OFO ͲƏQ8 <PuQGEjJ(EfTOfsdvHPqw"+#Oo/_?{j';ףo \'5&pgӿ7OR{٨_\ -~ӷqmlO$r$fl7fA0BLuN–cidbUpFgPgН?p\³Ph#\KlCCD5?| #0fX†qtM'KD%D010>U0& !Ʈl#ED7 3 hf19dz06YX,=Q--{Bol/,7ä[nD}ǟlN?gn\Iu#Kym/ ۜgX_)g4T~@q`Z~xQgë2&"#\>H)C a"a[} rQ*RE4=VTm*إ'#clv % e Nݠ>MxG ,yq6S uT0!D1tyoD I c77;ww_փRB~ ER=GE]WF]Q Q$P1p G9v[Rg4憵HjNfIhkTg4c@aw69x*-`o{R\]dr EU̹c(qSpxom?&@|5BigwtN|p.(ByPev3Is{ Nsp)v'b5'a2<{\|ovۜڱ [4>pj?9U~n7ܝ֛KbKڒ햭_kT2+>7!3t󴯂4!A7~{Qjl{Fc8\7=GI4BF Ѝ=^yQJD< PxOQ1% _KbPD3\D"詭Yt&zVDŽ~Xa; Ķ%lV荥gf\ONT?xgOjC_=2&TP2*j|llt\YvK\-ڙy+siu5DB/;75_f=SљikU- IDAT@l'Y˼͎YԵse}r ^J]oeИ,kzXCX<7\nx f:a4ђLcCc{J5eÝ9`{ +:ރuw@wIP""~/+! 9Aacކ\F:|?E2D 'c*hwM!h,QvwM;AAq61`$2,1!#e n,ZMkt`"Bmv&DL&Za*ݾKغ;V&\jSfr/e=Wm ӗP 2x#q@x^kߜӕw0NAElF]#@M:U2.\Rk|Ϲ"7`.3ɾ2[a3\d&JR GFY|y 7;tIQ]Xefkh7Tpr߹qsurt;j<"t\϶Q4{$}B{x9C_ࡻlz+{j:GƌVagfi>=hGqf˿Ev~/?yYCW/f̽uoX<+\x(z@|P땞ԴE:gCU0mpzL 7fwNqMqzI1$X->?g! 0y - psRm:n#BK*G!tf`l\#6nKjshz E8 ب@rf6<ٽC \m ~Vj[zABӲVY2f%tjhrワ2.Ƞk7}#Z^n RlewZԧΜ^ BQls zrj^Br\O;#.)4]Azm5EhF0pmF{6QgPK?n1. y$3QW aqdчeM B#[R4L.@]7n_ʴ0hz>_tL,Ru|Mu=TjXq|ҍR=-ON޺<$ŽG|o'mE)g(fxl5^{ y7OKUOL[Њkܹ&ޒhG^(2 c&;EͬϹL.|X[IfT b(*DzA5b"x6 M'u0FAG]12h,k |hI ED7\X넾Qv'0I<^OQz4Cba]D)d|nRi'HU9rW{icO YYZYcZ0Rb5w"H,Dk3?|#V5.X$iݡ3,$No(h29"~p@x^pkn 쌔ԭ0 _C݈BqeW P7$ QjO VOnTuE(85<;AUfm]jmf$߮'Xn&7ň2>%ёItԄ! n6_ >kW/~@SCޚW?ޅX(|vai-M^J> pzƳ\rƊ*ί/rmg3 ~i'W>>rk>܀Qy*'Guvx 4+-1|ÒGM޼O14qѪ5lKW 霿"و30YrZc8[AKuo/605djp1A} mӁkvCsԏ6pfn.fteI o[A&zCu`F*aP;/1_A {GxM)4}L/aNgZކqqU 4K=+5O.,}Ъ5{fwj^έϕlGoy9s W0U,&;RpBě?tswҲ|:+ҷ@~vx]8 <mIFJ2qޤSk e31escHITz E( jR]H pICc]7B4qYfi'' o3fb^?5*a]2$$4!q1 1w 挻#б^Exϣ„on5ԸC6/XC6P5MfQGDӗWfS3o3Šz\קgkO7:sۼ!=[Rњ| wW YǭTi-2C&32dJ{kvo&/@2xؕ*4+H]:qdblLhhhz 3a0t0IzUB+&l,7*=C6 ~m~RAw gs 76@} '],"ƑKjM:q\!>,4;›ݭ؞DۚftWV"v<:Ca%!TmX6'IUn]>OP\<0't=S1Y!jL=hmm^qq)H{<~ubcXXaG}'y6[r&-+H8H 3MLSy~o>@ܑag"Z[٧7g7ߞf&s8[5G5D")).!ťa G FhD$ki/y~q> Ϡ̑S\!A& LtX&as|ܣ)1$t-qJ% R"q@Ȣ=ճLGn ћW=YԽ&~~oo |,YSH{~3 Iuu=#hwJ.1Q_DBn@IWvM?}I) ejH\>IWaiI0n&Y<ٹj~}-^vox7VȾeg?{f0}fڔc'7{l"מXo D#냹Dh4pp@x^;(4jk̇!׉(!#G)#Bmc)фxq;Rn$1dNļ`i>,] D)Rk(nZf[$ք-xͥF6ֆWR;ު1@a=Xj"0!$v3!WdaszLB@gPJ5nm|1)byz#]ʰQ`O PDg?#BrG}䧿^?OɌ/qOV_%Rz>7*GYrOo&nmORn ~~Gߒ}}~*v_p߮+rwF6 hx<fa|:I#H6b,x}f`zHCx1=i/ٛ' ҍ,ZAbc~it:iN9.qN4z,9]?K NnD N'PwS_\+~?p֩ɕT; q佦>ioZ+ +Yo"9m8Vy';(BQ\70KThhTmA}jnGXPppƖe^15 y\oC1t,'bt0rjT'vXqFz; FtpY)TcZ*T#iYҷdHOx7o˗>yɽ %ƒ*go>z˓\"1$t_~"n [NagL(p'u{1V6i5WJ4 Q +~U*Y)2F4j$j*ŻWf6Gg- HmN?pD 覇9agv c^ ~dp1I`C[|?y/WDL(U)@Z A m؞G,dDw.aBiQh# IHFx~؀\c8"Bhȉe2g0aL>MlĻ@#H_Lйr% $CIbtWȭ.ha!}VIͤ1IۆW57nxZ<<\oOu[Jz/]Ռ9XAˤj]f0fûwg{LQdQʨը9*ඍm|lgd>QyiϮFNvnWRFjPîDg Hm ׽ 3ɠ z:̠DI0de9FZ Dy'OB<}{!r6iuIƊ5LZqɏ9?tV7GzW*f3.諾 qŻ!m2lk\XevENΉ0w1ޘ!ILnLt:BCg4xbY[@\+'<+ boW]YEAC*D koC bo+04t' 2"QnD#`k]pxD4ÙtØ9\0G?Drɂ5 1]LCC> :ٓ'R}6.H`n, SMeẑs_6/$CZ9vzhI/bS\\sʉOsۖ%-K]-,\K>zK37n/kD[uEg=jG)S&Qgʛ&@ePe&HQuzb,?l:(UiG!qiXeJQh>̣:|Ȕ,gZ@ 0؍X f-ͪYgmUǵc׻[Ԩ|n? & GQ o?jF?|6Z`l^YK2#NE}gjc ؜ z`pz/K:hHCl@wH4A0zc[;Fjub7Ao 2N^"<+3&Ejb/t`F/rx~=F 3Әgvkb랖 ۋ^h89wՋ"9"qg;c^TVH{yDu>SO޻ؘ+^_8ayy~.>:ۿ_ƮOPU_-iX5[N+T!ۼHl\WSķdYiLyʙMm*2?xxf#ckvp՘<;ޜ9KhJUD\[Md5[ b\HĐ;J#]GVHlU;OJCvJ/-pT5 ~NHxWIlAih7M0s.l޼]Ǟz 6zl~HWG-%=Z9qHԵ=O\C !DN^0"kȝI6.,j&&71'VClk듯>5 IDATlѱVS`W=tW6c[;1H3тiZ< &lDx >PnŹ;9;z$u%:b/[>y]t? 6!k8&*L/|ù|sҨT}ړvd5}[>MkK觶n]9Vo^ޜDڝ yG7mx_/%"oc$b2"EJUzÀxY!̮ALaZ+Yt̝t0׃A&&ޞVl Bi P0Jue@=?}8[{!"'t:E6Ȥ#Be*Fibҟd,o)q1I$9Z}(1ԈtM#)OKadh8,ϕjzpau_ 4-.u /I.T|cS}d};fŕo${ϫăjY>TjbU]aunP Io㠦0%nfF,ꆩW(h$?s bLʗ5Rhj˘4TΣhԲbLhBOUaiQ"ڹym]{SUo[ei!B~[?<:pɍowĔa%M~=dI#`C$7n .]=μw˃#C~dɦ{9N*bxςc_sge&u^Y*Qe5\G̗l^rzWm؍#bM|%ĈBHA]FNa8%"+nFT^:0!L GgNO! xcHEfO{ WPJ1l!֎=.ôA 3uJAjee) !J`"(OG8=B99oF'd{NJyZAc%;,ۈA"M2WrxL1gO# ,.nTgI$Ũ2esM sS11l BnEe ̌|@ ͧuҋjjExnT)b'~552?|?͞Kk("gUVԴ)J "$8Br=9r4正t?X:+p IJ#bL+ڗa>%Q\Og}i)#bz=1u6n6OXRV(Y?D;i?wT,9}ch |~pp{/ 8_tG>fy}Wϗce`;g͊XjxFmgG>|7=i|]ㅛ'jf!nD8-9;i&-7(;qɜdk$K EfnhM|nFi\w Lgi2XzR Ⱥm0o?sEAi c딦hoဩ"*=?[±~ d2\9l &Ęg1P(n;F$u"vi e^l2δ {~ hli0ABLL ϑ۳tHeTjcxD5ZUG})C j]67-7sEł;42hŁcy tBc=놎G.Nl;.2W|m%9͑p? E,N:_\?}ó+/%5ퟓwه*!@ВSGbPyFo˥j2h;qG07+ y\ yP=NپOWIGUiJ`Ӑ>ub+4|c}X<#ٗʈ͟'T [N((I\*.š1mٽiSR8,VrZ~u;5߼Z+?}{󛮂BKkCx3EgM5< 1s23iw']u)4;{N-R̋1>Ũz/ڪUPR82_e=&A Py{@n BfK$|# g)*UcxF3v:8͚ B(r $BJX?Z ^Hǔ[퍉ݷ|tr?߾n̟BQKn_V6 (@i.Nk;K[K ;)AZ1HK"T~~eن^T&U +85G :/y}\+7j]Nn ٵ–ɂrF 8c PC!Ȇ}4 a\A[drLbW-˧Ɏ< QZ?,< ^40}d tSאEMbІ@k0j@kv _a\ s@V>~7ݕiҸEr "JFb7Kxp9 jA|܊v!&ܥ%@x>߻sFou<_vB <ubm5Wuglf)uK4{R.K.j"PS뎊;qH+on@`Bk E;cˈA3h%@$0\+7)8uȝ%)aFxKأO"{ vl :k٠Pلh`S 9{[5##3hkx76&vP#`=z]A%~֎ nL|F$ r'y8{g1Eʂ@[<Pw<>! ֠H+ iu  ș}\.J2M/i}F $tQQs*R֐HT(eSٔN.cˊraf;VsSwNPv{5KKP0l|P)Cܐd9$F; A^26k Hq;x=z$SHUlfZG =Qhwp(Q <P &CNyZ8_-h6˦yKlƴ=zх?o_ﯼg5΂ =ugnKVmⲳgnFc\xk&;Dȯԙv'l?=0x}ߍzԧ`p\g;>>uŗ}>}MXBǝuY3k9U?$fi cJK8Jk4uaL*}b'GN8ebaG=\ExHa=g6a( p(й1q˘':M4Ft:HRҔ3W'X^WgPT6O Cf-l$@TGڣUr# c?;a8%D&)M‰G!vhmQ%ʆ8hCd57E!-gp6ȱC}Y/a݈0hQZiGp *WA=%Ly$6洁?+%D(Kx}T6DjR0Lr1$y9O,1Ҽ 2yԚ.V.0}?2]p=_՛6^Ho ?' q{MvC&~kfp; `|p{ҕe 3pyRPuΒTĮGAR``4z ӧfpj{bqapxR~y+X&)AfGC^Y e4΄JUy֙ѿP/pK-p#\-5CSh6(('QU1 ES2kpJ =3# JnHC)a_c;WaZ{MNL WOQs.ˣ26*Tn׆$On ވng繍',ii3ɣ(TT˃J-=AbփRŽgvV;A17ݷ/_</'<P"80IFG>\'6{8" ,Fw }gS'qݣF0N3ML8: IDATߝysPin}Wpǣ{`o?lЭMjWte.-|;K'|N(^c'GYYp,I}Ů@LE6n[#a|yb]Fxfv-EdUW|'J` +Tqh^'["MH' ثuan=*5;+7.ހ(O!v=~eJb>J ~ Eu.Yto w\ʭYOcXZ׹fGf)klVBXRhlw P @ Q#;HEڻH$׍,T{wgx6ST Y/I2K>1oޟ!0egEٳv3JSW2rE˩JQxDq V+2T<8hs0."] 0tNl2dc u)yn0_7/z||^P|'g>jДb, 0Fd4506k,9>YrqF g ]QC! ,))ekư/9< s\t3.umHdVkKjf솾I`%TA11EmTfqi'𭉣3L < cX&#߰?§pʹ?+Gy9|?ĕ6?ueFc]g{':qh×Z\@?[RW~b-?voo%{z1%tnoWpq{m moYFwpt>uw>Q-Ο\HOV8Δ֝2"Rz Ɏ1aҿ1n$a)jA[ rU=e7r!li)sj,qb`RJ&-k]ˡՃ~)`؟.v `CZDeh.ÕӘ~IqWnaN 0Ĭ.!+AgWDtFáfx] EUAdy+0+Ә2t=ЩX`_D&$S2b1XJ鈶2y`R=2#wNS.O>gq,\LU80rύ'{kQl,b,zKu0yʆ_B3GJ\0軤A ڴΗkh Q1pb?`Y24ASdfekx(OIFT0?NucɔA`>k F҉C3TrcN,-m Eoxb1 / ovfX}HkVNNaAĝ5BR3qDbbFOe!DwtxF?85=zT_dg8VbKw^YN9~[0⺛cH,MfIζ]-Ixc/q>6~UMI]W cAn`<ϩ72kcQ3D4Wٳey; ds7qP7\ l^fV?MhQ@@oZ;,[25F އ2-3eW3j呩EQ 5eDG3Qf6{s&qݣZ%3]RF|FGeOؿhiGk/>?Qo6.&] Rʛ6ֳ7wF/>ٸ'_8c8k40l˝n~$[<}kOBNI[ 匀˞oq+Ѝ7 ED5 4َWj>iؤӼ4g(Qt̘'۴ʏQ=*~ʰmHC)se|c˟2QinC)YVǽraۈc"U8 Uw}{-l⁉QU݋(p:_3lda>BLiڀA,QŎMߤK…BRz Ded # ]ERE=' '?qQ;X,4?MmLڨ@2t Ǩ0O=x.,eOq?R&"e^4"qhh϶Zl&%nokB`|9%ps 9dxsaU|13Ni&7;.,bs'NU*UI,VzB,m ԞlEXZtArJ|% f1u q'`"nus{٭]e JB^=' 8ވ\6G)N'`bLkⷄap݃$?1U?z˻xo/fJǛc`|!C4 =5ޑ+UxduW>כ (6/2H<6U}<t[r:`vuv^\es!K̯kۿ70%% kWþxLpIEyPశa^؜8@-u(LjǨyCڳ'Mg9'NXNטs3&Iؓ 2 @s yRm3.lUbkf/"vV48>c"uJ97G?wWb*0l",mwKD~A@Xe<'E;]T^"P%-!CH YV 4@! rXAPկ<^nĬywFLN,RJ1.G90O!?e XzXɤި[ʍr6eN[&TNh4XKIVB >JCW1!k/RٙGkb?U~ۑo=ٞxԹ{O_WuGO[;O۔5ΰjTRcʀbx*!tzv%=\q]`[xc=[84ŤS5c^y .N q4HA }( WQE70Q.y^› swpw0*a讐y+SC^١乤Sr2D]: DڇQbT2NZJi\B%Dmcr u-Q6SA8 7l&ЩT),nYLC <"UA/R0=#m#!3 rHZhI#)ؖɈ0C&,*fJTja̪酩 ڊ*)xව-}vg|޽ݟC]6q W^7}Z|B>G9&X{[>s1~iJ:SEGO%f̓ܖc =@ثn4nXJioIj$ޯ`{g=`N V}W%MK*hgQVMgZr<ω.ؙ-.?nJSN5[(%GBZ 6*33zI6)ZV+Ѭ[/ gc[<6;;6{G2UҩT^rL@(9EjerS/lWEC/࡟~G}׻ Cqq~ݟ }׻ToPl&Tk~ do9e}V_}fq qǷӻ'I%w"<>Kȿ}<Ɣ(s\k^||[3K';LNW;/oú1-Y:5gcұpvh X{\'G,s&& *e*8Y vYr߲W f}Wi,>Wv^ɨj~#Kة۫`^"E}#2먫1sj$Qj_eLj6S!shmud-ISE|Dc ?}\;Y#N#3 Yp7Oa@mDV3FQI| >.Y!N, Ax˻IJ' LP"Fg,Kd^A2U86s5eֆdX[e! 5Jh&Fe6Y Œԧ=>U]-}]~׬]U8ѵv$"TyqŤmz<:7 l.Q?F _WEYLsK 8ZQ Wl՗l,lM..vsɊlx;#9K^٭|nvKaИ/WV)k<9, 㝫+mmNYĝeZfP6tP7:!^P4f3XNc@S4e.|SrbSjYB$exONΔfǗhr%h_sdzǖ]q){q^yd[@Yu"$e`>޻??z>V^ y>'q|9x ׳/_N** :uU|lۍ+7zSvDV@4> *˨69J7 VK8u&\s;/75+~k=}}nFрA9VH±QHvRTTXU&EJ-Rdkj!,,$bhwgy6 $˲ު]쳇og<#d|ɅD 5c(Z9B"3aJ~>4pϐTN{0Ab*~Q?~vkAs*+tDEZ ;q.I_+< 9i]S.{γ<37!_yLpz9}c7ߟ?4d$e/= ;Rxk6-/Xtv6я|bޥGcFS޻r8~bdu?9k٣S߬IjFVTu*3z;-{#zb8M`[wL}S#& BU 61v}cvfA~aNf( Xk`N0=dQ+=Ri6NdE0>Tna=G"шȎI)RdC`G5i<9έ AQ="ɫY_FMXzJQXR!}H^ߢ_MॼdDn|u]O%v0KcId6! NF lA IOaIA(yx4krQB9( IDATDqjŐѧZ)j@ pKgaJ%8ƐէyU|1N(ףpǟ?}7kPBo̚rԁ{$19,E3[?s%P2Q[w>xG[^'Kyݻ*nQJԵ乯ݾml5I3mwQ0yz8I{E9˝ޗ;HWDNk!_yJAI\LbDfft/-b?WxmO34l`2骺t /<[3*L&N㊽8W=vj%FzXǕ6w.n^NNTN7]ؽʿQ~c~4i%''|oq.ǝǭN/83}ظ8FxU[M7>'op6+nߺh <΄>Y4P~.}- Tm# KY4BNiҠK)Uxd@n6;{8|-fN|tKҟ/] kW+I,jQE_@*+yJ)$@SPBgAk&M.@[f $ͤX:L+T9%f_t+\h;{/^uӳȤJ8 g&؝5v7CMouUd"m>MĬWLN0OjF#o md=l3L VMꑹ~&zo[xE\,"<U 8&2&A +96 59DYvW $yF F1I)1ӪPY'*Xre5 0R^j0@Yvn9]2SQʚʹ8sDb/>u EXMKMFxPBc#bZ]S8Sc-"+qB9n𫂖0F[ `-Ië'Pʰ5MraR]R'[՛&}kD .&1ZuIO|t\%$+WXgjJqS¯CֆANqPLr*C@#Q bgۖz=ٛeI ^@=k3wdZ?3!Cc>(j!.L9k`+ ;ģJh|R;j2@!U>> Č 4&̢K]:ROʎD:x ]4 ЍYqFlaϺ5GC19">g5$K}f[6b֖-R$ɵ*pZUٸ[[;t[-h{ΜnU緓FoBxpvwmXHGמ9Jb%}5hѸX@=*]MId2ĒC1ɤr$ENS)F6[neE| )Cđነ2y-3BiZ_;ٸz\]/dT捛|i`a~ȶۛnf1א6Nfɽ}I+^[3?яXӔo< (9;=Չ_O_};Ϗs=`ן*ϟW[i} jtP)x͸9sϳ/[xǞLn1o=ܨsZFb8\/1MaٙvSc(vFEB!A("\ F6 /VJSjWk `,x&E3j &iT#4s +cGIۼFK9Z܈/ڷY(Mp1H]Q?E^}}a{[Be ??{7phvcvQԄwp $Gb0NbTd:#4u"Gk+$rP. qQ*G-*N93o+@g 9d"@jPYZ(BPZ: + щQD !'q5Ħ#!7ĝJCgdb=0tuƾ8p +cvH(OOA~̚*SU]Je]~W;y5W %og"H_&0 X" ~!q_ժ\h$v6pM֛܋BJpxw ztVf\_ 9_ܷ:㦵䠑 tP1WZ{r9}FfM߅z0,fE{4&fPQz]F+ ARޛ쯻Dښ1jcR[D H6x;nc=k{1ك ^?TMs>i|'e<Ϻl2:=Ѽa2~.@Y:nzTAqcIHIJ)'e&U?8'|S4=uMvI {_kcw3K3iPH*xKOM^CFmgQ>  , 0EѶN(CMaWN66Hm8DqXTp]V@HQ1{HtFn Vd ZFZ;3jdL^$ 7{H-׬;H @߸8C h*ip;G_yt"izϣ&L. V~p'uľKBs)7E E=I.M抵Eƨ8"!i"1 CE'q4E.aJ~:Xm`ɱ8V˹oq.-3@ n̴&1@ mSBZMͩZ!94c|fkBxi#2H XAՍ-/'smyD6IǖYD\De7o1僌Age'R(GǏϿ5q XYN;Nj+  I#$H!0X+URa.p6 #4*6߹~{|S;jޯ] V&{wk]Ь>ɍk^66"3Lܭl;'U1X%fSd]M̠ȁUI;F팥7asa;r4K[+aolG{^]ncvD-xv=<}7LNidyS3(H9mEa23㌸z#i8'?g8Ua9~RJߙW)s\ΪrY~^ ,9tb'_űm1j[iu]˻g3L*(9O"k=Wci(рx_3?ûSsƍYKc*{ ʊCKh;CoREK%ªiu9rzNl]-Nl;)0*t{M֣[?T<XWHc'35B-s8#^YC,>";!N{s\|ܲ`P`҂]zhNamy3lP_9xच|X*<P0(EeرZA1B#'ZBƂ#lSS;,@\g hdM`p)#F2@b>A^Ub^(j :&+,]rcl^ͣϝ.0FN3a}6PwQ0^OI8ӝ| _{ Ͳ?J}G?F)|=%9$=-, hsJ>' /gZm]qb~75N.ok#O>8|_}_W o6x`x³cloTt:KIvȳǩVc0W/(j4 p(HJ1yDSCSs!S\lEi) /7=t񼗹P= |vSzP f3QGaJXC)bH>{f Dq8GC~4Y@ܾMؼ>fR%i Qy#k>M=й:^# We*NʩUEj(}^p$hS"4ӄtRۈ,f/R۔Oŏݠ"YRK)j|P!,_gr -\y?ֻLy Zҧ7zApoۭtwҺiqEÙVRSEd vةǢ#!.%.xwLX Pz.i~8/>%͉\:oAzqy2NW7=x_'ϗV)wgZ5hz.\|]駯Z[P*Ө~f'>ӺPA)MQםftejFE ꥚FߎlFn-d(aVclp`WKlxXkL18VYV{C{*q(s-zz{Tw^ޥ&V.*i{o29"WG9B{mA8T"=-z Gmu<װ>"P| s*{hxc*՗Pcgs*3'mQ@X Yzd;"AV(EN:18+?$.4dX-B,(g VkT1VkC5S:+iÀDPSUb1K1,k ,7D%"\b04"5 +tk qWf8$+kWHqL;ob;c5yp=>ϟ!191[]7(/fH&;-5QZQ>ɵB* B"(d؎FiNQ5nL0)jV+Joz oZz䴓7=3Nvvz_)TMҝkWVq,SۧT> "G)IdƼ[fR,FUR&+4śwꍗkᕃ#cjSI\}0\xl&6Z,jUPt"OouD# :&K>:/~.-vV.T}?sn;c%˜4HYͿC9koP!Rf,(CW"~wdee+9w" +_^Me8ϙP>ZKTirW3)$#eK& tINŴjpM6H#TAn]ڣy-'!ᐽ,.,;I\RU$yFmbN1tVXj]AV  auĦ>]A6wNuK(츁ѽ&WoS)K#< #65n'}|7Ə2"xyfQFb-411GMTq"/Yj[RaWIFIFj r .,EԙYM?w+B -z6Љ Ox(RQY£Cs1 & A沜\HZE].*DEO IDAT[k-PRuhœ97g|ҴBp<[O?rVP-QZcxgk1S$^GPF`r@M~rHb/CmLұxvFce&ϏE aQsB<!LX)KObi!%<ǧ4sg;ҽ ly9crg滞;ۙy\ެ{~ ul07 8v}rdC<'%pk!7Ƶ{[x#ߏa^ m+2/~JUmSVUv/S:8~ |םW*ң)١*B2ҥ$* d{g y\ل@;}/e4}$OS% ˟^CQWOp}p}{CfEZ7>BF[m4fsWsוqin:GQɟȧ'YV.(p"Ö f.`x,m 9-;F܍@:TXNLA ^8fYvl1J F/0!:x/W BqEs4'T`s?oeyu뷑D'Z&2CuWqJV^EAjƎB"zJA[* ٲ=c[#I 70oWfꁃ,% M%ra ([8~-cx DP$FB 42,LHŒ2Q(ʾ\Nyyn1CpYE+* M؍cN0CB8 hJBr; @[urE^QSz0S)`r=,OuVIr ǿܰ?? )^GY?K x1I@I}o[zWI1R.~qg"UBkSB(¦-;՗Dmf5e:R$+Z*:VŢw2HD"EI+L ?2Bg`q׶N=~Y,eW_rjWor 'Y'=sN`2H6۬uDXOD/7{zqBel.]ӭG;I;Q|g5yP=89C,2SX+&L;nQT!8erz_ 'ۈL(3@=*GQft\+{F(T,hʱ֥\~_4_G<ɿC"?/F}9\>i:APG=_-jiwbm4]q<<]4q]xE\S㷬?B$7ArJk-@rI=C!+ $v;n푋S fǜsscJ}xzAs(;ŶQwXY"13j}fn!\!pz3ektNoΩ.ԼynVhU>ef4 JO=/_9h;Gr'Tn՚o mԼ8q>V&-9 lSTd"r&U7Uf<.6rm~bGWvd׸uIp5gM;V/;ϸݯ/:eY:%Y=3ziu==I+Zr'%(7 i:eX=4ӿu)AuJ:J%8.y rѷ9}04WFS|cuqMo_7a `vv%>aϓen&/|ZWgjҔW;Y }l Q(Bt:S9`؞v6ɶ]d3ig9g˞J^׹JL5w76m;$=Z^ş$, MiKn ֯cVwI&$-(/"\3eqV RI^|#EGͲU2 #2s칯!Ҍv7bNYṇxk0Zϡ<>{rG#ZвNhhJ:+P !1!s/* `Kd vE"LAqQ0e&G \HMBl&bTrHے^ܹ꽰Mf *I)(.;Ea$v@{- "汒e},wui9CJHE$-8!eIAL+ @ 9a"ѲϐtOOo_k9'z3(/4{UFs~ߥΨGbN=jt\.^M-/?OJ?/g[_%ເWxjkI XuN?3J =ROQc(PϮΆW4ϲѨφ43DUBCU&zCu ~| {cJpT::͈^(9cT`3JFA-5e8T4<[nPNfpD1DeS.1el0Ztc%l )P|Thx37Mq}rÍ&'|p mN";3+ o2%:~R0D1E_@XYgP<-EViÀDhcH%AYg)7M. Ӫ!Qő/ܡx8Ƣ@48f`}̼ϼ bن,MFʒɜć~"*1c+c1b՛@P5q V;w|,|6#3e`GF O^ZG\k*|(%Gn^b1M~'q}Ԁۿ}m1݋(g:ݘGpU <V"M_,B y^o47(av-jJPP@cEXs0*r /Ȝ©#IglQwED82 h[q22jc)am"\:Q80 H"M`<IArLhdc}(u;Cl4[PLxW83!AV*$R)Iڀ. xX*4gDyY8ТK%ʰiX_"m)%Rxyx@v!ґj#UBUjтBPV$`& 0VM4 $D@k!r 4;$8kU5c֨-z/FC?ApăfYM]AT:WY1)F@$% ),sJcVRI ZՠGOuߠS7h~/şK~u35kVQM" ] VFtE5֫;fqSRA)0ɓt*vjJ!RZd|Lj~tI23f`Yrzݦ[PwlΓ |yEǧ%֛Ww?O)HzW~PO ~w?Rwxx>u[ݾAh}!ʲ?>APY<hjcR1'cTGJUMDr_%kIz"/#JP K AŸEBWt"$ûԈQէYT(xƌ}F,6j(k3[Ev5L{1^+WSZeԜra>q+4\Bt/1ex̌ɵ1<ؐG#tUӣ[93e<-hfױ13e;bs˘0O5 r TF0rXYRB& zߕHxJ2G`EšH8 J:f!L9yNk@PRqFԂN)B2؂L,XD娄g4תMQ >sP%j.ٔB׼|ߕF4TY t;#u}}L۫@/8AoG+wxfg>?CϤok9R?"%s |GEdB4 i@8 `1HzYl֯?.1|[ROIq9e?b VqˍF5ˌV~B>RVLm͇\w{qEtYM3/O;!+|0E~?iF=iP\!kdS6D7npH\nJ<Q<;V- M"tv)5eJ}C쬠$kN[AacQ OeSqj|Hآi\VHaPTliQD pA5:0],3WH*b]X(tLaul&Z[ISkD*!X$C9!$~ JA1%b 7''J ²*&'f`-8>&Qkk/>.V ocf3mSsYW _j2;NqHuzT6os-;"?51N&.Бiq{E~`>o*o@vؒy&8NY[0Fcx,-}%Pq#v'y"OC5rcYdYW견Ї 'p/-nDƠd~Jp)5Vk/ÃOnlzmKlKWVVk|&&(ٍN+ͥYŶz}9rڐ6|B{8;yIw=Vj[62ųI|h%w*k=jy.5z5Xz>[}[g>O֏~q~G;u3|w{Yzslk7NG (5HDn(?CP\jr(R֊,Wwf81,"΍XGC(8hߨ_!_*%vg8$졕cJ9dX5|  r;glyc!+.(N nY.K*rdW1CUzHMή0[ݥ"_.'Ut6t"vn~*m1,Uy;&mmU&]doc:pP8-Nu8642עZ$&-|*'jQU~@s%I'QN@ 'BIHZ!eSh!@ZuRH" "|,A!PB`:/ B| cpBζ?gFDX_"Ì{\F ?#rWZmcT-S IDATdaN` c16A!D+9~XZ8߈QQ*jvPu,@ G_>qnmZ;ҩaNY|&[jyM=5Y~ze?#ϞUN 5P: Ob׆lb}ֵVyd#ǏQjK18 .8;b$ qB CcjuYyb0{3; ړa."l5)f1clWM`vDicɘAUpq/3 [d`-@MWSҝG-pu8}ј^dztk X8frOF7l&͢1u w)#;g/-gsB5f~aoqwP S"\f.=qpIl.vQe2d"SJKlC["P,P$s4@G+d*rU傤>D* i8s'U֘cb\ n 2x0$g}Vt&4[m<%h5C7<ۧCێl*=mR ,Eis Oi&yI51ӡ;bJxmw~qmm-O㦘dk{t'֠]CS/irs<.6O?ʃkM/ոXz~OڵUQ9?^\,(s.G zdkԪKԑ M׹k*ĶOy)p(M٭3HASi`^b9`TV4ʈP-4!V QR#GQ9)R* "#pVhNB'r;;#P1DelƧiBQcc﷝Y/"R p=I ]-qA9bM֐RpanN$GR9Dmm }B‡gqʬ087N,?ܤ|Ͽv=o{c_dexJ,}㉍ )镳sL7S= {'fUs?&k4 iQ٢2AlFaQVˊNRRDF~`ny{rsFQd})wv{{\[]oą“ZE)2cp9ᄘ9fFE?i,#\'Ozm_IZv[^$Γ`0J؏v'۱X xvi}_,Y8[v;WOFcvUzl[r4fO8-]/bU㝂zcin*pJ-Yԝ'uVyȜ[ .Ú|'eC<}wZk9\. "-o<_CGf歜o%&T>ٹΛDQc!~Ytp"F\"5"l@KaKOh>{1XzuܱėT*9쐎a/q)-T W"Iʽ("9ylܽr8@&a8EK[[=dn+S}_|mVHN{m~#ҔK>El2\w̚_%N.l:ؖaRyp)@l4Q.J,eei Vʖ09C(AaSJaWE$@`+lRˀ_DRey>$h(UATT':UH4 7X c/w#WDrPy"u'f0w>֥>G`g{O:VɲS=:izF=E/uN f?6ʳ%H ћRaf3iOV#-y<\͂*N=*YZ^*X 5~@s#t]:},gϮ!h#Zjť;nt XT4_>ce ]ۏ#2+\Kg+Mf?L!KSmtwI3s)ur;Ϸõ2EOtKAXE1v+*#9'Y{*ҢGQZpE ))pGϱ|&{1|kIG @qo">3_6ozۏ~o$??y3vdǯ~h[]I°V-YnIКQvx˳b*]9H)~ӟ;^rLꉿ*|`3Bj0 |j4)buMo)qB1EkcdMʴ-JE.\ͯ7TXoy眸k+AW ^@ۀ2 FJE<`< 1u9U|wqr)Otl2b4v9C!h<ч+`\Xߥ?a x`kD 6O> /#њJ]9{q , I- ǞuO;h43DV9&;vU*qYI5UiJ$lN`zGk"Et ,eč9ZU@4juKKXLWdj[-uUc3Y-l3jmq 3#;Ih%A&$(Wgh9[εq5@ Ei'BJWּ6!]bLUj3)Y'jf N&vqS'Zk?g>6z#;QF.{omvkϷG~_}/~K~8l>=+K*wjsm6IHa<͓qǓYUDOc9A2(tnSt#tV=KsӌrEUïܹ78N'ǣIYJuM}zb$R8焵f :w8 OzVFi)s!I}=}.eʊR>f@v,j/h^4;;EG@xٹORBaRPsTam^ +z4>kD6f|fGȲ>QP%{2A)ds^ od+-n^A(ㆭ= PO!;`N}AL~s"?zQy#>cB *; 6Rw/1v4iWgKYT}9cCz<ym (3 JPBb, {@q1n]DQ 5Yc6dx)$)XK $a4ͲAq}6Mʣ=2Ւmr:ߤwPD;F'8Q`eՄWsv=N7ݏXνԴXnӯΙlf4 9_d;e&IȈ O0A!êw1.x 6!!f6D4q1rC+G̎=N[qVG3oUgraK# 36It0rh*BUA^h8K,G(2bl`fgJ$?m_8xJ _~ߣWNڿ 2Ƿ֎7|͝iR~ߦfxx4]ŮUUjIunV;&A#|-ڊ/k/߼M +}WW_* …Rz3/ſ+q; @Nnޒxu>Xꗖ"LԦ°ql?tJ=Q ϥKD>3?8FP/zI휜SsE֩ݰ3jг(B!*a\IJsPFά}6*SN[ٽ}ԗ 3j hW'z#r89<ϧRUDEq7Vb1R* w fMQߨy j*@E}@^B k.,`2SqШ4Qyh ( 4=cNx̔pDDC8h/(קrGT`2 ip3E^Y~K,¦ ɋܽ=Mlmt]e˻@㭑M+6D[s¬dOP"  &}e˚ FҌ9 kI9αɇ>NHz."L9:S) dhIRyQ5ׅdV~kPRpTG5QUΤA 9/Nد4Y*#iE6ݼ,?.*vnlReS9n JZxXsjOҪTvT$b:7^EZxE,U KU)}W_Rtd,Wn.xA/.eWV7ּv\R`|_](쇟~|''4ƃ .j٨җIoƓ zk\ Q|_]j㲼1!>>#/R+K,fF7DtOjfgdzJqed_[ nVhQ$?Mƙy4լە2 )!/QQjysbW%$;ẟTkO .WYM uvgG+!??}qg[ܡ]ks~[54j+q&!.  kkގ?j&0ԅ7k iG{ /Fu.ty=3_F8"- &YH)Klx[iER6(SCC@G)ؚulsvʚLؿ8&[^GahZ nuo0<{sTqb^q<^+/Z=Frg/d:aS~0OXKR/dJVpJgxmqV>#;m"c(f7! :ֲ% fkKv9M5$}V>cMu9llk&UFFuSU)Z Qt+C'pxR(YDɼbC ،z8 yhx=6+<lunlD5(ƪʫ#qu4$U(/M|4^ǽvH[n(1QʝiWI'5HIEBKJJ!|u Q*C>0X"ՍBNj"L"OHmY<Uy} Gd*h;w,K[|yLR:,lA؆0zt`!OMq-Hou9<?7~|eT'{???߿{}+;~o~Cgÿh/ IT}hT(GޛJa^tp%lZd&H'DHG`(& ɖ('12)SDrhr$qY~RݷsN>z-[_}Uo9ӳ[+">8T0_z,|彛/M$Ȳx4}1oЖJۋ$9b{nَ?oܼ6XtHgFoh54xwջgΠ[ RC8,diFYL5=>-3%|Ζ?P!( A ¹2%s8}pJێ['jsO;vr /rb/EF1T-U)ea_aSDjAQ|r{Tt,Zhou(+Me*z x%dGW?Dnf_kx Y{c|jۿo> ṏ*߭f<=$}M7ϨԊ><,%LG#,,`PV(c!r!ǀ* zBjp40:4O*r$d4Jՠf @!YPNV(Zl^#bM5W8²Դ(,iLJ :Dik0EA54%r^ZbbŦdslXM7 $F.J%_^̣/9Y84 E aX2Вn.pd@!LD ڐ" @jD08KVpEKdyCx*GTȄ/a͗)bU6#V#JbƢTJyvs^.H+Wˊ%S.(Qؤq/hRKV=*ʓ8747s*'$w-@H*BnUp@d& ߂& ԨG"@@ATU{4uALB*Ԉ)GwW/J/|?{3=~$̧(jqeOE/^dN˿utrwܯw뙬 nY F+fo6L#+05zX>lQ=:ٙ1@0-;[{qgQYxiqe}͌BpFu8M<\iU,/t'a;"+RSsewvkwbۢ6xkwv!k&7[r.rnEDEDk?OW-`dg@Wn$C{m_v{?ӾD˟r=q6(ms42! Z'E*U$B{ 0\!jBߗ$%%_pyzubJ:Y +Y΋^ɺ*4t~TCB&*vAb̊7*<4i#\[!b$e@b0b!Xa7_$~nHV„%<0x#\>CWnMs8s˼ d@ai *(1d€I&('YCҍakT#ƻs,kp*!ߜQRd^Y<'J%h&Z@ ,Ã*\XiJ:}<4HV8Vx}9=I *Gڳ|h. 3E&3X0ʼn u1~k^I':e>vȤlI{#H[Fպ熗JajS}B@L SJi$3r2)*4$3lxRunR5XSUj*+PhT,Mz2 i"DkJO cǖ -1Š$1?Of ~;_ᡨ?:鹼)h܏ۡ敓O<|5u]F::h8nk{{[I{@ {'FVrö\~I||\vFnuA~wնO΢/̓SnB ZN)e( tAU7n,XƢ9,{2Yl"[+/c(HiR~6T#kgiGfeаY9!IR)&C߱,o7 b;sruuyP>3׿-ګ%m`E8C)H=B `±(j{ʳN/|߿Ň?C0M'fEux6=PI2 ǦV 03U{ub y[uˤON^])/*BX\Y98S`KQtC 1Z>_&pRޔ6m8pxLn_/hmTPF p[fw.4!;uaSHgv{e)X v,`h^"p84;60;63X=jJnEpmmt.Ms%:W˛>ь7-47KQT ] q4Go^B+p;óF=crPYg< : P* %`YgqP 9gم/-W$)-:h{peUK gPȶl`UU*܌⭖RE.@L.g=n =<[`qF ˸=i̲r*eVghJ Yf;N4( 0@w$ r`[Pd43"@Q*P ;t,$ G,勏@H.NBD:+aA?*Xv蝠NVPdiGoLeиwtjïϋ҉BކRcvm3!ܿnB rY)ײ~Z<ދz{h4f0+2Nm/^ɿ݇Xr 8nÒ=],ɍvZ^ U4`x 7M64T4]dCsہYFY\JIH^:HdL)՜da!ua[Q\&:qY'Ƅp$]gl:=R"LDihiq6a- (i |2`Y!~OōsugTh8k*@}7t? #@['#Լ^5|5+$ܽ9bu$vYŇw>QRfYʼٛZy6F+BZ (R ðtUtb ֨ZVUX\sx'>DtJ~LH?*I6+iOߨP IFM^jE*Dr4-$*a4{5\@)s JׄCCtVD* [GJK fAfcwˀy veØ!jx8ޑL/[Zxx6hNq,">|I(iv37rA ={D! mρA}{Z`JugXaXbtl fds5‘D;)G>ȕcI2A֛p<51 x¾wŒ^c\ŬQR*'.&R?GQ]۝ wSҴ)wllt:;G۔2t'EXB}czeHj rf*/2SBUZAQ{quKYEtɃipUJm__fWB ~h zNp`u{o6^R.e`Wxn1;k Ӝ+A]/$߹̝^k&eґM0'פ0 I[K+yr=P 5񸪁O1 -טgOk d:2Brމ莃4LlFQrL"Nĵau⅝+*.+5l5ZD16yL~%2 "}4]cLU݃΃9yik;L Un]KP4z¬XS9T)Iaò \=+0WA9%rej ʌmZAKd9lGzKO҇;׽q31+4C!7$Z8E%2 i^Oj:跛1nZ"nMT0iy͟տ>ii= :]IaJf=ǝ7G^谑%6/_wIS}zk/oEcfL؇!4˲*rRAS9CnL!*ᢊ$[^6SybeZW/ӫWYr\SD$RqT(֨9I@ ^^+O T H(apKSО!6<0bL >l"Ksd&uwѯ0d -e@vJO<#f.”$,k lB7JQvb@ y5q/w2(/FD ^溁]ӪG3c^Iu?eAX1S\wG2tq IDAT54F`GvlD1; ]ڰdvl:!vCd(6Ā)?@9z&N_q{a;nvpEZh *=E+kZmæ/"h#!J.fSǐO׌0ePIE\7[^OjwBa**ԀQJ-`{d4y(3q%U!92*TM@msfLlD Y™",t%-}`<( +m06GfBC=E{tȰO= 0AiNbJ}dzC UT`C1EL䡋L +adnvp`F/ +џf8 5=a1KiI'v Tx%N7BQ,9CH3 ~K"StPh aO"adiOK-]MPW+ 2#Uc7rGU\< z~N )54!5BY*!<|sB “8Vm'RӒ@Ǫ~ԩd@yҷLbI 5Xa&($6"Z^B@AHv^(S%!bZՊ дE42MP>f׹tRkDc3{Ao~Wg?Ns(TxjD] PpqV0!2D8Y%(S 0r] 9]9sxe1Si4e:A?~`x/0v) Q=VsMK%5}6mwʤ;fk5>}:^,+O]?r}ӐLjfe5jA|em@fsCa{ 3-${ꝟ#N`RY*ecMsq~Bbꛭ7JΚU`]O^9((ڎpm[dy;x\wi5/gmSpFpAŰɋDwD&O(YVY4;DOx|~0qeC`S-s5NGl~Uu O tUW ;WqGޖ?&W>z{7ͧ_X>S}[Epvwtisi(d%fDxf" MkTCWJ,>4Q/<);yϪ ?ԏz$%W7oB<] W:dɣkh!A,T֊tmE MsʁSJ4GR2*r\CI+DhPpfXiac9Ghј-mf]4-\yn6򱉥GzwT?v ߆ɡO9:olCKȒ` y 8I̥FPќtՇ-`LXHN3CtC< 1O% m4f%v :COwq `; x (?xA t}K1Q8 %NO >C%S*W3C})g >gR>s!*R.g ǨxbjP%˘s19X1ڞ7>/G7v2H@JLD0T-iV_LX1t#PQ*[PB,G,/ pir@VJ>{Y uĞu[Y)U PCBZ1vX? r?G/3z UԨ3# (n\<΀U洵ݳԵT`2JUO,V Ӆ4æ3vk4NʰO&SkrX{Ui„VSqu0wOTLkrvwt YV\1liΧ[ܠ,J^-L7N2S[0 6O)m+QYE(%dM€tl}W̡] "HX.I]޷F[٢3Mf:|Z\_WGl>Ƌ^;vY3}Bw`8\k~MJRTTr{FmxkΚ\,׭A9.:{GǸv9qrz\f#P/N_c. /~X:T.],b1١9hUk34~wkX˅XϠfpRx^<ǽ9Dg xq[eG^bqw XC9<{#.L+K%Pb IrS#BLK"p3$ŝArFŢ]@#mb[,>=M1pSL*(2 0cܜS3ư(ѧjwcjwg*os1EoîZ./ nH o#P,Qf2ppP}x$eǤlr|ݠU?{Cxg>57:?GMH+1y@`YYs/|rVJYRJҴA6Ks( S:iQBT*)tFYj_kh9}ރ`,n<,kr]oQ6/>u=9!>bp˲,8=;0n۾_\X<:,epU۵ÓӔd{Bȱmˑ9/:ΦŸZW-wd Ȏ|I*cLGU,ʫgYi/3~ .OFX6$(Ia*lgQʲQL'h%ZG/%~6Q%>_ZWז>f}`V#|FxU/8{W7f*xez/K/|G1Z]6pήβOl6{ViaGǝ5r4|^xPSrIe RCWZê$2F(,hQeG?1 k h5C#e;o5Ӏ4F :5ڙH %# \(7KPa*p@IrESC dzCZ=osA1Z({ lA:4∘:^0C`𞁾4E,p4n#@ kTupνf눫c r:.xW}; jα"Fᴁ0.SL!vl@f)2V% uўU8 fL6e=oU4i~@p)NIeYkɱ`wqhZ/h$S wp}v1!u45&MvܘsB EyH9PM% *[Fu9{ʞ[e; WW~N|4 ,QQ\#,,z0tR4H:}iP@ٶL97@uafX\ |0,zs˃i׆Cze8jMrTW*IKZ<00evY/w4]Zp2$i oSe'ňmUDVX7Evz˻s#6襧ڷ2$߲C>g=u[towviyQ̡!+E9%H8&QqSYr Ҋojcc!(.QU_xMtۿ$+ګtWdV?\P_4 _|)q:>^|Iګ&j0mڑ<=/^{uDsxchW?.8c̉x?7Pyg VtlXmrSYJJߵ"`N3k^$̧MUjkf9>4S!JA%Hy͍z=hdsa!;+:j{B>/ (P&Q= Ya*hhŀhU.uQQY/ENRP"lQL0-@l0&" m!mX94Xvp79̲ܳ@~[KRCI| E0h-&)!]/&&j̀%nRح#,!f_!hP\i)4Np77p4{=c %L,;L:{˲LpSfX5I,%ݲ&UۆxcokA anXm`X @6^"W`gWνX:b-@RG t! cL<5ލڛ7\7tL%/!:gKa+[V Ⱏc޾axn'skˌ3+'?ojmqěƘv)rEU؆p)Y̙ X͌,fL\-GoX5m60Ow^:{ۏO.FЮVխ8s^-K}Q^Fu(~e$c5 ԨW'sWP{\] ӑ;ݜ2+`94tY ^.y86J)/-e"Ue.+yje(S* 泥?Dmj?N_~wo]pG?w~h&oFo? c:d&@7~)vF]緾Gk?>/?[(n ?/7/xïz˫eX݊K[ }Dy^mrGNzs){(9 w q ?0 x8aRo^zR΃|G -IںM44K&d`ez$yxo2 _^~~_1:2r]V1{~Ѿ?}yip8e QQb\WnbÍTX}L5[#C?n@p!o/r$lYjm+`Ń>~6b0%rD?Z$ʚ7|5ߔO$`ʦ.륯>|Rotȣb)'󄵌 *n"2FJգmg=ewٯ| nWɿwwckuxd4ϯFxz+?}?.P&\)Omҽn#Vm+ZOM}٣7`7_EztT[7ЕVoZ=< >crKҌ 2-jQ1AOp~:~G낆1vp=tsxƀ]/ ws$<'2 Z[=Ю^#N|s:BnvP_qr7A{t2Ieځ  h:D o`$(“@%rM Ip }kIEe)h IDATfF)O#ĩ"H@"z ~ 9ԐZb0(g0G8zİ b?\pO\ MΐN E },& %1tj H ͧ#߰ġT(0MRdoD9p6w vſ`{/Z$ %‰!z6ƧPLCQQ}@!Aq8J%VJ|9Ľf|Q`ٿF ?% 1noZo %쁻-m㔰M[g&1]+ENzT֕z_mXvQNϯKMmEqTщ?\Mxk ^C?jJ. m=/V<6o0%`!RSH`0@@A" k8Ā7^ل1Ew.kAY ;oapAg ODd~{?ߞg˶]:0L+봏Hv|fV0V`7 }ݶuFZWNd73S0wApzSk7uc-`c" %:J31lZkTA elrFiۨ"/4ϳ4?b&nuÞכ=clM$J{!8F3uE)u6U[ڹVuL~tG:7D~nn$[2lYW U wPTp ؖU yT Vh@(լEZ4'-8'wV2h0h@9?W8D" 5j6?‡J.j9X/~&? u}P['\%^{hX﫭SfE(/~3fJlmBꞛU>6J؋(xfAY_ہ?ȇ[Pخ)YThY>I%] 5pX!$e!b|9%a !q<&ЛۉnF*5x=9i,⴫j=ٍyG׵]w=<KOm3K=_<]H?7uBFL9F}VWv"ɘ—i u;y*uAljG 1{{wEzStUVh4P3AGں '2xGуAD:>9յi9ZQA(eksfN i?ލZ/[G9wl?y!n4O\ָ$&Ngwole/\iJJ?o BS˿x]TL7,f5+ 4EEe{[ԭfO/3iI)BOx%YCA˕umۣ}Ţhk; {3N.~3p?!*#CY6'~F+t?FC!D`08GY;Ȭ*\#4 JƘ Ÿ5X%}R`xe{90heq}Z֨As!i IC3=5' !2.|~bq5n~ޓ='MƏק`%"$qI #)K<׈mz\mRVS[bcjVs)Zⷋ&k,җV:Vz1.,ӧAL_9ƨ坁S]kb8\xn_l7ZUyNR JDS  #hdp#Ew㟠uF{xlk o@΂ATL햕v6Li7JnFPmo2\ڮŮh7!:N ]X:ןs xP>h-wh}ܔAb^)wjͣE!+FAgr4#fV\yۖMk` uۢlo8O[GU&8]쨟^y2}QG9mOfѪj u3UJgZi$.D'A.|G^/|xk^DtE*'??{9_m?OOa&@S'7MQIm$ cJ(RACo.)(nM< -ۂlq>> x[FVfƅ{gWjyz Jb?{Z5[NR`K.z$np a9EG'z@5 f$Sp"~IRjŰ5(K@ڌac+$Y K},NS6Ø1 L(k+ vc!n"ݭ `u!@Hg~&u95B@v@GϹgz$% 5:Uj0%puݻ 1K0R!j$ 8АV!`F{B|{z!Q_sazA(53ⱇL.,C/=(#6N8\_9ߏ.<_ 1A>vey>("FQ^X&H/`2 &}| l=YJ@ܟGov' B͐(}lQ]pHA(LzP{> Ql"j1&f@'R{58ǝ|E6S|1:6_7QHUgƟ\1L8G`暵vH툶u\XBAW4hFf˘/Qs#hr:0PRC»:TC5,Z̐ sֶ/BH yl#H3=vUP٭I Ʃ b"<cnvLF9:W[3v|})_'xǸ,@O#/^7xYl;4ͺxnnp_{ @%dJzϬ<R^Z\%3v Q ĝ2bCVQpo:)v;[MKڊ餯Ɲ:՝yUeiFy9N6= pBS)$ֆ{Q,mxQeQ۵0V7hUķ#3ZѢ\OQ'[ˢ%;$W7NLJ5[ Com wFz|y|Cbnl! UΊD@, 5s qZWdVpayaHG\R|_"=kUC,D0Ly F, |} 1 akvC:0df6P {ҁTS+ F<"D pio\nҜdSmda I|w 0J20?|7Xz-`\tYVVb7jK _]Jޢ3k[?5.Bw]`ǻ{>w:\v: >Qןun -@A+9V)ҘW:I"k5'$ Ji} 24VF n\ T5bw"Pg?k&!6E5G@P!zI(~*0wF?_!d dtK|?FZl5I*T V[\k#:z,5:aBc[&0,Z,/,%]b<jfpv?B5TxaͅhHփ 9  MIKs4W4rZcZ6`X=C6|nT-'9 EbW ji6ڕzVF]4 ~*A| =P0&WEتXVJ~{LtZs8e* CɮҫQ Pk")Bqr0 04 u Tnbp@Dl+t7Xhy-G~nM<tiw}In&|s 5oJ_377 ͍f No<^;pxUrƍq#%giI镇76Ipoi+W+o(q{U7 t8&˲dQ,ܯ_MYI{Kézz-JE<>?z"0y^Ms\j[rzۋ#anfeMexWӬTY.Ä5Gf<͊^ ]n/(Ncg1r]b^Ȗ,Y?R^_,[p@oӚrsqxo)L=y7"miL1Y/E,ΗX~K$ :|V5`oCe3_l#"Ǐ,p0qLzC[3d9EZJD0 :n4w#+5q`xڮ d@@rHlAqS1"KN . O'i%-`X96;N@979ijLSNtJ׏8gV5:@`4>. )Q@tLu w=  bZX]>i~3kP m8\xѽMe`E>Pq!4x,\)U big^( R(|Si4#B` #", 'vIY{àmgxr ʜOrxH`O[GD/Xɭ WJ׍<{Ėk_Դ`(i-No笔W.k!9˳dv (MbkY*zwڏix%\i7 ŤL-90VYVK1>,zW_n<F<]G*@K c묷`vDwoQX*c[fy ab&^ۀ7aj^`{0$dq F r'AG!r^x`U|K­]_rȁHypb;;qx<..Ӈoq i!.6$*E8ms CxApB;d < GJ L|, Av 9S%tB Hy|J0T]Q <@`W$=8ޫ0ǰ y:C5({||_-/-\af)Yiѷ=` F-p8Elp=ǛqI=U>sUc ; ~ TKC˴G}A 1n@O?)pVTb6/;k>Swy؏Q[}zؒAQqr,E.'U̟VGl\r=z-ӥ/l],)k},nIMd P, (CTqEO!_\ -$JI :0C!ծna|W.*0] ]^$wb:c~I@7A^Y,p]/}8G;?!{<>7MBg| -wDq? 筱!PIuSL6ӐN׽u^7k7 b%-"2Deo@R9(量OG:^k TN}EI%0D7FFl2oިe E{ L̏*+7blě$M$[u ,<=]ܲ~fADRX(mjav!0%ŰɺUߪl(jua*/͙us86{_l맧muv%hWWu$tsw~4^Aݚ6i dp˫ON^d^_ϾyV"^\Z$ q^{ :zXЂ! \v;*{X!!PJBhA9k>(»si‘J !s. AD"$U Qf}whx}}cuD& ^A&䢤uˢ(,Z6q]vsكU`LSЁ ;r fwt+a7A`Cq IDATkDX.\=u1z Rj)jѪ@0"DN 'd]!;.t74+~FMF6Yt9̳-z@U+( ,EbҠ]yxa4Z{#=,_jb6Y "lQ=o 50A{8 !xG!Fy"сR+D.(f4$ځY^r^C8qFte'_"C6~A3/>-Ea1o%jAgt;w4qEٺiC'L$`@FxE6 br^/y~>;>_-u4ieXrbPeQ9V֦mu}^P2HF0xjbI?^5, 0?G&|Nۚ+8K33$kW$9WJE Z3k"]lx4Þ-V!qل_ ?( H" w{D^f[M3YF`%gnY$U*9Fkzem?HK~Ue]?P {/7݋6iTfqIiQ, KyQE7Q,п~23pBl!C%=bNR^(GukYdX0s!$!RHya`p<ڈ~@yCd ɐ8``p J<. TC0jg8 /1wq*Ndao M ' ;Itm,b̴&t]w I7./ݮ 8…N,٫Vَ-r`J  * x(lGں\ǹ)jiDMz󿿙oLoXݗ]^L'ERǸ`}chGAdUB7N*V3Vg2f`oBˢhmC&&^"Xp0'NlUEq8Y"Qk\coqJUS/yo( 妀4XF~irS W2hk g4V! GȓHU]yrƸUӟ0xx||-e-Xޖ^8fdqz9m;]mGkp"EyV|?:9fgW/3V_! R@.Dt%^qu7-þ6f[}JEZ&%,hPGX?0 ,JMHVV/Enyp",{Dtq眽״SIil l#DBE6*fxHtB@F}"Pa@3 Lb@VPФAw 4%&h#$vZxq(x~#k1oNj4]d*Mg Z mfxXgE8;`^wZa MlBǃ;3=1 @t֓OIH>:Q!3 ɒ2#<%JF%PH̓/MX QHEDTJ 67!3\A !,'X[7Q7t\Es=ȶ5S=RV$m1t 1K꽀Qz@#[m!ˀt!FgoVH HKq6<Ӏ⅀.+|֡~ExG[DŽ{UFt/GŶF􀒾$e6s{.V2f1[zP-,G^b2T&Ï27EttY}ohnǝ:[0tbyև0h5eߌ LƣiСi8cF $L2l{W9GI9O˼Ց}$zVyD9adBSgI$mc(djds8NY6DUuvj]_meNM!bH]T>dHZQ=mhZ+,,Mۃ1uOtpo:.N09%ZۺON<`6jc"m̩EU/NJ?uW7q nϟlj̓TN>5̓'o6#Mv;q貴Px& $) 50x;`ߴfmzszșb`HW9g[} zS(2K{+p LS9|G<\" G=p۹QqRQֺV BlmЍt4M"צOɏf}elB;>IR2$D2%(UTe6u_+>eF^ݿ|~=9=)Ro:Nd[u,O_VĜ%F_cnWHN~tv ;E2Oޔ|vM^KO=^?쥛C{@P.Z# HЉ@* UKB SgJb<$D`\t FH`Ar;PZV  *AIFn2q}#>ׄC>hD^UKlmY"jdƮM `%m#E x#x0D@x6$#$v|kթph 0\Llj-4<"\ȕ&3E{RCH&%=Q_EZGG"K <ܛ6"Q[o<*@Ğ#W@K㓈U FhdAzw-Ѿ0v=N< }!3cwFy%)'17_R+F `jg1]= QHd/V8yڕ6rP[1쑆_,rѨ gȟ/$W90ZuFHP\O'y*Z)FITw\dLljg3|pz!ڪP~bedK;!])5mo)Z%:;F$2wMki^yÜSn,"&QE $\mٌ ^ĉw6PJo}@TNyKVMf!ލhFTg!XriQ1E:Z\Ie5_V1. Ŧ&O|v*a M"`%tLZ?))FMƇpSyQRn d qe wkEI5tjlp|0̹6?Xt١yG l>f)J K/QN뗌 aROW0V9cGOj_*c`5$n ƙgPxL)=#$2G9u!Moac9#>Au(rBPeCKqwpnl`^QA9i8i#Oqll !zKaڷ"\3m(Q㇫;zKx>0.ZŽ3@ /qc|Ncrc}@LxO`.9 yQ?CIwsLT $ :PG:8:k9!A+djD$ }GA]X1^$0M,T ?z7(Ƿ#d1C`hF:m[`"uo٣(bڪddsP y & _`BiI4 (/d=IڞfZHmk{>.G%ON+EN'ȑ+ԟYNS9γq"$IR.I1BԲPu4ME_wu\m+0R]Nn=Ejy˪6`p.yC22_LM߰6|mYf*WiMJMͪK?|M֧i}l|;{g7S,B0ӼծՆ?ЍM~~yE"ٓ =?fr$BY#1px =e1,ǭi`q"2hM@gL%`GJ2URT%e<)D9}z`ApRG'M[|%.GX~}rĻSz4)T!m?se%Wɷ7rDܝ08|羜Y `?w!²t= HE@.3Q7PGG%DVsZ1FG9Z ƒBx2瘦ר (&ħ T g;/zmzUEB(V A3WW)L컄.nez+y옩 AX")uQrԽümbkJbjz @E/r}bsD1) ]#1 5XR0cze_J`jxd;"X"'}_ 4b 9e P0cD:/{[.{d#1 2IKl{Ֆ[KE劦וђuFg "2Zi;Z_Nw*ֱz~[ xQϺi5r\D70pJycjqHĤQ_CkCF1sq=H(x4mB%FDy8#p[ȃx&̥wq'RS* 蒀T(a AT$ڡlx8I1 ob&nxHq'?ϿoJC/ E`` 3@ èKcSԷ}1{6ι[.H;WTpL#mGDdΌ*l[W?T)hrWJVci$SUD]fmIF,:kחهB'ZiM&$2E\y]$=4ZXZ8-iK!ΏQRH613 AфɪmIvQճIҌ| u{vׯ\N'lv=#c IDAT}zI{a~2NM99 =պy߷L[޿n_̅o%__yL {\}1L@1u5H|̷~{/4F1:1bFH,A]@HGd4H(t]\R#AGb"B6H!":¹u @4yM^?H~#\$ϱ̱#ǽgAbdV7@ߑ"BC9 -J(1,+@Ba:dQl Fe9D$a UqfZѸVvD&QPHT`.%*@@z!{~{//m}Zls?4!9 H!M翰<iD fk>#)։aA8jbg 8(8gk::; `qr POѰpPaw(`!=//[:yv2X0#s`iJPԼ6x~?.=sR_V:(՗ IPF#ov~d~y|;:IFEn]teS/6S^ Bx) 1:oY>clWI_@#}]P%JBuA[>s*>Ri>:$*=.7'''ArBH`DAs1O[\^w< Ž2x xX1V;TdЍ$BJgL"(C7i( xLB.#(2HґP (^o#:{ H,nCid⒜N:^F+F(c:!rGRdzU Wm9x"@= Gt؃'F@ "@qn/жK 8YTlY&))6(!m82s4^!M4ؓ H鑂_9-QD]9~% @r~'G fu$$::(ơ|RF"Oucټb@B^(vHe@qz@rԅbzEWKvNRFf7K1Q ܼ0 EZ>IuOY=^'cdӖxj~v;\aF5]-sTR-H7V}-m9gnLq鷸^eHxQ {;Ÿ#D#"Ywp/uxYK\:)(~>ׄlLZ#Xl=Ӹ5icɈT H'B9$$2$xf{ٶ%/V~F1C!jeP`s)sۘ3#o:2Mmc>:84Zog:) @zǡ5}/"\vι{nr _WJCOO~cUʹYa"@K?۪.zX皇H{`0Hr{ww]O]+Nx!tͦ ~YH,Rux)I&,EΧm*̓LgK2F ,=1!"LtM:i.ع%A$3HJ5$KEJ͓$5 RB $o[ m;Ȏ(>1<ϥIsmRUUII}7#;eMGy12_a$wj/21kxV!J<}%BTgRi&D6#C/ I^@ōo:C#`}C0tya]`]#32X$r +fF@{"TH YGP;>=h>PQTh 3*}yA д4 #4@@!!ܼ@Xzs}Ʒĭ앇5RK@{yV`È,)`߁BI#j $ILHRcz}n/{%(f&RZx(>רfByL * 縓SH,vLt1D2CgL`=7g$:Dؽ!tdԴ{о>.m'Z*`>s$P<ճvL6| G{ȍ`v=!-!z~P&LEpm-Z8-RH%x|*cv!IzchoY캟MKzmUN^4V҇V~DĢ4bQ-j>bhz.b&"32!Kt B􀈌Lyo7WJd<+Y@b .H4HgJ NE.")u -L;J#&a}>uq @0)B2 <tZPIɤ^HŒ!!4%_#J[ ibᅄ[򇚺YȳWV Ws-hHZFNt@3LZE_uWkuۻ}Ʌ1n-Lc~T| =69W;m˄.+0YfgxeKtwj+e[Kh@!0?l9ay0ԯA=u R9`|~sP69FрQ`GL20vBNc/GĈ_-#x= 8cHR4VBҐ)V [\ YH[@=ذJJTI_GA-ϦzӣqA~1%Ti^q9z6<cP{sV IJio+p@MT׷yd>w:9Isu6d=9SH#$~E$Mj|w>NN+!TJ!2&4׫}u6;iQwUMRQ(%ZI48))#Įo{3!ANHoZ@A)BK~%̔#霗m˔~o$rOW_.Fw8ϸr&pҶa?{Y͖/+hELXĶnpVz H`<&E1Kȵ>b jkdZ=]:WpGEJX 1H ÄJ>`Suraf 4hqX!@#1A2!Re ~YNO|#|do^Cç]-1?l`޴tR%nF%r lTo0-4xe{  D++ + 1C(PCp4 EPTL ƫ5DC^Kr .OUjn(H#A6(7Bt~B"2vCR c:;Va?) G%_yQJ(Iԯ| 1W{noԃ(#Wbth$dJѢz)iGʩn%1yk5 G~tՂyQgQF>u&FB %IYiboSZ,gx0D+YIIL7ܸ襔vZ@ÓhH塌Do&Vj0sFqd7YaI}ЅF{DR3bWcF>BddQjB#dO"GEdza ׎my%p09;f{kk 8B<`Q X#;λQo%O)zq?;uf[](6&7C;i@Ԁ@ATbs_n9{-y[PžL/d-^j{Lul~1ga_ٓǯƏd^䟗sOۆ/1{ eл ‰bݑzuw7j&6P.^$7cizTڹ,\߀d $gIP5}=wU.ġJMۖ4vN78:֥{Z^AS B: X G}MMۢ),C<\ 5$ sI#5]ddԺو+Ua(4#l BW?w.dCVQjdm u*,G(PgRhmҲ$*.v>!o|>nSq/ѻU]{ѳx8Ioѻvu⣧_޸zp'7{Ggۛ{f4C[Ylgv>i_ ޥF5fGSm)?cqIv$R\\J,vz>KE-ҢhG&M=tU9 E}$qng25r.{>l6 @cUlVy5ƙ ;NrzmA:RbmtkKƣ!`i(VWRK{+6dv6–gQKVM)"؀tJI$MbFQ$BUr`8bDc!L@wܝ-.+ 4h;"_dNg6Il P &@ XxyQr:J$,&^IOC|OFE4< ; 9\}i| WB 1/)6 AmnlolzX]o2CHw8 q_B/4_Vp{SjԂ01A"1-4 `P_L IDAT ,NxA!5ٶB@3D=UQWKdQђt?+7)5+Cت(ă%jZj^l@#p1;A6Q\(D˷fq%[i0X&F;*.o9N"ot}d|͓?K;AlFMϼ;x`o._1 kͧi7̚?zm6}n˓T:_\h7y:"NQ6߀m 6!B>x /'?XƷ h(^DW9t*s? mGV-*DDG:.VWu>-s: _Hkh+1XBJC2@,Kk"z]YSEB YdSLH=4EcoV ƅ9Hlc",B-WMmزg^OYϹX>B@9G+3 bBEJo:⯹3:[ ` 41ҲݙL#u]{1Axaɪ>~ܮa:u].{٦Y\ow=0Gע'8;%Zە7%B[X}Qo Qc\frklT(.y22DK$lK¾avҏp:f0@$Fg pxصB`P,zа+,bA-A bW=SD G b# ٯJm (PKZ>R+yys 2 m[us4z 6:mAl-&`+5!@Êi7\0DS&s& e%$jN\FLIDܔՑgj\y#CL'ض1vS˚Q#xUƣBD$gRŞ2 -+`Q\& oNanW,wD3 S{dB7 @&C;ц4COшvO9&FUxp&TbqK""?hoZʝHsNH ›!q+Id.B1C|h X8 rѠ b ) чuhBZ3fwdᙃ$?g'%@KHRcAA2swƺ4O+?;}ܪ4*ӛ{M3\̴o|hgGӻf?&(g7Z^f?sV<.:X~-mX{x|6jd 1nM lԙ, J@ d_paǒ┘ _xX@:@!cp&U @`W~7F Ѳ. bAuSŠE9жPibW!kCXa3̋ZwlV:dY,Wm{~iFmcyb oCpĢ>'Et#^*zJƂV_Ȗa*P.-)a CQD@]V]`xX A/%a:m8?Jv- :& ϴF\4:V:RaZD'5\.9q=Egu7+ ?ߑ?O_:)^؞^HA?/ڝ+"}뫏#fwv]7[KӺ$OA`$|f i!:CYҵ3" QG>y T `Đ$SeTsPs@֣=c3PnRd/ a7= =byVxИ `Q"%Urxr@7o,!%x RDAƮ  '|XՄpU}J8䢌d-Ek<)d(VcxG6#+OvЉ*:GrmtU<'UY7&_>}!\o_@}>wo?RPnio<ɳ5K>?v36gg|y+[٦xy}~p[C<7~xJǟ؟'^]Q?o4G% 635 CGI |xng] pC@Qck| ^ͩgk<8@C3 An]`0|=N aU~}5vhh^}E&_m{R7ֆ,BTaci#"#c&;ϛ'Blؑ[3MXЅkg=A0젍Ǵz"X0d%YH 䂣f]f8(cGIIvKbДu-BQB'P[;8!cl$p);dm` :9[cl!_o65G:!\HƮnQ8W;@a",y >k&Ҍ&6Z9_unElJ'ɐjL &, p"TX-07x &<"ΠG<@ XH+&4 /?[|qԊQq`y 45P0A `үД RA`EgX&P(l35bpIHXa6gfPSpxV!(I0hZ%"ņ>S'Нz`smn鑼f-FC.VtMlش}hЬ6N9izm; u%*ANIӾ8:}/V[QO\MW|y!.-gS-Ϯak_x\e:1W+?Co_w7_x,pQ`x={{N:/[7'^|g{X_ utt`c?-2)υ:t9: I)}ӋLUS`w 1x`> -pd}^dD_Qw~5|\DR[QWrx^J+3ه·:R̢$Qޭif3̲0djкtABS:AEx'8KU;A D'n`'@ I HZ5`=KdΎP),s0#,#>CED{S@%8G!VX;)J>Qd{!fKU/#0Sy IJX37VHf,$>aG4ko#մRoj{*Ծ5MhNTGẀPS-p;x/g-z %ZQ@4B#Qm Ҟ)1%Rbq@;?)Lbg-Tnrd ^ ?hT,kqo(j9 S<1~&u՗eV$2Cbp1+20hCFAFSwMÝe<կ|=dweڻӪ~|{Qu[į~H?t~ pvww/ @Cz0}?ſL?nb9'Wv@# *sWBr͟ߤx?FIЩnt)ꓢC-:18Mt-eC!ÊlBj@+cc3N崫(#"j񁢋sY3|ݬPv N *ҰC(Aֺ ~ !nZukLt4HE&EՍBXΥybZ@t4ղb gsTC>x=Kڶe0DG6F %IiT}a$K0jU0*DpN-dDHY+@Q7r*䱺TWk㭹y PC/i}e5^oNoF{_^mỢ~>,67.#%Yf,Q?&eB za#ԩY[m] U;F2Fr c/WEiFV l)F8dĥm@oA1u܃E;3$]Uz @&K!q`a^|L1bIAK64sYIeѴZ{?7Pj9ܨ3MtY^kQ9ʬ^ ZUةKT8`Zx`|I`*0iNaQy lo@l`@h26&s,0>]V(DG6n.{ǧi|5En/fԺ\DbwӜLؖP. ދsp/(_0/ml|;LoN`Y\b56&b՘梘d ֨bwZ0nW=1jȽA$tMeE+xEZL5RC:)8ج$Fh`=\SC+Kis#-{lB@ c]+p bE%"2Pwn<"2ls O7,VQV)pa5/%C _.xjNm(UhzQ6.LJZPxX--Y{%[M}>}}H}mKv wiTײxӽ5;+ oBL>x]Wo|i#/Nts7kcq"z]| ܄U !c}_|W5)Gx}sN;|x.; kG=IS\tvٿxI'D:dj`nI⦻E5xc: Uu/ͤ#'#o@BJ'ZÄX0LL)!UDI*kKQ#uy2{IjGUcghl:B1D‡m5rA* HPR9@ Z0(=^jVycF,hHPFD!J*8ULzPWMl6xg[})V*4[v)pl6A Y IDAT6Ƿ7rv_wE6kգ]9__&SuQ؞U|LuwsW4zA9{ !̬^B^A- YOR-L 4PHBY{5M$z{ ̀B@jLc $⍁ɓ\ DH=&ހxDWڧ!&i0P'x#A')vh47cjYt{%ev2s `<$ЫHDC1wfu&³]x0hJl/`QgHr0WST`zkL P`AiĠ= " ׋PMl#Ae97\W~49]HVX ~Bjzk,z"6Vxb2YI3<+&v@R#cE;cMNB$s3 BCDFk 7m0$ĖU+aٚ$hq,ɹOMwuToΥIB0v%FLɦRGhE8pvx-_]VѮRm%\{趮kZ|tzfݻ٣[ûOnŇtet:ğ7߾q{lw ^iϿxSEo\+ c?c-|+hѷ<>;Ey$O_ ]뢲\x\H^F5`9$Ԁ;B(9QC,&ȁewQ#ӽ JDL bbD 8 s@8Aiu3ТizG)ɰ7t0((Jң'Z9~FJJhQQ=/0xO gР:Z![eLc\}ytQZP/k4ÆKCk)=S,6\ٚ/?h>!p M ZYmy&\^;5\'B8R S(Y !PI ^~@J)PIPQتJ븹{:vTT֞{졩߷G,!tHa=wN9 nxH8'(L%za /\BH)#^e"b8YZ*ORR[EW9.Qa{2/}[T:)&FE_{':[7wW?W]<~ڟ|pV_~?|o8}>9E/ooZ;O>7WÕ_}%O} ď |&MFII _[<VזWäsu+c@B`mUͰ!3OK@`/Ik&ւ |@a+7 C,K X4{-V1t-&=iXz>!=Y@OVI:fyH0/OoOaT]{HkK]QҢάV2oHG!ՔjۻLIT@엜JCˢ@%3"]FD0'Q5yL3$d8!0 Wiw,dXv8RXւE ju"(Qv p+u II@*" Ữ 9#=b0Zdړ"`k!%788Lyp+P?ƅJsZ7D-%ESJ#:71kʒ6ES0ug1`aCvv,$뱽]at\o^B,3X"ta$c B BA"Aha {$p"G曙Lm`B?m_a4-_p&/.Vp&TlYecjYuG䢡zy\~;'*._<|7Kjw.z5O?(U@WfӼ ߂Sq1z)nӁ 0lAectvF0 $'08+xOCO^˼t0+|!j J@2PG]-]lDN- U3:W8wo50dҥP%khZwc:R^ cqh$D!t> (Rea`TD94ԑ@b+B+& T`'CX ^F1+ԝz5ó&9I1Z`2 9U9U]j&'' X҇3:t蠛BE jr8Cqqg`kBL b;jkЃ Cb) .hBOZhh8%AMʹ$D+ ^T^v=蝲!J}jӠ[ $,F>1rbY_#7|\lswru6^~)ӏ,;oËO;xƯIl^G *+_IO % 'P#=X?n-2ݷOpc?uEb\{n(A.qw|H^xWFF~A By2p Pc(T-芔~)[r5j9bP:vpSC8('ĶHlR ?~A3ljmc- mQT+p?do ?:Q( X9,B/r-ZTKnN"@/9Nt@:i ZLn%?#͎:T܇Gޗgm~DXX\wRԘ^v]5\>JX.VYߘ!Hq;Lll ReF{FXB-ƆÉD1&Jc4P}.}6`Ǖ+C P=U=Cn2PAc5@#qG9 _;H=Q$H|h=Cpk{ZcОXEz @Wʬ7 CG%vӘpDZ)ce@|_q[2! C$ CAoi< Kf 8 00gVU=3 X<EKjп4J?oJ j-ǥiHr(Af[75Q "U8!@Aqhm/Mk㜴JN%DiMC!WYmƹ=.MQP2~)$J'vXhiZǽ5;" Iׂ^t}c)*iߺ6dq6$~,k\7d};.caF,/*6+(^ɫ+JI_?KJO շ'%-;kEI J/=زGWO͈JLb0K=Yϰm>7<{KK=0'Wf h>"XvA­TB)Py@ {F?ःOv߰`@r>">kfDDŽ7 )uMgPW mʏ 逻ȆA+g8&*[o|g}+w#L Z:JJ rw(+3ļkt2DJ^Oj破nAuː@BgVgA%w6z_/$_X$C1.9"q }#ڷb0 .m]p7»#"qI(*S`{="t(FFPm*}kAo!dq Q#_ihuB}S2wG@`WјZz3YPr GRМ2( aw-D"8aJD,XD, 1it æ T!b#DK$$XH&,~@{=!lH "\Tqkcހ^JZ]:lc iĴ=剟맏(!N1 /ν"":f䳥h_ Y.<v7Gbg}J­ RTX2$st!.;gosĶX~h0i5Xݕ EZ)+Dv$ѰFNj(b!Rnۘ 45 &842>+Z*@u(G&c6D|Ik>QBkC* 3CV61.qy[Oϩw!.\ :?(flL4 !N}_ ̻3ڵl;BVTRI-'{ W}}6"fbuT3uТr41 FP."=Zs1"+AK1K-l{+a1֡yҽC̿w֨%bS!ZUp;xާ,<{y֟K@-~GVs~iY˿.>^Y:~0K{S#wAh1qB^H\x=&w@ :2/c#vQRaGHA!m #"B`A JKꜹMԞHāLu.ʝ~< IDATQ˰{$YЏO8gWt>I`{"ZxfEsP!(k\0y%TRm+ SR@(28DvݪX^]V.qLЋU`i'ŕ툚0$ǞAwj{ mgh϶ ""|q~}߻t}ߠmmmWjvOnv)6vklJw_\ˊaf (WďK]@gT1N@ Z? pluiF(4!A: FRm)u59~dGn"ybTȽz&I1APٍ1h6AD E HR߄mUOq0YgΥE"gǾLN"[ όdEp+aY\QaKݭ8X=F\O>DZmљ2ͺ6ڄThJo]4JK~之mm^{}Ԟ3ڒ3`{7Еgwl'yi`Ib ,9=%K;V @)píssT'|ݫEqKY"Xv:o7ulBirJ\|U03'`|)2VNBGry8(Uמ=Љ,{υx gY 5WNR_iD'SGgJ 'E|-Tx&E(keޟ*.~L1ܩ]WYsޏ%F)dw ey "uWEr%m&-tUU,3gms]o%W𰾌ř9)4<- g?|,e/zBnϿ{ I |2>hScE'f{ )R_&f x,tJO@Dќ;@F3G# %B #! TL",R xfYR΅ӭ$bhgc3nX7uMMo[I+I& \A:ͽ4t$I&T "n'yQt:ۮPJ%BcժY=𴐢6~/gz7O"}?8?;3"WI\tBNt~6['F|t4[VH?8{=ӣ*̬Wkn=sdӗxͻ'ϾѣX %^kb{TqtZLE ŠsQXA7}zd\*F]nnn,ae[A/t>3gLqMg Aem@a <Aw|ȍR%Ē߃Pzc1|0Ԧ}o}M"XMsU~c tUw"o-2–vmgZj;jP>)uW׷oNf~2Wik񒖮QI v9.o[-^ Ƒa07{ZOdLχƏd=}N*/YqK./^qL"Zzwqʉ Gqd)-xfgmHo:A=ʛ#.h 4.0XhHS04hH`MXqsazR呌n7+uZe&ĥ!TYRt Qʺ9gjv_#KŸF%5<3uKimlKhy-.uYΏ'8>Z?5X@"j 9 a5niXS']ww @vYLJJ*I&~I0udmY_r.&v<@Q =beRvGm/+ ȓt} >|f~>;O/xw >_?VЏ*#&ic~nlnx<|mlZBͮaic(q ZJ1@[H1{yR}b4Ẕ'T+˔u^Pwz\m*߹P䙒Jm;os`@Q!S(E J($/cgëJ56NuGgˇ3(vxnUnyȷٺقgk|OGEXe4q-٢y~g;^:jQ|m;{.?_z3?;Ͼs]H۞,wh/s {2] ~!L[z֋ND$Ǟn4(8 'u@FjIq=!ָ:~>_@,DV5{0+|+ƞy T_ 8נ継ulId兗 T 13HcqP:PnցuZ}&Ob ꀼVZ g!B 9&Yk؎x7@+Ruw͹_+m:p Xy\pCx˂wc7f4G܉A}1:r>j;$۽>|[/c Q qEuv|>?=#W&tgϲ f%,a6ED{މ+$ÔW_#\V>`UeJӱr)XF{D!DtVyR;:ס=3)ɳP4RC[VʜX)>1MJ ֚֎%!`z,Ph 5g ݹDZt% 4ZZE_= { `jh^{ UzW:ɿ]zĤFh$v'Xv!sRh&.`'Ky<$ F[d6Wm?>J*V;t=R1a8H38 ` [Y#Of:=Hb ~ス)6?YlG+leGp3u)7 v>_3=i:6s 6^V .I 0 <SW fp\J$'3pVp A C0M)HVЂ4IJUGgr;emMr@{iEo^XeEI'ZM4҉%$I:9ދA]:֑&@յ}Qڮj: gbʵU}&A F7lor$(#=R<I {90u6|RLbi}}o?v< vB}s'V߼rJ෾x:.rn~nwYvۏ$ڋ.ڿqw8r "ѐ¢FuKp_!,p G:1Ȍ!K] DſI1do]GBQծD^;Z8=G018*$b(ZG0$ا @F^#"4`4ai 6@բ5L"iEl VEF'+($ikŁpgp?z[tO]IE1P$Np{iܥFDv1ùb}\uת ^BؗMjJ䞡^p}W|gO_ J0ACd\l&̪ZDCps͹μvV8^6bs3VӚ eiY*Ô`Rqvt{BBZLFNz* ufj}!nAxh9=4aQ^tdÓI0*[Guu QbޜZbTiGsie<,Ww*_AYYƵ\e?[P-枕 uJQ<7byYy$5yz'=Y97:cq'a"bF›t2*Lw8B.1;C"g 4@`캳9xg3 /Tt[g]/|ax_6Gl{ Ҭ^&H`?/C|:<. ﷡66<~l\xB$`*?9 )I؂|"t!b.)b)RQˁSނ],k`PKf$4 >ފZkak8\j.gf8lCEYoӑ.kdA_БzU-ڦ{Wk-|.;*R%#+)0J7F<:z9qrYzV{ox{]}Oe'L?(Z4b?|ɡv{˯2?B5}*J#J>IOXARM@"|b~CV <ь` dAGbŜ g+]4fn&zŞiS+H+:&7u?H- Y"IDD""K0A#X#@*1y?&A@]NBXd XHox^S{ܛvB>Is?2( lpz2S`8T-iQK̨6VK)"a*+>t YW.˝^N8^`+$SgĶnCwAec}"="ծ1|"RyԴU~<.mOzHK,54q\`JX7*^WJGq_JgD3*2–;N-Cl"=/k{ -r mS<ȭ+r#o%$, rUH}<sͤٹɡ6GVdRZ̊0Xb %p=^R"ӀJMȸԠupY% NKۑʸ"VDHHՎ˜T2ƹ$5)^wZad1MDĎ] @X!N#/ؠ0 !ڞk opQg*yzvP{_oܽ/_鋇:_3G.[D4Kox@f-l)R ?xoSañi H*OCx`yԴW ?Ve[o7Ǔ ˘7l4|@AA63L^HL(Ď@6u˜ 2Ϧ͔sq64mq3Iyf֦yس|[Rw[FC YD }H ]r@CdAEIڎUU]_tM}jI2Ys>Y߻wg=.x^u2M}m CsFp;.LC&8dB$޺J&M:gIoXIz٪^U\i-2պRjȒI˴M%Dq<)mgvC/zJz]Խ2899pJk-Vј k7,\WUbU?||f,릻^y8&wy͓Kin֏o. on_AoRv65ekǭ]\h+ ']%i,s)wYidzy_RqjC(x`9vcDJf{ *ɥn`сň@(֓=I '|{hvD8{ $ ZCi ?!Yz _lo~CwήC1[se:w_'flՊdb|![RGG%O$ް`#0ݑ"ENCȇ%Qz(P"ze] _dO儭d/*j[-~dC|DӲ- ݘuaud˥0KqIDջK1dwCN,T>=v훘B r9H7^-_+{ mKHrq+Z9 zs6c mkE%W"M D ,=]SXn_2.Z &|v!c:jGKgLT$zrZOJs:ZyAkH2oE#냶V N;hHTGQeSB@IFdxٶ7 y=81 X-:w \*dgf,,C\#E }J#k!S j.?}T?,?:{ @V|H r zr?3^gf9ooo~[^]Xƻm> @@ ǭ6@<  OfSH@1!#2>|A$K!r@IX0 B IDATb5J`#|{z6X`]H=ŲW|eD ^2&H0cY&FDDM"aE EhmmluHI@95R2Nl.㜯 TL$Eh~Qԯ֛WKΒ~ûbN^^6੪Mgu>l(ɒ_yghuTLRmKjiq\ ^a{wL~{q"ve) vph#>J~ Hy?3ȪǑ}58 0U5uXzyWfa? BT(#z F*"9xDy1vJE("4 ㌃C E9OZt@Q [S-ZOP`QP6P]RIR:V)'Xh#N:"ۯZKr)l=u8FM~ٚbolYOX꜠MðYu&NvZHAq૞j=3*5u=DuǠ3\(B`@Fp@!h B`  U Nk :/ ӡ"]97@r\  f b,?E/nUb<Fcv%^PY;tbxTl}.|7~ˇå!m1CtLq|P`n$D`nj[@E0n8eO}6mZZϔwsFF˽&FrPbSRH. ׊\qL jD$H1j(O '=`;+]kD)˲ UB|JκU7fOvLcV٨s:םHd)^.\g^\NjkQ|hqz@P釓z>x;4zWv,g:YA:K_}-ߊw]\Jՠ6!F\"d?$:wH I^kvv ud״w9M##R ;5Ho 78͞i.rNdPpvz7s79C%xC5!g= t2H Vh%Sssu⭨'V^ۅpt)bňDTgŢ$z?-3zZ%= h/gU~5aɪ NKbūcutn,? OYwMѬR[pYE Kb嗀GZnkf^ V^ڰa̻ȏ'kKp}s\j{1\ʫtH?VaYl4&0C]xe9Y/O+M ϝ] qpQQ[Rk");- :+09/C̼,"N6FGEs(^;_2< =mBC:Ɗz>7 n4kN .Jah ~< [N7ӀAT-e&X~m!*1"g0(ѻ(D DL>@Jպle`h޺3k O<[uߺ :=<_lq/~_mo Su- ^n$W<7O[|08-#)ȠvTc@} ω" J۶5'`~pe1^H `>F2y9u: oƴX.HI*7mgwv,>* `P( R .z:y3ѡˤ;ΩfU7Rim~deP&jGi4׳&tz>Lj~#4Eq]$NmME+e)duֶtgl޽:;bu/ /O \?_C$;7nF|r}ǔ hǏwg1F!a!mө1!, {z{Nq8wPRH>땚֐Uq?}UVDbẔa=@͐O6e3qxN[GCXYDVwG4Nm)(JDyBg'%uGC\5qksѠ\;l FJnsˍIv Z8c8d:LƆ"_{9ױeAy0m.N~X@) ȦA$q)w(6+tz/a9\UtͦN%J/;z-<衪 W8&.Ji{?|L`$yn\ƒHu7nw A"߫E׋p -4<2b{ck<H/t%.f}er4|%u Ld^G@zS-MPL\djJ)(`RNȺ鹴ҥfYƾ zTҊdnhyp(isxM-(fMU`utr.2!-hձEkG LGAh Z " x蒈%۬8s}b6!Fr ` F6Rpmsq%;UX1Co!ӋBn%{Z͟$*gׯPgǽ.ߦooڿy#|^6.8ܡm$ro>WoM;+c}'|$p6넥RS(?Im)CcA:<(~B# ʉ! B<*SV:_Yw3q%6K)Z!Yl7XDD<]{'ziziF@QD{.4m'KuviAׂLf"-O91[Փw$֯Y a.{m8wPCb*Yة~>|m8?lgsC;}'z?6[nMղ{~Ze򓊝_O7G@1\?YAg,[_}/ۧ- r a3 >l6b;Tc Nˑ=^ "`}S߽4=%Gl'1 #ֱ 9D<)y1F|1(c1o5j  qp^V~YzQj Zwʲ65+zþNX]Mk&Qu'8 qBkS eN&pb͎@YP;*zSBxVUKcG-7'ɗSST2[F4-#>b]iY(jca7@! e*5\S\`pe3"*"X8+ `u0̢MՀSA |two8^`9D?~?wt;Y5NyŠϓu^'ouw[Â=E5@3^gn |7})fz1|RJn?'Dsc t-^` NtomQ' 7Z@@#Ĉ0BrD2 l0"IoѻִP:M8g.|Z7^>l1y1( hH>aDϪ"S=z3[fS^5_A(uf2ɬ|BJeIh~/TY$zljuݲT_>ӳҽU_7sW+]Lr(|YX4%j̴֫O2ф<4 dM =}ݦI `/Hb9o䰿n rĐ1oM2-[登H7EˁMuMhB_(M!Rf{EI횠;#gSl2?agby*B4bS~X:ple>7sFk ;YR - ֳxș+ b=ŘzuF<&|b| $;!RԀ1$@@zU6  >oy n+4k,n= (9Bʶ$?n&uo7'ۘoضz?vͧYO&KM3HCMh*ҏ/&^{r?sex[?s_ 3_\ly}c[Ym\zGY:>[4QfcuT/j[gP"eldCÎil$ .LD}ȑ(C/VsΆi{lX4q&p 03=OMl$Q[2,n}w(gvo-k|SflhBޙWg3lI$XmLKWsk#08# UgR\ ٍ:(FDb̓0G1&n)q:D/@hj^Ɯ#ӵ?.Yn"A}^՛!83Ug ΰ;LcDpN$)w4MM7W` e@_ QeEz :֦{ G:QN2POHU:1Ilw;&ΰ!`!1Γ%޷=NBpowۺy"S)ѫtYUzV|uQ$ݻ7yo׮aPLyvy%V\W1墩"`}u֠!~vvv}wYwPkfěɪ >a.W71bkƚgvpw|tU`uw>|piFK6!X F$׎h5c10tX%jv<ɤ滍taEWiu?'FbGj٧zfי fc&eΊH"& br)D4܍IRP&vt0HЬWp5KT荭Wm{Ɉb^=>ļ򛍖qllYn5ܱOC9e7bS:Y$|u)w(rΔ1u)h,xp$ ޺awUsuvsˎiE;o=ť=:WiC+eUh .taMc,=޵mzz6hZ`[)  g@HA @A `v ؒLEVD$] g}uo;k~ܪiJQ~$BahUSy>eIWǮF'"Q1 mCLSB@$Qxedq>[&jI)*.;Z!DA7eRXc  ѷ>"p %O-jEc^g}Y)`:&AJrbVW5Xa\8VsH?*;juȌӇ5‡/ZL~3Ӭ/*կѼZ' !/Fܑ⹗>}?@wx-S@DHkur -k5G5ȫb@Y (c av2d@R#kuY-\N#c~Ѳ8CXr@ϛJ5Hm9\|6DAroo2O7Λc4`!:Bh #D̽ю횎fwWfqNots\kfm .!7anٌsp>o<O2bikӝx\?)2ln\Z) kO%v}|5Ý W]>6F7lܾf=?]ڦvqU][=?wG ʋ fY(Y̺MQHkL6퇞UR+M3yቃs&r?W[ 5 Y|k9awNO&2佾ʗ:MZ\wɭL%ݾL΁º4`:|Cc6|9[XZՂL"T0mxxD;\6gHLj;M QEdq/niw1A.,Hws&_Ffμ viͣ}TJU3ìc)MYbDx‰oFÂ%,uunT tH{_%1D؏=8#TzfӕNu4b>B:l1S(d@>6`KP[ddO N<7)l-MVaE+p1 " G%,v{Zvg~%ǟ2>&, Vx%azgsι@ W72.w~xS|x+Ix(RnE*+-ȮX!DECDO0nsZ%y1mF` 1e9 Sd2bzxs:Ob9*z:$6zJ$SLKMgJ٤>M+\ _ dzËs!نl9S$f~\\ X&I Z|1k`L}LgeiN'G2%&2Wϲh{1}Djuersnrݮ>1OL+RONݾlOݖ<?]OEٕQW|q)`ڦEv9fu~+Hf.9KE<̀̆;ΟJbÓd'`*" }Ap 1@qvE6Rl f *b#E]bk!@;0.Bppͨdn(n/:{kY5880=(ShuƺF9qօ0xU"eq:*Xo[nXot ֽ5 J'S T(ֶ]Y4`{݉+T% TsWXn,];9JD0$9-<0ӮCNZP)pyS-2&9;TiuU~Ѩ~'ǣ6 붖l1'w,浐ܻ.sjhPwy5.OCY޲ >oV+{!M KÞW9[6]A_tQWM=(7׆u˚,GY{s^fω-!2Ϊ ^<9,בF.0͂O(kܥO ^Fg4r.Z7-#(׀GX~"R!vNV2 %ơ!6 phXjhF.Dw`lWn B7@&'P鈤+Aq*<ξVFp <݉{/-Ew=|e`wo?MZOQą u|"䐰s]?3*skNc}Uau\ϭ8j[(pě!_tm5@CCXZ;SS,UUfV4e&B/κ6$ w7wvcc3y4=}x2vkwe(z߫gA !9 j&Μ1$tit魵tlγimm).DaY}Zڧ&O"Q>sӿw74,l~p٬i~wʦe7OA.Oe~ws]ǻ9qq\v'=C7uy@!~W_`+6":oQ4G14o!-9刁(~ud9twŬhHŨ nanDh‡"!! ub5E @05Oxp<(<1v}+D}F0  Iʸ#0k6ɛS."_enL)G+5 K/_dta'6|oUjG\nILqIK cbLBKr? ҁ_2dTlk ^ [(ON!Rac8 ܞ +()*h bV9 4łRϒƸ3{n2(S^^7Xwh%^Do*.\CuJi?"?`.Mv|ŬUeB҆/K+>W=0)g#×y6^WPR\--Wiԧv^+g8{VL䒧IO=mf)?hϻSM -ym :zZH7RS eOE7rO 0~f78!ohuw YlQqyh3a\?kY b〗>$rk w8> ( H[c_ my3}>؜g?g 74^yf{o.?Hv"[o;=t_``y6?Ιc zT~Wj>0#x.bo_z|u=bx|gV'XB yעÞ?pzoD"x^.+b@P5@V>33:{7zzwv>Xo埿>φ"IRcdJТ9Lneաk;|8*Z֗#"p0{wMg]!|Wuaտ~Ӿv󍯝N7v7OmFO/fmnTE'ɋRF[Pt ح$V2g[|L̦EedlZz'K-K)/}"nvC.[P^]!ŷT$9,sʅ*=_(Jҽ"obVX#sD!Ӡ ټzZu8x*t%=q(dV;0'X\!DBh#0gQ5#d ]\ey8HX5Y F]C!mv{%uEpiPbͷ3&nm*j{jKMN3ʼnXisqiD‹đT*Z6 ]wV7:GUEXKM42.м&.dBvŦn˜WIZi5]'ղȳv{-c6FSX㫮E:&m eI98J?tOFe΢1Ƭ5$8ezITcJ,,(ڰACX6lNJ ޳~0-)bClg JjՂY٘pݫ e5QHbItZwCU1pK6Uk2/eEZbju:qG"ϗ^%7ڵGWB$m]BU>&SP5G$:q  )XlBH8EC.,33{p=90,Vݜk:@ X=M_=Vg|t]= xC#2@ßcz7=|9yq~4ӓߪ/ǚ*>%vg}?๗~D}^0c :+b%Qΰe]5bեHk9n*^@ȕ :,-P~2~rcϼ[᷾7{J:{k쭇FeiZ'dԹΧɂdemX,:xppLffsӳ7vV*vl}v:.f\-v7ݽ?ҫ+w+1A{$o.FrΎm/gAͪmvQTU,UM#{+NJ4^by]0FPݤĢ쥑;I% qD4LP: #1d'UGr.ґoXI\u.H+o1ӨЦ)Gظ8os Azhˉ iO詯B1QKBx@l4i!H0Op6e `:PB d`sFKq/)<3 Ƭs4!PA"o_9Sd0qt H4r%Mi 4`qwS_~?vˮ?}o^|2a۵ÿ=n-nH`R?Y?X?Rxu\+P:Vkc_}F#Pi H9@ 4XyB5# IDAT+SB 5KC.% /f][QZmdZ?’G.$O1hf~k-ew|~_{9܇omwу_͒MfiP.3>r2e/.ThjzG'|1;;8[##vY\s^hځ|g?wfoT^x8"WĞ!#E$(`A`X<y61u˜Lʹ el0qFdiR$Z݆vNGbhѫ ܥo-=lRSgA$OΪ*B$j0f 0'(8\eE)ZP?X)V'4)Q)v9Z]" 80E r5gh׀rB 񘯀5 `"W>paZRG V 9jr>(WiqCS+2&x^cpcV]]WO>p 8U˪;HVn3\:lEwk$ӯh5wXgodJg˦,2!qd6}\3²}!53"x"9Kdl^keu|49 L7jqf tC||v|ӽވWUė>Z{W},ͲKCN6BwU/s66;@8O`g]J%==kv{[IO<p[_rmYBzy"Qn߫}t$)L=UvstEEm|@3di?G`b܂א01h4pD z]\U=@Af8 :"05 ]Yدn7c L!> hU@QJpe_@;` LzQ=LwyIoN6QO~2Ɋ׸n}NgRظ||BR$0O=-Br_?M2+0Պ=X$u @"NCj(Ayߖ("t<A<ym|ֳ3k p8[c?|No-g?o]s~|GR(~yͼY59ӓgnϻu2QgmLlI=KgՔ<{;[]7&8=Nfsc]dfέ[dv~vf['biRO8gΡ1n #OC+^뺎lZpFү"?PYqSI}祿paLɧt}ls2["'_Y{i:JGK01^}ߘ/;I@ Ş3}1;0I4%ރҊ\;WYC8TXt$8b+!o>bT -C} L8!2F׈7IFvNdl,Z5qh¥,* 'Z>Òb]nxpd1*TRqЌN{By@<3SVX B(h!:v$%c:txM=hR{$c[#}tQ. dBS }4&H}GṞ]y.-s0!=1͔rdpKF(t4jW*5(悇h'.b9J\#.ОS. gx d9 Zo2X" Xmg7d9٘˴2&RAqWQ-ex}&(CBr8vESA%^wZvqz;[^S,i|?I*/Ei 293"xRQPbX^4X@iǠ=X*Hi h":nh1Sb"GrW#G`,DH /= }x 1:C >m0a|^~s-,Cg7Go}7_q?ttnGɇ 99YqkgoCpcЁ-+u7Ο}|yZLQ EJ 8+5r0uSEvv1k]7q0u6ؓ'>iU. \=;QÙ1Gvo&h853Ggλ$bc:t AH^gi)T/>L_Ny5{ |Kr_ycsY: o|~Vb|\ =dgAT3>f6gzok?ӵÍeʱ^0L@N#EՒ󎠈 *" 1BX^E+cxev1+gA`p6D#&169GG0 $[)q H۱u1LT4(ڈʒg֣T塡GJAs@& aywd[bYwmd;G}(gX*2i]jbbWH㸌>!fLQL KF"%@v #Z}?7'Aa,;[` <؅J&R"*4uDhx,$<0ofuKWB*9AD;04 @qE`Y IIX9KDPpx]4=ד[}7_~U+ gǟF}WQY`_{7O֤A/ſ-b(RpITπ$w`< MxSWcT#R?x]}x3~<tx̿, Xmaү=g\},9C^&)^hnZ=֔R,z{kcm7׿^?Ino}x ΰLnMQgI MSFUgM|͢Bc9֛B*GQeE/znf o ۽߽ADpݨѻ.*w6{7olB-ZB4[p]UWQOy~>:=Gvr9Ń)|˟xJſ`qvÓEc}UmR^,Nkh:3_h7r@b7 '0B[NeJ3;wlUwY /1$Fΐ2I¶. 0N:\|"\,! [0;B2%qT!bnZgi]B pXd \z0 yeBLCFJ7͘Ŭq> k:Z14GIC3Pf|u nVZm@DDL#FC+I *%],(n6MIOFt]%sCJ XH}eH S-&ei[Ϲ j5pI( iBm,"TBJ l!#.!8L,($] 1N jUG]]/EYº:et&HdS픤*()|Nl(!yy/)pL>ΰ-J";ۆ&ߛ 9-\&lױu+M]9Σhi 9Cj{>g& - ohUsN@{:,̀02^bhZs8DZZx-ȫlE&^&2(ܑi`ҁ' ؀ Ijnd4| z @o/K\_ߚIϦ5g~oZr W>K/'w\C'lo/O)4'axZA)B DU[uw_~s+:V#\{< x ",\lu\~+ /w;rl;s־2欗gX5z9'|w_+e2ڟL H L8 t9hNO穒ΜL=q/g3>|ml:GDtNJT}5PKtaAsl`,$˞r˿4WRV*~Huk"rQ?nfrZF{n)u^,N N>{+K%k|̍zٽ"cGzhb/FS)OV46(ͭGB&ꩡTz 'b0vƱ~R@hf:&+M ˬL3 ;DZ`Ȏ̒&*mn[)Y|@U~rDK9dD;LU/tXe)hc3|!P,wZ*|bA"^qt!u$2QAc/B' INH ,jb'3"lI*hr};\R2|4YtK7L%-0M<J&x*H(NHkD2 - $x B/St}L'H6kH$ _uN01!sLicdIi@F12V`p#'SI-4EUw67-Zܛ٪c )y;zj8窿?®^Q&[7^>vv?X< h~+M5^x"=q0j Ll(6dIAI @"R-c Q5~?~/I_'_~ш |7s:wa&v@ SqDM竅w0RS 8 PG`뭇fyem^}nsWosǧG;[7tqNVM:b?ϑ*u%!ȶmL}{Geוq.¨Wtn"ҕx٧O8ey1f!4MS'v푻'ے R 'gj4:[YN՚,.=RЃ>ɝ{E1}6 6<ɯ YUʤ\GfOMK*lt+EjxhjP亂ӣ^v6;+7cMlo o`4 (C#6gޝ{3,c Xr*D I:L#^K|w9 8!c"[u#Qj`kN(@rLȮtjvHj& xZ䑥s@BZ1L6.I@`W,@e"> u79k,m7¬E*| KHgYvc 𼁦B2mI@E,)=CRr@n0o+VTHD+Fljq 芺+i6+ 4׽ApQ_Kȩ>U;aH/,mPihE(({:K3nq:>5ə^blp̈Χ)e i@19q즉:QXlJىQy0$Vf!!Ks!uiߝmZg䜱H(/q}@Hu',lL/W@A¶1IgL}tW뎥~" Ɛ lSt:NuM4`Y"XSRc6%%\B -@Xs ~eXp`kAD :!;͍wqGy\$TAj72q]U C/U IDATG[O~;QOgW80ٟݻ̻;y Ν;/tCxG*a8POu(ػymAHH9%i$J[5}2RA` p'N~ +xa} n칄jk @'c$HK#={TBȪm;齕褞7x+QZ,qY&M}\Ώ>'w/~~xgR70FS_-D*I:F(٭l}{z:r1/կK]u-r,n ~D[w_-\XYdv߾v0MfMvN=?z8;ֿY-A->yS/όL޶^eX{_ݹZO_MtwcM~g']|ny-U)Q Xr+ɬZO2@8mܺy-a['wh?QT00qMve  R+uJ^ 0w{r"eY^ΤJ]&2/hALpm%`\${_6UnrhKutU,vvO;NAEA\d%5`ыjKd [R;H8G+QهR)^GbJ M$!F,W'=&|:K5Y4!Ay$@~X`s̴+JgV(k[*NT830 A{e[[S1#Z[o2ax+ԾpA 7}H Z qV I÷Vǽ6{|n*C#"IRPI D9N}FڥF :X[ԹD@a@Ѯ]3(ࡌЈTVjFDC4@sd MNZ'*G%lm)un`-iӧ ʿob+>}3e"մ7fK_?gLoc$1˧n-eFf~qb+2;c`/W|_`<ȋܭ^ < `#|ywk~^?"q*P6x @ʪG !A A@Fk|? tԵ,#ǎJ$GsGX^Lw-:N)O;UݘYq7,Kw2(kvL<S 50i̥.5.k'գR3~n.)J$R;2<-.nΣL"ٸX%JI6B$Ae{{u^{ ]ڡFj.l@+0IIH  -%^HIQ=!F!VY'垏ة[,QI4H8)ec DblV]7C)AK$٪JმlŬ0$9j,+iG9[4&202}a²>M-c,L369"cI6ҮoDV DSxJg"}̼P' ,Z($uG<}B+xg}yAKX#CEEaPfN Vn5 ]4ƺw> v?}| bOG#ع#ѥ|to 9M[5Oa-a7Ck~}D7UW_e/GL| _zǣ*?v#ʼȥm+{&v \;qH{D}sgQEYtJeSeS'hY&Y}y=#qWyz925ӳѦҭo'X a;Dc@BZI"ĺX @P$66+/e߉]cW3@ [rxa42'{R2(:ݓ{Bgi ,g J䱊cJN +5vhTHHyr dެZ4(oQԍ[aK489YUG-9eKfFt{$&(r$\6[x"9L`6#y2CgXe9dD@5jYÏc Q$@eo6a2vd @ā0SfCt tݢ-m B%72"6M@uQdt:Ql J@6tжL2уrۊrsܺrBf_w*EhۄYʛ+eZK7p, ЬM;] s+ IdDZ_Ԧ^+B BB(W$BԙARl5Q!"xO-͐6,icw:3hRHd9r@؎:D4-~} gД;^-p(EOcx,5BatZ\\N/,Zy K]>{/|.1c;O_,.|sF/y9f#w:k/q#bbTBM]Oy '_I^Tе= XXu(Եځ[p:9{ umrV1 NK@t@Κ %TIcq|$%Ikfִm_>>7]o_fޛk)55^#<1|Kh} l&ƶmUxN]O<BiwKJBfz $ ~e6=Z)FDT;Tt`}PM<{\i*V&2lc]/V˶͋Be#@;MԑjT/-eMgctIl{uh؀= x.]EҾRIosI{L$NF+1ofȚ%d.6DE{ԑ7ZKB5YYnE8*=*,r}%W  B~ #4fJ{9 6@=!<3\$`t3YbF$(rHɥB Udfa@cFˈG?q#LibI,zuVrX"`*գsy9ˋ|ڿ}<-oJ ^>؞j,}6dŸ.ET)=hwCs(JYAimz2vU]WYJʵ/ɱXPt]ZU9]խ_77z^<勜<>_]~W$ll {|#t6W;>.dnw?v|i&WE/(!x;}}HlJZE[<L` luY1("ZjSU1r%|{B"73 `f%9((b;" mTDG4uqmg) N#ѓB -|(( ]DʸF1%% PF(H8DFAZX$ i|%r(TZy+ECB),2SU}㻺*qPL{rC⦜Q )- r{ʺ3HK[A&p K)hK#Ab$y@ulUZ #B"TF1W") 4+iD  A( "J@PA]JT]k.Q $ⶄTa ""C^zjв2 ,|p}U#:ՈB&Ql'y7~;_Q|Sɣw;_ͯvxJdnvw6DNԘ'8K{>~iO=߳A[RϽ[_tyMc{?(v}Xsz蓯qnчb Jsd9_iKezeO]4 n[@ V*iٙĕHt}k'߽6inh6rw_7Utr)ޝ~|g$QeХ+wyFSSN*;XFw;=)"]nU)=HT8.ѭ]-`R6CtTKSHQJ%_$f] 6B JdAʝ#rcHy1HC ABGȳS:!(W6h@puADZˌ(X.%62 tG }^}zxR|TH N 5 \׉|mM=V 4URz1J IeOd\'!p* q7.:d2tuT18k\#^ @mO3 zRTRwQBrG1Tp2EI_6 .0bɌ` g \wM~" XHHI3"`XG"A[%`} F:V =@WQrKd!|΢1HT>0s) "1{=޽ /_kLV' S g{iO)H^9m/tO`?XPD'~~"9^? KQ<>|Ezq3t槿z)vaR{-6!gy_8ov$?C19 ˶tWݳD(ܲ햟xw.oͦ*-olD'_/i)$=v:|͚54Ofҵ2P8\]$ݕlյI߷2+߼{yw֛̎Ë{\}3RYQ`Ƴ_5Y֬ XOM.95Ztкx`ww$[e]no+Nl\t6/sWNG}9;NӨ':y_,3PU>%'6Q; F)Q %6"'"C1J&{TDf8i7u SG!Nq UZ=Ȋ7,o q2\-W6khRdug鸲/&,;IzzgS;hcQԓDqa9j u,eTh}0Hܝ"Fl'C3š4S!L\Qc@X5ihERRpLD[N3gi/%zR$wK_`(@^44,6x`! Z)B l(Et}R`,A+/h11mJKEc/*IYˀH9L1 ZW(yĆ;ʥ.z@0"Hh ltLX(e1](ҪjPD̙2Eѯt{یK݁(pM׸ Nj=7\h%kQݼs6mK *l܊D(3f rdU#K4ƪE6 MJlIW|.; IDAT\i:% HR4h&+O0%y%$qܖ $ zfPke:P bt!ZBb$e%֓SF}c+?#8PVU=<=3~-t(C̻ӦvY`4jAF}XW|4,"Opg/ߊщ$I*Qu3ro>{咏M8O̵ .Y\c?aPwcږQgm D&S#pĞĞx[@0'\5])sTL|CGt:JGΊkYɣ% J:R*+9EiT~,GDLoELixk|m}9-CG=51dc_}K̨։k8"k<<E7V׵<',G%GV+ۛZwť#RR ifE1J~*XeDʈH6Ѫ+6*:5x,@!Sl11JQk%aBl##BJ!lQ+ % ֑ A4 % [@g)Q a,GBqJOʙmL{}MQ$scHE_)RgÚ#UrubbaƦo5sg.kIJ]-E.7t86Y3@pD!zȓ!EAdi?{,C('B`ͪ@WX `x8PBБKZ P:\5B%+Z"qdutL L#ji7 " IeYK>H8HEH;`x!#Ёd@3D"[!$^@ՂV. 5PPDɂB-N6/^"\6?v)W7~돞Z*Ŧ׫z֋j!|f|pﴯ >eş_?s1KY<Ə_pG.?uWg`uy O>y=,J&IFC{pusk!K;Vhz~reY ("K88a/2Om;e^?Mwcd'iwm{ora<*F!cLDC$" 3όv#xhÏ^l<Ǯ??8>=DjGo}o^IZ\dD՝sfF@@%pw{2ʯ\XV+eL'm ) PR8y:˲ǃ^5^J֕ 1G?{9*I SNy3\v~lN${k˭EuVFvE)`X_ݼ}>:L}n)eJ^u(}jFу012sWN' wlB QR`vk2zQM Eގ{knq 6{k,/\[ӰgưZ3"o 5UZoOvwE٦o~A^}^faTJigu'ƚCH/MwХ٦2`9dnCKp.L,xQ{n!N,xN  :(QFbw`"(hǚ\ytRpXzbIq "PdF\um""ĝ""E1*Q%-l*eT>)ќ|LT,N1)\թMZ1if}맾|酛ң÷}ڙn'eP̹j bHuYXGE!vZgz5* 4p424 uZ;E1?H KRc/  Ţ<ܜ:nѳ1P`(]At-xJ #(8HqSx6I5@b.9@k{֬MbD,}CR gS܃+!pТ: >d^A2h1`7Яg؞Vh;|[fyJ=H4S.:ُYl/Se/|󲝬_T^lW_@2yW|"y-;'~~u\ů#W?oxrG{_?{ު$g0 #llBxy11a8?}tiZ g|y>| \WhtMY_?N_t[ѺS. ++9gܰڳz[u Z~Q_kHx\d2pYѽ7.1}]]RYi( ۺA;X gζj(Θ4ToVXhrpSz-3e'_NNNg>}rUcvs\`n//ڍ჌j>tt`*mի*c*rkS88r(@Ab-tGX>8^ȾS۲5>%I'on,wY-k\ou-+U-qtEDzˇ咓رtV>(KI\p>m6Q}j\^m}n,aNڞcn[] R[&gݶXT YxŚFG*ZRX/ocur 72 uPĬD8I^'HEBK̬t8wq-Q3% D4iG4UbkHp*,]0kNA-v&dYJ*Ť$RLۦRZDP)kL$!ˠ~\zBϾ08e_ob檤5e:bV7Mdh5AZ5^6( 'd/WWt6_иTo6L"LG&DO7\MAf;Rg B2Γ?NYtR0vF#>L-2deY)!3FT.(Ũ2,u>B34m4KiN h6 I'K^tE0b#qCm nz`#=p;U,P{ChU"Ychc}.zҷJB2)OCa;_+v_wf71̽:ͯOMf|vKw?YOoax ?Q~Z.t0<0bqQF.~pƝ5aY<>&rI5Vmk?_>J>Ņa=xQo'^QԬj0v;\wmקp|̧kn7>Hثqݲ]~0J'ǏZ}mԏ3u׽f2A8ܟ("kt<)?xӓG^|6Nj/,Za״`&dYKO|> kz.W?EZ,_?xp?i@^ >M4G(w{fFq-m>uϟ75E]YW:t@~K4聢L1*fg Dw%W3@4B- wUwzHs zv+"#}Ufr,r“5O5~mFӾ[mc.@ w} 5,7kk7C2nHxiG6&J#+Sn` km'zݘf._Li]YVj;QE:˚p6H>125&.(z"w/EVM4D>fK :F(HZ%D1DZ@DB ֍ `ciAIw"&3s. nu2Cneͬ2?wXdJ:]]`bMr=Tk=VULbrG*9汣l9nV 5*[\*&!N&dXLT/1tbGm IZS>"R{b+G 'h4V#p1XK t bb@{G$C|oV}gAL [ż:Tܸpզ: /a ٶ>g+s6پtqEVx1mz}rVFX5}ǧq"zYcj-skO.{ mMճx mPL̍6nۦ*\dLjPt!r,/p;޳i;.;rAL *9F+~ǔO1K{㧏ɗ>zx}3[WY{{C! Fd5U_|ś?~XMW^?/tjضUYgǑ0何<EPH2S&OOO:; CwKYw}U A#@+ALAX:OR_WY0bVBo}If%o}*'N٠n hWWB*#nzV+%]Q\yeC›T<]HTV/2ż͝Zq&UV~`2G JaZ:lRMmf7.fO\0g"q!xDM;lJʅhtIKV36X$0"pT &#Vb"-˔}@t Ov5TDZR\5-\.Rߢ%˜E\"9MccR1btެ52JI,'5d“Q7yoL\z> (i2@ǫPw^7Y᪏]-^UK|[Vd9r;Θ]6J IXV-.1@)NbBF} :x"X1 d H)V9bմռsٺK0Jv~J\"#&7$fJrFZdCa (I1bw$Q:&QB¤ SGrS@+T:G5IU;2VKm 6XMa? e">c2E$ #&nđv6_*1 j> ܫuiB%)%['<ՌkεO: {$D`꽏j%[Rx>icpPUJN;R(0K'IҾP}@< IDAT,+0+A6$ӰVY%KFЎЯ Uc %AG`bhÛZ+Xެ]PAZqh.]~._;y˛k89o=~ӌ&4}cƩh|(Am7/ `YOS]@NmɁ}C(~e2n>^ܶ{ZrnV\<>eZL>T ?<}j3z\MfuZ]}Zi@]Wy_zۺM|krk3z1ǯ|FHGBW7r61l<hMYqr+ h:V p;[`$>;'bn.GpYXQc~aV]JQy$&`Z%z㱖۫L%*N@>Ъ]VOB= ]׸cIH@DiD@(IBw>:B%sԃ^&a䷜XKEMkYQWR.QoEJo0+nJaZo~sjFǚֽi>l~0J[GЕai6fo{T+?"(B=WS-'=O̬T^-EUlϲC\KUIdiC8$ 0"`1:)RAt4:EP@3* p@RbĘ@"*!ƞ„ )<# R !I=1jxRBlolp)Qc6]%,?q0L{b)Z{u]{\F+(a;@(c';WstՖ]U4&m`|˰v@xՕ5eYpz/fۼ,gW0e?E>W[\ ޞ4yOoygmFevMEЉ67+ B͔ɉmXFE_1^\f:T&e)"L[I1 "~d@ 8AIZh/$Rs&.b2:哂(PyօK1" 1HQ*d F`kIv$Q0 lLDRA\BbגRK~ײe,sad/n6tÖC.;D}qܖnߗ _w%i>ȝٻkɦ?)|FxƩ m?=]^!Ix.w;YllkGe͢UڷOoy6^OGQc¯}lf9Wv[mߚ¨~PV9 ؓt}x$ reݽw!E5eEŽLUUHH vuguNX1!̸r ň=jӵi)mWWm3CDD{IArfGzoo?ן]/4گ8mƮ|kDUñJIחY[V?*vxl'm)JnS #80SMD!0?4)-g]sPNH $#iҿcoQ}Kj ۲^[d() mW:/Ce5eYJ'^mj.ӆ)-F/面89ftm\꒤30F%zǟxqv $#}68I:(((G@"'ԩc2ft4iTd%QE{H$b삣v6ϐ=vL(ъzmm2dָJ"DI.IW6<`]8b١JDH'Ƙk~ :r҆UL2{n ~d~2(RA7U\)*-1|agyчW;z޺;Ѯ,Mܿ\eq69B,ҳyd0Qu6T,$ꮉモa>pMvA)6F퓶es(n㶛ېW)+qԐ M2cR,i~E*BLH6d &|_') 2^HjQY([ncH/MÂLZɖ`GyŲ$ J+4Ze"F/S'QOcǪk4y_GpEO9#{M\>WEޯo/Pq7_3?g ?|v3av4N~q^pONr|x^_Y*\m,RJu5xE6(t]C4#۬r '%eڶF"hȟ&!A|Fأ址 &!?hDX0t+@oGKG@ Pk8U l$12)ƷBg>.FT]tҕ˃ݪ,RK^n[ZWCI.\eI1}e6G,66+|7& a(RdRZ'L{Eߩbۑ:nԱk PllRbH0 NBj;ݬ6 :@9J%DQ92!F1zm$" N 1 ReeVp)aG6A"'AI5#DDeԧ_m`Y9%uP}"ְn-H{BXyUJ3(r)̵*Rբ.Q^*NY LrǺ}`ilr45:Rļ'fQ[Nj8p8nmb]lJeZ]]N e>(tg 7'Μ 눢մWOr7 C6(I'lQMmY):a{8uRgAJ!6.hV x8r%FeHn͕<Ɵz퍸 ~put[  :QAj⣎,1-(ֆb-v#Eէ[U&[d^=Pޅ l}RF1<F6tgYq!!"D4@Z`;kTTQ^S:DeFS`B" vċSĥѷ/`` er, RƘRuV-ef8{ IZegTk6Frk^ s-8hdS7Dq9 ΓjZȣVDp:c[o6Oa9>Œ|E^Gz룯 |ri'{I#7c;6fXcH,ܨ(ѧ&Fz2$?q4HyO|]:2ABAN)1Q 0Ecn`9RDDZASp >mVM4i4&c $F肊l'B@u-0C  M"@ >epAaUܠErۙp_5[ݮ 1Y$wJ]+}=$.gIw_>?|Fx%+&φ_>+{Wg=Q`,E+K5+]L{Q^ 6~IoLO ^AR-yX t6u$B>O>%:[ULRjj充+Eb:֪R*t3)RgLx^^\#wN]7^'Ii3[B/Q)md(vBSb A.+1>9gso[a>1tU:r:B8>jT?@B>`h<3$׀OޤW,[{sy_Y,F%K! M h74g@'ڀ@O X -"eXdUf5YYe׿&{i ({"nċg^ (pfW7 pp50`  j `pZ<PiU憀nZŤՋfr)Z VM!#Wʝus\7=RB"o~}Zse|Ϻ %,U!84VU20D>%މT6m+ϯ!|:@( +@:櫎 RXBU"24)y2QEŀX Z9r6N© 92ˑS4nl<}tXJL, C>񫢶 V2o|-.7b.<L>ʰz  Z1Q3e.N6FTņeny%]f&Vˍ/f_J{ga.S8;'JNjϕA/>/ w܁v}y w@Z.{.Qs(%&ٽ!R;i:Wzeՠ7=ߔX(pP8uRJ#&c Hz0?V6wLB Q뽠z& H :'0?>[|j޲FFc  IQNai3l;B-MI5AO r#ï}=C<^qŶ 3v~?aǓ}PJ1dV闟O \t=]frJON/a# Cj7-qu1= YR)~ytRՇWc܄"3Eb!)R6!"Co/_ASU:Cmw|}K"/߽nsdN'5^P-1fJD`z:sdZ༯BP7vi5tyQLUa7-@]bK#{wl{9=z{F^DE :e$'$BpR3Rzסּ3PF70M}bM@Ҹמt#{7MM;7.S]/!3ccZV-0-Ny2B!,zN`_+?'A V"CIUM9h+fjLlP挒3  Qcv=Dz!&g@LdY[Q'tOvxUJ+5gcVMXgvOrGy Tp`,>yT }K|6~ řs)fwYy&).F3"%\7Vz3;NѣM_mqr9Yw')ʖjT5텿Chyҙ ~MՄg'C%Z?4 I>6s:`lܸ˶JY0rR;tCEF&V`ė#C%cTr)mzdcwIJNkqR!W1):rrbW$d- =n_ !gAշJID匤=HJ,PM%w%h(&gC ykkA8Qo_" M WbX/_O|WNNmUaY\N&n_?ȯ?~8)wggKCv/nljQ͚&UEm|ΨHH0qK̎@S̊i"3WU>QSbFʪ|s1 đv C|UPUA*^,zI6TW":1Վ]69a*{HsW-O]_nW~ٕxrSJ9I^S2e~8ax3&:jcHH g%m5J ^yŶ!Le1ĝ⚀O*½d h=z3cÏg|3M`rC߻tK+R}eK]"C= ~Z-l;DٓrК>H6 }o7q!zΓ>oM|RQRiz֘-4ۑ*^oshB\OTZ B8rB 9-i\W4K樯k^Mdt5 UQd+dP0c ػ9Pvns>Do3l&"ޒA8t o݌oUM m}fVj2y⎽n+? 5qg׫M??[ Ve(.e#9iX휛6* 0S@9& }cjoSA);;O)<ӓr~zI'dVۃ'߱:^.e^`Y9_n01FmF߿wI׸kwܛ.Nˇ.aӪ u]]L "a1z9d~Tui&;X8["nz?xO)U<ʹ`_K꨺lu͏TUB5m=M9iH>;):P"x|^N(=|/z?ٝg z =q -k-1Mx8"{gzT50Vum{q4U&D4)x珻mG`b{t>z{190DysLjZܿ@4#vfPsL_Ni(nkjDK1U(QPf﩮+"RNBP6TTK؛W˯FVZ|X.'/ڋ;m]5ƾ6x׌2(wLf\M3°00~X ~ ufPkJE6D+"ᥤ4ې[^+ Ȍm ݲ& U}5^_\Z+GV!JYbcyqJ8~X 3vT1O{{1@ 0-؏w1Spfӌ 'L$Wqź D(q+ q1)20X'0r!fb)L ![s$H<ʰ˥(c!uĥ%bQG3%af@q,(GQmI*s]|yk"* P|NЙosI;adÕ JMfEk8h Lϧjxv: g?>^t叆*S~Bʗw !n$]6?DݑἫ;pK<޷J9\9!!+z/l^C54qܥnc-ye4%N{SbS|#@;)u]{"b3AU1TiHE jAdGd90.oWUӞ f?zY~n2 AR<z5=ha`ZHUt`7u=z"vPBdLC3-!_B q,8g #  +Y3X'rZQAހ!ۍI2F qlK!HVoYW:kPZ  T͈&'0X_\g͟ %Q 3 FWDTVU ~#5P^-& B34E?7ӘZ&}{/IN44[}.GKoF \&71w d(3d0҆{>2УvT`?URx2/mZap#Pl) Fe5Z"DNHHa{$1O[Q~C@&8XZ4([y@{4h t9RٴS†kG' c1,X\>[i]J?yb֍=<9I\uo>yigM'>*BpqB&=JL_gUE9rQxT3`Y8ҘL'r~i,ۺ|}l~6wZ~7MBU_݋xrҵԔ("䄩0W_13wڎbJ2M9vgXwecƲHԜWu?v~/?(oAK|Y1>w[K0 ݃o܍E݋[>gi +K+o̓ơ[L4}vA/&TvMT괜:Q9U^hU8 [Þ7#jPיh|#NŹ!hS)k`ӫ~-_i13/ic׋{'Zﯓiѭ~&SGSuA<[ߎ6Z;oٺhSPx'^ u*;-o{*k@1+0IU9D% ˮd3 Aˈ lӈ$#Fmd3[r#zlQ [\I=e&+M6q8?1Ap~fOAfv_S;w/dfi֍Eӷ7}ϟzLO^v*zx8?e̚"ݮڬ:&1q(fFuU\ naL Dh) @ )YL`Fj <ǔn}˼ҽM7RAOf:O"墁yG\wh|> |BT pkjgCSW9j*xUP9'"߮@d[]OOntܮޣ0vo^6 OU~V~¡|5n݆?7y*C&$yhEs=\bx*?K'r )~dT~(x9EH yvpM a225Ζ:LRa$>ln};GONK"ڮ`2 Q)gs2!Ҧ8@‰䪮2Œ\]pkձ+,Lis'3tM8* ދSZ+'r2w.]XԾdkTfڦKt~~Ή䜝s b r.U|08%ЫbUTfU>fj5_0vêxhYxþn_ 6 $PuyNEw2鼉K(zIqazw{;o ZW8K8q$D_`Oqt8k (8 @ɑddzkH)]~kK(TT`d&'=j}L)IN'fJ-"<P\mĆ`f^@@M ]o&ލPL:xNf@!h{gاpx:>w:[]-w>{5.C(ƼY/*obo싗7?Y%Uܹu|Y69ooL5}Wfe)U; vյ%[z|cfw{fۯ8vw|881(퀘Fޗ堺.e<ՎWbUuھk'2rX/ 2hG.@f _$ plMW*8pǰq=HL%{Bt`8#lj4`z!FHdfš_ ' .{-6k{d 8 1qfqLŀ)^h.,CZn2"=Gŗ/7|WoY]|^7ʹчw}z3?囙{>O>. L'|~qz]}E=Y}.7⻗?<*kE;}!gA)6fضg|_@;87ӓ+ENgwi Bs' f:0S<$'#@!2Gq$5=.Ƕ`̛Uɡ0"a6'}ǯeefE־Vkr“NJo-Lv"FIeG( Ae78Gӝ !cNlqތ1 L;P(v"̂HcҪ:=rR;#qH_|{nK_=֟>]дbjPYlg_ܝެ>L޼h8;=)oų)'a:9o8o*/B*`5*Qۺ/Ϟ-VoצO.͡NCK_RYm.훴'P+U!!xa1 +$1gc99բ̔$0`~ԉ1`Gs]3of8aWO90LldĤ)&9;SQ#/*wuU!"3Ɣsvߗv=K!`H:UUz^qY㬴a&eœ;wקar;Gv 5\O3rrj2؉LWR>u8'ٍm\YAh-DXW?@H]M2R@3^J>ߎN\DjXeQR8p:f5ZsE HvfG5Uhcxb@eHX2S G;xIyP1ۖѱܑyCDDَe_cav7E"+"%*`.MutÑ09~k#( 䘈b& IDATqLf |^uΎ$4@0/~ॏ`r:c Wa:\]U_^zJ\ݿέllw&r5<99 Ӫ ]Wv6+b*5ח(S7-vFuxxg8l OJ2=$Q^CޱP qHNwUP_n:{K;u)0y(1QPoN8@F!Yƈ%xQm$ WhBC`SkJܶT]@5mz%"C\F3)%:b2Ҁg`@-)n2h$Z,[ VÀaC3Zz7O_⳿g_>f^=~Ǿ'ۚ;wg>ړӋRp{8; Uü P-t\89:6 Ljs6;SBGW ySU?MUf%J)c$f&&Bw"K9ΦdHn&A0\U YUIURfTz7Dwς4ȧ8bw^6߻WQjHՃ;~W )4|,Oy3G<{0}hϨ:K|xp3#zCli=τ Lg{y_ ,jRaUpӖLܼTɍ|3!{N dږ6`~9Q 5l,M{ZJ٤TF"̌IE8VpXDm8v@YWHtWKj@&"z`8F8"DZy %.İ#\! Y!df8VvÎocj~_) {)F5Byi+?P(=#@J"NFzg ΀l>?xEs (&#@մm|>9w;̧vl/{ki]7|Sa{4fp$`h``"[& r X7 I&$$ Hr8!g{Rwuuuuozܪ&)@!m9 7SOXk}l~edgZ TNF'*-ٝ—byGom_x{AFWq(Ӧ9b2r66LCڑ/[q=9Yj6oBo ÅCYj3r_:6SutS &J"Uh9D3~El#%M.H oZpB `d)EJ-"K,YA`Z xA-A|֎2^.],R&D$HIǭJPXDT ] 0g [Դ,DEWO@,B7$y@hx#?6vo~O?{:4DHPC$ %$CM RLt ȧ?j!9$q)~U*YAk\D@Q0kE%\ݒȐ&\evaĪsEHAh />¢:tdBE7ށT԰ghF _Wjl{vD~xL􊢔AרA1[NORܱ8XoϷ cfzb|2ۓuG":-VN,%zYaM[̲<T#0Mf~!ihN˕sPKlah  $Be%9B0P@PPcXBE* ЃAN",R`㓝(@fCˬ7zI,8Pb*1`pb ۂ A@ja`ȣ j9(.Į< 8눨loFgnF۳ 67Wo1K@Ǎٳ㍣i'g,ؗsgΎ+VLJ}>.MG}[}-mjlxJG45ّ}|Ԃe.{c)2;wme:JbjFh kV{7̓l} S G#"(q`KԠ-<KR0́m($2eqA"ؕPs IOd6.ZQU([B@PheJRDVX{ &_$Gn!+:G~(g~h44aR) {ٳsZvإ$9ZT:FG(ٹRR@,R>@!9ƘD*)}!{Yggzm ,_8wtlkk)x:|{E7g7O/_9AwWyvKҢ4MuڅG߸`J~_h0rNϞ?y罛 t-.7$xA[[[|<$9MzܤFh)b)ԐLb-&7AJûD5'x{.FhR7&m mZ٬* r$DqnmMV-z Ci;~As*K /fqJd@`)ıj9k@9 %J.aG=QOhѠBKT;n+j6ޒ\$YhpNp>%v!"LA[GDy`8`5c_S\?7ѥWSH<vn?o Pn׵ʚBTx8f^äq>L wxQ_T2]~xn=BG<_wϕ?F.}݇6l}{/nngJK4 H!:#}ci@$Q7 s ,(2˺irY$Iγ,Ě)I,C-5e@ $ }U,nTK݄<=| {I[_76ҬGG/Zi#A>o[f8@&J.˖'DhumdqA)̘[(g.puՁ#zJJ*>KMcLs5:1ؖBlcnHХz(j/Ef6߇9-DX>ós@$POuTkgˌꀊ $~ZlDB|>TBhDQےdZCZH%&yB#Y)BCk3mWl'`2heM e\jkYC{ ؄ lxLC}D]hih^QQn]X 66Uj7.+&I3G#͏n7˲O {qZ.OWGe_k%UVQT}-Z 4óoIHe+9ٓUҞ!Gn·Zc{  N(,d'ŮE@ ~;PИBA h"\W:00pXe¯H6;+HWax2a+lზs^ CIꥏkw=|@닺XBV i#QWi 'st-n^xR @Moy $p@_&3Ç ֍8 OD@ "!:kO`cd% !f#³#'iYw>gB'F~NO#(H&H 3ؙ@e3˺YTgx|x}2b[% k[6;N߾vtvjV==.~{^yv2*IO^h/B:푰 Hb,Rp̽b(U! pB? urhZ!F(Ⱥ6lIi%'8ݪx0 ?+K_-ظ0~s=hvحOQ:mus\'iUe'BpO59&Te \nNlC2Le$ [6<|FRJˌv-KC2@A"2@.Hd@CAAJG4)sL QN 1ۇ@ F2Tw@2 >8 @5e@"n5cg@>J=2Z Q@lc>$=% RXS!Btȯ)i, <w [ܸ?pc @MJ?|?_"tyg |wl?Ojk=d16EG ,SFDi(Cgwe|@۶HҝNsu] RE/s1*!yfmb XȂV]l:} "׋kv1B|nx F gbD x BTR! /SpD]L{8ێrk'ZYԁu=J)!֠,]qܼUS "P!DlA$ZIR%VC]rYa6\oxaFi5E?yxTM wzc{fcחRYD]i!F/661L-?^␬V&@Z׺ѕfwt~yMtn2^VmFDYﭚzf)|60RhQ2uRj LYVJ Y1BK±@M-  )-zȈ )$1R8DK4@AM ƺ0S(ccqQ>* IDATNɢp0=V5f7H(A":8.P( D,OSQ>n|yzO`gtww?F?^oⅉޟmڜE.Yk.J"GAB L)u~̌##RDfY HGHBxh>2 B !Fk[iLIDJʦA5bAZZ/""SAAs$q2[։\rjF0T"ItAz>-s;ۜgők[^qmQz{{&eSeb5>B{٭ot"˽4W>Au]}yxzV5M[]@ C#TA V_7FmsJ 2ֺUJ6Emca'*|)5HRW0y.ݵCOб<_Z?>!gy%ᙡ[)FU 27 ĖrNBĄ6]r"Jxb-[hhH(Gm3(|K`e!@&ks vI"@@s R؂+O'V.T+!HTBh|@YI9? d/_`^|979X>УwX>02;Y=~ Iux2*n7FR/\=.`<{oo-Vp7uLG=DLņ"PC2*!!J̟n JZݕĮ)B+,x2 @"B"1fl0RQrB-ҝshŪ츞-a' 犄ȏVȏ>D Pc› BAs Xƈ2U) 2D| ?y&xR~7U\ik; ;.~93s]ħ_xYD_USh1sx3WJLj]aȍ$+f %|PkODhD A;P s.BphODY$ ږ$!bcbRд4.{!zQ%ʎ>ёtaV34WJoI%P6ILz2ټrŽq4JzB;U;|{w/=μ=w/{>xTImNδmõʍ}+_Trœ_}ʕW^}^/kmL;BWU.pf4=[II{ǗI`z#->F?N~#F6z)B\r0sODE 'P]:[ !D)uiHgK-,H?㯠-X| p9Ak<_qcO蟣cy4@} @$@ugVrs>ȷy-@+)§B l\{Bxe<`O= fX8ԠZ$͸k @K,["\0C# $X3s@(ۣuEւ )Z-!TmxS9(V́UHDBSBƴOp3OyOLxr҆ f{Vpk;Y+,gp|1hʊF~}\$g6& W ޹+͝Iq`r:=iOwygR6q}PexV\cz;4v5],hܬM$kj*ry@ <A{K!$Hօb)$$ 1%V`0%V Hb]@U! [#) `Q(lC@?HEp 9DP) % d)1aDܺ=}.+ѱ?oRƢcN}R @>yoß~ |՟nwyoߊw7??wz4;oϣ߿|yvυ"@ 7vcV DtB:Rv<1BWD%%N#@!}dD1 (霿T4Z2Zm6,qŲ< J*Pt0R3sQ?fE&ܩ'l{kc,}cX)!t**mߊvR7_F[E]<({ۣ30?*sOG4Ml_$ljՌ6x27o]>3ɏO޻}޻2{41J"YrJiC)&轲n7$VfL-,5JI ZkMD,P޹>3Qk}]$4b8*SNcst>:V& Kt:/chNm"_@EϿdY :5I.NoB X';@ykx Ƚ|ͳ,~-O'=VsR2ouR] ̙%ΣF  CR8$BNF>x *ƀAwl"R V L{ c,P ZYa(W%U#XBY%0IqHZPUY \K?P=sVo?9w/U6-G$7ڽnx l9q.OLjH zJ'IYX5&1^ h 4+swQZAw$  @5,(g`kBKtkgYb݃j{s#m~/{lESMI㭍I`և7im _/׿_{egOw Iў;;֋t|u?v0M/s%qg2zժ·>_I\p5kz7]VqEy9u"ދibP& s ZKѮS Jq `i, 9]tet>BWǢ?_P[?iN?_5,yh.C@!:twerBľ `rq /j78l|m-߅&I3w^o}t^u=>/oh^"sP> ,j9k 4Iˆ$ C'j E)އ 4UThKvF`9g)em "6%rNtkNLL_*iW66ˍhRѝ,mr7_ڱz8o^q1wws_6F8 K8έȑSt'ֿ?F@FE[ +PPHHC JR30 $,C Ѭ1FDDD%kxؽ[jZp|odH r.WSS}E/``D/ֱ;XnN7׬=6Zoq)RL=?0{oʣr3EZIs)m]̯^z^+?O^ގ~?)ψw}}.k<ḛm k-4!m}DR AD)܄\eI )dٚT1vєb+N^!<i}rێR8[gݏ*<6~_o&n8|Oqlٍ99؛Fy]fTr&'[AH>7Y9I m˒OUU\5P^'=J=":&IߛqqQ)iu1[UamBI9 U\0NܓJ -'6Bg)tsщ'?.ѥ.TJ^g1F+~oy+7V6MA:sPov*ǯ]%ɯI@%o7\}b!R^=Z[Ok_7kC텝`M_MD1&]|qcR|Ɩ*KeH940@;X R* :O?MbBSA5ABÁA`4h (H4hQa.DXalXxJXgd@X & A "Њ i=-q:eu>dVi.Z1d]W#^7&雔A]Ͽ ]*AǸ.Gey?\.ǎGAJUVuE3%Ve'NJ־NJ F F?*+z1?z|["~..?zy<<ڸpN?}ej3ISk9_?vZ"⤪0wM03G&Ae]P̑$I&}M[+/B BJ뺓Eg:_`2A'ɺ` >! )D>1BA$1f٭oߟn:ZV_~@1$I:ō^Zd2}ڽ^FY7W%Ee8WO=xUŃOFͭq R#!FJ {霌7]N ^kk|͉4~9ސB uq{cOOVxOC@x.r#FJ $+;$AQ\jE|Cg002rmRIXYe[ H%V*!p,6hB3`01ُo<0 0!B($\G 蝔ҀSJUfR!`$M_^4x/oz\.O)>Է'Qkxg&2MK`Q1=D|;NXܥz<$>AorbFsoW T_\Ki,8 iշLwy@'m2e|ĦqJEHi\g)e2^ȶBڞ'9T0avƀjJJZ)&h" CHi1J6(3'8Vx_n Y‡F].-;ePk{ڻ)ez$jvgqK_{τpR(B[K6?w⸀8] J 8ϳl*ڋٹۊtae5? \"A`tG;CB   XBVaGe'~" gK@ TM;lt)RYMl,NV!: 4Q“&&%= \~ݱ@ |,\;ߎ s;XMH B!JtU?jF `a2DJRs2F @t=hi$L8JI"PH"Ւ& (! vwZ1 Zit>R9qB+;Q(QbA98?S-)֫ARBNWk MƳO=4JZg7u/=)%Up]hΠE=ݨWZ^n?xw7c-6Zq*AT;&7mowsPc:trb4-]sB痪gW;o=Ra9(82PF=8Xu(R"R[O>(]`u/`cwZ[ª<|jUԳ-N=~_x<1 @ͼix|e۬R^Is=O2 m_Xv"gE,$o whE'31>vdէqdħ?O,r` S`!,.Dd+JSHSxgRTk gmXCSBS" "8;="E )hQ%(A6WuҐ2A]5FVq{v ` m `"F P0]!QFP8 oef,  7r3X b[ۧ^<y,Nkɪ92ݵXKX0orel vQ1ow=?%2 Vq~Yq&:>!ҫJ6$#pmW aw5Q|D3El81818NiJ8MMc(w6%ijXC)E$NbP(#!SPA AKjEtu 4TR7-R1gTgcqq,'t7xN)jω gz]s`B0Uw;wV#@kxDheLavaQ5k6qtZ(JN <+㣛GlE[7r鉥V<#|xɦw ZQ9èρۨcV}YKB8ȳVHDgw&>=sK_nt|v}*5K>v$>qPE>'MXRkh{|qGCgw] nFGD~uW Axne?GN( aHB{0ht(!pCu ARm!)B0B 5 Ff]*0ƅp5c`NV% A0$10ʲvD cZ[I1P}0rE.4LDގM[[&{vM(J"o<|`mJ)X1Q~o-9ZOyikF=1زilG/rɿCLtVX0J .] Q‹%i4ljc;%ICj ɱwJ@TZh,xcW\|L{BRV)A3B)5wXW} ;ؒ |^sӋ.`P +_1]>vDe~#+5=;8z{K/-NJ7OWRk^=K'26g{f4}Uvi,f߸hg$-OeLvl): ’=!^zV\Xc;rH|6!$\֛+^+PB)l,ά.Tmp Q0% <Y\'&nr0lpW⫅<Bug,X m;|@ bYuW\iC;4Qxx1XWQ񜫗]X8 U6'%3PÁB!} `)M */7@ /^g [s*gW!;&JOt <;2I]0fEqs p.GL8P-8%$AJe }"iu]i Y s$1T*Υ#\ 4A/z$!6QJ'2.N3BhB"DӤh:-ud}x?¹&rPxk/S(]s繹K ɟ6css-JRB&G+K #5DR`|aiQucS6`PFaۄN)ePJ/ 1ۥ2wF }X.B$A7XM?{M XRVakDi :)$^:I *@->{e֐{Ξa ;|Hf>،kwez,^JTvr桱t]tjx^w&$k{7? ӣaX ,fÍU >G?*~]_;|Hr7a }$U%.f5!^/cJP$) Qָ3 U-P ʖ-3Qh>2!iajirK\ pxsez#пH Vm O?Ǿwo^\{8ݬUAdCkUvNy9l,lkL[2ie`=+^%HcUVs$/`o%v##4y\Xua]YKL5Ys7V8@1 ( h-a Aj-+5ʍN uA|ףJJ MRFh*M)#PM у(dK%L^q=قM}7Q#<F !/ jܪieT I^JN/̩V牭cۊAGn/U\Ysʎfomrv*%,5^Zn5ܠid|~05!RP,Nk@a+ M JE%ԟju{Jp'T@y )K#V6Zp,.$mk8}va[6`_],km I.3>v$G/%f$X3 kxl{@q K3}ײ5eO?Ov? ηԏ)kypMCċ`%;^)e P>&P $R/KrtP|D'(-ڨK~dk";k΃=y]ί܃ g5ͯo <71\?y.u夔 1[rhҭ]Ӎ1`ΘID(aMyʲ5 C8SJ(UTMXK]p[6aR%aHDo068NSLlM@@痖hLJ^PNGk!!gL2e~6Ӝ}'8…KRB$2|2'}XĖ,7i~jV gilXC6\7yK|λa׈3 ê'HV{rdηXcG4G"T*fFkʘY]+)4 MFx<+5[1d ,M σgw\tؑ޿{wUN,[/dǺ,Vd2+d)12a ~v<.X$@'f9ϋF Yl AZAPHW塚Ar'֦e Vt,'4N_{\+` <VXv aRiqeηh(m hw_ hA&8'ZkB@@ wa0Q0Ρ&(BAo0@_I}dg:̵$FFA L :FۡphFq:^,pXLv~$"'= *9%&gǎt3"Cl+k%X± w5^/}7sh$ !$0OxxཷcG~dѿmM0@a7 )*l l%X/.獆(.8zY0pFh  _bp!(ҕ*=t]`qZ X@gYuo$8îg .YW~XEK#lO'lBZyj8@׀kq W÷agΚ"Ofȃߖ#';>qo8!q,~Hj4@kܽZF1,U{4ta!%a L?}vF6j/W'M?D`vD/L>Oyٱw;sWQ=*Ou&5t~s^A ԃxk%Xc:Wt] NSFё]{i]U ֈh{/$gz}1"N![ M,K؞ݕ5ܝ{ ;ZQsnA:ګmfzw^ta}cw6 ~kFA{)I~(!ܹ+^X%;Y4;t cQF8砌?R!8u)\BjJ BHVY8qQ q!gbkP5twOl]^ Ţ6-Z,rPp<]0CWx^wΛbq4~?-8z\㭰̼bx ;Cgq^ ]XE* %c˺Ξl%Mh4?u6z KE 3MHb4^A񫑙T\G+Gs*֚TB)k]Yl}?%:k*^IyRJC#c"0o;7c1.VЬ S;"U|'a _c @tB;nVrns j l(<7 J7,*MAJ((n`Sׯ@T@B^Y+0 m#< 0nI*͎!ؚ:YX֐LuRH JمBvGw8 j^Nmn Ӌs0q?kZwR {nN_ lf7%Թ5=ftG˵Fk4BARIidR(O9/mRX&80p2R2!.D}N85X5ǃu)}U,AqaWA}uWƚ៿K%G9܋{.=g&43}oGbuua "wxZ%ǧó1lsr{Sw81ve#;6lؙyuMaӽٸmX< zwN=^ƨ|6NVe;^J>vdeqrj7C`I,,)/Xcts SRz}5,0ȕCĻ[`5oeWv|{f͛yv[J%Vч%qzDvoTl(<7G1wS8Y@$ (|Jٮp$ ˑR@0D F 1bo 40PE*Ŗa8N¾k5yL$r~yɄѰ,SefA^L%NNàR,ojѳ6줚{Fsn`p-Skx0TZ0`fBB 1PFH)#FYR:k=YG,0y>v8z|g>kjF}GN6MO-.\7Շ%"yw IDAT;|[`OU7olB8#{{ttH/rwU :OaMXaDmaӰqHW\Z>]Ev`ղǭ\Kz޽V9pUNnbi&ʟ&+zYUgz.u]dVOep{4?'LtcJac> W. <7G8!͔h$MkT)4w8V[Qj{bqoNsJ?ZkPJ0N*ie$@ցF)hh`Ռ IDQ A8q(܂i@4u,u& A&IF3q .b$BX0}׿,s7` Ul R'rT4PB(3$"+.x ńZ:Ҿ8j9j8M].ɚ``rBV@Sћwݏ>v^ZxᾳCedz @?S5–ʗkEt XyPf9fؔy8*lV>fNrNSi~UW߭ȈHz`뉌#Jhcp&-W^c-*._V d͓&;[2߼jVk!4l,nX2{'ngyU-Λ5KCE2 XX.-?ujQJ׼( :ss͵ s 6pmu1֚hbאjHR;Z cLUt}I@x:|k:)e\s AԟDRfghXԺ틭.XU, c !@IM2$IctF4cnL2׾pY2`*%rV ֍&tVO>ՉOo- #=XJby];Y~x{V~2Yz< %2O`Uej }/l˗`l?v`K~@\Vpvuk{-@Y='8_'5 GG`[Q/ܺ`v]β1;’\ ;'ze*:yq!XUm-;>/Î "VmgeXRXu.%/h_;$g f;ٟ5(<^(R%]ơ=FWE\!Jkc)004, Ɉ5GgGׅ#\S}BmX \EXf=^h5$L#( .:E'8L4IVdCjvp:([v/RI C{]ZI ӼȪ2pB!WL҂'$!)$I%th*dƙ9nO% q>{ױ#4?|h+dK@VcNSN5m}b;PwU,PR<󹿪Զ䭄v! V (h vX"l4 o@hbXEl&:gw\va{~ 6]u|~v_Uu=ѹ>k)w%@щSXe.=dpWZF\qfرq1*Q|<{/a݇}!ؖl!'M9DvQXVXrma5l,f1K2 K*? K6ع<3dpDЇVvTiWolVl-~vrLkDYG,ST:\ B7Wq#ah+5+;}}i 9w I 2V^oХ2cF ^Y@piB+z92=75X"Wk&`]gaX24F'/f]2~ݓayf$A%aQ6e3br7kkX,l^j}ʘ˛mn{STVV `x@wN=cG' 6p/lpp.h6UZe,f }-tgf uw}[:Rsyj+,Iga~/*\㨜}6X,4ъrU`2z͎}ᤉ|L!; foln{'#󳟄aaYV,*( B( +Bu+&cu]BPSJ2Մ1F)(\N3FApΡ I %Ln3qE?C;˝a9vCNj072iR .PF#XR>(jp ƢM@+@xR#vmZ $C[ܥDy02/l{9jfMqq;y|я=ITVdGNv^FmZ =!{[[~թ!-;ٿv\?O]:o lgzͤ&f@@Ae fqEF[^їqXEUYQ5:'*-@zBIgΜ~q?LB( If2|?L;~NuTzda;#ܝMlkO *fO2)oy@hRzl[{{| ʠQq| w"^A^8 \k\2pfZEmGxW0#" !GˈHIl|&4E $зQBcpakXGyqٶ ?6%I4l;z|нي"k)R4]x-c۬^oz{,F%^F/_flVН;2m ˲dYz3CMdo畈xYrrةYjcmA1{!q8@d֤AlCEo_ӄWr%ɖ= Bi^Gx7ge^ &lz7QF`_B1dzM(8@!Mlj(e~z)p94yK"'lMʀ7Gw9O7M{ݣ1ćL "0 3"" Ϊ&!6mdb+GIŇCa9Iߒӭ^E+ɟ|@Ʋ,Ye0wE'ğpߑ$9^ұw":9c!B\3vv&o?0ضN= k?g?Fjs{P!M ht 7tE |XsLe=suݾ2|rn#n?ԛ% 'TB`S<Wyފt2]R5B/r"E54` 9x/!NT]INym.9[4i˕z)=tG>Z3aW8P҉hB(\H"68⃧2K!*;dĦ#OӮ_Yn֬= !oXXM$]˒b*C\ڟf8}xa<'HBl@VQ{ ,~Zɖ2zujk͈i洿ڳOQ#E&_z kS}Uba_##UR8j'Df;^͈hk!ܔbZ9Nm W F7ֲQS}`Z4=lCD4@D7u#⃨'2m#ӴaMӨ Tȱm4N%$VɖbD;+*2}I\=ؔӔEF/ZXhSCjִp*H8CfIknܱzV=숝I+Res{ʨ0OQ<d"-؃./#G=i!^BךmΗJse`iq-Ѽ8p9cӼ z0/Y&:OhP H性E)ʋ9焷%N8K$ߓavaǕ:LF$N q*/_=h\3\jWze,vs{C9(ģES)ZRJmdɶDyLDhZWrm/K\ݤgJ0TUͩeVܖ}yLfrq^a`lpsӆq'"dt'hk"fqb啚%W^k,"HnM͒[&Bt$Ty[ɋ;.3LًH:rDR-Qrk;}6BdC\6h,qq+p֣}jlL|0-"z0ꕌ<$/!T_Gd(Ӌ 9;sGKzS:.nӛ&8;24 KSoҼ/A|WLNJ?8z !-ZnSAs75il "<$-Fv͔Ya_x*Uɪf|,IJv<zwDnTvM͒y˖0d^S$0b|ٝTW6.޶oX2qu_lPfQORnwlY W9{4ԳzZoY%{V,3˿gG23߇iPC R/{1pRO"1S6,qD(LyN&97wJlhJe5x!]^l~4]0VBA%{(yy3=D K;gUCCDGm˹L#ܚ8oΡnSUWNLʼXBSeN,pEMMIx_{\+>:b#Et6Phb2*C?!z|8}KyVS9aiXM%{7/MkR2{M jaGH _ /ߓ 3fv|ْx+D*%шGv'8!S5*$rπ:3†bOrrT (-^ yS8,|%82\_N8{m6%~`Z(g +[϶N\wq g&'ܟL,mx<ؠM]Wug++X3qU+eO:MRDl7HFTwFUDZ'N1 c9YB(]/(YrۢboڑGVNeW̦8}DȋH?)R%li)pDāUFu+vn39%x#/!DHOkg|?GLd)D<[z0+,4 hޞ[+~̓jV\3VV pyehe[v:)e#R9#/ԭZ)#yV<\lIy0{zD1XXȻo1H]c%s]۝"aW IDATR[}ϟr~'XS$zڡ"4ݍRagmJ`RҤ8] < |DCm2'`B@=^Ӝ Пҵ/I([{(@^bLTf9A`0[N~˨@ks١VA_kjtKEGxoO>dK]\\NW!ҸpVq)]H*I ◔毼|Kuz|qwQqDA2 <LFZ5Sn~ʩI*K>`2HK]ю3tRXX!3/PV2gnfosy&c{ٙw}oCY[>a@\LÔHN ]\NW!&(&6ާ.7)?Dq=Lx{-iV`I5I13ki*R3(t&ȿe,J'&D&/$N*L#^KMI Bc[ɝ^h?yRv~ljTO{{U>.2ImdY:p∲T !|dʼnYN7b_ WohtjmێH ȊWmewgydGlH "̛͙Dm[i;f=u_4 WHQ!lPsx^J:BPi@ȶWCSe*:R>{Ax2 A)Al+v3֞t6;a+|uH xxh^V,|x Drl}wٿ:8a[}C5[f}ЦڡM;WZr. ɘ躖1 I7M3jd&uŢ7?LxW39?6?]vhݪ*BT8pd낝 >_nX/,=;_DinLG\ts  mMRA+_kJRTW^,R݈ӻhXSкGX @:U&z}i. ٹzfVbȾ`Atā?k7`Z{ߥji87TEw;oo[}X}(O<7XxLAqKw|QnNL,WmJoD聓%^k\\\NWUt|ek8sq^j:O" i)>>HL@̣jCxO]!+N/أ5OH@~ H<@D#">&NAV4Mj5f;ܦ4FB< ͎*HF,oMfD~;-8n[G_EXE\b{QjKi= #;E!: wA*8.nMウHP6ɆkSz(% ܀=#Xh_(,KDP DdlR-uYsMtxWopp>8-J-+xbms<瑔 %q#ʮQ poDZ~/[@M͒DDfL܏"aDim>D{ǚے諩Y"ݱչWސH"$=i2 !Alcr.ʓBzVIF7K+sqosYd)axҥW7N{<7]{$+tZVb5@>3ۻwݝx$n(G 5ݥ~@=(F#VOLj<xe{gRzB?I+B8]\CSIUQӶ&}t@Su rԗ3j/GqPf6ć=)UYd7! 0Z*=ZL==aM l .5cϔLEK!LJcL?1Eor@8.Rt)Ye-OVQ>4yr*1l2>.0enTfծ99J`bpz䨀=ှh){uO>qJ7łnb[?P>{7_ٻog˺_ny_{ C8٪J.a] {+\\pϘvA *v%T=눞88EDTDLMW[pwּm2}yUFurqPe@hVG~ޥ3{P]Q؝2Z콇Z1?$ S2uŖKm٘dCzw"*c#pRj,}\o^*ma 1ރI+xWI/m_~ǡiH锭\is齩Kkoѓ)]ZIiF\\k2:mɊgRAӊ6 ED";,U+CuV[SMBA.8_&=M]m>NYvf3 =%I d0fd:uyn^t=d`ܜsڧhKkB$4MF1tΖY䬸k miJiקKj\0=+B4XSg'pBI{ șuѡwu˨Ǎ5U4us[蔢$}Bd,@T# [RAt$>>ƶD@mOf~-eVߌ)]xӾy/^j5K6#zxq<<}O]3veM+iϕ _g9ͰTKoXSOuzC+v+ <<喋+}c޿]Y#Zx*ȕ;[>s<2ɢ߫핊JR<W| _ۛ{D/K9oYU+d@`L ME;LB6ef7axܔe‡20~4l;}P:Qj.4өʨ@ ۄ6?@eQnEV Ĵm˼y|$$do$?z*EvZ_Jjcep,:pD*xQq6*y=AwSfݲ[2\l "fԲ#vuƀmf ݒ|鐾-7%fEa<.QR yq D'FYsbA $7<;"Ҡ.x&})l/sZT~av;*r[#UmRaV1Ȏ I1L i$.!z#KGlE畕y@oݪ/.[By*xAi''%b~E!T0?Ψ_UċcF={vCTgCzc2̓3kvf8=O ?|^hMOh-3Rb4g,cuβy7GeViMdDϿTQ2w@4IM~Я0mv޳ؓW坥[|8Rͨݖ)4qݖ kWib\:݈>UF,Mv䍞X} yME'u+B^MͼJ=oFDA yG'SZx7 -V(؞DYi*8ZX+KyñŠOSڝ%ޅK&%glR(.$hl VAc´ IW'RÖi"gd6&:VV*ڮ2N 6(xK|x&#Fi\LCB!R7=r! P}[dHoW[qQY?f~]! yYzh@ژ3Qc}+$!-Zi%`~rcgߗSu%% }C[Gu-=s~vz\\UܔÈ3Gc[ݏDt~f~YV&]\պU+҈ i ۲3_CJHHkyig%8}e F%8YQBV<!C f#MxHw09bhzV+)Dck>vzt8GՃ9=]ûJ!ZmXxhL`¾^|HZh_xG;7k ZtChҥ-8@x'-x'-%& K$OcbC?y/|QO[,*p>"j Qje1Ցxֵ);DX;! x.vTktZʨ_HEx_6"RdwT G:cAJ"O=7~7*0:H1yIH}kܺ/\3dN>{l,K߄}01Xs#R }@vX)TX nǽ%uJHaOg(";Ne m̀G[6& !!7kTjysNLk Ƴ 0^B~/{Mk-#[rKwC=ݿ}m͝n8or D@Τ Xj09S4͜X쒞XtB(% 20h2>%>AH ޠOnPO^Z/7Ӱ $oM+5)z b0ƪLA-@S5,T. И`[.DuqBހ hd^(U8 3n-˖LJ ;^tSClwk{F5}[!)vp-rA4{콍qO7Rh?Hd@R%x)DMxD`pm$ÿM-<唈}z` 0Wg ڍeihdO;"vrn6:qRT upCq.GݪGպU++e_//!eb%)yBx7KU5nb8ܻb[smo:u_w@ъ~}"\ vK5m B!fD%"zKi}Ni o$e{t9׸ ?x|Aip|z d;[k6͍Wlf {fUwQWXiө@ -؁~%b a{c&˔e6圸Eg{;C|ǍNf4K{-.>#{-Pn C:$1'Y\.o#Mc`&1.[[FS_zavՁ {&D p#!Wr.k,"UF27]q,%_ ZQ!ҏ'-x6eғ)cE Q}Y~UH>xLNլ^  'j/ Q?Ё^Mll41UFC#_5 d HBR XL\,L q07!b7*^˹[>v b@x'b9?ߗow,g]~ Sj "^1Dɸ0;ϝz [CrFxz(M=~:ސ( Fzdļ(sf/#QnvD(S?ƌCC׏;`'iTЇMcA"f[iC/I"% >Z#mޖ. + ^uinyڽwr }ֻRXrE{6ZQN7F%wbA–U6iؓ݃jC5TܲN3 kFhS`fiލA-\HZMݏ~6fQ;\|㤰*T89olkCɶ~@9@ נ!QZ/ ζʨ>6M\؛1#ޗ:Ѩn+o+1ۧzC sE@ٚ nԩ]|A!)]"zs745:X\cz|\^h3"=̯ ! Ƹ" $$TԗC+%[p g¸\XҀ$4K9MABEA|ӀssՄh}22(;jww߰'o2*pSn51fDw\gDm'KZGB)ۏ_@iA*ñ&ɫygz4͊"C=y}wPq8٢IY×M[a>yVaa ɰ,ےeI̔"6`@‡7g4a`ɮ+ͩpݩ}',W5\36^Bl -Eă0 \ >B/[F@۳bo-x~ I' ^ύHz/5-k\y10kdr<_H}/)K7@~E =I_홮 EƋf߀P@6 aRszM]RPi{6G*;}n2zp(ngiUF ƥVHZcu e`&bxbAD !v <=eIOK˅@d^4Oxt{_ "vV3 lIj b埙#D%⑉%x4vDO'aS,xs ++N kNeDەp9UZ*Dڪ'mv]sN8!qQ b!'4 RR1.~8ɟlɋM1d>8>gF Mo'vּWY?ۼ4|{+Kq\ӴXRM_ Yʠ0(`!`*ݪTܨ$磑EKр+üQ≠Tpt^h)/!NpfVK&^Dߙo4џh k5o}!E,SW~U==C72{y+&U))+푽XdpTx8:D||H3r2CW{<{o%\D{omYȫ9& !B2Foekm;0X#1. \鷋8^xGɏoz+#O= W%#mē(0>4r۲>¢lXù BvGӍ+xF7?E<xbX7j5:gUFLzYM6b 4V 9*rTGa2^uwS_Cyk@#*;I t#Υ:nZ,o\DXܫ;t3m KK'KN0_K{^pČ^ӈ݉N|a+vu\rp#<{oN0/l}D* =C!'z Ր%(DӠ'EQ#]DG'Òηd=KDzs4r4زZf߃{wB-36qPmKv <7Q7#&^|5ed!,D`$#:MQN㺟??2Htp!RUeX\P(^ g}͛[ b~P+DxN]9x!&O\,e?'qCY] ý<{oѴ֨ʨ[eTʨZxBFx\w"*gbCq@~W}wIHGT݅bU^-qCi xٹL}h+;R$p\FBDBh(G:;UFuA8MO6yԋ;~`y̛$$gˀOrIRd"H'>Dg o/ѪDOqCG)Zd 6[EKz\"e{-"6= e"#93@TglT Ĭ2AZ|ѬDx1n%XWDz@FNN3@m oGK{/w|a鄒yVHl,ZJ5t,ZJlX"..,nJk4\OsXfDD%$*S_:p?fʗF@Q"dE)ibl;>1\zG-EYNԨ1W 3O&H[-ReRt6h2qb?μ! uDQ[9BZ@QmU7C?2J <9q q"|8Nju1'R}$-T%iXK. qy =\E";w:˨f,CFlr;c*E}|YX~c2b&6t cv)"BPX*K7e&:lrto@h*(Sf-FF@~| }~+ΓAs9q=<@zu>՗=ň4 ؊eсD#8K(8F\,fj֩L umoмJ>4 έmtukmY>_ gz:}} 㢥g>}B̊)W$g-%a[:_8$v..,n~0h74utLFb)"pd1ޖd4ADq/O+:臽7ɐٕᅖ[**K @z۬\,y'W~zShO=uy_JrVh/F$iHR1Y~ ^AsUroN/)i9&zk]F2n\e~1=%СSO ?ze\,=Nu+]F*ңVWRUĤI 68brZz;ls V.Lw ۱9K,`ꢥ4oXCRzݶ73BogQ$X͈ׅ=0VȲ"BxR ZPA !L-e5n<... $Nqhw +Sђ63 =vd4Sp|=9 @o{{EoᲴ*nB2,(,lc{ʲe9{2cYXy=9s=L~חL3( gnP* <%]_#V#Tʑԝ3aʱ#(BxmנxA2n@ x-*8_Cmi妞Ԑ0."&x ؈=Զ8 2S!?j"_F#GeUN {~@\fQ QN`UrWFbuyJ'[eW W6Wd5ǶbMkZZ9⠂㌻?EŪXeծJ oOԍL +++ '}sgҡ=55!ʧ>)dوDc# c]A-V=s1eQ|4⣗Ceix @S:u dOFbM'8gV>YdU[Qļr4AZߺtzMeUká}y^x*wVjv*nj ~e+׮]j %[߭9ش27\јlǣj <6aj dnI3Ͳ3hn! D43x圽l;Q,  pMekڿ_￳tdeݹi$^~ގB4[#gZwfwU -M$gTLzYUe>ﮪѓO}htW>yurz"3 f>1)έ٥`zvTuu~xjb]7(e9PIC"[z'h5(ŌU$.߄ch NV_1<9@K9h2 | Z+DwYJˋL"1qR)" qV4PN}WTN}2*xLʊ]OTU^}[V${p曟.\=U {0x_ [n&[ZO^~'Zy8d,ӼBOY$pPd)|xz JyV 0nAg+bGE"ѽg=}nsBהꇦוw__Q_cwtj/Ĩ}ļJk6J2\MA;'=%W}>1As^*0?bV!q\6R3H.B_^ƋQԜi24Pc ;]sD+|59;C3'^DG9kIGiA,!-ҷgO}5SLF'-7!(HDj8v>bg9iidO7vS;=S=kMݪ_S =[e(8#Y~-hΦ5֌8bkﵴE2 k~yO Nk]Yd!ɠ xD8rIH!Ҁ -?û18%nxH@ oހd\^\t(5ͯH6>3>zb*s銕 \ivBp9}&5'>g[w a\4Lu~wMar*S?}rE!D֎?f$YS=0Ja424 p6v .Ԡ1DVkŠNs_'&s?W"׌2Ⱥ}0r J(%U(hyFnG tλ;E+Ch9W϶ieC1;->T: Ϡ c0c kxM̩Oda4 v/We`]H ۑ%?rQq<|:r꫑5X2ᧁT?3{gW"RO"15Q?PnZ8~+h4l IDATEO7~K+{_AAuOK?${_kiy$t<ΤF35aB9qᒂ:2~y Vb!1J0%+ok18|Yxmfe0^ӵk!H8y(o Z&90eفUJs}71AkC~+ #rQFsB\u5==<,5nsjYd{;{H0= UNW^hP'>uUiB c)E¸<R[%cW?-ݿ;]:G"#P7_ϕJ>,=;Ʒ:TKh=H$#1J!ui$|6ߏ"K !>~C ,5+ye~FWO.;+yA@-}pUK+Z`t3ͷ/ k=oA ;Hkz! Sڭ^"\ZF1ڽQ`}n Cye4@W?M4^]0Ő Rř"f~Ds1%@ ? N. z;|q8Ry@:'gT27 wB:[h|7!wdu 4ڽcnZ4Wz"4{?,qCO7)S-~x>4cZa VYaqI0 O7DU}KEWP nxZX[| Ɔe9'Tir3v=kIv 7o<'֋$˙"1/֭cf$@&gaɠPq&yTFb^@2O  e#}߽h=r ,r 3$[~=nS{Q_#7nPr.\0gP@a,;&x!j/M}CpJ3F -ˑaxӗ@PIGfȴȖqgUb2dw-)Wļ dϒ rɄ?2َmC@5hawmFO7E!8=L ?pnA= e c|kBy*o{cU]vЗ:TwhXs{,8>*]mha* CχH̫Ff\#1K&|? +K/9s#i$~w^4wd2ᏻ;BBxLy>`fQt#ȚەɄ_4W-.y}W!םޞkt#+U0R$}Cx'ѝ$]\נM [j|2Q*9:tu N!|-"B4%@4^I4p1\Ʉ?L)yW:lļp2D"$?k 53\;")3Lȥ5!PYp>Dv=O/@Wi1oie$n[p|{.hh=~iec z+>r0(ˑa6k@ZwAw7ư!3DtUg"s_Iՙ>T6S-CL*M,e,ݚ'Fbpb(dB1mNsxꀀb-FYil]hқj99+|=N#j #27!J⎳9Vz7A1^UOfb#HD* XdnIw=5DE-Q {F){&h"]{;nCH4"7U;戛דndEBbʍ%&4\"䍏{=뾍[=RIK=ݖn&x._K-vЩ@[d+sź<C].$/םu qF 1ƻe3d Z,A6?qy92/䦉UL 25DJ*#rYcNhtWDb~M")R[{#1E~悞Y+jt kcZsD2R7ϣ-GJ`:_>6_UQ_\];^6 ˔N };{nޝUcJr ^%djvWB REcA1OR_s4ch Ym"2AchO[jM>vԦ?GVO2OFb^ YFXN |WR6w#[­DB&D3V[`GB7gAx10J-]0W">MB 0c\J<}\|2hvKZ3]s+B FsCvLC`3DNWv?#oXD.px|n 9j0;/gN.|*Ai$M"քW佭 ~4_k w-Ǻ:҉ PS>$F;d$~3RhܣwVCDk8D$MkKoPԹ7xZx+e8r]Wrcn@s}7z9ilSeq-R8T|K?ɏqVzta<Ƃ"kB{h]W+/:#Q[ăWm^3|e';1TPKA\'|F6($2f}-Ċ+u[Dۑ&!J [f!C(|?> rߝ,8QS?ʾMȢA(3,p;?rRdϸǿz fL QW YDT1ڽ,^$ȢFڽS'֫ } m yrd-9 Fq#kТ<698춿|=W WX-@31z;G"%"+"1+<8 :Id kJd9Y({ f{{E;l&6氜3-M^tˊʇs|kϓA+ZZ9r-=-i./c)(FnFf -v@f 3CZg7*<1H`D A3!$zw\bLnCMUDbI =#(vH&d?]\ t=eȹhN!! `qnFby=ލ.Q;4D Y(t/n&9Uj@YguFf ]z9wWP߷?{QN0faǸbE Z;r̞R?. JDb*x"ZQd Dqގ.ƘL}Ni@֝f`u\Bɵ$W0 <\Y5hWې( wm4fSy35ɹ eh+%S̐[ i$|UHBVUHz Zd~ |(H&yO.* Ap °]n8JͿ  ]ux[>Y A#in,%&x%ewaar!}rh RK cYjdhDjdޟAb&(Ʒ -hfP?1J1;>Cc ̴!W$=Y WnABUd{=ߌ=[_2tK+Br7ϳ[PhvB|Ț1.&x,ދ h  5PW_J_*ߌ~b"ӈMf9:O*ߣX3َ̱-hzFS躈wmHW;qDQɹ9[Xы1RіVހ,g+Cn9f^FbX*ɥ7dM-\EǵȽ3FU  bL݈\/C, jC. "sl 4yىdļøH QBVj^NCr]"z,caBVZ9X?64Ʋ'PColL=]A|J xei(*䞺ŘVPq_dI!Aݠ$O&+o)CV:$GB3]`PoS =~ $|G(p#6qvZr/=<粶b;D0 dtuw{?ABPلЂV@=r Pwn;4 $ZWȚHBY-EKg>41h7"w|Y^\V`YG.g}!wi Tw&4}$ZiyQzI„1륅ƲbcI/@isQI.8)VJ?R:u\{~ގo +':2ʑ5.uOF"g_0ZHlvۃ\Vi1Rq88LKNW?qk;JxL8sBzؽ%$8r .z;mTs ,Y2 Mf 끷idC.PZٮɄ\9u"&xKt!)?'cڽҺNUYZdȷpEoG\Rfv% 0r}@q]a$pv !4\~kkY~nDN(Fxxm2qma,;$8YҤڷ+ҤYqia'ۺ /Gv|ryW +׵UdAB40G Y)(r9{Y-(8zyv,Ƽ1.)]TK!1h=I {ڽFW԰+x-`ՃY9kvi +l#V$\p?A?d *WG4JFH\r?)tY6Db%fQt`cO7.|?q'Qg8"Pڽs<,N[絵{m^={ g:Fh1>0K܁-; ,.J2?x FP] e~ve( ST"Z"Auu87 KK7Nt[ׂU\xpz&x9 4+aEqt鶟xk8H#BVnIz;[.xPl h}rҳɄ?yk$b5q-@wPoDq3DIy2 cf1N?bX[hYP>PlFk9@[WtѺAl^ {Um޺E-2p(kh\jZ9idf`[U/=|Kklrts >ߎbv |5b}3hqsgky(-[7oހ.K&L2(VJmYԮr۫P  5vc(JՎ` rP:cWQ3f3 4_nnļ~nC0غI&j6Eb^J]/CYE" ;K20 si@'ndg ITD=N竐x'ڽ>r\gR+L[`]fG Pp=O\ՈҚwNc}H hFV4CpCcvI+Q:P|Sx6mQCbs?@$U"Fbq`Xv:}ۍ2j:-ok8yhQ{vol]) -<>[$ (f(Jq+i  œ-vF%y$w  jYQb 1$P0J siC[mzKW%v7(~GIBي,@kلAPqƌ! T9kS>vlX#|z]Ɓ/!Kͣh;V+g0JfA8pU`Zqa0!kMl}6odŸfFa0Wح/Hbˆqb0ʼn_@uwUdE0ٗmayCm[L 0NX9O6ι/0 0S cq>m΀ǁkh4 8Y<_)Faa La.2 0:ըQa~\ nOC=s{Xaa zU68BG X \wiXa"  XDkn~Ԁ>u/ja) ,BQmޅ6;FO͗KNj 0 f,.,>_tsP>T_@`n9 k5 05Ǟ }T?vMqギ0 c`r#²4! C$s"Ӱ^DDADnKz 00BuBTj%hDѡu8v_q."6id_M/<1a5 H>hjWPe`j?R6޳Isa9x6es^Fq>0<"ԁQm_xmmHڼz-ƓG6mQmeK0n9s[M3 Dq~P510E /?piXqSmw5:hsK;ІWQrCw~9 0Sx@ho=7fhÛ6ђ#9^9Xqv#6{_A|A^jRtQmE!T7foL/\DD`=j:{ eCF}ҩh?3H&*4gM=>*>Mb/f 0N=6,8Ū6Ѹ ~9~j M4pp>H]7΁J8N7C"P \/'^o5I8%XG# tAk9v\ۉ5V.FO)#ØESNd({Gh*Vf]|z99۰9camDkP=vρ6 ksyxĴ81isڣ⨡1EBmQCVܾ.!9Ҥ&>Qdh}8mpDBB[g܋cO2VqډmǜM jqR(f<~b0>-gaenq5Ek `M_Cyeh!4gM jLq2~!AxuVn~ "XLcwPVfci [PPASfv0 Cq3:I JYS6 z&@d%:? í0 sۜ!Z驓h $}mv 7ŴPJo~*k>_}1O9i>4,GτNcD?hdZBMqʱ4ԅ6cݨ o2,',Ȋ Q.>7T5腏+~/Zr9f@ 02Tq6#?B'ӃxmXq!6lڼ1 ?AR!.#%\B?>+0F !i6r$%9qJ0cq(jK P:v|j1tNǁO.E,:/6aX63 8PikA:\Fyt֔=?g;_gO>C, '%@/I~D$ m1TJD]x%ZOa4f,. 9M^hsыC\.Ar_\Oc-8 DOiS(yt 9OE 0+'vToFB.͗:ym jnȦwBBM7 VCtD/zhjGꨆ n/kiiq1cq(:Ep:uQQځ^/G/ȧz=Gi|)awH "6?Z{6љ'NAD֋)|0:66o/~h8P㰎$IFE8ϐMCٴR@pLAv^Tg)HiKAٴyvsExʘp͜BgLnF@M3(~Vb4 S:k{k7"\D60c fxA46±ǣaoksM?mks5 V+{8\ vc!6M7?*z(yj;xxPʠ U$1ݭ#xy+ԇLh!vZ=m1sMW~0N[8=~;|j]HKgbn> 4*d@nݜ+*_,=)z\ƨ}\tM,К 0N%ߟs?@3r Aq*f z~ z.<+׀.ng"sEri ɬr$ Cf(e^?A9Ab1A 1e!r^y N<zi1cq gEeD %I%E wi)JPp-X{8H/,x6yi$hVhJgleaOD3αb2::s羃=$sq>= ƙD6mތMqs,^r Z4H>堒A#9>d%TL*pE.O .D2N*~BˌixLҵ:0@svѴ--nA!C~(CO@ц8q&(Fb 7_ btz4"e`z 3.0'167ц5KQm^vGXA6O )jw8#136_T$ā'j'FD@Q!.|z9 %\PT$TZi3mSyd]T6`}ribs,.,q=hz:DYhÅ褽t^6i7|xA8m͑oG^Ǚ7nw 7Ed-ESb 0+;26P$w6?[6_w,:_z'Ӄl,[ ٵfDZ#hY#A G%qF0 ͣYCNnT:wmH% ܽsu4 Dy:Mnʹ0cqaS-o.hhmD/YN,uè7)!Pbg)"RC̫&6D=W8GSvXz~y9gÁ X7sz \+A;9D b =W@x[Lfd. \-6Ǧ\0w*KG'Yr>}͜j(@k!|ȢCH#9,9zr*c]ҟno}ycriq҈P_lU^'hm_EMq2`l|9ca(Kѿ?;i`?&v3Gme[s935 t|p5xmngҩg{t{B0Ȧ>rhs;fvt0)y^g_x91upWOgǪKzvSow;8\թ`O,iM]utkxcB^FG/̧Px#*B|̵F;>tD¨=e Ao3>PZ:D^-"a-.q:N!{'h ):jR:m5<"Rv4"dtւ#ӻ2l-<\ќ&ܛk))[#!"H=]B$qS=+Guu]+k+!`0ŽcvxȦ޻^%sϏcc~^t<ڜFZ_2VAmg 4g]g L䐵G19=xL1x+gx)~x5ZerNELuoّ hb?A9=qU7ǽj+ B-=rJT,^Lh4r4Fԛ_G#4O/^<:!MED.XH- h'^μz<Ӵ-2ԀD٬@W:OД6%" a's861p5m=g p/!|6@"?!I If3 ղHأ+^L2Q&G?/hS) ,8/jG}Ѭg)zm86sO޸~7@/yO,L; nA3 |e4y1c4PҬEV6kC/~a3q5>yBqMCSRT" ~4zUOќg 5 esߠ9 9Tna1ߞP?0jRԡWF/TEg>q[\E߹͌,ܞ'l,Uk64]^ajGF OpW78LS+ܫ%KP-CJa hb\+#$''qg:SBA⑹&H QxMV~tJ RTPm(sk5/o~c&n|AtHkyiqzP$).} *L*_-nbv'nHrcVv) r/>zܵC闡}! v΍^D8>|VaiGd;/ ͪЎгy}js_ZFz*Ntj+iG#x֊}w?~p Kn_I?e)1JSMR* ?)/9H[7/j@/ O y}iXR@+]j{QmxNāy~u{ GM73齾^QKKqRr}ن59\Σ ,ы>:}ǀB=К=`MN= 7N"x.8&‰dt] =NcnͽY-ߜsh?Vj(>+r"޺quh<7jpz.\Yn/ϿXw4(U=L7TRn L Wѻ~7my+/~JiUVKdLB/Lx>Tryy ^JgU[bmxGQZ4@iYaxc\ &R(gZyM֌i-L=1&oLrPͷߚɦ;(if>zwsCP9>;_*^;Vpm 1 ,xuhA/?F=4*5~n`;j0BDh+׷Iww[[Ћb`Ң]xP,|[G;>I^6 x3[=vq& P=9q wQ2zADlE ]yx9r6w&ơ_y뇿}-v;Hey髶x G<'E+U!"A<@T[7dcwoC[P?t0heiO+!JBuJ5"Ni^ *4_EOF sRy'HY)ItN%q.AI#OI2JhdZ臎X+lSj΅ /HLtܩ$ ł^KCCL3eYҫS J>q$v1f,.)U"0]ߎN 8Chi sEAc#6}EfwO׈Hy{-&^]FssSQktq6i0uu:@'B~j8ڹa ӊ4)˅<K*0,OG\vO&ݗMue<\jcS?u_/__ ÆB=w`zp2iWٽY~2N] qؒZ4xB&%mH@-bXGYe2E〬$Ck4SJI~(UuiK!) @DŽR/iLā5]]ճ2qai֗~@g./`҇+}%׵?sDT3ǮXs6''Oq͝{gE><,28>%S(]8j{p u>l؂F/E{~ر,"x5{rj0)GrIv"mƓ6K?wpG,sZQ ViסUK>VHdeG-Z1CuV߬Jh QsaL9B W7CKA|G Z} ^"4094K^6+{>t1~|p|-[l=o/ C%U9i9xyEBD$D '1w ]W$"/ΓOa4R|hf(F$,6ɿshtڮ-|UhbV49'=0oUmGa48M%Hi5~8ϴfM]qw N=*-3c,FPfM* @5A1^ Hs9=Z1Df1F q 4%ר-_6ۆoHl{_rhՁ} ,Ͻ=A,9g'ty^7Ngz2"J9Xx|`EӠ3ы*Y4 'k1u:s?~57"te?2Xkn0jC9>c4\B:ly,ŌrFWI)CO}ߏ^}qL4=*1ڄ 0YL )A^h>WUȼN؅&F ZS39))O5 (&x9AzU) !ajѕ}4<ާ+[^w@~5nFJ%'yGo/3H,}@?p% x6q8ιF|H|6sǁOL 4e5i'3 ${!4ZΆfSh:=F[9.agW}5q۾!A `@(2pHFwoGLGgXoE7,_Nơ}Bi_[t`}4fHw M{e. T0b/g*jFHWKGBwy>z{3aX&НSwT`2WՍ*z&Z367y :.ML% xJmlSA->7Xݬ߳;sK Aűо/5SE ?/DѮݦaY<ߵt#|' %pR4$xnۂr}4*/}xݿ%uŕr}w/L:YWNf$Mb$j1cLk&J)> v5* ;k){K1B9VEU{G9 y螙c$ lBzرØ`,Mf'B% w|%@IVa'nQvM(-f뽟n;EGyE*ڮ{?I[H踎O3юѩ+;Y 4ڽg&61A.,ΧIuf@v'Wxq)hkFl 0)GDr|YtIXix~+꒾T~ƯKBv<&??<"Ю,*f ~odoWZ]s7gZݱlֳve_L*B7L!44<*.SI V` &-+.^\-qUW0Yxd0]5z{]/vlhlh )<:GWIG 5w{3i$!Bu ^הdf+>IJ'z9L0 ,#G nG)5C#| 9)友o_=;Z:^z N?5-T'uGNǐ96.#41fX+"Oܴ<zq 6)ɪPGJ^e݋ǤHJ{peTabd yO퇻=ab / 8 3%c6&moqpm)f,#.kLg:2jUP_+htۘ5%^nك]haﲫXaq~<゠B  Ck DaгlJ&q u$vƥe+RYv =7 ~޷瞵WR +EPI-EQ9H-Ɩ3l=1vX G#[ OX= LɴǶFdGd-"5^k_r{^!HYy3!spk3?rxh(ũŖJyIq^’.Z2s krdܛz)*:d4NX IDAT.S1A9i-bЌi;U5ܲ;td<1q4Ϛ11^:/Mغ3mc1(5kR8QhrB3mID-{Ƹ}ڣ^OZoA6~ϞK\ۘïmBRƸ<Sqm/.\zQ9pzT o̍z 2 E` /tH.<@4*I r X7MNOi$dj%#2kOhѰ i W#*_h 7+ MbM +"b񢗻2!+C(GսxX=!T8JqfY֟NJ qeO.OlpouK*7keͭLm*Qs[0i:'<'M!͍7m&Tپ! ^v[87{_f+~kjjjj 9`qIB^bG;I6 K(\ ph閂͔" &1i")J+2 .k i 8%,1 lșC7td1a~VaL4&XG/$%sYŘ^U!&`X|hhyU3:⽯r75"pܩb2ykռVN^\HÍ~E% OFhFaF՗QS/.Pu!D$x7 ,Y9w-+jjjjnN 7=4 ZסIM.mh0Dd-=="D;^$-~yvLIhoOKD588n=l&.V×.=RbR2Ag+1#dq_%h0;x;R&IΖE/$s1E&koįw[립\kT_QoSꓲۅOy;pҷ72709?82TmxJD8M5i5/VE*!ڣڽ< \~]SSSSM4qK0l~z(q7iJX-}Kl *ws؅] bPӐ1%;^ayѧAcdG▊ҔHpkbBgZVL)6w9Ҕ4P΄Nv ͕-m蚣ȸsqtH;X`iQ9D{MҦyeiYh/GK<@{)]oCjXs#^c1FFnDE@,QޔgasYRќgZkxuT3;zYSSSSmiȳ+lkJ &)tf]U1'IyunA-5IظuXX5b< -Zrb 8ǥr[Id郅2)KCW:°aS*\N@$)Ah%Ltp擘Ý}^jCTRHcɢQy8g'>&A_?ͻԫyO>3'GTysvyO5>Ա65ًx82$/gSTbyn07ݷ7P;x!$ TΨU{³weKoGY| Wσ_,P8Kp!~/L#@!\szG[~"zS lwi)F3Fauh@PZKQ$ qQ%/rCtVrHK΄5uV{+}^v֎mR=/ Ch/vi+Hm}1K)Rsȓ"-{Rmn(AįAsw~Ա654DN^rTJyTvT4 .RVqUï>=[9!ۙ U*az8Լw<U UN^|'CkBwteXD 6-(o3a,\?r"*""2Q))(LpDWp#4ENU79: )qt PbWq`c\kcŒ3rRptIsQfۃ]tFܡlhH'AP,X/y mS:)RͶƎd mL/gט7iZS_56L|ȟ+t\x/wzw`=>,6X_ioQj[{ًx ѿ8TB~JGT=yTT/OQe??(X'2AxL<Լuxqf, Sxg/G݂ iQV4q:0Uf8kP6@%u$+2Z#~ zPPX<%O8ICIT9;cFE<\cuա ǘG Ed?򸴹dx "4)> )z4&)xuH仝M=t[dew~'׹B ;׌3JwY_>})6kNW1ۏc-HY1ԛ"_kzT+J0U tFT!!THZX{ӯFA04{JnQ͞ ?Rkz55555.(nC4Y`_QAJ| i=^T`:<up}K(ͻp0%o,)CtRU8I#| d%Q1mZCPJ16u JZ̟+^1!si5:8]*u$?4%&,Nlj;\O۽xDO{d%z)^@=1?3?96=j :QQoo{~wYlN wG[@h`a{ܦL[Z,Ꜧ/4|iC%NFigg 7Ka!ħJIJWs^AOښͧgG§rN/g`vGBw:55555>֚ИY@@*nSIѮA.<+"Շ` mu!n"{9ĸ8-i#Vd8Yvi0rPBA)fgf Mhpe@#wD0kYRc C-1WrsV49ӱ{c|ݎ8J3. "aE Lmԙm3Ftעی?(W IO=*;jD'N1QjQѺ Ilne֊I<.#./jDAˇ?_5oMjXs~nԼ:NjHnJ^x*wNe{vTV۟wDg @ئ@vD5nlԼ?9>7,S$H ZctbqiS7!#>:;2haw]A윢mYseD0m^qtNM#91NSF() oY/2"q<3$%ga wYt,OFSbw!ly*)VJ8_뿝y(]ixZN'K|ElPWkϕwq.]I2uۅh;X\¥Q||sQ9`tEh[Z,ܲ!TNu7ƉٍF/:U1ۨ2W|=;zS恗gW7ۯ AW_W]SSSS C^4pAK:෎+Ϊ]y?.'A_Ɲ "ys8@y_#EQFT!›Ydž]p:/0ctQ.v6}*%BY68ɓ)eGqEjuDWJiA1۞β3#hʂ}P1"vTQ\8珲)5<:&/l- 3i?FzZpo~K3=66~oOu&y'6: g]L4: xo=XvuBf9LCnc*̏F4GC%?K%&ow#~Jt*y*JbB&u9jMMM[N϶!)TMA'b&%dw%Bg9ZGK0=nN&c ~kB%}! YE9[VLfKH ID)ptm%Qq=B)23DFHbJsĜ%.@p74@NSDlSОv! 6NFB }/M^l ShSgޤlw/!+⁳$wwf3~'>=8.,np.d+ ;"lWc$dӿ_Mr[Z,ʬP '"͡*MGUzq=ܘkR <*b_wn[zᇁ⢵:yYאQ }<`N8gw%wvz7'. g_ s~G~U#5 -/`%,\E0 1$#I%ihdj2mhIb@_,0m4/b&(I-vlpFHdYhވ^su%O쬟$z|W} snf̻q?MZz?kp3ٵɝǟko|pQg_;*#uY{w++:6A*yݟ|R|T'5$Q ׽cͫn.UPwRe%Ն Ub@\!7Fm*+R}'Z[ʩ~k#ZHSp`5pU²9p-h&؋bxh6A*CnBxkJp L`U 1(#)Ej1F'q+u$L8cZk5; 뒡`O W2%zX <h4ٔSeۓ<'&ÿ́M=hP0!%vpO]c5leh{$_ɴf4q;ƳC=\E4\Z8{tn#w;vkilj}15#U ?n/F/SOWM_7Xybͭ+ޛ[fCM%N[>ÀΩs02lRSe"o6!z=xi(kmm]SSS,&%^ ,SzHx)i>+ra؇BCs6bHۀet!VC(AvU Ȱi]Ԡ%2K4+q"6cH0l" ЭEv  M);O1+S46سkCn4]|B;,R t(r`'V8s[qrO JPSc}&r>lMTR q%>Np/M=i?afU*E g;WTW=ύ7S[*o?pFHZv2]^z#/Hȃu@@/c fvpn`څkSH0d 1j8)j:0.e >3E9nHa,# xj "h+i ;GzR3U ݹJnNNd] &TC&gH3],2g51h3&2vf`<[BeS-P8(]aQ9$XP( wLTfA<rYpxH J:Ek$Și?, ~3oZ,ܪt\8g_-]Q S8hlt7г˓utJRׄ6[K;7--hu D1pI eRdEXw'pt :x95+`Dd̿DyCх.Rd 5a B#ύyJM5n̲jpsK$ K/3ٳyLA]rٌRFG<ڔ2c*TX0EׅڇEKV!Dٝu2q%'--,pKZwchW͕ 5>XzW%,PԼŨ35*ˉMc$8j9n'W)0y=Ds({ejŚo?sj=8{nGhiGcRdUJCwZp*e0kH#Aw3l&3uPyP&T!(QXWA*Xh cYt|2SX@K đt@Yd !@my+Z$)w=iΓ[lP}:S1(k $,3u|t)$-gZ rJǙe(eF2؛KW 5,6'HaPY V.]`.!=>"w J#ROFdH]>LLT)*~qoWڨb- +T&*fj9^y'ĕ.ҥri !ekuxޚLO.ލ{++mu-ۿ+sɱrq3υzב{bVzzѸq?y;Dq{ 5Z,6LXknLBS%X B!VXXTa}۔RL(t"<9IxPd|xO7t<+΄PFAM!HX*G#PIȨЙ/H쪈qN" `TS$sZE‚1FA0i% $0'PoTx??w?QA IDAT/ڥGGߜc5]*qx\*G%x=KՎF9"i<;B'W}o9[_Kׄ͆;̹=WݟX'5/3l3Ib&6LxÈ6;Cy:OoM]d7 !<˔}@6 ™𡝣#4kH+ ˔Rh :A &K$^a#ti! ,Ґ ̧8]1+r(ڊs Ū)4M3h'8V8&l~ًOO )_zܦrPּjjXsq@5ϯM% O1UjPmUP , !k᛹ۑ?O~G?ƳNŷ9TFB(xSϾ/c"хGʫfU?~ 4v,0XMҾn  !m`WBl%7xbJf &s=rJ&VE=a<dFY݂ ~"k{(agˁ@,)sbM2PgR=B'/S,9\0MZ58ڴe:]w،`@ʦAQ`<㲹-(AQP9d2!,K9% E>–ѻA#d5dMEE\22R?eʓc]]E=n:6SŚ[Ya2*CTNB-oEnM/ !~Z[ԼlS;>S`pGn툼5pkp< `ԁ xh픔ǗjtFW`4EgBܐ 81"؃:^KpGNy.;WpјvJ.V7&l,OqbyGp㱦{:{X /Gj^Xe֖Pq]t6\Ic!kܮlPq{hpx|}6: #8uh<^߁*9G~}䈥mf&w !ZJ4pR &J| 5s\ Ҍ|*0N,o)lfƪ&ݞb9Yϛd2AY#kS{Hݜ@5{KIHTNY"qF4cJ박B@Ʉ IB2 jVG)BiTRtH$99 @!:88x EKT*Gg87bs>,ިsۈE!@nyN{*:S?7sm5o8/onًyKˑ?:6^ Jǜ x HVBw҃%K`|ĕ%])A/ao ̻a~ %k![cAdk 'A35f5b鄥F.g+؊GeμZ jj[3f L=y-^+xz0/|Ѵң(ic91m?CLi%J֡+'Li%AcJd`HCҸG2%,))ADeSI9(1X#Z%mI1crJNBa_a2YkTذdEm- jiq0jaVшQ {51^oJ8:{CAsȠљs6dj(-jpqu]9-= ]/g\P%;aQuǾʸTt1:x> Ŕǂ9n<}ǸÔRpDL#Ȏ,3 `9PXJFh"=0-pV.4B&q®=r,Wzd)2m:~oۦgZTֽ/o/r`-U|P}{'55o2}^ 8D,XsQ#Y{x>' i(zgAQxeC4%Q]sAܔQbІQd%|"SZ16)ã qlhC>d\siA |ܞZý+,-̳݌)t~xMhxG'%I81Ѐ!1D3sD58/!!Z `:`2"Ƒ3[VU7rd$ Wo'|Js,}`4aϦT2B|$7{ w9j^n8f\Hs<\:knc!+lnԼQ=T'5 VSyǽ>}0&<ùc'}^Aa:mZ,Sw{H8@Y]a߹&G=O!V.Mb"OYy$rE%Msou!6ƼaB)uO /Kz = towɫzP fğOO%C(Y8*r l_囈~QzpH:w5Le1V|:+VW cm!kq ϣ9Zlwы %_9]a9~uHjo2ˑV&.xȅpCr<* t-*צqTJN[E sn$AbPɐc+Cp(0xJ}.v[XB%")1xC9-A`aaTJLDxJb~0v'W`9,ɚS&KπOc!ucLc_3B|nOPnnxsĆ_*s} 7)+#!og.yvNu12:?R(j1pxua5_şmٽeuv _b2,}oHۆca)*8:)k%YQa(q O#tNŷ|,SnnrbpFD΀Ah*Kl[H%StbQ 0=HRSHJ4>6!sIXi$!"4Lq"2A8uuܣ_6y"I=IA{6u)M(HE]gLtcb3{YǛެ3nu dmo  s."QHa37euuq / \[#(}YoL{?"{@Vf `-tu& lY2f}uw ǔL!r=XC gd{4&kLvT`T8-v(3Lsݜ42] EkJ 66\Px:.n_gZe%"HHuA6Y&P{6uCZ46kMKCCCémwn+0i3ڏ%w!wJR C YX܆mg$~Bgw,#0`%j@U@VoP=esy±0tobo16!W8=_:@w?xfvwO:4d4JQڢ(cE;rQ8.6 #uĔ "*K.3Szɘ^f(2ͮ rBPJ(FȜ8xŊ,:ƷҶr #R8I D "t[YIXT( )!!:ęaH' b4dԉ-)fs0qm; (ge_1/F,6 .m<),gQvRۄb1ŸY\`!؂9cIX8F T!9T lT 2#8- d{`C%A0S2ƖV$}eNŸeDŽf 򮱆F,6<8pX7umxxUack ǶYu6MZ~-/}UX G` ?ϣ-:)z2deL`v&]urnL-á='|2..[.er̚:;_N#惊2TG^d< ]v=I9vV4A"eq1CN (?EcCql)R6xW9*3).L9i)D=0ߐsH=MA ) %%$)(\eh5'+)sS0W<-I$[Yt >s@;/f'ۼ%;y5h_.f'_X|&uqCCCC`|xJ$TAyк1נ,LiXwikrXq E`\ey 0˔Uܱ:7^DD#v}xJ^D߾sd;Xч Qk]^:_0M4{G9@Edtݘ!OH^j́¹1}#,\a{#b^.];#X^ *xʢ ڀ9UU V2T:DgjZ1G`ȤJQJ:Q(222]oaɶ0ho)xM)SA#JNr.rsH]?u  |:4a L.#Y`qgP_Gu[_Bf#U”pwBx{ +*`qܡTK#1N'ٷή6w9NhY-(tcwUoO9Z.><$]],s`TYm?'hϸ28e FTg a?N B YY79Aتzxf->p̺yt}?'cYW3¹2Np: k` J̈gT- pEEXs!aT*O*Up"7h %9)RtsㄼMF㌧IL6>.%9iƜd|W1 ӈņ9g&N&6scLSp-Kΰ\ϨY${ѡu3lg(*%ʭLEa%:b*\Z˚'ظzLd3\,6@Ir⠻9}V)QI=*-*t.6Lm")3q <*6; dB@[Ѻ?gGϚͧfcbI\[|:um :p;ɪVq}ò"XآO@uF01b0JuA?ztU#ʈostp%r p"v;ENZL| ~y&jmbѿȖol/Č˺g8>-fN hkNw-Xa ݄">VA|߽V1-FNJKK1Li9e){m掃{B w\DZ " sM91օ1NVS,\cD%*s{6f5'.3&$q4"xdIa@P_0ष=\>ol) ;ڻ8>ɌhQh;KCCCCgN!Qnq TdóXk[fȭ0dp53KB٤WFx9v OV>1U .0yzҡ/cNapby02c--^: 6}(pz-:LxXo1~ Ckq4L"A<`\&V<)8YIݼii _0er\ґՒ̡yU`$bAA5uA1+B 3fRihcGݜ@kL7G@O&hZ|8w 'Kr1Max:sg.m>U4b (>15_[6 n\"8KqumB~"Zt .Sr3G.^b%F˔xmPVx-8by&-lf*bJuFeIl4r(DE%. Y nW@j 9Bj5Zq?s,D)C^a"Da  P<9u=g@Pÿ?BͧF,x"|);Bࠩ*ꄺ"oޣRus6?lJ7rsCC7]1 &Vnr~yoCVS4Cܪ{BcDy8Qv K\NdkM7o"^EQmqD.qiw;!jsvK&ijYp3$g !HaMըZ2j9ޭ's-A.ڃ/ňsg_UXxw"E]z=HEB~"~5w孶 _b]X'u̧?<6g ]#DB tXGv:@1t N>XGweY WOO3sЩ{56&mop/;įpf!VI9\o 9*wPIo!R=Zfk(J(6+z;L=V kWĖ`TUMPsB Ā :6A&0*蜳][ + ppvrYrT:wU {&86el0Z"rGBpɺ}|| sjr)?cBp~S'_ !61݃E-o>M{ B844{R |^>  _=a>oD5>osK :mUBن>jeL]__@V%Sb@LM]k+؝,⓯`s1I+%IJΟ-Y٤B/d(ٞhH"#=*'ExV+m"8PaԆV,풩p7дT }SS9@0v#s)z`!P~@10{WhT4eN 薄DDAK): ے92 20:.f(\ *R2lll< % ԨIz=| z֡ͧF,:F H|/ih @NO["qi"7/rd]k !7 fT#HBժXisd[]ZbnP# ,t`uy`!팱y0G3Z9Âv$wk `VJ: `?#p'#5EQ 1e9R[Fq识X.c.=zoDfab>.Pyө|E}Hά|gCY!!zOoBHcOmxP!,ꓬګL}j߶h5瞽g[Pc]Q-ܳ|m>ż)aޕ׿ڕr3^jY;5[@B (Wt )H.3I6 !;"^ ςA_kItlk9Lv8ʯݼߥ;PVg *E#vy(;k"c=L+dUq.NGX g7w>{w}%)a,:9b-Lfm. NbljF$1*9AXj(1.(#IN cw5] f >cJ(\*TB\nb6[ '@Q1V)QD(ozL+jz/x6*&NBNY"ԕR7~7^5֨E%ANml3?j*74~NZb< },5 =0 7hlEਜ਼h?vdKը؇מ(! SEl6Z%XmLw!-',!2u١yYgn#Ujƶ,C AyLoҀchʽvڶH FD.I"Fs=zoi-BRFK)i䩁BQV.$'TᡋNtTIڸL`*c$ւPگC6 PQ! h:"f@JLsfoVIy}y>lEmϚͧF,>+4!!Ā:Ou_C]ǸCA=u=444M.SWxDUJm>rv_g _<ʕSBwFXK!,!*\?-ji%*]i\cXu1ޫ"\{]fN0qd'qre)#Ð^!ꔄ-I`R֝9z#`4o|+ fdzTwWIJv B])CSqڄ u젋D] aGГ_wE;g>yA\KR#HUʼԐ9tB7$mo+TqNYV,P1G66 1$Bd% .W?}G#OB8Sy>{!=Uzj){̨yM]|PMCCOD :/!~68V<łw<Ź#)I1%}ŗiEPX.#Ҋ%+AgyG_K4`.=@+>tM*..8!s L B JN1L\*g9l*+ !᠏%lk-ʲ O<ҹ 5b* si)wRZeGER,l|xrwLr63Yۥ 0sl8Ec'_Gc-ś ]Jsބ64+>o{K\RP.u!pd/?c/|J,ڋ]2ٝe#ZɢBpw6J1~gOܟ"I[wQSkOnc]e3_ӟz *2[X_ 9Fu) bN{gbi?c3Y"9R@< ՚A[^Ȣ[aԬwĭ4ݥ9 'CyQ$!(K%k.a3x9&pVt! +;vROӌ*5n+CkBYҞ!ƉO10cƌdfځ?PcV8_iBPOXu/:) z 0<%7ڻ,>|S744<`s>ImT/D oaHLAk!禣Z';c-~w 1 1s:&9s<%6懤{pg#8/D c#W/sv뤯~qyq)ɍ]J} zIrLjZXش7tSvw̼w%c̐|PiiDhS-p {hGcX\, zΌQDœ`T.~c!¤BU:2[*%K@X/B 0TiNqZ0'qdLh-ZhZLɨۏ6&keBNu/_=c` hD6?/SWz8qѴUx`׀?hbCG~΍_5tr6BRڑhu[}["|h8wIsUHJ~_F$D/Q+AYfH61;G- /&oY<5a?[>AF`fpet%Kr%be<Eei 0Z2 [{NF˵ie!Cì"t :xHLq¶HKwĺP%P=2ZIrP@fl= 9T=p0> V߃q$ĥIuSF#D"CSvƚz^Q#O/͍I ~ioE! cjȌ1ycBR {E-, 2*)KCCۣCmsW c%ܳͧ??}64g߹(jPuU7ԚzPgtϿD& vADžpWU+. 8<+-w5+d m^0<>6#̕Mu I!{$RPNn@j`UI30c-i ߦ@Y Q268ӭ]l!M`)Kٴ`#) .*#+C6y0Թ? ȭ /4yZ,Rj? (Wz}IEbф=xRE|:'昺hx7 gJa~{{՛$(WQmQ^oަ(ixlj@ǰr; Ep(7rHAOΘ2Zyd}9ܵWo=Yq2";# h;H`#G  ˲$-ǂvr$r==߫q鞍Nw}WuV{|obwYl iG$>znT63U%l>gHUdlit|3OaY]adVh1$$c_QNR4IvL!BHߒpߵ$g6O1kԃ;T6b(,ֈ1$ HÐ+z/{x%/HĄiBXAT jm* IDATbC"w,shDfJ̛@f;zH,]6I(eT6*bEqe |nY(P;nGGq}^5q 7sgy'q .i |Y<;3{oWq:%`` ģ{VAW/ x1+ 6p7p8!B2Uw~yohip<&wpBe7nR%#l"'Hoqy×^f;wR+huB$d9[-?Mjs #:Q˵փ\ $^4^ywV0S<5鐠P?+>=:GDØMCCHvȊBxQl>FJg$t$ V@ :֯e"Ud ƪnRRL`7T2Zٹ*GJ®t$6<X_﯀#@Z~*.N{ƻK/qSnM=SxoFPNZ4\@|g\3f|X}-ofD i}@>ч;"<~T}&Y֮wF_ËkaZ 4F PGXx<dЀ٨z(;{ G;DQ2Hꄞ{ vgvhxtCh"G|,a\H}ɦO&J=2XՌ^P5-6̗<_;|«nj҇?z&u` 3z#A-%Pt+`'od%BdžX{q@/%"4R[j[SJl$Mo('&|Jq1=m>Z8W%n~`'[.qqkb5Ddν53?>"ͪkJyQ )wѡهN J)%uvɷӱ ۭ؍"ˤDCb?N.sbS}c0GMA(41hrPkG6>PF{6X5BNn!:n 3).W(†Kf5X^- U_ڜ>_M༈BZ{-3m|ތmn:-V8)rx %-}xgk@>3!N+Gvh.:ى'(Ds'Y6OX2՘]J>Go^ض,-vzkcxs;ZYR٧鑮M J8g)P{(%i0P!R˼Hƹ)=r V>]*'!GhI~U ZJG

%lyBPln,sԾb%MErtgX)@g2/լMKnu(~D3V]@U@χ?n,_?rXܻxjxbM_an~HqB m0Aج⎙y_kMծ7ѻ'`8uC kRg/Zkg"ƌ&5.;p vϡjgVxɹH߾2q'MþMϵWk(*E1o<$llMƪIkݞ1;Ǚe;3MKb9f#C|Cע;E>4^̂> GWrZ-Dn{|'G %rZ1yl}F*Bڊp'{2>_Z\A3ڧ¾qE[.ư=Rdyw4(t%POf-Aʊ%e))ee޶O앐ntsyodz_P?UWVAQ(O+r{T_1RHSzoQ$_ϓ8o .l{[Y,f8p#x(wg6~O WpR n2 E}-vWiى9oڛN/OU=A 3fHx3pOÄۊeX Dԛä?L<6?@e'1Ⴭq#ѷN>#W`l_qV6F4S$^Y鏹4fu)6(:`J`L^QQ0אZLSeslJYkPfL(<}OH݂QD]Å.`1& 5 Q4$ɽݝe7i5`X'=F[r+ɪ!A.Qˑh73 pϧ7on(?PҖDV BЎl^!#PZAbX~=S*#]^lޱ=JŅˏGg=nG3>X paq-4;po/<"wq`[, !>o1Z.W'pJq-}_%tq0+p$UYHˇ4n)'wng̘.R_X⠖)*J_=LGWhs&Eob~bk_<:yDUd]S(л['Uv)!MF]b2Br1>Ũ P6msҝYD tk0šE{l 𪜚֔2r3c5he)v,XX)EIFW,.q*d+_SkE%4#(;"tұێDE:v Nmi^rFtW B*H ̃1TA#Õud9utK qU4zXFRrodҭDfo} oVuSj^/=+w5;h3{V#q#&‰].5Ȋ &M^W//&1[kgyY7N߆;F/ <;_F\S;?m'3 7Q8gMr]/5\گYk]cƌ%vmAk{X CW ȉN7BG5-t8:#nͥ.({O/^y廧9̬ >p詣f~ꅽ3=qyy<(>|=d6xeLJDU`E< ^""$ +xH"LD?A5; 6Z>vG`G=Q'[b i%"=@ṵ.1:sTnCjCQ>,.LL v29uA҃tou&,+rίz/lˆϼQ33W=Rja6:<5!PUQU^G]{no0qne' nb?7SB`'qp_]{ǀ.>~W B pƩ7q|GB.'nZ!ˉY;,aޡ ֵ&]#Of3fx|Ĵa11qZ.'] N(Jp6SX]P闀SՕ x~2iQo>?O}O i|"n:'i+O}ff9j_+Rfys]/igtj%J"ke*x@ /Ef k @8( [,e-KꘕR|撀nF1{;%^CrzO є4+uAV5,DŽ6%߫Ɣ2 Wj'~ACB/><`Hd龛. sB%B(#AlGvmRiiwDt<e"mYIi(_dmvlN^ka0@%S 'v\{K_:9ҙ曀[V,ZkK!ׁƊEpljy.˸y\Tn2yOr^vphC!Y;C q<,Z ky =n3-ts=4;>'pbj7Ji\5jhucWrȮ'ӵ(yz;qCDy0QGY(#6˟Ww:;8xOO}f*OL%q}|YS-^,;T18_̓+%s8#9`@iCjA)m lZ:Sܧ&ԂD=cP-K[ivVQmG.Ie 74:qc9 S+)^ :hmFQQf-_JC؂ЇP\و8Zqx4 CA,:B'D#Hő!IH=VZ~WV[6{j7+KE K'/Gy)Ojwfo-ܲbqc8u /4Ob'L]󸰼x??~zsPQ2KO<4oLB/!.~翉Dh|;W.vIp^/ BmἌ~͘1mg+ӥU56 )'@4Jc` aT av#a)Fy U4 T&ZM. :Mc Q(b%2P aAyH᳟Q6b%Zn]Ocql+ab*#D16nfi/k :b)HmW\|+|RwA)vjůs2Vyu&č &S4_Ǚ-j>hUL`r seqN Ϋ8yNsl͡W_wsT+B{*q"EmA~iaiQPljiu3f<\會%槞RL~0i!sg}:|K;-CPSG%g5ʬŧO?{\<ٙmɛ*9o4>Gŏ~@N&"w2-v@P䢠-iCpPńc/py|D7CtXFTZЍXZݣ.i/Ai z" cEZ MqC)j;]^MI7Q{WRr4Ndf81/4cƌh<{LCn&]+#0yalbM0-L9>KQGZZD<.Z0)h1E"O{'`kVbA7{E:W=e-.ehRN΃6pqTe)E%RDZt|H᭡IrcaPk$; 70B -9W =P1 )\SXcOlm7̘;=Ѣ$Qm̈́@bܬ1g>YUx`J{bilo(*!YyS~}ȷj3{<6 VN4mNc2ċrFr0v#bہtAiV+DnDx*h=ys׀fKPEV7Rw>= BQHEg_ΆKs+6WV/3|q{UQ/4X2 AvAegy_5N]$}9.>k;8 8J@K &9k += H=X1UDڱGd^ -~˒Zё7@!^34 /lW5N6PQ-ҭ-?dӈGcB'9ُm&WjɆ*H4a`s(zj҂@͑ёz濨>o~l[wh+_KDfp"WjysZ&|b *! z qqd;8Ӥ!6  &GF faO1Y;WaG{!\Lr(44t5(I%onH.515ΠFH%aqJ"KAF v&ڃ&Z0N~(!+ ) ]!J%cz֔J_ L+qurf6 R/<ﱪ*<~'RpySaXtJ~pWpŗqbdx1^V~ '&(Ufa{o !ĴN|wLK K0t3ŷSiieM{NR8zi&z|;cƌCmSֺ5jW*ZS4瞙;1޾{ΰwqƻ)%^ }->ȣBF)L@'9|L]Pl·MC\ko'8nŊX, 4,2?]|/s* ) \ߧZHqF/7FjZ (= S HQxTN]y"T3$cM+j^ 6,< ATIpG08WV`R&/rmX(V+Qk"$VQhEUn<@k|3",*TZKd1O ^Í Vq gFΎ('{{/iŏߺqG}ƻL,Z-80.r㽋xt֦B|SIz˳O\B(k~MgL?ἺkދUGh9oSFWxJt۩`q^w>OL[Ƙs\qwg^3n #i×qy]RiPY Le5>ˆ*|I*VCNYϤч+6AIDĎ6;*VdB0-c?oSm~s {b>/垣8UgM)6HII)2Ne4JR"u jI[H)- *(S +5c:ECĈ}T}boTa>0Vcz61QgK7#VE(ah*ΖF v4YQ59`}iw#1- VJT.' * =q)vGkX`Ҧհ]P=?{M!5IKQe~k{hmI^W@FMk}6&L1 ! ' khR pUU-7EP|LBv7C\%ݬˆxNU wTGr5l$^bYmB_wqS~i gm~1^ /aO$kCB<TW(5.gRF(7~7U,ql3(i%]e7 O|-1(ӲˡQJSd3.PJ`=R`@X@/ A#|% &Ehu1篏bIcg'ګG~cabcao.ϟ.曚glOx0k#\]zLIqn_rp%a\Xxuq4{-Bp>C`7EYཕ8UoBPéq'mZuR/nzyɷp^OWΘ1`Wgy=<[S Axs? ܨdoݳYYh̫7 愝sj`;:./&2 _^??e}HB2X{)sŌUcD><&:kM%.=9pӫ;^i!$#AH(khUF*a$k?nSgpE{ a5D F8hV=zHqihE(c 7}-% "2.kaّ+$VTZvUy9rcА^SG)JE?JoeƳ?sflx3xkm>ڍ}r\ 8qwb6^o*!7΀gqBL'=x7qN<ܱWh,λa;x=8z!Ӻ9yxK\U+>$wƌr_Eod:FLҵp#SZ"ɒ* I%-[bwR=чmxƜFHgw~}^:7: `3vy~Yw6Sbm1S6Q x:|h *XJLzQᛊY ~ " +kQ d&@W|A,G;Wl5h7iQqasy2z\r _;F4˜RyeY~kwU%ՠɒY`,`4<:it'N@ Cc`ƒ ϣfjUu3qsTW岤[u<{o} !^jFGMXfP  $V(rg\SLgFJnEPYQ*F*sMT)IOQ3^h4R/#GeJ,~#G2NÇ-NBι @D&Y&Y<<~?zWf[DZtr3'7 \ڇwl.RlFqlJM+NgtęNDfpέ?80>m|e|ľ,gED>t=Vү;o"d~'%9VLz>\7~>Kl{zpcU+h<)(򪸛l,` oԽEe8)pF.!IX͂؁Msj@`aÐj a:/eU2\oӨCu$ R | wj)$%*%.!^)2l @;JjV KT8zgNQ Dt9 $՞qԘ3a:A o~ލs}k.~.sXC߳u{wd7Տ: 8^DdG"OAMb+81肤zeȡt@U&e(Q(nS͔ʉ3kj ;t{~D'p*mRNõe+,fPUʆ5W=OVή%eeS))K4-Yl I :"Z$hJJaL;EF'aIјRb614UFUUI{~1NFeLY<;l~Vnl<8zo\)"7gD$`E$FιGc-^lunq-1c>Sm6;NF'洺iLf1 Olb9~jq˝~3/Ξ.h,u6ۿwGz5\>l>zT_x=-(O;ω⣌g,ԙwVgS@<{R_ \;. MB ƇEWB?虳ϼ eŊӎ;OoH7;555W {&/a䇪?Vnx4?t_VXu˼'Wf;?am3="rվ[on ~c/́kgXT}{xo|Vms _Y x!A z卨M SuRKх'G$";DݜɊYfI'>j!nef'rԱ 0x; q2el]#nBݠRHUQVcbEӔ6@#Jũ)tXG"̪¸,T*(zB9LF$Ӛ0)8Ҥ*ۀ/~9> ,3W}<~ӌuRC6B)J'ؖHњ}(,L;tnBҢv .s|sr)ޓc}gNt&3LO̧T\?B{RL ]Z-AĬ)(B>t`t;BL!rH.0V&,u 8!F G$@2# )-SmCCi_+FToj|RGQM`g2"+"r \ro;׊H\XD|/s3/uڟc9=e"td߃b8ԱPMM~~SLUx!2E%GO ?9zItc'ᅧ_\Jgu qқ mu"m޻H/'D.A hXLÓ{&T2.~qlyMDrPBh$ 2A"`RXS]s{gt=ݻvՑ-«yl{Hˈ i%ZfTSv@ t@AVa)+ˆJ@ :3͑CBeU!5ZPUXN(+Ce߉Lgs rҧ}~7 GX>q-0ϛn^̈5cح ?_mL|o&~qSx 3amV$8(t9lqm5551{6? h0ECذ+h1Wk懙 V]JZݵm~q>򻛪}'v|xFkDn9EͶyRrmh)&ɳeNp`ƌi+RŠ" AfcGp5Z' Vw`ZD7E4y`TʠhE0\ P <7 W2H8AkȪ8hL /#l D!DX@!܅ U)G@{9Q@m/kjgq;O%S{x^c(׀ \9,&" Ƅs/]=KxGt3{;Jg'N}#|Vq"s.ظW{%'&3bsFh/\%tz۩?w^6sI?)~oYɕnnmq#ǎ\>0e ΡkdwԸnEFD+M1)-{֖0=0m>l1Qx<4,4e y**(uP9(m82rXR\Fa4B0!OSֻsT:$,A)?20$b/~͗)5^o&N#Gees7v.&ѲZYd,p3 M7U{ux<.eK8F3x!aW2'eII7 3[}7~3QSSSӹyվ7{I4n`1Hr4G{?z΄ X zY=x,e ryp_߱N[gl;Hyk{Եr= ۯ嬶u6?ی' ^sd7fm8 ֘ٱ>N oi [Za6 v<2X  fd|ٌD̨I: )uݎez~r\![XL^. ijlh & TD_w)VPY$-b]Ua(mʩ(v.ADm0F(;Mna!*Ҹ$|؅ZsQ:1~u:Y|N2L%C"l/j3!^kV5G~d1\zZsq -m^ϵ~-7r޶9[#ffW K*)&7 @pFׄçEdCRAYFaa5)Q+g>[afq 53DAdq^:}L2NIJm#@_PAAJh(2z}(Gk $@9S#Q8.R#p*MVR@g|j.Bqέ:1m?xa­p΍O 3Ƈ9L,xKv;i8n.9neA+cSSSSocI5f^aNw`_=~17gh;/c;3j G P 2* 5!i,t0aޗ],n~]aF́*CMaHJh;rs!`9F ƾqaD(èڍͲv03iFqÍt%L c!MK&Ac#5xFLc 8p}ZD^W=aSKiR`y,NY<9޳qO||[nܒ~7jg>%rˣxmw~~]di}qV;w\^(w\U`JK\WGL[Zaˀ\EAg96 g[C("U,*\I3*= +U#Be-T*qV&;UI1භG罪8 xx!.=tp-gxJD?\xq<p'g ;[yWKkjjjʗF7}n>L8{dK[31\C|ꀄ*LvI1Zrx Val]t!%YfR" 31ׯd|J4 m'F60H(Ye5Ohƭ"Y`*Ji UD!TZ"[77 ;0+ t sh Љa(/PǛ׀WwwiIݳx_܏/I=0m[qoy_ij8 sqU*Nϛ "Sxknx>4XSSuN_zs%rx{ 8mӳrQ*pӐ$Oq$Nܣ@RIЩ&)NQ=:{)PE7SpR0Sv0cevsbwQEeh!jJ(CPVR6"2HNVP*,ݎl~Ms#pE0bı(iѢAA P(49. qq`9= 1'7wԶ ?)>|epRsG ]*߳gmա%ekh4$m@eb2Mc5e`wDy5l^*PmKisUBQc% ( Ј8 p|J I/;q%5uy,qH 2rոvyJa]T9X)1 RalY2[0B4nƽU%?{gqN~^hGᅁ~9-r555W<U^LJPtTȩ ڗ.ufniXZFS1iuYC?^ցrP}Zsͱ =װUaI8t4:F+/X'f+C7SFEK뭳*ciV#RG9kAիУg&{ mՔR#`mIWYB K  ?|۝mH,n,HmBq"r؉0|{ >e`<^gkjjΗFm`ػ}+v[(\ĀMTL$B7SSt"XKg"O gF(!hH`;̚!l`HlNQOϳsGG-;6RohRdx,T|$oFJCLV Zrp *B9KhP  (@"_4`#=~_ʟ<.\0.MlM%$&"%>=RUbQ˵vex! ܄YT\Mſ9_*T7ΘNzIRjK h Cb\HFfҩ*B.ҥDc J2kp,Yvz/QF!+bDXJhd/!iUMF=-ȆyFf,pׁJf6LpRFQ m2Hi)ƄX5R)@N4cĈs `3݀ɸ 2xb]K]ژy#Ǘ#ն vkj i|bGǷ[Ўk?Gǀ9O+R55555EnM7-+{o:v(_|ѫ56̬.cqZPkFG" E4m@hE5 "ʇ4{=&3̄08'w5TAJi)5dX'n#QKbrMAD8eIJRa#Tw l%X, U۝;X7]on~skG vkjx^uWr' ?r-?&f+R\I̔i_-_Ϫa.>\smdCZ r`NfmC"V8<=:.tcCB!mNi,#'I:v?ţ,(^;Mݬ20 V,fxms[QLu_kX"&*T Mm%༎Wh֔TyȊY~mwcmr>fn% IDAT{[];55x/rs? {!\Hqp ="1~C9T{؟e܎r':;w*u^ONWOa5iVMiH'Yۉ}{F%_*MGaeaml*/|kWCKXkXHN"UU9٦`->e#ȌNl@1W+@i,aQ! G˦*M)ѩ* ^])nTe7x5`n{6KMM3$_^ <<6 9==Uَn >yjlͳ>c8%".jjjj~}W#2:p~Gmvpu 5|C_yʆtzCf{+'Ml#!ΆtF}n8~ ^z7>/: >ᐹxTaebAS T i5JZȺ!01q^2rljl9#p G3*u8 i`dIS~E/lhd~*{/q{ m{v `/pl$Pd6wx \<;.;DDs /"ousb}yך#;OdDΩFə_>A*լ,_:ᅫss\w0QǾ%tި%r720$ /y1;[P4z1,XxoKe#ˍIU {NT9\jҖ(l%TFv0Wb9"Yj8nh1oIގW,h[9h x{}w6jgfp"rx^`?=,IϢï_+/9p.њˆT{qCe/DCB(q. 5TKˤ:/B}r(@hDR Ae*@W4CT h-ıAa}G I$@zeX;U}h%3SN;/[vວv>rWvk.*D—r spzëj\hgqR*hfEXL"gy<6U̝\փ=v-cAd!D8l4Jx#iP8RAIc6,I'G֘s2#RUF+=ꄼd:$ Òn;:Xi7+*J)DʸBB o  gC_FfmNƭm䭷sӅaŚ!*}x |ӽ'pqݜYp6UhXGd#""gsDo:"XsQ0qkwest(C'vgnLGqY6_:王H "s@R_ zɁ:猈3/F>@a6Nw5uvfKʢ*+1>tkv8Pmv8[(6wbx0;z=f)3: lW?LDT*eYۚFv}WE!V%ݨdiֲeZKSY=;X;-IsAT+i\Q@NhM"#󪩺i2Hu%e;~wлnj v7z;C/s?;>@ Om}\w!XseMz׀.9[.F[3ϭ;7gQxuy"@A%*`("Z+=ǿ'G<}s'>Ƃkjj.IvV.iUb sebWCћOòQhbN:THi:EN&a1Hᕏ~4I ZmM# F0S(`,AoH"V3 a7+a4\Ya6^ Wvi[p #ݷ]:[@6"J+ ~k!,ց5ڢoGcһ/?j|.8]+4|M؊;J-"n71d'g&U x-z.lEȸo9tL܉wR*ܥDq_ೋ\xP7:iI&q;Y9EQjcUFxrZ9|fyxf7~.Ԝyw)F/uwsqg_׼R=x-> `kI,'+__1diK:ft>z: _; d/:C)\ (HW*NMEun҂تvH+[iZhb:@7=iA>@"%Zik2jOfޛG[v wzCz5F $!0PmaN;6q$V:n/:kŝE^3WPO"eGU`1JBhU;as^JW|ֺwswwc]օ4};Kni/UȖ7QF9MKVz9',߆[.6b3WY`-ʗagQU AD B^{&o[kA | 8B!lw|@BS|u8ޙ:Ns[[גm.6hsPG*҅x9d[PsCLb/C{茆ó``Zp skVXh.H,+- boUG3?RJ+Vdi[ň}7,:TDv:DsBA{h ;D"`C]J7<.O'y2ݷsw/{28RNGT"sJY,1n,&lajnnY畮~hV9G♟zCa̓W.B]w箻`nt:S슆R &E(F֡əKL9[;CJ̠܈ R6vV|'4{ JRUk2*[RQS2rE`Zx ((4 3 (5( ZPC/o#ͧŽŏF+V+&V 1 퓟R*k~qmi<N Tٹg~*d6&9orڎDļC OX+s4]w׶)څ6 ReC0H=6ΨORSPazCmevkV>zś>s>sw߽R.nn}!/og6&u۝aά*Z߯nmwODr'5٥(>XFзމE)H!S(b9,)πQTCW }0gAzՁC%rpߐgV}mؔ$l=2vWFi":ru"KE0"% {HIǴ"!'q8@(n,ӥ6u۷pJW?^#aT2"HXkp/ƳxDܛ ޔoB.$&v [ }݄󕧏 7d[$$K#J[J_+xjw۩]wj~WQ9.飵bCisʣm@kOnDr: ,0a(Ra^39#"$ZP3^YuE"XLbecQh2BV-A0x_e%:"&(=>4}䲁`J͏pZ|(e֥al? |u ^i'U]?\Jرo#B~^%'f AdIN+9Mk w sЦ&i,Ryr~†Kax&X Bx{8K +񷁰1Cxg6抵'P"5% wsUJGz-ہrq|?/on[ZV-bHME~dk 5{o4;#ziW.KcŎ7ilzvW;3gO7xVH8Tc:֗U.]x+Kgf\ih}q[LK(Yn%ׯv:[<❛GS?13_~ e C+5*QOj c"<(OKt"+HK "RЎ҄"3@`Jȇ[b#pz°DKdob:MH$;Wy,E#~?86M,UN#WS]NE0 b[zSJ]55%xۄzKwM>y !D 6/95?v'{,ѡ7M˓aZf[_AjWKF853oI-KKK:׵ۺHVZ _9K u;3-=e?'.5V E|+Soˡ_ɍusQonjGZYTq'z(oQ[  PXVJEK\6O*6c;mo9-v0R>izzD'e6" Oa#J4mI="4b0 @<Gwa|-Aval^s'wy* `yt ?$[a0YH\~,֞D?0>GTs~'J W房87T0^s;OYj.dXGˀvt<Ө(ްqvO3m]6jnGOm4Q7,2=k֫ئ,l>uli=GG"-t(ZLD`pY S鬵)g޻nX!\OpTDi[Œ2 &_2:iwʡ(M0}tÌ.xgq}pr <ĪW+Z)l. Pi qR8男"J"$kj%J5\Q8Jl.Wj0n,?[NLیSDwMI};y} iNU|vŽݷ0'qfZe%T$#JH_v^ ´pFxc\XPdO’ 15Ԅu䎣tK]Ч4>A4>L^ux4/7W6D?~pο_lhx5Q;Zd,if[Gs&SEN G{u foWI7!ۄ(7氿7^;DBBgZ?z_7TN8 Xz5U?B~}8тI+ /KX9HU=un#󇄶/ g ao$x3^HL''Bba?p`E%J8Oz]I#&ȇih8v'.6T]}0TD` _q88FvDKZCIKX7d&P䙖P"/|o&MۭXV !_/K 3gfQKNJC=y]2Z^Pǹ彆v#n;>.$̍q#A(sVYĠACB{('xKFC{+Aఅ_Q1{<56J2Δn+A+cD$+2Mғ#ȿ ~)?<o]jFg7뀯~c6"n!& h1yEVš*UlvOO3 C}ȏq~+x0Oc.Go |'9dsz=/sEb-BnQ0ET׺[2n1:z.3"x9WJ ?k_<QE(co357H^^Ȇs䧀Cx#eY$DrꪃXO̲q yY ^ѢMIoC|⽧MeIYŁ($E>V\-X-ēv4,/.F8Y=h'G\|+.p)ڑ=N"4"CY!2o *Шmx5>Dh" Ej̴=RlŁ80b}Tp|ynyԙKZL,){IQ\tlYD咖 ^`s`CJ_䗺9w3ˬ0$ln#l6o'.l+q-'WWc$InAiB^>1|<_nB/!F*}xW1O8B^^.|Pb Z !aVUqamtʉo&9ՕOӄ|}3El\5+%|{y<|iCQa4zF`J* aX]!deev!ڂVEz4sXƑl{Td;#_rFK~>v$鯪!$I]J?ֿᮻ'mwpag/H=t羽\\F{Z%G M"@86q2=#؍!5+7 #Zi#:1̦L H+عv9-DV]6f0p̉VQ^52ȿmVQttf>cңZpoKPfG m>K쾝ݷU.7vSoxm̙>F,|~5$xSj0 9BHڀ_"xr|s6Ig6UJ^=!"O Ty 0nYׂco~U?!gnxN{#s]~;7 ycۜF% $ڝh;xUD^@vp`@?U$>][Հ mNazOܳoW{ ={,)W;wmyZ=Dۏ5+`f2O(#H0xΆug`sU3^TNE[mKw>Qα8eyuF6˹lq՗Z+cE N;Qo|z+J-*Kܳޔ%=o¤H+eƢf DQ$o8vp;niϩ_UA=h\T>.(oNz wߎ};vzWk? ?\GNfŽ@MZ+BPSF K:h{WWa(By8'wւt_lu к8"Tέ[fPݟNLjȺͦ; co\l QD6)X ?JCCK֛=ƷULw:_;?&\1;Tm.37}2\]nr+"v_{U (}=߃KN5x^nX#~:*v*H\/}[BQX2LſE@ZĦnDi7ԞaO&qep5&Ay:pD"11Qa{ǻrE]ĮVBNk_V$N,%X)mmμ'%7bIg<;6OR_J.isכogx՗!TQux\a`H<6jJGJYB*J%c!ogab=缸X:Ղl-7jWfE}3x|-&0(~P>z#AHzB^uir#DZ׏+cޢӷ~S\ i[g XI)Lo[6G3KۓrЎ9g"wuĆ`[($BYIFIrv[v+HBUV2 ʝ]dJGyc6EV]p2%ޮ$18(El$Z{oF{ʢU&! 'lsy^ObQqbV~{]{t? l݃m?=6SxH"ra(aa}A !dXd#I!zBC A>F\Gm֖:BHg- <}X$9Ki񝨽 PI1뵸^:6&gAr{w /]V;:0ܴw8^Tr5!갅TkBTX{r'|hsO܎ˮ~vZ?Z~܏z|P>'qK yOͧҡ6fqv/F,!?>`]qSXHK =2d8=Rl'dajGJO , ʎXN3 ì 4"ϣGkWjD Etz-vzaC:W[[Y GR=t:|01rv˷ʡ9|E 6,÷>=|FEB~^οu º<A^Z?$C KlcmX )^(s1rd79y<}ԥZz?cW}6cФv5609 g[oƇ|6N\vœf/s'˔bɖP .;/Z)> fԡWO}_2|iOͥ:?sѻuħ~ʖp{⮻k]?EtZE0رXW6]Z* Oa!egJJcT Ifƃ-Z{a+`:K C >ZgC%u#q"iE1Dڤm> z[d|eU`)޲:sy|n-O=δ6h 1MWQ'O&)B =S/`rp6P3KaΛ'xWk">^t"tZ{'IN綜x@NCl0ZH9uj˳|ձyl *>D/]DDN׻?Oz3Oq y@oGZ;ZC#Q0أ*u3ovkk{\p^yo~/Mž_SCߐ%,WZwcP\{pq+S 9)[ #4Mz'X) x1ZH+MPǡFiV I"hHK<3ίf&)eLt˷3xW%m3(D괕Iܮ1yot}{'Ȧ(Ze6F,쯊׬#TޭZK&\q$DLQKaӥ|i6;8C_\xGW{{{O~dtrM#Ob}{Ẍ́;jM/?rٍm> G nئնَJܰdĄ$٤m)HaHV4VQXa9E=6, ;3 Ә`/mo[>^z0č3RZ6Kbk\`ey8|ƿ?]=6m0t/e)u{޷3}.]x%fxiF, P BUN'CIe\W aQs!`1ԞI1VOW Jc_jA6R6TGLp-`g4Qq>a†VXX7; (;`Go$-"J$;Md6W5Z .X&lQ8\:3oW}C减ߴoNuM{a`CiV2~^_E#o,?F,CT-7ǁ?LɐP4dB/yaK.!!uMxED6u o.9~w>ZSk S8xa^KEs1Qԏ) ^ʧGMaS" y }āT{3^qn_gn;|2Uh[QycC~RC"r㷍hJa1^WDS]k7g0<-=V+PނZLG_~xolwg3rϬ3[Uې.}:`hu)z_wcQYW;uwuNaݛze&*uVyaSEz6u5Qu|ux.u<{8A>\ ,`;)OlhwĪ^x$$#3 &!$-} K o{v資?Τ󥧿f;}1L[Dj>g3ǀci<3')flX*xa!s` p)oe,mde?\YS%hH$#b[EAGY|HtW3IR6)[t]Jbw3L<.pRm DF۹e}]`C?mpcSK$juۼ64bUDh*A}(y]M> -%az~B#}c6MP`Oޓe BKv"6~ ۈL7AhF^¹Xx֛ʤtP?ĸ"龜jEڄ͉k<5Z_Ä:B+sJ4{[?}|P({۶I Hr_^;:[kyު副+%8Yl<]w7yL[_ćvua6pۨHq9R{D%}j詠(y]u:392x]o$) _&bt%R] HJp,%m"-&xa׌VYSᒋT1rvZnoH<07C |cO;\޲ |yҚvFON9I*9_ b 3[o'xqXHd(j`!#aƹuHg-QƢ1ơǶ>_=f|{xP62dR0BŸ8#̋vN Q\tqͬf?w.^NT:d6}@޶?wdݩZff|'=]t֯?H kțʥ EVd4x޶AOYeYy6 Qj<دEdZl_\?U奓D&6q*VJ֟hZSI""2r#N ܕUpI*UQkU?xd3Z(~1g׷?O:664W!# ndp'dz uJ;;E䏽gsl"m'Rź AiiKp: Q9A*G #yr Se]dێZ;ۓojcOY ^Ì:Eشhg0fVgp ;{0κq5]=I Є$d !x(*\p爀rE=cQVz%J'@ @I'=wk[N:$Mk]{^}?w{z!᭻Ti #>;4<ƹ{ꫡ)ycPc-ɼ5o/~]~w|ժ֏|kt~ʹ8G K8,mEc @cE c휗rq ;E|RTalpLUVfN %t.+A TK.QVuL>1V8?zIL‰FYm֎&FOgsPZe{,ߨ7p͛鴻a'Pc""C ۋܯ0-HLԯA<2 w)jYd .D:伇h᥯HbSM_ 9&b, Sފg8CeM}pS?](Ra"ߐݠiUz pQTe 1Kz衇ӎCG~*[r6G]>ms\wafss[}Uûﬖ&~dX?29=W?xq}?^t9Kyȋm =,0ԀہE&49^!+ bf'cJ"4zf-M#?(K9r.j ^OPJ7L: \<%Bo/gV)Xx7p#eSVڶr#yoU%O1 Isy.XX# 5eW8+G%/49NPsĶ,mʀ5(vir,'#a8ILô8qh}qj;: 0]4OOŢv4yxʗ?Wv󯈏!f>ˇt˻!szKZJEX< `I1 Ȅ;kup`#ҹ5-2)DRRJ'TPJRUJɶw6#=WAzMY@Q z3ȵ^k"?˓"z$ts3p6V8Q/|0q{(\fYV /]T]M_d=]f6=/9< BJH([1C=pL g&+U)5~8-z5 jOca2.smͅa{Rx7'PMo{neqWɍ*.'0t\zobO94L 6±UR^,MQ U ʪ޷ǛKw\`3$vdF Bhea 6¬}V `( )vD0R-eca>G^ zcNM|`bM,~3vmmlfnzjhtpzxzvA`2qdn#ALm0RR+Rj0kK?YT(v)"Ƌ`@MV5FsB66bHM#Ltrd}t-}߇f5[ 8:<"n7]tjg6SJ}zzیȻ_&#yvߦb@.JTc$b*2>u2?]v9˸|_x"8N !W}C܂_v(n۲~Mkv䢅zFQԛu+ڃ\0ii mk:4X$LTjJ꼞5>4N;tj#V-G2ei%lj+PHK*pCW+Mmp΢ڈh,ɛ fnA%*>dӲa _#|_{qJ 3hcLk0QJ!vz44eU63x.Cga[zbdfd)Qm6H1V goc!.n b/kض8 7.ccHUyǨ5SS_qZմ`r WqޢS4یpTViT%$VRVUnJL1WƫK=Gu4h֌ㅎh3;3h mJH@3vx!N⸑he,;Q (։1:*5bg"sG:GF̷״fa> Eq& ;H%E=8PS!z?#~o\iOEH!!#t<0bY}2RQJֵ؎L4\ˑkKB'3 ;g܃x# F鶦+sV4N? hDgYY,ç#,lO7"yq/I\܋]ۺ7xC?HܲS? )5-;?0R[/lei'⣟;K]n ,;e:\y#w 1TrqpFK_ < |IB;Jۻ[D9nm|2Rċc+vLmלؠ*c40_&$r ʮ <Lm啱\GZǑan1I6c> pəmR6pN.ɓrKp1{8 py~yf6!$ qܞ- V CƘl [1)֝\~dyd,E^F Ăf97]|.21XC<}ˈ@ȊC )s"Ʋrn͛/s=3k,c\2n'eHr1bQ)U>L0RKHC^{!WfnXGklQ |;>^ \qF4O io:2l 'b1܈ hJૈ@8HR@T[cL_'CWV`>BP{p<(# bDhga:GĢXD>E<8plC3jm:Ȅ+"D7+1,|:rDב}3x5R拈,zx_ oUe0L-mKzP֦P\$e9ĉF' 5AcJnn>p2`sr簗kJWٚG6l}z-R %zTRt3X!9vEQ$VO14R fiQfRKНT. =GFjP!B뺾~/֖yg= τnV }?ed!! °)>=ǁO#9P1=CּN wzە9۶G:rž3iji ρF4ذT  V6ILyrVkUxAiRnlW>Lשt4M)ff憻q`s/+"S9Wu# i ``x {R1yEfS%.2!A`4 ۂJZTpJ 8U;){C`؄)ݦ)UlyQ(B%$HjM3Z؄hPmmQy »fu(W7ݦIi7xE/ωI-KOJRj yp_S{RR"U( P KF#EV yX9<".ijhW!yYܬƑТ,ĵĬ\,dAw*|_D66Ya3gǐg&R;k0.D Ϧa#zbo}}]S\em[ʿw򣀾s=n/ ~6qaA!6J|? x2OaMG}-!2YJn~C*\S IDAT[ɮ]5+vY(2gpskjfuQȹ{$) zTX^:L]jEՕH|@Yk*zނ9PP{4=[38ӐXCvhpEeHlMCFi鞞5g Z`YyeE;9[AGJ4]1IGf%]G"~FGAԚcnQy{-;(Yc9/e|\^xEV$-q +0!/ $R1T8ٛd+6B&e5ScVee9~? a>[v/@T DDy>[EbVD{y!be2dm̘V%]eyM''oރKYNcEX bɝ@׼RjoWz4`G>{II\rJ hC>oom7؜ z5^kkfT>(NsP;b.DR #szO͟V}L\,y0RUGs-X 4#}}P>MAt> Kuj7YnVS JP@i3ktxh)(*^BLԵF |*ϸh,&y,mˆ86~b@6v(%aqnf}e:ܼ@Y}95X˾^f6}6W+5gk{YXTJ]ɉ&fZd؛ >%JH+s=Y̪-EOw܄xmY I]})6cTĎ"5SJx/y~YxV$zN6\F ȀDS- #wkLt<4g1!ˑ, i3y*ݿ!;c9LYH5t۽i-;/k_Ϗ۵Ezdi[ORJ7izZЇk\sE(D%hZ6`M[xxlsd?6|vRl|qn.|]߹_7fV7_˯z7+VqPiYњ])j1\vṼʽ_-cLƴi)g5sؤSFVe`.E9R@F06(R,,+!m2ZEgS\81]v hF\T|%zž\]'~D›f7%˨kcyo F3nų?p1O9ѓ>kPJ#mފ PqM'T`n39|I)tdr"X7wUYX @'#5qg# ze2e:]g_NENƧgiїBYhkN EP᤿@O!UQ#U{H^ŬRpol衇wk;Cw{ Wނa$kشLX0F8 E,.#f PYv*S~s27/= ODwYl(^ˮ/[/r.fرCiJ,s*)[v%ʘ =9b[ VG0l~;ĔZuG*";!I&6GKY ӆ@eMU:E R4(IJB(A=_i:h򸶲 T+ד21ſ38\=:< fuߨ3XuqLH=5.j?r]gwL,*1NqHǁ;{RFr #8XD,wȄ~k2kf@0w Mc3HqN|C:3cyt"x e/O+f5ȳF&ե~*dEq2YJקkVg#U'V൑Prp-ImMDZ ދFHy:LٮZ=㖝}7C^y}W?bj|~x&'_v{KsHltT)tcA'$L!z2Ob { J|] dN>rpuͿ?іݏr?6gY, ikP~,ిE?gx s0k&F\QDĆFW@9uʷ(* -͖+:4:Q: ؎RRTK (XvXV) *qse˶l׶[6s3Beɵ ;GicKo("kcܽHX\ka- b-XȁUj&nPqK'vf}NIxbΉ vrF5p6 Phtf'2$: (/;W\ѿkT. q-B DRFz8p"BFO6I3|^3REʷ Jso O^^LA{iz-qjن8ëO "Y^:Ā mYA,N s>dF3+ޝ Xt*A'7&0">2k>RDW|,=k;RCnE>f 7mqT%XuY7Q\C5*9e g ymh9B&-:mlWXXV{xEஆ[<[/w=Oomqs[*% -Ys'M[Kskqlմ5 خê AwC^Ju- Hyf\A3/h[vO k-+lMu\ >8S +R'K\λ:PFBuݎAad7l9fȽG<Ү|{Zt(0qo/Lgj ; Zj*C6?>tZj3 'B. "8WI)js6skNsD|)Zf )'F2^,"pC1x "xl!Bptiy"Y)Pd89G)b;2t0hBPH_cZJQDjǓmJ%7Ro7̞8z衇ƶk ^ hȶa~y# 놢qܤ8oFb܁421@A;,e"\7A{~;yn߼~1[W 30Oh"RU/-vC3I8˵W|MiW¯ 4ŅDxb5 Ι* v#OK04jT*vQض|ϓ-^eWQj5&mG~Z&rƸ$Q,/Vc6,TAuEh؜aZ|OdÇs$1# F$= \w/#su7ؾ7ΝY6?w#.EB{x^şDBV$icOHεQ|~!k=[-:?Be6t!*6A[~vIUhutBM5ڗo B]!_O_\AN#aG>=UʼnOȽ@"ȊdB5ZNQJU̪N1ӈc !e$tBow#jbTC=<3o˹iFnY_-;{7,O#u`ha }ԫ%*Fzv hNٙ3@`aTHl<"tEu7[*ozo#Z84k|\jHTja~519:61t5\vђ֯7ù#lZqIG`km6ga9a_G"hԩ^TCc'5kQՒ<[ЮF;IC~@,SBѪhkdzLӎeR6IZF+-ˁ% [Om>ul2q'#*aqHpO}cTkJVflp !;15|6c Y34o<ŏ#!zY%q2z-!msH7k}\،x3όKC*c ~|*/7GX9z}"7mp6MۦUn'=胗lQ0K(k1@vؚcs ;.2I!Ej(xq˶7k>u׭+o\us,rn'[QR.rpTK>4dz/ S*rNaX-116融?kZ=,h{\n&r%-5WgL`5L~Ix.9/9%Q%z"o%:=M:6؁ T)-6rRq^{h6.: T2mA`})L?l/^];н;+cpa{8Cq:{oɡ0ƴ)RjsC8Z A8ZXCX2ޛy5rByJ/TCP @(BUg9[ ˈʊȴy |뭣-FfUK "x*dퟃtS1Ep:b0rLk;WRYXTt Ȗ+y"=t? qG;?4C=/CƥqӶ!"GeFn}QՆ-N=F^hGk+/ewtL1hR/Sjˬo&Pk칟׼沣#}k-Rz6e qɃuC9,9;2efl GUJm7$vu?8`$jq71C3I;4mH7 `Xab7& Il,[U84zG=ϟVu° QoXyˍP(\t{o˸y 8cG~NglB6?՗f?Gny5o>E,*ƁF& O1&Fr*ةz2 K_E<(O塝 )׫1N.JOl F̓hAD}w _C20'H~D<Q5HjuΧy(n!ދO]9Zw#qB<DtȤt O1Ő?KiB=qߍ4  )ޱ==puH']m1JVBƕo;e珜|B3۶kMKer' )=ր䮫sΥVXBQ }峜+ k؛ Ö/!@.Fރ%A f<»z_3ars<ض86T0O-Ekl924mտd*RcKՈ z}Tk磆D@xsk86bE&TK|PC|t LTV[$$m0ILB,Bo$A9 ~*F´{$ѳ=X[hFcdSNS_.n>w96G3-}{웅O(r^~g{@._ {ʞcL9p=iӀQRy%Ax'6/FC#¡;$F?X1PJ9]-Lr\ҿ{L贾@eBP~qn"i:޸<2ݟ~V~ʜV#޼,5⚅t*)y!Pp3$i˕0=c2Df3UJ衇  }jBȳWaN #uY}?h} ߑah'RH*p IDAT7_y*A [*j(z! B*-52ҖJ&k>zG{fQpJ.0ZkeG}C4ܚ-Iy<9zh}jUkFê!LҟQ_s2=H9{F|zoD 1%`gęGժvk(mZVN_ikoxm"cF$e0$FG1jUUvXYlak6ٹZAj /{Gk-=u[?Fc?0"C%ו檸9.0p/yxŢRAJ[<Ƙc*8u3ZLNABJ۳hO$BKȄF&#UʔReEJ?X Yk%DD)Dz~!{aO=G2ii sBcd EiGg;B$NxJ}K%Ykvא6cA#Dϑ1~,~)=q@O=p2n9 y6iiۯpӶ rVq`}q_ݹ,{A -~ҩG~f]-Zt1`NѶO>EEkI]:^(0,ȃ{tMӳ0MfԔ,(ѧ\VԘXDD;R$rM;C397z侼^l;R$Qf.YWm[Voz&*=faYיʐ0tr[#BPgˌ8&Hco6ļY$hFv0g*(;>q fS9wᢗgVGGc멥V`mE?oQy绞Ԇ)B08~,3VyÓrs# djG;np*v8W}xt\"mmdiv6-X/#]Dx1,#-28"2,d"}ОnTYĚ>!*S: U:t"SJDEi$51 I  ,eS(r:Jw.-4SΔ pNj -Q*hcGA]iZjg"~j\{T|Ňw aQNƾODl͙kX;y/(RF.qsΉXG+k׮];udJ/I({:;$5Cl80&y~c;vIqo_y'v2a\kccdRᧁ_6~!?xٮM$2?%j?ޛYv3SN ]s'B@ L"$*5Qԋwsr`yO(QDA0I@]paZOu:$$>O=Uuk]w(06s-4|qe#s3P7 !~OoD <3L/!)K װQe;a(@ 8HiÐaNCzPVUhPD 1izQk(^^25 =bM#}t>,hn~h+W5.4cjG/u_W_|Z.)ņlȓB'~gڅt{wF2|':ζ-u[]$ bC0M{03d![0omdyθYX "&N,$$7A@tT F8VWZfEA۶ /W*=|/D۱yznH)(>Ӳ,=_g~wϺ|M܁7Gngϵ'|&7 y2}ͿvEt͓7Vms"x.b4}8'yTkQ4ua eX*W !2?|h !;A\Ц+QlS?#FQ!AE2c(2GP޽2C"Fq4Qe.Rba}Ă>~mMp;͐m*0A}!B%?WB\DM{-7dCs*kڅ /&S9!~SY^X4z_{X4ú,&o)޵[d;#3:< -B0"Zj@e߸JWշ W]{wNwdEEe]bx2^F !^}̏c+(Wz9/\j/;Vqh3پզtzG~r6-lۜec08nYFQ,)ŵ.J^m)+8޷׉Ћ>I, \GhݜEpt≧YJ9.8)=].d \B. ޠ? ~Ӳ(*|;>?]]l'GKٸgϵƞ=:={5[[gڿ ܤ}>wu=QXZns(ً?k7g)NݘM8NTgb~\RڜO0d\f\`;P,z%!Ę"C3!*Bg_3J-!߬s\Z:d-%yJA=xQ aT^OC4dA71mVP [-L=%DPiiNeJ8޾#" uP?Pƌ"m}Kжs uZOa}p'pn7QԻ}!*9 Bl{܍ !jqC6?ܒbϞ8zߏ;o<{_5ޯO҇#v't H;͟~kfMΒcOXȬAHjSB`+.ðСv>fTO$یշcWf'Gm/*͕?''?LO3ϻ~ eeO=ejgS޹0bܑ۲񣇏xe^޽:rmsEnXKn=z;<:G2pΙc{V Fʞ]A(b3m2qLQf'=jfCɝkm夺9e'NL6^6' S-}B.cgnoCX+ . $Ju*#ͧY<@EumE˒˫dletȈS nG[_#dzϺn] X|D-6 m, u6`O,x'.!FՂƍ !X.I8|Rw:5Ķ]-4yu1d C:S Ι(YBبmYZpw>|G.-dHs]\sŽRv CMH:M Cq ݷNoFQ?ginTMXN5&TyPC2Rx3E(@Lα ِ yFqz0 ~\[cdl@QZ#Of!Xd9N9zW"%b>#0"GI5hZ9-M/LiSp|2M:F\)ka…'8klF go^|O&s̈3n.*b>woCEĭnePh'@̯8 +B떫GD0RJmj4p͹\Hӆt~aznO[~Kjk;R3:D͘z񑃷)J )L60`P"Q `גa.IZa\?v-Omw<<7cժ%ƹr0REKfF78sX C`$rڶE"v-:F 9"t~ȟ3ƛ={} We+Pƛ+&g܌E)6jkB$*~'}ӀT:W5MȻ[FFm3,`9E|nSgw0$ y1jn^?caX] yZPC2b 1TȂt1:Mtr~V9w(Б4,x:~]{ DQ_@yv@Zп}(8jŸgnkLz.Ol۶QhT_/>}ByHD k~ RCӮs@G ބ)`LOlfG.lf:!Ŀ 0+h]"#IӆlȆ|rKVYF`u#R` 6Fp#F`#1#5I4 [*R@1j>d(02B]NUE0YoCכؤ@ " KXWf|'x'R3))؉O" ab_LmjX%3Ml 1Guv^\֏~/&^Z]'$ޮw،1)ᔾ_޾ddg.J91rQ mL ʨ?ideY)`ӑ2[9o җ.I26^r칶7I}͞+k|z*non"&ڳ C?ɫ^T,,܄JI#$jPu7w&|;*+Ň.lgA6s((2JZT>m(!Y 醽CW*|Ju`}aӬ+@QEPޛ#B n'A'{ӺwۨYAyKge7Q@JDWDY\}Dz D( _aM@lax0oaABnom(- X9VݷzAS p(ИRʎYHO?5>68&KPWJB, 0ϼabC6dC~@䖬=0> Ԛv--ϱc|wmt3d?M:A``+pM=7 gн '0v0q" ~>}b;(mPֽ%o:̮#w=~?(yH]v8c[パJe5:gZp\yW绝N/zN=|]mzeb[ز_\ڳj2aSPp\<5=Xf2qb,קZnBZXҠ?%#l޾eV5 +{WoW eno%\}hӪg$Io0~_)y/g"dE\(-?y\n5bp߉-P7:KkϞknJ_oϺWޞ9uvP^-%B"~-ɾ4ydZv Qe@QyfuJue7W sn~d,>G+P^i]势 5Qa5TO6Oǣ0 +E7 K: ];Pq3VGt:)啺K-\ɐT(>Qtu "٢?rS:gu;݆4ZwAwqQs# qfwRtAzkLYY_jw#-}/VHRʾl0%y <>MA'}N3k4OܰiNt_<lȆ-Y!KY}"cf~,hj-&qA֎%C .cQB:10(˘MIB?GQǞHf fܺ655*IRa Gq=ny 8Ǩxl%qɣ"ԝl'/!tαϻϿ̗Pt2jot%,, 8Ixvǣ;_W+:bGG Cr9oh+ncV^㫽G"t6tYd!9͍7ٖUGzʌ`؃پmon ;Vev/7tdRqde&Sh܌_zR:vnMNp#jv3p-<#'wr1 Q˔*0L9a)hٌR  x[' xCmD׬[ZpRRQ^ 'N E%<_}m_5}_+"bPERf4rmZIrz'J3tgP mXDI)*lAQa-}^ 5aHBdP'@XJ NW`/)nYׂ)sںo0@z\.B|jGBͱ5mtРXJ9B8B ɐGy7WޗG=5rR mmȆl\]h1D2ъCQ | 8dY Agu6gC oE9ˑ$3m2mu^]1DEN$5Aa  8 ,l9Bv|)(YHI%jhKƦ$?BQ|^ە_59SG`c/>ޝJw9A4pWJ>^ݏ.p # slζ\pYTKǿqWW-e'v7%1ߨ!hmnwVJ?cˮ3XJdIF*#e۶ى/FKK:+LMN/VeGfAyݳډ[ 0\v88H81W_U:((gSͩwן}?~zSkC֓ ,gVEmnPj9Kz)L0|8STiOMOY<~݉ )P T랏me(*Ä{=*YT姄E(h[L˨YC).D_D^T(j,JgC"t (P~R $ F QYԜk~K݇!Lʈ~\QϸvQFC:G|[qxsR !b]J#S__/e2ݥ>ʐ7}3!!ϝܒ=/Pd`Kny"GZLHI.m3QXd+ NjaA4c&--d2Ðvl|D[1i$۟9E|#sqeƃ*N;`I<ɇrG]dK&u7,㢇߽EGjj+Zz!ⳡk`D۱uyύY8هfW~bqjj88l)ekRuӱF;g_6=:nAwy9j^|yr*TvĦ5EL5xr2xq!V1H bnXaSޞ@n1NZ6CęNx!++-E;1R2iG$ (28(Wiű2ہ!vNU|ֶ g.ôDYf|>sl59²Nc8R"B2o}xo>;<ߍ=N7ڥ^mqx,/EJq3#݃5ti$)1WK<]] yABXMnJޑzތRJHW2&jۮ4?.[H־ڷGGQVC(`c6݋(U8MXEr3ۿi/Em*e}VO6ڃO9Խ_֓t2[9i!8E9U"1Pa}>P,͗-su?v2 5=pgQQݿ9w^^D]{TrQ&4ՔqZ+Pr#Qs| ِ ^+- s_dE־%D[WZ^" ` Vo`1 I39&F''BP{ Kl[fԲn~[ߎ L44ja υI YFx=^DK̀5bt mI;^@*xLR"@U"JQoz/_aI# EgL{n.@8gnJC6Ch)c8Q]~hA/y"%)u\Z֠0 k~؍6oC'?Xu~d7es&Yg%M+s{ZdEٻNfHvHlqc4J@9/θ"9X b)O8n^61:*p3azu8ȶ͢thf>V0(1 GۃnaC$#)DͶ(cGH6L0407ZCŐ!`eϞky͟oJÓ;@K'v~PdmmwiZщZ!"!ȇıAb٘H$ &"0DEeQQhEԬ X|DQDgAOY)Swm_ ~DKoE)XE,y+^v̽[PZfIzw*NETi 9F<]J?%ǰ,F]28<]E (`v`NĆt;ح5d 8)`\J_k1Nk@ QU1)8QmoEyOt0cU3eO=zŐ~5bv8$|4!(W]Y cǏ9j =Vѡd(T ˉ&m%@ 2` XMCP%XkA"QRnF7u:no>FP"=A7/"kD,g%6cnP1  #QϯgWJd." )Ov~G_- fn^󮧫/^^`qs)jnG,1Qġ& +M(Qw# p=:,8"Y\ebVmRGLε|g[~[^[X#cvppf{uttRڽIDV%iZKͱMуc{vח;F>7>Z4;j;_t51.˂c`و(f\!VlaTr]bǮ3q |J7_1ޏZR܏lByg yQH*FJ$뵙w5f{m-Z=")#0cQ4jc>|ŽM3 nyͿ؜Zb> D%2rދ͎s 6VMdJ(-4Y\c +bFAaia*W[&]>̶msj_\I*~N< LngFf\zpd|,d5=1>m>(#S2qz(:V6h5ӡaڝ <1&<'1M2;wI"^YY-D.QP`?*g(O\lf{6p A?g0KW ø[H)8=4 Z[^M)B˓ +SfDrYb7k VrH_Ƨqn^.ɦHD Fd'V?Ӳւڬ:nf՚}뾷ngI63' -~D6 #( tu]K)[BM/K)r Qj (/t[U] &P`qAi'Web!n^',pY|yoaXP$ViZ8}~"ocZ2LC-5R`7x0]P@=cMhw SG1{5^ 9) PQ~R2tP7ܳOdRuݍUA(R>֗R6,!˪w܆lȆ-_n.OO4w%$=$D@X j(`1@:Q a>VԺ:ی0 ~^{Fr}郟^#Uo x_B^Z$Ԥ*%9)b\VN{p <$Fdl4T>fARMbEs,tC?|rz"j#yW %Vxjb$NXXYg1"&8Br,14^3a}rNwbTQ.OqԘ3*r0[X?t0R9U-lw Ց1y*.ʤ J[#}WfÅ8 mL͗DH6!c"Ld"id9fq9t̍ 2,|Fٟ3B#3f*sv6ffa;뚎x2 2 %{I›ڭ: ;#ـg8 Ӵ6:tl&RvGHX3$1"f]|1tQmlgQ63'_bgB@yp:(c@20.P` KkMZ(Pޙ?Rt=(Z|M J[R]BmBTP`f EJ x<1`Lk44':Smj؎״QflFX:(`ɔP־vvƼtVrQCFi_N]#k~[p%*C0G>j3͐U6aqz]@G@}O{BW_}O3|V{5'b:"Tfc;|gvB8$rtFDI|yk}壥9rg['vZ| Cf`<`&҆<%ϐH)B?@,^zT4nRmA.ҝM+BDRkqP`%-y Ct1yjm3d<8z,&>ybvҧ$RʽBͨqȆZ\a)!Q R`t:?N[ԳYprHBԸ3,+q7@m$>kKX*in`N)}kO#rI)Ly׆,j) =5=1`N3#f{g;4str@> 7\ 36>ȜP! l'D@b|r/-4̟3%K 7}_x]1WH*-(3쌈+X"s=ױ(d$E˔H^Xa[VbfsEfQiYnNhMNjAk7V:iGs,cH9lN=9-&A,Üwf8f̷0R:n+fjmuzk%:KiDLa K%E# WgBttb" A~z-4nMGaIabOgеz@ĩnP | s0%5dϞk-`~|#E\3uK=|}ẙX"OP4ߺYB=Bt\ Ry5,6eHcb$g(oC(15ڌ6}Ƥ!b(!:JFy{1NYE)#ij;UR{l 6/XR. B7s"tgB# [,:Wvi@ltlk͐yT tiBm6F+zvx>i'1齌Ð}V1m?ۧ"8s~9 y}1R ڏ$ ہ!)+_sSd5_wKMkvrLDPJ dR(@Mi6j6OqԴjyK<^7WP?ʖ i/(T7׽F촐-^L0)-•K|m]9p=@Ęǘ_%oH,maaJXY$qC$͈٪r_|uc+,zO.ޒ_⣷g.Jd N~ՕƉRz(^,J>Oo٦0,AG&dtaHֶ G{ӳ<37rYq殭fsȈ+}~1X)v{8i悄U7$=昶}.x#{ea3}1}; )B +c-;IΆm5*_\Z4\mDG}#QO283+|4G-D1):I`&RJ+h[V; f痪d34m۔2Yi7-װhkލ:Xl !ah+[TdoCE݈2H/sy[FvZt|u2^}/fXt')iY `D!RQEh6 TX,.P Q݇IpQ !JiP]9">Z}>ω-z5V}LM-G°t*OTz|_y7C(PX@bzLB)FlȆ<ܒY"K44c."/ 9kͮK/BPD~骞$G6`A'5 '%Tzf[i@;EЌji`3%'s׽nQB=~3My=Rw"AU%ݿ3"NqBԀ%1fm-A"3 e^6s+Fg4un#^ dx@r\_+(#>E7X(UF%2Mu FFj]#6` _ rq`DI01<]n=i47<"b)lB T&3#8:=b Ȏ#d7-i\UH^gGc,GG$3wnbg'B;~v0rή/|2<;bͤ a$"d]Vzx-e Çgd&vdZʲ19zfG X83J7^ѩv}˴>}?lnly4"f#H6<ϢdR!WQv vҾO j+B-ȓ($sx7 R. !꺏)⡬Qd0c(P(6ނbLo.BL*;U 5kRE-`LK$Zz SC[]=RHsǭ',\ԟ =i>"i(hap6tQ-AQ:qNyFP㓲ں)$T-+rE& 1'5#uC!/ HlH zX#8 =2fءKzf{u|> {L_~Gg̓-w~{@I-:Z6*1D8eWeA,LUe[aC$3-8" 4z||ƽwnA4$U}=}~;taf%#g5Q$RݲqAk fYj mr7҉Lq=OZR.ޞ]7p ~<ȕyR~bs#|+ >I>XKv>Ц# 2nV􈟲CcqLf6"b+(7gT-jIkmodf\wX|} p_x{`_dՏrG+S2D>|sϋ͇`0/-8<Ks-2G Ǵۍ_V~csoG)Z5/cjϨ'@[( < $M (=Df\KU-{PӦ4;fx9ɘfU} @bRMz lµ3Jɶ ^=UZ'?dz-kTԀu}6q}YM_Ltf0{5 XY kyN6st- 6y=<2g猁J h\ P.z?]p-ܢq9b"Ct26Oz+?%8k߾ %3~uyW^"0 췳({v2F r}^Ff|d{Jyv9\vQg?ortտ;̎@̇nv8~s׌'a+^RRH`ml7:6̷Y vG=>$y6(7E,ۅ%K}rmX_V1;CcBXlY1"ؘ)M*{곽j9wiiQYY@Dri~ pA꥖ 0 3dLDcdwB?q~2i3:֏PD-ƣ.bm٤͆߰{ XHR#-s~4_26(}ٴEͱ(|5]%!AH~s{w_?o`h yoB}HKu@8[} o[\zQFm=)!- _w]X"lCh< [gl7T0e6ˬw<˚2F"w,JJc8Br͏ɢ\9o"+0E>v8b^paFXQ$TNn\&W֔\lU /{AqۉcIgYٻt7qz4llmƑEn Bm,J @YUHn@ɤuOޑGjoa͇uF 5ן;UT2PnJLq&}\iS__W}J5Tq&p+oeBM}\qyҷY-8 SI~#?:i٬>&LR4TUfmQqYؽ>Ǔ1ViO~2*c:}qꣴGk'iH71ͦVߗar; 3p#Jwo\#c>h᭾_\[]_[~T:˗>8+t'J(K2}٪)gF ΕkFIce;XH\[y>gߜe-!/x&~|4%CpYycJ(J[F۱6AݑM-8x|;ՆY6 S]h(@\1QӶX5)1yNeܶ<.j?6]sZ Fw6 O͵z{{|ՌbfZ.sw֫a8ڞ,,6˜ś Ӳ&\y|\u\kG֜,E ,[yaQ'PBVz[mB4q2غN$Õ8V1 q"Jpd* z_0yRN}%ǪSmcj!q߫dոNs'wTٮ•RW:e~b9\yfG aI0&o؉Jޫ*EDUWm_=ȫzl{lbNԳo_~;9,4Dd< 4D}kYy(\yY 'Y &RJ565\őWk9dmnxqR .3lSˏ^owks-+DkRK|ϧ`vsuy)*a1{tq#@HôͿͥ.<2t wfd&VwpW9bE|w6Q<[{p;#5p>yOppF2"G솓\eR֥86f};ǔJ`jQBZ6)S2u()b*rzRdx\Xy,>J|\+(V;8h'&W9xp B(&bIBoV30 7؃qS̫IeG/l24«MjY<%= A`,"EW92]= ݁\u!L(!ϲSvs N'|3Hݥv&_O|lwn 9\o%Yʬب i}Laڒ>}|k9D(xϿ+J_X3gEٽ)|uޞNk{t9g*\gA^'4r .)w_k H$Z)lfTM`\%FSaeV8 BZ_,RZvƉ5FllKߞ<*<ZLGDe)8G`4o4]#uGoufg+Fo;ga 9^+¶6E+*6"tMj0-G= n13q29 >dU=z0#ܛô?*A-EU9jr3m픓}=ʙ+dJ^wUhUz+q$rhR!pl"$Z0 3H1-}bZ<(UkU}U&;?_~hH5^/&'q%[c(z\\qsW> ܽ5Z>FUK[ P WKD,0$jCyy}@|lRϾ)}˟.7<'"Ǟr7gAySXb6ɆI^tN/f{08~MAr5:7UI|>ig67pxKѺeoE&)ixĈܐ+fBI""cA ( # O oe2YQZV;W=]0ӂ=ЌBn?q_Zd$z#/smRhm&=a 0"Csa-k4t cmlkCd'Tl]cB6o*|sCu7SG(LWjJGϡ8!6#\l~.VZdߎzygͷ37u/]_V>׽gO~6We]ޯ)|/il4)9B aGzTtwVf14-Wu]xQ..ʤ Kls.Gx%20ʴ=?&eoCfZP>HCcȲILH4%j) ,Tr.jzg/p&EVV2NdoY#g}C/Bdsw: ٙ˗t $9IAB 3_O鰿4l~yl_śҷ'y_[̏xX>?e:859\iE pĦ8qeY " Gj8ríɾcd_\6r<=W!ScLYm||8+T݊T:iF*3)prl@PCuN*Jٵui9K [8U6Ssfdž ؃tBW.Uc>|~+8\lncJ{:>JԤdo)LF,_RD0hW؇L\P S?1seӻ?|;?fG߽cֳ:`.Y 8 ^_zܰїidCyJ_r88ڮ;TcRdF]]}a-V cU,0!VNCOzxV#BIrd-ԺP}) ٬q^'$إ8:fVH+˴xQ̹S͝-Шf&/ll4bn(s*A%VOWZ.Mg,K O/^ohDuҢsqgƐ//?;ߦ|4JO_S[y0E^3g8[8"3/FT~-JT]\I kҧqCwx xZo!$s^h.Jx%RTi*Ipbj5Qg.bx)1LKKc>8xu"vS[=L.AN)9{{!5'Vӛ5f h<Ί&QJ-6^̖oו G];/ړ kby{g$^[Y[ #B U5dL#}0 aɳS(+X!𭰣qwk]42 !;8TaZKwx=15⦗Y a+5ڤӈZ[CC#nhOc&*lqt}Yl~bFoXkͿ/TkgpMa Poe>[JC{+k܊iEh}2)\َŪĵezkGs-"["UUŪ̶:TWLʋRAM,TUx#wg630̈́c =ul_itC1^ '^Ԟ9xOK RR >oż.h{C_xK6x~83ز@DWE4HOPDbv{(oy<?'8HQ3*.gV;Ͷ.`ߐOm' "<ŷߺs}6dZecgY(֌/|ZaEvȢ]誔A ]!R)(@ҎDY)=ԞedTo.z7%I1 Rgw6Fkk6+D140 |<7.h]ﲷ1c [ZI.Ӭ 5 ΆqۍkףjQ]dPkqTQv{_Ʊ޿xf[ O~+ ߷_!]?BQ&\aGއ `ѻj1H]%2]lq\f?v**X>.01Uވ~Ud;)Y4:\z5nz/j{#xNK[ͫ9< Y[aCΝ-E[c%QJZa1˰%tRX#B &iVx E+iyP̏D)'*.,\K^`;_XiEbq*(J ,n-l0pj}4ˉ7wZLVv{l4`ZVմ2rj3s?)X]\JIslNQj9@>Jl8KNo(f[2Ja膠b_2kM3^R\sq\pez& Vc \f )Q\|)иcj nÑdV}U+>>z4;xVk|XE\@af0!aSܾUQ_ {Ѧ".V؜ZKfu@ -tފsdQz` ӹzU Lx(A@#'kEjT>v3_?.6Ӈja٧t4s,k'T7A6}qX,@xRǟk߿Ȩ(GDFhN[% ew-}SiWO*!ЭDaHeAQdZ U/je'qeI(K%\iՋ{|;ieYhEdM=|1g<uQ~o1dyōM4GXA J'~8Jn/wDVB?IGuv;u@xRT+竃Kj G-,xL3d[2 &6y.Ǒ8+TE3gJ6pꍓ}ۮgTHz\f#&UaeVRQ>.+gj8XR>yUMp_5 3p}{~7._|+v cs9O%)XW c&kj"֋EΝX'Zȓ|D`QHŠDd cs9N&Es[2gfdv$8%[{*,k_eyy%/#G .U0-K]e^X1Q2Sqez"L''!.z1GJGpD2d&9/;-8xDxfa~$x;؃b[ؽe19o<籶|\(V@.ha^vKA@&}p-d7PO,F S܆,Ql~RXKεelY4!Yh618`86ˍv6zT noҊbGah>]jV`,2yB֋_1[N`Kf )h+) ƞVVh7 s{$I.i{N\?(slucK>N= Ƭ-c 4M1yILmq0zR ̔,ZӔdw~S3xkb̔,~ȏ8"V^O޵pu< ae8]f#y!S◃*VJjŗ TٴZz>ΪW3Y& ŵIY~ˆB:}?30Z!ޯ#V:flV ؑ8X_P.#d1/@s1I}xH]cs S{$[58Zxm:F%Qww(rAIۤc~[PD,ڥ6Z ig(0 Cb?@ A")0$IBQZf$ҲTRy~}yp;=FNcۓqIw m= G4VhXAY>uVR0 RDXք'E %z͝-.!Qϖe.(OB~?9я\ß?[jʼn&0)E}'dRܨɪ\p*qd*;S~LU9Z1UKLKZ+?W0 nR!3d'){9>/|+p") !^ɯ(L230 ?JmWu$7~E>Mk RA\w7$q&NVP(F`HPi$ Yh}BJ 5ab-;F;$[uN,3)f 4<+h4kR)_4[u]jqD6$zE^bmIQR;w'8~}O]wl'?!~~qo⭉1@']\;TB*+߿/DU~z4Jp5*U*U[V$wgr[Tʬ7 2w;fڏyM!ի'-‰ fa8!rRZB.A[%xGd˄pPjpm|b{sQk[0IR(Pzxx»~T>/ loWZk./Dqn>?N=&nj/~Q; eQMCFa)P3g: \P>yI 7GܔO CBBA6i[H"?hX!A&|s{_3ga)u:<90 xzs [½+fxJO҈ng/`$"VZ'X>GteaEk&> JXHOE~T:M8 m uODrω򮻗u-n5ib;16f'z'ΜǔZiikXQ>_{ó`w|x^l'?!qylHI%,6eoAXksk (TWv#fSᛘPFE6+X<}믇)V}U9oգYjAF+cr[ ݆2"ʪ[kOecC'2ַ 3pkGlk?62ބ((R{,*2đ"Ilh< Il<8Q[nP5Ǔ[g]i]:(yeV:.ڭ6V[zAg$O9j#bcG2+z^Qfh5BX! E#Eu9v 0D[2E؉M2!f^裵F Yc8Kƒ옼{8+EȬCk_(榟u%zJWN*$I3\E?`4M:q'I238Mkm)J%M bq=(REҎ0”UzpBJGUro"q\v8;[8x8Bl_Ǒ".z48eZi 30Ï~2NE,t\rbڣx(>R!0^g x.6ǙfF/ xb{4DQ6g00ϽO:7ǰ(`FC?WZI/]hrCKYXYߏHzV@rf-m4=dd ˵$c(b<#EI6Lha&Vh/C~2`\[^Զ|ʝ`Va7fJEMG,՗llF}Vg(eq~)%hZ!RjQT)I !!-,Xm#ϩc,}Ņow-ju¥n,-1nwg67?PLE'k/ư>RlֻxscFgYQA۩*V0)6^)zk>^/Ǔ}EL{ ӌ ܮU׫T-*?-.zC-;qǻ8xfᕢj_N`d ' ]9jYC\Y*bK@ +=NNf]`fzY%|Ә Bqrew:uwT>+68b~!mQS1KS`=mh `;i9Lǜ>F<+s $<#JcV)B2cZJ`]"0RkF2MFZa-{8#Y<MijI& IDATv^/R-N<,1[u"g0Nib$!+RT(9vlП6FbCQSK *jgA,Hұh\1ڪGe7<\oF~(<%Vg[۪)u/_{÷/ 6[蝣d\Rmpor 7+nY ~+Zc~.2MYᦃR^\OE޽U!ddmTc{ I˳ξgg00I"EQ$h^]9bkJ¶lSpXaJ6lZ+Y %JR|{X0 _ c 44qaAx,$ ]oRjXHOaҌ]F$Α4ύƗJ%Y>U;f3[z5C5Rp;\gG~'?eV\*Xq=b],[@ W͘,,ER ^XR]p הR4cqxaW2l8b:\cqE /5v_MR~^bWZk8)ў:B(j^30x5DYAB {=,{t9f\c!3T)%k%$xn,ǗW}~(Y76`(<)Jot8Y\$IJTnx1qG_왞̤ZZ&СG~#  4e[uT\*XqzK\aR pިw6N~yTѝ~%Wo5,8s[Z(Z5}veSӘ2(uθk)~NkgB5ʭx+{xqX5s+z#<@ @Wu-GQ-0r3L&F!ѬlH_KRոyP414/0aau;n4{ x{&,uz,Gx '׷B;s5@~^УAs ۆB* QB.n{:(Vy: |0W\Y O0 lF ZboM^l,mȡZy^Tf>S{&gdH{"$ZXO N7 f(807G;s9s~Z!a_cAk aCk =,[lw5,R rӄ^&D* Ymڊ-ww< hFi>uo<|OZYzCoi٨?cׄJ,V\|WC؋ݱ=hSWwpBVK/bX8*.+3sJocXd]K2E9\Y\մziߌ֙o^5\SBN|.w5zI,`F/"9< ԆAdkDF6Ol\k[wy}xxLYg=`=á(y:!Q3hϞY:CڵnCISBA8YA&!v{šBoέa"f ipJQ07" ICs)Ы7ZPVZ1 'h2P]8NLʹ*5,a&jZSQ0n)tWjd+yY0&CBj6h1.lv;\SV'WQʓtXN[(r]<{6:94)ߺ{[m1͞TAṋ nsbޙ_2,_I|7p_̝}>pe"Xy 7*UǬ1ewaW:TBvxNoeiMqq*aSp!p xxxjQkmcˣgBYg༩GF~ w~''؊Ua@I09*hNS/K3i[S%4 l:H~/xOqOm &&H%'(z9/ъ!{4'R9BFF(FDHPk0_oF2){mַKg\t ׆ Zl 7z|n{ɱ{})JL=ھbCupbUcymaQ0(|)yـ0)4^8be̍&/Dē ]#I9H!HtAlC`'z$YV[hAssjDK.RѪ71 <Xu@QL !a}?<͒ utn)m*8h|G31.S`m|/`<cWQB|b(Cw |Y*~>ƵW(·W|-} 18}Ƶz(~l4V| '"Yk/kނǀ_Nrg_~¢8D>v2,'Bj ĠKl`B@h&[uu^-E^cobzћ7Hifj*OQL6e':(6}6<]7cs8*{Q7LcV67Et<-T?!ՈI|}$Ef.#CSp9Dk-(jqBR6L7hL+Lk(v3Ȑgz2ߘgmqvmzwtA4ꝘCg" R3̅5(\%jnxIY)4V"ࣛ@LD~?_ہGp/mq{pn{+|<#mF㼾2/|l 1, fP4,RqzSȗ :8ǻjR"[=''j ԛ^@5Zmkf9Rj5y B,Z\iJ"yB(DA0kppg 60m׹(Qgm7~=UQq1|󫗾u 8mFٌhqˈ°s¤R֗k x\q܂% q8!f_rdݨ$ !㨨?y)&pDzo<84{X훙ֹd18F_/lfiIoɀB!DFXQ(whE '5=d] fKٍ"V'n>5`%Z`h4-$WĞG\^g8zu<}g&ԤU[Zd!b?]q@2țV{ayQǖEӏ" C9(F 60;> b^D ~@31(_<}߿yl5c k-nN>pC><3ۋՁ_}}JH%+GMXv ;8&XJS0;EXΑ^,xp= U;&ce{k1~pY.'8/0-jZd} |]*tZ㨨wɻZȳ튨;m~U뙰G`bmZkj8Yϓ`p0ޝ@I8<о/ydyri?YFΟ?8kxՏΆVgSk S4 |(y[$Z YHzl{R41:פ:gsF%@XBmo otk`uC*e4&N|cAL DSݍ8vJ)4QS^oxߝoIf|z>p8\#}z CG /㝽BW8`,>6ClR.vvpXvZ\8hك]1[R۸VI[U==^B,򬯉Z;q9 z|oBk\]k/S QhH6L9:IMprFt6so譇AOþ`J 4Y`ukV1Ct*ʯšt0|̉v^X[C)r WJS(sf} Z+Wt%#\CZG RFkZQD#XB*צ /lƫEqm3ڵk,eZ7!SV )!% DCroǜ6) D+EujIJi\5)&lҿpmm~Q[㸑bʋR^ͷEONM]K eHD9zl`, -ټ~7va&fg륅HZVX#xI2 v&K>RRKp ^i, `Vҟl ^^W!ݘM@xnnH0َ)H|M!< W9u3(W:+.$ d @^2)Yێ"Ӯjrhb&õl<[/z "M }.d:IW6sy.nvs: ~W-M,HN6Z."kuB%+cyV?B| xl}(eR0Yơe"xʥR6"K/&5vC75N8kGc^oG\|˝e )TTTTT\{z#!`|v!WA֚xRq9 B$2lm{ Ol'ז3L5L4H)+xqyMf2H^ ")tC֙V'Uӌg\(BA24Ě"-0c[+T{3jlZ_ؼ= gٖkŭ 6.+va+PivSi+#2W$ vS|Br1Ae_gTb;lU$^aꌺ>78!gb]2WqBYz^&6dW VXedi'86ɲH)l1Ɩתd>.ԩ;&e׾:G."`Й;W E mȴAym5ғ-|Zf!A IDATxt^x eH))`~efyla)=Vx4 =Q+Rn |{K:ɀX[B PGq#I4"^3TճInL3=ߐ6ړ,k6d nSԺI<,3O^\k VwFI.&)|<b%i+J,V|G22LXB < GHnť@tZ;e'T'<$ 3)kY|af)Fâo:-Ϯg]b>%ڭb(bˆmĞ WzvC4pG@[K` .J 9Oe_WTb*!hn(47( .pZ.FGm8hr 'l43ךr:T2C8 1)CjCƢ2e 8˫?;U1ddzY)->y">TZ@*<|ʹO84s@ZŞ Ffz>P{F=FmFjmXhZ34e:Fx8<c)aK*@ YK(]xgj$d Bm$BJULx~X%Yr.n^+d&AHnL ;1p:'gYow\6A3v; "峧ݦ`TGB7l·CDQ \6Ψգ)BKZa B<˃ׯreqˆً̚Y+8u_;s ߛ_cx 7>-`}w|/wlC4nm|K֯͜K#[exڝf O,ka#"7&Sىus`˜[f6euBwe+Eqn=TѰ΍:f!rXz (ȬX @jO f+KÍ1i7~ގ"~rBLF 7[_hyvc ')LΑ9' kZ]Dms9s&:ߤ{R=oF(4ű뭕"A/P2gZz xN`lP;UqZ!wqE !e p<pu.wW+׷8Zgqgj2p8w@:|~c7lUGzvݞ mDHy!X@+sje}%7׷oxYn;8}bƆwJ=W,d viF.ٔX 7K֢`-<@HQ`l NCwLE u3a b%P '`95vF-_;slf&Pc.hy8?֒k&>:T sJ,^y>ƸÏozW/b .t8c,Ja Ű熱-Pͫ -/e,wzwzZx\EW7M?}X:A*| KKe1h a$֦3)%'`"#z~c- #ȋmןd]ߐn=b㭐Lm ȵ09QqumB:ΘNy>5.5dTw*:13ΪsIKɛ(@kZU]{Yw9ZzJ;,w:0XXح##!i%M@ DDYZb8s ';J,^yB!>NP^q"9 }P&.6c N\Npr^<2{+L^;1n[QQQq%%MHDoC:Um\ *3UbѶ@F.!RV.ӕz mά7|Z5g6]ٜx 6;IPmu|V0(vJ >7t.92C!Ywv<֖ P{9r'=aLܒ xyY܌@0ݪ/4ºX8ܞ&0# ICz.h "1KfbpouF% 6`[k+x8 \CII-xMM͹ [j2@=_+X^n<2+]4]VTTT=4-=FOq}M5B u@ i}MIHb;yۃM={3M?}t~(؇mw#7?~6oRyl =muVcs1ʢoYk1r VE:1۹dYC#ib<`U8Ӟ?XC4 c!.7ɋX"?@[aEbc ) elzsWqQ+(W1u܅]C*/Ghq5$]㸢5eyΫX:q4N`\!u=D]) ж+**^c?=]뱽'JRk6/A6b澧מgOtb L#wݿ>޲Sʹ/CO9qѶKA%V!sp-(3`hIYJI&Ǥe0w ҈'ET}b]ōC%Ư  "勷R`…NO`x}|=/ ķӼzoKy˖%^cV/ckméJ{ GcOʢ b-b@[^Zd ыO޾[opoϬzGN;k4hE5 Ӆo<)eIHGW.C@X[U5+.ݷ§plBLKc939tq XJeӵ%[i$nkX^6****{d^C:ߨ!~?Qk/`.;W65xq 3L].yιʹ~Yڏ!=^#\*98s$o= 5KR c4h@yhYΒOzqA[YB= }Ifj S|} YAg5]8Ft0AϹ[^!Ʀlg|y&ۤY.W:͋މ'5J-Ba-ly 7m\!iP0;dwK*?1-S{(& NoWf0߷wir:A _9s2کJwZqlh6},B cPJn _n_x>NJJ,^&){g9!DZ[kDž)<⣣͸k:?6ǥ'gq,f+ hnR!|M|< -–w 0kW>sx@ͣvF H[ |z<6_;.e&o|e8;+u˞ɉVz~o?Um> x tPLzzڲ60Fv.z(p PZ$! o8_8=᰷=juM{9`+rxjq7R*BO>}nTgamV3{rw Ɋb+EGf3D7nJO ]? ? y;]8:{՟J,FFl)s7%oWq wB]-_ޥ7"׌./piQqR Ψ݇HTΐӀq_Ɲ/g%WN;yWqbq5ąߞĉ'_, ӊ׆+d3F9bT_elPU>G.͏X=~*CO@ad=?mgj3 GY# 8 FaQ7:V(# >BXTBP3H1 dnǣR3sON2?1E<;u/؊ZLMX:𔙮m5__:og"ۊk''kPDžោk"Ͽ)kq>MhWNk@qGԼTXזQ螋\ SGY 'Q<J[9 8YIJ1eMp~fww 5ӳyJ#WCs_iNJK].uik8;`_q8OK|XMdM.lk| ,(XT= M* ֠ 6.$3+֬%܎{ᄠ>n&(|g˜#I6(@sPyϳJ`T8? |yt|_> '=ɏ'z~vb}EW>ofȞ]>.v;Oܶg`V*%%Rm5,WJS*x!֌MW sN0,z3WoW kQk!丰U3Z{ wqP2//SipbWp . wZc>LFWq?lgĎb;6L{ W{O}m~{8|g!* +eqo g5 O5!̄֕7f$}v]s#5!5t̀>r E$'7!և "gѶq/̓3Ӹ M>sąi^g').bwyg]<($/o7pq6W702@Oُ*oc ΂:8a$6/F#iBԚco,暺W#(,Φdo 0bݦx|km{쬙kO=< O|øI=k G߶w?~QNK⢄*^TbKxƅ~*!%7Z[N |7ZN$2eU24yːҗ*)=ZkkR/npǹN]oُυ$d\***v~..D﯁gp±Hy+|c?ygnOWZ,t$LLfH$K5 g ##)V!ɔ )q^I[dqrg՘ɸF ynu}{&'m4,=v/<OYyjip9c~;~/w+n|&.}/r7sx84 ѫpvDʸZN!v(8nWq7T!&pᤳmgl֮_ +ڿq~GmUTT.9|{ג瓸P#Q!Oe*m~$}2؞XV)۷/@@ _jdm0WT~7j_bԔRoZ eoy~^ؿ{a޼ߘ Zog;:ݧ_~UqRyw0t?tooΣ>Î4Woe#Y 65BI 8N:VNS8ĖKJK\,!cjt6&@[WZIw5Iٮ<9g,Ν+>>ڈ6){ )wEC~"I\G\W|+3 )z|3$o"'NO1"4q|"QLO؞r+tg[[|}K:H讛WhBvQ9\"r' mh3*|\fyS3 S2P؊%KX<{s_| qGul~jJ+~~3^H$V9űSDx|+#0F~+koMRJ@t qK$f}D j.~H8Ug( ܕs.A8#C̴s=$Օ+K04L/g':m?o'+q4~*yo@KC"."bs22 h<\.:4Ds'+JFYt9Gz{ҘFw!ٷ3y%/w_qm)cD{Do0d((F|?"tw)WxL2p%}/IDAT3SJi)FCDDcDpZȔlp35A{y1ޜ.I m[0r2C 2L ŝsD]C87>D&Tni5=ZCiJl-r"6WJ7Oay˗ 5/tr<̓zc:'U^pyK[Xݺۮwc(6_[mDRy"S(9P3qx>zVD(+cg:s9N8H'n. kV೥ |h̴((|&<{e\?uI҈XU@3-48q3yie/8 3H/G\-[S2f$6WʒK&ww_9;qϙ9sh\P. X}GDlkG=@k.繁ydIYZm'or t%hcbh_apFWC|9HLxx#&ߡIhŨJ)!Jq+*%i.=A$姕Q 6'ұ $Ooclk;- qM8m%6`(9%@KΧ/'p0 >] Elfo(~anzai6_|= 1YkDdnѷ5;'rIcoqVKu8 |hbqnB"g ̀,ŧ2D)MZIkBGͳA|d+-+[iYGf`ilsl>|{DN%6ALȹHP(z/d87Ct!&wug!|v QzbokC߿;#(bL5,C'Gt]#x? ězN %.6"HԼц?9RJKRJ&v;{;QNObGe2]K0riwnr""6جB||)Q᳒yx4Cr9T*&364؉'51@ꎣ;]gYxH*_{c1ҐNQ,N D·4O?F.{=f"|-q4Zz8Rs匜.gr6~2^r6NljjqMNR=DbQNOwiz⢏ؼ71:"ج@ڲk&DW&bsU\аpf&!|\.Jl'v4Q")bsgK)yL8J3d$rRJr'ya`qwgI3qrYsK\4soJe(}Y|fɔ/VvG9?Rj)z*t7P$ivtmDSU^"beҧ x11rWeD2BTTn0&67r" ^N_>Qe ظvLlf?cA&ӓ4]&s>R: rqA <.$1n$.+e0҄r҃Dy{qfFr眇5Si$sN)UF|]R-tmK9=bq9A*93N^.-J9؜ U^e ;UrWwטG6"˩'XҴxw9}G=8Q޲TG[ ƶ:oWI /1'_j9tuĭ7$t>|bܣؼ}-D8"/T/fٻbskS @j(f6b\\ol֬1Y f}qyjEwMo)N薻qgWT{=đ_!n.^oɍ&x}WLrg`](9{D٣mk w-RljM*oFlnMoZ^/ܘwVJ9LpiL!]ÃD EtZ)e(711ޚs>6Jp,U>m[!JJL2cc>`#=sF(-W >bE0VRe4䜿E|s qiḧ. :Is>By:0o ƙL!DEIg·LHD[Ҥ&T|xL&If4ۉ'y`sJ鴃vQ 7ά/N%t7q Vhjl֙&v$9;R^4,C!)gal'Ԋ[Ch2RJ-cx3s·gwU"/1q;sh_iV"6OSչwvW%wgH1.D^TMJs= DQF,<3FbsVc&1|'3&z13YA9a6zJ&yNs_s%DIz:_w[rYzѻH xLOe$?RZ 9sT'2/*/9ͪ$Em뇈yeFb]ntZiFoF="wu?_UI3dqv1v'1_~ߒAblL%i ڶ~Ĩ3 g4o46kҖ 8X66k^1Y)e,>sjeI4Um[.*QJ R&t% 9Sc$i<by$͡ i% t3o; 4 ܾ,~f9]ës\FIPOg9ܲ~! ׶мx5z,~,α+EDL<]QI槞Ct m`o# _L.9+cs{Y RJM7ۀs}5]()5眝$Iڶ6̯/Q:Hmt5#mg5d,Rh8 ܕs>Z5-"p1;9{j&ILˀz:k;ҢRjȃ;\@yWq`yJiQ0;aFZE|K;ҢZw&y2AhdVfY$K^ <|ꯚu OV"KT_z: k/kؼ}[ZJ2!:$ųLy]|XLtIMR W?\AJO˞'F]-ֵw]lnHwmFv-o$'ųPO)DIwˈ]ln#I[۷mq8xՇKHcX0[Gb YZMn#'G=X HtVپmncsKvHQp cYdr}Es׎zx N$ͥ[;R p )͒f3tBJico".9fU$կ xL^{? Ͻ%Iyݡ wJCf9e'%h:e.xfIs7XJx5Q2YCU9睳ќ`#IRk۷%UsgQs8XTH$`$6/B$RιkPH)Ms$~wDl_mdm6, 0YT .~1px7dnXYc;s?[IT޵e.:\6"6˒Tc,fRJK9pI$խO?f +8ZӅIwU39cF=ZEzaH .IR,rοȹEITcV`R-",C$I$UqgQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$ITdQ$IT*S3IENDB`openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_45_0.png000066400000000000000000006646421413546205200254640ustar00rootroot00000000000000PNG  IHDR<. sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxyxYj_޷8Gq2F$4$$!`*ufMF L7jRPAOv(8T-[ު[9i,h08 ;w,\Mkܳs_H stZPs@8 A`;K?_^]4bGӔǁmClڹW!)HXyݻ*3[4ǂ⽣t&*N+9˼68%JI4H+Ϡ+MӮQ:4ŝ;XQmCT{6nJ=S7oȸ-DžzHQ 5*:qvqr\ɹzx]4b\UvUI<+zQ7zqF`d!f~2"7Vlwu+2%q ~b" 0V⬗pL 6ҚR )U| &;wpl{Am-3uVK[$7OGߜJN&Lߐw]^=g[^%WO2Vu4]ϙ*<,K+zTVh'Q]tP:O2Wڀ('O}O|`֛^g*L}Xp4J*ט&ĉڎF;DF:5ՋOjI5MW?gE4> ihʀUaXrj`np"M~ H>mnDxSOfw!/)! !;0~͵̎a^T1Pvc1.Ȗկ_-P %܋ƞONt5m'+|FZҴA<k1xo@;*3 Q<,}&c!`lj&k?H\/MP`1Y2<+`b^͹$걛f`1B*ʎQנ=NJcsPU+ ye}vG{[" wT 46T`jVۿpyWY0E:kji[d)Z0fӦD^IbjK gz a[Ԋ%)ߡKmjmշ`<}#' i- )`9how &4ހ:}A8{dع7ɨ3CMy4ʰk,sbxARn8300)0B )1cԓc =mLjXyuJI,o/}dp$~Np )z˾.Xiۑxcp /jio@cλ=H[38O! +DQ=)D[|Yy-e :>sfDv{G ww5יMv7f+' ^hI^*4bӚmH)9YXU=deǴ2Ow"ɧShRVb"=Soq%7h"TnGU?xc6?մ6c,} ^Rqdʱ{̓sO},&\k7fLgan$J 6tjZN8S]~lWn6`Rg)H: h,=ig\3;8"jQŶEp{gHG2baԡpH ۹Two|!`'{ڈ13i܋z߻Qٞ^q߲u({j>:aݹvp]_tэDH) ? HB4idڙ함D2&QCdC?P<{pR+Ҩnҵ%PָmYqAo |<ȾȾK3(pR  q$FV߼[l6*Z@bkPb-v6T8u  i)ww-ǁ\o~~86Ƴx`U<8܍Zyğ})@կYrLMVamb9.ݼ1}]O΅SkR;`F>kG)#| SZlDjUT7 ĮծӲ8a+aimNd0x!}|s̝jQ20S42Gib sUS@BՊ_"1ГF5M{C:YF@m]^q8뉻#xnwrk4T2 Ƿwaa$Iwy83R-0I;6J# ŦumߚXZkyşΆNw pFy~pXV2MjĮ1WQݒ~5-cv=ywQOQ)wކ&&jƀVCOfTf4M;s/]7+z}?W`pU/ mc+Y$>I0L` 3]ggn?cKŰ!g|&T@ ?Aպ*P>GfԶgdD t^fc씍L,d0>ߏ? oA-O˨BPu= \4Xaィ{㠮hMΑ]噭GP'P߂ZNچ:[1De8{Yˏ+$=W #10 ް*Kaz[?Qɔq>-˸ aToõTwl1j/jc%KbGM44vF_#vL8j)Ωޯ#KKӴe+!v[^qR jnu{vԒx2`2j> Tr$D`3YHH9a3񢊙hK%.~`>_ڔے0x?^Q]FJ:T䊑}#|,@8Epg*'oF=.Tv'zPz%O F^vj Mt|re5@ ZlYL4VzilD25)ɱ:y}>..Kv |cvT 0Rz\ IDAT͑WD)0?`D/%{{zpl"KXӑbNl'K1*ڒ\$vq<#/LbԴyiOvuz2#oXw_4t|?F"Mo/_vT-%Q-YWTm XDV:}$~RH%9i/%2϶9n[¶iM@un>}i_Cd82*}n9;˰_2?rt}fiэl9:JD^j- f$6{sNokv˨W]b3i&,PA&ԖF LTc-I@$/̜P=9m0G&Oxol1iM2fj9Ǖ2YG/{K%zV7mitq*4L"1RɶT(N&x %`{Go`KL?gr%MӮqU+\Tg DvڎP^ |dIW] 2`CGM~DG,vR1|8٠ _WD^/,$$̆NћˏVv(i8ڕ51vy$hXƂڝ;wh}m/=s-;UiF[}'NmMh דoق3u'tRlc_`ĺYe i 3ySqjNY &ckce~7-JP^ojNnƮGKvز&~.sAi:n;4Mh*-`vvabwF-a%,fv_Zv5` ;3/L;A5m2(d0ȚQ!! 8Xca"2"J"aI*g .Li*͕hfE1o5s'2[!zlTo8=P^QOe6ťCNcgN>L7gi0&eFs4MӾ \aeLN7Q15xVlrgZ_ܵ[ ˽ۊ۰Q 3;`8# ͆tDaL# 9IbH@ .o,bƲ͔:kI8D?-WOk]?xS.MӚx^RyICY N֓nId% q֍{$4M:โxs[Y9ۘ,stas0TzĨL{iըO{ŏ͗\oF~d )jԐ r0Uf6*~ vhH+|LLDNH RM0pp,S8VgonOwXy9yD}АӇ']'4`gcL Pu#XNLEPW5p)xtni]| W'i:IT>÷D}C,S1Ԙǣ#mv$ja`٦I nA" d@)ZoǴl$"a5+oTj?S[Q] O?fp dJHmto 4aHL$NJ`•aNV3+ob}|B<+Gu iMgxy`&ejf:+>,&đh6Kv %,p9QZJBhzOfoV!ҫW" bxMIe<Ndg_{0ۓJ; K-t! !0-@ʘ< qL<a n6Wq$M=+rLFEMI.0mIZQhTgϼN'Vϼ2kN #Znㅸ2Ed!Z\ÉBdif; 4D 0\t&B7:7fovLT>eť%U[Xy ckZӞHiZٴ9i]WHߐ){O>܏ٵF 37Nht!ΐ$&I ‚(^jt$aIԴ=x4MD:{C[sVuD2<д^^YravϚQމ&*~9E &j)U[,i )Hҿ7)9,@kB|^+.Sܕ/"=9jb`[ė aZۄ۰tW&,J +LPϺ6q.idV2R0ͿXܔ}sbkGxQ7E˭d6!Y@*p$o-6MQy1&ܞؤ2M #ƴ퟇3]3@eۀ{%zҨ Ť1'pv418TAYnJ$&%?"xT4l(*Lrjաb.96fd>9\+FDIڨ[!ҁTOb-~"ci؆~haŲ䭟{SY{E#vJu,0]Qy++ꀵ-2!+v̭v4\kͯ.g6ݴz KIxkLKH;Brz$cas0Y9hsG2%VOm7:޿ͮHuNs-Sdw/[ێ;keYYrҨI'ο.BVO芩n@z 9LG"IwXm5 m>YR*Z2 &)f'p]2\M@nM x S͌QоzbA<dG4JԻeM#j}%F@dNۀqɸ Ires;ATɒP݀f&d-{E0 '^ȥv%to N4ܸLl\e($pVMŢӡHFigXS| Qi/6n[o?Ջ;:Ȯ%4b2T)3-e pL2snFc4Th/ a ǁ2*B5[@I&Z_NaƍDX#AؐZ2x93xivʘt;>%y#]3Ml dCLa"BXشs]#7G[XV1[$$əNCWI 1X!6tpy!b_ō"Ge6ص[&rRץfVK+̝;sDAbJ2|ް[[$',ld/Ӥ[a9~r93VTzyqt`b̻HJ`p8u6p c㝯ʥT fols]@:"(Fe+HO:>!SgP@|ϧ{ Ӵnqg [=bMyH@RTS:eJ)&&N4R})#LwCv2! CT\;S'e/]4>} 47b m-`%9!-GP23"je% HJX`&MҦ:H}Ȅ 1Rr;'u;~KEӻv˱^ {>b}tohfĮ̒D( heڂKߌN~TWmIO}8l$Id%(:&M{z\27RzPr/[oh=2H֟=sBh3*]UyP|/_kj͜pSsH|e[LζL}*_gLƳ va٣"9+!i"7|fosvvؘX-0>i綯gB4ONX1Nr`T$/! L^!'W i] ݇})#F W" $]e\"ݱA0!3+6e?Kb&3eܔCRHږ$I:0';=Єgȁ "Q5͑"殨nbP3\PȪ8jZҺh4'}aoW5B vC 0=mlxoM|}O,vUQFC6u7-N&^,b5߉i :5_;[(_wF!RRӦ)c ) {jP-dj鱣lwn+ĚFi竩__wp6y6JOn 47T&e@D~#]Ci3^S!璫0I >oysV%|-\V3Aˢcow$Fm{*вOݏ-/LݽN111+y;R IDAT%#7D_"`5@}HEU6]]BQ@VHW)XN =a7YMǓp,P=N4=uE I4%hb Y m6zR^T}ݠautf ,4!}1l]z\@}1m!>Щi|Se8"#m`I m/u-5Wm~s๓sj_A%ʴ *jಯEN;+>TۀZ)6QѨ 61ƕO$} jN'$ \j+ i=0D/ŨjD9w;6MA@SŚ8Y~Q=|ohc,J˳"?k4FZ,Ȟ?G$:BJG3Ǎļ%Ze'43y%X wZ4%G˔xp{;ʤ}੢*#XQν# &ZQ{$DR,&??#5QbR111o2ok#)!QeC>JWEQ;vH`VujVF 4JYt?LȐp=А3J!JjWCok,׳:K3x߁,"=2$JHDZ%Ԃܸu5Gk(0Ak fℊh]UZ=!wTNGD;e &>0yO|~1A]dtwoCWj4e/ <Xخ lҭg6M}[\TI}Ot~8"j OwOʅŋx g xԟ~H|wE ~dSVmiO]BpQ5Hy߾4ļni}%*03Ek QTaàS1%f~i2x*ШV\kGؕIc(hԫ%)[t_XB" "@/F'B$ yrR!E%;Tu555DXo=Q l tt]'Mt)Ty9#PsDi3z7 KFD%"+29fK=5+7s^e j027q=q*x+{Z|dN_}Ve@.AWH BυjKkYÞ4O`\GP SOƊ- ChA:"@jD=`wʊX]cYGtj(JPCF+iUҦ3@CxR7fiytEym_jK{u%H =3ά*ȟw1Z, lʁAJ9R*y-dkOcbb(ւǶ'ֿsP Ԇv$^ݷ[WJCa~!&PsH1Dqu0Bg +idrgN?<Ҩy?u| ZmXT2pSEgӨz#hA;y@F?0dH$|,iݩZk#ij$K .TN*H Da بz<(Ta㣾@#T)BmvN~#!:U"݈ .x%q><P4Y MBSCp"*Utd494GVЄYư$00D136eq .J,tW(Irξ3s5jOo.ID&Uœ@bCP,yjENZ6kT~s@S@-H,Y_5fX6ھ'YEq,G+%;WO Sh$ꮮ)!NK>`fj`ƕqa6Td#){Q >lj]VEVB{;q\xK-. $Q-oJ45 /)LHj!٩r-TL=`JmJV TQz%4@ :׳93S_]$fczL M8PI6{6'ZsC 6(l8v32{J5R(Ht@[s%iZ7}mvɗ{EW8"P[}Mۖtщ|-;P֞S>vѥn+owٙ\{<#tGw+&&&M!H e?Vj2|ppC+  G P)@$e]RS9;5Y*80ǃLXc87l)725TN1 9%B_`Y.itQ By G'Da\սP!\tCfh5KHj)5$pYK2ADN yTy383SW=G7hP/9{iGR )=ŵ}3{}{ \-5^fOY6IzM HfpB (0q/rʒ>;nJ-Dٰ;oFK\dyt^4 euΦ]p1 Z5A&L I=SZ08܍-j]RiGrDMDrljXmdP|ަ3z jj*F#$Ut,,"z:\O2j$\~ݼ9֕Nܨs&TE93sg2d4S .йýg+7}~awᯆ;ʍHۖ%}6P0퉣O!,mjG8Y$6-ii"(%ZZa-_>M|sI~_d\ĂȯϯzǟNml;'-AF]Ku8#B 'O?.hRBE4";W51aCB!*O< /uzt;;8m9Nyz.$kb4 p9ӏQ_rZb/Eg `L R³ǡJT ?4oEcC;d~u`fN.DN2JJYn|#pq .OQ-vrnȝm]n2HhZI=)LEadZ?Vi?9tu/Z11119?nک4|c-y9*oqLa踚kvKyVkZJ FT{*- ;!QSdx6{JDcíWa#,= b'{jT]|-lqicWHTiTth{A)99Aiv)\Ho15tϝ̬9:6'Dnry$S޸MBD\ 23CL-dPt|=cTR3ad2(?w9Z>}]r>DX }2ZW&~yk{GmT?ɜ^\k_ J?;1=ȂYSZ;$\4 ixFT:_R s\#6-lک[G3Lv$pѥWsˋb9)II y-Uĩe |jऄ]Tk+W1,M^4}^+0Љ>49@QFWk-&N?5jCzgɕ%Y [jY_W ļ#2OjV-Z>e(p4Sj(e05KMyHh&hWsX[޼G#GTj-wtj`~ZE?NLLL[XE ;B`GRDS>J#)v-tE7}0"݁~1,^}yGJ!]~cYu-=a,02-hj?GF"pDFtOM5ychMpYP+MtHtVؾ㛷y5G)jT%#cvfk5 }+F5NU9 6&h@`*Le}R=b}Cl'n\/o7FRA4i iIl~^u8)E'\Ă-FCx1"K$7T,4/􄔁 Q z67?>| M,ͬ惚ipڻsH-<1aPBu6/W zmgfdhiu鴜|& ^;ҵ,w-gOu]s @챖ۦ'=*&Tu*RU0#}qc$“D19?.be.KK9''W hkVJ NN\%Ʊj4Ϳ{ ky HR\041P=fQ§(.y:xB4_uwƆiMK}8 ttԤ `#Wgraup~Ý:gާPohIb1] & dpIM̀]Z=Ye1?CR!+ #-I}tT;*>۽ɏj"Fɿ.w79*q)Q5PL(n8cl: J@fdeep&?ʌQ=sDygܪU.5nglqN _0kS vS,ڽ۷|w^릗‡W˦lSO=zSz~N~.}:~to8&4Jy{?joULLLLH,x.~(qcHe.˄O%1I Nn.s?au ʅ**uU5 KPZLR|>LN8k DudpOKr)6B5(̡15DƾTE"Y6$ RM- PJfhA+n;&z:\=~Uv=qLEeOt-󩖦 $c(\vN!&&&&M&.KeYhz-{ +@DȭusP&P4J2p+s3fq8K 6+OA©z?..nҿ6ϻNsCB7kyjʻHш٢GDJS2,${esfSɣ oBUGn_q٪~t6~Np3fч2*[z80|mlUG -rdKX\ Z k=E*:y֟OԋPBRBUސ0"=?P 97mDNcRh't~3KN?^F]k5<;MM1QaӀ :7\XȈ"t;x[abMc?5%ҭ#_O W [o"Cә}:1111ٴSkIPBFGP߼]b>ʜ|%nʨt5V*Z8Hl#$<*ft .?9oPjWUӭ`G , j%9 س_ =>qv}ƒVb쮯}ucluNOr11111Ҥh9q| @ȠVS療Mw,)gO{ mDOi%Zjܠ2R@G>}rqd6ϸ<@!Up@P'HLDV=;fǪ{e.H"BalL#jS} 2Aǚ6e;b5%?:c!bU‚lON]>MxcI 9+k1Gbbb. b6aH.bX?\9|Et@Fy:PETƹe?ګXKImh_ @dV J3a9=3+N^5>(iN8W {:^cbbbކe1u1DɒE(Srs(8*o@JRC||~* 5FuY \W7T_eSmTn1P=xFPK ڼ]g8su>*yra?׺夛ZqWpAېw݆ܴSnک9^LWlMLx$AzZdTPbvtط"j- `=peqLLLEA퇉SF fh_<\2PS O ЈLH,skZ~hCO6qh!,rUΎ];@˪kFGOrK̜FEB.TC$`^Vտ[6e.lda~#/=oļZbsRh@0Q6#(! ҋ@Ёe".0&Ue؍nEBg·Yc3Fcn95U^cзSY=j_m]MmکUn!ڒd)\GW+ZS+ mP80沗H_ һ.eR uy}ܦ=&&!<.@z_@i壩ԟKJՁ~@ m?aı djApbx~r^YxkJS#nhBnH9/*Bm2禽_z/!2eM D0aAքVY =GwMyܩB=43[ں.F7}-&&& K"_̩v:}%lN\,?+@4 }> pT ?nN9µhŶ)ڀ̎8"݁~qzY1FY;QTڄX_򅼃}2zaQZ6o*ż*gOshhVamϛ}^1111xx%@ťN}L[6r_Q3QY`*³)-[wQZ+a0GP>zdƇ YUiMm]KImit'N0*#) P?d9"Ld~cu񽎉(#<>!PHdrh&m4+"=a,Bmtx!.&!*EVhCEkHTauo{^ؓ]uW`lٵ"p;wymtؖQF;uLLEE,x.q6o@M;5m9u}סe=}5%2tWMd3h .G%|C"d|`h0u}TݴS;u}4;%aylu9'1[vi(?s]LԿbbbb.*bsR7Mj!i_&t@lک—֛)yhb J[M.!a YF{$T3P%&ߤrCin`N-Զz3bgIhU)=[ve!Cܹ!@9' KUiU=EpisFdGÈ=Zè%," ڂF] <rٶ)>9e hFc?-UmCKc-*(`I[(1TɘKX\ moکM:24W9g a(q ױwсA6%LYED\"*]DvzB+ WgNqHqC{}Nڼ;?}Q",PiO|X_{P] 8̝`LL%M,x.]@˦*#'$ X jk>0"C lZ_ܛ_NS"[ÍI1 [G`ˮj샎|xe*&uoNLLL%M,x.rv$/?2= C+#gЀQ!@?@&HbE$qA/rGDlqlՋ2Pݫu/?JC <&&mA,x.R6txq" "YGS2!g^ˋ0FCR-OӒ8ˈ^eZQQ@1.)N2|YϢ hNMߺ> (c^-[vWoBUm MLLLĂ"dN-2n'?,̦IT#yZY-1ƐDE!%f1>> Or6I"{>MtM;Hivbu$#_悔l|eˮ/ܹ lgDUM;SܹƝpLLLC,x.N|T7d{E|!r768 HLBM upT`] mU۱j&D^6 9:*Dq(Prp7X۽ʶT?`^bN|ǯo]\(󧨨 (e7G(as;ʣ~*?2^sK{r^+nhNjQsՉtLV=r&{ueA. 0I:Oa,*^.-Q>= T6oYTK-,wg(3od=0sjW\Ă"fN~95w7G𶮏s+aO&m4=-y`FRckDt،rY-Ko ;Ht wz0b}`=fINcE8 ;~Qܼ]^Akx9,TsO|ȽqW嘘KxxEʦ\oW~s;tpP * NbT+Zv1yJHFn5:YK:Q[MQ|l*TM& r;⊩hj[$jDU.~=Y3akEs˖]è8\Ý޸'ElxO1oA64i`ǡ[D%tcG_NF)f ZfKB!h ltj ~8"&@ (̛0r=7bn%ã79LwN׼7tKE>̯5ePqlu-,2^d.6%ldxJ\4)YZp Aͪ*oکP(|cB=^er*9i:SiI;0HZuRAP9ꖨZd&Ό&L}NϽZ/i7]$a BӇ4Me.1QUZ3+Q% m9VsC#75s#*w%vC{ݱz}hlgzaS%$[vTB <:J CwPs ]@V! FU[W_x%L< 2 A.hn~XAt=b~!lw?YScfdNZg$(N;k`c yŖ]sxq;LEEUAX냔jǎܹnp&I|o[W_NLzzu >Ҏ2R/U?4"cNm!j.TJIoy;{_ `y(48`0$~qO}k PшooM;5:V n]Eg5_GKV pBg=EP}wR@qW/}R믰=!& eƝlPXX/C/lԬۉ=Or纗y+Gx._5Mj8t#h$jNggI6r$%M)Qmpm.'\/WWtBBM%]"Yn \;vj63}{*vj&ߐy?>#~up,xO-[Pw9 wӦ\-oNLʅOɵ0\Ԭeֶ욘~ģSk{/LqŴlz~TE4_&CU Yۙ@Hdۂ8g_Tzۼ]ݴS3PяfTZT(G9 yne=8 l2|j"DSIr6>Iڨ"yhәy|heGӊ3ZT537?gkzrzHnj>2X@- gwG?m "Lx@@D8^:EF:"@Ut ܭǾN?| 8rV x22m%ȑSٝӀvɺ.2 vTf7(/hZ4"TߐN.L@F TSk;)\j͆>~2Lɵ#\> U)z .R0VT@~EoW㸴s$0\Wq v*o u; Ɓ;&a|ꇳJ2n.k&:͔ئ3&3$'@ IqI2 kwOW1|b ]24йMdnpHnFBe?$1UjאIH$ۺLAjH"C${VS_Лx^kY$uk&qmXoc`x}<枇,s]^݀FJ2$c3k /7枧?~ /ysygaKoɺs=33%(MqEWmPSn3$vWqtiU1X{UWÒ6Ps!).aǀI%"x{$S L @v3<`~Bfuv;YfWVXތY28$7 |V(}RBGuqg ;/Ne]ۍHjC5+7}~;=+@͗2k枇**|wkav$I+93K&Ω #J+])_5njІV>ӟ|_~x畗= ߐ=;}iZs͡UIoX\JfӡbURhVT5j]nHLD >ZR:5C}%NH7N>YHp^.S\)ݟ")JRjAf.W6Ų_f$Nk$ס<P9sHrG6$8&:JN/DsK;HvzL @f!@qzWg>?_k};-O_s@wϐąe[w:kEk4/uYe_5rq)V,LLRK笿i<2 s;ݓJkn\RՊ9Pܲ.sչ8N;8=Si{R,H >HaϦyvB0c}$3 v%CUC]FdLAVz p ,<`~RuB7damFtK-nn ^0EHn HnfgI Qb ${* $nGK\JLP^o\;: ^b:"8Y7M#N@:d&[mMۇ*0 z83\;UMm} D_cCҿƯ_3(Jrxg8zThJ0}&/z֌8}Cۧ⦪zV9y*&N+b [x9Ʈcbwr!H5Q Cgv>b6CF1L1Vo < K]'QI91Qwꅭv/I0x$xHAKDŽi ߺ遭`V<9M+x0QX6w|o8RX>\/-|gJԲmjI/<4 )㽋BY܃hIlY!t1}l^fzs.J: JQ$F D`C: )A/5 +zHi&[ #ek$쒾3^Iʾ6"am9$qڔvj15*LDg;O}=k$\}$8%rЅh5DN$qUzdf7~'}"n=G08FKQ^?WӟrFb0Ƨߧst~?fȺg,^]S#j'^*]'E:`ı8B*!B\yK$yy{IQ3ZOpQI,A+#vL`<3>68I P}[ ]4d5 EY\))Bp/AuޛgRҏ{Fb6sb.$;gJ띶IO[}-c|Őzkax+(ޒFurOwn[vqxkFo(H)) [U ^64/j]4=:~Zx5$Ȭx{*TPG3j5x;NCmԔϖg>>ˀkD\]Z3 _GN]$ Ua>^9 TQ#ñ'޾nPbj}i*Y"+XnW'=uL\󟱩u@Nk-:dȭ(yܹ"zW~JA/w0_S"*m塉N?s.,zFyo|u5 \tyd%kFKŧz Hn䵡acT͍۳ZRѧ-%§nǁj|_xj}&W)cS-Tԗ?q m< ;/:Koy͙9H%{e=m;VYƋ})yiqoWZ7wRjrNo+~Pƹ̓ali?ۼ$'{]Gp?b^ (%)f~K-=v%^"$Jd3OVBGE5ۺMc.iFZFX)T2yiSDVTZbódd0y!VRN=9K1#ߍ#Q Teys2yCTj )+elY{rS\:I+{jw{$-cSn*,F#q; 48Q96- Q$˪ L@}UZm0izÊ*ݺ}n}E<5(urlx,%$>c$Ίɖ4sՎ6S_<͇~ÅA૘1$ q^]KWN5/fǷTء]kNL&ѧс [;WGOLٹӨ7rzs/s^7_K3*1J7 f}B[u'uͮ r[̇yBr@yQ\j5s",w^B;VWa}f_rv}P i>\:v5cx%pҢq^Te}Xڡ[p XnbPXk}0W }ebĴKdԬ =C6~R1OӃ9`ǢDx=+&I ߝO$<$q;Y$"(/Wyn|=;㜌BF9% nb8]uUSɰ<3<\7nri֥/·}:]=  ]D 0=ѦlV @- BU5y9{>h{c&ofSo@ Khm♉tX4+:b{cg?O mI6U*!Y$ٚE8p_qPpljCefM-HiR(>sNq4m2qS X<~D=E: ց-ݤ1rm/2)n"E2T :%:G+lʾ>vL0zzvz{-Rgm)qk7thla(ptBkK$kɚ;h@:e xN(gߡ֤HtvrIնUt"̟XRH:Hbt}ۚjq`0|tU8]y*U50)GuTo.v^(2{1)#LwʼnGV^Y05 M>7R$>?[)Ը3Ӈ3NIfe$&d xO($:*z_DZF o#:Y'oM#'|H% Dy Q`T頔"f`s96)"9uF`>G8z"TEz:ꪯ,7bY+'PSwPyKݿ6e5˵myk/ Q:2堹T/+c#%] PoeM/M".H,a|CI?]v̩x$q7:9 ,ΏYn 1R5 ưҊ >sH|PdL8"FR7ঢ়z\1(NӅlg[~/)'Fus{9Re`*  QFMY6/DmZ/t ?BTMZlu Iփ]KMyx]ݔxG !~wGP$w8sC_>C).Xc`z1C몟BF7b)'Q  6rq`z&V&REf JxSb\ehӐ>@`msuw} u$TzZs[jfjڤ[%Yj;='V\>ZYjtZO,jJZ πz.|8^Z|bvcv$E1xu*팷+2$Jh`T8ٰ͈mӏ042n+[qع){ms&f"`){N$#*NBlҁ0̖yY/#u)rbe3 xn_imμ1B+fά3?27>mcG@u_hQ\NckԶi6UX`TC9*q7E(Oa][> F( a\,1AeO]l] Ӑj2Δ IMT#]夕ޞ;isѓ}om;\<%l] ۽?)$u* A\!s|$38/Dݔu j(%g˜ĥ.q0AdZ 9rވR^'eE .U'}T9NV&P!45iq%ЭG/Ba๫c{ɶ"u{sK0{ͼ 3xAϚ@_ - <<//_D}U>SF(#trfOi Fo&+/@6`e_K98&N ʠLB  T5BӏԚØD(|GG-3"T1V֑K:" %f +7-#_*QF(Q0+,3Ec$^UyE$2j>4Omd ,PJʊ0ú\HjS((V?̅N[KiXл32SoX'^vsw}1|O *J&$߻gYݰ{UWzdj]wUeO Ply-Gn@ykئE8HR-(P%RbQ Qiԍ $=ގ?y&*^dKdGBwb$KsYbLHݭ`UҚE443BDf'N:M_ lsfrQNNwnEuA caooik)kS #cIA~LnГdkzZ{)u7빁 Fr(U(A2xht1}guvws""~`:̈g2<կ]'x[{ݕ0+?X;oj=zGHP &7xP͖óK:I_g٘^4&Wzq-U 8V]7RB4 k@sP3tipZ|P`1:Me\Ud5BO#4" Q'8F6VF )y=Nӣ[#vfcjbzL?dRn Ցٷ:UiWvW^?6IeDAO0sX j̱WQFŏcjB4I$( ´#"hL[ :8'7\<%N۩ _?/RB%pNɰx:>|:$,BRO+H)|>S!mVRr dL4Wu#' h#j@0VQEyH0lB D2B:S}v6ẻ죨!n`y/l~z0 QWgWTfz`}ne)GǭpM/UTzZ_Y)ՌLxܙŕ^W>֙YD8qB{>8m ))6D9 y!$gdRRv5_h. 2xFO`wsSk8Y+F>ڨ)̏lCg=ͣʯg,ppx*OMLΙZp}ʐyAqeК@\=5w$Rsk+蠢Z8B}PdrH<_ vPL<<+ \c,7N1)-5=jna.,⨷4O ʅomz㶪'\gd.]T.ϯ2ejg?;N7+Hzg%zH=@ י>QSfW/UBUDŽ؄0t\s5"U~< .ÕcWl/СA{ M(RB 5"+\RMDz 06 X~X%i.O@s2()@R ?auA ݅zy!Z(TiT߂}6H$Tw@U։}t=WXDRXث7 S+>2LJ]WFtp{_e-Sn~r}w#ql*($ɶFۗ gL_#lD#{cھ5iFKJ m})++4ҟwqCp{Ż>[}5;=~jBQexduG;@Ze/@Ûv%r670S*q΂\&1?p t`w}…1k{0Wut YD%O*Q SᆯtBiofKڃA EhA{=S)| Me:(1Jʣ*8uXJ!c8:QH?cuȨR| ۰;Z+KCcOW/p~xCߺ}>-Ǧ QƒmpDD9=n*i#Iޭ`#bvp|ԿI- L b9`yX&B9!FIO?r$BG}ɤz3h/*n^5[=+T|]']'ŵod(dON[G(@j1<+'}1#ۂx LNc2*G+g΃d:XwoM@,%Y]r57Q(KU,@V L"]8)S7 | `{6fz8: `"7-\ &oıDt*.o$=սP%hUFfl@PرR`MtQVut5d7 "np3KŒkNj1|SeV."|QD5umDϥ+c٬Yk۹٭=-vË'˜̯#?tCI_8 BpT3"pxIW)su_&)@.b7 9fz(R_ ,Ehة,3&"C@-@8`lj'ً<@{jS}~mBp-yN(O^~1|"0߈~z9JJԦN,xW[ 9w-s'zK#SZ?D-uDPU+b=m&t4K`d |sx+)^A5a*"H =T L{n>I%%46 9&b|pa ,8)ˀC\Xׂ@;DY+p8J$3K^$d!ՁVޘ>z0yLq.DYIcI|݄ ۠((ĩ" Ku7wʤ< m JZR<:V?HQ6z$9Χ{(™'ʦ ' z =nGo0m؇FVV^>oǎ?~VUCGNQ5 ΁X__ ,MEKóBh Tu0 ~r;E8TE#1v"55w[Ξ5̦$_K|AS͓XVϐ$\"Ob]umL#!xjqWܔa{ `ӎQ^MONiՑ<*z)R8y^!Y["u4ؗ0AwmS?WALh+1"KD_QGu8 i:lU`k4pQ6`ui!ԑP5^l. R4dB͢(1Hw!U% 5bgUژB8y6WfT1ՀFRè r'trm'p|]{kHdZv婡վ2mmviC ǗlߖHc xOGwPHX@g:<&%yMt]*"֭v~J#xwqc!@3e;=xW_s@l阥 p͜Qvc%zԕϧk 擷_D/ⵋ ح$8}oX9sL`N{GWjb9 ü0}#-e<;"G{uW9SWܷޙ? ȷl" 0]w}g3w{ '[,,"X4Pڤh*WnU:Tqv'U \QpC Dcd˶&kzpps=lc["`:uϽg}}ߟoT<:5X#tPq ? C9D[ "(#C>D Ge ad I!)", x ay!;*9?D"BV&q KyQ?DOob2.ck$csa{Aag T⮜e?w~6:%{) @ɂcb^u#f~Bf7/o7c//aWJqG?7?f _> l|U;|k"򓠞1^ŀE >>qMI\WrΔNYі.#..+]0Y|;*Vf;s`G9N#> mAsDh01l`9?MbA >LE`&֗JwP>ipf;+Nĕ a0CX_!-C~UL (A*56Gqy fo܂ h]Dwg:FgavJJ5ՙ%C אfieЪs>H8/ F+CͲ(kɺJoήёe;o4dR)/mϧ}yOpCBۃzvxW3;j^pn_KFyr'Oiȣ9劎W4Ka'SJIY#SG_Nf'pDWo\t ܿUZ `S`[Q@`y;9еV97/>{ܵo}L:,F-r2>K FshuPvjf;5QNoFӽ̿|/|V޾Xl|e31#ׄ ;x"J#w'}O"7fS'b߰jt),ݧq'|6eϝz'Åv~ߓeʘz& utk13s,s!(&("C`مq7쏎03`aDNQeH:+Et%˰,du hrb~Á t~heUpbD.ҏګ]prB{7SObOe2YĵتSvF{MD28֞ NWiv"gKx&k2cF|)8JTQwә)kFJYק.˥Z޻؁O!;C p^$/- `Z+ΰ2øO9&C t" diJꨯNh}sy(ACQ(RHZL T .Zӫ?}[aޫ͝L/'g>ȫ}_Z(盿ZV4paMߺk/==^-!qO$|Y~Y%FGZ䓗 E<SyɊ#s)ItR~INav,Uw^_8f /9jnԗv}.YsդΣ46&k LXB,_J-b3F0n4}^sh7m\oM$#33/@y"bʚ 1G%:FVYq\Ry%Sz/d "+tg37.{VhVz~fL7_X8?;CqW`HX#Ve3*DZk9H;r6cX%qE;-.[_Iz40}ߣrD><wL̫\wi]M =;JIewWZq G~-@Pqsp%?'&櫗3S[$LnqYϾ'7>ѳ[|:-n`鹦k/4s;ըZq(cܞbDkX,Ti5Ke>~aHfǤlv؟2%HKZXFBb,=jh(84_tJ^91aꅡۖkzBs*@!WqHx^fY+49*WBiB- ҲԂhDi4aN,xN1AЗ _Q|l//3jڟ)LY uy~/g~Mp-o{Tvw}YW.|={q>jྑR#qh(l-?:5grۃm`}VV7/?k윃tYF]:u|p3[<\s)LϾ}yܱ8)? *|SS%at˦X*A6R.BT R@oVgide'w x*ΤOA DX!Əmؽ #x=?9,2e)# P S@0 Po-)28Cˆԥaz7oP܃r3M6[^d~@ib.LꄉTre S^ ]Դ g1(RSdX23EZμGL2XEaV"^m'h'jr>­▅5?pG{GƭQN|3`o~eo3 u3y\mr3s3pkyVdeшǮ-Rsp/!8L:\빒JZғi!`*b 0qje8-޳(=Muw[1Kzý] *\n푧*{:9,>%̓_+);y.=F@~^{i%.|0Tdh.eG嗢7E?gOgU|l~JGS?}cLckz&mJk`V_"'NKB1MfL֢cclHY7 Kc)49Eal? IDATW20M x$G¶|R࠙5!X3&A~)N\3 'P/6'2' '_OBm/O̯|h,~}R2Q_ۍ?e2ҒX?ُwR}7esj!1^{Yq>'o_-?Tph;GƉ֢S?y}ιT\o9g줓 G-3EDeFj0R6Jp'E^"QC?D) q&6XY)]8dҤ+`I69)1xUAB msLCJMpRyps2s[z˰P+=xϐ7O֞AC;`I1.h ОB¨3Пj.%+`)#52*Hq!GZK*~& ZQW0ȶj(2DTAhI!"$hBDbh؝ AMYc=VnT'.]WV;^8;ẃk؇WmU  G$O_?~ȵ;G ŕ$Wms<TñNUM?+7"Fq,N;踼xs 9E*䤤 ]00BǐW>KXo2B `' []\oZaJ0<.\-591 ̯|wrZmmhI U L,[ͫS0`Z7$9"dscL=r 8)5*@!Pð͋4GyK782NetR 0}U k1bf3w)Q.Bo*=,La4 রJ/l,6O`>b Om"!-2(A`8 #Pkc^X%lc7\@"#Hnf!`8 \SNs%iЪQ-aW:.tPENP67/` Qk,l-ñaftT$1"qTQ(R1c?OY}h@sNF 4ka`h{it Vq}>Fv6$6A-saA`\ӯ3rQuYEJW% SI\G&e+s⊱p(yMRFµC$!6LѾXYe2$A{S~~gGQ̇?-!ṊY䅃Wzߣ@ $˲<}d[x"RNCRf_v}n&Bwۢg.JE h8U-蚝혒[-h+#!הGurXc-N,fW^lu[af]_'^A;k`dAh~zxZBP]S1Phf%N@da͏~#w݇M ԽiZ[jIxйp]c{LWNXȷ?q}_ɕg-#f`OmdXt)*ΰebncۂNT. jFPIB L?qZivKdmik7λ/1|\EN '7qf`TʗفF/ _tgzMNn-XX,dVo"-Բ!:( ySo]1p$"H.,nb]K7q|^8]E>G`@Z6V  QmGUGD6a'u,!У$, !IyorDR+Y(hlrd̥lu \;r ЛH Ќ)R:KH  Ft&,D'8,31JٱKL2TDP%& +='Gl>t7*pHxbd QiNDH$E$)8Ibu R6D,#S=캎 ^1 ] } (;b`QXG!uJk*}y̔&ϕK?z]_~[08CL9} u7aymdX}j3贺=7eғ~#6p!+$g$\#_psMX qHUH?ONۺ<8Vs?1J(ݚM0?+fNYkk/sؕqBgc3[̹2YQ:[gYGL1}sG:Z ajTIzň91c #Rz#Uܹ85gyEV YBn@ }ZQ KWzB&nڼ)_Nn\CIiq=4s3½ƊprQC2M$ͻ!u b8B;Te 5-]`\ahIC˔87Xji/憬\"UhLl"lz r۰DFu&)Pn tЬo >~|{T> 6$d\I¡u鷊^9b)Z{*.kM$=4,1WIq&~LNGx `\ITrfNh0_Қ&?tt׽89-~z_~-|.;jI 11u(T.\b| 7/"DjWjeG.pX$|^g=s"NsnyҝVV78cTv^ ~7/b?Vv=wfrrtGYv +ϊ). v)[]ppwKmǼl1s^2?dNN BVC. =LqQLaj@6(#꘹mdo6tcd}rӛ0>ا"^U&g cRĖhȴd",ѵ]+ퟅw@x uDd 센&}䲈xE8$ݲi܂[JKAw~fdդ[FVEP ', РRT6H$m΋d(?UpXm֐ yL+C.!ImڻwQȆ%Rs'p/ºD9& )Ss=2'^=G03N1Opc.mM$m {&FD$1fcON3EyRT"%U'w>b /E[+tp2Q{L Ţ 9!ы$M,gi_ޠjaa=.l0[\aGW- -l`=n]ƫYr M ~j?l ]LDžD M8R?6n-TXo7U@?MM67m"- BsfE-ub&8fwYr4a X2d䳉 %))!/͢Qlg=~O_2&-9n-Oݾ s/\ 9J^7j^,nj|u騃hNuomGMnZ]zX]&[ǝ)~Bl9ԬV!U37gdٓL"¬ǢN:\bY9ao*0vx],_4-i#HK8ƮbǗFcd]B!A~SS JݘBcBP>CT.LYWE?u'VQJ4Kw3Z"# Ƃ ̦[vLb,B1<:rghq̎ XPG8*AT#%DQ5&"Ype.mCi`"2$6$+ b.xI>JkMJjyH0XdF ez(&,8R !Tc,jdl%׆Uy7ɩɕiTKdTrjtdWbKzD=)׻⻆W3`öş?yz;Jg3!! Sz$J2&y>Lw)]g;X^0UuXxr}\!WI$ dq}iPuƀ3@3q![gbR.VTnݱt_gWX~[x 0{¸xpf+B?b|ںm)?mTW}} s  r_a8LK5M_2W'$L&/ۅ#KtLƫDeN_TKwkVVW{oCW%/,aaDG[6veS!\z%}eu5Vw kM&8I"O(DpG0*bQ~k_*l,v/`s},%1Kэ=`m⬝@Vck@fpmUoxWAmZjDϞCX(56݉ASph6n- K *T#)d  )"7wc$Ӕ)f}.!6.uZAa6§-l-5qxӠNO;!Gn,+.8H)+s49Ҁc (CNdkz!FR{ ʯO}WK7%ho C.UKxXߴ G]P_:NKĴS>qq}h!ܳV+U2tx_(n -MhKGxZJKi#DH4a`A-1C="ZG~.-G]W9n z`c]ڙg>c~h|<R>\ <>;k<3N/zweyDp[OS@kG8M !') k" ڌIsaY\VZoʄiᆩI񋥭0M:3, χ%1fqWl֫gFzX.21uvX/0: 3A3J&UR9yAI0$zF.H'^&"z8:d㵹,ct, 4:KXQz6{0 s,f8q!~%S0xR ) aJݠCރ58 ]Z dmuN䮘 Fb a+DUcd!?e[h#(GVNY(S!\F~T`.= FIL uˡl.GF3lԡ*$%_rwhGUy\= "KWAwe+D~$Wܦ7c{O͇K1AC7o_IwogQ KD{-O:OMjaܫ;7RWj%ioh)h(ڥzV$Y<%{b'q@QR+=:T+ Ij'<21==8~G'IW g?!vʸ6䑪+ ;b"~}|߽yq2"}b??9l}H9S,=};mwq?! C <@~ )얬P]ly~co:+F~A^Jۘ4/4/ \ɾu30"LӔoV#ץсCqbQ찘zV 6YV}l4)IN?EbҢdE *~*p + IX'))v{ # \zM#*ƲK>zs0q0C$;3qK^Lwf3}udXtVH0ހeJQ,wa bޓK\I0@ARw8$|e $o h옉ձe;D.0&Jbb4H f=Г'jc\!X#RG *-7<>[~ gp*IbreWrBI]0jl 4m"Ҧ(Ԋ+Ti"HQqc8k9Z,-e6νs?~r[ IDAT)%*%;|3ww9sw}df_gjtb*#s<|YkgTק Y9V GxJ=MnPW /=M`cX/w&v^M3(ۛDgp-v˟d;:^ĝ`qy5,/_kOwug+I'/ vkBtҧ)-F:Ԛ-%Usb~"\L]6]ɴ'm5!3'(>٥VPuQߥ??y 0CUүǸ|9Mo~oÿ,ͫ[05|1.ZHE^Au6y6&}7;N o5xb)y;kqu<@H=LsNTȞ>"f&'+\+C:LN%fw].aKy@QH[Zlh :B~S5PU^91M:ୟBͯsQI2kdϚ7&r>w$ME1 NJYxӂ+ت+L"E$Mn<{JJU{ X~X=۷R\?|H)yCW7Fo< ߏRs9L|k>^f'w޶Vkx3xwY[{t޿4ߺ~kr{׼qG cYX;Y:`@`IA_*:M))lTk6ƣB(C,^Sj]nT͘1TqpSOQұPm"}t>֟BuyI%2&OCa[k\#,e(uUsY^T`peQb۝J8x븹:~edfbdv`>^|?{ScwF`g]{<\Rz`R>-)) ESNmFQyhnQ~fjCgwh 1[|sG~DL?sqJA}:|g\8~ϯ>I{]{i- >-֟zr=vw讻ر4]ZܲzH88qI߇ %΂4:+2΍,0) IeFZLH q#&\ٱ)v,^.jo xNgPXE9gN`;HM05 'L\:ۇL] !†'J4\$\}8z7w 6b\-wרD'* Ѫps5CP*s9g~WO_?{]>[n'mE+Wd3v%ﴏss?jz-]\ 7jIl)άD:aI [VJcq8 (M3%r‹1r=4ĵ='3`oWsdw1wE_n8GTZ@;AOh t0` p'mSP@P`&$a'i̗)-$](cΤ,O| DvIҔBTYX TBEWˇ›S=F{N̔|{7M߼*]O~y#XJL_~["Ig%V!EB8q#,)m g\D pB95Z0=GovOʖ^5W˓뜶JC (BK]Vqq/KO/WK(XQM*/cEG?SkT捧`Z:Sg4=#*G_C-}&1p[?@}CٯᇀvWLXgT}iA@e*C(@PH)T'3q+M\k(9LƢJ,|xL.}#Qq'\#nȌ=A/9\y`0w97Fgj>dBչd4}n>|vwY>1#wQ5I7VG#ݎRp&ѡxC˻ _xaΟ?ӅMys_QQv-_ 67JSHro|E l)!VO75]ݔCuc7i&;q^Б{FIpCh돠FN|ALh9Ľs+҂p̭8hzs{uVW/݆.=d֭u)鍎CVoH{`$8VD-^s}WZ&å3j)yjX;g@eM(AXC03H/_S |ԂT#QXg@ B D(@: ' GI8衔FV"F6Sކ\bDa"3蚈g5ˌ 0)Cz`2#$SUXπ|Zn?%%T:!fݹqʭ  ]eJK?áyx]O=r_G> X2Kȳ"SuPd2ŗRH+ ÐHFNO4BE")Y&!,D!}닙vCLgotSϺYXf' Ƽi43_F|kq?XhKŝJul'yg`uWʝlG`W۰:)zɽ탙?S]?qGAYGJ&),_9_fPTi61}QyHEM0%Ah^"#| % !_ h8vqFOM8/*46Yn9-eiʼnKр]飓&rf(%9'%jb v9Q}ᩯ xWRE$)ח܎grcd{ߦB1Bopu MTZ"B3wgѣIJ݅P u1fk,W##:fG(QXa({f&ERUWR})"~Y $xxi pCyZhzv!a|lܝ5Tm QκNx8d#K7 |o)[{Eƴ4V'iYTW#=߁-*x\w-?g/PEцW6hRd,' Ue;۔$4I͘Čhy< u2rtR@zSR^zx} T(4,aWn%l[ofns|g1*-bۭ?c_7d+חzѿ՚EZmGTd85i03p3 T#*OVj_>-g7v:߿z(::[kKZҪRR 敫_ cW\)Z@.6S)h1(򎷷$/NQ}uU%@W$' ө"=iZEy$OfstHH+@i62I@9[rF{7(S01. c-VSuv}""^εeZRpGf : -eȅY}3Ȱzǘxy o߅cQ_? !: wO_B:9&:bh`x6 f׈mN"BV^:ڑLO""tSÕY ܔ ZU*oTgUz*gjb(#c-U g89$E)ȨHvSz3I9 .CؖcR0Vӎ1 *ZcbhM$OrzPfU˟6L7Gģ,k Tb=V ?Ngj` TGZMe8%4BHLi( *7tq;S8 s^uX$EEF16$2k滊a8)&ҡMA!r_FБ' ^-sOs-ڃ7V2m~nWU2`[H$oVsLJ1w鿫۶?{n'o_dgB$=7?Na#^ Hwt<ڡ"T17fQK7} eql%C]VW'v-bcˑ' Sehdl*h4hN@@CRIn5eh4fTwҧa*o#@jh}WI,uf0'K_gŋ$e0zf]ݻNzIkgc_:=,1Y#Ϣ x'_|A1OSe)F֑ĴfdgbF ډuܙYGZo|ODqlFy/<#>Yܕ3ErB4uhIE2TtJ弤i!+27q! XmL#%ӯ%QxHZpJNc+8 AJz Dj!XŤҲMKB0$p*L؜fEq#dZ"Fѣ(P Ϫh?}CUѡH]$ 3;|C<5{>¿>~OF%}4zjӾ ]+&D6SNLSx.`l.'#"jD'6 Zz*r"+0 Ӂ맽Inq UM8bQat8z8fqKOdJٵBgw%[5{*& ʇ3C}Gh\2,7:יĵU{óq|ǨVDL R",\SiwR*#?Z8s[aoz'֗_ma7fs5n42s-ZX RGQg-$@ j5LQ !!j8 5U=T-h-(eӴ mh]zE2'K6׈(r5A)"'w=:d-1^d%̑"nR\1\. Y\ulYfR^FDh| S6%R>M Ø`((.c{V\/S!,EL\ pAȼJlA-}PnZ c(JMJlxAvl,B߁q!9=BkF y#IM> 2#Ԥ8D;5Ø+QV̖>ϻ1_kʂ*S(,H}e%S@u0X=1M/}wiadj7ݸ{_<{RjX)#&Zj0dODP%VH d;f?Ԟkۮ+KYl,ϓ?%qrqAo?tZ,̅K'RwgY 1ĒOұV IDATeR x*\,}Gx9uo?_d)DAФJi3Q,PΒ0yUTUYҹ[~!CPO_|x| Dj뽽{&x^!ux- DUekX;-E`Mŕ"2rƕiB;oÞؠ'1KY4cX30=CR/4tfhFQDnQd(mC+(Wc?*ʔ8$AR JRcep(Vh2!>89Pا=˪4}$"MLx @)J֢eFQΗ m PJw4`J R/{6`ikV{xse>x-~l"<%ؼ|5gY9mue_2ސډP"V^8X^ymh%4S)1CIukj $`\,'B뻶XT+k{R@ӱ{gkmŦ>9Q&fOv)Z*{׹sfEve8:_kk7ql~JJgϔS^X _Yn O(armS_QOOD\|7˴%raN7.)+\S>|w/N4)NG)uZᅉDyW9f^@pvychO3Pv⤇o=AF?NC,AY R^(5, *tCCLL8R ^JPVJƣ /' Z{$u$5$LďSl 2NB0ar%-W1YJP/h}D$9DK.'n^i0,sVX_G ćU^/LS3vTonYʶx#4NE_B 1FzML'Do3avҥDnWJ[Pݪԃ*ҁ^8k{;‰B潌ĩΜVHed>xxD1H8]jb?7]ogE)ZVIQ>={Cse'J"2G 3:GTڝ kTT/W{ǛՇrDyڇ?rB0~&z.܎bϋHIQ*4 sϫ|QHMAQ|(Gj|=R.c{u!fVW(:vgGԵ䜼_)sZW(cvk~Vm_#3 O_`-_=lvތڨ"M0Py}mhCԽʸP{wȒ7C9~|U꽘:Cd%RC\n;s00 0-BBQ9itjPHS۲JTfEW OYąƷd,8RdSȂ$ǐSUe@Y":v=e=4ۤ"7H0Il/c̬g(ҀF8!/Mntx;QyAv9ݙ?w=b 6xeΞN_*~A?lFc/p؆Ŕxbao%: oR bút 2}z:^; JҧNi("3)J#< ޕ"aŏҲGY.F9li%: @ʊm&iJXxV#xRGHa@uRc4B0ㄩ1 KiYX5PO@TnƢ#Rslk +GoW"&Z&Ȭ5HtR!'~okǍħ$ozuSK׷_}RXtGG{muynuBZvdxRN{8TeEg ۖ u i9ZAIM5JHH!Pf[-}At$wD4|çz/l;(Nmlm]|xp _.KWLt9שO{֞4o{=wGeQW~m`0WGQ|.R9*SJgYTў{T[!fU.Ŀɢx Nz&z0h,]y&$I|s=ϛ"G8Wza_P9'[[ʬj踑Һ 9;Րt$/ywт]o{'Spt@Q۷jX!h)DlXy^:W tDg2kq9{53:gFM:Y7ϵrxZL`gSG_:Mlpm OcSr-5;)N$cIӥ 93\%OzdV? Rء7:Þ5}<{y',%Oji #P!NSAu)j4@! (@: bؖ,vmV$")wμ߸ӸQ liO[Y<ϫ8C"@m@[-4\ r:c @ 1S) <aF"kJBCDIHJ`.Eeh[ 5$wGbLˏO *Q~u|΋\A⟺Vm>_\E~jKs1xޱxq ^4P8I\נuϫ簵U%q>UʍJq*QԐTSO8;5*8cSijFSG:G^N2!e4\ i0)9=PcmidjP6ݻg|s.A=F(,끸 \ @fxCGZBzQ[0gXq'_å-ةxug4-0cŰyQ8Ya2,##B>Z` k5V!S}傂?"ւӠ?%> ]r Rb)lb88sdG"]p_ ژf4#X{<ЗNJZC8.e{.)bT{8#`O`h5`ap^-,T {B#\@s{<1rUSHhWP8||_xG>Zbek#8meץG߾ʳr˟~ ?T%~ YCr5,6,ύQ v-Қ;B{G}4L\ KUfݠ$CXiIQc @79p "o n<7O{{s8鷏W`*.Q9/,rK*`zˇ?7ի46WV&(V}Er G妼͊+Ti7=qmrx,Z6sܨr6~叓eZYۀÈc9'iXؔ,>f%R@NO>?@r]9 Zpn++1p(O|Ȼu1hu|6ο ]o} &i}+lEx{AsbHm_@˰ʐ8+'X)/Ϧh@A-x`<[1lā030Zuǐ*3tP ,YAX([3KQ iZ!TPP1uW L Q +a:Sf%]6ڄЛerV0 CnaE() ʡaXOEh DNE0 auijsDűǭ=$s]TxQ-@]a+!WAϣxq~拉k=!98EΖ7Gh8ףB6GE!KCzUرֈ=Љbzu8"r" (ۃ Fpۇ9Y %8x:&76D:!&9E2\UHACYi6eIꖝaU묥#«o>)'~hF;KFcj,彝n0tNǟ͓x\J9Nǔ?8N'J}AUYcZ/tfc{}Nib_X'.Ed_8xysH}o2M:*__ן}k瞻+o~'g-?>;^^8&*eT ,QL<BQJa}:d "\ၜJo@ ⡉'F`U!B9ZJ!1XW9YFSN!^S][--ґw<TzHo !&&=R `@mo}f&5)i ^iqtDzH`K>J *!=4eLG5\0jNZhKP)E sjPk+*!@*RK's,SP9p 1C@q0([ 0 `iXcaB1 (|/& M@ke8B; ,%2137}*e+q@*0X@Jh dfy? ~'rl{A.s'eAʬ-eLx^`B^|$qf}GTXc@ t&%sh;+6{2Y kdXCjA!'FkP5: IDAT:tr-p]ǹѠrӏ=<=ȷ[^htaY?\g~p09"+5v7'T'5Lֆ1MZ5;yQh"`szn{ُDdY*T|>*!<:XT_}<%/~{؊fG_gމoyc@:L<1tNqMY?ߜ|GךKkxN͎zQRTG#3JqaXea;k52HXtL2+!-5J+ݟN.\FRao0F3#r(e]$rA j~." I"|2N̽ێ0!?^RBQkӊ"?pR8/t:1꺎 83pJN =WkPKXz_aPs|`ьc0ɡ co0&ZsԹ|f?[+|C੸%T0aR ׅ0nՏc*#(SZަ"HzÎsTUQzTdє[ BsDI XVu_S,S;>wy#J0mvCz{cTkxg3䬁FLh4p'|w2E[z\ƒ~#H%,:bRrbl9.bZG܋ zְ,=)(R[HJAPF"K3[Ts3M`ӺQ2$s 76/E1 JmQ0ώʱm GD,Ӭ(c)n)t1QX ND" k,#p,Af, 4` T8В.@p&Pşg| EnNuT` Uۥߦ_g{+S;})?[A퓟Vcܽ=^K)>^$϶~[?cr\OZO^{mzovѤQ橹T3fm#c&vsJQrhbۨᰄNOnIYZڝ;ᱺstlPj; "P;.h7rˍ_۷`8Ѻ(=gȮ7Z 5SuL&:LֻJsQwef[kMXrt8dSE4ʴ(]#Ccvy^n-#&<3uL~ meK-Jn_/'[;qo>6~O<,^6j, kjW=V|[2m֜^2y>#YH[=yU%" (=PX YK ~4B%,qQ *g$-#U & AT 1,uk( !4S~NvџY cpt0C}kz8;,C\w $0(%{h f~96ĠA#5a5 /6q!->C]A3nEkzdwS4 [H0pc$)-9L# 'qPAv D v@F[VC BKa\pR pZCp'nQQ#gna'\:FBLsTk3#?'%2,`iF0)kA cԣ4|$3s!pP27`,4gʁCA=WNK0.Z=@SyJUmbc?lE7.Wm*%fdh]&If]O(O<+-|,5J,p`N j/[Tu=T) >SiuA/X!兓ZKh)h{[q}!8QTk 9R\(OE;-cآ%Du;UgT^&z!%-ʡ i/wG¥%P`9A}lq -ׄs&Fp&-ʩAZ`H\pPy%&cLg ""q"( 37E" 4p]n ݴ(9Rs6晞cq鷅%ar ©:kqRP8J=ʈ(SB#VƼ'pX).(g(Au.B5 Qef+U\seH y jB2gqTZ!(`懦@K_xV*HpS?e' fqQRZ?}Sed=zQwJf ߥaF-hCUXkF#+Epd2F徭өApǯc'ojFt| 騳1F8w637pgXw[Ю+[EQF}G~nLs#G,M  W+)!Y \8PZ!)Nz,`U^z/wUU휶NÓUm4ʬ[S K=2<hz`z=?5%^Mp1ĝsK()$VW |3K;}P7֡Cllsn)p$ D֐jg$q/a:];`V!Kn+G% qx%7b\?$pҸ |bo }pHm rKat+@ꕈYn2V,GMq4`k!*qA)!>,0W :o#p)4p$A0D!2{UXlO4p.Kt,ᕚ1>WSŀ+60ҋspϕlS#F܈Uh,AFI95jXPC3Fr %`9QØ逘*鍆PЅk%`ł]eɸ Ӱq (A)_ْ>ק3/^Lx;h>Q Nv>_?ܹ/=cf7&_ZfQA޵z-XkonٛYລM~t@ҬEQ~Z׻"`֒pZ5춎d^×& aPE9Fq79`A 62>A/!?%Of(IҝUQ7g%VM0 ^!_ [i ̃,]D m`\30uj@Ir) @c]Wr\DؔPGP BTG2YI9OؠQKWhNd1foi5[>szHJpW aGi93RhV 80 D)9J=%BtF"S R,WΘ8A:NW*1=w f>(TQ\Y1S#6.J}I2$81ֺLZ866엾lxP.Bwo|Ao\5[{APwk!S|82d2룐9g:/uC猯,>ҬPIo&8p8^ƽ}jqǯ_k;B\cVA8Nf͵i2D4Mt>><FfS1k^^™e'hBw3 /-5fdc~usHsWFT^?;Nw20K^\篮v3+mi 7H((VcY`2J]t)smF2]8jö)q̤TEg?DU`7]~mT,>Ҷk̈́Wb='s]5/nvj<٘ "SS]M3(K I?fanlт* )1SAOVUxnGpJ_m;%@ (  Cj([r0>+ 3 sIx"G,SJO1޸{ 1ZOHd*D (܀-QΠ%CGބ[/06mtMt6P.5Em{񳘔!sl@lfh1n gf rt.r A0CAAmrI5 pkF OauN#8Dqty+>ן緿>w..#v^{sڐiLJXI}`'O٬.n `q98nr s~s}fRZd9g|RfݖxJv+|^(2'v|RQ{wKuc9Nr4<3n~3]z_1o> ՏoНJQ= |6l& S/lI+sOxږ'b%ҍ;j< <0Fp*#0{?> U=mk.Jt/8uxG*2Ek J(a-8oSb= IDAT43>hہNSZ"9)lIEa Rr 99ӝ :Њ(Zv#ThnbD#|{F2G8u=$,πespWIA p= Ge(-ua0K\S|akb.JyǸ !>G#.Sbz4N+(&"X*!ƱH`Y A=vSɘ ]8yS2zc LXx׭EF)'|:hS.H3C5JjX*zc ~l:EȀMNYEƇ@01J)WhA"3*-BQl>['ޟ}c0T{&5:ekoLCzdgZ{qVdhvq0M/<:KěZ]LcMOͥc S祹ug"?ZEEF0K _mIE4Iz{C"7oқG{Զ5ڎOcr2 p΄NSv2W9DrBh7k;`>hlrt.<ys]n癗fE{<ָ#4ɡg<\LX[n7 |"rm4Ofqu#8'  0M$aP;! uT?^y gXLf'憠.ߜe.Ci|_h{ܥO]:޹?{#|˵ n̟qYA` 2^^ަiIdM7/:\ @=$&hMlJۛ۔[zch@ pXZ[9RՁc@0 {5 $󷷿N h^΋L"x.Nٍ{ c)*/-35 pN$C&܇Z" ^B .ºT^BV0l8Az ryAZ >ڰQ'>>ت'azϠhkDG]*"̗<4bNdF-KcD&pC8}Q`> -2 Ѡ@"2L\  Eb;1 (8>Ӣc2iXNyP)dY n t§!sȚzG؍ 5PM@bceuĖ/ŋ>ubg6U|N鲚k?"djD#e RM1:$>MR2+"Uw 8L x (Wal.Ͱrdev E)ΉxxqԩbC 7 vqӇT}Tk =xRTd|Z~>ghOl׿Lۋ&|`Hؽ4"|>كޠlGQqmV'7:gZidvkY+ Eik܀9ޝnWvvd:<ϭq^<37Z]Oyd^]^6VDe`@ke%nai+XmtlQA2L][CU6]kQ27l8=ICZclzkn$}7c.7ז!ŵ$;I8MT$kX2$nvSgi)|XcW[oxXRFFBT. |t8a_ٯm[~VvOnSnս]Vb\`(rBONxB<` ! %@ B(J+TխW;0׾bIC=s}uWBnbf |L!yXְyLwj`Sw_W_ r~/B__$sV䃛wڃ?' i]377?O~2qvKHqO~-uWTI{9?Tw%LR2] (XiGg=݇EE֓:Yf U$Om}]Nh [Ze&c?vXFȮd$m[ym`]9d y[hU g5"8XL(hU5!(|0')jBEk_cn$Nɭ:O3,RX?.Hywܤ[h@1mk6z&ӿÚ;K+Y >m?E9`|<3$ {"8*w#ک7UODh7\CLuD+WUmj.fnD\_sPĖSÍkEW_RhO$ MYsEb16)Mb &/9?JMj)E\Lɼ)|#5p m QW/>{yzWdc..i]-i.T,jE5Jf?|u"?IT2 7NdXFoʵʚ(DnlCn 0G#ѪI'x$̐*V6ڧ<bNe4 2J{t@BtVtҀ|/C_;Գ,J+ﴦ[q Lo]_,Yn^EB&F~~5=|x'/+w:?vXo^s|N˽ȼIT{vݛ7U/M!NحbhKَ~ 4.i;۶1~=udv7owJ)XWgw,quݨz]GgӰ|xb,KqӜG=AFIo[ow}.g^M iȉCwlgazff. %&m)Kj7A<[k]/7*(-2 e@@%Qw̟S\͔P)7_9?OK%wB_uT'u`ⵯ~Q^4]֕r0æ4~?ZU.oVG gܤ}AփY<KFv$ZP {!xe PQWbrQ'Pۯ;zI=tLU#kh>l 5zEc.ͣB#,C%^^Cd$!#Õ =ZxҰ!_osǵzRpUJ^ɜb@zOR,cޣb#>=ǘ}531Ý=M:)5zHn $ED|ϓw'-ӋW${='O?_@qYAp4fY*,bѧI|4$z&Yzިc~kQQ#:^`t{MqrH&pSf96҇]++Be^47zEFfG,?ׯnVN-/{2;`77gy\f:YXujLo*Ě}_G.8syԌE4[0IQhh$1#[]KF ( _D]~a;w젶Hx_F nJwbhOI<nM:ag{g'~㯥.vغϞ\ewwfeocՇUvO Գ,,˫j\=}txjiҋr}48Q؈fJWiFY,IbuMM$ F$Q,#5I]J'Fɰ?@u`O?7NZ%X,~]|l_ztZlquч'ߟNTmf}kMӬU9IZO)uOzlJ^յ^aĦ̫<@XӏtlW(:ѵ)hZ0T.&_}lDS7Q(z{cA?L|$s9UC~i}"=mt3GSVW&.S_y$}`Wvx].5xt ol=2pT:A5@XإQQQN>;G,G#~%LۚyoY^>tre*0FǷzJ뮭kHVc)g$*tw$v~a7,ç~gٍcSdam+Hy={ .2BoPbCrNf\j%{_bSD,Ke>SFgW^)Z-0_5M3VN*|'hؾ|g;*6rλ_]ݽ uz),iִM\$qJgru9X_.^FFO_!BeNVZ&{Fy~? /^ z IRmONx-t4UFWxx𼸱5=ek5i}I}:)]2=`z^:h϶Ǽz]GWzH2@ ]AYyRF&Q"**mAM!.*t^SјG!2XHւT]ǟt6(0N2O; %fl-5 1):z+v8gav |iu@U+jV 5 0i]{q#򂱸D =XOH FFv㊂@hc(5u(FŠd1H)#ppS9^H(N$Jq?d#TW~Oq ވ7hJQ Tf0;MoWFUXUW1(-:^YQ y^5̳xl*m͵+k}&eE_9j2u+7'xG'ʝděղo~鈴 mWRȸ>|ݓ[ eGED@U1ID4GHk=UZtֶ[sq ^0 ]!9tƅfxwf4~"϶Ĕ88x5f!m*k@<0Y5NmYݶ'KB o^TD*N@~pz)}(U0˗Rrn_;RQw?z^^*m"݋#&J- O_?/y*VU] 6~F$fU>iX^b:ӓ;o :znL{7f.løjs)^]ucHb# U b+uq0h>}ry^鏖7>c|/.( ߙ'Pl8ꨬC@luyG d YQC"+lՂ8# /.A7P(P1Z%"hB(i9/\ISyM4Xaװ9v`&_ܔ+(r!w2@=U#^tB_^ .5_î1v8T}^ ON-:fh9)vيzij~]FH,fM2glLt4 h3aǣ<+s}W g+hKno˪n2j5V-n¬~0/1f3ZvM+A?)h{1ti&ёeV&E]6^QEhx & Cc8$mN"`Z`BLнn\+ nOOeN-B j@Z:@A)_6ixx~7~▼?N.I*@vocMS ٦!YUl7D'zڐzۺƓk˥@Pץ!uQG7?Tr=v&xT쌆 !!8+xryLP~<)f#P୹zv5q¥Z7/.G+yrȦ0:+mӬ|wOǡk6eR͖W٢O8O}ooZ޹3suSxbȎvf}g'n>MYWWoUusTUEhu3;}~jy֜=}Ld&8liPR dξ^jSXDF&E UilT^i(xqtmPcX3qDl#ư 4³>,Ie ]HW=5U"J@"jMKD* " J{Z`_ в_THP9)ה' IDAT)O\GL+l^ P[ZuI,mؠ:+]xAķy5vBR|WDم:K!uDpȸe ]s| iŸ q )hQ'RVJEiF.rܢ)8}TȚ4AToۻv5%~G,V$MjSa% 6k*wd=d|g(\\3%jDK;)j& $MEƱ2~-֞d`J08H4l׈hZsw)d%`&i+62-N%&k+GmOrU$w*.h]j E.M*p{pMZ*5)UB 6պ5Yܨ~WMd2*a٦7$]QKwG(깊vzY\BhzQ釲FCdX UPTH Rx4 6bhr{O-It0~)xm;$Xt w@A 61ɕ"tN,/JOe|kX| @JHMUwWN־n諐Z_wY٣7.MO԰D͝UQ~boZItI2Qz0Q?>?|tO*6q$O_|pm?~rz2rjdR7F?U}AmBFUC私Wّrɪ]}|y.W'QRZպis#hw1'}XBYGm*)dոAcCaX;{@Q~M NHn]iJN!*{F*Or n+l00 FfUKeAwH$#e RH8AJ`.ISg=jSG1uSA;G=mR]ڲI!(U~D7?'Mt.t]m=:c9V#$#ЂPhK(ݺM9'@ L9pAS,Y T dž !\i ]#n߀Q]òMHQ/ܐP HkVD)m#Ok䩥Q4$+KJb>wsEk +X>,8Kգ\%o&ёŜݩg $1>[3hf{aa⺞svըJyKcC;MySz^:ZN^⽨6z.f7&3[u|V0ڧ5EgѰ&w2(M.7qSHʨp&gFƞ~V@%X!KKX! XKJV߅Nyc4/xapJFIw!GlMt[-#tf]~W?[&x *;߂!w /coH$ e6īuiCg델ZoY) ]J'm2_h{S9&>eU8֩64E&/| B)SuE0(ξ?-ZzO҈?A#ԵlIUt26疳sZo^>kA@v̫Ey.BjԂBD˦[ŪVϖG^t:sX__p8y.ON˼(z?4g;*>UfsiV.o<D8EYb]yL4_ei?٘pKvGv4X6M[벞ڲRe`Q7}:#v<"|S:q(PsHNk#‘-TRC]uxO#@ kh>5< c+b&(%YQ2%s9<Oϔ .8GWj1LXW vdЌJ׈JC&wyC_$nmD43LQ813\2_24n9H?^7L:/wm @ jS7A#"rR!V(k1D\)I-d:J!i0BڹLQ6i&YFyB+Q!`"y!ֽ~w0df. )PNR|!m?-tߎH!isl2f`AݷOC!y`IP,5Qd=*U GNGThT0j.Hk7W-Zc O#̎ŌKb(KĦYA:A<5VOr!h/I҉ĸet2',U\{dWni҆yE X70<85 Ćx`IC|٩82zgCn-:rl?6%GiD4B;QKHr4!Lqz^dO2V)kNlj: "ahSge)s4]Ǜ:B%ZDoeq-d}_+%4#7U|ZrٰoD&J4!vɵWx-u{a"Kge?QyMFܴ82TBrA+O*WK4HpY1"jL D"d]睰Du8i@ Le GX;ƘOj Ln?:m?=+}}ߖ"tq۩?]*|?y4f]"t(.=ۏVyNAz_\ȷ8p;MR~lrICzv.8yO|,X5j6Qsg4߯?1>s"vR]6np}o5; mDl/oV_B hX* @b-=W^0r& x)\b#YK1kLkmD;-N0bNljr9`~h2Yؤ),&C`pAΙe5ΪglǺmKK[m{G-IOQP[P@!xh1 ]Gm"se.:X IDT`0iQ8v㡠1UXDB?d8ՍDQ W_]cf< hYl+sA M8-zU@Jb(N%"|R%D{1!hɳZS V ߆E;En87-QY 3M6`@ IT6<W94CzLNN2d!^N̜׮* _TKCk!Drh}kEUyk,'gv Os3jw#i"{yQX+n_zp7.  &.[5r:y<\9[?lm{w^\RWAK#R'dQʚqѢMKAbˀ#eQPhUH;b_+2&O(W ֤0f[ED k[Oa۔.6SZ~+Դ.tՄdGyBhÅ`:^l%X8F"1q7^(&S.feqre)&{ijRRi"C)Dit܏{o:M=  "jel yS6ZsfSNObS-%6JHo|q/ۻM/NcAY!7t~lJ_|vzq깪^x<14\d}Oj%DWFK58[Q0Z\ w&,7כz?~Qrryg~wN}Oh&2?yHt67 0M =6P.-H#E_/NhMO˶SyYxZ2 tZ@zbj R#l'&Ai}}ā`шb2%-2x.xȽ6I`B>[ %X&OF[ ASa"To4s-^kyqA\ g'HCTl룢#EW&@Tp2>lȚ%MA6 ϮZI؄!qoCd Z1 IRŻcnvnHU\xyEEưpj'*vX>U=z;fA*7u9nrW噛=,˫US׻bO\u-W2O ZFpyGHeP)1^ +JEsʐPF OlLz0XބW#Z8`^rb`RѶ1c1R6mw@S A*| o8]e7] =K (r4&6qUno? PVxğ h-^=DC Wln|<_PHϋ:שZ):VzqY(ִzYٺ=yQ@I~Z}z L=ٹ"GQ|Pr!:)tmvՎϑW>Q`7E^) tYKqTXh[ڡDZ@d 8{Sv^yvo&.ra(h9+xq9B$ [**4 xu@ve܂WʧZBl+6tjB3"5F>q6 I^iHlU%4Zq2mA]`dD@CIG0%{IoFrڍfFJ c2}$ kyH 錗;;pӠ#DF5Tb!K^ RE8\ zȢrEPj9}E B2IA[8VXDiJEӓ(V]CBN$9.)߇L}P,D٬-ry\V5;$@m/AN<3 <w3 JʨFX02 U4{撲i/Y[E*n.]\K;)D0QW4>ZtGiuB,M⪼j$cHXL2 ړ"7Z)!jp.´F[h8 00(+X5k>!g<ҝ aqhtT!MUW5pP/XvR tu:trgLcc zz\mdA"VCj;B!DŬ&aw8, ߽@'n Iގ 0dlr.aDSb\ŋ,/뇇I2Ge_0A?HUao¢ O>HZM4(}/&?ч'Y1EY*y5.6Miڛwԓ4;Á<۴ˋd7 ;q?woO/?_^!E-'#2/UU/WWW7nL69^ּ(}s~$kN~/ZZz:Lx$pILl &Ty%796T0WB}XADR}}AAcD(l|<_rtqutD>-% MBv >:tޣSwv_Z7~Sb fv~;W>R|~;cwqgeQ`AȵضA؎5QU,<~Lz^ZoEmWbSaX/}x4$_]Z08Oe{ers˽f|4E:|888 +UPݔ,kEڍb_=ט~;O~4J?|ck?9/g7RV'6r6UGT`>wIuuTF8'i5՗7[-]n=]IKDQch: OVj\ݩ0dŒbXJ"i{ ?F& Eӟ+=&.5}:D[2ѫs]J*KkdUJKSEIC!n+b1(ֳ!-CW̮KM:ip^V>X>Qoi2 ?5--qK@ w5L5My5UѾ Pٍ9I\M'k䇛]aV8V.Ek`15)uo 6: \6C+'TfqY8lwT_d2 8;3ti{MFMlqmFbZI-iv OdYb?C}ZXcVW._qxGt-T "b|_@ȷf}O& 'ti_Rh}-vi[U"e+EI^V5 rM+e/I%M<8:H7p@*klcݻ'mSU 4aMAm4]I<-̬9F' Fqy5l&2OvJPIll|Q}4LlYJUB]Niw]}+DiaFkf헼S0ޙMfpz/0>u{ҎJLOިpy ?NOmLyzrVk? u+>z7V͘x0+ QZ%{Fttՠm-gU-{^93@,;ų ?9A;ou6(V4>P0xrCxg da@s9ry:M.V=K*#X *Jh{ -Hch1~G$[a2T]Uz)ݞ q6ŪږY4eKbiiEBxdSnH𑗢#}¡ KUP`GPmk{54yN{f>0s-x! "rb-a}jPL 9 ]:bpG1L)5g?TW&KduVuv],Vxߦ@6CAs, l!AmTH XIOGo|' 5U~@ pEP <R2k18z+yJ )8]^WV4VzNš*sƺ7ݝNjJ狛&+a~>Iz f'#-fz# L{r! _IjDrQ^F*70_'L$.\nzw}W*AP dt9 Od`Ӧ1 i0'w&80Qaou7FyCM6bީ*7AX yZdWDQO]a]\'˄n~s<ɕaO"4;tjT@3:c_Ƣ9+ŃJ뱱$>wH ih@xOw3GϹ~YsnG'#8  XHR㦒k0YϣK )}M.#.uưdAbĺxI%z;22h%DaD a-퐴h/hZFI0 +/JQ'TYG^ّ+y3BniJ/CFx (Y6 5iPZxf^1>ʈ Kƿw;5Ҕ3 ~_ 4 @E Q(2+Foxl >}#`_w=jO3QW볾=YH"aqBzK~M5`7>e@TWЪR@D#6o|ڠC%B6g-wX%!ݲy qqxҴUȹ!ٕI3^qZLeTt0ה|^0_ۍo/7o'G]m6#Q}exQ/ZJծTEY[za2.gPǰPoqcmez@q]C x/}Х r")!I.ytNHF4fQK׽AntT2|o\NMcKKiB6CFzuې(Vǹ!iVh@:3mrCj>|ųӅժUeī AsP7!RLnb/xfJRtm۪0'nr2TC-mhʨ=6i[_qT.MCTK h؋257D'SbGrj4Y7:~K^ ӹSi7:cEN<]Mfg . cy6׽(9'Jڪ*= EJinUSTJsw~KֿVg_D\]f+j9뭆61$[n|z0-OYAj -lhQOfu-@Ѵqv`Z7R Mb7 GI3+|SIRȲ1ͤjU]@v?xd$(F\u%om&:yTU}-ƃllyf6X ºazϫ0 /"Ӡ} ޛ3͗OO6||t~4zE{}yGxsq`QDAkG>jGdaDKߝ>PQ^ T{D,G8(g?2JUmUf6H K~\ 2:<8:s<}`X'IDGX 춐떠tx Z,%l<"DI,#+=ձ x=@HA᩽tC-hx)Z_%wF _5.oj)4XC4NGqU "MS^9=Oc"'=DmCxg:n/ AOH3ش0v:! ;9#6("B@!r6v"C-BJ)LB-adX֛˳1uqZc6I5ˆxG~"T bұ|Bl{z#{:E2:Hv^^K{bx yVx^s2buE扗`kj-*C%y, hDa-#Ax9v[Ρ``mB6iMݦ4J*O]Z5n %5! )8 cmIE LKl!qz""I-5O T F+-EDHIozc:QƉogCŦl8fm ) 9Ƅ=^IoFbŢ)iDQ| .jaţC{F?7Ηvz]N1K,zT6_o~6:ɿ￘qB08١Z{q ~!](RIൿ _kY,"Mvc:n6N6|˙m?ARq0Mrp,PoL&E-(tr"k,N#xqiñKV`wǫPgw8Uڴ{DL^u8;}pҲ'7 ay/!2=qiIOVFZ@Ty* pSaw% H%t|t0mWM{;7/.+_oVUNKN%W+WZM~<Ӧދ;y|}F'y|Vo˒{ǻ}1SiKhb|/"/(]?n) UcHѵ~~{UeVXi+:ruu'->|m܏cΓgD#IT҂7>Y8y@,6 G->B D 춺#wb(L0@>ŋcsgC1Z߰m+Y:PP(en3T(gg4knR'XS-N MԌ]Š\F11"}i} y*p&2WJ!F=V:!gZ !.0'ޏQòr).h>mmoE@Ez*.]MB/m`!fy;QԂ(6EP_K&b-"e} Y̽5Zz.j+18I8kzsydBD[GZ*?Sv՘Ln|BбZxCO 𴜡P&-l5xZb#=p/MJ^ ̀!"d8^~8>G7o>]־TӵIVJ:g@ӧȂnCܦ7+l-8ZJ;^ALQtK?ŕʚ8>S\kcMq0M6hziO7(O:;)/qw`vmêm^.# Lo9zދ|"$ }7N~q M7эD֕58h5jVbJ]?j=yƇ";8xM`$dד{;aO֓)BѺmMUjH}Tgf^bX,u5OhJ Ne}$:ds=O9?n7qz$cs>-qAe֠Dr;Xڑ$-lFXHҡg 2W3Zp4i x @kj鐕vKTJG!M/!}}JI73^Ň1Ñ5٥`p3hR!>XzU鎣zBD \|Dy?td{c!b0ȧY&!pFk?W1iIAt?3 ]9D1:EOJFJ)Wf^ܭǜݚm(BZֵD ڇSsȠ91ytYSWϚ bqSC3,N( dø-)ZOULi gdӗA 11/סS,u}Yu'"ryۀƊx>rE%', IDAT6&>!ݏ7(} t/7+n*{nM~ŠUU%奾5|#Wy 2{.kE?`;dcUG V~/v/2E"jZn{c.eZ)?zh"O>Vf_OOBku|&нNE|nz@ZU]J\(s`.sgk$s4Nv䄏G`;A98_XR[RJ鮒zq,0Ei^(݃齼i4Y/7Arn&qoÅ6(!m/lQ42m,FfY0 !&S*ʹ1L|ZP{t]l{z뻙\'AxA,q%b ~L}gC!eWFR65rvP]+r$㔕r<ևF ]e9u]r8{[Sڑbt]2>!l&:G!y~S2( $m%Ѻ's9!pCB$QˈQZ`uggפ2R5Ę(e dAV5*ueqV;}!*1속rX6"4 ʕA]V*ǎ܉X<^PUIhkN#w.dYDZd5q*8YzR\wU pMֈKI6OhQ\nUBx+gOxVsOYX0&GYC[Pϩv\m>  3 Cr>º7Q Vp`(L KbZJ<561LcATCRbc Geo_ƢQǕa#Wv#6A # 5hK3F<:|>Fa*8MȖ2>KJSjV7OFE ]Zq~U DŒw?zoԗ?npM!why!v_yGnGwyx2iU]܌O;Ͽv {&{- ^|>I_xZ>1^m1]AYaa5 t!P:t-5d]W^nVn#*Hh}$@i<>/KO{lZHa؏@Y+lƉQ ۸ ?NXj2xUK<o@U60hӴB4)cACGM8~-VEH44Wˈ3c6ΦB*t~@>%GG2ܘZ̘HNv2Mݬ]WeL$|#7WFOϋ/ܹľF*;(r~5(򕸷Rgoo}irLY=47._e$=~ vn'\,I ۘ}+:p0ԴbGKK$3ٕ7.zrfhPZvďZ5@*DxpʁX/}SЩB1K,+zҶq4meb.nf=vojsLV |ƉST|Rs՟|-S,=&٦hDNlm%L lABn' / O4S@%Qg{ɥZ: /\,]~xs1m9\0ӪZQ@UhhHmssǯj+FBOӒ&+XNY0д7 $I,_U'Q8n&a^Wuz|.֕h.( *m>͢KV(*5Ӽnh~6JWwu!Z)V{`kW':JjjŲdiV!y/bΫxżuԋiͪ;T:/8dvDt{ઢl˗M6YV߸VP&xo+,VKW/o컏Zqx<[^'¯N3Fin5<\)a4 iBӑ\a0QcA=qeO:Y%)AAQm.)iKgUY;P!E@F-KQYݡ&C|Uxcx)6|ZBtP$)zqB黼wzL } Dv>᭍]*KZpsQkфNjNGo,x[(0=PYcڌ,YtniD٠d+0™*pC<2guj긅g|YxSuIYy:"90xpU`9GM蒘Fmw˝2fԇwkaR Bx[#z"͸>e[8&dLpdɈ`1d17k2}$>;xȓٍ0/O1ڪ:pT}B l ҍT1ת+Mg7_}%Z~|tzOb}wv&wj[&[N_pwjFW^h|oTє|/?P}zwxbpw ?>>7l7UZ:~G8p'r4{ޫ={'>ti6/ |fs hcKCͷn΂Lټn-Wu ] k[q^[WC5T;)UZalyQ⪮}"vg=Yow…zLE"^ս83uusާ!n/Nt8_I6(]tih):J%ڂ1>lAjX\J)! b"̩Y` \]/t,rvt3LgQ8ƒr~2:'Jz:,L߸Ϭuzr牢ؽyKϿO< {z?H8j\x K_1~oex߽ Ɠ拗ygF[T?IYcMv { Cf q%m`f:-JPGpe,G+DeBx1`rr> W@WC]Y~KFZ"Yq=N&ܚx eʤYC61.o)ѩkvn## a%sFw,[1m]btS(c vr [8ڌBMRr̃ʙzM+_2E%t ڗRIpUY擄Δ8<޵LgHHBj2.t:ԥ8kIihC˺NC۬楈Nܥm B[ߜHXA.mU~np2ikIotO9d٧+Q{6M]7K3ɽt?_~hg?{`<__7_+?7/A┽X5^eީ T4|#vy!^o8 O.h;N"|V+I~{𜷡&G0??5FfhNB#džRq8wnDHSgbٔH`j;ԸE|QJBYԮ6"\UĮ;F,YU2jwVV^l>kvnZJ$zI5G>!^W9g4!lJ-*V2Bȹt~ J<9IebAʭ|FtضMB{eܺs̑[-v{2y%ً݃Cm~w0K|'*[O4mx^~K5.2l#|X?#MJx6ٜKckP[AP\4+||#.Ha&kchC;dD3>F\~H o\_؀ dpǠ;BV=]9_zHpW5} k" >/c!IDī$qٻ-:߯SO=轳ª$"%$4%FZ!YwW'+O \22ÌiUTCwp&-㩕C+N;}QY)UNmUQO, 9Q9C"X:Sɳ?7YlZO2ޅϐ2;W?U}e~oߋO|K[tbl\k4!M9kL#BvdUDNF:P Cbo{m-q1(ZK$ D[uYqVGiCf>5ڙ(fYTeQ'V.~')h%((MbQJ"%89(w^VC XZ#q ( hIZk TJEYhEu5',sڰ=[ѭ{ɒ6yeyK~jAX'MzM)c4{g h04ܟVs~c/ȭ uC qPd`DG`Tg`(԰G& mB!Q8 Ҵ 3y+1UZ-d{Mߔ|oZBS3v q+P] {[lMsi;NU#> Hbpb)I(tJRZ# **ҲƵ"R]QSk !Q*KRmª'΀U猸bG9!ǝ2z Eu毉/E^LkK}EzI@/=m R .@PjbiZ ЎHh ^W$<иr> IqlPD+ںÆYh%\S1E^9s%DmH9s3* B*AДYV.G!j"nѵvY/yWPK}G߯Ko}{G{[}-'_W_|2ҫLtgc_+>?ko O}2{}+Fgڣk~js4\/рtzpVCnPib IDATjdsB@UBsesc٤N7ێHP=E錺4]CJ,I6vR:&em/7sqr!& K7.DI8 XUjN)J)t}x4-{a}'eif}}\Ͻ};7^8?'6xuϰtI/o~N+|󥏣^dLN.O&#&͘dvxܥ=΋4`!4h2= !ԩл\!qT$SB&b\ (V(eHk9Jk$_맯xΩƤ!tH;7Cq*W.kjT`!߼dtV9Ӂ;5ؿ-n/}vΠj;v< ]eYl -QTEǮ 2Q<`0e}:cgoGJ;?ɷC(o-i]`'޷c#~Jfn^h3iD@F%Դ9 #؎2b)>ޥwNϊՃ0N04^ۓQEW l!R'q D(VuD+-{1Ai-F&/Qa1ƠBG{O^WFR6 1"bϦy(0$6N|i5U:h/8{֎9я,vִ}y154x-.gy0D2?~ N+^x]&sﮡEmAlU{+ݝD;4%Ma4 Of}ϝ]EwhԤ2/L_, ۈޓ+PZz)Q8&Aʶ.RJ<E8n 7 BC c[ޝyL~أӪKyR+:}l][qYnRw8[o_ g+Yǝ E>u|VVjӟ/e8#o'dŌyˠ=%jT^x!jl$oTji)*ܙEG*W*#U`\7;Y0#9cք3 /js:.dدRgֳZM=n*7-{=?q~B7~pol<۹>{~w:ug:'G*╯k;2<򉏷i?HahǏ_(ЎaOj{-6ބ',"셐Ji\G@@QqZ%ڭyLW958֞ PIU1EQTj'|RHS.nG]Xfq::7ury$UF,@E*Fi"cȫ$&GU|S+wK pnT?47^&ΣbR^_X&v\vgHӀ_6ZSڝ'O]}76ߗ*W~O;ߘ{/m^y˫7_In_+i膟ԩ'/wg^`#<~ v̧ϰ{໼ gkJb/ϱ[Kꊦ-86\R)bYR!4{*{$ ( L`&0.UفV_TŝHWg Y!S +g Ofjk|T) Ά)fRu.j<'V|e 3P5 lO ہ(Fu~[2SEgd=畭k IgX> k2J'lxy?qMTU:|܈69e+ emy&nD <rIj]DQoGcUmllIPֲbqm/U}JsDH2pQMsB1G-:p5غAe 3NҠ⠮%D,X,{,RtST~O+p>]ﴚqߐEr)Bqwˉ%%C˰VX1Y;Pzj'Yuj+?ɀvR-wu"^=JUՑÔlLn]J+38Wg+̨j:XKvg-E`W:Qɪ:JRLGFuSzړr9 t tKzrJ"Pn-*#gǝnqk5ؚub/ S[4o?uY}{Ybl >zY_s VWv?Vˇޓo -84??7 9ڡQU;%Cͧ < BpLK$KkMpw$Ѝ"l'Uy! Tཻ7tZ4VENsx(P^YsE=%1;pŒ6nO$$>vlw$Mgk_Zq}m}+NWκ4Ihm$[q62/RQ(5JkzLwI'Ywp_]򉟼 ~ eiY3;ӡu@9WKS i@ВF[4n8y H"Kх#$܃rCʂALȃ0!20ǦuN1,&ʒ{p:6iqy9J%Z& Ih+LNe;AjF!lLkf. >k3c&jvcӻ$ u2#_Oo1/ĄU䱍0pr~F"=̉G$]!@B%gމk;g1h٥ Xr[gAj{ˬv5٧2x`\\]3|9^Jnw2|AFOK'Oώ{^_3>0mM~|GONOX0]8W3?_zm߇(ighoдwhNG45?,'ڬwIkG"*D)#&R(_y!ƈ mM²(MKNE qS> 9?Xh!T$ޒ"4^+~koe&Vՙ!yYiE_ETH8'G&h@8guMm- Jbk[Zc|]W2|򃏟Eh­[_^bqaw?d:>~^~'6Kh1@&b[wRNVbHj[HmoЀFCRӀs?-6p\ՊVڡ;›GKc Uc>l[wH=y`;BՂ0) Z';oӱ3#ՕJ)5kAu@ T:ˊb'r^XRw(0y@;gڸسuk+NX'4w<꛲x&xy$J=C'8*G꠻,kr $uӰg0ei4V4ahH x20Y9c$ipBt!SUj⃮r^-%?Hyj`mIoK-/ҢfHd{8ǷpPZ :aXu7N¶Zy KWI&m|R\z[kwUh/CboST?IBmLܒ[$蹉FzG}2֯LU㗾wkPry?&?ҫ,*/gUx#?{%ӡ)?ҿ;[|hL{3JE-ץcӤoK2[/pbNc iZλ4'er4oҔ :Co5Ǿ^%:RuNݹPBцZZjdN33]Sݱq72Q8H!Ɓ!/u^UQ#?V{ї7tBo#x[iU 1W1/}OM5}G>O8X<Κ!Ϧ(I&rI(a,4ҳ!)EՈpDj3t'I K jL.F|MLR1Z9k9YԖۛ{$bd\H 4 ^t0IlXiv>ϣj{#!ȞgdDAi%+щ8Jk*x2 !bBUYmu6\kުۼ"%#r"Xa Q, }&?O~$"dŖH{#߻V||;"&pP:Uuk1y"h/"Ե]{m6$D(ax7z1݋ψB snEw};w->}キvNV0?-ftP_/ӿ5U/>1}ڗx|x;7^dEQnc3UWknb&hr [x#>WZlj (n P&x9!Kh%׈$>2FSIOXnW ױd\Q:x DU om1瘗R+!B%6}k4U sA;زBjPv麓߸{Ǩ5*8юEFn :Y]ERcܵ8c;y$HΆxxs>ţݭ/ljŠLoz**, OO$A珣L;AKGєO0w)CA\B3 =PUg=@Ms N74IibˈaػCvUhP 40zDbx1='?n^ޘ P[|KO:/kYo}ſ9 ~؝gT,/>SX/g Ax3~C\ _fD``z@(z M '1.9i.PF5BJ:3:@5"* eѮs1 *ę'nwD'@H/ @\fM11HR("tAkF%>S>k[kzuUV!zԏ1uӺRkCy$8-%&1J+/Qh1Yl!bPDfr &$$"]$3v|3Q"U%Q$z?J+҉~.6*(1ęX=릭ղc}ۏ!o q5޽3׾֥5)x ~ώrA]3vw0y}$"l³¯~WO IDATo EhS=U:mFW6q$͞^؀Wib ~}zq OHԤMWE;E֧˟014|O|cQ ETV0(lHxLjU8 i6+Eq#geR`e٦uUڸqٕr]3 ^1`aG vDY=shmy -jͯŬZȨm7O[1D^#7OA{OCu#k s| i(QZqO6Nř?)¬7jt|vT|c+tWc'0i@.A΍KXH@)n9@w[ oQ剅b01t${˝N:^7!]o܋E٣烞ѝjmLo֍.{T˲N+i5 ό?L%29ce폾ChxP0`W(JEI3gGdE[>y/v8G}}}0eP-Ag,Cn6d" &܃m*n oD4Jd uߵ8MxB끙eOBJR\uE, L%BwQĮu x1{ݷϑ fyl^>Ͻ'(_7Æ㵸z}MXo~臽P2< ?[`{J:fSA"`J%f5K "HD#&qig˩ FO +DD%)MRh H 6."K4i֢~I\J NL/{[ô^~R*xQ(BC +DIf > fMJ6V"D1>u!"5Fih6NSO56 Y"T Ȋv}|ļē^b.]\Exz3SuSt'ű1~bWrSly6Ώ?wmy~>9[]\ܥ}o0B%8HM^~\>HZqu7SSlG=c(w>HU^evԍv{G" ߽Fҳi+AΡRC@ `؞ QMR*T   w4ZŸn&uUx  *N},ƙ"u4U&/I&O,D1lg^x w:P= :o\ޮ̟#Zr ˹U#%VO#ک7SGD k(vm T\˰RHGC̓5hlDZO'!&vMF/m/^.j{t?by}+[ɱ7j| FkS ;7ZB|tIl@A4Ȱ+#k(bk5v4@4jZ4 v[$qmR8 G`HfYz k $=CL^th:J#`{ $E&^"D%契bJj8O %ۄFyKE xjgm[2~+asbUჴ {xw@}Q, a@(D9+V?>Ď%r;15zUj6z!X z}1)W&E{DgwJC5&̪s_bR'& bLƥVJR6Q.Xn QX:&JhSFt*P LD!2k׈AAO$JZKZ|lVMF$c`F%sm-Դd%Sy{:ycӢ Y3+K]NLt7ӛwqm66d5w&{.h? ğמ kXdv^on/frΖ/h'u}>u `<æU=:F GRGlDq@,jsvaQSp$?-R\ofZqntY *Lr^@^ZQOWWѿ v^}7)6f3c_"'%A)8+X4Nw ωF,݊_bz#a}չS׶4)zHetfeʮpDOFē$lo>~'^s>zaJ=Hb+:i+i[J7'ҁad/,g",oܠA+4ꨋרa>RhrD\b-#Umf+G91FIhu2Ә þt5 u̓:{>=uǗVoy8&';W" "52y(j0'bGS;T^qyLKPf{Sl'g+\8SEM:&=Y.<~*J8]kI8Z渠abPvXv1{ t-`Ӡu,.\C-v}fKA5(uE*5 Hs!(YHNt!; yi<0>D.J *,Myny(dђQ]Ĉb;_NN_] 4'`̾R:dn)eƴ3kERb3I/Zܽ8Z!ۉUUqJ2.n "[Ne z򐀹SGpmѿ7wǣjE }kqzϹG%Bod~ai—??ضwۀח]F>z0<g~ǢY19.ER5 H$% TKʵJB!42/$@r2U :f6aI9(}γZ;i=3gRƇ' c"Dn֤5)[6(*Dό[=4Eўۮ+R`Gw6MӫZ˦{y~W\/ŲQ~jz_b6~(}Xe:4B8(d}K '`=&\k tܾl:ο96LOFԼ *Ϳg ݋bk/wvUn:*|F/6n J `-5 O-:@a 7oՕ:VJt0QemP-,+>o`6-X]ʠek&Q60P%i:bSD1ߘ4I|MpZOm9]u":%SO*;fIfnTZp>vq'Yᷖ dڈ+|cZxYS1ye]I-׬o~bJ7R>wh(q|\YfP"g-QwUPg"ڽ<6]նŵQ[1Bʭv9@'Bx9XOynS2[Ի i 9ەrZYg8;wzs_EKSr^PJ}]یe7,/s`8uȌW-uɁtPè +eEUڲ.e0,P =B\TBw :$䅺q+G=}!9hMB}:)P @yPvLq,?:i2n>z4y\/j|КZ|//VB̮QWA}(y6#FBZEM!I=? JA^,/¹Mlm:!qD4IW3$FQ)"^QA\0*R!:L @ 14XMzfJ)0ed=6cU۶0zޅ[LVd(55?)BՈ4YF^+ ̖{rz~qȝI^Ӌa;;n 1T"$Z-ψB[nH|Vǰ<.6`6<'|dw^|o+i;v߽E}<G Ky~h1q\roJQ2b -h,LЊP6l >, q6\l*.;{.V\Z9bF\;U&EFa^JGWb "U.5j1Sv'ҥE,tkŴ*vƱeƸXHER*U˵[yZKJAaM^IP= V$❊>*ls5?(/lf3?])>Z_}}Y@@`AF<4RFnhut\ٜ0)/"GV$$HO$P$#,AZg=!t`4jT\?/my$~(fR1FY侀621Eպ1ĨSES/5+5>$mC׀\yz_HxCh$̤ڤy=xb\g}t .Kb,ó3{lCgkI̼zQ-T=eݭ7OOfgq48u<(Uiݾ<;nYy}HS(-ڞv'YgJ̉!MaQ[q8$>6!-6 `s JsY4ћ(˵5}zNc7o*_uEw!JI5i*HܶYfxr+sF2Ad*26lЄF&Q zO9}TZAiCeTrrP6@ٸ-LybNko{xsq|b³,Ƌy'`z&JK2΂ Qm]kMY2bnLksⵝ3}x9w:)ƫez'uwV{TY3 .^.mEOd0](x2r=YAuS,j;3дFSHWӠ[҄b"? jz1jj꫺邢z)zFJvy&3h9J#{*o ?AE ໏ǸlRyzYmYׁ|@3T@ %닀[v IDAT;Kzq2 l4SN Y@0x3PWS;s73K2VpSTr6"@`Bca jdjMA'RQHçȷ_QcЫ?oѯ|q _z{0^ڀ1f\K4^>_P?t/?HҼ%=V?p:Q'9Lڍ.:#"2 XYTH@$ 7YW޹P%2m²c 8 qXU~^1 "0@`1 | \1бQ^`닏$^|~ G/!FiOIIlrǬȎ4cعA3Io5[vϏ'QRi} `'G^<u|=OAuq+sN? ^]mW2bDK7*Õ~$N>!WVYO+}2^isjh|ҋ{uZu:vu^xd"(MmP",fݩmąw* >5 5O\QHa;a s+ 64 !1`ۈ , P RhYt}a{Z4VLDTsuRXz" i {)T{-,šlax}]7ߪ,x*5!D p[,Og\`mL["&͂kVPtp4zꜛӸ:OI eTcdIzIbԼ}VȻ:vEnᵾlUē.΍|q.Knț:oNS^/W]$Ps_y3*oh_RDccI-'N3 )4aObHP?TC16kE>H ؀.ȸ=1EAG4Jwjl~A@FZGLT'v$dDy\KIJ:a)chbB!ڒN`I4U $1Z "=RP&Mδ}m0}xF-.H#BjZn|mq1{m1

ΆE 1Smjs͞~XݿOgZ3ݲO |dԣzEA'DV+}?Gɕ F:|7axaSDaQtUI:f[*)z"Q|s1 wHF,'* l#\ H$ K .N: A"dx\N8PIKbLfO+)r.!QllT)\KVa^?fVƧXy4Fr?j,vI=\i^,:K.cK.xWk[dijM5Ѹ=\„Rw w:.v+T}z\ Ty sLk[ ?3?bjm%5Щ^buԣGMEuDgk!0:*GMS;MS1 :p EgoeFs ¢tEi DTen})(}''}A%E@$42@ny䘁(ݩН <XŸ(xH,B9PבN|LA F!Cy ȍڜ0!:ȩn԰]l\X Y\~xt+_;}̈́A%Tpk+6F -Za3km6C@ ـ 1"Zԡ;7&npX1Leuۄ|3 6!I" hww|SzYbIY ZbbB_b۶Ł$Q%ڨ(XN 'EV]a66Jky~/[gVMbK}.L+u*B4~1_7* yU.̼ϥNW'מ^ ?y:?g?wpg$gWVK5Mtלh{!]T&B:v11eg/S6شΰں:^`#`ί0'ݸw-.fuyצvнYp/yWm 0>RG R;d7wov }cv}{m\ө RR$8"!ED`)Ab "d*TiloW;6NbW2>{ϵǜk4 I  XEL v8P{IwS &Ɠ|UZ!` @#u}(Mm&]x͒ηJu`>kϝ>nnh򓓮ƚ ŲΖZPZ8p>=B=rMd7%R1 '[{@-ݮ iHo%=dʥj&9Uɫ:o^i\'umh!5aҷkwĦmJ-|{Xܛvtz#)*\mwכl‘B d0h}:EFQeeЖ [ 4H U悱I+:3Kd#DRI|4N"6]@P޴C &L!EI B%%KIw hpѽZqg_fG~%⛘|9GHKoh?rI ">*D}bj糟oc`n$cPXM'1~/wCۊ!f&B?2A^=#8$UZEvBjW2r$r"zOm\@R4Mrvvo4Mc(D.Yj|GWyuQnv]7N>0)u:/K?xq6Oڪ)IuynIy~~<+YngRv]ooe'SEZNieio|iR^ߪ$׺d;K779Yaӯ N+I4B 3lk&Ox0y ˀoǂC PZa#q½5Q4[oU=#c"=8>T4E(҄` C%Je bmD$ % u &Y0 ! *|7AS$D=XI&a^sBl󅚤|&!_U@ o"ݭltJ5:iKe\ixSwA(Xsxl=PakSRLTxq0j'SXQKB 'wW3N9P?49]k/Ǿ{*1#ɽWz)j VO$΄L|.}Q]޳{-=[Mݟ|+Ms"¼\fأ?ܗlj%>"Wr Ed}c~?ػZ"~Rsw^m$B" Qf !N՟G h0lЎ߼Bh<"R@ $=FgO睓,@y}2Q $D"$ DB`l6frguCH1tшH,Xx׬Tzg6=JfJ[Whp:xMXNbN 7&O.6g @Dky\r;(noҷv޸3O/H,eW00g/q$c.ɱ#" >M}9&:PuD'^Vfqb /F7[p+2ݳrCug 8 kB7={~RzF>&lo)QlnmnM[p]݉xj'mOF΃1PdҟqDL=N#[rޤTV|uV "6YhUCf•%V}-_9V?/$x@ AtסRXyxCkz%̂:h]͚)pN's sѻ}rh @* c:ɹ]T46D][J]*S6f=$Z$2Ua䏡YY]m8mv~1uo'sǤ+wu=kk.;:{f2MvZgZig_s̻TONot^k&**+tuH)V)@|Gן㻮j ٨k{®'z# -#R&Lobz1@cs<ٓu;)T-ccЏLOen`1h2$RH+Z0CUG:%><{y⠋H!bZ Zw-FձM^:+Ѿ/b|6]jD&I|MLǸg="E!qԻg5iZFBv'">F<`:?xagG418]V e>!X(vKt+V! DNvIoCXN W8$1:) "U>'mbH樽iPyXEJ3;tMށ|t8 ~I>{_9'})߈n$~WOw'5oKKw]x~4n6xqx_hD).Z ;DU]}asb<}-5YTQU_L?-bxf]}.y{֔xwmk_ϾO#qu'F_$R IݩxYT}_OC8=z{KkXei#r@ 5tI!|MF6n^1nz+vlH#!WMxl]%CX-MzLdUNh,#z,G^21Dq MEYQ -Gd4ދ‰U]8D\hFell ۈDO%) ۣT( d@4"Q4AnƃU}8 @-f-CHR=){LۜY| ?}qsۿU~sY)oկCI/X;|y?{iOR C(3F"!>aa(g;:'&q^vߚP.DŮ\.ֶwQz<ڛǩ.baWV}ҨjkU+}FW|Թ,NWŢg,.<4Bnok'JTHG]_X {$L0WO7>٣pQ-rsSjm Bs%\d]34Ϩ#z Pa4 os%uO RF饴cP:r prl IDAT=r(;q-5@$.4. (kFjݓM\Gq6~晳^m-7*ݔA{PK 99 2QСnKC 5RK9 DƒHީR#)A%BA<\B@]p78sH[&*{ $ 0!Jt&VunPp܄TɬJƒ,"slڎGtt&czc;+[H<.# ˪_*  h")H&"&F,W T Dcdc0e#<`Ý@d 䮃b dy G0X!C O<9`2ѼGg_F̧~7>ܗ'+|,$&Owp`3 #_=}<OR c4Q*^G~JqPOn1.0j 0gƚ!~1`K<80"5'ç~W1|--|"a~c쎦N>a5LZRh.o;: Ix0ݎ4˯ aF2UmoGG[;c:["#%co,Vg'"]ٽXn%!eqF$L #F:qrgq:+kg#낔R'E||u2*޻{Jմ_ӷv.n=2_}ֈ^02z{0C\V|tEOXR\L1T.&]n\McfO*<b/v֦in}s qaċBTÊClw ;WPz+n& FYc. "= rrRj8zS>ZU`-@fG=BBNheەzE2SHmA JB9OLB6xfݖ/**+`hYw}>C' Xoi&8͖ %I5*EcSBG%tyj,G)UG"mO ު|}hHF)$zs= ^F2#BHr0dA"1T@)!rʯ kԙBHPQXi)^YJ4$Kݛ+S^B >^" c^de (&>Jv}GUc[(l:fi=!L22}\ z% !;M@T!$"b_+E.H_Toa$IK 3+Rz@=t0` OhV>`|y& ġ`8 @".@  (@(so;V3<"PNwpoӭ.CTen^tvCg R~ X?B? aH|oLog}y2_=܎?v4*̋H?q`qJ^uQ AkGx'>]{0{1-u>DI/[0ǧ|(az,Y\m6yDoЌrR TO : #l/…+Q $eY!+-:$=קWM; ;^e5_hJ>T;0Aym_*FۋX4H)e[NK!7r svI̔.6F2ty[T`s `\ uCu*c D|/3AR9XH\AW$$R '#rcj7)yzi3kkr$x&5{ϲ@v¯}@ݥ<*'RS!K5xe+ĕB\wBQW8j'ӈ*0l^@ܰE)μg{ &q%9[[sp~ΔKDIf LD2Ҙ_@0+l}p7#W*pu8*?:T),S Ͽ+js}qC?=_T܆?CR+0]0BAc@- ]yKϣY9 {A"(ADq+$Dhb;lcl齋Ssopl;c(NLID\r@(yq;[jj?lL"]}v 6m,ގGF[w0E=__x4-2qtqq{owNt6ʬZW\n⤭L{ v)tZTn*h}*~Mx2Yyu؟w߸7?8۴?텸_{C7?wN—q]<[%g]]kIΑ+yg۷N7F R<vSzF{#ֱx] Lpzl̼ɀ$ gC.!LHDP@ +Ұ4zS>jNf'-EВ`# Aâ&AQkƹ d:dɚg@UY$Dfz*$:)k* #REׯ5RNr7\tr20ŒioPgb6 RrDoH.a52I`9L;j@B9k0 [UA< -,A#0c2戇]^sge(JL8ܡ%"/=gR0BNK[VP4)$U֙&p^u]$Q81%d$9\ H$EӮϔxl\wo#R[!-.rVvVSjjHD0Ta_?_o]vp~4]fLȆ=^UO_/>Bu_4|~}f{cܸ^j0j[#1hd{_|~g~qdCI9%OCuGcba Hz{[owcCz)1:f|z0 L&c9mh%[l>v;OO$>p?Ղ_;O AG`B(FKQרbHuߞ>LXQ2:DV+3حlNZT!ӋoVW8>8Yb.%M4_5+gCEb V(fՊ?R:ؙFCS#OInU׾runL1>VH8`z\?|r3^K_D|P53~Vt ??ӛ_^4zbPS~F|np+ \dC!j K303an;^f$uK\ǡ$1H*GU A qQI6ZDr ȣ@t`"yK )7"pB˝II{GWn1S[ w>+Q;*Qeuzo>;wGs/Kv~c~/Sq^ ➹vrjSzz+}⍥76XLUV)"X GT ??pR=oR _{#)菄߮! >%w|!C˷ A"/lO=eV.uqx^nbSN+}2}<ͧ'U(Ub2uz,Hבxvs:h~t.hRtE[]j䪎o<-}{b1 >=eX}Qc-s>d_naҏZOV~_y{F>o`i/#-YҴܭI:$B18HSdQr!@!tGezPixk R0ޔЏ 80fAom곌wR 'b-!~tV9 C?LA&859u-%& |eӏhk,-A$GT.47ucBhҴnYEܗ]Ci{ VJ# +ixzr-M'fhRMz 4bAd9aK+| 1ECl0^BxͽՄfMT TD6(O[5a+@)AOTi@@) 3dQ" 0,e@] lgTB^r74g;y"AdJ( 6$JNa 8 і,,l͡]Uw3ڔhmm>\{u^{-NHv*Fi]m)]l}\q˗*u}2nr(t];DyݍQkMH+wn= ;փ׹ְD!% s½WfajGx@A'P$EXά˞Vϋb#"c;=fvgͼ!o~{M^aܩ53_} w>#]£ߓ['u. (hjw,RSdR cȤ4b"s ]ƿQ?.~g/e,sc\=ƺB \a ZG)kBT pӀ:H~{\drU^z?=&\jԶQ9ls+{l% *"3iɠBeeZ-ӳlU&crݢk}1g bMϖ˭+{v|3ʚ0Ve/nn7N~$W|9$$=QԲ?ya~]Gׯ]'Xst5bL~es'>R=#a1I.R\,jwq6+wF[z|5GVGN7_m 0pшSS}EW { dz |J_ )i[dS='r yM`ZY[&^<օoMöSV(O$DɉtU`*YK2| u7.:-ȰZSDi f^bgc@+B\P:A Ͳ*j PEE"ڋ#+#L&l,]б:>f_n{UGuQ4Dw)PBƄ JyBQ%:Xt N#M@YjSD򜭢6Vօ0T IDAT:]p.^E*b鎎d3Ll8ÝZ̋M13I өoJ챲}@@GȾBZ.@ڿV$萁XZ:@:H- #ze@tO,HzX둠my0Q0Z:"#Z?_OwxZ gfGo_˼~WVoǮs+ٝbj#3sOu[%؇iP୭#Qj=YYL+%*WJ0%Yx. o,."oL6xI:0C `Tވ<%t"Xc*ELYa3bI^" W&c{6_Grޅ[|[9h{˗wKD`cд FŖ&4o'[ wcc9R}sVx:[rp'"/oŪfQAFn_{\&{#}&ay뮍Ȧ0CNL qf&[1pt[V||盪]*;71ج<{o-W:/.{,uU$̇3LǗzKȊ'_SzZ{aw}Ap$ZIl>G6b. <_P +bZA28@ [# @/s21x] 4I4 eS7cFPtnC$v6zn{ ȧab2kovfTݡLvƧQm3 r?^d,b5W1n6` 'b{ Y˵6),J:;KmpŌyĐƕ҅0(&M= K77~>$2I)T(̅.:vC, bI"# NS2ɒD߀!1{e$R=@.6Q#=5>hX,1|ӬELfY4P#ǬcgIL'۔ai$ N&őԧSIZ΄ICYHrƊ/^V N2fd] KRsfb@o#0OW+BDCTri|$˨O-Z XzU2| eVAX#2p9>m]Tm녓mw5qgx{\2>,<[, Ǻ}c=Nv*)I8ybX Ta`MK".)*݋ x凳;W*vqzfQEwm]lՍY͍ؕw ]@`nkvi0yohO0 ! K36|Og8}ݺ*^T2o;Vj7t5LVgw\tu[t4B {x].mndI͜iv/٬az|]^z. W3 w+WD/<&qriCrw4~ xKLE;2._ c/@t1;m2rJ;)pY >x*tfR(c] &EYVe ;5dխy9ZFR}O")=jv[jAH(vub0TXU 5KvuUp7u׹ƒ- Z(&3$]`s2'R, h)#M H :S 3 ⁤fut"V;ClLj"oxә*Nՙrix)P5| E7"  lSUR_@3)\k6< eD ڣ 5@l"d`O1XE(X0Y75+Le㊟ʃki4\P2f0o`&uN?$$ }g`зon{bWdzlӿ?'jEg~b&Qs䙭f;E[<%2FF}  X% F@1X‰!P;NwIxC` z,}nI;.~h=~PuF& H`M#ZEٞwk Yao8bm9-kY6Ͱ?8}!7]{ZH _/˳;9X̟\;Lev'\y:F~iR+zIa+^ֲv M7X fi z؉`q|^g!5eU"*PrSVI>䕜MF|`a߫Ş^cbäQ{JkV3{t4yOf}GJjR)խ nVF8+K$*cnIb2 8:#B 1F ўT[O*#Cu 1c)f>SGE1e"@ܰ]P  I*$ 5NqU Z/ +c0,Neqٞo}5ep[ȈT\"^ ^}"b@a 'ȑrkp}7nmWsoSroy۝I6F*4tD"[ECV3߶㢚m_r ug'Fo~[z׊ѷN]҅KE&kBxf5Φˍe3#[7sEsyŐ|DfH=: Wj8G\3}hogb uew4.komgXF魼zh^(mKSvO졇 tXk5J++pN`Q;@"0XYs$9SOP0`dbO{jl`IZbL5SbYUY;;;(\ܥ&tl3op8Ɠ+g B*hmVMK)VVMHw()T]*E&ic.$ф4/bb(Oyۇ<kN jɦV2S&D1t @ޑ%n3!D3(SjڪPdH"t2 P,0꼤PLUK ̝U@_18&u - N&1`RR9P]dՀArA1+ZIA)ΛPĜ3sBt[IHQ`h5E(Zbe1嶃+ +35 A/0%h"[ 6`ᘐUeǹB5M_-2A=H @%]|ŏ|@|5H|P>xrz6?yv{sRe eQ.oȍ/LVGYEF|B %|C?]ߟR~?y25SQ{wo8O$?v%.I٩2:HdFk7Xx$%V{M7 Zonlჹ.mɶJLUĕUR|:g7]7{qۿЧa  Pǐ_+~mƉzy{33ōdն~7mTA9Yu|8-Yߏu1 ~t좿36|~|F9e(-csӷt(n⒢Ѭ+QnKG{W~Wq/}6bb~V?pA;*k`]kƻ7nn]eu5ejN칥Z{6d~@+<Ճl 6@d:Ez"^A:4T3}r+kyD>XtԞav׿*5:S0!N4ZMa;M] y6e3s*lN;Q2Tfe7xRZKJ0?v:L;/<.۽b˸\ rͯVG]&Zx4 Hy JT ʺ.l/gRPS N3 !QaO:km`uۑ4`o Y˪KHBIG ʀ0Ҫ]gӉ(u ZD<@Art(H+aU. )EN5\A=AP u:-aK(l9AK)҈R+ CPD 9 J .d( Q!tm7>_Mݧ/J/wMGvn_濋؇Gu?o6ѥ?<=+RFxZ~*srſ5D} ւ ]|O}ccG?8/"ސ^?qJ{ co·k]WCʂRU|<::e9#@kK" ]w@ݮϵzxwcWo<>Vps޵ӭE×/~q8ˑ)v%s軓,2t!9z˟I˪r蕫z{_b'G+S<ɍ&EnV"D@Q~3;޿qBXzkmѹUHtLd!%&[ZՍ^n<-]ptqy^~S[{v^W:.ξ㦾xP"ϟx[(<ziolX牨uUf{= ZԱAmG'{dMqZoůrYruvAr!>Vs!E\"[DerMP[St*K.oJ|vjc6fR+*հkuhx6S/ĭOϑ5(/7\řLV@DԬl{KRjCWuŚIAt$@&eG4QW@9+̡qJ]"iI"@fd'Ab`X 42%lL{qK %+@DGp Aub* 1:' -.2%IEVu!P `bh$!+w/ TB(4xW2Lmh Rv #ɤ| Mك[SYtHC3!3bn9 @lbu@Θ hEiBx@_1zE!`Dd#@8"T J6qM AM !19( t+`0p",(c}Xv̈́cCƑLaPs-DHgnbӯ^ܳQ׿.-ϋ\tn][eT6&g4.>wu5(z#ˋwx[pQ].byoh9gW`KQcGf'| UJ)pЕ,=7 !>6̆;cŋw+mK=p5o[AH1L66dXy< $IY笋!ܢ+Zp.vښs1޵Ky%]߽ڮMu}}u׮^|->QJN{_)iTG{56ѽE ' eGObmmSSހG ^ZkhѤ@}`kU VB 1t raPX>O6SuLQE>Q)20Z5VbNaՎMrflHcתә=#KM &˖Y欄qTު̰V7i%uT}A9om\%ȫ M TeZTͨYUa'4/,,q6G鄻TTuFDXLBREhf1yr. D Ĭp6:E2Dy԰LV1g=Db^ ʭhbG@8ؘZHFUHjVK)EңJ$*""pEQ)"C&\&Q|@mXk3)[ϩ&WZXiP:WIEߡ7n"<ԊSvSȅ%(} @$IE5Iג܌!9 b2 S/;~0Ⱥp4me@% aEyz:(pH0!A^e%цl!!"AuE׬Kuؤ1#@/5slz8q^aH]{x]}fWwvV#|o v؇I 5𖣣+j$G״oS-)FRg'/\`OX3t4^?q ?bݾJ򼡸^gs)`]Š ֜"E{* IDAT <{Y>+Rmc`swޫ#>N>..ԓozz9g8Z^9iɅi)Fj]'6l^Mܼ>]oϗ㳳op_֧'_xm9dޕK{qeOݍAq|zdg'ZvBWQj)tpso- Q/! `Qh Q_T̖g]07Ջ;R⻇w]ޛO<6ٹK֥KKVć1+JG gTM {]t:Kh93iDR:A EA>q"H8;>%5G@R +TDRJX!赌A\Kw2C:+zO 8?EMm^ERox#I]^Vt[M7G2o!&;::I"Ϧ$zm#o FD޹*H:EN)q/}g]d[^W{EW4v|j{+X,Rgs.mN†jYHQC8N,}&)JL `(BT$R) 1S(#4V1;5\E`sX$ZTΡ.er@ۦ^A]D.Ty2Mty(@@1*[!$ɡuS@٠<|Ҝb8:[ڶ"+?)oդX]_}wʫ|گ|o'5@8І~S/|?gl G|;ƒm_ǚ8p^6^$/ݷF. mk`WTd uIumLg\[YzA@̂!>O=r?2wӺ~|8*5M:*dyVV>DNZԵɟ $vΖhSayݼnNx%w$=?q/n]ۛ?rrWkn7=kU6bzf?'OG$(*]F.^en4 ƚ,[gxccRʖYΫ1pM؟Vicü={0zlC%e\KLSY,/BZl2م-\Ӻ)921 鑃 Yʂ( J(O-ҌdRLd 3VŴ";`J%CRhQ VH(0ZX1H,Rjm[H)τ$b5K$$i3 4I( Qsa%gو/)&]hk-M@eg#T9q"jJk ޳?pV ~t޻@v0!BDNH".'N1`HYN$,\CŤB e;Wұ@I(*3EL l0=S+@XcyF Ѐ9ÖS%(:AK h"Rt6Pf!Pft&C !1 \ 90T-¸Og]2jQA݋gf? 1>|מԗs?ʗ_t|#k2<'",kqNF Uֻ,T6WMQUjRt yY6;mVz,}O&~ plI+H}򕓓z_ FXm>;ޢ|65PjYޮ,S4Fs{zǃI፻E,Ʒo~uoo[LPcvl]nN&Ȳ ~Gg~$EgvusI?`vZMO=>;-_xF6ۃ=8^2a4>qx|?.I6qڼ<2SpU%3ux&DG8'j7E !]k"bm yy*Cd]БZuCzSWz,yWFg**e$&ZM+6Qǫե%c^LJ_bA@@ٕ,$̇KV0_)T)* %0m)JBYaOL3#uy2h"N10q P~ծ(H! MZ ⼁R^Ph mC V`ׅi\buHmЃA#aE nQ+ )K R`-1bB-4Y/;Uv2Ti3q*%wAgePG?eӂStfC@R¦ԐPB&}1&U&շOwl\-y"c*,/.%OPNר,J ADŊAyFP=` d!pBJ1l X(l8qPr[vE`\$[SbQɧCEC{_>7nߧu{rU8$$c@! d  AxHBJc%+$r5[?n1xX疱%[J ?ik?̵\{oo1~ ,hP1b`}  lߑ|(ukܭop7p(U Th/c;k?vğzw}/xqq=~|? ekG=𗱟Ɯ5 Г.09Kn+2cm-@NAqrYm /qϾp8yum<EU?ܚh\V]2;+]};4?XWo}]+ڛ Hb^$~[&Ӭts{ٸg.~|f3W͵tx֍N&UN*_i.%cwn[ˇk}ZaBYdс1F1|TEUjO?uCTwLܥ0ɾyUuwNn {yI}w~sÂW>]9VM ebO}7E"e }-FhQ)60`aнR)*>zR; F'7ȠB }>]au-֤C+;ۖ zWZJhZx</uEIK!Uмam~YMG.m*ͦu$#Vdߎ,'PSq~,{x=h7}"s.Y,w h@^)Jxv鄈s@, )3XMU,Qd1Q9%!DUcjUPPVÐh:!BUD$* c) %([(.*HܗM^n7e]_srߛ?xI #{՟\ޜg̰Bߧ*X0ُmģNg׾<«lԫfOz{ɟ|4?̳IQ^>oW2v7KܱO=l?(pa$Q|d_#C=rnWD*t>Yn8o|?zlhy {gnag.CWNsmKz0WcnLڛЃ'acг< }U {3my8SQkG^qS Bp/l\/)0,@ n1ǐ=-bϻZ}nyѴqn97M{(A1K@iBE1hr+'ǃ]IPe4 $QKy!+adB$Qa dB`XZ|/PF6) gHmZ(AM}dټJh`gC0aP&h搩B,y*i3B7P723Clwz se2LUCw*w!LTonҶ>{Dz*\ uuqQ]|q~'55~eN f'9#TEE6ҧwXt |NfR&29G){(ޮDx@97|i-r_g%3O"IG:^=| S~${ZeŘmAl&XG@m:}GiVyp-y?߮׼a7<, COj]pbz}K'>;<`c?ɶnR^atlN!Tx p2mcQĀd:[vM?0PN&n9޵"wT3Y0@2vIo괚L`M? ӌs,|z_|?~7WE9}$t2kom۸v-\d{x,Kbo6ޛL˩!n$jܦ;TwiQW㜬ohZ\ gTeɶk0iszt6tzTb2qm?3ťvz .N jd!xv P9OyKgx~/d%]m!ݻwONNlᬜY-\ ׿ky.t\N!Gi4N,2HQQo# e v @_@EЏ'0}C.iܬ 84*1%bU Op#yc€ؽTw4yZP/B.4:{6oRr%Zm]Qf\ta:xeesѵ4n cJ8%/S!s#f$cMDNS-I J0Re"c@{umKr roI'vpcf/urLdlПfjQ-BEC`U6@ &fI.ΐb:`2F K]JcbfSG.eƙ;:./s~kUPx m01[SICZ @2LKjYҴwИ`v|4õn} ){tڭ /f۝^zKJ/$ش0q9hVٍ(sD 5t.Z& qC"<#@5 @" :Bp~E ]1TXf /.3Ĝߛ: J:X%fo*qҁ,\*֏SrCg@0: j7NC_!;ٮV[` ׿x #)x>G>8~~^}kvh\Gϧوmn =ނs>[^UchvγA8Ʀm|ĜOgV:Jjރ/i5=_6~MYM:Y,C i$?+w^wwO 1㣐君 2φھos$h>9h0ʧ`<`R ̢޵ԭ>"HEO۳ŋqOU|p{ _|6u|:jvzۑsbBD\+ Lo+EJ 4gCWVW1bߝ92*v}h v+LJ _2ڿI4IdTs @*} :,w]h#tWw={&u/2ɜ -wnsW+m?fpDL"^3scf".@5EMtwx-äPڜ+Pڑbص:gk [=gK*48E;pɬ`#/b(]7 9QHI>..O4>X IDAT\BRE(GbDos Q]({h965c 8BLT{}řj\PK[ *wq()2:CfOYl[EQc1y$RȣPAac&:La%B r X=1@ 086`7{B"X(JO,p;f%`&wzqvhqtA @-O+< wz[dp_Ys?:~#W|/?ۦ~4/oD?P^n/W%YfJwt{rs^돝u}{o:_[Oi|UW~#(L<)*5r&k0f7?UY50M=u,s;stf ,oھcn:<|vO>òn.fxq}'@GҴϚ@ fB!iq23{s6xsmf|:>4mݶӉ !gϪ1-JBĪ2g!-+O>]};ʋY9Y$[6x\@^nnO-̚:`A)+&2QJNeP01Yyi\N<j|8&lH;c" tp>'y g9qs P:Z[c &,+IҜwYf7^kd$R" $\HDK6솤5غI:"M "k5bApR(,%nn@ ZTG| ^xbl5%M  3 B +JDQCV:BaBL$mL >b2]BY1o2%dYEQNe[i2[c\mNyh1;2ؚI|,lEy_DU'nB} !R2陛06A`dǚ'a'^Og {Byk]|:C'b, 2GeϪ|^T<_L&v[jDۭy|q5ف)̮2ky5~Ezh⪰ߙynó|r4˛чO o]_eOD% J,&[UͶ'D7m=dTݶɢJq0>햚b6ǧOf>zzW B-q"]on:j̼ﻦu1nӓd6*,ʼpПdqU*YGWUх yaIHk3ѺsVVKbDP@1s`<YdUO1]l*B7̘:݋prWP8IڝCˈ!>,EfŞI%䖁0B F[WAS!b9_1 &s#8gkKդk&& &ù Z c: 謳0Q#x*_KO s ` b s^Yk2 ")%&I)&oBhG $3 $dMio兀+K3@)Nk&xFt|-]QW  ̃8""F ( $ d0$ @(c<%]M9oLeC)8;!ye1iOEJHZm$%x.gމcIEcMtշnq2ssVȔ6n r4:m8PG,aGNI# `&(eqH'~cA{ai{*D`8$M)?2A d@UYkfMǨ@ )yo82 O|`a`E,BbIiFI^C4}>$l`Z=âʬw'ƤٺmmaY㼪U)o.m?;hϧ;/ARQ!yi҂M bT0ىwntrqpGV1J D7\^< )OiPUkB^Rh.?f5ao:ݵ$}Ƈ(X_ԙ׾\ x($ڔ]WM3U3 ƨc!+,{kWEHZ!c}1#cwv&Dw X*9[.mY|HZ@DfPrzabdb a43y , +a6A-Ei/6L|YblLМAL`$b@RO|J031܋ Y_S@dQ8.&NE2Sf"j!&qUPe"i-, PDŽ])k(4ϳf(6<*l5S]<\{ۙyUߜ޼}|uݫޝ۸z!]Ru%lB\F}߸̗i y/F| E0%)ːhX)܈W#5 67gQ\HR_#RYkaOȪ2| ȕΡMJ7@,%p쁡K!r DZz0d$##ĂC) Ph1zmjFx*xTlko"Va>DbXlq%.HJ=\{cw%7ݟ, 3ّ?7#)w_PZcܷCXxk;w~tyUÓ- v]Mm]koQqK]>]aWi5^{םOVT]RڡDb}#P2Y~Ȓ)߹1M]`!dŋkz38]l>3@f+L,'J]|<7 zGhtڮ׹wM6-8ɑƣEd15ƨ-Qu"nzWU(҇ Oҋ'v_z} <$<0? V=Ց1MYs;n6:`qdg2v֭#mIreiBQ |҂&J.+xKVӣn4]}hx l}:a֮M&]g [! { 巰WؼY=YD[N{c~t Q=0 z6\'I=وcܸ{~pN3Ө=15r/͜4"XMc`# Bi@ ݏ1uϨxFn">aPD, 0>$UE}dM_ S` QPx5Vv^MnF924^^gy=hh.'A5#B[A;` /%jdJ:p ~_ͺ૮D9Ovg]o÷Vlhqy2l jI)"k&a %dn҉c>܍";CTqR4dUtF#( yDSn1e$H`/"g2=q' 6ʩ 6DI]@&CJh[(Z0p0 AA:Gt⒜~TJ~WrWȇ\3倉GιȀ L#-۾6:Ơ۾#6fss]&MnRAdCdEd*w\ !06B3k1`#<ɬYz(kh\Ƙh"<~JQ!Ffk^S۵OadvЏgC? }g^2z']3L+ˣeGO&Qu1(>3d^ʼƨ)fv&{unm[hU|>p%օyb _vEqJax=i: 1 Qڬ#&P՞i21b2l=/a\])B_ 3^b `F{2q- Us " ؿbFR FT@a- T#BH !UI5Cd&(lj41H!;I5tHCc)}&M~i2^PSa_^#+F'Alsً o!jy s )mu$'d,EGdЕbiD=)-j :R%0 ,ssCT'H@P$ A""a@@' d/yͮOɠ$)YX\=vyHjXqx 7~?__+_(~EGa.$2 14UmWSƎTe%w ~s|N;xݪmC>M1@JCu|g޴5yѤ($/iYZ6snyϙό Q/v[ ֙QU*s h}"f5ec_&_~MM'B0:G}s,_=|fl:lRh<*"J٬]1R m[v;U9g{3, p&c8:$rzݘ|Oҋ0:>u9Z?{r{ǑjUR&S?wMoºrcdql}1Y41{Ve1ordΎq;>uW)Sfl"&9S✙ lD0>?#+55Rl a]c8M?zX158k- Xcn,7YT/N0Dy_~7d}jrGaʪ XqU!~ AEWfa_ @lڐQq@ˆѰɘai.T,o1\%uc'U  }b=Ȝ2EMkMf;`HN~`{m_-f{otGx'^~)aޑqZ&Vp6CY>z@ U޲KEs9C3uOR}G&h #IDf*~ǰ`3uj9de XŮazN<1$ aF 2^&7{( ~P-<@ (Z:#0ATuT&U\=zkںqD"} 0`h{#/ ? b1nܾph׶}T,?x4-?²qo>' e^/2yfE!L ?.b _dYbbӇ`ucT"8 `2Q%"6)6X^_cE.P;?ӣ#ˌNb|:ՃTUERP.r/Vd&yw)zjbUw$Qe n Z7햖^\ޙd<,d S$mKH5d7]]{573cEmKPsi;wƔ^6%)δin5i&:He̶ih4*hȌ(bcxNy#. gӥ٘!W.P $Q֟sOIcP@JbrgIg!LRaH?mxz,hTx%bF+p#u8ԩlJ+ؚSd(rLzaD 뭴 "X :d?(,I"VoC> CyArhu&[0mHR6FB$`#AJ蕆 (DF2(%!%d"$5@'D1 f)5&20 &ĶGFCI=2 d.) #sc>#劊iHqhNiE?4HZy$^EkRwI&kN =~DM.6i''4B zJ#Tv1f\z宖&-R7MPhgKIB194@.D0fc &R ba4xBHv -`Q:w fd41L,.E7a2!a:Xv"$RR2&NhθUu&<ǰ נ ZE+E{7A9|Z4/Ͽ+e!-g7}{y^Q8Xb"7 z!nMxO/Y;3>Y@(G0Jr#;2R!ӆ #8@`3yiZM砕)d2@Qk2Q~t l^NF1AܹuCIwܬn\;"IT1qïܸn_;Ȥ&lV;B*Zo$ ah R moq|v Q֖YY$1UO>{#ob)U\NGapǏǍ VrʭRITwIPiI'Bi"# UV]Fc*MWV=dz BLqt&$'F <*{.?CdR IDAT;D32e âQpf h3yU j!'mOGYhdDr"Bh$,aSKP@Dc!,GB:KV@dAEأCKȜ'-ڲLڦmBhZ<:o:YH_/E8^gymVki| N sTIMJ(ųpwk7ܺqX60qߛ'IS`*IwS21h{s0gZ!E"G](i_eƴȴ"[eS5]t2T~>>z{=| {o} ȝqt7軓߸~7~pqbe9|YBN)ch&t@!5b81|$@M2;:_ׇPaqmS T5A$@*-ұdBLVNOCLgyP.{[́hRc'1BVnb66ƮLE_%i42wOl3;T.Z}CH(٣%R<(='=,1und}]˩E.ՙP⩔D6 9u Nt35&ey>I T!P M ZxbF0i"\cPB4eDdž[\$hL1G5),`D0$$ K\HQG*D㪣nBYS,^P 7Pt? ?.//w8n_^ˮj[?{8fr|m4F>f`Es;blߓy(Yᣏ{c$Ie)z]'@ }lz/.W()i(/΄˪$$Hs)#Gq.G(}P4t\`6#Mi029Cҁ6JvEgų"KI(^y `1۟|m׾o;:_7g煒ioiu45QjiTeG J%FDj"HC@V`H@`@ @H ,hھv h 73 +y"_e`=r i[Ff{{vS" ,P""v@`@$ D>ǃuQꛅJ@ d$F r(e"I)섐zRJDl0PQbTfOjӃh N=sDHj!x YFHEFxDe&ۀ\h:6ݽw[GЗݜ7LFxchEl}_nvL5$-,̎TkLgfөl•X<򸘊ˠi텼l:yM5 &ỵ>R8ɴ!"lEH; _,7-s-~v"s$ 8j$ (H F $s>@ Cr8>Dxb ĩferdʢAb%k)Ĉ9\guYō=wÇZkWZK.֥MdM* ntzvBqTr>Oz)^ޜS;'=yO7^xxr|tYʪ7vyb2'Ƅ=4R w DW e˂`D)]c&9Js3ٮc `y0}1#sS ;`X`bE `K1&K)|C`!KFnVdp9`:Y h"*a-O$56<\Mdr*r2`-MN8J)M _|R;tTdQIDA(F$!-‰v-eYػMմQiryFEnfYJO/Zọo|l.^~:Luq~1+ 1[_e5sV>ɟ-Ta:*|Oܔu֒RfŪ!J*L2 -BCG.X2%+RNծr He48RC` j- Р?p8`@O0*b@O0 hg <]Q4W: hJ,>0PRn`|$ eF H* 5SW^vG ExfZ_yHPܚ2k0+bNy[A@+(0 Nq$ 2 :|Kω"9`# @ &dC2`-IrStZimPI'#&d4RN(Limk}Bi qqjױl͔֑2J2咍DJ$5dY˵/ԉgy^O2(զ|ɳ6u;;;IX.zhDfLibRU{};;6hg:۬U[WoB/{ /t1|IYaݴfCwuLT+qb_FzD^{@3SB.|~&TSQQ&qW 92qO..@vㇾ@B`<.>dz;-孃ʍص~l4WD"w>.Yja|v|2%wɭB".A֒TG$t穢mUp[{@ gw{}{; t/`>d=zҫ:%ph~F5|3C> &8`O/KDCY͕DG%?=ZA Wr!dkl0Gr vtGA1)2P9Z'aF98^A*l7FB e gVн EDv+*FNhc"ܶg,&AO*tUMg{y4MC|:6d?8+Co?(zP}'DiZe4鎸vZuǩtuyYqYry<Qגb͢TN/XC4oDZry"q)D) f Oc.m2.P2jbDhQa5h\ 搐`"5\d’h3Ɂ;a4pѡ%!S$7~C FZ49{Xck,G+w.rOAk,+; T~`gJ$Hu/_[CEoNj'ߖ%Bn{qY:K3xʏSi[y${_j0J+/ƣm0 aHHpp]{OB(ueSK4Ĉ#ҜDHakB ,/7 ""HkuU(Q=$6%BR)6ޮ_L4vZyBuO\\OG^O7`"7rt5g}v3:7s{poIg}ӹgW_ҫ}O~a^w?gxrնZb\'ؙQj|U)Y o%p7zWeM iE ipa* C?ΟaH_=s-Ї=Qf>/sm{R>3 Ct ]@ 0@xiHghfhM&#v+. Q*„]|v,=;-L\٬{Ou];Rp Z rY,/`;ݷ4ldnz|GDgF%SGв0Ezc6x2[.י6uݝL_R,8ݙ<}osmr0GO۳_d7jkeu}oBS# "X[wd4|GONNVu-mT~cSiE_IFRO'r4 E%ΖO_s$3gF_TZR9=:l5l+̬蠵 01$!ue*k s*# C w{bB,VJϖ#I\_u]zUYB1wt %?"(-gN$Y\1Pa,}htՎz2@O%$M!E"Lv͈nm^;Diy p.^BD 2dC0A0Q9?p`9T!C IDATZ DdލZdW.k4Ϫʲ;~}N|\#M:i^eߜoY.z.1e9j.B9@ (.C*-Dld})}<*km *\V΢4K}ֱNgD  Z@HQC %6a0x3uۊ9*4aE*xV#rm9Ap)rxA `i 6шU-WN@oY4AF c,(8S~'s'O7_Q_O[a3dt1ۊA#"Rڶ 9W#)W `{*gS iqm/] \jZix"5]|X|{' ho+>U'7JK+/AioZ/l@ s|bI# Ntuqw߉Goj#dSR RB;<7YfGMŪIβNզ^?9/6s:t9f}^o+<|Xӿh.! ^aD P"AY1\D0(A B?БϕvUG#L0T.j]N}iI dsLĤJSWұD%qNf,T鏻,ImTR*p,VFinĈBdI:D J'z#<A lQFaN!: 3P6pPPCA"B >S$1 x1t&"^6^<ڲ}|~B"W"^=&Q>?ˇ7M)HͅEG`-0*O$@cVO<ê#SȒBsH@8I"RѴ]>(3RgΆTiU7Ah6Ov&u`_z?x7Vw_;ŢmSbRW.TBYخOރ3aYj'ǧV._{FID>5J..7תGw~:(DF41I%ipqTTk l%R d 4_9%? K Bc ۿ&#;1vV֥b4iqZ䣙~0d bͳ{7,:gcȡ2kJٲ;~`av:ꏊ(ueY`Fs w|qqxv[j0e,jq|d3ZXs(OԣLksֹ:;J6?պZSwֽxB=x,R,qU1#QC-2'1#g%{~6cb 6Y[oh:6< %;Wᩳ33KO==,WN=_>-Yb|~T>SǓkvӝ1qN/M-^O>zC~z*z,PVk6KB,L'6|_?:8dw7[]7f׀kg>PY≡Bpu1 NkaEIIe43^ӓKcmYkAEbIy` JBa,(eYъ#-Hb彰/fx?(Z!l1,;f0l K4$ݵ0Iԅk[m"nE*Mge]p70mܤnA>tۮ^X]eᑢ{kM[HTZh#/44Y !Ɗ="DEԩ:Elb{)!ķ`@f}AK 'nu)B%"䔉a)YmS!!IM]%j;OF#@1dJq61vNb7Խݨq@x|N> rFTΎ_Տ:,4'T GBJ 94Hp7ŕҒRyRAR9g1Fjvy(l0i 0j8"s'4T&(IV}Z@5݈C /ԒZ$UMa)cmJc~j59}wy|da3x psϺ[VSuBC0: Fa>c5,v3Lfg$?aD3=3W/_Fr{ Zw'۩rP6aew'|tf<&4Ts E{ObFV?71<1p3K(y>q{ x|rp^?B7"jۈ_9 MhƳ'xd>5H3G&݅''3K){?oΞ^>$? aGh] Pq$ޝUVaA @YQ:h- R՚%4ځƱvUl pu8ж"kh~~,?DmD>xZSҚ6&JfK A[v6 ׷UVHi QZFq,LVNl9e ``j&D1EM-X4)W}Ӏ"\B#bO-m~eSdinbp q&&&t9J ȝA ASmS1g ލ^YM??z3~[7-GkakIlQ7%$R=&X IhבJf<@ñ8q$Kc(aHmd" C ~KTfFa$s.U'PBODa (IGÔdm`"ez-!—87mKͥ)X6'kv6 eB) w6{ghT7RIq\Z[V!Zܹ0Ln$ffvSQ7l/捩nyV VãZ &_LKHx§FxmMz cO룎>z}[7 [gQW^9|j OZGUD%Ξ6Ƹy"-՘Bu5|׋fZMg SW|FΈĉ4EGQ.N1B$췁تy:M/*?|fh4wcxT»3s1RUs@C%Q sC~"HG;EYH[뻝f#9t+}"rPn]f{l.lA۹vR`L[yXola>4_SQֻy}f2ْq4 B&^yKfTk`+l-p"]%B94#G_ԈPDX 2 U)bv лTV`4nmxw9Wm4$SvDsuj8"(3"KM+@Sbc'0[FC=r!s͎|C8w{S{ñofUa^ w<dfi|Th ѭ[a^CUꎯ{Gdu ۱gjƦfw?z9࿊&VO N|Xמ(kKz("SD~Z"/O4zREX>)w<_)dOP lRb1Xg 8}!!oBK S G2ؘvBĩ˭۶ng.N;jn<[ˇËeܨsl>ڝ,ƥygsGZeMGL!Б߾!WѰ8NIKu{aâ0<(G0\ E)*mAxYJERN_.R@^kxU*>]DanJG0Kƥ>jēh]6^CJpb3KZ󀔦.,zzʅ s~u3n"#p<Bp_?QfoU: U'Кq7BI#QTdPQGjDEAܨ9,,ӳYhu޽̈́Uq|y^ .Lo.G#ŭ nF7V9Fϛ4\s #h7.Sյچzu%>E8 ,O hAς+K+Ի;h"K(rl#a4.̰}F,SW;4Vq;paE|~g}cN/(1i' QВRU<1}M8)1jf)XW:XB tIRyF(\Y-J_֚z=0 PIPZ]y^2h$ڵN,L\ٮ7fuZi;(^5EgǏ-i_p|~~Zw7JoR76)\mu$/~ཏ~WVos-VP z׎QZf&{1+SAZ FyIOw'Rxb='eD,5S(JH$&d]bSAfȐ!"%c1 IDAT—٥  ɨ\:KK8k)fcqEV_ѨcF#E^M WIGoT5u㤈|E9;W?ϛ##?Э O}Gja7}aDU7R0>Jbq"EI5k.3&UhENEFY.$(Δؖe)qx#YCi3*)K2kEEL4#(הMOLFyc}-Z|hZB-)Kcg)O V8-B(VȋS ;p" LXVӔz=jdy)W>sxniz*`VRI`)ޢ^!Jf쉃w kqɩ3K ^T]'iE|2e~|Mu+|fi O;>{Zc&.0J-x1HX[>t,%^Q/1"(<:L)nNN,.Yq^D}w+x?s^?5_d_]:|lo֧>\hV~~|:*0ojR";QE|,Y)u4ftҲdTℿKWMvQ6@ -:.(þrs?ޚ{bJ(Uz}zƪ0z靓"{/N9['EC#'M3`@sipR?'}=xqo>&zovd_HDHRR預dĀ&m4M6#@EgR ='IcqO|IWc4~ OQ+`2w 'E|Ks.-qwqv;6%yc|(ϝH%%Ӎ6Y̾ +K-dBQđde9+@RN!I0e)MYAZE!ecK )m-RIp EJSXG&6HӴlM(ݺvF}ݮ5jMU{t߻Q DBZzūS<=1 YQvT}r /}qk?KI|[ﹱs9pN:z,sD>-%ƆJz3rQ=` ?))"<\㯀 eS `cA1g.Wb_%q-zwTg[wח,߉oU緶|f)OkΞ:{UYc?-c4]#vM'ks+cv{ц |x3zdˍSgO|f Ù0 >v D  0/˂} 1 Fu/6~܉ml}BQ1u*xΥy5^9'E|}O;(rR_8OO}='@ v]J@&`)h Fhj,lW >.Lf(+4@AIv`rnppYnIO7OGsRo9wAtUZoVbF_[wi1;ow{rʼDG]GBQhch4B*³/EQ;;v(pH(YoOZ-&:V=L(~nI'!mc'-|4+V{,Yq.= N⩳jOCxLj9ۜhz;v ؾSgO{x;lş.Np}Gw𩵙Cn>Q>igx|m3K_H#_ȣtH[n\0`>7[Ξ~{s>c/~nK"Ï|1 ^uLU UZ%)"̢q`)2PVvኂ/atEZ|ڳ+ZqHs/2.ûS𛉟>zΥ\NXU/=ߠUyWZ?xz[:k,2PFk<0<Y|'qӫU*׳Z'XW /G4(̲@ܤF*.7nKa毉 f4fp:#T#91Zl]8n}r}W^sSDW|'> xW)7GтY݃dZ )nQfh QV[Zq,<8k.D!"I9qF(j!NFjlm}|@i꺆 4eQlՍ[AYڪy,,2 ܺQkbYۃkW@91yxvYszW~(|n2=al );6D:@@EXKBJզZ/rFjd&ͬ,PP^ScQbƺY1Y>4]plSgOa7yCO=uRI#z/M݋y;|fBRN/&{MsVFvy`hSpΥ椈7Li=[Yᎌ0b&!RZc&\չٹy IX];#8q.|"QK-'Q܉ s=#,v,s.ʕCcis-xGZ΢Ddq^?3'Nly_~j޴o%?:yaȞc]:{S-əzhjk.tNfx"s~^ez;^PyyZS.*X>/쿉3{sq|L^8kBHpn0nz줈/+ 9|"7espNINy{09kd ]u.~'P絻-cq] ϝf,h>􀧾dnR|HƦCm1Ivqnxc8 jJEdyNZ؍:G^ԓ!Rjq`,/0Rb:y:tG=:.a[V.eYv'Ҕ;6ju Ej){Ap:@8'/soqaBJI1ԥqt:Hh%Mg])"u֣O]}_̕ K=OO?}.;}*/4r_.oBv&ӿ8:L$ԕe|EG 8:1|4++A?&?Qi+$^x4a,_RGm]J_b˅s.>)v-޹zΥI/{~WpnGqU/[3α@k},Ҩ8Jfw6Ǖ!9K !B R HЁJp,*&-;ay^L %P%SIz> G|;%opWSHMt>>Ǻ^>k[LߌwỦTԐ=yDo/|_OK!x|ҜϬWs/x㿣5|OI_y]U5n" 9U:'Es.]+N7դgO{x_h)FcTnGuZhw2[R=rߗ<p[B"GGG$aA}VZe B5k &o1:1Otw菆ޭW~)Z*e) FCL7 Ӕ "l6%;$K+J"WW3Z9#cʲ^O aqKc?!.QhP (ȅN91|I| ?u_)fs+6r;F4&^vo o٤ؠM66!N%nxR? FqRY7*:ɯ~ǻU~#ιt>8DÅ%”Xe"IwEJPq@QyQPz!WM&B`cmkZV y(ʜRHˆYZcDs _qXsQA$:ݮl\٣ʺQ9*)SR-e!Vcos93Ci &(DSZP!^FR+qS5/N?/~SS}Sw-`HMʤ[X+^F3Ԏ .>#}cxv!S$(^^=|f)ħ=z~cw<>smE^'E|OS@γ/xN>֩ d(?͓"Gsx{p;q|>}_A^[TPVڜ( O1h$NA+l[(CH0)˪%i! 6sQ*k,(ybwEEr,N͸ (#i}ɓz;|܄o?q]Z3EQcu{CZk};Cлû̻K(@V~CK -AbKǧrJbzkw(ϤXfd#~>}Ҙ̥KHƓm|8ae \; /:.̿aaaeVY5vAjdTd\ ZX$&+ᯒ8c %p鳳crEhv]Ґ1Iʐ4)I( Edp!lv,Lm.%ܕgN=w#o\zvˢwDmm+ϯzJ]E}UG3ǯ9c1:qm|R|KSz=?~zU:(kG2={W9 xPRtW:e E<e~Xt(^XPWJ 붻]#ҵ8f~jYZROJkqiz鐴p4ϴ&fupas.Btcf(JSKS=|=92"Pwn .k䲉nsjXk놳 lo}oo~by̥7(wn8>ş>^|=#2OG B , TzP'&sK_| fprcwo]sSVl{^wM|P8 lsiAmAEZ,SI#>K"gxm$~Ƨ\ƧX7kNxbC<^6o5Rf~U9 \zsEi|Iq@x |}!#:'{+eBJN[}@=xhvT(8ZgKc˼6֒D%C5#gUA8 SS0%z_|uHZ}m{)p, jfv<`oxO09wN3ǖ_ zƗ:?V~k/}{>'4}}zGUQ;_Ǔ9V>s (k~> ܪŅCaC]毼 USgO9^5n}nnꅵ^RoQ4ı|*u4[UyW gy)$!d"`"Q-@7ڨQl4=ضmn4*mKDb$a,3o}*BBu L}kZNk Ցk ~PJ:tD{]&JQ%%T85eT~zr̜G"tIybNn]i&tTG3,hhh)D©J/#̀/\>[ot&M*!`X"gB&ɐH~ u2q儊H2)q}dX8س繞Vxʜ[KYeAFQ2ڜ sQJW1=%ѯȠ7\z:&Q*)Fkڮ]8Fr[-fN8$ f0oאԌ$fJR\_9?\z=3`jsqOs ,u7zzWGwL1Aᑤ9kG''xTSa#ݩںk?vQiSe2 p0(M;t T(Ԅ:YּdE^Xe5э54|3DΖfXaj^Rzz"04 #y^4idsd6[k"{Ax",8>eBY\Ӱe! 4̰o6ɹm8v v'yH5HBp#9QCW~>@ϨP/EPnTtBMSZIJGVsb,sWrNyjA5+؎JDP> pplb'@s.2^c#=H՘9=Y@`-j~\OVGp`B6M ]^.91V]a[a.iݜ u`>QG^D+Qߥ`fy ϛuF3CCKʈ7hX-̊r[)QQu,lQT6=wt$Ց;uZͤHH(Rp`HbO'NgrDZ?xn6 h!ZQHf. oUˁƍ+`r++ͲΒ*3w]':;@EoJ.BǗY;S)I!m܏ @dou6}yMG5t>G!xhBʝ'&{eF r}`hyISvtdJ4 ?t߮ 3pI /P`Ը=N+ҟ ˳iV&Yex*D >Itw?rpxBGx^3jJԙ]((#๝g!f})1M3`B̓FI6' ?Gnr‰⢢ý骷©PuЪ@mőpėR;}Ot?103Drw'{[om?q枼Ȩ.:6ڬ?t8ͤ"p'$ G7YW/$}3i FYŏ$19MgVXS#g.fP3D5<{oU[@QivTFߔ9gByO8ހr k9H*,L?5#Hv9$]s`uYo.tI ?nK98[`5v 1%"8ż<ȕͩδ赞%d˶"q=dK_Sd_,BÀ] ╨kT ](KwAy.TfDЂ|QIP(D`.Am/6ɺi@ʠ9L!er@UBO<$PFjx\.hC|@hzgR$` vypp+#8TM uPd+ԝxwOgljq a>"E0ky~TWYz*kϛPj #h|:N5 5; c/ %{QC<è$jcXJU]_CmQ ##rrߒޜm+cxM|Oy-=j#/\Mg95XIJprľ;fMn:d7 [ZNCµ) 7R\]֥h:̿O,3}+EuG"<3ҼZ 3j|4܏:\ By:fdIJӰOΤY<jm nLBm0wjmIe2wR|c>J+gh+%{z4 ?k#HCȨfD ꄡ 9BqT9z'Դ8R퀗sd632-/hYY'=ZuaoC{,OxlwԺ 5ko1Z޿o8xw1$PJ!H8p<i/ ySzJcm` sPVk\_) Lxm0+D};&<*zH7Ar*\̀>"j`͔xv~+NHL*GGKUJ6ˌR,`uh҇\wk}{w>x>5 mZfo!n^W^-(!3jnz~Z0uvguaDcFQ$X0DH'{<,ʃю{f0֊-k־Xz`ϝ |)ۼtj9p3*ZJdQUoQb_qc'?L |tRԁGs2[r5fYN,Nuk>r6r6Z[JPgg\ze!J̴7n[-E&:Ȑ:ý eV̖BQŭ%}^D}hYWL}O%~%sӇ ma3Ғ%TP#V :qfoe +OhdGV?Ybƍ(U%:Q'˯ pXHf&3a3W`dL>&cu5_R+ tL8 eEa28o/ߵM91"dcGGKɴžT"r!alFDCၴ;l#5ku65k_ˁ1t2wrti_V^9UEnT4g.M<^(n/-K2.W_g)5`wq^:QRƍO^2s H6Nֶ~Zߚj/eUsXa_V.sW86OR'yh HzT8JLkr1P<0 2QA5&|%Q"ƍ;C6DN{ѭ֖ J`UQqƷnЯpP4\^焟(r!Ww\pZ[(AԽ]ѵڲ2ٓIȌFw\k{']E3\&ܳ h[7k' !J'jʮlo~cqՒB olȓf4C Zߓ6vu|joD.+ML7:3+9O6(r@WxFnzLZaEɤq FXiC棢 Sr܍}s`v/o6i{ "eH^["?|ׄd΄ci֏k.~;[L|w>+vMt]NעQ`©5']wf+HR9|Ip veA|o㬦%[ `qA~lD2(.I?+޿G犬j4@Gx67Q$p *>s6@O7Wѓ Hљ @y]NRH%yϞP]j6<7BTh~󤖁㕹mbǢ^]SkvL-_D@tiJL_F KӋ1 !2-<_m[-\ůX$^+}+Doc2Б#<|A,(vҌ9O|sA94>.U'^o󦡥ӗ87J/Jl$Qhz$\7U@1ʼ܇Z5n?h6 nܷ r9\`KӃ!'iwm_QDkֶ۰׬E5To2f7/~xrq{uiumojT0[Ӂ QYRPv̾l-[7d uc8> IDAT VX^>9[X]FrWDTZ1+tٸf_ZOu\.,%tC3Jج2nO/{jש\ 8gZYU&$NW *Z2XQB6hόNZ~JeyS?-8\pkw?8]Q$ixF7X_tvH_Xۜ|[_5h(-n.-h \n,fBNU瓛~ (J`j>֘ů(]yY#bsVU\XY}5 *T?XB5-x?FEaab ) 2DYI}XS[3jv5קini`baol^E{yo'#PYe{EMgAvLߨBUYQ*JxrȊdi¬bc@oQMZ3l]se=ǯ D X&rzKT,‚p}'Gzł<鴄C2Tsaeb5ZW̞7UJxgGXbkx7tiQW<0's^xf]%9Ey䴱F80Fz μϝIW-w|/%6Šb\b,Fn֖[QݑxgO HqpPy!?t*Kh.>ϨoS#0xRן7nX^PNYFTEMBǐq?-'', ܏}͸o v7}ُ2jxH8u?O6P0H, @{3+PMJ5kGu+\0Ά;cvڐ*b 8h#WNy>Ʃ֍=#&f4xEn3g~iF.dZ8Ϥ&WE@/]&dZ&!"YGӑj6.sWd}0PWzԝ+[˯ba Yftp,攅2˿㕧?]1RqW 9uDPeGzM!j\l8!gf2Io[,;Jg mi_e|k4Ev^{A&mb./o?#7tz5rb];N&BZ4 48v!w݆بnˣɟ$+ӝBsxW34;JV|Jr4C}ezw~OFz=ń<kH?nPݓ_F)H\@x?KԱJ+¬7ҋ !NB`3?|ڒU|];tA3uyȮ۰82/~Zvd"χ )vl46,Wg;;k =ȢЖm>qP*:;#ܵ(w,}4yvqn5QX\g,׬͜ V6 Z([-a`0-j)zwWnI0>֥wo4.?[RwQ3*ЂgMMD82Ss[v2Q]~_=Hu,Nn.(>jc6[D|5M'oUجhE@kr&soX㡏7%{낯 pjNCAW">ih(wa/ޟ/T9%{wU+Ceޓ~4|?ׂq Tu Q4E qDޠ<С\;P鯈e%| vz9*Nh p3 ҈Gbalۗxԯ?{?!Ӻ/?f9ɯ7xZe7Q=h@ tT'Z[:eʒ$2/*ϓ5k~z-/@Jꗹ+%j!B,X=)N' g0sO)Q<ϢyJcaIT5ߢJ |BOF\ ''KҧOV?lz^FM㋷nmNmuHJk**Ew jX/_Zh.Rd#$AuV:⩏ BHE-x _E(N5RPjH$4c Օ{@;/00*~RN,=7kh@ 7P;{ rWd[Bu"jmh.$20!0 qq׎ U>pEI7(H/G䫳zϬ.-(rhh1uKEp`Pt~CG/۫mƑ~:  a2ҋhЂgPz/;{౑^0FuZ:˗Oƃ_n"]vqޢنJCGx֮Ov;L%;w=Lx a-5-xyQ8 |m2J(J/y_ J dbi[T4(d_Q#jo8~q5h̄T5@rT4ь(Z}+π76bZ(i6d"~!%{t30kVpJ6onRq d$HmGݼOc&U˿ȩ9vƧ͈&oV#j[-e hE#~^yff&p%;YֵJgc{|{ l54oFt!|b˽Vg&KVMc?h3/T=s}|o3,sWjm pA3k3](m}˟Щ1ߚt_x׌|PG0IUA4)WD+6k~w2V$"mY 7ZhF?Ur8$YPhIusW]U;?{ПP,\gݰrʍk)qCs.mYRk6hڊªs9Rk1HvJxa֠h:3Fm>{\,sWHCt&;Y4J/Z}> OPr}߱d/~Ozo?F7\WhcMϸ]{O];:c=ұ]ǾMD[P-4A ˛?{6iΊom9nj2s t /\g\f#׆L7#8B{o+;Q~g:!=ps%]7dl hhD C V4 (~l7' fO=swPVF. *"d\U9r=;;>6Ҽ=3-יN 3fes2B4o\KFy5h^+^~d:Cg݆7?(fK5x;ub<(x( $m'fH2ld@2弣$af@ȬZьhPSXiڃC[@ #MwZ@Z@Db {a&!y#HRa V"̳E]QAr1z>whZh4Ci/ؕ(eWT5-h8܇n.Y8A\FtQo&8qψ%"^ L0tAD {0#mr+4(G fU1^r^ƈ}r,'v+U?uK@ͦ)5#F&be y.dê%m27h4c݇GZ&3vdFFX֋]_>N]U RTA# 2`$Gi TR-+"4h4c-x4>0S-ΛWs5U7$a!{Wさ>6=voJeXd,X=:Q=tZќ-x4e/pvpuW/?H)BXp OŽk{o{b&L,w qI'( a*dC܄oes5Go"1kh4c}f -|\E,\w]ٮxOڜ~ {?oʾkE@6KՁKőHFb E Ǔ3Dz934k&xF3h %w,~=%{++:^dheI$ hFL_}!k{U2^_旮 ƖLv{p=?baYTDpV|4!(B ci$4CfЂG)3iʞ}4 :։#e=εqsR!]–iLvD>r9v2pլ޹; ʶ"k1Pϯp{%PezFyhь<73TLn?ʔ^ Y̩JʏU?T4hƝn}ͿI\&g['M3; ":&ʧ` N֣h4-x4bWuǀ]kY6=.&bzC(vȋ3*7/\Ǣ.PaY `p g@yFx}x4Dq .,~xp> ١(C1Z̟?yGۏ\,f ,!}%" L@:aNK۹?ixeh^:£ь4 ^QD kxhw4H&>K2nf0/HIDg^ Tn{ ;D>cOJ+;n]ꖊ<3F3f`!M 0FwpJ)Sں\4[!L 9Xd'+Wu9ᙾ{wbR#F9-x4A9^9/6^Ըn4RfN>~KLbd'џm;r;l/Ap)p8LJθv)z|e zD27LOg*L<}@2tJKQ g.ϠFP9s xؐa0~*d/݋ JgLc m[ 4oݏn)cX%l*tZQ3fЂGX331+'?؛/ψ18+آtR8VG! Dϸ+ A>y>b(sWũԛ KVQ |/4Q< ߏ?]M'sHpdW8JZԌ8/OK_y׸d %+zc6ͫ1wd!9l8GEz*fl?Hp2'gQ0 4ۤl~DnZ!HL0  "(Ss :`F;_n*JcI3P`^YiZV-N-Ɂ}6-iϳvsZk|/0{BΫNy)hz̹]c%^ uKE]@faO 6`4 r!\Wijm16x{[K-}"Yu}x'E,FG-(A*GoF̈́2W難P{m7ӊ Nii43W!&CmA8~3@#eP).-WGCk'{ua$O^~!G-9uAwV)떊 ?U(O=!脮`U4+>ZT|ylpO}L`fiDCLXLH>>W  ҏIؙ;3Q:xp_Jh*|_|W.uQ|2AvY.mE=.B^: 3=?1)Yݞ7n 8$Z ǁ#@k6Y۞n?F JlG@;OydQ߾TUUkS4 C +_%0Ü5(cfĕo/7|eBh-K۾iT/&TvT 8pRjtGy#(?Z](qS)/kErjA AdQQ} uhOUI{g$?#/EUd/_`K1iR`"jSn[*^;(#mJ4J)L!Z=ۊA&<"n}(z^糅P{fтGsAycE׌ (BV3(ncP>PJQU+<Ԧh6iWQ2u7DM7Q4ʡwo~8EmۤWTיWƓϺ*B=AXtB0bKoh0z-c7\x Pf ,\5WC C4ܘ{gYVI[}W/ndݙmUZT,kѢ `Aq4)du n v^֣Zd]v q9AV5q ZNDHlD"1d"ZE~ \UP^najXm|dyG (˙MD*^h31oc=rk/}픊 LbS Gb0 Cxe,-^ -mK 1>@8bnu ,/oGo|1=Zw?FqUA/ݨ E 7p:Yr4w?D_r3A.nt}jߏ qMeYq=>dws`01oZ` hnlg2F)x-D\vzBјEn!345Cu@XNtϻl< ~ q6x+FB۫pYtu$.b8(G@{h /:ߣڗ'2d$>afC.wnr8Rꖞ/+UF=x{G{ Zwy3AsQv'A`t5 CLSgq x+GQ9 $rrHV߻Q]8ԼQ]ŕC֡Tg{8DH(-cv˹<ׅFW';AQ&nDbUuS ,f:p7f[~n |-d+vH]؍=J鈝'N0*֜)e3k JԤZj$+f sQ|ΛmQu(fj< zZ ,/t|9Cuxv {*d偱.D:yehlR?Y5 1!&x搭ܽڭTPzKƽ 8tB6x/8\ cO ^/ R?/J` zjZ m率m?tjtOxxOxbG o mAlS5݉uc^$#k6o'K碦u4,j";0_=s_tu2ՉWpȵue5a!Қ'f>vn[[tCNw2#ūE:v5x* FϪ*암K+ȭu -Y:C1:o(V4 ك.G=Z'T^>EY4>0N;Q*|<jz% j-(J/ [¾[,Ѣ]{-kg }/\;?OneH]Y+F|deΩ(ʒ;߽Hޡq. oA0掳߄lI.&ʐ<9֧RCO^C>N>~>)GGf19&x>V"'\7.mn.@Oӯhвhh+Ci>Zh1@Y/2tc :ś܉GeozKׁwp,ñWj6 ?0˫ 8RIYtc~yf}cH b{܉6U7>\\4XzRwE!Nx;Z$Po̭s)d rt?BBKOx/yyOx5X |9 ܆T&t,3 ]#CM: n~}`uǃm y|!&\Qd-]JEvcbǘ.(]ʟJ -ywY^T Ao!,W2zb! D߇:z!AÑw5pg#I%lTl3n۱_IV DnU(Y]z XOx㲻24FYމa7fx{ ,x;+Cw@&3h=+v:nl&vtu h>iëw7ѵd cJ["}Zn$2 Z$,}CM>'ln@YB%g\&$HT#WR5r夁/}|~OxXDquK4' ॓(rn+e$|[|,#l #x$;oAV , Nc#h>Nxuuml:@YH.ђt$ØkLDOɓ]+~x[Y]eq E!0x/r=M50oj= )I^Bq p3' oeP.kr/ #*<]sȺU W{HmA"+ٳn\ Qnە@h{ḯN!D|'x"LvC?1N&x q?e$A-}xRY:tC?%{ ۪-,WE) gjUxJӚ .}2V`%\Dgl~E14wDu$ C({PY9 Gتb/,.ffɻf@Y7/j`[ta(K9*v2y֖=ڊCrih[ h1>Q$t݆faSKQBynQ$&{^PAEp f*XHYa.!>S4Ꜿ W`P1>CVgqa}L2g,,^0cNL=tFOm*DOlh. "h!=bM!W#r ;mEq,eH$܀DOY5jMc3 ÉJ \]Q4Hա Ht#1y\\/!E"1tYwv}A2!ݧxK>Pr2 wثqP1&xp=n#\< (Te<D jV W EC=TG˥ dM؎H<oC{?Z,ǁj- $^*?W9nLPA9CE1Zً,7Hd3AoyA⪬w5R~b\}.Pڥ* n_nɏ D ʧQga0g` O9dفW!kD[fmdW}rw!1 #Ѫ3ƦNB1 Z$ 6؆\;ќxa\uDZ(tukl.Tq2;  wꤔ0cy-62A^'Z 0AyԽ!kA5a hQЍ& R̓ (T|gbyAˀ@b?Bg=2Aj$9B.z݉os %zv&/p*E E^B0t i/@ dGQoJͪ3 uEbG18(6$dFu'O*{3k}A)J̚C+_$u0g`QQy>As-?ϵvO EE$W5ZY.>ʭ=gE.8jwYuOY7<[xkq9B3qQѹ#s-*~t&~AϣiQ@~ < +kbp cnL/| &=6T7,ºW=$'=_Bt;@g96eʶy}MwMueUo@"zccT iOxdiY^!KH]L(p_:UXx4)FңʷM1m[r3<<A5 ܁b.ʀn6;4xeCVK=~ -נmnloa,gF<V=WQ3Zp8SÉjnC=aǂi;ב Ea'=} *!GP,prĀMÑ5n[zTVޏRk?33Qʄ[>1b3-IdYi- rN?|0f1f+Vx7(vXsW :*8'-^9 9$b<,%<$D&j9 t"+Oƍ'+B\eP+7I Ø&xS#6.)Oxw19 : tNQr9%/\Z)qgXE X V!YWOxx[Ox1#wZב[k71cK<) =I-8cದrY#.עNT#V ܏\c)@qVa,ḁeƂ!N](UUՉ B;g#sf1 C6 y(|+pJ=^COf X8~$CH܀ D\Y/,[$1cB 5j:}@q=Y~ x*&x X`8r%u>fdم_ya,LH'!ia5ņPJdQ%v0 j":E&.a,LI'aKk FՇ,?&x LHH'n$j&=[Pa;'eatҿ-Tgg<p9POx1W0 0c"#Gqs=j1a1b<(%ݺ1σ`fx̙ssc1c1f1|c1ccɢ1c1ƘSXh1c1,c1c9%c1cNaɢ1c1ƘSXh1c1,c1c9%c1cNaɢ1c1ƘSXh1c1,c1c9%c1cNaɢ1c1ƘSXh1c1,c1c9%c1cNaɢ1c1ƘSXh1c1,c1c9%c1cNaɢ1c1ƘSXh1c1,c1c9%HDȯȎ=c1.dd.~b18$]5׀;c1+jdG]U?3b1,f29p91Ƙ . Y@NxatKVt`JY2qe/=`0+"c"n%f1\Iq| 07K쒁1ƬB,.~,ȓ"bc1fyr>.ybdnc.,#\ =/nD]>0c15׵Y~0?K~pf1%˨oqSgWyV1Ƙsod0:{/0yFvɓhXB Fc@, LHyyg1\b`xx8cV`zdyljX7 "on2&tFshCg-O'6;Wbc̅bd9p#^{]s%i1Spp(עe3FWǁ7n "rsle2cy5hl>N~˓hl>B`bdu3+4Vc9/,Y\э_@r*hbngc̹0p])d 9Zr[84KzFZl6Ƭ]gqe<Eg# z\=/%Ng]c1gcAc44Ƙ5VWs.Q-G@-րCv^k_/QD.u=b7c֨ѽ.%G|=z&~70mKfKutm1+VW@Mh@DvV4LSw殾5cּ]'bG^ l&IW?g\ꑕ1Ɯ2:#y 0^vM kh43sr7KoЍ1Ƙ5W?!@'6C;AMS{9y8K>0 cV,vcdqMoނhPJ&8_;1\&6|3V4vO_?$1fMdqH u:c'MƀԒ\6c֬] (q9m_UXOhŊ#dicyٳdqehh@zq-G Zc̚֏$& D!mNׅ.3Ƙ5KWQt}3CyIJxXyk1ƬuG8xlEc̠U?G8ީBKRlYs,Y\MCq JW^I.-ɾ]ac1kVv!_oB+W=h>ھ^ Z|]1ƬKW[NsY/ڦ{ڞitOC7WљX#חqcZM7U?,hlG{ =']cy`xi.u<e3Lތp>t`1ƘMlql>|GD'EevX2y 4H͡%/-=ut}gS6c֮86o/GoCZ@ub6c̚!zYN"R@g)6fhbBWCz PDU}*zǡ-\rcYkFvI-%=SlC:h&G'qq eD4G{ leq8Z3K/nLdlE~W6̎0c̷et{[GЄq#zF?6ȹ}qP%rcV%+,:h΁nt5A4Eg/ס+1ItqaZc1kۧ|o@jMO!DL`FX \\2 |ݣ1ܥB9&4ymxs-]~c̷edh<| FcGm" tH mDh!46_N@^Ƙ7[Y\!ι\0(gh7Aj =hj><1'ODD$813ץ4>|]i@W5}]iA.ބnE'k3${'=˂%+#Kn+=#/Izd5䃀wHe9n^>D," Lmߏeg1'|݉)?1FWvd܃N6Α]R\ޡ ٽ,McqٽEdfձ2>B>8:^נ_@g8vpSǼsn? Vt6{Z|QqM}8_@˩ιu+1vc9".6@KP_FcXͷo.<; /0޳+CC CcWCcsq%̡>t3+’&"YȎ-2~6<ݷV眕\DhNwϥooCK]ϝv1ƬI#T>7 hlކ*h\C< ܊[͋+^1=V{p4!uE ~?ˋƑ!ER6pLxe;MYv,śMN Mtuk{/Go.s.#0M{hs%} MGK'su:6Kbs} ^R ]/:86?>ݸ6{*E+</t}kIa Bd3YXl6+,0NLߏ&G2q,jth3;XX ZZ~ ެVoĬ91F?g1f5h_G?pYm> ^46}nv{+v~'?F ؜wmx w}~%@vRD^R8$=7FYx~\hZf4|QߊBK`"tt@DB3hAg8Lh1/,"6g@[6tv]Bl>i"דOoZAv#` s+46_nF;]P,iB~yCoV*o1ʒ97v7BC;\|:S91UHviYO78uLzщNF?ό1f뎣;7;+ؼkd'6,~lݧo}#kw\2&t7ܯl14;o8,\}tBu[h8Ρ vl^8 D,Krr'ө8ζcքQ,΢ a'6?ڭAsjMN'[{$!\J˵-RAWAQ2H-P@N.- ׻lwaV7[~`CwZl6ΒuwJ@g4<\vbb%4Pmb+sVJ2'"DS,^^-yC{c.#&s$./2E薑X%bG% h1z|o5eNv=߲Rh;RTn$1 Asxr%Y.w g]܇YVg?b٬[Y\D~Z4 Yt%G;b!̢{hڦ$ :ԣ+_6ڿѣS>Ut"=ӞGƘ5owC%Mr7CiW;R?4v?DlNhl^5^S_.dcsIWH.uzQ@ %&rs&c " hf΁i Q ?u/\Z+GZU#Ꮋyd,Y||E^Mt_Z2g>Yuι/rfW=Nw% Mujw cYFYt ͟B6GcGi'.WZ4%׎u9ޗ=;'x X4RSZ*ʉ'ADcJzm .lfH(e!}$04p$~El eLD|4YrEn_liK.p:H f{p+ځjϞ,V_ =8zW1,%@a#>KJvҦ.8!Y.?‰Ͻؼ=%t5hlbs'Y\+Xh!iVBT[ 4H:f d)ddy1 ̓/^=ǷEbsWc;l)Kא%U;W%Woo=hOJpo%1.^NGG3;H1eΪS& 铬Z8fj㶗{M csBk cAQhL;$h}b5jQ7(UV."3Ͱ򵄮_['&XۑzwYl6uC][*OY<&/k',fHy`7Ed&GKZq_K4sb ' gjhVSa!6K [iq&UE5,$>q-iE 4Bje^jА; |Mict37GfxKQ3%=kˍP&[ђϠ S|]hBmq97ý0Yx؜? o:۫֝vmGcYQj2=T|ig;s;NW \5z~ );#B+,rI^jUvhO:ht.%/ OR0cBD< |Z瞻Xowm =㎻,6̒5:4z=7-Ȣ̀m9cc1/tҼμd?ORGaGq8tٰ=Q'W\Z (CKBDFuwc.gBh:{!V0 t؜_~{&I2;ܶm/x5=wr]˘,Y4i쬴ٛx&N&h] :Ozy߳/>fHǀYx]3 IDAT}1DT[R}7CS{Oh Jo%y$,)VyH&84@֢SpDBϰGu&G^PJ |G}$btD]sr{݇?1q̾>Te'':_k~\,LF *Ƙi';F67$<:u cߠnZ't?>y cy)D˦Ó=7}\T粮Ip ͮ@<=x'=D{sS6k,H7 =z4$dq S`8<&/a}uێ6^tK^؍1Ƙotgj}'<0uWsh)(qr=_1 @<( B.ˁ,8gH-hZ;G UcS42*"Lnov%^z\p?h2~=(z{M^߾~]#ńjջwe,bH\/|en,-tnԡIm(ΡNlW{c_r5{1B$kǏ сtCsJ9FBVw uz! Z< ҩ؃YYGLM{-@ kzïeږ&K&I48 *ݱ~^3>F_=kL}m?υ^uz7q} G8$Os2{f7qƼ"R^v<}3WFmZlCǼ .t{^Н;WF❧c9$E&~HmƮ6Z2?% {8ZDs(yDŠY;ЕFAKFw}5F9M1;<86B ]17}ó^O}D}ZZk[9zc\->S~=?aT\IA@Zps"+BE hxP+k'^`[䓕\{}_lWs ŜsF?'}c9͹a5mSb--W5/sjN;qK˩g)F8G7"07#[^44A鲏2ɫ-R'H e+=f$k8raރ8(u}a!.2YeM5֘}MhMmx>d,4 vQ3h"Bm^B ng59-1ƘL*;bs<ۖelc>>Օ i7 s NP?)q8j D>ԧ3N'=GO^b&%B3M.;TB}'0yxJ1̦ZBfqçDnH=Gǯ6bo}<Gl^KyDd]E|l} ҟ/1弰] ATs$oM4oFs绱1桭{vt9>y+¦5f7T2MﹻwYl6˒Ec΢sc&֣Bt%9M:>0.?\6/?vn^D|؋oc1dwy:1Y.*Y0u\. XoV]<;8KFe#2n r1:@+`c^b&/S60 2IN" fRyz!q&i6zk, ī9">S'XxxqJ߽%瞻zwNw{^!*GgsF1W 0z}UVKc䦂}HKۏEqehG:U4Iue/>1ƘIܰ<}ݣǻ7TG^Tu+}GnnbW- OCʹЗrS!MレkT0S.kv#3ݼ}l3.sj#ۚexKj/@eʭ㻌_ƟMxi{ -< ) P;i6-23scփy1+{ozg)1˒Ec@D&6\&h9}y)=Mѿk/$Ӎ7m_%cphWo1RcQ2_ RoGTRw`Myf{v)RBEFu%&/kBDR |^>yvB)*~VM^~ ⳾Y`P9'-\2SHҝrY7<-&Eif1rTƓXl6/jX 1fi'?6lFW>'B 1>◅V@Ԥvqi8;Ke+DD}c /7cur`40t<[K#lldKJm-mp) R"/e(}*͂G-sSgj=ۓnS+pTY _v~b3 4e]#e0Okx ){]iFNw, /CjY h9|O>q?u[*ᫀWrwYl6gdɢ1KȫWIOgYy[M?zE蘛a1,N~[ڻ_ \4c-X^ .эaGoM@cMHmrƗz7 \f#Ƿt1_X!Mt[<2[ϖ Iyڹ^&f-F>7*i%- Ar+e>L&ާ[2]kILfh}=a9oھkv{Ml.VB =r#pܷYVӛ]c+)a|Y_OX7;t-,iClE;Rktޯ1Ƙ k+ⵈ!8v1=}6{f'\zq}ާoҤAw u|;^1So[? s q6e+6=#R""c()Нr!@U2caΑr!*ͺP=Ru`Ȳ|. dΗ רnЗε^bk*>l9a6_S3`7 ކiK9cV5<CզvŸs1q94Bms'ch9_c1-2.'C;jrך d4 t-zi5J-*vH,; ɜrgc[Lۉ!.\NE\a^?5vVq^-"R1/SyXo+RNl5<՟/96;w+?5϶Ș>fsqĘ'"Cis$j !u:ZZr2$?|xwέF@c^6d}I̭xG/#xj)'=OzFDz|;RLZ4繙"RsxPH?9C@r@l)hLy\A[(K&) p^z(Ss1aElc_1 SAmNl] {y+f ّnyu: B=hgZc\7/ɯ玻,6e+>*?՘(.ilMD1][hv(ȹxM(cHFߋ/ZGH > a6 e b떣^-܎LU8 S:ը}#O2^9F_+2iEd TQdSǣNFX p@9c{1~Յf`Pv݅+>P3yA4Nѵ um!`}{'6[h^[Y4FøS'ڱ5E7ht qAK|IڝO_c1/ b09zx?qSz}$$ 1O3N*ɋBi(%E:0Fտ74ƶ5*=݇[-"V 9yM)$GNcd`Nix#hj–VѤLŨޕ nޏc L W7̯X(\$=q7[fQ/̳NџD̑!I|ț)A!5%&)A#X "ǧȪt7flfO \ Xʾ쥞d1H̦%$Y _ *;ƬKKDqCW;:#5EuxQuZDkcɤzi;4 GJPHGSJ_j06)rZp~8'< |\/27%L2cBsnd'y}gG=#EysP`c1L~ZgHJP( 6LT+bǡ܅]t:]nGWx e O~2)5[͓ Bkj<4 zC\33lam&%y^ɱk«BZi<{eEG$'(Y'6 47ѸhҵM(gsw.$2朰d\ Age1ENWaaobE3xhK=o z$4+ 1\61Gzl77"h08F")qa2/4`aOw0yX Id=I=)6OyH:rMB/ BRz4E$  =Gf7 85w 3 [Ӕ4G;n@w Ig_#!1{KƽY{+TS !B,19,,Y #=rm3HMĊhPv شzpzn/DCaL TȐ 8X z~L.aϯUxJX,9ͣ6ȡ)Fmհ)7)8 1GbeIIIIɃ1m4"zoA=$H+TN|/&+\Mp4śȵ+A,0·XYJ* id5C*n*G`\0ZSP)l!J:ٯLj&@b ɤ29(g( +(D@zP{xj U?wv72J\RŒQDJu&7)E{5ZB1 uIB4BA1#5ŶkIIIIɃ{ (bDH|nx$k4>*z){EZj0I/Cvh0~;'T H aj>IX^&D]Ihr\GX:2J<] GքdJ Ҷg]PKNʘE82c<,̰%M*e+4 IDATC?D(NH7%{x3gM\uϥ_H9qJ-bĶ9zNm !%8kV|Ku\Zۤ]N$>kLSfR#Rd= D1b'N-?&~aO( t4N?..Ht? }s.;R,7s e*P1p1{B ,R81E) ϶@s${xP8!5h2X>,56u-c>ْw̰b٠s0@,>&`\Tր/s{;$y DH^"Me]Gh !P ൦*|S*NH &`~m$)ӱd^x8Ij4;'5w4kuÍ)z&mrR q - XcOU'3.}*?pq̏"@Q$𙫴uЏ|'Wa? ל&>u9uvdo:L>Y|FgM<~w(X,9nL)[ۈ%SJ!]oRD nQS":8HRD./q/x?!~flIIII?ѝbm/Zź$/ŴʆQ 2Kڃ U/ ~6/UcR};9J IµP!5DNU2$ɧ؞CjAjʴ0G("D [IǫRm` T$$X6|]#wĠk)NF$M>]Pk1V-lLjUTh(ĉw e?]'ծQ[|?~0񊛓p(hgڞzyPR,7MoNR;3,c)D ((D)ߣrf"~M֒w9{ /T?~',oUS$:yeRN#OA[:̇j]`WÄ-I*bFUԨEF&KSli0"aB!eJ &7ub7% A=三Y$c34b' r͸=f1 N*x I7,uLK)$6ćq6h. р|#]t5TG\GYV6 _`N7$wqd&r6m܇_Z&xo\329F6`LL\7? 5J"箒M(':Brߡ<.PʘBHCQۘpa$A!= %%%%%*ܫÝ8'Y+ē?'?@x@g NP4ƈ9o#"_}g O]Su50qtK,2]A`CU<7AU'1(T.FJəʜH*B1j #ȸM8T\vbVqxR@ՠ}0Ǹ2~hP(\X_* b B Ϙ9Sm[3x]Xχ';..UF/jk7?ŒD"RKQAQ_W/~-R POƒ};)nLfB4)X[5ܹk ƘF_?̍0≹6^4PB,:a=/bk ZZ%?}>m_ZŖ~@oξ "I8TqWA#pu,2Q1ƗIags풌S:vn'bk999`^4Ӕiqp-~ozv-p([f jm\cWA j~n;O34CTB^U=з.7cVLJ"2[7 5,Ș#4.MM6P9rˆvOCo~)bqbB}w":CSދNv)"k }XgKzCşmu{)s1:Òw ?9iQ1߽1W~{Z>RcLeFƈۏ{U8q _Opc9Gӵpa:\safo|ƳN%=P 5m쨆16/p T*x̶ =`8 mYsw8OfVHu&q}Fq0;*eBD0a# SjDLJ3E1[ъNuܸvz3E` azN&ZO^fT xm68=hXʠeƐjAH4EaIqs^ߗ0JXrRO)N}}mZqBSD%EcbM^|PT:/ZYB8Fe cIIIɻ %?qE>MϽ+ug>LrGU:nf~Wamo];(hQ{fq}_@/(2g{«>2n\åQBcmO1M),b0(-㉠Œ]zŘS*{+ FOŠGz-6<з3&"&3~\ 긗~Ɉ&J "c `-&=^j#7:HUev Qժ " eNJVT v7|Xh`ǂ $ۉ8YZk[ аsY,9v!E4E!oPlgã7{YF}5ԣH}SƘ7KJJJJ:_}`6u^K-4߆qs[3҅ '𕏁1K!ȗA ?.!pR fV r'5r1!3hd:=^Y! }uۊhldVSY;xt1@ n,*Dٷq'n.D%V3WI Iؤ4=,Ɉ@g#ORļfccRl98JhqM3oF0νO/?edaɀm!POPE+ EQP\<Ο{o4J{WF XB1f OIIIIr{ ؆dIo: UtKlAisѸ T| `L1jCw !)s }Kh`UdyQGek0uƮb+,FZ`kA}R D\C<}ɯ6>e>RN|"~_rQ\ݏ5 ,0b1Ml7`C8- GcM9\-Ɯf`d)-4)ùaxgbazJC׉_UjbM2и`)A2[_]}F?񬉻o TqJ9Ƙ>ߦ4"bu){1x_JແBg_RRRazX,OyYM^ÉrK({bko,}b4v`%PO|H'/m 3] A ' yf}^]Bg4+1{Qsh O|ykw$h<>n$z|DO. keR63k*3uKcsJj*;l7"gўy2<<6+vHm99ZC3S⚃S`vcI- #iAz>3RZ;n᛿3/}@Y,9oQ Cqޘ)8Erz8˱%%%%! \7 7h07A܄;`%]De0ߠcU}-dVǙmӀ:5,7]G)5LL+ pRxka*b9gVxSGPXOts(Q|քRw 42M>)zfH9D&-A[ wE\us28 ,vcC[O޿=zudQVvS<cb!sM)D EQ8[o(+=o !~=KJJ"^Q~o>XGnYiVٿ2)j&WG%1|ay=IcHH FG,&("24 V`׆Jq.VX6uZdqO}M%o*6( ;j[Jـ-͓{T]t\# ց <+x⨍)))xGqZUP*"ַ!pe |˿:XXrhg78cRqj`'$t<:Cu 7ain+^s09Fc-ɭ>Jڍp3%^^U3D >1ImN¢"6)8x_s # x xtff)D9pbj:Y]H0/-WPP!@3>4b%q'-*FDJB$n]6t B;Xc zWog[ze[H!|!_:RKJcLJaLEУ֬9.W= !D)))a #61!p[R:U\\ދ-~  ڰS0CMZpg67<(">;SE^K8.bo2v"FY͗RVU-tjYkn(6[d,oBQ ZZM%G:K_zD`҆]4HϽHd/P8p1h`ٰay?ZwqEZئ" ed,sV kόqȯ?I#){>`a5+GS{1lãy Cd3BzC?F7$Ub+rĩ=qmʅģmUPzH?׸&KiFcrI$,@fr X"BHxB $Ǥh2$~ߔH! ag.%o67Y_x8!4\HmGol\mqp7I= Z0ުrYU˜NG 3D<\P(rM@G@@Clv5A8>[>ţa&V~KHpe.}ZC5[`|Q#Nͅ4E tJ$X DcadIbCC Q..$b$n8{/gN)y8bq/_ !#pФ茺{v |/ڈ?7W??qjqK>4WE8{KMBNe_:6JǛ-f(u* !6X 08Jߕ[r%4T(OOp/Tr 1DBF)&ݘ :gŒw/%Nv&Rc@ͿZdOl -X ?g7 ۋJy;`tp5*GuېU7Kw;C0Qθ߈i-9L6SnlVpo+[T+7MyD[;LN{Q`) l [*19gCXp=MqFܮRN!qķ`e58Hz߭aj4$7FZ C'B Ģ;݈-&*$8bQ$f@|VsYE& RigM\wZ,cB$Mc| IDATP|r({ N.Gmx`,j(sF=XaM_F!4Y~0,rk XYz)ښRTfDbڠY] de5. Dϒl"ȇ14v-Ɋ J"r,B&$¡bJ0R/j[MsKk`I1BA^Mav\̏嗀())}P4+9|r!شE3x86o#} 8 ) W}_#,'n *CoGT5&;éLp9 0XRr?%H) Mי@P9jJJJJJ&! X$:h2+w@+D N;8`ՈF ߄mACX6ʠC|4棌:2o8?I5]'ZLy]e _`^ sĕ1kM#;IQ ̍b&!/*)3;8cXI"GYOYij՜ʂ!Zڒ8˜"sS(-)#R < V"<HU P:ɩ3 6: .ddR"${_0UpW|3%b.!%o jĴ8Q'}i }TO°θMlզ,OZ$ig6&{ꨂm7x~F쎈FVa3L.%|&ӽ1q5hI1uحưekXd{F7]?W4G뛹&aF'!B!:YY!s=l3ګlxXdy b7&XiQ?Y-.:4 cЀlЩrR\|3>:K":^~W|h.DLW)ݹee Z /R;֧ġI&U6.2$s~0&N) U/DiM"- 'VdaTYp5u›cj1OYRi>! OCdœ |prP(4K Cj{G0mMQ!oȔC5eEk9[qbQt,/60 a2DrVCǰ r},baXs] "rt!z";KST_\|xt)CwX91Jwش%:3Ez>*5/ $aF|"LJ"H½P5O/!VN!`& R仚~M8UbׂPI=*=U 3I)l6N39r&Z-EH5)19PJ<" I'ridQĔ{0릜xko7R,>c+Jn%p91}E) +J8=p {5{r o{'aT'l9UmF{+C>MFWי6zoچpsg'4ioy16eIplKX,)y}re` )yCQRRRRRɁz b6ЂI ( 9X ~2P"PJ#]G#'PM5JmX8ދSј+D!ag:Trhf.Y1aà\py/'MpP8̱J6#Ryte̍e*&r붅'" Ш0SHɐ"+Bz-% sj7 ց}Efw}g/OwϦH!!DKؼP1)B"bG'e3N@Q$ ";6 AA 3{zz}]Y9㼷oϪA<[wY~[ID7tataFѺ DxE0F]84JA a30it3'XYCe$@Eş cLuU>AC)$5^$5w ;a,l@k:_!Yzq=7ň_duv!pȪ ,嘩k,.}K-X9F8GJ29f:^zѺamq˵<y8h $}ꌑ(|:9s=޳Ù,(OAܠ2x P J rgAJ2n|#] &|JE\/x5Ic֥Lɲ *ČD%l2fQCwJ&Ơg  .68'TPŷ:MBbu^^qGƘJWT/U]Ƴ?*Zg8S ֗rJzhb0,4ڤk\FLשq9;h$qfȎH9cWRۍhK:3;x~ε]sA}$[]_FzC_.8)*ߐIoOD݂|F j(j:ğbn~{߷3zL$-l *Fb c`<ғ872J"AiJUv~}];bR*xUa%.SCuqOPBy h ^bI@ ?$Ql֧S<Ћ)53m˟/~I͢b(b֮张>8c96f+r85Nf&4twCg1,eFH-3>Ԁ(n_#3c8hV`d$ ]Dd&#ٵ cEQH +iȉT,H(ol#P88jWoSSů1!rBLn` Y!Ŀ~uqdnuB}+\XRtvi= ߶a/hyd7L 5WD*f0!v` B1qS|w| 5$me g\g0h$ɲ$[h7Jȡ\Q-I1-ȇÂ7$~t<^ zpg])$IhF {Yi49յ"~ܤ{)Tb 7pk~lcm]]MCQ> XXTq[}lgۊw ?k\. D;wd4OB4o/{+y}ߞ-7JC!Jt o2cDru8"{b"a Am:fօn=Dyfj혋C<xpX=@QhQA9B +¡Ja2>EWįD%ɘe?E%sdc}n\E>f^ <QX/w\)/w(6㲃4?֣zzqRz3K̟{ggh7$jTE= {Aݔ #FW A+5Dd$\9"3m308,k|?੧=,YRycPA#-fEf 1\wArȋTg$LfD'BDag(VXHliwܤTP8߂Po^|[U} kؔJ,&PE+*Qλ~}\dPD] }}841䟏Գ~k /4F33 +N>|$WX1l!Α+Mvep+L,z& |?1^au2_,Bgڜihr4f 2yfb˵5ş%c n\א8YiRx̓l!'ƌW'.* )æj-vzɅOА`t p9!iLl(sA;$M `HH͈VM⻊3ӡƑ~#eE4C a f5>&+U[}c7Trl$CX6M6nd [U诨x!+ "C]@ t[WZqȁ6a pmՠ'+ U8ǟuZ*mnFЃDe>e?!v)/ȹs15:fB{~%>?ZC<b˨3-$ bVNR4 .*{A8N Z:; ;_+Y"4rW%uZފZI(:"kf%kW =\빀f3G2SFC~uqҢ=Ai5NA ']M9bFc? tQ {c;]q5@+*1| :mx0|/2\wW9H^}j.眵=k}\=QSmd&bF% ja{ޞP-_4-\elN& a(e#'Ұ1@눃鄤Rx9dYgLB sQIAIEC*{;4b2JVKGI}TD#]#(\bwf Ό&H f[E!R!^.$S"nPhAQ5w}b7WMhitJ g6OjA~kTZAC¤N 6"u?p[O ÕӦ#;v3=͂lR8Q=un;+2p=YPA?^ h{4tBΥ~zߥQ("^qpyA_w w"7f)6: ]~&$GN +^JʠV lDa$% &chV< .X)Q5.  6ig]ZrDtܒ8ȶKq Ayyln/MAӁ>C(W Kw^ӎ-0™Nk9R@%B lC`p ɩ1dLH@)m0$&E|!70I?kեTfa6 \Fl*jUxwb?cd+XQq ŷ`T'Ex-va>Lmȋ0ΟUr~;#ļҚѨӫLW{a8cWkm_!](1-VY I,M.upţ~$ڃ` 6K2?k6F,枣DOPgM 258~;H(x@ #`ƠC<:A9%ܠJZ$ |FYAd{#4.1ACNNNUp0zYڷ͵ɱ|֟27J,Gh~3kB02TÀ%x&1 p,;[d[FV_TTT>cގm6V(ຶɍs=xkpSm+*ppSF=},ž/)8ͧh?5 eڬǘxY/Od\}iYKz/ eăE{QI<;M u2݀EWKF%̶%S?5CwC{.1AUTD̈́r&be٤v"A0ECmgeģ#pDA2;#(3*uA# `v95V!L]jmu IDAT sBjƸPppP(4%L1b@BB.A~v;{ EYw0ȃ$+}T|TbrsfO s8$q: 1UcBu>V,ب9Wqw^k OUC1x;yH N)D{x6heR}蕿zkC)ɶ^c%B2Ȟ^9p7;W%gHԟE6%i̯֝ 3N 5zlIMt{R3ס+g%1\%z%yel1pPd+nIq"3Cэq`֦%]eI{~D#ز^_ bny/! Chfpd09r/VH.! }jh # bH uo3qVQ-r:s9+XJg ݥ!$~ HX`ݘ_qɱ Ƙ]REEM8_Ǝcq̿Gs<~gp.TOugx&f/;qg9Wb5y"9NjVyb$Mƌc(fJ0i dB%^3Q38enD8.Ѽ@ w{&1(\C}*c\~ڊ +6;]ވ~u@IZ1 ۆPb%,q>^#@{e;19-(SHB(}&/.l@lja)J4.Ha#ǡDK!E eXaS?IxTnF#X~}x~Zbs?sܤSq D._~#96ڸA5nbJIbEGi\M)gaiF{&9]B|JJa5 1C9pټϕP<#+n&6}f? F3i]ug1B!V8vBqzr=.co^˩ft@fuew9Yȓ@&s~G~w9>em?aY/_Iv/omO%u?󦎭r?Пyh|Lmvj-JA`| 8P)KQKo%HW FA#, )FepC 压wMQ5>a\f1 Ҩ>Z 4=Z5 }9+ nKLA$ b6q 1x@3.#!>jS :E쀆+ dX;d9`/ ;6{|Mz֟!wX?i.5[Bؚ+S+*xg0شE7<{GWUQQqx#ۼ/O51]k9ymly%b&_fgi_0fC?8(ۭ,7&a]7vdAX< NB$y*!2,Z/!vh$W# T) B lAP(a#ϧ3А)y\#9Ha, H2jH_j$6Ĕ W LPL }ա"h]? Hqf`HrIc&$=0'G:Z2@+u mumM­>'*nXng_> l 1uq뉮cE@6Ef.^r"~cwtU7mH^}onc0Z ,>v|!c08֠?x";7. /ѩ==^hA >.EuX1AJd\~ b5CVI)[x Z? %2 [~3F F 1@E qs˒a%LKFIp DjhgrahᰗA[7 R0^eCh9+/BR#.b%-Y/ir/| |jTd*h<"{TB0SOa=ү`*x;KRQQa$6Z8jna?G]wmaz䷷Ms~YFIsw1.YD28AsFzԳ<  1A̋8ԃ-<  oͰ3zL_sCHSDad$[\ɀr7K3sL !&IF r)(DBP@>'g"f 'msdT/ Lk]^Dwm+Jܷ_%l`5Wm>'*nX[5v@W~ݢ1JOK1 z57#Q 7٫Uho[xǡok ~Չr0!kO';om'66ߥ> }߭?"7h^Խٵr3츿~Q:›gA*l9xaJ01X4>_m2zݜ<zZd921TPh4eB F=2% #%Gy.3f,Tl(u(cPf1hЭR #nDZzF]k>fdpDΦ=iۆ4Nhd*I ߥ9ӆFޅT.m~Vw~YyTgz[k;MElJ&= C`8EGD4>Ms1`= ƘR (vJGyN&F)6(]71{wpm6m@ojxNУdlթ((: <)PƸ%!*Q]ٕ.; yFdVc >Y\2df䣜Ȃ/r70S"EoRg{~S# 6Z|9qEw8܋5mM?=;-]_ (pvX1iީE2^_A֔TcSN\'0|7֗9scdQs (p\P{ T%L+4|psiMC_˸ߏ@VhOPujNvz,VEǬW8ky.L̓L!BB.W7P2pc=A$ȹ:REg(!If8 ^9GSdlS eϕ4:i f[kD8sdgͧd+ E_zl:;LS]I6|JgRۊ/=vwV {Ʈ<%W> g];wXp ݂n`GP3N`|q1!ğb-6ڶ{Ccr!g ; ?P+_Fqg>vWQQffݔI鍭o@y`J盺:` !aev?;F'#['A IzSG/#i CHosyS61L](Qri?I A0$$\59-E)e@hsUQa4rHLQ4UcuAj3==f#q;-SEõ=eEM9$(4ZgyUaI`VR ! kPd#)̜U8-`~MjhH2Ӹ&2®Cv G:+ }| CP5zpO?fKWfu Ͻz|mVlJ~d~s;'n+X?2.ߍM{w/cw8/7{F; Â7la3ζ!B4xWcReL~kn#b2v⾋jvu2M}\}]93ud 1x/ 8s ejy 18IxJ?73T6EmAbMF˞+8b>lE>\C߫!C2B#/fkEa<䳀1\z>FJI' f3u ɤ)7( DnuW5,@KRSȺƸZIAhsf|\$f(("{5\>AK (ޔ*_۩{(fW*XQa6A@?9äLYd0̟5Eb}#nnA(g_kՁd֬S>w=R|Qڧ*|{)vY] JG>8Dqfp ЎAFށuz#w)47K.)e a~9}'Ldž k 15|F]$eAt7_޾GP|RE5b,^c +?k SkfӋHdc``G8 /]kv$eƒBBslRبӘd5'[C-ʀdM7CHsn&,?$1 dQ/K NV04[_8 2t!\F')k(O`\O%dOmpZ:#cO+0\َ0`ZNzx헧:J#~MZxSEȱьܢwIlbg]uRZnlV1}?~+^:VĊN.vRߜǛ ktTsa>=Mݩ:UTkIo n(c7J<7(.~>#y vȝ$EA̡fqw9Tٲ̢=߃ 7-Tr3c:dg$t;jgBVШz-4O\=7‡CxybAF W⋌aʑ4g ھڽR*n$RFo$ .qM9<4!ySSo)!*G !/ i)1 " HˀГ\bG O$Ny62c"+k<,qy[;DP=>7'[矯l*8[̿k |F(v=zO h}|;ya!EQ[ȤPmr 6nlOw7oܸۯㅃc* AW<˽JrX~vWq IDATzF8Q૸M\&+yu|>nTS;p !>ʫ^]!l,#^K^ʨǺt+&WZg=ez-x4 V1h=QRFNؿǖ$"T+GCf@ح2&גZۅ 7o`LYOֆ7kz6_ofu<6g{Mڰ,B0%³ģʩP XAX"o`Uv)tEh@E#h jjQ0aqՁ5!i3awzuV]TSVTUa& Pf&Mh%U2*RD-' ڀU1iK^QHNJIaaTU30{}'Am cʰ!Rjjjnn?Ys2|D{qΠ_6ɝ8˸~qq3g̵Zz΀bk٪zgpqmWsY; <[/$s/^^k3rrf$ӳ*" %0 'eqBDym,} V/Cu.~+$C$Zd')e-Z+B5B**%QeU=gK;6{;gϞm誔A9l3KZXc4~j'R TLp %F֖E5lyx )SB}8\aL`ٖcs}fD "~[>fJ)0|l5Qn1ƈ=r]ڒ2:*(* \b榲2 un].7Gree[I2$SSAn޷y6bw}޷=Qcɀ?s. ڍsǸ^{b8k A\;?V<  ʁdp.2-E%~2{xj6<.?y,7pkN~xbvcc]YeA`ظ$neD0fƜKxs.&öOruHk 2JKkR:ø*M,-abIWq/m[_lwx3oWI,Tw}ܠ[&q]Y :83C*)!IZT$$ $ , z:%ɱE}252 :1`X&ovjvֲ62S 4*cY[ ̐u"w¼I6 i"hZBY=Τݶ7(`g!U'8DBHue(ܼﺭmjh~UwOa2B n߬ ܈#Q`y=S n0N[߃sH^4^Xu\'H@p58 !nZkGfB|}W꛻z3gɭ½^طcŻ^~,}jgԁ9|h Hʲ5ؒ* e-"`@N@IA#M=f#rMh,bf#G6Nu*)X0DHZΉj5 P +D?!Z(V -KE4fX"*("Ҵ 8"VJ}xh۝ƛt2uv^;}>q̔TH̲ⱕ}ǂ6_exgevqB'?K/)sx!Sa4gr U܈{qQ"?͡U,⟒S+8=8qOh{C~w/)8[g]sBNN4wxog6?̓Ž9܄n ,*IRH(*@ZkknW[ 5x^VQ伉⿤v*_j> J"Z6?om`F ߚ5,/îms {+BO? AJ+* m.&{8E*ʰ9j +APF6Z"#Ҧ7tٴ h1YR#c iW!f ZH%Q!(*dQr]U[P`UMSin1'sݰ*Y,v+c (86LW!Wcl |W*zJ,.χqʏOp^BS8gE[w E\VqΏq |Ws1FXk <(0dx}]g֯5KuA{3ṅg=ϳr.*U&ZZ@RI,ObPE0yPCxje񮗯yMLs=?;Dq;n}{ދ+_ '@>g~Mje}do&mGGʛnj1UR @I'Vf9|JyE3%ڈaeLHV4QՈsxctUx$HOt6+հJU՘bec8 )AŰg05+FBjMh+ Q^Z2M̰F*]2Fet-UA0dU0bm͖V¢OT̬Zx_LJӳ \&tI'q/L✑x-~ :#? !WqʵBXk5uw^qu\||tʽN=-q@)hxZLoI{*Rj]A$;*,"i7KTRY=;? &NhD~]YJi)a-H'D $*0J҈CWˆ^=9b\E 6ZbvҚ/M1SiuX)JZJ"HRC$ (&"42M^+ D EtkzD"QT]BiQ:Ԛstz%NGkqwxiS݃_=)ھb%=j;_?pc/bZ{N%B'ŕ/?_M8ɥ~ob<ɰo'o> W]lW!V-~CPWgvg|deoϞSlk+ĝ ?m6yʰ J50£=ւ%ţ(VDMz #b0y_$<U9Ԓ =R-srH-v׋Xn\W2'tlFEBYk[ ҹHVa e6bT`.QA%@XŨSoYHbaFULLc.z7c׾mJS.܈`_:mDtɘ?$#?srIS}B 'gK;kLYT^ {]w{D[ ϰ9.wj"(6 eKY{ y QɉxkMFt 1p8Z6 9A Fj#EIB uB kBB3GqAgvfCeyox|3`㣘g&)|WxBc]~'ns?Fg&c0·1Vgيxigͭpx<'e2?~g)-Bj.DcTw￳? /M>fa}vٿ^Ic]By^y fA^E׵(AwBl,4P'#j'#E_2[aOdģ k̲2lK)UqLj!`x;*;gD}7f"FN>|դ.;_{85mByv]o82)8uٲb8 7ObLPJMC;?< k6g_.ĝO66湵cb;Y/Sq%fVX a]oT`Gm c%|vc=ΎY|QÖ8y|YR'eJuŧZ m&㋭B,ܲLt3"k $@Fm!R3 !hܧ{xg,O>>G?mev} 8c33G3Y9[m|g?ymƌ0Ġi~.|Ǿ7ɗ{74m2V_9`-Z~bsVF{y A1 uPiϓd GD:٠F̆]ܤXkvjaUPYNjn-ʐ 0i8UҍX,5UV aޘ2# Ca2#(>ВJM'KqKCy ;1WpO0=9M](b7'<9&4oƝ)KpY:{Wt?ӹ+.yi w(Mհa,EB؎Qbc":^ V@T$Uh'V)9,BveС$)G f f9aj 7sL!.̬˕S{,!ĩ*;|⡥VBk&aKu+gY; Kvګ^X{yf>ꅰ5Ǿ2 2*OptQ0I(((QPg f68!UYYh1jΑ,STv+ԊEh d%>nت T0N{t%1Dz'U @2+Uei XbeK[Ej@ ·!:L'n6<+3eΉ5Gc'D`Z۝̕;>Xd-/Bx s| ņCŶ#rS#&iQ Te'{bv#k~:;}t\!-8ԟ+ W7=OSlbzPM򴓥BHhװ6G&%EvKͻٵ(7.>ƒwws`4,/ Vec4{۵Hm[XI H`\Q!L!PU IDAT 30m (ƩḢX@>6(@1Xaz=fU@xҦ8Q p-5+Y\FxgѸ@n ,-\VqmUx9:zΥ>gs-;=\cxm9W^o}=6{e<c:[Ie{S@!@D5nļ Eq *%Msϡ0 Z؏UV""k`8`J) ؀t"0 !e UY`emL*De,\GVjD"%n@X˘xoR|f/?< Nnaf12tȐ<- \mSx]P7$f}~?%'Bkam0 l3QαR"tC(ho L#`U5YsL[eb@b ;HˊLE0T%4ؐg$ iPa@ r*R2 ,yaEL$"N4"TuN^4F?|;*;>7G]<܂MQ'Qx<k g=ɭ?J5l<@ m*鄢0 4ec6bj%*(RPǴ60307ay\d4&Z?0 S!K)Df˰1%vI+i0Q30>nHbP[b6$c$Bh*aKC("`hXRH`l1a$@HH& VG!4}wλ*ewϓ\5|zDpП˿OZ{R2x뱿6{efj#~E[˾~c7ݾi,GX0Tecưb HDJt(%9X޴͔稕l&Zy.[k'5t( e&T9cR515+SYAaF!E, l+Xk)uH ‐@**D` Rψ1BH8!Sߪ.p] \xg0S\|BwF$-YtX\f%Z?\P&}6{#mTUQ;w4Hӕ6lP-uGv%cc`4$P :i((i e!q0e(RFtz]f%jňzoa@])$SER`AFDD6*I i9`K (cʢD1h]!ETkrBhVHdj-*rS!Rr6]Zz\Kp20s}#~z<sW/m^ 3ik  شDk3 Ș²B "D4Qn[`PzJl<*+`ʒ54ʆG4( KJD"D^C`1 Y, DeN(jV0Nzو\Mj$IJh; )/z6{,z<gw)_'z<2&۔;wѕL1Kk[2B"aYn)Tw9\v$"*e@Gl]:Ns(q̔a27FC7UTlU%+ZܷH7iR'"Zdtd 22䓲G lIeJ EQ EE(B6S΢sqHpauaZ){.\x<O?YӃۿN4|]^ÇFGkF8X#*JZZ3b[Q9q@/ !3.'n|C$ A]-"if׆JV,lPA:ŘA#dя); q5"!AQ1N 0qDUŀ 0H$%9"8Tp0Ow޽xIN;a\6:%_wV!|Vx<$-],VV6r~I};˥9euin Z[Pe,-a>Qf4ZA@e耩qI B$;Kf\ؐ xh.%w|~YFۏ#Y]"C[5zc6.d ͒z6D Nzj+tfh%U2%(Rrq,0%C(-PN& 뎢Cv_|֭Wy͞E |~ܕy/nrxB^\w=L/- b[o,Ѿ[w G)Eho쾉ոF4Izui;O,PXC]JXrfO̓ef4ڀq"4VFDhOQKH# +J԰yE-/2 rF[6HeUYʼ`]&X6PLf|7;}m{+y͞E"1y SO/Qy<7U/,nYYfAQ#D xF9D\FQ H, :+I'iaJ M5{oFd9pU#bizD4?`vO*԰ B5dC^qj14b˜. -a@c" %ɥ 2`FGK늧_I#kQ~8seհ`x.%9nOp;:nVaZ;Bx.9?` z;Bk>sN! 2l=],N'!5bdHUᘼޤU% -Fsd+W7|Q,h| !! p{) Op}pKzDqSpDabΛn<ɛ Oy"Hqߝ;dxx*) Bҷ&s( &rQKQ*-ڀ.zϊ/ @$x5sȶ.5՜Z6 Uhņ xX+ROp(N);EEթw. zAm.d0U@f=V=&8# :ܹlhhh8%v5[kr{uv0D[,> &KhsJc~S0AƂr"4Xp)S0[㬩N"TB_~@G:2) + `4PJhU/p `X_Yit4e`vjٜ7m'96}"N/WowOeZVeZrq=Abphpʕ3fǻr Ip)7,3J :éy!oxUfgm\wp"<,X^%s>!@kPU(hXrgVCbSo,;D$(`d+0g-@L;ՄEgl'mߖ(^&Q+J)3zIB( >0W\YcOwUۇ4!> ܴm;{o+R7ҫV- u5l>{FYlhXPkpRXN Y)CSGWcl8Xk7X<V0i"2a= g̯^ʷt\\Bx"rSVAP C%=vE]ɰ+CL%{ZV%%J3' c=oh< Aa3EOzY`])"BQu2Vnȵ8]be$۶(2//v<3vV2nm8:Rݜ$ ۶3~.6n*iJVz~AϮS?wdUɹ=M .VO⒏<߿잩G}!4FQe ʂ11'(ꂌ-q?e_/+\-0)Cf =šz-6!ӞfMJGvQdr(ziP4dƧGd%X˄x%D$ ޖ&1Z-W?^+"&OAIB-A+'x/-)o`0 UDT]\p |L38 C+9Xyb=@AS[՟]SIurBS=wjY0B._ދ1n9\3ApsvqpFw|ͯ[:x]D~ŽefR&cdq]'^Q|kVoiI*sc?}~5MG]M×+i=+*r\6@kd,Xc@kAϹ$5(0Z}%4c.m3" 5aΛX˵ QW,!"m8O;k[k7?<}/hCEC _s؟_;ƫmc~v53Z`r_G]/՟w1\Vkjg:Uj4n136J (}xe7{;\ul72iKĨHf3bx"3d=gAkA hePRFz'B RĚxmLkR֚!S=V}igIj)0yQBY |b$p@ΫKSc(=7VD&{@fq΅C_l((&EIOpvw𻸰b-/ayv10u#  ɾDvq_y 66߁^΋x9V:W1t%.dÞSaqs"bT;7-p\=eqJU7jq̽q68e oYk3k~TD.\taH{0 __ldpӥ+⛧5MK[̤7-o߷8 [O}5_}I:CK].`=$FT&_u gm8kAɵ[at+BeUx3d=:dM21%b 7@P!(5ϜxJeIێX.2[aW/#(9P@6[{ ɳhB'|W9xe;=:5 1NDMƀW37<|bvvlG}Oxz\2c z6o]/(Jx&\1]8#!`|qp/y'OgqޅOTdžK;+pJ~wy=0 ?Bqm9kqJd<< <+ q6~U@&"Oyk킈\ ?syW" #nxs]룱k|[nMHŌ^ѷ_ y=ћsS c=C|hU"MOȔ亯ehq}&^Fa4em8蚫'ݺd@xeS+GVu0~~eM Ԁ.- pP F"ǐS V(u"-,]0~.Li̙[kc{biIiώh{Π C}cSh/RiYikŠ{JĘ X,C gl),_e\T}mFqITԊ"D _>#6"v־@E,erfq#8+J2g&FάMݙx&Oy 5ؖR<[+b`Rz]~j2἖ +mͽY0P_6{E5Z@CMQϐ5'# ykm." \egh*5* ds8Z(/('WRdk!b}ah_)+KI>(J V1J 9&Aٮ( J,֬8=~uc3L&l>}v?F6/9NK_1f"hz\A59X+' hk &kyʤE񤰑 xPe J qa: E u]H1 Rt%(.:x *#m)aNvuzlLO{wxoST,3,pOFS|v~l>q?.(g9?S=CkjOc/t}gq ̝?153".>eqZP;8o8OTCR녨Ӊc=W]U{8,г~y,Qy ~y U$"nelCs] 5n1Z+0De1\9/~镤r&#)h'mE:RBxUh߃Ba%S>ztmK ~Xe>|珶/evvQSzhK_!sibg{ =m0uy)i E<)D*FZň.FȵE>CYR'"yӲ"˥Bu*#bjX3jWC[t}N'>؛S+YU{sp NBe7tܶq~n9޶q5.Y"2 QwTZrb\ s9Ϟ3Abm q9y:jSUǨ{ ¨qI m.$0. \ůx+O{yvZ{Z[k&<`>tFа Y$ ~$MΦla> 2Fw;Lv`^a!` EUIҲٜ#HlMiJ1=rɂu,́ble=(t]MYk(K~rxwU[Vz׫2%+S1>eGuaEűMIxX JR! te.>Qp"IBk ^ bN]zšGuh&ƿ7IpJecD#8ћp/Jr4pn^Xk \HS5.=3PcxEDotꊺ-8u>uU&6444v+s݉3?80?C @RfT{vo+w=@rWxjbaVfD-oW(F6/w|˶'nXm=`K5Ia623C䂜Rф_Hȧ2j,m_!cZбKP䠡`^ Q{0'Պc1Ơ}_ڹp]3o 뚿`l-pqm8Զ׽i9~[p[i܌}Yu)lႥ,'T޼DcEGp =SqQt<1T}%,ZfBM0QxE9-'$"ХT= kW;rh[ϭ5Ҋ+W0,wsf5<ƳxQ<Çq3\V.q'vj==kmJËm_Ͱ9^Z]T3u^00@qBa/KI=*wQ+-")< <λP|P놆e9*%ZKjE &2$h~һ|Q~a:Y!ne[OHw%/o460 8*r>2"[=cȂO IDATK_Y(R(GDuM@X+W~p_&6Xy-R5F(,$%ڙbOn5[[{l=BH鉸)GuNK#mܜj9=4KǾ8- |n,Xòy WU?9,'Ⴂ,XkBggwF8q-n< +E8i r kedPgP鬮?KF4|VݡsZ?*ͮ}Ϳkqph mr Kd9#R-(aEY<ڑU3oܯ]QxL*EH0:U^r Ga6,Xx'W#e{^ zC?32*5LuRVDDPhr=(#)lD7 F*pbDZ:òFZю\?X.^xȻMfM;z&\]/Ⴄ,TUJ? |Nq^>'ߩX|yE0SNFƠi='9ܹsC\bzՏ=vRxNJ+6˓fPyx/}+x\KCCCym2kQ <W%0nAV J _!Yߒ(F$%͞%NMVLozk WQ'Zެ_[=pANMnp(1U('~NqYMMEDXU.o*fusb:\嬾߮iNx틡n1\_S?q#.$5ɽ.MDQ B # 2YM P*ehDzC5/F6/Oeߧ׏#-alLdt{g#yt!v*ȥ9#!A;@bAu\E BU a2nf- |@c[mIZNNc˲$4>tI^q㝭Q=`'5.?p&."e<:ASWq01m7mD$ ĸjKY S\) BdP}yor 3^u8.fQeV^ >D~vK]̦4zΡ}5NpÅWXmʰLץo}1+q>1W?gݲ ϠY@֖"!\H >\~\0q3ɞa Kv N)W:ԴZ ɕb^=)nbYNX{mn gKjn4Sk5R 4444\v+wQ(gYjTP4oQ. ߮py8J# a._Ao k#-PҪBߊti3r*ɊqX~@)gY(Ks>>!JV„ت7'ap/  E m|A{0AZQVng[TaO㝭*YWvx+'#l ~fxȵFYU"!mү)׸zٚe^?a; F6S[c}\GG/o4F6/7%voz-{( %ZێWC "7IQ/,Wv3LJLn1AMOw,y=&eqg\~,Y8sm2)Y/xBhJX%+ OPȠOy{xJ1Z(E!PĂV 0Đ#n{:Ѩ2=H9@Vq[>y."{s^6\L49(US2ե7%WWI pxt4"FGpN\CTnq Bu ]k^C'gn\7qV{p=>Z`e?S/ lhhh8V `T>~}vק~oXZo4s=ދ?ߕ}krM+F-QX1~hUSzjĥ=ɑ`n.8HJ6[%EX5%%hAˉ5d$w❲ƔyfPBIYbd:n2\|'pȃGMuoN8ΠQ/h𽧀3B]`P$l>R8S\NBO= |9r&uNd`0;èjܬc\? θ׃6ǓjhhhVlޝWj-kG~'[`$(|j_qoտ:y$,-^|w>Ntn鍓q>nAgXyZF%``-zU*iYʶY;OvW(]<찰 }NmW?V}TH>eOO-}1?ﭾᐬ.pngZ6*p'.g(n7xL^ralz藏 25uv<KanLR &}$,5&%&` ' @ T./”%EaZjKW^N%"{XxEo.FíQzztI77e%o<6"2ryS) \SvwvN?v^5r`L1E{$EYh n6KP1!,N $.fI `nleK,Sѹ&ɲ8|νsfyߙyO bqo>bH ͬU=hfwTfbR1!ǂXDG_b)|! 4:fk"i pLmZ8/‚AlNaR/P\atEā}nj^Qqٜ(2tKc[Me<[vSwcr0 .Y5"w!DbkW"q n7Rl-2M $ƺc]a}*c3.趥dY;~y?6Nl71G–l>6ƀ͈6IFd0{ %=5W->3n0#77]EiM*4R!Q hǫ78`8di]@ < K TFc4\e[aCa (^1<5DJ`Ͽ]1lbc` +Tz`Fkÿִ_b\,G ^P)uABJZh"^=hR4]O x#TDR'@Y)UQJ6D}BBʞ$1XnQ8vćWE@('k1I0H't%3!Bf"!D!ړ.b׹q݆.SꨨZhM(ߴnwTTПwNEꪭثt`vҋѶ15+`b3F5{qvQ?|p.=p`7T.>7 Ciڲ5$oB^ȢQFw# yi :Rjab̌ _h+H&̀[ˋAƁxA=HM XEXQ'I 8:%ǁȶs/޷$UD\xĵv!W{?mq7w76v_x`S.gmnx[ya{nKɱC@dk4`u 3 ⡵pl 6߶@yw<>Xz^*䬅\ubIrA{,K*_)M7) esi2}Ɉj8`50Un4RNvaV+Av#`6esFym=k}hgibebTٹ@ʭmw/޴b+`ZEIlֆl%iiYTJ] ;j&~F+H#Eȋ"}FHb@AەR8^ 1_B DHc;fJߊKB >w-=99+%S@-m?0}xn?62ܻ0aL=M.\Z+7\6xq;׾l;ߛ)QRǁqנ/*''.L tow{T71>U{j㼳̶쑝Q[kcVw"k,kTx '?FWNͩkb|:̆VڲQ  L 29Gㆪ=?HGQթTdF@E0?Ss߾Gd up͞ڪ\vUպMl6Buvnj9ⱔo6|`u%pZEB͑$uK=G7Y!&+R꯵8_!%{p5!,_ZB 30|הRۑ1jG{0f ByRG6Kӝ4Byd-ﻁ$NCȦCt ! MȥB (jǁT36ybO|?~|o~ӏ-Oܹ, ŸY6eXS6u-Hⳣ/~\f M0U0q;T7uhFU%Qs]r&y7zIbͅդ+ {8EcJ@\M<Ϧ=G: 1t-]Zc)ml{+5{(KUΚSδgx=H)i:yC6P20RB(0 o*NrRMQ0rc&14!il w뭷 }"FAk=ě_TEWJ#Dp?REHb"1D &I"dpIb>6h& ?oG y_mD0Aapz` -ppӦNNM~#ݖL!&Wy|C?ՍTi5_eHwuTt@6mg+JLC רx[(W_7pßxRG|>~QK6?oX8b5I6W?>ٜutqzr|ۢ S#|(D5ywB? f* ScyӹR>I]L#C=]":VJX#!+l6)ݽo~ `ufFk0ХBeyC+,9fJ>]Ǯ >Rhm8Bȹ4k"G@ssz36'nEv 9W_"k/~o᥅:aAJ/IXD&?Ʈl-an"6!)>JJmM"޿Cp B -̆6f&YkhAO|7@~Q\߽w^T0Q_G=7 QUk5~H 6]Ԫ//k spuu昲kTz m]Cw2`p;t!"kU53+ZP(]RP\:;_FP)G?3O"_(_WB 7mM5=IJT5l 6‰;3r9{xWe5]'&"pM:Y24RLDz}C/Ac7S-UסN\r^ZӮ.z'w]=hNg,4 u4ӄIdfs *ixf> tsV'ӮA4aJ<&s9B6̔gj>PʌZOv5rZ{HW NU'T(qUG׹j+ C7,gw#$tR%JI2Òxd^DLaΨZ܌!΍ֆ'[x)qJbRI$/_g((2[zz$Eޣ"DbPk*j-qqAY[81HJ\h5R$C&4BmI쎏;8kd.莏+!نXf1~qauXոv0Jk=Jn|!EHk;Jj>{WB ϊ6,9Og`[xnP#dC.MӔ(P #) N#dL#W'׼7uH6+ 0Is+k`rש_<8h>T=\Z }rpt j1M'cjDjxN2:aZU'zjV@g@)+6ƦǂZ-oN8ִZrLщGfU?rb%gYmR|K)B -| uv,/oh]##WFof{F:PEqQ9ҝJF4,Y;Q/Zk= 祭rƦ+. .>wWU23t&r]T20v'+)mV`ݺuv9J{ݺj[GND.߃-&Dxwl6\~,RZdzzd N'H';q*Zİ-HM8 .7!.F354&.UZNa]BBޣ:bI^4IBL rGg!N oB8$MRjeԁhhEmõmS堫 JT,\/e lahk jNfocn鶮9F'Mv& vR ˋBVv_D%dջ@RzY""g! *2="$ڐ;BOU1Rk!BvKabtg?p#58kVD!}Ȃv2~B /7mJ#DAבwp~h9ch ` Q͠rI64ɖDAfI ۺ'`[ԙJ>i<H[͕)$h/>b]oiT5zj4Ֆ0S蚆ek}]=JEڲ>PQXRig2_, -\vN{l7G9?Ъ06;6l>䝞)ߊ(aC\kHDIJyṣz qo|g!ks<~ֆGSHpmlI/mMsa|)nԇNq!mGlC Kp",0yĢs-pbuK`"eF\Ў}t#J#iB4B\ #)&?!#J|=@4ɹk8TsRRRJi8Oֺxm!bEƢ_)yVJ/p ->D6qG[d);.M;eW2Ƽ;uDH&xWq  : ~%BI;j0߭ʿ;"g!Ur (ٜv,7||lfݡQV5fԅ+ּkWI/Du*4#/ejItrtff˖w9s:|6}*Es{{{B1gV'lТGq׹ijI[P6vDS6w[mݺMٜ1dn6Bd;ͯG )ֆ,~s-7d'tR7G7?qbɾ3K".t!bqL2>WAKL)FI3 U«1VJI3vaX<8&B1GWCޛ-Bȵ! x`N|O0P'F}a]?y;zXUJ4cC$aHZXNw#sh@\]U euO~$ͫ3ѷ8][;Znm~ݺKEK򓹠mx04 kZ:|5L{eZfz2|MϨ_ίu/J<*DžC?ׅV:tuzn.̺Z]33Jg͑:6-G7ҷ|t]׾yyJ풭.Z0OTnq?\T1ԤiSZ xa۞N',4rL8 p!Y|6:B&hSnMt6<ߛ)Vc,V(X| o7pk  n(r=xKgu \c}gV2jj@xXz>+>7Soyn6diSaYDkS/$(# ǐr/d#7'+]80Gd=qV\.AJX8bUH{R_Z'I{GdT@b Jqێ/3}bq}q9IZr4CךsW޶ ,\WYo1˝5QA<4?-y'H[@i(rktD0ImLdܾys&gvn{f~=RvH0J8~d[ove}ǴzQdqՊexfbY7m;N!]? ,0Z \T[K65nr9)kkM&Lvd+zz8~G:Wmֳ66! @)@c!y3oFkWk>BNN YlB)eB&#-:&I܅ȚS( pRƞRx54>vg).4Bh⹿WbUFOR"w؎ҕz4B  B6xk0&c9[D3YB|SȚ%#gM zwKhR7IZBƻqaiefP|moOB ĥC@,B ' wgxu#0ѵ WS[OR5)p`&gv8oG`$Up}S4>t]?n?~vV IDAT WF/H6o?4KLf)t$VflcZ`&}0 0l`Zԯ`}햛?*vܳY zWU'}` .=2]!eU'>h%&umS^֞ϻ~thJ3Sv5Fu[N*8=/Z=XX*ܶeZ]eC'kn&QMyreUQSՍ5l<Ѩ(g+Wt9` oN=[@WYE+줫L6FvBE0P<$my܃|=-,C5 Ons24)n*Z$^/ SJZ7wCbgC<K'N0nB>MpW|B420B.F;H84ֱ,Vf&xB,G%$Gr!n>m,Jf-!H~qM!CjCmi]]Db&q-B 'H$؛F FpGG=AF!02!4tIČ+C>$.<1Cx`8#>qr"UQ ~cߝ^.3HlHl@P*h }E6eۤ*\Wޢw^UUUl>˴7^ >a =-gȩN_<]gY|橑bP=t)֫TȑRi,0 QlDZLEi@q4ZB!?vש"RXPs+A_:R0@Crwϴu>9~H6zGf] }=tP+wN"Q).ʖb5˅u+t0 Hأ٣,E cdm毼GY|!v?]Y9,&INe%)ߕR{wM+;oI9!gu1hzgL=p!@D$ڇXl$1-~4>AHN,^Fs͚=N[RӰ FH\1q=LY \܆(2Ԩےֺ4B FgE2nA$N"d|X#օr#3b^elYRJ)e]2({39q\])S55 SNN 꺨iE'D]흙iZw]zy-JŎk+sTh jou'wu%Ocu9zLKiOrxS Y߇,~#2v8{fqx!iC-&eRY~ObBܿ#n{.:0o!!!DqkH$I:?I29d2u OЬx,5CsmWdM>lJ-n0ZDZ8P柎{ttu6( i%s|v,p0~݀t S&8$O&Bk@3@|F#.eyL=?z!,>x(E^xb9-ZR2H.N 2P+u-7  +^ }l( w߽bjqб}zފ=3S] X8Q6 Tk,33ߴFݴtwsO*>XW莎0TYmx0 c(slgk5,Q:KnfZ=0M{VXjuqVi۴0ѴhuEp-FdQKuݺy J`QW`3r6WNsNS]2hiVkۛ͟G3VƯ|OK6PѾqw"dI`\\(_ʲs ˕X֑#!RE$F($m`4dbJב-9G6sۆ8E ܅pE,#qոqmwoN\HKt>rgZ,d&.b*^5804yar}ߵ1>^1'&LV0X^o0>2T+O~ ݶF֗Gds/)/|Yj{;wk_qquYe[nt9F|j-"NZ׏_;!qI;:熚 8K#"@^+~~$9PJA\?-$KhDg"ᑘ)܎DV6oz 1I Y@Ʃ@&n6ҟuxCjR^)r|(In鳃H_VL\q R=]Zh_>Ƣi\ w+@/O=elCt*h4p$I vM)#xdu^xs8{I=Y"~.%BASI-!K <:hp)SƞuK=;-?Ƶo6Nyt-7A2g["k4e[pu!.A k5U",d`SQ /r=o>nNmVҮx~jUkpR_cpOefˣnsQO>ys{OѮf^Vc#(QF!2iT 9#Hc!v0 E"Qb kymw[%E@}n9VIm&s`M8x}>~|Z8-7ԗ ? Je4-&hfHR!.6bq:QkBn ع*nJLuq|˜AW}T)Rj'$(Ik= y;{!fypoK!6N3Ά$3ciLHAHibLp KB,u ]E'p?ov:D[Zkw ҆B OԽѯ^k}w*ggƵfYctNJAC5lk@Sm33v>J)UƩR32ΊG+*jl)iE0\X2(r:$LR~J p`=QGC{Z+\TItQN ~hh-7t5m"4죙 g X"BfUMk\ȋ"R+ e"WFj~1Uo*+9fӖcX2>]YcAU\j idҎ\4IRuߘFVcpl,oJi[eLLNc'vb(:)-[ Ϸs"5bf p?zl0#CnUr7i"`ir iH9v|~rPVJ׿.{8&YH'<;ژb 0}  ^8i%!)v`#RH-s"]ys屮i"MSnT{oK/pw=w9J Wl{m48JqL=xEP~mlJo3$I20 ]MKHbaHIkݫBTH+ lth+d]ߡ&uY?X^z͙wEoWs윛VRzUB(^ftZç'}_lǵ=Ira}M5-Jfqz{*!p Y9@U['#/S&گkcoqF+*p F~C9n}c f Fs;)huܨb!'GNL+;HaD˧1aF 6x/wp-fZB[OhPB{,?9WC܃66]=ٯ\0wFri0S\\Ӭ;!1߼a6ݲ=1a5/}L:V/e_Z_puFC_[T֦6}7w"ܞa S/ą3XR2UBfl "RcB17`q-3=g3,{=?fxR(n;S+p `W⩁t(a30{v01u`._7X|-=Ko<ûO (+cYIE4 aZW mQgv7% ,O'jOOۉuxUv < ¢H\H!ԈHE~TK]~ ~oOJ}Td#i^>Jo뮵'NQ4:̮iATnıL$A̞mZ*ضO{Q:ZY -˕贒 (JJԐ :3@^D12nX|ヷzvb]< FH,^Yꕹ!GQ|CHWej'F|.'b`,,U@X1Kܗ_f?>GQ)oy+~dm y=BF(/Wn:B19bѾ*F@&K|aɂ.S``}D <21l {}υb.DV[[.U0Ύs϶qcaFxYz_yKSYn~:l- †G\N&U5k)S6θy+[msr۳G圛xR9@?L_ȍ*G)+W1I}N/`Cd.`sUxMն՚+B-q@#B)4(PBj )Eܘ)\,yU6?K/ 7~Om[fZ )ZX{j3+ U}oN3jEԋcл.X8$jfm$ Mg16n$G RZ"<[hʮY ,'"y h;Iʎ[].z '!ubnrZFAzزXtd$iBhʊcN?{-QGs3V}VWvrs Nm!3iFdܬR5pGb\__/sWFb "VbNK0yng060M y2lyE˵60ȿ0~F.b>3k_D6㈲->d<.TzHx_fyCGܲs1m:^cke؀ksO[TۏXN(9k[쑋Ĕ ˩f/am;{^-&7Юz]İFawջ*B݌dx 1y?f& 'U Lu*'28OVx`&7$ܪbp9|(DE0[Y,0/a@ A"AhP1NL2~Pz62Q6BZ$„#-HCʃ}IQ}v,G{=?||_p,o{7_07]x/hqe]nov@UrY6ke+ 3UwX.}n$ãiKad8P*ݰEЄH:c~ouW~W+:.cFv„``7NрXZd9"J$E)ES*UL)^[/Zhv7`_ZB> Ld)ގy44ͶbȬ=h?܄_η(-J{ }#ƚ^Y1olȅe`΢w[`܇1N9}??ؚӝ)a 7=r}:O Y"DpXӻOj SG)^Lj\vR݋ IDAT% @%"TEDbu/>ٹ9vJmD0S@zmo*Ǐ0̵,|2MS=Z=vD7W<3]vg<9;1sj-j|tӺ)Tc72%E}VU&4֩--ơVi:jUMʶBAb Q1QSn#UObVFQt%4M!V^^Es{AaZx kر+vlbukcq!bQ|RO9N%FQo9+B |S&/aӍ !F4X9a(^Hʥlcv*L>cȖ:jan2=NC[cي2&̵ 1lELq`Ogs|ZoQt.f 0m/FD )N2Bzx!K NfQlO a*ߞ;90#\x k;ݻcŇXMRӘRMm`#vMcZC羟K9'8gq p[;-ߏ}?j#m| TXWV%nNXSWoKN[lg,;-:Q80}?lAB_XUcxG';)U~q9%IڏRqܫ~|g6][;GB<>xZSǣ5Y)MN~ܱ¬dO}A<=c4򢌦\~z|{E/z]_n&6U'_]|Ontݹs7~Y[=8>y>+U8JD2= Zn6m66fA^5Q I 7"MLܐ}[#z`$~~֎%K~Mdm1VIPVH|PGkݻs:"?C~ xu8w.GJy5Y s5#cx1c\`X5z~i#nObި#pEmkt}h䓃خJ1) ^X tYlTYOd 9y=U)vEO׿M/lNICrIZmK7pғܤRi[x+Y[9˽ʃv_=EbfI(8m!1=9䉝޵'Gm8ՓS:O8}GpUv׮)y}Ȃpd[-m|`J\@o)YZl+t=*(>" +좋@)p%P/!L$E*| q o|.륢8qx얋ZGlnVXB3XInzrT볷cb )oTى)nZvX'!dl`J1|ZDG+" P$ocHLy%Þ_YU%c.C)La~o^Q~0cn-;a|#qLD21Lb8rMޅ5`ֺYnCLu qLԥ9>+Ze("Ao7rz{$WF1_%~Y60#<{SKGfcV޸6'JJGX@.Z:2DgTA%ČgF 0c Z7 wgwRP&Ǿ8#ZD$ V&XiEimąhS޶bO7beJms9~[4] 8ܼ|nlFL0U94PfZ L+xĶm7uKSBF8{SΫNJOs==ٰq3N`.B3Eہ)k&O4;)5 UtCޖ6Ɉ?0Uwău$wmTpcO3x)_OWb0 qk4 ֊ X$36 i6>P` iRڐ$i]B"@"tĩyB:mVAj*UWqڍQTJ sCgjF%<7"rn|5J 0gN.J╊:vA T&#Q{ivɣzs|QT§?ݣ#x𫘖 {xq&D߄ZKֺ}!έFZvv)FLD1FP1vNb"yƔߝHH慄J=A&4:k#Ly9"ss71S 1b1ܕsyd<\H]\e@# R)?ivJe-p݊IJ:UwHCq9_b` li>ہOY|B-zHƅu,tbsfK7 'Ko'Nʼn-7}psܔӻ*啯DJKoURa"ȡ(4JUt%x<*=r_>8gW,!ټGswQoO(Ipƙm%Qjr85߾~k2f~c,ޠ-q%EBMO5#69 =64"iXj,! A$$R~;r!NN` !?gH(q r#qj -iUJ2N϶i U\wH5ʖJ=kťH%cJh/ .͍vc/~ˀ rˌ,7z! &L.&MdP[bȞWR!i1"1oq !it1\]? &jX0,=w`6\WWqN-2*XάEc;r# s [n`U_] wx1ILr&GBqF⸣5X-ezظsf"cErPz vj@]5ODnE.fݚm֦G,J̟,lJ*Pu}|t';ęoQVG[FXSX( ױä.N&YUu˭Je($a5 n|-!0qw*شSz8|,º̐bvCrYߞ;/&shD4n|{œn*s儐}Yc"G1&0y(;ûn!qL^~LFKqy~IKA^y4&ws){bشT`}.\@0+E#DH%X 0/~<xa Kve=@mLVo#z4~z(@2-qHTL)tG_ÌKn{Ik78egۚߟǀ؎UH\ /3pn,{s{k9M,UX>DP:5B XF$OB7 #b;pĤvԎɽϿe鿉 }*c!y4'ۿOkK􎥚*>8|W{`Oq*cV4G-Ӕzܪ(vǢhXRX6i"D 2zXZxaշ(-J ƉQwySQRZyn6S[\-Ss]Vk`۶m?Wx8()(EhB DH ZӔ,\L4B+SAd G!pĞ}l?ZGC 1W٤7!+r+)j =`#zJt8KLO$w9O )+!#"Li?"FI"xyRCJ.!6̪z-Кˎg1bw6{\Qh\PaaML1zڇf,UݧN2̭<4}*bJ5YU!H80/6HYn6QuV8HifB/)$`),QA.'V1<"0 g%{t19XQR!k#ymFcsMPc~0|ZN`koo mמ/&7vmJr5U"`<}c^AT/WvW.VZxc%%iw%#VH1ZYSz:: 3~$z[~Ƕ*KkV:Q8 =gW9B̎4 6;Ea ITZk4$Mm(p^C APxT%&*UZ@&,I_Nͽ'O>=gC@ss'̟#̓X|!;>oDdI`\E|#2;DVtAň׷7bD<89)C[RGJvnǐ_j2{1LTRc0 mA^hB7VJͣ-ᅭwSa.#ܓ,bzOa|}̽Qش6yq1GaX< `UlSK|Sem'ٮ-YiLm2OEr([ߘE讇VOq܈2h"Rze2#kǝFh۳:PbAȽ/+RZ DPJB̓fzJV$ɉhW5_ޞI}ֹGݱy]mIJ{P D*vJJk^W('MvT+욙aPK t^]k[*S=8(exQvLG8$A HH4NH漲AQlօr611.^oA,5Z8d$9gPW9~z~7d5f^A)(I<(HQ@-.}@Լ2v fZDZzQit_[w_ݘMncmza[Y}q-eFMX|"8 !i#!D.oYg\,N])Z${!07am#FX$/e'0QcT)Kk7gq1bl#&/x)De`kƼv?dTybDx-b! k+|LA_a^.7'sSGSX".u(Ȟ 8İp? 1zb{oK5鮇+3NO2XL#YpҤ)Vv^[U)A(,A@f9![(I@Z % #4rN5ew'n@d9T7_Uo}s^~RMX9k&Q3q(g9Fg_9qB Tx +VTjK!eiTK¶5 IDAT,ES[G45i(rmT6ФH JI,1=RqǩH `fbrU9a'&hv;}W˶r%uHRM8?hDIf \&:60d\ىǚըmx[.Iynk-͔|!ԝRrɫϼ=#n&H,ZYSw/a{֧_|h7@ J݆g0^{sV0͋0+KgvUXf {9+??0B{T1eTaFxpGl@XJ[rϛaYo~}BƊ.'V7XivXhnR,Xlt /P+L0HӼď#*BFi BuH!dT*~VZRƎL)9tfKfS+Pz]Q՘/qd7L5a\==stq h^#ca8\dU]K ##|`$ cIRD2/qH(aV9N0,bBɟxFfE[60bqXLkqs 4y/FW>0ʤ,02^UXckoJO#0·.dX}dqI|9rkͰ:ïH(o^ _^~Nhw1]TBIB"L H!i8GUr$GA Gǰ.$AKL%n/ߝ } <=_Gl^u'Qn󙮖hmY`K)'̗3HDQʖ+t2 HASUJt@KAXM:i(+%R+MJR pCҜE)UD`mQ*MOu; H5Jq,ER&a3iJTFIIGƯVgWwtPP˶bLI Ӈth_/RI5~C͊wVL I #`mn;P}a ;y1窩l۵8FXm&./f.<ԁ\9(0JU1Wa3b[`Sr1bNk/q0#T YRM*6f &4p=`i"fWxj6fej$l#h D[c=:, nS\W.~V1e3\`ySP 'e9BZfhv|6&"qbi<$7I$!%+>OhRs=\!U 4!NR!$NMLQIDhoXD*GQt=n0w:XPi^Ss2An'n4U'Oq5JUq:d#ʓlPWW#b[Q%圽i2|lu+52^1^$MWVU@0a.˴U8yZy?Xn%ފP}aX8hBhiҲ-t8?{&?ȋKp0Z14B.~%; ǁ~#**`ćȞ]\^W kĜۓJ:Ld[/dб} kFᢐ?;G7 3]#fr/^2!|Nq88 HQ B pcZ`$BNG!hG'@`%0悲pcQQFTU%UF#-[xk~Nܒ³HiG>F1Rpr}(L(ǤiJAZuecIES6Hc)  e)Ha۬ĭ h\S ]ZHĒVԍan7.ΔHJou㖨lv̲NazrZH?@EEsR)E&D%4r)$r XҢ?#\*a[Z21<*[T4MfJ$a`tHU,R,>)Fbu/񱼨Z1"ڜsM6MkU(斐({ʞU+e|_K1o1*p`Fa GeE~GQ \[C^{!>w=$ xVzF 8o?sZAB 矈(XXSbWiuݬly)&j.jnؿЧG4Z1Dc2gh;,QtN0/ >x#ͨx[Pw=$cZzM ,tMѽ$!7h8]-#768ʑ^FVM&FNt"IQ'GJije\=@RAH<לK)/UN;AZ $!MQmO3|(zJU6g~2\ӛ0ɵLoyfac?/0z|/'kP7g[OS=O>"nK*Fg6B <, FoU%jy(E4z4j$9&"mLԽ:|cZީw6'q)_afZN3&l@yJ3~:Ρafu]S),1C4 j5@p=FU]I $ofnbE >(`^'k%N+"JmJdl ˷b뻐崻Mat`-%뚴嬰9(\p=|)vHG48:s4Q|c96 #l,z>o.狟 JStA<?۞*v |cv'p7c"[ KWObjnZdXO[F`ݪ yﺊ“",RuF}B'i0BPH".MBg0z,f8:5G lcg#S{Xhu)9.$//2ӬJb4K VLu$i`sr\?I4Z-iBtr C5yn>٤ڝ8)}}LlO̴j,.} >Ƈ%rBRH [q(Cbn#joGǛ>]6hs9lЎG6NU1њb Yyq>h9h4((ˊ늫U[Yg ؛syIh^k}ֺ x BI!mZr0=k(auOBρ΢GAwfI݉QEi{{ڜk6EE^g9Bz)5.YWɴfvɱyLЈSO0ld@x,vbN9Efu Qq]^q3[FFxa"9fiE1a!R4#QvЉ84():C(3tfxݰ<;H;a ?L~g5zYQ(Wr,u]JAW*:a*ɳ4p7neWg.9]0xdC;YЎG?ռhۏ۷]kY./g'1K/W2JqLυĈD.H=l"!oi B;u,OaX,_d Ymvܭ{Y,1_;B:"U[ZyPyMV}z:!>UELiЌx>NV7%BfmjaH7HjɥT9 * r0l0iӔr1Xdաⅸf&McF CqXhL% ׍HNã/ڕNGu[k^NuH\/48&hhug(Q¤6u4eVChp<.L("isv=t71Ms}ovOZe?#L<&EjaL7+k1vb|Wqvg'PjBXCk8+ʄ[╽4ܓLKtNeGp8N#Nq.q,Bf/R{/Ft*/8F%tP TKlZ?+67qhA50b $u\2D I5B8EHDIciANB 'Ϩej Rspp5(.v9/s-i ZkT)vi O$IEB%,164D!7t_H} IDATяPgZ8̣N6}k=Q [vӔLeyN+h_*㭔S!esK~(ǡ? I8Kin)_XNšEK"vx1~3vUXif9,ˍ[cs0FLYs#|>0VbX,k?@~, % LpˆaMD 'VjZ@ a5;G88d(P+Nљb$f"#|=y[6:O?*|y;^YD,.d@E8tNA̵׉3ED&$Œvt}k`hk^n[;Rũ'M7LrkvCN(m>RX6(Y#q<yt\x B)rW)5?pq!jYAY~8[>cY8qk ,EzgK*,OAu&i z9&UW7ӫ̹: bX,kw+}cgeS9$f:0ZuYکתH7]R=O%u~Y{< Iup)]^Jܩx.KϘqlo۟m=$*W/r$QwIH9E(%(y>Ή#*& A'KI{N3q|_9tQƣվhm@7:U?,qL鶊 PfOc ÌU4Hϑ! ,.@˂NIyL#KGJ RX=l!%"3`͢e-YΝy&c 3qSyx,ްm{^m1cʺcN{J7[+bY]>JLSc'q=iOU5DiH7'rp~hP2VB>:YFӊ"eB#GG:dcZ*{%-7\DGgM馅U*:j J2&!WMbMt͇!9i$1YRD WRPNrxq\腨J%qtaAԣsQ,ϙ6E9rA@r<&RtĘŢQW8AlYk-/yZK1XSqO]rcgodHyUL3z{+=[bX0Etq?cy B"K%PyFF ^}ߛtsN/Hm6Yy댗i-MqY7o](J)ISrɁ4udKpu;[^XhY+xc6zSJNF30}K11\L LJsXe]aUE{JHLbX,k0/5F=V!ysEd Ʉq|:ĝaNNJ+q⺒J "a߽qdE01O.4FKA@͉Jq~˞iO8{@S-!oo>@Oqjh%8RHFYڧ̴N^l0m$3Z2Xd`$q:-TpTeߧ'&,y8Ή].=S)1>b9 k-kjRǮBsbW1h-O_UA>V;iF, Ƙ.fjX6j\bY]vܭk3,iis*wJXD81}VF j5JvbVDQDD""S9 ߁TCf1}U4=128=2Pk훞N?K~8MD\UKmb,!f s.IM ]8I3X1'#h=A aFn~g3VXD|sAW:xϞx7R|rO gY85E%R\(8z߶zŚE˚Gk)!gMna Mv-9#[nx/*n9)ph& Z\ɞrޖ}4՟= {P)}|46fL9.xdVo $Lc?K<4O*'"ԧ8ҡ֋dy6|edoNiD٢ Z3RH;0٪_"u:y t4{QC4z,h1/\?d˄fˊXh&) ̲Bg.ހyLq=KKZKb\ǙeOh3%2)sBuNH)!U9q(7jFEi 1`\F YԦj^r@9$'!K|! Hn_φAdRG3xtV񉧿ޣ/̓AR,MԴ@C*Uc P,,Z+I/a/сĘl?ZgWjs|Mk})M,r 9.w/ʐz("ĸ W rbMW77\wlȗ#U[7 qht c#RޫԸRuIД J2Չ:!-<"9FkYʑYК,[JZY|HJ+tYixܾn#GE3Hߠj.7sC\?Y&[+|-g;iαfr]Ql]7Q>ܓ$bAm#,$OO5{T<<-^o/b-r XTmH @Ng0Eޭ)i_#_| :h/Utd]|\vJf-Ul;1ܦv0T0XjI)AeRLJ{yG8(Vn@fq^y+u͸ơ2-叽1\yp܁RE~ovY09ڍ_dhEN$$ V> pyԯ˱\Xh^9xx1Xi`?F^7 ܄1K(1ocƍ1{}^YsaйP1fbX,t.R/9Lgt:y4:2:DmjO<82X}N9jՂTuovG#tA%j@aZwfy'x0'RttB,k}ucIY@PuN(2NܽDK)t*\EZX'tT\PGp[K.F0|yroT㈲W ؜+T3;3}OWm7=_51n-7c%k#!,s[šJ,ܤ't_F>9[닙FH #Y*#]GJDtƤDR9*!ƥ,$%Ϯ,h 24@F%q$M(% _HO`IHY?k}Ǟ7;%7 ##_<*5Y-L9Fx8{1;1`tǤ8ha/0u%M;1FDOoZw'a0^~zᲽb\+VhO Iޭ>왙m}b?pdf?G皝7N.6; @gTA&k& =)Ti@y eHIyMaNT-Ltn*<<ζϑ nTX`߱e `˖qL1(̕^`DS놫f\MԜEavڞnjI:<]>&L6c:v1ǀ籎^ƕJ }cUy`\֑:Z2sz_[,Z#\Fq@ھ 0; Cuƍc3G&7?Fy/1)FbFa徔A|OO L *:&%@T9iQiQ+9@`İ9@1&iEJK]Zi~ffGd/yL +1+o*o2ebYN09 6h~9Րu'x1-$&c`RR&#ϱ%u]M }0_|'0GYLS~q`t.cM9^'iNC}E̺o)ns1zn4s|ių bX.l.s|O=ͣ|JwA:duDEOoV/ݮhAɑUb%ҥ_:61EOoMbkDyx!2t@k6ƍ/{=FU0ڼ^ isjrQXh.ZdDbJ.`-Nk%NL雏Z6b%3i&bB&Xx%m~=]1vpd) -9ux A !B)h l 4?e¢|ik6my#x^nK_rd:x1r1XhE*&BĨnn[12'E0rRQo.k clX,˚B?ͩx=rB+@7.,j ˴YC'%HA5-Fe@R^%(9FiL+U'RO#c_W.y~O0}1ejaX,mP9;1;a$%g0^aM 3]zނIV~bX᝺0GO1eFp" ZqL3e!L)@Q4kf6T]wswLyK =whs(Z(6h\FGS!(D^eɼ6?qLMHtTٍ+ !9j9-bYSwm~=:m1^ dC'FG?SYm\=X BLN3L+QCa.Zk!Z~XZk`X,K {+xOG1,0<>~W֎֤vb̲I 8 M;c>&/ρ1}`dXo YXE!Y0>KKG]f7ui)])xcfldbX,28"qϯ`m0u Ӧ![D{FxTAW3^ofYz;v6O`&>ՅN@IU*^-+>bX,m{>6Qkn ~qD]ީ:ᘇbf.N;c=m0[&4I®ij bP-Da1ubv1%VeytHAZFbvW7h5X,Ų&(R0%b].Wg-E \Б9\VUrjL *f"S?-J~LÛ!ߜrnU:gb"Bzg x˩iZLFW/0biX,uA1ux5L8]jBwvWD%'+.$e"9QC TY`͢rZc&j\gw],bYw>|cOf\Q8.weՋ[,5K"%a܅Kjv  uX,Ų&L0M< Uo\’ <Kx傰5& tgL-Xz3o XOJk}bX9>6ѫ ~V-禨cnLuA3V-k k/I֏^%YΏc/ߣW}aŲr鱻8Q=[_%Ya|7pU-(Z"6 3/-X,hHQkl|>k-eeܽ+w;lp5fZRhJIDAT4´ZhYXs,uRbdrN AqŒyurYfX,k6@Pu= ͖B?3op/˚Ěˀ]z]<\eZ)1MDSkȰX,2?(8H`?~-SIK܇[oi]Y,r]d:@0Fsz X,YrQLocD)lSbXs`!hYlFZEb9/l D!OQ1F&^bX,WQhs1$P6[,ybY((3#1S7ZbX, ld͎".+PbYؚB1n.,M11B`'vX,b6n/D76,f|jbY\wrab&fg;<[X,R!.\cCV-< nVBw%03gq4 xL]bX,'i"C1%i*|Xb\K)!rc{/]5Z,r] 8AF{A6;@B E uŲƱft/)ݟfeX,7xhԙb S8pХ @J׷^Z,˵MC=s,c` b  L,bF[O qqNڛ=9(2T@-_}_,\Z,˵톺BØHH1Bta܊e^bX,< ǑFkQ)̓XmX,s`#+Sy)w1EC@h,b8ߒ/HĐD˳Dbfq_8<t>$ǀہ;13-b\ O> G8m l˴Jbf hwb%m1jzemS#q3&bX,'$Fc[g9*@26\,b.f,hg0C|;+k`b^\+1vbX,œLhmz2)pY;f1 X,)Xx &b [ *.zݜy0m-bO>a"˵9ƘCwTأ5X,;g{ޞv{kJ&Vch@ g!fxㄸn% bbD!+;eGF%M[J e nwgw^]ݙ~&;<}7Y|\R:m$\7=mB'IKp9H) .^۱$I*؉%2w J$i sdoδ;Z$IeUK=iݛ+w$- RJdnmqlΦ&IRYef5ۛ%ie2[|I D}kO[ܽ ٠:IR eR'u;5JN*֛{2:no){u.wm_D,6M$C}n`8i螡q _Z{$3,"L_T%IRY=34xvw㛿}[,IMہqP_פfJiswuo~>{$aX4{7RJ(I)zUA6 |.[{dѭ؛%ɕŢED7@JiZ$ITYoM؛%aQ/2RJE"IRzsm,}m(GҫTc P'ṁO$+%+"bUi.aݵITFjT\[( y {(ORK/6GmaP)IQ3$R;$';L[{ xSuJ*'C-!OU[9%Ija$> XzL=X7Kj/We[Px>"P$Ie7 |yf'&>6XPxѧڛ%a\t0̵H$x~>sn.NR˥R[C7B$-{q.}gw7jOI ODC.[dgz3.ϛ$TC֛n ;xY_>d"6ao^E-}-)E#IRU|&#D $I,J$Ir $Iâ$I$)ǰ(I$I1,0OHE ?*DD FĪk$IP0Rʢkt~)p 荈`? Lrm|?Mm%#WO92y,)FJ`+ -NMFm"=3FW \xxQVt}:/RJEנk] ԛ)5$IR8JC0C$,p)߁?z!IRF1od9 .IR\Y$I$帲(I$I1,J$Ir $Iâ$I$)ǰ(I$I1,J$Ir $Iâ$I$)ǰ(I$I1,J$Ir $Iâ$I$)ǰ(I$I1,J$Ir $Iâ$I$)ǰ(I$I1,J$Ir $Iâ$I$)ǰ(I$I1,J$Ir $Iâ$I$)ǰ(I$I/rEIENDB`openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_47_0.png000066400000000000000000006042601413546205200254540ustar00rootroot00000000000000PNG  IHDR<. sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxy|W}j_,[ޗN쐄$$$!"hi-t?V+භhKEf Kv:ݖ},8#vLb۲C;3o}眣!Bg\B!Ĺ&G!!B{xB1IB!ļ'G!!B{xB1IB!ļ'G!!B{xB1IB!ļ'G!!B{xB1IB!ļ'G!!B{xB1IB!ļ'G!!B{xB1IB!ļ'G!!B{xB1IB!ļ'G1/]jAqRZ>!xZqHӘ?@>f',PЛspB !t\4 h]"  z ~|Y>}!EDS[R/+0CSb-s&= "Po'%F✪ QY3 \ 8> Ⅻ8#o?3|xRoޞ !.xD-(X JLN8FLOc> bL3 :Nj0 xPo^SB-x/Hmb*: e'$S~?bBO8#8 >!ą*?4&zcb (&=!0^Hma졪뵆86/NK]s|>B@fI.:f/ !.bxTC\eα`6z!*Bm2)B6%> <̾:kxOO#%dL.J_dC`BB]ez{.s (xu<'$Iۇ+ַOMU\Tfnͱs!  w&<_rP@IcŒUԕq UsC&}zb+ d~OZ ȿ10l!K>ӏ/~[+d嘆eRdHSRSX&|~Nj&(\˄Rĺl1s8;X7x|~]!.E#%hF&cGB4@gDhng-\sb 3kOyoe A{ 'ħ|w '[$3 8W#%!fkڬ+*I\ dȒg3CYA`*:'7/1$"Lω$[!cR`BMY[Hwxf@ 'Ř0ay? PHGK޼]] ) [nJH҅x 0AAmb1Lf.QO:tfpY 3nZ]vkLrvvB\x#>M_pQ[,.ÄccJ/`f*z?wUW]y}RJ$1.b/2Ӟ8kO_&oa<p9p%pЛOq !.Jxļ4MlXT7mEΔڵE̐jvaE.8:^#*Yeyf!#*& 9^we}rȺ O۫޻RQ~rb%3{#U!?iZJo3Kˁ@6hpVTd4!Nf~ϊ"SҠmБYH0I<r*pdSXfSv2oo7>7 _Lw|Q[7ޡ∅{fo},$K} ܃iFm4~Jz7y{vð))o8) 1+䨓4Z3de+k/3KO VЏi0:^<05|V**Ɓ2?>X2~rWAo޾GmZC tI~e}w,x~2%>,`58Ly1𜾞nK[jv,:Anv>6 $)$s|LUg S};1 f(j9fG53Jmr1U"׼oQ-f0'Bb6rMEvUQ ̠Ӗe3 Y02okG0IٽQL;+];fXL}G?kaAJ}6f!aGK i" |V1 y[8[@T4G 3 bRN*hׁ czwLQmpw7Y08^ z<9ttik몶_QL&ӣ~h&mGAY웢8ME (uv]'2 MrptlS)VTqp0ܳ 1N>ǂ! gzA:u|r|)ohu]3! `L/Nsv'L5כjk4) q #nҋڇiZn }(9#u{uŠ5'ar{ţN<ZuC5 XeV6< pN 0r\kع0j4>iQa8{PSɱԽwl(Q"j3N;ۣb#.x}ؘ!¦< ֿGcu!&oUVqӧwy{FQ}f*b;jh*v2J5,tRqm/Z.xq'!,k'u\.F=JkΑ5^L@Ⱦ,_ԛKNq$ VofFN=J1B#+VaFbyn%hP[6y0NeӧuO艹@ڵ,z[֮)@4>1ZWw2mt\ SGZX֍x&egޞJPm0Xa[xgv200JMadi!#.H+0ova}o>Ŀev<0u)2;i)k1fZlaWڲ<_nU]|kg*i (jCH&ԑ@; 6E-kQ [}"o`ySi[,᤾!82-]\pL%2f"7Lo'|Dm"4yMXrk, ©Fv@mjY-|3`Ewo s6ff0iC* `#aԟXj'# V0&clSÂOaS8uuO!Nq ut}>voG`צe;[V6_7+[CwY̰aPoGc6ܙ 7G}Ԃ>q5{4~-uzqڜdVԹ[7ml^ ;3L'y `S̪ڃ͘X-v7] 'ߩB+ap1)̚*qOWQ*u޴2rΫV;¼>,}?ˬS6^tO_X=&e+ 37YqFVSk88yM7q3.iv&|S0!m94# ӻUٱ}v[!8cxVˀ'1 917m$Uf.h5-6KY ^5 ^ 7ϧʦǦ7cϦD} =TJxzK911U1~gֿAm Q`9~Gai @@!PJWP׶q.懺xF1m%8SxĜ#tb3|3;pWjw«v\1+tJ|aR-z?vOLؙlLuF@o^V[~H^l92{+| Y dYbt밽@Atx%0g0r᭴ڵ_BLHs֏bܾ|էb=S+ICXp; C[$!ժ0Dm<'DhL2Wֺ .֖m:\GW=p;;?=>ܜ0;y7݉[NKDG.; N3:|3&5bJvOa. aSIB3 Mbu>cSlHXzeX1^r:܎y#]X'Ξ6\. ;K[$NT7yj+5`^?P[66~He}ڣ7o?y mM?F˪ߥ\D޼E !.]Rs]/Kt̪PQz-Ik[b͘N )%M%mͱxMX `].遺S->yZT#y{Q}C& ÓS\av~hzALU( :u#D>>#{byxBA3 ~̆LZ31>z> .OC> x23[oE$@RtRզz9ffVGHaxW^~'⒞bV%Le+#ayI8M᳤݌֮ z9nj9vM]XBxĜVMiA]WUVDk"+p%T頸_A$V p&!&=6;sZsmySEJaoHG3wt&l]lj3V׮I_DU?Hq ԽwY_Dy| V0ar5T4nVpaa-*86j4/}$*^(Iqqsz*[W\r{:xΜlVڲMqӭL(rsj[K }뮨T^XZ XaPYر"bzOh501+ 3+s9HmZ}?jl@}yU@> ^6c\عB/KN"mѕEд_QoCB\$״= ٘{9ir-\Sq±C~`nnMt xH~ZQqua k7] |}I= Em(u [:>8S{W=ȗWӸ$K~!nB0C3%TO\h6]n٦'l*<}0͵o< #[=u RP3]XiRM*G*䙄8Yp1'¤ĹmTKu8bkݢmfu>;+_q(P,!ċ"//q_AOKS۲MP5ݾQ̂s`U5l﫭lSq ɺiM:w;?N";G9_F]^FDtiQF; 1߰Khpoȑ$!nJ !^:C[iӭvb|SO8svRÍbzz0{'Rfn, l9 Dh$MUDaٳ">_}֭&ns[c06^Խw< W2o%9l'!ؙ<*Dƨ>(u?~ԳH9C=ݪX}տӭ>e~z.bW D<{f]NBT[E9B)suWdvU,K\ǶY[{l&~p/9jkx\ngZ$m[^A>¯OQ.DuD)R'K~!.^x\][ݶeϛP+ 1CY;0MULi`fìZ1;_fqRX6QLnHѾ:ߙ~~(7ٴy=?:A> _Aa"ZGIHI :FeWyK[% X3DbZ\U +b= B= Clw'wdž MUV+CT F)ǥh,<2|c𾷎/,tQ [ ]қGaEej\T}8%\zz]t{,!ċ"3x%?^) IDATfřYg>ӭ?Z8QҥLUz<˔#NIS'ufgn$K*v+Gd:*v(7@f=0vasQR[R/T: M(/q܊#:.Ǹ)ChEѷ!!^k4tˁ?DŽ4p0$'.t}>⹪pbmSvj2P&( mMոl28 j3!a* bXs/#M 1>!q-F/:mn<㙰Y&:iе"bI؜> !EMiV-7eD:f1oqgӭʽgSOj~G{oOџtnHh[ :I8@[M|խ=[i)(in:.0(guL7DauܴCiD 89=ba֚pW8nW٪$FQ`_uutyCyLoaXSkY}rӪhue^u;Sscm@U%k[YD1) 1?;/6GkPj w\}|"U j.rCzxfL(3Jr*zUy8 _~uQͪ[9-~\u[i1ݖ˒8 p5$ᱧ5$R̵*@&!e9D4LLCVa1պs\tJ*;gOnQo9|ԷF7`lĩcUQV{#<_GIrGenb> )|*ؤHxgl* a\iv-E\@SDQ^i=ޱȮ9,,"qI8N)o:5=8p϶ERCm7Emr0%y9M B#}uTL}{M&mz^p@W!O@'{?B!NTxN-] ,5XCgG)*D79qL &x[]R_՝7`)P}~MTIia0&7Ob),'m<ѵu:$!!V6Ji*qo'ףl+: M[~͹oᕽ}eZVu|y7G 9 crDBʕ@-öU>KGmrkI?^;w~|pBG*a]B4J[*p`Xn#P($"L%]B)ʾFEJqwTUyolc! CHw^k/Lc~7tɬekWcens Kݲ^w(gҲ1> ͥ9ZZdYkrq:7 [񹁐(!S몭HՎr\-'J|ګKo=<Зn٦+iyVy^[ڭ/].=FZY+ W%:ZYT'p֡Sxne$$I~b++tcn\$ 5]>M첲rvq'&dd'TyVjm\}ꫛ6"Ӝ_G,̞gEV-<<]y%,$K3,H8*\N] Uw2R*^8_LN!> <'V. [/WuNST +nXCZeQ*)i(זK@)_}xͻް {{f|=s[prשTǫ5+v~R?ҍY6:Ie,m*v-˕r,--@UUTsppq>G748- 8u$(ElnNJ~|8u{Zju{qnѹ~Z?)jIPC&C$~%`9i?à -^No>!|&CZ'itN]vR^ʄZs_Meh\dc:i7hȳ萝_VX*V[@f"+NkUMKO||a ©ATEo(&vBmYnR5nDu'`Jqq*SC7Ѵ!rw}+}qGx?;jsVbK0?k#ҞnL?POjZj0e.PNmG'֊T4֙<?'VC\jv/kJך%ZҾE'0Eł q](ɵJqϷ31nr%c{XjwN˒u_-z okZ4XxUye uxS26}u/Ytg8P;h򋆣kP!fLWZҔKCS^L?7uB\$[YzL}64PYT׽nWҸ:9P e78@.HU*euql 1޺x'ˎ޻ti-C ΋jG_dTf;ٟMOׂjWpuprRF]qyHm% 3d$#ϢSu^aaco >ݫ)&;_wφur]]2{M\8/X\}jrR~ʉdzD3OsU}S6<ß*Gh]oj(-Mm{}L>7Ma}[&r1;}e<ʢ²\t PQ/7F <',YpCZb{WȪ踗R[4Nn`ڧ@u%ؤ1C\R1Tk TpVccv[(ڵֻ/]n٦uO:6ۨv@O1@`i> =gf?P'Վ)ry ʤ?uڦ'}nZעt *KHѥIUDlzt=qeQ,M뾔'=|rdžoJِ);m{LJ&FJ+G7oSv3R]n# >4laq_=К5o<b(+P$8d Iw>oC S%UvFqpw/RM=$u.kQ!u"ȅWJGapN6_b hB.|Xe֠uatA%aw>GZ=ILk[ )0~`Pyۂō,n>~6 ߥ_OZ߼RF9CSV{1-F`e8X96Ii7›N'hYr|H\z:,0ÅAk1$pD =џTmo2'x4xq){C _V'C?}"Ԗ]֬>;Zz֒С#W\hmum[5uy;3v^ysyF/|\LCzw -@bvr*IYʚ'|[VBWxNYf'XL#k&UvygFtJAM)ON %#1)*/2A' o3ՄYp%pbpsP(fK `v p bO.*MMN0Q`e~ibBn Sh}WEx^H/괼. S- CRF`|A=$:зLa2„ Rٯ[r0bg>2췲ݩ#Hz>cCw+a9)N& p]o/ [GBPn^ozKڋ~wW2A7xw8 3^b/bvU6P 8-ƮskO Vw=mz*8$Q .Rai8/ 6d,&Cd暒&- E~$vI/µ"reKDYv͞AА֐$Bl&@ &D27+_rvk4~߸rD0x~&BjqB%>+^񦽷a8Dۊ=j lZYnA'U'Q,J^7S\w&=qUM$ FS sb%JZs+_YY.* y)uVcJݽGwkYcx'X}#Nvx|#Yk%Dڙ?8׏EqdϷ2Hct[e1ʨX=$\d݂|emR,1ID{Ri+IUq2^C;ޗ&tBƛ6=a&NlUOLmezNj}V5OL4_[E6\&FICc$N`.rT5qirUha,̴F aLedէuSRʿ"QG ͥb4+c*WcßR_6X< z-\/~Ͱ8o[TZjmَOX^I(`h@{~|10!C.>ى !ƒ_Pv~+lmVrfւO\7/$KYbWؐ9L9C:_!Ni ZM +ChpJ ,KRT"hakӖ0=q'\ۘt& i 2U(g 675XƟUkCP)飶$0%ρDC}?{.#N "^Cr$.j ӄ^EE3khW`~CGu0z/-;o~hF̰b.r?'nRmx%㩀q 6^p|Щ3S\S>B׮mtœFkIqbDhpZoqe&d9`ZYQCDn:?=]Wq:a7^rLC<>(؏KPMzz:89}%{uޟgzHjF` ĂFpI)ȹD!$7W$oHxTBX1XزuԦ9uuXh$wmy>GWBBVfl\2R?;4XUw̍O$`vӵ?( Bξ2K"f>侉(g9i4ee)x[ï/$Yh=ea,Lh)q *4SuKf(IƦY&DU-0yV"&bHpڱmH0g.ֶ0%A gl+NrH#R䚊x0\P[]WFo^*hJ `~ݿ[<+HIBmTa:8ʘs+*Aq] )N}n+xm;峌*)`pD_UWR̒vk{~nX/R9ifO\zA"k 21-- Ɠw.F\\)|HB!*8x}C.@XUU-') J+}`2 jB ,d֊CLPac5$ z[sY_D$1iS]-8+|Cګn[@ Zªjpjy9 XzY/׳!VlVƤ F&Ҫ.@3H}BX@V<0 =6w|xzhF񾅅VZQCdHsMsi2eӒr0&0 (IU1"h4%%+xMp?]جǩnhus8$ggC(o~`Ѐ G, #̓[:!o Fv2盚'X!5Ip|)EK_ ?"jJkKU&ZD' DAHHI`97!s56BigsXiGX ayIqvT2kaL!?;چQaqoJ$HYNOr*׼oS{SjƲ̎t IDATWX)H)+,%bb?^Hu)FUlc.Ʀkx42<6`H$ss^xU唍5U7wgpR44ș)8vq39ؠz`}]4T% nRJ'Mw&3_P\WBℂ̷ؑB|RjogZjWnQT6;V>ߘDdB1L (OJh4m֚Uk; OPX ,u:}uPq ZCX+!o(vUCǁTk҈Vði&eP^yS{F gJ^u\JK$A0 8et߷l8zMkzMǘai D &|=AbΦ䈐HDNL'ںj`s7GY;)qK@ I[2tJTc`1N_CeeQgOEV:Z_zw ~pG#3SSCL7ւ`y /VJ ѽ1ƒ7kh4eĥ[|ۚժܵZ!1iT "jfoIĶ- q}8jy sE@Km C=/WVC :“*uf(E0&tO)/i>5m' &0i$ LkjRAcD$Rb֯}>VL gljsQi7% X>jt)cm];2L)lʜXMb $BM&E 4ʠ06# T :qw8Xdcl~evi[XyVac4 >UlRFcT7ciHc=@ݔsy5;u",3Ldr IOPH)R 7%6tMԽlncdN+t. \:c{S"ebiYfƱ4*~hhy=A~&i+H Q$/~7t|Rwh4ĥ~Іz6+WyA"A#`0_B>KՈgO 1B$gUOxQCj8@?gQa=1tMV62U:Sjey"[ʊ|;􌸳:z9[AUc! U! ZEP6Bu/&x"ίH~.$/B!ay$CZB2n4?=}ߪc=ޣ'Co] OshÐ3vnu]MA}u?=h9t3A쬴8Gd.~6bc,7b!Aw/Nv,),F\\0q` jbcY`M-S*ܛXL  ]ǪV*+e⪅4Cd2(aj%]N+BI-PD lL|bWLȬڲ 7VǎJ, E ( i R1(u+z D@lg>cEˢ < YNhul5QA(w.ߵdu˱|51bf;hjTZht}nb*i3i~kD䷘eߏ^"|dfW7oUw)^:,gg)|d"J 2My=t;F<CukV,C[ {Wޓm\?[ "l̘4QK^ɁX1R0 r -g=A12n(z 8bdq-9a.i˥)ȏxC&R&$AD֗%XX/^w\ʹm{kw֯_D>7T֓RϏE xB<ЇiJ]N1/]?w޼2b.[NONLLGa*Vʾ>Pv<*9P; tyj*Nń"Ry''O[a!ٙa(iH봴Sv-*h4y%o]iTb.]q?>DCnQ%ICX ' w?X`R.FuI+}lA,YS{sƈ8-SWv]WfV];卛5ON3ObXC=eG ֜;|#isҢU=c̵M~mzrMG3dACIN?c8ղЎ|en۳fRz[r^p`&͐Q+jǬ1Y^^=oJghd*h)'q=yfgEXikΊ›]vEUG";n3Kh4yvl%mn0>/3Ύ;_ve0wpڶ!~LCL6G`43mANl,ˑMbr5 MOW+ʱƶuohW~T$ ؜n 1g]1G=PpШmm3ɕ@x.$#}i3_n \w; s+Ǝ4h-Ddk>?Q]xl(`I˘I*֔*T387(FPyN l#h8߻rT`_l9V&_ߐXޔM\?_z}.ͻ HHT)vO};H.X6tCӆ[+AarJXtaW ``=aGw<[\ùDDGfGlMdQtIgFg:G KE5̤"`̥BD>e&˔L4+=y`y՝_E{yz{u2Ր)]9xkqGAͯ<|mϚ h4˘Kxfm~ՌeY9wͻ,>W3ˮ$ٽ7X=P(as(CEڌ[W̳-?̚ |xeB9Uv*u cLtbw$f Rc s)B.I9tQ̑DWkL~;ӅIX7V^vSE-6җxGfU1̷^auNqƇ{|h4kyEs >Ym$D ~MOk ?|n'O> ֣(Oe(򢑞ԩp/9 emA汚fȼn)CBOLyrg 0L޸R̘+2\Y4HeQmnh~zq[>yqQGn̜3+etJ1W2 8Q/T` F\^hs{ :qEf࡯ZfBErBB TW?Dz!`ul͘%BAlRlI^{أ{N~nh {,5/6 L3œ&bXU:+ef\SlM6Of d2;ZQ98yu1Lе" ׼1 $[_g+yη h4kҺٺIN ߋHR/siƂ*7Tc/n_h1uC,UoxxjOөcLKyIɝc_}QJ!aj]o-HGO@h4 SZvp&Q6o]./?%b T|rἤswF0"ӍFv}3!TiEޛ"i`^ *)A =B#戄"ZPS-IZr̾'' pߐbw?S{}h4<[7 X*GB0n4WyM@BE\:P筨O}EN}9E1QGqtTp0݄8>~YUo'>Z c%R+Q9R MK8&e3#vuP}*;ߔ<9㧎m,KZh4<C 7Laڜ)$ЇjOo|Uv(8 `ypՂ>m<[uIjREN,U$O0`DƹqEDW &MifD4Oy%G_'r5N;;xp0}{j^Į2 X5E<)@zC'A~WQi>T4f!Xa{^ $8(Qr@ yoy< VZG*kcùp p4N+G[/STg+ζ;ˈOI[3U^tFPs1FQigYTTqTix2W.$bjD؁,5nM?Vʎ;Rg\r,Lut-yWM)1DUdhQi\Guq_Fsѣ%ƶ2~%Hj_/JnD nL/0&{/h0 b V@+*t5v.oCnm4<[ 93SaLx[憅V69~u*RSZZ0.G!THԪMA{uJ媜:##YխDںIIy5}dž8ؼ05Nc۶JICC*|_mCT{.[<;,umEph6JXwT*, loJi#FsI%ȶnFM|l)3ۺI4>}Ϣ%Hw[Ol~˜v+f'1^w4ݛAo,MmmĿ,+"Fln72|040?ET0g|oems޷xaKoK٥#ZG s*h4-x.QjRIT)Ι^(n؉dX3]|]~e{ߕatC}Yk3 kiږ3O,[zP)MFdЂdxlNY/D?*Ma+ |Xr8<*͡ھ=(ciͧ_| ZP5޻g[UՃh >[nb{ҫl5n\ZлTޅjGPO IU5R$Zb*odbfdtOjTڪ1r"wlW}Z#miGzSȍF\%݆`14YU픳[74!8z$*XW2IY0砚e2=[-KO3: g&d,bQ'y޷|SmTl[UA~dKv=h4@ K{0>rr&Q@XЁY@RFqDg: vgN0{yg/ԅx!S~(?(kZh4%Ȏ yqI#"TM?*\PjGBl5w`:(McOx/ŗwA r7x4Fhsɰu&`jGEy߉ 3jEV5U25tqCC9T ~'h8 =_}K8`džiE|lEP8/QgLk4KG s6 p#5dFѼȉ-bFݲh4#<MbllmXϔr2b˖  r=Q $ Ψء7aREo(GЉZ1>l*@Z| ]'d\yFl)c:&76-m;e|W;tʃP.Ϋɖxѿ,7~lƥ-I?l$cfdhOn(K-5B.66 i|Zk m,蜿_䀐نCQļ/GZ%x0iaw,7NOV*6͢$$^$`?roUk 5/ 7ye;-aH2cE>1[zg^X~틮?6k+::h.utACD'(.i,%aX*USӴ;Ұ01②^~gG.j>ě p=g66O;6z(Ζ^z*^qPTQI7dj:O?F[JO|T=?JM}YFrM.<Χ:;$W&@L`I{EHNŒƽM0j=ɫĩ5>0q0Μwt ~#ύp"(F}m^ %P~gFunOCn`Y-ͥh4$:uy9__‰BpQ$'O%WLEtmm<nĥ h"l qY8%Ww&u]p83s㳳?ո04l\O~a՟wڪJFʖ2LFl{_U#}E ZA*"eTF"0j\hLpEn/ywϑr$NQ6qF H)jH\+,>l57&Y;aF Q|zё3b2\l-|%SʴŭW=ݐ~lGǾES$l;N}`aWsZ7UouTL3 3^,[w?"3|Xn(i!i")A8(O _m 5&Z\  RT*k #8޵UO0TU);#H?3=rL;f"w Վ.AKptv/yR̕J%98ӉO j0ɖx?}ltTRGLew%d-lwdѵhLz0䎡~붟+'  gNcW{__7lC&QMͨ2 cʆAD̉"ID?0J T ~h4/Z\ ?S< 5m,K|5ʞ0(MzDt$<:?O>⟏KJSj鴯L1O~Uɏbk> ~W;AqT:0ͅDGx.>*-PrG0Ece- fʳN kw'Gdy,xWmU]I#̗ƁYK[)htʵh[ s,Rͭ]鶡q<>3ާf}>cĉOdt}&ʍ !yS?xsŏQf/#7'vt%f\rlADbDYDƊL< U@1-VUl+Q7JW8 *"0+Tsтps@+;+t؅)h"W-x[&[+͞'Loq[tdD~%hlT7V_p/}pa.]sS8@yU+/<MKMVFd-*5okG=/!lYٽ4>΃ ,vlt,'Tسǰ8TFFD׍1X @Lt qG#A, JQ%3$QJ$E%8 ΰXatKsЂUbn@Z :rd؉tث.09=[MVؐeW*>ls2X8IE0?)0EDe_y<W5бLe"\)܌q 2\q?$y>eCk{3o ˌ攻d8MA5ȟ;=vl#XJ)&8kr6Tms]!J.P/TCWL=v~cD:HTv~Ec +O;]㓏E\16qU<SΌx{o{Js2z(nlVű9r yyQ_L,=x^nm;#F||f-We_|v1 a!D2cUfH=%go,P~3Fnxc<~P>,i?^dҍŢmL^)_Kp)Ɔ:AJi_RExhfM70S}ȃ9^q(T i݋ '[)sd_ȍaso_Cbyr*# ܱb\-9췱t"T$A Y+_?q`}[͈_7]k-;wd'1OoWze^~b@3e\[MkmRZa0,IzKMt 9Vq FpJ-ŕ5,Tdx u>ZitJ -&א232Q9YSf [%]\ƽ5z뭟04Io1]Ȩ}' $E|$uuɑIJO4WeZb+uR2;~Luan)c3)ɯKZ(qi_MZSl /?N]ȭs5bĈo:WS^_yU#g7twK+#o7 C>zg `35mRם>"CNWݠ@)$*="2B,*%m6m%J 2.W(_#MW4µ=Z_J1e\3p2| wR3IJ$TH ڃ+PD:IdG)P1yχ#%%z@/׳yg2烋*0{ ǻKUr1#0jlQV6ֈ>0S׶|+w"B; rrSQH}K\#7s60;a wY@SH`BʑRqg,}Exu4g@ҁ='rs[Ӝ,zygR[shx֤]]+dAF q=chC }aZmM)WĒA^#REߎ RN#KZNbWZlbC n-Z+|:6 hͨza+#SOfm!>Q+/ #Lp:W;_*e](B 򑮋Hh.RuJ`*ФDO^/mQ'Js),zz]!b\0xĵhvx}pu$uĈP> FJn_^Q,H?ʿ޻].tΫR>3NeV۟GIYx@8KR"J.LdT|EF@͓,kL`mɋ4 43=Z3 HWn$vn綈o[{}c^710$Bߌy 3G0=PjqD5MWtoaHsfdURJ}"k"ψ;髻DRJyJz #:QEN+V/q:REEhn ! tHE̾W<mC;VQX8RYXV:˿f5=(Yu[GpK\Ko"=S3cf/֍A8g6&*Oc//mK&ǥSu>1TI)B"|B` SFX]VNP~L.KR\8Fg7 )]Kx*VhwJKncxy}hV,j]+BML@v >Ū"K) 4>ꠟ:҅%ҳľ N-j.٦饞rǷ977/M!~&4RuF|1<_}'/I:!Kd%Yht$=uHJhR@)4R;n>҈v (lҔIAG*'J  4nRضMf;);G9k`؎ek<7u^Ԉ檤W1;p)?qAtk`[Gܺs_<0~=6wCa~fH݈HX)f&vN6y#ZzAT4F{:lo,<+"{+b/fbKgGDDwm`3P$jO IDAT9ꔖP\К'a}6Ot3Z{lZ{b`}R3KG>gX^wD]UHȴ_s[Bݿ}p|RZ|TuG|0Hq}($)5 @F!Eٔi" DFI6 q7cK啹Gp_&izU\__?wXN ۗ`gOq$ Q{ $bqN}ҿ3|}&:|{'N8?˝ߴh7/? IU >In*Q1I0O xEt`E~Ö% man&2 ZF?iaj "~i U`ucC9,?X7M!NI* c gA!Va=0[QjSzxܐ~!w<owL]rs:*O+ \ן;G4rF 8 WS;5o`?]+ܬg'ۋ<#H kJ9 `c֣ebIM4F_b`I ۱}{nDv=2=Mg&C+$2;tf"] F :ښKA > rA|*Q%D0A>B5 ڎg)9ODf.m?s3/(|aoY=fWV/7Jٿ|{Vet7f$x^L뎔 @ǠE!WV0ReR1?iVW}uqN+Ey3~Fj^]VNm aD-duo?m41pß0XC[.7N~-o%#[/\Pl fU7^*D8[.sW!*lӜXR1㈶Kszz&64{Jx"jiX_@ߚ:Far-dX (nB}H P!6hsϳd "7n!fV Q (anO"YE[>DZ:h91IjA* BX/vJ_g*zqPRZccÂݽ˻S?X :G~=v^2BBQ]ͥ=YH]+kUmȤ׌wdo;OW|%{ u1\dԗ}RI,H/O2VR P /; K$^,x A*V;Y+skV[Lޤ#.ηekg߸_W>{}D/֏Zt{`r]go98J kٱ]Eii aiu47L_mFq^n$ ?FT܄#`s`y-,bG1A8a")!ŵИž!ZEnXJ"®6? 9, VH +{[{q4Ӟ-""oB ➍Q'*L]>f>ґVKӍRд?.~Gm7Udz[߾8;6k@"  ^J9|q19 -n)tHDCLR{yADο __SH=I:iQvҷN- @КgrsFbC۬ߺ3\?E8;;K_$J$7Vwӊf|ƗLJ> ޛV'l+Zgcp@t8_ #x,SÏBmzDmʍPBvf.n# "!*AƘn&.'ï`l#0{"zi|=:̬#}䓐qQe`c";Gi>}CǗbD5Bvɋ~z3wF1;?v㷺_;P[{bu!RetMϵDx6HAR#XkX8BDH)$Zbe0 VP%hg;/BuRkcoxAi~cKs-'ݙ,Jӿr0N*ͻ4 .?Ag\}ۓUFkìSKM#]11[8+1ȣ"3Dfat3L??& G ݇| h1H)P&*^0H\↋0ѻ` ? iTd.bC9("70b րX0ߊAcX}ƥqx 2-51XiJtZ N/n 3:s]{buٝ:b5 kꓨdR&2mTv 4-Wbw@ ^te'A(«7&R!5QHV LE6)3V\'i<^}rr m"]EnP캐TXt&`lyfqcLm!xz qӧщl lb1Y]Bvt[d`myۍ2"F m??zh|G϶lQ)z:Pxo3zx1#smHӕ͕J }DB0"4Rb|Q=Nl 䒴AvH*3i0Tc* ?E;|C{ei߫F%_wHOE]S[6o⭍}B^'4Y:G=M,1$)6 0|GX VρP: GCHck]B1ƳMI]bPF0@ 6o>,<3"vk8M4[Ih`T8) , 4dm r-Z``j~İ.G^&$VϠXoQ[1$79^_sg ̫~3X5~o[iTjhϗ#"7!P0J'e(wqcE*.(d"[GxRb^/B`K%m v qp%+!6B4[vyG $Y`ğ4bNJc7l?W??\؀ϣdѵ*cY8ON9] qGn"6[*S*f# qr3+Cg:T 9`h^O$.3pIGēHa+w aXoC ˄qHػ$i^% @8k!6 < TdGA} \s>.*R1<8)M(4^zO-qOvgo役ߝع#bv 5?]}KP/{BG|3<';IW"<Q:]3~)@a("k)#DRٲCG " Ƌ*nr&حI:ArE3А߻NbNJ?|S&<io߷{ŏ'T6lAy/͎~ GYx>C0F?arpFB:pMO }fC l魗4:io~N.Xqfc/Gԡ eHC&8c-tbAq *Zndl4SZ_3H/#pz[PED 㫈a|, Z/o&Πm"t)cbEj,°hU򺢕ߠt9EM)\zQjTx*l 75oWY=1]_mMzU|Гg~Z8zK(iR<{iF|1<צUKq5/e UtaHh# c_YALder-5r'8ij4C~C$Z5K5\/S)@Y4M~oq7ʩ17Bh)NG$kAcp .9F*r+ 'oYuDݰ }k7){v5t`ƨ0& 2ۂKGˆ\cg %fb*^|̟KI3߅ś$ wCytjywr{W%^!ЛHD!A^ ˃[Ė+٨Ex~ʁ,R1DmG-R=[ ]cw3s ۓO w'L߶by1_?.=C\iϝ}bewqмe|cH~ 2`|##sm$Չ_7Ǐ<)+\k@4FR($Hg j(lEe_+Jb4)cuVA+2QN1>ڹk:ynΨpY:e^#S<{Pᆏ sd>Esw }Jn <^Ξ{>L6*:s FRZ6!%!Нl ]}(@X]BA"nt9~LW%0N@9Dޅ\+6ppOt(62Ҷ!,f/F,qYԩдr59 PQ< {Ɨ:QέOҾrf'AꄁiZ0Csh)+"e,B{uo:6<1\3Zbie+Ad>LdA1%b$ո# ksc|;_W&~vUur9o Áҵ|?8hR$]T͝_KyQ IK]>ؑ23; /"~Gé~y'.I7Ѣ­LH.vP!އƟ]?|F|%!2d` ´GWɵ$Z Bֈ-Y]4wNuIDs9 +JQ]KQDPWc`18 Mi 8$(- =1$F&dhYҫ ]W(uOL3sA0 -$\K9>uk=.~@.FxyBCDei[igrݧWU?R7|o_PmP~cvG/ .$ߍϵ!̂䃰M۷|0RfVZn;i;EԘnj=$\QWzh*BQR`_T:稴:l4L׏XJ 6IgL K#RyL''x{RE|ϧ? ~yF@nj?@5O$6F@^s|27nSGZsF_D}8_k4ܥa &zeYt朔+6`[15%h!݃2x%r`nZ!-@!!:`%#=JG+]I=K0)h"4_#R6q_'[L2D&"0'hu5l'IhvaZ>~FA*zӠ$  @eU(8IKI}"R|%](+u g@(/V R lCHG9gG(#`8PJ8{ƲӳqXXT IDATr5Uݡ;Y8oX ôʷowķ66I_3 v<9x-]J3fz=ia)eHD̕Y\i* صk%4M'a|Z ~D0ʮM=p"Ȩ%E~NJ(Sow@- S0&$7o]?5jas ɳyj'Op X@xb nZӍ)7Bue%˩Y~i73NO 7jsf]6)2}8ׁt }r 2(O)8PG I7u݇Tbczmh!%en jɖ~ç1ȓwg@eQaH0MIu`\Xi,tBmt#T 5|%Uˣϟ ؼ5B -=R0O84Dv觰#7 Ic9p&[V;9cURza&v4sۆAC/̲+ڱ S =*^똝ɬ=&EH fo6ueE(S[#Ow#}'啔%rHDā 7LR#AdbD0mT<Ml ⨻/5 C|/" ki̓BO1kkH#@cz )М}0kL<q=&25sFa&j5IAAp} jS?7 ;%]cҖohrsn.o8\ f_.߾iM(l5l`Ȁ>j-Z#56jl L8!9E4s L-&= FkE ӈqmC,(@ i4G]"$ 0]8{"I y$4-n^7A[{7yDܻ\Ix.R2a0PL0-"ŮӚ KmVJPI"C튉Y:r D, b3 zŋmfө8߲Yy&8$2J>x_\\I3aDțfy7_k(|4-V맮F?3'yvޞy7rɟ7/[Q5>P؉U,l]d&Jbsś9Cc;ͶL~FhNC-1jȢnoʼneEj u\$vMn ǣd.U "o$> -7(,hyXI`FNcPB׆\k' oxR=:Jz`&Ѥ,BzJ 4ɐ5J$Nl~qƶ`"< }c?TVam} %HS݃(ΰnl*m^еX .gCVHE}(2ڤC (xWgs-2D=R./%* p-sq{B/IEtF5sO^/]+W֬L`;7?{#u}9]~p8ERI^d[ ZeD&6D"d76u6c#Q\Kk[%LLOO]uqڦD uVͭ{O=>Oq=O >9kxL6),XkI bc LFJqRH \XI`Rvjl_'#4e$GQNlߓnkiLmnLДzAԦ:Mwj"'5 N,qԻe՛9Cy>r%3@ufqX^}__]?%ZumT/խ?f7ϾWWD\z;ٸOkDTfL&b aƮs%_v8g<&2"! rcB%3tН~ &Bvژia~3yh``$v{8M*8a~Õyؘ%i!HasFK` &)y*{e :[`s4 2K 6CdU)Z}dmВ8mrɰf̊b..16Yʓm5Mg!fl5=; ROk'{i% 8Bi?i)a=q=YJ%KQAH9|Z(+c""¢LyyAK6;){a!Y92|s׮ů|g~nr5F@*|:85m;'h_8@׫ceWn~sT<S&7~.KK޵ɴھ{3 6n$Ճes4۝M.t b\G^AvQ@ )Hgqބ6sıyDԍ ‰\H.h R0 l:M ;yNdv63tF@DRZG(*5e  7/Nhߟ&]1' ~?p/NowUst3TB`wT[n{.K/ɗN/86=  `aO޹9&Mdl1Mmk^]DKj$L-tm hyZf cj 4ӲT?E<^u$` v.DPWofY‘K؅KgTHtPlq`/)CZ<_lLܢ:P!čƨf3=ʁ6!ȇ!^%AI C:((rCpR]2FK !=_LfP3ZMq4E!]i:qTeP}l^V5`GV W1 ]QKKD V`R=$;qNZ$2rGSG#cY;QscW\-O?g?S{ PFXva_LcBmJqAM@"$nXT9`KHWy=υtI|.K3u#cRUx@ 0,[0uNoM|n%{_(Eև?zح?7oڵ+T_?`y8Sed_7wZ>VY;uMzGW'1.^V*yOb;ߊW)娖luQȼ [Ca5D[+\vB'eGm" OP8A,mWo-Kg˖_ cxNP[HqrW0V(6! E0b-MN<ys1-c]ĹIz̗e/\Zäd-:  !TAcHpC<5$!to$ -cQG81i*ibkpl hF8irP>վF>/j&qmr|@qr3O9C\D3c&ndH*cLG➏%!˵^-Zs?칻'qIOް瑳R<91!/*)?Wmc%3BpVDY0Ke HYkU xDC$^| /1nKaln< LJ.9w{t_|c'lxl`~@sy3,oۥ;ɳoʑ=Wctm8_V^vbXT̉{7~K-ʐP8^FTUm +\3`ѽLKu iH;KB9@@U2@oqHI]@Ti'(.}LKҡzpĸB1*1t::IBh*XŎEl )X^:_<W0يhJcZ&D *x%h)!;8H}/1@1*'e]pD$}4/+ ЃEd֠bv1ǓN&Thm1&S8AkY#9f'%!i|Ylp1r]6fRMDJ4'/RP0r#0 &(2y`,pKh$39qjv/v6S>\ Kx xz4tmrD=xŷWܪvpԩx+jexlj4kl\}rXߥrk:Hvsy<2n"8^Ɋy#gaǏ.~|U ̘`i'fIKtzM+1535&b4#,ͰG $!\q .%k )h>jj@hIs6,mNf*owXil3Q9o%$ z2!q7d lXd5.9 2Q@+ F?X4>FxΘLg7VCi_'o(c , KXoURA jEBX!+^֨7Y#TLsẈꑿab$FJ랪&̲0=Z_ W̡ߢ$<1Mۘd;>>N?tlC?og_/]~Dg=?5M/ /l[=ug?O;C)^)ԝE]{w m̋HW mM\?'OAq@v~Lq\_AO?đJ0ܤ DZ-Q0!:A9 v`j@qqT IeʛQ2>Onj}2~dI>S I`mpӟZ`vgKZX$Hf^Bp>f)|Q#pCU鍮BNvY@ésܓ֩d[X1{rH#cPd60jU8G9HAY;S%ĆLd j(;W?]z-~Ç0PgfJݽ%1Ultl 'DuS4 n>%87/)UKdI  . MݣYsYL8T_*|Ћsnrm o曟W|G?vXģ~܇z A@[9BV,wޕEzWwlk^nHtk嫵[RAB9j@xـ4H1M[jH F,s狻 fpҌqEF2>A?י]& y0FNM2va<4:^7a nmG1tVMc(;E%͐KkxÜ>". ;_ydLᡅL|=^> `vgc(Ř^uioL(}L_t SXa ) [#RNEh-|q '("(ؕLmR1Iƞ4LFnܡmB6#l ls0 w- ˍyZv-E O- 򹅒) n).P{ⵉ7,XJ r?zJ$/UVKu}{-璖\輰wyv'IT!rmK <$;f3eF563X њY3˚oӣ{ GL[_&_=Ϳ6P &aם Uuv?݊as'gl:Z1^|{ {0|?syx GfVz׭rS0Y\06c%PX2IR*jYӫP\P0ۧA IDATlܼwLZ6֦z Qߙ}?|tQѯ|DŽW\Um(ygi5owhcna噥OJڊ˽C kO"& jh/Nh;*38I르E{vv{Pñ@6>g.cTbU0g$SvU4^M*53&x#b{^ez쀗c/݊cBbfh a` ,^AlA?w7RQJ6 n tץ1ثS:2f]#P/ 4%[pb r TΠ 0UTPF)R1CZ4pY q b9z5ߌv3*- 𩧂C(${p!^15 W~xrQ}.57xbň#VxL=rLxL^yGi0/>ӵ;[|*.o5No]|;wkOn%k[U{Iٸl57DJ<*B_'JV44j\gyI{idӽ}A{Lp}#gͪ'o|GG?׊яf|}ӉM ~ӝ>џrk,e^aѯ|;C5@AP}tvNffgkT䅗9O=Kf<#Ye܋SWQ SP` 2f7f,]bAv+_/:= % 3dznƴ݄%prFP~';w!(Li{<=n >]R~߾ kp-@&;᭵te]@s7y-rb{km4UNO~b;Z <qk7F^f8'T2&躸2:7]+Qlء1B?Co#|G? ye''LK]̃ӮܡG N~_Lw~h 02'p.4we*m=?y_n54& ma@N&h im]S'؋%ETSʮ`~TpSda駸[Y>"c@Uvii1 "mHf5xk0"D*nujDZ`#4I ПF,᝺:"J!He5A:ģJ-Tp IAz슘aX`V`* qwYW۬W<&"fD-W]L|: 99:nX FK_WTFqz{H$=16~~Ç^)vع$XsSgWdTs%4HPh Hyˏ[@qkViLŻ}c=^|_nԦN5%j5x@ )3G*ԝH-D15r>Q+SFi-RP"X{/TgՁ?o^x2;$gr-%فR'>o_Z]]_w:g6?YIC/o~3M۫ͯ?6h*?F^Z"jf2;]= qD{?yl:trP/ i];Ȏh&n"stQS1rژ&]yh7QESçE#)1QŔ~Y[7cB]RxoS@_-R?#L;,,. 8b1MC6Ԉ{kL!s2M^l86p8>}Bcx tRR+b8./~Z Ҥ|Okm3W>7Uo[`EuFin ~G*\16`T]Ln}Y!Dcm3@ ͠+D'Pg ɓbս e4ry7M|7 }L^N%w!͠> c𲡼L(1vO|cWhW/?"uGoEYV"6ͱVnE۞Osj%Y~Y ZR)[Xr/2,nHyIn>fo2OCQyVxf42oI$hG Ы!ϼгL?~;%?)ncJ/3$$ t.u)EF,tXA:ah3Lj$ H'DII7,De\ČEGYx"3 ^tw Ug}K5l~|x]Ba=+w#$a~6%Z/,%$pn|FȵiHeE*'zlIj]ǕNe?SGe|\)l+Y8}Fb٪̼ie~[za<{fviȠu]|sWƏ5#^EmgiٟZK1Ex[V䵚]]Ȳ7#eݼ؟{p>aRx %"$#M6'y9a3mfZH3/BQ|~nK;aa"747 γܟ"^'0˕#:13Gw{ ny":RtoB i3{Ҡ` FOHQ+/o1J?wEء}aUŨh6 ʑP |\c!1\ qgHH@yƒd!"+op‰K/nyY^be\\,;؂ʤX,yp&l9+h~34M!E@h0xƠ<쀽ۖ^&7nTSCA9u(Y(g>By=,1uMxN9Txq1dgʹ7O8d ж #T'm Ѷ lᏋ>*\9YN*=ryӸ8r+%Aӝ"j'Lg2Žu Λ ɱ_N/=9;٢?oS/?v~A&yMl!? y~hz7^/w*$;Lov[~',MGݼ^+jcb4e1IlE(25Z-)+(STj>@:XQ>/|?5!)Ghl8u"Z1M3Gys DZ5fqW$o[<[bfҼMz;MҴ\#Þ| ᯞB$ Rk\͗UŻk d6uK6j{G]0Ya"81#&,ěd(Mw}Clv0řԈHŤo2 S^w] MRmؠ #zۋ!ndtBZrR:,E8hLz;4)tQEG79%*\1Zr 9Ͻl4/ tQ"m&ˀ߼oL!dž7١h N2% 0oɋtlCR$7DˮE L 5NSEC(WW$&P_5b' #՟32̏U{q KճjJTU  -[}Gga,\U4OW~oޮ^=_>хAks^Ez|!(eUA.O@Q*+{o0p/-#~hiQEl&(u2 MEshTjQpwm4>'I^&@}TlJ+K -FA[X`NȒ6D>2rɫ9er1,'wt}t2UFሉ,),IR!bm {$;Bm4:_Pu=Ջ K[ěťUT*ˏ43 $aJ6 k3PHevɲ4K 26"\B[P5R10vbbp  $~T<^;cW(r\laѠϒd c-pR"+`DeWP0q 1 y5oDb3:`}ӭЁLw@aߣ:+āq.:`p> >Gԇ.'?1IJk3B$Zhs\r9u\AO0)-ʲJpB+]` l^DEnA^2T+2U_Sa+ / RkY L4_|z'1aHR*vl&"LJ <8c}r;b6'W~S?Ǯ]xs~go+؃?o>Nu]^ &vAEIFQ(%io'|doz4UڵӅ6iXew&>Da#CF9![G"AIj a0@X _FsDDɍ(4D2x(&eoQekDỉ%1֫ r4EuЃF.4Vf\"IJoiiRX9i⬽1XW m- =Rj҄Cۄ.ӫgqwW0&} |hlP 1Y /ZTY*X@k~Qxʟq8[/M"z?hVXre1N X "4c(slVh ?{o+Yz,Wv^{&g!C$EQ")ZFĎD@bqqQ(AL0(h#%Jpgzz[{o>}{CKr,:翄3^HKߤI#mmfѵ!!܅jYj .@{@ EpR&e5'M{s%|J g!rzyᦇØ^K'^fj@VtJc̽r*|CZh^'Ņad3iQ̒a-KNHj5PF_\XK3ڝBJd쩒-Akư}OƟ oݑ lS&ç(8"_%* P`@)hdZRN΂4x<!3IϨ]0emLke! tO@ZY|(%QYFn`)T@=G|u**X3] "RC],ِ'YĒ$EZ^cZ~%ζR &)V*@{O"T҂w;|S"WcУ*=6' Գ50|@dw=ׯQݼ¦δ ' mG3 $ 䐺h"8&bFf.F-\ˋGvgQғccD0;n*Y?5aˬNrx6×_WlɎߴ'z+b4?BڎޙCzlsKKRRւ>l45ah2o92\F!Tjñd5׀Feu9G1z69E'=Vآ߮^nљh_WiƯ}q{k9?PXggyAa=x/C  AdX#.~eø#^y-:jC#Q * RTP !#9ZD.Pb:p#Q ʙ9DcX$= XŌe"v̔@iKɡ7f Kʒ-8ڣJ6PJbj ɲFjZǺvӪ$O={}G}`0mRfto*6o/ԗ*l !QZ*)As BHsguȉQqHciRFMaMy+mk^+Im;;i_2Tp>Fbն/ٳ_c\Rť߿6{>P_)AC6VKs**e.?L?ӽWwoe2X?vn °I^xjXܤ&e@=,x7 cxZkcK$),Ale|H8@ ڈB֨i2nJIx1ܫ$.i&i-bԝR6MB{g/c:,K\-F=C|L&疑Jpy OZ ShHn. E*9Ia2Eo"$EyNZ8CRz V ˄jzk# W{9?z_3UPG-78YJ>/^=vSd6Oy;9#Vs5TB,m,٬l6R%Y&BS][`C 0GeXJv4T[jL0sSV'L<+\GBbra݌P\iyUzgA|&Wof0ҳ[ӭөZ=mo?*g5<Ɖ91<ߟİ93P-,$4}0YςnѺS E˥UN;4&{xN@ysb`@/F+Ia,oZIūĮQh?S &ǓM@GHokOS(`أ5z#L,IGTLxM-Ť+{CMaJRRi\R\IKFSA)%+"UG:5i0 "6kxyEf.e5AM$rJ0,Ko&\iLۤj(;^eL",9&, [u5]Q1ZlSZAu oϟ1{іԳ VoCLܻ<~~{ه, )jI\9~{&SkQBTKW. {}@-uhn81Z;wZtOjt¦kMa2m pIQv"΅ՖcDLǓHyVmM>߼P?>yL(?9n\g5APk{ikI)ĐqTawK#Q{WoyiتCP!DzsAe7 C>?oA}s?.'|gyzcE%ϟ^Qx kтL/?XgiGZ7QVG}?FW$T[~ QZH@@A83Lght_@)ArqVr)XUhZ{F4DuUrFB@pae($uy\f8! j1.'dv63 PA9C:ᲘV @ [eԪYVKWPR"Yx8J2 (0RcqXBH:m !T1!wbh[GŗNh,NC0sA&q߂STĔ T^Ȩj ySIzg?qoa>ov<3e=O>@?vS k<Ë3T-F|ˌ,PBRC/h9IZH!( ۤfZt<ךE%JW..ESO\zv1,sN{l{\;w0uNa3oP$i_[tn@uiyŸ.NQ?n{vݽ[gVGDc>Α*gc>cTgUTZ_خR}-֨={<ݾR<^{$( AV%Lw"XV :ݛG9WcQ%9?}EuctT̏+%@ZP0P'I%N$6 C-1r@~a⒭A 4My#dN{9Xס~ !Ý`,vlO4:;q[]k]cyI=:N Z$'85cQDdbi'eӉRVb09GQ|8"4qB0sC<(DZHKR}a%ī #]{;$4q_S DTY^H)$L\΂H3KOK<yqߪHb0,b@Z⅒(#S#4}>\̑nchѾ`ozZw(@> GS~}Go=g qT^<æZ)Ν%XU qL(%&&8-"=F& GH!&qQk7O *CC'ܽ-ߛ+K4њE97K ^c}i}֏&'l__GJ{T_zyK7Qo;Uɶfqȃ$Dqs:Bzfi8/ZuF~}sW~sOoG3QPTgQf TUWȣZ[Q)_v}ą6K/||$Up' nL=|nBci?VfWZ?_%҉l-yR:@;a5ܫ#AQ`QhY~#>.io;>(VKP)]{ȀeF*ώ R2khcV1ށa=CSCWokgN5+;Ia4[& ~͕W4 d[t2V`,]j(^ƉQ ;l" aSZNDS,|ABʟRX (TDʑuϑ:C*$5R"Pz +hj*\#FgG&"R#ʑ/BȄ("M9jr1 42Cԇ9Nhvtmʰa2Z9}Ɣ[U/[I0G˒`r#ٷɭu_e)@yŧπgt{Pz-v+lI!V?,77r}B:4LߘZYBVJ8)f-\aR~]BH`49nRJ!Ed6G9LMf )$NP <,9Vy̒L 'o6xA%IK4^o !,EQ?nDGV<R/5,wGwƳh֨d1i\gl t<h_ç׼N,txl4Ks$;V"./ltvo<6.F+'Ǟ6zOti~ml#üM>J{j"[^F'F))'a#syDVLI\Qs:eSM]YIr*S<ޘv2 2z6)ty.Ns6f=uՒTDWy$L-OBmHGjv֧15\uCtJq㵈<ۗQZ_0]3cUp3 1ɰD*XSD͔8:\U!oz 0'X&9n*isʚE[D^ҊЩ,f9eA3r>u!IԔD y9K5lQ f9NxVXR6,X?Q[P*)5P9!Sf6l?REG#uxǷQmQo^E5q/?vy|Pz\VFy#OwΌgJyk6ZM!K =Ojap$JTeHXrYIc(mI(mEQX<)rtᐽфak5.LN!Q&no=iaRٮCeͯY}`w3}ʝ;GpeZP7^BFq-8яwj !u3$N(,3<)p^dըɅn.W矸جy,NY*g;z{v>QCཇ4Ͽ䥃O7iU 1_m?=m}u'wzZ R:GXc2A3OΒƆ8q|˽ ~p얬T\v<gQiQWkB\&JNqؙīkI̩x>I`WѴѨ WewƮ@4%-URQW1pxn<+Cj<$/h)ƧrD#-t!jAk :c{J1R*e}*f=gWM9{˸P6G(ˌӢYx<_y/R=W敚OeYM,Ke1' +zx7>ѸJ}k9GֿG@LP N~3_x‹=z[?^(͊_[ }0}{z}N }J~8v2f8QkZ.cuU-R;g(JDݐQ_BicDQd+xJjsҖZ_\00aeXjЩ+?s΁8@MMW <׺\=t߳q7 -;rkw6s^=w{?`d:|ߥ WvoaM ?|r-ϋ|pIơv˽Ob\h5N()WҬ,$+vۥ'-2`& 1RЩ{ҹQ t@ON9LNufg1*v{}W}'v7[ES@H8WG X/ IDAT|CN߫FOn.G%Ɵ;S~#s7RoNdAxEHF+\u *TӖP Ŵ)p2!⬐B퀲?ÞL15vwr۫tMm35V]S!eL2挠=7WD`R18}~NmF$1Yt| d `?gR+>QwX Sz\+3nu&քlZB/ĶM5mIYD'X@9C`;Pn=kAZo8sS 5 r JC[rmE<}qaL`2X|=Jcmb7.{~h?4 ע^^i+7?ۨ]?Fuʯz¹%O:aϮ/ /0',Y?Ygt9u9W6l ,.77La,>Z[hL;T-ُ揑 D/~'O?K_~7smFur}?Kg~jc/l۵fn{=5VQ(/Y6< .  ҕY-D)`^ݏhIQ94 53ys')dq*sP1#W{7XOZ_w- ^eqgO%(zkLS%O\ i* {k?Gw&KLrfc-&K__#kD;e^)܌r$D ?G,.$'ܒHhn&#&Qc8z 742?IX<Ҽ0Sz951F ˶1ǟQ> th"pJvΫAI bk / W5Q>V>`޳$QaWE; rZb#ɽq7q()$ў3BR+D(H0r,)MsPNW*+(jm#Swv?J\UD{5"cs 1!Oo{?7|*[ޥiZj`tzvLr MĠxw} ,SYF(&tq)$I^)?1VFY$Ai1YZ2I:xB؂z"LPMK2# BNtZɂaܗᙳ'.qM݋A~-xܫdFo߳֗rF`?cj[‰hިk08Xojod6&"/] { 9Nx:jPiO`B=S,NKֿjשNa4i"he-_,ƝњߗI[>|tꚪKr?<ة/_||5OOv{F6b,>[Ϧ~QZTb*#c [;Ԋ>""{[# 4sMEd*:-*;S!W;L(`J[H^AR#"@g_lR1-3acBFQł hV7f:K͌."Xb&yecDLJk B^znBYn!̚) OLyvrXHv6#˕6f,(& 1Eò2`VqZh-^O 1)=9D1y5_0$6'w@d%NiLPs M҈tH-mqK/eZ\,cuQfvw3Y[^O(:61Nb x^T3z?xvҬkP:$gVfn}7"\bal15mb8#~=b,'] }$B:b8Q^ѷiK'OY5ȶQ e6E(ٞu4DK=~hP>3k/}8݈67֒D5o}/(_~Jk_Ȕ}$1ŽgaxW`2?|J-? %a`wPRDk7\w!$g)avSa֨]T>>Mpp@`O|3|5Jzz6O__Y{+3g'3e_R%xmjȩiԴ\)J!Yw f"ɜ Qs"19Z)G.ƞdȟZQ6oe)X,3eΤ%{iR J5:cؚ4sg9i ,24و}Ē8Q쟔6Or VN,/ N뚝٫NeF;1qgBz9ceũ3_s%o 4g')xHP+% hr4]ܰŹ-On,>cxҴet҂`-^2G*eθ ;j֧RzS 6Cܢ7\JQO=m]xĿM5.-|9]}zP^{F[?}SXV-<ެU5j?rq^~~߹+,zlǖA{œq<^LPqNz/6FhqsĥS'DjFjo4e۱+vQ /-$O&x`<%N,J]$f9gV${='^oo}q< _2+s]>u Cּkuye ZkX8AA>wwCa`OGyѵ %mp0}w>*WOݵ>n[>gG_X0^ճ|m*S OnmQbݛ~?"CՍzlM!TSϗʄvb#gChd#%*ѸgK( s_G}8TǴZ~.;\#TArƳU#0@rfc8YeqGEp^B9KR+:o=E}& ^9x8h6B~ٙ;:lOcemfc=m}xvZILdV1[n07!+]y?Z%t)^1i`V//M' )YB:K6!5EJ69e|lK[{,t]" BY{:lӞ\s՝mvLII)0[, @^%S#zH"%")bIkμyا/)F #H~@T9Xo-G@a_#C*- =#UfY c&=moiVa"49r?섩:;V@7LBQ%U.#}lj<()xB !|U !.=ƜqPhiȜ5B9ƥR_cE$-9&1e{a\x_%,R 8GVm1~'Mz:_E" w_?G"hqve4V<}ts%wfq\^7/<&kU/=wNZbݎ#*H,pZ9K`ejj%! g<=;oN& ;4%*aChYSbghWt0Ba`l ;ght'۝?{Sͤ7`{÷n y/76%gG/bӽ3'22,>S$ tvo NpC/Ђu[W*5G3ݎ~nўçp;8_OJh#^W?nx| /o]$ƍ4ݦ>\܋h߯mdÒfR5O|5FQ&=sҢ;ݫlw/pje) ~Kpes*2UzRq 5xt{N~08PO D 4 I&[A A(lLcӑ5zbx#ߕ$X->*10@A.qWQL]}N$W5`}`GAjjpÑ&H+`Ć`!_ԘˈO-1C5c.\yI/lde5'g $, ?;ѬLI+Ԛ:b|/vUKN ńԺ!nE+.[_x*@~[ҝ粆W9?͗PxY5x}͑_"ngyS_7/~;{?|ӏYVKfY!NRb#h. 8yU3[A# YɻgU|~5(V3 >+A<}݌-5Yw[~oo?:dZ{ ~w;d9v?FI֯jc?:O| {E\Gϊ\z:jÐH)GX9hn:YQcGu=b9^ֵ<)}OAݵ ~!WRx_C9Zb8l$|0w~-_0WO?ޱ-Eh_~S?x o,9p{Hpu[ʲ/ ]3%c2{v 0gǬFLz>'M}Jn?}b@6) ^xHa2s+C"-F_bzmUo;*oJ?ܭB۫)UW 5pԁR 9ie(E[TD<I)ϑF=`=K!ΰٟ`7Ƥw 1a ۄ&0CG QB* vW@JRa/10w5T1kZc-qCGṅ9D°󴇁%1FǢRx琂^+pnD5L|}CNA1X51)xC>UxNQ$ NEe%!RAot1jʻ8΢ӌ0Ӆ̩fYwD7FU^k:\)g4QPʽe8栤4 uʴ AM=b\˪O{aJX 'Џ&=Kp~fn<UҢ?v3~??gYqmg׏sr0PמVG5zRô&$~Ɠ^fn>R'M{tR<.^ϯ&RhcpH٭-aMU* f|p2==eƹ@j;wo~LFRJ5 #%*1gVDA] 81f7]Uu3H;x9WyV/|S_{?At{dp{u|W~P?}__R7ſ˯^\A"at +p & 1>v&gѫ5~hctg_/t` rv !;PCfBr_ʸXRD % 3+ycWK_{IXQ188ԗ~ڶ`+_'ݴCq֔3mY z)xJ× XkAA850BAPR( e 4E/`)h8a`pQ0_1 PkPIckP< ,qCRdS Qub,n k=@`˭!ݮߎH*,; P}cર0 ݿP#:l=jS-̺aS)C} m/['{1iwr??:Xg mV>q~:0ku!3 H㐪N P,kK)뻾mh14JmV H ՇIW/AX}/*OeLBbo}GiqIN+ߏ Gdgi$hDž&N.c1S% .ఁvhׯ˼eqt^VX's֖ {ςb5궩]5cѡL ~d?i<>@Ǔt9Տlof'wWW>RѥfEfBW_৞xЋOxc/2Jw( 6]G$Cj_Ꭶsl[c7FzB%4]miG}+F.w\E]lAOo}&Zߧ~N͑p/a`S\2Ɵ??$O6_{<~>wbHz/n&:apHw| -G꽱D-N e۾Gՠcl :J()%,kut{]qJJB3(}/t./,R8G=~0gw "Q`<{I7u[ :*uZNѮuZ S%P7? 5gvj}4*<.^-W <0]) " e# :>b]0'7S@%@ f` uqƁuA5Pi 3rhCuېhݝ9oE.ƴ cć i( *71 26L٠JNf9Nێ0.ȶSh5J8 B#)S2CDq;`aP]D- HI`++g1; ޭ" ѣpNaĐ$@10L98&ܭ3p]FG'^M<٢dZb: =DbQ ċ),(:G{ 9'5{pxxB?Pm n [f?))Μhp68,4>;X2ި@YIl/8LȐ .,`SIzT|G we74|[iըMsvHRn\Kx'ky"+9+v^Z<2 4n4˽IVNm g /0ʲAo4m|A9՜ΥCDhExq0>'-O\J.7? d ܏Q_×i RW6zR?.~ZtxJhgt7k7MT>䰼U͂ eHraXf(5/p]Jw<^V*Ez>:>evlbMWF+ ҢՍp:s֙sal4u%,dE)vXw7-v(kk%T+ݭc4Zx5&pHT$^ji0I1_Nk9XYߩ_oݎ1D=>7OM.ōaA{7+LDs'YI%6݋Wh{1R1F8Zsk,aMeei(kԺ?:b^)y $TyLYW!_ bakN'3wuf\6À8jjl }&;f"ݢR 8[ Gh_@Owa8E\L!8VVLa8\AdXv GvR"Xi R!ڂu[rBhR08m`Px:Σൄ|xE <D+ìZa.h(i%VRf2{),(t}j~vL{^cѺ\UgVYZ E VD>hz%" ! YEq[c"ÝuF{(s /Wj6%J?Ipa0e1c7d1`S(s%! 9+t2U|ѢH#;@Ph%N/@׸$P%`Q5StkI ^Y.FA d]`I!Ehdrv%ѠTԘmvYHc"71B06|{ 8xK_OOQJ=S1i|Cf?bh(!'W#J3IKU$qĂ3 FQrmt8.KYePJ 8PK%Aj,i%ir.,"NԮI%[ĢUc|V {W7ŏxC~ ȝigvcò2n{0$\dM=2ݳc8&-/gad6u]mwTr;t0|pChEjzto_ԪrYb JYZy5aUxpr )u3)+hCfRw+מZ.Ǩ׷6։t0`%bY=,t>%x5Xɟ|?Ᏺ~;VRhӃ`E?Oo~K>Nxt9;Q'F{nDd [Zrzl>!SJ3( sY6Ey& #'ZiD_r?~x^ :y\Bٲ~vaDԑvQ"6bESd}nz>85~'bK 2Z9^LJ 素o"*XsAbA7AVn9u"땁؞QaWP1'VS=)BAQ‚Z-$Q ȁE+ 7pÚa6(@Zc=bH:pn`;!lH!#|+8D}>FBspc{>A'pQn$) h "jA\KqAVgr@Y8bAm]p!q , `}u#(3'u !Xpu$hVRp9(DCthGRk !AG#;l`2l~nyl!0?p G)[ t>\вƲj`mb0dhQ0T!E6PĠ:s'0PPB^k ;J: ,ϰ([>f xƉVؽe<'nD*f!@ B p(iQa=1[: _1kQU#ysNBZPr~Q{9ZV,|\,K/{Oc߉VԎuibt˚gOA&i4;[E~ޟtzK=?UZ7SsӴh7ݟwӂٟ}wp*fU)sT杣a-y1 .R̗dO'֠IsμyxD1 (m1K+4)nu1o:ggP"~xqZ]}x탫wf;סYGlZO0}kRF0drZGl-/0B?OciZPB _dJؑ6f)ƛO{q7~N^ʪ9ۿ.w{Cs||X,k:^H`yru|fie XCPQ Њ'F[o(#ϳAL^U MA Ƹ3, ~, =x֠[Dm˹dɣ*)?ztUje"A;JERh@bx"" B#5 [`jJ` nEr1bKxBYYg%J#uL[AQ7C3PX 4CF8G Iu0:@u|h ] !rGKD)/PT;T!F3k^z۠9Yy {zA'j [8XB1(Bo9EU[6I l5bq+L@Oc8 %k"&=.Ci Cp'L\;دr_m+3W<83c;Ay?7U' qby?έ8& gd. &+ ̀*kR16݂R4&Aa1exzv~q<[,{;n'2¥s X4)o侼Tv074^|F˷Qv֛zyBmz .FgEUy兣Pg-ID߾˪|hfq! cةs.':&n:B>2j:+G#[Ua@nhd2sc36~BnmtкTKukuccxzD0wt, L]#ҨRI1i fkKUu hڅ*Y=f(NZŘJ C(THĉZ2H@ q0`s^8p߁NV_)ȶ%9$A8 0Jb>`%Xs ͶK miS^؀ V`"pt+g:~4o38>(-0"=R huaJk.*}1,p0`D"T/GL :B*}!e8"P쬁 lJ<̏#Fw!90{W5™݄l "r Xg/R { G1P!1 t~"5XX }dAY| '(:e !:hYc0>t\cw^vn7t קZMstFk^k wtN Vײ@P6MIx ׋,Z|h=T'7{ SB 3M.:(؞|Kx;fq5-8!`88mPVත%041Pp $~0Pk*:Z]U`R,Y-!Z-ޗޓӋl~iK6ίyY J~/0כϚn#Y㥠Z,n2wbkdܠc7t< ''O䌺Y#oh%1S Z s: #G^;`5vXT-\7$d DiJ /p^+hLAAA5ZYۖUVY8 ` 9Bp$R.Px!bU# [.Q:C lR6 ZSm6@W kWmC -)}p-wskou+ W{AdQ0prH\̈́:@Ґ`r ZVFXqlAA"aj@ Ź ?8 ʉ5}Ɛ{h&5!Е!DSx?OgztU5aޅ}^WjeN.Au``ï(!ZsCVr o7H3Ёaa΁_ct@Fw#8ĄL}px W,102SGUf޺Ȁ\Dw*熱 ZK:*;/uug+^\5OÁZnmс>iSӓE|v^?W*|iEY8/5ڑm^kӯj9 P.*nRif)"n'NZLb1L˚5ʒČe^8sy|qb˦ÚsTUvu^){$}MEϘO&)"=/dU/:0&6]-f=7b-բsJ/1J/c{Wjs;! dt~U*gƹ v}z9ϮF=I*J}xyu1ɷˆd-tҾUy9~|"ʪ\vcU^,9P/zp?Kz}Ƒ BoG )3Ejk  -$%mn1H0kA=}+ Lr18-Gpc\cйb : I@[ {h+8Rh=CI0]1;F*H IDAT|o_gDOHd{;'#~ Qp䰂°U7%upԇ!MZy{}<#*hVk?9-J[XLX^E1S ;uBp(hT@݅S,֠`Ns`r$h` ]sо,J#\HDu2QmJ 0^+]`$`d z!NQ"x_ \!;S$R# Pۍ93d[ '5 aS>G&p{C +} 9 VO&!i$SIT#ZSED1hYU%燙ۼ3ݷf6'ƞW&w *\([7Ðt m֪%91X@ F>(딭+cY=srO5if"0]HKQA` ZB!̃%rS2K\.@ |X9Ga  0ڀr&dXYbiEEj&r4ȥΥڳW:\^>z'_b6 iV -@ :{o+[v֞j3;f͙@[e)-) A$/3oCF$fH"F$K(3s߾N{\CvvK P>gWڵ!1JtvOt7Hbwp֪Q,6l#NFCB@thQ X*W ʇPx8[:% w6.C 9B4̍]݋_Ԯ.)fЌ32`Uf>(8s~.~~NNG}WD>hk3lnnFdI9ަ5;D+!AI*ߺ܏Nǻ ^៿F[\"I"8f';i/+ea6&~:wl9+>O9dU6]vAj|o4:=sSsȱ/t0rpÉ84yuI;wヤ,R,8:Za)іeQ)EO*ZX٠\B[RM4[;2 ୦nָ5'DҬ%/ / NkI&Ij${6m)gO?? _7A+Q== ߚl=GN%xҹK~5>c("YU5ye`9(K , 9B?˖NNkD',$2:R u7tW0ܵ!N^:&RO߀\=ڭ~vvoG?+Q%7MS/57ݷXXE[eX5&MHG׷'kU:Oޝtbbm?N8>UF~Oz/8QǃżnNO7W2:[Y&EyP*tJNCUoFҽ}=?bkM=̱nso1ڳ9ιBӼF7ݧIKx։E,մWi6mR>Zo@ b ;LfMQ"h2}ܣV WyiZJczeCB!cmƀE4Dv,bɞ9dsT '{$ E:nIkGJ14yLz94뼳ėAz_pqbx"E,q^\I[gJ[%4PV YT-A J{li1 B45=J:L$GxXa+Q[MG0[5ӒZGӛ+5".^0|qףJ:{ {BH<0f3-<~pq?Itd@^~?GmX.Hez~%lVy~8zNRa$bJĘBϮ?w-veQuYP>*Jzv7rQX=pF'lv-E_--ܾ/\۽/ʛ8xg+>?dX56e+,`R9p?_쬿3ݥƶh-{gRwG΍bi$5S-ˆ0 T'S8Q] CEO(e.DhC(.`Xˌ><~?W?o5ď*ϦߌY.4 }sqddz mﮎQ8ϽU M"qYHn%R(D褳 4eEJ-;NIkDeqn:Cvm"|'0T߶/|6s>n΃Cf}S -:Alo-%VD-0dgsC&|X.}lr񢪚aԩq[" J+9\Ӊ"θ2NL-7~km.47Ut>M6I^x痛]7|;>+7-"jB W۴hm0$]*B_iF~{޻*Ǔy/>'g3_}o쎟٤|9E ׍M3H7=޸;7eػ޻ WyQ$gO҂װ9zYc{bWX(_Fu5i}ZgFh=&m'cڳ`% ']XDIvПzm:uG0(B]m:O=&Ox剛nls7t0.*X!S.Aa=Ti˗ΉkvpogZUii[ S46c֕_ N2!VAnϐ83Vs:dqT@?\TD cKV&K- }]S|'BW2i' GN?'عs2YD5sUb#Aw5q vifU)/;EEl =2qoV*fbu'>uI%GG[0{`zdzXa_՘1ɡ"VGH<+=5 ( J rːi2$ԱBJJR\i1=< >={γކ:ʟӡMdvIhV6V!g>]*Px$PV-%MS ѡ}~˺T;<85%UHAFX)/n{2Z[ήDݏ*_%k倷-T\ *eFۤogCAfݚ%{KDQd3cݧv88딅3QnG2B1v1vm㠃-moA[卭gU1]5I,*$ZVvuH63xyNǏ֡ם-;t3S_(F"ѶsfOR t+"xQ WNGdYdp-~3w˟+ٯ}br6{ƓWv=H%x\$,ɒO|9W56woem}B{;^{~#ChkthM8:dړu7sp+o.!lfn=#dOoh>?q6rѦigwfw6T{\=Qt{P _)YdЊ<%0ma ) Rԡ:0JQ 1+MO7ܠSDAs.d\ [Odl1[788@I\,麌%82hu[CSۋsSzSҢ;ɕ(LReAkY^ôVYvbM$TF(-=R#LIDE hE]&Ȥ QK~ ?T5%ɋㄦpa0i\ jHQf/9&P8a~%:̉ƆH G.n)|_ٗE74yP4@Նji"Ϫ1>>d*u˻ApqW&uX08)fg!L^*3+̈́nxN8i5cO rns&{V Vkŋ=r'Kެβ Ro^l@%yM J*pkj"F,emv5!G0  cf#&7U͋g4%X%KBSHQ$0eF\M[UCFj⸪] N>F+x*H Ƌcuky i0xc ^wFmZ^RڲB*ІjEN[ݷ?ǭ/M~5eۢ6@|R$*(e~gVۋ70b2lGvgYZkNje"k ٠Ӯ[M=٪j r<,ζ=moQ7:8;~t:\lmȋ~W7Ger[%lHGRMkߥ}̀,OM;cvn?IɬW4Wj_1yK}[q}aA&ү/+.پYV)3_^§{?ʭO_y#O|{xa-+Rp cq`$LcE8K]9$ y;J͐zf}.DN(}oBWjur'*/)UGѣ;~2p7*U-*"}f cF鍛ҜHqF$ 7]S5qWˆpL)&abHA"Bf天xt> Ϋe1gM n]ס) n D@C̈́-MJDPRz9`զ Kbm\ǦM-.~n-dESǘS'L 9G')< IDATލ[{|r819FvRBN˺j<NP KH /LMFQs)b$3mb&uz~w͓kڅOY76|VuӒ֠c½݇HGR{ZF SUcG~|scǜfHN%NE(#oBf-jbmɠpux8;ZdQ4*G(U|rMW[՞Oz|1j~89[iz>5D!DXI7w?6uKO\پ{t6ݸۏn~+Js~;=/l;VY/o_|v^o (!u*o竺j6ֽU%o˓ɋgw'dokLmLd:)G95ĺ)eB YKV2֙Βfu`/,-٨XӤ-9Lz 9v1^ 0#pvK~C OT=5\.hs ̜|V1-L2A=;ô<@df$t"OR׼u`|#^iܞqwI^y(F e3m'͐QFk7J5.gXiUUU%P-3R:,8˩IhL(I3jq2guzA8JMFI_5[bcGD]@Y;:qM3(r^nS|V%&jI V5Kӿf stpP7S>RR/y2-vI82*\,1!Wm})=CqN$/so73DhC%Jw+x3D;_g!6PZyL)tɉpbG.j\G=uE<'9ń 7|,H\(dd2' )QyJ>- dޢU Ӯ@Xt2R"uR/ @\}5G cfG5hz/(IKg%ɰht<ӝg[4ʕ|u%Taw<]DRҎQ8DpqڊAUܵ^>wߒ=G'OWøGo7}/?jv2Z3:nm7` ɒ:No[-Vy>\vj9E6zJGkD2$2^\7J`t;/ ;bվ^LlzcQH6_^/ɢ8}Qn,2_/>.jYK{'~Nv`|QjqBۍ5 ~6u}|.[Wx|s.<> !ñM*v ƒ4(3vDa5eЦ; W2Rx d=vVJN!l}#mjq=z-+tL#ao(ўP\VpuǓcOё1KOjBdžSStc PIwqaX-Aٹ^HOFB`MT -+^1T6Ƹ:#D:AVB[:*df*#0bmLb}Geu:2?r2YD$CMqWU'7~0p*0ŬJ+jaq2 Xh*K )E8ZUNbDه`(e5'5Vp+6t^pxboM+vNM+ #x$bVW|ZC\JE}7t#PWh3:ENm7iF/q:1m@!m |jk@z )1/Ѱ ,X,HܨzF =Sn]xAV.>Ÿ[.md9X;8Gިep`1u0Q#!r@*hiq\NU!siOF~g| i#3ba]nܰ~[9#(wU@@DFh$fKJ"/kݪQ,FtEY81JFy/Kդ &y:iq [uo{V􍢣xYu/hV]LJ4c'+}k"q?VQ<8Nm j[Eq_*#6)p/ A8ǘxi}>[˿oF?3ZGaBG=?g&oV ._›/} KZ{+nzC4*MœxT7`p,W1 \e0mSL{#l9%p#P=K]x΁ e15>@` x, ^X;TCȇ(ԫ@\WNסOW<mE]Gx݋B7.mOOΨ%c|1=66e4Ai&1gQk%C@ X\T Z/qADdcpnʾB/o8nII(CBH!`9<qϳ؋U5J -5YfD7lKTOHP)Yzwi|dǵTE'ԯ,H:+?)=*5LB74߁mLހe(=-^Diay\T\D[DT&v RN*bqMV(a>M-.\UJ5Q _KAZ^/Q/mup)%)2@S_q胊umyˋFF e%+B .aVZ$R-ݾ5݃%͊nFCچ 96Lۘ]\ KFѠ:*V鿛}F꤈Ū^٘?*#/E?tg~XFt4G^te.۝N]JS>_I*m?L",߮v̏hMM7:RɿL|g|mȏ>uZ-piL4YeZIB.%M)%oC$B%|:tH U]@(_2d:CySX6:ݮu{cFO3YE&oxGbQvDxZZ@-Yp#$nuxE/*:Te$NZGaiS|Ze&8E+Tgt:,Gf6FtdEk#Jޠ]|5bU^g?|݃Qg2o;Ka=@ÿ׎{Y;3IϦ7QϗGyŻYYnvOLz!|UO_|'xts#O>≫{W\o-Zl=|2SQ)/<i']Zʯ6==G`D,3 #cx @R$'|(v4/׶.c^BC6tm+B8uduD٪ksDˊܽhżzހN]Q [aXyMp@]6+ҰD%xP7t.1s hUĤMh4%=J8b甽TJC-FRSJIٓL&dXq$4##P&ʓaѩ=2!cSBc)~R 5Od=[Qk ",Z)=qB K-{7.3.$D"MZ:zhm.EKo%=ZAUyHK'G ɆH P^q\ @aڙi$8&oZw@EX'pg9fNy:#V8R кG'IpR^pLhK`W{eɪZ?qEiDu8Se?|vW{aP/]g9Ο`JLB|6>>?dӟgы^ N7;?ֵٕ{~{ |OI,,OTwqחPĺ0s~#OHC0/6#֭O|K'IRɍqxhkxv:XZm+҈v iJR.]ަ^דq56QuRq6&تY^/zƸQ4ZQj93}XϚ5dIlϒgqR,έe7ib娒qiNuͦx6#`U͢muCV_эwa1Dz9,~i `u7j{]|bǽ? kLR6]./3a9j6_$zoLgote 7^s~o=xs?Io_7ޔ=OKovBb\z_#~7?Ɵ??AcW/{oc[vgsݚ^ٜMq%eQ-`"9@$d!+R+,Y8uTgC8nIv,;^ݪ{9uZ_3kT}a08ܩ3LDi mSN+ k9sT@*Hi5wa= M s a]+yͪ`僦1(T/a(* fiۏ(r|DK4Tk[ ТP6zȌ|5fz?CC0|L(7RpeL)Q}M׶Ey+3[])$%yr|+JUDH0λ*λ·4J|9'c,%E$>;ih um>ATPm4ᠢ5MCcnt.̎g,9il>1"#jtC:`CC5Ba[B"4I Yt1踭@cl 'ڤnmKieJR-Bj2X28v0@D Cf 4UO/!w5?n—tv{A|sC_ 8_Nn_%vdt{]Xߜ f5Pj}qVLZ|&DR[Ty? Y~sEG/k`acB"[ ~h{wI+F_K֦_'|;QuyqJ=c0nƁj$>eHFUGQy8IqWCyYF3waK=#yQ'ǣ#e~ܔ8uleU>pNYoGI3f3/mE68LFaTmW"k+J;2ֶ6"}CP\8jd&()\ iGM@f(@p4.ׄ zQ ~ŬFs^ήs5*~_T\4q z[L^VW$k_;n|E]#iWkVg?˝U._}MO>;GwM>/VSOlяY￙5}28XwKxOq-`P|P.;8 ʜ1/Z}0Zbx9:]t{.ĝp,-'kFpO0:uml%ūLs *s6F$4!HIl[JqSQGr.MJ |1L 5UXr/|'9)}޻ٿv/"9DfB#> xiǃwe] tgɊ :dm@=ǜDuQG %vxc <\t,p`\aH gM9% 5Cv7ׯ,wWTCl њ"|! ^Vl\At՚0dn1 _qT9^R8ChGuph1in8Lq:[\@@pn DvB+ Ǵ܀h{iM=j5U#NQbFI#Ǩ\c5NGh_KS3r[=f&D73vɎ()͠c=ȏOs>% B_a1̈jddT\(6ISQԹ"ԩ ReKeh;KM,+O4Sjn9cx2C*h,ѲhP1)WN_Lˢ@S%O׊Kqo?zHl=]7o. (,N>|f|H|[c3˻e}+ٽO lzs$)_&CP(+$I>i0@ O7?rf{CejgFq6_~aMzM8fv@v8%TR*4܍.?_]^-$V0 t+':Cgqh"*`bw8fG;GG=[gz Q'y7m$,tZ:iQqeUIE%"5F -`j*V'Q"CROO!:O%TޢY ftPcGTiãk+ ؟J^RW_ ZSt[-%єgBpqo@({2HbͯdO)bC28ĔqҾBMCƧ=Ǯg_y2|;y;^>5S<_|^9} M~XSĔz9\\C ]/dŹ_;E{]Wẁl0uCcLvI}l)H~YR B Y幸lcoXIV*=U8x:=M+@Vm 6j~bV8mQ($Z%iS+q( E;%x|j-a-YVPhDfAK*JP'8 J([jiޡΏ*B5w6d{lVm?7\=?w΄:L=6;)wi+"+{_o7ۃ{[z&3>  o|[Ky^Nlsn6Ctobuewnrc8=K^F 7:szҵ9b熨rtGqqw5m*;AWprD{>r#D<,cQjX֫\K`uՅτƍevlyK@NEEt7b  "chњYt7ٹH7xa25zz&e0j*紋 #! G!Uå׀yo^6E|V( ;R1XH䕲!!S$2[=|C`9JSC8ȡ-9 W)M:\g#tFTCmOP&" c&@^!].;9'TZ,d6ܼk+- r,f 9K";Z#/idKHҖhT^+'ѥx7,"NrL'"nv^StUDE 6sոMn?\!= V 7o>MoՃߜfĭDx盎!Tx4X|'P1x $ U3Lj-2ȲDC[=ufu($FGLsV:^JA9*͉_?%u@NUJ![ NY >v΅IT2c3!/]ޛ;+=?:]~_^ n7v _:J8X^3d6 FQKBt}_&CL F?|7n]<ϟEJW\_|qWɋ j{r&5jP\О\B9t4>U!dcWP=J{TT-"a]_a+"g49HZG\xm0Z̺ ɘ$76qk,=afl I=޴{&U@KTA'8|e]bE8)t9 gg*KEظO]9Qf>bK{ʌ 8mr{<9GBn8DM%v6 My>s :vN!x#L'"&85U@hA1`jJ=Mf %Ւց(!T ݴA⦥!yVPqD!WIJ"rJs<^Rӹ02XW&cξjo[o:k}֍y[$ͤo_>|4jnzp{eh7 T4~o_ܚCv|4;ݾ tQ9`6*6־ xOjT /-7ʕԿsSԩWF[$Q ̰N<h +<̄kcGu:bדgy4 MVa1KAlVIﺲ87E kR-Yj2UM$*bi#`ڌX9Ȗ%w!s$$_YN:)Xz˳beNyb^jv%SLd#!lO\cL$jNhNFU OCmQybCԚb[Q#Cuy2ק)"um'y1#!VQ@p$>sm)d۞y!+f/x_b9z/)U &T'%p&$z|hR%dWgkb( i[fpN_RNN޷l<#Qԍ-⡔4Uyӂh PZ;Bzo*>~$?ў_j?/ߛeu x=Ng,kֹ~rg= c/Q#; ssO?9>"έQWe@J=Y W+ԾtFQ-ED]1qr;=Lvdg/x;aRc#>{xӇ|=yw JV)8$ )%EDPI0!(AhTDU¸weG E*whjx6#n\<I9nKs6wWט7bzYՃ|%~Ns^pF֎|5`?)(>^3 u]#j{`nN{pa/kVmCc;G)/ ɳ΅77dzc_yU6k~}oLUЩ$| anVPX110)0UL s !Ste4ksw-y<%'5KQ_Q᰼_`WPDgYI% w]^jb; lcz:%:O Ǯ j$P)?x|xں)K??[ɘ4{^x󹗣7~v)90yiwK(+ORU/=j{]vKя%wa+x1 +78ATxVYU A= y52XCZ;P}Q -YMߌb)iFN*mG ;7Ukjl yV*wneUEJG=TFزy/ ^<(,Z*J:zXH{VP {uUU"9Z;v0 Bme&VGYF(LNѝL {v3W|o3b: @}UUAFbZ9l~JwQl<|:Fٿ\ޤNY>jlpL2 ?RmM o ɺB_),>: N(x83l@p"a7}١j'=e%aTTyN !=;tAVK;(4dqnBH 7siG%̚ #QlʕmńøȤ}em1n}ӏ}7L{>VgC&a DҀU$ae:;0*B&wr$i©\sDܭڔVL w${q.T/VEdr61(dL?i&iUkiŽtְ1 @wq@jXsWg+k}]yxˊ CCuX\Ƈ:,Ͽ?bQ~r,iY3p)zJ)q&u191'xL\HEhX*\Q*Eqg ȢZ>UugGw>ު| %ӿ}7? AxЪ[>/M{T#yǗ~ 2C}K<#n d+л\kVAAs\i@ϩ;A~'+Z^d1& HrVn^LH3o̼(=ߎv;i#Xm5Z8:Ead 5UU1qLYDݫgEY=N):e24JA@e#qTʇ1v2 bݯT68tlRj3ˮi҂_ݶ'OHC?)6Fh6+)*h(B!VItK7rȲU&Y umbܠE?T>Qfb-}mX۵vlu[Ws%JQg}mF9(5紋FQK?αܦL }iqF8 .q^' zź{*N'OEg9yo ᦢ) 6jXN(!)PJ#x/\yQ|z5y܅s^,UA]y+TIEXTb4X'E:5Ő ČK,fŖdшAOo^~n WV($9ibj{MsMΗ@"/MD%(-)}lCb( N. Q!Jᬫ zGtsJ-~6TurŖ@e^jWqju5cϪqY<۸3h;D;mv2;~5v'Gc^7\|e|!`=;ڳZ¹UPWECz;6p2+U-Q:kg go0Y.BuۚR7%SFaؽ9ΧՈ~}UOlio4+b! Gͻ=~$VPYEj1#/ɦv- bLPPHT'.8cD$U/5!YC\,PŒI0ICI69"V09fݐ3;c1n`Z1EX[E^ l5¥[ riǓ[GjuOǕ?=Ѿo4h3hl2[J^r |tDkv}nV(#`į5ZԏmCɻ=E FVq3U݁2=5;թt:BZ-+Cb,8 ӎPw0ӑٺHN9ހ \;7 1q̜P^Y4gTT(l! qLPBYPZc!u꽢;(+QR97Ǹ3cƤ/gAܳIDgj:扲3yM((T} a|ݢ~)/|&yE)ix7_UJօq~|w4@sv_; uAGQ]h0yԓ_'Ԩ^\<8Q2};1ƣL7cB;{摖v,@YG)vx IDATcx(sUw! AczWaNbOB-\rZ}t:pJ)2뼳vNh+恈0z ":k8tگL6n,0netbٷ<:scΣ~W*|s~4uxȳ󴰵,ጨ8{"lyC3@^w 7:G#".z8ѭ{%~Њo뗣gn-ۃ6ΗU(%ϧ;<ޟ ,{D .zO$Eն H,G?8f'gB kN7`nADSOw>(U*W VcX|Gpr>)rH3o?+-qUp>X sGQ cSB&@ Xq rp׫!{b&R& #M s`ghCskpBqHFw&?=B(k"h埿h6uK:IrB"`= ki*EFn <^[7S SWm㞰{>Cc^b/2o6#;'yQr{)1$/jW"к H =og 0g^/gǁ11lP`kQ #W<3;:h͈QL" -m t`9ζ'''(٢LTU(1v5Wga/YiX&|awppJ270 ͽ)`7[^9duta5J1kvc]qolGaٍVG tm!k\yVz >oY;sg*ô1  ]܂m9G9ػKӾ/ߨrώoF7iWڭݗ_>>COz?~ /V&Δ}sXQz31PJ"k(L*ݐ!p @Af!縔.̃@~x"=΄s"*Px7I mZLGe[: ;a>`JzVkct)  "`Vm.c'X8'v|`'beIqGDg<h)@Wl$vĈM{8J"% !6jgC61G])~|lԈQShH/xar&m/ã=D1Q_k?އ)[߲^} X2v4yϞj/ _DX+plv)b 8]wrNV+su|{c|2M>A`n"4zVw{-ҧܡQc'u"ZSͶq઎jO1L9ӕK]rmsϮl/[ Ph"  0<z`2yK@KQS* *4:Xum.jPAΗ6lwP(IQ^R;@[ЌbyNX"nMwʖtѡٟ %Lo}z$>\qrS ~{"MNLЮŲ#0K`G"NjFJu5$4kR羝KfJXUrEC q5'P|ONWS> =vk(aY~= @Xu3WZ xu4^ -QMf:HQjgltuda|+]4_ @~lҨ=z)JmPZ;-b;Y;byׯxm!mn8l[I9igUuܖpKNSFEPt;mIqZhNTLdg^p|ۇu>h.^i*6KX:v vDzc|*wpc@v 9Z<~;誉x8_oe!MԳv)?^^-nf[ĮG˙Z}=~{| ϥVEkWB`c88[,pn,|e|.6 N/?Nb$ OaG/dyV췲kWSĽuE|@gݚiˋتm*֋|nTlJʬ26{ՙ:I-f'ؽPx+)өBa 6Wۦ;U &GO^QiWSv- E0:^7q 'Z:-rQEmziy%vvXq&$3髝w/bsڛEgoc_x>OohY auC7Ӯ^{ؙT;kDbS.X1#վqTw̗E6 q 2]իq@`)1WNٛ;OO;ߩIԺb oζ`ƿ@sW{5j< _@ `C3] N 0Oo|`,,mM58 ЩqDPlzp. Zo  MDMw:auUʺ(O"<$a 4mX,_aNÇ4Qm]vfqb9*r u{[gq-E WJ .x=@MZ-qŜJ 1m16o i#Q(Zp⍦m,vRlY{Gu;PW!˺ǝB볝RZq6_EQiNwqu kӯKJoVo bq'O/0|]\<^kοEN,m;w{[k i>aH_bÊaA(I:k{}ɰM⃢~# NJ QQ4+;)sM c%Yw{ Hb6r\F?gCi `,Ц p'Uv78z)U nf @Zib=/. {5"{yb۪ؒ9Z g\˖zw~Ʈ)Lr"׆kYceE7}:!;4ȓ;CiцLDK`U׳ &{T9Qѳ v m)SVhzݹpjj'r'"%΂Ήmf6k1p W9s{1uSŵk'Jjmxy+u/_{_SgA3W?`ci6p\.d={湳]R)-J_l WeSUJ7χq%RVv㾎9Pkp~puX)KۼwvܚA5gܭovէv¸pn~:C5ׁ`;B&Xߺi |PF8,o}pw[5qW0@kho~m߅{X9;7ƿ ?cA| nXPb)U:,  špඦ{'% kY;00\'{±~ !.'b.,{{pZ/k T=ZLu Dk:sB,c5bXLEԺ ƹsRv7N8}w{ Cඵ1μ^ ڼG*9xq!2co]/Bq4 _V\.@pcxY#q8'|'ώpoMӧ߽ѭf?޻"?yzeLdx O!_9ENƼ;NE9M~CX;~9qA7EbۍrY#w kIR.6ƏXXmZ |znlۮNϷ?^NjJiz&|F?tW&>M\݌ޛǒxM D)7rBzdhMGQUVѳ!HvhΔCι>vhƋfoX?yi6Mq.<ijora{|/6ԳUpr-i=]Tw4{p$ڔbgDX&~9$OmaZ[_5MiJ ?zEEM] /z]&#g?tdO疶ǟ><go9j#=i5lb|͵Bv"kף¶L) .LRH짛K=o?聹:Z\l]B~1F?NK65klG8c+mG+!k~`M 5FneK7baIuֺ`BCD &x<- Y!Z__DbR-Ne3꨺j[=ⓦԶQ'qiyX.xHX$Ӣ;b䅒{OӪBP%/.s9l-+N\U!x+מSeyXZ8z,2-]<6tE$7wnH/Mt gg|M:YmbYT'v< M"kɃϴh?&Iʺmn{ggyɏ?`ߵ4~폾v#I"z7/>N5 sHR*<๤RˮuAiCnos8z^!Vg V:+Xmc-"O=e~N,=[V2<~ߺk ZVZlY$4RxzG.@Cd$+^dAQ U(`C@!^>ֆ9l :&veĺ͛R*,2S8򽈪SucHXpq%8vGnlLw? IDATdY6d{H}]TƉE!wJ;w)5fa\, ֱsǜ,O y9;nYpmw`U m?ߥ5qb?|;*WX֌ ki^q4Y^;a(% TE ?79Y:x!=Ⱥ}[>\p}By%lnwt ׷oƈ;{Ut[:3/"(N^GJP(r.z6M\]*pea%ô  @2բk )8+OB1(q V&`udk i5J 9P\i(AE:VU:esK3Hy\4 &` *0HfRqɝJp,  hQZR*Z E_o/ƨ1ǍUG*0x\Kc#pXߑ@1ȋ(TE8_ִ8X8'K3N,ϔRJTU*Rgtݨl|Io ^e(D4(N{bfIPT*+)xmha`yo= :YI,\Ri58HppdU:纭SYɼ\E< ېBzZhJ׶V18lkiiE;H㈖Xmj<}}?zqaVTUf?|x6}J}UE|θהwvvaU}U 4ˮs[ d't1*d*/gۋas{yu]ȥBe'_9A ܪtQţ< ?m{$s Nxa  $˶'e!|ᷗ_Uq> ҫOAeʵ6 q=sj:I~9HS(d<8H&9QB xFZY%ٛըNS".iM(&!p=5ufQηM,UC)95b\LaVrӞuAú2qQyG,]umm,zcO!E&s#) KY*&$6lY-6Ƣ];ΞϢjX*`aìށi9aH O[L =IGTЖdՐzOC GEH6WQjy(TL/"HP/#f9e T7pp)I@C. tStzHa fLhP~+뵰in!r! Oi4B$]q& {2ZBEKUDL _B8 Bτ覧gfp_ͽ͇߿?+/KW~S>>78>K(%! A_%K,VޮǪ1 ǂ˾O^t> ' *@`z4.I@F5)rr6\Fh`:^6ul*e"MJUD>8sVs.("83E5R m[K'=(!KۖR8q2/ ~y&e7T*΂k쀴|.\ Yy;@eM[ëǪwo0pqsSӰ|$"a 92tzy]rzi`|7-W|*mbdn-?uq7c;B>Q$xb1z|25PHU Opڼ#p1#VD8 _pXFK@V#)0 r@r#( #KA:Ff阁cst>PL g`?μ{8ެK X?\6OhK"i!jÕG\"PJ҅^C.SjnwU\9S7V'ÒQF"nJmY4"ؒn_D9#gաtDm!:˒A8(p't w1ˡ~[ɺ*ٮ9c,YZ9D־rA1S۪őpb xh`1p3X6~{wxx]Qw.Ue, u/,͓;Ǿ7CK(>-b~4}qq=VweUimu=Nuk~sQeFlNb $@Q$E _"DRA v,)4dLg6ݯT;i+?n$KbI* 8{9k}zR9sѠL}ޭQ@^M] 1'go>Gȩ}hGqvvHxC!cl("B`M,+r[BRb`mo<R9Fꁘ jmo.b :F 9r(:ƐH6b:[rh 9\^h(Q=wM7*jˠw.\ٵp|m*rRjtdHD,0C :LdYK.-ˢ!XxڞUFf CD]>{mKD?%*'}Щ!0bցJ%Pr 4*ݟJ1̦<$ +NateB3T\ȶA}@|z{CLkpjϑ`u 7w+^Dzc%?p9F`}dv+/~t~Ֆ|ЏX#DQ+V kII%Lm$AN3" N"F6=+ R/RdQ#e 1#qUh2:_.!qM@֣u}WY |p߹5(z߇*XVm phJGVɿTwy|8 L˃Yؼ k wGíI惏9Tw׷W38^/>k;ZɾoVc N-6S˃:iKy, ˩ 8:[]XfSW4)zbt2zhKd(}\yz^WYr+JZ/ FP$4;N"b=#~MdŨǬ|D~ZZ *#EDT +0q=a4遳m,gd~FX{f(ѠW@`/`ƗQf<-0l*dRwh}*x A,I:8EZuXV0ýB'ڢ8/\{߸} {7+_S 06A߿ {?䱈ـ>} j1I']†* c-s\CKkN|[W3 Ok zd2ϔ qJ IDATB *BzMkMo\589k23FUQU5ս;Qmؚsid2#w} hllZaJ\)ݢ |X"Rjøv~erUܻqvd҃Yg܌Qn;+'{9ݾLWW|opc33Wȅ放/kӓk7l"UijpHԔ{!_ @f5&q3,P5@b#vN65R6&6$.`-4%SJ$,rׅڦ%x/GJULds ץe^ʪ_KiڥL+|Y>-z,93J5OaRYJA%l\9diHyz>*rlY\U!Uܩ8r)'!&2)$*r ԇ b 9R)y2 zA1'g(T$JQєv,ofZh4,u(Y4ɍ + ;J󚍖]m8>( *⸄f-\7>3$pChur;_*X!wc$C ӡ` h&`e thm=:G`jP%d* @RXUG 1R!.8 ,@{jPr -q$@,iTst>SQX8W~O|O= M abFMfX!?q7_;^{GG h@Vہ f"1S0%aRJH؀$.-ě~\~)"<_A&3k/^B uLV\J2Rr YPs 1߻wbB'&HzTԭϛ9i8[6how7ys"nB-j;Iv;+w_?sMɺ ')& pxGbܾvu׆OFd"ϸfԔ}S0b[J{=w*HۡOY8 ]M@uuq*bom We Luy/'|(2^uL0!9m 3tz : RFopJ  A$'6ku@IZάDŽ* D"VP KNB22%|MD/JXla)3Cڣfվ>RJ.ܐku6Jm͛ TZ~udRz ^7.AuI$eR O 2LĔd„8Bl$УGGvvb'9**vPbL#H"\jNRTFZJLx p(1R9bF!Vk/zLMQOp]Ѝ;;~G~xE=m_7w! ǿ-p`'Zx` ,YbRf{{i^<؞EH-+bU`2 Z 48 1gK>>:/<}K */VzlOΥTJbYP7/9*cV>9m^/k LB5ѥ1 [ڽbu`W{EƢ#XeAkae]~v'y֤`CMs^"ΰa7\-"d+yOVʼzlļuTT[AՎB G,.3/Ym!36: ^ 02BoZ#6? Dڬ"4 Z0 Nh$%-N j $ ! #4RB@UkAA\;ݑ,DN3ƠJj~ݧis"(EŁAmVk),`,S ڜbkYiYU VeȆpԽ 2"l:NJΒ@(6@xDpRHA0g%T2ŔXX %ӺCtIo+2QFR2(y/ R~#q%Vi*IBn+ y\#^q!$lxTrˀ%k-0'DD@`B k HDB(z@Y;X. zaro:D' @Hu'P2H `R.2)90O/I*DӴhx73 b]X:)"8ҟ$E\Kawg;ɠ .OKe?q?s/~Ofy?1tG+/^Q?J ?J8JP/ ^R}a5.'?޿N>c_AV`\R'fAmũ F ,CC.KZ[[ DHH 91H`/Ey^ULwʺm*?}t X8^A.4)9,sz\ipbh(%a.q`sp{`0=Pqega__Wx|q1޽3!,jOC=I){a3UMM*)Xi!Bwm-v!%gPH lc!s;HVTD>1D / $@LbUU)LjDёU?jȵe14 Y)ǦB$:lFI!dn7Z9R`1G=Ȳq֘:lfQk\Ȩ,t`У]ժC (uhۂ3v8">* "Is;@r-b˹ls]0 "dp.Aw@P>Aˊt_T` vԈTyi';3o2 az!] aX cr%$;KI ձ ق rB.ygV5)ޚ:pY!.ȇZ@& WbWERcT%BL(y8`d!;, jtͨ$Ax BBN2E=<#-# `h rVKT] 7AQ]R[œ`û5_Td}|7OfY0 ?5nݳ .Uw8?jۡ0͂AeҳM;(Κ|z=3ߋA 314N S_BRλLvSOpu\GJ_]\7ƨmSz\Ur`TDBIo{ҁWD.ezFv}qJByqkև+tlN0{nh ò[t:s|h"3j4|bUU{ʎxXz7 L]UQˣ?VsHr6G=L]ctH WTLKþ{* \0y+#h;Oo_KͻT׆#Ӭ{}T='8PW)\yg6 yO7<.H!i+ b 2b0ok*.ڒ-b`TKL?TH^>0W Ul/c}-NXpINnE=w LIb^%DݏTHS/@d=s w|: |uJSz0wL(14I6Li+imI).tcv3"qSLʱp VcE6-xX* Sd$ 3@a$3 lcK$D@@*"I)SO+~2cKk)$`1tBsX_Bbrܬpt} 2pEɒdjw5 >BUFR$5y=e4V,rf@V$!j`G;fIi=#SN@ jvC# 82Pô) Y $M/"Pka_mߠV9END!sH_C!DD@Ѭ1Hb\ :vͽX~CQgS-WUuwi_Ϭ~o=s ήhGH^mq|ƟR7G;=; O}}'6g7oϛܜ7mb4cd=H0=.?vDo?$oY|"۲Ϗǫe9NٮFhQf8q[cq܋Ŕ:~rkf:(jzp6f)}o|" P/hl~^aJx/>Jw\hq->I1Q\qz_ƦCS9bܒ1~UD(>2HPFc %Pւ Ҧ&B6Dfã󶠇pI#P8t !ؘ,|-X*#}#J82sB<\1l?Іn{2ϻY綑đrAJ 1$)q,3ujDTt?cbWWg Yeih#+k_`BS4 e] (Oy~t[e-$A-c M.BƸ}FAkJz!LI2Je<(Ej!=JB XpHy ݺL"w/b@l xk?*uz7p!c' <#a 1$\J⠔;8ܿ`s/~oxxkCGcB|Ť@Ad z\r.oq&R*$D 3/?-"!B#A؃[|Sz`?.mͯ)<̊_Ę93{m%I3b (Ӭr$tq4|t֮4lX]Vׯ|hZ [U~׫rCb],8_fcIf'uvoaC7@b՞SC[kr{w{k{{"&RJ.:5ﭻ3$ݱmrnӭ' um5vdT[VmgUp"u(۶[ <~z U @ `(p̞V8*<|-ʅ vU 6ElTo|=~PK  )(q `( 6QdD"DF>]"c8Y?R<1-&?(%ORFEl*Ifz@b)=d,€,E+XNR-+qqu Ȥfd 58FҜR% BPy٥պ"nJĠJեRSfM$)sQ@`bY)L09WM.HMȈY!Qw/AGQyDao胆9y dȡDVlZy(b#@=@ @>NU_(HEQa+Fdb$pִ:!3%ODV>~P/Np/uԄT0{o5|G'QaxC@_W>]Ww˿jx;UϖOPD$)QP2^w^w<.3=? 7! + x HKo&ٛD6hі2upmVxT#+~ɑb8YU%THRB#094X׫kd ӮFD]֪,.Mv\=~Yhֽ3ţ<:YGb}O]yɹʄdUS<)_O-΁]>,4cc4~̕yæn''x϶P0mlHC3ç(yoKM:x.}I@xxaUy,B$ZCʵ)i)b"ilz[ȆRHS\BnGu){Rf>]*I#te݈.6IeVr;6.눮N++-sW&Qh)*qRIDT!@Q2HdZ&Hke(ʓOe`A-+R$C&`NC,Dԥ,pI" GPa k-δZ5r '2Fek̒Ҭ%ŖgE}L0GRI6R`|% ԹHOb:2I,KQɴzJ [X0˕9;;}ɉnptk??m9N ]xY0N"T߄% NRvbtr> 39tHo3WPHěY &NLo9d|. Mo &G> j9"=#z>Nǽb.Q^e&hݜl?৾Y B-'̉he5`]IRXeb0ڌ5q+ֵ >>*+_K*Y `ifՓ2z\Xj@ 4TH2Y2YFT#(lIɡJY Rc!#KC-0&H &%)Q Xcł)"%$R ~(u&Y,B꺍2\Qw"DZ1X^ZȜ%"$ȣ,8D'TڢaH$,Q$ZZ. ( p1QD$XR8e9A[2C90b@ [MzlFP/B y*J-p5\ڡ@6|J9Fs,I`[u)JyeI{}~AfǃvD5/ ſ]6=,}uÔ+̓]n#qS8Hx5?x'fQcO}zF6{?M`oaIy[M~8@.~Uķ8 F:7c{.6D8ym?YĬ'[jӸ_,~(WՠXJރ=Ѿ{O}jn9G˹ {@ 30L@oN:۾sbv>od yQvm`XqVPҜ/~q{o>ej: ܼoM/>կ_tmLwìqNEAڪ[֣˚ qw;.a U/RjSl [Hl *@a|;5AJoN͓ zz5a8NHUH@4," I]e+X :EdqO)0|PRbܸh0fy *)t5NJV!/R$\_eMv8bڧl܎UKʓe[˔|v{Ywd V{Uq4ŸQS )g1ECb1<;rI;J2@:(3Fyʕh./vEƵI[B&rt)!M.E>T QI32Q42mirh$bD$!4T6 B>|bYka( Ċ t0p7QRISJsr@tm@`%>[YciE#-HL 1p){Cnb#;*1!’ (e-PM{(D[|L&퀠QyV T0c o|o&hAZ2 wHs _2 8/  yS"P27PS p‘IXR5P F_ _?nv!Tt WAT >D?6Y#;JEb{_ޭV?U eY$Ub0W}"gվ#O}:>3~ޛZ]aZ{8}sU=7JȖԶnǶ 2d2$0? Da~0&GddMvWw\;q+?Ϋ&E#âOUpY{oooHnc#b_ P]!'T}3ybFc4`h(0z-`W^|K;2c4zW_US-u[g'f\ٹQmDUDo=N5 X5 E ĊMsZmzud#>.,yRP qnp]jm[i*oQ8rDs[, 9jWmA6PxGUmnxz8WJj؁afB.uJa!e!6rTrU$Zoʹkul=䬲ĮKjtn;kSp>WMruPcB `Eq]χӶMi]>^:6Fo?2aڰ}ܠԦ<. ,Rd]ٵj~6uTo}f=#w?±oNFP[apDLݪ򭯴QHVƻv !QkpϾ3x]^XPQfvy1Ňǣ>'iftX __ne/^K/6}d/\\\/$UUA U` @{2w @gwܢUg@ˆ- [ Hk! 7\3DI5SUa÷|?cZTIe=k3^]=̇1[.33Z;fS:]߮9M'\d);f@LvDd`N^kI_ѧյ*Kݣqc*Mb"7ƖZɖ7U@R/A2 Av:690C1bo&VAa;b!1-a MGNST<TdBͰc2LYՉ)*mJ؅|YDB GIǠ$=$Sq1(;iGffQ.6.UCBD(HZF>N@ DV(|9OBK(e6/XW PÕ6J5"9c\@0y&dC*޼(e"b$` #Whč1)dӀ BdFehL V @ X># =Y[+zɻC qJ0r#)^h[%1ٷ)bBG~/]Fz1{[q#;+o_ΟZD<ʏ?0[xYKꜦNϏXL~ M _=L.xC>^yޏ?[;@`ߝMDěF/In@>F `^/#;~lZ?9&6nfٌiۻxQ fArpVĪ)#Zy75R֖o~Wq1_9׫[ҦH!΍ {FAVj(OX߮(c BA֌Ts!D.Ͷve`p+W??u_hHmjUmwdN[loLצm]/A3 iw=t^G`EH{dNF@TvEX~h~ [nW^*|KI/;Bv7f'{~-K[_n񟏶 A։@1v޵]7V&d6j>Vd9 >?xfA"XF8zoW|:P` @ H @T O+}pc8KtAo+ HFܾ}Q}px|̖?u>Gm팧|mdxe# ǣZ楇G)Lr5pcTM2cהz:66x|6,xwyowڹ&Q\Yzͷn߹l<@a\h"5d |@PA6˺k6ǣ%F9OFGUfyܙM矺usׯÃ#1o>{ʟ˟җҭ枸֛Wu}~8ϙ/6ѵ+`PP|IPTROOC-mt}ѣ'a (-|tG_ܔ!wBJAwZ Lڰ{v i:yxVVqU2˦N*Mb! X)uZVIF-+čq[&x ]Xv!Tjm"TjҶX6QI3Vm,8.cDZ#H,7-9QQTƊ pW@I(MSױYHD`!9DA[M"&9"ʊ mPג04SAT'xDב˪Y{0."jdW Eas] CYy9.g.Q00`@Fr'{yHc-/Nc #^[Sf7۫X)"<Lj*h}T5LudDd`CI`7r3 m6  A}s''5>>l)6.0gyd`A܍|,ݝh{ܟ}TSşm(}_g׬LzϡleU"^ݞG}T:FڜL_MoO]{qo4D[׿YqϽ8.^4a-g'ct.w$-~>7BC";>(?X] 64[ HCwlT`BH4\egzp{H(/i((SIH }ԋ,Ojk(IIu )K,"Pw8*U`fpEPKG|I.F!h3 VN&||6*z:6*. .H*mCgŨҦS'bWdZDYbTƷa(O9xmjQ1p^-b0+FT$ք{(fP@֕mj<Q 7SMFp+!&.CjHH jH:tFln[$p홁 PrJ),yT΄Vr0bb'H2нp&zg3zҖL=V +>8D@yP 'R ioP7 ܇ Pt`3X]Ěna(hOՍ?S߈__%*=x۳-{,Fnf^Ov3g㝭gih$բ+_)zf7mo?Ừy gj?ދ<|wxy>=6Qs3 Cbb~\̈s뽼yH xwg&`qTY\m&݁o{UU_IW[C.|rK)mENAd1[GÍ|Mby1ٹzeY^-Kplt0E=$O;ˢ,I'LB\blWO,=xwos%#Z1/9"Mpîm)IL}io9Ln}x0J_!>zb .16>^'qA2oMLcst)][{ECm vMv!4ItMn'6)=j>4jHӿMWSa:k.N7jMT_4^Z-svGf[_-Ux맞ziݖpޛb$pIYZ(-xY4ۋ#}8SvĬfnF[3ey`mC1W.^ܼٓSջ DaF{՛휏bu8Fmlv+|vrprrCxqd^Yl&΋LRũ`-Iv'0oՓ"i'~H$}y}>zRc>vd1cS @J$ QkW$M+8AY"dL(2fw.\&!܀t4k*2KM L]5N?N:c0 MuV7tb5CC`MM&VVWLa NeUE5tKE 1AwJ;眶#zIEEDP*5 QDD\DI%OZADsJk Ե@ r|sT, "BcD=8kкh͍V:,硍&I xvH C3D-,i]{͞O bj$ 16FU[4L LEcaFCUg@Z AIwv@N *Ȕ6,4 hYw$-zZhH" 4x( k }.C#<QDePz]sO_ `,Sh"~jΒ N70y7_g?&]_M}1á*͇|cGU|!99:;7߼}w'_?kn}=|= YaK6sJyW DoCsj6/b>X[;ra> [}1 t|JeM%\q%rU>sZR7/lny^+_4RHGQqAggځs6n@_}boŪOa$sC hRjdoI954ݠhb"cbX;!BAı"4LAl;&Nk3a4 Ș}u1(m|o5#@!I41P`Tbt yL)htQP \kXI>LX ŠȦ`.75RCĩ쀐-,GX8 tw @C @й^f[ 8Cw4@p*u7\BuOf I!0PF+D$8Ec8o!#UΕf~'SvOU]znkoj,]"~x1_F{[˲'o+⻾y/ӟ)M^y*u[gI{\ɇ4MpZ1 x;.q&1ZUM%x)B o-F(-Pv5iScN1*eٴkh?[ǻmڻ|)v?rYYű&11ɤ4N͔DeA諷o_ U;{sO\on^xg$ٝ.ϦO~8p|n+<cY`"]A89;=cLG#ښ-ʰ "1ۚ]NӽmɵK"RU&y&+Z9LSEi66!/Uf tIUylZVe\ vDSKTG,ϭܼ&wxaEz?G:I<\U: ҉;)\HM,öq[Ӿc65(v EJd@f2M4˃` " CuPB|s3w㗊\Z5W{!c*’L2!EE1ޙ3@hu m3hASiDX*wL1Ol~$F`-t5eX3![V |`R%21: B= Pp1^1(BH(W^+1$><%J1 1;=H.% Qr\$8TaZHiUiDr8sڼ'$9梁zi(O>S/\ع񫯾 =c ˮޚ.oir\]I%U>],hbb`a! sfK)E4 ?us[2_)#in2.&|ˉd8 \ZaHZeFij WgOOI8rM5(*j D;#&$@e^.fTd &F+um҂H6$A0 Qi2RP\/@rஈ\k&Q%(*t< PJ&ul L!UgAyD80*%[  ߂ H51]tt$ [!CYTy5j@jV([CqiTUCBt& R[U׺]ntcy A-c ` 6Ct)F(rч.‰@5O,4},>3l(06Ej^]007P#CV+(5яBuukf!mFQ:A"H{:ǩ~Y*@Y=iXUÙj1aO04A˓q T5@8{;hDWQd۟Ew @uxHwJ׾x^:];fқǏw|ޅt̥E _9̗lzw꺩&U20M07oݽo='p]tW.΃#EXSt]0XT[ORc;[31 /༞څ ya;\L4ZE}8e5,$BIL4E]CQ PD1уt&zLg{p#(ۘ\`l`ʀײbrl6eORl h X! vMCwP4q$+44Auiڜe֮QbXcf E3Q."w0I ` i!J1H"pWkVdItptjc%;@и(r$PEkfբ 6)ĐP:ap0Γ¤%J"k5s{йcDZbt,`dzE1EJeH(1BS&PQkm]dD(2LgƱNiS鬍Vn='fe"@UbL<Ŷ 4 d\kB%[t%o{8K#*D桎9Z֨chJ\YQ\h40gZԼHu+85S({#x d2@n^?Z-P#wU3$0o3i +H bN![ 0EV*7V6 P^,W 41'F4&it)^iӻ#qPGE0Dae&hs . 9y?B ÿ]O8>#_3]\ޢ|=PZlb\o<RV5,Qo=98/ȊՇ/_7Snk4 #0I40YFeOom!mx\Ū3Gٲ,qyg :_[8>[MbG*KTYmL8 %`ư{MGTo&$ʭx8j$#EUm6x8(hrBQ*Q#; ADkZ/Nljeo 8k׳Yxw_]1[4^F-f>j[_ JA+e ÕN zu3gw[E]5h\ٌy1s_ Fd]j ]49M2R bMKXXf}q3BOh!!lz{pm4JN;p!8r46MaZo✢v9 *iiF\Fj5P T$bTBl]/j|nRB(iJq Զ(J )󮐎;pl5b:G*ʺb$H䡨b$g 1:ī#jUQ 1|ȓ&%(,I iҕZvRSQip>흵>Rf1eWvLwx󋈌̬̬*VIݭv56LhLْa` ؀l4?76$P6,AfKHIbUP9Ews}ŶۚCxx/;FsYgײFoXRML^ 7\X*+ >_^͝fD͆ւ26ѷgpbhEkuY3+ 'W@ }ڵ]X! j4r iH)[28TpX mavsmLxm1d`8 `Gna+L9󷃲9ԀJേ5 PmSOcqCv rU3O<o5xN_LƟ [_yLdQUt4f%<.E/Mlk\1qo̯t51qgܬ҄iK <hm'Ƭ4G!n=ʡ˚ַl|:͝Ab[A)ll6m'2a8zQT2_*$Gb|S>$d4Pe-8ĀzM5Z&v0ϳ:^o: պWl:oMFGWWz[AZmK㣰1&E$m[E FmB`j i7b> Z/Gw;tP-@βi›oWYN.\^NΞ?.vm`0m:,MiN/6<+769FR-IcuϷa_X+"zbVhϷm.unڰ$s1iD yll)#EPE,PisE+f," 1chE#rJ$no{pF|ЛF,[&8`$TSYE`pNScML\Wm.@"}@xY&2b4ΖH+!j-C?>n q NxPݶ]O?O\.ƫ-~jϏN7׿}o37>wrpŋt6tgx[\ ati[L֭?-Γu\[I8Z/ ܄0h%̝Q?o씦4I5m=MZM؂u^.lRTLzu$a&YN'iLBy֤N/01S1qZ՜% 6 (ܝ͖rvqj@5j1p43l5Qa7ulZ3 UC0 A%`i[8kA]5B@QMUͶ8`x2ՕI> 'X-2su3=~pw#e7,<ݼ}sL>n.?ê| L#;|gU8B]OzO |_9 OW},t{{~oIc]B1Gjxs{C.ũ i X?>:2H{}+Cv$RMfjdN1Vk%^eG\'&*}9OESj*&@;hVe۴A~ pׅMrZo~7N`pR`:c{ΚT+6ژD7En0)z05B' :ж~_~쒴n",(W|':I\?ݮպaLcsܪZ\\ĺE~}kl¦YE^䦋uM% !@b2s&Dg&ksYP'gdu.3BmIey Dɠ4gc9R5XjѥEn ]nk&iO"t@/dSČ#tUyNXQ" D0Lqg)@pZtFDT-P ܁&KDhZlC9o9&%e͊=)&!(MQjD Na h]PN$FnBUUVUz$Rf$u+`C06BmtǁUZgF5W}\RzCJbQEf pc3'Zu|+9nXsj,M $6V{"A9"{Z!)vK#HݥsPe@<t0+@5#4& xu==U-`].H!@ZШ`X 9m#Q4X98e9j"x8KJ9(0 \s K%4F| n{]O|*\՘0[N10:YbILX)#{yUUtBj=_a7*Z uoO~m qZ%%aVnO7PIQ%\;k%,EѶ ʓĦB+Z+8cGO p7VG{So`^,CӴ~2Թp0Ͷf%W%14{ = i J|:igb4#`@mdni6d6MZV9ۛ ׾moGY:f{ɬ {{Wr4?mQwU5Wqϒcmrgk$&^QIڃրRKh;s b,\Kԥ'CeAQ0Jm툴H]{ev{^s'RDO :с 씻m3l!cV*gnwNi_{@HME5W("h ] ؝bA$3fvT{tdyJjRʲgEDbkT 9)&J;i&'$.BBl⬱ -XAZٔcE3[cE*Iy@rn[rFQA[MEv3_ Tˁ_4TZ<216TvgH 2g֨KGRgK-',J |j8RiOQ1SSR$}hFFz-u2 =&k͜`h@MhCclFPkĤl`= ik{vD\ [t5bs|] Dؠek!HEGkob?S{h|->_yUz xLI fXj~%>.JC% aT8aXn 5+7f74GaLXݛ; kc97GNK.UNF1ff,53iX3y'`2Ѱldo:AD ?/lBZu|Hoܽvwƾ䙹?U^{L$*y"4X)bxX E/qz0)irQZgWyL O/^?[\f[ .`I,-Plu?֧ ɓm}xruIdo ׌nPmҤ٤K;c˜ J f(u@@{ J$U9"2T "AnjZKt嬝?VMtFg\+>-I{ìAbb!h?.. M0D3b YSФ&Fnf=Z5Mh^ļ(sݓ$ó˦}5ZV:F!@MLQk$Eu݁3 bJe޷Y8# w4 ۢ{S(V,81eYyEd5>L&.IaOF!FqRӴ`M41[E5ڶV9p:}r:_a]?> g\ =sy9KeQUi7߼1{ޭ^2M/w?*U#A?YmqEK2AV۵[X@nU.}):q$H6ZbD͖<:G󘵜^k!stdwwt9}tt7%_G^>~;ۍ}Rkڍ{}j;*\[B PP׷@S/)sz@Q:@cU%jY#];xmfJ n `ꎒqUTX7k:V ,&ζimN4 @`6^c2׭GqLׂ ~fCNϖP=V RӶuH2gM ]{]!J]l^ @^BEem0Dcĩ PHu&aηUM%HHK %J0ӑ&x4"Fh$ZYh @lZB}6B<dF-GWUoᓔO/Ƒdi~lGFseLĠZ'1½{ܺ.jRd!,ɀőEo Y( < 1eX 0h$uJ~ՠk@GԾAA/t MtN0pb \@C-U9\lpsܩ4g$3sޢڜ`#c,>7Guw=L ^1x~T?h}鋂n]<"r}my\ +ƓQRu誩r|12vo$SԵR45}tuF:MpIBT]D^R"s( HX͝9U۶@K-Ug}y; I`1ƾ ˲l<jZPlיKb2CD(1 !uM(vZr[ɼi;fWt}lUܟjp~yIQn|k2ƣi`Ѯ?x07Q;٥yvvqͳeB{m省KHbL?@Nkg9{N〆DQ×kֺtlIkt``w\LEghz;yx]}ǠsyD:r{h ;<ܽv{F46u3 ur,'dO4u=> uMdcL霵zN2JZ(B&H' @^]1xkji|bhdXlxِ[ Ѿ3횢 $*cRzMH֧ɞ5Qe[O2kmJ1{,MB?9/y;T\5:h\+u?.qix??бKBc80O*d M!A5RCꓥ!nQ0VTn)z$p#"@ HD]DZclXaH# JiD I;q:UZrG K('dUS"hXca >05؞q^'2<@BIoaV:sow-1kë)|OO7ӯd'6k-f/](?>wjk{o}˜\6|]ŁKhBx5W*caTܬ93)ݹ5u[tV AZDEhF&IS`"mz@- s(W1zR5W%4/&pq 2<QݼV0Z V DjEXiM`Ay[=4JR5P U㺦{DEO\4 'U^ g-%G&nBiۺ$"z~hYJͪJU5|mq*P}e5͒Cczh0P.MpCR"S&4 k;bVA_DW4JP(+j qm5]&xݠ_#):xXo}lA WiED5&{7>q{'C`^69+@U1R/ϢY)!"Ve'ܵq "M\☉Y$q*Kjz[ Sڔ[\͖PZEkAD(,Kܹm_KˇM>? -N6={><[̞2ʦiZdYoTs|׊r]%j|k)oh2] ( IDATU@U/ѕ-#ULRw&Tc pFAV7/۾\!:ݝޜF o@ :s3-ݼcM;?@=ecE(7~@kߍSe]IkD Гn( *cХ 0`h$ lPIP"Pl! :*][!CP9]kmqtU-Ѳ"f(H&QLр  ` Qkf%eXVICTZ(Y=e5RZkEܼT[onzf8h%OxRxX5o_f_ݸP/fӤ嵹 &:zZN$"TXDTh6P$Q56 . 14H\y E(;&(PuK1-TđТ<{,ЄNÇ*QذF"(@$4E5adPeďZlk, \]b-p6 %xh=^]b`5i7"ЙD@+_/__tQ h 7R[`qqis^.^gyG/e=v_w(Ѫ9b ViX55AA$qpdP5\b10Z+ u]#HUU񶮣&Rz/ZO5.K֘/_g:4&`2Y96EnF&]FYXmqAUE~,ϓr]L^?U^\ԭ|48cP[oj'1NkK9[n[EW>1[ :>=bC} '{MU珟on0/ݐʼWgRTUŹy#P+(JmkVKlԬqXb]r["TlSEiHp  ZyIRF@!"y#]h PDc$=G~0 c *3+hdhWT4؆k2| bPZ׼=B.WPC[UJ 9=E6V ~т`IAY8g%G_TfFV V.[v-: ލ5t<>{.S@<_ o}#'V^__[~k_/w__[|k___ "z|'q{A@>!~Ϧ]=[/zOsmGeELxݪw{t ]$+6If2V=s9jQ`I !@ 1vu@:tP .QoEF,BlY1cfJDSZIuyey5Lݧ;ޫ1$A! CR$mMNTc*6-ےu%]ʺCߩ}L{Z7叵Owk ,OBR?Uk5}R'* 1Iu|j&(-[-Cm!^O}a|Je/KmzOPN\ŮB&Ӥ4Fl`jTj/[v:Vs]ޮ̍qεܸѻ~sOzY]H%#2mdA;mIgO*侌q) !XSRDYb-O<ͫ%~,YYLV{I"Z* "C\J2 VfGd HX<"T--E"3E(,Mʬ4@f\1L_e~gjT ؕ)`R3|(<{9]"C\٧ 8]tȧ覝SvS|'?yܲmN'J!F_?8yMԟڶٹD~iVx)61zTMCb Y5B+mhꚪQ+5 IJTk]#Xt!V^rA4FF!όB:Țm{J{$Ireɔ4ߴB6pc|^:9ʲ$wܩమ]v0[e%I1^-;Nso7r3Nltbu`6 B.TIRoM_g''ޑ<֟}ZkTX7SI/ٰ5t>]ɱ҅Y qR2V-!r]u#_\>SK2w ߨO_=邥#Rwqџy `ZV?~w>}hǯ__{g??|7cߢMAͻ`]ϵ?4Yo/op}Z~?;`xԆ h *v>M*'^  Ric8(hQ&8EP [%beRt< P$2a=gX *kW૊2Q(;83£[z[ںӓQ1ci>V Mf&QlrmrNpave2*ont+Zl⪤s,R Fc3dMI;FDǥbj)`0(QI "SR/H>D=@"/e$yDD(d7uhHI;.k* [uΔH$Mݵ0+|0D̥,* >V?6`|>B'm1X$b͢я+ |&!<]i%]65t ]!0vsuӅClTu#,zb 㤗O'׋?'{AsH|OQ'Āo[G':OVc( c֢$D۶(B DWcg#ޔc*uEO=/69{얞 굽fe~qt4s"B%g杽TTiNmnzroz`O̞veth( X f/љ@(t3-`G)9F?lLr/r?t& !֪^%5^ֱtͧHձO#GtJX;sW.^:—-}9 (_;I0tpx/];@Uu0}ߘL ƜJJMS1чP}~{6d3_V^QFr c״3L@^4$A#H%%P?9쟥ROSA̐v AxҾ-u_&!:ȲAP eUdzH{bwkϤyH=%jX4EI<CP6荘&pm~G򘗾q5pNh孥Ṻ3+~Ρvx 9{']6]!ɋtAe D_/.׽ uyP,}I^R>Jl>zo%<+I1t)BHhJ#{cAi!$&6M#,ֿoY $J#\ՄI4RjQMU${/ kӶNqYGh>CB?v.aO?/(o h4=q;{2QB9y`8-i9vx%{k#}Z?1w!zW c{o|i^U.!ZJ,:202qEg8)jbL3ѭRm"[-*FD|Rc2&H6^H"!ŠbZ,^c5CG{Wn׊RnFǣix8'YӍv+ǾU߶y,6KulK>jC6yL/"B8S/L^'I Rfg]ʠ\Z̝0` `]#p'̓P:a%ud%e.>P앴[*\c"(̀V;c∇VԱE 1Zj 0 ]N` 9y"QhPSCV  \<(LBH}\UaH,2Fvon=ݝGb$ހoh _9^q/'Oɷ%~گ}Ax[}$O'ol_̚2TuӾmTSaZmx'!Ʈfb4mmuw5BweŠ߿Nu쏈2JV!:V'Z459|]Z_vtY}Hc6p/Fktj@g 6Z+'CYEiH B*2DAYVtb&{e4.AJ/rcb'fvzPtݩR$)*r6Fp;MtUnvcPU״Ͳ ZbHV8 `wRu*LcP3/R?ݐB}'*M#e@^@@Ǒi9xw$f)sFPc5w=S%ˈ%-9 ŮZKbRRN'QR% CM=769DelH#TT6U,Puub1t(븤ˉ?W.7q7?ݙMONOHW|nk :SHʺEgfOI~o\|{CJy'ChƩȗk[x:_k߰Pǹhk㷢RWI͞m1txU!DW[ڶJcQ %D&Td00Mc*x}ϓ~#7{ o}-碖Wou{;|,t`Zo(,OQ"fYQ"+8 Z8o]dL04z尗"[onY;1ArsN;%p?HD|MvYt*ϱ]t&WW΍}չuN%< aT[_tb_P#Ni89zy YWy"'CCۤR%Vʛ_mf٣jOMĹ5EMPk iwkz 7B9q|kGVE;út{7VY4,:=1,~eI m;\G5 A bG#"L i_#,E.ל0D^¸ب[;v$?.;vV+HW||BЅ>|']Ύ> Z(>O_QvWwwAf^ؽE|׏֫RM6Hj^mӣuL._;鰡DMׇY%sdi*1ضyO/Iέ$ZoR vcoĈpLQ+( !R*Z`ƭ;TQA>A~hE7[Y^sP7l6-za/-Lӧ$ɲla]bC,f '5I,H^il9qxmOog6bѺyz$RB, Plх taJq\Hs-|S˽.uNst>:󃯠܏b.֟r:IC׾‎ݏ.u ݟϟs҅v_rh@R+[dA&:B1bC@&AU6Z7Y(gYB[;KN"Q[!TiT b{[(mm-o\ Dk,IgFo&`m:x1r*EN^5g+;y'ƳgiZ C-t#j|%0y߷U{S>sz[g7a@1gQ5ׅh.0UiZKUQjz6v*Z$`HhH&ITGSF%sz(F&aTqW2B,{ei:u֮]?w_x\̓{ so>Wp.]ϭsk/mUE||}ӣvٍ~NnS 6BDa IDAT2o-qEF#br*C:Ψpnta0l`XǴ(Iӄ<D"mP-y"/$Y%1ʊ"^PIy\mTݴ`ycͳ}m.fbpnڵRh)s!G5FnHeF,1ٹ I I! ˪Gix|H(vGF]fȽ$n}N{I~e}ai^-o?u^^NK\xҩBZc?#S:U:h|Bn_FuZ} _?$zZlhй[+@U&g!k%[N*`4@jDD4 I .^yVxbw9ɽlz!щ^M3jqA*ȓ,QoVS1!Ģlg<9_YL_>.B/huҨLv'|I#G1BD5;hE+ Sy8>\ Y, fy)FcD@׀HIbJ =HJC,-OÅK $hkȏIe-"Xx̭tq")8<9=22]K,[\#Ҫ}ZXCbE)4 UZ O&񸱃JM)dÌn2uMx>AO{WԜԇF;է4?m}xXޮvt`:sN E+Z c]%-KeE! %eJ'IR"D6+DHFJIRpzP1DPUҴ:2j>y>>Rʶ/Zg7bܠQ/~A,ts6fp>~;޶3O T3uۄeaqu!?>je9Qf}O [Qunu#t*IǸW:SkOWտ ~Ҍ3< m=rxhk6xq9]zp҅[W.^@r҅Y9%]U񦜟/H?{pp {@Zt%iu\cg7(vP#hj %rH1!-Τ/t|(誺ɚ#]GRd:mTY#&dBV"QeC OIɂ)ఴx)" q(!EiY2g@D ,i( ` gS&)U&DU`0Q`ŌFb8P2f3gIMM ]چ 4X JTBFG}yS︂9uKxpv y ȮO̵Wm޽}O?>L҆\*d0r} 0JJ>Rݷy~y.H 1xb*X !J?H4]Hgޑ 6FOM!jjP,оĤz>yMیn-]&׾٥jfImeCt]"GLɑŲ&K5'7a_m[&(yÂu"#&SbHT@@afv !@"<LjenhGg!`I+z3WA pwK##c\ 39V֭UR QM}uuZ~h~?t_xg|Oewѷ=9ᄷll1zLA!9YSّ9X.J#]s¶6)1ZG!|B6UC\-ȒsSkq܊x!ĠM][&ZYe-jҐyt.B|Kӣŗlt!믞ryn ๗_|_^umCGrt'S}97O.9n!X-/锜/r_vyw-~9l/](yw}Q}W/t}Ş Kj2،e'@]'GbޅA%*IR(SoAAU37FŁ^οz,Pz (D"\zNtivm\t ih-ӣDX# zu+m2Ĭ,(S2dD6UۆHbVc<)SIbAQRR)Wwxj"'4p4dDkz058pMVbIj,uQPi(X*jEX2P jjInT Y(Ki?L[׭F6dڿoO<2H4Gi}gs$]}lтC#ѩ!* .H"(6%>t34D2It~oR!Bֵo.J0ZC|mzz;GDw`wuY՗hin+%nEK^yLє7wb5.ѝult.4ִVH`0_qKQ !\b ހ67V蔡X OB䍮+(QA M:#֐1t%<jۅ.obcdtyd>&KcaiOubO2[/ OoT0R]zb(KϊH %~ObSu[j V,gD dҧ24X$)ButH*U*'&CTKU$nLi4JKJ*ĬOÔ K V&eo#kj‥Dp5S@_v]/οşo?=}o}x6+c+@xﻪ)P ްqлv~fyP/S6$M嫏?IViėn&֝QBD T顲$[ٸ%y)!6İn|cd$!$ŌP =:mL_9._y.AG^MgPXF}kRʳonF(HS `W'g"!6M7:E,ə3_hmQ;;Ib˽;pcdۯQ{G\]־Se}GMw.)h1e5ՑPJ%1.{JׯI]#LObC D$x'n1C  sf\XҒ [p1CJ 2 I8$":-t3v$.`vذ7re "'eLNd!=Z8 F:q}y]ʠ^`^Zlݢx|5+/<ډ{ƵH)BP#^U,vum@ʨIx)Bk±?OBKuV:QZE!Zvڼږ:4E)6z g\WuYvأ״,X(Gh MXFh]ѰvC4 E9`m@GRؕ55}w! tѩ3ϪOx̯wI7zizex$BPۖ9IB++cZK$w}ҡIҮ5^6n&.Bu=ʦ9 #$rg\4[N1MSYpluLnHbVeVfC{0ش\JbL*dM{2za6j0}la'ޤ7D&"i.nP!zI#x_yҬљ}٤Udu,<Շt3ӫ> OtFwn yqtYv uY.lD6R_|Wo}&mГ].I?@td)|Om{@Ul}Fŝ;mQ[{hwH]^;Hwꇈx&1d:~&*jN?BZu-6ZUQg_|ʳ~2gK0ä7r)oܺ1\Ѝ1{3Sny1 (cw2d-!@De'P72A#R`1L{ A,Qo85Jv9_o1> / )a}9 Vm)m:G净/< ;_B\?2wY'&1ǁR?RS:uW;S/&XGK+/|P Tu}lttgNNI js3tD-1G?ZZIszX, 8lrM] [ӣAHIcF` jKGRxh.Ƣi t oǐ+< o՟#NGtqxmj|r LMy3oqizTMBj дm*'"Ĉo[}^]%ɒ%m!ؚ^3BfQٛ&8\>cؽe1l]X)ʕeU0Q,ҫ*!DXeYm- yg}g}c!$Q4ښ1&<Gc5-j*--VZ'K5h!,!1YqL~5p|ϙs_X"FbG´a""' aY/&e}?B!rx5Ol@uJ\z70 -9hFհ#f9⫰jr[oV!9[KqQI`089kTWwn&TBFK ^GB4 w'*>B覢C؃VkWYލ3, GVxx$Hc!lrIX飌$IQާttug`* CanzѨ㏸J*Tz'^l&JGt)z=QyZ4[a[L{u Q21b3m8EWCⶃR'}u+0LBJIDAH(#0MK 9vohx<{$-Gvv}}4VO ge6em784PYEyj`pFyB XUT#9,k/*G7:c#=jԘC FcN}:*ACq%*G]K!c+F XBq@E)op[Ů!ä o_򸔺Gko _V|hEJ*ń)>BF 0 ϡ6@Tm IDATBLkd~G@pMl$A ¶Ey} AF)%B\.KYlcLƱ,0$<|ǰ,RAF1&W(@Zx@yOPwxd|,nvwEw&dwo0|ATUT24ݙNNehBaf abP G<4cD18>w sy`+z0$,\ڃٙ6 H"O:ɓa~r0C!(HbK,(T.~*տ#?>4ZہYR@2&d1POa؁P&vPzhnM!kc8I, 'pa. l@^ʛ=Vsr/'3y. x eb~m#DQy>V(Lx1ٲspޡARU5=L e$RJi&aD")7I̲dWV#be$3ݩX%c1G\x Ҿ2ˆ], Ay9\#a 'BBɄHtct" # !{ 0, ;wuz;dwgGeEy<$A- K)<<^Dḷ|o5e+WD@JR;!uZ,<ݽgC]h?7`I5ޅQOmG *V]qZQH-xƐoiT#y&v@y4{PCFs(7 w*WA)GPmL6zx끞I-M#)qRH(vn5< (J:݉vGX,|y`k6";y̌\UXhe2T>ـ,T&GC&q)XxB`._#h4z+̚~ \/ӰU9)K/(Le܍ UcrphVbsGV$S~i6%ݸPL_p\P ?509/ICE,ʡ.󪶯h*>~ӟ74n?sV}0rRdOlj(4bu{ (.&gD47@[ҜBr2rvTgpzK|nYV7!7[uJ %#bbozb(A1` 5Q=As<Ђ0k;?̈bpϠ=~#aR~N[hz=TBn7@+׾u0jX}lM=Iyy:NtrZ&>ZY --=A֚??[xΟ TRIϝ0V@F`)g,;vzxL-nG"4UK7ƍ+GO+{ϛ=ʻcѱB.{Qql(]4[0ҕ_ŎQZA.55ad& ׷o-kʶP9Eqm~F$e Zo5/)JyEO~w8!@KKsys_ə|#3_َ.ٽS,x*!ⱦvDj!d3|D,mc^=ݮTn)@L7Y蜎:/{Q%Xdy$^b7vf> xqD&꜏B$2 xu|Ռ4(LɁSZ;JM:%1w0N8rmIS"Sy1;?Ado9V̚Sr=)&#gn_ѷљ\{~7*UE||w;>L\]{-wG0u[O޸e$S /I,_ޟèZ Qޓ8n`4Wy]bh-jii[ZPr9JXW{bXmH{I󁭨"hJ.(MqI,tܖg+V♉f-J5^'y#4G9ħr^G7ǹE޼W̪%˟ԀSueO%;P@`䜅@Ԫ9Q(g ڌLFtjNT 'vLEu.TG}GsG|T.*ԛAmpQ9nyTHxvgT nh{F@ 'hmǷ^]G]tcD|3KGَ(Zp?\rE me˫ROiyI0Ey9yɬoÑ^hv- GAڨ( $#n)gX }=Z4$bO6 UK#I-n\3)蟉 mYסċ_;6c@T83%rn(g]/0ҁ8@u޵1[K52hj5Ekd>hn9<9fGGh,}l2Nvnä'U];TBDzy"ٺ-'v.]MYkm@4 uZgV(sJLE&s(Q6H$!A :}w,hiiz.|NZdeW5< jF]*B«9Ơy, eA9^oӼԆ5|ۥ?'V^;nx#/~w_*L֌Z<=:uSOrw䗁dS3т}[N^ZSY݊w?ԡ~&m:][ 4 qيj^\%dvgEyvZt49Ow%)C^%ߟtdLٱHFc;>.\0}l3N̲ɑɜ15_\OCxbo7|ß`uf[#\<9y&[Z%#J<߱egoOx]DJOgQjQUR "#?C]rKٮRW .gD:N3~SkxP?;o8<^t4]]:N&׃ g>̪f'|װ&c{s{F0*":m;S7eneUק&:+ԅ/_͋L١|_z4?Ol )Dh:ڋsP(cBR)Թ5 WM1w(}>:fтgth{Q!~:^!Y6HJֵW Mu-{ _Sm1Ib5WKKtƅwN) 4*/Q9QtFid?򘗍zыڹ3kpP7I eⅎ󺁉^WLQᦈYVNy݆3SFo 묵[wBI%Y*Ki&Wd6<Fsq /s?t꒝|ꦁډ3)?4|x-s%w `=q7|PYjjrtfUWOSyg4Ĵx5<hB({kti$ឈ.n9VAK?*TxC~Yڣ9тgRGayml';eNeH;UVH>:gB^?O~+tb_굙;&:\"z<NTTNA#q`YLp:ǐVTHN?68OKyF|M茭 sRX[ ݙHo$,rV-YnTuze{r#L*eBgfb٤MOlJ8]!#8Q>:_QՠgB`q'ZhNUlk5|y߫‡|m}̬lj91)ge`?=|O/K->wgIq{|:$IF g5--͏5b)saRSP^F%&kbwok--vwNTp-1'|5__?Pȝ%`= HIzfU|fw`_ c_'B.իVK->hN >hcFhI]̭z H4EO :kBѮ:k[XRxbe;c 3BQA 3>95ET * ЊX 4 Yk&4}8=vϱrJUZ'!,l6'm IDATķ5 %PL%_(6+1p_ᆩg %vnveW~r0&ӣ g˯LY/~7?^zG0 )&WJxD W6 T#t()<㐇onz5t״b"r'f ;M36K-1jNQn/՜Uw,8s| n"Wff_]K.d!QB:Ph f>ɢuZ l5caP)6aG&kԅ!X^hokW/޳g_>F:5Zz4)u~ʍК8{ML*a}묵(OL o0;s9+e, 2LbCA6"ywJpy(fj>VQL2zB$é5',.ض |M~?a^`!(x́$(s&(|?c ]`e,5T5nF[rsWтgtboi#`[q`!G~`}a ,T)0Z`IS41 5 .N+?~9Lsj򊺁}&m{ٯlݶ@}4:F%ʼn Tr(i s?⇙|y?maHL#AEY0kbmQdI4KZk4C碚囕V%"aG酊 L"|ݖL"Ӑhke'9"؋o6 ;>"mW})xgf,֛sW**LO/X0t[Qf;uc(l_oSVLA:Us SRi=s غm_ o->0;P=\hq S`Ӗw?S't?MȥLݕ龪GΪyv*.n?+n`>6U8-;gz݄\mL +-XeSPh^{WPm}F:Ȣ&ZEBJ iD+xՅDx~"ggrf~}Hḑʼ&±QbUn>|)X8 \Y6PyHy(l÷Z<~YY1CӼ'Z60AYu;ݽwljь sL]yyyö,aMಕ+v/]D Ȕ偮(3|*`&*q9B}Ke!0ObՒdi}4BgsG4-I}ޣ̗7n}oV<˛u;oOf{/Oh4c<~4 f&H.EM Ǚ`aZ ]!D&3Dtp&Kcbf\Re&/$sRuNElGOyA)fP@smB|Vfl!Smmþ-4eEa~+e+W8cahj!цԌ0P%^Bk fF5EΜ-lכS&T^6mb%#\Α -#EoF9:><-th(UD-[L63χ(8,;OQ!jعt1 NFu[Xy'gb~DPHO5 `͸e'_J9 !bݻO{{}C֋AگGTެq5GNܕB!sb[sNTV,61z `)3q"+2儁I'CK PzrD攥+n_|ue.Ю7 {R : kCV*ݶl 3-?Qe r~K-iT]:XF DF!QYktAͱb xwh4Ge;vdQ9rs !D]mZM(KoZrE9t^~M`]jm_ygl(JG JKi 91cK-1@?{i`#PF(ja,A@hAh4)<ޞρt݆/ᡦwò+EJU4=\2jJ'0(:}Wk4`qN{i=~B?Ee+WL,["x饋oqpmh4I<[f^! e+WPᬪя+zT" [,m o4S2P]h4Qh3yjTxp릇/]8@%0ov3x) =rO*XF3Ђgr].FLmh ag(gl<~f1eۜYI_ь6P뿋njo-;w/~xF3Ѝ!p֥@|q@uցM8}m{[QKUYpX٦9uf/8UU[_70؏}[}vh3> 5̼s䳿LhF+pMMcidBUUM?=;?q2SFshg| xf||R@ަf``,ҜVp_l?!ߟ=>4݇gQ g fK]~5n<묵)TC @xSp9Yp GHKѼ=tHkSI',L{]+'et 3{-tg|B4-x_Zqh J`:&,끦nj./zÕW2.Ѹ*}A=` {&];p9_C Gr͇/D幀3^y52α6X {k#C'S dFU?OCF3nтg|wx 9<[9FZ<87z C9T/74T{f?7hFyqB1Y9jʷw֢c:k@`U]UE;E옳`3մ% E_k4;G{x?C]q.vp%wS$={ޙCoLqG%%ś (t]'77Vv*{9>H>bO32֗r#ܶE:h4o ]U0Ռ/B4 eȘ`BuӭB̷ʞcO<>T;h4o-xXo+hB7qc\DBm8 =i|uضX5cmFvЂg|p揵!ʤ(xwAڨ=7n6=<gқ|ypӷC4`Xb\gyLM4xŹ)XXQ^=LvHT"7::>kmcmF9yтG94Mc7={FMe&gkZpq{7#{]4h4-x4C؂x9oWYB}WB=~jOv 옐 lúbTRם5Fsh^6qԀL§<cxϻ{1z͓ I/䅁?U}O.wǻ:zjh4âFs(nFx %K]UF9R\/?{9g?t&vݖ3 {fcmF9yУ%4Ȳ+Y8@8sy16s95N없Cf2䁈q`s2iRh4â5KY`DRRpd\/V65g^a]hսk0 ]$_0M>~>eWwTŦhN tHK9Z/.}( )|TR`sv4A &5 |cm3݂Y-#"Q@dXXRB(xdyOoc5I<eИ7z2R G93,:;"= $BdHbFsdL{Mh4'!Zh4GᚁL[܋bo}d>W#k*@bcOcI?cB&~'=g @iը,B'nfFsFs4QsOoےikFl.`+<fi4 ?ۢ|`/:wGѼ tFs4yqiGqE T@eޙqwS3ɒ-d]>slc|4Nul$OȐ% Jvg'd$! Y`!6C,8fcl֭eiw*uL谤tgfz~UUog3 Z#s69$p\h5Hx 8!vp0,<kXdBsG{Hzd WsYtu}8J7?L c&mZt|x±{aYx l83e>4[b~7u8^kh|(O".w佉X!PlkOO:~-zPD IDATy0=&x ~8  jPsȡݛl'Žc ^t65JYZ}KaGj8k:p>FZ6`o`9f6;cLóv;ӆ12drX>wri L<08awsga%:b9_8/1uCbg5δVڨjYX.*P5,lAOL{|kYaxBCH$n `0:z < D8&?F@Ak Ga<3ڀ X.|Xޖ|kIgW:=qmP,dOPj~eƛ(^YU]ீ LkT Otzs$8;MS6|~GǿߎgBK(;"? K8izQO45 x3t֡xO mJEZJz9b .O3cp$TK^*o^_f lj'x9%8779'_8[,xptba"Fb!]x'pCg&`sN߀ `?k)͇WV'j3TRI/M? 14,ZU_4S(Թ,w=.[QY3.7Ycv. (d@3/f"ƽ@SgeqNt8UF(y*Al(p@APu` O2їbxAfXGrZZTMgTf[h"7;pNƘcxD4㶇\iD!3vuP(T@i)z@DgNފx)~s0}7Ƽ,0*[DQVըN43xy(ւ71=~TEP7ueK{kuϰi.rt㟍:>.p3m.B ia{9p@9ྟ;4?t6F/D)'҅H,D=<"{ yitS"`zhxvl:X!Dh&|YOc"Qv4IEATvDMY0=gsU#(ނ7~ tgl"/D#w_/Į_yb uyۋGf`)_35; 'ﱷ.AV`YeۛʂB rraByɒuFױ&$p}7V6/^a;cVLZN;P4nL<&ja~zs[:=ڿ^,z-M#x؂9hż 7 w|lBނ7WѱzgWsxH84sBlK/\ ܈DnqpڣĊS>.k͘<;Xb~mZUWvh쉝`."S|iH%=/p^F.F,xzH8z@Nd8: /E.3*5oE]jp 3%ѺKp8`H Ej>/fΎ^?t O\4z@bdKzb@zSDu1VC!3"5d ώ!֢t_mlQsBGr Q L3 p_ނO0/ L@h Pwd)p=?B֝HHGTȍ-^@땻 e&-;~+* sMOlM ?3DG ɝVZZv)&@19/#M! 1Bil@e49V* 0Jz}~އJo54,@ +ow2tqg1 ^6*Q-Wnm^^BR ޽ީ~3:/^p)U@?H*HLeީ&[GG?ZI3V$,}zM0-wGy wEEr)C.qN:]T0:-^pP9Q,kTsӀv!rjznƥQEԤ( ?qCmEtrK\}]>v~=(@`=oz~W# ӌ& 367#q4X,w*g?'64Y>LxoXcȒͣWVu+z 9$ Q/ l#i^g_AcW ϨG" 7鹝7t5 lz N$|2cigzX疁4[z-̜G;3{Hي&E)s(10=i 틝HXV*L8V؉'0#aGo?Y\'[8Di PZф3'MHx.B'ߎgp*Ulf.Gk1όS q݊M_O8."psT ԴKwtǨ.:z#=;@=.=x X1JA z_W(@Ԍ}(+oC.ǯtZ4}:O];1K/]?"UQ5P徼g0KQ<5/ASks)pyM. xtAY&OQq M LP)8&)e_w=`[j DGk LC A#Jzg:5;(vi }Y~& >& 'yG0Bͨ3{Y>!Jlj'To<.9>]|x7'sތL#qe1StwBp=DUFa:zgWc 8ߎ '^+T ?Iy3WtY F_~ވ':RI/W\C}:x n eGȭIz)pEk?0Jzx-mmcl6ӎ8 ѹe`Ah!C7S'|RLl No-bZ }lLMu;kЈ&ð^KXmhRAj"C>0=k$\S\1AY_DA?@K+A6ҁ;Qnx]zrB΁@ꃎ㣓2:zxQX)3,pQo(6) jO8q\;/<_ͲBY5(P p* \~}>j.(5vdr=Jz>Ggi-}M S\-}-Mf9;mɶnκt6>0Ɔ23DGo36͔jĴ"Bb[I1z![&2U^A(~(5M˒k'vEB} v/ZPhܞ!9F|% N` EKːjBtF@T'H5!dYE N<Đ%,.c@d0p? ='Ƥc|7]_?'P|Hyj~WۊB܏Dc<ZH »Cå {D bOz,A.h<&ۻMA֑8Rh##H "[X ljڹnoٸ^B$T|W) @GDR rgAg$~|LlO8oɕt-񄳽|Cr mPHoEbZ`(pv + _C1@KQ jtpGR_u-SPPp ,pw쭓 oq+@5Lhzmg6|W=֣:hr C( yB+y_*,}׃ =2'D/F E)sCDzYNztl 稿 P0b^Sr?̄K)e_K_Gg٦֐[k'OGQ$/AgYi$Ҥ|!d!v,th'Aĩ'&XxA 6t|б>&amEe]R*9[踟 j)?1c.&x?N0)U~{-}pӟ*^4 *4nBUHCn+.tlQ;23o-;p{,OI'V&7 hCA; K0Ud*i׉!6$,#:?Xs! <Qۀ_'gwHF`0*#,8θE@53Lʢ 5+NB"䮢PMkuA} =sfj^<጖zA*tנoz }oo@8p_5"GŔܒUd3>ZM<|23Oн޹ :r&J3SncɱMȫPLTraw1bR0|xZI{ ˠQE=Uމbw֢(g?K#+ϻ:#[N٥s7C.è yȟSBS@AlD=:AlO |Rc149.BmLTοagj-c:+"( z=| (,Ti軟?=3N ?}l&(ODwv ]#.>Ʃ`g~v*ɡI!DY&6nNlhek]b5'R K+*C^Am-Q `ΠbAs;(EKrη A4C#n)W1E{ 5m] c,hy~%+N%\41er8LZ$nWQnM# ZdRIoFuby *$v?fPd*Uq#,xAӨ>`Agtvhb13ٸˢ}6 >,=Feh~ oAD烩AxfTfl; Jndzbj,Cҽ 컦Plb#i2*=hkTcƀo!+O6e y e/C7/} w_gf?֣+rT7#SH4-C=yGhT&x)7x{=^*v3قSC!˓m<{)];r-{_yލi2/.;F| [`Wk4TX'b%rqQJhPIDAT"\ԯl/;R~%j&wqZnb?9'ޘdzCz z*LՇ%L7x+_tK5-{ma5&ƁxYzZjF=֥,O8?rk"j TT( F=DLt33FB J]W˶{ ØOKF^LOE?6n}`FMO8kU ~Jz nNYuDό +&5a56n>uo1PVL\@->@. GȺĎa`ظ{M4ј3RI/2feix¹$p֝ Td0j. è!L7x(z~jnGd| O' r;ʼ+ i$ƀͩgq ,0 x¹ s(fg͕~;ggSIϬa,aLaߜa;Q- Ø eƼ!pKSnJ%s0$0 f1 cސJzg{+|xc<aT{S~\0 0GfNd1''ac0 0\ZaaT=&x 0 èzLaQ1 0 1caFc0 0aaU 0 08R"4_Ա=CRMWM@p:37"ؔRJA LcSJ[y16Qv]®$+auƘVƪRJ] b;ؕŵؽacs x 2098$kګJ)uAӕf l``>ؽ'"CUJ). {i7Ɂ+YOISC2Љ*jҕ&9xŮ8^- +"5݈RJs'vcc,+9yl78$=R.M;AlN`u`30p-нp};RJ_p :6ީ*T$"w? <MoÖ^–..cufJ)ԅopH 0 o_(0db 1_PRteCGazav&| [ 'e璇T~J):K04ǼcVJnnNs9v vC} X8lb?ա!+Rvsx}Q7o&!vRI q Y):J e*El)6s$Lu+v8cb?RJ)u!"m<4++1Aݲ<8$cVJNdsjؠ [֒cDZc _Ů@.s+?\RWn bs|<>ϸs]*Tgi9vr؇ \cP9J)z&[Df7cW{ɍ-G]nR M;%ouA w=.`RJ%oV–n86?]u\\X*T'i9%v0&ok_>܈O/-yy W)8ql.`cͲp V ^߹UJds;g{#zǶ~#Þ471=I)zNrVt?kg.at#aCVJdsr6{CGڷ/k%-l8RJ)uA8yl.`c<rX7-rZk56+. ,vH^By=ԥ]Q K@{Sm|3G(RLK.6,qv_Nnj~k6TJ &%;؀aEm==c1`!]][t'WzJ)Ǘ|?[46Oc p y8Dw:2rZaj"sEӋfSb.绷mi\*RR-`H{R)^te 3K.Ů~ ،=)lPcwAl[<]Y wUJ)j'tKac,& M0`PYЕRjiyo\}Br8:سxk37_+"W찕RJ ,>bYnc!6<Ӝ8648$[PJW1~D )Ėތݏ8* n,k؆m]vLnx-vs{p[zbvS)RgO}86WY>66fO)잚yRJ]Y\E"2 .`KO?Ş[b^"c&WbJ)ԅl sl<=.n?`%pV nV-ݺͩ< :G4Y\E"cA̟8< `KZ\lb[vn_~='N?1FK^RJ308$>|( |lUlűyOZ#Txߥz*J)u4Y\e"r',b7l׵ؠܧU=/ƘyKD |spRCnX3dM$= [-5@9Z)cx`:ɽۃVk澻:%M"߱Gbmv+28vr}2=6:ψ o1FeVkJ)u "cn:ls%,?* |hxzW Ydx!6alb{:vM7\ߑ+yhpH$!=Iy}B(a̰W A5G6[Me2->Jګ17bnh0(v/ \m)s7sQ'M"7~[b3}+A!; {6c[4Iȁ;1sPhς@&; vdcL\S)^M6!#nRȦz#VB0,JO˵0" YL!LϞqΓ{w;rr&aNQ&n_v|y66;@"koN &,~I)M_1i;i8.`7/>~WD._T2 i] SyŲRJ3t'Lz'llNa_ GqdmM R{z{w[peqܔ16  }%)U = e ^]e|'<#" *g^yRg-x ̺0CDнo7VxLKcܻ|ڒMBKn\H]khǿ*ڑ:@E Z?z^JvC`+RO lۆ%Gی1_*7uGNocyJ)u5?[ew^0tI k+G]vv?rpd{1q L][r{j?urr) iqC-? rr xx> 1,*vc|N<ޛ>2Z9lձ'.j>_+NwDwsz89Q?Iᦒ_f~~WSYĵž}<$@r@mYL\R@h/.Uz@YokU7M/Ƙ .o "!خc,:LD uc"2:#~¤-ܼB9! wxl]8tIv%oӿw`'p׾ZQr S Q{0q<ܸgdvewq=s{vyH7V&^DD\ct>QLe=xjRk1?|;N kp@NY߅V%%Y #3 ^7ɧSIs#xM(DD8v5F^Yӳ5Ab[bel`˥@ J).tײcqs1,,n\Qn#8g:f98YNf\e9zC@ S^xlrTR4YTj5^Õ4cbhwc=RJ)j^Q|-vg!U@f6|2.`DO NaHN 0>TW,0;Fxm<v(a`wݣYszR@A-nj]]\D1Ǯ"~]l ƱeJ)Dddgov!/еPb.Ib '|D0%ނ/ \'qv$e~ {u=Jx@/?qc+]Y}A)cgXw`Wn/&( ^K=?@ n;6{Ť Yfi؏p:'z S,xN3vl{_vݽq:6;,.sIE:]v'DZ`; ̇vl)jx+F+zU{ۯs;Kk#zGZd+Ec IڻV v115 99ĉ]fdk[}=~{>67wR%[Аn#yl_4uX ."bWx$5U&`MD p[alXۅURJ)uNGn|_|_<:%?UH41Ђ(_ET m2(ML.` n@4yE% Sn֕ߴq㛀mZ]|d"u vbv%/gw: &Ju> p/mZ7XP۹7ZPs/T3"BgRJʆ?}߹e;cvnj1dxN1pA$8vUk+X۽S=E"bnܞEH=RFPq$ R ŭ,_ŸgBf q~N§eJ v:El$΋v0Md^t5ؐb 8~' `j/;8&DaNi*ʕ8 ?iű vwM~!w0+Y1@?]ԫ.L,*%?]T:|ۆ%"uρvqkߦM4׬RJW&EԛŁɬƳ>3u^->E[SShQ] ^LZvwHOVJcuD h>|/jb +!@c1)L`v?\ <B9{EDc6tdJ)ԙY3f[ \O}#By.lj| xѻt)B.,*uHADv>`tư 0؄q?fM"rչRJ)HdɆVΣs/5;άKcyTJvV.%@ClvdHz1P,Aɾ͎BC IdBCcH|aƃ,,6W9$O'y~#%tcm}Jf8~ŧm8|aWaFHm{.㛁-*}ǫRJ&'缷B7G}6ٔ6y9%/ȉB+64.Ѱe@RɡRrQlbJ>L+e͠a0 nh+u@&gK2EM7oÏx;[,3] $9RMW:{=|5lԅMnrI͢۰N끍žO,yhRj90×?|6FK];'JIj%ϋd`<.)M IE][f-C%p IDATAv>ev"9bvMurm3 0-!O6n8-W=~sWӚr!42pRj]YT,^=~6aJX%9!r%+MYBV{1!cLxǟn()O#Oq+RgA\`gq^_g~>xPvfB2!Vwi8 A^"ܤTCG#ABzZd,t()a8I#d} R`$8N16'k'ܒau~<2w`d)u4d1ɛXR"r7!NWWMzDDKcؐ|c ؋M6l2[ԯۡ*R"ޥI_QȶjC ᡩZMFJ+JFnhH.0= L =-F7uţ|Hi72G1I<*VAneZT.{bOe3{ z|68:ns@E^^Mؕ5%LV3S#d OD|cLrhb|tRJ=;:C/y]`j\tMrꑫتrh}FH) 3莸wz|&U{oN_u ]WX,wSJұ5 &Jc' l& B^V!N_;Sei$`0tŲ~J)Թ'a΁W߸w&6֧J΋ؚٙ_&zab k&'J˪&Tz28T/ >=.q"d,g]Z_}]|:weZUz#$qم(Rh)&v]+`~ W'b0~&?{EoމMVCER+B{ ^7-LW-$^ԛu]A\֔[箢\)N3Y h6kT$\*GE5,6XRk8[FiGJa7I_rt7_D$GǎBc]sjPD4?dE졋!6T5suhsߙ3Y>_hǏp6~Gk9aXdQM:{;+vz.*JJY(AO?_=c=0z?!-bg=W)J o3jU![ ?z?ԼPsTw8=t/&Fqqo,*N aEmrmp,͉+"N0Iu/gϨjϺ n4~gPz/[*mȷ*3y%4 3381-qIkkJD4;HR,H>ciBO >Kw*Ywt#=TU Qvc ִmUpj|9̕h ]=YI/"M $k2uf}(~-@ YKH/qxv4g[Dsq73עY' 2 ܞ.ך١d93ARv'ܸ>b†nE5W3Zfnl_\yTwWu8]4CWjKB GzsK,M2MrΊ1FWRJ#r_7^)Pt^G GXn?值Q / 'sz>cNΎpKj ^Rt-P10dٖ QcŐ>=yح̦2FQꭐokmt, 'i[dk? (d`Л8Pey)Vm^2lmSK].޷8'oRacsP MF~wl Qck)5ė~ 9օx0 nja[fLw%Sr ~Z4#AYdAG, @\ H/ɨպ 3fFIa|CZ)x)T/H46TϜdss-sϮe93Ptu[cZ5,*e޼ڃ8W ,tCp 6~acLr(Rp(0փ@zCIqf)O A83O )WsH$azQĶ"WROPKG$wjR7 yb@c2ʅ*s{͙pˤ3G{AQy nflZC/i@gL{޷ "e=)]C4azJɢ赏?V{⮨ DҰ87qJ^<-_dQ)*~)K*7!3xzUfpncFv"@ׄ rcx]12[GR+Py4f+)Fx8&I!y&iG"ZqȦ NT%Os'L4pU|7ϚSi#4#Od1\;Ŗ9[0:u17Jٿo]A#*{q8u6Y/"E)舕RJ48tGKQ _s^6aЭai9[" EJriA'g hD緭gnFw4֥edldOΡn02?ێ2LD/wp6\&-ج:BWۀ9$,*.N 3^z,.&ˀ'`}>/tbJ)Tۛ @.s5n5 x/CQ -[hv f7C:p-/Ks,#|(epq  J7G1pZQ f)ߘ EͮEbsVȉvl vl/5.}ymwji-|=k1*.of|٪` PWrJ)I4zqY{9&*7i2)$;{&dRSExeCWC3Aѐ<ǐൊvShϻb2CytHP=K^8Tfx 4hݴ͍ƕKl <3:d@&6l๐RܤE!Ž(g~ ۽\dQ]8α7n6W l` Q): ԾQM<-Ƭ=XJ7IaSh6n'L&*9L~yT-& n`L, Bng=H0- a\*c B2`>6H՛,ly ;x~ܽ u=b7 1g*iL5$M.щ%RR\ηaKJ, +!s^dq0l:[{%>Ap{kc]?LMe>|R!,},i$QDQ-Qt2L ͤǩo1KpQb1&MF6<\t <6v.7.[|7f+ӝV7bg^δîD^D:0 vb;ϨRJ@sR8H !\8T%36 4tHRc‹o6K1GRhP̋M"TX(&@*^ w3țzǨ~,'n1njh.v5.6炇mt˦+Z3`,YA7:(q=p`aw1 %39C.hSI-0*F!Ķj ]Mlqz{7-06 UɂYz=}C&-8 nVEKUk `H4xcN'~ ;2eXzYoS$EH\B&co{1F(PQRhv1~cT*g2E)i`0O*khcOFbgj=rN)̅*b&Hb:54 E)B A"0K ~bjJck(GЉwV<Õ\qT3(VGeNg1;KR3j6d%+N(fvLZ@nf҆FYDZwBN /~V6Q#?E4Yk)r I03tqЉ amf\L 3ŴlrtƼua]*%1 C*ZnNGYbo;1&Muq;,Hc/_= Ew_* B$g_1q@ rAr[Z"HХ뜥)Q${eGg#r3 *s)~Y*JK/,AnPaAn|hҢ!`diW_~yLc}>{ےԨ02Bj i DsLBt8Wi ѶFIFclȅgGxQs :"r1Wbvs^#Cp%(*7Ֆ$ V\Bg1V6zj HO垤W(E]sB{=+|"6~1XaW~@b遴W)jlEg3sA3Q_yl8(EBucL">>=RT*}#X8c}0|raWПB_r(ZgȥM\k"vxuc[yI&!6Ɔrrl`TDRWRh3B/P90՜ưJk1#oMl9KXlu",^%#Yp\0O ;cHx{l޾@qY^Omisi >bn. !P)҃S,Q{¦xnE2СIx?9EybѧH8%OS$&rRTzpq-T]G r={ҍL6s&-A !:1) IDATಏg94 q*ngaEa#&yipMC*2Wҷ5&T:)fjcx±a9Dnīb&6dB5)d0!!s,ҞF*0${xM|;Gؘ>zڒn\F-Fx1UzdMOyf歙.[.UuTų;_1q <+=gOQ,Es^8J1A?hjӥHE$KQ_HʏQ2V(.I>c )^1!JRju8}zy/o{{SRUi$sO?=NSk0+ȴGl4qO]C8 Jn 3ZDԆ yV'wC\ppcR,)9e0XAe5[s-v t 3IvDoZ~q/v-'`pc3D:NeڑrEg.ƞ0c; !BBHpr_o|rf@2$Bm3bĨ@Q߶(: \))')oY[>OQ_*J" 3rsGҵeXy3K#с>6NnaCo@A\+ m$qAn9qMT%Ů a6 #,[Spb:{CjaSS\]'ى豦4Ҕվ]Gôk$.5 E`AVkP! "l2sF4u+ر,^$*#sB\aNЩUIޓ䜪U+㺾Jؕ+I/v أ.M2@Bm~e#/CtR1BߧXbYhgWE}qH_w)%e:%Ѓ61~}PT*=ktV7ul-u9j9E"c-N`obֱ86l< AD._7-Q+k~>M,slybTm $&TZv XYN!n,?d3(|:E?V hSJ(nI@>yX'9MBI0kA]U)x[7Zy]Dm+;hm4 _^!fL*o ]Nck?xh>U&1F !A1DhH" X,Jxrkv`9黓k~U>GBɐ8d'j[D1>c*L41$&E16u *;!}CZbh C 4z+ c]=GR?<('¥|#eG}LR[[^fbvP))J) !DyT*=HW܄a(ȒxCGe#o!881#$&ac9E{!Ad.Uë<ɱpv^nu^c]:!Z}?bL6/Ns ЮTPq(B;qJ!^3[D gՈ4-`y(2)v<MPEFm@5$M]x -%h[)k_G?P;ʓRi1&B2>>2~umZ2Th+hȹլɰ2o*"*)rrRFHjR$cQ q͔kqĭo1ŵQoS lBR'$Asɪ0T/ |R~NճiF]8l5iB\"0Hd2oiXc8F)1䞇A&~c;}4rfTzk])a*B*mBT*J011k$ {8 {7ʆ_=2BTmWi3*(V7+Hs:j4GzS2܈=7w}=;[$!FbbfBPAL|"ɌYʼRoƨ,_t O;®)ڄ c4[Iw2fFX69YBO ndާșcLp1(&("gwolDF- kw-L\OU F!G_l b^c#UʭeYnN|\}JRSf?̽<Y,D6(zo -A2eXũf8SLc T>)E1lq 'fI9<05\s҆&ށv;R|zeAl^EpTR 1ͪkOSBLKR7Vg(ﮟ;sh;zƐV`o KԆzU/ zs ]=J Kak.Օ!ݧnb<lDUATbsԑZm#{SL-Lq%H`+"b:tiPJkHr+÷@*iv:)~xѹӛOg LKڡHHsGn=T;עHlks;RT*a;?}U:&yw헃f*]@.iˆvl@Fq=ZL0?tw{sAQ:S#[$n ~|}`ޙI[lgr&M3;TT.'\LGv ' .%GHV}FVLtB18/7&73[da*r+b=S)6cXH$-^;0¢Q@|,>)drx z.Cd@B Xt`a^(c!؃w@1HݐRqQ;Fq cXJ Ƙ\r_ƭ6e#Nc~[1&?JW(ǿd9챔>(L }&#$PKMҭ]5X?^qs*LumћĽy-wĿFf):Ks$mͱFT z.$#B/'N[!fx&U6 w-4ޙ+ZJ>Xv]j.+nX#1sl 9pvr/_ 505VѧF'iq\h1u'SmQ`0xxHdv1EڛA|{E!Dۿ}sTQ,NK ԏJP\Ҭ|G~R*^8,0Gd6{L.ŖN㉮ Ų4J8+)'ԯA<(6l5]˔̏EH ɯlI!k\,r:7eO9j3.kC, Q(zMN**V<,v tB G`a 9vHP\$ΈJ HPh4EFsF7 "ATț޵CcU&r_(Ľfߗ~4O, NJҏpRر{/{Vι\hQ77NG^< *ct#3d(Y|{*F, ho?J% ȫ75$L5…*As4a hm̸Ť`rEo01) !:=h>QZP֥\Y4gu7fе }r@_@ yb`QB|o(E[($1C9 `18hR<r24 PDpfT;H:p.;OkJf({9Jǿdel.}FEѳgZchJ4yhС3X4@&P1O",M}*2a{5e[GUvΘU:ݳ,ۯhnjHVX^8)Ì]ȺA5.^qCmv3i꫑N0B a,I ޤ r-[Kgؖ[$6ArKR8$Fp1'}<ObRpF{7bB0)EP@IQ D&P$L3466{lݜ-z 2B`Q,"sҏY,J/(>ʎ^laT*JwQ ^r%?=@a@U u"D$=Bfp4,̯}}ˈܐWr m9eVm8\\Fod3SW³b`C*$ZrJFoA1<XDރ'*Y[ Y4$ lL#vh1atg0'Ap] {9(ARIU/'qqe3Tb\E{:ăFb,9.JwB,J?X@y7*<@T*OaD ZPMCkUHL5EF 1f$6w YP1+Kl}֓*n=m͙ǬW mqoXRFM9tMSg\HX܈<ߑp yBTv>G02\G\hu1 ' %aҙ`rI5HZrH4>1Q ͐1)<$ivQŨiU&dJ$lbpۿooaPdTzB*pbycF~Ix)J}__X=V5LA ^T,95-dpm',n4ب 3D&Hl)14]&juNC IDATVhZa_kp65~1e\l#Irz X0v yu<Kꈷ!ŌjP8vKX#$NmĤ bbጫ MCe>) ,KQVI!B)B XMIqqȑ4ʷ.(W/WN=]U&[>D(vUD1\v4.Jrte_<5+14} f9 Ycj o'UZ,C0%$SWr_y("v+I 5ī'1Jjz56ݘJ-g+6au ?)•`q{흌z->OMH"xݐCt0ZZDuJ@N=PcAHI56֨N0Pv'$i3#d\dd~B: 2aP F>GwP{Zy[*}B&R+~^ֶNRTEє>߸h8>uqjtJ4ZD" -ɵY*.6!/_ m,4bLںL==lj/ũl91/d۬m18"d C.[M TSgH7v]&;$B;Ge9h9Ŕ䣗UI\[ *uHiiBܑXЇ֨i9-`[׬⌿bG"|a@-I ZEcRjhIbRbWbB#S@`79`0ѾHieX*}B BQStD]XZrRTEQ~J,uL~/צ4:cyu0dxxld&.%ȼv Fh  \HpxiUf3D. &#m,2a"M֪EdW"@&҆#| My ZB hB*<}\#lCyn[w玌ҝT&?&0 \'N1?W1!tg)_u0t5#-J0?yTOWelT_t"BuG#18wҦ TGXw\tq2ЭVsfHx" A0Llq_j%\wz\ N\i`vvM8^=eh po8›p2(= ފ}ePm5iy9՘0?I˫6cE>x.b H+5anh\vX8B &DR[30m9%>8EZ5k.?Ec0F&ӤLoFTc!߶hr?gWt(N+_q+Q<B_rܽg-w~sKRiml n @4Ii5CgIl,0Ёcmm!²-[5wÙǾ+T%UTUO{:k~K!]`x#`*gکk-bZQQqn@$ҟ=?!#COo[->Tϗ߻i5` x3HOxgzOh y vnkG`Œv֏7q:5vyZL("'8:f\b8.hw1A)M. BCPI.&L84UhG×FkkɃ3Сlu E)1@ɒf$}f- 3W]6OjQ ҤG nObdI_!P4RCIHl(hifHۣ#lͯ*R*g0 L;1^6ކݻE ְiCyy>cS>AZQq ҟ5[ i<L }hťD-(ބXԵM?>8rUx4-{ Vn\"G:kSt5 '.e|hHr7!mĔ6x4[ϐOF CQrb$Vlz4oŧra >A9x>fm9N}@R;HSÈTBϴXڈ(g#z¥.cRL.Й5C@0IYО0Rj:8 袇9H76`@ EѦ `7/HG}~*|US9/NKūq<x&KXvZF{?h +*=w9 oN`?R9W=/W< ڷUFuݑ{3IFyY<)x~sKQmnȣ0_iؓ!*apȠohsb j改 Ɉ]ͩ :pX,(H&B2OC ´d!n~3s +g$MEYO*gMSlJ. ӇRs0Zr 2!@'9ijpDZ*ʨFJT]q= O2H3(O '&k,%A;A,(F Dȓ'p޴)m꩜3_o\&˲GWeƦbt-ھWmӅ>{_j6O h*xoV)Uqf xUܗ+;?*So2]ώV\F'p@/ =~hPzѳ7,5'N!rdOf+J 11xQ!3ǎ:YXP]D5ݍ)}E64s@dt2bfg[ocؙڠ7 2D+INSd(3/zgt\QW94H`RMيA4y0Ԋ40uːeh9- !1%%999cӳ6GWuQqyų lqe\bT͸Ql egWb7|*OEEeDsWM=BV G4ZCm, וxsGraO4=|Ӊ?rZŪ;bQ⣖BרܡHbr@X0<[b|_Q]5\If,k E 5'hBZHIMC`":fb'@La+cbω==5cz%%|aQl?f/)rS$i9 I.Z8 !8!S#3.`:5_9),b@#$K68g6_T̳m~` Y&\*&#Bǁc`+vPxȰ3%i'_ݩm2mRJ\1*|sm^xql0Vz'̄\]$`80)9MNhAH|cʪI t4dZF&ꤡ [+)bȲ)8"QVGOdG'U0PPCx! bbbԸ2y vZ~^*ʧSx&oĦ+BTuTƘ.ql}MXGScNSQQqܵ?ax>\)ش;nc{z65;>]7~4>Bsy09Jɐ5nܘ}\ @YBQ"n&R:l)̳'\C٬ɂC{cY\-H!Ac@5"܏ }Fkh̫U&&$n[9ZBlSBL55B7'AQFi#!%LqDYsݝˉ.m=?WnF7 f7;(}*~+;hswۊQ{2PN51jB; (؀(fyPá xL)FTRns[#gS7^G3VN,;"ә# .qۡ (pK (^"1% 7 tf` |A*!J~@z5R7f猦<"6INJ!"0dA?wI]3_qE3i`R\vہ5B9 7ƬQN`kYĶOMV\!"lQt~U8_Qqh遾0^cH+R{r DRxcs;zx ;c΍zHƓ>FE|]Hd6—jfҔ ^l&cֵCfS#"pɈ0 >_՛c,S`1CwL0{c7CG NCє.l$0]f|Tb5C:%!2!DK` i@3Ļao- 3\-/ld NF$kya R g+{ӯv g'Pqy3y'b"ֺ-]W {gf>6rYdɗIc_]B]`%[[eҏc~i'黃;۷;/NW\nnl&Gײw2O vL ofNb҅ D9X pbJW;$0B8G&MHA-8EQ' BoHc6&mL,X-qW`3 j 1^'H$y^N:}JoO$ O{iH$l$Be9;r. mowHґUd%r;L~NTV1StB @p` ڼDx6 WW\ 1B\c"3fkx x5_=6Z{0A_~r+*=wX|astK [uo& Tz5+OMMG>"[ L-U:mS-8mI1!u[跉.]CWF2r]hA-PK9Z')d8 %hVJԤ¿MF 9yDX'kz,7\ʺ"NIس(uh#5r#<,v \@ rKJ0|A3#01o<$Cҩt^cg1Q6"[C.T<ɦfU:p/6j/=ugǰcEE`'_d{g;lSk[c1OT*g9P>M)uDŽ,8>Gl0׳ӐyF81RtgҦno%%"c##J-8ek"k8yυcJffi' |H1jO gV@CZ&4ZZQ4504k @,HdqҞGyn0\5LY Ae^ibfłNbEQ"[`˦1audM h}e+z+Ż/vw_:'1Ukqvy`Pb x ^EE]/ZVjGn)I5ًm,67w_e\|K0;! C?8zdV8vf_ghA.()]IԞtED;1$ã#F2@lH6??DI0mJF]ț9qgj|ϻOC/PАA($w%ƕiCoA6l,:xjE0vCQʑf mx+Qmfcʴ,57awM^ȓSѿLRkYbo|;:][$=`粽Wsr *>n5mEEŵYx ]m4i2$l5ƚ1ZgYVfj&d_Ni\&*% ! w:[BM IDAT;Kp%Qg7w:a@hTpц`pX3`ܒvIaꐶIHT@V$nI}h{)Cnҗh tJ(sʾK b^_sG =CpQll@øx+k'(eNMvaT 3)[[ӭt_۫l5NUW4^1B8B RZB%vJ0q$yQnY{ђAfKqs٦(⧱?.Gs?EWkLtǿy|}TiFEU/9( yb3 T!a'7(ב7+:_ 3A +_LzzDs6):5 WCI钗 :8hOX-$SJV Y,~c4 +uxiF=tfo:үi96575&wvvq~|deL ؃M/z:0%()1 ca_*.=[ {+.,c'mcU:ŊشnY7$[F6cm7HOawNxfbi;2ad׆Xqx/{L0ӏeo~h%=CPNaOC㗆`H=) # n-_k|ݒlnp,=:uW!-t ܖ0S8K-̙ uM>|@ʬCbid-= 2EHf ;DQS /~2(mVt1Z^ށY޹sxOgd{x*+^CTX߂ur$g/;!NTYY4GXe7ac Fy)گ\+K؈b{< |eܷ> wg,ٞ"(ʧlOC &l:>QoL__-Bs&&ȣIW0o6BV37,^3LMς'UeFr20+}n+^=*gqObS kpF`Nc{ x^qD}*^c2!ģx )vxa q56z=ulcJYXh36Dp}¸؎e>g64J :` S`XTS֎gyԷԿhcG`m?XI,;ŭiD TOGE{cUO3hR6)_(cT4 +S@M'EBCLFG\MHԨ!jmͼL]j`*rd8 t$yбK?O RN #P)@,yG+<7rG$.F2 ᅤ6nwMs`~}a|dէn6"Xw'Qp~;H |+ 14E! }T31؈aUQc{lEWM`u?X*,&(!s0P譔ی'+oFP?h X{<`:"Mк郳x70N(YE_'۳>pl3 ~ (]nUm5i>48"=A՘+Z͞ɂxP?LSt81H(MѐHgSW(i)\8$;FNgj N {P ҁkbʁ963_ԣmθ/\:X5H\: b7|-7BcLzbb1/)n9* p3\װC:~jaS$ˁ._8PVTTX慊HlQH@Q@Ys:ʛG}mV*~p]">O \Rj*|8zav⻒Z/?<)΋ph*^!DbR B\H= H帖^lJNlE:<6p1z:n} W6RŁ:[uk-w.91ڧ>2AC8 I)E i * hm';wl%}!jAVxg~%m/M?ksL%ϗpT[ۑ¾΅v1uKTi(]ľi6]>_]"\[h7Z#?Ŕ$ghTm`<ÆA('tui/4 ю0kYMQ\/&P PA]SG`"|e@ɉO?(GjqR9g,eڶi{;plݦ:fㅦO^ի3>oJ6rQS4bdDžD;m\gxS ychaSQQqu4k}R Ÿ{GzpIG#| UKXo,mtRQxn(@B >D^:aBnm~;mAAZ_Z_o;#GzVES=Gr3"_DRvЅDL.mNP}dkfeD710LS`nƐNY75pI-1 -XxJ:Y<˰mB)m4bc ۆa}-sA্1a߯si(,vr!XgQa.LjGb#_yxC.Q؉7s^0BYǓ1[cqF8\giI֛Q't\p\+(H}Am짳|xKu0'FzN _mكO,Xݧh6;eF1^lEwn18%8NF*Xs<#0)nm ȣ&e/'}. )Ћ|K]~BifF},QR cdRu `=cCea . */]:Rdծ1Zsdz,{o#w\Ww٨ps*gLhsyI&NH},`MMIn盝TXQ[Ƙ]}i$Ƞ1&BAΒRYg\޵,=L[,_'_οP[/6V<x^9;p@8u!a #Ol`(8QT#"W%̍!Gh! Xņ StsC15?`5:).EmC=YDY |x7kd!Һ ;N3*'qiT-Jx 3zO|ɇZyw-ʹ[~֋W:ra1Bc›/ßpfsl9}l-"6"c[lx/YyI:`f7 !~rb3~rlt[ʞsw - )d8ʳeEEY1w߯=w>SoC1,He2er3.i :ĉD)Z=b?$o71}Y b:>*B)@a)^/ĝyhbs|g ptM:.ok[9ny\f|ɍ~1H@EI>DLG9Dbyo_9i,!U!mdFT8r͍*, 5ȼ <”Bjh!MaDXb8Ԥȉ]01SpRzˢ>JrI*\Q9g+i>֡[FaE>ֱNE{3oF(mek:}|{Ѯ֤v[k} r7SNn2ne1ZVTTT>p/pѹ 3b% (V=K bL/5fy]K6xg8jFY)!]v 7fNKvMfiF?t(vid wqWAIags(QVX 4Gk(>@ wb¼!H&}4e6M䉇(Z8U  ~jy9P:X?֍%Ǎ1_/lM:u.8{ER \4YYWD,g'wGF5O),g}߯l1Nu k煸lX9i:[$-hqFy`[pQk<̐D9 J FJ%dkP˖/h>ivvͰ6hppjD9,ЧLp* RG˲iKP-}22i9L1c\)E1 쮂 Bh/D)dmz2:|sx>W\TųsxxeضkcNF!Ľ>d}G۶]8I5o#˳l{ev>s/`QEEEKqkklvzVZ[GHHvE oGܮdY:Y[8+?~;lڂ`p"cwPsv9i2kT_)$߉}ydŪ( ! 7k\m>>Or|S8_=F<fLTׄ:vKEG>ʁDg$bMDϞNq+O8<8KkOʽ,BJ9 f5F qFarF !ѣ dF\ Z \'PB9h"'ZLebnfb $Z.q޾Uqr-F;.Ħ$ʹ(3_ΐ>R?G 9n`. slEA l edHkԢ~ͽs"6RPQQQq|}3͛#ʌW*UFx^?|9}=Ϳ?}bZsn=3{l7<m ~u`>?˶x߰S=&;tBd7F0¸J Sd!'+78:&܈ Qa4ai аAHQZAq3O!=,AL˲&A 'TUZc +Ҭh$QCS$eN|Zm_xA.[Tss [q{m~+dsQ32,cZcLr/cLa9||&ϓǸ|fDEcUQNГH+lo,R-$hٜepsQz'z'K_O[u +G1ܘގ+?%ĝ/6G}doUC4&ō^,5p?`{] CYHy Y.7$SUeqN3~nne50 1/0P̀45Rtᗎ!ֆ^h" +%IOsM/ H{=kl$m8M,_bKv lecY;!zc`7%~XgKp_GknB\ʨ}EE5+Bw+*F8gʒ O7R7t|GWdO|ĻgǘJ忌C7Ηg魥xOMUb0I)p¶eE6dYO :Mι#f剉&ƈI/b\[:{4gʱ~ad5R ")B(! YޛKUuߵ#9ܼYY5PD! 6(϶橥ڠR <춁Ri.OAZRf(yy8ffeeVfep3s?y8wEĉkߢ3IP֒UY4\i IDAT;s)N Ext@.C'Q' u[̷dž}|F`a.HRjDƇYVȨ.a6]mt vn\evL]ƺӒ\̷JL`[%͆6UR?i]48Đ!%_{p&Bjɤ(ih/DiUUh)D9Xz QD0SxA s')BDaTfy6lp(B싈D"ͨMM78EFI CÐ6}J-;v;#l-ATXZ@-I-^I5 D 1&eЬc\gzaf53àɠ)f0;5bsSBB{$`3tې5F[N,OKd@JWMð@e4 Đx%)yl&VD"fOPv)<?Jx?0)"1@'t{4gʐH$rUW9"GMj*4?5wMևٟ$qwTIŽuc@cZ-@.4igIc=˳DRy}tb߆i/,.з”c5|!)kBmG4تSHy% S2[d)ZB9uʿHSb4Ns9Bq-qc6b"iD"Sҧ'z{޴tsdW7}:~Op{yw_-NKgͮy|(.!u|OB(Ir$C#!~ؕ~C(CmŜelfL- Mobegdl}2[-&[Lߍ̢H=2(9,% B;RɄik. GFB<@F{0ƇP$/YA$$$gPj|2HJJuxۻo1X< 0{c}zwz 2n4G.DM(?} 83JÑHnuoNnyZ2'͍k7lz}om @7X}0^no?k;WEz0zCC!E1"S)}25փhpHviyKCyX+XhȰAVʑ ΃EHxz!t~+e;&$HuJ3R49 :8gH$, 5V2PnA%{1.rFnp_:⽏"b6a^uovkړw7#@ OO 4,B%P~ܙ9_J[fj9bXD"Oo}aq޼O_ 7Iɕ݃ݻn5翬}޾lT9}hl}ǿϸiR%YUPl=`XQTCMDQwWP=z$?y9402e8%ep,WvU*AF0l4I@m_ә* i5#8*S-Izanº!i1HQx76r*9,>EF :sN\|}>ba$/.#(Mxz0 aϷ!HG9I۟sF$*l*K,۹?7_l,%_7猪?;/_T/fse4e2RtC(Pj Sf&3]ڑ!Ӈfi kZnHV׬kP,-6$97;أ*=&)D|ZtkjD)ƣ$t RNpiF&).Mxpp!M dR._m*✿9UK " <&A!u d{ #a@6yhx~6$\}9웿凮͟[eW'Xw>S?\/3k/Nl_3ٚ-mغ4IRY X.5L,q]U]\U wR &5fEJqއ/1ޯǹ5M-X-SZgdaB<3&Px$-9(Yo:cqx,^';Ô|79.+i, wv༼9UK >?Zbj$RJP u+aCR*zw7{c_P$9+͏N6Œ[^{##^!r}φYwUc:Y7SZQr,{d*k񆱙],VJcZțV3ճ;ZK"aMw2Q DF1ɺT1KPEl&Jz' '=AA2kRA[i@c}N&RdZTB Fp"P |ox;o9b4//WEk"G2/vO#zx D"?oy 6.MИyt+5S캩8o\ڜ/R`]3)'ziΆA]drq͹jf|wǰC#$c{qÜ5" (37ٝt[2#Ժn?t - ; 7ss=fi<К-99knP];C/Y0)*7,e-~0ϩTVx]#vK9EeZcN%8I t)1-2GBX?Zܭ-gG,!8Mt ૱4kt? IB7-St,%zтIRrprV3(Z8MՒcH<4 U*@ _haɑ8b2=4r*`19{,p#p3A&.rG_&ΖD"-W0g[vv3054Ix`AEZ0T\9:/q:2[fY)e jth- m=RNՎQ,lXaA1E[N(k;XPjŢ:K04) %h!+ vy̧^sx# 1XDt&uԋ5Xl &~/)hS$D"Dn l;M {9ߨ<65WV~rن˻(\1WLS)W޷Aq%>eX; M)]1r "h4G%Acx$6fAtoYk QNFL)m-ﮖyz"RI\$r> 3/f.^02Ag-py*D"3$1uzuqq%&2 ٰϳ*l+r{ aT1yFꄩmk1Jnz 6 345!%Xk<^p5Ԥ(g !K4>wֽg#;{問\pb$r~p۞x7i1KqfS$DVe^O6v[EQ- VA`ز1*ࡉu=;PWclN7|n@ri.^Q+Eofc, $)PăF5 EFA*`<Ѹ/_Y/]OVD.$beV' X.aN~D" WbBqGMD7J CcIfI'.EwAf`z2((|y6=_]lMXx)>x%9@یPj5\$ǕFH$Z^ dS/yi&ݱ^Ѐb<4K$iÁ jRƒGfXlY"E9pa]02T[{d"/@Lz<'x!Z፣v YWbA}toUO9r`19w\Gز{}D"H? ^ݶOIv^@|2 \Eܨ!%GEСfuTACXPOq,`6yU$R,-]cɷYX6$JeL4vz үdV/3+b$rnPъPz } #H$rEM\/ t)CC5x4 2Րa!W 1X7rqllLՐu*cy.oXłtH7l(0-$xYMI@q>/foEzo<-. G$Z,p?\,xj`'3D" wwpPSS(5,+``$ rfC23ڽ]܁ &z=sx K !k8Ӡ3P*M8-GjH)}ʹ]󁷽kYD )0Ex1m<D"S!iæ8% EԀ$c R д5$er[SP*E]<Ѓ%֞Y9lL(h˦jk*4]i*xNcTC˚cIڤվ|~}^A,F">, elO$D"{G=׌)S+ Z8᳌Z,5N /O.<ՀPx01z}Ǥ܂ha@Ů0dӰ1uL'5+C%mN I PcGyvغa/VHgN{~w}sihY+"p#Ꭿx>w5Ṭ)F"H~@Xվ=/7~얗ɂ YnMcqP GD~ m}`6(A g+aflR1O;Ty΢$ Xx5Z.8KU5 +Rh@y F(An8ׯi#F.HD _|uD$6[9V O eW3$#H$r rg?iGUD~|u\OgXuUhŠA,iF)Lr4P [׸G(C-:XzʱTYh\Vp(VZ3$0: C;@㰀 ˺ɗWhw~"W_n^N*D"'bpD.!DDk}{ΫQONA PS:U #H$rqg?~4&`3x}}՞OFh"{H@d@;q턮=.@%: `s"t~qw}U$Ť&5=fiO0Pԣ=y*0P  NRT-.ir_EG #"k?#TT{Y0*CVCfqdM˄} .rƑ@KD~ZDvEmW$(X#QAԊow:>yoߗmB'%x6%tQ,8{ӊzBZMbSdlX'-- kCXhtֻF<+uz$UDc HPҮ: 3W7* yˇDַn7?m;mmm 5r! &@xWeu3oյA80>= UCxn9Qux7ac y{ӸH$rA&Ӥ6Fyn]}4tvBTC48Ȕfhžj[U܅l!H/5 irĽ;@۠7ShMQ$MM*P%)6Y Ѡ54eƂN(DU,קVgdJH&üG%y vr0?h_-7_L6;˗*7m;/ }MWJF;WFKLbaGz(Oû1_by&p+/"7zw{"ȅzAW_U޴8Y/ BAa2F.2QZpNF)-xh$ εqk)YGks ӊނe7LbFhLV[uT>Z AܦLY A_MZT=i?YhCͫ떷`0oU$m۹;>lճpD.F a^#=xV[/C5 6/ \yCkH$X g _='mLS`\K@* 4L`*& .l# IDATsyPj9J/EXaLJG"=GlHH'A:qXeMIԑ%iuX6rJ@ ^CU{ b%p0KX_*Z$t5.6J2|ULT(ɔRuc`V@bQIL>xy}ͫm~}v|9߼Rd۶_sc]IOGuhn>GO}*x` ADx﫧iS7 ^OpBNW;;&F"Y7 kseyINK, .T^A85B#2 񒲱*arl2[CT`jgf״Eg;dXn$NGuC;?Wa w>5Y\(5Ǐ-\H|\.D.ZDDs -A|oάpDxo#qzf,3R3o~PRXRN:b`$g^u}7@gS]f&(DM&BP౒FiTѦڀ γX1V5Hg7B.؇RN2ĕ5ݜp|091b]^l%MO|$#jNBͰIURR#[y,cnjprq! 3509L&3~?gTtD"q'Y{g9>g~a 0&^V܀XTxEdP{d0`mHh ^k$U.t /"R 2Mb7~Ge}qBo^Uh_|p P4u5MgڶH, "Ȍe-'dXK쓜dcp`BWG&,DUyEd͙0&DV{vG{+ɱ\QfnIDz3Zzׅ04CxOFXXb< ۀ.aiWCAMaNK׶CZK D0Αx4PU DpU?w*Ɋo^|yzr͡خuF`^m;gھ'| nD"+ANƩT:h2Dp֋c9XУ) W5R;M;"g"#ڧ8CϘaH$rl_ܺuz\W4Vp%EC w(ZPIJ\_)AL%4Ywz;*M$;e̞5r|CofCѩr=֝YD+e$ǬKp';Dzn8`%$n2zFJ/E륟+D.xgVku+7;9͈RwRqƕE E3ek+;SuUcģ,Cƀsl2Ka} \YJ$5ʊmm|s1[p(yճm;NHRN-l~TocxgY^N.aAgCd,zur!ǚѿk`#熑MZZv<=쁃3I~ŕ0[`r:Z-}/ï=츏^uN|sDKV8< xfT`hTNik86Y3IWiFiii'ը+Ck`ϭ-U̶=릡?i~pIЍ8']KI"{Zӑ&)<@ B8 #1-O8B ;En4nr6+WQ]v,%ǑY;I?t莚E?}YM뛿Yg?3Ve+2|]shFZc|8RCARM1CG9JՙJ_4)P|Ck. IG?ђ4Vce"H^Z\08RͷD>{_SdknU>ǖeCAp ,WEQ k)!3At++Y Y&cÊ1dF)Zcb:%$uXȢ${W7+y|3m6} ɞ|9!fW[g2H+G iln`/E$ R҇ትmuegrddƓqDH*![t'[p<?;cr~e0 y"Q$@T[\x\Q;پ;>97~'w2aj+0$=Ѧ`:dqǯ8^{6uiougn],.tu4%4uȖ L\>KLۑo^iO% V|?J e&^+t ms8:z Q7wi)hFf+<"۷mgzy;xv--(eYR-RgXZ-v$+yaWG9tAC84:vyB({BK 5n٣3RrqP} bHOq*Yɕp-5WE#iC,1[?&=+E"^"88`*"KD}6ԽGkZ>Y~W~M_th{Z/?wY\;~roژy%ÃؕWC H3R{(?zC Tǣo>x xo[K->Sc}xJ𣝁!=؁ C_\^UK75,aC5P*@0Ⱳ7ldc3DXAC:WtV z1YӹѶ?عz=#ƶ~*!KB;Em;m:zo,5;89.Ɓo"d WzݞsBrP .'< N)J0;k$ؓ":s"a_sI'y8Uő 6%<]ٰU~xϣutN.)o3OUWȥľb?/]rݏ3Kcd|}mTc:"zemz^N^nJbS9\b0{dkx=|͟ywDgǀnSvVHtL~ro. Yu#4IN9wkit8qzVyq (I&eV'4 (8Ҷ2027̧47/rvfhz%P|sgd+čgn|+B |7!$3qztPXL.qNޟ 1$Fuh| ,GJQkBFoEDYv#,~du2*^rA0 "ӣ2̈́7kl> E.jn' Wm3\:p?qq0{f,8`}tz~c8]\<1L,,]۸v'V)|=k2zݘG@z.c۴MQɲOrǾ;ko>xn^)7,Ϛә3d"aAE ZB-k^mz[zo-Z*VkiVVT 0y8'99{\?޵ObLo>sr^ûZ}g%õDJ{tgXWp RlEB:H- д`66A E3f\,RHBaqW ] ȁYaœ:mn}t*uָ5\6JS%ͫb٬xi Ix2&`dKr!,#8Y2kZ(Gij719!82t06#Sb}ӿۘKoҟi 3UB(&t؃<^|CkK0cWGk؏*'p]`c"`0C6+Yz u\\ 'iX%# 5`:mȀ[|K]PN0Qp]fA80˩)c@Pq+/[D3So6o }5U-h4tk99{-4Zo<>zt/iU;t:j1sʗyI'@^a* "fdHf*%:pUjf p}c$>tИ{@$}}f,\=/Z9@='N 1yo^wi"3UN1c3IƦ -mcc -T-$֞83jcY.ҺE_*C(%]JF(ǔ4hk A˪0uͳq={|\6Jt[#K%VV (ܠrw;jtv"$P* 鞗yv< ?+ f6cUVIt̬4]*" \!-UrMW]ʼnz Xq.dw;kspOϕ@ H=1/F8X lŷؔnLcwcHOcQ < ܆)8ZtfVD-7d}R1t'>Z{Q:q5JxA 0US7ϩ=cW7E/boUD$ `YI8R.#LT4UC“U2qШvӬFݳbUۈU-uM`9Oۃ2l;߸7[_4ZTݮ%ou8(x˚{큕ڶ#=h^,Yv05Z炖KvPZŦE(Ym1{2}/1![tv|';'''x7U'+-HD.C&$M[Af4 UUWK?ZEJg^mF.2˪ vj SYJq|::j./)ΝXBŁPoE" UH-bQF1B@@Y0!6E4VKB,A$ض%V(x^'3<+/߸g{jLwB|:ɜKQ$#'Dk= `e=)Rdma-M]l',*{ C5k;1-Q1&0ypeVG*^6^z§1L}NKfs*p핌]y{.V-=!Imcѳ B2zbB4hIxz0gOfCOjӏέ*^_#r^zƴ4' q][AUxd8Le'K"kl+OE;$P6 60aXH") :j_(- mϵ} AYal ut)>z,iZ܊ } \_`9"reDkLIĹXM`rb$džs<dcd!jҞ?5ΚZgM.s^0ycW c&G1 H=ʕc:01̤ `DtgġFg:W*J =b*S=u/'''DQ[:+U~buJY'03Cf%,Ek~ؽr(ӿ?UPw'W=¼P3$F6rY`MVքX{aB5ZcEE-)YĢIҞTuDOĂZY4ljzVDNcR"]@\hccV&mv-mLȩ&WFA,Zqʔ}5F6?dy^r JݏQ>P/aBg1,6HHSo1!0&a}FQQ‚&i!n^|AlY<Yuݑt-÷es{ORar4Y3q|2Qn.wch')̂~e6L gylqd_ʚu z@`2<0^agLc<3Y#ϩ9Is n"ƨki'34THNa%zaSSy۽w7.(|3aMDP kWTe$R,{0J[7T$a5i3L=)h7%:НQr^xzfى0SYFP^"R|O` Mal|{1EQ1HkGØ}'9"v*Lŧ1m@/ 9999#J't:ڑYe :$#m:cߩ3@~ i͏n|/xVrOVyzSTmxDeTRi]y:bTB6maٶXض-mٽ{!kGI ʏKq;|.]xK/g#EN"REjw1 *&\0 dFqS,kߐ.<'pNE," Zk%"-LCpڏYDt ռL[7c fnNNΩHaMwq5;XR4rQqIl˱ݛW6zĵ΋B6ԺryŲ~+mrfh7 KCMGؾ!JE5a!h%TZvECQ# M-%帩+q(I. #Z۲DjA%`dsÅ)wP[*&nԛbEL6;}g5:*9%WOBO+I1ާr;,a&M~Dwq<Z*F{|]H}EV -x~XԦ;QQg$a1[qczq3'd)S(hzANNNƵWMwϹsMKhfzZ#N⠆cY}ookdlV}Rfa"zfxTjW'q֎$BW"+u^[XXN',AOեjEA(U2DJ+'j6jr. 纫Z:sU[Vb9>qLHI\i2BNaPORM_b%:ˀ^#m N15~*fSdY`UsRr4ؖWtgG>q%xƟz~뮐="DQ *H~!)8v F)Xvm"H-^}*AD#N"-mC,`k#P4ciӮj>1CM{ֹ֖MSg{9v~&&i#tNa='1i"bc09m0a)֎Ps~B\U%"!S 0F{\Yii3%3WzT ƻ(}~`8" 0srWo˶u0ZMӺbU='IX Tо:C6yeI0OJm~gC=з-Mը8^k>~*ĭ&Z<H+RIWEj:l[M׎7'T6ٍ!iSMs\&"c;8p7*MK؋),cc<:3++űs =L;nL)L>l7&tjX 9}0t:/bXsrrrNy;I+C<`|/~nر|.jPIoΑ#JMM=f5۵UժЪ&D[VBY"RQ]$M׷$IJ-$I  Sٜ hφWEM#;&O;g'{kgO[X]6/)ׁl욋Wwp0iJWbdyNAɕSF>&$83o0yf9'y;&w1({-g1k/q0Jm`<{/aH7Sa\ ' d9CU߁/ɧ}(SNNN˂kd.t}O}wSݣOX9_O%^wl=֜oްvՏ.Mў=5 +PK[ ׹ܼyH~^}jôjX8a`>\6|g:~XrKhtq$PInB0* @ fvvZvPצ P@8yI-I5 7<睷ܶoj/jP%e=zj ,>Y{s =ɋ|C4H4iLH1NL_3NHc<[(jb-ִTNKuo owrrqN#S1Po*)w hwY6vO5w%:=85]/ZIVNT(ͨu汝36-Q||YB l+Qv|oxtf0#gZysBM"VL'1V0p ^u8 CkF[dO£q-E*0-"yգZcd$&dO0PؘNz ̌1jLj؍Qt\YUޭ5|wbdv&s a97.l㯼Kťz  Cy{O]a?skxd`䑿aۗƜ۱m9?]JGZ].uKqVgj].Y_ǜ0Ozmbt1{M ?v-l)lQSD2[mm݉:~\W,ۦj͞Z;mlZGh6$ FɬeweآSDOϔ8BSh@?0kW:ε0Bk$}/o-#s9Lx/ѽ M\Cyeo[ﱞ| .~F˄@ KzS_u˙GB}iwPY \ 8 ٟhtynSS Z U{F&ZP+>q0V|)0p^;#IEJkzVwtbVK+E$R,~_$c* 4&;=nsGZ;xk@Y,/"Df4ZZic#$l_"10 Dps [~AZEm&9L^0CWD16&qNq*ЩH73m}(z~:G0ʡO_799'7+r]ܼ^v(Eߵ\tX)XڋV~ w'ᦻ6~5^cW5ʵEw5 0{d&w[&ɈҤMG{z# ;byX/F~aS0^ Ocû6VJ2g6=֨VqOWWka TK.9 S=]B+J麈]/%}[WwǤ<"Z vl(>9ʢRnDe w.v/b[b? |(aBӄCGĵ]Vtzowou}q|q\kz#Sn&:P63cu:*{ٺlǺnPro)ZgM._yQ% z pG,)`&"S#_E"LYLG%Q::Y =81IxbA=ҿ˘a:Ept9m!9#gZxzƒ"AΛs4y`K1F0U?67P|\6=S9}zI]tp\ QQ38NLn}~yt[RQ+{sM"R7.k)l~F6,pZg/9gΉ1QEՇVFO dEr0ǁ{0"ĒCɔ.Lˍ4hC`4 _}pnEdz~o/sFb|?n_ILۃy5&DcB[JkiDYymz -RH0n/K;lwaz2V1vfa' 99/5Wp Tw'f~y.7݅/aܺ_hvkEX-N|k*Wn{+{C`OOxu8ΖoZx*Gb=/4-4ۻPعci @YQQI76fSUg_q>Q- IDATmtjXۮ$ӑe{,_lP,k[8Gys[sguau`. (/~T6Ej|:jlwS.+]Tje횵]V1Y#cu|42e± C!oGzB7.=Dse}wӟπ`&Ob ߼.#&"0!_O^4H=m9FKގ1La E 1xbLmQ̼9#ϰ$"t~i ڲ3'|^FFA|nNNK?FO6~v`O=;Vٗs[lUJ8,†+N3.S_^uMwx$W\fqe\qYu< }-u๨;/ ߸W?f]9$1t{Ak,V&"^/ABC@x()c;Y O-%]fۋ͏\[bKDŽAJkalNME~wL*Wm~k=W==ZSgG]PŘ( YܰYJY:rp?&&z'F*U:e83t7[~4͓[)1߇Ka,CDzDQR =݀a8-Gnt5FaL0Q`_41:}:I:Q }1JlN=N='4kEd^6PQ̋M;/;]_uGT\%x޵ ՜Z? jo=z]g6"Fx=LnS?Κ.`ZgMVE Y OlX,\-zˣzˢ=Oޱv ?U.KEW>m+#]Xq:iˠXAE YLm/T컕Jm%Qwpzq՜VL7ڑJ/-flv)UڅW]v~gF6+V [WPѶ.Ov\2g_}oo[D5i-#3GC&?Y3'{k5ZgM1/sjr_2L`r~-_xDMCM4N_gc;1FOObe^x ٲ0ɺȸc he'&d &Fۃn1`ce}ڪTB 0/k1'}mgqpHB>\HGQ=cɦN7)r3ByNgaT/BDޞ*+촢 uorrN2~x?#4(չƜW|{{Y o{ἕ/j=JSX%56-N~/`z7 0yod4`j.F6?{ ]-'s5{-]vG?*kG{G ]=Xy9sq\^sj[gݽ0ujThQ\ltX=Ml۞ VBD>XyGoUɝYsG/Rpsww[NUV_g^?m7O6Q){0EZ묹2-~SQ^W'g_}ėỎuor^JYLQps_"VY|u*`!a ٿQ# 1u"R.#4.;-I!"O0ŤQΣSTt: ƥs1JQ1Jځ`tg `'6ƅ3,Iw0dZKɻWqb#wp7cBqsrr~2,w1`3\rE\0pSIn^w7;!=:' Z*ڰɞk aHYNl^L}}IMl øor N'5LD1g߽\r\w߸7; \#n=r:bs_P3\[6HJ3fM׊ +V/Ԫ[/ػqCߎrjH$C{3>JD*ڞŶHDzxtٶkIQTEr8J:AE vVan%'ˍ};o~ldqVѓeCDTkӞds-u7۱}9ǀc,^q[/ Q2pƓ}l`<5A=ߣ̫Lt FY 0 bcm*AD3Ιao~KD0b<}t^Qw``fz͐+F|50FH("shވY5_暋&1b&^Ɵʦҧ]_Ӝ6g}y3J׈S)/Dgf6f,?"8~XJ6nNR1 t[oy[o9cj=[}?DwF{gZ0{mk{\AS VqeJ+ϔY_U.|˱KZ v_*֖8mVJDC;ީ_11~wF؋~'f}wΔ~]ڱcٮҮ}Uꍻ[%'3t[ŧ7G.~/X))Zq<*f 0c4S~㵜+"TiZ_z+w~ lL`BDc;JGnO8}BbLhQF!}{E0J{0N7=q|mOs1FVa6q$s]'"rrc̓zE0&ϱܩęoJ87 ~ml,bpb̧Mk 6v%OYzߑʒW\*-R0]6wƮ5 ecB'_-=<%[o%c dp4QDuْXKh2݌7xTqZisy#q7l˪UK~ŋU乞M[36νC:Nb&EYg=5`*gL6gSq:},շ2j_hS-ZаB^Hp6>.u>+~O⺵Κ?O~^^걝j{8Pަn:Z1y.4%< Y~AھdFt4@z%xz~ɞs#$bGn_z.&2Y&i(=&35mac,GYCg+o-"?AƘ3!drk.zWrE:;VIJ2-$\}Uo<$QQ [f8u0&-f" jw{f~#^ޝh}_a w?;xEgӱ &g[oy<ُ d~GES { ߾k||VPk}>gv_O9(Ś?[-Z8Npq?{l^\RGG;wXUZފܝwN˅r($XARʨ$5.~gop.0rQ, .pE8Wލb/IrTmjy p.Rλ }:k.Y_Qs!ǢaoZYkwB_(0!n6^g p1U~(Xcp|h|N"R(YK1W?FᚅKaLyd,1΢!aBf^7{NzySa?{8-99ǒb\y梿ԩțZ}gم&L F 7Ѷ$x!]҃f6FμU/D/ԅ{w~z慣Iu_ w>OF:Dql[jJahWpׂ?[Ppdgun`mzUݒ9uWmG޸9JUDz}=XEZ>cb`vZ;wBdOJE3:CYTS7 'm Q1/1.^]}tm[ۋW=ʶ maЏBgc\,%q$ |j(yV)y,@$X(DŽ.~\eM꽙Ռ[ 0(]Q|Z% ρYD>XLی몃l0Vн&gqmYR|trm$iÏ+Y˔yfr5PƳ;Z'iNxCGk<;1^AL ߚ>cP;<99ǚb 3rgًGvgl&h4FK,h{ >5B8ϲY%20Z\B/jb\ mulyVk?٬D)'=zg1c?+.7o?}-zjkW͚;B3{qt'k8 30FX/l.7=,.'6v\mNrBW=;6 $\ݤbUXpDFFi+$4υJHv} W_nHnIX#;cw[Θvr.;( ;?>g__o.Vyhp3tRV֜KZ}*"{uH*v@:''pEӘ69/1K{G[v]ev>[9,KecLMh~jlxڭ@ rЀnclcA6lY8ʪR\7w\sTJR)r1θ'}v:w[sopuwgɵ"qA A[^QG' `_6]xo}?Kskq>1O)4XbGBqA߹`-7/cj")bR@~aMLw}7ްDƱѻs{i]k/<!!쩭"@laJU`Y0IxD @@EG!1Lj&x sC8Ck}.`w!4a%S4>B) L-bL(H8֠L@FR<O$4{K7sg~?Rw Тə/]P(aT;!9sBJLfلi!fuo8[ps񆱵˓?Ο<]n'75E'֦NoIy茲}wg1]kyi:ݎdZ9,Kkzyy0=<ȱEs,%v(dHzd.t֨ !p1cPPUflOF?=qf}g?zߏ"Z(ݖ̿[?>bsix17;k͞)lL&1I-|N 1]y9mhV{*FB>s[% mB܅Iqc>L.L4ps$~tb~#.9=0Bw73~뢯Zgi?b!5$/N+~ۧHVK8umťy! p 5x]Zk!$RQ#ڄϪ8zNUg]&7Mnyȼ)Z=A>ɣ]e"R˒R*AJ'>%9~oճ@㦭RsC../_uZj%wD1Y\ v2DW:#W?- 7o{zg1LPps1ȹYZV02QZзl,ph/,;OVݍzC:m&fz.k[0MV[k`5whNlZKQr&c^QJqヌF=Ԫv@r6eDzH.,7KGZlέ;vɓF&ݩq#,'i^eiHq٪[}y9 TZ-bׅ4/LX觘qȐ_ŗ.~f&(&*`nFoM,`ʷu?O;urqɍ#( -* "rLa k6부?$糘yLht_̲k);iϏa;h.csOr#lv|!`+BÉ(!IJ,*#RMYexXj#f( *c;Fc"Y yD13Q%B+ԎD#VU]ωX̴>ps]n.>7WY͞pR\Z0}Q ĥ9 ۃؙ &G/:4=9a1 Z;± ˲q1K ҬSY'2%Y[pڝNږWw*%U1azi\)m<_DeHz%SgKUUsT κp8%w_,̤=hzW)qs$;0 0s0__y66+B˞jΦkF40Mu3F-u|AZ8ܾL1WazX>%CI E!2ϋc׬^zsq7؇;߿gsS<17{! !b?g_f5N]uܬK'.}Acm~R~?mf?;2pta;\ѕ臑':VJ$M3̆ 93`ՖIE9(ẖU{Ʌ? IDAT9{=92Y^kunٵic \w]X_#<𖙱H`?%adŏ <6U &W<MXoB|]ֺ){hp֫Bs0Y,7kT"8X⣘t͎$O,܄'=,afP=rO(ɿ3d<vLb~gO5Iv9KIC 1/DWtZǽWڹ\Au;pGz$ǭVYna͌iH9⽨up2,S}I`}o>/V#fnJ]# yUsx,Vps:ZF#X -m'%A,s}1Tґ=tf:iFn%Hu':Ģ212TTJ16QUu_Q,LX :>\!سu\\iQUl ]4Ivzaxc<,մЖmdKLn`[@X_!ֹLRnA/괿8/zu]/(Cn4C3/cɫ89J7c"9}S\b ѯZs/OBLN؁-.F`=ucoSb"|`=^^D癠H;Ĉkd$4_Ϳ}nwfb:CTqkILL:pK(Pc+<}27 =ji~5yDRTubz6TA\j|xэe UGoJo7M1)pXJ7%d&/fw$mK'iOdqGKArj V_Eǝ7^TQ=fW.LzBO&II\&l߶w!8CedաQ-ժ\l9v칎%_剣 rJ2Ū⃶we}ߛlH4K4)Ha^#lLL*s {/x20}OJ0Ϙ4¼mylyOhX`c3m>O0-c~3!3gt1Dζܛ?O|1C]?Їܐr]JF´p\Q6&^^( ! p aPV5!f3$nb@:pD#}o}K_/| cxŒ;].~/]!DFx=*ћQp#uzVD#Sk#['Vsw>yJZѤq'VG-,VV}+ AJ%cϖ4MHaNYl ׶2QR`G2µQNMoصuk9mqJt[?>!#;B8{^p?{060^x/g0<~:3jNq !DK~kW0g ǵ)\l.` _+T F0 Ù:?aDX1 PBXdA=2E X\, U_ăѻ# d/!bg+yluɕ#F}e]cd%tz&J9dјICp)INb ҜQN.0Zc4* C2<,^r`R?һ*^^}7iV+0 <)cz ~S.^dZ"zl¢beb٫:]U)B*+Hmm+ؕD8n iTvK NbOXKRrQЊIǎ0=USՊp=0JZdR[<2RDZH\/3&sߡ3 329|fd&Pى@Bn߹;1wCc J1|+{~ C  B0u_;89A>t0@]}{:m39aV!U0oGe_xqjQұmD׵4cRdCu!=ۏfG gZcd %'m"UmzJٱp dbFZ`0V;⏜r;Zt~g4ʄW"jvm!^qnQ׏سc rŒ!Rvf+S(vWRMo۸YZ# O/R/ۡ=T͑V!cUv:!C"MQ(M! # ͋x q瘖1oƈ_ȗ-\ɞ.F\iO_ !?'ީRx Pm9i>&1.oN0u?(pⷁ p &0QG1Q9L Ĉ710y9OŜ@%Ԙhq}tg) S^h1C '/gYNϬLg)}Ih &b͎^}5˚:`'oa$( n!Ol1#i&*Ĕ-}v/03^H;kpQbD$^-~ѾɁ5;*!7[G"kko䖛R 7OjG3viٖu pQl\U;Xt^\ [o(7,;v^K3v9c;0<;M NdJ~)/UNdXE|ciq6󈓄0863ÇӨר*=q4V k԰-ۓZiZ-5^ܗ#XRF?ܼnVo]M!߫r!NX| քa4q?e%̀Y:a)0pW&P #>}FHgfA{Jz灇Q#B X`f*S`zL$Y'TD SNyl:'351Z8}cXtg!yGVwǖ>WscTIsl33ϐ-`!JH]ٸjҵ6)d>'=]gp}lS%:v5o{J @vXaqƙB|8|-<*p,jge}<=* Il% r\&)Vqy)SVm.<|ROt|5nù_'#uU19jjdkkp;͝}Eoji ++Zyn^`@HPV\R .*P[QkE^f,{v`c{l4Z9hu4|קZ |.^~1Y)S.LMBRLOv[Tz=+-\$yWV8Ygz P,pcNM GS?+Ģi  a |#Th& ]bng/- >:12h|&vW !W}6 8Bf1OSynILt\Lm10Qum21*p4ä Qm!xI'go\|Hn- 4ЬҶQ Z97>0 "DB1A F”|D¹i7ƿanX|jsغ2}B1ͲYF<бzv8W;}g,7/ J\Hhq,Yvc+SI>|k_Pg=|=?.&εݷuۍ㣵J[KjamIv_4}pʲcbh[ $dC$q!3"YZ0hqD2C':j\^57t}'oװˮx9+&YAGlۺR8B"ӠHۖJq~^,[G!Q,3IK$uO39tVPFYj#z5b#7N7nU{^KnKΌ36Z.U޹)KqmcS͋۔=A٩RN2,̒ha KN24m![4=D6;Vڮ7|~"v_*el7Ľdftķ^/-@ʲH(\mJ <_tHY"`0)mH)%l!(d}VG6Y-[ٶe?./Lxc&b+q!^0B̀[PO!C1"0Aw* Õ)Ze 1ⳇq`jI 8]1.`(95-4O[bָyzq6 05x@QD>PraXԺ9f"9φb!O|`do>ׯz64>\>f彎B!߂>*MD(K!GddjÄ)$FEB1_lE{t.3>=y,|Cλ>'[dGiԤXq՞>.(P‹ǑM *V^հ(Q 7@)aW$c" u\L?vJ/_o~M:7N)-5o1]%oBㅅo_*?;W&˷}cEo譗xb0RJ@OS PLv\l)e6)IߣԇTRDf~c5 [ITvq⸞҉ -/WǸGp15Ɖ|? NR&e*mEcݨ3MяcJp$Q092Z..뮤G`okx nu=͹P܎7>r!XBˠ &`_IL4(?plax)?u}b0)&Yk?.Rahח|[|c)aV;/`1~`"h;B- E=nˆ0Ý ҀW=S_tkDC3Hu38I~2?B?waP$0"z?7= ~MzsC 1O7b7Z5p41bXesKEॼIl"Cv+ [VE_*4$AvJ,<,>]U.G杺h'ssa@ĠRͿ`wczbk5 K# aDVAlaƈ+ŘH&JWd`"x0Oi-sN|?s:otC}{N$x>gQ$|e ϤӅq]D+ f#!"c`~2%h` Wb&Z1Cn-:N+N7kRSH&]ݘZ %ȼˮ/V֥JC$b=_}WҘ2Z(y-:a&0ymmG.`'K\IҬLg1x+{J_y;+ƤCA`k ]!j \)џğڳoGAE>z/]{o1dr}笴z;EiS=Z~{х_ W\ڗE~ڈwNpN0z[fv#i \BS XdIV iI\9>t'5T`;qVt,baX%([\Z IDATxzk 56;d8C$*-, stDҨk?Nez|D&7ەO~4R {.ydf P_jj//s_^;n*Wy^ !a(_`B~ӨX'ԳrnPS^o1!j/#Ic]Ƥ )Em_"<# !#BF8ޜ?4&UgPw0KgP7o9q-oF$tX߶"ebu)<S:iے`ƜYaHx_?-Rb!N[Kb)r8ngT m=$͍ΠڂV %2plH{@2zK4XR+#ӽM<2yMzG ě/Z&gL/5cM]ރ@ieP' .獾6hner^ԛg)sDH]{9UM/gr/{ ^gsfq|?3~w]/_wY'v]qA;?IvE~y˞1/ҫt%Zel\G4A+R&U :yu$F i,N-i"J%8R%QxZnTK֊Fqi@7:r9[Y GGkTXt=&G:löll diJ%TL P*cmTҒ3afc}[朕d%42/SHz ~##ʒGHn[:iI~}rQ !(0e?Lг9π` Zh{0^{N uOLj.F1ga8D7c.vY:U0/rL$9 Q ]' {(Wml&Sg|R_b #FcS$\ m&-pri!D}(b׼etv޵?N/5XAܩ2^V*t47S+l!Ԡ(P=~cY@9nbbO>Op>t}m2uk]KH +a ٤j']ھķUU:={%#GzWxFe fREۍxFVG+N-`7j@N>17/)o~ܺn{?~qJ_zyp榝{ǺDžLnrJՑ( xtb M1V 5l$aH$dYE1vIHEHiI8N([?LmsjnB:mmґ? ˜qbi {ϨI@BWX`믮]VMW櫿 _>}y~%յXoo|k}#j.i*=2K:Q=ㄞҤQ~WFjcXB !%ZgQ1MgL3)XBHqv^⤯kpv$V:yrwq/ػm/c[27m=k܍la"rrO=],{F<1Y^/oaNAEfi ,(&D>p@Q"wZGBjn#;>[E4H7]"eyQX1HEJ fƄmPB@k}l!c߾ouz}I#vQݺ} ~Hnwa^cg?=Z}7pZNjaB`nj 3,E<2\jeOe?9qS˙I 8yu]Lb>9e*j<rL+n;nJ.@ .Snyv7;nEQg^KK]0Y,;6aOY<29;(MJR*ZZ&vr w l|))Y0C Miud2 bKwI/9dbMFNQq}}xĶ .ۢ홙]s2E'pDZ NK۲!JHZ6QS͒x~{[?t6S*.J9\%*w8u7'w % 1z~ÜU05Lӯ'EnCtߍ}Ә]5_Uae)܎!e:㉠0Emhcֳ#|_ʿ)0rl3aP'|0b[`+xڇIѽus~`JGA+1ךS;I;̐8&[n`b!mZ1f d,>hB[R Ud'kX:9*>/~}8Ώ|_eqN"ʡ!Y#V v/ YSw3_GZ5xT`+IϏ,ԧ7Mp`&{Kj%|vj fY{eg\hv^'6t.oܶԞaUdlock9Az12BI~)jE:fLJO+ ,2 AR4KIKjuΨh֚Fe~v#nJͲH={\hZ[#ӴD"V+~mlk_zZu/I |#6`"d5L1~ي{*uǀ_˟Z'yTDaMLDΠ }IۉIc݋BG?$u#7`soc34½u]= B(s(`͘Tx ͛33~ c5azv0e-3Ol1Cm׀[KB,fru܋J:rc:qd.DsgzoLRwQy`sTѷz beHA; (~(>t~ogph=6; rQg7-/ꎇXDf!+;䷶Q퉴Rm:(4ȼSJšرL' 3BbMkHvϸ兖n(ܴa:ڣّAwMu/zl+xhbN;UkY"5UCFq-8I %י&K3%:`"]z"(A8 Y][UiRR_lٶzY|u"KRlǗ&)APƵml=4r-3HҘDc;O `aoƉ븭V_i+0cs% MA1ɫykg oa-Ͽ.cW<6(}1fqSؿW?<B%}R3\n19;«WH Ew̖O9ԓ_၈^l|ѵ?(`Re,g%5Bpz0;P #ƨRoE&_[2 vYJ7ɫjQ0d4$Cq犨Q 1iKɄ#Ksv4#cwV^7['o`F*a '6-,ՃGHRMDiW!I/s6,`ɷbO³.3@J2v`&mz}y&˥U9l;;K<|{0ݖ3+j3mҽl 6 ==#zozRǮ.sbnQ*XPRצNq"[xרxT9 ?5K+>k'<6߇-(`_ 1h@xs^e:WvuL{tcihWTm :`#˛F1ѤWnYdc뭦"Όߨ 9E}Tͦo.| 6lJjv#4}E§zڼwR(&F VF".,2T*3P,P}d֔E\vD+j(h.A ) 5 )P.T.Ɂh4[oos鱅i\qd 0$RhchrP(H eq׭,y]4aVok58cGw`wlZbUD+D8}N&l™}ƮYij6c {7 u:Ưw/O\W.O4yaϔ~MgM)xTcg4 JOb4#|Cc7ׯΣ@mz0Vgs0v"=H$\TtqhK29|ʨ A:-41t2j8.:&Nb<BݬIul_!i!3Ӫ5osq] p=f*x]B՗x4E/` ,+jԡPrnDSd Fe\D1E/00T0{v_+E4fЍUr K sۆmjjzZoO%4#K5JAkG,.i8:E)EfQ1,|%=/+7?3smCb'}m4^ֲiƘ1am{Y ҿ/D-5ӫ#02&t(We'y2_v?^"l[匰vUx1 vq~5V1ly= έnJiԍeZ_2o*347T|+Py]"wmh*hD|OEw3 ҭMc%?1a]^r/,BmTpf16L3\m(z.A+;N,cFMyb,%N+taRox=q l0JaRR^wb_(& RQ::Y"hCcXԫ`XT $/5N(K p epEb>,ix&/G1ӵ%LAkCPR-+K㠵f^%NR:Q ƴar'~ =xޗч?}7aD/6_4?r?r.[qI+L\:MJ sݯ߅-~7?LX~B\+3bC~`Ún+Xk5ЮdƘƙra+oPJ=QJ/ÿq!U*Rc:LoF=ġ; :kأV^8OmM|n.`hcƳ`?]i IVk>~vTx`.$ |mVZ5pv&)TpVoioܰB!dfa lk TM6F@LuC5n4"Eq=;MZQsQjkR)(C&xJB㐤)s@)I hu8ip7ӐF*((QP |EPc+h"#۩Ay'`v(cryyɃ;>?U6w|#pae%)Vqj3{91(-ǰEox-!,[U֫j[svVqM1^a+_+B+Q1SnQ\d>ȉm|z أEFތ @`ffm88Ag;v0-u"Elt{=}Є鯃I.+$W^> K/ݨ.N^hG)6ݫiF䯲β'7ϟr85aZlQݾkci-O!N]{՞ʼ߁\^5Xa~ޫ^kWq[q(q:CqlnIdpPxiC^ Րpq p~ŽԬM7TpI/7Hdif3"Q\X,p(0ԇvS3s RֲE RRM%,X^g1L9)9ND\3AJ% fUqDd,Nbƽdz:m~Qp=(eȠjf q૒ 5mjmuH] Ba"R..\TF)H>c̤{JcrMcs4+uc%97lR)&'i6y\Id;^eKǀOJ{r2ƵëIBg' /fwmܶO8/=%wPܱEl8>&e?Vy;QQytkЬTHwY*wof aHe(s=E:$ރ :>ݞ$]B`XfT!Nq:S$f[8Z1ȡi}T[80ܗN J3V Gj.Q72>@Jp IDATp 3mp3@)њZEP B< &KFyٙ#?|7C4.9Xǀ!}816cG_)[?gdRӫu5,vfWWA(F+}b:O$l({Ybc xOaFvGeAc:N*0:J%1J1_[cD58( C ZA 9-tGBO%'Mv =w+W3>"IiB:j,i_,{10ݝ(llf.w.XnK_Ǯ(-ujS왎 q[{HrwYJ3żʫ>kWG~ T}hZX8<=˨O )ԓV[\(39xnr~ +:YL&N, <s>q883p(>ypT1P(>Bb27HjR4A) !,j48 09Y/Hg4u-tc9P(BO*bbHSbL9#'NL֩ctPo)>yC][& eh\J F&Ty$[FM+׍m`RT;mIRFaY#9.3*%;qPL T}4v~ӬjR`{Q|lJ{2[o~epHӄV+Fm-1njür{yn c8)̮cxI}L^}~x ۜ?~ϓ13R6/!O6Gw c{y9O`:8h.|%X!f? Bq%xiWClgNZ۳~>h,D /e2U$"/9%!(CEd34|LWf9bQkљb\&J V$IV'c\1n1a}wzf@\ i& (8V=淪ϳ(/-Rv LK(3N s\N Rh}>?`` Bj]GmR[-ԗ<0 @+)gv3кuUL"= !({T^||1&6aX{.`lyb t}n_+B>x|y?!` n)oJ3JLǡ6AG(:@eCƎUf?pVr&0>\3JlΑyvnkCWMc‘Y!J"0*׏V Բj@}DZ[z4F I(9 `Q !uT'J"`b0 WU[G:8‘,ұ*ӎ#Zu|wvQ8ھo_gvLJlLū.=Up7,_}˧Š{Y 8^6\+9ǡN25|>!nm I1S.ڞa3eSQ o(m7u%ĕ~jKdMc׷aoxlSJ1TJӋ %͎9pDnj'ECB,` jXN4þYhѶۏ tӝ$Z3P,rYU&KiƱd`3x:4s왛-|^$cb`M46De"MzY/fG+r,^1oZ1&; yE}#{nR+ !|Qszaz% ?&8RgP Qh{N旴) 14 ~׿ߌy'ײW"p]RF(33WG W}`/+|r7),"pC-59Lwn&C+lVs F<׎NSMˋ/q<ğX bꭐVlN P)cTᦉM 4foFf,()7.$)Ml%J2j斍L򆍛(z>% Qxo5vLJ qQ.-tB9%BClB|i;O4 .y<>ߟw 8.!b0Jn6W6W q|;j0 7I>F0:/QbsݪAchIZë4LeJƍ;.c^-|ngEmy^eWF!}J;Ou{3i- [k()By^L7j40@ OˋG>ov}2%5O-/djjqBk((66 i!jWD8+c.{/Jugb6bcJD@wEM0"x y B!s?|xe9GCOC :sL` Q*MS!3Mv6Uhn"r{cnܷԢ9 J{#i$:B_D"_뷚٥6y֣3M((yB93t{rt!DE6nfctXϱo|K_?>q,N훷}vȆ{ sO ŏqJg!EEqtJ! $^J&l:o[kq+[lJ+'&=Y| p'1&?PJ^vMOϥBwk]p;O8|CȻaROk j/ IݲWoS{qCS}~3X(34PQڱxa64Ԏ֎Цp8Ϥ:lH=l=rTg| Yu*d s^7:L!(`'pub߰cr߿a;@clt9`߳g#ɢbcRwɿUJNlu{dM+ksrI 嬗׀Ƭ6i{6vsolY~a?ay1r^Q!߭R7p|x=m՗YF7R{P=syCo ?tCO(4#t43oT*1ȴФٞosOr+e'y`"L`Μ+h4(yN*NɌ"bϡDU*on܎}/c2~ 'oeǤBEqE3 cle_a_77c ގM&We(˾^ȋmUqwbW^@9۝m1~Y)+q{@lNn$Mp^V!uld\MQg<%faK'ɜ>wfGuC{VbKvvPz#nش4M1f'^`lW:f~h2E\ SO㱹[̦^M%yh 9M4Ϸ+O1Tƭۖ柦(<6oNr56c2 ~eŪdQ\mZ7y@)2/$ivUg*vCzycF8B1=`ǑjLDYDС12~fѻ2:}* `,k1&SJ&#헍UxElӋ+Vp{xzA6(;`/8Bj[?{nmlhƞYBPA.QP]ҟImEZg䉗4ՋA)*$f?Q-4:&ɖtNcCv1TYlCRxCl 4[>o[L{@Ka~/`8\]i'ҋcyl}:v u'ɢf,;_W}E>d~1Ŗ &}\>~bόD[_]W!Xk-Tʷ~i[Yy8gw|:؃x#qstW{6o)7j`*Pl8\kņÀ* 0 L(ZPNxKhRcH-N,cmqזa9d]@3/\;Xw+;#c.(s[@(~orQbLĮ*c'C[;&7sWrBqSL$FJ~lːI'cm/DB!-Mc @DfR>1liFӦ/,3>:fʕ@ݴmsDKE6Rn76'J@+joz8Un1!5a:ZaTƄr،Bc2#h"%`Lq֓~Îw[簵>SuY@b]ʢ_}nCa[:iT}؀3|q U~{BoBeIJl"sDI«SLjv|d=R5V$C7%Z`; }Lz  (QD&`2:L׊;Z~/#vO `c_ʹ9@C/(ĪdQ\˞Tݾ{ࡀ=_P RPǞ4ۜ\Tӫ߽[Q7J1-aW=cb < \/B\$D!3SxxC;kP$翲<I􋇣NX\p=yڋLLvOuw#\eCy,&bNSF=4~y|FZ]vIu^1(6 oPyGq:_}h˳y1_N3vqs  Iг#@d0}:/6w[YǠvi))Ehjݡ1{GVc I5M)};GƘ 9*7b+[ZO.R?mB^ GbvF) ͨ}ttpqg{|= vll.`wyE>C˞$⚦Z j~_1i_v߆=/^-Z#B\ GBr O(&.Ml^ #㐬M+.UOe!.9I5M)[f:IKƘKmaql*kMƘW/B5e8z=IWe%&ߍ-lw>2c{VtBI5-oca,'+_]̀o4|RK!XK!M.6+&Nmsx~xێq%2= L5"}5m3_=LHc~[uu%~KE!WD^%l3H#U |h%~CEqdQ%{NW[w'_,ֺ^~J!X{튉Ͷǻsi.X# UːR|˪ l6tnTB!ĵKwo[)Wp:79"9U,̃(+$9(Jr 8<|h)3+B\;*c }bm̃RT!Γ8 ?wbi|8{m伢Bv>W_~[K`x h̃bde:&b`#6Հݫ?4!mL/6c˫˄B9RV>x;\, 'R*Q!|eYl֤1g ;6ll^cC>BsS+ޒg'>H/'!=²جIH u2aN pb !8 IOurӳ#~g+wc$QB!.8d8cэ)Рg~CE!Xنzo?سy2jv;4!t(36Y5&4?}轿K8N!H5PJU0΁9SkOl~x˒c$ˇzefz>IMrg? M{6 pڔ7ֺp$o|$i,&H5uC33=$I3r|RG?gp?)I/7`W353|*EIr}0N& T؎$R-x[yI.<IGKs73͡ lfzoi{|~[b6>d"sNrc)?Ir``f?ٲl8 e-4ܴޖ䫥b@5ߜdOpensoYc6jܔdvhsVJe7 $_Y%#I[U{`'fEg/RMIN}pf#0l[vܜ90z0kۛK)g'&yIMjfkeُ^kw{r]9f3 Ҹz,wS$Ydf3e,Т,Т,b7TN2W ݛ::w4"o%{uzorL3ʢJ)&ٝZN@ ?dt4yឝ®sg6wi),Ks02vHF^M6 f䁮g>e)&^_^Kr{wһ2:擒ZLw 8)oemIR,.̊U9f6u.g7T7c~ߛ厲@}{dz<g4IIe#J)%ɳyZw zmr'slvl0 HQ[Afa /$;" ~d"o*v N.I2$ċ&RajϕRƓ1'InFɩ2'_K}Q{lw9rK,=}$Ɇ,,; #egJ)gyaK^JXSGf$Ogai=%{B4<#IH5͠9UQLj4+Ҝq7ɺH2:zٵNSee;<L)eE^[۩y(e,sIfeY2D<̾S$Wk>KHL55Ͳӹ$c7u]sþO 73rUGlHg i'bܓk.C},̥luDf`ɕZO>(J6ɦ$ ~ֺ`SSGfifDmvֿv ePhQhQerl"(H1ɿlJ֗: =59UV&ٰK_K??Ɋ1Y+ I>$ϗR4\W֯߶q;4DٗtÝE:UJY$Xl`S?W/\~ͫ6OG ,PJ1ɮ$I>q$g$<v 2e,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,Т,_עIENDB`openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_52_0.png000066400000000000000000003025441413546205200254500ustar00rootroot00000000000000PNG  IHDREXsBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxwg}~}W+iUmY, ج! )q~7FBnL$D8&qtB56el07Vߕ6ѮdI^Z~Ξ9sf3:3GH)h4FsdFh4'Z05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05FZ05̺UbUB84ѡS9[%'p;F9:`j4Ǘ{_==Fst)lAh4maj4F3 `j4F3 `j4F3 `j4F3 `j4F3 `j4F3 `j4F3 `j4F3 ƺU" #Ѱ~# >)E}Q%)F[h4ͱFw9[%,`n [91UXd̽3 XH2?Z7bw5^pN+YǬh4cH:(NY{/O;. H[q\ 5m%c5!c-$Rf_,zkRiO_yc +0<½髟~šV<wi4id8;b`V׿s2^[% b [e8 TJuȃS@B(^`O/`-Kڒ/|v4sV8t>b[5WxlS-3Ub!$/-Fܺ_ForTWoj=fP{%X \.s0: cK@8< [ B>k7>ڳ4|ea}fG>-Ak$c+o|lU-3ĺǗ߽sf1`JL+jKPہPVc;S[lYeӰsrI!l! ?v=^<~ʥڎ?yuќh!֭g>lF;.{(ќ"!0Q塉QuZ" Cg+ܨa5D四_Ʒx4͑e%3$-QUM] *!!H#XJJԷsIFYCm`@3 ޹F%{M9 Ѝ f9`bLCYI# 9ªIJC-Q;GtH 4#%@F9 Z,5maIOu@g]%H-DB#7r7L!۫+">Ug'}~ ˑO\Fsʢ-"1Uc|$(e&ɺ#R-xNn|"iMWbf lplƯL(1SO5Q:C?IRx5D ̲b63Q[3A0%@ 2>u0 DuYS@^0)rދne$$ߢ/A2gp`F9Ђ9<@2ϒ)z`jѢh&K +_LY Sn*K4S/Y&Aغ7x( aIks }/n8%)F9т9W 3L#%Wήi+l-Uʅ,4&;>K&BT*VC@=cE\"?~z͘Q+w ]"#[Ҝ;][>-2VU?zC-Vȉ43''A`1lhb3f,17b97δ4U75'WXha_z퍙Vsx߂{_Uڌ Wz ս`أ9α78Hg +/cπ6(ĸ e AǂNzCvԥ%3HONw7/tu˺E=r AMh́;]S_ <-Vȑ#|F3K褟T PbW-Gg.n-X`aD1]ҹ0B%J`fy}SQCrg1*~eeQ%g/^{} #KYڲ6 IR06*ѐH+xD|*Lfnh $E,/fJg ?9_ns`YSk+|09EL]bj۽${P)_zW3[g`ۚ0guɥ#uKg}ݐFIb!aVg!N S&Z/ݳ/Y"xXmψBӭ4KmeeKC'l/h"$(QO3wQR$L@8N>7I)[`U&yk6čq8'&l'lӍ۶\K׼lTBw "A!ǡ`X0v4\-GOhp"sHO.;w3 Z'8߇7GUU$!iOLf F\G6U|0i$*4馁byĽ]<>D xE.+?ae3?ѿoʑl`'fFL[x7SL  H*i};vO3R|}O<:9b|Pap_zmmfƿxL"y5I:[Pg?*Nq PGz[]ݢ0z{>㈞8n0e yWg`Ǩ3~Fw1XigKiVYhYK8 ܱ_[ȏJ lCO 2B|/ǎ8`HxP ¦ KL[6517'h/xfJ yY%P f h|qw.OCѼdXjQBVRrZN2DmmkBÆa^${^T<i&c2}9%q0Ii &*zQv>&#-''-tY~&Gr`<ь"zΖsG'oH")hG'/,RϮ38ǨخҚHBҪ/f{e?S#Uِyr~e>\dR35!VfO7.!0X~͟D8\}eNP*ۣf -3Ll^70&G Cbߺ9; 7r< F6r+:`&ñFb@1 (B.Q%BlH6&!̷3MBdVY53T:heթ(4\xj4ǀs\c47 ]adǭb8\7 hB(1sJ @iu 6P_mO??n3 ȇE_{Q']_vh2ǁm^zK'؛- ;0( S_ZDG$W JF;ׄhj2+ X2k*2`i%j3HX%)N-ox~*tN,b a} Xbfp!#*>UV[񢬹FJ8y,vHW\*ƈ_ %L:gӼWPu:߀3` %(+r> u)hvĵI;2uutёт93xCݒuM}ΨhWHHNFLfĖswI\'iA5F 陆9Vh4'Į(~/H M| +ÿI-h<Ϛx_2)Wz&C&rj-RnJ]ݢ (QQ Xͤ[N}e ^=˵ u]mGa~+2gh_ɗj= ܴvuz{,F'=9Ng` J$kΫ_GdHXNR[ZB <tBKdL ؐ1f.la /2~Ř#iY[h/ , &Be<$OϮ[,uuW H)~=mYsؾtu,J̖qDYYg^̢QsFȽ(OLS1?%n=sމ@|yzz''(/LZJ( Ik_'F;o`Z7ڑ;Yzdw6yGeUAE{YĞre1 l3ɤf`l̶]" O>1ʄהsGユOuɢ9IAbʊ[r:Lytz("2#k/i''֏ Xd*&Y*=9!RD*e߀7-y=c-V?+O0֜wE{EƕhVbbPU\ssݶ86o O/p- P3:\g8em#p&nC3N\+BUqk);lɀҊ_I>Mke(+Q"硬rN~A㫡bQLz{}ut sfxlQBB5tW߭Q<z==w\O_U+^~ƻ2_&z?gsï'".$xyK- ~{9ړ?f-_>veC7tm]nemu^JhN*c]UMI"&tK:|{?m%1x :C7SHK{{dPqQkKr <( ^ʲLPx=Ee 8">TV}t*-''?A o"|ɮuӢ*'zri@|_zT LhiNXYD ?;gwDGGw',?]Ɣgrs2Dً$hJ*n"xZn]8gwaz׆|;t|яTeUy߱KY0TQ2`UozXsU{l{𣗰''Ӎ@GwUF%i4/fQ|,Յ櫡tRtf)Y~={#i=!JgL$/r?Q\gpkNwS@$BCX"2¿!l2@W&XjkZgї5ij`|to_O_貯 x11-k>+XjN99 ;:kghUoZ+as3zʼnsF  IDAT/(jz=_@K %/1D>W%N\{]ץ_Ǚmֻ[^ߺ?۽g7ڝ;?=3Us]'1u{.Ը>O2|| T`/` g1K~'n[|1#M5Z0Ob!PD09,h Mh^"Z0Oo׮ֵYF3S褟S^%Fh^Z0O vf?Мlyt};mn5ch4GΒ=X#@Ռ4A>tso,sͳ=Fsth " [+o?~o$gӯߙ$A )OltvoWǣy!ãќTh<{;>}حYU g{v OѼl.ɆC aBMo]7ұߟ39 - >cXo_9Z05b7W$by{M2`U872ch%:GѼd whENH>9P,{͟o^5P&|֘505Q#nMs[M]wW_. ?s/3`ޜ7~ѡ-LF3m sPB9 T?nZ=@)\~ QMd bKKϴXjN:`j4 ?(ro/}#OM?+KKsҢ]fڈ g{,F ᫷3Cwmj_%9mp4Xہ}=#!n6A/_ѥ[,iո5SmajN oMpQqw%:X0e{i[XQP=SPQ77;TVTvj+pPJڼs͜%-lo5?};hNW`jN oZEg!~@;g DQo@.=Zݶew<}FێVwHW(zk>Dy 72ޮt/> L s-թ2!1(q<(_̛k4 Z05/+R1yx|?Qۗ;>@|ixaN5ER~$о[;N94 {ش\6w,cmC JM5y0|rsɀLÊ,3 \:v 4~7 \}K"' z3Pn 0(PAAF[{GZW6hsi/%$$e'Ob9{Hf[ .Kh&т9uQExJ(bz‡qFz7obmρCT0vmմD&bliWq0F\'&~ВTe!a&au Ք֙Y&uG?Z^z~N.-;VDgjN V`.*ׅ%(7}Bl;">z/!ݶYSqH+I(>y #;5|lZ̄eb0dcio*) R2 !Swכj5֭Hҿԭ43?Z$}lK@`jNnVމźEy%) H2>a %&T9^_I-]d,vػ |bp~V!̋JrɱhNv`jNTuA]bYFoD $ Jۜ!1w.Fd!CvW/De Uە~FX#L,Dň0GƐSB\(Z.`ܭP5I5O""18Ɓɬ&` %ckW uC1`p%$&Z6D^䁰ܺhf\6|%_8b톧G^F3}XCG;O61hf,ήan5Y2 IX9!|I`0=C08̇819ܰYHog\xTd,4򜛴Pq8. [yP''2>O.rEPPK`uȠ@=1b2T}د@fj2`X_C&ɠ:ToL#od48Npޅ*o{Św.+ќ008 (&.-AT0Y֘-ZY@ (a "Fmv!?{ 1= zp'ZR)05x>h/+'Mj&L- @`CO Beg'>57B80͂ژjb rnҿAb >`'<#`vϾYE\`sx`jf[oq﬍}ҒVvk0*u7JX0 Id2H A֥$6.8ljD 0K!1FdȆE }$~#m#,FkY29#6^I0\Ă/$ r0*s"  BNgiƬacb_Y fNiH2*mBƘT`Gٶ dZQ~_ dzx^'պ^bvx,5Q6c0;~#j2r7k1d PO.O] $ [ͩq|"ϸ֜ߕ $~!ts˸#a2&GS/F ̭۟x%`y6|)EIP)dWq:fK./̙UvIx%Aʒ!U%cY) HTh~iʍ<ә,y艼h1~q#0֌큲n=yLb qAt!rEL0wSĠȅ1[iҴIޘ]G$y<}Qb|G! A9_$<p'ҢWf@xmcM̛WڍQ_y_iGyl2~'mo̿g !ޱ=wp[_z;_; %a^ŕ!J`b{hԦ2n99Gma $M ]B5kAH'B:5=GȈ=3cT념 99҅||`YՐV<bdC5(+&T.Nyiޅ֨B7aH_g| %_KЌ)2Q!K!+)75*.mA LQPz4AFsMOi0D[iQ2J]|FڲhP_6$MeDӋV0joэN\Sͧ3bo!v0Х]zj4<=t;|:qӇt~yh9Rq}1!+oGWzS^ 4ě4|>[5T( B][ m7 i G}n IDATWgK xsm3+/>s˻7=/U|SN(5/XOK l+ j+rBZJg:b9fe1&sJI,&2TD_TdvNb|*pHBDJ uT ?EL2.^8 S LRQMVH)VTk0EZ.ȪI5i >VYSwܘI2edhUFYCiopaG/2kE Zǁ\MbB<96HVq̕Bix٭:]pj8ܴL؊~6&f}mYdʣcCmr}][.s+%=tŝ`?c?"k?h}_* }FvDqscQb"dg gZ܆}5lMxx[Ͷy0Bf8X`NN@5 1)]O"(U$ebC="q>$M @Gɤ! -105a#łAkD*B$9t ׇv45|{yr GzR.*7DMSڙ&z%eswM>) Ry+&F feOf M=3M3TT\Lq$=qBy1IГ'TM8x[}jg_U ^tjZvwet#;B>iu;' ۿЭNM ެu;ZĔ82+_9o"w ~%qJv0V!?2ssUW]bMh{U1GH:J#k%9{ K?øH-JW/1 QxHSSc7!&"ZQQ^زb 8Yl ;BfSt'4c/(\0xot r^&0}"5 *aGh *i2g"J6F^nO*CZ̰1y6t FܮL!4%f-arNjcZy&Z]w. pT/NN֟IrF3_i;Y-FƤzxl%H+>uY? qb[BiHg,1_/G:v*.^d}{뙗31|Jt قŒd\tKi C"  >bWE ɓП/rm=hgv)jW*L] vdfW,QF<(D 5ގgNau8b KE+`b¼K&#jeU&#&aHhT?^fvnT _&kCUR3(UmElLVSɥM]9M pL^+9Vo-Vy{*WSqUHZ|b/ڀ?ovDGQp8'"Gz}?y,VRfW *lDZ5yUj$3%6djGSF)}|ak 57VN5b=za^)!.B Jinv%)3A1C<σpp$#NmiJfzHB4 x~ #:8לD Ijb`Y:B%p$H'n #LN#\]čElC5jrH[p3JnhY2z+SODQf.׮vL^8az摇r %co܊g.%1ƤYV6 {sy_Ϟ`)+9ipu/rzcW?S>#oW:a}0cԁ )4xmt? ip>mjbV~ˆ[M*U}_4u%?y/.+CߚrQed*(/l+Ѵ[燔e杭 RVXAK03cĊ <@g L'`?I( rl9"]zRETI\TL<2f#(Tgmyjy# |2BQN gGyQڡT>װ<E-F6 [1~gH:EkN 6/ϢCT'uI#6BOB+B$ gɻYRM'TjdU#MBieB$}=jV\& UrݬYGKhJLzbjvdBVӾW Qg/?3ArsG]kZJFAvw ݕN+QVD4 m&g3Fuoo z`]T j^R/[$X`B\oΔڶf`֡ ؚ3\쫴7CO$*:v?/Rn;"磟R 1rU=C!jVERZh2KȬQ !#+0QrkK 0Wӏa9$L#ZBm ]ێ !4BhVƬN$! V)8 F2I%m0! )k=Fv&/òM4jbwa5Jv?ܯܰ v[M֞:7iZ`Op_W~*˵\w`;w9QI@ޮZz@Fs?fQKy!{}0S#;?_{EAu?ޮޡDe'zq D$*& bT!HMP%$~k5k>|q˻ZP;,;}q1ROc\tbMY C= Yp0}3f\9奣#0&Em2X ;cLe0]a4M طK ĸdd1B/@GH;e8u#0óLDllkp@%$(#œDG7KD:nߊNPVpzwPD$6&6 G!L,[n)ٴD[A&!u3Ua,?k IZ#ٶpԈVE&+OjNζ/p'o7e{I _')-u஍un?t++aR]?u%G;rf5ȗ2o[߾P}hig/hҩf&fĸiH JA{$fi}tBxӅi֬J|2|WO SIa9UMj8bIK";.xBI] x-z9>ǥJ/F Fd롈o-w_RwBB⭗Xݖ_}E6>B0 "=@#Բ+ZeA"zeLΝDni 8ҔИ%C`&D8.A5! 2h8Ed#w]KuNv;G;Q:Vc';jS$AQ%X0ĉ2H9>]TFaoa)cf D\^Z$!oAT*df*Otc c{]#s'w~!w~5➃I7/UgFƿ[J,<>f<:xx HE)TḄgg)"P(bBRB` IB iv4iSMyJӢH>iSɧ ܓLwjPy w40rUxmrvn*ap+#y n‡wט"|zպ> Di%.Fck-iAAv}[QBI9GS~J4`W!$LXM84`Kv񛐃U&7%rd7 3'' DK)DESyr#4 wd33/#bN[!UI|S1"s%Cѓ,p.< mXC=ΪYԌlk4(>~\j*SD4n5lnJ; OԜNnjO:lR 'CFǸ6"S_q?EX;Pܡcu4͙>'K=*M@KZS\$iLbF%2JGIӥqztMڛeso }%PML>tZFuڟ㓎 ?vqs-k!ˌ]Jy!wޝp8ϵgb7:G~2u5DO^Xi䩔3g–*axjr!{qwidcYEȌU2<2Ƥ#&X$H70kD- Y>NcP5:壄AW)Yh bw'P"Nu܌ **9Me- "ϠXZނohhD&(a Ǟay,z螥dx4l=Kcy֥LM0H="팿I+U EpM Z_;T]f{+WOaQ{~J~3Fc6F{f , ^ TG[Wa~#X7[c23EGICTF0=`RπH!JG)RW v$Z|TA2ˆc}0ء]oI6@d pwO\to݈˥p0_JrBmA `sp_QG,S]u_^SfqXؘ늹n;Of9#;;{xvQR2ΧJ!f 5܈$Kbx+UH|ޞct41HAv㧚XaV0&cl4qpiVtV(d8h4tit|GGث`QƎcJםg WG2C5#]Fcw!iM; YE݁M$WM1,Bpae `\F{ѐȦ#i 6L"߉cmTgԛ<zԘN_Ng^环*wΞW*cWCu+,<ԁ0~ONv6ܕx`RIJۓПKco44~3VN!-3 Dz0qHX \ͧgTќQSl&ݡAR.#n`J~J/Vbǝ} Yfෆ^MC )mЕ )VqL ʉsؘYո0D.!K!Pϡ#詜IS<]lݸ"sq!Fkmk s΂E#J9:y)mV">L%#̢m: 58ˡhS$^F ߼@I̐x d4:3G<3PSףX3Ti#" |J S,]9( f@xF-"% ~ -f6IA%LGyc,#F:a&@S:m@*ϊGLc0CX4HjbJ;;szˀܵqߍK➃gnzSfk&0oؼ7G*vVAiqb!%d al*2U5ZH3n癘KCO˲0GdPk^pn'Xhv^c7q?ǯ}{=ow}5]R=:pb< |.d s 7;;,=ՙ/RƸr;JcJGGrԩ05Cwy0#xݰ^[ȶQ,Tpz@OxR ;9TmG^rz^++GK4\%;!߻۬3)Az  F)Warq.t×)tAc5D70g$ ҮXFQgΉzmCtSY`'2a[)o qq9`Lieb2sY3`5`vQЧ:h$!iNI! !xT'`I I"l# yJADvr 7u/ ,>"B] L)fh휦3:-5R=%S#kT)\sN K loz9NZ<)9΄'.NU}]$ުO}Oo3&-:p3;w}?']|/-H!Pӥ TT1qt: ?v6.8?/F46Yp1G'QB94iv'Z.hSveT[_og\%o44*SSsHI0u!lR 0tcw]C}e /f.vHjcP)p1.nz@BJ0 &nݿFHVNдVf_q-QʟDmA.'0!,Xk [& Fv@1WGS%팲)b|@{j BuYSLl=ܴH>6Z!ʝ"+A dewYBZŚF Zvڤk]2Jg|\NB5L1Uu:~%`aE4Njz] ͑pUN-o~oylO\%ׯؙOY;~I4C5KoMd89prY;#?2Lh+JX" HV܊.gB?>NÙ;gdSYvmKlI3g%9+T`Gʾx&'+Ezf+q0}pqi$N&q"m|`K@1b1 p2o_î/w'Ŋmୠ/ 18tm_#Vz oq,wڙ L_;'1._edh- K5=}jBGa8<9˦VmKXAjvK0wS'sb4,1&F@DyN.mmP[PϳR_Ddh̉}g+*:}nFV==2Y8ꤦs18zPejG4]~㡳=.5{ g/!;b_2x_?R7 ި8]Z MAҏ#tAB3Hh70[ ⩉OJWo;Tiq\]wOvnyfDt}NV?В~XեVnHA+Q񖇺?k_yVCUxł;csqlNҤ'\X.2B:,g { u6thBB q=V/,7d.|p4m@0ajLloÎCSpu\6FƿG.*0єɵe`痘=m"`hTL3.u H-'It²"ߨrG}ߟqjg'mt) !M)& 7 1+Gh$RfD\OXsŠI霞roe ɹqBJ&C)Uscذ oUfdsۄ-5^Z?{̹j?x*\OÞ"q;mS/pndaEb$QH/j8/"1q1oa|X[nu't/_hqlU<#+2[O8zdǭ'zq=yH7o;uAnyE-7xkY] N C/D&}e˿(wȯDPf>iQ,x ⠫]_m@& 7ҴFFo|bykzW!plҨI L]^*n8MclΠ-2 J=f9Dhr -U-m4Odyh!""pz`:D =S[0K#5"y|;aE"Y%#qF.L= $ `PrQ >M38ttK6+ LӒ0g%Q3K-AC؏Vօ@0y?K} j sƛcr/=w,~qI&a՗0U<,Ej|12| [߮e#ZvP1QN_V1f'\{MkKι \M^0|Oeٷ ?u`x׺*6{KZ!@=\]G3J iF6T9kb?zB_}!ls`$޴6bŰdR*"2Ԑ]N;O5 co $)c4 Z:a:ݟyC7Z^ ,.e۴.`;[cK_7najg/D^6gmzq4v'Έ=#.H$pqY+_ynݾx>e?﩮_`*%wMPw\ϧ9s)[fv±,N !mTgW"UD'71瀇JfYWxҨWkv'3JuWjYն*A);rS7֍ L 1Ilފédv^сn kE{Z{XBd eqNlb R$mgbT-<,1`ATp*Q2M(JF},화b~B7apa\(S2nqh =Z&?Dfp=L\B3EQԲu‚]4=Eh])G4 d\HHDAzOAx' ii*Xi~lݱ)=^7էiE`3ೀEg;W3\MaT$IH7kݼt˹4ۆ;Of0^`YCz}kx|y/>Y4 vifV/wQP+[5M69O ~mw"ٹ~Ѿz燐vоHp0-a1—=`%R&!M .JRឹ4bW~G 7^]{ ˮs߾ZYKUlmwYvaco`p f`̀g{z1ЍM3L#c --ز%Kjr}o9g/JV~#22*_ͬs;۾4Bi0rKƇ"aKbq-_6M?J*Ax9?FX@b-zi^J5EVܩӜM\|Zt*%)"&Fg_'qw]G%Nu)k;1N %i6Uʁa8sz>͒|#7{-+vwz'\<EDVޢG  >ѱo0Ǜ8)v4bs/ͰNvɇ#yѰgDosvw;|l2jW-|ͿV`}˯7+aV֘ <xKI 2\fRm$&i18dCQٯ)}ܗp9dt<{Y̦TUNȵEų\5zȞqam:_؋kK8S!_!MX݄C$Ti:^/A7xYUzMaGrz a3n#\EzK l,*Zf{ѥ 7R2Ek@V@AK)"G+U7$"4R=vM:%ɬ浙z-X !BaL=ȍ{3M -y[ ~n?{×Q|T6,G:U^K*us?UW/žH퍀Tg^P{7ZC=kie:\xX>?>Xq.-ھ׭٦}{&ol+}ok&Ӻ3?o . <+rF<"Ch-ap5.Hø>E,C;9kIgwMœt:DX{'Xn(FWv.VtٸK`SQGӈl&tg*AH8XP`zXfpÀVߟcKyFm1*l@)ˣXuSlѓnYGIA$?N82 od['…88ky8_ebE?J/j-.{ɸFj\x,)+2nM5A.7)1wh蕥d\Y +7,x.Zzv4aFz,'%)SF6el39_QN:ӕUA>to^mS4."'Ϲ.+OLVwVd,~N$Ab/ڠg*tCivnB8!ކv.~y0b 83jB"YR50AZ0d:vo 1۞a.4L?2JZȦ%,62NKVQH3%; iM]5;Ҽ1{EVDNdFō\a1XyFN:o>I3\ޢEIJWgr&UagX̟cW}a<whBm{M'zevMͻZW}G}m /~򹃖熾{R5ࣛ_j&YOUIj`o6gӯ)rIUE~U;wZw?0۴fw.uXY3J`9:(/8Hmcjf|Y6OU[ nG8E 3~Ʒ(^jAQyM_?^wŇoOii- <[Jyb}lYtFs8= ) KԸs9{pu,Fct\۸cwZH"RӜ6han1\D)>_LUORu5#Vu !2QI2>16S +-]Å-1.gT{;ZcwY}*RI6 SdE !\ KE"SƤ |7`ٮ2[a5rKzX3=xhmJ .{̻ߺC~vS]GuyL)?+obju ]b6u5^ÇN]ٮE+-j;=ӗ,o|A#J8 _Vd Lr$nQkgl53ꑒAe1e ձa~'CkrNO51ͥhI +âI>k' L=Ӫy |z[}qD4{5B`dt 1*Ɖ̧]Rs7 n2!h"v񪏬ZRF3O j#9NHv2<<ƾ3gQ4ęr{1PHX3 '\*="hf wLG( FMVܷ T%Tq*W'+ldf.d|:YWռ_ ָvutJ⏼3&[%͌TkNIql'+xHkg_s1߲OzZ2ar*jrكN+=<*zl M2-cU0ꒄ>yo6I&ml!~кИ1YJT+BPAID`@*a)fHr\T٢w~\SQrTv3s$V0\LH4 ^F+{\5{y~ N3hM\ʩa,!KZɍ&x'֎M\5k4DzZa%5XyFO+!4 .! ͐avlv}j\[($y1dj``)V@"rrIЛ[}jAEFCL"\8@ v+2uB"˦fftY,5:}}|oCƍ>P@o6n|qo<˿_|Y]Mp!|Q{7?2?}.vjmƼ5{敖FX%@M4(E|EQgq>U`$ES.YB,j.gGܻkػI⊇جsW<"7S1ӌn1g84ZlDE}"7 ϧ+s,e9bT@v9ľ sb=Y $ƸEwY5AўiFVcoHsBGp,zY< fST@XjDxPBN+e RLS `1Q^@ 54 I$A?MaX%tzǛ?rkS4+1M IDAT׉x%v?&R3}Ԏxʚ+{/ 6OA@FPo;gbC5}O>9-Ǐ]_>9娹T嚨Nz}^ `n}9E Yy_✏'ֹN9;g-WTDQ SDO~%66&m\ D|+cAv]{եOᅧ\\~ ~h/?y\Sd7K"NիktSsqxҗqq+r/gyvLWX dWF\u?.G>iOp|e,0v6hQ=|.sm4B@aj geD?P q]}s۷"~+G@tC'|;8|,ĺL5 2[b/GO۵o sq 8dwlzO>QZԄ唭(rsi2CVlA Ա g\W$cY(-Qv$vNq6ڏ>{MҸeE1S#Ζk8Q3C. v i8#0:U'-=Ry q W SᢃE"ƥGlJO7](vv. }6YI# CF(GHǢ>]a$L NX '9v8]HDLc:5lRv0$:B+DK/>Y1 {Ù8!˳IrDp3p.Ux-oޕ9/[8!^?z({׿]>yE$ /Lw8{1Vؚ\b )ؐhᖵ;M|3rYVQ>=mejo[4$Rba+yWm恛p豰5Z^(W ބTH~wwk> M4`x Y3< ivh،WK$oNEE.z&BYl^zoȅRBd__6DF@"[qXdE:ߜ"'F\nzcCh.4eJeGccMsK>M*RRrbf&Bf"Rr;7>;׋,'P`^LQ+8 &~M { 84},TUv_q\\{y7%Lpȩl#^p&Of){{hS72'\~iqy߶7ۄ-}ubG<5[3|CtvFbEln*ÔMoO ti cu[e$>xt1Je9*-dcĥYE{YMS{dRX56cٞ5>Dv!ĴaR2Y ĠŀA0t0vʰӁP3͢!z)6g7Ԧ$&tm12XazMb4Ҧ^΋  Q]ry$kJ ˳ %[5-7sJNłnIbh~ kES}窜e5q!I3Z]Ez TUtdNlɮ(d;ބߕnYZwib}/7}Ž/şS L:mn}{ `Xԇ+S;n:|z0{:Qb2.P4_.y#E<֜c01A4 yz<)e; 57ְ&}nsnx;$@8nGĩq\ X.pF%*nGmDVf] ٽ1T{JϳNoblk"}~Y2a$vaTF!hu`e#}Lni!{\3{o^X&4'3\'7pq6lH=lGiBv ͑-BZR6Wy@Jm<7C^:d3.3gb?CQtxujA ##P)3 W $dQ9gn72EJ4CaݰXLab.|yRߛn1ޏ=}%%JOʡPzj^;7"пTG #qȩ/TS7N"Mw%k:gW/a>l ,I"8V6anCMߐ68}d \B ?b$ Y%@E~p Ѯ`F)zİY.iDMj!Stt:ײ#8kp!K vJTǕR%fma Q3v˫[<Ǧ7G*Jz-?+]w%g⡽>:T2FlHz/9; t ,[HaE)I@46-ðg7u;jjcxm9(TlhR'gY_ {Z<:pnoyhv'h({v<'f%-Pk'rB*?|ngyù箛GT׬ѡ5gF{(UfЧ-W٨.7X;FټfϖZ(uh\L, <ޡhik34zM%IgnQ՜;pTV jAN" +25ЗIitߒd!kǝ /CV\%x#80 Etu\1l<ƒOO.=jiD+ <1"))ԵHSyr8ְF6v cK Q?Vo#$>QaAP?f^ 8zy_u޿g-τ*>~ss55zaJ\L*#0w9Fb0M\Њ~M{N_X!SKMKCnIvt(rOd-!YLaWo<%Ӭcm$ P]f@[rUc@ (%',"6Kx. JYI5cD#i\(͐ f$ E,;e900#AE; %7eQ?Ak8p/w,e{0IAԡbYled^u`k/Cdҡ΁'>IQ\Hl81D"mEGNZI$]&B+l,)e:X-2d"&*Rc),:,Slm/-cG>^k~? # ʟR K8OC6BQPV)o!c{`zN<!?j7n*?;:&ac&#¶x^j-&-89 ۄ,;?Wj'*WzS7yL&?)oC0PsM`hlz(\ ^Y$VN SbFP]%gI- 5}Fff"1Έ/WTqsGy ]$*T>- SƉX!q\L3ax 7-–MXL`QnRA#JM\Zġf51=D%{,*Ϋ*_Qj"&ZnY,>>Ap% z dXRdiB2ǔv$iIiA9MNN!\hP#%\9#Ƥėlh-Q?O7w[fa~]|weS׎5Yo~u] gL;qr53FYУAb[uRc3>O"ƕ1MYgG<³c, a y ZVKe1⃱EG`j Dgx!]Cj^jgsuLit9&ēf:M qCz{8xs72s(+EdPx}}Olf7[RT%%ZB0  ܒR^gACh3flYg]bfF#c{LkKM@,&Ip"!޿PT3l r'As}l9`0&_?К9i]?RL|"h7EE_~4='6Stю"io~LF&)imQiB#q`*-_Rjn0*vq3,\l OzY*3V>#LEje@zbmCfY cq6 qcc#5R25`G5$&j=" afcl)){&2`_ѡ]\L2\C(q sv^SI bLt2 Y?qu}W~?͓/t)}uEvcF%;{ե9$[4)tK#$ CW#f}9Wd l@XqҾe;mC5aǸAy09]E(LePRj;5Pa%f6Ue:\!2'!K0P"q*.,bНfj9”NНՄ1%иY ۲ɺX"g $]]02lU]2Y0'"‰,ԆĊah0} 68g%qy䱅`cc5.]JF+&ɤt+B@I/lM1^.2~yo$6ʳKspVn *(Eɂu' jDL˦r8˲jezpzWl=62TzVmwpi5ȼTy|Ҵ.+τ+#;ec07޼ԇߕ/j=c{n+]o챧)w2AJd5XEyIGԁJr,%`VxȲF!*!R p NAWѷe'KX*Ƕ4p 6TҔKL;95B&N9%|TPCbCTQУ#;1ل: %'Z*A3D\U%ZYC *,Et #00b""8:ø9`WL(05 )-I^C !,' 1 F>HE4))2ȁE␒ddLLLBtկDɁA 7#'mzcx~^+/-vf6i.wiԭӍ:>H)DQ\^LZ&5dJsCbQfELif%rGJT6&ih#SWU:{e?WBZG/h6! ـw>? {s ?S?_H詝֠ `WXx:KGEPlx>mgŽAcQm4q tV`)fwAXgdհ EblsҘ|a1 +X!x:hcU.$D@O Z%VN$pQ,šǂq]7K 0&_lKbŠW$qLkv[½d5ATq|v >D'ܚ@91 4a[^ia0m ㌔1zȐXHDtLDDeVΰP(t>]D~O>x /{|swLEjF[0-w婋c?+3jIf}yh.`tA^ e Y.0+:&QI0\r)pS&j.W#h'mnHJ|eQ?5W_ n6a>K0ӷB?S/im721U[q@~SEH,TD4PN1 ,IitoN1HE{B4 gjx q S`\IHr*x׻qI!)5D"AX36搩ULo)RuضT1S; ,T0"{ +nJt1$9~5עZP!>7H"tP7K)qL\vQ3w=ZSiO2IΐmafV rP Mc5, y`$wkFxe3aa$(ͻHs4I؍0hIŘ1!É\!%AQzDeC7B1&306~K9Sq&cT7oxMqPx'ŅB3Z!dt= IDATDgu . ]dAJHܧ {t/ltj>臁?6Jl[o=1Cw|!pU֯X{}./aIgܦk D\ oDR栋9G!&L; ̪eXV!쀑]fPIx"L*l!JƩB<\㬤ԃA?Ft.3TMc`d mPU~Ս84ٜvNPtAcAփ<3U’)JӠ@((^vŌ4f&cX^L4FE0"¦J>hrFuI'E_4E'MmK-{O2[J0UztN57Hg ! 0+]8|hi^DZ!IG8si[C^Rā3|ZT|>/ )Q<.wԁG,;[~W6a> qśxt5hxx?d{׽kpa19 vFU̩s#mVϷ0!dnYT|P"v1P͝4}XjB %TTGٖ ^2<+ZKJ=tTXJy&EF\?Żt[Tp-咨y>_x;ROBG_ǃ 9;djyr璝D֗κ~@O]| 8_{}uo|./VK wپIkS]L/GVNjMNVMUUbE~v +_c2gۧxW}_IZJx|Y7x>~n{& &-5 Kg½llJx.(0 eʡF6'QJ1HSSDS pTc-yhOae]/QF*+Au,y6_Y%u0kD/%$JnhBdeF%#bMQ%8O5.M,UA(D>pNr|/&h :HR6v1b9! *# L0.uj#; 8p=KHb|]z5CRN#(lI5-gaф)M^鯻y*x/]Շ^G]ݳ.[ 9n--{H@ȆS~(`긖Y+FbV[ɳCjWĢ_o 3):  ` itS6K@R_;P-~ qwLģʂHQkm`⁙E,J9 \[Ç}̛ޥ·fug|wk2q>y "LUK>w ])Aڻ .˺:d|x.Ź\2dLń$m 2"':&ws {҈g\⡻s Qa(DXIK<G'TY`VԾjES5ДCʜ&sbƮɪY`KA'^ed@g0:ihF9@M+\@V6 K mxR:eo8b3fisIlT2TD l.S-@k kf3ml` |5J~1A[(y|Lj#Jd,hmjJ^bYu'@5}PY'mZQ0P3[ZQ =A8( ,],5yEKD_/e?ŷm\_?=n|cgWfσw<}`im;˝!*m4!/3> >XhHHULy?y+RNC~ǩI""Znjm n'T5uCb6Gv2*YV*BI7-Eң1\Өo4i,Xd6&"R2 CĂ9pt(X6}Dd-Ψ@V @h)pT&VXӴ<&)Nm"㏁[%0eYDxA㭥:nˈ8nPN*H,fb)2S4iqTh,%%;4F_q,L^j +Z?Jrr?!!]U"K8΍7Lϒ4LNVIO,j*}N^P%\@+@(Kx꘦[aHR":dXTNXMMp1BeY2Hh) 4\Bm* q@ PCDF cGؐ&qT[ä+H0y8g8hqZα2Y1HǘU ɩi2OEQ(l7׳0Ĭvr WG?_h \=? ?Osm \Y/}.1^^fgN%V_X aOJN%{fsc1xC@Kߞ-ZAE^g6xy 3GZ X U}2ES*UmƱ%ba\҈`DLG+C\Eg`hD2Y!(6jRmV?Q sKc!6+JIIJ\}hQBSMeGSLih $ E/IHᙛ]|݁z^BYhb=fV2d^zXkp|uwcxe`o뽯Ӑw %;8|I^ys *q ->GxRW7$NΤ(.2a-y0 x@Y oPx5 yXf#_O}HMU(9ݜoR}=v!Y)y| 1beHCN"gqsKRp2O~AUI]Tv5ցzEw2lWh@Y %T!W< 3x`K( 5G~&;*NncklSp8#RF~MI/`XBRQ |@Der5# nyO?#C97u_w_w.j0ِ{'uwqKs,.>=a>8ϲ.i+{;H+dٍ M.λ~ mq?h "#9E9& *H J\"ۻ@XB`$bJ:[nB$rRc,9q Mb^Be΃#cSItDe)(!K{ `#GoNa+O^M#r__=BضS&)Lɚ RBȂ|XM#rW}eb gǓQϹ$'nc_ߵ?r }>SO.+g8o[n?{gz>am`A8']$ -.v2*p u޾]}^}=:A=rb~s̈́p~3!XB2 "4z8ɀbhM{!L0G327DKMWC& zi\hMUɴcJlqElN8")R9]np1B^?.WLE}E5_25C&O#"R_egB>_H,lӡγ:o?O}I+ؼy>Da&8µ> ]vrKu:dW0D4+qҽao{6*@gwG?oݟvƃ3q>t)M-;43 k_;9SiCIÍ)tDN+i^l8+%V.Dي~1\C$NY)bE4؀iHrʡ42P N͚^ʺtFNC!DPv(}NЛcǒŽ KJ?B% ݌* Ҋbu{Jܙ sk9zp:Kz o_"5Oo+*2rD/`Z-o]}F+akR򅑕!z_Eɶ/y[vbe\zϒ(6;u0 }'>8{RmRxeli8%BRY I]a}-X37@cFEK!es%XhU b)#qX֬Ń 2TuUv1.@<&]@ v`EIFe.&RhE<a-O:SSD/fBM66Ľ2T1Yѓ|z;s?7NǼ#vyS}?~/J=cgA/R r̋{xbPĢP$ O|DSm/ ޲>p}/n2} ]'}V\[G̎=*x(g_;e1{H蠰B4iL[d! ͠"Jr8%f3E t&x<6qѝ£9n꠭P]|ւ}bAUkPbH {#R$[k2f3 YICkC&5YuPFd8jlJ6+q9 rv읶uG}Ozaĭ>P/.qC;aڒ Hwޓ YWLyG-?渲]?yxuo!(D-N)SnYVKGY-vb˚T 6{>m/n9ձԕ0;hz D!PVD> k IDATQ(*MB=E4$D6F50nE)r &,*F[x aG2tmiN €7믭$v,(J7 PGoM2[xJj:У'4pz *I<Ӵ'_4F@5.sg#_SofP[|0yNۥ[w6m\S 6^|9;xMA|yoޒj9'Az] @)x<AP"K*T>}P h%#SI4ϰj1Z%*cc 6\Z4cx4KRv0`=]8DI:J ZIJ6 BzkW@\TV)/j.b1&خ&u s]?MS'WmOzv9ٟr i*_>y7oqB xXv{n&_|.Q'Lα3_S5<;}+ɩ ncW0/oz鼻a._{ G Ynt{ Cg:B(I5171HZXȺQ>pHr U݇( %SЭ.idhl}=SOq׏RK[+$ÌaU A'*Hd.H*&空[ذ #љdx7;aVZ0(QaC80Ѝ@δWUZ{:/ۢH; %Vz§v_-L:eiT71R>k ;ߺ[}$#1SGYMpl7vҾfrK tj*fuy~)s~neW0{.ݞjKN_az}l/ګNz!ﻢ,mҚL{ي!S @jpF]֦=Aw8EgnNT NT-.[_eIC:'d=U,ID s(̉蔢D(u"'k!%]Y"!- t2,|^! hL{j(%AYks2Nt~o%Pa*s{CS/l_{N.߶U &@ORA0ۜ͟9AM""ޢM".6yw~R_vm>Byw _v΀_Eqc˓Ȯ`EV`i9)W{~}c--2q`4u驟I)$X(u|I3\N.Zs4|H_4Pޓ˞GJJ)*9Pddir X:TߒoՖ$Tj<4  ڑD$F+qZТL¦.Vq?*v,F_˱Ef19U@P!alʃrlWdMLЧ32 !9aA:[q4B3`Tդ̥VRF`+\Mrnݣ cZ,@{Ul>xj@hM둏Fm(qY -W/E.w툴vnsS<~M?o[}~&Dw5u՜Qԅ?')R=;g?um׵/S ]F_{vsO]!{ \=c=+*o|#Rl1c0 [?ND (T:B(H.h q6!m=G,aCq(]!:Ɩ&mߢ3ޢRF`jrRzs% ( ' XBP$9X"$>޹TXSPSiªHn;[5>6PGB0 ucQVJK9J?c#?.]o}Unٻ"~?#=n~~:<:ޟ~M'~~O%d?wsPvy]?uHZP^iEä-t"z |%C %҃uB5"P֢Ef~!UX2 @:DxYBС"5>Nql-LMG98-S5Vdž&P2^PPYҢ.ޥeۊ$ؼv\Q#W[^)pDIT%=9h0UJB)Yu"̢foǓҩWsJLY{}o0߽yI]|Uc0+t|&o|[rV~OIgl0UVt\~Z8_N^lj]~ x'˯vsA=}@yhky/^A|{FL dHf}cFBaO6ilOG7J^ :GO}zMyTe@>˃cll/&y.CZ_A#PBy0aSV0 '`P7H@HowHВh*O$$ o2+uJ6Jߛ2l2!4qT_!Ɔ:|5j/=2Yc38z Ij ymoBJ/ᅳ@$bywGj-ԅYo}_.3vsm7xGCx'\iQ⚭uٞfYo ̹1 X\c(Ⱥm3`4F9RN2 iy چR.ue8ad(ʙ6;*V2 !"hQX'DJ* 2ee!0#"g&s +vLd4E Q}~ظ[d ._ cjaYfby!Yobf\qA"z>SLj˂QKf-<ÌcCϱ?/YHƊM$d]7`pp4ESn*u/?p<3DRf6_O"eHvs"/-ΐ{dc{Pizx޽8 eԠ] wbW| %}k\;>Ʃ}{p6 J:),hsdYpuW&hUt#X_!Ix:# }L5z Z3EYB\A7!QשX+B&$UPS54҈H*]!j=8w xI_fΞR(<8i{>)ԼbdϽf te ڝdx4@fqOǿ|"u} aW0wy"DԹ*WR{4)Ɉ|^/<ll;?Ábi#M•גǖћL+Xr+D*Œ9=ɽ‡휼fqe]EyZQZlsHr0ɤf00Ih .Ħv"CE^:*i$JQГRy'uVrǒE@^ F(=n44͸4qKGNjV&RݹW]ku VAvqYƷ*i5kQ1s*W35䳇 ˋmxi "+A hd)Pe΁` V,sˇȐ(8(OӕC~J-IZUKVSeǂq@aY\Xt#V yѕsҲB PDٜDG fA9X+hGaQaBKM@:g#!ώHbj[kQ"(fC*K{"n6X*CVM9M{ Xԧ}𮷾}fa(%:}LtyS_5:W?DO.\*<#zP,~hx%"w5z/;O2xHW>u!&lLi!rc,`3sZn^(bscOeɐp9G4̙۠^g]=8c80d'q3*W[&7|?sλEjI%o!$0Ʌ끁,p|g8!'$q8L ! `0coز-koZۻsZFU駪}ToiRjK, Jі{j'' h B8/~P{|_52 gEF隺l`LMz Ek4%Viꜩbq!kq˪k.jǪ ^ze˯W>~~?V4m4[\|%zd4bsB0_h -ͧK{>~sm$-oGΗ{x#-o;.ל9wm:o޷kNl-!C!" Q#Ơ˂hP٢*i2M[Gq=Yhf,ϴi,laQbƊ~ʚ6d:mMm^Clɋ%t|Lt4="x"x_ĢQʡ#PaYUUXRj0EX7F c%VZ [;QƢD$ǖ,u kPPSQ]A)CG(@9X4m-h=^zeK?=_~=]~}\A8yV5(~﫡x>mvٞIw[߿W>_ ႗;=>[AW_́_30lg+R+r&]˸3bi~usX7sJlɰ,?v Sd9WU-V'lhSOYn"뗘V j/x?͘xb$2Xܪa TRѾӍP 4BGJ`҄iOQR:j ",%M =g^Mz;ܿ+ݥێ85?k1Dӫ#B%Y᧥}oO<끝زcK]}ǻ}ٗF80fJNV ؽ\OXw"/j%OQQPRCaȕ!PzG#*$f`U!dɡN @v~y^vɑU_o㇟#,g ꛞ~MVm*'hx|g|v~caK,^BEcOԳZ)S` FA sH z2JA_B`m#:y jZZ3x&VJ*j3Nde#(w 2-ȡXz, *lH &(Yn k{Xҏʻ- f | IDATFBhI\CU 9uX^J!a<.0c>=WT%S%ܡN=VQem ΀Tn CblJakGij+h񍒢PˠѰ-`] r8 #45d4BHϬ `zh;uoԶpp5SV"*cEJW%W`ę0CV|3bJE׃OQ~1+V.f&p5ԓII)`m▢(B)C;t-8cj򹒬Ш`Ed{E41{#ʐ&>rs=]#FY~J{GGt|̤utvo%±^Wۉ/9ڴ?P_=u}_e~۷NE쎵v8W ,֬&*40@x[#uwh$4a/J \1n9Z%iǂ72T"[ZO_72aC"t#:s B Ǯ ]q^ <: b]KDCx quӚL[;1b3`>7mUS޵ߩ:N@IgdiH?P?=m9o>ZNʗFk=SR7goQ>x =zcUW'+TXgQ by! xZPWpFB,3wKNYmW1wB=Zb*,E] O G X"7CqNW v4և{Jɪi8l@`P6y`p'sv/ⲑ;vĈg #̎%dP Ns/,n:_M?l$ߌ{ѴI>z̟Hd{YGH2qa~-ve i_P"d\wa֋/0hI fQkIR87WD *YM AJQQ aQ,DQ , EYH"bl-R֌3WՌ53Y^i5k#Hj) (Nj"2ob%%<Kh(M"#F8H0fv,!5<. `/||Ow7ھ[wwoPٯPW% XJo/MҭH&L+~>& lx1`?Vvݼn~硒 |(&J`-Z֚R9Ά Tk|U!qYLa9albѷTuDU 4H3l#a;&,Dѩ+$R4MR1ކ4]>8#Fxq xѷک|˛ft,ę-o˟+_ÇOTֵE Uzb?^+ytΔ.'oPgsqv?*Mt7<31{>nP^w'-︯39P*Ve۲9WJ1.CQqclI>͗(jV&93zS 37o^xi(bSCJԡE8/TAUڻ5{Բy_#_^Wy.CopÇF%"#F~({Hzn;aP9h4-9XyPn$u{'VZCYx_R{C [xI*τOe}g9;nܺ=kԟέKٻ#?ZĔp*\CQp߀BZc>`BVͲٳDC)1sPIjOV ;מ#k]*٬zq1 sT ЄbT޷L6tg2#^n71~5>ĨdĈGyuN=>zvb{/wGbmW+Hk^yy3?s{˗woL`^٥={r1g$Gg1On3gK{kvgӍTKޮKu69Ϻ?W]2Cǻw/rʫ&Xx`9uk@ 6vAB) dCX#-c+1M~xJSŦ!5}GAĊmK%hĕ%wQa+Ea ؅6VsD+ж {DX"Cu"YUw u;Bv/y쏀+19(yp{"n}K pLw?|yfh/m\'twi7lqObzv8,PWԘFL<6<.Ð鉄a] $E L Ig5:BO4Y򺬕˗[ˋio "_J\:T\N,,i˰bb🌴faKl}\M_?NpQfZ˵J~ddv,zFc}WmGRNl'vU/xfKL^ٸmL{KF4(. Ei)D762pHE2YQN :`q i"h'V*\8hI8Jq,VpXEw."bDy[RWVZ^#DZ}҃~O++$ݹ3Lx!ZparڄLΓ=lFkbǎ?w$\^[v:ʹE*>~0%{+} lcINSYST-^:Y8PYAQfOPja8j|R*rpx0CgQ)NL{h*Z > iEMUivICRI9G潊bJcS'q98QFb́?Nu[t JGuɋݲqrO޿X>I*t]zVF9Ak>vx?YNG~I3z┭bL!l5U"fD 5A,W~ zj U8r"<q[޵fTϦ<.kꊪHo v"ﹸ*4Ү‰VPJ{K&QDžk6ih=ԓu҂gQ"e.؝cy-KE0 ڛ?'.|iSny`~H/@ h(N<9Z Q̅ 5ǪM]w_;iлL|Иp&p`ÒQ8bPz*kByVt=uj7⟲=_r~g赴B^a0V|;jGF1ŕzzKw>=yhp1g}.RdPB23pBS 9땉T%ޕړ* ʃ ^8WiT-d%띕=Bs'%UJc Z8~ev8$ vA|Ak㹼v?Ssԗ9yq[f}?O߬\uB08y_5sxߕ۷szKM_3s5AlK8ZY!hXQgo,d*+&Ҕ`9v Րڑ 8-*3qE'Zf[ƒwҤq@8y%U[zW4}E:(ƺϼ~kn; N:Olm7ndgĈ#'Ge' ,^ =]3 ,0S &KO{|1(|=|" $$QCԚ4qcb9n1g%Lg٬_^BJKI6VCEZ u yƉH%" Ci@k.*=.k.*}]EP%9جͶ39bs91BKPkCm[γ䏂alna2R (~Y~OS}fvt#F<\#,P՞kvBe8+Xeχ5/H̊4XaWj77Q5 "6VS"޴ ;עʜLjj Ԡף;>Q"幈TCD}/W۽{k%r=Lh>xqEs@c1l9X貋 ^C(x)=#F00G<)&ҸI JBr`MX g3@%mjmޢ-+,Zf)&odxX&V:{ʉ &d${qB?N! j=K$gzr×^qـ'O'|iWu/}yxD2^<?>Fedaxr ! E5ڍNl}'zPz tLbE)" ݾ% p:n.**^4GL҇BO9$z .i->|E YJ h(+>I 8 n(7q˛g.fu4^mSH0G<9&,/8/=U[`QPaxr)X4tՙi)qPP҉6J$Tw("<1vU8#VU $oHփDM="R,c)1Y8`˯%\OOOr 9p'p 8_vn7>;\v5jp$1ڤGz9#{sē3;,-hc8iKnc疔1.y? yTX6䖱HIhc&w03L7DCP{H% Ѹāhu6aB 56X6 BV 6 FiYNL^*!38)%E!Q]S< #~`X,^ze$e"C(i {%5ටHcsQ7jYc;oijwk5mYXaZ42ִĦA0qCC7 IDATM]=Qc1szsFխ9*\pxfHMo}P-a2;sc -&x+Js"$1>Rcˋ<\@ J~`mT{mjw.*FhPvmj6TgW:תs_9}Gz=#F|,0;#k$?}ōoPz1= ]̤Uf'NV6xӛ)Zu4RoI$Cɫ84z))"H4+*P1>tO@4"&aǐ n(MR\+c J WG3E䰒 h̖xPs1,c4757J7XJCmEcEA]ؼj"N|X!CI0`lATO)6I[͊yfA1|8P'թnp BZ`0Ĉ`=ۥ7NI/ӎھWn\շ?xիOy'1B%QRtuREE_b9D&B Y qOG~ Z;^Ap !0-}ޱ_ɼk[ZM+_:cWc_j5QJc"# LlN҈Hq " % cfh./waŋvp0f[v-:^q"f[J% |.2Gx1r .,}SgפɍU3g#_/Y\9U6P W@ZKEe8n&RtT R m Эn S|k7ۂ&ɺ76NbVAX!$ށ82]࢝бq̃Abb,|EKEsї jU[W|oե۴n .ۘ?=GVtijY|vpM`X얆T5郑՚=|c [kx@VP/(,! ' ]0~ßZÜI[3Cy`cgc҆c)sA4.j@"-pm D[z+ŋ=BR"Ӭv~hlk~{dk //Ͷ BMgxDt7пqM)`x## TSSxP$c͹{[.r%%lUbwǶd&JwW.m#DCacj 5~?GD ok-Y(BI;9r𰡯Р`ug ʖM2p1&)\)f5ZA};ud{S^qi-kA+@wጼQ5uV_1#Ǟ& !yh0Jl aͶ6]:~{":bĈ# sǶ-s'l :6AI29TvPR{''}-nӚ&ڞ=Bj'J$$ N>GTPZpK@gk:'BWxWZʹƸ?Sk#Oa/xt%QUw$\c8${yfn'GT{ngwo% Ol#dلYˁQK恍s6Y.=YGKnz#`da~9]3)筮=Dʤ-߈\i ǿO;}Mi#/u*:1S\IYEY4E vd f 4L@UJ JIURJqPqjj D`Z@HEB"1 5 q˔oݱ'qkM_v-Y:I&_ |:0ӄ@xAwy|wSvTc3vc(#F< fE TN8'X1.@BLawj!#]&{F x^*rmE"| Xzx{jM==#{Vf^T_Fxκdw_?މo~^lnij7>L7<}|yj +a!ÑəVр%fjI;a^B&զq@ʄ>T4^Qe` # ֪5zQr]k }O*ƍ,QinHW58V) )c4pJ0؟˶wVsy_q~l/Wn<?|"Mγ(={06øC 7f,09& %EFVP_v6N],CLs=qhg&8-W6.0.uT#DU "E$I8댒IB ב` ~54|sq3Eb"?OD F~<a?,"OW>Ώ W;bo7^u_nxyE {`e J] $  BtC4WY!HJ.#N-Axhe8MYF0.FƸgT8ўmsVpLw;Zbl8$$;0|E 2n4T-7M/5)Z(#ZSfsv. C*%[H Jģ+@S uWa\/+k˓;~QztWcbY4+o{?[Ytf|Gr ?;~ُ8s-V b"[]KIΉ+T>  eVh gW*jx'F.?NoZZTgF;̔o wȟN,qb`a-,v 9jR#)FQ|ΖR4fPmQ "3T[q nyBa#iYsL[W9n/_ZIΦ"NK.D֬[u5nc?1l)ն9gi9NPҌIKp SFo~4_G ^99ΏMr_~ ?%焏_|>xm!;ӾVM&˗y?/m!N"PzzܖOzD^ bW-y1XqiE'իc0Cc=IYM=[DFq̢Jͮ\yɮ{¯%^huYqIQ{g֞u狺шS޾wurѮ&'r-㒡 Y8M=#9r5q[ki-a@PtH2 0yՕ"g3Y>l\ST64szR7g6xJ2(a0 ȓ^^RN@"g.gZFnSj>*Q[/J=:{qğ"⻎˵M7`ڶ+gȎ՟C^ݼqHqjpmv]3S=8?v _ޫl-}x0xb2.5%zβt4zws4Ȣzڝ;2в$A iF!,W۽{"rm-&[61FI_muG>h'kM9T0V,OxgRurF  iڪg#iۧkI7)-RN+sS) J-J a{gc-Ed ٳbv;ŊtF|? [VK pk KFJLwl11Y,aRhS jSpʠVBHTzr>y-;̑#p{k䮝モ7o|棿#0oG~KTg7{C8?V͏gv*W#0e.M4I2Npv7[_MmWB+Y{pkZ+Ҝ *N@ALMB0G( fYȋSƶ 64*8  <{zT(Ƃ<2UPC*"X젟2JmJLzJ:Yƒbaqy43 0oS,~zsvLrVWc3;'YGw6>dSDJV,¹ c!bF> F o0յIQg&'Lo#Z82SWBQ;Lf#b\oyO l ;s o  G,7gnwǾ׾mrT`h+Rl^!eW2.@HSrQ O.Ox3+tXXXk:+U|8>m'D^̄-DQw2 cV)qT؍R,KŲP5HVG IDATy<>լ4& Vex0NnrbBb&C,)Qٹ-[\.7]GՕ6=@yR%{79@ezee욚gʞ`+KBfҨIUvJ(L0fIH"9!DIѰ@72V z=zGv-\NnG.mKbX-NL"wNtiߎ\`OOezc_U.d ]9 }M; 佭S8Ώ=tTl~Ƕ~GDs[)707ME͏%Q*X`7\,3W޹`}~`C2.A(%N,@ow/u/<2>՟{MQ5+].chC$Lt/iyDݥZ6 cIJ / gTܢx?۪,1ᰵku3X+,xg{~w쫡T/8q㐘^WmTd, B!^j?Iet׎Y94kΜ=JaIxh`ԒqtqJGN\heQJz0v* rZ ?`~;?~6샷v}]6NȐ?8Ww&F pW@\49ï pgdȇd Qm/w)o'V}1f9-xlEonB~};n=.b{i|t\*[ ^myf;jWb99fpb)ض!@/ C$eO~;8KjVbJl?GcSQ-] 11>AFֲlf'l CDiؽ/ beTsJ=ÍR `hɤ+u{ Nq'Zi+{S,M}v:$) c p5 Fb2fSЉFE^NO]@egW^AO-aE;vQYZ@KJZ,z2@Ilk䬶-PZX'ܤԐLgZ䭣 @(\@/[9ȭG:~tB.Aȑ[8Ud&YlY?vכ;kiOY85‰d{ pvWbcs9_}6גǞ\1wW^_E2Guۿu-xU\:asvm?}?Q󣍍?0w׫7M9i'tdl؀q Hv> !QHťH[V0 L\ :I x.7 Wll[ 'NM_B'r:"v,gvv%{ĸ*L*I S"D.ɒD,#:N_Xŭӈ ךJ=h2VrSHhpSH& J$hLj 6eu`dېq]OK%!&IP*Q*٤Nʕ˚Ǟ`ظT>4{mrw!`׆e P0`5)ܢ!x~ͩ,#Gn;Fȑ[&?9bFa @8=?TD,ӝ/^.dFZ݆oR8 3vY?.g'_ FU45?[l񵼢)s=,J1!Y߮C?8N~wkwl5N#n: 0 |d@"9/ e(jA4>Am?>Nu,r,Ig1Y۵^:a 'A#꼹RtcVV0WMҠ=@uH50C`H,jɹ0 VSQƍp{px)Ï&a4l,IwB?]Snw.-o T?Vg <3_e+E) Nohi$Ӄ?]3k^vLM kjoW,<Õ5PJA $^7љWwhΟʴ7i /!woo6 Igb:ER}F7jE,L`( (NN Cry^ 62'գl%]4/|Vbǔb{O1u3]DLOUktRVeAHC]*Q8  e|iM,\dm(x" 2f!3 Qݨ%]wP^ސi? _M)0L3]I]l\;/.?_݈08q~E)((AEib %qc"1&άTeP 0 "1+rji'W.(sqe:(m^לߵ0$v "37ĐK^NH)n+yu?Ds|de_禀S/Z>xmч?~_?eŷW2%QL\U|p?ֶx)|7xEƼ4F`7[Mb/`~3 bzȠj3YEE!# $ ^C @@"3Xxnld fO 0B&-00ZN_:-ʝMr(C#菍"adB#qiץJQζ)3xN+yA het][tD,X^be`'pqq~m{qXJGO:iP 1h)) Ɖ)Y&b.Up\+6>ma6:4v5J>æ-$$,[\NǾX`fN؝#Ͼ)>xm[bŷWR096~]$߷mD}o:5J)>wfuA֩QgHgHMv5Ndb%|'JI˽ -c89PJl3 y˓0 |TH%-R"upcB!vj #7C.Ro0@W.8:8ݘ^6o^RFN~i-¨dǓŠ :]{ _ 7myF Җ!E^1Q?R} K|@kM,01MyxCb㙂w5)DQI%- tG 8 PbH 5!%^rᤗe\T0br >A0#mi" 1AM1TҊ;KJ,1( A_Mz906C3iH# d>K[.N6?cN? ;[Ցg6x0l }Ķxxca~>";dptTX}r^qtB^qA|C>:5cld %ƨWB8+)QhDu XC$q V!uOHj#G4"tJp,g'`L@2CK(BAd@ 7sB )= DZHBgFsc; TxyWIb wHA_:qV*x{=aBBJԘ5 (++b a N2TTK%-k]Y*%YXq(,^Xf-DyCJÁo^+ٿ\=z"̲{}bay<{QCCblRps g=ikx <-ί@<|\x3Qn߿@8?Cg^7l-^*oX| 7[,8,-t^}-7MA /v, ՙ֛ՙ.0iPFQX: )CwOFhӎlf$1 rJ׽V S"7I2 "̆hU$`MZÅiLRM,AA5a)p[2-akb4z.@5(Ht&-H҈1b ΨC8\ 3`Md JQt6Sj J8c׶z(Q)Q/`Wٍ>{E&5b$ $j^}j!`& Gr80ȷ}_,F:8lԕZRry\qu~l^VYg)8?F=`>Ώ׳w  =~.WUiؽŻ)Η{'cH>8(zn-xago3]LA9歷tεW{]ç(^2L!|V"0X t}Œ ᚄpyx"2AZ; tFW1JnDDA%TfĠAI cBH`GZh)d&4.`RPy#ƘT-+_ZN (UWdA Ȳ8iؑb l[Afcui*3^4M4$dmVj exS5)vm+~s&xfq-ܑ#G\Q[pgE+Ƚk_L+ C>l5C0?v (uh֚|תW1fsSQLoU=wWZdke˘A<fY +/Nq0"&y#x~1+,BVL--$[%P]=qzZR˶PKn﷿Ok(Jp_P2[u2)HNߚVD\1kawԋQUU.,7l֩Tj4)RX$Adz8St,A1V)}d',79Z(KJz EIJ{J|)r6$G9UKwr=Pz@rχ?Tqۛ>|C:~I]ƣ7;R6r~8wjK"Kc^潇%>g{&vjrwO5kwfxA+qIB@ܺc  f V6=GvyUf1D̆զb7ǎ 79퉞,w&JA8֡\vvNLؙUCK㮷lQ^l,w]4^RQ:Au0Lűp\QKl"} #΍TRJ!%T <`44I4Nv<y5idA>vz]SNjU$lQϝ_bjjkqDYZLD Hqv3L6t]ڝ!ofTaS zQY6뒌F()dUߵQs(~X y ?gst[eJ{lce_[\@.m]r\wԚ'Nt[wv}~S_o!\^D".ƌyy:{XPK?}`Mo#x{^~ARd?|'/"gˎLkpn+0mLi& m~jj>:@X66 yY>Jxn8}vv/2;9C4766d}Fm/eh)?z>z f_7~Qz'SzvHXL RK V;ü:;] DJKܾ['*;vU¬TFr+6SKGk}BG p+xVIgR5)riA꺞W 8B^vZ]\,'HDnIu]IB;9RHhbRDS) ݐ$GZ!Ks/frb?r0tNI b#R2Q 4~PJBs$(W<\fDunNƪ:qχ{kLu8syjn @PTZvowwO4)BO~}AQ SPXE41c0=ܤh^^ Q`y?ޟݾ_%->)ශN1d+|@E-JǁW/0VT'cQW 6}6e:Pbk"` /PD1JWp]3 4q<J;:s(ϞxJfFJfڪZqy+y-[(ڷ0?|gA_n;~)f7O"~)n*xnjxe{ݼQ\nPw5{U]wÇBw)s?r!`Ke2.<=8 ͽ,VnP*bIf0^櫞=H{nDae0ȍa{LH%xO#ʰtr B)RɰDEl!/GÄ8͈F)$3ѦkҔps2S_A1`3%A]܆V12F\_pxQxZ 'ޱ0ﺶ z#JrϘF RRTXhtAc!ΟeDDI%(q`^ʥ SPBFkpd buds|8KY;>SłQ?SՇǿL6E| N.h[~%"^k~_Rлr'( )zQlU2ɵ0=,W'.EHj#Qa OT}:wmQ|IWfOqs\vq<1Q{p+uI杛 oشwwʠIy2Z>wRPdV8scPuY#c+,"wrATE$٩[p3aQ"(jj-DSP<@))IK FZq6#lBT͌U(ho}(bgNр Q%L<\ecnXH)QRF ʥ2T2aBMy!JMR.kJUr,v< ;F\'7\qŅg\r#;Pd9Mƚ-boYR{1Uctb3?sA,d?c\s\;*{/)> {uݷǛ*p3Bی4ABB;Oˡ囀R4lyjbA {xN'^NCH)8>/}''m Ϲ \A-s]̈LL ǂ#-ńJQ+ȴ;iMR&M3Ø",㑓2ٵ ;ƒ= 2K8uME8锬eWnMrUK.%Z96K*O:&'7`IDۧV*:3l/I}jRH}rʫu3ZUQUGۦg~qZR0;fabNo+;s(@[, d__e:ELI+( L$N?8w4ôMlGƧmJkk46tJd;%G#lE1(F =Hᩳ+>rK{qћ< 1*ׄ@DBg$%w`DSZUR!@bWWG)q.ŅMj*#%^)Jsg)9_;1;D>N)Ri)Yg[\KR&`C:ZV4`,LM41b%(Y¶J -[ Xlg &se: G#$&R)C;Zkya$@*qXkU._Lt^9;ݼ>8o✟ }+V;C)6  1/Ns{)2HK1O:*SpW RȭkAds_sЃ?sqNmË ,HkfޱdN*X2ӥmxy@'T|t<,O[Tů2 LSgis)cI1?YgVxAdO/-Z~I$hsrEl8(cYSLيI[|'nDk.xM\,Z#B`ZE1J[f鈝I,ETX3%)^r9OrRs8w~Kkw?+aޥ5J`wM|·b@89|2ve}-'üjV^YoمڜJw=#)HmsFy+̈́c s,&K8ߋ"Ͳvf^Ow}sM;Ӓ =fse)]8YHqˎfԬ=OZc0OR GB.s\)QR1_o6hSC+ vڲ<b~Aͯ蔩J#ܵ]mw2Ӝf Q%$yNYfݢGyF\,n&SЖrXZ4!7uRhg~򷁇)D;?s?)./u>xGk/Ƽv2+qEX}(\-?R,0D0pp~P#[J(mY(Eb0BqQluT|,BCudh\T1q#$rMo8g5&hg ]SbG#wb`BkIFJqU1}o{)1}wЃt_['I'-:؟,c,1JSl;>ւ%$)6K!QLyCC3}STڿi<%8Z[]F f+]IJU@hmp<,&JS4f{sQ#FiHlnr{\ sNdyNn4рr$1a_HtҏFTJA {fIA xރTjMVtmu2 uJ8,.i.ZO$.bԳrDn6ΠdAEk5;dyͭ\ac\\[ïc'=aY 1@ %ݥ cZЃ20|MIAƒ㋀PUХn!'173JYJi Zs˜h{nK+Oݶ{iJ/Jr]: ((^ٜr3ȱT5\ I%%J:#2rmxri4e*a1p eJ VNO6'\,qXy:ԃrdM\ז&Qz2‘Bm\Jj t1tq (R5"#Pi"dK VL%% @)xK! RXO ~$9f̘1/k+`9ź>҅}L̀<6K{)&z!5r~l/6{#pJ4UNj-QR5MtzYJGk>2"g3g'B?xnzTgYĨ4T Zg^lP0כkO03Z!M:c4D#TTAAyL =YB)) J9OR C\O8'g(W!-aq$\3Q q87pJJB:1B+#M),*8mQfX0Qo8RȦL$6.+3TX}^+}xw̿[z(R!)95Y-)|X{}j<8a^5P2ۨ٣K+Gϝ>cO>e;[g _*e(J%0(<Ҿ5Q 57(>I |je\&^ XM] zØr he1읜tFG-"Vrx3U@Pw=DưDBc F+5&%/(ԬυXJbrdLZK/ Bb !ާ$IW_aq5?fWµaݷ[_3~i?p|_X PJaȋ i)ZqLg,X^`QϜ[mIҵX?TޙУD'$v:=P{qYDz IDAT%W1\(d^}Jn{O<:oLv+j|^DG@JawvSlbrMץ!cd,eJ<$VRgm1b]kJUYkui!HWV+iU:~Xet,u \QRιXLUF)ӵ[Y*kuVxJ+YfEV[K B%y1(b/3},cLkxez Xƌz^RHrGVOر\oϟn߲;"ҝGG*O|b|bk}G?gYgFZE=)g1$)]M v̱2EaNǡg4#4AuH<\)P#5,m cBΗKz-חY*P>ip}d:%m !)Tc042R( , =P^<歞-ZRH)q\mmV]ޒ|8XaO<&R _tBk5i_ne8 c&Fv͔fNlJFO'E#&-:qoG,ougS}b<$E/P\bk2 =Z-idP24~lZ9@M(,Ikɍ!7!#!T4H A[GJ"<7զau}d֨dlmo^#߸k]DYNgT2kZZX9(S0:|ʮO t$8ZS/]3$Ҁ2sٯ 0Q}cƌ8|#YPa Ӈ)>M0JZIk^kzܟ懾m|>tڸ)ON(ӡ:o8j3Kl现`yrM++igxQ2^  ,\~~wQ(_Rl*3 s{-Ǟ`AkاU^mj1&*5bsl}gꭻO,tF#=~c&ɓkB\/(`,:JzxU:й8hԭXWC*Bϧ5|f ob !^~!إ"cX<=3Ke:,q P|\cR}<$\y7O;YZ#yl!|I[>DPA&fќ,o}R KYAg4d,ꗭ?NZT0컌G}? 5aȁ(z_X+LJ7t)j{R{v">kN`rF;,繘]Ћ=6Tᵲx|!!yv,Z-m1P9H%,8oܹƨǞꄈ2\TDiƄ_;&C:Ø֠-cRA"<4 o\r9.P`uNZ\L}9n 'Yc֖@a^7lN Thm3kkH 59ZS 5d$yn۶=6t,)Zɀo?x_8uzR CR֤u$hC0Z2f̋d0Ǽ(`RFI1l;)"ufUVOMLyqzEvik,!0Pk>yt4] Xv-$WgOq,4xjev4X,JGIDSN9n1ck5 ӝwaG\MaO&-'! Jc-۪M,E9ZXuHtLN`Iv~-Z)uT0ͪ&#_ iػ wڰG3 T-wLaRQBV $*Nлr 4cƌy昗 {8uɟ1/ ;C֒U n|ΌrkpNIssKE.iV9'*3R?~'K?;zo۱gZ=u& Wa{tJa?.7x>,#,.<1clrd æǼ?Q~a`Qtl.9_47qϥ-lh K|DCJ&W:J%JǍ;Yl]S3禪ߐB|,.|_motۀȮ'UKŅ> X\Xaqᗀovgѫ~1c\\9`/^ui}DJ<~bc-,n3R.@w{Xs4K%FVqj3\?jgO`qR۹Ķzs"@&pլ9Y\9>m.JLEaLnjsWnICdn?#[T%q{"m9(|.ʋR0358ͻ=VgnWrtIR\wdۚWn_rƌ7+9RL|2xHuʵ$I~,xrZ3Ie8J3{Pp\MUqܭLns)o4YI+W3 l .)`-ʰ?T`A;nj>f̘Wrݷ~2kx\u/޸wGxjtP *uR5bCR$Wo֋X,l̹ZfZ IF}?_:}tv+!)|i@B!7^#}a-umɲRhgߣh$=w'Gj|?r_×zcƌy%gc^ ;'{߾3'Na~vE\xn-P+Wփ7iv۹]"\`qB=C%.9f+Wn[ɘ /o^SK EG bkXd}~$ۿ7wΘJFBX[Cr(=/{jcƌy12Pvo:o&UI^hsK#3Vy%93})4Plt Ɛ szz͛;^cƌ612O | SG(p YAZ/d=.t+&)oٽ'vML~WA +!0H/<5%]U3`0_鼺ݎ$}y8 5a_a#fyL| x7.w\ܐf" T0 UD:~#p+8~$@hWڒiUUZgn=h:u29윹-T7`xQkǜ2RQ@҄$7?BBK~|/9=r~>P`O|`|KK=),0otsx. K& s` B'O4`9P'5/8i*Ecn^w{$[D3M}'RhݍfGǖ1Wuտ;U,P} 7|+ww/44Yf|qi8\\q )+ Ԇ/dol{c'!Ӻ۱Y1MρNjKC96:7o=55sq0PU _; 5Q( Ȍ*zb kcswz,ֽ^g%֑+Į;8RX&W/X47f~Um+*YnE,bo0>eNǓFbSDLl\:cLw ǪUa3mؾNcj{TV>ܕtίy|6 6bMlvy]MRDJEK6 ࡵ/+;96nW8ͮ3uKԮZHީ9'<*\ `U lfk{D: "R&4 *kX]jX\=\yYL:]FfWPllً:3 _GDB\n7/@G$_Lz_j :N4scX"Kg2HZZF>,cճC7q+"3Qs%4>WEBa}#1K?D^l+^Y-^0Д RDyz8ɓ2uN?9O&Lޭ!_A( `/""DD3-FsQ݋]UXL-fY嫋UX^ ܁UU֎+`= UQ:fED&NYJ0F,0lWҜ|HӜ O2B0K'W,/2vkAjźzyn=u7G:\DfiD?,fUعpYrp#G/, >v`5live.C:&7ueO3y)G r!.0#ml Kwb+"",Ü>ݿb-ںUDUD-XZ'-ɖ.f:8va&` S(0K^5g)d^=XQ mDDd09=حk %5ؾf2c[U P`-`uQjۯ|Xs̑jOiBsVaG͒&Dt QD$9< %K&&0q*""èJ2F˥ 2drd"Hq1IDAT{-PEDD3rf=k{+3KY2HQAlҙPYQ(0/ovy9r%B4̚)@EDf.-^͏c۱? zخ *3]8M{ysK=.֬`oa@e㷟z'M$*3Wz "SMK"2!eKRCd)0EDD| L""">(0EDD|P`FřoqjK= &+oq`U.ֶVKN?R-NV KxK Ug;K9. .aJ _>-Vj!LED.qHYjk. d7*4ER集7HQMO#Dqab _)e'T\&'wyx"P:V"e%,`sJ[.v"2&-JCaX8WMD$Rn~T/[%S4)Zoq؂8z/MD$4ÔR|};NrqXθ|X-Nh+"bmgS Q<`%Ņ xHP(0 xCah)4Ed(aJY8ne>f"aJ{% n&cP"| L)7g QϷubrwcsVXVYaJ*4"8)VS4N Oa׀9XP_ 6Gץ\- q2鷁_b00'ZK$" 0,[*IlIfm^c`R`""">(0EDD|P`A)""SDD L""">(0EDD|P`A)""SDD L""">(0EDD|P`A)""SDD Lbh9OSIENDB`openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_53_0.png000066400000000000000000004044161413546205200254520ustar00rootroot00000000000000PNG  IHDRO.@sBIT|d pHYs  ~8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxy|]Wu>5l˳'qf 3I -@!@KKBZ$iiPb)%@I 2v;qٲeɚu{u.Dz${}?;h^{mcE)RJ)ϙ(RJ)z4YTJ)RJ)uMRJ)RJ@ERJ)RdQ)RJ)4YTJ)RJ)uMRJ)RJ@ERJ)RdQ)RJ)4YTJ)RJ)uMRJ)RJ@ERJ)RdQ)RJ)4YTJ)RJ)uMRJ)RJ@ERJ)RdQ)RJ)4YTJ)RJ)uMRJ)RJ@ERJ)RdQ)RJ)4YTJ)RJ)uMRJ)RJ@ERJ)RdQ)_2ƘRJ) S]46IɢR `=6`dD)RpﮍMo&{,ԥɢR ` P3QJ)m֑J5,{ǢN]Z;cPJ)RJ)5ʢRJ)RJh=cL1^c_LXRJ)ygdE)u4YT'Wgo(R -+M.HcRӘ&jڳ1pdE)R/5λk(6QJ)RJ)u]YTJ)RJ)uMRJ)RJ@ERJ)RdQ)RJ)4YTJƘG19cW&{,J)Xϭ^[&{,jz&{Jb.(R L e]U=5=ʢRxin֞9QJ)lڴc[ѽoجRJ)RRc?L8N&Ƙc&{J)7}szq2?ܣ-=*Mա |=1i-=RS5d \gϾmr2w?k>m &{,JMU,C_Q<ʤB)ԴLtMXN~X8Q(̗|kNM UJ3 N@NWfc[kwNRJM/o{4g@ ^'I2e{bdIFEuPV:q,=Ƙx=IRJif_|]7GiǼ> f3=&M:q>:PJ)ЖN~l8_;LX &1&,v[k<RJ}0WhlV0RXUJ)جQ2T&pP(R >46+uT4YTjcRo(RZ W'{(JMZy?1:)RJMݔ??9p: E `IhRJ? DTLx4YTjbT @/-RJIG '{j׻<14oǢRJ)0ׯ|*JMRJ)RJ@(RJ):&J)RJ) nԴdV_4cZoǣRjY?Z>Z籅k"8n:hcj/A1'jNRJMkW;eOHH\sřy64p 2 ej0ƴ}%I cLj3x㌽<2QJ)5]V JXm ,l-; &bddozzı];"uҕE5c2yW? lC5T%='q,J)3Rc1! clo#w˲lL0;כּHZLxԩKWՔ~3QZk˓0EZMxRJMekWBP[Ѳkm`?ro>Nc1*, !jRʢ0I uU) l@|RJY6Vbs)rQ ;)goޫ[GuU9/ *jh,c̛)Iv[k5Y|qZ[(X6cb (G.á@W{kl>F>dY}HcTFOPS1tA{:ЀtA`>SS1EVH RǦgc|#k>΃opMsg7yǹ:=AѹҸUH!9ǸܵNcTɢr⮧"o&W < o7$Z 3\ Hs N>@tR/nBۢa?v$=YJP| w_LG#yxk| ;*sb7cs؎_!c=]46O,)'>o!! f 4Yo-9M@0ټZ{lR ~q҆OX[1BdUܙlkZil>E-^i2½QI@ɕ߼i\UEzR4![ u'&j1@VfYuC_!{oֆ'bj04>΁&@^75CD6dq/H@` 3) q^ Ih)}EJGqUV)5]忹s\{,B)`/*UwLr,6suO1X{,'&H-TGwI.-] ZgΕͣH|g\ldQM9Ƙ끿?U`R_vc~D^X p j c)DJ^B$g9t2*?Px&nEU6tS\]C.$ ,'OcUj58N["pW죵4¸WO_{?U.+G\86Z|c?Z>TlH | X|Zy|Z;qPƘ&O}"~,^k> >z7Ԓ?7QJ:|V+W,y}m/\S(R8݆ T8_Os~}|=};plW{*W|X <  -P/^|~]1gtG"Nrux iloH0DI$+w&z1 Yϊ0%M8x܉0~c0;,<خeJʏVprg.׮*?BR ـI RI|NGgCpu=U_<h^wnH=#f`ny"難s1RX|X# a':6/O>{ГZ"~텸Zqԑ<]xDqYH|\o#ݳ6]Q$[+/04nz1Ic̷^ D1@=XIXKE^[҅tT% dq-HCg{~x|!bu{vRi4 6;G]uOMGsc֤pc`P)Tv B*  28W!>?:[͘w¾9Π-~\ >k_sbҘS@,rsk~ *(WM?ն.XlM>?6W\Drs k?bB.dǷ#;K_Hl<~3<%yGR/Bc̍c~x{ ;sFF6h {.(îDV:/D:FƘ}hOf_ρČZ@@ 0iY]܏܉4oX|K#ƘhWVD8߻౏}S & s=?bG1Rˮ>۠z62ܳ* O}r(pu{JC3μPc$V//cl"[KA; -Xs[-s(XKZr`f9n`6 Xa& 2k͵Ѽ3n\izvedQMk'1Nc6Z[46<|.!C;ȓ^~G6rV5OFq@Q-bvbK%s[|uqt@#AkҔ&l|_.EH XEJI Em|^_76QJM >o/} efkW&6*|T'ȳDwkȞ]3R/S 6q?} 7dKgIcq7̦T1f<  @!@bjL-Gk LnErs!w-gˀHln_\iӵ6Q 4YTZ1H~Hį_?oD8Ö_0Y| /YI$&: {\-jvIƺM/"~kר|y^b9hz#ZkmQG)ڼO\|f.QY23_ ,rggmT%Ȋ5k29?Ṣ-73hl>6~ޯ?1nB3Bəfh lK $# *X #£BDRz\@>omBX,&l~ iz WƏe>Ȅ^h\ ,\i^ӵۓ9=i5c^DCթGV{Ó7B5c0ދ I>~x&GvNfWnv +Ƌ$ylBYrY܀ng#$h%p%92g 㣔RGW CDevgGw^[z8w~R}\{E^w{?o6Usehndk|rs +U608N TqC6p7x]ҤirRn qy|l.B\49|6<ې Kd2'KOTMՔcGJ&7Z|1n}r$) oƘk;.DW!%!(}lclv7#vHId;A] Gvd2k=FߛK&RJ@'&U>!}2gr8 })N}ܻ2`o^ejR|6ZRc+YCm<]apaŵKMDRȃZFl>d5_flIZ~36woG#I|$63EV#vsؼzkݩ5YTS^<:{^cZ[?9UlI"IeC<R>IAG&$IEʋ#Z<閖A"+$!|[C:vƷoAY㟁 RjX PLWx/@~2aLս`jRRxoIh+u}nqr_$@ ̨3LҦlAM-667"%=*zkΕfcB^$O!+| ey!*5G1 es > +t+X5I@'Yk(ƍQAīr7%ֆGhT&.Da|!Ags0V8ce.C9Cc|x<1|kW/qꗑ}_DPގ 2qr!P$0D$ş ?AV}[mH@M9R#$]VDgYI|Ukm:{`6kWwqu;JwjM7~t~qxgl7_l]bˆWUvJ~kO?[_pgz.6-H5=L'Ds;u-nLXlw!]}d\d/c>9rlLJk5YTSZ4z*޵%dmx=Gcnj:ek$o;p:R  H F6oEZHIykA,X[tYͽ>' Iz$݅r6 q= vc̫uRD)5鮽H9Bc!޽Ӥ`W vۛ9^4[~R%߿$M&v-[?۟x\׷cD,>]Dl Ifu;+0 H Y$vg\[tҽp-6[L'}(I۝+ttM9gQM[qBZk+=*.mG~`ϏMQ;i5~ <d?V$8Y|0o7]? 0-HkY]CVqw]H$! v@>L]`OY]4aTJMYkW˵il>s|<[{xf^74"{ۛ=6u{A)Vܬ w܃LGbs6#sH*[y;`>4H2p}Iȱ9B}dM5YTӖ1ffZ{غyujKN/ 7!eH| Hb !+{ /Ff+$P]>$3dq 5u8]|z8yRk|(Ԅ[z62Akج` kul"5n2o%>4F d/H ܎Ln@&sAd؏AV ^tY݄H>੮uvCJ"s89;I UM q# pL '"Q!T2{ lXy38|Ykcrːw"oĐAJKA"d@^F#EHK^d24ǝ~KȖH=)Rj&7n+%]hb ?119D@7W͠}!+@&R?mSIŲp]C9hl2Y f,6Hl^QbwڹױyW .]'el֕E5%ī? +A:cjGtXb}eUh_<޽ȑ 1/Y 6Y{/Pt(]$b]K`\$) 4y74d D/Eֹ {Z}TJcqvcykkCo0$r&~pGZ7-<]=/bL޾Zl.!*8 y-Ҵ x46曾T鱡ts-N-?g̊z?{ (WjEOhR`̋H2y SH\2y{W|2ab]紡ɢ1 7'='hK՗_7#u)G:`~%cGJzJ1\+ oU}"lcoaD>`QMCP\X{!fkG܊\p&{C\+7uע7v&1Z?7yO2;}b>Kc_G}oN-!$(W !l mZփt?,w'}2dW6]ЅU, IDATWL*1+ߢsBb^`I:k;Wi&hbfd bsܵEoJsTуH}C:c̹> )Lpt"ț? 3+ޗb{oB =ѫI?ծH9t'2u< d&J+c"5K;E;POu3)bV7dwӥKɪAgp7C7>>IzN')8,"h7  +/Uxgď+\ 46w [gkyv$(OcAbu*HlLC戱܎ l$yL~tteQM Ƙ4\(@o"@6H(DqWQߑڽu'>,GJ{2ڙO)gisI-9jHsI3Y}=ɂ?mgzŭ,]zI XvS½o+N女,5~^T^ޙwĢDXFr>I14e/!}y\iSgH!yIwO!j#PH =TlܵΞw㘲4YTS1Ox0cf"C?~dRdٹ!ڞCu | u.5GxtlֆƘE/\XDf1#uYwNjzmHsiV;֞x|+s3y=ݝuI7/XfpN _~{0ucpfPfa GfvPB%:(ǝ*F6v5Q=q#;3Iviy:!ۗ^Hl" nuaJvE&vW +%s<"kJ&q&RRr:IrJ- ij2xlyC9cMkUH)Y^4q Ч4{oW|@Zڍ בBԄ'(ȘTs`7t&7\fк/s%CF5@&Pjožv̂nh BTĥB$9ca|sʝkEuz\i>Z};Hl^cMȖr׺_݌ vulY>%b,)3;&#og 3F[7O"Dᵌu, kM <ӄ" y~cLw?AZTI)5f.ؗn]: е1޴ɢ:# BMtX;+vq_b*{O9J)Ԥb ~ɷ7ې:hرt"ʙ!Y{J)h"jJ4LT<PI./_kD:lLX\+[v쮁z{mN«v ۳SyAWŻ6s7zhlVM) 4p)mD ҇$,Hh㯵" ~ j⨔RdU;ݫWM\l-MC^#YR#-X7Ď6cf@zgI*\Q5Ajznty tέ`yBMb W]/JǖL]nwN'hvo#P.擞|~;R7جNj,)/>c3EV]%UŃƿHMHlG),Ƭc56c7T;sgo2¹bz&t쀑0;:$KILSai =HtTsFiɖ!-{yb%M{1~_L{]fK_MRkn5P_fU._m-ȬL)eTSf) a;Nh` RB=*I2c\ {4nH޵ ٽare}ahN@>#MT}=cILZ* I?Gz؛WilVS&/1k뭵OLxIہ /V*c2*R:,Hǫ"<6?SJwwQ}0~ONx*c$?%ƒWR@p)!C2 IU[:K?`)J_9$6@-6߼Jc dY)]]y1ip^o&$ė#)j8Eh_Z c=cznRd?]0ua3G& BN㭈N)qTRG ȹ !LpRCՆ%e+&M;6T!3Z!ɢm)ՓM.ip{3{H!Z@ܿ yl*ȶVZy ^G8e 3w TvsWI fh̵Ҙ13#U(}0A`,Q27OglfyC~f-J@dC?뀄~ρͫùR4Y|V B,~rRG4Eť/_`lޯӼthgLTA^:+jc(j@FihsDBkPG&`YChDpF戮#ɺ,ے>wwy /~oS$ RDbQ|R#ō`-ZAO;[pq`vܦC 3Wqn,*y$3J@ea2=#Iq^g=lB yV$"p-Һ~nr,Y}HvsЕF5tϢƘW!+'h"[ QG'@ۀ/;^k~7*g̚WCdqZc{HXeϦ ֧)ZdO0c=Ҏ.x93==D7dshi`{qm04B\%׉׶^5 {t } ! dEo})=G MɁRAfyta? s:El%Fd"B-_ pKB>Q}4_vU\FbvUU'&3ƴ!cH3=4Y|1*ZcZk7NxRJHƬi⬱RnX;$>P)UR53,>JOj7#i(g)SiIsF OG Ys7z=m3&3 }CJ'p1:3Iw/~}Ѣq6-mgA5c 3$s7 `5I}:Oez2<Ր'I7J%U lɖhN k & D`*71J qIQLT&2Z˒Iʖ?陣WiG8j4I _y1zfRJMOY73%L[-L cjZ,)-Y؛$I@Zy #{BYnXbGBmnwmo+U`Ykug<a(5SS$]7)dp:΂gWRr FI;Ik`hwA%Mu1 c p7FP0!ly.)0~'K`Еh(ۈ!¶@-+nl 6T⽌<}Q%>UċOkİv6GXMb:8"swڛWilVJEu\c<ʉ(?U'ƵlcoiU<2ְ$qI."(Yޜ$4e"(M0FQsC$9V%0sf+:thy^QPr]sIsq歇Ё``9Phq{U<D֣H4.C6F0@jXӳI2@}a*`do+MNiݹ f ]蕸ϝN0䁛b4|NPKBNg JT&ga2~Fqh*;t3_Uv:p\f:INVw"+|.+;p~}~LZ"32ؾF1x:BF6,p$;Pl!_c00g?JY;-韍0Lf.HBd`OT"cEgzLZDF_ێYV-P/h | ZQB-q G9-9,;E<n8N l8T#d56 +{PflхRVV磘YRXm/$/_%Ujڼ1+1zԶ8}&[)[7}~?6΀ʸGxTtD!ƫ($.,g̓,:ϹgVVe%ZlY1a f nMAan"1t 30螅h7F,M0.ˋ$ڬ3_W%$[%%ODFe]~w~ID$}:Ν֙+Q:L0uʈ{pXnzD Pn~4-@kksgfHY q0 5b2""d,IUB!S㕁aYQJA@E0$dB- 'fYb,M*Mdd>t (֎c2݇1M Q:۾?jG0 2\S0gTxLnso gQAt_cyu-PcɹX99x~Cfs'_cyuxG* VvHw8hgdעa9XUñ".EC )02+u`{Zfjɪ{Ae_ }.̑7"cgk}F.O͜ʮup \.ۿ;֯ 0~ǒs͈🀇T./n9a;y6B @:[DVFnj'4ty)ZniE6ioR!AyC [le ѧY۪Ai͙"~cI)?MC`x!Mv8$!n"م#ęX°жcl'SV>zT!h[Yg8ĒZ8LE~5F"28bs<, !S#=GVkw|@ҭ#'70 $a'rۜsٵ" p!ww5h+8yP$,Sٷ:oSo:~a%1 {I? \Ǒj2iK=2Ҁ]0Oet1G׮c ||{ۋ83|Em4!6'1ĥ̸B j0}$FFPwV|d()SO2 L$I$Vj,((˩bF=f:*4)I &l)|uhO ݗ0`#ߣ9<<6 -J~Q4HAUXݴ DnsYBuo7pXUJ2ma*Gz$3c%!bRM'٤ q $BWEQGPRB(q c߬0\q+ńĴ'AP{(}djgNh`Y6Q[ ˝upK{3@_|N]̹*g=PJ}F&uMe.Bu7rP999׍zn`r^p:TґHIEhv@(*$/$K{ {3G/:COጯQN8qؘ(;](j ƷY@ILS Qul,H 5 1۝%G,Ҕ4 q g&Ċ͸eb ]0110m.h1^njQF=r3?~TńV@sS$:ػLzՀG$e [cЙnzd"ju A[|L&CⰏ}H&L5g!Rt^&qlF%BA7qL MpI\HQVF"Jz׽ #Ә#v^NKMtM+ A@`c88궶+A na^+ mprA ^/99W䚧 !_BmR nrv0.~5 9Wݻi fZB8WNNήC'zPlzTGc _8'6R 4lsκm _9|A.k9xQStEKoB҅*Þ[RkYɏ1تjeS;,BD珓~=6Ҙdm xx3)ؒC!鑦eR:(!>0юҞT%Bd1$KL#iH|3|ͺHB= yA$03RDG ^WS*m$g%º u`]\jsi]rw˰vaxRhݚ}UzLgѹG_g#S6⧀BFjQD'XH>7oW6 =c4pgNNεgP{0ltxӫudXI $RP$}L'$V8 e!L @xlD(SR$UõLrؚbZTefE(Vl)5c9VQS*~%!b]ڼPH w`I NYݲ sPT^BUЋl, ŧﳜuITJu^g:Pzh%/x :SqD)5;.tCz}=i+޷u|@/ΰڗ"G~/_6OK F F USuZ?xAq7Rmwmq90V ~<.w$G&A`b;>O}\VmeXX1v FlL<3^o`5џRgeàN~j/ V⨀(T1WR$i&4 q `,+c6"Cke@ya B]CX)He2!B<2uV&FAn%&m؃ ,el|,V*Ca}eĢo]KEdU4MOOBo~c"0tc.e^z=$;& _Yyrm~5'p;T8xE9r2B^\C&րtQUyI }3 /IoL.7MԂPq5b  (͵4:>=JBfåa̐6M,\%vK_<׏lb~=Ey>9׆*=wz1CmZSav*^0 Еǜ,S0"<ـ%uRԾHcIp9d)8.qjd O/(Eb-7{G]EA)Ә%.6)cKͱH̔Q@aPm2)usbE5eM{cW)~VfvrA&m5xA>g^Itb5KV҆&m@6(9qѿ9jP68u@ N0 ,T^1 mg?/n8r3vKj~*] 7BGE hy8-xB)5 ~ mr"Bfs^'h`:َ@F4νDg%`}T—1*B<伕n46(, kd=y k}N)񍏯_w~q;u; !.yd-xVlR6AڤV30 : eSxaOPOr9| Gؘaƹ-1YXoQ(jaLUs@&¾T~mNKU܈+o&񅋙CiNzQSR(#!s7}OػnKLKV&fr?Abzt@\q=EG~}rb8 tc^&ߺr暌6fgLi-1R8y:XA_:b >o*$]8JeH(kEg5Q99ousev^Ʃ ÁfuҤ3.y}?n߭Qϗ'l>2#?^U(HLȆ~~0k= ŌԝĨ'>S%HL>PLTgS/u٘s0.ބ N\/AE"ptu](e>>w;S40} )C"`b`ȔPxY s). J*%─˰18o eSlT9BG)eO;v4kl9"4 HFk^\w/޻unJ`,~|!e{7tcHÕ:Hʔ4!SSE5"zϕYTJeB_~ tSUOk@;dUl>{B?hC^89?<=kʼnD;{t>>!Rb8o8rr<0P _^)^ BtzG+# $ w\ u0 ~C`ض G>r=.9]vjCI-~}9k|NRT*cm[j t'%[q [JUT#ϑ>aJcD=|LhUg9LV!P23ښEl I*Q`g0cn*";ED&(p o,c"AUgPX 8?Hس9ME!% ǕuR\(EœÞY3U(Fڽ>@7q8oh,D}lzgKKnwj޹g#RґU9YQ!Di2$M)v0ÿ>r~V9o1n8]ϰ0dWЫ~R!ǜDz=xeDSDOCGϺl/o')T}t$͵ |*ﵜ>'lg 5#~̾nW0/r$3&:q(78 ^\-*7[(VSbLB6h V3Ի'yn>S)IQabǗyu/k"]"j6=N96'x u~o29Quv$-% 3-[ bfL6Z04:ҬbY`GAҡm_d-&piuc&1s}8zd1Ak2 c˟Z=6MT(sVWFv68qz"=ADcN>H|Ȏ]֌'=Mix 7IPF$3I`j; `},,Q|"r07Y^ :贱~ (t 0.#cA`\>j؍;Qziv+yvv.~zTC_/n#39\9diS\GQoQJl ~֜] _XWS8NEYW{?9%ǁnġ|A~?K7dx-9e8;WiI1a4xoR)Ha/L{}CntXy@祖$Άj'ia!gqr:V 1Ƭ>&% AMqޝ"͎aO^)M=ءGQ@-9(c 4@/o-#gWI{$g湥b6 fWM1QU$e$YUuBL=4($.iaR읧Ǭ̖mR p =X EkT whvcˤ)jl/2nV1Vn)E|)~ۂ,5?x9Bv?%DT&ÿ5kSONYeqg}\To VpƐyԾ;wvFF `8ڌ|q^]pc$?j0=o58:Zrq+]O::8RMёetT=F[u5>YG ),9tmoa8. $;JkNN[N7:G聠dT M{{`LJۭwJLջ\$W1spffޢ氣+UXe,0oÑ tcTa_CNL\id(3cxC)>f|#nU-L/Q]L6 kbTcMl޼$n }Zx |V[brεg$U@׉.1t:g ևhYͳho-SB;GaLJGo-:vvav gQJ]ttn8$X>R㜿V6reDŽ+yg26OzPH%ňr&E&ww1'n@fr_)zO])3Pf̥n([Q5v8Dc9`P,*ꪁ٤1-w,1"*B|+\+p;ɸDb͞A^dG#Q{pdϺȦs9MVۈ,fI8M]!h)ڤ)zmgzdVMYGL (- }(%ʉgD )θ X'O"JZdb[aPi d( 3(Xy/yd/΍dI&^w$L7yL|mx>mh$мcؽfkǗ;?~fviJ5ju\m9? ? [v"1wBDx~LS`m@ia1EʜmhDqƫ&,n|?Mäպ4Usldc"S଻&EJsk!JfV.P^3lS]|2OU .bOze1 ʝǴ m #MqEz9aNH + ]r3ێ9h_~Ls<[7\`ÓJjl'i2w7@wqR)YJ[6ՇS8SKf/~VWP{|+otC. A.x{ uFg]d_Qo*IV !R73w ̸^d:D;/S73l'-t]xx_F;.zF#NUmSO!o{PJ'o!CNIP )$20!WīTOGpЂ4O'ǯ{`;E.dkh8K~ 1{[G1|ת؞a̖QaB?DY6JX$AJdo)6MFVhf_k3=sSہ1V%`/-%q7Iǟ$=Cڜ41(HM̰JS@:S\ Z]nUc C,W2 ޖM$2OPl$t+թ\T-onH2!~QfDY新2K 4Y_խR٥7 ZK`x|O=6_3rÍ6tGu\|e4{x:/}~KL[v.Nzdr r1kN"ۖ,ڬ1vE)tA;UB_R*~s!D z%su1ؾf Dh5k Je!V1mvsR!ĨkVGy>V ! yVD΍@dս^•l`; :X%SH#RxwEWA.Zϛ?dBkSY>V^lя{I/eEH #. Q/L hYaMq(HM S0f -l[Rs{Ocd]X귡Inɱ=rH3a84%p# %F@_(#fGhY[Mlx)"$+حP':kk8Dհy{ җ@Q?iP*YJ\d}}Z"P 5$NY ^thYk?qnV-umt\X~/tsVw]GZ]<,.Q$=O?;O| OSaHno8~߱_?ŋ|mލb ]D8Wb:2Ji*pgH)^z}h`qF*)jB}B} %gn{q $Bl#wijmzR]!*+hCy"8 7ioTuD /e)6ʨC% I^r ,/ ha[l+mOݟS+҃xw&i!>lt& >'ařpI0+-(6Bh'3"H8H`bp6q\"A|ʾʙY5K&U؜#bȔI,vP :?MXv8sWc/ZxEm$NZd@L:x֠7Ygy:[KY 68)-.~ >>HRXII JXc3,z$^A>)t>tܣ^)a^lH+թiX3Agd5ϏGSdSw1ۨk'F@nVx>X9??:fO?]N< 9G_~:9SD{U/5#>qtu΢RJ !/l몽QJ;1tϸOr^B3_eε#CO7JFGv{h'F+NgP) !AބAB<.%CB*yg@S"DslJ&:>=!B/qIP܃R) +F3a/߀GD՜}gzFf ľ`N qomU@́IRCQ@b%!Ŵd2(fdP,rO"׸`W9eG7']d(v˭[RoU[U97~ Pz4UZQϒ.3ilFxKq.}Mv|~z'֓KO O:j;較"F0,V$M2W!:ahT%JnعտN'0'>~zsX۹yEΗrŷ8_#C Go[ԛ:{Σ?wlNpݜČׁz2 }ZBS&']#e/h\n!qѸRDFK**,o?R?Rb@iM\3"R|k$zw}L_lVcBXJѰ#2<*Nrns=Ѧb+jܶX~"`/ÞE3nQT| s*"NIgMiiMD6MǹHv}eUɰVbZwզ@HkXȲ$٧Jpkdbn| m䔼 3VAޡ'YwNNW>]&ByϰUjrsG{_wy։xGs{mJj[7*@u:~UXY\ ߍ:x_bIb$ 9x0<7k??|7W7_q^a!a;˘Ә*_?=>; !d[D.?ƅ\!*Ƭk3k1ba'FہqBa$ZgBXm@S1&RaиD5LpxƆ?BQ$QwZ>ND<)̦s}l"M8T }6%ON+C9N@\r}ޯ7mU@ő;}kgdGOCڹmZӟa&&y.ْS#@{{O i ґ(< ]!/AxOJFHhtpe^@܇U:C@Dnmx=w̿bwC?'MOޥ4;л=:k|ݤ\5 7?q"|SzEAOg#bj> ~#|sb2WE=Sد`df=06cLk^ ү(0e{m:hl9bϵs{)~Hq% 'BqPN$tFV] H*]p$4s-7Ȱ,:WA#ѡV.|ڂL1ڡ nǮᄛNߺM9//Rd̶[}+}eWԝ`N MVBCdBvbP!$ "}.)a.rZf*˕:37 B+%I֤vSdv'R9k1j/*kN3v'Kt}GK΀Ji -qfW9*ۙyy)]wƀ-{?o? 7W/Be`1F>xͮtcc̆by܋bRmL\Sf?le<9/FT9!luxFsN$r@l>UX-ag,˵8x5f٦CECt³>3d?‘=Uz4v,CD䀰V]^|y3%>>\L&r3t;zm A~ҿtY| X`77ܙ!z\ 6] 9LkxBL2\4:n`abe6¯bD>ciְXvtF'{&jň?yKFZe.$V}]##!,Ka|ndd!,B xsP Et,'q-7,Ρq=X ]1-mLF/Swu4R9S&P!jAZo,DWO3V9۬ueDyYdݘo!z`s7` lۿ-)YIRrbzt"z:5 H 1Oe6S\l&6-E.m$ݶXy^郿w{nމwO<7q_q}kFK'X^JO!CB&%\ Y&>:pO̯N1PqOM^>+)3.J^J}p?s ^[q03 /0љC[]-I*ﳩ;vr2ˑkәnN`|#ktnSx.^WAҦBDu =S>.Zzf[`m"j'\(]`UgL² psb19t$Vppȝ3: 8;+d%yj̖tШ~Elv)Ľ%!Bi/`㯧][2?:[%*䨨dDۭҚFQe 6 ᛋ(2N&LM3`P*Sxn7\|Q'-唄tȪ}jg(9M4 DSz QլYuHm`շX 9et|7mP9Kg(]GUԞӨn pTf0X*R]KkS4A3(gPuR?*,pێ%im>zbgUugGu[۷_]|םj>\:7[c8!vNf0  "_`Er_},0n?,4!g}"̦f!4qͅx|K|_;.؍q>ʆv`z_M!><2Dl,+r؁ = 0ɜ *0pѝ8`e.):k?QJoW oM DK#CZb~w8|PoInvM+ɒS,NoX-DBۆBk Mw(YVa}0Sy2:fE" *PAHYqe{Bd:s\)띜d0Zj,@7".aw+pV 3Ot.7`!CM ɩelJ\X!fHE )b@wr34) A+TVPКЛɥm~Vz[>o,̷7͵}IvvRMo5{AU:qsU8?n+0^=,j K|11b7G156.^=\ocmtfuǔr½&Fnyt~c·q:_È8[Q=Qҫ} i_/~11r> |!|/`W >p3be@ kn`q0U9M~C{/o<EcYt@?S35MWfo~? bہr1J_TiBURfiL2w5gI)Iq Yʹ2B]t y~&>:ۂifUa $"\DU']TKI T@9 Ze:}RmZYzˍ'xݗO>]ezg~<%baǙϣkt'OgX˞nJ&D'4ɨIIAP>(j<;$S-hǕh])S--%,?q~[kKc+ʩ};BJ[.>mM=] +a Gd8~,T1?|ܼ2~^71d0MLy)%TKm SBwk(˄)Lj0P1 BGrwys)D:g1Q^֣2O^mHb%J1׎~QZKx ^|xf*rF)ٽFA(˔A۪o^kݴAiYj7ZF,XYQY9ƏݹuU6fr p dcV%/=-l-RkZ = 4ZB ƵT)n(!&+M݄4Cб=dԬOjddߵELijsg(#)3Vsv08֦\gZYPz"n'I-븧,i aJ\0 ]/TJcP9:\. xA 8FPP{Bnvʪ==|gn pǾ&kO2-+w~ m!,FR0,'yx\$qCỹ,ܼYůaH/V897amgqcP?O1c5|q0F=B_߂ObRu׺Sn~?|3y 䓻{ŶBMA!J`   qm;@ Ox__r;$ۊ\VA_X**RIBB !,H ٚęu!d 9 e @l\,dC fPvqNLmr rbq&2ed(R#YW3h;,dEylÝ-Yeiau:X}iJ80եp̬Ǥ+P30G=G<^p!wWk"E[@&1@q֐I)(2 fʡ BIJox<գkN9R=Ӗ*M#]?3lAnڜsP]}7gqa֣!_!B-G2%ȳ qi\٢`x~ϟj֢asM}H)Vnz#\{G}* &0W)q=%V|0㔪veZK)PgCPOg 1f!K(,*K딜!K;Xe+2Z-:\Q ͠GlǍc,AHG2'k(2AJVM>2`ZD Q3jM"rv[I(>n4c:*, R" k#]] ETO"zPh<\Ī>{jM@lX9ds LEif9f}Z=;ᣍ=8`k51ܻ]w.]3ˌѸ AkѼ 5q{.B>oPWVK<M,js!0Kӵ1e̗rSEaN~_8Ex ǰǔ1Fk(s{;vv߂ ~ǔށq[ڄ-UW0o;aWc4Vyytwͳĕ gM@{-ۮ9kQ![Txs3}Rk@]n=GFm'7:Ǐۗ E9jNlһn>ӻJ 9NOqG'_ Z'nBR "PFY. 4MpEvqk=K`g*qqsA]UI[u6hxv C"o"EmA=g& i _K \+EalMF$|,EE!UR`'KBؕ$u[ /_۷7C ]k`窥8 `MM_܉틅¨Q/yMLp~s8;E!6ʂA^"k}z R4Yn#2'?Op=7HJ_{/R)-XGvt!Z֪( iKAX9`g sOgj6N" DV OK!*Q"#SbrԢ,eC{ =tf1֚5n 2'Tz'djL-f|VOYT--=m#_S 4i} )8l($ؘ踴 OF7c蔅B30tZK7.OݷfǷwbr\Q۲|XuHYMJY']t!>^*imr_y"Գ /OWRh1ۗ1nnSŶJCS^M%="m(ڒ4"WTA"ק^>*%a7kk<lXZߐ`Gj~%hm-%X#qZ4dŮQT >gkb>۩=:~ō ^>h#Ӻ3:ݛo,HJbm3V']d_|;vn.7olqo⼔lbMxncT#D`Q|j5BKKqMp%@`ܯb ?&f[9LBxZ>'AQ¸qZzXJdI'tzBW{~a9MKgdg-y_vP;ik#ρzkڶt-8I$7֟;hi*ȱ]L`lM@s ƒRt Z,xEO ,wachUIqɃ [h)T݅¦wD{F"f !ڊ]mSXڴg(ا (DT 94y88/ɁpPSწH2\;ˏXD0Fdc'aZtюvn" 7oBl#VXR @r3PqN(tqITa.ু7ˆMe䏲zXk\'ts[E1lhLclj`olZR !&6Z&F5Ϙi sxU":k1K? 00FsυLl֜/HI>Bƞ?T7ZD%YjT_Y# 7en DҊKgpIUAт0lkmk@4@ALu\4xȹ^z `BJz< ^9e|>/|I588or% O}N:\S(' IDAT1)TJ |[fv iC$>F cn0unDmwrX8D4+\RFFK(pNkt.U":A *;M1ܡ{ljLQ]pzOaiEA=JQlM?C4i+D&؍ACl4Ⱥ@qHzo"| xmѥ 4,m.Bf%m{UlNxFB tͽSɱ78پT#%V1Mw3π6o<(Wv3=h! m!bi\!Ja"/0"7 ƛ?!o\7o!od"eCu+FL#>~ճ&x!p0S@k==~1Bi/ &8}(aƻ&:;AAr*;ɽR5;;W⹮;wR:SGf3 AD;[yw5-?Mo?|ছ׬;&)8zIrN~vC뤕ǞX4K NQRˌ)`+ByJBئ:GX6ijaq<)ks+ٿvrӫLY6įXș',N(}AKV8q_ ,},gau v91(= b4AIaL'vFx)[Q(r,#Pr@=P,/8(Nrny\:UvNVNSM^փG>WW~N9?pGnN%,Mv!sC4ݼ*ܼ3o6^K6%&k8N?ᠷpcAaJ]I1eG1NkGO+fdL]_Z^#%cS8NЌz̞ux4{Bzr-xS:uVJ~|:y<55}ԣ:&"[k6Okk:$Ϯ4e4/%I?W8u#n@clAnvtqtqzrju7Y Hg5K(f"uCv"ȔQFu-@@1@sHMRnH!A@th Ka OqD-E/.y fgQH V0X ; s_6gQXg՜19| pVFQAզPr3=$# `dDiJ335jDJPi/,5|jƔx\%t`8N3v;3KO?:}߹GǓM7;3ؙ}|߉}n;|7*7oFgɐSjw%&%l14) HG?DkU 0i*|*`s`/0sB# BVJ|c;13 ^yc[1| lDbt%\ߍt#\bv\6ӫ3ks%@dž׽/?qȟ_JlV|P CU8DsZ~p| LY$2:6N($d.j&N:عDyvXec 8]NJksF}^SiQfYj@;2e * Kg|+HY>*%A]PYVA Vm1#  T`*NeX2`):F4+!4@8.ebEbɜC+DhSp(3m6y|VeKᙌ~K&fRdn\Lpa'c鱼bth_c2$;xi98Ƙ2za1=@ JJq\;(fU̯$Xww` ~ʘݟO0ˊT,p7/{ĹI+-DT',y1}|l?џ:a KvZR FNɧ[x#Sߺ| j/axlJnŸxiG>..3~xbrZlT,q /x?Dj(2pG͋CXzز0ÖRZY9旨DvĶ%JI%5!L"!lͮMִ)E*90"Lw;,)B$b(sY!wh ->\(TԀ6'*\R'Ǟv!5LPEM58d" Nx]8NYJaux}.+Npuc|+lԧqsRbzsa0sF3 ~78cBsWu5|,*:Z߯^gO0wAqtnEZҴ iH84.7lS2W2?;򄊱.N}p5yTv&GMᛲ77}s~sKB*H.W"xʍ+ވ}Z7( PT,O`$I@Bj9u9s望RUԩϧ>uks1cƃfN[gJE%C܁nh߳RdG$haEqd 9EYʘBU]tWW]9LsJy"*A)a+N4~K\A '2L4qA{d=AbCIʳ) CSX )H{H ..X!-516* @PhK/ @=W{G\Yb _Lxo!{m^EMDN%=l^l Fa !DM+\>aS.x !BD8J1 ǰCTz`F#C'%֜ey[ r/{ÞtטMy(%^_U-E0tWk$S[ݘ%L[dTWD3TQYmEa암V`J,S]0)n ?Q )W% et):'hdϣ[$CQbCԂGM45ezCH 2{6RA{OE~mr@k.yR%&%  !H钅LP:k*|`FA ߹M7R!sĈszv5{kbQǁ BO= ~~1x)l+wSprh<Y4^ :Zx#Fqz1Gh2' :P۲/ta'ȒS0|PuqeRirVN6᭷/%!$(?O$Flc 5ym%/7??JmV=57P %skyr|uc0yvD_mO 4IU=KW0yRe%D{^L0E  }/1!3& 纴nZ9eJV .cJU4'ʨIc z(S.ac΄$/𷂗A`L*LK|YA!Fj*=էO k9HH2beb&a %'?dmRL->/u,3Sse`Sh!K `y[{)cw1f1 n8ML:fIXG^~4u1bĈ3(RԠٌ16 ֌>CqEd2\1Tfҕp[UmϜ6܃7_%_]w޳'ۿ<tO3"4ﶡہ$>pRCUX8@\uAHZD^XeL;+,Z&L.G!JlZ9Af:pRM#Y5!)IͅlNSF69G"Ҙn7eLm0|[(BWy=r+\}EjT -9Ս lrl@l n 2v 6E;m $ QcWhS'a>#`&/A+(:60F ABLF. m26?"\ϙ#.0`O8Қc!4AB1gc̼πTj0 =Ն0G4qlu [tF1b-Ï8C,ۭYJ卆 ijθ'LoF1ݘ={?'{}^tՁo?~Ɨ>~)EK^~Ҷ9~ѳ/:)]g qJY ^F& \JqJ39MFL[r\SM3T]cK҇F~M̓G>A.)r|n IԫQ ]?%j'6t \D Bp2.VX(6opʚk肭Af =K(&%o@ 4 $ HA-kR+%"h5YA` Z6NG`p\#h \GcQ($CANOkqYB,c l_/`my7HwlIT۟ B2p3p5P?> ţ9 |;G11f y< xq+^ct쯉,ss{yF޻̊}{_}r')4YQeB6KklZ-(EC** $ ,b^AF%ӬtBU<K{FM0W5*Tm:Xxׄ)eiTY@a#DFIBQB#&}4%4IAT1봠>=pP Y:0V0g#63P }{G *e:œy!1Ըl63Ɯlӈ#F*X{:O6xRV8|1.fo]˻cuE_}93<󶹗LN6o7[YjP-+L1_FE,kQT 旄!HjܤNiAj1j C܉:lM!"(-٘C)Y%} z+.nEV I4GB>4L:wez.Aa0 U8@k|I$Y7m8LŠJ9N{nxJ!e #@<\bP!.!99H>II(E%2#91 fqsIFyyźw1ZW؆ӧ:+vFE^`x3}"#N a-B+(eĈg[o7?<І,+?;= [n/b|U)>\z`>W]KY/EyP"ZJ2\X7a-sEᠲ7Nfp V8M4庤Xո" )0)B)#ꁧ`LI iPI1G88E.OO@FFAN HѦCHDxzAG~ ܎sq^[< {:As6H́= [FDAITW;"ȃmNg1#%@e$@ɅLTI)-#MLK),+Ah ӡFe<1pA.dIpJ.E,LSǐ+T7%yP |qH%6T-1d :Qu0r̆4*eUM3C6(iQBJ$']%;Ϻmqr8_~w x|1cmss ~=>z ZK ^Hߡ!w=*E*7s@8KT|و,L617(fFBu QLbupmVPkeT QuI 1gŃ9FLrNi: P+sz0څ@"{ @Kf/"i&)%nC)]xF{zAHAXPH[r#Xɖq<"2UBbRRb:)5%"E Gyy,RQSetcQ׶x !>bt|`bC j3 ۇbf )12F#F8zQ(z;`W!}`23*>m(ioعuK6|b| xu0n0\%h4iB: AVHS R tFqx 'za22)I ZdTdi\ϥy30P]")P8(p DY]hU=JZR4)11}\lX\e_V ;޽mmb7{n49Pq͕sX汉igZTd%LOqEc+IVuϥ],6lǥ9y UKgV ivKex%H ), (R 퀤1IOsJL,5R,uIMNoEt!>Y[z61EKA=NUk)eAy=6%0-mM_ p+."K?cZNIO 5ḏb Pd8(r;`L<+;';Vۈ;w!2u _>'us8S1< !JO{!ėV8>N). gTF/Z^5Jj>/^Myxh(\c!Y`,q+4/Vik@8 I߯-7ҭ6Χwˉꙕbm۹wl?17+!>p,ȤSJBM)~jlknjǵ x%^4M(iM57}tx$U1,BqTt@\^Pb!cj_ZCl n)=B}T!23}H3f|h+2%ѣBxe贡ח.F%J(iS»̰y2_f{%03]|A)ș e=z8CP! XY^+hG8JJSDS(sDdJ$Cb<k՘}:X0WvlXik7Ťx6МE;hJy9*$ !İmԸ⏁7Ƽ4!t9Y(u׳۰=a`ZG'9ƄlP| v#^>#FFo1>az%|%0J@go=+>wRyvP_u%*}ïyAdߴ~K:;sI:Oݗ\oxV(#$nX,q~gz%U0g7YsG^3$-JznNJ "_aկ(7f0iQJ]gWF`c09z~g&tDs +>-]]Jr. DF#=i(TSgJ.`""&_U6lN-GD'Nc-L$I\-A*qQhRZ( CEAwJ܄آ[á-Ga.B{Yw;ݒuP,lʫx^\h"X~OvȌ1'|Ƙe`Ye@Iq1duI[x8Y΢5c>6ԶE,+wssMcv5ci8lz/dNlq:qŢkɅ? f3E8a9BN6*?b@f7 ı܆!ƜQ2Gi_{;%ZBs~kw?rڃGPR"mYU$: dDe!HkdNgjǭh= 2VRthS""#'#C࡟d9q4hkp߉"c;]|?viE {?fePIAAdFu;wqB..(gq :uHlTcBFctO<\ɋ 8Y8JE@8W3t2ƬS1BX[`Kmgr8~9&_];~B4w-~ܩ?^uiS )CuRmiԋ;ٷ20S[Y[_dajx[hm L&Zdi66Zedm^ } @olGWP9Yjp(8T0.j̈iV/$Y%SE,"Cbk:=5=8E_⒏i\k$la"my.0B 38'`IRRE袇C6Zݨ;v Ԁ7amLTÙ' v6}ęBtu`/[#ߊB1yy ->^i'"11 =NPBxE8Y !:{8qM6ZeY#.TT 26瀂(Tu~򊃝e)oOfW5o㢷3K /~ + kr@;zՆz3D*E,]:Cϭ"U+(m1/! J}eTJdr>}1aNhf`ztS&+K"!(!pt.ӥuo OivbB`#vu|`Eld&c9sCS uCz$,]O'f KK;kx%YR gG `#!Glbwp|r VB'C:r` ʨZ-9b^63Dq2P'@ ;a}%_DZ"6 ~3pD1| c;q.vcnQ-n0!O"L8x$ڃFnH y_h~Un3P FKeT/~,I:a}RN6\ܢcv>txˎ]5u|tB7?vMgkbf6G ;ƘǰFZQAb^uXOwap[<^|<#y؇9|9S?ak#gqí}ˍ dw'=zqc$S6}eS/wsS~gG}ʇU: A-Kh_|.ݿDyi'ƪ\hg"_.-84ؘi&Cd2 5 RΩ1.Ji (W$ViD\qq 3%"BVibupbR$rvٖXlɊ$Zҙ/ K^bjytQ4*@uIh$>2e|4e$)Q7☿%]f&x!waG̣W+~ o?r{cgoQ8 JmM:K}:i 7qr@6'DYTAp's~'Xrjlꊤ>`, &S҉wb'yb ߴf@&l]gvr|#9roV. Bg[:LA])CtNY1#-f X`{ރ[9|ϳ9B m̒kϮ gعK9yGq~W =?-VsY~#O׽MǵͷnZ(~7청6_:Pxבn+d|yyײMWboҊa Eu6v6AQ$33s,i%>QE3LW5/Ȋԏ d|'¸0UM$K  H rRP$ U@iB'3#>o!ppӧO]I )5- Y6et :οAWzR*@wbj_n}A0v96ȁܹ vtˍC4^5xU7'{+^+OdYvE!?u;sV=;o?9Yo x\1-dpރoƶxS"bY߇uX+4Ԝ(]yB\.BLs]ca]XY`kXؚ=ѠX#js|+SSw(/x.TP}Ƙճ^#F)^;x\n4翏/Bgv*\( !11!ӭR]6[Z]g=7v|Ҕ KȰ `2̩7¡w$՚ٝD^;h&ȈKH4[/)^X^~C?F-(V5t Coʶ<@`Cp<ϋt9 WGZns7MYJo!KycL(S"[(D.Wɍ"o%?ptH th➤/#6@jpϝ'mciƿxz/^+E|];=;yݗ?wDQm^vD75;*\ξ#tFǧjd{cѺvu{yI̕_[l:XWADW q1&!\h=O*6sjBPtx}G>O;R|`E^uQ JTHMэ uEV9^`I3M(zDQ!Retr|'w<_CeDaN0^ҭi&dz Š&]VwuB>ʯ6aë>=bR- M1H)/o͔iXǢpЌamlq8QF?DmwvRϫb9:NJtmE*= n|'> ~ox9 gqPak ~S;96<*J߁]9_61>p1L=]{m5#)vL5oNcQ}@n1q1Xv-B\ɰ]7Q8.6xX'9uccLsmppߎ?N#F\0Z$~6|q])]X/hOFsS|g!c,8HYT\.e5;.\4o&KYch 1h-풨gR$Sh![mwz s,1|ՍZ=q@p9G-\mEFy' SI 9.QHA9, f&qCOWJ<+XiJS)"SPw( 5ǂ,Ki<ƒI<$_ҙp u(tKQ(\|4 8xͼ2LAu| +a"i~IkEx^%nI9675 7Zat9!]#L@FہJc9NkЎÖX@: .EluW]ew}K.0BJM1wn}ճtza:pf 2\\Q:>N~"}c%W1G=y(cLO`c2XT~[)뛱J|upmŁZ[ϡqG`9/15_׽M'm}w}n痨`-\E":Mgr9}WĞwp?oI6ͯYQ #m~ţWpp j&k荗d9={xyOr2ϙ 9 /pPc!#kxrgCʄP Q?šP)rCCl(%3c#ArcB#r1J P#˷(|Iօm~_] l76܅,ZOL%?T&&×V­p̡e{*c9nRW |m.$p p]%!<80罳( P,aZnzьSS!vbؤN_hB? .vc'OY-kEk 9CD>۰w}i{?4uF[,?I (+Y1&4;b۾S\Ϋ3Ɲn~t"]ّn$)B@ q\WW.(O6߀͚8-6EəqM%)^OtL ARX*.e<ޛYVxzfATMrc&1L фI boܛ\hIDTT[<U]>]G[SOwgk~)?* 'AlZGZ5F"Pv\YkOkmA>oVJ<2'(>Ε* d͞{H/~f,_ܵhdv-/sGs|<Ȼ7;l[E*ŵ\k; g= ײ^k?+E4['_^*Tw.2PMJ mFbC0И_6əQe4q؃XlrrQ!HIIS6Pȵ!GZ4gfq}#̸FT)LՑF#7S)[$ꑧˁV;ֱVynHҌJ=w &7!ulo7NgvEeZ!q$dNf5-nN/UA*6Q}U--Z 2}0vw*İK;3=$7޿2u㕆p/)4xvR9VpB׉Y+RWcfӶM) Տ@ 8Xũ#}߾̼pBZ;I)ud!]RJ=q:HnX {SDB# 6*#=1Xkg+="LyBy S?|Y@ƴ:y+.oeUN҆y/oTP!PBsE↶ֿkE;iE bXs{W+E!]osY5IP\[03bk8JrŴ,Ժ2K=S8 sq4I54QRk PO2=_-QqƝV",Vx>8."őj@[quX55S|Dqh&R9E4cJ, C2md[FđgJ%D3ɷ!ܐbO7[,9lҦm"ܼn[kckUtnع{A gXCsX+6'5 cfX,\C'gMNKV,"b@|Xq!PY4ںz?"FG~d+HA7GCf ZQN[u^ЬF࿀>4V#ַH(s I]gOPeFh#N86VL7Φ 1"#y=1Rif_uA3xq<` }q *8zn!\hW۸ w*Pʊ0339E"?By:,1L( :C4ImБnh *Q ̂B+&W֭OY*gL7\ا;cZ:R򲰫l } u$Q1"3lo Jb0pدjon>tOKjǃ0c>^+Ozi=a\N`o}ɨ1h^>Fv&ywj4(!rif*{}C![y:clW#(rPJ/rCTG H?M9b/*a 2T9X-GEDѴqҜBhuY.7NM/UyGWqjuD(ՑQڽ Jglz<*Zk#n UJ9sYk[yr:26GݎW" ADl! #7?B_lb: {=\cyH7_ȜQ!=ԿQ w5V9/w꫞A p]Z,C"SP{QϡV:x#gˡ6LDB[& =ـ1JՃu L\Cc\נ]H%⦥b"AjIRi0Vڙd*JQb26e@7OMݮ[3h-C᠝[*'x6nFZcs S` W#}~_i]yR8&T'MU*97m*dתA ͻm8";'\ouI;ͩ7Y4G=B>})e[F_n.#kg SiﴩѐqJcF{xT%X\Mn;3,ܡC<Oa(~ i+?~9XP,s +s5f)2kGqm''?xRj B&{ o#g+i}˪xj6)NC)HÖC,0@<13^Y\o !"ٱb)IZ9:Vix.ހ|.;gKk:x⚇tBS+{ggO*t?xɿ/iA~uQ}Wf@nϗ"6C@_{?8F)ERY"BvO;{=8Q#M"D֚ܒf^ivtt *a_4A3c"wq-咃x^ K{Muܰ% S E7H 8XEe\%LL$Q+"cF|WX2qC7j?^|z[Qp OFwQ!\$w@5z i$Z.Fɗ-xP;Ui>`.@]=sQ~,9~i8ҴyѲ#7>r3MιcMyxcs9=实7O "4ϧ\*|f"uͣhjcbKY%3)Q@f &1XOɜ`f`X{[?fgX0m>X~~mFB~t,O@)UBүYkjCFqG>d[&xќ, YLt70"&{l RqDxH%{ G]mx@)UGhbur3"^Ct;1 ?* bth 7xLqNwbX.<^CE{8#|4k%bNeX[ӵ~~uzZ/ }Oݵ*G_)yf>nF8A -ob`-q,~EǕ@I 6 @YA9W//m$d }6ze-ӓllhKFUI>@"T.&ͨE'"@&`3FhEldTr_?b3P:&Jh28+myN$kҰ?ꍣG7lemwZB.@B(!+ FÏ#֦\pbtW&|o@ۏn7\ /̩ygH$PBkyyF.l[[z[^/,C7{=C}t>T)ђu28ew | \]ZCg%RH&%D-̛\(Omj< @#xɈ"Oq & ,ҟX,rtQ5@Q|9XD!>fhyURk%pZJ` [l5מ|w;/>ky0&{ }͙])@9 @_|.l+/ιg~>zZRj-**(TJ:.QU ߧxV➹Ds,Hb6)22G*|)}N@01EIJ%5Q\?4$q@H&0JgIb,hUZ[ͱVN*O H c=*!4s b=4Z{{Ϳ/yh~I~s/| [S<նMmqs(mf7+`N¹ <=RA aMNY&Οv3 2pڊP=,=l⳻8䟖l -RXk3":^01zC&3nutЭ,hYn= |pcvE~,#>xIXk#h~:@Y)ujGyJmt~]t /ůi1]_#T_(U |VJQJ-* VqdRY]T^S:^NV_FBF#@ZlWh-#|Q@c&<jy;ǀ{.4 M|υ]j д_{Cysso7ҭ(gٕy|Wܵ#~,k: T#=ỿDP/4 {Rޞ>9XxnSАZEdi`ʀJ,qs3 :uSIB(+XT뤨RΠ7OЉ!/){,A`E{Iɲ\*Ùs(DQ8Zi:%ll!K=ׁP- b mclnK {ϢE_#U_7BQVsUkUԙVĬrmmئ7o`_!a$t-75~W.ئ]WRd;qz#m+J:2y>4HeAf<3&ZFOM7YnJ~},MHM6 YZÐ:%\BjA䧁7E_(*?>2#/M:"LdWkGzFv-U< bE;\َc9爥k@S)5)bf-*$TBz bX,6^]-ތ{?/!yA,"0#O 2ve3[Gy6 簏: M_%*YVB(J /˞W,1yB%Up[?]tex❧m}UdzYi+mE f!U.ō+Te@QnPϬ#Iԍ!m)HٽsJ΀Q):0tt eEsMn]zԛ-b*B "Zp ] h9$V>]yEі#Eˣ$`*2Y͛)NWO7_tJ bׅWmNZ:QZYCK%6i 1F I#%$T1Y&nv[e֚fT)s̫\C憤k<n!tʻp XlHv烍ȼ"߇ċZZmɡvp82O)iBsPRu"?+2 uQ| !"RSJ  XF !j{U$$-H| PJ"C)ERt#EޢHn#H}. iĚlV֊lAŚȽ9ZbV|?AC1tA ?V $egynkFq_}3_u;zw.OW~?n+,?usKY~bo=<#0`<P5PF9>^^W%U.e߈h!Wd&3&v0.JD*%cA9X"3qJXece|D!2J6%B/ }.8TzwzYڹyokkU]¡6/>FpƙME]*H~[!/gR'uRJoFˮrͮ5mUb]iVyߧ\6ݞ獇i7#v=E3Ԛqq V{*R dͱL4iL5Hi' s&8i&|{:΢$ɣ>{O LPx;zr\ڰR|88R৑pF~2C@ -rz</&#c)2*tkWjg˟@L{cũBD`km+P`C׹ UGad,W?rha"c42Șm@k?RGVnE <<t !}5Zgs»^ O~޸t-&\0=qT- Z9qTѼict'Oox'Nw?U-E[c2,y3D]\}U;7_Z5d>8-VIA;5D ù{8%=1?&\t) vJd5%Ɔi_pKUMKS ^h ih UlT m|< ܺܧi1s͆%KO/mgu.L#Rj3?"TuVv,~X>SȄoXk{2AHt"?XZ/u<226{7J)HڮB~R!w"!1!۬:tlPt2[e.GHF$p^qwb-ES粊EȞק Sw20 IDATTBكk҃ק%2`~lDsOTOX@>}T^~aܟxͺ+ꜯ#`s 7ߍ0Xb!ȳ89Q%5Q/_".8Ahӌ<4`noJ :@t5sV͆/3ъܸ43ˏ-&Ck,p]E3,.82x|=9![n[[src~ E`#r2R\eZ$$ⶻmBASͷKWlg1j,$[U{<#S;FuΫL nYzF^RS:BNFO$SP$ӚZ>u'=v67oo;8x`ݣ:!Vː"E׿D&!xŢHXĔR km w *0aPJ5_Cu x!Lbe< 9bfGj# ^Y"os90ځs9@pYkcTM"{& YIm#cZڝGzt=kү9N:D/KW_7儷߂,m┨==Zkj?^Y*0}OpUM/ &ͧHQT\)DBdIH].͜LxNV ծ(TeeV.(*PRzZM 0(3b] X <\8`QXD$f)XGJ;洸ele>H"ZNLaMIpi5{y2{;wK +oZLTF4lyNϟ25`磉cj{Ҳ'٪_4E0S^EKd8*?gr`j`P9X^ pp^(Exi@4[U=eλDM:xb;"Y}c3O#֢DG0G) y9,t!:׷B$i4CG(I$1A, ^ri7W" Lg1#FA1-vTFBtqJEn Hg!q$DdL(*H=̄~09z5J=젃^xn1QcG/7v/z]7)9{Ƕhh㉪,y̥;}4w{>iBXKp8^IʲF͊G\KRvS*T87W40VQ)&J{RQ rqPkDs,E(OQthgo 4d.N)'jjck '3(8rs 1`JTvTIWR[εwz/@^KoGZ2n$Xio钗{F?s}eŵ(YiAY.&^ kwV.> ުvx٥]h@{ðfjv O&%cI=Tf,I5i8)v]L'Vk'sWTJ">4!L <2s)RV ^|jpH2ɥb+t89\1li^HI;7zUYrE@y6jsi7 :VlZy:{Ox黖>P+ =jp'{<;^|}N.?_8gO1xR0ꅼ}G]j2'SB[ +O\^?\OzsO]m>^6O}ƌƻuHe*Ā?_lW s>ͳKxh܍IB!<6R-if魚rAQPb߾ -1z"V +G*<iNNƈڃKr) Bq1{3Rm= skA*U XŅp5Rq%7}Yg7D*m֎>SJ_l*,o!"}w頃f+Y~=^=e~J;7]N/3?}UO2KWX F)b-Pqb Q#fJ8eJ sXE1}%rpJ%*MMa*ͨZ: rܢ`=c[ V1x鋾21T#ہ"{Zz>J)0wHRp7ڕg׍o|MjUv<.,]h@'hlg {uۿ_럿,J|Zy7׵4ℬD_kgdXaF |r*Kd^| Rp`\^M֏~zʛft;vNiL7 G;ЃƏo:hGG,x/<x?zv#Y.>{1SL{v\@x[hYfSqh"ˑeȄ{P/!b_>g7q/J@ @6#>D}1fbA$'aj12kUJq`/ yn;h{UA-xyke:蠃Y{.4'7o2b LP]xyƭ;lyym+nN q(ZZ QQX)`D1~jEY|a_ɳ9yU\٬ }FpS}{7T)rŻNB D"%Ϸ!q+(s )^*' ny$(IZgO_oW_^&sBJMr [ qB+0mZTƼe¶|~u.&}SR%7777޽\mWc=Y2HdOOq. pץڸ{QW/m3~{WpY!]sMZ*tywoZ9[{IoXikB#g!R=o!kR:PJˌ. G To&NY;1=G(f6}h姵pCDMco1Dl2Cs 6l };2g 9x#EƳezȣE] W*)mmP݅x/[GNG$"RB,;8ٖ:`/m~͇u_wAt\)&!AZ»,1 S{x0q0JE% u{'N޸CQFs2wtf% ĕ[BF2+ĀiVtFf6&IͰU %׺V|xw2s]pG,$R+-[nz~Du=?a{{6ڋ0S:{̗|KFx駓'up7O~/;wXyigf|;ݧGog޲=1@ݤ: pÊK,zɮwW-9i>tk2ҮoUJ][wx:8t8ˠ:LXAq]QJV=m߁L/C%dƞ_|D}ZU{Y^ ss-ev<|炭7^z1"^ZmQC/!<+̔hl Bu~N6 )?69aQ\R_U|!@w"<#Oܛf3imSJEH!BVbKfatfOqMHoƌEYi/3A{UM ςr\wy{蠸۹ ?', oZy{LFAYi宝=~U?}7ZuYPio޸F˵突w6݅8"m6 J}pAf=gfꔍ3K{ 'XTJek//asYGIA^jBr9Rd}UJ"t721"EE.f_O޼cu[&cF)S)`Z)kk4oGv+z-mE.Ç9PYH'ȸ/~y&W"C'p^y?R+ɛ<+wD)1C"fL V.7Bfm߬:-zY%+?_s9^Eݓ]JV#y;/H`\Ϛk[dGHedI9=x)`#s2o$!|ڋFuS$|(9YQSIRv$V"Cuq܄`]Ke SyFU4nuxXQ{[`(r렼L%3 8yg'']w.njAo[nyI|,oտ4O#\.C+PKKql. QU!b6K?~'o?ߩ5. *Z%w[=Y:zit9͑ӿe|cT\4[}G-v{ jms +HnO tזs/{e\qf{ٹscCyicO?k;u/_x+W~@h7ԻV9s8Z(>LnfDdH+A\;!9tе#w"kPQ..E:ěY5XNquk}؛~v:=R)* Qtm﷼c=GyxyKB|ɋ|>PJ}!=ߖy|A/!|>|yW{l8羏O=!֝;>eiU}ɛw wězO;~Ͼqv[2bƘvj_JRkbC!.Pi?!Ƿ5pdIR*⨘*$!EȭTC*Xǵ5rR7H\*lS; 4aJ۟)"p(=quj$5`8pM ;2oտLoq !!nVX-÷HPmXϥ}Ƌnc4 [= H ^5]+s'*==Ҷ6oK~z}yfYo a掛ntF~7w}e,esN6q/JG&^iuj<;.vcQa֋"Gi }ڛR#vf- !ַv W"bYZxOaPv/_Ec)?Ǹ6NUcHdN(9Il)wS`1j!^6wx:WH,AQXtʺ.ӥ*Fd&xJ5 GӞ$~[6{ʇLAo!VW)y+TL;:xuNP,1[nV|;Yt-og*˻Ͻ$$dԼ *U^U[*VZk_+V|UT@#IU1'g>gOkq}NHBN {]:z^~?\+i9[+K$<6sLҿقIˆ͆B 7 M@&}wHʯx-2 IDATfŪlssl?06YyWPr:J&%X?욠N[u' w\u}ם_Dn1dHB~"h[0 44 haz9^̮ =p<ԙPYnF}1.Ȉ2nY;dw zK/"d<AIC赲i½Fe t<{d׿y Z睨c={G&kB?$_`ȑxOu`Uel$m mnt$w^axOfk$t]X{Hq` ^UFaبm I*ba0ݳi3Й) t5FVf0 R`[b"q!)FCl 69l$$ة~K!l;?,.7@{|JI I|aM-V6gvZ9#W9Z89ƑYkF{#X&`JPM4h;_> !+mVXEבYjWڽ }+FxP«ezWϺCuci4^B(%u@Qu>D)4*Dl,m˷ڝr,)ͭǕT0jpDξ_N~?!FNN~J#cVjtLڞXރ.OCkhFtA)ĨmIхs=SIE5ζib% MKܒ AWs8"2 x7ѻC֪4CoD 1CMev<6DFʶmαYftVc9`͖͊ʜW<:ƫw=> v{ X;vԿt#a1x;|,ϼ/>υv BQV^&n8Q^SϚ?y1)N!} DH1J8&QQfqiK6q1ij -u!N 26\[@M[6V` n=鶬#Ƿ Zu4Q՜(Nv'UxR-E3*I$Jk˿Fw1sgI;6`pV+/2#?){m|p!j}l~wuAuҍ::|i:739Orqd#h)㔒6ݥk.|e-YAorr/\fĝWT:7;7 $-/hXu0Nvai\+K@p?0 xyнYvr6Xؽ5`=$Yuj*oO%gOzX Z=0)trtحƘOƘ-""SޞFEa潦O5Lz iKirVT9U .s~}W tquZϸ,{_D\r@s8y .\Z<߼Yxvѽ۶=趹?w7=?\w'0=Cvx$0ش}(g+(QȂzNuE-maY)gKCCDHmrX6mev֠`,7hR~G'=X+ߟ#fF3 ߄,yQ)5mLN뙄86QԈΕI (|\G ($]c$T(vGs{zA{7IOmx6q|f'ehx6oZ6> !)H VH3{x2\V.ns$dMp|`flUs|0ltf;&hNmz6 ([LYf;ч{]Їk{k;\4aL Բg41epɔ(8UR=/ft>?BkC 硤٨O, ,4y7^E91ʑoX>oҟ]gg_ .9hW] P/^|)A>JՔŎ@vԞ4msq1 `xF q@"G:ƄM؍:e<ߥNwnYʾDr1;yW^2Q_TۛXaoe+k.5mN^)G;S>o˩n>"pɤc )$ 0c[R_:MZv'HSZMҳflQ`5ga;]%{p/BHc0HTwQ&ȉÑJ/I}`K9^{{\ZD1EAs_"@S2D4奌ڕlzi8L((*FS {OH]S4TH;o*D\=eTDDnC@QQ2Z#zCc~)"/E h ts*P*kc2z/9iF74 {=\h7mϑ|/2߮wm޵(kgBPmh 54(7k:9޾BRo|qF6JImL'H-XbQ #!IPAjUz 8NaWE}1mhuq}^xȺsˌ'>(Y+f'FI"Qw,Q6mLO;m.36>ϟVKVmv7v9iD盷v5u )/aYRed|7o]K8ټ^!PuD%^JƄm%6OOAQ4=mcfqD?B"(9 !!ozɝDCu5U")ALf/hkh~ ڨAȈ"K3/jT==)/gg1_cLbE]gS7l4ƌcESN{Pv}#tώ%d3wDι8;.cȑc?Qׯ-[>\WJP1gKxdA-w-G7z7X / X9 jm1AE+SbdqD7(67 +Myi.U:2Ix=9=L^mΘ bHK/_W]ƙ3_KT[6a`M.iım򿿼CO9.\qeW\zY6XatuӤ oe:|[ܷn4vԼ?ȃ?#iF@cSFʣn#pG:uSY`J=|rGLd1#A[-T?F,~{k1pqTb.#4T@#y.z0QUc߄CS:f-~5g;YPOӹ׋H1<US!wQW7,8MtDĞFiӠxmSi[bIEdmwL=qf]<4J_8h+7H^#̢͇6Ȏz:ڏOG#;m^P5,wPwg`,<ыB1.m?8Ʉ0YP&[naȂvYPԂ0 #u)(4%1VeR-҉>˘8jF3,Upkh PU ڋ- (4RM798?~tJm^p̽=?Z}4m@'Am~euϨEޱWDul6J^( kC.[?G5Ѿd%ͻ?fulbVzNd9SuYԣQ'Gosi>|":n%'9r4nENUg5cK?9H K\ 3<΄F` >Rf 0o AHRzHb#Ul& X^+/58)iŵxkۀ۬-ZPvwԎGv ~-FK',oc?_w'q>ѳ+|m=Ivm񫯒ݿ#s[+uܷڣ/MLO{#Ķ ]['* NkXjv\\ZPۼ:\ -*%7۾Ԙ]R[D2]CQHh;Z"Y$0Ĥ SiU[Tow6Mm>C^ X uV5 qJYMgbNw/D_x ݮwO/~M3<s[\n%Gܹ[(S7mSFn(ԏMdPry hVmAY$ilxJQ.»l jwFє?;S~4F͉$CPqP2;x'ڞby>per6wo)LE1 h*:oQE`9 m3{@Mwn2SM9r̿R*;&&^sP?SEa>9A6FK)zS0 Ha@/3A `[FbɎe$mː%}PD 1ΜǢNe]p~/xpnƧD&ݥ?$dbg{Q>:T({o׫},S~Um5o.σ;PoN_wa4v[$ ITCͱ3zZYl׬AGc<딠)=( %rO]ԧGs(}*LzEd<2dPI'*Tgzܘ6Zd5ǁ'yCT(G9f^`9od\ǚKӈ{z̝eh/О;P#۫:j/>l)|E;=)08 uR2SGFXcC!Sj5!HDJ|{}. [V e['|g6 sl`~Ϯp*I_pťEщW (S~љ㢞o^w=EhO9{`7V;N?1 4ԧ;4;3Q"Vex J*h T6iVH^(6i$c"*VEk ɨ6,pEN'z;=Hђbd0&mqۆ(҆`;%IR/N:sEld"i^AY8֕ O +)1SYP3=ƑY|6`굓=ɽYx-˂1a ԸӬX@4H:čwNC׆f4oJ`G |(7 %Rq;mknӼQ*cdC%_5 9Kjd8SD\M>QC0pp ~tsFOdXupf. PS"KruvjF󖢩;msIE\iTB 읮aAiej.D$QH52&C.FKvp[t*z8Ukǯ]zy[%hMnfK U= D11t[i_3+V>!"AIDDNB^W3޴5d%j*X3=zEr%6-gkO&駰ktն#{m3JjoE6 1b44r&TaPH;hR{el9zMk,W>scs7 io沴H tJji<0Q@n|op?-MCmXAjH4C- `bbyEF4(aMz=%j@ 1^ iP$$Is m[!@D~kQΓN_TLœ(>=ݧMQ$2&EйX= a\"I`Ry4qPb@ehU8)\~.45(סf2 ":?!-T,ԃiVcL^\#3 W c6^3JMÁ\yWh[QHLyNĤ;=Oi'hYl:V@hIjmL$ vڠ;EJG)Db4eܶxƶ ""k1uu.'m[8U;v^cfٰ9Lsju;FhO'uԽI@"MóF's#7_1+Vțcŧ2 E]Ns;o,T4ia/ESL-DqF `o- 'ԌZnBIb<(Ec*qcbCC^~_:Drگȑp5X5k${fw=>S[7qݙ͎$M։: Sh `[)\ m@݂.[mpmAef˦`ġa4hd M!m[:Sfc7Y  Ȗ,PϮΏ_#Ƹj99AgE7`P*jeC&Qr tUbUnrAV ™=~&Aq!@hhd-{/DSBi5wZ8qplފLd5?遵av4RbtRDn7^9zMX4Us_6c:a۟Ϙm6l[aس~D=8zYqwP<+W[#]%^NGQNg#*IStwl{@Ml @WOcmZ)4A11^l$@{m?v,&.* ѱ7A90\h"U=8>$({VY|Y*wZ'<ާB,єK63'(A-NZ}C4e+JdCZ4n CM~9p)A=GWq mB4M/:oۿ7i.ٲ]TkDX6ޓI Ħ[>Tj1D)$n{<.>j ^ v,@݇ԅ_`Rbaюsl mڀcN0\,D)Sk;vڌ#KB~NJډj'6wMw-O7\u= LҸ]YZX,ݩLnVSmLD]tܜ9M=}˅;m~#'O ,C͚EuQb(D]8}3uA4؋GT+{I2 {'d%SJ?.E#>4 Pxz?V/EAin~>Pɟ'|`-ǡCk}3H/¼`/|TV.lubVe#KT{`x0 |WV.qއqoZwu**f](zg-zQb=[P"68ǠijvZGΡF9чlB޺Nj4~ c:&`~&TGjq9Y|a%A!(:g%4Chtl4U?A4b|x=48x|4o#l˱nZ0](fKk>aK~kN :k_BLI<{| 9 [ɪ7nς l[%V9EѤfw&ƈgȚ ;I<^ Q ǘ> mv>0Mj /nt'erT_ H]F;ՠ{Vܐڇ~tY6W%19/2{ѵC)'XV_9*>\|#FgǴUFy|rkbՓ;" cV\O]nٺqEq4ʸ}84uowf,Mb4"wfAI_S|%M7hPMrETSpayt[ȑ5j'L'$\y0mp:X\w(gw'Gӝ`[c)ZjHljܽ֔-2P]CY վmdM$uJH^cb;bc7n-iTN3lA9@ "`n-RGK?@$kgm7]~wMB#_5p؂j]Qo9˹m~2:M4qmO,ꀀ7M"Z-O[U:>'6e#yd0ho*r iFX6@#~/D1Ek?J7^rqNm-~5Mf쩠8B)c̩~oe"j""2E BSΉs1wKPgF9rx=gՁpF$/LH5HW! NOqwq:;>j2li Rcܐܽyn/ Dcv?'iO[w.s瓮 4 s&.N-Ďu-&tvM %I(4.fgQ_hwMRz^e9jv\3pB./m&2wۤ`cx5",ADmu<ߚŧ7$N.YDzqZ) H?^4&ڶC"4v 'O,>hD\o.Z׃*JKhߋ7%хmy:mT`d6 ކ>j$6J8sMf46>;PᝩB6>lٵ86{Tql090l8a<.G%FB~sW36ţmoNG&λ'?}s ۟KRa| mbhe͊;+_n7i7`&G}'!yRNhW~ x]PS ~Z"v^U= cV2r)%#ΎkTpLjR1y2YLwmmy ؀ݨm"9a}3Dun_swSX'g9^]Ncl ΄ai,&1ןU*apu8zii)rtqH(2ڑ4Aı綨)cfJIe!#x7XuۻR݃۠&rc`7A=kj- \بχ@|V'Xmt /.%TÍ̈́9T>q~vÿV 8%z0ׂ ' |J<9Pq>{ %zf@y+PJ AP7DŽ@H%00'u1_<ﰒd~; _Ւmx:*tG2$c+j$˼nxt t=(Azc;TҭuDQk#>s@$\EkBțcCw܇4Yb7:w (YM{_A9е(%Xc$A)(7m"N<;=yb~•'Ocmw,6D8܁e;ZI4˔.#U4=Z>^^-B;mYF}z(p1)Ԕ0;-i6aҾl׮&qne+.([4T%([6=aA= a`g6pSݧJKïu@=nm&$*>/W@9Z|Gepy?lP%3PJC45&?[DYSPm%ԅ J؍ApbŘIiCcBDL*PbNNCW=BqL.-DhwxtfA(h5ڧA`jqL=_hy MPlLڽN ؿV+a|!<~q Pao|A}6WR!r~jbZ@]pLJ PߕS8##ZpvDZ߮c@;e_Y <P]-TF@ٯmk_zY/}zپՄ[j>D}3f1`:*b8 rñhSVo<~u5'fHlMjMC]wŕ #"*F땵NÝSn3V]X1dn$v϶k%i@5pNw]iZuGMZFUH el'5 ӽeW=4W!zim;9-?q&iqwV(1p{}J"l .V ,|urec5Y' $T@F*o,IA .W 1{"JMA=Ayo += L=rznbB &s2 aZ&m=?%"9%P"%xJMƺmx֓vRJA 롄P%L9Ŭ'r.By9%q߃K)${@=XPs_}1HW5#!.Q? #`>K(;in-D_\z:6{h?yN,#򡽟-9'awwG?q'sO^[Z ?}1=m vv# vK!`cϜ;>qA~vǝ̼v[/.1_,:XKhoܿg_/ hVϭ;Q9tvg"Ʊ<^AbV ;}y7|u?3YI`eU7 PWnrm6PNW6ށW2B9P%%n9Ac!|JX] ucmB%Bf2Y )Y&[D q(zaF9aP8RĀ 1h%cx+i_G-GL[pP!'3ٮAnAɜ"a'sɲǒw&C ۳ɾ7 -~=nI?;=\"|CPU/cS{r gK)/JFF`e;n-CC%X4aCaԄ V`336_xLۭCc_Yz8յ9Ė[ڴ:q[o(:c|ۍ-mҙp K@#;x1ba""/0_Պ>Ql>32?6i=e2K ovy G9KW;Cs `k䮽b++VTd~u# cgqn[z(P;?'n3^; ~G+4J7r jB:SIXjf/qɫCWl A,6L2\dP(VԍP"m 35 B !B5 Exm-Cy<(wͺ1JYjyP!4ٗ%fP6.v]Ia<(\n-CZIs'P_r 1,3Pױ, 1%"$/HB9l&dBP/U;*^ Cfy+~Xco9&x0WO-DoBbjk6nn?ƿڙ_~|mosgFͩ]Oߴ:qϟ|_yMVO}sw;>:14 Dsj=ft귖Ӣ:[sREnmlREob!f'f"lW6w?swG 5RgA۬s/,Gn/L6t= vceK2#eX$l;W q/kBںys0~tߥy;ķ U+/ϭ}{u(GnYo O֦Nx:d o9|9DeBE}w~|o\J4o{l+$K*֟JНDusMWJJ /1h^Z\B)Cg&o0LZ4LKnc;8ܾb%PJbz1YKS](#LA*̕B P3df( %(wAr!L P3T`#5]%5i@}wWrM@]'PxW@4h$攀DBZ4 "xWA}kO>󥷌֣;y֑Շ埜{kc?;bc9{;lj/ۻzwy +vu=Ku$ гG\ܨs R5Å"@eQIU*m*85M,-w6V;К;&i!dUS}_y"|Tsl,m_kPl6?u|~fBՑō@ ki."ڄA(Jcګn(Q3[ wۛ }(JpaW}>y!TNꋖzCkA 3iob*&t15L[g C%v >sxiTR(#>%p r'BM;T|-'ߕtGI] $ !'|4-LBUt@evR)I(O")er/##? &) .8K1$ >ڻbq] \zQ,=tnΏM/mdowl2N=ѡ~g?w;=;LME b׶6aJŅZC?W**M6{R7t*z}R;V,gA(FI6:9#ݮgjt r4E"~o^VDŽ|mTjkRT>!D'FF\f~H. EYbU{w6-@{0vqewAMV" H1>ְKO@=f=ۼS6S}2.z>IϠ"ơ&Kd " @vf@By-(w'Tw%&B/ d~N$SxWчaOLZ%dd;dh_i語,Ǡcɘ>6`AȫAD 5s;]ԓ T8jj8T)|99p~ѝA Bʡ OIiR2"|g^d\ȸ$0DE`B&كGU4mw C܉r r+:ݍ:{%z/~\Z9>7 Zՙ`F-'+By)Si4:He$mPW`Bi;vug`iE Ons16J2a,5mu|4p_ OI8Oiۚ2J1pépAH\-MK#pq5({|kss) o>}$b@C3(|d> X 1!ìAy 7A "@c*lP.J Py($@ VCáDY1Y׃} ؤm0ג}xCd]JI(v"~ ]7 ;Z,ӐM'D-:~n4〕 D2B7#BJn -R0 f[^tΒ#grx\N1\tg4GbjA Q( 0MX74LbNzQneh'N+lz!(-JZ26r,~;|?ϯ }Znͱ'BAkĨ :GV.Nn5A :ѿnBaѷ"\֨֋ Usy(P)8N n22~@PK!4*3Ǘ._@K,z8Q@:ޜN?T0ExmokI1@pbhdHnJ9%!1ނAH5-CB%"9 *,%a W3Pn?qlҲA'/*W.BǡE$ l'e!,^ݗ ݋AG3 D$va[ʙ%J b)8F6YW9Ό*e7^c{0l.f/-i"d^pFGD?9 Ip,/OԼpjt9=SOQzX5s<nt=bZwIU/K7v3[]t  BfhШ~liRh=Tö:v,ۍ\'6gvv..lMmPeUR326;Wd IDAT̳$gѼk1Ho֭c 4(*T3rAhڇ1 P4˓Ӝj2(ٞAhXiI B?Yއ2Fi ȯx29& *q7h*x8_==BHڦ#"ȸ"9Py)ח6Lm @J*'V1ĶQFynL?=9d,s-EyhxͮtZ}ުGvse,ԌKf7g7 НX2ќF *͝#$6M1#̡4GN lT657ۗNc^ѱlմ.:lsPFA1MC$6ATeZǗ[~h k lNC=z\~qu7N놿a<:kg?#vNut~;?;Ss ecD&7 \ .hJt $BqJL7';P&@e~Zݔ (Aɒ}CntTER .94w0ȕҡn2V:qP!)DA.w^!dU4?ADxʻ qEFlV$1BJ< ݰdYq &qhe>M Xi99GXN€-Ti[ 'h,89رJKؖocymSQqh@jYbnTAQZ"G"?;Gβp\lLoz79&͚N_@:PsƎ~/lsn1 usU'@RQ6RHZOv^@zEzDž=ˤ Lhfw͓}bqD1-7{8hPu jrKpmdddXB|bl(!-H0}(o^>y?mS.18N?ه3tzWT*|d*_`Pu5Vԃ1 Ӹr"I0nƅM ׿xBL~m踾oИ+Ci, A>\ϰ.*e@GjwLvcO>.|ggᢍxGc )Y ̘9>:]y?ۃq9ؖA4(ImtbJ ڍXȨtm]_1zbPsG KiuiNjb98&1VcL-Q= =۲\H141|8J3n!!B[LBb >h঳t`ٖX$w3Cf]/E&/! Ӹpkb^ 6Qzh %&p~(0(q6D% C 8u@4I^Ucc$NO@y8cP6- {T" _Y/ UR&m J^Y_p'#### CA] 9lAK/*UOcM8ah|ɕğ11Zwx­33#5F@r 9ֱ1sr =1k뵲Qrȅi~n-;N'Z}A)cqA.fJn&d~o.Yvsk=BF׬ Lu oMJVw9ꦬvn Z/"1b}cebӇiyDRP f2۝^Qz`bQv|w/W:yxS?w?{)/%2<^EU+B_og1ȥ&2sɲiQA0 [ B^SA ~]-Y7 -b%HK%B I2hcZPM}C+j2˹24MP IkuCt b6WPM0X( (& 8QPHa"S7ٯ(,wf X(bՊ;u.>>'MQ$sϟfj9A;,% HN T'[ !ӨMFpA#nr+ ?WuF]ɩ!F SjaDZh> wCa2Խ&ZaN48 +'^,?s&d U*ի/{|>96M{vzhb)""$@9b0B6`!M]C{ov~.ϙEp[ʎ yHFF Bm> %,64_0\m(1A'PI35_E4-j3,ib,šd 7XX0~/:1y_gm1d_?Ps~! Ō+Cۡj̼m1 XEI 28Ajndf; o̢ssz9KnC':gL-XVI7j3]A&shZ$-o5ߗ.Nb9_@!bIhٌRHC#$ &Fbid<5 \cEZ^/{Ch"<{Ltv~ٲ$ZbxMǞ:"eyP8"ej:#%ll0s>|c]xI/]04##ґW !$*}+2h*r2i41CӼ?6~^CCyl ZX'2m%?69/H IC^>CRnrZO8`JW@ȸ8-EIKbC?H2yEY [0d~m\cL+4=Y9"͟4ƣxznNbQNks%琫5KBz.Yɨ0e6ň$8=Rs [l1oiJ9#nB۹9mA?v74YKtLЌHu"s k@qM=\כ6jZ-n,>C6*VǙa7jlCriKhf~hI#hQMm1^XDf,Ls hR:Hȏ5b!֐ MEF0RPݲͪ9GjBNGajB'czrMNΟa52tHOqP8zPVO:M}jGZ_V|B"k}ΣQF\eWo'4WF5Mq`:̢l/p=谜=vc ϭ.c.sڳse&5TC1t_@GaEfg I8AR)kVd^B€%5$r $\j1 6+ԴMn;6}Q훧Qa~yiֲZ-lai  :>IͤT%{G%9n.l*Sk?82!\qbʛ/]mPa PP޵!^RCM_,{~/=jiSC7R_4dž*HVMRvD?j1woÏBuP çbJG>[D`L@J@RP*! f 1  !4UF]J 4B u/*SH+ Z[ǟfdz5B0efA:-KKu8@ Yp,a w gL]Hٙ)m2n3 @ŘQD枾-o^񕒑\QbA H)r&ԫX7IigZ{oT$K!0(6iE6ʓe9/E@1,w0(ZaP\f )St\t/JNPLTdddd\vVG޺f~չI8JV2ӢdX`\! Ae X 1{z4bه.#FޙQo&:r]n'hqk2B\Ѐ0Zb¹Na w^f@r!6[8SopJ+EhԲ LVdrSP,MGZ7bÄ8P`y,KV.=O,4ZLV*m3g湐-+6Z*oCo&JIb<Dqӳ =88 >(BΟիQq/t!"Lö$ ‰ lsF%|2geJDT0XojI=\d'XĠ:k*RKm;=w0M[|0 a&i@v3g>6UxÖ==UDscv%VXmv1])ˡA8c4Bm!Vij92aL#5=NuBAK 2灀KRMO9Դt 8oG=Z+TbC3Aըl;&~lvʎS{(ɮs_gU$K,2a 6aдih14LLvfl8,nӶ1`ȋl˲R--%;܈ʬR*e-sN|q#22{gI`jjz8&zYZav(7%]U\Ydika!Q h:#8X_[TRrs͈ELicֹ't}?` Q.Տxчmq6w#n\r0w2^c02>ȝbt]!8ƥtpt}3;y޳.UZ_gaXw//bƼU*~`b[07((HT'H2!U$>-g#~Mx L>J],Of]s6^ZRR8$[Mּ.=I%~($^ooB;EPmEG*ɳN/,aիa=aI+i % :>5ף"-"Z ")ȣTRUYJ:Vו_.Pް{vS7"URaec.UiFqd ^OnۜX:pH=q%8xh?n<~Mwx /!"@o~UrU IDAT26\Fĵp;Uc֛?#56hy*2v9*bܛwav'0/Lݸrg`S`[(3-E}elZ㎾GFkXs#]å`,))Qi}{ޫ>6w w߀>&6\2ENf{X@ 艙<52']iVDIբ`qp\/Ȋ\zи§!(rf0 lb[XyA5pE=NƜX[G:Sř~ݞB~7rR0ᅝV5md:li^,)<z[]6:j!/@ǂFO@E2H!e3zJU~vC(9t s2Ć޾z#nrZMln9r >? aOeMwоŷH~A6}~8t^{y=wjV͉e{RjR0\>̢⇁_Nio1ߏg'_]u Mqx;&s;Et1A0mܳw1..3g?b>S7grXA86is/L_͝o`ʡt;;-'ob~+֧?zW/o|+&6_r\M vS?mLca]^V'>Q .%\^@ Atșl9y PP!E;wo/1eqM>B6cZds!B |o`2Sc1Ά91d5#V*.W^)Aٞ;(8 ~ϕq*lg3w^sl1fZ%%%%k C֙wUxa~)Wb<^OJ<# ,lO[nQ9cN} t6?K%ȕ&U9[ْ:ZSzlUl"VU?Jѓ$܏LM43?k.4VB,)7`px%%ϑB,^+D훀܂)9Cw 3χ*Ku;Kd [ϸpqk>X,߿6f sRod?oa4I+HQ4<'k%syZi0H{lDmI ZՍ+fEgČSǒ#<66g }B)>8feE ~+OϬ'N@ղ}SU.3AS̈́U)P0J \A8U'@^h@s\1ڟy 2%Tf3 mKZ\[moP5wݭ=?pNJPЁ~֚8YQYʆ-8zpe%YʋRN'6;]^ragXܢQoPJaS, Ƕx?\R( b<0\\Bw\qOgm{>b-:l"UcKhrsŞoy|&kzصI/))))y2/}q`:I@)p% EDAOwV0fR:SIud꺢"[\iFBݞ?g-Sܵ\X\F7{ʩ86QlN$8LZXO]*WZ7y~r-ҦqPŶFHo$Y b6T"M3CQ3Ȅ-y^98t(=_;b!- l"ĝ!BwC'Z:"٬ըTB,KZsϭWO 2lۦV1Zk8li$\R,(.1*M.{8?Sc2\ɯ,Q#8v87?Hcy%[/x>+U24`Tk8Q#NX¯|+G ȅ":zVUP@!Y>aE!`|)NR +ؖ]&C(@gv89NєTb& YbQ7Ou?8W}n\k<\xd9vܵqҦ.m?1mG=3]+fʖMxEݯƴp€j5|gHk֚( ݨ`8u]|mRH)fE.;< m836>))^).#p0Ca_^)0߄>d,.WFl܇8=v1qc㝋1kr t,d/+0sbJJJJJ|]E*oo[~xʷȑZ7oHhz2+66㡵@AV(rVkLӸgߥo*8ũ5_\ǶUd̑Ơ^;3K;cBFP[ -֤ZhNXBi]h-h϶Tȋs\"q-(EI. ET[5 x!–jes˵ɳ˕r떬!*V<}{((]jJ5 I9"H129A EbYgK2ym9oZczw^_OQIU:Zn ^Wcv5on&/r%l;M_2^!u\":^]:.Rˁg?|Ω%%%2ȕ ^o، xu>߽znom m!}ԃ 2Sc !2˱$Rz4T WH!{2}$hLׇY. oZ'Wx|yIv86W@$ltFm|$.")Y]eYN 92-5BW*8,µ*˒hl%q-ccC:|@4r`jJZWEu~|F3zez$b-> l۲Y,!TleYHt“;Ci6*=g}v*t,Hl΁B'62mN(6]>:[.eOJ3a;.˱PKse;5K%8%8-$iՊTCxA(4'z>IrrsL 42-Ls@H ??kHD ;*W=fxީbU9m^7@JJ)*5{c1}cr]2&u[q+q%J+9-LV1Lj6Ls^qVq]1+as%y9BW6JJJ&eYRCeٮgVG3)Z_^e Ju_~끟g e M`WPXvzc;M8% zQsO.c''j7Wܶ_$PbG4h)@ . U&)rq7ZGq]!r KS2U`KIfX#u^pbG-1)-լoMM!gJ๎K hU }ڃRj4B{y\ / YJz8?!%qm4*,ųB|XlP k4mo86~6ccK8/dOu`Iɍ®ӌ_` xcb1Y{)0{f4v)jd EhwmivsbzS )y0-gQ)%HLe vǝ/[SC/*+/_!EKظOFô(w;ԍ'gQ}i1yb3| }BƖ$m6aR8#taEK#7R\ۈDXfo !3zkRhK8g|Μ_ŷ4첕=37Iےa&ºGGYyH!-4(EuDJIs$ߢL8;kR&-r4~4ۺmkmƱ,k591>o?&6U*cotT$\;s YR2z֎PP %vdʤt 8t/%|Olu=Z7]PAĤDK: PT0{" &L!T0󯴦Po5]~Hvxb'RK-Q{&&pAѨVAVK RP=.kg2jM#43_ỖIпuv[k4ar)lE%(O7 xEJ`S$ (Zcx+;i1.oev>{_qb٭NWo aZ.Ccv^#ZdHx%z_ |ܧ ǽ 4GoRRr97y >wq4K l"rАS\Rf_Զ-lۺwExZ4YM;Scq?RuWzrf67ιXh*j@  pl4xR'*Q:!q=Өc&HfE&,LN2SQ=!3\mSn(2jKLNT3Q:gVJ-'Y6ӵ&T!$5ߔZBrUH.4JkIL'bR6ء(02rQ|%k~Z 6 SӄC&%8a1<&~=5$2j 7IjmB"PN1]{t84NjP`6ˍk|Zɮe]尩bzK:`y_aObbF 1'1؃bQ MnJ,>&%ϋ_3P]]'%}ncw*xdyҨVi>GזȋX`@H$yF-` A?N8 *WnBDH?dz=MI>HkRB,h]ZYg`;6e`OѐSӨ*0䉍u,#*rf,a6QBrKSYl-`~G;KUMͧqx /=xM otkw{=W{ޕ!K+Q+>ǻR:t# Nx)&\#2υƬZq)rSiۻ_u\O'o3dz/<VwV$1BOh0BDa"mi.6zCjn#Qiue;o:4gi]n}|w&kozJ7ȱ[2m;R)Fnw[)uXK)piWi{@Gܬ뷒h_|_?|é: >-- `Ld0 IDATm q>+iNh,C9.IX {.)K6d{[SNAgXT-eQhR2*1B,zrOk,-g[ڵmIS]|&@T*U7( C-\in8`M6Jk:g;[]AJ qT֐n1A,h #˲qb;CǟGFodnϺe:F1Fn1lf-; w_jA>`P^ v2Ͱ" _^43^J- %#)rAя~Q(͎ǑxCz W_:u VV'ɰ6bmLphr Zk,CF9-Z {B75' GHqDeǸ ", yYE ^(csInh82yne_R~QK)5̀<:Ř!AG1_ut[,/cL:b{^G)0g\(k{w^`^k^& ()dA d&r\|FY_y텩V=giR")N~[=4?ި.Z\N `=ؼG ϒPAN> $-·}\Dn4V{zBE7M2z"vQ,zP/~JSJ8٠8uOЪ RKKR٢ǜ\'pƀgOc·Fܜ"?,ƱF' ?®fKJFnhx>ovML轘tml=?Qbf*.aFdlܼmw!&ĔDc\yTe;{YE #5F4EtvfuWke |D!bIIIՏ`𿎿,c<" U-?ZoO5kṠ{tXV?8ݳL ϝxtS z6Ǵڇ[ =s}m@(͜tud"!cgѢYSwuG"(T%ORHy[$oe=x)KJL)AFB1!QL8kX8naCGR1Řr\5B1UL0P~M$y_-))))N̏JٶmK:|{rhs`e h"i-B{*l;4t-)T{@b6z]`5$csqMUH@}yJ R Ks-R1Ԛa3']7J<|@.%k^d(y> EGl ԼLꄾ0\X봙7z>iXf$R6&ju,"s<8_,*L&BKJ. %0B0;G]72!>ىd `g1#80{(.qoE}۳J5~$Yӻ=*ZU?`~{.'}Mo~X´~Wq` J9PX S`͝߾65C⮅ 6% u`3MikBOInIyFh[jųG<,MLzC|~4t 9I3@XwXo}fp}=:V?7VC:F1QoTs,$cYqfF{Rȏ>()aĢ/1BL9cF! #{i0T7yL) ٯ\GzBgq?C#:ͯ0y-OOMߜl:ő4$^_Qic$1_hVqy9w#VFـg׉/:oZWqaū,43&"e=1UBq,|Xx<_w=꾏e 4 'W*\ϲxY$˵'k5X9<`thz]lvڜ8{Jb9h<ڣ G??Yp݋ELvt~jZ_B1 UxSS5OAٛXRRRLD*o^ ~Gό7N f}; 7RX `2Lxf/d*^ xny,bv;2- "xM/c:=)^ $ Y\cUEVk*V'u?Z6I*a= ES ZK'~ӱ՛7\R,hcjcBz1y.hbȵx|%ff9ƱY&22YO컽_r}T7nZ>O6ݯbqm6P=Z4Ȋ§%bb:&ae[F^{+ulj <.p_k]i*)))zn~ѭ񮓱͟x?#o/c3 : N( 99&90F9<\u],#Ĺe)*=|[yfn'E^$$:zY wϱa`mbjҲ™VYw8JIM=+h4ML:gۡ$J#zb:y5y6dk>(0HoŔv1B1f-c9Fqg1ElS+#V8G ~>sӞ, ?}05/?yt=؜-ʁہ?~5ٹ^1=s4r\`fI60^<ٔ}B,嘌?^;~~l.F,̐Zo]ޫHocc ΦOB9Ql~-JjM:рyƞ[T%=79P|eeY23\:Ӣe)]:viiMl4uBrTkR=,)lۺG~:䞹uyѝSOOZ/mwk6GrLFIɳ#ӯqyaYᗀ'ψ>rݧQzE\uv!&jT1'+w0s1D3r=b)>ʶiNIIIɍɻSQtm邔{0qo0yG0B dѶ"1؜ \$;<)- :כ4le PEƴ岧V^^i PiGQV ƪ iM qm CPE@^z@ X϶'gTw:z(:ɻ?Rk齤Zv=Z,Fk]h~ hKY gH츀 5arsbى!)ÌcaD^?cw0Likhz@KJJJnD<\XtB{w'gU{sꩵA \!F8f;ADs'^3"F3Q ĉ ;N ".% %!d%I'ZTK^Ϋ_]O=է+{~gZ| sfD_:W.osXl(mR\EN9ŜHÇ.Su8uWsD5Jg~}~]|5A@2VM&#.LT,p F:){82Va?Ԑǟ|lݘ̶޹}ak >Kn[,EU,:";9sz}{=6w%$U<ɖ=׆.^XX8x&)`K`&P""bր(D Ŏ=ܺWn+W^࿀RA.@tzA:H dҙv=0yHA9ӍD9f.]Hd`tE!^NOL&_Z(Dr<i-dq8،5u:~V"r$"Ytν{{N;G:*v 6E{ 5;!˧ufvYDS|a#s-/`16eJ#*,qި)"RY5{YܣXv9RA~@77 #-%Hq"bl>kBeZ y6qÎMln+y&cqjc Gƍ\Д >&=bxM}m*w.9,Fcr@4w\Zsܜ􉀔+dW3l{_[7ҐnWz_,'bS#~;hw*#p*GVn6O3$8r-ORk+x5d4' 2 d{G9_bqΝ휻9qp$}Kg{C0,}|"juˢ<2 RƮnǞg3^V_֎yTְp9Y8kaǖ5Wtǀ,]`SPuj3!jT}i"`+,^%ю˵TdGdZsQH@<)__WwTaXm*DB6} FrH!_\E]cUV_ Dy2J.J2%9,c񶩩`5wfWˆT|skKckj0|kinKО"]2 "}{sn{^v j/:NFn;`@'GHWE,N-B(%v|~l$;sm@D,Uka[, =6b1>{~9W`ݴ w>SW,6%#|!\xxH@M2:K$T-LtAvtA4QĢPLudds5C,'Q8*Dc6چ A4 .K5OLۑ q΍sMwΩ:T7tRl^?pV!< IDAT: {O ج :<'7|෱EzUFDYFu(vy gib%(+W?|+@wtUÆ<+F2SGQ])iI"K=6K_ˮ@Α,Q<6D+-3Ͻ>~o/䆦[&cʈ14_'W݈ŀL` 6{=zFlM0ڌH{ߤ,[*3uؖ-SI,A ׁk ⴚ[ L""ͦvŘ,Yͽg6y-Xl>UtƜ.0" G:% /Q!G <'ҒNwyu56NjLy صa>9vxCSkg"9[z&s f#bWsB]z>L Y=k8j{E~yc#JS[s{w#;DWsw5"G4,vhl=DcIW`]ι4:{Si1,1`;Nª~Ku0æQUD]9[31 ܏(`s;ߍsj6{߬J]5`{,Սgxjs]"yT'LHH\щf- uT*pziYl~M-,`z+O؜ y,6?u` $?8:,ƠI㯱e6Az>뜛ܾDiflh,; +s,`#u[ӱ7ωcIs S*S'{sj ;U[^_i`xoiιbEYI1:+ Ūt𳧜r={1>l16oL¯?pޛϩI|h,/ Q_( O%YlH)6oX:Ͽ /:7cIB,67~&̟&l?A߹_uƙO^"'~a7f)6qeFs.&{JgrΝ <Ƒ<7ag>{⸲soʅXs)%e K^8"XqX{Cxx-X {i$X8lP.|lH-gSS NȰd5#w߇ Lge ,i m˗գ<5`vPm?c3zc ˗u<Yo8wL{Obi?g}5=ޝ o o +sED"sw]Ws.C mx,=f4,I+`8Ǹ ֫^ۿق_~ݓגs'8Ly9\yT9T.J_>V,x¯\=yf8T=gkDؼe󓏏ez-6ϟK_d[/bum+vG&B~-useHTɢ aԋ Ӱ/+yח/]-hzX"*6RJ#zwq#sTۅakJXo{3p$/ӟ)""Җx7np ~W~~1ҙ&}}"9QjN=.Ŋy eǻ)5_bW@#v6\cӜNiK7h?ܼgT)%sˋQm9R4$ 2K8xZFsCfb(lcJSDD%-Kgk8|<ΘxZbA4)LiH<<+TElf,g} IѱF%Q<.lE*c(UD,Q%S1,a`(}8Xvn Xz|K߄">s?א9""2 I;=D6>}FI%X-),^S]z,6}924Mƃ5d.up Fǯ?XE[+R!JFs=O[qغ}|"jZCpܦab6?=1 FDD_>%ۚ & WT^u趍o[r۲j_rS~c.wl|EW(#WWEWLƊTB"֡ Z^२8e;5.6w>Їmm/`qΝǦ܉U>$< xlYDDķOMcjjkd"9T*UO1}l~;Pk?{W,m=7 ;&N 6`?d[M4(Є3~(4YX5iOae بjo`&.wS5liml{"A66GIX,sx-y(x@U~2OvQXln*g< 9y*C.@ysf+$mq6]çhjXmr V ?(%҄R runȑϼs-}lnLP9 xC!joo hĻ.6;7/ l.m!{m++k(v<U2. U.~4pxl.V+< bDTsKn[VU]X2/jNk]9T2L͑?yQ#2X-Sgt<lK1I,(&eKQb@up`sVX :IJd}j\U4<϶DM?Pmvn^?у )652*ڮN)e:~+SߩܧV0ێ=3\UI=W7zr?.[r۲?yb5O_jbs6) v([]Q,Ɩ x2ϥ7SlAC#2*v&tfXGS=kkt u\uUj*;Kh@h7}+csbͥ[mLX],Epds@H[ۑѴZ ~~pf, BMn8'3""2D#}2X4b]ؔګ | Vך$[5mcCdPP(n:Hо*X dcl[x%`,p*uؖ[}a6UDDt=6ǰ\mƞI˶gٹy:ܩcG>+Qr %2 x3zSz/}&w~9t}~3hx.lF^J\.|Gn,z7͝aٹys6?R7h@,.*`[e Je zRۛp ~R#kׇgoᜋ:F;;(>౲Ǹ u5XDDo}]\l }lֺEsͫ++F*#U#eqG^)RU,@r'Ò94\s28bi?AsXoHiGD-;OtOoɪ \%GMY2Ԅzv%xMWN <%:ADDhf.q\R%~]ȝ|[bE \ە7\],UMbĺJ`PWfI+I~NW޷`6H_K`X[%T͝ӥm@fc_GE.L6 yl>ʊ 9`i7HcbDD#3İطُ?Eʸ{va{G[Oc$ X֋MWJӀw`ҾnJ6Jzݽ+y-X9[""}@j/5~O$$R8 ^tp:j1ɜEVJ)&_X/|6t΍V7,p| Zy ^sr9]^{8;7o-`qZᶈ;%s.MwP 0 Tmm0 GwXɶH3$DZİ\ VtNK{ptqOlH%(Y`smbek?~N8玫hKDD\2 '&O_ ?'͝28Npd&2:N3]/Urq\ I38l {?~:fŊ̢̓s17}mnHb9Rι7?soQ5ד7:Fz``$"f.I1s[(qVxn+G,6Oecij1 < OY_DZ/o~ 4aA* $_iQDDf.,6'⏰%"Yl Xl> ͿhT#%99<=E;1X`Ox󖊈 3,ͳ3z8`w :Rk\;?܃s[ ؈b nz)L6hcDDdp:nۚkDh HaI([_aOf|,kg:%s R5vW{〫/c:x[,""=3`3% ۰X}}"5KIDATXl\16o.vb xJ \ :7_Js-| Q H\kw`#'ftdK0C""*KMM`ǒOuDDD/\n>NB9#~ɊQD(Y$s'Z VgRCDD\2x C@2)vNxƾgfp,"a"z?lH>t(GD6D _*FNhA$,b`q,s'""2YO)#|hg~g -9D /xᅬ-H$WiJSDDdXŷa %,";8M)is.0J3L'""2\j P^ =5uS +nRlAɢi""""""ҎEiGɢtDz9 |DDD2PnRlVY𳈈T$,6_醈HidQsn$6`*p)[`sE%""2MSxF0#7JKDGɢT3/`\ꀝmvMg:pC$BPtDϢT%s@#pJMDDd(Zp+~A)6N_vbH5R(U96za7liBxYnRl6*p#r; xgFDDD<zJ4JDzF#RUQsN 8{o Qg:p&e7)6T,J6'〱!o9 W1.!,Jpűjm)vd0{V ] np lFOд GN^vbHȢT,1w4G;=EDDZs6HW?.AYZ(YV8 L9w\QDDDzQXG2]m:O㞝,2?Tt9~-}!Xl>CoM}*1%Rm^bof{6#d`^6KDDdzvV1g}bLEE:%Rmuͫs˝s5}8!hGYbPv]pm .S(Ni~0cŀn]|xl [)"]dQ7zxr\yģI EK}ܠ7"SJ 1CDDDLGJry)Z GDE6zq:.#"""]>F8""HɢT}9N>JBIDDwf@blE* ֫;=~nȠ&Ǫbs%pO[<6.]Dh 5l?^5&M@kx^ V""";+ok,6G:~l%R^nw~Tv2}<!9na,6`mFkEDDDDD%""""""ҎEiGɢdQDDDDDDQ(""""""(Yv,H;JEDDDDD%""""""ҎEiGɢdQDDDDDDQ(""""""(Yv,H;JEDDDDD%""""""ҎEiGɢdQDDDDDDQ(""""""(Yv,H;JEDDDDD]IENDB`openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_7_0.png000066400000000000000000006600601413546205200253700ustar00rootroot00000000000000PNG  IHDRO.@sBIT|d pHYs  ~8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxyp]y{ι;}#]]v[ph;mT֊=N9;$δ't'55UI{TIImǂ$NlKvJ H=s/"i|PzV]XkQJ)RJ)sn(RJ)hRJ)RZ,*RJ)J)RJ).ŢRJ)RJ hRJ)RZ,*RJ)J)RJ).ŢRJ)RJ hRJ)RZ,*RJ)J)RJ).ŢRJ)RJ hRJ)RZ,*RJ)J)RJ).ŢRJ)RJ hRJ)RZ,*RJ)J)RJ).ŢRJ)RJ hRJ)RZ,*RJ)J)RJ).ufIc̍RJ 320#CJ)u-#cL 4kQJ)4M7ZRf 11Xҍ"Rjc3#Cgs^RJ|X> .kQJ)l޻OY).Xko5lcaYkRJ ˌ wųY0zvu,i P+^ףRJmtoV(ڀmfdHY)a2!MnrZø7”RJ ʌ 5y>("5R7FT_2^)H6Wݧ٬X1p;2x R,ẊJ)P#; Txljd:*Ԇ{q^_ `>RJmHI$ҔRu`Sa bK1 P)X}U$τO/E$ Rӣ3FƘ :%drrIE]RRJ)u ܁d,ET0tftAx@1F]RJ)3sGؽ@ޠKTJoxe` " ў,.MUJ)$-*ԆPla6DF15G+@֢cR@M;*RמjGdS uEά{NjTF3X8j Ā}c-#J)5gFb,b ܆ <Xw߹<2G:_RJ4XRR22Rϓz1 !AP\rƘ\FRJ]Id,39Gf1#Cy3PKx.RJm( kF?Đ^@)?fy3чX KR}d_hJ)eԻ 9@ 6qdVR)RrNb ~ ݎdsi(m'lVJm@K!EaQ$rƦǁZB Ywf~6 YG9t=H{E+~l^ Af-B,^Ad_a ,q`2@hYkH,#ič1n8'|\\XĞAFSRJwP܂4WCf #]HQ(*b243ȖN`x)4ydȺ ) VGրhoŮ͉*Rf4`ls&> l.&:HXAh1UXQ6~ĉ3h 8>쏮ٵ{cN U)n)Fg+aى$x~ʹ4T$J%M34HM!3Sމy$HEؼ~GRɌ DcF:l쯖__[X6AgeO_ _#3Hù9HDEV2^lRJݴt giGG6Mr]-: _9)Hq%u7 !3r[nZcLs;*Rhfd(ds;0޾>AhkG(ۚ'pV[A s~W~Ng| iSD,KH.u] tcZRWJ))6Q6?lhC(e%KWH~-@VK^ _ca4Ɩqo0j6+n)Z,^pgY҃[C h> %>~Ȓ,v/|$?"iRjC0#CRu#$[Ų9ZS5]X3ҙ/.2";tmscmM4dƘxxbmRJ[Ȁk/(l52٬rg@ժZ9P.mdڧ~wxnzX>8?٬zb2YkȬbI|IQM2|i KJQtLw8*usԐ mƘ}RJ W@rw!nH֞6\ 61T 8 T5qh'xVOK{cQQX l6kzeWZ[1ƜFCd rBb( *r ܁vp$kW‚N66)}e32>{ܼl Tx]&t`1쯌~lf=͏'X hx1)dH [‡D{ya ]>|HsH) 71eUjٌ, 7lRJ)XSӋK?fW9$8n-m9T ?u/}/f;.CLrϭHőExF7 j_##wȆuc{c.3 URd$Ȍylx1rWgW\/vfm{i7ZjgԖn7gz`~sƽs2x{c߮R73-/_90H\}vs4> ٫_Z[#]T Xz5$&Z뢔RJsU[]/5~ ^I6XX⾙IA5{Vf0ۚǏ=}_lq嶕PG>?o}Oz.mqo,=of-C^cއ,sqy˨Z)#}dF1FM0 Tg(s Y:SGFLURTfdl> >Il6tx邚+s3^68YM{l<^gr]B'f[x_c g,#=?fGl(HgBdf+t)G=Fmwsk}~fdy"22 R0N!tKk] #?BQ);UXm><[rAs8-à  QE ]^l ŖnwWJ^mݭsglwſVYXGW'w% Y EԭFŷf%pp7R(7O2ReEd it3~m`c"{}1HXG1pQ)z' lw!e8Re*cir8ւ_p`:6S\Lt6_|RRP=gyVk@ۭ{cqoLY)uKeoAZqGL.˹C%:IcL )+!#{!pnF:NE~%|孵!VW.J);E)d)h 9~듰@1I&WbcjS'+'Ƕ:7}ܻ--KS-H6d (ڽ7 w CR75Y|k)AF REg4])%dq-|"R$D $1ɰynuZ[W @ C)z'ȀJ)k9N]ztJ1<J5kJBl\^=p]nMݒ¦J{c {eORFOxCUG#4RYp gR KP[i,)M/@f ]d4$ME/Zꄷ~<[șHXk-R"ܯ؆,E$K)v9 _ 0ga҃n\c)wo*fU[{9=y.ovϻXT7ex~v)]@p&Zt|&elVJRX ܅gE$wdf ?d HB[, !,A,[k5'׽~^[TJ)es7?p7R8bnٜ؍wPPcƴB6gt!5ЩRo m+Z!(5⫽L:[.}O=ʉ Y)nZ,^Z ؄B 3x{"sL,=݄(ᥖ؞Rgxyd3YIw_{g>쏖ǽ1fڧw'v$ǽ1w zKr|M+۫y]56N##dJ'RD#PAԐXx` ) ~n3\I!RJ}Y!|F%^ u`&ds[7ʹ6r{+>dps|l I<< a6{cOܝn SJ[,h؏ g9UHt\>5%~R0B էq~T #5ũRJ)u3#CݻG2fdF+氉cbʐ.YQ쯮ۗ|Ω޽כҗ.?}۞^3Yɜ暩cw.zkHgټ ]<'ǽh^RmEXkƘx 4RF4tQ1 (F29 ԭ1&zO t aE2n )<^ ?) ]UuR)-W7#Cxx SH^v6'Vf CZdHZ3T 2s6@VzZ)Ԟo|Gw?3E#wTچd l~~iR7--1$/=d(dhp ?$ t7,.!t[}:|adoF)*'itOCUf~Zl&d]kWJ)n(32|<{h Y9 &fedstTlqL5q`·^ b-e0.yȝ?ט,&6S2Idxes.\C82. o°?r~kJ)umhxȌ|u?CFH\,ב(ZQVTY޳>3ݵvxw'vO [DHON?0[lOh~KHV<7ZvkfǽfRꦤ|d毎4مl2)uA{oBJ}iFƑk6:5k P2Ƥ{cMnRJj~G>Uʛ] IDATa=1qR{ޝ!8-ulҏI?lIǘb¸j--yv餗~p,%ln^\Zt{at({#=y;zoͩLt/YmriixVdo`l ˄]mhe2 h={md0Eds Tշ1>cȈL|VA)=3HN@K-3qpl4ؤRJ j|OD2jR#w'Wfd.pj`?OQD>ٱ(,:;vI4R75-*ށ  E ^Mh`bij@FIXRLclxmH mHXM[> nzRJB\<dk@CH>^2+RuuwF 00WZu',l߹ԟG 4#KT|CJ)uSb\Ⱦ$~LCKZ +-<4"2ۗE,2R +?5d?𢵶`_D]91y+ٻZqRJ]WϿKj2NX6\TE 4DdDyjXHch2ׂ4qaWLRm-%gO>t=km3=בƋl:~&D m3}Ŗ:7'^<K,"|32.; >~J)u~-EhzW;X _)R,%Df5w) _@f)AKd4DRAЭ]2teUJ)˴sm6Zima 5ƀ9n.cYC2}k*R jٝ(:?l$RĜj53ӵmCf*[hn̬C_Ͽ:-Ͻ]G^*7|em)?v7PG=ݿ'gs3>oZ,68HSGilo ?<xPH~}YzZE0kx OSF,5DHه,ѹ B\R@΢KYRJ]gw=uG\y\\,o6XT2Z[}Kn) ^GpztjwkOړM{~g1]7>}rOvwn3@߸7ܷ~SR`8?u6 {?d.O"\wRWB6Xd.>X| )~X.~ udYaX+"_o7DއY:>`1u`f1|^~dR}4ooksOU*.rmHi6{֐!G:F9L%fy*kM; 5hN[Bޓ}Ʌ\m?a8O ,NP9H8pg_"2%)ؽqo,@{/ܧ?٬zi"I.p+"KQ)(V¯ {lFnǐ0짱GFU]$Znk>Rh!gYn g \C)#v;ٵVlB-:MEߵÉ,JZ*XhiRf, N󇻈-: Tɦb4p ~,׳oNW>:&U&?Mn[[zǮ9bv(|zg8 Lg,~[J)u6dhiA ShcxxWE .dZFkEf-BȬ_¨ Pc#Zi֮?Af"z%mRJ2EVǜ2E4ԝnu[ENdl: Td::R(YiV5j^5нd-9S>\xoSه[||V/UMM˱ ؜{$X;}oXϙw37 ٜF2J)u٨{8AfZ=HU|Mx~ndClC:)95AFف4Zp oxZ{vR&eLg~fRMszo5$-)chr\ gVn J)Mw6-.p_l*BcE)'-M{"k} e4ǷYOR\#9ĺOB)M2RO&ꉏ>tW~!?\K8qg77&Ns+XjY=\kJTfk8O'Ki\-Z{rVz\c:'Q3kwJ-w{7VcX{5uL-$sHȐ Wf? ĺJ)u3ذ"Wp""9;wsőBGY:}w#GiAN!A҂[lFfDk}k@cȬd*| 2ֹks)RBo/l*wcI3ֳ֔vi\IL&XD!I#p 2MEkCr\.Ck&pKmo6}/޾Ǜ6Z[T1/}ĽA#e="%`}6 wfRۨGg` 9o~O40V#b5KH 2 o/߯"E_.ftp1}+s}km,{.c6RJ gld;U|fs +?ܯbHht)d ֘Js-$zVkElnԖ7Nί~+Ϯ}w"hZ݌S|Γϭ-+]-[Zs@=[K߲qo,ps<{7 ?K]~XN"ȕ I6 i6+n Q/ڳ,I݄oyd`Zu$ 1+k4zY:ZF $2>v r\X1H _V/-3RJ2/÷Z^]33JsgK/J-OI.x~ {7FVa Ȉp j@SM ܚcLdaq⫩{ ۪GWv7nԓ*?=vn3}?|f؝{@i"[DXH;@Y)umb1ll،`/ Omx[B4%k% 5 J2 z,9c4)>CcN!g6^x1Λތ1)~K*ڀ6;~~Dbs_v'sKKN5 97e쵂f5!X[; gIdSNTXwjMw/Ѻ)X z^rqy~/]񥶕;w\=O=ټw_>lok6+6`T+ۑCH֍[?x4AV&4mdY"{*5p)&߇tkuncHqf3ƘVcL?:fBfG'%J)6.X۽`_9:3s-g{%n mmRN_t p>UJUfXjJu+r, d}yS;ӱ2|ʋ>+_O|Σs3=~bN?t* eb?:3{ci$5R W,K1crHFQ$^BX,#_ɯf `Wx$rO]G<ҹ3Id1̆NXk9&#,$kd.iJȪ'R|;_1Y:uWZ+^C,Z9H-NHȴZ(cb1j~ʧ|!UNs]] bi)4W<Еi}Gz=yudNU{nԶ<ߙuvPW#ٵ|ghKSzO2)al2@5Z`a^KRh6+pŢ1A؅,GEfZw.4vKkEW}=}!MxR̟Cc KhƘ R pm;t|&ڡWO-e 2R6dO[]/)YKF5Z-Alme:.ۚݣzy;oNw[](?ZJso=۴}gfd??;#l~3,M o \ۦ{omʪRj8F +C!TiU#:J ?KD%τ[s;1uI˾g(GT8sY YBf K)Ri彟X)/ɟͺݟ~mduNd>G.m˅UZ4kA@2 /zj:u7!櫯2y{s-`j-~NVWu>џX,;Bf>K.֜M><8ɝOռ%^b-V>{c?zv4Mj6+n 7]U? 2sJcbtnﵚ]^ ;ʰ22Df'Hhd*r&d[x)4אeS@&xL"]@SZcL<|b10RJ)u$|si2sŚw>ۖ.y` k4#Zs>ͦ @Xۿ90XY]%r+[墿lNV3ͳ‰&_~ó[Z usB|KL.n[R_z'}F'R`z4Sq)̇QqJ)n|1V7|sVq媡˚|j#qhljq0rpbmٮUJAk X^NלqWW֦:mn]ܿtKu_&]ٻcӯ%VK-ұXTL\~?PN C2d|4R7X,Vјد"Ei;RM" "іb0&?؎;\Dg[J#|Zl2Tyc1 08jhtRJ)uͬT*mm^k & fڳ¬jn!>AP|8.Z*[hJ϶AfR//YccsTO=ٚX^-ʶ=JvW8g{sRnLm.wwLT+sCUZVֺj) ƽʰ?Ǝ}#0NxFE{c^|F6ԞEcL^:;xEKI 2 3}5d ݣ~Bx=A @xx8|%Dv?>ip3,_&#Et[æ8J)5gnw֧&ҫk+׍NHtqܶEMX-*a _TR؀N$ a)Mج0~X/ B jg:Wrɕ`pgo|RO>D8[o@{9|Ŗ-'b1Lx城&J-dOݗ8V˓L/wFg]6{7!})]qoZwmWJ l" <I$Yڐ"1Ic׳\[2* .וE:9Hn׀G ^sB dq)*]lp6qc[΅{&RJ]Si>?Ujwȶ'65~ +z u'QjUc'b@,3+Xj>O&s *MTlk t2?z6{^؉ϏI\IzW~Vܾ|tfNܕEfWƽ 6%ݏuWͽU!:J)mb9Ec݊t_zgEaFՊ,思Kya/אq)<aGH788XE[kKYKJ)1ZZ_(OXַ<[s;LS2h+q<O U WVHCHȴP,qlX3wtcOɦ3uWkd[ګ$1NsSKk}muZ,Z3g&ckg꫽we<ʋ/MOSYtgn}ASuΩSM[WK4mNTs׏ȶ3;'e~/ aTY)ulbdeBŋF{n^^stb!sC! IDATR`-RVcRHƁ##?qإ**RɶkW_`bl"ZNMZ,9TWq*^``X6`*JLghI8RԨ3EzMfubv.Nob:_?; xYǸX-iuf [1<>4[,xwWV;fgWMx|9u[yi1]Hxxvoӟݧb}Kہ-0G5R7wt.?M"Eas\oB0`[B w!KFEfƐem^CV,߁581])>c}/[In{{tW;I$8cymW)qvLxw>.'۩HWܳ_mc]80+&A#9HBqZfiZf&ő5o ]]#s~b4i^طpҿķqÞkkdvm҅k~MwRNGeg-e͢BBYTjQ)sGCH2ۨk>Izhh5TJc8]gY1\RNB%!KMyH߼ww[O4\!-SZ5۸5_-jdVqd2@{ zL 4eo0|lvɈ.g~-"-7 AhڒV//jeYkO[Q?X+h]gadׯGNBk'c{8ב7\;IhxžV:: _ 4+l7;ogY~q˚EAu>TMe(x; ,x y?ǍaTwdshDmT ܉hS(伟V 42U't;tk{YEbR0-|o $L$x#fA'J3(葆 $C":E6bsZ ؓ8dG˥`C۫Gw7_k%G7Š+Yk+÷>3Wl?W EҍR3ɮzn{'dٙTj]Xʭk7dj;ULQYD6|hf^FE(CehrY@r0PE:eB+t2P1@5Fi,ؑRBxZ5O ijNNNNNWk<5Yk >!ZrsmVd 5 =Q&O @ύTd.1S0ƌG,GrDP  R/l {L#6%i =_rw%\tg2jV8E:wCn熗tgQ"'WÿǺoF^iQmv'/V%>Z?jrFOp`[F׹ksNN·ƭl `%a"o1Ja4>Y3*(a2sϢg3]oU9]?]2 S!<v1RAES6|bBԀ⚔2~`^pR;j$#`^Z<}ݎ: \B:*JژGu7M5;Q[D( fi| 6)$25meYN/*QӅedŤ{DDI`&nS/,*j>eQĵn;W3RP kiLͱ+ގZzm}}pt<6\Lg_fRx{?o?.m'; {ltZ_:yZ^fWht[n͜[;UR5m~ SO `}ǂªksNN·ƭlp8/liQf2vUOwP55/]:fL装RJ)8TBx"b/tnBiv2l>Zj,Ǧ?8xgVykdXy? `qj$srrnqnsIZFSLU*%Fo8J'-լ&B&pXB6PU'( |eL(#=2$bznG0o.RwAs \FYtz6:Bۋ\W 2N,әt {v)C"G4&CQ24ʅe1S*MO#&^m s@Dl␉j˘Ghv3gV)KS'=9~z1Z-&v;숚pЍJ\ ur'O0(;h?KͰAqʑ.r .a$Gz'e\BqHעΰqorB,-on6y҃F{9^mo gvgW\uP7P}s{@GgGF%=65Ru/L'{= [y򜜜[[,0H?YDp#8e|]:2y-4#j8!1c(z m@J:* l| nLЍv#-oׄ0ﮊkQ&].^EB]Xwq ːv;FF RKO"EJS׹5ǖ7:&fn%GH `+S,#)$u ]DN$A= 3#sBru=8wgf 'WkuOZbyŇCoUm{b[SOΕ;f:g#YDiGw|RGNNΔ[, !4:3}0SN )7JC-AE;4:* Ft!) U TngJƶ?3VM]mťz ½ݚ9L/e#s)ʻ+Alc k{r/lwyJ0k9 8D҉C13pqBEwra3xEnn֫ԮF+_{O998iniK^?>rU1˛isl0?MLHTOڜĜV3eTOR9q?FE 졢Q+QǧvQmRn^:G!pPir~B)P$xfC'< ɼv==E2EXed)=?8Z \HITf4pj i% K4d:AakAha$ )85"]5zvZ,K F޽j<^պ{X՝s|O},6Kn4it7hLs_;V7ܶyLH px;% T^34[gGrmPev0sϣ̣1PK*:|e@Qd}:/jXsrrrrr32&{.N&ˬ.1C"F0*!3&«pqK ,OݵhT+l,AĩCЙ%e%hϊAgzg aYrB\je٬M /R xqu8;'7J:?ޫ;}e9BOۦtJVX΢4 OuWz|KW/|WǃWEis-''Cf0T?E2C@JQQ:2*bZFTrWԌ50)PsQu/H)d'/DH{>''''F]ʲW[_B*2C&Nt 2"+]4EXEŭ- 0TM6?GheʦW1_ jPQ6gN4gqk$Ge]f[+lwq DFƏCʵ#F%陖ȿ&"8TMIz[ѝ#w"z6SKM'R!k,H Ҳb%)DQe#ó&]-w77}wmEsϟIyOP'ksNNP[ ^TPC> e{e(ie$#T xjh:(nژY))aLT:뛋5ZeTr#''''f SkGI^l4L`9‰$ Vlk籶=}b!(NM׮q"+˫fPZ4 EK5 f٠lTfh=',tCί_6D5Zl/,ѽ8qe̻߬lRcS`;Ƚ& EZ`)^[ZDO:։/l?(thYg?1~^(m[?0''ͭdK4Nǭ3hr2FQB2u=TQ\_I-ꛋWk= 磞78v=)V+ŸcZnҝN!jKުK`O=6[]3-7YrIk(q3`By8jps3_*^G&(@EO:QPkϡj z"yD (!CFB]J !YeT4SNKQ1'''''".G F̼sq3 ph_{c;B c@gɲSfKY_KxuGjXh,Uk #^g+W 3i@v9Ō㶹y7a8SwKˢ폙K2Q#,AL}7 _ 0CxPgpMo겵 ׂ}H?Wl^7MQKlGܭ?ʮr{keu!&+%;,V6SO}tkNixl6'99997:F݃L~ʔ-K @u<=1T7QTD?r#j3O wP)GP1/֤B S,Smu8ddtVQzd@QXt\}H5`tl(4N-r~]_H4u:Is* ˡoqlmQ4z1q=k%kfB3'=GZVv[5*v!ӄv1m<&Zv|ahr~qٗ=HZa+ Lmir_2Y^ :ZGuNgj]98k<=GM90X{쳏g\srr>0n"e\#3kF3iLQqt\GQ"b9Tt?ǁ_@ER[Խ:j~y hJ)enh6ʀ'|:[0ɢ0 ޶D> !Fш   e;fXxs3&&G:Fez[#9 YTi4 IZ.''۲q tjF-LCY[q5M6DBˣ>#kw|d/5害U†7z:+8"dO5ww5zx?'ĺN /h?t4,6qm?;^O_n|쳏gG~j̽ͼX999oV01aȢ@=e#+:g!*6=!>OO e7yrrrrrn 󏏣刂p2e"иM8ped^ԋ!/D#^?!5RRNèc evw1mO?ǖjO;l>1)e7ajA{4dw< вMzN}ᅐLd~S t9x}oe׿ٮV v-KlkAPh}ӗw4;>ܟbQvp?W0ZLsY D֑43#ǟzRoBO~᧪6J7x:999anx+ssvB?ѰGe <C!L^hyTb0*x|!܏_s-)eN'/RJ)pSJ9zۋTiki39999dYet ٘\ҋm(،=G]I{O'\R6,8hD!8>3GXa46, F)Y )LF\ߤU, a3[#Х`$&` Nաz$hdbC3+YىʹVj3+Gz uv#%EԎ}Q˭F?vFTWME|gw^ Y/v]"Ρt@廿sF߻۶ ?\OȆ{K_~'>>gs6OksNN;r+ [܇}" e3y3P/F5/qe uT-J=mRMMT}b86BCU@u[uSKD)"jF8=ײ{:oFJ۔sQ4Kg{INC 1qk`' ®|saE{4j"͢I(YUYF*tcvKA72ahb:+&fIr=+.:/&%a;A@j~$N"M19-Q.+b2{yjQ/=84g-0O8vJr9J,R2)O}3v/ GKCSO=sљ3Xg|o"O<ު/<&QsrrrޖzԬ4Qx7sAc  ށQH+(s(Qu'{TӃT+(92+(C6FOx^Je)% [ dsrrrr>HE;lW qQ"N#dbs'턊]dmpׯBXF!W4K*6xELЁnu5J3nZԋd"\LM|̚MImXrq׸F4ݚVZh;nMKQ}w-a_U\3X@3e#014;ZYBY4֕T|Q=_}({I6cͧϺ,i/h m7@=ŜwY(S(PfLC5P=;UTQ˨E|UxU0DE'(PQ"pqℨOSDW K\Qd;XK& 8" FQ5$ᰁٔ+)3v/16ɐ+c~VlD)S.XR0S.cqҬ4uA Eh$\Ih ]Q7E4fQJ !^@Qެr5Q4Q?Pvu*G=y?o(PQJ#A %)&(82kUeF 9i̟:BǐRv琓@]}}S y<9DÀH M`7a6lݴ˘Ll P@Aȱ;ɄcKsL=T)2þ4\ }|1 uRJGKU]V2Q*W *^1/;]=hlTw>`xxm5Tw eq{&PcםssSE񹓛^nP`2s /cǀZGEQ;=QAʮ"e2=[S^rrrrrny$y}SDqo4&u6cv"6maᘸh~ĥvƌ6;̜F]^zs &%@ IEFl^(*CJeNM^lGģZ[2)G_BfЄ(Io'Y1VRInpDT:2xxw#ҥ_-',X(nDwm]YVa7žlqlx&0/Pq;z#.ڜsEx>?Ƞ2n@p+}f;aTpuoQ,:]έsrrrrni Rهױ4^OŮ1+VIҐWcL-6NI90K:kկstv.xH*0VXkiVnQ@I$@'#6F}2 Ǜ-,!Cߧb:PUEVe&ģH%͖VCOz8 [c8+ٮ(Is?*uDl INYW=q{Cޑ.ƛBު͜l>o~ϕQ}V ETFo ڜ͢Yࡌt;Q!yqAPCT1*85}oxsi xWOMo 8ti lMzrrrr~,$]Xe-zYsǿO٩haQf[Z@Pax.~&S0yy ˵y>tl8_gZLA\Bd);mJ"Q`;iGLiL⥫Pj<3KJy@O5FGd"", SF,D;v(TG[7topV8biVǵ^ X/ɾ/͡kt,_P޳O{/V&uY 1Sm>k<">~>/<^Vo  m~ 6_fF ȁMQPQ NEN_/2:n6a?t>bFZ{뼛uF9999?TF&㬠ŚNŬۀ }&QS|Gz+Sq &Ֆ4(tc89ژ5 0 3 ]F2:{4 ,E&}mpH66qahͿ8Vnty$I粪k̈w=#1D&f!Q/n{PZ殘+Y/OM/9z} 8|ƵCTFg(x03#V]>zڜ>rӚE!D UY9H==Z.OQ5OeP0EEE9s()%A\G=C5QfvVnZm!~UHK5A2dHEUnG[jW8yBPȘչ~EcR BYB& .m_c8rqZ2HrtF} ed@,ךt&cF(-rKf%iQ>c&s,/.SfmP/Qڼ쟔QX6?~b?*伿ܴf5R5}݌HT7R0&ECVPtez܈F.eT`u6G `!v~BQ]Qq QyWakE)iU&i$ Ce+ޙx!;FLjWa,)P,G8{zJ1gizB8SJ9Ԝcay,UJNW酡XF&׆!Jc%dJ?H5L1EĈ4M L0yDiEi d[sָfucCˉLp +A6/F݋_ N`_m*.zιk+vxz- mxFsszJ=hsnsrnfAE*UgxPp4HAQi%Un|_먈`e"P&0ADz:e`κ3]wU ٘6B8$H)|L8c9>sqyGv+UыVIWM& 6࢑;N(&GfolFQ*UBǜl"I2pqw ?8BwO*,jY&D2u . tSpam},(L&a@/Q:QC6dzd2QY{t0mώ q2R7+2TT1Ɛ܃A=DK`82צK) p.^ lRW1os^>L,9x p!L#9999?|sW;w}pӏ 5zM /fYV Ā x~Ĩ A u¡03KL7+;?oߪ^܊MfZݒlyXXv0Ϥ <@c00ɘ MLB Dt< 0L;k,ّeYdudYg8͖Xy~{~ۀޠ'4@E4l h5ZͧHMRԷ{͎m0-J]6{2 HUgg{Swٚ%Q:㙞7|.yޏ\;S<VzZWDOH|Hk1P'.D>eNL~/_8SږZ(uƜÈ6W__x^)a3'OmNHg`#BQ[TUPp%uTa gn[qA(i]TdVøu3ڰWC]a.!!! :ޕ{ZkEesR׸Lɚ@6)5<}7Ecev[0-R4=.vyr'ЅcӓxnCMI$8e<2suxggb&H~;1B)]a4_z8d&AgL7*4maeEaܧx>em?)X e60Ewl7nm=_ʮ%q9G[NɷuӾ[gfMd~)xVZ7]j 3\s9QlgzI\b!vm~@!xM8D( T(P:;XBuC P4|VP:I)baB@c\j#!!!/*~쿿]kL=Q4E^N0㥟ha`UReDf/=z-A$9PePfY@J1AY vdK|a;pervq; r<.LAfZi)q3;9J=hÅzF߫)yG#V 6xʋƈ֊FF(mRv,'x˱0˛J1!5;m W Wt z!xQ<%J(8wGۜyy/LlsBBMg3( )1QbgJSBQ7#DypuMfU•n۴)# 9;"p?6s|Ӄy[n '=Otr >-Wl*-;+{?_b§4i؛͵ Y ((QLTH jcUg q(C[ʡlXj7q-/TB|H)hTTar:@dBBBBgp t dF` |M6}G0PtMX喰Q1L䫌eGDL[,7qZ\Nh:K9E Hv:Z0ˌ`Yd0 O,0;zde}+ZlzuKiκ'uK1!= L8HuWZ63ֆy|Hyl+~Bˇʒybf<"Yl[8o}W7ҿ>SZEpsoyyN?yL3G͑|cw8wԑM2o6~䝅37擧z= U,Qo حx:d%͠oʻg::\ڋ%Q9VQyU U7PYIl笣D(jRޔ;q )7 ijޘ;Q_gpM{'!!!!!nZ0qC?hkgeky:ngD;cfASoz#.o-]Zf8NcY*'}2 B`RasrT"S%2H^Zf=_ |-T2t_HgzhuVYdS%H i셙P ߒ;,L3s=wx۞O.}=]槊 V6!'L b=Jn8Z茇ƅoֿ@mbVy7o(ܛ;+g.s􍶹fg7%殜<MDzd2&Ffnt3]YYHiV.,{$cn۽˾xSsJ+hʔ-O[;ZY;hִsv*}x牋7kg/(|6F ՟ қA4w>1xerV$D<+&3 υ|˰[FM6= Řٛ%$$<875>{@yҨpQ<UPC٨*0B c(*\qK(8zV>M)/>k;ޯ_ߌbGJٽRvRFؼUEԸ*-F1(.g/jX ?LHkјpD'G`t[yZڪVjG3L ld̐3G l ?qY'cj"Sh:!'tL'4I" -2lhI7kAZܰ R"+MzK_Vcqpq(bOv kBO)}GkX/$$$$0|Jf>HbIKKd,ӠnϏ=@#b4F䭅USOL< ~DGBx>!"aH>C;󙚙AH"( j} E>(:z2`C2UNחvGΦ xkzٝ2jծ;oɃ|z-)Ԍ7{Gƒ g:c,n;?IK'3/Ͻdg7lt8Y~퐉èXh1w, !Ɓ G65`L6J]+LwPC 7Q#+C1 ENY`U%5T.,,TA<܅8_1},lۨqR[A JY(؎׽CdIC wQt{RʅkgYBoܣ`r'POk*݈R !R)XQJ)۷*BB|/w5q*׹Gb!kxMUti-GgwM2HQ_(쥝 S]{\,cޟ]IEӾyΊj<|G. VMn=>lt1Zkw3/Y@tm; Mjy/7msRy1LÅ?9"w9+^{kq66'$_`gk gR),lncvXX@xf9ѱC7 ϚFut jV+|J#Q{I|ii~nO=;>FCK`GXdt߯3ɳa?sNQCN}n;o_͜;=9N_9`y29w湳28yJ,ʨ 91!!gh'A J ΡwJv;87P93(m6x]ſW#֍}pa ܜ8EibO}=#q.fBBBɺfd;X`\F*ӏ0p(Qxm%gX봡csH1FڴH69_#kڔ3mt=RK,F6} @2;1Iԍ 0::a?`<=.r5dZ6-{9'B2LkkC(&e :Vf܉4ixfò#WM+I+`յW':]A牷׫G9 M~-SxSWmssn5yDC{!΃KGSB̝MlsBWb(~bXz+ (/b%GUEQ`zb,%T +( P^:'Xa 8Ќ^Rʮb^;W+BXFQD %))Q>6 B /X_mA&ʙm3R^kd q{|&ƠHf5)"+ATuYx%14S\ #b% B8f,6DGPhl1#jNaRʤ8<:i9ȏ )1zEN-rroȠFV#絝Ԙ\YWmxȟMt ]=y7mhkٮJ|^K͍gze_}82u?/|orǹӒSlls[kݎ0m>uKEܞ"xS"TL~<%@)t* (/~_%mT,BPF*s:c1QlQyT-wQq5tF\PA V~i"BTf!jޯ< 9B;ZLHHH@]?nHf05<3IF+v:m]Lhh lSK۸uu;m H!dLFH[&F P(fm|@*#V• .m1b#htzovv`MU:Z v)ȭ5ZM9h u2{|eJ ~'w:;\J !#hlssƙR[//*q #.ϝw,.s/<' g8qbg^G[IJG5m=愄;<|2ﺇk5}Dza%7QiTQErҨٿD*d* . E1Ҍ [g {eT(-g/P^RboXz@^[}'oq}{ MHHHGȵBC#06a!D&fl5\7:H s\\3I V~FM{ nj |=Vt*~P ,cY) Q lZǠ$(f x ldINeE'EPwݦDM$ZZ`I]lVQ 3=l7`)L]Ζ4؆>Nt2x9eT N^glRksg?mڨ]\=Fڳsg)10ϓm;lBM}IX-ɻz׷Є'UBz'B ᬕQD݋.H`yՖJG#)0/ bJ=g&s{\0L)\W+>oUm5zSCya[w+@+(Qcvk[;*Jܮ n f;jQ_<5۶k~#mg]Gg^ˣjl/jslrm^ֿ|N)"Dls*sge)1@M(bOߵي`RCJmsnyXCb>2M"J0yAn9%Q}o IDATPD{ %csX*d4>wx>N97b4W~'Gh(vXab#̌z I!@!3 cdYjk:}[CϚ4zvkDYA.t\Z~1ьi9K>9'A;vP6SM'Жm syGd-YA8oK\hgW+NiTͯX-^=ѥH:=`ɍڟwm~,#_6^6(ۜ{!xQFcg^݃srX#a^NIs+ҫ8s{J;+{Խ2[ϖ~ ~,fPwˮQTQԽ-n IBi cKq >^wRJWqY8HX0s8l|()QnWlBBBߧVoЫ+_ԂMlє[@T`~bs0Ne wRLvΐΤin v`gMt!iC<;ixhvu[rл^quݒC4iv] r j9A-rfcvimsfvU} 7v?9~?oqًs{_Wn//:_6^®md9W'bx+[fw<ľr~ṏ6ϖk1[/b3)%p!(a5kWAyAQbu 􀣨3 Ԁ@UKFetOH|[p=r?[ š{=\ڳ0αPBBBd>iigr 3 B ƏӍ>ǹWluMJRq8:Zmc621s4#x}iLii~KpGN 'G.i?ǘV MMh±pCșӞufT^yNVw~_9q=4K}f,w~}tNӢ'F_g?VSz^_=P\#Ɗ ,~uEUęio;Ș-]h2?[Peoo{Ojm~hb%A Ajas>^>,@y ۨ< 5~+^gBӨa(8B??F|URBmTHGp!wd !rbyG^I-&$$ssͯ"aY^ ef|QsCXX6emO>')FE"_;,wxj( lSAPȉ2][LQMh{]vtF1f(ìij;]_n˗aFc+Sjm#[^=rVʿbF\Nu=|F{r{yj+c'OT3.am/J(aڋ= E8wg',s~@)qn[/Ģ2BԀy0ΰzx:p.y*޾Q|]DyS(x(Jd-BIGQ- Qy춰(!TJwڍ(.S{ se7w?u~&Np%#L(aiɻk& Ƭ#FYgc8 l3syھK/>cY7@)4͠3;9n V;Jd)<{ͮ6YfT *olo8k0*]G:^Ю;9Zih^̗#Nqҹ?K ٹz;K>h٩MwW-\0gwozGdzw͛aT=5>.g^ng^fK't7k?ڷ.&B1!fXĩ\UG]_0>J <v6:jPBU~ FZD *'[Fy-5TJ8u !d*3Ή6t.!D B,~;a-\M< 0-+2f12v[}-{[RqSh7volO #a%SGS̖.kk[ǎ]:~4]:rgw9B%y+@4RLa atb'2C=JaZZ;z^;Җ*++WTBo8fY,=#6/m[O3'_:oxFao3LZڼwwI=>Ox^^^8AekoB:}=t_D ̝@oy3D?(<%@rvE#\qOVo'O%nt: 9yJ+h#@ո ]LḄ_{lsr RB5B,DC?苸kNa7n|C':X<.`M::U@U6A]@yFy"{x*҉ŢDRu@Gn|7ݴav-E`BqV6ʫ*8?mT(o)Qϰu[UT [=~cPݶOHHHaym09ek}ƌIZ$=Fs&Q[#k΁")ףm# Q5z 4Wd,!<6pf9|OK v ʣBx}]{>؞.-s哿8 S huPԽU\e϶)0wVT6a>zėV6o_|kS.4yAM~msstXUۼD-B e%ۜ/bq zP>7p';Ύ Q8P j_Dy!kdQ9*7w(kD۰ 0*BN,ta Λּo?kB ,0bh5PV̠<ۨP=FCvzʪB2l6, iES#eZEEvzdl19^"kCֈF&Ai=tҔ29>:|)G4]M3 DNtk:iqa[ZDyK)tY;`]<=ZѬGMD6sw3S3_ZO{!'Pn{gBBBGƷ(Zij̈9M'Y/3;BJAZM(O>[%{@"AT r@ҭuo.+En1deb؂fg)4l-NGsND_ΛGaNO a\bm*rϯ>5STc&=~9'#mnΖp~?gC0UwU\5Pz+'n6lpIb>$E,P-!\z5|5Qp9^(P`cjBR>J CByM1;. t=!D#kB^ߍՓR6lEJB|%6owNĢ*oZ/"ʵ].eTC=n,5YFyM!DI0?JHHHv%=:òJCJf/r6[uja!Wg34gͣ+3o΃Y*`Wy1*qɂЬAI.EOb<οtv(h9je?ݱ Xf I"COFW1<¸y+;,~:Lm0smkL;s+7~@;,j_5{;O?-:~dgnؔU>|lA|m nвL]%X耍+QD_,"&О_{&], Z0A ? Xdr,pQR>yM>&APB*?G7x\u\XJi]ظ!7g(@Fsnu ?8u/ ku3:-{m(!$,(wjʄ?kÇ(+pOE{Yt2fڅp`#`zSY+hHV]Uggvu͟.3j#y:iA*6T@ltCT3;'W)u#PJ-0iWCF-ROG+!{W2hۙ)*M᱇kNCRGޔ14q6<#;@wpbfz0%n 6 !2g, j3d?N܎T*nC/y>&Oq;wxxǏP_cQ(sX8\}A|$`"r&‚XZbLRQ `"L(`B3r}=30Xi0 }+40S,]6q.΁M|,i:1[i?$.L<*)g螹Y}$O]#lyRj^g,=s.=uWÇoX$* I#&q Q=+v!/tC58Aڲb(QICEtT,#ESwvw 5մa.\b@8 )N8(i8zjIжRUkՖ-NɚO ͯ'V"sRV/ݸ,/~ύ숾P!{j@IW|C9{XO>/S'nj[>pK0n.t-R'W>gMfڧE]㜋EmEl!t SE<i-{;H[eFr0#UB{ ;r`V3>\͛DT&}xx# ?zuW/(x?7&`5m&^&LcL|)F1!c,5@9z`X:bb thn@RS)"ܤY i IDAT`#A[( &Q WЩ1W/ՠ4.,0jBH l)kvEDR_Σ R" hzؚq \jJv_ft$Cof[%>D'*hϊ6fTh7?V_zJ=c|hgl.Chd%gNB :Ƌ!Qn<=-vȾLq)0 lqxb6l+'p qt wdl@>77h)7~h(7M=G9|\.kw+_kEx8`+b`#F؊^Z");V_S= <8ϛ<"/nѱ4y+lہ"xloxk.O3cR_"N!f3֚{H `M\xz?o[য়? (ӵSAKX9hz!usX[UNr/A VCދjR#R(ÉD),:@rl@4Q $TzQ.NLVp%"$[HH*Rٴ~; PJɎpPAK%+MisXxw잻sMW2&k{,bCkKtI0zptLsSȟB6K{_E#d0eyz o~o}?gd{=+NH*1m!|#ڻgY<1Pq*(#َz37$g8P/3H1T I!+A(0EZ&%DZTTJ ǭYNF8Ge[tW֫npDZ yU{nKM7ծ]K.5Us?z{SuT:P{W` a涗Pn ʿ{f|JVZcuUxe﬙bT[*kjO]E{9@Cr笡& uXtq`ͩ"ZRE\ 7{)OƾܩWLKwf/ZZ|Jx L&L`#7yi%RE("S-ǻ-GHhxfpYGKl$8aSgw ZA,V#-0P< &cy׫M4&0LsCELJPS'o5a|5~~B6i([Ӱoɘ !DNeyl:[|yQ#F"v kD: >|\nD"9A挳QA Ф H*"$=SJ50t`w6P@L%6h(ޚ@2!2J1P͸s[P͖& )eLrЩC$ B{sR 1$։Vu di’Y!Q{rsF;M#V-z[ںo F *=YJ^ܶ*\&[Y Xwxueờ,3_6dS-ek yxr_ꊶ__:#Ʀq!7`]L~0ɋV[ם*Jsi ]!u3Pz~ӯr>p ?;!G"mC$6|w& x XoI^,sD}l&(d k˔rya7jm{2RJĞM)]{s|X3Q&h,gB[`E0; ŭ[ ,l2ñb`p;! lewU7,f)OkE v`>|l@t˅;K'KO b6>Bt)n©O!$o2栌 v+"tZƮxe$` *!DP/Q)N$jrȔ@jf ڔJl@b4Y/r 2UmM㢢HL $ٹc玜e4MB%vG^ ڔX rhKƂ+wݲ -48{G?1 T`cb>j{'F FnXP[kpMϕg&{`|?"ȥ 2Ռ#Ń`~0q"`;pn0RQSE}&zǍL@ih1<¸1ևv\E.$8.L`y&Z"90q ^ &."~ΏB)7&0 ~"^_AmFh!u~ `B#,llm,mVVkZtr_9bms99\< u:WXsmUﭒiQZѝHDZZ_$rĻWntZuEURa.:1P?PzX::JAIa9e(H @Ț5Xg!,Չ\X2b QEU" Lj@  !Hn9Aݑ4ԜXS Kfbx*Viaa""DW"-`uYYGmEmTj+o5l.A6M47/=1٣l.|9b}@-B+G4yAA 뽡PK؂;W+rІPTƏf #`n#V@Gn͞0 UnM`ZUnn&SEjDPEr`9U>|ŢW2XE=m,,KD`&$/6zfGj6NxikL^Cy~>kox`+!\8AS>^O!dzLS ! Vm\5koј^[Iƅ} " k;1_,O[<5lD*G*($fI* NP`4wZ! B8܂nRD4- [Q7#ҌX)D2*Vn K']F&LKnz@Y+LeQTjF%+r^ĪܧĵT_Ym"^I/|ACv#0$:VY4Bzas@FuT425U7!k(`X:U QnBZ a1!2ԜT2[4r-iW]&Zeٖ-'$=z%M{u;b]mr 5/?6JvZW/OmrF*h˟z|^xq0~izgJpKǂޓѩO:c2j Uo?yDIE{DIv*7{xfuGB3kp%w9hCf z!oRB81Kxs܆-4lfmls2I#XuXǟ|LJ)ej h0Tp2}s}QpيEe0e`-u' L9 {_0Q,Vk`;`ײ5j5e!A011BHd* ~>s| Z!ުb!dKsVBR楎l,ρzx{C V*7 crk VZ+\D7z#QJ-M{ &:&k[qn.~x鵗Ç 2BtjҮg.Hu URj0INJĒQE`bWUC#\"nVat`^7`TZpVmؒH@ "T\# Us܀I We񈘘堥4 &'i&xRR9 D)5۲t1b9+] JwўH5%!>WXE屖I.*E:Βs9) *bW')&"Ʉpڶ1w4?  ]|\:ޓЙ*bK?HG7Jǰ룏KJ*8j0=nx_7:E8t]?B+"f8*by1 &gU^;7ץ\+.J|a&I>|lV,m`#Xs*MЭ`}1w&΃mukR9`m`"ϣz`Ns#;FցRZ$X`b;&W{<'M hr,:J[ LԦN# V0q̟#ίr: vdaa'k?L0m5 λv7[☭:īÇKȍUwAfQAݭ"i!va+!T ͠'ځhBd!oBQ2p292 Dr4VC-%Z:jYGu-8r4Vhb;:uHȑ-Q6[ [582-7WL|dzTu OKv. WꆘYqo3/CPg?ODns>TRyz7DŽwyI}b7 x~)+(c &FW @NG[n2i4b8>psUZ|{K{-chp{~n^Jk6f/yJ6B -{< `S7-g[ @Fns`";F ^xf42*fL3ǿK` k`[&nIly~NtEpOUs\0yN, ;'P))Xcpx )QNRJ+>!MHA7?_KSOܗX۴A1 9z9^p=1^݋ C1ҭÇouCMB*wy;1эd2b$J3 A8׆NP4\ mZ]:]-ĆrQ#*,+ДlGwED ("P0M"!K0ef^Ɂ@,ōY ZՔȬB袭fyb 9 o&}n%;[r~l/=3 +jr߼hlQ:lX<7Z[z$,>Km,V`Qi `K NVq6w26Y^Ywpߺ Jj*Bتj`hLi0@;z#tl IDATwX&N?Vz͙0=G%-a} {Z>|lR,׀^ϡ eh.߯isL`-:F3`&1Ug wo4ҫK4S/JvI<긱0è P[%,nLnK`Jjkkiw[ & Kvֵkiڌ 尾Hlյ1Ço8P,k_ oA0: ea%! )UlBx%IiDHcX$ehr"^.B 2+2T:Q(%(fPl9rDɬL0IwЦA"+XŞWk H$~*,4R7B`zSQ[;>Q׮(1;^qK{q$۟$xOF,=a9_X6lq1%=͟'KOk]í^A͡T\%$n>d{7/<#ᗟ`h%I@@4`3 *Bzs/n^ Bx ?c͍g>&F ~-Y4DSE<~d=7v$q+">|؀R,P(`4lD?Q$dσv`N6zΧ/4?yM`L'N~u@;oY* =0,e`l}U%G/X#2 c[kVS{m(Ѵw<4`ɥ(yBHu1$}-uÇ72"A)"%V3 E[sABQ7Pj/;/Sk+*vbQ IѡĠ r f5Y6,$$]ATU -1BP[r\n8%7)M̷X)mąʐ6U&_ԅ>i)x͐/FU0ݕ@+Z; kPYod--k;l{ڜ }ǝcfw7 YVES{Ua&p7l?L+GD|.n&jP*MRߜeO1O^ 7;XT^D~ CcX^0>6Lr'ȝTy--cj0|ct}IqÞ][s f>v*+`+D5r'|~oz;*`eIKm2R}lEoA\UZfk3Z`{,.ꄐm6Lj| 2_ϥUuA?zNG"-ڜ){$V\` P *fUvHD:Ħ\GhjnpcPs$e`&P8v]KH݄(յ@4RZ70Kǂ`=y]8;S/~ikrٺr nꩽ?:c3ZŮ艧7$#J6~b&UIOCgJ'7 s>?|@jễi ~x5}U.y=x~x# [{sz<~]9-M&(76{O{iWϛ"el̓ b}~52Rޮf04 Eq3OEn}|xqE O&[l2j`n`/&$m`i78V2Ĩ)O}=) U/ $,M#S45&TR+r_hh!-E4Fc*%yӛlH6WÚ7Vo"޽"!xi!}xHLg3 )CP$`#&xZ[$|L?HݩWz[W?,݄0iG^0@݅R IDe$r.u Lz֢ )l GQ5+٦p⠊h@ݥR[QP%ZP()/Lh&P_(j:PZ)]]]$7=WhSMoO?{p#i?ݷr]][fA Zx'`;?Τ[TBGqұa_ormXZxof(tX9{բZ`L /  F/uo(lhh84Z+V1@Z=ng[lL".8z'T1+U+*71z1+)bBf c^3>0x=]FC'+~iH4Zx694 %I d#l2HO44xhL9)`bI摶``^5i/b-ZYx{\,xn;1Dyvha`&1 ǣ`5 [ `߰gF\.^$P@)73ϹLcl .gY3x8[n^L>|l %wDBЪv#҅HAK "<:H M6Zr6 C2NM~EPwV 7(hr$6X8L[m@ :PnA3L+u^M}GRTE{ZB4 uSf W%Xo=}濗KdW9m =NCC?p\Ѿ E^Doȃ%gdx5oJtL9.8s`>@[k"C;~\x }AW\pbu&%L-?'~KnmX;|~[Ǐ\-9UI!~>j[9͸,钹h &ofhs (ڮ3`TM Gh5?&\vE)g{=ĬA-0[2#_,UŚ^~I6gnS00j%և16q y, )BBo']BH`4TBHkisZ{n<ʗ'lծ"=hLSi8v՗4^i;ǟMXD]e0Mq!lǷ# ǯc-ji=-ɥD[}c;{{#r;OPjCK ^t,"vYئhZ;jmzB:لVbÒpIZ@BenRVl0bjg1%Ȅ<gvMH|o_4]5sۿ$Z|o_}y'R>JxsK=ŏORPM^m32L|呼?MB1ɀkDKgHFf/T>ah,Ѣ<4VyDZG64׾#UDk0mw=h]_yroy0`t&F5n?ÇƄSOаj(clGg7n;<¸y+Vh*B#1-S/^M"3H(V6><2ǣzEn;op&mZ}V<5kk7.`eq6%$.! A2Xyknu >|xש݁n0>‹$ LP{v́Q7C$ *ĨL2b0 4XMa.d׆aBD!Džk@QWUK&$gVE$AUMC i;!Ě^2x>4͖I)pFL./>8uK{_x*77\{h<7z0!1ZhCRi;fBƦW1 />_G919j~y# &Q16!.܉)ο\`4 (?6?}W[eyS31jȥ 2~dhtRuY0Ʀ17 yK=UИH%a /ܐP޳d9|m<[FC{ :v6#dq>~p9E/ 'Ww&R< ,& X`,*8ͷb(FF6Xk5u!׃==5#PE2 &b"BHkH9x6ݡ&ǧ\ i2!9bn8oXzAH"$X Ef8+7Dz @ !d Lk`y6/K`+:`" <Շ>.^k)FC 4! \hȴ"T"B$!*RRHB"Rg/Ry1Ra4D aJ+dGʼnYXN$DƒXUzJbY=$<l+V_:1Н׳\UB.\aZUO=4NM 瘋F\ɶ52q׮!G#-yaK!ދ1:W^phLğ d8=u>pqa0da&~z%jJ"+F5v>?~_dyF2Tmꃿ҂ioY9;R_[ZSE:vks+vp>TTZG(!;q>}2RELf?"r;V~ҠXǧ`"LX= wXlULU1Oaͭ46߂:Yh':` LtyZ cɈoKLm2ƏBlc{M+\ `|Fyyʥf6QyުޮqBjm׶IN/Iޏ g:ɣNc $n?2x n @i'g[>|t⫷- U}TiWBKetD*0ڳ P};@=<\C'$ib`!߬!48zb7nyD>v,PmИVR5M@:R@ڢf臥 =s/hNoؗqhn+C`<y03.X}ulOwhkg 01G#$WܼSM#nLlnOJ,B`o`\>u">`B/2K+^d3%{cObC3{bh ܬ{ղUD`k7`znifc3Φܜ*"@zBG13(8|0XM^?XfQًT2i0M7oe%؃\1(XJ6H`ik&x`+L` 4H!<V 61KB |L;m8 yBGī 6e ~r"F4 hX}H7\SN/h+Mz9ޏ"QPgH#tQ Z#v" cUk8!{Ag3zfz*Ώķq(sE{)ی5^X[cŒ X!֢ hz}Ϫ}XUfm BjYyϩSUx{w*xϕ{p.YBoOs{O k:Ɏ9~{7l"pJOdžU鳣64gV968W͕l6WxÂ4Ul"jc-0 F*A 0~L{pfv"44_A>'l Up,ўXzxN0 `lhU{{R# z_Wds(t ApZ3 D,m Px]"jdkLSRyӰRAb9P.ikn_1 QhHSA}\ˢ\Rh\X[D;Β4ݨP;]@T䍪Vv | GoM Va2u@ݜyÝ71ѷU0Z~\-aR]8gO% XN^cQZ1.=3)/B-j7sx`lAh> ua(bdJy(J64XQ0n([pRNkz53\&Kv)-;BM7,qΟ d#SR6nkkv54VfN?ű$N{gSjLq_i"{2}Thٺ=P_ cOeC`];ZA=ql$V~ԅllFȤeSˑgA5O? ,,Vb`-ۜ 32' s|' z?ɽ,߉qviܡn`ER [ag\wMz~@W"5e1`gP`fvb[V/y7"‚lr+b&RFg-☷S* CV]jdA9D3^{5fJ86ўPx*P^ |>9@ + v2篒4XAh?a Wf`s_MR)F7frguC UlY4@kWB~&ʵB hMHCD@vHTC9@!&(KY{cEsXˬ dEj b @1ƶBB48cuѮQI$}Ux$P쭆7rSR1bbzAy,)qMX̧yte?AdC r|\H5L9/ڄ'/R;qVKpsJQ@b3Бw+=[(yx(Vx73+ڢǣ0 9+b& +処!es mGm@ȡlB:4 ` O(T- |K .亷s4x3"Zڜs#˟XI8Fjp ľ;~Ktt[-%Qk==g?zAZƒX&#pQQrKoŽIض67r)Ul%cYtx[Q%Ai_ ?w6M_Ab@u  ޛpPhDL .ZT=+-V;ql vq &Fh# 怒-ՊY6[mXs(2[q(,x͠.H9cL*Hr;z >WMP_eKE23No dnH%h ieuHljUZk_ żޞuQB-J# >h8$2z+h,ht [A: <@A `BvNj~oZ\/ŽZs+hug<,'T0EZϤ}i^J.|kg' o4._x[w^5yzP;=c'|?(E[E0^z*7:"/nnwh,F;;*Bzބ]7l^+<糼R8FÓ^@Nqذ#}o"SgT>#1DѶJʼneᜫWְZx޴$=YF4Wb.5bkd17Od}^hPA(\9q~[\KmQ6eUNDNEsUI89 qVG(=d+>X+_d\³1 895孼fڐxhtOpJQ"*r1Dd (2ĸU{J0ƖA^'7cl¹Yަh$*(+UjUAZS}Mvq T&RJmG ufC O1bBA\UёI %0^A>ÐbE M+*^ Ana0]k\.Obp%gܹmf::jTib3%s鸲iavƅ3sLG_I {Lc/T]ûN$yntgї}w$kYjfmݼ  `ވ̍ ğ јUOhBFx5>8o?|'.Tz1-#o-6.|K[|W/ZG*"/҄^5FM&@6ƒKby<جAcOJx?X^w/ #JLX\%.~O3?[{4VZ6Elmbs.9b`m>eY D:Z yi ZP.cQsA9b@oq㠕z!u9x2|"Xa8/U+E(i3(oquՐRLQz@^($Tx 9{ֈrI0(4 QJK/iX^ͰfY-=|1n9^4|rؿ4q×@K81\_/}-ҷwQSߥ(.yߟ6J*%`)Kcӑ|RV'gǂe+əDti4{`w0b[OosaO2XtxKKLy1f5،cr9C9*6l^0uÏw FTb,wzb3:]A±aw^=AXcd0D-'u`w5׵e1x-!7aj90Aȧͯ݀ym.g=4e`2˾L-.+vay{Zsܥo"Xu/a\Y߽l+Vƒo| %NZ =4 C'l3M# eq{^F~fsD&h9ʬ]o# -lI-̣嫭 a\ȗQ."B!7U %+G&7L# XD]nHρjQ4Ή^n! ‚>d.^D̒ *<c虄lcI B`ޚ} . {EiW<9qQnGuayXdy9e$D":Ef8YeUjUhOik>찯%8 aW K aPxtZDc@u)rDl.¢j!k@ƗE'@R48*7PaRS;)Ӗ+,Og#: xWciXmׁm^STc'}e8v*-l돜>G{_\lQzל61)cAPޣ(~ѫW gOou"?,dž9Hzxp-8ҷi^XXa!|{xØav\2Obc8eI_ɥ꟟c9WX&+fß+lKvK02Xg㘛 K>#}0 ~ݳtоB!/4P)3`jBaC\ l.b#6~ͰyjHV]puRsF9' f2[؜E,f#f9,/TWj]2dIPH)!=:h/@qS2 Ix @dJЋr QMVO:enn"I`c,Є$)ofcBAF4q4J; BˎUlDh9SoBf@+nb0Jʒ Ck Ol<*Ām ΋c {xvv6{md)Ƨ̸zfM9xM~ڙCGzop+l6$]O_ J{tAȠ")Ȉ6<PQ2q-y ڃJͯ6bs2:ܺy&Xw;z k:M&k /1mι-R tU,BshOxpzDxhfZs"TwQΧɅ"(0c psx.(j ZDƘ|WÌ1V=?:c+PUG:TwslsR,W ! ;( G@38;u{l 6ldn~sw{ؾ،E\Sg^@7hL{r q뱹@mt !R@?\"_n{ vEo@ywWE wrԞ8; tDylX.r(6(i5Y86<n[sl7ԗo :Xk~XLh?b /<DYnw? _* R' CHdQpqlfں)OPPV@]yR'T֝-a-UlY4A_=xȢ$>0 ȋ r4ȼhMAQЊk Ъs(VP.jxǛndXE{QJ 0N( f (ľ G;`T h`Ѧ i yP8z q!WڤК0Aj@9@8ea[)uz15L-Т7=ʾI,zT lcltwȐ_T3/1z6Eqs|bۊhTA8*B{#x!siVXXn0_y14ݸ B6tn&n,1px .3ɺy 1y٧Ԝbd6rL/+vw}%tE|d&ܕO]ȨGyռK d?-ITnzŒ]٦Ou"|ه~hބfv>DaT*b6%DVG rH4"qB19V9ʥ?zqlV@%(N/p{t;nה@؜U9MG%6σ`X(%b\ܭܙ`۵ċ;k'>rk_?Sӟ?di]\n|LМY\-`d3i*08" rJB(H1 }YۻZ g,y]12=?ѯ.|cSC͂l)IHo N/ EY'?T>9t'[o=$s|h?iEWy* hG ZӮjA:\ g2MF7mfqQ} b߆K%IqӠ 䛠s> Zm R ̃,$97PuuQٍ7QAF*LI9bܶJdrs(pq3F}V1V!*Nmf3Rlbm(e<{2r"65+ՀmDAVhJ)3]qj.{R~1ef zZc#c󢭊 rnhQc5$GEDE_٢#אBLoqZժ8u,,d䲗{iDT?Cxمp X8h-e⪁? cRu%$EK!`i`55 ļK)Xi6s SN>\Sguf`^}4)r'X3y;sOey]Uv6;i"1T4T |O?d<=flϼ%lƑ5k'&¸312F <|ݥ料_o2Տ *j0CtNH$*EdžK8ҷ т>0Wc698 CPOp{r dJ]ۃr}ǽ|[ ¡PwXJ4_T:.~V@[S6s, C]\vR *Dm ^Ob1Zajf @aRO NH*6 r+f%/| +4g؜S^ ׼)C<gj]˚A =!rA9O~MЀ׿4`- qLFwxD0>).iT1VϨ6q&@^ ρ10>3@ukC9751K2$1f\MU,̓Vh0*a-6<^@@3 ͝E9T W3ƚEZ.XPOcpcW P΅9F7 =~Xc Dޟ7 zڕ σj]ڠwa*D>r̀]"8F[ :rXP.$:(/e-./@!R)5V%Uڏv;/あq%\zD lbN I"ibNCNס7@0cL'/ ݕŢi`Tf!`{zkjqqr|7g̥+x2aXg;XFC9u{*ϙ澞xGnL&V=sG8RDz|8{ЭIֽ\&2ߨuCM/0O(>o(k_>8HW~,^^%*dž86a&Ђ3Rzl0k \$qM Sdb>@H_b 7?k燯6Gԓwo_|BgO'tOdMح⯎?X ^Ya"$plx ,Xaמ U3 `鮻ys9wUs5276\mgDȱ4 nf^7^z$ҏ\a9ԛ|1m>v9#n$\%U{;,m2~Brh{PοKm3 Ū@+:IR |mh2*&GYj9#!< AqcE{&+66AGz'l.1ƦE{9Yw^ $ޫ Ѭ d"Wz6R|E7 z>G*\y!}}UPVA]{Q-'B<[Aý %,Ka{ Jrp l^OGэr.m-r 92\7JyK"2m'D{NUIaժV5 ,g0&jq$'Ѽb)HL%a"+5|折u-bf-O,:(yAG>(Q'kI%^,!8*]Zժc_DXs!O@ /b! 0mt+TJڅlZ2l ʐŠt孳k Y %Zlgg2hy=ιTg|ηJ)=v럌,r>py|WGsᙼRrn.X"<Z ޳~Ӷϕns.sq3h#!.H_s,求5FD^_HFz%6']_Y r)v Hǣ/ٞAsmqKh3{| !l͝憢ZJptjɁ8At+_I[ GYv|&<ΟRc,_ǒ:Ac+؜E 7[<H˪R'9!Dk u%6yf2WRQFjU{NVЗ~ 1$Lo ('@"%ψm6 "PcA|6Giq[vc(6ؠd4^)Ε|gA=CJ:ɑb4 hkՀ\HJ_D֗@a+gwg\('<\99&{TȀaŚع_?\  $PCc!^ ߺoDwn;17t|Vϳc+Vil7)@a .'Rwʼ5'?tA?U>}7W>u MP>kL&6f֮Tnj:XXIu:BȦ;"B=dǒDJlN0Tj^o0[bsXl=ѡA>gbsAHBHqha-rQ+9@İ_h?[]r74AkA%;\d?t"rBv n`A+JςnZȘqAI**hi :4`yPL/,aay&A+g!-|mF`ďT]m2?W(2R@9_UA}2-(~7R*JzQ.w!I8.r#[䅳Aael\Hg)C{-!Df4􀈰5*= @Y:zA. !$@DM朿.Bk1h3*|1fȧ=2Qc yA%dp@ ql&ZժcBѮSIRA uPtMr:Jv B膧d!l, 34\>nZ;S,hօz3sۃSJt/ƗtLޟ6u {k u֑m_=@I͖zW3 B-gk)λCƕŬ?*ËEY6йO`)9tn{#LWyÍ^?ީP{GcXئXxֹG(\DQ*42aKl[V<}UڰP}|91\̕0;#IPY{EpN D|ٍ9լaڊs[ϝy9Ns=J}32`5znH,yuB="/МiH~՟jN5D#@s476fQG \X/`)b#ȴ46 rG w04,!feW#TQeQ; h?A+#&P.)f=r~fA= l^AJibs޲] d1 o#S )嗕@ډ@H*WEV@j6hPH)0;Ad ұ^ЄfM@1v~}^`@DF֬ r974A8A!"l>Z-o1˥? ]4FDLȍ;`{A{( Ad\p2̷TjbBx_ρ<]*{eVl߇8%KeytJ"(-9_Adi+Ö+(2w"LAL>WA}Q.VSwC+EPΧA9 z@Jl6||.[|{ժV)˞܍ic[+̀@=\VEł@)GC-zP4l%|RyF*|lmA}{)yJr#׬嚬DɊ& ׯrvh-4g +\ݓgk3 w^{$nRr]/ad ޣs͛;KwB*7r+!“<8612GBUsZY^/zůA=o%6g12c:^mp? TsmV0;c3W7Rpva @Amk}~w`3 {q-n۰lp1PD<;1ZÚl91HRp} I7 O\{0+-V~Fa\`'^%1GW6{㣏d&5*ExX 1m9^+hnհ|MLXZAft@xEK>"1i(i5ɔT^% $KܺQ&qU=œÁ S{wϽSOʻ]^- _,2zn&XKl3T3ڞu nKO~d &SrOR 8O?"~/uZ`Zx7$G( =MXIk^8W86܀0²g+Zx%ڿtO86:6G:=#? gp|y6NH¯}ߝ -ƒw/+\y]79W(]\ni?j@G;o/)(?AmLlacI4,O?щ'NO=y7y&=DBO:IܦyN=wĻzJş@ ZP0&adxX3(YN /*6KBg%< zMq|4 l:~6)B]75AoK\~dQL4;ĿRXmh\}AI " Ѐ}[@<PT”, hŸAa,"t쥔k($}ȼfkL7*` *. 2(չb(K3Q e/o°2dVY-BSeun 4='3+ E2W@ԿYw19\-T Dn/)qL X$̅<;[%гT]wGw^\KnT+?# ʤX-qΜSYotR镃r$'* s>Zժ#c|?\Ƿ m(FIOn|m0 E-Fs̡|tS.@Dd$Q̛]Ŏ`=YcDf/b:kɞ)UQ'SݵSgGBrp-5^unGx+gP>yE[7{9_4ܕ zZj#?Uwtِxڕe2$̶ tx^;^ǒPBD3{wpo džDžܶTGȎ=9߿X'3w:[j%6H _K Zσ?tRJx3$O{T<=ZNDN$qKw/8l+ꉝ>Ŏ7z|e]_=żiץq-Ew =ZV3d5%sٲ}sG~=٫{ֽ+ \SA[84S~ N<ܻc9z=*CӠf IDAT eltŽKEkAq,6AZF@Is\ıPq/r* as i%y @j*obyq#}5?)=2VDH,o@^\{"IM8 ey{>a@Ym^l/~TDA`7-icql͵Ƅ($E[[UG>m7Hv*Yw%2n9%{TGq#V\lw 0|Y`"kz*` 9u[VdbGn6k9ioɩvf^k k%8B㤫:J;ηM=>xxΐk6y-Ĩ'8M+|-689:P Ԇ^HGF8hL_#`sᏱ~ "kz4 icT΅gO:_Dq-}1s+ S;[Iطki/>!:kޛuUgϾAc+i_&pMKTfDx~i/g{Kuc='] Kf-Hlo' 2zQa9羋\׷hPtcG={mkٚ {Gd2|94}x_dµa{t?W;OB3W>}ٺ6߱iNݏ7%fѷJ\>:W6wH^*x=- >yhT`s CT;jm \J45br%d2+HB@cԱaFkG/ojkKg Mx nq=޷{Ŝ賥ZeSr}5{D3ّ]j[8eF=dܶt^ \}^>rm \t*nhn^x~+n0h;35!a~\=#b(P+_fz~:э`/lu]D)s UN gE!bp7ca؈vCFm,Hk/\dpe@$qhBlZ "agup\ǚhhMk xjYq" ]#+(Tx ϐhP:I0BjzA@%Uvbm `6ݤD_"_!5BPh&&6 8i!.*ȘiTa4TPY =@7)]F"%rG(ϟ8a=b*; /~cdrrK`EWm8f҇\lfH=)yZk>2LR JGbHG&Yjɧ)1wjf- O7-4ʩjF?5t>JVsy{veQeyeX*4 4+G>PU!Wax <HcOp<I>P Qelk0u] ;@֊Cjkէ:]AtlWn.irN}Yg*fZ*< qOEuC_wwTǧ#xXSGb%t> 62DJZ$=`P׏ޗ?5˦,lJzIe#u@ 9fjW $o߷ie-⎽sTZta.܂Xf ^a"LFu{0>@ç1 )ٌ}/l:Ae;^14+':z߳ngR]Y¹q]_,pΦh4wnYP~ JStR]baC,w6>˻{;e%T1x~+i4WMup4}/"I-S6[CCP@8F&zp?)=D|7TwIʴ{+k @擨+Bv5kV2$Kyvpy /Ŭ6vkBn ;)ו'_TTπp{tlۂbo!te+m;nahmju?af\G`\3lTQ.Z XKYdQL2F3^NТyHMT$FKoO-\ zeh -N DSih)Ҝ<:!h"QI( /d_MkؖQ]sV4-ع'EIo 5/|Q漢A1I@ 'Wje+$~2?.(ٚٯ}4N-YS @$v T(:eLW*2a4>eCbC)9J,oSݞG,I#jdGsy{m#l+u\Rón\i\6 (U -1lC~+2#86?Z,ʝeq83^H [ӷ6L:~qun$WLa{f6:2_=}쩮\S9YQLIVhkbj[K.[mqh ah9V±!_vCqwɍ?r( e%jQiaslvMjeΓ$reZa`N+ILEn夛3<ӮJ?Hiw 2m}ou_?@yh*6?'dɾBި@6o-&'B'/s@( >LG4rL9JkpZWIʲ=`4ikO0N`s7IlaS ZV@D0ƒyR]*6{@laPM?uC[qql.sKج.$zdo$Y4^|E#.j)QWQ\"MM9b%˞.R' ŷAD !r%hkADC"4/Rďd󠝤o_]I$jF֓ H&$M$%N\2Wa8Ad% I0(|¸ ('v @0Jj<ɥ '9{ďwv1x_dk! "}g+( ?&CY&yhkXLYAh`.mz5:ڑuaV znRbTUM0VUR^UUW=ӭ5[__{r񺹿lK2cEʐAAƲ)Έ k%}yES,lĸۈbF" 'NeP$ OycV0(9:Pʊ$\u7\z8i?i ntYڳAZZ^-z^X&p#n-@B|e199]'*VS͆-tuK۬(aJVyֽduڙr@IvꖶgÅdkͧ1ɛ/nn)oXjHHмK j5h~bXb0͉o}JCbm xY-lM?w]Fc`S\v϶Ź?{RIUrW[OLxu1?/i9˽&?1o5me~~JWdYys}'CLzz갹V+9}R0*UA/yZ:o <ϣazo%/T`lщ, ^t& cLCf4t0lspGI^?R uc(3\ײ7,vu: Zte &BCJt@Mx'BMu?՝bV<3:8VUĉ@ yB-pD IHZw; 5+B<.j#BJPP=4R濯%Ф%&`q ²eѭ@^̋P3mVq5ș<<NXZDNb*hUkbMBg(,r&Ds&L ؘp0^=hBxCE9Qv7_]Fޤwj{蓞oa*' o,7h-YQ/^zE4lwiíWnrf×Jb@v*&83qz,`,ՠє WHe$hFrH2r9Ot^ϥ*ڹTRU*:\dSjeEʵ7c^){.ӏUtku K5fN6gl_=@OwstWiRs֩7wy-]̸)iܬ[-Rht'Bݪ߷h+b Ea69~毇Θ28K,6|#ڐ5ic5Up`#Uup|lw-RI@ IDAT:g+BP e{(tI[F){ Z?y@C-|}>2 Zk&OfpYf^_R?rp o7,^ z S2 g?o-I RC nDpŪƀհDcLI-&z9y~B*x[16G̉kd=0"A£Zc+<=\CqNZ@7t7 "pbPaSd1:Ὕj+_-g; O@˙,o{7,n jy`SEE#ضhUf{/s~)U\fmZٸfkس{TjӬ[0 \+@{PtJP,6ؤ((TV!זQ5ZbdHbls7% Jd5*j]2}dԭrE֗Lb̒g{|.]I\Pp¶:`áQZ5gM8ݦJK6 #Uu;f]%ő6EAOnQ7K:CYbKXnev"_`>p8Njk;:\TIgUECch@@zM]s88|j^w[ҵr8ĚdlsZjA!vzCs;o1yoofu0rYz䠪up|pf.'^Y\Z,%9rͥoMK^[J[mB29IT;R9=TBL'OݝM:omqG2;hw4܃?Y~ @|~@9Lz: 2rPPLwĺíyKQ@Ie6[I;›<`-c}+ˇml'غRmUb2ln8ڻo,/yl6NKD~u\%cŽn͝а|Ak29l~+(doȍg ^T*V b;hAL6^JSs?XZA1I ,=} ZB]0GZd+yܠ0F' 40F92*矙 մf\ |<3&q~) 煏VxQ*Յ 󜪪YƘ{ qZ](J\KQEc3hiϿ)H 1$U+2^3 fl.7rn `Fpxt?nއ*(wۙ $}9E~n ڳ fwcCMZ^ޏ".0߭0p{l~E7rkcuM0Z&N#q߀`iRzqw#d[:F (6%0QGU"܆""2u0PPJC-0TV^Wښׇ2cGf7zS:+n͓)L bUCgvS+p1R߱\;S秮/o[šQ?Bߓw_MZ~{_}`\U@n[6uטlz|mv>Sw{b*}`&xi^~ ts8ENsW"0Xs'eno.tie,>sdfll͍p( c]&jcf;hPnipTѸM3TAwC5}BOe`f(AіmveIls֒ R8cժ<$}ɮ@̖\)JMZ45-۔-Kq{:ωhU>qr4l6Y@᧫%a(&WƑj6 U6FxA@6Drڋ+AF^@/1 i sWo",zRUCO 2S1傰=Dj<Ա@L&T7;SY7 |ߣȄI˜VHj+&9Y^ț'#B9I޷DYA^䌂ohy%~Am#"ULH[D1m0ІAN<6aˁrMU.7 {?>37-}г1 -UN /9"kAK6/DpDZ~ Zx/?_]L5[7vKrw ;WӨ:m@Rٙ$AF$Uax-UKz+JF/ (XXvʲN56fR9A۱ 9XC.g%S.c`5%aJssH9ӷ7NmqlX\@(Kpz+(/F*&j,ާI-5Ǖy cV%5y,j:>zד9(Fs\Wk9ݺ'1Q~TF 5<\F5ШA˕R_W" 84j8,1'&gm1+zַi`ْ lw{xZL޿if|̾WS]9@InU\PͲb4Koolp־{}E-I=MiNWܮK:Kj8WVbc<,p>w;Vk;Qei9oƻ||)U@A @cx:,6ydX[a6=2 :rP +z@8m`N&Ɨ@ 42;m2/ֶкZ3xOpC4ϥ?i!mXvFD8 9a*6A,qEYdnZX*X 5(&m%1E%DLD2E[7QPN렕H@+1` `d@l} G燧A}]D [W9UU}iUU1c4 o(m M1*v/C =u@ @dVe `"4 (?to.0<882"T\?"dBJ6+xZSUu_gPV|O{^~Z9 M Wl=/ RyJ-oAvrNv>L1;۾u6޿üO.`@j8P ԃq b̹77*M"68BLI*p Z@Qyٚٯ^,]].$E[}Qzzϩ)tkCR*aF]pkA*A5JJjs#Cj@FuXnAU*Э!W%I*~7r1U/!#஖6U z..cr$6(GK+_f6dl,V|+K񲚬f>dZT蘎i^84L`jYW6׹hS-ou/aTmkbb7lxoRkq9]~3`5Mke{=Yȵ%gi4TaJ`h]΀вqU*閥$ b #qfIq"aK@}а`,y аy6E )6C[l.3Afl9ky5HX+Mk @@;1~eJyXzρkUA ~~處AX@9g@[^PgEg\5Q}"crT;d&szb[5[_=P]/ W飑 )!o#Fk!((,囡ώC*AjX-HiˣkpVae:=ԭlAŔ\JTys+ˉظs7+syH "#VGQ^^1gK3?^< wVEsԟLx_N^xhKIwMJ^jEqU\֬W"k2W-dmO#tKdge[O6[8;/ln)SZPz5z1ށ-TЀÈr xٕ,X21E5B:y]໶)Ue>'R1,خ$pU 5^|֊><ġ, GƘq)i5DgoDI7cK6Rm֞-g4xвeha-^ZĽRe ;{@2xmW_>4X#Z׵}KҍMcu^>S.'R|طۗ_َK5w^h1geCe&o.NPwa[_X$=0 l. 33 %({f._|h 4'G Lf? ai=6wl> "li䠚#a2xJA>GqG yrƆ/Q0=R@ k]d8%:o缝c ,*/Eog'+'ev9o ȯs.zkY;+ 3h{݊" _,fKPhE~M_1bH]Z5 .S-tD]4*j5MƳ}sw?\V'84jisN_,yI{:_,SFVXfTIrdefTM֣bz]=6 YC _=ѫáQJ?}>O P}Wmxۃg썅h|w3fko׺ݸG"oo{ h|ʮK̷0$Y&wA7gig=~}gcp8?YJ=卋 +.m\.^{ ZTC}El+g6? TyX,j9LfJ9Q^4`q䠆̓LMHа%~sf:t6/Yl;@k,eh%>/GJkؼf%{#Nh A/ zE#y1b4B T@^<; 0fyH4O"\$GAE^>阁 ck)&x{DmhRr:WF"" XFxX* 1)V@$PHYUUiUUkK*$3~OlɳDUUp 9m0J^+<2z- 2X_ h/Q0 |㼿 }| kXQzv q0y79= #coo Z;"!f?!Veh]n`y%efkfFܻ%yt("97^t#p,d_ 8N/@fG`Z"գx&k16me$UEFUVMy}ger>Q{̆YTj ?iL0u%sw*%3mML +&Wod hl_a}Ip(B(1?H`HGVmhmi&PȦihs3v!}%.B B3 b_ZÖd Bg1_/*W@>y' IDATژ)cZ-9hyBUmi k?/F:"Hsvp_]+fk?Θ!l'br eB 7a ? bĒ,a\\Dl9JŒc5S RM@0*pҚ=+0KNaeEWE˧twFw*EN\^l_c9sɧ FNZs|bqlcfbOn:x⽥6ܹaoƍJaLr yw쵨kPv/XvG1aH{U_Fn? h[ʔ;Zl+Y;J;TfQ^W 48fF*4JR>.)ܱW1.̎<t:<YoNsjokK9lR&a0 )zs]mEo~` d WJY,[T%Yo'W^UwlCp^kFN5>$᳑S߷'_fN|O~l7̺$dB8O>\JmC&>>{&O}*v$o5[j榠~ϩJS^[*,qyEb{Z 28j`np%?iP4|J*,)c ?L͂68A++ 0P6h@Q_:އ(C  fčw9S~~%Q@{C^5[55=WuAn0A$ tM( *Փ0BՅJzZ Ci L yYf=5YK C.5(AݖH-g2awI&7L6kRoWҞmWjz<)y)IIX+&щ>Jy0xf\-?7~3N60ԑWҾ״bb#z14PUfl~o3( )v4ßrFzR S[tkapu34e`u`*>ujQp_]zlah  Cm3l4 j} Ylʊ3c fȊ7S^1T04%c -Lη̋yщxCv'aa&'33`or|.oJ8=?(L>\''ڷAR WL/Vl[ 8rewz\ھw~ߺ7Ue1=Өa6tv؜SӵTj2{5dx8@X WrO$|qp7Nw\’,%D cgEM Rt] SR‘ffDFEi*69v*)KZE\&P?xD ̀n?nEw^h6ͯyla@́^~3?_ _ίqD Iea\jR䍓PCoC3 "J|fy^'"A"4Qr= :x׳=~AD)X(ɦ@!9hD=fhޔ*LW %UjKr,"\v?WmX]qѻQ-sIbq n{'aSbdv @)~ Te\Xa(ug}JܞTe]{1@8;e8pk/n5ɨ)|s'M#[PfTسSF6U\Z|/͓bYnȆr;_=Ac ~ VИMַ:(eP2 N֠g͂Zr6Le, 2n Ud_~ y07+|;ok-"dm+@O05йB`<YY̥\@t7^Pㄞp 8ٵ@} 'GYSs:}u 66Y ʮO3wZ(+70(M(hL6`^MQhs`}"VjF[`~ko֒1^"UFK3dX\e ffn!*n"نlȆIYvAm ,A8?a9-z^b$(I"68*uS ӉJFv&p bgdCQb3tY-ykU:pY^ NL71jflrvӽPN}O{2!y][?U-ĴRštsj=v-͙66򤷵)`G({zvDBBc,զ\]aR9"^R,V[v q:8\e25 -t P*1TlidmÀ.nPZ%Ա&+>]iև KnD)6J˃I.@ VF*˶=#XUUcdtr0]1!kBbx 7d 0nu;Yx&SgAJ*AȪX}< @`ɨ; vjwA|x,Ā" !;rNp+@T丐`lANz/>c x% 4>Vj鼽'ؙOW[P GpL8m\= 8rJM©%{*b=aU2WI{}]çv7y ,k4@]q͜m?\୦buG-PaC%?fFb^۶v;'eXL*VBvOS]<ɣvq!g-o[mϝɅ–'J+)/KپQlycM@̙ hO%@z{YlL~-]ˡ/IRUD }|㥔K"lm;L7}nXۛZg'n?/*A Y tuؽf^]= ^m?pQf3;/Yb2yf!K$T=6{ȉ 7^wDy[r˞5ё~oS`/4goxVј@Zȡp&(5azƧ}~$cgӧ>85w7=J;flޅcˣGTqx% ƤŝIN=4+}`ǏXV{Tjo78 U-f3zt!#$  }%iYn;@bZ t3σn쳃/)lx,Q2-Jt ̄R]́nwPg@3XnP:-SfPt- jYҲ@óCO8XzL_`\]`RZ̢d8M~ ( gx(@I\!DF5ȁZVB-a @ l96oZR#/tK FP}vS@?[!/1m.gܺ@"1Qh =1#,%YY?llUUOQX{X>LF)>_5ty6'; ">CA%qX5&Ijڐ ِ 1 bVS=KI2H|֤ Ֆ($ٍL'E`a-uh Q5%L .5 8'8&k B%Im {ck|o2PPD/6ſ=9<}{cnq6ȗY6]ZKbJݥұMu3Hsٶ:c=KlZwqtOMg?)靝\Kf`^\Zڅx%{nC2xǛl-:WG=;mvt+d J՚um?pmw[11fvmMWS|˶|oS]_qMG5$,`&5x6C &1ٗ//jPP={ CrL͘'u]! TGwȃjxx9Ais~4Fsf<&gN= cASǁk?>_+yod-2I鄧5G..{}M;ܪg@ӻqzM- Dwk?Y̆w={O-(ۿi/\۱?uojK_wy=s76ڸy… p,߭n6U#j2M7X{@u4tϣ@yX}Jĵ7F>Kյ&0 y<+ NC `@l_] ,yV~*ge`1u鼬2C: VF+t>1E,v}թsmb,(lc,&PԲrA @ySphqS^Ӭځf =vUU.O.BHi+9vOAв^aj.Eg:b;[@g28h 궲t+q6eP˸n:lȆ ! M$MvB{ј.1NdmU$eDK4UvM JPmhg:`)#lO!Hcbqhw&WAʖA{=Ni1P n.>xCY1J\,kol9TsKç1ˇM_5pb}\Ow*]%t# n]^_̇ A݆E\G~Q7k8r?kɇo]$GY9޻gCo٭oGx\^'q9 ₧*6Z驁yN8p.|UX]P8]6T(s?jܹ3ќ b^-r5Q7]쪏Xh]iZNgw]֋-GA䶨مٖZe9qsje[,>L8ݔ!gL,h_I ]wn\L_xoz\֯[z ?}i;~4\?i|c(5OOϷz}N# M[CkZvk`=5.a\+\gWS$ׅXG"l`j^[+vC3?,M3i߳Qs2織)Is?~s$}CGf~?rFg9_sM;²AZ3j{$Y^ 8-_'AAiԺ ^ G P>-R P/9[k Sx~<!*zwJE8,sXoWu݅Xދl0'g! p>̈́@چ׆0 IDATt e+(+ V/:lYpD@MTR68W<ʣAlI/Z82ݹ}}7קjcsDAuώmw*=/{>i4y D{v5kߺ=]5l_/kP8aR_.l'[[ԙ7E-.-(KXgᆬV .=]׿ML)_]Nv^ii͟go+u 1dKT75_kd',M״ߓŎ ]s·&Z^f36c~~PzJAw~-;)M7gwjۛ]GLj٘[>GqtTn#ʥhSt8ɥϯb6[x̖?ZϿKH=3bؕl]Vr}%eT,FG'H,'[?K2)Td3ɼ`8|rM[bf[żJاjݱB9*wOt86qQ_jly*63yQr䱜`+sP&ͭ_{H$0Ayyg OX;yhۇQ5W=k=#jQp{xhmjuZ!~P}:(06VF ~G*n-&-AݕhApgyCSyhe$UJ7c[t^k~s`j5 З (LfP IeNAP}Nڛ*)ZvMEKBU@+@ 'X{TP"/(J.>u0AB%P e_q5(bAR}6/Ws D;@-a9P X>y5WP s!UK#}lL2fPzh`g@'-QGZY@9Cנn~u3VpGs3*4,ˬ)gT W VO6dC6"rA//Ayf^K ÐDK0ZJ14GPt`%ւhreb# k qA0I]J̛(͈ ,u-OhLYv'Ǘ*%!]&lTβoV} ]sJ1DžcyuRb\֢_ }h{BlŰgAsm쮏jm?<o&[z{nG~yŏP$5>@Lҭ7yY$3*^kSgXjGTV5js`]L(Q6r &oe1:3uV|޸2!!YznԌ7yRlphh{Od@Ӗp`],Tyҡl:;k;5u1:i_[3{:FGU6ϔTO|IX5=F̾ݠg-z/ -lLH8 E6Z:iusI,0ka(![bP @$TS ZaVlUWr'Q/MQ$pDuIP&Tfp ERf`zqa0偂yL<>M ɪ([Tn]U,崭R[91v皗<_6<ݴ6{`^ES☷?рlHnB|׹[ۤqo[>vo7Ε/k SIࡷfbl8( _\޾Mա'-Vlh}ٿQz~E77vD>dE/ K,QU!b-(WvTf|+!|-5.5^\ȡ ]_[F$M%\d_E$އƒwKZ-b_aYyts tϾP0اn|I_PJ[,͞uSved?$r0:ίԑysrξcՐ9jTw6 Dӓ3Ͼ@1T{{'7 NuӉ[hNZmm L잊;9Q{=sUdg\BnT=]xo+@Ͱ`(ܱg-zֺŖ֛vGi"<9#:VGw}ÇfP=r <;Co To:At~Fɰ'؜Ws ГZA;h(0VA n6@ Xd_W7ka[!W,jV.Qh_͵Sˀf.m$@_tv'^ˊ,(QAcVA7x:R" Rtpeϱr4ͬ¡tq5v!hd3+;w0QLsEUX xE-je;"ɲ:4J0*jaߋzf]h5s0i֞0(="k'(=PnV $Ym2tC @j"%hY2SbMYUPk6;_Tift>3,i独^ W!V(A]7dC6GlH2[GEc: L-(L8` 4 f{/6R\na5PDNزI^M8iyYEo*qs9aZLYKjk}3u"ڧ1iaw3ջo_]!Auﴸ+eE Pڮ P*"^n!3xnnklA9ᑗ2(*Fl w[lX~:#(@_6σ렀dBLݧQ YЗs Բjutbct-!^ Q?k+@}ڬ$t"nx6i`e]΀[4PcU0"*o,fyF`vpw*taky<?ͺF#C`SK nQ6]lNWsJM2ҬZk9gc_Q0/*!IbzؘAA־E: !E7V;,4703ֈC3N[56uBҞX<zc`fMݐ ِJQ*ߵZKչ><w.F1R+$C)bS kɈ^ $ h@zI'` IXmTmX9ۢ9Tbauh-mu"|+#dVUܹ^o.&=pS!=ߩw3a?Wz"mOE:B[W^H"p&KCǥFe4YPV8 K8Wdijy^bSU<Ád)zkVd^;j|Gl y߾֠r}PNkߔ,ݾM}u.>]h׹;DuZ(a*h -r|zgb*~ 2oS:ad3^{+UQ,ɒ \9j⍤<؇F5PE4.~skh询P:b%w-SoYؕP:_w.U/oM\Z6p B:nKN=/Um"#/yZvĥp׏fl@.m[]p%8--S/ I&Lz=72gmǧWON}3'21oNh0辊o~y ,p@˃BT.VGkxt7ivZb9@iaML7\us~0 M7C߇B#$ sq}* (n0XU#nf<2ؐy9P-]_(*^!JfOC'C\D4kP0"A8/3ˮ]YyA-A턐(Ufv9Z BͬLP PF`Ac!Ucק@:(ӬI֎UUK,&)BH2kFf׍6̲Y[Xm_`: ȆXyZr-lEtc;~7]5o3yV DKU3T,kh$6B*(BO\’,{$`Ғ@³=ˠ lʬmUPcc]aוYm6VZ9Bb@t7dC6bOe7Y:U* ў* $bbl,_Fԗ+fU4y^x4K9Om3ڹJSO1*)tfo`H6ifugwZqIޟmjzouW9̯ .[RVX[k͖JpKE-\W]9XŇ7I^XMfBZN&ٻdN@t,KFg͑kT1(j@;E͹JESJ*&͊W͕R;cɢlOU{ ;-}qc̶@ *Kr5b"lbZfTNs'?4K8:@CW@VہkBGi#*"dȕ"_o>|eFmݓŧy;>9GzᶏoӢRa^(z_s<9y]%mKf.Yi@VyM7k5D#w_j~%Uc>ӞvQoÎ RSWZ1n,8rIV~Ҽ[9DZGx!`J>;1K\.m(w%%?PCf۩yf O,w tK_l[,_<<< > k #=#D R*M7@ustRZGבbZ. = Ub]-Rt㪹 HГh{#/- jXZLKe`ew:gd%蜅`@7ztQZvڻ৬m fyO٫AԨjl863&z,vmk' =mx9A kbbcYS@e爇,BX8t+ꮁ 6B@-,hV]h667dC~{"MB=\Lg%lFH(Dey:&`v@Uꆫue|R(@t6AV]8P#":V9CdF+^,\ɼS=eqM*SsUΪ#UXM >1:N{58q9l[k}ew&|19ܷUpsgXwve4oa1mO??|ƮLŤ{rN|f'+D%=u<;sg=GrzC]|o++׬Pl9 Y~OUѝCRXX),tvV>I4[SE9D,R{gwY[i{tgb 0X4zDUpt ..~dyᢷ`4>6M΍[ǻ.o?:pp^+y %BT-;w gt @ڱs VۛYn5MnW(qUM] 9n=rRFwv55v̿6|zxP;ʼnջ_̇*-Ε-+0V0\ ORS[-xONT՞3\ܡgRf|bf/_Jo$ 7^c*{8(T9VaǹI~ 83x$P$R\|'lXkIġmb8_xL`TRн,~?2'ZΝ;2Q޹*@(]=$OvY#$@5kGԋ#$ \[btsto!GrmC?Ѹj/02rO 0}JXȾo9瓠!{=+yЍz#lzvVB!jʫZ2eRΨ"&Y|TunFx`@L=iDx[NN)8C-YM6}N̬Z6O3"]Ds nHNSafsp) =ΥZm^Yf@wM$\WXa`_ C[ƒ\x/o24V]xUsۗ rfEr9X)" '}oC$cc?vr{?㽮 +*ኗK#^4}{Ou go#*0%nmuԶYnt05w >A\Uk*^;"zmZ&]MV1T+xa;ditrj%xSۯhжYy}q{VMVX-)[A[TeO_כܾ fIҮdI7&47%gz=_wH}7[llӚ^XR{|~,cC,n7g/ǽv̍>qpu*\\\[}>͹NJfͦG>bdEF#jY\awtk)ŀSBas^G=᥺ =VG M7kASkw2g;NeGGnfA nfsC7EH(3vsyЗ,@NA>@r zl]<( M5З\s;ԒhE1 6lqVGtn44 Ǯ,^ TY3]ZBKR=nٌZy,<,NY{@A6^|bOZd@{h\iqK !-lNllobYc2fP A{XJ1[gR>K VƨjE\dhɂ8- lk:f-n|Y!ڝ]ԵZe.̈́+ef+gKRUi)!DsY4GIPې ِAriи %,TuqYY+#r[aD"XCM6PIQd>l,HJV&:LĔRՋy>{MqU-kW1C):8ў,B^IleಗBat\M94u:} t'|vR:K"d`~9\ XP QPxÍ ȗh60<͘\M];T-v&p9ű;v xgVo uI7#Bmq_ݢ XʉYWl6#[tgh |6S{~Ih5ZhR9zz_?qϬF[7&cwMCzVʭa]Hv^v}e2('=  n<5lS ̷V>7Zs }<vrw M[qgg?Q;2nq+7fL4}5-[lzW;@;U'[ !Rg5sR<@;?]õzW8!~iKP 0J]vi \nkG63|Ȳz <%Ey&(&^W͛'}!ǂ]8-w1iy'^'PWf)S?9j\]MUn]aRd㱶]E#d@4ݜ!i=zD#dL7w.s|ԑUts?fC6EuZPP \@Q4.MݜW}UV_ S֮&U%Vj~%PK^ k["(8jdPmnB UYP ^BqU[4.󠀱+a:&1Ld' j vAOy7ԬZ^ @'/1qxq熞> Yr*B[8O+ZK<gheh(`u!UU qNUUPҊhūES82p UdU n,+JJ`5N]֫%A)߰n6S0by >G0rTUnhp> f %(7^n^wE jRʦRkʚZ~ *{-ŞT]7ޔ' vɺ}l[7ḘcVw&$ZVqKɌ ^ivv*pע`!g_XC+RY*>8zD;nOv5Y9>1H2TǶVRܳ@ʨ3mv7`g ck*-9aaP7UM ܜ 60z> `CW?~xH#vo;F?{ c&Ssxҿdw- {n-l83DWNoub_UIquքPB]Q5' ҔP5h-vJ OW\`1bV!{kQa`! ] fWf$na&2rTU(2аG^3b/U~ñ!/ , Fsao뢅j>F)+~塃nls\V$6"thF;O>Ցjt@04rT-bww-mz,[KeSPI׃lүc7dC^XԀ?#U4FQUZA^rBxb F=:AzPbkTƛX= F[:KzZmM ޲9zM hi4&i[LSa5/'м}t ǡXmiN 63 sz :g#="81 TU5L0}jc4LoaYh` =8iJt  :Ƚփx35m trsofz|f L r, Aj1kh 4S }7KUU-m,TUM3 3F SU5CBYB t J5 5 ِ @>-=˴|_Z}MhX0bX .(_"z O5BQB2s.^SH*(MC`#pլ8ȭzIHl%IMR1&R3e).{:S^SjUMefdBi9ޠgv_j쌮qYb voY0[kfoYO=܁FR^?}3ٟ, ;**zٕTb ykǴmbxj@X0,DBLR!@}kM5a(ʗzwCywnƱ9(#>&3<Մ ̨FtDoox{,J$.}=f7a T)`Uplԏ{glTmz)(lr\֡-;[4pΡaH4X긶tܐY[!7˻?9~u0<>]*ȝB%~auѶ|ګ뻿Y/~5]/ #'?ޔ0'*W 2m[__l2c^]}f`!;ۗR9`d8Qc7ckNҹ?}Y^z<4pl(@<~) `ٟq8c~b='(dFM_7so0ra(wL?} R"=4̌= pl䨚:l}|_CÌFY9Faf9[7wXT@<1c /@1a6dC6?Yd(_|ϵgوqll)1F-ՔrcN$Tyɩd196Wlm'9CΤ=&6C,jrN5:Ly:U)TnQ7,/㐪1Zwݓ/: 6HJZsKg-Wj :T9^vӎ|Ŵ sGUJuqqsdA# /M#dz clz[s, ©D^ CVS2ל0SUPxz'ZlU5WuSy}/IrKTK'o,A-Vk9l*`4ʥl7XnwBts>9y |!2 8ba7؃5g[}}`tZIfR %,4]IZF^:J63t\&'Kk^D:n?}WT9(ӹ,X@Z=O%wQNfd!@q6 ̃n$A'm1 }Dt0j;O* ,@߼BNdjLelnȆLXdլa)Nݣx-rK晷T~V913fWj-gܨ^^|vl cN6uqLl@XjuRnyE^MmR~˚rnIp%|Q'D}v#Ns.YaL/5,ֱ"#J&s}/0{%wW eP{1w\95lphv]J^NT՜fʑ܎ՕެuyL6/Ey=lˊ&Ou򓁿/5?oh|*B]DZa5Eېky07tfKuٗ`" 0p w޻= F'6LX>=zWmh5qk7,SvdWZޫr oæfB 2-ǣgxӭ2ҹ,g46$= Z61zwF޸x}{DtG6z-0c5 f9Vvo \![`? &Nd;Yg|JL{Bo6iijybŤZWk[V͝mOdӴ ENwm"COZ@!Dni_4R-N"=f^_˛|rt>r8\1Лh;U}j+ܲ-Xbϕ:뀩u_~W+I\!*:VuY^nr>TU3鳗 -.7j5E`y80ujWR.7L>Q.{*M>ϢD <?JjG3jtT7bl+H+e 9yy-5Gݏ]{8m8s k䬒+[%=n9V0pCUÑO9fIbzHrݡCÌ:rn o^"5@g@ VA U<bpk ;z*! }YS#y:艃xp s o^hE6H[^ m%@fx<  uX  G€О@?ZfΛk}BlXj%8 ({;(ŝJTx^@ m Լ fm6N;Nǣ#W@tmtAU(0B'Aji!E`|=[9@@# rA@qz~Nǒ^xW3,LCi5Gmm ]cTY7@+< h!Z:[t 0!2!N'5BbΆlȆE+{5v[hѢZ+-NzK5n+$2a sRjlWȰ{/=_:uձT*pfsc{T|%TmoԝJov}2Ԡq ս 5e *kKXVˡ= 9Zo<3E7e70toͳs֮p'mKRom/rp0"b>ݭ4u]Uc2Ǥ,泗,w\1NU)e즖<̚cـro^*0{e;[e.&=6xNjr%?T:ApExhY%5Go7f+lDb)߂ǎfOwl;0x`)kխݝEyS;Ϟ?\XପsşZv ,MFEM74̡aet~o{6l7&&9K Z *`r VwI}uj}ڜuTN[rLoL+ѾA"̬OE! ۗ{/57)eU XWi](Mg<~oADZ|TjnKsƬR @awۼr0oS̎ZQfONQm r1[vƛ>'&ff_Ҙ{~~s7_kɔD|B_vWKKEiëtۓ kRŖ h 9hyq5w>R0pd@7_ /ZX\\qV*##8y \5noh&__۳ "0yM5lPe#,C/bK \ ,([@ <i`Q'xrR-o1ݴ~2Nھf@@ R^#)@65t̶KM!'t@/%fkגAC zo$H-B?\57H65GU9A^:7N ]&t!  q}t>Ct=xUUi}GUsBύҜ<z9Gw5zYt@UP0cZ kj_d*&%6ӥLla,wڌ13| Kaao&n!R]VڒCÌW8TJw[A1v6ׯ|yk&k!s|M#^ťgbbS]?)drn|Et|΂K!ek3 w/dqkmֹDN~Mf3aW~`I3rն}o7}aTرQM>?p_vu\)|mi{y-pd ;ҲdYKMs+PZraZ9\t{b^gʾMbV,|K+u'痯1şcmujՂcE3"oq5Cly-꽥MmgR:V%߼w)܍~1.{`+G/{˰hz+yu|f}}}-VOϽX5VW+N$p};T(CsCP b)YKE T;&r %8Cޒ3#U#RlqKE/n~n9fd0pچA,^-s?bxet7ijR 1 硇D.x  a |~ mX<6X ?8-$vù tYcK@/FcyQ,  V" øi;چ d yaIy)ô*=W&:0e~JGWk$Ac4 />3AEnVaY6Zi3T:~ ˁnP퇞k壡c auӹ>Ar?}i΂Wjq1;a>ZYy{BH(`йp QmXABzWz8ЕN+{E%S?O+6ZlȆ+ 5w#=\augM_x.ߤ> b$^~$_7kbXfWW] k/JOf왋9)\{|VޜS*+ʌ\8<,ȶ-*\q<.rr_j,glUwY8V s{Bے;b:ð[N](YRmXȣ63S=#w=7e 'z= y|(3>PO҉V82 ,-Qou벑57T[i36G ? +y&8A pVeeYd۸b%Ujtꞏ;U#0 vPzYOG4 + \!ůN 2;RFIk"z ᤤ*//vњbz=+69#Kԃ0`ѽ&آ{gb"/~-}x7~UFxfEpcRu].&!tb,G}Ss@ݨ;)c(}'*~vZKc5.'7o{#>M7@ XӋJ%̛Jx˜ 3Q6Vy"\=Aҥrx_7}&4&[@N +@@NWv^q6n=Y)^0pf-( r­1Oj>k_i6 iʫ$nXr\_v:Yg&/aL!יbwcnӳ+3A +;}Z2CGpEK`[8ۼ͐d3uzSus+j4}-G9| <qL0+fɄhV ;"aɳzӾ cBkp@`^\|Olv/C'2梲%{q]5^N.l<ڿ3uKQH f-U(q;\|wJQ۬t>wύUaЦ_*l.m Enb d2%ֽCL5bk|'>W'>xg!+sJn\SͯwYT_L}kC_k}SNhw?b?ẆSϼUBɂkuzlb\NMjMWf6 X=r/ƲFTMb%F٢YqVBIJ݊5T(q>P`6)J֨2Հ컲RiHٓ$zSpi,0>$u2|5gubwQ 2{<عJ1u>U&ʊm.sE7  9(y k8h誓{͹Geo7Kݑ_E*P 6"`[ +拿CL ̇NsϾt44\8yn4&hW*|?BXמljaqO;{T7 3v )%>!9 AA)yĂ ;AvgAT& F@1 t h^ ,O?ЉY!{/5G#ɀtON1c kofhCEZv' S ]JA Lhd7熪 X#/HsV@NerCuCS7t&P-2F}(ơ{M+Ё^gڷ 6K 2gzm w+zxHdb3H0E:m zg rO -fM 獨ZR` &ږƖBDiد'wUUA#Njz}Hnl_5o勆3x;|0|jɆŐ.%IAo@߹g_-z^60b84-GnWsج032s5vb )FK{0 NN) kLvLt. Bu:f-OXU01to Vb 1$iz tژ/B<`!!`bbEn83X?gI;lg@t8rk} *Yj)5Ŝf{(_nުe.ۖ|Ő?.u3T1k.sz.z"٧Q Tm+~ѩf 4@rYRcVVS^f.X; jCѰM)SN'?{ >YM,E;WSd5` [>WoynK PDŒ9kg3*sdb*2-{Hma} x[5O-5k}b2QBF1DNdX8{3e]ŦLNU 9FaCWcڷ zq9cm$Gnq\JBי_`1{Q s'f(7=eu$^m}͂o=^ R&S2K&jn?^Q{+W S m\asNi_B(͖:Gm(m@gJ7ìjMۖKSc7?~C m75rN2ɣ`$+܌Y;R^VkU6=Coz󳝉֎fCAjJT+2|ڜ+ <,^T24%Y$O;,:˶\}qh1Iꊷ8M]ZHq曛]eWa䨚<cTJfDX 'A*rte:E0 c󺪪jŜ!!&_rN.W 9:P 'Ϧt ḯ|y GRK)񊷇O_ݴ{,Js|Q'DXQ%_2ϻ_;lժUWR='ق;ٗe⮚:뻫WJ ͵sJg3Y1jl|kM&4'/)[!&Ӈ"yEJ5fKWK00|=v]Y埸rr&cOqO^]EJQq6z͵{z6Tҳ!3bWuQ&l YXgU{@ _ߥD@6O#h lx&u\Zy Wb.0w; N.q*(a=86M*Um˷LvOufF{ݩ:tq=O?oy^esLt! wkj?y~Y0ᯛ:zlq?UO T Gyݣ=)Ϯ;⶜naXdks)etDZofFֳYDs1yuQ?xF-'i-9tΚO>l`g/eE\i3kK7+e1pӮ'Mf]5D-VƏ+|#oYQ6y:on5|{O^kWÇo 24@t ٸ!,9^a B4OA"+4jI)U+UP"] u 5 z^s8uOOpX>+ n< ޫ>0PhM#e=|C 2HHTQ9x~aő*?xF}xMTrא1\7~LFݗO:e $`eK`LBۖlP'GWb7(fוO/[ԩ57P$:r&VDI2lm꺟M<:VDRxq֮fo㦤-Ŗvuƽ)ʳR٠-T xܼ}ҿy0۬ege{!=4AfԸhSޗq^GwC.f^J]eP >;p=ڟ᫾X#}&6}ˑSmőiSR 6S`TGfc0F>l@=f1$?|HfƼKrj_}+6 ZKMv/Yۘ\9uD8WuK$*mEKdzåȅjִ.֫7anYS`*$m/^u#ӶBD7{s9OҞnz欻Y.e]zQ(~/Kw:?k߸? nQmȿ~˞Sd\l7k1̿FxgO6 1w=7靋Xusk/386 t{Ozه}Q|;GV~2ْ- 7ϒcT|2 4~͊Fm,x̏݇yä\ 4TaTT2|0D!l/(EgcCYp s cs`P.ŮwCcV.^ɸ"IC1%Дr:S_rڝ&˿-m ;nk⨫XlbK:vYZ_^}H'Iyl<[ھb+=Z:vcӬ\ݾZo`ݎ N޻T[7$¶au(jd,-;]rjgRIYx̹Lgl[咹o.?PwFHB^5Y+A f* ~"PS?o:Ro2ݾDzZ Kf_bo¿;֘fmܨͱ<Iظqv{%ve.;/ܺLĻl;#~gpPdEPriDZ Ta;5?~ȩNT =CZlc.\s:.d3O0ɏG1q~sOہdz7~GGi_wܠk<5^z|>JZG?䬅u 3=sW_w;҇60hbW..am0+s2k?g,/wN$7 k6yHs+XZ L(o$f(ܒmBqdrkm@5y@1 ,\.vt, {A4lZp ?ܸA`Fk<WQZUFb9jpcͮ_Ti[G@Ls 27THҴ3m'e m =h&@W1ya͛j;ͯD pͼH۽Mh[5gBNZMkAy8O4Do> ƏB}mBzuq;uwAD @߳Zq"f\Nkzi/04iKi7h:mXcBM[ݒ-ْWy !?1iFK:3N-(w.>W&<`V,~\lոMEಅQ:06+:xw#Nq('YY=de#Ϧ^4T7yr쾮rK|KG^f{^!],lbaFf45|bk#{;1"_YZD5.gNڞM!^XUj ]umA f>~w EC0p+)Dgۜ`@=!l< |eѲQ4 7{G|"UL}dr7 oUUkS >S*YOn\KKyȮ)E~u.:{ǷYyp/m{I2\ՅTmi>4eM^o|-~ϺiVtUň8{/V6ՇW_X.GtaSTLl.9?w3{YgvqNA=olf^(v~).qzzߡV{vFdAhӳn-{jW:s/fL9#+s3D֚gw0c&@'pAZV*ݱ3yKq yjڴ:27,ܜH~sxoMk}ObG_nM8yl}ݍ/_擏3]M[?|[,ﯩ&Sݒs@ i@E 60t@`15> ݃4pi(]9!iMOwз{۽5O?=ȃɃؒ-ْTɕ}ȖbpXWpsx*~fQxa3/c=g`I8CC܍\& 0uQ3V;0jle yi禯S˫1WY(i۾wTFke ʌrN au;#3ZA$`Ci؜bC)^mvH쒝\jʞיΉ kau+gYYؐ>j1띮FP .փ"k, X/IX 6h ՑZ]U(MIs?pbjՉbYv}e)?<?:uml*7ɪ}|7u9{ xBp ښNXvAѶڼq5ʛ(j5Yf ;]]{քw8ۥT~>}#Ste+Y]Y7jG39bz.tlQwכ\ cɚڜ]0mϮ~O4lS7-[晱GG틽x*}+L6z$-'ˣmNEr+ٗ07Td(EKyޡp59qju/] l+j;7lz}Ů xҞa t3-$ڡxy(kЎۼ*Ri8~ŭ'L`EȂ~~G 9 Y@Uh8ݔo)w,3 V:y@AF 0!X^J|II='蠲1ǃ̂h2N5@ ?5jM0 0%My>Ui Nhô$^zmP{^ кMY3Awhy=@9Q5L8M^s-7E;TڄR>nL0&/DD M`߲).Z~v+ cDjmS4maP99ǡVٷ-ْ-JD~ ݡx쀊wr%72[Z ~QB!og׷BnYY)>r,7 x 1wXZo_nq1t?zM9xwHQUS<7 ?6ә={7n<Փ3ɶYgg!\=>*̿K,i Ț yqc#3E~ƙ'&bl@cO) BM }n1~pqgp}3J?"v;' s?;ū[#$[^7!9A ^k;A4XZg@n\m&~d_APfz:As o4}'6@4ux,Ur6hUѶ [~M>ҼViv9{rBf٦zltQj;/ZMQq%c3Dڽ|b00>e>VY5c7|Fy(;>$+T˙6G9;u ʴВ&a={+|m1x5v'y諸(ܛ\7 K@hGw[){R:Wx^R˶TyyIm58{J0v&xIUbdu}ƿ,}$ k0~HRDv9%>-lG*/ >1U=*5Sϧ?u$&)SjC~iJ_ߵݴ\"Wkq bX . 4op zx洃Mz< K^@cC ڠؔA63@34.Z,AAP t!k-E~i޵* 2)hNll EzUS TFv> \@\@똂M3}OcoB2H@@Vy ςq@g}^}4mC݂"Դ;iB zr݇C7w.NT }~SZX'"2~ڠu3 S4uhKYb5MgMݡ"A~ ' sYij  lZ~eEӪ5f9/6.f>xev2Z?1o$]WJ欒4#u8^޼w,7uc2yc]N#pUu7,sm*gǖ S&sb|Ʈ఺Ӊz5l;nfnkmYʍlnlՍʒcYyet =6;pehcϛM5MI_dp$ B-ZV| N!j(U;ݔ}v]"CNj.Kiŷr.qцVú=(0c-ldzGʑTi:k?S8j?P|`Rڄ9+Ҟ\)Btyw_y)+*ܺȯ^s tˣ~o՜ 1o+uob>r?`/kx:/uވ$L m_hRj%sZjDkQ!c-< gl:?b+kPOh S\t55dڽ'rF 'g,핀e4}&o)%`O} ǙҙqUDR7#KhQقŷ,@M@<]4ǩz؅M 'Aн:Qm޷A0Ӝ @2́FD ,E͘搤 .C*N5eږ4Mm߶EZh"@ V㙅i;@='A j\K4.Z*tMwihh-8Q|'fq1ƋЁ3Kl;A XJkRb ZX8;h^ã_B8 x}A9ʸ-]pXe3ZɇV*Gk5idãFo5X$פgJn) $:&Ř`C>a3XIm 'w !v&H۱Zo}}qt0scFjDC7<a͵ѕlu(ڟn&ϰҺ8z fwL3lGM)LT3N6];Z2ӌ߱Q\C+ŸyȌ\f Jj5;\|pzkψd2<|/[#v{ߓ|n^L!xn}`3 \-sdWus)fٞOƣhgv=U{l ʁMs QbyYk咵t{V?pt5mIkZx\4dwt[! ,QT^7=+N??67 UڒC>: 27K6Pό~u W/ "/c̈f~dckn-X|{ɂ@<yi [ ЦRo>I666Oe@kG?yk6qā?m}z[o34z(,jgA&4 h_k_s [:ah+ݖlɖl ٿ'/7@2,;@}Cf]gjl<7@` Zv&V0pقpXJxM#\mlNUyJZC/U A-+[菘q:>yقFu:f=?!]aqxIhg9*3-DŽrP*f5 _~:.GmQ&*#-|U8*U_2u`IIYt-B{67L-mҞ3^ 2[JuObq̥BV_3yv׾9]W_p_7^[U%Ӱ5?ٛ1-׺;XUý.E޸lƁL#37S g lyX>X(qnTǷ-y{قu[8J%yMU09 Rd=# ƒ,[ aK pǀ@ ZAz\钴,}~%^:ex :o.7-FVh w/G۱ $Vgr˴ޏ@w\I˔A {@ Hے]-֧H}Iw?}м鑶h_O뒂E5ݫ{i=S4|ַD.zmr(Qv=݁C3AADE `^'3 /%R  c T MCo,LSe -ْ-ْ5y3hg/ztaI[c#Yd^PַWgB#wjaױ6A ,}kPk= +uu'g#ƻ7}AG[ٛ)Xmn ^߃/<7?zښ>jEJ3CחH#muN,]8Nl6 rI0onfF7(9N[nK ZIM\U7^b4MDYci_vf.:}NjѾ;J{zssC4rCJ,xc_cJ`C/zDl•^Jfc+)ҝmK}58^ï W*k^ ;&+ȝjg9^0V26c}Yk1u):^GՀHU sfp5f;s˚is'@^/O%?m?н.t\j@j dY觠Ge4mmҭW9q9Mf6&t- | $ ڳ=hr \ƳR%u@AӢ@ZkcĔAh9*H^44 i봼t3v$AA۸C&m2ȃT\Aks٢}Sp_nq"Nh3AG E%^%z2M̧hA:}@m:m0ȸqRMI@nɖlɖJƅCK iss|$$~52 2m+|> ϸ]"†vji,mr VzMl^=b}7y<.Uv,ďaz%˕Zrٜ.Õ#-3hYt#MJ1iOntk|ˮe)f.LDz74k΃KŕvN+UC1k,~^ʶPZH{溱ɕʆyK䁪RΊ gk~6㴶I$6l- y-uܷx̓͝xS7%~gT#Učw3ƣ`fmdru=Y3 aVWD/$8/aڽ-s֪x+y,qe.N6mES-l\W& W5ڝ{98J!~ ;&O_t|4k딖WKLAl*a\ 1ꭲHK!_taͻy wÆK؂zwv'3v\ϥ#jV A)$=5R]iv<]cu{`]Z+kS7ѕ5/q'Z֜MGvmjl,+e zTbW1bħ6TNrK~|e 8i,JU[ qvxEBCl^'A;\L'K oy"uȄ*ļR-5Q+C7T@7I?2 k /KB7BqDcF& 4BӘ@O6l|A`Hh.Gih0@ }b2c? }gM z:AKۧiδ8ۮQy9ڱ5Z? L7F2TV`fi}4:d܄'3Um P_SU50.EMa^k IDATn׺SmcW PnHeUU%lɖlɖ E5ss@_zEBW2VS3Cw,C#]Bykvlis vAbahh7\%\13`59g}.-(ije v;/.q33=.YS4ݽ+R8%jrDvjzQ|frE!(cqZbe͗vo/sVdsǮޔv+=~7)R j)0vvNd,׶G-;\0zX+fRF,HCtaP-6*( r9ظ޷f[al{RL%#z>s+]RSU:̨lh9q_ <~+Q[Nxm/Fn0 TiKd5%ڑ՗12yM3 Za 1x=m@W;yhIZN!8xq!ж/Ҿ)?gA,(giz<=Rd솾&_=D@vѲVh[V@UeMZWMɁ@joQs1k\xd,49pн^8z@c ͵􃜷%[%?E2~O)|M)5]ZA`èKhJ*;>,W8Q*.]6b! y<"T`.TER%jT3?kEKbu\Z܇NXufM6v"ծt&mUÌs-6a\psPz~I7Dr`ml[p-69WuZAǺ}2%]] *٫&}!!40hR"oL:Z -]s*wdF1,_~ܥ1ǯ3QKf {?—/1n5Wn,bk}v!$0'6|:Sjg˰W<7[ 7U{ܦR./GZ'n,/ם/o7=g4}9%g>`=:\smlfW\bn[}HlJESTRׅ+'FS/:jD]^Shn(;ʼnxbn5{7C+/c*h-{lܷCUnX=(im3YiGqdU4svZr k=ɠTs֢__ VSw̓2}?_-D t)6י5ܬ.'P`񇔷zs|S ռ1Afv@ t-Rtd opBIh2N ha<">G˴<v@wA;q dh}q PbicW LXihZdi۬ 0&TcrA}}=Y'tYDcyh> +C< ݴW;Cy <6tGGvg{h:dY{e-м\. $d4h?@Ɲfv\48en2 #BR_,p|0 <8{#IӨakSUv!/H\Ǜ]^[lɖlOh~ssFXإFi#7]@nݨd3c喇>~`0+Ou|P4;Z _zACԲb٦:ܶ#aXWҿwxk&p&2eŽ]!guo-H7Yi]se {~Gpzx.ez4󾮽rCΞik5gVse= *y%Aq=/A2TDcJl0 so8$ ,)]]UEKRٗiMN.__te2#;FOn:k @|DAL-2  t1A@4HG*@`!pv@pDu}O} cO58an m:=0h}&A m1c {4]-'GFAӻU!M6kX,HHm4o-&uyjq p +СO QgY,he;i8A5 /mv b^ctSNzrisiw׀q MaN &ێ3~aL , cp>GE' e*%[*k1;~Jǿ+ @(0D##0T!Rm FvO>m>R]\避 m(#5{94}pW.?* TS%zJGiUv9>;Ѻ`U>ɷm(2m֨wo&&0θŐMl;R+ ?սnf[7΢\bޔƻ x2nR[5swesi[S}=17Ǘ]#*J+P[)ކR7:ךͼ?g3^uŧ݂ˬf| Xi|FE 7*Nf .92zS>6t}Ev;Gp,Pkմњk4&wyeGs f]NCGʹf+Ż?gvq^ݳVqڝX; !shLTjΝX_}!Dc A`aN;7C-טD{ɜ#Zh.fXddn¥-Ö,?C w./'ҜrFۋQ`[lpP6g_UOٌ"vNjuՠ9sRym #~z1tVۉ k n_+/=N|qWUN[Щ rbж7ښ7, 3o8mr R1M1+sx{D# n`A@F,EsC`i@@bzadq}dB@ PHI@ }O߷h мyfL8m*A&twx XXE IM|ziнv[TZhރ 0jH9Gu]9 t^}w/ s=WUPUUd& 0L棨bf-j>PUEXj.mOK%[%?a'^s27+?Ee1CqIU¬kw uWԾU 1q+L&.[oUGùmޱlg1VooUKvc'Hl0fA `;h/vlU.OD-`6uATM/9ٰn4X̽C?zzX3F4J|8n773s*9n Ho-*So,I&g`P~i,\/~y}&.İp$Y¥ͯN~ rzg?g=@7 !^`9 M 2 rrS.<́Hձ XFș 㹓΃WekQx7 t":/ Ã\(VA`³~} R6OȳOh'i<!] H;tȅ}8$Ki䢳Oǥjڳz" ɀ@Iy qctwt9_m]Y5'M8%=ѵ^@~imz9 cN \o@H摈WðV1ރfp\yXvMVA~˸YZLA"-Sބf(%83k7 lqI1-/KD)_?t?ngim$al*kݰӵB*ştƨ:^ɬ@\^؞Lw^Y>ZS-mC;fT8c)-|te1Q5r2??;^BcA ™ҩvUwң]9T5y?^/,_ _ufTVw7R=47D oq>qՂHKʎckO "LL7h.L;7' CS~/}px쩭y-8py{8QW۱cl_ЙȚؖ5[  twZL ?\,UY[^/v[DSji{'LR]ݨ}oe+эfکƍ̊uGva=h~|n>x%cwO7OcţIrgf,\bj> ,?Gaou=Sü r~ JA r3 ֫A\E%>mW1>% Qyi }iC pQ6 P2SOX| _ezgяo,I:s bh_^I m7PρNct #ςXt̵ic OϺ?~fiO}Mo+g  + зD؂{ŋ1j.@ԥۇZytckxc VC7AeȴmcO63#%Es@EzjRP(#?&y ?eUt !e|.*Y~ՕXEȟfq&83hu_nɜN ($a"j&T86 ‚H8h$׎'SDqLFew5qSqؽ⭇Dz]ːu |2$&+V,טÅ]SOzǧH=%3x'r9Μv%?~-oak!H]=nu̒+ܡ"%88%2]ΰo~R}#bc=]36wcf#Z)ddbits趰=?!8XPC~@dˮpB`13w[VƇ݆[dűʫ8N_7UaPW#P|s6zh/ɚ/Zhk=t5?_q;9c4_"ꄟMCLYmsTU %䆸#]3d'˹poM?̵3x}xZbȉdí>M,SP뱎j-s>{i6 qWڙb h|?Z7AGŅK󙥟n? `+^\;tZq GܬA`~ MU:B ^6A,UoѶ9Лu: Vq+T@׫+y~_ AäE6 Qڞgph{m$K&rLRo @= @ ַDL:Ih;[ WG$9O kmkMt]+9 P{sƟkw@׶ 0L~#DyI3XN=(=@)Xbﯣ f7&+l nlͣs zZ AnxZEL'Y5{_..|+ Tė;6Nwݳgg@dJo,W݃li0ĻiQMv$i(`Ξj5b͇0Ȇf tjsꟲ7y݉e5ˏXqMǬR#OVnKFcapoğtt1?Ytӹ6fwuH٘{_R;^򑔓928^Ar֙\q^+7o~v2r|"%{V>˩n;٫>v*#%m|$%VZTYBR: O,o q .A=M>٦FG~L4焜_U]w7»A }gv#tRK`v-;H#j 95ft6Xf$/<.C[/v/ wxoJ3wXw/4tTO7 TsU00́u{xxp@D@Y \An |N_a'Ac%i)aB  _m7vX ~{'Jg'3 -X1я+ӹk(A uO@FWA\b\4 tX>G OܖA.ȏ@v΂ :K*; IDAT/DrΡBoӾfAb*9^0E:^Mlf/+YqO$&z ĂLӵǔ9A,zʼ3tޠoa;4q%07A}I~0 5*#%D'1V1f0.Y_Trƅywvf&9Ote7J6xh? R|fw󉚹> o%ؙ mLKmje2nJj#m!m6N Gg{ùHK93GR^7wحx׫աZ"J ;TXɗR4i^Yfld8OcLo?1\( ͎jHӯơ7$ qEWcdlzeN|؛zHkTd./qovخ/xQB:鲹ᅶaFjs#TodЕ{}-\4;Zc1lUҲ-}#2schN7HoĆ7{z9:GJ !VZ뾜6l\OnjSfDygw8. a e臣} 80a7s+ͽҶyUW o{->܁R_{,|3O2Q,\*Ќ&=&%;⠟a   E@n@ srs]W: v=\tXDzAU dѼKAz v6?p2zqq^ V{@Ǫ$@? ; 3BĂ`\4]`γGt AWm.;<]ݦB1DU@{w)c{X +KS@Χ}ayK_2A; A/5!ikz$i?@kt^ SԳ X݄#4ng`z p @>H<ւua2s J@> ^CWy".~j$' ܘXs! ;>.[l0e4P]=j {l+R|9oO3ɓC3wIGP^=띜Vޚ}Dza~}gO9y}؟ˡsa5bn;dDRs Y-67Si(vB}wzNn<>(>ŗ֫n/y6S(q@7O c)sn$oV؛wCaV }7|[cCͣUҩ;O?xpUmoLRʡnmR0!0#ڎ%;|o$l{Wk`(bb͖P]ډ#u2XsEoXէ]ڄm!s=s'@=>QsrXYblDΗWfNǫh<%YK=T[N\>q8ފhJ~crHjzY%Z/t ,X9 wgF8%7sb1 c]] [ ?,L9 + ֿ[A6 nv@\7@`y @d} @w& y6WNEĒ3 ӣ](S\hF@4 x-9tYY `?qڦy6_ K&X^GA,^ɛ Ki^R]./~J㌀qkd@,^i<[KkYt-˓=B]=kqa4amzީ8FPA`{ @2|Fc=a@+=:`t~XZopPmKLA7W5{xh|/un~z?sfGSl¶֚7^~t.7bu BQk=AO7뛫 gZCclt;775\;ܬ}Cֈ0\0W9&bj0:hB.F ejkns2zܪcUVSQf;_# +NU_r:2TtW4BB۶;r=(ijBuɐ/];R}DS3Vaڪ U;E߷|;Ce\hhFU9mT[rÙ@϶y! wpcw[ѝG9 s*] |8`a8Lގ _55) <%ԞYgBŹ[#6o@c׫賄ǰ.\o@B5MVwM3Zbqޔ;f8+P'hdI\3oNa.۟*GP f2fj>u}^w@t!.쑰=^Cu3.ۏ罡vJ?9 `@,Mej),6Ek4NA/-) C/ i^aa(>\yuߤXQDS bUti?S 0 ;GąT 7\?Oj:~=_k} z@+ :tѯRb#EYvit>2]"c E]yz\^k1:%9X'@\Dc } ΀窜PD~m?L]}̣Ue9b-BsGc `οPy;N0; MT.D(t,'a|,wPsq !_<~݌6.MfRىEڸ HE*RDQfTh7WWW ]?^iiĂG5G͓4FRD;s0M= ~ܫ"X)%|SȝN9cv> sϴa0vc>)d=5AF5y5hYܐ̎<)&Wn~+;e. Μ?Q;9$L\P8:,YfcQ7Ҿ~+p=&ZqeL n툙Ykuøn@y }䆿 = ӟb %Й}@--[*ȍs]EDܼ@ 4C=Bק >/_^b$N IV =L~6 ݻtm:h/tӋ۷wFhSZ*5_IVǷ\Yok  k`XGLٌ9l Gc9 'J -cZ~};|?dVuSmNQ&ZHfN\R ހ]Yv;yQaG]}DG LuR9mu6rf55kΔɇb{7N4R̶W릲^RRnmXNA4Y߾Ut>C:qq5я՜cۼg[hi y/h XRAb'f~ 1 .@]M x&"FE&%E=ӫYsJi@7Kۚ2FMn{|y /̀~ӣt,{֬CdQHo{}zs{_\m2ȗοO7K]|ruu^O7?W.C&EPp2խʹú 0JD.:&zR),Ua;瑋ߏf/JN6(HsJ}@rpC'C4c6rdn'") Vp-% ףƳ_;iIrrWr£5+ɫ(]١.it,A0ǐf!-6Sݤѵ{iYw]{0V-浬\zXeݘf|[ UMWNCuw8zJoT(w>~{;w9GF2}^eoxG[њ3Y5Dd'Bˌ6x۩:.RgI#HH&cwyPz>.dTaݖ\#6ZLv+\%h[ˡ;1\qݠIA'` ^ؓ4LI8LTx3|ㄚB)Yѐ\4T_3RC\l3 YsX=@m6|h q;&g_{KܝOy5iM~nCoGm#e@7enֵdf6$[ݙQ Krb) @(_C  Y!EKDA  Y8UC-!Nafm@H^]7i*ӶG GNPCC qyY6&*xp3A9@nb[>qoat]ou5+  ^&HUxɇ@K/k'[7K|b MCƠޮ" c2|4kkX?a:XZ{ ACPn=BQYu0TŮݶ IDAT컼.Oⰾ2{U^Xoc=Z9up9&ύu꼎_e? 9Z*Fzꉣfޝ/9kmy%tѐ.2;IښoQZhQ7]NL3a&J7}#wʾDG D(X{VZa,;?PВ5cv !j1}-^՝N[f[k3 Usv;cXwn_ "JvXvU`@Ďn -3|;5sp>j1=]E (]  Ӕ,:" Y2te T.]ty St-1mˎA$0!A/ʆəpm9е'F\n{ QM("-^ӅK$B'YeZNZnG;TjPM P!Mr]YBUP 7s h^ \T,C em9+ t5wKyV} 1}IvtQǓmB5dzS@4a=" / c2xn_/Lz^ rbsHEꈇ!  d2lQoh#рaZW$A_88 ÷{fpR;mkme up~r4#ǭ-ɮZDRjԓXH 0J<&>j[Ȭբ\f45mBUF{C%+FȽ|Ol;;wz"Q*WRt7P\!i`ޚSN%k hmM)'owmd#E+u3 ^/ d e#%ecǎ7k{lu=ݝN 4$±n.rIK88NؾPLp:s 1#( ݵKXÄ .FD73fQ )pUTtYuDяa2 MN^5i_K+ =B }5;O>H 0@\tN]@ǢGC?㩗P'>H{cMqU z 1 [88][g0 SIBvZ'@΃;˺۠kܡc7>RH?h^8dd eyptou7ӳ|M,]6VXEq'-8{'`;P#՞NN 06b6r3[, ĩu?,{oنnVsBEgDQ j]AI\0M32RlGFM̉IaLW]2lqq87p(׵L;LfhC2I'wrhy'yѹόKP&GW l  t^R$o&V_;vs[]EG_\QnW;|7;֊d7N)ϧ|6k%9=Gk[l8R%4;*vV/2mԑz*:5lۄa6\`O8TY dOR'aBD5 igk)]VNKmDm^FN<;b/8F}th1V7Yk7w~- K,]t֛i / hf,]lrb=,j)$p]?Ҋt¥&&X֘gP c庮gUqiu7y* (@ 7@j^!MA : .C  I<(1:@;'ѿ8[6 p֤} PGL琶﹡ +PcW@ k V1: n6f5Pя|oy?8$B"nOQkpgZE鱓韆~ 4tҵNcKо& e5.6FնC۫3 A?1R5 ryX-:a=}gvL~ޚDo~p d  {kWW&¥XHtC'%"-kPKU߳Av_6} W PCGts>U0!eJI@3Oj#A] ʣngmNΥE[huj"2.cXMt NGʍ,:gqtbex%'tZϸʹ]A5.BBhDndppݡ0F̉,~WJj̧p0fc'~ms/ Ć*W+'SF-sF=|0`ʴΎRZV>k庫Yfby[=vWz\+mAPE{M״֊HŶSbxeߌ LaeR'|zufaĪLr-Hl6\HHX &X! I $ JI}l=Fc Y:2gc%4xLM:Ѿ_/R=D?NAw :?}4z78^ ɏB{-Xq;0t$fsS,.Hi]/DIW{t@Ắ0m|a^ d ɽ75~Ľwv؋KװtKLc_$1;/_> Ghf];Oؚ*?wnC !I&kU^}@xXyTnϨ xѓeKumDn_X% Pw]c*Ɓgc-ϩ˻o_===fkΐ&)J d6a$AA #D  8@B%DcI(ٲ,n"gz87Ko/w֚?Ω{uNs^η~{O9~P,NvKZgNǟЎVbmЇNfAמh tf=nt#ɬJ͜&0s4۰o..u`[-paš9jENp׼3hغa 3s0Y n7]߷111&ALL@'1 3#gdtž5sPtj~-⣣1ED ,ugo!Ƣ_X>C |d!6HбHD!, X Mk i!DǯƐ0Αm!!ב5@Dc[O ؾ%O>j^SgN"# T9H,oD3Iʨ:TQ%%Cj#Abb0hyC-u#5_P]57ב:FH"|" h?Rg{REFy}=,ߏ9Gm!A=-(6=n:ցZ$O<}|?߾LI*iw@ Nrqݙb XO, ijSԊ3aVNԜM9c8]*qd*b=[ t-mx9E#ퟪ[{1:Ց=bԘG^^JX(7&&ɈAY:U#gL;(gR{jh{lDXxD&;ܽnkuۇ(?*r. ܝ"[O7.94|b*t>[w OvN+pZ96|ݏu\m[݋CsTA'judzE;47p8MȄi"a@K D3f!ȲG 'Fg:!C|TO4mŎ;K}q|lanhdF/tޮ֙#WЬFn4s3zmX~8+WcE~3Wl!z5 ,k qI6dfŌ|\oۿt$**=3OŤ YRD_,\A&raiZ 2Bw fIIa>ZR/!9$x $@K$F4RehHPDvGSVcH4)N"#[wIm t#9LŽXGMLۼ\>P͐- jI@P!A/:U$KdI 5= `LX`u5U5G<&Ld9yN}^ͩeM"p8BDHah쑲y>7!䇘Dı$ 8T0/6M|ңx7t)bsn]ne&k4bnb]w  zPd(drH`3 whvL橷 |)*.Y{ikۗ3en4&G'kd!֩w0e:a4~) )m]WKgt;c C?l6[/(.x}cNT?3ӍG4ں*洓 53g-M5'Lvfb&ng3F'Ι،~Բc/S܇]ecj?>v=-j?jn`gFlZus?\/'{0{dod麹w}rj{*﮵N|:gPa/:DDFk}}`!!6z`G.5BtͲqF,ck) qh۟Ld'jl_~K+;9/3* `fg \ϽzG''gw&1u Y7( ]32><4u4{ZoAn?1›.v2-fNv:(y/)Pk+tV>Pzuʵ u/H}l"IF\KǾ`qlIWK} IDAT t 3Y8T xMbbRexiH0GZD KHpX@ B"#p kEb>!hYV% FC$!1򡖈ѯ1ͪ~%upI9$A qIkKH@%|.2 V3;y_}wL5cj_QH0'ujI}\0&IjK"9DU,i1i 꺄$ISxo I5l9N( !N)mG# hP4U6Go2R~\`Op硍u8hj|%倕kg,Ϥ2kz3L4)(&ge S)Lrz^\f3w4zl_A ]NV]e;.Mtw軷^I9wvF,τN,sg3y(:fX("(UT|? }q4wrQG%ϘB s2[x5vmWm|};'<'?^Vv66y-5N&+!ւꞯ{/vj fǑwXjGɏuFV b8,"9>~d7Ն;1h PݬhBd26"C&>11xY@AkcxǙQeqLB;4fܙ0B81,ۡGo_hy/}7s,pfh-_;Vv;5s~<+W`DLzkfVc,nB jԑJCJQ?]Rre$4$H%D9dʤd@,a7BjFm:Od\$ <4:20&鐟V;0=dWU+H\Ϋ9#R>RSjϞj3ў,6@F Rm9dt df^]ژUc|@:)2%t^".ZyI ϤưH*!ԛLw)~yؤ{DFvF8Be-YSjy i6donG1 lQ[C+nnoRε)dVNa;TM` *y0a&Ŝ3777)<ΟIXqԞZns9;6B 2=&y!BDe>bӘ'_Ecn5X\ q?Ww~ЈqY^vQ 'r7]v VNVnfw3~!o&ߍ}„Vna/զBou~#gvѽo9?o[Gg(ީ`ޅSoEwެ{YQ=-hԻ~7oOCk4ӈͭs;Sgv&AZMOknmVk[u|=f!bf8F mOĢ<0#"LfC#D#$""t-?Vt b 8,ha"v|tX@p!7->fF@̇fzZp?}bCOWYrW~ዕ/)%YTǬ\oŽc1,-D=Rz\/ !6giw[ ʤgۤ,%$@0q{h[YRsP SLHGT,~ȈS h!DZy䢿F*CS6UB>2Dl"#yO2V8u'ձ-R'`C #l~D@u$ ][AZ4%NI kz#uާ1T~sBQ X'cΟDM=a9U2%FsZ!LwH]U?#_"▚"ҩl !ZQjh0R@TlhB16Xyگ۬\K}K7A_^p\;_b_G  Q,It+z@kr%ի1W}VOr65c !~5 XH%>|<&[ڵ:|#K%@ැ~H$)K꧑G9P ê]$:7.,콊j$4`M^W%S} ~n $[EFI-ϪDrHKΗԦ&B xڤ4Ձ'Ljj5V4*1g"78Kj H=uj֐j|HtD*#_&܍x_1T;sȩXNCʺdU}xlc~|&׏{ar`m/zl2v ?rFzO <@9Qh}ioV/:o1QĴ=+4[4e.5:t~BC]& `)Zݷx I:<װZ`8h[^<;aڭCݏy#;k4OˌNW*0lӲl|^t 4ЂT 5L9`V;'ZwNM^4:FGeyާ2R|nϪN*jZ_\=|Bܨo,֞Ϭ~kP oniɇVq'G̽԰*2x4 a3ZhD5" bb"<M"rLa0(۽`{F|7 Ȣ9ca)Q,#FhDvh2s)Ad0,w-7_=QީF$D x++y\A/HuڳmVr-r=oV/A* 1+זիcc1Xۿ}HLt\ H04q ?DzTfT|ʹ|!K$@Fv(IElK]դ$BFu󪏎:vG]O\m; e VH҅7}7UVԛjW86-տ}Ͼs N퇇^ao3f'j[ j8-Np}fzaQZJav]`t]3{`L5TDzoНs͓Q@zCs0ْesTr FXc)EfCǞ=ѻTʢ:ual^&OUp5Z ̰o&}5喝RȜr=H9i9m@88rZ'ۋkܢ;KɝVvپ5x]9+/[qy:x\a="E囹1@L2,8.Z{ 2؋"Љ ݂.t"t4 J*tM?Ǣ&dX !Иd'4p 'smw ytAfdt,dGVh9?72=?s_C}a_b]ScY> e`FGʥ1Xۏ̄/8P@!/xJFB\*u@i :<FF$' [࣮ 5uHÚ:O @ʫ>"H0T Q42Bv-2ydHB-$H>;LM?N*WD$@}]͙%dlE?rOͅ@29RijjIT#RT ^BD<+z_S@X)=2ԏj. o!\'{R7]RyTJ6U>/_C 0vyҀ|&AZr;:-'<- U['DF$p&DjkD+6UUIi$ |FkY_ P~%86<8H@WPTwT~QMՏ{yG_N>|]sYT#fKIeQU z,e3!9 "-+&}R]F,&?R&6EUͨ>'lY[|9p 8BGҤRfK(Byk"G^;4ml?. ^/_xE`xb|F>sNL.Ooh_5iL#{ٝ_(d|9MY3]v#LWr,Mh;7( Xu,dcߡZ!~(cXCX8 *g;N1w2-y* v #+`kYU3U4v)y|~ļhֱos6CabޝZܞj }t ^*al 6v?zvh@QPv*fڰU~AMˋ35'2GVdę[ aţ8ЊFXDA9|T&w\+) Q \S'oO͟x7ߝ֟laچ"YtDJmk >YWn~#WF)#=f#a@D>dIkHГA McddFdURJ!@m Q@ˈ`My&OR3}7 $ %?H>j3!ȷ$$y ,s5u tdtq AlMվO 3ƀ&)yPc L!!%TFU&.&eHV0ns/@߈8H]^wHݿվ%u6" ]6$l|o"I+rarM\|3&=\_VgHe6 2s+ ث89E'1ǭLC!76,M}\31Ua]/ (N0ur 5)n/M>\4h弋 'prnɼfۖvhZ&ad#GsuNb2e< <@1#Xz F\e IDAT8I* %V&x uktC=u`ԚM0mCuB,FD[U 5'ؘ\ٻ`\^vXhb.د5ݣ g9U9W?,-ƞMNyoP񊃮ݏ EFhh#V!@H'6C3ԣ ~-qABF8-cW`}kc\{o/q/4kN~xobc._#?!Q[/">^}7r> &T=V}`ql?z<kՁf} 2.P0#W"I̩>l Ñ: i @ZCWBWH04Bw${FGͬSYmHGKs HYէ'~?dS1.#gH5 L׼aoߗR$:j Ԝ>TԸHSZjΒ")i P憎55P0H}5GF^'UէRҠ)՟Pv(@|(%BcDmlcO&내df]{ߩ5.>h"Q8r]G>myhϩ.06>9hqcs4 "9AX^+}sx| ҈.ٌ=x~N#"DC8OmocGx$ c(`BvoS^?Fb Óg*Ҿs3&c%43=0F_7 k>30odBzuİgq]<>L]_|{*`gkp͗ŁZ]vsbP8sqk\wa^c_ aj~iX(y3 M囅zeȈJOа]m4n;b3bB#䀘 g%˗HlI}q{ ?{b=E: "g 6y?=WcZիfJTi]F=~%b>G)Kfq~0BZlj VHEsH Z>TA会juh`R7$ۻ!,J Hlu$x w>9Ph##B3Y@_GH"5uI~s ڒy;FO>r%$(iR`|HdA,5D>#Hu5oM$!PiG2qyHD@5jH`&Olڭ.=wݵa|~ãTpXL?2}]zG \_d]N̈́Dq!L,cH'eitΓ2LjhDZ")[ 3LR\3Rl+RN/m? iȈGsŠ4;';vW|3oF"Q{{V徻xh; ?bGl;ߛTnwj7 ~bs )tU P_8v,^.:zWm?۷a>>0] Z>3'GNlt (cX6H"(ؾԨ6VҤ[Uod"ponVD3ۯ/1FD/@ tBr\[cj"cPfR5MuoG\{4zl?Bű[~>I:A@و/NE@Y3|H WE=$X)GH-!p >{+-hFm'+*9$ wF8%̠9 .տDa DqFF47Q95|&LU-!9tޜ]5Y*25I CF.?'H&E~3h#`ΛȦH]y]P}&R ";FScRVڢj h*4V$)@K #3ml?>{'~7H-d\VD>ޯ,[kϭfZ t"apa^fym=3'huK@Dqv1,.:D)bjXF0\m)io81|84 \У\h3MC/G3U ieK!8E;zH6Li*td8lGKLPr5#Z-aWp-\ ւ٧sl"iX]ɍ.d޻ӷr~&c%#f_gzw_\}7.8δO=dg?^r9={{CMuf/iB3"c3[cX$ ,_bd΍ƮaeX63 h^^Z 5Ϗf){q$u "zkc wVuFϚ7V7'v.BpkW^ @AUZΔ⣑-u& Ktiʵ|-m c1 wݦRg#X #AŇ@ JH"XCdz$8LJj2}{ej f'2SǷHX--OZR'M|p&:]$ػ\!MyΛ2<'u~'5[ڄSH@:&'Hu,kOy]XRm#@򃚻HQs0B^Y i AԃG⪫:GQIG^g[UyQd>Dc!Dϫք@ymR5H6e>yzyJ.$lC9^-/j bȧyaF="%>rgrEft;~c@.cGM3;5L:BF4}co92 ~ 406a+ɋ"7Gbc8Y5kq" M]}FH02bVAK#,@8!hBd:C}R NXW! 8@F<]R ZG>@c$H棪L;IS6g{$0ZAjQHp,)1y5y䛸CDbVg-l_|@'D0U_zՑr& c_]"~յvTJ%s]5y_ umGk !ُxWN$ 6޶X+ VYv[#k:1-~;!ASν&MÐ(z OEwд:WۊJ)?GL!ǫNV PLO5_b|Ǽȯ4) L!$Ye: ۠;qLGЬ <È3Zbh1nG'ga Nj>hV{VB~qb5 ~O8BnyY-7.3sc.vfbb_ v+o~tٛc?;FgϮwr37~Xs[8Lߜ6fs?[tDf>nFz, _DQ5й5AW00QZaųh5(z7Sd8` ya%\G YEm;-i-W[o=>a43\Mի7z5 e+6kjM9~bPAW:H0HRSU'E&iaP{ϠX/Gt2-5D?&*> 7`fZ%))O"$OW$O-p{Q$#h vT;<gpyHPu3IAޛYv}o}L6n S%GRɑe9UNJ**F\N+qHʊJe;VDQ`H f@L>o߻K3wpVuu}{ιo~oSc "j|;je !újC'Bj1Ϫ:/F9UED:" ukWZ'é@vuI`0o接}uױ0)˽A]wA>~& c wO`c,9G1jmv{_fz$`&>g >{] d6CdRa~7iv12%:=K7TzWSj7y%R!*1-65fX$<], tGڴZ[q 0omBv7 4; R;C4un`Eڽ r4:p2;v)9y&|h3p+DFx]3kI24$#Vk%ى7*_)*%Qc-L4p"'nbZRq޷;W/Wn3;sXT+ꅛmivۢ.Wb6.HN;↝ ףK$603:nis;L~D)UNUpb#aoAMnz63kWow_f_Tt&"u"o!aԪԳTngQSɊ0l{p'q;\2HR"[}G?%b[O<@ ϸ;_>n615q앷y{ Huhv*S>D'[?U&b?lR(9WUhuW. eO3OcѼ`"W6҉װRH>.{9rHUvUchE"Kڴ{YJ pjsD]`34Cqh7ZޣtzC$c$y4*B{7OƽX.A"T2 Jkiq7p)\Bqs EwN_S?iljNv5o8rԋ>E̖YcVb/:gyEaL'eb܌նoQ,t넽h9R=bӕ;9')e\m#jMyf-Tl=m:3Sro[-v0V"5?щc96$Vbw]߿{%}`s}_2bR3 ADA z9岌DMDXXxZF,(<"nfNT'TQu}Fo!"("nGA3Ga'pCхu"w!%;2u'cb1sUߎ : 8B8È}Y]{AmRaauo"q]͓N:y>/P>^'H6KklxOq&GPr ˪O&9vM 61})qIꚊ 0 ߷s5]K6>裏y,2#Y_xF{/V?/ʚ7Jw_YU&alȱBFBm?ko/qHDG7"7:NC<'K9C:`0Uc0{ZH:3A!bvGӔjqI]`NN=I3 ~Z3s11thAiN'C $bV ǀ&H7OøL&0(&f/PmT*K p)V:=oBT2dl-u5k/lt8OV{&*hiNm;`uGϴOlȟް1'BgԆabNt5{SXvܞjv㋅D)u [$ixn*obE1elF®m?w#vַӵ'/@.m4V=q3 o;',=Qkkp>6 C,k̾C>/+r_=Fm#/:QI -6}P"&"JfqF8얮!ll">]>"BG=$A_]#O tfPo@Ty DN!j{dU*jL~Xu&$q ~B ?Sg0R"SYF;%.BYD_%Ȳ%4I_Cêh"9Μ# d7AuT5[TZRU|:Qϐ1 cMeeu>裏w}>nND7 \Rj|u⫋ad-]:M,~yFgLJNoښey+m{DÃ$"yfF`a'Ѿ~A:&$Q;C4zTԛ[=¡(hCX@miq4Jdz iwp*fʞGU- I"5IUU+A^M,cOa9"ܜC1fdZwf.3ןf~0maͿo='togp/}{p51S(f;m[;·]:ح/V/_lZ݉O.lZigdDb/^\rLor< 1tD#ҭNI#]zfWM[ۏA,nݽzڲpu^o9qsKӎ]3tP3#K]oNa.]?;j7NDܰyIp~MYbvl#$UnchpQ Gjo!GF0mD̽!sڞ Rxh"fU̮:ADAmDOc I335VDKAL 5֣:QD0uqC,_'HQ.Ϩs2qD$"n#n#wsTeKP$e{/\S Rm$ԸuMth,>5jc裏Pl!Vf(-x2c>,{G:|՛*)<_yl0aCÌӸ.c^HG"qiw{q68nxslF$E XҸB6i 4ضPfjs^W=]S6]H !l+A<Ϙ$.lcBfLQ':EzԚ=<y M7FsomYpvBm~f6Fͧo?688ݺo&(f;SLT[N4~>5NSyOEH{DCѭ\+#rs%v"Hg&o$ڱ5Kfex2pIovn{!qktw,ךbmvґ;Go)_pKR)PX>"V3zj~W[mE>c2A)e#3 ~n b7k9-G}0fUD}p$&U qrKb@JWEMu0iVI*u;lfh2q;j߻&#\혝{zm;+/!/<~cyjܟ{ޭ{,[4f?Aλ}Pzx0a8 XKD^~0>k7APp`"ˆЙVǮ!B*,n@_Cb "MP~~_H>@4 uN3TV}#/"DSW35jgUi$) b,Xǐ 8aWصoB}@$y׀/#1"1K"ϨgS ՎVs#FĪ&N(]Km\/(ke6e}Ǐ$ȻY%IpPGu{KwHrS_}m$M*.xqz7I%89"Y޹Fblh[5iV3 &'uzE8x zMr4;܎xCt!wh?SW?+3ݟl͉Z>S5{ߞ+MO擕hej>4T7o&'ܐ[D_fdu6=L:^9{YAx* FKÅdr1bK/\i+^^]F L}psyߐ:^ZA~ nVʔOXҹvwT8"[b^Rөwu==0t>#uX]A̮aӈX G[Bz E;AADK_G.P&Ӭ"([WWUMv ` UT1"1&"2+jk.g!B["tH /ǙU+ӤxYSi8MP[q,#5- ;-D0!:&έK߭aC~0A cƪve}GxJYᾟE&AUyd4Eď47Yę*b[8sEؚڨqPW٠a B>,)2u7BDI#ڙgLyGzgv쌓6b bf!3õxmeXoOVGo>d7u4b'2Q3x3X(EJ~hz͡F!^tXȱGvƅrR>T6;O/?+C~@O77?1O)!*n>~(bfE^)ƽ°ŻәRwU 9)`EǢS$H-ձ&"h*"<} =4~d "~t ]Ou"ػkZ\ !6" "}md! Sׂ}"6ݽ'-ƺ[Ogj#"HSCvGo>zW;-AQs]515G5߻ 2Nt.bw=]v!}{G}#‹[Țz,sX8cf^tK_y$ge]8oLu*ˤsi_px"OiU:\'~B!oV*6cC!cd)|za*I!d"vBx~+t`*i%\J>U ; n_ukx %nviv:xD+zh y "p BPC`G87x|A=QIk㍑u3үItcGFkr߶fF96d<ëml;l%:cC':7vzc{[Ŏv xi./nV{xzg5Fr#OĮdkW*cdw,'o4:=og}Sͪ\ FOoNw5JNλ} } ,"=GFaÈ*r XtDm !d6R}yJWsWa(aj1 #,E`a0YT裏t5{<ĕ8kn.uuLgi-GyQO%Ps]ik} #|ﻹ" >Gzfe9tUF=./{t8Yϟ}onNd0~Uϛ@_]W6{|䠒g0 {Ljɤ6g kQiZ.1kTΥL hۣF\Fw p+3٫X $QMFل(*{W .]g9p=/Gwi$)>n1H^ab3Njo\kb~CkGg_8cNr}4؋v%Ǝf'kٕƱGᶝ g/yQ#ژOv5~;^5Vךu#$1})q<Å/^nNGzv^>q&czמM6.:qi·h/r7=ljn?SO} Ͻp냭pY8y{$)V_(7by(8,w}Tqw[#P/quJs,Z"o$S67s- ]$ŽX=uA=H۪c !' "UC}yu"jcKƆ",#«(#[jÈo!7cZBJr b{ÝXs Bӈh+W|uɍ(SVj/Ė^-[jub$ɕ ÈgI裏>~" BqYnUU9 'p3_ޟ^Y|-=-&0@: d^#lF h^/nyf7D-098ͶƲDo PSzcx@ZmVdHEqiEExE 5X uafҫAJR)\~PgD FLY?'[ Hba7JCCNT/ld"nxʼn8^ e;^Znk 7 Ϙ*E*D'V wpiD̉~? i$Lvٶ5V"^$%'RpӼ[ZqIVHeD߿hn~{+Y#7Q7b~>7b?p]`2a+!ˈ!juT9BX.'{YCDINbIqmq'1߶;I˰I< ϫ=zeuBBӪMZ-BTۖY5luռdL!V:HŻ@g7=SA ˨>ߟQ?Pn8C BtAտUA$ Cz x1 Z(!u8>裏TȚ۸X wc Ӄ|vVË,?{xqxbJG ~+zCvbcROd0-Ghu<4צ\Y!2xt!m 8A&mb6]'Pe`PcyvbSib4նNRg!w¡]B8bg%7-`jtaZ@؟*KJiyx'f`urqw^=2Upfq72x>vwn3͡6Px`hxZYa\O:Od?|3'=ɿXGʭMd1 v` ʈ(qRBF DleuUd'P!{<=9DdmdLmcywE!<&8~EDYjlDq|\W1{ջ:LU Hg{b2"VT[>ӆ-O"C]WcE\T 7 ԎDJ,{"%l=濫Ƣ[Ny!L;LnT?n+F XK}O&tGl9xi%.]uxbf$﷈']ce' Xxm\5!L,b1;4F@,2D> hnlN8ұ 5G 1!lJ0M8xl£ڰ&]A ƨT1b]/cM`OSmN*1At:" /{!)\ !BoƈA6Bs߇ͿIͷN|Vn5i3GL&P: `O{<9{pX HSY9T9t蛵hcNl;1j}k>i؉#|3`3{/\m=<]|W|uRK`O)C;KTc>t g=u@}XBS@XuNABh1,q[Bk"D\6qu` bv}k `|a# T;5Y%:&-H"jX3#IfioJZA{ RBnZˠ"@;HEuI :jΫ˖U}շ7Rm0FטԻ}GgZ|/ >/^ezdIe~|K} ʍu`.Mz&a{L2,p l H&iHX!.mIvpAr8j+% qJg5Ӥb77G$-'lcW ]$y_AuɭDP:dO"T79zrȇ-rlECc <[eK|2JSđN1VZ܇>i'V]MvF:Z;ǽpFv}5}v#W.4í7]7}so)|G.i^6Ž9z*{ryjh7~L_F?B?)P4MU]}o)~q IDAT!9,Ed'Avtc#Z,Pg7IF" -kjXjA"L$e$Kimtg_ 0BWT[HjڲSQo ApF%D긌՗= o"5hZH$"8 qC5ǾN0E`-汫ک!bPϪڈէ2uBe5GmWy ȨyAbB>HMUylq`< ~}{IzA׉o6q̏lan\N)fG1Ǩ51I3b 3O[` 2(m,ƇitQJ˄DivST4I"Q.yNr"U*Ani~!:Wy=J7 B6rgzl7?9~'yow[۰ٕ7|۬e7;Ƀz򁥳S;ɽ3vukpyBo5]+CĕЯ.f wmFV3́BT~scsf.oܼw}WĚ8O'.u>b[r+ED8 #R?h.]u2,lEv 7'Ȃ| !dG[/눵P"B=+.a!UնxSEB$xѮ5F!!ծ.F |K:!5u5r?@:wSA)+ϖ:z1)zG}x/^X'nj,p{]^y 'g8:mÏop;2xic>~%W"ãOY]qǯ7K,=OSx747oUx~%F&vr维p>O/7r}n/  ʒ8 p#1Cn } t "u#BsJ #4u b@SX&T:p>ziVgͻSm=Ɇ#N 8ᶥ=nvk鸋UEt c0r9 1Lo!q=5GG]HQQϬZDr5n(ktEGywl΄tױ~"?tm<$ <#뾏pOXMЌ:ͷtz6ϟT/C6YǶe)B$׶F cNBk$cv!7-whu2$q52-(C&1 wkfLpI!\cn:HHK`vD]V  Ttasj0#FyYQsn?dG} ϟ|͓y{3Gn𻯈+A&0C|%,vy:9zJz|z~w=d􀍏g{xaK7]|PX7߅~6>bd7rAX6%4,mΧ$WV@O IUd>|4۫# (PDvuD LD 5dD2Ccpb0u5t~@}à V?Ψ66 Js>{II@ BKjv&HMKzaYc7|߿'- v}߿8u}GYX8ys֊n.˟'*D􉆣? |SC`[ilF"%؏Gh;[jͧhm2 lhN`YuRk1H @Zp;C޴5p nCZ[Ӿ6;1 7׀\W}3,ߊn;]{GwNY52}TOU_^z#p5˟|7%57 2/k,CFvB>p \+!eLS3,j=>n~-ƭǩ7 L3O"kAK@9mZ"NԋLGfo ܭKWIϹ~oUnPJL7O/~%6lz\s.=gOfO|qq-p'n}By"wqk0._ aL @6o"{[ngA5,qBvkgo<, $+ Fv:N@ ti QuYKBm\۸{誾;q !d1RLazA^Nc_UuulIidDZ~t4*~J !Pu aj~"@a5m=ލ.AYavi}A eNG};xf?xtd[KK/)?>tza*.c)v6$$c|}HL /SJ7i;$M!k2rjko0<_\1c-"7V|lB#}>n$ù7;'ao F-61~ AdL/!fs9B]y߉fAv};t7X,<\ER$K m˶rc+UIPܥ:]ȅEWENr%RY$j=+03X[~/{,3x~U][?lV8Rgҳ3֬IpJ[Pf{!6oPۡ͌Qԝ 载}r쥍y+&okvU/M< G3CAcOshR.뤊 # !oю P6Z[FR$v &{/42`\ǜ4<1UWo_mz`c/6{Hpڽ -|iSEbێuim6_B+,[} x=UO[˓t>uF-g dWaUkP̳:{!Diռ:6I?Ayl}.PW1Qrщ91A[~?|a,`_s+FU3"i(woXjK;ƽs6/ς mרƗJ2F+ͤ~J AP$Cs^.V؍ .H/agbA5/h_ɹU Ν]V_džk0țllll@ˬR;y}RiOP`/MVVTza6+Z3#\[^JM$҆㤪ފɍ=7t@l{PhJk$Obi)RoIqEd}õyxj2YdHOٽ.^ybv62m׹OxG34MijE5h5r=tSVڼFY}H=hh3 4 b;Շ u<&<ϗl>133(-GFxɛEie{ MoZFCW8l׽m{Rm3'JE̷7! )dPvI4YTf+('빘URy@bT!LH<+n)O t9he)tծUd' rcȩؘ" xxhu u`sgl>ήSsmsv\|M~N Tim^dqm}:ۆYY1ll~'9֖+v ]-<}a+]47u^b{cZ/9L"]$i\ZHUO`|]Q]!Q~}XR;Hem=H#;Z[VBƟkp7>sXNjIWG^Ĩ-H~|ݶÃyI(GC c1 waX,Vޅ<H<_β#˲ARϟ;j :ܶ d_CI$ h"=&Ht}\jZp&Y'QlC"eX njظM]m,srPէlq{[v^GFg=/O"O R5)nƎ\EHAp_M;v_XA:T(6o1>Ly/!j;|t`|۵yR[!hi`+om0]{n9C45`}kLaMݥ[!Zk,MrտE5Re:_C"Ig iky(& wi~TW G^בFv߸j{Үqiմ\m٥/QϯKȠ,ؽPNⶍQI,ðg0#ľok,͠U+@%˲lw<+VT ɸEb|ݓ 6 c"y<,"s\s{ER&AEkZx~ێ?4&0`B-7 ~ޮ m?tͣ\XCy{5vjA={W[PMcCǮ=`xcb|PͲlZl4rjɫM V$y?}էI( *g-$b`LO7I^ ؂f{y{&7fߑ&7` $(Y5"@BTwP#jZv x||eY!$ȓ<<6fO/N7A20^yF(-3>[U@w{j?Wyխ.JNHG3Z_e_]ZyIi*ַy+ܜjazj5gc*ҝidBN]뤺 vWD7i4MvnB^FM#m=HlcZ mvC\<քޗy S*Vޟ6oisMA`ޤXGKNJnm{M-hBlEb0GjЇ&hҟCF{:D#!X@_C9K٫E-,hky_^E@d u;ъ%{n;>o#boyWH#,*"U={ ?+A}(L$Wl,hΒ} >PR=/7] lm?EueoTZ-p&s8k/ՎpW,> K'YY{J'׸9S`| -laR>$Zum^GEUܨ^cQvZ1{<-d]myΎ"mC!D9|rhnWmH3qvj2ԃdyEz_,G UK]jք&*)dKhr𼪘BE}Pj u4iϣIX~JJ@M;md|"/=țHm6jdD~ y! ib6cDj1Co2aMxjrIj2e~ޏ[{ܱ{GFIMvH :Qfu2SJNSʗÿFmnFI7(y :N-/I[\.r}%0#Cu.ئ^kLγ2vei%TԵͳX:tiljF[=_ѵy iXI 5^[{-ؘN/O#ny#'тoO*-ն WԆe'[H\}Ӟ={ Մ"ۺHvr'*nlx>۷,T;ern#g}Yy4){+ ;md̝sA:jJ sU""I''Wo{L ' q<%R>U}h='қz(^C+-HH |EI42dD+W8^Do\:7 ſD4﷍,˼Ep!q?20AA([{nRlN;[[P-*sv&K|*‹q6yA'/]iV86K8:N-JsYjE=#/5܂ #y9Ԃ4) ŋùYmҨnP"虖shmbV"7W\)Q~țH& E=yҞHUAh\wrtq' x0â XO%\ $hCo\@+z NRF2(VH# hߴk5s䉬=9zRHD枼$p-Y$vYwH&˲] {/ eY@*4i$˲.T!wG)o ?D=4_ >.dX{*`:0,Gä}U y ]̯sp9ӋP-:;XZPPNm|CHK$3L*C^`6{))#k$oH#Ug-"<~~wa`΍MwZGgpn_!jvcfc|1E*lQYc|3a,E Ax6ۘQqh-!h>PaR/>Zx#dl"h Z{Պ8kA°&- Y@~;W/O'u4O7lLO# (BSNA9$Ln w5m? UHAW=L"Z$,y^3O~{;)Y#rVA|_m^>f}3Gk>|?ngFV޸}N.lW\kPerڿ}ϝA4g7*=O0L^Dd+mowQ{A:ZC mFtmo˶lA(m;%h! 1q oRuf{佼cQO Xb|fF/Ƴ[[1mg9A1?*˲,s^y#&C05, KKM1huQ;ԟ#2T7E|SۚIWEmlfa09WI+H Z͜i3^ ͔2Νsl<[7,]`O i.5f?U)gFr]Z79v dL[*K?'z٭XHEҝƪ=ksHG?ϫvS'HNDQ;=]GI=] i>R{ OF vWp؀mwm^chƶu =|c>3g1!+{gIwÏ3iJ-4 eRh|M붉z 5}߱;l&8zڮ@ =uC=vMT޺ W{._ќGb'YD{3v붿f,o"VI<i{HEߴ\GWE NfwИKoG^1*AȠP= ڼ~m&yJ _)ݢR۔w)uW \Vi]dq=t_nf#, k7BRC3Hf6אƹ6wB&i!c7!RfI{:{.2S6Bхx?A;B g<ڼ7wQo0'˲6vM^QynZ۞y%y㎢n?* pJ$X}m4dh?x0CdPz'_A] H<סUl=4VaWz"b LQR.F"{E.(Ǟ{ylHj m!::gY6ol&z`KC FmA<:F|{?*]#ܚyR& PY6WoZ*޲Ҩ^HϺHyiq^Qݠ\Iu5jI)n ^k+ʮ<|E&=4zˮUuLZ cRa;hBF5E0zMGdpB471yYsT N!öy:7HyIl'%!Q(=W@wmGBGjG6s@evߍ<A< T0miÚ7!m.ޤ4GB#:ܣW5r뜧Vbe2w q=45B@ZҦfy["cN]H+ISW|w i/µhB`;{D=oZwES<vsh@:CZQ_ HϗE=moA8ٮ >cXjuwx}QEN\ yN#%d,~rMaǞDՅDt'{a|Ҋdc*ZC"jKjػ;H_3H{/Opv՝"^F"|̎5~Ĩj**kmff^܅y[ GRJm9Oysz5^Z˧ԞT@ ?+$mnE:NZ(#m _Whd֑f<G[v/34T{ SٜWGz*ٽسl!h@qeAu ȑ_Kv:IgPId|^. NMG߳?Jg1>G!GڪG Q*dJswx M"!&EE{,&HX'J(ZoxK');{ UF{wx%7NZep'c$TݯIIZ@ň횗ml%yRs$24;*; > ;yn䊵wh#oUȸ jΌʾR ҿn պ0G40\:vui/&ct{25RAI/b'ZG:kې6ϡNksysݞ{E )"[O2>nZc1> `zw]2l}92: \TD UNGv{:xG佅ĢMȰJvBPHK2`UTPZwU|mABUgkCB2cYBߞymvY jRsR-by|^ئއ ,t/ xԑg0Ź{8+%ޞ8dXy;_:@S~w}[\fr&L/\dcf3Gp{non)z)d5k wbj~ i66-H+H#{7QWHuRES/3Dj Ɉ{ _H$mJv;oҎ62ݣ|N< :QFﲢy5˲w?^a2 <*gC(VdN#çW$ӫHh"!y N?2v # n>D!#T#)z[I^,"*U$$^{!]!6۹U1{ˤg~nA"8Bgy|3zn< xhβA G{ΝRkǫ ,ㅧϾY.xRB*@ 3'흢Tb]Z_ ;ٮո:PO%yd?6 *5}*ұFC2GkT`ڼY2&is Z A:}is굼i<"_سyOm̋4h32ׁ+{*݊@b||T]y=˲kh=iM~+hR&4{8[E2ĽWH{PK /ֳd:I=~ 6]t ۖݧ J }kc_gZĎAczcڜD+_*H0NeY+v`!4,~(BaErl6&A>HK:MɋOb^zsb_}n錕u/RG7n*5FУfkZ>Oop)zQ< fシ`}1vQm,kv$Uֶm7^ۤȤ (|؁ fo5Nm^D:i܊ ~?YOcgjjAe$iFy>enURxh"^A8OoGu$4n<@kB"UNבxI2W^$~HZ$D"Ipi>irAFbp[8ZI[Fj$2$[Qn+$sl3< dLO= ]ʲ1~A3/pL+pwG9c{~H\$NX+![9pM 63>Z<֋4m[U#yH𪒴y}?jv2}2ZA $m>nvC;_GOXh/ػ` 7^,y^|%˲Byl<m΢&B6z,F;&v4_CWUF>dyryZ"OHb75$,Є܂}^A2 ;6~dt۱QiC8by%/X o>wٻR7Y~Tvm$7z[`VIDAT췬UR8AJTE(̲X1!Qwi;y>/m_΢CtO.=I5hi(Ho5./BO S*ҵًxTnykH琖l,'y+X9㶽[B*b? p³<5,^nX<+^MHqh~?Ijqq% Mνhklcm$6.on#ld.Za%JȈ+"a+ިW_{-#!:d92IAb齤ml tڶH!vM{ mv#}~ik_H^a|tfϿ#JAʘQ@e-w }<~u+&y.F3ފhل ;ed(s^~ ˸lHa(n<\ ($kh⿅]J愍6& ) eö"qC^GqC{ YA#f޿l-Of0z{la m66Adgo< "iWN_WW -^aHOt-:"ReaoF dQHjouQ'qiׄ >GF޳׊*X æMF{ճNd|Y~URK[ DFsHF"0&7m 2B+vho~hB Vd $2OUF4Md`~-=k߰m_GQsQkۨeʪغz !,G!2$yAg|˰WK]Tk+:+p]jkyi]r iW$:ҽ~7.R}x"@wv'w&dBg9;=jui;GH3?dv ݴoy+ͮ~Ps.2>ޚ QGFX f qW:I=pIto2;yofYv2~4矿w]c}so;1cs [tq5>iu\W@׃yPHwVﺝ7h, m@F`zIo`)0H'Om+]ic i{îGj:&@i R-TԆ4m6<`HHjs #r!fK|4n" yv$s(tdMWׁ>NV.k~[eh%r ̹܏"! HH^DZy0:6d۾}rlsHL".=/ۘZ@-*ny,kB-NAa_$i/cZ_pcjy^Qtm!Yv[m|2Qhez5:Ii`lx<kHqjݯ}AF=4js2@?G ɮ*֓B&~C}d9aBg5;D5d> j9 tUvmυ؝G+9/ي UH*HԼ ɦ훱s:m3 I-3Yo{fDbcI69wvosg8w B"xm/*9O RN2뤖}vni%Tm -/ @l_"NCꧼi*mO4 q꽙"t^Dgi]Rafԛ1ɣd(Bx ɲ`F^ |!]@ӇV3qф܍VWd}5} =&ShA2PB~>˓C+**Aalkz/ ߆D˂h&Es. cHb3[ka0A L0>:m#/u{ o2gbOmH#isKXAql\gPčk#v6d;_B9R譧C^Di-nB|AGm%Kv^d|By GFx Hex,A`tIK!B#VsA: \!Jhm(X/ XCB׉} 1 q9m{z);o/ #@ꍉ[llvHfl&vR~i fYv VSA!jZnxUOQ{nev^4εyY $mnA 6sAx<Ҹ/#n ׷a:>n# 2".!}^]hqwݞYE{ X), ݍ:˥#R~ڼ/T%C³'~Fv$$cHփBE; eEo[Fy^_u$f/Hn!! _ ɦ}ܮWql~h{~(Z9qodYvy_|  a>V& x9e() ND<^e@0h1ӵixiH`ZmBRHUd6ގH7C~Ϣ>; 0:&,{spn Bf}Nx X~[| X9|7H!"hhO -Rղ&Kh!9&'+v~ou#AkE!:v 2Ъ耝VZ!JvYy) k,o,˶P p‡uŮy ,)4|t m@^˒;k-ΓH/!X{nl mNJ㸈ѝ&U{Eɷ6{ņ1˛ӗWzޙ{ 7UCX 㧆&_aYh^Er~~8, &% 2Zv_@+vNBe^"idhmo#A֪6F/ yh8I!(tHY<A#6[{c0'Ctm!ݫ#G&$b5 sg?m"7]o#]kPCf(6(uGg/GYAAH}Hwtd?E{x^%a.d{{|ݞ3@{ֲ,F&'QXd u$8ĥTLHT.#?k9϶ B7v~rnN 12CyӞMdCbyԮ}$rXo3Y1НeYژAH/omJyQ잓sUD" ^@sH^Sa,Ai{m"\EsHI~/T'E5K ,VPB 0^FSA"҉ X.qoO53֑M9E Y}'`IJDWZ5^`3ur3˲BóA{ŵyyP@nyQ |y`Df֩N e~2*\SBjo nF ;׶uRNQ+ RUMۗv%(OzAFgvW>&c捼GemY $G@'52Ķ<_olAraYY.|sM7m5RϦqρ_رE Cxe](ׁYVR#=x 3>ڼ l֨ #h-8vH,Q_CFpm^g|tQDڂ @mZU@yQgP^uj ɰܴg/b;H$m m޶<օ}8avrm~g1O 3|}BMh,3 1lir̲lLy^3v_@4Da]y쎒z?wyeY? QH= CHx(lb"j3kϑiֺĮn= v2f3Ĕx&XTCN5dx<6gfGk!l 2_E ,no i8-+3MPޡ[ky!xkH}:_C Ww c1G,z,&ޭ0y!vmfYvl׷/fY7hkA'ؕeٍ<v2<K7qB?")?rvw,CeWt#{7zeSHQnZ^z!#m9G=\L_ti pǼ~ƲL>^ug|C @ W| yژSذyK6v?:6F.eYc,kס2JwPC},<O gBR$ >85~NG+;r w6#}}I댏^GF=mf|>l<| <]@܋ZkTlӔ2G 2ې.s} 5dx>rd{[qA!6afl(eyT۟an{V7cAaߧ{97{ L7 ٬׊tyϘQ{YzS%T5G>1b;y@m6rX`|t޻Z|Ľ#g1OU}"˲Ig|F۷OZuU U,,:,hmo"^Zk&,E:]P5E$Z>dk1-Hdo+3 xd=Ay2dP?O&屙 =lߍnRzO8nm.E>KH,QWfϟtm3C'r 2AQ< Z5t`#UԾ#G}w$[[E;h@ٹ3mhȲm׵ymG:L& T84A<=ނ#Q;G]ƌXί#ٹ3Ƞ{<ֶZۨش9P >@;Z|{~+2&GWxMܴqխG y>AA@p~6屣 ^@63>Z\߽(0 _3V<r7yP'@kjxރUY  F YyAd!&q|Df/s jrݎk>2cAehC+7Tq<ݕUwcޭq TfǹDˌ "~T܎"~n<08wly5iuTA|<#g1pO+yOJAApBp>*m^G,#AA_cEc͟0EX xZ/  >y֋1h ^H2 >AAEhIJA7@I" @   `Y   bAAA0   =AAA!   `a,AAA{c1   CAAAX   bAAA0   =AAA!   `a,AAA{c1   CAAAX   bAAA0   =AAA!   `a,AAA{c1   CAAAX   h]IENDB`openTSNE-0.6.1/docs/source/examples/index.rst000066400000000000000000000003721413546205200210640ustar00rootroot00000000000000How to use t-SNE ================ .. toctree:: :maxdepth: 1 01_simple_usage/01_simple_usage 02_advanced_usage/02_advanced_usage 03_preserving_global_structure/03_preserving_global_structure 04_large_data_sets/04_large_data_sets openTSNE-0.6.1/docs/source/images/000077500000000000000000000000001413546205200166505ustar00rootroot00000000000000openTSNE-0.6.1/docs/source/images/10x_exaggeration.png000066400000000000000000017110341413546205200225320ustar00rootroot00000000000000PNG  IHDRXrsBIT|d pHYsaa?i9tEXtSoftwarematplotlib version 2.2.2, http://matplotlib.org/ IDATxw|Wy=niB 5(K] K[ؘ~ !K"B $ !I;^dqٖl}^I<3sg{B@DDDDDDDDQE=QKDDDDDDDDX""""""""Д44%DDDDDDDD)%"""""""" M ,ihJ`HCSKDDDAyfj.LDDDDX""""""""Д4IK`Y̮1l̬n7+z|Cbf7-f6df7MDDDD&͎VA2cf>B43;U͡ZF9!X;?pFD5Sppc`5kffCNd dW-0V3PĉKW@OiS?$CCM\S{ȻYLwMxDdb''|1$௓8CMm=U+()'0Ouis46CVrA-f઺+FS=:o1Vaf\(jPKd< } \Od?C94ptb$aCz_!'%? |d:""k2_ x3pб뜬q>*J0ͬ`f̬i_Mn}̶&Ko1j73UQa5dzfqےcG3N̎q0_'M6xmR ~tyQ\o]WI쪺if=fPZgf6朣f )sjf;ln0kolJ~`2%vk>8(9c3{ݜk^A6yEa'S;3fvAYL~gPs9f\m7$fܗ&fv̮2΋ɹf6j*x2^d{{3;x?0 !3{p;%E?7cO8Or_of]{ ǓǽQrg&_K+k`1e%_,&qKo_5sBƓWoƗI_|?G{xޣ\ n !UDDdfܺc!% !| ycH&~ wKݘ< \;Yq[!R͹'&r2{of!^1 f> ؆QS}11yJҧ!58gg9xL F.ȡ"0`6 zɎ/.j<( cpEw|frWSx቗c'; } Oԝ2c<%ݫk}j$9~<:܏'c=";yu禀lݱz5&QW#ع5ˁ_b/]ߎWKTw:Ʃ.¥&풯;4|BgZOof.;@Mf'3cז~\2ՍyWoa R9M!LnV0/+IPws<7۪%y>3¦!JH`%eǿn1 5cڄϚ/"UfvJc v8"Swy^ !; Kxg.pqޖs'<jmc5`fӁoj5R|uqέ^2U!k_XZk3Q<8 BaWoOʳb/|cs._w8E 0)KCl #;?NO痟{FnrV~19f։W*8("?k~>+Z$>cB3&^w_4>8'zv$c/&qSD{~~Nޓa^k)pݾ,cs<]B}WYsd,>n?dibLx}>x ,:yם7ၼG"rx0t|@.PD&Nx*{}ñ ͬ koqNc ovU|((2XN;sc6y_r9(׋AbR!“C!!n!\BU9CY~|7 ^|aן~7U8Z-3Ww n=A])6UkYrړ~V㼹[GyJIQL3KB:+9ٓ2Z`>17~D3ZB-Bk)J^Lwź '\//".wxל,]^Ky1S)y.° lw'=<3;iK5*#31 Û1%{1n4ǫފ'jc_ٗ&?7x >^jjǓl[7CVJfn߁_ٷX2|7cLcB ߭p !`"GDX"}I/GRorY_>ifOwݹ߁p#'#fv8|(pumef/rf*HDDDGpfU-BXif0B̞\n1p #/?2W>ㅋSkBxݚ<* "1)>#_+Nޠ'J{O5C5.*Y-"̬C{lT2mB|'"""3B{M8ٟM!LDDT$!"2nfR|okW%3OEDDD(qN OR834 O@EDḪw曏7{P `x &KX7;o]%"'}_!L/%DQofzoλЍ'6=%?uC>}AB /~'P !M=DDDDDDDD44%DDDDDDDD)%"""""""" M ,ihJ`HCSKDDDDDDDDX""""""""Д44%DDDDDDDD)%"""""""" M ,ihJ`HCSKDDDDDDDDX""""""""Д44%DDDDDDDD)%"""""""" M ,ihJ`HCSKDDDDDDDDX""""""""Д44%DDDDDDDD{""5J389XfED}B1,ՌYY_s5+# op("""jTY '1|`p??M<cɧ$\W\<8+4("""U <hmx|`M^ `%0Ǯ!D ,L+o+XpjWgⳅ z:})"""2MV<J /Kkj w?nno^Dz`[5Gm-0>aE&n LDDDD4`d`p>œW'[,=~ŧ"7U`5%6lkU-_ftX8u>Wjh[^?9X+'1xxҪykp#+?!"SDX"r`,uQkvdk~cH_}NB{mzOmg&y`;^ƛ>~q""""2,Um~ |ZW[Ez8K8砉WE)%",zn'<>u{7m.WܜƫGHcyzav}(uW o_[뻡OD"J`Yzz/KNk=zYMVt])|k"kYViՌ o09ք'=':GFUg3mM*^Ӏ~x2j1t=JSiF6|1+c*J$p+Zk>D{ tm %"""2ŒOWqU pHA{ ZOe.Cw_i2DD&*D`t8>kF; ۏ)>} ^Bw_$U:8o_> x^;ߡiU0'/c܎Wz=8 r]7U]mJLo<'DOVp=d|'%ݡEDDdYk6 :[<:aL]7'k^< I)kX܈nnǫ Lux٘nP(@C:1``x44M" <1s1^US OJ]W_-[ʞ- I^Dm/o팩~k }:+n.;&^'p^\>'$W'j_4#I3VQ5ǫ`0[$Ju5tRODDD^t|8 C*) *e(B,4O7658ud9;vV,3 =&HIWx6`OT՝.+.«6PB,2iE3K O|pWαsͽSĊ*6|` <Ͼw 꾎E#%k@%G$'CG=DDD4/g<?6}/r1SwNGGٙjoOx&ȯ_9PT(:+=й48%DHO/?#iI2^q Ѫ-ˏ?G'Ü 2M<\&c089 x=ş=1W~8r]ioU)tm\W&y^Մ`2Yu88£ C΄UiF*Ҍy̴ܜF ,8/nog[kvEZJ`4$qxB<ؔ\7;9oԥb˖m˖Vin>@K~0vā8sG.2r]2Ʌ@r]mxcEr[Uc3ykObZ}>l )Cz$t&"""r0\`&|'O1}DCr0HFzOe/Sl얼:o)YK+q/O'} d!n-!C4^ūCwozHnhv-c`as,_ J`4^ڀ//ǩy|)JwkM# IDAT{"p^aԌWIa|{௡o4MDDDZ虁,jsASvOM|Ǔmqxe;P$џvPiIK,[&9c8 ionзl)C7:o0^=і Ex{1 oՆr]kBǖ^Z*;>K'dg?\x)s&k;8JœY|j{\\KD@[h:\Iq4ȢcVKqqVgfCOb=8r]06=.(T &<%4ɫjBѿw?1PL. F)(7WEhrǝ"2T%y /rv"{=E< |wm _>w[3-F|޶↗olH, gpZ)e{oݑl>£R $;3s  J`WE?A xh\u:՞cg}_9l6MGRչ0xthPm:Ivg p׸力ۀ\v7L ˖[7ԵOqa'-ՖǫRClەI  G!1`a$yeXT }2Q^$\B -0Ɵ ](%:joݏw'ucQ@j?c< q?2lXk&F9 EdĽ T]j4>}/v9ŗ1U_]qsdžxg0=f/;>lL/;zzIcn w;y`F;llL#Mn_FU/"hJidPvIU)2#uMӦ ]-kh4U%g`5p -ymD(% |Rxn,?|<)F gI'=\ ,[=X<='N*xH1:#࿛l/X]Y_4"`r]}{7GPz[SD'͞[_a&,Q8?M۳ 4@T mtPxHe&BcD_B 0(ٕsH+BMdސ}Ggd>yءHcd˗#/͞yxƓL{~VxeKY3G.v=>ǃWW Wi)A}=7OmQk.7{(t z+nn! v(;o."""uUc>ϋH]8M![H%&3J+T5 | +ͻ*1"ф,@$fObylTDU#nwn }iP ,If/vL ˃[mgd=LO{z)[-< Y/—~;Ɨ _v,,)y5~cR[ F*`5[m })['1SȥGcGGm=/ҌH }JE3m;3%j;MQZ5;YR\UFέM`EOcv{-$W ۉːڎobtiٖ[M4LfWJQE3ʡ\?zW㉦&~\ _W/U&p+`ZE}H8pa}p4eJ%eu=9PSrl, TJdbHԒ$Ƅϖ`8.[҃m43KX8w7~2Ry;V?t_F ,˗X'T/_1"o^vʉӰWBNėM{ӊ1ppoO/ۀ˖q=i \/s+;|w[._#0 ?LN'3 ,Qc~<oɯZӍgzx߻]H/X+RvFw)6B(&1hFlY!X*\ ]eJ-_bigVUe)&?πWM@EAr][xp?gG!E Bd W0% 剪窾i<ٔ.aaK]"D›ݐE!6CjEかJk˗Xdy݄.5fh.N=3daDŽ޾v(V繑e45͈Z4g2ͩ9NO.Y61LOXLߊg2 VWIc9%)jF6 x%cf)!@+-_b[gy㏿Lhax)F݃kao^{WDv-?*̐eeV3&;x q_i dCw_e%D=#D0=YI.gw?Kw#u|2Vgq&#NÇtEf::ꨔϘqd4PYN&ρx@QMuXB~?+Hu q-""rp x3}%qfaGѨ_K9tiaSUR֝Lf8`oBZglgCXk#(ɱI]!"G ,R;/o/ƗY":itxKW-˗XW/_9&t Fm[֏ zUU.H*ךqk}Þ@wSV`kSSGaM_?Ar]^t| a59DM5q*#]=I2'ϔ̙Z ~LMxTa𒿟GAy]keVG%,=*Hl@%xs^a(.8>.;s`qb3Jqg./{93-8bBwxX2TL\`o /Dq9xP\g_ 2 /_< aQqEd)_!T8:qW_sFkYa<;`+Hh|w`\'ƕJzaM,Y{?KsU[VNA* 6i d"tONn汈L0(Ibi uO OxmmQ*Q+`ZJ51l)+I*)cˌ=l_n{`(mB#RqdžL:.ŽueB!tNCG}m\êyPK^d#vFHQgv&Gi㉠S&"nZ\]"4ڼ mbT c|5羡yް~:&.KQ\4;zzǓl1r]iNvz}˞/<xrlj)U[)ꁨ6GU`HDDd,$;)&°j|Ҍ zziZt$k'z=|ΗHuYaQ:P|n>XB'i(TRLB&S?RpO1!=fK:sƋ5T9#=  wk3|'2qAMC)'+_PJDDVIlN:b\R@f̬CG=<\xPXk=lgP8buleRGP&M%L5S.ҕR%NY vZ7@?NRqk܋ Wdl2VY!J ,xu;0EiF*,8x<58zJqAϳ.[Įòv !7nJל^|q0SM `{zO//Lg |gͼOΦkY>'+}Ƶﭞ_##L?6D-AFY*\6m%2-? 5B=K+^}]iie&XUȢ-mοf9#3w`~6,*gše# Rx`Lu eTtzMA@!@)oTfD5 c8q[vޡlBDDa=ot"pOrK۲ &Y/LdВ2ܙ'u*kOA4m:Z.;%$ mZo/n}v߅**Qlv̬KwPT!l+l0$0c[̽Ps"ٲ|53O~c/Z33 'M7Ow[|3~n0K`j[̌|Ylg߹*He s([  UFcX2]o?^V%uxQD!],IF jjS"9$G 2Ι3 뚋\w?{z>l=+cr1q}[aӢY+s:z}aձΔYxkI,? OB'ā4F0Rr]ӎ""g; XwPyxѷKUT;𞬫I=jW?j>hp!*>uk>sb߃@liu' v`Qޫ>kÕ6u~+6i2|gv4- ә%gQ,lҌdy<>JݧcX2] 8_>pJ]ٱL IDAT,3_ #&O )"""F:/UFc_yMɚ4d*p*p%WW<8oR^R鎺b.. (:Y9iq,lKh*VC&jۙ֨?lL[K T}&"Ձ pW""gZ ^zz.×&ry+ 𢵏ǣG/u~<#ɬr\@Ne9͙cJ;8vQjok&&Ja_?{oeu~gskժ[=sϹw~bl: ?r>0۳Wؼݎ܍p$ai ^Nn2cAmOx3''2'r.Ou"6:pV;b *)A߀s<䉱, 7=F'/<YTOԅFj16={wdkIwdPҀ7z4"ٰ>_pq:◝i&\Cty^6dZ׾> ױˆFa߹zrrrrΒugq\mq^VrUK^tosi7),Yl.BiϿHOܼA=\//ko'ιرY;6رy^ٶg 8hf; |:35cZ"b0K섪Vb܀8H1T|朜K@~5cE!?Ip&Z>?f?w-zL .nTVBXJt(%U_Ћ2zB FvgvR you5h2r RR_ 6Ƙ 85irXw.L]ϕ$E+q }Bn)4Xil?,fޅ_с>k{sdV1pI2_.Կ+[MHaE*s3i0O;F(Zu24Bh(]Tv]?#vl>ެ'e }+(W*cF1WfjJ}ў&^T>Rz99W%VιDq"x1do4{[%^NJbۦx[Zu%dfāϳD( Su ςi`/Ü{ep.Հ[v愺M#sndq9[ЋuRyG`B2(QeCYи22JJpH!.'.˜2mo/)SXۿdyC0v^y D)l*>xn/~m&m?y]raU?kCrdĭN,D6Zh$%A WBO*5~ݮonĎߡۉrDa~7$,O=nXp D'8}*EϠ4*RkVdA,z-d'MmL;PBTT_IӌhJh~d׸CUJJy:* +\fa9Z/*:X;m/b6''' OV>Ki9sI}(˾kTuc]V=+[Zs}Sa v  vwтW;NvƎ6 Ď ofX _abWP/j4!,@qID!d8Iu;\Dˋ\ 𥳞5V4 Fӕ%/גO licE+$8PtRX*aqfSdpYz)JE`5p o-w=[RcBX)8K#`eY eAmdzV O&!LNum pʪ/NA7$Ni[@q^oryE Dc3TBYJUCla#O˅˓w}x CƊw̕T3zn,&yyQ'W-/O~K޿G>->Khbm%DcPBuR8ҍK+XS5;w}^zon9c k6sˁSd|Ű0bIp/S^he& eSE\ ˴{(Qg(ׁK~0ٔ9,i)Nu"kb!/:5o|\? :bogܶW᧿Gɨo\˩ f)}RHUP堝6\PUJq%yZH >ԝc8=8Rc[]A"IצmcϪ"2Ԭ ^H3̾2a*%srrrrzzwl}K5z(^2߲O7qIɪ߹ x7RѤ#+kB~]: Q 4}IN$4-tj/ Gg<|جo+Ul! ܤuzRUR}ѯٶb#e r7HR\ZiBq6%A@7ю(\Q999J.`]9v}c湢: NRR6y{; dWX1@綯?8}8:lfmfO tECg,6ԝ_Ҟ=ߚg)XpʲzovvXir|[Ru3Ɣ_J2ObK uI"6頯lwOćH7ޔh_e/z^~% Ms J6&x*v+/|)}U$ތO0NyVq4\+.͂ Jl~&ݾU??oNIo` cŴ38Y[\Ajv>;xTpX6`|VW6t#7wj_^ҩ$o #ّE} q*/Iug69V>g|g<%%:ZJg_BT8@X5DgSUd 4-U8W ib|b<2 Dp+xOm{?+=Ssf_/wbD'51`B`&J&޾Uҹ }hԚeFP) @jW3"DQBM3,Zi.v2><}M|2|s>ã0}}ڹַMҏ^;6axZО$]BP ( qHk $ oO#T{>W99W1u'M,Հ;^LS&VfAJmx)Wձ%u7ip#fv+yݝ 6[bEoWrXyy|Ž LAEXl'sw `Il6k$pD0g J>V#/HMty;^ _ 餝90~5ItZ =:t%2͑bW<@BBBA#8C%cEpO`ku%}m0-#A;8m9go?r~Nd~}_dmw0lF_Gpf F+sܥSvI)G㦐GY[ee)ѱطew/;wQf^q4 ]zmDILuu$puHtBTůܡo{rd~vE9X4yƬT[ F@hʇ !,.oh=irrrTrAR8u<=4'L9j= AJRƉ FQ+ wKw<ءP0}*~w޿,v:eSÀZĊO )'d8a=d{D#-K ŷϜ7$۷ %4F\sSBdb:ei\< 8jJ}no(E%z:#uXH XYMʩ"$欎B8[O_J~TsvMw\eTG!w ě[ ҞPac 8\'/C<''*$L2VAc.l؉ hsB5&D'ULw|L =xR2FV !8}*mjU< ͫ =SH %٬cV'9Q\Hq XqV%mI^ҝ~S{Be 3=R*Y% ᚂ* dYEq46{-SWq"5:_}-|#oB:˰-}+Qw^Ffp 4Űc6}h)N,X0|(P9(FCq`E-x|( xjK˜k&e]~X6ڔ˄+ JtU$9!>0WJMāBMRϧSKR1_Qs\WĎ͓X/1\ %gD\2vHgE7I1߻\_W< X'p%2MG5W$\sCEU+]+6`X馌(bьrcKL%׊R'N;3aA:[w37LD˜t_f۷9a[rV/ hW#|V< |S, fl)#:I3BPS}C6Lsx8}хx"U9D;iP?w?׆aH $Ap)DLd֬_>nzsmE9].;pwsU;++FGW9gXf#BK}JPD5:7i0 o(;gh uS|0''jaH S?ieM/UcHCdw,ԓjSrȾb]ۘ>;KbkxzKuw9NME&Z8J)(.8.6bi&ESJ r(~hSm{RQV:-]_Ŏ̶͇=x>P$Wr% NeH999W1u2V7c=n"`ݖ9tكw|yґt)Փ7eTbOW3™YVy)ic 7]ў͓GDOGh 3ݰӟg0eow@؞xBu]^&]D3+zlN]H^kƁ7aElO3 (^i`n |]ι=1x#Z4Ku!]bue-l2BZHML'm]n-/δ2AH ޶m,)y IDATie; f۞WӎTd҈79iIѷhTڻ*Y  ,IJ½aǟ_!y N ~Qǻ!IRwO3߿7hlG+An|b>h7T$jz>K)E>d7uFrۡkJ6Yf o6Ve.zgbVb'"`ff^GMaz #\D!%CvhTW4-3D_c_I xSC sb}U#vl@,_R/8Ci !vVZ S|WNzNN\|3d뱢--s+ vY9>jfڽl)$&8INEnҎ%*h2\lwUGo>y1uF#xg8+O.KƩmS 4xb Ihվrmb\W˾k:;h&l8zw+p]f뮽l+,i.5u|zKR%dx0z1Td<3mbcTy; ) ]H/;+3S+Ev3`8O l 6` VؒXaFc@⛫o,o/#+GM/w+& IfzBh*F:R0|t ft:2 G Gx{oMGcx{̦y+ջ%'''`ze 'f\"!L@<&'Qv#J<LXQQ0H4(c5=cE}cAQ\0`5 Z`|v1$J YX`!"BQf:GitI]xsTW:ا]2C`5ݤP>_[JwPy['Zb&8Oϛ{>g\\79ρ>:ޙ=) :??= p5D,$ =Z;$>ǸzN%QZ+Y(@.:8͆-knT: zx,QOVF>MwD=tSA;?.~;stVrXȰ̳б$L}Ye: U9Ju 6:-tЇs$`Z^i{}'5]Eձ~! s_>cŪJws,&+`XRGi4;3c3XirCTG٩N~Ћ[(NLcJpkؚf 2h P>)X5n1Cĭ 7Rz,KËO0l_+[ZE=gM?{)[/n@!?ʍB'9)JX1N[Ĥ8&eU#֑̋ReҕUG7t {+&/~^kJY0nanݨNT&ŀrl.XX1Mn< xMNNF.`]li8[=b8.ӁzkziMr (kİm5+$hKy?:;[ 9YW 0:=1hqo+c;')/ˆ&B#H yRRK܊{oRgvvhoa /pn^N7u _ <͌ k sȲHe>&bݐBH~lZΠ3*-eI++Vvۢ: 瓩#`JZ ^>UsBHLtESZ=5+K. ,`E*z^Aլl,xwDOGs-{mu -F7؝*fih`pL Z ":PEf԰sAt$ pcŹt~rTdPs~+ >Ne}P@HZӃYōRZS玭v19\6Qe6-9mfmYf8ZN=B4-H0WHwS.2۷@ vg.iToIgJ(War-AՂV&6FF*pN#Kqm,WȑT@ٶLj M&1"׾i[zUY.r];Sg9Zn1Xk|枏\ե999 ֕Ea=zpn:iB`^9' RkxJPHQ?[8cE3&ܿۘ[48X|Np2!3vnAaq!%S}-9Fit(zg߮wξ=( #/d_2t ܸ+ X,by_H U5i4Seڪ+rbB]R3+/|-竰E Ձ4z?cdƲ&Y%{~b`ł6e? =IE%W+. vw3fe~Ď3 `-$k 媧JjCE.#k`> 86 )RZPnW G( M@P?IM8`J%3xr*A. `-89999>l0PjL+6`ھIg+CBkcI-L2Z%5H8)0K߁AEU%? 8}#gcE--Ef.oBJ*5|;^JiSCoFҊH6qC:iS#t/*+^ދ٭"vl.c&plc8:~vVp^[C$5IɡM w0-Dxkk~|'\$r"8y&޲;njV Y0uSC7BB!mPkbwٱ0@Mv:Cv'Q&drBC[B,Yyt 0HR4i[") [籂Sw|w.0tQGUn:Lک*;Kb.*u[Z]lN3Vw^|R H uI{u,h ;PPdq"Ԩ4TS)ӆVhyD@ !͎w۞qIeB[pqD^!-P{-OBt]>ò "M`G-8j.i7P.r"q:oMW}[VoJ "4jPr ehT12Ĩ^(7 q /Ύdy| D ~|7>oz9WV4jn rrr8'H*|?o^L_@Ò?v+jP"IqT-[vM8MXKH*dSF"Mcf3S=t}c6=V2Dl0^=SuW|Q n0luFH#nj6hT@g膞hQTR:KV֙!9Щxwkvb{Ou~%la}ԯ%kJþرT6⃰ʵ@f챉AD L!3jxW?UNNI֕`lh)F3 h6}%aa&N P[%1Qv/8y%F;`Ts?_7S6N`}Nz5zj%n炲9+"YSZq\=+"V|x!| NTT?3:Of"۟LTef&F_\㺵x P7*S]6)(7MB\arCS`bt )p: <:x_"57;\^[40~~ANNNS X>a "b>O-1yVNK [0岎ҸJ;J A9)Fu{ϤvB$`I>'WLu 1 F:ErSM( g@XfOq[j5U gLQ/lQ?XSlLd ғ"i`<QFV tح?4!:tV<+>W&le]2v29wT48R&m4JB E4uWqvͧZϘ ɳsrrNK.`]y| Y (M/IɻYM,]ӑ->7{`}"ɻ?y9}}:۱ЫdQHHp1mŔ RDhHmI(#3m<;iGB߀݌|*E`H UVNWXݙ9Q \8):H ;(E:M1r8>/Q^󆇌Ю#H&) z&(r3.S L^z8 蓨*(b ˜91YiDigOV1NIP>&SkvL=b MQcϟgͶ=Mc8(vfk#nUɞt+J+8P IDATt/˯WkjB'Z}1WiW%-Qc7{_#'` z%G`db bRV, zR8nPBppsq 9MiPGģ96MJ>@GbKVV]v28·t] 0t‚o ( DSH IN)'bAWӟhzޮBGVy/\a\Wg5`c5r(q ;$t)ɲ,/#_ZȦ'a_}gRKt|H|P! 8Lٳk-bc3T깍;Yqxy|1̒-wVoq 8?5͉য\AN\5^y~lK#[;%t0 : ̇AB^JЙ[fJbkE$u: >vD0~9MA%i+T_3ܲ}p C/=oƩ-z MOnR+զSϮ5πxP!# \""2lz乳\K4 f(.QҠ(<8%a>t5N}UĒJ|G\{YؽKw -"?CWoB;QkL<(/l9Dr -"E,k ʣyrmJR \oFdUlTX:#i*Zi0c-p6kzϊ>{_hK{ߝK* UX%`tFG֙u{Yꡇ^zO vm, ܄]NK"=y5mF'  mU)m W1`!3E[4Km8C:dEs]W{쪩`:v6?s`!OHrQF 1(1I˙iuvb^Oyͬ0:>~FJvję׷:y0],bR7q2MO/"E83 ɑƐ/ ~0~hE8dD1*H ]xnp_=÷zPܔ\%W۵o.:?v)E{i`7]{W T!`bDo?䉾)ݐՖV4*>}-,u$]S $AO_ slgdhAlF&,BR+kdm]h4(XwlS9=ͽ筇~g +ƗWm?,$jntYX!.M qV$$[IXU%P\Wi9"rXHvvUhGG-ZcBKf-+IjD qzt%8]%5|]I\>.~( 1#56k^%Q Z3آ5)Zh+ <**GS*<5SZ <0+GSM]j'@U$}%6ն:3%/ l̷q6ӱյ߬oCU @%k4r>hoyC=h\t)U eσM(Vx_t "OflN,U y%!iL3ST8utܾȥ(J {s~Gk/$XGaxxhd<DF2{O>ӝw {6in8#ħOZ{ydB"6Kj͈M- u9+.]UN[XMj2Z<9_E:p0rF}um8ZWj⤥$ݟVJ%.;řjklkx UefadiY~EfQ.R ZfЏJҰ1L؈Π<:_S(ז($ιH_S*-&L5">}DH#)j]TY?'څ'T.cE4:zpcS{<ٶXc޲6A Iwp8'\nFI˔p,BH%JSXZ].lI1.V-e£%OVbQnVܮҿ8[;.py壷+@˯sg:M}]B@]E#.܊ ЕF6f|(#t{SWevr.)y\eR={<o'? DAOP꫕Eȋ;ju(~pAP#^XIMʐ6?J --& %,k-MQH|鬧"UHk-Kf:I Ӵ4eAI-PC[;TQۆ?xc-e]heP!>1\UuGCX!haY{&VcuS͗aI_> nxx8|gZ#@k6=;6ڨ&9gA\JsY!F6O&}e$~>&01*Y&6 }3 ĖZZPu 3Cpr; H_) KRCN &an{䶁oMnTlR=Ϩמ~5>==q3};$koY K=\VN8v: 2< *̭Pa[5DU6.սtUvWgQhd#lj`2ZpTG?0G*G jݻC>n67TdpR Z-:so]B4yg(c|5;`:}3:dZ |-h3̖ÕI?ZV/f2=B26ic}& v)ˊh[J0'=[']Ϟ?p;ٻivzu歯<_$UkQ-|QʕV-%V zs=m꘺^{)S팳 0$U? gP`%(  j#5m@pJ w|bDCh켲f=I\woʝ;NcNb{bnk` [u9cAѱ< ݺ܀1rh6Ř) 4 W ..uNB A#BRϰgN]`D\Wg&jrDQU ,T.hGm Tr4eWw)`zuFQ7^PE)P#=BO$-ŬQjRJ{#';{ز{X~{nN &F̟|J~ߓ~H" wĕkpO[;]D6E8y]ci!;K Cxs\B]֭)Z&zc,#j+u6d}4͎;|jƼ'} >z{Ÿ[pֱή65_~IPnkCar9\W$Ċk\+&Օ>2lKMxpּ3Xs]x͓QVeIiMjjg|L ׵r+i$#ء3Z߹][=YZ (-o o3D: d"N̻z4#lNzJs#'Qyy ׅsJD濹}"]JiX5`{]zĞW͝;E8 \1;6é%{,PP`mѩp/)N.2"",R /0y`^B/J]U4올:X6 م>5k'nv*HǍk8q ^iP#>ۤn@F[SUxHyk (-XɁ ܸ{-Pi^}w#Zw'=^%9mx993QJ\ .BVUf&0D!*0.V͗ Y93P\_u._xdp9Ў"Ob[GȗN yC=L7vm,+.0DxirKN8]5s&D<3 \rضtMoF"ŊOP(J&4A̲ն VӠ@Gk(' ӀX]9Yֹ\>\em%Lľfca)5 7!ه+]U^G_ϏDc}Q56J5)-wtq^ܺB"?`QB嵌J|ːW #% ?G"D~<ɱ ҶKV"|>3T񡱢2}#;nnl2\(|/i4Fv #Dju! I|9?#{e.G-p{ª ^VyB+{Wڭ}sܹ`&<`=0{1ĖtGsXv>~C[#Gĥ LIatuyjZ"k^{P z[߈C3H)Biٝ JSj3ed6>}5EFLЩgufoY\j<и'Q<@^•Z\"]nC+G`^q7LIkm[-Tn}'CΪZg/D 2Ҥx֥xYDA*/bʥ+? rgˠ};6A"D^ݶV_N]x-LrX禄PFz" |ᕵ=O@ii 9zx# ٶ'E=ͻ+ I"VWԯci%X!D Xe4 q< LTsE)7"d :N?p*)aT9BKk2(e]ǞNͣA`H<$iTZ$O)|K)6j$GLAELBǾu]{=o./=֎\xi3@y)5˯i#;i !bl׽޽-)g{! 2""ح:B?-y3oR%ә85|m~d:n o5[Q^}g۟-O( WSK\O_\_?2<n‰oS=ٳ3g?uU|j*agjVY{ % ć\:2XQ z97T :4H,^c)NuAUv 8 2#t|!| > \Q[hGt GA*#'$BAtq!~g7>MƷO{ $.L#q936 á{own@y#BAxlbDfi5[¼b\kt?[2pL;P ge`[kw,?62uN&EE./L`(.G=kmS{" < yO٤*G rMЮ$rCgtN.1Xx_lvPlKB<;$nu%:ؕ*<ؒ0j=`~cSƚwJz+v83p{oi7k[ GRB&.)"< #|ᅭZQjGB D!0)63+͖Y;c mn\U+J^1+ (H#3«,ef3!^`1\kN?y mU.plZ-[^{G" wp^Qp]y8 ڳMe@,"'A+g$kZ B'Y&3p Q|EkJձDW_~>rx1U> _L qn OPW*.4nkk0,?JT1Nꭉ8c=Cz֫=}o8NQ1ŝ;~dw޲2w 9#X$F19P(gCum,m''A@A!uӢ@M#H2#69rY~;tVZ$nlqcaAjr}z(so]EW7ȴ0)9S̼XH )GV[o“%!DL6&e-v㮤/̩ ˊ8W\ $eGOڻ⣷G@䋰}s}W~'ǥk4qRPGԆJEz>ҴExEE3gW־8%V{ÿ:X`hnQ w{AW{rOv z׿W8o 8nM-[MS)7}}uǫ`%G.M]j=EmҠ(d^P*j2~8&lǜޜrLm*Ѳ'ܠfY .Ԋ:EmK7k{rGtk(jEtj!\cl_ZUS$p씭մ]ԙ1њPY|ɸaɳo W5_#xb KWOO_"?f>;wrzx q˞{=9GygS`g^ct3l{WKKxmCrsp[X_(B;^H@BUݮx 7h/7鶥HB[M 0Vf%Qz~A>+|t;UP!f1X\Ithm|}&7S忮[GoSh_dB6 „\PS )1(.ǁKBJ~4!32z衇= g.qJ/(o.+w:ɞGqfaP7R+0`@U9zZ!, F6sF|Mz(4i̲&Q܄XĐUrsՒUt+tؽPWWS}vTX!g !3꫰r6c-QteBUURH'>/N)gʀTe&* SFĬ6=է5B۴u l?ߦ(D>?v"~X/xC0:bKc³*Qhө'^ARr|(P߯\%_&>w{U]~67~9ZׯN֞a^~).aS,tj]Ao5aljEҜ=.X8v5K8IQWVnMk0$qL/ŞM] f~*E(|a=_qPBiW=fBЄo!' 4~_' ƏѓnnW6|3zkOX{& D3SoE\=+f6Ȋj܀3}^ECf,f3QjrCp+@)1 χ pʜ8]@ t6buQw;HsB(X6 yXNߚ6ddiNwnݸ>ٮ/|Uhl4ǧ&wCL}~,M9BQI(|aPa1x"dbIIZtQJu^Ck-[ׇxր  or St6)+ĘBbKߋ2mP q- "H"e xCvl]ϼy99˸噦!@C(ubxaCV]hCLO#?1J)Vi A 0Ḧ́0mq8X:tg;4w{ˊoo5o=:)뎧G `Grj3Д-[[48atj5[Wͭԑ-Mn(qr6(ʂQ,rz},72 |xRQ5^Tn*O~O6~o8#gni+*6M{ԀRlq`yloUYRitbhvCS ;N×/[lTmCKd2BJcOo{lnGd G~0 ĞwpLy:Kf=MtȬ]/pJ{cP/3z7S"VEҮ mWi;یj.Y,I!_;]ҫth4,D9ٴP&[ѱ ɴ%/,._rYypCD\[ּe'k~*.n #?܄ ,H aVH)BǍuVqx=C=pX,f [Wׇ#^7tvZ[{ˢ;gX?#ƅ.!K klR6ByrCvQxB>\Xe:GjY$Hxn^r 4⎨{Xf7"Ah6Noj-.l$Ûo%g*Ɲ^PTY+ _ H?p/Z"qDPEDJGZh(*^6gǛ&nf627뫕R C5!ca?Zi둤מ&~40l㗮%i㔋 7~'æh~G,P~h2Pύ5g=_LJ ނEK]f8SA}4p- G~?@I>l\q>U"B$(IT-0UK}ށR&VTq"`FA*ol*qS/RW+sp wGٷb`wrs}[XpgFp]ܓ=zj  BlIeo!<.>[HH%.z3[8)gz}_O̪c=K$V1oI[`z̚QزSBoc -\\2HΎSi*!Q 0 U4m+#_pHÌ\kb%#͹wN?;rEo\⽺&|2쎯ԡ݅'K||}JoN 7m_;ƶжE4Ն}Qj-q/;ݝo D+Ю*ZʠE^>/z@W <^z &gN"BG r#fd@l yKj V*iv @9hm2>Ch=5xO$  Cs*5;GT B;xwZomXo6KF} 3 :76ႽgXs0.MZӧO>/}!:QN;?{^2S. DN|o^! Yͤ zٻx=ϋJݙL6`lHIZE(&flRH+fȠu֦랶;kU*(M~x^ S~{403ZdD\ۀJk7>v;K!RIdʼno%J :YC%DĦČ$ƽ4hEBusXF bn;LRu_pAH;+P)!*JP]:Q{13[?xܗ<ѫ_lñqǯ ]!aYsbwbްLU-w ί_To^׹T`uFdZCw ر+o\Znz,@VG_-E,ҰKnC] LE˄Q(cBm ZN宗P7]ldnɀFZo !? &kG2Up%9"I 58<]Wi0e1 :Ipb@y-E3)?b'6#O\Dn9eDdWDzb6&Rh FB`EaFƅdŭخ3lgj-n7+V1X*ΓM,OlnNN!"i{D\hcg ,+_Z.fj+0wWl,_\O72 IDAT~G?Z=0.3_~a>EyWXv\;L? q0oJ0 d zI&z-uL]߶ PplK"m`B HR#Ai`-dy ag $dc|40&iF0JuL_,[ό Ai$h3F1Nd5w2ɸeѶS#2lL+̢ @ָg8##s[>}yi X/rЭc㑽Ezt ^n/l-%ݯhmC?/[fŝA7*!w1xS 8J`J0,\d6Y NwB׾~v.?_y?//[~s?9-t;m"73膦U.U//afH^yf`5>tŧ^w,zf;bB!"e9&c!S15lS0&7>D&OLs; 2#º-㕰T\oy#ƸXǾ!: nVe+Uʗ\lHaZ4E6,Ӯ,q+CkR(tnM}^u6TK~8VjaD f \SN <VXF)tS9t"F ڈ(5ә z"ZM[:A4ThԳiLmfejKN^ߺP#\r} Z,*=%-=wUqut6t[ԝ@VoE|4zzV Я;3BB# ́MπRCCk"@$Ui/Jf*{*@zG TV⢒wBK^nO.{UCtO%&]% N!13p jش!"X.HtRsUËv|1 C#! %J4cma ^X.?pU/\kr]+m>w}-ӢMN ( )izA-7'WJ'ipT\灿EgNh79;6Yg&v䫐cU\Ӓh1{߫}Oy=4[vj;-w6}RK2.֌`CCy1n s;Yz7쏽##!c|TQ]P-TY(ҶKT̰(k[>%8)jfQˇ LoA{z'!(f[Vm  K3X m?+5 bZC&+eX+H[Ym Zt!89DjܼTDANfK!$*0W6 n1T=ѮIL€=ʵ6t3xՁ`nڦCˣwXP(\A"cRosL9! ;[%ou72fNG2O#M4!Qݒ-b@Օ _4+Qk|C)YO oib@`V4GG(*I^ū>}y X/}&`9|$5ƴ M x݂4vl솲苨ZXC`}ػ{x~Όn-Vɝ1 ԻrB:xeGv<. y}]JSkf CX`5|WO~^"$z)2BgJ4Ϡ,Iu2fl:p}K"ײut'"i_J'fҜ*o# @Ed~3S( Cv_DIMoP#RZB,=i{XJAlhWJ#5Dk*rVdK7!˱9[~uRlb讯!O^N:6C{T,dfY>n+; qt~\}6FAؔ-4|B)AthA'?`Z)Qءi؋E&Vbf3zע'Ȝ6u[ |~8,w(񇉸mϻѭpտ_D%ӌ,>'?J^~RUGv̅sKѭr9-s2W2;֣֠.c4 DAYȀ0$t0kn>aE#Phk1VH hIO $ rP.$-{Ū.cV 3ly1 DYhpsU@[Tj옒*#ǔm2ۂ3͐4ηʳDZWDI$3A!Zj+/[cI9DW|(99#'-S >vqj@<1-+DꓒStNb>x M9O#|x-%yѷX|^u,4Ea&b:֡QyR/B1*i:H'nCϣ+eDqE&a*T=7Wb7¼"R 1(‹NQQY9יY\'ee{ll1&HXRi̓fYӧO>/}ћWUzN^<@m $~ڶ9Qjof6oػ A_A;^^3#bt.[o* :ws8;TW*|1 L1YhǔmeE3 ^U{)njW=&4 o9[ 2"w'Afΰ7w gUIHS|d>@ *7"!L1it34==G0"Ca % WāOA"&M#E JI VldvN-dq a_KnZffk C:Rxͻ `w?~_%+ϯ XK=Bܶ^{FvĽ*|LcWҘW i2FȔ#Ð4Yg`Z)$t P[Ľ< `df7v+w¡ GZ3>\?uYS+&O|'ꖻgU'0:xng) 3WXAT9L<ȝ} tB!*s ]͋ēB"_X$tl^^V nJl$S+۠e,4JUAhed Yc[ ]#99ِ?\hܾB>ͿtP@$1i˘3)Nwc"e>ώ^z9>" T?zĐoo/gf婇'NzS6p<;Mc3LHѵn/ItuγU%^(o{V[y<dN(ixlݢi7=Li5M0'(hBTaUݹk^]3@{r+/V,Y RKIj›e/`P/oDi+d)Oko[0طgٻ@;/}>WFGXeQ3#+׷UʘS s_-~ 1({ٺڷ}nhO57R\ ":{$ 1H{Um l`$Aqhet%j-XC> hcQ(lT)҈S$0+VDK3+a0LCc)$AY\t0\i6GeP}ج\T: 3c㉍Wf0Ь;DZL}jetȣn*O-󰇵>&_O Se /Z~u]mqƂ ;xQFTmry 2^Kɏo|Һ# 5P8@`;P\AZ8,`ٌmRdJYGb4Yƥh KȖk|S2n[s%h5M;zKfŷ?;?MsY/Nu&)Oi X$*!C J>ȗ9/;ww_^Cu`uGlHi\!jP`x患jmuKګH>deE, '}^z5hqBͧLx梏x{!݁gz?{}s}.!#`s'E_Yʁo@{ĩQ\{T߽N*%ߌ<~QlCVil o١k )P*=^/#]RzƵG :f*Bo@DuTИUPw7d%yTX%qfQVFўi+eDZRIJ "wzH"D2Pwdrj3\$I9GO NKm386{yO-SzO1ܱ/0i"؉ 8Gu  "ɏ7Щo#l:lZPIxi\Ĵ_^ I`cX }ȭ閸`zq}.!~u]Wy ;?M~?s3BCoyϩ>%Wۆ:Gi9&*Īܘ/eͅjaB$c; TfRŦdɑ X a!Zb;a'ƈC$Ԇ!q2 ᓔ!pFu6VK#Cs~ \#PnMpj'=qfFoº'K09s6u*Q(6K8 "!M1E\TypHv|ȯNNP섄Qj:>bU]$%;^uԪ D6=pCCJw?({MIA?<|}Gza $4b2} 4h_KSi2!)v:xAڣso v#h O~p\Ĺpo1܎+|(:2bg{}u+cU D+MkO^ќj;+M]ZpBJ7@8i-ɄuL#XvP rG|_a!,:/F̆fk6g mgddINvnǻo;9O>}DΦ^Z֮BlqgwO "r" {wy=۽=? wfnJ c75|G!A6PlP('u3#I 2XbN/'S7~[gêLt6CB Igs: 셮ue6ΞdY >Tʯ*5uhD5<*h+TC9dC B8Iu 3(boX%v,7c3Ox좿hwՙTmI_cnEH* c7zC WTRaHs{yi} |T'k6o_}WޑQֿ秛Ic SEe ^'Y;*nR{8X<<WD8*t9J9**fXb#)g9 ,h)bk8q4Lky)7 &; Gm5yض*q"24I&K5Iyy>} Xϗ}'w'N9ۧ՚+!{=_֥G_${w7W|l[CڈB$1  K 67ۨ,ꀕp]l3#j׷3 ߡ[onzƞ'#d,@a`@m^-܄-BYwJ7x,X(.xƓu,!iB1p j=&2;BBp4i G_cUZ 9GYXot%q,00 !$NoZF &Vgpls#*m(}IxJhD7戍 Sgl$\{ֺn[j?Wރ"Ugb8nVO\28 r]PPb!3,@Z&B@ rҠM6C8F;3j:q p* ߆>#S?VwPwm\׹ 4JcΧ2<ƶ'Զ wncXȜmU\Z0F{dZRL eȥ1,m\ha$hVFN,ގ% O^ V7~ Ұ늬vXٺ>e3m?  =8Mi1 p~mp ; [< .iѲ5S'\=a 'iUjeLA) 6X>,C^@Q"i9)1 FB> YyUá<eD,gZC y{ ;VWggFG?xnkRzp pK`~~Ϻ/(lrKyC59=gMq?hy({TV" z YZyoӧOىΌͷ[yV܄^@mm~_b["m>o<ޖj3%-ndQ W)[ߞ-~ݪ)sZP ̐tP8ȮZxx.ޖ_GQ87B7hYѥQ"\+:(D2[A-%D&N 4$ңNȨ('IR@Dj,Ȩf̘Hٌ0?$i7P d X>ne`.W\G{$zr/p /tkO/;CqaDms ooMy_fի߫-wSB>Po1Ё7NǗ&J=2Jl\@It?%6ذDg%Ө^(l1sd(YY,Q>8[K8zOv O.ɦs B6F #E_߂Lj̄heǍlňœH$VlfAt ,HJHS s"Z mB@ʘ #lue!s:4 WՙTXՆPha'! 2ު[&D"̣Ldb`p C0z ? VF3ൡNfX&t0N dLhXKJ&ur T bgL:k Y 38ԅi8RMՋJw\6}*į\Sw\mg:V2 wG}|ww[]\Ͼ:fXAnQ@}?-)j^,! VK躜G^٤nm|%#(㻞Dqk r7&T3K`(K;$l$rHZ:y\kt 곪>#|urq{t2a.()V:?x7foq_ӧO|طDsg4==TA۰̿>;?*$H.aOşR([gDm+ϣWy;3*3#r[K(6zsRG6&t*@!HP$DH ~ F;؞s>M18gV]=p c&:H즔Ѣ`!21VEVbfVFQkQ0+b\NDI؞aCg̀CJHi<5[D"]Cu4Ɩb(, #[| 0:<L :EpZ@Fz:βhFOAG!ۂfA KcX\j%d"Ce,ߡuq^1(Wvmdkș#03̀8lĩ2$!y 8U[oGrB 6:@>_xƒ 'd>_#jI$폞:*̑&aq-`iw-! u:Hچ-8 vKe<3jO''s VD+q0eױ_Aɇ/8{UoOvפ];&+L?6~4! ,FG >v&FZڥOB܌"@"0P,d"xعk=wV #Ifٹ_ߺUePGo+LK8|7@/@g|MK?,Bml8˛5z+ɥF(=}--)nӠJIiXgsp#Eh@IAD"c' "céf잍CHr;D'ad Rt:]m<%-*--6tPr M[q,5<&΁[GG1. -mvt؄1hg8 :ƴ 87IIiHf@IZ=!"Q 5nk^b֊jƨN2vjyn8o)瞹ﲘ"C?W,n _Յ>CT/2dȐWh1cds z~X!"HEl$IY}W+ѽntR?qgq܍"R,Cv}V8[Kr*iէϊگ o_LFt.@yVI@ W$R:p7eo-ޛ#n<*cCF"0 nd%٦HURBGŘVm#eˬmDy3NعbI姏}[k ( ʨhay.ġ~u?slȐ!C^AD3% Gѻ1s5θZ@W'$IvK؞͞DI'enwW?2.Qv8-S+YOv kO&_yc3mFrnkQD&,[̘d[j.fmQ2(TźΤXv) 2d[5͂Aˆ7Y;t٨ʬ`vLAquPfnim>]ߴB5t٠ۅŠ.5 r:'SA`ź$;^ЀPplz0]赡};uj`-H>T+]EH8a%0bDm CƘg@&$ 4< n,ffZXbUJ#{!ӌJLT<#{YVK}??cuŰ б7_%VыE\!C yBb4\}C4,8nhk';AٯSݯ6m3 F7?9wqa1hԣ߄1Ds\]s Scn[rn"sY`™%1vFjCx/TC }PzyA_/&`m_rl{pm3ո![ }0:y{mJl9JOϿ!LH$j{7ʔ)AAW@#6LPU1rP4w>:pa! E~[yd+x4VE ,%Fb_) ₓ'l>?3M2BUCtH??n#eC~w݀ !֭k!}I3wI9 W[?z7 H;Y_iSV0l܁`ū5y}|mqLܿ]~7޺L_2}e\?<).ȲR ~/JLo] /2z"%*( ےC)f-z;e~VTfGq`lRpśtqnH5O/PΦD)v7%e$5A3<+J=lBrT0f (#F@8DJ}z9Te\W@1ՄB+aev^} &薪3'wCeymn^VNx=Y]u!@gVwǡ+RK`@3P(,vM Ti|-lU6%>s7'tiQ(mB nL24% <eQ(v(MFX.0ՆR/CBK491 ·K;_c^ow{ݩxi Yb,t~i#t IcF0@$6BHVYSF_#>j{BE IDATw!|&@ 2I J7fPJGɍ>n~KnD~6P=Q(}MZgr)[DF^)"1U;N*\җeNTYm+qfIa+ X)jejeC "XSm .{Z7rdP>Moc(01=L Ӱ:&Y2R+H%aMXF s@yI]P Bu8!%lAcXa(4r1c=NDŽd`sP\[܄s{asΞ L^{!rzte"[ =%h#6=!O)&#;"? F*&>2_wYegoU<5bfm$~6-_$+_42dȐgRasU]iER][k^+!| h{%O2=~bl4gzTdDZ=uɕL,7U:q]~lя}7 C 2 6"+ k.[?VF /B3Lfw9ϷX?>Nv!1B($עWΞDw2>K]Ps\cۥ#4vPjjeq0g{h6xu11 q`K`~*׫ cl)U;!d F U#4D+o* ix/ў/x7鴳jU6Ԋ'd;<}7[VϷ]v#8od|`n]gdM}?q5H1wGDeđ J`{Dϰ&*Fs[BY.|q 0M S{]JxoVh,WTT^ w%;79{+\D6rH7U K9&[^*3ҌZ9I") \)+]CuQ%%)t'uG1UIiaL;IJVvP+$4% I3:K2{ uh借(i̝"5Tb:yxڴ=ӂl&}$iJ8s\{tXn IBɄ1)T*荬ڂoy5P.4~ 2Q$H6vbLV҄xd#tgNS(ai2&LLDKƔ3 O8qS);Ua~aft .?޿l\y6&Oy'g6ΆX囮uџ/gmȐ!C^E<9ϯmt:k"` ֞,݃cV #pc2n?E >q;wݔvn=*b=Ra ,ǚWDzh0Eauw2>C 2{Pzy܂.uc`=؁ }T=t08pmX~,ܧ^,q:P= Eo`T ׈?P"TsaD( S+,XP/<~X jGV1]C. +E!A[O(N?8߆_ k3 vҲD^߳G\Am4jǜe,H 3[]evK@fIshHktuk%*O_ܜ<8w=z{#gY&ɄDi3_G Ds{ҞZUAcD1"7W!6 Z-zӯ݂ˎ߃E]FW*`!;C:lQh@Y$Npc " ̛E" 1q=X P)5+em}JΐyUhӍ}hs\FoDfn+D"qa-zeʹtI$df5y򱝬{yvtOz*ʻ+n8{sa`m22B؉;dȐ!yD)]8,.x|%R)RH/^&j-++q#RF@ĻE}䌺ϓ; l;m٧zgI+"If8RjcȐ!C|0.=h]L=CVv(z5Yp3ZDXc::}|-\=/ vBG&һ6ڇə)\UtnDoV1&:wguN їhJŽ PHɎWʖ`S[RE\kS$QԍSiojq46)\İgG.o]!\["IrnŻz@5~ᖳtc(J"a`HFzki3IGӍq:MWxzR(E!w?|' r**Գ5cd*K1cԱ' &7 qגD$*zN$v;('yu}` f8Ye<ڄ<8r1@Fi#7B(08נ v(= fֆ+$-l"-ˡY9DwP2p"Wgc 4 BQDiB-XpNti{"/ce'gz>q,V[!v%sU &s+d6fpI=2Sv20Onb"C")Ξ4gg^ zvS*=6JvkyQj?婏tg!C=2@pE jC/VxJ__H嬟}sKK⮛֣rU$+N؉8N"etW"kdC 2u\]p$B]Rlu*$ZǙEgv݄:_C[J-)ȳJYL=)4wݔ^48Q>Y$/~J @I;0Tl"RI՝G pH?5] >S/0zshP~'mTc!D!'|$kGo1Q @$x33l{ƓNz3d560,LhZa1n; wABnMTux\ CWS8mm8q2);aa @ɡ<&_ 2dC҉f9D]2c, 1bV6>?JW.;.SOEζķ`GC)(g#|H0e@ I%W鋎8lsj`2$dp5\XJ追`Pf(>T6||rYX#4.kߠ߶t7jk^[fg?/!tsswSOÄD(L'l'N N7>Q>i|ZHR=pAؑ{Z!I +}h QO 2{;fߴB_gV_#Q !:81S`U|bB|82㟳Jm/SPD*Ok3B*l%bTHznE*yć(AhKX!UQzNRˁaec{njBaiX uz}ZPXoĊ +3@IH:Ej̈X'B@"RDH, 퀕``5u馑,5K\2`kV'k@xI,1bo0S4lHE*ۀ$ dc|M190H $G{ay`,ZfSs;-=TUXM,N͏><&gob5%%;vQnޱ;}F\L7kN>="6w9+~ͯOꁿBJS\>|Ü ggOss0jȐ!C1.nT|_0d`DD ocmn^Iϡop@]7hѩ߷Ak@^yQ_/uSPGzE~G筦RJXД _\\Ϳ {Т[6h'8 x4tO2?ףl?zL7u42ObؤIB`ЈBT}s-3}-iJ*A'еp%dް2*}Q1jyn؁ ;õGЫgQ=z6uw>&#Iu#=#!;L2 b,iR8LPIZb6=bR"R-sMёYNFƖ">.Y*i|{jI4z<کÙl\')sTBThىV~[C,m.p{nNO]iC].>>p7'|㡗0dȐ!#yaEeEC ؼ8,*0oC7'lA{ĝxF!tW8U;{{\:dȐ!C1^>_o:0(-t=;fз۟7@W_=OlW" D9}6қkwzT%3uT]}x]8H-xDjO_yyܗ>_o{+}$1:8f 1Ʋj?@o"#MIy9$dZ$ݐ4|ɿEglO?!gfJh<eșߵr4'wՎ3 1~ūPiO@C!%)D"6ѻw~ UuDh348ASzAy`.%:*MҪbk5o}א,Y81?{J=ƥx ȺNdB/t+UNAtNt9T UT"9؏CcX `)I2{uCNK@^ C &~[$Q7C"4EqV6yh0Ų ҵD4RMKي,Ă{Y|fQ㝖$`],&#AI2͠F7_^o:>~7d)1֧C9vÁGyN37jC+l?sLi㲧Ï=hkbfӗ;vݺ1HxI' :2d%2^L?qXHtL[F/4 ŀD<+n_Kb6Z3" vM˙w;7|À}AXҊbQJŲ;:3suxH(7޿OG߬j},gP``Zo1"vVE$M#Nm;d[vpqrck22㚠jVzjj <:2N?4]ai3X&z`t$RJTژG~ DB5BYTpe#Ƀnc ^Ht{ ^+>歨SW /"?9?Z?jVkok:V!XIlNKwuLO0sUGg8`"AkDZ 5Iw*Şy6nZ(d|8ro"#Q d},FA,`! E8$@ֱOÌ]Y|ŰB ҃P~ 7N0hDw- Kl#xRQ^ᵠ@u%jdV3@s}6;Ph@[$IML Nȇ˟",m`Ĉ8} _E*v=z+DO&ƪ^NN'LeLKH I{}/jle'm+T %DbQTX ٤qu=\*Ml kOQ8:  &Uhg+?Ʃ^Lܟ_'N>s{#P}h/|4wSl/f; 3o%׈=#\塈5dȐK+ENw$fq[Ӧ^kz4zn7-]_| !C a(`}7hq>= }_Aw$ZERr Wbn=*Zd^Tq.T}4}sx//zT~Hwy ޺^>s/0[ƨOq]0;ō;ӌwZmv(ȍ|{?GD*@P1zZ>}5i'=nlGAdkY.Ƒ9U=Q^,1R-y2%ՅV99y08(,/61fͳ91s/_AzfX {mxx @lu?tD4ӑ)ȄL I:+o`itp"1ϔ6sխGu#eCL?`$ HN / d z/[$L8g?6V4YzgǩRmYHUo{9s|]Y|jߦ*ոfk,Ǟ[2qq*B6qQHG>BV41@))! ga8yVD)i%X5oX$̙o 籍&t''17gQBPA$Z[l >\Y a2R3C'adh0}y0ӍY}S|)|hخjsڇOaoEv$5I&rN_uMҾ_"1bĈYXܩ hٽG2jP,^ tt/c0df?7Wa!(>} eĈ#F|0xA>qeHp= \ ?݇_6U`RƎuOFp[Nsd-qw9;.E'>=&?l}6wZ7q.KS[Own> iwQB @u"#ƋA@!i&tLk8gO-ÅCՌdg &[0IWv*Pؘ`m`$jl f yU |j m ]uK~ӢMHKd YH,mrXHxr:EОZa;S2,nWw1[ۈn2hO6P^0q0q\}/N-zɞ;.Xc<62ey~ӏ8ϺP٫]N_:.<\??۪WZpHϝGk??.1bĈ£X((I !=ES_2Règy3ˬW;9U!zo"b1b7#cyU>|4Ê˲Oxxy}xؙd駸an\lSd4\7O1A"kk 8d؝džL=6sLܦ8T9uq?oog^R n-WwR[ Zsd!`X"l~ }@~g=?+=ï?ufZP&S~#bðsϡ'%=󠧭8);]?Uvܽr`( \SkpX9m$vVQEVVrBΡ2d 50$o̝G.]Mca?Oi/:+rl2^E;%Nċ4֘*chI;9 "AI깐|`N%)Lܬaևs3~_~\ZN? bzGbJTggn%kO;wUwd fO\0r.O}wd=!m[lkG񷘟Bk}1دM 6!S1{@ {PgݻGVp+8|c?O?#wab:3L0d{U1Vm߲7vW?ر?\AA="(O g?&NPEI"j\yxE_ ~|WZ߱1*y]~2Nw7c+WON`W0)o#dq?hcI.2CZ~CA)Dێ\y`T5D_>mwT@7O59qyµjKgz+SK(稩 #͓;u|z?:;7-{3wvZe1⛌΃n.Le`M`9n [5,XIaqia=ͤEНߒ笄#^>KջU{7˅U?~i?-N;/K=G1[s5 87 cR /.RqWbRnPT% jL噝-׎[Urѹ6¿V9= ]M7׾kyڇ |,Z+jj̒[*Dd p " M MLbb{lv[K2B#K\h5ו7blyZyމRm?.^<(:ʡ+?5>[ǫ+GGW#F#BP,RWN[ZF䛷5.9fM.zR1g%лKNv6wyt>OF.^80]99$pG[|{#F|K2x仁_09t53R\P\:>Lϥ%zpݡD8԰nHÚӣ*E3 ݩB̤y ! h yb='&~?އmf?jܳU`bqeΐ_xN˞8A*U J(o1 OgHY"ST"mZt^u86aX 7Θ=yBm0_fc+fɏ\R &X=lɉI,8.ZŢX/wy[G>?oo{ 3Ah JPY 8~!b>LiD@B%3WmSF-{PvTf7ܘDYРq9W\n/ ;0%sl/sP*u!; d*b*-PPi:5րde>%f S?֐V)V r,QJ4 Y= b1K%Ja&l`k/ijc6։M6 ְB~nu3r!pf pJ0h"9^(HZkU3L Z V٨*Ln/̖_#v\r "؎(>c)kieiqN|(~`*,#H,S}YFʊ4:k F7+F뺃kUҕ|qo\XiM ^PJ;2 pEY_9v`7O$#%ß_ٷ؈#\ƼUB`bGrr@5P&e 9HMɘAEH 'lql:E,wC}CmPɏ3_델Z'c~+d'x[%b2@}XwK~/10W#FVf$`Zx䇁ߥNmϫG ߫=\+4Hz]i@j22r #lYd.iܓ䉛<+O O{4OZǶuRiDouKF:x12gWEț"fe5_xrv\C"rkvHj9Vzf=^Ic+od|N}lJ dQxAT/?a`ny(Z$\oRqY`" +wâ͊ -+#R ad#.o\r-b6溈Lj*=re!NW1Zl%Muó;M: ,yDt&HH2褚4Ψ7Q.ȩgY h3^i1\yd>by5fc R#K >VqXa)afrr3T#0sfbwy+-ꚰ3i$6{2glT.Y~\k2iچq`k`On^3ޝhnk;|;<з=21BH)=x+0&Hdlh60%dvX+&{>Dg[lt׀ O}3_w!Gw®I!@}/p C}//^b?zᓿ$;nhNN#w#F2^ ''xo9Ճ6'rO8^̸Y6rc|-#KjY}Ii3A4[-bQ4̈9.ib)+0UL sL_ &Ծ05J QDArpSt¸]f9Z DNE7ഛ\'^-PRvdMR#s23&LU0"V4 #`2̜GyT&h).Q˰ i)kpb+(Dl:h]fG·IDAR"RV>ggvW͘ʦ6)QQΐfj]'7NSŐ p'3=FmъʺBWZiHš=DRQ ^nRdsJ 7LY1N^:􉅴+d7ޚ3fp~W޸|cvo4DM t+?O93+,tjkk 0_=Tw#ş:ֈ A~+#zfL4 H0%DckV2ڂ@ti6s|[<۴9z(} #}b īm|ہ7(fOSTr+|"R\]2AOYbYFZ^q=#F|1^%y"n_7ģǤdՈWccD4/msiB0@[H"m9?VDa9¦!]d?5_K_OgYWYB{H"rl JFὰN16wzŧңX+b'B`l8_<OC1FP6s&3l)cLV؀"@幕8"i/liYӛ3o_4kz̛һb 2!O^Yv;6Ŋ50aangZx轆Z66ho+?~vdaKShMa'pZV2h{)k ECt$t7ю`=tuˠ 1Sʨ27BJUfVq%F.!3^#$\ 4Q͋d`YN,3!.pņBZSP U keUК.5$<,0S%LL;GFuM42Rr jslmyuQao~A:Q"e不eM"~o͌[btdicVp sZs*K0kTG%{j;+W6'=43fi;5D9f@P$2B*:D,MDo4TfQ *|hVr½UN7R6س%yK׵疽~9$SWۉܡrjr-խ8YN#.uz<:ԾnWybw|GϞX X#F-am"CZ(sب?)֑*:]5dQ]|; KeHRG~`J@S&Gɕܦj{0="QO}߃<Ȝ"y`x=}C΁՗ԕOW?_|+CF1ox70Erí}8k7]RD] aZ_/$8|amRA~OKB &M}-\y}px=8DJg) &ԩ, ̈<#`C^w9pGӍrNRzsBk%}خS6yRmZ>*nzƽGEcST&c&B|Q.DV岙݁:pۦe鮼>G>t*75*qPum{? 9Y"v mDuFijS;8JKX@kB} W(%M ^0 tm_!@u*jVP&hmܾMm,eDk 0W aʥp7B+ >.zۏOAwEЙE/# Irde\4zUA 0c$$^[ZGH֠yNrfCbU6%sI;0rC|?[ ud)*mV>a'10Í4Yiɒ1$!@1ԃ!%HCS i'/L7[ΖO"u-f.^Gr:"o21 f6 AW;my?2͡HI cPiZ=̢E=@XsFXkx\hNozZ$( Ld^([ًͥ=7j2Qۓ-{l&Mͅ;cnU KoĜ6KFKa=(Ω"YNU=Vn ,0D~W9y` E)AGhO_q<uz;'Z^"_va|DXAum?AF0p"v~D\K:u1bķ"#pSh_wE"ӨNzD ~p$Ѥ@kDk׊H %nӗ{2p%JFEgCvS6 !#[ĩ64ոB&k*C= Da)B !E5 H3n撌Lsbn̉ck"smj1LAR!Ս Î^&r1ZL)eӀHT}3FśDI2ƈ[u%'89ht>^_&qBJi 3_P׾d/[m0KVi$|u,\9f*ͥFΘy+F*Z D<&: : K^4p$N M]xǿw=X^"B;5HT'iTjv_3[3ybwh_xov۲F+y7ƷC/}וW6w-sx9jzk|PQu3ow? )sL9Vk6j loRCD.8!8x=LdlE]E䄲 uC!3>T bʱ{[ibxP!#x;rGt'll=+5 N+*04 14֨Ydue <q-P&$BadE)dZ`9 03;ژGTscfUEz3M!sPc&SAw02&]󰙰Z*v;EȌϯ%Gױ€5m;Y:f!M*WfdS! Ua|c->Y Z.EM z vbrܣde&S6&T=rr+6fc$قtu{(NJqefWfRmD:ILhzhϊVe+ò+LM:%v)!.i+&W)gڞx晙 ][wս>W{]1BoO WT]" g0ɴ66-Li>w8KDV](/j`2IOٰSk#v KKi#ܦ*#4"\ea݃6Ġ[BNiЀ3ɰzNO_,,-gBSPdeEo\WsG&#Fb$`}r((: BF>xr,2}3! bb(\qx4Dw )rNҗ&&tdc<-yfyƓtSUda"].l0KmJe!Z}mw X^nmJ]ޕxY) {->f%T,D.K`:Z%OULӡ)oWfPa'm t}Ef:*z̡07]  %՟?aѻR۷#5gn_tn5?}qGƬq&co15'iӥQaExjwfRjR%c2/B^@cKмHd&`(K#ENdoEuctUxR|N`lae9Pe)/+% Aƅוm^ TQR;M.Gku=ak]ӊX1SՈpYn,.IxԻn}O72Sj1͘ǒpJtY qFa s>Bd!XAB3C>JCvd*ҶRM4#*F[2$WN5Jx2=a (|`j(2[I)OL2eʷ S[jpI,k1F2'$8@5&E1 Qh?,Ga~R1Og0b#@,IOhS(kvXHe%j~9Mg&5 oDE<(6Ĺ::ә*asL46v12N^Kܞ%!wNF:Jm}"CP"M v&=S΋KBW<t+8{r 'cuwl["s j,̴F ;CP*3.Cm[~Cc ؙ/h?LR&upIJGMp`HzPJ̓NqsiYz A.ETaWwHULܞ평GFh0\>nW~9M,驗"v*11s YaE#.[UM 3# ,gJWqu!qM"UiXvtFa! t<#Z-Z3nm3NXyT>aC+'NU<1Zv?SH,Z‘#dy fcq1a; mxDY[]K6`pwڲq[ErN2;dC|Ϟ`{=.ބ,s Grqx~&Gٝ$iU `"2 :Hڂ# rƌei|n[i::(;{t53KO=O}^YcVQŪL7~v熄żIC}ݡ:CcY!j4G~-bLGlS(hhM6ҌҐ#YWfT1bj" \u޴D{Q~ўQ9^&wtthURN,ep)ɑ[C1{e &aXq}my"9ifWt][^(ra0O䩴ʪd-'j8 IDATo;,m9|OꏉP)ɩk>>tQvýa߹s 33fa,J$Am='xg4?atuQSN8Ns_%Tu@ioVl*(ɪcՃ9Q^s0h ~GBavC9,f>ƸԬ&8f{?v5Lc 2TqnTݟ碒x$KաM\qdN9mb#Y^y+J9L{ny>'LѰ89l{m_q2%!r]ciZU"RiPyI=CTѤy\wX]_?OrGm^B;p?5'+> 2*sX۷}>|_)'ozn"Lj__+?u۳NLmT;do_HVd7m͞IL:"i"(/ ;g-(ץ3OJJeu -JֳF41m8ï߇ ݦkT41ќ~yw2&sLҬ .Z{ƞľ⏿D-xsQklUr8㧼CbWy^Lbo'േvV|<TϋE#igO"G:VUOfn,bSMcaI$b<|%:w5(zN_ j o=u 5|CWOΌN o|ѸO@GNĺ<}hL75UKl1,H\ *;Gsl 1"r-΋:c*R ՞Sk1nҀ7P2A/n_؀E~$a~@ k *Pn\hA#"HH]$Z,lV@nni>y##Hr۰<.VW!yC la;C1Hu(Fb,9L#h;)t# d ||0kFW $f1^Dئ(G^K5Hc&]lo&i;dB0{_)5\ROФPߊp|ugzM1\hCp /sQ w3<:qcjqI7eJC&'DFRW[3#sey&'U鞞!pC6PUWFc$ %g51%NlD_go21ʿ=WnC?p$ǗӃؐ5PBr4!^m2eʔof֋ľKa?zͺ}""DDh l;{n:bB l[ٷ@KaDk?>-\?Op><{c6҉5~\] = ~xvWۻ/{ x[\zwy`F—YQؒ.3gX}X(qeT Ys l\$MlkX"_&4N#p p}znB;0Opz_XL<@-Emcj,Qa=ѲD"E"k *&BAc ҫbhxfϑ^T1"0V0K GQnq$:vɃ7* eB,s@Oя$]r!hn!fkx ~)GUyrDNP^JSW|v0!MX28;ǬΨg5Zmt B9PW JwВ$s_BlwXw-͝1aQThRekX&[yMRla"<M(>3B1kj{FZӔC gfiW[R{qdt8&G@6֌E6SYYXgAΘJ֨́F$87kRt I 8u L-nZr]Tců2(X (!,j{dZ? 2лs}Nlĕ3Ip$V0b.&J9PETڱ=[gk3;ӈ\OɸlRrl' pgWfx6w偫끨ŒAZ2X֫R5RvƙYASS y(ic"ۚ^y@XAiv#|6$DI '. fgsbN sP@@>kf'Ezh8,Ĭ=Ϸmͅ$)O#JOH I-@؂}dE]ZFAuYX!Bou0hH2fQʕm<\ !in2fVœ`>nO,c[n8Q٬IzsiF }%fӗ.;kPV!CDYb6v7ic5vZ戥!tV dCL{L*diL"w簮jhq@z+z$OOs6DQ0SNdK/0~*rFb2rqNLjl)]k(ZQO$b_# W\Z>R N-p5(wW(s2k KIXk/Dhy&T\L*d0??_3#AO>3I֗GcIl S'Bnӡnsiʧ=KT?~wا)SLy! X/0 q/=t?yx >{7!gRA2.%$Յ]0ٖ;drwsixew?{aғ 'ѻFLJXYq/}?ZG_K94d. LFhrdRtx^D 1(0nkNQ-8U__V9<)ID-.rg.r3H4TblF6pcaKv< lslqOe$vU!S7Iz,o ?@Y }}V8Έ5e..5gZ_cbhk]M{룟|׼cYapx6wē;+m]zpf;roZS ftu(A}Pk圢3n$EspFfH 3p5;:/ ~]Yl #Eh/V֨)j(16znPu(|>5y~ZU'KNFڿo+3ZÞC)dDb|DHhk;_ԟ}{{5eʔ)/$o+ī_O9~vog>]HFI5yNAY/d^P&ONa(w=!hRNj[G ʃb5[47J]b:SJ3Xbi'0+۴dDf}"!c`)"Qf-yK#|N'n#:XV( '$|! QS ,k~ll~;x=}f(ro4NaDփͨ)' ~ ί=|CU[oB @ߕ:Z7-php£m?pdiۋ|-]I% Teod $;0rn-cgz̋"dxiIX<o4}ǣQP/Q/|.IHKjjt*Va!E.Ag8mg\sfP*ar dOt.7UE5<@o]MM݈Y'^M>' w <,KA,? D (tKd2n%!v91Jv V;7e2q%>\gU]T[)R>F s y R;"cqNĨġ:w\R *lфv{)򛳤PHiLLpIo84u[ 8dlsЫf)C Kk /m jL4MH:ʖCd[bгv B fn;)@Mf*]E *}̯%ef3u11dyH' W@ %۽};8O2[*EybDaK1גωցh4 *ihyIjȲ95nr(Gh!!;)R[6?&BB#Wyj_enGxO6eʔ)L܍p9~cZ2v1H3x$CWtA磰&E-/JPDc]F71P /z6l>:X-,+d}}p8WyL1 !*h$M19F!}b0\7 ǀGS|%sFZgٔJ("u&&f%FBo$ֳM[9<2wcz)79̥_?g7 G;|s} |U}|UQŶ㽼=_8wGK-錤5gJrʘ՛V"RRy̔R/ xFTT5#sC[Yxh#xhT6FK%d!m TORy5Gmk{tЭ faX ^3aXev7rJy/@p^U==jhr` gc[Z 7HVp+=Rq`6iǸ(gD^(A dhT71 5vEw[oS[@2ؙeCwL;D2epĹ.bXBm1\/ΎG*EɁ{ܤCVX|;bNR+0A O9;|>7ŶOSlw#󋹃hH,hqN J@ j/t]'Sw]̹Fc_Y8W2H%!3XW!N/Xi^TVw+M+cvaPsҗrwn~p9`Sh9jNDlbdWŎaueq*B49ev\ ؾ˽SՁ-},Eew}> 'lW͏v IDATŽsQS/98Q_z,F7ows)SO+Ass #pYX6+Ըdۧ.x|w_(Do'C}>eh=73 'urXP; C+Y9sL%ҡOx݁xnTj3z`6lJA0~l ^vv_v]':`7~m/qj x'^h/:f=+ܽUK#촭 ;tJgĸ/52#j8f0ZmYtjWprxP +]IuEԦDH>2x&;H$;MEATqTo!:Ud$XmT.3ȝB )Z~ekC60ɫW@eH~^7`0iy{%R;ȼ u6#p (z2ffR0N'\&]q6U_uALZxbm-q:$7k:B Q6V aS2T;.KKYAS5IY+ /!0I@a0'ZMeѥA[: fUdo'/~Fo$lD+CfR" gk=hی/Iu`'nU5o43J, }\P. % Wq)-nn6K+7ua & wq[y);wA9\uỿL+XMM*,SEwE!shCrA0fpP>I/ @I3#vgDqUٍZ?vcg~,) ]UxΖ<۟^|Gu̿reqcwni'՟/į1xw~j_욅mk/j{m_ W{9G}Gڧ8ǻk/X;2a*`=G,"=]lٸ>#>xIoCѼ=ϸfW(T'Pva]_o&A6}LR sk? :qK$'ޓ?(6G{_șsKG4#bo4$X_X8dX6ɀ)8.f.M:71pUO>mHNoKZ7G( ҠRs*z{~H1<ʕ3'>YHx85o|LFR\.$e2cmxٓn׾//!MJwz8=kOV(X23AOՇ̡z]&} J)]3$hZpjϲZ']ΩRy4^pbE0:3>}{ȭ#Au f`{F(3+8io|U"/L 5oe(q\W*nS 2 rѕ9uʲ7 VHCJZuI˒FQJ KVp0uJ#=jĝY \lD (鑐m6lzgak:c" (yq9 !(5TI' [`bSK>([)S^dzW KX0G{uXTl 0}Mqha ߫8Tt ɤS\%xYGGi @7ڬ}pyq>{nhTs 8W6c}כ֨W<;w Q /~;_?җ^qhxIR/[HS"W\FH\98Aar†%)BL`*`=,.|3=Eο_KUR!F[2`3U̞'{4yA^Y$0h٥5)R28sl/QM3[Kc383A)C|a!4/Q2?B>neLyuPhp%>KWf>dVE'&skYxO K|R8 ~D)ֆ RԻ9N!Ԉ\1njdehHT(uL$j¨I Hy J3s'kCN|Vb`z2CF ǖҜ]8\ )ሡg?Ucx0˷aF:WzxƑKIns <y{51~+{%Uה6fⰦ `Ta\X,56IMO@%p2eʋTzX6BBĚeW(n `j=o(nN#~F7dO}~ Đ@[dƒiTAIZ P\:*"Vc}4=Lc43[?{oeu~p;ߚzZ-ɖVhG"Yߌ?/gywzéHJģg_*R_?~fyV_~??[OEwN\!Jx4B0'GaSGmwI'$*Ab$6#cvcTRΉu!%o`ʌw Mi"u-p.ʂ{/Yu8ߒt睠3!t]ؼNOg@8oLjj;%Rj0 )x,%XM"SjW5Ʌ*@X\n.ﺃleSZQy+RT{8s,+oo!=Da$TK,WXSŸ)>̛E\iңR$%OPKPlaڔ]Tghv@~ uŃ8?   Ygp0=&fj470YH)$ ֑a%~1K$nx "(2ښA^ T}WZ I:XeF΋6 c0 ʤ a"XQ'ley5˺cs_|d<*2~ԂEM TWĩ󸐼-)p}RT<2./ iX{nIDst7nfI]U0P≄|S;!Y`)TQR%;cev^إUkEvp_lpcn?~:擗r2n/|I…w7/jb16?+?n,~b;~; DL?"b/ ?p} |0 IL 4~aԨ,?mP X> *ex>'@~8XTECo)L4c*_Ƈ'̤Kd bW`2sRS@s=ǭjBvOxߋ9>,@*=oHQ-=>љ!d= M""=`L$2SiVB 6r7O< ϐۥJܵ(|zs@)TvL^yɑKA}p"CR[\6iԫuHF>aSf%aS>Oi=w3I74g.9|v[[lFL-o_Pٶm~"mSL[EIDkQNSoӡ1ؒN( 2毑Fl-mZ]`q7 l%]FgqDIR >.aFYq> @ һ M䣸NJKP`Vbٹvi'ROW-o < Uz ReI W׆',{ *"%1"{njC2hn`8 "MEya0j[kuuob]Axo %r4v+GAk6>B6"l}E4B /Cǹ&€^E>V<|1cLEQXhQT . FPtж$#^19G<As<'=#c"ZI*Я#dɊ8Xd,YfV+gڃrGK\`gdrRT]/ٹ!`.H^&+M*u{W #am^ 1nGX;uO5O_[M' &h}%啌cG/uhm&hHK{ci[WwN<Gg~\PMPrU&*R,mng^^>ۍk^;պƺv{~֖[[-ecuwͻ]E7[?5zR,|c/7&'=xعРM;(0PU ¯x, -6{!>Sf  r~*STwS`4JO[Mp^ux@ۼ8N\Bpkm4[2;9?kYR/fdY`pگ6|l;vݧ3aN7eY$&><7(l+E1ARsrLk6)p|2#Ԟ MW*)|ǁPdغ` F.%ٞÍ,9y;]Ӱ.] 7?x)3Sgv L 'p[~)\?j 1i|Iμ)j+y+5&Yp`M8!6m:,lPgT42*ɜ@0.}UK: D{)/ԇX@RcĻCQp{>,Y:" ݀xE VȬe [4bW#-`%N#1c6X%6!<+b<(W4U(G.^fnx<M2CR;fѷlt,c5"wQ0 JƤJ7mI6H9 =-XNzřQa–N*_ؖb u0u_,{?^u{wJE~ EPB"ܮD6vG -<|a>uj}a(y촸\`:e=9{3:fW0{n_pQq9b[❯}}Ͽ%3k!l ۞/ 웕YFs׼{Ϗ w۬\-̿F?~% H"Fm>LmNmBm-h .\0vQě0ͮr B4liәknߠj 3W {QM<ߑ$I4Nel ‡> !U2T/8"$#CaV]8pF(pAx5(WFg2;Y~dTۗ;O,-D"7:tlaǖYtw79܄Q e:A_GW%4! кD%Ñ[Ck@Wg<Nq#[b}$? ˇ'=:ؿH/ 'e?!F !JҺI8aDXt !a,kwf9FJTa<¬`Sͼ}f3 UZIT%hbCIsX`oٛ%"}A}Yf|N*{Ty0_F*'ʢV*tY U3dݭ|ɢ -D| wGkg#@֛Ľr5 Oļj.L/xk]gɺ([+rЎg[&cW1wه^#[?=ݕ_s߈Ń.DET%>|컝=u HsnO+-M/>m1r!1]&iIBdF5}jeG +aFC"juD}?28'@Oe"Q4dI's7+*Z9b7rG^( 8 2-sJ]@>v~>lOчk!Wt<<Gq^ ]L;iL,. lҥXŭ^xa7!߮9c%?¢\01X?fo+LOh֩Ϭ>j80b o G i!(B9Q *zVG wx@Yx#0Y%j5|{>eBf1 rΆتSc= d FޮNHM䎞(v] uT6$a奔ώ=|YR%TjWt̔lJEgX[BܔHqVuj;[ 5as+3R=ogu[ܾϛFkWٙk6/( -fFPd!Smphm@œ~ہˍ= , xO'$pMu_wSR1f"͝9.F^C%>G`FYM)yJq:&e)H 14z3)#<]Gj*6%%ʪ0%,ծKm:ky(oS.өhZm'ԀnY +sԍNa0zG}[l JM~U**iUPyITw߸;KnI,v|+9xo}dңO֛DmS>4L/((}ͿswuD(aCfݛ?zwOXu^˖uKmpS 4Պx }.^imt,Dn)*}M.2"1d\$a.iu'BWq@=~Òu%(uP*$LZebȴA]% 04UkgVڌ6") K d1lbe" IF-DZ#lm"jdMf v Z{ t)7; kRRR>ؘ i´>Cs0A0;Z9 \#v1k9y)PKR6'.Z;0Y 3=Kq9^>xR&y &"5D91 I(X?@unA\>Z=>{a=uaԀliK2%TОs!p 2kM3܆ RcPX%ҦugԚC۪*35!flqYH0$1p:}e>~ 3d*Z(Ja]ըz_ 'vw8/Ry7*#}T{-{e{W;}?IE^8oq!T\n A:dn,W۪Dǟ@^h||Nxz.ޯ`G%+f :\9m`p"xnm}?+uo EÛ$/)#/~s3[;vy_Fٙk6KQxp3=ccv'v_HG* ")Km7#{Ie_k?|Iv7qә/X8+pAY ΃WNI7 ,^`jSZ:& FZի ̸H!x-U/p_vG`}a|3f<9 wvm0MJk7Ё+[v8 iT)pJzݳ ut}@%:W n<=jd.K9!%u',3<\zJ >Ƃ 'Ov'f==<#ݦ;]ш߀d2}pGT~EB jl^za 7=ƃ>a"!κL1-Z(Ȧ0*rrUy 'M- 4}6L0+CȑJ]?yq;lh!?OY >,#._tՒr`6ˊtkETM qƣb0]:ռI?ᓣ֟43wxSfNn<BӦư%([(6T0"|Ls<|ͳ5qbUR 'Zwd &f Yh〵<lrO .!&:˫dh;WOVZT'`7Qq=5S E&tTzJMfvy;H[oX+)>7$ 4B2jLsK_Oo%ՠO^5vCxH`8/+wjV.~{_9FGw-K<ve͸053ua@ (q ӕ^v2bUPcZ.F2.=ڌh1&ʉcDO Ve0#b&p M&eC Ɠ$Ih _2 p)H<kc2@8^S9[ÖC0s@E? ވHܸZD=O%ؓh%$ %QPFnZYV>\Sg:+ i?i.TgwA.GN~C+in&1K2/%u9`DOٹ{F]l?'3 6!4H=>CF֕SXGO:Xl*Gr%ݞ)Dj".WPR: y0v= `&r*1eSwhGcyosZx{×;j+Rs 8 5̉J3r q(֡f)d3Mez-"}Gfx$*W9zo}<^\~1n?>þɓռg6π_N"-_ N6|GW֯Wb->ί2J`k|:Sˇ?{STNlxB91جQ 5uaꐌO kAh 5N1Qq䳀@'(kՠmԂlNB@m s+!sg=ż^I#.cYo i+ޱ+mN r~jBZ(FFfRQڔ@xv3h^[Mo?+'{w{-e?w|Ng +2/)emRiX-%UCfgS5֑vSkMnåT9j pU~-Ɣ1=Ou>.຋}vUc@Ӣ^(,;vҠ#T%2j.%)=%2b11C !s-2"?K ”?l_2:G9͢L} ?C^Fqo5-߀azu\cEtm'#j=zХ`$a1$5Xo¥խAm %8<62Ys[;@l=D{ A4HeHB©ek 1##*RaOmD]cAw%]4EG('On[DE[*bxȇEnmxR`_%r$_ T}!需D𐾁p%F0hksB,ʕgs2,6L'̧<[: dshs#ʒO7D 3EGHjepʒVV$(%T2 |*M3iw|]!pUٻkZ7DW+~NY-_|QR?:C51 8W跕ld#Ƣ=hշdYie15kPqѶ9za=}d ٿ*N ; Q} XB_?<ٓsOY׆|/uY$PQZ(3M4"9ۏp ȯ\OɃz2hCǧHÇ?_|k_=/#yj_|jN߶}F}8|J:fvyJi~=CD+@QTDXr% /ʩMQ-Փϟupo}1?[ SR!qzx7Z|}d] 8tׅ/=l&&|6'P,,^ rCKN<]Lߓq_]SRe&`S '%FV U5XBZ|9虈1d 0ǘ%3besr6Hh #&c7ˠZm㒠9T!t|&z]}G>eت8|K4yN3Ey#28:~qrR,ƽd5F&KQAsR:B>., c(wJUhn8Cp*x 8} 1xrfVė(a!^%l`Z«-!s[r0[`+Ld]ƃG$d-ޤ%5#1.,c|C9U(J0)j%BH,RZ La$2iRd%N7XdNٵ) \yPV(PB A 9 &b/G+%#bl=ƺ S'^"sIa89>#xjrحrM.s+m2T.)l0!ʪoXBe.,TƀfSbd* RgHT0T::]Z~|sZSȫ=;nUs׻Q ZYjH!QRfF۫_hva2T}i.kKə Ͽ.PQ=+#a汕 az'z0"!mgk$4;B_M``)-CqWϝ8:|c'VC~I^|/,nfcn=~7oҥ_KOk =5n~1[wt=g~igS㙻>peFs,1<}o9p6UUDTmɘp-yd9([3^?T՝5 rµR'A = d/^BF( qD. TINbJB-a@Ab!7ȚDz0~R&poӧ6>x׏|k{VRi?SJ)N VVz[ HQFxZ+uUR{5(8PfL.*m?ƨ+!y> #n Kϊ0-rd h#3&d}@A$d!6'e6ySxrVl#dux b.8K^A0'scnT#vc三ho}c| XES!Zς=JCnuyD S`Hf"^N)S|CAE!{h˲3"Ő#9$d!#6ֲTR{QE*nX%TJжzUP-( Ɍ&9Fg{x/##R}׊s睳}66x߬q NaNy"iad0^Csvg0A#'؝%9}TB`4m3"fYZStH%9q* >4wcxV @ouEm"śa$T- > p!O"~QXEZ>8p@Q&ʡI'=7IP)JUt!H$ASTcXpp]f`kPjWʡE7ĽwG#=sĻ2{B1ΖrS*צeb+즛UOwPGh+ƹ߬GJ}Tl7vX|=Zzhzi'$ Н4g^ =v$i `λky\D  smɇ'1`W AW>1p**V&16b!FXUFIB^};~u$ yۖUi` (8"$V UhiDf9#cU !{i'X1WE /;{.?]'BgQnttveH90NޙXqp%2ePADHXfr HL$יK]n&QD@ G,E D8VH-U:1+1fE('çޓZPȌmk@d F [ESH2ςGŻj~$7?,~m7ozxo;ü!ӳc&@HrqØJ'ko=t~PyY?ݼ>* ;'vm'_~Z#~71kI䍯{=Q85(HmfFG/`xQrrQ n`λJ+EE A;v[GӧNBQAJ bkP`:۬dc{9֚-J#$T KQ΢QiczGP C]V#M)P !{ Ne PG@g'fe׸Dh*3zZI)C\Tl&*2[ӞYp8&*C [P PjZ;ПM`؄ 1'IO? SjY ;c󴗶"4)#*Yͺ1je2]}4(e3М7w߆p G4,mD{ t6."sȝc~'J o<ʹ:Q UYtH) 3 D."!(pHL 8yR8$#$AФL )&eU8c, Ku]qެCKgƺaRlE r7u2J5*Pպ\VQ<'3^S:]Zĕ9g 3ⅽF88ijk x+=x_y5L\E#O90%!.:jd'Ei72ڦQSI#3;F\2WC+C.a&Qߎ 4Ό\<~o5wB<7m~q2}zO0-M5OH9t.߫dֻ(Npݎ8=5 }э|J|EX+kH6Gdkf+-љp'қ= OEb{'O$֏<$MWY߹/ Nt~>hnm0 !sR_i* VCN4URxvрQvӠgqKA8;¸h?(k@aGA.m@%V㽴G.ܾ|Ew;NC¯v/xS9GVdX+%J:_p8IS >^2;$/;6. 1?xfJ[ uHǍm5aOtF Hzܾ|c?ȁX=]fQ3k@-qow"Os{dIasdOg(<}bɈc5RZH*bR9 qCDZCBADA ՚N@ZV9"=f1V0[dK\z8ְDwo=lt^|Ä!dKO\e1U >'eh$J8Tbs4ܐ0l}.nJ:BY(Gd@X,xψfߟͱG^.[G+*"Ϳ׏N^s^} '^:O֛' cE:h5I8朻tNJJaTni6ȽPC)!@wApIԑ]ds ^ h PPOpXswc%V- $Bp1X(b!<18(, RSd'e*H[u!ͨŢN- jQi;)݉tueW/FR R} uY%"grCxcjv#-^(!qusp D-LE١n0NIfhcFG:x3M 6"%AM?1$[z{78VbX| COSz!*GKRLXɣz?[rq1|{wultӣt_!!ޣt{GyrG O9ܵܔG7?|>! 9ζ$H]GP8j;YۈQJdwbs[A#Y]!;APFlw+;Zgd Z1]od :\Ss%xL;@$SLkHDF0# 21tŔPZ \Yge;}ۊ4>OmzK [~TF)$gq Q*G t/Z>5V>jsYy:\f򅥚 /?7^{7./iI|$8> s ,5*nO!g/%R#@L'e*F'}6<ǫ*&+bibͨ.HPxOC@fy 0xtZj~s`@gϷe1=9GOm޻x ` 2r-F={wlvO<#ߨo>sw \=-O=.P>_siξa MN>u~q\!FC *!%;bQursH!M|6ΘP[DQ$p(,ٔSf\9` jϸNr"83qke \ qcIn`+lFC j:*Ƙ"EmQڈ8"ϣSA5%Nkq U F%>0~_Tv=jz÷0BTB҃m{-a{t4٥ Acܣ/X \ʉw"):CG71t9KAAPm'u`4e.w`E4dL%qXvyp=fԄ,FuM $IJD 'yCU'bBLWgH\g_)xyi@Xk!NVRNY$媎=HI*%/6iY7SY3r$2'PS:T"gPI)!VJ,y@,`օ1 oz{^6?y̍qO_ĻVÄfv^Aw9=YtXU!C<'1`IV pPAi0O}֎u'?|^o}o5rӟ;{~և޻3{2^}=1YiݱX_H^}5᭭PC۬Κwxm_?Q8mTiv{K,lextޓo]GmNyRXy,@f6p*%GtށQh(Ƚ}Ip9%3T`fȌMA6hr [l: #34)Âlkם#Y$'a: Z3k[;y.0mqM75Aļ3坬fRʹv?ڐ\&+YU- nD-#&QA+ T1.wPF0S; #3 vDcs\ `-(K!24Y5&I6}TޣQ(.K$L3.\,DxԴ +19HqqӇ`g υJP!i_h# ETD;$9;UF! le "*ib5d\fL;‰z-mYmr6c+0k*|<}D.957 9&j 1>{]wdl7"\=m⡋r89=!w5?Kd~X# -&IJ 3z\ p `PȝK o!+K~$2rU0R:o˳{~|><}bucNQ:D:GI?o>F?Ͷƚa6ΟvVKbג6 duN~#3^s9NtI#* ,V*o~4~AS>OL1QYU(w% ?ʹͱvQ9}-TT{g՟[];fu8֡P5QxAO>H85Ki")t5L7ӟO|xr;!tj6.7Hqq :a;Q_TL[~>oy_3?g&|eQHGhl}'?Ԯ? W \0y ܪi6gu L?;FV6|Υ?MYK6HTp]!r#Qǥ]Dh0"C6XDS9\^R&O9h1GnL5 67n&LZIgf)+E@I(: .=dOPvD$FXGh,l! f"J NV] qf`!L 㖗#^Z=63 Yla]9z# ;(]WLDQcG`LYXpMvNZq \8Esypѷ'AtERK5"J$y(2 yMpŤ,of?߮ od|i륕.u2pPpC\㋸'UrB\ +7UD|Cxz,pd :ofn u^%BO%6Ufm>1y Wk˨98ޘ[Is|cO-anL MZjĄ9`$phN18z{0H&EIޜ]#u lAr1H"zf];+\GBX>J%aPܱ'Ѝ=, NaO> v6)VOHd(ie)t/;UlFEq`3cPIn$& χ8ijABW96%9Lq9QUJ]{\eK[c:T=E,;rO7?\rodshoA5r۝8OSʛ8DA5w6!4U9g Y?z^9uE Lw/T3I2l@V%.O@hK$ ȓ&Xa#$c1hBulb_a&%Yis]cfqbG2Hd E^[FEt =*s˃h2/41cuÅIeZts(D)ư;r5 NZ]&YRFiND!)pH$єH[rLEuv`2kg e 3As'iw.2zQ=LjD}(dF(W1m4*h` ri;SCLhJj?ËWDkAS=It* eJ&Yu4VrҒ,7duFT[P7ipH hVr2lN^k8'K T-(YE!XA3Hr,cߩ;}fMgy dtCa&\\Dp!8BA{E_y1p2xOm=W+M`J_:'d 'j(rA=C&g~%6 x_ XHGCsx>_ehM_wlS^o@m2/*9å#MwIU0%tEg }{}Ok~3S,2C93')sMP6a, SO3pVx#61s#6;;8`[/=A$Ge;it-gJ=Hs0eT4Zw٤?}k0טk!$RP$bjs骻e*&!z{q!9ŸW< d d1j{!w9S+wl/z˛RF&}bO.Rw!z>FABc}9cLHx"gN55{`4kU `W7抦-kZTbibMpŘJ0 #å&*co}_W7Mȵ+޻K{=p疎O[~F-luE X}D->z 3C^IQ#Yij*QW!0KH9h1mWּ׺m5# ',4׀T2gJRc% $Xm!3hR绝vB;2- ;t`0ISab@F\蹐3_di"b}b\G7jE,9FAqv,M=z8id ,(e- ,97*"9lCO3/.ʠ}# ҭcЛ(B!)Ā$F֐6@YlC"ȐDŽja0 m/پSv7q3>K?\]"QW)g6"BB@{DiX%?\zSS^GO (^1h5=eOÚ FEd!je@JΌ hNp*m5NP' `H\%,Ҥo+z4_]2Cu"=hd J`ZkZdhTRD7~>gA>|"jd׺Ro_ΛmJ:?!R0k>o}0&wR8MW*j6/[MĜz2(AedO ^rsc9yn {sՆ?;_^|_9ėbR8'n‰K |萂[)Yce,""Ema^1)4b4uH ʧH/|apYR*T O@x뉢 9=Ill}l%#m}Ϩ C~BZAYèeOPF]DLB 2",$&u;YHޠ:Q"t0XTB#vQݾ3s$ yI`-+[~8,6R3LiZMsNW -Oh=ڻϧRAaNhv\tF;q}f#'yϯȽk~Å v?HnpF{u4"ilҔ9F*k'jsӽVRٽ=k(p zՍq[`R[?ا8p [TzyvbmT UΝ1ӿxnn}~Vy+ˀw4w[,W `kB^%_ezf~~Wzw-#;}-#;Se+kHQkeO^Xw | fteZ>{j,i@K bz"!E P7Q1ۢG˸ h)'9⚤[ŚZք$]€fImSӋc.2aFQBYsiv YuI˜!dGN BK:ɞUyt, 5I{kuct{soWS} Q_dAB ?IzYU8f*h ,GZD93/\Pj9~@oaWs\eVBB#8 .Ɛ`HI \;T`J@k׽$3&2"$hK 9TcIDOrR4NrGQiq@U,2'iq VL^)ZR9ƭu֓R)a0 B9 J"%n#&cיZ(&)ög5h=)/?T\&?Woh-]׆F)w&Vow)ZZғWJH"(I>8#)bz g8s{ o'^s{߿l|_8_},E/Ώ槁#3.HT0pBͯ^:EvrYaÒȐfɧ>d3Ћ}@*6!oAo-oОư{#$) Oz]yó{`#?o^œӧX`&qπ.`{]Gܪ=ҩmQVsdqnWtZN"|5֬=q^ˏ?w7h`H}ԲWwORMDeS^j\ Zm3J)rǴu4r@AobGjkdZkS5G6;qOVOOK0{)?g kV$g?uv82 b1aYXj*$GvI AL"]UpXja@7%M1k Rh' }#AZnT\O m$ꔍeD/rHpe?]bp>f?S3fL$nǁ O?{x7i^^ڥ\=,(ylT߳ sk/߽U3׀GV87KxE/@5nP{yKL6$̲Yed,pmk&0f 6We!-R# I-ҨkF$%#f=B1`Vz 'Bv A#/uax\JWH06AA4ŋN5upiOka -" J aH[2\TŽkKȅHos Щwo 6I2iE7#P6&SMPk@oShA$((uA?&C/Fiހ[Ej@2: ~0ɐ2LC:GIX¡$#,9%"ecr2Bd4kM o-[ܗNBYG׼\-؆,xϕ wUC񒯐s7G^L U0%o1I`^Lotש:0La2t6A2l,̖ LQAكƒ^mx/tg&55x`d c sr'uQ>`mAyZc!6B&f9k0Ӗa;//NF/,ǧ??<c]yr-^>lʭUBdʲ<|kJxa$mB3M+RD6kB(7v(4%x֢׍59m:p Ȏg.r o~ϝ&cRׯu<_l50 \oծn6ڭ'PXIJ 0 |w<L"nrJN-KZ$)LqQ%o'p3'~|v<mV3S;\b ϊ(Ya,|^X)pECy| Kl=,mǜm@nԡG~rC/0^ًQTMBpg ̈́[D~Yn9e]N>M/EP>nydc!!n@A`P( >]360T@SN$ rF4I':r҆6^ׇ6 33~" @g8bfV= 30GS8+z$% xi /w?WB "$nivG3vQ#>Z:Ą#`I QVf&[2/X,A"1]mVD z(4U;i%3yrmr0@װur'$v_w.mK^8CaԞFLD^$UXŴ Q"@LL4zP @'h9JkGƝ83K:y|!AD=H,n@ 1,{j 0!TYwֿfMp{C3:<񍎝nk>lӗagVතk0:OCSWQ I~ )'PQ[Q]j4ͥjL7ҵrX IDAT=_rܱ~DK?xJ?>/8_1 Jepew" C?Jqlʂ!DBܢSar@)W&87N%B*VUH !0 ?Vmɋg8h_HR6OLC]jNFE?:_[_?}}~H"e16i1خbJp]GS9eQRꄰسIr&ݴJ&/$^舯7h…4TY9qI$&47~SzFK. 0q>OO}[nn2hMl{}|Y;͠Pȯǭ |;GC+G[|K+:k_G:nz}qj#p{q6#Ϝ`hC]j7mhw~T8\>񮥻_sm,XY܊G\MĂFNHfCzfz@GlczIBLdSjB3fQn6FGy¥)y,F;My^A J0Ql8hk_-NA(XѦHJafE&ۚmc~*MhL_$@]<Ȥ4^Я&Nv@lcdNeȼdfV; skB; r fj4PCྏx ހU~hָn>j"5%YaBQ QQ)%rWUCk}w012#d!WY5 FֱͲmm"BPb5V\WfYEx&WIˡDR"4k'nr]o L[),`=03/rj[7u%x/W8.I6 ,݄"]ߤRP U !>&YTYzb.&ZQeIT|a86؉ܵv#M瀼:_ɽ3Lz+lH>#5y%<!CϰpbwǃO}O~Ǩ~߂-d6g@6$e5Mhx[ /uwUX:2aA@{ 3NaH}n@ _ZyM("Gri7_#G+1 `80ZtVZwXc&='hz塖 ƅ*H)I>^Y@+Z8dḱ&p9^+;3B=i_;㫴sBo.Ta\>.(8 ,K߽Mqxٓ\i0Q^S+w4XɫwkR' tYS'c`Ԕyss6l=;^5~v hll@brk!I/u2 `cBFMG$P)U7lDN WՀ!y1qr1J]>k,rB^)u)fJ.N4pTzs~xV?7?yY߻mE7$84mB K7̨18"UGGwG?^3y} 5UR34Y\s:;@'q D6i|E:b̄pS(AC"8(2B\1c)f `=D*je k\mQD &Y麵[42FW:\bӰFlPhP(_cbSv 6`Ui qJPְ~! BYQtWO}ej\B[;(2!w@w{EcB-')Ե֢ U򠤏RU:pG`HWYcµȱX ! xL&$B(D0˚h_8z z&DBT%(7BMxxuXTFoe$Uuؓƛh7TZ&]_ys즏-Հ$е?יm\6}(M+8;c?*bMuKVɬދV@{-oA`y &*J)74Zj}5Nr/#s>12>(pO 93_>ܑwSrh~@|Q|ۼ[pY%|Kck:$'O> {]RqCrc&6w)fL-@8%UhI5eyYva ~ZE" ; B ݬ2f9x^C͹3(y0P@2kvy6gAG΂؝sMe|č.;N8lAoM*T+ am }[F9FR3iwD\؞+ljL}?Y(;~1sިܟ:1h]̗U'Jf.tggi|bFf~k`XKֲqKy{ﴗGif;|bWGv>Y]7}={ϻPJ#gvut{I(|wd)4eT$!p"Qu3 0~]F)2C)Ͷ#&@5sѐ#xt c,N'v>f`.4yCsge} }2jgBN/@ ^_FD "eb>z* u|. y`MBz._8c6+ R:|f;J0lhp+ku]FEĉg/; 1ey4!4Yw`z(Jeؐn526klz-kprޙOr'Iv޵y*w kSej XkuA}@*kC80Bw b& j [2#cX05.WHYO D*G<]ԛ$`Z4T}r7Tյ9 6ܪj"GÒ!'Mq$ֻq!Z+}_zF#n{)S#T^%Zhkl!DRXUd&^/$hà) vڬH]<;w4?|i7nO^,,mԑ[Z}tNS._[Q7BrŒ9aX1)ۙRG%J\VU0 6d& l`jPAv۰޻u 0 tpOW߭ Z)hڅ'#Ɇ+v7% :* n9-S:k3 l6Ȃ>^O? ,+inq 1ksWQ? ė7Oyjmgq~g.]ozy݆nJ,gwҲ| 8|5\)mK9ʇ}Ye) ԾP)$jZAG` iv#0Iw}=`T}!;<;w/ 'F8J}sŹs6*V̢YJxe犡+㚎Qa6x"ťA 򍗏Z (&T.G3ؾ9$phRaa쓥4b66/u+ϼ,ou?w]F/cR_&W7ф^mBwhq:cb\qw[ &Ref.O쑩zG VWgq5Isܺ3žw&(qU!ԢLS$, Z Y'I35RY~Đ+[^ #rLZȸD슽lNƄG6r/as1pVi]OT2{e*1Ϙjvi 1\"arS%0PQ} E.XLuF*% I^U:P(puDupYu+R7Oz2ƻ9#JCWR!>~(Xb|,6485%?er}-1bXyfBw.ZO# UZ $]5&\M(&^m]?vٲlv+S#:LB2y+N/{8xxe`LMPZrΕ-H)2c2+ॄBTdWe^,Nm4Fu,.}?:{+Zaɝ~!Nr7mR0lΩ)x~ĵW]d]uN/?Ɇ@c::)Y||;?{GzjSnnti֟->[ ǽ൩Yr{̝Zk= em R#F-9-F·PKyL|\윷a{VOB6avE xV{ZCz&tJ+๯%XKa0 Ǟv-L|p# .'`s)R)AǜJNe *| }l -GCmJџM̳:"f5Z]~ С/F0{&ە«;;>>mD6>阹{_R'K7[?Ju3Nyq$40o[aEd4*,ujo92*hm,o nEz|-oW,cU+Xڶ*^Ou~u^ǯ+y>PA\%0`"*0PA LhhUXD~PzV%х&0JQMODX3=mn\fcU&'\XS ٯHl[zKKۙMf7swRfL{0ne ^=t`LI~s+KWjs~įcWw~?q}uYP|ڠuJhGۇdvA{Dqx/* lb! rrzn<ZH(1&OKеfAR2` 1J,,(sBmL6MCֲf5{NjrLK )ReF/h114i6;~ ^F5EhK퐑a1a峐O1~BFY0[ؗ~K(c& Sy PAt(}GR"WҝEI y2F$I8E jC[)ѽEׂ`_wg"PnuR0n9u5珹~hTO}o@T]nv =i)P"F10V!JEB88ZF̔6M.PmHeCI J4hB'L~垜I *0&7--?2PdcpK N5JQwK`=:+nTS,5q|S۠P#tQҏH}Ve$Q}j2]wk`6r8~{>u v7o "^D/hǷ璬|mI@,"! hO'?/ 9(֯G8|:_=~Պ {R>nN=K}F}1~MwV?%յ]z=>Ƴ;Qw|E(t2ˎPbrW6¬a-l5>$SйTN`؃Z6OQBi Yr Q\uu@$ g 5? I `xKnON~_K'ocsXY X?)X_pϹS.(rY!ŰJrO~K[RK\`TWtM@=O3&D=Jj! m IDATF7?\Z&5fu%Vj0ƁǡA6+0֨ 'Yl2 iP)QVPܐkcȳ y[ԺDmYz'%2Rh''5_yupDǁ7_ <*_w(҉Cw zyxeVNw;V\/FiCy ~n7M$we|*Bĥ% О-oB$fQEUB{ -٨5O)J`H:%S/Z#VJ+%dKP`y [ڟ?Cޯ GG/45}Lzn&p$۫tI,Ҁ=+?~>7F/k_}Gc]w 7P([a7<9pOD86{ 1{ in>LIyEPeyeAqg2 F 8(i,eeZ2ךyy"-Iuq Oa딣)sw":2ϑY ;Skx1EcL9GH2Td=K&&1%>1%p/Ω7mܲ<"Pg マ m<=u-`:?F"t+(=6BJ:hZ- 8&=9?[ŤI2w jYPEE{DR*-\(MG 7w}1UkԱ5 L.bu K%)>-RPX* Y+2%%nZHZ |gii/m+4iiĵ1U@@b`a/8Cn~*S[j1!kx8~ΚOǏm:(;>-Cyhh7 mB7h4`TBQy>1[T u1 \?yeZאg2 ` 7IZعr?2mpͿM~}/zˁV[_4/H{) yLq /Ň%u`vD)@ϝwc?h>:#[%vjM-{zڙӡK q'bxQ4 ڨ-AGWv^:݈zC{GGC, D?z2sݚ'~  !&(Qń RհzVOgP=TYI `{hvD*]eZ-kx؇t?< ~[ˈgGw0*S?]0h#0yRk1m9!jh*CudSMOXVfxir8\(c5vFb7x启L^w;Lihm k,l{9>TLyYq X7cl';qEB0KI)^Qt&- !KSۖRFQPOsmdZrkg"z) S{JN1" %0̺:O)X5g3sޡqyڥir2] E{b^ll֮k&:`BI CE$ D5!41pa)J>"MkE=iWs<\OiL@84xA` Ē!x~/G?՗~B$}fIj=:I^Ei y]WM\GFoR%NVF8[" *ٳ ?Iz; 'OU#}G1L#ƽt^Ҕ ];fݓTȻڟS46g+Lܢ̎^Eѧ/:Ti!xIPH dtC=rd#KjCVT1fC酂{-:T2X̛By%J^=lCpl00%XBsՃ\Y@;+#*b^ܘOE;Q@9-XY-ct!Q+$kMD?%h $c峅2&y[dkC><θ߶?zǃ='1o}z2OVQҜA[|\E7 ҧ>ln3x7EMx7{cq/}wW6s{꘷rrק2|i9emJPA&"P# L1 -5 Ig@)깏2˘*c跠GCQ^,aȧ4`gqa|m $h8Sc\ 1At 9;akt,y?C4N4ҝHu /~<үb1D)ߡt>:iZF~9gZkrыeN3ڂxF"DhcT}+݅D4Odzx4Za&PiݾL9+ :8=8i+&L]üC4S=w;QzOa@9+2؊dD ^ d ı#1 "iӛC{{{qnz`DUuN/o=7R@.ggWVA+ =r\w?}fY0x\3z^h}N^T8TZl4a?tdc\mJj Bieʖ0kؚ,L+e 9k">N<#ֶg (.g?S@75(t1+$3{o6=&gYi>ZJg՛ǖroL}J>9/s{^>m־΅eM@^7g%^oZ]U[B_zw2>k<B*̛,uFo-3*nS< ] $cF@>tα<(j2zt-ZnbQVFx% slF8BrF1ebjc}KaҴP v {-~مULё)j%:f^~5;gY}=$"*WSKRoqT )Ds7h{^ae}ppK@JAiu<yxF ,("S>B$]!1b"30V$)tVWI&Rsp+e1h1Ơt|FrGۜ(Y8&<`] L9x*cx,j|%>I%|Lڝ"Dv)KR5g zHU֟FWN'Lݫu'XvJ¢nyh[c;{vqa2uqlA~p U܅hC{ Gsml)p{dӏ{3rw^86@4&$IA1ŒK%r6 oHk.͖Ӳ zTj{ÿg5{l' أyz*,N_2u?_g!PagkO5V>c`0y\f5N :ʤQLVGÚ)K]4JI{wΗL=y(!᪵Ȼ2Ye/HQ،GlRp?okg=ߜoJ$Aݞꋝ>zrZxZeBykGZSSK1גs7E=>V4ҿ\t)c37<3h> WS\mH|c/M?D1@9У;* G'qHv ?|yM[{2Hѵ1S oBeɂ= .}K2w{a=|MW~Arݖ7̐_?(YŔ8 |<} 9D05V(KQ]:g.uie׬R/.BȌQaUcmC، AsUo{cK.Z%:A8AUBEh."^B2SJؙA Z"%Kc8Ⱥ-&W ڋ>\13B-Ĥ76jdw$eih7{=矾$)9|iύ P#+SYl9 W;`TE`a`vvpUK%l/BJi pH\#u1>ikdwb)O!#݀q ?yJpQmoJ|=;NTNsuԩѵ wzOIv 춗)sV3.bCMpH'RJ{P6wN!>(/*OqHJH' arնPLꄘw(^, 9._~}=/|[~XNW`U~2LYN;w[ eԅB>W..[Lvr]N!4Ef=S~ٓoyCT };>);OqF{ړ_ƼI}z}>~~>h=?l8߲LOm;XZ|fʞk=`JJ_TR/y;]\5jsO'ጲ qSC k00[IߡXo@m`{(` v4/[5%R83qۈ@ð^yFI5hn,ctRh Se}d]; 윆W9Hjqvi`@ȭ *xAY.@\TYsƘ=J"d ]6j y0$(t 2mvlFGp.dF9lҒE}j.+~ΰF/ tϜH>zmfOpͽT36aRjou#m''ֳ@ulsɦ5\=FeަZPT;kMFIj-uaMRۓN?p|BY"SKdF<7qe62שT"ϾNpl}%#,KES2_֛_K i俼E|nݜ}[ ۓv[=\aQa82]\ܬO[^,ol~>SQ3/.j'x򉔠p k>s?/Y!@-< n`-MVa9අnAE|zTʼKTtg92o^CcxG/tS3W;csA~ D%O\__ seoxxV4YB7P} 7g!xaGҖH@ۢ͘\O<Ե MJHY$N,.U2#ؓE b4 Di=F2WMg/ :XIg*}H$GDf^l9.ѶM7 YDgN:#|7)uv7O~$L%ùMI60]t,qN5۾|_+/L5ZK)ٰg )(י Õ*_-ř}{&p'IG+z< i YUbJԣay*qL lb/]&7I^cϟ!X|c}='f'ǻu㐊nނ`Jy8+p9ID6GyTi\'IUl\VT8' Jp +9B'lkO|)xP*s+,>_2O|}k qS[h7k|2z񺔦Gau@8qOS,krܙvSX>9^Uo[%lW`IZb'@ COz%yy㹣k~VUQ YCAZe396cM \Lظ(܃B7Σ{>\pY¸S[9\9 .F|[G98C.}2+Q=5]j>`c{âb)/VzQ=!.*/iaDt'~j /Vx&`|I!Q3%(: +Z {-HPU^ 0C(|M.)h*ZD!G1qymߣz9,^]Q yZw T+5p=:KvQzcFV`˒3ഩ&IT1͍SRQ,ԬfIYnȄ lu0%dYLHnB2a=Y 8Q8Yyw{ ,ͯ|USMFN^tm^8uuTvO⡐J#!pIi͠(dJ.sUD_x1_ a1PcÚRF38;&nxtf'[ZDg#,K/o}q%\տ]7+AQDkMwm亯m}7ç(1fv1hUM/t9w_w|0̏ Z}r*=(7c꿔z7?je:z11J>zޝ'$)ghXfȜ<ց-*"ˣ"֨XL^R Q5?>bN_C76A>#//4ڤqtiq6ey>QdLS+g]>;%uNxQyTĬvctƝS>NNQOq\#US^`ɍ H2rm8E[:EY4)sm׎rs?dyq?8G'3h>פAFgQ\Gls85}*C4qKW/| 6%dn=I' I30,ˑe8RwE {Bnn%+3>A-a:&Ӹ^ dlQ!eri}LqW!/"JV&.S4sxq$b;qpDbin4i~UUTG[e*4k+n9ʢV OtTfrgU`@H0_JNFrRr C qbDW䰰)S|ν]ԉTn+fS[:WꣲmUJ+uaWʝ^JTKZEx TI* mYf{6nG(oOp? a95kO=5`܄CU}9+80'H cn2W_#?SkykQWHS-F3ڞyj#{r'_7.j;U'&IxeeV!(͈tv%Ɗ\i^]i`!VX|$ Q_q{A%-DC6i2`&(i8aSx>3(IQVDP{F v+5vA4 [*Oe0wa 8\pi@3OjBk}R YY@%"Jlp+zXDTbk < K <]aS1ZW%k֊y!5h$M7)Lib,G)+KLQ OH!7G+ zT"QSEF!RTߓۻ#o'~GOߢ8!#h`b9`Х"29>0j>5ꆣbNEtA7xI dy2M-@sϪ*l'؋ՂLƙѠlԅs CJyrܹ=A3 ϧhv]޹y7w$7Kb#MޕY|wzB_b|\[^w^po|w^ 80Fi-? ~mIin_ҷvы'E 4W2 p?`whׇ{@y;7g߲ۈF88 ZA{T J@M=9>u{ޡZ=OxGY}bɫ)/ne#3?</Q½ݿ zF=gkJ!h]&IeƄ8fbpg`Mn'#o;;n2ͭ%+aXR~rӝK lǎBIK"tchC&ug_,!G $ hel|alh2jn@pzi_zHFa]D~y.t=\2 Q3ŏ6x, qPc xO#E;sO8 J pxX 帧hxuWw6`-9  ,E0 %MG] %N(̕K&Ue [R% p3~wVy¯4 -% ǖKV7I)b ~9eŇ>^lkV˘5O6^soBIJ0e.C)~tҳ8W% >r>R/K_ ڢ =^r#?8YL̄|GɽG?7M"-q$Hob$TUS#FtN螊TwI>(y76e/̠hb'혦'*H9!b]i^9gaq@3^՟p'߂8!ר ^;.T+6ՎZwNΏȃXPB@; 8i]Jz#šuM.JQoKR&P@ K"`=Z@4L|˯z^ʢK!b|{ \@t}_ˎ]v k-o&*2S$[M?p Z||ް>:E?9? |?'~UNzqV?~Jݿc͖:{b}̽5:=ƽwQM$?UW]+k-<^iW:lCrݝ*4rS>y]R/)^b]I23Eو1نJ+IOϼqaex m,ϛd>yιt,d'8NJLXK;*F1[#y6(|J6d\Ʒ"i}NsˉYV)Gh vBi"Xbw_]KTs.ŖAHVE-!":4VYpA<>9uLŜ~)S>xq^.{!ΠGF$koC8 QYz19GlnKW9 o`n _GV^UiUUBMQը) [׺ }T]s#ki@bj&rK'+%kRpP$$Wʏr #OrA Mf9Y mG} +$gXtbN@E|3E\;u *1G؀"E9*%ppcO}~"SS"E-8Ѯ=Wv 74oy )Y.۫szT0*3l9؝K[QKLE_ *Iv]Kn`X'"2z`iR#@l;9t/"`V4aAJ>ljUoJvknC PJJ ⮺+I(v(#qM,eo-r#'~$2@u9OՓ?gUN9COiU=Ej\B o0Ai('ѳywu<ǁS-pֽw j‰ oɻ77$7%c o5@ ~e*2taAlJk֒ڒb kӢ-K!S >xܰr8qtwo]۵7KL;O~|OX X;p3̩í%+G.qRbL ]UW__(ބqrrT׹.MIjb6 Kb 'VYBWpf"C*C5Q:Pg&2]hmTfeG t !P>zY0:WJ#,nTTwF,^ _`{6;YET%)=!!. P4@& 2H[҉Q@gr ιGmoZA;c-M(}2ƚ"3%cXB^볡 yjm2NI爇 ˀ28F @>Aα>+Z,5p\֝3l2z3# -^v:@xq6뭕Rd&Y0xz;)*XO#2ZjB8e}wtUmv"V\3p3aK4Xɦ߾rc{.}G<[NW>%R_wª4+3u{\YWW^3qKJU@FuL sxN:ji/ORP—:){?3 |?Ws оIu8>Ӵ{Ϗ(M`=R*pm?9 IS!i%GZ(H" *-| eFXd(q)̦-+5ͭu#[2Ip"ڕf{P"##r)Qr%s=p9=hṗ̖{hQcrt(_"">!NUfPɒs:ÖG|,O}u޾ք3;vcțg37؋P=#q}F]j Yp O6bUj:w/sW="2 :+cRK/Df"*1zL4EeQ%X[pi(  +|1)AL)uaH.f$IR,! }h%ֳJ7Q6j=7(.gJÊoJ1j&ҋut5P 0`d_X` !b,N 5iۮZ*qTDUirLbRr2V9U_Ϲjr*7 iQMoݳA^Mq_}kx̕,`wbh|?6plz,O޶ES]N))aShYlÛwaV_56zIߊ8yqB`0f <$x:i $F)1PXq% *rJP ̣SM; {Q:Z[ y9Оƛ4cjE/J#D~3X<鷩]%l+,%2!cU SNN5%zJt8W$~,w6(mnոy4߹3ܼ' \fƾy.z}oy[ j~@EzB5!`T&NUTq>yp/3z.yw;/}6#T?p&u:2 nRe,9R;T4_uM?9e݌Zݠf3~Aݾk IDAT QIU4^)4* T{ocYzlwe{8CR"iJT$Sd+lŐB $ 0LĈI @㌍N"bX-4H!EJ"G}^jY%{{HqPUsg=>/$wxS9T !K ZAJOglط D[nAGYYٚŸ#O#LL{u\xcwxWP/0,SnL_ˈUE֒QRô o./|s8" ի;$VjҦ;\ag$#rh4!&6zmI&vD$9>!H98[w9U_j2!ST [津%vG=)vO{=<; jݔ6+9qS$5ڈFECP,S H)k x8i1QDLd%/$)_U GIA쨔gc1l[ yŬX<=v2B U5Rz).o A #ſ>mybm@Qg? 쯿?']g+Vl=Y.nҴ)L![GЙsœm Ĩ`FW!7*!^Ͱ^y1@;Ԃl6nMGm4U/__K}xF`ọX,ղܮ w+^ZfVK- VT#a/}h?!*Z*' fh$a9-GG8R%uFK˧܉:l#7Q{H7Ǝ7UXh4Fn]&pexy3]Bu vЍYx0C+ׄZE]pQ 1>jI!ttۚt"dᄠDEU -pv(*GDf$D?㶯*'pC/H|DZFmUzJjw nx1h8Z0K+)e*S\TW#B EFI؛vf J[G+Gݥġ]oO}߼|megIiޡ8#1NZ@XO*$~^BuWeOd$pZSZ9DG|^:kuU(e.  )42k %UmOp C%RV[uB r)j b(%;):{1ɧ&GFTٍS 7g@EWе/+K.b#е#"ԛ-. EaզGz!k:{/|Ӡ?AQۃu5x jT}E4cG|iAnѦyut-=c\զQ8{>{~X o g;&D:a4(pUɒ!!-cS oN^{0TڝK:GJ:9CY()㬇5q&%ROKSah*(Ʌ#S\Zh)vxUR:燵``],Che08=샎AWx~1~.?˝ ߫8FAz˽GQYʃ\,򗒎SRH+swG #tD %-dX !2ٟ^Et*C+N!FCT6KʁYQ _3g4NCLuOJ=ݏɥPpz0*H H᰹E; 6 "zFPvlZ )3+hKiIՇƌĀhMqE vq |$,rGus0JjߩXE8hG.kr$佹z1ݕ^*`S%sֳR]4$s#\0 *2cɧ J '"nLc %3$*Hʰ #ZEH (.r{x=Q;)lȓW~+lJ[?qVR XlQtPn>yBV=}':tJEݷ"?' KSཏT4EeR'9o [gIԛqoyc87k]h^ ̎X* Y#{*5Di; xb)$+uEFOͧ/ ~4lK?+.;ե_{ϥ?p8P NB2ƹWD=<6qt_ SOGI۲$VF׸Hm]!d^~xO b;6ɵP\~Gd>J ⷣ |;Cq]O6ׁMUŋR{62Hf蹐[aXX+$;:a ^r/,OIښGxJ{B*U}R~BkKxQ{z:a<8ZQ~%u.U7ʦM!L!3g4) \c$Tތ#=Bs ݕCqDZU]$R7@ JQvWdEe&]_+ɀ0rBvE=;4R1YIKa #{րFL>X)>"ЄQ,r4󘁘ؒ)]GQ%RzKC9GMw$u&x Qr =eTBaBpڼLRF4'3k_AK͇qۗ'P[}\n۠eZ4 .!Yc_UF!b.xVIUXBeըy W  -$' Ε3zUB QRU\4qDO$by!B2)dhtЗMo8S^K,2'c>e,`4% IR8[9fUghga޹8#nhygs'ɂ0,14'BsXu(lB2VJBїNHO&xSz=($l)z 8yQv=AI7\ʪ4Emm65 F67g/f+dMaf g N-Gq[%^|0?oʿp9,˜bz)޲m1 ^1Fa/wko|jeJ_>I7 GP>G{6_w<l~XQᴻn9{Fx!=A8xZ(e}}QxmjN9PKL% >϶ŔAIotn1G6\~?{e7b/ 7wj6i׺+ m6eϊ/xYy<&kniQۗ98(s!VR|_\XްWg*x]QN)c4-9D"cdkm8_mPňIxhHcD"D ɳAdxyZt O Dwu+($ a"ݔ@(Ţb"}$11b,E֦xf,D!Rв*$+ ںcX 5t%Zcr)0R2BJkϙڸZRTxoX8!xJ/x8O.]Zt]_nXqóN_7lhӥ,2x+0PRF9u MUHIƅ8ykJ{]UjW6seEo'wu̕.f2y^=Je%8! }~>8<{q! !Z`2;SJM 3sxOoyK)ís+]Zv?*=_{ы# +Ar<)B*YpL=}涴Ys>ԓ/S_V 5O'mׁ}擟V+llV"ml5)"k*'u9@.$~J?%cכlT6޸7Z/eoFyN!2)PP szL8bZEٶF!G݇7t~Kns{| >gh5K=sTyQfS, OsK3|: MMQդW\j(7yL^}zdN:Ҍ}=i؎oYѼ6`+ZWVTFQM^Y=7c8lptåVAS yw6q-%2Z!38seSvXI$Q֦i\0TG"Mqr>YOYxRxi97p0?^FGl->'~cBJhU/(`)柪0jB6R25] U&;Uǟ@bIq4Rz%suή*L(T$ĩr ]P$aEl-d40 pbC5?m SI ċ~6I ('uԲ'BO]W1l)XuI=ݛU W/֗yh#J)>6Zb|#h]˷-^*vY|-2)C4D(q:B *W[Aݖ)S`t?71R^JvnTc$GE~m:wxij?O-5O=k 3]ad,3mh|kt2yO1IH!Ug-bDR j^| 7)"MQ͈\u2J~FRA0Ldȗ슜]Y񜊙"6tᔵdY;[/D⃟=^YaK B,aH/Nj;Ξ{CiSG6iGa,he[X'ϕBH4]QogZl EvXUb !$Q}Y8b^F B!Ġ*+YSab66 OnaG#*õD1G! g#s_LwF|}S<ߛ6CR1C]צP @}M,.~8x~ ڥPFqLTOY&`* !pW_ K*QPMw:Ph}r2ItKML-4xIUdeޱB @'y4;9/>%מXy<7d*O;DT87wTꥰ$TȹV!B,)% hr?qB8FuTb-y9F9\ٺTZ(D mx52iԷDt[w-S彷B{?k65*G%rmEPمI7a ZۀU"9*)9)^.u39Il}څuux$ HUYRxe-E& Rzxsh' mU!6 [!0V %[QK(  о)<ʣȹBzu (?얣n6j:;q/'k?OUoO=S5/__}w 9ll;+p{{k֭w\U#iv;z6 RjDbW'A籢T2 iLR0,h/>Ļ/PE2$ƴX";!#%ntICG;4SQT)!0 *9D|a#q{nfMx((Z!{90Ay+pj&StV`&FpʣDbs@0 Ǹj#(%viVu an-c6m0TS2ra +n'S2i9B`,]Zک t$37ܘz lO_p1.4j8Hƕ.Z"D`蟼0/o`+Ȉh`Nn娙{f?Zd\pJ3 IDATg}HjA   gJhB#(J)He~PJ daK7 ~yܺyk3d?-N;"-L],?W^?wEHQ74XG8z3o ͧ3}{X-$Z>W+,Q= J-in~3| ukߢ670 BqV߻uGI;zO_}FxדWo _du׸0BBJ/+UW4 o^l'aA}+sc1̒6}6z=ѕؘJ1}Ieqt.;pq. K*uaK7L~Κj=hnj赏mT 6P=Vϲ޼rr8p ^w2nF{(XZPBdHZzƌ& dnP7(e=}A -bhP#bd 8~HJuw\ }tPsR-H? uP-h%08 D:&xO$]+>I楉pAp$h4!eT'2 & m#Jaױ-qwe!md|Y[a^{:-FrXh+ᄈ@-8玿ɓڢH ʨE2q<]`+Ǿzۦ""N x7! #BQZRf-KXkK(flrxdQ(ﶣӽBiiշVH%&ljzNS?YMh BR8lyE!h2/~]$(:RƋsmBSO:'xቧiZA~8![=-YJ~J^,7(˟=w:QҘ^j}.ICuR -`޷*"lW v4z)U%![:ɜbBJ'ix:EY"t*!~ W(.?8X-|X]0LbB`ܥ:it\:=8ZxQ^Yl U(H{0>p1/(9I"WaTA.x]Ыi 8 ATJLg{ذCU2Sŀ!*'>qhUBBca+'Tؠ' _7~pEϦ?q}3\'K֚FAiJJk /S\`Q/8k5 ف ؔ%y8"P}|}-dH! 0ۇ7R]dVm fz{=v#"s!-zzwv$TR`ra(뎕RP>MA~D.=HuA>t/[L}o$ ; mZO z;^Z4)X94fbUc$OO}ڻPmTgß'z򀚴|Zf *AooXIxaWd+W˥]_?ܾV5KεV!];0e;@`TDD`&.SUG;#hbN֝p(W?lm?~@}4MB9<16 +qRڭyE1EmYٶɥe9G^N`'\ϳTla6hЃmR6"!e%%IV*0e2Hr:o^Gz ݂禎[{0_5Hˈk*2m 8&Bhd;kQ'>ta" TCfA4l^LJV͋$tDs.& K&LFT ?W+TL"Z.nUi^kg`g%#_;/V_ٞ5xzlI`ihR{!ݥ\q3LGΜ&WܚҸH'*!bXKaZeT9c BM| FsTUZI8gqĝ{ۙ BQk TeNc (ԉ~*k98^yP$9p{4J5mWܒYf/x\Gtw0j%Del1()QoܑpUUU;kNK*BMooX|S?׷)/Q+~7S+g>s|NPv{{t(X$jbBNbАN;WI..3ÌsWF\K Z$[E*B\CUZQ%`xSv$р6ΌUxWU}N-o b JG UA܅\MQ(UmEhVkhfHAZ(sUŸG~i+myqe:+q.D!W O1{ K!(d#fP '#:&Nobd Em=~'9%]?sνyYaX_42pYA1o<ǘ)1#JvÂa6k6QE"DN'sŠJB ]KQ^ J!*8B EwQnU8( AeFxEG>Mwz>Fʋ& Vv=MJ ߫oP AũIbyDז\jK]l]WC5& 1K̟g3}=}[ ֐g$;gjS~VVF^x +76]r5L/_x+^kA;X-l1gRi[GΒ:(0$ à*zkv 1X sV!}r;Q%K.{Vns8mo,/}${h"U:Åx[9hHyS{_gb%tMԣ{% O۷KavAo811j?3?ٝd?KS h 8~])f$ ޹_z*gA6"+Jb'z |[Ԥܷ g^Z#࿧$x9<ïekk#vגlZ`F'Eཛ׺M ɹoQy1efReJTͶSdFa@neB8JpIy ې [!J4&\p4+I匋O :r;]Я3JnF{'-lj6;dĹ^6Ig8ݡ4!K5n5UI"q pE3P *d* K͜5QyC!)f5oᶛT]-ܺ@4b!T˔"$T!Mlp.D>Ƙ%J#IVdQ.d-O[ R TduĔ|:q;nO-w:ž*pp?&≄"yiQc!̔o6$"!EX0sBT3.cLY Wuf:q D@U>@p=w-_@"BґF H!WccX17nW3R[dG-J75>ϻξ b02ecLJ8U66R*RA6UlEQ,,aY I%Āfs}ogg9}Q~}}C'M4̲'cާd -}J *[U?l8;#_൉#k{͈ T>Hz蛳ل,; i[Jfd8&p2F*j r@+gĺi%7mEmvX12+E"&zbvij.H*T#2**è^^N=Mcd<($©s}N6;;YiI0j㯜@R<1Ijx V Eʸe Ӽ5˄%:II-.v1h p]bkN :H3]֒Cy)\ZGa5jÌ+1e?xEaX(FDdWpV|/=Y;xNa̶ M_H!a_^zkO }w/pU|+v vQZר&h2TW"`y!v^j(7kgJWS ϯtziv҃ǎ^HH- -?R>T ιG$J8CMa=0U'Q1T4ˬ^Jy(9!ji6o\8[Qқ]\L]\7 T^wt:m,)<_= vs]Z<ƉEhXKi}$NubsW7?ר9!8/|A@+ᚮ7xDWl-8J_)sJϿ0}ÓbaqzR.S#^BR5 $`W:TI#JȺM`Jcɭ>TBaI.g)^T6 S0C l.&&g0h1{`gipRx2Fx| s>ONw%ꔨk젒!a^cz$,fH]"FCF! B!!^L1ƽ,`eur9MiCա}f=Л ve9@J?@(H@) GP8IkNc!\83 -t %ZTra`M%(zs7-0U(@Ǖq% (ف$F"bMQfb{bޔT&2H\BPԎ27eV(L:2N4#.=c]g ެ)vhVNjQn>;S=nѕ[KIuD+k֥1&c©<0I%4 t@B#q#C\ΐlMиLŕ#>Ax~B_ B>>*e@VXqVܑK/*Lp n\W֗േ#k$T6>s_m<"Σx Bs WU0d9DJ =&1_kN0P,NEa$ )%Q)jYET}a-,Q_X3 խnw7OgڝB xN3ƺ( D~?1E6Voe t_qp;.+aoWa-kȱ=ڷ X#yV=za`V_yKoE|Ǟq/RU|j IDATne)S=T@k,WZ'S355t]pXj717f!a0 V5%d25J F%3# X3G1u_eݹ+>KVdq}'ޝ&S>OHsxc{p4&`0Bgؙ5h԰S)2< IѢܮ㹋P @ef@󎒔He Q9 y!,%7 (騘WLcVwM畏o#-]`M|蓿zG;ۀg- <HYi=zc/ۿgH2N>rs엉,w{LjFqs'+ʽ,C$:X(4K3[ˍN_}衸r(bЈ9>aєtZ[Ƙ0P^' [Pk*(-b:yqZ!h"$6u O͵YӲ^./n,~XZ\gj؜} :{_TX(K.^ \%Ti(! 1Z$5ѬQO>,r;>@}g?>W}u*}J[ߡmT¤7Ń;57/6I+λ=Z7=XﳵQ*]JhyO`]B d.r]:$b!CD /!aК|?_ aKs-9E.Hݺ!Ԃsdڨ>) )&tި]=梂$,Mӏ,iӊ,~{1R k2t+vb#гtY} CPBI#D=staBcԳCA0"Y>Wg.վϊ@Nu(a`<Rxt$P$TTp503>Xׯ&7J'3GC$>>vH "d$)ѥ(D`BclaӆP"{2asį/ZhG b?tE!1x0TY*¾*Ln`XFN/ST^/} GxD'~N`cA\QBemKJ\;p(k #;(,8T QXWۂQVK)X$g;qqB'BG>Z=͇Gޞ#a~k߰G'֞~n}} o_:Dwzw(~ߦb].Jnu}േ#7w֎x! erf`u"d"'}`XEѺ \Y[)-64DT  M…_|`q06y( Z$a|ew'礳\ e@.Sʄg/A" ﶘjoj;yܞs]\uP&NK#z;pJ[Xq榗{Or؛.GT4%p@WT/JT7}ܫ#|:qngF}jmjc/ss?;&_"AB  MߺnL5u+zQHXBTtU*"'}݆g7n+46p;r!ngժSћ[ .E/41] )vmU 9+N=w_j3Mg豤a)=^a-A<|s|uӽ;n ZE*+wP +/o.cSbL[]Fd ly8#d wsҞ`TH# ݲVtm.fdFC!Q[S3Yv'huFyaPW6=;B>Vq&薍0A^ZuTA\ZC RWxCaQ*@X+jZSb PRPPPxvEF<7e$v#KO7WœU5d$ ,Ң4t B @+"PH5XBwHq2_x{+;˯eqZ怆*cw5S4&Pi??3?uw'AqF`%P\1ʈ00:N@hƒJawУ;H4:xile7H$EDZp~^?0,"].}T1j&ea~:lO㯾qc?W~s_~ l^SJ5Ұ}JȇWUkWgL൏#ke@p~[oovȅBw!;QjZ3=\R#EV U?B@ej3EI"- DeqsKU41cfk&3CdbYQh5,C~K`"xU֎bߍ .( 7tۨd#&g1fIEXWgY' gĵ =yבc=-ɵ@i(hDDKX2h/'ָopbşT &F+=- .h&DHF(Dh]iȥ r刼bVsɗ8ZPLy؏L3cJAidq=LlK43Hb>JU5}u^=L(W7s.mT-W*|G0 +r{msv|nJzduמ_W[c9z^ρ1 kۂ@BJEj s7B+뇿vN^ gm3e}jy~_¿|[{|7F3p$${ugWgkLؼv90X[R(TXI tZX#?{N0tQ_,y%UGeġ#(\tRcX,LOk4M  OrZF# xMӯŵ[6 ޘKZX,ӞB*Ǜvpl4 mz_e[U4caG-ɔ"'%:p%Bw1K0)x JKV(6md .`1WP9%w"Zk9[RMGm:פ5DCϠ# <;wVRRVD[L8TDkw -4wprP-ڹțh^}Z;y@$af𪃷դxTKա ĜLрU3hYzEɆ+I$!‘ 76c.3%n`j9_KkR!Щ),Br "T츔&L2TM1.C6 e?\$"FRؐ q/cb+٨'Y "x#K4a#0=P%y 1@MdjݤE(4h~ֱi45 6Ug:eCid٠Yލ҅>yӋ9ac@;c^2J-QTP@_^oGQ;/O郟TۘԢ}SǎHcF0nk9qoh;xJ>߿Sgj3;7 q%Vo7G-}V:V֎mGY1D Yn4$P8Q1SÌ8 ґncsP=8C)WP+ȳ)bvidh+"4ZqKt\[¿:+'Ns/h59#0e}D; h' D}XB 0BD{qA;9\XVd\['- 8^$ F %}2drQH4fXr&ӗ< :$Sbx!AOHZz;)=9NjaEbxJi4Ýaji=wqW0NW_:cwN==yv|bX3*\.o}ū#e Swv;gw;Ί{V=Ӏȯ/-,8ߩTT?jӴsg]8{yˋ_:|?VcVT2wӬA?{-{};{2-MnLZ`\ڵ̬ΌgVc"+͸8BDڿ_<()L0u*g=o{E=Scr}}4ZWl/!J.'+=~ B|$/d׷` J'8„H{ [Tfz|g6Ψ+w1Ζ_-u~* [iH ˪kt~$*nM^2iv<껨|ti"Pcad&Xt[I5 'MU|$*'c#I@0ՆD Kff-)AȈ0zHk$pZZ})uCu %{Ctڋ\H"ɽzz *Cla[kTb)=pU.I(JM^UY('}@BP)&`:&NtE:Ve5VE#eH,m^%KZH|%rxf7|Z<|ѳ?t+[-=EuK+;,_UxҤvاCڤŹ۾+w'\گheǏߏw?r59I8'Ѻ &(na߳[obLWAY^r7<^Uۜ/2@XU]$Ɏ\Ask\pS\X%SuT1Emt9MpZbCO_b,ZHUn.hJD<ΐ2edCK-hn )B '0, JE68P5sk?JQ!Jb"$}2Dr&E!Q)ml4Pʹ Ir reQ_ cLNx~)O?z|=􀧀~ϝ7=Gbd?x}QzW-vZSkQҠ]^ne2]iηGγ?>gY ,:YT*(RY,/NVOgh—_w> b+j3Yp׽F>j3A_DxT-"L)q[Y1PϭU]N]cGXhҳP B>P1* +6CUr\SX,swmm~`0_,~S{vsͼ>9β7@0(aQ92uJ*n!s…Ƌ=c,ԼT}?NH X̄ @[X"`#Q߯[+|{|s/XZ:\>!{s SL;vbU/47T TޚN׽(x@!v-v;F N4=;r7,!""(UN&jAQH ̀:=jC<ͭ?sNt#=7x~byD5P੄r(T2 '4ea8b)"ʱ>3 <VtCk/|u#Lc;SLrDc9Y5=jMŎKi/ǔ}3'wZCNC4dbP"dʨ!O/p!~ɒe`!}@ #2 3x`Rpo,"ezte*)$ё%g!we Fl<`=,Y\ IDATqUkAc$=y}נ;m@4} П˻2u%.<=p-T<=~aגa;tߜ}//Oy~ (x K=kk'#C]9ܞIֺ Gsz7(ן{ɂ |I sg)T3;po-ftW~i-b~zNeiy<ihVq,pӣY"˄/ /°v93u tyNfaϸ|pz_xSRy|0^`# 5IB0엧 ,IS>t) ?1}~r8x.8o`w%kI0`Kvv-|{w_ .- <#V4i4 *<*fTW7-qe\rd5nhi BYh&[$J QX2_M&CTN gYLJΫSAWA띩:"Uxpkӱ<#V-kVpF41%T3±Uzr .' /=j]L6b' 0lE3`J|,q} X1T_Fna$:)=>EFdTVF{ErmZƳ4a{sl;nXu1%@@B"Ä-SpDtGJIirk0#GŠYx% )qD(2{4*&`KOaPVO \(G簊Hl .G&SdD,+(dBji+ 0yRv5)U$EZ'X"cֆ~(g6XW2Bnd/U@ QyI=+€"o g/z֫2}DT僛'pbXiem;;xclJ5Aϗ2kZ,DtHb#+r<0"u]T({asr2d ֳD AYp@IJpCN_f@Dܳ@7;_&zBjr n/Eypujչkcu8ux97W>(V,Y$1dD'o|(9wp[8ܥX*դ){Jn°O$Vօ5GQ};+Jn4X$fP ^F~7ujq9 )q"2:CsL9dR{YƂ:+g9pVMB3#fL7E+A\yMq2BDDŽ瑪vp*'?J34-$G.L]*Zģ)Qh<`2'kZ,Iwtn\4C H)Q$(J[..Ky@ _lɱBBRq~`yhrٜ`mo衇_7Z^phk5EU8V鍛Y /%BUƥO1eU>GXA%D-H OYFUG;\Щ)´N{|-ђޚcN:HG)b;Qq8aF70nr>z"_a*u4 Cj]z%$\NsOe!*gj d6gD#6AzYfdR!fF)Aӝgt{X˲﷧37֫fThʒcQTDɖQcf؀8P Oa;Nd%Ѧ&dw՛x=s_f5͐h׻}kk_xZuB:svn-G_Lr뫃;rDJ|=6a4T3OUp%҆DI1gA{.!l#U|iTxFb,- jQM`X~v/pQ]{˚!e Bӎ4ȴRBUU^Fv7P&-sb_HD ,BznuRP[uV4;ۺVKU)!mH;:%(ɷV:<%zSl[SLp]w?y>"B`{X(f nY[OJxO׽$|UфQE7"fnRa-"#DchˊZYI%EŴ*yyߵ+Bz݊?LZa`bԕu-wiͰ~S)ꩪΏ;WYlk߈XX4ڤ[4$ݓKI~N=?p/KإMGrj| OiD1OI.ZRT3@jB7Y+B- O,n7Ww[HC]F|)0Mz<>eL0/o!!{la*üU1/ԯ>JW7)@t WXҚB Ԭ}#ዌc¼l4lӟ0۸żn!y*vZmjhQx4[2Ţ HP0J]YTPSw4\h4 MwV,j^@kNa] 5C7rkqt][ڱ'/CLN/uO)[;o?oOt!ǀJ6>Wc>0~7h{},/\exJER%gþqĴeWMgoVݯ>gu?cWwǓ?"?E —6 # 03=W-?h0q ʚ:*Y_4*V"EΌ'E~\Te;9I+M?r(W'՟Z/31+ -Q}RM4տyމE>k1]>ߝ$*|k6F/JP|֧TmWiT"x ~|?Oz֪յj8h~cN|B;i3Id5(BlmRUDk:)b`av"7_0`%BAQ.BbĪ1[ś*:5F-FH$ hB cH_ hl R4D M1 % <g~_swVFj7^zV2-CicfYh0K'5$Q~E5!ha>u~(DžiZnq}fQL9A+ƈIg'7߱E/Ty^DɛjvBV<2*2zMTVeU1_qm?γw4\e,p5ӱzMEh[5{e51ϯ|GGw9>l7+zj'*~|ymξp)mjg>/wvƇGje6!BD$wBEdI 䮆 dz7*q-?8Ax\0E73 V sV|V S8GkZt8|pEs@C+L׮QJMH.6$'a6X^C_2YUQE*ɪRQUBi=uA;VPg9u(hih+7f<3^T}_ Lhȫwx{}P'"M~y׿R#=cZ++?셡++6ԟ없xm[Fgw+? dC0 u qw-9y&7vTKnLy%% %#45 +6-N;]5D+Ұ& GrC̹W)^aax2"O9gb}? /!Zb|ZIŤ2dt ÕCJgxg1r=fljt 7>qVr⢦])C EY`m@8"Pƺ9ZoNe[@3Fy=G80&AIPJO]}xr:x [GweCE`("b2mjch *c"hwA^-k;uSy7@//$ϵ-ڟ?|~gﻢ>>&phOF *j_x?ڑm"iwY^ꑭlALAU d7\#1_ٽ.#+oLqEdևlg焵"~^ E1ۍa%*6@k##'4u<Gm!fSQ[:ʃ+be}R^iy5P&a`͠)4e^abI-l*9O?pq6zzZ!YyfQ,g l N~?g '?H|lflʺMsFieTWLHP-~Yxm~V9P+7uדB)ӚbWh.;Z]żV=lY Y-*T2v/C&8`EnUo^;|&GIĜܣIPٱb[L9 sAtbryXRTqAwՊN30jdQrRQQq b]jQ%%EMGRWHgThoexvRʩ$6d59uv7!Ey~* %o%;=}m2*gnxӭo^>ݖ,A~1npHNt]ϯև‰4 EOܦg{gAu~H?[nY1\+ 4 Vg #)@![:fI=i&M)TV^!1!5!:N5HB],(/5,L e"-)[E ]_hɮTK%vJ1M2-q)LVQ7 h8ʞ˓IU ^uj:Ize/)T; ʺi 'j)Z:LpJEe5exrR{ %+itܩA۞Qko_g?'>M iGj d.Ezx^Wx)9HS'TWon=qg=͵Km\t|w["m]7F_puʥ??DMPn@c]hpo{!56e0< kl UFK | SqjdNkc8gx$1l$soT3Sy*!i R7 }Ի\'m'$QHnS>!. t1!LllB$VwF\7چ+ƀʗxT1x\'+uyP҂pW!o;wŒ@ |-` o> -Ƴni7j?hBѼ(!vT=**!UD1-椦樏|O:Xu!XI69*RDMbA^ .B-2VViN\ڤdwqۻYq{6ZW{C ؑ^ՊT>+ֺ8l6QUY^*I(!z.xYUg"ѹ3^|WiH7MǽF[=~IUgcr{D:Kw'<Ս8JۧH e !ckg~m%pY6rw\poqe 9̍`;\ me kRX ʄW!ucw9,[5^Llf@1>Z+ &!z3v;<, 49LkҔ2ء=h3zxt->=cڿli-zt~ݩ!1}9A^eVJAK.H%M UFXI܍$PC9tF ʥM3b@E ' fCyGhRkHJSLhA &o*W7ϻR4D=Yv+.98 ا?~g'o?~t.~i&]/x`qȻ\= u.4m8A/U1~_-NYN$4RWgQuڎ۲WKre92t Q'4b H|Pއ&Ue1mTf[žJ״Ac"g'-^Oэf7LbūJQOƁmd*. gچ7&tNdiwZ&@q/j24ZW?ɛ#QLᒦdq"O#h {OMMt*e__f_;W'>/FjCy>p:{ɘ*8Q{8;!nd?pͰxM#nc~ zDs4%Jk-'$إN @ &R IJގb](h-b*4ekB@n qN7ǏJ90̢N7 "bz85Ԅh.oIB9& f5c~'p2$ Se/ OwW_IG]~41*u>:Ջ9l -jDz9nW7ëS\Ai@4DH)( #֊ɜfQ Tg)/yLxw,4zӴ Xo_͵e0Zagn\'nnivڭhzv^JZuX_=}v\]S)Oߺ VVoϋ8fC=EB5NXD5YVHZMqE/c\]s圧Rq9.p6 z5Ge`Zu32ܲr󪜭^;gPR»tt5hMdFT^XٴZ_׶BSA- ;t$]YQ)2h2}!y=N/AX/HZ v'ANQtBj;.&ۆb7ϴnkjYݪ~d#nsb~ecʯnN(ZA_L{hs1 gҒ 5-hJ"Zv%[1yugX]SA%ރ̹N\@$N8T1X&XLczHQU>ܻ$nޓQJ)vg)^6tę8%8 a)\?EҎQB49eY'U_YވH4DfWܧ:=5tC05VCUH1O"THN8a-oSzN&Nށu#c|>اט?؟L?֓zVp@:7F/y+\{H(L2W=GȆ+`"?&:O,Ҳ=hubCF x>;QS}ގ׸5ͤ5C@D{^{dupyzS7;z՛jjuLZF ,+/241Vyt )ZJ8v[G+ڰ+xZ)s?'.{% I[BFӂ ɕ3b[΅FN ?Û,7Ao%NQ'+vfhYY*^toWkje w>Xxy)-ҕ܄j1#&+<95rNZG$R#NQPX g񋈫_9/XΟЮ`1[c~p$3^ctsn6d鄘yMun|1`/樨#=Nrh;8|m}!b7!1syɭg3IuY*_`7]5ɟ` p->ϣV82Q Fs|߼Gٯ'?s_ħ>ҐDO ([Erjem#??O\~C飇tƳK[_,Ko?{@ey+u{ zޯܹv5?4T%Uy+ޘ;k&&{]Y8kpP ,Jpy[9@Vf0K%d[Vt.+:B6;o'^t>Օ.5\Vѫ&)?R)TV_>^N>5}ʉV3(ѵ8WJ-oYrl3; Y罐L{UzQf/&4Ņnͽ.QAIv+/M6.xhYu2]XoɊ:Z'(yAIxs|gl`ԂSo(ytNfm}=񞗞@/ sdz#omxkI 7>i@motCBU579\&e ys|V HeX6 ő 3p)-K[Z׻ pԚ2aeV8voXf+MBm.yZ2;`eO:"jX:ҋldͱ([?x ࣟ'?<I؜>/9tR !~_WQ?_vb񟜋V"9֕?~dfӡ;dI dNv,JB@@x} J",jN$b LX;2 b1fQ:'[lApRѶI-+AO bo|*1NL4eypzj@QDZ`E!{ocz==\}%"زdfD2h2E ;1 0""@PB$kF"|T)kWw>G$H/Pkڵ?|}&!,ZuL!Z')dnTKxꀶ/1(!Qm-j51|BTPu<`(kys{°:*Ib3=Mܩ&\W-ĔMH <ٳb~⭝iYI*~F&fG/W~u7ziC/ȠJ),,UQ՞q`CـNoYOb#Rq`  x.nUFt_/}{=Oyo./ħ~C72=Ju$lPS-^Ex0(8g,$'3aXntP<3yU!HkVeZ }_Lw,$ ZJvFC^jo@%ibӇFճ,ow'YѼ}NC ^6e:|ev5݋b׼QQ[z}Xb鄲 IDATlWR MCHE,C"#im٧88tqO+e'A ΚtU: 8MfmK(bqMO/V޸Q m+3,+:PTHݯ.uOw.hlXfQa`hZjGz|Wc:<`c>13b?+qfO/&:dIƌAtX;^jY-ٶbLBhMm_(t>dIbyAl%752UG^20YY'Qw &"Z!" E"6@6ⲕN!p)a7zBK}E 4+ؐF!\猸akYTRM^[lka&גr" -)ZX/=c~*7I.cVH)ȴ&Yrj" #X$$]o+0 JQv)?ilGïcX8C4  dz糛T>ζ`8O*s~%3~oX˹ p/ .\:Eǽl7K][b ?o82LOJz:&n%͊(vu+HFxXT5BOiR/P8 N{ N!5V1ZKT$Kg|Lb9 ToM7B| gf {.k6N 62FɁa}c!P?Pl_+zMScr0)FFMX-T7_:1ΓZMaCzeoqY'cPyK.7xuǀ_/S<_%0h5]K`NÑzke{.Y2KPdIY(8jc8zEڭâC7l]pr[[쌇]krǚ"GdZ)j(8nekyy*Zat8S8IS2왋%Lmĝ6tLQc}JėUUqr6CJR,OJI%#$ 6!^KGx`8 Zs]|=vȩ:Tak$?>|Sf˵pOl)Ep=Co$oFG''@챖%$uI M .漪uIz]r,f# 97m*_|;G-ARѫW)9I%I`6.swǓ0Ϧ9[!f6uTvt~Z8]jFA85nY>a ;cKnG Лဣ'T#]WcTѐd&9ތ~o%-,AC,5&-vIDKۖA ,D#o7G J9'-Þƶ!>q?Ow?w֖;.>yfHotEJSy pr7_<83O{[ϊ.7-o\=er,~>PXO֗_ڿb#h(0>2׈=(=>nvŅ.Y,G]9p`}^#[G Q)22+ 1^#Vӄ1d&f!rk+jARF4#S3Ļk0:ޖ[d(H'P/xITر2[ -5h ED昨ק]",zp7hw#uBd,@d> +u]Le/^x(6ZCRv|\qi5KkǖSaW>m{S|ۋߌWS/ՃodTG,6d Lҽ=W|8*i"`ƒz {#!9IMi Jt(n":xmh[ AH)@ջ !#.V"Ƌ7# B_FPkC6xmxj6jx$bɮO[2rN-9;n, Ŋ;tr\*UH&2p&E(繫Z#)Uwkf3XlZ6} "+:S<_7\d?SĽkHB l tE6črPH%i뭯O6%$yU)E̪9M';noQg5'Q AJeVh))Nn+% % 3jnAr~{Եʫ|[1wC[ 4 63=PR{:h)˶u%`04JuYZ YJi yްZW$XXՎр,OX.,5p;3x]ٴ~o3/e$[_%[K{ޯ}'r>z֋#~9>$QZdZFdV^d2U57dT0sz˒!Q,jwqU,zD"?0qa=&bRQTo[!(я+ü 8>Q+h?YO ;# ȝ!gg#*K\>įSv$ NsIb5N5ӄh̹ȨU\ϝc+Yv(vϮM4pL=b #f"^SmKµ-o|=Rh2τ)]Q{GEI@o5QFRH"P3Q$ItGHa蓑jclX1UqMܣ״Qo k"T q]|njRH E QlSc%IIL'+/,+$?/~_v"XшK!zHiH M'V{F*ЊF4q?XwƍJN{z>c̬Pl\ݭA(E,ZNY垠Du>qީ@'{z'=#u˳)7߹ګNT/k_}7qU}T=5oL.""jnWWۃ<3@K~c|}{8fnJr Ӳ@VQlWϽr.+wdE[%N98{y6L cR;L5,p)nCN#x>4ԑtpY!rPsxy#'evXdB /IDQ p{Ŗ:[6bUA}b8Q>0zO} z;ѳ?[;?_߹I2Lϼu77E;˲2ԎA_gn8v]l^^5>db5~Z8["fL*#P:Jukt]Q䵗b!D0Zw.' /)K_.٧x*6kmLdpjq΁{vp~rK2GJm* ˦ .V{jogE_zt̝3 jݜ^$0 ; z9Z+Ns&`\xBPmzK"Cxz4e=-Ә؏TswAoqtA\c}Vᑈ|<_ΖU>WZ˨R3Y^$hXuH5!XȒ2)7_=ud/;묵,I*tH(n?9 8~qAG-_˨:b~s# _߹8_w^= #yjyΩc[{»u#!/J#0  H$n_gGFiV3*}H/E%\ruGxS!tqe7AU_y=9V dm@ Bt%T@-Q_ 28ؓ^=ʤxWqq\sFEA@ ڦ>[x͸0oN6ìs t5,wXfDoRHPD)#־g >;D^7FHDࠕ($En ᱬ]=\vp:⡮)6Ϻde[8A ^R :wuBWaUMg#J< b]o7cjceD3|\tqѥ+7s]#I.3`*T뇇֟#:t];R2Mh%Q]V" yuYweyB^wT xC_+5J8f&e/Kd$cQARXGTuIu[k@*7HViLdFHA+G|ѶxhΙ8jg GӸ&{VhFpL΄X-[eW޾z_~f0(ҶAY h'n46QڅeAOJ" #Yqkހ^SqG(]Oq|`CcR~oEtYK\fƼך~Y@vU>@&6핏 !GpzpK*Z,ђVsY6Í_Ō dC3{a*j,FC EFbG4%Чo UJN.2Ҳfo;om/'Io5[ӦOv_ua1Q<uҲ m4#g}4BbL{au$S`u@-8j!glSҩZ֩eUܯNIbE0*z{4,SuKh[RwN.^ͤ\E. TK3[6?]{dDu-W =ohERE dĸ_k͢AxH*KҶ(82&qm{H%J* Q3%}]iL:K^n'2[^fU$::trQ6=X60W~ ZH<գAbYcڢda9[@2`QumB).8˖Цu8M^2Rr?~x GG=uFTk7?ڂM>YӹRpѫ:~/jw(3mqv|S^y@O{;g;֣JEQ=z:MS8qڻzDY FJTyzJl[Jf]PƑhVt6-\j +kޯTj)Pi7:dKr+G 1*XE-gvN*2SSMℏۻLĈfw.v(cʝ{4ɂxA|Pkn9tYf%/M8Sw"ҋu`!=xsɽG]9vN5ײ##z 6_l/"lpfMjbz WR+rPڥ@ߦ>DžUTMIcT,B?*38LbqB&XK- m{As kj2 \P3 U :N0D֖f 0ilʭ.5 H(ݴzp H6)ǣv)w1<x)nԆ<\l ]5X>.Oŗ^V£XxLWY>"%5WX0oݽxL ciD'}'kp=ƼmI$lDS[i= Z\ !moiq6$i ^{E^P[jZOeh(\{O[7~=S| ?h5~;aqJXsaz϶|7Gy QdovjXU%|@N[0>8.;W7ϗEh]Kg`GܴL'|ŏsz 2E*ɨMyeISS#f34'tN|wW\UC62eΖ[G{n|v6DD;!#$FTLoeٮ ]R52bb7Uow`S'`vöZ5Kۗ`r3'EIP 'KT[V{X <K*7D Js>("ЋZ APђLFJo󆼎1ro IDAT0h9cdidBg)/ζ4RՑn n:6I|s}ӿ뭋vtq-3|ઑw$nhv~IQ^#KR]\(% 1M.-2 z󄵶-􌢧ndjJ"ὕN"nd= icBȃcBdU_~_GDt T2 o"C :X$9&g8 ng׷Lk!_!,3-犺i\i,wi}A+^JΝq)]'[VE3OW& ߟ؁pqw=S?\X4'ٽf=9o|O>3dVo^5y`2ne] I$S ;%01^A?M*YT>qdXwRr2c\K/K,W|R6Ģ`9oLrm/'^kTF8}}1_䡟Yy2 !O T6FLX*]))q` FyBx͑Nil],DQqc^ASVQEKCw!Z UY+iS-g_ds6˾u?g Zx\Si ӟC{S.?hs]DgZeWÃ0n e9npW#gTME]L[] ]BtI3(\ 󆵂:PA$ @M7 (V(۵kCR;2=Bg-@co9>*XCwlK{pK.]_c;L~o O28hIl1f8(\(3+8:1<`L>"e :gσ .5Z d,lȽƔ -˶.$e )X)II!IRs8q5j9xx9 ZEz)&S g%YYL{5^~C4gJn– xxp9|Ð?0Z$OWGt%85 rA[|Ž)&!ʟQ@B[ȡ0 xgrMaU746W+].'?#*6HD5䖯&f7' ^e҈TXvC@f o "k{sץ}ғb5l(O$16۬ZPtLwq'zOُ{rE"(ܜ-zf M,4u(uVuz cqMt<Z{V.#V 5 MJI.!B8,qf}􅥷[`uCfANҸ}5SY=)[um|t}5DrK;bۧw_ 7bPoD> dpd Vk CgBt[ӁM K1&X@ 1`!m1mKT3\5-Nc z2yuyT H$TuW!g|c۴+YMtyH i# &YSZ"E]^1x#=$ޢ{AР@E ŘsO"߯X2ޭF,tM;1[&cTy"A9EP{S,4F}*2GhSIu%xUq-vFWŀ S}=;Þ"%P.w-33}hcA\s/MF_)AJڒ vnŐ" (Fzc['9^W )LXB4GIIq& .J|4"vZR)q0M <:ZcιGtm!%D;b ٩ERA9\X%$ƺmݐWݨ/gOqnO?hůl5[ߛ)?FWoA;bۢ ϽmfP ff*<7Q=t/geoᆾaQ !V56M ((-Q8amEah[xҷ!*Rk^@Bl|7O? 1?SWtf9 esE-g~j>f'~o[:0k&E4h%hs|˛gyB򺆲^S#j~QYr4c sFՒ2僮FAWBgS| :g laCml8J2 գrwwl`(5cȾ;I$ B-Z8T"`\0V$Zc{`JAMJ'Lo;.bklo~Hㆥ_LMڝ ֡I.Px},բ|in}n׹\C!BM7Dځ6:J GVFb11;KOYL+nf5L-lWbORB#ݟ 5L.]VـM%Z` ~8qcM~,M3;}#"##")MdR7`BF [Ë/ m0 m!2ڐmQ"ũʬƝ靼ndF%+"E p7p1EGSx䩨y4m\uY4餎A,|2ͼB7vU".3:9 G@ά:fK'5y/>}7\Lz rk&yJG b0|SoTk=acbT҈B!Ow?_ٟ+W BPN"[\SCl86} ktۏ\n\ɽ71ˋۦ->kx?zֺv]V\1zS=k>dU"mmOs"J_\B4"KŪmQf D!8MR $$e JA4b&ȭA A i(&O&ݿ-A9lhW327Z0W0 \y;AP9T+$:j|`J5?}*ݝdR=g_Io |*IBύ+ /xz_==fM^/qxe\_뚞݋.;3h4GaMD Ҥ;*<3V:ZRY/YJ @R!ъe7̓H>re5}ówߚ_p4Øg}x{doA9Y3už.${CA1x"(/g߈vib1h􍞝țuEB1v#$nM4)]SWJ?zoe/OzS~S~n=қx&EgKaLq^̨OYuD}+lHNj>2B )ĎBhk lEHܥ%>-L*4LH/owEz?=9'jP )sfkoʇxq!Y > ]9$4RH"O9tdka./tlG(0D:^d[H'#.oewWbm ]"ki⊆N,3' `@ }OUn:j H(IM?].guUҿy|xt J$op~-叼qb;N[]ܒW竪U޷-ջwHY |ĭ"RkoXI)Ӕ<:Ahȸ,GO>p8ǒ?:]q<#79WˊiBg⒵ĪO\f[""Yvw?vmU-9UWOj>$V_3亪.Ę֊ӳ'u#,[ƣHBH!}pHR)BbV+QmvI)#;@ Mk̶>"DM%#J=!,'}utD!kP>DE~5,,D WtT֛*I#В)ꈏY}HeЛbVlFF" pr}c.P(G+IQH)b*bV&3}#FۀپkZRN@I'Iq9JZT:-4xG:0@y &vTlJjm%Mu;tbg5}< lƞYBEJU)!r8M4gy5hfϑ:d g4NׂY f_ 89M08W>/\m N mVtn=m(lSfE0Dv*pB/'EotL"q. ._6`P<'峀O|޼I" &/O/]tumC>>~:}BiS'r*^讕 kyA41?yElWu~Ǚ$utLd&Yu;]Tv-rD!gI/vk#%&ZCf27Ѥ(bJo=f1Su^;-ߺ}}"wҋp>_Xx_Ooe̤۬#M5a|ypq~4c$va˓Trp?b1MTŒtHS.X4Wnd^PO<ގOz{ePB*DKQ$ eUex%!FŊTuw?*F'fI! AeՅpQݲ&]Jz6p œ/2 L?ZH:U6ÏTsL{/)VR$;Ҿ}t(!*M $lq‘~ʿ=owȨOՓA L8t>oRm8UiۡլQvE/k_c;=d/o| 7 /D >=xq!:H?l7YWd޿m/>Wg?g6YIɳtj]m^գdP%N#ZvQ]-wmަo=Yˬ FG7]-'CNޓoޙ$MfXU; p\Q-&Eg|GнFIIƒ(Rg9RD&yvSn0św(w8"C A$Tr IDATع Cu;mղe!zwu&fov-1rڦB՛b(d!IqօHL,ڮvgI'Kohv yVu@YZ*,A'3r-Q: ז4WlLL\.уڎj"ZRh=LȳT-ש$Z\$)B C E-jMCV 1nKID}>z*Mzk\KL\Q"yX+:ϸ̆Olc} `Ӕb&ւmzC!!!"foՇ_OME$J-* EߠEbFcqtU#ܖ*( nIA"+tW#~mq q8~=c<켢r] XZuL ]AӜ /)ѐ4"7:b9g:T' Hk; A=IKz8C^D* o Re3补l%`yNkuEwP 9!]*Β.a 2 1tm*L4[][,q+X+8J;L Om"v\ %rb8LtR Eb4) )#<ê:iw[&QCE%{+٣G1/[tGfd{ji5~{ %$G !ePKef3oLqhO&օFp֫zYmNjW(F](߸TG=sn#h1qq/7",RpC*v3KWsNDcH?&K)~+h8]7Nʆ? OyJ(wH ~nnLۼzLJn]/o}"Y9cgWJdqog5VH1YЊ{6Y'i㺨ޥPsm_hSiC| zK\$J|bȗ. nY h.L dޣlGaAȆBtdɊ…:z1t 8XUD5\ i!%RSwjD.e95!u|py4Zno׈$[D7NmzaҐM!|rdY3HgH+#lm,J0[ PĆ `rY*=ds)q l8dܓ Ǹi Ō3Ֆ|DtvF ]fu,%~hݱRIh!b_A0ަ.P._%D/!EB FSN&dp$SQ(4 hptDR]S35.>  !EPso}Cr-#R4yn6rT`YK~t{:q/c3Xu@wUiwFyMÇ_EYHFi6EBT}08JxQu]J)`H88Y}?#zvjqH2""Mt!p˾ti;?_:%Bhu/;V[`0Z6W7glG4n trr;JϯW(ϗG$7 Dse{ gJxwl Ǔ B?zӫ/s`^?a,s^;"`STs6!l{F1H}wWW正$ʠKٔE+CSUݶޝu(6u=Ywm캨1JC zY}x~4x Qĥr'mR!QFR{Oll't?:o^dVRo*mC"`\HZ Eے!jW%e -E}Ii$ҁQ F;Pph0ɶI!=}NF_-;bV'{w) (FloSCu}^(v_yr.Ih;C+ hb $S; euo2nru>a F@p2Id5A9'lCWk! ap1\cg*|RìƯa&K.6G! )?RY﯈3UtNѽJ.SSp0=avB嬷-bP:Ie>l| !9SčfaN[m>jw Ygri3FϮ8g~FF )]16\#Z&9냎zRp'̊,WKTy=" nSStmIi;`CKA Hn%(RQ,iέUޣЭV ڦ6Nvs9%7EnٷZя3ۛRxxX{=~Ivq޽ˮ)U|K5O}اonq *Dssŋ| j[׽׷x!S˄8+ltgզꝙ%J($oძ $aQ2(T&FbA(\( -\J.4HIQ$82$RĐwOG9}Hľ%UB3z }t'U~# oU ; Y*0(7hbC_ai}sv]wG_[0*m,f~޽}oD k{ɶ)ڃT!-~؃G):x{ -Oe F4=gFPtmJJD#ww3.r|6<.'q~kǁDV/`2nbsbʋLr\\񍃿blwz7f#.-F #9x` ;zvHH.9ǘ\\Eرj DVjσMhMʜadŎ;aZ4eٵςꩅ^!B9J4e 8۰!\u,Gk!\$l؝E^*FBryh1bjqQ-q,ǻ(R#F`rV`vǚ[!H1I+9|&\2Xdv\h6JWhRL DJ!ѯŝQ_Σb3$Qj-iZ6фϖe'l9X_m]&YK}2ޮ7҄)FA$Y-xbhXtIwqpC]7x?sB_]hI&BE쭟j -^o ȜC+?Θ'׋~1 [}ITpP-Mu($mvLjy8w4 ԏ_Ss@tb=Q5 J5狍h;l*hID/NّBИz`>gl@#0`W 쵨#|OǟW{ `*մoӃW/>חA@'w㦶X%= }1ՕHAL6:Ķm:ts)!9lf[-$(~_{)*A&΋[x)Pmf7"w 0_i|W< Z|wֱ{t'q eԭiK`9Mz.}\ǽ~}LzT20}ܒOƇ\G5@# IcRHoӃ=hB:lgF!zs/77?T{>ޮ#S|­pݐ/RXݐ~}D  8hd"`^ر -acF#Nzk8dPԑN]BH] {ҭ#̽V]!ɆhJPq{IjvlXdbZiLdCx{A*<hz \93qɥ΀Qg\XG; .,&䵤I23a8l[T H4G4<7t69h}!7 2'$~ lΒqiClJJ^ha%o MQ3[ Q, S>B 53sId)rWURԅHʫ6 T)DYE>>svKiҝHI-T^4(F).f?EZT($W oIf9^]AX( [mwA_ȟrL(֣S_L+eLRKn&BM/z,rMuR2֧ݶ$M C6/>R5]ךN -g~"Z%?ɥϋ0~_%>b3.8`AwAAh.ɼ o.l 'uZ./8'iˣ(vR%FbV&zOv\n*޼}$,6[*\76l(q!pqŎ࡜f A<3Yfev 2evt@>(=| ϞF pHt#O[]vLR6[Gj|VaBLViKdQ,z>S哺g"_ܧDNC$B D>On$|&9K_PUm^tz48ƻe!ZHh$YHEi yq4SL¥zLL+"^(jYsWՋJ7 1PC"RN-(IЎd" 784$%$y1G3Xh"^X6O.YWO^$D0BDnɦΙ OB#F:EC3 8~1\EԳGke',D(IKBۄgmƑ1M8I 1HD9C*eP)!lhkQ$[AZ~t6oD6Oݓs皫]ᕒq֍u2RG'Q}$A^ mE+ePE.r*^ wJR琛G뛲̈K'u/{YB9b`1q/^BZ xd4&Eo~C"MYB ,ܓ!KrLaEAO#|i?UePYL_!V/^_~zr s~b\T Z>ؐGUiK$RM_Jb8ZKÎG ʓ),q_yR{Ǜ)"![.7D}ٹq'g[7.MO9J8h;ܽ{o?I 9`GX"iߢN-G$ELv 9 :M汞2rX%yƑ=9T!x&K=k$`Bɖ.o}߁DJ ΑEAwI\No4GFydtޠ=3IIu$ yfE$d45󼡛 e(8~V"E`.-,=yb9Ȣ* 6I+'3  $ٷurg:4ܥ /$.n)P2mQJi"y.tnk1gh1ui$]֤݀7҂2Vyf>W3~˱Oni寿sT=}nmrAFizMlӄmT B_T>BcQQ6.cT2iLc`4F5)e5ㅧG+4/iQ?'JE(~ן E<D?~gC?mNA83Cڭh;|@) yVT> R,6[$aS7,H0J2 co|py M5,e< xѣS:( d6+6Ql֐L% ݃۔'b̷۷GvZdb6,\\ǓvHՌq߈MWTгɮ!2dپydzY\_KB\^֧=\6XF3xl[XnADB uwLHV?[_/tU$o!qPpw)iR~(hץ{xQm=?pk.SBR$򞭥!D'm넝+2d$ R)o Z"+NS۫s`.)ޕMio#tF!{Yg'7 0Bz]L{P77 J/-Ci= DcIi 0uP`u-4:(U.z#0mS @R 7D;B'p)m[u4 * @TƱؾ/-'FH ? ,5$oeI׈g`JK:Hv%hwe{o*3ӆBwLhqM$Cƫ-" 5gGdZTY'MŨ[v_\-DH>kt%ς;yΤ#pʉmb MT me?ǒ,;|)#3+U"Y$h5VK@A@oԮA A@+A%BbKͦXCs̘<mTUFsp{Ӧ= IDAT|ԉ͑}e]#In]2PP{ 1"· VN3싴C}]CT^6eݯlv Ċ4)hMN{v Lre4::Iv];t7\ 59RPwny?7@P̍O]N"NdBg|EZ<36I twͤC%:p kU!M#oR+}h\PFMlUzdဆ|N7짅VRnW&⯆O -4jG/'ŋ;szѮe2òn,Bxc> 3nOj/"O00}za>b] a6'?<~{/?eKF}jzp7rd'/vqs31}Z#ǝ /Ӟ߷ j^~ -AJtd߼h+OŖ!e!Jh#K[Fߘ=y~e#f49ga&;y4pfWl)yWq=*l>+18gp$~ˎ=gV=uA2Daw$qKЉO-ރ'U1N/ ε}F{lx WH;=W9M7+M1KRx IÂO i =)L*Eet1m,4'TTQa;wnJGG# idCJ$4jvynV1%bH 01{vJ M!JE RJlVwb +^e5o}g1*~U<{9aH<9 ˱ҽ'uGaycT}İx,fyiHY@+>RRQmt3g=~F.QDY(xh(`:ueͱvjj6m򠬄Iwmb?rϹ$X!L}Ŭ2\rLsY~{ &e"϶b?tֈ/6B 0Y_3O@fLؔ(clI}\ڗ.qy%eܾ`AA.K̼D'Q.k/!eҡK#;*̰!}2nP|3CBdA-6YƯ& *`ḦgG;lu!ks;w5m`!G z'kqh#LkR7 ,ɱ.7Nfg*InYSibC 9Q$KiBr4 &صuF>Nɱ/EY4RإMtB9<^z}O+! ,PFrL0)_Z^iahF5Zk~CU%i@i5z *wC Pyp"ar=w^>cZ7Go!R Q+//ݫ}!/J[ z)K?ј~P}ur]dtc\G彊!l A15]=H=D2 tSDH*.'Y`5B%*̴.&G +Dz8EXN&d=N.*q-^?n c/J.F>[O=6^cBu+mTlj@##vCNFKpˀ-/{,Z5>AG3j{IuwPzLf ǀi%2ȈZcdv~hĵLk|4{x*e%ݰ~!C%תBx1m}Tc+}#[NA;OBvuE2U*0I.:|%;+&"gfDʶ7$f h[Rt$%LD >҈O.(U,H9B">6Tm1Q  ZnkmuEŸu*[An:Z:=/3smwUg6:EβDr]2%*6Llm+rhɷIw{?l4^UD;F*Ң )$d9Ȥ|z<:^r~獾+$]cc/.ulO Hޱ],HHhߴD`?=T$snJ3#Wܛ_so6HGyp<''d> ̏Ed l RH9 !m6 z#LtНf~qzv=u]Hg=-aZEk #i|kɖ,(M(R6$a&RHGL1;B5&d< ƐВ"ZXo ^%6pr0$BqcMGs1~VЉrdP2vҴؙ1YIu,(_ϸ'eR2|mI$:Wg_gPa3l @$^Em^M5/Si@L$ǒ o((DTtj0VDeQB! l9d 6f֬rI3:byVmŜKl`qH1n\-Qҵꓠkd۝AMzf6#j-э!J0 4jbL=RzE₾DWv[z8˟u8CO: F\uow~7yx `}>v3)2Œ\[78.?Ld (rQe^QhouE}qA37;\NA@ě"ⶪfY5ʒTMԚ,ڶmvB$eaG§V7fIeY|sx?OgA,HtN?Ać,+j۫oh{@g0`= R__P:@e#m$Le%8(C/"8:rbZZk1+_vKMQP$dmnAS6 /2U V.M\6:f4k.fE诮@Hξ )BdbXʑFĈEdP޲i:iDJHIHi6THMbSX蘕i6`e3U2j'ucůŀ6 ?j(_pt\RkxlEmϪ;(ɭG( u.S:Fyje hЪ%䊶*Cc蠎?^Rjv/<ŎXzC ylxZcG˚jHe ;3l C6+N͐&}*]j>lWBƵ8t㜃YFl* E@Gձ&ό-:(:"AkdI0$UmQ,iha3Ŧ & J-!qdA)THrJ #TYnYU$fͿ۪No~E,Xmu^l'66}ges9("g&鑤2z:$tB $+VN ~SAB^Z%#SI J$5)?sF(p_e|'lw~u:Ĕ:0q&AG4F_/H1Bf0*&kUӋ+79k: V]̆#.vk>x8FɽG33N"^~@J(5IЕMdW4uf8*IzXmmٞ NM'=.U5,NS> >>VF cfMmk [oʪܹQQΧ3ίVji)DW,6~p;Δ7 f3l22%w7N^2 Uv }\%1"sFh`abzf2xr2e{TV|.f~plKŞpT_i١(*KHx(`4PЈ`IV?9@L=1{Cq=OFEb(lLC|(qH A` cG_㝠ɻ(-clU+&Wt{zg"S$GBgh7^X̉|~ sy O˄{09H,"]o(& $6Ү+tEY 3)%&F[E$E(>TUئ8L@pb#<:yT2ZvXВmgw|ɪe4 $jp[U?/x6x{a}mdowIh8*.x|iX:-rH749JS &B3ɒ&)7z[{x;tsŞ2h߿[ofu8w~Tx} <=?)y?g>=lRi?(+ P-!UkBŘ!Jir2 ՗LKFdzVS"``T'ɸ(E8Vjc|50mx2V˒-DI'͠L1e?ґ?Da%J;XB|vZR}I&Y^!肇CAZxGkx1<@<}F^,>CݣZ#i?e>J%sϲ  M;Nk40ve5!KlH{p츂1X_()sL뤴*Uw^1sq`[^|ivߙAz!z5қLEdPBs ӑh-DBDΰcbPvG%NXrʜacUdBl ܦʻdp^`vI+)@:j$,H<-[*u5dSx<8;ov !P 5z̆,;P (qvΔ_B] |+ҡJIDB ˖B]⩆7ajCtGlwbhi^nqiFǚ@\iE$a.ݛxJ|P)ckux0僚!i󈦦"Η F4/Z s @Dm 'Uu' R zes t2+L֨VR%3))oH@DHtaIMW(YR)jP1>T&uUV1pzUi.WZ~mTǣ V&Uf{l4?.'cQ^P֊[qq٤`&72!j*W10"o:!r]eS ҶE.Յ}PGUՁ$3*{ _GGנzB2,W_dO : 7}Du\nmIuiyp^`)x`XyvE]x0CpM, %rC ?4jȅ썇kȪ=v_ΦT;韗6$4@[E˽d|u~=zumOΎo~4 ^;4Ӌ_K |.icMtzqd9 ǕVFz]S)5b2p4z~,aDJ #^zNμER0ѡ4q(Q`sB"K)d,-`?={(q *q5vdoN#Ш)ɬSݧ-'z DE`B=vfn&!;,-zb{}>Şy9 d4x4xTAL+2RfX0DQ4E)Gtyewp;tX jM" ե{MC^gq 0F#*3+A yb$H IV%'3⃎s_P}] %Mq,o[em?) l<8R>L8aa2=Q$FEC&Mfo*\HMG0ItiY1O;o[6]ע%AލXlv| V׾GMN%9ZZ!yb@(6*wYvb弊*^LEf0A!Lμ d'a+~7NeayF A.Fn˕wkV=v+) }_0WA5}_-x `}&?=/9ޤOL <Ȏ~N~kl>$eםzXè"D(gݏ;>;בH&~$ ZR~ !F%:rWw÷N|*i9" r!H2J:J_8Tc~(cyo1ERFa& /f\B`XAG>yNyӀSBcg.~SrRII"io 15)&er̼M9i&Cb=Oȥ[݋]Ypӭw=UdN|f3T0(㕽xkɐgEJC0_ eQ*G]% E%'D*S) *vjBk sDZ8YNsBҴK@AuB:̙FCw:PIa%j#P[ {D*h5l2qTDQ؋lyh<-fH+T#ECè:!;fAWYCDR=S#-\YWDںqr\WI.rH!JiaZn?Z#;3ȑ3tu7"B&9 HE ƌH9!Ɉr+F!bUxv4D ۱gjNzR2$mmn 'sٹxY5>`ڐy#OųYp^'&dJpQ1?}{ ouIO hl'6t=%73K~ve t#a)3rf]+fw=%%C J̢(<"%*= Q^VHJcdQWf$ ܨ^>w eM JbVx]` PKE1DLP͎AsI1X2n$NخK7u/=FzK~)cl%L>UOYAId3#Q $גXG\=P K{KO mAΌ'(}K79*z}RqDNh$KK4k'//QVlwCu^ D$(BfjIi<i(Nu>D]TH!FM)i4jF\յ!b (uTz9RT;2u( -sϺr eKzAB]I("X߯,3kñ{p}PkJ֥1K.|Lϼlڿl5~1u?^wP!1]kw=8UۇOHLH)_gLwgvv?XW'҆MD;d{[ϟt%r%)iDCR;'gBL캖 BJ10VĜFF3}jk7 ;%%KΕ4,^@&qҚjO`9lgg^KOt>T Bִ10='ط4/Q}eFEVAZLXmfI*:`҂@ԩ6a&ȱrN$fI HBb7|; ĒBd2]mu -"8LC5Q;jW\.3š^ (|k؝C 5LTUq)(!dv2H{(AĞș>!FLt' u?NE/S&"tN8=l!JC {n۾ɫ=DOw 6eVX.&tag1kr7TT%;vp6{#9Ѻe3,9b|b)t[v%B.)b_mr$*U`݀^ը2d$ZXfC2 /e\yu$ogS~kTWg*wُ_oV_PZ2Iv`=>$(jmκۉ-E(vM v4\q*`ph/[B7qwHiG̲USxBo[)փ۾aگ|ڎg |J_^wWdz7ޚU5/M]9A1JՍ6#vH5;gWϋ,&ŷM;pbs%>uXH&ts$wZ ~n/((2)z aѠL۟Wg^/͊W̛Rj:c1yYM8"gbICٕ%2>@ h@4{ߦ%1C RQĮϫP;Pm&cD&da6+,alD%2\"s̴@JB~hL!1 *CɔfZ0[h> ?=R]%Ua\Дebwok\W5O#  }"Lc}W/`C|~I2'RdIUv; ^, I\XN4ˠ7e3keǶ ]fX!GE`Ѯ@lErIp\F֤m*LE31;XN#[!VaRɢoh{z*=ν`I ~'SVq* 3g .ru@#i@F"e  /e-p|Hi(xP[ට_~=[nj#os0w#K+l(qX;r='dRB"VJ7KǓjSߜWy)Xp!SЊ>&zSB*3EG %= ig&PJ%8 ckhva{6WϠiÏa~Jl侪B#e0HܟxQ H)1z˃jXFDGteE> >A ݆uQli1g=kdIk|G~<~DPØ]EwSoipdXDRJtT3-<SȚw7QS|bKJ%ۢ$Q]9N\0 'ӏχc{\G,!ͦ(#L6 [7#Q껐R׫d,,ȫk4E{kc?BmHɲD%hM?Z3)#jH:P*|lR;n?><_ӷu4ׯ"m; mt>7oj~vw#u#Iza}mE˷mVV1 +}I-8VY;'oV+,'ݡLTZ: PRFo.T!pv7-TLˆQE# fIA6@ע-z$HYq LvpuAOv"p1QJL=gDñSv u=HG8PCMq\ 7tӎf'otfmƊ`xH%\F>'H ˬ.Af\?lKw$1 vאfha{ UIfm$U ;W%5a9d8m '7-~[^;֭UEHD&đGa+$y $?!~ l@؊I(ʢ(jniwa}ιU(R;¹u^kf17Q~x]Ib'f1 8U+~ۍ8+[O\fDiJ[sWXu-٘Wke*%"e_ R =^X?{ `}7]`VU/]}{_|;VQ**ۦG\[p5żk}'Cd1E:B G6#"Ft.h0Z߫|K)9< oRH1N"rb'(+nfm>=;ϯuKwD!(:T*jxŐʬeB7y!& B%${Q(\sf1E7Ϗ;#L`U9.0H"ͫbDD?Q+-) xu]]/\v+hyM7ǵ#~^x peWmE0thQ34 WF&PAEDL̵XJq# Ux"y/ cEO)$"T@h"\pC+iڧC= T~tyFاPԇ}EυR97lQSt+ :I)"4LRyBK%aD Bz0%M kDUЋ,4nzTmHZ=O[NG|e(' J$Tq>^b>1:L̈Y!OOd MSN٢-agW \ۦ"K2SY mu\fGXZ *6Mi|HZ ܤӍa(QzO!E2,ZePrP3O OSM#E7 mw{ʍ^.R78hUhPmq >g3ʇȆ#M]ҧ1>:FMę,*zn9޿n/~/|JpmCsU E#-;RtHab=NJ:}=܏KF^tKAJS ?_I?Z^K7rz@up9|طm(]ǶOCQj#ϫ-Ǒ1 +A %%e1e#{"DI)%<ڀ҉ tmbLHLytRldtrdBD~2H0N!"Z*g&IGO޼xx?Oγ??:Fn9x%/wxuf?9S=lLWrL=p|l>k^oLǟ,2R7g8 'h Vcʲ`wHFVҹ(>޶ m{_FgGQ ѷ:1 +i^0Đ FIױQN$E5UUV*d$8?'޾Uom ѵ=cch(<&M4 r2R@j0,[tǧxY"zSvf2듸B;5LS5Ut$E,-H 9R(P?Di$ ڇy_0 È8?=3s}3 )gx[XK3"ⶂD9L4Zԁ`KRp]A|SX54#Ψߛי:f3ّ~=ISDy8T+o3gG$yQJŐ6~ЎQyRjX{h7"D>Q0CSk6$a-z:Ƚ%:GRN=z;IpEXv7LhjK}7dw g}YlqJiVdIr2ɗi~L{ `}&IG_F] 15~ eMwϯvNUЃc~`22+ۍٷ\ď^`>?# B!̔П< QR@JR UJk@x\{.8[NUyEH#HIz B$H$TρW1%:3,qJ 0~K~E>BDێ9:ӵt&C! G՘b Rؐ(bPoķ/6nJ*!:O-+?I^͡OԳ.Ac$ )Fh hHISQ]O_LwXub.,M[d&%b''!'S b@e d S_uBԎ< t$!AW3xQF ,̒F9vIJ %`+5d)|zZܐďiC:1mBz͎LJN(LBńI#]I)y:aC)ȳn1$]Ȑ"1Y*( !Ed4 VHǁK)sUsIs WLf2>zxuQ#̮2+g4Wg͔I}sdjFg/m}^JwvgZ ЄPImY1:'vT<4>;zE~'ֽoNOX(qѓGaaׅe{{AI彨ȧ:]|E FEՍPvM|xx `I+7 ane,mh^d >V(IHT}%.EȴqPV J)SMY(-)+Ѹ=H*rud>q1C_\ޒ`<|U&hc(EleU?yx;]_sΫӯB|v2/y|(vX}p_?$ᄃ&6/v*$N 2?@,.M TO_i }g7.V7LKP Tݑ|:k.j0u9{͚zoMM'(!K,Sxuy?Q?2E^T"hLO,ģȈr"Z8tRDC":[2 F'O89"\HN. I"C)V#O{RD}0kE9{4)ҐSi9tMDUFtIٚ$#̓aSp;} K҃=EH֟x yf&09hN:tVVf{8i"hӲв8I6ZXW/XŚ.)ˈFs<- nsv|:Y RFH9+[yԶV|Ԥa>jEP[vt3( %Fʢzåාu_<-M|Rxiɾ4\~)νrajPRO n /3"-יXR.Q\} ͒UQG|ZҀ?}d%Rj|45ՈzALy FjC3w|n][]O,̶4+uˊ]!Ev<rQS$G.[P6 ; oN۾V7I: b0kɅcWY\{PUBQ]$b(QAvw֌LZ`g}^8!owpF*Yst7K_N4B'E(QIE];̽GGB˳\W\ 1C;!FqC$` ,0;ZXcݝ.!K}(''cٞ#Rq+e>i7iNB\ݣJHqo7QMz{QjiV>rzeftx)8z_[_~Γ׏{~V^/kOdraoPfk4dSi6hrmdyO#HWb~tߛ 7yM`+ل+>9ޏ/4Eޅ qΧC؂W˚x23H!Xt}  -%>F*)%ALtޓ'AV w$0P`D˿6{ IPTg[ $8/J$ +I'M٦nCjF%{,Utd3*Gz VChkuGOq Gg$GH7J;f $*݌xaJ)cyr7;o+yfdp8/PTxc{Zطx>S!Bˮ10jKQEݍ0)i/}(.똢)C޻O"F라?=;fIp{!wŠjurm$僋~_~̮ͯ7oMl5)ݐn/oRYGjgZS^6(Q=~kt i )]A[X9`Ekz$ սz~7/8K{i-x9`Gq#6z+4l?y(\9!D6Ql{3ܦ>yַqg TYNiȌf+uh ͩ})DzնL+ H"iӊ:ϩ#&3a6#K-i҃׽Űx,rUW]@Yd:cu|:<]?=9;l}͡2U~ǤU*̍*;Sʭՠ,Nq﹓夨tWbK:_|sNgW.; r%Ums1Ӌxxv᫳.N&S,g;+pƷޛ?uO?FZ 8z ],Gy4m{;H0_ xͿy_/nM[M."B$F3O&>\1&+*Ap Z q)bnݜ jbמhD$>+ةg|p`Uf6{wB(O ]WȍAB!5iB`66ؤT}kil.feF_ퟯ?(pC;H',!eXPH11 SoDM,CI*6TpD74jBDƑ" iЅAEN m+$EtvB6J&5;DIˑNAh8 ->3zQxAb  ,` `jRH(P Z@I}{˺#mvl7d}| QRM{MD= 9ial FY=۸)O(Fy]ȌpB YK}ZzZF s Cj]A>adI9D Z1~BXEfS@ B#RHFh9 cTb+;w)+{AMЦ(&C+x&Uj3%NM?Z<{?,ŹLg,R /A+R+~y_sFG 8ޝ,ɩ\'I#7j<.u6ZY\NLɲ4zN^Rt TۦV.i'sT׫kE v~p\Ć};~=oŝ4^K .cEU.$L 1*#HL۱ C^K0_pmZIh F В#>x ea2FjƦ`Z8@u238g霧4#ylʌɤB "B #%᜼]0bP8tu;jݪu|LKt<|8Wޝ bU?}ptV=Z,b2mމWWw_~X͞2+mWxZ{Zݓ^ï#~+WIL)1猆Sz|y_\&~DXؾM>'EKl C Ou6b99V%%SG}rM#x)dlWb$o{+]aG1geH B*]ꑦ[9*NjS:%2%[ o!^HP\$Zrh=bA&ZJ+ (12f$Sr]#pNөg(pǁ{F4z%D']8@ݣȂXH=4T!m.8X&IC&7khb?*,='l>s@;DՀCSq;EZowL]Ƚ"ʊ5)(5 O#o !xس踬 8M&$!wWf. CQ^4C!z22wiBA"g"$`jc iPQ,P}d A a1-^) %B@2D!w:oى%%/2zXSDSlGXCCTȊ 5Bi25L7Ddk@KWHyO|t"Yalu!;txz=Onѽ wVnwU$ מE)Fds/Goy%V{:{6]@RNHaP25_6)^B T[$3z=@6xYAN/"$`i&>1^ߴmoNJ|<>T-7ē3uCۻOthU>;&FC-EGg5ub.Dm-yE_\ú}ŋ,`%1($'z' C 8-2cgia@ DEױ3-LjZ͊#& ;٧L>|G ;y4{wi>&oMřPtF0ੳ 5{Fb&BBȑŬgE(T f JLe ZD98;'8V\ IRzHH lpavX<Ê-WW5C]!4R+dG$g GfZM^b,4քmWP=&]*|%(1|sm9yq*#wOp%O C.A:2@D:gX1mƒH& ,/|XF:Д%]E$Vi%rסi"IgBefq`5w.,1bXD֗~.-aAIDlZAKO@n@ŀ"lՋl=IdWAk)#> ^ߊW4[BO*>)zHDJ.U2G2=> % 2*!Dh=j  {hk d lc~H̦IHE$t"2 D B Rͩ32iN4FrՑ&eŰզtn2g/onrYӠ]20d0v\([wɳ{r,>'̅cФ|~>5)Rns]fG7:NLN "i}t IDATC :C䱌D)y' {Ϻ=H$"M4"c+49x.u!? ,N^bgyȯ͓$/u,}U{A6\yqDAmx߮l*^md1kChLJ#X,95.9حܞ1:њ1$!.;NC[w*"Kf"BxJ}`CmpeXܶ."ub^,\̓2ム'g٣Uᳯ|a_)wq(hh_wU;_w׾xvRGOGy-!'+v+:hݐǏΩd Vr|*9=Vs\%"ۗOKp};4/vњT}e" {Պfo\˾VDW1xRrB A'ˢHY!7!ZxE|nH!Llh₡i5Oʛ4J!Mf}(-e&[;Z]&S UB$KƥNiF?4֖x7{l"ER i) 1  $I#N0 $$@>@IK D*Aɞ_=îs}I96w;N9U^XyFSS,@KlՎE |xbaHez,,"}Qv%iJtWPAۓۈ[Ef~{_yϞ|ĝ\3<pJ];!ǵ<] ZT!5#;1ElJ܋5mzbZOKN@"AmA\&, C'HU0kw 8+mAwSQ3H<l:G^w|wk7՝F=̄ VA:20k"]7Ғ' bʧJRLdMcJ v_/[>aXn?E3.%ml%];֋|#¿`z$~b˺^II -Rc~դmTJ)I0Ss8`0lG_{(։΍MY-y}EnRMcpx( WAY@sA+I.^Oii:2"L3l%Xʌ2!-ݥM,y1xc-"ʟkqH]G^Wa|d`'/ ] ~%mffQTDJ%iycf.zNcDǐ}J[LtSd9 Q^-qAGLңiԪcdhJLlCq-Eբڌ},G;eZp ';=0kneْ̬nWU2o iZ l"J"| (BK6+,l mݒ45DSA&  u&ι4sb{ZC ͝HO(SeT @w-ny[Bea$۩5Tgm ^tavbi؈#D;CD*Ā h(V ~0]ZH1;H,cU)yME3卡s˦| qpmn,Ʊ['1_>]-A:Z=l?>|}ZJEP7l#_)zwVlKο;B7H2Nړ/FM M !6PVĸY ߔ+#j?uP4E7 Wsƒ$ͻ$fgI5}\CO_C>Oz/iz>r? R`^:!)~KIFѴc^7cw4X闵f.B #x4&JtRըhH[\uĎ`%ӶBagVfՒms+Q pc4<vxؐg- kYۏ p+og|#یw>wBJ9O1ܺV4Ǽ},?g?݇&Rm[N9:"󌇊³,d9]x# ʑKUZj< "* 9ErO<JJ|tc$DTҎ*CsbTjsOS D#ǧ hLF9a\ iEd>[ N9Oxct#At`-S6,{dJB16'\?yD 6oR<)&hOt2@' zc5D013y*3CiES°t#먫x'pv,up4lX:X2_,h; _ uaDzrX'vqk=?ܻv! (%hmu`@LRѺ\flKtꜩ  5 8$e$*TǤu1`eAQatjHŧs?e4vloKی慽jύգ{Yĩr%y` 7ޫ׿p~3߻6ܸf)0y]c,-U sdU9S1iW1__ euBХ.JH~?5Cm?ʗw/-ٕm@uFj}| : U-rk6~LIόQ(?ʵަiy1?NhxgBGj|iKid*B D.2+s[˳Z43;/E=p[i"Pd"ϜpG%`[' c%[咑Tdv@e !3N\kBFƀ:L"񽙜eEы+\^CrB?a%cgh9 S j $qL^LR 9u4@KXԇs&`6YKj%XɈuB2ژ)g‹nV4)9cP6՜ 9'"^ozB9g#=53xFi[rR]āD-,@h1wA仑l>> N2zd*# MI^dRca,:%l!m+{e>5IK=^$Mg$B*dQRft+#b$3,P* D[/ EE,+}n7z=b$\3bDq*0"%3SN`ݘf!>̙ a8gnՍ6z5ѹhB~,-yͰo׼qǪVtw>G~V[jk{ݛ:l2q;2:tgv#FѵZu-ɘ"7)hWtbVŹAqVy P\;[JOHU>JGYRoҿZ>>{;4D] 5##|{r*ZG5`)K-iJMaJӪ"7)U(4%.K|]KkD#wv8M@L1eT{!fBPRuXxne[vx< ETB$~Y3WiL]8fr|{~t/}>BfV ۚOo~4ȯPUg[lo9vroٜV٢ar~KY{k{;w:NΗlM5Wjf eF,h-.&u[rMA"ІB:]D._B+kkD6,ж+R I9~ rhkXZ8dM놼-65g!ǣ)ǃ \;?blIň1XWc7|)[g3^r'lChhBD9S>z;4ж >jVro4&Ez2_1o-]4e$Ր-Jt颧}(AJ&w@e VBWC|8sr##f"<"x 3~:-ҢDt{>F#:B  mZfH,|GB)(Ȥc*Ef8h;Q,00"xǹzFVbB86Q1@!T$+'3ŷ}pogAhrygSCNeWٵ@qCf[5Gg_ 6ڇy\6*B )7@;ޠJCʥm1'G+rO+?O\f)!/d?ʗ_[CؒFLofSq"[뮅»U|⦵QvԳk_!Wc'?$=I@٧H%ʣZ;5EvJbt]("_!Pn^04RF5< !ٲ͉cʶQVH#hY~cXOR'DVb,}Bw‰Q/ʧLt)T><oǛye|{ڪĎn2I7+탁S6y E#h^f⹰B̈Ia'`SjփHT!/nfߒ.gON׏<5\}q Mb^ EI!Ii[ AWڟ^ )b ^8 }5 S֮=+ɵMc,9 tG z旺Izo~G~rr+.,RWx|W?QrIfln2[;}N+5BjVeNc.ur}0x(J\\++4= Z1Tu F$"(1Qx0EfEtuε‹4KPN ʑE[ĥw ϰ\ HFb7@ۼTvJQШNZƋ<6t!cx)thhA]I޵bF#S.)-96LDw+Ds f={F\NXM&&ǃm?8BzО5,&In] Av cj=x5pQء9i)d]VK]uz 31,0 "u]pLe2)v588dG~pUpB-cQ(n;(aDƆ XIYc^26- ]mMPgIGȇl7峯{~氲x;`>mNpK`GӇo=_}Y/ij3Mt>('otFZJ0 41W?O6ʎ;MxG}!qkY@@b`Z/ ?P{,%?ΓZt]J*c=&iԥCHYdG>OR`k;l@eQ تf AX*6%/>~ֶb`*(Rs2لోeBҎR=Ϗ7iFL&i"9]&uܶ=!sښk-h1pED; ‹!;|4aRF>$рY-ʏBifb"On̂(4415%pP5 ܄Q]yf'tyIS4VSd̶ʹK5y[KC7C0'!W#ԾXqice8Je}"+z&-J.5[ܷ`(tIISN^pԅAwA)MYv=\,%,$(yP15,SL c-D ܨOBw^ 1 ϻS&U(!>QQώ0e0&j2Zt n,'t^[p^[Λ7p`x^6OJwv;59w?>,E9΅byD: (11(!hE.AՙT%=' d=Dz6\}ϩ(qb5o,A`YY C/8=iVZ hf]\ZzWEo)DH%!ƹw+#Wy raެIZfTbD%3olbufbV: IDAT[2Ka>#%;\Vfو9 jU+bI 1 x,Z) JKk,U,`O-8cXwL icnmL$_F{9r'Aß>Mqt8mD+ oݸCѶM|XUbXr:ODNjcWŐGZwضFQ``Hm!Eڤ*3 >ziS \d2RkFhrFx.Ȅwi*b$J:oQW211T(O++F!2lYơNĨ'0P4rM)D~5U=FrNv"zkXx*cvKPmIh*h,I5.& E ^2 u_t=e-$&̺܋"ȓ:Xw+Ly}XOPK]eڽOYLd"iZ̀ls~,⳴Fgf6ԣR6Fe"z ik8ج0[%vC)k6ȕ3?ZX:.Ur蘽",C`4MK3ۚBk2W)P]bu5)1FML!Ҝ 1: 3NLh ~;kePn_R֦Ovt~?[Ze|Uo[ޓ \=~n#CWGU )unŽo]Gܾӯ} ea_h##BLFo.3??K/rt卼*H&{VV,I s@Ѹy0|Z!W~0PUKFtUa)?Ҙ֥dj= \RnfVr9fk)٣;T>yM Cr=)M{͜Q{J["#n{ (q|dHJjZU sPȎ*fvX՘9ijcJe Bՠg,o]˽MJtԣJ79^_g]_zhg["ݛY7 R_y6"ǚFNgέ}hGcA/]wUdi\<{uy./KF}*oYalbl*iWT(:|ҕOb^Aq#:9OW6p->єd|]'%f,߬?3˶~ju'/ݹ{qSKWA7C̄Eqf7ͶNoOdc͎~8=;o9诒鯓w lc#('7y{yCo_Cn?0Vip:ϓЧm>! cRH X%.K Un !b*յo\k~_Zͯs).DaҿOZ;\VkoIZ1΢RR.[/=qb2 ʶA4Ιf9+$I>X;b@Ȳ$B EUC`{0zqz D9'Bu}ޭĤÅF9 MFY}]]"F9؜k6~Ӌ2L%B$X gW_bٴ8)E7{o-\Y%VEdr4fovƵ/qpc傡oYh\);iT RKŌm̓=6JDdn,; '%58n;]*fI8[>iY(-,d%ZhR9jhS%SS:\ ',@v&AG^H:>! \Xk$`h")zT22=ݶ5rO T́Q:E=u .n\"cȞ֟cO/҃ "!$_RID,ľVfpN*u9ܪȣ-V@ijjԢEU>}<yV>apQ?Lm@hiHmќC {qŸ9ʁ֍tvEKt5YKLaȘRM(lքnSo[ү[L>ߴ8d}u>sdg/oD7vΫTbe^v>u/҇R GT59ҤWaW}٦k4qAH.Z{OH()rԿ^zf>Wڟ e5Vt>!D)kYYTb"S7Q%ň_>OA0G]{D @Zh͢8Qm1ư;Pd#ek) D.oo{oD0l>/i,-IwUd%ӒynEw,*>L-?=` =%<v9 (z\i PQAk 2%!)P)cQm;:MY!gעQ,p.+'PǻXt×o>R9Ʋ8Ջ)g~%W_ tg?4~w5ۈ^<Ch?ʗ(Isc~&̲~IiZ'37; #jpy~ q3Sn|3tR!D݆n`c~4 ~7ITG$ tEu!"s(d$pוwU_G/“u4vi#[[ nB E^Z_g 0tK |h:C*bxɽT"'j̮w.-iɳڷHpm,jiΔB}aJr^wP]3E-݃ΣsSb:I rj%8: no7^۟+O8 ٙ_>/F,a;͹i"[W*wĐc4>,"g0hCjE֑֘<{>sҚNϳE[NeF6?_KCǀ]LSvg*͜aD>ӪS f¶= RR>dP|u5,1>:"5Tc4JKFEh;3FC HT1__Id> $@ۥr&Fk\:Jk:y^db.d N( :Xr|?[y[z[7ΩSgv+ംƉl8#?"?!)€8&ȘLF1qwcnt3שi5Ƿsν}xZNծ=|k}kyy꯿~~.:3?u ?!>l!>CͿX&r7>Sͻ| )x׸`C)_z%fwy0x)rμ1 Ielz.&!f ײRJC" tm[ڶ5eQ"X<*h՜]sD>^=mT&]: +IBQ˖j{EIjF3~PrxMZHW59fq TO i>{NcME8Ҧ{<{Xb-@izc0] fUt]1k2q-&W9ytSju}y@SD ѹBXkv=ѨQݍ¦tjH2Kz;Jd!]'ͣpu-FM=ٽ_l/.xQ˻+V]m?W[ #Ԛك~w֟x.gt[Ɔ >}G[L1|~Tlr?6R ӷ[܆<mǎ.qmg{c= 6FVK3Z}zV1~]v^>|gG?rnC}pAĆ06Tlk 2vN+}h<%/RYB/$ƈ>JQFI:R_K%J\$"1Fc bËZrAS8n4Z*" bF8H+kW+b/MZ1Tis(WmbΜHML]4 :-4AQnDNy,37lБGYˏWN {ZcgkynqӋZwF8EtU+H\yp+C߲Lsh+VyAsLSe]n^"$=uO3tn@)$ބ]2c灕x)a檻RV O[ݸ(d6؈Q5"V$ 9q8z!**egGPyˆߒXAϢBԲ'kECaK=9& ^ hc<\ @ {y VR냖6 4` 6@U(v=4#x$5bÜ@4[$n:!o5e3UPԦ7?2a&nk!HFt`-np6fb/ac' :D8no |!aꗃ Ӌ;89vJ{)Fi?E*Elw,1>Ո^΅\{ cC2c0˘ȕRb.))Y`E$+oo]_Nl7;GM3uYu/E?k/*W*||⏭ɋ.=XUuv|fn j% !}ma: YޝʀѸm%\lW36$5= 7ۊ/|]F'Z瀗g7oO';WWK_;; |mӷO5G?ӱ{r67;L,1 6P-HE$H,e<ǮM@GLJq3 G  iڶ 4mǶ)L-H^^pm!"T'1bH&@c;VPx<ZD8aKA5PGgK\yWL&ws9-dotJȍk?NRwW[͋,z H޳VeYTxcFC͢5x,J#~H[G @J#l$&XK6XE?g;>դ,R_:Ii{ -&xу, {{*.is-oYh]p8Ma!;sxKKmg7\ydPo_ȖY^)4G8 QD;pBdpo=!ޯqKaGQyj ː]5,_h=mK"=itƖذ!D8eC!C ̃" 27x{.fڦT(3 }9kKrdۇȇ˩+qz\V)ud: #3ۊeD gTJ$Bt&v5W~iN6/If?w#dz|P?J99fd8U"b\x:R_gsguso[W+mR:oGG o.|4]!7g`v(xx[ IDAT_G]`3wE{B_#-xzI>,F$FC0ʤ"}0i!d(v] Rp$r+F*$% >q^pZut5d=jH5H2sa˙L"I2<&*K/} H gge~E+0&|;GX#Fm(2dzr#پkjΘ!۴RۖԵ!??fT+W,2koʘ(*D~tn Gf.I/KL Vpqz'qK,XDah~&@V-ŠBD"Yǟx(k|eQfxB[%1 f\D05)Xt,TOA;3Lfs*=%T)14^FN:d"J@; jAL>Y!GHA)%QWE݂Bg/ZVA}$1"VD\ cPFW>yi0)?s=i\_<h|ӕ7;3~KteUZ?qjp[AAB![} Q8;n֤}^;-6^c;x3~-)otKBpYes!d= > cD!iK̦'ɾP y q0t.hG5HD$a\kHHrMK hEJHҔcbMD y|jX6B cZ%aM:zkc¸"ѵ 29e7.|cw}[woq+}ɯX/)i+ InI>}& G-zT n].HiGhk7>MgV ISdVi!*6!HQ8JGI6SÊd/gϗocωu>T?7fF{Gk12+DYatB=Vxh!ujHk׎;Wp4=[rSzmh`n]C+9g'쮖qȢâקjSv,RøYqY8iʱgelP-7)6tȪaNXf鑡5AQfyj !rf"iBu#اG!Xߣ! =BŃD A9XvA`o©jOKܿGZB#FI 6!צ'l-8+ =ߒ!Eg54UِI EeS15g!A,rN[mM'k VJ4ť$FAV§^aQ}m_-ek+GU/-{Hm`|w\-{/lt@;pnM6ۋDD<1kvsr"yԗNO^)u"C.8SuMp>DzKhgƧ}7bȠ\`MFWK"鼇vsy|X)x#iRtkhxp[N1OS7Z泟%>ڋY`(D?x!X Fz ;*׭h,,XRI)Y/C$#d!BFD{nH)IH5NDhQBHiQ‘ h-訤f6ƣiX!dَdm*ܒxA)y9<% 7XL(<Щ-yoE6eD RN@7Y"‰o&oXcya*1'7):ftFw {ܽs΍E^-Tײg{NjO;M +dsQ(Zh%,Ҭ4- -+-Ew! PQ+{ҁqH-Dkb=.X E>[߇G)M@X);sT0RNF-deťפë^y~Y[[!4'|ITyj21KQw.n_"j 09zŰ[MΘ>>3Jr6w}\{v4?b 9'M6I(!T8nôZ^ɐ4^U7Z*::d(Б"i;~C?Hx93=u"i k}Ec "vDk4!غ/ٜ~(zm0`+FJ\,[M! 〘%248!iw69+)>7 sEE5 BlM+ jN1*4 j*SEVYY@GmDm$K.e3T~hVD\ZDMĬ,tZϿ[r(?{z:VCD^^ن{ud4MC&eDjIZJΚڰe &+8/* YrzJP=aUĻ7><F\\3.]Lsy&ܼ e$x=J7(DxHD&IѪQڻڳ,A*MX/D܀lC"DwakgSut8^&M88hJkrۢm:ߪ!dDiE EGj!teZHdt<AUGU'_elH<yoNe>L1S!']7k2HAA@!*|X_Lѯ5gLE=3Q (IlȔŴ'T&'PA%,Ekҽe|=LUj|a0 Q1]_QI"((V T mQ"ړSS“oq eeq?Y+D/'ʎ_^|__=/|*?o5R2×+r_}_Juh* n,_\$jNwҥ0Bg^c"B\xTݿ}~`+WN*+ pO3:v1Ƿ%>[}!L;8OϷ VhVca;Q8lnǍSqo; r~?ZH7 yNZg#k ej"Iߒ Zhx׊KFdb䬭)ұxJԧ˥LDCbzXsW襉z3wEc[rUC@T|[G)P9 jH q.&F 21Hb- 5M[(/ XBϏ7&LTȼWՂdMpcob&I=+BӀpxnwD6ޥ8oƣ(b<LbҬmlptM,E|ꑽ3=`F$!~S-r@lO ($ ]Sa]}g,y7Di1YӒפ Y'dWOLa?J6ln)mf Db+H2S/0%_#-Rk3cBT h M2ȋ?B -,e-"D<8ݍRob xB}ES|\ebu (M4Z8X81|O#[@Fj%#ok^ 4"U2 IDATh\m}2$h- >ʴmY3hv}V%FBfry|!4-,7ˋs~棿3}eP~嵁?"fǪs?xgnBHOŠNl~Z{~u*E`D5n| g,?Gw 3@ fqEC*3\,C-j6ެXl08%'_5Z(/L! |T6ӥ z4'y˯߸Pk_\ŧW#^PoJ^&u t]fQR`@=0uFcE"8D Ϸ%Mx3Bv tܮrnkBz;ގ?芟<{c92FNxHiRLtQb)!MST<Y۲UCBd(yT@3(9uemQ 3 Rkgq?%Ʌt#%Z)YFF [D>%M'ٱ z6=H)UJ+Viځ~5hO*f)2F'kD[c5'UM j I4j9Km:K]\ݡro] A1~]|HH)v-bІ=K /_>wc>i_T*BH'=rhA i@d٬/tXW65mnM[n*HN=i\M$M'럱eQDu[WFlF>ӆ!:N(NT@m$L-~oWz\ՌOm@TohUj"a)ٙiie/PpE%\*eyoir!Ikj"RsHv 8J2 ̲2}W\>7~oDh=9Ŏd$ptv=;pYPAiBO12tbi+Mc) % CP#<[Ԙֲ[6daJay]2ؗʭ#IVZ\>1벇"Sي[ab 4sOn\КRpض(,) ï\s!bFZu i.SWH5\7ܫ\Auޙu"ʧd&4ycrI^42!m 1h 'TPʼn,VcS +yOh}H7s+>ADWߩA]9_?+D޿G|Y~gm?G?x7mGOlۜGxΫɏRپ6/ٶ:tN<@.$Hu?ZF{ڒ) _M^.'&J~+Z(C &y?;QBZH1N*aT#]a9iuֺʵe3m;\+lr!D&yƲѱ5Yrd(LFQk^]aFz=`#7:q6H FnNO͎.:t'GM*:}ZYb$Y}{QgrVbHTf-%KFuI,0Kq$RwXJVeDQsI[W%G)sFfR.(UVW7SYB`-e>VH)P:,;Griкw\K—*Wt$>|OTv $1f8Z$$4$E]8A=MXOJ`BnoYAzd~H};סDh3! AӴ넾Zˤ ;ȅ !Qe^hB]^k"U#RJc; Im=:7 ]ǰ[EV7l>kSuG!qbZ+9` !@"_`);(! B $ƖmlU^ݺ&VUw?Ķ#js>g n^N/_Z_NobWG/҇oz ٫8s|޽sW}pf}wfO~ E$Rit<,J&9k!(QHDB1ChlG0x4\Ӑ)SGa4F'yIiV H6̐lkV&(hC@ \Io$$!@ t!%BK c$*,^1"Osf{]7H-E]v[e]7bjJn #e{6Vfvo:()ªu箷NΛד񠸔(y%3 ΦD#h"BJ}:f'QOdIskKߏ^lY%epSe3nGJՁG7xcW5wBhgFc&^{4~A+#/ZJ?l8Av\*-d a10ԭÜeK9d5MXv01TFt[mZ2}PyZP?6/H$e6JSƫ5MDNtB2l؟v()y6 Syi9丱Wk*은5k%iSe}0kNKu YӀ : hcg#F;@atd/Qf2,$WKSLR{ d}&o</l/B(s#S[X =^`ؑ 9I8yy.ZFF#;wp(1x1(85f W2cDe3ҹ &Ms+^]o$S7n$L-Nx2a24~AKdS֌JGDZVcJ] lI|.5@H-<:cu;"0Q#<'Q,YY ŕKĞQZEjȎo+VuaAE/T>\*6{ɴ уi >RK$ϾG˪F:MUR |@G$‡(ea7#ݘg-oU"βh?a/>q?·Jwn$/OդN{.Ϟ?Ex7uka#{8/%~sO>wgOqUꎌuIJFEJ6p*PPP_;r$rX+9 bS+km (8+4.M9(RDbh:L/>J7Yv19v|J=}zQ||3+5jJ"H!MhT 8,:b)\17@0QL:Zȁfp[ D"bDo~Sژm.O/$#p_G|zj9襮i[Ȅ`d(HNx,-Q8q 9tDP I+y *" +Ǚ`&h-1Zj͢BbC FQMj ,Qb%>)G@PRnlܷۘUќNzg?Xcg'nMHo{ǧL~ỎU\ M;lԡѕyݢ͚8;o&!;gZPAþM\g]$<"@ns5i$qmܸJ7v)`FU ϠhrbNRgq*>&h l0FDllfon真2aW::WRb؈4PL99iYv0:cǃ'v8rt]sKG&0>o֣ʎuA"j\b1reTldp6(^]& (|MޔaEg4C5f9S@Fbu:eP[vVcFxtvJc$2gJ^)ܽ{d_+&u)dH6LHSKz 2u)DN-6:e9]fw-zёrb\<7{%wa^j!zx~CSy*<#&twɳϝ∻H )!bh҈<3Re8)ANEU0V$޹mXᖠ"eGL}FjVEK#YS&W<%lln|`6!]|i~׶k=  n@u;" YOoK}zo_(gϤ_J{m>xhãDZ W- bF@m@HCh!:!(m7\H"D"d 4xBCy_'r/ eמλ6`p<@hoC(ERB;,*|ye3HS8A(dĮ#ƈQ d SFpWTߛP%$J-Ӧip\i2D]kRBLـ( yz }46OC6<; ?隹QD$ B(1fi .CC ޻KBF6kpDKD4AHt+x%)'ÎBsPNTҵEh=\l8؈e:X2ϕLN*N3A[+vfZ[D\H6I ڈ $҉ 4LR^o ڛs!0q]`齽ö:~ |k G.g>8$^YP]냧xB{n$J5&yJ C<$!=RS~I FE߹Vc O4@'tJWCq_|pi??bAm{?=i9eMŜ%$g6DV(@{JLk1 ۛUMNX(4JA[p.P%BYs:1B2qny}uyJ_w^28ߴB/yޮ?qA1ub'D>PjIu$21!^k@K@(,EpX yR4α lӂEI$c b0|pFeo mKh$B*0 7ڐ$kBtC@-b cND'M R*F܆AD!$t $JC17!HgNY@:Vs7A-<୛>.gbqE;:*B(lW1'CTA!W{ܠ4_wtuM^rv>R&P&  Iƕp=! " HEBf/ykTdEC.ApB;3{Zi(pO8ˆ)7THH%X Sx~ba7$+t-R c$hs YDhCV-VYFpRe:I$5ڷ43Ѱ(GpDǞ(s qDK*iٺtQz L%WG#mYt#M$3ˎrRzzzGZ#oE脵P vvu0'#7-6$:" P7+(X#[e 7dzKDլuX.kZ6h8PVK-h VUJ^@nj>|yS5K5B<`k1yoEe e 2 f1'1.nl)#dT'8^ Onq쾔μ|W7n>VLN|+Y>`=FCO`xL!\C頁}ƿl @X&bkRV"SD骤Z IDAT¼~>^qXM||0* 쳤ex] Ԛh^M@40U!f#\~hEwA = ]@E(6Z UcݒG 7@=شaNvFQ40@Q4 k8O@سG& dQpuQrxq*)G)!e#[,ruU8ɖh(Րy28ۣE3jlWKKrmD뒬M`yN3$>r;\*R6)<0MJNfdt[-z0ml[ʅ@\ I\`lrěgq zYM; i J6\/F2V$QA eysXcvj^ۑ1J!J>D.@ _fD5{ D`*-|p`S,ƺS%gRˇ㝼Y̏o} V}_oݻr۹싟+6GI+W>7?|{4(!pk0B..<ypgU ԏ=_IaVHH|%pA h~#WRk-}f <]Dn ZO~.y>1xW&P`g=O>娿 ߯0h0i֔![+\>?޸d/[Z#"&搦 TOpgZ<f],`-a3[ t1'['SjL*eR%ꉁ\2 "٘ ѱB@S!Q8r9gWdT8$1*"~!zc("@J Qc"@l”zndFϞ\)q"I(O5nP fPYv F){(3;q=u<1m,Hl^8NƦ8kD2IiFmhTt}a;N L.}:j:=>B?U7/tmgˤyrC"@/O=a(AI$RL\X_kHks|xʸ|m7ݛխkagd jKߤXҕYQODHVs4#ՎAM%y+O%dr!|I$~öy0hrip&cjuўl 3ìfonٯjWr/e8Q{}F F8HqQ= k ?_LGޒY$xft!8Zۋ $na;M8fC/ G]uz2=lD?VLA돼ć?O??cU ٢G@ D: ~R((Iua3z鰪pI6RIDhI%iSŀ %mwUxKQD(l[?Ĉof}ĹڪB(AOCD%)1#DDCQZI-mmv=a.r12g39]S/4ErkYՉ8B|[׭l>V>K&Me;T;ɝu9v7rB"v,8wϸ`-l8sYdRv5'0pYiIMu\HB?Ld1"Ⱥ^UD 7+xaitzxaPZ8x,5>x(2i>QAldsmC%JFR6 ihBѨ@b:Ll2EB@4#AjW j 1 gmZskR XEE5T“؆S5D.=1uK&%l,}B.޾ VrByQ5"+awyU&g=fkH:UM{x%+bv6`es]cLr:O׌I'r9J{.֨,Ŭdug̼&33Rɖ[Ҭw0ny)D2ּ0~2 U} O9HWu yaJ>x}:M>R"ΡYɪ*s優A;ϨkNeX'5sJ\3"yc'Z<_~{|foY>}eqPUhF2g=??YXz};R9wFS$ǹH+Kj^*DPQ "y|q]6|\Jily-I2=/~e(%2m'd3okWEwoݿQH, ( <@VrF D8M5|5$4 f{dmĻ0ukP,&ӌʊ&K1u-Wss$mVi_p=g`8S Ѣw=br2m:IdpfG\JDsE 2 YhG*RR @-JB"CՔ~Ǖ-,#ZyHǴ++k_*7+URusٸF ֎:;ìmP<е7v 2XsUG2 0\&PYhq_Ix$9b!D!qآ)Oks a,. * /_7|'1S .3ivоob}rq6*e Gpge{cY#۰5lX\K7Y*tE{wpvT>OΏ{;Ϋitpi8(Kt>s:P M~`: $[F#*  &lZ6,w']`QٚLj!e>N3fg6_rٶԫgro^+}&U\߾7}ZẂ&ԈƣYvaWAydS54ۤZ 2%7b@0s𜢉tNW1.gz|N! жJycQ3'PB#~#pj"v-ເb 'c@C7m@$7G'Dd,iz5X 9υ 8_FC9v;48 {IF',o #]͢Z*&ŀ$=*@ͅQR"xThp'#"i۵.zFJ+B o|d$*uCZV ݲUn|1F v).Y-O/,O+te ϛO|*WOJe:Ot8W6ZZӬj 3XL^895^5fq{\J7f):v˦[tg{s# %E$ô4U>B C 輤 lP@D*ѯmqLFpO\VN R߳ dVUxQA- ඃR0ΝvZS nt$ZS,eP ,1Ì1v\sƠi tّTr1-j)͂Dvܱ69VudDt2hTd^PcWT13%"5):Mk#2 5\chBJ\ERQg5C&HFlCܰdZfQ=(TYH MNMUʨaj**ɓvh9g'f}Xػ1$4YGY tb}]B\GMͼM2ǥy+VMd>pJ3R]@XáPANYdvk]SGKoM'wwRG>4eH kn Ɨ4 X,{vvVJxA?oz"7J߫2cs:Foq{@!M3V o%11{F43ttҚz _l`Wϛ\wmmh;YoId쬌P*Ux$ Q Hjj렕 >`@"5JJjo9ڶtN)\).֝;{{ѯ'^~_ zd)OFH>t4IR &NV=jK=N#!6 g(|\pOMB iӌ9EtBfvheBFC(%vvȷY^ٹ=TD۫sRZADg{*-k!ة;ǰ]o Bgny; a3Do`:,Q24(!G/o!ιWF▊VE~lbuH|PC-u7 {6T×TI ܃\~A"&DhƩņNzН? R%8/8Σ4o~\ t7tZ\n9v@|jKl g7ĹE.u/Ny{'b5;*%lwZMsыèmRJTԯ{-6?VZS]}w'?OtF M7c;߆`lʭW?Ϲz?g=fŨU;Gy/Q\ŏ !OaP5C,<}B^(+iIJU"az?iaBkdOvrNnY V-([;L!ʻonxpVL-r/iq!jœCEEM ypׅtxx ,T -pEE~(u])nl¥DFl: ڇsH/)~H2*AT~hWW6'[_>x?`S"J`΋`׋THƱ7+4u/?޾VwGƼaN'ݻ]5"V*<̋Mzmez_SoF& {z5;k:IuRj#u;C\M8x=CkLY:2wL#E@my\Z9%  ٓKqH*.0{"lqؐ =L`j\$YJ/T.p`&d蹿Q#tuD' QCnZ-r5KRI'蚚~li :PiMm5Wbn,јp!Ӥγ.(%'-%a"ŜfEcpRx9"_n8rkF&0:5dGPRcZ6+ВIt׳֤Ilfe,ЬH܊d֤ݜVdiofbQhi( bcX$)ukH]SuՈ 28 Qe, d Y.nu<:7GIv{y^/}s[^ݻW)]lbmFq|\m?C 8@qC&`,pb9CA !}Ep%9$*x }\CbZ2$U> 9p{ZN; 8O89Z1W=[XX'u8+^]!N59:ףzpzŸsMŸk)v fΐr()ۼ.ǘXwL%! f[[vp aLZRFu9EKXԔTbRr+!%&%|2%[CpU(lELYT´gLOr/G(ʎn#}9!Vuh)F[$t6wݶZVg;R!Wq$)Š,E޶%׻)5Cs˕ ;mЩl6ިIw,»jw|v[ WZQ@LǺ98 IDAT{UdGQCaQ-r#,FKubb$}- F樾an,J:m肧ђݪ'(K RѦqP[Gы>n+>N^&}ҍl~Uwz6IA[~;/..i/I=.GWs= HdT^"u]s(!тq݀;oP[ykPUvMږ4:?qe<_Oj6cXolO~g>uiƻN!~_wzW+ Ð-<IГ[fHI"!|k{.aۿYI%bFy#2ۆIAc"!K0rs1N)!Nia2@Cq:C`lF^%tMzT]m1( 'ӉN)n,ry; ?($/wd:$8zQx݇僇/zv٣!̫SbzkYws]JJXJdܑ BQuȻA)9{Py<ӛ4&zYwMz=?dؿL2[ϸ<-JD0ݡ5rAus; 2<{g'#1h_>Ҡ)RAyKD$7n8.2T/?/NTJCf'{bސb7;{B̼.)!)#¢,I#GP(I"EO$mKZAyh If`SJ"B)euA/ئ6Zkua:fǷY<rj+1ȷoIsc/;4p{| ]Vd}KpEɽ, 2EZRbySҋClʒ`- ?rP׍ o5oA@DLn^"-sy*D4"L:ѭ muz6{ ceSL,fԝ۽It~ڛ]U칼O"hzWMG*tk-a5#s W~S '2peSCX?ALW" }U [ /T?7SY8=I{_F4~jF;{nދsUWNr6^qso8N #))h&}>& nPwQEhq[on@ZNq"'`R0s(#G~#ŝ_Wg}|?_꣧{dokw7ʔ6fdU{v ђ%xZߘR .@ TtdmҪB`Ah9h2PI۫㽱<-rBDd!*Ѧס&}z͝Ѝ|8un\Xu7&JB!ZIl#BI֒.~HC )uB8D*S nV@I(Pq X܂O-V:0$, 0Fӽkx_ϮNXL_?;qOƻn<~Y1BJ4B|w$%%B@02䥚6.,D! [= t2j@nK넏UkbD F]dLVh ] )}DtfZziUF8OyG-qjVm6fV [K*OT+3գZQx3 ^=)5s*!&siTd_rzJ҃ȅ'!J~Sod;;)\У^h6HdA28a#!@1MYv\**I {1\ѧw(UO6~ՁCHCu o)8Cj+l@0D f)EJWDȡĹSxҖ )oyIyA UXyO_;+ r)M$u BX9͊Q" IJK$uԚ`{Նqo1g52JG )Cu0&T)cKCP-5DmXBbI"$HS)ɹ,0!jWPiAUW)'H6`ĸYS2OQN^{v.Nq,GdCw-Lp#ϽŨ,RFƲ2 (dz)EvέWMc>Х hC5^d(*@rMj^LCrh7X ݝ%8G?ݠ&;"T9H:h5P 'V3YnKD4,ʌ5X#Xk-6;4U7ϮkOuվQ4ɟx>}0g&FFt=ߍ? `ag}~?M՝^24kn\2?0ٓ8S42\2y`gpxE\\WT& ȵ)Og)l\mg l{P[ .f#\)snM1YSBmҧ;}틿kG?O3{{|r;YeL/k1mn}$X~~o.q3?|ۗ+I=+&!M2]wL>I*V> cB!BR UV>>#@Cax24 QHmB>5V ҦC_{4,դǫF/Oy; RR FiMw1Qu;ߖo|)^hM%o叿u:vDFeB&#ڄ؁ M!춤&;ɐ[PmOw;GZLh=S5Y`bµQ C /#uLyi';3H~Hn·D326o?{:߲FpL|rOsx[7|wO$BX D<'~MloעB #P^ί}wt #ܬR*?WkYR`Ht4~z9'3,tu 1伭jK7ǻKW{^Y}ˋTM}Bhy;o{O\ z$z6Ͻzq<.随о_8;xYO>l~#>K!eJFmKOww&K;lHXΔr*7":V Ra=Ȋ |\z"BvoBFeM!E|Ugd:J)3%{LN?w(D_EKGB1dil~KǿQy;\}DL趣9*H>f4E.g_Qt#| #6N1 zR} DަloVݐ)򮝯$n' *uje;ţ;xԤk63LW/̮4kZu^ѣ{ٰ+*{'Kgx3nVw8J4Ap:6)у\}wb wi{8w>v]|ϝ'6 y5oy>F\5cC)V cO"#w3^N6hmju%{atd4[l\ǙswM*}RoZMt> ET̜coߜ<+;Ͻ^ׯ^y{}ͰLIndoqOzK?=Dl1ޔph&M+#mG s (Ah:_R.0vMA@^4œr6cv1.=' N/ln݀~-ԧZx7.481baP 6v'* LV[ӂ/oU;~*!x,nLv"zVyƦ肣#bh(t-]͵J2%y؈JA% IiD} R&|%I8`GS3>J%U[7URNݱ3e>t*֕X(}>{t3>|++w(3z.S:N)޾V?"H/vR*!kNT_Z3ٰL7B\ڸﺂƪTQ6Xdv i0k$|j nG;;m*QA+A"I $-;>_[R86DVbj"^yPON~HV`ǁOPfrZCs+1}Qɼd,5m:=;>2֞+2gQ O.}w3&1nAu ""1$v.T*2%zʣ5{Q4kȜ S^BZv3@V nGq(!6MD0tJ*uWQYܣnjz %>j$v"2u/}hHms&e eAiA(IF7861޻s,IYV5" N!Z}b6"Q GR;U[r [>^D i9lh˖RQ/dB`)~c(^jF] ؞-'\ͮy5sftNO}Z;OMſ{н/=I*S`V ǐa.<[{{\z]7%!މ5*˶cvQ4&*)6P4{^ȅskCyuj;y;R[]% R/uMzK//]_K˃(]>=N.GoOL.N~pgK7 $B߻_2??? Y|k+Cg2ז7vZiDܠ2u*nrTcb=i74 nlPjtD/Ƒv= N.IR( !*j$rm_^JӇw >QAj'a7'[ׂ'S OV )5; N yp//(}O /nRcb^ k*p(=" FeyX! &28h[3v+6+u66Q\7;‘796ol*ՕAگcAjcUFЬ=L&Œn[~gg}77{y>]s;fڵꨛ33WrU d?SYWLtQK!r7)qZ4*: F.a!l(*PfBB18֫llhAb OBЮJ[+!餌^&Eݕt;]y}u_lȵۙ>?3~\.xɶRS=(}ڞ PHbE2 A@h![27kQ^YXb>]!}R D]Wy)$(ē9)qA^Cb˾3e/ IDATn<1#Kip.ɼ{.KҶs쪧wv_c'uX "<Ȑ !seMRVi&;9ʘΓ@N&)Ee#XmN0$I)E@&Kfd*43[ZD&Rل]Q6֎~ sjӹ5g1< R'nVvPo*bE]Uy39Yݟm[FwBX}pyX1(<0^]5HWK ނ$R,P;#cw25XHTwCd^2ˌmA8; T٨ˏNEYa_WyݢG7ZKo>epX Q0 8YbٷMe^???],OAzku8Ŕ5.W͗uz?S?8~./-/|_-m'my}ef܍/;4 !w,FZ$/Z-nhQJC=qXS7u"7\ 6WPfi.5BM'>bieI{/SzHJ\|]A`F$>/(iGy`{[cw6b^ :cXtZdm86HKJc'لI;(:]e "֮O*BbnH寘*O s7z5v"Yy(o\눽Qub'X֭r6 A&y7j+ (Ġz0C d" $:#I*EeA-9˽RۚtIc<1뚴n^XH% !wc@ :*hoS4:TGD^O9v&,;dj=v/`Oӽg|7/X?QHsP=I5!::!b::Yy}Ar$|M"|!݉!Z9[|jbT>m~] Njf:ߣ,t҄CWI+_=N|Վ 4[30o8l"r _v+gnlb ȵ%aRXJ$#H.,>À5 ѷ91IexqSNiߍ3cR|nB2F$yalY$xӑH B!u7FAf*$ 91Ζњ#HΡsKa .kV**c:\T>TF+c> ߻XMBЯ;\˓zE+nǺmpaջq}0Sv3^fZ[ۇ qcSut>IZUoșS‘VGYb2BJrFQVmS> idnzK'nKٲDu3OOfV=@-NhH}$XY)Xdü%D9R-;rHp"n[k#ѧ׏Ng?nU0K/wx@/ĭ~ 7;R ϧtc[|kV]D(YvmxRS>5&deeeyg/5(C`%W"-`]j50l D)R`$ 2)#K.m}7fPvzlsÆ>C=C_TX:%9\ȯ7RIdtFRb$ј'Q"1 .)VYNVqW|6'3ic,J&))6-Bk^u[]u9`,H&G;02]z5?W}i߱w8zʯ?{tq!#I;E:SQv]2 ,6 F +bJ}C3%^ TQguʑ&Dl<@zoyk"{m] lE710],b>xB̽\- ^$aFIU#[5E$UV\:Mrk3ub,Nvܝa*+әC*K;#ĄwWL_\$V8#Ar<jN"q7>ϻ^Nը:Dbeוtz?޺~;+p]>iNUyIUr;r>&u!IB-&?]+D(ݠ8Dt;vHm/;$.7[h' rHT;( $'] (7]޽1mV o#<94 Iª۸'eJmB]c$7%p0 R"i~C +we")"r@e %(Q3[. &;\><&Bmm`Kzh"jU+kQ\׮ A'*2Rh)I ɃO: GP,%3 d@DzuMIQi_he{C`H"`0e̓=e'Zr˅`#ci"QD"^tGY"SWˮMBɅUzBfٽT,D Qέn<ڭ˿w׳2kTn. )wVlXt'i ݸvloYޗHiu+=cO;U"wWSTKԛ%^(XkxU^"49{[d3V%t6Ag2()H3'Tu7iWٺ,}HRYgLR)0ɽzA5lL#oRt'MBJo,D̡IxF~s/=zBڵ}}#yy V*xVEi%E2Xy%vZd.٤s&GP$N|$If5!|kD)0 r n0_QW̗^oXGwcܿ i9[I^k1M z4wGf;TZ_-$-Ys8|Z8~>zrݥp7nx=;sM-zXKM `NQFyA37.Ly+t`dd[h V8JVԶ0B e+2L063MC܋Nn$\\w≮[9Q$.?|33voD?uʓRVelRI[MǺhNs>m0 UnUfO@9uan7&f:0XQbaqA߁z_v?qOr 2Ҿ6n*@SOL[ʫؼJ? &a/+9NtB.] :\E_T 9i^CH1WHBdڭnQ30TeyR;EFUP1]* ۺLt:'̌m:۵AU37F|գ|P]>:fXdd0>Zp[FW8>ʹ~P[ģ/]HiQֶ  Ňc=ɎsUR/lBɵ_D{ 7< 1 HK1lg‹TfZ05 dD2) SuZfLFR9AA4g -webhMv@{v"*r1L']`KFM WT ջݍזXHN ׫/")$"Rg;PH*d/"fy٤Dq5a`d+Pdh)SŊzXG-C'h:2ab/\R 3ڂ&ц,< 3p %O,۵eQ]_&csvdW<^ô=nBCbFlԽX!Ķ~-uu;/h!H[}֩~ k+ݪӬ`Zʠ|MW nA$E x܊P 1!xG4Tz8A,YxhqǠ<kžAzPDhL hcnAua]\fPԱ1QY$Rn_rc5ԘRy~ =y55 c }T@}ZkmmJ83+[,0J$yopi>1. dTRBi!7ԮɇyUh~Z;?oѷapUvw .`E$ȫ! vMhɃv>;WT-Fuit8,MQC7ImIJ Tw͏=va~cGgF<?z&֯(fMHw>+)`/g%|:+w7 02OR,StA O"losϝ[/~3o*T諓!2kkڌsxN23!6ӖJ.ն >X,!$Ĕ mf ݒf)R:hB,6V +sٮMP-eZQbL˖Kwxve@с=*~*kIUA]ϔL[coXo?gh&!tSHU6%nslmPB@.MDR{XێEx-IAȠg^*:ӤE`<md:#DX[::0Xp2_uZjqQvUn]mL^;{Cidw˽ЙXp|{ach:WlRKD͗n^+N';QjXd2jOf U#.H'V4l)cnEiƚ(&]>y$]7-nwcAL \]xѬ B@i~5(عTNݶ:o8{* 1"FsLcYiE= D!EfPE!ײ62D}k-Hq )cGdPXvHWy]lUҨLBT1AKIJJx2'&e`l2 1[džzb;Dzaؑ8W) lߘ\Law g7@#g6TFd!2(#I90}/x e\Xge$N[CQ5×w,97i("#@4Ёqm9yF. 2HTB@[z0Raq"J]\@:I聠8H(R Ẃ!dJ`TBY@ Dj!rYEjzJЖ£p^f)K{] jsZ5%ʹw1'~o=sDQ-PRonj~(tA9?]Om|XHUiU] /Atq<;z}A#!{kikkfsz'En'ywa<᲏) 2*3JVCЙۺv:"Bq[0_!݀XSzU<5j蚶.:Zq,n/קP6%h*i<1X'UkY7HW?Elf-)D4-UeeW-Vy'"mBcΩEss/uĬx4Y\$h+DAZreb3PE8"XI#^1tdOF|,Bac[x ?(B#)SzicSwHIRoj6ͯ)K)7ۄh򬰕òTqZ TSdBkVtI5kRkFT>uyVh(^})ٙ2c=ֳn]UVI.85/d7~d V: 0ԩj6eG=U(npO\y)/G7gdT١Κa1Q]i_HٔkQpq9//NceODyžQ%zv K_e1E"^һ2 zaFh ͅQg!x<(fJ~<c0Q #MF1lZP8#ӨHӭBbD2giB$ג 1 IFbAKJe4CPBJȥ@xA, O!+$>i\'VP:gw'0&|"w-=Ll,!Q%\#e$P@aG"E@[P㸹7+U > BeⰊM@ DJjZaB$0/q:G0]PyXGZDB#Kw;A35\_$Dl(CH-14mFPPH琡2"]''`ƢGӋb푹gݔ{u U)輞*d̨lM!mg?<pS? qq/\jW#Q͜z6} uU;:x/<͢s>5bMx$>>P+t׻GzO=6,=UkA,l6YNkĶ!!F+䡢Uu@ r+A,J/nEAv(wLo>}7Go}|UgҹָλnU+ڮyŦS;-K{QM<)E S Ms/ JHdʨ2AgԢ}-}w'gבbh]0s߬wZ%v>QO_Ý =ESͳ| HwE12  YXW9H;,mlRem7VN^kX{l}`^ZXRDS,R&옘gTtp [ TƩe @nx4[>p ),@wx{|g⳧)E*ض]t Wsz"4Pc.t\({*'4+&;lNu݈E%)LƠ,iMb~vY ǧgA; )F]>td&Nok΂wtEE6Ef5.Olq>z|m&Dân󮖢Gޯe݌Yq1ABH6/SZΗ Q>ȺS@ A]n}uzg^sHl-s_q-0C P40;`B;w&tvgW+aDk)dJv\ɥpRȣsiJB`v~3-6,2B6 +6&;OuT.NfyY^G%X{g>MMصL|U⑮fC CӇ. Gr^Z,UX3ԍ1NqN%N1CѶﵨL=qKhJR'Xas *ZKEsivFP[.rs֧ҵ\>9zMٮy:NsWI)jΊBHKpc,̬;W91MJaS^ (-ѕ5a2UJos,\)\ 8hN.b/;6UU2+niҞhث^#]ƥ;bd(4TKf\7^C"9>NFNԪ y[^iO㜺E3^cJZLna󫜠Ǭ{_x"\߻t]^zZOs%Ϯm>G'a|d0pKL9 RmcmZȌ&Ҡ&YH1SG)Ů@ dHL|DYHYOrv8< 1Hog/PڴS¦pW4u@6GܛR%ohzLHbʅRqݪp< ePH-àȒ-{,V2Q Z 6bfY%JViumHY:2Ug{(zRHJ5]4Rk 2\+ݠe;HR<+y(F؍p©})F!&bP@RTٞm hA$Z 2@N$e%2I#У/!]bŦ#+b3Q vDSkC=ls )WҶ / &D  ʵ3:4B7٠QIAWZtlYZF!m[_X+ I8D)! CDմ pCŰԖIb!YJ( 3tQJ- 3dDr2'$IiBZNA.I\m#§ސV'x lo oAའ3 u/,Bhr+Ib$$cc-ˮ{oWU]=w&)5K# (6%!0da?#B A6?bŁm`"+h(Ss5L{?νݜ"*t/P{^[#s$H 1Ԃ]:6Q,`'6Ɉbi*Ì=S!=P E)EȰcXed<KkNg>~/{xHʍ<\Jbc'es犨1 nfeءl9iyNUdiz._ӊnźU&U+Ƿ_uӾ5.>#:d #G1H:Hl.0^8B[fr>ȧb1"OHbA;RMgV$u]Y᩟UR?~w?EoV/&l/\:يΛPlU#̯״+"kۯ tUJ د] ^2Y %KӨuz_ţU;͐P3 ­}m3Ƴ[eŒtڭG^'<%؜tQ'$/ȎpiJ2mHnvz/O"H (0,u+>#@:0d/&>:lSI FE,QHgKT8=hPkpv,E4%L=x IDATWvQ8n;g 5 LFNkkN+׺ZpGJ*ַ\ ڤӝ[}H.*SxЛP[v%,5|'wl??$㕍44'n=\K9bN͉.6e<51KĀ>ɥ$ ߬`^̛Nn=qOdiJa?^H?3~,hK,YV YLnf~SY3+~+*^;qy6dFvN+5@sOQr,ҬNDƴŔ,/1E>Ĉ I%Ebށa}#7qɴ*tff|CRs p:|m!-gqT`fP+6No@S%M#Pì\SMNjX& TMA>P6qc{kD ⌜Cl9ESj{$˛lqȼYpjܴ/-'fYMV'e@uPCSq'xz:4U4V4U ,Ѱ\?-7?_67N0Ł酸Hv"R4B\#Nq)q)jRD,gdB*ВSnʥ| V{k8N D$(EE͈e֫5G5Cy oZh0IFcQem$cZZ t42,jP,Q H[mf ˩z!LH-$IvHW_vWXUt]ՓDk01K>XMжE:۱Kuv+zzH[E#^:C?T$sŤ@eQ-&:m ^HE Bm]bfTC2k`8yS h &Dmޟj`QM\r_/p20AϦ+IPw.A3>`/ ~,A`)lbHyE1ٲT'w6?5-j^ӢϬӟNٜM:6 i)K).8 m^zA6f'fV8L6($oFUɮ?/ IAu5ۋ|<;Ntb%0gXkhz=JUjWh܊RSGXyG5`kA=##N] f|dgbڪuF  {&kk̠Q:ghKб$Yb?rSl"h%B!m$Yo㡎uԻR3uzZL=~/oѷ4V@uPDQb4:,{4MFᔘ&;Ax)W^L.9Z&BjkҋO+'e1zlKWLSl/ &d\%<{4ޅ~z6Pמ޸իhk~a8^('͢S.ai쓐.򝀥p&#M,8g>\7s:z$O ֯^`H78=z Bđ&!P#v>M]7]2b l2:?I|?ܡdy 0.V4PB- 3Ee:_7C? ~ ?֫WKʾtySNy}'=5D0݅ц{ySlߘ|}Nei$1pN̒K|q`T 4eCD9S96' 1UM(V5c tVs::{5*(Ɛf &d>M&Zd4eU6BbHN4 % Mz7IpDI`fxRXfdzOb m5jLp;ΐ4W3 kK)MB*qDk:jg-bˬw, 8c5aCb2$XbPc!f 1Qb7PU1-mD,[n8N=$YRi@#2|CK=R:; ! шkWUg=&H;O5tV[敧 J"A)M 6Zu8e,ܾ1xn!K%T1Oh3W2IDXտL $ OhgD:gbQ [a$CdEBZ hi7?a>KI֗= $IKw>![f &ziA3&!()[J( kzZ'^>:6pƅláݰGeD;NAE`姧?:}[ Mɜ%v̒%mCX]k߲N7 .G[wANV}\Xo`bscezB6*LWn-0# dW\O;wq7 _IUղ2hzdD2enuO~ƈi_hҷsxvv.w4}"$Y3(L5X~aWxh?߼$~2ucrc [gּAogLzϡ5x$@yr`,u؞°( "? 7͇T|Xc:77)`rDtkSLq K}eV&} 0ĭT'z1| ?vl8}^]c7k7>#üڞΫLVDP bo}:2c `傪vF*_s5r3 cO=r…iK:{5*Z`֔R*VE~8rhV7V|UUBbW퇲M}p+aL Ӑi3I=rS"כO ~_ay9Cxg&~VK=h?D&s.ӁOW"#P:2<#"SNGFl['6课^\J]-ZVՕ0dq 8/_ΰe=.ŔEZ!CNEXn$%Υ7̲KI=ϝ̛ utSqbZvdB5ydmW`!ʥU>?ŝ>dce,6o 4閈i<1lQIX㖐 } S9+sdD|3F0mUogMVƲ\_w_cv(N˅W7Y}-pqlɯμ-|OZ2k/4 *GZdBær9v4r{bPl^?:u#T[xC(Hb1\"Oxo1޶ ~+|iwmӬ^qɳ[P~^qu3\X\ ·9W{)>D4ˏ{ Ov+@O^GO' Biq6ANv@?{nL7d3mn"6CA+nⰻu }ڗ}eyBZ7Z7C?y ts^RCuJP][]k@Q#Ywd Y#nAټQ^aC}l1h6g ' 텐hTWg5b%ckz,ǐQ'Q+:狁u6Jc.sD/q$,BoIBKJ"]q޾:gB`шO0q%΂1"*An05 "#BLNCʡ},Juϝ1P{hTN+lҵ4 h U 7d(G8J#TUhs-Y9Ry jA5Lۜ*xK̾:a2z &" C~[W!;B[/܉.u:V]Wӓnĩ T@zINgY3YgϹWebK2X`,95QLYS'8K$|o[o=[A%Z$LJHb̨*CMN߰wEhzCuBa_Yk-Cd=`rDEj7PF"ҪScxi5K0&2=?Xͣgʸ^NTkDp C4"M &b88Pن4OaZcj@b; g <"_[8J텐@44VFi#eO-֮/}stWq\ :a)\/ҝQL} S?vwbƊZGrOI%Za\خf\I0Â5-Ɖ2m m䘭(3@z[y?{b`Х o SޘΎN>_=^ZIjA1z}K7{3?s^v)v&˗<ڨriDAEE$樦=0G#3ϝn-qFgBԣL[ٴkŻwe7tpuJ= 4(3ZE'6^e\ǙӀmUAtÂ$vZ\ r?ߺl_=W6ޏږiIu~Ϋn6;H(97P0[&\H/ez=7N_jЗdS$>&OGA\߱ϰPܺ1蝡`oB{sA_S_"8{_M"Z18ϵ&EJ(װ"5$J'؀01:J`:#IWZSofuܿ{7ZC?1ηR[2\Y[X-iOY*/ň/\fk![{Y<5T+m+^s0;%V\mXXżjݸ^p۞J޼9?9L[׼ĦImcv`1Ɋ K5Q b>M%֥DcȬMN3pw[o{R}|8k/|Q}mVͭuVĦ9Zh͆+5PY*`z[Ak```,h Ԡi}N9)HS!ĊvUo8jSA;4 b!hj :L-l{#3R7M*#&u9;kCLbZ(4fBԔEݐK?qB046HT*\ I"xdLWr]e@;5B[2wkiEtٞ 3ݜoLR7 k#U똦#a8jlRHfBb !+VθFtUMy|@ j4DL4Qft[t<-UH*׬юÓ1f6-`P!sׯnR:G\~?~`D>~9>ɭN?d(JW"YԒ l|v97Dkgu5pw.>x% IDATjnQ6g:U =kkD\hBxMO#B<TQ :c[6oRN(C :X6rkdiݺup,go\_Lg&a{ExqsWyo{6dCq|w$1؝6d  F&8N-d]Pe-j̨12zs2{(tc2^7mc|DU aGF]Эv`ؕ%v-p tGXw)e=$ { FZjr+<Cq1*P$!`汄~j2S}*1$u`3wW̒/ϯ-o<皶 xsrg6X دg?ږ?}>&}gk"2 ']yCwd^*zm23-w\3I]|[.LS%NLs~__ڱVRpDڠG ;#WyQ>/΋>}D~~O9O?ljᧃ=~蘳{>9<]1|w,#A"PvF  b]7W5U|U{?0^ŗ7C?wo5=Pt%ߖfYXҍHctIWWgm7*SfZh e][~Y>y#IF z䓽mzCF#RNfREYa.wwp~x`Vl[~4;~M&=MyƿFQ}7v1cWu Z; _da6qv+&8sd#Ü<4y{-m>keW:l#8AL#Uw)~#ݑ5Zvw^?=u\uLP_xw$9No0vWA'ǩy`~:,>?u/;CyhẈ4,3kԽ8vA^i 'U쳿Lg_|c$9zF]R~W~⨺d慛˷v \?$*n1j,]]io3ga+Ъe6| dY(=G X+n}u^ͻZ*@$nWwX=ucd\6J,ޓst֛-Ї+w_QkD\Q8*` '4x¢nƳ;rs1bFRCvE#,R/mlJZ)slpI1rYvv7٘ &0u4EdسX\/I2$eۃqZ84sxa7:#@T:p0$5LS?nj(܆sV%39OM`@R $Dy%ҤBMeeYOC]Aڳf+؁E>8F%Mڎ {TSu P6uP;y> -Fr"I`RAL Y^?B9Zx/+WC i V4ok%20mg]5-|sݴρ,Ag~e!ݪUAK'es͉P[珘矏Wͱl_<;^ܱ/D?yo[؇,iŧ3g:hh.yLƕDνP3 zv#Wohv45w{_=A \spEQ)?RQ_aSr;٪%/+lUjQUY8C'?\m$Q;I|`p赲h9;֥"*R僭.w K 0(rrgQTuIBL9,/rtc;4;1KKE^(#MOٯj 9緇%U8z{DruyDR퍆^+"Zdq)sA*q e )~Ol͈fGLDE@qYyACYJ 5-B/V)ƭ4rҡgIB`7@XzIR! !٠ 6!"{V*%,B"T@`'R)$BP M{lv;KwckҴ"8pZ@?xB%0VPU-;ED)? oA!lށ BH x0q#yg@@F='D}&m3YxPKA(Ōd͔ @4`*|h#%XAD12.@MM@<-SH3ڠ Km-*\L1 n|2l|=5 ]RV"!|Y%xGW|]/w`4S'tQz|w'`> ?ys:B0~p9#>/6\J6;5QsiBf`|jslV*BÄsω8ndPh5G[(^Nbckd~Q=rb߳(Z5n@XŸ@(qIy12miLbٛ?ank"Il@iWO$_*]|Łg4;NOwWdY ~.}ZNW`ovjo#a/^>/G;GOC{J|.OU7"/636~PrdQ &Ԗ` 3) خ ) fA[A 7P_e`~_af*x݌gr[/`mtRipee4m=tY A(yлhR)uNfeoeyN'}b^tkj):F{I~nw6<ןУ3w]η?3\=G/boOMcS!SVJhXPD`NSX"rj-l:( ƹd:6d]K;*Zn.YC(9fxZ(Pm"$RM .U,1uAi=.[gT9Qфy2`uAJd*qu\E|Xkh=&d a pwgIQ%!)S4ӑ#1i+~kx] *^^z6'ɓw}'WT O_wج{tmmQqk\J8vgǺE)%/i=kѩ{&ْψ+2\} gm2,nK$4妉fY2YjKRYMQ^SUA;k#GTPܷlݟ_{_|Cw._w*oՠwNV>Z{B5~s斷YzUyԶvpwtL=~P ¤QӒ};." *|MGv'6 ƿ»Cx|os1⩊Wbf R`.zh?GcJh\f׸joufk]#qr*\މTV@no kʗ7K$8HK Rx!8?ڧH) #C@HvW֙T91+Yeo:릉Fqk6, -ȃH&uU_L9^Ŭ&z$TyH‚^\Y2tڬISù,H∲8^ IDATM^Kw!/ksLc*_&1keI^Vn0IBm4Z22tE$6NK8@F +D%"LvIJ:@ J7OҌْQEJ@gQ5s5M:_\"B#7!XJ\.U&vDTpڠGJAFy ٤뺑##(J;A >p2ɔX$J*: vR 5#]STJ8hk| ,X/,HUvfyR5vx"PMIƮ Զ!xt&J ]#QUhff"l+x e 8QQH5:z,)kjC3hp%T4=z DF H!8\:g%©fJԠ[JIn;G4ƴmxY@?OES25a B0ZQVYXh5ZJG uԉʪQѩQ'IRΓQQiQ2chR.L.oO4&׸k\thg/* w)/_ y"/^|uq{Nȯ)+ݣYeL҄t/>C ]Zf$ߣ!v斉ҎzB 1:x]UׂXz[#E5A/јf]BhPURL|h EB0)a"1 A?I76VNzE$Ur䰜 =Kf_~-K4' L?^;2-ܗÛ6ϫ ! <5 HkVKB8[#E["|-f"]Cx KSws[rt !ټ j-`ո\cN,'ˢt;TuVoZ6Fx8QQeR.l,P,TxQA . TyV;Wx@aO=LEm.,֕~.Zݜ|MGgYgv?_ͮHC'أsa1%ڔrđbsV費-)PwcJHyi ׸#|i0p5C8>T6+ ^N)Zd`l%9BWF3^6] 2o旫R=Hblwr//<1xߟ?3yD쯭tGzn_Y}^׏p9iW^rⰝ\[k{/CVOOWJ\:zϼ);} /vlT _gDp|$%keWU4Sb:(oi2mZ7OXLCt51MP\^pDMfRh_cW <[w;>%^?$MbJ/k㝣;AFHR%hKU.YL] 6笭sSwɾM鈛WqK+bk<¨*Li2[ޒKc*R\,kG7X^X(э]te([iʡx1+Gxr]EH%X[e{q^}3h"bRUV+RRJ+RNE^\.,⢈xJ⃇-P(QayÁ#ZBV 3>? ʎuLV+q H]Jt}D%]V!iSK#%$BkE(Azf"SֆɠjOZJ).tpi %!2 !3WR|8w9PF5T H1Fj$mlW{wUg峱AWs~19 zjMyǷk28{b٪>ر㏞>u/~77_S foxp˥Wr(!\n.S3s1"mHʐ Z8ou% ͹kL˿0Nc| 3b1`*A[bzI8!am"A: 0&B+k{Cs=덯{};K};['U'?WI'RN+7?䲃oFsLB^ؒ׈ԝi`{|z|Vdkk/i%=B &;&JծW}%Ve8*I<~n;ݹ'MZo=>i̡ۅ^mthm슦/[OdZP91Eއr NlI5մԅ-sb$FDh13A`j$wH|-vh;X?q-FoUXԏ#tOWw1Mu(XLZ%|xNuNU/~auC I$S*|bɋT9#{8^JO=ѩ$>H!a`R4 ՌGM!H_NRs :LGN^5c;>O ހj:2\N[J ƕ+ qꨇe͑.7&+&MO 8HCߴI,LqbDrZaE rjT #({h_`&SO-1JZJ(#YdhBٴ {0 *WةjcRS)V:Š@ {u8!Ώ2or !"є"6*.E:I`4Weɽ?}8I'O*u+aPg3/HtG;.u"Y;L'y-HBR"*4THr;" lZad4H$4y%ZM^O( 8|1׮%2E$\Ђkg++n {ܩJ Mxqɓ$U=#4jE!I^yYKj2o~YA Ƞb&XU|Ii2*)*z.w/Ntui.q'qrjFI~UzW d{E"_]Z&i)#EȤ5;Jl'XBd*pqHg佌2Jqĩq=0[BG+  w=e J瑋C gFw݉229@)PJv$^y i0&[rDiF bRܼ-KKd:& jpNJ(Fb"jM ۣ1G2XW?-~`Ȣ$( #m150 xI<-Y_]*KVZbnCs.}$No"ޑMd*dYr0BAz6&EB Z! J7IbR BC@Ή+{9⢄Ɉ b{Y֕ݮ؏$րDn}(OUŀQ1LEi2*?. RS t\K&q{%"7zy&`b FQGqfsj4(:h ؍j\ `+ޫhb>H%s!B{!BR_\>nfF hXA6 0LsY]lB /*ȓ,&rJ:4h27XZeuXZ @8BTzD#*d:Q3%dZ\J[+t)AhP'|M\zQM V;%2iHTV+vN+৐]a' L Q`[`@*O D??zC A d i,|PhdM-Ts\Ewڠ(q4i!q L bE ߧIh-԰/Hh< gh#r`'aH|շW v`)L?ߨ3Ip+=ƅ 9Uo{o~y#wC9+Sʗ3.x2ϴ)W:x犬JV`\BH"Rh&Z/ !JW,Ȣ6*coEj^ڏW>5`'<נT@|lW2)B*&K\\[ٽ/SƁS : ˕27'O>~\׽~K_z'^r^F@jlf> 0{֐R&Tcda Uܣ}$[|F?IݎmÓn:\R.͔P@;4 M0=MUs?JQEPM;V{D*< 0ҕjGAS{XsN¹֨Z$n&>e^FXʞmJ;5l "歋iT_<hUK\l/'$z?pr6/hAocF;:*I$,+aw*og ͦԛDcz{/ Q 1vKi6Pט@xr|a}5>x/2=ώK-t(b|̨]9] _zd.yW俱A]`w|KlOo_V ~M8+* rw5+BrƗR^4);=|'~'ϝW_~E<ofmsuXQ _6*^-":ҽ o o/xP9>,zQ!f (}49Z(pyϐq7waG ?iaiY*ӃY}y.![&R(!Ȕ|J/MBJ!sֻ=lZq΂̢$Դ(BXC#}D(u)h' X]Z`eG̅&9'lr&1N(,!q5gOGKM+KIӘeFIb22]f:"rB [;Bdi /ə3tFBU2NT%ZnִWAn()'QN]m}p[A$le 8IӮ2.OVSTT׵rk%J0JϛT& (L!.YIER?'Lq:Zm0B`t#Sk)HB+5fDtCzŒg88,炱8r$Q*MpSh!R"pđـ79@M)E 4$LxoHBeBàP2)=g[: eDJyHy}S-lU*GLlPDD hxTc,AGȘ# t$;t(tK v( ^B:P5K*T"Tc%eQ2 UѤi교DQU%h(DUc0Eۮf>Ê0[J&t HML83uU}=s덣 o o>xg\xvk۞}`_7>?|X3i/K++ohLc&ɺĂ6QڒBYw(EբJ\y غ+_禣GwM|[(l]]z-sh $JPumTBX*ibմ>}'7O=}` ܾ}=`'6k<9VM> BZ5 /ӂt*G$mnobZ1xc*=.xLN>γN~}y\HcsXB|}WanfNW!ʲ+1Jfy5;<͠H F!/8]ڰ7@;q5Y\}6ST^2!/"ZS'`$\b6e#tnkybUE-5~uht2luD%~̱.{weK ѓ/-gBW $6wC{|]Gx:i`a/o;'=5$ / bƝɡo/sGW%5MOyKC)83,Q-\1lϕ&$Gѫ->ҥ:ُ\~(l+3$ 9Y^r韞<]녥['ϗq@LgqVdبJh. IDAT eyl7iTE>1 o[>t(e髳v&-w|[uu[ ѯVk=>R<ĺFmsM/ά7p0V8W◮e~OݱQąooAV PsNܺ|vqiuz$P  e'I¡8˭.1NFMČQQj:.S: 2EJ@aZZ֗$\c?O11V!e񤴝ןV'$n.V9j82bym"vԤT^`*I}0עU#M⼒EAZR vϐC E)6 †J*mlԄH55u]B&t]6q !td.͇RJ47 nJRP=wʹ={7pŢ< Duk+Z#$,MjV#dMCʀ`(-G)TuD "`ԳC DZ"d k'6-i!WR+E%Ha+FX!Q2%mOq~@U84N@iE?b:b'jlYTK`R5?7-=~ߴ=ݷGf5Hdd0k$.Wr\O!'RbDLcHaZݺ=q=79BT:^{k}BUQȳ{ cbFmvA%@QsE2%M$0@K# HO%A9oMuâ7#AkN֝|/دR[hTa$~18'?-^ m0)T$'\~r]&hs`Z\L!aGs2d"KbSL1D 戢KkR+Rh5`n xݤn7Q1Wxu,5&&TS؀hD:9 C^cs%Hnd`>4O{;O{!xvem2gWu{p=YYz~]g.~zkr_5P4=x@VPWqh:10x馊G /;'N35r@mWzMm rRP-mY44i?gU7]FB-o%Zzp|cI|o|{o^ki':'sym7Ϛ]8)oW]+QLgfӺޑ Tin~U;>ZoTz˛DCI~6R0lf[]?y}Rvw#XvmROҖ|_cE ?^mӴYe`{DKNKwN46nK p?gx/r_VZ|w2] ɼ9zC᾵ܿ~7NCS4Qf3ړKUU4'2'0WO;u[[;\=*Nr=w2trFAa&kO a!(mҨaڛLA$V(J26ÎNy7׶NO<_NI6j?Ipf[D AcsJ˔S]Oaϥ|cĕw$#*=s& mwwq*Lsêt<*(h!n aŞPa63 QlFp| jfFK$BT ȢXPV@! MFE#qbhBA%]* ʁ-.(DRa"ԡ&XKMdYH!3"pӈ~ԯy礵 5C^m܀/Wksޫiɕ[O:[* O`RwhtF0%M@Y J)9(灟7Mi2t'Q78HN;SAß o;1|C7XG壿(ZG;M;9:UH%m%v0Ch[ ka_¯v2"$Rs6k-WsA!qSmTj*MFvnS JiK] c_*K)EbaIaHi@ mH=fOu=0O2J90ot^W0*&N3p0o,wN-lpmgSr_e;R_0 -0;П Xƽˊ: ,ō疼ʼ%w p۴ -Ҽ2h>N. tHgVNם|D\Xr?q*qtNX岢(iTu.Ǯ ӶRk᧞)~E׋D'I~?w>O_no}Gv8~;879?%z0}u( ,MҜ^Ο\,ZU?4on^~xJ~鴛Z՚Ke4# >zbk>ᠨ٫N4cڔ!Uyl^Y| CJӆ-#0Y7>YKI#OSv;&yq8!q I̊0VٛV\CS:D [ḆDpִAfUk¥Q "R\U1vlbFuIOrȌ2]H\|]>ς_] `ZWA˺orMFe"E圈JYx* !i"^6A'[K]Vi?e5 qsoE%,uW Y%M^ D4QY!ąu|\:[BUG!)J%J ƈ  1ک(PؖSGn.6Qn"NyNOK|㳠p֩,Q8U9j? UTj׹vSäҨ*@'-+-ɆFНUH !2jyk1V犤5YjCL`QS Q0PPР nhA i,Ж@ q7ABBO3$(FA$h b4fPе%Ӗ $ FdmIL, $b(IjJyF5ir#wo6^ _|c_~Vu+F䖲ܛfX2ppi;1^k{9ɕiIM~Mf~uV˻;S vZ4 9bR|u\jg8 -;ݼޛM[e>(J,˒W l}wxgE;=?c)pvӳ'1+WG;sr;v ^0 /RoI (_1FYxӎɥ1;R&R85 m"ji{It]\%&F aw ֱ`IWgh%_}yA}@+gʀdTR|ީ߸㮕I1L;9>R~$IG!XmB[;K>n:{Uu1Q]Wv{Zג}R1k:w5|}U!aMvʢjr^.'})şݴL& 櫹Nlz*|hiڒO ^mƟGwmpOr?(5Z)7il\fR4Wdp*cG guh|'9.9 mu,' FׄI*(Ӄ԰|HݯVN\JŨUDY(TZi̸Vo>]~QiPI`,dDlմ3#dh}{5mO #і@qJt\:AƩє ^ 0GD iPUkm+ VBVB*~*mJ!էI[ah]" Q& ®io]HA yR Q9(o[tk1}M P5&YOۚy0\C=Sv0xz4)[NWq-OMem/Kk)˴nO]X`:s;"VrE+u=DVecOnVEvzor;(g{b[^/v䨵@PBJHx70_w+z/_:dK3T{lH4D!WV{@k:*Ͼo|]$|?zֺ1>oڪ뻕/^*;( : /.OjG[W0ٙWo[VL6jR Q憰e!vh$EDk?t? m%R椞BBB!%mnK_Pgl66H3I]QYL 8t:A9%L--km%]bp]eMJ:Sw|nSzU|wLGMR4]Q\t{G.7O6 "Um &d&\*O`^+rHq|fL¯XB]}ovov>;^^,o|ouh y<=md\lQĒ|{ [Ul>u^E{yV&yz1Ի㜿G>$DV*9MNxW2᪏ovj>q63gn>JOD]ȻgHP^ߦA`bZYt&7ݍ"EC%(NITmbZysoc?潯=0ڇx :$Eg}Qt'Y.R563~kNxUZ]5N: Jx}/榾۸[@Mh?Jx3& aipe6fZ4ܽFrpeKj4'1Gr?bcu891vl:.BlwfEAS{,v҄).]Fؤ* 4t{y$O_BQdYjǠ?@+ lμ,@'1PSyFX'2dc}n"IM/Kt䉍4I+VéXcM}y/qlw6ZuUUսiFJb$iKd)q>4Vg)Q, TM-vUh\FMhv_gn "s><(ϟ=wx^rf˼JM=HЄܢTD5`o+JPQ QYƵbrՏ˄"JkMVZ)a5Ƶ><QG!ѡ]ZjD0˔Z# IZu2׷߬*or-cV EcT::X_&}9pZ,t3YS\O+BoOJEF(8/PdYJ60s#d@Т6UPP)3*P-fB`t8gP;' ǁ$ĩcĄ{ A|} w2>beK`-TXN|eJ7c1 N\(q{X|iHuJ}WyA4SJ#v޺ ?-i2C+U@Pjh'kt?ݭabJUp=7$ڼ- W糇[/F_B M<ɬ,?}a'N rQqmd/FJ>ㅙ0? |NPg1d~w;UiDDc Z:Rt#ѻP[-S) XP&BX5=ٌ7I Fk`y=uV+S"xsg[(TkNmG\g"^.lL V V g&WtWꮝ'Ȕ*jt hڝK&$nI'E+!~Ei&*kw|]k?s쿡RAF`"V\,xeREag /\5 .{M#ў f+Ub_Fszn.z/nm8 TȾyQk&֡v%kP~'糷p၀ {Z/Pwuj{{&{gԝo0{뻾wW<gkX5:~JԗTf錝$:"A$I8۸PMlnsPڌ$D05eP4H4W&{sŰW2 Õ~W&"Ux\"GH?WI!cOs1C񂞁1 QDzHfZ1̃a Y7П Z/-"h1SEajG(Bˇ&"x`XBMDb C#:Tq$!hAU"6S&p KȺoF&wwL}PJyR |P1m l .v3R,^r41~t&J'>OVuYpWƯxe@AN#X/ s(:]r@ma&[+_}Wo~G+v;#nBmo}μ;Wiv;t+tK_x>c;bBbe3[?~q3ޤPTO|1Ջ 6#!4Ӣ|Y [BF&6GHJj4V_)ƴ缢@3o meYf<)ZlhyT-eR+ ;*KhK[Ɏ̦M[JdL 0sҶ,S8oQQ P63q0afocsrVRz!3ejs*qNv:**nE=u˽뿮R{9ʙ3MwkOVߨsz*|5iO\ÙNy2HT񈼂 *s=+,J=ͤd&Ģns3'YWKSZʞ_eЦo+ZvٛHo }n*D]9uWW &ah~=Dtq""!U/}xV -_ܙν;#k"<1ݭkw쮼y ueeT]:ERO>/ӱO;E QÇ[KEmmm8"^v8EmCi[>9m‹N /:cMjw>5'`(+=s5PKFSe6x2Mx sS(r6Zk^7T!atbKhP YT/*+ "A$F9R_-/kƕnH$b=l`s4:nX@9@Z> Y,s)(Ȳ a~O\YqZ![ΚnBqX](O#F1KkũMʼݖ Ay(DXXSl(&6k|U9FsǥF߳pJC)R"J 5͊[kȘ jb"HO|q3^]aZD+|FMR6N5~jHσPAk-XA}'0k (BF ouT.8C P&I]h 41 C`PQ)\UhҰmcj8.(V8Pbp@-`~n-HV٥$m k]q#BX.ԡ`Zi fM;FYFx6!mz!:ctݸ-ܫqFKVv4:A[sVk`>=$~{wMϬvgi'*G~ oMƝ~|7>O=ڳwԹ\?f}_ݭN^o\)^FУjV۾/dNbo6GEnA^=o}:q fl`d6~"''j^bFj9DG.ll֑R@X\ˆ=]K2۱ k6q7^Λ݁K}X#sw^S6<4`X9٨JiZ)I#0vLKi7.];K~Z->+#jݼ1w5O]Tj>*V[olǷ[Njֹ ÏGUwWbgؓfݐvڢnf$tHM24/G))P UuxncdgDBҺyt4vW/:5l8" ~j&_k]A'I6$(%4;Q_FE-];/?to5m6n&6 0}kלcc^ߝ-&&j-R}g|IŽMìB[MfܹN5 eU|$#McK _s]<[ׄ0uts:YJ3/ E5{c|4!1 Bnft5CL)i|bC#,R+-MV ]OY֍sYEeUDlU7v`C?vԕz^U^ky6XN0)fQ Y{2j#s6-bS^u,ˁ t%~EVd#:%jI3uw nO}*:XrVsOY(V8wlA)oqS|YXUo{r5 d( ](uteWκF7FUC-Bao6[ݨzڵEsKBnZCx鵛õ;oKߖ3?o:y\Xêxs=v]Z˿J2vX;jU:4^pzle_X]SN~(8,0_ @{1k1`ڮ\{wľGgӇnK2s)*ő!xjPtuU7x>8aʱϒÀ&2V{~I,7\WМcE i4EWa@|h<9c7SHT;+̷X)}\󮪬c ƀo)E4=Cl҄(h1HnUs~sHQk͠{͓\+ѯħ~{ޒߞX6Nd!yȈvd<t}QQx$X*`԰+m2v" uꖞ &z8 v -ˈ#mxQuZiH [73L&ϳχ._X{wwTּܹھ' Q6h4&|⇿ٝ'.]J= k%O s:V]\$ryV)f2q,%iڿ䵪8Ǔr1WvHuLc_ b=B]죴ǩ 4XDԄEJIxlPxh#٧/f@˿owPCﻂN{"w 4MmQ#FE!K$A/}V,G˓=&SM\P5ziʩQSɕ>y?N^cHVNY)F| 作nK2 XY XA[S@ 61MӒ68kNN+[WuVSWM'`&A1vlRv|}bS 룁\[g2ηwwn&Ɋ^ͳlZ4*ҹpyѠJ?VK+"KTfj9@2avyD{QTuCZ Z' DB{5iƗ[H.)tQV6@BA 33fjWB*9Ip( ṂP.I&' W5wbL **$O5#5`@p鍆uF汬=BKbH6n&&f& (pQKymj}&cRZ?JF$"!J4܊|I'7~<|ct/Gӭ_vM)uqn*t ~ p u'Q;N^9ONĸuYoin]wnٿ|c.e{rҕY{ŇNm}-\y&'7Qx"q_紦rvq`яl[SVR'`ԭER˭D>s0Ͻ@_܋`i 7m\L̐z)b D"}QђS,=UGeYUb5=Q!*E^g)tԗΏd˝bPyeY-EX$`d c1ybB&10j@b @H,-5jʪzo?νYՍ?ވ̼7{s>6>³K8׈gf*/"XjA ҆ZtRC#nH"׶"@ &كh#|>gϟw7;7wM3&-WT va3ӉB[UZƾ7HpQ=\-’(MOA`BQ ='-d8ut"A&"-r]_gb^@0 ;Ωׂ]5ԧCqadы>{Wmq3˷!/rɽ[fT>y=M ms+}|-lg({xO0\fغ:8)4-24~Fc<0+qLf{{{'xcuUNƇ鬈vTVzSԍ,58g"sE+iL pQ,:U21t `JIU T-Ohg,*W2jZ֐v~^-"gA >{DM[JGxJV\ 0}8.^,^ŗKrQ8m|pIGqr %hP XO2`> @$iJMSFLfZJQGO?6+襞$ַmn9JؠB,mUVhlDj9`sIiS2 SqsaJYaIc\LjBۇ (/D=zf- "N# J9Zx1*3jCpUxEz&T*"8EZYsPn7O0-^RFƬa5*v +YK`ə8{MnkOylw[_ s_VZj/{㜗_5CT&))ˆX ߏF{g$]Fom}[;|   OF^ޙl(~|/O׆GӲ>uK&nDH!b K~nNR#!?Sהyy" qDco:0CY|9)BX׀ܯ jWɄo__XpVTiCpx?_x\ !()AvlIgzu8Ct-O4F3oyGΔ"o(X#PNҝy)qwਧSpUT}(dRvY˧4ݦ(W"b'Ql&)~!LBqeUe痣5@s_??ώ_|} {z Wï횪\>Ȇ6̇+5qf3@L}Ƣf8B>ZT;f戾ȟpϟƶ)±^B|< 4q Z (k,1#ސ'Hݺ| s_o}}oXz??/vW_88x?S̀5Ktwܿ9IN@L Bujd._~͚voɞϓȎ+G՝-C+mFZw\q}o j08044JF b!7^`YUGD+7:WʿJ IDATd$pCd鍱~,Nq )p](h #F`}` B|Gل=JV=λt /UvCޗ4տ+,.oG܋!Z&&p2)Kފ2CΟ <$@"ޖۢוǧ.>$.6h!$ ,M8kNcDB݂P"` 0sL4B8g(ʚ݃} :qM[v:=ak{8I<_Ȃqil7U%E?'ݽ8vt]ԝlRgݸʢ,^#WWp+vήZ98yV kx2!x?̬)&F j_uUEZ2',uJ̗/H,+K$P5{$ngm,j~5i4mq$B]gw#]~/J1qU]N'2pZuu!7@ՊDʴ`p$nNtjhV TB8/vj#ߋ#eۃIк9JPWgRI8i %XK#57֗xt*m J 9AQ(pҚAZU`~Z%E +mJb?7Dvr_߶PtzEeɕ?ʢ8ڳ, CggrDxu8`<%#1lZ}q*UGDz';3 c0Vz?[Oenra,ҰI^bHf@^qq6yCQmZ{N- e0'.=`8'% 's 19Kr.]o26=->VG&0h*@hT-)ULd VrqQ$22$u5 _FZLfgWgtm'>c;#_77jц<3!tZC#ǝZqޛ _MTGmu[*v&==TvZjaz+P %ZIϠ[z<7ksjr5ƶ줼x$Oߣ*w;os?"ٳSoXiwBOu|*BOr܏V܊xIim ,p'D&[%Ο:@XYUzjw=7u[ܝMUX$U'OcT,1d=KIM|)Z/SpGsT]XkaauO@m*ʒo-A~-2u+ ]֖Pr8~i[u#}b640N:X\#",ȧEui: FX;) _6CY4iKm5Jy^Hv$6}^/X!D[pBx!xE}BD8G} *9YZXF5kRY_}ڵaPm sQğ"mPǜUX9AR]I-*.R({VJ@'QBXE*G$jD#ZjEلcY xyv6XdU# $RBd濇oQDJT8,Q i֪"X4EkY6Oro3B!"Rm%j)},~hԀC@aM˥-.D闠d[PwgogX7]?~;Ed<|dwJ+ik^ kpjA̠ BB^],Df,&ȴ]kV?¹mhM\ |)Y57?sۧϬ\]zs˦aBTxWK /Gۿ>L_Tw/_}J1|MF7FISQz?Ԋ=F[(z4yDLHj/M*~O Qa'a ʉ ' o3J ~C=[DLA(-hT;U|;|R8fF_4|\'wܒNTe@ûV|Zt{!eXDOo-%ikW< u7!;&BIimr6,QBp~u,pை$N9Iƒ#I(z%eՍS?+RYQTuކ{,j)RN@76Ơ,] D&,B"n*qqv+s?!Bh ܏W1bd~vL fmhD?瀬NXs*:Ͼ=~9uFQ^w~^J.֜hE6ADw.&*Kլ32 A6kW쬩*ꇻK&ΥEc&Wм`\8E5n8 {zNZ`֮K s'`Z`IBSUȒ B*3Pndmnv+DE^ *4VY'X^9Kٵ\^! 8Gc[]),;ˬQDM:ݶƯ{*m1@{X9*W II;fZW. : 4)=IȨ# Y.&VYqd W =R<1%WT.YeLtd:R2|pdZzU٭ʍ@w}Ge]dF—HL4b!h;jMG\#e@(4eHW}OEv29,ucx>2LE:_ķCeoz[]Z/X )IlJŕKꠜ «ozx?z~7o,]#kBVeb="2zEYFlkG{ZGWʪnWgJܜ#"1/N/Ta#M, u%]4tBKs:CRk >*gUQ-g3 %@T9y$# &@x}i=D3o53U"FxKP^Q,A9+lad,ؒV QԅB &W)}R,S!I8ATa%̢jX.d3FS{O-Y3%slsRnSA5ƢR2Tkf>~yb5:ٟXLfaGۧ,}DWWƱxv٫!n[yw\*qG&}#emp;ߩ`j0E_#_'PH Gl`AT\ cf! .gu*I-dI}ljWp@2kGͳ/m]t;MgK*ޛg̶gŨ[Z 4S&X*|Z$)vT[/P~5|ڭA RvP#:%t`)ǭx߄HgSS'җ$|'Jbo674/[UxwickWWLTYW[f;UYy88Z]ծRq|ъF3DQI9y?HgC//^5Bs:с|_E]ZRZ+5o_=.0_5zQ@Oʵ'?{Y[[) ul> @R^-7[,6]e[zf+^+h;Յ\?NͶs,0/gH;;Ͳ; ѠUZV"PP-XRr^Z7E^o\^XSZII.x9lh;y0!+ĮXKS>a87N>hBfpyvxg( ?$82!D@N9YrY:V30kd(ֆ^?P{ ۫OyL()-y ST8i :A{ M/b1UctsG"ȘP2#GX 1N cO3WqX0!b -܊[qJ\{royr;ql m=S%8.tG$yD⢘Ia*|+O6[ѡśju%eҜm=?#mL[>{҃??xc.2bV [B/aPy"Rݾ8Jti|`~xG>$Q~KO?I`;3wwR_D >4̸wBv{I}?qd'Mg4_n*y<+y&M <( l9 @=¼R]㶒%#A'!HvET값F H}㣸ho,ӲTJ2 P7BH ZW[L;^nT~.Wo0ۿf'| Ɣ%kB_]I/ɢӯrQnz~ºqtjK$yĴ'iGq8v*2vHfDƳ~&'ˉ hD %J  ]OB_^6Dy Hܶk^y88Gw6_wq+asK"֝:epMf2=%q" Hz~$h(qH;4?d/[~!N+2n}MbS0i#/^ҵvi|~)|+nWf<%h@+ZM+^MY4- 3>N9Uz . qw>D]|{90, gCάn?Kb"is78Ύ~\Q Ya:hIowMmԺYYW45R͝KWxB'`li$RbkgB/qzf!'$bCDV*fIQc]@ (EaCҮ$5PZ~pYscivv-z/׹ 7W?f}tj'{sW$g6~`w"äb~=33L'̯ˬ^EMWDFUHm|ǻ{-V@;nHdHC#!X2ՑR ;A r' h&AU Tzi+gӮYLZ8;hr{UlL&A w{KU1NL9 Q"垙= S!f0$>Et ǎ"0 "8PQCLuwc,X|fqQP+00'(/E," [F[3eFхX B5bn^ @ gBK{Ƣ=8 7`b_1Vxm^N;s W)M,PXǬ*jT4'H(ʒaSddQ2N/9oy\pg2m+;6mwV}Q^}z+NMٿߏӍk~t/^i9NTK{rۉOsevcFEΩW{O~.]j=ٿ䔔-hRu겞E9^J;.-yZ4@K{ιRۉ;Gg.]Vݸ@: _k mԱK ba;=Ż9(HrxQۏPHh7Jk"i|_/<ܣcGKF @v9Т->* Eˇa?A3EJ#:PKG&,C*uHPOJ>Up5C,ꊄA3!] i=cN'9VQbȣur!p53V)'t)tĽ) IDATWُsIweN; nzɈf9B Aw\'}c'd骕4ퟚyJٿXޘ'xWvJ{הNQǭMPɍԪ皰z҅LDJFkWG O *$\?m"J &:0gkxÉ;1wVkAUV &E "_'R\Y\0.'R6;trU6:KFm w$RT{O:4z9o H EĝU"/pJv ;J v;PCXE3cF& ;@";Nj Pk&hb'Khќ̡3ɹw*sjC6CxNG~K,FItA9LQdt1D$Xjv%`0XvG;9O$V%ֻ̫+ZaA- 0a|l8ښ4V)z)Sg[m}&ٌDP",4;4L: 0. f IeIrR*-5:`K;B2w2b0htZ9'ʈ6KՐ"JG}ut|JqH*b_9];JkB53^,SPWH1at`ʆlN&zp^4Zsm%ZN k ۟ޭf{wg驇_>rwt|z}'wztyU[4;/,:$*CN`X%ɾFHnM_U$1:".VZ_{NR5ijک݊L gr #؟$ <_UWz1I4S+31:.cQT%M[rGtR:9!lcK&f[# ɉ ۙadsZ#P>Eިމ%"%TQA^0<4fB##S63 G p:kJ-( ;cB=Ne2 FQ7ٷ_jipAySdp0 % F(*cUE#A=h8a3sQzfxko^q.q?pKNx+ǣX`N:V4~x=DEwB@kmhkL `:-d~P a?l.n?p_[]^y1r\u9N7YJ^XW%Z)b}#{ (H!hPuU)&Qlcch$Y(c B֗&g8>KK%hˆY(|U$J bcl>^Q)WJ.39čJl}g3<(8Эo(ؾuy:hv YeLhWZP )Wx7?XR氼']M~l{Y.i}5~ν-&huqtV42U$n u( B#')n*! )ŧv7yhݔ?ZPôV>|PxhX$D)U#/'m> !BIdUkɴ 4ޓ@-5LQ6j$t]T9eoy] zS4(e.=3Ϊk{NwhIƢj9Bk$5 Y*#38ߥ OF#oR$ h|z] e)}B{S|SW*2n Y p Gh \ PFlPBYFJaq4K47,;{:ӝ̬,eJSIj $ p FA66 0V8pm0 7tWw n5%!YZXJS̬t38ef% A(WF}s{o}ŢB']$V3be"ÐhЄRΩCӍm3V܊[e #>w>-cpw:Jq6Rhv&C+yse2vr!#yQ87aZx 3w (!D"*սT׻ue"ծuzşW?WKz;vR)R:uoo{GOA_ά]ݕz< в4Vd%٭O>8\ ~o<~B!J -%4GI5R<|$M7iɴNW}ge/ }?gi>8&IKIhì9p] ;$ /#]ےwjN}.s|D7Դi#1"rD.D2},c 5&M?&5)T ѢN^,~! D4*!i]!xrLN䙬ĉƐX e.i ^KeD(6",REj)=LbSS3CBI['L%:*y9XY)ifEH:L.KK&6(9K1w>ȳ=D[ɜ.+=7c. IBC 0!>%^x!yak7[ع݉}L?wʦxS|Υ?'WW^4CqJΧ}QY?8|YuGY9z3e7, '-Iɕ'=Do#_K)U#ρy jT.ړFVk2+EVLWF% Ncfb2V+BlQ56^ 2m+(<1ʜhUĹ Z\I Kcb )Ğ3>Mo7.ux񘍈l*̖ze?vS6]p׽K݊[7>aAڶ٥Q<\~7|QrJs ޞq撤6^$?Ջsj^x]/e9/H.1 ɝi -"͞X'$?HHuۃg bVVId22ɥQH)\\eylf{(E|#ѱS]DJ-]LgR:th=Od:rW&l`aHiW6R]v?>M˪ vj. c͕Wb)\+i/ӿdRq)J(;p9uGon>OSOzalWGo, {'Lcx_oL! Qڊx@R|﯏iuÿ]]l,'%q.Kݖ܉QQW R΃J ;t[yQGDɄ %:FX", .cܐLY'ĹF$=\keLT^-~܍53S.¤2 8a>()\%\ F$9I1xY R`4x^lFu`0:!*dg]5QS$GB†4k4& KxjBMf{4D 53*J,3 Le1:bnI$Wnŭ3 #Gwܨ ?ȑW je:@7ϫ"K+"@zd(!8|8ZVU1}6?.imsNغn19Z* ѧ-wwsE[9|ڥRVIX%=) I*DAXP5SԡLQ>-,Seȴ e<% !Ufh6sjbW1ڪI3_Vrk}s c5p\g &)E\4:eT6GN63㩊۵EI:H1ΑXٞcgRDy6C9nli*ңPda0r~|CYr` ܬ(I1x@ÉNؠ/5*rYԯTVx|@aZ!va|DHEU I0LR$Ժ JԪ| +^\p{n<8)Ց?KH~mׄ"'ǯ|XHMA}/&< .["a0)h%#>Lz/(^[/],W3- .~_ΤS~:\ȵ~Mj-ݑU8_k2dJ x x}e 4?>$tDQd:ZsځW Ua6$xJSk AdbUYQ_",P׭۟T0+g䝜%CSH|M0 i0  eNJP# qxDP iCO-,¶ Um)9hO0@DR4D1 4H%5c,%pc `iQU<59G\h,frvW%u5ºnV*vpٿ O}]W~c>ޙk/^It!6?`^VPFtAj42¼nS`k(VXλHHd"zi|Q'EOZ "rm;ZNu=~w+6w|cO<9{FO{V/>u - ZQ%C0Ҍ?ŝ[~PtViSw鏽si%9.W_onQ%Z<")rj: % nwg_}xtƃgΞ?|w|t$T";B`zO$ A·?ݡ@L9.zO;y襕z9X:,%ƧQȵNӗ42kf"B#򢴤N6n*"wc* a^aНHz6N 걂aEEg> Z1ɠ0w("R!Ź䭘x Qt{  ÛYӎt Q'FY‚_%dBdsBs)e 2v^ppM9Fcޡw@$4ɂc09Fo?_z>5wh۔^sc]Ӈ^@aSRYئI[$R bM 5/I N5VW_$hMygel_x׽WsV|c\&-s{Cm~g~{~{O\Tճ_u|eӳr2xe.ǖ4D ^} !w!ҷ8Pdu$BylcdV^+%JkMQy66%Ucի{(27ڟ(-Z$I ǎlw0d> 3P6Lr IDAT'tt3GB =Hvh'c&9 i?>-`uG==e7H;=_ӂ.=ࢄOvy[h՗i SڢMo}}+Ӌשi ›"Sc$^ G'y7?=;lx-؜>_c&[_Iӝ9L'Ot6W3DU)ш V'WVymw}k\i*WK?7ٴ8c+ߌB +]}|kyEI!c欄ػsz٥M mF[FO=ɵak[tV-2Mp"ET|cȧL:qrz/g.vQ_ULTHS}嚬3NwٝAVcf&Qi>ʅh!uYsT>#hLBbAh{ 68Bࣤ{A{2^ہNAHAb)Oʅ#LztX" p2%d)!" KHݢGZ@iSvrh)P^Mkq(@VGN^<ynpsvLZDN&bJRYwLu`Fqd16)F vǭr&"V־gߴ uh77d!h.=EDVz1zZo~Rg+k:&,V% ~xoπ|qvw^MR/O|P.E`luY`d9/J/U%xxZ'BD5)x0BX0 DRC [>TuCU7rm$:ݎ~ғ/Weڅ~̄mpu2Asב`Ex̨k)ҬvzMRIxe- ҫqA7$ǩ{q9N=MưI'8m-Y-kK@Pe饈1: (]~$AͫeR"%D5 Zz I֌jiT$,%PG^biK9Z퓅@**ʓyܚ^ׁq>`;/ q)jYPxG=`5H2ȧC#>(f*Co:%({ Ad{%R [f"};nVc9tY&H UкI&8!KΆqkGUݍz}laݺw}w}P]́JNEY{,"օ$߽pmŌ['sij{/mQ,uJO""h2dʹ7Ew@ $KI@I\٥S1 L^>`g)S@ M}9HńJ& i2K%!ћQɣrM,ZgpBe W Q}UO)D\QAG9"տ3NI#zύU/$҉r"1ca^ËOBj.i;-Y03)R (=aaJQp$p+~"Ę&Qd =73T!#-.ڶc)y[v[f$2Ƌ.;\Gmiܦ޸{H{OYq=^>`5nϟ(ɱ׼LɁ87L=c~'擴J2rSy5u u,X{hePp1*D[ B#ք"dѰGbwtS ծN'E'DiD4|I։[Dh-ƣ= طU@qM1ىIk>e#jVZTa$ӑ= Q3dJJ!E1Xj5ZC,Q47+"4c@p3T(;A 0aHEEXۺ|XJ8xCD2y%|;F׶O_}ⅯMg-V|5 `޾A=sit>ىAǗݜZ_ISVw'-$4Ռiu"I[=$Ee^q4vRR^2O?xs>K=Noű̥swnlg3R;=! W+ֻJHU yTGZ6Lm3qec5j藤'^؅^xǁ㴴 3ZQZ ֌>xsy#. ?x1 ?׾[k=O<8FF'/v өi)W`RJF9P"1V!Ffhue0HgYoW3[>ͧ-Y$w9`N^H 4Ka4Ҍd=ő~pPFvq(b`:Fkd^<ϭBec>wƦ:+ɫWuEueڨ,bVxĴ*xR9b$'jj+Sc\4‡ Ez cNҲ>wϽh]7-sTF-u^I tY仇祉=ɒۅk].-^桭ScQ,Pz gaJeU}Put_Hh)mZꃴ}{_&{ p-}rvP2NzH$$qCKBCɘW%u=>B)ی#\pzR,g]73WN=nOYϮB|.+[wvL U/V&.dB LyӈTk辢L/j#L=s~+*G~op+D}'<3vo|[?Z{!n/VRdtȲݽ}sQ UtJT`6'ێmmFWdڟ(}ߐ~4wK%d Yr*~V#_Ÿ`k3,*FJTlO &\}i0N2"iMC/:IC; ?؄$Zg2sV*\ J K^I:"7 D͡2)d֒p:Km FJqM Yptj Q'w%"+2@0. 8e@I{oY{ $LcP(P̕!/)'q˒#Eb%l&fNCGfXMD5u%0$(C96w+.p,Y?[z'񊬧R޶v]n1YLoDܥ) {u\dAL/$A,m|l:QΤɠ݋]-EgNZ6H@f8$DT"QľM=A3(jX/)d6PyzeMR8*ϺDjЊWBq,VD"c #G!d/$ٚ1!>H"XuJֆ C -Ȓ2XC\*A~eiXeBoe0)B_Z?µHJF)z]_m+jg?Q.}NA#R2`v% y]CׂTC5U^l<_2syػ!|z Ḟzn? U\pVV܊TqV+F_>hnܽ}Zֺ=:=<DzheTɴ`-:-BIYcd6"EVKSv4ZGy'a85>q, !ȫ D&*3l+Q pʳ?1@׵VM $1 JF% +i5I (]EzI 4o;^ݍb e m{v <ԳF8tӸ}kh[ɥ>n~gեpq{J WC&3ʫM9lGFSɘBn(>qIHp/zX\h ~TJ1!n2B0 5&!IUR,xQBYeI=|Ag5}ۄȬP7DIߗ!\Rw""6NƪqA؄@2XBj2VÄ[8F R+|%4MH"` ^2(2C-סX{ĹE`SAABW~DfL̈́)PcD1Kᨯkpxy;6{X"O]uw}nuN&^yW/N2&Y)%Ju0nc,t-ۤq!$7 'WVđ~ce&7 W;O<ƙKcib6_w֞խ$}h9ͧg$e9Rb'VY3}Nl צcOYqc=K/\/=Q%+Ez[|?wu3nm9:?8%GloCv_vz{G'׾)M̯/ƺ휋5 MӷS%A^wZ_rj>Blnz9^~qνP)!DXxvN=MKJ<8<#m0 mhER U}L6[W.DXZ8*,ewH:3uQNZ):d $@#[>W9%Xd (kU#&B0 /bH}o%NHld9ra+Ej>54*c41+OQ8 Н$EZy " #tqA Ψ)A~a nɩ : ]TfJ %,uU,|U⠻[ɣ%5d2a_"q~S9.{hҳM{8kU]-UhhIȀ^tVPC˱,B+e"h 2aԶRknP]]㭪;sM;D IDATHzjuoa߽yy:P!03LWn0NOvcz1nɖ!ȣvv[st|I  aB})ؕ%k+zgii}ehhzZL4NWvS)ЊR*5UfD+5TmnWb/ tcMt#yVM@;ʣjvL{QCan^! lJ3řBzYEF*t/ɭ5H*Rؕt"Qظ-%˘_8>қmbN.vnā OUOb8 ?v <ΧY+nQ)@(3IwȰ/}B.:y]6\ՋPZAH}}jdZ[vӬ&np\viG ׽q9/dZsdDe Ӛސ\[+'՝o=J:G<1#m%gUE6T{yM JDߛ{$*!1*o]gΟuw(P7V{z Lg8{HBkkPLt)%%˶;=MvF4;; f8<,H3ޫ#ߗJ)UIh=N5 4*+S44%#3!cOq"SjDɈNRnun䆺o *Z?hr%E`3_rM5 \˕vWn~|&yu_ nX ww^W*5̳CїHb$b-5 eW:Bx"PZSJqrJ޴.>v]&Nϟ=/໾Sέ/ܽ`W9{w|8Zn-v4Hέ-|wH*g§og'A\n13m-RKe:|c۵T`ӧ\"-Pl_ (9Z}תrRJ`i A*c?L 񎚙y(*e`]i ij)C<+pUX3ڇJohR;Pd!BĹdM Nh;Lx(kWtStDFj[z2"\z5Ǭ9mC@H~*6\^u"i:M>u!àx~Q%1K.qΥvQy~nTm; Z\nNfy ;_d+81[: K3ܻx$zN ǀ~el 1hˢ@ A6%. ;JA0//bH\ZcMup ;x75㷜,Nj1߼uс(d&LLjŐk~^ `_ u@wiwݴFW2{a 'ˣG_{۪Nz ZUZZ4:Ĺ_/Mmϐ~|ߎdٟ9y>ŧN~}M7?ËϾmC!c,vl jygGrŽ0.bۏc󑡆\" iA؏: AfuJy . Af_! `aP{ϗ'd BUok`L[ɕ z& J#c}5:Y Dg\T9 !3V-Ĥ)4 ar/Q+eXl'(B SDHWtJ rB4Jͼ" l/F{ç(@-V\TD؋FM0=5*gfJlCXWFB Aw;7N^Erз Ƶ,u{ȹ%" a qd f/ո iU9zcY5\i9fEx榣7v%bgT2V=V*3˩7fMI7fqa\]P:ip ZTtcp92kyCHhM0FdΕTޱNppʭv-QQi̲" < ,mVܤJ5\e_B*w;;h #2L]g WN\~7[WgrxlLCE ɘ}?3S Gz;mCƏ?bK:|~0/`\!"Uښdm?b#$.-T&BF_f>j!JTu,ϲsO?>C;n>xR볟}c9I _۾[Ḷ|v|~Q/H ϕ50WGxwiii1ё;&lHlGlS/.@g>mNZQZ'}XܝRn.OxPS# |߾WVxTFvau.Ѝԝ!d95RHT(R2J)LfpMj$^lC_/HE͐=3a]sr馃r{_+{m]A$$CKbAKyx2R.z~=7{䶕_Ń.&U`7> gw^y7u"c8XüFoN9ȀZ.8Y<ÏGj~>RX6HҚ^cUޫVX? x^nAEW !R:kxY٧ jMU,PQ;aXm#eM le'o' A'4k =, B`Q ]M@LPǟMč1Zu]$ywֹ?>yE*!,J^XB"(BK!EA/3,v ѡW9q .x'<'q2\STG4I`(\_v."L]xtjwkƢxP1O{\Ml}ַWȫXR';zAGT= Ϯ><|T=׎1z|=gՏ5I68H>&$ՄdIL5@\]9xI!oՑ_<^TB,T$ZX;jc?Y))ђc3{ky~:q@i}'RS)‰}kRGt<h3GA]&ͽ2;,/p #K۪;j 'CN2kMܼO~,+~O\_.АUO,7 ɸ4pkl_İ:/6Y ) YJ1nտ(]QImJ=oyot!~I9N__8w0'N&y7l:wn[_?{E B2{, ʬ`Ի6>?~+)lNw_|ULRgI&G@HDh}tW={~s>}e⸭ɜ^_tMf眖'86wvJk)])Njr_gGKΟގK|||yߗ-x8l'JA!n4k`pnD-<#Z*~tHĐ!p X0WNv#}!̈́TB9Q 2gLeA.CR 5A8.3QWBy0DeQh#UB/Y@8-ț2I%1l r iTWδi5^.̤P c93ՂK!|vD#F*(\ @t^F·#r>jZyoӹ%`{6fAY+V+,"*oыahл6hd3 R)Xs)n}X@jCEdeFD-0QQ(@yJ8E(zuh 7"+a{hw%ˈ^UlFh2D*]E &26H%&Lb@@ : gQ M*P3 30g=fgdd 54Se V(.*$:\FQA8kh7 @7 r#[ c >Gł}T *UOp|ϥ!ނ4̀N2;~+ŧ2pc<>;2CR 8IDV֣naւRqP*!(H4>f("c%?2wLAr ? <1Oc/x W?!JtfN<i i j^=W#dzƅH/BCKEf2fut=ϭ9ѕ?z+J $skQP.u %*gerZ!zUh{ܱ2u3V=uѣZ-Q-/VVIʛut=c0`gW|./#ﭝŶE'g^Տ\$Yd#%OHo^FJPob'Hi7%^`}RAqM~9cBNmmoz{86V"4kA㺇cs4Opl>|(!^|#{p{_H-9=A6G筤1dLNNI53UCvdJ9JMiôqSx-i*;neH[[5k{KW?.D7#N&̦RݮmRKI2d/H3rm&BjKOr|Ĺ{.XwR^]y;_f\*ŲG.!z[7/~Gh\XW-Fge,Φ6Rr]u~oݫz%{zYypi .J~:;IM?x׬#y x~6?o{9rPY1;?kw2O}rK*[d~ן.hFtQGI=QJ>I{uNhQJοĴpFWz+ڹhB( ۷i-~t=h=v}x_!!rXuMS!D `{A/+ Mba]d qLoʧKNMC!ybW@b?uƈ{MKAZ2(jxw}VF6B~ve-Q6DF! r2Zzh~=o߹IDֻHϜ;ϔ#?5*Vhf7HJo^I4sx8LI$ϓ: n sw{_5ǟe;5~H2bCH.G O?M*< e>`JDripxaj]-KA;l?n:x\.?ȣuO2e]J٧-2f"z)F%>L{1H5(=٢pbi,TPt5(*\衃ch'4RĨ\h6I2K/dZ UmWppa%t=XDQ3?AQ]F-*o \8+%_!zn g:6`N/]41(D Zq5vI$6ĭ_)iӆ8 G̰#;&+P]2W/n%.De4]TD {m‹P\|$k00"mAzm}Q,m+jrtsb4 `Ұ])_>A p#a??&FH(2N; ±{䀖L*4LM|Y|s ^l**\Y݇BTVg1r|LzQO(!9)ibabuڶA!FʢObd(!^[I(BI&$CDt"^Ef!J]zdȠvzQ̍/77JVy `KXY Y،U֛Ab;j:6Ssf{H.i4')y*R)tG#R;Hj$w6FBkOZ}tZ&rCq(24 GGMŔJ<+'.fubo|y2!x m?ݫ> Ňc|wpȉsOBlfZzq@HN Bdy0R[ 8$C Z7R?Jb -^ }wO<~;<3]]&覷/kÅgY6Z[ɍ98q(Uٯղ(d6M&R1rނyii2z$s(-fkkۅ 3K 6͟8ur)+H]m61 w8ı;ۂj *ކݳ} Ib^ZP+^}^nŹefm M_ їI~b{1x^?9ܗνȅ&"2Jpߍf's)?tJSe%n*⠩E6x;aجd#ѱ_kA{l;eWy&fM->?ohes:ym.Y@]PgF gw\Q=EĮ5!YF"y)F[AKУ'ZH]Ee)uRT{`MgH'ʷ  !T<t(v ,sQ9m"rۈ`Ί^-3U9;[w*Q{9yы EM4ԀIX#!Zl"e@hm3T!jNbBk%!;#e0:ЄRA;!xpS2Enm{BTX""$n@ּx $:c'UAu |r/F'`A2vl˅.Z֨*燭]'%lǬ|V\m'Otӟd9:&[bzca:/~?r/#fc73ٳBJG)m_:m_c9$Dstb۫>~/;EzEq9#-BHqQiTxdˋh\uxkZ%ǤMM|y/xKɥ^=!kl.3"BLQYצs.ì G"N GjuL9[sG Q ' ]Hy4&fխ ̵!BQDBH PBGxs_&jߠKwrSÉskפQ_~E# yGÃEܪj~V6g?cTJ"N^I"8 qKRX<r"BA*C(Y'#&%OUǦ>|>QHMTȕRȚMRfdyWd$]H4㊂ 8 )y=n/vr/'y,E' C"yc瞬ֵx4(F<E0mC&.ף&M՝w@*ߛm#$sN7g|4?_>mwut?8CG"ej[Ku4傟ݚ.JG n}9$W4#% RF@  jŤ hk[B]GF ò6ʧAkÂ҈=v ):g{jWk  .]\)yr5=%g茏E(ERk/72iy!' )":sGo~dC* ]E V71ḧI|J}d9Ye r ^QMZ:f2%d7F QeE{uhhԅl0hAƥr"U0x-)*A#+$] O%8YE"iz!Q!I)4!NԎßq1(ZPFE!dENK&omlw٩ɕУ#MS"^5 b$H2 FUG3"5':Lط,uNFi(\2~ؖ/W78&~os`Gy{6y޷cMA'jI?"C A6̡׷%vWE'-cLDlD\a#Umb UO/_7q7q'$-lA9!е7:*mC`A)Em ex,J:TZ6P,) -!*n= 10lls~PruCN^dUnS"ő)bYN^mf'*DsͮʒI"YvR1>ض=}],pv7ڶ}5LVmLǜ1rDg8El˻_kO픢n#gx`L:~dqA:aY[LD p[7pX,}CvͫBsG7C*h$xխb.=wOO!`7e)1iNfb$&5DrR~TZLؒ r,k "u7[y/LTR0丟JF]іcMTf(18+nّ CW*nj:heT̈́fS'4M7X颎tD0Y]* ѶjGits ZWM*Gm5Ҹ.am$-ΞREk`!@O`bˡFX^]&>g@=]de\uQElG1۲v\U"͐`QYH BlSFdJ.31ZYg6}3f(tPVM2z%L[?f`aP/y PWV)20 H*Z&MQ(LZbW4D5W]^2O6` L +1kZ4"YL֐^U1Lؿq,7.fX'utϝc\ Ӂ/Ͽk~}5oq<<.]=ЕlBۙۼz)>{}V0U&m wFQ=+O]8W:}c[L_]^VՖͰ}h9}OLJYxhR֛WgJ穼'ibc:!x;s _8Nf19979GF ;|ˇ8CG@WYlތ7j$ _YDs|auAV~/?y_{WnkW\o '3Tn jɽD̝%Vf'ar5{Ƅ$̥kHL;ƐͫǴ/o?s%|&ՎR7,up>T5:'z 8bO^c' ϽUu7"vl]]!wFZxn>4.gʒLn!-#cyW.m5jvs{'3Ȯ-H! |QH %Tm!`UqY.Y^9q~yT'f*} OމNr:`flq=)vV0:ȍVy7۾‚"6P_ེťz1g:@!Q*a܈iIplV )l0!jϜOwhX@rc SV,^lӋm+ 9 K1µa2 Sg,寧A^̿:00IVh] |ATt`\ݕYM3PCӢ8y9`C,Q;QY_6SQ6b1B%Z1ў`FOK0u+(gbǕvBUSeB5]6M7o3Wei^W#%nr|>'Gwr0 #"CkXQ $[0&29BP5I~?6[(NXJ|C>Kec{:Kw?R;31XvCRRpe4;BK1&fMCĸγPXkx\29Je,K. )yv(]d|I } ndImH17]lH74+jꃇG:uRtw~<<ۼI+~ҁk^ђ0ymF^?%ǀ"e)rltɃOo|`PwcgG=o{;v{Y@o:I ;)Յ9/ 2+IDfL+GA&ILܞ^ڸG=a=xsvss:Ά<=Ar~+1|p8xf|`3~~rehss9 ?ErS_@n>`z9*O{7my{òʼnE ٻ YzX_sWvMyg-k%{_$K5ts /KQQ|ո(K!\g}*au]%kO0FIhclnn-I<^/xti]8ZwIӇso3 fMb]USrvƴ=9izrm}|=w,t IDATZ_IUT+iXtR"(dͼ{1:_g!ʛZ Y\Œ-+⍖v)n/gc(R/}4w&5}G˺-:=⤱6j D16K9{-]VH_ҹ2]-ę-Ixk=> z2i2r v-d LYY x,(nlfR;JC(ֆ(@Ue4$l)у6LӬzuݾfRhKV@96`ڀ@p5}I*".dinzRL^zؘV^ E7cPJ6I‹^/]_k%7 YўrDp89en\1톼r`#ZúZmȁE!]#aH&L8"$Ej Iwc*@t+|ۏ^^3>^.:ROǃ]lE.Ed@| x ._,?=QA1qh {3[X U;oioO],H)dN@{$?4!RfɄo53Hs5RWed,ŎtJe;?ru_a`~ԩ#Rn©S'al^%qkxzexrR/fkfHVc}\}(-d:Yi2-+GW w[Ci|_<%2 Z=SDz]v2ba+Xݟ[ؖQͷyqsw߹|U/,ͱok;~O$˵Uu lJ q]%UUiv%vq4Mlvϵ,b厕CZ]הF̃)%c&>3Χx>ޕrO9R]| kfv+c++Gؤ`T#Ol}v]h *rc=x,+q;sin)I~+d;jW3_W;;"r l#QebEhWi JEՇԺ4V3Zm]ae\76Ƥـ1Df h)%=m+̬Vv|߂釕z "n 3DRE ŤHlCX.f\#>uidM4 +7502'Qi Lqސ?36K&SW)N;; %:hxAvV%gs-gY6H5X0Ɣ6x-c>cUJ"̞C z爝g{Le+,yR fQA* LgmM%Jζ-'k Bf~mI3Ca<v)QYbbJj*ibtK9<13Hh*l*=:KG³G8P1Yd!~l5}cv镩]<:zmz[}'sUK?]9t3O/7`%jŰȘTi*mA6[J⍪@(bPX5QrQ1f]O]$>J'}/^vf&4Ƈ /F,+7uYn?SN210&= |s}Lj$S=jyz1󳇞7ϙC HAvMbx~tzzz'ug'z|y/L*^Lt^]䱋ߓ̜ y5w|è ~|4ǀݚs[%ȕoEѐEWdf2r2nӺEOy}8ZQȁoΕ:}ocC CXO_xxm΄^9rCɲO;rarDPqZk>xc5ݴP%1ŭȲ>$B(\HԴ]דR>ZFttpeUWeȨQY-i2~},ˢ/⩶}IS \ ߥG#\ƍ䁕o,?1~$j,/#k1'@Q|%bP/|_xt:/{0z Va5 0Bmoz\%L}Ď"k@c ĎSZhg~EgJuma"EotdmvS qV)n@^!Z{BHE)l-4+,* _X!҇HzӶa}szu36Mߝz~8"Zё+ J{[x r|m/]3x Ǿӫ?SNӧDݣcMXQ8HVw:l{k>FжBGY/ hNJulT1[3提^خ@lema{4ϪaL^m-|5ln]o5j\*z}RO?YLՁncf }(P_{ߟ?vsr}~?<_?Y8wyue9sে翼G <}ΐɖMןͯ>|/]wo"<̀་]AFZ5'euΈdihGVƘ{U5hJbJı#ˋ ӹxܾuU֚JhHf(BR}sDV*{^)t3%|a_d̝8v*"H]2b"\`ĴLEհ*B^|>H5Ά`[eĹr뽺^%Y+jA1cq҇ͽaV1efYWp)"Xv+XBR6]BB{D*+9,wDLjC)iHJb[ÆhE5eGXɦHq^-wK펼` &w86ɨi 3%q_ ZYse1U]3VӬۖ"3Oc#$Mf,=xnђza}ct_vy}c_>C~ EL0d[_&P5ɔYr~mQUsbE1bH OͶ[Bx4N 1DFUMm^HvJzw>+zF005"WPoXj o8p=u5rϰ:4]wzhm}{Ǖw~9≍m :{ YA2W*’X*dKa<<~6@睏/0[ka5 74|6TǁIЛbyD]};uvÝ;pݼI/V@n]~lg-nqW-;4V~\/Ʊ [F>[ϑWR`WfZ!2}xt]&jcP4*+4fhۖ:"xUY, t֘8Kz !G۶=Wg<&jxo1bb;K_{ ǛoW#ڻKgeY#˱:Rh4&Y"b91^W kg?u8`,$re&q"-. lw S14uMrBoImwEl%ͪW*EaB/Z5ɒ R1ﬨKjc>#TurKuX҉y^VyelUKQVrI_4l Ӏеc:*u%dJ1cф^_KN Ni:K纜M χGJe:jۨs5WQn| [7L ˖j Rd;wRĴt ibPFcH D!6ZdMfBM2Pi2nb˥ʗ5%%Q#19\02Ƚq4p tEwIsUNi ]{w~aηeAi-};\[i!\ *Cmgx&~~7n( $QËXy'ɫ}ˈOt<};ȕ?onD\xO8u}<!YEb@ TEuѾ늢(.WU󚅔jlˢP0*$UЗoxsg]Y29wۑέnn<8o&f7Ed=u1rFV$O \:}̥SN!kX?}ܣLέ8xeSMыd{Ya&/X ٜ-r獘}bfwmOwϭ?%P?ːw>rmz<0T;Hve\l]|g>)%n`볭Q#7hȐ$j؞nW_J˕>Yid7.lTZ]"_cW֭cJ(o¿k~s/\[^WY 7>r'zLL}[ovuo!3Um'CL 3^DjS.v!H,VY`[DZھ+JO_Yy/%8KH1#cdb,;V~nF3u]d:~zUFVEĜ"9`3YD–m)Mg KK^p\0L|A*d;aKJ8/d?@ _@Jb 1Z"Ί>""! 4*Et H 6;jLȉ3~eqbQ,Ӕ" Cq]lI?l9 `ϥ6"5]J20joCθ`L6` Ķ(8$h*:lPt`KͤUTb&hiHאpa"*d.6Sj*'s_'9co*@ $"诽7w>vÿvWӷu.IBuXS?nryrvO SN1!r"l ;xFѻ=6'plCdQzZr{ONU^kll.qmO7}?d½dYkosx] <;";|/FZf9]^v{sOgO/D$?O,^ I_J><'nPOWo*yŦl}#'>uE<%O=_8ME djkVU>J "ʠ;Y9:Cc;{uYkC {5V)JH fGd QhZZC4` Y& $&Z Bo$em+㵋\%"ew vqŔ`[Cܱ"0I)e&!:UIzT Q k IJ3’]df:meM8 )4Aj\t3~HW(eJf`4.smh}߇>oi]}߿7?_˖>e' Lxd1jS=WUBlo)hMJYMܨ}vl}&zŨιCQD N}Q|tW.&>}̔ms >˟{>_'Uŋյ.?[+"۟8ڐ39 /Ⱥ%K&p'?y !ƪ IDAT%w_3JW{j͵ l\oq&7@!`Guר,Q53B1R*^zM44'Y5Aߚ*;:du-2&Z+R""{vT}׶9b.ѳ|PYX0m QBI a/ cĘOw8IXvjɂ1֋lJ酮4̬êяҸBHiW9׍U AVjS16A?`7 SY1bkggңCô7h^#r0/i{ȼQě1^Ғ3VA "$#V:52WbJ,|w X2 /e^"O5/w Ph*AQ]f@.ۂ9@Ti#~0⁻ &B&اE Z-̪nt.Q^:V2dF ZL0L-c:5Ԓ(ˀi\n@A8SuE*0_a:2;!z0g$n N?*5ƔhiI(=z>؄n)AJ$@M>=3#X"=pT(*b"BAZAO^6x~{i?w?A9t#j׷GG{_?DGVf5UJߓ[͓_'+v5dMI;]դ TuUD>G>G&gggOGGKi^ls/oJMm}_J7?u/qR3^Xlq=^lfWNadu< 0;}n}pE21°Pb$ߩS' `O3[ ; ~I vN@'Yz}$# 3vɲڌ~Jc\%_{3((m+KK·)HmMZ#`=ΟڶpÈT}/Yŵ#bFRY>r3-K`!@OlPrh_ n #IpJ rϝ(-F`1&UCx/i".  Laȥ7hHa% w@۷lABC ϘSQqQK,ۋaU>ݯwG/u1}Jg}cy y{cr`dxh: JO_lNE -"!/-Q#`ew}gx=U|v/8\j*Xz;sc_dQF=k k0_ X]1(zwAnN [nX;#3,gnF_#O:ur6ԩB.9R:urX+? $+m%{$tr{> r>kVnrx@_SeS죳:_Fڠ@}ugܳ#t2߬Inh}^mk1F&wpė-ꯎm+* _S';ڧ~Dzo_3!W  7|W]zaQSREƔ7aZUʂjPgu1cFr-,.HXHa1ZcX'풱kmk0HjQUM(lw6DYul:=X5ٮ&3 9t)4UudbfmW84X&8hMy˵Kqj/je6C҂p3a= !B5g( QkIϊk%Z.`b`R) ޛۖ}gûo~- I, !rۘ8124vc+!@;r9咓!"A!@C#ߝϴ^k^$Lu==k}aC.]Zani}QEkUW s5OنL9*ȍ##H!!4׻+Dc̾ -]s}EMJ"KIUnD׍bh:rkoX9"|ѵ_$X4㪨D+v7!fR[zZ1$ʢٝl-U WnvI HOq`kYG(=S/9npir_ؒǏ|υmG`ir|ki$J&L."TH#ѹ1+{U2I50S;"'D:#]iYd;%u{o?Pd&J_egu>BU#{Tp9f\Үҟ6$6 ,ں16T]/fi7 @GU*KC=OS_Ir`Q,A{jz#q_#T@!iףA⹡v!Lq1a W-1en )ⳋiA`ub:DPQ;mEi'7ERPIkcY`\eVd+Ȟe%hID,w e"G 81 }uj4u?ף OswkxEcs-ڎ^"6 $`2*mOuy?S!*_rUISo[XCbl ǖ#Yl/K#Ҡz'rjiҬ)փ"c{p6.ٓDn'Jf -:F&.NJF o!` >~ܗyM&^ sS@+f,qoXt<~^_vS1ouHʗ.ܴ-))E᳚@$1̴S彖E@( F[Um|Qܤ R†-!)uu UYiS|&gzkkUS7MAEUVz=9IUUmg $c4e)qcG]1|iK9VҒ`|Ea$n{gf=4-ٷu>7%^^n{n'T3;Y${ -`cJ=e,N=V1 $ALMEfBytJkS2CScFJeA2um}zP_E0"_hR3Mt1C4j$ VyHk 8ӄ`>^LWuj'k}!YZP#v<r>,Ԃ`󮳋'`IdmnN)#bIJ| Ti#DH-]Aɀ4 :6=aIl_M =Gb҄gr{G#J51w=j9M"9`plp)}CQWH' 0F`hh1:07Qmm\k&uIw/8!^xo-{_-n3ʻ|*T}#XcyśWN kSWM ӗX~.Ϝwk''H)ȩڄ0ӭ4 d`v=q5Nխ|/-mxQOt|g.;_?@0-gCNe45 LO..@&}xS /b/bv-?䅋3}C =[?2L>?XŸ2zξ0\;5WoXSa]);g%lvK7P(V}ub͍l\o?N)n֤nN1P$967WsɪɚLhrT9o ,t?!^8$^89k{f{Xˈ'1Fb75UY#;Y e2g}E Vwj49^ Eo,pbz6MҊSJZe6x[[b+{Yd6,w41tPXS"FwYޮ1bU闾PЦ)h$v'=åDk`0EKb, K-*]uBSBY/̊nNd+Ԗ/=/() Ics4OOMDި}F\ zyTyd|Ԕl]Y/StơW,Dki[GL%qbFJIDYd @KZOBiSQbL\k Ww,~kwͱSB.mn-Py%(% 6=P8]:ˣe;e jqqJǛS)qUi~룷 W Z(AHM%sEy])T8+Jŕcrv>9 \=!rռn=fS'skFK T';džK-\:wg+[>woKmS_x~:< \|/H[d+'MLVdy,xc&uoԔ6ɶQj{'+ղ)OTgKg>ȘtBDh49NOokϬWL9uxyˆ4f3ЎșV"דs~n:m7}EW.Wn`8!qk>(dt릓ʩސ+&˛m)JaV/_8>,7p̌<̙Ksu-7Pג0l۩j7bCX_Ic-2"<zWx>>rw?/~o~xslzcיy] Rvx}?(l+{½[u}d|hOz/"x}p)n+n;\w"xdsy='W.Nծ)tpsWNʥl2Is IDAT1YH .ɤԟ#C<丵{L1yP[X`L*~ ж1`9 <]SaCQĔ `r^;;y:BX "6ؼS_%@D{AY1ڶ%uzorܟA|^\vGU{HOU]ڂCIɦCGYi5dތxGE"2e-EO,7O$A,j-n^< )Ɓr5-Tgm!M5d!xZ XX{1b.1ۖTS9l,m)bQ%$+Q̵ˤ#J<&1 [J[dn!y6ҧ/*f  R,yMOaUe_yQ8o FY4mat)K^c[ڲY'{w΍2Xܑihm @z `+Gj7'ֺ{[1Z6\ޥEјfN.'tb9"--rEB۝T0m[;:Ü e/a ِ+iU[[0PGV'X8!^㻾Թ~ҳ+:,Izx=J|BDւA[W/>w|4|}=^L*G:maS.72 6%bWi<~&\8-3ryk?Q/=T~/۲)>.A=~oNٿO~5/IML~| 3-i̎/~]1N9I:ťD"+vb` ܇%a8!8$^Ix?S)2$0eA,D[עߞaNm5w87^gnO_ߪFSpy-͌XmLf\oO2kN&_5#[#ӶuڕI1:wKG^k2ԵG#++uٸ'ܫrU{-O]nm{vndEr},s䇨 KW'<=޴Ϗn=<|H^}q vupnMdq~f "GT_yuco?s߳?8dz?| 挅ij7QwȔE`|H^+ ɓ~-w֏\ j ?lwɅpw9K|ǻχy3?w;{T_ȇrv~tB'с /K.fp~oXX,,R S7/iz-t(ȹomjc4bzWlϦ5eaiS)ynk#>7pnOC@E,- EYmӀ1 \U5!1Xk 7uX8:| 犥sѲP#`]鲠x"ZAՃ5|spyOnD`a/ߋ,'sؔD8q9*=g8jj"R&i"h"nlU={N@f84;9 4MB5gLyfbevfzy b{ j1#&A6-d AwBHji'˾M19=̶;{\g<\}֍a-7JXp辆po9w}uxkH_S7r* /'rˑVܙ8ڌ.G)Ȋc2d$ڥ̙=4F&<0X~3Sx#"kbDXd>yJ}\Ӌ=?;O|yBW忾qe<:^Wb^o],ragweI&no^ G7_{=^VmAooWvWOV3VUWi'_t'>;?a :|=/=&[~֑ gkG#sǺ3{_>ѐ-dA䪒9۪h^/|'۹O׀1wEχ| S>,AN3;[mYd,v`Ŭ` )\(]D^kⓅdLU8ʺ>f"1$8U ܹ#Ҋ$kD]A4 b~IPbtĘ ~ID hB066Bpxcbh[J/`G3O挳bz{N &[{jx`HU- ٗ&H`˺TU6x yaWlTv}ܘg/Oˋ=m,R8Fa1hbmYk6)['h=;@Aג5<`A^AFUYeZ9 \kq>IĶgSdTmrP ׶ 8Hm̅oݩ7[ʴ8k'#gJ'ɸ\7HNA,;؎@Dm=jF̓gjڽע}v|VW[OoS?s^D~H}w|]`௟JC? 9in쒘D";q:޹敝O+{x+T_xo.sˑЏ~DzϿٴ?[pj^/{륝zo<^Wk>(e8y t9_ LJL$0|"Xٟ\WxHVU.=tƅ'j<|3>rՀ:/~/~[ޚ?>/[yu???ϵ><$qzew9v^~XټfVׄA¬sMiKm|cۆǝGOFZ&RJfYuPHkUU~^ƈIe~sy'HD|a : 2XSO?WcO5⦴XZmJkKY[M>h'O[lOei2 /8խԗhJ?5S0"!aXE9"<ƚΰPjfTK$VDV9DJ*Zp׳JNhZOBUFN@'-8DKM 0+{-iLWɞ.S+uMnM'{jTx +D mi)! ~):$8.Z$ LasZTHM]שHVTw9J]5(m /޳%`{лЯڵqkhV%m+[xua/Kj+h~"Nl{!goVe*}f>8#E%ꎨ v"3DH-h^G?IUKx9ͳ/\<6pf9BEqgOսwY)VNOɅC1ٺ>:!7wVQUF,`uV5_io>uį7_Z?'P"k]4n'=@泥ssd+e_G:<{f|.r۩k{FM%Ւ;/'Åw|ı3wO˟g~zƷeO3 7aB ^=n{=?i}—wͯf_)}RYrRlX<~o"L3^D0(K18cHlϧLۖaQ⌥Dl I. ~b$4-F}gl,i+KvrNT3SU0k˄7D?C) YoGbn\1W/߾ѶY) z8C93l;zeh+=ڳxK9x`ݗdZUB%'Ҧh)SF T Z7)!#kXkdEQz^OptщלB+؅Iw yB YsE,Y)] q/JTJD`!Hz9vו+zoa<4/?\)c%S_/G-nAu6I2cRsz6ؘiF֒J)rm-E4ۜYm/mivݽ\>OP?TN/d:q ǐ*L%{JJ Jj$q,D=kRt&F-\ᱍҮt#phI4Z%l&҆|De1>#@ Nə^BR$I+ʽg1u ]>"9% ީqma\V-4ވA\;u&DBOmqcV";J&F#*Umd!̨p4̩1aaN}y%XFCQq}eL, Z꫕ 4=1鐼:!^Yxj}|=[y߯Etzz羼zӁp_OU[mI[;~<,ۆQmd`^yh]N'&kԣW-SU3腲Z .E4T e!?{9<;߭?)>7GLYb`ƦgNpZgn>uǘL_WXe|~DyJwmURDň=(W ZC)oDhچE,=Ӂ.!DڶAD\@B9 f~sAQ(*=1h-yjȪ+U";Q~.h&!NFK3F141؅G "%iRFBhytne+ ٓC-ŽYzݴHI)0h$Uze/1N5Hv%g%\ã9McWv@S#RRhK3727"Fz\pƷ!CcIxs`{+wk'ZT{e6TτzP4m)\})3YoiVdfǺHC@l-YE%= IDATJ#!xR̃ȳ}3Zg~ds&iX  R9ptqcvіdgu;C wx{9WVJB3P!HeԴTnh @nm,$vhd$JHBR5W wq˗UTHʂ;WyoDqNčc{71ض5Q1 Y>x H"ÄfV1sk`k],yWRR@좆6mb:j^NHCz0<&@T&Jtqܕj :VJTH ySxI8lY9he((x@;kU k,mY -z)k]TǦKn,YFۭƤmeI2$;8m*bn$נ$"!&"H5vW` X8g:oxVkdr={W6:zl{=Y7ټVG>_b%Txg^xaǀ{x['=;8{~Kn?1c|MCWmc[jʚ Ôl^,6BJ`VIҧ*QA`7jOh#o-ۿ& p(n˾9Й{wط~[Gu_7ݏ7w%ીo#>~{ Mipೌv%1t-[ mOi(B\3, BJuAX#j{uآmɅN}eӶc ^3-h۹*Yw AS[EՌrF(y9x$2*;VN "I* :Xwf 묨f.kNlnpt&wJSo&5"B5NYX<\.)^E/qXHs:]2!tb&*a4LIz$'8| OvM+ĹIzG`A$xcT\6E)Z1f[; ge`=AqjAkWjE$s 99=<|c)Պj%J<]\:ѕlM>o;ڟ?m$w%XIl}=c˹-2xB%P狪=g۽{|r@}V=SOӥΚ ؃Enă3*2&u tG0~|;?{ O9?3tT_Խ^]o~o'Iwh~zvGn$|ҕ~NYZ= C^U2 -szylʥ<#4WDjjb(+xZu(w8gN,%!q3@6沍H%9DOΉ:A]9pxpvG;ܪi*1KiA"]:kZb%߿e k/]䞪3ahKO۩gKz#τo/;ֆoOMlu{pPFbI yIYAY? K)5~kyst4o~/67/o?NxsO?C:s)F+\Y/>֓916XK~-GEKY֥iM6izSagvkGE!yS5UUa`E(!1&"0;P4IL #, Z+EީmGC(st7"gRVfyrrIb]TZڭKΣn;|d|tos?&_"G}h{@ތf-_ǂ]6'GT5!ΡQ/8TuŪ L?Յ+cY&?}&jѝۙ-ީyqHf:g#UR5Qɋ"˜uRdٰJߥ1`&I#F+Wdb{3"t=?ÿzSN?/J.PUBTܤѭ*AH:{FňϴQר)tnӳĚݏ4q\:N&236TH5Ի 'ggsgY$cv 8;f3VQzm?\l9X}h b:MU/,ޔ,gT1(P-x%kC7c":U6G|(IQb ]2\UaLvNW^Y|th Z̚B#:J%", ~~1=b׀\mm"6fHOJcI1'D; AG"*kEŢYNˌR1:tK^/m P :tJ1Z-'bK,³OIw#&gˏ>unwKlsqʿ󜻾UoE{#Q Z7VG4i-CCb춁,fAؽ/ݛ!tDlVigS ya٢(\U7bIX᝻m;wv O֐VV[Nd_3ipq'*g]زEn}֛oO_ o,v<=SYOgU%0c>_>Μ6rd]ͅ~YZPegj+_TB9C.B],M=R{e^E~[P j{A{ ebۮLUռ'bJ(Jf;U+Yfޒ9$ʲW4G`fb.Kwf/ ȑf)hkn}߼󏏌ZW߃͋kSc6w#-i?I̅p=fC[rbBW.e'xr፷߫?}]ѵƀf'kA>4qw3x`I`=YqI@%<yZ|ö90Jh[ܺHn(}ZZZ9ϴ;Ĺh wjBDSJ* ؄%3fQB][:ŀ)+YHow火'Q}BwKr4pN?lBd#ĨalO$lh_{>a/ȡ{޻yяnnDd^^BCWl᳛]y pn;93ӢΤn[50ƶίHjZ52ҜPM9bU1B( %.PV-WxɈ#bxu"Ud-tWasno$T I=udizY]J1<H2FzLgŽg(ڸ-o{ۯ|{O]~̾ ױiSrᾏYV騍4vvnl9z0jwjN{3QT1 ST@ +o)2 )"! ӶogQ.;'ɟڌAɅ&!kLCU#zNe_ɦKfrly~Qf\."4Y3lRS΍ qCl"&7i Y%-2XYE 4a`b\kXK:s)'d,L4lŒPZRITs^">0j`Wʡ#E`ł4?ʱڮ+9 .$~K#q )qv}zDOdxV@ gskkzࡏ[K,{m߿w;^'~^[ǽO֣G]nKEƒz:z埽H!s@S$T֢b g *V*x2ˡiR  Us1E4)&MĔkɼ_]iN%ARmTEzzks 1vLADOi8~,+[ /J<9~߼WzqP48CCGMw'3%@XeM6sZaL2ځnS_u, 6Mײ\Hߩ&:f(I؟ύ,1̅[ " T*pSb_14.WbZ)cL*N.wDR*AUI:9i3"ѽ&f/ V/|yo>]~ppn- MfAZ4fCuQH9iL m"`-XlJf7/pC/Q=IcIMT3Dn&Uu (g5G7 c{v eVeV눌du[ ʙ0f=TwrYZHm)a{J; FX!J5lN0#qB=nT{jsJcdU JMOM DJb6YnvJKq@@IA3'>VKK =Ǡs fPKb())D]ada殊fzd##0 n Si7_A tV)9Mk3ٿX?s{ݚxlK,̠'^.m?;sFnwww~C:`#ŃH$_?𖷿UFxPMn{3XG)[o@qywrrEp=Xb/֓#M3c$%UrMv5#+vbdZ3( \΃*5~RJ̹2sYwJ 4mK ቑXmt~Fmh6k4vtWgn1YbH !7+V7-rm/^ ;E郵ɧPnmmQuwu/_Gg|Yvzqn=/ڕ7&KS,&@*CkesT5`x!-8Uo#kXוv~b˂ZHȭ;n,F( CGZ-TXFڅbӆt~բ) j{ =".+R# ƈ 1@DUm؟ѭd Ee3zR ƚW>)u4?XZBsrYɚ8F[q[79'ݡCRKI]E3ps!Zb1+YH㓛;N۬TsYfp^܋خBmcR &틈 RbYe}ۏ[閶7qf??,٘N9 2DXY yF0$e-1$v.0ݝR hTv@I62RV8NTk:z1bHc)e`Gvܒv><njis*As~'y`> BCCFeQ>-aA1O LD*-&3 )at}@JmnbĐ8W_uwd;#Xb%.^dPƋJ:I;k]%B"alt)pS&MZ9{Ԕ=ݽ3kNS~{;}׻mGR.WV+/ey_j(/'%"cI`=yRBȞϑشDCMCìi8n53OeLښQhs1ygؾKH~YOZGHi{жVA{jJ7[ԑ=ٟ] Yᜧm978WccLib IyUPUZ]8|4^p1BmĴuse -f7 Fƒ/7+|TqxO Ū@9sR,Qrx9QBC8'1Y%Y@yɋ* tq3jT;oTvE.%b*`[ŧZ`Zr%JN/ȱfؑe" CLbDpipskko=ۋ5=OW20 MMRrzۘmLA," T۔DcA $x(.^Fe/oiCABF[Bь7n IDAT˧/ J$THټ-DR]J` jZ.Y#$'P 64TLQ'I\`($"5(K |x$@ME 8Ȝ:J"Y L48ĊJTjлE͡X H&dPzTkiO^&BK,ė/^_}迼f[\Q6p-oK?﹞*т*zKۯU[G{Ux`.N~dC3WggT $imܱݮ?fOo8)-V۲+=22>8<~b$XK%dĽgv sВ964<.M c8KNW3~ݗU'u6E;ҫҷ:5+9Έp>ђn rpmgehYiThQ:>;DAڦ3=,$Q EySkm1UB?i2LqD5H\/J\|q&-\9M2Ĉ`cE]E޺iШfcUrΊ?Yϒ+Ƹ["FCC,@B]k8⶛BhO<è )&s )j,Gf|ṙv8c{+M6]|dE{!+>Tfug2RCR_ hX =ΰJ1q[7]zsiP^ꀆV2iLpgxό3 wk!5_zV6V!0ݙ"1B vQ_RjeErJrrBд ulP55=U }xiJ4PrND &d^T0<5tŃ ': ;i88";X ;rX )bt 8f֑%/ӠZ}c5˹G^{~Y_wDK, 7I1H-o?=BWQp4T-ppwڈ>!Fj宯 OyH1xyV:=~]v]||_?Wc)]0eX5cΑYKw䀷`aJB oYNUW1hZ!;@X楩J6ƈ{r WJ4m ًa.3p縑3cϲ6'@BjY+"eUML)fȉxfgcu6f+Fd8l@4"2M#RBĵ-J;|.`Wg ;,dNiژ4ƘY00H BA"BH$I# 8ū-p  r5`Is4O$Wɱ")Hg drskC/y_9Jʄj1ôELk;NIrbXC RIGӳ^Q!&EWKMQO(؇;`&)C&7H]M9hef2f\.ŮvvR&(| HMhf&QZ ٬iLJt};2(/sY4Vp#ƘLw&rJbMSJu3,ψtG$TcdMͼ,+46cZ-G^ @$; $#DD"#3s4BdDbSK; S<䭊>,Q_M)ԭ'#@brg2Qfbkٴ[%O?;[[rJ_f[W~:K,qI)f1&S=y?2WI o>ܽul1;ww^:/y.s>%o?Pۮw[7韾yU1QDLMuO%XXONtf=_$lFL)+EI6/+z1EҠsTM b(UVxawzU!@h]9h"ԳJ #Xq y!W*ĹV.{WqYGδ=}ؾO@ih<fgtCW}5B28QEmS3jkl sgŢ!Po4!#.>=*8εE5I^긬R]X;R6m8{fmF0+ƍZ)@@P;3m'08gX1k"&xw:r#зfF3pnEjpع8"xv hMJ5;[crVf!Td.'n)]ѳ!'gʌܪ]3DDKDh(JM @ç6)zy b$LwdThhQgm2X C[Vhch.i,Xa`'h6[ֶ8 Ȫ0&1'#Öc?qSno|P\ظP^xK/K|vfӕ6Te;S>k^]'X;>P߄D &v;.L8ɿo&$uEozu%iX⩆%ČX^}P5 ]MMn,IZ6Pw ^1\YgP=Z jjipiG]2\beYroٔ6M+;G;zʴrXJ\ )Ty-T򌨚X<[8RࢵV<1*"nʹSG1E*/y.xW"K}&]|'\XJ?u(woǧ<8wWs:DNбJ& f\C G{>KH1f+%,4'Azg%`!" >̹!Ub!u]Z :ح,I$cpus*YG9A}IG1,f5H#"}U=""Cs̴a׌$hH6Ci2g+Ra}c܌lD%Gtm" i3 NMivZ]Hdrc8.Z6&Y5bMӬѕ4z :ϴ ]ŁG{)f*8aDH~ W63^\J6 9}[TmTlqMK;Ǒ|`͍L_L3Б2tF Xgƹ![KK##P5;EE89.K'@v 88"52eBCI4f Yl0'$hdlt-Qsdbh'AY>ԟ÷NgQ"]4Bmꙶ>zk]&o9Iɰ闎IۖK,C#\7Fe4Is|ߺNW7u)gNI,j;=< pߗqK~n3jzUIݞ%xaI`=qȽg+~COK@sV:K ] }zޣ>fC ukmmI)!."0Ν/6\1EoF@WZ,bmZ0P%u]yvCqJ2 =Iu[xREcy0d@A!I}uO)FDBfrN=UFY]4U>#1׶A:Z{2wgzeȸ].LV0{{,w1\(3Tξ]8oy?EG۠CJsru*)E51I(уH].LfEW&t&jaa9b8o;GdRUbpLdp ۰4~{HLtɴ-+) !^lS/B:@,&ml5LVDi4_s̐(=Zø+HLdY";I-f q&?"h:f;?N ?Ll# $SaP sa% KS9jY1kRBtkJ[Xo;tt4ijxS= f!bLQQ!ԔM6NDb.5KAIBD&vʂ|fq4̘۳,k ӂ%*";7WS2%r0[cj$xvLɌ H8IuNvNƆT>p{p^>vۑwGÐdMN? ϩxHJ~OX.Y1xË~zc%XXO^ )(3=B#2VEvkNՠ}WM=WZ)抖&۶ˋ=U1UiZjuXҶaB0y#x,9HI{Iһ{<=ь@\a#&"o`a0֢X9b^9Ң7xl,2BayØ]A`02:ft͡=?2nIh@y}+*+3++}=^޲4Wf7L*=BEDR^D6]e㼯 k[mn-\i EvfX܆C{{f`zϕG~ gnZ Z׺̋aO8׋5SO7ѣf )HZ*6}ί9(rPW^ /OAi o5!->+H1 B̥4ӤMNfv)F|R+Hyucb9NsneH@&d;ss 9"lTY8V Y]'ݓX\XKdJZxPuFQ_DD;4\kÐqz-d*@yG;4vҨ(䧽hԖ@v)L dMï~2#@ _TAer1*Ѣ,4]ƣCZ Oc"@g:9 Dg;s6β 9 X`P(,”C=`%56H:t9s`F`9dʂ)5#Zg<s %%H#h:3$1\IF c-dRlg*|!bwuL0F:R~o.3V>JzV~'-3$Ӈ/r{>Z<*K m?~K?fŊ'}wI3LXG_kѻxዏ;rpx ;[߲:Xb󞕀eApק9rJeɭ::ՊͲ-,r"ĢmYGJ&H9I9QhCq;⪣%tJ9Ĩڶ.Smeq}Ū,>`'ծz7ư Glm>cÆ4M uj wvxݶH)~IΙ i@tEbl6X򸮏+3O0}^*xTWj;K 7Ѽ?tV@(4P;pK+Un]v0_-'ah>HLnAⰮI}cF$*-2lH9J /賰`4(!]$}!\VI ܠX q wbRr|ޏ4ND2$TРnOI rkf AIUƶk:)4ZYLNaH$"4B dO:䲸=ՉٲٜTYmt$b8)6CR jwqIwkW("鿡u@9DaWzY2-]rږ2*7,kc|f>]G&쳸bD;!VJ*465_dYgjj i95 }tGOZb$E#,-g> s4R!`ThlE&̙)S'dR^˹fpsC*>Ã*5߶(.ͮ6yw>[P7No,b`d~:( jtH)h*2cyb~Ŋ7~W~i[>5{l{L}.&oz>0,wfwXbŊ+.ҋ ՍwuM4$|]yݷtL5d0)!9xUöw)gRd,,!L7F"4MhrHʽDŽV*cd0Ui\׶ԻTBQ=uuѿ jf׍W*Y+t!B,kDU-FiDY lkm֒,X__kS{WY1UUUۺz2xྻyzΙ;N;}']vL=~֓.^.٨^T$\xr^UYb;2l|x>1G~͐w$鵯h'~aU++/7o Z'xW|ZO~+w{ͱUρ/#qaX!`iʲw3uDAAm<:cȂU}fU=0Zѵ-]}wA(B+u}6I.%kեFeYS"x/9Zd!eD_;~hYW(T>$BµPʣmc?xbR^ʲMl6c:SE{n5Tw>MiG'|\+` [ChCnL&/*5Iӹ6n\U'|w +]hdLEߢ$YޟS]@ur.HiK;o\wvh8>{?JC,4/9].BBI*`Or|u,[d!yeP WU7J|z2^oIDO|` IDAT4Q "qlc7[rI)g\bۜſYHZ9z *$ZsB쳸@h2PdbΔRyC1seB@,9ft͈ Gmz #(RU|u)lVNS3vXc_:>i7ebXQH)Ŋ8N;_ȎJiϹEWJ 9VuyOƆOJo h29-VQZ:MKO=T9@+I$9$ÈQ.LЉL&Q sу9Fc% &I>IKg31Jҕvhy\0QJ|7?lܶ.=z^I_V>[3u{nǦ^#XOz;yZ_O,~tx!ďďM}ϕpu-ڎ lLx-VGg[sd}\_1ߊ+|3G[%wsc``i2BZTHw i9R(6 @=l#r셮6xblI_7um UQ1kBh-C5WJ{-;ZH9FhǦZCM뚔RЂn:uj4MOFsYAKB8Tuk.ʹm#;q:_p0VB(*zEMywo!;L%SF3Ը1o":p BmU{(Qw igrx?Pxct1i3._ Zo\>8C[E43;?o ;s(/Ul$k*kC9sL BiO]N.scKHM}uG1 s2&tIpUஇCj>G:ȑ!CWA 9I Q^YRQ"הQ 0Vs E z/F)0uJnkBQirmLVQ:!(:4!;  -?k0݌bQ4G䘢hG@;B1.J%Ov͑5EbP PEP ƊTY'/[jb6BSHn C<\eiveeyqӱʍ:-Lɱ66Y@)1,e0b2:Т)ArM4kL . H4I,BHO&b))(F[#C췤 d5*mj l'uQ=;[='8N9{vϝZ[]Gg9? )ws>\b瞜_ aC q;8jnZ;qSGj=C'5WxV>:{qUԡ9usA;_oY/w~V֊ρ|3O&wUUE=)1WC˛#!rp|NĔ12Ӡ(3Ji֊jv4UGъ-jL eubQ.2> DhjݻnNbHv{79f 2tֶ= ;SSkR's._tMӖ{{K'sژ2Fo޻>ypcР $ B4vsdVh>yӗromN[kF{9V)% n"m6bBI8 /9?_H`?Gb͈Y/;ҁRr~mK?q{eꢚͬ]e;@(K]2RS]گjgUۜ.di8GU{>yG] ݥ&*ANJIPދao;GH Ck꣐nhи|AiG":_D% k1%s&P3OTp/ GD21`ȸ֑֨֋dmpS֯CO#@]y俟I"%"RVzuzOi*˼ ]`c)BY}505nq\+tOF0C=u$aAAu4IL 1a-eԌzRU]d96yww5>>M? @#wd+VXKRY7uG~ǟ.ЋZeީuP'5w/vWW>_>uޮX|~8VS͏9_v;Juzav#O}gc]WJza_ n'B*31dVVt1%֌3YehQn:y AYsdH(Z$<##-9D($s5Ħ˗Zu)]BP}yA;%ۓ[=TDUU#)&0w1cǚ\sxPQOM )|xӣK{kv ;B30Z`csmc׫ɿT9"E%*F>QJޟO{'O ԗB/*BF')tq8y͛߰QM_:x0*5֘ 1~Upimo3L[q=dTIԆ:TY߱/b33 Fd1e:J2'e)e܅8 2eUGΨMdrJI^®>`1JiADa襈LRԇNjV1љ9%Q1e2t#.$*FmAZ:QɐKI:Rd~p?liŨ,Y[c5:ń. R JSD2v,{&. k4eDic\DvڕM5)VBwH+?C2mYrBiʙNDtCFL@&cP"/iV EERh1CBFGRxAL&˂9s^|lfyӤ |ٓpnBP\oqG.NN}IU;r۱bŊ_VH櫿^{k_9gx{t>ї6]Í ˼S^^W<+~_O魧g| R+* xQZ+;[2)9BϧQ5m.l]#:ǢF+-^/\ ?ˢ(:܋`SRlc&u 2!Y]hK3oɚ"W!aD94)|et-"zNvKdxN3:~3eQ I1U|RScKCoιx+;J)-EQ:q.s)2Ƭ&M$2LI[romm~9p&@̡kMiN r)_ wz8wr.K;,v_<\qwBjL@)&SOj;ȉIBe]ht)ȷ){ ZJRdE&rUˠeFֵy=\JCR6c<, ~;l*T4P(ǡϒo.|pPHuZEҚ?=k \b|0Q".KM7q?[lUc]Ti?2Գ1KfM+2Q}HtFn"=|4zV"Cg|9YwEt~6 Z̾]KsTbRMr} d:~U>7(gPX̘5(M5EFeqNdiqDLXO֔ñ`N|VÂʜV QMacI;3⧦:TwHu4)rM_vLsд >tjŊd-|nb++ĠG?*)_|qW9o'7wiş`y+?sq#|q3jaJq(ݪ|uŊO|3sk9q~O:I&erV 3HmVZš"Y >FRJt1C`RW.s>#Lʉw +1ڀj7Z>t3N8NT e9D)PG]S:$\~:uUٺ@S9S(Tau(W>2W~t4o!Ƕ6qeu`6́z|n h^Nz^Ӗ=kK[ru. }cooOF^Kulu|ծS|uعBԔʜӧp%\K W@^4 07,]Z7oXzJSu}kdOSmۅ-vK*p)%eKT)HHm gҧՁnTr:46mL*$ϳVL4siʶ  L7oH]Z jm36"D'bߎ!8x:H,:J(! nU[E,hL{8M;\5IY1uobI$ i֝t)!GsozRWON3ft8ƌYcAC:pzL<Rl,1p3M"H&D|BBg_X$c5ꄥKɟ;4a܌:jd7^/o6~V*.LĒsKAȵbŊ&-v_skUuż .γW^._Ztw/?>{;2^>Nj غa9tsooLj7+7۟7_2F&w6sŊ??|xxnC>#IybxbzT @j[ TC.v]4mX03 בRfS6# e  `LjĆ-Z}ZEXs5>Y1Fq$ ^T4ZQ8h3&񈺬h۶kZs7snEԘmSι11FsumS9dc1C$Sg#OY>ÿ eӨ_X,'\s$|z9$BPhBD۴uJÅJ3[[kHV"E26X+1S$+w9$j 9 @s7_rҴ;Yuw^ʺn:ӈRsl2X^s8!^aP?R6h:/9Ee0v׋ S\pA–fD7뭘afk9E\he~/CE"0Cы_CTô /-@4Prw-3#9ZDJG !9g-h;>Pef9&S69vBԢZUdV/<WçOWVXŋ̩f@ 3A j )ÅEήW<g|Q_ܕ_^+>/X Xg9o^ޙ<< uε1Mc-ƪ,H9cˈȲ$oTYQ{]eķ3H !H,{hA5YK BY7tH16n\K++v1^:1cL-9cu1 z]ǩ^$PƔD(]J)9ILQ\#yo+%[λąLX1ꤍ74ko5U?W+V1_-+"JBNƗ+!puf{|})`4ܕ7~Vnş(j)Dw%~ɽ:Xyl?fg^{Ow;}4A$ۋF)ivb8Jmы3k~^wTfV") NoZ% (-pChQ&K!cw>֚Җ7=N7pUIQܐ-2Q)ݲw^*@iN(> y/i Pdyb:s y]ı45ݕݽؤ%&uEsJRt+]pX_Q7-P =Kum񴞤2K +RY%i;\m .NG:L +m,͵m+)[xg~k{[@٢{E7!X ƷLbjp_.a?iח_X07΢ބ as/?ײdsȐe=ղD)3(ȶmRb HQ9U ͼF9UUvɾ[@X$v3͂٥}v^@@Ub1hw(<(Uz:~>)cRYNTD".(g|NKCI(h,%C m-s`lpQ}]tĞ(oaxd#QC%q"NJiJѴ49iӲ 4QQ#qC(E/h:B#}UQWY2+tb0)'Rd6eg|r]f{}KX=?zpoln} Uͅ۝ 0K3~3#' IDATz =I/tG ,ur]Yb< ս8AΫ_*/;_*ewm/x MAdKU R#IBTQR3p;L<%,H"A9At&qR^j m%vR^kޏQ ܙ# ;"ydёJj՗9N8 gtj1"9n8{y[ncZo4X3rt=_;ӳ[=߼ +)R_?}ŀoV%؟wo)y͛S+.a^9Çݵ\bŭX Xc~pS/'FŨ&K71ЏvRKTHJЋ)ˌ _awRθCʲygT(o֫r)Q˵<'E) g_Z]Z,Ӷm9Zn#"ƨrNR"{<6!`骺ֺ4Zdmp>& Z~B>l=3a[fv:I#nɍAZĜqѫH2 .կ~Пy_d(4' ؏$wq 8M$)1Ouoe4ˀ_-U|"y=oʡnD [ o~x^Ȃ^)n!qw8)!Bt>;D4(Z䥋qnbV-$&Vгxڨ Wf(eϽq{,v[i> Qx+Mfvx=c:NPiL$><+LM~!( XÑX0w<Szb/Vy(&J4i9LLipR,3$xף-32$ߣgCIP/P|,[ޖ4$[3,Ʌ&AX#F"Uy1| bvZHjKϗؼČb$ v)Cxۊ'5+65!_7^g `<\sG2_UF|$XΝ1>ARNEEŋw>BL5@*}&Wk^fkޗs|**^T ={O_`A[Y_ZEpO>#NcJoݐϬʟ# !XEgp6a/?vpaugX"뜈 L,Zq8VF*:Vq2Fi-7I \Mup)FU !9缵6BB4Ѩa* BeggCuun&qsKW8(2w97RG2##_,uZ|Xq69`:WQÄsLMI@;&216^:zmRQL0*lqS/g[=0xߣ o=]%)K|Kgy}tNaj^Gf[S{mQ FX]퍅U{^/qxqy^if55xl8up 5JL-E+9pq ޘ$ OiuEz1fx8)&ָf-)ږXEز( E3iti8R(JQ`mS6Bx(!OMi(vŶm֭`߶m#?6^_xjs%~I*eu&Z"Eb.!^F-qEBJⵋY8uѝꮋnH K"&noa{)bc<ھ08B|kRk8΋6sBD[CGl5YWl+ާC-FmĦlhĀC; HntxTI 6[zLDFQZFhЇ"bIi(ᱢ'M 5Kx ∴[dXʽ:j>!,]z-YÒsNEc̃_/TJ;.H|a n >(&csg|6 =紿<]b L.Ui4:{oǩgױmziΩJg}/n>G_|Ps_RmO?f!@ =UQ^H $)%Zm PIW !RGˁu=Qo?z sCؿji8Q9WJILSQuSWkհ3#1 : > |js&4Yօ3# 6-{c@&q8:?_ y91ˏ'kD>d61Y%9)첐6"sqK@PV;MjV}?y{gZfr_@:C+߿Fn9ܨΜ8`CuV]>7DrIf4x@bQQQQqwz),G^4W\_qoWo[=g0  hz>4CxUQ^ lh1 Eò)7G)BM!ߞ%g ^UX +nDҾ,PsUr.!XIqReq;d={ IT!$\YZqu3I )%-oZG5@< !muFHL=NvDQOMZyf "Q^8 !%5X6& x{s0uD>ȶm:;]m=qdVD_2,>=|GŃwv>S'ﳿη4:Ǖ/|ȵ -7ެ sœ_rpf8Xز0{ct*U"H1ew/ʳ&4F^ qx7TY3Ϊ# "Ͱ ֗v2gp񀎈\ KI^l;EK{AC8qݗj "4$q$:IH!@xVhGU50/Na ft8Rk.wb(`\.òצH?bqK~ ,=lED*Wth>K/W߷jm>iZم<%o3׻HWNb+***s߯=mx~0/O|s+w,x)8˞G6_%Ŝ0-̟UTT\XNG"jQj~Q5ºO0p8)M,Q\,ei=&ʢ{J)#wBc-Z(q쏯Jr"Қ(e(Rx&LEiw;9/8ʹD dyADQ2 :#C頴nZ3><{v:jQImeo*kp'8!J,YTB2 g)8N[%ca q!ޟl '?Eq$Ю֗vp"NBJ!4eY`h^6#m5_h-袛dS\J'!e()赺R|J5CZ\1G`pzqdt)(d9u 5kF#*B :M JY<(6!2)0x"$e(ژ:ޖЫ#_LՑ<ӛ*'s͛[Y*C_'$fv.}v;xf7;}I=DHzj7~F^O\z\Zy o(z˛ >GOxN!?=Sej`ˋ4_uos:W/*–-vL8Yʥq;دPOk Ns',IXgEG΁{kK眗R&I\~sp4 0 c%1T>A- et9yT1iBI[< { )##˜/ҙhQԒz"#%z; usVz|rMعK{]Xۯx~wQb6~=牖m/={*T2ڻ/^i;\WEEʼnS X/&lÎ^ \ X'XL>jo݄Zq d*ŃsNFdHS-pbGzu 3>,|Ǯ0&^2(m,^o X`q4^83EbB(Zqyœ$ p)hI| $nηڢWYa?AHЛMA'1^`fn=31Ή!!İmBEDP4Hc"Nko?Lpں:Amq˶_i}-wcsJpnȀxs | p~!=gUJYDM[<0qnW {X{/\/36ꙃ-Bl#oOϲCwj?VjٌED&JQ4=Bz=VqyTlWV*cBS3D,)GVzJ,-Z]UB~W#+C!chD^ c($c2":ecPdZryxu"mيs:6}8tۘ kV( IDATIxhsP?4_}@EEKw͂[48v=/ןXs]Alr/)W6gPa杯9w/(*Ɩs1u%GvW@D)g$"vf$U$ eʧ^G,"ʲT87}V]W^x֦qG{Rꃝ=kYjFHI^dnX64%ʒS ›J{_&~V1%;rE`?EGBdT d :y\kYeT' #D$ )\K:NF8 AXDxwc}Pf^6/=)vݶm7և_Cg:̧$G!/F.o%'Wo{$YiB9z N.Q: ԀLi/6Ͷ+/0Siuk'{]U[wL3o֜b3Z[v,ʒEx$%ea 1Hvm&zzgn@MQ i9kTE J!""GmӠFՏL/ȱTKDb0:=)cywQC8Q*ʖ=vL}jeĭ3cg4fC;}]YiMC 2ҶhY;S덪I8/RHBi-XlL%.M-sqwP;Y.#?F,AzJZ+ʲ`!o#wQD Id,[giQl׮YV!5Ys.FZVJpI`^$&^쵍|>lgIx֭}r۶Pk>F. #kH%6u"r8'V۸s!St{ 9BkZmՐ 6* qhͭu#s$W]W)w0|%A-wN4~aV|!Yg]6\!e,vPTfw~ JHEmBMjRw\HHK~>V4d3b&dX"4#h$$'C"EFG$h":8p'%Ǎ7[-?`e EGv4|.ᄳsjqz,mFn_L|=a5BgD0RZm77_j^QQ|\iEEȞ=L}Icdݝwmy掻W3WT3[6U,]sek2BI2W^18HF訐m]\ O")e)|6 I2th~Vv}lXIRzP=Yzy.!,CX-5k w~YQFkn=5F 7ei1q> Y'[:r#dYWFZp+J;?o,R$ Rk|j<яcWB*]r^Z!k-Ḷm۞mz~PD}=po?Om6۬tS7нƛ#hz:H_sLeƃ -ͮj]|y?C{^^*BXv[NϷF;Exkvk>YJ)m/COi+!CcoyEcjച#86T |;8F]JR%:!/r1A59;]Zḭc)Y4<{ݎҫ"/+'7߿6WtGQ콧& f!ZtKN,8Zt{ūPʗmz$Y3sVp>g~oc6+7[5Wlz `=R‘^ֳ7x> |YhgR} uY<'flc4Xɗuoh_P 5T8]g| v<Ԯ89͖QSHF贠D{dpCDTP8ES( :tG&GIN xbR4:5ZQIֳ`3u4 ][;o~xUQQ/|GEEK{n^7F:̝TT(9ǎ}YpcZ_*reW.sArVZꆒ8_l(+[0je.|W?X־_N/mڷkߎl(ilbQĭ:a3b7Z$1yck -,(!1βed(FާjɅ|ޘ86OT֚j|qcHT}A !1>Wd #B#wvk.*yB #ć"UۿnU~^%tOJ_d{^'oWymSsGXBSyrTֳ@xɽ >JI~H(Px+FϨIDi +7/tԹs^ƆO|nh0e)A^^ddeΰEJ[k<+ӻDAoSlpJʬGgSz(:F*CBtѤBRSСXbKA09Fh: )9b(gDxH]W}Ur5P ܪٳ8-sXwSEʼnP X/asU?w|wyA'+QKB4%dt0ƸV%{Fk0YkwsSN{%u7ٲq̅[.ZЁ٧d}qeYx6!kreE(kۘ?6(K;zE֊8B୧S0(ޝnQZG3Ń&=yMGPъZֳMS@=qdDOX<ٶm֭EE ۶m?*MWu?fj;?z`p<x~ ziF-^eXW1؞4iO'эs etkXσpg"*=ʴ&bkA-,0? Pcbmǚ2ۭ]z ,_tkmLo\ ܭX|{/ +|c0]7[$#-lg })K92 Q %%PgOhM*Q/lEENBxRZ8¡2g }J*km "e:tD6r¾G'g ,|@ڊk!C6#r(%b1PզQLSYKZ3S$NY;լ5m/L4GF6GcQ}ok{owܱBGk `Unr٪()1 $e^0?ܼ8 qwF$TR(r66<&3s3<ڭ94?i:cWgE&4 `<&*Pr*:A"㗁O&^ӕo]$;?ztGd&pϔ9tgƁlOw=k^L]dP},f[nS{agB5Q3K-^PP[zv;7{%}VV;wHJ%!Z]/[1 і]c=ּj11 49# '05ӣ9gQIy7}$3BU%Фcqxd#.-CN)))\AO!n+/;I܇챲xc:™'Yb LtМ˓|9=NvLR2u(UT0 $ԈIIiХC!,%%% !YPH] F( ^3-V2SY,ςϠue7"#qg*oOå=B]vϕ.ڳH;VmE Jz ,ڧWD*Mkc jGT(()WaBfhfshQ2RcWkb޴ӳ8ثͶ;{yLj8Ν%ܠl]y}Fp5 n39BjGivnƛSBC礃"ۋ=kZ{&iƒ{%^?:/ć>Iœ+u?EWf}/?=/Ɩe\pfIhStХ7љ (Nq&qԀIFЫF@()$4-DT( On((>˞X|>8y Ɲ ;NVUEEEEEEbݘLs";\YQQ|S X/eltL9R !.%CCǻ[|j$ZPSiNVZI@.ᡡ7O[:R;up׽O)Ȭ󋝢E:j[\(wQ1ysV4$>*̬Ls[[+ hHe~9!\ks,[6ou?>bzvf0K#b [n ۶m? P>D|w:y/:tJk1i}T3c,;l_\M7tgA%2=k/g<5-,fc#pSX;؈snoq{LXܜD4IYzrB\ta[Z+s=S1=;hڔw3;f[^~ӷܛ|ƁKl>)\_ȟvmz\tyc4H"&I}b,gnXwT= .}\v+5w7%9Cv|;#_?_,%2sqD+o6Ux\xthH0=\UtXsx;;~cX##ύk}]_ '=Oަeѿ&\[ 7Xh'Bc ~6&{ί1mj*N\FkK9MKqڔD:UAI-CsT rۗ {TTTTTTTX:2^PWrhG1zL9C8!*or?kQc4`bE,,= N5{ ]ʔVԥFM`Bz盝'f{b䞋s4&KIw-tA!+p T(nzS-\F;6$CF K=o.>o|>{~}qcNg_\&``DXJz8ztq 2 ek"6&QO[u5&K+3JJi+****^|F$",͵gm IDAT}E ;ʟ7>ʿxP XKk71A CR|hrhf4 t/`e+ OXizuZNk-v;ڥ/̗#"N@(+|F=OYw?p=s HX@p/y` [ʩ}޽mW^qlb~<;$;ޙUnYIR@"JU82o0Ψ`EJQGeVLnEEZ.6M9=7I6]hG"deb5e."d߸y뱨O"]?<6ޫA:IoLȏFOޱvWw玬y-<3C&'1bi8rjvɟk裁w`td4('BLɣFCdI &O.(.FƧx _v*5ld¯46+`8(GJV8ӳ3wERzja Fz3"wFWP$ um]ݽsB"yZO~>m86 vblL,x~Sy4;wuG?K|n嶖:`jmuOL(\y@MĬ#s䂱 6XkҾ7J$ wsO˶?6ߡCAm2`uCƑҹv]h ,-b4{#_df]k_Tnzu)v ;ڎF\A-uDL9q9jhm[YL.,+SᩔoV(pIaQ Ar{cog])}?6<K֐/@ t'|k!M߲RR#b73Qmv"lJ6 _eY?zDUh_ Ud%(5CޤdL^pB2ӥ3`2y>oj:㑉Ze:[bE[5+:h&8 p}<[*K'N.zȓ& 5(^ F#zbۊg" ;9 W{%ϐv pdw ўu'U}WW?a0 c7+Skkߵ$pf>|kcL&<jmy>Z} eٔ5|R~̍[ۣZ=Ĝ艣Bi8F޵mjND` 0"D7; @WgoT[z Y/9#%nrKaI2qƳ&LloD90qZu#.r];i)%KKӭ\pbׄqޘcAB%hBgքpL{-zM~wk^NehO%_( )`q)y_6R.j%S9q79#ZtϾ`Yx~qto֍ݰ69?SCbW :/o]"Tgc^Lg=za7MAXQb$ߒ'X/=GQlAgHS)XX p+l4%||$o+P>[iw6e: ɬ{nEړl}}LD|;W]={ oZ2mۋA9,ӄk<<ѽTceNΞd:B# eWc1>g]dوJNZ>Zӫ-ScCEpp}4cz=F ~u}X"[`$՞"`)D|nze>*^鎹|39?ZrWVoGBҊTվ|(;V$n VιLg /s6F\gq%x&@H -b"l ppH6-1VtU6dY׺ VHYUPrh1tbx9ug6qAy-$7w_Z[WuuM8Mm[ 8I\5U EJܒe-%KL|o3._[lIV爵ail$8ea-DJkY0_^4UUvڐDzPg8@jŊUqHKh1_KmL}!%Үt'?@'E;>]df+&T_mwM|e}ߗMr1OnT((`yáj@=,n 5xcHglu!ⷫsUۤ +39p${u?nthIw16@Xl 1 ojegjm;κV+e|0V\>_PJF\wmYQK)eA#gkLx0ގ qFbZk֙Ύ?ؾSpTJGľ],/,.щB|4ӝNVڛv' \c&>t8lK,\v<,Y6}fw, wSmjTʵn>F_YDD Yp2.~?pm_**!ѻs,Sv8iJQxO+`+{ĪEYiIu3ZV#T_޸yWU$/'ӑYͫN3;>}?Ph?0B[e:{@.Б<[d~ }}ΔFN$M^*{k[JE,xW|WP# E1, 2 x]Ҕ  Û /NT~c{(x5`/ת+k{骶5s:g00awDNȸ[V] }۶1:.8.@xbٞ %u3m=]]زyN tO뀁 WU O۷Y^T""K yѳ;_ؔy )Uh[=͹=7ش.yc^\dYߡ{C߰ "'J%"`I =Rm@W +^eX ҙ{yV2Gt"BVed!#Zp<_iПY"(HG_{L]|M;HIbn(bm9.}b;,5_R.¦0|6U.xU BħjE^Ũj]x:1n㢩AĪp}Ӏӑc;`  n:Jxgv= 7;wVs} KY}U|M뫋S,c5 #`gjld]f F?;=ߏPa.AT̚~t+- θ1cǬ3v>M*^1ouQ0Ӂ(BApGlH)y  ¼.,?Ay4T,t&$21AԊy :un^d>M\ܚL𞂣"Svy5=Qc"J"ثî+Qc7??\6ylt(T),3"VQ6TNY@&q9,D0q[GAx' \ڛ~fw}dC,]|x_\4A&W|؀C7+-TauH]t@x[ģޯQg#YOQDϙB!~߉t m8ϧj |_zk!>1;ӕ~ttuf3 ׾ttTld<]EQ`0 o"NY[5㫋dZ'uwnߺ1[7gSsO]pɳlc󜗞Z!,X#`Zu+1: 8hR)g`Pzp6Ⱦ.+՟{jSȁ.>{UYsF#F>ԡXtBr÷4Bxx6|cenAS1B{i,=`0ؖٶ6ꧦٲkw[=䷿wvѶWTAV2mV`xD R8փ̏F$o;1 oe1]3XVRD813C݅e]B}Q:RII>qIz 'cir:flaÈ#++Y;,Ӊ ^wNeʬcZ:p8)zR7 I'YVQHG]Y& Z˧0PcX+Nybﭭ{}OF||#Ŧ C]IJ o犚G1{Obi3-+˯(@so7E?5:vҋNŶotV۱,cWTN"ŃL]'moX<1OŞDX5HƇ $ݻqoU`)UߒjGaQ"bXPh~z|/ضGV9"=V bc  cm0 75}FgmM s//_uvQu*57`Tz Ūߐ,ef{fN8~Ӈue`xb,ȘZl,Ee%QqK^e߁]~ \[M粏DZёW߫hY^ p8#CāHNo֊ږ>6Yqyesqy <ãx(U"|O3Ze :\WtF[w5)`RN8".-_[yufk=W.joO &69r+{떑T D8~ĭyav"?*:'=i4 #hs,r\3 @>rH TDj:3 ȕgnU+qdyi ,VRa3dq_mf=>M/v(yUJE[:7Z|FgڱHB4Z|N'u-| 1QKĊa^7S=/.o:|gvTf_jp=؟дeﲻL޼*:x ŕgt! `gW9*V+^lh1)#t8RB1+`mGZ>rNW"Yi=oprndHOFD mK*Vg  60Hr鱈(ֆ~}u( keU|5`0̃LE $2CI"t,NKBw@A|ٯxБGgs.?unL&=>zrU:i/Iy^o N/}o0~0Z[Pτ_+Kr>f|u7CЈ(K5'rW`0L>yƝEq6ۅ1.:+_=aEhi̜]zg(^k r } ;nP_ج C 7\3g@]n4p>8mY]LW߆8J&?><<%eNn]ԶCJ!%UiD0*@.  ƻݖv@1O<؞M(V'<U>8RjV7Y+qx45.t8qBI魣Iez,E6)C64n85(Ua)t,K5#N& IDATp>jWV*?}:v$Еf֍kc:ut@KwO0Fsy\QY6_V44颷W`Lg@<SCKU!c?9'_E;21/6{ VGDbyCdg pHhuQg?q9*ul%{pNx[#&ԙvYv͜ݝZFt(QCk1ލpo30 o2exs͜, vAGgGGqg'u^1tsASQ'ӓG,۲sE@ gy%;fys;V8{2xxj" D}r#@ pd<q" ҹb¡Pu3M-+u0Јj^nߞoZ߾n-6vka;Y/O%z-5{XHN~IKifLR'FH5 HX~y1p>:Z|@P<  4/_$B&Ӄ燎Gmۭ2--yP7`0 =QT=.u7ݺ(f{zʶ^l}w9`a!1.< p|Iu᲏`+ >6tlܲməL%kGosjR; f$uD) ΑRK(RX(O[ƺH G.3 +L \ _\~m\}BBmū"PqVW}g\{;r un>- :1S/bY3sf2 =8=R|{ӵ2!$Dkr˯ZU~?l^?!G'eaCb64 mC:!) vˍdF} ُ\No!+,MUdB[,og0N.`0'DJ}S(pkvy/ԼVI-1{nr;}LE h<Ѡ}8cj>6 Gޘ`x]f)~r* OrTwwGcH~q7@X)Ol(%S,=3Yܮ=j(&!t",UT9v%+DPF GZ,OIG+ˣoǽ$PǟEy/rmҁ8c/K!p(ׂRxTĪ/[ף3r@& g2~ \1}PJC# =?3@BrmtŰː=@)ս X `8b>ѱ\pT2v8:=gR}pwSXuot,d.w}vh+ O-O咊/ `0|pعf쓁;/XeqtX;Uȗm?V[ڨm+[TDh8C3wAKv([k85@ TL$N,M.ݓ]^I'ki+5c1i9lPؔOSA5"ep 2Tx|D>\L^ʁs{w}VO@#h,fkk'7P˩/0u uj pC=eb G ~FC#7/-`0 þR⤺妛#ǏOk-r*.Q 4U(4/DaD[Tph{ʵF?l01,a9GO ^DO w}OQH{c>F2\L؛Ky (,o#c2HZYŵX ,6"bGYGw"V:f1P~+0XxY(t"BaSUbΘ 1Ծ ~Oe*cl~BEK5@Cc_4/3jmkR)lhA>|a+ErʆaD<[ռ}w `؝Hw;+ԹmhϵּŞcmʯBWtګ9C}=yEoG4xW:rv5$(wSKMsM.nYW 7 Ɓe8\3g/7'p݇|iwE7jY` gy-i$7TD(c=YD@,o~oDd~;XHQ SRt1++K& W7z~dҍީS~ B,B,iD4 9OYH䩈Utա?v%soh䫀ۼԼlC#?V(^9!q!rOH mr˧O\.pHYaѡvbY'`0 ֖eFmqS Oy86E{'5\)¼ Z MIbo{M k~qfi`=/P"n)".̞@m\tpmNX,"%ieP}:N";Nqy7w>A&N6X-MPC}SqY0CPP`_Zvەft*rxoK |5 6 ˨p(TXbLJˇ' ,_(2KrZlܪ \7vi^_C"B#`x32~D>gߓQ3@5cZx |/87q.hZ[808M3UL5"`NO"a3,B~/>O==lbmxr1Dɼ*%t <x)[]^|Ԣy#paCnu`0@5:Vv –藁͇wa7s2QA><2{c8~v6 GƁexݹfr`/7yCr,88R66 F0w+/W3tѰP{Q?ܯNM6"l!ПsU _*}Dϐw#Hi'-=[tWyLrT2N.'8?I"F!.V="'Ju"r¯}SAJl?G=7 TuwFa'Ơw^8ۑD c *mvmrsRXCAq˔#Np_<ԃ@9_^C,o+_F.Lae\M`>Dh/JQ"rQFM^?Wz74ju%X.6tW.;P<tY7S=ҲR{Hű۞yڟ?#e42Q)BXŎH>)p s4v2GMnRѣL,]{`hˢa}Gz+_UI\*{/pp?>"Yp󐬸1%Ak<_/Gmɫ?뛗 `0 C73{?>būu~#bHwϮxG2 pλ ̙= 8;^y80L{X,;@ݾF= YV8ˏC\1Y!Hft榆~^x"lEJO 8aC ""$=cK$V7ݿxΣw"ΣYIS2֤󘍈mp-῵j] _]R^.5;Qv$ۈMCekY59y)(}01[zpUT9rl6/}?)|9c YDh-}uId pWC#6/`0 0yT"楇dqIu-d둛.G|3MӒKŋ,1M ;=R/P6q[pdJ$ n#Ͷ :q"B:wwF7Ul0PqWZrWJq"|m>|]!dzz;:_Gs#BԻfTUL; 'dr[xU$O:z8qc)ęT}j變 V %p," v ǽp"RQ׾w:+!K97{0{UI;ekGwe۲A,*cg<%Mï=Xe#H+`0 aoTI2c ~d]y/ؖp?(ttvD`ɊZK+0w&ƄX/P1b+3AUXHIօHG/P匀Y6$hvĝ4"$*f`=Fd^}!|'˥_ٓ]\q 7$}m /!sS:_ԫ tԅ;hsV!L3X;qnug:D)ͭRH?ݤ*rS~}pO.ATkcLF.khdFb=e9*π*`tɮ;k7z"<65~ w&f98yg0 ad@DQ]{ۧx~;KWkWNqԀBtx:Gfs>`0ސHf;%刨3;Y~?\WUO}KnBEfys+?B^bcq7@'O?Tdf/u u׫(U!#H~Nz৬^7aO1 aSؑ*$El+"Gi,8nĭՍmRHuu3ժz pv1_~LQ./msU(  $L>8^Vmd͔C˜@)DP@V}̶bV"TC@W4ߏCɈPЋ\[ˣhm~{U <=J/Qc7YohROn=8-%ȱm. (#*ub!BP)C]+"AJ !%!X"(!d/% s?evu3ՖZL/?@}Luprt4 MK}BZ83=4F Ù9gPa>*O a*; F:8;CkAN!suHΰpH1ao,f`\|KFOņN@甪=0pj@+tR\`!ѩ L VyD |]1D*k TPEfys gm@$,1WޭƉ%бXn,ϋ GQ \D|᝖nQg~(+ }`0 O"M~k{|K8ewAJ#dYL14 */t9"%Y:ވLڊ5m|&Ё:H߽3q7 F2fG[HHvԫӀ]:{J]xQEJû?.>V.C?|,bw',ZKb!.SOAOr IDAT\c[TO3#FRПqg|6 ez9q@VqlTc"$y]{F(>8?[V nnE0.^*N.(;~tVA p.PcL^!|ϋ^@D(d \wDp;NNlh#kkЈw76/eAͫWy0 `80,"$z‚~ʃ-5j_o=Tnl4h 佞TCݩ!;8:ji 2/Ǝ e#S <8)`R^[&ĭ~]=t(/sF.{αhDX,nTz'(8ԁd,گ} ȅ _]d? bTy)a&VFXnG[W#ev{$}zE"TC9h^JXAلێdJie1}{ɥvHVBwKCWU精]Qwj>uǐ}?ݝ~CH 4mrу:yGvWg{b܋\06eL JhJy D$7AI+@^ "@Bl04ܷ/;VIUr?;ҎGef{g4sssc7~Mw|گXmaF(u?)jwf5LZ(SEbi6e{&?ⵠRXD~G"h#A4/7qv18#F^岵cc /WW{j0 pAe>UYM"ؔ p ^415cw{OikfdrO3b y(6j\4r^"$ȫlnG%Hzi*$6& Ѣ}x}=LG_ $xֈg,E^;w dvt<\maFY4wnOϣABPuYsTٮ:(QWxGHO~mE} pfnNx͵w]VH㷈le_ʪVcИ3.=%)ZB?9g]kB5rpԚ}EWYE'2kE3v|8>7\_}3_̩jedKDx)L5v̉Wsc]l%iH9čq;UCI}/d%B>iK_8"6A!_ HУЀ!}= >Jfn:[?:d1(m@WAn`C<<<>1糓ǙCuF\rGN&\,#@>*8_ $ll9dk6w{yo7uxqjy4wޏJgc4A'8/,'מM Zsbm^Z=BRY;T*dUSiSQ0P'0ɵ{:dtTƊ 6!O:폠VTЃϙ￯ 7Z [Gˈ- KTYt`M'! Dj f'[Z93=ZKDඅةVp5nzx"ˑ!l4 0 7DJ+Q:vP,9L<[)| xUa]QZI|G"yT۪5.vcA/TڋpGUM~L$Hnd4 2$ZZمE.ANi|s&6T$} ,L2ͶFd8uos11F(8{'8#B7/JbU "s>$WIxܑh>/Eע]Mb$dD"Uh9k.4"C!`;(Z~LmAPr96 0)Q,Eت܅m'}ARrЏBޝǙ(P6 ųVmO!c\江< 2y*?}pjjy&&q$lh[W#PFt].ʋINr18@l^;%ls }*>QzQHy%trW=_}roߘik\2B\ZǜSӃb1E%"v+Q;`64}"y,$= &pt\֢{? hOP $DK+x!?\ycec*ffE}%vaq?ߝ [Z9)|TդFa{%OdQ׏{9MwxMw%{spa{ָSsC{!Gx|WdzB*}HBxM\<ќd$/F}oBh '̍&j&'')3/^^wWuo6.9F"J<اj۔J{{'([DpM=0=ަL_yXϫ'y:wg3UrdO*Š9gtʜ`T4iia46eҊĪ6}x 8;rv;(o9Hdr?/ 0J ^z w?ָaݷ.h 𽚁fd36d ۿVMdNXu6ή>k8qqa"ًM?wٞi%WQt:?%UvMNXIbD\^WD'G 5"&&&eVe XD>[}F'wrT߬R sixu?[dՉۺݫa{@S>Od9 Wnz|,a4tזrˑ `V{H;.w3} VWE5L?J{v&7t$bX5+}=- jmT69vůWXdL{{?/$&TԵʅC |M"|_5򖛧Or͍>|߽m 0!S8wc,똽\"Oύm|bs޽LK-G>C(n؀*Գx6Er7Eb}XO >8:m9U (f@ &+ZNac"LN"1.^& <$ccq>1-J ]tǃ #9:ԅ&|b{h!7]K(hx'j2{Rx~\gBR*(ˡjZBJq߃wx\ bnc Dk!jSJ{p̈́^θ?3n%,2`B{>]szը jPsbWPSWlJ]UQ S۬ +7p3GmfI\?g߆JI`sglOWJ %UsAś[+trVRފ'ג{OD@S_fzS~6>ӏ!JM,<>uՃ %pBB}vN yF/Ħ+y݈.75rzج0{@KïO?E&w{߼T0wvV{̶ƙ'K?DkB5p/k}>ɬ>(vń5N\n@],Ǔw{-kʋq{$r*FsVFEȎŚOF?BrHdٺm rb[ZИ+&`m^ 6ʙCR T5 K |lw.w_%_`l7_R*U5]rJcfg?׻*zW!|v x⪯*$n~.񀷧2s<>|U- ;?37IQ2jp. ~*kx{b|"ʩ+~oVX0rHA.G[~?|`wlA=B Y h7#wLc%oτ 'wbLJ\Ǫo~/11xQ\M^ d8= >ĬCgA ^u5[~pFe,yڽj򷁰"j}VS_ 4#! $JԢ hNŭ?\6*1}i pR*8bdUFТH5|eu!_JXV/DpbHXUw*0= ;͵5֕7(s| jv=:_;Py2qcJDa˗mLh\͉{]2$F[Ukw9;_Aҗ.FZZi<4wz3TEOy6I(f8C*I9eQ8i*ܽRī"ނϳQ{z(huK+?FzTD"1L:+2V&`K}^A$eP{Un^LXa]GE0S3~sԚ [2.11$ @m*>*n؀Pz4P`9DXҾy39[XiiM_DU;ŸJ15" WpUko>D8u= Mx>3 8(oyE~c6$_^6ziAi #`PR-Q˗PAM>p͝wLyI*E񷔸֢x퇨BH yCcdnC_ J\>gg=OkDhU_T-| Xώ̓GdU+2K53.F+cΘe,uV#!!MGs:4J*`yb|<:zxMk) FǢn2HǛ{;5ܥĠ_\6Ϧe Y2^U 8^D!ڋ[Q#(܍*Cz#LCC1rq|==YQ0+2/d>#x5G2 2l(f79ay&Z7"aa:~e!/^gOI/#Hy4VL'ǂdSUWL$W]Y_w| xeU"0e R#߂ z%& ZVOW!U <斋V7mh4N$p i,$\UGE=բ; cm f}\7rDJ5v{&]pTJ$(߁2>z^Y 0ikxxqH_qdL"p]i.l%jiل u @*yU.5%.ڌ |&Ty=߀b67g; n͊EEVעs>>>E`yDcU_9ʝ29y%_.XIh%hm@M-Z2xB[!d.N-oD2/Ϗ~6W~*ீgv$q2Wm(Pp8F( 1}RYUX=RkxD,gT>l˕LH${_f8|s>Ul݇|[Cwb~^J{]^2}=0R0\Ĉ"yAkz-'{b bz>0 0*W QHDn-GQ7%1K5W6$ IDAT·8Ĵ'j1,1fPJ{326;*甸=bqCޅU)Hzy(sC(U/\Qƾ/;+7q=ҊWBH@UXB!~ؼDo1gL22&h nA[(08LF퓁ppz6\ւ\?M+erه͝ʟ_P9V*"'쀎T[eBg(0KFw7ѣw%`^m4ZUu$:!q%Pȏ:XLpz?E +}K tS*OSi-L%矈>O3w]%ۙ#|Hʫ#j? J/ n_# \aY[$(@<',?C ;O"ai/\.BގW|&Q'wCFjB>;hblkP8MRWlߪP2RjBtm o*9KV6:xܑL?J{"q/8Ɠ"2d0bWÚ565n:>xl$JV:7c X5^ [^,9X%PI$DG ~j&P|FPP<%8^QbaXە#/1[9=Vkө& iieZy~4J>7.ߕxE3y ̼ݨ5<] {#j Aa@Aʧo@ lzf."|oC qʦ,&5(#VJwފXq]\: (`Y `&U_OA㵔T lýY$znGb0x޵(D:(:oRbhxw*fzy}9\y|X=b=q;kR|5JN%I`$7vg-Pr2B9!T/hiC<9IЀ&Gu ;8硘W;6pwIdd}a^T{y )״=D5iZ%WT=$:Ad:^*7@eł8Gqt~\ \-~=x.?svQ .DH8Yb$@BUU!?+ƝȏhbQL N>SURL?J{? a"ajƿM(n<S-pa*Ţ1NO/N܏.Tն%9o s5qbz AXoB_-)@#r]>K$kDF7f^]k0 XyGЮokB'[Т_,O+f>1$8W1J{Ο F4q&*^FP4!`|cp.SHs-0yL?o#@ߔ 7qy"&g`܊Z݊S>n_^AR2( ̾ԠBat5]vp 2E/6F_+Ѣ?$@;[Zَ<6ҨM\J\o?CXaݫނ֯nY-"eyʥ(Zc(yo%8sy#w/:5r F;ם# EL~$*9nA`(jб,?083Cɯzd/1-/x UFP3i 6x.,벧іVBFƒc M%'o8Ш^Ԛ<.QtGXҥAY @ uϣsJM0]Ize9d Z ;?a{g*N ]+=@Gl^Dt.TGeʀ}o/Lhp=C|, D"W%7mף,iK+?@l M܊|3Bx0 cLsb|#OG8_ 탠sS)Wd' uS/Aїǐu=J4dO?Z0UxZ3D* m3l/ _>X#(r>0CUOOZ?V!dWi8Or:ܽT0;<r(Vx3}UOQv#]۪۪ykpe<&L.N^uo ? 8P70 ØcC}:£myp \4VdT Hܺ%6ج!as7 NUd ^t "1Z_Mu(?}pss4x$z#wz MTwƜ ,cYO{?B 8 ".[ʢq Š^U9/Dꗣ L'v*lF?AƧעv  89߀+RiqT4_ht,낯!>9*. LBUKŏ@Baʜ:VP na(V r8ɌRS 0sOxc[\kvw+ьyM[bNbzJMS2 bTڋsl8Tkt钓n::sPPt79ŻΙmz BqO)oUv+ -| kx0a^ PwE ӁT;hnDwT } e,p=l;}6 _Jޓq~,Vt?AGf!3Si<7]`SLET 2ÿhUb $5*Ӟ3_ܚJ{ TH_[Z\ՖV> UB"D 2Z;4wz8j`m~Dr^'TCF85ϓ>*.ѹ@`?e\3:`;F浞8GE0yRlCHk jQ8(z_9յN :<_а0 cI6ۏ3ۘ4b\|P8JŘ}Mؽwő<=B/;PMM5NL=u^P-f^1 XO#PJ-4'  pu7:kP l0 X"lk$󺗢VJR+; 4w*'f/;Ffrv (B ÞD$\Hܚ@bW x\/g[7Z9.lK+J0XVؑw{"'oLCȼ; }ʶ h_8;^*"O.DPUΞpR.h>qvRܿ =7WE( ur.HJ1$cq&0c_N[Zz[ZAV}=*.1a K0qm܌ M-SQk&~^u2[Pq I$j@W[QbUŃ/M/棕蜗Ln :꣔~nblþo{ڽWFneϵj$ly\f{Ey" &2S* j^7B,̿G7vwMx9t"gM&?Ǹ]de ZН8ZЏ27Yq.PR-a>2<Y( _`i T1ήα ^ t\)}J/0 8GnJe-(#b9buXx8%mQ~A8e gzgTF_{ Aυ&z8^ k>ʽ-/k!4-]m޵|)waLm)t^HbQym,Z 6AG>vLsWXcT>h68Qp+ |Ip,Ri/.Lѩ P*ځ^aqy<G1+;_Ӹ$wzn&^l2U`U \">Hx8!K2Fe|+,a<~NIcya\˟hk>| F-"$B|ZN.bUH ͝*y℣؇2 mUX@xw,5( u2P x.Df7  $"dzo.:%5Bh va9sޘsG+Tgh!Դ{ 7V {3c} TS]|=@W%yp (&Gk`a$j]u(x F񭋗tmw(_ d1>0j#\ɾEGcVEi<5u<~VD4wFшK#(yŞDq$|b@A5y&5~즟UrL# iǭ0 X.{%[39G $ͫIDATv!]~nE7Oz> rI7#1p C Q~ga,϶{_@SݞĀn[J- Nu.=3W޼qp%h!_#In0jV1s5ⰇQE[{@jŲՄhM?İp?<-. Mo<0A =Փ|UX^L{Djr/^<*bL?|PTW31> .De lH 0 CYeԸ(J9@! kڽmlORbD.c"61c6 &^E/3O L*TQ$F}:U\=g"+C&^|^d c`jhџl. ~STz5QBS`݈~ݗJw YOX傛3PAfQiye6 0Omއ;.z`p Զu>r o$9V[uCJ=FljsˀqGmK!jX͉Ų{V$~e~ caaNFF VS\pa?#FlVS.ԣlg⃕T6 {>*j|ř 2T;iAaw{OBCM.nT_0 "I$D`)(zůGXFML &^ƲZ <>v.pcTڧV,U>SQʰ.~4T^ʥBg0 Xtw.zԊUxw|2 V'v Jfz1~$EjU]=|[ cbaD|cM0Y9,gς} -lfQTu9ba.Kܔ| c ^jMBUU*yO(o݂*z7"a <]{׎I 5$ĈH<&Q| ƿ"Ȣ}沏 qΨ ~6 0 cE?M-ž=Zt@U~kZD $䧵 Za,oL2%N[^R7Z5MPu1bQvp6{_]2 ɣ+Oh^iFؙ?DbV p}6 0.jmkvLgDѤ(νXHX׆a,0>ɪ+3U~֯UAMZ(GY_cIgzT}3k'BUG&T{$Taq\,xppۯ~/J#+|@~fzCa, 0>䓲lc#:jFd,(cY--w\^D~ 2r?7mq>-ӷW,CBe }0 0CwU3QV`+8Ԉ:PbaK c3I|_a`.R>[MQ1$7Ҟ}aaϫ# \*GC- %"_%E`a- m ^ojXǂ!z e߱\BSiC7(l)d&z-2\f3P7,saaD> > bdq]咊aL2 'BUXܤ~ n"kalN<0 0 cE}ib&<$Z]ĬెaL2 cYL dID*0{V1 aaQuO%E0 Xa,+2#=BL~&6!_0 0 B2/r>MnBa+ XvdzaWhYǑaa12~k\&Aٳ_a, bz 0L2ލs+ ^ d20 0 l*q[-gu 8瘀eƲ%Px(>؂5y>y>0 0 ~*Q{u< ~7[a+Tڋ`x2 }%a=Jh{haa8Ri ppQ׷AXaeƊ#F HL IiQ`X _2ᳱφaa+Tڻx, caa+TڋkQU-󁫀%Vnam7 Lpw0s Xa@*E/u%t90L_8Wiaf ^sx 8eaaaa,iJaaaaƒ,0 0 0 0 cIcaaaa10 0 ر i(k 5`M`&XX ,k 5`M`&XX ,k 5`M`&XX ,k 5`M`&XX ,k 5`M`&X weWIENDB`openTSNE-0.6.1/docs/source/images/benchmarks-umap.png000066400000000000000000003532031413546205200224410ustar00rootroot00000000000000PNG  IHDR4ΨsBIT|d pHYsIu8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxy\U)ApCpApuFGq}C*F1.q ((8.(5F\pAqDF !! !qnYkT<}]Nս:})%$I$I$Iل^7@$I$I$$I$I$А$I$I$I}π$I$I$I{4$I$I$IR3!I$I$I I$I$I hH$I$Ig@C$I$I$=$I$I$А$I$I$I}π$I$I$I{4$I$I$IR3!I$I$I I$I$I hH}#"ӂI@DLMk:RcqDDluޗ$I$I hk._RD>VPJ˜_꧵qsqJ)m[<}#brDh˦8uvۊuDCGӆIDL>  Yx#'أݞ^"⎢-7Eą~^S>-祝n?,"vsɾ""+OG}9V;M9-"N_m^"⼈xڸsD.".?G2"ߎwF#lgؿwqdivI$Iꥎ#'88xҥkMC p _#XZ1<8-"K):~L҆;5w88j@SJ7t[9ϗ Lq|JiIRJWG`N~sAz{SS#7)c7;l”Mj xJ1="NH)}mN^`)#n|"AR>b:6".i3?-GG;׽7p9fJ.d7i^y!w縔_[m*,^C0tnk;nVر^W RJ۾$I$w4.guxQUUӁ'&_$~D<2t[ RD|PJ~o"|S-/ |fDDj_L)jv.RJmώ:9b 9:pp;p?p|CB.֯x2b_~Xκv!Kn'2C{;O+{/\L,79rr@鼈ؐR"b*]YrhbRw$NM[TDr;1Lߤʛٍ|"tܔRB3zNv`Vx+9M>|,"mϱ}Y_ C>O+XR:Y"bwŬS:`;سX9ʏ4$I$)ԕTL/|-;X`˝3_;Meu3O_M ٕ|!1/N=▜cK-(-w|e|ܷ:uŲY U瀙mypu>O]־M9P] u' Y}X0G.h=t9`svsѦi-=6y(?)mD3?o|Ģ}d? 9koߌA룀ةkh`B#:3-VLJ(޿ػv&,m `vk̰:]NNNNNNNNNNNN[z}+Rs4J)mSrX:#br ]wN[.>%m{CxcJl(wVK|x|~1 h||6SJO)H)];,,:j$QMJ)iE@^ǮԲ/CwCt^J0&wȈ:xy/֢ߔp/*;DzMA7cWDE_J])nK)K)I)%tqJˊFd;,4RzWd RMۺTrY#+!ߨҲ6#<WGs[\j:0cR\Fw_[zӾq_)"v#b{56VߟFFKC;AmU Jj/]Uo:3HJ䡉Nn󘴷Ns|K)'sЈߨ($I$m hW ,"-q(nr rvg'`{[֪;1En@_j)#Kϗp#upyâ/BވyD$pJ[RO  7w15">H>Fw "bwm#۴&tR.L_~zbD<*ՠǝ-P\HԔp4 o#g:L@];^m#⪈xs/XtvdDM~ku?:kaO=*Oo,q O$I6l ;CU]FDnRJ6QqU]y]vED|LDl& ro4]jTfG^o)XH.p0jCk 6TB%u_Y|?ΊoVW3w 1FC</I)}E,L)n#nr]cϟ`Cǿn8< x?ܧ0ϿCU[H)mBIS>= ۵~ȘieS#EqӉȵ1CA˟{d 8"A6@C7onAw2\_/-T[ r3FTsA??">̫[HRJ RJߏ0FmӨ;߉yηr7=:j8l;&<m_%I$P/.@J>j'S+9Rs݅~vgjwwj/jw3},i䈸Z1#idF&4zFӥH)^}iU(k8TD9Q+2=24(8X|տqWcgP W[npѶNn< &VϛvZ2[7i2"G F:jwBS1K%I$i0CCԪ~^T<)K#Ϟ+䌀fm.Ҭnt{6ڵPF)k#b9" .^ C)?G(1]RJ͎G")Uq"nspjx5y/?$NY.AHx~7Mγ{$)6?Ǣ! ;Ϣ[լgY+ա(ό ̣YU3hh ~D-h~JUz;נVXƾ*FP_ȶ wG & I$Iꕵm/׮mܑ^n]2\ؼ:ȗ i|whz+7;DE_wߙZ];:X\lwצKu~6:GsMx?8<:`8Cxex6(r̔#RJjQ7|eGZ^Dŝ }e|f>D"jC \OEؕZxb)iS 3=:RdIU26(]- jRy[njp> Z-R1DDm8$cIu:1sr4 >fyIVm71tc:*KkF$I$uCNi[P=ϯ .7R_0 /|#"SԚhMPQZrpi'Fģ0vAbO/v?R;UU"b;ץnzwd,gyTv{]`g'/6Tz~K Һ_.Fh'azԭ7RM/yDLjC4]Rjl`Y]#u:QFs@a?8x~nJG7GD!bTraK)m x|qj{FU/>tǩ%^lpVe[|ɘ[Kpy.e=nl0"N'qWM2 H)Zy7AwqVM}f[iRJkA gu6*j?zzlR7Z/h"A"b6 3aN*^^4v&WN)u|)a t!bFĬaΥVo㜔'zI$Iz nnt[c_ÑZQEmEWY?,=?Rw>pcxt\.ZMEg#bF")aX^;}7R?{nvQv|pg& 82 hmDB_@l~{5퉈PWya 1=Z2`6hr&"R jA0s-ԅ|.7n..^ \s#qqTQj73搳eeuNF& >WتL#E (}Q\O3, R"0NW4xd>"%I$묡o)M^2;)W gD\EŖ_\@{9pThrs0c)p?!=j) 0Ӏ<ۡ\x{ȝp ;?RŲ !WtQJW_Jxp L=x)M)5"Cp ઈ8|~qjcIJi)0ThJ遈` gidgP;Q8<|ΞF/ r;ރ\hQ<4KJϓϟ"O$ךٷXǺ"Qr93~@N^F-l phov56បҐ!RJwF䋺DG)ߣPxN)5,r[Ѷ^C-0xE1\#?8xPD|Ms:9S￴~仿90c{|$ED=r|߻וSZ| wmEI{X c~xմ<$Mvt>6Ffj䌣NqCs*-?u^otasdAwJ[=yrc"98 O]vN}_CaLGhCC~ə*Ѷt!g4tySj|Gߩeg4[Z9;`#_ gP5&&U){!gi~bhjA, 5UcPS w #ި{JL/ȁ?_%'gj~ə .|&rͳRJǥ&cڏPXS,e)'`2 E!棋. R-t,(rRu ؇RnѦ[# 瓏*jg#RJ/H) s5߫N؂\`!uσw8_A0[J.P}ح!/u9SK)g F"r y1ó|!~U,EH)]RҌb:4Ҧ.)ǤޘR1)#?m[x#9[һ:ֆbهo$+v=SJO)I$IVRuq'".#_uQJi1Y;<5_"UJS `v]ėCprerƋRJ7snI$I4bfhHczf˂ʐA1, xޖaqu5K3$U'go-'`mDڭF5M$I$iXАHQH/&)4XDA0<ΘFĩ䡊 <*tXO'"%הyl10?%ݹ<\ޞgf\)nK$IԚ i2&є}c ;u/.H)eƊ;O!g},f`w)}IzEvk^\O)Dzm$I$  Iq."C.^!E~|+tO)I$IR+4$I$I$IR߳($I$I$I{4$I$I$IR3!I$I$I I$I$I hH$I$Ig@C$I$I$=$I$I$А$I$I$I}π$I$I$I{4$I$I$IR3!I$I$I I$I$I hH$I$Ig@C$I$I$=$I$I$А$I$I$I}π$I$I$I{4$I$I$IR3!I$I$I I$I$I hH$I$Ig@C$I$I$=$I$I$А$I$I$I}π$I$I$I{4$I$I$IR3G"ꈸ$I$I$Lu4ȞnH $`Jrk",B*7P jߨT*HKfhH$I$Ig@C$I$I$=$I$I$А$I$I$I}π$I$I$I{z gkzO!sI}C~! e+ IYRuT[RJzI$I$ICNI$I$Ig@C$I$I$=khhnPTz*UKToHC/RcվQTZM3nFH}b"OTKP i(XoHr)I$I$IFDF6"VEğ#68"~qCDWD/I$I$IҶl9^#>`I7ս/Zel$I$I$Il\ghDD[`F&Τb?S/Eac$I$I$I034NKf$XPb1ԛ_J)_zq5G`7`pDW[.I$I$IafhDN_lhg#pZ);/GTI$I$Ixx)0x~~xyJi2>\<nVRXA24B~!5V$m"6>ޥȟ{Suw.^~(4Ų)9l)Yَ$I$I$I͸r xB^"█87"oH$I$I.CNETrn{o*xyJ{rql``Ͳ^T*nj{JҶg;X|CR˯T*+KW-_QT+٪Jec4`nT*l```'`˯T*KLnJ|Sc=N;m{~:N8Mvq_؟qHqjǩn~Q?ǩ SS8^Tn.IƵq`? GG;Ӂz |9"K)Xgzimxl+Ğ [ڼ8peWmZ,kɥןiYɥW{7Y`מ--㣀 [,{9Vu:pR [z83K۵p6SU?'Sscy8q?5qjԘǩ9SsSM'?mT8885֭Ԭ_x:coôAq/R 1yPI)5)^>:tmDA )/)FƇ[uУ $`Ҷkn i(4Bj7*CNIirȩf/YXⱼ~mvϝN$I$I$ ׸ hʩXJMżE"^ߌ$I$I$9Xq-p}igu+=Z'I$I$I:6?6tԻ#bZto(`3p;pjʔopI$I$I4ظ h6o6Eij"b׈xpD,Xn?*^2"#9/=̧[ K۠;}~! fߐ_HC/ƪ}CmZz݆1$g[ld/H)o/jzU) [RJ=I$I$IƋqgx?@]Ҋ6x5X`&083]l I$I4L&.]4gC"IRl8RH6000 8xi i(4Bj3] K͹$W&.$6$؅'~!7P 4k߽v_C{&Izɀ$I$IR9zޒiԻV޿K#IR?0!I$I'YTr@cN;lw7a$$I$I}+z??m$$I$I=(+X<7~G'IR0!I$I#EVL`0X |8w$А$I$IŠɁk4v.)$ nƅۀMn'6B̾! e_hPdeNfjW ̘cM$D27"VҬ^E$I$uWee<8( >r}K͹qKW~S$I$IcyK26wS3׭r70Izi$Iˀ$I$I9zޒ 䡥rV〳u8(3cXI +^E굁Ӌ `ߐ_HC/4޴ʘHξxkj-|4oA9e~O߿k4\>e@CpyO!}'R}C~! eи&+c?rVu]\71kivˁ7q%Ik4$I$IEVF^ARZe#Qb^ۘܳێ'Ae@C$I$i2fzA䬣#Vx pmibr`}X]ń^7@$I$ikVdeAhFΰ8 )C`6֒ Xh&yF$0CC$I$iDRY3֭v;Vy@Xܳt33$I*1!I$I4LG[2CL=@Va{׭m`>4oJ:r0dEsVi%IJА$I$IPہ7Z,܎:YƲ$m hIDR ,=7P 6Ye(ێDQXtќMv]$m"6f-$I$)k1x=93criŴZ 㾱$fhH$I$5$+c9|\3[KA j7e%IO hH$I$ip ءfSgU۸z9!I0Ш \ ̩T*w-R AПB*7P "+̘l3a਺nN?q/=վQT+c?rVu^EpT7b[+.IU4$I$I6MV:U.-9scrjCLmK-3!I$Iƽjr`Lu^\[7 9 ꥋK$c׍*r>7P 2 |عns3 ELr͌U!YhΆj?$I۬HT""nH)u[$I$IY34ʘ|8n$ۑ<6+UKٌ$IsfhH$IqÀO7~xC`-9qߘ4^$5d@C$I$deLNH>jpnY o I0QH>J^E굁Y-KToHC/-L1PˋGsDzCL ߞz9[tjߨT*vaI1$I$iAVu;U끅y@}b&9<ʥ?FA$u$I$IjYZ7VƓV8n$rvGu;$ hH$IN]V4PB$gmTm&;u@fˮ-uEs֎E%IА$I$I[2v> UM[GZIC4$I$IVʘAhq$9bտ, g_M&1XI-3c$IА$I$I}V]\`ՠȽjev%IRwА$I$I}ìVX^7"9+ބ$I[4 wR'%e i(4B4H ʘ8fۺRkƢ]T$m"%q+@JiV"I$IRt1կ&9n@d`~I46А$I$I}yKv&gRЬ -| tݲMm)$i+b@C600dJr"!KToHC/۶&Ykj`կNmŴZ c]W?ƪ}R\HKz vu#>;O/P i(6؝\/cWP+x33 2=Vkk fvOղs!$$I$'șӋkȘ8n$Rw?pO[S~yrgm%I hH$I- J. VbO"U4rE!֒j/[pD| $ie@C$I$m1feLNDi*r cIMO"LTelG"-/CMI I$ItqppP'74r`d-98rEsڡ-;80͑$oА$I$Icì^Qu?"P_#ZKVj¹ȁOz3ǟm5@$I n$I$I݀]387p!p:`b!VwmE flfڎI/$ie$I$IRVFuNb514o3IL߁<Խjefe |\jI8{6IEdb[RJzI$IFyK&32 uw9{bg?խ~9CXlb{`Ʀ5-HrJ&1~Ek#Iڦ!I$IyKkeG _v$9Au,`h]hNv:}y]˳7VϞ^M~c@C$I$Z{vlC/-[8w.Mw+{f_j}R*IR?1nXDN?R֢BoHC/["+c'`*9bC2&''ϫN.hŴZVƺn-;𚺷>|Z1{wX4$I4$I$i:x(pW `'3 ,[8w'K7b[g$i,А$I$I0+r}rUə]B'gflCL,[8w_;cKא3W.?$Icŀ$I$IHYϬWM6?\^jCLߝֲs%3ff\`$I-А$I$I- #+9:;!rEI!6˩36wl_!ƪ?o=q}PH$5`@C$I$5u%Aiu -ؾ~ %g)ܻtќiyo-[87gەy`B$m)4$I$I 2&8+I/l(]fn-;X ӁUzt$IښP7aR'%e i(4O4ʸn:Q/B&߾r1.G,[8w E: $9e) IYҸ¸j$I$i=oDr)9Au t{] g| 7UZpnb੥E`Ɗ $mifhH$IN2x9cri_99ZQrMj-[8w[ș(+g_읥$ I$I9C IDATaEVFVF5+c%C2>^7ZEb 9Prt}b¹O <4frQ?g_|o/&Ixb@C600pJmzm```WKToHC/[^Y/"g`( | XBᐳ9v$_{XIweJ |wX;MM~}R;H3n8NB&Q2oDz=#[ҫs^HzCh^+Eπls$А$I$$Y[>`ШW A>V $%R*"oP*nƲbG5=$i2p!VRX nK \| i>"%Y 8 dOTgeDZq?qJ^6>_NJ\9x50 ǢVzmHԵ8rEv8{.$IcUOP 4 _Țd 2bTȭ>mCHMbr$IZА$I$i4dev2?+c#!Uwl"`WFNJV*>D(`lqB$IZ hH$I2*zY%4>jJ5f #fh'6(rRÓϿ|R$IZ hhvL&Sn\v+]ׅpmH.\k!+c5r@ 2suɾԟ&BqJjVd6_h*k#XZ hh)܍pՎ-)pmH.\ [i.+{WqpkDɾCrU* 6BS["6$IZ4$I$IZZʈ20Vo>s*V&;7@fw$^ ~!$44$I$IZjdeL }~ N8eCm$K& d[䗱R!xEC^O|ٝ;63Eƀ$I$IT YO%d`&KZ_$!de#^?aY*rˁ?O ήlر$I+ I$I$+L(U++c+!ӠjfBV=cdm=/sB$ b9@(U$3FL'I0!I$I2?8S 2 \ܵjS=u(gEM~+rL ^J蛱#{HА$I$ih1+c-:ܪˁ9Ԫ8m B Rÿ^"IJg@CK7͒Zxz궤!纐ubVgT̸Ρ*8ҍ.j+@ ׷Z<&`,/.!IR׊((pcs7wqtޱqm)+I$IZZ^\ KkCO0[b"eTE {ŗ]lѥ$IfhEѻu4NEEc?3I$IJW#+cP{\5C9ԚdQBɑλ1T^W=QmaW6_蠎$IO dTԲ`jmE2̸x!+;\EQ)/tWr -&P]\.''w]Rµ!纐ubV_>%.%sMY3Tm> <15< m+oHemd2힋$I }qi Q1uIqW|#?~89=fBoBڐs]HuH2*MDKT@(=1CűX_/F;,{ %"3FJP$!q$/%R 8EO~\qLg(I$IZ15xp:?lrd:BH ?>Sg[j9S~4u=O k<]]u4k9'SmGLtWSUsh2}ΰկE̽"Zu LWo4wZ=O=/yJ[NT:O vy\yeDxjiyyZRuQy y?'whC{hf=Q pSs.{Gc^-ko\w[>N9+7fƧnkw TT* |4wkfv=(:m=e$IQNWqquc>>%pXئF$I$#B) &oLUlG#5~ yEucOo~t08|_iJ\"_^FȖ؝3$IRFhc?MaQ&4lߵWg2:HZH\^Ohh N i>ׅ4r_GxpnnrMrϭ{ɑT399o$bGx*k#|ƒ$unh<5uڪFlExœoN< [I>C>lHmrBdPj/0jJI0~Ul5*$CuQ7ϵ@f o,7$^DqpEo~x;pd8)I$I&Hʘ~V=w\GLKM LWR!wo*f"0'z$IlԲ8y84{OL~fWq\\l%I$Ide< x>56 \ Ae>ˎ2 +V{PNL  0.#IN h {6B31g\o'q4%Ԭ|xW=j$I$;KV@@\pr)`W gc_%JLo$IБ8@ ؗ$I$iyJ26O?8j[={ldh뾰/r7[lu+$i hH$IԬ#\ِ!d tR!'2S[R?17IТK2X"[\ >뺐 i>ׅ4ZGq pU?~=/{6>;E{["ehܖd2{.$SO'pC.u!UsmH.|]$Y'e@2}7OxD3$ǚ&d;5Q*R!# f|x6Pv̨6$IjfhH$I:NQ)4Բx+0P5~3R{Mz3!1lTȭ>eCE8bW$I1!I$I(U26'ûz$|~r7JTG(15C ukoR!wyS^)BF}H$I$I@VFfqײx-Q8S&F Ӂǁ_dvM$\4$I$I+^\de~VO`a#BVF!Ri}wR!{vxp=0:#I I$IҊuYyEs>'8jB0 !pRIBɄoCj7JLo$Ig@C$I"aViT_Kh}c&_җ CQWz]"IǀW2I ksے׆4Buʨl dd4*c,M`D4p)v= :Z!2ˁ+bWN^$u(:vYh;@=I$IZͼXS?+DU7:4R{Z|*r %|d3m$I0fhH$IV^B=#3N8QX 1`K@B0{}+=^I)I I$IҲV#+c׏a3o.CJ7 dTA_* gw/ LdvM$u.Zr` h\v+w!\ N i>ׅ4B2!4{U F6$?2w GO <!+co;֩*k#JȖ"VB[K!IR2!I$IZv~f2U5me/YK9@&-1B.^C;!veE?$1!I$IZvO=~vJh!d`I|h& 9y$0E0(r}V=~kW6_9s$Iݥ&$I$] z8w:SO8&0 M#`au`$I:А$I$MpXdeeêFMLB}sj,&d \Lhd=TlBn!$pe`O6_o$IRw2!I$IjፄM=_Bd To^ |}D J/>`g !ؖ./~gE?$2!I$I:Y `tU H2 S5i8`5!(rP^+JܟJy_~<17I hh) K8aMTnK \| u /3qS'r;ϩ(JM~cr `/_̧ V Ah]$u(eh;@m+I$I+I*+c-!CXh,ƿFwGM J0cwR!5U}ծlk&I I$IQU#+cz/gg}ikceT3F[|xfCC MG̱$IR I$IQ?8lJV2NS5#BR1+YUJGJ|}拖w$Img@CV.cL&sڭ\.onN.kCuNw$YO}̆u}?ˁCs ߝnd!rl0/SJSãKo|qs\32LHN4$I$IKfY'^uͭ<4 MNaC3l c_Ba0pM%dem$Ij1!I$IZ deq <4sBǁ$i@v6T=Ѓd}j:o 2cn$IА$I$-"26|iFy*6;>(=%+r?拇17IF hH$IXzBc!;c?!(piu{}慟9f` 0l) >ڕ$IZ. hH$IZU/Y'm]s tϋ}q_ IDATg]3BJ[|xRjxx+!`PfʌIА$I$dY U5e~]f()6L L5tR!wW`SdvM$U4$I$IM1N]QO<CUNLٚw|X@ppzj5x6_4$IV Z )#R% i>ׅV$+c3!;Ҁ pȪ? wzOܮ~Mrq+IZ@C5\S05$u(}\DQ m힋$I$Y[̈́ &i0-y~E 6*6Bҍ2R! s !3v`w6_l$IZ6А$I$-h܍Bh8K69J06n]TE>$+veE_?I"Тf2fВ:Z\^u]H ׆4BYwHB~^OX WܷĨ5J\ɹU PfjXMW7#|s$ hh)|8pN" p]Hi i>ׅJtVNge^Cѐ6 bc?Oi[8㔍u_1P+G7)r 2>D }Ʋ5WʿQ'"IR;А$I$?8l%BkYgU55`3I[׎R!ww* e|fV$IJb@C$I"2z3~M?0 l~mFZx~)r wK !+co;&It4А$I.ȬΩ8(59=o^}gS6o]TIB'y$IRGi$I$I^qB@DB6N f<*s0@ɱ~ M> W07__^; fHNd$I$u$+R^j3$jqU%BVu-Le3'iV/TEۀWU=ol8}&It,А$I.ꕱP8Ds2 1.!5 x i%svc#C3r]tR!QO|$cА$IȬ gU.T=/GlZ@;PbRdEuIűo,Qmx["I$3ew%+c2Bh}ZՄ/GF%K&ߵJ= o|qs$I:А$IȬU싗@(05 SЀW5P*A8)5O %9$I$I$uY}+'YU/nia*9L0+/P*3xp+vM$] hh3d"[\D N i>ׅEfeD ?R[eҍ=u ^tv̗ Y.bR62̥힋$Id@CKa გ.4׆4BKpx !+h%+#\ׅ?8ߛi݁wT_Gh}C ScnJfѬ^B7UfEZzmHԵ8}rEv8{.$I/{#!;c7~pQi]O+*%ҍmT݂R!ǩC@$017I I$IZfߕtf'/2~Tҍw3[b-(rw?5<| {17IĀ$I$-#ktfH ki.j i>ׅ4>qN fSHMtG(15>(rO81= <_Ʊj.I hH$IQ4ܬf! \Q5=7-Ne-sK\M X ^׊OeB0^$$IKĀevOBZ&D嶤!I/$ûFk^Hh^x;QBVl!gw3.J+ "5s5(!a֋JzmHԵ8rEv8{.$IpĨKs_?xp2nhq*yL~D ^ང`S %lmI%f$I$-VB c sW3W\T.>@YMmms#T*6> d2/aj$I]$I$--1uPbg2S5~=2W-N'9~snq?J 閭ϗldI$t4hry;pN&){.R BJ6\px!{UK _2C?ҸF5W/Hu] oOh>kUF&D$Rs?|Iݬ&*% i>EXDU5[JD%S*vZvT=,rkΓ^$u-$It+}2**iGq!C0N=VMl)/B{6wI<} x̰$I1b@C$IZ?8\Qn=J(Uϙ^wVK"4K(/5~R*""uU}x5!fw6_4`$It А$I'Ҹw/4!#4T[GO L sC)Bn1௪,eeߓD$А$I&WzTlL~ggWq}SKK ؔzJ S&d&I$ hH$IRC}m=CȊWrx*`Mj|x?0ڰ{#"d L2XR!wEB1B"dem$I0!I$Iuo zN'':jBVƵ-Nc5!+#& lq?Z@{0x6BVO$IZb4n$eb7aMTnK \|e.i߽4ny`mj<>%)םM8T= 7Bzไlji׆$I]+N|2EQ m힋$I0L( 5iw'de <{:gq%$I*i])/Ui=AhrWc˄o0+ 22,ut g֦.=4Ov7i]Gd2=id2ׄTŵ!8zj4 &G(S݄Wp5`Fx%+P ZG.JW?n"3<Z7CcQݓP#08O{b <~bݭ:\\`;2́d5I6NH&‡Zf2O |peO&Km!4few&ٛl\omMzBɂZ3̝YCry+ Ze2]BҰL&340SmNfe`|l<`ζO:_\}nu:4[Oe?#\%V-@Ms piiGuym)c_e;l{Gu+{} WJ9O%IЈ9~ ajTf5n'SJ:*^GҮǥ1odl DWںEܳffUG.>̡o_}75'w?bOw̐$I q( \=?w޾^m=s8Xc} }r};z,Gr9NtgJzBJ6\?8p!zB#Ä/$k}y)U_^A_W%Nj @:E;"p("࿁lXEUF&$-yFE$ԩ|4ZW@7ϵ碞lɩE-E$ύ_?OO~)J$I:RÛM^v @WU{i?axN% X(rFto ؝ ,I$u% hDQ TB 82_M:O{3`2/NcR͐$kEq|DQ4XG_ئ4<;O.ἶq\]W$I*ߛ {<.p$n36JZU~OJLp WJW%˫Y{17I$ע+U3Զ \ B2U82!IV8 g^YJ5g5!p38UJGI7%B)U IDATeh$I~]X hH$Wd'.NU? \_jQr~ F{8JܣǧEhkB0P;&Ia=4$ELfsڭ\.o^u]H ׆4bVUפּV؝>4qص`!6>|dT%!f͝=gf9m`fԵ*fd2xJڒghDQ/q } xCt"+J(1pj&HV.7'w]Rµ!RJ$}Zwx*F*>f2&FB.^Kh~{=nxKB0ÌuʿL&j$Ihdh%A࿀?N?E"I (E$I2?8\)ƍW/%doT.=6O#3G+r}ώNDxoiN$Ih<.nFQx#I$icnB٧ #ʸ~!~B cR!/㱩l:~O^ З={-$IҲxU>h|'=EfK8Su"I$iIDdO.2?GX|Ä'\0ccrS*sh/r$IRMG#C-USߌS'3> s$I $>m~{ze5"CD{a!>ހ{1M!D

l7?5squfv=p*(!""""2dϦb9[68px3x)H~J.gfN'JM-kR6U#zhf,o-uNDfwl(2o% f1XBdf<`S*#&e%KFhDɩ3u? b{}0Jݳ@lJ"qD3[xicl"k|OJaD+$^ౖ.=w""""h<-i/I7`,22ډ"B{g :6D4fLDorE,p.IDy)%+z:5xiR7ݿN!24kn1{\!""2|al(ݗ'ݛrDZ:q=6w il{QYP "+cf,lLd ( | ,𲖎~iD`ͨyRrp""""""4GGdN,=,. t/i"$Mɾ@_T,LHʺ VttqȶG/6_\рZۻR.15Hd[dg'K6 8h],GVt D4HXC6z@<ttDF4^ cf]ڊfvQoՁk0\V'B$cCdKchmCL*7>hS=Yo-;!cHXˁSҖg֌BdIfF4>ܖ.4̌淹 T4O{;B|)Q^CȘNI^z:ka + ݋7"oȶ wlf~J..JWfV7k=,iC߇ eB4~CLOJ26{Pb("&j.""""fL"-Dܼu@\DDDDDꨵ{13̖QuZ|Nl?rVƊζ4VXx;i">DZ:]#""""! $H3;x.dK*DR CDDDDd$%6%'.vm~+p&pI_KdeW* g] +!""""a&-e!+Cj6qܦ+lod"+Èl5D J4ݛVttˑ4n_xDDDDDUݓ^iVem~p`M]%j=>y%/kZ3cW3zn k26 hAk{w'#mR3om\\[cSMo.+ {?{ p2+_JۊFFƆ DƈG)rq!RcCdK 9.r#ρv`QȘFJOYGXxcn$D0c( ~!RMf{}wf"}N?c_6f1EhEDDDDƟiDc&xXOH?Oޒt p1] t"cCϴWhP*#}}'%j.""""cRÛQ8mDa6BdeW쾉4? LX8{YtQrJyiɪ48 XOX]-Җcfs#*,=3v'p29yUV"#[x&zvt3th{ R)4{H=7T28flPʨ:OƮR?x|7Z:܊l#I%xy^%l0'gWxaf_uN^x N]nw>JdrMrw7Fnpq݈M ط6){.XIirHݒMMX6Á.HD0D3}MDDdܽ LM~0+*KR=fvv/QnqD Ɓ~HSޝdUtv'Vq}^ƤWw?_<";"""""cU{G"3c"`fQC`ƟWr0cz_rˁGJIf 8X 1e?VkEw?`~|F ĄEj+i0C):POk{,<"ȸ~/xJf ǁWm0"c=mKz:t{* SKB'U"['uc-]"""uef;Ef;3[ffkl}̞Uc̓˞f]lf7f~hf3֘ 3]hfklf4,3:?yezcےkJxݝ֚Y~dfَUV0l=`fכxuxMOsg}}fnf2[3[\lfǛ@\lf 4^r?Z9BIDVd'i&25Re׳KYM$\ƸRTK3r:K-] +!"".3{]bDbwf/^nfU0(ܢ{3Shwm14l٧p3;) ݡzVL3~KN>A ""ʃ)?7z]k156d)ɾ_Kz:ۖ)1>+ ~C`z" ,U0CDDO3'Xw&N6vD@KWR>z\e۫`Fbdz}Fv1sWe#jc[3?_ifo5yU}'2zǢC]93;92'f=QJ-D0cp$u"Gɪo >I`d%xy3۝(͵p?o2 XJc124j-pG{,""""2s K ˉ/Y#N`n(-5XEddVt~R \Eh_k.l#F"| N>/,? irӁ>']BLfC6G+s\[x_=?Bbm~ ֿ6"Nd/p1u#f/r'/r-\2׈ӆIDM_;[1݉S {`f67fn_F|8ݻsOi~OfĄQ*]~a9ͬs$Q`w,2vݏ%u/9xqOl99~p`dvK?{;qt2p`r9.p!<@|< W%'$Z'ʆ UޭضA3xc`@.~>E 8^k3|ܽR0#yasW-%O%O4+\ǖfubζM֕qT,< 1AA`YKGWfߎ_T^)O\wW֟_-pf_Tv-7% NThoúO65g6p(Z}\O'ʈ F@Ÿ h4)P@D8IH ӱ!𡾝&%sgNhO i=:?Q"j~-MČY$%ߏ)1~Rp!p;"[c!xf$~!RYzls2?|NϩΪ.ck'Wl+w>w?ͤOM}Kĩf[=8LiFMk\u2EZm5_c&wT34i3}Fu"""""# i=1]g0o֔#jv)\LJ.@msc~  ba"+ٹEK4_ѥRb""s/Qi"wT.oSm#y{KGgo&=s^/qr3Nw_N3j v"03ܬټSs5a'~⨎"8~ =DW},F4B-}d?밿Q 3A6x$?QzEDDDDdw7A.=crӒڏcɊ6[D4l[No$ʷY:=Ε#/͹E]y󽬥VYsG{S~wvd"͵Dj fv/c6K|#N?juFh|*z*-5֫2g>v~f6H#6Scf}#o2խfv?$S_5tM@~Ul.0{5l͘FC#JN]e_8=b$2*VԾ]n֮{5D|_I>uwW+z:j5qT,L>+h)1󽬥K}QDDd8xQx a.|´WK1@~p0Udݽ_/1w2k /#>/%ݿx-QV $}X?(cdek| x;19 ݇)|[r=xT[&S2cO1kc/@3k'~kf'cZFg:8(&2&`fE[DI%G&~a8]A|V,w<ޱDߕ+=>o|LjTazO46&u#ˈtfqn/jP x%fW*%f87K+S^3;8:8 ^ \6 |ȸ謰ݿTe72T1V${g%jߚr%fv9!lvBrCO~G^I17VY%ϧl*yQ~Y o Ӏ$j*l?Aj1 wվ2*~ECd8&ҟE$ؐmFk{T%R. DՋkڔI7۰ČɺFDȸV*& iEKiVƶTNL"e 1o7}Ӊ̇}!O= IW'wt43888Ĕ>sb,CsFNqfUKGo0ͽ}FFKRA3^sQO [c4䔈6#i=( &Hdmd]O4z^*)G@X8(ǑDI Җ.="""2Lө&.3;8QViH@'xhBdAB$KdžL(IlDyOiYKYmh9ӧ6 0e}dlnGJT ̢"-]F~c/Dba:OiV7cDD@]H:0v\yHZۻ86A+;6 XΕk6HqeDv6JSkrځ+Z:VDd* F&z*fSqUiDƇjk}iGDDDDdĴwO+cȼ|86R{'s׮4=c==yHNFn,|hGdelX\^HSYDd.;4Ѥ62Lqyx7|S ؿȈjmNiR"sn'2f'ii `R |8285Yѥ"BXD7&jƿx>VlRh QDDdLiD@#MjEDDDDFL争ȼ\d9|ciD dձ}kvR*^M})D#=6{Jd"1m3,%UPDDih< kEDDDDFDk{4%e?͹M~<ϒm+{:VNk/ #T,.N-Z |>/So) S@Ʈ7WXuѧ&OI3ԟTDDihF.x ؿH$Pn6 |x}nu'ۯ'2z:tR{( [(9wMDƘR0x_x:%?W5ۮy@KGXՈǁA3[J.V DƈU1,"Adž IIbD&r7ƒ&"Xٶ&q*)A̢5D3Y{t\Ȅ4NSCޟ<Dc@}!""23に[޺?df}ȶ&{1ps'#dJ.k̍6M J:-8Y"ۦ$1x/J/L'O FAG9?ҧRu"""=afi}(`+j^3 hw+c0Ȭ8 Gn7pʽ2t|337h3per `iKGwmPXD/)Si{BQnvMDp D|uKG"""4""gCݽR6C U:9E5TgcU*fObaGl9z+v%tRd@Y:6\M0RoI&23Vk%""R[#W ޠ:qf4z{{477XDF[ooo3pgrSDžHBdž5s+h 2oZM1 :.ƇR Kr󈓒[:V&"2)nDb~x1=zXCWttcy\/7wOGf"ۢ&'b[i(_U֙GLFj#&&>߿cF3!^C0t"""""Õ4NK#NHN%ff˄>AOGMIwde=m?$_|>j2`YKGךRD/ffk&|8Ѹ: d`\z wO. cu32HO6fv{pfEDDDDDFDk{l" 1Rv:?n ӓm7MӬ 5p`J¡Wrn><@4^9cS*# D/RFdHWӿwղ."21{rM}UaWU%z y ^`oqD Ɓ~HSޝd^K93w$l1pȜ'u3 \9V@}2Oa8,"Adž4\k{w+#ʘ teZ=ėt$"]} 8QmubDI3? j=*t\Ȉ( 3@Ai IU e,$3@r7!"2?֊ efEwog:XPe2?:7A587"q4D1ͫTQб!k=a~>"HH_A9f)3zJ+ _-8(G4V(q!̙I8{ OLD>1⽥kfؐvEvT0ͽ2ng NNId-`8 x  }i7UfFbD/e.wFLv)s2C?H4v!.~m5MgfU[3΀l'+'&^O7vTn <(6x-p p]1:$>B|y2pY)YZ"8tW27YwL9DyGrip hlf+=F#)!""""V{1{v.Qdg3r(;uOr{2}~ mue) '^+ ="L? }x5QZ*O 5 }^|f=Q3?0&Nf/^nfU0)5< 3;[c Mfv=[xzr9̎tz47w3Z3b73ۇgkq M wP"vsBlfqZiDD/rOwhXGܶmDŬ~1p7ffq2<ܬҲL"cCBl3Fj32k ;Qbdfėܬ;(RQBT,4g&[y$fN.뉲Rit[ܼh"2r\"(Gh8({;k+3&Aafw/UjbF77>Sl Y7%32s#`D_/ ݭDf f#10!D F84 Uc눬06ʿ x?0(}@⵰6T"wl8q'O,vwf(waq#JWC:-<{rDw6|q6 aXfQj#Q:.7w Zw?1:br`l-3[@ҿ˵Dk-O}Ȭݟ6ߦI?>_tU'W {;9[N|tw6W=Zؗoz98nq̲%D2w_8s 7$Cv%=v\-N({& gVLȾhjD c:+ʐQ*fr#fY'-ttm@D&R%f;8?C^Hl:Z:jmޙ _DԃhOg[I{GtLyJ93{7qbu:q*!H&2?v fgg߿'f`FfV^ofr|0#~sr:98v|d"@"+3 {,`_\j(6TOIhEw^t.f>Cu"8Z0#K?9TO'zTef<ֹ~of$b@dax }n=ẃ)6e """"Uw;Eb_ֽDZ}>8feT*MxrnD/+T _db( ӉCٯ/'N~eA'N#?Oer 8:8?wޔ~ S#yr@3_Hwj<IɌ%jfYe{\z8=JyƺCp?.&%vȬL{fqw W3&ff{]>LiVL!ypQf|"hs݇ndf)ZvHHBVR`Crx[}K2?%\и/Q_t05L!fM 'B~8Q5)Y2嬌=mCNT,L&f~YklR&"\RVn8xq/uYSĉ|z7:Jg1om^R*3~\fI9q 4}kf_%jfp Sxi9 (n3q}&XAW xt6f33ED wwh[Q"{gj+vK:9$M(Ov4P+VIX%|[nIOgŎ &dfd/aֈ^Ĭ!IYVe~ܞM;*`~OLbaGE?[RDƩl^;qBnnU&f,/'&U-]:G!Js~l(|+뇱5f~ޟUUMYXcR&TB`31[| wߐzC,{1/'1ƭ4XgF>{_ADiwXF ՂP>Rnj rT{e\f_L@)DÑZtDDDDDIoG|ICA͖|8a58felaJAK#&$Y/I@K%eDƧR0?=^cjѷ"KW46~VcߪH'v㑙֔oyn5LOb`Q>m!DǙK'ݗ=ӀpalqѯbkFdCM#^ h;߯#/}3^u3iUDf61]T3efl6}^9}̺A3[kf6koxh!2FܖlwKsF`G#fH%ou/cf_NHn\I|xZ_~tnƛ*kv{Je_Hd %DI=%$8(kx_1u<:'/\ XC*gf-{TY߿"ػ}>ZWO+cD[DuvSϤ ʠ]NdN'CwUu7Qm#wDi'K_ٞ%g{*N-Rwo2k#QmҐNj">|xBG49>G9}ߟx1?'YVpi h6ۈ/i^[! _b&+/4{:F|fDNb(2*NTT,L"NmGy:?"ߦ'6Al c"^Dbϝ ^L6{Ϩ2)1888^wx̪d e(1XC3}Ϫމ!>?;qduݧjw?)@klc\ͼH6phv{F<(i0>~"⃆ r3wM@Zv?"3ۑ9gRwߜY>xрIC@V׸ h|Y_ e;:.DKdž䵶wO+#8'9:bdDc%q2kwaUǿf; meqavƥ]' ,#B m("(B)qtnݮTyzԽIrs̛=*WYTܑ8q=<~"z{+-Ӵt\4l :5O}!ˉE-s[W$*FPj" $zf|qW ʏo&ˁK· eLsH4'XL} Q_+Tmwg_o SDҕx7H)y;Sc&QD5+}{H";@`+"b|+0?Je ^EۚȀ|H 8Ky}j0"P!T69`fVڏv̶lv~W7 8ݯ1ր Fz,"#m;bYOdžd͚3ĘFt}Hz])?U)/d٣z"KEmi17-rH_2un2zʥ"0|J D?K2K6^N2FCAU96 Bo4 dlJ3 GxHRCJ(wch }<`Fc'Õ:fvHS*?Hf͙?dLkVFD5D"f*6z\`lʥ&׀w6=C1en뀳EdKID c?FV\D$22V-s['IDdqv3;Fz3x(7""'zV3MkQY/Mԣpfv7Z̚32S;o0:bDT2nUnHM_E,ֻfD+Fr,C\dktDٷڜy>5z++N~mmm=S(VM&VԔֶ6ZU(9ݯh/ 3eyPXK%BaYfzu^__M7:uHچuq:u( ҕN/\=呧:KO]zkw6cL+__/_qNJ5ƭdƤO6}ʄcu޺)?C1~…]OMm[l5q)QbjmO5ϽqQשF5YU2äi?qMɄ==ף8zlk|c)|/7ŇMŎ1u=/"2Dspi@tCq㨦4uD:ɪɮU@'{zUATwڠ;'oߦ}ӝQ1m6Mڠk|$:>d$ P&cV':ur:U5㩶|:U54hf3'󪽶/??T ŗO 3OBo.s\*ǸnqL&u6n|ݺ;w/ܷ)^CSm#:ukX?^lߞt;NϋW-&MZ}/[s5u$""4=kf~>:p1Etfw)U""""YtXÏ~0}a~\ڙ'>(c_Tx =~m-tIrƍS|KXXs-z[oXkWcIՄ;?9:"2FM4Ȋo==af'+0ӈƎOMkrAT:ٷ^Y\/6㩙CF#{XtdMRΪ퓩J':G%2ןg3l~׋M)ӹ=]S:WSU#N:jiKDdT( x5gYmu|ܡO-Z!.<?~v,6WvfL\Z|jX~w$M竖_|DB\g[|';tne6a"7_xs N3ӺL^3>p =I^QWW^3mųS6qjhq[iS7Z7nƦzy6i:6}l\pwX2Die~BTS? VSШi4ͽ(ruw 1Q֑.̛1\*n| ]Wc.&&&m r8p \LMѷDDD߆*ПtaM1sv3h7v.=l+lVјfr3ݻ XXUBx"26!&m@Džz:6ƖYs6!&^|6-D6ƳD0cɂyVkc(^OE-s[#?.]TLGdM'{W ^A|('Tbenk P( gXDDDFҠhfBwqeqW'2۷*}]fT"+cwbn>/lF[[ \dдmۥ(;wqT2,7[uz\KDsrdNX245VF@ xvsr>< x7KvqI2"cP( knF3V34n3;VD2R3 V^?kfgÀf!`i9idLbP@~mYDͩ6 JLiz+ΣpIbJ<<)o:Ot}lҵwz߈pqO=f_TWl'"x"""" l֜2+D%HBMcKej^ \|QQ M~r8>8='26g6T 8x *``K2"e~[qgAos; """f͙?(u0e`.w"߷:e2ƾr8(3v&0>p{ITKũg}͉R嫀T}/ъlhL4NN4@ė91kDM"`+Vߵ.'ʐkoQʥ{ 4D6}{BEFH:V >ͮ+v*'"""`L4DDDD2 zv#+c!̨de,]0oV7Twش̦57o f3{Y`rn%Dpj|hoۺz,"""4DDDDX{cWG5+cQvRb{-QP_&P.wDdI'mUaV.%DDCҮ?+㷽enHАp5zGDX:s]Dip.kn{9S"MUFq&I l|%%IPKcT$eN|8;/#ӿ!ztеѷƔ=6DDD+QcHEDDDGj9azjԥD #9K̀/mZ \G4U`U.'WM_:ژH5Q d,Wi8+3֋\n7Ucn!"""ҤRM/W࿈qu%+C5՛@0]`曈6VaR.3Y^]EGSmb+"'u;3sШcуt3;ݯۈ`=t"74K}GP@CDDD ͚3*!t~/\C8.!5:̛>CR.K?ىWH:-s[ EOT4"b38"nnv]Aޖj]mD-%o4}a}OݧN>̞c <vH!/Ufbw ϾBy/' uSQwߏ𐤎! h x?`kKW/w]j<2tnf Gz,"#mK`~B$ѱ8f͙?8ڛ腰gn [涮ˠB0{Fw;Ą~+#3 xS"͈'0Nw.-#VO'zНڇǬx'30տy`}ٮ| N3+Kq I@>NR`HBAL"u :69zO΢7%-#&,7[=F/u:{Ąsj%/CᎋT.bӉo?' =\ XB+F cCj4fgHH Q f!z )'kEq3k{x2 3+8'7iO\""""ҳ18xIn']+yݖ.7{0UFPTܖxs>B5+CuEPT@4FLM#/&2*N2~C }w EdY`3{3Y a/z*w٥D@cGgj 3{Ȅޮ7 ̎$&"<7L3lmTx~mNf;QD|x38-q(̮>4PdhTҨpDHEDDDd͚32+  h~=`5Q{{0UFHZFymTZ-2Dʥ$"q0RX0x>Mw>"t IDAT+NbbX@v0Hz ,h'f7&K7+)H*z[&t7c3?MSR{"Jd=o&񻤟wݽ^#_SEP4!`deKx8i%/y5$ʥ9J \K‹ r8~ p Iw;"\q#v>"Ijo۪@5KrSiA%;cp/1fvwwЩxC`IS-y#_LCٰ:f6tĹǭ|w#3;1MPJpPN&y!/"G3GuWw'^4P4*!xlA؂8*G*+E5+c0UFHj0]ŹͿ'bXen"\*t "ZtmSdRPq{`蟱1|MޚTnޔ+ut И{ ΢I̦P p&`s۟2/*Ls]2plB ^Vc 3EXuZ=[32y{7_w0L`" 24X4/""""deG#˿6^`~@'MڜMVXEx~jS*]#2R@qQ*?uDg ]}4]cً ݗٵDDR@5hܗPZ^3xؚ6S@#KvHDJ+ՆCtyk7RL,,Gs}f fS|*:N0sGƋ%xQ2†"q9<䤬sfDVr"+CDZ DL.RLv#1%yF뺹Xu)8NqTM]+CՓJYN w_Bjfۻ=<ރuJ]-NGeoN6%}+J'uhT]@É6]R5YHdlA,X.Wch|b3QSbı0's]D!2k|#Oԍ}anfIdetK̛|8)\*N$VK4z5a*#e(&-D,6l rbbq%:]TT"W@kT:дYf68*ݼݻ v)Ш|Ǻ8a0 akkNuj/\wިP@ E@c s93:! m+ _14B 1!cchd2 O~[KĿ 24Y([_NmzȬjy)5^KID cJ #VE*?cA=//31I&IɜC|ف8чCşͬVG)g`mlם·e}F}_;بqo1ζJ/-$hPCCˆ|#"""2DVn]ԹPVFI!ʍ6_C4V=~A&gӉ Iߝn ŕD C%DQj=K4řO}{)x{w}qRz^/0u鬑1dw_i8e"jj2fb>} T￯`dUߟ 䣇#M3RVƖGxٕkot-1&R.gs;EGXR6"zL%O?'ʅ,% D`Q3zhoګ"" ";=4Hkν1{RF^1qP/1kxܟ7I[1>}U^JYFơ="cI[[ Fz,"#m;t\cc2Y 2vw"+~feqJ qQ./!&Mm T{eh4"2ԛkt"x\Iw"cP(hѨ4[2gMH7v_'R]p]og3[@|kfg`[1 +w=lK~w8V|Oh6w=ݡqd z@k~xHߥ+~e6. -TY%y9E.$eHKJDS^nD9R ѷCofvp3_wUT,_؏t+̈xz f$W>s@} s]Ym/<+ڶ`fvŹL1^||5 fQnxdgHdeXp/qr]~k}I+wA|g=Ddej Ɗ"RCM3Ȁ:8 |C|>_m'>+*&"c)i-feb2q Ăo.들NB|zQvjoe@{r7To"p0#2F,x5&R(w_efg?ffn'nX)Q>k=wNO̾Jמ%]v^-/vOQ]ޘtpl}4Ratr=4_s}߉$2$kא/I"""" $jkTWU24r?r{0OdLHf&ykȗ$+O{V}>Ș7#dhXXqp >3FGÝ@s.'wkf'1EDL3 ^tsWkmuDS__uA7'v6!?5yYǥ10/3rKw}1=ۍXBMEF[#Kcw2Y$Ҷ?W)Wg2F́ĉ so!^ELѤ^2nSrSWRILT&q:ID CQ2h\ىDi} +fj7m}ȎHjhNһ]_x cn?gK 3ee4r8C"ٴ QJa B|^ dL#>BB.>Kdc\C4 NenpR]IݕkuF<_6Ѐgz;F؆XUDzUDyRv2KTG2oDV=T/^Nck'"7% +UCD,Lj,Oٵ:f70X|~#=P@7^t;"V&H eHб26"~ >"ҵW ^; M2hxxqI4p͆E*68hF`\f,ix8[H2ځ d0"L/##ʥDӨ=IZx992u0 UDDDDrZrJDDDDz)b:."X,Z0o6r8fATLd9ݻm '6ֱ&"""2Аkkk;P(h4KM"Y僲_%ȗh̛cX\*nJd_A7ˀ},UKETJdd\Hd>uuʯ$F*&#rl Gz,"""#i@ 3;?5H<wFprHxqL =62Yo$2rx21u9.ʥD޹} jj4N4(/RɶiD cRl2t={]_dT 遈fh̾  lgtD54DDDdJYI8t:Hiko p&ܚٴ8XDĔ&YEQ.&2,++Kj"ѩ'4ЀÙfv+:w7`f;+L^[DDDddeZlv#DZ%+C%M/_=ܦO$&[mUKRqbf {^AdeTlinqw8Q)ee|(6_i@J4r8jU6H[+E2ʥT`+$bA]:?[dQjU,tw0Q/ee _"Tn>džYj܄ʥ L\a$<}."Tz>i8~?Q`f7>Ct&]1%"""2 z@;B)"""2&"+eDc]{e(+ K ltIm׮WKZ涪૖[TL"z0ͮe/WrUMDDDd0%K43{ ݷ鱈H2Y_ DVFĔV7r8XMJ:"=`!j-^TO|N#_L'^v(cܑ[涮a0  eeBd_m^B8~F׬ M5r8GOwAeVIS+@Ƒ@xR67F"m]5#  Ɣ8`,˯0+c2ST8&Q4r8xp [t<{ZYM|ή ve744h h|P(,鱈:.D|l]/=4x+uR-ټ8%c>vF7mUDž`HlH׫lSSrR5fQ.U(:cI*9%aQV'"1qL Qwld2 |X=uQb1AdehH* =ro>C4.m]ֶ ''ҵL:,_,~jNh&T.4 38 ݭ`f.J7uro?1 z-чL~u_؋ |M,"m==^/6@hwo&WꁎE6df3#=fHN Q=k%]2T6IKōӉ3V=4 Np+% EFX+3 ؋(+u4ɧƍhA4~^X@HS9AwOzv`Fz h1(K{@9̎sf6"q5ggm~N5#Ej:x3I3ݗx""""I&+lDCLjɵv`2ST@.^| ^}enkPKDɨQ7Zvy}v;zۤxen4Lf/I}x=z~Ȫ^10ID>fV!Sw݀W8>ws8 RSP@CDDD5gD)s#N'NVV. a BԢJ󗦒~3^"SiŹ_w6j}y ?`:J s/їqD0|f/Qxwo6ޔn7"8>ݳ V>lfˈӁ9}x̊w;ksVl^ C33|e^~F,)wߣ)~kfg{ O, ʗzs ۴=z}?}Mϕo~%hՏ^,GCHY"N*)\ƆIDkbz$Gc"HY{N_,?DMlVƚ4r8 6?MwGa ({uD"8RhB{cCY\J|_?>+T)7}UXG*Kl>1}NEjk]'˚9$L3h > 8ݗc,OtFbDvDF3;! "(u8X 3ksC+]ĹJ\o$QB]>c~Qf%'AÜNr9 "aCh24IP!.i3[@E+C4BBH( O:&DrHY'jm qf#M)QF.k@Za<$ IDATo̸8-Xz(9 33_"[ h5@ ܽ>hwHcBѫnx=73?w,x%sâ%sa;oK/3 3{"y;}Ԋ. m4DDDd=+&2~M Z2ZT@XKzX]'"P11XMieĪG'yd3UD^Tܒ([+[ L<2Kԛh><c/3kw7ME"(}+Z|l蕰5ѧF Dؕjz:]L|OdPD.>TA9o n~.̨0e&\0#X`+)u`KbQO}i.ШUJPdhL|=ݿ݋N{XDDDɥpz'nK̛rʥtdN 3 Ӣ4>+RɵD)$_>O }&I|g쭿||io}8;]1 &R4Kfv/Znzݨ$2sYf} 딺ZCdj1- ]z݈lwW^CRmD9u'Z{ätZyצہfϺ臱Հ_l.2^X.~{U=.'2J]\(ֶ9Qgt\7Fx111}pnDVMd9/7[oM*؊EMh iTiU buT_ Md(x }q!ҽʱQ(qgW@(3h> }Ȣ0T_ݕhTJS}c]Mw3,3H4qfweƲ/vLH}Owx:^.v]@Nμݟc*1L"y)YQ;n"hC#=18&@DžHְ߈H'$1JdeK̛9cƕ&o$&Qwm zjy ioT2'3ˈL ,= U@;|ʯ^iW$w'j).'91h<1wGz վ6'B|竔)g`mњiG3>F %13s 7*lTkwwqo1%wt"S u#|Xcfw2RCl(ˀ͈7Ӓ^}p>6l#VL5+c2W݄8q801y%eH}dIt"q*zr,Nde@O'cJ&1굋H&pUUzr6}037!zճi><ϥD@uߦ/b:t{a:e3蝹wf/A*Az**5hRQy ~ź{֐J}mOKEd80Z\a*Wo9d"/ATT-5uIDDDd+ a:@`Q*؝8(^">ĪX22k3eN@d#V=URc&{84~?lewzY<'q=¥ͬ KI@PNoZs;=}Geo=%NM߂}fCSf6 x~>}UNۥ"NyA"ݼZR`^~?ۇH|Ӿ~wlKX3;)DZ\|f6jfFH~8Cc{h5M-5//1=| :3"hn~ݩ^ Eߊ Q/~0É[ XDDDd K2>N|3~H|yHi_`E f44p1/#x x "g12VKIR@d>3Vo!B= <2u""RnK7?`fJ}j t"@vw23#HH%L7믩%*)~ t3z;Olnߌj6b|\T8qؓ2rfW_'VT~YmK} Ok8j/Z*6D* 8J'F ꉈHfv$p%1qӝc}8ke*&0-}U*"bG3ݻ-'EMߌ6ړov~;O63;g9sfڑ8'emڊ<}NwZ=+=ݨ$cf^lLD8/z~V_#^Wck"|l̚3<6,+wT2*+K)bo/m Z{ٰP3d4}p 1 hRy󒴽[I2F̚ e 7#dh_E>J#I7ts9И@L[Ḋ[ cf'2$^lCyp/p pq?zdgΔ"#c+}}2;; 'f['`q,>|#>4mq#p/ܴ0"b<*\&zsfǥgnf 4Ys oM ^FzH{^`쑞Vo |HټHY**V*+ĄZ"[~qbߙ~V@{~ygf'vvM6,3{56ddG4 Y@Y{6?DXAde~+]4r|x,Qʥx"@8tyDc*x$*&""x~|1"B|32424DDDRVG~K &2f'|_#j8g] |(I2#@F>Ld "PȾ[ \ \HdbtJ@GVi`fv Q`ei43xE1f+@z~PGF[[c~Bm"2 ]馎 /F8X$Lʸh)+K D!&x&* ,%aUfHo40hW$cjFFHq!ҽʱQ(RI^oZGxHs>C0=拯]Ə Dx☨\Ыc#ee%MlZGԐXaX`l|orRq:ј@Kn/OPmHM7CjJS@tQ쾖<-k1AB{cCD4c-#=f4 3 0P91~VxxQnʗ;R)ˀvee4r8،(Ia~  [0ix)c:Ș@dO%W53"c9d)CY2lGDDDƀYsoL4j>MQ{蕡I&W./&/mhX&JLK̎418(vD"Pqp/0q'QRj9T "q(q_2/w|duIٔK6UPTA!`ū^UZcx\dEA0~pΜL-}^$dr`$3yGDDj8[^|?Y 2TQܢbl \)pVF5ڑ:ˤJ#'aoc l?!FP1ѵzd.""""R&shw^6-"""5 .x='؂ݦT\&5Nm~J URq\6SX1kؐo=EDDDnEa[DDD~y 6P(IqU cW[Zk7Rq\(788hfO`RCH@lvƋ EDD Or_5}SX18 fe*ιȻx*(T Wnˁ{M{/6N9=La5H]@X_yq~TbW |,RlL$( 32nT!2ﳘZs91}Uث5*I`!F+Ђ݌jz t^=7DDDgb/=lU\yXDDDfC2 <. Q?~Afe[:h{Μ̋l 2Y,eˤ XрގUXEsy* DDDDDܜiܸ} 4DDV$FQwan%1X#JˤZY/m[ ➷KNlnay#UZa5 0ѵZ $@<,#(u,5(/H$V/2E>o"XB꒫S:`|a{yw'b yU!nb U`,l+?ªy]>ߌxn5NNjêX1_srTN<x+*[C]Xм03f6Wq<]`b2عB-MZsnx1}\_Ue ;V8~ 2a'aF+K06#ЬY0-("""+. -i؂O[V+-d23coBG [LmbJ.j L`F+6BܽX5X%Q0{P'""""2"'p6`ܿ8wl߱q={;GtYN!Kc^?AZɜsؼ77c0Y,Vcb!E;sh  ܿI2]5ئ,8WtwjaOeRؠo`|S,s ='mvw> XцqCLP]@<; xy_}_"""UUe$bEl;pW3twjW/^U`,rv1Z_҃ˤ`b?w[^d@'N~Ǎ`Aǰ1DDDDDJk > <<| 6ʳfxDDDd$]b2PUDgZѳ/Ve*wLXk07cU8b vtL 2S ֭^ib +"T-WJ,#*.Y3 չQ'\UX~]~/$P<˨ Lrs[ڰvQ ?Ok6Ub cAH>GAHqsCDDny mY=*y:67j6p"SU2&`g࿁RxEl{.mZ=\fbcoKGUctt  5-M$>r SRUn7*2mйQBuXƢIla\,8+΋ڛ'c16?\`{ n7:v s#H\Xc)'2.zy͆Z}Bx Z߈ o[ު 5ȵY m~ª3zbt^lڬ8 1^ ̏J|lFgŞ]g#B} """TyMQq@+Vqðk?}Y)d:K&istwB+GS|hojlH5X٩iv^N8;Z1j Cy/ >}׶UpVExwLwy޷Ǿ8xy}kp"""%2^Ue'geR2ys^) {\OaUfveRX5s N|s6 `^Vc:HDDDDhunMm}Ezc瀥9}bwyއÌņY| y9/cStv>*B`oOwf g^E{e bu%l\kH5cO^]d~2 7ª1FʠaUԎЁU$،̈́-,[%I5`؀7`߅UZl svgwcϷwFT!""""Rv48Z<+8E},DTWF*cilؼgeLR2F` ZlXчӂl\&ՂHb^BXVhĪ6ڀUj rEl_G<ϻ8<=z I3"e2G6_ƪ YsHF40 IDATeR@_MY@75 V֛ۮ Wю_9 xSkЬh3lРVW6"""uIBOyaC-7?ٶ'}j9{? ;?5/qUoį##Ƽ88;e6@-3qKWo7ͮX\>5vC5:6U`aa,[`{+V߷GX]uDbAD&b +M$k#/ZL?HlZLgC"t6tDbԅĨD"94D">ߜH$"q q*Nquőɣc߷ 1ZIowОqFOhnzI;y1_N,8׺uh+=T\ݞOPn~[&~E+ lyNym^skmZ.lXX"Χq*NqޏH=@CcW͏|<|@#˜XjAغ۱ӹk8 8y|( };[o]AXsp >D_$ kϟa43?)ǩ8=Nӫ}zzt:FƦ\| Y__l̋'10.hkq~`}b}￑\t旝~Mâe|x;pQ>=qՆ Kv+rz)8/{ִN?ĝnkl:V{ 7C޲u?20 <iׁp,~q*Χ =N[q*esCDDnP?'-px[H5"DB-DDVPP=k. fy؛}#Vۑ0CDDdk$Yx6jQlجGEޞNmrT3Yˆ+7ut<1qLÂ؅<{O{p=.S DDDDDd{V sy@Oݭ/faÆ̙d:/CئXۅ,0LTeHepW/ڒUgDÌx{#.< V3][ū1\6[ } R/m]`Fy}X`q>dVDDd*=X+͗bacX>Ue<d,*N_|#0%, =:Voq.Ԧ\&ՀUc㝀{ | p/bbϫAUly |7}?c\/{wц- < 0Utv?lpKbazgeHJeR6hcGbƹ*KG Ǟ+*5|Xv-R*^1P6Oy`}/J= Ѝ =zY!`jUnZDY2dK0#2qW/ĮO/=7^6/9 ~-N݀gc/rLP;sh76?{>݉]&z{F "R&C9|,͒XᑱMO.#ʨun( > <خhweR-R .ZN-*5.Q3j {!R\[oxMbe +iy:{3{-o,s|SDDG2m|j:7XUƓBt2[.aAs^ {6-  ]\+v웰vdͮXڑ 2C!ՎZ 0CDDd4YlojTdcA3Gup&V3Ud}]rT\iFx}s2,Vc (JCցH:rH][AlA2]Ue8ij`VFU/*1L e 8n` ҃X1 VQafpW+dۀyo) 4KGmV5^D"QDDPp)coE΅G>"6|6_du@oOw@in]"`?#I@Cl, мʵ煫Ƙ k ;]+LQ ).zn-"""%LgF=| *cć'"I`AXKm8AEF_Gꑒhڿ 8X67f_M@'6 ~mBȎQ!""R*cXh%VݩTu*IÂ=o`cX0և`"uڕˤXq4S+v^Y~Lbϑ;q,ZTDDDDD 9Lg->?7Q ZTQB"`w735.~ldjEALXz=4.v|oCaT5"""s$6!iEªXx+vu}-RXKaEF_Gj=wjip]}kaBcZ=9,"""""R 4DDD@2}pئ:ª -6d,vX\dˀ3+#XTd(Ȩ1ݚ t3woA0x9 4DDDf8̈>cUQUF]eRXXU"P=]uTdhJ cmnZL`Ex56X x5_)2rH،RG쳰g6=lê>7ܕ RoO9܋AEF?vսkD.jlz ^³us{m2zln{ 6cj5XDJ]A[}q UhXx1=/a=/DDDDDިBCDDd$`UOmǮz*n2) 2®(7[vBъ-Xj8 9p>FoC@/­=Zʥ@CDDd;$yUѿ׀M 2cA"އUd [f! 2{.{aR<{/߱ c{N #zNTE֖S|V$5>rˁGݧ:/jT2= ʈylShs-^cAKEv}6p UT\&Մ=ڱtN(sCGi+m9~5aϋc4G-R9j ΍D"XDDDI"""[)ζ5F6M?z;GKRL aEv{ }56!^ @ 1amXq&ƃ^|#k*DDDDDD@CDDd+$aUbî+v}2x.dYdA1]ߧ 2f9\=֓ xx}}an˂jW00CDDDDDd 4DDDf22(~UeˤZEg`a1EvTdP;:`y7|mT0C x=‚!`iՑj"""E%YkO;vN/=;)lEv{p0 f06D> *syG$,ޚ@Ӛ.u EDD* d: |x?4]u ªR~GWQ[>n7h aAxXfM.j*,Uc ,f5XK!B1:VwLg^4b-p6b?'G'DDDDHD2}!p6lb؂u2j 2Z _;?ê3pW jbgM; {쇱 # :ZFD4hp7ž^c/>?75S  ζ_Ga1L[XS5,I5aAr࿱ac/ A0d*ˤjvla [Dڽ`bþ[J */yӶÌa+bՠ`zoͺ9 4d6+ATu}u^Td:"*}c> _c!n@x/lQ;j5`-S 2L8 4Z*>X{Ī1nB«hG 2y!͵V^AEeTVi>u06`x0?2="""}UW ""Rl;L.m*c2jxvy\"[VQ\x8  +&= ;2CEDjT2A˨`F򢁰eԘwV i+ہb_[ <x ovLg®_t;Vq;9PcpAl῰0cI]p^iSoҝk1-$ 0b2§v䡻{Urڥ@CDDj^2]|I55>[ެ۱B$""RhHMK/ê2 *C 5(IcW_lΗHQ!""5).ª24}䦞N]]crTd|[V\|T\&5r2p*㻄Wnz\-ejp1IHڎyvp^LYYQ(h>5o5NR G>0ƢLԜd:r*cئ[{QUFMeRآQǁga{E"J_ *I5`a1RU`2 > tWcEDdFEZF-^4`  б?z 6b*⢍1>WkS@CDDjF2] |ئ!ͪʨ-L 2Ë\Al7 2* }*" UdVی 0YD\˨b.c6 `W`=phsnXe_Pu1! (ư*oʔ9Bs9@CDDjB2=!Gl_!cAp{zs %Ev[` ъ-pW&Wxpnz)""eZFM^kՊڟ07͏X ϝXxzm~~`GPcAVW]uH )Lg`4|k=5*v2f,8 2+F*"\x-L 2*,8ͮCXQ1ZRDlP5V=DË>9c l^@;aXDj!}͹H W ""R l=FlVƿ#ˤbgrAFP ˤvmX yXl;)1j WdE3Pw.j? w]U xmoeUy0"~+6bLs.DDD**4DD$ٝ7omCXȇaЍ݆s_#Z 2)Bw%6TtO@Vc(nE|Pt-Z"huS/< * X^t=XxMXmc$a5FeET!""RU+y6 b*c&QX-z _FaAAOAFeTٜ8 6`-zŧfX %VVDdy&wT>Ur_#/C%a(1E\HP!;,Q"+[>_}b%٥XUc7纏t]s a QXEZVdeDp<7cq$k_;5-H a5VoT:/JcyіQM@O`܊{9`j@svQCnaD>rR!қ XL X,I'vmxUeT:7`=۰I7| "f3X 83O#nToT:/fkUlEhXhE IDATbh@0axzύ tA XsC5""R.ɱM}6C}@).ȘVdhe&lZ x-`UL} #TcԈ".E3aC {xmqcDtb."?3-s̹Ib (Lg_Ue,mxЂgs6NEvH2-*Ȩ L ڰp*n;#ǵF1DDfMQAh@ah.6_\1La".+0vQbLs.DDDdk(Lgw_ |?Ő2\.jǮ? o)v癄W{AF/?7 mk̫.JVakj S""E$b-(w1 /ZG,Xk]VQYhxbs.}/"""MT&uXUF=`Uz#\&>ؚ; Cؼ!djY)eˤa.ހx0a@1vw1gmmZ=-FQf]gN7V=1FBF,n0&E'"""2hH%ݱ'6m[/InkcW 2n={dh^ H]\-|-E8$Z"Rגl*6*^UX^"bxVansh"?w>vQ\] )w%:7j*c ʨjLо[/])=Xu) 2:Vp%&I+sXFl؂HF18H]*2"zλf]~U]̏: gWUT==B8]T0|p@Ls.HHY)Аp#`!"v.XLgb6.z{;-JD taE@fdZ/\&Ո- x96b&l+U pjDEABS‚G*5yXp̺hv‚ k\LjUy熈H|_Q*yk|ߏnűWê2t`eRXq 9Ev{ 2 y'\2eRX[VlvA 2n';U)"".fj=p9SCmcΈ|-h l djŸEH5QL28>i7XU`Mv\.j‚w=\Z=TCH5F(nw05+6 ,C]uԜ]D[FL2*MboԝL 4Fܶb/%5R.j0)PDDDD s*ƷV$VYo[_Gm=6lid*(\&aU?*ecYlQmjR\IіQJX+g],n=, 5׃9`>aE|#Zu1vQ"""Rh'>rKAtvcئ@HSU&I5` 8bA+ ~״_z=܀Nl ݦqX$< 3p c i"S(2"_.kݷZOoÏ$ 4q;6.CvQUc .Gpn$הXDDDĬ D*DvN@n࿀n^x V8fwsqAb s {k]p@3_q^VDL˰Oǻϣ: XFh /1jϋZF.c-B@wn~kxm htTW Ahx1JB!""RhȬK{aj^۴ 2.ņ~ksqm`z Zjla25{ \&|1xHE[mMw_u0Ba˨%>c_p=Xp~$ңxZEa*DDDD nt2EMOP7tw*ˤڰĂEv!6ws΅-@ jƆ^mu |[=K7 ut+QM@/^뱇%xɁݮAeuؠ*yZl$sS+, Eū..JDDDLh6sUz6 4aUW\&Պ ~>`iݮ®d CW΁\&5Gsb2{aMª0b(\s-ͻyA?9·6Ĥ7cXsgQvQ w_ߓ~'1s.ԋT2]+"v.|#qI?^8ͦQ2Ӱ߻@65n̖\&Ռ-8k?l b#,8:/渋 /جCvQaSK}}wrc٣Obwa$V%x^܏}5*-vQ-.J@oH|_o+yk|_^cK`  3XUF2*160@= CB.jΫv:Jl߂ 1]i,ȸ[P{d7ܻ x5",]M5p6*w{\#c#"ͻ(Axq 6)j6O+. +*6cY†uac"h /4BDDD~(А%|E|}ZE2=8ئGaޛTQ\+0OA> kmƓG Ͻus.J f$ @CfÞؕT"bžN2m> |[ XyÀ2* 2GaaEv{ 8 kcKa1CݫVnBl!do| XgX+Ǫ4 CVݝR>̻쏵ZUy1mے>o`F0ιt?Uu񼃗z ^OߥctZ!""RhS+McU7`UBB `Gm-k1ꝃ aX+v[nîV^Lnj {< mZFwᬋb?,\Vx}sku (Zg*hU]z!""""R!""$}ocQ>p6`mOwgMkk8"m~6}$pf9voyp c{,/"[wmՀ‹J-YQZ +c )-9(""""@CDEK}CoDUgbe<k'up cDZS\Khn[h+v,p.DdZ̻hfVa[aPl{c{ZKL /vQѪqBe""""2'hԩd:{Vo/ S,,VpAF;vGWcWQπ_b RX%@my˱߯Ɔߊ- cU{L&\ ) C "24.Ou~{[ӰEUX 1}(Ἃ k 4DDL2]8!~/BEeeR\700| ,*2JxUG2$l.Ƌ8\-A7?"abhN<%wxJlv1bxʈG E=U *+G @CDN$6co/u^&`sOwRqrTVQJy]ưRbTPѵZ6[Ʌ-"Xq,S۰El1pC3JDd:.A{`iXjV"фUEET<\jLDDDD* ͊ |\X{M/c j/UrT+Pؕm]&9+yEQLj%Uӹnޕ^*Wş}búg[,ZF:֞. 3{6b VǰCs.*x΅ 熈H|R_""!v`AKU\&$cӣ|RX eLpwp 6Cf:ab };V\#nXwP]10X < .B_}m>&‹G9ѶPѪ]DDDDj*4DDjk/qTKl'P{*vމKv%C P1\&Մ ݗZ?b͑oy 1-:[E /-_*ܿh^HuC~HMS!;,/H$+u/7Or^$$VOl&R^j۰9ˊv=pL ` deR Xhjl.Flð*f"h2';,625h*-쏅A̤h`aX˨cm */bZ /t^DbMEDDhlxV"QqI ^;Q>K KU$W1fCX ,ȈVdTYsÅBa'bht‚^y+z0ýGoJAUDuq(2`_ C{ Վa3xc]wLʋ /B} """@CDmE{E*N.j|ػn"c诒 rK`qX%F 8-6Uh%WsúV!w Ἃ}~@.y#'KXxGa{ c-T """"2""U-$oRU j"݅7V (Ș>8 1N-W`3c!lB *NƃflFb`fE;[ycX`1}$`WPËўNF 4DDP2.&?cTEq-`>]w?}wX1U kt!7<ݚ#S,ĸk3%l)E 0{/;?'M#Bp'r"`ZX]wu?<;^ꪻ+1FppwHHҹ9GOϷR5=23~>HOwuOe'XEDDDD Q} pirmBJk4 Nl56#c(;ڥ #ýcA#vt V[ ["XC |EF<úkjpxq4+>CNXϹJ """""#L(f\Yxk/+^ otpzf!~E ǵjb-Ϯ=s^܊Cpo~"@aUجpAgb; EBhxG9~x.@CD5:?rK(dĪfΎl3SXQCa]{F`֢RB4XF$6|!FfDv^DYaݍ :G뱖QOcc¯ފ ߱"""""ŧ@CDDRWc"w?-?KT[`Dg?~lŪ1d \ M?"O1sL-RZ kO^ 4aCXv=^ZF=UbĵT]`)A 4d(l(N 1_gng!^*:Yj/UBRm- Xql %VU؃-敖Gdž 19ތ-lR۶cݸH!;֝]pV.u;wρpx~E&|‹SDžȫ>6DDD*VRH$xwpED@{>X*lç{&qX&U-Rm-uX[lAМ!+ka70鷔1!RDyuWIlXwYXnCo3XXy{*W\Px!""""2ʩBCDRO^1GP{jkc waouXEF7Z"T[Kр绁%47a!ƳXpB N)ú#h5 |{`0I2_CD+/b p]MtܞT[K И4snAs?շ3 _X@[[|xPO¾(HxQGP1 8`Xo;7r۽pghE6t‹25Z dwbR!CXgCB_wq~$]}X5^dꂃO%/TdjZkwRm-XLlL} oaX.w]Ԫ){1\WyQLfs/j-S=<)`b Ue:.DJl f֐HR!"2š[bZm/(^j9j/U\A> 2*22Xk 2Rm-UX1݂Æwb]mh@[ýE /8eqX˨#۰,6arV`mêc!E{|AxKn GqG`gTsUӁban­:Gj?)֒&o IDAT*y$};7/f+-nv,-b *r`"Apa^ ,\.b+/?/T("""""R!"2[`o/U K 2X"cWӢ%DRad"W`Xw8 /FE[ , /TX Ty /DDDDDdmH$\5xו'bg󼧇vEd۸o_ͺ-?u^sb6{ 2n' ) õjĂާb TjAwwӢ%t:pp 33oۣ;c"_WvQ3BᅈE`W-HH |xW"xy)밾"}ƚvuۗγ,(j/UT.,Z~͋E2,袌 W҈y3bMn4_70؁-vvaG=;~_E*ޞlߘu@[ Ks oms* -6a+HpϳCEkj*i}!/|lTݺ Db6}{> 86`F"q/ocg-cgocEx k,.af[P^M+I/DF2dEDDhH$S~~<[H$@8y$D @ِKk]֋k/sgWSMt\T[KVpp ?lUAF[(= /#~p9@vy=*CΦEKha/@35W^Tc3cXuV Rx!L/Ddž h 4 ]O *,И;v"2JR_DK=|^9SV#^+VJa]XK9~EƨnjkB'n`<6*l;x‹:ruGŎ:?c5e_0eB`6'zYxIx 4M ]8 nUf:s9Lvz`z'J&ktz<0d2)T8vt܅ϰmdm[ ,dC2̺S lۛL&\ qEd25 lq$drWhwNѝ˿ZfagHm}Œ?kϐN}_>vl?LRr=ԍYZ5co]1렣Oz:-7}Oѵ ^}eU{21qDmjz ckVM9/QX t%IP~<>)Rvڶ+SfWGOhXsjo7i7sJD:>̖ÌD͵UU=?*fTk^sI5UDc]muc[cӳ'6w-]ЋN1*)u 딇ug)""Rhޕw+zDbmgb-PF wA뮓EB-b>,._ߏ+\K݇ȝv9;w}&rm-ؠ|$>nN+}+Vh:vrmJDH4qkk"1^N^nzhD«sF jMG}T4޵fߐgS5{*\7}DUU ^>E )Oͭ5S>R_[;cZkcn^Ա kv8P Θ73.`7>iMkuf˄ӱRw^g;vz䆻ya|gTx u/ Nu pN"""C*WHr#9P/O5u^ÿl l}vvOSUN}7dt{l o͓5LS5ifO̶%} c&=1fCwգ]}5^oV`sӢ%t:]ϽVs/O}^5S+[Xq;2 g7n:tƘ;~r`eϿz5}5=^ =;U=@7U'{6dǏNW3eB}/uۖ.^^{ERuBS^N N)Pn7(ADD"$ 6;6h:.Drd21"""eL ]?|ms>3L2ܳ_DJ^skx,W;.^wN˫jkI`.< fa_jk*a=a<ގ-vBEKJlWsk{5E5|^bqϋ"?.^#}g/^B$ xcCDDbB#/agy9yv_^ysFehڞJԣÑ7c 2䂌{q`A=-ٶ个Fw.U[ntbggΦEK4CFrË*\(JZ,}Szpx_ g<yo)1uDbyq!.]얈cR M;R2Rm-cwb6YcT.iZJc  1nKwKA1g Dž `&x,8x :]X%V/cU+ */bU /DDDDDDF ;ï*Db:i=:'"µ"qr?K=!jkibd>okq!F=Vq26\ h8x x;-ɎCd0\xo$]^,v\+yj"CXxE'n9H$&nz?>wR E6G=l?&`>֪f.yl9N&M(6ķ2;T::Zb6Y;S;#B1 \{,ջ3̣!FhpGF(0;A6Oor d\8ϑXׅYrg^䫼(ɥB$l$Ӌ/"""T?$"r'=w(ŒQ4lBR?^+Bʡl/Uup O?b@7YîIb!!c69Wbta?2þR T_L*N!=))&cGn=/㻱cۯ.2px‹""""""RhȨ~]Y?oR#Ƶdj{lQ5,KU.tRZAFb.Z87ww)͒Q3A,~%=\yQcZDDDDDDʗ  bX{$X{"݁傌COB^:El 2n *ofg;˽w] 1 cc8sj3fFij8tz2ODžH."džHJx/Z*Z.^XEF,j/5"\k$IC&Vcl [U õj`~s<$ ,م:#]DFDuIXP=6,Lf`+Xx6}\M0;\y@ᅈUhHIknm?k/]Yl÷j/5\1 'l{\?[ ܂?dbU4cY@AlfCbUI(P}, }1^;0߱Q+{>r mT&t[V""""""RTQBT!p |RWOQR#2k ,Ԉ0E2\.vR 7`P4`j, 6b- f_>O8I? f (vË /DDDDDDdQt:$ɵ\{wafDK-vb{t:}0rX[V`f&`4H`Aƈ. 1.|g "Cý-}vl=8/AЁUӅz x=޷~чjVb*,"c#L&/"""Ť@CDJk/#l8sKT[K 0x;6Иł;ûE2cpgPfVa  1n }bAVGjy q:jV̽IXxaՈ "Rtͭ퓰R!}X{԰q&ӁSb6]ҿ"#3BJ 1fa!iboN`3X ti F'bIA<񑯫;^d C@CDƵ{/DKWЄUc\ɳ Xq#AEƮjFX{󰪝8zdp/ W}Aާi. rE-Y h X0V?g5|"붏V_HDDDDDD* )RGK} ،K 9L*.^GuXآΑ2Rm-X1H߀-gv=ہI!6cW`+C |$Vs/N*+.a:9=v?GDDDDDDD 4DdDR_KEƆP{ZJހj {MvTdt~6}} 6ܻx x/ {gcg{S}Q ;8{oAgՁwO kgH 4DdD3&6^jhZXkcwoɳ܏[Zw7-Z2lX,ĸ훊-8ou_׹| [!FHÒѡ싱'8}awcXS+3 ]> 4Dd5:-rW)2nhX;˱J|^Īn P !4!bFwo  {wM=$qXM}ы͐1X(?ۯVo !!~^]=RU9†{_+ l. Xm)5,gZjcv u  ^aE7C ^a ̾BsȭD>@uϛ^HDDDDDDHh~Kӧ˓ɤVX{x^iʫTt:]!Cx\`"k)oBSXhZ$3UaC߂x;3_vFMKt쯌1vc-³/vs/aIU/CJJpt\d2yEDDhPU;"#ϝ=7o/u N崗Dž 3RcF./Xq7V5òC؈U\3:llAlټ 7!/OαS}῏`NªOw?{v+u;R[EEYUI 2B$l='""Rhȫ~2^5zbR$R r8a7a?hK!3fwcÀKݥЙg?ǂ< ?3ǒX`V |{? ?#4h>s |RwWSk)5p8g_fb,D[J >YP!;[Q?ȥWasN MSއL~۩>w0s/z 5@^DDDDDD (As KMܽ"^jvMcsoq_م7`g'=L-6@wY@|эXs7\po-0zlP_}񛍯tLb0`cVb-ȇNCDDDDDDDʔ ^.bעR k:k)up`7b ?b6||5WV^,ĸ xMZL̾N^L=$X_bꄺX[ObA ú3( V_FDDDDDDB([ۧ@ XK U:$+R'9O \!9Jz,Ę縯*~ۿm&rCΦEK2CR:,['aT 9vlCMŞl=}x CJ OC{:JDDDDDD)X _'f8T^箪T&bqaKm=fvfc;x%w6ʺ@FýGZlVNmo'<,Ko=ѿ ǖExĥKhnm?2OP{lTm[K:D!_+VWlNFCÈ 7xh=p8Vh ȉT_L*.QM{ '">؊r~gJzS`#"""""""%p20ܓ.kذ'cO/TZC:JDDDDDDD^Uh>ԔP{~\ۦ؂fg1r9W܏}&h%U}fV>{RwiwNjZ$jOO,P"Vl0k ? #\}Q"""""""RrhT^h{;a3*kTN2`Fͳ܉l:瞵VR؀No~uųYQ`•ʆwo HT`x1t?n=8A먗]XPŪ/s/T#"""""""%OHknmzR_nBK`rl@>+c)h܍-w`s1z}?Li^7`ވ꾗_] XUټRwc ]:CJL{-^ϓ#q}q?~㞪[G=U.Qj6J 4DTskkD'G~ |; K6N ,܄>KEc,wjZfayЦg` ?ۇJ. ӴhIż*R}O0{lڿukn^ `d+@C4OKR穠R  \ Lʳy7pVq[Ύ~5A0'a!ƹn!4 5ܾ,'020*,eyf_TǪ X{?䶎 ϽP%-"eb>Lr-b]F"c3A5RjKc8Ɯ/c0^Œ.l.FvD/vdžno }x,6%:aQ{/:JDDDDDDD*  `lR$ͭ?$ԏRۨRb*6 Lr+U|/c!ƟpK}xHT՝UU9y٪j<1N#J1JBSQSCH0{]}/ơ^7{ ¿.":.D>6DDD*V.V*Z.>&1wWL{)7x6?`7p+6~,Riђ}!h%$p0k݃?]٣Jbp9~ \Mro?u(6Tޯbh~9DBCdr>|p2*T[l~'V/ b܊ R^@NbXtB,Ę,'{B?~E>`ZI/jRsT/akWZ<Q(Q!24k/u|nw(Rԁ<‚hKA-$X1x8 =0A%F xUe߅/bN%Ͼځ;vQ+Qbm­e[)%"""""""2h~Kӿ>L&{_YskL[X5Bԟ/P\K[|,0 NY-0G[J ~+z ZPlb!۫s6mxo˚qX``ۯP+8{}I +}!q!KDžH!""Rhcm;"aw jk\]`'c)6T;RjfX ϸM۰ACn@wS>m7lP+z`:g_Lxn`%V}{?3XxP("Q!R[-G݌Ueߣ~a5R L\ -Jـؙ]b?ܻ`'WWa:8`EB†y?܋Ë=@WU\! c V}q"*}/:g V̽x x5?;\}Q""""""""%@H inm1.rw V%0Kju*1v_-:-{U'`Uq$h9U ,'CJ*N.U_Z}g_ X{mj>,x 06Q )A 4DJDsk9X{9:Æ#ngr-bs1.fĪ1nTCt`!BޅT[K-A1 X 8.=;ܟ1j_R}Q,pw;ܖR Юßр}Շ. 6EQ7Ax% +sS-/jj<܋'4 B$ xL&բZDD*  Qx4>UK)`+Aqa!agGbds&|lv™n~NͭIX%ƽ{0Ri%Uڞ>gb!->iaAb;QSH2J)2"t\ """Kq Ɨ"wo [-RT#6@36",hX ܇-fTbtJ4bʼnXqؙ-^htaAF/AFZI[ّ۫XpqVQswc30jyX(@ p6`GnxQUG""""""""2(ͭsom@(T[Kp,."#߼>c69 XZjRm-Xƹ@뱟tu7]T)qY5AUE 6Xh1a16*,06s/Ů @Cd?4Z ȏaƂ^AXK6 1ځXCRׅ"Sm- gKV)p V)ϓX+{0En,(y .Xŭ3{ zmUknW():"[hXLnm׀~+Rz b\Hvf,X$tXq4Vr=A>,F7p?p6cJʵ(,p8kևULX[8``ElLʹ(R@Cd5:;rWX 3JbA ➏ST4܆ŠجEwEa53ox=_%4˵TL_`- ]BՃۀ/`gaG\hy""""""""Rh Rsk}=vLD{)RPlXˢ|V`!RlA 2Aآ1s:p;Cw0ʪTGw6QWSŖ\.Zb?jp f-`*އ-DDDDDDDDL)Аp9f,3| 9`WKRKRolnx-Ys` Mg_ٱWdt5-Z2+BmNZjYgb:XKj?"M0;U[Bn߰RObFe 1갪j3{O\mگ3jy˱6Hn{TwӢ%Eo5XͭUxhbPg7jlp@(KXWXxX{QH^ 4d郁 d2;%-l[ވK=Rm-upˁ*1ڱnw{<ԙؼc/:ܶ6*QN,x30Jz޵H Bm ?6cm^V%2n16pGwfj^,QTt:]Cж,~g/"t\d2"""RL 4d( [O~+mlzHrýO{_- Vqх򰪃7aCO$wzV ñ`wIrm~C`@>kX 4E]ӈJTaW`gP`.l-ؐ=vt/p-S0!)VR6QX0s6ȧ!XJ Xh"3sy[}뉈 )Аھk/uJ'w񇵽}A1=Xp p+b$Jkj`~r ֊.n?J1-ZMD> x{P}x-XI AUE76_n .Fg """"""""")А> :^r{,aKZB˰f`wL^{)P ?bC̷3CG9MQr$p`Cas=<}nGrӿ`-^ )} 4"EfeLܽk/9jk9 1$w^x ؁U,=$r/j` X%4602!FI1/5WS\Xq.Vۻ0ɪ_0 ð8hMW#%ј׼à1 b"j^WH4j,kK%/ ""[XaYǹEߩ=CT439x1VߍMSI$I$If@C600p pa__cS}g#/]v𿁟s笜8 أE# dOӴyqar1rb 0FbO<W򨎞""/q݁l?-G[ ]𱓷<;`t_HUdߐF_H/j}ݮ$Id@C䩈G^к]}::`9+{#>~X"ygl O%u1yC:[4RE rQ`o/R&jVE#h ɣ5n#/RYSC طE@W |1:q;y Q^iVŪWGG_I)!H.gdmY$I$I6V@R<~1c^b<8h Fz{GEN.B6Ȑ [ƶQ9.Rq/]D-Pq?9@h I$I$IӖ M +VzeO%h;Txzr -o!sߝDM-2o<-fr`(gU{aUÆed!4h ג&W&Tj G[H$I$IiPX 9` <*Yyy$k':D M?7qOFE9C`::F xV$I$If: /ow_}ʥQ)_Z.֒ף5E㿷;|Ë~wKaC ka _[inm>Qۖ7h rߐ$iƊZ)wu.SiŪ__:RS7sVM^T"1SXCTJ=[ܧ Oh^RlGQ r`jVOeB$I$I: u͊U X{b{;9>g#1gdXݴ-vۺ[^8mҚm\;D;;,g3y$Fy9XϱQ/$I$I$iQ!3iƊUOxqݮ| p/+Jr4%H Ӝkn\͇r϶=FϡbϏ=80mw60xy1z[ ׻M5 !I$I$I#44aom+Vo eאwh9w<%ԉ ]\&/kn+.s~ˆ}vӂ֧{:H^\M`\Gj C'400;pVq~! i44Bj7v]$I&KRJf*ρn^E(>l=]~vܮ=[0\ |xp4gxc^?l.>-ؗE9<]6FOȋP{ cߐF_H/j}À$iF3с88ӀF{RJ}V?~rsכ-}?sb['3W}``o78EkF 0(OeB$I$IfMD~L7M'EDf OC<}E/[䆭91Ҽk"15^"'3Jk57}Z]_o!,Z@&pJ~?pl^(6Ny:5ujԜש9Sc^Ny:5ujnS.Ia@3QSLջoᡬٝDZK9v<#~pŚ5|mObU%I$I$IӔ6[ ߿UƔRamFoT+{=}my!2;ȋI$I$IIf@҃qtD,?+J)mB5%I$I$Iv.8x" RWM$I$IÀFRJGąہgKsSJf*`ە*bOԶ%e i44Bj7$I":wu.$I$I$UkhH$I$I3!I$I$I*544a ؿnE궁e]]ToH/Rcݮ$I I$I$ITy4$I$I$IRА$I$I$Ig@C$I$I$U I$I$ITy4$I$I$IRА$I$I$I7NݮTDm[RfߐF_H/}C+RJݮ q7@JiY"I$I$IR8$I$I$I<҄ \׷um```.pTq~!h i4Xo]HM44.vE ؋'~!7h Z߈nWDnr)I$I$ITy4$I$I$IRА$I$I$Ig@C$I$I$U I$I$ITy4$I$I$IRА$I$I$I)nA tt"I$I$IRА$I$I$IS$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$Iʛ hz+}]I$If5)] I%RJݮJ鳦u*_H۳oH/Rc)9ݮ$I M)eݮTq7/z i44Bj7$I\CC$I$I$U I$I$ITy4$I$I$IRА$I$I$Ig@C$I$I$U I$I$ITyRv$I$I$IZr$I$I$I<$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hh\"Ĉ菈{#bsD_gun~"bVD(">?"b0"Fį"qʛpS#Gq_Qmو8l|ϲ9+ʼ8}Èx86"weߍPy"HE:c&Ԧ#7"Vч"ⱈxD,9O%ThDs"qsDluqcD|5"ͱ N%""o#`D<EYWeL~q";,"*Lt$kYD|""n**Q69(7~WFĻ"b^eT$)%iL 8HMpfh^ E;(kB|EMs>(y jSƙe_/Swku~nBg-xx$h=y.N6s}pws\7^/d2͌ W(}w` p5{p6pˎ Fp-H`a`VԌMlи 6cM93x ڹF;xw'/p sM UyL&4sR+`> _@Oۀ9ݮiz[?Яmg8_@`O^Zq?w\m9T?nO$`xLS]J?wkm7[zL&ifW4}&y^Snٴ$Km'>E פwx8Q$z>`JeҤgsG^O'~$4#@פO\2U6=I_n,q}c< 1}^ʳtq:,e:]z`YKJmSM#rJwD" K-]_乺d2fVrQpIm)+'*i_>ke\RhRJi'(CJ^Bq>-mwZơ-s5V;slw!/72ED_ .g:le2T\aG;MVROE?^RyJisJ顔?&o5]KUiߨKf$O[888~D,hRti+UbTa_[:=i7쀺hE_HAJ&'}y; `;RJ`2T􋪴zտl`jJ2?'Fļb۾˰otAD |#SJ禔nI)=R>~UE]:|:?KiGh2}UNVĀ:`i{6y)n C ȿyKJ-92jy:=Gy%Q12Çs7q?mz*EUTh6_%- Rl7Zaߘ"b6WSJ4ʗRvMg)Hm_QdNG2sT$04ԑ=|?M'71wsRbc&}wi2nly:=v)yeO)[*<=,/n_Q*}c7MF}MhBmz4AE)_GZd/[T7Zaߘ'h][vZZZHUWFhjv$<44*nm!"U_/ue7eJ:<|B3 yve@^PsT]w\q{{J&eYSL_tMO{&G =[+[ F;T,c=]ڊ#F/pL| In\YnvyzIf &yNj)"x蜔҇PdZύ}x4}8G^eȯZaGI5^06R{r1UōUUThr|}||'+77/ne?KiG~4o_sG߄v~HƆwMJ}Uy$I3IJd(K_r?;0u6Mp~vq1 _zn^uǞP{v߻p=}{1,^:vhYpggyRwïh?X%^/d2͜ _bR6n4R,EYj_~E׵8M4FTh쟆RbWҹiZP.XDYoq٥'6=ifZͮij߰otPWUg)S6mTw }g8ʽXئ]mt(= 9kSJ^&d0MD ueYݮi1Cۢ OTG/n>G!tOxۊs_1X0ﻊ:P<PyNI-W. p|XسKu=MJ鉾^K{?r=km߰oT-|? h%vm?Kڿ10Q]|xOTx]kt(-p-y}uź ﰞxL&ɴH)!I$I$ITe=ݮ$I$I$IR;4$I$I$IRА$I$I$Ig@C$I$I$U I$I$ITy4$I$I$IRА$I$I$Ig@C$I$I$U I$I$ITy4$I$I$IRА$I$I$Ig@C$I$I$U I$I$ITy4$I$I$IRА$I$I$Ig@C$I$I$U I$M8;"RDH$I$I$I$ hH$I$I3!I$I$I*π$I$I$I<$iƉW R="EĻ#6"6E/"U8S#qcDlqwD\F ̈@D\E`D<WD#bFb b4ȓ7y ۠E]/mP;RJ5ؿ jPn/Wlp$I$!Ifw"5l.5#bws/o^,^|8g!rmK |;"浨*P(^T'-s)rL`# "nQ(/r0A`p8x|="zꎟ5$I$U#4$IҌ02"RJ|xOqwnJike %H)]3 U+p5K?L)}nV4x^Fx脔 \Oj-8.. e|:uϦ$9h1P}G\)ok I$IGhH-P fKµpGz-Aow_&;<36`FQƏwG.j"b-ݳ3R}}v $I$Ic@C$4O/m$X|}C)-c(_CRoEĝXiD -YRɾ[K٢[۾y6)6jb!;-Rl_6K2&t $I$I/$Iv"VFF4RR5">/+"zRJ4:=}Z/][}F%lc-dHM;EޚCw!5$I$U#4$ILS h\RZ"_-y9cSȋz 7"58瑧zyEpSZƚt[jaցf6xl@$ITQ4$ILsdqt@Dgd)\Jk "^ \ιxIq)?K)8t{J҆bc?a ȢRt 5$I$U I4c'w[M;@oJ)ݕRzJr/]o.e-{D3ɞ-?}{e.m<50k I$I0$i&4PQnj c\C0wB(Q6a ";Y'ؿ}me^L~Ngjs $I$If@C$$QC5-צrxBDixxyd ʝ |&.pDR`D;),\qV#b^DXx$I$ hH~)-tSos#qPD^|Z䅶ƹqzD{EK#^$O~/%q`D|<?r R0"Gv"F X`10@pR{/^GRn~.;|@$IT]Rv$I4Di.N)H$If А$I$I$Ig@C$I$I$U I$I$ITy4$I$I$IRА$I$I$I)nA$I$I$%GhH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$I3!I$I$I*π$I$I$I<$I$I$ hH$I$I dhFldIENDB`openTSNE-0.6.1/docs/source/images/benchmarks.png000066400000000000000000005501341413546205200215030ustar00rootroot00000000000000PNG  IHDR}rsBIT|d pHYsIu8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxy|\u'Mt_XEP qU ?7UQPqYD T@)^AP޴df4x#sf֙sf|>B@DDDDDDDDDDDDlMc""""""""""""2| }DDDDDDDDDDDDꀂ>""""""""""""u@A:HPGDDDDDDDDDDD(#"""""""""""R }DDDDDDDDDDDDꀂ>""""""""""""u@A:HPGDDDDDDDDDDD(#ٮf O&5q{'$1fof;l]F{["""""""""2q((rXX2iWCs$zVٿJ !l'OnZOq_6ف,3̞ˬ4g$fp~ɜyN/~ٓfK3O3۶͞~Nv6ϛfx\[l3{Eo)o^affaf%֑=Op&@[f=9)߮u}.r3[gfϚoZaٹq}xx, 3ۿ̲ռCf6zƂT>}3n{nf6{3loT23;0ߧklu2cr3-R3{̮22 ?2T""""""2z*H1kC:([mg)fB2Wհ]B5YsBXSfI3!GjJk5,߆/u!!ef q^vsَyx jd389pso/5w9KϘ!K+Xg9)vy+dfAۑfvplahO-lKq{Á_4v=v)\r i!o'fzpC .$*hU:[^v?'}]lf!K&\XlsJ, sl"3L?WWQ>mVffsi`Qt;""""""uNAX`:~1G.H~ˮ8Xw"26E|b廝K'ٕŁxA8]$Rtt9fCfd>s4>fvialo4|Fc#f/|F=q ?V&̒wA0aHC 8.?ͺ3~t"i!3$k3ύWŇ!v0̬/%%""""""uF=}DƧc_ΊBD^!~q /MIEO:J,=YQp}J7 *p\FV%> !܊_ļ /&Zz$BL+9׳\_y}|fvޟ,/]@ \Ywr\|8O$5T-3{8ylf<8gC7!VB.<%` FXf#yLVC|1u|?_$$<{1i $!$(""""""uLAX8=N1{͛:4?Ss?i?m&~}̮3'l-1{Lfi3;- 3ueܕ ga%K.P CeYy쟚7f3ۭmfmf٣f#3+{7>4>xLcq33ĺv^'GY T},'y k=KdU$0BafpҮXTי{xd%pΨ  J>oɷj6bfGf>'`l]e3<@:2pU5UӃ-Gda@l~e+@JBXg;n3;x2~6H %eW !glUir7׾;}dff/ ,/Y@+?eh&laW6_Gh13;̶ų|G loOb忘%W9 ?|^q6i{̬ۼP{xyZRG2Y-#~CyA HG;c/]jf3l3ѼXfnrY3k!qAT̚yq3Xՙ 1odrpkf(%=v˕y((^JA5bk4x] !<z-Ecwq2:*+oODDDDDDꛂ>%7sz|;^3 ¡ָZ헹I/:& /n6.ۜy~_3>3df_ߣb f6?ױ$f|̞_~Q^<>SKΕ#4f!IC?ϛ!Ύ'z٧*| Ϙptfvk̒\afwqy_ow,fL`crĘ+N>Uvgz#_$'"""""R~4Եm쌃c=͜ƛN!,!7G5Qo5n3@U@1ޘz|&s\݉_tzͪ0pPV>3;8/aby!(BKw\32"v3G\X/r`'2u0pFsElf~J[016N&m]ڇ*?Ii޼&%fde_mmWeR@̼*g6BIJ +)YR< 95o8q7j53Fz>Sof_N,Zv $!V<ҾWrLSHKHU|;޲=VYU6:瘙.,sH#[/|!&JVHG/'lҋa{fqTjQOWYf62+0U+S3ޕu-2C,] \9_Cn,Sz>;cr-WYbX+x~Ɩ YS6T)N*jFIfvbM""""""2#[rMan!?s9Ulg*,) nsUolPb0>%J(B^h4poP^j5_KBdc23{Нw &Q}]\܀ߕ}RZ|m4v̚MdMo>]݆ /c54[Ї_8>4<3!MTv?'=͞}'L? d0L(GgW^;dʾcfW]W^fTyLvÃI¢'M%=o~eJ ^uL% (&""""""5Pl Q({F6P1uu0|Ex 7K3R2se4ޜHo-dn[r b;5Pn0Br uA-b4?cXg5Y?*H-٪!nsZ/RU۰:;ڵLǐ6HN\g Rh(QCH*Ixx,7,>7OUlAV%8;~~Va?p (nWxy<{jzE!ϖc]6U|e EI2w5 7RTSA7+w6{z)i05/HωWo(˫ا.g/H̶K{Y 7o_m$U4;݀gl"s˟fT5ֶ/I37B%M%r[3wbќ+kU۪D*HesGjRmr<L\K\_L6}cd\2I<n.4ط xC9~?Q~߇z~ "]_ӡ.o;kfO fCۭƃ!GkXnu~lx?$m=0bULq/CX쟙 !"| m W_;3bz~7Sr?0n !a^df/ʙ21w̙KqVhL'cvGWdlM}#pj O1YC,wm uyxO+Heų']hi KU|3|$ lUXlđٗ/{|"gf}j)އgl3*Eg a#~nfl<  <q=#~E5e2BJDV&*+yYIe2pYJxɴR0i>^l ٓ;sTiלx<Ҳv'08V v̓ͬLg',j 㘬e[ }C !T1XXfrͤ[T1ef-O]{*&MɈlJWBX #NiN6m*Xfw0iU!_dE,SO›W-06xyUNZYn186Eў嚀0JzXl?e}#/O 4 CkKdl"x\\&1`89}55s!֕< !?}a -XK]OM |^@NO,iwBb;8cmn"=!|UTtYp`|4Y2?vb҅BXH#PGYIĒs+_Uſ{?Bx>r󛙑L3mٞx&xYA_OAyqe~!Hi ]ZRzM*X,)hK!_{gU{knn*4F㖙 -N>PXSH3/W[')^JT ϮgUlųK}Κ١ cP~8L&}f+iBq953{lf}1ܷj{7߁3KϢoM~gs.0nkkjWkI}Je'cٺ*0 kO4 }e/1ļ?&91)fvBI`53/ lbCR$2/n]p^nG(#:7l1x;ܶF zGKJI6Pw>)2 lZ`G\!E崶/u5.%-=Gkf{ 5˃ 5HGYװo~|#v!=~r{,9qr:,ʮkiC鬙7do2 'f|/pcy싗"p{vlf%oM_*N?Bx>_\̾_] ςJA B7v(#:3J\>G0>(?vefoKUm|x|9^I?WߺGsW(`3~{}k\wGh-fгTxmroOx8;kf' {mM߰$I93fi m?gJ \`fWqߦe>L\:.Y?p46ںBwwW$2q?rgynA~bfϋ_秀c2 xZ<&+D<oŏoªOdzܞ|!8?v?KGc9 W}{ `<'~ ZlYf ^D]??khdi8!t77bk[JsK|*uzy> IDATvI{⥞J--&TOq8@S.ɬ ^Yx+1ߝqۆ^_x x,?t xw4kKl4<=,t{%+E2-1q;ު|wXupp/svƒދ*Yɶo*sm?U-5%CSxU3*ُG򎥜>=eM}u#gfG`}'u=l|;^&oEݍ&><2B݈E&0ۡe/<(SjE+񴢊} xH?xQkX\a馛n馛n馛nMݤ4iOgG??gG |A},TReG<;aDIAb}M 3eݒ̃ܲn9wpv|i/'C3b.Cˁ x0OۇңgױO<{!P(Hε1GY%}%NiiL< !<B8ނ_@_맀!Bm$]f12O;CFsp~L_?NWWBo*gL 7Y1xx-߾>x*9gg7ȏx6$~ZJޣkb_zB!o6N !!B !6w &x@x`l-g+\@w`\zsgKB/!|$M""""""2>YaADr٭x+Bq# R]30'@`q1dfx&!U]CH GK6ͬz/9s:e 3{`d{$Bdz=a5lP˚f3GyWEDDDDDD$C>"㘙][Bxfyǁ߄^3ۓL<s/^jT?d,C>!=ٮxƇ%Q{/M=JxTg|{,""""""278'z'އFk[/? sd4w Gs["2#, o¢7LAo xx%peaXH#SGDDDDDDDDDDD4)#"""""""""""R }DDDDDDDDDDDDꀂ>""""""""""""u@A:HPGDDDDDDDDDDD(#"""""""""""R }DDDDDDDDDDDDꀂ>""""""""""""u@A:HPGDDDDDDDDDDD(#"""""""""""R }DDDDDDDDDDDDꀂ>""""""""""""u@A:HPGDDDDDDDDDDD(#"""""""""""R3fv5!"""""""""""WXzj c/"cw'8¹mg, Lseger^dm>`},rV3X ,`ڻ; ?ZX <ѵ1o="!)fS,><kHύB`ow-4~>qo2|$`")<@zq09ܗq_% \<l~V*#""Ҹڻ;'N&5'^eMoˀU➎ """2w-E]'+Z?Ҹ&]Sg_ojݎlڻ;[mi,{ewzidIφxz 9˷QFiXe2^jSK.<˧X5lڻ;g f?w/Kg8>脸,\il*জy,<}9yx s6Ki,|pL|`u|J^KV<++w4}d8/m#"~.Z$%*LDq?<@,Ry)ʦ韓jK '˧QO#wၟH#à M鼐q/N|fQV0wxI/z:*?=7DDDN)#BPXxQ(:'\r~}=>-L5(SW:wkm--O{wTw wnp wf~xVrb&G,鷊/}DDdپ(08ӊgwldpv4s%%[QY[3װi&eNt<`k&ǵ|DDD@{wAO쏗}:3tSf>`EOG~CH.}d\}xEP}k8ٰ¹&M2wLn뀁Z34d|⼘g x >6Q4)sb\M)I"y!M{w2YCwp6Uf{>(eYOGWv@TŒsP(T"""ehvG= ;"i6`<,3 ԇJϋ$WW%8aE'#2ٔ 7ڻ;[mo3#H>2>k Dɹ!"""uL>""2^%Alg_V"/g9ӐΝ7ҜY6[ q .)Xؘy"P<{f(I]Cx(1gADDD*LZ N?n+d[:PЧQM.Pkus̯5[l~x?q&~f#{ݝ3YxN ޿g[d>Io>|@"|DDDR*<_} Y?g^.RΝg63hÃ15-ݙvK%n³|z:6lҵww6)x/Moe {WTLAwΝ7FMʵ%>(˧1+f 竩[{wr&Ѻ^C+]ݝxf۪x;8p)V<|6Ǘtt7TMDDdS># XΉ rVU~ڻ;3,}xft[\v9;ނ"""" ȸю0?3Q6}iSh>,&yцiƳuvL_6y, X2ڃCtn4}DDd\Y8w^Rs"[-jKM7yӲunfpdd 2Vjj!//͘#""Rڻ;[)xiexHl8x6N718+hEOGͳ"""qw'BLz{{I7y(&2}PO*s^3ťvL*6kk٧3yj)p~|XUK@IdHٔ Mݝߥ}08s%pof<-lɹQ(\ͯiw@$z?f"n{h󢕴\[byX4`# ԳRER-/`-1g6<0?gj3q_cV`%3DFZgy!s0 i3u߬ 3OF3o΀O"""Rǔ#""M';*7!_DonŁcp6-ߖ\\Yd|q.³|VY>H"""ٽ lN_=|n<> ,ѥ 4 xoً#(x4@.Wmݜ=~pyf|j􋈈lڻ;Iuh: ^8j;,""" ODDd܈IE2͒}Qn~Sb~iƳ|f~_Z q)""jl=3UזY IexI7|DDDdT)GDDƓ<igOW"3:i(n4xkf ?֒ >xII<`࿭:GDDd 9Ο=$O>gx;`I9 ߞaEDDDq%)V,awOƑIxef:g6` 00gkwweoʙ XJY> <y|Vn eMnJ;ފMă3Tipt7?ˁ=]<قlx5+3xǓQ3MNA/v'ω~C=Z&MIT`l~>@AFyj{Y0f9G?3IGl. !2R ߳h%>,<6#γ, U˞"""RqP(<> 2^ @#-x&nD6ӧ5ηw{}Ve/R62. \W(*u-Rz{{'ot^$ADz|;ъRi}f{]kz8ۻ3|a&ц`[Si0|7o><8yN||mitF)G煔Kngt|x $<<ޞ>QrnHSsL-^+qo~}NX7nv^*gIxpf+=n,o"""[{w4|0Lbd_aoix<~!g6峼f9Ξ|+sVф|6{:VF=}DDd""2[1J"ϧ UЧA <ܷۖ~,~7[N*-ÖdYg#.#""2wwN63%w/>|H {:*3*&""cjy%=ių|œUޭA[V)cp?_i`g"^ve`ࣂY>OVY>YG"""2bLb<ۻჍw$2;qOG׆QyL/#ZmωF9/^+H{d3+fTڭ,;uU֯&lTX.{^!3}U*k,_%g*0 x;{s&<^>24wH%t^4Xu6>xh+><`p >~Q|sCDDD2}d\( ;>Bhs")VC .Ղgk(8&-˖,Y_Z>,˧;XG A.eɘk !hL;|+`2*|02 +f+z:o>:7DDD>""2Zl'siG]*8&ۚ[lHck}yAG0٤Q/q33,Z2`쫀pB\~]\vYOG*DDDD@*&""c-Y'}E4,¹&V7W?.Wb音`~(Ζ86!峺kkͭs^m=7Xq K>|DDDdKLz{{ |P(,}k3P¹4gu|x/?߉lOZ_aoM&Nz>w5箸1xxh&XZJ,{0؈|di Jh ݝI9,k>A}Q1O0"_|v`Qrn OLs{Qfvifk,ۮC,ffsfgfsfMn3̞65f5k8A-"~.@c-x ;~>_2ӭ1vk1(Mi N=vԩǶlݔgEx8Kh8љ~܂_PZ fR;CR:/\{w`kwV%l[o@;>SKϳm0] IDATsCDDDX=g삏3bSf Vbf@7#3kW#W-URm=ҳ|gp|>XڭiA&K%Y> ?3>ZY~5f͚x/:ÿۗtt-+"""OHC*\f~Z*|xr[WA!J盤KO \jf~WKDdKvGfd?['YAPЧQL"ijjrfYCZ:0W{wD`"|=n?,VttigL#,{wXEi)>`yOGʶH]bCOT8#xK_VX8yiכ]xМ MD^ly>9 gDRbi$:` Qj$<೮%0tV|| KMEDD@3/O6w p!^xI[Vz: mOŠ‚$S97YKlk8%gW~ԫxQ>Od/'Ae4Ǡnkum@Mnݝdp)֫WY>*"""ww6ܒ=,r2ͦ&uzUA$!يg{GBoutg4CHdˀG2 4[QS ˗{frok P&G OG  ?. lfݝxAeo/3Lv< ([Yѥߓ"""R깼mf^WR+!zoH/g/+ bX[ *nqalpU$Y0CM%Y>/ޜyťE el^ݝmxT2| 4|/~AjAKԟODDDꝂ>52}|B(g?X#xg!g?e<" ?'^ӊ6fIjÏ xKٸfϖxnߗks_)Sәyv||5C5wH5t^lڻ;3]K3>2V=|}G:O>Ks~roOq`RooN%P(흈T(; O/eP(<+|w)+ Ғ@y 8o3}}* l( ϕS_(g^ʲB"3')_-ӑ}jv-9U6,_>)мa%}='Ou>ms^03 ͳg=]x^Y7~պ+$l\zի'5oM7}>O̼?w3$Ϸ4\n%e_JþO|JwRoooާ\}o$ߧy>0^Χus߯ۈo9era;jض<|@߄=rƁ?5kykϘ`MfM0e{~!^O!"""uJAaq.œ٦dbr?6OUq!iۇ?#xR~8L Xfvx{Xu*u숯SEϢ3c^`|b-^]ܹ?mzs6ٻߺnå#E]:Tv ЊX7=UzחY7n] :)HiaN uLia:#1uo_S/~aw\x~A@Sw2b߯{$܋Oh:Ӌ㈈H TqG!UߋåsYC\ۡ~>ѶV'8o' wFZ~v lR8~}"""& 8N6,[mOy-\KR޾""""2}8ہ:/s]6Y,=~HuS\j@hGqϳpSlu:Ô~ß{0S;>ܠ, 40 Bˇ;w]ʎ:5y_5x& 5u}CRPЧ81ܙhap5ukꔓSV:~Z1OI\4`OlÆ ?w^P`o~[?07:Z$[<9ʮĥ'n> {{ ޿w{&:oTi#%U:-dSSeKu;ӑYSff'qkyRm瞱nn 4u?xoO })8Ԕh(V8~ҽ!"""5qd8y'( ܌5.q]wƎzop]w/V {ƑDz8֢|>;5E+_ &fy˚t&/H{wGTVng6w_<Wb16`2e[^ |V8Vfy!rt_TL.`VPJg~࢒VJ8Ɓ3q{#NH Sp  >{O]p+s=v""U'N+ GKT:z;>_|hqFp ~?+L.V?L(#""|2lhzb , ,c=ru,ўnfuO=}qf{X"zuy\=<-.qNDız`:uh )g:~89C ) Nޝu&,GϙF*"""K!Ʊo?vRױ+7/poIJ{AAE9Sn~:6u=C|َl[`L,EDK_Gg Qj`)~SOI,x3Z]9p$OlU8D LT:~&aY>7V{6R %eSXfg{vGLyE*&I)X4tuW|G ,q$MY[p]Gyn,;(cc3O]׽OWD$>w-x۩VmKQj૰ľcP3LȱQ, f^| &hx9Xv0խ A""""zgչ[:y%ll)뺿_S\+Eyz7Z}R~>=mGP?ZW|\~~$ب9y drYOnn <*U cb J&;{V=8&s@(òrGz5I Qj|8uVDdutư[Xy^mr;p:xwwԨXel6 ۰mޝcgrM؀Ӈ{oX ׽XPiد8$/>|]p  tu/+""""eJ|g} ./T?O k />+ 7L,w'V]DY>"""K 4R^-]{O |\P)88խr""""G`^'deǂ9o 8->'uSiv5SS:e0x?c `{DDD2lˮm6b>`z܍ |gs@#LY H{V\DV[>k3}_UhIuuX&9xmkC7輁}'S \XU^3W3CXy\6NO3V՟T0< |߂}2|t{#N;}."""|=r03 <低`a}~W^>^8FE}( *J˼-j0])CHDDDWN {6`馽կ5+^se*&""+P+J'm;%vY>sOP0sKb~?u؀RwgK=zV*<*GDDqMXOLǞQ9LR9A`HcLY)~VGRms m;K)8$ingO ,_Waܹ|`DZ,eyۆMhԿŲm? <' p5СX8;=3eJ>~6 XWhYTڭfut\x=|/85DzƦ v$QT4yx(GDDdr( lĞXd>c1?S|F <~T Զ)0[0s#6XQJbK3;e#X&O31_BcdOWDDDR&M`g(xxpPMwA,g3D/KěTцMhł8$e B{Oa=C=]ݓw沀!"""5JAYOWD֊t:=è%~f?=Z(c]~'\ `ˀ7wl y -m0$Rj!X\6NO36q"XZuXMyRߣy@}|\(4>53IfAl׌^x| XIY>"""G ˦ F,3Lq߿!8ֿG0DDDD>& ;G"Z>Ozr_`iAY`wjSlJtR9=#JM7\6xls&0&Rj!x|2l݊MV2K)v0n7}Գxu*m6}d_`gODd ،PE_GgrAyO[<6`$lG'onķm=R?*K~Y""|}~fPЧVݦ)/ +Xo2ũJ&,MX@ zLzQe'$Xo„.p{=]ʴYa r>o9Xm']n5&P-LV@~nYL.u&pY`X$0|DDDe V9<''Tڭ68+웊ԥ}}/v.pq`aWC>s""" [=̜;QݮlVo,vH""""KAYNqJ3D!wEAv˱k_.]$:ԡAGFmľü-$I,ggk\6|ecXv,@ E]/>.EDDDDV>""~>SB<*O-JQ KChcDa25~V`/`2x ) +ր=7 `zsF73cHTEDDD}dMHYJ  l7 ̵T v gޜ<9|]h=pWvg{` 6C93|ò|g`Ӌ) ~$Ϛz\rO+s5pp٠zl0VJu) Ȳz@@~`HAvs AVnꓯ6Cxk`}X?gBP"""e`ߵ)eiXg[""""k>&wO ^>o-V}p8L?8LڴPi'`KCKO|m޸֧;Ol ,T~9&5Y2/ &(|,X^ x0,{!`1`;)$U7W\DDDd(#kǀ4X'Ǽ|_$O~>P |ޝ+tnl.~Z_ |¥D5 n VfCd}gRZE&3{g ] [ x g!Q,3c{L!Y{CA,:l ^AeԞ:,0 |@)|8YJo|uӌnecT }DDd9}\ XI]q4>53Na}y2X=Ok6)UK\6NO3LVǀ`AC 7H)ҩ""""UDAY~8D>qCAZ[n.GmSiss+1'vg-܄e j(-""Q&MaYXgRE]{/W8\+ 4||> ȒL`ϗ~>EkOwJsՖpH%E߿9 @7nã{AiJEDD֍L.4Q ci&\ ex3W/(å4bdN\DDDD>"">.~i7(/vAVKjH_Gg 襘_^{T֤}bh zINyxto 8h'\{`M Ë n .c tuk2HSG֊A_ vOM 3<>~>TSvkމnwo?>5~q~#{r"ՠڟ"ˡfL.Կ6NVy1 tuO#,xoHRGքt:}jZNUyOut&)vNg6DAYjl@F,쳁Ip5""R 6plg|;5P#n1,3Z %f{NJ 1' d `տ`TtOW J\iƞg} + c `Vz[QDDDF(#""KӡrGp(˧9goׇĞ c^C(GDDO36˒4 9c'"""R[5!t:ݿ"Fb5 J݂5|cK- vdk4Zfv۷1^X7uFWcwO>7"T3Cd}e#Xz,X3K|.0zǘòFzEjoW x^  / 3} {jFutƱ`~'XڭZ_w5Zܿ!^Xm׿>^s69.RŪ!q`3( \eM.c (n0}DDq v jN ,fYT:G@SåbE裛[Q ' 6ԬL.[q>X,?P{w"" J ~/ ׈`O`ia3):msV45ǗEDDֆL.`9 Xp P^u*p pr`M-قM>=]ݚh#"""(#""W-LV?5:g}jI0W 6Tnhj!:ocuJA\6i5X96߳ {^<:F{GuC=}DDJQ*. |% v{YsȜC]k,-ZF7V mۨL9\6 l:ؤ`*Z>`p'l&_ C ?ǫ 31r\{NՕ}~OJ> +6H((4mi{%_lĂSgEDDjJ&3{g  j}1(k  tu+KVDDDdRG֊OP>Md= R C9e?2|S;*QF}mT!ʕUnf+cuT*/DVX<3DVК/=-8X)3VxNMXq;{CDDDj>&w9tz{"eDY:e)SB=ŸE64Dx$XO@ش3N]Y1UYkO=e~&-ižXg;<!"""KGAy<*>~?{(ӧvK NI'64ݒh; 9`(vEDDVC&c0qݾ| 8zF,Khg:Pipv`9Xڭ/Qّf@Sej94VqvoKt8UI`pa"""k^&Â4MX0f2Ce~'a~lDS Țjji6oq-~i7>}ceLf{wj&j v g(]3RĜ][gΥ}nIJ6e+YQ7D[kE&m‚=XI~G^=K/~q>C=]ݓsRk{#No_s壠c ^Uv>}QJݦB4gh6,T%M`S46'wĝ\<Ʊ"""U)FtX40HyypXoC倏h PGDDDD*Q֟p^]:*#U&Tm<뻁[M kRȚe2| I2>=x![Q2|T&WDDDD*RGDDEn^ǰޝ==Y~O(埃-~ЏmD!B㞭'$" lU1l *e M3]i̝7識- 8XY8>@c=]mDDDDD(#""G+B7 NndN`T`]W<1P8mS'F` TFDDdP`KpEh/2X, g֊̣:,2|.^.`$ TNn?tIْ`6yZ`c=CY>""RU2lS|@ {]xox73MPS9R 3<WЀ?b}_-By96 -9X? tS.,Y,L.> XI1`"I!ۭYG8Яg }d`IELS?S˕J{wj-4z.tI#gn݌~\Y¯O4S)Ĝ 06n""R2l k&/ 1/O؋Q^<0խ_EDDD)GDDƑv!YXv~ut(D)^͉D$g[@V=g-""2i5E,{āv {8w2_EDDDD |~;X:V'Y| ˤ5t_utƱFRN ,_ZG|j 96b%in,D`kq!2{u`]ױAA`pk}!to̷\E&bϹz+M|x&p43=*Ro=}."""|b7ЗO^K??eg|F>Ej}Q?Si< LޚND&./Fm?c#Yjӽ!2ߒ\6I)ç |>$O8d hƞcXS}Oj,}DDH-T- jn< $;UT?;@sa)i.-وege\iƞwnJ= X&P%@#6b>\? #RAq?O4Xu뺻X߂}뺿_d_>bu#9(u l,K%PiZXO2%ޭW5꟰)31,g*""8y{Z(qx3 }~h ĩѨ٠VP*q'(/[pFൎ\#"R^>,#(GDZA }X[Zl۩ 7F[bd$shǹ ĚU+GDD֜L.WXgҦqWv~_r(砈,Zn6~4y />|cX&5 3Sۀޛ GCFVnHى7Z@u^NycE9s&6ou_ݮp]sYq,(u? LP*\|n~?b{Nժn vk,@x*i;q:\owa1 Yu\,؂MCI`i,3, ;n뺏!7goBq9ƿGDdg(a}(˧utư^O#'>N4M%Ծ8Gu]Dz|HDDd%er&,@ӆ=X)q߃}5;矈,RX\y^w`3EDjBgY>`1vg` {>3Qz"G7ş0w^ㆹ V"gʍEDDVE&uLV|*~;pL *? 6a[Y""""qfot\}q<=c'\|;c5azut&)vQ>AAj . 'JFZ^gu clkX>kYctowE&>-X߹ʯ= x!AlBDX+i{ tu{CDDDj>=1!,6rv8ou]wqkMH{VD֊t:]=πGCXVH+&U3}_HR>_sD*ܽ%~l<\Ĭs*ٍx{Bdн!2ߑ\6e4z?#Ophs',|\,3խVY3YlSlߴV@ X p畮N@:Mb |6ko!3tz`7KCӔg; oms󾌓뱺 KӇVօL[ N%5ltn9zἧ5$Oo^`]~ZR]'Mm Tx}Fޟ(N_ QgWae78s뺿9sg/hۨBJ0;o>ȶ?p/_dO ,࿼8uW,^ {w,7W{g.uJ:]t"8-=;6^~ m+NM1yomh;nwv-5H]K9jN~j8)$wlf;}w7wm&ui|]LCMמuyKNɍ9N|~?S:U0]UuZVi_S^O7l\(qXVӷat|Zə }p\nUX :0VZD$AյPs{\e g)< 7 _9Ly#iolnAgM;8Ly]שN,Eߊ_,#?eK6ymapߛݷ'&TLt_^"Rש;j޽mG\}ؿ~x8wc]i,^3t~=m(q$i638l_O 7ߥtJV::UTi}qpr ? 4ͩ' So;B"?0~FIWZM#q`OW?EשDV:}ߧDDD8{D8y'kځ[7Ey+pNW_u1B֢|>;T[XPoĪ}uX/7xG d`"6ޝ_Jutn>Ӕg PpOm|Nr1 )Ow{NNMO"@OWwFGl-"k _~lRM61e6 * s9X 1`{r-Uɿ7ʻ0eTvod{x=Ā*Iy?eݰYߡ<;/Q} {BYO&lqɗ֑RTd 3hEDDVoo˴`c(88! R({ۜHOWQW}Y.>u݃M98[m׃x %P 6a*owfV:J`oAۅ 9%yX}黦o:X&XԍȊ;X3 a~)<)>qXp@YkYط?8ζ^˽?]c?F*lۈ‚>{gEDDDDPMq, xxPσ\8\8ǰ<`=+Y?qƚ'cT{)/ azutF$ ~1PX^(0,'(+%Xm^mD$gk<:NgmE鞮}!dreBqn8x$гhz# lׂe1`٫ U,n~_u&9G^?(/g4\*pd^nD1qgHX_Gg=kP| +X{'ޝ|B^iX B>Sqǹ͏n8 bvt؞w.썈L.`A?:GSʥH]) HDDDDdMLu8+7gcCy]?9L\` pZu5#^D֊:,'\mr,:ϴ>U-hQ9h.LF~0pxl#Ml90se#zl Kx!7Q)58{UDDDDdtu.q|(yn X!X T7?.ws/~yV9l'~pHCDDDT&clr󿇼xaIJƱ=]0"""""kVM}zLC}."-ϷW2<l?_{BS@ *vKy?AE#p래HC4nr }!ސ*a8Mw!/l{jN]{?!*6ܳa`A2DVHӕH xށu`8hs"b&{wnڗ&(\3O_ gzT™NDX,պ/D:dr&,çR +sŹ<)&Ğe?3DDD)GDD l`8:Snxa^~/sb!2^>[8љO'ltuϮ銈ze,ӀeOp~7_) ogxNJXPOW&HURGDDؠI?NNޝy_B=M7p-ErֽnѩD=!R"",2l zLb=|^|{Yu"sWT8l "hOW2ȊQy73<XT/ n,%tR^e}'zU\wG0qڀٸ328""2l4cY9d, l; =**|Fl0}DDֹ6d~?`pch}{Lnծb |n3`ԴfbhqيȺeL+3@8q=X6{3xӓ_T[88kþ =]#""""5ADD$|\ ؏% tPL{NeyTPi,8_pѓN3)}պ""d=-X[ X.lH~S_w ²{EDDDDV2}DDtځ-ev]~i7@\3;[TuŹԴw3VEDverӌz >m.:݂}&9PGDDDDj2}d* % vv֧AT,渥#SN=p#9`CPKκÛV+v_TRS2l 4`A1?dxzdhfmzn)lRCm?0= /"""RuWd 9u}."ut6kJ͐?| {$o9̧*ˤ3 l>XTIS}f=)E?g{csmuS}읹;1:g/""$a&lB¡Rˁo(e;pOWn""B^L)KE*V Rصo`NH>vω+ob(8>StwԷ Nj3MdUf|6z+#""""NDD֩8H`u. ~Mh} )<ҙ6 7SSHg՟7^m{e٘l+FQ]y<2e4`Y>sX'Xb#E|o2SƱOaIO^DDDDd RGք|>S#N_sYm|~ 7 ^/`,6yp8j^i6z,@kq)q.jȜ{iOh>:2Wl;[!+x_TR2l ZfgWՃm [9S_٨)mW>]=]%=y*?3}."""|♔ w _/J1>JU:3bؽ5Q?$lKto::o3H}dqne0+{W67*er$6Ѥaw?>My,pX$ڐ%pO 3CDDDjzC} ,xd׿dJl{NIneB"MO&#{){^8\(6/""(6`%ڰ,y+e>?&T D!gcu33@pJriY::c@oK4l?'<<=) 64$g܉ƉhxqVO =@wQ? |(/FNi96"""""2}DD֧:g:e[I v~3kHθ͸u~K~2L޲㱙#ZG Fe#XvO 3 q6=|fwOO &ܼ#5$""""n(#"ut&< R  {w>~u++H:+M5$[ JFR3x\a{wέyHq`3Ќh&>a{2<"΍<~T=XapR__m=m*laO9 cXk2[_+3>9{3X)U@alI%y׀[l|e `_Ej;l"bmᝑ{oFچb9w~;5Sm%0Ґiu>[0ϫF ;M.\{ML-_v!R6{3a`XsyCRT8uC|;}=eS)/6DDDdRGjB*hAVRQ`D[ 8,\ZڭȢ^umӆM@6zamSXbhͱ+"{ٵ& +Frx'^gA-p[jRkҽF,+6  [is|ξǩ]!""<2v >%yU$YC!lopXbs?}lîm%}sXpk_DDto&Ӂe䌲wd, 4}8u"r=*5*""""R>""GQfّl %L, <}"Ko8b{1WlkIf +Gry/ ʹb<+,ay:p>d,SIЎs>"""""RʻIMQTž688x$ʼn]T*TC%TZڭ9su׵M6i{gD"{}Y'n>8ǃߺg yfӅc؜ S,_v!R6dtb%:@d7\dO7w6PhVbvJ9څHyaHRnΝEDDn)#" t㍑w,ٞ:]@R_6%GѶW>dsq L5Mؚ] ,3,~""İ{Z40R[31%{wp" l00Sz^)CA!70 ɑ,Bݖ$x,e}ٍ9LcqHk짍9OofA_DD"ҽ&ZiF+u3 nľLcg>RGDdy3<%4E/+rC&ױM6s:5߷59}X]3bojrcyJL ̴c1vaQ_c=+:e 󊈈H ,q6la( nIkRӒ@|g{H?B mM QLa#'Qi? {3XgVm(X -uQkT 6/np_wOn.\DDDDdPGDdK`< Y<&\Zڭÿ kNyGU +&cyݓD29Y>lh& &/w$:_ݴf2K #fpM{D}0rP.Ri?+ݛc>mc}/ C;I"""""2}V<ؾ!R#cm"|~0Xi7LSGKKEQЧ݆;V6[Rk=,M. ػn;6~q|gMcWV|],%j`ҽV3x4a8ASli>}=HWj"Eۆ,Q HMHRYkT* WH0wiIG%ۓ(鮁əħ]XK}KyUٝ-ַXe O>~L ~ w],j{:̞2K3? |*J`s` uţv!Rچ[ iݢ01;'SW6O6MƜ{qlQ2'W,9Dҽ ²uvw-Wi8}2|vwGDDDDdSԄ7#D8``E+z,,vK`gP]۴ajvɩfoGŏnr8,͑;:p5v!m|Jf@O+3ǁsK_ |;ϗa,gg_wςv!R^6TiCDDdiSGjů{BDjZM${gl<~YI`kA:cI iZ9kz5ycwlm!5 %rZ}=_BdQېy$X&Nƙ.m-V푑u Q,7 uߕv!R^6b_,}DD[36wWdS8;{"""""YXceBga:rݦ~Bc6l{|b+kzI-S31OS<]z$_YTODdKfiev6Ni3R/yT΢c%Ƃξ}Y`žYJEo~_I_ܚkcgbO}r8|wup{535_:[DDtoXݳ$>oϏ T$HءȡiF a tb>,&*Uj\wu/׵V \νYҽ&,ӎ3,gdbx} ن€O`YDDDDD>""KOX-D;Yw.+9.WL{_O5 y#% {g$O'E o}!Pf?~vC}=9}DD})ஒ GYuc<.Ċd~==Y>ޱ#rXWʹcV`\|duf!i&=r,G,vc5|>u-ݛw?/׷4>jcG8?+atEDto&i>,S:9=%T4>GvuDDDDDd(#5!JžZJ:Dgs" eoc&jop+7{Mtt{Ok*1Q?rX 倚h ۅȒ!sIfUX/ix&}N2F9~{څHyj"""˃YZ/ݑfyLnu-ݛIٕzx<]hpgc?lDnfg$ɮQ""Rҽf`-ŒnY \|íywa>k!#"""")#"Dlڰlfv*-VY:#Zb/ W'|M-SԎr{r<""KD7Vc=m\;BN~<1n2{~ a}=27,0w088KTjtEd  ]$KE|Ν$,:ʹLV7pgͱh*d̕~3]5;B څȒ!{3@;Â3[7a+$8X_wv!R^6RE}-"""pZq!ЇY:6.#:1{>a5Cc v[l'na|o)#W9fTdpΡ,kYtE˷DK ז9n7[aOL&~7uG=0U#GvJ̰鮁!`wL3iʺ=_\]9DZ>3DDDDDj>""KCv4ω,_c4O,z 1k{gJf}Wd˙c#uEDT7ӂl Jj,o=`ɭ IDATp_wO@"""""RCTMDmڰ1NnO:B]HYL |M9Æs[Oܚ}o"XHA?:t`%Va%;(xoTľ[ aY>C >eԿ$ց?eݶ,!Tڭq?>Ķf׺r$ȾwDnٮ~u≈ԑJ,{g%ɻs&3?a`g`YDDDDD eԱM6:,c'lih9l4piݭ8p}th~x\)#D_bf~""u$ݛib]%e*|9XgXLŲDo#ϫvv<;X*WmH=jDzu߳у_6n>}Xoث.De$ݛc=mcol. o?Wvc{*uj"Eۆ,Q HMHR[kTjm"ug\OXiPVK(p}2:U,\u=6aw(ϩn~.D #ݛiʮu`;v2{߀LdWS[ ae_ǁ]<څHyj"""˃>""u[4xYeeL3]Zڱp+c~}%7+pzdhObf~""u ݛ2{V`afh[ O,9R,sf,ŲF{EDDDDd(#"Rx#Kuu F=ԉto g׷Mٔyff"umL b✂eY7 [(vuKO@A ސJZDjbvv+~&`[d dQGj: [cαP|ڟWr+SW?څȲtcb5"h>L9ۅ2{3q` 6O' 4kgS9aYCCK)P)/l""")GD>%f}΍<~Pa_LVXف +& C ͌a\erEDdߥ{3afO'Y? D߳+Y̞'*}a]}=> """"(s뱚FI\ |{u"ðpOOx0W."KXX-Z{1;cc'|3Lz>\7Zb.>'B656CEvYҽeڰ?X4 =gH0ݣ࿈nsp=+Ca-I;?;UF\?:ױks/^,-6la  .-94u Mu HYgk yy5$#όU>8QOD{31lNZIc9ϡr!8orn ιks _+sγsq97rmu9wsι*\ȹv5y yx܋s9~圛{9qΝs}=7eT;.Qo G9{2up!^@jWF[~e];_- +C,ݛi3aY1 6,{xp `9\֏2  ?lp{5b7z*s`v]R7xzdq/GJ)ӧ K~R i׊wfqΝ )X<-Ov`, J5ύ,_LcsT+%5&(ӎ}A?/\ߐ8?O].-Lu Ulwtbo}M,ā)xQLO[pC Ԥga= U}^9W4Sl`ݏ{s.1ns㺃;.7ϱvC?䜋/H TͻN1X4I%d%Tz6࿃;1."KWP-<ݞL?]-:Uۧ8lhTx13/G>r~݈"IfZRn+af8눸ذa8˹Mer ⧪ޯޟx^ȗ#%Tޭh*E |8><a/qI'Yư6>JR,v Yn:Pi9HƜ[w7ȡ,9M|NڅrQgN,ر"Yo+:3:_?.Dʋ YZyъ{A`ew9E}-'nu/Uq ̯o^|iE",:b~g#8VcNr;ey8VWsYJRXkTjPMݢw;Rp]b Զ鋧]oOtLgG8w8.8kv!lmԗto&Xi*J׆3Y\.DSېZ 8~ |{C~,}+6((7ЍX˜pl]'esoWz{ZTXOz"wm| X_aXG !,swIPzl9 eW%iB.QVlv`;Q`X a=_Z6o~ {~&3}*ZYeU'GGK5u7)-'"Ǧ k?+VQ1؆H8'\ߐcޑkt4S͌cEAsY 2^V)<y$*x|R  688xlOR`pۊRԞI۱ÕLRmWc_ * FH*R`FlH5R\ TjϨ-5J"ÂNRu}:ojUѶuk_6 {/޲jǰ@2x~|iݺ?088ݧJj==jC'p>Î5%;V5wvJkΫ~,vW#O3qܱ)W6Nď=088~}L{>TSΑіT˭k+rgܹ GZ\=G=;uխ3\юV;F$VBSqV..vݧ" t*Z)z;kaY5XE*\὿ҹb :06?;ṉ_yP?kO> ־*h5(wX6ӱ̊/V?2[ywy <};y E{ `࿰s@$V?Qbžםe< L*T)@ˠ#kZl _`Eas w{?>r)SNܟyιO{'H=}gdU{yk\f}ٌվ֛sl<2yd\bV[qϳO0;%F 4Šo= A We߿>+[ewb5COE y<-x~e-L)'1k\igUuOT{z=wnc_Jސ>/?>Uy/i\UN_)?88xTF{Etj=TSezn?ۏس>}N6%/ӎ^[$NէH_n{qguUE2t[Ȣ86 <$Gι_bӏ9r%O_Yܯ6q ܷ{s`\K]܏J/_Y9, Us{c/v9{o߱s.|/|{?ZmJss{pޠ0KVS>,^<{-m\i{)~>v{"ۮs=*9-.H쒒C><>蜻>W ]3sIェ}m \뜻 F_Y$e؈e+6zIf _yη7baxEIgw5W| ι7cWX a9sQlwo9w p&v*}f+'>U/b)yco> <m$Rݣ)">GP;YJ#oFs/Q}Wzohts'<=pk G*o`%s]Sy{V`C&|ʩ7ʏ6[f+Eq>M~I?=ܴn'>a{iO)Yݧj=Nn5?|#p}<u]+Z_@+O1`E_ |nUsakN'Sv}SS ݧJ>}ߧd~TuT< />ߠ{}dސ'R9S:m("8Z(}S.PmNۃ?Wwoq}˼8 x  = 91q]✻{?~ҘU:4^u mιMX)G]]eY`)9^QehQ2rAir= ?'c ιa7z*8ū_r_a8wx|z/T բA"{j JRnڰqE-Ok#ZM=5u|IfV`RQ^3<#Ϭ]m8j[n Ժb``?$:t b^HPۨ=L>ފwq(s(uWu5ow_wx}= ¶JTm< >W^aނ>d'km9w5lGF֟E},XWz{(y9wW*k={_یޟy8lk]Ҍy'a ޿#X B;7T㜛yzy?=K?\~oxMq`rYo}?eޯ?;X,-=̞d_?I_g?- 6.U j3O8.5:b&͝+7`bV  ηCY"scW,U>sU G91$*^,꽿{㘝Xi^r縓bp$;XXvG\Rkf]R#T={O ~G$DιRmDYޒlS, _ X hkfPK;62a;s#m"XpٯgLi X'+}r46\Q3ؼc}=#tn͑oP\e0s96c2b]ygs "ܟc:羈 R|sn_ agb_7yS/m?W:Vyv.$}jʻܿb_{_ك}p4.sZ_xP*"ifF㧙ٝϏj؏\ kY/s Sf)unmϢ_Tb)>34:H\ZZԫ0? |sĪ{v ŁؼP-TtNF8P4pU8u96Üss)3s v{{o>,9Uܩιιga#a?q(M5,k i gLbA/M\.P45Я0u ݛcw|ʹ/K_bp) y!H 5XXhpc"8n9X0Vd{_w26څHya ޼9wZ>ι`q fSntΝYmG!`d ?jCsuΥssf(q }/s,0C^/&|gIuΝ]ft 轿Gsj;:s*<&FェwÁOTؖ&@{W߁ 2xDM R.5ԊT*5|k=bFLf-GK9,H4JՓv`-6wV+FVs+FsS $/3]jbוh"ő$m.0b1;s`.}{y.DSې/X6gιa&ƁGة*\{?t,;` Txs/>{fl@G`o} IDATnsp {G V2eKG)Sh}x=9 5c@f{?=n\5vtΝEK✋6߻?sXpMι״ >+G?#'_|9!r h.:$@ݫ@kv+mGG?Du)X~"" ݛiǾϯ:)fF/DDpq,[hw_wR{gp963G<(;*X΅e_rc_U}3VZ9[縎V8 QV{@"s{㰗;s7cAgS%]+*l\~_Kp=u5cP)5k0s؜G`LOĂ XV6;bRPH>U)i=bY)cppHT*[kYlᇽ5;=MxFdK)n˥ntGElJN:Wrfbx1_n 5)X'l>/DjN7㰠NŬa'_eI, k>wݳ/YRڅHyaHR5r]dy{06s㱁qc|{}S6c`%я>o>~k:loQ<\}?0y6˩ؿ} V`r! [9׎NjsxԽ"[b, 2I:Nބ = ہ` ?k#(؍#tmX{ 3n\y>9w/ž188聣SdOt+;k_n؁?y)5п)rVciFws_?=Ŭ'2{(N؆vt o_Ћ?Ģ}^졶qh{3 Xwg =},78a>&]}=o]T*ZDK0/jdԜ"_ҒsX9cyh!Ya~f ˲1V,6>5.1ށ~QdO[aC96S`v*9ہ_GXZYLL3i޷Gػs%V+}gS6c)lH_wXDDDDdx"%Yx>[RCb}""RkjXm-JvS\-XA:FÆg9Ds19y$5 "2/"ѝ3_c.|ZpXI ̋a?9žeg)>""50S,'(o{lPJH |ڱ:̝˱r@ۧ9 ;gDž$v t`>!|;C߷Cyu(3WDDDdx(">9Nnȗ#eHx:֒}΍< V6Ҋe  |<ض{BϏ< !:#"rJY9y< .й;xj2rh_w<_Ȳž{96Ij2}DDj{b1<;xxdKKnu!ݛm3".:WOؚb#àOf$N9`L{]e 6ǀw_>;Mb ې>"""""!6ltxM ir=ІݳGMaI`5x} 3Eq(_mk#""-ݛi8Nͳ&`Xf5ٛ={$"""{͋}=""@DDjKL7Y-Sl.P G-i"w@;'x_Cߟ;ڍs9 L ;)sU!S]N׀ X8G"""""Zqpb_H H\SWm-:w,zЎu&FGj'pdSvu"ǔfLPKgE\DAJf:X } <9d+*2K` ,Shw_wڅHyѶ!"""K>RRԽ} "mӆHw4-<##Il_y"Z=?I Gi8My@ֹ~T*BƁKf6N+c>`*Ld0f^u̔OڅHyj"""˃ ,@1ls3zՃcȺo`Y=cب$k׈^""!ݛi8j}tW94s1İQ0Lq|DDDDDSԄWR)uZrG֝O "DK5aMS,,zt.&l$Y<d6,%BFXYZҝaBdı M[h4;ej[+}v!R^6RwZDDDd(#-d,mڰ1āDCOM4q|!n,'鮁!X9Mua?u0q+5B ,;h9w>+@"Qj!ݛi2y:N 4&>i #ξZ j"m͵/w ,s!LN왑}.gv ,,4cȦ֏{L`i3=,kL'6ϯ9Frn Ȣ[L\x0V/{-Y=x#ǥ`$XvK`NOZ׎}6VGؽu/VIz,%L8N+)o^|j ΑF{egrq= IXU9ιky8""Rݰ­c76/$/+^MvǀɾM웥*st,9}a5""ss;`CF˜. αG/DDDDDKy7\sιub xϻsuν94$"RGXi7eahgVO [JYwk,3 lڰQ,N& y 3f"HDx_wb+€UWҁensmWGDDDDDjA}s/ne8x <1XNl{c | 9.:vSrϪsyy _ҽ6@[dӇ1`'92DDHfZ ˕XPg{ߌ`]ZOT9U)Ԁsۍ|r v""""""ʻ9nuF |<䰭X04 ^GDDDDD>c>ཿ@O⽿ xX0ID"Ag޺>ynhcvaL Yx~gcؼBP-'LlJ'ӋHv2HF ݛa=Xg `܎2U&(s6,(, g~R.Dʋ Y6|}I,8@e"J HJqa/?)}: 9$W(%eގ4='2l&D. <=d@KY*Y!RBmä{3XV,;gbF'=wVXeT,8>eYN`Ҭ#j"m5گ{+9Ki8Ro667w1f}BkzUVاx\l:;ߝXuy﷔;VsweӧqTg>"" R-M Yɶ Jծ0mؗk )+1@kN'f {M(KDHfXb6e +C2ΒMXtmg: <##ޟ_N\ەι?ǥ J}r璛p0,[7 w{?ٶ9wXk/.k+swcȗ_ZKZ=9t=2H+ι;&s:>KuHy;]dYشac#%"N1Ӏӛ,:,80Jդ`6L+=}=W۹#wᑕd7ekvATQ (;`C ADu#M ETXj=ms~LfL<}n{7{^ !H/ Wˣ1k!O~4t83I$[&aHYMޚƅX0=NMؾ Yܺ,88""sD",./"n0"rGDV"cḩȯDa}CD.Y9pR?QD%"RU?'?6Mˢa1(JdswmDe%[{c8 E<yHDH("Y"E&"r,yMDnf<~E伴"ED:o- u8Ln"<*gΤf~xvoe$-X#]c. |ښ[ǻTNvMWKQ>8$-5Xޝi=w=[Om\aW3HSWc8S&DXDna"r~a]-ԡpڪoWT9JUQJx_ 9\UmWx0E`ϋȏU5[g!"ۓ-Gx29 p0~[a?Iim oMncHdyHsqsaUC u.L۶>m °3v <wE?Gzgx"g0K|5POᘲ'JPqQǼƦɘr0m=uP~zc -{vV~OLoc &_ ˫g~83I$[a"칸#A`AvSQIm >8S>Dd[ 8p(z|n{֟lqDcY|0`כ X"I:|8PH0a8iY~|赇/(<//ȶ"R#"[ȧzUM2"r hЉEI:m([1X fk6P=l|>vm cߚ܍ > 1']BŮeu&"Ez°;qf/Wxe!RH(Tly+&8㕨{@Fϟ}@.dY[s몒VqǙh57] {~˂.i`Gu:jBxK3=KKw|ppZ"rx6"rMzAzOlm"&BEXkh?ZUDLpF:l|HUKD} p2f^4T#& <~o=ɴEz,3"r.l LPr;VՕ jO퇉_O+$&]1z gXóT3Vd 01uZ Ϩꥱub;:Dܜ*WزE sWU_H{82]]⥪մ^ uԪwp-"˲i˟ JPqHNk7*+b@gP^~3= -U9D-&()98΄"l-fq*XcF-pOQH4dDd#0Li!8'SkqO|})ӊD DX|3̞#_h~av\R{1˲(yu1yDD.p_Cw\Y͛ (Llr ƇHjnI|mE=(f`)94g!N[_M|0*C':^U؏R>тI+5z9y56Ua*,zsLԩE]]Qv$-1#  Y iknME_7S=#{,83 Yt9p p'%l[@;7uq;HMf ߕc7XwohaxGKDerlq/6FUDaVaҊ ADCHى!"DQ},ȉX'&L̊,Ͷ1a Ja)ߍDd)שjF[ fߙcKJԺgs>ʙw>"ϋȑ"R?7G95)o혺dX`[q;\7h;Z%"nv 6 5k}-hc:|;-ivR!4 L*8KF(ҫ=xg 1ݵH` !ӱR#?7V k}n&cQ/5W[g|1 )kqF[Ŧg(?7Ge[{o{ ؐ̑%" S& 0 "[boU5[Y:Ă/-ZרIn/y>"rYܒo0_8c]!jHg8.6pO^D%s &D\VgH^ >qks(R&9P__v'x6ɖ2.:I}xH^SE--mknNoׅ 6FP>[4#ɰY#fsF~z2Y󹤭c d#,:wF6{cyf~&"l `>_`߿-"]UW1:G?Zߓ&v88^DN~2# l4k!.蓫`({ED|˳9k:@D g܁l"wp- }.ÔHD"k 2?;.84ka+,:"%sRjLO-+ Q>NسpLَ@_CG93nI$[zbO'tWfXoVuqF.i|x_E**`4^Fn;iV}ącT&7'c[{R6c5׶,Vfg+ "udcO%i 0L_ J${r^>r`YDާYv?^Dkg=\KRCDdsL uqeD=XD0+̈>,,6^~ fz:P56(U in&Dġ 8&lz Fl lIj=q2l[A*B|qgT3?6KB(bi~2;g[!"-O|[9 <f S՗+Dd\GH0ȕ'66=Xf·Hݵ^V,n~9TnUYEm_ͱ>%V TNUWՇ2]hކs8οtvv%A 56 Gx@k%u:g|7u2oLǞ]q;^t}1d-kC9x,ӓt΍³Ÿc_ddL}d`gt&_@LKښ[5z X.TDF81$L_B"7?g+NNJ- ]QsUv< "lfb- #Id]2 I٫ޱ̟x398ߍTu )[9 X{IEW'd)Gxn!'OJ"䋪Vec ĭ" Uq@Ŕ)U'QQSʺ ]$-XΙ Tʶ U"^@J z:MDg?/g c 0KXL"XC#,Nh,qJLtm8Ψ@U`@eD4vʱ&?>aW"\rYs)"r$DdR\f?-t% Js8" fdD䭤:w>XߐZl`>sUUDY}TN+IiģDdl1Dm]r'V ѵؔUqǙ@ĭ"; i%!똼"BΉXU7b=z0fU&?ODqdta&/D=lr9ޏ`1ȸ[omq|OV"k/zID>a(P\U376~KDncDds)"[A"r>/R"hbG,b|GD"R'"!"K,;Xd[ O"5DdZP:k:Ȳ?ȗEd?VD>oXW HH{a큿sz;[sE@RDDv"g8~oR[ _/ar5p\."Ddl'"NJKm7%\k;B= eso2IW| RI[b=~; ҬݢǕ  n%ݽ>Yf^FE|;`uY,sԋf%0d⢏8 a h0Ř~94An*,: kpXܚRqqQ>+"GbѼ! _bgKT > $pD (Qn-ÐfU}a(?D&|0 q>p^yND:A*'"r!S@_PPEUIDN~EUe:Iۮ'\ b0 C:"e:.>I`?aF$0dNXΤS%}Dd6HlUz#yXU=8j~;oj&GM]&4{'%Ģ|&kGbCLnS-r] lqdK%3ρC+1L>Sð.nknuTqè}"f}?x sYms-Nt?T[sԡ8CD~ 5ow𿲴"[sp32.YOI> "jhU啙W>BU3ʨD^ 0윮灛H彉oE 5of`m HB}%3XU{;:l6"IQ#pg}.H%p Սq=|/vud#{9UlnoO$b1Ӧ ɮC.3)TMaK_Dd&"S=+`EU`¸8mWVl&fQv_࿤r L˰w6lQ/dj'0]/fv~wh;)j`DD9SH1g)p>."S DqkZ00TQF"RYUq] t5fi_t|nD՘\ q1K"R 63gy`=/ĖM"0A|e[srqqPՕ1˹3!8e,:̎)% XO&uqԐvŢF"nMVxCc*v,?[ R%]c0DvqcZ5al;㔓De t6v\a}J[".b!.888K%rY8YZIUstL)"}7Y~QH<|k@uF%o ,d,\{vEuQvbQ>GV]EtaQ>źyMQN*Rs3/x0n,ÞѴ8ƨ6BYh^E,b=`ѳ(ELĞ־'qFkqg\}٢]9ȱXޟߔ:)Bާ;fG @U2 ZU2y@U7/w]g3K kԚ~,A5De&v %y{n 4 kl"{"K8o,Q槄}.sqJ"RELń5XDd=/cj-Ą~L_,˔3qq9nWqA)"}a׫j0-q5XJR^0ޛn 5)`u%ЕCt t"pgLHT|˱_ p>L{j?Ncښ[W888E)r<ƇYaqDnqҸ#az)qFӱOƖ0U`L\>U8cDe 8\BBꙘ)0, B|qɅޣ宏8h>muE]U_V0` (pm :;;آ>8c`V>(wXzOлmR_!lH'b츮ִ5M<`D΁vYFgg_h6ɖ`SߟVX~3XE<.mknMqb\3ڈz)w]q)}~ %ezHD)EOcUJPqrY ^(>ib#QAgPf`EŖ= ܁ 4k0ۢkHzolD**6N ֳݭ3$-嫛 >k730OOiUӍS5,Yqqq9EwS~,gMx 8k7\z8㔙LnM{`"b ? H$[&cT̪(Bas%umTc+&:Nnde\p+#q!نH>V\E܎ٷ"V`8883pgLHT|0&G%lSuX,hknPqqq)Rػ+O}0놕s` f8c̮&[ M(S 1  5'z/V5j°*,~gQsqQO"2mf`%8L a9888yP2'BUWwqg"P( RE st2nKt'g2vu</*G*qgԒHa`͡1a<d mMqqqHEHu6bqg$ nUXҰ8V,m5{9D ㈋) qqqRH1+æFD"~Eq:X X<+p7n5Zcv#֣][F56U` 5"K8De0|fbWֆʰbQAQ܎888%Tn2B8w ] )QGܾߓjDކX5M;#@" v)qw$^ˍ_^!,mؒtg`4k5&Ƣ5H3q26q &D:? ؈T`BQfXܚWOqqqgx [dYAeUS l 5|>2ܺ8c}GC#[ߘV nh_ggg5GĄرj cz֮ SE{E`_a:Js2ׅdF"RٹMŞcXNׁS#o-`+pƟp q#jR65\~]^.wEg(klSIv>%$z@!vM_#Txģ|?ŎRRQ;n6/>nWEyO~u8)ڵHL[J`c`[1?N[SȠVohwʅ?3'3ѵ9Q"mχU"V-+"Ruy!˛ (pgUN$Os!zgPBj\`/U{)uz4-oO$DDh7QUP*9c"GbAbonZ.8 j_Ka)Q`~eHE9e h tV]uHXE p9G{"#g93*H$[aXַ!A)Xe|q)7"rwV>D19XgYmy"RUgb"}w))UU0 )Z1İOU H?&LS|^;jؿ֣%"Q5ih/˧3$D㟎-{{F&\>Ls^-XqgI$[ȞX3G=uiN cѫ+mͭlsqg(ܟc}YD]o΂7cko}Fވ8΄ XM#bEQ(ne(nHTbF `تci5|;9y)6z8e#4 u;|/3o>m2Y=Kۚ[88pX+]|V\YO[w";,= l |ADRK. >U},KO#"Er)Z"r>XnPUtʱ̏B@N~ݠL8še[$xǭz5} =8d0dl az yYU3e_d؋>㔉`o9nfcϰ ުq fU1.%e3qq-"R+̶e|/q=հ{_g+"Xqnpx!.L`(w)awvdQ>e$DLNĬ".„Uو`e]n8N9H$[&a{"g5`XW0{0_G&qqܐeu.pI90r#"ӊRp)eAD*DQD^.Y("D9WD~:wRyTD9:?}DZyQDzDd~mv+EY)"Dil^> sj>ǁػȖCVUIXks9CWƦ*^Z5~@k5XlB^\)/QCieOcaq+ޜnX_11+~83$-5,,q*{ 6k8 7s+emͭFΎ88ewH#ŗ |,>KaU]Z֚"gڪj,}W,zJҶY= cTApש2z.>^`0"" U-}Ed;3(y~s%ːQժ2ۀ/nWsn}93v{Dj܏Dv{V&BZ1sNlw{J,'c4i)n>CX{I=o~cqG"2 ga^J|>@J,88(̊ز ݁DdVTU%e199}(joe "u1 ll 8&%̿5Nk'MDQ˰w:SE|;/om~p㷢n2orGr9^9rVdb0A+Cߔ<,T k^<̖]rMn?mi7 /; = IDATEd#L  pFm9 CXXLl <|/ڃs4mDNnL8:oxU Gwf)8(s@)p|dܜKHy+:3TnYk0Qo/dKv,>]bQ>K6h {10OPk;_{kadL(+ښ[_88TC!z6_jؐ5ǺeػLPE,dǰxC0kUua}JDX"|My(pu}Sl-*"2{gMgĭc6>|UD~E+",?qVS>/V|8BD2Y穪Xٛ 袏ޛg_ȕXs]R!"ov Sռ,zD*qGYUzʮ: MXbTbLޖE&cjRANK8svBq "lLJٳ;taǾ|&O WKۚ[K88m(̛OX`,:]Xn(t"r>箿uEalzqR#;I 7WyU}$G$&Tbn.:/RտaA ̮, w"'oXO?*g} t>nLU;KQFggg{RgL0IR h10z$ǩ  4hGggt0EH$[u&D)B,ʧQ.YI17c VQ>C¯ Lk#l,Bc~>"^rEĢ|`8sт?3'3ѵQ__rqp3?("uDkÀLl?ur_b8k9[ɇ$ģQ[9K 6f)57}y"}(b<1 H%DՍ56]!"'EXl r ^ńӱl׿+?kztdbq}}PvK$SX[__N8^.~QFes tUlPUjwީ~ͷ|PC0_E˳~M**,Z~=ˁކ΂l[̘U=[h UwsV=䁽:+*PVNkko~a;?N羷 7p"bq_gg?μZ`7jY<^R*^;xwQUY[_sTgmFsjfUXA)o;;J?N) e:NE) ~=eekEIz"/AWD~彙5`_X/bahU`3bӅv:]|,eruuoEMua1N^'ܾȟCIt_<%v|qVDjID7 0Zx8Ǻw`Id_)>2q660w ӛ` :GbxT2=Qz|?>a]w}? yqUά⎛֭;gz#yՃ[8HԜiWD+*8 jgqy_OߎE U+fqo.ڳWUf|Raf={y㒆Ǽ^tRƓo&x!#oTD,xN.rCT\b+cӟRb9Drg0.R/[?lTu0/êǁEOG"8oj쾵`գA_*Gഷ/.؋z/|zGd6MuX_k*V=ĺڋHɓ{sq`i;~-> U2b]'Ҫytqg}R{ "gaXO|">HhgoG)ojbՓ6:{{LߛA bӯ1x[).^@PtF'H8uBYٰu/j]<(;&M':q5Ov鲩= 4tG`ǩ)ES"2Ӌ3kU益=sS߆4kśM8y|4ʤ o_;=Z ',]^]vժ)k>{!qX&"}o2G}/h8N|NӺk7y}k_>8^pʤm;s_j,4h2Sfqʎ_O1R)̈cqRr;l8x#ʯT`gj!"ҊW? V<ގsc~!XޥEdU}4K(s-G`f{ElUM'M;M}*WYlj_hS-Ŏ3Z4 k([ l? 2 :WYAu1 622Xܺ0YTۍ~jPaXѾd(ׅdmgl_37$a 5X>Wۚ[eg Ltm׏g "'Q{zO%|U=/m얪:bߚ|v,SwVrlIU!wM,hCCy!r Ūzvr'ݫ.YMRվFcڝޘsdzU=>m՘ؔoH](;˺zd>WRE'] Dnˁ7buE؟8%f^c`=JjH96VC =K`/t-[\=08_jۃ)bg_De1[T Lw>>'OaX=ǖ588(1,Z)@\ȃ Rvnr| {0GaOكMՠCs/am H5{0!"j^Gi| 8 $EJ,om"5uc11g9!-GD[H]EX{`([N}¸T8I5tb'ǂB)"a"@nشvA PIʪ뽱i e:ڳpJC[u9&Ԭ>9{=8 C}XO{qaouE]KU  pfmSY'5qqqIh5։0)$m-af*pD4fDZ|7uPDH.R\3@Lx Z"̹,`_̆-E@cs~ke˱.:nSռd袏V ^jLH7g(<Î3qC#,Wai[sk88S"UL_U]Lb9^ؙרbnR~DLȯDYDv9"<o>/Z`׭`j|>SExGy3b^~گtf=[ > Dgb"bRX;FRLt &-tqq"a4=] §[vBr7N cC be'è8#G-Yl[ƓEn0 GS{fUkkn];gvqj1a9Ǚ$-30">+C~}s;!888CBUȷoc[4by \U8c>"7 fC3SU/(e}Lf3 Br3Ǭ*ObnWj.k Cggu]Dv(BSb0mhOo9a֯™[4p'ע!D9 xK9TD E g"H0 {70#emϬ#!-9W{V`b߇ ?3'3ѵQ__|8ΘRhKU)w#!yaU+cu1HID o:GUWc΅lux;hTC~qdl0ƨ2QKJlɖIX'S1KEǬjH 9bcwrfbROCGP"™P=f&։2`P2ĺ5ՕGr1Yݧ5qpį qQn`rcE8ΐ(#"aȞxOgq6QG @.0"@z^9lLa!׍2C})jͭ)D3xp ,b',~e[_d,tt(8883)EW0.K8cyMXdO {ZD!͑0m#H"2I#E,V #E$-boj,F.8S dS spfZE]k{Ω[ ,ikn888qJ!8f):uyϊu6;vIۚ\qqqǙB KoqrExt#0("}j0t 4&D\ /ʧTI1`` q[?$tpv&ͪ4*ٴ{pqqq&4%0Yqg 0i_MJyoȓa:n6Tq3710΍˘:&]{0'`Aq%l6csw |^Ţ Dhө/:888R>%ط8HS >5+1q Qɖ};`GǢnVCnTťN,8N$-դ0\L{8 xgcϙ%l۹9888(EX/= |>h$lu0]{6t^g%vMDNn{c V{mͭñۋݢ@5عPLKqu+ɖ)3ob=3xY׆㬏_888ETYD5X3UA6q .3 na1vK(r?0eaCG%ll~ 3,ލp mͭC56`C"Oa8&:_93I$[fbOJ XCYS&ev&+sq,$-,L$Ä~*XǂmͭJ_SqqqgR {[-f{+:3Hv{ol]p[t7tR5[ n5Hԋģn6n2pcX69f&89 l'GSZd;2uqqq*}~ 8Ntvv ]__Y8Ʀ= ]- 𸃔G| fVz0EbQ>GV]Ef49Nz/@OCG{0ɀ_X&l"|_=ksg&,ikn͘'ӯ Y.'3ѵQ__v83)裪'{΄`3̳qF QO&" ;O#L/ŋ_Ɩ\ϺH5yMWۈ #(ׅ3&I$[b" `{  Bn,RqE[s`26g}pį qq)q[5-+t5t{rbLb=Vښ[qY)&e:VEqu3Uf|ax? m&D=#Xeqqqg࢏8Nc56 kDX32LŬΈ-{z/!e7҅lDe:3/93,~5 8DZ0 :칳9qqq(wqF!Q8k!Vkh#@,DZS\ pQϰK56Mľ(W sk-"gEdt"!" ɌsL㔏aEWiU }YDp)3ֽ <kUxcH25zl3ETc_/=H|er:1xD8D Ļ`6"@ =GVKۚ[y88S$D$ZADÎY"r~#\A̹ xm7 1i"r+3U[Uudnay .ȰP\qkl,ªӁĊ܀&cђ]ۈHTb9/NzG|i+ۚ[anvl`?[N3I$[301yx$U?'{2geXEPAVa@@E`$qA}5E\A@)A$ڑMAE@ED̚ٳL8Uttw:QW-#:}œN+E܁yGs:wLRZ:3LŤ@x 2(Yy*6Q88lTB(7occ{s IDAT>b_KDd;FN#v\ߩXX2"^ >("SŪ$GWEg'2l9N\u1aIB9d3:7ٿkIO#d6FV,_-ihd1xxN);Gΐ2sKGS)y&Nl2zˣ{U&i'"aލԫn.R9Y7Tun:j+s`=^.Pէ2110{EpvVD~ > q&Qi7kA л CdLKg[5R{Q(% X v+$!`٪`trIHKg[03H7aS`&vZI,+qq)I2kQ?= Z(e'󩌈۰JĜD1݁cmRl9,ЧHzIk7 "r\v)ӄliz5b;=!"''ol{:HbF30T:n ޏ)#gbF,޺#Ϝ1#v >a"`)£*h.i[qs潳j(dO-&6%Yc\/09J \hlyzŒ?S?!X$fڱ6g8.'5az8N x \tdYcW֨z,O#;EdVTU%1lgxWB#|"riAb`_,UySDj0Α.,fK1k? dm[{%fЍD$SWLhuȱ> \  ŢIxsJ>8|,Ř}:8 C9gRp`E ?'1Uqʚ1{I)= xkd}tKqk0EKg[-f;딁d~u\zH($ :0:~OPո[)v)-myN$ BcK0)z ).'/~[Ny=>w͙& gOeDC:,+lx5x, !ES\lj ӁY _H(";Փ裪pfh3F,2ɚa._R/<Ƣ_Th_Ƚع9u퓦ӀTuudX[Q3WD`x{d pdR={0iTPvpυ3)',5Yn}+ݢ}$O* X'뽑eb~/Նya4j\)o&, )6ϋȹX"U},!di췾D 6UU93MDޮ Vs1ML{H坉E>"o. r)8S0izEH^<£93j1O^̋>P-TMHHcƝ# ސ0+9g >EtQ9uBsq\ijW`Ϯ)XT”e=}>R\>C<]ͪ:lܧ isND&o>#"Wa8 UbdCVFœfѹ?=$$~,"j.FhiKfYUM;/z캘rU$5k~rʆ7gU,Ƣ>,"!nT\$gƬgꙪ|)sBiP&,:ٿC9"Vɬ}1x5&.9(X.% ÜN5$1^`Y԰qq HKg[#f`痘7fكݧq)5M,`y:7׏,Rwl)Hz=Tuޫ D;9[Xmv&j)x,φ9_+H\Gcۅ9Ӭ tzV5JߺOB"3 H)Qb'-p_^Eˤ"2;}$;;hT|UD2ĥFA"K35A㌊$i^SH1:8@i򸼆.`ȲߒȣSOf/0 cdnN $'obc۽m8p]8xuS{|&f ĞK˰g҈\ /"_`FM|Z^+"rZP&/"r!f|YAlL;zot&郪!"nDd2l9tɒp?@8>ILҧKv TsȲ'1Ϩ <:'v #֑을ud`A|@83$g`c0TlRl-6j(Eԧ83 ԓPϠ@ `uP梎31Pե"r51%\>ayjs|3$"5!M0+SDfjOrMab1ǪjΎ: "Gu:`fNx sl>Zc]&"cJl>ڝf}xުӔq&(E1=&KC/䞸˙@tww6Xq 慕$V: $&K"@eR yջ>vVkZ; Hv;6Nǃu7fLv3 P;PT`bajlXڑGh8vGXx B:xχ}&l8S0g7cQDG`Q,{c"9~QȂ[D=bp E5f@YBAâW>O[` QXsm&/Tu/8D,e.(*EEB."r'9ݘfLZv|""r36q$ x֋c7$mzFpL ]kgu?Va@Da}HsM>"6ZM'ʜLtaA9 @5sgJXn-6gam&OuO,{  ?$I^/E܅uk:ϲd2 t`:rrZ~N`s&#əݱ8N9H`tXGdi%0Hd]X@WkG1n/UNXDH\'nsqLKg480jߌWnqq IS{=>!6˰\ >ubLD|V>No}~8U]待D9!";9qu1>"qc 0TҫDZ,Oζj||? s0yaNk1'H[0CO&1}g\&X&yǢVxYwvW83jbS1גpBʅ<%? \z߲ݴXqlj#,f죪u""$ rgjY#uJG1> l$;'۝؄ zqE#+CMl?Q?vcޖ!&pջ sy8@>r6El [3$ו8=zHHLP I`k8UchļSFbQ>$F_Y;͒U`hI$Km$S&b+śU@|s:Y Xj09 rqFp >X`k{`|UWkG>֎8Mj&!k$609TpqǙ裪ځ_'Upa f`J򸄓=W1iYv-& lj/A) E> 8㈖ζLfWa.H 4x6q'o㵘w4B Jnz*ñÂ$Ӈ90\%;83(GDN&vS[WIִU3nx)*q8M jk GKg[htbdq/Ё6`Š̨s4f z<) -m0clZYA£XWX1^:m8p]8%#SO"_O .`4e~\96d̓7/ͱ>83!vCW^#w3 b[uϒ}<8 Ya1bݘDfppPd(ұC_h{Od ge6Hwi2` gt 0,1٤bgvYUa%ۆ ۅS 3ccD /> 8qMmؼɒ|_Wg[qq0@bl8 d`:>U؀@^ t{_,^}YҼ p:}~&Dk8vጊ`Ɲ$A_V,`s{pBPEIl7gzG1G qq&(nqgPOBmȺ"e6 ⋊bx"^{1٥bRl0.XK9NYVE!8ZU2(;8x%C43"{Lp,ճg2=5sz$ =Uqj |e/h^v8{8΄'Im5Al@rS=\&qMU?'WR(hN'E>@h/qRV24k@֎kZ;)8Lh0_O5szN); ߬#b*/hO qq& nqg2[TϘ^vC~ӂ\ &dCԂy!-gI*l^v u6(Qȣhl"|.cxH {~8㤥=^Sz{`#Q=NޙzWXԐ Tj虈8㌀}Ǚ D#wc^^䖜q"HCN#-(RL,&و8C$rLrnuؘ`5J,4ezbQ=FhJ3l\|A?q8' EݍM%-L{*?SXҼ{bov1x'X_fc(ѧĤؽX݈힮sq Xsr:f%KS9,7cdqqgF\ .% F[ȢH,(X?gG"&pJ0IzFnQzk3De" gZ:۪0/!#Eawʮ֎j8vz@ۀ=SFr`,翂m܂E_jgӳoMhpqgF,b'u I&iAżbR }hlן ҃IS[YwK| v ,/F)1]8#V ld p66o =Yۆ 䦩=^{j~`@vYXҔfʕ$MۆSǁ_F-VcFvWv "WG{٪zV!6Hq~sFU%i;v= ˘Tu0i۳oFޑewVg_|,3h.,6ϵ~:/4|:dBDyުW)g8D&h>{vISf)p-p (*OO_PO86F|Qrqay|_"vD"""z.E_V`rb`eYEIUgY>#8{Ɲ;B>C{IO1#D佪zXW(f7c} ؤZ:TUw̰ޙtww?4bc]gzUaąuauwJBww< |w%Z3l|FG-3Y[4%X߿ exoNzZ:*Highatl.߳#3m8p]L|h0_O5!pގz cDR<=YX,v@)8yXd9)xd+UTuXa,PMTD$ĵAUSE`/U]5ٝ7%"SQEWfQlZUNS2̉: "nUg_#"_J2zx杤w| &l XOQ A5X?7c"z왰D$IrDnd.Vތvᤠ a+,>[ڱ5xpx4kG>}܎Ì>XxaU)OFo ۢmqʑ{[Th}1Ռ3:Ddg`~!?+=1GtIc$hp f8PDv* nw1]ħpqJD V cHXF,Z;F--Q_h{ N6e8%$_ v:ַ2Tέl"2q"{j~}53dG1t;cQ=a|Iw8y˙n,+)ʄQ>WZDFH`/"dUbJ| Ñ}A4ϏlgoA̰5C&R?3ؽy`1pL1^U=TDN(%38֡ZH -s# vE"r=c3U1 "R E0^SGl{f=œs/QՔtmZDڰ'U=4iCc]X-pQ&>+">?)'8JUGy8+uC%åݞ<E%SMӁm/GV~W6RZZ_h 'nĢ 鷾Eڹs GkWl`Xt`eWkK/:L2q'4zgDn5ǰ_*a^Wâ r n8N! 3|DDΌN‹06ؘuUgIRT 0I>+"Ǩ#˛^\2_Luꭙщ#W}n ,vrE]SߗaJDކͯ쐴j^L] KI`(UupfGVaiS"~U}$ánÌ>`}ށT|#>stHdݢ(шg`{Ixb/mTڭ'\__SZ:3N cgG2ݮtv8Sdsll3Q|lBq*Lo">`r^llQ<=3YchTa4\3^ƿ. $E.".ÌZ`֝`Q|Ac0 *"{j:]\0ۄ{F|/8|̀?ٯRX^ZPU~o~^7E3CT6H=v~Ɣ 9ۋr-`ƿ:r1 ϭ9543}{^Dޮ=)f{{4)s%Њ=~.Au+mX޷T4ʊb}}>^};ddI $r# mw*-mXhoȝ+;aHT-݄q):@2dI 8 WNSX}ώ8x f X?~|Hק&CO"X?&,_\s嚧)ǺE湮֎$G1'vAU[IQU։H_dhƺ琸v>]IKȆ7ywjx~ȟpJf LBpUFRk/?zYȝAXDPS"ElLjS"L/|@U6:f<<:,mT\al >X>Y|{TOI뻁EdAD>|]%0Em1<-MXE@vQD >&U-iEص31tjc=AgLZ XH4ލ{DdfU?Ӭ܄]ȏ võwtQtM(&؄qucZIHz( aDd) >o"ؽഈ'̓"r96~Ueiqz'i=qI Oxr3kǢE,:xs?ER\U]~oȯ2F0Ʌ"qۂ""`Q<?Hayk81DADnrsBDFnB}_XEE4uZ)"=L8.(z,(!@𧘵7iXbVRى%528|BU]:;$&*6zNJ-E^!WrG|j1_80G1*IHy4u!f4 T`A-nV8ħ=. HURzٞET(3܂M 9lȔ'os1)w:˽uo)<.L` >0zkhĞ'JgT[gneG1>?sT"dH%pR1=+0-q恐 I|1- 0+EUͷC:hgo\8'v ^k ]nS*jX(XsVx` ga!kK8[ObX;0I7-/ǃ)=]8 Z:P/aa =k1Ǚ^lroMWkG  o3oeJg <=P/Ng6hQ,G*^ۮ&grDmSf> 3nהC"U~rɌFgOStadY+2ADhD ;3 e",LEȩQ,R*uCs^>iWl>Eʆd6$7$Ю9""g|êz]eX7 ^ED.9hAAk>bu A= i~>3A[Jzbx<06 OLwDQU)c0i:L~Σ ]LzǭH"bi7扷 =^yF߾S}'6{4 (Wbϖ\$<=Ąm<8ɓRJK]}}#'"ҘvidOϺFCsP5"3ٱ"宾,C?C/g$Jxz^q(VNFBT$9Ha6؄.ȞoRIq^,VcFJzd(ilOcQ!?B ݞFaϒ*:86.@qDydY|3G&8=^|==zH=y2=ϑ(cLs8E仙"kl 8MN6?|n$7Epr<|U|&c%AUp fEVi i+D 1 tƘLKyG2lǜbؒ`̗N)EylLI34{(8慡A@.^*-m؀|w4ЁuVZ;J)ͧNE,q HKg[5&p)CKG1iu]kqzgsJ7'&s,mE܉9WqUg\7lk L49lb#F7ޯ6w|5 6-aFyi@3"440=cXzmsx{\6P׫}0ÉQODUvEd6 ҕS՗DaIIwwbu]qK=6TYw ٛy uOww$zr=+#X?uQ9ל3V裪zcȾ""&DL3St'O'v.QZY7_24~]BNq @Kg[ 9 밁 jp/jq DS{biZ ѧXIꈜ]^!̫-`Fp,0a8D&Hfүz?CDPտFWR,|ފ,eĢZ#ܨR*USڗ;)U5Z\3l볪VSDvUyT$pPn[w < ̑xpjW-chQgDf}"r vHq^DRu"LbLyykl.~/"_Q+O)_OkʲED걨č KPI#"r^Y7'92y&>[vw\k:?7_www#xb.DǰE:Vb A*`Lu^b $BS1ӺGVD:OX$ o=B(0}88C1,7Vcs_#"rӘ!b o@5˗c`sm#p~d#X6:1#W`"2Rͪ#C uf\yg{>K]>E .%Z{3&n`\]?-<<93(26 U]$"b*k1]wl. iʔnIO~c'L/X1<~Hm$O%Z6)Cΰ@2y{1r8j4\›?c7K1t\%yxS-XG a7tC|>CߐLScrfd{`s?=w&`F2 <ݜlΓA[s3=/ Ɉ߻n]ODrƧgȍ?=5o r%)ASuEmu3 i5S蛏[ZCm~By(~QcyJOif}=~lB=?Bu}*l2g6n!{c]gO]oW<{Λ_2z4X?0^ry3b:@#{X 7đ̋(il:{yE_75>+b˺ cCVmo8΄wS_U]Uj_^^Ͳ+8-'0fJ񷪫c C$83ܬ՘wo0PY)e6 nǿ./`TWU߮8dy2+ǔTW&40d$5"26)?A\U]nNys 6a|0,=IEk{0}@g܀E` lI~PUW&oG\Ǣ{ܝ")ngm~FDb<{z1+T*pP >pRJMDނ]ayc`K]?u5ߘ#\'\q8UD.REM t/`AڇL"F[R2*!;&-˕7Q&K~N@U}@Dn:U.֐LL ! ٗQJQGm$.#W^rO+<rϯ1rݣ#Oe$0ify#սiU蜁_]5sfOwup\qM͟f~$r:O3P.iyֽv*z ="'\i5kf6.j+KҞ:KUEj.yv{^v;fWMg5 gҷ ᾷ%#B<%(yjl`[f༆ه Lm\S)uyRݰXj0yJPm})G$󔌟KU#rqj}#{9or3V W̭2v>6/_X)spV`/׏TTOU0#ԉ1=at̳N ^n{+i -#lwh ɇj^c$Kmcp Z,WG9 "XbSՔ "X'U',3KrY?0Ttww+MTqaIF+` 8 yg.6Z (g=RhRe.Z:B* qp76^,L% +1O@P" z.|<\Pʱ]L6Z:*1m`Zޏcm ätvdo3o=^ycQ NF1߫_rNDU3B0ٕCYVCcq%{g¶J2!8y3&_B:y,(Umw;;eU,"OoP<.,*l0 ۿ8@3FDk7Ϗζ vC >bF,zu >u$<.J,l`_;-mS0/`yS0cLۘ88=^{j>EF`yzN$}K՘$ъ,Q=_j0cQopl;8΄@U9:0ϝ40Oe㘇I}f)7; } K6ѣl l|˨j;93"KVc^5qՏ`z1odirf&cȲX'`w C `?^[ׁ3rk@m7 l7_%t Emq"aDT_6c>IXgu]ndg䔲&գXm-C =reNDۆ838 s|_U_LHDW'/F3ѹdic-õD>6obXκMbo"\ Vc؄ o1hl;S8(O+n5uГسn]Pʩ]LZ:۪cBeZ`mWkGNxpxȎxϛ`F5"O"E7p-6nFrDDO?[|A?_ q.Ür"(q}2s;&fk"W!"rpd7A, 8_"2U}?q&4لQ菁E.#t`/,Y`5rgd !E2~8Nt.`h F98yĢzH{jJ/s ַN=XTϝXLӳ88S0U$"b&ș X;Xu%ͤ'R`B`6QRryGV{le˥tEH (+eDjD:,4)̮AJQD֥%sx9L{|&9{μ<G+m k(,H#؜E UvG{{hŝkݘzQdo[&jsĥʘMPko,g9غ܊ YkJp4˶zCG8L6D`,[܎yNO7vO:=C@87\iqq*A9}DjrU}?O p~3ee>pfZ~:MNU'7XϷ=lVaN"m6%1XNLi^N3]"MгoQhoAJ}!v{)=GEKGg3kl {yЏɂ YꚒw= AL/crn/bE-ns)qꚒBz=g5x j0g^Yp3^>zLaxqq!dNbKDڰ{U@D l ]2 Kwg7&% 4߃'bObxXrz,ˀ-ΠJm9|B Gc*?'~n¤hcs؎^qꚒ0GO u]"R5c} /`$3uzF[yqq!fPNU=OD~\ | "` SEvfF־8_U:qD68WabpO )%C? UmQKcq#3Tx5 S8,ˡa1G&m^qqLȻ'p(>q%Mm%=%tY>e朿$m 3X,-Y7Ht"ZNT0Y>N8uwn>8OJ x8%D]Sr84sNNLE{"9 V&L9[ f(::=88eŐ8}7jkk3 8N:PmvЮ˒ڷ,QbH̐p>V-2,u[1Tc146;?q*B,E|3t?n2斵46/:Őd 3bka? 86aŮau3NrNOQ)sqqܸqr"*V="Ukz2RʅXt#0i Qj:-g"N\(%j&ohHkz8ˎ |8uMLz ż'y,) I'Z' :=88;}) ҤV&v3D|&v%N<(o8V(d&=REzSrnNk.iilv8NkJ 9ac1gYx3C{:=Iɷ̧88NN$hoo6p]mmb)YjHvmz";F'-ӂ_ 6/bp`"m`HL,ҳ,1ɧӉEN BK93T Z 3-nil9Rp)yQה9zBg8RYˋ0Gϱs/J'WLT|΍ګ=qq ǰbqfbԎپ3b!l-I؜(u?#%ෘ,HȺ75'},GgI.Wy ռ([̽ 1 g0GwT>7g}J~^5%G5%'c`-Vk0,Y<H32ׇsfֿ?gfRwTpADN~;@{$8 <μ8gdAo_PDE"ZD>E> "3{I~C?}N%"!"yi7E3o]Di5"[1>&4 %#V)Cs\q*X">[w`pj^CJέHtǩjz=5xzYIJyg9Jl{38SI,ִ_~1A)]~ .Kz:D׷>l |sx}BDTuNbB "";b򵝘I&~L8|s͡s/#Tb >:.Qi~yn+>Mmo͘`9IJn$*Iqz% z <Pխ=bd (hJU-tF>CUչ)"Y^.TuPv>EnUFex< 8SD.RUV#)"..8Ni>VFvX"3<|',J-7tŒ&XVOȰ`8T4I؃,{9yEQ8S5%cY=c8R_ְtstIDD- 3޳:)g~ )";;{cANL>128|z/ᰗaAt dH*:",vgbGlВ@U=XTy`NO.}<ʰ@q~2KC`O`H[+ 5Ddb#M/ X?dv9)Pd;%O|g 0`;|qȺƘ!e*Vsm7!7f|&)O-4s u>zoqx x$+YCY#""rC/4xܗAV"|Ҏϼ`%9;LDN"t|yADWD>cDs:Ed<'"ȔO-"򦈬yMD~%""BD^Dd\ 9_6Iɦ^*"l'? { !Kh\YK< |#Ed%"]"LDȣ"ri?UTf~0ZUO$Vꋅ8eKپx {^Kqȶ1ᘱxqɺAJmvOhΑyT( Xm(.> SjqbkJF\| h Jgz^e RuzVuzǩn²!$"GDDlɰ_ "I6ƞ?EZ@Nv^gUϽ >]0Ր憐)x&"1U7g hfϡp̵X@Θ-y8n!8>x kClpDU{GD,`!UK5"siGak`i|8 R ԋ0Qf2ڀ"qʂ'YG[}p:f|.|'{$c*lN?@j,4BlX\0*, d6v#.V}^ D|(T>؂CJXs8N AE]SR\gfw1i)^pR3nDϬ-0Gx+P;3\l{n?bebY7aF|9)pHZ-3ץ<2,CUUD l:8]DO'{%8ݘ$߈>tHϾaAϐWHؚȴ.K0n?R k|.Z >BvGDg굌2m* IDAT"2zsqv[LOχXD%q*d 3actcJHs&E m^qʟaA}2Ē4ƥpmI+brP$1Ŝ` 7sOVgsK`Xȝ,Pէ2l94xWaX?oT5P:*xM2t´ 6psa0-b*VBD^dED` /bhpf:rQ- ."2 s' gB8} ޯc 0Lhoo8vA abF"mT}*)]΋>RL %)V02zf{ubF0ldK[U0y%K,CXFt漝]46aЩ8a"S]z=SęX `0wӳT.ܨ=ΎSDTu܊` v] ioQȾfbWf _٫o,}f90As11tW}p'5ܯ_RFp s|6Wվڨ/2u72*x@ͩURΎgqctR<&mig݀` Q߼~U]"" m{_Dlq8hנA0k6//<{{n,?;_'K:ԉ|+s:WDZUuu")mK`,'Qi1/8ڝ>%#Xz`NRu;Fb]Kȉ0MSvrngWJ#Ŋ^Je8NkJFbXb&.,fLmVوuz:̬8[1~#G#0uzq '?#X">_Xk "rNPWT|#Ÿr[83hVvuK]Sr  Iy[`83ĨA~ƞ$El Sy,`8p\I)س@BD~݇^lbl@D.1yoQ;?TU131G՘Cbqy`Y1zRP${1XƆ>jzR ,S`"[/{v0G/tmΥEd2x;G ߡX|l܎e(esog%CaU\g)H2U}LD> \|>h`H׿'_ 1qʒEO؍ܳ;EMnBt)u`N!a vsʄX">n= 1c(8yӰͳt}@ 8췊q)+TuıZ@[~iw{\F*C!$dh,ăW_f C@1qvO4rVdta#SH6Xns|&vȑ$@]BLuzfE8NU9 8 BA>yv$cCjtعT1m (M%!c96'ŸDQvhd[+vZɺCTPmdgVE'oe$p.U60pBPה}供8d~k7[W`#3tW!,: 3$Ƣ?;:=Ng8NU[X'kۘ:_OlɑIэΞq)"{_1i)XV;XA"~ |WDfga1~e?k܊%&`3oanWDdeq;Vf~Sz AU_#fXC,AU߈G Sf*<$瀨$`]}̱*ivfvIN` :% 1S7LnFcEXD,UVKGciJe^E-- ~Գ|iD cFOO%qB 6 HS7V28S5% %|,bXf f 3޳q):" u~q#"`bVC2}q@ 8*=IJk;|z1,_FC{'Zn`XxLdFbFPGqJX">t(E [9Mq)9 Ɉ!k8ɶ,88S88^DUbYN~%WP8NI&f K.ɖ0,Yɺ(@( 'Z܍ VMO)IbX;3D &u{!Vq9qO]Sr8v}83d+8 suzN88%*"]!99{HU+ 0ZPSRU/+xҥm`ZRW/V&MELs3,[Ѵb:˼ 6ĊᅬX@C+m #LјݱA$LyMжwU"> ;oac>KIɹ\an8NkJN 2rq?i+ֲǂ: [E=AT =qʓK::U"lj "cw98}Ddf;}Ͱ; ioRNY2?:'[؜dL*jȶaX&AwGXs` VgHlsӧϼrbTX V,g"v [: %=7'5%k0̣1gYfm=aI/\xypq U 0tJU_Nޝ>"&c2QPFT=$4/#=${Rnˋ1vkl_!%U%?L%2y 6<]V`Οe$8StꚒaP.X/d pjMM6p"uzqqj>?<ˀ|8CTw0:aX0sdʂ˰z>!ciA[K9A,eDTaE-.8Ꚓð£1gϧt2+_% ܹɔuΝN[|a88S5ssX;SAn5k"_TGdGgE\9M Rn`;`HlR՘X"9sPZӳ9?IeqꚒ#S1t ${I$R6V;88NRϸqc4v-@`Z,zs$z%⣁oE6 3@rJE|:pSBz:,,d-p-Sٳٳqz)9j29lVbA!XNO{{{G888K!>obZ]8N_JEmp`d"%L~}o!`l#m aMXRwb|> Q;S!I}RepC IT&L>u+!c0*`\ ݺnw9"sK5= ?c8 (,;ZQ, nY9zܱ888Y(*D`< N{ΐSCJsCXO_M,D3D|$pQo; ;dʍE,%XSmӓu hsc7عٝN1(pPה'ٳ}nbMNO&֟pqBɻGU#"r)&z꽓Z NBiլ/蚞lhڕdΔ\C. a ζ75FaG3Nc^X">Ϳs0c@VWbyPqB175%,m's~,;t Dd8TAU_o-"}'PU-x)=a^5fAB@,9fDdݖiA( s\9 %l. #szR0C\k h8Ꚓ03ݲt[IfގIzqqqȵX>@NOx֍C >tY< >#M_c))pEb' ]V5lN=+xM΅ZuӓyQE vP1gb,g88)97g9EgF]SrW </`$y8ioo歗>V88S8ӀӃ_^ vNn6 a;Ah_KO tLOVc&؜+:>G\QnHI-Nk[e{)Us#}^~K7n6\e2sNeR8٨kJ`mgf[+|^8N3gĨ/z=.qq C!2}Čtwǩj)in&GO P7;rd1Ye*)_(N9=RKď~F6]9_46wax8%E]Sr;`Jnnj-auzsg4l&  {cC[8Tp_qJ kSdkWVRS": 'K1&햩&`) D|$6COI/rMzqܨkJ ƁCtn;Vsf{3}W|4Gwwq NL::Sa0C 2;??{MdOw)oY753_'m[}ҚI-9 uMͱz_g6d|[OJ͝S@hL'"Æ6b#~n8S7 l^q*a8f  #Rռ^yRdӋD2L֭3n^+0?sZ:9&ټˬTvO': p3Q,n>FLzuzhcubs=b~ ?R)]D$|nĂ6N361s6`s^a왡T0]{{{8SY \)S47꤮))p"p2cnkzbƐesf֗[}3NY0wF0,[o.b1Y.Rcsq93x%[ y x |é yqDmm3$Fs-;bL;,)sY7HLb z[':9=HX"~ J;F_Z]F)[27ꢮ)9òze:p;oʸN ;a4&؀(Gw܍euU}tSȮ1pLz=,HNUC}GccA1#\>9EUoX'`u`1U+h; ZUoNb8#"ñF``*""U-"bk#ÍkT{ƲOƲc0TxCUsq`>?!n>78FD>9CžUصx@U˴obYIa [obj}{'[9)0:=ND{~c3Ul܊OOÝ>CB!jlc:S7n 5؂! ӟ^p)wKća}?l_lAQ!uFn,.Lb ,gXy<XJʿ>8NFݱ/aL,ÂonŌ7+N䟹3&aҲb2st_I 'SnYw+"n,x9JwMkGD>ҳ6 ^{b[iarLNUqB "vHk*"G xa"r^PFc5Ø<)y#Gy0ևz;N?x!"`R㇧5*`kyi~SHsȗTuv/cÂs6̦eu0Ed{U}-׸SL7wֺrSA,6O>T{mѻ[9^ ֓v[i<9jNNfbq%[`QjsLҖ }NuPה9yN>bѲ X8gf)h r:3ˢ,òr-=w똎3Dd2pf}IwZd~ủpV=J^"2 z3cGae6>ȡ 7?cNry^U1 7bY,a(,7 'uX]oDdlI O`߼""kWKlk zfƀ ~}/ogsp#ky:0?35߰uWRY7eaYKk0';} ;}RK%ؙ:IIc(0 ;rǜj~{yҖJ(<{,beHl)ؽ/bxPFwcK0Ϣ2n:UOsélꚒ#1 lxo`[ro>/0wFϳw/2Vrj /4 "֠-&%_`*0USZ{; )"csuc;]@WDl.z2짪oD?ǀ'11X0Ht3w>$x@D~ם9kٶ Cɘ һIDy!"5EwEs8}>4o,Y'UuadEQ/y W1Lj@6ui?Ca?%KБ_g'8N F`Q4-X 3q"^~ټ>;`-͕b!%YE6N~:CD,g5 |!eIKcs8ǩ2Ꚓ`=_ƌPXEeފ:̬@) hBG:57< tms=;m 5XH%z%٣vw<$g~AD, saQ289|֡/m`[3"2VU==ᓋs>!1M>vXص|3xoY*!RGj4O\SDvQ 4O31X^ȹ"4m1zRNGD>o/1ǩ8,.,g9k:on`k'lX'm II-4߃*:9#E}4^,}STuM /bƙ\YO>38N;a=3m./c>"=RYlI3U2beo|` RaC#?ߐ@D Ɯ eqHaGD$W@q]!"{aObjd|=}3X35Zy-&}<^is %"3{ǍS{pg~ ss6y0P#5It! Ӳ;yd>`'Fn/c8SYN1А9ܯ.D|ivCXvFRQ5ð"b1ӓN%w:p06ԁcBǩRꚒñSLtw0٦[0 Jos!eX6:avmouDPEQQ" V{1AU0îaF?8spW'd̥#?Y/"3@HBN6c8 O6JCόo(/UzOQy"sm|UhGˡN!$NaV0[ukBŢVUZeCL*fv3&_hmb:,N!l= ::;+&bi46/9䋺XFWdхD݆IFp6sg4l9Z]ds>9zeyNqXם@MNdZ$R-K,mз̦\lƉH#ǁk1C1gBݓWeabwn 9<-ǜ>yT-O4 s jgn=c:SYDƜAKBiX">6=u9.eH΁`οӓ]C96Ͼ6 .cu*VEk5K1iOa[bBj<f'd3fYEDT| $q8PU lIH4clJFaDk{\}!a:Gc5Rզ#Jgs`>kx(Ddw`jFU=9CɢCi:?/tcL4==wWUc0`O"E.d1EX9}gj_g89XlٲsDLga 946Wc8eJ]Srpv=k d_os1wFr.o`<ϐӹU\Dl$nU} x hoW[`ɟ]ú?cEdBI9"p  U!1uUKahݪݰI_o/"IV:ԧ`p/yjH9|}OtʄQuf 6f.:$aTZR ;MٶP"*6QG#LNd!O9v'頻tYv_:NYCe3VQ$kIJϙY-j煓3ºGz%s7:z9csqJU]+"?Ǟ#uj|Vq8~0Ak<׎>I8a`bDU."`_2Xh6+KcNCEd.m^yS5 s~ fFUSǮ?]T?Š|#v[,ϲaܪ24]0i7;w#\?:b"v1X R{:0Qה %;Nvm ޙ7Vmte;(" YD("'ĥhm7ȚB8,&(T@T00PY:{YrIei>՜sޓɛs{?=(g> c5&v#[S>:"=6 c1 ؾ{ˢ{9w$;<YqEA(k. u݆'sG!rr\JN QDC (w7ܛOi7s !϶=`zqACϸоi<$ݔY'p~Wwg(S7Ewv\eD~+QZ1Ĵ8賱Q.;oUntj :GӟA3*L ,CL\kf6ظ=5&*$@co>vYd݊; =YX,ّa\,M߃2vC H{T;ބ2oNFP"T$x#=srg;xG8NGQ}zi2xuι#/HBo7cp}8?{}g_tz9'F3ۡWL!;FG w?/~6S<x fPͨ)P_(`ˡ7npuǂۄ!8hjscPy˂w<AV{sn\ҡ"}Y{`X,6$?HO8t! }8?2}fZ\ِLB7*l޾3}26{p*~AV6Kk3--!NMA6Gz.-XW0J9g3}ۑA YnAf6<ظlhTD_#{[QdzYgTYĆc#uaGP]hvTF:`Gʑ`2}~w>@ɉ>07;8纀Q ϼCM() Y݃QeL&"m"jtG_Ik<=SQd45 y /#?t dmgΦQ}Ǿ=qw0 u Y(*,䉟Mȷzam1paSPmm4߄7@B d&a@YCKX&l=J(^`-(M1A5 >-iŰC{{A lI,hoo koo ^߆E>D-"cXZ!buA1b AcZ?HX,,~{$be,[iWYx&0Ս[KΚu}ް͘zaPhy:7tؤ׾׽Y]3n7a}{ݟ]4,נcƵko v{'ݸq=Uƭ{S/*wgzsP럝5NCv8V;sql58$~l<fStyv;Nhɺvk]Id~Lwi7yଅ;m;{옪dw8gS^Jw11ATx`{8Mm]5}>i 2S_y0{2M9we ι2ΨbDH_"f7<#ӀϢs+wC : v(BiokEȏfxyYG<" KEBV 9 2:N݈ dwi0Wm'jӞKՄ /}jl/ڊ?Nծ z5k?iK޴^wy[s81L)(x26lWu Onja|7l ٨1~*+_w7dI/4gL2㔥$~l<f8#q*󡧖nק`c9w>Y\8g8S~ 3 /o{݁شq ~7]cӒ_+EǺ S[q2 xo9h.U2sŖu*jr} 8˺p{[Q1\ `݅D;[!s5n}}夷@3*}}ttF2^}ɽf1HЮyY׿y[L(1cwoS>p_g9̚?a5df:cs(RLaıX&Na̸.6t3nΞ~v;NߥÆaaB&S*)9[]tmJY?9y:9 CH{{vzGk VYp p-p7v`k~Èң11J'2<`p]@-V{ac#Ud]f0 0egvS3sgxy-|zV̺PERfd:59Sʷ_N4&%Mk<1 9Q$g4p @7U{C2: OC~GXM&Oj\V kLSF#(~yq;>6.JTއ ճ u͞R(;8ѱaaFbȲCyLjb軕QL@aߐQd^Om` @d p9tY\TTLb]0di'ơf=7(}j&NM~NooPͻ?!Qpus}S=fӸ0QאBsPޚM_nϋ[vA`bhkL<@aX܋PxzL\llaD֋|N<`+(Z*`<<9.BtWtj9+QvM15df#L`z5ߵsC[cbos2T:l xn֒fa1l@%?W^]gζL",+h?6"磂=HXT5 k?[!4~VC6n+d:;~-pp]|e_Yoa[B]Cf2p*yw#Z}YIFx;_q6l$ l}s[.aaE~Pu˜{} E49`oT\oRQ} b(UBkQݖ; j3S7paddm ٺodRoTd}oo[T2**PȒLfW'lI?h, 8ӫLƅalull!P UXZ\2;q15&"P.w ?d3gm6Bc#gc0 0w>jB,>RINdd!9nG6=oLjP՗"4lA$d9 FDODY@g"{?wc#z茯J2:Y"픳@#?;U0F@]Cft9 س@ (:*C@[cb2p,m;]b_L6DŽ!:6 0 èPLVg-~@`owT' cveK`gUY}+G$vuSv 4tl1`(dFCר#N}ߣY~ˀϣ(MGoaT.u I(h yٷ]dmQvh\Eo\BBϝ(;c5 0 0F}`qJFdqa(1uЍ߀ʌFW?@ +kV(g*X˭Qyzgz=2}[EFt Dx/[Ŋal]s3VGffikLlNFUE/FABa} U0 0 cSt/*^ntj,mT&h{`j"fټ^"˯ .$ 8$NDB`#ff'haF121d<xSfPuM6 m95&?l./ۑE6aa%>al)w TtcH+lP6D` YBrY1?r'NGΫ0e9D]iu0!3AM(``95&B=q`>vyY O1 0 (mL1 cK Yx 34eDnQ52|ch - xk1k'N MnWq@xys}S% a 3u !`V$t͟È֘SPv!6[QvO'zzL1 0 (L1 cдcH8.i ":џL#lo5p3 6-=X_ieJ2r -Tg=״eؤa!y ۩|xd,76M[cb&ʼ=x.DMdy0 0 L1(,+B 8ޑ 8]XC"OW̦[+јAHG!UHUޙO `5*4Wr6퀾vO0w$ƅa e;62ccs $Nj(*ff1+yNBnAש?N=- aaT({ ȭ$s;t_ʧ5ey|E|,ʹ DV'NMD{VmNGv+Ik<ᐅT!u|FE@xC`qm"FtQ[ΦъJ24 cPא =g 4[Aӝff1P}SS(;.T6!4Œ;1p΅sWl8{_`q [~н7 4sAz[ 9{q,nޯϷe8nF5?HL0Em:Ȧ[MzdVQ$ө*w@dH'`{&L܉*4Qz > F|ʋ{%V&bW77Q5df!"MG=K̾0D[cMN%$}$܊j-6ݳVl&aFIc"MiKsc M4epL89wah"\5ܕjL1 cL@n* tf*~h;y X˖ۺd$LCn"W~ ?DB2E @Gs}yQL5)28Ye> tϟkmh$ !-@=d Ɣa1q΍Cn5<XιS`TA4s@Vtzgs2w0( =K,[4}1M(?YIn$:5$өqO-r} ]\4$;/[u\5(Cg:ىBEftMG5Vk3_.$өk=o!_\ߴ#Gr\F)Sc!7pdn41}^kmPQ㢿5&SP"{p(,![ǂ3F,s#I2! t9e.2|68{|d۫ιQ&s{b~DO:.laa5~?fb!$LKE&)2Bn t57 !e _ >SPLKYU*tjŸY| x='145dns}m?}@y^0֘<'c4_E܃l<6 0 8ɗ>xsj$샬⌡(gG;光>a 0h4r.ĉȦ2*֭5BDòjE^f"^,VfZ*{Pq{pqկZ,c0^5dTP~bKPU?16à1m; xGwVttΞku (s]=RU=ENG+d^_1NAu`FI-#0`=B܅9/=,u~s(x x<{^9#>= !(0?gw}'s ;vD: ssppxU]hnj`ww0{Y@snW3(3xW4n^FA!z8B~vFǡ,D>;y60_A@\}>>D0C(Y7߃.|*d:UAwMs}S%DUQNPsѤC.5}fZ%d0e t/G(}us}M:u ݑuY.&עfffUAH {sVmVO7ٌKaT ι|NF~y} oiv >D}'@u(^{8&3Qf"Á:N/O{$da\%}te< έχ}Y= 8!k9 G:v޿M}w 0ιy6<}9MM~{sJs"m> r-gwA"ι{￙gAH9bba5~;" !k3q#rp *m :yOV(#[;6@QH0X2gISC !#(r !3O͡ u5fffDMEJPGpw"$tݳXasn:+4,xGLވ&sE lduی oB";ȾF{lyG"ʮ%s i-6T7ʪMsG{s BA`7m? t+QNH 2 ݋.. އL~뜛](;Bn*psA_֢ހzs z}d{.qnBιCv5 F6 os{"}hAğCdEMsi4W ~$gOab"~z|GxeUUgO?102Y]LT[2Y ],V{Dk={loO܁(brExXsuGz@z1|ځC #:8 <NF@ߵy^w)t}e$(bg\}Sdݲ<wyF9&.Zی `9~Ûsl bf}'IV9ɜCˑ|b:ڨ ✻]?{_(kpȺAkgP@yxs=s:}%Zܓ[b,&1PjIȺ?zeMP#4I^n3F/:gf&T6.?xZ^1R%N9HAQAB^3 c˨k섢 Bx؈nҮEϋWZa,4Yv2pl>֢(zz,)GG_1k\2$JC_97H0|s@ҡnh!:q (?nGAbx79 Y.ٕ<4?@sAo@V}isn ppxm,|y\( n\O!"}|DV%HDT8?3J8^ ׊v j |7J^ۺ-Ac"|>@O~ƠBBW?, >&(tht gEV{ .t[YV[c\F92طEw~ (^Mp76sF[c!S@WByAגc2H1xsM{ wyu놱{Ps3A棫KDlB9׈ۊ6L+}Xg9MEЏ{*!!(+ι?{@_v'w;E+yb&>9oDA`QbGFqZ l&My6fZ*"7:J_\43uHa5.!&z-T+tXQi'NEH Z\90=. \بkl ΡMG#o3ʅ:g5&Ƣ" $.B෢>OfkY`TL)DsJx?'F׊)6^kG%Tp ),gpI'yqٷ$H9lvs>6 ȺmA}g܊=6 fZ^C[:A}.^}A +.Ț0e56Z+Cq jC@-AE?TőcO>Vܾ:%<^?000^ vKS{;%R6%fc7E@ؾH[;j3fWRJ$ө*$L$kwJ":+뛬aT(6y{SG>_}"$yr c@5&V4AEZ6I6R 0FB1sa1w8nEsϛ"{@: r}bR4/s>휻{oι`>*^s?}ιÃ9WT/=KeM5y} ʥHyw{sWs7f8f P-b`psE;<Ɋk[cFݎkC*zt\[&J]YS2uMT<}pι7Iιl*b67w:~q $"{=9"&x w<>DKι_ot] < |$F܍(>g'T_#WPnHhuEoml (q 1<$&vFQqTb-! ;ъ>a%A?ܵMDa˅Pnι7yZL$GaAYD`o@cGx 2Od9nO4Aƃ >>\*} [q,3@7eoLb&drDeIk<l@O9gQt]^bZwt3DHSס(f& BkTG~ FYnh{sYp&erb(Xhj{ʴ ?'+vwEC=e1$8%Ͼ78Fu2b^Fu[s8; a3~1|p#o>,r(h Q8xw vŬ0wOD)i J7=XZiY":̂0ʜ$d'1qⱙS-G cb[ikL8tdmz`n v۟M>{_h>-1saaNG{'4T& } (F J5>>n#,{kd:5l}N Sd=UE-"m<YLA)kj3-F$өPP͟!+752!㐨{3@ӥ(8*[;[ikLT! o۳͚>ss>{gm<{nea({2M9weιP2NYbQXzy2 ?Fݏ<;{j3-^{r) i&۪Axb"(꺄ߐ zAvpVWELjM/잇QvO ؒqaL]Cf~ʬpq*V:bfcè(っu s@_u'= w ,)0ᄵ}NAUFiq:_]lY>D0v;ٴ vKSΉsm!f:ޙX?@BB'CPɚ౼6b.#H2j%g|k@ikL@vg:k&ϋ["ikLLC姠bą2@-"ہys0 0x>ޟ4}(wL1 m*C>tjH $WD53MFh%i<  | >&$ tj ʲ{ӉAbOY+h#o; yovϟIlhkLĀ躳Q(pe%3{nKgaa@0( ? ʊ5.#F^\ YY\4}ۧD(LDmYdq@RLd2Yr+kD4< Ys'ۨ;FQ*5dv@Y=h U\Xo+ hkL8eGאs;v Aplbt_ 0 >L1JyݐAx0`Ȧ;sf$ө7[y)YBИP "É{ >Cb^GmƢeGd:КqhrlTX\4Z#:. cĨkȌCH栺x>Aڷ0JD=8xc_w = ̱qa dž>aQcF>jЄsm܀nˎd:Uz<5db%44m>5rIJe6566#^~Hq$U*X\d0JH9ئ@@3emF%֘ eX<[g=3 0 0 cD00|L@"{"z/-{>pHd:eO@i'ƣ4u@Cl 3Jkj3-k1:tjT`եXeխ(a2",<:\,?/nڨ)(mjP;PF+z:0 0 0J} E`68e܎nKm@$ө]oEVEne'xiTq(#$ϢLQ[q~LaQd:u,䶍f"1rH05dƠ9(PZ*?BӨۣ<'Gͤ:OzNt ڶٸ0 0 0!D0rnڏ{xX_iLR2`Jd"` ehO8`&#{bD1m*1}4 L> t47X3!/zfh GY|wffT m@ =D'XHYs[6 sW 0 0 cbaL@YMbڇtOt+zOqa:fD[~Uk2+K^(Mw Udf0Jt4$\@xیJ1m(g>vy]<Ìaa00i' p+Svntj&\ tuVk<1eLGma6ȁu۷QfSz賬6cő&t t\&GVw.GbOY Qi5dQ]9(mU;r46hkLC=';˿ =OPk 0 0 ʘc hcd ݎ{ x.SR`wE[sZt{`ӎH4'%碚0tecSYL 9R͎Iq J2{ggRxMn_,֎} c 2F؈x@A/<@%El0aaT(R&7 "}_y[zwKk3-eLA!E:UdkLni>ady*v˙d:w1  0F(]E>z^?/nBQ5&Lt#t yaaa 1 xb,M8)z#\nˆd:5EdU'U>֭5pLd 6FMނOނ$dXe#NA"OQ͞Uqho22UȾjU2@׀̟1k-mP cP b2Eг؎aaa&2ٻE^j3%a1="?&@=QO/ɛ,נeLKY]L :NGm pus~O9%FYSא Y .l=Ordf(;<sp$+Xvaaa&%A{{ 9XlY!'A{F6݆n׍DK2zpAdkJ>2},UʸD8/+Gfd fZVύd:4nEXgi5u`Us}^Yu (( [X= \DEbfcÈ֘DmK$v.GeYƅa X#0 0}RTd}d Qqu(s}ml ZUk{90 6 YA h2`eDcXISco"` VcQc F5dNXO@ o{Lllr'#w/!P5dE0 0 B10F9t?n"[r`oPehƆS VE}BowA[ф@X%өFV=X\T6u 6AC~̌,#<eFMd#tG!=(kU$pjeE]Cfx/->$܋Nе]baa>1dLwr4Q])d:{d.X5"$bڔw*idiЄ #fmW+<\$өOQ(첎?QnmoCBχ_>V7#!{Mv%O[c"A8G:?Gk0 0 0F &&#D唡p6pxdefOL?uʍ;I$~*n_ʹc] Ne 쓳kMR?0ʊHyKf3o06hkLLA;j];x,![вy 0 0 cbaRr|>FF&Z`=e"$өEV|X\T6GkWn>& ){u=KX\TYsk>ĞiՓr6%N[cbgS2tl6-y,ظ0Ddžaaނ* "#ti'f3Nj3-G{&NM ĂUg:Fc57ӀeH| 4@BL4XZFYeC2FX1yh2t c kTEBIȮ2PO}Q5&DyG[@ABh5 0 0  c,%+,.ʤ,ٞMKk<1:c#+j,CBD'YnВLvBX>;[s7ARאyz>XYp+p5p'imF)֘x?9޶X܇E6@eaa%cK>:54VVUt#l&17"MD%!3 >1d:.n.#;hlhoFRא $shϟ9hkL8`$TOSwkж,] 0 0 (}L1JEAXXzc 9' Mrk3-%mWLt67ERk<1=Q`Yh2TTUĦMG"dɬDEt8ނM>1`eD]C 8%y({J[dy"߃U"}plb17 0 DT (1)@ /}r G_B;.89ٚfiӖ.CVEDv, c KEQ@eT x-*U'`R@"`E`Zf׹s*il6z!Uhz6.>:/D=7DDDdR#2Gx|*lVBWXpӞM`_Li c|ʝ}9>0QP^z wT}߂o&"໌>>-D6`4^U@ {٤m<"t۶ܥxxL۶k5sODDDDDB#]s ʧqmp,Rk0~ gV4@WXQv lz y*|3±B*cnzkM v㊼ IDAT"9qƂ0z5frjaoHԅVbt.<~O<"""""o }D1U>,bhĆ}hvPSM {Vz@1<ޯa)'REdnfH ; "cCǶ9%` E".Ze!կNo|Kԅ ȴm{wqVͳ7T#"""""~GdnE9~|O8 g י9mJX38+<޷7^]-P[ ZҮp۹]|=[ObE0_8hd_X_/FU.x3ֶL6fm}l>^T**l(-]e,ςy%)(-*#vгW p(?µNs-`5Einڷ=9Bm%Bp8{vKwjbcADDDDDdFP#2|y_-ХC?[UoTr|XEXmyޫOanϡLE*[=oZj@-r=xFj`z]QZ"Q*.]}X5ϝجlU󈈈Ȍ' qV,:ǮBQF.Vs6pVљہt6Us{n,Rf<َ]oMRvJlxR;w{RԪ2s5Մ1kB] Ąc_z6]j鐶[SM =m/ܭ=7q_[dbXdJX.c&jh|9pVUyt]_ڷxT$uD2m.X۶3dB]T!"""""*}<<{xZ0*۟bFq2-Xu³<r'JnoF枦P!VaR*8-|gB[,dVwF x6L N>;/!Ī:碵^}2g Xsgtbm5{]Q uݱvmA}[wy o#SDDDDDf5U$qw[MR< ~<=Jxn =x80JywfdO2\fx||s`a_Cp,R lh "7p[ ,ɴ{>Ҿy%{^t;Cl!s/6> dTNFEX |ʲ:]rvl<7uBt5eHN,yLȳg6"!}n>:IT*8Ȅ>GaMXp7q8unc}sx|"{k,z[me@)1F>Os^>@bnX(5u17] \ 4NdY΋l| mi\9=3Ƭ87uEX5ϙX;\Z{lff?Чja"S }n8>: }v}l*z85G&W%8 #,鯎7v;I{6*4 ڀg=Ū pXRP\tLE O`m3=7Q쵶~8'h|?|,/ǮOc۞Ҝ.L>8mZށߠ yT#"""""RkqZy@;R~qN/K {nJámp,Rg`i cs!}nnh 4WGtEam2榇YVO6]d2@-cחh]8לP9֪l}vLX"m EDDDDDơgz>NUӕTjgdZ%&b8) Y]yS~ 7e!ϿLX_/ P3X  cKsu\ X;"ܳy.M^Wˬ˰j5XK,v ܓn鐨 U [ ^붴""""""g;8#FWLWuc+|X\"}E -@ L#L)d%@   md_dh =ޡټ:l6Laa>h?šwӿq [Ғ9e@I:=OtE.=O>yӑ gOߣe([ ֯?ogoX?e#ÅEKwv.n34m7~S\HAђ%د^{-Wy!=O| "lCg˪=yq䀢E˒NOd {]Φ74{1{~e5۞K ߎS]|;WeBqr "]m>x=VyGbW~L!,A*Gd%V? ņCe= *;Mxy\uiky8G㯹·{zg|cixd?{[/#޿^+t7yj 9{| O}O7R)604jm,:뭟)ZT_1yS8# K*>ykȹmiyy9xs=OS7֜xѸwe3ϓw =OyT>O"""2K(>>}!~qRdDm(ԦB&ESMp˿Q<vKXE)*r=u D'<*s.U#}}ýt?W?/ɋ}~jxH;c

kSP0G+"""""2hq;R~8\M:qꁏ8t*J_E{2o̒T*D|* ;UJ[<k{]E< <20[=3֛wOI ^qc&hO0}p, E@ˤRzgK+ʖ/gTʷGMuAYY?+,p<i.mɚdkEVݷz7+?-׮®RL#OX'~eG񆖧7cWt;qv|2wsyc*^V0~E{~;rs 84+u;+m]7oO&~yY3ӹ*5dzʺ?>w1: [L&T= m>ݿh spb}繛xLןkdF)kw̲p"ǁuWԌzW<7u2TL|yp <]ԿzmdΘ"A熈ܠge{ N:ql>PzVB&!lhyEc i-XPP[&`;@1Ќxȶ`OG a9c%ρ[nm߮v"~Xs.{łuWQfD]h2!)@DwƂ>U󈈈B]8b0gܼ }V;sT*/y_o仁[@OeJWWkr`/jLb?EfWZ; |&l/L&T?ͩ"Xٜª־liW4ϋ`4p>=1`л蹑 }B؅ ߚ"S%}n},"""2ud8AT_9n/]p]~|[8oJR#džF¸e}jZY-$݇UK|'߳^m*{!vC =`3psV:޸ab2CYyE*m>-47kqqE0_{}>)ǮXk ^0ṑ -ޫL5_&S"k}L$f{4INDDDDB8CնMKma|{T*q/b(N| x8:p{4Jh*ˆc-ݺr͎ɗp,R\|kEP)VaaZ6bn+ .B:7}G;c=7Bنz-ȌKw,ɲk/ptjNLD]8L5Qek'x'g[œJ8X@t5\JwheTnlkG_|Mz_#M5"BX'p4֞.x ֶ |:㍾M6rP[`4^UU,̲0vX[-@dH h}gwiŪlª{kwl>ٽ 8[Ź[}m~m*S/J.vX9flT*dKW|[O|.lz iT*tN3VsF_!mtW}}A8) /y@gCm~E҂k<}P6ÂF4G&A.;s*wcg 5 O2<bg}=dJf|}|P".OjXX_o];p,R%-CX׶nX;r,dk%}܊Va~eX} ?'U0_ xM]_B@Xszd%B7bmJuv܏Uǂ>T#"""""2k)*b!Sm2B?xkTc:;ȳ {f?VZS8qGX5Tލ-^2GXd?nڶ5MXУ9=KuȷbU'sX5ϝk: }DfOρ@['xfc/z6=\ۺXӃ]9 nr^ `- .<±Hz cY'|Cmfol. 6^}XuhND]x= 2] v?pC~ɚӯjG&lL,]Q20^@*RXֆz=M5bXVD--¾v:WS_?-8pVуU d"`k0vngıT#;-QZoĂ\^$v?yGHƇ//fR"{n,Rp6qg#@*ZcV,n"3 h76 "~t5pICm&T,ڏbs|R5X[mw"wx/[C8).uK{8 jF$yž=s8zۈҜ)Pp$j6,h+pVQ2y6ȫT#2I>XM>EM/csDZ+Xxqop)>Xd?gIcnjfr!U8"Ǯ9=uXгܥ\~$kU)""""""Y)! ru=7]<V}]MZe@ҧm`Bl{Cg_cWbG Yo<^a߷?>2;dځuXs?kND]ªyNd{Lֶ_<""""""/$o_~, =i}sہwy6;OUSMh>PTc;DZXRWuqR'$*3"?fxm>R;7;{^blF{YvĂ۱I b+r?.X{ ylgL%"K@|L>* 8bWŰF_͕" x6㦚P1Ta=;[6)*:Z*sfyE>U]P"RCm} Md{lyx, KĂhND]hO2<1q`{6 |Zsj%{3:yn^2rWXl#; Q*N SswB۰>`pZR#s*j#7zwuWQ±I=mU8Trӌ\Qo)hk=±Hb/P=2r&9=ulVjUXp6%6'XQ-*9YEˀcWa0lj&K&ds/C#ܔA{гUXHNP%Vy 0]:k`s?>"6,6P5 IDAT}V?6W7±M~ϪjB%XS-`jnWcs0 5:=z>E#M~. TuL`4`{sSfe4G&V@Dx k׶k]ڃ=z_P#S*3,/q,2cB\uehST*ڒ- VtXV>]= "%X1=`waT#,H*p$М@.T  z.Xs'ps֧j+>c +QUx|~x] GC y:m4Մ,)r3 Y?ӥ;'ϋp,r4VsؘxkƗ`+/-IFf}#$BȄĝeّa v~گ4㥩&T,AԋXkڕ-r9SIE> lJXuX Fp$U2f($VszdD]^ޞ~͛a,PI x/jBv|eX<[7 WX,\Ϧ[j}St+`F0j>P:3=?Xx` ?*.P[ߝ#$ކo{Pel>uؼn,Q- Q;g wcCOc ~`XĪb X Y+LOb Iv^0wbA9@ np?^f9=27%Bqdy^;]mT#""""""->">XXp,rpgӃO֭&T,4J_aÀ'Tuyx8Y|𘛆eXծ&n;88Ǯc‚szdHԅoB73zxwaAfT#""""""2!>"PUx 9ﮎjp,2qM%[SM˱ gbDc-@suqVVc3n{!i?0`4caOu][uyҜ9.Qrñ @w܆]Pзz\EDDDDDD|OHU>%-v㸲 8_jTae%67;oiK6±Hǘz?Z}܉Rl,y3Pe>luTszD]8Lг2=*znfGDDDDDDd)bsUr,g?.TcCMP[ߗC&TAb(v.ҥͷa-XЎp,rc]p qUHZ0/Nf]LkıF&lF`ӳKLD]h?2!)d <]e:7DBd|sCDDDf)'Ŕ8RԪ|ḼY|saL8)-"M5yXpe+ َ}kڪ|S. ycL/8cA @uXs:p&pw? d26XQ9lu|0 >#"L4J` cۦwX2\~"!;t- gʬ?b`4^Ukk8~l° zzly!;.QZ}q],|x.?T*ž*baawa :8+± ԆXj:U37ֺ<9v}  }GmfD]h_2syN%{׿ד?wͫ6*ALr`o#X@wX ཞMk֭&Tk+^V1.Ćշ;gb`xp,h{+i)U~h{884@#V=؇OIԅ`Ne>qX%=s3 \GdU>%XXªjNcr'M]@;OU>XԵ ~qw m}T9; T3b,-,`7nCszfD]kӖy\z \\YE*{2VVlϡd4Մ*o!6kgd؈-]F?U;l`xG9Y/>zG]i..U>h`,9+ǮOb!O#"3k%B{9sֲy\WVL> X9p0lcU4y9"p,R ̜ y=P1,ĮZxgGʗ*Z ~fp,R½s[Zn xV=K0_n;x]]_~\U3l+uu)dn\{mO8r?Z3o38@%PLe-2 ]<>_e.6>l~՜y4F.T ) |f"h8Y [ZNkUC_ԇ1ߗdq8k.` kF2~:c/udBӁݷnnu%lf!2t^/}nN"""sB?{} S8 [K-V7Xd/fԳ9 P[iM5X*E]%(©Xu{. Ck+Kvb Fs <ܜӳ#b"Q*Ǫ!knv/V\N3f:/DƗ>7|L>"SSsgsVә3Xd!67fgW峭[SM {6gxvi~j:㍽};#xØ^.66w'lħr<n½ǰʺt3P!y^O*1AX%@굾&"""""""3BUw:ѳl'/Gc" |4Մ XO6dlCǵ Z-=±H! ૌ<hw FX۶Sڍ߈Urx+z4gqKT,ȓdh\l }D3]wcO8qgz6?|>ox-2, ɞ>DФ 讎7PwH899~M/nm[(;&oAu?| tE>ٜB/IU_0w`=r6'&lѿӜ#Q* uL iGd d?"Wx6uOLSM {B,9i{U|B 3b\*zϱs:;4gIԅ גs"۶bk+)ᑞmW[yE~Mj!j4ՄJ@  g*`>EJcx76;*K >,/ǂscsڌmkGszfD]hO2Nnݰn FL"Bp{/>FK]Q%)"""SF$R o n8 Hȉal-DܪQ-X3UtHuO Cd"Mؼ,csZXeO;ӓ;z^$B%Lt˶c\?c" 20g33PE`*~Nb엒xnKϾ)xfc#_jU/`?kVXu\؄^.9 sIa$sy:4GDDDJ0xC+ݏX$v6w0_h<'[Q#2ʀ3mxc>j>P[ߕcyUSM[^|ss!엖6:ޘ+̕vB|J=kZ==뮨e[.Q 9l$3Nk,"""J'G=7I}9;F:ٍ+M9_ߐH }%U@3Dzl~ +\\ٴ 8>P VS \Ia EkvZE{soW{d%| ?/%/q?Nȱk+6;!`d2YiS;m ¶xڱVl4Gf glKL`4>ZkEX;VY܆]7ԏ7o/3y枽.,]y ~DDDdFQ#~q@‘ITUl*p,roub yT*cSaUPY*䋅p,R |4͓Tݳ;'`&cvaU%YvnќJԅ),>Lymœ Ч<2=Cd[:/dLzVxCoBe@R$^y:-؅/1\^޽= 8CG$VI-~¾ZMyqCDDDf&>".l~50±k2z??ݘ-± [X+_`z_ss17=\=~`B0/ 3lfFoS \vmgo$ k֪<"""5A",]Y\; h(]3nsҸmi n\R5oEa[E+ ڝ ;K* z+1[3 H-rΑҽoJs)a(U /rJspqR"""BQ ó@Gu|ñXpRY> iV-s{jA,$k-p,rKA&Ъ=mqvν %<'u@xL6D]h)p*g 8[Hv<"""sҡN:V\JZ*/+WL[#/Ut7/+\^(lm+ʜb9E%PG+U^0Kݎ,I0!J  T.{!>(~W3SEDDdr)E {6?\4Vcrag6u1&TQU}s/YC썖?oñUt ;p@׹Ľ2s_ ?okN$B'c!nGz~ضr"" IDAT"2(ue]9V3--±H!p- |o:!<,0Y@x 엒bTڀx/ñ<,Z}r]=S zal^V5+ڰ-7bA Pp̛n_<끦sꪜ/(=^:X) HV0zVh61۞oGN_vv,-]R5y;za3T\pq3\xjEl:yG#J_;R[ S 1ٛƞi?Hp,r"(XRX\ C=S/59`nwaCWsz'QڋLӀ%qgnN`w.O26IDDD|.KtS^`Y:xҕ,#X[wPƶ.kt3PYpP8gwxKIΰS QHӷeٻ6:Hm9i&=)-֔@wق1}b-]v7,9ʱ`~,?.@K[JE}n$VıKwdɎ#|<5Ic)Xcy|Xo1N7Ba2!B!($G ;0Ϝ/bmk`Qub ]9}߄U`u,Z@mkgMwb&HlolG8Jͱ p) Se5 ܍spӳZ"5e䃞Sfp>syyY:`?v!szUWK$}kvQ?혳WR8K/8oq8/$rNS l _Y3W7JzFcMJRU>sz+TDe-mF!㪍=ȹgCA%\myr-c~Pޣ띁g60ܜ jeN!BeJB$\ ʀ/<2dEۚww,ΆH&ӊ  W,TUTh[S5*Jbux 2aljp2 収r V>+;Z3R؅R 9ˆdBŜ0:' efa΁nb}C!8>H3N/:sFIZgfpK|cB?7(fi-ꡪ`ǀVw[-ΆH]5[6OM@_]h[+1m6NNL8"T,[١+F%?@07ü dNϢj0Vlk De!™QCܼJm&B1o$=/>۟9zRZ@s<xw8[m_3el1XǼ5Y,uQK-P\i1> z`8T| sfb;0=/vTN<_ |ӿxL90Zd4:zLۿ^dNςj8*Ws!.` ssy qX/Ooq_ral+W &ہ>kkveVuk:Ad*}mf B%<9ʵʵl'8gxv:W,e3 |Tc[[d4V`|<|3cplh[ә/',> |ut!gJɿK~0݈9)V ؅Y0\r2W+&ӶHcjbɾ!ᎉZ.ԩ|#(ٍ@t;;j\>}j NJPUT[ Bj?_PM<P%=ЦdNt Y°a*sz ̙B!) }´f`@|WDۚk^{>w&:"l-p gbf Cu XmM 3xGṔ/c߾^N}1> ydNjƴZ=fp862G!=?: Ƃ 7a*sstrϸ'HmL rSPn ܠgk]Yi%vsmֺ7Ys~2˧T!B̀>BN)p5g|3fDۚ^|`Qx#|>Lt6DJJLk+&;g.ֱPcmM!!࿁aZ}5۲\-F^i:;$2:̼2guDJ1ըmf1AϯGlHF !˄ƴMیb|3愱T !bԗޗ8+TQJ7[e Z^Y\Uk&۠5Zo=}"w=?ʜ o*B!OB j`1ȯ`o{bw|47.jΆiVGO`zR [Yompއisga#ͱK>y &䜊 Ӳ&NB!*y򄂯 \0-U:e@;VSF*t [UV者gJl NmSe%+xYGyf) W*srmz0G!%¹N)u*PJcΨou|q7mX}*@<azT~㕘v Ťpo+Wv꒞o|^"Pϟx<^1 ã};o}6}p8 ~KY76:X߭Un8{x Lbl̠bᡂ *GSp^#UuuSe _߹|~VgSnoĪ =0-RWIUPVNx/}j /TČw*iKxE3,EOYۍkJl^XV+"p8 x,i"&}wo>|)^,Tyu}p8cx<>Ռ:MzKu*f>ޟ ,)؟9^N[i"y&7W"tmGta[)y'He<;tֳRC3a p,kԋUVTdv޵bgʭ绁Kij_b'['[_5ePŶ;n澭)§: W(pB"X)',sUSR=t狂Q4s)Be@BK0gf _)fzGJ=S\sֱ)VDSQ?3g .I[:+jREwÜWLy{u9'TR(ur)W|P)H (p褪M1)֝tÄG:QtZ\(|t?Tp^i".j6nUZYo p# g[N^*|ʼnU3Rz&Z?XJӒܟt/:^'ש9{ǡ Ls}7' lPZ@sp)+g߸ݯ+t *s^ܕ: ]UePjn㖗;bK׮}퍭Iwfġ4w?=@VVw_a R<MR^.GFgy?t Ŀ 5aZ&^lJ+B,4tP_ 7?teY\z -}QkW7m0PRef\d+J2:|/ZjTEJ>~W۬O-vmB!qJBy:'AA o1?_WJvgpEKħZʾ|,r9k6=^{- g{7_0#3c硺Xcw۾3 6Wnl-{b/beM<4 &\ޔz$S5+JUcYUE/ ${SmkR5J^'4_O^~wWs4u $k#xX9V XjWGg/ƎMk#I5ei &tΦӼg.֩ɦ/Hyܭ*V9ewenK4~&ir昅ؼ*x!h]"0S ^hr:UVJ+y؃.|hea+MŌaUY:{`o7|ෟVqTz{IJ2w~9j*om>nOɑ)o3BL)PJ}A $Gy_>_Pk=oL la62@>5a[ǚΆHXi+0f0Pm5m\1>_7NzBJ\ 0Iv(&Q y8 ]- +kgp]fߖӞ( B,fg}U:/ƴ;9£R%ǷV+rȄ߷3 }f`ϗ/.yLӤHPk)9=Gy/КAGC8Уn&Xr߷l*h!B`g]G>yXZ^O)y)?Rf!baVT.C-ށ9ja%>*a$c'`7bZLs kۏ9svH츦A#.%> `pBdN-Raѿ 3% s-Lcv5g,رT z媀rFfaMv[yC%*Yi|YhR0G!B,q,ނq/_/f/aϩh[V¶nZ"A he'Tp]X`g?['/87J1g\F=D1ۮ07k2 NWK$i]乀sӘFn k&B,?ЩƄ75lW8[QRT[c3sR'ּWTY+\S֙ BOa=K%Z)UJ7wiM6kb*)rR +pW;C9=s !B$Y|==yp82\!\WDۚVbO,nغgv6D0%\>LǀLeœ5Y ~ZsK$3x< _~y mEV ?ILEŔZ" SAuu& Σ[ H3/XdG9kJM ]^X#}^Fu¯)S>/JĞAh3l Q (Oɦ2!n5#~J߳5Dv1\ݷ_^2Gp8,BeLB?yѶbm`a !RR8Dۚρ|u[9C%p&Z2 5Vmk*a!ڗqu'r?OovM)EVӘꑟcͦ!Y}dn7&p[ج !oI7qaL xjU*VJ+AJSYÜ .:Ug96@JgjLCa kemeel\Jg(t\)ʜA563GA\ﴏ Wڣ 1=C!˔>D)jE[(^ǿYbi xya†9ϩ҂ŝsXG!'[0U7gN=Ѷ7afUbO˭Hۢ[1bu:ZYLUO}3) εl{ n6xdi'bN708WACdVUYIU}W/T&PRcTZp?cfsW76cy3j&C@ Ӿ-㚆L?g'1!-or0K!dc&^ De֯&J%F H? c+/u,9<Wd)ξ\Liz@kRbXkRhTУQc9 V}5 -;v3&B!- }y3Vaƴֺ6Rn*CEmkaS\=Q05\fxXGh(t*uE |RmR}}y &|.־La4 ń;XVh?pf.¯ge.Bc!rrU9cm,pJˬԕqw#@]׿(̕SUr*?%l)Q/Ĝh6%7:%O)pC*:nxW^̜C+sT!Bq gZKv.#K[^ _ph[: TjK!RT`[U#jX<_!LShL媾9m|%/}$ޗ: ۀ1mF1AqDWK$ ssy^D0-'܉ g#W}sdJ%[duZrJa)[z,{m+/9*?Vg+ΞQJиZ)yFj~mNLtZxqGJѫ  ڏvS(B!XDQ) :̚`u/iol]3(;"A ZcИ?#uY~/mk:?]㭺9v"fNϛv:i瞡V?b*znq `TL%b#_RƷv;]Bc[}s"9*'J;-V *sӯTJe*qӂj!=yݷ#\SPir!zkѧZ/ULHX 3#=V#WB!B$KE3f8ah[Ss] GgC$v ||pTmM,X~19V4btU0sz~:Kbž9\WKd#JWkfpg0-nnz͒s^YaF}s &Y||uۜ*sTʱ $ڝ9wTN69mv\2G!9*+qB]_nVWd ˟Ȯ.kU6`~|~ԙ>[szxXJkjȞJ{dmn=WAVMh V sm?q\k5ՄB!bNHy'|}.g;lؘRL[70gB 0 Mmk%&[Jef}s$L7Tlj0/^9{*)LspזO!A}sIڬjU|͉_qicVSg%S6m)Rڙwdڪh[SB غhh󋪁2 ()WvpHEֶۚ7DۚJaں}SEtrj5o^2ŪOaݛז[7) }_,]-M<7cڶ Ǯx<`Z6"_,}9C o{5*+B%֕T+J%V*VT[ JTG>yC: WrfpזּJiUV)P]c5=~eN*C=ތv]5{q\eNaUγs9 !ܾ+&Bc>b l+Y o\2s;t>ELpl(LR }>9#Vo  fWmk 9TĴ_O]Dۚ6cZ,0uQ0t6D۹b®& &Ϙg.1p&"Ycfc퍭@|}sd6Yń[aU<%%R\Lefp!xX!aN&ًy?+t8=ZgWj*dүʩTeVLktieyXGR?fd5ըTh,m[Mk-ZXq/xJ;{yeV&{s9G4 !B!1GB!LEƉ<;5Uc8, < ̙'8_0A0Tc-> 竜?/[uO}sӾESӺZ`#flj |5ESbnn:lHv !ıs))SWZC'WY*T*\fV4]U*+IܭJd8ϻ5yV> *VhO:V^{0>Bw=j_B* LBz*G!B!'8u6D179O?<*h[tغ;"vn51 | ucDۚ.iW1=˥ovmEVv? )Z"'o2Lt܁[_ !G~S r a5zU/͘Ѳ;{؃9VJ{,jkd6ۗVr=<׭}ЖYa4e3{:wwv2+='˝ y`!B!H#wf/̦eh[:8s!SF =;f &mk*LN#~>VLEϛEVaڷqMòf%3(4b*ynôl{V!ͱ痫mjr+L֖tmcYJUV*JUt=כLFjHj}V:3V(9tϭV L&yg>^j}B!BLEBql,X㣼۫13rrlol8Ά7 VӘocØ*i+)mMuM!LuϝzuO}sӾmL=qL붟=z Z"K ΘF;BWඕ x~l]gDۚ^\Shhlol}HHt6DB*̜Uڳǀ?ۙb3;mk gu~S1h?<4,xEbjld|31< y~ ~YBv-[R+TY*VJymxe{(+Y-W)Jn+-+O~߭Xg=j;O@wO:׭ѡL;@ks!B!XdRgCd%*'L53%dww}VgCD@ |x_IuYt= W.4x/pW{c19o9v*m달no-m4ltD63>9y7} \!Rl[e7OOPɓKUzcJKUJWxj$V9o}l/3 3^Zٔ }(}@z,g+W>[wɧ>l|nw{<7 !B!OBTl-}-R8 h[F`&#/*l8 LS8_E퍭G\%Xc5Ugя`ZoK㜞}}"j(` C3].#9ku۩徿Ͽ`dR%U8*3g]yֶ<*%jf)=>r<j7%:Tv`V[nmWœx<'~?>i)gQL!BeJi-'.'J=Zb{ 5_0-^RmM/Xu[dKgCƭS҇ 'r,LdCÞ4Ѷˀۭ1.U_}J0~Q>mb~Ďk4?J>0Û?(S KB!|o7;(S*sbHe6)Sr UZI*wFW l @HeBgbgGK֘ P^zr+H`"X=]Inyڞ^E+OwYIR#B!I8}|# |lLKzy!ΆE~vV! =vduGۚC퍭s[͟0XjLeO1Ȇ8yNg|%ϺqL%k }Rю `*_r>569Ѷ(X7M!ȷr| ljujLP]cSѶLHRh퍭ZH1+PTk=N}R3j1~{ruO}sLY- ۷szZ"pyr!m@Cި^`4EPZ;3ޗ~.5PFjem[hDoKJzj J'Zߑ\(o6qLgC. [(~ oV\iń< $c`7~;qMü؛K]-*K1! ØyXPy$ᴳ{]xJM^ɋuI-CYiI022K R;KGtw+=,U'uY[.y9+t$Y| +7BL) r8ݺǃΆπ1 ULnmk0ZW7>hc*>DZ` 0hջ IDATaOurLLa%VnH{cP[i6Sz/&K eWK|% 0܍ӖN!G]-Цz&Y:o0z&AY,VC9nՆהniŐ.a+a+egfwuYtJ;ܒ }JhjM72$rM!B!8HX:"["9:,x!)3_6\Ϙc*{bmMA1N-Jb=ͱ *Lsڋy ~y;i(v-JCcM ܿe{15H% 3l7jlTA:UƸW&+mؤb9)mMk03ur>ڰΆ {joJ&MDۚ,o$ |C{c뢴Ao)\LLUd03nTWAͮƇ<ϛM{ɇ<lޑ&BZ"%C!l_TlWQJdcgR+r=ܩ&OwDv\VOeԜʮ:WeyXY/OP :ߥ18ݘg4UL>GG1'=C~!rF8boB!揄>b e` tI3 t6Df>J߀w0~K Њi6Pq< ~<=[ͱ&p{+f]d@;ІĎkLKWKd#Cm3LGL'OB̫H)ūr ~1Wccګ_oF̬\oJkhJ^@/ج\5*ل9%x5aV܄i urU:{1#X`!dbr}CB!bG,; 1}rU3~M׀' cXGbZL ? }o{!m+: \gÏj%Yr 6qŴ%,:!JB +SBA J!H 0$T[6.4ƸVVuwshfv9ϔ{w;;{yωY=<=]m'7Snr~ D ܷ`Y`4tu5Yf vuxqZR 4 ; SY ?r&=y;S$Maw20won)$J t8^gYJ^oY$I$Iy}Ts"fTx/Xf.YվbPwfmkt@Ӊ>AϤ}aC.(.^4&u4h&f|Yrhig bUB/:1W <`GUWGdb4's,}/<.gR8>HO9∽4/&i4a?; Sؙ9v\`YP>w711Cg G1#I$I&4>+k[^ \{ ;WWa%/>{j/U+~7}i h  /!f51s\rq<7焹x0*SCFբS=/uyn:q~1' A 5~/1vq hJrP~h,[\ ' v,\'e%G LI~>BV3U:|۳K<.` 5~yz Y ̫q/$H <`١WXhs"xI ~s$=gxۀwsPIlkV(Xx÷YZBXI m^uh_&Zx cjX5!f\B 6ȣH1+j-:8M"d qI}?IɡwggaJ´iM;:`΁q]se_2_M BʆZ@1(s8 =?k̓f]u2%I$I& > i#6\Ku7U+5ֶI,%vŋHv{\ {KumjWEK;@K<|*!FmbSj)ӻ c`A 6-XϛT)$b`i f3VUHݗ4ݕL;@dR´Œd[aZr_ϱwNھr04:σ{,Kg9t%~P$I$IȠƋoW/_عzGW. o޼}Š.@mm<Lz98'}';[rI#g_Rn`ϢKv*OȾm{GmWG[1{*yn}!S^lAs"q|P83r p7wzZo ƽ{?8wی}n/LKNo\1uKa;i(Lao21!d] }3t;zgp>S@Tt( !IA --- vݵm HfpeU>\{{}UC8w&ˁ ;W.蛡=}U+F$@z \Q>P?NmGb2]mgRYH?_/J_O l]ld 6򁛽 HZ'?Z', fYbp@غ?sw2y-aS{w;miasa&{ؐkyČ6M6wǀ:#Tt( < I}Tba=e+\b' xժtk[ۦO#fWAĹn/\}H6KSӧ| `I=!~=*pH6f<Q{)ynO F^WGN~hWGJk K3Q@ϣ=={6jZ;{ڦ¬#ݓL>±?/M5iBD6ciA1c(+oa<$I$I& >L+rO=[W.y.qX~իW2y~8Xŀ Ĭ/ZrɑWlM=GᖾH\վ@?ig!@ϼ*շ&zn$3F]mS)f\La}~ 4SlJY9ۈ$ 9x69CBLBCm6$ %vl+Lۿ0){g^뷯92l( ON nT<::IKyt<_$I$IA ݷ---׫g>s!)W.9 hɼeU dck[&b6J#_ر3xӉCh%"^m"^>|xecΆt= |8t]=OOWG4b`'9CU8-f`wyF:_\bfe[|& bVcg&kȕZϯbdyt~qXQl.L?&`>s9N s> BNyt#M9t^H!B*/;7ZZZ.}$I#ǠƋ ֶ& \rp1&1L28tx/ҝ\r< ċ>El^վbXMK=R T}@@O:OE,n%ap1CV*u{!f9ݟ:N iS-;\zb_ɂ$'2ib^g: ۃ7qw'SC8xB1P<8tcN f3tHqصusyt #u7y! PRysC$)>^O\L l{V)!w$q G*n;vN {CIYj_-We@=Wb cmq,Y]l'v~ wyƽtx@ͱ,XzZ/hl`MVB l$I6?N-l{ML9'3dNo036״ױٰgdN =#I$Ia`GֶfsO WK9bU7j_QScmkTEFbV­(.\}p%ӯ\<9^վ⁚^di|UoK |a tu sui02wֶO/7m"^ջ\|%C9LsZվ52- T d1u51гc:ڎG14CrUɂGg5 :$I$I hIo-2_g%GsOm^}Ew 7N 8"}GvYr : LJjJ./ <$-'Vs q!z:R <xJnpe}tu lZl#o6[v=ٜU\E 'r vN'or~z;bYĀgN&f=1[OPmR <̡aJ$I$I }45wvZrɅU+~Uѵm8 WKob')C@=i >6utZJ.9)M8ˈ)9e+}H8w-,<2i$f ttY@C9ytL<:5['J$I$I>/k6b%w ;W.9d?^msiɢ-{g]rGΟˀoj_QS$~.@O2o1@OWGtY!^Y3yn"Wwhk"V?Rf(ted%+ uH>bybI7129i?fy-\\@H-.}6ybFΣY39m'K<7Cy^H ITBxHdXp[6)pK&yQnoj_Qvm @;/̌oL1> /ho\վ! Ϡ詔"}>г@OWGezb0d=vQahE189}J싍2 X\>yW~ lZվ)S Znb!Гs&}jĉo'fX` ;IWG[1@S.psłew1Xa~f>94kb_;Hqޙn` !6^):\EF\b'oNytR Fe>e$I$I4* hBXv1 s?Jxcn%W2dֶ@l^5ϟ~L)Y(+kzai=Uyi{(zOm2q|&ODl#xn'xnv.Xz]h;x+fd,suWGۃ%G1b&}د7+g}/LK9)37-Zy5Ō#ր\b)˸Is6mɽ|٘pk$I$I8aGBwwwh*<4Xr+OjUCYD~1⿉A'>W=5il0dEj_Un O%yWy{Jͻ:ڎ.y6ϭy8TO;,[]53i6GRy8|0g3P}b 3Rb0c}X{jer-l @Nٲfy늴N5H̸u%Y]G7hwzg9t`Ɠġ !B*/;7ZZZX$I9}4mm %vy%CTxaU+~^S:3R6qų_qI 0Ъ*]=gTy{=iZzA= Lߡήf^-1s3p=hՖ,A nz!jF9}CբHfmo|OZ8O:b{hiE (3*w.ZY\103t'|y,-U %I$I$i1裉'/^DphSsϿsUk+mm| h#f8v=|WqhW<I=g<'UČNbVO@OWG[1;(y1PP=(ve$jhOӨRVֶ9T畗ͽXK@Ϳ)Y3#f_yi_ 4D`O>sf]G ]-YmFWǗϧ0f-'fN}j\bY IDATYrVߒf4/8gҬ-e&K'I3j r)k-y~.9ftb0:7~b`ga ,'̝ %:$$I$IT >O5+Sru:Y> ֶ9`b&8s_>@[:c,s|}UO&y.\eS \Mhkں:ynP;T[WG[3':gWo;ʮ~PۈC5׸94k%!N#J˃}!fa־fy͹z7t60kyz!|"I$I$IA \A@ϾbGWܶrrozt8kiv{e۾Sl~`:#ޅ|D% G`9S΢ ߸9͏ΤGkv|Cww73b&ύiF X}lfR`5àgoC`=Zs qHJeg~CO[>^/˛ܙG+'y,}[,oT$I$I$.E!Ge1H$In}g>L $o__kI.iޭl?Y7ANnȩ6Κض[f48}@ xc63SvvΝr͖Ov 9d,f=,hIXд!9q+ ! PV`1q$u~0eP>[~lEK;sU)ݧWקe !ӈAt#̜lΜGX3ݓ,X󖖖 ' < PRyٹrX$I9f;(|>< pO!$I56{9nN>=q8{pw{o\eV 5}ם'?N{NϮ/k`M2N4m`Nv{LjNx)Ph>IxpoҼaKaGzv}%9 dA_-vv6~i%mR}9-8|\s99xt E/1$In!$`yI8>ΘFE?z`>Yw{l)Mi# nGv{L=Ĭ3m)+I=@=['vvG=@۶v*ڿPo)R-31a r>N3Y-&I$I$IAZPd|HdoJdÂv{ﻳa_snaR/!9MhQ8i '6m) >^QHfCૹ&s؞3gt`{aͽ3{7f5<;{YS6f0@ñzĹ|jd`:yb3q9Ҳ>wL6:bPK$I$IT $Ir{: @+ag8 tV7aoc$6f0ͽ3\t b)flLI_ƵaCmXO$I$I4 vĠ!$ɡa:OϏcz?zLN;´B|[pÁdүq=@sM3\drC $6(f&s6YZ:N@$I$I03?^VS:Ta7Na祉\c'Swv;{ᑞS>yކI L;oYɹf7!lml[IMaQ&s5輷8Zs|QKڮmك=liiiٓm"W4*u{[ZZsrTes\aeg~}cKKdj[ZZ͵=] GwR8qc<'ϧF8mx8Nϧʆ887ǩǩ:NEvTSEv{/%IR03A£ĭƯS9әVÖG&SŻ&5]NwsκϺ,8]gk~&fyك^Uw_by#UdUU>U$J's(ex.ps?]oD̆qǩh<'ϧF88TH8U2x*㔟WTX$I$I$Iu$I$I$IR0#I$I$IT H$I$I$>$I$I$Iu$I$I$IR0#I$I$IT H$I$I$>$I$I$Iu$I$I$IR0#I$I$IT H$I$I$>$I$I$Iu$I$I$IR0#I$I$IT H$I$I$>$I$I$Iu$I$I$IR0#I$I$IT H$I$I$>$I$I$Iu$I$I$IR0#I$I$IT H$I$I$>$I$I$Iu$I$I$IR0#I$I$IT H$I$I$>$I$I$Iu$I$I$IRhBwz?$I$I: OYc$id$Izt ! f}Ƒ,y!y^HynHʛ$i$I#L$?;"!GBܐy!$~9$I$I$IR0#I$I$IT H$I$I$>$I$I$Iu$I$I$IR0#I$I$ITB$c$I$I$I"3}$I$I$IAI$I$I:`G$I$I$$I$I$I}$I$I$IAI$I$I:`Gc&Bga]a_WBi !4^BM!!!m!!pr !\B~B !5WYvgm>nBhWCm !\Bx|`BDŽ6\U:C!›stsaO!OE3.h|h>B7;=pw!g] ύBh !|$p{]`aKBƄ+RBpKn+z_-{S6Qv?O]<8#w z/HH6(9x*mݜ9kvn;F~0=.jCsӁy@;.] 8ek\ ۹\_J1;ox*Xޟ93O[c&j_Rmo[Orϝ< X@1xXLl?Yms^Ci;;7~Fxy,b2;`9JsGeG@Cg(?OL1~Sΐ'p_]Uf\`c|^ieW8fb,hj=H#~YFsi!\Qaٯ225.h}Xptzn{nI>듯P=:o}Ka =wE_< Z^2?Kk~Yk}x},b2;`9 }v^:su^1l|&׷N,|/S-xwu[]CRf+sm\Rg}$/Sz8;>WU?>M1a30B#>gk\ O!2ywz>?=7ܨB) @;N+&b_RC+rClkN[:Sr pM㢏Z*Jr_bSi@]/OoH u:֑%F~|?6nLB+s_\$Iǽ}ċڸ| Bo%ok\mH}:0xq3I}qVn?ǺOgkiO b}ύ<7C*uzs7,(}RIB̆kAaJIGdB',/$IuàF۹-*$IyI}i8Qfgh]Lfz.I‘yk~z>O !&?_BZ~O&OS 6 ڨŘQ v]$͞ !4+(Q7c89R=w%&J_FR7zd}*qXrmu3s-/+xy$I}4jBO!0r{L[z8>_kgVU^N賝BqRԁ1+Вkyy4Vk\g8tMm$IX_Q ۻBω>Bx$eܨކowB‰!)!SC1]O !Ӏ1s$~V Gb Cejb0$ 7 !\R҄F6<7&$I$·qr4Iw>ߥ4ڿ裵oM 1/IA~ˌhHBg'xe GLvFs?G /]$ɯp8/K ?!qn, FbfCO˭1mxn$Ia*ÆMw)hcxxՆ$IuàJakY.$p qW G>>^y !x0!45QxOϋ-%$I&I/II|c5Whk񲟞\}92N"(l `|70B3Oox. I&<>MsVu]#/: f Jh$InP}8`Lζ39mh)}$IvW_p8/K ]r\'Ir KCS#߆!2O$IKdk$w%I!i[}"Ki$  Gտk6$I}46~2AHyzp.]oLUV966Sl;nt;;s҆q $Ir >=xӣE<~Iz;8-Q ύ (0 xIY^$nH!h"Ki$  GGkg`1/IA$IOʩT?%'IS5 !5Ĺޚ$ɗ3Lqw?ɶS6l'Iqؖ#Iu6<'+BI{yץ G4BU>=Z*fܘ&P~fˏ !̅ W.r_%mYQ 5l*1{$I7}4~^PB/$7O/Iq!$I6ƉIiFwDžNR$IThkꯍY\熩O⤺M{P΋1ӣᑿ}tzesύh bOw)o4U*1}?wW[va~v%IR0Ѷ&]Bxf:/_ f8"BWESI¼ m,L_\B *ژJ*mY W 3)5-yZIDAT٧$ }ؚfUk84@>=W?J5 <7&/O.4Qߥ4#' d~7!oD1#~,3IKK$2j "@C]g(@JN|jm O&^K/Y~ x![6YtypVkH6Ra|Gmx>Aɝ3WUX>> 4Y~&W AqѧG324v̴2u^@u9gFήP'o5Q ~}듇mTfs my~ ~ן|>^iɲcJֽ8W / ~v'cckQ<=U+?Nl'fG[kbX,R|,g?rxXeb*Ry]?_]UYu5e/+߬UwQ5mre .#RNۘER[VY\݋>=e z:z*_XܨBHS8%Ā+k_Ru/-6^H ~sL(5}zV]x,b2;`9| qhNKGY&^?m^Hr.l ^P|ON/TuJ| Ɔt`?/ HZ$I$I$Iu$I$I$IR0#I$I$IT H$ BU!$-GfB--7pkCSB !BBBx4pG‹ʬB!|4psas`ak‡BGWUk>}|nv !|,0;[BB!lgaaua]: !|60_nc!B!{r}/BM?c I$IX I>H$M8!C뀯U~i$+ՂM)%zeIv.n>|T/H] ,,S'^$ɗ4icG7i CI\f|8L[?IdSu $I$I+fH$ F;#+7:H!#$^  ^|,fbsS9S !L>7/Ix/OK $N>B8vN x0X քVi!YMRlu  !4?c I$IҘ0G$iBQ*$IzK|xopr$jl{1p$I]ڞ$IT*bwEe^ =S'IΙ]ϛ$\0I]%u^ |/mIu+Bb`;m2ӁW%I:lĎ$I$I#LIJN7FRɯ6M[7ݫ* |+}~zz, ۹E~ҀOӇw2!Ӈ(I%K1$I$i$IS${*kIo7'IIK!s!!kCbxRf$²sJ-U,f&*.!̨T5y%m H$I4j$IȂ>(f)OeOOТLZD_L "2 DtHwb #FA7b!ljKZd?ALi\s3}3y瓜{ys>{)w/h&>Uu_'9睞nb/yȽNlwHv&ZZ.K㑶K/]bϢ:9mQ026n5f7/ɽ$O$UՋvxK{O%lK]9YTKrvƂƖ{ܲn#d#9Ln\LgLw?S9]Ͳe;)U?kj<{$o$?];ݓkWg4jSIi }SS^PUWSF $Ir}m;$_.\Uyޔ闻t'N?\Uk_UOKϸ}Ys_w?FMv{oTՑ. PۓTǒܺܝ9UueU]RUysh.KUܪnJ,·|bbM,SUG5U^\UoIL3t`k,+}v sӻ6|_O$kkIzRkӱ 9&gܓI^I."8{I>ݏU,*]ސ,3-czty*ɳ! ck$I,5I~3x`[=TXBvy }f@0B@u9R0B̀`>3  }f@0B̀`>3  }f@0էIENDB`openTSNE-0.6.1/docs/source/images/interpolation_grid.png000066400000000000000000000347661413546205200232720ustar00rootroot00000000000000PNG  IHDR #sBIT|d pHYs M MέN9tEXtSoftwarematplotlib version 2.2.2, http://matplotlib.org/ IDATxmeyp4؊HTD'5Bj)RSɨ-j* $*}qմ!GH-PE, hSUpCr8p9wx5sl{g֦i 8t[@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@hډí5ɡY :dQV4MKװZk^[umZkgZ[uI[[4Moȭ 4Z{pcWHrnc?rKI޹4MOקib}O>>MJF6M1>I.$>/_Xa^qc6?6$nun46ֿ4|cmާ$gOOۜ\ǿK9'4>gt 1FޕIN7Eiٗ]ا-\w$#9$ܑ}ZM9',.>mcN~wO L<^N4MgTrC5ȡY /I>1/YE rC5a0`vZ;Z8_lBP,jCA+f;pQi rC5ȡY 3 FI,GuȢ9!0s `vZ{":dQE rXq"49!P,jl?h4v>=ΫA װՠJWvW|x^ILTƬ9k}cF 0&~5;iLr$ST4f r8v ,]ñoLԨaGR1QNcb'x Le_-$9!P,jÊS[ȡY :dQ`0>$yd9rC5ȡY I>/YE rC5a\ҥɡY :dQf@h7tZkٻ9!P,jÊ>N$yo=&:dQE r`~I݂KCAuȢ9 F0 `?.$rD5ȡY `8f;hl=Z;t-#CAuȢ9]E5ȡY :dQV F0 `7|e^9!P,j51Ak㭵edrC5ȡY 39!P,jÊ@hz/K#:dQE ri;4MMtHivsiCp:5tؾ1QKoS D젵vvv$O&ycY!c5{DvdL,.55j4&v4M>$y0ɔ#<7%ۏ9ߡ5ĘXzj0&jeLc`:p[y rC5ȡY ZOK#:dQE r9 03δ^hYɡY :dQVvP,jCA+fI>1/YE rC5a@젵vvvvZF&:dQE rX1؁jCAuȢ9`0>^Oļd9rC5ȡY kcZk,]P,jCA+>N$y_=&:dQE r `0>^MyrP,jCA3w `[kW̰9!P,jÊsK@9T"P,jC4 FǕ$,GuȢ9!0q#%ˑCAuȢ94}Kr\X49!P,jLXAki԰x Ko_ j}5aGikx:ɓIR ԰ՠJWvɥ Il)sjPC޾PijPN*Zk-{\A#:dQE rXq IϥɡY :dQff;)rit=CAuȢ9 F0 `?.$rD5ȡY `8f;hl=Z\ɡY :dQVv஢P,jCA+fI2/YE rC5a@ qN'y O&$NZkLSk?zuI[[4Moȭ3N|+rnc?rKN\[}cWZk'y4]k۰OIH'\ۅ}N%$F-ۼOۘөMޑ}ZM9}gVY\ۑ}Ɯg/q?Ȧi9'Ʌ$y //n|^6?uӼэuot}>bN> O?=/{aS4yWo$y8ɷ'߰<I[O[ӹ썉7]اmf'Ӻm=YeqyGis:{oe` :dQE rXqpד|!tpɡY :dQffcڅ":dQE rX1؁k jCAuȢ9`0>%tVo`rC5ȡY kcڙ YE rC5a `*AuȢ9!b`0@hεv>=wjColߘQ߯cF b&?S2MsjP5,}5ՠ\I>4]9¯x6ɔ乮 VC]C'Kp5jؑ1`LԨӘ ]E5ȡY :dQV\0 `4}yrP,jCA3 `Zk/XɡY :dQV4}H{.MuȢ9!00 `4}ȡY :dQfZk.ͯa!rC5ȡY  `?.$rD5ȡY h+I?/YE rC5aFK#:dQE ri8帰tirC5ȡY `0@hUpZ'<.FuȢ9!bIϥɡY :dQff;)rit=CAuȢ9 F0 `?.$rD5ȡY `8f;hl=Z;t-#CAuȢ9hxg_wivkUpp|KgDL,~55j8NdԠ5,}5ՠ4h&<5aA 5$7tZ{ o$i^YQɡY :dQV4q `[k_ɡY :dQVv݂5ȡY :dQV F0 `ד|a^9!P,j51AktkBkҵLuȢ9!bw :dQE rX10 `4}\KyrP,jCA3 `3ZkgedrC5ȡY 39!P,jÊ@hjOK#:dQE r`0f;hm]l]ɡY :dQVv஢P,jCA+fד<1/YE rC5a@젵@kK229!P,jÊIϥɡY :dQfFiv^ jX5Tھ԰4}(NdU4f ͡G =,]C5j؅1{`LԨǘ '.`Lf&<ק1kñkd}cF ;2&~5t;c`:_-$j%ɡY :dQVDȢ9! p4q%%ˑCAuȢ94}HyrP,jCA3 `璼.MuȢ9!0 F0 ` Z'ȡY :dQVq"{49!P,j `_N\E rC5aln'?Z6Mk3INi&om4Z{ ~#yu7dE_+iZ;koLJk$X[}+>^[ۛ}n}gisZ?6>ۚ}ʭY>miN7lLsO I 6__X/m4Ƀyݣ.Ϳ㱍/Xq߱9ߥ}Ŝ}l~z^i6${|{ kI.>mqN7&d;Oۘ'7wdmSN*;Oۘө$wnoe`@(g^䷒|ȵebW Lo2,DuȢ9qY|&/9"cbE;znޓCAuiMId$stL| N؆׫t[IMpl>n$Jn}P֮W}k}#ӏǒ7|7ܱ&:dQߗȢ 9ݺkLiZpH3ZkgwݺۺCAobyĊSIҼ~w뮛! rC5;YT`L4%/6d3/ Igwz//Q DHO_[YreƣI~.,e?hUA$Ú/pP'Լƛ<:CA]ڗD3wshGy#ϼ$uȡI~#n s;h޴Git=w68s>f7M#-dQ,z=пmnq_?7/w 2/?8?~Co8;{ޛ<>׹rAu횻c|\KrX=M$_G^{ߟ ;úhǷ\!OAX:;sp'yb^}mqc,jC=m#'+ܮig11s0k~[g|nĹS}ֳ:'I^>^sy*{o{wI~#O;=ˡY ;ҩ9(6;{=ǿɏ[oeҰ$?vm cb5}H?^#^9 B5 ost#Gxe gudn*w?cbݸ.7>7<#ocG8_}779wGynaqtm}Kzc}gIrOݽ~{mt~ W|x^Vu;DngϦ$g~!QȢ9^'rlq~~N$Gy#ϼyyЃ11v0Mӛ4]ͥk޷n(7nީt_Q~M0Y Û\|(Ǔ|j^-v⾜g] g\$ʘXq ڃIi.-]>v~{ǹFpO%y2{V[nszKr,jÝ[7ox;۸wY\?dT `?.v@sm({\#8ZutE rX1r6;;g$묋Wؘp(=45?yO&0Gą=oS OhރJM_4p}xiKةA ߹?cflkԿwo ??o~濘o_wo= ` OgaOmK s'k_= .6{n?W˓?__]ټ!|%ׅW~Kȏ~{^n{JdS ?Λ9^ɂYvLX ͡G ǵ{p=OlkySO{7yT]go?}ZKLwY14MoSVk$|0M9R?o7]O&ٯ_ɏ@t*hm5rvwG?h'[o^O~#6Q\{I$8ǫ߽~o%$t^.I긻X1$?wWq~7=7xno>kGҿ麣hl5r/gΜ39P6e$Oxϝ_Nr!?c7wK11s nwF$[od "6/qٸ~mz:ɏ\GIDAT'?yzf͙c0m)3c;owjvtzߓwSٻVDne\ ~oΧ{{8}SЍ5{^yɌk36^Pz,t`\|tNGkR9aIoyMFU8&v8J75O7|Ad|T!δVf64_L3=1&bK4=8MV]OpTw}۞ͣ`>OYKuj8mFu5f;>٧:b~L$GwwƘXq $R;=xk8GyINo~d;E=ep9z51KPqI#ϼw$>/]Js7tL<*{y1I~=*:z?7Kw yW:|];cb0rY<^=̋ߺSI>p}I~&"6r6t^_~v!cbayeo:-V}-o=5z ?›菤I}Y/gu?}?;mz/k 72n$}fGY+{` }OCyYߜcM7/9jvG kI2/+9 i7<{2ÈdQ8J}~|_7I~1G<>X_cb`Wا 0Y :ŁgF֞%ٵ[F_LvcbMӴt [` ǡZOK#:dQ::׽TfL\ y?Ӿq~=Ǐ^3δ^hynCAu&qZ+f;pWQ rC5ȡw`Lh;9!P,jÊSq5'%ˑCAuȢ94Ltu 4Tki|҆9tk}cF ;1&~5t;BAklkbkILTDzCǮk85ȘXz]j0&jiL섓K#Kr!ijI$uhCzX7&j԰ cbQC1܁jCAuȢ98 0 `'yb^9!P,j)`젵@kK229!P,jÊIϥɡY :dQfFW|x^9!P,j]1Akڥ ,DuȢ9!䰢DȢ9! pN.]kĹڵi^kIrjGNtv6}k_ 44$7o0i.N&y4Z?;_ʼOo>Zk;O[͟ol>mcNǦ]٧u[O5ا-ϜMr)#$Lk/l0yK_Xh7MG7]c_?6S_ۥ}u~isOi}zb奥?>o#-7f$$ve9kiKsylڥ}ifOb{cW20 ` x9C4= 9!P,jÊ@`0.t1rC5ȡY 3}H{.MuȢ9!03؁jCAuȢ9 F0 `?GP,jCAq pvZ;Z{l~! CAuȢ9]E5ȡY :dQVV|Z;5,^W*m_ jQNdԠ5,}5ՠ49Nn$ʼ=wjColߘQ߯cF b&?S2MsjP5,}5ՠ\ehy6ɔ乮 VC]C'Kp5jؑ1`LԨӘ ؁w :dQE rXq `4q%%ˑCAuȢ94}HyrP,jCA3 `wQ鹥 E rC5a`0@xt .OCAuȢ9DsirC5ȡY Z9!P,j0h4syH"JdQE rkccK229!P,jÊUTE rC5a `4q#W%ˑCAuȢ9\03o=ZZF&:dQE rX1؁jCAuȢ9`0>'¼d9rC5ȡY kcڅkE rC5aE$}^޹ΧUWrS-]C5j1`LԨcbGhkx:ɓIR ԰ՠJWvɥ Il)sjPC޾PijPNrH34M-]ϨP,jCA+@ڙ 7 ":dQE rX1؁w :dQE rX10 `4}\MyrP,jCA3 `K229!P,jÊUTE rC5a `4z'%ˑCAuȢ9\xLIN6MkkfN:Mֿ>MrkC4Mo+Fs/-Ntv2;VߘIޱ4MW}O>٧]ߧi^Ȧi9'Ʌ$y //n|^6?uӼэuo$K9ͿGwiv1'dۧ'?fi6$HpoOa-OϾ.>mqN7&#٧mf'Ӻm=YeqyGis:{oe`KCAuȢ9 `0>^MyrP,jCA3cڃK\=$:dQE rXyːCAuȢ9D0 `4}\IyrP,jCA3 `7|}^9!P,jLǹ=Y܅˒CAuȢ94 FXAki԰x Ko_ j}5aGiyyO'y2S*У5˜Xzj0&jcL섓K#N$yogLIYԀ575t =oLԨaU1Qcb'ia|9C4]ZQɡY :dQV`0~9!P,jC\03NkzArC5ȡY 39!P,jÊ@hFK#:dQE r`0f;hZ{vҵLuȢ9!bw :dQE rX10 `4}\OyrP,jCA3 `ӭ K229!P,jÊUTE rC5a `4q-ɧ%ˑCAuȢ9\03δ^hYɡY :dQVv஢P,jCA+fI>1/YE rC5a@젵vvv|Z{>\P5;5tt oLԨa'T1Q.cbWLsOLIN$y_=&:dQE r `0>^MyrP,jCA3w `[kL9!P,jÊsK@9T"P,jC4 FǕ$,GuȢ9!0q#%ˑCAuȢ94}Kr\X49!P,jL0 `4*Zk-{\A#:dQE rX1lj$P,jCA33|9C4]ZQɡY :dQVFK@9T"P,jC\03Nk\ɡY :dQVv஢P,jCA+fiv^ jX5Tھ԰4}HyyO'y2S*У5˜Xzj0&jcL/aW"miٗ]ا-\wn>mcN7sx87wdmSN*;Oۘө$4}+#$Lk/l0yK_Xh7MG7]c_?6cis?K9'4>?f `}Jd>Ofk`0n`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@h`0@$\JIENDB`openTSNE-0.6.1/docs/source/images/macosko_2015.png000066400000000000000000004766241413546205200215040ustar00rootroot00000000000000PNG  IHDRߊ4tzTXtRaw profile type exifxڵY8e5(aj5[fdݞ5pp:׿ۿ.ϕrmrO?ON7~x;wF9?_߯ϿU'O}oϵ_Fr~{ϛfzN c]"O[||+b{wmoۣ߯ۡ??Q;7iw=?S]_})~VS/yt4ƚ? =<Ra7'~_klg>5/lą"_~xoo;O>Ӣg_WBp?ziC 2lĂ=^bWߊ#?t8}63x'gbb i쓟/-"B4,40VJC#ǜs5(K)QƚjjŖZnZo?=a^zc𦃗'Ƙό3<ˬ>}VZyUW[} ]vv}tɧzg淼moVZ-c_i\ry dmŞx8Rz6CPqvbX07aZ?ەnϿgKoXtGe(tOliiÜ/}O__|*/ms3`.Ǒy{1XM='[O۪ai^%ߧQ=R Lq0<.Ӊx9:-b1icg4y1ykϳmrɹֶCr POUxgyqJ-=c33:-1ͺwl9iTC tRA6l[|/˖v[xes[+f™s5!$pC!n~kj*ț7Lg_Yޓ0o__ ܧfLY _wqOs\e_ zLe{ͯ.\ kQIu}?wf{s.Nf|woaWW¦i[AFX `ɋ]wcN;IXn'.d=71R7Iiۙ%#ϳ_^漧!k$|;]N()C8{z'\&tijL ;N*wsOIza*'*w{޽轻xT^:|4o\֎sZLVdoLe,kz1@pA~ߍHzҹBsr{Aּf o`gJzعu@o/M˩Dz*.3;v5/!t? R_ 2&m5Iy٥{vpU e'7A4"9l(j@dgЬ:(0-\:>yͻtg& ^t5ɔ;|2cuhVQΓ#N]wMV; "Gk&6qk!7S0HM`ċsNBpN#i43~]d٨ܟ}P/L tV0 ԯ5<,;.L/O CaEX.iy'~W$WPcdɚ7 [!yVe#/Aʞ>>L&I~{|HF#:^l/@ L*dTĭ vHf;θy|;;]@:yGgp Xrle;qӳPl2 s!?JpDNQ|'g D7I䑢g=KP PN4Sd'T^8-fFI L]/Azp 4yp 9eoH@Ś;/Ct?s!|ec7jBc1|"7̖FAѺ*!@qt;u²N m ?$ W%iީl;o l޵rxSC> >4<AؼrsJ6إ'&7 "2lLodᴠ块~J&DaeLuvxY(6X[Ay ԩyUz)\C{#xX.4w-2Oe_S#Igz`Oܗd Q23Mֽ[^ ʳ߉_:8T*74ķ>!`lDuˋ67*S>lmٟ ^x> Д8Bq}} oJWv%g +Q(F6\#닦^B@1ay{bEE4&yBt>He%[:CT{Vx:v Q%J; %A/= PH_@)ڰ[<Da<8t djmU.N4a|8z#R6G J%X?s_m><(Hygł8/9LB@!Vzag`=eҤ=I/R0v(@DT(R RJ|vI ʔZ"> o 2x3-0(IØEm6-+pqKXS/kHd"a"-Am-Yd`~G%c 'E2>@BC&U F aN$t}/_. bZx'Ck*dOX]%]ǹ4B2CF%&&~G `8!ֽȿسfBVҘlp7+x\o$/}uUk |H|"ǻX2Ag-{YO Y,J'(@EРpyBO&a6X{ 5쳶dYi:L !n*-/TE4nB p> ˾8hd9)#50Ўsy"Pmj$ӣ @АK@t>p'''Zn+"F5/@FU0ve" \%"vl^#hH ڲ1ANp8|~k #/c-+% uJu'rG^3] vuE`QIR*۽+CMo<+@Jpɮ XBsrf2iM,tX?YWFE/Y[eXH{CUm%Eۦu/XYR3$cz4T,)|t+9'"aԗp D8"GPlȊ:,#HI*_>&NʔpqLOötPFW,懃[6Zˮ{@ާ||"#B@c9$ EGZ@H\dc;5XPd2]MԏHx zא*X>ò"Ǽ`ivMzaiBzS2*wСv#3 -j)[Rip˒nx^θZ n D|LȆ^o!"D׽90#ll,0S%JTRww&m~_avC~i }V_Re!`GnAR(FTo>:i,v(V>Pdw5qL@/'q&TipL ̋"d %Mr}lW}spI8 -Mu! ?vmwR kv&o<@iek&XD9< md^0%dMhJPVK:s png8a`vSZVC{g7ؾMP@ q(h2ST"uəB!~W9 h]¶A6C蠍u-~h1oLFH !:@@s.%$&tZ<ϗj ZJocMn$81 $6?e0"J>J* |Cl<Fu>Pd%_}~+d7k %x팎| 9b{y=0xjr˕_(q{_T>/C[#Jt8H<dỢ ]QVrTZ>% vF'OV~ùޟdP Uٶ;ÀBL)Z?$,Y'<'̠NPE0pn8$ 9ҐüIAZKnӁ l(8ÂM_R(6:-d8!&cРSBHj6 BiL,`u.F#daaLB"+9;,APAvP9`#V/²cdq"Dz4+߁ nꮥ~ƨ-xGx*I}RQ@gKNn?|Mt|GwDzļэ!DGT↼I%{:ۢӾP<0MR>$a0ay6(-e6@* oFӍ*`ϸ1GIIf[S_OA nԍm%\I8mIfy?RDgSHּ$#2ĵ3pvG|.4s/@࡭r^yI>Y& ~w(N;S-Aডu%9&AeĜMdu)_?Zgcl酌=4$",$xLxmYm=Ȼe_X\)\k80!Fpmtݹ0$ؘLA'SWd*qA0Z#G#@\4aL F#QP(>&6MNdi8zCw1U*CbEl(81ɤ U"=RY1`Ao~-m:sh7*P)Qme8hZ8(BɎ,A;Y _fS/az\d(> '-2r[F+9Bu OlszX9H7$aNW;^۞N$cw}N$%?uU *GFHmWAfY?BKx>`Pz؍%hE_aL= &eو.*ⱖ|h!r{!VW,5|%pp}nv~Pe|K Qu'nCC#8MhhF$[M?/Ct`X z^A_,(\7r]j4:Y9z`6 NSO0e(IHSd:}hKڒ2 B!"(AkA;<[3r#K',!;/p`ݎA79eN \FH}P!,SuJ P>T7^-MԘQ2"@E(nuoa ɘFC&Yh25o,UKH-Fr .fdMwq :8ʹ1<*LOdP/w!fTi9ޝt1<څ=܄ Ix7PmHirkZb7m88vd ̄C;Ά%rpYTrm Q.~ev;Ͳ-ixG(r]ֱ= >0j.w%9*9.e]]vlϘ"~G>Ũq _w"&Xuہ:Rf T@'*uiy/Wgs)P7Vsӣh/SoG%8ze! eQg8yǝc0gb/vimįlZ0yx_f5$lD1].hV(i/ǖ Eڈfǹ6\L[ġ"'v'8q+r-;@+Gc 1?=bghq:|D#xrP-, V+G*?$\Yp/xԮ^Pw|d8쎛_;>6z^v1s瓇?w w)cWikVOn~UK"V`b0Lf!=vA"0Ivqk$%1C3& -ԏ]q²nW2k#74S(T v9ujâLi9\R 7h7йo8= x.]=X' η=G=2[ b77Z2v7ZG_/Y),H-گsh].kgU`Y6p8#p}5DF Zgap:&Rf[ Ǥ'xPUu+JpĴ%#Oy-Bb'b܋"āT+-ν~^LP 3C,wս=]4/YWm~bG\uN!e}v M'"GlWXUGz١A\UK 86̭ i;@s=p.@k}#Dž#2 KEFl"d;S;qPv2by/Dy ݓMlKg;(Q.|8m؈qHÙ%yu=B7clN4eJ.{YwL;E;/U%&~=տCNۄ67!G<3=us|=yȔ̎mޠ=pr'TMX ΕD>Eml^  Jz{I kg-4-NWOӛ0pnp|x}aiP<vTϳqM\y& ñ>G؂r,CՋsiWy$8lЮ#s-b5Ŝ;sq:Z#A мjV`x|&R4AoL: DҦ8伧y>cʨ_=3aΏ!/I'սa߹W+^8:8g[3^}$^ύ5PB I4DNb6b}ѠhB!d$x qi;P-]xqV9㉪*zIb×fvz\[M3hQQG_$8jG vsEcGzSNsS`ψ M7x{Ľ Ėo)J['$P^oYmX5;~s>M 1][-ásTrsQbk/з`^מ;o %u>˹`̀[q 㤎ֶ%τ1t<5ϩ~7]!(<(,e>#Ow$"W%ሯ*׉C%GR"D"jbaߴ{Dfb Xe;'ټ57%O +L8ݱ3\+Kێvg\Ш' BzyW]A"[(IT"^N $,j0)?RD"4 ]Uqc%eثE"~^JfGo!bN0+|U_'cۆ❓=dZV D84c'_'"Ҹ-P+3 x)` -vL( yO{g|k#Y ztJ>*R>f!tu|QYxnzpy!t>ݣ [2}+{@8XFf=8!Iŀ_uP8Yv"jfQX88(W l? 5~})ʠy^Uy& X̔CpC8 ґj=<  v$Žy,:9F:B9Q.ys %_D/kX7N9fc8GN~f3(N780^\uOXuPB9{(;H ?\W, k0ϬbyL&ޕ]Ͽ;ҍDo!9>q;~3;xc@``&".:)K6 _rZ;yVxJo5.$[1#,F,ҡW:|es"x|ǽO&#Sx=)Y,YF><$;^мГ"a޽ 'ʫd[*~ (/K~>^KBȾL ϩ1dS < g1T8"29/hG 䍄M~'<|`29 z?nCF/szsSNj 0M&/a*O`$![;rıdvWUl4V_{ocjtr,^+7}Ex^պH5' zaA-?7s' xg*WRqԯ򝽙+,x:gyTZz?/gN|: 6gd#؉[&e=3Ʌ,1IF"nR1iw4a8iTq-'7˲'yX%jp{E7D-^1`lD̫dnD6&M+ dqѪHOo ֵ t腒2%(e- l MۂSt.xw6!pNu@oxaP}"zĖbKGD pHYsaa?itIME -ƒ IDATxw?g ޥ" V%v5f[MѼI^cS^b, Ui]uo^c(>{ݙ=zAA>8r AA]AtAADAAAAtAADAAAAA]ADAAAAA]AtAAAAA]AtAADAA]AtAADAAAAtAADAAAAA]ADAAAAA]AtAAAAA]AOA3B*?N7iCWN BZ΂ 1w+ T `u=,A8]<\Iq+<#z/3%" | y2 &ܶi9[p wu  dgClk8 lN8댝;IN D$)N<1{/aG{:p=~_;*WA;$u>p&JrUҿ j=)(  .=Nn?QwNOm^//s|'xƇp"08e6v+<.þPu7?z^D)8* t {AJ4պ=mf]rA]e6j͖үmTN-b/p5Ug͖]5:@Ӿ ~o|z PjhUI:b2Igu?5)m;59<+$" | ًcG@ϥ9e JzAݵ7[+\KVFM'/;BPЄ1Y9W3Hj[(1tHQ='9;j2279Z𯊠  (k*jGv46dn\ EhQK}cNŎDqF ]7\믱/)_$%ʬ`˨ij 4'Tol,WDDAd@Р:tIM;.|b Ќu<MmsQ}RtP:ᵌUvX)]@y!Fug{j {{տkWDbE7uٺxԑߎ+I@'|puj7~[m >lyL6陘 yAR&1\Jtm %JV~kUp aNy@ԊquR߭4)x cŹ?&e 15ozNJ  ;Z͑"FzT<#oٻɺ1'+gȼuYtv L̤*G]"q(>kg+嘄NrWV+q`.$0 ^tA/I27+~!܌zN璦{Я"Ѩ;ȉ{]&b-smD+Ӂ&:* ԅZR0q8c=G/GS* ywȅ0)&Z8/ \nj줒at~62QYrTqkEކɄ:&ЎzOzƕ^c?lϰZ lvN*lLS. ._L4?0׉.U|A<'W:5邌r̤(3j|^퐿loۖnL@05 &|J { PT !e)`%-{?tW _JtABrcucQvpd>~{l2/ 5&ܤQE8@Fnmy>Vbza0 o֒ϱB>0o"^g?Ɩ ,V73)~kIӨ*"ţ-d,@UG*u] kb(08$u&:^+a\oaJ|_n0sS*+=a$х2]|EC`8m*4P8poV6zקHd[%eJEрA]oZL"m9Ntli!8Z]Oh:|k=;@S ~>{o]8wGuT^:XA)v )CK`%֊#^V"i=ЌԚcd30\)U"VNM/xeМx65%fȕ-5yt,Xm:ȲsAKPcۘX}YֹinC4(/VikA KOseٞ&L}z+++v܋`_<LB|}ƪΘlUewRŪc2K[~ɚu29AB?d>9 OZ\Z|kvҿ^|DcE#:28=bŴ}9Uթ:9$g[U֯c;dŷmjL?;Kt8s`noy(њsWroߕ^ aC], 0(惀aTP,/3& tI}ј;gc1'ݴm ~"PR~8{]6Yq:`1&=qGj ;hOK1T3ګscэ: ~ao*=}lӺ;O3%%LkE mIsgn>h+7G"c7鰶LE^A8;ThOz}4:0%ޕ5a^¾ Sv`\D<39K(̹n_-NϾv޳oYyÀA 7 Kg駞'_D yh&FJVwhM7|=>K|'*ίW`SRxn@kH(M ĵOb\Vгf̄,U zgXs1o.䣙u+GR[1`h_R~M_mEA~\tAA7Zk.mRVZh#E]fȂz]9!䀂 N]]' &g3#uJj{VhV|oDeS@kkuϚ z&!+(mpz)~Q&]uS\D]m&,>S&֡kҁ 5g~)o`8G2'OXiYnkQU#z5ڊm9&[ t`3V ,1Dw`DZV[4ۊl+b."pX1V>Мh6{DJ)TvTEtq4ܭ_vmh⑒T5 YӺ[Kzdh39Q9}+5*(k'1d0f Е^E3 ŷn޹EnAAÂ&`E+끅JQŻij37g&~ζ |֑ J?d8 5uTen=dww" Ծ̱/.n%;^` $CZ<`izk,#phRӦ,sW:>'TƸ]k];W{ ta̽}f(RhnTeY'O7M "p3w#p8 WPޖS^T 2 ޫmj;kVfquӞVXZyK%-M-֚=^5? uZX S Rbb}3 A[͔_oQV]+Hΐt j%>r .wA/O8TA$}Xz;oF23[;/+/r8G8Ӛ5 |>32*P oGe* L"Dqjw Mvmgtj-ݮ hd]pU{ufaIzsMs .V̏O|KQj*.]wDs(U]jGvshV*iGcb)Lۯt$XE~d`-&- er8굧c>7ӘNexN0{?ݵ߆*wy!֨TrA]gr1s5Svk[bYPj*PTmNjtJoy4pQŸ8,'KbCsVi6?Z`ڳ{о[K>lnL&OU-] 64u/,IE&ƫGtu};˕tA8<+Z1IT<~lJ-@dW]b^l87Z_/~BGvқΙѽoؖnMJLb[X>f(qo$u }3/9qD/j4OyԨNrc)V ZnM΂ 8el/;WSݘY+ǸƸϟfY|{Nx{SkUN*a\G2w[?'0fݍ';OwT(М̌L ^NpT0sZ*? ON8:Ryz d3*[KuR=dt\ rdsA ]SԺL.rkAf/={_Z\Z&BnNzkZE\ҳ=S.{u xS6/WuV&G՜+nɞZnyf5AHazxNqѥϺ؈Eo&I?ih=n ;<ȍ+.aَ6YA`}/&lUg{_gIO;B}ӏܹhKCϘq"y7}^.;vu+kj>{G_̺̌w8鑉V " |m2ZQLm`R3}sIx pTyKr.&+3L8{|[8պ޿msamdxtV5\t{L\T#'7 .? ldtn[([N5I;S^w04lbjuMÊwbN+@I;p^]';WAa?E`kM,{LL+oo߷ }Yc>~r0n}qZ;>TǜՅX^ҡóu\=ֳ0u-qq'"Θ_L#y R^{>>sָRg>DA ]>_D1-Z1iw*H ".?}&=2>W8r S)Z07 P  .&@=0ӑ͗" |ذLZ9 "y%+`׷ uarbAA /؄u4_o gKb6(_MY`!ek)ކ=litshGˉA,tA%:{t?zvŸK{TעݕYzsC[Nʍ"b gO-ZSƃs5*'ǾAG}P,93~bGŸpxN"" Wxm*~@ ;Ev5liyPyѶ6'+]Gu)ZKtA8A`MUpyΜ/^^s'h=DA,tAH?vH8 Illii=X\V͂ua2#'AOY/p0u>귞6E.iœWCÙZyکQh:걼p`?8QtA ]8)JwnYBͼf _˫/5%:鯌}8{DS`]D]p׾\֊/Rg$&ל3kЅ/=579'_e JkpIo{b _wZC2o|} t<ˉ NiDw'C!W.?fhbpE(8teCUkvkN< D'5;5@<#)kߡ5TPE`/Ixw{-m`9Panjq] G_{^][K)U! IDATg GϭL3G.cF 5=Z?Z}9$tPˎ9:|o'&˻k^T. $.Rxj)cqґ>s @+Cp@? w9vG3 }#;RO?{xVBEu\HABro(pF\뀮IO_.,< t92}}> å %ڈh.\34?wl ]ljM9ƮĽ+Y'4Ϙ;cvtK6u\o1.ݍ+-ǔU&<C;epP <4/b [C>ppuek*p0%!Ӊ pas 3ZgY 9= .@Pv?vj lV`}}ln]sXFėaJڒfGN:2_0F0( 8Jkt}x A!1tqÁn<`OhpCn+'A KAӨ.ޘ>&yqvLk]-/cp<:f5f*ЄTkO b _zy iP)9C[1 lNzOϾre ?\SѲ[AJ.[t/065 |̤"k]@pyseɌpyG~kx{hmix  ;+~t 89h ):5-P&aI?y A]}Zjpٗ20 H*Ԧ}?j|kM`SjLԞCo{e}`Ԕ։@WY+Q]J7lmd N֦μ;& 3}{-Ր'5Q:ҝl073fNq^[n[s G|ៗkbBUJAAo=qɋ@3vz3D&#) ]+_Z x  4Q1O|߯WP =s̀o ņ2{r{vX(kK|hX&G`on@?0\)ijAGU|r`+)0y[k"9KkO0Y / Y/rQ 1|`ji)P5{{]QN8[䄘﬽h}CmNQtcK?82 _8ʆ.D71osgjۓ_s0Imgcfj{n2}АuMAGy_AZ0DA*t~N~xλkv!WvA]D8WO W0G,|O@pa{@P\Q|ull[ޒ̫npRuSTy]'|qX襾vOZmVQ$|C׭`t|20f_[6 {;PxMhSI~)ZZuMtr' .[f+>j\>+{ZL?~0`zn |kR/f]{"=goZrA~"P(ZOґ'WRZL z;Z}+_Sr+Z뀿Yb:ʽ < -p)p$lHyQ^m{s GxAWyO^=p0s @Qǽ%'E8!S)y '-VSa.xrz<V)_Oz~ٙI_ido\G2i=J8k50v0t@=]7`ŸWaf` c&tsV+0 ^v`Qv}FnճZ3_ߋm5;ֱ5:Ybry(mfkFj=,*7?/uT0<U%E)IcvJC6-hΘTuR ); ([3=W0։on \(A]tͬeh  f;m6jl[bӺ 8Ml+s2(NureIOﶂ~:& AL> b6.Z.]#p;۔_s&\ ƻ_J^5>P)o@,[gY$`6LF0ьq_ d%<6^խwy}%:Ɍ< xvŗQ*i\:>beNs#XA!4!-kwOރG%u!9ڑGtFNLf+a겗cj.6MR#-& &qWYQeoic:[^nq f]avXg&) D(p1aZVᩃXpNMvp&V;7eEX9j^ mb&^Z}xE0}okYO]1ᢱv{^  ms?) o"|l\&=;#=3bgq=sHz:7֚|ka﵂a\0q;01\IۊI֪-b?~|ߊ6+bOb\o3)S)^+X}\Ti>ݶnװ+46k|Lh6:k?wU k* 5?o<%Ւlk#liwƖ8tǩѨDP݅J@܊wvMsdB "#Ee-m\ooj Բo!(&Am}XBb-Ŏ׼n4q_a܎ab8_"|Lw3]%NdNY-L//rP;xj{0nbL+1 n_[}}ЧY˼܂K@-twlY\Ѱ5ZkR]Ywf~1ALJLbTL>Am=ip,7s|XOC/+ʙXҘU9ձ7ZÌ}k#otG1ӄB 0 W*RO9V^~tDקm@z` ~2Oo ?}6U?u .:~_߁ALw Zksߜ'd=(oYL9%gY;mB>>O^5Y_x%q!S1KtЄz1k,GbiZ?¸Pg_;b@"|t! >P7Z1Uv@^Lپ>G$s*M:x=_8z+,\;m?]Gt/ݷQ9w1ӆ,_Tv:s4S<8taPsVzT-T^FLbj ߃8h\צ zv*CPD}!Djy8 "¿Rĵf!IK.o ܏O20Cu2E bS|Y*+f53BkN4p¸bzjkzqOU8Zly2<}J+q`ybuNe_{)~f>pE>vr\)A kف8f1? JO1ѐm|\s?:^xP.0E2",&]qG&8 }>|=nk9c Y1Ͳx]Ⱦ?يsLbZP$@EOø]nZ;@i7S#dca ; Xd?_;Ux_+NRieɁfᔹw繽68掽ț ބ/FʧXBڎp<t_7ДXO$ЍF=o/_RiAUs[=i_3{.?{(GP4RV89KJE(Jwvtzo*k[&kk=ݭgZn省9Y+ݱYdM qЊZ{[vrNd*V3׬g vj!cl{\.&^NJ|rZ`Xa{4?z+X's6M#;[WW4e@Dc-ĕKg?1Q d)N* (^7՟|{n5%գնgov9C %r؍q&1k-v¼S&qvnqv+9iĸۧX> Q@wT`eKwlOav{N7 uв]⍥-{6oR(7猾񮳗|hpC:z>v`2MU{i9=whAP8fnϮw]gQvф)iK":a?϶BL{GֵV/ +/q(I[n;01@OY ybSg47xmk8Yw|U%G&2"1DЅsFeƬ@۰߾9š箛޵v0ǀCP^#oߧP~2z5PۣеnkEV*ةϖ6Պ[Xҥ*βh+Aڳqu 1jLXjE<]|1s307~nyKw ~Y)[eUob_aBw9J:ho^bH3pEL0s"?tɮ[⢃~ o?tu)Ј0EbTR} (RFXT MY^Nhʰ\+toZuUX1T ߅zyήģ.&F}#.}27Xs7 e}[zA:;nYv0QLfb;G苉ߎ z~@4ɦH~SdCL=v0^:hLF7Cp*U2MT=\yT=Jg7$9M( IDAT?!y֜fsA@\-wU\sV.Yrnw܍;ZHBՐ@ I ` C c`q.[jWc |_$vGϮv;3;399sNi/p8=Є@,4MɒpT`[&w 01  C٣_r6 ^Bj~(&jEesPvA9鬷7E,KB$}^zFCD_;gLhK } BѾڽ Wt_Eşj'(AuR =$|4 K**)Nʐh:L;fOSCGЮ.+x8 l9X(jy.GO5&8gHvA\lNJfCs z ICPKN1%AQӨBK+ |6_ت =:#l&'~V7gAٙʷ>})@̾ r\!ǿ;'~ hy(lx <_Q`Hb}PD?{k:DI|߮Crt\4p-ylFPtw'8%}Px DS\6IyRޘ;UEJwa{Ә'NcF=$vZ@#nZZ~ (m ydW[0j)d8nCgHy[ @cxN0-#HvjhPGJ ɑf^ȏ΍O:& lK aZ(=P#|c!FkPGBٮ= hFa+ @#Yr G)htE,]cc;QeD菈3 @ ^1/NoPs.]l`~'V K"@FF;dKֱ[/ .qx',^!!pS[/UZ̆m[1v[H_6)?gioXAE?\} B!) Z!edPQޅ}qZe@>ʦrjj T39Aٴ'A FVrsCRprB\r Kr SCϪ{YG M!@F,ſ ۩/B ^]2Uw[ه<s bI^),YH  C|`])! z(;>5j(/xcT’($׷/mKVGqܧS!!BR,$/hgBk[ b~%/^6ɡCל&q7rU >-o JisK(6>cYͼf8@A9~QK@L3Ssu3y5HLB۶(;C9@s(IJ95^>Dؾlqll':'x-PDy{xRH@,V+1pYl}!q$M?_hئ{^MAiJ ۢVyϬ|_U1ѩpD hZGҩW]Ӛi~͊/V L/7_p]?Wa|ے<2>/b$f96ҧ<'}pKW/ku|z;vJǵ.Ywv v;&hZCYքĂ~ XeeSEo\MgM]j>]2; (\ؽ 2 VjzþW 2=Ө7AQق2("ҠBOA<@bG;K=@:L~z]P42wy9F(Nn}ߙI)PcK‘+XyW{Q n?Vv?օM W1ZR.z 8"n\i#)ץ p,u N _TXzvz-KJ XRewnk,Col>6!-&m K7 8$w?y'p;nxj S~T'0,隰5%C5xCj_&GjvE/m'V}(*%?5|NO#Ǵ-MtRk $kFZF. A*m'c&ӦSS͢ƟRJ(=_s#Mwɨ ek.f; GPv@svp ^PA!/۸TPx8E{;wJ@ϤБINe}8^p s R܎e8E%J-Zl,M^6$`\K靍eMcuب f\7?RfK:O[7!,o@/0yp2g+[[O%Khx#ѧ0W (篆/ldiӏӄyI80!u,g8"+ܛ)ɓh!ĴЌY1mu%wf6z"s%Cͧ"&rzx{I@9=s5>#d0aJJR.!k+}aI)\mb>B{d:;=Q!C:6 c{3 (g8MsҎ.Ч(_BmNcFC 7y>g;> nW ׳mԄ >S"%ߟG,#ZuO'kpxZy/IgȉVThl#['aByo.BُKww nu^'KzzMk8Ԓu>dMGV Q̤/u3{4ү*OUCBiQ]!.gPv8.C@¡]Z"ϛ|_.gz(oVx?ݎ%[Vn7֯}5PU{OhÒ358KA;*}Hp蟩mkzg%~+%0`PkdecaIȚNzbHW}> t6Gm T|꓈%4idQл Zpw@y8 ۷,.Z(@zA9~oO۔4].?7weFȶk.^ӛmSپ?ߨ@3,]f>[Bi󣻸b7d(8,D/ rJY8Է64J9nN?߭:)ڤ:1p3s]7 M0n;fBPxC5!Ӳ9;+[q>|eYɉOq^e $uo7ΩDohpyûO~wM yj䄆)5^W'5ѿ1RdߧaǛ8c7c{&{0f@4%*RHyHz8~ʍ׸ٙƼWM_g[S"߬Y 3ֈ9sE _zgە4 ͬG~2ʷ1K;H@ׄp'Ɔqj@9 PM wӵ%Uwt!p)L\i$L(o|c@n,_^,)<7e; wP2AiA$Q=A@?Jbqmͳ+.n8Y\:/&? pQi/M}<:#nu+=ݦ&)x_u P& 7كu([ߛ}fK.=9$# Eѯ҅q#?%5.@ D2%wOO+$݋ 3CHN\hHx_Ms;ڞuҗo# /ٕn/]1PR |y9RcqLy+ԧS{t\RӌP>$P~,I Zhzl?(K%'5 p u$yxBhx}dH4Ln:8V$9u['B״k_g֡-:Qz1`~d'^sKFH`/oX6wQZ5ȱdϰp5Op 2wZ/{"HvTzjmk;Zx>T夲 ;\FFVk$, Hi ;RfnE'^7}sLΉH 0~&k1׵kG sBl>{# ;~~} sjm̶-$g7CԫJ8)R M4,1=*= I?<'k?ϩ QzCZ^<ѯ.%;gj*\1m]ڬ1Ѝ)4&b3fp rzS@o Ir[ G@JHj_d.򇩀~hW_)StGEs~0(%_r1ԵEܜkúmCd /"|?myg`(Pㅰu_Q.xRw%`G<Ec6F%]PTGJy-Txj ]"" &s1{H'Jg >'@"M()Tjvm*bNuq3y] iKBMmϡ Z4wa*u#`|ARvxz>3*~xXGm3.Q~BK)0M )OA j .+ flƔ|@Y N< ijhBؑ9X6<#^Ӿ}в}UrcZRM]:l/6LO?֣#(N]+.z>焣)d>Fi F?{H3C 9G d՝\f@# K&3bЅUu5axL^!tq6˲_d d’Jg_cj RhXR8`/%w|ʟ7ҠWnP,%><2}wBO')D4D@G4H|۽{CX=t P 6(K0D0Izj('Y@@p Q[-vJf.7AM܏APR+YwU%{l6._?OE,+a@q7(idPviPlW,LuR;Md(,/ = {\yЗ@ g0*R\TM__2-dX9[[})VF+}wa@ z4|K}в}I˥4k>~ON~Rߚɪ1oK)l߮520J!̻ jtsi9}J-u. gr=#Ok"񝜯>Άբ=L:=wb}XE7:MdE&yOx}Ym:x~m|O3nG0WtCuisS+2B9I;N^W9dr3G[jX1nֱkuhBHKʗXAME%XfV^KMC :`+>--RsPGi ]e,h B*)A9PN@y29'̓ IDATBg\[)$#<>b&Ts8b={P.f?Η=ui,J2A <ه7_ZKk$Bvt^܈A/ Tt+-q}5ȩBK"Vڵ鉁1s|)7,餠<ђ@P+L/Ԗ8Ksa5c)A0LGOOZկj@z RjjnYqzN5R@B,a=}=| y{nGʿ "ILEr}TP? z-NIu\/r[SYMxrFAw;= xk_"h<'BeMvЅhS]ַ7w.d[ثjni]xO8YUgyrى}W#5lj5ß#PGF̎m\b߅Js^BX{I'"&)%t+*[ʪDලL3"sm;H by|;VM@.#6^'(mݸӓJ('dNܣ AymOy'ِ%cً/THRf<  >57u3I vM5DA#: @K`;idذZ&M3[5c/7MG'£˄U.D!T/'϶r EK'F_]'x5U`fcK +/bxOK\ ܸK.~.V^qHW8;v>gzáoLv^;gf'N m8y%J w;r!JJh oQa}5[<9FW|w\[u!0wFϼ#+L){Zr:hs{)u-^˧fn;ݲ̰d%Ep X#O&(saQkKPZKͣ*EGN(ha]F5._[v%SQ l)7jӚKt'`}Hg^G=ʇ,=Ԧ}h*#q%s71`m_ydcBiP冲]hdP;i (OP,{5ed=.' ЃM{4[X* J_uՐ'F0Qȹie'mJ޻n–QijۢB"׬\) նIߕfNם )$ )QS1{r-4yqزoKQIC@yG6Ox+b:!꛻OV)]nΧ6;28Nߘӹo|~r= М-5R`ɐ ,G:mFTiR8@lI4a%ੳB݊/uS 9/rżU.nBzڈn6sJs@aɧu G9z_u}W O2P1#@*g65XP_AٸPw{7bAe@: @aj 3I,T&C&š GECZDBjC5Awd$4;hZt2sUދJ#3^)} +@ G=gފ'}:s֌\,]y@PD~yXl2.h Իdխo77 5&E @VIh(08x_#bGk;A HVQHO=A.0 AN`AB4:# r3';'G >(gPvxkĜdzE> f"dq|CὙ (~;[[l׾ Ƨ12qnXŸwMn;^ƨN_'1Ivk2qZa:BCN*t0۬HRoAgXׯV<3W[_= |8Uh=5,9;}B|ؙs-G"tM 8=lg7ĵd/徻G~HiaHr+A(T0SIHZszQ(>O'>tä EZuV= 1M/P!;+/TU)4paIaI E4X7'[g%76]8j=''*+xc',) BR[FׁֈB,"( pvXFg3$.H/ x'x$DO@s#;XVnTy-lII) F9jkןKK$?Y̍% *$`Z)XȾ e/:rٖP ;G-m[4[MrБBIcog?Z8lpk{C{X5 /)ڟG O7} ЅISe+wT@ܒ}oz !Á#0.=3M&& ؿ\K ڻ>+ >I f@Faw,T-6ؙ85wM-Ëi3,TZ6 -\aRI(tQeZaS&pݨ a&n<_A; n bq/i,j(BWB,[)5PhF/wyp^_CPjBsF-B4Zr5;>|5^ ޞXTԺӤ -T芚<e4&4’5\BCB,VP^U#{8}o޶e(ǥ~R|ecM9vBȬ j , (_FLÕOjĶ&0=3OJ=3n 78hk?yz T yL%nWW+l" }#P`I=Rv*:@NF-q5Lͩ,inv E|*BJ!yl4x?:mǷۥB@q7q#zffӗG*L'j9lc+:moxL9)Ѡ,c:VF$ʦ^gF)%H{ j",p M T ]2 \TNȑUoa}뢿 ,vx}Z)#U--ü풡_j+_c.,yC]T{nZqtEK`4ҫGk~K r3XqprkuYM9,i&]F;0GY8'/PuCהּ;LK)`YD  OJzmSRpfYkeMErkvhCۆbtVsi/89gz᫰d\/~N=ӊHϭGm j[P"y*S%` ngǵPIgzR[]BM.asn&7G"(W VrQ:,Tn|IJYԂ֒Ouv8V =Zn AҢݟuEPh?݋|/tGx_.BZp ۶u+xvBmA,6m!`cuqxcvLFRu/V P#IUVx֥kCנ QgJ~ClFRp|5 X{tBg&K%1swZD'k.@>q-J+GgMM\65BRZ$0ZfhNŬheˠ @ ;|rJF'N?KNdou}ܷSé#]-j>%]v ʈCa0ܓ^jy oMKv*j 5S)4ϟ^ `Y9#<}冧֠5bs2f_)y;'o}L`o;M!Riݾv@̋G94!1x8-8߷P+=`~A/#j k< @M9 aoMwX-x(I#ӋtE̮gx@uKfu똰? չNI$Hk:)gxws>. ;6@Ɋ{9˥+dp3ypIyg82+Y[[5_{'W?bg:8>gӸr*!`/b> l)K7ǂ{CO6"~1ԙg~@{>m, }=H`n?^lAAY*Dn2X[%x\۔v Ը!52Q'q,hW f'Ƕ7DE>!vο݉SV!hΰXG#Y PO "L),p;*)eW3D}rS_u׷bO_[[wCpwz?P'~ko /4̜QXYAm}/}ev;'_ MV:1gJp>#03p|\2Kп^ET)PHγEc7P!D3`&';/,_ltZǻ<ϸ~S97{PX/|kIxy'O c(Sw";n3W,k f=TJrl]aHt [el13Ekw~ʃnWٖCpURM-Ԝw|X(X<Ԝ/:Z@)d :Q Al/p;*+7;ыHgB@Tw[ ܆l:tmԂkY-dTPG`Qt8Qa{ ? 7QNᥔBNv'ϴSd:)P{)O؆C&(c9:SӺ5ϥY.6.s}E6y`> 7 l}.EB<`M&Q a.o`=~nںȯ/s3xo_5\ 6qM6~F%K?P8T!"Mu39I KUI Sx\7.Cel{15clc  C!NTC{(LxSŒ&u="f,wnZ_WYq?]8Yل܌$䅤BTqżUiP)A / #5))p.NW=-<(RpF(篮eS+ @UR{ZM$ߣ~'bGҠ"u%جལ Q(OlK: 0&pPnJ#mN!m6{%@ē eԢ-n(n.o2< - Zg|A 8LƠ?_#P)8f.䳖3b[[{G~rY؇w}j$$fɑ7[WW[ ۆ_\Ρ\CF3Kk#(Uc..[jDНC\ܲ k3kǂ==}{s[\dN =vU:_ej>29CiwZu"HDB9\+R{?jdu:nFTg/-ɛp~*M-K28 9^vh "$,)\W G|wx0t={~1ᗩ,g2Q\CHUB@U=eS:* IDAT*+$ׅh1tݴ~Η'6LSM7>w٠xkw>Kz.&6$^"(~ e+o&"]FS_BPtsn5$݈9Rځs(ЦQS%}~̱d#9װ^;(J b9S=44]8lׅ.<-PZ_ӒhAv6_'a Ԉpܓ.Se+'NNkr̞%H&^ xmnF=vUckVUWـLeݱ&%u-y.XHFBO47@ȥ5hX9ðhЯ9:C9n (0q 7`վIFMCktݵh * ŀܷ-:Δ]/ bBK4gqTE^:/q@;| j|Sb~';qF`.$*5<1\`o^K@NӪ DsB`J_ uD:jcjG"ǡh*xxWSS3X)m:/RQݓmkh%&@e PaXWT0>֔85v;̝pmTXR#OJbI7)텭=6y Ӷl2mDׯ޶ [Mmo[2)1ݿ]/ k?LTkG;%\Dž#1i7˪螶d2_#i泄`w'}sMc;ܮk5aݑI4Ef3-$]:l1% WDU5!t6pȲ/ n ~;CWK` ;dI}i}6L-j7PiɪKN+PMpAv!7z9BWQaO *<|ib]n=.PgX=hКP(}2 x:<\PZZmIkE3L` u*68`VZYq3T`d?bMrko$'Zr nt t+]e5()VXk~ЪOs]~QEώ+A )3\'%޻gCo1}=o&]uZ1&Gڥ %qjoFAMrrK6/yХ/9c5vc .)#bOţW^õӪ#ZJIIqű w5_TWmmӽ>wjphJm/*!rT\ 22cGa0 /TC!kA9.qS/C-z`h̯}6W 9QP ј3X SJ]ׄ]qfc]an0 j5bF''}7oHgfN'חB:k#],U*T4xB7"uH}gCm&VI# Bc3J@mgݮ%n]ϬԲo Q.7q+(GӪձnTrn&"]v]J@P1Ȧ"SϾm`V]{+AJ A4?!t6cN]cu+6 lyQuW"ۏp3}3Es^W;O&qm!P>?\7B.wDoJMGCLV6CB!?qm%v-ɑQ+͈vy=F0&|\7 3mȩ_h't7 .̔Xz6l[qpsu*).DTTQ߾CHEr)OurHW]#4d'< d^"`Y3 Tyoʯ 5S) A%pI'@(uMB`Z`E+=P`8՟Yp Ao=IQyI9h$-=S74(w:b s_PH'1M? u, ~o HzyJEJ#ysv>ɱ ˔xT}La(%ecdjBZ}1G\d{m/CMkMC?m Gwk<^+g>twmdir_cXGͼ@N2ҙ%?}Z%CV{Ysk,=&UV/!lk :{Rj`trDcoۛfM/0˂&b)usk"љ? =u[缛mќIAsAܲiܐ &5(HLhyG;-޴~P&f1ܤV{̂:t XοkPeM4TNC](<}'7Qb=Z'㠎M%5@W!m)z[ߛ|'D|Z)ԋIoX"tW8ڻ E <u޺V|8Z.+!F8c{Be\n mf4-|ts-BjP%Q1.qߙmv-rZ7AH:ϱNR#Ź*z~1r'5ؖ2@/%U''/TZgruP~1PG^&YC.F%`7ۑd v}8bmN3CRg,>c`P k&T_oRHd'9g>1T*B`: \%pIMKNIX4g\Hkp0mo@@ЪimQ[:I|w︮̔z@} 䂓ycxýw&1[yP%@IV'MJLJͭ)6w #5E8 !pkZ0B .놔|S1 ϯ>ZiHy ߴ3N-3/xMm٦m ^1*O :ȳikF\64o9%'Ŝ1׎=%G>`)-Bd*SJ P~ ._b+pPħsQo( ٤B,i.Z) Z-W"3q\?=CvؕP_ ` x{MkJ@O3TF0FJpUƌM޲% *iB׾&Sq YRnC;3~ TXe[#@ZBͻfNoqqXD(m}.?mzCx)VhJQ8!|C9gY`ӽBGE_7S? 񢑳ɨmO +=+r2|,r0ћ[\ ~]ŦMaΒ}`5kl@ELqD}";SQ0w/?͛ c Q`ZRӄ7n0gK=u06=24e!ڞ^uOtGk_B@j)Ty"hMp:̚p;[mAS[q*vLނV`#`xb`JzOw`f Et{MMЁTVAƶf@J@@D3:pql϶GZ u\J2YJq2:8M{"T́ʯpRp-Zn;d׶cc:@7^3ۭ›?_6K)N96Ghrjk<@D.=Rbizͭ};3J2&5>V2SΏ"1 ^vՔm]YeAӾRBTyR>w8q.|]s~eQv VՖ~ЁO =Së" A ga,p!{!/?b@;M %%HѰ W?ݛ fos0%zζQ@@C T:Vi='N! ~Op Ri I ˠAv&B{ph$P6%G04A8m"8Z~VI2} =99/R)xM~;BGӒN9d"f)t)rOprp˩p-bЕs񲱭zWXM%p_|{# ыq%&Fj5grPwœvl 9x94MN9d׈J'l/yDυf&]Ζ݀V5q,}Ӻm#Ʈrю dG4TWbd}n5@S%=] IDATO[&܇76NԄc^ {HirsY2wRo^ > +iY~k ֢HX7M~E #A-ZepѢ b|יP\m§I{ఋӚ.t* \7`y ~&a'1 n TsS|Ч}*KUebuhsޥT`ޙ߽w5A anN* pLpP-=*u~3L"~1 Y*g#;JưؙRg€3K391iqӼ{]'ٺsv= 0S}oDHϤYo,Q!ȪH*o>5K>WU> CEs.8Ͼl0Tpw-޺Me`oS>/WszvZ>@ӂ;x"HA=*P€*|ú`ifug|$(M[ `z5- a"YE67Vfn2[?7$n}\PTw+3)Q'v0? WQR)e PA%P+2 (q5#Tmq3)Y}1*^g}+Jke"Jp=cw1}ۤ$(Q憒'.К}~`\oXx2`:`t_(7c鎨!?z:h:]=md" -GCށ큛/q?^t HФW7t蝝Z|$-?f ]#Dx[n674LY3%s'TW5w.ylA;2$EiT{tˠ&,u5 h L c/= nSbBu!E, *Y4BKʳb"p:ٿ[E./⽏@.(8ji}AVW`̢ʡN@y!G#tgz vݡ]O\FB.n| et3k?\[XcR9s[klRKK5H"m|Pټ5-,$[ʮPkФZgkȁ]&_8{\AB"8MOvwy3N gץ2[r:u@`]QNIп9\ңOxrW Яzf<3@.k]B0#%KNL[ uڝSմjRW#U?:mR "(;&޿k"5f|Λ hJ1NOIc(鐉ѵMl?H e~EKNk)J_'~ <:TV`VCvm.5BU$N𶂾"gvʹB l"TV|Z~wݺ~CPvnyTlw6Oi~1Y$ n?{HKi8*9{8dFsD3ZSݎo%\~eJw;jJp4tp&qe|{{su-8E՚]9 I9lBAuU#0eX替6.eskzWD R!NTٴX SqP)Cq`{tL3O}n`wȍec̄Zͅb9S=iv^ъ>]OvV-3i㕵GiHh A~ChVЊ {=}O h :VWSIs>|Zgb ζzAgoj'uD%Rdég86ǡGD {@EҢwPjdL!H'+հqVHtazP:'cqKK#{ %7>_r2* .dEq=VMA3rĠOe_n=`nZےw5Ny] @K]\+冈ƋZcޫ}c#R⅕v \iJ*m(>"o&SJl!aR !@}+Jort-o.O}/io ~i=ᙍ;h15u0_/;&$H|&u~{me[NBmP~t(?qwM>߽7fg!5~( A;َ\Z[ؗͤħOz0ىw=;m^_솖H YgR^( K{1 |KB5` n!G n.dTpVq̶s̖j_ٰpIa?/Z"Un<_iM_CEW둀e82CnNh>kB+'m'/˥ᆛr˃_._xXR;7|\|O]M)`HOK|ygnUʸ놈u s W;}K`hyhև)|7A2| >; UXkh"IN'мL@)y%8Kl[J_PUkHݫC.C>)Po9tԒ&^@J)(⻷PiY̿]CSg}΁$،sZ}@{; ;.h8} -قe n;xc:g%:Ay};"@{HE]F1Tpu*SSLe[q C}ʿ=cؑ@>JH6vE$sށ.:k uK+PaӪA$!LS"@9~ }#2Ò+;s71绯>wǷzwS/tFVT5h)aL7<>C8t!d:c 葎sMҢV"(AÄMWiQYNKO('A([\zHwQP?K >Z_{4Hv f( ,h=\ _Zp6 (wX-`.&E| eޖ*{ - )Tj/(?v:-1yU/k##H5_Jġvug#vȑ:k: O+ [SY:߆Pc"imC+x3:`{$t%l+WS(]~-|[WavA(Oˮ6!@ӫ|.ĞA8!p$ OuQ+--\a40˟%e( ,:Gj~o@;+|F~F|sųvŒͅgMJ7,;TT22 h6leFKT>`VZ}RZ9Ηђ{Oh#g]蘬B;i* G J% !tEFpo,Tnq|*;9F )75Ru"ؙ/c?Ywm"$&0lr?z[> >[#E6BLJ|]+tAtphalT.RP$d[v>X? I)*J_",#:5pTX:u(}*ۨc]ev b\] 7ʮ.?漲#AFi:{;O~~iH Mۏ{ĒmmB- <{y]+ւf})[>kqMغ[?}_RuL*3H0)?Ju \fI衏kdBׄ4'.٫Q b3x5V)CEU2M*ZvucX7 xֱ{c:mBGـ^[@QYP—j,ZJbee2OPFB@6DW5%lBSB {9J>8m G"=ЬDw`T{BIhCސTvmAE埾Kdz bM%ѹ| t\Liy1|ǹC:X"gZ3{H=.]pxn˛B:7׹/KVM_uo\CO SŨz`\mhԄ 7h7$2X?/_*LWT~;O DGA U T[T5Tg+W~C4嚄[I()Hs@I/hCR)Q'`@Q֖0i4Z3 ~*zt=x:j Yb'T4uWTT7W&g:[TiBIUkՊMO4MNP7<.|Os# Ake.Zv*;#{*ms-TpU6}z?bJZZk|#*dQ[NZ[^89=-JRD\ 4;,P!C@sOc߂Wdz D 0+~GI~@n߭rI?7t[=t'rۤR2dCj?{q5u>. .M_`jm+8Vd:̔@9gK`_Y}S+KX >M5ǹ l-mԙ^a8%tD !NG%kZ1MEݑF2vuu;]!:Ц 9so ]+5xIpҢzg%_Qƭjq!vo<7Zhn 3Ѓh(_$hL=wJM*#.B0HQƑ! US6T2h!/'@( ":2뮡܁T$ʏ6 ҏ,FyT*#v 䋷l'Vp, )Sx#`c]̱ =9wld/ _9i*!I^5@!FOmE+2:BRv޳o-Q\7s7p{ܽ\3| 9;!3~]\d5-;ZVkvLW7<mA]WhnkM+,{.I0 WTщt,(wGbZJmeskAJze}Ql|GwF0{El =࠮S~̔XeY %~3kj4!̅s&-M ĽwP EAR}2TV: 8ja43* ʟȺcPe (o?V(5g(ّa0|ӮTNJZuz?0|2R|Sk !rZ?K!4 P'*8ɨXC7zCd: (8'^@:|# l{ cs,>* J,&q)V >s Yq `lv\sn#6Mx޿k"*ZTJn?~喗֢-L݄| ?N=o5̔ݺGM=8`k[#>&+}Ǖ)IqZM7}05gЪ Q|){vN;wLyPc%!Os7K NHtfv@P&%Fغvu/w&$Z-9ozPAdi%hQleݫ#[t;ێ쳬ѲӭRQ8w!xAUʺ7cE|B+m A}:;lL)FYm}<*NA LrRk72&vVeR$Q)Mkeh[Z 9}x :uLm6ݢ uk\!a߽cBZ xxtBJ`d8'?__'0w6'*T|x)q M!q?LFe:L SrmTC.zD| /2JN(C'A}i灌M_wS`ᜁwJ55G+ݶo&>oMV^fpc]VfZ&$ԗf=R/pB]C˨NB"mƸ] ^tph\v➵ju4+R:B{ 3H/;<3(?'{vɅJZSx xuTi0 P~fK˸͋p6UR @eޓNk=kT;c&u= tR - @t1: $؋`$oֱ۱]hs|' twx F*Ū?4(Tq>b IDATHF s7N]||H~/ob,+_"4$y_\o3ڻ= w^uK9)QkB݅@#iJxT)l 8>cb rr^v/ݖ0UN!@b:q4f NHC qhY*A%Rl[@]TVTĵ&P'p>Ϥkf}^5"1A cRyϮEs&f6ɂʖÚgĚO_f`BIz%Z76ꧏƌ'rŢͅ"4УS"n*t@!y%tY s=O[᰺8pt+ G#d;&MLfh66 v.j2?nF]#ݛ`ʷT>ҋ/nmjsY^Qq{lL,ZFݖ4kYY\Н-1l8t㥝&+soA '?)ɩ/aRJr'#a Gjh2|A # AS{jjINiruMF9MP8I_I0x`m:/W-ԅR~%ZS >#l2MGۮ)]ʣl*' #QM1`(vJ0- L?`t UoJ&B@N hD˷$ j=;p-d NIKo</Cit$n>!@1o\H~7dJ OJ;/g 3|Juc9TV {bգp0m^n2YNJ6lRW77E1>tBKBpf/8{x3ƌ1]Y>)A%|Tx=Ap]\붘.m xn3Y jbFq>}@rw,3d~_LH4a-HR3`8_R{. r`tq/-",%8Rӵ@Pxt€)`JBx2wGR4q`ǐ&E!}%- FK j}H>L< 2Tֱ ~mhl}Aً K>´7h*ja&M-CphϝrQ@ee>>J .DА2 u0*~'1rϙT`LKD!۟Kť ) &0R4 LCvSPs EvM\=JuPJ꜏( [VۮzÜRT^8'ֻ-j_LKZ)D %2gnl s2;_8"wsJ?V΋ b C kXÊ_NhXY{MP9~]Iۨv`S7Obu.tnv C{bTNkՖ4>t׶C%@֖e;5:R6]1@ݓ^Ios aΥ,0)=Aa _] tm|>#h/`J5^źL~+E^L p: DK"Y.Aas>-4U6PX"oPk:: ϥE#H GHoEm ײ qX0APd~GPne%r)T`G;nu"kNT.{zw4ra{RZU! gꂿ}Ux히pgG(q+_>tӫ %.:~aβ?O)_/;bjљ>?A.hlx\ d=pq_6%rH*3$J*Y#>JDdϥR 4?xjNXܐ?v9;\Ik-AfFkFmŸŽsmL9zX@?9kE-a@~77XW7EN!A@At G=2KZ7;L wsH[,x E@5Vr  c|:*n6:#du5`irHR ֳ ~Rk7 iPg#[P>~?&=T$tpa٧nRZXj;3}v-J\ )FR{sµ^ʶkd)M~%Fq˻gRɪ&?9.sq/FAQJ*wu\?%tXy nϭ쵣|rqLۋ.6m]ps+|6߬#s٠bj`Tzj1IO}*%4Wh? ~S y`:$Z^tJdkt0z$FuIA^ iZh.P ¥C:wJ4řP~U;SXxiZ TIUIiO O7aL#7S)< ˿E:=Nu9ԑIZV_IEXD M?-5)P;R9?u`?#.dQqk9KCdɀ КQ!HH#]B:<4;?p:Ң[¹~'r=2$`[2woڼH)W:Z]6-RUÃIѤ!DZk~!6Y y2¦ 10q2il.Zjzڨ^vޕ9K z;i=K~\e c i =#h}Me[pM[2l4__Obؓ# F6|<b6?% ?ԝCK4{]]튱^HQM ^Q @h Ik[_=VV5w`Ym VLzm䒝eJh{yǤaћNw5c7VwlK^icnPK*/(UjѼU[#Pl!Fodzb^y/t@4C6G'*jH$[hvS=C Bnunי}POD%`]@PcH%0#PjX$(ГŒT m&Ma=g:_ 5iEIWsa p}_H:E e}wuϾ҂r ~NSHȇ1n5XZ:Wݕtk쁑:AQZf~xଢ଼M o x% 'W_W ZNrB/9 O?~R׳O9m O p$KxC(8ƾ?j wq$1Qq\ɊIt\g.ߥh;g|3"RΝ |'z.taĹ`B/dG~`?s(w~x׶<>' Յ+ 4 9"o3u`!K8"%pG}kڍ' B译ɧROf:_#8te~\]ae^7dLl!N> LrjT ~gL #(c(Y@a5xOSDZqK[Jӄ?ϧ?6`yO:줔&QCgدGY~1 #6TZ5<$L:_a2@.17Bٛ^|@$$Z;:TnNE_p5šT̃NS$@`]x@Q-~=\?ϑlMݟϙ]"|0j%p.WqqSήzr)7\r7$jgH˦QtM=0eD >adHz,GZOż { kܨf)QK1IJeKSa "qBFnS%0oTH,/ @'[vzi #+ֹ; gQPs)d]Q -0bc9;焑  U9U6%<ͱg'+8# ڣaF\TLf5'(cߙP3FԺ52}03\8Qӯ|B`6 >v(/-y54A/\.5 ELG⺧4B֣N`?|bu(~/BLp&c}թVaEj^ɑ{.Ϸ短_ԚD-@!+U67qWr<+V6A"s?֎HFp]_n{LP-\߫[K嚔/lc_f]~wt\W4ND }=/3TD? O5]q6?P#7ӻ%pT xEX#(ԟ%v)&FQWC!ڶyo؆(mdBBrZ>H&#(b`.ͼ A (?6[c4$h}3IlCԈX+9W IDAT؅t 4R$X^ p~Lw@q3$c$q9v.P:n{#A۝;Vq<8_7Ex?\иm<3Hf49{A&X%Ԗ)xLzZݽ@0u> s./`;D$FYXK;яu6S{}gTWR>C@4#|>uEvt?+F^. Hi8kbl30ì$( :`x^w'ǫN=}][1K% QpzK^m&0ǀ.>+U!N746z&4?D M=DfdIsO>`lkxsˮ0b3ɉ"%.u(py^A5>;4:"(žK{3&픒S3rWΣf5TOks'J, ²=Gtys~tWWP&T)mr:[`Ok|NUx O2 ^)]M]&4)ћB ?O#h4M_ 8G)>#%%0|׏SK4z;ZBϺ={RO;.FpK9[BT t3&k{kFʛy%v?!4e;+7Bլ{*ߝ#h$8R Z>$ؾI:/?`?PZP\g^1HXw\3,\ ا&{^ݲag{+|%XcS^?ᨆO(%6'lڋ_j_y^9ۛ6囜V ?Yױ*yjcܝW}1?ˮôz첶- 4MɎUiDTFF07+R$:v\`볈eov2w v@?Ԥv'Tݎ`]/޷v iW["פGe?^ Z?0#:/#i.\ dK lkh2k{zT`3gwE]6I w UjBA.Y$o~F!%'G$%a pv2f5iHP;#\%5+f@.P,/u9z}no ^uti`~S&,<υB?#G'Xy(ͺqY0;S|v  Σ& jf'ԬX3yҦSxk[a;QNA?pD-ߙI-u#1H"0%Q=Y c  s]#1FP/{hJt%뵇b{ 5mD)W>w7-{mR(5]>jI^&OILG[Mz }r{CqҤ;{A%O$ Y gXb_{1.@`%pito.n'J`ATzoA^ՁN0CoXgr#ᬇېhEB`a<'Dp3q3 ^|[ʈʮ#^:iτ-W ܈@tǕt$2Gӛ̳+nš e#_ށ]{h}nfF+oEHOpg9S:Y DCKⲆ#e-Ao$Y@EV"tRN32.\WPO6m.' ݇ &ni+u(0ԦUȹsXf{,Χ8̖Nf{s4w:^%]Ͳ'Z7M `וnb9ݟ ܝ?KmEJ?joHjf߁_?i~3@*4DY>T>G@& }evE4;T|.'ZįLmȋ X,Q;%t)]pETkސ&" R`-g:5-8S=DsuϬ۽f {<$oۦ3'KZZlvq]q@tmBqL(`4|?'_<+T%ID*vL-+[+D"ZUI6b ?C c/'FOwFPh X9䟃f !C`9ubd=Ǒd?a;DX yIH ߕ+}&A̘7F@}ay^ }ߕC?Mߕ๖85ꮴ|c[j,*9H@յdZM`e&eȹOޔ׼#+3MWu^GtD)8*$$9?_/cg>5@_ }ւzǸXnKoe'G5n|iaaҨs2-e]!nx$*[ٙV ;Q#s͝Cݜ6務 -x4]J-濲B:-r3<:Uq!AtRį/}S?vvi~9TQrz;I\3W/vFǫN3!?Rn/! Oм&S3b(e f8q?d61M>`كFj^7iP  #6Yd]X,u8 cv M3f`&=y\aR=wZ̆=LQIMm<}7A5k/<[L`>@+ q;?H+FFk$6 `l VmJtTHk,FyWKiՎc'BQ] 榎] EN5%ȬOΛǪ N]=2X%5q5:rb[HyiKGv'WCi)GkȀG}ëEDss-| i1IE`O&Zi0FH*q~'IҰ5p.5+HO_?'anEu8ldMӄ?2 TViSnm\h7<X+co|[2rqnfB@^`gםMv$y*۷P[Tjȱ4F D \s| M*AO᱊Z$2Nlw3Yx?M+`vbv$&amb7d~3ڕ4k#iEoJ;T`$Kg=GHBE4g=F+`%#/,kI\ K*U_i!ύWlβ)iRuD@OJirL"])+Gtyc##G%Ts ]b41͡@jb5pŔbo(SYR[kXO`]k{=/i]yPN" v cnW\($l+{Unk/p2\Z:HTc+* N -ۡw?zOJ)ExȘP|`KuRk F .iMK 넴Z.賩Qdg&{( Vv4Z ~+v* nԚLc}σiߛB{0`l1;HH.ylo_V4;I"+ avnL{.@fZ.%x'4]8wwO'Xrt9uBT'~5+AoM' wcQ. ~5hq7SH#XE-5dS36xwE0|j y[oFLS$@6l/ ?GlgLY?ol(*;G_sp*A}%1QX&hO<>~MKiYC->V TƾK$HDKFLo)0Rv&az}V4s.FF >o@4X%U.?tM%lMk'=0C=a P3[N^F$ Z?Y> GWKW?|~3Ƹ-6-3bIŜo-k:XYpV\ɷ+x.s="Y rToNv,K}inq8'b;A3ZB_w:W[Ƚx"R*"+PMVzfW*>0cTU=n{. PJ;w'[HT kB6T l31[[[l'H$j- ?`E^FyT۩P"m:LMڠlojXGJ dj'~7,`e{J`Ÿ$ H{ X{:`lcJ_D79rjj$|'l };h.eDo;(6Դ2yW5dQ1Z w~X~֯C}}}u'K ƞ1APaS6/- v 34̙˶~Dg${vmKwދaӿZg'yԥ}7?8(GYKi1z.2X'CPLj1$ޒVvu'sZWt{'di;~n4"7̙ :v59OZ> Gq[95㎻EKmuV1p؃p}]R>|9Q794u2`M逸a)GU !\9/i]qt5e'E rׂ ޞJ} }.>Z` v!\F=DZ-0wPo& `%pB BƏ LԵ80`jfAM1}tXFD2SAkel&&SPt|H3q>fp;8<#(>۟Ta!5+$\~/g_JӃ >ؿtԐ'H$r.fޏ1p, [1ZI צK`}P?9Ts3ّJSʻ!K'd{礯] U]+V7l/ih}hm όRiz* Ǜ_Wi;>(h-QoX9&rx4M|tr>K ,Ӳ&LsAYI\|C]ǥA7s10{Jh޲Ct{?RPAJD'/n?6-sC51y'S;[X(B ;z/=]^߬bFE)6{>ˆk?g-qQ#-pJ[FHY#0şK|aMͫ@1$jSGRVkYgu*ܕ ~x;M7cӧ֛,}l.nͭU]?N`։fp>5f_\ºPs6ylg>ڳFaR8 qldMUޓ?>ej٦B,o+A"`ٝ3Vd/o?B*-C>ɧf&NNsua(Os ie[e?ְ= 7YMAuv=)ˋ>Og #oSG 3;](pr@EZmcqs$Ťo3-I?s,]V9W'#[iVu FfM""ЦK< PG씀 -Npz0KOhZV>[>dGĸ Ä |NSN{U;:uSkѤ/UzU⓼B?:3oMKCb`|6Wb)vե=Qοb4: 沐Gf- Bj[FV6[hijJHu؆iJtw_V.Ԁ^ cm"N4ɻ IDATOFw:Mqj FB`LM3B L¬JZ[@At!AvLc[n!v&`JRr%IJMo>!/Mfb\{5չ|F`5!ً~IjXTc>B|jX!y~}#H(iSzR.>B2qtc?R",%hbn"ШK]b}F()"/KϘ@T^puӱ+W!Q]B4~ߕ kBi:\uRcSD;_NJ鎚B{XY rb;}}z+s%ILX'rQ)ZP8p!4Ic-[Ij a!@1\)P&`5c>M[P*%B MS+J$xX1- sZ`TеMYql{ΦЉefxw'\:=޾!I)p=WA@BWBҦ}?p'C_[FXBS8U> [s5`HM5zJ^wJ8)ͬ yG C9)$z 7}i{)R6}i[¡oߗHA Y^^dڍӟd!6C~v5Q`vQp ZjvP7#::yf[fsGA?Df c](dSV]f"bX|Kha$s{pV( hhݙ̿]o-c׆ F`c4gB$YD֥zJrSK8 ƶJ)q0ezF C8er͑<9O[LAӇf]_^0HvI @ut'M yb鵜Ӓd$7Y8{~Rͱs H#e$pUGOȝY +Vᚊ\13pmӱ_N9YEGm`3TOXHU([i&c*3,~2[dވ 3W#_/Z |ymBEΘ զH *ƌunL)M:/`F@96 Lk D_)B貘*EK̅/ ւ}$4&II(nGM3|hŪw J R:hQh!hjEJS4 YQK kR.& \;o-1n#Wmzͭq]^9l/Z"G+9lR5$TJG倜B9>B`B]Ϊ`WB+M6#널g D a޸S.t}m>Tݠ;vglKD)k5m`B.ZK/-n#0 F|'0MLci*M'Q,BdI-Q0Yݼ JAX c+xC44&]X7J; +kڙ¯ډ <60RQ6%꬗ckzoy?D ` $8N=`[GMn$ '`qj\;F@w#-ΣXޗh~dنbjV} lK!Puk̮.q;&GtTX xSTX |n!!i5z>,#v_o 6KIJr r6cnfO>_GCAڣ&G?zGRO4>ƅ1gb=;3b]sbʇa'*mn|!@5sl#St} mJ՛ !OuC2ɼ*IHc7^]'(:mx\ IwHЅcz"޿ۦ 5ߒйgAvRbeO])`$H+ [JmR\^nkBJ͐;?c\|4e.5`ϲbț6jG=HMTF@:Bo2I (2`-i?,Vp d-/M} C9-ƶ;1oLs0?Cjxs(` cu>lKWCv^v  ^ӲwV=RkP4y"J 4}_gdh@K0C<#.A+ e>1&ӇYκ`),Ή-D$d؍Ps*lcCVI>SܜPiImA[۹%Ksu-O-`Nxo{I.Z{{%cz~/|zm`=dgf>G⴯06ry,t%u픀>Y)B' 9fD8_ hi<0hIs4p/џ2]t@xj_L;HQ98pMxމEg8ݥ(rTE;헦p0~D܇GӖMBjݤ'e'&כY rTAOؚ6=_Ņwtj})n߂ :ap+ݓFͷ0$Ow&p;=φ\ (9T2]0(t#31"1{jHhƱNi0|] v Lj;ٯŴJ#SabV P:C 7fX !z;$:f;`mW0&ul#Ʌtu !eֆ0OV#kb~ J%I#&Ec' y- f()}skDZҖõ>`ٍGb=}BsLgŤ{w:v$}._~:\f&)0-Uczy35OluMmHKɃqN$_fwU'uڤG)G޹ܰkG8T*ܳRq2#\ٯ4^6b?- 2R:t[iDzCE$d'<#{MAn.Iw PY-K(@ӳ໒\A#vL@;A{!}>AKuҤO1ZBJgQ*M9KFP{-v^GԟڤɄl+OSXG#x b%ld$`@ 7x39~SJAzT(!/XUѲb"IuuZV@ΑvwsfƲߺ`=l`DbD +EH8Lo=I6z'.1.߾y{lX^ZI{3 KA[C_̓(T\̹'$#5Yl`[;˷M"駯y 2խ9ֽIGqвMNĢC8w,o/*JIwgۜ?OmD6 Ъ32uttͿJ|^U8e6?~l걗BHL*eG|y-C8jBߛ Ѕ 'r.On QD*g ]"`nV`: : Q7 !)UPvG>`Y{h7WPS|#y?CR:5X~ONv`ߗd[WسMl<_Co5|g"Dduy"r{Jca$C]Hz:ZqlG=h2_#w=}ÚmO<As,gǦDQ}IP>OU3ŜW,w~־ꚽ G?Q)v[9 O+Kq ZBA^IClRZgl| C\Ldck`̖}?ϛ~Z3mzYK{MH@-Zcy&lQǘ_JۤMmz`{oüU;40|RmUc^(BE'%Ic-"x\.8@Ia2CG8V~gs/LAp8{zȇ.I3)``^Ҝݙ5*Zz(ͽ-#^#MFLkaV,7@՛f3<6H!$ `D`/lgʯ:F'؏#( hʰ@bC/>_BLR׼`RLKاvϫ!aFR҇FB?f + 2fH ?I>O }Qv %Tna_lE h.x=XwA 葤}ds2bI4 {a&u'#Y r KC{߿w+iyDC8-'\9a.SИ*jac5HY1MHhN@D᝝B ,ƹ>{|'+h@fD.cVqŻFG.Zm6Չgҷ*0+ ⛅е>8PH#w/:34:Ǹz7)#Gu)Bu*vdu oS\̤x7L$Bό.&Z8d[Rc¿pMºS30cf4y0+v8M(icG Ԍ\:֛Bu/0|ױj>gWf}C ԉrLd$YUMޥآ`cuƲw\c{ Nefdz o=<1.ց.w[iuC6RIsKW,7vM+\eB;, mImfݱU.Ҧ5~ALw8 zgq^RG+_aНȗ$Ο>:xN`&Llf0A-It%'*Ov`_tKvWcږǮ0wD\8^ĩ*dGyWF+R4'` >wޒ㚃|\Bt ~"GH+87T\?'~ިxGIT'ӣ>`0l#99FDek sU!T>o-1% 7Dg;*XoW[ ܑ!ͥC~V4y\&j.U9F#}O &= rrkF *z7`$j# r'(Ns aE7:FS DR֣F3|f-42F#)A _a=`2d? КR#ps4`݇q^q\f}A~7Ο$ٶ yg"a#9yr%pFt Ksm0=e`ϙpq~Uk4?sPc}IN5* "NQ8B.Q3{BBM`FUA]b?mʆwmSjB Y$k@یxw8'ۅ-#?o&k&^^ٖgdހg?؃c 8ysjB1ڹ-V&Q & `+U/77] 8?A9B>ehao;z> Bű rl;ރū2;g8;ݏjg}*1DVvfp '!j:_I+C`cwm2#ٚM`d]9zW="ѩFC+K$[]W]Nyh)/ps-%Hen}ѝBZNzOY2w=~Y rK=2Vkargv$?h { 7cG'\Td)1ZQhCkg/M`}kTs|3PSrLwZcIJ]W w0l] #[{gZBjPi8ZcSK%ysolhEjdyww}IWp.U|:mS%˞^ş#:Y(bsM?`WNpu&8`O¥hlyuR(t2`DVj̀+ou d*zF\uZ$1qYmX޻@شs~t!(5O#z>Y$AYH allJ8Mѻ摽lIR$8M7( ¥Ұ=OX_y>UeHhVE>+"gUyhA:Os^M;w`DUZ8 A2pMp@QdDm!_j‰ 1.VngvU&6?r"]կ~mpյptjAAM> DO pk)+ FʱMrs{V C4{{7{Y^@ %Y,]Lr&M:@ OaDsFrV>,Zkw?>gR2ξF?o3AcPH0' 2;? p %0Dޟm3wF@mg$V@dJRPAsy쑰# pQ7<>3 8vVR~| FFal6>p2߽}:mMaT;Rbn5 +j\* 4F ?mki jy׌?5xm& `a Ow;sǵ3㣃5UD dϥ}%xcq71s? nU[\$Ql$\^X {V&<nr*9-u"F xi۰3g|-RN[>kk_u<2ᘨu!f7׵G; 7X֚=ʮi IDAT))<+Z3o-:1B z\B =teUsFŕn$fP Qk )R)X( Kj5/|eP<|T7 wUP X^35.,'EH04˚:0vVՏ>aDH4OP反 Qw4#e0Su L $. =Hl&Xk2XiXg$Xh]YvzcQG:FIRڑN伍{Q3sK6l:IH/@ nLHEʋbU_h( LB 5$l3{%s]{93OˋkJ1a<u=UrȐ]B@=BХc &2XN MEɰ[oֵ_~!4MpY=M@rf)|3Ԛ 廸g6U%S"Kơo~<.lr ( u8ݵS􆜔cn?w;{q0 +XZՂvXL6SKI:R$9~<+?'ݚGfw g={u j0/r|%eaDZmE7L{U=0#rӟ3{Bˢb;#o49xU uLS\H}Ǔۊ)^gPai$>+H?E)H'@Ƥk8O®*g#oUI=Z3,9+ͭ2T S :25BXg.cgyD)C_zvk͊{v @uΧp*C3g@otwx"nyjKM,**P`Qj5 uYK~#0~j\󛑨Ȉku4cU Ce^ڝST\Oo)?=oR[ ͋_H[ vSjE~o{Z} ޶cGf(-sp4&եI1#]gv]s*5P2XLbn(U4_$.> a ]<%8v @ARID0H~37}!1JZ"UNOn䧪eJɉב` D=˩Hĕ(׸k_@s75cQ`k1|m[!u> vorǺpؐB4`tbq/JMlDD_ӊ|o=a$I0O25{"I4Z EFr'\ ރ7c"W!JX~=4iF9zq P>k8C2 @mj:歘y_:1&N;a`imR mX0k3>U=kgv*h`J)ixC&_~`t=W~md/= =aQ a|ډo? To…< g=,fD3L,{CM&IKZC?d yc($fϪ\s+2zUtCm`(U04ͦXNsԊ!8:9$v.KkA@5&U\'wp+aG.;X}ULJ!jDj9]3V w8v$UiQ!p*a$ϟ7s] VACg,6Cb}|D_tbмd;/]>L]M:Oq~r gAȔD2~8p-]ǟ ҮGxKt+xe͝m=Q= nF;m-@/RWUYANy{m@ @n#M^#!.%{}nhV&A2L%tCW,9ݩ LyD&oE`QwQ[F[ȃ\)66< cop{WKd3}*KBY+yEA'1G`U];](TP:e74UXlZ`98/`;)pE U!K$%r߲ɬgJDr*p=缇o&"pCM+ MffiIZq˔Hq#[A*|~/_h"ҁlUO:6fK_#"IN/HLs;ý)6d5{ "g?x@RrBcAݓ̴v::̆ڮSͅ3@^ E@:Oa4uҳb,z)%?w;3}vj]A?,rD}8%aG!5S}L#mx)KwKv7 k>aB Ǔռ6a"MJ)]y>VJWUSE#Ð`يyv21rj :%\2La':iD*5=8V  +}˰4SU /1_: EWJKGcsiS V|kl'P cܧyԀ~o3ϖ7ֵrG4k<#eZԸ3]"JUemps}kxò}E2XvedTm`yDgk uKp>;~kcYz~Ghf}o$W.cLg=xlm)H/_Tf9sZRkש 'HŅf(q *j5ygUuLCswX iM7A: 3qƃ(VdxRHiv<9^ٷ;W ]|]#,߁C\X*{( gP"yحv3b7?yϩ{e3P @Ua#i/~ʐg 08qcKYI<]dz` A_ݍOda+xLClco12u4`#ԛ>\Sk%;$73LBZ6rD D_Zؕ^yӇnZ0,_Gf8ϔ@thd%N8dJQ1TǸvAd b òR[d@ykOqhLj>^&ӵ@& 4C<FCTY˚?O:R$3[>M=в/YSSsz^ףh٩ -̰n-9$;f\fJΞ( ұ-n >L@ɲv" z\'L8`9uJ&%3v,u=Bo쮄@B/b0kUv A})~ϥI[ɢe65 `B2kؕT8!e\ddhP) Q_9J9!p ϢJ6:֣uh D[>TtEєD/]מgU\TsU{{I;]dqn9[޺*P3Y{vE@gc[핮hCjW%Bdl$D@=? x)tҢnsF {B3Se0e. oP஘;/{p=]W?uub厊ܕ;*U;*"5m9z^VڮTJeT!H|iI.y1l,XLA)`T͎ݲlfw>E#HUqabQ%CQy DvT2)C*z G2(# o0r^)y'LHfArr{8|VΔw TU62揨]y'QگZP+Gm=(ku-t!I_H@ym&S6m]עI=Iq4gCcWMqօ?nZ`\>i0Uu! * ~ -fB :D3[DjO,Iߗ }^OB;@v~] d,AYXJa9v]W cSl~>Sy|3QZSY甪{plL`RUqSK=ߕ~,Urc"TqCskru{`cafLpTwʃY5xXZ@ tX%(()K3|x\%{ls_ivsq|2;dbd(9/5S\uR{@4sN@)>4x&j`.",XZֳ\кUkbRL,\ZK78{Ǻ[ vuY^7>OG{N\ї>0:@85>+%;*I\/^l'P]mT5dm5`y_O.MUkxA(@u:V!L$niW}&~J̾”&U嬱l[m#o#0(a~6E!vP* 쐷) %Z24av`B,^l,^>+%tKC87LFYm;[_UL{j>1^H{Io-NR" ?@o x.*;?x3vnXNcCaO|#(d2P{jT,"JЊp.'r} :m|@Fϛ΍< sO;Гq98w֖d'+ '@ubS⻧kۻ7-w|GoPn$o И+nm3T9U#QrA*,gBA=^I^r"6ViǞ6|CY.*qkNM_>bJ0l=gҚ]N5{^<R",;Yhײj%`9Wn4ҕ[q6cf)8X.KX|SRG5=MFR '>8 bhV@Хi!h /DNGpCB lIrONIם5kw<0SG`#}@Bz1VRTavS2J\R:VabJSb?dN 2 ]l{9Lp!RXL-K&o"XR!j"ݰ.M7axb0M pq.+Oa'I6JפxZdLGݑub6aJp.yh}ݬ4!%d@B wKј_4x8[`yj' e/w(_YF"9`9e;rJd dsS<'7b@v! 4SjY܏dҶƩ,dRؐ\2y"S>(-0QS>q#۩@&531S٭M}yΙ1 @QS-\q@8JG'ܩ:(Kɚ)"f8 vs)B3.₝ l># YWypDl.5W}ŘP޲ J_.,+f7KxW{ŸGκ?ЯyhX½jGT꼾@7#*Kn}#MTr?@uv*Ue2Եq ȵA0Oh!9 73HNזP1fdp}Oبhd da?i&Y$C ABm2o%_q E<;øG !ldb- XI! #3{d*}Ρc1ic ^w,5f2`}[(&f8"q\ H9ڐ8ƾ8xȬӭy0;4ѵpʙ*>hk8/sq箷T"jv)WnZǫco |>8V% X%vD4 SD)$j.QKSG4M¡G!#?t(Z6YIC1x峧0NM4J*P[^>R45kߕ\ Ӡʤ7*νFW1~ } 2E4ݬOhp&40]Þ͵;pIΪ+\oZM> df|i8}jMObih `gtQ@^9:_겾 )ggRƞΑ;;oןkVS|.pCn0Kw}^P.)eq>@Ԕ[HĿL1f[`٤UxEb_;KD{to C;t~)~LEB#REd6l%C]q}^HI8y [@"8VHL< JͅlR[B {Lr,?FJ%,otk\*`9,pLj sølPaٓUj)qk=ƷB\,29X\Iv%8ʦW ǒ[b5iaR\iiF?3ٜnVR'==ȵƙq:lfq\kRsWArm<5̤muMy/MHL<ٞKp)Bw#w1~j~N[u|ڈpLd\/ԃ&<~)u:}nyذd}gsb>칹U^wGX$1IIrcAGKѤl-n e)[]fݺ=w>w )6!*G3>oN*бl&3 x/'[Z~ձ՛OOO`쒳vj }RI.V|Ul8aJ:4h!QHin,,/W(_):BO~ji+ v~ $YqҤ*qHjSPQ*N#HJj<`"6]dTX6lrvXa_MiX䚓>+\\Jx_X~E{o@H ­9r^z|;W' X bNcޣAa#Ч;)k`K@`Ăpo@oWp-~;owlV;{tB*<'aMZ{0Ѥӄ 'Ԧ5J(!?Yh朦d,]B^]c@hE3-U#]m-7Oc|7xÓg+'jfʎ M-%j@w;rB9ͽխKG0/G+D}Ёpjѐkh'L@1A:¨!Qq8p[/nJZ*Hꎑ)kudT%D© O?!]}u?JMlJ{/[=rb°qVÿSQH`ߋw5ˆvU9OI덻Iق8uz<_J^Fb#.#',de?j$pay5IvUc|!7sʱivXwc ˙*jŠ[L,jr(a`OsZC,45c7\ϽyO8`JLl'9] dQYd.p*4s)~nO?U6ut=8!pf_p)qphw&MLtګwB©XI-;^}u] 5wH@{*,{x=`f~3F!dDa^r\3e ݝiQ'D7I+tfq8tN:ROio8pI͉|Ǽ1'T!hQ:fwyШЂw*^NqW?H,m >S}Bkɴb| ib"C7vTw*oHV%imC?Ib^z7,gIqjT؉ciCN\+ lң*"Xvay%= ;sb:qõ>z'dK.vP0tbkl6V6.eWLlj3p~Ifk'NA v~#ŜrTT1J*{1 jLƙa9LX\kUi|ג s/e?Ɵ:P vr]llYh280|̝qY9_=.a\Wx3[(_Bug{g5eItנ9ZpΗQqQی齐@2(6mN<޴.Xf!@Ó_iz\| r%Ӌ;)^^"xWIZ0..ʩ:y\LtG R2:35Ӷ$vqɄ|>,Z$ yU:g>T[:̐0;0gq6H8Ý5eo J49*Q'H$ts{?Ew+g\ >7p\'.O{WS3  H!Zqh]Pu-5]]&}Pߍ*X4/}f%)|,#^GoL u$8:3vC!(˔ThPhUq}cWh'_p`[y$|6A8DSm"-c&۫b%(呈gȪ*i͛>QV!wqXMk.X S2.uvǒˡ0@>DN2Q~Jy(Aj5qhpX|NJG86 sUaye;1J|[yͺEgw~έ .X)!R:.[4ʶn2 "Arr@r+`ggÎX ˧`1&dNx0G;59qgE~Z2;THOpհ^\}N}y8I9BvVJLKpy )> C} D{=?7Fq„ {M2RxapҼ;.TygOVw.gOHw.TZb#>iv#%ؓZ{b\?: }Y9d9uTz ͞2SF#,~${I#BZR}#z/h醝 uT1/ 9TWqhT=$haS8*I",Ǯl($Pq$ya;}f_v"`wG`FqݷpMz>q@(fnb_S`򧃠RHFq]&U "([:;L x+ + XNw|7%Fvd9Zɽxi3۴rod]}9q_9fv -q3rR,;xmDn{{ fgݸWQސbJmNG9vAbf'˘(_3IOGFK@RSt1@^{q?JMb?P8u.oq-H6⍶`gV[ØH$6M4VJ!-S:'C@8$ ok&5Kr3AZ]ReJqM/%iW^G) qSl$p`ŻW󻭔z@q_UU%3\œ ǚCHbU#eSJ/T#lʣ <M&8x9V~k8 ^ ͿEP9 i#p'~',1dk8V6RQ˿wt.CM&c.soZi`ǙXJ9 n7VUSlT.$=5B2Te1P)rJTC& <[A.MD "e?|vH2ss2VlN! liw^gx/$y;4B7wucsfD$c8 1/*Kd|rQ+2|&b/ׅ@9H.@Lg!kJQu4Ԧv_;^I/_{EG <oVl9\ ̝"_V!$p1dd} PuRXV@ 98qwPH#JbO _IRw >r1JnGXIU.`\Wjah5pHx !M >T:'Rh2 !.$ IDAT"ềk& d^M:!='pd5gya9(gs/||F΄]INs~,RKaV)6-8αkVD&^s@x92xv: ^)䜽<3*k;8 44-ڇS \U(Z\f2[a9EfќG7Z{] I'Ůwl? xi̾SY<gq&8F=aj.9g/*}g̺ k",^ll=Z̓KO ?pom#nY28rGE.,{y﮲?Rx: %%MG3wZ'&l3Uϩވjx ]-%5WZJUzNC|1;YI—G"BV9Krzn`uvvjr\C 0p\}ȋ J~KF\ fTdL3 ,^Xm$= V?@dA+Lp3b8Ux;a{i伞ekuQR 0*g;v A83-Iuj&8)IXf]y2%\k9zbZV9C=WDdJwH7a;'äZɸdP'4g jy$slaj!k> 5l4qԭN{qC&_O@Gt,R3(ǂk6X6!΁_JijbD|I79\3#B){/%Fw85=8Wtf1P'$WýM-̭)UE| &t*z-(*r m_z4<0QU3M@B-@'*U~Xab(1Ur,P%qtEcb-$HJd}$Wq{a9}0icg1܇a  <>ג(t_o? @cK}noo{; 'c=z&ώҶu(JG? Y&Urm`5{x4]4eJ{IE^3é7U_/ۺ\X0Q֏|fPR(_BBAO I M`% z j=,d՟7{etV!YAI]FH𷃰<@)%1Ntw~Eg&臝q.@S6 A>_O, }|0XaUiJEfe":Lg@~|Z4rA>X؞?|~Af4ծ CG@5%_drp8qf!Pk?q /ΐ JwFԜgKN{cQ<Ͱ .T}AF s~LB~~LNu)qabs5r҇׋@vPˆChM^aMBhWՊԫ ;q@pZڱر=$tQXjK<,qb?AATItU\!j>qUoN{[5\F&e(n=Լ w3F9lF8f7:m:m9+Ę딖#~"h|Bj Os}RavJd&y[ Yh Šp`D{qBC)MIC\'ˣ%y;ʤ[X,>ҿ_?q {!?#!藖z7_X(7c{ Ftei5$r'DamX6C|H [I@WqŽbU$WP*OdUT#ԠT`y>Z+a#?F$Qm:@w*z؟*m$X>U ɰ H i)"ٜsWqIFsrhJNp=;Vx*LL9A88~-2E sT{pqGZaSf y|{A E; ҕd,?c(gvߦޛeWn Bo7pIHHBr)!!^nc7ɖdYxE7[j?}$evys9Ku+3 nl2$VLSj0f_¦歧kvTVMǠBV k9JA2ͳ@;$*F#t c@}1$Q 7 ܅IS}2^ X M^@^B@Jcf>A\ҩ%wҺ-?bJ%&bC`3ns1RK`bgv2Q=,<PXP&iUJ]6?dDPw byONBAE? O0{`!6zɓDWsA`Fb3f_kyn-0 UJTb|Oy!lS죛@חk'V4}6Iڲhg#î\ $0%\ w<\P:6_syU1^#5 ;x^[arqS)1C-b<X`*gj))z/}Yi^._&9.2x2˵ |qTwIi eڌ"!_>kmvz:Jm0 ; x]/$q♒`#D V$%'8d  ;=(*(M~vp/_3f凚7Ф`G0aH>P`֗&`DXAhQV ĺ`@'X讜M\;EXYGTS]xp/.(簂T4@b{\X_R>:Կk{p.W1FMמK@r9LÊ]M6Mc9ǵ(=)Js}?8ak?g\sHROA}%I8v|~C&:qp ?>h >kN()|:ꥁnf ;ciϷ%ͨW5 4~?fY]쯗: iX^#Sd[v) i]]H ;<|ז EX|]{k|ܷ`7~5 otυ >9+em!`On F+`.;Lo3v3#1iuL{=?+H=\SB<Ķt_'cA41:XF $x$$=pk cO[hzU(-s Qss?vz5< @3P$G.Ι ƺ:} #n%`\B21Lrݔ*o{1&"s$$/2Vv&A@W9K3\!1̉˩ 㕉%Zt˥`~o.DنR1@73;f_<{f%qxQH< ʳVBt;aREp 7VtߔEU@$خ|+*r\ZDjo1&HL~Q%A:~_e%J9Vp~XATBJU(J)DCﱊ'*\WSѫG:q̿Jb$l| $]AVJ\όVQs8ot/u $tidG)s:RGH Ԯb{}H"H ~DoLb&-+H&pGa'r*Z$3s>Q;V5LUʽ`zy_kk#Yyxt!B5{5'(~ٗ;͵G7HǗ9o?ͻ3#'2B 58 `g\Ү@I&]عo^HX)}j$'drn]`=$U dZH0FA"#0+CUZaūD \~rHQvC8DaT$K\8,!A̠\\ߛ 0ēHu硅kE22ߧoՈ@"ʋ˱ii,8u2ݑbI!BF -N>-˕mt`Ow7tM@O)=J{kloΛTl~X5=u?]8R[.:aX,o8)q>Υw͉,\V4*JUh9i!7@:yaSڦ99DQs0yTOҵ<%Y:A9:*xU.Du=+要If3h5-(Z-صҏ?LH=ǭk+heZhITls vb:~v$VA J]J][ Pù޿[J97}VlAZ9u:@ /Š:D?Id?qbC.[{s6<V"Pܽhڀuw|1K3 ]^H BHgᒦ1ZL,# @7ڱp%`㦔[n:exk[`JLp)qJQ)W`, 7Wkӡc7ō0%Pٗ`RdP_{Xc`Ƞ ~:~wF#) aJТ*i$vO` fVVcuHA±Jx3&ڭF8Bz `عmB\1Mt 2*ʱ܂*d&NXeqlw 7eɵI).W^0sI_sA>0M @ct8p~#e-}`HZ3ÚKHfUkSGxxx^i㮪pDmC =H\vAX'k9<ɘN~N/ 툱sӫ?cؿuO \IJp"aJ(3iO{6$ S i~Kvn`ГB֪/bxCF h& .6 28lϯ^OU/5R!2G$ u#ّjdENXffHK`&KzP3&7FMZBX8sc'޹k5ۥq8b= &o_4{{_K  ]xa=|C⿈epCMgP>򬅝s]JI+Ao@7젨b2I6Bt',rUFZQ&BC*k'`p1 _{ia (dI x3`i MD&x;"Y e"WPQCv^k;RT?m:6.O4\ա&/Yb(fjӊ=+}S;*ZG=zIqě锅^G(.";(0vBjaGʧq֐d,7zZE;cn>$oS)\[P$x lHʸ~NSÑ!'Z@(i/QBbw;B "b.g7S9E|`ɤ=׼ !G ,k++ 8n{K@7b$Spib>IQ-KÐr|؎'I֔7i5Ri5{ uXL;?|ki}'v ݺJk,`<"ѤpI@ВUk&xM8:@j9vs]8(rY*Jcj,+[tF$!pG ]N^aօLuc|1?84EXQ1޳r@7Ì꫎a=+VCyCn-˕d\- A#] \e}] jeua b48 8'*QO5D~(eqA|1R?XybTJlHtsn4gd0AGޤdUeMXA|ɰ2y8ozkN󽂅ޮpev ۵8;97Tz霟Q\_8:m|U[t?'s/~)D ᡥ_EXI߅\ו'W;ˢN͵~83"4ڪSzʯ|`)?BsPq/?~)rgwSM˝ mU8T*vqTύ|oB$*qI=)7dW.c!rXg6r7xtJ{&{3Y3Og :"  2T{) TNwU,% :SbsbJ~uf+4h͕mGZS©ts0X xV3)Fݔ\SO~ H.ړ Hr!öt2* k˵z)x*o&q˟:s:e@D>c(iS5!@r s 0_O7bs±E:eBQh8(e<#W#<+$VDCV k|֣b2baNG}j0ڛ2,9`\_/waj#ϿoroIK 3p򽭕X'h5%Y9C3#2irr//^@t! )o@D+@LJLGt&_eOg]f ]]|Š~3)3?Rt:}̤;VonLYފaWɺV3=# vj[AIT vQu++2;Xb4Gt&H{#^mcnO--Hp>\l&dS]l 2AB%r ֻho\&ͷ*;+~wr+CM=ޥ& >JK raHRZ?&q" ;_zhLGBSWq>U>_(bk=T*h[&}ΧNS&Hl*%L5V2xjgޞzR ܻ5/SeR[!uRsŒtaG#; a3i5ꍅbMW[tz&I4"c$u9(w)1ߵKI24x }$w8{;wQIdN<?VQ>,GIY.]?qѲH YYMaO25;ا /(__|:]g,:y?UL1z~A($@ȿH=ֹVTOE ]kK0#CjBwV{؉[΢;IYdpIk% G*?GmX!I@XK`aK .|,}ƻĪkE)TʳK14Dwۿ?Kq:=*1wa(2*`#yG F~3vsk&Q geq@ z R\ehx#8orq;كNHk~Tn6%o]V)1Jgžy3 -{3]hL63& sR)(\N>==_p~MOalzK@AIktpr{׏fnxAu-`񳟃`p5dq9sPS`)HuxS>2Py^ EH"^KhU.3T%oSx7I"FXsJ4έaFjWԵV$x -*SlgJos3 s'p}@ٰ"3HQn*>C xDuv> 79 TFKlXtV?n"e;bԅ2;JK\ζK03.苚Vi~95Ѡ~=8qRF=O ))^燱dGS#WNó*o0W>2ɥC-t/=[x 4*Skv,>>ԟR!#<:"*`[IQߧ2 >~/_Cbk;u\mF5;5Bhа_`FEBrLiKYzWeEi~k …홚i-y-;WQR<4Yeq[3DOx8 {W4Ik:_?&!icS/\$qTk)kH w>A?s=T^QЕSO&Hud$!䯓ߏr0`U'A JB0@n(ix7jnJدHWD lOsz7D@J|~UqlB )&LwKa[M`˾_mw Xa dI}/B`K)2KD]]|vꀕResbA?=jAvZO>R%p]}uyz[>Zx#OvCZ T2*ka!w [ZjRIm-d)֞OT`Эy}zXŋУ-];k*DɄ[W"yG8%iclwTlX}n=1Msq(wpJRp+'AZut=g(K#إzWx4{SQ{vtg^Cφ% 4:j*-ظx;:C _d5ȦrzLqAg$bc꟏Z;xL];8;'Ip D5=<HgRP焗/\i.R)WIRr`?,fIv .'1DTa0?zH @+K%o A ag/|#e rR5(p(ښi:Gv.[+.[&p\Zk^5WeJM ͕WErx-~i>#s&Vuʲ͕(H$oHuE9N#M-k i/Ucz#SD>ѵ(esf.wlFe2"tyu7v]P|TmiqYIykמiSf;ѲrokWww SZߥLRCW8ZY%`CK?~+-CLr릪߼`,on}TZVT\J:JO%N827H0P{ͥ2k-xZ܂bozr}vA޳Ymrm$iTF/Ev)pAD2 Υ_ }Q?t/ǽ {"4H*5.l/Z$ \#WDq#u/8-?Z xdJ*ET~̹pkZwsS[ ({|_q"U9 _/|x#Wm^xS-eE]MX9\sќm|o؞|&ga r†^Op1qo?3Z[7;Ƈ]s6&OIdԯKU[K͈ @2'Ɩ& `&n[cr|"Kaߗr}z`hmArSy2I/')%YHqm")cQmq7 ^z!#1Vd 7NǍo:1'=M>y)ߺNyYr IDAT5Q!wJ4T45Ɍ ]pN}$q6rm uE+4WQ TƆ#6(HV0NF}|ؗ00Su3G<]@Xq O}ucMwBOٛM9юC@ m0.{j-t!`J bTu.& G v x7(*KOw7젷Y*UrfoB; ;xfb'&磵= qF~>BNşHm<zχ}]EwS!(euTh䩎O\O@TG6PIn&QE gCFrh5L\Ú@ؔCOQzXqg^Ͼ/a[Iisn|Su3Zzl ()883t}t)R"+ ~'F7~VFekn4ya{ ˛64$^SB2q &N$Ż;fnNpja| @,w\hr-?/doeIX OjA7ET&TITݩl 3O&iY vr7වa'9ZIΧljاVl{9+ )$_ɜ$9\+f| *`z(4u!&Ys#EϠ[i=Luٷ>UU:Aʇc ٯiqIq끗nuR۞6iy.\glg\fARRF xZYCR;_3pVf<1S{o>;' _!3&:K^~'<0A "dM\MD[QQռ g_gSs0qwMt 1pb3G`|,\Օ+R]!zV+'3Q넵GVS*cQie}9CT~Z̃ Bh%-n;k(gT1qfr;B*T#}2g!zClW39.؅qQ^;( zlM ~Aoubq]HcR,HݱَT[YX /?$&N}t6Ɋ,a*u;P2DJ&*+iq"wO!QHca{tE+E~2 n1IqZ v6B9Hz@L~6v@_4C/4 #Ҝa8N9),ʪnxz~O[r+<IDSB+Ky&$Jw~S)ޔ$Hf"gH1|= }xqsm$;@` GC$@$9&Q5$I>#a:gDW8^ |[iJr^wcI4+Vi wf &\,0_uj/4cHOa`M_ڴ\e"Cظ:nPS;pwox|ӏ#|/0Rh;]{/ձ+,Yܘ6:UȘWﯗF `Ob F _†@|7g|-TJ;^t9Ox2ΙQ7Va%B+Y*TUk"iY W8cnE蘄R.$0i^ 痴]E~!ghgWYTaf5TOb?BA>vYH[-;DK3ba$jt>a+w8r2_NIbl Me&POzˠ (Uq `R9!e.c Y_d%ARvʥIΧ|gQ38)TC\h/r(tѪ_^meSK9-$<" Z ݏ/Sc/8$IYꫛϵ'm7b`t$sѤoW)ɀ+hNcδYi}U܌QDW~x`eDgIt5g{;kVH%-BHԾx='x8^=/F&uز. t7H7<-x~=N :eV17G38qUb;Ld˨HB :L'#N4Eײ3НF4ِ0Z㯗<664+_1{krlnyO_I2%vm^W^7DG.REXp]|= {9>ITG;g+Z"XA` `w0tv6SZo(+oX]k;@%gĊ -b8(4dkUey5^ُF9@>.s8Hp9B i_psW+B\@q%[ȵM'Bw2_^@E'8L98ùs}LȈP aDGE(U&N-Wא(Abk)#"v/'_xlv't#1ޭ\:[^G 9mgs#?z/:B1MZ`493@$t3SbCSY26 v"T< O"lPιmurO8 Tb!VqyT.>cKFuj~2?M'TY `(`{:1ʫqRJvyZu=记!}ƷX<ĻD Iء`|B.a=| @wZZG0 6 :^iVQ4OHĂB<čooաBDSOpj}~J74NZ& ȅe ? b4"Kե_qKDr&lނQƾFtc"aOUPh?MBCH}+CS;SRs ,l?sFS2"gH80X@C,<ҤsvF GgK=͌Z׫"-ۆ"9ы}ޤ7i[v j^a\p w±'|:.||M:ss&($1 O\Vt`E`jA)H*SQQ\Vs}W)JAJx(﹃m8b}ݨc͘1(ղ_5BV);l{ld>PCG`P zc>[<[C D34ipimeBޅ S ~w$ KAs&)Us?cK)oU@ρ `Tq+P +ҽ},yI.'\H6.êys7_k2庋obXGw28+sH"sEb|rÔ0VlU:RwOY _LT1豊 q7-y:Li)4+cCbԂG8SO4|"sNRVkDSˮBW1w-&Ƶ$cYmA ϝ tJT 'IF+vn.lk[㣾kW8ǻ4^J+寋1]*e#Ǵ*_H fhr, 5[^Ժrum5Ji#/lUWH/u&J7\ XӕPK`W^H.nK T|?H >*[G"AL3_NqBqOj,(4y־R8 pE9 Ճ0(~#WRK/cjHD $V@MA6E1I ;FRoE|"tM2G}X 1Jݢ5S3LV]̛42]]ZJu ]TkLw l*-eT̷(*<*n~naWS6={`E=Ghٽ-zfX } ; ]sh-#ȱnCSr<>u]ut%_"N{}TEbv,#"6:1Ss=g9_tm94<@КHy;Nw`%]]i[H|fouwYQd7M΁ a IP]kuf׈0`9&$N~[5~~33vWW:Iu8Lf q">iH(:Q@ B`Ay*n`"lpp 8bΣ ֪LεX&>򃉰$熶%ѯiWndpmӤݦICՋPR_'6 5fO:츰Un߁!in3Mj .d62me:UL:.#hEQȷPz+m)?`/0-*QMߙNEQ 1!`L8;E_] L}-^΍?^16LB .C ҿ P(u)MP*XoPJ~+!>~@݇N%J%yiga8JBXeT#>EzdqCI_w e?1SyLyҤ45(8t bl ݫ)\EzV.i!>եT/k.MZb]3Tz宖R6nXJ jCoch}Rg{ijdljpO'W'MkxDq`|Ѿq6YH`֏5stcHFQKW '}>O߀ IDATUƿUkVE _ϑsd;C߸fǀL&4dHZ%/{l A#*?8èۛaF)川s 2Sb1oC!M4{&pUg0&>G澜 QOP@V>#fMvTj#FB|XThÌn0&c:['ԎlC.fԹk`qL.r0s  Uj]!aU8iu?Mi<0\JA=,+} Zn[w b&D8W89J~µB+/̹Xy ^%bSKz4[uFN\hO4>+k&npeTM_MI73;5-& `)NۥHDR`=iA~ȹZ"u6>""~$ri@agSQ%! ꅐxߔHn a8N<\a& P8`śTs čv!H򾒏/ZZRևLt ۹t&D 3,Y+ wffjQKRpJn..N'LvIӽ #ZF۩=Om/߹}7Ax5cҖӗv9l#Z>L~>iu>A36 f``7p2M0K09A|w*c5u NK6Mpfvp1ԘI`izLf?ª}#)`)u(j/N0 )d5~ V"㓃hz}T)vr>d»I9dOqM"+PQJvi2Wi+Ma3!Xq}'}?Xa&No9nTh~x | X6hR<@q=J)rι2Y!J'' + ;壚C/'|>|܈*u䪱V|_bcǤŗ /FAh?!UFo%Sh%S0;':+W 7iФgH†<.7[|ХK4ZB<򢅫R t)urcs&0*M/!Mk,hȭ!zX]\nYΡƦίUT20n~f#P©ɴ汭siV1ځJڦs5!a 4I'(8}/ ~9UZJAMJH8Ǿܢ DX]~i|5 34U~pNPb,$t]Z45dr8-U7K\ vpTnr-ɴ3Q j $eH[ae~K۸y4h5($ЃpW x5aD4H?v:7[;Km < 7mQA FP RM"Hةq:[ 9ѽy@8in!s Q5o=i ^8!m9䲄ݣlFa)-%$24}G29J$E B4}A~wBE2g@Mh2!`΃k?ÊR έC]\3;hbfU/]Cj ᖵ4L=QXL:@*$NpOy3iG Dk>'l0i`o 6,~1\q~ wO`( 2دΈ~DeQvV́)Ğ. zZVlcO~I6oXwف:&IKyJ`Q& hg %=(LR a4Mz B|,ndrCFXG;~=ύGa08R5D)q'_ ص7e2n86]wz Xh;a.%ÙK -ׅ7bBH3Q3V~T,7m&S8fl.2k`{ǧ̕V.KI17}:Y.lɈ,_9cԗb@#BL!y3L"̠d̠𞠰3 wUXZ!s]S:Lo,,ҫ?i3VKڣp& ʨBp9C옚/x/*ڼ|"4w%o9{y,/$y-K[\:WZD 4R\\QܛE\g6̓ }d@DѺ@)7to,.HlPkfkD۱Gu>ݏ#8F_=K]sKq|iz#RݏcDVtf݋pr~Xg`aUh/yǚ@6Gˑ].}2 `d4pQڰ=j)TKNҠCwWXrycme-!&* 'k4.ڽve$OO,G] N*yA4B)\*!Z}mZ4ym@a"E4 i_Mt0@2\%3(ه8Mؤ'w. ˋm'yX_*4LvޔAQu5c.\hSeP)p:s\3>:ZFHFPl)b-Lwl@Yܺ+l=:"7 F ?FU2jN;ۡO]YHy?$ߴYxw4u\}Q\+4CÃa}X# Akk]†IuKhݶτ&;aBr@Aicڷ\25n+h(?2t1Bn0hոvP +/W#*}U +c@ 0(T-dv;F3\o#3O3 ]F^-vCL jm) TjTC`5R(RrP^jC̮-#|h%K6]D`FRB-\um*s)~s|6}A-N?meq9]]n&XmE<;BT" Qdgq\iTO%uI$(Hurd9BJV!A -HoG<4Cz|Jq1OkvR)0O;UmYVon~-9GWwH` oh:. 4;Juhi2=9.(apnQY.lXJu0{nOHB~yq]eNM(@3E7RFu{mN~]3 ?t_:̘(:R?yq,ܡ ri` ٬#kr;Bq,8B {-^֤ޞ( 낎K].\>)10Y?S`3yV͉~r7#'6 q\6Mzuc A8)S8s=N1GvuMW(?t [9w+-O7{*ALS9Hf>b0iihn#d)*A>~_D/;KPGX ɜ]daɗ]S`FZJDa o.@ :nimDզ# O $*M/j}#tQ\Lu91͉ȃ@,9@JfA;n=*րF?wQHy"](\3!Jߝu+ևPOKQ;IОD:L)i/N.QͧhpŠbv:i8_O@ 5HC)M!FqڟaJnv'U5`< Q`G 6 HYsH.w@LPQXNz}F'mwww@mڴYU[fNhz|U0 Dϸ>޽cK҈B'|G W ЉLSq> W!e:hщ;[Ejv'@ڔ3:=v~ktt^A-&*h{5M4>CEO{+*ˡa\ M=-\-Ȩo&j RbȅfԱ =<9$`6{Lԏ߇tک|27JMC"aPw;%W[fTjvIA#f}%PP3tQyvw55D5f~IZѲw/\Q@t=jb7}!0*ޟ-fК:$aLp6t g_Hϵ~ja}; `%bI%}GI\_;ݗIwTfP( Vi7HJSØk7Bl &{'xOsnuOohx I}xsnd>؋4ZmG^ԥ{3;ڎgJ5ͨ(04 QO:UoOd77ƸO8.>#r>+o;d9M@ƈR?ʊ]eflOq|ލ3+vV2#l߭%6j^q +djé!%t+.2<53cuO%30qﺃ3EZefPcr&6 *M2X%"M 3m϶H-|_ٛ#󻔾sP_7FΝIBJPrm*mnՋp((%r,CH0`UKU]ƱG_k,TOJAPl1>M8 ̮V[h8>L&J#kjB 0fl&(G?jD lHٕ)"qM~,v\ -Lj6gpDXO-fʥp@oj `!P9} FhVFUk0?磽B&Ҳ6E jј+X$g6%[0pa".y#Üx$pkĸ*n:cں77/f2u"hH[ۧ BeӤ7`Guw M>J .M*J2ļy-tgRr?N`igH&}6+R| N%}mN9&c+2 Ԛ'R{:\+@pg)|0X c"N@ʘ <"w+ '-Q|pj^}h9@q;D8Pp"L'7Sx:BA3y̳~ daߤwOV(sy*8U~ytW˧tSD}?<;$ʸ?FXU㿗!.tCHtZ5nw|2ypޤ}vS&df=K 4j#Z#5P췑MWª#~f jJ^r'~nJUL B*Y|^z\J%B!0ڕNv!3`IO`w@@$f0g F(.5<2g$k8iSJWOsvt1vTAh:5r ضd|ҷ"O0wS8 ʦ=У멖`ruNjU\Wנ觲E>>棚1fP`]>DltCbх WCۧA|bʵfn=Sg;}<h`cE2- IDATF M1sQDmW8hp-u/~();%+W1_\=Okdޓ;߹Y'@za\/h?}t5t{Ǹ\;Yme.=rhߝw},M `2w5C(͘7n$Uy~jMM!8Ax~-|b `'X!uRPZH5*Q@vH4jQ!8j~hIZtT8l+|B/ hvXh>Lnqi2*۰L}w3a+H/[7nfYz<7#s޳lLPw`‹r ?NRhz4(.9.`6s NSέ@÷ufXw SпK294 wW>)M9o0Y)n.wW}0zE9NG>|}R/R!9` }ro9C^j=hH]@/ܜW}51X]*K%E&&4wi>ob#h¾y] eNc}T~@4'm.Y,4tq j3Hf2{$Lin#ḧa%us *2MQRvc D,\tMĹp\0JCA-EojE0Uz >gls+'-=*_sE&t/sLp)5o5}'5B 0 ª觢ħЂ ԯm*1:n}n:&|H{;-g]4Ǜ"Y4]]:.Hy|ẃF 2kl\EH͸Z{0cJ"zI{pP) ѐ](i<,@ί0 E3vm9}Ʈ"Llh<чZM]|!.,z"v[!1Uêmt 媌M?Z1c1n`j•ŋx_M.%hWL8v*D! v6{z'P URS;̧{'t=dj&ky ]ʤY|NRuL9'})PlUSUr%tq`s҄> JӴ< wNE:tz9`etM^/I:3UA` 2I=g0Sx __\ŭpVvZvsMpbv}xEGΑ>p=(}R=ݟt]&RҶ7kOuk~< k˫ԟgP ܵic :A) C(5pϛa} 1:uWUS@e}S HMSƚ[5ur)N}Pjv7 =qV猆B|4?&Al&jW|ԠI Lk&foJY<7kP*?P[IĴlH{4 ~p盧VҭF">C|UO,@$)ȥ|A3r?vp&W]}w8{d`\/NHxpEreUkӻӓbpƨ&2_[j0Leu@!ZY]3R+ =yp}LwgEG*CjIСKN鼆Z 3Wsc_-d*>`(A6_"<qgu`N9xih{s~ @p Gsױ^TL Oh͠2?#7#\ ?y|I`|d}]G zQa>u . V*G%3ǡwjx?/L>wJYu9{v'~NV:طEK ceWwOqm5ohsƬ HNvaxbO{OCچbB2?0HatU:x[QX؉ك¤D/]-c޼m=ý;zK 2,| 4!i6u-a0WsH5r0"` *y.b> }UIS?n`;L0'wAZQu2 5 kc|Gɔ 3'y&L2VXyU~d[v\ >OPh + > *h5i|>nT#8]JL69`)6ȯ xZsM} `v{C8ȯ!Us\l>'-RjS]Zf4lgʹD$VZ GUdJVd\H *QX b ?R</)sK`pԼ*A ,ĂAzb.pv¯K!!1:3V=w+D8V"A]E4$E/#`:mqi,eٴ)GU lWj# ݏqp> 8sxU{F-~etuSjdgDf]>敲l~VBx&p&_]3BJ?\1©pA'fCaHS(*z)]zsdAX-La6[p[ fij*FX$*ɼ7;Ofk`6UB7Y%(S{+`ߎS *Б?;,q+X G$$**bVW_fdm0}pmG2SqyV|EJ>VQMrjŗ<)P_CWfhQ9W@M}2Xܶöp}!ua"Ti8碖k" C&[@rʾ56cYME㽑=u=ۿHQ|"y&)# a*綃V.b(=&;wh(wr9t1;eQ(pC6aH͎N5P/u,SJ.JB[q8t7c^P֊ZDRlI vW' HDb삗sszU!$ 3$ |O]5%uyW~4ivDOs~.di*(5;ɤHJC-7XeC/~_힠V,,)XH4Pm&&P #&M/xH'V@*o:z GO<̀7ir=y>zX5Xe{ >~X"6Y?'K[hW\\%<*7X@ |4)WK{׊4s@- g_4>zڟle|kΕ) YBo++l6THa荤iԼJ! ȀQV-xm[qq#&Hȯw7X[-4SGeCiC{߹k硷vzvU&_Dq8P߹O؄l~C:XCVQ֒ WJ` h7$z R+UW3{#Fp!3VuGÊNe6¿q p*|;/6қ438V )uӼ(LdR3o"Τ)QDXPd2L%dt0c\#攏S[]咎8CLRVj ,G6a]!f^9:nu9:v/'Ҥ֐r)@f(@Ӧ>Pp`Htvq߯cpZTn# ##6:*oj\h]{*G ]U;ܚAB2pM@.c:-XK6C M|LS>̲@ȴPj#J&IP!YE@b櫦|m߇UV9eг/VaevR_ÊY]զS LM7s96HB΍!}I_q#F*PwavS`U=R \ sEw*Ũ 0I"`~+of3f>[Zx"|9xq57X t#fj{5!ZHoh[}~j͕OG:J49¿^z_ _'$`Ϲ^ߺsKOh(IYW9/ D ITA.cry 7s7\zyj0"{w'EM-./8Y^Ʋ!seZ?yکxu[E7h4E$|rN1 ~{5KSkoqٌN!4!ʥ&s"<7]2$`Ft i`M]i&A3ds n+ \n26F LeJFX~JuEG`؋|9ڷApP{{|g&jU-O5PA1G BJz=3Up~WqP;PI@uSh(uΣJmz sL(a<,_i(@Vq m|W6G3(;lo]JP+HWxZoj%N^<9(Xi vɜdBxm􅾿x7%[俋QU!MAm.\EE'5؋_r}G|m)N ^r޸ Z F&l2BhX ]3}aN~t|IN;x_@ޱ)z5pGנݧPKr|ڗUY872K$`t`2`Mj` ~ B-|C p.M`8rXA)ZB4fK "jcg AnZ ubeV!0 fj14;iYG #DJ=ۋ ~XEwQxwe_ܴtpn.ʦsyPĹi_#;) UMZ;[|aOqi5VgHL#yU^-bEDCgk YG-or~I 8c`$Z/CÊC#\=w||*/^gg_n4itѰ;gH嫋Y7Ll>Tewn+E7؋fˀ vuK B:J3:`E+͹i-y6~yk"MȠa`*%7<;}$_ 3`EPxS`u zZkȐAL6% a%4w^V:Ɇ>Az wLx=#C--1];ZyBVP^H~$ta~(_cpgM؊evp Gw]zN}43ͮ!O'?8NFˍ[jU9g+[Ϧ֪(Ӫ:⦢ X-?#y0#֓h-0yLplq1<'ҏ}&f;Vmr'1ydIrq"k9H8<y7me0ӝPFJ8_VӅԐvTY0U r9O*sV>^&p\@6U%֑5U' }a۪ XU"U[*OReup1O 3?{8H2['??ʻ(Aaw̭/p'޹k+~|='iui&#Kj>YX{@A^Շ`LvDC a#ͤf2/E:4 w4fP& Dׯ_h媠!|lG,"΍ς"8- #}&%$R ;Li ζ$mU@VDb5hSE+)4`UT0 &G ?!!Zv lO OPhU0W)i0#GRIg' ;)B ٥7 qrl O Jny Gɒ(Tr. IDAT }.L{n4g2HKv)]!䜵q|q#D8Pesᘕ%3D^]iC #aBOR ̭v-\{q$L;(zy4/?Ԯ0آQtcoW3^cop@Q^u/s_~G~k!ZѲ0 \M{U"e4=o1/~0'Xe=0^R.ȨR͊qyO}JS˟NPR -ES6j;`9Q }E{ i[~\L }4 Kx 3J=NDd'|g6]{a 1#mi.2 V[ Lh xpq 9Z>W#x&c`0v>̔Z|>UljAM`Y{)#-ԱX?i^CLEgO_ 2JpNA!=hisќk]v=v&5Y >LP.nOcm1Rj|{ ڣϱ٤3or^<|O! $ mR ˥xNo E1 G6#o7ɈT4a%=T_?~]) sG~{ Ek 2޺Q\6}-n\@qp#O.~d5 hH522pXU+jka>魯{DZrj(Bj/w4o|'l!?-L?pBas  50%RwRyFÓnNo`NW -Ge}h'BPE| 3aչPp:qrqlO-/ {)0aBlkrX55 `9 |-鰒X~>'#8fQ\Jrog)yĨﬖR<AF^'z +x)(.ĕԡRp]Yi#[ xl%׽q(|0\j`:rpW0a9PmgX:OV;:4#fC` t Oy=hjKy E+ZI.\J&;wӛ`U*S0ג!#@_qL?aC"f,j SӮ%O:ۼf$w;\AFA4j*_=0}6Ƅu4Mi0sRH.8AqqN!b,q<+~ XqM= QYNˢAhZ:MaBg̐t -20SJ'>!sOVGH 0XHԑJv}l U) ZaT@IEy9+Qp:j#,H@*7:ၛڄ죹)Vtl(2U/|/1Np%j!1 ʍZSq@f5souu>h?/04i!Fhcs}63 괾8sL%Ee}W]3$QWν@u;.g4JgNAu/ZMR̼d|zA-;La `BJ)ka&1 ~%!si}G D2*'$d#b`9B:]"LضPhаiGfGMվ*c_(E~d@v@?GKwfݣ\`U$ͣ'>ϙv–Z?ٛـ.8hHvI?c}QXY1w^aH`LʉSb1|msU0iNKj`\dľ!0}sɨ?pe8JFeGYG.%(۩ڍ.yJZu4"hNsjN=Ar (h|>GyzL;BN0}ènc(wR& Œ9k_DZ?0,y&=|Mr.֊I<={*+VPP r뒚8?8P!uL'Mdk]+.Xo ~D~Uu:Eē|"Tn",kV\vlAZ/׶ xZd1Xv #ٜ]-C$9-t68Y#"lM`9oJQ6qW- cr9nUQ!ӈ, k `^˹=L<059<^BEy^;TK@:m Xz\rTua{}Ŏ0%k/3vqRE0Dߍx&si--:/Wsg=k}z$XPlḸ9Y l=.lXs`q)3k2Ÿg"X߇pfH F,q>!W6n<_ xxbCs5+Hi<;BJOduZ6l Jƕ䜋M㛇K0?G<\R[PMF`qFg6_%+FdCY(vyD9E7[,ic7rM dtdb8 l`lT6a?7\-m|vrW{pЉWά5S*mn Sޖrnen/"T؀濷MiٷD\錉-'Bm"Ye3wm7Aܱ[R :{VZDPoQ""fMzMGiZY*sqC pqӾ1UqGDTn":O;US߱}q*KDx2?|ь+#'H%{3^<1#|Im}Xۙ DsTD9Eg8ɍx;2bݽOO) C.n*L"B5}k4.㜅E19_b>%r fl1g7wԜP[k3aHq*J%9{omH$OJc*W7y{X̢M눴Uӏgwێ9F]4iU)PHZ <=9h՟t*Zx@h;^PO܈~t<#E\ D* S+ISDN4veѺ q˦ ^-foXge2z\ GiGɅkozU>l}|kbo1pu%HOچlrb*֮FUt0rˁrjKGَtزc苻nR٩k{-7t7MO*qZ9nfCXˠ29+GrfߘG$0xvCC}{7]t䳣 k)?+6Viy.]B&Ǻ9]\히=%'垿 X-风#260?M#쟀;_ʎ,9e}Δ 隒>UЕݙZ5S/VP[$ysWֽ MZȦd>0~SSrR+HvXrs!a徸X%8.Kξط%pŕ:;uáXSks![CrE}9?U1a`mسr}e@X'c쐞;KG#mu}k˷lH`";"оmHuZSGŵ ٴֲ&'Y!u:yg-]&Gnʆ]ݨV {_\0+ =Wo1*_ /F6;l˜zK*7=VDec;bqL\ۃ)Vޢu]+ 32^xfK:q1qVv_^߼W*ejۀ~rܞ\Lk:x=V\՞ֹj}dzsXE3sxXd183Ԝ9e;$5g^:bn;Te[[ko CePd s=çWUn-oE[v ؙsEq tXXUݖ~=oLwE 4 -w~hMv_{^Z9 u։woL2T, Y˭VX8X|%.>3So&q5M;؛}ByuïvOZcnݖ79l xvB:ߏb/b,aSfHn9nXibXNy]b&]\IXGU ʤy;+ TJn*nLgYIѯ07Z;ޟ^!p2uӿ*p.zwKrkO͜3ʏ&o[zp6l[UR,e{HK,yte!'.d3n}ٷnZAO饵ϭi\p~_zssvgPnU9bhK_ ם/D͜3o>5Ug]L8Z^;1M]3?[6Gd{&|X3$.wٕ5m~o߼yZhƎbZ)O)kv /av(fqO ~e*76-/4νdc|{eB(] |3W[m%G7uWLhoAѹIoߘ x|wC 7. ,yI* D\}^5֜"RX[4c66c<D}nI*nutڿ2LIկl[ܾ1D~ܾKH\' _dTug ه9!/B],]xe5,O͜:feuB\L%}x38.{c{&|f4.; Z! ]XSo:D:7aN-bXsbny|^_ntxM/_Z'[瞍մ5S3̩cv.+t9Ao֚n~ZV Vrܻh؊+"AxvO\L]{ ء>ߑdx؀e)(1{1sZ$~[}(R޴fZ%ދ%$8nӚuf:M늗ioJʴz-rʽ\흞L2,igZU] ȄJ\¨VTaO~X=7{։son}n1R~7!#tT sų&g%c 98\;!S5bsgedi{+i{ҵkaPh{Sla }Y'V^TģP mon}p18ϟ% 5MϾ¼(Lק|}|skЮ+9ӻ="Gm~ F֯KI~EC:]torMt( -^iht&l~+Zwu޹@yzJzٴ gܓ3ހhܲLm]0a\撦}+ E ;v厽GXv.&~K%D Zj1BkL8sμR,k7OA,5w *v~ǁi?G{:+ l_ZW~vfJ;ĕehxSW삊+3= S$wQjqSzI~{~}Wwr߸v5=rRzƅ(=1e\o+ƘG߲V׶GqEz ^#o.s+x+X~BU\6π?K<dmaыVaI͂ KyװJu IDATfh?=g:qW<} Ⓕj d>D̙8m֦"3^Dny\eUWYWH7og!YsUZ7}:.O!e@CNOcӷ\6lSK]+0o*enZ/p q84Hfj<!٥7>7x6'Y!..MxҿuҷݼA9=ґn#ۛVdcSAWOW.GL,C|E19\st1FM>vIn#qC{L&'y`^uU-y IɊqcKt`uϋ=iFV|o^xоי07o[]~H3gHu087.oǏ{N{XL|g#FBG\ukKGrm0ai@ Rglh"6cpՄ8κ=ej7 œL -:(* 41S'ѫ>ZC5љ{8'_3k&qɚz#?~w=O9z\HƢJ\}Ɣt+C |ó -/ +?tޥwGP+u^.|rhwy^3{w3"y%wºDlwK72nybZ5)݄_ӭ'̦1iqSǑHNiP WbMoİdEOn x.SFt応y!|/߹hCX9C d-sѬZKtqYcXr&({@qljhnhN4O՗OΟ1ᕕg1KqeM7Rߝ8y&āJV'1_K%b(rL2oxb[cDbE$RM̷ۛrxW|AV}_Ac<|Q̬9󞳮LG<:97M L-)A l@Qm`qjoE+eS ,g𬃪0h 6sbUyN dpq1H m܅ڷ8''LHݱT2u?\`b(s\|umx)H/o1 ?ȋ%ҷ(l0p b٬~1Xx{`lH,\[wibXX^Ns0gaiZ;9ynvt|TzBկSW`';ݍez3~A1`MQᤫ0˖_קW'2nJbؙ6gni-,YzpL\-r'p8P6 j":#Lpfſ=h!!X1Q{j_wj罵pm~n|_VT> <=(0ÏsV"f\F˽DlNdۡKBqwz ;YU;vJt0za/^1ਆkzV>O,`M#Sl.N(d}|kTm; %MQ4>[<ҵ1 Fǚ#2')>G2 M~ķ/_   ]J\R]D=~ \-q>s9JgΙׯ# zcKBdi2/I\ہFQiU4Bj (ta#HNp7pր^t@֡@4xW<24|#Wj]y_\6 љ Tfa?$٣A&o`yo\\0t(ү_T4]Q_ft0O0OG"`>Hn[<<Ԇ:aӴ>nw"0k1Q]Pc]ļ؋Pk O]QEٵ) 6զM#֬²nU¢!{ `Um.zͯ<ٟgz! /tYXnmHz1k 01=YL6{+lTڀ[^}gVIq(?F!Aq ;OcCLy1k[’ۼa܋ko%$xDs&p2 O ? {vr 6`Dr}Kט\j1$Su+V3'M46|a|1 }i䧾F~ؐ/ 9ݦ;uև (I Cհus3Cn-T`mNw*oiQ(Vz:^{ .D:EM]QEQ (( ((*芢(+(((*芢(+((( (+((( ((*芢((( ((*芢(+( ((*芢(+(((*芢(+((( (+(((gFDOw3sμhz&q( ( ah}|,^?O nzq -oLxӬ>(,\ EWYPO{3cW^0Ĕd2[_ZOh\X)Z;^a}}``R{ |ʊrcXOߢO]g_3۷^_n^ ^WvG;ڀS\QBW/:Ce6} [B3]ư; ?ڤOCks}&5[lϛ>cX U3ZQBW/6|` b-K}..,@2}歂mؐG7i~^yhYl_QBWS7& =1{+oU^bnMSWfz4~5_aroW^04RậzqZ= | .lwG \Zcꇺ^8 X-+E(5gx ~r$2e+o(gq,Cv# M:! Q(,ά 81 $#9+Z芢3t<Z&' hkbO n|nȌX g#PiM@obi~#'37+/r X~_ l^NAU?WE(F#6$i]$sv?/ht!+C -!4G7FvZvk Dhϴ<*cm@p`ٸvXF(_(澾?丱v>.ݐla"+OWh C??b[I);P}#~u>8X+T#XʌϦX1a߆Cj 5XL~ $?Ž+(*d֜y=._ <ÑG;-}Xn |C,q[vb׳E_pAcq *<`|n_< ;|&6CЊe40^qE]Qv#,ĀH ?8y;?@ݳjQK_ )Q^,qI8x1.Hˁ#@ .ym~ xΐ?> 콚lJrY^mEy芲;}a Ik{ǡec/z qù < 4|.SA_]0xE8 Vp؊|qk{BFUTlղ~0_IJ)7L;cqnW0:k-+*W({?BzP`0Ri[ zPXjؐc ănwshWtEby! _MKwo`֗ײnEHb3cLfdQɴۀ\<Á8;oᦓ w)Y˺v(?>T_/}ꊱF,4~@<sTF4;/_=#nn/x+kY+bкw kH, S#⚃Ͼ^0x7.#"u s%Mù٣r }EZŒo[j%n~Oi19U׽x߮lk$uD֜r+ϒMcôaU|psyA*L.;,p*a)Xwƹ=` +=>bʺQ\\#P b!+ЈseWb?1Cr?naUԊ%M%y-+ |a(͋9yK{<TIm1D4㸌Ĺt'd8{-Ε>KmㆱqxV2qq bVD6ϑ9-:۱F ~YkA^DiYvX={쓺;sų+06Ah܈izE]Qvf͙eRNN(p2px1T J9pTc%ݟ3CJsGDRD!U^ &bzFuފ˞ք}~f] [ْXO~G٣F5qcP| C9xnwd\D;fT~w93:cۅng}e%=Lo.n*rF~b]?"eȺPPi Dž f& QEQAWݚWַO^̰Tض.,Gx'98yN@цfݻylO\L=O~Y7'=eUĻZr-AO^(z;0?DIc5v.xo FPtEٽIaRŸDb_܇%zHg[X=wq.=F7爨gײet^JPDYD(fE<"}ޜT:=e ٺr[;@Q>9^gAQ> oqW"j!opmL D@wX^2\z,7j \ÕjqyXq\{#om7@}ֱ5o3&q\)&~'6,xr]yzd]Z7hR|aX q"]5k%{wp>.| J,M%%݇j?]![0Ξ.QXzY#E=`TjWkE gqrowXכ@Q>rWې7E@W`v6nxOl,m!.nb\Lv?>.>"z짻g!192Yvx\C`pǚH~0M&z绢+!ދd[/`~O\ $$J=baYo%Kg\aR\= 7έ^^'^9cx zvN\9q"%cohsh/E]Qvʽ0M4At}UK7Y%s$;DD/xZ!.~n28@XIY>!Vt:ytfz;΍~4m\[F,ouO..6O\PtEbù8*ževȷռJ3Dt+q p=5 xWR6 18b7{tN{{SXRcxFm7C)\;e4!NQT x% 'o劰"""=qIk{MKE"X}p>tf׈P}qd'[i@\`S|#4L"{{j/&ixz Ǘ\K=K$k<]meP6x6^/8 \߁m."X䕲At&5׳M:3ne{Njo1?>d0oČb(j+ +p_^A͕D \Yˊ( 2(y/2mP,%-v3ߎ#) ,:_yփMkBQMa-0!b+ 5u Uq#@B^XEHJ\=pŞDp pnপ KbuOXF2(>2hxf.kDY6=ѿgoh(?i-6˩m"u\,玈ϕƈ?$:E,qXߒĊ^p7t`8K\D\OEoʀ !u-7t(=-Uùr;.f=Yk&+;+Uu{]>ZDx(=N6\ĪG EğAB "li}.^W~}Wg)0|@guEQTefak{ nyKx:d׆K\[#EMćcz8_uٯqO _/@.q-%\-V˺{0AѲRțDsrk4.?oiT~z&n+3=mIH4b/BnOe1\p87"YfX?5ڼT[9v{S&Pe}VӜ)..~B˚ܞx$wRXpGpEXke$0Ҟ)t;>K>T{ [RunXe[~`.-f.?R^ȳ"7rlw*&ތ+$3PmN~ΦMZ"0_=LoEv[Sϐu-{"/Cm*uj06 %*V}K76q:pȘ+S+DkIJ$ù2܋{ rjt{{I\gqW˶3)o$/͙IDATקG׵͜GfPeGB ~֊MwE z) ’N39"un"+Ex#8xB~ 7/[Fx.{=EԭΓ&|K .f>L1xC+W{`| כ@Q>%K/Tς|FRƆyѰ"W )'cDE4f;L[of{u9{W-Z5!BɀezN5CLur Y/B ")"/=lUhIqr R䷆iovX96Ar}$6Q 6II"/p"-][v!:-#?,zڳ=[Eہp oE-b2KwhLOQ>#(!xܰ*O2}YKzkkqlG3"6IM`GH"-?)ހlr[lgp..ފ|O\wcq`^^AE"TV>"}( |AƱ "m6㸤~t6OMnׯ\&e]& w\n?pts Sc~dqn.+x \2 ƣplxT UF+gN[S0ʽgۍ-rGcLn+P~ŝ[qSڒLw\~oeree⚵z7@+X) ,d\gH>ֶ?7$roJX (*AUeSFm4{u 1[Sm8wFx#ٮB~.>%nN28Pޫ%aGd"ԔN9n Xxo׍uמ,ȴ3m .}{z`E|AxFNTn)שׁusoS%9bCz!c3Dz1E+FStEQ>/X {YMK\ ,gOO+(Y((( ((*芢(+( ((*芢(+(((*芢(+((( (+((( ((*芢((( ((*芢(+( ((*芢(+(((*芢(+((( (+(((g HIENDB`openTSNE-0.6.1/docs/source/images/macosko_perplexity.png000066400000000000000000013235701413546205200233120ustar00rootroot00000000000000PNG  IHDRXrsBIT|d pHYsaa?i9tEXtSoftwarematplotlib version 2.2.2, http://matplotlib.org/ IDATxy|\UיL4-kd2"82Z|Y4#EE1 " k؇d/}͞93 m)4i~>yw;q{DDDDDDDDD@_Ȇ(%""""""""X""""""""2)%""""""""X""""""""2)%""""""""X""""""""2)%""""""""X""""""""2)%"""2H8pDDDD%DDDDDDDDdPSKDDDDDDDD5%DDDDDDDDdPsnsιs:9纝s+sO8.u_8p99朻9w@_ԟXc808x9wX?^ bι;^s}CD<Cs䅉HS$"R9< smf9N볪Usy ؁)T2|97yιyιN\\9z!#{s{ι&*| ngߏ91Vnx{ǟ{&|/^p}q=snysflynpΝ2|9xED̷Y@.s1ιmzeg7Pw;7yq@9Ns`j`ι}qеl+_[s~\ 8s{qPpsn_~~ ޷9ι˰pv*U+J`l7y sn pasP=wkܤ<9[`.U xx'J8.w /y̑ιs}mk:8Nv^R~sιιu\smB Vr=_Gɫ2)DDJWxWlzWEmQc%眫wnmdbR "-ٖWa V#y=")%_ª:@dz` %ιN`"p9Gҡxoӱ_cK)&kmw.pK(n5]\ 9w{o ՝_w{snaX;%"P(,: N19 ޿]|9)9$`gh+6 ιGqν kX|dhW*oΌ{s.,(y(9"lKU^c[Mdy:;m_8RZ1ٔ+_ -Չ;{2|mi@U}FX0qd`sõ쎍 |{뎺OsCJv뽟:x?֓lMt8%L=ac^VOzX{fsOb=>_j`}}Jc7J#"[~/pνl6}9+8[%BD6/=s!삕b/ */~ι B[{s +u+G^X"ы@Mp]J+ </p4/|+5@ٰ!P*'i#7xΆTvc$s>~?ya"(J0V y2z}4VdX1)ι'^RwK{`}~+}^ltw%L>RhMop2֑v cO4ܻZa ] F6ow2sn2p%x>+pƜODN늑x?ὯȀQc9lzJ݆UW}nm]- [ 1%"[B("[=빒Fo䱖w;bKCVY'ι˱% tc#'Y\w7ӿ|9u'0WsCۢu"\r6Iܭ5-jw/d-[3`XJsf X8%9-ι7c+~~i1/Vms)a2y]"PKD޷QT`%;ت7ׁ!ߨ[==%8{$6UǬM_sΝ l= *8ul.Vyx =jrΝ?U\|#%"""@o%Vp(p6z66Efeιn % _ªJ9wp Mljk/"[(Mqx!8{ENs}5#"""[J""oMB}x {/DDDDdR$"ιaxXIDX""E~ 𵈈 6Dd j%""""""""X""""""""2)%""""""""X""""""""2)%""""""""X""""""""2)%""""""""X""""""""2)%""""""""X""""""""2)%""""""""X""""""""2)%""""""""X""""""""2)%""""""""X""""""""2)%""""""""X""""""""2)%""""""""X""""""""2)%""""""""X""""""""2)%""""""""X""""""""2)%""""""""X""""""""2DD4> jlh lj < -za"""ۀT&瀃l:@_`} ""[1lwUVs>Q<l6-um"""[T&7x(N*~r6|~.QDdPQK_&lK[M K^pDScC˒8-E*_NZt6)y׀lo:VZO=DD6^9-]K5*4#DDD0> g*KXM'ͦs7ulT%"nÂͯ 5Wmq1ެW$""ez02>/"M'뀩L T:PDd[X""oRs>q -l*WԆDZyEcCˣs"""+ɍĦۿM'nsꁪl:p3X r6=ȖDX""o^'$`J_-yfA#=X`Q*`6\eɕʍ8>Xrl:U+YDdț=`W-~ 86J^5#klhim'@ocCxEDd[R- }|(Fͦ]op`cS@pI6\;*T&Ww -j(+""[l:8!|ǒSwaW]U`=@ <}#NHer1`'`^6|x ljT&w~6|8%w2dn``&ɽ|0?ݚM';=/J`yU'JKjEI,8vXk|eq߃l´/?ͦ-%j5T&% UK; z*}9Np]T&׈M7%~k*Jы1,&"EQKD+kbk'bXX`(Nصa+=X0^ rbyUcC˛EDD6T&678WsUT&/Vum6lZSR,T<> h/Z{1}^X{ߕ\:Q3x Bs>QUޜO =-:bER,bGUY6=,;uZԍ G/ |k^[Z܇%<-"".\F`#ڱ{Ln4t,b8DeરJa.iUnKT&w66|V5B8_6? #3N=ɍMereɥSܳT&w Vu p싁5%S>)to=y˔Eb(|}Es>~Y拏%jjҍò#Y??iY766|b(6J 6R'lƆ6ŋlƒB TXT&w(eOUT|1KuaUSeL Xr_%ϲp[siwcS+ETʱ*.`V6k0]+Gvu,ϥ؀@ ,lBcCK98+ϏX7;46)ќO8,|H -]O&,@n꜃I)p;[R7<]+ ;h'vilhQ bdOer@e*!0 w]6lZSǦucYW{bWcUV p$, '6p 0*u`pcRMXQrl*11L5}U*gɞWp<8|,p{[xwn*"(%"V9 {uhlh5F/#G6{76tk? ilhW%FGaAkstTiklXRE,vhȠ*FaI1Xё֭xڿ{')XEs7Ʀ5h5c @3V-R5p;^æA3,z59ɝ7'o=zzRB&NFɭdGG6=T'"]w䒽_t߱;Ly|bYyOݬ>[-_Hl:l/u|(,H+- -x+wXPzmcC{;4`M\X;1^>|98k{3} -kmW* 6VT&6nO`(6]Xl޽XS%pQ6|XcX2il89XS=} Oꀏa1X+jP[MXtcmrXoJQГv,vE6<;l 8-%l:97+aIK창:lDD*DhrF`ɣ}=M[[q˯J1@e*̦O u76ڜOc^N@9HcC3oᰇ)NO4 -oǁݰf v_18-scC]-F7]FqtkV`Mg;i7a=m@ws>ocCc;Qs>Nlt9xk2Ɔ7"""ݓތ}w2sVGupzWU^%  k!*yթL.bO`:cS>ŗC^bI_V;\'lտ`S_T+ ).Y@<ɵ}ȦT& 4{oxlmV70,Hh[ܴ_MnXXl5lD,inm}MnZz} ;ec_92 ȥ;>cSE'kr>=Xgw6 +_B/e;-ڻ ;cXԃ dK7:9pVMLh_Us݉GT>;y -5 ;`MKGZhiw&j~lޯ:,98e}Ib*Vcy˦{9"" 7K͝L@B`dvo܈  *^zSXU,}!<~ ?KPM=tؽtv=zK`caUX)ŧ=LlDZ8cVcoj(=/n"l_`X[`q/C6f^ @M)=Pޫ1ê7-T#>}'-7nLerq X<7Ū X[ѽ]ұ >eqO\s -V%룱$X25Xh(Z 2o6VEd{`1\l⯀%6*EV}N6^45] X >|mPV%Fl.m]`7s/D#V#ߡ()(ɱ$$7zeF{Ne#wo|U>+:\r&P~di$5,ruXs 6+Y OxlYv/6x+$9GOq;&TZZ3[w&kՔv0nWvjQ2)mĈBy+xEp39%Ɔ/9R+Q'\m|vaUXi6klhV$e`s>qв껧ymC7)\M'Tʬ@,*}eE5=+:K9VqϚsӶ?~ş˱wa3U|^[Յǵbɴn^ŦeX9KdMp4i+i+VƆcx,ݗgUL.䡚ڃ{;ݏ*}"\ûJay!_;NxrtIJ0lAt=}[ q=^DJ`6M;[a ,W7vC~7 ##`ɟ'2c])Xu߁/a Mnm&7U/!X>{#Xok~OVm,zWĚܴ_:GTU-n+wE=fQ+`{qwW*p]+,T&w5^HwVwn0bnH5ta# _.=ŖOsvx^W56l'vX]~%plemwTRk6ch?F&Dž]cXgKN`퉭(AXp ȥvgv \׻S'g|^'Nv`J`# ~>Lo(TA(nUaw/ŀFőJӫ:Rrnr..z`97|.M3n➹f{^~jxppP n%HnþM/ htHoe3ul]G疻{ XhWwbe WյwU]vsh*y;̹û]6e=5Q}ԂXOme/'tu#ee9МO)h|buIuG5ځf-}%˰{`}7)63Xl_OLDDQXy/p&T&7(`oߋ?8 K>_uԩcGT,wT?? X+l,޾#l_a (iCʊ} x++}ıV)X-PQIQHp5ca%:{w^r刞9uޅRg9Vu!sX]"]wz*.9g+$lXňut>Uhu}s?vCCǦܴY;֔2 ݈ =}0JxV}gdzIҾBܴ;NzQNX)‚vjJFݚܴ!uZ#\o)G?2;^z+)TnKj5{&Á.:`,\=Y^7o/}d.\4"X:Fו&aU*2N ƆE%b_|1,18KܲժʕlʨPt5Vjlhy9X\AYPz1Ɔoֻ ApOVuؠaʱO.XSMMޱ_zjb4s|pZlh*u1?fɮuwe ?vKw.# Ǭe""E ,BB `S?}°!^Owm~+:5ܴ;luܴb7xT!P\,x[:9|w-VrǒORkkW}6\MnڵX58,.su2tR\S j(]pu1+㾧-\Ҷ;čzS,ˎy`v']Sΐʮi9e]iXttq"}g=߶.4}9V-7 p @jGyӗ:K bXla߫,zQcCO˱ l+y^EDd ~ XRؠ߇gbWj#R>`*N><䪱^Vvvv>5;Y}lQ]^,9b%T #dSt:b7Vꡘ"W'Z^ta>;9Qr< sX֐pz|ŻV8!A5җutu;zԔwBCYGOYOmŬ.emj/쩨XNKzz{}9"BNKو'^>{_t /׾ aB*M'[?M1aNj_㢥h9-Bs>xeA|46M!҃%2U0%ɹ1fӄ5xמ?yՁ!""!X.,ɦ]L>*t L. Ux2Fx`8X<ӂYUX~bv>5I颾Tc1)sPJ{f*'v`aUx/`1abd5c+!ƱW %|kQUrm5 #+lU*O6|ݪӍ -K[vND-QKP9uz^'yrU[^ F]Z5Պݠ_)kǚUƂ(&":)y9Bxk% G[hOބMMaUV.Q~SѶ>xOX~ B1S’T`uj,}`/\S6M<[MT):(g>oz2M',"NJ``nh5 ?]G,Qd@ V_ x'V.4VJ+6VSrOAyx}yWbPZuU{%.= * X$ vh+GXe^wmōcUQ@썮ocZf֏oy+;*:➫vUV;gEo͂Uvq0Vu=ZpB}e$O{[0u;[W|Քݰj{R`% UfZ.k'NĂU0~8X95i{cGa?GR~~~~&6;66]iB|JWQm^%ktcCˠ9uڽB+?9}=p6\ Fc}x 8aI^ET&T{ޓ᱄G+HD1)dR?L5VbװDQK0E 壘-AƑPKcha}x_cׇ)bg-s8:b߮aap_kc%Ņ:yux*M'qNǯNerjg-%/%dЙg4i?nJXme;< DI&Xb$lh6()}XC'6Wa@7š |k"F#g[gXaBy,jyeمe]CʞUXP%r^lG} }Ԯqw\=t(?+tx*p[|ݫ+X#\:ΊcǹC_X1C&Vk;G{+㱶C㮭|u]\|;ƽzVnv V)6bH L`Ɔ6>6Gئ SGaiG?Ccz#3&ƆW9a,gyObEx';76窎Prm$}v*NŦ%UUߏ?Gb&sh,N5*TQyxx, Vϓ(7]*k¦u`aəPQzqVP+$݊M+Cޛ`=:^a`?ZhbEaf &ZlmR?rl=EE)%t?č='=T:Q_0*lMuG,QИb006l+M:Qr8pV͵sxQcKJ>b5\p H \SU,:vH0~Ra݋()QMtx[ M<6U^L^?ج|[˺ ޭ\1爝=dI5x[ak[hvu#0ܱzF7Yv)_Q=|A텪a=='O$,W <^XWk,-6h5S.뮼#G (mmǸBz\s9`?ۻa1i'.llhY?Nun_(.ǦI."" mqﭮ\sƆK-{ ]3~;^._V z7EAmXg>fKd2vo%}zZ;XwJτt;vTabSńѪ5+VRV}Oc |$j_וc}X EWxEs?l{C)4[ ,/l:xgؠ,Ν ܺlj4gDK~G}3?d¢x1fE ͡;+ [&]X)WX҅Q5Xrl)ĺK4ccpYX5F.V*K/{*>#Ut]+ Kb}';ZT4rW |յVŎz&;fV }u>kX .GJ<Rq#GW_16T,k_(/S_ދ?c` UXȸ%N&5xo;VV BƆK7ű*l|紴ϗ+9sܚUQW`Ɔ[K3 @ 4n~ lRX8j춽S3q=U;q餡u˦RуX5~KL¦X.<%NW)&ʰEXR*U}ׅx}҆=XU(V9QlUf vFpiը߅}RE@\ko*vgb1CE*l0.oe;lr%V=^6/WfuZ,W*dZzޔVK)Ćc7Nlr]o?F` FVϑ.l)jYCY{4ph8^'bqyXг*\ӨUb݋2Mޏv*{;_|KZm«:oA–?^ Fw9q'N6&?XUN3:d{d| uywW^]WV/lM',yT&[\SϦ״Ջ8KF.j'~ǛќO~ࡷxYs[S(XYGb XrPnuLjc{{-Ç, t6c@܂hO‚X_ VKDdupꎑ8 ]uA/޳"bIocfc}+XQ, P6lMer*qX׮F:*J0`c҄U{FZ*X(:^^{Z`̨_hu^ؽ),FDEVPl=Q*u5-4"6V܃![iol:Z*-Vu3Rܾ?Հe.kDWaN7_c?/oBUjjXB* ,X髋8jIrH͒9*(t-]b~?c$h!mWxze&}[Р=%Inɦtr1?-9lq2D`VIg8k(ٍ'MLuSL@EUOQLk'kc!X >B19XѴݱUQ'<'jN1atpx~*a :3ϕ7g9Ier 1Rt-iX[6LnVU\M'_fR Wk>\N 6io.~>V)%RJwc7H;ŲtGP\0rh+q;a\c QqXҁ5aI=Awkf%,/0 IDAT+zݡX0K Ò_WǍ*ma#mw`ɋ*l$lppvX߂/c`SXߢT2MnZEսV/bK݅˦zeIߜ o>BppFl.ȦD}ޫXq ֧b՜O$;g(56<ҜO|{W[UYKuS_ aA":flxNJCb# .`ߡXv.Ɔ\St(HmT6\SLk*)L%PM/K=_cU}: LerX4HOg'|^/~̓eκ )*vش>we,n-l4ZH(USLX&zõcI5=P1>a1e;jic{}TY >d7>a|l``_ƒ^m@e*fH lI5 KNbj4lN=EX2X݀Nk K{D#[5ьVgy.LFWl9bSbX )/JcЍܴ?c؍pn,HcIz;,c>Z10zdy n^ױ f4=}K-[4x'FϽ ޤƆߕ;+' ǀ2oP)lkpz=Vӄv -|$lehwO]ǫǍ|chݒ{o`S/N~_Вh'&qHq~밊9UQ*|H‡lB2jzv/{a`1ԻJ'ĺ#64> B/Qh%6YŕX% Z`)nU(m+Jcv잸f Dϩ2pj,&ZIke2VY((V{SL|bqltMql U%zcnŝcqXXϫ X.0V}尊:,H \ESV*<g2X7K ajޟ`s "/Ы :MnX5TߕVaF+6q6ؒ{aX#a7컱J6t:KGBU CbP MlC|z&й[> ,zKªp_n{lj/~ֿeL=K}Φ97zI:)khˮSerf:xP{אbp_t°ru^ |K>| !,lw\uwf7=  ȗXP\6,(EEPdl(Ї*5B oos'F $k[> s 3r8ԩy2Wxw"N7yP;Q$"HJT>v<&Sݽu6)'ppP3 PQk:s d0X0ԯ|ERl3׌&ml[ckC_ñ߈fXe>gbw ✟@f|U!>g{37(; U⋟FGOĈ#zg`B!| NT# Bk q蚮׌?v//Ov9z,nIK2IƘ}g,S%{, n"07?WL}5,nӉ#F0鵮翄ճa9! c6HXLXٰu!8ʘ: U,5|6m\ngKO'}eϽ+2I> +=#X5¦9u)FHT4֣ITvI4ףhFB,$fII_Bj T-E$`6cƟk1aaٶƌό }4v4]kpz+F_=q_sЇvJ@f_˅gTdލ!*3ז,Y[o7r9I.~dz%IFeC\1KY I])WLe3¥a/l4uƈ#Fm yNQTQ҈ۭDO%jkAzF C\eD+n1Ɏ Cem+M-l_)^'m/Rum{zr+ٌ.ۘͽ Yo.8gD?q^]rW{YP׊tv|  "^Fc*M_zmbYn溞qs^wF*GqϦwt= 駶1bĈ X1*8Mh6$ִ!QI8M0 !g7$F%|:,Fd%}e7Δ*P;J^&Q94ߎ&jTRu"[s]}e+*]w-~v"fuf\7lt7 OuQVBٽAw=zNDQQv 2:Ӛ~ >Wgň#F'El eqϦKGũ~$:u!.79HXBbRYeW3 hUֻlʇOnkKԔU }z}&Bp 98 Ky5,wh7`zF׵\vḫ{8#uyxoL1G+NBy[!W ^fI* Tt>~$ Y矄C;8K 6| nH`f ?gӱWk[b+ֆk~8 M VmP4'NjZW8M>u{Q:y" P~~Dpn>c¡h_JJ> ~i 18`9@Y@0k*GAYb_JG\F܋va^0U=o'_5<&|礜s{9Huw@M>ˬs"k/V1bĈ>DFǣ+QCQ q#UFO@d){?lJ?l>N[6]F XAg%HIX⨽hv]CCZ ͲY`>/5r9'ۑjT2ױ\Yϊi#{̯>3vy!DU փ?Jg]jsՐn2~." m2Ñpo X1bl[nCJExH;EYVD!/Gz-NS@pB.@Hp%وS@Nq^HMPFl9hb'~[ tIU}w4Tծ1n+i~eszht7Ìќs WLMAblA `lHf[߆nmxK($ GW0o!}1bئ 8QldF3Z. Mp=(c~fE16x؇{뉵²]|.VP`~z};"q SVK5ٶ5C"מ( hFa`umW!#Pv;]ĤaIg;ö[vV(^q $ms31Z}OՎak# %l bj λ8WLmgRԩF]Aُ1bĈc;TgNb3g] M\ ߆!nTk6-GD @ |6fy%Yۇ5,5b(`5eh@‹NH F] { E}@°fmՠPpsV*'hCgt/% ۡ1ز6 Qf Y[nasK&S3fQw4p6⽈[a>} fIfg7f] e9d2$5>\7G?4\1 [yX{ܭބ]&?g=&Pv/(u fnEf&>&]PV474vR ZL1)KQh2ň|LA9v#"\ÁON}pΝ]}Wu}{A(+;rwpP49h]Fa)Mz5CB=@82,{+d5"ne\1ՌoV1bĈ㝊&ͼ`-!h4 ~oGbĕ0_t Ng77  S-E,m9a_#ѲHm9h~`v40@"C"٪%u*(:|{ }\ r9H eh"$l1=̵~[cY&+HTc[".ƜeQcug/k\ϦM2t?A7/l^Tllޓ;%7#;mGӔj:g8N(՛m];?D'hGk u*L$$5h[%.AҹHp9?GЄ:?&/Flcz~߬]d(e6 %iKt0|6M\r$EU7ck(8-bg%"ޠg(}kóY-07"g _nj#F[-heA K\ϿqDFoO$@6QЩ [}fl =yf̱z0iH&,s"? YߖFc@e(jS>BΒ@"{Ț– &آV xҌg:֏xP@@Bm< tQt` 3?8&XԿ!~8|6*;|6LcHX;q2e`{3U0_-^܋vIl'*#%Gr>lwk4K$Tբ` 2c<5Zħ1FެoP]JGg|EH8|]H8ЕϦq/t|66Ggoog>|6={YŻ;?)n1blψ[(]0տ>;I@䢉}M/lV6<c42lCK&֘QyE=ZQ2-NӍ(kj4(('3,,^iZZE;u?+zL5c4 ]A@;a=QJs}Mp61֏98N(FZm#=d助Q@}&Y& $ s#Uvسg8*F1b. f>Qs< =MvB((wo>~#e(gg",na{"z>RӐb30/5G;IGl͈ H nJ1b8nɬo֭Xa} aZs#PFoH] ?f3AAAKgxWHQۀr >u^>~Mo1ۍ@,39?7KݶM%#vXU9h  <"Dz$ pP{rI1}\RŠe[7|Σ?7\e Bs/[%^O]REW蹛l^FPg Ig犩LйA+$ 助Q4LC^ܔI6d4s–\1O&YS(w zP@1bĈf](h/c\o3/݄%oSQ(#h*{X0|7~>nYm2vTvavvknlQhBST 8L"b%PX¹{1״XHn>y3U0v1@vV(Ñ7CVT)1lkRxu= U@E{x.|6=H˜& ׹? hg #kx9v?rŔc0L6ةoƾbx"bl {DL:8MQPM|yb} DūvqYJNCL.*H8DpDZ&!B6H@MAF-NIAk4WLyk͹ZZo4 8M{$Vw;{*j*(e"$½\.O-m*ںa +OkZ_O[yxUBEF R263C>+f!ajbjO# Ո('P$G(ȇf(nh}ng>mHG/v&̊@¡50z\?N(FٱW ftL+k6_k~lË_|2z?tƠq[e1yxDDWr= \?7M2~oQnE9&{}_m\w!~+FXUddhq@)F摍HL&d<r*[? E~&d}a G"|M#Fi5˺QYߐ 0p"hܵ헏[?^]vS V>L&\kkQcǶG3PUNP^Y-NSHg-n$:I*L4WL"wŶ|&>@(Ђ WL$ '3Zk?1WLEDFg9l)j̄]_6o”_"0F1b Mz~#p%\A"J%bԂߣqȮJ +̰5,$gl';5YDw#0>S^hdeODP$j>^PH*CYM22Bn.sV ,C Yv!6 ge۱*PeA=*[b>׶Ƭc2Y]z?1p븞_l>A%u6ʜL7Kuxrjcݔ$776hZ/L0}S];{T^;kWO3{7~18 1~8M鈼|#@4–HIJ%3w * Eiyg&Q5֗Lݍf5AsCsЄ>6va]+Q \@y@ِUmAކ!bj"ʖJSY4lg+IS 2)d}&5eL uU]da|}owAՋ?1bĈN{ 0M_f=*X>ϦͶSڽ(ytV+uefG"S/k%a3,CfلNDrFЏCPi>4Fl6evYTH\f1W.#f vUf:B$ 5"N NϦߡp=M8 q`ޚ% WQ^[6dȘMᚮDN۩1F qVmAi)@.בg%64'ФE>b~CعhBXc~,9C;i:9h]4Z.kZ91n83n*[j%Œ[{֭_),,Dk@FY{f 6Y/OOCDv%D(j:Z„Ϯ[9F+JyU:~?یbĈ#';Ͱ.#ӌD _xb}%VFr3(3q:}HY8\ԯʊKC8}@GYdH[ @_As]g>sA2zdUJ؃eϣ q^eVN(՛u2(~5UFhܾ So^lWp";dns1_?z_]ќxnijA ^d܌74^XOʪюC'!|6 85/݀xu3r=|6b]8l@od1bliZiq(EF:9E)h"jDz|'v@8y'dhisQ(}4}MAY=@UӴ*'9h}y\Ak7pBt"-;hķcl[ r!.Cdr:pV&YES_$ +"YVp( e1?LSF 鴾'䊩;~f٨Iƍ|) 抩yP]o.\1bĈˆU7oϳnD֋Halӈ5 7eau"VCo63`Sad;Yz>A(ٌ(pm~ GvEB(I( A%e;)9ys99H0j00l(ɖ6"߰ǀ$=, ؎8K~+]Qsوym7C E S i];3np\x)M<*Q&K=Tsi;Qf}9kElfy5z|/M_=OˁTgNu fuSDc[_$r&êi" B_B5U(Cj:!3OAbi(rubbT z4i[b/ef(5kUJD6jNDsz1^# yVQYssIކHdCrvK"$RLpS£^_@$87,r0ݐr>h}/WL}e7rda0cĈ#腾 &A)4]:KJhJn;]5^V"u3 >׉D ([P<Λvne=exLac!Q;Kl7YWƷaT?@Bxa{м[F$,59N$TV"qj,v0cwg 1eO1%dnbK[X=hNu=yD}u3Eď҄>\mH\:~[So >s'j6 w/-?2c8t g}lI}Za.KфȘFv ^ؿ?BWl% r$m?[c EL 23C#lQ2ԇ&9hl a$n Y^ G;+:E~/gg% rg +UdayjEѿ#<"_BiyBM$ kLj/b1~͏VUU$:h U,(a#|įrbQ71bĈ}0w#jag<$ Dp0q[LY4odph+HG|0 Ǫ]Vݗ͠|fGUf6Kn?֤ͯ~ެ Vtd{+u"^RKh>\6TGj`5/vHWn;9l ݃ q즢 SpŮH۹kaV=V{WؾuSz2s(̎}loo$*s}Vjtks/)}p=kl|6-sMl;^s=fob+6栵4]& DQsQ*\4IVFMkfH8 [mj8&$l절ƙ(X4wɶUcl$"gQw{|nT"3daLpkd["S!R&Y.FӟoT(//s1M c/-F1bz~pZ!M/fϠz௮^w!mO 6*KjZ 50fdaFMH"լv& uՂW?Xekh85F#a>_~+F-2[Ϯhv8bpn3ΕÄ Mę 36NqHȿm툸(Mi\2zEX/ rϦ_6wgKky̮ΜOe[Bw3w&zFE D5_c !Mwh-5\ϿwVu`9T`ǾχtϦWwMb^0I`!Fc[D9j\]& 2O'FQФ6VDY?'D .,R4}>CcوNT |bFW!{O$ ˆIzs@zJ7^M+aO助2R "LgEtˤk&W%(Vs#rKK("5egqY(J[9k䊩@sW+ƆcĈ#PpfKza:]ٟܭ@sr`*jClќ6`~2/EY2UHYфG6ʖ!~ga2ETК\G3夶@dJ՗ }\hY@ܴ @؞-G=ͶHZx(qZ{(85,s~+vHE!^) _1yg>ufJvBU\Ϸ gmE`f (ނ!n?P1h34t;Mmz"M-EtFx6ʴo4-F6(Wkt= zqňNBlcCt>lzmǐ@5M/Gs(AKӴ؍6,"$/N,o@ךx!WL5 V!SDd!ەRzd!ZzD}}@/g;rGa:"B'e}@Y8κHC(yńi|fSkܵW3Dmx}oIVo}?-p*jz{͞ޙmζ1bĈͅM( e\|6}ff$/GY>gK Nv#v+p`ІYGl3l+< )+X5Z?-%DCE ,[֦v /l&VގBtV(+18vY!nYGa6įjs Ccy$^ lfBej}ϐ y^X,OqyΓw>CzJ6E|6}Y *y}{н)M:dߕH`zT淑q5lzF9ev]2ҞAt1МϦ7#cؖg`M%$>%P4Qbp4Bα,I -NS\tQmQegQ`^QVݝgt=/yUxϺ8v=?Z/\? e18[i>8M9mpBMkZ;ylj` \G"8oPdo&pv-,ds1,.$ չbT=$ Xe$ ;ȹzV 'o;+t0H}s<CXDn.$ OW>W8k]>zM1bĈpܮjψ [zQ:!(c˕#VDevZD,[g%+B=ec6YB/E( ו-糿'TNbpƔӹvNY[7e%shwD{L1.!,A%{khQB%yQ` ͷVTۇPCh\$q3;G͌ %4ygp$L}TP~#&[eEBے#T\ϦL3u1yw=0Y*m.gkO}|6=z̾nG͹-a0nD&^?7=g"3ǁ]ϯ2۬^6Xۆ:c=~ 0t#Dt7$[X͡9h~40D`lMφ0:MA d J[B/c/FF8M!sؿ,Zzc;_gocߣn7tg2ubBASZJD6*`[-SQ=+{jw1lHLнC-Pd/F+*Ptrdq "2>$ v'տ IDAT3\1us4EXEhHT 2BYXmh>Dߍ`"iѶSV2vZ1w(助2‹&1bĈA>ޜ ]Ɣ.e@q+sFƕl)ԌQfH5 Vڎ{!d9Z}`ؠ\#(seU;aVܲͳfلYXAY%j |jn$@h>܌ˊY}o[:9^f|VP&"ï c ZK}+y;Qu=ʞ:, =>ԡ3hIsi("PZ#{ An2a9LgQ$L0~ Ǻt>^Ϧu=s,Bl [lz1h?|> AC;MVE@Gkq aF;ѵǡݞe.0Y\ϦEuFr6`izn&[H<+Mͭb.VmA-N]!2SYie|a \jC"JhӘiCi/7d#WLW [?3WL] LD *䊩Z?',D#z,( jg/L~? hn6)d+Af}d}ǚ{+Ћmmg[82iq^H1bl0X#^$ދxY0PVoSDYʚW$TՄb58O .w4KRIaƷCd=믵]oYm0c`Utc6Dj{}H[Fk`Χ FK WDkRLG{1s/>k /fEbolRc@<k<Y~ĩM2`_Gsj7c2TqIVGXx \ Jx6s 9+\Ͽz6g ~$ݝϦ_4kЅ2n痣ts/! z6V |6=T>ɔ4Vm2|6"SVz]f!;[ X1^b+;A-NSEA>V;`;,Biuq۹ ףq&J~a#~o 2u=#-9 5WL/,q&YxB*p֒\15uHL}I!r9*#L0T] 3&c/|ؖ\,$ A>!BMl6SGߣ2«"q|N|D!4@m"InDoMcĈ#6k4F h.B6i!QᙄY((eAې`M <ۑӈPT+ai=K|D0l-`hcefp3FAT޵/fN֖N";0eo\$h$̿6͞xsm#4w"H`Ku0ݸʆyHl b+; {gZ} RT؁EڪhZCclapo`q>ts`D}n _|{=LI8(bٞ+~`փYms!\/B{抩k2ÈxFĒډA_^s_ϦEa#]ܝ'<؍J^1c<{OepW:EEhPΜ#FL[~>g-^`Uf,;=^8ԚLj#Fm}W dABMH8 h86ȖՍe~KvNI[j4ZYc_%טulrٿhցʭf gfLY3|nnޕmJC5>aya-#s-UAB\h.e_+#jk*o;&>boCls_3 oy GfEl\zy Gkٍ.8+H(ze82܋2f,u=lʧ563up='нqCQf5d]ߝk:A%sgsxu 9XYf= =A߷:$fE:C b{EwA^zVX߶嗀?FA,`n` Oz-N(28 8MoZ_edc8Bq=lƷ{@oB$&Pr\1u'$ 犩S2lDzQe(*x֠gu{c?xMl/WL%PqX3iӟ|p./MG r=4Z_(" ZE$fJ?8jO\tyڻGG"V ҶoD#F[1\wQ̎g/v? 7T|!!bُ-߃P%w/QQv8@Y>f$fE3 ?Z NjPvlB̖ 7ώsjb}!X1}6*YvQ<,ah7_DM%!|L@,o ( S}ME8k Yj/"=k x_5qkLtW4z~yp9'M?YSg0Ǭgӿu=csFYV7kӨ@^35 /L$5Iy@^͌d+?2FUcFPgι =s{ףC朞1c?k\/FFznߋF]"1j%l- ~ X1bD`ZkhȦ1\LBD&Ji+,1~S,%ےÈsԑdaNBhH D$6)2Wr(wH{ؾq#O57g=^xs@x=p1zD%CQHXD^iJ y]z/\R㈘$s>X2–2ُ#Fo>w܏D݁3| D 注H5M$TGVL̬~7EkI=@-hF0UH8C 0gl1HLx́6+ȶ(1!t^9G@MU%4qD3φzg" H2oK?F]і3$D툸RUmB\u/aFSύ@b3 }m(K;i! e*Ģ;7&Ar l{>ԲG8"^Ҁ@x:LϦ*Ӧ{y^dÐ7Xf/1H0,3*[d:z a `OL B1C<.Yoq&k`U>.oO5AV6+pgJ"as{ v]j>^Io"b+FVeѷq\f2pB:u9pDoYUo.5-b(#|E523 3b(`a5+͘rg{Kew!W'(V\1A[#o}'¶~Egv (*<0ǽw^0f+>o;&"d}1Db*F1=|6Y|!3Y^v0 ʖY_&3Eye3,B/Rz腼 V(sɎgH%寑W*Uœ_'1-K!4~wDowq#~b]n(d)槚0˖NȌ(kǚ[L2 a)``I7aI(Sb$4ͺH0lA\:4ĬƠ,̵yJͽ9Ϙ<\? 5~97 ?p=`)a_ʌo; l2.6L"g䵶Y~7q ]fK-KQ Y>Ѯe*l:0Uz4v>Ϧi7{n~_Zob+F-Nq:z(4qi}9"$L;:D?g݆]MD3]ֆnDEC+?ӨnwD̦֠ˀ3B=Vs3ikf>s-@exMEYz/x)Dat?D%z,\Ϧk_n0@ayIRV['l9dv%9Hh$ rYū^И0^1!4b"EQAыJܜT?ndt|>PSy9Mgg"l? B QD@3L)BEގ>? ˤÒ᧳Ha#IyKC[H`nvy'twG?.2K/=߿#[:o  ;6Mhg5Hހex:8?4xxJEŽs7E`b\&ftdI m="NABȏQO~W n" R~oBΩWЩg"U6 Uȩ4TNj k,#R𪥊abWe;߷ iF! SCJ?WN;{GA$ ^#B_.,ѩH"߉yɶ6196F3kG 9e;њk%ϯOgI;DDQHQ8ݍjlEo(S#pz:ߕC($t6N|#±[ ;).ci? {툤icKD : p~ٮy+䂵Q{P'Cdczrߋ^tW?ۼgc%]֏Sl7{>`:"go}M0u[Og]c E\SAml~s.k,|ɶ7"cs >D D^*+,eR( rӎ0Z:ѷ2/v|h=VhmdDdEϸuG7 )~ t .eR;BLw?*6ڪEs'pK*,Iݚ$H/(4˕Sra+Rª{Kţ>?h뿽khS?"PSE .h=bJgFJ! -V&%#!l*DH﯏'B*B kLJrgIRn&"IR*2榖L^^;v IA:p5"Ngk岪ADG ED`C@ZU65kHWQ袛M [=o)Nfڵ@QՇ'у-pdⳇaa@UȪNk+A%P~ ":lrR5'Bيb)89ܼHkc}`^Q6V7l:k[MC$";.\TLyQnڪEjVP(wټS'! 3sg8:c;%Ȗg H"@S6rJ blw䚏TGD: Gzو 'sT=8P0Zh/B+^rz-[zL׺?f[nAl)q2ET6p|:*[:?bW"r*$ޛeR\2vzcȋ457Iem7L8uҪ0E>Ž_iH].Z?|ky|8Љ\~f}ؘZP HlsGi@ASs7mNoB.}s]Rp8">("mmLELiI۸u2p2g"w)} К?a= &D+ -M=h3f܋ӷsECȹ9Iu2u(hg"٪6'1r`PuŨp? '.t IDATA7?R=;%-wǹ=H>T*t#U;U ^:Hb:"R=_5 |x3"#ۮ֧O|D~JE B ֏:+b ӶQDpqۺ!z͉SQ -Wh!Zh/bYQ6,+7!8_N;Iza9ږZR*DaP^EVCi%p.@ 30 Fu8XmoCyƠj[Q(4AtX!l}9߿alvf0G3){'x+ҸoVut6?tO+j!ZhoL{K#v>$%/k"0yp\IZnT?5Hr1#G )uujXYϱkdm `UDr9F#F&N_m?G jj]o?,hK"p˪ȤeDjFkhtFl-fnh98/woO,l~HfLPuCg7eK -7f󊓭2R:Pˤz^Ht@kI\&]cLC]++0Rp{͵Bx@ /!bjolmA (T=U]0˫u9Gˤ^ds',PMO}感<PzZE=-׾^>غcFՎQ}l o\2i'5vΩkF+K_zbmHʕxG+~2Ph8Ço[Žzb>Aj{aG۾ ?w.B b ;jq8h9*ˤH 3&'l>.j$S;zI؃pS` EdS6Q5ZDh#υIzHA⽐)kש\n/6%D8jlMnBߍ%s7Šm6 [ Yt64 Qfm݂m^7p(D 'u׮o؂HA;v]o-q j9Zaaj}4w#BNua=)o;eDtPv s[n՟#k;Z7tn1TNnDʧyw&"5W1~{9kkZۺ܌R\&U6Y-E2=eRt6RfkݮZh/B+X q-~PqVAyU+-WHރ}fei!_K@B kw;rh~`w "cS(2ml~$J?t:5[?G"$d=cmD>.Ƞ pQ ߕh8f%DS; 6/{HuduDx< 5;_NP rJ!%"Lov*YEhD B{ӏ*~7Vނ6ft6MD rS47xH񬍽7h9F Gr.pL Pk&2{-l>ˤnyZj}8 aJԟ<ֿ< MCQ~Z1P)<|/ MIXv1EZ}+MbD>8SA*GmAdױH*LuҘc\Xܷ5I7 8"!qv&\kO@A.Db+PCp<|El>j՞vCHVF_ mc`[lm@l.G36Jk>$JcDt6,0Me Q~zCڜ7yx~y-Fs/n];ܺ1eݝ=cL&6m1b?eg_Q@t78.7uvMX߆YSRVI ojhڛ}d]qXD~ȄSJw:z\\y}aeFݗh? ½*hOrz7}9@©8R~2ɝAɑ [=Uj)[]ʃ[*!R9f%"]ލzlKCAxoL0 y8_H3(TvM>aS{HTQ!q&RD1#u'",Da~ȧo|lq^-}oؼ~)_g5q~#Ea7>F=/EM/yWD!(R y~t"RPH܊pTW3ƝB掶ykB ?jc !5^é^XccwN݂q#} xX vawB %[H`ڛ׊F6l|*ޅl~ $n}$H݆sׅl8\"ڨ#oл^@23P.Gy/I!UW{ J1{6ÑyӬ$/4oKV#/X4Y0amCK@9eh熡~?鏞v5;S7t6ߒˤ:7lчI=Ir_9SFo6y}ȣ< 7ChZh/ٺFlCN("k;Q)ɏ^0ɃO((FDst$ AnBLUY].SyJN*$% @i6r* \ " n: ].6uPC*!(;v."WkL@.DyƣO9NîՂ:1܀~Dn.B!{?Ak ]G_Gh9 v1DTNON甈#"T\t׊C"<ҿ!>d-{׏+i(} Y#)W((6 r C{u-$B ujeR^\&=\.V{_PeR6}m-H.~&$pP`7A~ L(=TyBףM9sPgnCw A5~f"qKv " oz9-Ž!@wyE~ʬdhaU^5E CJ['? c¹ Oo/7bU@7ngy.]?'Wk}_ݣRѳ{@؂[kY1B -gLjk:_TV\&5GtslfDF1'H,i-Ij z؝b:|PG\s4][>H9x/0 8,:s9$P5UէAeS2FTu(ZNM`3RDM>$ 6F@T,ΎDuK*Y !ID"D&a}x_Uź ”.kk#Aki9> WȘdp Mfv֗~7fA!J[ٱv*oGt]anB_k/uW "fϴqms&PN U鈤teDvrUF~:Ai@j+PtX|Xk-J~Z{ ]nߨB{,$B uhlC1?6sԣ|vMF)u*Rq.Zm? <`E !6ccVmr[OW𜣯Shs儉2=ujl> TrTǑiYmp)px1~o~rx:<2! =&Îq2.,ib{ ݺa Bj=w;nm8Hބw\u˳UDNA5WW;PxI#5{=hTFhAG5y?Iu!хFnv#yŸ5"6)ȹX@7.\0qKX{ӌ& cGO`1ª{Z?HJPѲjf>A: /?pzi7 U]vf.;Ω&ڼ# w7ŵqإi PV=ː9k&T[rgk24I*Kg{ 5eRO@)Im'^<\߸ᇹL׺?o_ݼ#Cfx[}?VALVnh1|m"B@"I@@A`'^c7.DlN k ̮SL|q/eEڼ y$W06$/nG+ګ 8&\Su~5t\&jh 44RŽ}4<^_[0v;濐g. \0ϸƊ{yL/{Ҹn,[?Um_;o:nxtTY9f$Hy ?ZhY}Q(OC |/Ixz̔_#H pk.JgA2h6M zWŏ ǓJ~ewPޞ~: )I|XK@G*8SJqdSk΂@J&2T.O̽hߋĩK5Q۬?quQ]ÅD\wξcɮ]FXk""q#)t% [;K{lȁuAh8SmLz+u*Zv`_#ٓ:2ڼ.@sIe;rކɈBDL Sg5VEHpI7Y{ \~f~ZD8ks£c TbϮӲY.hm Jo\䜮 v3"c|뭍ϑrl߶Ot5%pe{&mg1*sY b]k 9<ڱx2XZdlt$e^ˤ̞P4צ IDAT{öi;ʕ_oՂD٩%#/6z>b:I vfEV{2Ahi:\69{3?5@H܁*oX#.ݧғG&Qٸ}Yzf4R}23>ͯ:~UMO:fCg|-Wo{Hذx@}3kښN`+?Z0=Zh/|}=]L@,Q7MYhnN4vt6 l7ڇ'}sMCk[K2~Xٵhޏ S=P(G@8 , 9윲ܵQF6DP>G$Q# gl [P2w!5>Tq;"Fpl\eUJA`8AR'Qm%\uWY_sRwOZ~*" *AxqpZK^Ȫ'͹6ì=m69a^WOg6A"ףMR "Hy/|y'|a>DGdA"8JAwCN2R"xFKXHMmAև" |_>\QD\d)nV!݊N'l8 6~? #l衜V{)L0@y.ADNyGMﳽR|} ':#mH s!# Up"EAa6"H)Bcʩ'5sm!!G5W֧֯VWw9̊H6م:b Ƕ}6BYW'o;CAz OZᶦ{W& nkӶ/#^B+}4|mה̑ۘ#;'W.}jy][Qww !@*j~hszh؂BXXQ>:m҃6 K6mV]h#\S6M#OwF 'f4Vǒ >y"| "6Mх=/'Q>Yڵ6h\<I'>+ŞH_M]B(\ag݀@C/kŽTsY/Zdٺ7f=-]7h>)kk1yC'2rǟZhosLxPi[Yyiw4o}b`'Wq_"k-F[:O%DDsaG[$ 3 -vwv[{1=c˱bs&gΦ?]~f>S;sT{=I  ۩bxszH'l/_"Y-M/AϥL&H: m:^EXMu q![mG$w Qt{!9B#O2ք0J;ׅUl̅=:y^+ t/RdǸ D~ˤ=Uۄ['RM}>) AbOvr`QEZ^5"$Tq%9ji˞MC)[^D쇈ڶ >o-HOrXeID+mmщpWXq>.|"&HނȽP"|G9@w݈7MvB^#|])eM6OAas+">8wWx#cOjHg2-簐 Op~SKOڧxQTPg֜gF93IuHsoxT#цׇJ~m؎pI`2o?W{=G.apo$7t6y' -X'UFޥF`h3 +F'TZrbَLIq $b}܄+ꗼHO# ؜\]umz· [Kڂy>|麟y]k-жQ,,uX[}N.?:6.pxH_U#GV*;>\?XzԷ &ܶE7o15[fOIt׮ڴ~^nC}p0ƅZhԆDKM'z\??Rν;\hgC+}/x}qpHݼG=m9oJ>aQ2__2)G:FJ(p]yT{7#BD{5ʇ#uBv%"ȵ =TBT '1lAxȵOtPcb_7s&QAj^~֗^}ASNu5lb]ebc/v_B#ZU6 ?!2opU>"nE35֞5 Ssvr r6~pNӭ^͉rc[zfDb\m폴]オss Ub56Dx~vݏ#3Q]v.PNgn'( Hʳ ) o[ :cU9pN1:bs6@]HAvus \p f"r[ |ѽrOB k .-紐 %O:ڏA_uTWhmvxO/^1ob6d7/ڋ?ߞˤ6@[CGj4py+L\&l:?@6Fj (뼌}\5h#rnǎA%[2! `J:߀9#`0Rs CjEx2Sص2;0vtfvx[?H {Gxo@ksHgPlB5noUGsw[w]jSk7?lγ]UIHlo pđm/D`mB^ܳ:h< ILghQAs"*}Y@wet05'e3ڀx۹Lj}:KDr|ErI]͕uKh`t>; rwM$Uqk<Ʌ"؎Cc+@G(ɾxB`!o$v>^E[Zdd^B$R\a!Ç,~m';A^ +K[u>¢?z}]ы F#L&PmD{lGȷ " nq9S]2\QNcBGuk\hݷ9\oXgs¡.[m|ylj:DB[%su߉e5:-Z}J!A!˝=[܊UBi.0eky-ֆ.o^Vh/f{#e3;=$j`Qʵ'4~{G?AeR-I-NIgD^ u/PȫBV66t6?2ImLgG!rk>ˤY$>ΟɮT$ocX؈Ύ@|OgBgdҏq( : g叨! @J({}pwu>./G6&EJjYG_eR*yb8ٷK?{ZFo4P}z㎙|#9 #[yǔk%.e;{lrܵzĩu'cLXO,0eR/:L:?n#F ڴ~tMXlZhof;`: Hnowǒ˻_u/jО!uU#~l}4_D*Tw~עއ |G, =~k" օF ·NTwU'vO ң@Jk$sMEy9oT qX͗ {7" tD)"2=iMPup3r m@F窵O+ 'G> zrv8"nX"g աN_c:vlI&B7&e>eE|U||Ꮽ]t룟{GNu73}?z2ʍ?eYKrvo<[gŢ./^$8>{I->c_ŞJ߄F| Zh+ R/_ju2[qz/Lg!BĩV"l@ Z>@qEj K4!(NS2a]=NT:NQ:PN vmS+i*U]CVwLD~y"fE8ƺ$αX)QwZ)xg~4brSnYdǏB2PV7cB冡$_5JۃFÔe#0@5C S_ 97]R §Mɑo$ bձQETpbׅ"[]lpGdE k8}wLUDSȠmֆ;y{O j}YF`Ɋu^=EX '@@nb9h㬷c'!*"X",ߋ#xA.z2}ϣg0 IDAT;'*Ƶ O@_}("OyاW,.Is3{q F7{ LAs]bl>g]S_Cqӎo~|c~/+kYZFm#m,V<]!ɖHqP,H/{^JE5jȃU͟^o/"[vΤHb1GO0xMd^v+}_Zr]N7I|*}Zb,s&tz?|yq6bTw͟G=/\HEiMB."`GMmaB/ٱPnϩ'/^ F3@.E@ !ЈY@^%cj]dscֶŇW5DD"Q~DC;ц:=]ݳ3q!MndD܂шP"G^Fн=^iaN} vYUcsyX?6 sG!jBfV8E=vY'ֿ>BfDK W'DBh;+*VwEu7@:!dzps| cTo{Z[Z_[^)3"=%uXA{q&bsB-"W.<BӀڅ0^)aDϡoе*eR"/Xd_X%ʱN~~zG?s7e󣀶*5/R)cpKH Rp dEf=oDm K-CH~ڮgCLFb""nAuw)Ð= KSf"@4 䥍Ad1I-yᖪQ6v@VV]Q Dݽ U=UQT68xʭȢn%\&ULgZW|m O5kvr_G+=ȓ>cGowo~M[wȏ#ël8v?g'hdugϮ 5u5;/~gOOKW;xBT:u˪%Q2Ͽ#:nňkk;=Y ӬHE*+ Dy\T]pu:CK:EDcaFE>F$dہ qr6ʽ="36ېoHgPe}6"5=j.Fةi|ӯYm97V'Ў}d9x!/Ģ*v!LZBx8Ʈ?Y2`C޷=9(%#Qzr8CĠ{#\A=TD4 ߆lܳ޽NG96n2b"7{7_99i20ww(EJ|]};7v6.</.B.dބ\:mFVU@>CkJt! H!qx-!<>lky(czl9>~O-umG;Oudݯ(E#n/~oa>e9Y+" U-ԡ#%ѹZB,.$c;i;v\-_rl/./jތL [l?*L/FJhJ$ '!lVByFD|mC5In^?3r[8(m=REHI=E =/W7PھYT-Gkt6/?ߋPjm@Pm[ycQa@-gn5kq/Lg@cPh c=S~FGas T_#lg`I}GsTEEjG~U1=Ký6!a&!%,pC1wlӐGo6{o,+  5(Y6% U(Ǒ{Xǽ؏<~p@sP*䓓#bpx~M֦A?A BJxRhNZFJ*.~)avN]gb@$ѵ$zrkEXJ*CN4Os) {&"k:yBJ Dzk/"]/{iCCeRl1Imb)Y6;[w 85ش$Gm_],(xQ軀-X("2"Br:{W$zͨ^bs]I+RT"Ϯ2[^C7!Ϭk؊BGH_<'Ts {&!}hD^V"3@B"$UĽ H7^XeCaB5ع5"Φ?٘ub}L L`w?npX6>n./~IZ+X!O>qSUz@OXkݍҮsr>-։ ҲΎmBʱP|`vs;XAiIy@;P.z?Igu@ϪX|u$r{~ yƧO"u##sx\~b^`߰&AhBē ǠwXC+}heKtżl7βVT"rݐ[6|2EJ ?79Qv @Xa!r7S'!нZ.ڍH$g=Bq#4a(OBjuGXc!vd sEG/Dˑ`>pN{_C2CO8jcr'~bhqFZpjOhByT¢^q!pظBʽ3H+”CX'Jbmx*D6uq>u@&zǂ0_3uwl{L:Xpt^IBZXKVՕZ;n~dD@Umsy܃`s!zcF%v{(t6')-~t׬.n [T<܅K m]A!OkvOĔ΁Xvˤ3wQkgD#EsZ:]-rOWoSJ O%p_`ma_DBlLD&R9\&t6ߋ,4ɖgA>we}v~ 6'"eSTHYƓAϏ B9@\5"v@ O}^$Bѽs7 V@#{m;[˩T:D\rOLNsAHAcDqBHY[KWkD"B^.U;YGEdgHE,Bs KW<㊼0U G;@!lhA$2χwral6,9ai"<ЄtLٍ!DlͽKdm%r .KP*`h{;Q~rȉ~G {p'C9ΰq'#\z%i,uoZOZz׍f16f1[쾝 hAJ^7wnOH)PĎ/wȫ>^a4hP7SWjI ;RU\>گ6A8l \(;H%xtE:٫G}AlN:vb6#7ߐ?L~,R0HgbC$v8 >T `{"nE3(A ? )s /&x+z]~<|Gu;Bo4oGL Q,ߓTes'˿avQ- o()Կ"@vB φ(Q &~{Iv (o&!0!%C `ߟueROg?eR=>߈tFY{y A}h>Q& tżP,{dJ Ȝ>TI^dI-q{+BO"^.ݏt]2_KgEzs?M:O"}|$*Fk ݣN Xa۬-*^}p;᫵CU5@ p `XB>QܮCQ /ED/"<xskDZNajpڨ{pJo':D]o{zPj!x3(T2Ȝ7u WÑWֆdŁaCRsT펶)u/Ym<\܀Y6#<~+m^"L[."+za-`כc{{g7Z#眓 6B;VZW ^a=q2Z XvBڑvwۉ77b3׀=Cs=5"xѻD KB'g"B0J{EC+!D?w¢($u&>@wJigT=t|tSՉS5(k$둲݋SkleX"0ڨ'v%Q+y9 YbXm5WkFtRH H!T@sXG#eHkFa$X,s UBviR>^(ե^9s? )XOچLKd3-yb`AJ $m:?(&ɫxtYs`Ub Ek4(F5kzo:zW6͟gHE,Fݷw_w%тoag=`Ey=0eıg[ehzj} PCxj;BFL!F یB"AG^+BEQBDl@8 ӌ#bj:O PEvs _/9(,ژˤVe}ve]k(!lی33m:珈 2.Y^[yT,d٥MzջW4aOl2Lr{2=g2:Y,Ig#uR&5{P3NB؇IH]H"@B ,[zUHɍ&X*!Tqvs9܀$HmGlGxî )$~{ IDAT;v)uvH#cW5"~#XbP▩MePt}pڻllNzmlHG_'"`t6nDFFlLjѓ5 bp%94q(Vv $=!(|wĤW4rvZ7[ze\yȋFD Q؎=6+u,x9pǒhBBRwvg!C KW4I:]J]}#׌݃lF~t6 ˤC.ڑDXüC|?Y]N gOs=`q/#$u/&$ގ0HӋm@e2Rg`h89) p;g}/25B;KXm: rɍwypî$Bog ,cy] GXڛwES drt']6_w{./n@.<ߪavN$o}0NǰZڈܫl/"I}(bʽaN6Z IvWZwg5zM}ϯUK?A L̴{ Qx@X;C䆃3n~^otoG} 1kM;o=z m̟pMh_lc;Ү3;H>fFC@sTYdBSIe9p5ƊT"π,Ԡ=Euy✀ޅÑ!hY$ZE!%bV}!ڤ6-,]~I nyta"ڬxӒhՅ+"BR/=)F_.tk O}:h ܢ" 9;ߌ0ϫ1ԇ<=/P[*18QDaFBx}}Ih\B:*r3tɏyt'OJh#F~͟ΗG4pMy ^ZUs8I=Jվ IGŎ Mec{oq-dDCBBA""&ظ݇Z?\xb~1˓>z ^PCEB$h=!r;­Pn59 |+u3aIk&Ws+ˡh C{irXַھKg!ϧl?ԋ]֐d%h /\[^A<@mAxn(24'{Y|4wG*П3Lj3 UIy.."ۉ6ўܰ^"[۹[ xΈʯ7*p$>gӟ|:p ૹL]ݭ:DHE$IU#ec܈>d繧Heh7JF^~BH6/7#,n ^puDWY=dR*#Յ[B;\^ )Y(2fYe!K|v]v! ڐ^ ~VPg覝FJ~]6k ၗgd@pdJz#b#º.qBVh ֙P ~e}T"yB3Ex/Ds< o;&CQ(iowz?ODH]-@W#Æ?wS5OdEZqIg5@ ! ""t+Q 12뫑i? 3hmA$¥nn`lFqzJߒūsQN^yʃ='T!]К&xZjԌR"Q"F{;Uc4!r1* yE>l݆m&6u_u aZ4[)MwmN]7Ԥ(vag;GDuSy^$]w%'Nmvhh}y܅a5r9%VL g!"э_I+0CO7Qg|=z<ѽ(kI;/!_ǟ!^}'ظz/ؿsSЃjMNZQpD(;dhw6F #ME^R!*r @P8R:}sdɜ.j--_ # wb̝t9v<7G/efAj?^濰W^f@"j(9ڮ1ohG $#D󱛐u,P;A)!_8B5*D, Y @W-TD1iqq]s&(ݓ6w#R )ɈlzK#RF (K-c翭9g߀X_6`G wI:*;&I Im{8ئXTH,W^sVSX!LgKYV~tżCQ2veT`W(^V*Rm{[%ֿTH^݈8Ydf.jlZeEN@!wʪ" Y LgC` %쾄@^y>{0; \.Ƥp{ŔCIeyW'(~<){x|z 3/?n6#U!w,\:- a f}MD rh#yh#W!(!!zmlRbƦGny--/Z/)2}Lg󷺂}rͩꪯ$z=M?'$|L ;gדK=lh AD[sm]b^3z6Οl$f?KW@k}~W^-nEu!""Iv~D*u!%aBCUR.Ekۈ0Ɉ|K{wF95aC Di->k&Ź nӠmGGc|sWڸyY vi2v* ؽyjB2.'YcjBh=ׄ4ژ@)9;a35PA=U=Xyx%܊?ϰ{Uٸx]+ fr³Cxac܈޹*vi;Wc}KH^NGua&}=ފzDmqJוu!lϹLT_R*VE's>|qK z+(趲̥rw i{^79SscV1AoB/;ˎyy5,y7 01V"i!6ũx2GdTEDTzv\ *Ͱkb7#EӋVo}r`W9]ƊK%mU?+tŶ6N;y]<gX`0A^s(qܑ֗@M:#L9N& BKPKgػ'Ia)=C㱾b)(bFy64CBOBɗsS3Y^}74g[6/?##Lw{Ey@=t ERT,Lkmv9!%SL >OLSTP/nI \M$X<@/K.j}F}) NyeoD!{Ð.ÿ62sf6nB D|݇Z V"Tzҍȋ(Hb+G\sBnz/p.KB$P)12x!I|;3|U#EBED' $3#v{U}1S N!š] b7!Ejamj](66yJuKe#mWPD#X,VSГyRÑ+Pl;X|œK*f\&WܪH:OL⒖wMil7sԽl(챼_ =/{ek4/^q!kECV U'/69EH ܻy%ޱ6/yv^Y|2s>(" +z ū^^h!dw]ęH^Zu)Ռ< 6">=ygu6&};ϙޑsmh]xf!Y$xu؜ %䊚e߯~jG>ݮ"sZnk'"[@ ʉ5p0Ǟw ~LO5ck*]<[ω?@ơbzM6'Ռk{9"gd/~B~b2Jg2g \T,gFwNDRf7V썆t$!D ۦSYu4*xD\Ĭ/2; OGR{m0WʝOpSi(LP9\&p:w D@]KLgIgW[_ 4 m_-<)KgN 87aFD$yHWNU0PJ^ mQOus)7(!7@&po&}W!,$<|@fcs]ox,(v,䎇5<_DoasX'l^|<(ځE*p~h9DN@!4nkL{m<"|=W>ch-8Ir/e,!'>z)Kq K;(9G5nB(I,hUiž,<װ)@@Gۖ I {F{Bѻ>.4oW;tOE^R!*,_rNz})vU:9O&V? t~ `9)0!nܕ[LDl f q368JHR CYc_D_!K5B=-/UpDdVźbBvT "pv;W1Hڝ)5o@W>O9eYuLj}G:O"\6K@sعl@?Flby{1}z[P}+E$矏2βt/|`y s=O^E*%т{zOO`t/#0^j+X<^cEnq]뒔N OΆm%} P4{dCg!-l*:6:XEI ՒhRa銎|ީ1|6"& q:y~%IGw| 2;7N!lfay IDATʇ{7x~%{J"m`Du{ z8he1QntޥuhTb%(E<6g=Zĩ/yuV`#EP ԓ5|.Y |iQ]3KRE'\y."\3!I*nA*yn$**yn+d_!k!{( :wQ*-_r7]zʳ#l'p\&!Bc:4ɵ5y"m1u@bN{XOy$DqeRw~"~AsP${$V ŷyM6mGJi\Y" GvHlAF~DN'SeD jֿHC>׃\d,hGJyp%_^Cf~ocvRBv[_zq6OȪ|#ӧ[ۀ't6\&A߽ ˤ3ӱ} eRC|Ai'p\.zV"yeI2҃ދ#8p 蝹`qG2Eʾ. {'6t4 'QQSC4oX,o,%Df¾ƚw;!ڸo=Cozh66{[j~#5ћg|o믅 KW<lH:Gae{ ON `E}?z>Ȥ#gT||4I=dF/u'rYrvn׎u CԂP '.@zvq6"=h0U6媑wzBVnK&E$xyzu#XS¨sy:wu;<NxU}v;&>^닓#a:ya=(g Y!\&l5L{yH"<@WكR~.] .j}߳@e4 Fy aт^ؚlU~臟_mjc]uGi_O^ 10qD!Ekd!̠1@@Gh^<(k] 0۾A @2Dʸ] yj t^ZG`xB'C#rg9G p1%IeR++~}{LWv%/䕻`Gy~ wmB!Qb:4O+gt6?ˁ] :6v ]۶6ECH;۵ѻ1+W#ҿ%K7~[`<j_.A.*&d#DRI,{;~ɖ!A1V5=X~i UJg\nD$ C8kDpF  y~Jh[y6!a-(|HH{nsܻbFzt a3~<^DB#;X 9C[?FRrкͽTI!YhcD|e]BNF^ujy^K܊?֏mv^$h x "b:GK"¥v*w m@^\X+7^IX[6 7Ro(O m#x"y 'N.C[%:[ =7LU;NTS*R$"NË9qy2!_=~Jlmf㥁Dav$;tb`'0[N8!LD0r$Bx~ǧw2;;~"2R!*,_ܒDJx ۃ<3NGJ4"^]m]zFp\G:?eR2UlpZ@ 7VDM'Xʟ?gRX07xCD* w P҃K݇13pB΀xJJ~ގm$T_ӑcԮ"ȴ 1@W!z~7ڱo{C(r]Z= aXSm,B컅H9c6N7 rhO!D-E`6++"ALUl~(^O6qּ>A W5sݿлQX=e\;"ȋZP&"_^}0AU.̽rO .^u}܈,ޣPFozN/k{$b{xNUb9յr۹2ox)o{ߵ)&i=_5yQ ={d}8Pt6_ ̜t+x\wihU<_,$S `=+ǣd+f1ˤ'ͿQl~IHv!CFםk@e3e[9vZt!B?G:&s"V Y|8w"ƶH+_1w5ڃT#=jCzuaT.eR߲P~wWg bg6$%nENt*Gxҽ~|@iVlsyЁiPx׃u6g5虾rR=gy=m=ߊȳ# D\ǖvZ2Nkϙx]"N=<&:_3!?}vh(ǼX{~Z$ú]584n7oFZ9k>(b¤c= DR7pϤ$Co{Z(}U@믜+ONy8gy3N/@r4Zݏ"|ksn@*VEXdTJh9)!˨#._rB)]D%N_buw#+p>ͻ8϶"`ԇy/D>F`c"M|Mk/kG Œ*HL&)WnsΎH e!$ vo?eR7(l~8rٿFזGv?i;\Jh4Y>_d^}!ۅBnB 6Ϳ_{a˗9i,i^M:/@7vx( UAjZbNPې.pO^oӻߚiWH g"^o_d'Ħo F@/ BN̎]@  6&]0~`w3H#AVˋl9W(݅D%TظތH}Xtk N8CXQ_dx΃>vxY9)84dtˢ'0rؖe\Xs|ɠX? ǥըʨl\\7jz'ǾogMυ~i0U9*JjZrq "]ځ<=W.b"#ː.6G2@lw&Ϲ5:KjLmes,pb {k"M`Ԡ9ym $V'#ú3jރp G 9DL#~1a"2lBfa^Dd|qiˮֵW X c*#^g."b6Ύ}\.K(C-?<*O1%Fk/2+l@ ;혞e+7>p$ǫB<:o+2"Bq+h1*d=OVx" B;QW %!Hy";c@ϬPUdw?ueA$6<{pbx"(l>R;ُ:?L$z͘)4:HNH:c4%b@V}HM RAdIK_8c!v.1Jd- 󩈘r ?,֝|jA!5`qkem#]\6u-RCgr:rY-? Y?"^=?*Y-Q܅ͧKq$"V!=6Tnu_=; O 7wqM5]DFpP!nݳul'~kg[6MlrgWa +k]ݵCIIUk'7Տǝ2>5T@k3;uϔO-7U6x*"#9ۍ]٧Phu? ne^ y>:M][@V+nmAvdt8FE\B۾qf!%o!5c>DG99'!#RM({>\P av w@:y) :;lB.[Fړgcz?xEk_ KD|7Kܿ;=n5-M-sj?Jc|x~}#yɽZ~p, ǂ9azjDe &3/ {!ǨG8gTJT7WFk'Zgq*" I}o[!$#[l.yh~r2.ecpt_FX;tF!S\#B֣X}#!GQ@+~j%ay5h;=AJ u'P!nu=%OaSaY(ShI'SL}u6"Z@G!3{%$R iF@(9x -kJ \މȯk{l,w܋tu1};2/P9!]{ڙplskcSg/J89'jNMJA(%]6̈́FJ HnP@3)W(E(泿~+҈xK!Ri [b>a(G_gBs"DֵӀ G!! X86gT"*Z>y¾.⛓y!l }q[ZnYA+% }CeȌ qymv #5 #_¥)"A5#Țy}d)Ux-TQt`yf"ҽTEdw{ IDATbPn3o4J\X JL܃:1Ʉ<dBLwvwؾN@4r4!/;C{:7lz^㟁0d֦Ʉ>〚ښ΁oAt4"MjM6^!p2h~y-!W<}qٱ^dDv!wՐLE߱&$ ,J}a=6+sDGQ8eUaxp|K#|cLJ[[XJnޟ~/؅)C!Ibk,[Eg"Bq #Īʾx_PQyܨ(5*yRwZ|1p!j B|FF0;W(#CJܽy/RJeF7"p& Bg]D}\'iC!Y?D|"Aa'R)]Hlksk.#"Zi9=kK}? H]n8ƃ8 QM9ۓ]oc /\-vxWpF`F?VAYY!u~}PYl!r\!'vFm&v-|7W(}<+/泷dU[pTF1U6`Yhx!#5%S<C T,YM0&מ(:xFslj" 3<";/\됡;Qm_cY;l1&W(O(DȝdJ/*ϽڐwPʃKk?9|'2xyhh ahޅ \J-'Ṯ&k8^B$Mɫ(OFqv=<{aK[UmϺפYk6<Ԕٹ0 EdJ(\o뺇"Bx':`8mַ]"Qx6b=# [><`bϪI[B$xĵ<b:PzjpaNJt8nC3+;Aօ"Hw'UG =v=`A(NXF{k]hu'XOqYrk/x:8r܃oC^E?Yr[׈ڊErҋb>[|cZMP^eY:DpœFc6ZxșgŶ n&!9W+Ȳ9d~""keyo ="RxxI(TF`R2 :8/=fy9phREjmE=o# +</fmv`4t!޹k]wgLBcѳވMk6?A$ ;xlSl֞:;h>EAJ!6%W(#LkWUy|k3>W()7UW!{iP\|a=-Ru% ]Os*򴛈6 ݑ,d-vNC@vAQb5ͷC8׌浇cm},|/B]{8(,Fc;Q>w 0D0D:A{ 4lOP:yz9րlq7 "^ud☭h-t"i;{HdvC;6#Ck_I\ϣP&<&}g=͹m0ߋ+u+"ž9*kѳwROb%Fe+7|Vn\{aq(brmE Xr]{a*`\By@x=p}'Y7eϔ®׾3I1ݞZ{,{[g_{UK9M!DD" 3uϫѦa1hLoFzوX}? ]W?z֞|a9H~7ރ"f'#<kZ^e%&u4d\:U>#uͼ+3vuv~~ K60W#PD,^n?YD(6^b4Bd7^J3Kݷ=^ȗ?!mEOkBs«.Ɋ^0Y0*BDG7oOߞ[{=wm cVomvϺ~B>6P8OQ)Fs!;vG2/ {%l7*ž[Y 9< iG\]t}vn NnD~k+nk{wuB%;f% /]R.$'2Nm=x=_K< *܍ABvBvgsyv3Q FF)#֨<,[Ay3⫕h?Q#涞CqBi(.W(,W(yeH JmK y?݇g-Rb!wS!D~lF xdz-R̷+6yom`g P_{+ev /+ o@^K"4WOJ Ey"gm|,|cӋ@蛐"#+ɓepdYbk_( ̩\v6a@6c}IlPR9ܗ^&BŠ~fl5~RgI,ؘ݀<slߡ! @K{˃&[rKg og%[ooXjӨɽh-uoFCFS#jHqõy'naՋqê+Nܳxj2м fL뾦wՎ-5|2\b>[F$X{HE 'NK*Z(ǠgȻ+݃ŸWgĒ Ȑ?(*G"b/"U~߃<W6!lӎtbDI0y2 4 "cМ!MezƸuD!x=ٮUDbnG8r?gOkWWc{ai'mAse?)G!2 HU+POZq|}8y<DPԺ "&h.DC(!Bt;J5chrvOѓC܌<9g^BQ͝*w= r#㤒{hznDBS f0_U;pOV{t#W$tf}ᖎH53)OHPMڐ.p}Ja~?`-^9y>a!DF.Gs=h~-Nl"g TP:&W(<)e4pT,[q ;6 q˲pfa W(]Ƿ"%"t?H"+9js,Z C Y!HQyD8,AQEH1G@i)Z %pzlD@{jQZ\ aw9^L]p4W,k -e3KˊJG+# RPKQ}3R@ZBW[ {^!!~JU +H6p?Y6FjB-~b>;+G./J{1>YP ĹBz%)Bvr)_ex)G+c}\aG8YlOE>mړ+ 4٭C2 IDAT哙w55]7X\"^Q)+Z-Ϣ  P⛩xpdxR>[ k/BWīX-s͏}]H7pWo'==~(Z~ww>ߊS'w!I?@w\t"#||v]q31/B$}C߈8E:9z揇0y_=w3 y=6TeQ"껑af*xZl}fv}ckO]W6~㝃{ SymG!l¢uj_/uqսvΫZ4UoׁE '!/6k=PPj?TC^N| bAmS #`^!˗hT=*wezs\di<=w oCU&ҩ]-  5_D1:}TH"^}Ϫhoņ"#@Gr488ysFxM W(vL"Wī=]eD5E|+~gU ^+MBzI% MBi'ϓP19MG氿O'{z.@:>4)_&1|^nG^ItM^ب)!]=mп@lv ~:ˀ(ͦ뚎Nq4zgre1MN&ZzgވHvt;g'14Z؛M}GO6FIMb2T,AJ-1$sGs>"!2!ɩ'9o>_ B_!@$ 4J\F8rQBkAPi3MBZ^BqHz?ٽ<HBSՂT )YJg!rѯFV3Ob>{UP6p6`cmwsit;g<eU lRo8ӫ7۲QCˊxVE˟ .gyEyV|#Ϫ(,t`j25lRQݕH?}隻Gެޗ+ *gAAy[MTZyNgsh##d ٟZCC H:1fOU!؎6h,e. ik d~׹oqv*`Bi%~P㔡Jm-q<RG*R:u=T5L*^4Tg 0~GF@MM{3]Wg}}Oto?}@%J1=Y=xK$M3gx#C뿐af[{v-mםN,yu=ê p#f_'7 $x{^6Y;yB0W)4W洇Ld~D /yWskn-x0g^0"$w2=&>FnXpގpڟGR+v-w%VF=nAz52Ўd1\k[ fFu6&[={f!WGrMM59=$N~,^Ev*$BW2+ab>Br+(Țw['1ƿJRd2?M #ը< _HCZ16PI'|~M2chkKʍ?T&Pʎ]+KGbo(G86WOf9yLaWHń0Q{6s99&N hz"(/=OC!xG!^0z cM-߇p]I&n>?vVY(4콩q=}muSfdGؤԌ\ԧb7;?vxnICPhBi.RJo7JrW&c!R` "z""lj]D܎0d+pAP "cvS Qy"ɪhy y.@FhCt>AmBkZwCo< mZ.ED\75[j(Sw_ Цea-|Czy\qHoރ!#wy5}{xt':hylh x g >@ȓUAUS[U(nk,#B'l?>}OW6IqD{ a9HxmcLz =c_q#yXdmnNl5EEl"Kˈ2Oчp6X@dD ^Xɑ50OKGS7HHw1Ǯ a\^ҋ|卛bl'Ku)cŸǧlbk{AyN=!%=:>Dfͯ^n-1u$QƷpYuLdH w3 4Z}ED%ThE 7c%r'\MP##ZonDhO`r_B"W*OGgڐB9) HLjGpܬZ?_Ȑv|`nA 7}KʟxzTdۋ@]0Fo{9piq6V_A$66*a׾EUS^H U|vz+3 "D\e [B(Eoe"^{IPJ#b>{k1=d`PjG)9qsR}jS-G(W(lI9Z3eWJo.ڽtFԺa/ڳ?ըHkF Ds<yAI W^-_Nl\`%*BX=[6?a 0A{Za nƩAm-֯A7"$@ͳZјD;zƵh47ɮFIʣP0V֞.{9cmM@61PƄ>L"T l@NB>i[xzrm,Z;x{iD~aB^FM]:W(-@Oꮣ(: ۑdVY,G*D=w zzU!OKC.^ Qs=RDcO YH)mDXd%Xw=@0@b"ZVY|Hd s [] O;Yr)wDC=vNYʈZ `O.ϲ19"JqvO 0Gz3)_V6MEXIP+Y'?Gm gTF '+=_:??e]ߚ|{1h=Ghq;T?Z]\>vByszE{WX-z="][lX*W(e]=sܚ7xQ %[|?5W(Ix A^-}z Bg 1Lͻ 525Lyя }aß",E;v;Ϸ~ w>w~+!K-^ ]mw1)J\Fsdx݇6S{0^j 2 %6nj?A^c.gci9 ϘńJ/dDFv!4+[0tukq"i ?EDh'+Y[RQ|QOrw4!y{;J08'˧xLϒOmM# u/=(l8*U l  z]D++D$ h6zGϋƯG#֎8y]&v?l翶:q8 !ʓTF '̎>A44pˆxW&>nb,"[DpWEJAIr+%yRB{{T,G WR nai"x]r^wus/"fu=^B95Z!0̈́ajc Ӑ"<@|#⹳DU}H <:T:h{ DJk%y۫6fIjj&Xr"4" .]6-Nu(9BK_d>]cZ!K_+|DՏ@o츟X{(, J!jTd)(y\b>{!u10HoZRg7qsʃ?{__qO^^7qhDuhPw*3;pw/b uWAZgRrR4OJ(泿{6=Z De(j2%FQ޿m<0?HwgT6?cۻ/_#]x =|3iE񙇼@'W9~sWE׭WW_*Z; |+꯭m|f (hrE۽;סqwH?Dv\ad5"! yN~Aýv!c8BBkͯ@'ކRϣ &{GVq~/ -h} ӑ'Eƻ.3Ϩzv$l~:SoH!q\F0RGX6k[\d7]<É+Q W_l환Vr"8a*Y?`0{'[{Ɵ'W |^)zMy0DZ>Noqw"ؽp7".=s(ᐝӓL˕k6D¾յ%@y'4ߩoڸn6oGzپ;1 ŨÎُ RӀݹBXDLs;L[ 9ӀB@# `K- Oy]1)W( |x&Aen mڭm$+G,o(koBeq]f~zՏlL8W(=oޛy}voV8ܙޱۺ77a mE2XXBb>0VolWgJKI3W(=;g>R娌Xn6rƆ/~Xwmw?km烙4> /lEmʪhya20P4xu #~O IDATPg TY\m61*%W(y;3a9rR"rD5Ͻ¦>,g"ogDoIA#c*ĵTm}]ɽcU{y,F۷/Fl5ܕczC/S}v;Bq}"/!Rɟ6*-H"@TG~w"r !D]p8(=mZr$9C#5`=?jc3{c "B[=''h3]=c8o6>sH%1s*W%lk,#ϝvBy{.olmdwqBx!n> m''k~:!sr(xFw=h:P,dI~4$@ Bk;zgRB8dJbF_@8tX$Q#EdhQA'ZA/SMơ㻧4|G2,Hw"/ -:)s" Ѐ7&E K֠{1=ȱ?!ϗ"9}fHH\' J 9?9/KJ]j%^F_zOG,g,l@9\1wFDdDtU92`1JUC,?T^Nc*'_j}m =:ZS>Lgxn1 |gњv&%هHD~6ƩK6$_Ћ&B(h>ĭWGW?W T9gudY'4?8Lgo~Ztvzw/t<6As}9zOnNϤ/JRz9c'W Wp-ށh9`̳q٬~fMKz\jEzv6л헭o? yňK#]qB51hφ`C~) ϻ NO! zߞJjQjT S4tv"'O@8,mpYDF/Pϑ>Q]aNeymF9;AX + # 8`Nt#=~pHkfŽנ56 vO/Py} Fc;{e\=Z2tI'ؼzg<9s*'~^Ҷ\.(/sޡȨeQnsA塶ަ^BuP{ `vr {Z{2"`wlm>a>n-o)Xo4E#{|ր DDaՏ. Nk4?3re="ޏ—qrWKۿQdRC_a7Qxt߆W3&ܱoO~ˑtY&#pèͭ`9|!CJh7 JU9؞Ch%(dm4 Լ_c =ENXyhV"3և{P2sLkvgvo#+牄<\7ߨV7w/U5+g#!9 6ڑ(ܡ#QHфRN\t#T4,"@?Mtv)! 0MiLgQ;@r [f-wL*ռΰa}XncB36褕K1oEk>m!fgoD?sQKɾae# $e],Ckɂx@C$6хBny{rD&xYTQfW_9_KϟP \=XٲjִlAגvG:laUWD:VsQ^َtWҊ$G2ē^!b=ԃ֖<ܓōub߹qu(ɄDҭ] A{L`@sKԊ޾]7 u'z/!(c26H7{5l i?ھ2b|S!,p@@5QS3G$Oo_e{GZGN'ǁ&ϕN9ƒ|/>t4;l~|Zg0QӸT=É$_ac6] L0`$xϹs ZFi_l$DLkR}P6]p#^@ nx1fcGs:Z#] y|羧 piSP h0!z~ [[bQiLЙ]hshM8:{ w LM^O",,{({kg4sz?ZQ>b 7P"g;߾fTY R>O]j@0EVĘ+}_&F^} 8 އGUG|"zy|{ yzPyG_BX9)2g.mg:{$+jG7"7)!WĥpX3AG#߬%1 j RJ=(7URlټ,?HAI{ hп錝[m7"LjnD2g9K{2~\I%#B[}ey=.ȫͫvدhw& HѼGL /E_'d:I~#?\wp5!ƿwBx|A?G|"Oj޵}3bG^S9jEfKyɤ&3Q=C-OAW 4qlGˬ$u>(oJ(U?jWڸh߻Mh{},+BzEyr>BEHF;7 ӑA@(7G9 CzޯCFo_ T~'"H =G4xE,"bho-Z24hpS&wԼ߃;%?!ܳ ^7݈V Yg$xw!( S;1t=2\)cǢOvl/)ybGµVړ FІ )֟}x3əȂCDfbx>GЅ+qǶ%$ P7x;)sb9H}0+'7JKh!R0BnBRwk ҵ)&qR܃xa?D!mL~ApɤO#T6Dylr Ud|\&w缰RzDWκ[뇏B=-~>W [w 86J &av-H)?l}R+T;RD=B 磹|-Ey2Z33(tP A<n1D6V'h]DDxXDҭq`u22B<>;I%v& 2LgˤwZ%.Τ*I%^msE)JQL*q{2+Jj5d:ϤVd:; #t;H{U3!:XpkQN#hR<'kɏ"㐧x HfR5 jd$tzg%g<BD8*ҏg `Ϣ }UJT sbd~ xn<2N]0k)օQз7rL 㕳}κw !$t2 #!롢N"+j^^ n^yۓ?I0HEqؘ<`s'D0G='x9v#APBɓ/= aUJ 8 "njg#l H>N$CB[!^2HWbE?>k#7=l.`Fƛ.ߐI% Bi!Tg}JEy Hzeu~MVg 4EoC qEK_rm$Ζ!e+R݇,ODDw .+Τף4l`MҭiN4z%|%B¶=.<İM5ZGy VCZa ~} yU%J%٫"U-%:H2%J\g_Lg?fa~<+WF9?ܗ#]؋ȓfFA: >!{"jNH0vFe hnG:Tt oADWd:{3 gy*ΰg^r!1f 4+Y?FeH|s=3M/}1`}?7J66 v#o!q(,]gs9σ_ޞ3ʽ\{oC#S|tO^sܶuVfh;k 2P s3E~5Zk9kݠW;`tsBЉ%'lfn@3iXڢk[Z;GWWokiQ_^4e^,ʱmDPh W_}vhye"Cr$gln_W6b`S[H1^աצtM/eR2rKUto==8HDVA>b_5ܩ"Eב=nSHbŀs"h4i-,M3"n4xDm(W Un@D(ŠC2-I5t$ds` Ȫ Q%L?@2څ)Ym?6XƣyC9!3tIP8=wB$C%H2qK ڶ#z&G B(SHm,M~3OHy;"I }T#$9楗Ӡ)nDI! f."β1[ `#fنH'rEd csE-CDFxDd`{28b;lc|tudd83"5lL$JJNw k)GD&BF&ߍDa{er#y"0{"9 aQkc1)#%~'>yoONT9DFu6bmBL/̻b{ ´3& AKZ۪8pAXzUPXjcp8ҁ@KHy!H}7ʮ>۬=hm=y9v"Ckܓ汈 ֯χT Tp)֗c80¼ދ\y jpy/n$6+FOk V0D ͽ^UDfhJ4lFZNA t߀l 5E&xOۼܜLgP"[6ўF륁n`}E%6 0O"<:^bz|) [xhHdh[ IDATq(,ezԜ^7zX|4F5E)JQV!'e&ȤE|+ݫB=H r'ELMQ顈فwu[zUQȫ`D[{qӄ|krg @?."^D#}7"|&QQ20@y™5EF CVD+!c9uDz?ZE^kvVBGW# y7"ehPǍ6 FcvEjږn1۝i#QvzKqݿKbmxA#\EyI1cj4}֨=Rz4&'6E/[t /FP{J!qe*Jl݉7% E==\o!3ʞ42 B2D+iw"m \Kp$TsL# &X<DBjdb-Gkrm=gTg8f}]h٘I%ڒb`U{ Prm7Z.wi򺪵qO{,w~CH~>Q"q(WGwg B`u=tq`T2 TNڎz"w>4^H/,}7[cέV^DkO,:/}ҮF،FW\**:ؿC=LrޱȒ~5">'[ 'ɤO!bU6!`^%/JQRzU9hȫA^.g{ r ,j"m+t{![{HH G7MDuHi96+,ܘׅJT@ba}o^pS[s%ãFGh"1P(fO&v>F(:$k[Ww Bc(їq~ O ȸ=o9GX-)E H_zaCec߹/$rg[1|(?p zuƂ%^޽1:?)Ҽ:?W٥l&XZn?~jO.Wؽ\@?XVi!X$$j@y*PZCk+:Cx/!`,x4G'YDte&p$k+=rE)JQREN 8u+{2zOA{y3 D{=oA_"\!x?HߋR!4Pc=ATCvFq{mE:x&«UH?" 8yJ!}xqw"\> y?rxy-' : Og[?f#ҰH4NvFoCԾXY~Guɞ?ΗC`3̛O(".-2 Nс?;,Z3=J krԃnFFO'wr6~('{>F xsz+#;_I0\;Y兾|BG;? >wz '8"@8Zg~A9|VUKqylDggzBm}AA :]֣יT%=4ًL*ˮ-T7~(ͤ-)E덑1+ /FJpͅ3n9Γoln"2ox_ m^g,X̰:?Wۨ)bE0s8:A ,B~=GPɥ} (,5(>L(3{" /uXz)#EI;;Gzkr}]sR%ֶ 96#r.3')Cb5x!ys-oc< "wϡu}3*\FQnNEy>nڸ貊B9*lOgQdۀHm֗W366k$Pl@V&kgtq0񣈔?@c#Pn̤I󖵹&$^h DO#eeK+H&x8><EdAXw_ aftc)r݇HxJu9=&k5ƣ؍x5ڼDG(E)JHyuBlx^D?ﲿw }s,h/BjDݍt'ߎtC !6t a|}0Jf*FsI%I?EƍZd%xEKdy_@uH!xLOϷ!E'R]ӋąT|'p(l3y u㱇 zDz\BgY!nNx]vGq?Bzep L^$퍄H{̇Gԡhq~"~NTUWqdtҭKגl]Ī6Xi2'_E)JQ6BA:` 9t BƢ? Gy% ^ȑ8pF SpODxg`Fn'YqGt=J/cH/~O!$ac}")/gB [ H=񶗓ahg"Hvَ3,y*I1(H'J/ϑXLCcTP~NI뼞+F`Ȫىkks=q':w[517+ɽZۏBzhK hBr)z#/"IBBZguһ̱yh%xFʲU-Uemnx/*[{V\eyiZ;ֿO s8"ZF링cϻ'r?f)a2!ia:ނCi9g-!:^N "pCB[tCc%DށXpA8}}볶{Z:9!ilol>֖[\"Α+Dl )8E+$@HA"6=,g; w! 9F\w Ch1_!>m#?#^e`EC.AdQ>jc8{ d#Z ehRwA I3zKuBQr*twC%h_m$$fDSsR 7lD$٫kQZC4(tΚLgI%([R?^Ft߭c Kzaw| ҟD8_-C㎷JӋ0{’Ũ#Rٕ@O_bdи8yw19y꭛O.X6BfH"v RSѦ\楔\d-.EhW毋WOpv`q-Z%6FՂ3I$ݺ),R{ >м]!f[ ~D@R乖Bu \|3m5 B%[2StDd.zg&x}q5;hmGxg}w?2D@2=bpG@8y\ JlN& ݄jDKsW%r+ޟ e;l| Esn8ݮwBsexU٨RBqؼŮj؄9i434zXJ^>dYHlZg(E)JQNɤ%sQd:{ ) [CHn%}=wS={4"Hy뽬9v7hPALWxv>S IDATF{G^9@FHda^ 3% G aF\in&3~)@=lybЩD$U r{7wu9=&)/3vD]D ͵{ mrX<d:Y{r{a[QeUߓ{V5Z[kr.DCy!ҋG 1'C`==*9Ya߆;ۅ,GW'KDfD=DHz#!w#zwM6/Ӑ?(s_6+y δe/2{WrJtn@c z%(t=/bsqce6fqG_>B(&n{#py~iv aMvnϤamPi^=)}F7׳w!Ro"Vg $YO\4JtR(Ey5G$NPT'JŸaH=mЮ˥(YHO#~_09#=gNE^ޑL*d:[N t!ɼH2繷3ikބ7#b3*AF/_B8ʓ ^e'!=r{ve;"$}t7a{$DXO:\C^@ ?Fl' O |E Q(Az0z G0@Ʒ@@mmw6On|Q k_BkqVw5-|QŽ zB2e߁a"BBo1mX{wa,!$ͱ 9ƒ{5Rp BB2ijS(oq)XLBaZ^rv mnцffو~7:;L)FEu7| T1t~^?|o~rȣqgF=fG IJb ~i(m0MWϰ~DˑՙDaÜ!o4yq㭙T"Lgl<gODW)Akd#R rZ cyߎB\Z6DGb2R,HI1+OQ0El@C@>4G8o=B"iE@5h]Ǒ{ t n}~ ^##H"M}\mYlfmOymyhjk~OÜoeSPG"i6,m>{[Z&+I%jݙps.Bm|&xZ]ޯ/vϟ lF{Bka:Cgʀhn79i兼YcA^T@w>Oyj2&Ώ .QNG8J k Z,G-s۽h DlwZA7{ӗLg͙T-H`iFO4w:zw1h)zf&֟FքztDy..&C?=I%V0CE1Ѹw>pFb} 7qC"|YY]gyiIw$28X”܊w:GL:$A)6I&'#| bj%"%px6/֗d:; k}/@h= Ce)s8?`!4uvz'-9^`FIZdV37rK$VBf6@<`ǡCgu!p8"jۆ0ؓF=0N|= B*>݆ /AH4swyP׋Z+ݝri B^3t #v߫-~ h~[v ٕ]*Dj8HI6?Մ Ga~YCng>=Jcel}1'(Zn!cl# ]Nл16>N0۝ 7\f͙|z(d As |g zK_ۭ_mE!?8QyjEl>['Kcx* MN$B@/b~=>?Ck߱khM:~G vKl#rSp8E*I%$4zGwS$aӀpl7~-H`>rz|=E>6C'@7t/1ԠPtP ilYfd:{9:_I%n&@~4'،=&޽ TGDmWmNb|w_] ^i/++^j~tIH]F5}N2j&XLg/DsI%V%K[| x>t@N 9[d7wz]'3j !uNT^(W~  xy4 V,SzdU=iDč];ƤF'DD6mnXm <@0Or{ݟI%^0c*"1NbJ߈~%=ҏ5E!}f#Hzl|!$tFx)j]۩x2N `܃'݈,3 hL*q[2}Q~k{=3֮/L a*ў#?f;!)j/F@k2DEa1u%>5(E)O !O^9 sQܫ\78ڥh<6uKĆn~#zy-gRN\U‘ ƪ6q|5r}߈0M ^G6={d{#0 LgK'\"~I%~Lgk컑CF,gb&';yf6SFdes"wJ (02ΖeR( G'$#17Yn4B с4b5 ܊heNAʢB -.@ AH G!֒M*s]Z7yR"R Z.4noAp?Ӟ݌H&d:ۅ$Z "_{c TVlUsdY LOpj;jqˣ20'+)2x-3nM#닐t=Dc,?P$D}y[ =H9#)ۣ TBA|,fv=B".GfGX Ko?pk2MAZ @r"fYǡuyX[~L*џLg7#9vhmZj"Qvk[uk 7WOHk4 Ur:_@{/f1ԓt ΞocZe}zȾOp}welfMi%BD@au +Vld^E`AS#CKM( =l/3y]ܳ⧾0q챻3OuWnU\{=@d{{ Ccn'r+]o3&~߇,*R*YGdxDZ\8ZE]_G 49}{\MKu֗jR~RNBq67erw6ԀYWuF߽*6cGADrN> _w~-{p-n˕ 6vn;q=A8YV+n9Z#SdkulAnw=cebj dD*}%RFe#cܘhu?AGGX-+܆o%lzYu-'wYGS\#r ֟Cс}h]F0U?uh]@Ė[x3f&NXG|1!FTB̳6pMݣաY?_Hz%)ڃgͻ7+r;"vU/}TJ ʢxIhh/]U?/dQc؊-+U%G~81PQe}ŭ@)7 HQ(G>acȘDT~)?pbWDDHx9woB3)Y02+#7G^AѳH. ޝH1vd( q>n AɂN%fO?{cLI9UȞ DH~vC=]=hx *H~wW#\KK=QkGձeāلm9Ǒl<Y7E]bH!qR:"l{-RBBpۗ^~o ҏpTk;Y9"(|{x= p o =Snn,GqL(Lk7ʞ9`p)Bdc ]R$o )%֎њz VNQGD$erȲh܆!"¨O : p lgӝ\a'A\)R!9~HPE܋45gH -A,bi}}!ABU[V34?G3BAF56 p E4Bj"1@B wK2`%7~\g!B`5y=9B&#gLe4Wyf}l>L0i g3l^Jx= "anV#0-Ow#g70F툰: 뀧3HC4C:)$Xk h}\aĮ;y3Mcp$|k =xu{α(!"ϭ*0ذnם$#DfJ2D"YÞz4 j IDAT MHƈod"Mz-;`Yq6='=@k&WƮ C$džX[HaGSJnm`Ut1ɥN&J$z>V giF!<2`=$RС3z+uK힯!lu*>SYݜNA`8ŽZ|}Pu7 x3AU>`J%) /Er5z#,;޹˞ ZaKKل1= . !slse7M͓-sqga{v49AҸ1-Ng$ݪޕD.NNEk!}tvֽLo9:}Dl5Agm<*UX8kJk%jM!ڣvM lH8{pZ*%j6v8h,{;tR}a+Ҳ\jU&0A]7zd1ß1;zu\uo\ ˭vq5<џ?`|6وگ"h;pSY4bW;"y"JLk0&yӅw!Um3RF3+&d";XiV46کV_?gmFwQ[rDdEMz+`{+b1tuwNN: kQt@'Kgw ׿+Йv4ΰ]gu9$7y=tn|}|:Ҭ$Iu9(BD EBZc >ca?H.M&dp`b>6+3w a L s@[&W͛c|6}}&WXAjs`ٍHhxJ9IV1 trwKh| y/!hGF9 lSp8cy]fxyH"A !;:LucbW3dEBu\@ZC1 U[R6[xn?7M2HqnC$PCN m=g;2›F:icra8!.Dηv@1I8@`y.=&Dh9 '<{u֖CqY3,<]fzug??hu`䆺_k!j,@X[ )wG)/?~:>XxR*R^n?G %d]Y^)n?EKEqDJrHxm`).GAHKY+ӳ\K t"ߕϦ;3B.YϹF\Q*'lA2EK&Wa?!Y,!Lkez pE1K?=v߳a~k[&Q@TwXHn͵kтdR򬿣Ѓ,d`{?q4d}s6 GЌN{~iGp3޹).A85MFD$M&cR5UA/Brvl!Ϳu:a- =nF%J(mXe2ɝqz8Cscl95 4a*4W=Ko3BcOޏw<3&y { L ʕR),k 77wm5|.RH]J8`a#; H}^ lޞϦ_2r&W\KX1Fza20Wˑ 2UnGA#FࠊNv!s C&Wwc%\!DBf Y]Q y[C&g9>wݪo;5Q{ !0ym4Żs /F[!(~͵[P}4#2oVnC^4 K z=vAFit!gե XLa60%j^VXCd +A :#O]r-!o\{t`n,"=y!I:toE$>NȚ 4>!@4x}}+M?lۇٳ*{=zr$ Ƽ@y6vYMvݑȍ ji}BP6nރ@=!I;!ѻRϫm,γ?+"jaUdUJT+drF/ yG"L҉N{vCH{׷#`G-SKP& ;E y[1&W?O͵.%dk)2wdrOPׄdY(FY fb}8 H.[_]c!XD(b^g׏&$Dz;n,`;y'صN`/ W ~ M {"|J:<ኺñ_޾5[=$dBxDb}a*7ߞlk.!g`UuRpuwGm3G/.\ Is_}n:xh{!}.{މcmF[b{f$)Bڣ]Uh?鏁Lp=XX2t6K!ÅWmXGiZ'@c/6 GhDH+DOk=z!p=pPލ6(tT:qeN^Iwk+} ,X(pK0G% gvM3cv< 4^ {LJ $DuFHqB&WhBD>{<2βB CЋhnF`dAH3"x d("$."bN=^xV'O߫z3qD 2t6Q.S g Y3rIǁv{s6.7Zܛ7X޷hEpl@VE3X"{ChOmݍ4v< i A]H:oyBZNxAb];/wk o|z4Uuu{b<ԹSnϦKַ ֏ h.;Dhoٍ(F+-em!m-g|s%+$&%/Jb.7WZF ی0=}}?)kv/JCxh?Y;+vD|6<+<0d xƮgrQ*~:!|@+dV_$P !/f֋d8e+ BڃpM. :4f>FCcq'=hq:8"rg )=ot "G'X}{sYBlМs wOm/Um0D>d +OLSBx6@ 4jC8 ;'ߒ>όaj7u3&YGg}јO%UXso(?m}R~/ZX}܊I8~]*'RH\-_^{ CwY>~0+Ԡu$ ʹ'0+|(MR^B`}`>hQ{" mUوـ@ЗҮG x4:,E T4ڐ7fU<{gWgr;k/q4g"4%bj,]@یaHqTW+/ k#!{vh)k ahδ!!&t@5dj"8)R Ճȓs ! &cw !(V".%{svZE-4=?ZOB n!; ﰺEQ쾑h`Yr5|6fܮaYM"0( N:~{F[6c-9:5,vo']A{[n}~-"XjA4nEcu|~.AOw]ՎC(;+\f6=`nmWaDngMG=.[ or|2ޞMȫ[#쐵Kd]Jro=K}G !E5T>W`ɥVϕH"{;<_0'JIofr7"xh>nu DQ#h*k Dg?洇HE4|I$(1m"|:|#+rhAs5nC.櫇V>Ʈ݈֬v:NwW~A=gGy{N{.mh 8[S3¥*L}!XWʫT܃:m.V,˕\aC \|6Gɪ2B xi&d]HF2:яȂ^XɈy8̞J H^tOgryV#uB1ݛRDaJ߳w-n[>d@)+7"~lZ5 kj'#`"ZHLnn t(j$`9'?uV`kr3 $E'đa5ȱ"݃H`}Vk+lz @&W/L~ޒyAy}h1NFd?Q#)-H[ԉ.w}8WEBHړ&]݀cdY~0B(ZzSw',!RE<B,+EdeS $[d Z8gQȄ31h~v#c cC8xkgZ1hDŠϗLJlWGXGYmSaj;wJ6se1H0z3?T5L7=u&dz"7գòk6mBhRhD}}"FQ770PEq mn=s2yfmY""M"ͻ5 iGo+,mr A hNmkNG@Љӫ*!g[䔐ecE$/a,"> @S `w3EkD. A|A֏2$'[^n?Ҧ MуH 1q35#cmAs`#0֖;P-"cd>HϠ5{""(صLP@ IDATaH ݌LDfo@k=ͭV" $&wA<\w^DVT%(D>~Gس}tWh&}53‡.bM ƀزco=Ã~P\HԹl++Bl7WR)-!1-0Kr$;6 3 \:t0_%ʊ"=СOS|p)H!,Ӏ,6"TGG#yK:"QL֧H51Ucem*#WW0%%joxzÄǿ8ۧ4۽S_B'-ވpZl7,.3lXj$.#%,OPDr %D~P.\6y?c=)qތ?<sqc͕1v<_h9c)SjfucKA Ȫ (7%Z"\pD>"W9}6"toe><x~l1۾Jhh{aoX8܁Hn٭Fo !ޞWCMу}c} iCrkS|NH =*08 ḱ.y7ƣCևס \G(W\d:dMC{\v tEGޞu/(3bTˢ|}BLx3?ыKUg0 ,_UV#ъ@[m@G} mw2ElzM@Yxܠ=QU:nG1) lٸ{;uUe\D]Ϧo Ax"~Fs0w1oX;wk^dRTMGk)3vFq7Ӿl2!dr$`駭:A\uQp! vgSS`l{ 85YS`>Bs9j-lEG,\)dPhLaBh=g˭RV2HdX>gx|.o,-QddQp(tV@I w9m" -4whFE@| Eזh9>?Oi\A>kZD@ͻV 9@j]oԖ]cF JѸyw=`3v}h^Z[ A5OƍHi;1O1Y5kb-a6[;߃@D7#6PVsP[P:Mu4"?C$ $e Qt8K4sNĔ:oDk2³l~v M{^/gMgݎ:=_Lp)a!Bi.\ňruUIy k˵נys'_@{qXm' %up4fh@kg":'[݁$ڳ XFh$ 4~?Uʉ"B-YTbtwVW>=\l*b8WJT?,.@(˃3%eKhHK ,GnW{[&WX⛑RYCd5HoA {WZ$XmE|;"H{n>/LĽHO "n o} +d/'ؙ+1}zλ^uyȅ$ϦD |6;+f9"q.v>J{_a[ .vik-G8ɡ=2_]6{8fXs(QfW"µ8<Ypʱb=%m ϋ|Fp-WB,8y#W,Fƪ~{^uLS}q_q|6ݙDsnh"5o~1R%ѓh<+^*PZ*d{R-{ȳf [.$@F9Bv=s%ZSuOgYyfx'YouZH!X ܓ'$p5*JE.vV7HR!Mߍa+UR*Vv&Wx/"BHS[F>F0BCg@M:ϷgeD \ fqmdޅt2\SDʻ.4*wI[ГHB6A7žS߈Vבje:O"&dqa|*P&@18Nl<Yַ;gmKh,9q<ܞՄ44W'[=o1w-m'ƽ}!ӑid ͱCD}v:Kfer#b-l*R*? >BWnZ//tY?[j2)l#+~[bY@x\g;ɉ!yU2s7Rh!Y?"8F օVS,ȭ\x$/E!<)# }ZlG7"V [ԴXD&#Nw/kCry< 2ֹg[O&ĵDJÐv"i![CHfE zY n%z9fzGyb.b{څ.%(FXne(!H6ȷcJUk} !NlQ7wgxX$ NA}4_ƹ {ya!\EgjJok=km<@w' r oc3w\V7[:coEX;Hyr#¡ڑ.S^848iu 3+n+qBhNBTQ*VHgT a8U}؁.{.%jMg졓k;jMwva&/Ӳ .t|ta&W:40h=HPq@Six7 ;#=t^vQȯ%P+?w+8zֳi$ "2~D\8 };Pq ԉճӁTd_{onk ueH8}`5 S~Q,$+[ ؘϦ Fq֖=vRfD#+3»Az BS 3L䍻^EcGx$gAqd<a{  #,= v4 `)40Z%3?nj<`}ٰ!+d=eu:A^"\H`sZ[">ߞpD=EP\gøc j1518qLʁDM"Q_|^5a$=L=ho7W+W -koZ8"L3wO O =(N9r10,ϻP鈲g[g>]Gsdkмa}c܇Bt!|gzKH"5\$~ov}2|/ZH؝N,n mvXSF䖛y6k<]FOL\pG]2b 0XҨ͏LlL-$w2,k 0$LrpLwr"#$ANpkEX-=G:<%XY݁b͓%$d#ꪑ6*]NBB` D%@Ek;%f/^\3]|4OppC^O90qBdA3A?֠֞_dkd0֟[L[|/} CN@<6DX~>I8{ qÐDd*?DL"wbVwBX@qG. X9m#,"+i;~2VGx:\H71u-gK: y޵YcM@`]&Agڗg@dX;/8zAFU!Y D (n6eCILC}U@v%{o)ލ]7ȕhnGV:{L.$oh}Z 8+@ NPnIvcX9nc5hwnz{H6՗yL1nA{ר]-{|o^o.V/ 'J,E$Kp䘣:yHg}m*Fʸs ؾ1zt+A}n 1g ^ž] /oj}-r`3+LB > p#R)T(-QkʳU9Gj*e*n_mZ hA&'Co) b!˄[\E8{|LPfVųS2B ijWK>,+܁61\a5!#Q݀,CGw;bÃ=:+(~)9kK-\8VVN :g& 0D!ad暰H8"Hژj1 ?a 6mDp<"b@D<UL^Y^Y[1&;?Eҍ,yAs>뚬QelV$h"6DL6! -u#F ?,No/}O\[:ctw&W%Az [Ye}+Ma=i}1n:VnvԓFm5ErNh<޽lE`g=k,g9J҇,|ۑwg\Dd.Z@ ܭG)Jq( EJDxLGUc¹NR -mk!(u'+TC"\w KEo`LGDZQ[_Du$ xg܎:W­6|݄+ʕ)B|74D )%XmGwKmCsx\@qiLH> !7Lp~>WgjR!JiZȔ8k;EZq5-QH \*݊{)DMvZlBF1ߤvr_췧FCBe[2W7dr_mn5w#hlGXDlBh=Ѹ$xDи!1{`jLx݀@|$#'ܚiճݚăZ^(B7I(A'[[zs>'jo%"ah\X,]87P\O;*5E0zHoEp9($7 Ո'7OBB 46yu W*ٕ(Cb:"K 굺Bv>i"P "i.~&W P4D{BDmG~KD|? ccՃa?:Ͳ&':\|wuOZmcp;t-!V7 OAPwk\ Zˍ-˩iGqfg?Lp{R)/%틣@CeI$RHf$>qhanQu޷.=;IG"Lm'gӝPc^FܮozgX=>p*aR>.n1tzAQxel޻2+\)w,FI@d[GȊ^$W?dǜGa-¨+ AW^0|$F3H^կoKmxtR:f-"rOHo=J&q2V5nt7Ua!f >&܅7;K#O"KFD&m #׍2>8XBmVI mYЍ /?3k#>w%pycЭ<X4nͧ7Ե5 n|wWI g$wmeRlNuvk'}qU{{V4`fc;A!T& |Gٻ';%4Ǟ@>@FE ,Y5(믗 -6t86#2 _*1HXeȲh#x="0$ܺ-<^u˚,M˸$^ 7oWoklH )~Λ뺋Tn =hc]GHmPڎ,NCka&H=,HF} )lzT'4:Vmi.>PBB^B`u'&aӎO静\BD1E!TBp{ػY=}/iI( I@Bgr[;w1͘9aшyH(FX$ݼ9qsK9$Ef"2B ]/klgZy~ iw~UVONk]n~~}MUknfR)Uıl:ظ1^NdyCEI8f8"S-CDyXyvln+"(>Gq;I ݛ"Bkn(+ٵl" s/rt'" {,o#8 !'Ap"؄8_@m<^hCo2/ʵaWuO,7L{N'; /*%Ht"l^ayDnwptr%M_GY9{%4 xi4\jڐN H6k/7*?gcQ:!s8XH n%˚q+@؁dѻЏ踚:F`~9&0BŒQI2ﺧOlIEȚr! w6z"b歙@Tm#";eDǞҿ:BJ}%h-("?"ύ#EV@cТwJbij8^Q+ȍ,ggV]rrI[ ZWIZD)a#XuR"Rw!Q,9!B@w->"Aρ6YO+7g#^(|R" M=, _BDї ڽ _BrVy}&|%q6&?'X6|S3s_҃-C#Me(.q=v;DNOp;w '|~X:"拟DA֞"s_6Ț @90RnMlFsdhN̲qYW=c]P6߶cs6yz""Fv>[ˏ -ޥtD-^Z6_ۮҥ01⅍[D{d;$־gQV C~ȫ`֞#Bz"YGDk׈=kچ7!Ú{#$BhSܰ1tʋxHL'k [WlP\J%.U|0{HGGH;'XbWv~(ɔH=λtllϽ=6u= 1D:A{ު9e*WOٰ'!ZuA/Y&`-78} bއU@ o!T\dugc1b}NB"֪r6j zь:mS !DyDMkll>kq| I~Bg=1"9A!})D&Unjn@~GH On+`>@@D9nCdm,As }SZ `Y6_"lxV!y!/#Wp;&5P.YyetUh=} ҅x:D)˄'_b)9|x~{hxژ{њivF:#lx(S۲!5#=vdՎdG^DȘ$2cu+œSwG]6vKߔ,~22ļ mhm29_P=HE><4g!]K .D4"&›fAz`j\Ds2a"f[ ]ܾaY Ljm.5GȸT6_, bT{9-C֣llڭ-%EO!\P :K_~ϓ/jJqط ^$x}8F1PڱfЃ˽7]i8O` INjK=-EMxE4;ӄȃD+uK˚s^ٴd-J@b)ZF9n2zG&8jk #!-l/cͽXޏH]9&X;SgΌ7ۓά`7&7vR(PTCOeׇPb-~1AݑBuw껅he~]7-Gջ)oHE o1Rb!U6_=,4=u"%Ї " c~j#jh:8NTtK%w+!w+2!ϯfoZ[_C6y)@|G PO 4Dxetsy$vcbkV[y3̥0֡'^/m5or{DMxQ'J*;p&D~{ڽW9i4^>8>w#~#ϭ}N ۘbPFk&k;z̷1T!w@6_]7vU\~GT|/F`XFJfGݏ6[sƩ#:;눺 ep.95Y>R%qԺ`#j;7tD]o@ 3hQhB+[C BqQq"7 h(dG hGs+y3ƻ@Wխ `R\f~¬DO/U =Wqى7XMH ͋MEk[< V"=q3C7":[ڳ~0w430 $ރ5֌CiShk1Dk'OX>aIOO[]E7zV;Xzz-^̱6V^gO~4-O'G f?꾋j}t!yGZɗqcDNDj Ax>R 4gJ\sԤ&5aHEi@=-G0#Cch㘮#f|B.܎n`wϰJ\7g{=Z\E9DǬJluBw▞BFA_"Pȳٽᦣ*E@"B`{d>, &x'ɷ"}l(#ׁA Tc~{67O 7aYyU]gh_s C ZahA6_hZ-0 m>꯲}[a$l||܋9w,!,syO Ǥ8N9ՎdK.h̽@;ǽZXc\M^~N'`ev1ýr4ogHDL{i¤Ţ G|bWص# g[b}յk*cm 0.^◈8m3推k=!ܨXL%zd{=Z?l O(]o`KD;^׋nWsGsjAv](6 ߃<>H1"nC:DEJpg"!<Ⱦj@!zBs<m,.BZ#KԎHW 0wD/WToG8D>HvJ-{'G|6Eakt>z"zѼ?1c=rݣQhޗlJh}ޏkV7gWٽ{<>^M=TG`ߏ"^Dn $+k7}Q^/'>\O F}ml#̡ FJA=g yh ,~^iQʙ6VѺ㕌'X/Iy޻hءdECDјפ&5g'E5 n:Ḩ>7J7{l8 ]p6](28=/ f\v׋s#/>?4ϯV_kD{.Ӈc^A:r!QHW4"dDmG:Ŏ߀0{{NFdBH[ڮ"yH [F̳=N@kN<>K=J; vq0sOB>DXLkI\ a{1j)‘[7<"{ͤ=ӱ.AAF4=l³X6_gbsW|q!2D/Xa3h܆ihv"’RW魁 IDAT#GA:a;OߨA(⨾a~` ȱh#z-Kcu"ZЇy0RNvuWϙBJ+u|\f R`ƒlr'-V>e B.sp0r*8mX7vm?+&i%,Wo R\>&i0"Nh!Ok)O"qgxS9&"12Qf XN=NAX|FͳeyY?Mgc|\1!-c@ Bz7b"{ nwV#`FeC`$1щE:u? c MK24N@Tk2By$&_`Ϣc4w޿IkT v"!43Cs,4?]N^6B{.;=&MpA"bw h-O$)ٵ !#z5}`4hml̶[2" 0>٫2B9.E[RMqڮ Nx>х\~7[[Gr!WwؚA.Yzet@Qo.x;Z&,9GR/3(2m@Eo܃}[!Tu^( |!/f8÷!}7y5rA":Dm%§#Ch_FDǭ-:z{ GKO\?E'b4"EkzBTo*a>ss7OO]a4C(Ž=/U :DG#=_EJ re|(C Z{N>"O&XV-v{r7F A4`>E@v>ѻk=6Qu]eMgqRMw1l4@ȇrK;w"XVd/"b^Ifddaw!xw!,ṀX!?sM?d6"wr/xoob=Dgze#M#v^NDlvF#E.gH~D}EG#bf _ZdAABE/[<-(LĠ/Z\ -h~z!v9ֿ޷~|iv!RuNmv >9̞p@4{ k5S$A(Ox\z!`4֟&*ͮ:g1z1)J?^/}60v"6ٳދ;s^ԡhzgG= 욯zuDeO"/2z z7/<ywm@ќ^h홍ޅNEqZH/r9jdWl"P[QZ_kD"nR냄F-=cro6=WX3:GZ"t-c4)"^J0-h_b?01 D\< ;!>מb@^h1‚ȸy0WʍUe$[M^R#т;a͊^Ka]93>Hm@:!dtcb!YNXWKv Uh=ڸ}x ڹbu>v`/zU<`mzmG FA=c;RPNݑrN}/G@g"V# a-zwkk"X^B*hJ:!b=u~Dus8Q@4Wت L1/.AlShbس,tGڗ"ͽE7X_܁|چsO}=z"zN6RF/"rKn! +P 'F v+cJN4|*PGquc*d]`zgRг\f(/p~B.S*;bU}Ԥ&5zoݎ1RPMS,|Ȣ5^y K?ZL\o ƍ!TVٵ윍<@v;ópL;]TFy*=:ý{u$FB0Skhل7[+Dej"d2$1Qpݐ>׮>2~UcKjcPgm" ? xy&5ّ hQmڍ;ﳍ׫a|uh3w0#wvuD]_B^(Ђ})ZV!;Zf>w~-lid_ ]hchRˏBmzοj-`-2yt $sPxW([Ngt M'ќVI Hx .>C ]EXgENtyrÅ(T92X@Bde 7zR^1"Ŗ#1O~BF߽a\ aסϡ$ŭ}BlB$~=+v:RuZqug&!w_L޵vK K=x30ew!C& 46%J}z5hܜ"{yaGc<͞\l՞aZ:W5=(EDF2D>֧+|@wK?=>S4#h~;D@a~mk##+e6l}q1 $RC -!>A0~O"K5(Iyְ@kZŷF L͞B.SA8|q{ε)5IMjRWa/2;w<=rǫMHLB{ ]#b+oFzHG;zۋ ӄhwX"s&{Ȱ2֗+ozAVQ%^sFF43cZМ1H\kCy;.J0tG0B/jOܼC#BjIFіHS0qCxўlx=%haB&5H ⋮e]-N8#Z_q$狔hrmGL'kyB$B ;Bœ{?(FdH pY!Y\b拯E fr&R;@T{yh,'6#Ȃ7ص&Z^DVdϭD|-nJ6ڍNFVߌc"8Dv4Ɏsd;@-F$eנ9Djxյ(D9|W^.eȭ3YD`D¾D C8Gz;R @+lX@&n3}uDtHHƕxpUX7!2B(ehn4 7ⱄdͨҷXHrY],߱1? r Y?y}d;֦ šR&m5%0r3CuhoB/i}|5{)nMWLYDJ}h^_rսI{FYm| [NXW:޷)B.2WgEOX6_ trGl;"xUʪIMjR@ ⏪*m/=%2ymC޻ Z ?  ^Yn6X?#/6YBN}Iu _>4r`|l&5#F`I ^J%2;Q^F{qAYT X'nbNXТԊB('nP-Z'Thw^t_X~1 E|qrIBt&wM~#|)qЂ:d?,U}%wEro(4>;44DAJHqJ1JA"h=)q/ y9"c{ @4B4`1w r^m~=6UϽU_FPhGY?O~+HCeuu];֮y0hZO &#Hzx?D\䡧3ll(sW6_ˮk*4/G]wCY?ܜ;VY 5Y{[{>DdZ/0<`aO K|p]=7u"O g=ǽ- Gt|I0YAqk\՞CK@І`Kjx~hnTp&?Pj}yDIDoElB.+WDsU|qoER'wGxhO?QD2?'*qr$/ NuHg%t.~k8yWHL_FI;{_Bfx9 HW#w19v~=تP1a}y;&a)ϓy {9KsxLi|щvzaFBԃ Q!DL!Tti S s-m}ehEd6DxOH߈oC#|$ fk> GlXˬww13!,^7Y;K|m\jR? Q)u}%v7a3vw7tz5 -\/r"JFPB z=9'~)PoC5f' |;/=F=|i{WsihtW_;BvB# \81uhSe~ߌ6\4foEDv5hAoܓ{%_Nțn+CYUr]݉Q,wϪVGOoG˰'[[[ WmIMjR@LC9>QeVlwg2},2"g0"BrByU?4:! t=&Am;~)z#9WhgA(/xte!wGרIM\^XQW|-D2cz`X诡B"gЦHx|1DۻovD]G-B A4RL~R^68/F Lz?EnDÁogŏ<#,Įm3o[&N?Gd#B7֣q!Ң -Fdb~{Uʒ]ǥx|""hUc˞!<_Hv%T,#/HᯰZO N;*&Mr`\S"ʅ\QG RxvbJXuUI3 ;ϻ4ZzG"Fl BD"_Bb+6!rhT}eZ*#+#Q8fHso*'Ƿ,tF[A>0HkE' ` •K=y'#D -zٵ/u¸yш%H^׷ rm"  QY )v ޖJD*4Mֶ={ J B7#Ʒ )vMgPB.l )#~:p?jՉ{B6_>ۙ@p%@,=H9^R/bs=h-XH^D,/$WlYxB!ȹֆA2dEϝZԇO/~")2XCNh.߆8Bk0Xkd5Ȓ=Кw3 jCX_BU/D+ϑ:FFjw\Ls mΥ, 8/b!i;J^g4?!)Eh]yhw O*2E 6^S3=ZPAԤ&5_5dD=p!GEED XBaC砵Czᣄha =|Bhu#,BzIi>JH5aB';VӛKi/H;nAd`a3횳AȰ՟v/ 5 =՞\##_#?Bi !F :wun`lAXŽ{ z@wOcW X{<C/U ǣ(hhf ڴ`N O~PM[FuZ:1F8o%yhhe?na:47ET!mGuǝ1mS( r;;xn;.I(|SP^s=? iK=LܽhLw k~ u⹔*q7k m\<,9j9Ghl8m(2B.Rd8ˉH<6JPص m[Pzxa#Z=A` rP"LބgvWq-Hqǀd=-Ib'!!A5 )0/ɁHDP>1=\=;mc_9(Ib$qs{)Da0Po%+ߺ'('[[E^TARo}j~+!!4ޏ"@ꤠWl"N݂E]# ]?WD~DTE$,kymv~"ve }_̴gb} b݋j="P> 6hCp azm`Du jjRU$f@ ˬiHm&xd>6d(~GՓaeAcM=%ID^5#59sKHEq[1凐~ c0n9FR F02 3fr]k~H8 v> B5CAuaoC?s*cygǺkBH0N,!(>{o2-pg}@(3/ߌP5d42Rl8'HzBg=ZԿCFxk/M6$!3j3oFD+# v7.+9DxBOhOٖL5UKֳK}@Vɷ,h[{۲(14'[@ 7 B@<8xrRL6%KwA֖U ^gOd AB"BfΟne!I;@bbǝlOG˙\kDn{̈́\NR8}8"-r9BoVrvGA@}%Ujސk]Rh.5>jɍvF= Yb!ƄʉQs{3R8+ߵו.J$" ͿVL"wsOD^/Ekb?Fy=ޡer2Zgv%-6"0Xg&{"086_5IMjw#ydt~!p UȭK ݱrl8BFg>t>4cxknxkr^^3p*HF EHC:="!2n̰I #'G#5齧 &h=|6_a,׿)ɓ)ܯ 6H)'EdeL˝-A0zy8d`l5\նPj_#ъ0F=*2؁XޫZQ&d6-/Xp/)(fhRW2urxż^QYS ZE 5Mh@7hwǝy]\9Xate4X- 'MF:uh-\nYfǵȮi 9A`@mRJB< } tRxHf~BeLr n>s=׽k[153V ~Uezdut5"B#Eg@ gO>%7#t=WDf&ȍp܄@=.ȥy \ܡyF #p8RGH=d}LFN7+52M@]6_|;z>AX |1"d"_IZ05foYr]|q-3:&5߉dL_DM|?P)#~)/@B. OA a5pOJ]Fa+! CK3wb\&YM4%RI## 5?yr^BF-Hw@^$v܃h Z8E9^g&+UEHz6tk}R< AI<~ysyq;[zFv:Bᘒ=`qeBfGj>^JϜN?\#|6p<'`BC#huyDtBϓ r "ϻy6qGd^}OQ⣄H'0h/IMXXPA/{:@8Rv_kmU Nx"N'ծs E]>" 6vZN(,D= 2BTc$Te+Y~Hyyb:9J=VȚ l7rvw죑Z6fōxz'M#~+e4smlBe=$v#%='lA[[vUv|}~^rWX칖XlmE(P&xc-?| k4V-7 9/bp7"ݦ9qΏ gzak]+s@reF~w5`aNy@V%P%^:.FX_btjR+MuY{btaA׊lAyм0u(oZ#*Jq>-2 QԤ&1?roa7~@^m߇6/EX?Sȣ{vFQN!ys?1u?>lDѳ&J#֏-!Q7;/^P +5v(!L;ق´b^H^d)BuHGI Zy"} xAxWقc: )PJva$$

kyE,ۧ";vD%mkΞS/$ގy3=} \"d!ILs0pDdE6G kܕή@u'7ˆI~مj#ZC֞yfi@WLt6(Ǻ%  W=4h7ڸ:!BlGQ< 7Xˤvy}~N&ۀv;^ށ/xpTS tF]ژOD$@bttw5r˔"({A.ktF]]/;$V6m:D>m2{g ,Q[;bex}5e;HzղFQߞ-B' ۈX*"3xX6(I8f\!"'9ԺJOpxhmīR$XP3`3M!?;If2Jg\f@ fОV}{twpB\[[7Q.v 7'ܴT/w-=CLuzFBcҔy@Xv߉H0;qS26 Z} бgkq'dߓ`3e{pJ:,F3>,"Cv&k%Lj 2 _o hQt8%r,$kw?4]o[γzi3K\M;îa(-Atc\4緢}וK>d»Cp+wABCw9-wYL ֯g4%tX)C Lree.:h~JJ-GTWctu{sԆF,1\&-ͿheR y! #oA }}Ydroj%:ialB#&d+vkgWQ IDATOcQ\bDiQcq&FJ$n~M!E.b7 Qm=dյa*td1BX!h )y n.!x@3VN \An0W'ٸG U(!^ciSrĭcN{=A.A5 HAx(NFZ]pǸ/[,mXo}؂N| 9IC{I [R)R8Anĕo|p@fΨ5Ժm>kPH4[w pHK&g-HVײP<;Xz{A:ЦZ&|@_<L='1jeQD:afDTxC{Gɻ4$BTߋ%7Y|Y#҄m$Z+`1cn;>'bs/z'Z XaEﷃw h8ىȚ8.DdQigmln@lx/+4n| 0yuq{8+R\*E 6"BPɉ& Ҹ:X~l֮bX %ؗݓ\ A\([B~$>uTB#c$-X%hi1O{hvC֗М- =-bmoR*RO ֽ0eH[}i롳A1+qOeR۷b.{="߲V6oB.g" 0IBD-=9f-:Y.DK)>dM\qS/Y+ݗh{.a^Gã*ŵnC"@JDȓˤV"0Ff{vAkԵ1Zs 1Mg; AK*x HmRoDa($""<,zRGس4ŒD Q!ߎꑼ؆DE`drS鵾"r­<(;R:$sF<`EȺ\Drc;@î+W2,\N!Z.љ$bms}6v k3LFsm^!og.OLaȞgBxr CYƉaVCUUi(nr!nΆ~Nl{yc}w]̲ WF VlUt6(!+Ny&$7ΞnDL"yVDD eec AЕˤNgW!f'DP` **7nqjDZYs'"DAey9"kmNM\Wn?8&!YnIՏ<Οe׮+B h]gV@ƺI(.{{֍JVM[zۘ>،–\`s1p&jDBt6ߊH:ˤvtF] dŸaxr6z_AG?5R)Ϩ<' ,?@6y, O|;F24Mҭ-BB~pi7#YBvu[XzK_J:?FcVtPOd{&/oABыkYnOV3ꘪ4!oE$$}Dm`͵#`AD-HW%[?8~@?"v}/3V}En=Z=6|њq`_4-G̪GvtH?YnpH:? "zrq߲1W 6#'@sq^=l-ړY,%UW Vh+7CnDtdϭ>d9}#ɲ<' hY{],llyiA'"t6."o:Ѽ_^d{U41l{fDLMꕻV՜7P]mc[+"+ o=:ac9VB9q6뭭nT:렛ل<3Y_HNf[ 3h}բ}`lvC)5%\ /#Nrk1*=IkY)R) ^^_qj5η//}*ER:I|H@߷ EW7*ٗHx0rQ lSU,%[:Wt0$IPº!y;r ۭH Q#5QDdt[}} a fXށy#$¬Ew>6εj&?fh#WXLWTdC%L"BG_0_[mֶQ`Z/O$^* {mG!:V.h݊g@~Ю5ZgqݍsfߍB%쉔PbrTw: %t6a-OUiJyFJ`.$rKyw;`6RBy8Oމw}^ˤ 1?{p #2X[w\hˆx0{t-Ҝ]Fܳ1К@@ves5K;r˹hLGZ?~սZgsR\ɽ7lmz<:,D&{Vdx= hM8u\y\%gwD[a ZV1Ɔ@Cp|Z6FX_Ei2!kGml5Bd^/zr:~$YGpS?hcRCpPi$[ PE]k c &'{Њuqǵ={+"Փw+99ˑr+P UMQ5|MA1Dfu#J`D¤q+64a)CH)r<+= .s3 h0M4:b<9T2H8HDOwar; a0}uuZ6 F$tq qÄҎvo 1 xW&]$/!e"N:1XmuOXyɨ\pA6&9DWy-$ug:ôkzъ jMEm; ݲB/4.@HTe㦧R**U"4`ioo0f rReA~{ !H~cxtȝ6Cz)H;Q&;A@b$͟ˤnGdϱh\F b5Ȥ4~47=e%:{fPyLk>>amz)UH]dužk&# 4o~/2O4 9d7HVT }YVp͓Qc *w 7m﫩xz`ή ]ȋW"!{n@ fwɞp~Bmϗ7LjG:5֖6khc϶9Q)dջ٪{36ƭy|><}Ǟ  i>2h`5ksf'2#a}&ơ̸ri߯9>|'"_к5M;pDSh[XJkv?ZϽv=6"K]*޺xI v2R: X^'a *í=6OF *.4ܷk- Aµq|;! P{Y[2eh=kv[ زhJJ[0 n,0Ig"=Áwn:F܄HHM Q+LN֐@rɷĥz`g0Ƀ@ND .3 pӭH߁sR$Cw?;ɛwo$\):6oGе%Bs]Ļ:A!9߅sV0l,& FJHߋȬ}l l_D֭<6; 8;I{+60n|m h7|TIg/eR)qi*R*߬\_wqt@meRCl+H?U|* L֊pӕ#Q޵Lgl)&'1{&$'G \<'Lh,O@I.vq!0xD2jɫ{L+ ,y\Q7#n!}Y#|fmuKtwCw!wh=- dvbc3K6W0Y]BFAl f| xa?OSrl~1+R$ʊH<.3~-7܂6$|B VGy*wOyzcI(~4QWêx?ܒCcPZ2"Hg`_0Ɋh=vZ7 ?#w O ʮ#T<~Jt=jjV4%>`3nυǒ:NHáz vB$~uB'jG 5Hk?A;yv-oElLOUNA-n d}\GeKhl{:xĮ"^@MҞbn[l o8K\wuEv"2z ܻ~;q@W#p~LC{CKm pjMKd Oa}QVN{~pFDRjϵ[^ǥ>D&dJ0S n3{yi6DfcKr1q׭"By1ǡ5Mw!);îqrK XL~'8WP[+uw["6x桌nq:sԺt6"c,zZ9~)!GhC4_UݹL ;lBwtJd.|m`ziBfvԇ{ZBrw/"MV<$_AK F$J+6xĶ56u?>?pMV@e֞sлiَOgeRO1ne%Bu% ; 1@;&pHFޝD tU61AyPWivH~4{/!Gȷ'Swd{5zظL=`" 8uvO@\NT$Gv Oh}p6@M 5+|&Z"|1zW&X9gD>+L+mv}'!KIF@x>=bwwLB~ls0=j28c\>Bdk ckRmJJr3o/ ~orԛjyH^GZ\&.M#94ېLYI{d#aV7W,b%X']F nJH>OMɵyVG[j}jAGq׮w$Y\pFD:X5VBC+^E审Al ށɃw~~^1w euDlL+Ϳܶރ̇u&:OF 6ƫMkc'#m}dL[OFv<Tp7[zlHuOYu=jq"tjJVq R)R)b.{ϭs& #=چ=QLwOh=p$Cva!XX9PQBJC6tvBFw}/W|W""`Wh+,GHAm\!LY(!HQ_@r1܃`2g"HM7[?!N SDJ#Wo.N|>^ ݈jF{` _[ˤvɶf2)zl>zCPqQyXՇ gGHAg5HK@&t0| ABA/I$V`SDtߴX,c.^!2ݯ5+wM:F31@a "^Ech.N@l~ :_GZ-iA2DV\ȭ$`Bs;j_K߀ r Bb [xPuw/!{%cֶ" Y EwIs!LDxIlk~n5lddQXH[uLl~A J(6Fnv,'dx[Ljm r* D:%:g+@0-w Q7l>iz$Z7}hdcZoˤV`,\;|=MGq{JEfC$'R9!k"'wD{2i($ݗˤ ٷ 55 [j~A֖_Ԍ_砽cYy2r'DQL2Q,]C~q،wg8!_a֞C_ X|R)R)bkˤ S)nLj\ 0#lHK{,x!NsRC7_7,0˺l(!"B8C?@}VGPY Un EXpk8q״_#˜#̪Gr\m^n` U,5HV_d] Wj'0PWh ~ye; r^" pn=5 Yu]PnI8"m@]?uhky݅1 $tV/t(c-ꪬM}XB Y,^{D\&uavO,SqeyXBT ڰƣ YSWtF]נlSG1*ܠzP:d)q@AR(<8X*^tL'db\xDxr:_eRHzFcلUh~NG V H=Xft6/"x&3~h.@BpD f".Rh %mնxFB> ZYwG;p(,z~+Bw#p? ?@܄4^ n] 6|?JPl"V CLRt6 4H(md৮X 2Lnߋ`%J0oDy`sRJ;dlw3' 3mv~ʶ"fV:OY [Sً޵h}2w/n2ZM'tգc|J&>נt@0kUQWкbT88+7XޕR^_Dzar]Pn u4!(~/:4\}-H(54\*j\GU{њ [O5R)R)2%/BXJg} wZuI 2Gnw+&(7!3?}nD d\DwrA$[[;宀N4݊dfɋ^ͦH.w+B/Aq#|Dέfw! De{#!oaӐ"2a=<YFGRJU->m"XWinTM #Bl2,Yrj/$ F{ދ'`Z5n4 osQc~9^uWZQQ~;=ؙ5| 7/@g 6s'ŭ{bCLR*(uk:-B$Bߙh:mAFȢd:$6 ]vƦAc<̆נB]KN$A1YlD@Nsy zODZH0} м>7!wrTޞ,z/ 8 oК8[ f`0iSfM\kB܎MYܚr"]$j $_CZ7J,Y~ |)ImB.ڊ<$lG]tP:_ڮ!m|+ X[\~$')"fy,!Pz,zhv>> RDsыb#WDi#뛟#hlA. K N^ōO5eu=m,Fs8b}grrQխ5}Սb]Gq5'"m`"&h݈\:.`l,2O(hх* FMT`RmK#gWwس<>H+c-Z>}?AğkݵƖ'Zȅa.!6wh~OBWY+GTJTm1OMlùLjn!_Xd7pֶF[?>E?£G3ld~4 K=$|= sX=؋= Fˑch"4~V!1ΞMwPvX=s|6`mXbm?>?(QT L#b !8kGўbϝ`Hf7#|V[Y dlUg@-@%ΕК,Lb<}ĥ(]x=ۭ҄ʾ3 Y? /AkdI:9® 3`cU!l={@H:G.ZR)VyX3Ѧ3(D EAThz~/a6Ƴ{wv' d>DWˀKW ,߶Ag\z#!Il[roms"pp:eRHg3p̖@30Ey8)Eڤv|r'܆ifULDB`|-tXߚP 'MGd5HH}ڰOY{KH(5r{{r>1ӄ~"ͿG@*I3F-"su~`e6D@лGڼM|!"~KxX߄\6ߍH_c}Xm|;ST;3qLHґENA3AyՄ3mC;d"|)76"`1`WVcz2alCeu %a! Wlkl65 5-Ϟ[~EX*.1\ؕXW\EOb8ž߈ 𛬏=r"DI<︹9a_~ s@J; _Ҋzt6,f> dR*X:d3)H]-Ó좥"wC!h ~PVжHUT͈gp? vcZ&.`\hW 2Cq)sԪt6!Z4nˑ Bt65_Q\vy#U[ #YZ"w:;xz vhn7XwM~B&8RBBNdƫ9.DbXD^4nE՛B.=PhBk{=|d1F@t6߁4J#A`IrKID X ٌ7H;k("䰍 ƭ<ΪfkQ,1uсd`7F$[-H摈pMȄۃ}EkVd37Ghlơwa8$?u+ $a#hY4u[;Zwi(¯tm>7D ZUs?7ģSFI#~D Iǭ/l?#Ԡwu|3Z[Bƒ=]Qrduz|oFV\3V5Ulh\&N+R*R  IDATg wC6D@g^}7@{ya3ml~6PM={jgNC-kG֎AN;,nY4s'w@2pK !h^b>lksKl$ҧ8gJ<Y?eRc2!LIHXmA!d @C.ڕ AրƲ K;Aq"bCH܃2]kCD8$h7>HHzڑ`qt9 мY4 ~{"BVjDl}3 j$^s vt辂濑ˤ:EȒi.A۵ ɐˤO,@tSX\Fq2އ|2*AHkx5ANg߫ Eml9i%ee.p$w"B4K: ./ÄLʯ'Z/GVz5#8xlv8x:Bj|{@m.fzՈ@@Q U κIw1!JRBywpE56lٽca3=0*F^7Ns;O? z#(JJ// 7NnK^ή{Hȅ17{C@E!Y?h_( *> =;xXKBN$k: R0u5]\ad ,GUHθ{}ep&B2o؄DCMN8P,t;PRÐ̫qRRV `om8#xUCGؽ, ,$qiD'4vZ+́c #[]:~ރ$hD82ԝAmf㲋@'(OxrwU]pC}  A=mDnmh=.FDzfKXDq pBˤX]'""ly:0IJys*+ bbD;6H8܁q\Gj~Uj"IظЦ[C⅏tF]#aډVBsR $͟j%pnY.R$X"$\ˤv՛FZ}횫C`(g}o;cȬ{ #FZt-wKX{ϵbލ|U`}#!Vؽu8t`mA{XzA:FA0yIgS~' ԥo 4ɥ$z,SUއ5(H.{Q{IjuvF1#;ASǾa-_kDvo:1BQkGpc}7PQO$ZkL;q1 6GCYk6=0g\&uK:_MZGs|-?T=_#l/Gg\&5zmfTIvƒR)JX*g6dC`=D MHi9F`qU"LvF]gwF]oCZ.7Z/⿠,#(g"7磱;N:_H8mB} si;+I},ͷ! l$@,3>eHyм]kZlXͯ!(co@y% B݃7"A5lc Ak =ѺrT\<6S!gfm; $8=k6ƛpd ^DG/`EaH|1:{pj[$JYa kK<_y)X{fs\4OƑ(w>>fn*z=h vd412Tvd=,睙%l6eS7l( ] 2 E{WTt{CƣT0":(H'@ پ;3}D<z42u3O{|ßкvG[_uim4u zAdcZcwgh{j@ sZ(Xc漿]j.#s] ko]7Wn3hk/ @]DD'`czOgv%6vLƬ@P%Cdwt{#fSh7u߸ZZ_XÁtn5!,:M gLp@&W8 ؆p՘WuL1M#pHWtFrh+1n=];)|}"VϮv`׸v$g#<܌BPl9hl=[ ևyǭWPB,Ʉ])ÖM37 ᐍVBآfQF#~B07ks۳kFqkY \]3]W\bؗH7%;z#$L 7'ktDj֖Cй( ,oc=o\)"reV{$9X~R9ڼHP#xJbU镫?TYEZ%B+ZOݾ{~ 6|_=qI]T닑Yn ,=Su͕9S2h$XGk07 w*$ף&%$vM;Jq=pn.tpvW=$D7F9yy_GNx']y{;ʤȹUZeb9p xsq鯁̏!priIԪyp[er.4 ކfDȔ N#|0 -үКa#'\SdtZG`Ev)wXO-z2<X=FZm~~s2 D#%ºc 3xV!PWG \- uY>^#*DTKI7W&Zz@ N_-m 2jx !ׄ~}"!1kh=GCkǵSʳa=3e|6}wOTKT?o}q qxgӌQx@xk=;7z&gZ2( EHypşC2:|1?!pK( <@6$#< hwE=H8qE7"(V d?cµR1vc>O $Ar a!9Ϯ=).Bf1–CgAq|/mveE'BqƉ Niű=eg|`1]G5dQBڰ V+= 8z,!Ah2NeMpt a:]lc{);!d磤Wdr"eڵlR~pK>={T˿dy.Y` _C] F m=H|(MD-H@9h>ȵ? |6}b1k@ccW+7ݵUωlzm.kM-!*FT E9ߕup 1'"䤦_?jހU?!nkQӭOYi|b"x*!B+"WVb;^ Оd㈤/$׮Br<a?…+T\e]M=o|3q)$kG.XMxMHY;f 켃xY){[;Vv% 3֮gftܽ?Aks㙿!M:fv<20ϗ'$[hZ mMud+PՠsAZG8d}vy)63 $6aD:~پsatNI %W igu%g@{&W)E_g to s{.R-{Py.XhLAϱMiky5Y\xܤG&hdǮ1W\;䦩v!˘/_,P7g/Mߏ׼l BӐVC[9s6"ұhDAkژȮM ԉV$wiRCkcA GI ?QPK'<[{gLn%EexC>{#22Z[[n (")҆/V;8-2?CBʃu,FD^`'*jPdr~6zo'@80 >ޕf<UX""p}1dIsZc+ΫyLgC\FDXRe=I:Ԅȱ+lh8ӾY#@4dm#u[k+weMb+A~!:c>2B Z3FwyJm$D}K&Wx+k[nJK2i6Ngs 054",đ}vJg#,؁ẗ́(EnkyX+߁0d1w50Ʊc;ُdp!v ;1Y]ه‹ J@<£ f9Fe&Qb |}[m\ql6,F(U&MȜ쮔I owy%(%(}:=iu| )<&*|6}Ɏ,"{&!"rP`"L@ɚWR-oO`uE}S5nD*zv禧h,BYƾjE1]77[;nnL?X, qY28.gӽlz"+g~LpR\"@z \4,#mL*CV.:.D)F2 aӌ@#a{}"Ǒf< Xw嫳"W< >hu}.8JKlAX`n&Wgӕkb#8?M{ zRWc'!p1"6" ẼOyXec@;`"#ǑK6VoDwd9 h3W!GpO'7Cߠ9H!5~} \k P.D r:KӐƚ|6%$kZZ2}G/=>$OwS^-vUY,iB++C{ )T=LHҞAůG{Q" !1` x7G ;[6 []s-H{B2"iJjɑD&Xa;̄L߂u֮iDrlfHu,ح_@8'zOAm ɠvCޙ^{X5t y=!.sD\8(̵/{{!AL{ڀt8C2}a/I$Yh "Z=n=-HY֏NBh1?St[ ??y&VK\z| kE6o`!b Z;>{AeQAׄȒ `\BlL "v&E#cNlEdkU|6q<#\LzCvWޓ&twgc6g"0{'"[@Ak!2 g޺[p A$b5ňdn%(m \I?%|n2Mz\<xﲱ>Lz#TD2DlΒz\3ZZ+yddS( F/D7\}K]@S$MwBVb LQޱh~lDB y{|nB ah_/C6^ +/2kZZe+lz M_,􎧸Ϧr`fT|~D:C83w}a$=͈P@Cq0FKl$X^ȡGɪaBU(P?ರ`-{!t6iG犳YGi퍦 @Sk IDAT"~rh5_CF$< Qg!% Ph6!y"={JyRn>{G}Mj$/bh,EUMH&>b/g'q3кk Bc6/_g7"Gqbck^BHrӊ< V8msݳe&W8]nOF<jQ1ԋA'vE?͛F_ E=qԥi=@m+%=qgrpj-ϲ^'s03GagF;2a7h.==xqSc?M IG|'&4v{Ɨƒ:ugK u܅OpN&Wx>.O L'oDĐoFֿnEdj@h][\ۅ;  ڭ0 eY^_ʮ]N$ 4+Pݟy>#^esAdND'ĢgҹeUodfu%6g!r]48QSǭ3}؜,`RZc}W1Εkƭ9] ](0g`D{4!ۡ_[馉}}9ǁc?pAߴt@.[ j:|܎׌y]*Bxc@[쵗+.ُDn޿z6K£d=%dJ!qOj{=;e㴠Cjj=U}ﳬfao5Kڀ˟8ks9m -#ے-:x2ճ ኩVzB.HO}SH,''@}rɫi2TC1ID`=jקñmK{; q3KyCR[Z'#ܲi{;RoIk?'}mN%$ۢd\P1QW"L!0-ZӡiE@pK?qD[ AY? Qo[ƶV|F_٧nCV6?Bvi/4=Hd.׮لtd9SK ~=Uw(Jz3cx=Hn.u٘~"Jz ٟxgN5YRU SDi ߎAٓ6G+"!~F O56>N-E r6;N(:hu[GmhP#!J(u)6>} <>ž?CcǣVWEDN i@;+8m~bB*> Ykm/9u!pEp q]3ͮi?HX/(^ljLrDdZZ; ,CZZ=H\D "ޅdH h[X:Ёw4uh7!)=Kq?+HvLBVͮAe˽Զ"o%d~͛g:Zs5H:bsQd};,lzuſ+ѻB w?yΕpC`eAY[j˿ UN.F!&ܥ&hsm+yR4'.GMhsjG]2Ap+'zh6wo#jKO:j,꼳8|6_}"~-,\m{HL3Y ːd1AjOC+쾟Y0Ϯu NN| _Hx 59LpV$|lD -AQduncw#HX{,$YEޮ"ke5hX3Ÿ  &([@nǠ!D:1#W8|yL9g֙WKub<c#6[;Y^'W;tB{vD]-QB ZDMF@|_pSSm=GJ}v(B쵄,4e믻&;Oj_y:.th#CwK ?#Xeu+Dp`fO#zgD޵=m@}Y+g}!{n=s*":x,<0BP# ȭOF%Ĵs^nCy%DRWKTKT(v1+Gʭ5Z٭]L!ĭC噇ػM{Qo1Jp,)$[ٕ݊ B+|`tk´.ULćc5DGkRHy=Iɗ6,'Y)Ixz("o;-m%x>ᅗ"CfeV2'*=#Q+FXpJY? eb,ژ"(#LwOZ_lC)8j/Gʨw"dd=aM.C2?p ^KL{AY6fwsxQ>k 콻~q1ͽR-OX#S7}e  \#%awovܾ:K:G5.@晄,jT7قX{V{:1jj>Sm0c!ھ_۟) {yw=ać>ڸ;> h @D y}e i' $ۭEf"bt r Tt^Pȳv4ߛ=K >hcr0rl/#g:F͵F6>-lnE@!yE<3V!7ZDXmΰv ʒG @dPF\| "6&X`o ""/,X]FkPǞ8D6&!6dkXd4EQlcE{$ƎBTdQ[ Bb1hB! ZBp$Z]Rc&{F{f':{nn@EDSqֆ홞*38r{}6N*> 3FF{ƾ܀L?>ޙWظmD` ֏kl^Dƺ[[=vqkGt 6ozDʘ/lz9dr *Ƈ$I ZZE)-D2{Y-D2d$=0{e}[̺ H:fd? &$ =TE!ŨY!(IJVνɽ3Ft VccGPQi/10o\hTG2,`_C 63p=M;uZ$G/~Ʒ<m|{K4nKͩ_LDɝ7Bxϓ*yh-4uEJ.~<9a볓ֶ?# {U1CktF֙c |}[??@seJ\a/`)cGj |6퉨nY}R-{B!uaYb|'AyY8f|h>C󑥀T&Cb1mb -& {hcʅ@ QNF$Jc_K~gE^erMg ֤b-H|VjcH;F08 *OD8mG@ h$)kkќ;1A5\VRfd]sW@Bq+xnYV9/AYH8@ۈ  w5:0>h=Q@UG?@l\66KyxA Mfr!QLPDP3"xԻd8CķPK|x5vͶHLi(Qo/%AkbmDqzԸhDYbc0_1"ߖϦWfr.B,k͕[Um1YMF,cke/Vbfxa{Ⱥkڿ6kȃȻIV|w-:k+'2q$҉H˭;wl A mOt J"z3,TkD;Zm6WCH?.FցKoE$ h/|6]47l mu9M?Q%lOqMTKT˿}ѳlzؔj_6t$Kl/#<ɀn@kHBx1 n ZLpl9,uZ R@9`=¯hr-cL#8-֟C]둬uNKcd\?NFPSDrkUq<[(A)hvL$]>,oC}!(xh /j)EOWmXҼq L>jBa'DRop'#okFuE~&1oFM_Δ;CvA &^FgR&WصQ]duNR-{\ L%p:DPe`kOGhyWW|?=%5eŭKGwMtLAmV0;mL;V_ {gE_ {gprbC 4 6Vlm{ڜ^28t u H cAWeu_'8_g!"&kсt!"1X_deZCc{ZV )@ qȴ~Bv8_W;:t?m֖6) mDd dgEgylьȩMӎDbnv>. b%3/ =㎻9!2-wdF60"ƶ%jSB#4 baBV `Lp'|*G@γϮu i(CțC(d]Yĝэ<9Q8Lp]0F4qh}ۈfZ=`鞥/HYޝy\Hlz0lfˠS"Aewx#RVGpC~ނū]c+К&0 (7bE^D2I$+kk}O>"ZZZ2Lp~Ej;ӐyA3pDDCK#x0%B$=?pʻr8)$$u7HnxƿNL9vDJxϞVUb׍Fv7b6Vh XW{7&F8epU]k8 '@O0hKG)&̌̌"bsVa" n%B2}ޱ[ky aj}U-{l ,#]#/zok&R]I$⦚ʱDcSJ$܆ܳ",4"AE;ډ+nF/_.< -ᚱs+?Qw>b9Me"Lgכõ M}s+jm.>gd : $(OA @D¦_ 1ꑦ0" |[dhr]b8y)Z_Ci0;MaYD2ykmlzS&W8a?E>zVlzy&Wn` 8lc?OkŶ|5#͘[;oCm}_>s@j55bq8XQ@v)h 8ɱR1Wky'w wi<ZXF$6Gdf"S-gͰv6cK=qDzی@u#:kQklf}unFDǭeV eFճ.+\1Q͵)qL\xv~O'!kw׏[{=O~W[Vؘ\A{,F܁E3fҿ*lϙ\4 gw>5R-R-ϑҎd$iՁdCڏo׹ ,'ߏG2åHʠf$dc[%B < g&]C+rYRȍ p[龘]Û"ʟ7'l ~IhH6?Dm{ֹcnX, côGu"NW!DG9rYDt+2p@Ǒh'6"1Yy=nYm< 7_Fc'V#|UJDnf][Y[ 2Y6Ev wu}dvE0L=0R-Je#̵8pкmKT2ȅ( 9 qW\VO7z;:ܜ6^ك68҄QlH@L!hi\p!{yՈY\Fs9+i6!&lDqHO&Xrމh]xf95H5']ݒNB` AGG\o'Xb팕81pB>}7 bVk p|k{s"cjnG.#{ A~ g[|lF[ZS쾉`E ->Ahҙ\7X EX&W8ᕽQ |6f/S3+ SY-ϴq"/&k޺u-olMnM2r_ *SQD]bOG^E8`߄_芖6g/늖w)/dn!-N{>sdrB5>; ,#L<4dW!Ѧ$`sP DzzÑU(ri;n$ zDIbkZhYǐ0|,Q OxlX&W8{hgrElz7>B\a"\k\C 8Vfr65|6{`׀زދ,E,C2©9]5Cli4#x|ߌ,^ i4"_%6?{Y}Ohy@qlcD*"%ڐE]HӃ;vؒI$<K]3ϖwy_"ӟ҃Hv]FK=!eegEXA@W#@2L 80r m\3E $kI9{;09 "N6|AxH屧3bu1@ 65ۘ;ZϷw*$g~:w,%HhsV?]0|6}O&Wdli6unsOʾi '"٣!<ZeO$@!|6y}YyҶ֔=?|?58'AWdmso]K!r5 <{XFÊ@_f9p/=qat/Q̀_!;`LCt2ހGYÜ6IHP!EkG~ؾ> C#N#=[jGwAK;` $wJ514!B̃c#떋bՇZP\CiD4aa~ƞ@AH$ח,B ' L^DH}ׂX ZJ2ZkNem@3 Cc'^_q`y _w#΃:95 N-&[ {z%QCT1p43ېt;]m{*byiV"%JTKTsUXXJ SZϦu+"e#xR*lB$+=콠ޓR*3\Vyo#(*~wx]^0ΞޙBOY_#z5dM#Rh 3~߄<\l~i҈Hjmt7/$h|Ed66Ah3AKYom ֤֧/ 'h=~Є4fnD1ӮA$oRd}'#z?m3Z_g9_hn1[fힼs}o#KZOX[:}{4drF=sFqNjuLk">b-] Vƕ],u  HEhɬmHՐ[{ʹ#c=kqɯ© nt3@Xevs lq+oWM$9_dsnaȺݭk]Ho#ĽtWߢ5r?+I#o9K"Vzw J97h\F"+H!xB$Okm@yF*ƃLhޗϦ2-l 0#=Œ/}3çڿj-{e;2;އ.}a݈D[feCBp6;;cѡI]bmo֝(?JZA^ME:B%uABBM4 ?ym[Wmwo\YH241{@:CRd`uFT!ހ/#0L0\܉L]Y;BVgwW HN@7ȇH0epJ"#Gkm<<;gW&Wx 5D{-˵+>u5 'V Du!'F$->zo##왏\Of=3eu<J&Fl_?<oBV>oZ?.gZ]'x,19 g3B/_g{RZZ\Gkl*z7 fr9?k fRbN"1NHu=n?OA$3!~@$e$+ʧRiFkaW:$\t#d[GgkA 5/F~6"VDq1"$(vCHY?!Ie`t'&m!"R+F盭} B!SPKhnu㐺Ɠhmk{>ήo'?v9AWK #+a v_z'F6" y 7BMq/H9{@ĮQ64ڒ7}? bmGs ]4x]}-qA@hmz\"\jV|X S[]8!kDZ)+̀z!8DDƕ|˭ׇm-υUWX&Ql>_1?g \y[ַ[\A[Gf~wy#Xx}jjhg#TU\aG#F JJVDwص%]q4|̾CHvsD|!"B)H9 ds,hu D^ #U-lK` 6Mu\gR PoxqSXԲ0뗴Xfߵ艻W??JuM; |6!+| so#X<,>A̷2B<{{ߏeȋ5!m$DA"z vDrmCTw_C !@B廕DZ6Dk"+3 V^%w"׆'`"py;ʬ?fl"ANpf? dt]{1gCWkom~uoo7TcX3u͋O@N Y} ơ,!F$T=㟗O5k"p,i75lY*ũ"DzuV*f~/@믧TކȫOWwc >AkBDv|Aptpuݞq { c֦zd$A{Θcox6ل^G53WrϮ]#Qh]{li6)kC#z ?7BW@&WhC}h&EAjjy*M? o!, "1Nя5ώ#ĢJVMH&%G?s )Sη $߈Ȝi֞DD2q1<ޞeaa2v+`s Rnqp]Hn96hc$Rn.+wA{f r#$V[{p1K&nG1zd84, @R=;wٳX'H=+M_B`@Da/VPEƻ^AhTfR?+F2?xvT3){2u#:C1uY,{&I:uRq0M&#TtiK&WȡWw2HvU\a "Z^ ՆJp>T('S3ː5[O mYFBc%"u\aN^1}cź;vy͏o=at|ڡ)|-yYȯ>BTע.B@c!1wsqk#$2B KF1l'#lfR0`ߵԋ,FODij`މZ͙wm^kߕ֖"=~w|. !k#h{<('/g!g@~<"DDxdWb{݊ k$?<=Nz݇H~@n1UF@]:{8@WZݏC'hd;a7վh֟1F٘=i>gLZ!jjBy :3wq&WDvD$Wkc #,7X9kJed^3H N>pdD,Dkp Q3ȡ ʮF!jtX#`hy"#!)?#a_{@1H(>74!Aw~pwh?z=@DˀI$R%FJs>g#)lVAݦxv{,}?H "mk3 6~pE'?9j'(wLoGAKBmnZ9ז6'Gx߅5f[ȇʀwYH|DRBp/p3`Ieؾ7"Biݻ5ь2b% e 2?4;`&WHo{w[vWXwer7! X{f=A@?g$[ULOfR LDK̖iDtWQVW첻]U((Rb% =<=~N bV$s]}uw{ysI;Q{5[Díj b@KJWccP^Np{"Cz?Bsk7 ûE=F&W8}BmL^eD3g~g?.uK]98cǕLDx^9O!3uIP''7lN I)G12"ߎk'amvo]wﯹ~NU/ |#Ijto[0c}_%tOoCv`ʄnDl{//c#Qj|{Ú(bJM %ُ{amu;!Tx]ń!hm\Dh}y%z9{c~a}z\|^^jƒ>g((sGFFs]Ӗzk |23g@9y\'#%t_yK"$Dc0M) D cRG"RZeBe-v!v ]V= HNFpnD=!~;l#sTѻu܏6Ȼl YhoFl"L® ogELD׼h5{i/=QTN.>}r[TI+кHye6#Xϙ^`Cez}D s>_IއBO~1oW'n}6͵4Z[f5&F0_SOz/Z_c  kh !̒ =QOr{R%MWlz4= Xqd}#{< UHҁȄӑEÐU\'OT܄tHmo^]p􍇯=K!r8 v'Y6ɔ-HL&?N$7 lM3 ucpIBaWD1E'<=vxK(h20Y{ntZMYqϺ>w<|eN&GzvϩfG ۯIۯvy3Ǟ}/0"|x ‹س? \eBN1ߞϦogk+B^^?WO bjgӗ?my[>bbnw rtۧ=L޸'ZQE7'118y\QI&W\'#ehm  -"lINQӮ.2~EHHt9+k׉WϦ3{"ʹ{ }tsAڌh$GBWB 9qLSHĞ'Uߖj*TB1&d9>Չ14g4/pkϝi#b$^ Ewi{1kש8ZI )(bnݐPp= 1{)U+ݝhLcrlSB͈[Il>ڱ O>BF8фHƶm wNٻ Ѿ[ڀٙ\?hѷ~a{M _yd1ҡ(cRbt]mڷV[v*8eƆⅫW][~t(a{Zfd c zV/{~LLB֞l5j^@hcRDyHprjmCC 4!{ 4"׎AQ#@:s( Rk_ )p9iٵ@D\9YB#yD!맃t-"Nص$X{m%+t"H9څOvDx5 RsbmZeǶ4D(]& >^ sMGhchN[#%6f֌h܅X"bOAsќ 8]k[w}h/DvBw'Yix]lj=$G3߱qr{ DT{޲ABn=6;#σw]A`Uј_﷢w ?-}[^)\(ݠYXu7\o&vYttZ=U y8VJ;5dwFI5\'sJv Wdy%\!q";@x:b#RDHoAx "}]F:y9֮nB|6}f&W8~V9H'' `k{m]Ůvz~:%} ^)q*ya>N&Fn@xֶ8fdbgW2A8lͰqklN!(@0c|6hdO&WXJ\Ϧ.Ok ]ke#K7\q!+(b";o\z u{G{/ 2l35TW*=mn!fxНϦ>!x(!O@ a:`C]Ȣ䖞)>,"$[`ioE[p86DHGĜ7,j"r별*/QՉvBl#X"+ˣ]On/$VD~p29`ьAY;%I sϨQ^'Ϧߧ#k,>ޔϦ jwh [ѸEB]@%͕8;@!<;"n.FkG mzL,6>`J0"r~ihCS-4/mRQ2smq=`Xp}ߏZȽ͝X>Ϧ/:v8FeF#lzK&WxJ]%jArlCiMCDB ézE)h>x!ƽ.u3T؟O33¥c;R&SQ6-H;!d9sxPL Gœa3a#^ݟEQ "=4 }?P)<\Bz] UW5[yeYN!o ODa6P\g K#S'"F/1'} mvOɎYw"۫zBfE45O+ȞEh|GY#p=„A ϦoCc]bd(pR+mzյRX:!}Tzq)E|6+bGs)C"$3ꐓ$hFo46gy'xmuϑOLpW#=?mnA+/B u;ȸrE^"NEK "_Zl/+ 87y|SjRi-[LžxKo Tt2Ȃ.#(/Fbh]j׽)&G!\ȴ2RBm\$DLކHy9o=è{f{MSqdG!0\ ήGӽKF9!Jl\!;/:dyϯ~`SG;w\uVy9i_׬^umռ/gNme,>q<yrkB~#Rg?Dae]s&WhC^^MZyynnkzJGOi &{وsCI("3P96#lYw'uߒEsɞOugӿ@^OYp#OsZƿнh {h x5=Vh]K]LЊ黟,EtdE+Xڑ1-oC`)"ç6 UCu$a=k x!US37~ vY 7"/!'жńm,ی0<^nCc( g"N< 3AƈCw !T;f!lƬHwB'YF"/n z՛ ]޾1W6@"!P^^h>dyN듫 %lL0a4k|6<vT&WWȑKmk^r׮3&K%gJ!|.?q>VlHA݃>JJ! EAsOU$Fx]SĖcއ@Og R;L<"Z#)ڍPuV#Pj^Z6utKΎHb_R}uTFJB2]g"E[BZs\MLoI]G|ssy% ; M1+H:S,-G`eݫ#9(0'd$GԎ[ lbfѩ@:+,A$RnۊϟqDcC:\h^{[c:[_ p6BsKb}u=ӿHXczxY6* 2AB  7Z_JVkvQn+Ic[9C$BȁVBgpRM} Vn!|-Gdrww1mnD{W/.M(-_@3FQ2N(P /A~R]Rm2Wh}y+К{Dxe nGd;윅-HW7AVBrM-ݘt n$ 9G7@(>:$໤= kaVPj~qGs{"Om D]d}{ 둮O jw52v^@*_a}y"yyDn_NG k>L"\\`}X |{;ž%* t߽6_k\ϦYNpK> /D8tu&WHerYܣmȸ|Lu--m̓c'Rcuk[*Xކڿ܎'R'dL|LezA+R?ݎR&H? B-]{ D;!h} RvȒv4!@M Q^ D@  Z}ks qǣu+3JGۮ+ ɰqB'! 26~j!T^H/y~dm[3g^im^Ɣ;н˰1GH~~P+1x\׎%s= 3gkk1D,&XH'!w *Yd?j5YDT d|9<:yO4xc5N]H>[κwMtk~g 0N,Ao~,=w95mޒϦX\ ȡ ~8\u8)NU !rkxrY?B`Zp6ԂϦ6{Q/Ѧ 7~8"^&`&uhu}~ymb]RlGxg;h7|6 8/+|q: y֐+>hA-2"BDzұMBgNp ]8 sur5L0%8}M1fdD]L=:<|y8&|0Ԧ)ՔykxDO[NI'o.~n-(|RӐ4Bn/@^bF{,g[UE63Z%5u"=R%BB\sT"$ѷ@ CHYȻ N>}3#I4Bsj|6}O 2ClϦo徲>aO&WHG|6=-!s'_N&*w4 'nDF&"$K~h.?kN˓-GVcə s"B>^pWJN8$MP1!/.\.Rף/?#bïX3BB~ ߠ{{""ʮC1Y_Ϧ g N:Em? drbcɹӑ)BG:ozZD"F,BT(VTRD1Y|ҘJ3!~!x~53x.xM1y3?ݙ\ 's+(О컾9iE9&Z{ IP!QCG2—t+Mڸ44ݼI6 lϘvbxlʤRK_?E""އn۱E{p3"bD:<Q^c'6K.4o߆;Gπ2wWb0!<&HM#I vH'x^kCk؞w/an1?viw%U6)tdr >L>'z4砹\4>3 [jsp>};R6㛢>p҂" != f m{iWվ a '&طJ \NN}v9mgB x€%Jڳ|'+lkym{}oCן;M?kg^u⛏7иzյǞs`]+>⛏f^u1:U'U,y[2¹&BG0R@/GEmkC\<7RRRcd5rj4^i j<)F`Wm(d0B>2.{ΡE{]o|'XnBɑLp.+܆v`ƾ{Oa=먵l.2{XwkK?BADȄ{'TY$GVq:FuJ ʈPOW 9d{1۬Wsj{=7D6ឳU΃6N7OlnnJdm=͏!#w+~Ⳑwai`v)pX#D WY:7_qUY@[[vGBҭKb[қi9N֡$$ub6]j4O9+l"E8z++VD*>'Kv8DvDF&hk&4ㆢ`xb!9ޟ捁@q0 Ơ.uSX|L.Bƪ]x|̋+ہ)4#>o>>" ^Zm(O%>N`$_@al#_Ha6cH᮰/JY҄rRbw O+("o x8`)\a:Q Xkݏ+7ȑc EM:P](4 $H(_!m*֒#`p9y)23yD#‰jv/#%Ft !h;!YNB2oCa@5J(*'ǔ'tK˞H_Asl2Y-@$\W40%2B^_pQ&W85Fs )1߂W"2v::rt3sgܳ3՚ٻ|Fo#bX067sxUF{I?gZiz'Ngiv<^c>2ߞt J1,4Ǽ db;hy~y-B{TN"{ٹY? N]Ϧ7ԓг=@slȾ9kåX;Z-Fm3a|{a_ԥ.uyH&WD# |:M!c uE>ھ_^O[Ʒ5XN[x Z~o&WR()M< Ro%l2EJs"4 $h)CHy~ x'Jjz2 {Wؚ@#%dEJѱm=j%V]C+@$ݱrɞDD&EqLDL"@+/Dn` |+M !yPZJp."Ȯٍ@х('BvQx?{!!tNڣȀH=﷓چB hkfmB֟7ٸOyL sމh ">M]gI\jUq&Rm%>c@󫅐sk5{niZ_o}8 zG2D }l:>粻{JbH=<{9]"ȺYODBm92"v4N 'ݻ\0Eۋ'$x5cyxmsڍj9JRjeK_cBz^Mq'R<N&_xE2…l'L:uvZyi mHt!y'H/\QSEXEYM|Jm6]s6Uʣ膆e7Ps. #[#>&<OF?/3\%E$LZ5(;%TpaƜWƣry(9X-ԒXSHxd7l",J1;o%FȰu &UX&W0Ƿyc> Bx`c+dduoY7ߊ.}6T"Xuy"d}B&+Ι,/@6a&$<)! 3rU<.')ߊH4!qR/"$Ë@'11" #3Qk7XI Mĕ;onSt.Zޅ<^pt`C\aj"qQӀB~.v"|>.grf^[A WmgqKpoq8VގȽLG4ס(V=;!`s -kMEɫnt37Xz2&B"VBX'Yg>׎qOnk"UF&DA^*s27Q3l(Yq Qi rUgCz~/|_%fs{U4D} 3#0&7\aYԥ?XqsסHWX5zOA%B~U}Q.ٸyv?v{o"hx^V{~,w.#O#k"wg}Pn*nN3INDC?A9c:='QzNഞ7ܶ`{ɖJKe,aTNB4j1ቐ ZB&Į ߡԥ.ȣuNKZ=y^<0 g6`JlֆtZJ"Јt(J dz"bK q[s6tUn)&%,<"}{8iBzu)kߨ`SH'(RݷiEFua/%xt"1NV '3hY6uWW*T^EB}3\6t!pB6D!;6i{Yi6$+EW/+vakp aplXދ$D.G?2"g!5ț[8|6]F\D} ^fx+¡cJOՋ#އ޷=N`剔;BߎLYUH!)҆&dM:Mnmϙ[e7!^[5{ip* 3BvC&x"{5C$BDK;eoS"UmK4*'Y;UeE%F"둲-z8! ]/5=NGTGG\]Mބ;EܭV{AiqrZJRwy?!sB|֥.uyJ>&O&Wx52 |/ގzƅaMAk"\sCGg}!ҵS@E4NY1ZsNzщmvoO/롇^O"T@z+HBNmCVkRt &Y Z9{]shySɰ PΎ;f?.q9~2?)U6#oPǃ(uǽ Fyo;IL4U^h甋Q7GI+--JCиg 8Z>RI&PAcX'n/߮WuK]L~`{<\%H} y Hgw Lv7 Kh-wBRo78ap!ދRx~*Zk ,G^/ Gpryyo#~(+G x."лmg ("Y{Ǭm^}ݸ? iKkb/YD v.DJ[b.ڱCa^5g#v܍| "Bn,L4h [ټLs^qFL맇 IӏA:㘑<:sxnw] "Xme|6#hE`Dx!}l>=Q↎ʑ}S)ON3U.U5~fcv/pތϯAوk 垯'(\́$9dfaಈ1 ]f|wBXTLN{g V} i vL;!S6V/ +/ӑge;5O)cU_2`ĽR4Z&% .%@1f<S UFhvÇ!x2 d~Qh2D|45 '3R{k&W\L)(>eם{ǵ6F>vLOY9rD\} :Zwڗ=uŻ9@?D 4+hngz;o="@8t,Bc'N~—Rx~_f5D-vLB['+{qv$|}N]:(1W@$VfWʴp|&2/ _g=Q| IDATB k>ޒ~-6NLFJ=0@Pw!BɽR;B~{':mVDh@w'Rso@y~QU|`Ӕ(b%ژ"(И9Є#+-͇PED(ImrwYEh^ 4݄ 9B>oC`W^m2̤Pܝk览\bHQ҈WIZdr8cϴ=G#w9'T&RŅ;FhY\R(Us^2L4[4atn(57LD 8\\DkQ<Suu*WY<ƈ}f˴t!JLd}21oh^(2\*{9kOz?o@+^˄#!x v%tm7B 6\kцFj,F^#+x%+!^Ïun˼ԯ67̥!zyLL:F}c6bE;>} i6}G~ov U+CxRt׷ۭ_#Y<Yw2ЂSooNvߟY_y`0Go M?62LDz]Ϧ' q 2`> l^fm!퀥9t]Y'ui"\%Fgo/뚶-;_E#E|6j2'B9"KWˮ)msDJ㈸8M( aBq99Ɓ\zpnF eз.w"˺!"|s^wo'rJh<=Y[V yr.v~;xZb YnG{&a3; 7!-.OlZSOr}`4:Z!cmwx#⭇`Gc>F, Q{㞨S'X2BE~8ѡi i<Qe<Wr MN.sgk\HNGj7޳кp_I4KAk;лzH{Ӏqo<c^>RhEjz}Ee~x'H>3Z24Z&w-]7kR G 'І&@eՃw 5םlgӵ֥ts_O]DoAd<)yRk8Ⱦ+# B"|^Ȉ7!J|3"ކ\w!3`DJd [c9 m(yĕo XRV8vs2RUkk'T#M6~ 筿*H$\F+g}7FKQ>&'F=Ʒ˾s+iN6DKӶUS]qCLj)=<8Yp"^u!@f`AsB{5h~>E}p J;B)@CK;zsb}ؓ. Eяo#b"v߲Ǣ5 7qCzް%wڹ'w@43j˓:q ohC5Ui®g|,C c{BsA+' y#tjxh[ 6Vއx Jw-yU+LjTNZ*Qg M&'oXxڮ*wni"˭6A&_X%SѺpXOԷh>W{hAi(_VG)M'FG+"jc`GӴR\J& {󱾝]Pi(ѴF G LQ<XV3$܍0i/fWmIo2QJ֠QJJ8y^;2#}(a s'C hȘVElNBxuB D!:xȽ"ȶ 10= Q_ks#OAG&D[&ǫN Jq;F8zwa1B7X?o%?:DVڱnr@a2#F{~c7#w iƜ\Ϧ$cu\4 =UJO4GqB1ٜD.uyZHz L_Ad'c,$l6ɦn`!B5*#"`xПPz( #mH2 -&@ eSȦgw}wvLB@8+WwisЁZDwKkr9^3as/U,3 {D2Ģd:{$A>cyX"nV=:!NY@~:# yz?z_j{ J)cWU:ܣdugs0A#(\h'# TO za~%t7Py jm\@lX=r47x#x] ?'_D2 B[[d6F'tӓϼy|0ؾxD{_]K )Gnvn2`=U}8 p1\5"XB:o$$w^/NmR|wG"D9ȤnͭȭpD)]Or"x¤}^@ ʬ}`iC + "/3ƞɮ#"Cu]7)uv}CVﱱYn@dU ")*1֖Syt"̘Av:h9"J"v6k_wT 8k?sH5Ȣ]{“3jyv2d#:V%d:{/:;~=Wk:mm6gEj$Ԫ{yX2yQY}xuHp]+3ع|8w}׉ȈwPP:~$'QCDȻ6TpZǣvZQD0vy6 d .C";wH2ާ@[2=`o{]S>R5ܿ18aw"Rhtվ|+h5Z=|z#1r4N̽hh_7{PGl5ǧ8 juh ,5PH:Z=!<m-3=>*wI$ OxOaIJRWrn@}^tO܌Ȝ`և{bk6{tL_w$J%ړj $%_m"x⎳?#lia%'?Df]Uth~'cф4 <,h1!6P g#s9F,M2vzeY7N{/G0:F Xm#/ZG|룄\n08=c5vzw!!Am k'A-;y( GdRqVu]|y0R~z]φX|tr÷/fq"?n}&S߰կ;"u'!"c,ۑ7ʟp]I%vr+@RDH8Z)ZC9$}!ngN=ܦ= AR{mx' ^x]v!1D:45aȋ\}"tE\֡u9TyѓÖgD9/$VDzi6[vv/H.ϯnnLK{YYfd3|ceʪ6bL." *DxBDs yO}s\dW,zW[f5 n$Z%+f_%)IIH&M)*M7#c T"G&#yjBuo[62fr{w炙~Y?,^о{$[snC=!jEkxBx'rdYk>~?Wew h6s~ ]nd]gm@vWMX;*nmsݳ a!S{$ y"~ EK%Zؙt=0M-c0c7pQ[z2d:{0I%FlܗI%"%t>ps6={k^`sʩw}o}wGR[ )XIe ^|wvtPݗd @ָƓ!rx5L7#r`B\9)1)zDΕ#PR i%\TqJ&W#: T"Lg@6#HyKH*ك?{0z݂מ Y5 F7,Մ&^eӭ)!$Bݹ{r2+A^I%7%?S[;m(Lye! m-bpYEUݫ*>jg =P;n't oB }Ο|ƨԷnza8ZF; u?:T/o4l~ܽ>D#l=pU[Ui.IIJrٿ۱t<*ī9l:!k}au8d,!=oBٮA!ht s қ+q(!dt R3^}Ko{6^p~1 R]I)X$)ѻ\Y.'F \ݎ @ldB>VL*B ^*<_Dtx̤%۬G!M[)kN!VDE}>Acy845SIl'#pȣ\S~!e; ;ڽ>ȕ!ğA_6~=|ЫBD6-E-t3!!AfWv'ẅ́ 5}}[oAB 6[7y]K-]VUU|PVÍVnTl-ƺdd:{y.'U <1ֿ!y)ecWx^hcp8L*1Lg?KHPzTBieQ{l'T c=hm&u~ + B@%hT8ܭ>oh!_^V{oCDIw y=޽y>4ފHYT%ŗ"׷HQwEsu n*'a[=ix;=P!B Dqۀޫh \BXog@䰮's]OTwoGVDnEUiq/ >] WKd 3U,}hQS7?ΕPO B:b;!1MwCD9ڳ@tt1k.\Kchoh:!9Gy@{f닽 ƃ~|Mm$NJ IDATnBv}*!ch{c Y1_AWA9`нk<C$>6Gסt7x:~ўl9T&z=*m6YI?BXԋ6,~zq l?H X$쵙TpD(Dۀ=ofgE&d%LgT0MgUCt`x.LgEZV8zDԍ6D`k{m ނN.BdX7PLg[":B~hMos2K up]asuXXZ> v] lh1 kP3mlڵȭcVoO{I2L*qi&+M^Z >9}޿^wNi3)īB_&ߛQz~vB9Sh~K{*Ѻ8d7YZSԅuE%(IIJHL/'2~!^LgoA57ND`\È(Ȕ>+ nWI[0{j Rc/J$Ec_{؄m{Ϊ; ,C⋒L* `%24%Vom[rOP" h#rq'h+~%h,6r 32@ Yz;Z?fe$g#RÉVBFdB)PHȓv;Xzh'{REƴe HǍn{ўE/u#U"LSF{^r: F±](|}AGz=6G;5(CQ7!e:\{]G٫}8Mg<(EĬsad:[%dR˥v,kR<|֞h&ظW '<#؝wVE {!v 8Ȯ'kʙԺu۫Gkg~kWC`b0}6>n9۳ېu7FZrK_1g!+DLA`{.֮{3!S|\̱ X=ԡ6Mh]p2>@З @a'Z %d:-D7ʛ>i#\E 6]? +`v]\xUUhf'6nXc>skލHO*$֣C8LIQDk2݂~7ZXۯ9Ģ>h͝`ɤw *_Fz[XWB,j5DDs,jș䕐%Fс|-:t 6wM~1:|^\gFd4vV@~#? )EBfajZWXt!xhgAg)dn -C(ZlEí>=hޯ#eA"4)$"InN&ߊ|z҆E'd7H 2YO\89T,X>\-II tv:RMO&g""D@y.LEJ=p;DW<py5o KTӸ{ M9bU)c&ُ3sH#1;}-u5\dt.cW4$ُ"@mZo٭w_ȱ8Іs&XLgWƠѾuUhCsޛLgYwP'"QyFk?A"T9mhN&꒣кW!pZ%#= Iۜ4^޵ZDZ ~|oͤڽ.Tb) I~^1BYk,Bк%}<4 O_qk/#/m"Ԛy >ŏp_Z{N$/$)ȫL*zdpvr"GU/%tz4@gQa9A : uPxfB&gӑdVPT Kjٹʱ~w aC:\0DZڇ@M֮v.G8ҫXSw @ލÄ$H?oo}~ BE4&R y֗wOl6FƍFs϶ޏϷa'yܾ{yD췞 ȟ\ÙTb{2$PI%VaYm QX1`x,XW%)IÒLg'$jO'짐 AxFډǒB˽[b]H||)ձn5HDocX+){𮟛Xd:gL*v Dʲ:Oֶޮ+ߵ#%5+yy(q*Nj'¾jHM. C!T vwyi P^;gLg[=HD w4Lz#X{[,[᨞@sByț֩ADl,z v(Hm;kSӐP<_BX:^99d׮AКF{;I ;,UI%!ϰSVhG-Bk-'8?悋$Uк |DIJ׽XK : ޜDFn7ZsqʋW"^LCK!C{*mH84 &!Ru"E>3&˽Hq℗Wm@+x`k_y|<–^c~]nm?nHpnq%SHy5@/zϗU( WBtx`(W@TGb\``@ءDxX[0@"lo@8)fޓg"5n%Ȼ<^VОጻz`QoQ?^8H#d'! ܆ Vm_t V=$F6y5L*$J"^C1t! @@[P8b=eLy'`B`}&2 *-94p9ymu" E н< \9$졗q47@ ֞=s);[W܀oEQ}L*K} JY^avAp<\H!Ri$܍\B豄br׉@asmY&",ϱkDz<2R^SQ[10\ۄ>z"H}+t'<"A$IY& Q3uh'c;gozm'a_2m-lyQ9޾"j#atw&#v֟'gRB&ؒLgGk֓AKo&{'trnWT= t }"oA5 Ơ5?ֶl Jfj!F"*=t8uz^w0R(P̤I ?LQq*$E!W437T63^+ mZ"K=eȫ$K6x:ΎC:whF:(oD1gaI[[޿1Jl4!vŞ^+jj $"]Ё 8Sfўu+yLo7"h+Ln_s Þ5dۏuE^[ȐkVBգ h|v.DGxߴ! 4}Cc";[=W jG! y9kkߊ}H7L+s:J\m]<ĸv.E׽tm敼t7IlLg#c}ﯯLY|3Xtn##Xݤ+`Q{[8[ ̈p~W,j+Ѷ[]<= NEXwߺ]<} и!tF O=W %k.Goyü Lgo9LG1ΓuNG lBJ G )0HHdG]-LgI%VY$(.*ZBE[ @)Zw??S8Оm^`W9ODʱoD o3:0#$mTkD\HHO[K"qmm߆MZSn{w4mB`td=sǭQh4@t.B͗ ĪOE@FKz86Ξ ABY1PvXް(?񈴚wњYjǭ"P9'J̷/6g(Բ-(bWVٿf1f=tv=R9Nh6/\j&U ͘oろt6؜m?YǢB=zϼqn=+*Vn{fߛPE:(̴{>p`5qO+'\ooF Cb5h ~acጪ2- IDAT16*F6eCX`q5\tFgo@8l=:;" QWjJ+tT4Q*^;j^z>H+p/9eڅFKŅ?ϿFnSPL^Z?`Q6/xz8VJ(tv2ڼѡɐ DHFiRhNIW!ӌ6K*i%A`(@adBp:_W>:0=a={oK}X)[r"ދߛСb4F3Q($F' xUu9AE0{cwr'Ek UbrUn5T`T^ܝNDݏȱևO qdBEϨGԟh}!^D^ҹ@$coQ27uS:eTQvutv 7! #3 CW QUIw{$>i6W4W7܏^. (ta>zA+XmZqUK 74S;Dz1NF$*>tFAZBErY: KBQ| s!JnƵX2!ZUјPtA;@,GےFz䟁?+ݜy<?#+}h0s-pW'Ƈ5 RɩHǕ#]9s*aI2 <ye ҩSE pvq qe2 BaB <`e姗7L32>e,%Մ3^VO8I14+GTWbǑap_'XۇΫ!:>28T ׭ # 2B-6Fo&%ؼ>cԊе#;pn2&J|L*1Lg?as}&xIЏEqސށ_.]g4?pCMU^Lg+PV+.  3:~¯ YtC{p=Jg; hO DD5闔BWNEiZb xf4bosHygI%>mW kWX2 Dcha7R^6yĕ_ȍGxGMM~0ʴd|$΢=34P"Jl/MQf#R'~)?\`- =t9ڼk+v9߄cC_DDO.4|{Mo+)t<{/*Ǝ܂Ξ5|>di|Y{" 8PA1>Kߥ֞# yIh-dm=Ҟs"G"*oEV$'&xh8h({ͥj;л$0 mO-VٹevQTbw[,)t( Vtε6h6Vzql=ah O(넹.V@1p׽B琂Fp$36q6Zcy -WCǓfڄ(AUl_D!`}7vD8%韷kȀooXaKR=9 OC{W :[[q[ÒBko$9Hgx'׋f#=S Ȁ!^ o5ʧq*6FH=ÄXpQ^ ?Kl fR~phX?q \E2ķƐL**zu#lL(~+{ϰvT#74 pru,qYLksyu81z,X@T=`wY>m}T8O3H(yܟ >FP^2<czoGe%.# I%&Y}َIJA2=c=$%)+.1AG-<Db:1"tXt\\myvAzdRʹHW4!ڌ&H/?0{"^y-3u Ro7O+J-Akgy ! 4Ξ; rd҆XufPm={ yH : %0sko@8a18ZcX#!md,!Xݿ` yF,:B^M֎z~msC2it+hY6%_ԙ֎ǓEYy&}h{apH{sc.졼=!CȰdG#Ǒ*g.GHx*ֽ=Usm _1(-3pyf. )|޶c%TRы間'Pѡ4 0*Had :DpWGy CH~0o}?L#Ex.h&4\/EEJX̤oM@dq8h8J6{¼yAH_h @68nk* W6| hv6dMs7.Nk\܅rt=CkA'$siGkDLC-W<DV8T#Yԩ=`#%y6)hoFMn؆pEkb4|^NPfs?A9]6VO۸VojB8U0k$X"/p"i'xPy 9Ȧ^9;9FBQ={?Dގ*݋ y,!O\[֜i-yuqh=u ptجE!kaȣvx%w=%)IIJnʙ(񆹽XUaTu3>R(pA8=ȋt J!d:{%y~PZ}H?r) r B:sA^=陉E#RoC:P;tK-;chrq{n'? UBX$и/MʁQ_ra狌#Isd[_ mb]xU=۸+̳9Lg߅v!7 xc2ȣAB(īo\ UႢ{IWGuh?yՄU$gRĝ{Z,j>r>=#ȃ@FBD?,"P.%Cve=cE w]d:;)HOZek殪PtfP': u'w冶Ŗ-V?sHHa~ LGcbps=of!RBDB\At7"(o-RqF#+\`@DrCJti W(Ѹ7ўL*q6}{]{?"NHGU!Gy"4}Y:{m,=6B<.9Ov05= 63Dw2ʍu?(% pF4 rwS=AAz==6~P(yCsGoV9NA˜@r#!Ah=5עur3A3fd\g{dIJR tX^B>Gn}5 千[;1Ad:L*1Nl tȁ(đhA:ah&#,✏U q&=#{A5 ч֮uhG3 #l@A"cnF쫊ͤ}cj%ξWdR)DT"_Z2d:*XM~Sa:KN%٭RkPSICjLg?I%V5y2~)F9N"/\H\|snGڧWٶBB8mjO)Hl:qy-"/Drw!KdtPEy!EYNs2j[`_܄6O@|`Ǒ֏'I*?vϻm dkl $GFjxrA"MYdo}#2D| =,!_`FMǷ}pe}ۈ ֯6GsDG=s"sLgE@v ߷¤Ǟd:1lChCk;pHG.~9Z[:?9WbB[梨=d":xEkkNF"rNA{h"wzVafTb_I%:?Db%)IIJbbƭ a.u-CJ" qbȨ}n"b!Iw4YZ TZvzKq :cGOuE9 !Gx{ ǞҠ afܳ+BΉ&^VB:B`ApPq=i#]%~`sK9! yf}BމZsը+*o<3Dw.O_#(dsok"9 xA#v1Ƨd:ks%0Q oIoѹf#z+f|_F6^h~|R[E7@pMki͍bX|S3m핣Uz5 :};Z;w*rHGgͿ`{xkV$%럐d:;x:,z]ru` R^5@uH5tv9: x8fKpva5{e4kGu#rxX/ʉI ?[ͳ­Tw](RUhLB}<4Ϡth|И&䉒2ۅ+\gk+ {:̞>3:ߎ6wR9aM:Av};[ h=٠W)p]jd݇Bfa!Vڑ#;89j%ɦMcp  uw-W\^Tǂ#!eSHo۔{}v "H9׾vwgϷ9G,߹1`d~5 +C287J1ԁ@ `mfh൲V|>wyhY v><ɽk˷\(h(ɦ<72feU'Cv7!4c=20Fɾ=8dxnW8־j4v"m>.vmة6l"ZCV=s=KѵmkZ}2?rO[@V(Ln *1@1؋ttuN>;?xPEEB+ :h;Q-XĶp-X~aGf:[s9>:|uF<;*tN=^:' ?ā.Xyҋg#+'bK/Y肕}/D_eg)XDq؎n IDAT_BҬBjԏ{!FՏAj؆y|8DπhY2 ڄ SDش1{'*b}k*@:J6&HEJt&ɽ &ԝ#p(P@y h +=Pp ox+hmp@ʽ #fΉ%8۽Qf"O*e=Xnć h^}2ju33=Ae1bW؎]К>_Y[ q1g0ލѼ>w##a&U#?] 'g)Qy]ɵxfH$doEՇXb55t@h@\:~s84ݾ Z!˨ߌXփ!^3^5O%h)-_s?]7=]NT&qJer_e)KY^qUJ?i(*N/)prbP;`lZ85-J W67*0gUԗpXU9/ڗ_ F6ddKl^4b! nWg-Her PF.[Q^އu*F6]xo3*d(tfdeg9,*baTn S~ Vʯlld|D6$(=ƾN# )ß肕EI祈g t8BRQW+)xĪMD`'wZӐ³6V=L}[ V;%zlЖ#lyS#pcR 8q6^qG9 ,M'R\UXU եBp:AƪVM'ST&wU6b]gj|3sYRb0'Dã!^Vd[;@R0<8wEiy |eci3 U㊭S^59C3p0EtJǛ"k- Q3{ (܈a@-Z4y;:t1?K]{vD0g}-"5P}i`MWP4 J3@˞!0z\ak;qi玨 cv!9o>G8ҥQf(2gkؑM'?HD@^(wֳ][n3`,/k3N%noʟ' HPQqmWuۣ~_WGE|rF7 윯z>̽]5OI^,/Rdw*>6h8aЄOP #҅w)лzM^(sԒ]Z -؎n1hYMlyz43k <:CZx]ށuW%| `=ɦS\/R s/:L^QZ g~!bI; CaHM@8mxlV- %%i&.  HbQHW;aI>Ԇ#6Ө#j2.0ފUUӺӵӆO b$APW=p䓻j{ ۀ}omGo:n9mz&HZ7j3V3=d| y/(|]P@s2[݊ :}S{% 97@7x ,:W"`⧸*PHQ<21҃~NcS\ m,E7Ǻ{njS\ l:vo3/ɦuD$N~M'?,4.{PIȈk8}׷kAOʑhܪYܽlo:2TNOkeuUEshn͓CL@[:*!)R44?v_/KY>^EA*cfXJdw- J%Jx6@kPo 6Y5|lᭉ"d1h^M'G#=׍l Px7vhD:"j"3ܐB ^gG%hULA߀W59֟M'dɧ=.xa 0Kc-ͬ?J Q 4Z\"AE=L:at?Oc,>V@sIGK5:v"hv)'+-_@}R>O"92 .&s T z{}bau5 |Ox?Ln>j"PO$>%f}R5?P cѫBWP!K}pev!_P +JnK++zkvC s+N6"6*NKer+qe9Ḭ˻6_03h|k AOer%tyw>M'{Q^];/d7z*kBݾ`Q.hLWkyt}y{3g ՟P34ǻaS;IA5l4߭cT3폷? , `2W~ Sܫ]Bڂ౼#l +KY䪐.gd^@>ed^4eA pgʝRmE]5h(U׷ 0wXMՆvڳdeH* LEYHGU%=׉XHޙU@l䠚ף1g㝞+D\E4Csl}eaHCfC6vlz½׃G7!0h1Cd #CdGB6ϦT&iT&ׅtVdSyYʦ\aF|]Xe)ˮ9?Ƌ, 3΅sy9 ngw9u5Ml?h )28PXEot߿Œ,vՍol7#؊0_Ӌr@KC(lA-p]8'"y68׿O$? 8~Ϳr'lD#yOw}9ka(Ȑ 1Ύ}b?  0ј 3" QXi}>mux6S-h$Ln.Z@SܗOvXi ș/bNs)hٲ$lۮqa ҽ|JHԎiZadĨ|6 gek/T=s*9+^RmwĦ:m>D'eQTG5rmmEМ1H/nt7 ڋl ӱy#{W>Jk,6 {|Z\?YK˟Yc❛S cCn/x0@6 dM(iTplynrՙM'Gd(KYM"[ cq ً9lόI$ =E̹5VSy*B}hXxo| R8ᩞS1{о1x3G箏,,>)X'F]'2cTD~OQ_A0Zl@,;LsAt7/+R A6P1:47މ,:X Q׮:T&ge']ܛM'/t} cpp*{]6|g;sE xއ4 U{WD,jdLF pٟm[[^4]Nw0%sE@/gB*ƽw& }>62(ڵ4%mrӫо}_GLnw,ঞ֗\_ . /+3^~r<ʧ- /5A1פ2dAd߬'v87[ FgYU3Uc a)Q[v% [EǠzw]~{[O1*Y)*ѳim=S1h,jsZ7{q[ 469y +{ ݷ Gi('a7Hoq`G{fJǸ{mԱ>B:q3>]XPa3]!'t&vO.E)?z`aȔ,QٌӁZ3:¯!ݽσ|tx塍l.C-^Qef5Omac+\hF==dO@kx~|.յd_,@\>$ & 4< |#, _0aLw"P ` :w12XxwyO<.GéL}bd=e.Yۺa7$N>NUtTke? @ќ;vD}1eb9EϷ}@㣈fѕ1ɝV|јԡߠq\ {tp2$o`h.3OD曋nw,>cGYp3T:aKSxIpp{%=K˞K5RO+j++2v5.fRB CډQnjZ BĆ!vڎ+\xm|]6=xi[s*vf^tt?e>oxs3&S:殙5?y1 |2D6죨^۾#:W,Ȋđ]dld 9;i3;㐓1F2Q }mNl-Ɵ |9мُ#2 ;l:n)_1NM'2WzsQ~ss.ȦZcs0}} Z6ӆJƦi tf%_^TJ[MNvW߇ע3Z@)˿]vT&޽ & nI Fp"n/R(Іg Iq9k&;H0 ; OM7# 08k\(dXq4sY t8ڐd{F!E@ݵR(:0`~\#dǐ7泭hnh;q ~X~ v})ZosSBkS~>}<!ɁE{wā9PrK|B.Xo {:B*?:}81ؐnFͦ׺垭@Uh^Mۖ\",_G2d[HB &an~Nա5f)[x*Bfd,?_T&7=NΦpϲC\޳%YBVcMǶ7, ſukۂoF/uhu;͹^?JTWX3Ti O@Zm[]av~R IDATOZ=<4?ߎR`܏&ׯWh@,{ꤎ7ʕÈ#4>=pe+ǃ g{f٭Jݮaཌ{+|QuO*{ _ƚ26Z[!ɮ/׶o28l:T*kC@iv!Ln|XE݇҆$]p@hmm3hba =GC YgYB]cxyqx5-W5!Ͻh-<޵e:bҬHerϦ]g7C$h, -U ,yvTbNǽPaSA8D)-^./#1Z]AyB{0XǦo=cx͝m~wCH͕@z4W 4[#:#֘]q<G/yf42sd@)q*€rknQ7&=vdM'Ker {y*ű/j.ɍMXY/I*Gŵq*NޙކǡsX`A*M'Q;pU]L~*^==>Z? l:ڕۮ>΂v5h+ {9Ջ/e٥ )*ĠiD xƃ4rG p?w%:>M'oÖ"%t h9)\[UhOed ^z$h&CCσ ,* ֈ1@H4f{t"a 艣94ц܇/Bdy oȨOYCcѳSq4׽ p;Ƴ@` #&I 2@(+jt7Bݏwވ Z'K!)!vnÏy)-g\w6Mer2][Ev'r<Zg F8׿G\=,d~%ɽcjNy갦vp>?xl:y>ft8ov} |5 ~v!03.<00f-E;c"`NԀ]Zƫ$ʦߏ|BΕeKNڂ 0:Ĩ _+X!pt Z?$Z4V j' l+Xߡ@-X^@ik h|,uGؾ|lBE OqK}K$ײYh{Zl,WIT Ajd KA!ۀZMwL!~7S\3ұHg[C -O0XCtϨXhGancvB!mOPn .Gzo+S{,~wfk D:0Rb, 󱰢dgp<Ҩ*W"hW+QoCx D7y⯰tU#5Y+Nue)H*Fkr*T&w}6Ler3l^/f291N/Fs qZcRE"y :C{]ɢ VnG籲AREpc2z)"g`xyRt=qUF}#w4R@It9iq-0Lj< z ɽm~/>E E%zA"GJ^;v?C0[h $*"Sϋ[<V cm긶5k8O5/!0(8_Ƈ^w34;"fdoAP% [Ner3`zwF@*\xl:ٓ~~ % JϖwkDd.D{uhCys%Rx}_r6bXަ`ӇP:uv0J A s 6xG1PWXWiE$0r|<', vG u%<ҍh=Xj?]ߩ,/qFk:7sG71اF8|@R,X32O!_Ly=LX"vdL!9 PbktksV4R!{*:`ju|}lоeot9+{Y s}hv6fh_o⽕5CXԉBЙMgGl@l:%݆l>1M;(呂T&H=BRtd|ٔ@ީLn^GE'>_@bMI N>H!_Rglr1]c92zjuG#c)ڌ'ۜG{`B#ؼjp. c+J}8#tu`.4?k&o}9;Q eOvm۔䪐~{! T6K}:bYIrKL xO*;NM@@Ґ(M@og.ѺUh]|\:_h k+{zo ^yњyPbXs4g29cAF87k%t(mW\*[M'ow{02mE|!$~zoB FՈ䮏?O>wpoPT&l: =];#N8ډ@UC9OSTV#f/l:qt,K~0n _ L>}a}XeS4} p[4tlb݊@;Q/ lH_;v_ hkk,B4xaK%y?:D݃/\>!}6vIp~9)-h>^ڱ7(x1[Y$X@Xb8ȶx5Ap6sNW8B䘺阃Qκ?,/i7s% s7D =-=cjw7tñg^EK谼ztǑC|(j=9!-Tcء RbX݂v4=#N4'ORT&2퍇"h;t1骁;S1Y9w:}aI\:4l=J#@Ih^oFkg3`/7vLܵG Eٯ#LnT6b-FsDe&ekQƂ[QSs’P_qdZka z퇔J|LAFS?bYW#CZL>->ChL, !ćuV8}*DK]Сrd8@Y"g>Ƹ6݂h1Ȁ4&Z2&|K1յsb&"j%ܷ(ཬݑ>g Fhh7 oF>kGLo?zߔ<4i:]g*p؄C4G]=y<Ĵ[y7 ۃ76=1XJh.' )1ʣjm 1!돧2Ǒk\ۏA빵5\^>,. -AEkRv %L`zۛjkKG<^a㍍M6*? 7 [8F%*==2BOŤGGccrmEB^{Ơ$Ƣu b_JA$dIxY%K%e"h8;~׋8}!{Oܝ7{Yj.WZCW} D*ECOМ2"2": Bs:/t ؂w8Oc}Ow}h,㑍~+tue;ϧl:Y 9d4v< ЁW'J7 d:GT&J=鍲 `lP Q>H! t`_G)>l׷"1Q3!?˟@t^^Bت$M'U5ȦMer_+R xq$5hLr3!C _h|1a@ 20 >Ğ07"p_۞moXDuMZG F~V,ָ>³gzbOoR CJÜ0bAB>v<l h/G׶a}P9>b;pU2r͌οd>ZA0s _m@X _ak᭝SWZBTS~{Z4?^vמ2=a{hK=;5d5ۿ6\=XSǐ9#V:][pm4а#shoy(O\4wGoKy"PcnAk A>;v&an뻎I?s< *"ۥ6͋Q*!{ |Eh^hy-O$?RT4#VFH??l˦-D c ;Եc#* tC.- ѪT߇tVv4拐!8X[aծ=s!7 zc-x *vXVY珮M9{đ3t*< s#x(E,65VXp~-g N^NFy$+$]?O]f,( 5h^Du:TwN1F6|/nzܷa>?_ CHqB(Ԍ<[YmhLJ Xv]@g* d(Dzb-\ꇂK0twBw]l:%m@EPt4{Y5^+AP7!luM<1 R7cInSlna~kKX"S `Xߌw4Ҝ||Rec!c02}ggT%Y$lA- 'Lnb$BxGHtMXR r`>C0Qn{= iFJ!ƸDΖƒM 8~N*oZJX>c\*aޓvgOX~.>z"LG )=^R³-s0qk!VMnM<ɽiꉍ_XJTK~{h/L4o9Qeh>&Ё 6^9ߢƼh ] |#U嚏:t<}UDŽ2''=-҇&m)@Hj4 o99VR U'}*(6U aqG=qPTu3*Ű8{;Qx+^GR!(pW%[1>|ٮŃ5нwv!~Y^6O[ 8 +Cvw%DuˮXqb,}7P<@*E{T&]0^n~@EF]Wlhg5xGcԋ5Ռȵ5 IDATOC)p ;۬k/Dzۨz<$ a#[c`<xg9w4l.G1q'~؍*{Ll 7돞Cʭ;r01wp.aܮR)޹b!= /#쓸&'cq2 |72F.iz ĭH)2BJĠ*T&7%,1HBJhbbݳ~b'6-4{x M 2RF7*7@F[ A>[AFV@)"qzj|r*2RVe]߅aaa9Ȍ(bY<""E7? ̈WF)O@o:6-tэXg!J41j7#!64@ĹЦ*qHb1JR ba(ZhΠu'@5Z/no!;cfӿM'I_V,zue(Kٓzk0=[ܳXތK>RDFyHٯ 4OZuhݳL+#w c͜1VPhȮ1MG:ϚY6<{-Lq #\e:9̺D亩x]=nhU/ ގ9fzܵ!s4kd|899'NGL\QRbi Xk ;+}WCtoÙӈj1w!?G~,/܎bd3 NKgp[ LnJGEk?Mzs*[[eyHY,! ǂ,: oWUs=bC l )"62L6\V2Pw T&U D3 4 2W#^;cloOk[Vی^BT4 #)`l*M'Ler8d<YFȻ=4vӑcaY[ߡx1:pVy r>ֵgڈumlnCLyf]`D<[ɒnOFs̒nd[ZʵoE[uM7+߅w/T5K0v׾#=-D|o)vU܆ iԅ C 덈E7FG#Q]Wa@M;W2=~ؓ!s5"L(:]`4sKݥ",7!֣PcSOZy!qH6ʵ046M'@럑 Xo>/XLnBr^Ϳ{mۛUӶZ_zŐTh*4߿CaXdQhͻ7~xaJԠyo[B;w&$ H7.mIt kB\ьK\/ /+eyFqF+;yWbBrmlDi<#sD`X)f7Yg2ܽdZ9+&72 9?K@5LȽޅ^.dJ(trU*-gRCR\w= 8ZgUbcVlc`y>Sa,6P<h_߷ꚾ=4\0w% ekg[]:ڑ*y=:$W R뮫GOLO@r|brKccx 9 nųŸ9`ST&|)5Ll:q/܇b{!v%#?Q_E,?ڄ2nh{pcc||'o̫67(7?FV2Z{P׾:wׅr틌Eq0sF,y|^dMqmMapa!l.8-H14CX1\)*!Rd=q^D*P^DbKĎFWCjf0ªg lmBՠq= r,t}r]_[~+O&k{zlqm<tT&蟈2JgۗSP nGJӵRs{PP4'# 124/E]q%oL$=,Lʏj-I8 |n:-GhhE+YѼs~}<Üh/nb9QhY@#lT]}QͦPPcrā/ ȨTô{,KS$DiW?MMUBya;` 6fEk SAw5٩Lt~e+LWj ku] SPCOMX ;Qu= jMAces1+d'^[*c}PhXtno{mA@gǑ޵%g/Gv t2 4ly$,ewH*kF, (f>|m.lEl{C{A:[JM̭]gמ&+x+d_ǣ'D|xF_lݶ+X 7|,ϳQHE_Ker+eD+ՊoUNwFHc(oRC* A A-CGMZb'jiw\UǿwI67J@ ((RA1 K,8^W"HK =d7w9kBI`g?s{~z!a:9&9o{leϙa'Fjٹ:X/gE~X  8 8U2azL3 8 h5o Ds/Z@Qu Ӣ҇QJ!7Ŝ>)Դ 1>PUֻ2񭣑uz(0 jm}sa~9qȹi\_t&_`j 1-vX(Q_q澏C$wMێE&sM7ӎ6J5?ȉ ;Ɛ=v_E@xQD,4O1NC oxшJfǔ@-SoGFqc>,e;@2WlX;O 2wTsך_gL"{l%@[qN xcKazemQ#~VgR S,r *w].6ej5g\n0=o,-ZviX$'xl(9dgu,CA$[cHe^{5mQ~w1⨂c-slۯA~/Xh̓Ks;Ƭ%>&;d f֦a)Ԍv浻hEۏ,dҙT``5~iW_9OJu<3ǡ.<ҭwz_0p@{[Zc{h 0bv{iS`` S^PRK[4<~\)CQĭy +cqe+mڑ#܈3"P9JAlA+Ҿ tb0"pjLKGmsmac6㏢(z?ѻD,|4_sQhPPĨjĔ<'-OC Dipǡ6k! P[k򻐖Qʒ|6? !,Y ٮ?H9F gLF Bȹy?rDl%E>%7rއGc55NCQ?]cǜ513/E10{/nzNi<^hmAișz@4rhDc_.cmke=w f6!U-~Ӝc/Mxo܅>n3 f}*O. 0U(/kEA"2Iqj;>궫c?/G&ym}jJA2c˺یtef]q)_ԡ~c4 +fxeQ[( Vv_9]݊|Uh] ;sxY; 68+6ojyrվVDUg\ꖽee| ~]޽r[;''Oʔ׎Ɠ3zSÂA_SӇ^ٿmOTo4lim})CsY@ wTtU;[Z>B`lXKks͟xݛuj&0bg#x1M1l~3Uj>?"ok]X&#~>bPd.j< p%ÚIΒu=f;3?YV3Lr1mU~ԅƬ: !>:^ۈ滍x+j*Y} v̫3"-ߏ;Ft~Ɠa4ގBOJ//5 *OI" ƓshB<ԶPNdheiΌDLzڅ ]VOv&C$P IG o;[Q_) `|wyI"_ƓH[tȷ/ho\?ogҕPo &Tk?N`.Ȁ/ӷkb_wˑS-ۧuVfֆ8S圿|Q3|N6WW~đOww]Kk-GKA3ZZ?-ƚw|K(8֮ WmB~`6?̟xKlWj4FAΖ/--~%^k+X<^zxbN9Jv??Z1*ФhS(j ZtMx_h3M6%&s5hbUQo.z)O4%b?GPf jQ(RV*0} B Y7g^dYJoD%x)v,P}sxbgt<^z(oZ]'6<_D jssLD9BT"'zr8 zpji4܊7)X[RףOmzT#mBԠDԟ!gُ`_u]=br!08t^T[>gǜʜE.6ԞɴUmе;N.`KDηx/EƓA)u>~(b}(Il7Dq+ s>'$&?5U_˲h|nDߋ,E+1] 2xse/Z;9HncxW _.O"dW{DIhuMKKo;isR 6⤺CeM\61?ށ:#jW]\Wa9gњqs-=CrylM7XKks3&nB_@_g]`m$xKk3-"{-Z[Bk9魌Gb{ Zg9ηDYm}*r:Y轷ۈpb&y1a!7[XU(D)L]fxrr?±٘qFFzh ӔʘZf#TrhG`G^6G48ZYG1DQ_``m8R;k9ft!n@}fi6 \-4e( Zm>t=9͹G4{-5hE1p [ٟ΃[f50B]'FMMCl蝳bVT6=h ]@k3J %> 1j F qj;uyɶ|bEV! cWooL^:c\BD4j4m&`4|<z6v1b[ZvD,Yx -'6lJb ^IZMDϪ 99'/ey)mEċVkx#ѳ4\E}h8j~0rFu-pg"Ux^l݂CP.{A0mӆWh]eΓ41rgUaڽĴw|l)xbלӶZ@x ?Դc/w9h<J"TK(M͂O s!v]MΦ16E!E6DP?M'bdF՛{i\ZvGiV-в毊ULN js|B^V3ףqv"ļQ٧ vj BX??څƿ{N`%ivfxzI|kYPڠV, "O|+ꣶHV`J4ZLBc90}H)rtzG!x#3Gi4׾1̵}޷(9@42ZT>21+oIs+ݒE>k@x*x:Zcq̱JQzg(BP { {FQW z :m6=T Ejx-R>7ujC3(齈O}ȑ}ܿ3,z(í Ex~Xw.sMECQi7U,auF@i$z@*D$6wEo6}ͺIqP>~j`FJ]z:GB{pZp -qmќeևYO)44lqnlcY=Dc{?}h>-ë{ !6&'гFm_+s,R (iVĽ _=B6B@TV68cw"ߩs7/(G]j{nͰپl}kd )_?_nH"?ƓF!Wv-8elFUn1=&ߣPtx䰵 kK"oƓ!0Lo#۪>?ϣX|x +Ae/Џ' Egh<9\o[>" Kl;S۝Lgtt9mC BA~1q~MӁKhC`&qvٯ\ռxO1ϫδ* IdĚ t0s+ )Ɠ.ri̱fmۺD,=h@^<nK"/⼯etq>o3rg.˗%΂~_JP-!9(es{V cKEFC{3]>|N9C}B~/oӥhN'ҝBcs_|h/2t^|ٺ-P -OY:Ɠhv=o?j -8Շm[Y2U?yr5ݜߞך(}6pnz6n<ΛpQP$ M`%b[ jǢ`wc?pΤQ̟x+?oqf:8c|G3^T0ޱy3o7'Umcҙt88xB4I~x [WlSp_~ {\,Vqͥ\2`>Gk̖i`Ie*~raɣ\QQƌmp]-K[ci1(F F>U]9b6nþw|~ÿO}Պ͊`~C{|1lFBx!z# )m'H9\A !j)&ża-4 #h893ODo&b3C[ZxBc3daJ+k?@ lDvEj@̦Xt&~8JQ}yjM׫Gk?J9CNXcxrby%^-)꿗QA{%^ȩ'PTRzƤ2LP0u4b?tWhʁv;׻s<C|OjgG{WJýhg:N@ s]O?o`Kk_36!&qڼ@ޑ~_ nr)j.8Ύ!VZն|ew-.1$hz?"{ |^Na<Ǐԏǣh2 t7<}5(sLYD,i}:xr{?b ]'ENu:2D R~/+kӊZrV-!ТeFN'Cw1z^gEjCșZ 8OW3? i-2uQNFڌ"]Y$Z+e2.Ѐ{xQ:=9'2y4Q<!_B@b%J_cRm~p7D,I}< 6-2M@` sY<6KG #j ,<' LZ,&2J"EYhEɋhqr^"o\W3Uˁ -xBtl_!pقW׾Rk̡K)Zє>{/V:>dKm Xτm 4kα˳u~4lI"3tG d &W-wmOm&^y=caV/mscYTIJ xՑ{= =@+A |?[|D,فS͸ho 3Ƌ9ڽkim>wCcwփ'a}Uve&VlXY3wk焥`oy.㸟CG<چc;姞l1%e)%[:%+3 ѧ/;Nytݒ# n6k[m{4DI'bZZp_4ΏLx;oB.t+˶+J;3S/{7ޛ s`jChGZZ+kYi>eNYv4?4[ xi~Pry># ڟZkl(H3 /x_[ڍ%bL%QEG3hG0l p ^ 0M_>fA݅Gގ"_maqf0oH6OK?-/DmCm r Gnym_D K-d!'}„hr,3G!Vfat*VRBea|Esѻs>Y 8L]_ґ@ 4a!_= cDwB F&bgMhw#cǘvFME kEpa:^Q{=LvSъ3]ԃ@g: عDdu϶6<5Rzvr"Z 5.a=H9lO#v^nBjL&7|48?@J"EoƓ!e2aԈL-PIJc~OAa5m{*g4T.&@L-UgŖ1(:rCi5='`\Ds̷!}1"d:yXd8?jje# ('8S9w0+i/ `g5Zkɑ_}+z]h݌4|fO!6Æs4 #iF1M4;hqi:\% ]{?Kić@ZR;T̓+B49Ap7ChX2Mځ|bY}Hd:lp(RÉh޴mjE7)X5e"xD,W{AF E 8]2Sx`u"7is7D*4V!g58B BqbZȲʣgVRu,{Y9UX ? vV?gfNܧ*Z^3 IWKks{mh<*B̧Bհۂ]LCoSmH yPjmmvep-s&fsXI[S55|n`cn'˛[Z5himRл!hǡ O$bDWF_\8ӿ%7Qѳ9r eα=ϭ9iĦ玭a[/h4\ 8{jtZq?^-_5?Ȏixj}e8؟Mgþ|ri~7/ |qNcZgthL)֥hi5`hV([Z@_[3kDx ]3D'eChb /E\somSȹ/ls淭\؇:p7@ j€[EXF=M[f4x6ujY~9^J]D ? Ӏk%hz bN]\܎3-rD`6s9IoB}er_4WW(Cބw&boDKsiGCɩD8J&a< IDATF?CFsN.A:E{ڭ(Sफ़mkM '2C-KR k#ymECG>Pal9h;v"J۟Ɠ/ƓP[oAHMMȇ)lT.;\P!@g-z1v El O$ڌm|9?E~S|k9_-o2)Khzz'NK=,фol1_ nømݥ%~_>H-'QP@^ 1Bٸh<9 㯘}Zdǽ7P/])pYߘ);=CJjgȧ[L8 d8GVWl7Zl2e7 *2fw9^v$rOJ"7zJ3ԇX|vNׁ#uE.C6<([u$@Ɠ[F?݅>wڪQ^f>[=8h_Z4`\S8 V#F\i| (EhE{ØIo@d_gSsk6;[YW+\'8"Ʋ\eNE`3~[ _vVuy[wT(gn e~7Mhݰ9 gFZx;Yu!opvD͊#Wl<4ܼc|?S?|sW:W:ej֐Δ7P8"ꖗ:9hqV`]C~@ҶO[>*sxƛc5T}jѸ@@X ޗOw79q6"0Xj_mƎe)c"fw!aC{P';^~<#[%t]F OK.Cux̠CQ%f*T+Ћ^`UKoDK1|h^-r3hrCM6a`Y "ݍ@G86|W4U|6EuoC@xo9g.1m@`i~CTU0ι|wyV1X(jEsݥPtdkNQ‰(J$zx"Y\<w)廢 Ce)tu|8ΙxroFrFrh޺}Ŏ}Ce{Mn5Ooeաېo )ԝ &b[)Zހf.Dlq F1|u]' fPPw(=zsVxb:[9om*owGPoE}պ+/®`F\o ()R)s?k+7NsyC'_8uNeeGQ% T`C2{vkUG?B&v5u#óSF?bҎ GTT@R2[{vRiM`_[65p$ZG, ;EU·? ҾX{b",BXC ^6~kEk-6B@#Pg 2Y=M#*puu܇"}=-Ӫ -+9WsZ'W{p+Vd?+착UL[%S}~H"_/kC% "d:u,2khPNTag^%d3c9^1a'\mD,Q DO"4PoD}-XvB:OV=`/d~NEl_~sL[ݜk9 jJ0*|63gQjj60ED#Vj<ݳMۥ͵}^k3hAXd0ONƣNAt饦m]G~@S ?=9On2/H|8[vȌHȼB l6&iӳ.-Gw].J/tlCmo 5+2Ҟ9[Tp?eh}܀r9mlU[+pc'yKcsG/a)yY/Zo%z1:Рsrƛ1yZx?q0쾭/Gku =ܟEƓסR@[XDV~o* $֪*w]]azia|\X?S9֙SlM\*<.Fk޽x3gSٿtOܽBs/Dɋ S gMוk{7;D,2n,O_P4v"y))sR3,ܜq8Nͳ_^?{+Ӧݷ|~wܽhoF3sb4MD`<J4#;EG.B*4ݍXf,hrCS 6 %k 4n|5z' -2ni-`2~ &{#}Aw/>?YKf9|Ͽг-LтpeъFeXiUGk#g:K|]Ah>vmہ2#W{H?wQ:[Rﺾ3]|ݘ%Wn 1'ҵKy_z/ہ|}Կ׸)ɕGm[j/T99He3l.T2GLtEY0NM2 ˀdto0jǗM]fv]&l띐 j|\'K3U[J=5hҏuhs5Zb%#̏}f=m܂^4Fe+X/ fوt2,YvN?D\&hlK ߁1ph\EzuDa'CHh9s];Q]Z˟ɥpξGrp1.t>.vO^@Qu rKQHhYUIn@ @kqnFIhIah`ʭXŘ.3xo=Îwxr&#J" l 0ĞB:n~hm·K/xҞ6?_5Eo)Z\vK>r0ՀˌpD`p/@*K¾>G{Gp{*TOUi̳c PEvYTdGF恓OJBℂ&nj*O]ߟ?o\퐒Ne- Bo R+5;$+̞+K-,ӈ@ z.7NŠ˴D,'FQ-ȓE< '觑(H(&GiM"xs3Jc8MO dh^ _ǝU:"UN5ފ^jKzWw"cJ;׷c~bⱹRfCQ^r=zގPQFy/h5x Ap9=ܰ4 5!u"]v܄ލTॐ7akGΩMoPzêP"my?6JsǜÃ5| }D,r7sŨT+j݁xvcF`tӖd50#OE =B{M-Gf"7 `z4&BC3Ǵ,@[ʤF;|ro_Cvnmo[R}Ӣx9WzgVommtZZőȟ욽 +V~hg"h<9YFf(XkPQݵ[=܊t~4mEccH3$.?oSv;cƒCWUWSi[H{NMv}>=l~bZZg"3} ǁhۡ܍u Ξʲp@\-^lRAXWV^-G/1hj.E/(8 '* %;Pls ޏ^#qPm^G!Qx-׼қ[܎}ђhXV^ j:z"j#DM(AQGr|*'5ۗΨ{@~t [Ɵս w}I"[~Є@_+߅wuƓM"%b'ѳ/ƓQ<մtntDMEl5@hE+Z3ՑܔE6cWј"FU5q 5-[-p:UmmЦo>,\QŲ/@; ~ԝH:c5tp~.C>*!//Bs7`%b EڣD_O$bvwa4}֢hD,/ھa&5WX59=K[[%(z#3;K2ٷ鞮)mmPTeDqE;D\Y,uW.QdDAdI &i/{靆Ѕ5+]g9sd8+K[K/y_SisDW UTgSEiXA糳F޴QZvG̹^%XQiI{rġP ;c\2?ydpX},; xNC2c!/WضT;mgXRйgeoAw!δ[v` :giS\]o܋ kĮ! T D\F@=M23e3&<)4HYkkVJ0 {ֳ uM;2 `ģXcȆn#Lcwz'9o턂`"vRa, p!HVG1aIZ4K%~KEnGD,ūX?uU8ns@e5- vdDP{] Y0:2^gX5!`D+22`|lF;T/##JzT{H(ü6Dy{:>.@z8=/N1ZȦ/eID'Kv )َZ\KFdDH,qzO/#siīGez @r4)a!t{x4H,qF!^͈WJ=V!@cWi>H,kD_DFƕɏʨʨMadpdMWM)_5>AOs-"dzi?퐔x4re#bD@"d ݉뺞a;hk]' VOJ茽7rp=LuX \.P%qtQ@,+ w%>DW!G8q=c %9Cl.0 ċZS֖?lk\o>#\׺jpi3]ehl߆W+p]BkclrcsPU㘷M? !z6CZ~%Kѿ5%FѫyC]S'?*?}[w̜0rDx+,0g`̎SƕiЉ?nk?w Ko ~_,?00g|3&6' {Wnf#g] w-Rُoksއ8kmX~O IDAT91iHPH@d鑩tꆺT_646kk:l X ٽ թ<"jiFΟ>+ځ-HtT, .W܎J*o>;RDwGbǣH,q9RA,RCxaCԊ@۳"Ը[** U+GÏGb ]yYdw ůC@ЦODKo?s0 g"DDVYMUJb~zF]JӅmꚶ[oD< vM4\I6|Lb L3DNRI&N!e:4o_v%G 1ҙq( jx9)7Bk H;XC6sc'hx=oPѰ%\[[qiƣg_ANsLL\O&/#B\F,5F'8 xխuz⨌ʨ7dxv_!\L,EWxtzO=Vz;sAsfEȾD"1Ʀ#+sik+5: eƣ_8 #D=~y1>z2 1TuɃ`)!K'O#H,q~TnBtUw\hۅԝp46[)9򻆺&i4I9_禓}]epšw Ko;毕[G  4?v,>8˰m[dyQW+7%3'>qd]G3i:ZpOC]S  /U8qߌM{L^겖lXV00TQUAlS3rc ]i!z$g;Kxq]1dMWV4``n>~ =n=߆ت0MQ4v8*XsE@6b}w M%,Xr^x-Ld)G>^4S³r$G+\Q&FʓZIZǿa"(.[Խյ7)`-Ë@Q߅,i#BzveX@j$2RiuG`P:n1 |EnG~(EKۥ?)hcx5 96GRtuN[,g'Fbx4麖`87&?ه1Hǰ-7kmVۊDmFxH,qJ*2*oģᇐ(tX%dkS*!/TG_CZ#F*"`;O91J[#Ȟ9R4 ؆8 40 䕻n ,@1巁UxȇUGUM<;dlR1j: %~:K$( 킒9iG ^{ 3^>66ח!6邚NzYBQB`B2d|8% ӵ\4E>DZ&Y#i5n[: ikrBK#t)c_(о``ţ+7-^WX_VyNYq>+e"U'[=@gٕ]Gd@ki㞻m*FѐLCÈ=S8o@?*B>d< ʸ= '!vg!_;gעO`:mKKcs҆ X.+O=})`j0K{"UZ|D9@@4РϙruD !L바H,GPH,q)u0Ax)txCSAM0 ㆞Bi,yyHjDK#È;^G/h}Ȃur$RQb@/wSmdԿS+*DrM_ 񶜆dផ/ZL{LVV/D!$?_Q$KhYTOA-9L_-f uP $=BdS9&K(k0Bh%~3 SJ}u,2B(8ޅBy_J|) ^ 2 Y2vu]҆Aqp4+ۧY̖#dm_p\]˲N0Ǔ-PԅSs"GM_Zx iňȹw/ ix;D{ b#g)b܆ݨlU[3bXSls]M[85nZχ؊}>\W'7-waM^\"h90ضz«|} t]Քc&ۢAl OTj)Ma#H$XxKH08 "+ "ɚ$vva"CKƧTFbjc>$)Sk0bAoh"Shn(Dgܢ>s!ԪD5A],bw{?l #x4lGb9ȢVוB׉,<,xN# s<883Z3],?xes>2g}$ ntz©iCqWL@p;"U@*6v#hٔ/q/d#`hG6ʩ<*D6w !@B:Vf/}H+V?B=wu$0TAG]615hXKYxiC@z%X=rwժRtEWER](7[1x _'L6J)v:5Fqޕu?}?Sa9`1Z>>\cDLŇZf!"^h!!ZyѲխQhkP%p\.ǕB[#FhVFu4"595FK%juk[At5H NWp OM?7d= ?QEbGp>?sW#48XzTѥT_F~vs` @$z~Gtų1$ro ~V x \*5 IcsHNdO" s]th$ >+kz0 ,%Rne t8* Ξtژҵ %K b"ҺprZsm8rhwB@oq"EF|tb=Pt}Ϫ8ѷTaKT(ٓ:jg EFI݅BK`>ElC}N6c\޵t ϼoȺy)^|?zWLWo.o}vnK l؋vPdcsRV)X)jC 8MPD\&+:p2(r)/B,H5qݧBmށXA nǃ 9 t3O"MHe;!~:PDTH$QOq`C S J$.]ZKT&vRyi%/cQZ~+ye3!7)D ɋVkJm_Z&CX>3/ţs#pP} .2}4T;ZZU^0pj_0XY[RdOGq~NdU?*ٺ(yIz=Ʃ{~?RD+H,s|!N<djP޵ٷ2/(uz}ݻ_^ #>?} JG#EA6eЄ3#j0_%D^?@yȯSʁ_lhx0K& .*@b+ {jl#NE2N)G-q oe?́|0Y5 $KTEb:՞/ lEH=8^֋lkjl DClA/[tgO`dgO~B=淺҃oê1ݔMߗZ~P _2iYjb" k^8i޺`M@yxAM峍1nqeOjPpXI`oQ|T!8 ^\>AjɘǭlY#d,\?q瞬ׂc4x. ltRv0.kukws_S~X( FU=\P< BwnTHдjרo )2*%H䲎2\q{#@xzZ.he vF3%+hxc5ǨqXHr.K\Y`qòQVorD_wOrbCç &eRZmQyƲq+է 9nN(_csvK:->װ}'*ۼq!sگPayiô2/Vk#z rs`wẁo?vQU"ցOPad@#' }vk3sюW d6WD:IV3WEbwz*> ~x4hw?@v‡|IH,JwBxޫ= ~j#,Z8AыH 3UےF< A5ňl jDaC}w& =s 訧T#)R ֭ڝRlEۏGWDb"բV 8-4V1tf <M2(UVR=_3ޑ&RytǫgB6iX؎#Ěx4,o|ɕ]# g:5{H`.L!^s*=sިw]_eޘlbo J;+~'?q녗&8Öi+0]w H\jmp-3vS~gE3-1WϜx!cV9A]ގD9سg :H~IߍNʨ^^Wc%߉!@pU` X mNw4546qC}y^0% k Rfل.ߗn=bێNn- u75_׷$?8ۻkCs&/LJN7Z6T^Q4ƂuREwX\hkڡA썇F}S}w;PuG5'=:4 ,}CFێ;Go 䥲/>Xg|q-,4iAjj P#["DQƵN Ԛ;ĠFcl&/=IC[xϤ5CD/0Y<KQD]}5ҩHM@s@[GLku ӹxQ~3H"+KLAx ^/E}k< 1,kmыϑ1|=6W|LMWd)Pټ֨@ZC6εNEbƣWDbЩoG" H|d9Q}>Qd$Khְ/XfϲS 5Ծ@ǒR7kl̳\+$o^4^߈&ܛ"0tQXZ21Z򑹽[Mӷ2*2 )E-W g^0=Zn0r7S"čnQ?;۔TNCH,q&8ćȾ|p"DϢdGT H>UUN{7I.F}]r21Vo tKDOWm\o 6o*9tst]d0Y,)hzQ~gtmpN[,\Cm]VWhu*D(y/BR%SWo9nn2SxtK2`ۺc:7vSu~_hSS?8vP)tK}䛶q\`o{ʒAUtϚed33~ƾs֗C)ǟq#sEP.70q ^y|!eS[b4]3}B"A* M("Y56klSC]> 2 `Gë"W|*x r2bLDz 1l4@n(exZH,qbއC` 1L g.E@MuXb)|"F"1 IDATUG1 yNag>+@>GHoB֯ Q?!QD!کB揎ܲWߚ[i!1!H!Q_@ƊI邚 w1yO#O@hJAf\kuZ2Ș<_T{W$JhvP]?KdUɍ&5QjA`<~uo4,wܤsx;w(;} "z_Wy{" oQK Gbgq/8 C»1Tp~XOfbhs`\EmDڌW $=#CCtk/]d=/V6Hv ˽%\ty=N:+:8fU{wtݴi:&z*(uȭ$4!k~r!խݥdʨF#ii刷jw 1+ Hipk'&ڦt(v8H%>T{WmW?${<Db EK5J<>߇8bEb+#EAWg2 Hßk:9"[iNu҂8΍7ǣuoSGGR`ީkR/Ljflk/wEs.ܔ Lwn1yKz"!l#3K"g)`&H[ẏvt?rcΩ,Y__Z$jA셿;S LZq/|<{aPX'6Z}ԒY+{efzt ),ޞL N\tlg=QfbfNܲ;@Y`/.d/|:)"ص[il/EucdCIF$hX$5HdE5v]"<Mn"wmj.-GKAXP'm+]n*Cv*\?D%׶~ʨg߬kIYB^J<:*[z+.=)/}4xq$>_PNH{E?HUd1|7CnXHQz|7Suʇ}&*`Bfc&acD8 GQy3D[YElNwgyOXveBly_vn3mʩ\qmыkϜكg?t555]%N@l7U@A(3K^ fAE۪ȣ;K[RȞ0il7}r ǣx4܍3?XȎsyԌNtvTY޸s_ن툃oRf`eɆ#+2kkflrJu?ky]_*ZL n[Rn~/ٳ3M]<!^Fw| >8$p&!Xb"R}!D! /.x x<6RD9kFPٿ#HvV8h#_" ՑhxV$K|I܄LOˊޕYB3뫏>/vnn ~c]Ya:( m,w`yw VewD9!^<~A]s!2#c\:L!T鎓M7]:iz}e6'y/[}F5QG:|,2g7d¨ʛ-X"W"{r~4zZt>vZjDۀxg!ڭ:d$K\8~VtEbK5j/c2KHǣ]?55}s8ϵ7}<V5*[il~iզXbd<r VdtTr(Ks\ ;(V פzx$&24Qc|HWQ)hxY'Aֆ{u:Wv2s'X9_:4Zqx4zA"Qb!zʂmδqj/<*eM{  M'ţ"' qlʨFW1О45mml߀h_; t\\P4yˆa|\E쉯Mqn1RmoBe .f@WuJ,%#R8r4v]~?|+6ZΫll4퉎c56#6tc۾mk]30gMHA"688{SN? 6EsO^'~D:M\2xѽCH|y Ht%une+"T&r ޝ6Cb7 c8T)Cݷ,KT!is ?U Fڦw vbd^פF@6ՑXOSh7pNGXV +@x4:K)Y5evw2+t6I0yuX2:=?6̐1'ol&T:xlWVl쿁q 'ZtQ5߳oiukS5F˵8yWތHV6[c|Vѽyv!GjRlTFeT,:J2rnu#24⼑vW=dXAcdv$x1"BMG=[i܃L9d5ñOglz ۲r2R]_r$ X%NAt186{T3W|=IE.НD_R]ޘ8 =0ٓ]WpÐ!ׅL6XB`omJ;z'e})|=|#(il_W+%Vz?!t涫 &&[f,)͚` ytcϞSQ#cZ;ophxkc5Alp9T`sy +}` 6wMB]8!6TDW 6]h;Ǚ]~HVaw#VGsW3L˹Y1iqNpGU`q1^FdnQMA?%C&f%pHKQz!2G{ A@O x4XʅE/d疷BRT:0]1xuԍ&oUj-dq15al,xXHTQ/=Bgm<^JuHc x+2v cdiPj.c!P0} Q/qIȆE"ɴ^NCJ#@FvO=d}T9$B1T;69I>W fBשgBHv<Ht ͺA_Ze[ty(U[O}~7o[|dC^%k?** Fb6OːIuDbҜe7ģ|ym%i2vxxp̑l{~sy'Wg}Cýcq ;nƊM[TђU/Rhx="Cȼ̕<\Hd4<`FxƲ{/ȺhY־rQ9\EGRaV{{=&td~[)7 ՅHSHqCAnG"ΫDNӹ%,姎͙"{xp{ڳ?u"7q %[`%>4}4pT뫐i_\G Baz$8 ?T?kdn8.~1LaPo<_u~rLY>+.P혣P5$k@U uMͳ-4wMfZRhKJC]])\p/_VꘚC~Gzdb!:Z>/$tkƂMV(ZSr#%P4PQѴL k!N'5#@^C]ݍJ+{i uMT_~ؠXC]F&֛,hx}$8pHx2IHtN!l@6+x<~ h !HH,x4<_q8,u/"@Filf0ch" n >lĺR*P?4$w-H ս#K?xJWϞah+ ~1^E^d|9سEaܲ>tcٞ}^d4muq;*(o {Bnָ }w3f}1en2n}YnGtқGHk7Bt,@ ȏZ4^1Z>־&wTFpH'hAݥ򀌜{#86*Q!{X(=ow;ǵ)4LVIы#Dzn$Fa`c<^%ˆv4vTIyK}/O75`nD*G(u͘aۑƘ5LQUa(݇lkY]mšGRقiL-uaYxnO]P\b W(v<]b8.N{׌m sk5j-Xz ~gI \8CZ=|%xAXo䤿XdG}XNdrwb@Av*yÈaVYdTpHd|d݈>$'\?qv8s YyE> Ҫ8t_Fq ] !|\'fNߌLːEOahԡLL4yQP }a:".HYHg^*^3}+K_ZdSy4 IDATV붥x$տ~Y$넜gvoHfz4H<}u:4Vu#jK.Iـt{$%Uu!ủgTmB\bZ{@܌3x.1Zne*`#S#?Fr Z$ צIZͼ v#ї# 3+.h"@8O"_!돁(0֨كV#D /ziW]%_^-9X /dO˫$W"*ao퐑_H,)D-B/#ĬQ)xTmikZ\_Wg-f MCyCt;w'T8}C}`oa7_mk<χ*SM'>{Ƅdy'/&_QyZ ilJ`Yk}նmeEZfz#TC]S]KGWo ]8xC]ӞU\6 tᒒ>+ D>w}!Ӥ^肼vR/F# >/}HuUMWظH;UB˓[x\]{Os[ uM#CRFIj$H,$$]1St+^U,2H̅Fgţ݅lX$w{6Z݈ wu@Rq>mA*᝺#%HT*Q(E"lpڈU;뿤߬߈aSm_'MDG|KUC͓ER!@6yUI@Ɵ/>gFݯE=?=iYM~Xb̯R}Isx;,MZs.jbaF'|Q ;p-:&Z~ЎH,x4ČyrEݟG95Fճ5+cq>c3k󐍹h/C手c%iukk8z]mB]n熅c[q FeTFe7R ƣH,ф8E]iqJdnND2]'~Q٭"DDW[IGtNFhj߆W<LJ{ǻ]wٳ-VY@DA@"AAIT`@2%;*BҽI޿?^wzzh8$=^uޤ9N{nK磷W2K И73,d'_emz5U8(X<Gf{{Cuɏ W*pfέaӸu9eTjyyt ;x*sV5?imZ9 ^8޼q+NG?4.??{w,:8).F6gzph kZ\w0 imlL磾d&&d6 :B蚖/laD| &qpb>?m>Hzr?Y@$͹j6FL3׆Wqsn]ĥD-h5RyADA⢎Y&&6^H(X "I{Y<=gJo/X2T7TgSVoCrlvZ4tCD>_C7E&o|;Ϛ2jy؆gj=r`k/Ou{TFbDZr(enx~nx gPmBN_tQGۥ4 ~]a[nYd ]tWW=3jag@&[\=,Y#EG R>t 4-C&[Oemt)^1v82ѳvC&ֱR&^p7E["9ĺb#(#24#ˉHne:}ṬJL**J:_{ҡJx?tQ"Zp~3#\SScen<;0fO:/,׳U -\t/ڛ k6#q5-c,ÁJ-BMѺCƙމEoyUɼNF|ff{qٿ,?\4׍ADnBA_K磗m١Q#v ܂W!c?fnq W w]%:4hY22jn+ quT]Ax!x=(eAx*T$cgS6x*{9D"iMFщߚoLWN%d'8,ppAU%鸺QH92޿!D4@K7=չ!6Sx=sgsCQ7 GO5wfOoou'}c / 93P$9yaD6 "kkȫjbeYm:+ ?߲??63=nL*nG"C xʏ@UFO}G>-@X&Dq]: x䓌։&ֻq a nxVVQAD`Ug#W!lҀ'~0 ~a'9'5>M"e#âF`$c/_x(E&񈨰 VSo$cK췐p_3ѶBOdʠ6<;+>GrEd-Y gn7T(+Q$Gf͈rPxKw]DNHtea4ZҨE,G(jOJtʕCъ65(# 7r9qx" Oe?jt.D4,l4C~qpn&csdx*{J]`.>[ SNSv= v(zgHKңVTJ2zcXx<Bz@&($UtÃF`:w eDk0FZPkG&[%3<ۈ[k3GYڍ9T|Dkxbc@j|e#Gx*{S&[y#>ԽrD|.1:_}W(зO4|/}=73܊[]b+%"- V.9&uΖEqzsKpE״/4cJʺcD;pF:oV"[GA6YՃZW{9`֯%CGo6,kyiH|Go82S)P#v k25T /GP: zCTi#&f\48탗d;9 @S]XAh8 )"plA22]4\Jf:U.<>9D [k^T Gjl3Mni_hcprE7|J&2Hb:/>Ǝja"Oe_FnEDx IFeP)A4(?Fb1CU@ DӬ<GBNa[tK(a|4FJx>hlz>}&IײCn_,Hwzt>Z9fÎ譾P@]ơ޽>>׍"nQƉx>h.k:6wʲj>>^[|4me&J5ZPWߠN!3: z][*hP *r; 1WtK"24 4*XBI9fx* *; 4Ͳ'X|7beS?F(Dr~DX&!<ʆlWK`5 l9!dQcηd>7od)aS4Tz'\Oe/$cScg͈j3ǻ8]QVdr<-T=94F_? !n-xyjt =?e~4`ddĬ@E7%|!~]knw^cƯQY-PC MdOe-/a}iL@sU 8^]-y E7r '!n3жBsv9!~ 9/v?Q_mKiy$ۀb ?r ܢvyx_I;V}ek LPW8L]0\߯Ec54S4@i(@}Ӑ1_Y@};lc"n}\(c t>jueښ_ӟGFKz|T䫴 ն o9wt}Aj\6d9u0ٟ5k()u)@ZA/w^̛'h :b%һ1c@GcQH$f|lq4W7 Sٷ!v ݟ2BCM={aֹ9_7PAk\FD<9@&;>" AJeIx[TF%\Õ({Hs]{L26Oe0DZ;"'Ҿcf"\q q.jV`";'f"膻N㽿u՗;7Mh>}yr Ol.mq"ю9h .'nxZC 5dG,4ºrNOFߏL2vkaT]*Z7B}E)Q ՑH/i!i 97#}.Q7d%З BBN,cX)5KzVMDscPnO\>ǘGg;ԉrP(}^"ozV؇LBH5bu߂/5i$!!qw|6|tMnѽD$דGOAU)QĆyG\) ʲxR2,nkhn{iCcGE IOHN`]+`_ ?F" 2ɘM5>jQ[w+""[7ՐeJ= (OSSQ%DfϠD=JϼSt2)]%W" Ƈ1?gQ^ȣAjvx*E]To[g';zk;ˁ_k֌YN@\ RL2vC<}s2Tcws~D$c}OeQdc{>^V ,`Oɵs}vD@>>n-ǨB /4$CxujPC oNdh^  moM Eq$BN_tÃ!@DW,GDQ)Nيȷ6ʏv!e-k]uzwq+++zɺ{ 9s)2|P%n^_ U|G:m4ۯ\t^Ft>z&W#2:F>H [XI{]vRal)_o m$ ܍|Y;~`}eCDVg3=-^KՕX#}G!^eqQ#vdx*?x5vMv.QcS"oG QcۄJ8%4 * UPyf?An.ZhOG(Ǻix-jʦ ,:#M5Xxrs~U5(AhZH YDνM64H~|nKC=2lmǑL2E" 85 ?&ߡIpc= w •^r W{+luu;9%w 2(>`ݐS@)|5PC ~kYJ 50D Jdl=A)Pfax C~ɹ?GoluV:o <2ڻ PB<ࠊ;Vq@w"ʬCmZ5zٽ(`;P`&_4aL2vFW Oe' Cr7tIס j?Cٷы[T&GM.S0O.CYLF#*"k5lF+ū$xA40QG]": y7 RfH bϠ9AZ)G$%V ba=ND=J.2uF74Eg2X{AL2Vwq3جx*{4"2F;SٓlXuU.S8p\Be(x%6ZU=yn.wk)<?DFnPC ;침wR:0Em1SȎ;97Ye{7W2s[~sr h)WMkVicpQ9Njn= o9Uy\ l:oѽP>(> IN>GU dx*{5dlRt>:RUQ'>4Bj}%"|t׬GM17k)Y[8k"볈}j9|elgc"SO!H|ALT"y*s?CDEV'Iw֢P1"Gak{"-:T62y!RLADXsB=O< E++JCq|֌42>mk )/ IDATɈdk7;CN89auy#h0}'Ъz%(B`I'"~iPmΣ;4C%49nK3bsOī=`zcA:(2MTt&IjTvUt#ve!_c#χXIVnV&Vn)]jx]kI6]F% J kr ǡLQ`ykau"(;W),`m[Jxjpd,k NcP9_Yp,p~)9Ss4{F"i\$҂yU+uD$At>ڐInF j׫!VvBK׎8|i>|\tnO?MDr](o=]㰇q?J(KqiWCT9x ʠ|*لmZl><>D]% ݉"|<<^B)]Bzt6|fV{ f(왉ن 쾈ze-WsQ4^[M_֛Iyt$c"pYG T"ُ֋Hq٭w>e.Ͷjl.SZp @dBN Fd;QL!=p E"Tx`z? NȖ* 4_ t 6Up.}/[I.Ў2yr O)z'lD܋-`~ë,žr _)QBCN!n0 @c}Hqʓ7A^}8-^rHnu:.!uu9J* \F6~$Y)H:^kJ$G?3 |?2W#w$Ӳ%'8L9: G?F D^!?Tt#R"wgϸ X_DZvs,L "NAnYm8K 7n^VV"JIJ]xj68jaq%du~ek Uh\IKNEϩ\`QoD6xoh_19&J{\M6zYb(sʈOe}0|˭숋|pux>Ǩ|)!S膟ߙ,cP޽OjoԖG:FE:w(k>.` nB~Υ(!T< Tں-|Z#wDG"t>zӾ|s{FR!X?Xǰp0[G&~iەݼ,՜G[6qgAaggQ|D;9&V[.ڟb&0JxD >"a0'uZ Y2i#QfVEm e\+p\@US'ueK! SH sA38c&U_݉H_4֏y?v 7m  [8Tq9*^Fs{_Zr kz_9fߓF,LDr+J$"|dP""NB>+.t>;"QFY|u+\`^:="2wooG:aD$wJJX{Lw.QU#jةa.SهL2S4Q1oEP.Aq= S)Gh"淡Mv"uViAYX*cC ;q/Ⱦ +z_;yQaoDP5N&3vSYku"٪[715RZ:f_.JSf8/n.]$%Zй~ϿW St9T?zv{ol'M"ErKlMA1ξr g"c&^C 5P.Sh.ƇEE7lE77k: 9;M]Ӕ ?6L,MxLW)ܞboGdKm0aþƲϭ8Kٱ]mUu#e@ 4>j4膷Ndz^ƌ)}8s1yY>r~f7F5 ³l ȮHI54x*+'"h =Ȏ[@"CWOOneD$WH bS%!RD$wW:e;Y'"l~]GҥAQLWUv#& -|4lfu=iQ#jإ`ҮmiGAn`[iDb,E%y7+ b':ĆEy]6wa2* 0Pdyu/ȤΉL/kϣzskb/ [M^|72أx*;̏7۸ /e؊1ZrTyrhWMVmU_v|ӟhO.~mtV),nӍbDޫ;ۍ[t÷!GTENΜbϺZ~G[qlhضCx7l$[EG:m@姦ы !W]!g8|2.FlLY<]ՄSY+E5J=OAzVTwwJ28 47kܭ|~ϖ\5]FL9/u웆n/Eӑ@$I<])[ ؂ͯH4˯A%jGTٮ/$`1E|QFO?d`2 1,0Oe/Gfs]c`x*{pUT^CC5›$hq "N.1UQ e Vc3Oeǰ62tdYo{ r"%?t{D)<玖r 2v@yg}5[Š4"hEZo-xM e;!^e 9CN΢)bkjxSc*elȑ88`C͝'468uڦrYj6, 6ueM6Z*wXxK3 j1oBU'N-׵|S5?g@)b^~n-F"`3\5 |Q̾ٯiYp| 'jGD$mtH'/~~ێ5TB7~%t>GĄ\I".F}ʬyz2eC`%"O}&#e"E`n*EV:99c_"ɭI磟Et>1GݚHu@Gw3e4=;L og!)[!`e)܌Um%|:(f1mo`)\اQԩeK,jb0?.m#܊)ٶ4YY/r Q.lSpk 5PC 5l3݁%p [7!>PLhhŁr_fҮ $FEٜ5:cpZZ*~dT9(cQfʠww\rˑſ_'n{+mE7</7_O]vKsfڷ{T6D$wHZ4ZSeNc,{}%q.Xf/w-p=6bS1 m|H<%M롱u罫>g6-C]ڷ S+eu,O#t>Gcѕ oieJМ#UG~e'DaWQ> /h 9B]#Q*hpDjA'" {9!<)ȣx;"m?\v*u "@䙝G9D} _ϡH§l6Tr9VmTv,ps&+dTvy"UP$@Qߚ6 ׌gg+GYnSٽQ5:Mdlv&e 2.F׽psDQ5lfex;nS8=otcDuwP%Y{4r BFޤBN E7##PC 5԰kFz2ǔm ,{i'X$Կ0RErTV(dpyW̘5ǖ,h]bݚC#; {~U-s*=>VjMvE[yM;l0{1:[}-ܿ/u?.9nl-F" (0wY_OeY㬛k<>b@Ǎw3 O$c-ob/v{_yk|}+R,6CS\]he&P?F[mvT-r4^znuTT-4лs؟ 5KFY4tkV 5mf#ķ f:"m~[Q,DLC4;dތjyDBT5?4uwT؂{)l_4P~1߁Z3L0PND`έ&aä=kʺ c+ 2X9uOeǛ|O&./tzD^]=rۘiuLǮ(Ts4m6A)ZE!YUuux䣵CmmQ!p:ZGhrGE ߁Qr onuBNq4.+PoCla$IzWRL5DX5PC 5l/쳽$,FdcWi +K[Bf5Rlg`;-P?4>gT_eZZY7H7c~ VTIЎz=w-o[dBF%v6S:DPQ}+NƥVF/}x@|O.[&^@x"+%"_G=uEYO9 Hi2mTSC-_m 84 Ci*49?ns G\ѤRg$`yq 8TYT.L2x<" H`4X`9"ΖO(kj@9_kGCw>iԸ-7><4jhe14Vks,e?oyxL5UA`ǚV>FeFb٭/-漭q3c>{ΌC zݧLupψ)Lɭ|x1~vd'BdWuE~Ҳבj39Vkz9x$r|T\e[tY˨"hDaB&[OeDTq"e& 9Ѥ<  G =;h@@Ee[=Ģf8&6D8=Ǻƙ_;pjPh-T^@ l 73|SaalЇP!Ͷǚ CCq+e&wzdfL@ ",ؗ p䂛HncZZ}'CNzn:}srל]ݔ~ءlp0{˨3x di5ÉHn[t%A4Z2^w߉$uO t+X/mgAaF2t IDATSAi4[m#jf2*6O$,2X1ʞD/cXGzC+ԣI9g6(%wSYeLh}`s-:t{nx*?ugtf_sE״;/:lQM x&% w6'$=H}۹kr4ܚGNDrc^X50/d9Tm@{ ZA$N"A4e(r""lQ{bхZC*4x O:(i$]Ci am;[1/2&xʜéx}F__F+Oh;p%)[yFO| xem/}W<ֱY / FdJ=ViImi~?`YÛhD$7oST-4~IgZvN <=㱬IpKA|7`*t6"z/_O/kS<ejxarlmHf9?^fI4^؛dxf4n39Lk+@n oiŜHK u:ZߜK k5s7#<u!|$/}zlZgԏ-9u={_ '=yT(]Xtp8s|LKK$c#[®E"˰tމz&n9Pp_LaSBj=2ތzT>}Kost/'3ڈP&BΪ2=7onw>*Nupef&Hj}X@K#=>W?;>-7o.6B}' .DYs}ϘY'^s+vYMb!<:#k6E75Mj73Nwt)Km#{zcߘ{;0G:mG4͇ݰ#:w:w?`݆*]*>6QaOy* .Tt=!p7ODZ- ^Uh|x#"ќ4A4r\Q@p+ f_OJ [Q4C$bTR-k1*LCsz5ͷk = y"UH-\|5(ӡF`հ3c#LGx*{*j)j3@Xr "xuv0w"!^?k"&m2R-2RFuA<]H8+Qy_'aeعh~ #!s+ރو:ќgBeCYOT ִx_=sz_lbaNy#Ti7V,I/":gY'B F:mK^虀J^ 96ݗ2+=t][F8t5Vcs ʥ%Vz5)8,;={ףI@m[WjNcws}/K2ɘk[f)wܲ^'~eDoWQqYv ޻!yH_XFH-Sw=bjv)u}*C:9 /.}5s망zr:C(Q leNU#jٱLOݒVj9.*ٚf",Gf[eoKŴXމȌDv?h3 oh7HѠ3v(jbCfͺ~z(mfVb(è׬g;o"^f䎄5ihquhew2)óׂ sOKNyuׁSG3fu@ ܏BKNUWV>V kFn<}+Z UZ|mǛme=}/Mhks|gE +n5栾(V:+eʽ nZ bsPtË!E솁w$\ "-A Ql!)o PC 5԰r 򥿎^Wa  :䃾a=-z /օƕދ22'h.S nf- Ρ3X~LyRd쪐S(liq xn6ඐS }^@ ~d[~def?G{ o}˯WѸpn4Uy|t)||Ά};\v[/Tm.UΌU" \Tf;  :硌 =ϠIAD8Pf͜to1b3Y:0[&Ĩ"r9d "6ۛHJF R^fEiQ7وz EZA2"\r.xw`KL2Wk<=_V?|((ၐSH6<yD]61nsαfݰm̳QйdLEA"iOe(4=5_!N@]ʟODrwn:f2A~C5[G`1-mv*$"5oh%*x@ޙQ#jiI0gPMp PZbOCNr\Dr5#{Ȭ{;"xJĂD; G7uPYSh`D]lSgf{~I0Q'"c5"fग़2N;89ri;7>^01HDOˢ"9*ր 8R܄tKI&uS6e{73Y6$$$>;o繟FgQ^FOu2Ӟ_'JknJjDbl3kN- ̘@+|)RD[@ƍ3Drd8l0`_;=p2"E҈dmDW]/ ܃K+(2u}u,@U⚁55@ʣC.}\߱O6\LjEHvFQ0gDF:~^{>{wa0t_b)p+znnAʵ{! JCSyنq_Ň>|^m8BUV-=" vr}5o}@2^ܔX SbAD| -QR~rCƍ/{}/l >޿>|sA^-J\b7~&fOgx'l(w#xL1|-W%,Kd*ƘqƠj}wk҂y -,{SaČGd7]7<{KkZF~ى";lV9H_ 7F3G[ HX ck=@T-v>~|l7~H}`C8ԁw4pĚ_b_(<N`#PQ*/MDUᚡe50!ofPG l Q{/E(I{/=8d*degq3nWLƍb'*?B372n|b(ޕ( % Dd\3N%ނb.?3W}]lAr+Rdb#;*0 3Yu~yicCK - Z}~vOCdhSR ZmA)yU pA<46vƆO`8HLB/w"ilLE_)=:u#UL-E h@XX)Iy@P=H9J*H 2:"^D|Q !T:Hk:^@_5HZz ((x$2rsStu8elnJu~47%1S@g>?"ƻbyjCU] S(+.Ureu:vݏ`#A w#ER` q>DPȌ5e_ t,;}1Gen$2:<~HI;бԣ孈 @  ~5Dߘqyyy Ç>>fHyq)g3bƹP<ϏB߄:{ʷ`CR*ǛDFmD"xT VDRyy \6!2+JJށ2'PB8*7H,ߟ2l"i!تMUM9C5ӀPM 3sGФX{^Gv?]4 C 㒩t}sSbs28Rd })Ld77%vœ8]s,Ƌ f؎( D~xDe/oWY$TQ^ /(|دcb !mJ&%g 2idE˽e} I\DC#Y=|a b_BA8D4EQ@"ZSCb(W'S鋚ڪo@KZ\ÿXY+mډ.fɈDUz35A{5b ɢwxۛ{lθ^`ʏB4SyӋknn\yw|Y`LHO26'\[S}[pMǒ /:ϕSPl6P8K[6;X# =Frx1*F$Pj>| ȸ~;zw!%m%۟"&c =4FĀM1B+ ѳ<շ՝ ]I7!o{%HvnL57%vf?7,Qľ|)<17B1M"n7Ɨ DT}(bmqf"Z-kad`fnjsNTl>0DvۂyA2 D_ם=\ђF!5ٗpcC˞i , b'9}:hx}@ԅloFAhcT)q{2nBAG+Aʺe֣cQ (T fluh;_^ĩvCnDZ-NCd֣hH:|Kb44Xwd*6dX]r/"vhCuUϚTܜͳۚ # E$."Gs앪z44)\\@.Ɵs9#e%6[x*m "\B5yׄư VUt6ypXǟ} ثU_E>'vwE[wQM&NCu&fDV Vƍg_>Ç=:l}(̠/nryWD':Ǎ ysa =⥊bvIBPJҳPyF(YbWDȪ`q IDAT*vGќlWphSkwbg uG1ȇPe*=scƙQ86f#pJAX/7|8@I8zankX!Zλ 3-?yi \176|f|hDKP9b2ɳ`[? OX(޵@~: +h#X*G:RPP44C4n ,p?ݰ7,A0hf5K#WSP?bwQ}-Ϙq!G7z^} ŧ?@qkꐁ`ݑ3z2et,'f'l( g*L u9篿sݷK 8`I<;8ʸ6X^ֳ+ (laN>^|Ad*}*ZG#%F4X{Rq;yh NsQqRDl?RMU!ysWo2#rd9"hr;+`2|[=( `o$t0 ),:givU,t8>+6jt/@TtͧČc2n|Y8 :W}T/(x]ht;еfƑÒ/_,}-J"?TM%}辵[BnTu}+@ya@B{j&u,ן(2nQFĺ֘qEʹ*a?@!uҮGPF>=>|aG#sr.5x<"Bva.dɫ`S&3mtO6T׳,Կ62:R?U `m6wJy %^hnJƌ3%vc(TLƍ7+8 `uC( كFiBHMTw4E䒧 ?>fG Էz2ӆ"ncqcv] s)oC@DnΚx:d%jq=3'>~Zpҵ梍~X|?U?"dhsSbwHq# olh9`d|Ad*] ܂^4NXU|y̱>ikd[QVJ,#7oAA?CsS톓L?:v"<ߣjPmicC˺d*=wgx#qBASľTt rd{=՞@sSbC2|x]p(ۏN^k g…>7X]2;? OW\۝EFgL<+JOknJGsSd*Ai( bJ;0f.gx.Yx|4jilh8qz%1|5ٽgv73ΤqoWwL} s ,;s'TDzj?Z(T4ۛ}RD|%KQ@M _AJSQiE2QB^DyNe<ht*+O҇٧!2ۖR (KXȬ $(e~<*;[ȉvňTg{ؕD{)t2PܔxD+{;JW R6K+\sP斅C<"o4ז ~Lv9 {Fr!D(0ؓWD!J$;-lY iqƾOr\s)CBImtdx~G|8ʸv42n3f&T5^>|cdX݄Jcݝ`/z6t/Rb? |؎G8QI:9:6-OyǖţL747%v7UP\xu{,/Eg?n1vO@Igv3V%fG$ѵMWU"6F {KW!0[ `y- oSt#,Ŋ/@2`IcCf|X:o49@ycCuD 2 Z煀@cCڝƅ>]ѝ k0W > Xj>"TϝT ߉Hi. Cy O`/jond*@sQCʣ`3\g!jEJÑ|'hCP2| lTeQ \ty儛gIcՍ1"$h(RHғP׶#бTS,ѳp]0xl>Tl?3ȃ zD\nr_hqcF(xRY[;es?ԥ8f3NQBqkE;z~~o /oeÖ5B8m7q]뉘q*Q2ʌ:>|hBkccƉ#[vMA>YerQDb;j*rJ(Y$ݘq.D2nƘqktn'&F1_P<~DH)`UK"V~Svw{WE}I/3eN8OW|siV}]oh#x[||3 jo@QgcTr曋3^T0\/ᵋbJ;\yV&}uA ?oN~]56*;zcCˊ=O{b1|"*{IORY .=+zQ$?P>_/`) lArdh%"E7 轈CJTdyӑ l!}(9e.>TNXu(T _YlnJ䒩 yCt`L^YLBAצ dIdd""lށG{XOB4c[@ǐn:m?ewySc.1+۲8;c}>~wH\aF :-v1wd"?m™LjnJ,+݇q%Tenг {Q&q㽈PÇ{3r ئ4CcQu<8^yEy8d+2?@-A 7^jq3Rudq.zS&SSunDcd*]@sm ")A,$>Yj- D _/ l4+ U(zEɾӱC[CS5]|{c3nN>?(~*6^6:~0 olW`fJ9+f8]ԉ2n8xp!(TRs{/.h+4?:~Dfdܸ'c'|V)j6P쬙|밯;O2]=(+VzTO":?Sl{&\/ǠAAY݇_oBJ;)ќLSh? vިGpUT@g# @Y2 wQW-[I =);Lt&~'fs0uvAz$1  }7%SY]'~wlWи>ڿ!> n ̙@=hmWdW[S x!<"n}~ 蜗b}?TnuC!Dq2a׉Ҙ,d^u7МI4Ad1 >7)R`e;xe;77| N@$i(3R` pB$3a';8u(G4dLg,"N@uP 2+Ѡ^O]wbDZM<`e}=L뛛7'Sw")ͮ#2qȫmF{'Sh|W47%HwU2> yUst=inJTH5|U{BO{֭̌/3?eAݜy[­p60)4ytQ̂}xٷ&pP͡#!5t_3wQy/AF-i2R臀ey>|Cƍgфuo#񽟢 hm_ȝ)HmW,&b";S\77%-1LbّU L\uk|6mnJOf/:׍okVO-;a(ӓ)k 4=4 %S} jWqkKvnD 8u8?FeQ R_a (;  "Uz{8#;lSۛᥛ޵-[yq`e# A$V)@5%MƆpۂyQtOLE wz)f65lANP^[DOG_nP`sl_wR^ "^xLJ-^DnUT/zh !R4?)e!v Vxw>%(zL}#vinJtEƍgc$.c}?#fj`9uCՇ <5u\vpK7.=QytM8֣ K}{岳GzQ4 RBot\o]8A.pf "g2Ç2f#5zB*Q֢ *Ggƍc#v@_8)fk3n_8RXϫyYr{ Z}\;{Av8HX9,y8px_ʛA7-ۗsWh;DA(4FK>kBBw͝OU1)?h tBDE8]TB(H򲎯WF;|d*]qn;@OsS=q747% H4\ɴeCvH|q T; !ܿ:_}*bɠ`4 vPSFP_!iG( {.z.3ecrf흵@i7~D_?uͬ<}c> G;^@1󀗤)~u7vhEl14ɸQ0 c/L8kƇ*&ƞG!ˬLjTOZ J߻{YYM< q'|cVS0]xϗ!UWY–#lDˎKcƙUP3޿3 Uo}qsfJ}5)\g~G[!"P:h!;R81\As[9 \D2ue}E | s'E7q{B8 w=w|Σ?{q֚;ܧ퉿LGf˒DS MOBfMH!Od)}e 'D$m.G.ʣAH(hj@Pp}cNs67%xs8~'O67%'Ιq-%_zwfgS(:=WČvi@$53*(6 7#ifƍ_g@8[(.@טHf>{gw^9ؓg45 3>|–}#7gQLsHYu +J_ 4Uqǡ1ÖܕSk]6c51q2Bڰvy~ ȕvlnJt'&Qszr}mz`7#6LSsS"g/]?q=JiP0͙ȪCj+f*w/vӁ(6$g(@qw qTɰ]ƍng0f2nmǁ6Nf{)tmllhـ||Xn;i"*:1{)o;i:r k%>p'ӀĊ=S~`SsS/J |aDΒ FHplC8)D|Ю4~) VG G>_M m1 VnxjHvudl/qhɓޙ=CS:BkW_BfŌwGQlΐ@^GH~w8f+dL7?R]_7ؗ @7 xɢČs<݃p〥l4!MÇ>vEs#^PI7e@]PݮA*sp`s8crOLƌ3)]L8_ڙ'5;fZ4?-%qv"{jF` z %z1|Z |KȳE/AI1!n8_,2nٝ xndG1sy[6<nh̩=UmlPSqDٽ+"Q?P]6:;{Y4Z D5PG\S?w|T:ء-5|[ͬr.k&l hٯpf;x`_ْh3I  'w#8}wxc @rC~Rbm}nߍ8ҵuWK |X~pz/ooBJ"&DteYm}N0G̏_vWqtMsSD\GJ6( HǣA?WGkU&`I]?uisSTc/`A뼣 -gƍg(4Út k((znV!ϫ5ԉ@85ȯ Hވ\Wt_*)]ERA/cyNJ;P^>|QOq CqJ5 S1y^!`2氁S7*FUh2O O\q/keYC[C k0BMM7p+=#&T^LW57%^U``'sҹ\_0(`*Ow/+C߀-o~og) ܏b1 ?5V" ؘq X7ŲR /{t疼e%/Jy\X̾@ TzDL~RcJ;QՎȽd9>)Qs~LIeݗ{ Z#q/K5y) Smҏ@~("J3s36q84^1rcKc3n7fQW?Tjp]g7"FR.D F(m&ԗyh@&3>|ؿpkzH fҘq. û*bƩg_;LÕnudtn&оo_xB@Ŋژ 7ܼ8%YZwts܎%ӆpCo~=Wm~ۛ唯9i_C섳 L`մߑ3ả{ƞTNN~h2¹k{H 3(.ZɣqB@Վ ||hlh) ;>'ɥg67x2H7&TW`Em״rtT!@ҋ7$S*ی!5t;D~=4=t* ʵSdנkT&B״ ,P'L2LYňX:Yre|;gDƾ 8.ێNۑdD} W*C$q!jH:l+!mz'#CmÇ׆q( ]^zy"ƻ`n!JW6偊 Rrc @ /Pud}A5͔r4=7Df'mDiU뢓B:J֯u dB]>,܂1\س,g kFF =lgpЖP_8?nzu"5lhŘ8 3m`baƍ/GÇ >`&{s;^t/A(!̤cTAP@ъ&aL#SB)v,2CQmU 4}mW TMR u؟T: v56/hnFjMC$ۑ e9Hx@js5)߮7/F>kCM<\o6xaӶ重]{Ș{Vp!oB</5z}*(@d4^=ƺsQ># u(-CQW_5m0 4t{WFfpU.1)ѹi`Se7@$cZƍ? Lk/1M=9i^?HDU%I1<1LiE1doPTzRHLA} iTK|gH]Q4L/C~=3/ ( uD캟E*AD5 3όR)ȣay 5k+kC0. Z-CC(Zl_[TlDe[+vƆ]M{~6\o@Aף{rUPpz3^Dѹ<H{w@nspH0\Y=LBg(vWK1H@Wƍo^\@ RLx'2Čs:R-Avۣ]>|FTJ5?ЋW3? Ԅ}ͣ ֹJA\ 亅lP0HؿOB >H PdtwY`+>қ)ػ B &`E>Н 9e Vߦ/uO|8Еq D1F,SsQFS\}BʏjhH2 ,EUV3,Z7d#J-"Ce7R4nE^녷#ゑ.|$\A86#R럨Dswu -z"=/35(Jܔ /%SGG'EjtFxpG=e_Bd5"p,"o䱷2n|U8cGGA{Fྵ|b,,k3nqL8G쵟a.!f蜌Fjm'= a8>|cу*/68<( ®Жף#_ɔBk6?4ᐾu܀_Fgdž6K+P%N/eZP_FqTz$aw'2n|y8`.Ő ޟq?xW} X^8lt~U<v[>̛fO]7<>x3α@:7"2/ "7ƫˇ1|ǁ@Kn`=ʡ E&\9S\&Q4xO{HBBR~QigNClϋi6I`CsSb`=6UB>@n(.:s.# .nlhy3ZU}r^^&w߁)z&0lƍƌSn?n]( ^)YGm=Ԓm(s8S{s=UyջPp9=|qcIRrbyj( <32n|pV#h,5}+{;WʶFJn-Oy*kf pE!T;?ھ0φ!7<r)8?Ձ3ntAcd}Slbƹ(7dʢs=%M7U۾ʄ9D &>wM(Q9=;&o/hIamEV uDX3;K^Z3'|ƍ4>|س ,:B r4&Gdǣoe>{@^4 hʯ3lU023jjlhi_ȲHЌ<⡴SfiɚQ/Տ KsS~ܔ<.hw='+FU 1=3Υ7dخԞ ;V6P`kʸ1Ç>@C_9hEQP::kD폧n1v,"95&hhD2Ͽa%<46λ )B|y]aH/R\AlGي{ e(ȂyoCYqZ--Tz3 ܔxCm|'& {ͻqחw<*uD1||1ߣ2IH8fl4F%JƐ'Ʈ@~tX$qM;H0ٮ̸l |5R0MAcv@sSY pa kq(*5t{ C!kFmydAP#5A D>QYOQ^:l_3*} [8,^Lk?2Aqc: Q63O ND| ֞D+G o5 8NfI8V] =gy: UsѹCǴ%uBd=ecCWFUٖ`p([dBARDӫ?"> r/x-G cg3(XHx5@{bƹfK ͨL|}o_\ siJ`Tn.E4Y[~gyUV)kz%mB 5CAʂEtmOTDA+A *"8RV" K2@$$Cz2;qef'HB&ɓ;y|9߃Z"5|BH'i+5ڄ1 a;HuЭ! zV'ZnnꖬEX[9?~s`C8=GC2oFʆp`)ڈ@_?_C<%x=pwm<ٞ\6dҍHt͏xjL$jҾto<;w,H1]>M=f!l|7Cn㩴 AroKVQ؄ܽ>/#"k|FSkks_'dLsה_ň@6"X?_3%C+: ab#dxd/ <]-c1caޚnȞo5?_#8P#jûVU[c]wߙ,.r&`!v,AZ?.u["f ?˟g;ʺ?6'ېXʽ^Ӏl|2C/s6<&+KҺUg'︯Yl| ooN`Ug#`vkM-w#V14Y;gN8kE/I!+HdɊ{/d/r_w!r- bY ?oj)CZBYHK*dvs _{i1\F08T˿(s '"GFg±!+B>\iB>'"{1D@]\/͎&-(;G#bH$0J\[93kG~u9,|jI 1x6ē."kCßA>s{ qDLT_T.dҒGr팀e0 CkʡX կimJB/^k~nx$vr/)NrV>Mv0wUlRrEo4KAÐ[[T{mR5^T{_[9#w#{l&'+6V}5*ImsK#-hma&e(`%Cv" U&E 69dp[\cxܘ ;#`GOG4T`xsϔ~) aPy#z*AO$y|%p0@~f=(d >oojU"k.BצuC4E†pl=@4TD±{{5"C2"$} LY-&M-'gϸy[.s;LNu }n[tljux^S Y5HwIPVȗu1 PT@ޗE IDATn(WH%\K;Y}} "<|?v\|u@>Օ(Jwȗ 'C@ a'4{\=&t![9^oBS$CA_( K"[Sv3 Z8tB[_+-Jޝ\V4u qh%"'$zwﱕ3HBĖ>7 }NŘGTR[oy&ڝR7U"_@*Fbe9ʹ>]'ַql eIk>8jgGw =#`^Gė#LbT^x($h99XlSu"_ˑ/D)HELʛZnEZGXv}Sg| ye~k O?O! $T#b§F8|Yw+T4\ˑH|>d:ɿ/"h$ZG!ƺ͚iq%}kn۫qt~g Wn y4,[kCs7/Pkn[ <Сe{s[9ՈUT+<%С˷ɸSd+"`5d/aFMF mqliB:p fg"O#ߣkZ dz-*Mj{D6 m9 <`+g_ ϳEuO)Tc4O܀uȔǀ[_.[3mW}W! pDD<@_ {r~TLyCv<˥@Vo/%t>`FRfY`.eBnyH̑Q YY|Â5(OmG.B)N+Kg 2 ;f#"(d3_ǫ,d* /X)#WA^^"|٦ $t(Gu \U˘FHFD3v3O4,>v6jKLO+79sfЍ_,v^ڶʀ,Ynkz}!YIGeڲֈ wLz/\bKD4q±jLNTz#9,ri8?C7z}X~9RT@=_ CQ l+g&\BDXܶF[9}Hp=p֐! g:T[xE7}b$+:ߒС;vĵ0 Nc"b-Q/Ira`wM} Oʏ*l*]vwȾ=rCbEWhےC_GpJ$]J8VCXjf׵lҗ-IYnF9NL}?+N྄~3;ҝ| 0sŃU}sy`WRjf_⟵|oCj5 {YVO=BpO#9ȗ9HP U)̭eCmqCJKx υƥ0&JU}Gϡluߧ.vw>l//|g>d=w.N*n"֩?||jߺxdjJSm,^j,7xX-9 1r"\ :,˻y5rOPةke˔řoȽ$į~]g h0l!U#ݛS-%J!m r.I R ׾яHUHʄqףۃm!`0v3l% >rqo_ʎFH`*aȉ` H_T{;}ۿ!mDĭ=Zl: wH]Joku4t^5XwNJ[~XDzU۩SVM2i z-׌X#7|o{^Hj)k#C{}Sd3&mҨ/mvY˞? 0:tծZ𿂩2 \ T\p6)W&: d~dV!e$,pl@6CNE;v%UHW(^g됬e1P5޾U{(K7%ri@{_b/UYP~ט)_]UU:ӟ.ymC[}S#:F|Wݷ2Էn܉߸g_xh>DRqU~L4zC8d_}СŶr~x}i]W.'XEiTeń=>ƫ\,X+)W']Z# NRXw[}/Rs<*8W#^&I<P3 Cr*Azj *ΔզUڀ"5'?IMiH7F!{jWl|}Z`d8Q6Pg7DW?^1mËtFw"{\2]lwMędP Zav+eh_P=d{} 9^s"8y'!U9ٴ+_mL&7E|sF~#@d]95 T32Ckw =#`^F:(:'![\ DjA6ST܇qw"bbI+gDZ o57- 繳hGD RvJDD_:RFΥs.[1/y W|n^\ԥź7Y|d_!w% dWez;c F]kksf^h<#Um\C8 :[%+ۡSmQrA"[n$`7݀TuL! w,DLL (/\dא~DO`}9@<9NkWKGeS'տ!AOZ&}to ReгsA$δsBBGbfmbIСn[9u*J/*[S ﻌ}kS)d,?À/ʉW't]%T"߇6w =͍u-?Jd@B6B/HŃy0_M-ӑ/sHysg*R"\rDsc&Z~|x7 )±t48H\~ː7":M%?ӈ0}(ǟ$)+nӣk(R~ƺe&"F<.F㑟 }m8VС[š_VMdT{ "ir ʱx?yڨAaI,EHL+,L -pB~#`0 ;7A')nojwuZ+>Ñr{^G^t՘NQ-*+# CʮbdYT{ֺ7Y$th'S*2dR_"\wt82` >Ci̘tJF%E:ې*Ui.,Y)P=sM*ZKYfVɤQ?G~EG)תESY]DkC1z<ϥ2wwZ ڤo;1BNTCH-)C`R%$jGld7+ɷAy2p k &U`س\۝v=b߆Ts"{Hw-"B|"IL\[9^\E*:}KZ?5=?A~С7wP[9#*dBSϑ$q+1MBh"݈LrOODlSV9ppk!viDVN$}_"ǓkCVB^3de R}.V7`[X^i - l4nN!{'| ؐ1b,E}S(VgxC8f؇; 6"٣_"c/=lnKZAmM ޺ݡ}p3PG7ULgO=t@IQt亷W6cwofhT5acǽcD~_tDRŁbO\&d(uƩ:mY}S5͍uZѓF9x,x !bg>) X[xds/ !j[9@i=||k_c i'N{#!ؠ{{~&uI:fpw^$6RJ Fkwz kinz  Hx *^h\K*a}IvMpo%Hqy:CwoT% g`$A,LЭrf!UT{1=H|{:ml[ˁjacyOUOUir}`:1a9!;g5?;OHa7rDZXW1orzt5Vg ٞ{@Mjb58I~Z!-e| F2it!-n BlC%ȦB OT!?qkn{Żn9aPߠߏÑ,Bzk ٹ5r:H0X !;| nhǶmSc׎\1t/ǽl67qh?5+?/U;98yԵpog*ݼ4!'OxPr9 I$#|$| $E22 sw!CerDI$rBm `"`أyIj !#`-Yr"P"݄x=}V`>׍TxV$uTńどrd޹JTU 7:}=~ReKPVΫ=_/'Y2:,>[7@V УB,|]R^}A Ca/|-3Z_/-Z@Uu*j* d LuˁW:[2p# ۈ {i1N_OIrEyd:Znhn`Xw Cj&ЌןT\1k_6W4p7k#À Y͔:Isc݆p̹jX+/ nzThd2ޮdmјڷ1U+Fέ. ?<1.BB/?;ډTND_m ؚ:t$C~}ϫ/v/ ܵʹ z˶С䙣#Wyy?&i>Lp S=y"`0qm圍V/F]J`*h-_T/ĸŶr.q =U]B bFUV:֛$vi R H\4.5zj3jHbxjfOT; @ŀc]~_.VL;W-c {D|l#A?$ kHIIrۋh {Bso;?{~ :#>Soσ68rNnEd,M-쎅E 8B[YX٤%rV A˻%@˒暍5zeXde9F2#`Z,\{%/O O!*$'Nީ DSLLBܞbh<H` G@+o}?J}p47u7LTsc``kMb?K!'V?oD[S_A ±s3"xp!+?ʉ-&>T|}%C;|K HPK=R7~<`0 [9c#1MU=($!v ⥸DͺϩBHBje$G"C+6"*ĬADr+ry5=Uy,F4~VN 眍ěIeV2C+ܵ] ,$bQ{v#3ĉcUHuY紿UR thIulD@|MZ^,_7|V gb,Þ˸܋l $u"`csmdtsc;H֫ug! miT$3X}S/1SƺB,"Zp#75hMKI¦Ae/yi1[=G&!Aܵƥ*.^iⅯ p,kX:T=rŗvSts碢ZI[9_FZ ":xBvw6`0DDB p Rꑄ~<"+y[~nK萳8~T3PX} jϻu0Dv_;_J"V:ɋ͛m܃{ "[RwMy%3*QeX,c@yA>aUw^ -6F2#`ZJrGÐ>dHԹ͍us:[M-g67W Y#̭ G#t`"LfM0HOkT*AƛB:bnH`xƭ"[GF\ IDAT#Rī\mwƻtfVW9ycV1?vEe}7ڷ'a}Ȇ_T^y cСr.@2^u4[9c!:.Y`0 xZ i "U+Ѧ&"Ov2N~y I7b&ߎ${}}&p'CHs.^.i+F=g"1鈭 'thCۺ--n(\_ [#&X^|!!7 {&F27!H_;F1\IF^MDl .nmR)$kX)#<78%kfݿrÌWm]TF A?hth5Z}¾yBꛖ54/rDŽyiVQMG!{ r&/CDHs7 Dp"u"cqȫNb=O[9:,>C [9_2lLx4( 'ʿB喼c MxG"1­V2d($]|wsc]Vγ5t7$E>B|ϼ Ϧ9/B?T!"[9D>7a˰'PdJݟo."g]̪ojVscֶ]lA${>R-s{4C~-Hg(YwZdԉ#0iKR`}?XEz/[c-0rY0N)VK)ϕt\m2iM_Z*u;k31$bx܏}Rۭ+k;3)Ig@ƺo5A)dݵ$0n61`0 EZFrC.I萗귕SD"mc^F!PhCDvZ[9W&t [9 P["dy/ AR |VN.']$~СA7ر=H0}pjHҝEZ[|H5Ȥ"޿0`@wl,J:4䒜PXݞƺOUA-Rf!AW;nj{ rȱ;ǞSWİl4IҾE_J}{`@$usD­eVQύY\:k/];UKZ.ln[ëNq3mr-d,՛,iPzߗ9 h_S;ɿ&>yOL ϥ{Wo 1/RYnqHKp s-`M؍V/tf8B|A:15 a%Cw'T$QOjD@ KB {?*$lLP*΀)~ңm\СEقZa+NA"6}CVś5\xgm$AW٤Rk(S2~)ǯ|r/V4r.B95 p298ń0T1aeN1NeSL ɬ$l>/?hn\͐'3~hfV[=w'-YJ&oQyI;h9?YNЯ 1f$SÖ7T8z񄶮1{7t?cƗߨڅ볔:}Tj &#%7c GZssyOOfL6@Um5cY"!ߨ9(ȄC{H6 a[HPVC@"&tDֹvÿ 3ll UoKjlǎڧvOY!t`/h+g"I1 !տxX-^Tn+j+|1m22"vCĬbpe?LE*|ga,=0aOp,(*i#ssscݛ`ۅh<2?S tR>5eTmK ֶKnZf'K.T)dǏh7 =5T-tZrPj]}eeEn/vLK|5Sڧ[]ۨ*V`?lQX ()aaovdҾ3±'Q҅AJ+7ܰ,סSe&kG_ۊ q ǒСWl|W^TNL;ܟ5䛶D⹊ո]Sw/hȩ%oVg27%tV!Hb_%710ϩ OR)H<5ٸSRURC "P-F*jUOg# )GHU/2R< ,f 0YSZY~D+Ţ@򞉣޼}{G잾5VI6`Xɣq\-vzsbV4Qʌ=ly[5湢@GZ;Ǐ--ntz|EX3Zk,P5ٲ? ±`u9>i7̮Je*:G6釿/`0 !ABYS7ʹ(Co=w'/+OX Ygt pH64<_Blw|$ʟpo'D:l!f:ߒ? ?{6_{Iq?i3mC)F3=^Ry.d tuB~AGf ZO6cC}£˰^USlFay̻F4W H/ɰ +-u#=oWmoʝnc` YJNwд.@,qxhdxĤeuܬ Fd̜{9Dkd2 Z[+^Z[7+&VΥ ǞɅȄ!;.ΨIULe$}%)]xUpF^CSkIr-ɰE)|y3A9!4v?eS ?=Y͍ul BOF㑇MOHFf%A:}*BE?YDlx8Q tQx$}"}W,ل7ߗNB֏DC8G bq",wݛ0yҙJe_OLW*=< WfԆUgTh>[biS_,-]tG0mjF-mm|j.`0 w刁v,ˆW2DRPч[ɄkWM3V:U60 iRb<`m YxSh@6" U 6ZKB*ĘQ*`u!3=H X` og rWI5Hy "X^}C[y>:m̙g F2ָX͈9F6o7477֥w⶜Hϫ2RRy+]pRM?Gw)mo{,*ΨzIO{YqwkQŦ-]KiI{su?3!{}m]y7WYju[c9iKxI=/j|`0 aOD!UHu{ p70*<D,R3\b6S-z{m -HPw^KB^s "@MEA~k+3ȴCv=nv:;؁lX@ CIu!_ی%dA*!- i E*7 W&t^aWӨ|hݒ1ڇ:R:[9 "V C<Ѳ(cpˋ=S}Xc{P*pb+k XݙHn`  K<_,p/"lO !!il3mI ߣH9!Wxp3܆\nzRƩʲÝq@ wױM3CJ:?aDm75ou 𿂭Ȥ"[9?N R5^TI] I )*`OO't螼cir~СW9$tHʹoX ')R!~YCΧ^W=_=WF] Cb} j@W i<ӽ;F>B~<`0`,n;/Ԋƺv/̻oU"e?lǶOfht4 =3飣 l{;JV]zc_^L4 E 2PZ>1$qX(T 5W^Q9"|M@EHЭr:{sʒpBeVx=Ck]ȵ5wOfi; 1K?+~ $Ya>-M1o` -99K/Vh0r˰[X|ݛh<E LduRG㑟"H }E%āւf\|9neٺ 5hLwSCZw 9Y*5qWӞQ{b޽E6*^aNy `- ,A_LM"1I<&$zI䷿%tVuH?_UB[9_CH@*[9oZ w XV?v,>e |i} rٳ%HIpa0Q4XLS3n}rՂmۊR|X)!HEXaGFnFX k7)cGW>25 ;ʙ?+lt{ `0l+0= `+'`+:C7(cRDdy %t d%Z(C>baO1R-b0W:=;{ugJM"^G&k?+6?=ݔd0+E"k{sp=Sm圐!3`(XDlr%i`닟o#±'wqƲftkji+~n;3gň4Zai5e3@Ew^< +M?8nb}SAHXW82:h?KtuÏzخhU v `0 lr~ Zi+>ƏTjFĿ*`Pk'r"n'm,EolHKH-p RӹP_"0$l F>1@GsUX!MQx_~{g{RB`0 X.Ij@|ٴ<|P[ܕd?~ A䂀plץEUiTi*l9|}%e-ZyOZ[LЎSMZn؊i?`0 K/He|5  b$6kۡ+2^D ^@*է!~_0IJD>׋5 R6ӞС2Rո, 'w oƽig4P`(XîTrBU>{{~д)WߡD&ۯl/N&t^"AG8!u.eZXY ۝3{+bWˋJ0A#%Fw{:m+9x x77ƶ>Cm|ྵ#`0 Cʹ ` Rk*}vZqsǞiY+E{2%E~]Uړ-KVZikح6t# u_pj+׈uH6  8 c*H,׏L"Cǀwz߇F5)w~qהDFZ }Wɵv`+Bê$tȻ (Eym6m/'`/V;Bޛ#m|+C? {F2v-!my,,t]ۡ${+#:7V<\S ޑk#'ϭ]2#VvH R!۸G"moDGT/{ K"uYN}t7]ks #{±#^fx†J-[9E۽elEɺiGp'_of `0l_HR5"r܉|}ܛ/ة+ IDAT͑@"B ˀO |b2C$»r.nURޝU Ow'CK!KP(bcǸ Œ:8ʨ9c *"8 (% ȞztB]W]UuT}n?MN%Ӊ=̫!E"~ IǣMJ+X'%' "g>`6k姼|\cX_ +vϲ>Og`hh/kFLQIme XPߊs+j& ,s#zy3G $ifDa#xpub:?&<}/`iEUHӈD' 츫,[ξn]:b箺j8l>i?{o Cln1uĀlϛv5u }վEPV;47nx-0v;}t>"ST彈,I@.UD@4 OHMw̧u1}lsxV.'=Hz߽dQ ӓ$|RMȅZ/_r+چ/!J% 2n;d-`0<̔t C=n{׶ڽM R}Sۈzʵoc R۞%IN!iVOX}Nef^ԛi>TH"֖?j7oל}3yW 9VUB0Ȧkv#̮|3 XJlPso#[=Hڕ%M}F/![M QzR>il@ u'DG, M#LNٰoy;4ZGvwVW̾G{g?2&X}{Ͻo nbxB؏XÝAdT(~mFH4 Qm"J I&HTZ/Md |R-uYpdUJv{4#7.Һg:a`{h/X蓀^zWwRg1D&8Vw{x"2S$GV+ZoeY>g n{pL!ig*$I㧙 IsDPhBn Yil&"bB87(<4J&֞Ed qĆqcCzSs 1~.4?ncb,jCvb r^~*jbѰ9! gњ#]<[V;篁cGEMQ Q~P\R)jz&bwg; ԋf)7}($Ig=ыH\ן&#' F}h' k!=c6~zG7:g-՝ݎξG\BD3-+M^MD韛titouDV',gUM{0qE,f򬬄Ȭjpj\~£[z6ǃ'IӬ>Dcţ|&+Q3-V IDrbVf*ٟ%oXnjkyj:Ĕ^b6HG|s5o'"zhukć <|:\U$u[#\N#v=ǎ~w XS-D͗ G~Sl}l5D9c}sL+CJqL I,`j3-?.kpkcq$p(fZ\[H/ ]U'NcCӦ2@W$ou WkXN?L4OV"i!_uB.fZdaKR9؝j>ͥrg[u*Zݛ<'ATyt"c bOɧppvl2gߕtus =~'-IʹrA;O!ioF`!Z-3Yˉu]H$M-9IDYCm wDo+ڲ;ӭ-蛰/i<"N<7Q)l+Ig_|>{nˏ.R f%G2u<,jNth_J5EzyQ8Lk G>' NWTJnZ/I4|^}o 8Ck\B͎{Zus]la*Z}^gm$ fKaJzfbt0D&@4L3?& jos^Yi1Ģ(&&)M,.nJWJ}$IQH YwY{) p^Y\w] f*hm^dS <;E4QrDSX%vzVUJZ/C+{i㸟cZ/ gTjxk/F$I[tW =ORǩDۉvg}JRu9"{FbLbM/ZJ}! o0x%mNqI.Py6Aρ',61kKĂdIJ$Mne5DO[Tt'ӉLA`p d:pm.xE5ˉl|M;0$$1%S4xX3c~.,ʼn>II6" (ےN%6N~ \\=~~f߫^ y:6>(a<xּ9iwa,IZ/O!z[M9 =Dke\RaS$I_ ]z[lPH#!6T>e"j-0}ٱ]DZ"':wtafZ{Ү ,Id/Z|q*+'L||Q$I̥޿npޤ} =_k5ڞ@p$Q.][ף"IB&USi\%?n\N$IN!iVH($7~ ~bƼ>X=v" =D+./qJwd{&HL4X:ɟ3vS̕ /jS$I$s"$`!ᴼ4H~9Ι:gx޽/<N>׼yƜ5GLL!7p_M]JdQ僉 ۇaP$&lcGHX:uD*|)qJ\Bɀ,I$mAd I'ਞ:dc{zۿgpU33lxpJBd\}h{YUĺuCdrd.: `I$gbgj:q!_Ozx+Mc+IxQ^zT"/IGh{ IcX %>dxfҕKo_L\xdDOGO( ?ؗ< d/^8Zcީ0K͞ #׾}xg pbkzoO_[$FOܺCެIttdIۿ{gs|_;o}^;B^>h9J6PH?xk$y3-b`%1p )u Bxvv|pm@dy3" +;O3@׌BҘL],iyA0o)$s{LIOӚ28Z/_X)6Gm[*ڊ:I$M%\ [EWʹ"#jp#zQ28LҿWeG'!*< giWQcI)=CW1_JҔѓn".[j4ّ*=5D*ZsOI$IǛw$ԉG IHfZ\LLhACr^0((ߣALJiU^x4/QJ88|wib4UJ7U+%+bwvHP$RY)GF^NؑR< II~"`X|8 x"~p!i}C FSa.7uIb"Sj6t7[֝}0|*!s#DY3x%m*IӎM,;E/JonYF\@u j"VJߙK$I;_!i<D(]3-B\b:ӯ&P#D+@X |-m,iRX <CAb|TdZ[+Qu$IVHED0Nb 4@oώݗ(!}DHr4qoϸJmG5 M+gӺD PYRg$IҎUHOU b|b#w`9& <@%_]Dy)yˏvD3 `Ics _ $.S Qqq->{nq=QI$i'($WeZio'!Jg !bo M)cMNhU?<"/3x%m,iM!l:w/B_h tӔ$IvBҘ ȠIT!R7OMv3R\)CmO$Inz/pp D fZLk`կiq5Ʉ>`У|&>߀5[ݐXnRm~Q e$IdBT`oLbjaTsgeʹ Ip)얲Xl\J4fZ63i7eKڍUJ:>I$i6 =847b4"u"p4Bxc3->Zg/#J գ'߷! `IS^N i=+XT$ILW($4iI| brL\]kja}|n?!{|{{_=)N4ݞ]]^> " 'ೕROO$IfQ%XAlخjţ}$xH@הQߦp2X |L}҃W?0w΁ޱ`w:uZ1x*1>DueD0fZv3-[VYzy!ѴrQR8h|~N~7X)nV%IDAT$Ij nS\Z2e?%3G{'zWI夐W^YkW5e;H&X6Z/ `]|p Q?R 8mI$i}5+I^{V?9bY nKlvK4K%mVT[ Hz% Dv*b7)>%p ,&I$Mӈ&V?t3>&A)nKe%m&&H*MDy`)W\B8 (?(I$7p-sݽ7̍ݐC4&R[x(/~D4 `IVpn=*,%ImԿ|9ˮr$k۹DjYkh/ 8+N9yig mu+ #mbXTeH$4[i+MۇWv? [xʹxKZlwҮ?(%m gUJ{Xgwox&p$Iݾ CˠW+o4+>Xk\fZ<쵆%m;J!\shF\)s$IBҘLkDKÁ{L'[_;mPq `I"r"5+ڢ =1I$i'($9D6%ʹ80$r `I$I$K$I$I$I$I:,I$I$u4X$I$Ih$I$I `I$I$$I$IRG3%I$IfK$I$I$I$I:,I$I$u4X$I$Ih$I$I `I$I$$I$IRG3%I$IfK$I$I$I$I:,I$I$u4X$I$Ih$I$I `I$I$$I$IRG3%I$IfK$I$I$I$I:,I$I$u4X$I$Ih$I$I `I$I$$I$IRG3%I$IfK$I$I$I$I:,I$I$u4X$I$Ih$I$I `I$I$$I$IRG3%I$IfK$I$I$I$I:Prq IENDB`openTSNE-0.6.1/docs/source/images/quadtree.png000066400000000000000000000267501413546205200212020ustar00rootroot00000000000000PNG  IHDREasBIT|d pHYs M MέN9tEXtSoftwarematplotlib version 2.2.2, http://matplotlib.org/ IDATx{|Twfr$bRĮtE{Q{񲨭uǪ[[5ڥ ]VB*X^(-@BHe.ǜw2̙̐I^ǃ 99${8#OB8p,`! XB8p,`! XB8p,`! XB8p,`!Kx1#8(I17iq=rA Tr1X$?UKZ/lII:Wfۂ7fJZ9pq}qݾ61ƌu071~7p,`! XB8p,`! XB8p,`! XB8p,`! XB8p,`! XB8p,`! XB8p,`! XB8p,`! XB8p,`! XB8p,`! XB8p,`! XB8p,`! XB8p,`! XB8p,!d16 #èڽ<Ñʽ4L%Ȅ78SkKH>N81Kzv>8&?+=^;Εφ *IHZ ߦ W5oS$Pk>雩qLI}npْR}o@lC}qݾ6f$\Jq3xxO¥#8*c1fm0 XB8p,`! XB8p,`! XB8p,`! XB8p,`! XB8p,`! XB8p,`! XB8 HRciҾ$'Kvo'cˬ)`z3;7 .qN1ݷٝyw벱??cJnIҎxuY8Kil7ŽZ:mM̝i$ XB8p,`! Xrmd2)aIȴ26K8 ̞iL j@𪾞#/ilW-iyJ䨭6K8会ڵe'_pjsTv;/ fL+j H䴚ڵM=V@;[Ǥ@IIs1 \7GRaczAegԮ=\+  ^%$ᱰ8 PNܮxteM9k$])9s%KNLJ;l{$^ );</_gJ$O\~h[ͷH{;At{*[ˡfT@Iٱ)䨝[[$c\I/J:qnc9Au)?+=ARIϦ]E1(b'JO?&%-ԐrƔƟvgcm6=-Nj'^DgJڞv%SVJBilW)imOcY+Ε92}\+^::1ݷ,u1mIZL_;|LRa'eӐ0S gn[ݖNVq}S neznsۨISb끏᩾ A  T+X:H@Z#Ŕ)ں-@ZF5YWyM_5mi~n/X0+NhzNSSL]N}Rp\I\}~!| BcR\3yNg۟Nxݹ|Qv<:,i9v@I.d$2YQgÏWIVKnV,ܡ57hҒ:V_h¦ԝp^-i1JZ><rO-oFӝlcT̐`$V J &%I %5swgO͏ѬW!#Is{"ʞ=ТHK"{㏷U[١d.լ>"njp%9}߉FIWSV CyPɼY[% 3*/+(Y JRՅwMZrSDL0T%ے޽~5^VP]w۞esPf5U0Ijc7?'bW Cn۾Drvտ/3ʁya‰_0PRvl=Od] *zG߾޹"}zk9)ّ+UP(H7qBO#UU[?cђR yJρPԗU,0nj-ϭ] mSw[T5cg1}pSNq.tIbI +X^鶴]$A 7#隢J Cϫ`I74ZPH9}FX qUi`y'vwDҕ5k`p@$}KtL0н8Ğwft(>5`ddT= RqD癴{H9˫/<䄳.tKGWE*͊O!11q{wwB)ҭ 2AY]Ր%vⰤ`px],rVo3F?E-lPJ98je~JAI<(>b9ѰWIV_jŒ~rV7]$IiW% s 4X^SikVۣa'\F"dEݝkj.]?ᬫ;J..'yύv:\{ťuxoʭp* S=.TcLP47QTC/&I7.`ge??IұJ(Tl=D>ZA7ā%)8fBqBfO'Zx$5>9{#媜 θrkHk4ZPܠp[?mC3M8$+,ە- `risR Dz~b*),!NJHg]q͍"OKr@)]RϺ &)eT#_TO x ՓFJLA=&r$9)+)ং'%S"u:w"Njk|V}][.t urնeC8ww'|IfcoYL1`$5o|[n["çR|߈ƹc_2Ah̡2o堜 kӹYIZ+IW_Q+cfiZ Ǹ2<BUryC&Ln 0s{{1惾ݘ-biXG|y˦ߝcu=' 2c ±2"z/Zxd~S8c'G%m2׌1h{5'~%eawiWTn{1%ylg4;r*"Irbqـ\9U4}^(P:~Jiw玗^b~+LҍKch[t8E/$xNrƯEw~()7b$Xߒ6NE3>C:YKphǶ A5uF t<7ѡ׆BRZM2t?2jJ/mAQr3t>:G nsnG$}GQzq]8f7sI&:\@~0i2{sPFI9.ysɚڵs*|. Ig8dMQ$=Wɳvvv"]-mNu:\uѭNWWAk/UqR_t-K}fJ$3MXَz/טb$mttr5Zz-_:ǦZƽ GI8γil0u+^DDZU{ܠy_y ^_ۥ.ٺ۳fYijk/I/Ǣs0 *]g__6IR}W{ۘ`h2)ɟz/–vڷK`A0u oQQ;{D9aIŭ{~ $|Ө)84Ki֦e.]}Z3뮐@ ),e?4]><_t$%]cIRjZlRutb]W>ДԮ=O}|S$a1$Y|kS-,0ڜ2Ɔ|}5Ax¥fIRgv9NG 7lVuWRp"] Ziiپ:4~@)Ms*(cUuyS+$)<"]v.pnTۥ[ѽ̕T쮫z 5$e!knn5ˎ*>oY/%=>T2Q"e$}~X=~>*w9MK%]K?wK'aEa(1?;/ m_Ǣp0TتHNZf KQ1EcbI)9_$8\n.I *6#U=]6nA1[(ci+T_10LaI/UpyίlHk3#>%R,zמn~no:_{u_^c nT3I++D**[f/h$8qc=Kj{ת\r)>1Ew,->ĶXwM+( 1)f8aI xI$Ą g)|8iߢ/c $s $&X`I߰+|]A.`(ԽK)*^$ﶚڵ[҃CpLZIAm{6+>n,P0aJ$JʎSc r-.;mo/,1`B3Q\$4Ƥ:]_X5g))ð`rX;ZPI؏~iI7S,9Զ7Ƙ}ySWRś9]r3$LaJw $n?I@Pɹ' ;8+_h$m jwI8cL$W]:M8:QLrHR{Ov]9&AX NbdGV8Kݎq NX?\J&Ƙ: ȕWJmNV-\rGwg ?p_~p|?XT1O䳁㏎5mؿ;Rǒ~7~tῷrZX߅nGW/#H:ܚ|JJXl$;j%ePBGL\?4-[(/(:q 8 )Wx_ ;p%m7[CJ]qR:Igw$#'?]2S#u}ʝ;60G[ռ}M⽿O|aX{{ɺWs%x\*4~򽒖L*TX1`ߵ3/z䙥Ǟz*V ]9oGۚvԮ}KnJmws'%i[jK/|%j^$pz {J8qtCcL{1݄t<6G =ޥS4i\%VZػ)4b[C啟T'm:wl?WInwyTb} vX84?kzxBeWOыMAT7h.7lX}]` I$)xښ~ugy~`s}߲(,>UJn¤/}.IG) $ǝ8Lؓ$iW?̫Rp$Ul{$]ޮ)w8sFLI]O9ZzMFۚmkVQݽ˘S>?XRf8 Cj g\Mq(jdb ڶ>$Ё(i7yTZRIj ^DmkR$~ 2A1h bwS4O%Ng[$}ΕM!IYEJ,rlS 9IDATbVհ:U8"Q,I҉'ϞzOA%ֽ⅐BeL.ڵt7'Ҹ+&Z9]ɟf9*Б8tOn|DqUŅIe\z1>/i ~*AҚHڵK:2Ccʍ[4/*.I8mlTݻu*^֗=$ii_#. lc"-׀M1wz; =ə!)w~a%]{mϋF0RIsu$+ Yb>\Ckw/@@sv+)weAI_ճȏ'$=%0)>PHdy>  xׇE=3o>E";/zr@ʸFħ-Zǡ__ћ2RyͯNZ,u`; \돷?l6]{>~q^1aX? =]nteoynO"I M%G.oز g\qYP>|W "-~Θ}b n) fm˜Wԓ@gso% :JG*~ ,̠[&2=6I}/5?ɒ ;=o𫧝ζpnݲsyB9Wt~6:[ΓS%yI yw#>27c B8Ox+$OLIdƆ&}{w%SH$8-˜ζ2}SBI )_nKs$/M+^- NBf 7ojRKwQ<l:Q=S SX#uumi|dEDv)b⅗nJ88γ=vǕ4G4:k ]nec; >d!c5iɲ/BlbQ y][:󄻿l+rlAɇ90RԮ- rI/+>a2 }@Nm``[ի8TPvRڭCFLxgg2!7 Io]⥥3{DaG8*&\JHb1&Ud $ XB8p,`! XrmóW٫J񲦧H>Iؼ虒giE<鱩RL6<+aɕp8V1Ԍ1wҭB/*x$c-VLbs8B]=w¸Wg1fam]]ؠ1Nc̈>m]ۯ6Ƙcccw F}Z&l7-Z;ZZ+k-}1f1RT,ƘQgsW6PMc]oGko1v^B |h_һZ;I0Ƭh]9c Zw,*S^wsWeV*x3n9x X$a,okn&0 pB=~fW,a B`0hU=|9Y6gcc>~z>ϩ)}?ߺΙ ycVc=Sݱ:9`cwyK0`;.x~xyH]|_U}GjpTE`b5R~ @&\=[mMuێ5^On^?_}~?zR?箞,x@ZpO#7N4"D)HjzbuXop>p"XIr/.\,Ԉg\6rH2x_]"s)ti ,.ouDG]-5<5nZwsC *@kw>Ο >r<8\0F=@G߁5v5޿ttqίg?tu97ZG3:R~y}mp/BmV+Q3pޞw ŅsQݜhbk\kF~TJk Ew?Jk?!Mk{"Nvoׄ ־k]`sŲ?[kNEMk(6ݐXȍ}9Z; %Ϸگ shZrvv [HuISFի1cZc7Ƭ^.dϵ1 lX11p _.f&kݕ`cqkFo9\w. Hꪋ1]pak:ս`1U_K'&`1f cL;cMըF|5 tgSXہ16{#Fc4vs?s}1fcLw^DZ|zf&2^\hF|Z{'v.F&"ZkP`WMHF@Y>Bz݂\EHOv/uUw s~'[kkJ,9#1f&^fΧX.C.j*C$޽{i+57)v}"U@G,a]#^#!#ꍿO"mM';Zknh}_"p|Al}gXi-F |l')o  p'08=Ԓsp.СoCvR4TEDɖ@9i@4?.wJDRfmujfRf}4V>qΕ[v^Mp߈6_P j J,pH^ LMT A,  l -f"7:`^LpjbV^|A5&E(Z`4_ov6OEbA 8h4o8htS;B0|2#;m~ lW㳷q JZ 8;/9¹?_5sf`r|!NKf '&r!oIpVTJQ"WpdW876 `%-gqmɿ JVJd6F~O^sv̩l7M{kNlު(9 2W}Ǔ5fqOO >Jpd3<4)ZJ9AN.Cڨ\XDVnݿf I¢DACJdL kVs^1֘<~~LExo>|k Z-Z2sf\oA d(h.sc4gqx R=?fJ/7 =`Ӑ Cf Юsg^ AO`RZYMUS D=׊qd"W创~p?&i>Y88|ekJk=V-7%~iJuTa0@dԆS(9Oe9'T4T]u')ԛj JF1xܶElqNҙזHQq>+x/q8rp9]\&P5yȓ~6lFEZb_U/7,ˣ Ha\HL;XUin\ lؓnā2HjmS%P5J%}׀Kˀ/]R ғ|kr, ub9ĘUUuD!ҋu]O#أ8eNp 4TuR6umYBN*vTupMF#l.Fk3ЕtE8OC:\VM jP% X,qx C5QQ-@ 85_TN;){y46I,@T9 G5\ccnlZ ` YN \'  ١*Kݸ}3XCt)z@K W%[w㩳 GfSfsP'A 6 q~.82DΗ]'Hp6;Xf& P5\IYK*\I9HèVDɦHگ&VQ\pC%R ,@LLG i݊tReiMQ32z(R(YUQ{q8sHb .wS-Tl {HpjW|b;y3p|5 PlWwC}Ġ0 X -!XEX g(jKOC}.E_Ddj''0%:Q2g4?ʓHc(ƞ}"d08w0Kg6whI( J@zmjso] ]Y/^%9- •: SũrNN)|nK+,e_uHI㐆X!V@_$@S}&xd!4Z9m|JDmzH] ^h JVDJJRo/̓(WR%*p9h2h w TZ]c=o8:2uog` ;)Tz5@:|MYV ]h.%P])/7Rn;UA솜OR/W#wQ`*Tŗ!4?RUoQҵ\-±zhj,\\#yG݀W: Qw< R %"=GZph[Lc%PMN?s8uMߐ8w0~p*QŗG#Cpa SaŢ% @,`$o4({p|< rB5^ԧ"@7U4*8 lDIroH ^K  AJnN}@O* W}=2$Š,5E(CunUja7y=ZmK1Pq8H=(;Uu;wX\< -DiCcx 8B;ݭCVK *(]5"A!#y73\oT㌧K(u|%ѷ=j*<4& JTHs{P]|RIio. K2J ,9܃+3:Ms r4>p.;tHJs2"ܾ*3=e㨀@`_rS 2MRf&t{5VGA D}s8Qu JVLF}tlAlC{h^XagI`c_G-+;xxo4)duP0p}6_ HByjɢ^cWcZ[tBb:Lh?'#'W/KRr(TCNGJҿsn(/G/l4-Yd-Xy֛H(sի.w0M{f:th#AN+kaA,;K1ȱҿ{ ٗG/d xYˇzMמWQ%"qWӶ/^ZZKa+}W1$LGcv3pۇZ/Z'陱 @x◦? %Y.7$tao'tX0JW ;,ǡo?ʸe籵_X dn@kA߁@!=Fsc `#iX %`W4О; c{^`o?uWvB&X H H 6&UM/+˛ǀOAAtLNm1.l/G%4x45w5ׂVmF|ۥ*l; H2 `%]8;r-<lDq4l"MH e!EH)`WhU'XWj5F{÷ԗHmHJ`w &M ` dXd2&):0tyQ*Rx맀}5ȴ`YIwwˇFQ!uV,@~VAy)?^&w~@Wԃw; vÔ|TQS(;$:ÅV!$Id5ҸU,H *ǁπcݱȳρet>m$)UC+ ]{#Z5&`EJX78 U {k5 (4IDATnm[EuUz"5^czZL*DI+H=%}RL`hrm$r 3B88&ݽǗ±wL6w0Y<;Qwu˼Mʼ]B;yen¹1vK}Tǀ Se;*pF߁%9F_ `J4 `c `iXZFp!=VMes "h9F6jŞҢr|V]_")EfySmVM+X%#ޓ{@#m Hp`T8 dcx1NzBQ:H ˱ xz2ns]4qUA6{8*+<h 9Jv[5 ctTNr5i{Y[YQFA DQi~; 2.A;JDI߇Q {8<)$ j'02 Grwz&"UCtRIAtLJnj±S)`o)VDI'w+i~SmT4g[QJ6SH4CXHdX^njq)hT ÃscdR tBJWJU XH4AlMdK8w0yT9pӇ SV4?D x5ܼX d}q@׻xM3, Uy )\w0+-&ec ""= 7tp[8v&v9L䍛1l`5dŁn.w 6/]_Ek) G޿uy8SQ V&tRx[Huɞd{U{knU+iPQ[qDU _EXeᘽRu&DVH՞Q !$wcQ ;Jp,;Ȁ;a]-EXW*#1{5M34 q pcZ DnaUHzXw퐪 Amz\£-RxU'O_0vƶ80[b`M4?4_"r*1KoুZȬ8#\UZf=DɪZ:tiAT\9dgCs+8吧8;UiNF1v*[AH}7w3Vq[/^TUMh($kGп'QHWyU.BzDIՎf8ߜp`Gw]aH!WksRRViEښAď#Ԫ4(.Ez3^VWM0DjC*=޿:_~܃fUVj"%XdM~H '4=]TV4!c- DI{!>H#|A;RKpp/~L%=&K$>A>ƬTE n:ۀՁLq(0 5U{۵is3w<2~CQEpnjP%}SD!20}Qew*H𗀃(Y盌i1=|F`VE6S!m dŘ6FZ8x̡V5M,}+8 JR}R&Ñ~[c Ɯ|д{ʸ`m֋35nٶD`2Ɯ1gUZZ\kcr+0 ;ٞCRE+SH%]W8;U^얓.G#_-`TWv7J>GdN^"i~>?cng.$ #i揳1BZI%+!מCRE;@O Jz%vD9i5W~ݏ-)"^wQ c"H;[@oǙ5U]&Wf13zCpI` "%;8/ `+GqTq8i n9-u*\Tq ky{󉜱`V-<0 TaRZel0~ Wmu XWz.os<q`VKJ"!s\ }D% g\}}ǣ ]u4(9N0a5nX}RR:2]GN OɵX 1RJ~V-, JZ#mG/ *w02o%-܇ Z[\8 ~⓷yּo?f75k!10gya_y![c̺iF_ BT8@{0K%'gŔQ2tc׮QT%o8lOzF R# <[JWK.C? X)e灾WYuu}7w> s{>Hur-+ݿ^hpiG#=~5˩4!(?Q!f~΁Y6?.I_/4,Gq8w0ʯ4CځUUc9`GDeƍ9@RţUJ)R9%J)R9 RJ)ThRJ)3*RJ&J)R9 RJ)ThRJ)3*RJ&J)R9 RJ)ThRJ)3*RJ&J)R9 RJ)ThRJ)3*RJ&J)R9 RJ)ThRJ)3*RJ&J)R9 RJ)ThRJ)3mbPIENDB`openTSNE-0.6.1/docs/source/images/tetrahedron.png000066400000000000000000001054331413546205200217030ustar00rootroot00000000000000PNG  IHDReZ_)zTXtRaw profile type exifxڭkrcr;hZ$˾v\&) dG"AwKKI=8ϟUݯ/#Ͽ|ׅ~Mw|u!8_}pwL\Ϣǂy>6x5|^,uzO|_wa}}~*g!ܽweswnӍTGy_T{[&9u};XjN.z )0 }^a1O|qE{kVc>Kpcn۱f|;~Kx~+4iE ݫ <+*VNy ל7C?,MsK~-{l.'5B_`wf0X_P1 F- 6fiQ=5?ZXl*KmX)e⧦F l9s5(VRɥZQZM5RkmѬ[i{{.=xc8mguXJ+jMﲫm=N8I'riqk7|˭~ǷU _iݪVW-|V,Wo%$kX+^tԚRZ9ȑU YVL'|÷}r\NhVi+t_+ɪ) 5. N q|tn.qu`<1\gqvj;CNܠtF˜1U']G clTQ'pu?:i\U^50 enB8]qQ&ЕIYiS+7W]-J=1FR,f+pX?K~;Z=g;3sm_"}.FOQl"#,JOw/݇9O<ȇ\s0c¼5z&f}$$,]\  ,Q^G>0ܞ5,B ʍk;*1Z7Y)M<aU;-R1m{g;L b`}7K_蝆]e!CTEX0=kT4r؏5aHxpɌP(Op Do (y߄˄ =$h\;7`0E::s0Sd'{#C2pq < .ą\ 3!@GΈ .XU5TC#4ɵR~|؀ih1p/\ hպBދ+g!O}WXh*< !y:*Ϩ 0udbMᇺq9I F;ާ`faF9.Vbj* &ՋT@\i kЊzRK&:PS6O%KH !4Yvǣ`*x1 w.{)7b7wU,UNL5 9_G=m^p:HxN ŭDN5&*8$_ V΂&$ϴ)J}#b8ɹ=H!c(D5`و8&co ''ѽH!8) ѓB ljDs{.H^1hE\*- |o+:WIw>4{V2.[AP$@-o.: 201x.iZ큆X c"&`z[8e; Ɇ̗S\:">"Pl r\ ?S/$?O4geǂ|B0KSu cG'_笾 p>p[Sx;LŘ#"aqcm}3BK7{n׶hI=ZVܹZff,]]8A0Ap#zGŌ06rTcM XH]`3 AuӪb,dn-٫DCUNj2oޞ+B!pua ?n_},pA.`FXnb&.lMU :ee_ "lumxrFh m1TKCҮ_3ko Zj-PX6۩[`kac>V֖3/ሂ}:YS5$Igw4lbGԉ~ E;"98 A D*![-(Ȗ-x=|M4e^%rj8/iE Qem$`d^w"sL]_Ki1GX2 y##A 3sx :Y F!mt }y2V.P!q2^o8?f58-`8!iq bx>f14 :Ii]XVB=a[ 0unzxX*S KxF+M%+$Beך9wRN|EWFS,S B;1|i  A(#Ţj׼,Ñl)p@QbԨ%/VԬm]plKYa%gyEAij.ĎuM(ϋCNή봻X㍇Y\PL+kuӕ`&(p<a 6)d֧PVwy h# '.c θ)G^$IG5::T {QҢp`䕼Zfd̒M)\E"AkKF6J\gأv"Gx6h:{T {,` ڱ4gHNҲ e'&:!0KV&3 H`m3XS4iLL5LmihB8:@&8V~]3^u]HX"(䈜yxptrA5C/h[SϔH Or{z^q^F W%_rܭ}$Q @fUˍlIAz!)cnn"-TT륨!/h7"XG᭐;(aUAӶ$60 @|j#A}՜pQ=;f5FFRvXģ* >m#[$76T_V+Q5p7/_Y%m"'\UdOkGwF#ytx d)+[`.T׆AOQ&m`ty+9j.% %URO! = ȧ0<Ʊmb]0ָ+o`JOm0*eN*R(Ja&4tYޱ}rj-$Ǘh#TYydZ5^UlF7z2έ6݊ʻ~a (E)~H)>2i|@1W.Qi_ Ѱ#2PWE|. }@E/P:+Ðv$T jR몇`.>tTXAEUqS/0 nz ꊷlF:v (=WÙSjbi8s:)]V$1,LGh iS ,.j^:[K_rPQ[wg96_0>>H, :7_OO(Wk)P?GGvۚ6)קEaمQg~T1& r0 Z*x(0V/Mj6tx x5YjQsA<7V<#:.(83n!!4vb7`tEM«tJk¼X)e6 ~WeGE :SkK/wArSfpĸ\bTt9@}3@ F1ݪ,KI(jj!H:43RX1E'WCѶ(t(g0SxNS/ko* <Ftd֩ꮃhioz]7 A]@ژrwlY"dnDPSY|,&qPP [Q48d=66*5x<[&p JS Tn8l8uMj'=/^pAguz oYG⛬GCIhgaQ'yމ4h^m!l;-_skM;LOs:bjH]{i|d/xO8E}؃T3HYGQ6mjl*l]tä+"Y_ىXASB Sw. jhW+ިՠ~Y[U)n" vo?ծr$Fv#?D "83^lvdh԰p0ڦTw,l7(ԈNE$M_R";vJ2H!o,< }g ':0giuns&d9 R[ 55@mqu*mMGmI~kA9", Nڨ7; L󼘙w3K\j[ +^js8,] )nh WC#A+h* E!@ʻ 6e "Y O"LRx;q)N䪳ezdjr-,頪!UCOG XF $Fu3z5J9XsR&F*!߈gԹ\aH6Y7Yz $GFj5%4W@!"МjobM|ȳ*eҁEIG%CkL>k7UƸP]L;MM6 3u5kj`agvUyj79Bt$wZуHL jpUxY:fC{fa`)[a Jv3mPAhH7&O#^S~i:5_\:1KnAw9@;M%}Ԑ&(AG`=U#2GA w* W>l2j-9ui/(:{lNI4^v-L:A۪f)TZ[oFTЄhNej\wnT[Hj(GժT:\95XCVB3aGuHdB鴪vX>\# ULﭨi{Ĉ"5e+Y{|h Q)?G2$jp:GLS\pdѰ"QH1}ڄRHXRف-GLҚ\j* (#m&kI>"SOǘȡ3?]H;g.jWE9(' :!E "#^sS}/Z)NU7'hK?ԮƆNP_`dTqXahE8 1UT/qșm`\üBW0׵r'Lx`jyі\6oB!F̩[bKGD pHYs M MέNtIME 01U IDATxwxWŶ,nKPK` @a ^LK@Bh!ӋL1pM9-dK򮴒<ػڝr{9`5a | |'6X fC d <l}5f]C{3l!_ xDe#q;`~Top`g`9rL0E 8%yl`te]G5@/(x.av) Fl JרZި ۘ䧽G\m!"@9XϰKa0R6ć>; x ė|w(HwWإ0EB!xx]#/A P-p~6(Rx.- GFy# F3 󀛀q#}GWv5 FCI|:=,N&>l6vVQL5}a04۫:@`GQ'#nH`XK,D4Q Yff88(Gsd쀋03N&5Srghu $n$F>8XMn a4ʬm0l04"I fH|t*\ߏ)HqkP8 YCG 2jc kOO3fu. N.@k{!o:14")څ<, @kl0l0r rBEq2q*ad߾Q>1N&a#ST`h Oِ+3 8at)p cdbU=O|"ĭiq21Lo0R6+[R'kR [~Vq21u`J`X3\cq2Q\M@~Lf;RV 0 ]v" SS^aO$#x!UrZæ?ۯ]-m@gUvF5.F? -ǀQC_+f0R6gB.BZ:3puCKE^Dmk)BUW!]WU3)36!e3Q5ZNJkidEm1ܬ*rw:ҌkR!Ӫթ ,`,|B%/Y.$$onFltr  y3ixAH3ԾH]H*=i Yij KU6Jk1!.U` :1)Z{"57F}[!p HwxLLN$$*`O\8)[/ -]_wg-+ޯAm݊bXПsd⻵8)QG֦*a76`0ܱQPRVU<9N&[kCiK{os7 Yn k ׾ݑTLnW%sCF:+w4 $q)~4d M{$#88FHٰ&B1TYN-OX7$pҬv N@? Z~mLڨ3 ̧'_Gaׂp᷃ )9SU&H8] )R{lOs[%佐M7BJu~[TQv@),"W#Xg+TtHol] Fj0j',6VU\AٺC7XÖ:VO<.iO+ϑz̗m0d) 0z iT^DCzR>Cu_ &Ċ "$"'ȢJn1k )eCk p$cs{t6q8G26߇.LL}T3| CRF"/_!hFNH7eʏK_'KU)P3Q!'IJ wo`"N ,md}ah-xNU&ƬO;sm9AuϛV8o/ $b$u?,y ZQqgFyAџ0MF2tKU!Iz Uq'Y>iVѰ~->?9)D*+DܳJ 8 iz8 6_2%*pc6 ق M%f*7qEz9;rau˃êO% vƳ|pOw/Qwlo> )8b ن M}oY|G`ww w-眅jU^Љ5U=og-x:jÂ4Bz3dmp 0iF !t9@ߗ#q- FʆRzɻ#twē |+B#k(YTl~wWܻ's*8N>^5}r+(dNex{ ^A` TEgG#A l|ʆrcN(h~jӟ[0_.^|ey~9=KqSS_ׁ~ t9+U#ː:(pUT=/BbwND!Q]H Q"LA=A l v mkرl=ޭnߩӐQ[atvUrw'y3 .m|\Cv0z#V:`ğ|ZL٥7)riq.ꌳT6,X0}Rp\^~!O(}UƕS+(Jz5<7Iw,j=rg#>%πd:N&WU=`8q@s%~Ϝ^RG^e*pg= RsBU˳lL)3~t}? ~i#9Js!]{N]f+U vWL%S綇/X#"a`sUX \dvenE: 6x4R6tDBdkpy$+ϮoΘGRS7s:67وD`8h5S'7I%䣁WEŧBFSʆLğz4;sn?A(@ʄ>xIF z=n.:D{JJ|(8x҆Hِ ܡhܪy@ ;{ #x" uWGH5k8nC~46W|v4R6)28xGɏD"eV%d㽢r-ܬťHʟsxdqfcl#`l$1|}8:z-NY(_;]|,V*A/ gh {uvS\"ÓW p:pRScukvv޿aW͐ 0!<7 ֶ: ܇$"3s3[ާߋ߻9ٮH^q\L5|ww,%HfOz~뻏wa{m0)ڝJjx{T#~ҹtH7۫Hb}W{ιQv FʆDHH׏m$q; ,΋%Zdur.P Fʆv+Fxg60XWML|{ĻOv.UōE_2;x﫛 :q2Q>V/{@ϣ mҘ-fi?Y#ea#}ah8 ![B43~vY mQ%YWVrYT[iW`JV݀ygE,l0R6% `kGu|.` -'v}ahJnh n9H$'M&1L)ZWCZ#|, mɑAfb`g`Kc0)ZE%ouGR 53"~3+s HȅH*>;[#UH b}Hِ8{يE퍤hp]~!Trk|8#> iq24Kb\/ g}ah$!;9`o`+p Dς0ꆔ'UY_ڈ0)Z'1)2eg6* Fʆ">Hg$/h ZCFa>eCc'?p~kr.{5`"SʆP/{V0:#͔ Sʆ!HLrpQ,v`lhIFUo%淀E"!?T(S`\XEڱ:{ۨ1R6dp0|RE䌫{PFHِM N9=ȡcy9 CF/ uT$&L˗б/xʡcԕZe`JِI܀9;9W)Y`lȤ >?ai4n>.Ў%a +8be{sfamq0L M#揁,C "sn Rlg[}Ug-9b` RJt ]SʆIJ$WUW!\ol98{c?#AfC/:J^] ޗ"8"ޫ7L)[HE#cjyk``lh#* 0qrmAFHٰ:B,ikB[ Fʆzq.zg9FSŦfCD-u8vREVx{p:'Hm1olL)RQ'%BnjQ-v`lH9X:f#7t2YİZd$x>Fshsc*Fyڨ4RhHWRJ%Y`ܱq$?~(8ܞfC"^%w]_Іϥͺ/! k$0-r{>~DjX ;JxxأwiJYϣxlὟl`Jr'Nrfv5OG;~ l\=y 1 lԴ6Dy] t`fNΧ{?Fr#dtRUrV- MVE F@y^Cns/;,b0ER6DHLvv~}v^ h6lhI=r;Wsu?E Cm*IHِ8 {H!*}&TP `!zڡC/οHvwR6n!V9:Z^OL=#eC+CGϙE:<1x89wYiBHn*H*2`*h)eC jEEŢe?U6$L)ZF m LBZ):M{3[G)29 $旁}Eڸ83EΨdkfaCbfSʆßFȆf:d1R6%YL<L~alh!wFbː='\fժec.kelhB`#*f5s`< Ro"mPy0kU< {_aVY5VkBC`n H q0脤R!2+}8a01,"ڊ`3EHLr9gYQv3E앪 ]kV1l7 YTˁ3Mf#eCe/@H_ lb e73ZgfȺJԹ],"KmY30b[J  iՠ+3.Au1e~o ?R8c|4w 6@|.~뽿.{[GDrq2QNHׅv)~:"pxM݀Kq2/28HȊkFm6Y7F1>*<$@`&!lrrsxL7U=n&hش?Uk YU-fQ7-y_R >>}cITj>"m0I8x˔rv 88دQ.FW! #I%}?oO/Gl;, Zh?y`]2, P[`hU<{{ O=+AEBvx$n}]rUղG w9*C3QR,$.[v HۨH)C+r |tGIEg)+\BCN&d"ܘ< 0*ô)D ̟FJe[,Ux=`0ZNCREAeTF0: ۴h z!) e&YP{/xa!qވDd὿c 9p 40 [L.*DBV!!nVʺdhr9~L<3]ML,\"*FC215ιr< fw5M/WތV.URhg@>q2U/HB}lc*3Atc8 ܢ]"}}>CֹZY)BL$Lzن|페AFsB ;%b]rG$eH7$-&ܥt.$ ۵|]|v_30rMBLTfwV$̦5 F"e)?I/VUkԈ!|# Hx6k|*tźjB_.# } FL_!@Rfr/ėUs /3syBfq24N&>> (_nJLmoLMONNРarfd)1Aj1,$LD+ H28HΣU1u*s x,N&m+pҠyRa!*W.$N&kQX'껩BٺYPB~9P0R62yHM[ѣq2-~sZ%圾8)>ާ%!-Su|dgu>RH߿KT;RVϾH^EF'YVO݀ۑóbS1O^֙wFۼFˀd9l"X? >1A]ry]Z2y6]GBQ0nqFÐxAȂ2`]8Rben{j:/C?iT n)p@F|י7i5pn/xAC{H5SUu:a40SJnDaj ?|M @!ґd jBl7U38HŲ'U\2tBm:YT[ uXǯͶd*N&^Fjt-N&f}h0@F3;(vYFgj4=ฉQA O6yfSx$LC8ZIHR|W%霪)R.6\'{3D/Pb0$砋)"n/S%鈋PuT坧O__sJ(}}'N%+>|f/"=1x:N&>Xza4Rt28YQnw<]Ol>@|Lql\⦅!5P$, 6``@V_qVJݑ ?WO]3WUʻ?Uw$8OLLU[gq&tmrFA ԬV~ ~i<)SR^!Qbh}XV>D)_)O(SzOC|ߑ܆sBSH؄7Ra?_>U?Yp 4)"!H{u &!*55\%dD[tNg=2Rg`"egح3C 0UfU#2SH-S0EtwJb{5[s}f%} TrTۥowuLUZ f~BIYm= qn& <1öXl0 #f);0߭oH,zK_- q;ܕH%4>kD ߥM "[H]uF!X-^j)JT"H%R8݁}A} <'{!g깬x#=`04W!ᨍ _b#),U؛!~"dQU$;ϞSuC\Xd3RFHvH c P P6l32$_+p[#bZ_^zq[`h}x+s'!!w;v^SUL V"O_Z&Uљ+sLN[Hb%|:383'dbrLFaOS;CN{ιT՞]=]$||y*AHι{dbSF:)/}󤬳4CH75̥6MxU5B$\ WN@;>7g~*U!;p>Ia4>!>) D2 MUOw%$e5F=o7&W{K3ҝA%JODH w7$]vZJ^qOzWHcTлTYBi"mY {Y$g 'U+'N&xx8ON~VMF*7PNY [<'5|uka%RtHdC\d:e2fȎH&sT5/PQ[_iYkBNJA BB c22!S=6*;)ޅ.r٤G)+Vc0ꡤ98H;%_hRIcy=o0H Jhu۴)a0fA Ԁ{ͮV_ԃ.TA5L_^O9"7y_o,6&7OIj12Ez}֕ao]?f΋m]6:!F褐rY, kuC[*iK6z+=fSVHT8x x QH\*7=fd'aQEb|jWx; 毸̟S[cWIWU*:_Q!TLۙ^O~GJn6*z2c0ZD^3NJt&)V#} LU?1@ŲwRx֞=NwHjy$rI|[gqɪG+ 5z.74 V"r R",-`[+`۷KG7NJ nG௫jZ?PNL}T)D Qu!i/D6G utFYY[ʺ VXUѕ/y,xȊϟzP\Z=Aa춇SRܳ)rfnw6SZw9"&@?R1:)N&JCBF=-{ 8vFt/?L>Cգ?šjvNsCVAB_DAcdi zHιJݐH;`>Gc{u(uٓg?}wϤK^!gl`.T,ǥ߽?jpkV-[Ȓ^cm5Օ)s/xܨ_wxo[x]^P9j}{?ZX:99 eȂ]';Q }uJUU0s1Wً6܁0dy5w[LLkOߨvj [W).H$}DA_7QYL,g(^ҩt XZ>qdaq/9pB_UQ7o~Meb_]Yk*{\6El0txߥJ,U{y vGZ|O)"a,[iOCTdR3 DZÐOaui߱QVRs1W.x+˙GzŇ_v΁x`"l/){n1Py]{vʥy]gL!X6fu_11h%霴# x&EF(1uA\ʜVu0 6|#p J9,֛mz>+<='O{/oǽ\'OWI <4n`Pu|ͨ{W.pIt[*esYX% Nk99 YL{AUzHXPlSA7'5ڛkoB8ART_P&rW.p8 :P{J%߻k{_6D+>%q2=2 vOw,{K`J/ZQUV-u/.W58%^ɷ~NLLHWwjC~{^v_4½Rs:lg{,+w[sF`;{}1{3C XE/ g,@OĹ}kwCimsw@'ϑLyiu]8hպ8-Njm򙝑Ct=t*aOT͊q2 q#vBb΍;8g C Ka1B@$V? e5JW$|q}b0X5v^/Ӑ& 8')d:kp? ppbFRr~x }QB_7uM!m^_CyPw>b<}Ug+>_ₗFn8x,/FܩHSϜsuvBKhD;j>Q xs#>6RO=?Ol(01n4$\I2Q)RѲo^0~]@yLrP$=QH,%U{_?q;(i6 #<;HtŅ ?ΩWR^VPRdn8YSiꅔ"$buUE뢬/4M$zQjfZt)O }?{xY9w8A=C2 #eF Ce2 IDAT`L^Hw)'LCm,'9H\s1R!9Af{H HuM<'+Z§< !o;շi> ):'PCѦ9 sguܿ]pp s,~Aݬ\|'v 9C 8{_FA zV"]Yu"n))Ǽ^A<$/gUkvNHT"\Ƣ/z2DU q֧qua|+<5𮪮׳gWvHN[\ۿq-].u53Ajt<'_LkT]Y$_Sy 'SZ.)k'TG2eiƦ<:N&'Y1V?TUCN%+5V]$G8v[`)Bgo D5cS[")iJ$ZG~^BAMi)djog_<<9^MxJHI d j?yg>T9wf #c; {CD`AFQFl'6] 'Rq2BLG[ )LR ԕ5ۨJ~z59PJrνu{:mL((翿yōE#ڟpمA='KV}g8[ $_! ~\je$mwj[׽K,uC*d`SAӐ8ę:So#Q7y˼ yιs]22JÐ5嫊=r_Udu~1 jWu;|Hqaw$<qY+MP$=۴_,D"4$72>F V@$d/=G?{?9 p4jKW~/?t ? z#~ikLm ?vc9Ys$XE)N0 } !l4 Io,js@BXS'{?YH=9Y kR_S믭)]^ȭ_|MyS+ު|WT'+1+ōRtUqPxR6Vq RR15uιW&7{;N[0`04K_ۼU߾{Lx WeINxJ6V0ꓬӧٷװTlJqj )3Yz+N&Eu&2?N&l h`jVιtErl:Y'05 )w0S z|ɚ+~.J`efոx$c^PňK6D k6H񥸼~\^Ҽg0꩏ }  n?{ #eC%Pu}6k>2tou_JD^dI&h;$:sx[4%"!7 : t?^OZ,ֆxݑEȪf(㽯_tLYTs Aas# :5 YK 8E Mq_DQwf 7mE+Hwlؘ_mH$7Vl!T$_wJ^ BA*XH1);qs7o:.j>-fV%|׈;8n8%HI)0NXU\HyƬT蜴'#9+,tmRO,';kcV3zgb7߼ǙEZ u+vGI,WW 2*>SS]>8x>?U-/Y9j>#LnC~;nYC;'C?w[2!+6VBV9Lݥ~h`/AwnDOॖ,F89WIx*qWu8{t9}UQ[q >Ts>&~ Yx_  큐7GOQnج 29貕*@WQ d"F# 9%kC<"]vsΝe1͆6L9wҦib`/}[3B_H~HHYkszQml }ٵ@%>)6U/fߴFTrH$Drb%泽%fVt_2Xdq,9wsYǐd9w#3FF퍜FZ9YƐc1g=Q7{YH4q56`C.sΝ|$B]潏:F흘+E$,t=a1!E#xWu;9 ly|Tǿ'¾( Zj]q.EU_[ZZZ7DZXX`uxQPDM<0 KB|?| {gn7gsAwD`k V t\*_NsƏ/$ob4p8m$"r5ѩ0][ecօ9nFx; 4mTgu&Q7Ex XD4yADظ0W8ƣ) "FJAZ&LEtš([&ΏF/Pc}ldF `$8s9ea¼8^fld4@E@/_`6LFss ,V4F، 񟴮㜻LWo9Bb#clDOA rѴ¼978 (kDFb^DA7TsB\N'߆Ŋ4Ӝ(Ѧ'@y. 63&"X2Hhahz-Zi"+wo3ڀF_"Xde9@+4ȴjlTk}Hw DS ߁ιsubll?q~iP)b§QA. pνa&F/たQXW]2k桝M yOs/DhYs]{Avl EB[b`Z9[}5o:ollq~]}?#ի}t\ #r@ED u-ola.w΍CxBDnmFܕg>jHwt`)vsfhw٨"?'Y`W** "2 ] i+6M-K^Ѹ36ru]+x|ܚĸHDG7cs9،6Q6rCksWN>C 3spEk=pqƄGDn{f!f&7Vz.~{>%hTx"'I@QS^<,D{}єb?mrج5Ls_56 (Ah- ?f1QCgtI`sE/N:9FgNȮ=9^$H~ 2@x"q+`?&ȆrPAR7HO$rX6\DG7R D*Ԉ+ppsnLDm ssn"0̧"ҡq~<=0XO$G7eTfGQz+;'K}s b( jlDEd` Żrtodsn!uZj B{fal'_G+ ,"c~NeE0>{QTlhKu0\'y.DDY u9wivD,dDhi\휛?W6FcY^Ak~'wCo7ϧ;nx.'FEËQY/)|*#%Wmss7L,GD.ȀF<`FHy^'ɑe!2X6Cs,@{.xX$85ι&Fkq>}YD&4ب+V]fpyŢSqlƱ1`IF)q:! gr+R[.q]圫;m(Isf w&F.An>9ι+}'| -WAFa0{#vέFaPiwޘq?Ddsņ2@aDKc1%n|Dh= :0Q6ڔ0SQc[hO;Eڳ"EZu,A@$H:6Jf眻7]vewaqC|Ėy1t.[ X<+Ho  j vD>=իDIc[b9eى'է5t3}bⅿD)iF_(:dj>\V\C~s\RtASkQ[pkF\w&F&gQz }9ꚛ}``\c#NQ`y^I%_z,cN+0cCO"ԫcsm)`Fc[a֦#zG70X0:Hn.nfMMiagI?D<_n9sj tqWhstAn&{:W A{ln0)̶JPw|8`!pdv! 1 CwՑ])O0x]#|4alwFF R E͐z@FĽuH53۝4Zgl QN~> KY`ƖlbK}TYza^ۋ2F\als,llmcO`*mҦ>ejG0߈M8 x"Y DKEa*:;X7v e#Wy[JD@w0x-+il 8ׯDaa #Y@ԢZ5Ok NBmҘ޹/'@ 7&%>eQ-3,R6r9R>ۋ_jR|/{ýJ`lߣt#"]ѵա5AK3 0al0|ç3I>>>:ijG?.)x"y)ZQppmW3L$P\mN';{._{]2iwǮNXx"yi.9chg.-)Mʿe=9:/ЋhZ5QEzcȡ7rz?-{aah:nZ ٤ oO@n2Q\x+ v eP$>L=\;SEvhzE9( Q5r fm477UU>P3QӠE8#2AO4,R6K37؛ \LyZR+BDa-kդ:RzclT\Fk;jYkx"il(F3 mXڞtTD>9唍VC< 7TyFs٨Cƍm:1EF! 8`ҝD@?NfѺgnPnd#jD,}a6Ɠp"^L9X<<wiQAp;/홎(|ATKEO3͇>Or)( x$H׉ )a^;w al*v83 x")hMr<$`Z2͋o(.= .-glA.2$`|s/^t{gPNءZN78aliA3+FNA ]-ک'c0Ȍ#=.U|5B/ 6alzzkIUK{f1\: l؍րU_ۃYhǑ:AjMC验x=Я\Ὺq%jo&ʆQ>|08$ c*y+gyJ`-鲹6Fk/wKvChTqHw 0F0Q6BܬEc0Xjg(arʆa&ʆaaF 6mx" |~=>#8\F?`a"+<IDATl/ʣѭ5@0 z }: 6Fsb9e#9ݖjMc_ _ calM64)J߯G%^ D0Q=h'M%{iD0Q6& XԼh50]S&4E562f #-"]*8+ 6:alamM( ڨii)݃N$IENDB`openTSNE-0.6.1/docs/source/images/tetrahedron_2d.png000066400000000000000000000161751413546205200222740ustar00rootroot00000000000000PNG  IHDR #sBIT|d pHYs M MέN9tEXtSoftwarematplotlib version 2.2.2, http://matplotlib.org/ IDATx=ldyᏻ,5A\(puY&T`ELa"@p.E(X[u2ةffRp̝srrw/VUǓ/y @d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#|.o><"#ղWd2"^Dćq)`zf%>"^w~_-x-0+Sduݽ}Дj50#S@d @2 HF$#@d @2 K_x#>"n^-{UPHm^Fċ0">bW2Lm:{lk* @js_)`jsݽ}@j50#S@d @26P,>4 %g @ %j @Jvx0F:yƑ @hE/cC=^`,lWs%)v}zys1T,#sݵZ@ zD0b22@zҼ Oc#NHaZ7"l @s{5 ȩjYu7u\bd5rS2=M4ʍZTNr*Hޞ&QjdU5~SzJɑU81b󽋬,RQ7FDַDpk4)`Jw\`"n)`d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$x#>"n^-{UmN GˈxF>&}FL #{{=  b֭5`jk`FFa- ţT@K ţTj1 MBۖ;ZiжYw4Jczx¨ 4lT&Xx$/@(s YXu/@[,d&p B˺ł @`LՍR@JfgZ ј&b_f &=%ė @( k/3e\h^Ȣu(Qk@c.4/a:5}4D{&"~'1ޓܧ7wFD%!8d{dYDq\DďǍ0H]^|Mm!#s8X]~MuQѧkcl8d{XGp{u'nE^k),[v{#czMgxjkpysy<!bZZ1HS?fqK?>hcݛ/T0Yz}ҏ-ZcSD|P Ʒ 2"._l^,LBe6xj7wc9u8 /6FyX0uj/n^]]џ#83ؼX,MjMqx"⯶ڛ(u*j3Mu]{4DB& JnL[֨OgbgeӷWzys͎GC TP-9-5$"gkn&9i<{R miѾЌi˩!Ꜷ xQ94"Ǯb={~&mL< }CoiJX^m}}C?+~rжr^yhivӸC/1Md$*qLonN3Mn`ZERw^xGB&|2~} *GS#|^xUFxIWv0RS<*` p:B3j}L,;c}m /#@S Ss̨18c-zwgj /K2 %ř&"cFwbAo 煗%@ L֑b7> ]6VYQ(4 p _ma_mdK%8jȔ1:=&'paSTm4 @`<&=Sƴd4sMo2IJ_YHKǟ_}@`*cn{v2 GWQv`o; pmwv'Drjk{WQv`oq(^O&"~NцIFǻylyF c ~p>!,"~NцFv< y6<@!cl?~=btCoc}ߗ6eFf0liA,dp?I)CF g7̓uD:"~""7" khאȩG)' L9 p{auD|~P!#3RNg$s,NDV]^ @dK*`=L3PZFƺQ#nqPZFFQxQg uS\g-J\eDYg 0ZeeZ7FZF?k&)3}c`b97LLBJ2=so8AP.kmo8Mk0!kjo8YS0-3e:#l |!~LeRo$ TΔLQ-8)wVԪ$ qLsZV}'qZ F@`t7wD7'K]RL*E\,.'3SEs1BZvS@!l²l`vOHf'aYac31 ˲1)`d @2q!@ 0'00'@sr!@Lsr!@)`d @2 HF$#q4P˛b}籾{իe (ˈxܽŮ1=uEق>"^w~0S@b{>#]VHF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 HF$#@d @2 K_("^F󈸏۫W^p/#ED|wl19:{lk2XVc=aD`XZVb{>(,SSݨda a `c=aj2Z&sZfd  5S90 HFiQ*Y@J5([TS9g l-NҫF`"])k0 @v)u#\L]F]705:@v{ X#w 5NٱiP\FR>ۏaJ)ki@f1Ҕ5~ۏqysM]`r'JN0#nڽL SvuF2!kXL?85Laڽ19N`) Y7>g .V@JXq "@8 Zt]  jw6lTKҼt@riNd[Y@# +#~Wkz CZ'̈́4z]&ӠpxOc~`@ 174m~ܛ>"6b My:vh qO#n(G[z3LJkulCmh8 ŭp]u~Hܩ'  ]cc4wD8 p}HCkul'ObWp}fزֱ9%z(8s@` Zv(9/N\8@vԈ^_q /Ϙ>f=!Lm豏94C2>_Gb}P%H >>/NӹF81 ߟ{ >n|eX#{=w4CϹJ8?4P>z6jCt< JϹ^m鷱 fsZHky)n5nu\/%@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(ExYfW'^ۇ{;O.['d?ɭ$W>} FK{{ \@%r}f~ƥ$_ n7$"0iLd@ŲC;MW< 6&~h{Pұ!Xa=o#Gި4 /&ӾQ Ȇe0wܱl;hzf߆knޛ[wI5cWFQ[P<(9~f? l #w|]pa72I2:#L@)#tO |6 p>y?2Ǧ2:]Kjlqr2G־ IN/#3{vgi/ΓVV_]6KNn}~Ի4Wx#/v}Z'Ϧ]>65L`Qs)sUӻOqii{;F`#S%,݃4!AY߭4N&䏒\K(ɝΕ $?ɒ0ҍC?"}̓u$#2y ۗZ7\(tognwqo$qxi޾9c+o|!0ApZ#xIr?bGpD~['4gOj&Ib:8Y045'q&$HΓޔ4׍=޳lgoosr0 98-]l]Otr2OfQk7vX.5t˰\4ý^7zMN =jeg%p4k$2v3IW[Yl~ʑG`z G|{hW$poߟw2L\Oc;mW w~Ap ai#KCNU+}t==gt6WI~\{Y\ҜӃmxtiD]q˝$=K}8xNu&BN}l*N[LW&g 9\ d"zkqֿr<./PQC6O;1/P)iNίc^8mu}GI#gs ![x#5Ismୌ識u !'lÌ-u !7ײѩ^Ǽ@ps-3 bDT/@b.]@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1fW'^ۇ{;O.[ SS'J>@,#n<@,#%y4𙜁@,k^Z^  v 5`k>_w&ddCȝ$n0pʮᆐI~Aı0/Ƭ;r-ɫ'61 @`̪] !M6 oG  @`̪] !5$7g'N3I^(}caΑƜ;2~o6n Y'YmeøO%y8@#Z76;}&S[9yL}I>۱ @`YF$o$=ɳl4W%>C9*UkFz!$yQ$g(s#Uӳ|vG;-ܭ (NA,~ݜty J7_w`SF(7iks;~I3 {!.]4=?iFŚ'441Z!F gi imei0~gw.FY,?X4!n籝7r< 4%| Lt#u:7w?ǣpYL:%2[NAt4)c?DXfՙ|7XCحO+a\r|J&\ yf7 4Ib~}kϗ@༌]^sgvC=fSyy4w޿&M|ـ9yM!D祿l;GݕB$063rw.[r /pV6wc~z3捝#ȋ%Il2Fւ~Xw8p颿d @K3ͻVLn5ZŘ(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P$In$ΓV"'bm?ɭ$W>L`M.mo_gHio܆&N0֮_0%*V|^l_ko6{ii$ykSomg$?Nz'&8}i⯻$+۽<|E4. K`y{Q{&݃994,i7ܱ{y{]~od>0IMWibw`?%(`d 7i_bi&(wIOfSI>t ep@En'ywiFM3Vm%y}Y ;9y7|ߤ}>X'mE83k8poΧiZGNΧf{YLϓpLO˪CW=?Ag/@![f$5gsߧ]vv>ؘ$ߤY'8z` dQe1w]YCط46E6S0\׹f ߫]?UNyV{ÃcȚkGOҌ w~ڻgf\ LvI[w\nkE?O3b m0 ;W&0 5{]݃oD4qdAP_$u0]Uǻ_%8͵_ة4SF?MuϻXcp͵!wh[$gDO|fJy+ͨޟ&`fEm e0*8=bJ'iwQ;i^: L o%,l$o$e{YG Gnq>fGip #7/D<͙fn7׫. h0p3,)]nc9N=chLn;=F^ߝio.4J Ew+iAпFgp<ھ:[1 Onٱ;\4h6,ϳc>@ py/1l YNe0@1K @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1P(F#@b @1F^IENDB`openTSNE-0.6.1/docs/source/images/two_clusters.png000066400000000000000000000204411413546205200221140ustar00rootroot00000000000000PNG  IHDR@, sBIT|d pHYs M MέN9tEXtSoftwarematplotlib version 2.2.2, http://matplotlib.org/ IDATxy$e"0nz *=;δ.xzX.*2R 3#2-  ڀ GCw5}VYUTeUfV֛Ux1F$I9ݮ$IfP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@Iv$Il`"R`固[+ $Ih`!soZmW$?A _b]g $KX%IO+1J#c Id$IRfr<.n@\N@Tqa X%$Uy\ *$,fJ.' I0 $IRf$IʌP$)3@I8 D3E%I@$G₢$f J#LqAQIz AK$e.`IjK\D0\EYR-*˶HҔzPUեZVwI:CUp K 7pICUϰPUեZ./?mNOe@TE;B롪geIfj<7P$)SHAUG$MP㹔 t)5I@20q|+I@Rs$I}*IG:}p%)/lh:}I $>W3`w)i#CBCvNd4E@IfNȍ v0"'q%ijWW^8s,Iө#Ɛ5tJBx/0,$Ƒ]õVl9Փ(/%IRivk wp\h.as犇$+E>Bxptjmo&}_cnj)I}[C6r2tOZ둁˼Ï6g޹?<eI,nxmE7&?P< q{ Ije_oI` ` !6`L҇cVYR)ad N[{  EǐMsj~FaiUĸ[+3H_(~Sg5JWOetV9{#/c{Ú*`׆t 1@yO&M|K i-1l`բs%+І̷qyQ8 /5?]gQRWl1xVQfpWx*1^MO}1d#{!F1ƗZf/>K@܁|`w3Nq](uFՃ"1ۈqK?kq<с` 1^T'0:XLe^<̕xqQEb(sbWCϷyO+ay3p\C}/&Ɨei+SʼVU{; ; ]G Á\X)WRkR.m=X N˯ ;HZrsÕ2OOz-ܸW~VE>Ͼy^o;w}n{_Y)s*\֡]U)s,gl{k>끻8G`1)?kE1n !1mgv9x jA$i>w},g<Ỉ'E'h(s1^U9xhC]. 2ǑX`Qb\Sڼ`$C Nb<9+w42b|CQdU e>I/\F34xZ CI,% |aZRXx1/ e\P%aWSߒ"m5)`<~O/?ۊ2+xz %z R櫤uu~Q)a~')|4-a9Q{ 2@ T{'#w<-_:B1Xb(V-b`[q.xChy]3膟upΛ[Nju=U1|aCpB-!CbkC/+Ϝfۊc:#Skz p!S< @Gbt We^KCuf%[6.|Oܶ#r'̈́pvF{(6ߐZZb(ʼt/?eF w2|ytҁzRCHgϤn5"RKk|H)uwM:ם !տa;H=1 |sBL=쨔t]gI{i[2PjhFsuR#SKVTޣ[=9q-G*e~H̗Hݏc9 8m2'=gw7VI 8es{*So鞋ܯ3`3و 5"TZ qz4A7n+ZC1~Vp>00w?Kˤn/.//n\y9o"vlol98B.g}K @pOIzc ,enNdtsO tK̿DjN5z}-RI]McZb qO12?{S')Z6VI*sSQhH]3Xć]M]f}c Q)xpzᲘ!Jڹs7߳V+].:5D"\s%_!{Y_MHan}klSVMmcRO{169uo-+ˬ7Nve{u:c+[loSIcB-{p϶ÐR5.qaRW{xgg#y@G텕wBUS\ie+qv#-zR %c iXyf#de2Rtr mؾR=I~{C9N!lqԇ}iu.rË닀v{$)=t)Ʋ.:bgT'LjG`s?{ !|u] eNbw}WZHZ:hY}K%uI=netI76l_GbK76MjS^>eT'ؒ'ͤ5mq^5#0vct单202hW)C#IK S?jW}v11]Hcb~Q˾N0jDƈWc5*?t-𺓀OP浤rFZ@ka)abܱs1·^^aC}&d{>6L.X;piэak$%)y!] wUJHc뻼v ]{7ZtPSNڇkKf_lY ᅤ!&b(Pq8yϷzybEPr;#/+dc;B8/:0`ڝi^T<>x|LWNFI(05 :1wy)diu]_!E#CiZ)G k讕^`4}=! HE1E5m }cE}@{T)I=uFZ]2XN>jp=8Cݵn4|$Ʈ-iMn"ǒ&=x,!D wo .T&Hp\hGa!z^HxqqؿJM`Xvi{^Oi9$)n ƃVElq\S_{-9lѶ-onںlݭk]\`#a,"c9Qݕӊ }$Bi+QBmb`H+/ <<1G65ձ"fF:}$)=sj՜1eZ\ZRU+Ii+i._W}mޒyނ o0T>~ =8tb,\;5 3ʞjms1jI` sV8㐗w\}x맿s4'upXq>pԛH~Fik$ɆB9 D՝)Ikk2$.0"ř\`SqOڷεٝ*I}ok2$.0.!8qHRϘ>ǂɼ8ܟ39CzD>׳ש; =]KZ&|5M8qH jw\k?5aMZkav38$ifM{T1[z̫Tr3{kdQZz$fBDžK|n{4{S-l,M]5 1nAl`pfpq$ 8 kܭttHڹ8v g(I*'i[Izs $IRf(I%I"R1J4u^^M}.`IΥW $M쩯,IԹH$e@Iz35 $6g"IRos:(IRos:.`Iz3q$Iʌ]$I1J$e(I$IRf $I1J$e(I$IRf $I1J$e(I$IRf $I1J$e(I$IRf $I1J$e(I$IRf $I1J$e(I$IRf $I1J$e(I$IRf $I1J$e(I$IRf $I1J$e(I&$pI!^H:+b_޶VN!,vy]¾XIJk_88xnUJ[Rk B1:I:+PF!k$^5U$͜2,cT;,N11M$B|uV@H-/=Cu$Maf"1u]E1v$IAN$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$f$rIDAT)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@I%I2c$IʌP$)3@Iw SlNIENDB`openTSNE-0.6.1/docs/source/index.rst000066400000000000000000000046331413546205200172520ustar00rootroot00000000000000openTSNE: Extensible, parallel implementations of t-SNE ======================================================= openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE) [1]_, a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings [2]_, massive speed improvements [3]_ [4]_, enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations [5]_. .. figure:: images/macosko_2015.png :width: 500px :align: center :alt: Macosko 2015 mouse retina t-SNE embedding A visualization of 44,808 single cell transcriptomes obtained from the mouse retina [6]_ embedded using the multiscale kernel trick to better preserve the global aligment of the clusters. .. toctree:: :maxdepth: 2 :caption: User Guide installation examples/index tsne_algorithm parameters benchmarks .. toctree:: :maxdepth: 2 :caption: API Reference api/index References ---------- .. [1] Van Der Maaten, Laurens, and Hinton, Geoffrey. `“Visualizing data using t-SNE” `__, Journal of Machine Learning Research (2008). .. [2] Poličar, Pavlin G., Martin Stražar, and Blaž Zupan. `“Embedding to Reference t-SNE Space Addresses Batch Effects in Single-Cell Classification” `__, BioRxiv (2019). .. [3] Van Der Maaten, Laurens. `“Accelerating t-SNE using tree-based algorithms” `__, Journal of Machine Learning Research (2014). .. [4] Linderman, George C., et al. `"Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data" `__, Nature Methods (2019). .. [5] Kobak, Dmitry, and Berens, Philipp. `“The art of using t-SNE for single-cell transcriptomics” `__, Nature Communications (2019). .. [6] Macosko, Evan Z., et al. \ `“Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets” `__, Cell (2015). openTSNE-0.6.1/docs/source/installation.rst000066400000000000000000000026231413546205200206410ustar00rootroot00000000000000Installation ============ Conda ----- openTSNE can be easily installed from ``conda-forge`` with .. code-block:: text conda install --channel conda-forge opentsne `Conda package `_ PyPi ---- openTSNE is also available through ``pip`` and can be installed with .. code-block:: text pip install opentsne `PyPi package `_ Installing from source ---------------------- If you wish to install openTSNE from source, please run .. code-block:: text python setup.py install in the root directory to install the appropriate dependencies and compile the necessary binary files. Please note that openTSNE requires a C/C++ compiler to be available on the system. Additionally, numpy must be pre-installed in the active environment. In order for openTSNE to utilize multiple threads, the C/C++ compiler must support ``OpenMP``. In practice, almost all compilers implement this with the exception of older version of ``clang`` on OSX systems. To squeeze the most out of openTSNE, you may also consider installing FFTW3 prior to installation. FFTW3 implements the Fast Fourier Transform, which is heavily used in openTSNE. If FFTW3 is not available, openTSNE will use numpy’s implementation of the FFT, which is slightly slower than FFTW. The difference is only noticeable with large data sets containing millions of data points. openTSNE-0.6.1/docs/source/parameters.rst000066400000000000000000000124331413546205200203030ustar00rootroot00000000000000.. _parameter-guide: Parameter guide =============== Perplexity ---------- Perplexity is perhaps the most important parameter in t-SNE and can reveal different aspects of the data. Considered loosely, it can be thought of as the balance between preserving the global and the local structure of the data. A more direct way to think about perplexity is that it is the continuous analogy to the :math:`k` number of nearest neighbors for which we will preserve distances. In most implementations, perplexity defaults to 30. This focuses the attention of t-SNE on preserving the distances to its 30 nearest neighbors and puts virtually no weight on preserving distances to the remaining points. For data sets with a small number of points e.g. 100, this will uncover the global structure quite well since each point will preserve distances to a third of the data set. For larger data sets, e.g. 10,000 points, considering 30 nearest neighbors will likely do a poor job of preserving global structure. Using a higher perplexity value e.g. 500, will do a much better job for of uncovering the global structure. For larger data sets still e.g. 500k or 1 million samples, this is typically not enough and can take quite a long time to run. Luckily, various tricks can be used to improve global structure [1]_. .. figure:: images/macosko_perplexity.png **Figure 1**: Higher values of perplexity do a better job of preserving global structure, but can obscure local structure. In both a) and b) we run standard t-SNE with perplexities 30 and 500, respectively. Note that perplexity linearly impacts runtime i.e. higher values of perplexity will incur longer execution time. For example, the embedding in Figure 1a took around 1 minute 30 seconds to compute, while Figure 1b took around 6 minutes. Exaggeration ------------ The exaggeration factor is typically used during the early exaggeration phase. This factor increases the attractive forces between points and allows points to move around more freely, finding their nearest neighbors more easily. The most typical value of exaggeration during the early exaggeration phase is 12, but higher values have also been shown to work in combination with different learning rates [2]_. Exaggeration can also be used during the normal optimization regime to form more densely packed clusters, making the separation between clusters more visible [1]_. .. figure:: images/10x_exaggeration.png **Figure 2**: We run t-SNE twice on the 10x genomics mouse brain data set, containing 1,306,127 samples. a) t-SNE was run with the regular early exaggeration phase 12 for 500 iterations, then in the regular regime with no exaggeration for 750 iterations. b) t-SNE was run with the regular early exaggeration phase 12 for 500 iterations, then for another 750 iterations with exaggeration 4. Optimization parameters ----------------------- t-SNE uses a variation of gradient descent optimization procedure that incorporates momentum to speed up convergence of the embedding [3]_. learning_rate: float The learning rate controls the step size of the gradient updates. This typically ranges from 100 to 1000, but usually the default (200) works well enough. When dealing with large data sets e.g 500k samples or more, it may be necessary to increase the learning rate or to increase the number of iterations [1]_. momentum: float Gradient descent with momentum keeps a sum exponentially decaying weights from previous iterations, speeding up convergence. In early stages of the optimization, this is typically set to a lower value (0.5 in most implementations) since points generally move around quite a bit in this phase and increased after the initial early exaggeration phase (typically to 0.8) to speed up convergence. max_grad_norm: float By default, openTSNE does not apply gradient clipping. However, when embedding new data into an existing embedding, care must be taken that the data points do not "shoot off". Gradient clipping alevaites this issue. Barnes-Hut parameters --------------------- Please refer to :ref:`barnes-hut` for a description of the Barnes-Hut algorithm. theta: float The trade-off parameter between accuracy and speed. Interpolation parameters ------------------------ Please refer to :ref:`fit-sne` for a description of the interpolation-based algorithm. n_interpolation_points: int The number of interpolation points to use within each grid cell. It is highly recommended leaving this at the default value due to the Runge phenomenon described above. min_num_intervals: int This value indicates what the minimum number of intervals/cells should be in any dimension. ints_in_interval: float Our implementation dynamically determines the number of cells such that the accuracy for any given interval remains fixed. This value indicates the size of the interval/cell in any dimension e.g. setting this value to 3 indicates that all the cells should have side length of 3. References ---------- .. [1] Kobak, Dmitry, and Philipp Berens. "The art of using t-SNE for single-cell transcriptomics." bioRxiv (2018): 453449. .. [2] Linderman, George C., and Stefan Steinerberger. "Clustering with t-SNE, provably." arXiv preprint arXiv:1706.02582 (2017). .. [3] Jacobs, Robert A. "Increased rates of convergence through learning rate adaptation." Neural networks 1.4 (1988): 295-307. openTSNE-0.6.1/docs/source/tsne_algorithm.rst000066400000000000000000000410741413546205200211620ustar00rootroot00000000000000How t-SNE works =============== t-Distributed Stochastic Neighbor Embedding [1]_ or t-SNE is a popular non-linear dimensionality reduction technique used for visualizing high dimensional data sets. In this section, we describe the algorithm in a way that will hopefully be accessible to most audiences. We skip much of the mathematical rigour but provide references where necessary. This way, we hope to bring intuition to how t-SNE works, where it can shine and when and why it can fail. t-SNE ----- Given a :math:`D`-dimensional data set :math:`\mathbf{X} \in \mathbb{R}^D`, t-SNE aims to produce a low dimensional embedding :math:`\mathbf{Y} \in \mathbb{R}^d` where :math:`d` is much smaller than :math:`D`, typically 2, such that if two points :math:`\mathbf{x}_i` and :math:`\mathbf{x}_j` are close to one another in the input space :math:`\mathbf{X}`, then their corresponding lower dimensional points :math:`\mathbf{y}_i` and :math:`\mathbf{y}_j` are also close. In order to achieve this, t-SNE models similarities in the input and embedding space as probability densities. In the input space, the similarities are given by a Gaussian distribution. .. math:: p_{j \mid i} = \frac{\exp{\left (- || \mathbf{x}_i - \mathbf{x}_j ||^2 / 2\sigma_i^2 \right )}}{\sum_{k \neq i}\exp{\left (- || \mathbf{x}_i - \mathbf{x}_k ||^2 / 2\sigma_i^2 \right )}} These conditional probabilities are then typically symmetrized to obtain joint probabilities :math:`p_{ij}`. .. math:: p_{ij} = \frac{p_{j\mid i} + p_{i \mid j}}{2} In the embedding space, we replace the Gaussian distribution with the Student's t-distribution is used, hence the name *t*-SNE. The t-distribution has fatter tails, allowing some distances to be less faithfully preserved in the embedding. .. math:: q_{ij} = \frac{\left ( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 \right )^{-1}}{\sum_{k \neq l}\left ( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 \right )^{-1}} Our goal, now, is to make :math:`\mathbf{Q}` as similar to :math:`\mathbf{P}` as possible. A well-known measure of similarity between two probability distributions is the Kullback–Leibler divergence and is given by .. math:: C = KL(\mathbf{P} \mid \mid \mathbf{Q}) = \sum_{ij} p_{ij} \log \frac{p_{ij}}{q_{ij}} We have now fully specified our model. We have two probability distributions describing the input and embedding spaces and we have a cost function that tells us how good our embedding is. The only thing remaining is to optimize the cost function. One simple way to optimize differentiable cost functions is gradient descent. To perform gradient descent, we need to work out the gradient, which will be used to update :math:`\mathbf{Y}`. The full derivation can be found in [1]_. The gradient of the cost KL divergence is given by .. math:: \frac{\partial C}{\partial \mathbf{y}_i} = 4 \sum_{j \neq i} \left ( p_{ij} - q_{ij} \right ) \left ( \mathbf{y}_i - \mathbf{y}_j \right ) \left ( 1 + || \mathbf{y}_i - \mathbf{y}_j || ^2 \right )^{-1} One last thing we have not yet mentioned how to set the bandwidths :math:`\sigma_i` for the Gaussian kernels centered over each data point in the input space. It is unlikely that one single value of :math:`\sigma_i` is optimal for all data points because the density of the data is likely to vary. In dense regions, a smaller value of :math:`\sigma_i` is usually more appropriate than in sparser regions. Perplexity is defined as .. math:: \text{Perplexity}(\textbf{p}_i) = 2^{H(\textbf{p}_i)} where :math:`H` is the Shannon entropy of a discrete distribution .. math:: H(\textbf{p}_i) = -\sum_i p_{j \mid i} \log_2 (p_{j \mid i}) Perplexity can be thought of as a continuous analogue to the :math:`k` nearest neighbours, to which t-SNE will attempt to preserve distances. More concretely, the bandwidths :math:`\sigma_i` are set such that each Gaussian kernel fits :math:`k` nearest neighbors within one standard deviation of the probability density. And that's it! You now know what t-SNE is and what it does. Accelerations ------------- Unfortunately, a direct implementation of t-SNE is rather slow. It's easy to see that that computing all the :math:`p_{ij}` and :math:`q_{ij}` requires computing all pair-wise interactions between points and has time complexity :math:`\mathcal{O}(N^2)`. This quickly becomes far too slow for any reasonably sized data set. Moreover, the normalization constant for :math:`q_{ij}` must be computed in every single iteration of the optimization, while :math:`p_{ij}` can be computed only once, since the points in the input space stay fixed. Most of the subsequent research on t-SNE has focused on how to accelerate the computation of :math:`p_{ij}` and :math:`q_{ij}`. We will begin by rewriting the gradient update rule in the following form .. math:: \frac{\partial C}{\partial \mathbf{y}_i} = 4 \left (\sum_{j \neq i} p_{ij} q_{ij} Z \left ( \mathbf{y}_i - \mathbf{y}_j \right ) -\sum_{j \neq i} q_{ij}^2 Z \left ( \mathbf{y}_i - \mathbf{y}_j \right ) \right ) where :math:`Z` is defined as the normalization constant in :math:`q_{ij}` .. math:: Z = \sum_{k \neq l}\left ( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 \right )^{-1} This equation splits the gradient into two parts which can be interpreted as the attractive and repulsive forces between points in the embedding. The first term includes :math:`p_{ij}`, encouraging nearby points to remain close to each other. The repulsive forces have the natural interpretation of the N-body problem, where all data points exert forces on each other. The optimization process finds an equilibrium between these two forces. Attractive forces ################# First, we will address how to speed up the computation of the attractive forces. In practice, this means speeding up the computation of the input similarities :math:`p_{ij}`. These can be precomputed once before the optimization as the points in the input space remain fixed. Only nearest neighbors ~~~~~~~~~~~~~~~~~~~~~~ The first improvement for the computation of the input similarities :math:`p_{ij}` in the input space comes from observing that points further than :math:`3 \sigma` have nearly infinitesimally small probabilities. These :math:`p_{ij}` have practically zero contribution to the KL divergence and can be ignored. Because of the way these bandwidths are computed, it is reasonable to compute and consider only the :math:`\lfloor 3 * \text{Perplexity} \rfloor` nearest neighbors of each data point and ignore points further away. This means that the affinity matrix :math:`\mathbf{P}` becomes sparse, and computing the :math:`p_{ij}` values includes summing up only the non-zero entries [2]_. Approximate neighbors ~~~~~~~~~~~~~~~~~~~~~ The second, more recent improvement comes from a theoretical advance which claims that using approximate nearest neighbors works just as well as using exact nearest neighbors. We do not attempt to justify this approach here but the interested reader can find proof in [3]_. Previously, the :math:`k` nearest neighbors were computed using Vantage Point trees, which have time complexity :math:`\mathcal{O}(N \log N)`, which becomes too expensive with large data sets. Replacing this with approximate methods can lower this time complexity, allowing us to compute :math:`p_{ij}` for millions of data points in a reasonable amount of time. Repulsive forces ################ We next show how to accelerate the computation of the second term i.e. the repulsive forces. As previously mentioned, these have a natural interpretation of an N-body problem. .. _barnes-hut: Barnes-Hut t-SNE ~~~~~~~~~~~~~~~~ The first major acceleration draws from particle simulations, which use space partitioning trees to approximate repulsive forces. These are made possible by the observation that given two well-separated clusters of points :math:`A` and :math:`B`, choose :math:`x \in A` and :math:`y, z \in B` and notice that the repulsive forces from :math:`y` onto :math:`x` will be roughly the same as :math:`z` onto :math:`x`. .. figure:: images/two_clusters.png :align: center This is true for any point in :math:`A` and :math:`B`, therefore we can compute the interaction for all points from :math:`B` onto any point in :math:`A` by simply computing the center of mass in :math:`B` and using that as a summary for all the points in :math:`A`. The Barnes-Hut tree algorithm [2]_ exploits this fact by constructing a quad-tree and at every node in the tree, deciding whether the center of mass can be used as a summary for all the points in that cell. .. figure:: images/quadtree.png :align: center A quad tree evenly splits the space until there is a single point in every cell. Let's now make precise when a cell can be used as a summary for some point. The condition compares the distance between the cell and the target point and the size of cell with the following criterion: .. math:: \frac{r_{\text{cell}}}{|| \textbf{y}_i - \textbf{y}_{\text{cell}} ||^2} < \theta where :math:`r_{\text{cell}}` is the length of the diagonal in the cell and :math:`\textbf{y}_{\text{cell}}` is the center of mass inside the cell. If the condition holds, then the cell is used as a summary. :math:`\theta` is a parameter of choice which trades off speed with accuracy. Higher values of :math:`\theta` allow more cells to be summarized leading to worse approximations but faster runtime. Note that when :math:`\theta = 0`, all pairwise interactions are computed. Typically, :math:`\theta` is set somewhere between :math:`0.2` to :math:`0.8`. Lastly, let's look at the time complexity of the Barnes-Hut approximation. Constructing the tree is fairly simple with complexity :math:`\mathcal{O}(N)`. Lookup time is dependent on :math:`\theta`, but on average takes about :math:`\mathcal{O}(N \log N)` time. .. _fit-sne: Interpolation-based t-SNE ~~~~~~~~~~~~~~~~~~~~~~~~~ A more recent approximation for computing the repulsive forces takes a different route. This method is quite mathematically involved, so we won't go into it too much, but the key idea is to shift the computation from :math:`N` data points to a grid of points that cover the embedding space. We compute the repulsive forces directly between our new points, then use these as interpolation points for our *actual* data points. The idea is demonstrated in the figure below. .. figure:: images/interpolation_grid.png :align: center The example also demonstrates one of the possible problems with this method. There are far less blue points (60) representing data samples than there are red interpolation points (225). In this case, directly computing the repulsive forces between the data points would, in fact, be more efficient than this side step using interpolation points. This highlights the fact that while this method can be extremely efficient when :math:`N` is large, it can also be much slower when :math:`N` is small. The method splits the embedding space into equally sized boxes. Interpolation is performed within each box separately i.e. to compute the repulsive forces for point :math:`\mathbf{x}_i`, we first identify which box it belongs to, then perform interpolation using the 9 interpolation points (in the example above). Clearly, the accuracy of the optimization depends on the number of boxes or the number of interpolation points we use. We can improve accuracy by using more interpolation points within each box, however, this is generally a bad idea. In the case of equispaced points, interpolation suffers from the Runge phenomenon. When this happens, the interpolation error is very large at the edges of the box. .. figure:: images/runge.png :align: center We demonstrate the Runge phenomenon on the Cauchy kernel using equispaced points. The errors oscillate wildly at the edges of the space when using 5 interpolation points. The Runge phenomenon can be mitigated by instead using Chebyshev nodes for interpolation, which equally distribute the interpolation error along the domain. However, we want to keep our equispaced points because when the interactions between all the interpolation points are put together in a matrix, they form a structured Toeplitz matrix. Toeplitz matrices are computationally convenient for matrix-vector multiplications, which can be accelerated with the Fast Fourer Transform, reducing the computational complexity from :math:`\mathcal{O}(N^2)` to :math:`\mathcal{O}(N \log N)`. Please refer to the original publication for more information [3]_. So clearly, increasing the number of interpolation points can be problematic, so why not increase the number of boxes instead? By increasing the number of boxes, we also increase the number of interpolation points, but each box will still have only 3 points, eliminating the danger for large errors at boundaries. By shifting most of the computation onto the interpolation points, we have effectively made the computational complexity dependent on the number of interpolation points :math:`p` rather than :math:`N`. The computational complexity, therefore, reduces to :math:`\mathcal{O}(N)` with respect to :math:`N`. Optimization ------------ The t-SNE optimization phase typically runs in two phases. The early exaggeration phase and the normal regime. The early exaggeration phase is first run for typically 250 iterations with a large value of exaggeration. This increases the attractive forces between points and allows points to move through the embedding more freely to find their true neighbors. Skipping this phase may result in larger clusters being split into several smaller clusters which can be scattered in the embedding. The normal regime follows the early exaggeration phase and is typically run for 750 iterations. The attractive forces are usually restored to their true values and we allow the embedding to converge to a stable state. Embedding data into lower dimensions ------------------------------------ This section is dedicated to the problems of embedding high dimensional data into lower dimensional embeddings. Methods that attempt to preserve distances between data points e.g. MDS, t-SNE, UMAP face a very tough challenge. High dimensional data sets typically have lower intrinsic dimensionality :math:`d \ll D` however :math:`d` may still be larger than 2 and preserving these distances faithfully might not always be possible. To make this clearer, let's look at a very simple example of a regular tetrahedron aka an equilateral pyramid. .. figure:: images/tetrahedron.png :align: center :width: 320px How might we create a 2-dimensional embedding of this data such that we keep all the distances intact? Perhaps a direct projection? .. figure:: images/tetrahedron_2d.png :align: center :width: 320px That doesn't work. It seems that the points form a star-like topology, which isn't what we're after. It's easy to see there is *no* way to properly preserve the distances while trying to project this simple tetrahedron into two dimensions. That's because the tetrahedron is intrinsically 3 dimensional. If the data have even higher intrinsic dimensionality, this problem is further exacerbated. Let's see how well t-SNE does with our tetrahedron. .. figure:: images/tetrahedron_tsne.png :align: center :width: 320px This is likely the best we can do. The distances are somewhat preserved quite well - not perfectly - but probably the best we can hope to achieve. This is one of the reasons why the interpretation of these kinds of plots is difficult or impossible. In all embeddings, distances between clusters of points can be completely meaningless. It is often impossible to represent complex topologies in 2 dimensions, and embeddings should be approached with the utmost care when attempting to interpret their layout. t-SNE's objective is very clear - to preserve local neighborhoods. If a set of points cluster together on a t-SNE plot, we can be fairly certain that these points are close to each other. Nothing else can be said with certainty. UMAP, a recent and popular embedding technique for visualizing high dimensional data sets, promises to better preserve global structure in addition to local neighborhoods. As we have demonstrated, this is simply not possible if the intrinsic dimensionality of the data is much higher. When using both UMAP or t-SNE, one must take care not to overinterpret the embedding structure or distances. References ---------- .. [1] Maaten, Laurens van der, and Geoffrey Hinton. "Visualizing data using t-SNE." Journal of machine learning research 9.Nov (2008): 2579-2605. .. [2] Van Der Maaten, Laurens. "Accelerating t-SNE using tree-based algorithms." The Journal of Machine Learning Research 15.1 (2014): 3221-3245. .. [3] Linderman, George C., et al. "Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding." arXiv preprint arXiv:1712.09005 (2017). openTSNE-0.6.1/examples/000077500000000000000000000000001413546205200147715ustar00rootroot00000000000000openTSNE-0.6.1/examples/.gitignore000066400000000000000000000000051413546205200167540ustar00rootroot00000000000000*.gifopenTSNE-0.6.1/examples/01_simple_usage.ipynb000066400000000000000000021205131413546205200210150ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simple usage\n", "\n", "This notebook demonstrates basic usage of the *openTSNE* library. This is sufficient for almost all use-cases." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from openTSNE import TSNE\n", "\n", "from examples import utils\n", "\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load data\n", "\n", "In most of the notebooks, we will be using the Macosko 2015 mouse retina data set. This is a fairly well-known and well explored data set in the single-cell literature making it suitable as an example." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import gzip\n", "import pickle\n", "\n", "with gzip.open(\"data/macosko_2015.pkl.gz\", \"rb\") as f:\n", " data = pickle.load(f)\n", "\n", "x = data[\"pca_50\"]\n", "y = data[\"CellType1\"].astype(str)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data set contains 44808 samples with 50 features\n" ] } ], "source": [ "print(\"Data set contains %d samples with %d features\" % x.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create train/test split" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.33, random_state=42)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30021 training samples\n", "14787 test samples\n" ] } ], "source": [ "print(\"%d training samples\" % x_train.shape[0])\n", "print(\"%d test samples\" % x_test.shape[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run t-SNE\n", "\n", "We'll first create an embedding on the training data." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "tsne = TSNE(\n", " perplexity=30,\n", " metric=\"euclidean\",\n", " n_jobs=8,\n", " random_state=42,\n", " verbose=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------------------------\n", "TSNE(n_jobs=8, random_state=42, verbose=True)\n", "--------------------------------------------------------------------------------\n", "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", " --> Time elapsed: 3.89 seconds\n", "===> Calculating affinity matrix...\n", " --> Time elapsed: 0.44 seconds\n", "===> Calculating PCA-based initialization...\n", " --> Time elapsed: 0.10 seconds\n", "===> Running optimization with exaggeration=12.00, lr=2501.75 for 250 iterations...\n", "Iteration 50, KL divergence 5.8046, 50 iterations in 1.7123 sec\n", "Iteration 100, KL divergence 5.2268, 50 iterations in 1.8265 sec\n", "Iteration 150, KL divergence 5.1357, 50 iterations in 2.0626 sec\n", "Iteration 200, KL divergence 5.0977, 50 iterations in 2.0250 sec\n", "Iteration 250, KL divergence 5.0772, 50 iterations in 1.9598 sec\n", " --> Time elapsed: 9.59 seconds\n", "===> Running optimization with exaggeration=1.00, lr=2501.75 for 500 iterations...\n", "Iteration 50, KL divergence 3.5741, 50 iterations in 1.9948 sec\n", "Iteration 100, KL divergence 3.1653, 50 iterations in 1.8672 sec\n", "Iteration 150, KL divergence 2.9612, 50 iterations in 2.2518 sec\n", "Iteration 200, KL divergence 2.8342, 50 iterations in 3.2478 sec\n", "Iteration 250, KL divergence 2.7496, 50 iterations in 4.2982 sec\n", "Iteration 300, KL divergence 2.6901, 50 iterations in 5.4970 sec\n", "Iteration 350, KL divergence 2.6471, 50 iterations in 7.1508 sec\n", "Iteration 400, KL divergence 2.6138, 50 iterations in 8.1424 sec\n", "Iteration 450, KL divergence 2.5893, 50 iterations in 9.8184 sec\n", "Iteration 500, KL divergence 2.5699, 50 iterations in 10.3756 sec\n", " --> Time elapsed: 54.65 seconds\n", "CPU times: user 7min 53s, sys: 20.6 s, total: 8min 14s\n", "Wall time: 1min 8s\n" ] } ], "source": [ "%time embedding_train = tsne.fit(x_train)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(embedding_train, y_train, colors=utils.MACOSKO_COLORS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transform\n", "\n", "openTSNE is currently the only library that allows embedding new points into an existing embedding." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===> Finding 15 nearest neighbors in existing embedding using Annoy approximate search...\n", " --> Time elapsed: 1.12 seconds\n", "===> Calculating affinity matrix...\n", " --> Time elapsed: 0.03 seconds\n", "===> Running optimization with exaggeration=4.00, lr=0.10 for 0 iterations...\n", " --> Time elapsed: 0.00 seconds\n", "===> Running optimization with exaggeration=1.50, lr=0.10 for 250 iterations...\n", "Iteration 50, KL divergence 214688.6176, 50 iterations in 0.3767 sec\n", "Iteration 100, KL divergence 213210.5159, 50 iterations in 0.3881 sec\n", "Iteration 150, KL divergence 212270.1679, 50 iterations in 0.3898 sec\n", "Iteration 200, KL divergence 211592.6686, 50 iterations in 0.3881 sec\n", "Iteration 250, KL divergence 211074.3288, 50 iterations in 0.3814 sec\n", " --> Time elapsed: 1.92 seconds\n", "CPU times: user 19.2 s, sys: 650 ms, total: 19.8 s\n", "Wall time: 3.89 s\n" ] } ], "source": [ "%time embedding_test = embedding_train.transform(x_test)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(embedding_test, y_test, colors=utils.MACOSKO_COLORS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Together\n", "\n", "We superimpose the transformed points onto the original embedding with larger opacity." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 8))\n", "utils.plot(embedding_train, y_train, colors=utils.MACOSKO_COLORS, alpha=0.25, ax=ax)\n", "utils.plot(embedding_test, y_test, colors=utils.MACOSKO_COLORS, alpha=0.75, ax=ax)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 4 } openTSNE-0.6.1/examples/02_advanced_usage.ipynb000066400000000000000000031575061413546205200213070ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Advanced usage\n", "\n", "This notebook replicates what was done in the *simple_usage* notebooks, but this time with the advanced API. The advanced API is required if we want to use non-standard affinity methods that better preserve global structure.\n", "\n", "If you are comfortable with the advanced API, please refer to the *preserving_global_structure* notebook for a guide how obtain better embeddings and preserve more global structure." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from openTSNE import TSNEEmbedding\n", "from openTSNE import affinity\n", "from openTSNE import initialization\n", "\n", "from examples import utils\n", "\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import gzip\n", "import pickle\n", "\n", "with gzip.open(\"data/macosko_2015.pkl.gz\", \"rb\") as f:\n", " data = pickle.load(f)\n", "\n", "x = data[\"pca_50\"]\n", "y = data[\"CellType1\"].astype(str)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data set contains 44808 samples with 50 features\n" ] } ], "source": [ "print(\"Data set contains %d samples with %d features\" % x.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create train/test split" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.33, random_state=42)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30021 training samples\n", "14787 test samples\n" ] } ], "source": [ "print(\"%d training samples\" % x_train.shape[0])\n", "print(\"%d test samples\" % x_test.shape[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a t-SNE embedding\n", "\n", "Like in the *simple_usage* notebook, we will run the standard t-SNE optimization.\n", "\n", "This example shows the standard t-SNE optimization. Much can be done in order to better preserve global structure and improve embedding quality. Please refer to the *preserving_global_structure* notebook for some examples." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**1. Compute the affinities between data points**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", " --> Time elapsed: 3.78 seconds\n", "===> Calculating affinity matrix...\n", " --> Time elapsed: 0.43 seconds\n", "CPU times: user 19.3 s, sys: 794 ms, total: 20.1 s\n", "Wall time: 4.22 s\n" ] } ], "source": [ "%%time\n", "affinities_train = affinity.PerplexityBasedNN(\n", " x_train,\n", " perplexity=30,\n", " metric=\"euclidean\",\n", " n_jobs=8,\n", " random_state=42,\n", " verbose=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2. Generate initial coordinates for our embedding**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 448 ms, sys: 88.3 ms, total: 536 ms\n", "Wall time: 86.9 ms\n" ] } ], "source": [ "%time init_train = initialization.pca(x_train, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3. Construct the `TSNEEmbedding` object**" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "embedding_train = TSNEEmbedding(\n", " init_train,\n", " affinities_train,\n", " negative_gradient_method=\"fft\",\n", " n_jobs=8,\n", " verbose=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**4. Optimize embedding**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Early exaggeration phase" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===> Running optimization with exaggeration=12.00, lr=2501.75 for 250 iterations...\n", "Iteration 50, KL divergence 5.8046, 50 iterations in 1.8747 sec\n", "Iteration 100, KL divergence 5.2268, 50 iterations in 2.0279 sec\n", "Iteration 150, KL divergence 5.1357, 50 iterations in 1.9912 sec\n", "Iteration 200, KL divergence 5.0977, 50 iterations in 1.9626 sec\n", "Iteration 250, KL divergence 5.0772, 50 iterations in 1.9759 sec\n", " --> Time elapsed: 9.83 seconds\n", "CPU times: user 1min 11s, sys: 2.04 s, total: 1min 13s\n", "Wall time: 9.89 s\n" ] } ], "source": [ "%time embedding_train_1 = embedding_train.optimize(n_iter=250, exaggeration=12, momentum=0.5)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(embedding_train_1, y_train, colors=utils.MACOSKO_COLORS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Regular optimization" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===> Running optimization with exaggeration=1.00, lr=2501.75 for 500 iterations...\n", "Iteration 50, KL divergence 3.5741, 50 iterations in 1.9240 sec\n", "Iteration 100, KL divergence 3.1653, 50 iterations in 1.9942 sec\n", "Iteration 150, KL divergence 2.9612, 50 iterations in 2.3730 sec\n", "Iteration 200, KL divergence 2.8342, 50 iterations in 3.4895 sec\n", "Iteration 250, KL divergence 2.7496, 50 iterations in 4.7873 sec\n", "Iteration 300, KL divergence 2.6901, 50 iterations in 5.2739 sec\n", "Iteration 350, KL divergence 2.6471, 50 iterations in 6.9968 sec\n", "Iteration 400, KL divergence 2.6138, 50 iterations in 7.8137 sec\n", "Iteration 450, KL divergence 2.5893, 50 iterations in 9.5210 sec\n", "Iteration 500, KL divergence 2.5699, 50 iterations in 10.6958 sec\n", " --> Time elapsed: 54.87 seconds\n", "CPU times: user 6min 2s, sys: 20.3 s, total: 6min 23s\n", "Wall time: 55.1 s\n" ] } ], "source": [ "%time embedding_train_2 = embedding_train_1.optimize(n_iter=500, momentum=0.8)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(embedding_train_2, y_train, colors=utils.MACOSKO_COLORS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transform" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===> Finding 15 nearest neighbors in existing embedding using Annoy approximate search...\n", " --> Time elapsed: 1.11 seconds\n", "===> Calculating affinity matrix...\n", " --> Time elapsed: 0.03 seconds\n", "CPU times: user 3 s, sys: 192 ms, total: 3.19 s\n", "Wall time: 1.15 s\n" ] } ], "source": [ "%%time\n", "embedding_test = embedding_train_2.prepare_partial(\n", " x_test,\n", " initialization=\"median\",\n", " k=25,\n", " perplexity=5,\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(embedding_test, y_test, colors=utils.MACOSKO_COLORS)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===> Running optimization with exaggeration=1.00, lr=0.10 for 250 iterations...\n", "Iteration 50, KL divergence 226760.6820, 50 iterations in 0.3498 sec\n", "Iteration 100, KL divergence 221529.7066, 50 iterations in 0.4099 sec\n", "Iteration 150, KL divergence 215464.6854, 50 iterations in 0.4285 sec\n", "Iteration 200, KL divergence 211201.7247, 50 iterations in 0.4060 sec\n", "Iteration 250, KL divergence 209022.1241, 50 iterations in 0.4211 sec\n", " --> Time elapsed: 2.02 seconds\n", "CPU times: user 10.7 s, sys: 889 ms, total: 11.6 s\n", "Wall time: 2.74 s\n" ] } ], "source": [ "%time embedding_test_1 = embedding_test.optimize(n_iter=250, learning_rate=0.1, momentum=0.8)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(embedding_test_1, y_test, colors=utils.MACOSKO_COLORS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Together\n", "\n", "We superimpose the transformed points onto the original embedding with larger opacity." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 8))\n", "utils.plot(embedding_train_2, y_train, colors=utils.MACOSKO_COLORS, alpha=0.25, ax=ax)\n", "utils.plot(embedding_test_1, y_test, colors=utils.MACOSKO_COLORS, alpha=0.75, ax=ax)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 4 } openTSNE-0.6.1/examples/03_preserving_global_structure.ipynb000066400000000000000000140474351413546205200242040ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Preserving global structure" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import gzip\n", "import pickle\n", "\n", "import numpy as np\n", "import openTSNE\n", "from examples import utils\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 220 ms, sys: 12 ms, total: 232 ms\n", "Wall time: 230 ms\n" ] } ], "source": [ "%%time\n", "with gzip.open(\"data/macosko_2015.pkl.gz\", \"rb\") as f:\n", " data = pickle.load(f)\n", "\n", "x = data[\"pca_50\"]\n", "y = data[\"CellType1\"].astype(str)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data set contains 44808 samples with 50 features\n" ] } ], "source": [ "print(\"Data set contains %d samples with %d features\" % x.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To avoid constantly specifying colors in our plots, define a helper here." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def plot(x, **kwargs):\n", " utils.plot(x, y, colors=utils.MACOSKO_COLORS, **kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Easy improvements\n", "\n", "Standard t-SNE, as implemented in most software packages, can be improved in several very easy ways that require virtually no effort in openTSNE, but can drastically improve the quality of the embedding." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Standard t-SNE\n", "\n", "First, we'll run t-SNE as it is implemented in most software packages. This will serve as a baseline comparison." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6min 4s, sys: 15.7 s, total: 6min 20s\n", "Wall time: 53.4 s\n" ] } ], "source": [ "%%time\n", "embedding_standard = openTSNE.TSNE(\n", " perplexity=30,\n", " initialization=\"random\",\n", " metric=\"euclidean\",\n", " n_jobs=8,\n", " random_state=3,\n", ").fit(x)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding_standard)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using PCA initialization\n", "\n", "The first, easy improvement we can get is to \"inject\" some global structure into the initialization. The intialization dictates which regions points will appear in, so adding any global structure to the initilization can help.\n", "\n", "Note that this is the default in this implementation and the parameter can be omitted." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6min 7s, sys: 15.1 s, total: 6min 22s\n", "Wall time: 53.5 s\n" ] } ], "source": [ "%%time\n", "embedding_pca = openTSNE.TSNE(\n", " perplexity=30,\n", " initialization=\"pca\",\n", " metric=\"euclidean\",\n", " n_jobs=8,\n", " random_state=3,\n", ").fit(x)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding_pca)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using cosine distance\n", "\n", "Typically, t-SNE is used to create an embedding of high dimensional data sets. However, the notion of *Euclidean* distance breaks down in high dimensions and the *cosine* distance is far more appropriate.\n", "\n", "We can easily use the cosine distance by setting the `metric` parameter." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6min 16s, sys: 14.5 s, total: 6min 31s\n", "Wall time: 54 s\n" ] } ], "source": [ "%%time\n", "embedding_cosine = openTSNE.TSNE(\n", " perplexity=30,\n", " initialization=\"random\",\n", " metric=\"cosine\",\n", " n_jobs=8,\n", " random_state=3,\n", ").fit(x)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding_cosine)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using PCA initialization and cosine distance\n", "\n", "Lastly, let's see how our embedding looks with both the changes." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6min 2s, sys: 13.6 s, total: 6min 16s\n", "Wall time: 51.6 s\n" ] } ], "source": [ "%%time\n", "embedding_pca_cosine = openTSNE.TSNE(\n", " perplexity=30,\n", " initialization=\"pca\",\n", " metric=\"cosine\",\n", " n_jobs=8,\n", " random_state=3,\n", ").fit(x)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmIAAAHBCAYAAADKNtc7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOydZ3gc1dmG79ld9WK5944LNr2YZkwdSjB9IAFCCwktCiUrAoQWSEgIaEIIIskHgRAIpi3dBMKEZkwzzWCDC8a9N8my1bU734/nLBLG2MZNsnXu69IlaXd25uystPPsW57XCcMQi8VisVgsFsu2J9LSC7BYLBaLxWJpq1ghZrFYLBaLxdJCWCFmsVgsFovF0kJYIWaxWCwWi8XSQlghZrFYLBaLxdJCWCFmsVgsFovF0kJYIWaxWCwWi8XSQlghZrFYLBaLxdJCWCFmsVgsFovF0kJYIWaxWCwWi8XSQlghZrFYLBaLxdJCWCFmsVgsFovF0kJYIWaxWCwWi8XSQlghZrFYLBaLxdJCWCFmsVgsFovF0kJYIWaxWCwWi8XSQlghZrFYLBaLxdJCWCFmsVgsFovF0kJYIWaxWCwWi8XSQlghZrFYLBaLxdJCWCFmsVgsFovF0kJYIWaxWCwWi8XSQlghZrFYLBaLxdJCWCFmsVgsFovF0kLEWnoBFotlx6DUKY4ARwDlJWHZhy29HovFYtkecMIwbOk1WCyW7ZRSp7gIqAF+BpwAHIYi7ZOAvwCPlIRldS23QovFYmndWCFmsVg2iVKnuAB4FugLdATyzF0Z5nsNMBE4pCQsa9j2K7RYLJbWj60Rs1gsm0oHoB6oA14EKoBKc1sjkAnsC+zTUgu0WCyW1o4VYhaLZVMZAewO9AMmAAuApcDN5ueo+TqhhdZnsVgsrR4rxCwWy6ayCMhHTT/5QE9gKFAM9Gq23dBSp/jhUqe447ZfosVisbRubI2YxWL53pQ6xZ1Q5OtiIInqwTKALKAKCTPHbJ5Eou1C4H2gsSQsq9zWa7ZYLJbWiI2IWSyW70WpUzwKSABDgGogBeSgyNhq4Blzf725L2LufxKYBTyy7VdtsVgsrRPrI2axWDaKUqd4OHAwcCLQLoRYQ35mfsaa+kYHalHXZAYwCHgK8Jo9PA9FyGqAydt25RaLxdJ6sRExi8WysRyKxNUDQLyuKHthQ1aMxqxoFAmwpSgCNgz47ZJ9e1PbPocQQhQdWwI8CNzQEou3WCyW1ogVYhaLZWN5ELgUpR2XZq6uL4zWNdbG6pLp+z8HDgcWAll17XNZuUt3kBB7E6Unj8dEykqd4sGlTvGvbBG/xWJpy1ghZrGsA88PMjw/uMzzg11aei2tiO7AypKwLARikWSqc/aa+qipyE8Bw4HbUedkpM8r05web81scJSO3A1oh0TZOaVOcQ6yvji+rjDb9fzA+fbhLBaLZcfH1ohZ2jylTnE7FM15BrisMSvav/NRQ8cvO7B/BzKiLwEnt+wKW57Lji07qkdm9LZYffJV4CqgEBm49kDvIwuBj4CRqF4sG9WExYDFwI3AHsC/UddkTalT/Ex9dsbvajrn/TtaVXe+5wfHJOKubeO2WCxtChsRs7RZSp3inFKn+MfA88gD6+c1HXKur2mfO6PzJwu6F3y5LANFeNo0nh90Wr5Hr8cXHDl4WAjpYd7vAK8B01GUqy+KhCWRKEsLqggaf7QvsBKlJ4eWOsVdS8KyxlRW9D+rB3QqT2ZnLNiGT8lisVhaDdZHzNLmKHWK2y84ZECPDpMWjclaWdM/Ivf3XIBVvYtqa7rmZwFO5cCOb9/72M9GtuxqWx7PD7JiVXWPFMxcUTPkXx+cj4r2TwKOQRGxbDTa6K/A5UiIDUARsZT5XofGHuWan68rCcv+vI2fisVisbQ6rBCztBlKneKM2sLM1zKqGkamok4YbUxBCseEhVNAshEidZ1zw5quhdVzT9y1tL59bgy4ORF3Uy259taA5wcHATX7lzy3O3AK0AXYG3VE/s38vj/QGRm6VgDLkSh7A9gZCbfPgNElYdmCUqc4F2iwQ8EtFktbxdaIWdoSVziNqQNxwEmmCENCRwIsilJpVTEoii6rJrOy7tn6opwCJC62+0JyUwz/FrAncFEi7v57Yx9b6hQ775WeGAWuAcqBv6Nzths6byuRkes5wJfmYTGgwNxfZ37ORQ777wGVRoQ9tGpgR8fzg+nAbxJxt25zn6vFYrFsT9gaMUtbIjerurHRaQw/jyWZFQ35PCIRUY4KzIvSGzbkZR6TP6f8SSAT+KfnB51aaM1bCgfoj2q00vYR2UYMfSelTvEvgH/tX/JcwZ63/Ld6t9tfnQf8HzAKpRqTaKTRayhK9jegN4qSrQQ+Ntvtj6Jki4DDgIFI5E5b07t9faS6fki/xMR2W/YpWywWS+vHCjFLW+L/gPMi8GMUwZkDfIWJ1oQQhkAywuqVu/V8b02fom7ALkAfJMi2W0xqdWfUtVjk3fZy3/r8rI9DhylmbuS3KHWKI8hiwgvhnczK2hG5S9esASYCM4BjgftQV2RPlJK8CFiB6sT+h85fDEXGapEYrALao3TlEV3fnX37Pje+dHy39+YsKXWKr9s6Z8BisVhaJzY1aWkzlIRli4FHS53iG4GdkAP8P1C6LgZQ0y67IXtV7bu93phxyvzRw0PUJbg8EXe36xomzw9iWSuryKio3SVa17Bb32c+fbGqe36n/AXhsozq+sbm25Y6xYOAy4AOyBssGxiczIg0RpKpXSIpbgJOQ075vwFuRcL2JBT9+juyt2iHol5RFJErMN+/RJGxScCiWF1jA03p30Fb7SRYLBZLK8QKMUtb5GkUmTkSRcTqgKwQVhOL/i0CRwCT9y95bjLww5KwbLsv1M9dUHFnxuq6n/Z96tOpmdUNYbSucWh2eW1NGHGeBE4pdYo7IPf7z5EYOhBFAWcBA1LgRBpSWQ7sBTyBBn6PAl4GpqG0bh9kBfJXFAkbjM5xP7OMdGdQB2QLshC4uCQsC0ud4kxgBPD+Vj0RFovF0sqwQszSZjCptiNRavIx4BLU0VcCFEZgVe6Kqr1QJyDIF+v3qEh9u6YxOzY8d9GqzMr+Hau7fTz/h8CtTkg/kuGPkPXEzqhxYSKq8YogJ/0Y8FVUguwYJJT6o7q63ZHgqkbdkJNRrdhLqCbsK6CrWUIKpTC7mu/DzWMdIDRi972tehIsFoulFWKFmKVNUOoUx1A67QEU6emIRu/8CEVzUkhMpOcehkgk5GzzxW4F6jvmP7a8KKcmb3ZFNfL66ocK6V8GXGAcEkc7AZ2Q0KpH0at2KAKWgTovi9HMyD+hc7YGnaedUBqzE5CFzmWGWUIkCR0dndPMCFwAzN8Roo0Wi8WyOVghZtnhMXMNnwIORgKhARWL348iOOWofikzCZHaLnl10ZqGT7NX19ejVNt2TyLu3nv5MWWVXSfM+Tv6v29EHY4XoTSkY74aUbfjHKB9KkI3J0WGowL7KKoJGw78DAlaB3VDrgAuBpYiEbsM1YAdiIRabi1RJ0lWTT7VDpBdEpbN2CZP3mKxWFoxVohZ2gL90AieChS5qUWC4b/AFOBcFBG7cd5xOyeX79XbbWyXc2Ei7s5pofVuFRYeNuiEVMSZ1fvlKVdHQr4qCcu+KnWKT0bPPf1ekELF9FkNuRmJL8/Yq7hw1sr6Xq99eS0Sr4ORs3460lWH0phFyJLCQR2UOSha9jFqiojkkqyB6hWOomFTt8VztlgsltaOFWKWtsAK1KW3E4rqFAJHIwuGB5B4eKUkLPsjgOcHd23vXZLrIoxFbli5R8/cvi9NuQOoLnWKfwtEGnJiS5KZ0e7Zq+pC5IT/a+A9JxUeO/DJicXVXfLfBB4rCctWlDrFDvAkMnNNG+EuQBGzn6KIY3sk6Hoj4RYBksY8Nw/oVBKWJbflc7dYLJbWih1xZNnhKXWK+6Hh1OkoTojqnzJoSsfdVxKW/bxFFrgNKXWKoyji1RsJ0TXzDhvYL5WT0b7Pf6bWGbE0G40h2hMJqSRqbqgGHgL+g9KRUXNfNU0pylpgHvIQS3emJmkqxH8QSJSEZVVb+7laLBbL9oCNiFnaAlG+bV6cYW6rBeJIlGxXmAaELkjwvPtdhe+mWzQOzC0Jyx4vdYpvRYX2KSDR+ZMFJ2Wuqe/hwCvIuqIfKsbPBJ5FBfvvoC7TT2gSW4PNIZagNGQ9ErWXmO0BAnPb58AZwM5WhFksFksTVohZ2gJRFOHZnSZBtgSlLH9aEpZtd95VpU7xuchWI4aaD05F9W7rIgPZSXQqdYqno5mQ84BngFHZFbULkKCbg6Jb16M6r07IE+xw1OgwCPgjam54GgmriHnsclTk/zAwGigdx8hPy+kQO5L/3ZdH9f9Q8X5Q6hT3QBYhr5SEZXa2pMViadPYEUeWHZ6SsGw6KjD3kYloFXAccFBJWPZ+qVN8YKlTPKwFl/i98Pxg0IohnW9IKQrVgCJbK9fzkBQwHqUUnwMOQkI0gSJXXyGLiuvRHMouJWHZ4yVh2T3I8mMMsvmYgERddyTmFqGu0nRa8j3gn8B+QM/hTP5xb+buNpc+45BX2P+VhGUzgZOB24B9ttApsVgslu0WGxGztAlKwrJK4FelTnEC+VtNBPJLneLPkKB5Clk5tHqGlb11cu7CVf1TEeojKR5FxfHrS/d1QGashci8tReKcB1dEpbdWuoUZyNz1cOR6Hq+1Cm+C3WWFqMZkpjvSRRJzEGNDj9Gqcr2KEI2BNWfze1AxdL2fPp2hPBGoH2pU+yZIv35qE6vfalTnIWMcyfbAn6LxdIWsULM0qYoCcsmpH82AmRX8+sDLbOi70epU9yxAEjFIlWh4+RCeGBJWHbsWtsUokjZgSid+D6q56qiyek+A4km3is9sWf+nJW9er005eOiGctvNPfnA4cg8ZVC8yR/QtO8yHo09Ps5NJooPUtyFPIN6+zAIkdz1N9FkbUBRnjthqYZvAU8jurRbgPswG+LxdLmsKlJS1tmOYrOLEdpt1ZNqVO8D/BaCNdWd8zJIxaJAkOMuElvk4FSjp8BVyEX/WJUML8Yiar0kO/DzPfM6q4Fy1KZ0Sjwc5qiXkOQ99oJJWHZX9BQ73eR4GpAFiAxFB2LIBuLanOMhSjSeA5wJRJrNyID3WuBQlO0P9fsa4fybLNYLJaNxUbELG2WkrAsRGm07YVdgZ0dyMhb8nUmMgP4TalTfENJWNaI0o/90fNqb34fiArzP0IjjarR0O3rABJxd8od0V+85KTCXwFXo1RmARJX2WY/oNTjD83PhagJ4ifAKpRq7E6THUha7NWiWrJxwFFmX68ALwCUhGWXlTrFl5vXwmKxWNocNiJmsWwljGfXluRVIOqgf9yIxE4RcAIyUgVFpypR6jCCBBLAXsiy4m6gsSQs22vauSMWX7v775+4I/KLY51U+DhKzxYCI4GZyBV/JXBeqVN8U2NG5KRQ+5yLIl5RJA5rUaSszhwvbZqbadbXDTnprzLrur8kLKtJPykrwiwWS1vGCjGLZStQ6hSfksyINFw36KZGzw8Kt8D+imra5zzcqLQfNA0lzwaWNfPmqkG1V3k0Odx/jOwqZiNR9LdSp/jezFXVdy05oN/JDfmZF5aEZfOB3yG/sUoUGdsNpQ2nhHBytCE1wiimBKohC839+agTczqytkhrxXqzpv6oxmw31K25YHPPh8VisewoWCFmsWwdDkxGI07uglXRYf6rr23uzlLRyLGx+voDM0dWOk6HhrQYS6EoVHNh8yRwGRJDoEjZ3si+Y7DZvgA4vuPEhU8VfrX85mht4yUmercn8AYSTlkoolUF7OE0DfOuA05HKdFGc1smioDtgbzHQlT2UG/WETX7S6935OaeD4vFYtlRsELMYtk6XBWtbUxl1jSSv2jNXpu7s9X9O8wo/1GXupzhVREnI5VOOcaQGHq52aYfoRqw6ah7Me0zttz83Ah8CEQKZ6/ctdf/po+MNiSPQOawrwJ/QH5jU2kyiM0CVjsw37xh1CMxFzNfi2lKgUJTnVjU/BwDVkV71E0tvHzhikinhlNKneK+m3tOLBaLZUfACjGLZStQEpaFUbgw/fstna6+c1P35flBZMqFB15WWdOlfuWUnitS5bFyVM+1EImm5kLsamSUui8yZx2H6rzORcatdSh6tRxFpg5CdWMXAKuSMadLTee8kXVF2eXIb60eRby6ILGVhaJkHyOxtRqlRz83264xt30OfEBTVCw31r92SMbw6v1ifeqGoHSmxWKxtHmsELNYthIlYdn9NUVZTyQzozTmZVx8U78b2m/4Ueskt+dLnx/X55kv2q1a1aWqon/3c4B/1edlLG7Miu0GfD0VoCQsS5WEZVOBXDSOqBZ1Or4O3IPsKCaiVOXDSDhVoZFG5bUd86ZXDujYb3WvoqFmlzGzzXjU/XgfcAtNqcaJaMB3ARJqS5BdxnSUEk2zuu79Qlb9vldYPzHvIzR43GKxWNo81r7CYtmK5FbUnblsz14nLjmwX3bP1758CJmXfl9ShbPLK2K1DUUdP10cDTMih1R1zD0pe2V1vzBKpD4ndjTwZqlTnAdklIRlFaibMQK88l7pieOB2P4lz70QQn8cnnZCGoCzUFdjBEWtuucuWdMxq6I2O1Lf2BGJuBiwDKU105YYLoqQTUb+XyehKNsX5v47Ud1aCqUnk0A5jU5p4+zsU4F3SsKydCG/xWKxtGmcMLSd45YdD2Ns2qkkLFvU0mu5qe8Nv6ruVnBZVkXNEb+bdtO07/t4zw9OdxqSN3f4cG6nnq996WSX12QA+Q44pnVyoaPasEOR7cTRKB05FJg66fJRv+0ZTD+taMriqQuOGFzR7qvl+xfMWvm6IyGWLraPIuH0Niraj5rbkzRFzmMoopaNUo6fI6f+RpRqdJAHWZZ53DTk7F9pHvtT1NG5url9hcVisbRlrBCz7JCUOsVvoPqn498rPfGVRNxNbeAhrRLPD7oid3yylq7+UdG0pX36Pze5PjQ+YY5Ez0rzNRDVZ1UiQfVWSVh23yU//Md1PV7/8sLsZVVLlu7duxKH3M4fzhsb0ZzIHshyohvaVx2KgM1Eo5EaUZdkHuqIzEAiC5p8yhahiFiIBFnEbDMTNQ7cgkTe8yVh2Xb5OlgsFsvWwqYmLTskoWYeOjUdcp4DXvb84FOgLBF3l3p+kLFyYs7tGfmpSf/926jWPmPyQuD8rOVrbimatrSbkwwjjRGnIcyI3ppR19geDfOeBMxCthUdkRg6Ezil1Cl+ZqBc9FcCN3f9aF5H4FPgZhTBcpD4SiKx5CBR1gEV5J/YbJ8ZSGw1ohFKXc22dUh8NSKBuMTsa4B5DieXhGVnp59Qf2fqRcgi4/pZ4dC0A7/FYrG0SawQs+yorAQ61hZmpbv2TkVdg/9a9UX2Ucvezf95dpeGJbT+Yd9zgAlhJDKl3bSldVVdC7K+KB6ZXdsp/9epzNg1Pf83Ldn99ek7xZLsh7oo81ER/gBUhH8wqt26HBXdZ6IZkhFkO7ECRdLm0eTztRsq9v+Fue1Ls00UFecPRmnPRmCsua+X+T1pjvExSk2OApL9nal7AwWzwqFvIPHWy+zPCjGLxdKmsULMskPiwOBkLDKtYNHquTlLVp9a07VgJIriULBT3bKGNZHPGqujfgsvc4Mk4u5Dnh88PObWE8PT8rN27/bm9LLazvlHpDKj0aIvFl9eMbRLr6JJC7Pzlq6JREIa0f/0OShK1hsoQ12MC5EASkeuZqOC+n+hbsoclH5MF++no2MZSHSlnfITwHkoGrYKdUceTJOB6wo0czIbpSWXANEoDVcmySjq70x9G/gtED2Vx3e/yaka8gl7zR/eb+KDjV2jqaEnfjbogmtfsOlLi8XSZrBCzLKj0i7amOoQbaTD7ne81g54Iz3TMJIZntVpRPUgJD5aPYm4G3p+kEFmdM/cJVUrOkxa5HT4ZP4nhbNWDK/Pzwob2uXEUhlRJ1KfbF5UvxTNhDwSRaxOR15i+yJxdRyqD1uOBFlXFMlK132lULTrBPNzxNx/AhJbPc1jrqDJxT+GUo4OcDjwidnmhEN488LXOHLSrHBoQ6lT3DkF99WSeUIWDU4/Ziz7Imd456rK/DBvdUVvFAW0WCyWNoH1EbPsqKzi69nYfAW8WuoUH2nuux34NfB4C61tU+gHXFndOX/fdp8viuXPrxhJKuyQU1FbQMQJF+/XL7mqf4dw7jFDa5NZ0Vko7fcBKtw/HFhREpb5wE+QEMtEUavjUBF+DkplVtA0x3Jn870BmI8iaRXAM8iJv9rsow5F4LJoEmUhSoXeAxTvzNSnZoVDvzD3HYjDMQ3EwgZiDfPp+9jAZTP+Ozh/6h9+8/t7rAizWCxtChsRs+yolGMERaiOvkNSEVYD/0vE3QVIIGxPzAAejtU2XrNk1E7RMBpxOk1a5KQcKJy1MiyYs3L+ooMHFizdv9+ULhPmNkTrqgegovy/o5RjmsOBH6Ao1yIk1Aab3/PQe0KIhoeni+2XIxHmoDRkLhJds2ly0++EBFkuSoNWAbujNOU/S8Kyr2vBlhzQ738Vgzt/UleUs3S3u8ad/HR4tk1FWiyWNouNiFlaHf2dqZH+ztTCzdlHSVgW1mc601IQpqAujDis6dN+mOcHeRt+dMtT6hTvXeoUH9HspquAPyzdv19D9oqqhszVtWFNpzwWjBoAEImkcLq9O/smwvCk7JXVNyE3/RtKwrJbgUeAO0ud4h+iEUhJZFHxH+Tz1Qi8RNM4ojokxiJIhN1pfq4y9w8E9kJDvjub33sgMbcyBTmhRFw+EmLnlDrF6UgZs07dvbF81x6PV/du/4S1s7BYLG0d6yNmaXFKneJMIJWOmhQ7t78eIbXvTAYcMTY8/f1N2afnBwOAvwHP9X120qPR2oanlo7ou/eaAR1LE3H3t1tw+VsUzw9c4KR9r32hINqQ6gCc+l7piX0Iw3cyV1Z37PzB3E/bT1pQm1VRu3dV14Iwu6ImM7uyLj1guwE4tCQse7f5Pn9f8Mt4rKr+j4Q0mE9eabuKp5Bg+wz4K0rZdkKC6n3U2Rii6OJ7KHJ2OBJjSZSWDJFY+wrIrWuX8+XyPXse2fmDuWRW1b+HRiP9BMLxGSes7r+ypucui7rstGjlnn3+kYi7mzx/02KxWHYUbGrS0qIYB/zxQI9Sp9gtCcum5FJdkyLS2J6KNZux6yXAf4F3/Ld+We75wc+BfyArh1aJ5wcZwGNA+8/ih8+P1jasrO5VFALDSIWrOn00v33nD+YMXblrj1truhXMy59T/q92cytORAXy+6PnPM3zg55AdSLulnt+kBm55sg9BjzxSWXO/IrP81bWLEIpy0+AV8zPt6K5lN1QbV0Nsp1IoWjZQKAdcB0Sex3N78ORKOuExiGF9e2ynpo3enh5fWF20P/5yYeiyFsPYJ9FqUEDqvKLsvIWrVq1ck8WbuXTabFYLNsFNiJmaVGMEKtDF/QQGZN+DJSWhGWbFA37Ljw/iAEnA58l4u73HjW0tfH8IILqtjojYbMGCaw1wLVFkxYe1OXDeRM7fL745yVhWUPzx5Y6xVlA/XulJxagwvn5ibh7kOcHRUh8LgP2TsTdxlKnOIYiYL2BBxozo9dF65Odw6hTlMyKNTiNqUWx+uQAJMhyUfRrErK2mI8EWDWqvcujaQB4I/Ij+xOyuNgD2WNMA16YeMvhtzRmZ3bJn1J+4j8eOLtyK5xCi8Vi2e6wNWKWFmX62fvcHpqZiYBTU5Q9oLZD7qnIGX5L0xO4GImxrY7nB8M9P9hvY7c3Y5iuRSnD95EoOwVZRuxbsWuPn/1+8vUXAv1KneKLzJBvrt3t1l7JWOQZ4CIkkKYDSzw/OBEJpV8Cv0rE3XTBvAP0BQZXDOr8y2k/PWDgkgP6vVDTKW/V6r7tM1fs1q0GzYVMz+mMofmTnc3+omjod9pzLL1PUPTrCmAXJODeLgnLSkvCsmm1uQUTGiNZEyqGd9ucSKfFYrHsUFghZmlRVg3q3HXOkTtVJyEZAmv6d2TRIQMdlDbb0sxFouS+rbDvb2CiW88ACROV2igScfcB4F4UTcoLQ0ankiSBPwNTPD9wluzb54bVvYuuT2ZEHjjvssePnHXKbleW79J957p22ZVGbN0C9CeVurXDpwsu3r/kuacScfe/6WOYaNoPgINjNQ1fResaplZ3L3wiZ1nV0wVzKxZ3mLykDp2ntCdYlKaIZQcUJcs2t41DzvsvoVqychRJWwrciBoF0nQBDgBO+14n02KxWHZgbGrS0mJ4ftADuDu6pu6AYX9+o0Ntp7ysMBYht6JuRe7iys5pA9btFc8PrkHpu+sScXeDz+W8vz6av2xmt1PqV0UHFg6pGxk20p0IQ5wIjuPwRyRq+jn1yTszqur6Fk5dsnj5fn3bEYnUI4G0EDgf+BAY3entmUOLpi85uXDmyts+ufGYcU9ec8y3PLpMN2Mx8hO7GFj9+aUje/R/4pMHc5ZX7epIUHWnyW0/TXpOZRzVkB2IhFsGGvZ9N3Bf89fQpIY/QNGy3yTi7q3f43RaLBbLDomNiFk2iOcHzoa32rRdA0cm8zIbZp26+4oZ5+/P/KN3ZvboYXds7yIMIBF3b0vE3V9vjAg748/Pnt6r02fl2YW1D2Z3qL86WUc+DrlOY8ohmXKQHcTFQHGYGb2nvihnwvJ9+1QRiaQjU6Bi+6uQYLpi+QH9Lp97/K5Dazvn/aVo6pKnPD8YsvZxzXl+F0Ugl5aEZStWD+i4f21RTg+z6GWooD9tZwGyuViGOiXnA8eiMUj1wDvAi8Aja7+GJlr3OCr4t1gsFgu2a9KyATw/eBLwPD9YnYi7m+XttQ4eBRbhOG+v2bnb74Fza3u3r6+FKVv4OK0SI3Ad4CDIunXqvFGxSFZIzvzVdcnO+VlOKjVpYOLjHg0F2bG5J+yyP2pk+AL4GMc5glh0V/Q//G80vPxiJI6yge5EIrH6opzI0v361SdzMl+BdXcqloRlH6IoWppHCuaWn+OoJqwbSj1WICFWgKJe15eEZfeZiNpnyIX/NWD1+rzBEnH3Ns8P7kjE3eSmnjeLxWLZkbBCzLIhhprvW3wuY11xfccAACAASURBVCLuLgOeBPD84Fhz852JuPv8lj7WpmKERidg+ZaM0hkRVgocAuwMsVyAMNlQW7Sm/LxyJ/86HOfDVEa0R0NBVh9Ub7UrMmA9B7nWpztOlyTi7mfApc32vy/Qjlj0puX79ftTIu5O2Ni1JeJudWnJc9cBZ6N04yxUvP8lcBmKzo0FRdT6O1NTqP6rcVY4dIMGra1NhI0d2cMBnNHjF1pzWYvFss2xQsyyIXZDdUcPb+XjpB3vd9/Kx9ko+jtTOw5gxqhj4AwHTgWmlzrFe5WEZTVb8DANyKYinVpMkZVxUfnevV8DjiHifD7r9D1nA79C0ajbgIeAg1Aq8VFgYiLurlh7x4m4uxBFwH60KQsrCcvGAeNKneKuQE1JWJa2m7hrHZsfgoxhx9HKo5lGdHVFAnYE6s79MVAzdmSPvqPHL/zWubRYLJatiRVilvVi6pse2AaH+hzYB2gVF8IDc8Y/zGF5R6/5IC9VsKwqgiKD16LI1GZjzus1JjI2DhW7v5qIuw+ZTS4C8PygEFlSVKKC/LOQ2erCRNz9YEusZX2UhGVLNmKzR4C3kF9Yq2TsyB45SMD6KLKYpKkbFNQJ2oNW8vdnsVjaDlaIbQd4ftAXRRw+RcXRTwIPJ+LuH1p0YVuWg1Cd0byWXghA/6zZvRZ2GuykcmLRZjd/vqWPk4i7oecHk9FcxrOb3+f5weFIBJcC5yLfrieAuzemAWBbMSscWg1Mbel1fBdjR/Y4CPgnTT5oDt9+75sOfKur1GKxWLY2Voi1QowHVftmKadi4OeoU20JEiw3AZstxDw/yAFSibhbt7n72hxMR92kllxDc4oqKk4v+PcHH0dSYbo2bjUyWt0sPD/IBWrWElKXARmJuFu91uYxlJJsQLYPp6H6sDLUxWjZOB5FUwTqkB1HukmiG+ow/QhYjJocrOO/xWLZplghtplcfN99TkNjRl0YRpIPXHrOZhe0G2+th4Dhnh/clIi79wJ/QReMmebrOfQJvvnjHOTdNDMRd/+0kccajmYOVqBia0sTkWgqzDY/h6hQPQ/NYvzeGFPXk4DfAm97fjAGiazpibj7BdDg+UE3FPl62NR4TQF+jVKR5yKn+y0eldsRGDuyRzfgN0Am8Oro8QubG8nehqxS7gRmINPZW1DtYw2qAVw+evzC2m25ZovFYgErxDab2vr83rUNeRkRJ5mxufvy/KAr+vQ+En1iv93zg3cTcXcScLXZJhN4rnxSTs2ggs8Hf7l6eFqQDQcuQZ/6N0qIAYehOhnbLfZtmp+TBuDakrBsk0SY4R/ofLcHTkcX/yTqSNzZbPMz4BpgtOcHvzPbnYIiNRlINFxgRiFZDGNH9sgDJoSKeuHAaWNH9ng03QU5evzCv6LZmowd2SMXeBA4Cv3tA7QfPX7h/G2+cIvFYsEKsc3mwZ+fMfdHdz33VCqMLt0Cu8tHhdmvAHsD7ZCrefOU3ZCGysgbsx/rcFbhoNr/en5wWSLuvoBqdALk6bSxvIDG/gRbYO07FEmH6kioQm4H/lYSln3vkUueH2SgovtCVCCejwReqN3Km8vzgxOA/6HXuxa95ncg0VaIImcrgHsTcdfOafw2F4Q6Z2k+WJcVhemYHIGGlOej1yAXOf23mrS4xWJpW1ghtgV47PITvS2xn0Tc/crzg1NR5GMMsB/f7kS7J6MwtUuXkav/UDi47gBgjXlsI3DMxh7L84NbUfRlfGvy7WothLHI5TSkaISGDLhyE3czENV/gUYBHYdqvFagC385spf4O3C8+TkLuAfZeLg0dfV1AA5F6TXLN/mfAwtS0AfAMd5066A76nrNBRqRIG7Evg9aLJYWxL4BbWM8P+gCRBNxd5HnBwehlMlvEnH3GZCZptnuEqB7Iu6u3cn1B2CPnsdW3p2Iu3c02+9+yFn95kTcnW1qxnKBcB1F4NBU69R7A+t1kHiYnYi7b37f57u90lCQdXG4pp6arvl3Xzv31k0tjJ+OjFDrgAnI12t/JNBc9BrMQWa5t6L05dHAG8Bs4GSzTQFKY36XwGiTjB3ZIxuZ7S4AOkYUbZwPdDV2FemRTNUoFXkIMsTtZW6vAM4bPX7hc9t04RaLxdIMK8S2PXcA+Z4fnIZ8i7rT5F4PgOcHvZGn1n/WfnAi7r4EvLSO/eaji1K6wPwwc6wqzw8uSMTdL9faz+2eHzyFLkbroy9wr1nXRcjrqlVYTGwtfnjL2Myh2Rm5kYYUde1y7t/U/ZharnHNbvrI84OzgLeROCtH4ixEr8NYFPGqRFYWFahRYxAq2F+5qWvZQbkUCdqf0eSv1gV4H9l8FCBxtgY40vyc1ezxWeYxVohZLJYWwwqxbc+TQKa5SD/p+cFbqKW+OT9AF5YvgcnpG0106kRgeSLujjdWCD2RWJqMhFuD5wdXIM+pmSgaMNzzg0uBWxNxd3l6f4m4+9VGrHcOEhN7APcBFZ4fDErE3Q0JuO2WjKr6/nNO2CWM1TSE/3jw3C+25L4Tcfdzzw8ORgLscCQQrka1YM+gaFk5Ou8RZFvioALz17fkWrZXzrjt+SJ33K0Xt4tmtctK1gWo4/H5sSN7nIYaHApQA0QlmgyRLspP1+aBRNlbyKPNYrFYWgwrxLYxibg7dq2bCoHHPT/wm9VqTUbRkPr0RsZhfX/06X+u5wdT0YUkF11cJgKDkWD6Maoz65eIu2s8PzjD3JcPfC3ENnK9IeB6fnAiMhddBuR5fnASslL4cGPNRT0/OAIoT8Tdj7/PGrY1tV0Kpkfysu6EcLNNSj0/GIzMeP/azBeuAgmwQ4E/J+JuhecH1ej1HIiK9ueiaNgf0GzHYYm4uyXHK22XeH4wJKe28olkRs5ulfndmTjsFM9990+dxo7ssQ+KNJ4G7IvGGDV3zqfZzyFK+bbD1FhaLBZLS2GF2DbARK7OAV5PxN1pJrIVQ55HB6JP70cCaSHWHTgCeMbzg+KaRbFoZnvn6mh2WIRsDHYCfocuJI3AF0iQVaEOyCpUn1RlDFvnAuesayah5wc9gaJE3F2vP1Ui7j7n+cF41BDwPjLDXG7WPXl9jzXH2ROlVKOeH5yWiLtPb+gxYyaMcpZV9Nl9+ap+n/72tN+tV+yZzsPjgGsScbd8Q/teH0ZYxjdnH824FKUZP6MpBfZTFPVcjYrwScTdeuAKzw9uRMaxjYm4+7TnB0cisW4bKkR5Yyz7g8rczjtHko3OrtOe/2eDE62PhcmpDtyAUvLXoQ8tHdZ6bDoi5iAB9m/0v2KxWCwthhViWxnPD2LA74FfAIs9P+iDLA2uRSnJGDKXfME46ndFnXaZSHCdXzElu33R8Nr9M2j8LJbNYHQhORO5gU9A6cfPUErm+UTcTZhjZ6FozKWo9ujRtdb2I+BmYJ7nBycn4u7qDTyd11F3Xw80iug1VCi9MUwHvkIic/HGPGBlZY9/FOUvPi8aqX8cPd/10Q7VB222n9sW5lH0Wo5vdtuDqCB/QiLufkMIJOJupecHd9Dk8N4OGcnuhWY6tmkScXcp8FPwfnpN8XVH9f/0oZecMMyvych/N7dhzc1IfA1DzQ1j0Lk7HkXHqpFvWx1QOnr8wsda5llYLBZLE04Y2kkpWwIT5eoDzE2n6oyPVLrYeyTwMqr5ORMNHwaZhV6KUodnIlEWR+mrTkD/+tXMdCLsHMuh1okQRV126WhKNSrQr0MCLURF+vcAfzP7XYrEwDmJuLuo2ZrvRULtWVQH9ozZVz/g3XWlHD0/eAB5nF2NfK7GboSA2yT+Epz5y9ysit/V1Bf84RdHPv7b9W1rzn/U2Hhs15go5j7Ah+hvagTwwo5cl7epPHbEzg9EUqldchpWr3CUel+A6sQc9L+1K+qYjKMPEr8ZPX6hnSlpsVhaDTYituXwgcuRmEn7iqXQhWFeIu6eY6wruiXi7iOeHzyNPrl3RHMEf4oiTb9DqUQHpVfILKAL+oRfgNKAM1FKcxUqyp+DZlAegAq8T0ERlFOQMKsBEny76+4a4D00y/IIlGrcHdUuncs6BnAn4u5PADw/8JConGX28Q08PyhG8zLXK6DWx2XumD+xkVMCjGjc7kWYYSQahXQr8Gki7j7cwutpteTXrfopeh/bCX0omYc+wByO/vavB64aPX7h3S22SIvFYlkPNiL2PfH84HgkuE5JxN3KZrffaW7/MhF3h3zHYx9E9T5eekyN5wcdUNRsMbqQXG42/xxovp8MJLLqkODIQrMIByCRNQB5gs1CUa0eyC+pFl2oHGQi+mcUIYsj8fZPc19n1HXZ2ezrtfWN0vH8IA+NVfp47SiUiU5VmjV3TcTdb40G8vxgABJ9YxNxt8Hctg8SgXelb9tcTLo33NiGgtaAmUt5ImrMyAJOTsRdO4JnIxk7ssdxKBXfCaV2FwIHjR6/cG6LLsxisVjWgY2IfX8SKM33L2S4meZPKEr17/U89kl0YbjT84MJibj7CEqfFAKjkUBaY37/GDgYuAIoQSKsHF2Yc1DNy3AUFTsV1cKEqAYrRIXxd6Buyh+jurTdUMoyfYxVwKTmxrAo5TlzfSfAFJBno/o0Z+37E3E39PzgWhStq1z7fsNJqHZnEhrEDErLHoCaAd5a3xo2Bs8POqFoXY3nBz9LxN1vRe62BkZk3oYE5dubsItj0QD3TFT79706Xds6o8cvfBF4cezIHqNR12lIk79eq2PMhFHpiN60M0eM224+MFgsli2DFWLfnzeQ4Lq5+Y3G5HS/dT3AFM0/jETLJUiE9PD8ICcRd1d7fvAoigS1B34JXIiE2Go0d7IYdUVmIkHmoOhZEgk7UBo0hRzZuyKRtgB1ko1DEZZcVD+zF5pteLs5zkZjuh9fNutIAqWeH1y3dvQsEXfLNrCrfyGx1dzL7EaUlnvn+6xpPfRHUcEY8osaub6N7xt3fFZ9Q87g9gWLJ2/mBfHHSBzvg6KL35d8JLRnAOcn4m7tZqylzTJ6/MKxwNixI3tkjh6/sH6DD9iGjJkwaj9g1Zkjxk1FJQJXoGh4m2/IsFjaGlaIfU8ScffoTXhYBrKoiCLx8mPUOReYNN5lKHKTLr4fhjotP0ZzCDsi8ZWB6sF6oRRkn2bHqEWf+tNO4j1R6m8i8D8zGBzPD15CouS5RNx9N/1gs44jUWryKvOYq5o/CWPD4Zpf05GwnyAxeGmz/ZyK0kHzgYXrKqBPxN0Vnh+kzHOYY277EBWobxDPD0Ygv6j/W0+B/kfACeh8TtjQPmcu3OcPK1f3OqdL0czzGcELzY7loAjVW4m4+/hGLO9faD7lOg1zTbr0cSS8R69DaN2PhPKc9aWHLRtHaxJhYyaM2gf9fx0OzBszYdTf0AeENcDxYyaMeubMEePWNZLMYrHsoFghthUxF/AMY6p6ADrfu6JUyQRkvtoTvSnHkOC6AEW+Uqjwv735Oe071h8Juk6oXiwbRac+RJGulSjyVY9Sgw4w0PODm1H6dCiwC/CxKajfE43WqUKfxmtRfVkWcJXnBwOBZaYe7mzUVLAURdumo0hb8wtdLnA+iuoUoqLzp835yAJIxN06s+21yPX/jOb1dt9xLvugodjvAweh6FkUNS+scwamETGvmK8NUl1X+HYYOvusquq69qD1E2jyA/uGEPP8ILIOsbQMibH1RfZ6o9c2G53ztdc9q9kxilD93jLg1B2hM7QN8yM0T3QGKksYiD5Y9UETMj4B/thiq7NYLNscK8QMJkrxHPBGIu76G9p+I7kNONu40H+CRNeVSBydj2qB6lGqciiqpypAQi0ttlKoqL47EkuNSGB1Rp+iY2b7umbfl9B0kf+teeyxSOh9atZ1CBIXMZRSPQ35gs1GEaRbPD/ojoaSj0MdfG8gEdQBRcJOMY+/Nv2EE3G3yvODy5EIycCYvRpRuhjI8vygjxm19IK5f2PczX+BUjcpZO1RicTKu8038vxgqFmPn4i7n23EftNrIxG/8ilkpro2LyLrg9fXetwgYKrnB41AdrohwAxZv2IdxzkUpaYvR/V/WYm4uzHPPR3dXMU6avIs2xV/RqUNKfS6Xo4+YKVf12EttC6LxdJCWCHWxC+RM/uBNHl8bS4rUarxKDSnsQfwGyR67kNRoydQA8AlyPbiv0gMnIPEWCkSSiehVvwGVHAfQxGnBvQmPhKJqD1oiqI9g8RQJ+DFRNydCeD5wa5IUDWY9U1KxN0pnh9cgqJis1C6MBM1GHwCYKYCnIvp5EPzMNsDl3t+8Ef0Sf8gJI5uAGoScXe6ORd9kMh0zLqXJ+LuW8Bbnh+08/ygtlmkbF38B6VO2yNx+KAx91ybbCQ4c9azr7TRbkl+9sJjsjPyDsnJqqgeM2FUhzNHjPvWGkwE6oh17CaKopEx8/N3RqpMNPAJJKD3TsTdndD5b75NJ2DN2qlKM5/ydORRt0W6SS3bnjETRqU/gI1g3fMvG1ir9tRisez4WCHGN+wWypFA2tjH7YrEyG3rMtv8qKT3hGEliw7M6dZ4NYpmLUBCaw8kWqpQauJwVBdUi/y7ClCKrwF1fVUjYZEuPq9FIilptknP1OuKxF09EgUL0HzILKDO84P7kZDpjLzLBqK/gRpzDv5u1vQ6TULs+kTc/Sj9nEzUp9aItiFmPwcCg1AUaDCQn4i7V3p+0MvzgwtQ5KsSRb+6ohRMWhTmm3OS9Pzg/GbCbW3OBBYhQTrenLNj1t4oEXcnen5w0kak77KAg3KzKvesrssgFq3LounimP6bKFrfuKRE3J1qRkQ1Nj+eEV3dE3F3drPNYyiS15mmBguaPaY7Si9P+o7n9eIGnk/zfUXMY2x9WeuiExq/1FyETaKpXOFJ9H9vsVjaEJGWXkBL4PlB3PODWs8PHvH8YDdkTPpzVGD+t++xq/NRR+Nlnh8c6/lB12bH+EGnAyqfWfxm/oAw5Clk1bB3Iu7OQG+4i1FX5PFAGYqKLURpx2wkpNYgMZSJBNIglNKKonTZXGRbkfYYy0GCLILSk3VIVK1CzQC/BL5E0bY56E0/GxX056Fo1ghgmnls83M22POD102aFfTJ/WokRD9GKZe/oajeU54fPI5Sl1egGrGu5lytRmIqTRJ1eI5AkcL08WKeHwSeH3zs+UG22f9vzXOZb55HelvHNBIAX0ew1osZLfTjlZV9TyrIWfZJJJI6bK0i6WOAxzw/2H0D+1mYjsx5ftDF84MoOr/3mYHfzY93CBL7mZ4fzPP84E9p0USTbcgXG1r7hsiMVT+SEat+1UT9LK2H5hMo0kbLabsbB/2/7LutF2WxWFqWtvpG/SsUETkdRZv6oTqovkgYfAMzqugI5HK+qNldmUj8/Bxg+Qe59/d3ps6BcN9dfh05oc/Jq9rVVzoznypxz1trl6+iYt0KVM90BU1eYp+g1Fo7JDZ2R1GsL8xaHVTn1d2stxZFxdpFI3X1qVQqGpITImF1MfKyOtbzg0Ik9nLQReAR1Mk5CBUH/8wcw0FCrAMqKE+bYA5DPmQHAc8m4u6rzc7P/8y5uBSl3/qhAveZyDDWNWt/26xpvnncTiiaVm7u+3uzc5RtjpWFOgjPbTaY/IC1zmcJcKXnBz9JxN2X2UgScXeV5weVi8qHLTDra848FK3YKA8vzw/2BR5Dz/dZNBTd8/xgTLPIWHqANyjqeSJwk+cHBSiqdjBAf2dqIXru42eFQ7/3+KhdB7x8QjRSnzFz0f7rTZdath1jJozKQtH2dE0n6APVkc02+4JvziS1WCxtgDYnxEyUoAaJlzvQxb9dIu5OXs/Ddkb1WQ8B95r97IlEBCjd1Lj83fxBwEE41NYsja3I6lCfkVUU3tvs2BebfT2IDBz3QWJlOop4HYY6GAeiaNVS1ECwKxIv16POvX1oSqUeicRTMjerfFnPTl98OXXe4eUoohMCt3t+cEhWrPLIxlTG0mQqpxxF2i5G0bd0N2dXVNf1PLpA/B6lHdd4fnA3cvE/Bphkui0PQ95oLyER9aXZ5yTU+dmALiy7IdGYYZ7H4YDj+cE4cy57mvX8NRF3V6TPlek09ZFo3geJwnXaQQAraBKk6XNtCvDX76ifiLsfeRp43rjW7ZORwEvvLxdF7j78jn0m0fkOE3F3skm53mbOx2yzzWMonTsFidXHkGB7C1Jf3vzIFRlZVL7e5eBL6lZOzPlJh72qlgzv++lfBl+8LBrNDHsBf0jE3W99UGiO5wen52QelluYuzS5c58366WvLa2Ag9Fc17T/XhKJ5IPQ/37a4ubpMRNGHX3miHELW2qhFotl29LmhBgSGVXAxETcvc7c9q2ZimvxBRJB6Q7AnmiESheaCm2/GHTx0l1Sdc7i6X/vOrpwYH1PJHRWeH5wPbIz+BmKLJ1t9rknsiT4Lyq2j6JIkoPqu+YjofMkioy9gAr/h6Ko1W9RJGcRsGx1Tbezps7r9gZKgWWZ48cgOTAnu7JDTV27rskUa8z2RyPhtTsSA5ORkewAFLH7Kbp4vInEwwHm+FFUqH+IWesMZEh5C4rc7YaiPrOQ1UPSHGsPJC5/gCJ5jcjnKz0rs9c6zvutSMR1Q6JwnSTi7gOeH/xzLYF0FXCy8U37J1CQiLvrTPttpB3EQyh1tMDzg13XrglMxN2PPT/YD1hlRNv7SJDOabZNA/o7+hqT6s0DMl8uO2WvxobMPXuevKo6f0BtMqdn/R6FQ2rvr5ye9Xz7XWqT6P81aUReDyTUV5lJBum/w1U19QX19Y1ZH9xzVLF1aW8FjJkwajBqwMkwN0VQpHUGmiebrvl00P+fHe5usbQh2pwQS8TdOs8P9uJ7pGzMhfqNZjetQB2JFUiQAXSIZtItmhmGw69avDsyNd0LdTgeiToJf4xEy5lIgNSgaMlwlCJMojfptAfYj4Az0Js0KFJ2PhJJp6No3mokpvqjN/J2SECl3fcLIdpxeN/XTvxg2kl3IzE00OzzeZSWvNs85idIKD2ERMRy1LH5LKr9yjWPaY+E0a7mGMtQV2Seuc8F6hJxt97zg2dRmrMYpV3uQkLuuUTcvcnUfz2CBNyja533WvNc14mxqhgATE13hDYjiUTclUhwhp4fDEvE3RlGtARAeSLunsbGkZ5o0BO9Ln9fewNjUnsZivpdkIi7X0fwTGp4EDBtLcuK54AzIdI7s3e0Q2ayMSeWHb5eNKz26ar5GadGYuHIWE7j74DpzbpKL0VdtvlmHTcgUX4S8CDEsh+/8thviTCTAr0Qifx3EnHXzl7cNixHo6oOQn9DIfo/CVFpwmCa/s5z0AeXZ7b9Mi0WS0tgh36vB1Mblv9dnXOeH/RHtWWF6EL9DDJq/SESNw3oQvsJUJWIuwd6fvBLZElRQ5NYWomiPlXmq785xETzfQ4SSItRSvMDlMYAiaCnUFowhvy7DqCpEWOyOcYjwK+RwFuGUon7AWNo6uI6BdlevI0K7Ech0fRHc+zngb+gqN4lmEhcIu7WGgEyBIm8seY5nIKE5r2o/u04FMU507jop89jAVC/AfuKb+D5wf8hMVqILmh7J+Lup83u/z0abB5Dr8MKYJipC8sz56AuEXfbr7Xf7kCD8TlrfnuheS6OOdbMZvdlAr9DKccVKLr5BrKbmAxwamnw7+r5GSdkd2mcEc0K03Vy1yLRezFqVHgHuCIRd6c22/e3DGM9P3gNjcRKojFV16PXoiv6cNBlXTYXnh+8gF6HENmZHP9d53dDeH7QA1hqzWU3njETRo1Fr9sUZK48HgnpHCTSe6LX9CwUPbsDuOHMEePub4n1WiyWbUOb7JpcH54fXOr5wQzPD65GqYNZnh8cZe5b20xzZyRs0oO4F6JPv5XoIvwMuuhVI4EDigjdgCJbz6Ji+MDcn4/qvkLz1QmJLB8Jilx04e6HUl9foCjUh0hYgMSVgwRdtdnPEBQdqkVv8K8DU806PeA88/NsFFE7FkX6XkXi8m3U1dg5EXfPBkYl4u4EFFUs8/zgD+YcfIiie0ORDUdvVN812HwVorTkBWudx8OB1z0/2MvzgwGeHxQ36yZs/tpEPD843vOD41CkMF347vDtdE4hTQLTT8Tdnom4uwq+7mA8HjOuyfOD3Tw/8D1NEXiFdZu6rkb1XlmoW7Q5fZHw/IE5j48gAf6B5wfdAOpXRqNLx+fnVS/I6IFe40IkWo825yvDnIeHPD+43vODf3h+kPEdFhQNZj13ATeYlOx9qNZuCU0psLV5Hl3oy9EHhU3CdIO+A4xdx/+E5buZhM7bj84cMe7RM0eMm4dMlHdHqWbQ//ml6G+jC+uJCFsslh0DK8S+TQESN8XozTEP6G8iAC95fnB2s23HobqpmUjYHIrqkYYAXYb3/XRU+aTsD1fPzrgdONSk0o5BRddPIJuD/6A04S9QBOwBJJRmoahVI7r4J9HFtwt6M//M3Pee2e5fqLvwUSTqcsxaBqIL8zSU8owgkbYaRdbGYlJV6E3/d8i7bByqo7sAialzgPNMk0I62tILibYzUNr1TCSKXkOCxDHH6YAaElajmrP3TVQqTYk5xiiz/jsxYs3zA9dEHjHblJn1XYhq7Y4CDk3E3Tl8k1+Yc30FMtH9Bom4+2qzqNy5KCr1FhKSIz0/2GWt7UNzzPeQMMWsb1cUBd0ZdXqmu0Vno7+Jvp4fnJfZIVnQef+qBsfhn0h8L0Wvx5Xm9zp0Ed7DrP0EJLjTx+nj+cGbnh9cBFwEHJiIuyVpoZaIuzeg0VWnGWf/b5GIu/cl4m4sEXc7JuLuRo19+g6ykfg8GhhvauMsGyYd/ToGYMyEUdnoQ1AW+ltoLmqT6IPELlgslh2aNlcjthHcjkTO3cjB/Ql08e2PaqJO9PzgRRSB8VGqB5SyOwtFoF4AXuj5g/KH8vs07N5Yw+noQvtrJIoWN0t1XYreiMsScfdgEwlyUaStHSro/wGKsHREkbQ3UfrrAVRrcieKxu2OIk9pD7JeSHj9F6XC/oqK2CtoEj7L0QXiWSBpis7PV8/qjAAAIABJREFURkL0Jc8PjkHpy/PMMc8FXvH84FUU+XkPCclF5lx0Ndunvc06m+c4D6VoX0DiqMLzg7eRWEyh9GcZ8gm7AEVbhpl9TTFrnWrO83Rg7AY6IouQSW6h2f6/69n2NvS6nYUuhjXmfK3NGajOZzqKXKX93jKQaPoZSoc+BVyciLuven7wa+BQx+H+/P71IRJgTyHh6yLB+ia6ODvodStGqeA6zw86mTTpnkikzU7E3f9b15NIxN05/Z2pDvH1PNP1YFLEazbUaYpej3r0uh8IvGdsOs7atCPv+BhX/aPR/11yzIRR76BmlIFI2Ibo/yCC/u/3Mb+3moHlFotl62AjYmthLkLvofTTTYm4+7axDDgKRS5eQALkLOQDlY9SfjPRp9h5KKpEwcCGr5wYszPb0YBSfHujC+9xKD3ZiDzI7gZO9fzgdmSO2helRV9FouAEs993kHDpgoTdGagerRcygixCtWYhEgcxdOGvQB2MJaiu6h3USBBD0bR9URTuNWNK244me4zhSBzcZda2HKU5f45SLe1Q9+MVaG7eQppSqylUM5Uyz2kP1CUWojTcPWb/BWZ9RwGPJOLuSOPXNgN5n/2fp/E/D6Lo2osbIRaGodRuA/B58ztMKvLo9O+JuLssEXdLUFPFC8ABibhbYQxad2r20D7me7o+rASJ2DOQQP0YRRu7AbsYIemiFPKlZv83oMhpDvo7WoHEa9Ss9WXUuHCPee6/McdK+029ata199pPeN/9P+zecd81r+/cbVJxs+ca3RhjV88P9jdr+S6LkK8x9Wf9USQw/Tqsd2i7hTj6kDcD1YiuQZHoF9H7QPq9OBmGxFat6TSsrj57BYr+WiyWHRgbEVsHpjh/7TqhNeiC/hmq6ZlB03iSCpS2GociIzHgr47DkTjcjKIyP0SiqhwJtyoURToWCbOZKHrUw9x+J4oQ3WB+v94cJz2vcS4q7l2GrCOuM8dvj6I5B6KOwjlmu3bmvhxUa1Zo9tMZ2Ltz0Uz6dp2YO2vRPuetqOxThaIe/0Sisj0SCGPNeu5DDQF5SPDsB9yXiLtPen4wB7jR7H9flMZLO+nnodqsD2mak/lHFPW6zOz7DRQ5IBF361FTQFooHIn8yxKY+ZfmvsHoQjatmUCbYI41ZS0TXlBNXqHnB4MScffryJcxjD2h2Xavo9TiAYm4OwldTJ9D6WTQa9nbnNO3kEgsRP5g9yAxPwJF0WpRarYQ1e51Q1GwDPO4hSjClEBNDumavq8A6ldFx2S2SxaY8+MDoz0/uCARd5/2/KAXcFj3o5yCVH1k78ovsz8356Uvsj9Z6vnBsYm4W8N3k65j3KjunUTcXej5wWiURn0pEXff3dBj2iomGpbucn3pzBHjxpq7fmbuX43+Dhzg5QXLh934+exDbztw+GNhVmZt4Tp3arFYdhh2SCFmIgBHIwFy39rRk/7O1IJIZqrrV3XDZqz1uA5ARbruxvODfVBqMj24uzcSJs8goXMvirpchCIE/0EXpiQSZP3RG/AhNJmmzqVJ3BxKk5XEb1Ad176o9mmQuX0EilT8xOyvzKxnMop2LUCipAIJsBx0sT6KJp+zGmSf0RHVJBWilEce6sKclxWrrsmM1Q5paMx2USRuL1SzlmG+3kd1a2eb5+MgIdIRpVY+9fzgAJoiYRPNc6lEkaLOSLymkIjrB5QYIRFDQu8sc5zmr8kwFHW6HwnNETQV6afxUYRtoRFNy03U5jXWzRtIPPqe3PirTEp4P+CDZp2AM1CUcQ18LdCfa7afR1Bkch5Kfa5Ar2cWEtqZqG7vKJpeq4PN97lIjNWi12w8irBeg1KfGeZcLenvTB3sRLvfmNu7fszQ4qWdUETzGJQqfhr9rRdntgsLIJmX3blqL9P92Yj+LpawjokRa9HJrGV9KdxvkIi7y5Do3qqMv6Ofg87RXuhvpzfwwMirZk9c7wNbAWMmjBqEGmwGmJs+bHbfUejDR3o8VxL48lfH/f0jzw/OTKYy2qMUtcVi2YHZIYUYuqiNQG9sC1Ek52uKdq1+OJaXPO64X73+1xdvP+xygDPvenb3iJPxRkas7j0UpQJdDLsj0dELRXdq0bijtNt9ldmmE0rPLTWP3Q0Jq8dQfdNf0oLQ84ORqAbkPZQqPBylCL+iKQXZiLzIctCFfAYq3I2iC/xMlC68GnlILUWiIMt8fxsJi5XoYjwARdMGmMc3mOOPA/46f/kuixYsH1YWEhmChFslEhZLzPNYhiKB96NU6pXm+Vebc9PBnIeVqI6tIxJseea4kWQDuySrIrdlFqUazPZTjKfbLcAtibi7s+cHHU1jxDLz+g1BhfB7oehbLk0domnuQlGn3qh+bRayyPiWHYbp8rsERcv2pEmgXIRqdv6fvfMOs6LI+vBbdxLDDGnIecgggoqIaUSC17TGtU2Ys2uW65h3dV0zXnVX14A5oehds67aBkAQBVEwIEkZcs5pmHD7++N32h5ZVHTFRb97noeHmTvd1dVVdfu8/Tunqm4Fbva0cXe4xlilgc3CmlBv4cuHUI5Za7vPQtT3IUD3s7a6EEFmOPnidhSWHWf1yEXQfzT6XuZi4Nv1ooW7zf93vXZN914z2PrmAavvZFO9DkPjJFzdvxZSVg8ASlOJeM3JBcVW32zg5BprmtW1ejXbtM22ATsauAaNv9COHz2k+BU04aQXAvkUuvd90HfgK6RCNiwpLfvOS9evaPug3E1Q33xgSfrnIjU4q8axT6GxYpAb33ScZyxjGfsd2u9yHTEv6b+PHPNKNLtsSs2/73PKqEfzW1SdmFu/usrF2D2ViE+4/Onrei5Y3mlUnfxlk+4+9by9a5TVHak5I5EC1h8tA7A3etO9gmiBxjXImY9Asxi/RuBTiZSG/VBC+1IiCD4TOfyFdo2DkDIURxtdZ1k50+24POTM5yEI3IAUul2QcnMMCtOtRdA0AiW4t7Lj5yDAbIJgYWaoAHlJvzXQNJxNaDMHz0Ag9h5y4KEq1xXle1Wg3LdaVqciBGM3IhXuCuDQIKC8eiN1Ni7LXlbQsuo4BCaXIYjoj5aNeMSuU44gpAxBWgv7uSta1+1DNjFbzf58q28W0Kvm+m9e0j8HhT9fQ0B7ZSoRn1Dj7zshCBuPVK/zgQWpRLzUS/r9rS/mATekEvExXtK/BznY4+0+i9BEgzyiHL1yO68VyiFbieDsEevLnRFUplHouAwBXRUaMzOsL9ug0GdLu8b5qUR8ptX7IwT1ixHQr0DwejMC11fs58MR/D2Fwq+VQOuUbVhuZdUDVm9B/t2vaqOHFN+Hvic1ZxWGyms4g/cqBMBx1Ja1axwfAP1LSstG/ioVrmHDxvWtY/V6CYH6O2jbrnfRcwUE0BOBfoP6jFq7uXIylrGM/X7tNwtiFs46Hxhja1rV/FtDpHo8kkrE523mXIdmHMZRLtitqUR83rBxfdsDS6/adWgFsEvxoKUTG/bakLZ1p/CS/p3o7fxr5PwWoodpNnKyLyMn/zqCnsOQI/Wtrg1Qom5vNAOwHlLEHAo1dkDKxhUo5HQGgpQX0Rv/1UjlmoPUqK4ItDwEAHei0FYuymVzyFF9YsdcgbZLaoVA7G77eSBydG2QQ6uD1J8pXtLvg/LVRiAVphCpZH9H4dQqovXJaiElrcDaI5xscDxQka6mfrqKNdl5DEWJ7pMRmKxEIcDLrNw8FMbNRqrGEGu/m1OJ+Ejri7NQztYE4BRbhT8ML+faueGirD2RihSuf/UJUijn2Fiok0rEV9tEhccQWFXXWrx23Y63vrNq1h+2m7ygf6e/I7BZaXV/2K4zEMHTIKTctUKONVwipBZSB2PWF/MQdB5GpD6VI3gtRA65ExpDna0dFluf74GUnsF2vddRWDYMQf8FgeSeCJLHAfcTjcvTrc63AM+mEvGz2cZt9JDiGFJ/w4kSAWqv2vbzAtTPu6Hx+AfUHzXD1+Faet+g9mzxBqd0nuJ2b3FqcOGUOmzY28GlJaVlW+1hOGxc30LUN2PQd+1y4Lp0mvTU2SXLps4tuejZwQcO31rXz1jGMrbt2m951uRuyPFct5m/dUcP5P+YWQaaGZlKxE9BeVc9gfZe0neD+oz6ZlCfUatR2OCl1dNrvQsM9ZL+m17SvymViF+EoAD0Nl5EtIhmGIbqgxyCZ/UbjcKjZchBH4KUlJNRUnY26oftEQBl2/U7IeDLQbPyTkf5JG1TifgeCFq+QSBZaP/6IoXlXgQBaxAw/BMBwKVIoauN8sqa2M/bIYhojHKPngVu85L+EQj+6iHnX2T1aW11n4VA6S77dxyCtTcRFHazelcBX8Sy2JidxzoUusxGeWKfISC8BOXPPEi0wOks5GT/aO26V41uvN7aKFTUwr5djuDnIQSdzyD1aZm1/e0oXLS7nXIY8KyX9DunEvFF1vZDgewmH5VVAqe2fW1yU7vGUKv3zUiZehQpmIcg4KyP4Hyttdk8pH6FMxGrrB797eclCKhX2/9PoJmlja2N9rK/vYteGpZamzSx9jkQwT1oPC5B0HwWAurtEXwPtzJeSCXiDyAwvNNL+jt7Sb++l/QH24SHbc5KSsvSCDYXoHv4m/2eRm1TCvQpKS3bgO71JJRTGC77UA3sVlJaNh292LwNjNiT5x8bEDx0Yxk7v7KSJpegnL2tZstWNT/z63k7dVi8skXTispaBUHArsAt7008o+1Xc/p9mg5yLvGS/t4/WlDGMpax3539lkFsLAKdqzfzt4/QW/9mN3muYW8jIBoMvGkrt8e7nLewHbH02Pzmle8ix7krMMASusegB34ZetMuQOrWm/ZzgB7+jRBgnINUr0EoV20oAos3EUj5yGmsRWpHuHbQ40gxaUcESqPQUg4NrYx+yEEfidSS+Ugl+cDOfRcYatvszERLSFyIVKdnkfM5CCk0i+wzH0FBcwRX+xGFeKoROK22n6dbO4+xezwAhc/ykQpzNVL2FlvdeyAALLJ2ClXDcIumfBTarY2Ao8TObY1CO4u8pN/b1mAbjpSR14m2ggptid1vmJtzB3B6KhH3EaxuD5xp+VXN7PdSy01bb/24y9z9umXN79vhrnE3/OFUq3sKhfgqra5DrK2mI/WqHoKcFQhQ77CfsxGoVSFYC5cmeRcBVWMEn2EOYpg3tAJ9RwfY9VogqH4Mje22dnwajcEQNG9Equu+6IXg3VQivl8qEQ+XmGiKVNanUUj7IKC3gdnHXtK/kG3L6hPl3h0ETELj8kjg3ZLSsjAXcEfU5h7R7M+AKHzZB0H6ogJW0SFrotuQnc866jKb9uUWgt4q9uHkP+4ze0nPnInTDoiP+eLYq2cu3KltZVXupIfOPmEBuPOQIp4JS2YsY/8P7Tcbmvwx85L+k8h5eT+U82KhqY/Qw34HtJhmX6RIbEAPyCLgSaQ0PYFCW3sixxsmvoMUkO523EakTGQj53kjUjiuRSG1j5FTWIugYg0KczWx6xag2XPVdp1KFPaaj5bNKELJ9rNRbtPzCPa6IMB5H4Vz3kkl4nFbD2swWoIjy+qxDoHVc0gtaGV1uxw5+XBdrCSC3r0QhDaza4FCfr2QY1yHIKM20TY8pQgawz00D7C2zkIgGjpZh/KZJiCV83YECn+2z/5kbVMOPJRKxK/wkn4t2+cyy+pWH20mHljO13AENzcgVaq7teuNRLlYDezfNLvvUAE8GuXGXWCfPWr3GU5QqIfUzmus7HuQmrYawVUCLcvRGCmdAYLGHnYfr1o7tUcQN9nqMRIBVR0UWnzQ2vRgpMJV2/+3EIXnHJECtA5BcWekbnaw33PtvEoEqn9DY+8Goi2aOlt7D08l4hfZd2MgAsJPtjR3bK/DxjYoX5TN+LG7bHaP1p9io4cUd0Bh6ROsHrejcVKB8t3mAueWlJatGz2kuAAtBnwr+t6lETz3RmP5SrvXjl+y+wET2Ge3HQv92h0qPpvyYOUdywIX+zKViF/639Z5c3bJk9dPbtvsi24z5u2SXrCs2wXpIKc9cPnm9gTNWMYy9v/LfsuK2PearRC+DHhxU+fhJf2WtpffQPh2Adf+aBHPDUgtug05rBCKaqO8jm5ouYcTkdP8BoWpQvh4AkHY4WgGZW3k+M9GCfUXI0fQHDnknVOJeEUqET8cAV8BArivkYPtjBx1uAbYYqT0rLF6hXkvK+38D+zfFXasQ0pH/VQiPgM5pFvt8/cRYMyx+uciNWeR1fd45MiXINiaYW0y2O7rc7vfCxE4rER5bFPs760RGM1FEHc6CgU3sGu+j0KT4X6aWUh5PBwB0iBr2yMQmDk7Zipadb8pcKVtrzMNqZvXWVuBVJP7rW4fIOXtRQRAbRHIhjlYWdae7ezzNkQbZ99ldbgKqZSNkEJ5IALUBkhRO9vap7WV39nabhjKr/saqZdNra0L0QvALgiGOhNtGfURgoiGRHt9dkDj9By7z1cQEIb7iWYTLaR7l/VTV6RQHk00EeITa4OnUc5duMRFGLZ+C6htENYYgc8oBJk/al7Sz85rWDmvfs8NC/Y59f1fYlb2YPSSUIheXN4vKS2bg8ZYE/RCFC52GyBwrETQeTcK57YEWpWUln2D2vVWCBouj7V5ddK6/fYuqFi/b+BiZ6B++MXtuhcSRe2aT2i0bFXLqorKWi+kg5x7Uol4IgNhGctYxuD3u3zFsSjkWOwl/UNQntDzBl2lSF1Z5iX9XSyZf0OYkI8c3iPIuXdEzmgFgorLEfgUILg4CzmCG5HTH4BgIgc52wC9uQdolmUFkVLWGij0kv4eNgNuPlIzViMQ2IAAYYl93gE5lbUoJPmy1dOhXLhKu69aCIbWWjmVwH5e0t8BAUO4v91MBAnvIODZwcp6AkHWDUiVWWD1aIoAqZWVk4tUwyI7rwFy/AkUfn0TqJVKxBeZUw/HWszKD6FzDAopjSTaVPtfRCCYZW3zLFLibkZh4e1QntcaBLYxpDiVe0n/dKv7TAQqDRAghzl4862N4lb35QhAplkbl6ENu7shJ/+EtWUSKXKHItjtZvdxj7VFEwSsa4iAdT8Eo/WsH6vtnA4I7i9AYL/C7q8BgvQYAsM5SLG8Bak79a3861AuVEc03mciheg5BILhAq05aLw1QnASjsXXUGj0OLv/4QhiDrD2AY29+4CLID3372+dMCAvd93ks/s9/0NrW1Vn5adXuRi5WXV/Ebl9HZHyVwg8MHpI8T9Q2y1A42f96CHF5yCgjKMxXoCAdgTKkwzTFKqBwu582O6sxDOWk3UKJVtxZ4DFK9sdO29p14LOrcb6nVsPP3JQn1G/zzBExjKWsZ9lvzsQszDVC8i5laDZU33QQzncb7EjevvvYMf/w0v6D6cS8ZdRm2xE6lEecoR/RIB1LFKHXkKO+T7kyBchRzcQAcowlL+yHqkxdZDzq4Uc447IsVQBT1kdHkChxj5EszAdgoLudnvNiVZi74fgqY3dm4dCZ8VW99Eol+hkFOLqiRSrlggiwk3DB9v9ZSEn1dz+hXAXbj/0hl0zhsKAc5Eadq59nofUtg/sXgK0yKuzNplu99QVTaRYjMBrCAK3NQhKvrRrHWXt3BVYbKHIGIKHRtYfJ1o5S629H0JK2g1Wz6+sXvnW9uVWr/ZIDVyDgO70VCI+HsBL+tVI/epn/zezfpmHHP8GBOO1EeyWImc/G4HjUARTQ4hUtmKrz2g0KeFm6wcfrXy/zNp6BIK8agRBRdYvuyEYvAuN6Sb22UHWDqdZfe9GsJKF1LTP7NoNEYQtRSA3Ak10aOQl/T8iEDsPvRy8B1xRQ0m+x0v6jzdtMH2fybP6PVuYv/R9pFriJf1ca5t8oHcqEV9n5zXnlzeHvsvFCI57ACeVlJa9OnpI8V+sLRpaXQIE2u+UlJYtRd/P0D5AfTCBX8mWr2n1bN3aSwoXLOv6ROKAhzMQlrGMZew79psHMVv7aANy9gkEOecigNkeObXPsRWqU4n4NC/pewhuPkOK1yoETSAYyCHaiuZ8W8Zhe/tsAXrY706k1oxEjm0H5OzjVpaPQOZ2opCWI1pJe7qV2cHq83cEMVVWdhGafXih3VcVgqpm6A2+i5V7CILEYfb79Xad8chRV6YS8StrtFkxCjfNQtCWg5x0uH3SSruPcE/LD60OFQjAqhFsjCNad6suUJ5KxKu9pA8Cn51RyG0WCo/9035fgZSbGxC8Ftj9VyNw6U0U+myOwq4QQVQjFNYdZfdTDaRSifh0A7/3rY/2srLL7f5idm/r0WSPswE/hDCzs1COYGcU3nwOjaVHkLKSRLCzxO55I8ofXI5ymV6xfnnbPpuOICEM/SbsXmP2+wYEun+yPp2E1K2HrC2mI7VtNQKlOXZfN1gblSN1bCc0ZkJHX2XXCSePbLA2KbLzJ9coex4Ck2+Ao6wPO1l/zgGuXLSi8+n5uaumZ2dtfLZGWzVEYz6GZivewy9vA2r8HM6gfAIL+dqq+93RWIvZ/W5AfXQi+u59azYL8+9boZ7fa8MuOGwJUjQzlrGMZew/7DcNYjZ77nHkvC5HQDEeOaF7kOOPARNqLFqaDQSpRHyCzZrLRWG087ykPxs5wIkon2sccIiX9Cei3Jq5Vt5tKER3AlKazkehrYkoh6cucooHIFVmsNVlOQrxnWTnj0ZQcBLK/8lCM+McCqW1QcpNU6SYFKDcJJDDuQFBZxP72x72WT7q21ZIQXrHS/onoPDsOgRRLRFspK29colmR36JnNmbCAxvtDqUIcgJc5bmIABtaHVfY9tCxVFIaSlStz5Ai8qOQZBVD+XQdUEK1tsYMCJQmIXUqOOAq1OJ+ENWr4Z2bhpBRGj9gVwv6T9ioD0HhecmoPChQ+pVFgLWs5BCmQDSNiZOtzLDZQxaoPF0odWrM8rnCmH4DAQ/CxAMHWF90A4B6+523UvRIrsv2D22RCHEM60PmxHtIwrq8xX22XZWn5XopeJo+/kDFJpuQLTcRRbfnd0as39VNX7Ot7/nopy1l6z/alubDDMIy0EvAItQXt9IcIdsqKj/8YaK+t/uwZpKxBd4Sf/P1i4Ps3XsVQRaeQhGR5eUli0cPaR4N6QmPogUujDf1aGXgMZofGYsYxnL2DZt2zSIlT51/R+ysjZ2mTGv799Tifjm9srbgJxizWUqbkKO5XFgeioRP2CTc64D2nlJ/yIUhtmIQKYO0WzFU5CzfBU5v2OQo5uOHO2OCESK7V+4+OoqpCoUIXApRE4qsHNmIoh4GEHCX5AD+RIBSkPkRMN9HN9Cb/R5yOnGiGbIBSjEmocg5xEUGstGEPRv9Ob/uf1/JgrBNkYJ71cSbc4dLpAZOvNw8+lZyFHPQdB4L5a/lErE7/CSfgMEBI3t75Psuq0QAOyEwGFPBEQnIqipjYC1tbXtUAQk4+1+rrP2+wp41yYbrLR7m231ixlIz0ewVAxk2Qr7n6Ccvako1PsYUn0mopBhOBFjJwR77yBIqo8A/iEUGi1AOV73IfV0fwTfpQiUTrZ6T0RQ8xpaMqISAXaetWc+CiUWoXGWj8bWPARs2yHo62fnhJNEAhSaHY8Un8Da5WUE3Qeh8OoqNDbC61XVGAcxO8ZHELoKjcuBdm+LrP/C5HesHo3Qd4BUIv6mLey7Lxqj326inkrEb2QrWklp2bWjhxRPR9/rcEsjEOz3RWNt0+dY+D05ePSQ4tSWLNRq8JlXY8unjGUsYxn7VWybBbET7x7eoEf7slfzc9eQTucuhPiwTY+xWUc32MbQCxBYfYzyhmah3JtN7RsEX+2R41mNlJ4JyJnughzYRpTzdLYd1xQ5aR+FkeoiOHLIyRYgB1dh9djfPgsd4yI7ZyVyiIcgNa0uyu0ZbXVIEIX0HrZr3Y0gAaIZcrOQ2rUngq0AqRo9rR5pq3uR1dlZGU8haDsBhd3iyAk/hyBrV5SHFK5LtgI5+AoEMUcBK7ykf7zd02IEDYchQKpG6lOoyj1kdZwDTLY1zfCS/ssohNrF2iuNgOEKq1uWlf8kUM9L+nuimX4trR3Osjo+aMc9g+C7GqmZy1Eu2lconHWanbPa+vhs67+FCMg6IIBahHLt8ux6H1sdK6xOYbhwpl37agSrR1mZ91u5eUi9PIlo4/B3UUi01No6DCWutXZuQhRayyNaT6w7AvU3kSLYHEFRYPfZ3I4NQTosNw+FUN+347Ls/AUIYAL0EjAHTVQY5iX9JBrP9fhuHlU3BLXnoCUufk172uq4DLh29JDiYUiV7obaZiOQroZaMQtPLqQ4mMzufSe7vVqVbJky9negxEv6R6YS8alb6T4ylrGMZew/bJtdvqKislZFZWVOUJ2OBc6lx/zI4bugUNJRyHHnI8A4cdMDv7i52TOV69wyBDhr7V9t4P1UIv5aKhG/1n5/CjnV9lb+hXbO6SgM9TRSkkCQ9hRSGI5HyfO3o9ym9QjEWiPl5WPk6PZC6kQFEdi9gNScT5HTm4dmEDa1a61HzqgKKUCXInC5FYUkU0hZATn67VAOz1NI2ZtOlOj9jbVXY/vsZARVi2tcawYCl+bWBoMQrCxEocOzkAq0imh26Wd2/Qqr/2tWt7NTiXgawPKP7kF5avfacV+iJOwWKGx5rP2rRPD1IYLMRQhsSq2dJ6PJBo0QmA23NsxHIPsUUsbC2XeLUZhvH7v/JijUF0MAm4fAKwwP34VmbNZCAPuitfuzCExvtfY4BqmOpyDw3B+pR70Q5DVG4dB+fDeMGCCAbYf6NpxpG4aKp6Kw+4yqDW76Zzc0e231jJyPrW57IYC4B43jCjQ2ZiM4WYcAd2eUx1hFNMO1kmgyRHNrh3DmZBsEbjVnR4YvFZ28pP+Yl/T351eyktKydElp2SgE9n2AjiWlZTNRH6ZRCLxnJTnl6ION09h5+ZeupHG1yznqh8p+fMhBu704ZNeXmqRnHJEbrO+SFZQfupVvJ2MZy1jGvmPbrCL2zMWHrvOStfOA6tCB/4ANQ8rdz9e1AAAgAElEQVRPHOUbrdncOV7Sb1G0c8GXVWtjhdm1qxc4xxDkdM4B/uwl/VZoZe5w0+YN6G26HVJ2aiGYmYKUkAUo/8sh0GmJVJ0DiXJy1iOnmoMc51IiB3k6cn51EQwcihz6egQbMeQ0NyKnWI5gIBeBUFvk2AuR8vIacri7Wl0nIxhck0rEn/WS/uso5+s4a7PaRCBTiCDxGgQqVQgsmtpxvYhmcU5FCtTXdi8TkEL3MIKN2kiJeRmFQDsD73lJ/0GirZdGI+h6GYVge9t97o9Ck88jsPjcrhnOYH0OAW2u3fOuCGDWIvXrC2uT+dY+bwB/RXB1nx33jJV1v9X/Piv3EgRq16Gw1woihaorUtvOsf4Zj+AlbW3UGYFrlV0r3Jx9LIKmUxGMHWznz0djNhuNo1o1ygv33qy0+qwC9lw/P/vq7qUL7wgCVhLtvPC6tfVZRPswrrLzp1i98tAYXm6f5yFFr4W1w3oE9suJ9g99r8aSLqDx3Rapf4+j8fxG+Ecv6dcpX5p1bsXK7NffurfvZ2wFKyktmzR6SPEJdr+g+30QtUNFNunRadxujqA0m6pXITYQqYjfa+upM3wRxW3WuQbppsGMqvmu02Ve0n8llYh/tTXuIWMZy1jGNrVtFsTg29DjllgWApP6yPF9H7gd0mTPdXVjOeAcN6QS8ftt6YgvkFO/CM2aDB16bQRJtyDnNRfBSAECMB8pObMQEOyNYKISOd1qBAl1idSF++wa2yOnPh057mVIvamLIO9g5PheRWAxFS1R0SiViF/pJf2WSH3rgeAusLIuQipNCVLoZgK9vKT/LFKKVqM8rvvsGrMRCO1vdboEAVYlcnDNiNZRy0GOPZw1eiQwI5WIz/aSfhguW1nj/vdG4LPY7uUcu86Ddn5fotDcRgQmYU7THxD8rUahsDxrh4n2+aNIrcwn2jKoGYKLjgguGiLAGoVAqJcdv9aOH4Ygqbf1cS/rx4/t+FVI+fyTldnB+ukSNIHhQDROVqLQbDlSZFdYWYutTe5EuWHzEeR8jRTS2gjywry3VVa35tYGDuUWPghcX6d95QlAjIAGKCexj/XvS1anxkgV7I3guCPROm+3ohyrPKTYrUfj5Ulr8zMQNB+NQrvn2t8AsE3Vp3pJfxoadx9Tw9bPy9mdWPDXjUuyTkbQulWspLRsUY2fg9FDit9AYcrLV9G4+2y2WzYyduzbqUR8Pppd+b02ekhx/wK6ja4g5wiIrZvjuq/FZcUQkGYsYxnL2K9i2zSI/QSrJFo082DkVCu8pN8eJfn+05Y6OCO7Ng4llT9g54bb7oSr18eQMlEfbax9NHKyqxHcLEFQ0ROByBT0wH8VOdlTkTMEKTqticInjZAzX4SccGeU55KLAGA9csznI7g7HTnnUUht+wPRYptvICc/A4Vg+6KlGNZ4Sf8fSOnpYfff1O6pJ3LCK1CC/6l2ncl2j6F9hqCiJ3LW05FSF65aX9v+XQPU8pL+FCKQKCYaVx2RM/8EwUq4LldfBAvZwOR0miaVK2K5KyfXurbpXusLEKjsaPd4WyoRX+8l/VsRLBSnEvHTvaR/A1JnDkdhyrUI9AagNbGWIwhJodDo+VZepfXDUShktytSpE62dt4NgcRgq/9AojXfqqzuxSg82Rfllz1i7dER5YkF6OUgbffYngjQs+2aDvX1o2i8NCYKUVbZ8VlW7nJghHN0B07EfZsjGKDxvr318xEoHF1t5bUkylGsQCBdCEwLAmoHVfyxYk2sW62idG/g37Y1VJYd181L+oeh78kzqUT8fPh2J4rX2cTKl2WNrFyV/UGQ5oNN/7aV7WM0rk7KZ+26bDb+my2fLXluMV/t1zr2VU4nPp30ELcPAnJt8/eMZewnW25e1sLKinTTHzsuJze2qGJjdbNfo04Z2/btd7PXpK2N9SRKTP4bmhE4HcHLP1KJ+Agv6ceRgx2Kco4KkUP10AN9b+SEwkaZhOAsH4UQQ5A4AuVLtbFr9ERONUy4XmB/C8taihzluSgfKpzR9g+kjq1ACeoNUVjsAOS0X0Ez4+YhiOhpZZyPks8dUkOes2MXImXvaqTcHIPCeqchuCmyuu+HwnO9UGiwNVpkNAfBwyAUvtweOfSJKEesltX7aAQozRAMdbe/Yfe7EeW5DUWK0Z/tHopRXs/7CHhygUnpKupXradF1drsT2u3qCq2MhoghbAEKUKP2XkLURj3RJSP1cb6uwiB61hrwx0QABVY+3ex9j7C6lqFHHZL67twqYZDkPIWLp7b2PorzK8ajxTTNkiNOgyBawGCshB8YlbX5jXOX0wEXKEydiYK6QYIViciGHsdQVgxUtTmIBAegcbY00ShzTXWTm2tDyrQGAzBOQuB6u5I3RsUpHFBQMugmoqsXHZALwF90HcojdTCfDQWPkkl4ruwiXlJ/1WkcN6ZSsQv2fTvv7SNHlJcF/W7X1JaNtXWjNsdGLBb+qUHd+PVDSWlZau2sKxawEcB9LQve1WarD36lX49/gdPzFjGfsCcc8FTH+31o8cdt+v7BEHgfvTAzV/jMJRP3C0Igik/p4yfa865B4HbgyCY/KMH/7LXfRR4NQiClHNuBHBJEAQf//BZvx3bJhQx2zdwlYU/fq7NQlAzCzmQbsgZHYGWNfAQQDREoYwcFKZZj2bVhcpHCGEOObYc5CCfRA7/SAQE5UiF+wo582kIHuogB7gWqWoBSvA+lCgxPo1CkDsTJW1vZ8fsRAQgva3sBvbzGqRynWnlXINCZH3sntsR7YH5tNW1PVL2jkPhqkLU7w0QXKWRqldp99nQjl2AFB8QRLWw9liL8tDCTc9PRSrQiUTJ5UvsGvcicBuBQoH3WnnhFknLgMdj2ZTk1KF9Tp2qXRG4NSNazqHY6rkjAqxP0QSGVlbPP1n9r0Rh4n5I0UojqOmAoOg5BOdHWB1iCMBy7N4fs/Oa2e/HoDDvoUS7DsTQuGpiZfewazazdr0SAdaedo8N7NxyBGXz7fe6Vl5HlAu3xtr7XqK9TLsRgXHcft4Tja8cBN73oTE1Bo2/pkSL8U5CAFuBxntHpG4NBua5GPPS5Qwgxjxrw10R2IWw/Rer1+t2rc1Za2uTxt/z9++Yl/Q7oxDqValEfMmPHb8Z64C9ZI0eUtypibvyqnXUPbuS2l9+GDv08UsSd//Q1kub2gT0nQu/gNnT6P3M3Ul/YCoRL/sZdctYxn4tOxbl2B6LfMCvYs65rCAITv+1rvf/yf7nsyYtt+hjNhPu+CmWSsSDVCL+SSoRX5ZKxJehN/m7LZTSBYWn9kPgFEOA05jIqRUSrUBejhzgGPTgX4ecdFc0U+8N5Ey72XVWIifRATnbSUS5UqsQWI1Aikg2yi0LE/bzraw/IIcIUqfORCHPWUi1ug8pXTdZmR+gCQR/Q8s/TLfrvJNKxM9FjibMlRuKVLECBA4jrA3eRksqrENwcw0CqeYoyfkRBEvFVt5cpCR2QnD0lJV3AVKSkiiM9jJSjGohsNsfqXmvIGWnNvJ/69Hs0iOdo9o50kjJKkBhyIuRmjYHKYb3phLxaSj8eClSbV6wdh+KEvvfJdou6mYEwQ453e0QRG9AKlmoHE2pUf9G1ubHWNt9bW07zsrJtna4E4HW7igsfSaaSTkVjZMiNEngZhTyXINUwGboBWCW9U99NBa/RiHiY4hCi+G/5gh6eyLFrwopn5/bPYwmCnuOs/5oj8DtIqS4Trd7u9ja6vysWpyYlfvtjM/aaJmUm4EBqUR8oSXr1wISFrLES/rNvaQ/xDZb3xlonUrET2LL7GnUryO38PhN7TP0XWkMLOoSjD1/+2Bk4Z7Bs5+lEvHZVr8WXtI/317uvtdWrSnMTafVoQ6oxgXrqJN1avqyk0cPKc76oXMzlrH/lTnnCtFL+mlEzyicc/2ccyOdcy85575xzt3snDvOOTfOOfe5c66DHXewc+4j59ynzrm3nXNNw3Kdc4/YsZ85546wz9c655LOuUnA7s65Ec653jX+doNzbpJz7sMaZTV2zv3LOTfe/u25mfvIcs7d5pz7wq53vn2+s93HBOfcm865790uzcp41Mr43Dl38S/Vzr+2bQuK2CrkJL74sQN/im0y42sqgqczkMMttM8rUJivD1IKnkaOsgypC8UIfOqiHKK9kGPfAUHAYOQ0A6J8nG6oXdvZ3wqRSnMCUnE2oHyz0AmHi38ejZx3LsobykH5TS9aHY61unZFjrUfgr0T7ZxRqUT8TC/pD/SS/l72t6kIhKYgx/0Ryo3qbWU3RVA2HPXBFQjOzrPPxiJomEC0pdNyBJSfotymaxGstUVQsx1SjP5if1uH1KaH7LPlCN72szYIF9/MQo52ByIVsggB1Vzru4m2YOvqVCKe9JJ+T6Q0HUC05dJtSKlai+Al7OvaSLmbYT/nWl8MRapUSwQw4YSL+nZff7F26GqffUK0MGuotlVb2xVbH6cRzJ9r5YaL3XYjWlNuAXpByEL9P836J0Dg2drqdQCC9WoE69Ot7eJWzkS7Rrjx+T4IeItQ6LYahaGPtvtuS6Qo1rY2us/uLVztv+ZMw45Wz2wra6jV46BUIt4NqaXYPqDt7LhewPDNzFy+BS238Tg/z9Kov1sAeV0Zz8bqPArd6tk1jtkOKcFf8N09Jr9j/xx25ejSU6/s6BxsdDlBGd3Zife+yaZ6RyKlPGMZ29bsUOCNIAimOeeWOed2DoIgXO9vB/SMWY6iIg8GQdDHOXch8h0XoWfRbkEQBM6509ELbQK98K4KgqAHgHOugZVZAHwUBEHCPq9ZlwLgwyAIrnLOhTm816Pv+B1BEIx2zrVBz5Num9zHmeh5uWMQBFXOuSLnXA5aLujQIAiWOOeORgLKqd/TFjsCLYMg2N7qVv97jtvm7X8OYqlEfD3f3U9ui81WVr8PKSUve0n/XvS2MDCViC+2HJJw0+cwvBMgxx8m+B+KEr7vQIM3D9vaCDnas5Dy8wUCoPZ2znvoYb2YaD+/NHKwLRDkPYcc7SFI8cizvx1n565DzmI1CueNRE6vk11ngNV5O/RFKUZOty5aw+oBFOoZDOzmJf1m5YuzD8ipX3VEVi5NkMO9w87LQ2B2HIKCKSjf6WWkbu1FBMRjiNbhykUOfQer51BgfioRv9RL+pcj6Gplbfoy0eKiF1rdr0gl4m9Zf/WxeylEQLSSKJduDVJjVtg1v7H2WIAUp8OtXsOAbC/p3w2MTCXip3lJfz9rn/es/WIIpsL8r2XWFg1QPlu4l2dLBA+e9Vc7BBYfIGBdixTPg1Hoeg5aIuTfCFDCBPhOCIhmodBnNnrBCGd1HohmRq5FeWWv2M/NEBhNtf5wVvfVVr/9iNaFi6HxshdS/VrbdVdYW76GQCcHgdJsBLG90AOrDpodW4LWJfvcjh2VSsQ/9ZL+IdaP/YCnPYXovkIP0d5ADy/pryJaxqS9l/RjNWBrAAod72p1DRct/tZSifizSDX8WWazJHcCZlRDnU/KBzB+8T50mT/y+NwhxUNKSssq0IvFWqIV+DdrX33d694vpu3cuWe3CTuudI1rl1OHDzh4TF9evL2ktGy9PTuyf8LM7f+JDRvX16ExUY3G2ruD+oz6seV+MvbbtWOJ9kp9xn4PQWx8EAQLAJxzX6PvO+i73t9+bgUMN6UpFz2PQC9w3ypsQRCssB+r0bNzc1aBns1YHcI9lvcBtqsBbXWdc4VBENTctWIf4L4gCKrsesudc9ujlA7fzs2ixi4em7FvgPbOubvQ8++tHzh2m7b/OYj9l7YjUrO+sX0iw3yeXPv7AWgm3aUIEsI8rXDV8tYIMO5HTj5ctHQ0cih3IAfzBer0wSi8cyxSPHZFD79myJnWRw5vOXJGRVa/+kTLEYQz6vJRmOwb5Lzbof0oB6G3/nDRzzRyfsUo12slUtcG2HkfIgVvTyDvqzubdKjfY312u2NXLrB7G4+A83E772wEJlMRDJ6N4KQpGvRVKNdpFyIlpTUCC4e+eFfbVkk7oLegwQgo41bGrQg+JvFdpfMJFH5bjUAnnBFYhmaQ3m/3PcfaK1yzrRSBmIceDAdb3+7lJf0yBGHh3o4VRHl3RXaPoeK2p/XVTPv3KvoCf2r9tdw+O96On40AugCNqYXWv58iOA8T2rugMG5Xu+8cBGavogkCWdber1gfXoK+ezPRAyzX2i/M7wrXn8uyviuytuqBVMYd7Jw2aDyehh6wb9jnfe2eE3b+QXZPi6yujREQrwGqvKRfgL5LY9Cacg4Y6iX9c1AIdz56cVmK1M0ewIhNFK8p6Du2PQLt/ybf83utpLRs8eghxUcspOW/CllZJ6u8PNhYXvsDqx/oO3ktgsL3v6+cmUHXcfCvPUcPKd7hs+r+n87M3snlxNaef+XgO8NdAy4E9vSS/mmpRHz11riXHzILreanEvGyYeP6NgTSg/qMCp0jw8b1rYO+z1ehlx/QS8fBCNQz9jsz51wReu73cM6FfiRwzpXaIRtrHJ6u8Xs4exukON0eBMHLzrl+6LvyQ1YeBMHmthcEqAyi2X7VNa4RQ6rbT30GOODLIAh235KDgyBY4ZzbAb2wno18yvepZ9u0/c9zxDZnXtLfwUv65V7SX+Yl/QN/4NCXkUNOoME2CoVM5trflyGnvgq9RVQiJ7cEObpVwOupRPxSFJL7AC0J0QepMflEC4k+gFSotuhh/wJ6g7jMymuCcr9WESXw706kPo1BMxzDGXUOAU4F0UbWOyHFYCRS2p6ye1uDVk+/0Mp9yOrjrC7XoXykdV0vXFjc9siVTRHUheHZr9Bkg2eJVl8P87fCCQFzkRMejJLGr0HO+XArZ6KdU46A5UL00J+AwPRiBFp/QWCwIZWInw0085L+JC/pz7T2m0OkgOYgcNiZKIwYbpy9I8qJ2gGpHG1QiPAGpABtRID9CTC2ch1PVZXzKVFYrdza8CKUh9bP2qGWXffUVCJ+l933GKSE1UJf6DpEm0ffZj/Psd+7owdgB6SSVSBgq2/1qUuUGzgmXc20II2zPgz7eYld7zwE++2tP+fb/2HodD4KUzu7n5EI3C9B0n5/BHtT0Zja3doMa8cU0R6icatjLaBLdQVry5dl7WBt0gzlJ15m9QsQ/F9q9Ywj1XlFKhG/Eildy72kX2LXIpWIz00l4jdbP12EQrVbxUpKy/zxHLqqWa059O/84ujbX/vbaTX2k5yPQHn+DxRRs6xJ74054IlVq+pVN2PGh6OHFP919JDiRmjszEPflf+F/RW465rU5XXQs+nbNd2GjevbCuXV/hMp7GGq2xT0fcjY79M84IkgCNoGQVAcBEFr9DL349M0I6uHpROgdJXQfJRKAXwnNPlz7C0UCg3L2nEzx/jAWc65bDumCD3HGjvndrfPcpxz3b/vIs65RkAsCIJ/ofzpXv9Fnf+ntq0qYq0RAOWh/e8OSCXiYzc9yBLxx9myFGchuXN7L+kfDtRb8Xn+iUs+qv2H9sct65udTy3k3PKw/ejQG364fZJDoZ4+SNkYg2CrF1KI8pBjOxo5vJuQ065ECk9rROZTkQo0EIFFb6Sc3IVmsYWhpnCz5mFIqUsjx7fG6ucjZzsfOc/HkLLR3+o+EQHTKARBxwC75DdNF9s1TkaQ8D6apTbNlhv4CAHUOpTj1cLqsxEBwlq7nzyrVzVSpJrYv3KkphwPNEsl4t+E/eEl/eesTq2A2y2f6zhrz2xr4+ORstQVwfBq65ePEIAVWJt+iiCiHwKxUfa38O0sH0FzFvD61w83OaX1YSvauqaVk7NymY4ApQR9QacgleBdlAPWBhhuuXSdEDz2Jdph4D3U13Osf2L2fzguX7a+aItUieEoN+IVu0Yfu84DFauy6gXVQUW6gvcKWqYHE4Vds5ByOAqpWvWQmjYRwV5fonyq7ez3q6zN/oIAvRkaa4/aZ8eivLJKNE5bWZ3XIbDtBmysXMuGOS8WNa3eELuk0xlLw9m6VUQq1nikTn5s1/sCmO4l/T2Qwvy89fMqL+kPAcpSiXgKwDZn/wdb2ZYub3H3k1PO6132douTzimNPk8l4jPR92iLbe6rjU7OLVp60YF1nxyIvnPPpRLxYajPf1ULw73tm4/7qm7Bou3bNZsYTp7ZYdi4vl+j8HMP/nOm6oeD+ozaIiUhY79ZOxb1f037l30+fAvLuBZ4zjm3Aj2r2tnn1wP/dM59gZ6xf0Xf859jF1hZn6Hn/ijkK2vagygK8plzrhJ4IAiCu51zHvAP51w9O/dOlFO8OWsJPOKcCwWlrfbyt7Vtm11HzEv6J6KHfhcUuupl4LW5Yycg51qNnPUSoHr19Nyvln1ae6e23sqGsdi3ag5ESxasRjB0FYKTdsjBNUVrUl1LFB4rQopWBcrB8ZFaVI5CXmcgyJiHFIEXkbOdgt5mj0EqSm275sH2eTs0W603csDT7ec0emM53urTDznk15FiEyZP74ucdCl6E3kROfjxCEwXorfkm9AXpKddM0DqTTgL8DEEb0uRcnUICk82sXNvt/N2AT5PJeKfWtt3QcpdOLvwTLvmQASIf0LhysUo9DYA9edsBCx59vlpdt4aFGLZiGCnFMFUCIe3W53WIxgIgIUbFsf2Xj291qwme6w/1Tlmo4TtixHcbIdgZDEaGzuhh8MFaIJGA6QOLgN2DZdWsLypB63fz7S2e8Hq/hmCnXIrK1yzbT+0pEiXNd/kjJv3Rv2D01VufJezFq/LyuNIBJWPIvUwsHqNQsB1OdFEkfU2HsL8vOsQxA1FSkix/b01gti70dhailSuPDRmG1jfTLCy8qvWu2D+G/XKg4DStkesPJxonPhoZudBqUR8upf0r0CK65Fo7IY5gycjxfQbBLIzU4l4gs2Yl/TzETh8+kvmW7VzUw7Jzqq8qkG9xdfcfvlpZcDUGqrYFttt7jxXb+cFL+Q3WNu0eN+p/YH6JaVlP2UZjF/MvKTfF0jk564o69hi7CntW0woyMr6j6jFCqRQNkW5rP1Q3/UBnhvUZ9SWLmabsa1gv8Y6Yhn7/dm2qoiRSsQf95L+EyhUtvL7IMzsLpQ8/VcEEJcBTep2qvhn3U4VPgKauxCATUMAsgoRdXsU9guT7dejt4MeaHbeiaYkhbMNw7yjZ4gSuqemEvGLbZ2kOAo5hSHASgRGOQgunkB5YC8gxSKGIGS+nXMCAow9kXM/y+rzJoKBNsj5+UhFmWP3U4Ag4CYEf30QJIxHuWT9EfDURs7+3why90Qgday1R0uknDS0fyuJctreQI59J0tmBjnnnRHcdEMgeDCCstcRHD5i7TYCvbl9hkAt18rOQVDRHqlKh1n7H2nHNEaTMmZaPb62zyehMOmw/CbpIL/J+nOQMtQKwU04Q7OZ/d4RAc9V1nazbRx0JArTH+Ml/W7I0b1t9dnX+vUppF6FC6neihTTV5Gy2BrB2Vyg9oJ36nasWp3VxOWkuy54u26jlgeurnSOFdZ3YTL5ajT+QG+IHRHgrLc6PIhg3Ecg6KxeCxBU1rN2WY8AbKG1+Z+ROtvC+uYbpPCckl07iLX548ow3+12K+NCBMA32v2BwLAQKadFQHfnKjY2rT/zggZ1Fh71tyOvD7ykXx+N22/NJtEssnUBb0bAW+Ul/Xo2Oee/tjOOTH6+fedP3MbaBbcuoLh1c8pO42e8wU+6ea+dd1n9+iEb8gvclLq9jpmydsATJT9+2tayjcDa6nTOGeWVdfJrTFALAJdOU1WdzrntpD3emVDjnNSwcX33Qqrml2z5rgIZ2wqWkxtbdNyu72/Ryvq/Rn0y9tuwbRbE4NvQY3ILjnu0xq//8pL+WAQY76cS8bSX9Kdj03aRI34VObXTEdhUIpXhG+RI30cQdb2X9B8jUm0WIMUhH4HEoyinp5WX9LdDjjNAYJKLQoodkOoRLqTaAznTtfZ5LeQIVyI4G4agZA7KiboeKRAx4NxUIn6+l/TnWJ032vWKrewC5My72OefEC3s2hhBRBkCt8MQdC1Hikgd5MzDGW/jrQ0dcsRDEWgEVt8XrD2vRgrV8xb+LECKYIH9/WQUhrzD7vk4a6eBVr9wV4JzkOJ2prVZHlGy9fYImF6zfrzR6nm+HT/H6nkAAuoKu58/27X3RVBysfXdx3YfB1qdsokWMj2CaCHb3gjcG6FxMh6tiZZvbVGM8jbC0F64FdQkoKjlfmumV66JrVgyLv9rsoJ5SJnrARQ3L/rqyOysqr3nLevqp9M5MQToH1jfv2H3uysKSz2FQqWX2z03sPp3IdogfC4Csu7Wr1+h8GIna4ObUcguXL+spd3rAJR7FsJxHSDPS/qnoFm/rVCYvho4oXOrDx5s0XD6KTnZ5W94SX8JAtFwoV68pN/OrvsyUun62J+yrR/H8QtYvV3KB66nQc8Z7OjyWRMUsjL3x8/6TyvPqvfYmAZHugDHzqvffLip+2YQGi+/uqUS8Y+Gjet78ezFXfaoU2t5CwOxGJCuro4xfuohi+cv69HrlbG+2+TFdAx6ls3cTLEZ+xUts21Rxn6ObZPJ+v+t2Ya/zYCPba2pw1E+z2XIod6O8oAOQPlel6JExZtRnLkDUqCeJJoFtxsKVdZHILIdUsUuRM76JeQYz0UPxHLkfNLIIU206hVZec3RAzSceVYLqSG1kHMFQcdKpHCVAa95ST+BQqMFCO52wR7WSF3pjUJsucgJhqHACjRrbjSCsg4IyFrY3/MRlM1Hjn5XohmTtyC1ayFKXh9obVcfmJJKxDulEvHLvKTfEAFDjrVFOVLDPrL7amzt2xGBS7iR+t0oF6DC6rMOgcZs5NRPQbD1ivXXBXaPu1p/Xo0Wtv0KKUceCmnWQeusvYug4zIEFHMRyL6OQOImBOd/s7pMQ2pTBYKw1xBshWrlS0TLOrxOlLc2Co2Nw4F6BW0q9q/fvbx+p1NWPNVq/zXbOUctBFhvL1jeZeailcX3x6hO2b33RxJgThsAACAASURBVHlc7yHF73EE4g5B04XoZSFcdLfQ+qvm8idhfuNRaIyvRoB7iNV1BlJNwskai9BLQ8KuW2J9djaaFFGRSsT3t2sdC4xs0XDapXk5a8cU5q8ch4D1ROBWL+mHKtoia5PR9vvRdn838iNLSmypeUn/2jSxu8spcFXkjm3NjKrP6H+Pl/R3/qllBS5rebXLqypkWbo101zdYPGeP7YY7Fa2Oq0aT21UO39NdRC4cAHoYN7SbjfPX9ZjNTBj0+jAoD6j0oP6jJo+qM+o/9XEgoxlLGP/hW3Titj3Wc01fryk3wypEJ+hMFcWCt0UI+WlJXLYrZBz/hqpRKuR0z+EaF2pT1GbPIUcRzPkRIai3KW7rNxTkXrUHDmyMDQzBTm0QUjV+NjqcBZSIkahkOCf7Ny9ibbPaWb/qhF8NULOdB16270MAWMxAqK9EdjsYp+VIgWrqV2jNwK0mUiFKLByU0RJ+GG9d0fONpxNUwfB2BlEq+fvDPw5lYjf5iX9wdZ+d6US8dDhgmbvdELhyZkIPovs/PusXUqQsz4fwe2hCOoesL5YglTAYqRAzkKzBE9FEFRlfXEnkJVKxD8ML+4l/Q+RIlQnlYgP85L+xQg22xNtKVXHrn0qAp8nUL/mphLxlV7Sb4XyqfaxNmiKgLEp0N12LcBL+u8h8Am3jVpDBO21EKiPRIrfA1bvWQhMbwbnKirzA8gKQ8jVCDqPtPaYjwCwD4K7KqTG3oLGwXA0Rpoh+G+FYHWp3e99RGDVBKk8O9rfwhBzUwRN4RpiHyIVsgFSKx/3kv7fkXpaD7jq3IHP/RMpwTz/vj8ahYJ3APp4SX/fVCK+1voGAFvx/hdb8dpL+i4vWHfkHLrlrXEN00uDltcvd60uWkqrvVH4eMKPlbGJ/QHYryIn789vVx3faU3QKNxK6ieHOW1Nr45A2aA+o743H85L+rlo7G7Y9G+D+oz6+sLH7jxv2apW57du+uXL7ZqNv7qyMn/DmvVN/oYmb6z4jwIzlrGM/abtNwliKBfsfHO8U4k2wK6F1J3DiTbf7oXUiyLkpOqitax8BCadkEObjuBiJsplmowgLg8pEa2QAlEPJX2/Ysf/ESkNJUgFOBMpBasR8M1FcFFlx/RCCk0dFJLbYPXYmWiV9hw7vjly5rNR2K4bUlTGIkccLnjaHDmhAQgImtnxQ1F+0fV2zU/s/mIIzDZYe72LwKQpUprWWz3KUb7XShQGLTInsqt91gS+XVX9LKKNsz9KJeKBl/QvQ6HlfVDe3O4oNNcAKT4nIxi8CjnzJVafZggyL0WK3BCi5UdKkWJTBDgv6U9CIa+vEVCdAwzwkn4vNE4eRHlsI1GobbjdW2+7vwYIZPbykr6PlKCYlbWPte0aFFp9yHKi3rCyOqAxF1i7dkBKZLig65lonNVBUHUU34KbCyB7FVIVN6Ix+gnKidyAQqBrkZLbDI3PcxHEH279t9bqmkTw04ZoweJ8u264wHB7pPQ1sfLyra5LkWpVRbQxegWwry2KHG72/goC7Jp2mN3rEgRwG/mJ5iX9PwF1U4n4prPBNmupRDw48rZ/D5rvOr0HLA1iOZ/Op9Mf0difcPQdrzUMcHWevfjAsi0sb7WX9FPlVUUflkM3HNvxExaGNPjqilTZxqhf/sEPz2K7CWjhJf0TUon4f6hY85Z2fw1w38zv/WpWrHLH8o116pQt6kUqEV++pfXKWMYy9tux3yqIHYgc2D4oV+l2BDk7IEjqixzYPKQuhHvHvYUUmM7IES9FgHAKAoQ9EWB8jJzLIqRo1EXOOAxRhY5/Dgq1vYlg5wE7rxI51SNRAn01Arlwo/AdrE7z7D5a2fE7IWc20O7hQuRMlyAIDPPAeiLH2gg57rtReClcfb0hgoUWCCSfR2rfYqTKVVidnkOwGK4n0wAl809E+UjLUJ7V9kgdq7bzFyFl5yE7rx6ChLl23cl2H3sQrV91ml3vYwQsTe0eh6OwV3MEXe2tzByiXLqzra0qEMi8R7SXYi8ixaoRApGu1p/1rV3C9doG2LXnWf1ORArTK6jf9yLaZ3QEUuzuJVJH7rM2PsbOOQHBT6iuzbBr5SDQWWl9+RSCmYFEszOrrJ1uQU58rJW32tr2IisnG70sLCbaCWCd3c81s1+oP3LlF/lf9fzzgkOtDWLWDv9GY7OJ1b2l1WkVyoWsj5TVPREgz0bwF7Oy/2L7wD5JNEnkFC/pj00l4u9YmVci5W9vIK/mrEgv6R+EZr2es5mtjmraNUBTL+mXphLxRj9w3LcWuOxYOXWXIvg9M5WI33T6/Y+NbdFw8pPzl3YYWF7ZYMMxd76y0zMXHTz7x8qCb3NR59i/nwJheWi28X6oPQNsH9Bh4/oeB5w9qM+oza1n9iX6Tm+2XVKJ+FJMdfSSuR4Q2xywZWzbsxyXvbCK6h8NbWeTtagyqMrkk2UM+O2CWEv00EsBj9vsLCxHaShyercjyBqH3tznI4BpgxxHDDn/dqlE/DAv6b+CnHp3onXG8hGY5CE4eQtBSy5ylGcgGHwHqUqv23HriUKYhQhOOiB1JgvlmuUiAJuGYGMKUsWqEcCF4NMNOe7HEcDdjcIp7yFQOB5BZy0750kUemuNlJW5RGum9UPAGkMOoztSV1ojaAgVxTvt/7+j0KdDEHGSHbMcOLyGg/0Q6AzBdm2bTty3orLWzV6SNghgltt1L0GgE7f7ehUB8BvWR8chkClCMJRr53ZG0FvX/t/V6jECQXEO2sIjL5WIL/GS/jikGh2LwLkIQVsdpLDVA56wSRyrrI3CCQCNEOS8Zn34rLVTlbX3UgQvtdDYCtdGexdBbBg6GoigahIConDNs72sPxpau72DYLDMyulPtKL/R1bX/mgMdkFjKmb397fqco5YMrbwwAY91n8IXJtKxN/3kv7uaBbmITXacB3RVlmr0Tjqh75HR6H8uQAl2E9Gys55dt9t0fhog15eRplKdmsqEd/gJf2p6DsQrkcU2jPopWESNZL5N2NhnmrDTbZM+iGbh777hwDnekl/Xox6V9YvKOgCMdYuyq9u2Gr5ki0o57+1AMFvPfvdoTERqpjdh43ru2BQn1HfyelKJeIPb+kFrD0yWxb9RqyK6qZDonVRv9dK+efPzkN0zh2GBIhuQRBM+YHjrgyC4Mafe50trMtFwNAgCDJ7s/4X9lsFsecRtJySSsTLLWdsf5Rs/zYKI/VDYNUPOZQAOe8FSH3aH6kFpxrALUJKVrYdGyAnsRI9aDcilWglgq0b0AN3FsrjuRSF2M5Cil0WckaNEYTcj5z9KPQWnYdArIvVOdzSpq5d930UCjoSOcwUUo822DW+QoC5PcofC1dv3wOtORUgBacS5a0MsHKzkOLxJoKaYhQifBo5zlV2/rMIEG9DDj0Eu2wr51QElvDtxtppmhdNcxsq6uQuWN6tWyoRHwn4XtJfTxSW2xsByA1IjbkTwdJCpAhCtNhtc2vvfyOw2t766UsEPFOsvI+Br72kf5Ttj3iB/cNL+sOtbS+wsmYD9byk/y+inK5lCL52tfbphkA7bvc+mmgF80KijbpfRlDc3+r8R6Tw3IIgoTmRIlqOxt6jaJzGrE6N7PqNkUoSs/YvtGNnoL5vjpSyyVaXjS6bpTvdOPfAIM0OwJ+8pH8SUs7+isJjDRGAjkHAeytSkaqQ0hlC+evW9zORWjYGjYmP0NjtiSDMobFejL5jG1H+1wPADC/pbw9MNnj4CqmEPzZNf0eil4UtslQivhi40kv6b6ExsyRNfjBncY8gIMdVZ+Ute+jsE/8j/+qXtkF9RlUMG9f3CdSO4bPUof45GU0a6Y1CkRnL2C9lx6Jn0rHoefN9diXyOd8xp40cXRAEvwTgX4Re/jMg9l/YbxLEwoTpGtYT5QE1QA/+/ZA6VI9oO6H2CFgq0cDZGznewUgVaoUgKI2WkNgHKR/vorDa7shZTbNrZFlZjRA0NCBaw6cJUh9qI3XqYgRIuUgVWYkA5wCkai1Hoc29ETT9yeo9CoV39ibKOQnXVhuOvmgeUkBOR1/KFkj1COyaOyHom4Wcez5SYN62ujS2ttkOAc4ryEkPRuDU0/72BHLY9ZEyVHO9oseBUsjikxkHxdLprCoESXhJvwUKzS1ETv5Ga/9/ovBhHSvzY7t3Z/W4HCkp/0ZQ+ncEM6cgUOqMAOBSpDZOB8q9pH+1lbsahSVzrL3HIpg8HCmB+1obr7G6rbV2H2f9cpW1UYUdt9ja720Ezw3suGKiba32tbZ6F4VquyH1rqG1x1OpRPwfXtI/zupQiGAmjYBrspXZFo2vk6w9rrAyq638nYC8WDaTiVS5XAT+C1Eod4Pde47d20l2L7UQEB5l99QRhXJX2H3WQfA/xa53NxoHs9A4aY0U0NMtX+6WVCL+uZf0/4Ecw0XW349a3/xg3lgqEZ/vJf1r0Bj4SZZKxEcAI7ykXwixI6qDwkeDgO3y6jL2yNvfKHlu8P6jf6yMX8D25j+fo/WJJsNkQooZ+8XMOVeIwL8/elZfYxt4DydaoPtP6AUx3zk3ET1Pr0Iv3x8hEeNA59x56HkTANcHQTDcrnEZirSk0fP3AeC5IAh62d872fUeRf7mPefc0iAI+jvn9kUvgnkognFKEARrnXM3E02MeysIgku2Xiv99ux/vrK+l/RzEBBMSiXiy35mGY2IVsF/G4Uly5EithENznC9sLWI3hej3JyHiRKOO1mRjyLnWkKUczYCQdNC5DQbIwcWKhrVaJBdbcf2R6C0EQ3IlUhNWIDCFi+icFwrpFwtsOu8jABxF6SC/AMlfP8bgV04w3M6crLFVnbafs9C0Lg3cuCrra5ZCOxaWTlFCBirEORcYPVebff9JQoBNUXK0Koa7XuN1fFLa+fnEfR2RLA6AjjYEvaPs/b8GoUQd0bK0YUoJHa9ldMYJZ0X2nELrQ03IghohwDgAwRP4fZGFyGoeAIphTtCRT3ImQXuLAQk4YSL3dDD6HJr6xl2Twci1akcPWCeQA+iuJX9CQLIilQifo2X9LsjyOuCAO84BDIfI5jphh6K76MlG45DqsibRBNJjkfjYZm1zdk2Bm6x+oS7RBSgB+USBNHhFlAbUbj9LavHQLvP9TYeGiNA24gejNdYv1ejl4lwVubx1ieBldWKaGmQ9SiEfIT9PNXK+BJ9Z5dZOxaiMXceMDiViH9mkzrCCQKNgRbhbgy/pNm6ZXejPnsX6Fm71uJXq6vzcnftmjoxN7firbP7Pb/4l74uwLBxfXdDykSYg5omCrX+eVCfUddvjetmbNs151ywhaHJn7WyvnPuOGBAEASnOec+QC/6/YBaQRDc4JzLAmoHQbDGObc2CIJCO68YvQjvEQTBh865I9AzZ3/kw8aj7/yO6EV8nyAI1jvnioIgWO6cew+4OAiCic65G4EFQRDc5ZwrA3oHQbDU9n58HjggCIJ1BnThs/cDoGsQBIFzrn4QBOESTRlj21hHrAdSh64MV2v3kr7zkn57L+lvkWKXSsSXphLx81KJ+N+Rs7gczZ57Bg2+2eiB+SxyqgFSFR5DgxDk3KejNjkFgUID5LxjKCw0AikKeyHYW4+cXWMEV61QqO0SpPrcQeSk3kHOeREKAfW286qR6nQhUppOQYrek3bdP0y9t9EXQZpD0b5iXyBHHC6zsJZoD8356A2nE3orqkbOfZHd247W3p2s3lUIvHZCDvNSBGsxBJmHIefyjNW5AQLacQjeDrH8vL+gWYkWouShGmsdzUL5VVciRfI0BGJHIHAch1S9h6z8uVa3AdbWDdBb10PI0c63Nu5i/dAylYhfi8KWgyDd1xHr3anlyPHoLTB0huEEhxNTifgaNA7CZTu+QWrfJ0jpuwzleLVHuVcBChd+5iX9xXZcPwSLeyGwamT3eIL1dSuU/+WQyvQiGg+XIqgLF/ptgEDoJgRC/Yk2pQ/Dw1kI1m6xfliH1LOOCBQ7oTEAAs9cO2YdGhenI3VxEYLco4iWE1mIxslSlHeyDr0E5Fq9W1s/NEIK6/ZEMN8NQd5TwIJUIr5PKhH/DCCViIeboR+IxulYA9hf2tagsTsnlYgvTiXib6erc6sqqmq7iqr86+Yv7fqOl/RPs/UEf2nrAsSCAKrTzLXPqoF7lqxse5OX9BvV2IHid2vDxvV1w8b1bWozSDO2de1Y9DzG/g8X3z7FOXct0CMIgjXfc+6sIAjC5X5KgKeDIKgOgmARen7vgiJBj4Q5X0EQhDN1H7RrZKFZ1pvbh3U3pNiPMSXuJKTur0IvuQ855/5IJoz5H/Y/C016Sf9QFD58CT3wPeAOL+nviFQhZ/8f+j3nNwAapxLxaTU+64Og4AnkHOYgB+uhUNC9aAZYPfTAnI1yZ/oiGFmBIC0LAUgeEVychNShNXbuMqSYXIXUiKYIbF5C+WB9kLO8Hzmyd5Az2w89wLugAb0BzWg8FQHBLmhNtKeBo6s30iVdGXu3cnWsTW799Aqk4qxBDvBZKzsE6nD5gc5IOZmNVK0Ca+O6SOGrixSx1xF03I8A63IEBqHqttHKuAmpTe+jkGVPBIU+QCoR/9hL+tMQdHVAyhAG0rfYZ+sRvHgoWbzAyi9AatdG9GXtY9cI1ckQxtohub3i/9g78/Aoq+uPf96ZyZ6QsCSA7CAKiiICEbe4jnut4lg1WvdSq7Uqo3Xtz7pb67hb16rVGq0d3JfquNAoKkHZREQ2kX3fErJP3t8f33N9Uwqi4oKW8zx5ksy8793vPd/7PeeeSxCr7SJgnim7g1XmlmqfULsZC/a+1NKut7bci+AeTBCDejxaPPoiRnAGAjfPod1iObDCAEQJAnUd0Njsae3uyj0GsUer65ZEDo8UNB8UyaG/55GJzHw7IPB9DaL686wuzsH7AASwsxA4nodMhK4Oy+3vX6JwHkPQOCwhCCjbbOllobG8AgGnvmhsPIRA7ZmIpZuB/NXGop3sQGRGXYTGfwnaLDiTJgjMbWfPNVm9xgHL7Wqoaa1A+OXWR+6U7hq+ZbEThv8RpywSad49J3vJEZ7nrZ23dMBBiA1siiVSQ7/lEBBPtbR4l/n426bTkTbhUDPA3PLSynNiidSeaIxdTXBDxLcmFVVl+6K+fAJg/QMB37VUVJUVoDl3COrbY4DJFVVl/wLuKS+t3BgY2CrfUDzPa4c2qDt5nuf0lI/mWBlaHx/xPO8W3/cf3UAS6zYj+1FoHr0JfOj7/oasVx6Q8n3/hA2UvRStcTHEnO+/GWX5yckPyYhdi3bLdyAQ0AaZ3xKoQ0G+TBuTt4GJsURqYKvPapESOxX8fpFQ7b679HmhJ1IySxB4GYZAyWvITPQMGsxNyKTjFrRmy38ZAhY7IADSFVM8aCJcgSbBTchMNRkp69fs5wK0e8hCIKgZgb4bkaJvtjK8jhTmE0gxXwZkpetCj0dy0w1NdV4zUo49EIj6CCk43+r8qZXbXX7uTnS5cAbdLa8lSKFH0GTYgeCezYMRIzPf6jwOgb5n0OTph0DAEgQuqgEsqvq5CDxcD1wXS6TcZeX3I3PfTOQj0AaBlzeQs/6riC2ajFjLtYiVW4DGw0toATnA2qcTYmKck/wvrG2HANkQmQ+hFgi7wwtrrC1vRbHBPoglUoMQyF+IDl68aGW/CPm75Zkf4hUojtox1vePolOANQhAVyLm8gxkrusL7Fg7P+OX9Usi2y0Zk/MUGuMFCHw1WB/UWxs+jsaRDyz2W6jzW2j0ffIQaKxEYyxkfeVOfY62/sokYCwnWBtn208ny7fGxsW+1hZHEfg7lqBNy3mIlT4I+TdNRYDvBHSo4nXrl7T133LrkyXA+GQ8eqm1/1MIwDpJobF0LdAnGY/O52tKLJHKMteD9T8v2hjb9MjZJ3z0wIjTbogf+tDd1XXFRyEfyiQ2Xr8tKS+trPv35FNPr5x80ltN6UwXKsOdYluImNMNha/4NmQ46p++wL8qqsoqKqrKjvkuMqqoKgtVVJV1tL+7VlSVvYX69jHE4J+BgPl+aF17vKKqrGRj6W2Vbywx4DHf93v4vt/T9/1uiCEvA5b4vv8AYq52teebPM/L2EhabwPHeZ4X9jyv2NKoQv16mud5ufAF+MP3/Xq0Vt+DrENOqtE6A3Id2dPzvG3t3TzP87Yzv7ZC3/dfRvqwtc7eKvywzvpnI+ZjOlrkhyKl56PdfDIZj573Je9PI3AcJ5ZI3YxYpUqgP/jp/t0raxet7Lc7WqxuQgohbT/jEUAYgtqhCYGc+QjgdEVmvKWITXOsgHOsnmTlmIBMNnchRdYHgYwJCCAsQX44ZyPmaSqymfew99siZu16pKRvRpNiL2BNZlHL2L5nrihEu9+PsJhNSLHPQAq1N/Inm4eUfSNikHKtPPX2dx4CZc5BfA1iEEvQbmd/BPLeQyBsKlJgbRGI6YZOg0WQ2e2EWCJ1ob3nFPCVBJeEj7Vyudhc51s6biK+YHVwp/cqEHMzApmNd7e2mG3PHWTlOhyBmBwECB5A5sqL0dj53NrgdsRc/dbqAmLTChAwqUV+X/fGEqkcpFDaAgWm5IdbW/0MAbEGAgf7MxFzdBdSQtmIcbqhaOe6Cz2PjOyS5k7I78IBpr9a/2VbfQ4mYLTGtDQSbaimJZLpvZFZ6JcggLW/1RMr7/n2+Wq0YWmPFHID2q2WWH4hxIjdjcBqP8R4uhsGqtH8ae0HmEbA9B8InOagsdDR2jVC4F/4nvVNNJZIXUNwCOL2WCKVBh42Z3oXF+5rSyyRKkHztlMskTrZTktiwXpvQJuC178sDWPnbvymZdiUrKruOig3e1XXrEhDZ9QH11u+n6Hx+F3JVWg+D0djanVDU+6cWCL1TjIe/dYulDbmay5QVFFVlkYbmB3RBvU1NL93IvCTc2FYyiqqykZ930zdT1xOQBaG1jIK+eCu8zzP+UCfbN/dD0z2PG88YqdbyzNofZ2EdO7vfd9fDPzL87xdgA88z2tEVpPL7J3H0TxvHWvvfntnoTnrnwo84Xleln1/BVprnvM8LxuNj5HfsP4/WfnBgFgyHn2bVpR9LJHaFimRJmSS2dRpq9h6H/2c4CTbHRDyPv78wOUtvpdGzFY7pEQWIPA2AinHFsRuNCC2IQsp6mbfp7C5jtxwJqtDEZYjhqYrYpciSNmHLe1mpKyOQ+zNuUgJPoMW5Dzk5/M8QZyzAnt2JgE7OQBNpqXIjHYBUrazEKhqsTbaC4GCWUhxNlr+3e35IsTSHIaYp8cJAoS+Z8812DMeYtSOtDJtixbXnyPgs4u12+1IWf/J2qE/AkHrUF/ujVibZ3yfoevmhZfkdk2XhEJkIxD1ir2zytorx9IGAdZ/IpC2j5VjKgIifa2P9rbvLgamJ+PRC2OJ1GUI0LS1vnCHHY60tr3Z3nvR+iDb2sGFGCmKJVKHo93mZKRELkenIKdbO9Yh4L+jtdUyBORvQGbMw61degKDwxn8HvBCEeoJfAzdheqnW/4dEWBqtn7wq2dmn57bo+HWjHzf9VmE4LDBKgS+9rG8Qgjg1tt4KLK2mkJwaGI5Yl0dm+UAobsDtS0BuzfbPh+CFldnJu2PzJPu9GkOYtv+af1Zg8z7zWjMVqMxOziWSJ3zZYFIDewWEdyt6q0XR2w8UvRvWx2cLEPj9rtim76OPNSp7czxkUj6BlSXKd9TvivRpuoK34cWnxlvjj89G3jQIvZvtjN0RVXZc6hfHasSRj5EDWhTMQeNlwIE9J1UorXiCLRx2yrfgvi+v98GPrsDWZU29PzF/OdmYECr75xJ86INvHcjG9687IX8x9Ktnr0Tba7d/28iUmV9Kd1QGbeK5Ac/NbkhiSVSY7DYXMl49Nav8HwBAga3IgU1AimFSUiZ7IKU8aOIxZiMWI46tKD5BDGXJiJF1tiSpkNLE96KD3Mv77hn7evAx8l4tDaWSOVZ+VqQYuqKGJK1CJSUIlYsiswihyKFsgKxEr2QEvw9UiZhxAS5MApLEVA6BjE31xDcMRdBSrMHwTVOixGl/DCabOcRmMBcHLGlyCQ6BSnkHSzv462MHQmuyLkJ+e+9iUDGarTzHo0A2xA7EdkdsYxjLY0/ook9sbnWGz/pyi5925euW9Tz2FXjEe39MwQMrrK0lyMANADR6XlWj5EIWA5BQG+w1ft1xPr90n6nEMDZx/rhOeSzsgOBj5wL+nobAqPHWpvl2s8rCDy3WD/2tja42/J1kezdqVQfgeg3EMjugBRSG+uTWmSu+Zs9n7A+XG51zLC+dKzsvta+na1eUeuTTLQxGGHtczaSuchHK412qwcgMN5oz2VYn9fasx7Bvagd7Tvn9xixdMLWhy4af7WVtz2aHz2sPI2WfhqNp7SVMd/q1A+Nu2sQKKlGQPTEZDz6KUAskToAsaZ59m6e5flrNFd3cxH6Y4nUc2gMHJ6MRyeyhYqZ7f4GvFteWnn195TnkWgedWtsJisjTP7qmg5L3pr4m+uAezY3En9FVdkAxMA7F4exaH1wzFczAbh3MRdBa85kNN4eLy+tvHRzyvFjk59qZH3P855BRMD+vu8v/6HL81OTLTWOWAc04TeIos1R/2m0UPwdAY9M5P/iwM84pFjDiP14EjEs2yElugOBD9WrBCfceiEl04MW1javC6WLd6sdlIxHb7C8c5FJZBliN9ohPyYHpC6wfLsg0DEGgZXLrZy7IUDyLPKxKEV2/m5IKXVGyq8ImTyfQ4qtA1L8/RCg/BUCkt2Q2dJdIv4sYitus7qvs3pNRMAjbHWfhNiirvZ+G4LgqX0QCzIYKdjH+MIHi55AfwtP8E/L1wV9nYQAV1Mk178F38sq2aOmJ8FVUPujRftWBDjvQz4Nj1le1yNlPw2Bh/7Wjo0IbLyAmEbHbA22v49GwPYqa7MWa/ffWJ+3R/5LM5FCGWb1exiB2Y4ITHVGMt/aIdv6tQgpninAVcl49F8GKDrbMznWDDwTigAAIABJREFU3qcgE2Y2MunuZf0xyvIHgcEDLd32iGnMR+zbXgSxdlqQUosRXJvUiJjDvgjkXI3Mg+lWP2vtuzBi8OZZv+YThD9ZisaTCzLb1vrtLDQf3kMgbz/EPF1q7XMTOtoeJrhcvAn1exegRzIenRBLpM4nOGncAdgllkjtgMbYX6wcWLnmWdrO39GHLw56nPNN/Mp+AClBLED995FZRVVZOwKf1VCGQaOC3OUk49E7v+TVryNT0YZvdXlp5TKg9+Njy17xfQ7xPPA8IvxnEFtQ3zWiDZ878PQ/JT8mcPV1xPf9o3/oMvyUZUsFYnugReaTjXy/DVLS/ZGyaEY70tORspiBdmUPo8X/KATAJqAF83i0SGQjIOVs6vVoMRkHfBLKoF92+5Yn0bUuFyBz3c/snaX2fAQp4fcRI/QIYmGiyGTzGgFouBKBj73s2YVIQS5pVTYQc9aITraV2/uNVq59EFD6HLEpjqVZjdiq41u1k1O67rRM2sq6LwIGbyPzWie0mM629vu7pfm4tWMlYi/mWlmrEHvxsX02AzE311h71APdBt88b6GlNxvR5xHE2hTYz2+tPe9DoMEpsz4IDISRIs9HwG0oUvzOXOfb5/egfh5on01ElPxUq8sR9n9nBJbaEYQN6WNtEkHgz93tuQ9iAQsJ4nEtAJbEEqme1n+TEfAZjMBVGwQsJiDTYgcEYD9AAN+38jyEAFongiuH2qExWmv1/Ssal8Os71rsvdMILodPIv+N0xFAf9eeK0Rj5RwEEj5FmxYXTLgdAs71CABG0Hj1rW13RvPpHgQiIwT3ebYlCJIMGofvWL92jyVSlwDPJuPRJyzSfz4ydQ63d1rQeJwPHJKMR+dYOvvwn3IccGoskTovGY9OZQuW6tq2/we0a2kJ7/Vd59XLm+btWR57vbjnot5Dfz7GA/A89Vsk/O2dRCsvrWxBc+MLWbyyz7Ki/EWEQ03VmRlNLWgT5K5Gc4GFQ4g5H4gY362yVbbKJmSLBGIW2PWt9T+PJVJ/AuotqOZeiCrvhJTzK4jpykKszdlI6S5GQGJnBD7qEJDIRwr7F6gd5iBFOoOAVVmKFMzJiJUIIWXlIabqMsSWNCCmJgvt9k9CCvccxBacas/daP8/jEDdY0gpDrZyvY3YoCeQie1kgojwuQRXF/3a6ppLwCKMRqzbL5ADeQg544cQk9PG8iwnCNZ6LWJlLkGKLxeZWGciBV2J2L419mwDwSnNDgh8vo/A4CSk7Heytk0Q+Cr1RLHVRlgddkJgZ1sEEq6yNn0HKegsa5P+9rnzv9oGsT23EMTPuhWBiP7Wdv+y9PZGJrVi68satFNfjcbLNDQOliFgcImVO2LvrLC8PQSCH0DOsk9bO3SzsrRDgHF/S3cnK8MS68e70Lh7w577LQLq7iqr3az9iu3/FgSCxiIT7Sr7yUWs4DT7uxMaU9cicLQMmWqvQWDOHcpYjXzo6qyPzrPydLZ6LUfs6mjE0jlJIAVbZOkVI7OzO8UJwc0L2xKEwuhjZXvCHOWrY4nULdYXISvHGuB4d0fsRmQ6AvBbvBlk9sIhd4G3fyTScMt3ndcfK8/+Q/Kq0wZ16L7I930834dQiJeAI79rx/gFy3a8b1V1lyGFeUuu7VL86RwE0icgoL0XmgPvAeeVl1ZuvVFgq2yVryhbpI/YhiSWSIUQ+9KYjEd722dhtON3Fy1vjxRMdwQK5iAQcj4CGg8jcHEWYmAOQxT/tUh5tCDl0hXt/j9CJ/kuRyzDWqTQc4ERyXj06Vgi5fxD6hGDdARwd7fiSVeHQ+nCOUt23QeZ7k6yNN5EjutnI6DWaPl0R0qtBgGDIgQKzk/Go3+PJVKfI+W/CjEfJQSXhF+HzKUZVrZXCZRjITLT/Qopt65IQf8WnRLc3sr3ZwRa3fUsd1n7bEsAZAqsvH3sPcdERYC7kvHotbFE6hD7vDsBsJiNANlKBJjSCITOQYq9HIHQR5DSr7M+egaBn/YI9O6KnO6LCXz05hFcYbUYsU+9EfCZRHCVkTO37mC/XXDSCQjwPmtlW45ATKO1ZQZSONshFmkRAk+FVnbP8s2zNrocmVt9xML9CYGq3gjYv4SAuYtyn2XvOgdYd4DkX9YXXex3PhoXuyFH6P2RmfBIxBR+hBi64xGAmmnlWYtYtRxgYjIeHRxLpLrYs/mItXgGza17rI9XIDa1jaW1AI29tdaXmQgEO/8yd1I3ZN/9MRmPJtiA6Doisr7sFg1z4r8Xneo6xw72bNHyNS4s3yypqCqb0FCbuUtGVhNpP8yM+bvTpXja9MK8ZZeWl1Y+/V3nv15ZSoC15aWV9RVVZRlAuLy08nsxz26VrfJTki2SEXMSS6Q6If+fv5mTvDud5mQ4YlkeRDv9FNqJj48lUsMRI7EImT3CiDXYHflY7Y3MR+egRf8ipLj6ImU0wxUDRR3eHTFoa4CXk/GoW/RuwMJXJOPRfwL/jCVS3UvazolmROqYt2zAgemWzL2RP1UGUvi7IjNlW6RcZyFlNwQpzFEIcB0DHBVLpEZbOe9DCvwOpCxT9vzJSCHPQ0zaHOT43RcxR1hezyK/qVXIB8hF/j+L4ERd2tphoLXZuWicnG1tMsny2hcBkLYIKG0fS6SOQeCu3tLIRgzOK+hKoPaWx6cICA9GDNlyxICOR0q9r7X70QiodLC262Xlexwp/WcRmGqyclRb2tXWTtvY889Zm/8JsWd/RGD1dMvzJQTCsqzOY5CpMcvqsqfVZbV97syjYQRABiJg/CoaDxOtnrciIF+CzIgewcXudfZMAeBDy8qQl17b4odXQ2gnxLTdgxiu6faTZe90t/IcaeUfgrFMVrYF9jMKAfAaK2eNhX64giD6/q7WvqsIWDHXxu7YejfLN4yYwdloDoH89goQuP0QbXgm23fEEqlIa8fxZDxaQxDb7D/EDn+0QfPpWKvDFuuk31q+axAWS6Sy9xn4EO3b8Mes3MZbAW91dfue9Y1tyIjU9wWSFVVlF5WXVm4QADupqCorRsGLnygvrdwsk295aeXSVn83obH3Py0hL3uxT8MmnfU9spa0+PU/SX+yrfL1ZYtmxCw22AjgwmQ8ev8Gvt8RLdj3IsV+IQJb9yMm51TEoLyClNRhSIEPRwDtSQQGJiMG7E/INDcAgZNqpJBmIV+YGsQU1KL4W3cgwJNlz69DjNMd+TkrHsmI1C9ZVd2lPwJ9i+yZvyBF1xkp9m3s900IJE1GpiMXLHYuwbVCIwmi+/8fMnGlEehwJi3ncD3LyvYvxN4sRYxUMXB1Mh69M5ZI/Q0xdY5l+wcCZpMsrUIrw2qr1yWWdz5SvvchEJhr7/dDAGK15T/W2nOMtVEm0DT5ms6xHX+/6PxwFvsjoDfZ8jsJAcz+yHzq4ljtbf/viMDCwQSmV8eILULs1lJk3hyE/KSet77raM8vQ+DqURsHCSvj9va5i0N2uP09DwGVMfZZHgKF2YhJ+qW10yAEvKoRMOqKxkc1Yix7I3aup9WvCvk3lgCRjHBNffeOHy37bOHAuhZyuyKg+7iVqwqxWp0RMHE3JDxq+XdB/oadCcDhWmuXJ6wdD7bnVlp/LUdMW461rXO8n4bYxklW1lxr67nIvLiE4J7UDAQ+u1p5bkZzYJKluwdiWq9IxqMvsgGJJVI5yXi07sTbR+3X2Jz7GvirfTI+Qsz1q8l49L0NvbelSSyROhbNiYdb3Syw2VJRVebNX9bvmlkLh57ase3McL/u7xWi+XIxam8XvNOxz8PLSyuf20haJ6DxXgw8Vl5aefq3Vc6tIvE8z++5UdfmQObQ/5veNZlG7Ldz2fit7/vvep63DXCH7/vrh3X6qul+cS/ldy127+WLvu8P8DxvX+BC3/eP+D7y3lJlSwdi/ZBzciIZjy41U6S7Q/Hh9X1MDJg1IPNQe2B4Mh5tjiVS7yLluADt5vsiZ+hTCS5VdndH/h9SZvMQMPkrMkstRkpsHkGog4+R8nPmKXcdzAD7fRVi01yMoSst/aWI4ehIYLLa1qrRYPlUIeX6IgITjYj9uMXS6I8W3weRkh6LWMHhBNHcD7I0XSDaOqS4cxDwex6BiwL7fZCVyYWy6I3ASzcEYDrY7zDBBdMRxGb1sbaFwPF8pb17PAJxjU01oeSUGzqfsM3hq/bvuEdt2trwEsTegFiVy1Foj10QEHwBMVl3IkX/GTLjtrM8PkL9eyk2NhAwmovA1bOW1uf2/K7WtkvQ5eQ1sUTqZMR8DUNj4iHk81SMgNrF1gdnI3b0bAT+DrBn+yFQmYEOh1QgP8EcAn/DQoITpJdbu68DBkDaz8tesXxdffsGCKesDM+iMfMYMj/ejsaTi/tWjcZBDkFMuF0JFmkQ8zkEjT9nDp2C3ZOIwLHz/VtpZVqFQFvI8h+E5s4gGwMLEQidgg7U/Mr68O1kPHpoLJEajMz989Ecez8Zj+7NenLGvY9e19ScfUoolD4x7DWMrKlvf2Qk1FDd0Fx4hKW12YtTRVVZPrDuu/KfOnDPZw5os7t3f1bnxrwmCn0IHYKA8qpkPLrZMcUqqsqKm5ojM9es65RTW5/f0L3jtAiaq6PRPNwX9d9raC4uA8rKSytntEojghjpP6AxsBDYv7y0cjpb5VuV7wGItb7I+2DgMt/31z/o8rXl6wAxz/M8hB2+EQu8FYj9t2zRpslkPDqN/wxIdzRSRs436EWAWCJVjpTkC2gX/icsXlIskXInxzyCyPLtkXL6HWLE9kZ+SjshcLEOAYkzkTK8EwGTx5Dyc/cLXm55tUc+Zj4CFJ8hABlDQOZMZNKqRYqvC1qs29s7zp8nEynVEoIQC2ECNsKZgCLIB6oLMheOs2duR+bMOAJ2RxKYnLIQgNgGsWbbIFbjcwRkLrP6t7ey1dtnHVFIDqeApyC2xZnrCgj8r9ohp93DERh8z9IvRYBhrZ/2XvDCfpf87k1lVrfu1objEav2K2SS62/tcSnwWlH+gr226/Ju5txlA99fvHK7gQgkrLQ0Kqzc5VbHXsiXahYC3d0RQHofsaaLEHCqR3cQ/gOB0OmI8WwiiI02CAHppxH7MwqZMtOW5lLEOm1rZXCRr3MIfMhAinIRAks/tzLOsPK1QLhlXX1JMQKH8xFwXINCemSisRu2OvVGG4Fr7Nm49d0Ayy9l/esuRvet/xdZWiUEMcE8xEI68282Gi/uJHGx9Wem9X+2lb0DwbVXF9i7r9g7qxEo39f6aVgskYol49EkraQof+G+DU157QvzlraftWC3iwtzF7db11D0grXlZvuFVVSVHYGA/LXYWmGfZwHF5aWVmx0ao3j7Zfc3tO/ROyOznghr8Eg/2bHt7C452WsXnHbPsp0f/k355prrlofDzRdlZ66r7lA4fwdgWXlp5Zv2XQt232tFVVkOGtu7AnUVVWXZrfy19kJtEEZrxS/KSys/38xybZUfXtpg8SXXAzenIl1ZiHTE333fv8qeG4lcMgAe9H3/ttYJ2nVEzyFXjwzgCt/3n7P0X0Xr4mC0vn/e6j23UXR3CB+A9N2NaB3IAu72ff++jVXG87x9LA3QmlX2JReY/6RkiwFisUTqBHQacETri7zXk0XIpDINKXksuOr9SDEeiXxYPkOMyr5IOS8iCPEwDpnrugDLkvHoHy06+5VoYVuGAMUqNNAHEYQU+Jk9U2tlPQIp8EGIFQEpxu2RgvsALYwtiDF7AzEpuxHczRZGAKEHAgZvWP4ZiIXYFTFOUaRAS5AJbwpi6660+pYg5dhk+e5MEBW7PZoc7j7NNQhkdEAT55/I7HeltcvZiMn6hf32rF6XIOUbJfCZciFAVlm9d7Hy9kVg4CCrwzrgoszCdMEuVy88CgHH2xCoGGn9NwtNxGakVFqQv9vba9eVFK+s7hJq8UMuInwasTwZ1hd9kCmvDTIjzrE8jkJK+Ebg2GQ8elYskUohcFFi7w22995DPmt5Vv49rN1GoCu3njAfq/2RYmuHAPe7iKk9h4B93MvqvBK4LxmPXmUHTi63vplFcFH8LdZumcjHqjPaJOyJFlPnuzcWmTmvQkCvHI2Xs5Af5O+sHjdam25vZZiOxlqx1ScDjZ8paJOQS3B9UROaA7eh8d0LbT7aoCC3RQgsh+27EkuvBgFUkvHoLAv3sojgUvNHYonUomQ8OgYT348c06vTxL6d289457pfXOMDe8cSqbut3R9jM3yOKqrKPGS66wbkVVSVtUenqN9HfdanoqrstM0FJAMO/OD+5euWjezebWqHpnRO85qa4nBh/oqcrIx1vWfM3+P/EAv1jcWYvP9yy9jIc38GqKgqiwGXVlSV3VFeWvk31M8voQ3Jz1r7dm2VH53keJ43Ea0zndn45dmlaGNWC4zzPO8lpANPQ/rHA8Z6nvdv3/cntHqvHjja9/21nud1AN73PO95+64vcIrv+++3zsjzvExkPTrO9/1xnuc5H9gzgDW+7w+1K4/GeJ73GsF9zuvLhcA5vu+PMUD4P3PwI7TpR757seCgd6Dd+4GxRKrwSx5/GpmTVpipsjvafa9EC84b9tlryAH/OaSInkXMzxWInToGeDaWSD2BFEszMtedihifOQg8FaJ2WmW/1yGA8QekUGuRf9N7CJi8iRTrzQSXUZ+H2KXLgJeS8eivkUIoQIpxkuW5FjFxvZEirLYy90GsWQdL4xRkqjoaKatr0e4ky34WI+DUhJgPdzflsGQ8+iSaID3QicSDgBeS8ehl1ob5iA2psjxyke9VDnJy70HA9LRBbEpvBCrc/ZCPIJbk19Zef0HR1V9Bi8CuCPD9GzGeLv7VwWjX9YC1dZE937fFz2g7c+Eeby9dte029nkhYo/WWbuMRcFWd7H2+QMCRo4pPBeBDhDAPBUxPAnEKqwBxiTj0ZUIQNxhZfyVtaUXS6TOQuPqagSG/4L8wl5DzFAmge/WZdZvXYDCWCK1HDGQI6zsswn8eo6zPmqLQOS71sZ5BE79OQgEj0SMlbvrdDt048AFaKxchxbFXmhstbWyuVAcr6Jx0Z/gmisPCGVGqnMzI9UZ0HI4WshdKJF90SnaJjReCtBYeRn5WWZYPsMJJA9tEJbZe9nIpA5ALJEaMnfpwLvfm3r8ivXMhpejzdjXBmF/evE3bS5/6g9vjHzs5lus/e5A/T8SbaKORGbrk9G4Ot9O+31jmbDo2K7z1pZOKC6aP75Lh+mrVqztsWj2wl1em/r5fo80pXMnbzqFzZOKqrJDKqrK7lnvku0QmmOPVFSV7YP5hAFDt4KwH73U+b6/i+/7/dDBrEfNVLi+pHzfX+H7fh3SmXvZzzO+76/zfb/GPl/fZcADrvc8bzJaV7qgtR3g8/VBmMn2wCLf98cB+L6/1vf9ZrRWnGzAcSwiBPp+Sd3GALd4nvc7oMjS+J+QH5wRiyVSZyMl0g4tIH9GO+KTNvD4CMRkJBHguteeq0Vg60GkFGYjhTgWAaqdEPjKR2j8dQRWhiIK1QXs7IGUaDFawIcjADQNmYFWouuKQCBgOdA/GY/OiCVSZUBGMh6tt0uPD7R3fo5MW12srPvFEqmDkYKPIKXcSBC9/C4ENE5FymIeUrDzkLkyEzEfRxGENHgLAaBhCLzdjtiUEIrNdSvyvXI7jBiBySoCnBRLpE6zNnQXey9FgGoMArQ5CGi+SGC2cgybC/Lp6lmIWMeliAaPAuNjidTPEYiYYu3d29J5wvowDwGmpwgu2G5DwGb+EwG/h1E/FyHH5V2tXTpav9yFTHeHoPFSn4xHb4klUv1iidQQa+djEFDIQ+MmgVgqEPh05uNBaKf2uKXv7mq81tqm2eo+A42fXyN29BdAZTIeXW2+jgUIGN6PdqW/QCxFe7SQdbE6d7e6rLayOYbKjfFbkam8vfXL/gTx64aiMeNOfy5FDveZBA75Awkc8HcnCMbpZ2bUhTyvJdzUnNvV16bgA/vuEORbuBwtpHORj9rjaN76CMgut2uJPiAwq5+ANiIN1mZODkFz7w2CC9n5pnckVlSVRbt04P6S5sxOeJ6LA3gJWjN2QsB8FFprwtYm5wA7V1SVHb4ZYRduAzqEQn4I2LOmrmRWfVPBymQ8+m+AXt40d/Xaw5/5/V7+hnl8mRyLNmTvo40QyH/PyZuIrWxA5v/PvoMybJUfQHzff89Yq+INfb2J/zcmJ1p6g33fb/I8bw5aIyAIDP5VxQPO9X3/1f/4UGbO/xLf92805u4wxJ4d7Pv+tK+Z549SfnAghhR8DVrIi5ASydnIs7cgZVll/68muPy4FplSmhGDc3oyHp1mMYmiiMZ1l2WXICWVaWm9h5TFvmiw1SDFmIuUx5uIdZlGwDJNQkro2Fgi9UIyHk0D6VgitT0yiw5D188chJSziybfAYGDKxCz0gcBC3ek/xGkBLtYm+xi73W28s5ALIi7WqnayvwkAmfvITPUamuv6xFD9AyQZ9HOI0gxX4yU8/YIzPRHgG20vf8QYgn7Wh4l1j9rrZ0b7btmdMptGYHj/rXJePQhO/lagPy+8pHCcJPtj/ZdWwKFPhiBrb9a2zUigOFMY48hP6yeCEzdhEDv7tb+1yIQdj4CUIOBp2OJ1O72f56V5Qi0UJycjEc/BIglUgMOPe/fp9ctyxnQbpe62lCYDxFgOB2dVhyAlPnnBAcizrE+WYvA9Rg0RgcBr8YSqSPR2Cq3358gJnAbe/ZIa1t34XwEsa9LkdL0rW/qkHnyY/s5wH52tTZahkzZexEc9lhq+XayPo4gMDnd2s+ZPeuAjJq6tpGMcP1Sn3A+Ast3Wp//muBAwFoEiKvs+wxLowrNj2GW7gA0n0utPcYk49HJ1s7OHF+OGLpvLNc+d36nbh0m35WRQVkoRIfMjEY8jyY0l/+M4sZBEFS4NXuQgebKyxVVZbeVl1Y+z9eUZDw6C5gFUSqqyuYcNuy2rsA0LTlfiLt+6ruQONp8HFVRVfZ6eWnlgvLSyqaKqrJ6pEA9NE7dpmmr/ETE87x+aFOxguDaMCdRz/Paobl9FFrDWoBHPM+7EY2Lo9EGq7UUAksNhO2HNpebkk+Bzp7nDTXTZIHl+yrwG8/z3rT0tkNr1Mbq08f3/Y+Aj8znrB9aU37ysiUAsVuAW5PxaEsskeqLTBOfxRKpqxCr8n6r01OT0c7237FE6iBk/hmD/L6KkYkphPydHrJn6gnMiyGkAPORQv4zcjC+Gyn6AgKzz0CkrH+LWIc2CBhkWJp7EzitfxZLpK6059sjoJSPBvHxBKfVqpEyeMHK0kxwL+AaxIachhbQqQTgZzJBxPcwUtRnI+V7CAI2bRDIK7A2PQWBhTcRAGiHJt9INLjboN30m4i5K0RKyZ123AmBPxcXLBsBg90RWNsegbhmZMJ6Gp3CdKc/19rvkVYeF4G+l6V/vdXDXT+1j7XRNIIwHH0QYzKG4FLwfa3t5yFA8yvsTjwr06XWtv2S8egwgFgitR9iQX0EWF+3MnVAcdqGIkZsx6Zq76hIXnNHfBoJTmxmWRseisDfO2jMHG/lW4bYl18hAHax9UkJAtnu9Ol5aPysQ2bSdogZy7efkLVRe+tvZwJ2TvIdCSLpr0Hj3IVByURsWNra6QTE/E1Ci2IRQZDgJjTuCiztmWr/jFBTOiMPAc2fI5C9q/XlYsv/YTQvj7J85iCQsxMaA08hH7brrW5LkvHojfynnItA+NXJePTlWCLVA1iUjEcb+ZpS35j7QDjcfLjv49kdiFgdd0UgHILbMGj1v/vMQ2Oqf0VV2ejy0sq1fImMfOzPv+jRceKDq2s6J64cfvNV7vN73ooNapPL+3YHYwWm4D7z+83Hrh2rqCrruHhl9wm19UUF0+fu+UsvHPp8XX3bn4F395cFuP0yKS+tXF1RVfYOGvuXVFSVPVleWjkGWQPcabpGYEx5aeWqb5LHVtmixPmIgcbuKb7vpzdgnaxCm8auyFn/AwDP8x4hIDIeXM8/DMRyv+B53keI2d4kEPJ9v9HzvOOAOz3Py0HrzYHI0tATGG/m02Vo3diYnG/grwVtNl/5kmd/UvKDAzEDWb79PQPdL3cKUmYHIzOZGwz90aLpgkvugRTiU4iJaX0UfwhajJy5sTdScI5teAp19lWIfRmKFu5GxEa9gkwxNyJ2xUdo3l0NMxEpxK4I1OyIzCBLLJ3OSKF1Rwp5JFLoFyGF3sbK5ny4liLFHLF8wwiI1Fje/0bKujsCi5ci5VxMoFAeRQzRIIJAqOcg5mEw2qaPRIphCAJi+1kbLERAswMyfaYRu9GMgNcYBDS7IZahGin0UQT3dQ4kuKw6bY7t71n77WPlPc7qtQKxdGmr38sI1Nbaz/lWn2MRUCuwNilEjEweYnqmISDRDgGGJ60v7iCQNVaPGuCpZDy6zoLkDiS4H/N04MrCfg0zfOjmhYggdmk2UnLDrO/uRSxWBC00N6KdW4XlVWppfmhlKkFs0mI0VgusLKcSmM0/QeNpGAKY3RFAPsPSbLC02ln9VyKfx3cQMG5BoLuF4ICGY0J2RgBqqX22kuDksGfpucMUzfZ9V8trTzRfxiF3gAvs7yjaALiDK0vQ/AqhsdaUjEdHxBKp64C5sURqpH3mLqR+G/nBVcYSqZ0QMB6NxsbXEt8Pj25uDu8biaRzPA+7/vq/fF+rCfwn0wR3ZabRJsed0C0g2EBsUIoL55zdvs38goxI3Vlo3hBLpDoW5h158367PBg2fThjI68fk5dd3bl9m4V0K5k8qrExf+4nM/bMGf/6fu9hpx+/iZSXVr5cUVX2MfJZ3A7N1ShB0OEQGpNb5Ucuvu+HN/L5HMRCO5nv+/5/gR7f929Bm9D1P8+338vRmrIhGbCRzzH/sGEb+Mr5RreWNS4t3/dHo7mP7/vnbiz9n7r84EBsI1KBlOt2tDoii5TUNcAbyXh0SSyRcsFQMxBwu43gVGEtQaykEwgCkTbYdycixbojUoa3IJCvz0NSAAAgAElEQVQx2v4/HIGz45CPlo8GaAECansgBugspGDPROBtCDJJnmB5j0KmmWy0I+hGcD1Oo5WlyNKtQQDjI2SSbEEAaRiBAp+FlIULQPoSYi9CCGxloN1ICfIVe8vKPhXtcFYhn5UMq0NXa+e29nMAAj+jkT/Tnmh3/yoCBxF0QrAAKbNqxHDca9+tQQrv1whMeQjAjrQyHWf1eB0BrfnIOb8aAZD7rV9KLc9SAnOnY2Betbr/Du249kfm6GzEGF3Jf14YPxGxVW/b+1j/7GFt02TlvNoLgycF7U5njkPAc/dkPPpLM3Ufh8DLPQTg8BQCBquU4MLuoxHoGG31cmDpAIIgu/9C7FMn1N89EHO7GIEm58P4T9TfdyAw2B6xlCejebDY0nfjqpFgDjSi+TMGgeJaAif6sJUpjcbHGmvrCGLAdkRjfwAaO+2Q6bIvGu8T7P03EACdEkuk+iDT5SP2XoPrDDMFHwYQS6RKkKnU7dK/lqx+L3L3qI9OP6Zt52Uccu6zfQg2M06aCS50DyEfxwOszu6KpiVoLC7cVH7hcMMRy9Z0fzwj3HhtLJHaA7XP7WvWdR4/adahTwzq+4oz3W5IPsnLWdWQToezQiFCuTk1PXsWj6t75qljIvzjm9Q+kPLSys8rqspOwoCkmScXoDnVgljkDzYvl62yKfHIWjKH/l8psv73UZ6t8uOQLTqg6/rSy5uWDTR85vfzAcz5+0wEOHZETujrkHL4N1IyeyGlMh8prV3QjvUjpLwWIOCykz03HSmiAgSaJiH/p2yCKPD7IQU+jyDSezHagdYjVupyBHbSCEi8i5TSn5BiX2Xpt0WMSQFaMGfY5/cj4HK8lbOAwIl7LAIR71o+lyIF6ILBHo8YioWWfl8EaLPRzvsNREGnEYipIzjcUIwU9KdI6R9vn72bjEcPNyByETI9NSPH7UnI5DgQMVRTkS/XMUgxOlNNk7Wdh4KddrVn70Cn/Q5GDN4oe2at9cUYZOZaZv8XIyB9PWIzHAv0d8QK3ABc8eGF3T4A+u5y7fydw9n+A9Z3XZC5OYxOtkas/p8jZqurfbcQgaAJ1laPIGD1F6vvgda2MyzNyfZ+Lhprp6BxuRNir36NQO1HVs8brC8n2zNd0Ji6F7FNz1idLkAg6l8IcL1sz4cs/5VofNVbuosQC9kfAewwGldt7O96K/O2aNzNtz5y96z2atVnDtRsSxBOpROaa/PsOxcG4yDg4mQ8+loskYogoDXc+vIq4J/JePQGcz/4I3BpMh6dy7cgvbxphxZ1XtZ80bOX32z1bn0S0idgQ1ehfjjNyvcJmhNd0+nQ6bMXDen9yby9n37yvJ9/aXyxiqqy3wNnvDvlFzctXrX9dcDtyXj0hi9755S7/3HwsP5PPZCfu6zD7IVDsrqXTAxlZjTj+/h/2P3+jM/8ft+6D1lFVdlxiFXtjoD97uWllR9/2/l8hXJEgA7lpZWLv++8t8pW+THIlsqI/Zf08qb1AO7KKm58KpZIbYf8UZoQI3IFUsqTkfJ4Hymh9gRxnSYhpqIQKcw+SGmdjpTcy4iV+QNimI4miDJ/KAIA45ByrkeKug1SNnmWx2TLL0pwas39X4hAS4m9dyQCNMfZs02WxjQExDogxRqxOr2IgMBbSAl2QmEKHkFKfBpy/nfUtbtBwLFvnyIz1RAEPMZY+7xg9fmZtUMZQTiOx+z9acDHsUSqCjFKtyGl3MHasYeV0yM4EZlGoDeNwEQRgbP/OgRY3rG+OxkBlPbooMMCBMJeQSxPb0vnc8szH/n3uRhX0xC4HY9MYztYPkMiuekHVk7Kzijere5VBEDOtDSeRo7OHazP8y0tZ37uaO02yNJzkfFLEDtUhMCQu9NxGmLdGu39XyM/voMQ4+Ku+5lnfT4fAfa90bhYRRDMtw8Ci/tZntVW3nJLZw0Cri7a/z7WLwUEcePGovFSa+n7ln4aMZvuRoju9v0ixBi+joD94QRjtomAkfsNMuUfaXU4B823YuAfsUTqDWvTQTYW5iMQ4I6tP4wAaQ9gr1gilQ0UJePRL5R0LJHa29qu/KtE1//M7/dKRdWIKEHQ2kkIaDmTfYjAL/RpK1cegcl2dovPbb06f9C7S4ePr6+oSvTeWJiHiqqyNmhT0GuPAU/VP/32H3qj+fSlsq6+qOusRUOX9uv27xm9t5kwrKWlxfM8cjwP77qxI3pD5cbMma3z3gGIlJdWbjIsRkVVWTcEON9H7K3zDfxexMKCDELz+njguIqqshfRGlcNPF5eWvmdX5K+VbbKj0F+NEAMLXbztjlkbW9kkuqGFOkdCEjcgHa9PQjCT7j7BWeihXkaUkjFCCDlI8XTiNimzxAjMtLSPBstHBkIOHyKFhfn9D8XLSrbWXp9kYJ2EdQ/QL41zvR3D1oYlyOQsYM9U4+YuxzkzDgXAaZ2SAGGEAMTQsp5PAKKSWuHWxGwOsfaaQICN33svW2tXTojdmAw8mfLQoxePgKfHREAiyLwVIMW0p72TDf7/UsEBGut/h7BpduHWlvk2Y/zY2sh8LMbZ//vQWACW2ntMh+B230Q+1SD/L6eQ74GjyPw0A/JVQQXUC9FLGEXxEZFC3eqW9Rm28bdEQiahkDEajSGliIzbE9LvxmZtWuszKuQ4o4hwLsEAcezCMbESnTR/McWKPZ6BGa7W7q9rc0y7HcWMoteiFjPBjTuhiNT+O6o/1ciYLTM8ilBwCgDeLN6Vta/lo3N/X3x7tWrC3o1FyGQ7eZzIer7ejQe+hEAZWd+zCMw63dD47mP9cE8q++2BLcmJK3tZyJfwU5oPDnGeH8EsB3gWYMC2U6IJVI7W7oQ3Av7mP1/LgrpcnorMPaypdPMf5/q2piMtbbvi/qsk5XfHdRxm535BIxiyOqeFwm3ZANeTta6PODpiqqyY8pLK//DfBRLpNoM7DP0ru4lE9dkRJpOAZ786tcwhR6es3jwP3ft+/JzwLpwiP3RxvAvwISKqrKCr3AN0wsoOO325aWVazbx7AoU364JrUdjCcKzbFR6edPao/Xkuc/8fqM29fyXyDC0YVuNxt+bKI5cBG0aqiqqymqBBVsB2Vb5X5cfFIjFEqmLUIynOzfxXGTwzZSjnexDSIF+inbtp6ETb2HEnkxBTJKLC1SLFGsYKZ1GpCT+hkxn2xDEVSpAJ9u62TvXInBxNlKqByNfFhe64iik6I6xcpQi4DELsT3nI1AwkkD5TEMMzCIEwkYhxbwUgZk8pPjcyUQfLWhHIiWehxT2DUg5+2iRG2N5rEXKczUCWydaO9RZ2TohZXo0UkhDEEPxW8QwTUBALRcpY3f/5DHWbm0QGHThEiBQvG2RAn0Xja1eBBdQT7fyubAgS62dJyMGJReZrPojs/Jj1gY11gd7IQA3GYGJhUipPkFgblyBzIC9gQcH3zxvewQg1qC+T6LxMxgBxkbrw3etjdsjEOUAfZ2l5WIvhRFgdRd6t0MM256xROp1xHDdY23Wyd59zdp5IAIWuQjI+NYHfRA4P4DAf7ELwcnGuYjROAiB91pgQiirpaPf7BXWzMxZXNCruorgVOUqND76WJmzCZgQ57i9Pxp/21o53KndcWhOtdj7V1n5B6Cx2N36xvnQRdAG5u9ozN6OxsXT1r8vACTj0S/YpWQ8+nd73sn7BHHinDyBWMO7+Ypipx0fBaioKrvGyuzMsm6da0ZjdBoCBwXYaWLPozUYGAqMqagq+0tNXeGkNes6jbvosAfWAmVTPtt3aHM68tKn8/aubk5n/X70pL+O7lg027vm2Os2FOjyC0nGoy3A2oqqa3qhtnoIbQ7/iub1OSj+3Qaloqos28qfhcbgoxt71tqjFriloqoshMzcC74M6Nlzt1zwz5La2467KtdvCW+ubtgFrQUDfZ9wUwPHZmSS6YVIow3oOMRw/57/odNxW2WrbEh+MCAWS6S6IzCRjiVSd9tCtTHJR6fMStDC74BUPWIoXkFmoYMR03AX8tFxSnRngmP9HVG990ULWzMCJpVod3ovYtpyLf1qpJDfR4CiGwJlDiT8DS36C5GCOQIt/mfb9z3RDn97e+diy+8oBIJ+hRTiaARcuqMFqh4Bi7YILIasDo1Wh3bosMCpyGS0BC3mlyDQMhKBDXdSbDlSPJ8ixXcxwX2XUxGI/dDK41ve9fZ8B2sD56CdiRT8UMTc5BGc1FuJwOx1rdJpb/VwV+isJAjfMRWZLrZBvlGzCMI6PI8YjTesPnkIaGQgQPKW/bj4bDXoJON4BIJdwNhlCAw5FucpZBJ07dIDjZd+Vr8CBOwmI2DXjJT3BdZnba2fz0aMWgEQ89P0nv9i4UedD1rTHMlhgX0OAj7HWj+fh5jA/RD7dbqVaSZiotxJ3BACQx4C7L2QafIS4OG8rk2P9T5p5Qw85lt9T7P+bLJ6rbUyO7+9rmgefYyA+yIEGl0fP2ltvbv111nIR9DFzZuOQOdp1jYvWVu4MCsVaOysts/+moxHvwiXEEukelt/vGUx9wBIxqNvs969ksl4dATaRHwlseuMLgOWlpdWPmDtsBSNWQc2fTQX2qON2yWof3xrr9VAke/T3NTstUTCfnff926sri3OWFffxjF9u6dbsjtNnbPPYT7h84EGz/fPXL62e9cbn//NvPEzj4w+NfLQz/lyuR8BXMd0L0Ys+kebeG9bNC/ms4kTlnbHZmN5aeVrxjZ9qc+biQdkdei+9JBrxvxmiRfiZS2J31hy0VhL+z79IplkpdMQ0WnkCOqLHYC7K6rKDm59SfmPWcIZWYtbmhs36awfimQuSTc1dPo+yrRVtnz5IRkx55MyZxMgDItO/lfEOFyK2CZ3+XUhWkRcwNbhCLDtjRa7bRHLU2rvNNpzne37q5D/1a6IobgSKUl3kXEIga37UKiCVch0t4+V/1mk0LZD4OpTdAowEzEwoxAw6k1wCu0vCBxuh4Dfichx2EeA4g0rjzsYsCcCFjcRAMnt0OJdj5RLMVIuLhaTj4DhCLTQ97R22gGxHO8hs1AX5FuWb2UB+Zddjxb+bZBC64FAqDth14TA0L2IvSixspyMFPZ2yASx3PIoQqYJx0ANtc/mIDC9H2KjnPlsLQLEYauzAxnu7stuBHcxrrW0qq3O7RAbE7G0PkHKqycCFIvQ6dtjCJzed0JmyUzEQk2xNpxg6efaOycjRmIvy/s+5BCd40P/nC4NXRemCl/ufuQax5juj4BSV/s7hZSQM7EegMbErGQ8epQFpHUhKXojgFBHcNH8CWjjke+FGIiA1vYIwO5odWy290eiMTsQjeVMNGb3QQBgnrX/IjQGG5FZykXAPxexv92sTWvs+XpL7xkExu62uh6EwOydQDSWSL2ExnMMMR+ZiOk8hW9X3I0Mayuqyv6K2JguVr8mAiYQNHZuInDid4xgETCxsSn7soXLt7/FC6cXdGk3dbvmdEYv3w+7iOKzwa/xCbm7ZCfmZK95qjBv8bl4Lc4fbaPyp5fO6tmt+IubEWrKSyv9iqqyLoD/FcySHwPHL/ikR/qTt3cuu3y3aU9dN3ZELuqXFeWllbUVVWX9UV8fBzRUVJV9iLFPm7o5oLy0Ml1RVXY28KQXYmc0Xj8BuNn7bS5Qd6F/19c52XUT6pdZnqd2iegIRQtBX4QIXCZ+EkCspbmx4zE3v7bJ50ZdeNAmwdqGxPO8Toi9H4rWhiXA+b7vb+x+5q3yI5AfDIjZXXL7beq5WCJ1IAIZo5Hin40YoEPRQtsRmUsykXkujJT/75GSOxMph3yCGEI+Ur61iEVrRErf3Y/3D6SMXXDL6UgBHopA11L7fE+0QGZZus8hhmMlmiQRpLzfQSf0DkJgbzUCTf9HYDpJI2C1h6XrE/hBfYYU8W32Tr7V+3QEDF+y/10YhwuQeehABKS2QUBmF6R89kWMWBIp4BKCy64d43QxAjaORVo/fs06BGA7tGqrhxHIOw6xDR0t79GIRVmETI+/QAt9GoGaeQgwv48USZG15WzL/1DL35mXp6KFuxiBjbfQQYMeyBdmOQIT51v/OED6DwS0w4i9yUZgzF203Q8xm7+ytu4MDE/Gow2xROpEYEDHtjMfralrF11X324GYsWOsbqGQmFy2+9an9N25/reBKHVp1ufZqADEW8hMLQEgYV9ENvVK5ZIdbE8Hdv3S/vxkJnYxblbhcDdAZZHZ2vj3RDAyrQ6Jq1NXiEIw7EzGku72TP5lk6+td1NCLwUISXvLkQvQmO1GHhr4attqrI7NY0t7FefFc7yWwjMw0dYGRzr1R8BSw+N6dkgdwMENGd8dT+rDUt5aWWNhW6oRqB4MAKvLyDW0ie4QsuVI43mgoeAZSZQnJVZn9+j00fveF5Lz3lL+8/Nz1lZVF1XvL9lVQ9+RruCBdTUFSxvbG77s9tOjq+sqCq745PPy3hq5KEbrceZ9/3tEI/dHvX90KhLjvjLb1qV/Sv5RxlQe6P8lrveWjC1526n3n7bjmhtKwEmV1SVDUas61HoZPZRiOXMR2zhW18lj4qqsgcbG0LPhyMtH//9/TLmze7fPO+MXWu2eXb2hcic+pXE0qoFpnreF1fwbKx9xn3VdP+XxYKiPgP8zff94+2zgQS3ZWyVH6lssc76sUSqHVr8hxNETN8JKaIb0KSuQ7vtXvb3ahRj6hYEDroiRe4TxFcCMRzvIwW/N1JsryDl+Cu0oF2FFrB2yBzTy77fHzEh65DycWEWihrXhIpr52deXrhDfYFF2N4HsQj7IqbiPqQw51mewxE75li9bRHj8Adk7rwMKQvn6/QuYiyG2k8GApRHWtkakFI+2cp9JWJyLrbyD0Ig8veIERuKTsG9QXAoYZ3lU29tW2ttm2dtO4QgIOhwK99bls9ExJ7MsHdGILPI3fbZIAR25tj3E62fDkCA4UmrQxkCtnGkyCsQ2J2OzJi3I/DdgsxsaxB4cIq2MzIBgsDnBASEL0AKNwMBkQJLz5lvTgfmJePRxlgidRsCIPkWmf/kcKhxdb/ulXutq2+b/uDTox9BSj6NTki2AMd5IQ4JZ7IagWdnanYg9oKmtaGzwtkt14QyGYeA4BnIfFti/f48YrZuQKA9x+o+2drsMDQ+b0LjameCUBKPWH/NQazTQmSWHYN8ik6yPO5AbMc5aI48TRCl/ySCmyo6I1CbtrL2sDYrWpRqs31mu+bGSE5LU5vtGuqQ2bTO+utmNHcPQWzyaKsDrUJWPI82JldaAOd3kvHo6XxDKS+tfB0UvR5taF5Fc9XFR1uG1oG59v8U+64dAv03oDF8TCjU8hbwfNuCxbtlZ6wdVt+Ymbrxxd+8V1w48ILc7LW3b9tl7O9WV3eedOmRd620vH1Kv7x8DY15C3KzV09PpzPGfNM6ArTvtuz+jr0XDu45aPo5VnbQGKhBYzAX+dh5aL27G7kcfKlYiIk7gD0yMlvc3YI0Z2RnNHfIzGpsk718429vWJ5++w/+EcP+dG9GpHEfC3S7CM2FCFpDANaUl1Zu8tTpVgFEXDT5vu8sF/i+P8mT/BnpMx+41vf9f3iety/yu12O9MmHwEm+7/ue5w1GejLfvj/V9/1FduH2WZjbiAN8W+W7lS0KiFlwR2deOxKBFRfB+v/Qot4dsQ3ZSNFNR4MpB7EfRcg8+DJiICL2XTZiqtYgsJBCZsNSNEh7IpYrC4GBNxFTMgE5pd9o6e6HlP7O9rlbRAoWjy5Y2LQm3LugT304nE09QeT+exFYc341t6LB3xP5K7mrk1ycqXWIWVhgfzsfsb6IXfkQMVnPE1w+vhtafOdaXd3tAW0Qg7gMmcOmIGZomL3XBoFRDymk/Qnu7HQs4rGWbxECNiG06Gfac0usbfORwu9m6S6z8rjTU7Ptne5W9u4IzNxpdZyXjEcvtytvdrC+zULgwkM+U7tZv5+KmMbbrB1dpHis7rMRuMhBwPQ5xJr9HTEno+znAQRMPavT48ApyXh0CkAskTrZytgx3ZJZHQ41n5oRbliHQNJca+MUAi0XIvCTRD5fryCGcEdgz7rFkflzR7Xtkd+rflCXw6qPRWPzE+vnFQgInog2CVcQnG49wfL7GIH1XRBwW2z5P2T9cLX1pbu4F6TwrkWnRNsikHYPmmNnWb2HWRu6GHg59jMXOZM3ozFbh+bkHtufs3RSutFrzO/dsA2aD9uj8Z2ytPdCgLouGY8+wX/Lamvv9gQnM78xEHPS4ns/q15XPCgvZ2XbmuUFHaa8sWtmtx1m02OXz7ojX7k9EWAfZPneicbhn9D4/hma56+Ct3ckks4qyF3TafbCfr333rmiF3DCstVd23+2aGB0xP0PDrh/xJlTvkq5Hjs39hFqk82SO/981RMVVWVpBJrcSeQQ6q/1TaMZQOamrm0y8YGWFWs7dsqJrPKzsxsJhWju3X3iyJy82scuvPGBTZ3S/C/Jz1l+/sSZh1yz63YvpiPhFpdHIwEI89H82ypfTRyYWl+GozVhIGrbcZ7nOQe/QWj9WYg2ZHt6njcWjfuf+76/zK4nug7Nv0uAXr7vN3ieV/Sd1marfCHrXwXyQ0sXRKmfgQDIy0gZJ5DivhktmCXId8VFDgcpi/MQYAuhxXUAAm8f2rPtkTkkDym4D5Px6Hn2fG97dioCH1cjs+VFCIRcQxCXyAVjLUKA4xNgRucDqid2PXxNQTibfIJLs/+AWKAsBFI6EMTEutJ+aglMaIuBx/0WhjTV0H15Ve7TSEnnIEbi85z5q/x2H3x+K03NzyLl30RwurAGAa0KxABdauU8LBmPvo92zPui3XRfy7udlesTZM6ZaeV1Ds7PI3CWYXVfZ2WqtbrsYX10DWJEqi2fj6wf8hC79QpS5isILmN2PmENwMBYIvW4pdEbgbYmK9cqBFwOQAo1ggDK3ggUZ1i+LQjYfIhYLndZ9gjgrmQ8eq89c5+lc7K1/Qo0hmYAxBKp4lgidZOV8yoEjPLfnDCi13tTjz8TsZ1D0K5yhuX3CcEVRO52g7OsHSsy2rS0K9i2PrdN//quwFxoWhAO17pTqtlWzgzEOu1j7TbB0tzN/gctrieh8b0fmjNHIIZrqJVlHALgzqS42uo3Go2RDta2GQiEtSAW6X2VDRBoO5ngOqy/oLFzf36vxicKt2+4NxRhnPXtAcCVyXj0SQTIbkRAvNaCAK8vJ6L4YSMRSIxu4JmvLYtXbJc7cdYhmQuW9+8YCqfT2+3+Mb7nNSDAOry8tHJ1eWnlAuQDeRnQvry0cg5iGp9A42RSXUP+SxNnHnziijVdln4yp2z5rEVDbrR2K8rPWRnxPbzO7af1+jbK/A3kLdSfLgTHWLT2LUX96A4fvIMOr2xSyksr01M/Lzt3ymeH1IyfFZsRCnEi8Krn+c9feNjXB2EAbXKXhldWd/PHzzz8abRGvIDGm2OIP0AAYKtsnuwFPOH7ftr3/SXIWjDUvqvyfX++7/styALRE+nMAUDK7q28AjHjIOb9cc/zTkLrxVb5HuSHPDV5CFCdjEdbU/UTEdNxHbrG6Dp79iikcPPtuUzEMLkgqDPQ7vZzpHh8NPHDaGGtREphG4I674iU1zSk6PIR+DrSfvdECtqxPjch0FCHotnfhMyejZZ/Q0Z+S4x8Flm5rrZ69EWMUSYCMM8icNYNsS/5yHzwa7QbORTwWtIsxaOkoE/9wUgZ97RnQ3XbFO5dV1IAXmgFWpRvtzofi5yWWyyPywmi/UdiidQUBFKuRwt3CwJqJyF2bC5STm8g09qhCABCENJhBwRa7rO2bd/q3QzL62+W956t6r2HlX8JmuxvIPPoOwh4jUGLQ6m1WQSB0k8QoD3Dyv6MtdU/LJ3fIZDSjuAOwf4I2P0WKalzENOXZzGtbrFyP56MR18DdjawEEnGo02xRMqB5v0QqCpBZtp86yN3L6g7QJBj9c5HPmPuEu82Vv8uwDOR3JbCbQ6u3hEp/JkHDr7vxKamrJ5vf3TKxS1+ZrmVqdDawI21Cns/x9pnAUH8sjvtJ4qYN2eGK0WgLQsBWHcy8Ryr078Q6GprfevuXByMxrS7pWFXxKA+jTZEQxDoCll990M78Wfs70tjidQlyXj0TeC6WCL1MBqbnQkOggBf3DFba39/5TAVm5JJsw8+uyBn+XzfD7/XpsPapbkFtQNLejXHgCnlpZWftnr0IqBTeWnlMoDy0sr3EQiloqqsT2ZG3eoOhfMyV9d0nten64TdOhQtOBytLa/X1LXrnBFq6PXxnIN/qEjxR6P1wMkqxNqegdazGjRPL+QrbLYrqsp+DhTv0IO/vrx419LmdIY7LPPs5pgNh+0wKoE2l0vMZywfjZud0ObpvK/I1m0VycdoHfo60tDq7zRBPMGPfd/f0J2ShyMm+2fA5Z7n7eT7/lZA9h3LDwLEYonUCCxYZyyRaudOTdriXBlLpA4HmmOJVBwtLG8jJTAHKdll6JTYzohy7Y6UznikePdCpo5+BDGP2hMcY09jMajsQuK/ox3xvoj9+COKFzYfOXw/hRa0jy3P3yFGwUdgwfkeeWhnsRYBjjXIL2I0Un6nAw3JeLQmlkg9Z+X+OVLq7VHQ0QbgtXAGu4UirPXyWjpbXr/H3SPpeX2IhFYRCvVAzEsnpKiPQCxRI1KAYcSu5COgMxMpzOFIMTqT4bZW3izLw6X7IJqQLiRCaxbrc7Sj7W9tU4IA3v9ZepmIoXoLgc8uaOe1NwIMvS3ttvb8YHtuhfVdD2uLF+2ZBALpD9j3CxC4G2j9MAYxRYX23mR0KOB8e+Y5pPivQGNmDfBJLJE6DTnEVyNwOofgUvfBCIS4OGc1yA9rFNpVnoEcZXdEY6zK2nQ6Av0TkaP6LgjAnWPleg/wxk8/fFx1bcedW/xIjT2XgcaOWywbECh1h0H2sHzeIrhhwPkXdkImxn0QM5mFxmKOlfW3iN3qYX3zCPLh6oDYs3uRGdKFILnM8mtE4P4ttGFwF/14tR0AACAASURBVPh2RCdj69E8uREBrm6xRCrP+vlStOFojiVSj1paczBfsmQ8+q0v8I/9NrYG+UQCUFFV5q5g+g+TTnlp5asbS6O8tHJWRVXZ4f17vNOmprbw183prH4d280ehMbY829/dNrbqM8n/D975x0nVXn9//czM9sXWHpdYEGKgIioa18VHXtBnVhWv0aNXWOio8ao0Wis0bHFFjtR1zbB3jIqumIbFAgoVTpIh2VZYOvc3x+fc70rAQtRwV84r9e+dnfm3uc+7T7n83zOec75sev/fWTc9IO6bdvjvYbc7HU5aLz8uH79y0srX7HLFlWkyz78rtOSJgegsRvxxG+PXqGPDofvkTHg28QOGDQHqzPQGrcIyLNYZ1vl+8s7wA3OuTM9z3sAwDk3GK3hxznnRiBdUoY2Gv03Us5UoL1zbjfP8z5yzmWhd3kyUOx53ijn3GjkP+tbd7bKTyibixH7CrEKb24odEUyHq2OJVIt0IQqQju+AQSsSzYCAAVIiTcgRgCkUPIJzGYeUqjVCFD5AVonIV+ZHLSIpRAQOwwp/auQIrwBKTMPKdGOBL4ZDim5JoIYX2Er7wLETCy2dnTCjtXHEqlnEeDIITixdjFS7PtY/Wc4x1qCWFKXIfZoMs5NwLmpiHHb2dqzG2JH6uz+CxCIuN368GnE0GyHAOV+BPkn26EDCdlWp95W52eROcE3edXYPREEaIqtPx9BTNAM66t2COhMQiBof6QMVxE4GI9EvkvOrr+WwJR3JALGNyCWbx/rc98p+TNEvx+GQJpndfFT+My05xaD17lV/kKvIH9F96+WDWqJlEslQZLzUgReQgjcnZuMR71YIrWH1dVPV9QDMUQxBCb+aP3c0e71MwjciADqYus/P04ZiG0diObR3itWl+xj9dkDzU8/fVYCMZpLEIjf0dpYYn0/wPrydWQ2PtDGpQAtwPnWB19Y+xqsjN5Wz5etr1IIrFYl49HKWCIVRe/PozYW9QSR/fdAAH4mmlNvWHuvs3He0do9xdqcY235mMC38PfWX35IiZ9jp30WAhqnoz76XlJeWlkD1FSky+ah8Xqntr7g/tc+uagd8nv71gCuP6XMWrRTr5U1XUYN2+GRw9D7k4VY6HubX1deWll76+u/uaKqptMuU+bu8+vmcd3Wkz8iX7KfLAWSHQb4Gwq1cRrGhm6V7y/mZH8UcIdz7g/o/ZqN3qtC5K7jAZd6nrfIObdBIOZ5Xr1zLgbc5ZxrhdYUfwP5hH3mgLs8z9sKwn4G2SxALBmPvoIYAwBiiVQnpMDuS8ajabtmdSyRugWZndYgFuE+tNvtgRZ/kALOR0Fc70EKYB1a5D9Ck7UnQRDXl5ES2BctzNciEBJByu8PiBm4ADld+4rWIcW/FjEor1kbsu17P01PS6TAW6HTh9MQSOqHgjmOQwvmBLQ7vBUppnwE/q5C7NihBKe9fPZjdyv3n2hX8wAyi55q/fBXpCi7AKcn49HHDVA8bu24FAG+z5HivxkxZmuRssTqMgaZHrMQEHnLntUX+fbsiADdMVbOJ3ZvCwQeelkbulmfLEGmvkEIWI1GjEm2tS+DTF71CGx9hAD2BAS459j1/W2MJiOWZjoCPD7w82NtvWfP/xIyRxW3/3dTQ6ZwAGIJG63c/dCcO5mAEXgUIJZI+ebUlQiQNCIz7IcIDN1v4z3VfucjxsihOXawte0YNBe723d+QOLF1tZC67+2CLQdad/7+SUb0DzrgYBPF3TKtSUCUttY7LEO1m9lBMF5XyII0fEH+3yujeGRVpft7DnLYolUa+D0TBPvNq5xs7NbeqcgAL3ExnA6msujrI5++qqBCAiORz5puxIk3U5bH+Ram/IQ0H4rGY82N5n8lPI62vQs3MT7J6Nxv+u1jy7cPlTfcHt2de1NaG3YTOJ+Gwk3ZhGwTYsQkx+pSJeF/JAYFemy7A5F4YvbtZybV1XTpQtsGIj9HObB8tLKRovxtuY7L94qGxXP875Cm6f15RL7aX7tu2gj5/9/frO/x6P1Yn35rw+UbJUfLlvKqclrCYJpntjs8+2Q+eQrtMAfiJRTNgJFRUjxLkXmRD9A5UuIAfIjfr/pXNOBQFfPC/smq1uR4n4Xmc3mEJxqnIzYg2wEUjwEXrog36TtkfKMWBn+Cci2BA60JVbHemQy+6uVtxKBPF85f4xMo79FSjeNqGA/5MbriGW40u4BMRP7WV+8T5AaqR+B78hNsURqEgJe/7BrPkXAttLq/hUCpD6rVI8UzNvoVNYqxADNQSCqI1Kk7yBH/EMQKJiIlPVHVu5FBImy2yEfolMRWJiDTGcHIYbpKQQYFloZQ9GCUmNt3AmdXvXsXmdjAN9MMv4l8pVqQKEbqoFuEA5Nmrv/nz1CfqyzGWgeHYmAQ2e0+GQQW/k0Ag67WH8MQSB0vP3sbs/OIIB6pj3TD+3gn/L0QZof3d9DoKcFmgefWP+VWP3r0bx5BQGWQgQgDkdsVD80331zVC5y0n/bxihOwFBuZ335GzTfFln/Po2AbD4CVtsiVqscOMXz6OJl8Jrq3Z/Bewmxlvug9+ozBKqGWV93QfPnIgSapxM4+z9gfTwN+eOdnYxHF8YSqaN+CnPkt0l5aaVvmvzeEkukIn49R77/p/GIydyr+PVJ8zLZ4cLWXyzchZuP3mxALBmPrqxIl/lJ20HzrwVipt9BzBNAKBxqyoRDTZHdBz5zZEX6mUnfI3DsTyblpZUvba5n/1wSimQv/j7BWkOR7MXfdc1W+d+RLQWIPYTAwFXrff4o8OZO/Z7fadz0g/doyuS2QgoohFgn37zxHmI2/IjvLe26DgjARfYY+NTKJSu7F09bsNcN4KqRkluEQNT2SGlFCILHnoQUzt+RWSPf6ngSUuIgxVxkP378pr5Wrw4IfO2OTDnnIFPbewg4ghij3xDY4ZdZnaIEjpW/RmxGA0E8rzZo4a2zZ5yMWLcJiM3bF/mzlFtd8+0ZlQhcXY4U6F8QyCuwer2AHIGPR4DwdgR6wkipFtizDkeK1yG24GQEBN5NxqPRWCLVniBwbj4CmWH7fxB8HUMrg5TcMgQojkBmuYUIQLQgAJlYvcyHhbDVcbLV40QEYJbb/39DLMG8jJf1YDIeXRJLpLZFvmKzrX9eREzq4wgw7Wn96Ke+GotMslGr22wEdJYgQDQcAagMAibL0JxZh5ijwQig+UFEV9p3dyB2rw6xRzMIIuIPtesjdq/vqO/7jD1jzzvO6oFdU2xl5SLQuxYBY/9AxHzExPpBc79E89DfzCx2jsKmOjI5RZlWiNV7A/l++ZugWmtvawTK8+3/RmBZMh5dFkuk+iA2N4LAY3/g6FgiNRD59/2HeTCWSLVCTPEKYOf/NsDrfyOxROqIcKj+/DtTJ33cvtXc28OhP4Q7tp66R4ei2d2Kz5ocX3p8vz8hk+/mliVovrVD78Ie6IDNVxXpsi5Ay/LSyikV6bIW9v2VQO+Ln7z5/tmLhp6GrA8TNlPd/7+VrWmLtsqmyBYBxMwc+avmn8USqRsQGzK8e4fPj2lduHB66rOzr4dQApk5GpFS+wKBFb8tEaSULkaK43Hgo2Wriv+yuKq3H29sKlJ+S5EPzWFIqVyIQMo05Bjug4jlSJkNQoq6Hdp93oXYrWOQEmyDTIP9ERh8Aim6eYhJ2MXqOB8xWbsgB/7JBAmKt0WKzgcyvi9KCJkWL0MKugYxcOci8NiIGLPzfLOPxcCaiUDJIvt7HGI5YgjYdEJg7FRkipqAwObniPGpt/7uhXyKhiET34EI3H2KgNX51maQz8m2iClZYM/ww2AMIwDTntX5UwQmmhBYOQIBpDEINGyHAFETQZaBIgTYzkDgsoX1ic8QnYXGuhjoHkukqhAT2smuHWi/n0LjfT3BCdEZCITdgcb+BgSoF9jY+ZkPcu2ZdQjY9bJx7IbYvBLEGmVZXReiOeUnTP/Syr/N2j/G6jDHxvMlBIwbbAxyrM4pBPJSsUTqd/Ys/3RcLVLQHdH78TZBsOCb7HnzELhsZfUdadeWZBeyGIHrfRCg6mL9H7F+TqP5mW3t830sp8USqdPQu+DHODsbHR4ZiOaHn7FhfenojxNwZCyRKkGnpn8WoBBLpDoDK5PxaC2wuk2L+cWFuSuGeR5ndW337+kt8lft2r5ozs5Z+XWTL/buvu67yvs5xFIS/R69q/nAseWllScDVKTL7ga2qUiXHeN57k+exx9DIS8EnFLS6bND5i0ZNLkpk93uW4rfKltlq/yM4jxvs20+v1ViidTrSAnvdfRef8l8PmtYv2nz9/g7WrSr0O65HzJBtSKIH7UMgRcfqBQjJeon2XVIsb2DgNVaBBrmI0DzB6SUliBAkYtOrhUiFmgSUmK+0q4jiBb9IgJj+yOwN9jqtRcCfH66oHZIeQ1EQKwamSCrkP/XcCu3q5UbRuzERKv7UQgA/gmBhvcRQBuCmC4/yno2UsCzgKxkPFplffsMAlI5VnYCgYBbrV+zEaMTR6zfNkjB+2EO0si/IExw4vBJBBzeR8zHTOQ8uhiZUvexdq5A4PkI65tbEGtzt5V7g43dXfYMP/H2lXb/6XZfMTLv+SeFpiP28hwEcP2gs28j0DYJmQKLEYgahcDO8TaWL0Jme2jMhkgThK5HYLsWmWvTCAifjEC+D0KmIUW4GDF3vhP8vmj+vYTMsC2tjl2tbtsj9vQ5gjASn9vzdrS6T7TyO6L5M58gkfgLiA29g4Ch9VmzLxGztjPazDjk0DsGgV7f/N/TyvNPPk6yMmejeRVB8zLPyvGfvQaxqwNsrFYi1myNtdOvx4Vojn6E3skXkvHoDNaTWCK1JxrHRnQw5FIEBtcCD/jz9qeQWCLVBTHybyXj0dsAKtJlA4CPPY8WcxZum5k+f0/aZ0+bM2TIe9Hy0sr/qP/mlop0WTHQWF5audD+fx5tGONfLtjx9sbGnLZ9iz+sD4VYBaxbtKL3fhcd9OjMzVnnrbJVtkogm50RiyVSIaBtMh5dGkukeiJFORqxF12AmSPf/1Nngkj7ryGQdC4CTP6x+RbInNITKbp8xGp0REphDQIJ45FpbSxBuqIwgZP0TUiZPIoYrdOROSiKAMdQgoTXHRFLUIEU71jEsPwDKenO1swHrV0XIGXzKwRU/LAafg7MiQgMOoLI9j77sJNddx1S7j2Rwk4n49G3LFl0GAFJP6zAc9Znc4FwLJHqkYxHVyGwsiMCSMci4DISAcE6BDIaEEg8HwGdw+zaXRGYuAyxHWH7fwYCWB5ihLa1+pfY7y+sXtkEDM5S5KR+p43fQQggzrRr86y/tyVgHJ8B7kjGo1cbk1Fi956DmMLdbawbrC93QqxeH8TwLbVy6hE4fBABihdDrmlQVrimvq6xIAM5g2wOjLPrq9G8ONHa8BGah52tbT5z1B0ByJ5WZw/N2Rwbm6esTnsi5tTZ/REEbk5AshaB8gaCVFUFBEC2J0Hsu0PsWRm0AXjaynnQyr8OgbU+SEEvQXPtcCvXEYTbeBrN88VWh1bovdne7su19vdC4O1a5J+ZsTasQ+CxhZXxFTAnGY8m2Lh8gQ5OvIZYQ3/czkJ+e39MxqM/1Sm7FQiU+zHEOqDNxYfOcWC39pNDM0b1ydQVZt4qP3OLBGHbz/i0X+iR8+L/Lg/21HVo3B7u2Wl8zpraIjKeyw7h7Qssv+igR5dsrLytslW2ys8vW0Jk/V8BT8QSqf7IzHQ10CEZj65OxqNTzV+kP2KPpiGlfB5a9GuR0hiBlPcqBGBmIGVRhRakKchUUo0UaA1iif6JTFJ/R0prKuqTsP1+BzEh96Fds2/uuRQp7zUISA1ByqMqGY8uQAv5bMRCFCB2ahoCVS9YfXpYu45ECs5DbMuFiKm7FzFfoxHTNxEBx39ZO3dDynP7WCLVEfmGlaMTfZ8iJuhM5Pvl+5Q1ASTj0RXJeDSFfPKuR0CtGCnezxEzUotA6bHWnunWx40ItK1CYR/usn46CdglGY9+hhT/GgTaiqxvFhKYIn2fr8kIaBQiIPElYjRXEfgjTURgbbh9ngUMNbPrEILYcPtafyy3/1sQHJyYbPUpszH+AgHRsQTxtkozXnhJXWMrB9k56CSlHxB4gPXDUwi8tbf6lSLWbwQCKFchVjRCEM3+eeB3yXj0/5Lx6FcI2F+PQMa5NvZ9EKCMWX+MQ2B+OgEr69Bcvs3qsTvwm2Q8Os7adbyN/anJePRGK8tZH16ETKcPI1P7APR+RND7U2v91NP6vzUC19XWP79DzFg/glOi3axfb0TM21Q07/ay/liFQO9KvjtMhT+uZTYu16J3oZPV94zvuH+TJRmP1ibj0VuS8eiHFemyLPTOnQHsksnQGI6Q2e/ElzK7HPl+cUW6rNd3FPezSkW6LHvFgnYjVi9rNRqZgX05Ga0TOZFwE60KlhMJe8vQunO+JUjnvEfu/fWZDzzy5oOVR1xbkS77yfr4f0myXGSRc877rp8sF9lcwYC3yhYom50RQ4vDGKQAHkQnr9bfseUTsC27I/CTQbvtj5Ci6Efge3Wh3XM7Uu7FyG+lBYr4fhRSGr0R63MIQWyz7giY9CGI5XQ+Ms+1R7v251DoB98k2hYptqMsXMRoBJQiSKGvsTbugdicweYDF0OKahViX/6MwNI865cypMw6E/hf1SEAcD1SkkcjpTgMmJiMR4dip6ZiidQEAhPqOuASYx2fR8r3LQRaz0UA4BWk0K9H7ExvBGCLEQj7HJlVixG70xYBoAxStsX2rHsQsNgLKfQwAlt+nCL/uPx9SJlX2Bh0QSD7V+ikaTXwRDIevdMcuh+yfuhm9fRDYDi7L0nAHK1D4KIjUuiPI7btevv8KntuPTLHHQuhIhsr/9TingjYReyZ21hbaxH76jvXVxI4xHe1709FjNBLwIpYIuUDlBJkxt0VzWcftEWsbQ8igF5OcJp3GwRU8hAgvdTaNxggGY9+gsy0z8YSqcGxRGoIAvJfIKBfbP200Or0IQI8LREj9AAyAZ+ANjqNaN73tPEOIRazEkveTZBF4EYb12rg/GQ8+mUskVqKgFkascq7xBKpE9E7NiYZj9bwTWmB2OJCgryqfnxBB1wWS6Q8BMivAB73w9z8yDIYvQs0NpEfDhGpq89ZkptT9yLql77oPdgipLy0sv7kk+94Yean/U9BmzUq0mUhBKQ/Ry4S/vu/BgHms9E4PNGx9Yxrlqzs1R0yfT9/Z8icstNfOeqwi559o9uA2X/bnKcrf8nSSFPHWzjvO6+7hHu+82TlhsQ514Q2p74MByo8z9vdknxf7HneYZtYdk/gFc/zBm3gu3et7E9/YJnDgWme5036zov/h2WzA7FkPPpvxCqAwNjs5t/HEqmdEAMTRkr7JeRI7xCI6IWAVR1SMJ8jHx0/r+JbiGG4HSmY55Cym0sQO6uLlVNs5d6OlN0gtIAVEwSELUOs1hdI2Txlz/QjxMfsmnYoHEYnBOoeRgBjbCyROj4Zj14eS6Sa0O47C7EA7RGLcq2Vdw4CEs4+Ow4BMz9hskfAqDQRhHXwZTICSyBW5Uor61i7/kjrzz2trEeR/9P1CNiORCB3io3Rr60eLdGCvhYxKn9FyjwTS6SOQH5f0+z+U60eLZGyeB+xaLMJch5eb88PIzD6iLU1G5gaS6T2Q/5rfWwchiNgMNiuyUKAyg9e6iEwVE2Q3mcGQcqlQmBJMh6tiiVSd1i7muz5sxGD6AOCJ5AZ9hAE9lahUA7XoLkRsu8HoIMe/QkyGhyN5mVrq3M/a+9gu3cyYk1HIqDnZ4xYhgDeQWjR9YPU5tg49sLyT8YSqefsuQsQOL2ZIDbbUjT/KtB70BExap0R2F+K2MzzEMgaYn3pJ1lvZf1Ya/XuQpD4u8p+v251LgOWWAqpW4HrLV3UZ1a/c6yvqmOJ1EHJePQjTJLx6Co7dHAUYgqxfpsLFHoeE+uWhzuAa5PbrrEXzWIQ/rdSkS7LBYrKSysXIZD6EfCbcIhIxsNbtbbDK7k5895Hc3Tej/XcH0v+8Y/f/7nETblmltffB06HE+SXfQ+Nfw80H/ZBc30RQO8un97Ys9P460M0vvX0lWf2Luq0Yu+lszq26TZg9kNsDbi6pco6z/OGrPfZ7hu8ciPinAt7ntf0I9bp22Q42uBvBWLfIj8bEDNfsNWYL9aGfD7smoOAqc2cetsjxboC7YavQIrwIWTmuQAptdaIpfgAKZpeCDwcjxbuAqSIBqGdbQVSHg8gsNAFsWYXWR0/RQAlhJT7XMQk+b5rTyDFv86ef5x9PheZIjsgwPYVMhPeZO0pQAp0AXJQL7YyDiZw6r4Vgcg+SPF+TgBmDkJMkL8zf1n3ezXgFhrT9mAyHp2FFlPf/OyfUvRPm4aABosifx1BQNp/ITA5wvp+JvDXZDw6LZZIHYfAUyfrW9/hvT8CN0OsX3OsH6YjgDQagaAeSCnPRObL3RBz8w4CBTvY2I1B4KkrYrnmWfs7Wb8+RmCGzrNnfIzA0iwEHNra9z2Rgl1hdRqFAPMb1i+F1vb70CnBjNVhnPXTSfb83gjEeNZ3IxEIHgVc7nmEVk3Knls0sH6WPXe5jd2eaM59gEDLAKtjBWKxjkAAujVipC5Fc7YUgbn7rY0NWH5EdLgiG7G9ndDc+gDNXT9d1moE4qqRf18/AsZyKfIHOwSZnfMRaLwPvTd7o7nWiMY7TBCk1TdNVqH36HEbm+2tjGcQ87IXAgKT7Ro/uXM+ZiJvLsl49F+xRGocWrjbIPDfGxi9bombVzU+L9awNux6HLXqD2xi+IiKdFl/xIDfVV5aOb8iXdYN+SfuUZEuOwy9wwOA1c7R2muiYeZXpeX5uWt2bpG34mU/UOqWJs1AGAQncRsRM70avRtXoDF6oLy0sgrgxF3e+3tFuuxxoMFrihTm5Nfu1HGbr6ZtTT30yxLnXI3neX4e5pbOuVfRGjcKONfzvIxzrga54OwPnOecK0XvAsBDnufdYX9HnHNPoo3gF8DJnud9Yz445+5D73gekPQ872r7/Ca0njUiPTLS/t/bOXclYtAPRZv4RmCS53nH/8jd8YuUn5MRCxPkzPNTEK0vw5CP2MtIUUPgAP8MYgr8/JGL0US6Dyln/xlXIrDUGSmsNVbubLS4D7Z6+CcG85CCm4kU1FFICZ6KHPRbIzbhIWSG2R6ZGx9tVu8zEWMwDymc9+ckW9+Q3abxvI57rd4vlMXfkHIrQMrFV7gT0YQ+3u4di9iFm5CS/AT5xhUilsl35HdIYY4DdsjPqVravcP4gbMW7dSurqFwITLvzrLrfNPmPKR8m6weVzdL03ILMpn9yurkg8o/ALXJeHQaKNuBXe+HYiCWSHVDcdcmW5/5AWkvR8BnB2uLn6R9HTLv7mJ9HrUx+TdiNE9Ec2CdjeX9aAd/NVIs7ZH5eKn93YRA81wEfEcgIDbc+rUHQZ7IvyFg9Ufg9Fgi1Q+Zos5Bc6zQrumBAPowxBCehUD8vogFiiDTXhNasK5bNSn3knkvFl2W321JbnarTMie0Qf5zY2IJVK/RazX5WgD0NXqkkE7xtesH560sZ2FTKm3IJ+fAYitmU5w8vNY+/xJxITcj4B6D4JgvX6cqfb2LP908QL0Lr6OAFV/BJZ9f0LP+ixl47YfAn0Zgk3CarQpetzqMhyB6hfscwzoP0IQ9fv3wBWxROr+ZDz6Os3EDuz8EZmN59vzhuV18HLzDqjByxC3fv+VPfuHSjHaLHSqSJctA57IZBjgHGudY1tr93ZoHniNTVmuRf7y7LCr9w+x/GRSkS7Lx3z0yksrN9mZvry08ouKdNlwNO6/RmvVn9F646fhOqDZ9WsByj1WQv9Us6+2ypYpec658fb3LM/zjlrv+1K0JsxBm82j0Wa2APjE87y4c25HtFbvgnTEJ86599Da1g/4jed5HzjnHkHr463rPeMKz/NWOOfCwNuW73IB0p39LRVTked5Vc65l5C5MwngnLsMKPE8r845V8RWAX5GZ/1kPNqAAEFJMh5dtv735gP0JFpAHgIocVMO/uzi4l5IETYg5fQntNiXIGTt58SrQooxD4Vd2BcBulkEDvhtELBqQCvOQqTkeyPWYBRahJ1deyLyNzsVKYVtETBabc+ajtiufMQYtEAMz/11yyJD187L7urCXEcQOf5KBNq+RGBiONp11CJQdbZd8ytrX38E1Hx/oEsRQF2CgM1q4JOmTNau+bmrenRq/WWl3VsZS6SyEEDyT+41IQW9g9VzQLPun4x8lt5Hiv8IxIodB9wYS6QK7Kdk/XEjSBS+DTLpnoaYkBQy3+2HQODHiLVZZM97C7GZfsT4TgjUVCGmbIB9PwKZOV9AQOFBtNPyMxpgbdsegZsrEAAfYnVYhADQQWgenY3G2s9Z6gdbbWvj+UAyHv2j3fcEAjgzEaB/FwHC9gQbix2AHQp71/YuPmrF0kh+5i60CPa1fj7V6tiWIF1Wtt3fDs3ftgS5EKvQwrcazcUO1gd/JQBbp6C5cSjaefq5GwcgE+NYK9tPX7WMIPwIaO4db/V/BQG+SVbGGmvvAgS6hqMF1vdRjFg5fpyzK7Fk9mgO+D5nZ8cSqUNiiVQ4GY8uQuByIHpf/MMYG5LX0IYpjcbzReeoxQMXIqex/uucp5sibwEnlJdWfgrUNTRmTV9b16poRXXnVui9PJMgibrLzWnIDCp5tzE/t6YQeKYiXdZ1E5/7rWKHBFKICZ5akS4bW5EuO6siXdZ3U8orL61ssFAWz1qZT6CxrUcgdGvQ0V+urPM8b4j9rA/CANKe58000+NTBCmLmtDhNOyz5z3PW+N5Xg1aT/ey7+Z5nveB/f0EG055dKxzbizSWQMJct/WAg87545m46btCcCTzrmT+Hlyzf4i5Gf1EUvGo0u/5etfIwXXElhZ4qZkEZwqewgpp1uRf9EwpKQ7IRZlDAJDO6KFdA+kvahdyQAAIABJREFU7Kci8LQCKSvf36ojwXH/C9Fufk+kUGoRu/I4ovhXW9kj7e+lyKdmOvI3y0Yn5VohxVgIlBYPX3lKVsum21yIY5Hy3dmuW0xwCs73JRqDlMugZDz6vjkmewjYnGff3YrMSIchlqYTYpPOqGsomDd2+pHzEPh8BgGRHKSsJyKluwCZOBuRCesTgFgilYNe2JvQCzU4GY++at+9Y+XMQIDAxRKpq5Px6LX2fQ+kQCpRuIGDkRluGlLoixHI2A4BlrYEJ0ZnI6DW0vp5MGLs3kCAo7PVuQ+i06usz/IRMO1GkIuzxMamHwEwXGSf/QsBl1EoRtkEa2+9PbtTMh6dE0ukTrXPsmKJ1GEoPtdKBNxWICDpmyZr0HzKsroMj+TSsWjb+r2tjmcjcHMxMDiWSPVGvnXDrNzd0YZhndVxN6v3RAQ0mxCjNAsBmB5oTk9Dfj6tkIwz/6o/ILa2JwK9PQl8vbLRycMYYiZXEZjbX0Gg+RK0oPo5T30TXBWaozT7nUFzx885ua/91Nl4tkfv1+FofZkMzLKE036uw5PZiFgGhLOAFXZi+vhYIuUyTdSGImSHwhQRgOcfJOaAvrwiXXYMcGHINY1cs67VvFaFSzsg4OyX67e1Fr0vfrDgn8pk9yaaE42o//2UaK9XpMtOKi+t3CSFVV5aOfPJT8raLFi6bcdObadmRcIZ0DiXIhC9Vf7/k/XfC///2u/pF7ax+wFwzpWgdW1nz/NWOuceA3I9z2s0c+d+aK05H61368uhyCJwOHCFc247z/P+5wHZlhC+wpeJSPE8BtTP8vo3FHSvu3zIDfP62mcRIGyL80VoMtyFTEldEAOyGrFGvqNvS8QG5KHB3wYtdhGkzFoj1qsdAgm7od38CKuPn6Ko0K4pQcppun1/LGJZViOzWSGWfimvU+PpkXzPZ5BeR7vTM+33cQSnz3ZHk/dAFJahF1L60xAITSIlELP/89EEvwmBw3YE8Z6KEFNWg1iRvwPlyXj0nWQ8OhUxc0cl49Hnk/HoV7FE6jLEzo1DitsRBBclGY++gYBOW4K8djsAxBKpAYh5dAhwLULgKweZYZciQDgLKezWdu0FiL3KIABahYDCFAS8HkCAZxUCQFMQE/Vv+70SMTT5Nr49kMmpGLEtPlvS2tp0IwIaLYAdkvHoAwTZFZajwwCL0Vx6GTFDO9tzcpEvV38ENu9GjOeNVjc/wn8VYiqvQqljZqG51IMgIn0/ZAI9C83VEGKTJiHw4kfNz7LPZiHgeKj9PQUBhN4IqDWhKPQLrH8fRpuZBJqfdyK2tRCl0boGvQuFCCi1QO/N81avCEGw41UIlOajOTebbwKyPgjgHo/SZJ2A5tpT9szBVpc/st7hm+8jyXh0XjIeXdPsfy+cRRsvQ1UoTC0bTlb8Q6Q3sGs4nLm+Y5u5PXKz10UImD5/nmdQX7X0PNohIFy4wdI2Ua59Pl563iP3/qqxMZQmSFjvm4+zkLKaUpEuu70iXbbRTXNFuiyrIl2Wt6Hv5i0ddMny1V3ned7XS30IJQfv/WO2ZatsMVLqnCtxzoWQnhm9gWveB4Y75/KdcwVoPX3fvuvunNvN/i7fwP0t0Tq/yjnXEW2+cc4VAq08z3sNkRvb2/V+fl2sTsWe541Cbi+t+JHfqV+qbDFALBmPjkrGo72T8eg5fq65/hcsWRDOphQBqI+AvWKJ1O3myP8+MlcNQ4tkHjJ9+f42vrNvd6Qsm5tDff+SCGJyxiDm6V6kbB9CyiQfKcvRiBHqiSjcWcjXxc/vl0YK26HFtAExV3VW5rOIQRiPdhjLERtSgdimOjQhj0NMUGuk8G5CAGAQAoUvINDVFvkanY/GcLqV9RBiPT5OxqOTESj9OrdfMh59DHgvlkhdajkBz0TMmh/U01cCzeUmBEzKEKD5o33eF7GNCavvYYil2hGdtIzbOD5MEJ7BT1hdYP15JQJtfkLxxWj8zkDjl2fjko18nmoITk7OIXCcb7Ly9rF7MvZZJ6vzKPvcB7+90JzyA/m2R+PqH44YnYxHByPztWft3B2BY18ph61OPss4FJmFj4wlUn0R4/ChtbEV2jDkESSRzyCw4p94ykYAzfcBfMv6qQHNtZUI6DyJNgA7I3DaFp1+/SuaP76P2VHWftD8/wLN4w8QC/aZtXUxAkv1iC2sQPMsYt/7J0ghOJHqM9utrP/HIj+9L5Hf244WM218sz4nlkjtYZHsf7Ak49E1oTAnrFsUqZv1dOvbS9yUgu++6z+lIl22K2Lk/FOyvqnYTyvmiweEFkwudh8+vV944fSuKwjA6H8tj31wQIslVSXvRUL1zzQ05h+O1iT/9G81gem7F5ZurSJd1qLETele4qYcd939p/esSJeVWbiKq4DH7AToN+Syw+6tKuk0/q+hUOZzgswW5cAdFemy7Fgi5WKJ1MBYIpX/Y7Vtq2xWGYN02GSkp55f/wLP88YiciON2O2HPM8bZ19PRc78k9E7cd969/4bbdynoLXCN2O2AF5xzk1A68xF9vnTwCXOuXFofX3COTfRyrjL87yfLGvGL0k2e/iKjUkskXIIVftpUz5GwCLbmJxrkcK+DS1UlcByU/4nxRIpP5jqlwTH632gUY8Ak++YvBqZV/Yh8KlyCFD0RornQWSGewstktORQq5HCriYwPm/jiBxs88O5CLFeTIK1lmL/I3mIlPWxUhxf4j8coYgpZpj7fgEMWerkWlhX3u2Q6a7g70MTR4MCYUYEEukjkFMyORYIvUKUJ2MR+eil/QUxGIcjUDGQuQP8BZinb6WZDzaiBQqBLsmEND4GLE2+yEANh0xJc8n49EHY4lUS6sn1g4Pvbx7ItanAjGLS9E4TyHIG+k7iPtA6WkEniYgU86xyHSbR3CiL9f69hrkSzUQgTyfUfyNmfG2sWeFkE9ZC2tbNmbaiyVSg5Lx6OexROpYLOp7Mh593ILnRhBwPACxnWMQIG1l9fsbAiYTEMi9FwGwIjQ37kHM4A72/2o0xnWIFZyFgO4sxBAmCJKRX2K/30CL5iGIzXvf+rGvPcv3CQQB5MMRo+sh5vBsxGL+neBQyxD0zs1AwM+P1+fHiZuFNjatkaPvHOv/AfZcP3xIQSyRmm7tnhdLpJ6yvjrXnn04myZLV03NzXiNrm0oJ5Nrdf6hcqXVtR7NySYCc6QPxDwEstvWrGg54e0Hj1g84V+lV77/xcGzN7He/yHZWbVr2rea9e/syJrBOdk1vQneD4fexxeR60EnND9GAgOPufrvny6d1W27Tr3n1aH5Mhy9d36i+v+Qs/cd+UBFuuwhtGn9DdB9WVXxtMVVvW9s02LeiytWF1+DXDEe+bHat1V+Gml2OvI/PvM87102whavf5/nebch3dn8s9mI/d/Q/fs0+/uUjVSvdAP3fcA3/ZE35HP2Py9bcq7JYmQinIsW/X8gtqQgGY9+HEukrkVK9MtkPNqv2X09kZnvn0gRjUcAqwKxUG+gxW0vAmfmD5FiHYxOPd6FANgbSLn+EwG6N5BfWFt0EmkbpMy2Q4um73dWhxTOHLT7zCfwLzsSgbowcHAyHl0TS6R2R87aNyFF9gACOq8hJXmP1f1htJPwEzZ/ipRrjZehC5DreWRCYaoRcIkihTrIyj0CKdF7gKeS8Wi8Wb8VoROSteboP9rqHPUZyo2MUyvEKr6DFvpXEbjaB4HO25CCewYp/SVIyUxASr8eMVY3IkB7k/VjLcEp2yfQ7u1qG4u7EWBsbWXtbGMyyZ73BQImc6z8m6w+xyGgU4SAWKH1w/3rtWkkUoo32E8FAsW72zNOQOO/2v7+GM3VocjM1x2xTL9DbFInxGb1R/N5jJVbgsBEnbV1sfXdQWh+fGJtOBqBI4d2lTOt7X4i88sQIHrNxuJj67/jEVPpofmTjebnTQjQ5aK5titB6iLfvNzF6rm3lfUSAoszEHh/2p6zrT33AxvPMQS5NP2QKDXoFGRv4LJkPPoEmyCxRCriZfh7U72LVk3M61ZQXD/u1Vv23fGHlFGRLotZe/ugvvHDuvgmST9Uzly0ftxUXlp5y6bU93vWpyuaU/5hCI8g5lKj1asvGh9q12TjeSHmLx2waGVdjyU79Xvh5ZDz/KC6A9Hm8nrgsvLSynfXe9Y/0HxpmLtkYFVRweIO9Y05dy9c3r9xxlelDwzf88Y5m+KPFkukXLtWM+/KjqzjrlN+/9sfev8PeE4LYG0yHv25YmD9YMlykUWNNH1nsNYI4cUNXuPWQxNbBdiCGTGkMP0ExKUoIvfkZt+/hEDNywBmanuD4Jh+TTIefTKWSJ2ElGZLRM33Isjx5xBQ6YpA2XykUM5FDE8D2jG3te8XImbiTCvHNyH0sd+rEdDw8142IOVaixi8UxFo8P1oRsQSqSeQAnwZgYhpCNDshJgf3/H+KKRs8+Frh2U/hEINHhOa6tk5U+cWZRd5nyBQU2D1hCBo7D7JePRrutoc7o8B7jUQ5ueU9M1iNLvWIWZjDmIMBgEfJePRt+37Uchc2hEp+N2REl6ITrveisDPKgRU97XxGmh9lkCA7lD7f4X1cbGNXwu7vq3191rrt75WX585GNqs7fVIoZ5sZVyPwHkNAipfWt27I0ZokpW7AJl6fbbiSwQIn0TAyY/R1o0gD2PvTBN3jvtD8f6zvP4rYonUJwiUtUPAKYoA4u8QgNze2tIBzfN8NAdnIiC0CwLezyPmaxoysY9BgLQnAmvLENC9HL0Dk5GPmG/OzbI21CLQe7b1ax2aQ2cQ5ELdnSBrxftoh52P5ut0NFduR3Op0MbsY+vrWiDieWzrNZHjQuBCtLf2twEe3lQQZlLiQvRvWBnpsHxMgfMa3Q4/tIDy0srkBY/d2bFP1w+2a9tq0RqCU9IQmIzboHfvDNSXP6UsQ+PtB+l06H1YSrAR+XqdXlNVkFlb1aJp7rodXg+FvX5O43eOlXMGYh+KgH9UpMv2XS9J+YsI1Nd2aTu1bV1Dfrht/rK92rVa0G27Xm8vBvapSJddWl5aOeWHNGD3gY938zzOi4QaM7e9ceodFx306I+ekzOWSHXIy1n1Sue2U1dXpP9yJNr8frylZQDYCq62yqbIFgvEkvFohiDo5tcLgwV9LSZQjO3tswFIMXmItfoslkgdTRCU9RG02PnBX7ORIuqGlPU6tOj+DSlsDy2QKfvdCnjUGKwDkGLyneWX2j33IJYu18p+DYGZwQhgDLXn3Y125UcicLY/AgBjkP/OUPt9KlJ8HRD4ORyxab7C8CPIj3ZhTonksQd53s0IXM4gOEWIXbsTYgGan5h60eq3TSyRGtnCW/5US5YXLKDPGpzbfz02LI58tlaghb8UeDSWSH2WjEefTMajmVgi1QmZPzoRxPa6Bin2vZHiW4yc22vs/5kIAPRCpwo7A91W/Dt7edHA+gEuTIlzrEbga2fr62sQeHnV+vI4BAzbWvlJNH9ybGzPREDoEXvmywiAnxpLpG5D8+d4BOZmWL92RSCkr91fhED3rxFgf9zGaCACiDVLRhd4wCMlbsrxO95KEWK+2ltf1CNTZmfE0PkmTGdjdRYCgC2t3g0ItB9AEJ6lCCnfm6xeTVZ21K4vQ2bYJnvuhQg0PmrtHoDmzrPWF3eh+dgLAd16NH+LbLz9lE8OmeLXoLnoh3LIt/nwGJrDqZpZWSPXLczaofWQtV6WPLn8KP1nxhKpZLP4dT9UFgBP5nZseKt4eNWfcto2rtuUQko6jf1dy4JlPT2PLOf+42vf3D8K+KK8tHJjYTZ+FCkvrayrSJcdgEz6PhjzrB7ZaD7m2meZtl1XTmrbdeVt7eqeeWZ5dXGjc95QxCa/iDYOJ6G51RV4tiJdtn95aaV/WvUlbDMXCTdmIuHq+WiedUSbnz7AyIp02TvAg+WllX7Gk2+V1eva53comuFlRepDi1b0vYIgUOiPKWu6tftiu/ZFs3PRRi6DNpsv/ATP2ipb5WeVLcZZf0MSS6QuiyVSU+yEns/IPIbMUq8j86LPDr2JlNa/kvHoCWhBehgxDGsRI7I9WuzmIWU9Cy1w/imzNGIwshEouwsxO/ej+Fq+/XwpWiCfQOa2TmjhfILAUfwqq8Mo+6wEAcUdEDg8A/m1+U7kechH6z0C8PYmAoJhYKylg7rf6uunOfJjWfkBXgchc1lvpCTrEAjyo9z7i7Iv7yMTiAc8MMD7oGBX7yVXyMpCBEz8sWiBwE4BUtj7AYWZJnatXRb68yHxd+6OJVJdrX6+T9l7CCh9hcDlq/Z5F8TSHIYYqgcJnPnDSCmtJfQNwFlgvzsAw5Px6N1W/v3ItPwn66uHkRn7KMSEbYv8gu60/i1FYMX3wapGDNFs9D60QmxdVzTHXkTA/SSrQyFiO8vQnLjOrksDwxa80vovwNtDrp9fYu040sZnb+Qg39P6rhaBtHcJTK6DkVm9rfVHHdp0lCDw1s+uX2PlPY1YrCiah0dZO1ogZdXO7nkRgaQp6JTjl8i/rBgp38GIBYoQpDDy89llkOPtAmv7p0j5vYzerbOBQ5Px6EXJePShZDw6B+f+0mpAbVM4B2dt8LNAFAKvxBKpXdgEScaja5Px6L3/vCR6dX6XhpznL99/kxz2c7LXnt3UlHWtcyxC75r/LjWid38VGv9nKtJlnTdWTkW6LGKBWP8rKS+tXIr8NpeiOd6AxmMR2og9jJjHj9D8T+Tl1DzSrf1k0Lzzw+nUoo2Mn+JqALBrRbosXJEu2xcB+hBB6JyJaJ0IofmUaz99+QFppOYsGnLn+OmHNEyYccDHS1b2umuTO2IDYidCXTIeXbNdr7c/6tRmBlbfauTesFW2yi9etmggRhBXzHc0dEiBL0QK4a9owbgnGY/WIoVUHkukStGi9Q5ScMvQi5tGyuhOtGilECvxV0TZhwmASUek5O8gcOA/LZZI7YBA1MFIcZ2CdqSnoMVvLoG/zMdI+Z+Hdon7ILPfA4ha3xWBO89+P2s/SxDIfA2xH+8AxBKpMmQaq0KL7f8RxNS6AymPFQShIa5Bp/qqEWC9ELgulkhd3KyPL0As0+XAe5Pd7ms+d3u+WkPR7Ygp8VnIEQjEtiMwxUZciJ2yWmV65XZoOge43YL1no183hYhU+VwxFD5uQyx/rnQvt8NAdt7bawOAjq0HlRf5cI0OPe1E7JvqvH//731ewyBub4IyGyLwHYbZLp7HYGpc7FgwQTKLQ+BnEUI2CxFzOFiNP8eI4hhBlJuN1nfD7I6fGhtu3PHW+YP2PHWeZ+Hc7zlaB4kre9LEMD5FAF8z+qXY+2NWD2aEABfiwDV79EY19nvW5D/3jsIyN9nbb4MAaT51kcFBMGNP8T8JZPx6Aj77hrE4HYkiLIfIch36j8vgpi6m6zfj0Sm1WHo3VuG4su1sP7hzbv3fiGndeaIVdNyxswY0TbTUEMRAcArQHP6vxILEL1Jcu6w597xCN26orrz+FU17f+M2M7P0LsTQutNC/Qu14JOW1aky16qSJf9riJdlhVLpNyyqu4P1DXkvVSRLmuxkUf9EFkJjFyzMjddX+tqCfK49gPOKS+tvISA7RyFxcsrL630yksrJyPT9clovnVE82sGGtv9CBhQP0OA7382CgHP99G8esuuTW2sohXpsj4V6bKj7cQmkXB9v0ikIWtNbZuRd51ywfiN3fdDpSJd9he0fl1ifXwrAWDerry0cotJwL5Vtsp/I5vVNBlLpA4Bfu1lqF81OfeZtfOzX/3wX7s0N4VdDFxhIAszff0fkLG/C9Bu3neOXIV26vugEAT7mhP6fWihewYBu22RcnoTgTA/1Uwv+2mHmJEcAjNoZ2RCXIxAyZ1IEc9FAO9UBD4KEGOxF1rouiFfn20JYjX5ATvHI1Yog5iOfRAIeAIxFiVoB3w7SuZcROD8PwMtupMRMOqAgNPTVs/XrR5+fsYV9vydETC7NZZIDbN2vpaMR+tiidTF1a5dspp2o9bLBerZMyPrfbYCWJdpZGY4m0VYKoxkPLo4lkjNQIzPCMTEdURAca7V+yuk7D5HoLQXAr7LERCIOEdF7Qq3LrvI2885nJmR5gHHxRKpJdZHDYjFdIgVWIGATi4CW3vYT9rG8LcIDIYQABqNzKLnIbCQsf450Or3JgLO8xDjuMb6cD/r5yIbzxYIWGeh+TkKOMdY3F+hufRH4MZkPPpELJHqgBT+ajRvj7Rn72G/l1lZDQi8v29lxBB7MQ7NzRBi2SKIzZmPlO27aO7tYe2eDgwxX8p70HvTF4FBR2D6bINYkm72vAUI1F9rY9UDMSuLERhfjgHiWCI1BW0I7gBaOMf2mQbn0FzfCYHiF9kCTud9tbz/kNaFXw2ra8zfFr2b89GmYD5aJyY1N0uuqW1VFAnV7rpqbcedF63oOzM7q2bw57P3ObZ7hwkLenUZX//f1qe8tHLuQ28d9Py0jwbt/8V7Q2acdPMDy9AcexRtkvqaP9ToinTZR0CkvLSyedqlq9F8Go3WrzgaXz8swUw0D3xXhbVoTUyjTcgBaMwdWmOOqUiXXYfF8isvrfQq0mX+YZ/haA6eevmzV7y0Zt32b+RkZ4+uXtsp+d/2gy/nPnx/x90GRH4XDje2QOvINQRx7uawaSdmf3LJCbtF9Zmv9dFGJTvE4romb6s/2VYBNvOpyVgi9Tawt5chXDM7C68p9FHLPnXR5sEc17s+B7EnY5PxaLV91gkpUD/Q3L1odzgCMQTHoIXlAwQeBiOFc2YyHk3Z6csSBKD8HF17IJOZQycNn0IKMIlAWw5axAaiRfJh5Ejthwe4HJmlahA42g8punrPI1M9PTunoHt9y0ju174gtUjZ9kEMFWhnuggxHVOQsgijgwc32DX3IkZvkP1+z+pxOvJf88sai1iq69CCOw2ZsR6wZ04DHrM4YxsUYwI/JIgFVo/YxtOAOsspGLFwF1gC5yF2z7EEccHWWf90R8zKq4jh3BeZCiuRcvgdUNxQ4waF87yIC4FzXyfv7oMUzhQEcvKQOfJq6/cCK/NzxFy+bte3RGbKaTZOrZE/YXcrYx+kxD6y+iy1+sxC86IaAf3tEbOWsT59GQE2P5/lomQ8OqZZ3w1FTJiHNgV/QwD8FDSPettzFyFglUJA7Bzg82Q8ulsskboFMbFPojmcRr5q+WiOnogU7dn2/Rv2nFOsz+cgwPuKfd4WbRb2tb8/s755GoHK0WgD8YH1ccb6czHaQMxHvoDZiMGdbM95CvkiLQYqMvWuMpTthdBcezAZj/6NLUBiiZTr223074CxNxx3TSVARbrMbcz5+//+9uz+PTuOeWHu0h1y6+pbfN665YJemUwkf1VNx0ufvvDw2zZ0zw+VJz8pG5NpckPfe+ygxUMP+zha1GmlH2h5bXlp5QbNsMYURcpLK1eaGfUTgkMYVyOQDJp7Ht/cTNWgud6PwBSaj96rfDQP+gAVVTXtDp2zeEibPl0/XrR4csf3Ovedt2d2Xn2X+sbc0Nq6Fo0Qufb8/Z65/sfoh1giVZgdWfNq944TBw3ulfLXyBYE7gmfAXuWl1b+1wD4xxbnnPfyHhu1Zn8th3+wEM/z/tND8bvLb57gG+fcKcBOnued/wPKOAIY4HneTT/0+Rsprwgo9zzv3u9xbc2GQnD8wOedgrXZOfdnoMbzvPXzYf6iZHM7658NnNRYy1kN1eH2hT0bOiDmZGOU87kIhNyHBYxLxqOLLLbTWGBeMh4dGkuk/Lx7KbRjf5MgH+JFCEyUxBKpUxCdvxD5S7RGfmazEcjYBgG/RQgIDbNr8xGAqAJeSMajTbFE6jGCdDjHowjr1bFEaikCWquAsZ7H8FBWJtxUG6qP5GaykSJ7AS02JyD2ajlBINSlCEyOQAxOvtUtisBhd8R2dLa+G42UeZQg2GklUpT/QgxZIWIC/4BMbCejqP69CXzbfgOcnoxHv4wlUtsgv6lp9oz21p97oJOef4glUlcDMTul2hYxHw6ZEV5FjM/tyJy11MpZgsDPowg8z0NgI2n9sSKc63l1K9z8vPbeCAQe30MA5RjEWo2x36dbH+QjpqiTjWmx9evJBAEzhyIwcb59f6qNexu00HezvuuEQEgTAkwOge2zEHBqRPOu1satFQKZ1bFE6lPguWQ8Oj4Zj46NJVKjkcIbigDRy/b8ntYPy6xtY5GZtTPaQGwTS6ReR8rHZ4DLESh8EjF/tyMw94n142XAhEwjXTx4PRxhPzT/ahCb97zdh7V7EVLcr6F3Y5T1w7PWt81dGGoJcrQ+ipiXr4Df++bCWCK1L7A6GY8utP9D6N34QafxfkrRIZToHc0/2xgIO+vBhw8e0rvy7vy86oIVq7s31jfmZTq3mbzO8yKzOxTNvn3Tw6J9U5zjqdXLWvVaMKWnK2i9tm6XY97bGW1ivo1pugnlj2xAY3MNShxeXZEuuwSB4rfRJs73ufRDdixA68X5aB2aVF5amalIl12GALrvs1nesmBZdkmncVQtavtO6t6jj+45dGr9vme8Wp8Vrs8vzK3Kys5q2O5H6QRJbX5O9biC3OU0NkWujYQbxyP2+UEUomf7ca/v8qcrdpny0Cyv/5zvKGurNBPnXMTzvJf4cVNcFSHd/J1AbKtsWDYrEEvGo9OBq2OJ1HVthtT2RYv33A1dG0ukIojh8RMmN5dvLKAGjL5CbMe7yXj0n7FE6mSksAYgh+VnkbJbgxiTHARWdkRA41h7nn/8/jikzHojtmoFAoT/Z8mwr0cL5o2IMSnEwlt4HlMbqkMTsltlnguFOKigW2PGy1BLEGrj38j0WGV1TlrdnkBMxgUILIyz/z+0732fllvRAYCW1jdJBFZ8M5t/rD0fga8u9jMLAdM9EDg4GO2i90cO5oOQWTaKQGorghyOBYgBaY+U+oEIjPRBwCdt9XrE+vgjBBISaOF3iCU5DLF0e1sfH4fm5QygbShCKK+954OSjsCCZDzC6uIfAAAgAElEQVRaGUukjkALQF/rk4zV0fcbO9DK/CMCAI/YGK8iMH9eh8yJeQjQ1lt/+ocy/B132OqbY2PrJ9JuRIqvDplJ5yJgG0aKrJggr+I51o+VyMnYgTcJGntC1mnILHs48gMaR5A5ohzNycsRkPwSAaA9ESB8HpkLW1tfLAS6eB5dcbh18yNZhT0alyCfsp7IPFxt12Hj6WcK8AObDkMMRB1BwNOQPScXgcH2aN6eZn35dCyR+gK4PhmPTqOZ2AnoD/kFSkW6rHP3DjvcnfHokpu1uq64w4TRi6v6FELWX/p0Sz/5Y4ZPKC+tvK3ETbkvK7eu4PXKE/xMIN91cnEUMuv/GQGx08pLKzNWXgZ4z0yZuxMA6qXAsqqaTvOdayprVbD0Ov8ek3vQOjQfzeP7Qo6eLfKXr8zrXH3FwGFja/I61q5cva79efk5VZmcrNpapIh/FDl6r79EPI9fNTWFO2Yy4RThrw9TNQDp6qWtqt995ND90cbi5h/rub90cc71ROucf5L/VM/z5lo+yFq0pn9g0e99Rqm5T18/tNmfaOX0QmvdmZ7nTTD2qbt93h24w/O8u9BmoLeVlUJron/IKQu40vO8F7+j7icjVyQPmOB53v8559qjdbG7Xfb7ZgnJN1TGBWjD1whM8jzv+I1du6XJ5mbEgK8db7/4jssOQUpkDoqw3/z+JebIXmNsWB/kp7IIsUAgALUG7QDbI8X4NBr4JchH6FG0oEUQKLkHsUNPool8ElKsLyEl2AZNlBBiWM5FrMRE4JpYInUlMG7twsjBLuMGh7IzJ0TyiISyqCVIxbMEHR2vRgvfTuhlaIsU/wqCk3EX2+dfInBxP2KuTkMAdRoCVq3svgor9wq7byQw1Uyyp6BF/l/I/FRv5bWx/49EpqsXkNnvTOt7P7p+DDmOv5SMRxtjidRwe8Z4xN4stuc2oJfzSsQwzbQ6TUeM470ImNVb+89AAG8RAs7nWj/5prtbYonUrdYnM+zac218HrZ6XWz9uA0yj9YQJAtfiNjN36KTltn27H8hc94tBMzBKgQ6a+1ZTdYn4xBgmoTA15HWtqSNp59f8l47ODIZAdADgWXJeLR9LJHKL+n8Wa/8rKqSyfPK7s542SOsv7ojH6VdkF/WYwj4+GEtDkfALAexdVUIBN5j47QNBv7qVrgvc9s1+qmYtgPaex6NwBLn+Iu14wbr9yw0b8LIjLqP/f1vtIA32rNaI8DtWd9MQ+D3YGSqr7M0Rhcl49HmPky/GKlIl/lBVGePmXLk/R1bTyvp2GZ2UyTsre7WfuperVvcnUp99rsHrzrq1h+9fbO8/uvQPP1eUl5ambT61gLz1gNUvpSh9e5mBKQLPY9n35940oCQa6o9dNfb/1yRLltZXlp5u5W5miAN3BsV6bJhwEjnaMjKaVh3z+1Xn/vEx/v0rq3PLw27xl2cIxuN/5Ob3PBvyvlAp0wAcXOB2xqbsrzqte1bF7X7qvXwyx+fUdBqdej4O/5v/8am3COAa5Lx6PIf6flbsuStB57aELBbfwNGeJ43wjl3GvKVHW7fdQN29zyvyUx7AHieNwTAOXc40qsfIivHOM/zhjvnhqF1xQ+t0h+B8xbAVOfcfYiBH9SsrAhwlOd51c65dsDHzrmXvI34QTnnBiL9sLvnecucc23sqzuB2z3PG+2c644sW9t+S99cBpR4nldn5tJfjGwRQOx7ysGIlfh0Iwt8DVKmS9CA+DGgck0ZViHkfhZamA7CfA3QxAsjFsxPOeMh8+I1FvT0NbvG90vqhhT/F4idmoAUUSmBQ+wQYGYokvk4kuvtF875OqXKiwjUXIIUW0ekrKchRbwtAgL3WJ17oxfkfiv7FgQAdrJnnmD1+xCZUj2kOA9CTNQFQCoZj86KJVIFsURqeyujLQIQw6y+v0U7oFlWz0GxRMqZz97XwTNjidQNCOxmDISdYOX0QC/tTYiBewH5Ul1qzyhCi6p/Mq8T8m1abm1/GXg5GY/eZc/5EoHnsI3LWcl49G075BGyPriawP+pL2KstkVANBuZMI+xcj5Ffk4h69/pCIBMREDfP2nW25631L7rYm3yzYc7IPDWC4GxZda2bJsLDs3BQ6zv/WwDBwCtzFR3XFHBgptnLdzphowX6Yrm4oVofmaQr1UJAta/ReziWOQ7t8qufYogj+YH1vc7A2HnmJTX3htqc8MPJPxOYy07RXLo48FjzjHf6r0WKf8mBMY+t2e3sz4NI7B2JwK5TQhg74bMRS8jAP0pYjjbAFfFEql/INC4PXCV/95a6quDgWe/LWvDZpTBaCP3SijU2Ds3e50Lh7yI51HkHK4wr/qQo/f6SyFsGUDTWLmNho2w7++tSJc9gpiug50j2rfbB69OmrPvSLSR+488lc3un1WRLvPnURR4KhTK7JCfW5Ozrjr3lZplLY8Cbx82EYhVpMuKEPAfVV5auQo4wDlcdlbTOmjyU22F5izevmnWoh26lfZ7wfUaOr09sFu72rm3LVrRtwSt9f8LQGydD3gg8Jeyf3dDp/9BMQ7/2uy+5zzP22BGAudcH6QP9vU8r8E5tydaM/E87x3nXFvnnJ/54VXP8+qAOufcEtjgwQQH3OCc89MKdrXrFm2kTcOsfsvsmSvs8/2BAS4I9tfSkotvTCYATzrnXuAXFl9uiwRisUQqZOaM5nIvGtQ7ml3XG2iRjEfHEyjORQR+Q1Vop/h3xCb0R8CnBJms+iClnYsUSzukwEHMQnkskXoLKduRCPQMQMq7PxabzFcmxtRcixifSqtHdV6HzHJ7Ri97fgaBhylIERZZPfoj9uI1pMwmItbmfjSRfSD3LNpBTEUM3pNWpz8gZXgZ2j30IUgr1MvSP92MFONzaPE6Bfm0XW5tjiDFXIZMo5/EEqm9kPlqLFL6+yMQ85H11U4IhNWjRfMhBGqGIMZlB+uPF5G5MG7jUWs/Zir1cNTvftp9j/erXtvpbrTDc9YHdcCudlL2UsRmTkzGo+tiidRziFb344d1Q8zc1daGFtbvvREzsBsyJWdbPbOt/+aj3eVV9ty1yKTTiNjFRgRU6hFTdSgy9z2IgFEUAaMmZO6+xsp+13JvfoDm19nAneO+PPJDG7MdrG9SiFU7Gi1e2dZ/3QjMnUcjYLcTAkkRNHdnIcCWQxDL7iy0yPmnakd5TaxEzJjn3Nd+YxG7BjR/WjUbz8+sD7oQOOznWHnO6tEb6G8nmZcjhX0smkOv2Pd+AGXQ3C+ydl/Glif+oZHfDu3zamcA58DzFMIkk8ELh7f40D/fkBI35RR4IOf6T87sgoXo6Ff8Ud01x9w8+pZX/zkmL6f6N3e/dcLe5+//1HsbKaIJbQLWWOL0mcD5Nx9+S1GHkq+O3vuU10+sSJddWV5aufj71KciXRZB605HNK+OQ3PxLYLNTC5BPLPGDkUzIyHX1Logd6WHxmdE68KFryxa0ff+ZDy6ScF9/4dkgwfgDNg8C5zhed7CDV2znjTffDSxYQxxItps72jAbjbfAvS/RULArp7nfSOocjNgtr4citacw4ErnHPbeZ73g1N2bQ7Z4hYTi4afNPPG15KMRyciduEk8w8CsQAfxhKptsl4dAlS2K8h5fE5cL8BuqeQs/WQZDx6PTL7fIZAg0cQLXwGAkuF9vlNCFS0RIrnS2RiOs+evw/wq1gilRtLpA5Ei8bLwEnJePTIZDx6nbE7TyGA0B/5Tu2KWLglCPyciMxSy9AC46fR+T1aoOJIyXrAyGQ8+pm15zQEEN9AjFJXNOFnEURn3wGZFa9Cdv+B6OVJ2b3FyNG9BQJFFyOQsxwBEZCDuX/isgcCH3OT8ai/6F5pdfk1Anj1CKRdjRbXmdbndyGl7get9NDL/IKenVkVDjeGata1PdX6JBeBsc/t74sQIL/WxuFQY8eakLJvj4BlIQIgjyNG6B92TbF9PhQxoc8icHU8QXiSY9C8uMbqsMb6prPVYToC4DegHdgQu/YDdMjDsz4/FoHhl0Ku8bkRH+5/SI+O49vb/W/a78kIbHewsepkY/E0mnsj7Pt6An81P79pnrXjE3TIYVe0EXFWhxbItHsgYg4bgDeyC7nRhVgWCjEazaE8AlAVsvaUI8D4utV1IgL9pxEkH6+3NnyMzNB9zVeyzvriMxvvW4ATk/Gob+oCvUdNbKF+Y+WlldXVa9ou+3/snXd4lFX2xz/vZNJJQgi9g1QFFZUAKkHRseLaRneNuvZV17Lq2F171x0L9rKurhjbWHeto6hBUCJFEAWl9xogpJA67++P73l9Y340FV3WzXmePElm7nv7e8/3fs+555bMOrLd6vVdg966b2CsIeEGD7cgrP8V0sOZ5aAN2E01VSmnorVkF7Re8u2SfQ9vSCTfua6i/avhaPxyC7nSVO5Ec7QlYu2zCvOLp1/2+lWJo6/+Z7DvPtNTgfPOf+rhzuFoPGUbquW5h1yMNhSXAZ8XlRS8Bqx3Xaqqalq4tfUp3oGfpKyMtcEeHaYmJyXVT7Xn1vfvVnzyMcNv/skx5X5jMgE/CPeJ+G4kW5KngH+4rts47Th7Hsdx9gPWuK67YQt5lKP1xpMcYJWBsP2RztiSjAWOcxwnz8r0TJPvo7mKfb77Jp71vgsAXVzX/QhtbnPw44/u8LIjMmI1iFHaFJLthvyB5iHmYiICThV2UfWlqPM7WT5z7Lk3MVOWpeuHmIV3kZLaDZlKeiIFMxctOFlotzYRMQb1wAuxSOiP4Wg8ZHmciZiU25FCezMWCb0FEI7Gcy0vF+hosbrOQacePafnbAQWhiLgsgLFgEpGk/AQ5DSfhZSld7XMYgRyspCP0lhkKvUcFkHAaiICJ4Px79YcjV6em6x9VUjZtrN2zkQM0moE1qaiAw4l1gdnop0r4Wh8BAJot8YioTrzixsdi4RmhaPxeQi03W9tOgf/Aug5aDF2ERt0GCTNb529+LZV63sei9ifPZEZuAViK70gl39C5rkbrD7vWTuORcriHXvOu9bpUATGDrQ2xxFA6Yf8/t5BTGdXNB9AYCwFKas/oBAkDyI/wtWIpcq0stsgEOWZSJJsHOoAsjJKD3JxLs7NWtpp4crdh1t/LUPgsdD+vx3NhQttDA5H8/0RZDacgebXcwicpqB53c3yWYnMgmPRItTO6pFi/Ztj/bXa2j/J2lxpeSyz8tuhueeg3eU89H54BxYS+P6Iv0fg+3doPkwDjohFQmfzQ/mBCS8WCQ1mB5ZwNL5bMOmPu3dvN7UmL3txKppHbwODAwHuPWno2He3ksUvLuYu0RCLhJZsQ/L2yDdy1mn7ffAV/q0JAKyv6Dh55qIRr1RszBuC3rt7aXLPLHpnwb99YjRAZm5FeWZuhQsENtZknbe2vNNhDg2vFJUUPF6YX7xZU2FhfvHyopICL6TMUPTe7IxcDCpLN3R+Ysb8A87q2Prb9D6dP/fmHmgsuqG1uy1aL99Fm9r/dbkA+IfjOJdhzvpbSuw4TjfkU9vHfMpAa/sNwFPm1F+F3vHNiuu6pY7jjHccZwZaS+8E/uU4zldondniaWnXdb92HOdW4BPHcRqQvjkVrYUPWT2CiKQ4ZzPZJAFjHMfJQXNltOu667dU7o4kvxoQC0fj6UjpvOzFANuUGIjxgMwwZFq73i6qno8cOedY2iOblHE5/kmvFQZ8HGR2q0GgpA4pjYEIbCxFu7Fj0CJThZRSnf2dgQDNJEv7tBX3DQJBl+PH1soDBoaj8bctXT0CdC6QYVcALUMmozcQy7DOflogM42LTIAdETC7CR0/f8TK8HY8z1se16GF7Fj82wNuRSBhJQIXLZHC9S4l9xjCBrRIH41MSC2t3dehRXFva1tP9DJ+jH+qcRhiPS5AiviAcDR+meVZYnVci4BYVxu7UmS+W2yfVSJQ8IQ9U7hiXb8ViAHcBSm/e9CCW4tAwM1oHnmBT8fb36VWr26IsWyLWKusRn28EbFiJfYzAYEM7/7Fy9F4lyP252Br96sIyE6zOXUNAnVXIAXX0so+EoGkC9CGoQR4tLq2xdhFK3etW7qmfxt8xrXG+noaYpVaJBrYZ82EzHnVq4Nfdj2mrB0Ce2da++63Ps+xOk6wNh5ofd0esbcNiJlLtb5Isv7ogOZjZzRfDrR0LewzFyngBjTHOtl35yBg5vmFpTbqo7iN67toU5Fm47rA6kQ4Gu9v9XrkZ174/atIOBrPqKsMPBxIuN37DR6XGgi4oH77DM2h+7aYwa8gtqbdi+bzidvwyArEPi3e1JexSGhhOBr/ExbcdzO3FuxnvyvQe7t3UUlBGWKYDSglcpMCNRkZLdaCAsL+oTC/eM4m8vLkb2hu743maj16j9/KzVr+u+7tpyZys5avQfMqCc07B8379mjOJqN1/H8CiDWNweW67tOYTnJddyG+v2/jNKdu7hk2bxU7qukHruve0OT/AY3+LmySfNimMt1cDDHXdZ9BFoDGn61B+qZp2qfx29y4TvtuKu//Bvk1GbFvkFKOIsW1LTIK+U79HnjNfLFe3FziWCQ00/vbnNL/hpTE4+ilPQyZYkrtszca/X4BMRFpKCTAtQjMtELK9n23gb8k6hh11M3xfYIZ7I9Mic9ZmnTUn0MQaChFSvwutBDmIF+vKWgBXYDMkW0RMBxrddiAgOZY/GtkvkU7l9uBvcLReAUyAXpmv+vRIQHvRFsdUpwd0anKaQhMeoE790dmz6VICffEvzpkNjIB7oYAWT1SPkEbD5AS9hb1DxCQG4L8o1Yi5fAxAi/DrC8ewPdp6oZ8t8oQON4IfBKLhJba2F2HXqr7EBgLWFtzEDhai5iog6zcKuQT5l1WnYcOUUSsDxajxf4uNAcfikVC66ys4/HDnxxl/fY1UrxL8K86us7G5B7EyHW2NrRCDFEDUhZeGIhcBHaSa+oy35o+75BTrJ6nI4DYyvJaa/U7t2GjU770vZw2SWnuvXmDq9pldqn7xtJ5l6MnI4b0mFgk9Gk4Gr8fza0EAlrfIpavBdo9drZxn2/94h1SKLO0XiDdhP19nJXXBjFwcfTuNVjfetdTeeEsGtD86I9A5zFAv3A07jGuf0O762EIwO3wQCwWCVXtueekVU51embD8JS3U7I2fone1bcBZzOnEn9ViUVC7rF3xx+vr+HVo26P1wZTaOfN503JfLefi+/PuTl5KCVY5YwaFp1TVHJzBXq/Amh+HIivsDMQCLoRbU73QvPBSU2ponfnzxPpqeUJ9J51wLdKeKdRB6H35dvC/OLVRSUFY5C5PYbm2+lA36RAQ5fu7afX4ZuX5qDNWQA/DpqD1oT3i0oKehXmF//HTZQpAVYeMX75NkXW/zXq0yz/HfJrAjEvMnTqFlP9UK5FJpBXvA8suOgxwN8bH1e2XeJAYL75owxDZqeOsUjoWEvTBSnkXRCw2QUxE10R43Qq8rsZhXa/3j2XuUBFXYVT5+KmJWqSDgpmNOQg5d8TmbNKkZIE9esY80+70spOQ6DDi9NSjAErfHPhFKQIuyCgNQqxNPsihqUzYipOsnJetPpeYWUGEIM4FJnRDkYmvRcRC1eH2LJr0GI6F4G0dmg3ci5yes9FIMQzd6YhynkMAlu5wK3haPzvCIj+CS2wSUg5z7D6paKFfBLawWxEi3OylVNoJzmnA/uHo/GnEAh4HIHmcfhmWc90FkBK/knkRxVDhzKeRIq/FIGb1QhEpOMD0xIbqyfD0Xgxor7PQocqeuGD5BrgfjO1tse/nmdqOBpvYW3+CzIJJuD7GxJSEfs03sqsBKrs1oFHECiZhk7DPm1pz0DgtyyY7s7N6bexJKNzbWpml7rz0S7/AwSkLrLybgEeCUfj/0JM79uIwaqwcnex9s+w59KsrLMsXar1lWf6rMS/JinN8llr7RlgdUizfvRA5rv2WTsb2w8s/6VoIzEUzceJiMGstbr+4lJUUtAFKC/ML/7JZom1U1qcB2S/8+Wla1BsQ8+0ut1PeNqBo9OAx2KR0CYZq03JlMu7vD/orsVpjqMDE+Fo/GMEVspikdCuP6Eqs7IzV+WiedEOhWlph9ajfvhAzEHz8DE0V3p53wUc1+nVaRIbKnO7VddkbkxO3ugUlRR0KMwv9pzAe6B1rhdQXVRS8B4yfy9A8zYXrVkpVk4Q9fksZK7auVF9y9HaHECbjGx2gFOTzdcWNctPkV8TiO2KTFDX/ohngsBbsUiookk+h6DFvxS+D/b6HAIlUcQ8jUNmlc/C0XgHBLyGIqV3iOVTiQBPClKKe1gZbZFyfgApmJXA8uQsd3XtBieY1qbhTASIeiHWaioCKPXIxOc59mP1G45AzGir28exSGiuRV9vQIrtT5bnKrSwnIwYlBwrpw9imNrhX8q82NrVAim7SV6fIVZvDFroHLRojkUL3ahG7d7X8lxgZXex/7MQK9YZgZNHY5HQv609NyDQ2icWCY0IR+N7IxD0R+u3deFo/M+WZmf8k4Ag1uU6tHBeE47GL0Sg+VwrL4iA6Z0IIK/CdyTfgFiwNMvvemTqrEVA5Vvr/xOs3NZowe5v/Vxt/dUdAbLOVh8v2K8Xv60VsLqRkkxFZsfx6PTkYQhopeFf0p2NwOgkK2cmAs9efLzZ+H56p6J4djmWfw5wm5NE/54nrdsXjfsVNmaHWd/ciMBVprXrQqtvH8vzQqTIvCDFjyKQ+A5iNN5GG4dd0fwpReyGd6F5R/w7KhcjkO7F7NnN+r8WgbHdEVu5s+Wdh4Dv7bFIaIL5DbZCV5FttD7e7mKXTl9p+U9A83kg8HFRScH1hfnFNUUlBYej9+eRzV2JU1RS0KFibYteX7w24vBFX/V8Yb573JfhaLwebU4+RgcOtledM9Gc/bQwv3hNdubKnWvr0odX12a/xWZMh5uS+W6/xJE3Lg4HUuiflMIH+GzwT1rTY5FQVPV7tjMCWl54kgPQe5JrSR00149FwKpxeS7gLl41YEVZZceyobu8eBWaR971O4vRGngP2gScZZ9Xo83pAfj+iY3zbI02n56PoufrmorelT9uyR+tWZplR5dfDYjFIqEV/Pi7QF5HFxUPabRbfBMBnwWN0o1Ep9RczIncfHn6IwZoLGJHWiDl9jI6dbMGKeQ0tHPvgRTO3ci5OxMxEB2BVCcAqS3ddARSNiCwNxsppkzgvVgkdBzINyYcjd+CgMBSjLGKRUJ/t+8H2Xdj0EL1MFL23l1vbdCpvtFo4XsCLYYZiNlZbW2bg5Sp53t0jaX/AinWJYjtCyKFmYdMU23x/TsCSGHPQYB0FVLa3yEQ1fSY8p1I2Y+xQLqzEBC4DSnnGxGwDFr5E9FCWolYmrPQ6dJTrczRCDR7jMvxiEnLsX5tsD70TgNWIaAzEQGoXshvbHcEZr37R+dbmlHWxnbIcT9hY1yOgFtrpAi6I4DR18p7sFHfTEPzoydSKA34ke49/ynvpOsKa/sG4KxwND7B2uNYf5xu+Zxj7WmJ5uM6NBfH2hhtREzmzQhk7IRMiBUIJO2G2KsKZF4/E21ETkYbg+etbu2Q2TULzYtsa0ua1fFT67sgmmOpaC6NsjEIWNu803CvI+B9Mn6Mu5tjkVA1/NBFYHuIhTrYGfXTRPzQNCkIjKcggIn1xVJgbFFJwSir4zHAXUUlBcMK84unNM67hzMrc1Rk/+jAUMluAw6Y1CXREGiJxsV7v79g+8ruiOUOAK8eMOjxYxsSSbV19RlThc+2Xd64PvQqfG8N6I7GonxLzzSWvQ/5/IzMrrVX1JYGT/nklb0/AyjML16C75g/oaik4CF0QOUYfNbd89NqCsJKXddZ3KXd1zfPmbr3pwHn++vhsLzrgJeKSgrykduAdyJyIWJfvfA8VUBqooEaHBoCge/nXSX+FU2j0cbgo8L84q0FA2+WZtmhZUc8NdlYvkJK43vnfrtUen6TdDPRS1nBDxciT6n8Gy3Ug1GbZ8cioc/C0fgApGQX45t3khAb0QMBi4Ptc49JuR75kj0Qi4RWhqPxw+2zWVYO4Wh8Z+Tv1R+/j8fEIqHGO+uRiK3yIrR7YSVSkAmrG1qQ7sBnqRykoPsgdqY9/t2KIHC2E2IjrsK/F9IDOC0RqKyyNk9ByrwN8vNqaWVMRYxJRwRg64HUcDTu+XN1tPbVI6D6IWJr3kKMUQ4CibUI1NVavpWxSOi7cDQ+2NoxAYGF3gjI1KMxH4APAL5AgLAYsakBe/Zq5OQ+3eo/HAHAefZ3GX6k+OfQgYLZyKR4EdA5FgldF47Gz7Y2eI7npbFI6ExjWR9HYP0m689BCHDORs7LwxCw3YAYpzsRgD8MzZlOCCCm4pv83rC/d8GPSxZAYDDXxuYc6+chiOE6wZ5ZZOM2zfrMQb4/ra3tZ+JfLt4JAdqv0DxpZWO6ELEZXvymGgSSvbhuz1l9h1q/72nlrMV3+K+2Nr5pz/31Fw7MeiliUT0Gshq95y0RGPZ81hJoro2038+jjdmR1qaBRSUF0wvzi78/kZ3RqfbQ+JNHD92wuuULXXaZvygQbPgXQCwSqg5H42OAP4aj8W8sPM72kLnAl67LHWM+L7ghEKBvMKnhu2BS+c8JDrszuA8mrytdFpy/+l4ITdqWhwJBWielutkEyN5cmsL84uqikoJeaA48gDaNCXyfTdd+qoH5n0w7Zf7a8i5HTr2mU6Kk5vGRwFWF/39mXIXW0mPR+9kevct97ftMIPDp8wfWDxg52W3Vcd2HaI1LQ5ujyehezB0ioG6zNMvPlR0aiMUioSu2Md3icDTeGzEtT4Wj8VNikZDHdk1Hpr8L0WKdBiy2q5BeRKCmCO22bkOA6gVL3wEpr/uRmWIuPquwOhyN74OYrGQUO8y7Y68r/5+2Pw7dS+kgf5uVaPHy6PYKfMU6zcq4BN/EloSUoHfyz2MFliCl3IBMq20tjefc7d2vmIEWzgqkfO9GJoeH7FmsviuROe14fJ+3DxE4ehEp9jBS7K8sgkIAACAASURBVFEEuPa0/ppv+e2EQMlXVmYfxLR1CUfjryGG54+ITRqKgJB32vVTBGx3QuzemFgk9KCZly+x+nyNItz3RQB6po3hMQjIdEMgZl/7eyG6lHqtxakbATxqJuPJCNS+g8yerne6NRYJXQDf+xa2tfaNQEApw9J7/XuljUsDOjjxBGJmdkLzbgwCpVdZv6Yj38ehCIRejdivPdB8mIt2/btbPl2Qr9pliD2bgxTW36wfKhBo7md9sNT6dAACqtVWj+PQnPFige2BmJ9OVu5Jltc+6F3xWAsvjMsbaO7tBxTYrQuEo/EL0E0LD9n/56P4cifFIqEP+HnytbXFOxDTAvX/pk58ZeMfKuiNQPIa9A5dgt6ZN3o4szIufOFap9vQE04u29gusTJrjzefufziz5vkdR4yL69DrPKPlsueu6MwPXVDRr+un76M1pgbgBFzl+7ZpkfHyS0tsO484IKikoIHf+JhgAUpdWUly8a2PCfFDbZlG6m1T/899M7hR352/7g3hlVvJelxaJ7the8sX19Xn7zahezkpLr1jsMVG2uyugSTNmYBiUSt82dL/yJi1hz4wcXqr6PNH4jlvRatI96VZq8M2H/qmQ11KcvQOrAr8hOdvCMDsGQnuKKehq066wdJWlnn1jf7kzULsIMDsa2JXdWTjoDLMgS61uEzaB6YGoKAxatI8T6PnPY7IaX1Klrsj0SLTj1SqKfa36vQ4nA2Uohe4NOhaDdXg5SxJx+gxT8Fn8737rx8ECnM46wObyIlvB/ykbjZ6piCFM63SHn0tt8Lra47ITZmpNXVu48x2fK7x8pdg3+5tYsUihe41jsNWYlYOdc+z0AgthsCpf9Ayj9g9chEwDCBf/fmVATMpiAw9Yo9twj5QQ1AZsH2+KEi2qLFt4P19T4IqIxEAK4G+DIcjbdEzsHTbSx2RuDj9/gHGLojdvFoBMjOx3fcPwJoEY7GH0a7+h7Ih8oLWJqLAPWLlveVwBnmI7Y3ApSHIlYmz8bhAfzo8HMQ+P2L9fd6fPNprdVpDfLba21tbW8/yWjuekF8o/b8RvzTol9aG1MRWzvTxmi11fk8BK7+at9XI/Pmn6wPZlgbsyyPdghw59pPttWhrY3TIquDF4+vEinODPQOvGt9/1o4Gr8CMYPe/a8P2e/b0fy9m0bXY/1YMT+wMVb24kZ95m1gGp/gTELz2Qtq2x2NtedzFAS+6eHM6gg8Ou3dIT1rF9EvyIaJLUYGJwFYMNI6Y/ieRu//Zk9qb0nC0bgztP/cWzLS1mUiEPg2Wgd6paVWrHDdwN4uidVV1Vn7JCU1HJZwOaSopOB3jRm7bZFYJFR51W0XX1Xfrt+K2o1pPypI7jaAMNDGYxT+oZQkgEUrB77nwsGO497dOmcpXy/Y/6ZWWctXP3zGee16XDqre5cBcw8+67G7BxWVJA7FbgYpKik4Gx0WOR4fSP8DjXFH9J7MBl5q1an0C/RuHYXA2PWF+cU7ZBBgT+ppaHf39/G+Ny+X8dBWwdqmxHEcF3jOdd2T7P8gArITXdcd5TjO74CdXde946fkv411+Bi41HXdSY7jvA0U/jfF7NoRZYcDYsY+dIpFQk13p55f1WsIfL2OXuZaBLhykHnmKaAuHI23RspiT+BTO7m2P1L6oIWlFDFPzyNWYgJSoHMQUxBASu9rxPy4CCQtQIApDymAccBj4Wj8KqTUStDC2weBgjrgKDN3DcE/nbYAMUsJ+7sMKa8gftysjvZ3FVKKwxC4+NLKboMUU4qV8zxaKDfi34052NLvhNgtL17aasSc9EQAbyIKuRFAPkaDrN3ZCODORsr6MSvrQXSqMgmZDo+1Nj9vY3QGAnhe7KGTLf9uyE9vFgIEWfbzO/udh0DOAsRS/hMxhV9iAQit3263OqYhcHmxpd+A5kMrBLZuQizBbgjkvYRMeIMQeznD+vwQ/ECoByL/lizr9+4IJHkhI/ZGTFrA+m4nq+Mc5DC+ApkfX0PgcRwCQT3wWa87EWjaxcZgLppfuyBmax2aE8fYWLWwdGMsj8HWzjsQaDoDAW7vGqtF9nxvBGRAwMJz0q+0Ou1h47XaPvOYJNfq7gG+oxEIL7X0+1hZIADd+Ej+ZQic/YGfJ8Pwo3Z3xn9HvFsfVqOxLEHvQm+0oXIwwGBSDhxQmF+84hpmtQTmtO66Kn3ggVParVva+pZbIqPrw9F4DgKwVeFo/KBYJPQtP+5w0Q/kmOE3Z9TUpk91cYMJ19lrRWmvw3KzVoxITy1P79zm277oHc10HbJr61IDKcnVw/GZ7x8lt199bwL5S/4SMh7NRy80TzbwZdd2M5Y2JJKXbahs+8b6ivZlaSkb5iXcpLcA5rv9FhSV/KkKAfcGtGYtQ3P0KHyw7KAxK0Vry7vAxXbx+GcARSUFG9E6Hv+F2vffJJXAAMdx0l3X3YjWtaXel67rvol/CfgWxdFdQY7ruj85JIvruof91GebxZcdDoghJd4nHI3nxyKhBU2+G44UJ8j/agpSXuPRIr0AKZ8RaAc1BZmJDg1H46ejl/kfyI+oK2InuqPFoBYpvDswExWa9Eci368OaLHvhJRqGwQkPL+VVegF6ATsawFoCUfjDyGTTSIcjd+Ndi/TrPzfI5DXHQEIz2zkAZM6/Fg51ZZ2H8QK7IsUtYuAgncx8yEIRDxt+be2th2CmCrvWHgLBCweRvG1jrPPX0EMUgcEuAYhdulw9MIXWR2+tLx6Wh+VWn1LrE5XIBC3LwKJTyI/kEsQ63KfjdvH+OxSBlK0NyH2ozcC11fbczMRQDwJuC0WCY0AjrTbEtLQQl6PwPU+1sa5QN+KBcmxtVMyP+h6zPosh8SMtrnzFqxc1+tJBJjuQWBkPD7T5138/SpiyTzF7oUEqUVg5S6r6yvWF+eiAw6fonhqhwCjYpHQunA0fgRir5LwgfDV9nMpmpcJq3eajUcr+90Z/5qj5Vbu1VaHVVZWAm0wDrL8SxFw+trK7IBA40oEwBYjcLUBgfkqxF62QUDuzwi4H4GYrivQXJ1o7bjN+uK4WCQ0l0YSi4QeRazjz5Xf4Svs5aifRiAw3x/NuX7WxoUomG5jMOOBsSwgVlRS8PitE3mpML/4kqKSzzO/+Xi3lya+WvDMHqOL/9DzYj5HgM07wPJzJdVx3Jy6+rT86tqs7+Ysy0/s2vODpPTU8vfRZiIFSM1MLSeRcMqSktyHN2V2Kyop2AtoU5hf/M52qNOPlsL84n8B/yoqKRiUSAQCVTU5d6QkV+WlBGuKkqn9OC1l/jwzqfZq8mgMrcHLgIrC/OK6opKCUxEA/Qa967ugDdVSoLfneF9UUtAX+Wg+W5hf/CSbCOz5Pyxvo/U4hvxHn0e68ftLwF3XPd9xnHboHexpz52LxuI99A7vCRzmOM75iPF3gVtc133Rrgx6EG1cF6P37CnXdWONK2L3SO7luu4au2zb8+W733Xdx3+Z5v/2ZEcEYmPQLnjZJr57EC3IrVA4hcaXlC4JR+MXI4amI1Igh+ObJvZHL3868rvxFmgvOOByNDnHIFYjgdiPk9EC4znyp1ieLn4AzxwE4t5EfVpojFwD8JGBsP5Wr2q0IxyEFAj2zClosucgJTkH7SL3wj+t5uJf6Fxr6RciRdoBATPvTsQlCBychMx4pfa/5xc0AwGsQ5HibWlpj7c6gBiXXPyApV3RDsy7T3A/69t0e85j+U5CCn2BpU21ul5pz1ehxeNqZDYdiYDaALTr3QMp/ztsXFajRTnbxqcGhchIQYyMd+S+HDFgv0fM6IFAnetytpPEsasnZD3b9Zj1z/TsWPJmp9bf0rLFipG3Hn/jXIBwNO6ZZl3rmzYIBNYiZucMpFRWW3sTCAisRgDxPcT+lNlF5L2s7+qAbgbCpiDzaob9ftr62ANdK62cRWjOtUfgssr6fz2aQ8VoHr2KgMkZll+h1cmLOp6J5kcS8rHxAgmvtj5eikCca+V3QADrI7T4Po3Mssn4TPJ9aCNRjsDPEi8Q7y8kg/BNiy3QnChDPniLrf6Xorl1NrCorLJNP8dxk7Iz1jQFU0OQ4l9SVFJQDLBkZpf9Vs3rmN6+15LfxSLHjzXW3LEYgD9ZwtF4/506HlQFiWs2VLa/vF/XT0r33uWFk5MCDWVWzwC2/joOblKSOxEoLiopeB6IFOYXN17/3geyikoK+hTmFzc9qPSrSWF+8dRwNJ6Vl70wvUV6aemefd76qjC/ePoW0m/ED9/iffZ0UUnBy2hOpwPBwvxiz5WkcdozEOtcjdaGZvHlBeA6x3G8Q2hPYUCsiYwGPnFd92jHcZLwY2L2Bk5xXfdzx3GORfpwN7Sp+sJxnGK0ke2ONqFt0Rr31Fbqdbrrumsdx0m3fF5xXbc5rMg2yA4HxLa0k7bF8f4tPH4KYqQC+DT9mwhkXIAYHhCwWIGU52lIEXnmmG+QI3gOmoBnod31WqTkFuIHHMxEi4kXuPR4BAhGI3DQFpnzFiEAko8Uczqa+B8gZXeDlT8DAY0b0c7Cq2db+3tnpAArkVLthnZF+yHgELRnj7f/r7IybrRyKq0vvACmWQhozMH3vdmAH3x3jtUjGz/kRdDqdATyactGDNZ3CGB6ptJc/NhlHRBDdAN6oesQYN7XxmoI8m/6M9od90TmySp0cfdiZOryrn86AYGs561fPR+o8cjsGLW6fGNtnrD2y4yPcdyxwEnVtS06V2xsWbtqfY+u4WgcBAZHW3362Bjcbe1LdRPcU12a9F3VwtQDPnp+n+8ve7bI9sQioWOAy8LR+BUGulujOVFq7TjS8pqJ2NReyHy5u7V/JZpfD9lYtcH3ITwLzbk0G6sQfqiMvRGbdwg64HCyjVk1mrNBBPYzEUhfYOWOR2xlCwTwnkVz9EHErP0BsXNl1obrbfxusrG5PxYJ3QxM7OHMyu5x6azAfLffVoFLUUlBf6BVYX7x+K2lbST59nsDmq8e+9kDAcy+1q464FzXpfvX80e6SUl1zpD+r3p9ANqYNAAfui5PujivBhz3ypSM2vo2PZa5Pf+wfFA4Go8Cl8ciIe/wCgPzJu4cTK1dNHXZ8MaxDLco4Wi8bbd2k17v3fHT1hnpVS5inb1L7L2A1m6j3x6z3Nfa9z2ALCopSELzexf8Qwj/MYlFQuXhaPwvpRu6rYuedMVPOilbmF9caX9WbSHZ39BmfMxPKeO3LK7rTnccpztaB9/eQtKRyIcX13UbgDLHcXKBha7req4/+wLP2/crHcf5BG0O9wVeNrPlCsdxPtqGql3oOI53AKMLvsm5WbYiOxwQ+5lyM9qxz0OK6BLkXzQCAZ8H0WIYQMquHb6z7zpL6wUQHIScndsi5iDLPk9FCqAC+ZTdhfxy3kUK826kzDsgP7bl4Wj8QMRCrEOAp8HKKUXOqRcgU+q36HRmDdrh5Fq6CstzPn68nQACY/0Qw+L5mUWQUj0GKejCWCQ0IByNt0IAra+1w3PST0OAphSZtLxo+wMQCFiElEim1W8QMheNtzRtUTiIOVaPIQgIeH5hryBW7xZr+8P4fl3vWfu/Qi//eegwQSYyWT6DgOwLCNi+bmM7AAHedHs+2eo/HtHvS5Dy+gQ4xnG4scsRZZ91OaJsNFC0dM2A85euGfA5Mq96/lPJsUioGJhofbWL5TetfqNzdF1ZoHf2zlVLj7hm7FP/unWkd/HsDUBWOBr/wPr1YHzWtR1SNH9HrN0M+zsTP3zIOsToPI1YqIUIQO6OAPa9yBy4DvnfHYLA2Hgbk9b2bIo9uwYdmkhC8/1ZBNaHoTm3l9W7h332odXVC7GRYmO22n7mIQC2xsqcZW2cCnDIXz4+p9sJwSurVyQ/g8Da1uRCoHtRSUG4kTLemsy3tmRbG56x+p6Pby72rucKOg5JfbuOIxBIeCZ70LuSC8xxXU4sq8hLmb1s74PuPfmyS2s3Hhge/LsJB4777tRkK+P7YKIDW0/cfdeDJ5a077+s4q8vv9fhluNu2abTervt9O76jnlfdkpLqctE7+9ViE34E75vlMfEe2CmE1KsGUCymeYORGvXu8hHb4e4TzEWCU3+pcsozC9exQ5wr+cOLG8isLof2uz9GNnWd2+bxXGc/dB8Hea6bpU59Kdt73J+q7LDArFwND4QgZFHPTNBOBo/BCm/82OR0CS7NmgAMDkWCbmxSGhZo+eDaDe9DP9Kl73QYr4BsRN7INCQjFieExCAWYEWvXb4p7DWQWIdNPSA4GBwqvBPFnrMy2q0M5+BzKcnIqAyELFZRQioHY18qQ5EJ5JykOL9M4rFdQ9iiM7Dj6nlmQY9P6rXkUL/A3Lwnoz835629pUh5XSpnTrMRoq7C1JcY5Hv2+EIKIxGCuMEZN6LI6bmBgRukpHizkfO0TGk3HriB7StRmDrbXt2ltXzYvzrpOKxSOh7pR2Oxg9Gpw0PQeDkIMSIplvfeubbEGJ8PkYApdTqsbZRv9yAAHAFAhBnIYV3vOXT1+pZh8Dk7rFI6B/haPxJfugT5MWq+zAWCb2y+4Cpt7bOL3+8cmHqEdVrks448ub4wckZHB6LhKaHo/F++KEerghH4/9AoPFJa8vdFpPq32guZlr9QKaYt/FP63qmzzpr11+s3++1vgAB02dikdDqcDTeHT+20p+t3y5Eu9m3ERvaAgG5IWhOZ+GfjsywMemD5m+y/X+A9cEYNB+noE3NYTauE8PReDCjI1ektanvVNuhvjvbJlGg5Y8AYSCG9AYECl8AVlfVZB+VllyRHggknESCmtr61Jq0lJosDNjkZS/zWCYv8rqDNinZrkvKxppsZ+2GLm3D0XifY4bX7pGSXjskNPiR094Yf/XCxibJjJyKxS13Kqta3mJIVu2i9AcQkAJkekxPXf+7RCLYvaauxVWxSGg96D7FnTpyCY2UUMKlwgHXcb5nKZ3V6zuzePWA+n5dxgUz0ipB4+q9o89anXdGY3QUGpsoO4ByMzP7gcC1sUhow9bSN8svIk8B613X/cpA0KbkQ7R239fINNlUxgFnO47zDNJZBcj6kAqcYp+3QYCvaAv1yQHWGQjrh8iJZtlG2R4Oqb+U3I7MJyMafXYqAlMn2P9XIlbl1KYPW+DX0xHAaIsW82+QYhmPlE+7hhpnfX1l4BsEfrzgmO0RoFmGFNRKIKtV1pJunVvPDFrYrS8Q+LkVLZRvIkX/F8vrcKRws+D7gInjEZNTgEyZLmKCdkVgoidyoPwbAklnW33+ac96wSs9P7cj0BjuZuXdiBT9rkihexGx90K+Rd0QWFqIGIWZCDTUIwBzLHrhbkNMzKEIlLqILRlu7RmB/LDuxGcIU60NHou3HDF9L1neXtDHYZeMuPeIu4IX9LQTsvcg4PAwYmnKkAn5VfvuBTTOy60NhdamasTeTbfvb0Jm1ypc9wtcdyhaWFogtq6HtaU7YjEPAna2OhQBReFoPBiOxvMQcPw4Fgm9AvDljEGrWu5Sc3RDVdLFeXtWOcF0uiM/LWKR0Cy0cE1CjOzR1jf7Wn9dYwxbPZpHk5BD/RtWtwsR4/EQUrIHIGWdjBbADGvzKsT09UBBRpPRfOuE5vK+Vn4qAtpPxiKh99Fc64RMFDfjm0DX2lx5ysa9N5p38xBIbWN9vgqBuP3RBmMBUBGLhOoDQW4JprsPZHSsu5BtkML84jmF+cXbFGy00TMvFeYX7/zquGuPKMwvHvPquGvnfPLlqSsXrdwlAdTNWTrMnfzd0dnVtZmezyb40d+D+KbA+4BPVq3rMWfavEMS9Q0pVWhOFgE3JQUaFjb1C5s494DS0rzdB9YmMudAUlPweBEul2ekrj8mJbkyp9Hn6aj/HMBNJKiprU3Nqq9POhH5+CUAt3RDZ1as7ZM8Z1m+5/u3Cj9IbyZyg0hH88AzSe4o63UHtJakbC1hs/wy4rruEtd1R28l2V+A/R3H+Qrpqp03keY1tIZOQ5vzy13XXYEsGUuQzhyDNmNlm3jek3eBoOM4M5Ef5/+LetAsm5cdlhFDpqwDkPnPk3MRWPJOY3yMFM10gHA03g4xNfPwT1cNRLv5KsS69EdKMAd49uu72tcnpbp79T1/ZctghpuB2IgEUtpTrA57Aisqq3MXts5e1DIjZcPEqtpWSchfLRkBgznIZDMHKfpbkY2+HJlT1iGgNM3qNhgBrRUolEN7/HhIXhDKSWhXcy1a/FaiCX8gAi3ViFn7JwJxu6AFPWCftUBA6D6kiE+xfliCdt29EbAKIGX/LFK071jZZ1ueyxCAmYwAYmekqK9EyjnV8o9Yn69HYCMFAZ4ytOMPOPUNp7dYvK5tQ2rwA8RI9kT+ZTOtLlMQmHsUgYnrw9H4KcgktwT/fshWaDc30OrhzYnqQ9zH5wWpSfk3fw7gBFOtr9ZYHntbOW8h4JFu8+W7WCRUbxeZH2z9/JLlicWVGm0A6DZ0WCAQi4QSsUjoQcRKEY7Gb0KKNM369zy0mL2JAOYaZEp4Fs3fw20OTEQsaBaaf30RO+WZsAII6KbjnwbeFwG/z6zvR1q6e4APw9H4CdZPf8e/I9BjL+cgAH8dFictFgndEI7GsxCo/cjqMsra8SFiFndFC/pk77quphKOxoehd+Ci7WHGshswXg5H458Cl7bKXjS1c9tvegPB3OwlKQk3QHLSD6yGHiO2Eb1PAfTed1myZpeLN9bkPg6Mi0VCsy326WbveHzhoiMWW7zCpiElot3aT5vepc3XpWfv99pC78PC/OKqopKCU9BG8kgc1geT6vOSkhoy8GMKur06fRFPTa7q2zJr1Tq0Map3XZa5LlMDASagOebdFuCdAt39J3Xg9pcnECu7wwZW/a2K67r/j9VyXfdjtJbguu7TiETAdd2ViG1vKgMaPeuijeRlTfJMOI5zqeu6FY7j5CE985V9t1+jdN0bPXboj25QswCKIfKfrsN2EzNzvYUW4fcRuLkeMQZfIcC0FAGZu4H3J1/aZRTQdY+7Fnd2ApyIfxn0HLR4L0XO1RnIHDYF7QZ7IYDxPv7F2esRE9UbBehsYfXphkyQ7RAYeBQtrichhb0CMSw9kElwJFKahyEQdioCYTEEJgMIGD2LFONUBBbfQcD1FQQuWlravRDoucP+fxQp2H6WJg8/cvquwFOxSOjCcDQeRxSzd8dbCXrZpuMrhaj1zYH49wA6CIRl2d816FDEOGC3Np8tKEyqqZu6Yr/ePS2/oxD4uhUxf2dZnW6wMh9EAGZny7MasTUJ64d6q/dpwBnZ7uoWee7S+QucXRe7TqAE+bBNRwzlzgg4notA6WTrjxxk+rzI2vwHBJ53snLme3cpWpT/pFgk5N3Jt0mxeziPtX6fbvX0AqZuiEVCHcLR+DMIjN1u5d1lfdHYl6gKzZv11i/eidUNCFjeiYD6NASWihArk4d/0ncDYram2Hi9aX2fYuMzNBYJTbPbIs5AYC7V0mN965niX4pFQpu909B85kYCb8cioVFb6qOtid1E8R5i5BYB+YcNuXtQMKnuXYcGkpJwrK8cwG1oCDp1DSmkpVSB3td3yla2PKDk9X0X5rRb/1L+UZ/e9+q4a3cD1sYioUU/p26bkqKSgtTaurTzps09+LjObb7u2yFvzgK0VqTjXxS/Gm2UdkZA+Fug/vXxV16cSCTvevDg0c9nppW9gsbO81lrAFoX5hc3B87cgeW3FFnf/LxaojXiLgN5zfILyI7MiP0U+QABp84IGEwG3olFQm8aU3E0UvJevKTf7/m3xacikHQg/t2L7fAdIN9AYOAEBLxeQEqmN2KKHkDK2rvPbzEy68URE5VArI938vJT5ER+m+VfjxbcToipeAuBnI728y1axDshwDXCnvkGgcApCHwNRODCC2uRZmW0Qkq5MwJ7pQjM3IxYHy9UxhL8kAenhaPx56zMjxAA+AKB2iR886MXduMZ/JAOCaQ8piFw5B2WGByLhF4GVoSj8WOtv8PIOT/L+rHc6uxFaf83YrvaWBrvNGeKjWGx5TEd+XSVA0s2OG16bXDavIfYvEwE2hoQ27fUxiaEQMVsxNpNRCxlG+uj1cg8Otza9gYWy6hJ2BQAwtH4EMSgLQPOjkVC063cZxAA9PqslfVPpkXu/xw/7t1b1qajLG19o7Z+a+17FoX9SLO6zLXrvXaysr5GoPX31t7OlrYagbE5CIB7sZySrM/XhqPxyy3/GxEQe8ACIQ9HgHtCLBI6vGnbNyHnI1B92dYSbqN8ZO16LBYJlf7xwXW7pyaX17VrNff9nbt9+obVNRuo+XbJ3knryjsG9+r7hpuavPEW4P5vJww8YMD+X75QXprTrzC/2C3M58ufWpGikoLO9XWBJ9yGpE9PKfjw1k0kadWQSD68ujZrl7Xlnco65M1ZhebURsRcvoveCYCUxsDq1XHxlcCy97648L1jht98K3C565Lu6o1aEwhQVlRSMAKB0jstPESz7ECyo4OrHyONma9m+WXlNwXEYpFQg5ljzkZsUx/EHMSRLTwPgZlTEdiaj8+C9EDo/zngzw3VpDXUOF+n5LiTEZN2K1Lcf0YnmFag04ZjkLI8FDFg42KRUKk5tPaxMs9EivgfiOHohoDBHPzgoCOQgr4EsV4pSEF+iBRxANHMC+3v/ZFz/QTEdDyGWLDzEGOSB/zLPt8PgbMFyA/gNOQ/Nw3Ngb2s7QuR0nsYKfMG5F/XHYHDHAR2M/AD67a28quQCfAFBCgWoB3/BwjofmeO7MMR47IcMTknWvu9ALn5iAXMQyzmJVannRAwqbK+vz9n1ornK7vk5tZnpo5EzMIxCDQVWRnpiCmqQWzTKQh4jEQs34kI8ORb3tcgVrHYxnKytb+v9c0PxMx4f0SAeAACrh2A3uFovByxTqsQ0LvWxmAR8tHy4mONt7JSEZvoXZfksViZCDwus7QF9n8QM8kjP8g77bu/IVPvHxDo6442BzvHIqFKix6/yvrCwa7zMRB2bw0CQwAAIABJREFUKWJeV6H5FQhH458gsDgazcWtSiwSmhWOxv+I7hZ1fs6F4Pbs7d7/5/79sYxBvcZdu6asK98uKrjpluNum1xUUvA5eg/TWmYuT6mrT1ubFKj/AnhQdxsWjN1Ynn5Uhz5LZv3UenhSXZHax3UD+1VXpu1RVFKQC1zTOAhrYX7x8qKSgtP27PvmhSnBjYvQBiMbjeUItDa9s2Zh2+rytdnrvg/OobZ+H6epqOTm54B2c7/oNzApuWafvK5rsrLzyqmubXHystI+x6WlVHxDI9N5szRLs/z3ym8KiAHEIqEJ4Wi8BO0+TwEuCUfjxUjB3IyU7Gz8+wN7IcWWQIrrfDdBatnsNKfiu9S2XY8tew8xCCdY2vVop//3WCQ0xXyGWgKL7UoUTwajE4pRxLbsjfzNliJT2HvIbJWBlMjliDm5DQGxNPwgfQ8hU+LvEDgKI7BzHQI5QUs/HSnjf1m621GIhvmI8foMAbWVCOhch4DG3xFQaYMAwLHIPBdFAOUR5AtXjVg97yoWz39lL3xm7TwEyE608rD6VdhnQWBYLBK6IxyNz7a61NpPPQIsZyNQ9DcEkh/C9/M7C7GWJ7cfv6BP2qqvdpp+yf7pbmrwEmvDEgTasiy/W5C5bwQCOKn2XXvLZzJiKEEg9TNkCr0dGBmLhG4MR+O5aNybSnfrq3Lk9N7K+uk15CjbGbFINcDt4Wg83fruJs+0F47G98YPs5CBmNaE9Yd37dAGfCfwOTYuq9HcPsS+fxfN55HWXw9amwdb3TeGo/F8tNGYbON2NfIFfBOBRQfNlckI+PUHuscioflsW3iKxnKK9c25CBhuF2nbcn6wZYvV6TktVtfv0uPTqXAIhfnF3wB7FJUUJHVsPbtnx9az5za+PNv+/nR7lD/++dBHS2d2Gz30uLE9s9uUd0tKqg/ij59X3iIEaj0p6+HMSu45eObRw44b27H30K/7ZbYqf62yLGM+Ym03JX2BQa06rZm6YGqvvVt1WttQmF/sXvbcHcWry7qNCibVttoe7WmWZmmW/7zscEAsHI1f3SZn3p/6dv30kfFfnfS1S8CJRUL/2sozJyEF+LSFsagHHrXdfAvE3jyImLHOSKndgJTQbMSGHYnvf5Vo0a0mEExPfIwA1PUoHEADMhcCHB6Oxt9FyuxM4JVwNF4D/DMWCd2PH7HcQQrNc2TsgoBXAN9Rvw9Svi8iQLUWMUpdEHtWgsDeBgQu9rS8O+LHfqpFDuu9rawXEcg6EYGg1sgcOA4xN6sQU/IMYr9WWr6dEaBIQmbPmyzvTCtrF8svzdLMtfruipR4N6v3eMQuTUZAaAlivJKBReaAvQaZAdsg4HKO9ffd9lx7xMQ9iRiZxxFAfA6Yk76yPGd9nzYHu8kB72RZqfVTvbUtAwHoDvZ9LQLAQ6y//4pdoh6LhC6y8SEcjc9HjN0GALueqMOxd8efWPRay8q68sAXvU5dewgCRecCC41VugdwLajr4wgsvWF57oYA6+0IFO2NTN3X4psukxBoaYXm2r8QOBxs+ZyGH5riDGvbUMRY5Vk+ndB8HGR9dzvaNCTC0XgRPqt7P2K9ys0P6yob00+RSb0vArY/8KOykDE3ApPM1Lw5GYsYwM06wv8UufHYOzZYjC23MdgCKMwvbkDv8y8mf3/icreopOCqyd+NOiM++YA/b6zN7lmYLydmT8LR+L5oTXmu0UnMnHlf9O8974t+H177wUUzaqtS11euy17Q+LmikoLhwJCq6uzMGfOP6paZtm5pny6fXb3HqM9XYLHb5q/Y8zm0iZz5S7azWZqlWX492aGc9U0hbMzOWJE6uO9rX3807cxvEolkYpHQ8Vt5ZiIyg12EFNnnsUiotlGa3ZDyjuLfMen5dD2JWJyJiInxTii5iL3qikxjpyFQcrZ9V4Z2whkI7NXjg4GO9v+dSEGeY8+nIlZjY6KeYU6ApU6AdQiIVSEmpRcyJd6DlNiBKBbXdwig7InAVAg/wv/ryASSZH87Vva9CFCkI+VbgNii1cisGgMebqghw0nmeMch1XFwEWAK4If9WG6/d0LMy0BkpmyB2CQvXlMlMq+9aPW8D7Fo/7D+uhH/suovEVBYjZi8tggMv2P1AgElrz45iLWptbwzrF9H2ThW2xhmWx8Ms371rozygGBXBF6qEAi7B4G+RchUWxyLhE6kiYSj8SsrlybfuuD53EAg1W3of8FqB1gfi4Ty7PtB6OTSamC/xo784Wh8DwS459rYBhEAvt1+V1pd/4UA5x5W514IWIcQUH4FmZRByng4AoOHWvtvQPOzwtpda33YDrGLY9AcGI2AaCf8uyYftfo4CPR1Aa73Qng0aks2OpU1IRYJ/a1pP/3WpaikYFfg9crq9K6r1+2U1CZ3UVV6SvnaQMDdvTC/uBQgHI1PR+B/aOM7OHs4szoD6+e7/TYZpb+opOAq13UOHDv1jEHlVXnZLVssTwzp/+rhZwx/u/my6/8S+S056zfLryc7FCNmrMKjG2uyerfIWP+nRCLZc/ze2jPeJcnHIHPTachvxxPv9KCDfw1PawRkeiMF3B+fkehk+d2HFNtgdPLtD0jhlSPGItPyrUFKuC8CeFfFIqGbMKfzcDTeHp1m+zwWCYVCZ39ydFbP2sPqq0gkt6AYsV9DkL/Rt4gpGooA3ZGIBRmNmKdXEDhZACwlkagmELgmFgnNNv84EMiYYemGIWU8FwGoOmTeuxQxIrH1X6U/m96lOim9rZuwtnnmsQC+f9tyBKb6IzCzHoW8yEQKfBFS3uMQyLrNyrsHgdJpyFzVgECHdxvATohlm2j9OAiBhRpkRp5k/eD5mwVsXHKRyfgTS5+GQGu91TEbMXrvIfD7FfKhW4vvN3V2LBL60sboIcQGhoETw9H4iQj4vRqLhN4AHkzLqz+wdX7FiMxudWsR+9f4ehHPrJtjz3/TiMntgEx9DyMfumvRHFtsfbjG2tgZzaMaZCI9DIEp7xDE6QhkjrA2vmJ9F0abhe42fs9b2m8Q63o+8mGrw0y3sUjo2HA0XojmdFvEiH0Ui4TGhaPxM4GaWCS0hiYSi4Q22HO1Tb/7H5EWQLvMtI1JGe1nUF6Vm1GxMTeYnbm28Tp1PRrLH9wLOd/tt8VTtsD9DYmkZyqrc6OBQMM+QGpZZfsF27X2zfKLSj0N7e7mvK2mu4yHtgrWNiWO47jAc67rnmT/B7F7kl3X3eIJZcdxKlzXbWHXI/3bdd0BW0r/E+t3A1Dhuu7fHMe5CSh2XfeD7V3Ob012KEbsx0g4Gg8ger4VUoDZSDmsAg5s7K8Vjsb3Q8rvBqSohiN2KweBFu9OvR72ezkCbfcgv7JaBMTuRwDnXaT8kpHC/DcCYL2QL9MS+7sj/iXii5GPUt9EPWlOgBMSCeqSgqxHACHfypyFHxx1uf3dCh8Y1SDAeFHLGcvn1WckP+3Uux+V92lzMgI1XlyvjcCFsUjo+wtzw9H47ghkvYJOI+YCb9aVOy8RcNOCGSxwHNKs7k8iJu07BNySrK9KETBdiEDAreiE3lTE9pRbW59EJ92uRyDlXgSI98APULrK+rCt/d0XAaqE3fxSA85tiCGbjXzzhttPmpW13Nq9HIGbauSsP9LK38Oa3xnfWb4nKsAL8dACP5Dsk7FIKBKOxu9FAKYGaB+LhCqsD7sBpd7/9lnQxse7Z/MLe25PNGdeBj6IRUKVlr41AqJtgS/NlO7N079b395vc2KojcPHaG6tRZuJfMSgjUSm3xprwyq0AbjJ2n4emvu5iP1KRgFZZ1iZ7Wz82iHftebYUFuRopKC2UCvhAu1deluXV3ax2fv/8bI7VlGOBo/DW1KLvfCpjTLji+O47jbCMRwXXeLJMNm8q9A+mSY67obHcc5FG2El/xSQMxxnKDruk3j6G0u7Q0YENuW9M0i+W8GYgcjMxb4ZsE6xJrMQgEuOyMF9BViEG5EjsnjEDPR1b7riRT7IgRCWiGFtxKxN0sRiJuGlFY6AgE9kfK9Eb0Mz+KDwlLEZP0RsUDvIBDUA4E37/oSz+ndCzz5GmJiMhCQOA8Bwr5IWdYh09Ma6huSA7UN7RzXXdiQmboPAmCnI2YoBbg4Fgl9sZn+c9Axeu/eyfaN6tMOKfvfIVPjQ5ZnawRAByLWqwyZHfMQiHkUMT/nWl/tjsBIDmLfPFDZBQGG9Yjpe8XK2Ato71C90iXQA9w6SE0B5sQioX7haPwxK9+7rNpjkrw+XGT1fR0xdf+0/qiwMZmMTNTXIuD4HP4J0eMQMJqJzLgfWd/XIVYwH8iNRULvWv+lIcaqBB0IWBGLhK607061MnsgZnABmo/lKGK/G47G37Ln1wF59llPNC+zkNl1H3Sgoh6B112Qcv4A/3DHhyg6f4P1/1Abpxn2/FuW37xYJHRZOBpvi+b0hKaR5G1zE2xs1m+WH0pRSYF3mbsXSuVrIFSYX/z/Qpo0y/+e/EpAbDQwxXXdmOM4/0RzcLjruqOaAiHHcWYAo1zXXbApIGZXH92BNtGpwEOu6z5m1ybdjNanfq7r9mlSjzNQmKb1aK2vcV33/CaM2NNWTsxxnOvQTTDp6KT/2e5/K/j4BWSHMk3+SFmOzG1enKvdEXg6CCljL0p+L2QWbIkU451IUWfYs4sQO/IEAgwXIcXeGk2am5ET/yPIAX1fNGHnI0X+rP1fZPn+CZmirsQ3MyUhAFKD2LS/IN+gwQh8BZGSbokYjhyre3dg/1gktF84Gn8YmfY+s/z7EUwqTwSTjkYgMoaYOy96/t7AN+Fo/CLLryUyaV0BfBaLhCrC0fgcBLCet2d3R4ClApmsjre63YDATwD5fy1GwHMlAgBBq+t0+2yK1f8B68dzrM5hBHrWWn90tLyzrY8fAG5rl7vwsDUbuo5xE+66Bpcc9KKDgJIXrywF/9qkNOvbXOu/7tYHdyNwdyACjl8hh/9ZiLEL2DjeZScD54ej8UPRAYxRKNCqF4n/n0DLcDTeIxYJVSG/rCcQAF0MlBpb1j0WCT1tJy1fRLvXuxEgy0Sm5un4JvccBGTXIMD/MjKvr7F2edcdnY5/+8BFCCy3R4DeO/jxCZr73ezZ9cg0PRbfaf7xRn3SNJzDpcBe4Wj89MaMX7P8QI5HawfIt68BeKqopODywvzirzb/WLM0y3aTF4DrHMf5NxaAG/+E/Y+VM4Ay13UHO46TCox3HMdz69kDGOC67g9M7I7jdESb2T2Q3hqLv0ZvTh50Xfcme/5ZtL5u8RDe/5LsKHeX/WixgJkDkZ/P5bFI6CDEDNyJFLWnKO9FwOJRS++BrBXIsfphpOh2Rf5gufin/FzkfzQFKU7PGd+7lLkMncT8JwIUo5Ayy7LPvUCO8yyPGgRovDhQXtiMJEtTi4DFGsQYpSLF6CCFHUcA7g3kA7fE8huNAMNCq9OB2F2KSCnviu59HIRMX8+Zeayj9cXu8L2J9FvLoxaBnQb77GOrVwYycfVD82d3xBblWfuOQMr+DeSj5J2AdKyuUyyfVHs+DQGsaQhYFK9Y1/fO+ob0MxrcjKk2bjuFo3Hv7sxPEchyre4NiDH6GoHqUZb3Snu2p41/Z2TCDABvxiKhj2wMNgLHhqPxs5DE0cKSYm25HjFLdyPftN3C0fgNVtYkdIn5tdbmGcAHFtz1PuvDoxBIL7Px9vyEjkJxoO5Gcwor50j8q3BeRIBxOgJYVyDz6wQr3wOl8+3/gegd8PpmA2L+xqNTvsPRHEgFFoaj8TPNF86TZWgO1dEsP5CikoK8opKCIH7Q6G/Qe7ELGpPEFh5vlmbZbuK67nS02TyBH/qp/hQ5CPij4zhfIj/dPGS5AShpCsJM8oFPXNdd67puHdo8bk32dxxnot17OZLNh235n5T/ZkaMxr4TZtYZg9idq5ApZno4Gl+IlPGniBX6NwIRzyKF65lzhiAlWYkufa5Eyng/pJiqERgaj0BFApndHkSAK4YU5PvI9FOOgFAN2rHcjJTlrfhXx+yBGLt6ZJJ8A4G4U/FDSLyBTnsegQBMyPI+DiniyVanbkiRekFCPaB0jfWJdyFyDlLSVQh0DERszQz0Upci9uUge243q0trxKBMR2CsC2IY0yzfgP2/EYHcQcgcuRD5Nh1ldeuNAKDXp2nIlLkR7erKrV1R69cc68cUq9ufbMyGWV/VIqCVjBihQy3dZWgedLGxTMUP/THP6vy+ted1FLy0LQJQFyNwuACBt29ikdA7AOFo3Lu4/eVYJHQwvjRYvrmIBfsYcGKR0LfhaDwD+Y1NAmrD0fgTaK5d5/kyWprnENNVidjeR2KR0DnhaDxqbW1p/ePdAJCwfNqgd/kSZHbfYON5kNVrIGJS97LnH7BnH7RxeA4gFgmNQe9QszSSopKCrmjOvVqYX/wkmlMUlRR0Q2brhwrzi1f8B6u4Q0pRSUESkFaYX9z0wvRm+fnyJjoJvR/+LTCgNa4xwZLGlsUBLnBd970ffCjT5HYZN8dx0hDhsZfruovNfLm1ev1PyX81EGsiIzA/qlgk9NdGn/8FsUXPIzCwPwJjYTRh98YPVpqLfxVNHTJn9UELbwVSzk8iwHM0UmwLLf1c+6wGgY1WCCRMQmAoGUi265Y6IvDX3dIH7f+PUdBXz/nbC/CaZW3JRSyex9gtQiBvpv19OgJmq4AjYpHQTDNp5uBfpZSJ2KF6BJYeiUVCHxjrVon85j6wdC8hM9hbCBzloWt/llibulvb30fA7j3r2/etjN4IBPwTMVWes36d9Yd3n6KLwJ1r/dsWgZ2PEDjsZ2WfgADaoQjYXoqc+Dtamvb244UeedPq8aTVrSfy+xoQjsZvRH6D3yEQeDgCND0tj3+gOXUx8Ac7KZhqbUkCdrWwKGNjkdAKfPZrsTnf/wPAAv5GEXhKQXOkFs2/bhbrbjfkZ7gEzZs6dFCgF/JJ/BiZnMciE+8kxHrmofn3GfLnS7O8XASy86xOr1j+rezZCWh+PYOY3V9EwtH4XkBDLBKa+kuVsb2lqKQgDc3ZB4BvC/OLT0HvSRYG4ItKCgagzcNw9K6s/M/UdseWhMtja9Z3PSj6zhmPdcibfZtuOWiW7SRPAetd1/3KQJMnC9Bai+M4e6DN8ZbkPeBcx3HGuq5b5ziO5xO9JfkCuM9xnFy0sTsW2JJZ3gNdaxzHaYHWvtgW0v/PyW8JiHlBXT9p8vnbCHR4Ts+PID+tuxA7shop1oA9vxAp5tcRCBuIGI416PTgGQiIDUYTbA3yl6pELMyLyPx3JQIvQ5HZLwOYYsxHADEW3qXiFcjsdIHldyIysZYh5qcMOZDPQy/abZb/+chnpRIBo42ITRuMmKOZ9uMi82Q6UshvIKV8HFAVjsbft+/+jfyjshCgG43AyOv4hxSOQxT2owj4pSMQVIAfvf96pKTqEFD6K374kARyAE23n2TE2tRaOVVW/2TEhMUwZ1QbrweszaMRsJuJWEbX+qcVmtcHIwDTxuox2PrIu7PyVUt/AwI4u6H5McnGfREy6+2G2MarkNnwO6vDGOvv6xHQ2gOxae+Go/E7YpFQA5L21veLEaXfH52IvN/G7SBk2qpH7MqfESjsAPQMR+OdkKKfhYDtd1anZMt/IJoTZZbmRmQCXojifeVZv5+CgrA29iQ+m+0sPZxZDtA7Oad+0a7Xci3aaGw2DuCOJt+OH3BSMLnu9i67zs5JSUsMKiopWIHGqQL4uqikoBfa3O2J5n1nNAc2ewn6/5IUlRR4/nP1biJpVEMiuc3Clbueu6Gqzf3k0+x3uJ3Edd0laA1sKq8gU+PXaJ3e2q0WT6IN9RTHcRykD4/aStlLHce5DR1UWovWnbItpF/vOM4TSOetQECuWRrJbwmI3Y18bD5q/GEsEpoUjsaHIYX0fiwSGg3fn2zzTvylI6V6NzJhHomUYXekjK9B4QASSMFdhxRzGJk0eyCw0BYp04mIYQuiSVdrf3+CQNx6ZGpsiRi5Z5Hp72DE7vS2Oj1lnycBSXZ9U76V6V3EfaPlvxMCD+vRhC+29j9gzE0a8HgsEppop+PORQBgNQKBAXTv5lQEGDKsDaMQMzYJsYkf4kekT0YvYR4CU9XohKcX4HYjAijtECgoQAAvbG1ah4DTKqvfBuv7tvZ8K+TzdBiKg9UZgeHLENidYP24GpkjH0ELQwECSv0tv5bIBPm85dEVsYvvW52HoAMD9WgRuiAWCX1nLOHp1hfF1kf1iP2stM9eCkfjYQQUS+35rHA0vgExqgcgf4g5CAQmEHj63PJZDDxqoS2mhaPxc9Dc2s/65EMEnP+JTBH9Eeicbc93RSDLRUDe85Ubgn/NVSsbo/Smdz/a6c+an3MfZBPZGXimriwYWBbPvr5jaMO8rT6xg0gPZ1b/dr2OOjk1oybvkPNfXt9ttwW56HTqTOTDeBtaF/IQ49gSvdfeKepfVIyJCxb+H3vnHWZVdbXx377TC713BxAGwQYCVqzX3qJXTYgaY/80qPFiTaJRozHqVWMSTVQ0NmzX2BtXRUdQGQtdBxUGREDpMDB97v7+eNfhjIQumkBmPc88M3PvOfvsdvZ697vWXmtIyRYnLf8R5DX0vu/hXPqCiqo2Z8xf0rvH/CU77YX6rEm+h3jvC9fx2dto04n3vgpt7tZ7r/d+NlpP8d6n0eb76rUuX1PmemS09/5ei2P2LNpE473/faPnndHo798ivdEk65DtBohZWIHXYonU7rFEqj4ZjzamSguQWWm3Rp/th9im4HRaHmJWgnyKbdDJzNZIMf4WsTXDCCOoP4TA0ECk6LshBqQCAZW2Vn53BAyCU3/fIAXcE4GoR5ANvSsakyK7/7VkPPpni/UUOHQ/Y9e9kIxHF8QSqfsRu9IcgZRvgEjgP2eKd7KdniyOJVJ7opduOlLmY5DyvA+Zdq9DgMVbmZUINJQhcPALpICaI6D1AWH4kHJkLkwhwPANUl5XIxPfcsTYdUa+bnmIzemAwGOu1eceBKBGIrA8AQGfHojFusjq0QGBoSuQAqgyQPGKtb2f9dlcBEruScajsyxZ9xikZK+0tmK/a4A7Y4nUQ4jN7IlOff4CmXg/tqCmLYPwD7FE6hQb+yts7E5EDG0w/oXotO0qBM4qkH9WGoGzU5CT//4ojMdNCHyOQHO0DLFKU9B82R0B2iH2jHo0HzuhTcNFhPHRelhd3kMbi/2xBdbm1b3IhDuK7ylFriw4zLID0HLB6y3avffa0G3iZFTvgumduxxZ8VxDdfM57QqnvJtO8y7qSxDztSN6N4P0WS3RpqV0+JCShT9SNX8H5I0uHXbcppr5LNxG3drpoLaGjC4ddgzavF6K5msdek9XA3tHIv6FaeWHlYC/vTB38fW3vXrWNyOPGLVdnyzNJOPbTQnWmknGtm7O/r1z7hDCNfu5/3B9tmnZpuKIWeT4IIJ5McotOa7R9zkIXNQDvRspykzEQixDgOxqBDqKkSktSHgc+IY5xGCcgJiITPs9Hynl/QlzOw5FIKQF0DrdwJz5rzT/fbt9V0/NadVwJQJyQ9DC/TYhazYbsXfNkd9QBIGCfkhZBxkC8qyuj6EF72wEDl5B9v2fIyZpFPLlOhCZoBZbANc/I9NZC2SuvBv5Ey1A4OEPdRWRBcun5/2fT/NS+71XTycMcfAeMkVegwDRz+ynhT1zNgKRp1o7uiGFdQsCTy1RjJoia99KQuDXF1Hbzp71DgJLFyLwshIxURchBbSEMPBr4E/WGoG7o+z6k218/mBMYODD0AsphxHWd9fbePaz+dCATMSzEQgaYeX1RGByR6t3KfBqMh5dHkukdkAg6Rm0GLU8Yb8bVj4//orchnT27QgI7YXA6quIKbzI+u4JpOTboQXsPjTH7rR7nkzGo6cak/sc2hicbn1wBzJ1riA8ENKcMBRHETrR19H6P8h5+gB6Lx5KxqNzAGKJVEvri9eS8ej3PX1FkSu7Bo1fe/toBnB4uS+e/X3L/qHlwOHj8wu61451Ef/ZizcddMbo0mEPI4Z4bQnYyHvRBq0S6Dl8SMkmBbz8PmLplTKHDyn5ZK3PC60eLRAwykdsdw6am6/b50cCLwepmLZCfW5BG6w6e0418pmbiNaJ8cCDcxf2u6ddy9nFWRnVH2Vk+Czg4eFDSu7cGnVokibZHmSbYcRiiVQ2UkLtkPJegYBHYwnYpepGIKwQAa5P0CI6CCn+loS+Ne8jQDUL+eMEp8sqEXj5NaGCec+eswgxRF0JHcZXVM3LKlg6Kf/GtnutnofYmr6EC2IRYjAORGCrr9WpKBmPvhJLpH6BwEwJWtxmIeXa1urTGzEhF1i9fkroQ/YhYeyuZrFE6s/IhyyT0JRSj8xcCxDzVQU8V70o89H8zrVX1q2OxJLx6M7Gnh2EzLQvogX1KsT8nW/9eZX1/9HIRLYcmVaft7r9wv5OE0a072JlXI7YxK4IUBYiYLIUxeXqhBjIiQgQjUNA9ALrr2JrSxqBkBfsGQtsXHeyeGAv2Ni0tv7xhL5iryDzZHdr39sI1AxCAOxvNi96ocMBzubIArv2egRoC4BjWxXO36u2LveLXl0mfPj53P0OsjnypfVBFwTmLkJm3kMQsEoic/ohSBYihvGNWCJ1oY1ba/vZHW1A7kGAs5nd29Lq5hATFSFM2D3Vxi84jPLHxibIZDy63Or0vaTIleWjfs3huye4elrb7l/Xfd9HDETuDIxfOzDtlsjY0ftUxhKpKFA7unRYFtC3vjaj+osPdsrpveenLiu7ofHlrdBcbYXATxaajz+oDB9SMiX42xKf34w2EEejdWl3NPaNA4Wm0bozHzGv8xFY2hpyM9owHmj/L0Jz8iAEBnsBh3dr/1nAENWjOdp3Kz2/SZpku5BtjRE7HznGdkBMRZf1+bbEEqldEGjrgkx2FyOW4BoEQPZCC0Mm8jkKGIpKpFCqEajpghb8eUhRt0PKdwkCCN3Q4pIHvJaAj/ZcAAAgAElEQVSup/uST3K/aLtH9WEuwiRkXltu9yxEEdZzEWv0KQIh/0QsyWcIwAxBeSn/FEukHkA72UMQw0AyHq0z9u95ZOKbbz+3IPZoF2tHBorxcpM999a6lZG+mYXpg12E0+z56doVkXOqv838k/fuqdS9wx5fqx+7I/ZoEFLybyGW7UAUhLUe+TFdgxTSpYil62VFzEBAcglhSI4lCGBFELhohUDjGwg87ICcTJfbc53V/1zr71FIuexk9xYioDoFAbPVSEm8YuUttTr+GZkbr2h0byaKjP8TxFC+hEDYWAT4xiP/uA+tXgGgvxoxZ3XAhwW5S3v06fpe1xlz98qurGkzjzCgagyBrwCo9kSg5XpkNqxD82ih1fd0+7zB+qq99VGdtfMDuz/TrsGuCxjdmYRhVJ5OxqP1lq6pNdpMDENR9ZezFaTIlR2M+rULYjJ3sLoE2Q5mozFJ2u+Z5b74mu/zTNuUBa4AP0vGoy98n/LWFosXdue8sm5dX0r89KiTrrs/s3XnZRCe8F2I1oRdgYU/ViDX0aXDmqPNQDV6z44lDFUQ5OX1jT7zaP7uaH+fD7w6fEjJFmVOGF06rBPaRD08fEjJxNGlw4JMEHtY+WPRuhCx+iy1OuWjNfUbNL/fajpB2SRNEso2w4gBJOPRv8cSqenohc/ZAAjrhhwImyMW5e8IEHRFjtNViLFoQIAgCPS6DAGYzmjhOAgBihoM+KE+W4rATQQ5Qwe+I0dGMslsN6R6R6S48xFYOR+ZMj5CgONbpIiPQkp3N3QCZhoyewa+VSTj0TNjiVRrxAY+m4xHA1v8oVa/akL/jCDTwEcIqNQhgHgJcHrdalfpGzizdkVkQU6r9NUIYNW/cP3BdcBPYolUh1gidSLwcjIerY4lUoORsptGmDvzcELWY3e04A4EHrFwGb0QKCxCwKy59WUftFjPte+HIkB2IVq8RxCmLqq3fpmAFvkM5DO2E1rYv0WnU+9qNCYvIJCx2salBIHmVsjE/Cky9TYgZfAvu3clYvYuBva1NEAH27NaIAbyGKvbQYSA+lAEcncE9ltd3frziV8eXYMYqrZofnVDAHo8Yvn+gk6WHoIYrU9tjMqQKedQa8tMNEe7IWAYHPwYZP1eYfVYYW2ttc8aH0Q4CHCxRKoEzb1Z6GDINQh4vsTWkcGETNxnVpdgrB0CniDfwKUI3G4WECtyZblATbkvDt73NJqTDfZ7q4qZGX9VNLQsNzO7dq+MTL8nYnpmIPB11/AhJYsRuP9BZXTpsPZoczYZvTMj0VwI5nkgDahfnkMbztlo3TkKbQQAyjcVhI0uHXYqUDV8SMkzjT5uicav++jSYcFGdjkhA3dAozo5wnUiyJPbCXhqyqyDf/bsuNe+fvrSwz/dlLo0SZNs77JNATGT95BZbEMvcWCWOQA5ySeT8ehjsUTqVQRSmqPFahQ6FXcqAm4jUXiKnyHF29qudWghDoKCtiYMQrocmQ/3QMzFcrQodkKLYX/7ezBiKWYhULUcsTAVhCkqipAp7RUrM5DzkL/KkYROkUWEqZHKjCXLQwv043bPnYiJ2A04zDcwr25VxuLsNnUdEfC5DFgWS6SGmnnnr95zDJ7TrK/GWJv6IyDW0dr2FlrcH0GnbxyQE0ukmiMGZwkCXB0RiAp87PZADuu1CKR+jUymnZDCPtP+P4kw7c9cZMZ9Czm8Z6LxPwoxh9V8N9J+YG47BfnTLSJMnN3SrgsSfA+0/8/FzJTmyL8CmdMKEYi9zZ7zUatmc3fLy155xsJlRd/Up/PvQ+CjDIHSVdY/QViT1ohpfRixbG8jEPq8lf0cmm/59vkipHDnofmyOzJDfmVtPMv65ivE6GYjQJeP5mm+Pb+VPft26zsQkC1DYHDtEC+bLUWurBPq/1OtrfegcdwZKd4qBBaDkCVp64fnilxZ83JfvHITn9PZyk6i+YbFaYt93zZsTMp9cTUwFl4Yu9GLfwAZXTpsV7Th6I3eGUcYkynPfgem/y/RAZCH0WbuGgS8H0Ig6KPGps2NPLctAu/p0aXDng/834YPKflsdOmwn059a+CIfsMmX+dcuiYjwwdjG0HvegDAA3DmG/3tgJbd2k1+HJg5uvSmA7e3YK9ZLvObeho2yVm/ztd3/DHq1CT//bLNATGLz3TrRq5ZFUuk3kO+ExmI3QApvxzEhFQT+s44pOQfRLvOZkipBqYrEKD7GoGhvZDC60uY/Hs1UjyBz0YLBAaPQ4zaBwi8fUSYF3GIPSeFTv09j0BAS+D3FsW9GC2ok4FVsURqQDIenZaMR++yXJFfGgjLsHKC6Pr1iOmZac9dnt3cD8xuXnc0MidkI8alJVrUV6cbONZFyG6o597MbNqhHX/E2v2vRn2zyFJM7RpLpA6xfgiYiRRibpYiVuoQwgMQ+Qh0no3AciYCQ6chJucj5FgMAhZ3InbwlXQDt7kIuU5Legvru4iV/TZiDSqQ6Xom8o3qhJjJaVbvfjZO82w8foqYowMJwUMwL5pZHTIQ0KgGLuzfY2yXyTMP/U19Orc3MslV231ZNqbY+P4DsZ4rkGLcDymlN60PvrAxPcH6qw1iAZ9GDFMbq2N7BN4D/7W9EECrRmCrGrF6QZaBXL5rogpyVaYRwG+OWNPvG27hOgTCcuxZZxEGA34fnRxdicYKq0cm8v37DJm4NkV+hszvQ4tcWUa5L/5nkSsrRAdbSrGk5uW+eKuYWv9bZHTpMIfe+wZso4P6OQA9AbhpIGT2Jw8fUlI5unRYKQLhq9C77QlPgG6KLEWArmrtQwjDh5SsGDnm8q719RnUNhQ0NC9YGZghAyZsNZpvwSGYRfZ5O/QurW6evzivf483d0dz4arNqNd/vdTT0GETk35vFKytS5xzDUivZGKuLN77yk28tzNwl/d+szcxzrmrvfc3be59TbJpss0BsQ2JxX3qjhaGg9Bi9TlwjEU474dYgWKkNEci5+sMBEq6ICW+zIrMsu/yEWsSxG2agPyM/oEU7QCkfLOQv08LxAZNRUDjZ8gcNg2Z94Kkzv3RgvU6YYLnfvbTA7E+rZFi+xNauA6xcjAH/0xjog5HSroS+TE9ghR9cKJzqrU1iE0WjH2Ffb+6oZZZmbkU+wYWI6A2Dp0SrI8lUh9Y2UG8r0CmoHRS9Raf7K/WXz0QsCxAQHCYfX4+Akc3oV37noj9y0MKtxYBqyuQSbk3MHTuy81Wtt97dVVOy/THkUzesfH7pY1xtfX/t8jU2RmZ6ZYgBqkCKYJc4PNkPJq2E6XdCFmld9C8udaeeS4CLY8hZrABqBk37fQvWxXO7QuRmwlNzw4BwiBHZCHyISuwuvQiDHswCDF/9yTj0UWxRKrM6pGLFtbjEBt2oc2NO5AZ8QtkurwP+RTmILNpG7vvPgSO+ls7Pkd+RO0JlWKp1W1rnJp7Cc3pCFL0vdD8KLQ6BHHksq1tdQjwPg4MKHJl9wMXl/vijTEinVC/tgZuL3Jl9YhpPgv11dkZhfV77rZb6dQVk5vn23fXlPviL7ZCG/+T0gyZxN9F8yMPMaHB5iPw+8pE7+Ng4I3RpcN2GT6kZCbw9OjSYR3QBm/M8CElY/79EesWC3Vx/vq+77/PxCszMtMHZWev3MGeH4DCNHoHO9n/c+3/RXU1mYcundfWteiwLC87rybDOVw6zeWjS4flDh9S8utNrVuTUOW93w3AOfcYGqfbN3aTcy7Tez+fLWeSr8bcZZpk68t2BcSQ+fBhxKJcj5T61cl49GvzfbobgacXkYKOIAUdhFYoIDS/vYNO7RUhhqs5WvwykKnvPntmF8Sw1KCF50i0k+2EwNArCDB0REr5LgTogvhXB9o9q60uh1pZ85BJpg9hZPqRhD43QfqcJDq8EITeeB0BrnH2WXvkxP9HpLgKEdC8EAECb/+TlUd/YJ+sPMbZM69BIHIoAgLzEOPxz0Z9PgrYP5ZI3Y4ORTS3OlYjwFeNwEopMlsNQED0HASQvkCnEscjgBCkGlppfdYX2LGgc+2odJWrohWrrT7XIqYpx/r5QZRcux9ikhZaPzSzNpyAgMHRaF6chcDwTxA4b4dAa+BbdwIa5wmWL7I7MvM8uWxVtzYINNYjoLHA+nUyMr+2QODDW/0Wojl1r9X7QJTM/ad2Xx5iyl6xNu2DDie8TZhkvYOVNcjqP87ak0RsW2/Ebn6CwmUMRTkyGwfR3JqR9L9BZuD2SAEvRwqhB5rnGWj8sPp+Zt+NsO8jiGXdWNiM36J+2AMpncuJ+DRpvgCXAxzesCqSuWJy4YHgXdu9Vg0u6FELGvNtVoYPKVk5unTYXchEHqQ8K+K7oKcW9WMNel/ygJ1Glw5bDORZ/sujtnbdfnlgavno0mEHIDayC5qjoPenZ6NLg+wP3VYsbFHx+l9P9IOOGZfTb//JBQ0NeByRSHgqvUk2X94FdnHOFSCXgwGoz3/vvX/eOXcGWscKgQzn3C+Al7z3A5xzGWhzfziaS/eh2JIXee+PB3DORZGP9QwgzxKDT/fe/9w5dypyAclGxMQFVqdRhIc3HvDe3/FDd8L2INsbEFuAJsUUIGVBXgOZg4DSxwikPYNOsXVHAGsCUoDVSOG1RyazexA4CpxjV1kZGQiYzECL4O6I2Qmyyuchx/xuyGz2JFpExyBAeFgyHj0rlkg9inYbx6PdSq79lCPQMxKZpc5DICdqDtif2HWHIjDzB/QSLkdm1BgCWLcgBTjNvv8GsRm/QmBsaqNDD3ejE4+XIB+w/RB4wMoYgABBxHIvnoCAUh46wZWPXsxM5Nt0jfX7edav0xB4WIXMgmcgxfwo8vkLguS+bGM4y/qtb9s9amoRqNofKZ5dkbmlPwJhdyFz1cVoQbnF6vsz4MJ0A7lV32SkM3J8MpZIXYRMqH2BL5Lx6OeEqUDeBoglUjdb+66zuq6yfp6LAGZ7Qn+zbKvzc9bvJ6O5WEAY8uRjBGAvtXHbEZleu9s1Y5Px6MuWOeEde8ZgNOcCNqSPXb/CxjkLgfwuiHEMsgP8EWiVjEe/iSVSRcj/8elkPHoj31OKXNmBVv+LrP1TCYH3EqtH2urmkZkrAvTC+RYu07fwdZGFaLw36uxuflovAS8VubKF2e1rY77WDfVp0vUrs5ye4xoQeM5pqHK1zXesmvN92/lfIj3QPHBojjWW4MRxHdr0Bb5Yz6C5+v7o0mGnDB9SstXSCo0uHdYHbRBuRnN2ClofjkbrSoyQFQatFZ0B16brkpp++08+v8tOc6ojEe6KRNbEYGxKMr8FYhHtj0CM52+At7z3ZzrnWgKlzrkgRMlAYBfv/VLn3A6NijgXuUDs5r2vd861RvribudcO+/9IrS2POC9f9E596tGTFw/5IO7j+WnDPTGdKCL936AXdfyh+yD7Um2aSA2unTYyYgtGDF8SMniZDw6k/XshINUR8l4tM4+eiGWSC1BSmocMgENRQCoKwoBkUZIvwOh39V0e8YxiOF5GTFghyNwcwACb1Pss9ZoYVqOdrcHWdlZZkptQOxKYFIKnJwrEWDsjBiWZoRmmlZ2fTDRxybj0acAYolUsdVxMQJBYxD7tD8Cj+OQefIEZMp8NJZIXYvMiO2tPnONBToGyI0lUn2Rsp9rfdDf+qUFUhb1CNSdYP25v9W93p43ErEzD8USqR4IsAxHoCI4sDAfKZBlyJQZOOXfjXZ7LZDi3tf6KIhgvwNis85HprIFQDfLSNAX+cMtx9M2p1VDy0gOR1udxiJAt7aCC+QaBL7HGugcjhi3nRAgmmPjEcS1OwEtevfZ70IbnwiaA0+hxasdYby0w61t5wCnxRKpqYjR2xeBmlIEcOahuRMEwTwPza+LESBdgBTxl8CZyXg0OOULAmcd+C5TsUVS5MrOtH6Zjd6VTDQv8xA4q0Hj1Mv6JPBvigD5hUVVNT1OWj593istr14+Nb+k3BfXBGXHEqmszm0+i3bvMOWjkUfcv75I9QV1FRmdM/MashqqIrWsAXvOofnz0eo5OYe4DLdnLJE6dmuHtfixZVlFp1FTZkX7d2oz48g+XSd0wlKdETJkdYT+l1mAS3uXtbyic6tm+Yt3zsqs2STfoc2Qm9GGKwv5aO6A3oMgLVkxepcP4bv+ijhHzuDjxv0cgYd5wNThQ0q2K7++H0kCZgrEiAWp3o51zo20z3PR2gqQ8t4vXUc5hwB/997XAwTXOOceAU51zj2I9MPp67j3YMTMf6j0lOQh1v9FoKdz7i9IL26yOfx/XbZpIIaUz3ciLcYSqdORIj8/GY9ObvxdIxAWyDSk7MYiJfYCMkdmI7NdS6REbwGOq5iV1eWrZ1sVdzywYtc2A6tGoUUxC01qh0yOe9rPXASIBiIWpAdKoVRpPzsDZyfj0ftiidRNSNE2RwtrmjB6ukeKNguBpY4IiByGFru7kbkrkDmIyWoGtE7Go9dYOI9jrb6PIrDQE4HDjggUdEAMVY75LjVHu61d7bpqxBBehoDqOAQqdkCA63cIoI1H5tEJaDffHwGIabFEahlintohoPg1AnDVCFQsss/aILB5A2HwxxUImB2I/E4SCBAFJ7RykKN7LfBELJGaiADDNODhSCZpHMdEMhgG3JeMR5fFEqnjzF9sbwSylgAHW4yt89DcGo/A4u7WR4NsTAKzZB0GNKxPhlr7ZiBG70Drw0vt/0VW36lW9lGInRuBWKa3Edgbi8x5R9s9pYhR2wmZtRsIzd0rrJ3PrWPOTzZwvtnO+UWurC1aXCejORacyt0Fzcc6ZF48Ba0luyEg2UDonN/G+quhZnnWu9mtGp7s9YslbyfjA7/zLhbkLo2uqmrzaGV1izeR2wBFrsx1PHjFYb7BLfj27eanAE/6qozL66oygnVrFjLJBql1jvH1kZzapRn5WYXpSitjdzQP32gU/uK/ViyGmR8+pKRh7KSz00DbZRVd3uzTdUIVUoqlaBPyFfJfPBi1Lx/Ye8WqjplTyg+hqOPHXXp0mJaN3q2tJX9A8+j3aDPaH4XyqB9dOmy11WshWoOq0FwArWdLgKQ5/7+7Fev0vyZrfMQCsWTdJ3rvZ6z1+VD0XmyOPIje+Wrg6QCorSUOeMh7/28HLZxzuyLddD6yDJy5mc//n5RtGogNH1LyNFK+jWVvBDLWnEoxFubnyMyFMWck49EVxnZUmrP5negkWHu0yN1KuIjs+/k97e9tu+fqaLOeNRcTAsBDCRMxj0B07jTUt3chNuhAQqfyz+3v3YABsURqAGJAjkYs2+UIMLVErArWnpcQUNwVgY03gam531ZcnVlZO+qM/3t8yqrebVda/e9B9v/CWCK1DzqleL397kN4inSS/V8I7Gdx2k6IJVI7EYbcyECgYyFi6va3ts9Eu+LxiMU6ytr2QTIeXQbsYyc5+yKza63duxjtnKsQ+OxoY3gRYt2CgwwvEgKttD37HLTTuhUpHwj9s/6ITKqL0Q7xNmQWjiXj0bft2u+YwhpFZH8MAeUeyJn8COvz9sg0+Tw60XkpYQT14N1x6DDFu1bPwJm8PTJHfoqYhN7W/ysQeL2eMJjtDQgMBydz0wgIBsGD90M73w+AYyzG23FozgSnIlsj3wxiiVQ7NNcfScajDybj0RVsmbRF83QQMtuXoJ12DgKfQW7JVYS5TvPXKiMIglpRtzQre+KV3d4s98Vrb4hoSGd90LbFnGSzvCVr4pv1/MWiq7IK09fWrMiYb32Rg0Dr0aiPX0cmlmZoTrm6ioyGsrs6XlTuiz8ocmWZiBEtQBuiHysn5BbJ6NJhR6A5UAL8xDYJ56R9ZuAPdg9iHfdDqYqWji4dFkHtnwg83Sx/cbPenUtzWzVbEJzO3mpiqZV+Yf/+aa2vK9G69zlhqra9CefKn5qCuP5g8jowwjk3wnvvnXO7e+8nbuSeFHCec25sYJr03i/13s93zs0nPBgWSJ1zLst7X4fW0eedc3d47xeaWbMZAn213vtnnHMzaDI7b7Js00BsPXIpcGMyHp3b6LMj0OI9ACW+PiEZj86OJVLtUXyqLxFYuBiZ3CqQYlyKTC2XAvFd/zD/X+mqyH3ZLdMDkRKtQCDiasLdf5DQ+RAEwPqiCbocmYsmI7akJXKqPgHtHuaicAhRpMyCUAj1SJFkIpDxFFI6lyTj0RlX7HFzl4WDux+eUVUXgJsWCJh0RAr9eBTD6lZ71tGImWuGlGwOemHutgjs+yClfgdhsuzjkDlhFQKamYj+PhMBtpPQInxfMh4NTGJBqJFPY4nUZ2iX3BUoNLNhcLJyBlL0QcypeUi574PYltGEZsdVyDeuEPlr9bY+udjKKEEsXjCeLYAPLTbYQQhkPZyMR9c+EfY7Ql+4J+z35Va/u2w8JiIg9CfEKAYsXBVi526164+3vh2O2Kpc608QcGmJlGp3+74GsU27JuPRCsthWWzlL0DgPQ+BrMkGwjIQK5hh5Y6ycoKTgscicJ+BdrhbJOW+uKzIlY1C70R/pFDL0NwKTsadas+u57uxrUBzJ/AVq0Fmzcbx8dbIoyNOWIpA1RpZ+Xl2p+rFWa56cWZg/n0NAbLjkKIPYth58B5HA95lAC2LXNlI9D7eB+Ts/qe5nWOJuacBf0/Go/+tsat2IoxVCEBY1yjAjNGlw25EJqM3AYYPKXkA1vhvfZKZUTe+a7vP9gGu+jGBz/AhJXVoUxrIh/bTJD+83ICsDlOcc0HO2aM3cs/9aKM7xTlXh96Tv9p3jwHtvPefNbr+Xrv2E3PW/y0wxp5XhywbVcCD9hlsZ6FJfkjZplIcrS2WFPkxIJGMR/+2nmtaInbga2R6iwADkvHodAMD76Kd8pFotfs1YjM+QkokigBAL6QURyAGZIqV9SYCXQ0IuF2ETErvoEV1JZrgXRCYyETsTQB2ulr9Zln5BUhJN0NKL1Dmswnz2/0hGY+uYQ5+3/23e0Zq6udPufzgVoRhMR5CSqotAjcnohN2bRF4vBstnAG7NwQBzraIselmdZmOnHArEPAqQcAuAD6Td735jX/hOCWrouYPV1bd+W/si53uDOILfZOMRzvZ50dYv16FFHstYi4fQUq9xp67CIGvpQjQrkJgoA9ivv5obRsCPNTh3ZnNchdXnrqyZ+uzl+3aZRlyMO6KFPdKa9sDKNn1evMgxhKpoQjsDSdM2/M0co59B7GAbWzcgrAoE63fBqB5VYsAVRv7fKR9fhNiHINYT3+0fn0UKeJXkA/ZYYQxwV6w/rkSOfv3RfNjOQKLexKmGboEuDcZjwb+JJslRa6sGAHs/RGzEUFmsTusbYMRkF+KAGgzwrRG5fZZhbUtG5hU7osHsREpcmVB/KyvwH+C5mMa3ErU9yeihb89Yby0ui5HLvuiblVGp4XvFVZSHzkXAdGOyFzcYeAtc1e6CMcB/5eMR2dtSZ/80GJmydbDh5Ssk7kbXTrsOMTQ3g3ct/Lujs2yB1dcl9W95qXTjnnrTUv+vRrI394CpW4r4pzzmxhHDO+92+iF/wFxzv0VmOi9H/Wfrsv/imzrjFguWugLN3DNCsRifIMW8TwEekjGo5WxROoE5GdxIPIF645MWzsgM9h0BK5SyFx1FKLnH0Z+UFcThr8oROxKb7Q7eAP18dVIUdbafScSBmgciYBHHCmyIC5TtdUh1z6fiZTuq8B75uifBTzFxfsXIUBVYuaM9sgkthKxB92QQsq0OhxpPwsQQHXA4mQ8+msr91jEvAXO1gXIp+0mxDZNQmBvFPDiqu6t/py7aNVPFw/s9n4skXoWMWmdkNJ4xQ5KdKNRupVYInUlAsZBNP4aBG6DeGoeAcggpMSHyOzhEdi50/pqLwtoOwr5Ls1b2avtr1b2Ij93yWqPmKMvEXi6w8biTBvzhlgi9RBi6YLYcVj9WiNg1A0BkMU2bp8jIHIpYuyGIZDdDoGQtujAQp09J4JA8euI0XkjGY/W2AGJC2w+nGz3LLXxGWrt7IQY2/5W7iEotloRAms7Wr8tsGcvs2f0BIYk49HvY4q7FbGIWYTjsY/9HeQ+xfoiAGEegfP+iOmsQnO4FXqnNkWKEGP7CjkNC6iJtAHvIKMleu+CCO6NA5rGM/LT/RZ/VBDDu1cRSH4dAeg/AJ8teKP5yZ0PXfniWkz5f5WY/9SGxmw/9K58OXxIib/v1WMvzu5X+av06oy9gaGNTkg2gbD/kGSS8e2mBGvNJOPbH6M+myvOuY/R/In/p+vyvyTbNBBLxqNjY4lUVzOBbUhOQwvYB8CDyXi0CtYwNbejk47XICWbh5iHXsgh1dm97yPT4Y32f18EuM5DoOoKxEiAlMOHiBE4DIGwIAL8dARAMhD4SccSqceQuaGr/cxDDMsdCEQG7FQ/tFA/YW1JIVbLI8V5fSyRug4BzdMRqOyAQFQ9UthLrezjrN459n+DnTLcHSmyt61OX6AXcxzaiZ+IFH1Pa/ujXx210025S1bPr+zU4i3EfnS2frvCnn1SMh791trZGrFAQY5Ij3ypvkDsY5wwpls3+z7b7su16wYg4DEXeMDA1C4IjL5R1bnFdLwfUtW5xRKgKhmPjrBx2T+WSJ1G6N/yiPXxsbFE6igEfh9GLOfvEEt1rI1JhtXjZDRnrkIgZSUC+/dau/MJ/RT7WT+2tzF/z9qxwEKrvAYQS6SeQYD8/6yc5WieXG7lTbA2f4WUcUcEwHIQQLwjGY++EEukDrPP5lgffx95EM2ddojdCoBPAWLD/oLGI58w32XE2twWHUZ4G4Gh1cCrRa5sZ2DaRpzm56AN0d7UZBjrtYY4yGp03Up79lygz1fJNsehdzcH5aVMF7myBgT0Wy4Y02LwgjEtJmzj6uV2tDaNA4jkp5+o+yKvNxnc85+tVpMEsq2nLfLeb5S1bpKtL9s0EIM1fkjfETvxt6qRM/YQxFa9gLFhJnnIZLQaKch9kFL9EPny9EEK/yHkKHsjUsqfINNHB6RodkXmxzRSpCnk50PQ9ekAACAASURBVBTkrNwFOdv3I8ybuA+wQyyR6pSMR6cDB8YSqR2Rwh6GFO5XSCFehsxUbyEA8BgCCEEQ1wcRAzEPAcgeSOk3R8pxBQIZM63dC5AiewwBi73RycAbEIPxO7v3fcRmfAyck4xHfxNLpGKEoTYWAr6uRd7MuhZ591qb3kCAdkcr7/lG/f0EsG8skboKMUpdEfj6HTK3tbZ6d0ZMVhD3bEfkR1WCWKgByNn9TmM1b7Cx6IeU8+s4dxACWitjidTvkakvbeNajkzPrxKmHgoA5O427hHEqvzS6vWA1ScAH6uR0g+ivp+DTKgrEZCdBLyfjEcXxhKp6xEwvhDNlStjiVQBAoHL0Cm0y9FcDEzgOyDQ2RcBuSCJeDcEwgL2NAfoEEuk7rd6LEGHA1ojB+otlRnWnifRZmKoPe8xdKgkMA0GEkRYb4v6823E6I1H4PYaa9P+aJ6uU8p9cUORK1sNXASuAM3vxkxxkMHgaxSyoi8a+4C5621/N6DxDaLQPwR8VuTKzi73xV9taadsjhx12disvI719Y3i9H0vGT6kZD5qMwBnXfbSF4TO803SJE2yjUpk45dsWxJLpAKzyCgAWwSHIRPWiGQ8usaBNBmPrkTO1QORwvCIHZpFqGjaotNuNyEFDTLFNBCaolojkDMXmaN6W3nXIOWwN1ImBwGXJePRIxBgaQn8NJZItYglUichFqMT8r/KQ8r9N/a8BcA4O/F5EvJlewyZqm5GCvgwBCx+i5RRBAGGL+2zd5AD+y/tOUsQWJmH2LOHEUB5GPkYvYrYl/bAheb0fiMyCx6MQNyJyNx4NwIruyHT1XnAX5LxaGOHz70R2LjE/v8bMsktRwCnFpmSPkeM3yUIAJyAGLQL7f/PkXNpYJIO4irthfzZ9rO/A5r9H9ZnJYgd3TcZj56KmNBjbGxeRkxMrV03BIGyLMKTgt8i8PQZ8tUaYnWciXzUTkIHQ65JxqMXBWawZDz6d6vTKsLci4Gp8QRr+5NWfiYChb0IWa1JCBD3JgzwWkB4uvYfCMC3QozitY36eIuk3BcHvoG3l/viQyM56R1w/tfIpzIIMuoQ6xvkGmwgzIN4AQLMf0XjdQACkZtiLp2EQGiQBzZYpyLoPViO/PGCwLinon48BW1QIkWu7KRIfsPJkA7yMHa2OlyxRR2ymTJo0Mc7ffWvVuXzXm1+94/xvCZpkibZdmW7A2Jo0f0WObcDkIxHlwC1sUSqZyyRatX44mQ8ujwZjy5GyuIrBEr2s/sDti2CzI7XI6ARQ7v7vyNgcXoyHi1BimZvxFB1QoqiCwJTkxC4eCqWSP2R0L/tKqTAL0Z+ah8ixiOIxdSe0E8sz+o8NxmPjk3Go58n49ErCE+kXYsU0YOIWXoPpfK5OBmPBsf4WyFlmIUixt+BWKurUSDXvZPx6AIEwIKgpvUIsPwZgYM6BP72R35AHyPz2VfIVDrbnOCbxxKpY2OJVI71YxBHrQgF+xuMTFj/QKDyxGQ8+jECM9MQmOts912LTgJlWd+eADxkMcA62LhfjIDJQdbuGAqDsdrKqUZg+6lYIrW7tacPYTqk5+xZ11mbe1g5ryJTY7mNxeOIffvSvrsPuDUZj85KxqPVyXjUxxKp1rFE6hWL4o89dwowy54dxOhqDhySjEd/ZXWssO8aJykfwnfNcgEIotHnlWiefWljusWnJQMp98VV5b7YxxKpwl1+N//u3A615yBW8yRCR/kaBF6Dd6Xa/m6FwOT5aM58Dry8rtAV65AsxOImrT2BLEJs8zJ7dkegT7kvriv3xePLffGL5b54CTp9eEPLAZXXgusEPvCpm4NM8z+4NFRHanya1el6t2zjVzdJkzTJ/7Js06cmN1ViiVRbZNIajEDEEcl4dLx9l4NMQ6uRcu+KWIuRaAc9gDDxd7Zd1xYBnj8hNu1VtDNvj1iaN5EfRxQp87SV2Rwp/E7Imf4jBLweRzv8OQjw3I0URzlhwu5hQDIZj562jvb9EjF2QRT/1xCYaQA6WvDSwcgEWI4A09kIdP4cKfAzrT33IgZoJgImgwmTnwdKd4mVdQjw12Q8+pHVI2FtO93KPxKB05UIVN5gzzkMOcA/Zd9dBJxrJtqgTW2sHn+w/n4Ai5WEmMcbrW6d7FmLEVMXs3EYZc/ex8ZmIWIEO9pn/7RyLkVA6irru71tbC618ZuK5kUEAdkKpMwX2OeD0SGEb4BHk/HoTOuHCxBoawByk/FovbWrAKWh6WNl9UIg7rpYIvV3a8tKdDr3t8ikeq6NwRxrUwC+6gkDCT9u5WzV2FEAgwZ+3Ll6acY7lV9ld8VHFhHG9FqGgHgbBARbYtH9EdDOwoIab0owVQsgexUa90MQOF+C+sGhOdPHyl2F5uDjaycOtyCub5NXn0d1JIJ3Dtw3iCVMBnUpcmWZ5b54XQErm6RJtkhysiLf1Nb7jTrrZ2e6b2vq0tu0P1mTbD3Z5n3ELATChcBpa598ayQVSAkXY6lAYolUxHzIgnQdPZHfyafI/+sy5P/1BFL2SxEYc6jfOqPo573sviOQYpyAlPhF9twvkdmpDzKhBKEIdkSK/f/sFF01Ag9PINPoSgQY+iLwsBtQE0ukBiIAczti5M5BbN0oxDRFrQ7L7CfIIXYOAnOPJePRJ80x/1gr91BCxuB6ZNY7C5k/X7K6BO2ejhT/HtjBBfPJq0BA7STEJgYnPyutP2sRUxW3+h+E/PiejCVSQQw27NRmTwS+utvY5CG/PGf9eS8CXq+jGF7VyNRbY2Ocj9i+gAmLINbsOAQQFiGA/TQCSgfb/AhS9PzNfrIR8znLntUVAaN3gQI7gLAL8tPxQHUskbrF7glS8EwKQJjJsXZ/GQKkVUCLWCLVEzGxINPbiwjIDbR2T0Fg63w05+rs3hoEwP4ZS6ROtmCud28tvySApRMLmoPvmFHQkNNQxWLSkWesHsG4BSfAmiGgWIR8maYCv9yMiPbnone5Em1azkAbgE8RwzePMIdqEvVDd+T7lYuc9D2at82oyqxFbG8E+VheBYwpcmXHoHRktUWu7NJyX/zRlvVMkzTJd6W23nd495YeG71uv8vnbBSsrUuccw3ovcpEm+rTvPfrTRXlnDse+Nx7/6n9fz1Q4r1/Y333bKCsA4CR3vuNxSj7QcQ5t8p7X+ic6wzc5b2P/cjP34EwafoBbMW+2OaBGIoj1Q+Zky5a1wXGEvwulkjdiPxIXkdg4JfJeHRVLJG6DJnBbkO77CVIsf4WgZDAWboVYrB2QYxFAzpZ9ybyNQMphiDwZANicQYhdukgBAiqkCJtDYyOJVJvIAD0IfBZMh4NfNGIJVKfopOHVyMH6KPsGUchoHCzlbcIsQQnJ+PRtw3QTESHAB61a98Bzo8lUkEoiG8QGHgYhb4YF0ukDkXm1IcRcFmBAEFfZBqcg3zMTkOKcG+75gFC36FWCCjOsWtGIL+qN6zsOYg12yOWSL2DmKyKWCL1EmJCuhOCuMWIOfw1UsCZiI0ajRTsDMQ+rkT+atWEZkVHmI8v0z4/ADGUnawdS5CSP8HGeU2KqWQ8OtKyDHQESpPx6Cobk0zgmlgiVU54+OMr65+5CIwWmg/iGoklUhF7jjmjE6St6W3PXoCY2yetDj+3/sq2OoxAwKcegbwJCKjMsUfsg8DivdaOrSLlvrhs556TZ2UW1u+8em7OLnXLI8H8OALN4c7ofQnSGv0Ggckl5b54c6L6j8Glz22376puVfOzI6tm5uahMSxCwPkgZOKPo3dwNZAucmV7IrbrniJX9g7qBw8sd5npl3x95GMEcjuhzVNv+34aWzcFUJM0yQ8ta1IcOeceQhuXGzdw/fFoM/0pgPf+mh+8hj+weO/now3ZdiPbg4/YZYRJuzcoyXi0mjB/Y22jr44mDGj6ewScbkQAKIj43h4pwO4oOv49yBG5BQIsNyDWrKXd04B8p9oi0PJrBErqkWnqHgSeDkVsFcjcMzWolPk/nYCUdVdz1P8rmoQ/tectsrI+Qbv/pbFE6gFrzwIEuA5FSvMYBESORAp+CVJolUipB/05GSnyW5Cy+wQxCKMQU7SPXf+a3dMRsUDnIebvc6tLkPz7MwSSfovMnwHb9BICg7cjQDECgerApPcmAjarkf/cofb/BCsz0+o/yPp4gPX/CuuXbMTSROz/FdbmHHQC9RZ0WnEyYlgeQozOOcAFFt6kDJlHJ5hZEatrH8QwnokWudsQk7eHMa2DY4nUqbFEKhJLpI6LJVID7PMLgLiBtN+h+TbP+rc1Ami/tLo/RChp65OgPRVW5ydR9oIdEdg/ex05Vb+3ZLeq/3lBUc3YjJz0BKvLAYThOgIAVoeATRs0FgOLXFnBukv8dyn3xR/1PHXpAe2Grv6iZf+qKSirwWI0z95EQPxQl+kj4GehcB+vIAf8dogd7IpcBUpzO9bM7Hb88jtdZvoJ9B52RAB9NtrI7Fvui6d9j25pkib5T8r7aB3HOdfLOfeac+5j59y7zrli59zeiIG/1Tk3ya75p3MuZvfMds5d55z7xDk31TlXbJ8Pcc6975yb6Jx7zznXd7010PX5zrmnnHOfOueedc5NcM7tYd/d45z7yDk33Tl3XaN71vfsds65lF1/v3NujnOu7VrP28E5N83+znXOPWhlTHTOHWifn+Gc+5f1yRfOuVvWU/fB1sbJzrlS51wz51yGc+5W59yHzrkpzrnzNtL+/a1/J1kdmm3o+nXJNs+IJePRV9BivKnXP2f+R42B2DPIFHeWfX4VUmrdkYIHKd+FCPzUoFAYNyHzSRAPqwYp1l4I3M2w75YgBm4oUlK72TUvIZPJEgSmioG0mVvbIGDSHSnmCVb/tD2bWCL1iLV9iTmIBw7gQxHzdwUyX7ZEyqwtUubFCNy9igBOPmLOLkaArR8CK82ROexgBLJqCBmn85D5qbM9qxOQlYxHZ5mpMgcBi/2sD94mdIq/AYGJXIujVoJ8o7B+vIwwSvwcq8Nn9v9zyL+rM2I2exMGQF1qfZmPAEslAsXYfT8jzJE4DZlYd0ZsXgECQ8EpyV9ZoNggZpq3flxtpuRfIDD8UwSyP0Ks4VexROrPyKS4DJ3AvAptFkYiMHBJLJGaZ2043eqURGC2EM2Nz9BC+7Xd8zIComkEXnNQjLRRxn4+aeN6LjY/tqZ8/PGgaUWubCyaU4WI2Q3CwwS/VyLAm0ZjPgf1GwBFrqwnkFHui4NUTP8msx5p+3Vux9qrMgvSQQ7TBxHDeC5AZrP6hm7HLavJ61Q/6dNbO61CiqYKvbfXozl2IdCvemlml8UTcxt8vduJ8HBDENR45baQBLxJmmRd4pzLQOtyEP3+XuB87/0Xluz7bu/9Qc65F5A5LWn3rV3UYu/9QOfcBWh9OhttPvezHJSHID134gaqcwGwzHu/k3NuAGKtA/mN936p1fdN59wu3vspG3j2tcBb3vs/OucORzp5Q3Ih4L33OxuYG+OcCyxKu6GT7zXADOfcX7z3awI6O+ey0bp5ivf+Q+dcc7SWnAWs8N4Pds7lAOOdc2NotJatJSOBC733451zhWwBy749MGKbLWaqbBdLpPrZR/MRK/E1CqdwNTJ9fY3YmW+RUv8GsSo1CLztixb2IBXPcqQE6+3nDOTMvRqZTgrQYL6CHI8nEPqNnYyYlXw0OR5CfkHzgXaB/5uFutjP2uER8DjRwOWhaMdfgxRPf/NP8miCPYsYn5PsOdPt9wAEZm5BACJIlbQaxQzrhkDcMvv7z0jpPmF9ciTw22Q8WhtLpLIRCE2jU5vX231nIl+qTxHj9CxiDEEM2j3IzDowGY/egQDKMhRf7EoEKDshQHcyesF6IPBVYXUJDhsssXIXIeBXhsx8MxCgWYpOWM5AJzYXIlD8gfVHV8RoFVv7fgkc8fHIbg1FrmwH6/saFPrjFATSRyK/qRttTGlU/mAEeLGyj0fmu9PQnPkAAbHKRnX5FoHYAQjQzLB+eAstdAVWVjAP7kAgvIwfTsYTrhlpe9ZCwvRPbZAZMUgtdHK5L24cy+xG4DZLY7Q+Oab6m+xRq2bm/sZOQ05BG4ZHgLN9g/vXV/9qnTv3hRZ7IB/O69ApyQGIHdgJzYUDqM3oVTkzvwDWaJ9qxG4+iUzJTdIk25rkOecmoXWpA5Ay5b838LR99w/CXLAbk3/Z74/RRhBk5XnaWKc7kA7YkOyL5ej13k9DeiuQk51znyA3mf7o/dzQsxuX9RrSARt79qN2fRlaKwMg9qb3foX3vhrpnbWd9/oCC7z3H9r9K7339UiPnm59OQGtaztuoA7jgdudcxcBLa2MzZJtnhHbVLFEyUchH6wvEGDqEUukdrWgmycisHICYlm6EvoqfYLMbEHam33QbiRN6MS+ECnxJFJGlWjnXYmUUhAS4yLkX7MbYp9etPLqEVv1IFIUw5By62rfPxJLpI5Gkzcrlkjdm4xHz0Ng7x+IkTkKAagpaGJPjCVSRyIH9+HAckss/QgCBg8i5/Un0Et9ADJ9TkCg7G/I5HMeYqguRkzM9YQJvxciBqdfLJGaitijXRD4C8r4CWJR+iNwthSIJePRyQDJeHQRcJsxOwGDtS/yF6tEoOtaxJTVIfNkK7tmGvJ9m44A5p4IPNdY3X+NGKlmVq/dEOtlANUvA1dq7X8NAY2PkIn4HmBUMh59FKBoZNm9QMciVxYr98W1xuadaH1WSeiEvwgdmjiRcEGcHUukjkVm5dcJQ0AE8yZByD4egRxxpyPGdCXyRZyJGNfXbazXONwm49Hn+W7w3B9C3kdsZhz1Zx++G0oD1LcfAceX++L0Wvf/FcjaCBM1ES1sa1jucl8chGKhyJXNB2orZuQvRfP2RAR0HRrTDDRHg3hmQd084dzdpdwXr9fBubEUubI+QM9yX/zaRi9ukib54aXKe7+bc06Bq8UI/RNYHviObaYEp6yDcEmgd3ys9/4n5qD+9pZU1DlXhDaog733y5xz/0Rr84aevTWl8QnyzXmGA0Z471//zofqi38T7/3NzrmXESEx3jl3mIHCTZbtGojFEqmMRpH3OyMgMRbtil9Gu+ilAMl4dEEskeqKTE2fIeV3Ggpt8C8ECoIgpjMRIMi3sjMQ0GiBwFmQL3GuPecq+/0hAmlzgN29p1vt8khZTqv0KOTTdQ9C93shUNQGsSVPGEi5hzB0QU/7XYLYkqeMNRtjbf8FUuyrgGeT8egaJ01zQg8Cg96BAOI8a8fPERAdhEBCD8Q0FaKdSw5imQ5Afkw72u/dgPvteR8iNvFVBIz2tGcU2zNz0aGBEqTU70WAchywayyRWoFYtRwEwJx9D2K77kYKNQirsQj597WxexoI/akOtXYU2bVBnsJ0VsaK6e1bzmm2orLjlFVV7btZWzxiIYvRqcr3CWU0Mk829sE6EQGA+5Px6FXW90MaajnJRZgZyaQSzYnTrD8GI1Y1CAfSGrGNIHCVAexD2ncgnZ5HZkYBAnd9bAzuBu5KxqM38SNLuS+uBm4ucmWvo3mZhforCOz6GTKVvLsOEEa5Lx6/Cc+YjXwZgTXmzKWDbpvbFqiEbs+g93AKoS9asIn5EAHfiH2WifqzGs057zJ9bmHPmtRRl4/t//ItB25KTsZ3gXZFruzycl982yZcv8lS5Mrao3mRLPfFczZ2fZM0SSDe+0pjYJ5Da0K5c+4k7/3TTvbHXbz3k9F7ubk+Sy0Is1+csQnXj0d6caxzbifk7gFybVkNrHDOdUAbzLc3saw/OecORRvuDcm7SGe9ZSbJ7kgfDtyEes8AOjnnBptpshkiY14H/s8595b3vs7KXW82EOdcL+/9VGCqc24w0h2bBcS2W9NkLJHqAjwTU1JvEDC4Ail9kvHoDcl49JTGoQWS8ejXyE/oAaT4miMT0SmIeXkaUZwjEGO1uNEjg4jpVfaTjUBMD/t7KDr1dRrya8msXpSxYMEbza5tqOUkxFp9jlLgBE7FUxEoO9/MT7cSmkc7xRKpXHPg3xPoGkukbjQHc6yMNkhJPhVLpHaMJVKTYolUEFn8L4hxeQKBmtPt+T2RUgtOiWYgM06u1fESBIYK0PwJgrJOAWqtD0+2PspGoKUeKcNLkD/BRQjg/BqxVCeicBgnINAUMC2gl+wnVtYSBKIPQS94HQLJO1ldc+yzwH/vGwT82tn1yxAgrAAm5eeunj5vyYBeq6tanW1lPIjA5kGIzbsfWB5LpM6IJVKu3Be/Xe6Ln7Mgp11iiVQeMlvun4xHr4olUtmxROq4VbOzetQuy+zdUB3ZBZkSx1l7llo92lvbsgjT8wQBezOAwryFFb5Tycz2pL1HoPhptLt8C5gTS6Q6xRKpXfnPSB5hANe5CIBVowXo83Jf/Nn6btwcsbhiY4j4F9CG4feofzojdvQSBASDU6i9ERPcne9uMmvtmm8Ld6hJdz16edvcdvXDNrEak+wZvylyZRuPS/DvbXCN/s4pcmUDjGXD6nsMGzf9NEmT/Jt47yeidfdnaJ08yzk3Ga3rx9llTwCXmRN5r00s+hbgj865iWwaWXM30M459ylan6cjH6vJiOEuQ5vYjW7EkKvBoWYWPQmt4RUbeXbEOTcVWZLO8N5vUixF730t0u1/sX5LIT13P9Lzn1g9/sGG++ES59w059wUpH9e3ZTnN5btNqBrLJFqj3yOnrIEyxu6th+azM8k49HJsURqB2RGeRmBrT8SLua9EbN2LFr0d6ERy4IUfb1dl0ZmyCIE6mYhBbYn0Kp2pVvlG1y77JZp7xx1aNIdBDRPxqOfxhKps9EkWJWMR1tYXR3yYTsFMSP3xxKpPggMZSH/ozJEk76HwMk4BLwuQGCgCE22Icgx8Rir0yoEMKsR69UWnVabj3Y5dQiA9UUgb6ZdF4SV+BV6aaYgRnEeMhtWISD6eKM++LuVPQ+9pH9DCmk0Al4F1rdp5Ef1qPV3no3DvgjMTUPALwPt/AqQD1tvBOwW29g1R0AylzC0yF+RE3ih1X8lAtSrrA+Ptj7cDZ2KvALtEI+wvnk1GY/eikkskYoCD6breWD+680Hp+vZscN+q0bntE4PQsD0aWRqboaAZZWNT5bV8RPrz2iHd2cWNORlNywe1DWNc6tRyp8G4E/JePT5WCIVHO44KRmPLrYTnQcA720gnt5miQGGn6IxjJT74rFFruxQZAppjfr8MwRGuyA/levX8gvbYjn22jcHTru50198rZs16LavnwCWfjyy21QU6PUgBAJzEAt2LDoQ04LQJBksbsvR+/F8Rn7DYV2PXv5B2yGVd25qPxW5slmE4VBKgOj6MgTEEqmAQb7345HdeqE5U2M/8xHg/6jcF59c5MoiGEPeFFh2+xDnnN/EOGJ47zfkK7nNiDniZ3nvqw3svQH0NaCzuWXlAA12UGAv4J4tNLluU7LdArFNlVgilYt2AOchP5//s3RAwff9EVU5D/lOnYyUZTMEDDIIfVMCnzGQ0vQIeLRGCiMHmdHGIKAUoOcKBAjq7fNSBCRaWd3GINDxQTIerbKcj78CXkvGoxMNnL2FmKQ90C7hKGTWG4+U+DEIwD2IWLYu9nmOfXajtSFgul5DYR1+j0BDlrVvNVIo2QgUjUO7mKF27Rz7fGcELC5HQKcVAjEN6HRMH+vvKrSD6mBlVCLFfjAy/X6KQE836/erEGt4hvVfR6vX7xDI+Yn3zF0yMbuoWa/adjkt+BQBxuAI9t2IiWuDAMVAG8c2CIxVIH+tOxB7uo/V/1TrrxIrZ5T1/xrTZSyR2hOBwBvKH299ZPXCjBN7nLLs/vyO9XMQwD3c+nc6UsgfoxOqeQj8nYFCfAyw/l1ovzugufcE8gNbhDYJKwjj16WsX86xfvqokVl+i6TIlZ2LNiENhLHfHif0AQT5sv2p3Bff932etbYYs/us9yz65LJuZ5b7Yl/kynZC7RtBaNqdjczF7yDgnYHmRTZhPtiFaKw/LvfFz25uXYpcWSc0Zq2svL3LffGEdV0bS6QObKhxzy6bmrtgzhNtcxFIzLb7StA79cT67m+SbVv+FyPrm0lvLBYsHbjCe7/ZrJCVtSPaUEXQ5vSCwJl+e5bt1jS5GXIKclxfhXbOjYOpZiBzYAZiam5H1Os0tKB+jZTeGAQgvkQKqhL5q6y2n3LESKWRWepLpID3QWbBJ+z66xB42RkxYXujk3KTkELcCyAZj1Yk49E/JuPRifa/R+a63pYn8lEEGr5CrOC7COiVINDh7JmfIFalP9qZO7SDH4eA3EnIHFiH5kowXzqhQwktkGmyAoGwrggIjrHriq3P4gg4PoXYqH0RM/cGApjHW713RfTy0daHbyFFO9H6MQ+dTHzA+uhdQh+lUxDD1xLo27xnXZssefDlIcCTb+M2FwHdvyHlegFS4DUIbFyImKXhhHHfuiTj0QCMlwMfJuPRa9cCYcMQSHkKeLn6m6yLiLhB+R3r90BmtFzEjr5C6Fu1m9W3Bs29HoiV+xTNyX8SBskdik7Bzra++QeQsLF3Nq4B8/gacKP5AW6RxBIpt+v1X3fJL6paZn3wIeGhhMAsDHpvsrf0OeuTZDxat+i9ggen3dyxAehR5MqGIQb1EsJNz0K0ichHczVCyErPIEwYnoHG9aoiV5bDZkq5Lw5yr76CHJmnru9anyb3q2dbRL56tmUfwnctOFWaj/wQf1LcfuorB/9iXPkhZ717xObWp0n+e6WmLt3Re+829rO9gDAA732F934P7/2u3vtdthSEWVlfeO93t7IG/y+AMNjOnfU3UcYjNuQb5Ei4qNF3aeSD9CxS/h4BsJlIEbZFyjyNlHgGQvGzkUK+GjExJWgxXoWYtAYEwv6GFFoDYsA6IPDxAFLWAxGr9XcEAD+ENRHag3AJn6P0TosstMUIlMaov0WABzEIS5D5sQ44MhmPzoklUiORyW4pcr4enYxHH44lUq0Rg9MPAawYUvAgU1TgNzPT5rGGJQAAIABJREFU+iyNzJkVCKjcj0DWnlZGJgJ2xyMw9MGi9wtObLlLZUFmvsc52hkDsi8CE2VWRh/EEE5CLFwh8mE72upTjEzKuyKQOhBY4RwLs1r4emtrD8T+Nbe6/MXGu8LqHbVxq0Bg+yUEND9AjrBZyXi0FhSyIpZIHca6JYhqPzAZj3ri1ANfxxKpW22sbmzEXp6JgpVWIH+2UcjH6VrgzGQ8+qoFaD3ermlh/V0US6Scga/Hgwfb6c29rD27IFASpNjaUNTtDUlGZr7vnd3ML6lUX7wA/CWzWX1hut5lp6siaQsL0R0dItnq8tW/Wi9CYKYL6os2a13SHgHmQkImOo0Y6HIE6svR3PgWSJT74i3KxVnui2vRHF6nmBn36KyWHWdm5Hmym6dX1SyiCsv3avXrh1jMA5wjL5Kd9tWLsoayBT4lTdIkTbL9yP+8aRLWpKy5FJicjEdfjyVSnZHJbjFSyjFkpnoCgYXJKOzErxAQambXjUMAJhMBqQoEIIJ0Rt0Io4/PQU7YFfb/x2ixfg2xWLORWe9hxATciBR1EMDvBULlc14yHr03lkjdhhTwy4hpuxyxbF8m49H6WCK1O4pPthL5YS1GrEw/Quf60xHo2xMpjR0Q6zcBmQvfR4Dlr9ZHM5Cy6W79sAiF+miHwJhDoPUAxLq1AM6dflv7I/M61B3Z4+RlkzJymIkA7DmIbWmGWIcudv8SxEZciUx67ZCfEAho3YCA7FdW75aILbsKgbYuyOz3BQIqOQjIHI4YkwZgqOWO7IrMz2OQX9ijQfiKDYkByUOBscl4tHKt7zKt/3dEpuaHEOieBFyD5sAzyOH2DzY+/0TsyRIEeA9GZu5dNxY5P5ZI/QTNk8eS8eiIjdV9A+XkLp2UR/mjbRvQ2F3WKbr8gJUzcndY/VWOgXzngd3LffHkLX3O+sQc3duhOdUSMb0HIpYzOJUZpHtq7BZQBTxU7osvtDKKgXnlvvg7Kae2cl2HA9eC74lLO7KYTW3GRLSRyCJk5tKYWddlpSvwbvdZdf1m/1D1apImaZL/fmlixCS9UYDNILnz3xDgmo0U8p4IIC1EC2oXZKaqQcDgOcIYTjORQs5B7E0HtBuvQEBnPgIMM+yatogpqyU0F56IwMEHiO1ZhBT4PsgX5k37rBABo+DZ45ATcwFSAAcjxXVELJEagBzPcxG4Ohop/yutju0Ro3MlApk7WztzEID80J5XhEyADgG+w6xPMhGgXIlYp0qkJFtZf3RHiigTOKnn6UtSQF5GDi9ZXScikFqGQOHvrB+OQ8orMB9WW91BQAvCLADPoXht3azMZvazk33/AgKRuyKlfpz1VT1KXr4cjfVxNh6DgEWxRGoMYkNSCJCPScajd9JIDBy9zDqk0cncIE3RpWhuXWH9VYtMZ5lojmQjM1YFmn/trI4vbkr6omQ8+mwskZqMWN4tFksJplCq8HmRK7vAw4BIbnpC231X+owsz5JJ+dUZEdcJbU62qli8sYX277IiV3Y88nVMoPHMRf33LQLkzVEfB2bwoIytcopzXVLkyjqj96w58P/snXeYW8XVxn+jsn297t3GcsECbNOMKQFjikyvVug9IUAoIQgCoYTyASGxlRB6EgIhBEIRoYcQ0YsptgEbMHKVscG9rbfvSprvj3eutRj3RtN5nn1W0m1z752Z8857mh+MH+uvp5m2KKjDK//U2nfUANgWXxkQC5nURYUs/wUpyA9XCowYEI0n90YsxaOIBYkg3yEvYnIh8i0aRD4fmBdBNxyt0K9EJsVmxKgMQoBkDnIQvxABjZ8gpmow8lWqQOawWxCoCrnz3YJAYUsiFpkfjSd7IWXztitn1AaocWYq9o2O3SZQkXu0zcCGmf4iDnbHRhAQeh+ZSK9BTNJBwCXWUmUMTyDl5ZU6egsBPhBIGeDuaTFa2S9ETNdI94wGIOf6nRHQmuXuuRmZD9u767+PQNZ5SIF6Pmk/RuD0p0jJVrhrzCfvRP+OO1cWAdBlCDxf6Np1h/t/BmLv/uSO/RiBmSMQK/IFMglejpi3RYjpux8B4JcQID0LJSXdETGeByEgvNTd54xELBJmFYnGkxej1ApXtk6LEo0nf4MA3o8RW/NzZGrb3+1Sg0ysHRAzOhqBr+nunbRx7bk5EYusVyLSzS0hkzoYuN9XnPu3vyx7ir8kV2Ezxtd2cF1D95G1k3wBzvN8FrdSe/ohM7dB46kj6mu7AGenbfjTrdSOC1CUZAl6RwEE2E9EY28W+bxK01Df3YF80tmlyPx/VwGMfffFHyyen8s0r9NZ3xcoWpBtafre+IkVZNPkB8OIRePJXyIF90QiFjl+lc3vofxehyCT0JkIhExOxCLjW53jM8Rm/Mbtdw4CZRejyfVutBq/HTmoT0YgYSpS0J+hydozo0QReBnsPqdRAlmLUh18gEyY+ydikTnReLIBOD0aTz6HJvCVIfpznmm7S6A017nDbsHHexyy4jkUxXIFcuIvRaDk0UQssjwaTz7dUuv79Zwn2w6oHNB0bqc96lYg0894xEzticyVDyNAMwmxbncjZutTBIpuc23oS75MUS35xLkHuu3d3XPzI9OnFxCRQ+CjDwKZL5MvSN4bgTk/AkSeg3gK+dx4of/z3LZ/IPbIiyDd3l1zGgKNdcjXbm+kNCcjANzVffaidBYCk1xNzw+dqXKMa1N7BBCnsIq4ElPnIqB1DWKwPNnXtaeTc7a/IxpPvoAiMw9DrN1eiVjk6mg82RaxS28iABoGsolY5GtOq9F4sgz1r/+1jvTdQlIOVNkcQ4PlueqWGl9LccdMsMMujcYXYNnCtyrKQpemOqRteMk6z7R5pAH1uY7oGR6DFj8D0HvaWvIESp0xBDG2QxHDvBD1q65ojObw2X4VoSYaFwRzmVq/3x1fgVwOHmbd5VwK8i2XXKa5y6gx/1vnfk9cOnKdYG11YozJork8gPTFqdba9Vqcuczwz1lrB23MtQuy5eQHA8SQycqPIhG/Is7cMzYaT+6CFN8xKH/SWGQe9PbLROPJjxBb0Qm4FZnprDv/TQhs9UfAZSkCaFchpuN1xIQchPyvWhDLVJ2IRV53lzkIIBpPnosmca8eI4iNuQIxPIsAG40nf56IRaxt8b3eY9Syx9sMbJyfiEUedg79Xob5VxGAGRyNJ79AA7jB+AFLNfJVOoy8Ca/C3dM/kWHKums3oElghGu/QSbaae5/B/LZ+I8iHx1Yj4DVbPKVAeqQKfgFd963EXPYhIBjkztXDQKFn5AvY2Tcs+uGGKyX3X22c9fr4e5hKALKn7h9xyGQHHff/a6tQTQWrkMRkVn3DjohkF3p3qUX0PFnvi617ll95OpQtpajyYMwT+Yg/y+vFuhbAA4oX4OY08ZELPLqaq7lyXYIBHTD1WfbUpK24SdCJjXVtvjq6r8obglUZM3A8xbvBmQnXtf9f5la//Ngd+tfNSmXa/TPsM3+X6Zt+LUt2KR5iF319T526YhOe9VdB1wz4dJexyFz5daS+chUHkc+fp74UZ++Hhc1WdIpY7sfVG2WflhmFr9T6S2ilqLFz9qSVm60hEwqAJSnbbh6NduKgeYCE/edkgYvr5Yx5gHk0rCxATkF+ZbIDwmIjUIT92Nr2ednaEV9N1KUp69mnxkIZFjkc9SGfM6wQcgB/FnkXzQLKdouyBSxMzJ7VSKgcDUChitcIe/ZyPH8bgQQFqH0D0TjyRMRmzTQ/dV42wB2HTNnGWKaDorGkzNdO09F4KS9+1/v2nZ5sCJ3QuikpVe6/Q50/3siv6si5IvVz93jGASknnbnegYBoUrE5nmJSTPkox5vRibC15CpcwyaNLwcZtsgNuNKBOyuQcBsAmK2Orv7XOqeUwixZQsRQ/cxAlV7uWuehNjDKGK89kUO+dcjYNceAaIyBJ6rkbnw/EQsUu2S556GGM1JLiHwA66tlyHgeKF7h19bgTrwtdoJMRGL1PB1RWtdO33AklWSDpdXTyk+Ndvgm4FA9JrkA6T8J69ln80maRtembbBpeu4B5i+w2XzX6z7Ithz6UclZUvHVRqwOyHA/NoWbIvF1Z+MxpM/QqxqYHWAYwvLmSgqNoUY3i/QAqozWtCt9A1rWhxYvODVypL6L4or3L4BNC47A31DJhVGi4fRaRveXMDsXOCIkEldiMbHvWjs7dz1gOqiqu0a34jG5xy8ITnnovFkEVDs+nVBvjl5BzGxGGN2QuOxDM3lZ7n6jrsilxnIpxXCGLMDcskoQnPQKGvttK3Y9oK0kh8MEHO+VHetYzdvJe3Vp/tahnB3nvej8eSfECsGMmllkNlrMGLT7kHgrxdiS6qQia0csUcGMVu7IFDwBQIaAxHYeAalvfBMo/eSjxYzqH7kddF40kTjyevQgHrTneMBxOxdjfzV9iFfRucj5Cd2O4qwvB2BrO4IHHpFtw0CekciX6ZnyDu/17h961v996LCvNxiJ6IJoQqB1T7umZyAFEIzMvHt785xFfBeIhY5DyAaTxYjX66r3PNpAsoSscgvvXcRjSd7IzaowV2/1J3/bOTvd5P7PBUBtycQUK5Cfml7AmdH48l7EWi+0+3ryQoUAPBP1HcCaDLbZMYlEYu0ROPJM9G7fmeVbSv6FU+usVnTzjnKr+kcFvkCfhMyGQUuvN5c7bvYZugWbJdZHGib6ZipN000++7cim0ZjdKMbFRqio0Vlxn/dLRgmUu+1mwQLc7q0VgvA5ps1rSp/qzMh0DbADTGgmj8vYBAfwMaa+PZBAmZ1GDUn7ugRch1yJdtFFpElRg/1l+SW2fwx6qSy/A6sE00nhxYAGPfjLhs9geQj6L/BypU/box5gZk7r4Yga0LrLVvGGNGtzrFucCfrLUPGWOK0NxdkG9IfjBAbD1lf2Reegn4++oi1Fyqi4MQy7MYTXJz3eaPgdnOvFSJwIFBTE8pMv15BZLbuOt8hnyVOqIJsgaBqOHAGa2cvp9Eq+UZwLxELPLTVs3qilitJgRu/utyS/0VmRyrEfvyd3eei1AwwCnumtMQWNzBff8cMVAtyL/KAzgjUZ/5EplTOiFwOR35tJ2JlM9gBNjeQhP/O+5ej3T3vTtiyLq7a3jPJ+1Msvu6c2yDFNNo18byaDz5c+Av7rnci5z8q5HZcihirZoR4NwR+d3cCPzb5QFb7J7hPxE7eYp7Rr2QL14jQCIWWRiNJ0/wgiGi8eS1QGkiFpnJZhJ3rV+sbluu2RfbXNfZEpKIRRYjkMyoMckzmpYEMsEy26H/GYtzpV0yV/qCfAZhQiZVUdK1+aKOw+reePPpPd/aQm3Job6/tSWCWNUAAmA+NB94n7sgBfcu6r8XIma1nrzi86Ox76W7eYevLgY2WEImtT9aQFSQD7QJILAXRCb6Uxa8Vtm08K2K+ul1O2xQBYa6OcFiX5CSL5+rakdsy5hUC7JGKTXGfIRA+2dA0hhTBbS11nruLQ8Ajxtj2rrfvWogDyI/aFA/u8oY0xP4d4EN+2alEDW5geLqUv4FAYp/obQW/4cU+edodfsKUvClaLKejsDEYYhp8yZhr4D3UGSq8rKkP4/Mn5ORuXRiK0DglZHItvJlOgwxW8UIAE1IxCL7uW2D0Er4cdfeTgiktHPX6YxW9J5ppQqxSOcjJdOHfOb0FgQaP0EAohyZ6SwyCX6MJv4b3X2/g0BiAgU27IZMn9XkGaYsclqfjxSH55Pllan5o2tnR2RmHIAS2D7i7q8IsZHtXbs6IfPmXmiy2gn4z6qgOhpPXokYyT+4d7YvcHIiFqlxSVdvAKasTw6xH7pE48l+uQzHph9pd13X/WuKDayoTpWc9/bzez4aMqmr8OWurxrYOKf/T5b09frx90FCJuVFJnu5wbxx7Zkjm5C7QQ+00GlLPpcYqP97474F+ZzuC9yStuFnVrnWOv25Qia1N5qbtkEsnCfLkdLugearJrSoCgLP7zpmzn25ZrOjr8g+1Drady3XGYQWbYm0DW9SGa3vmxhj7Ho6629UrUljTK21tsIYU4bY8McR8PrYWtvb7dPP/b4/MKnV70OAhz1nfbffYWiBcI619pUNbU9BNo8UGLF1iCsAviIRiyx1P01FzJKXbuFnaJJ9EpkXLyNfhHox+WjJtmhVvD0CGi+hVevtaEKsR6yVQSAiiFipPyHzxyx3/ay7fkM0nvwbMkV2d8ctQA7z20bjyREoTULKnWMsGnD7ITZrBAI/nv/YUNfO9ihVg5987i+QspiNVvcfISB6PArV39k9By8TfjECUTsiEHgGAm1TUKWCXoipaoeA21sIDB6OQOv2KLBhd+T75T3PBAJrJ0TjyXrkH3EqAndPId+vE5Cv3jTgikQs8mg0nrwgGk+OBE5rlf7heOTsXpKIReLI2dqTAYi6/xwxZ+sUFxyxHzA1EYvMWZ9jvi+SiEVmhEzqNny5E1vq6Nswu7RtLmse3u/kNw/vGgn2mv9SVWPTssCrWxqEhUzKbC3Hc2eWbIdAjWfayZIfLx7Y8kzmpe77FGRO96oBGLQwWYz8V8cDY0MmNRSoTdtwKmRSfdA8cT8aP14b+iGfxrvQuHkQLZw8YOi5MRS76x/rvv8DjdVGYOjSD0t3LO6Q2W72k+0qQpem/uXaNgeNxy7AfWkbzrmam1VpG/4ELXoK8g2JtbbeGHMRmvfuApYZY/ax1r6J5sTXrbXLjTHLjTF7W2vfolUwiTGmLzDTWnubMaY3mksLQOwbkgIQW4tE48l2yO/qC/J1HrPReLIfmnwXoVWmRabK36JBcIL7645YqOHIEbwchan3RM7sDShybkm2hWm+AMONYQp5p/PFyM/jS9ceH4qOiyA/pTLEFL2IfLJ6osm3LZrUK5Hf2uHIR8TLc9QfTdw7IlC1HZqgS8hHPx6IVr1ezcVl7j6PQoPWSw1xpPt8DprcT0OBCknkE3a8O74PApefIlNus2trGfLn+hI54Z+GnEu7oonhCNfmZQigdUesl8cidkM+b/9FfhF3IFagCegZjScPRyzbNohteNqxikchVuApx3IGEcg+EZlualFJoS6JWGQBrcSljWhYBVj0ce/0PWT6Xac4Vq4tAoy5de3/LZcScr6iumkVPoLZFnK2aOnE0mjo+OX1i96o+KxxftFmM0u6bPlFrcsVuVqUsZBJXZa24U0y7a2nWMQAv4r61a7kQVgWjesi8vOD55zfj3xJrXL3uRSZ5yembfhDx37dQD6/XgMaH95i0JMuaIx2RpHQX7jreT5qXq3RoNtvb7S4KXbt8wHMeabdskBpzjYuDN6MAlr87vp/dPfwFLC4qH3Lf/HZ3gPafHJWpibwihcY4d4HhejLrSvW2g+NMZPQnHU6cI9jymYiNxHc//uMMZZWzvpocXuqMaYFLchv3notL8iqUgBia5cViD1a1S/oI8RgHYUmqs/QqvZENCG3QSDIMyM+iSa9OuCXiMEZjpibSYveLb+3dnbRzV32WTGlrFv2N8hJ/3MEOj5o5dh9oLv+eAQa/osm1C4IWIUQmJqPnMDbo1XydgjgnOz2e8xdowtis9Ktjt0WTcRPIPNkIwKcOeQc+gWa0C9BDFwtGtReYWMPqGWR8jjH3bvHdAXd8/qXew7bu+P85CsXdEQg8Q13/jYIHO6MTDjLyUdnvut+74YURxt3Xw+5a89H4PVC4CeH/PK1rDHBG/wlueSzN+9/eTSe3AlFks5BSs+LNJ2EmLuvFLR2wO5viOn7Q6tNs1zbTo3Gk++6FCJlyNftg9WYRjsgcNyIfK2+00AsbcPVIZO6GYjR4v9zademgRU9Mx8HyuysbFPgEzYiR5ZjnWLAwrQNP+B+CyC28vSQSb0O3Ja24dno2Xu1HbeGdEGLhCDqIzVoUeEVFT8FLTYeR/0ph8Z/Do3ZexF7Xka+MgUAaRtuCpnU9e6cpG14AbrnVeUdtNBZTn6h54Evzz/V++xDrO/9CNC95dr180yN/8lMjb8DYrkeRaAtjfIlZtM2vDhkUr3a7dLUC2PLGuYU/TlT578/ZFJX9Dx66cPlobLhdZ8X34cinwuyBcVaW7HK9yNafd1jNftPQHOpJ79yv9+C/IQL8i2QAhADovHkkcDiRCwytvXvzgfryFX3T8Qijzk/oh0ReDjF5Ri7CbFYi9Gk24e8H1Q7NGn/GLE5XomW4jYDG88s69kUKumc7YMA0BfIdHkEAoJ/RMzUTmjF2g+xYtsg4DeKfLSWt8KegBTEawgQbY8m4wrXLs9vZAgCHHOQ0vBqTg5C+bIGu3PXIlAz1V1/FJrou7jrLXHbOiNn+P4ov9c0FHzwcwROz3a/L0FpOi4kX/C8I2LRZrnrn47AZIN7jm3cMfuglX8/xEDlUOb+XV07g8hkk0MKZX/ESoz0F+cqamcVd/QFVk5oTSgNxL/d87gUAbjhCGzvg1hMT6oRQG9dHN4rvB137TvP+eaNcvd8Bl9djYLeXxDIrU/Zou+I/Bc9n9cmz9uxbuWvGx924EcMTmPIpB5O23ALMtufjYD5WcDRIZP6JQLlV6ZteGbIpHZD7/yOtA3XreHcmyoLEWPr5bWrxyVXRuNsNHIhuA+NLy9iugix3L+pHNBQHGyTXbZ0QvlSMGelbXglGE/b8HvraoBjoJY5wHoJWkT5yPuaejIXjReQn+YYNNZt2oabQyb1qNt+pdv3dMTyPQ6MDplUCIgvn1SeaLND3Y97RZe3nfVw+7Nblgcbm6t94caFgU5Yeq//o/v+ii9QtGB9krX6AkUL1rVPQX448oMHYtF4siNiiOrQ5L62fQ1S1PMTsciDaBL2tpUiNiWHWKHr0eS8HQInDyO/p78hBdMPreKLi9tlvyhux7toEjwKAZf/oAn7X+4Sp6EJfVvkL1KEmKkmNOl6SVh9KDP7ZODxRCyyKBpPejnFrkjEIte64t+noQm3yh3TgFbLf0QMXg6BtaUIRAaQ0/weaCI/Ca3mZyMWYAcEpl5ybb8PAcUbkZl1HmLF/uyu0xkp7lcR87cUgalqVF3gVLR6W4wUWB8EXIaQTxq7gLyT/o7uWX+GFGQXBIJ2de38Euhc3CGb+vR3HX7W76xFi11Jon3du3jelY7a353rQ3e+r5iDErHIm8iH7GviajyOQOByb/LJbNutZvdnUeDDM6vZ9p2UtA0vZQ31NtckIZPaF/WFZxwT1BHl+3stbcNvOpDwS9Tv3kSm7T8jsP8I0DfYselp2+LLZKqDLyEfxWHIX+8RNK63hFhkPvf8vDoidtNLojwYMVANCOD0QWboBiAKtqy4U4sp7dZSuWxi+ds2s5LJ2ti2pF0bStF48XxGH0UM/vnIx3I3YEprsy5aPIxDixqvXNnfkIn/E1wftRmzZ92M0jZLgra6ZXmgDOx+yyeVk63zZ8GsND27ubAsEYtsrSoL3xoplC0qyMbIDx6IIWbmJcQIrUsqEBBbBl/L8NSImJwcAgsHI8A1BSnufshX6RLEABlkPvsEmRdKkTlsKgJJZ6EJPReNJ689cq/fdg/4M7l/v3nNe2ilnUUKqxiZQJpR1v0BCNCMAo52pZ12RPnJvBW7QTnGsuSjZhajibcWMXa49r+KTLPvuuf0qruPNxDT8znyyzofgbh3XdubUf/aASkfEJMxwp3DY4tucu1dhkyDdQhkekDvU8RutLh2D0Xv6r8IqDW7Y/ZEpuFG9wyfRdnpSxGD9Q6wt/Exctcxc8ahrOeHI6VVAuwRjSefR+CzBSn9MNAtGk8ejMoIrY/58INVvo9zz/YrkohF6hDj+YMVF333LOon40Mm5ZUZuxT4ccikdkb9rAiNDRCwHYGYygBYk1kRwPgIAHOcf9W9wJNpG57LFpK0DduQSZ2Hxs/eaNwtQIDrj8iNYH/UZ69CY6gN6lvbBCuzuSXjy/3+0lyDzZgD2u9ad2U0npwM3LWy2PqGteUY8sXFy5Fp9AI0Lm5DwNWmbTi+muPrQiY1Fs1bc8m7GQSAHr6i3DsDz194zvT7OvRrqQ76lo6vLAWq/RWZuUUdWvZuXuL3g7klZFKjyno2nZap7zK5uGtL4OiGl4Y8dfWBszfkXgpSkB+i/OCBmHO4Pnw9962JxpM/oRVLEo0nvXD00xET9SECLPcis8rtyNy2MwIuh6KVaTF6/p+jlXIpAiK3ItBwLFLgnwL/fXPSqYN/NOihCcZkb7HWfy9ijbwi2GVICUxCwG9XxJy9hepAXof8TX4fjSf3RE7tExBz9AH5aMf7kYliBQJtXvtmosjLxYhp2xEBoE5u32PQJL4iEYssicaTf0emt7lIqXZ353gY+bf9DzF9UxBrMQDlONobmT/3QKD3AaR0mxDIHYKUzFwEss53z32Z+31H8mbKRhQp6pk2+5JPRou7r1+iCNGOrh33IF+zwUh5hRAz0Ms923Uq9kQs8kA0nvzUtcEgFnKtZqa3RvcJoOc/ae/LZs1a1zW+R/IF6oeD0XM/CbkCzAcmuNQIC4CHQiZlQia1I2I2J6O+vw+YMtvss9ZnpyIm7BXEPN+4pRuftuHPEAM7JmRSvVF/a+fupQqZSz9DC60d0aKvT3GnFnqPWtpcO7M4Vfdl8MoV1cF+PQ9fXonGzCOsRz9bTVu8FBgAzSGT+hCN/3+ihcutQDBkUmenbXg+rEyJcS5yH9gGzQtPo8WIHzHQXXzFuZtKu7WsaD+0Prvg5SovTUe7bG1gn+aluScwthOWE4CD6uf553fYtcH4S6yd9Wj7MVzNcRt6LwUpyA9NfvBAbHUSjSefReBg8Kr+O4lY5LlW+/0OKY/D0eqzI4qcHIMmsRHI4XUZAlqjEEibjHJf7YFW/B8hhfMzxBq1ddtHIdDXo6ahY3rOosF3WOv/PzTBB5AZz4eAWHsElrZDIGBbt8+RSDmkUZTXocikcjoyF/7EbV+EnDf7I+Ax1t3XKOQ/lUFgqD0CYBHEJA0kH3XzpXs0y9395BCbcRuqBHCWDXsXAAAgAElEQVQHWpmDzHKeabgZOS63QSalBsQkdXDtmYFMjSBA1BYB3p5I+XR2z9vL7t8PmUcfQ2aVZhRZWorYxLcQGP4zeb+4UsRE/g2ZULu5d3mkO+fvovHkaeuTgiERi4yPxpP9gW7rAmFOQogpfYavBgB8I7K10kCkbXg5sF/IpM5Bz/ooxMr+GjgwZFIvAX9J2/BjCCj8AXgxbcPXhUyqJ/IjjIIx5Ew3xC4PQ+PqecR6bi0JkE8j8zPUfxeh8b2X2/YF0C1T48tlav1vdx9Z88tELDIRIBqfUwK0T8Qim4XFS9vwWyGTmoiew1K08DgVeDhkUgeg5/QjFB2ZJc8sH4LGwkI0zgOZmkBLptb3yyXvV9zjjvOh+SDXvLjopwhgSiym7eAGAuXZxim3d11ZEqsgBSnImqUAxFYv+6HJyEt0uibJICDQghTJgch08jR5f5Cu5BM2lrjjfor8MfogANONfNRcF8TuBN35DgJGZ7IlD02ccWjWnedQBEKWIfBT6Y79HJnTwq7djyDTyXiUE+zHaNKtRQCrEbFKXrkhLyLxDgQO+iIQ0hUBSi/Z6mjEpI1EPmKvIFbslWg8eU0iFnkwGk92Q2bEiWjC7+fOu6qch8xMZSgxbg0y2f4WmXY8oFnt/o9FZt9eiCm7k3zwwAr319E92/koMGB3xLb1cdn1e7jznoaYy/7k035ciICYHymdz1CQxAj3TppXcw9fE5eBf32z8E9HZuXp67n/FpFoPGnmv1p5TKCi8qchk7ombcMT1n3UZpEkYrCGoQVQW/ScvRqMFYhNfQioDJnUCWgBMQz12WWobzSi/tsJgZ+tCcRa+w1WoX63r/v+e/c3BHii64ErWqq2b7S0qhHqzJEbDMJCJrU9YryvdQxda+mD+r1Bz6gSLXY6oX7fAz23LGKgvYAY676/g8bm7pNu6LE7WhDtQr4KQBn5Mk2KCrXmneWpkv/0Prz6qXRuu8839H6+6xI0gfkZsut01g/gX9BiMwV/soIAhcz6q5VoPHkAYpTuWRcDEo0nzar7uKLbXVCk4nwEMDqiCXoGYp6eRybEGmQ66I0mwWfRBFmKJsauyGQ2DgGG3mhS74UA3MUICH2MTHnFCGTkkHnzeXeuiWjFuztSUIMQuFro2tUPmU/6ujbdi1i+Xmhi/hsChW3R5DwrEYuscLnWTkKAbySQTMQih0TjyaMRg+HlN3oTKdZrgVsTscjl7lkdhJTs+wiUViEAdwECecZtO8a1w/Mb85LLnoyURify+Y8+Rf5Xu7s2H4yA2rXka0yOQgzMJchkvIs7z0JU43KC+/6Re+Y2EYt878qADOzw8SH+4tzVTUsDi0q6Zl4fcPai45e8X17yxTPtzkjb8Edb+vohk6pEDNbP0Tvz0i54Zvf27rdJqH9c7rZnUJ9tRn2hLxpjHlszwv33TJxb+j68tCwhtECYi/rig4id3dPd0+hdx8xZgRi+q9Ynk/06rvt35A92f9qGz15lmx+Bvb5oDD6K2OAdkI9oKfnyZ14m/nrX3h3RPFSEAG4TAnKzUCT2XOSTWeG2NQN3pm14dYutH4wYY+xozl/nfpdx5wZn1jfGvArcYq19sdVvFwMDrbXnbXBj1/+6fwees9YmttQ11nH9M4Ch1toL1rLP0cBUa+1k9/014FJr7XrVbDXG9EH3OMgYMxQ4zVq7XvkgN4cUGLHVSCIWeRmtaNdn39UBtelIISxBE9TlLiLvKsS2DUHmgploYjyOfLmTiDvmXLSineD2fRBN8tNRvp4QmtzfRGCklztuIQJ0xSjX1VDkf7UUsWfNaPLNIsbuHwiIfIAm1pw7/nI0KVcjhu4E5IT8CWL0TorGk8+gdBueeeMVBAxBDvNJBGhnJ2KRe6Px5HtoYj8mGk/e6O7jPQQWp7h7HoJW2CAT1cHI1FNKvrj371B6iZ+Rz/5/FjLNHoMA4Hnk2cWJiP3qj3zrdkHK5BWUrPIJBDyPcu/kSqRcHkzEIu9G48m0u9/dgYe/B8lXV0pp15bB9fMCQ3JNvvL6z4OHVKdK/hCszP1na4AwJ8PRwqQD+Uzw3p/HzgSQj+XBqLRWE+ojI8knLfZyxRm04AihPnAxm1hAe00SMqkyBGpK0OLmHsT+WmTiuw/5Zi1AzF0dSldy12Zsxm8QC/y7VTekbTgbMqnt0Ni9DI3zvuTz8ZWjhdUziGX3uXtpTz7iN+V+34F8MEBnNC9UIiC2BKUK+etmvK+CfF3+hd7li61+OwGXG+zbIsaYgLV2kxYYGyFHI3eXyevacV3iwNsWmTPWJJsSMv2DFxemvTo5FCmAnZAj7L3RePJHaNU/D02GxQjsZNDkF3Sfi9GEPQ6ZDR9EZpi/oFXobYlYZBkCDuchh/NtyDMJXrHfFxB4GYHC0wcgxqwImSb/g5zzByBlMcad/wW3rZ48WLwaRSk+kIhFHkfsUwUy/yxHLFZHt32KewZxxJL9inzW5uHuOnuhFfhw14bjkEKpdb93Q4DxEgSwitz3TxGj2A6Bss7I5PIUYtUedM+gN2ID90DMXxqB1afd3wx3ziZX8ugt8sl5d0dMSxfEMoL87m5A0W/d+R6Ic373td+1fkxLvU8myIANzn683YnphzrsvxWb8ibq751Rf8uSr794P1ooNLjvByGA/yViTC9E7+kUBOY+cftVoYl5IrBXyKTO9bK/b2Z5BI2L0WjRdAJaSI0gz0y3Q4uRSve9BsRWhUzqwJBJ9fjaWTdA0jY8O23DF6Vt+Ms1bM8hBRVGz+pwNP94EZxL0TzlZeL3ofHZB42lQYiZfBotXN5C7yGKQFyF2/a9ScPyLZYEcJgxpghWsjjdgTeNMXcbY8YbYz41xlzvHWCMucUYM9kYM8kYM8b91sUY86QxZqL728sY08cY80mr4y41xly3agOMMb8xxowzxnxijPmLMca4318zxtxqjBmPFkutjyk3xtxnjHnfGPOhMeYo9/sZxph/G2P+a4yZZoz5fatjzjTGTDXGvI8WN97vfYwxr7j7edkY09sYsxfy5R1tjPnI1dAE+LG75lRjzD7ueL8xZrS7h0nGmHNWc48jjDHPuc/XGWMubbXtE9eGPsaYlDHm7+78DxljDjTGvO3uZdh6vM+VUmDENlKi8eQ0oH80nnwDGLEKM9YNKW0vFNxL+LgYgYNRKJppKHoHc9EK858ogmw4iixcjFb2YaSQqpF5AWTC3BMppu0QIPGin36OJsnrESv1HFIE9yGl0RWBv4uR2fRZBHQeQE7wXqTVaKRIqtBqun00ntzRfb4VmV33R+DSh8oFtUer5ylokp8AtInGkxcCzyZikcvc8zvH3ZcHDNsi4OZHk7vny+JHK/6FyFQyGym8i5Fpcrl7vhPcM/i9a9ee7llc4M7fiADfJ0gBbYdSU3iF0L3s6PVIiS4HZkTjyaPcs4i5579ahfddEsfkPFXaq3Gb0q4t89qFm3ZeNrHMkDG5XIb2rCOf3uaUtA2vCJnUIwgk3IHeH+idHoP65TOob7R1+7VD/c7rM0+ixcYUNGYq0DgqQX5kjUC/kEmVA9e7TPWETOowxMCOccliN1TmoYXKTcg/8y0EYBrQwqp1FvQW1M9ODJnUPQjsX4NAzGYP0AiZ1IXoGdyBmOIaNI4WIHDVguaeDmgR5UkdWgxZ8uBxN3c/MxCIK0JzXAAxZjembXi9fCcLsvFirV3qgMkhqN+cADxmrbXGmKvcdj/wsivw/SUaQ2G3T1t3qttQLcpj3P4VrD7X4erkDmvtDQDGmAcRsH/WbSuy1g5dzTFXAa9Ya89ybXjfGPOS27YTYmibgCnGmNsRIXE9ct2pRumOPnT73w48YK19wBhzFnCbtfZoY8wztDKfOnwYsNYOM8YcitxSDkSkRrW1djdjTDHwtjHmf+RTO22I9Ed+12ch4uQkxM4fiawqR6/viQpAbOPFy/XTH01KrR1tv0D+It2QGWyFM00egdihYQiM+dCgeBExNgHEAgxHE/pCNFmf6H7viibDt1Ch1zpkvluITG//567fFnXwIFrNHolAWiUKpe+CzEBZ18YUUlpXoqjC/0O+IOXu+v3Im/C6If8Ri0wSOyGzzFloQn+i1fnnuHvs6e5hm2g8GUPKdbk7Po5A5tVokASQ0qhFE8lg174nEPD6FVK+SaB/IhZpiMaTJyHT7DYIrE12bZiKmLZx7j72dtf8HAE8L1VFDVKeHyBmpj8CYzshM+yHiVjkCr4HEjKpDkAHsNuV9Wzu5i/N9i1qnwFMFswKZCb+01Zu1l+RH+CPUJ+sQcqhG3mG06A+9yxy7P8SsVH/Qf1vHupzh1RtX/9Y1fYNpV882zaYa/K3R8BhN/KFuS90190FLYbKUX/cUDkXMF5GfJfhfxIyE5a12i+DzPpHorFwPIrovZa1BwNtipwC7AT2aDDTkVlrdwRUByIF1458fUzQnPYZWiA1oPGcRQC3u/vde04+xFAfVgBhW1U886QHxH7ifj/OGPMzNH92Q/7Jk9E7/ZtjeLyI//2RvsBamwWqjTHrC8T2M8b8inyk/qfkgdijazhmJHBkK2apBFZWYnjZWlsNYIyZjObwjsBr1tpF7vdHEasMWmAf6z4/SH7htjr5t/s/AelTry1DjDFR970KWYU2pj5t2lr7sWvjp+5erDHm41bXWy8pmCY3UhKxyGD0sE9ESgCAaDxZiZT4L9GEfy35AqxXoMnvF+5/W9QhTydvqrsdDaYpaLXdEYGMcSjVghfJdg4yDxzkSjGdj8DHK2i1+wVSZvujVfBStOroTT76cAFSDoeiiXlbxCzshpghD+VPTsQis9BATqKJ/DnkwP+su5YfrbR3cG2ehyZ9Q97p/lhEr08B3kjEIv3cNU5ArEcxMqd6g7yLa0cIGOCy2nulZNoC/4rGkwNce6YgpdYXgbFy157P3DsaiZT9Pkip1Lrn3JZ8nc0d3OexbtuVyOT1x1bvNxCNJ3u7KgvfKQmZVH/k+3B5u53rjy/t3jx58pguvgWvtwmgCfsmdO8dQyZ1fMikfCGT6hsyqSFboj3ReNIXjScDaRuuS9vwWMRk5tC79/NVpW/c/14I9F8H3JC24SfSNrwIvftBwOvNtb5QUVWuh7/EVrhzLUD9dDEwPGRSVe68vwfOSNvwcmeq3Zh3GgmZ1GBYmcvrAfIKIIfG4t1o8XQgAo+L0jacS9vwa15Or80pIZMKdB5R3dLvjIV224vmt+151NIhYE9BjNy/EFjsRN4XE/ILowEI3Na4tluUl+0ltIDysvYvBX61JpNoQbaYPA0cYIzZBSiz1k4wxoRQvsYDrLVD0GKqxPlpDUNz7uGo761JMnwVD5SsuoMxpgQRAFFr7WC0gGq935qqWBhglLV2J/fX21rrRfi2rvCQZfOSQ965W5/XABe2akvIWrtq+bnWsrbn0rrtuVbfc2zgfRSA2FokGk+aaDx5aDSeLFrd9kQs8nkiFnnDM0u6Is9zEJu0DD3fw4BwNJ5si0BCCqHvJvd3EJoAs6ioNogd2iURi+yDwM6lSAHtBvw2Gk8eg4DQbGCFK0Q9C032lwGHJmKRbREAzKAV0iAEujIIJP3C7XstAjLbI/o3jQZyDfks889F40mPqXoTmffec3/nIhD4obu3W929W5Ra4ihkerkSKfqd3L2E3L0ejOjdi9EknyGff6wnUqQdgQOi8eSvUF6mnZCP3DbIH+d5ZHbNIrZrmjtHM2IgY8jU+RfkA7cTYkL8SPmnyOdJuxUBwhuQUh2biEVa14X7EwKPw/mOiQnYOl9x1h9smyn74INdx9bNLNktWx9oImc8n6zjUOTiQ6jfHQb8uv3Q2r+N+n3y4Wg8uc6w/PWVkEmZujnBx5uX+xLReNKbhz53f4sRML8AAezWE7xB7+eFtA1/7s7VBvXxcqC8YXaxnfGPDnNaqgNZBBgWIlN0CXrXLaDi2kBDyKT+jJjjFzbwNn6EzPi3O1B5cs8jlx2JAnJWIB+1YWhhcHbahhenbfjnaRter0CgDRU3X3UJnbxkUJsBjX3ahJt8pZ0yjeXbNBfjox3YEP7c9Yjh9hhGi8bKnWiRU46YY69mbAAtKvcDW9Rpr5qSDrvXen6Z2S3ke1eQNYi1thaZ6u4jX/6uDRoj1caYLsh0iTGmAqiy1v4HvUOv+PfLyL/Y85mqQouVzsaYDs5kt7ok5x4IWezOHV3NPquTF4ELW/mT7byO/d8D9nVt8eozezIWLdxBVqQ33eca8mmc1tWW89x5McZsa4wpX8v+sxBzjgO/obXsu9FSAGJrl5sR4KmPxpN91mP/JvJFv0EdY4n7PUBe6f/Tffcj1mci8gnzI/r/FOAP0XiyAiUdPQTZ0YcgZuc3KGJwNlq9RhCT5Dni/tVF+D2ClNmhrj13IWBT5fZ/AIGqDDJ7/tgxffVIEY9DQO0NxEJcikBNSyIWOcK1bSyaBLojlu/PyOwyB5mQTnDXHO9+n4UU3rvReLINylU0EIFELyFrADF/U11bvICGS9GgTCPzz3ko4OFVxJB0d/c1BYHIHFKWNcjEmHbXW+L+5rtn2hUBv73cs44AXRKxyK3Aj6Lx5PmtGLBp7rl/50q37HLLFw07Xj938uCr5s0F+OCDoU1gzkR9oAw9iyYEakqBG4s6tNzbcY/aJuMnBPhCJlUSMqm2a7rG+kjIpIYDF6+YVrJ97cxiL88eCPx6ecS85K59XVu8ffwIiJ0RMqkhDghMRjm7/NrPYFt8fZCC6oEWOx74+FPahutbNac98qNpx4YHYsxE5phb0Jg6GWNPQKxzBerX+6Ggk0s28NwbI8OAZ9vvXP/3ko4tjxkfk6c/0CE58++d5pIzLe2H1jaXdsl0QWMxh56pl9rjKOQv9hpSynXkn1kQ6IUhWNG3ybQbUu8lgn4OMcwF2bryLwSq/gVgrfUquqRQ0JKXP68SeM4YMwkt0r0++AtkYvwYzbPbW2tb0Ph7H1k9vmYyt9YuRyzYJwjQjFvP9v4f6kOTnAnv/9a2s7V2HprP33H30jo/3oXAme6eTiUfGPAIcJkLBujHmuVeNF984IIT/sza2asngPau3RewcSbMdUohj9haJBpPjiRfxHiAM89t6DmuQ+zJi2gVORqxOAGk/H0oOm8uMl3MQ5NgOwQU+qCXvxApG88MWIuUyG8RKCtGDMYFCKCMQyzXBLQaegyxYl4x4ABSJM0I6AxGq+JKd74yNFnf7dp2Ppqk30YT/tVopfAPxGLc6f7vh8DPnQi0bY9WYAlEjYeRwishDxAHIyXgJaStQKayIAIGS5BZ9iQEch9HDqBz0Mq9Avm0eHmcLkWrpUZ3n0ORL1qTu7ci8gELxp2/1O3vZXj/SSIWmRaNJz3n/d0SsciqdSS/cxKNJ7cDlrZm+UImNQmZpT9B778/6pcTCOQm9zi4Olw/NzBj2QeVjyAgXAWcmLbh2o1pQ8ikbkCOuDcAs9M2PM/9fgL5yF5PLOrrWXddz9RdgZjXx1Hf9IreQ57tyaD370Nj6si0Db+ymvbsXtS+ZdcuI2oee+OJvb5WF3R9JRpP9pv21w5VK6aUjXNtuAe4eGv5UEXjyZ5I2ba3lh2XflAyddajHZvImaOAxZ32qpldN7uoqv6LYoPGpUXvvBMCvy2IJb8Igdh25J24c4DPV5zLYKnJNfsy7vhhHjNZkC2bR6wg318pALF1SDSeDAK+RCzStJZ9fMiJcFoiFlm4yra2iMK9GflZvIcYlw/RqrILWoG8gxzKLQJgU90569HkOAiZGme7fcuR71MLAiDd0Ur3daTgapACeh+xOCPJR0hlEGsEijjLIv8tHwI2bRD4muy+pxB4fMC1/xwE7urRqrgImewud5+vcJ97ue8Vrp3PI4C30P32qmvnbLTSGYjAkkWALIgYNC+x5GLEdLV17apDFP1O7t7Gu3ZWopXMrxEwvAEBjVrXjnfdOyh2z2A5MsM8ggBbwCttFY0nH0Usze6rlrv6vkjIpP6FInxeRED/VPQMXdoI61JKGC/NxCxgVNqGJ63+jOu8XiXQZnX+RSGTug0tJjwl5ZnPGhBY9pGvVNGETI9VaAHzJwTWd0d9LIcWHTujQIyfp234g1bXMr7i3F3lfZr6bHv2Yh8wMRGLrDEnU8ikSlB/Gpe24dUCtpBJ/RrsdQRzLaWds/0mfzFkwer225ISjSeH5Zp5Y+nEsuDnj7bPAlkwt6PV/56ITW5Ac8hMtLDyk4/unovGXh0C5ZY8M+bJP9DzPXtrJMz9rkghs35BNkYKUZPrkPVUvj9BPlB3ojxZrY9fHo0nXyNftPoTBDh2BTJga4sCdUNaMsWzLUEvj1ZHNOl5TEBvtOr3IzD3GFI6WZ2DejTJDkcmuKloJRtEjNoeCJgMRRPwQgTQvkQg0Cv2HUTKOIxSYExH1O9jyJfrqEQscm00nvyflxHcVSE4EwHNRgSS7nVt/5B8mReLzK8tyP/HJGKRhe74m9w+bZFT9cHuXv3u3pcjcOjlETvSnb8cgcJ7EAD9FVLIHRDb1Rkp6beQQpnp2niv++3nre57PHC38/db+c4TscjxfP/Fqxt6BKokMIV8GZuPwHRDQR/LUP+ZC9wSMqnj0jZcGzKpq1B/Pj1twzWru0Brcfusab9fIN/A7ZGyL0OgvrV50pDvHwtcu25M2/ATIZMqdcd2c/v+FkUCnwe8HjKpZchE/XsU+l8aKM825nLcXzOt+KOQSZWkbdiLiF5VdkSLpdvJ++esKtMr+jY19Dh0eWVLjf84t+8WF1c/siPw2K5jGIePa2qmF+1f1LklVNatpWj5xLKzwPTBl/uEnGkD5j3ABsqzXTP1Pj9W/jvBtpnSTK2/t82YOhTAsxC5JnhVNZoRK34u0FIAYV+VArgqyMZIwUdsEyUaTw5CQGwpX814vFISsch0xNRcg4BAN8Tw1BcF6rJBf1PXru2njkK+JjPIR3m0QRNfJfmcPWmU8mIgWpEuQmDsHWTirCZvjvFKxZS5/Xdw53sfmSPqXFv6ee1B4KXcfd4NMQBXIYVynPPrOtD5r5GIRbzcWjPcuZuQ0qxCk7i3ig4AMxKxyN9cW34ajScHI/+cGR2r0ieXFy/C0BR17fZA118Ry3GDu7eOCCiUkl+l/xQp7Z3ds+mBQPFpKOeZl7ZiPDKnXoKA8Rx3rlpkTl1bKPT3WUyr/33JA5ldEah/GbG0d6Fn/DRiIR93xx2OmJbWebM2StI2bNM2fGzahsNpG745bcNXo/fmgS9P/Khfz0CsaNIdfytijy9G/o1POKf8bVG/7oX6wx+BUtvsM/VfFgU/urLnLtP/2vlW4J2QSbVfQ/M+QmNhjU79aRt+vLhTy0u1s4pzjYuCHde03xaQKDLHFyViEfvvyyOjJ4zf7ZDBv1pwvM2YN9C7GRmozJ5Z3KmlxVeWGecrzvXoedSy8vY71zUAuWDbTHab45eYjnvUBMEWI3DuQ24Oo1AQx7PIxNtYAGEFKcjmkQIjtulyBIrIehvwRePJ4OpYtEQsUg88GY0nD0OTW1ugXSYXXGaMbVlW0xM0mbYghupB5Di+j/vLIgUZR+DhSgRoGpFZb3c0Sf4IMQBPIcbnIATUBiHQ0hMpsJPdOY9x//2uTWEU3t7LteX3CNAsRQxZHKXbOB+lj/D81Ga7Nl3ojvWCFJaQd7bPOqf3AxB4GoGYu+V7bJdY/PKbJ+eWz6xsV9atpTTYxt7v7mUm8lnJIEW6DQoC8KNAgTmI0Wnj/sYi/7Xjkcn2F649yxFr59XfiyKTzK0IdDYAgxx7eXoiFvkh+b28ixivDHqGBoF8P/Lzq0f95irk17c3eV8yUF9t4/l6bQG5Co2JXq5duDYuB5sp6dpyWa7JnB8yqWGoL8xH/X4meq+gAJczUT/MoH7zKJjhTQuKuiF/yzZojHRE/f0r4gDdq+tqbO+jl1/avDTwaUmXzCaXMnJuD6XZJnPaiulFl8z8R4dSsv63gRNcugxCJtUNLeKsa+NKScQiE0OXpn6KxsK1mRp/m14nLb0//VCH3sCAuf+tajJB2wgEs3W+5S01/rpO+1QvXvR2RXusqUKLzJfSNtwUMqk3Af9GJr8tSEEKsgYpALFNlzsRoDgAMQfnIsf0r4jzFWtx+zyG/I52zOWKP27KFc9G4GdbpBDPQua3qe57HQJBA8nnUtqHfFK8MuTjcxLye3obRQCegRRLCQqnb0AKbAXyOzsQ+agtQ6xQd2TW2wMp2hZkSg0j8LcrMhsCGFew+8fIQb4CKebtkULr49q7EJmPtkEAqRixLTPdPZ8MlD737mUjar8MNAbKrMk2+QJBsuXIX+lniKWZh4AYyN/N8yGb5NrQD5murnLvoxk55A9OxCIPOIXWETGKv0DMTleUnuIp944+c+/guWg8eQ3wwtp8A7/r4iIOL0cLiUbyPljL3fcSBM7bo/dbSb7aQRZ4NWRSF6B+Ns1FQ16JfO0e8IDCpkrahl8JmdRpyPfwFsTOzgPO9pdln87W+3oFKrItLONl1Ec9P8NlQCBkUtNRdHELeSD2V+Q7uQABySBwNuqbmwTC/31FZBZi4zaHnA0c3lztG1HRq6WirGcz9Z+X/hiZ5JeHTKoXYgQ7IsZqzKon2HXMHN+Uuzr9vnZmyYHBcrtDea/mbuGLFvwl9aeudzYvDXZB9xvMtfgWV39StqTjLg2VWN+RaPy+6oE7l7j2e1NntSAF+bZIAYhtungRh0WI3em96g7ReLIE+UWlErHIz4AzXF6uXyBG6mikABYixedDyuR1xGSVIACTQabISmQqWIAYqp2Q2fFL5BA/CQExz6TpKcTXkJmnAa102yEH+Dq3b5Hbdj8Ck7ch5siL0FyAzJ9LESO2B/L1qUMT9HEIAHnRiuXI1FmGGLCRiGFphxiJqNveHvh7+TaZ0pYakw1W2ABSqM2I/RqGAFIA+XatQKBvAgK1JyIFPdY9x2uRHzJauEcAACAASURBVNPB5PPMtEMAtjYRizRH48npCODdFI0nn3a+YRciRbYtcFvNrOAjOw/5ILj847IrVmUavkdyLNAVbAZMEeorHujKonceIs+QeRG7C4CDOuxWe1Zl/yY7979Vf21eFjgM+QTuD/QPmdRv0XtasBkSl76B3nd31P/GAfNtzozIZXm/aUlgEWLuPHP8bajfjUB96XXULy5BY6kX6h87IFNrM2J6eyJQ+UDahldsYps3h6SB1JIPS+uMn2MaFwdrgKfSNuwluzVokdObr5puAYjGk73qZhe9Z7MGsBcFKrIxYIfyXi3t0jb8GlporSrVxHhwi9xNQQpSkK9JAYhtuuTIh3i3ABdH48kTgcNaRVCGkAKZChCNJ7dFIGx/NIlOQwzRCWhi/RwBtKOQye9TxFQtRf5PXi27KmR+nEm+vM/RSGldycq8SixBPlYnoYCBIxFYyiGmrQyZAQ3Ki3QpYphCCBQej0DcjYj5ewExesadZwX5aMSQu08v8q0OgbX5KL3GOMTGTUd+bU0ooq3IGJYVtbED3LkOQg7WNyFwGnfnORUFJsxCIGI6Anw+BPRADM++iJXzotaGu+fRJhpPht0zewpFulqARCzykkuOexMwdcVnJec2zA92ce18jO+ZpG3YhkxqINhSsBZsE/iC5Fmx18hHzXl1TEsQc9YXMMaHNX7bkm3wnY1M4s+iPuJH77oHykm3SXm0HLtWGzKpae585wCTZjTs8FjIpIYi37+d0aIogLLbT3bX9wNT0jb8YciknnLH7o4WKx8j5rUU+YB1Q2PqOheMsEWSr66vJGKR/wH/I/b1bSGTKkLBO/3RWPt1yKReWCWadWdf0HYOVmazvmI7o2Fe0am16eLlxR3ql2yN9hekIAVZtxSA2CZKIhZpjMaTY5B55gbkLxVGK1QPiE1DoOJT970HYl0mInahKwJlHgvRD5kbPGfZO5AyPA05z3sRijsgQOdDgKM/AjHHkM8yPBWZcaoRo9DfXaPRnXMn93kcSqDZFTELHyIwNxwxbBcjM+Hb5EswGfKFvxe4Y8e7+/XyOHllla5BrMPURCzyIUA0nvyDa/vhCHh9jJT4AMSCXefuw5LP9LwDSp0xGQHIvRBQe9ddpycCo48goBmJxpNJd3zQtfn4RCxyPXw94U8iFvkCMSMM+/e4DxG429CM6996efj94f5lczv8yBe4fl4uE2xDAMhRT24l0Mogs/vuwHVgWzoNr/5w8dg2P7IZn1fInsXvVdjF75UvBtMbpQtpixYMDWhBUYeSTG4WceDxWbT4Ge9+qwmZ1L6oj1ahvm5d4tZpqxyfBq5wBb/HAY86kFfvSkBVIpDZgMbBt1ky6Bm0R+0OAi+HTOqstA179f/+V9qtZXS/M5a84o27ghSkIN8uKQCxzSCJWOQaBDSIxpPPI3+Nj1ttz+CiuqLx5F4IUF2EzINht3+AfD09H4pYvAUphIuQieUDxD6djswtbRBLtAiZJb0yPgPdb90R4FuGnKz3cuebgUyC09z2NojJqiVvyitz+/ZDAO5BZCZ5EzFjOQRS2rhzr2QeUHTnMretApmCjiCfxZtoPHkkcqC+CZlBd0eK24eSyHrpO25B0XkeWFuKgNkhKPqzo7tOJWI0bkMAqwPygdvPbRvnnpsfmVRXSjSePA+ZSZ8A/uKl5nj/7d3+g3yLvo+yc7C06e7t9plU8emrQ6vJmNkIxJYg8PwWckxXVmwfvSv7ZEr9wZrq+S9XlSAztgV8YHohUFCN+p0PsVNHbgnHbmeWu22Vn5PIh8yLAF1rEe+0DT9PPlkzIZPqh1g1Lzqz/5pyhX1bxPlsnQQr/f3uQCz6ykShiVikEQHkghSkIN9SKQCxzSyJWGQu8k9ak5yBGJunEOA4DgGVZcjMNgS9lywCQgejvFdF5LPun0DeH2QmKvTdCa3gj0Z+WAOQcmzn2jPPbX8dKZwEYp+8lBCzEXtWggDMWAS22iLA1BeBwT7I7DcOKes9yAOscnf8IMT2DXbHZxBz1hk4yDGIeyEz0IGIMbnNtbMJMXSdkDKtRs74n7vrPIsUfX+3vQPK72TdM+mOWL4eSCE94e65BQHHg/l6QdtrUNBCFQJ9i/j+y+SWhuJ4Q025Qfdcg9jO3dF791hARUbmDLMeaW9tzjSTLzME+Zxr3kJiDsrr9pQHwkImFUF91quI8B7qU/2BurQNz9gM9/MBetcR4PL1PacDMF5QgifTvu0gbFVxTOGFQGwtedAKUpCCfAulAMS2vtyIWKUW5FMzDYGAmQiYeAlJy5E/1DgEfHogIDQagS2D3l+pO1c78ubBdogZOxVFVx6NQEzWba9GDEIl8gP6Lfki5eehFXQcKahD3LUGIBNkLwT8OiLQ+AFKLlvufj8DsVf3uuO3RyCrAgHBkci5/kqUlT+LHOTL3X1f4/6PcfvXokStJchX6xlkMrMIHD6nZ5PrFfA1tMvkyl4BMxSZgbsiv6ZgIhaZDRwbjSd346tFpEH+X4cDv0jEIj8EEMZJw96oB+47/2h9D5lUT/RcMoh1PRr1j0Woj92Xa/a1RQxjC3pfHvPi1S1cgVi1W4C0Y5kmuu9D3Hl6oAXAINSnqkMmddimpr5w6RU+RX6DI1EViPWR61GQxo0IQHYE3g2ZlNlcUZ9bSxxDVgBhBSnId0wKJY6+IYnGkwnE5FyFTGk7IWVWiQCRFyaeRSzDMwjUXOj22R4BqjsRm7HAHT/c7T8QJZjdHimXBuSjVYYU5jhk6nsfmRwPRg7217hjFyMQNIt8Dq5fIyVaTj7D+oOIPRmDwN+77hwTEeCMIJB5DlJ2vRA7NgxlJ/8SKfcjECPVQt6pfxd3b9cCf0DAqtG1qxKBrSqgD9iACw69BHzzkIn0fOQ3dlkiFvlaOZ5oPFmEWJmpQFUiFvnBOjCHTOpS9N5SCCDVoaCNwcjXsTsCWYeiftSJvB+gVxEiQH4x4EXTLkf9sDPy4TLIfNYbvbulwO82Rz3GkEndhBYOFyPXgAiQTNvwsrUccytKEXFz2oZvGrHTs4/2HfrZgG2GzNz32ovuWWeVgIIUpCAF2VQpMGLfnNyAzI0nkK+l6NXVyyFTXBFiJOqQAvMU2kjEdJ2LfHjOQmxDMYqQfNrtfz8yfYaQgpzmjh+CQKAPKd23EBP2AvlyJne4892CmLsTkfJ80h0z130fgfIXvQh85BKh/hQgGk/ugZTun9zxS5FP2NXIB+wgFJRwE2LIRiKF/jcUxdmEANdY5JzvsXmvu/Nu7+7zXQQSZoGvv2vPEe6+L/FAWDSe3MVd4/VELOJFQo4ETk7EIk+u64V9z+Xv6Nk+j8zm7yD/wGMRq1iMAFgxAlkeG5Yh7yhuUF/+EPWPHyFHcpO24dNaXeu+zd34kEkFUb+ekLbhN0ImNRIFdSzD+WeuQa5A7O1kgFNG3zmtuLzhAH/AHg2FFA4FKUhBtrwUgNg3JK3AwVKk9O4kX3i3CIGkcuSAXoFYrYNR6ofPEXMxFbFG81CSy7vc8b9HpqV7kELsiJzyZyIw4/lseck5RyBFdBUyZ76DFFh7xHSd6NrXBTFI76G+cxBikubQqjyQy5F2HPIDuh8p93bumPPd/dyKFPr27rAg8jdbgsxF0xAbc5jbdxfEzt2EyumMR4p3ge7TTANzKsqvNsydu6e7b48N+zkCaAPdPc5DzN6m5rjaIhKNJy9ABedXdUzf7OJ8ov4aMikvTcp+KELVtNrNS1XSuiSS5yMWRP2qEYH1mxG4PgHoEzKptui9jAb+mrbhOzZz+1tCJnUx+Uz6b6CUGWuNFHT+VCtzaZVV1c9w97ITBSBWkIIUZCtIAYh9gxKNJ3+FzIGfoIz7EWQGao9YsdeQKe8TxFAdgyIofeRZiTMRYBuD2LT27rf9kMnofcRq+BAouxrl2ZqDWKyOCGR9gBTplygi01PI49z+N7h9q8mnzKhJxCIrk166/GheItVfI6DnAaI7kFP+2cjn7VcIAD6M+uEV7ryTEQC4BPmAlZJPxdEegUWDAhKOcNfaH/m5efUzt0dMYgXwC1ekfAUyjT5BPqL1fODy1vewtcWB1r7A9EQsknPJfzs5cDsE8EfjSePlOtvSkrbhJSGTugUFkixDwKo7MpFfgd7Z8eTBmFeTELfPjegdlCDQbFCwxXDUlzuwGWpSrqHtM1p9bkRge0PlATQGxm6udhWkIAUpyNqkAMS+WTkOsUxfIAUwyP1uEQj6M3AKcGciFhkfjSe7IBAzB5kFe6A0AR2QwrzXfe6FosBeQWbEsUghDkNs1kXI7yqCQM+5yHx4HjIL3uz2r0EsWiNinrzIxb7ID+cO5CzvgbCJrv0HoMSw/3D3NgSZEz9MxCITcclRo/HkMGR+9CEzYgz5gv0EgaqpKL3H/1B6iRxKVdGE2L4XkH9bZxSRN9BdcwkCoH3cthqARCwyC7FquO85xPB8k3I8AuOXIxbnLOCoaDx5DnpPJGIRu/OQD3busm/NjhV9mh9PxCKrBhtsbnkT5XDrjKthiPrD1agv1KG5owgBLuP2CSKTcQDVJ0yFTGoM6n/vo+CLh1plhf/WyUnD3sih/rbF5fTT/7jNoP0nfHnZ6f/MbI3rFaQgBfl2SgGIfbNyFQJUf5twWc/Dijtk3hh44cInguW5sxFrsDgRi5zSav+BiJG6BoGLbuSLNK9AJrsOiDnzMtmXIDPe3e44L8N/NwS6OiAWrgaZLY9FJrvuKJ3GUe7385EyvROBr8nAHFfE+2coWtIzqb6QiEWqAKLx5J6IrRoDTHLgaypy+j7Ite895Ku1sztXELEZTyFTaByBy94IHKQRO9YNgbEVKB3HiwhEHpqIRWqj8WQzMnd+zFZISRGNJ7shcDyhNYPlAPQfgEcSsciz0XiyHAUvvIVMwX5gejSePBq9ixeBRS4HFMf+Ljm4ctuyhwNlWa9W5utb8j7SNpx1Wdv/QL5ElkHMpgfE3kMLh07onXv+jbu670+GTOoIoGWV7PSbDYRF48l+iJVduKZ9QibVBY2Dp9M2vNWDMUIm1QNVsugD3OSVTdot9Mp5dcv2vn3ulJ5jLzud4Vu7XQUpSEG+PVIAYt+gJGKRF73PoUtTQ5oWB3svfqci3e3AFTuhifsjYLxT3P2RqegML8VCNJ7sSD6X1pcIvOUQY1SEzEY5BA6s228IilZ8mbySHYdA3EikcLsgcNDHff8IRSHuidi4DPDnRCwyxZnSTnDn9cBHZTSeDLjEqLNduw5FTNXLyL/tNQT+5qLAgQaUODaG2LGrkY9ZHfBfxHT9GgG3eneuPVEpmwcRS+eV4LkYAcft3XUnR+PJgcDdW9gMeR7Kj3ZWNJ6sAgYvfLu8V/W09lX9T1+6uzE0RuPJ49D7GYWYorsB/4rpRbeXdGo51F9iG/3F/AcVHr8gEYt86gtwertBjfU1M4pHl3RueG8Ltr+1jCfPVnrv1U++7ukw8v6HWbfNRz4p8TbINFkSMqljUV/bFznTbzIgcmPithXTi5eFTOru/2fvvMPcqM4u/huV7V577XWvssHIgKmmg6miG3BQ6IGEFggtIFqoSQiEpkBCDT2hJSBaaAEl4IBpxmAw2BZuwrh3b6/SfH+cO8zCB7iw6zXmnufxs7vSaHTvnZHv0TlvybrRt77l0F2Qyjgb3XMdhi0GTN6l1541hT22q/9fGyJ+DVK1A+jL0S0AjdUl1W7eydWtKvtB1SuzsLBof1gituHgRqC4737VDUjlGQzcaSy/W8zffVEsl6fuTEOkpDuy3CoQ6VqCrKU/AhW5RoqXvld6Rc+d69xgIfPQxlqPH6szwdhdz8ST6WnAvYjoDEdKVFUqEXs7nkzvZd5jLDAonkxfjTbh9805u6GN+A2vOj3azHuiLM8/IOI4BwVyhxHxOg1t8DsiZW2Umc9wpA6dg0pjBM1rJiGrssSc/1NU3uNgpJ5VGqWuyoznaqQmrkJ2b0fhUZTBuS2KgaO4b3O0qYrCXANFwWJ6Ow7zEbGtR0HtRwIV4a7NOwdLKQwEKEKWdRB4PJ5M/wN4KNwl//BrD+/+8Te9aXsi4mT6oESPv5t5nIGuiVdWBUTEW9D1DqJrsAyVoyhC90QVIsm1RmHbHpHOXMTJnInugdmm9tW6oB54cM4TFccBV0SczO3A1KwbzUacTADFFoaRzT0RmBdxMq8Bd2Td6FPr+J7fioiT6VnQ3XmsZkZBafkmjXuPufy1paGyfHcYcD/6QlMI/DPiZIqAlqy786MRJ/PEws/KrC1pYfEjh60jtoHD2F2XI0VqOFK4bjHxTsST6dfQhvlbRDpuMC+9BGVRntTayK01MwuKyyItbrjUfRJtmCMQqcqhOKApqKXNFsjeuw1lL26Osg6fQ/0b56CYplNRnan5KODeRSrUeBN7hSFDe6ENfBoK2r4YkaWdEBH5yJxvG6T6jUcb/KaIeL6P7LBVKF6sL7JMt0FfJK5HdmaVGdMEgFQiVhdPpoegoP+TzdocmErEvBZOHYZ4Mt0fkcJ3W2o5buk7ZRf13K02GCom5zgsQ1ZqObInLwO2dV3mA4Md58vCqN3wy5jEU4nYhx09boCIk7nJjOuirBv9qyEOi5DC+h5qvVWErslyZFsHkbr3PFIxl6Ds3ueybjRnzluIYgoPQdeiF7pWu36fwqmmaOxw837jENl7GH2BeBIlgNSibM2bgRezbnTsur5fm/f9suCrsXF/UTK44caSQc2lgUL3guIeuTOLe7dUzH2u4oK6OYVPoOu5H/psvZp1o2d83zFYWFhsHLCK2AYKY/mNAd5JJWJnm8f2QqSsN/B5PJneAlmFgxFpCiAVYDaKJdofuChURH23LZoDjkMAlYWoRDFgzyNycwxSOVYilSyBajBVIvspDxyPyNBg85oWtAHuj2LP3jcNs73xh5BNdzUiFr9CPS6r0UY+BsXO3IDKY7yDlK9r0Sbq4icGvISq81cghWwrFFC9DyINUxBhCbcNZE8lYp/Hk+k7kKpT094kLJ5M90NK2/888mnedz4qBXFk9NxF+/bctXZBsIABjkMDfiP3nyF1rhcQcBwqkcoTQCStHq17C1r79ULEUI2vPCLJmLEsR9dkNFIqB+IXcPXacf0HtUiqQvfRK21I2DBkPQ9AauslaJ5bIuVqnZtrZ93orIiTmY3uqzlmjAGkwl6BSGIZUpl3LRvaeLtJhHjZdFtYa5i4s9siTubxrBt9xrzX0eAEmhYXBPsesOrsha+W9wqVFnap+yJ8a6A4NzDfEPil6cnZjP1/18LCog3sfwjtBEM8XgQWpxKxE1d3/BpgC1Q/axzaiEFkpRoYE0+mP0Qb+dZoowyjDagaYwmhGl4FwF8chyBShj5EQfBFiLB1R42DV6DA9htQ9t6/kVr1X0SidkUWy4n4CshJmKQC4Nl4Mu3Fgr1pxn8NIo2VZuyNaEOsNOc/yPxeBOxuxtYFEYFGtKFXIcvyZ2Ytqsw8uyKLsgyRs3rggngyvQ2y0q5NJWKPosSA3yNFrr1xLCKTpyKC+xV0375ubMOSULfi/i1vB4IMRqRqN6QI/RaRYRdlzDahummjzPxKEOnpCZwRT6YnpBKxf3bAHL6CrBudhoiSh6UoNu8mRKT64vc59ererUTxfj1Rdu0q4OKIk/kY3UOPmOcGoPviLJSx+8esG11nEtYGJeY9ipByvAm6Pw8zzzvA/KwbPcYki3j3+zoRMQJuV6co171i84a7txr+4U5jL0u98cz1xw+t/yI8t3LX2sUtK8Op/gdVnz/vhW6lBGgq6tl6fuOSUCgYdpcPPWlp2YqPi/cZGp56sNsaeDXrRq01aWHxI4clYu2HMFKIKtrpfFNQUHtVm8daUQbgnFQi1hJPpm9AFt0qpExticjMKGT7FKPN6VRUOb0FEbUQCmj/D1I3JiEFbRukbsxCts4cRJTi5j1uRbFe25l/xyO75yoz1vmYkgvIUixDxGomIhojUBZmM1JI6tHGnDd/b4/qfLUikleOyNcR5u9zzNgOM+OuRHZXApG+cWa+XdDmjAma/sMarfja43G0dl9pMB1PpgPAroOPYjDQNRBka/ziuR62MT9dlIk6CZGIIH5wfHd8UnZHPJl+sq3ytj5g7LcnIk7mQ0TKByBik0PkswzdZxcj+63APD/KzONDRDgHmlMuAv6WdaP3t+MwD0BfEkD3YDmqredZwI1I4b0WxYudxTcQ59Uh4mScQFHulwUVuTtwcFZOKnEIu+dNemX7MwuKG8ua60pbl73VdeaqybkHtrpqwdC++9R0nflA0WuE8mcBvcPdc5+Hu+b3JR+IuHn378CMiJP5VdaNToo4mX2BfNaNvv59F8PCwuKHBRsj1o4wmXLNqUSsYbUHr9n57kZW4JGpRKz+a8/1QxbhJBR7NQFZdSciQjYO2XfbIlVqIYrXmoKUsJPRZnQSSga4F8WBHYY20jMRIboJBY9vgghRAJGsVYiMXWbOcQhSRq5ACs9EZFl6xGx3pJZ9iBQrzw79BMWITUMB91FzTAyRkpfNHIeZ908hYhlBCtLDZrxPmzHXAKFUIuZVfP+u9XVQjNPi9iQ4JkPzwXyeHRyHEC45J8DvEQk5A13TAkRg8ijG7Wl0vTZF2aPbImJTh4jlVGTJjgU+TiVis1iPiDiZMvz7aRG6jx5FCl8MEa1C/CzLajRHr6jqlugLwM+zbvT5dh5bEVK3eiAleBn6AvMept0WcHfWjZ7zPd+nBNyXnbA72gm5br7BqQOnBMcNdIk0uDWzS5aDU4c+Fxei6/UzdI/uCCwLlbVEW+sdyAcdcPIomcCrt9eadaODvs8YLSwsfngIrP4QizVFKhGrai8SZvCC+Tm07YPxZLoEWT7PInJzHKqk3xVt2l6Q/C+RNQcqHZAzz3uK2ZWIYG2GCNUliKDVm2M3Q0rDKvzyEAGkaIWRUjMfkaVZSBE8Bm08IdTjrxSpJiPRhnMaIhj15jz/QyQjiTawHvibeQApcHsjxe4elETwe/P7z5ECFkYxZ/ubea+2crsp/RFH5DO2uuPXFPFkugIRq5fyLcxxXVwUmzcQzXkmWuOFiISFzTyuRGTzXWTt3oxIzDS0SV+HyOw/gPfiyXRxe415TZB1o7VofbdB12kMImYjELH3ash5NcdK8ePMXgcGZN1oj/YmYWZsjegeWYzUr6kogeQh/GKziXZ4n3pwznJbAiflG4KLIPApOM+UDWlKd9+pIU3gy1ZlE8z7bYHu8VOQFe+01oZbyIcwJKwVFfQNowzna7/vGC0sLH54sNbkBoB4Mj0IEaEmFPc1zVhqtYg0/Rr/mz3oP/fp5qeXeXiiOSaMNvgAIlHnok3Rqwm1AKkv/0Zq1GikcMw1j61CMWIHow33V2iTrUDE7ipEpmakErGVhhCUmnO/Y34OwydJQUTWVpl5bIPic7w4sCPQZnmkeW0LUvAWm78no031jVQiNjeeTF+CSOWryJqsQ0QgZMbVBb+dEt/RHuheRNoa8PslrhXiyXTYjKEhlYh5hUp3QpbtfaFChiOVb3Nkj+6DX33+Qfzm5zsgW3YGUhWLzRy3NfMqRJv0hfiFUwvw+yquFxgy9lmbh5ZHnEwGzdn52uEtqCTLUODTrBtdpzVeCwxB9/ZbwF+zbnRqxMkcgE8OC9Dn5Xsh60Y/NXPeBWVB9qrNFl9Vmy0eh2JE90NfBFz0ORqLPgtTEEn7EyJgAXRt90G2fi1QGnEyXyDFuSnrRmu+73gtLCw2fFgitmFgS0SkCtE3+yPRpjwFEZbD48n0baY9EKZG1+7wpb1WjjaB99CmeDrapHshYhJ084TcPC2BEGFEABJIWTocfXMPISWmr3nNlogkhPGLqg5G5QG2Af4UT6aPR8H9X6BNLo9IXRlS055EylorsmhGm/O3IvuxCFmMfc38F6P4naPNMQlU/X8YfmPuB1Hrpg8RkWxBJPafZtxnAJ/Ek+kLEQncLp5Mn9O2+rpZs1Fm3d5CG+a64GJEfj/eafT7x/TZb9UfwqWcZ973gFQidkU8mT4cZXM2Ag/Gk+kRyMr9DyIHLrJTC1EJhtfNY1VmHWvNcf0QwVxinhvAV+MH1ysiTsYjXreisc1Dmbv7BItzfXuNrmnoF6upSiViz3zrSdoXnm16ELpH6H/Iqub6BaHmqmnF9fnGYCVay/bAPujz8Q763AQJ5n9OztkCGAnOW+gzNR71fe2DiGoj/v+53voVoM+VZ41vjz6X8xCJs7Cw2MhhidiGgVeRLbg/IhnzzePL0Lfo3ZGK9E3ohmppZVEw+2VIUfGq1vcHWurmBxuLKnNd3FbKg0VUIRJyuTmmBalqV6BsvkmIeGUQCRuLyTwz4+uPFIhhiGCVobil2fFk+hRkCR1oxh0w/0Yj27EBqVyvIUswg+zHB1FywL+RGtcF2TXNaDP72LQAete834tIPbwQWYy1wJupROyleDI9APW7XIAInQsQT6ZHmjXaBBEZgM/aFJ9dW3zS2uB8POOengMDofz7q6YWj6gc1RB0HJqAm+PJ9ElA35Zaph90wbg9i3q0pIJFvIrUwXnIxm0yY18MLGpTg+0tdD+UmvdaigheNSox0RFZoGuEkcM+6lEywHmiYXH409nNm5+HH/h+QsTJ9K3Ytu6K3nvVDGQ9hj5k3WhLxMncg2zBKQB99q5ZXD2z8L2W6tAHtbOCi777DN8NQ94PBD6HgZ86ofz0cEXroualBTXAPeScAgpyjhN0l7sN4b7o/9YZKEkghTKS9/jaab0SLS66Hx5FxDZOBzVGt7Cw2PBgg/U3cMST6bfRt+SxqUTsJfNYJQpwfx7Fad2LiNL9yMIKos1+pnltoHFZoL55VbCgy9CWVidAAKkpnyEiNwyRoDtRLNKfUAD/XqhEwyhE7rqiKvblSMEpRwVTZ6FyAcXmsX0QgexqpuFVWseMc6l57gukFgxEytQQM47j0MZ1MlJZdkHqixnrKgAAIABJREFU3q/bnGseUqNeRqTTRZmTRyJV6Wlghdev0axbCpHDWmSZOcDjqUTsuDW6GN+AiJOpdML5v/bee9WoskjzwNJBLdTOLpwQLHSv6TK0eT8g2rg8sEe41C1pXhWYW9wnNyaViE2OJ9Mno8blDYj49kIxfwcigjwN2ZP3INWkFcWHPQpMTCVindYaZ7vtPriqYUH48oKK3LiPp217wDcd8x2W8HpFm1Zb3wv7nfLmoNbawHOlg5vfcFucSxe/XbqwakpxWa7FeZ7m4J7gloMbIMhEcoG3UQzgjeblzyMFzQvE9+LWWs3vQVRodkzEyYRQwsqirBu17Y8sLH4EsIrYho8BaCM+DhU2BVl5uyJbbwy6jlMReWk2/1y0ueeAmqLKfEtRZb7InKsFKTGlSIH6NyIzlwF/TyViV8eT6QOAS5EqF0AEzEHWqVeGYYk5x6UoluxkMy7PEnUQ0ShAhK8RKW9T8XsTjjBjLTFjHQ70TyViTwBPxJPpbZGa9gdErvZEtl4I2Z9ZRN5KUYzYaGCHVCJ2+zes5XVIaXgPqX8R85p1RtaNLosn0w8ueav04HCZ61RNK2xelO627YBDq/6BrN7+TsitalgSWl7QrfVpoDyeTPdEAdwrUQmMIUjFGYau99WIUG+DaokNQRbxzogcV8eT6fNSidh/vs/Y1xVFla0PF3TNlRX3afmmNQa+LBvS6WgPEgYw66HK3oGw64TLc9O3vGTRfv32q2kk76yqzhRd2drMFHBOBOdOcmyN7uk3UH27n6N7sgjfjqxDn7dK81ge2CfiZDZD1voY4N2Ik7kIEbINYi0tLCw6BpaIbfg4EcVKXdTmsQAiM/9CNttQ9J96MyI7AaQYlWKCgJG16VU3DyEiNgdt7vPNMdsBvUwF/+vMOXZH39zPN7/vZcbgorpS09A3+HpkOcaQKuYRQi9xoND8W4HqY12NlK4DUcbk3/Db0lwWT6YPM5v5L5HN+A5S/HojJe8YRFammXF3R6ras8A9ESfjFPdpPjFUlp8zecY24wC+1iboytWu/JrjynC3lsKabIG7YnIxvfaoqemySWOpmV9JYVc3W9i1xSOxu5p1GY7WcEcz/k9RDbUdXZcRi/9XdnVRr5bB3TZv8irYg4haK4rJeiqeTG+bSsRmt+M81ghvv7pTFtNL80eDvPNRvsk5p2lp4GOguLB77qLIMSuf95I0Ik7mMfQF5Hhkt09CimcL+pLR1qYtMY8H0eejDn3+PjB/l6HP9GgUdnBnx0/QwsKis2CJ2AaOVCI2DtUEa4vN0LfuvyJF5x+o/pRnt+Ugv0mX4gUrc7niYH1zj/dRsPv26D/9MLIHb0V24r/M684zv49HNkp/dI9Uo01iFrJceqP4q5Q3PhOEvsAcX45inkKI+Hnf6HNmDL2QAnQLKl8xK5WI5ePJdM4csxswPZ5M34hsUW+j+gsKxj4bZYNW4hObj5B6dxcwPVze2s0JuzeESvLLkC3Urogn07shFfBy4JHSwc0jWj+jsWJE4wvdt63/IBBmDIoJCiOy/BmKi6tDQeUVZty9zDr1MfOuybc4M5e9W9ardEiT023zpo8QUWtGluumiPg2IEXNYj3AZH2aFlnRalSi5evP32T+EXEy96PP1p9RPbHyNoc7+Jm6jchuHovuBQdfOeuPiLeFhcVGDFtH7IeJFFLKfoZswRvwg75XAo+B21TXUNmrvrmii3nOIzOt6Jv4QBT0fT1+pfRxpl/k+/gp9i2IJBQiQjEJZXV9CJwST6YL48l0NxRX9lMUG/YeKq/Rw5x3ZZv3bUQq1lZIcRsO/DqeTI8zczsRKQKbIEVhECJyx6CaX0+hQP3HERkJm9dNRCTlduCa6VVbruy5S931lTvXXr/uy/ydKDTjDANnhEspq9yueeH4f+16crCQ45H6NduMMYisqd5m3t7aFuETsj5IKTk/WOBe0jdWfXn/A6qfMcfOQHasRzqnoGv3cDyZPrqD5mfx/fAwImUZ9Fnwvox4P7vhk67dMT1H8cuTeHX07PW1sNjIYYlYJyOeTBfEk+lR8WS6YE1fYzLrzkWZlscjJaUVBbEXAT+B4Ko8hc3g1ANPoP/wPTLmNUVuROTgLrRZDDdvcSMK9F+EgsXfRIQihGKW9kW2ZgYRpiqkbPVFitVmyD4rxG9enTNj6I8sTK90g5dRGUX1tFqRvbgEkY5Sc47HzTwuRrFyF6OYsXFIDdsFEdJ/IIuScf/c9dby4U0vmjpt7YpUIvYaSqCYAbzqOGQdh0dNsd2T8VXGNLJjn0V26PXoemUQqXzWPB9AyQtvphKxD3tsX9+joCK3M1LPckgx6YnWNYIySQ9CZGy39p7fxoB4Mn1kPJm+1PSBXa/IutFxWTf6SNaNTuo2sv5AJ5SfiuLCavCD9L37vwEl1zSg+95TxFx0vS0sLDZiWCLW+dgTWW57ruXrPkSkaBWyJ5uR1fcbFE9VD84McD7Hby7dHcWWTUTq2WXo2/glSOU6JJ5MX4cst+tRsPFuiAxN0jnphkjE3oiQHY8CkmcjUrctImRRM85SpKpVIUIRRORtc5Rx+TJS9nZA8WW3IjtzElLyQkgVOwcFrRfhZ22OQCTvFWSZHowUsdfMJlyJrMM748l0l7Vc39XCKzWRSsTON+9zFnB8KhHLoLZQv0KZnrsiFfAOVF+qG7oeRyH1sMCsTwXwUDyZvgvFBXZDDd0fRpt33hzjoKxTB32GN23vuW0k2AbFQBZ25iCGnbT82Og5S5aHyls+xq8NtwzdA97nYQki7fX4ZS3moy8bFhYWGzFsjFjn40NkwX24ugPbIpWIPYUCtp9B/SH7oKr3vVH81+1os56ENvCt0H/2QWSDlKDMvWWILFQCP0GkoASRh0oURH422jweRoRpFSpC+SIiYRWI4LmIhBXz1W/83ZE9+SnaFKcjchVCqf0uql/2N6RsDUMq0XREUPZApOR5pLzthUjbbHPcgeaxIrTR7WHmeC9SpiYjNbBDYEjeeWgdupiaUwcgcngxujZbIIK5JVIi70brtxmyXN81r98FWbxliDCD1iaErmtPtF6r8Guk/QTVbrP4Kv4AFKYSsQ679t+FeDLdG33B+rSkf0tpa03oF+jzUI4+k83o81CKlOdD0X27CD+O85ZOGLqFhcV6hCVinYxUIrYc9WD8CuLJdCEq6Dk+lYh9V1D2e6huVxMKlv8psqw8xSSKSAAouH88Us0GI1JViNokpRFhOBkRsUr4svjoXxHBWoI2iCHImuyB1Ld9zWu7m+MXmdfnEXFoNscNQYSsElk0RwJbI8KyI9qE3kOV5z01bCYiWceYY/Yx730wIj85pN7lzbljSAFsAZakErE3WPfK+WuKBkSke6BWUWVmbF+YsTehNQ2ZsTlI+fNKF7yLEh+qTDP3MUhNq0Fql6fodDevCZvfG9D8C+PJ9MBUIja3g+f5g0IqEWtCa99Z2APdBxelErEbIhdmpqLPeikiY15fzmp0z/RBXyyWoC8Oo4HJESczIOtG260pvYWFxYYFa01uuDgO/ad99mqO+xOyB19CBOd8/AytBkRawkjp+ina5AciouKgGmItiPz8Bdmdi5E6s7sZxzaIQPRAapdXo2wgfiByOdpQbkFZYAXmfavQfbY/2miGIltzGbI9u6EG15siq/I4fAI3FKkEM1OJ2K/N3P5kjulufm6KyOVstJktNXPPI5Whw2FqVZ2D4rj+lUrEapASdh2Kt7sKEeM8inXzSnq45vc/Av+LJ9NbphKxBcDHaL33xm+0DiJyeXxLsgwRsTJk01qsBjc8eOLOZ557Tc01d5z+0Hp4u5eRxfw+gGl4PgBlDk8uGdi0sMcOtQ0E3KXAZU4oH+2xY03/YFF+e3BHo+vcl3ZsSm9hYbHhwSpiGy7eRUHy6dUclwduQ8RpHqo39hcg4OYJtTZQGyqhznEoReSoK7Itt0FlEb5AhGd7ZO2NQfFixyJl61b0DT2ANobTkQ05ASlNM5DadS8iFkcjspZHSpdX96rQvNcmiOydgYjGjoh03YQ2qcORqpTBL11xdjyZfg+pdeXIfi1AGZbnoBIB/c25e5r3eQd4K55M/84Uh+1QmHixKW3+/sT8er+xqMah9bkFkcaLEFF1USxQGSoyewxSBc9F9vAwpKp5RXS97FMv0xVE+P7bIRPbyJBrCZ0//Z0ty8KFzUd09HsZS/Tdto9l3Wgd8EnEyZwwaOyqi908h9XMLprTvDzUvXyzxsoe29eHWuoD1M4qwgnlydWEQffEKx09XgsLi86BJWIbKFKJ2DQU+7U65JGCsjXazI9F9ab6uy5dAmG2a1oRuLuoR/4XiKwdhDb4WYisPItikZ5Cys1MRGgmImL3Ftro70RkbDAwEtkrb6USsVw8mX4eOAEVYx2KYrlGIlL0ErIRVyGrrRSRq1PM+2yJLJj7UfHW2cAjSCk7BDUxvwCpdCeZOVcistkLkZlfmZ9efaYUIpX92DAC2fdApLIB2DeViF1lAvLPQarihWjdWkFV6ePJ9FwUzF+BT8K8IG4vA3UlUhwXAhXxZPpY4FlrUX47uvdf+vMDznraLSptTHbmOLJudNq+P1926dK3ymqal4deB/ap/qz49KblocZ8K26wKB9urQt4hPunESczD7g160aXd+a4LSws2h+21+RGAtM251JkAaaAX+dzlDatDHxQ2D2/IBBgrDm0GsXN/AZt9OlUIpaMJ9NFyMr7CD/4vQEFu2dRoP5MROKeRC2MliJlZwRS2grRt/fFiFz8HqlEXuB/EtgPFYediDIn/wecmW/mi0mXDVwA/Hf7m+cuMeMoRCTrNpQs0ICyyuaiEg5bICISRApZAYpHCyLrcwlwZyoR+38WZTyZ3tWM/dJUIvbZWi/4N8CUIPFqtW2O1m0AslO3R8Q2a9bqUURs7/DaAZn+kyORurUF6rPpIgJbhOLM/ovW8VBEVl8DHkNE74/ATalE7IX2mM8PGaZn4+ZAJutGmzt7PN+GiJN5CF3Hi9EXjZ3QtfwEKb9hdN/PA87NutFM54zUwsKio2AVsY0EqURsaTyZvgQRn4+B/oEgY4sr8yOR8gT6dl2MbL8pSGnKmtc3xpPpF1Ejba8sRCMQRxl5ryJSdhFSxT40530QEY8ssieXm/foiRStKpQJ+ADKJNwVKXe3o4zBGLCf6zAfKTv1iGSFUVbhy8gGXYJInoMyQFcgS+8uRBpHofu5ABGcXoh0HsQ3x4odgKzR0ajq/TojnkwHUObiaLMmyxDJfRuRpCazTi8iklyMbOGsWZd6c6pKvmoTe3XYvNIWQbQ2d6BrMBMlY9QhRfOXSOm0kMX9Z5Q5eXcnj+W78ChQknWj0yNOZhq6h5KobMVmyJJsBHpk3eiSzhumhYVFR8ESsY0IJmj8AQBDqo5CKsoyZBeOQYpND+Aa4P1UIna5KXg5GtUVG4D+43/NnHY3pJANQRv9S8gy9bIaVyEytIV5vh8iDLcg0lCNvvH/C1l0DiJ656FEhBDQHAzzUbeR9deHu+SyyJp8HRUtvRKRrqVIaWtECtE1iEyej0hXESJgXmB70Mzzn9+yXNcj27U9YqtiiOy1zYIMI7K1OVqrrsjevQ9ZkkUo/uuCeDL9BSKbf0YlPB5EpT62MvNegIK2W1DB2vOQUrYIrXcslYg9aYrJjmQtS6FspFiK7s3qzh7IdyHrRtvGgKaQWpwxjb6ntHnOkjALi40Uloj9QBBxMucCNVk3+uDqjo0n02FU38tLjx+ASMkktLnXIXtvVDyZ7ovszDPRxvUQit/aBSkuv0bxZyciG7EPUn1mIcvkAkT2xiBFB3RfdUP2ZKP52wtM94q6NuBn/zUCTw07afndiGgchl/A1GuT9CZSzCpRHNnLiNiNQAqbl1HoEbEcUqOe+qY1SiViDUjlaw9MQ5mbuyPCFcMvulqONtc7UGLD9Shmbjhahxha95OBF5Ca6ZHIVWhtuyLiVovs5xB+a5zpwMx4Mr0JKkMyC5X4+FEj60bfiDiZ3ZGa+INA1o167b8sLCx+RLDlK34AiDiZMFKrrljDl+SQKuJ9iw6gDf9NFN91FX7D7BeQ0vQyytC8GhEBzyI8AZWJeBeRnr8gteHPiHw8av7djwjea4hYNSILrgIlBXj2UB4pWheav12k+IxFqtseKM6rBZXl+CkiijWpRCxpxv8T1M7oTlTGYgB+z8mZZhwZ4OZUIuY1V+4wpBKxL5Ca4dX7CuJnNDpIpRqD4uqONT9bzTGfoTkNRmtyD7JXFyCV7Cp8ElvIVz+zDlLEuqMK8sWI9P1oEHEy4ccmjO7y2ITRez02YXRRm8dL0WdgZcTJVH77GSwsLCw6F5aI/QCQdaMtSDE5ZU2OTyVi+VQidg0qJfEUImBhRGpGIMuvGKliI4FNUonY7xAZW4iIzBj8Ho6e4hRANudhSE0qQgqMgwiUZ7eBlIgalNkXQWTKa4DdFZGtuYhglCFC8QQql3Exqr30e0Q+GlHMD+Z8y1G8WIU55mb8djF9zXlOXM/Zg9NRWY8kqt0WQOvoBduPRAH2ByDbcToim4chG7cfImgD0XosQjFwryGi2YrWwutYMAsF55+C33C9Byquu9Eh4mR6RJzMrhEnMzjiZIZGnEzXiJP5DfD6U9eceG8+59y4YPrA/SJOJmyIVxIR+65sGJmzFhYWFt8Ia03+QJB1oy991/PxZLobUrgiwCOpRKwKEYK5KA5qMXAtIlKlqC9iCca+M3bmNojwzUabu9en7xOkunyA7LBSRAZORcRoKSJm1UitKkFEYRyq3r+deSxv3n8CIiFF+NX2/4Nip95A5PEAM37vHn3DjHEvRCJLEZl7CcW/HYz6XLr4WZzrDalE7N14Mr07iperQ3P1Mt6WIzv3OlTj7G5kE1+DrpnXEsrDuyg2rhgR2GL8EhbLke0bQnP8KJWILYkn039EqtqfO26WnYpjUQZtL3S/nI/IeNHUcdsOaG0ON2XGb30DWuc9kT3bitbrvc4ZsoWFhcXqYYnYxoMn0eazCGXrTUJkZRPUa/EFIJdKxOYDxJPpN1DM0e9TiViTiRW72LzmAKTgFOBbh/ciMnUoCp4vMs+DiNhZSNE5C5GHz1HcWS0idqVI7emGEgMKzDi98hObmvOCCOFClBFYZV5/jjl/CQp+PwNwTKwX8WQ6hojOvxHp7IyA9eORxToRKXMeYaowz89GFvOZqEn4B2jtnkaJDl7dqKNQhuX+5jwgUlFgzvk6IslnIQK6aSoRexet28aK51AWbiGKo+qDrOmJjbWl90x+dafB6N6vRbXxGpHSOAXYIuJkarJu9PPOGLiFhYXFd8ESsY0Hi83PT5GlSCoRmxdPpo9B1tZ4tIEfZfoZLsdvpZNFKkM3FDt2KlK++qKWPLsjpeVwpGS9hYLNe6FYrSsRQSpFCs/OyHIrQWrOdWhT3BWVjNgWkawGZOfNQWrOZKTEea2RepljbjG/TzLH3GUq2X+JVCK2ApGzzsQ4RFgHoLXwsAqpMr9GSQ+HISLbxxzXgmxIr26U1wWhEcW6VaH19todLUFrm0PXkXgy/XNEcM80PRY3CkSczCkovvEUZLWfja7z7mjdDkTrMwkR+T8hMjYPKWMZtPZ1ESczAKmzU7JudIPOprSwsPjxwMaIbTw4EWU3HuQVCIUv26wcgeKH9jQPn4DIzT3ArfFkegiyy6Yg5WV/RBTmoFimPKonNgwpEtuhlkKF5ry1+OrZDsimLEckrAeK49oilYi9lUrE/oDUoBrz7xxEOBagLwY5fPWn3PzMpxKxa5HKdjJtbLx4Mj0knkx7vTU7BfFkuiSeTD+LEh0uQeOcj+bhogQCL37rdES0gkjhC6L1LTS/NyEytgcicItRnF6xOe9kFMu3BNm4R5thHIvi+ryacRsLjkLrFjHk6T9IcXwB2e77I6U1gki7pxpOQetUi8j+e8BvURmVK3/9m8u3eWzC6B0emzDawcLCwqITYRWxjQRGIfr0W56+BxGkx8zfj6ON6mBkgQ1GsV1VKNC9GRVkPR6RuHpEmlqRBbobUmRcRABuQUpEvTnmTaR49UP1xZYDJ8ST6VvNeWYiQnEIspF6mp8/QyUedjXPe/0XHzbjfgCpSYOAq003gbuBxfFkeidks3pzXC+IJ9PboaD5LRFxOhuR1r8hheY0RFyDiFR9ip/hWI/m+SyK36tGpKEMkbEpqAfnHBRXdwJSxkaj+LD9gFe9uaN13NjKH5yIYv72jziZ+5GVux9SvMqQGhtGaxdHhNZF9no1Kuh6HFJ990EW+E5FXer3R9fjOBS3aGFhYdEpsC2OLDxb605USmEhIlnvIVUmh8hTGX6W3m5IdXAQaXsNkYCuwI0oDqoPUn+Wo5IXvRGh2gIRsTEoAeAVpAz92Zw/bN7/PERQbkdKn1e5/grglFQi9oEpRHsLqplWBixMJWL92nt9vgvxZHo4mnMYkV2v9lkrWtM7kG0WQgS1CwrmTyM1cLz5eT0iwQtQdf0dUIX92Wje9ah7wAqk/HiFcxuQRec1Wt83lYi907Gzbl9EnEwRIuGDEHEKoqryc5FNHkH3i4vuszCaaytSEOvwCbyD7rkP0ZeBF5C1eS26/8YCt5ZUVDd17bli4sLpgwPg/C7rRqeun9laWFhYfBVWEfuRIZ5M90FW17+9QHekwixAcVgj0Eb4BtrI3kUB8NciwnATqht2ECJk5UhpyKM4syMR2fgMqWk7I2IVRBvoSPM+ZWij3RdZk/siO3QQIne9EVHbB1lPm6USsRSQiifTRfFkenAqEZsTT6ZvMec9BEi072qtHqlEbLohhAcgG8xDCKkzcxDZbEBV/huRfXgcWq9FqURsOVLOMKrh3mjNeqEsUQepNyuR6taKrMneaM2KzXvm+WF+pkej+6sIEahiFAd2FyJRg1HCwyQUw9iI1rMc3SsViIQ1mr/L0X2URSqid77DkSI2oH5leWv9yvIpKDyjPOJkLkD35I2msKqFhYXFesEP8T9ti++HfVF19r5IsSGViE2IJ9O7oHIKXgxWzBzfC6lgeVSVf2vg76gMwzyk7DyGAtSbkB30CSpweg3aOL0aX2PRZveiOd9f0CZ8BLKgqhEB3AapY4egxteHIdLm4WTgiHgyfUYqEZvNNwTpx5PpSkQK70slYrmvP99eiCfT3ZFV5iD1sAgpfnngP+a9j/raaxYgZecRYGU8mT4cKV2DUULFIjOnmfhFcWtQF4Pb0JoG0JpORsTYa0PVqfFy64gJmDhCpACuQP83eQR2H1QnbSRa4zCKXRyFlNl6FBtWgt/qagRSCvNonXY2v2fQfbYQv9fqTfixiveg9bewsLBYL7DB+j8+jEdB97ea7EmvafUgtFnlzL8dEIEaiDb4EqTAjEEB039Dm93l+FaQC+yINtVXkKIVQhviDPOzBG2qVSjLcDoKQp+O7sdtkbVXjmKkjkcqxxdt5vAWqh+WM7XFvgkfo/ixa9Z2gdYSPfG/0CxFqthdwMWNS0JHHf7b/46PJ9N/MhYmAKlELJNKxK5NJWJzELH9i3lNEiljDyHyMAJZscPQ9ciidXHQerpAbSoRu8kcPwCTkBFPpneMJ9NbdOTEvy8iTiYQcTLDEDG6DymBT6P7qQaV97gIEd0WZEGehlptFaP7NIDWYa45JmAed9E9HTSPOebfp+jeqUf3507oi4ZXisX2dLSwsFivsETsx4f56Js/SJ0CbYBJVNdrFiIWQbTZPYfukxa0we2ESFQ12shcZEUOMufrimKbRiJS4iKidQRqg1SPXwH/dKSiDUWkozt+7JljnsuhUgVHxZPpM+LJ9PaIkOQR+bvnW+Y5Gakp/177JVorDEBzypnfg8CeuWbnqrnPdTtyYbp8Z6T2nfstr69AZG4IyqacmErEFiE1DEQQxiBV7Uq0pl5W6efAVEO4jkVxdJcZcno1IjEbJEzbrt8hwvwcShzxMknPR2VRCpH61wfTDBvF3O2GkiOaESnz0ITu7VbzdxF+bTrMczsj5c1FMXlPIUWu2YwjEXEyw7GwsLBYT7DW5I8MqUSsNZ5Mbwn0SyVi083Ds1D1/QeRLXMJUq4KUSxTCSJirYiAfYbIQQWyibzeii4iCgchy/EFpG59gmzJU805/oOUhzORytEFEatV5jxTkUX1Hn5T8VsQObsXtT860zze81vmedD3WKa1gfdlxmsBVQ28FQi6k1zX2SdYnA+3Njhvzf57j56RCzP9sm50wdde/wtEqrwyCifHk+nZSPXbDtVgyyM1MoRv8w5ExONkZNHtn0rEXvROGk+mr0REdINDxMk4iHifj8j+1ug6f47mdBpKRngYkakD0P3ktcNy0H30KrKxS9s87sExrx1gXucisjUf3ev/RkR1KFIZW8yYjgPO3mmP9/cYeMSq+lQiZjMqLSwsOhQ2a9LiS8ST6SNRBuIURMY85PFJVhVSKPJo0yzCb7jtkZJ68/ujKOsxgDIrCxHJmoGCz89GG6pnHTXhB6Z3RTbSCLTR1qHEgfsQQXEQ0ZuRSsRWttcarAviyfQdqDJ+AJHUbRC5ugKY98GFA/+LbNJ/Zt3oHV977S0ooLwRKYILUFD+CEQuXETuHPx6bd0QqZiHLLw5wB9TidjEDp1oOyDiZILonqhHpGcJIkTHIvXqp0gt9erJ1aB1qTZ/V+LfM54q6xEwF92XAXzLsgrdP14h3KfRGgdR/GMExaAVtXntos0TiyYU9Wkh38KMYAEzgHvb1uezsLCwaC9YImbxJUyvxF+j0gEj8AlSI36z6aVIaelpHpuOCNQoRMgC5pixyCo7EfgrKkdRiEjYJvhNsQuQKlJtzluB1AmvMn0WP0Mui2LQ6oCHUolYp1pv8WT6NBS/lUPzfR9t9N2QqjMA+Iebp2rV1KLi2Q/1vH37m+dWIdt2H6QWXo3WMoaI2zj8oqw7IsJSiJ+R6a27a94K4mLwAAAgAElEQVT3AZSUMBfY/usdBzY0GCL2J3SND0Wk/xNEylcgizaArrl3DzTjq7E9EGECn6wFEWn1iFQOXCdUnqtprQ5+BM6OiNDdg+6hW5AK66D19erk9Tfnbeo2sj5XNrS+sMeohmIgFypmm1Qi9m11+iwsLCzWGTZGzOJLpBKx8aix8v9Qzat/I/XAs84KELnoj8jXq2gDW4Q2yzpEGIoQGTkWbaaTUMzPBJQV6cWgfYgqnT+MX6ssbM7dhDbWCCImdyO7KmTG8kQHLcPaYDdkLR6Lb9NeCNyKYuneAB52AmxfsWXjdtvfPHcPVI7iAVTl/SggkErE5iKC0QeVWNgFWY+vISutAD9+Ko9PQMajrNXJ5jEv5m+DRdaN5lDW7T2IxI/ArxnWiNTQd9E95Smx48y/HvikbDHKdjzPPAY+QQ0Qzjf3P2RlQaAwPxK1/HrAnK8PInmuOU8YqWp98S3P4lWflHSd/3LX8MpPi6ieUTgdP2bPwsLCol1hFTEL4sl0F0SuPkslYm48mT4EZek9jmykgShrcQBSLbZCZKsFBTifgAhbT6Q4DEQlMmJoU70GEafdEfk4HcUFzUMKSBiRrHpEwCYiwvWpOXcTUn1ORF8ejkeb69hUIta2dtd6hamq/ze0uYOIUDPa0D3rrBGV4rgMxcDtj8Z/HMqObEbzOxUpYHMQ8fovsokb0FpnECEtQITCs9yWI1VtCfDihmifRZzMIER2piBCfhYij/3RNT8fqYEDEeHsiuZdhk/Y8+ieBJGl+xDZnY3iFb110ZfLQC5XOqSprm52cSE44/Gb1BcCN6MvB0cjBbjEjMMjfRngGHPcJ+heuxx4IetGx7Xr4lhYWPzoYYP1LUDp+4cCp8eT6V8hQtCCygZEEMlqRUrGa6j+VTGKsdkPETMvGPpJRCDmoJYze6NinV5A/37ASSgx4GAUUzULFeQMmdf1QgTnCqSwjTTHHgs8g5ShfYC/xJPp01OJmJclt77RG78ALmgNQPN8HRGHHki58TJL70XqTwHKLvXKMAxHhHQYWsfu5lwN5u+27xM0z7ciAvFP01N0g0PEyZQi5bQXsm53MU+VmceWoHXYCq3DCkQyvfpzreY5L2vXW4NRKHOyLyJfq5A6eBhQQD7YXDe7JIvu3+1R3N1fgN+grMn+6Pqcju7JO9H1eRkR41uAxVk3mo84mYGouPEn7bo4FhYWFlhr0kJII/WrDyJPjUjRWoXa9zyJio1+jojS2UhFaEBE4zWkdG2HCMJ0RBAOQy1rzjWPfYEsvD5IcahEG+okZEW1ItL3BFIhmpBlmUQqyQeIwL1lxn0kflzPekU8mS5B5SS8z1AexcZ9aB7/NVJtHkOq1efmuAMRidwSrcG1+K17vCKtLeZ5F62nVzm/5WvDWIFIXZANF/0QwSzFV7QWIyW0CmXGHo3m9gAi3m0zIJeZ5xaaY+cgpXEAWu+2Gae/MMd5iuRw/PixenTfvI+SKcag+/ZpFM9Xg2zyYmBI1o0uzLrRPEDWjc5FXwK8nqcWFhYW7QariFmQSsSmAFNMlfhbEEHaExGpk5GV5vUBHI9PGsYjNaEIBWD/C9lFS9CmWoVISAlSHv6I4s9GImuqCSlcC/ELo26LyMUgpIB4ZQWeQpvnyyg2q9mc+xBMh4D1jAa0VjshEtoTEdiLUonY2wDxZPoGpOYMNa/5DCkrHyGSUIMquh+Mn/Hnkc8eiEQ0o/UZiNanFqlrAURuZiJytyHEzH2JiJPpgpTWHZHddywi4oWozVALun+2QmQ6iE8qQ8hynIPI20OIuL1izjPfHDcFWb1d0Vq8YM7ZB5H6rDmum3n9pYgU7oGu37OIFJ9qnp+AvhisHFY49fz+h67qWTqoefzLt+zVmnWjNe27QhYWFhaCjRGz+AriybSDCEM/1KboUPzsvBZUmLUPIgMz0abpVTBfiTIkNzXHrUQxYmORSlFnjmtF8WG/TCVib8aT6X2Q0vU/tJGegBIFlqAaUhciAnIOsjX7IptoS+DxVCJ2cketx+pgCsy+gshmAVJtzgKmIfLprcPuKE7sKVRn7S4UB7Ytss9ySDXzSn+E0DoH0dwr+f9fnGoRuemG+lhek0rEVnXIRNcSESezA5prf/zYrSrzdDekdCVRfFsBmv/fkVp2MiJXXu/MJvN3E7Kxp6L7Io9U0m2RkhVEKmEZivO6CWXuOkg9rTFjaEL32VhEzD5GpH4TdO9+3HufqsHlmzV0Ke6dezNclj+rTc09CwsLi3aFVcQsvgITrP8BshArgHcQmXoQEakd8NWZckQQlqONshcKvHbxY5uGog0zj4LwI2jjbQb2Nhbf1uaxPZCC8pJ5v6cQ2VqJiJ+nZLyMlJACtLl2JsKIpD6BWjttgiywZ1EsXR+UFdgVZUouRwpWASKezyJFrwQRCO8z+TAK6G9FSQt7oTXrgwixVwvrLqRYbodUtDUmYvFkuj+6Jm+kErGj13rm340PkKXtdRSoR/OdjJSpriiJwytS24DfyqgZKWdeIdZS87MAqaNRfAtzd3Pe15ACV2FeOwQpaQWI9C1H6+MRvFJkl4Nq5l2H7tUguJt2Hd64LNw1R7Ao77X2skTMwsKiQ2BjxCy+Caciy+inqURsDLIcn0UEYyhSo6ajOJ1iRNTORapGOX5MzucotiwDnJxKxPZOJWJDkIL2CIqRSqJNNYCI3Ksojqc3su9qzXt+htST48z7FiLraXg8me6URtfxZHoIUrXOQQTieDTXQmTr7oEUlt7msX4ovm4MIgd7IUvXQbbYEkQYWhEhvQiRia1QXNRsRIZnIVUni2KcRgPHpBKxWWs5hSPN2A5dy9etCa5D6uVcpEIVI/IcRdc6bP7lkfV6Cbp/euGXsZiHiOV4lLgxxZzbQY3jH0b3x/to7Xqb51vNe87Gv69W4n8hcJCVvASprw/ifxlwwKntsknz1kWV+SGBEIea97ewsLDoEFhFzOL/wWTg1QGYPob3IjtwPrKa+qM4p03x2+8cih/ntBIRp95I6XgllYg9as63K1IlWtGG+xek5ng9Jj8FrkJB70lkvb2ONuYL0KYYMq/9Mwry7yxVrB4/ASGXSsRWxpPpX6ISDUej4HHPCutjfg5ERLUBZRAWIEJwj3lsESKag1DpkPkoBu8jpBjNR8kNZcAZqURsoRnL4nUY/+2IpLyyJgfHk+kilNU59bvKZEScTD9E5suRxfwT85R33VrN73m0hpUoftBTvrrgK1ue/XoOWr8ZaA1/ghTBd1C9tg9R7GELsi0bEVHdDJGxwfi2eAgRvK1Rfbpe+DXapgGj25RF2SCzUS0sLDYeWCJmsTq0oM1wFnA9ygJsQVbaCrTZDkcb3mzze3dUtuI5RDSGmdizwYhwLEeKWE9EWDxldhWKr+qPSJn3fpujTMRypKI4iLTc3pl1s1KJ2BJgRDyZLkglYl5R0TJEnFrROtyHYp6azePDEdnohdSuichGOw3NuwGRgSq0Pr0RyfjCvK4S2Bc1aO8ST6arUolY/TqOP4+UptUinkzvhOzXAFL7Jn39GNNDcpCZ68PA52XUjNmFtwtnM7R2FpvORrazSkzoOpbg95AMmt9noDiwgSjQP47WshERN0/9WozI2zEojrASWZd/RkH4B+E3kPfez4u5KzRz2RLdU15w/9lZN9qpLbMsLCx+XLDWpMV3IpWITU8lYvuhuKZh+PXD+iIFYRmK0fEq4rdFDbLU/oaspx2RPVcL/ANZl3sBO6ON8BVkU1Uh1ezvyMo6DFlIh+FXlX9kAype2hpPpgvN7/ejfolNiDz1RsSkhq9aYzlUz+pzVCx0AVLCNkHV9Y/FJw5bIzLajCzhhxAZHsdXe4J2JIrr5oe6LfuoKPjhpf0vjTiZbb7hmARSQicgy/WBPIHNAuRy1XTxuiT0ROQLvCr4WisvQ7QBqaJHI+K1hzm2GRH7hUCX4r7NEzc5bclLwZJcL/yaYpsjwnU6WveAeV2uzfvNAy5GStrzSO1dgF+/rOdPrk/H4sm0V+/MwsLCokNhiZjFamHUrIdQkPUHSMX5Dwp+7odKXZShDfZu/KbMI5EKNABlD16JAu2vQ6pODYo7K0ZKx2RUz+x6FGs1CJGRENpAZ+LX2to0nkx3uqIbT6YDiCCsiifTDyA7dS7a4B9HMVzbo2zKqajkRSuyvIaiAP8+qIr7bHPaWrTGsxB5CCGCNgsRiHsRicuh+KiOnqOTa+GUz+7s1WXOIz36uK2BI8M03rqp83Em4mQ+jTiZEebQcnRtShFpH1xPaekrHBRYSp9SZKuOR/W/vCK8XtHWGmRj7odiySpQWY9DUND/fSgL8iJgWsnA5v6h4vzRhd1z3fAVrqw5Tx1+/bU6RF6zaL0K0bWYgUh+M7qPewJbgXvTwv92ua9xSeja9l5HCwsLi29Cp29kFj8YTEQqzK1oQ30NqWAu2gjnoo1udxQjBgqofhGRjWJky0VQzNdVyEq6CdlJlah4bA4Rsg+RwhFARO4yVLML8577opZHD3TMdNcYWyC1MIRUu0dSidigtgfEk+mtELkIIOJZhebgBag/YM7jKTgg6/EE8/dctPbPADemErGWeDJ9ILByfVXUb60JzHFbnVZwQkBNIU3bNBP2kiT+GXEyr6P6bpNRfNgdiEyVg+OVLvkEkcmXUdJFCM3f+7mpeX0C1WYbiq75oWiNDgV+BzSt/LjkjJbq4Kb188KVSM3qi2//TkBkcAgiYDMRwatHpOwc1MlhGCp94amZzeAsXfp2WeWyCWUhbmjHBbSwsLD4Ftg6YhZrDWPDzUMbbTMiDYNR7JeDGoZX42+2XqB0A36wdgWKCTsHlbzYAr9a/0Jk65WiOKE+iLi0rSDfAlycSsRu7biZrh7xZPpgVG/N6705Aal/hSh2aTHKKB2O5teI5jIXBah/gazbvshWAxGyJUhl/B+Km7sf+DSViJ23Pub1dUSczL4ovgtgUoDmPoU0NjdQ3gepSwtQxfpq4DbUWSCPbOwP0HwPQPfLCnSfbG6ey6OMyCRS/qai4sH9zTnnoXugG1rXFeiem4DU2AFIrX0E3TML0JeErvgxZZua10005x1gzufiW8VP48es1WfdaHuX9LCwsLD4f7CKmMW6YDDavLwCnR8gwtSAyFIGkYgVKGaoGb9swRfIsgQRMRepYgcjlaIAKWb7IUXjZbQZd0GbpIOI3G+Q6tJpMCTsH4iEeeNqRXO4AmVNTkXKy2fILmvCJ6S3IuKwCtmSI9qc3qujVYF6d75I5/Y6fB8RoqHAqDwFDzZQMA4RrfvQ3B5GBGcsshmPQ+roduj+8GqlFaH5FqH4t0MQWToDZcbORfXq+qAaa7PQemyF7qE6pGRtad47bn6uRARsIooR2xLZoHfiFwk+HZGuqeg+9ArBvouuVzF+z1ALCwuLDoclYhbrgjkoTmk3VIJhX5QluQgRqNMQ8djNFIjdFakVeUTIvN6Ag1H5iplIMepiHt8Gv7DnM0gVOh8/9qwQKEolYl/vvbi+0dWMZSEa4yNI2eqNVLEgUl9WIIusGBGSzxDJ6I7WYh6KrbsLfSYDSK3JI2uuH3Cc1zppfSPiZIJmfB8jYjgDZRc2medDSHEaaMb6JrIQZ6FsxhxakxAinqWIOG2Cru9bSB08E9+KLkHzH4rU0i1QWZByRP4qEenqj2rPnYeyTRuAUaHS3FFdNmn6Q/2C8EtNS8MF5v1PM69/D91fbZvFjwS6m76SFhYWFusNlohZrDVSiVgT8Jt4Mn0aUnHeSiViC+DLIqdHoGzK+fFkuhgpRCvRhuz1Xcz1r/w017tidsHk2Qds2porLDHPX4cUjBEo8LofCtq/CpUkqECEber6mOtq8DKKacqkErGfHXzBuNsKK1tPCBa67yCCtQApWcci1aYVv6aYl4gACua/Cq3TYjTHAkQW3jXPvbO+JvUNyKNyFauAk7JutCbiZMoiTuaP5rFKpGiuQEpXOYrp2gORTa8m16tI/QqheK9Ts270RYCIk3nEHD8K2ZvXIpL/ASpY+xKyZxtRk/qP0Rr1Mo+PAh5F6mq3wp6tD/Xeu7rvyo+Lixa/3jVgxtiM1vFFFJxfhrJW90eFeYdHnExV1o1Wt+fiWVhYWHwXLBGz+D64DwU/7xNPpociC+4C/My5kWgzjSLV50b8oHQKQw2hooLasOPkWlEsTxBt1K8h226hea1Xkf44pLTUpxKxZ9fTHL8LVWi+i4+8Mb1v9h+9dyjp3zwvcszKmxAhmYvmfxgiEP9FcxzKV2PCQCQhg1TFPfFrX20GTOjMUh1ZN+rZx21RhBTRrfGD5EPIYvTs2S5oHgGU+ViLiE8E2Yw7RZzMOKR2voESPQ40578AqWLF+PfSTxDZ85qd9zdjOAfZkR8jha1L/byC7ZaOL/ugdk7hh4hoLUakMGreK4jI/gdIKdsSlUu5FFmsFhYWFusFlohZrDHiyXQZ0C2ViM2DL/tSNiMl411867AB9RF8Bz/YfjF+s+euQMnsRaOC2cXbNbtuMGAe88qpXGJeU4fsyxrUp3ElUkA6tdq5KVlRbhpsvxlPpiucIK8P+enK0IqPSs5IJWKvm+M2QxbuHGS9vYSs2CWIjHVHBLQEzTeAiAL4fRY9ktO4vua3hmhAxNsrlNqAiFdXFHi/KX7Nt2Kkqg1CBHQqUqMeRXFZCVR89eJQWe7PufpAuZt37kIxY01ITRyEiOpByK7NoXviKFR3bSWyyEcCz7utTsnyiWV15vj+KJEkaMZ3Imq/NATFmi0y45yDbFILCwuL9QabNWmxWhi162YUB5YHNkslYsvMc0NQltxKtMnugDbnS5BS0QcpGPVoMxyENtEGpKZVIrKWRwSlAsX+zEP21nHmHF3NGB5EG/CNqUTsg46c97chnkyfiAqOnoU27wRSUrogC293RLLCiDyUIpvxTyhYvAgR0oH4NcW86vKY3ycC/0T9K/+ZSsT+tB6m9p0wsWIOUqsGo5guB79IrZesMAsRZu+5VnzLshTdH5WogO+lSG2rCJe3Rgb9dOWA6umF+aVvlk8H/pZ1o9eb9w5l3WhrxMlchAqyOoigLUWWp1fDrAjVuEsDv0Rr+QDKTB2ObO/NkFq5zIzhz+b3gqwb3dAIr4WFxUYOW9DVYk1wPrLXys2/tpZaCtmJx6FYHq9n38nIgvKqqM/HV2BbEOnogwhZA1K9eqONNICIzCFoo30cxVu9bp4ronPV3Jmo+nsVyt68AY03j4qD7onsrWORXVaHNvqfI3tuBVqDFhQ4XoPWdSUiawtQnNR0c75D48l0eL3M7FsQcTK9kL34MxSrtwN+zbMcIjw1iHxuiq7pCkSgK5FF+y4i7bWISA1FpO3Yim3rfkM431g/N9zavCw8A5G38yNO5saIkzkXeDriZLzsytlorfsgteu3KKM032YspyFi9hAix4ejchm/w+/5WQw8nHWjS7JuNG9JmIWFRWfAWpMWa4Jr8UtPNCErx8NCRDS8voEO2qC9UgOueb4cbch1aJPNmX9vIYXlNlRiALShBlAQ+xfIXqpBdbnuA47oyJipeDJ9JFK15qBEhK9UrzfZi2+bY0fhty5aaOYzDRGQXyBSEkA1qsKolMMipM6sRPZeb/x1G4kInoNUm92Awg0gQzSHxuvVitsezaO7eW4OvgrmtSl6FLVv2gw/A/ZzlC25E3B11o3mIk6GXnvUDB5Y2bpi3jMVk6umFS9CazUc2YhLEJlKIiVuBSKr/VCh37fQ+tcgktwNkbyliKgdioiXizJyT0HK5KVZN5pp/6WysLCwWHNYImaxWqQSsUXIhvsmHGZ+bgJc5rqcCHRznC+fzyFiMtD8XoyvxL6PgqX/jRQWL46sHm2sIOJ2IIoXagHGpxKxN9tjXt+BAWY8Pweq4sn0bqlEbD5APJkuApraEMFTzDyGIPWqGCk025rxggja4ajheSMiszUoiNxB5KEOBY/HzPFHpBKxzztuimuHrBtdjuZKxMmMRaSmB/o/JIxUrmGIZJcja9kr77EYkbI3kYL4GbrOv4k4mUrgZ5/d1jtaOqh5Tv38gk/Ma0YgsnsPsjPL0f0zEJHztxEJK0ZrWIqIvtfbkzZ/P4nI4MvAiqwbfRyprBYWFhadDhsjZtFuiCfTPZdPKpxePqypJFjG0kCAvm2e9oLSPZUMVHtrS2Q1jUTkpBDZlgFkZ3oV+HsjpWWLVCLWobWeTDD+WKS+1ZmxeUVa90KK1Y2IsD2K7LprUFzX4Si2az/8pt1ViCgsxE9C8DICvcruXs/FecAvUonYGx05x3VBxMkUIGuvBK1DX3SdypCV2h2fZP8dWcsuIl2NyOI+FNmxe6JK+1lEqo4A5mbd6ObmvQYgNXAsIlRboxIZk1G5imPNuasQMetpxgOKD/sCZVmeitTZ/YFjbZ0wCwuLDQ02RsyiPVGXqw8tXvR613nkmYgCo0Hq1nPmp9vm565og+6BVKKu5nHPtvSq6fdDZGUOavbt620dgFQilgeeRWrfZSjW7e8o7mioGf8IlLE3LJWI3Y4Cx3+DCMEwRDyXIbVrASKRXnxbmXluYZu3bURzfnBDJGEGITS3HdBYq/BrnvUwj4Hmujm6pp+jVkT3I1vwSdT4e5g5rhYF198M3BZxMuURJ1OBsk3/iBRCB12POSj2bj+keq1EZHYEUsyqEZndDKmZPZCtPhQliTwUcTJeQoSFhYXFBgFLxCzaEy29dqubPfDwqhmBELUopudepIDsi2J2mhDZakF1tZ7Ar6lVgwLyvVY+XjNmF5/E3InfIqnDkErEcijQ/jiMJWfGXIAI1HNmnEvjyfTVKDlhAFJ6GlAsVTdky1YgRazQPD4Dv9l1PYp5+hVSi67r6LmtK7JutB5lIq5Ca7MQEaJmfPWvCRGkUebvw4ErEak9DSlfy4FxiOR+gbJrq1EW5r2IqG6KVNAWRKy2Q8kLcaS8hfCt4AJE2JeiZvED8fuSViJy/DyKW2tbTd/CwsKi02GtSYt2RTyZ3h5tnn1RoHttPJk+PN/KLoEQc9BG3hNlB3pxZ/3w+0dujUpDlOATH08ByyICsMw8d1ZHFzqNJ9N7oM38ZdSC6ESk8NQi0pVGqk0YEZJlaO7z8FsYBREJq0dkMo/fBH05IpovoAzUzYGHU4nYBlfdPeJkTgR2RKRqLgqEr0XXs6c5bIH5PYCIz+VonS4wjz2UdaOnGOtxMVq3GCJbZyFV9BNgZ0TO/oFsxcmIqHqZkcVo/UrQvfOmGc98pLQG8EtpXJB1o//pgCWxsLCw+N6wiphFu8LU9toBWVDXAXxw4cCaKTf22a65OrAHIiIvoPpYHgFrND8XoezC19GGHsYnYV5mHogMHY027Q5DPJnuhWLBbkDlFwYh8jAeVf+fg+YaQgRhlZmfi8hIAyKMWfNYIf5nLmd+fwMF9z+MVMNzgEfjybTXd3ODQMTJdEP1vk5BStQmSNn7v/buPkquur7j+PvO7mQ37EP2IYQIZnUSDJeyIBBBjUrw4JiWqhVZpLbYVKyYUrDqUAs94rE0FZWupsKx9hAgRXygDqLhKLSLcvC0WJqgaZo0UxOcBpOQsCG7yW6y2ae5/eNzrxMQjig7ezf4eZ2Ts2QzjzcJ88n39/19fwvinydVzgEUfj6Mql0fQtfoJjQDbm8uKH0DVUJvQsvP7wJWof65eqpBaj0KaV0ohCUT+xvQtR2Iv3cY7bD8LvoztAtV625Cc8K+lAtKH67FdTEze7G8a9Jq4Yfow7EboL5pcnTxyqdek22utKAAczMKXPUojH0GLd3VoWWsJaj/aILqIdgBqsYky5er48n2tTSBerweRo34f47GWnwcLZPtpjqy4QD60M+jUJJFS5MRqvYkPWDZ+H3+fPRDsZC/A6Cnt28L6qnqArp7evueSHZrzgCLUfCdhQLYf6DXOQtVAXfEv74YuAVdi4/F961HFcKNqGerAb3/blQ9/Bb6c7AQLeOOow0NLfFzJH8Gkh/JsUn7gBtQSF6OmvtDdI33xK9zEO26bZ/i62FmNiVcEbMpVyzk/wftcPsIwKv/evcjDR2VrwYZ9gCrioX8ZlQRSyoc70NLV19BH6QTVCfsJ7ssn0If9jtR79kOaigeU3Ea8MFiIX9tsZAfil/3YyiA3Yt2SibDR4fRyIpPoz6pJDQk0+gb4x97UUhoQJWylclzxs+xBu3WvB8dn5T639F4mOsqFGoG0Ot7FboGFRSY+qluxLgSLTPOQ6H8L1GPV1I9hOpRVUtQ+FyLlhmTw9/XoqXebPy4o2jpdpTqJP8s1WD3dRR8d6DTDJ4EDsaHip+Ndnuamc04rohZTRQL+R8d9d8RcFVPb9/VR/V0bUaVrTPQ8lZS6WhGy06NVJclQT1B56AP4i+jStVVNXwLS9GH/C09vX1dKHx9u1jIr+/p7ftd1Nv1XlTVSc5UnI8CyFD8epMxFcm5kZOo8tePzmPcw3OfIZksryVBNe2J7+MoeM1FvzfbUQUwGU+RRderEt+2Eb3/T6Hq2HVoI8Miqk30Y3WzJ59o6hpbNPzThtWV8cxi1MC/GF3HD8b360ZLn1m0HD2KrmsRhfKPogb+XWjzx14UvP6kHIW7AcpRmPYwXDOz55X6v7btN8ezGuuTo2gG0Yfr48BdKHQcRgdD/wvVkQhZ4JPFQr4fLQ3W+uzFDcBqtJPvBjRYdGX8PvpRKPxA/Pr3o/EJ7Wh5bTeqyCTHNRF/HUfvL5krthztLnyGYiE/hpZhzy8W8mmHMMpROIDeZxKM56JjjvIoaI6gxvoGNBNuBI2Y+CIKZ2Wqxx9NEo+WmPv64W1dPQMjHUsO/zi+fyeanN+Krumyo+5D/PyPoMD1B2iWWD8KXw9SHRnyb/HrNTOb8VwRs7RU0HiCCPUHXYkqTBGqAl2Hlqy+hz7464DX9fT2zQdGioX8T2v54oqF/MGe3r5l6CieZJnxip7evg8SzksAAAqvSURBVJtRBeYGqqHjBPR3KRnMGqBm/eTnyTFPEQpi69DS44UopDzX8x+K7zNT3ImGo+5GS87jVHcvJlPvM/HXe9B4j9vR7+Xp6Pfv79Fy5m8DKwKC6xvaJ8OnNxyXR1W2T6Hl4AtQIFseP3YZLWXOQpXUC+L/Poh2rWZRMJwANpajcHUNr4OZ2ZRyRcxSUSzkJ4BLgPfGoxq+j8ZCbEYf1h9AQz7bUaCZhT7c1wAP9fT2dU/Dy3wCBY4RFKLmot2A16CA0ImayRtQD1tS4XslCmb3oTDyx6j37XK0lPZZFBpeB7y91gNqX6xcUApQeDoVTdQHVfuSg8jHqY4b+Qe0pPsddE3OAU5qyo3MCz/6ZHjcgtEtwArg03u+33rHY9csGI4mMh1o2OvVKOwlQ1o748dvpzqy5HIU3CtoGXtl/GMhWqpcO/VXwMysdjxHzGacXFCa33bGoTXNrxh78IRlw7NRo3UWzZK6FR0C/oe13jXZ09uXRTvxlgIXo0b1U1EIqUNBIOl5GqU6xmI/qog9CdxeLOQ/8RyP/ado00E/8Nr4PM8ZKxeUTkc7Wneh/rZVqDl+EjXHdwA/KEfhivj2H0dLyFkgaMod4eT39w/UN3LmY9csuAzNFxtD1a7dUHkDVPZA/fGoOrYIBbExqrPkAhTGhtB1ux4NjD0LLfd+rRyFN9f8YpiZTSFXxGwmmjW4qWly533t5WIhfyPVMQjdaBnwkmkYXUGxkB9HoyseRbOuHkXVsUf5xR6kJCzsR0uX96Mety8/z8PfClyBdhQ+NdWvvQZ2oh6ss1BFsA0FonoUxvqBebmgdGl8++Tcx73AjR1nHX5NfSMXxeeERvFjHQR2ZDny3Szjx53ErnE0zHUJCrLJUVigQJYcqxShEHwt6rG7shyFS4FiLijVdLacmdlUc4+YzTjlKHwiF5QuLkdhchzNnagH6AwUArrRYdDT4dwo4lvRJDsz9ZyPerweQstv86n+Y2YcVYt2oWrP+4C9zzf5P16avavGr30qvRm4jOo4kUG0u3MSVa6OR8uT5+aC0kI02iPZmHD2z77ZsfUH9yxN3u/L4vt+Bxgap+HKOsbHDtE8gJZ6kwAWoWXKcaq7ajvin18ff+88oC0XlF6Pjkz6CVCo4XUwM5tSDmI2Ix0VwigW8vuBd/T09t2E+q3+axpfyrbxg8HExEhwQiYbdDV2Tr4F9THVUQ0HoGAxgEYn7Aeaan380jR7GFW3jkf/32hFO0v7UQ9XF5qlthmF5nUoMHegKfyvyAWlRfHt34TGWYTAhRDUTzIrGqSzAx1TBKqMdcXPE1D9f1UyU+wSNM5iDTpf8jDwj2iDh5nZMcNBzI4lX0f9SP80XU9YLOT3n7Zg0x8RRPPmhEc2nLj84FZUhUlCVvL15SiIHAbWFgv5x6frNU61XFDqBDrLUfiTo75dj8ZFfAVtWgBdB9CRU70oML0cDb3djibmb0LN9O9Ey7B3xl/fio4tAgXYZnQs1nvQSQTJhPxk5+kYqoQFwOdRr96rUPDbA2wpR+GaKboEZmbTxs36Zr+Cnt6+t6Pdm0lAAC2lDaHxDLcCu4/lalguKK1CxwZdjZrp64FPoP6wj6G+rPejXi7QcuA2tDnhTOBvy1F4X/xYt6MxHUlj/2oUzi5Bmx7motEUXahaNkJ1I0Qbus4TaFTIXDTS4wJUCRtFIXAYGD26impmdqxwRczsV9OCglcdCmLXoeW3M4HbZtDZkC/GPSjs3IsGqL4SVbr+Cg3aHUWjOf4mvv35aBPFFhREv5cLSjeipdvfozqg9TBaun1r/PUQOrNyIbqex8ePPTt+3Er866egXZRHgM+Xo3B7LiitRWFwUTkKH5nyK2BmNk0cxMxeoJ7evlbUh5Q06EeoYvQQquY8ndJLm2r/HX9tRv1fS9AOx3tQsPoIGl+RTLKvR0cZvRM14g+gI4raULA6gAJsI5or1hY/fhM6QWAyvt1Q/L0xqsclnUn1UPVDwIZcUPoiuuYreJ6BuGZmxwoHMbMXbgRViJahcRWTaMnuzcVCfn2aL2wq5IJSBlW3hoBXo+C0FA2sbTnxwoE17aePdGxdPf/uymhmF5BDVawMOiOyEP+4ClXOlqCdkW+In6L1qKf7GWr+fxxN0N+KlhhPQaHv31FoS0aInAzchjYIXAHMKUdhTU9XMDObDp4jZvYCFQv58WIhvxz1giXLk/fz0qmELYNoHZnKHWhn6A703rJA8+RYZsnI3uzZXe8auAOFqBw6+LsFeCMa23EqaqZvRWHqbtQDlhx/lBzz1IAOe/82qoCdjI6TSs6b/H0UypLl33cD3yxH4dNot+RtNb0SZmbTxM36Zr+Gnt6+e1Hz+rnFQv5YGMj6S+WC0mnUVTaSieraThv5s8GNzRejyliyS/IARIcguBFVBD+LAtY+YE789WvAP6Pdka2o0f64+PbNVMdPDKBlz5VoAO7y+LZ1qApWQcuUPwKWuhHfzF6qvDRp9uu5DGh9qYQwgHIUblnUumkoGqtrP7i94SxUCUs2JVSAWRA0osrXhqPu2hbfdi4a/PpaNOx2OL7vAdTL9Vto1MRB4AsogM2Ln2MQ9Z6tQ2MpWtD5lvuAN+aC0vpyFM6kQ9DNzKaEK2JmBuhw75bu4QcObZv9pojg8mg0879ontffoYO2B4D18ffuRKcHnIjCVtLmMIaCVQWdHPAXaIfkCtR/NoZmwd2KwtjL4vs/CLytHIWTz3pNK9DB6f9XjsJFtXnnZmbpcY+YmdHT2xfMf8uB+UObm06pjGay0WimADyAwtMgClY7UU9cBi0ldqL5XhPxr4OWIDMocIXoJIQvoJ6vzejMzve0nDxyaec5w53xKLYJ1D+2LReUFj/rpf0Y7ZbcVoO3bWaWOi9NmhlAflb7+OeaF4+cSIWJ4e2zz4Ygg2Z17USJqRsNeY1QU34G9XEFVI96ilCwegrtMr0YhbQAWFeOwo0Ay696eH9QFw0PlxvuGt2XbQF+B1XS6o5+UeUo3MQzd1uamb2kuCJmZgB7hsuNo0xk6k5624H67JxnrBA2QmVffA73QtT/1YT+IXcCClpH0A2G0UiKbwBfRUuPDwDvAL6UPGBLbuza5q7x87qv3VNA0/ovBXLlKNxa4/dpZjajuEfMzADIBaXsnO5DvbPaJ9+974fNo9FEBrTTsRmieiBDdvIA4/URaqYPgF2o56sNVckuiu/zSTTkNUT9XbdM+xsyMzsGOIiZ2c/lglIe7Xz8DNrd2I1GWFwDlaZMY7S0cqRuMZp4fx7woXIU7s0FpUagsRyFg7mgdBeaC3ZROQqfzAWlOcDngH8tR+HdabwvM7OZykHMzKZULiidgULZf8Y/b0fLkveXo3Btmq/NzGymcRAzs5rLBaVMOQorv/yWZma/WRzEzMzMzFLiXZNmZmZmKXEQMzMzM0uJg5iZmZlZShzEzMzMzFLiIGZmZmaWEgcxMzMzs5Q4iJmZmZmlxEHMzMzMLCUOYmZmZmYpcRAzMzMzS4mDmJmZmVlKHMTMzMzMUuIgZmZmZpYSBzEzMzOzlDiImZmZmaXEQczMzMwsJQ5iZmZmZilxEDMzMzNLiYOYmZmZWUocxMzMzMxS4iBmZmZmlhIHMTMzM7OUOIiZmZmZpcRBzMzMzCwl/w84up5bpbDImAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding_pca_cosine)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Summary" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(12, 12))\n", "plot(embedding_standard, title=\"Standard t-SNE\", ax=ax[0, 0], draw_legend=False)\n", "plot(embedding_pca, title=\"PCA initialization\", ax=ax[0, 1], draw_legend=False)\n", "plot(embedding_cosine, title=\"Cosine distance\", ax=ax[1, 0], draw_legend=False)\n", "plot(embedding_pca_cosine, title=\"PCA initialization + Cosine distance\", ax=ax[1, 1], draw_legend=False)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that we've made a lot of progress already. We would like points of the same color to appear close to one another.\n", "\n", "This is not the case in standard t-SNE and t-SNE with cosine distance, because the green points appear on both the bottom and top of the embedding and the dark blue points appear on both the left and right sides.\n", "\n", "This is improved when using PCA initialization and better still when we use both PCA initialization and cosine distance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using perplexity\n", "\n", "Perplexity can be thought of as the trade-off parameter between preserving local and global structure. Lower values will emphasise local structure, while larger values will do a better job at preserving global structure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Perplexity: 30" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding_pca_cosine)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Perplexity: 500" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 28min 32s, sys: 12.9 s, total: 28min 45s\n", "Wall time: 3min 41s\n" ] } ], "source": [ "%%time\n", "embedding_pca_cosine_500 = openTSNE.TSNE(\n", " perplexity=500,\n", " initialization=\"pca\",\n", " metric=\"cosine\",\n", " n_jobs=8,\n", " random_state=3,\n", ").fit(x)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding_pca_cosine_500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using different affinity models\n", "\n", "We can take advantage of the observation above, and use combinations of perplexities to obtain better embeddings.\n", "\n", "In this section, we describe how to use the tricks described by Kobak and Berens in \"The art of using t-SNE for single-cell transcriptomics\". While the publication focuses on t-SNE applications to single-cell data, the methods shown here are applicable to any data set.\n", "\n", "When dealing with large data sets, methods which compute large perplexities may be very slow. Please see the `large_data_sets` notebook for an example of how to obtain a good embedding for large data sets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Perplexity annealing\n", "\n", "The first trick we can use is to first optimize the embedding using a large perplexity to capture the global structure, then lower the perplexity to something smaller to emphasize the local structure." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 28min 39s, sys: 13.6 s, total: 28min 53s\n", "Wall time: 3min 43s\n" ] } ], "source": [ "%%time\n", "embedding_annealing = openTSNE.TSNE(\n", " perplexity=500, metric=\"cosine\", initialization=\"pca\", n_jobs=8, random_state=3\n", ").fit(x)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 10.3 s, sys: 644 ms, total: 10.9 s\n", "Wall time: 2.01 s\n" ] } ], "source": [ "%time embedding_annealing.affinities.set_perplexity(50)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2min 6s, sys: 4.6 s, total: 2min 11s\n", "Wall time: 16.4 s\n" ] } ], "source": [ "%time embedding_annealing = embedding_annealing.optimize(250, momentum=0.8)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding_annealing)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multiscale\n", "\n", "One problem when using a high perplexity value e.g. 500 is that some of the clusters start to mix with each other, making the separation less apparent. Instead of a typical Gaussian kernel, we can use a multiscale kernel which will account for two different perplexity values. This typically results in better separation of clusters while still keeping much of the global structure." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 8min 28s, sys: 6.88 s, total: 8min 34s\n", "Wall time: 1min 19s\n" ] } ], "source": [ "%%time\n", "affinities_multiscale_mixture = openTSNE.affinity.Multiscale(\n", " x,\n", " perplexities=[50, 500],\n", " metric=\"cosine\",\n", " n_jobs=8,\n", " random_state=3,\n", ")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.98 s, sys: 140 ms, total: 2.12 s\n", "Wall time: 115 ms\n" ] } ], "source": [ "%time init = openTSNE.initialization.pca(x, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we just optimize just like we would standard t-SNE." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "embedding_multiscale = openTSNE.TSNE(n_jobs=8).fit(\n", " affinities=affinities_multiscale_mixture,\n", " initialization=init,\n", ")" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding_multiscale)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Summary" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(12, 12))\n", "plot(embedding_pca_cosine, title=\"Perplexity 30\", ax=ax[0, 0], draw_legend=False)\n", "plot(embedding_pca_cosine_500, title=\"Perplexity 500\", ax=ax[0, 1], draw_legend=False)\n", "plot(embedding_annealing, title=\"Perplexity annealing: 50, 500\", ax=ax[1, 0], draw_legend=False)\n", "plot(embedding_multiscale, title=\"Multiscale: 50, 500\", ax=ax[1, 1], draw_legend=False)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison to UMAP" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "from umap import UMAP\n", "from itertools import product" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ppolicar/local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/nndescent.py:92: NumbaPerformanceWarning: \n", "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n", "\n", "To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.\n", "\n", "File \"../../../local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/utils.py\", line 409:\n", "@numba.njit(parallel=True)\n", "def build_candidates(current_graph, n_vertices, n_neighbors, max_candidates, rng_state):\n", "^\n", "\n", " current_graph, n_vertices, n_neighbors, max_candidates, rng_state\n", "/home/ppolicar/local/miniconda3/envs/tsne/lib/python3.7/site-packages/numba/typed_passes.py:293: NumbaPerformanceWarning: \n", "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n", "\n", "To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.\n", "\n", "File \"../../../local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/nndescent.py\", line 47:\n", " @numba.njit(parallel=True)\n", " def nn_descent(\n", " ^\n", "\n", " state.func_ir.loc))\n", "/home/ppolicar/local/miniconda3/envs/tsne/lib/python3.7/site-packages/numba/typed_passes.py:293: NumbaPerformanceWarning: \n", "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n", "\n", "To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.\n", "\n", "File \"../../../local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/nndescent.py\", line 47:\n", " @numba.njit(parallel=True)\n", " def nn_descent(\n", " ^\n", "\n", " state.func_ir.loc))\n", "/home/ppolicar/local/miniconda3/envs/tsne/lib/python3.7/site-packages/numba/typed_passes.py:293: NumbaPerformanceWarning: \n", "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n", "\n", "To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.\n", "\n", "File \"../../../local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/nndescent.py\", line 47:\n", " @numba.njit(parallel=True)\n", " def nn_descent(\n", " ^\n", "\n", " state.func_ir.loc))\n", "/home/ppolicar/local/miniconda3/envs/tsne/lib/python3.7/site-packages/numba/typed_passes.py:293: NumbaPerformanceWarning: \n", "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n", "\n", "To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.\n", "\n", "File \"../../../local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/nndescent.py\", line 47:\n", " @numba.njit(parallel=True)\n", " def nn_descent(\n", " ^\n", "\n", " state.func_ir.loc))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 22min 41s, sys: 49.1 s, total: 23min 30s\n", "Wall time: 11min 37s\n" ] } ], "source": [ "%%time\n", "embeddings = []\n", "\n", "for n_neighbors, min_dist in product([15, 200], [0.1, 0.5]):\n", " umap = UMAP(n_neighbors=n_neighbors, min_dist=min_dist, metric=\"cosine\", random_state=3)\n", " embedding_umap = umap.fit_transform(x)\n", " embeddings.append((n_neighbors, min_dist, embedding_umap))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAANYCAYAAADZn0yoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeXhU5fXA8e87M5nsO2vYwiYgKooStzDWBa2KO1aNWqu2VWusVRKX2tZabdUSbLVp9WcX21qnteJSTd3QimPcRlzADRBI2BKWQPZlJjPz/v44N4ZakS075/M8eUgmc++89ybk3jPnvOc11lqUUkoppZRSSu09V28PQCmllFJKKaUGCg2wlFJKKaWUUqqLaICllFJKKaWUUl1EAyyllFJKKaWU6iIaYCmllFJKKaVUF9EASymllFJKKaW6iAZYSimllFJKKdVFNMBSPcoYU2mMOaG3x9EVjDEXGmNe7OJ95hpjrDHG43z9nDHmkq58DaWUUl9Nr1U73adeq5T6ChpgqX7HGFNojFlsjAkZY/78he91/NFv2u7jx90xDmvtI9baE7tj39u9xsnW2r/s7HnOMU/Yk9cwxhxvjFlmjGkxxrxijBnzFc+93RjzoTEmYoz56Z68nlJK7Qv0WvW/evBaVWmMad3u3HZpgKnUznh6ewBK7YEq4A7gJCBxB8/JsNZGem5I/ZMxZhDwBPBt4BngduBR4IgdbLISuAG4skcGqJRS/Zdeq7rIHlyrAE6z1r7UA8NT6n9oBkv1GmPMFGNMhTHmgt3Zzlr7hLX2KWBrF4+n4x3FS40x64wxtcaYK40xM4wxS40xdcaY0u2e/y1jTPl2X1vn+Z85z/2tMcbs5DXdxpgSY0yNMWY1cOoXvr/IGPNt5/MJxphXjTH1zvMfdR4POE9f4rxTd95uHPbZwMfW2sestW3AT4FpxpjJX/Zka+1frLXPAY278RpKKdVv6bWq/12rlOptmsFSvcIYMx14CvietbbMeawMyN/BJuXW2tm78RJrjDEWWAgUW2trdmPbw4GJgA94GngeOAGIA943xjxmrX11B9vOBmYAacC7yDttz3/Fa33H2eYQoBl4/CueezvwInAs4AUOA7DW+pxjnWatXQlgjBkNLP2KfX3PWusHpgJLOh601jYbY1Y5jy/7iu2VUmrA02vV5/rjteoRY4wLeB85t0t28DylupwGWKo3zAQuBy6y1i7qeHA3L0o7UoNcND4AsoHfAo8gJRq76nbnHbIXjTHNwN+ttZsBjDGvIReYHV207rLW1gF1xphXgIP56ovWN4BfW2vXOfu/E/jaDp7bDowBcqy164HyHTwPa+1aIOMrXrdDCrDlC4/VA6m7sK1SSg1keq3q1N+uVRcC7wEGuBZ4wRgz2Tlmpbqdlgiq3nAl8Mb2F6yuYq1tstYuttZGrLWbgELgRGPM7gQMm7b7vPVLvk75im03bvd5y06eC5ADrNvu6zVf8dwbkItF0BjzsTHmsp3se1c0Ie9gbi8NLQFUSim9VnXqV9cqa+3r1tpWa22LtfZOoA4JmJXqERpgqd5wJTDaGPOr7R800ua1aQcfz+3ha1nn3776u14NjNru69E7eqK1dqO19jvW2hzgCuB3ZgfdmIwxo7/iXDYZYy50nvoxMG277ZKB8c7jSim1L9NrVaf+fq2ySNCnVI/oq/+R1cDWCHwd8Blj7up40GnzmrKDj5M7nmeM8RhjEgA34DbGJJjOtTgON8ZMMsa4jDHZwH3AImttvfP9nxpjFvXgse7MP4HvG2NGGmMygZt29ERjzLnGmJHOl7XIBSPmfL0JGNfxXGvt2q84lynW2kecpz4JHGCMOcc5pz8Bllprv7Sm3RgT5zzPBXicc+/ei+NXSqm+Sq9VnfrNtcoJ2o42xnidc14MDAJe38tzoNQu0wBL9QqnDnoWcLIx5vbd3PxHSPnDTcBFzuc/cr43DqkjbwQ+AkLA9p2fRtG3/sj+HngBmbz7HtKGdkdmAG8bY5qQCc3XWmtXO9/7KfAXIx2hvrGrL26t3QKcA/wcuRAeDpzf8X1jzAPGmAe+MN5W5Jze4nx+8a6+nlJK9Sd6rfpcf7pWpQL3O8/bgATJJ1tru7Sbo1JfxVhrd/4spQYIY8wHwPH6h1YppVRfpdcqpfo3DbCUUkoppZRSqotoiaBS3cwpXfiyybsP7HxrpZRSqvvptUqprqMZLKWUUkoppZTqIprBUkoppZRSSqkuogGWUkoppZRSSnURDbCUUkoppZRSqotogKWUUkoppZRSXUQDLKWUUkoppZTqIhpgKaWUUkoppVQX0QBLKaWUUkoppbqIBlhKKaWUUkop1UU0wFJKKaWUUkqpLqIBllJKKaWUUkp1EQ2wlFJKKaWUUqqLaICllFJKKaWUUl1EAyyllFJKKaWU6iIaYCmllFJKKaVUF9EASymllFJKKaW6iAZYSimllFJKKdVFNMBSSimllFJKqS7i6e0BKKV2nT/oM8AhQHVBXqC6t8ejlFJ9zVizLAE4FHi3wk5u6+3xKKX2PZrBUqp/GQI8BKzxB32B3h6MUkr1FSWmMPUy89sz3bSfBpQAvt4ek1Jq36QBllL9yxZgMBAHzPQHfet6eTxKKdVrxpplSaebfx43zxT+uoXEHyxn0kNTJn547/ln/XEV8E5vj08ptW8y1treHoNSajf4g77JwCeAASxwQ0FeoKR3R6WUUj1vrFl2+TCqrziNf3njiPz57YQZ+ft/e/Fpo1LWbau/a+Q5wGRgJnBrkS1d28vDVUrtIzTAUqof8gd9NwJ30hlkpRXkBZp6d1RKqb1Vlp9jgJTZ5VWNvT2WvqLEFBqkPHpzkS39r5uWsWbZhGSavv5N/nyWCz4Frok/uuEb0WovkdUJPwMGAUHgd8AMIAT8Fqj/4r6UUqqraIClVD/lD/reBI5wvowBnoK8gP6HVqqPK8vPiQfuABKQoGA6sAGYAJyBNKD6wezyqgd6bZB9wLl3vzB15Auf3p393vqP2wanfM0djt6UurY2DOwHvFNkSz+CzwOwfKCmyJZ+2rF9iSm8CQmwlgOXAblAK7ASuKfIlj7fs0eklNpXaBdBpfqvrwH1QDwynzKMzM1SSvVRZfk5BwKNwCTgWKAdSAK8SDba5fz767L8nOGzy6tu7a2x9oYSU5gEtBbZUuutazkstWLbrEiS92gTicZvO3D46alra0ciwdQi4AIAJxP12hf3VWRL7wL4RerciW1ZiTmJW5oXx7W2h4DvIZkspZTqFhpgKdVPFeQFQkCCP+iLWIsb8PzpNd+my2YGhvb22JRS/6ssP2c08CdgJPAGEli56LzZ7yj5BQm4bi7LzxkF/BjYOru8asC1HC8xhfsBPwR+BaTE3K5bt0az3rjYPPjxNM9HVetPH1/fPCnDlfPUqk+SN9T/G6gE9gdW7+prVMyZNqNtUHJh4qbGO/d7ePELSFZrCvBK1x+RUkppgKXUQJBuLY3GYLxxDLnvhROLBmW2/bogLxDp7YEppaAsP+cg4ClgEzAVKQ08HintjSDBwgQk2Oro7muQjPS3kLLB98ryc340u7zq7R4dfDcpMYVuJIv3fSDPwrkfDpl2PmekDV+3IOPQ1lpvZERyVeGQ1MotiQfWp2VOrLo5YWrLy8BhS1fNWlWx+tCqOfMXxgPhBXNn7bA0usQUmjGZiSl1k4Zs8jaFVgHLgB8hpZlKKdUtNMBSqp8ryAs0/6ncd3Ocm7sAPqs64ZeDMsueQeYdKKV6UFl+TjIyz+csZLHbGqSL3ShgBBBFslTbkGzMy8DPgKuAg5BgKgEJsHD+TQCGOdv2e86cqVuAQiRT1w5cVhXKyU5o9oQnJn4WCtW6K7eelntBdWZS1uSflye/+fEpdwxO3HTPyNNWpiUnbnVPefn12GcXz1gWyk7+HRK87siUhNrWgmFvrXkNuARoKrKlL3f7QSql9mna5EKpAeL6v/64zpjE9K31wzkx76G5BXmBe3p7TErtS8ryc3KAZ5E5VpuBU5AAwiBzJLcAfwYOQdazO2N2edVGZ9ufIXOLViCB2XQkmxVDAqtNwDFABcDs8qp+efEuMYXfAmYj5+NcoBmoBda1Ej/JRSw1nvYWINw4OiOtcWx2bFD5Gm99NN14CZuM9G0bwjbBbdqI/3DusS+EspP/CFQTs1cMeasiedwTHz4KfAg0AUOB54AU4AbgdqQc8wHgt9pFUCnVXTSDpdQAsXaLL/PwyX99c3B6xcGVGw+c1NvjUWof1AyMBjKcr7efUxUH+IHbkLK/S5HMVIffAo8DS4FxwItAprNdMpCOZHwmAqGy/JyC2eVV4W48lu5ShBwDgBtIBNKAoYmEqoFU56MudW3d46lr684DvKk0bm7D666rzxyZTtN64P1D7nzpqSJb+uqc+QuvBgpaB6V4YjDMJQFVLTLXbaizv58gZYHHA5cDvwdCJaZwPBBXZEuX9dQJUEoNfK6dP0Up1R8smDvLejzusz6q/Hr7p2uOuaTkuUuO6u0xKbWPmer8a+ks8QPJQAWAHOBAJAu1HslOUZaf8zVgFrDUyUydhWTAypFAIYYEWdcCpwInAf3u/3eJKZyJNKjwIoFjxzyzJqAK+Mz514s0ongOeA/YlkDYu5RDsrYyyB3DDEHO40ElpvCbU38TeD9xY8Olk//4VrVLsoYHIueuPOYyW6MG0+4hBenamASMAVJKTOF1zmu8UGIKD+ixE6GUGvA0g6XUAHLdSX+uvqbq3gqPO3xAa1vm/cC03h6TUvuQA5Cg4WXkZt5s93EYMB4om11e9ajznA5nOt97EsmC+ZHGF6OQBXZHOPtoRbJeLTilgv1BiSn0AH9FAp+OrF6EznsQL5JtqqUzoIwhpXy/BG4CXJP5tD2c6nKlX1TV3vpS+pDIZ0k3AqtS19RGp92zaA1ynpqBDyw0ReFbrphNwYUbTNRJJrYjpZqHIMFYOtL4YovTIj6ryJau7/aTopQa0DSDpdQAc8jE588cPXRpeXssYX5vj0WpfcxfkezTvciN/iYkkIgimZNBwNyy/Jxvl+XnPFSWnzPe2e524OrZ5VXNALPLq6qAyUjg9RmSzXoU+D8LDVHIaPWmvOQ01OizSkyhKTGFuUjzjnxk3tmHSKC4DWhAzs8HSCZrOvARkrVqh8+zTjEgMoSauDGZ64ib2pIUNz7kRgKzp5HzMx1ZE/A14LtRzPVuGBQBjyuG9USsQUoSY0gw9xOkPfxrwN+RUsVrgN+XmMLh3XtmlFIDnWawlBpgLst/bjXg6+1xKLWvcdapWlSWn5MK/BwJEL6LBA0dZYPTgfOQgGI8sGp2edVWYOsXdvd3JNBYhswdmgJcDKQ1J2ZHt2aMzRpbvbivdxWcDCxA2rEbJLiJR8oly5CAZhASRE5zvnem8zE76nVfbqJ2pisaCyMBa0p0bXyo7tbR3lidpwo5R7ch5ZJ3IHPf9gcWurHDLVgPuI2c+47zH4cEWgcga2/lACcjgfEiILt7T4nqLf6gzyBZzhjwD+fh2wryAtpVUnU57SKolFJKdYOy/JxsZF7WG7PLqyJl+TkXA0cg2ZsrkOzNebPLq17bwfZpSAnbVUiL8eSocaVWZ09asTbn0G/84ne//LBHDmQPlJjC84ATgTlIE4uOm40wUgaYSmfAZYHnga85j6+KuUzF8m/lzUja1Jg+5t+fGCSj1QLUIaWTYWAhEETmpc1AqnIakcC2GQmmEpxtNyKZxFHOcx51tstBAq+NzjbLkBLNx4psaV8PYNVO+IO+bKC2IC8Q8wd9RwIPWWu8W+rGjE1J3EZSQsPzwPXAsoK8gN4Qqy6jGSyllFKqGziZqcB2Xz8MPFyWn3MOElREkdK0/1GWn1MIfANpANGCZLhao664hA3DDhm6Yfj0wWX5OccAwdnlVa3dfCh7oqMb4HJk/pkF2pAgKBvJIrUhZX4Ap9OZaRpsYnZI1pL18S5cHfOxLJJhmubsw400BjkReAwJZFuQzotupBtjBRLguZBAahGSwWhCAq0oEqi5kPLFGHKe7wcml5jCTKQLob/Ilj7dtadHdTd/0DcFKAX+Cfzfhi2T4ganVw4KRxOylq2dyZDM1Xby6NdnIUH6Jf6g75WCvEBtrw5aDRgaYCmllFI962mknO2j2eVVb5fl5+yHBFMPzC6vqnGesz9SWvc80nFwOlJO9+pnY2b+9oxXfpwK3Az8CHippw9gF/wTaTc/FqhHgi238xFCuiJ2BJcd5XsdmapmA6lD393gstAYSvY0xzVHBrkkw5XsPC8EvIWUQx+IBEprkXb305DSxM+A15Gui8ciJYsWCZqORgIriwRWIWebC5EgKwU4G7lPeqbLz47qCVuR37GL/EHf2x+suu709OQq15H7PxreP/dVb3JCbUenz/VIuepUZD6kUntNAyyllFKqB80ur2oH/rjdQzOQ9ZnSy/JzfodkWZYigcHryJpYM4GoNxr6wz9uPuPpsn9flYEEB8EeHfyuOwFZ3NeFBE1u5/E2pDQy4jwWobN80IUEOmnI/Ul91EWDxTXc+bojMAMpLRwHzEfWFEtD5ro9hgREDyPlgRXAPc7HL53XzHbGlOq8doKzv+/EjEnYduCw5JS1dWck1LW+jZQTJpSYwnygUjsM9n3+oC956/rBb9ZumBI37rDlc13u2G3AS5NGlV9nres1lwvPoPS1x9K5kHcr8ntT3pvjVgOLBlhKKaVULynLz4kHzkFu9ouAqzcMmro6qXXrsPTmTetc2O8hrdqvBu4DjgEem11eVYdkwvqq2XRmiCJ0rnsVQsr4OoIqA6xyPg5FAsZjYxLwNLtjDHU3h+Od5w0GNiCZvDjknF3rfN4ATAmlJbyw5tT9n8t95uMt3qbQQc42NwDrgP3ozFglI90HE5xxugDaspOam0dkpHha2rMT6lo7yhY7SjX/D+k+qPogf9D3LeAMYFDVihEHLn7yGAaPrf5n+pA6A7gmjHinCngHWImUlrqRn+/ByP+9bH/Q11KQF1jZO0egBhJtcqGUUkr1krL8HBdSnpSEdMRzbcicGHMb48qsW7MkPhYai8xjOgcJAlqduV19WokpPAspE/Qg5XsepIQvDskgdcw/a0PWpcpGji/BQjRmcAPtbvv53K2o8++9wI1I1gFguLP9M8AJ4eS4MTXTRpC+qqY5eVNTM/AgUm45HgnCxiEB30hnTBXO4xMAa8EVjXd7Xe3RsCtGIhJYuZAGGM8BNxfZ0o3dcc7UnvMHfS5gBfLzNe0hDxs+HR0bM221y5jPS0BfR+YDfoaUgA6mc85eFAm8X0aan1hteqH2hgZYfVhZfs7BwMGzy6v+3NtjUUop1b3K8nO+CTzkdHQwbrnh2wI0R12ewMNn/PEnC+bO6hclaiWmMAtZB8xDZ4OLzcBo5+vngOOQTFWHNiTAagfcMah1S+lfR4v3FjozXQYJfuqdxw4AYjEIxLzu09zhqNuZYNOEBHJVyL7mA8OQLo5eOssXOxps1Dv7TkcCq1rn85gzvlOKbKmWkvUx/qAvDvgp8B0gCwmYOha17tCRqewIpkDm8XXMxzNIwL0ceTPgpoK8QF0PDF8NQLrQcN/2JvBQWX5OrCw/5ydl+Tlmp1sopZTqr14A1rtgq1vecTdAuMWTnPjikdcfA/jnzF84pHeHuMsG0Zll6ugCmE3nje00JJNg6by59QC1BmJGgjOsZJtiSDCUBkx09lsJXIe0Vd8PKRfMdMHpnnA0auQmegsSXLmQQC4FabqxH9IqvxVp+95xI95RwviIM0ZLZ4DXkeWo76Lzo7rQ2s1T7q2pG3mjtWQiQfV3gMORNc4203m/G0J+ll7n4zAkyI4gAXQuEvifCXzfH/R5UWoPaIDVtzU7/xqkdKS9LD/n8V4cj1JKqW4yu7xqEzAXuAVp3FAR8iR54yOtw6aufDEDmT/SX95RPwAJSEDuNRKReU8dWYSRSKCzns7MURNSspUADA9lJQ0KZSTEI6VfUSSD1YCUHGYiXRSPQIKnjsYZHRmzduANpE333ci5W4BkJtYiQVsdktnqmCe2BbnJnu6MO4ZkMzrOeTzwkxJTmNYlZ0h1mTh3+7Feb4uJWdMILAaeKMgLvI+05r8Y+BnSkfNN/nv+XxLyc/XS2dWyFgn+Lwee9Qd9g3vqONTAoSWCfVxZfk4L/7tOShSIm11epT+87ZTPy01G3tFcCjyTX1yp50cp1S+V5efEfTru+JnvTplz87HB0rgh21b+31mvVvy9t8e1K0pM4RAkKEpFApeO7BR0ZopwnvMX4DykucVZSMZoE9DcmpU4PpLgqUitasx2np+MzOFyIYHRUDrL+8LAu8CvgdXAn4ApSKD1OJLJuLvIls4vMYUjkRLFNCTwOga50W5BFhwe7ex/GFDj7HO281pHAg8W2dK5XXO21N6YM3+hASYfNfWR8YPT19zudkefAn5fkBeo+uJz/UHfHUjwXIO048fGiGJwGfN5FjWMBFsd69RVAQcV5AU0c6l2i2aw+rjZ5VVJSMq6ebuH3UCkLD8npVcG1XctQtaw+Bfa6Ukp1Y/NLq9qf3vaNxdHvElvL8y/4Sf9KLgyyDWrY12ruO0+7wis2pGb1yFAPrI+1UVIQPZX4Cjg94nbWttTqxrHWMiMgcdCNZ0lgwuQgKijxDAOWTz4QiTwetN5nXikxb0FxpWYwouQrNYYIMNKQ4N1wFNICeMUZxzDkIxaCCkjOxApe0xESs9U3zAduP/NT85Pcbuj17OD4MrxLPJ7cynwipVZd55whbsCWTPL8r/dtdvoXGJAqV2mGax+oiw/ZwiymORUwOXMxrVhXMfNKV+/qFcH1weUz8sdjJSadNRLtwHJ+cWVsR1vpZRS/cec+QtvRbI4Ny6YO6tPXrxLTOFM5I2u8UjAEh+FmMsp3TP/veZVDPg9ksFKA2Ixw4ZovCfd3R69yxW1xwHHRQ2bW4emJLYOSX1g8NLqRiRDdQcSJHmQtapeRdYPm4yUAI6js1zwWeB6JBtxK1AIxEddRNuGpMasoSWlujGDznlbw5Dg7RbgCeDfSEnjh8iaZZ8CM4psaZ/8GexL5sxfmA4UAM8vmDurYle38wd9I63lYyypkXVxz8aNaZ+FBOkxJIjvuJeoAq4syAuUdfXY1cCm62D1E7PLqzYDBzktfduAOAPG4nq5LD/Ho+WC3If8cewQh/zR/VvvDEcppbpcPDI/qS9bg8yBOQTwxAyxxlEZ7oRtLcQ1hSNuue/oyGRFkEYTDUi2yRNKS0gxhvRIctzNiVtbPwTCxpIIVGd9smk20qhiCBI4NQKZFmwoPWGWt6Gt2mVpa493x1uPq97T0l7hsqxdcdGhD2w7eOQ3gNIjiv51NzInJ7M9PXHNqoJDm8Y+9kGuM6YIkvl6AclYtQB5yBpkhyHt4SuAn9O5SLLqRWfPvN0LPFaQF6jZzU23GcODGGbFjWmP0FnRFUUylx1zsjL4706XSu0SDbD6nxRgsYUjDWDcHteGtLGPA2f38rh6Tfm83IORd0A7OkF1lIzcVz4vd11+ceWrvTk+pZTqIrcA9NXsFUCRLV1bYgofbRyVkeJpDl/magm5E+rbjLHWujpLrbbPYn2fzrkvr8fXt00zkkVYicypmuSCQSkbm2qQbNcFSKneWuAu4Ket2YlbQpnJeTG3yU7a1nrrhhMnH7Jt6rD9D7pnUYorHM1Pqm7YkPXR4rNMzH7ivPZTQFtCbeu5B92zCOT1AsAHSMbiJqQs/wgkE1aGrJm0Ecme3eB8/8VuOo1q193X2JI1cs78hc8Bv1wwd9auBr3HIOWpc5F74c3I71YSnQthx5AgqxiZx6fULtM5WP3M7PKqBqAgDCtqEwbb1rhU6jLHHD9n/sJ9OVh+i853RKPIRTKE/JFcVD4v94PyebmZvTU4pZTqCgvmzrJ9ObjaTqGrPXpqe7LX5W6PxrwNbcQ1t2M61yBqRzJXTXS+KRYGhrrAGAgbySTtj8yNCSE3wG8jFRz3A39EAs7hCbWtixPqWqvj61pdwKyRLy7zjvjPimdc4Wg98NTQ1yv+kfVh9abspVUA30YyWCOQ9bM8SFC1FOl8eDnS5OJFJHP1ByQgOxC5+R4BvI+UCare9/jazQdWI6Wbu5NpeguYB7xTkBd4AfgecAkyzw/kdzWCdB68ouuGq/YVOgerH3vs+EmJK0bN9H+We0ywKXXYXf3kwtvlyufltiJlMx0X6nbkorn9xNSL8osrH/mSzZVSSnWhElM4EXjSQq6Rv8PblzXGgM0WNsXcZpIrapvNf7fH/inS5e0/wCdImd46ZLHfjcBrwBJkPauzkDlXZ1m4DChwsl8eZM5yEtLwKBuZe/VDZP2se5Ab6ABwCjJ/9xEkK3aU8/3PkFLB25BM1nyk3fsHQEWRLe3V+b3l83KHAWn5xZUrduG5Bgka1w3Eeclz5i+MB+IXzJ3VsLf78gd9VwK/RH4nQ0iXy3uAmp7uJOgP+jKR38dXkSqd/ZCg728FeQEN8Pu4fTnr0e+d+/LyVuQCs6+rRrpWQeeCktuzwLXl83IfzS+u1Jp5pZTqXm1AgpHAKoL8DW6mcz2scMvQlIneutb4cLq3InFba8f6WK8gmaMgUoq31NnXccjcrqnI2laHIZ0BPUilwtOROJfHelzxnnD0I1fUvoqUf91WZEvfKTGFk5zXz0ACrEJk3a0LnPEci2SozqazDLAKCd68SObsOuCDIlu6fUffHlc+L9cNXIOUR1I+L/cpJPv3HvBAfnFl25dsdiTSFOQutitrLJ+XOwpoyC+u7NctyBfMnRVCgqG94g/6XMi5bUDeGHAh91inAA/TQ92J/UHfGOBE5F7mF0jgPxmp1NnmPF7UE2NRe04DLDUQvAicb+UdThpIJ516kIv6OuRCuliDK6WU6hEbkXf9LwcOch5LATpqZoYlbG3BRGPGRFpHIzeMNcjf8o4by/2RltojkSzWPUgg1IhkFf4O/Ar5O5/ibo8lE43ZaHxcmau13Yu8239DiSlsQQKmViSYmoHctN6NlP/5gZedMW5yXu8DJHO1qciWdgQsr3fh+dkbE5EmG/HIsZ+GBANfB/Yvn5f7PeBaYHl+cWVH57sKJGhtLp+X64+T+wIAACAASURBVEXazFcCDwAfl8/LvVHXjQTkfN6PBNXXI+3645Dfh2d7ahDRiLmptTHp0vbW+C2ZOdsSkeDKBZjaquyMt5/wzXlv7I1Pl9x2d6CnxqR2nwZYaiAot9IxEIA2UkinPoy8o/Uc8GPkwrMKqeu/Jr+4srZ3hqqUUgPe0UjHPTcyh6oJGGeBmMyWjXNHYlFgoytKMlLWnYws6PskMjdqGhJsrW5P9n4DS2tcS/guJBvTAjyGlBSuBb7rggXESHK1tschN8cuZL7U1cgNch2yZlIAyUhVOm3Wm0tM4S1AY5EtbQXKnY++KkrnQrgdDZ3CSCbvcOSYrwc2lc/L/QDIzi+uXFI+L3cqUmb2HDCLzjXKjnT+va6Hj6PPKcgLWGSNNPxBXyIyxy8M3FqQF3inp8axdd0QV1NtalzOpLUjkAxwBKfMdnPFcM/6j8aNHjW18gbkd1n1UToHS/Vr5fNyU5tJfCxE4okpbCNEsmnDHUki9lAyTUciF+3aZUx/awyfXNVs00wo5gmNcFeNzy+u3NDb41dKqYGmxBTehLyxlYDcyLcDXguRlsyESEJDKM4dtQaZ+5QKuKqOGV8XTk0YPubZT2ImZmuRMq3GmMt8srLg0LPCGYmLDih9bRSyiHA88DHSbGICkq3yIMHDI8hixXVIc4pCJHv2KlJeeF+RLd3ruTq9oXxe7hRkIeXrkXMQRoKjjoZl7cjxpiOVG5ciAddRSLOGidvtriNICyMNOw7VLFYnf9CXQ2c55VEFeYEe+505dNSrOacVP/LsuEOXT/LE2Y5lDdwAsRg0bk1rTslsOvXioxZph+Q+TLsIqn6rfF7u4BAsX8KsEx8zPzTvcFrMYKM1TFoMrrdjEI3A6BaTcNBbnByN4THWuIl3x+LbMeu0s6BSSnWLOqRJRBS5OYwinQG3JNe2NTvBFcicqCbg2bbs5NZwRgLWZSLIje0M4MfG2qkJNU1uVziyDpln1I4EFWORhhabgOFIQ4uRwHtFtnR8kS09FJlzdCNQggQbucALJabwihJTeHqJKczr/lPRpaYiWT2DlDy245SOIVm9JcBJSGn835DM3SdIEPp74GfOdvHONgY5N9drcPXfCvICVcj6mh/Sw9Veiamt97/60OyxLXVpDyGNVj7nckH64IYktyf2oD/oG9KT41K7R0sEVb/1cd2UtWtC0xNsLEx6zibSqbEptGyZwjuXrGe8ezM593mGt7hcmVFmrHjp4H9G5oYHU+n9On+jkUyzrj23Or/vL9qplFL9SpEtfaDEFIaRuU7JSClfJVI62IQERtDZPfDUsU8s3WhdVLlirEOyVDOBLcayefTzy34K/BvJTGXQuRjsd5A5WcuAPyNZqrY58xemAtEFtrTFeRyAElP4KjAJmcuV6owp2OUnoPs8hWTuTkIyhJlIgNkI5CCdAocgc3YykazVdcDvkHKyj51/T3P2FwHc+cWVi7prwP6gzzild/2CP+gzgLcgLxBCgtE0JCO4rQdeOx645aTC/cY1bU1rT85seAQJ8FYhnTA71vpsRgLn1u4ek9pzmsFS/dKc+QtNe0JWfO2QsdTGRrOfDf7lAN68FDgiv7hyxXsc+/xb5qykTRsnGNtKbKJ9N8VDpKLCHGaXM51VHMKn5mvxC+bln9rbxzKQ+YO+Of6g7w5/0De+t8eilOoZJabwDKSUjZjh3VBa/NBwgmc6ckPfjjRdaKFzWY1kA3GuGE3An5CFhH+GZLHORYKFB4E8pJvgeuf7K5AFgIcjgcavLPxl9DMfLUKaYvyXIlv6MHAyEphdh3TW6xfK5+W6kK6H9wNZwD+Q89mGBIlLgTOAE5DgazbSaS4DWST3QuBeJJtVg5y7t5EGIN3CH/SdBXzoD/p+5g/6vu4P+rzd9Vpd6BvAY/6gbxRSbnphQV6gojtf0B/0HeK8nhc4YPxhKxZOO2nxOrfH3o7cp/8JKfmMIf9nTinIC5xVkBdo7M5xqb2jGSzVX2VEE9xkhDdQNWTYmoDroru+P/cvyzq+OZIVaRWkMsyuYWTFhvOAj+tNjo0Z89xSOzM3bNJNkmsr73Pc4H/MX5i5YO4sbXrRxZxWs/9E3nWbS+e71Uqpge08JBja0DYoOdick35ke7K3avgblcbA6Ei8+z9Vx048Onl9XST7o42pVv5GZBnJFMQjlQVpyILAm5G/H5lI4LUWGIN0EOwoj8tB2mivAGqTqhpqkODhfxTZ0ibn0/52c5qOBEle5G/pNGARUhY5E2kmsji/uDIKrHSaOv0cydjVONvFkFJKg8xLex0YVz4v9/j84sqX6UJOJuhHSFnj/kjW5UokaOmz1m2e6tpcN3aoMbHMkgtvWoeUu3YLJ+DMRM5JG7L8wMXI7/XJyM/4R8CpyP+JMJK5XdJdY1JdRzNYql/6Qew7iR7YsDl+/Mbm+HGnL5g7a9n23z+At/ebbR94YSSrzswvrnw8v7hy2T+KTl1uiZswZsqS4Zl2ze9GeJd/b7lr5uXAOb10GAPdWciFHCDeueAqpQawOfMXjll/7IRFVrIl53jaIn+umzy0Jrm6YXjj6IzBFkwszjOjYXx2e+ug5JZ2j/kUoG7ioK1WgoTLkezWMOTG3IdkuaIxIOR1bUHmd1kkSIg5348B1QYuyPhsy1NHFP2rP2RLdpnT+fZaYDlS4jgKyUatRdrMXwHEtptbXA9sQN5IvwXJdi1FSiNfAhYi6zudAMwun5d7a/m83MldOGQfcLDzuUFKRW8G8Ad9+/mDvq58rS7zzvKzN6/ZdEhL5cZD0zse8wd9U/1B3z1Olmmv+YO+VH/QV4CUHb6PnJ8xSEBVjJynE4E7kQA1zVoaozF3BFhM/3tzYJ+kGSzVX9Ul0fQUmJFIt6n/clzx8i3IuiD/ZcHcWTGYtYnZXO2s/r4eeLf7h7tPOnS7zw1y8figl8ailOoBrnDk8lhC3Ll1k4dc9/NPf/LxncnXzUpbuSXZWrzxda0mZmj2NoVG7H//6x4TwzaMSs9uHZ62OhIfV5PxWc1hyM1jBFmjqaZufPY3rTFD01fWhIAEg5mBZF6WIgFCNXA78C+k1XqkxBQejgRc/9cb56A7lM/L/TpSDvmg828lMsft90gzjxHAPKC4fF7uH5HmHuOQbFdHJ7owEpz9BMlovQs8gwS1CUBT+bzcHyIliHN2sGjxTjlvpt2B/Aw6mmlYYLg/6BvnfM/rD/rO6oPzs15BAvh12z02Egl0Bn3h8V3mlMmPQBq0zEd+NslIxrYKCYKXIUvOeJF7k2zkOvrRinVHTd/SMCY2POuzwh+feU9fO2fqS2iApfql/OLKlny45tK92Iez+vszXTWmgc4f9H2CTJ6uQhYPtUB9QV4gtoNNfhKLcaExksUyhlPRAEupAW3Ya6ufHh5YdbIrFLn2u9/88+mj4z3eIe+uN23Eed5jOpNTP6vKatw2xRUjZiGSuqEhLqW6oc4dsR2dA5uQoOB0YK2nNTJs7SlT4hJqW95O2NpypCccNciN6DVIhuQ44LvAR8C4ElO4GVn7aqDdhJ6JtFlPQ7osPkNnlcBPkYqkWUiZ5ArnealINjCE3LSvQs7TIiRb+Byy2PL6GJS34/1BPOGRyLm7GAne9kQWEkC4cFr0O59nIV0L7wb6ZPMLeROWtV94+EXg7YK8wG6XC/qDvtOReYSNyM9wCJ3VY23I9XQaksHyIs1c3kB+rh8hc+R+VduUc1gonDxtyaqTq3f7oFSv0BJBpdSumhSNYqxlRCzKZuQC/yd/0Dd8B8+vRd6J7nBet49QKdWr7nv26sVxzeEfWLfJTF5Xd8Lmw8esMXCHCzZms3VNWuM2kJv+P1qoIGbbXREbj7RQ34hkpm5HbkSHeJpD6zwt4X/E17U2GmhzSRbmLCC1yJa+j3QWvB0JKi4Gziuypc1F0kFwQCifl+tFshljkYzGXGAO0jXxWOQGvhUJsCqRroEpdC5EfC9wdH5x5UVOO/b7kVLDbCDyGYe89ai5+YR6snOc+XAu4I7yebnuPRzyNmTuUCtSYbL9m3AnIMFGv+neWJAXsHsSXDkmAGcjywQMR87tVuTnVIOch9XO4ychWcg7gPOBq4BQe8SbWLV1Snp98/CnF8ydNWB+rwc6DbCUUrvqVePMojIu3MiFYxbSevl/yAXJTIlGTUdJxeCeGKRSqncV2dLXPaFoQdPYrJNHLlwxvikn7fvhUcmrJrGyzSNNFzYCi9uykrY2jkhrAw5AgqMMpAxrCXITGk2obxs07PWK/WoOGXlU1ONKROasPAuE5sxfmPxWyRmNRbb0OWfx4ELg1t445m7WsZ5YDhKshJAuiIcCP0QyeEci2b/bkUxgKlI++RfneVdut7/pSND1O2DQW+YMbxOZM7Z5hsaclFIM6fS4o+qEr+Rkpk5AMpEROoMsi5TFPYEEhQOaP+gbC1zYFk6Mj0ZJQI7fIr+j44HfIOWus5Eyz/8gb1we5TxvHfDzZ968sQx5wyG7xw9C7TENsJRSu+rscIhNHV+0hrwfIZOoa3a0wUVHvLrK47FPOYHZMH/Qd2K3j1Ip1atKTOF04NBJDwWNNdRVHTtx1dqTp6ygc4HbocB9idtajkjZ0OBFAogWJKhKQ8r+vMCGmMv87NNvH5FWcfZB6RuPzG1bd/zEe4ps6Xfak737J25s+A/R2N0dr1tkS9cU2dLNPX28PWAEUpYdj9y3xZAb7geRTNH/Ae8gXegSkKAmCQnIpjrfe2i7/S0H/tBG0uAVTD8y3/ovy/BUR8z+4VDd+IwVyJtm+Xu5+HC9M47VyJymduRn34QEXA3+oC9zd5of+YO+dH/Qd7U/6JvpD/pm7MXYdpk/6IvzB32pX/G9Lx2/P+i7tKUt9bltDcMOWrziDPfKDUeCnIM1SGnm2IK8wC8L8gKlyLmaj3TefAj5mT6PvOFwopO1+iZSXqn6CQ2wlBqAnD/8J/mDvj0t8fgfBXmBuoRExocjng0NzZmhd5af+xKyLktgJ5vest3nk7pqPEqpPuv8ppEZ19RPGPR3dyT2jXBWUp2N95wVTolvQkr8DBAx4HKDx8hclN8Av7EQsvAtJAOznytmn7CJ3lsTtjSVR+M9qVlLq64E8LSET8r4ZGNGQk3zmt46yB60FVkk2NLZaj0J+DC/uPI1JGg5HD7PksQh57geyXZV5BdXvtmxs/ziysb84sq/rmbalqA5Nf5dTk49PPLvLYNrqhtice7i/OLKt/KLKyPsnT8AK5Hrw0gkOAQpHc9A5mE9gWTedkk0wiuxGKWhVtcLwEP+oO+HXXmN24GbgYe/GGT5g75hwGNIUwoA5sxf6J0zf2GmP+jzANdGY56JVTWTWnKHfFAxJLOiAvg7Ut56OvCAP+jLdTb1IPPTkpHzcjXSHv46pGSQs2fe7jp75u33+IO+73bjsaoupE0ulBqYHkRuUqw/6JtQkBfoqsUkW+LjIndForEpWanVpQV5gaadb0JTc11CY1tTckpbU2IyeV00kh3wB33xSIvbNwvyAjvMrimlukfU65736XeO/KunOTTkoHsDf8p96sOvh1Pjk+OaQouR9ZwiyNyTcwE3stZVXlt6wqOhtPjhyRsb0zztsZXAC8CQI4r+tf6d205+pHl05sGZn21pADCW34159tMnfvPvq1f00mH2pLTtPm9GSiwfAjY6CxAX03k/1zGHyjrbDQUmls/LHQq05hdXNpTPyz0OiI4m4+GwTRjzIUe/nWNXneqpirZRxbOctneD9Qd9ByBzxAqRks9rnPE0IUFGBVIG1wydVRFfsb9MICvSzpC4eHB5SQRGA0cgwWR070b8lToC2/H+oO8HwH1Is6Z2JPCtd8Y445AJh/xwyeqT4p547ceXnD3z9stj1n1tVtqGA4dnr1xiDLcCawvyAtYf9OFsjz/o+znwN+CcgrxA1B/0tQIup8xy++VnXEiZ/cZuPFbVhTTAUmpg+sBKcYeJxVj50Osn5lx69It7/YfZ+aNfupubjXB5THJcZszUhrN+gNSadwt/0HcUsiZMAhDxB30JBXmB7rz4KqW+4J1fzB4MZEaTvY/d2PbrH5eYwiykpO0jpJOaBymHakIyVRnAEd76tiPiGtoSXRYL5IZSvIcauMTTFG45+O6XrrMe9zNxDW0jSkxhZpEtreVLlugYoO5GMhxVyLnKBY5B5sC+j8yH7WhqEYuCy+IOW4w3jkgi0lDhBSBQPi/3WiRDEv2Da94FwDnGRmiyWX9Io+aT+1wPkr/34z0O+I4zPjfScnyQM4YjkLlGIeCfBXmB1l3Y31zg8PhEbCgcjzGWqGlPcbvs0IK8wB61kt9VBXmBx/1B34vImwFHIMe2BAkUT0UCrDLg+GFZK0atrj7spYPGvegF3OnJNZekJ9dMAMIFeYE12+1zCbDEH/RNR/4fvFGQF/jU+V7ZDsbR5g/6vkX3BpOqC2mJoFID033t7dLBzxgMtn2dP+hL29lG3aQ65nKvW7LqZDa3TMrc+dN3nz/oG/OX1477XV1t8n9Wrj8soTWcAnJhT+yO11NKfaVWZEHUfzhf3wl8aOGCqiPGxDUPS00KJ3iGW7nx3gYkR8AYSHBb2g2sJDlaZ7IjR9qYzQaGx7W0+7wNbXcZ2deednTrr/6GzNE5FvgUKRNMRBoi1CBZQZDyS7cBE+CcpmdMYUsLqc3Afsjfw6Azr6oYuHHB3FlR4CqLe8k/zY3H/dn8fCbw1znzFybt5XgbkA6HbyOt5cciAeLBSOngfkiFxdiODcaaZSPHmmU7ukb9CwnKhno8YYyJYa01zj57wunIumLDkHN9AvJGYbO1n7d0/01ifPOcP1558U2DM9acD/wSmHDL4Q8Ov+XwB28ca5YN/ZL9vg9ciMzJ2qmCvECkL7a2V19OAyylBqCCvID9Vn4grr459R0Ab1zU09KWuOovb5xwZi+MJbp+64HTvZ7mjRnJ1St/9uRcb1fst8QUxpeYwlH+oG8o8GCozV6RlBqOT03aQl3jYGIxDHByV7yWUmrXzJm/0CDzqc4GUkpM4QnIBP2vAzOaxmQmtw1KdjePTPfGXGYskNE8JJnW4akJzp3jq8DYtO9uHDH0mlXx3mgoYmRdovlFtnRFkS1dVGRL96mbzPziyhfziyufRjJ+zyKLMBskQLkXOMKZnOWNgbUQGkZFzE3kIzeRKiRAm51fXPk3Z3+rgQPL5+X+yhd71CbQdEI268ZOtm8dMdiuSbwqds0N5fNyE/ZiyF9DOhmeg8wriiLZtVFI17y/AN9HgkXGmmUZSKOOm75sZwV5gXeQsvdP3S67Nc4TiXjchIBGf9CX/mXbdLFD6FwYuKNZx8jWUHLkyfIfHz1n/sKRBXmB5oK8QKXz/KeAXyOZw0HIcf9P0Oq0f9+8o7Uk/UFfXJcfieoxWiKo1AD2veP+necP+ua2hBJv/GTNsZnDsyrOQ/7496iPK0/ITohrsFsbcscnxDf+AHl3b29djit2ZqTGvX9zQuaIhMStuE2MtOQtGNOKxQD2KGQislKqZ8QhN6NJtEd90TjXMe722ETAGoiOeW5Zc8uwlNa21PjMdGvjwwmeWHt6Ythi4loHxTYl1zRPAOKan8sgfnJbuwmZVqCgyJau79Wj6hvGAT4ki3cn0hzkXOCYGKZ6E2NyQyRVJNDQOoZPjxtnP8yOp62tmbSjf++a/+Cv5y/8GjY2+lp7xX5GskmxGOaEbLvhyHS21o9nSfZR9um3kbbuKc7+98QPkMYWjyNZn0Qku1YBNDtzdz/c7vmNwJNIt8EvVZAX2OoP+gqd485EyiRHISWT9Xs4zl1S35wdTU3a6opEEqzLFcnwuCObgKFuV2jU4ZMfTclKq77KH7z9oYK8wEpnrBXIsXILy54Enq2wk3frXDoLFH/bH/T9oAvnUKsepAGWUgNcQV5g/gX3Pv339OSNF1dt9f6+l4ax0rhiRamJW34zPuedK/zBP/1pTxpQzJm/MBVoXjB3VswkRoOJp9ReYNNcI5obMmluy4gOzlgbSfC2xIO8jYt0bLquS49EKfVVxiCNFZqIc59bc/CIusGL140JZSXF4prCNr6hLcXb0NaClAeOisW720Mp3gcHL6me4YrZUmS+USy6LNm0LEtuQRoKLO69w+lT3kAWrF2LZIlGA38CqtzYaUk03xNPyLppD0DMFUcoq43E9//NFRcgAc7UeFoOjeE62E3MBazLYfXtWVSboOdk87o9Y5gv+o/T4wmVnly8ZG8aBDU7/z4MXG8tacB/Ljw88KUVBRV2chQpHdyZ5cCjyO/Dd5EmE3vMH/QlInOq3t7R9egPgVN/4XbFrolFPa53V5xOSuI2Dhz30nBgqzcuEh4xeEUiUrq5FOma+F8q7GTLngWqdUgDkG6dY6a6jwZYSu0D/n7t6VXIjUuvWDB3lp0zf+HfDxq7MDsrtXoCsubNbpkzf+Eo4H7g8TnzF/7168+vvyApof7omAVLlNXVR0S31I8LTR39otvt+fxvW0/V6CulxEqkPOp8ItGGUGZiUlt2UvvaU6cmJ21qtKkra0zziDT3xmPGP3bYzxZmucKxoUPer3rMQErUMC2cmZTsrWtpd8cIIaVwt+1rJYE74syfqgQon5d7LpCPZIrSKtn/7NUclLTSHDb2EPvS4zN4fjMwsorRd1a59jsfyQ49cZx95AQ3sduQTGNOAo2ftZH0/IzEZ/JjzXGjGhk82EXN9eXzcu/NL67c0yYiJyGBX7g9Evf2eytmp4wc8knYH/RNLsgLLNvZxjtSkBfYjJSf4nTbuxdp9vHXPdzlNGQZkfvonC/4OX/QlxvvZZLLkGgtDM5YTVJ8g0VKBLOQbphLrMXz3G/OnnbLI8u2VtjJL/mDvsOQuYXbCvICezRfsCAvEGDnS6CoPkwDLKVUj1gwd5aFWb/Zi100IjX7dWDveePjOYUH5L5ihmWvtjnZFdGtDWPfSEncts3l4shYjOHhVjeJKdHmne1UKdV1nDdT5gFz42tbc0e8uGJbJNG9dsjba3PrJ2Q3bjhp8uARL69wDy2vSN16wLDR1b4JM9JWb504+vlPh7ssl5lwpN0VIx5YBPywyJZ+6fyUfVn5vFyDtLn/GnB+DFO62JycGSLpkkGsH55M3cHA94AzR7F83YK5s27q3PY725D595lAahabx2WxuYJGWqK41q7moKEW04IsUPzm/7z4TjgL7x6MLBRd1RJK+0UoknJNRkr1AUjHvJ0GWP6gzwskfTE4cfY9C2nrvxSZx7XSH/QdhGS0flmQF1jrD/p+COyPzNu6GFgAvFaQF/jiG3vvIetcffAlYxgE3O92MQikA8uEEYtBgquOgN+NrO3oOuy08glNNenj/cHvvg3cgWTxUvxB36+BtwryArU7O241sGiApZTqUXPmL7wQWQPlhgVzZ+1yALRg7qw6oHjO/IVXQfSM1lCGWbNpWmxY9urVxtgRB417ORUpmckCSEyJAmzphkNQSn2FBXNntc2Zv/AXQ4JrjnNDzsbDxpjMFVsS3B+3t0SSvDckVdWf4mkKH7PxqNzBrvZI66DFa2+LGYYbC56m8H+A44ENGlzt0KFI4GAB48JO9dl/rmwnfulg1s2JI/yyswDxawBjzbJUpILhlYd/STkyZ2k40nwiPQYHVzEu1sigtHhawllsvBnpcLcnJgI/RoK4+6869snX/Mm+cqQhx4Zd3Mf1wEx/0Pck8O+CvEC183gSMr+roiAvcPWc+Qs3AsVHH/C3TUMzK3zAO/6g769I179JSFv1XGTe2k+R1uqfK8gLhJGmKl+mDlgQCif8xO1qi3o8uHHOd0soyTQ0DyUjpSrmMtFWb1zEnT1qS/Ocnz50NDAe+DkwBfk9vhMI+oO+uch8sYudc3FfQV7gwy99ZTUgaBdBpVRPG+d8uPdw+wQwkUg0Lry1cVQ5cApSkvQBMjF7+y6FC/dqpEqp3TZn/sIpw15bFclZtOrX9eOyV5hIdEL9qLRIfE1jVmJN09eiid78+vHZo1qGp2W42yKDwinejFBmUijidf/eA1cYOAO4rbePow9bi7Qtb0Bu2remsTVSz6DjwyS8Z7DFX3i+B1mHLDO/uLIaCVKeiEFNDGwT6TVtJA1eZg5PX2KOTXmS749B1qnaExc6r2eAi/xBn8fplre6IC+wq/tciszRuxSY2fFgQV6gGbgBuNsf9LlGDv7w4IyUDadtqJk8DFlU+UFnuzQgHgl2DFJqV747B1GQF4g0tWY+/Mlan2kNZXQ8bABC7Wl2W9Ng43G1e+I8kaHRGOluj33R5SIFWeftJCTIOgs4COlm+24sRtBabrOWS2s2DX3tzj9f/Ad/0Dd+d8b1VfxBX7Y/6DtWuw/2DZrBUkrtMn/QdwxwPvCAs1jinpiGlJ8cgEza3tlrjgcSCvICHzsP/cvlitlYzH1MWzj9wYK8wGfAD/1B3wnAN5BJwR3rX/0AWaRSKdUD5sxfOAT4XmNu9pxIYtw7iZsap6Ss3RZPxIaN20TbMhOTG8dkrM7+sHpJe3pCXFNu9gmRlPhQy6jMS+4JXP+Cs5s+0zHQH/S5dtRGu7fkF1duBu4on5f7N6Q5xAkWc3gUt3szo0ek8OFMJAADoMJOrh1rlp1/1qyHo+XzHrkaqP+juevKg+wrtalsNS6iOXUMXjTdvsCn5B2ZRPNVbySc5lob9C0APtnVtZf8Qd8UoND58nHgjoK8QGR3j68gL/Css7jvdODjL3zvI3/QdxFw1oxJTzXEYu5RLld0JlLy6EUyc8OQduoga4b9vSAvsG53x5GSWDt1Qs47nuSEeqylzhgAmjJTNg5LT97owUK43Y3FbRrDqWekJNTHW0PiR6tPuGzEoE/ShmSuNc6uRgI2ZsFtMO2ReKqbp6QnJ9VcHosyGbpibWdAlkP4BrKQ9HtdtE+1hzSDpZTaJc56U68AVwDX7Ol+DJGXPe6WlEP3e6rIvB/lQQAAIABJREFUH/RN9gd9h/iDvq9ac+VHwF0d78qdPfP22jOPvuuYs2f+/D8L5s7afoHGV4FfAGu2e6wdpVSPmDN/4VDkTZNpGcs3/cjVHnlu+WWHm41Hjo25INo8PO2zhglDhqZ/VtOQuK2ldpNvwpyJf1v8xLYDc6LrTt2/x9fo2xl/0Hc48C9/0HeSM/+nrxmEdGw8zEuLezirGcVygJnnl5Qdel7Js/458xfuB1BhJ0fOnvWICymXO+qb9if7R4gLN5PW8jZfX+0ldEoK9afM4pGkTYzJbh3mvcrVGns8Y3XtcbsxnuuRhXirkDlgQ535VLvNWVQ36GStPuf8HNIAYwzvuN3RF4zhACDVeUoUyb6tQ0ohI8hcLfxB34X+oO/K3fhZfuRyRb5bsfHgFZtqx3yCvHGXAliXoT0a83y2sXayjXNHPJGoN722afg6F9GpNQ0jP67cNH0RsiTKEmdMuF0YC//P3nmGuVFebfieGZXV9urddVv33otcsGWaCKFjRInonQ9MCFkTQoCE4NAtqiH0EoICZDG9GGEwsg22sMHGHdd1X2/vWrX5fpxZvDjuGEyZ+7p87Voazbwz0mre5z3nPAdVjZPqqCIvbwuqRr4/5Op9MNdol+uSA5wIrGUXUeoPuVR/yJW22xea/GCYESwTE5P9ZR1GigRSgH7A+EMuu2twx24btg+0FmStOQa4HBgFTGXP/bkeAVK9zmAUQNe5IBxJGVlV33Fe2428zmDUH3L9E3GFArmpFR3MOE1MTA6KWmTym9Rxxso0XVEmKrFEViTTEavp0+7/Npw2sNraEHkqnJ1cUDGsYxS4ctk1415OWLWtukX7KdpRx5AUtaOAu/wh1+PAc8AMrzP46OEalNEEeCIiKuzNpGy2Ee6SzTYUqAa+Oku/94pmUiZ8yu9SWl837oYN8Tn3dbkciFuJnDSGd5YDmf2Y1/c95UqbXW9qCJMSTaZx67aKXvSNzLekbm8YBMzcz6HtQL53LUh639mI0Pri0J09XZEoWQ9EUESR2qxW8wkVuVfdhrgM1gF+47Wtka6njHH+D/6QSwUuAdZ6ncFPPL7ADGBYYc6y8rzMjeUbtg/qnZVaFs9O335XQ3Pe9ZvLB8ZTkqobku11cx32hoSi8Ntjhj0dRFdnIdkaFwCv6DrdFAVFAZuqxfSuhV+3jrczkk7o+Z7XpQ6YDizZTSrmZcDJ/pDrCmD7/kYkTb4fpsAyMTHZX1qjTDrw6kHuY0hO+uajs9M2f2mkW7wILAXmt27g8c043mZpviseV5+N644w3JoJbPM6xV2quSXlzPKarrnLNhw5ajf7TyCTogTSyLL+IMdpYmJygBjmFpcCKUuvdcVzFm9Zk/fFxgE7xnSJlI3vbh31p7dOiNu0MLH4qg1nDK4HfhvNcHQEziwpdv8Um6l+hYjGTkhN2I3IhD7JH3KtBIKtCz8/Mt2aw46rFn5zxIrs/nX1Ky1j+3fQV0WG8pEtgSWRRPPmdmwM6Si1F+h/XSpzfKHVev3Cqf8N9NQXqC5ePWcRRx5TTzoLOLZCU/VCFT17UPPsF9o3bL6X/TCmMKIj1yB9m96Ox9SZmiXhQiJJ38vIwYg2nY648nkQ8bQaqa+ysHMeW4+Iq2QkevUGMBAYCrSmnk4GNK8zuFtxZZCC1ACuAD4pKXZH/SHXq8AjiYTazmFvstqsYQUYlJm6LWdw9xkVNkvzzTZr+Lnta9q/kd2hIm5zREBJbAdygG++Wn3C8/VNudcP6Drz5ez0Lb9DomAW49q8Y/z7Xhifw8faPuYPuSxII+qYcT5O4Cp/yPUP4POfWurrLw1TYJmYmOwTf8iVibgqZQLnGu5L+3qNgjhKrW6zYrYFSCgKNuBioyfKIgCPL/B/O0LJRxQOqz8nrtq0uK49gKxKRoHwPe9cFc5IzRm7qazPwOqGDnZVafnNjf/5x+MDus58XVPjHwPty0vbTUxKbU7Vdb0hPbfhht3Y8pqY/KSZqkyyIi5o25GJ4qafSx8ojy/QGVmRn9HYOesPQOceL36xZMfYLh1RlWbdos7XIvG1sRRbl36Pzfn3l7f99kqkj1F/ZOL8k8LrDCb8Iddm5Jw0JGK0DuiF9E36wh9y/cXrDC7yh1zpQNTrDDb/CENb8dh//vTekP6h22rLU6xJ6TXkJW9sWsoRNX0JpQGXApPG37B+j5+bRiXzj4uUYy9bxLGXoifyQR+qkNjewbFc65a5KFe3RFY9uOmpNUDRg77A48CDJcXuPUWi2gHHAs0fPXHyorkvu0+87j9/I7Og+i1A84dcA5Ca25KDqMnqi1iw25EIVFub9FZRFQXeRpwD+wHvAo537j/7wY1Luo3csrxrk1cHrzPYsLsDGELkImCZ1xn83B9yXQW03bYKSFfVhJKfte4qBX07UKcojEl1VA8DbnttyoWfrVvYu+uQ4+c1HXHxrB0zQtfM1LG8UlLsbnlz7ntfZaVtjmpaNAcYA1xnXLPzfmCRU4CkR4aRrJNHkRq1VGC4P+RagLhLvmdGtQ49psAyMTHZH2Y2N1usNnviK01N7FejyOr6/OnRxsRpibCqP15/suejRb/PTbJdl/Nb50Pqjpqioo1lQwZOnx1QS4rdy/0hl0VVbhoa3mYZG2mxKymOCJoSCUfitrhC4jVFjTc1tmT9PdVRVZCeXJbat2gu26u6Ja/efMQVLZGkE8OV9jX1tRn9K+meG6tAWfTi0LpZn5380Q99UUxMfgD6Av/SwRHTlLA1rg9jP3oH/UQYhthQnwtM7/VCaJO1PnyxY2ttvLFzdr/Q3Sf3TFtbUd353eVnNHbMtCIRovbAgsM56D3hD7k0ZGxj2JkenW/8tCBucb38IdeLSDqc4g+5HgBeQqInnRBRcUgnrxV0cPR0rd/aJ3Pp1hXlwzqGPncqHY9fljTMMSsBRBB78EwkXXBPfIWYiYxBUT8A5qlEJzY35eQ1RnIqv4wffyZSr/UaIiz3WCfrdQbX+kOuycDdXYau6fzFm+PjmiWeitikn4SI0g6Im9/W/T1Pf8iVDTyPCNwc+NYqXUWyFJKBJ5AMiPbIe9K4paLX1eu3De8///UJMxIR9U9AM4Zl/R7IRFIuOyGRne9E7bzO4A5/yPUYUKip8Y8N4Z2LRMY+BVad8IdXR3/28jFau4E7dsxbdubxOmo/5HNw76lH3P0RMAWo9TqD6xHzpR8Mo145ExHanRqbUwta4kkTMpMrUMV5odVUYzySon8W8OYPOaZfI4qum6LVxMRkz/hDrlxdl35Sug4NzWn/TU+pn+R1BnfsZlsVuUltrGnI/SDFVnmcxarTEtb00orhjeu2jowM7/XGnC3lAydsq+6xKBzJiqhq9KLTjrj70USCnmsWDSjevqVL555jV/6hur5ww8pNR44Y1vOtjZ3aLa0JLLxqlc3S2MFuaT5ldP9XVXSdxnAWNmuTXrslK76lopel3taFDjlLSNFL9cJOVRd6ncEXf+zrZWLyfZiqTLIAX+vQVwdaclMqHRWNhZP1aT9pwxaPL2BB7KiPRXoP3Tdq8psFcbvl+fIhHcpLzxwyH7ACHySVN3SKZCS9/upNvz3YXks/Ckbk5TGk75QNqdtZgrjCtbrWxRDxtYGdka46RJA0ACd6ncFDVoPk8QW0UxIPP5XDlrO30e3xf8++dkfagOhtBVmbbWdyz7ZkmlMTEF7ChDs+Uc97vKTYvcfPjccXsAH3A+mjEm9+trm5901ZC7cUbCjLq90xblhFcmGsEngYeK2k2L3XSIvHFygsyFo1o3P+1/Ud81b+FqmRehox4lgN3OB1Bg/I2c64nzyKiPZkRFxtBfKQ6G4d0nMrE6n1mgWMm7PEW19RW2Td+FbOtWWfpg8D3l+v96nbx7GKgGqvM7jX7dpsfzFS23St1xn80h9yjQVO27B9SMny0iMvC0fSqpHrFjK2PxZpT/L03qJWhlmFrU3vr/3GH3JlIHXNXYDjkAhft0jMlqkpEVQVFIU4/9siJQoc7XUGD8jK3mTvmC6CJiYm+8IWT4i4Aki213vYJde7DUchK47jMlMr7tET8ip7Ulzp2SGUOnbAS1mZqdt7D+4xw3bUkOd04LnfjHg0GThBVenXa9jSS1wnv+MozFnzh9qmwmsAX0HWmkZNjfc8fuSj9yqoL9lszVXRqLU+HtUaLImmuFVr0Wst7bUdiQHYtHq2VA6E5BQFWT01MflZMVmfFgP6R5ItzTtGdGTbuG45UZu20Ugd/CnTHalxOcr4fd3iG4/J3nxcr/jmE/rZEPERB4aF81LvTtgsmscX+Mmekz/kOgsxdxiAREaqkPGvQsTUHCT1qsL4GUfmVBo7xZcKPOIPue7wh1wXHSInwt8s5LjhYZKbOrHKPuutCffYc/RLhvPhpgS2lCjqglL6hWbj8SIRxT1SUuyOAO8Ds4cRGFCkf60pEV0f3//jeEH96vsRIfM3vttb8Dv4Q66O/pDr5IyU7WkN4WzKqrvF3/5s8p0btg9RgG+AucAI4CV/yFXsD7me94dc7fd1ksa1ahXra5AoVBxJb1uCpNh96nUG3wPeAv6JfO66jej9xhcJ3XLRvFnO1ev1Pq/sTVz5Qy6rP+S6CRjCgfX+ehepy1sK4HUGP/M6g3/6yykPh/597cQrSordN7aKK4PjgJORGq+9cSfwz4PsZdUJ6QvZERFz/YFUqxbRFeXbtEqVnSmWrT+twL8P4ngme8FMETQxMdkX22sa2lVv3jEgK8neTEJPVPYrmj9/D9uuAT5CUkKOsiUR1nUciiIrZxkpNQkkB7zObm36BChtiSbnOuz1CSAaj2vdFUUfraqJSdsqe18N9G+JJd+SZG/sBnQ+auizlyGrbZHpd567sXJjYd/zfY+kh6NZiqbF0VFQiGO3hkFuNiYmPzsm69P0qcqklIbk9KZopiMpplkKrMSfQupEfqqsBl5HJsBHArnhvFTn9gk9k4BP2n2+/o243eqsHNYxYTx/MxJ9+HgP+zvcVCPCIgWZK+UhEZN+xs8UZLK6HUnLaysadERoZSMpWCOADwCnP+RqAG482LRBRY/ruWwJN5B1fz6bHwEoKXb/57L7tg79nFMuPIe7CzuyOrO38sVzyxj/HYMJjy+gAEcDG0qK3WuNh48FBr7CjV1qU/ILlaNjel8WtFzMnRkP8dQ7SGrf3jgFOOeYYU89mUioLaVlgzpX1hW1+3L1yQ/+5ZSHz/GHXP0R0d0TcYvVEeF15z722xFxAhyCXP/W65WO1PK+i5gkgVzj/0Nq42JJtqYdJcXu7QBdlZVJiNHFl+v1Pi0eX2AY0H3RrR3mxZtV5Y75VBuv7wVc4Q+5bvQ6g0v3MTaMDI4Pd33cEEa/ARZ5ncG2/dz+AaR4ncF9GS+9hUTkDrh/GPJetiCieIKxH4z7b+v1ayvy4/E46Chxi6a/chDHM9kLZgTLxMRkr3idwYSuO/LC0Yy7Vm06Irhy43FurzN43x62LfU6g3cYOewvA70UhSrkZrEQmZhsAT6urOtkBf2+L1efNBaIJBLYlpeOG75k/dE5yGTkbeDlmV9e9RmSu/9X5CYYBazuK98qVNTE06qaeKwofxG9Os5maI93GdLzHdKSqyuR3HwTk58lk/Vpes3njgkd/72kJam5BeDCqcqkn3LKazIysSxAnMsGIJPqSmBWxqrye4reWTZi9OQ3/4jYdj+BYXDzU8TrDAaQKBXIpFRBejz1Qibsw9mZHrgU6T20DKkNajZ+tk5mNaSe6XzgPOB0IxVsv/H4AmkeX2CwRmxoNxb3TWC99kH1qdzW52vU9umNak5pOZ1u0IiV9NU/f7Ok2L2ryU8B8Be+K9T/Dvy9Vsm1O/S6CofS+GEyddcq8EpJsXvyxPFTLpw4fsre0lNfAW4C3lXVxKNF+V/fObDrzPNKit1rnvz0lG71TdlDEdHdmpamAGf5Q67U3e3MH3Ll+0Oujsg1fBW5X0SQmrGrgNfCDfb4K7deevxtEx7u7Q+52iE25z2AJuBlrzP4eJtdHomkQY4DSHVUXNKv6OO/53Xd9k/ggZtHPdkAXIhEwLYBNn/I5TKMLw6GnsCfECfCbzFSD6v8IVfW7l7k8QXO9vgCl02ffet7XmfwpQMR4P6Qq5s/5DrF+G8tstCR0WYTHYjFIhZ2rCuMJBJKwnhsbTxhtyiKYg9Hkr+3k6HJdzEjWCYmJvvkuuNejCM35v3GcIva7A+5RiA3nCMQG9+twHHZaRs/7pi3JNC/yydnAo2KQvqArp9SUZebDGglxe7ZGIXJ/tCU44HuLU22FbGIxZeS2XR9Wk593WWP3X+b1xlsuPnVW4dW1HQZk5FSTlpyVZmmxYcdTA67iclPiZcjF4emKpOGIEYLKcB5U5VJMyfr054/vCP7X0qK3Q0eX+DPyCT3cuNfJ6Q3z7+qBre32RpavvxL7dQIUA786/CNdr9pbVreKqQ0ZN6ksNNo4SbEPvweZIL+KBLBdyPCIpedDoQgEYaXEAfCo/ZhGd6Ws4HzYor9xoX8pqyBrM7IdWzlRiDpghveLvP4Ap2Av5b4AiuM8U42aqi2I1HDTa0vKil213h8gcVxxfFFg+L4vKTYfR94ATAawD8PLAdu392gvM5gJZIGiLFt62stFs0x26qF8xMJ4qqKzk4HwP7AGH/IVTHzy8vLahsL7kDqld4B7kIif7WISJiONLePISL+KD2hpI495yNrvyO/ug6JZPUxnmsB1hmOjmcCH8KTXwAPIAt8jOz9xoz05LLjsv/2TeLt+857deYXp+sQrAVCQMgfcl2GCNCrga/36535LiuRz8S3xjTGeB5AIkoWf8h1/m5qvVqNSZ5jDz26dodRp/YnXWdwOJJSZ7c2pqoqhW020ZFrt2jRB8700PQjOxx7xZtzeo1dVgDsaIkk5SfZY0k2a1MRezcCMTlATIFlYmJywBjuWkOAFfuyQvc6g+v9IVctkvLxL6SzfW9Nw+ns8+bLyOrk8UjqILnpFQpwA+K61MrLzXWOcVtXdT59/Vc9ex17xTvrgUBCV46+tPjBuoGutc/W23MeSHHUbtW0+Oem5azJL4XJ+rSVU5VJvYBSHSw6PDsl44+jb629/6rDPbZdKSl2L/b4Au2Q9KSPkDqQN0uK3VUeX+DlyqEdr/f4AltKit0rDu9I95s7gM8Q+++JSI1PBHAgKZEPIOlvZxiPv4aksM1GRPHRSDPcOFLnkqLr5CCT3sGKQgH76DPl8QVUpGH6Jxh9pSbqD64FrOMmb2hs3a6k2F2LiBKAjUgaXsI4bus2unE+36Gk2F3n8QW8xjm0JQasRdwG/wcjCjUKEaLpSJrkHK8zWAMkW7Vmh6KgGj0Pn0QaEV+G1KjdBcSG9nz3gVmLLs1ArilI5kMqIsbsiLtdf0Sg3glUO9LD2e17b1Ta99mYhtwr0tkpercigutioHG93udl2vRtzErb9i5wWrsu5WMuffSBi/yhB2Z6ncGKNqf1rrGPg3LuNAws5u7ysIIIwM2ICP/Wyt/jC3Q3rsVTwIKSYvd+iyuD9kD/5pa06h01RSPa5650WJWYFb41tGhB3td/d+q/4fia7Uti7fuWpiJi+P/SUmorkTTQfgd4XJN9YLoImpiYHDD+kMsZT2gPR6KOJy8d/96z+7G9itxY8pCb7I1IysZ6xIziwUSCgAIqO/PFiwDV6wyWAowZHcxTwuHXO47ZHh596scPF2SvG7V81RFnLl50ZFG72LLEMee+cbrXGfzghzpnE5PDyVRl0qAEfKRAnuFTfc9kfdqfD/e4dsXjC2QA9yET1SOQlfkzEJvtO4F7S4rdMw/fCA8Ow3RhDDANSX8MI8YXLYiI+RSxvX4LuNPrDFb4Q67OSKpzB+Be4DZdxxGO2Jqsmv6KxRK9HJl4/xkRZG/sujjk8QWOQmpqbispds8CmHNflycRkwfPuBs2/E+tjscXeBoRG5fCt8Lqe+PxBfKBSEmxu9ofcvVGLNivxLBQX7d1aNWOmu5zR/V97WNF0d8D/oOIpGSkRmoKIpQcSOPbIPDf6bNvrdzVpdAfch2DCPW+iGAcj1jfv4UYRjQi0Z6b+K4r3sdIhM4FPOd1BqcY++uFvH+vIMGFY5F0z4e8zmCLUZ82HFhXUuyu+r7Xyh9ydTDGO9PrDEb9IZeyu4U/jy/QE/lsPFBS7A4exHEUwJVI8Fo8bsmERKOmJdLjcS1htcbrEHFvQ0TdXOSan4Wkvw6LJxRVUfQ0VWEH0pdrb9b+JgeAGcEyMTE5YDaX91tbVd/eUlbdffyl49mnwEJugDZkJS8fSaM5AhFcOcCCeFT9k6ImpipS7aAA61ZumrDyzDnvzdN162w1K/XGqq8ye2TYLfGVm8YNSE6qWx3R7c8kWetuTMoO13299tiVXucPdMImJoeZyfq0r+9TJnVOKNQrOpaYptxwrzKpjyrpaH8y3AcPKx5fIB24APnbbkEmsTlIztndSJpb7R538BPGmBx/5g+51iMRrRTEir0CSdXbjgipC5GarGe9zuDGxz85dV6Ko+YCBb1IVSkHOtqtEbuqMnzu0nN6NIUzJxw19JnfWLToRMSdcFfr+tWI09+aNo+9gURt9hTtWIh85yrGGL+TZeDxBdojKY0vlBS796tfoOH4+ETXgoU2f2hKCZL23ZWdKZNU1XfY3i5r7Sn1zVkD05Or1iKT+uWICGpC6u7WIM3nv42+7eF7eyRioNEVEZ8zjOvwmPFvPXKd9XjcQixuwWoJ66rKCOM124EV/pDLhaT/uRGh/7nXGfwGuYZt6QHcp6mRt/wh14O7Ebq9ENHm301t2+44HREypUimx25Fbkmxe7XHF5h4sCJ4+uxbmTh+yiZVJUlVY0oiwXYgTdW+/Wg8gkQhT0GMZdYDNcDSLRW9X1u+YcIz7XNXLuvfJbgIEa0mhwhTYJmYmBwwoZVnVCEiqXxf2wIYK3gjkMmCgkxOrgMu8zqDHwH4Q67NGxZ30cvXFyhDTwxRtqa9pXxNSh8909IErLWkJZocnVoifQZ+Tq9uC1MUIjUbq4aPy++9obKmobdStjW9CzJBMTH5RXKDPi08VZk0IWrXZqEo1uacpJNTdjR8DhwzVZn04WR92gFP0oz+VfFDFOU4G7hT16moWZqUnNwhssmenZiNRA5SSordNYfgGIebMnbWZOlIvVATYic+AxE+l/pDrtOAj2sb+3ZLdVTbgXOALxWFDoqCBnSqaSg8qiWaOimw8IqKcQP+Mzstuep/6kZLit2bETH0LeNu2PDe3gZYUuz+J4DHF7gOONXjC5xTUuxu27fQwj6aB++GGCRe691pzv8hkcg8dpp/6PG4Wp6TXvpyekrldRW1nWenJ1fNQcxACoCIUWtWAtJzyh9yPQ086XUGP9nD8R5AUhxPB65rdfbzh1zdkVTNwUi0auLWqp5/TrbXjk5zVDTb1MgcxGiiL2LgUQhcjzgOforcg3bHhjRH+bNHDnnOK+fKI7s8fxTy+Z6LRN92ixFRSkMiZYsRW/894vEF+gB9Pb7AG7v+DRpRtQHAlonjp9QAStuaPY8vkAn8c/bX584fP+glBVBUlQ+R/lf1wDtIhC7mD7leRQTWMmN8f8nNKH3V2ff1omjMvtLrDN61t3GaHDimwDIxMTkguior7dDpb8Di9Xqf/XYe8jqDa/0h133IyidIIfEz7MxX79Z5YGmjxRb7uKEh/eTsjhXKyMJPLDHL/LtmfnX1W+EFLY8MuX7FZ92LvuyXnlxh0XVOGtRtRtmXq0++SUFfhPRGMTH5RTPv3lMKk0urXioMrj0jeUd9CjLZ/Avy+d96IPsyUr6eQCa+h6IPzsfAnPAO7fN1L+QcrTn0wJApW78AdpQUu/dlT/1zYS7ijHgKUrfSGp2PIY1uz0BSzVYC1pZoyuW6rszWND2bne6CUWDz2AH+r9dtdc6urOvYNbDwmvtarcV3hz/k6glkeJ3BBQCGkcXlwPMlxe51e3hZH+Tz0d7jC5QjkcTGkmL3G0bUZK/Ng9tiTP5f9IfuWKTrzFOUnS7UiQTUNrXLbQxnX5Rka7pl0ZqTchatOfnh08b9o1RV9OFI1sLGNrtTEXG3Wydrw2nvIeTzdB4SpW2lGfAjovbkcCTl6PrG3MY0R1m1pkW3IumaPYxtk4HtLVHHcRY1OlrTYlfvJZIU9Ydc0xFL/R27Pj+y9/T3cjNKj06yNXYF94quykoFsN0x/4pzkIjm37zOYBSpJ/490lh5f0wj/o6k0VYCQX/I1W9bVY8zvlg5sQrsHyJC82Mk1bLIH3JdbhyHPp0/TV+7ZVSsMZxVH49DIqYpmiU+RtV4D3h9+uxbV502bsoYf8j1N+AFwKPrqAldVTU14bBbw7Ps1vBKwHb+IyWjHfb67CevuHiv4t1k/zEFlomJyYFiQ24o+9XxfheeAq7A6M8BRI0+KRcBL6iqbuvYd7N/ztJzkrvkfXlMcqJc+fS54/9oS1o167qXX31U1+mbSKBG4xZiMTuFOataSk6Z9twhOSsTk58DqlLU1DUno6G0unfekm33xC3q2LjDusZW33IwrplRZDJ5SCJLJcXutR5f4Ox4s5YAdX7hb6qbEMe8xUiT1Z89XmfwJQB/yHUv0tfpNKStxGQkDW0d4j43Gxjeo8OCcsS+/gzEeCGORH0WZaWW1Q/v9XZPXVfmnTvq032J42Jkgn2GYSzUGUnFnGUcs7UHk244uIKIlPmI2FMQo44a4I1WceXxBboBNa11R0bU5K9AuKTYfY/HFxhtjPv2kmL3luUbJozsWrjAnmRrxDCvWIpCSrglLbVb+wWZyfbG4zQ1uiiesFHflPdkRsqON7zO4EZDNGV4ncENhvHRxFax4w+5Moxr25o+moGk+dm8zmBrvy78IVcycAtigb4AeHhHTdeirZV9R2Wnb03OTK1KB5ITOno0amuw2yJ6JJZk+WLl6ac67HWLhvdsfhmBAAAgAElEQVTa+3qgkbZ43e6e69RuWQwROa33rtPTcmuuaKpNjidnNKUh8+kossixCumjtkc8vsBgJHXySyQS2ioij6xtyP+drit1SL3UU8jn6ThElCY8vkAvq6Wp34mjZv++b+dgtKK2U3bVtlw2fd01npLRtLr3EcvGVNcXVABTauoLY9np2wZFY7YjdJ26eSvOTOvefsGJBdlr1mhqvBp4X9cZlZ2+aXY0lsQzs0/se+n4d9f8z4BNDhjT5MLExOSAMZo3RtfrfQ7U8Qh/yNUNyZ0PI/063kUKpV9C8tUXl5b1e0HX1f6bZhbmrJrbXz/3nmlpjrSYBiixGETjDj5b5iUe12Y+f815xx66MzMx+WljuMrZSord4dvb3TixcnDH5+w1TWvbLdh0/2R92qGIQh3oeJSmbRbv1vfSz2p/fF0kuUOsHzLRnIdMgq9DohdnlxS7G37s8f2Q+EOuo5Hvrj95ncFSf8h1GyK4VgOfIyYNdyFRkXcRN7vuG7YPTNtYNnBBekrFLUN6fJgASr3O4J6iUK3H6gtkeZ3Bz+BbIVQIbCspduuGuHoOmdjfs0uTWwAueuw/hUcOeXayzdKy0usMPmWkmPmR9+e/iNhOIBGY5pJi9x88vsAJwNU2a+MNJ42+/+4VpUccmZG6PS03bWOjzRbVkMl/UTyuKbpOlcUSjwMXeJ3B79SSvTTfdY+iMAA413AZbHtu/0LS3873h1yDkDq2CUhU9T2vM/iNP+TqgxiMfIhEvv4LbJg++1ZlULcPfp+esuMvmSnb19usLSlNLSlFC785JTKqT0mKpiVYUTqh2Wpp/ujvZ9xzxt6u8b4wrnEM6FdXnv7f5jpHp5zOOyIWq74duBaJ1L28Py62Hl9gAiKeKpDUQyvQOHH8lPqK2k49Zy85P6br2vLdpe56fIHbrJbmE8b28/euaSzQc9LWN6i6ntXUkKptrBmuDerx0QNfP3R8iX5E4sEhI99PtVvDJe/Ou15TldixzZHMOUm2ut4pSdW9e3SY9/WaLc4PR/f9z7Tt1b2THLYG2mVtvByJwP7T6wwejE29iYEZwTIxMdlvPL7A4L6dPrni9s/mWDSNRn+IBPJFvHafL97JICSlqQdi8VuAuAq+hhTEryiv6e5siaYmRp4+/fNhJ8wd7UiLfftdpWmgac10yV/I2mWDpx+6szMx+eljRB7CAEmVTZvtVY1zM1eXpyBpRj+6wAIyk/JjD3Q8pTZJS4pvQWqRrEgNTG9jrNlIzcyu9tU/a7zO4MdI+lYrdyC26yMRt7u3kcjVZbqOe/UW59pke+38NVtHuyLR5N7De7/9MDBpX+LKONZ36n4MUdUDuNwfmjIVSZ1bB5y4rbLH8x5foAq4paTY/U3ra7JSt0VqG/JPAaUImdzXIalpxyLpjnHEGfGsNod6H/j4pNH3x4EiTY3ZU5NqozZbtAIReIN1HUrLBjWU7hhcftSQ57Nicetgjy+wpjUt9HcPvfmHlKRLjx3T79UXHPb6bzMfjHqlIiQKp/lDrhcR571+iFCcCHT3h1y/R6zb+yK1RflI/a934vgpq+Ys+d0b67aNjBblf9XSu9PnZ9bUt78/O3WbM56w1GlaRB3Q9eOnkfqr7+DxBTTk87p+f9IljVriDkBGel6dIz2vrjU9NA8Rt+mIsN6w62uNFM9ipCZqBfBbxIjjv8Y5/RvY4HUGJwGLf+8Gf8h1uT80pRtwa2tU0uMLdFOU2DMDuszUqhoKIpvLBy0ryl90nKok9Jpw+/lxbENrG9tlJ+rVFwrnrO5kGRUJA9kt0dQnQC9SlegRw3q+3VTflGtJxC1H2KwNEyxa3K4qCayWFuIJHtdUqjDq5UwOHlNgmZiY7DfDevxnal7m1mMU9dsC7wQw0h9yHed1Blv29LquysouSNPROXfMx4tYtG9GVro3GL8/jKyclffoEFrT3JK+ISm5xUky2xI6HXdUF6mJhKYX5qxTALoWLIrcevrDj/2Ap2ti8pPmT/FHQh5fYLGlvmVQpxkrDpdle62qst6WHc9UNaKgqxBLgKUFlOsRN8FsxMntF41RG/OUkQ73IWLmA1AVi1uHpyfvyI3FrNWNzdkPFmSvOK+xOTOUklS/HsDoH1axPxN9jy8wHBh/4uhk7NYmJ5BppLf91R9yza2s69QPiQB9p8ZpU/nA+JbKPot1Xd0xe0ngfiQV8EOguWmLpcaeGzshWmN5v2x2WkGRpzrb4wssNSIoYXDjD7nGNIazu36xasDYWDyp6cghz/zebg2nKwr+qvqOFUnWxibgr8s3HDkZON3jC1xUUuyuzk0v7ZzqqOyaSKibjD5RrfRCaoOqkHTG05E0OA0RVJuAORW1ne7YUDbkkl4d5mkpSRWOxnBWj0jMcWZO+tbuiqIPG9W3JHdLRd+4w173NDC1fe7qTwuy166et/zMizNStrf07/rpWqDWH3L9DnilzRiOsVkap3YrXPgSuL81EjEaLI8FFrRtCGwI2rcRQbgIOBJoQIRmwvj9X/6Q6/ZW86Y2ZCBiMsv4fwDJ2HjSeO1ziOBqjU5yyljLYE2N9VYUHP6Q6+T5KyamQ/8pOWkbZ3Ut/Oo4oKJnhy8jiEBb2Lnd8m2d8pYnFq89XqkZn59VpC2ygP4N8FFJsXvF3W9fsygSsx+bm7Hxm/ys9Xd/tuyMo0b2eetMQE9NqlRSkmpQxYAlF7kX/+zaKfyUMAWWiYnJfuEPuayZqVlHJyc1KboOcZ2EplKP1AHMfnLGCWse/t1tWmN1+tT1ep8vdnn5ZaoWG6daEucgtQrXIW5G873O4DJjm4d2OV6rW9S18Zj1tpqG9kM75K5UAF1RSCgaD/ygJ2xi8vPgi1iavXy9Z0jFvjc99JQUuxMeX+BqVWOAEo2XZujbnk9kWDondEtLQzi3saTYvSfXtl8yS5D0yHzkOywjGnOsXr1lbH526qZ4mqNycvf2X+ZureiT/YffvHSpxxcYCPiAfyJp0/tiHPDbT7669OrjnY/8u22jXK8zOAMnMzy+wIO7ppeVFLtrPL7A2Uha43FInVNmtEG9ed2Luf78CfXxHXPSjs0Z2XA8Ig4vRdK58fgC/eDW65GmtScA9Vsr+jR1LVzUAtRmpW29bGtln4XxhOXimsaCvg57zaBjhj51uT805ckx/XkPqU/b1dRiKzIPPdK4XhVIRkMFImBygf9LJCy2usZ2NEdS1eSkql6l2wdXbywf0qtj3rKVg7t/2MVqiQ7rUvB1FvBgZW2HuiXr3TnV9QXnaVqirm/Rp6cCvzPekyFAwB9yNcn53bqiU7ulsc75X7v8Ide9bVL77kNcH68BXvWHXO0Qc6b2xjXraZxLBBGEYcRQIwtwAlf5Q65KoCPwjtcZ1L3O4II29XMYveBmGtfWXlLsfs3jC6geX+BaxHWzZVtl76PSkissyfbaPjZr+PJeneau2FLRrzQjrWy5cZxViYQyVFX1DxBnxyxF4eT+XT4+NjN1+7SC/DUDFEWf2mqM0jl/ScwY9z+nz76lm0IirpB4dWtl/95LNxw9oiDrG2VQtxloGgpwCdKfy+QgMQWWiYnJfuF1BqMPfXDKYmCoosCW8p5KXuaWtCRbkwb0UtVErSb59/9j/fu7ux5fa0sOe3uMXDnU6wy+DxQb/UksRprI8OaWtIr3Q3/YiETGRqvqn9efdsTdFwEdrdZoVdfCUKlVS7RXFOqQWoaP/CFXcusNy8Tk10hJsftHT5M1ancuBd4rKXavKCl2LwQW3qddO1TvGq1tmJS1pDK948MN4dx9pr79EjG+4973h1wvI5GAFIslkpVka2iwWSPzGsNZL63ePPa+5kha66JSGRLh218x+iTw+r8mnbkRztztBnuy3S8pdsc9vsDjwLNIaqDHmppY2FJhvWXH7DQlc0DzaUn5kWTEmKMAQ2Ah6W89IZFIstbFUpOrH9a02CykaXC7itoiG7pyRjxhKX38ssvv9IdcrUIuiKT9lZdVd9ni8QWuAd4pKXaXep3Ben/IVY5M+q80jhFFemf1QaJFQ9tlrX/xiNSXzrJZwgWKQnW39l8sVVS9V3pyRTKSGWFH0h2bwtFUh6rGRzrsjTnHOx9Zg6Q9PotEnXKNBtC3AcMnjp8yA0lF/PMudVOr124dXr61os8dpWWTVhTlMxqpS2ttaNxqT29D7lct8QSoCigKYeM8LkHS4D81xkbbe5URhfwt0vfsOo8vcDMiAo9BhNrXKzeNK7NZmvOq6ztUnjburmc1NXJHp7wlm9ZuGXPP4G4fPfrxV5cMikSTHmqXufGdqef9aTWAP+RabrVEPuhSsCjU6jZoPJ6MpIXOleMot+ho9rc/v+n8JFt954Ksb2Z2yFmZp6okG+e01B9y/RV4e9d6OpP9wxRYJoecqcqkMwDH4Si4Nvlhue74t4a9OM91bmNz8tOVdd3WZqWVvwhNJwB/TM5oWlxXnpVYr/f5nxSXAUd/aUVughkA/pDLjtRdbQNu1HXuqqjtnD+mnz+cZGv8evaS8wd3K1zQ0XhNYEtFz/wNZcM6dWn3ZXOHvNWXAi11jbnPJ9kaF/lDrrNMkWViAh5fIBkxJ/ih3as6IpPDMo8vUI9Yvf83+boJX1kawun14Vz0uLaqpNh9wCY4vzBeQurQBtssYevI3m9NAp7728T7ErSJVBk9qm45gP2eAvT0+AJ3lxS7D7jBdGsdn8cXmIE0/v1mvd4n4fEFjkaiSY8izWnbmhzMB2pTHRWpI3u/WZmVtj3d6wx+6zb35Tf3jhvS4/2ZNku4n/HQdCQqNQK4Mh5X60IrT78JuR5bgVJ/yHUCEuHTkPrbTcji2UmI6GptQzDFbg2nGNtZUhwNYwZ0naUj6eWPIREqAHv7nFVaftbaxRYttgpJ2XsJmGkYa1Qa281FRM9YpCa41RkQAK8z+PC1zz9SqCiJS9plrr8JERzbjDGqyH0phtFkOZEgKRbXFIsa1zWNDUg0KQnY3DbFcBeGIJHI5Uiq5OUlxe4rPL7AJUBLSbG78daSmwdoSuzU0X1LyoALbFo4LStty4bhvd46DfhvTUOHDCBtQ1mO0ma/NwK93/m8+JnpswODgIcnjp/SBzFbmWrUDTJ9diAdEXIp/7524lp/yHUF4uZ7DJKeeQRiOT+SX4gD6I+NKbBMDilTlUkq8HyLTU293XHtc39tfsR6uMf0YzFVmZSDfKlum6xP2+/+Ij83zh8dfMkfcr0zpMeMZq8zGKFNE0yv/m3hcl9gvdcZbDaeegG5QS8F8DqDLf6Q63ZkovCsrrO8vim7rk/n2ScrCsOG93xDcdhqFV1HURQmJllrLOigKrEU4Lbq+sL5S9Yf265d5tr+fTp/dpQ/5PqgbQNGE5NfGx5foAfydxYAbvuBD7cMuAyZKI9BojRpTR0yzoeMJUB/JHLwq8brDL5tuN/1RiIeBbvUIB0s/ZEGtK39t/Ybjy+QCiRKit1NJcXuKGLh3soXwBRg7q5NoUuK3brHF7i/MZwTz0rbXo/UD33LsF7vRJCUSA2+rUdb6w+5wsAziqKf0q3gy8JVm11vHTv8n0v9oSnFiEhvj9TgdjZ+PxFJEcxDJvi1xnNWXUcFFMMe3qgN4wHEha8dEFUUHBYt1mzUBK9DnAe/g9cZDCCpgq3GGQt23WZMv//+RddpUBQuicXV1yxaYilS15ZgZwQLAEVBsVniRGOWek2LdUHSCQuBSbt/FwD4F+IsuQPogNEnrNUu3x9ypfftzEXAeGC4rtMuyd5c2qPDgr5IumUp8nc4C/imzX5XA9FozD4cEUnPI2JSA272h1wrvc7gVuBp43q3WuPfiPwdNyB1k1bgE0SYmRwEpk27ySHnPmVSLRIip6Ygdfkd2+7uf5iH9IMzVZlUhHyxWRE3p8LJ+rTavb/ql4k/5BqIGFY873UGX9jdNh5f4IKBXWe4e3QIeY2GlS2Ia9E5gJZIQEIHXdewWuIJQE3oCqqiJ4A74nHL+LVbR3TIz1r7TkZq+QjgCq8zuHJ3xzIx+TXg8QUKkQlROXDUwUQ2DuKYfZAIQRMy4e2OpFx9CNxtRGZ+tXh8gfRTj7jTpanx85BIy+z9sfDej/3aEKv+A7K9Nyz+/428R9OR6MQDJcXuPRoUHQhPfnpKR7u1qfHCsR9Ve3yBXkDTxPFTmoFhwLDlpeP62ywto7q3/2KZolCA3CvHA40Y2Q2IaFeQe0IjUu9kA2iqTVIstjjWpKhuiKwYIlJGIILsI0R4/m1P954DwR9y/bklmnRsbWNuv6zUsqjVEk1Haq2sSP2VHfnct4qtKmPcNqS+6vLWCJY/5BqJpBnetj+uu/6Q6y5j+5nhSFLylop+HdIclS+0yyodbFyX673OYD2AxxdIAdJLit3f9sLz+AJJxmM7jP0djwjRG73O4I5djjUcEVxWJGq4EWlKvQ1x+k14ncH5/pArBYm6fe51BuuMxdRuxva9ELG25VB8xn8JmBEsk0OOApkxlZiio9rrw7mHezw/Eins/HtyANunKpPaTdan1R/GMR0u1gMvx6LanGv/+JeLh540z61brWsr67rM/NOJT8wytjlizZZRqT06hFqLnhWkOWMjkK4okoehqHEdsT3eoCr6IESE/U3TYiN6dZrXgHyh90PSXIBvzTHOAJ5tW/xtYvJLpqTYvc3jC7iRifePIa7SkIavOvK3+1tkwqwC60xxFegCPPbm3L/4S4rd5xzKfZcUuyPIBH9/xtEX+UwsRt6rz5EoxUgkFW8OMMfjC5xk/P+mkmL3bpvk+kMuJRa39I3GkjSHvWG11xkMe3wBB2A02r12WmH2qpkKR+eM6NXr+tLygcuAV5Dv46sqa4u8qhrr3K1wgV1R9DIkMmVBxFU8kSARjtjiyUmRQiCq61hjcZvFaokoAOsX9aKpOi0x4tS5GxCzCSsi3n4HuIAVSET1tf25Nv6QKw2pMxsBvDZ99q2nIhG06yeOnzICKKxvylkKSjckPTAZiQQpiLjC+L1VZGUgZiVLgDdbBZCBA0lFtO3P2BBnQBvgDrekPFyQ9c0Ih73hJmAhIir7+kOuhGFgcQMw0uMLXFBS7K4EKCl2hzHaOQB4ncEPkIbY/8ParcN/n+qo7JyfteEbpBH1jV5ncIshoG4EIv6QaxJST9YR+OaZ2SeGlpeeWNCzw/yO6SkVMSQK50Aig7O8zuCT+3mev1hMgWVyyJmsT9OvPv3xpKK3lt6a3Bh773CP50fiEdqkDCCpgh9NVSaNnqxP+1Wt5nidwQbgiZOP+dfQs+94+llFgYQe06Mx2+88vkAfY/L3xzH9/zseOE3Xob4yKZqeG/4vsgJ+uqLgUBQeAh5HmnDGjNWzq4BjjRSPVrbuMoQBSGrJx0iqiYnJLx7D2lljl9StH+hYFmTFuw64GJl8tkNW8Nvz3e/CXysDkFX9Q5oqaQjbR4HZJcXup/bjJbcCaR5f4FSj9uoRYz+5yHt3IyKyshCxsTcB0KO5JW1GZV3HtI55K28BpmWmbr2tMZw1qCDrm7/r6AX9ioJnqUosPydpo8ORXTkcESX3A9rIPq/XJxKWOaqqpyIT9SzE1EJNJAjrupKsqXq2cSzrjpoiy5otY5TB3T8g1VFD/wlfx5CoVRkiVrKRbJmHkIW2tcbz65B7xx7xh1xdkfTBKkQchFQlamuXtb7joK4fnATcFItbu63b6tTSU7ava5e5yYr8fbWNWCV2+T2BGHrc1iY9HgCvMxj0h1xzDyCV/T/I+9M9M63yj0gq4RbjHNcjEdF1SM+yWci9bk/1XnvE4wsoquLOtVpaQieOfuBqINXrDC7pqqy0wZPWO+Zf8WdEOE42zrUO6NsYTu1d19hOq6zrVJaeUmFjp/B0AdX+kEv5tUeyTIFl8oPw2OtXRZEeG794piqT0pFC2W+JKxDNTHImVYcvQ5x7fnWcVPyyntBBUyASJV7dUPBS68q6kTZiicVAVYGE0oKkB/4dqenK9jqDuzYltSLOVuf7Q66/e53BPdkZv4sUZ2/4AU7LxOSnylDEXvqf/PBNQuPAHHtFY3jo3R9dCXw0b+qpDqQx7ALA/wMf/+dAORItmn+I96sj139/a7nuQSJYu25vQ8bY2ibj38DLRl0W/pBLBXKnz77V2i5rzdg+nWbfsWH7Sc2pSRV1RQVfZ2lq9Cx/yNU4fqDldw3NWeGmcOasVEelLdVR29LSZL1xwRtjLyzosbl7XvuyHUjT5T8n2ZoGI9/LCcTcoT0i6pJVFVsioUcVhcWrN49ol5+1rigrdXs81VGuqoSVeFRVNGtiBxKVyUKE44nhhqQX6ivTH07PrdHtKZGPEJOOY/0h11tGrdGeqEMirx8g5hfrTxt392hdp5uu82Rziz0GbNa0SCebtaWLcc3j7BRZOiIOrYiwiBr/ipCMluZdjscB1glvQZoQ34CIxi3Gz/HsNPXI9odcl04cz2teZ3DWAez7W4zaumtbota41xksBfCHXP3bdfvb5B3rOqTdPOrJ8++Yf8VpiNhbAmTpOoVZqdv0AV1nhLPTy3QkcvgFUnOWjiwsvOsPuR4CPvU6g+HdH/2XjVmDZWLyPZmqTOoJfJVQSNH1nctb0TQr9vpoXIEZwDm/xnRBf8h1YjyuDtG0xByvM/ip8Vgy4jrWlEhwhaJAuEUhya7vUBQu9jqDe4x6GvVdTyOLQ+furu7q/Gklk7rkf3Vm36LZ93qdwXd/oFMzMflJ4fEFchDjiddLit3f7Gv773GcJKSIPzR68psLgKdqeuauXnnlES4kajMLOGV/GuaaHF48vkBnxA79i9a0wKnKJCtwTs6jazNswxtP/XTxxdlNLemdRvR6M7pu23BbkrW+uV+XWR0r6zok8rPWr1FVOkVjlqpYPKmDRQ0rVmusBYjEosrippr0M9Pzasu8zqDuD7mOAM7XdSUtoatHa2r8KaTX1QlI1KMjUsc8vKqucIeixDNS7eV6U3VG88q5/VU9rimjzwxegQiXZcDRwFnvP3yGb80Xvf816NgFzePO/0jV1EQKssBQCFzldQY37O0a+EOu6xDREkBERL+WqM0OusVmiaqKQgwRhFZ2Cqu2qe268XwFYijx/KGoBzZs7q9GonQ9kXteSyyuqrGYBZstoqkKOjAPSZW/2OsMVu55jwd07Kc/e/no4R9MO+ONTmfU3ddp3JbLh3R/j4LsdaXIe3V2uMWmNTTn3JObua0nIijzEAfCCHJ9bIgr5F1eZ/BXuchsRrBMTL4/FsCesKgoCR0tLosWlqYoiqx2/QZprPuPwzjGw4IhcHYVOQlk9XBLa3JFU2M6818eEzryog9m7GN/S/wh1zXAuUiu+P8QjSVNqKrv2BXI8Ydc+V5nsOx7noaJyU8eo/binn1u+D0w6orGIvU7zZP1aR9c8MeSa5rap3+I1MRoSERCZf8jLCZ7YKoySUF6VYWRaNOqA0k5N4wmBiOie3d1efmI4+RjSAQLJF3uwsZXcxbZhjfOT+haTTiS1rk54rDXNrQ7LpKUVF/flNu+qr6joutqx/a5a8OaFitXlOalmhrvgPSlyrRY9dHpebWneZ3Bx410sbn+kCu2dP2RV5eWDV3Vq+Pn/l6dPvchn5dMxBziU+C1jNSy6eiKtmNdQUZ6XvWwzgPWZWfk1zYA81qt4f0hVwvQ+7irXy+qvuXyq2wDHc9ur+phb5/zjaIoDEQET5ORWt4PSGu1KN+F1jTyyxCTmLUWLZKvSC2X2tJkrfnsZbe1u3O5pfOADaltXhdBRFcMEWfPIdGaQ5WWvhD4EpiN1HXlAw5VSejhaLKCojck2aIzjGuWi9QvHzBGnVUHYFubCNu9Y8/5OHXa/bd96fEFiprCWSd+tuzcDwZ2nXFtbVNel7yM9fHO7ZYXJNm3TUKEVTWSIhxG0je7I98BecAof8j19K8xXXDXrtomJiYHwFRlkhs4HyjTook4hrhSAMvOZAAN+PtUZdIfD8cYf2p4ncGw1xm8BnhQAeIJyMio1b/5bMAHN496ckxXZeVev5e8zuACrzN4vdcZ3L6752Nx+6VNLemDkNX0p/0h10h/yGUuJpmYfH/OAK5A0r8fBGjqkAGKYkUWTW5HrKxzPL6A4vEFjvL4An/w+AK/mnYdB4PHF1CN5s27chIyuX4IqZ3qfIC7Pg2pW+24h+eXINkEgzy+QCfjsVLguvCnmVPOHRW85ekrL5gKKKs2HelJ6GphRW3XZ9OSKxZ3LVicyEnfolXUdqqZ/fUFnesa8wcpCj2RmqsEUtu0wB9yjQPe8IdcvYFLuxZ+NUFVos5NO/rcCGyLxS325pYUZXNFL1tdY+50YJCmJk7XtPiHhT23Jadkhp8v7LVtZnJG02tIRAQArzO4HrBploTLe/cTb2TnlcWbmtNjiKPlYEQgnQK8Z5xjsT/kStr1AnidwZeQptn3APd4ncFzNZV/qCqPKQojtn1T9PyyT4a1bFrarbX3Vcy4RmXsjGa96nUGXzsU4sofcuX5Q64rEeOQwYjwa4fUdeuKQlV6co1u1aJ25PNRDLzyPdLwhiKRt5OMz2Hf6bNvLQWS/CHXoJJidykSSXtixcYJVRvLBndcXnp0+5aIoxFJC4wi16IFmfq0ulTGkc/CkcC5hpD7VWFOOkxMDhKj59e9SI+TmAIJbWf/CAttCr11+RK+e6oyae5kfdp8Y2VSm6xP+8Hdvn7ChBWFSotGDhBv33vTgNLFvc5Ebnb7tLHdEyXFbsMW94mPkTSFB4C/Iba5JiYmB8+/kCati9o0M44Ai4EeSCrTOKQg/iYk9cppvK7qRx/tz4ezgfM9vsDvS4rdawCmKpOuZKdRw0ZEYG3bw+v3xDNIdGW3xiclxe6wxxfYgkzi3wY2tZ//tQWJhgzwh1z102ffmgFKYUNz9jNAMij/sVkjC23WyMuAsmZr95stWss/LJaWdERY1SNiIBcxGroESetLAC80NI2oF84AACAASURBVGcdDYqa7KiL1zdld/9q9YmFKY5KtbaxULNXlV1dvTh53fGTXutsscUvARLlNUVvzF5ywYO79uUCiMUtN6zeMvrItVtGWVffn/6vS/95z4WIk52KCJRHEEH0NPDMzaOe7H0zK2Pr9T7L2u7H6wzW0qZu0ahnmgWAk5uWfvxoydDfzh+H1FfpSNRvIBLNbeHQ1hwOQBZtNwD3NIYz8jSlpafdFu6mKDQrCjajtYmGzDXaAw/6Q67jDzJKtBkIIv3QhgNTVTX6GHABIpwuAapVNfrv3PQN3Stqi6oKslY2WS0tlnhC7bR+64j0tJQdGflZG9Yg7/t5yHvQxE7XxVsQo4xHD/ai/BwxBZaJyUEyWZ+WmKpM2ox8wTUlIG/d6QO2FL27vLs1kvjWurX1G08HqwKTpiqTQkhD0N9OVSa5JuvTVhymUzisGA03c42arF6fv3pMFdL0ccMh2v8H/pDrK+B0jAbH/pArC7gTuZm87XUG1x2KY5mY/BooKXaXA+UeX6CHxxf4M/BQSbF7iccXOBuJksSQie18xN1zKpDW2jzVZPcoscQ6W01TeccZK9OnTn7zlYTKkXG7lmtt+TYNYutkfdpzB7pfI210j3U5/pDrrN6dJhR/s3lsVNctG42HrwIuRCbIqxGjDgeoj5QUuzf5Qy4bYmX+4er5fRa+8deTh//hlb8uTEluPAqJtk1EUsQUZMJ9GWLVrgMLNDXxiMUS7TCo20dVCjiSk2qGZKZun6PVNypLP+zbs748s3M8piUstvhY4PXZSy74PeDw+ALe1nFPHD/FDoyvquvaMz9r7R0t0ZSnloWP+jIesZ0ETbnsFFl2xBzi4ZtHPbnBYou+H49qtYig3W/8L1+zEK5ZuMvDnxn/DjWzkfegERi0aceA0e0y1+VFYuEd6SnVqUgUrytyjiDXeTTwtj/kmofc1xbv78GMnli3d1VWdrJmRid3v7Bybkon5iP3yCZjs4TDVpeSl7m2qEPOsuja7aMdKzeNy+7efl7e9uruWixh65yftaEWiYj2Rb4HUhBxtQoxvbjIH3I99mtKFTQFlonJ96M3kj/+NnBO0fsreqjRRGv9Qdv+GK3hrH6RNPtZWn2LVxWDPT8Sov/V4nUGm4BFXvna3bj3rQ9432V8167XDvSOx7lShwef/tQdumxCYNShPKaJyS8Zo1mtG4lYne7xBe4BLispdremb21DVqxbacJkr4z689thoEdC4WUdeuo6xJNtoMV0a1O0GjFKOqQY6XLunPRNMYsWndO380zdH5ryBFJDlY5EaVYiluCvtemNZUFccwd36LPRe9btT2nhBvsXKZmNVsQwo4Wd5Sc6cBwSyVwGnHfdcS8+5M90KUjN0sARvd9uSiSUh++45KGrM7Irsife+tzXNkekdzhiawFLN4vW/EQsbtcWTi5KH3bfptsVhTgi/G7PTi99rK6x3aK+nWb1GfL2jK2IlXkSMrlvPX47YOYd86+4ff2XPdPqKzO+Etf4nzRWJL3x8vystW9sLu/3UJ/OQQciVtPY6SLZiJxvOtKHbhjy/vw/e+cd50Z1teFnRn17dW9rG3tNMWBAhCZCESUQqoAgCIQAgYQWEHwJIQQSOlhJqAlJIAkQhSJqQoCIUES1wBiMbRZc1r2s196+qz7fH+eOVzaLsY3tdZnn91vvWhrN3Bm1+95zzns2WGCZVAxaVf2dK584uqis6+XrLn5ovrzFhWjI3xJJ+J5Jpj21uZw+tLV7SJVh5HWXI+3aZ/zzeV3LL1bj2hUoAe5BRPheyPOfQhZcbIj42imwBJaFxTejEVlNGqeBZk/mdKWqdFi7GUwOOuzwn2W+MUWupvaegR8sLgK2mNuXhRBJ+BzAj4HpQW/8jUjCd2Fz++DZHd0D6UpWeu9+8bjuK457sai/x2lhsS0SCMf2Qnol3YTYMQ9EarFmAAGkLmifQDi2J5I6uLFpbBYiGNrah1d+q2xRC2jQNqrq05oZy3+GfL+UTtYuHXK1cd/6bMc3lgHAiEFV857+5xUn/DaSmDwIeW5XIGljNcAPTznkpldMB1iQBbFIwndKR3flj3RX9uZRe83x2J3Z4cjX3RwgbhjcOXfpfnaHvcc+cuAMsz+U0zC47ZKH/3DBQbtzMPLd2dbUMurVt2ecVTvh/Pljxg54J9Ps2HV1Taa5JZUuGdSw6OD/nXDg5JqulpIDPq/61XnF3UtG6zWuf38y13/wLkOndBW52xdXlS0fbRjUIGmISxGRYS5sLgBGIYLrqrpJs2cBL23Ga7gl8CKlB68BwypLl19cWbo8j4jHBiQltwpY2rm6NO/0pKqcnrQNOf/ZSE+zjeaa56+dmU3bX7E5sl/lgPhXt7NnOhDeve61EdmcrRSwZ3POdFdP1YiKkuUZt7M7hYioY5HFZxM3Ynjx7UjC91bQG09tyhi3NyyTCwuLb8YrgJaD/ZPlLlfehmEAaZdOzq6R0TA0lOmFRE86XS09z84/Y5/jWnYbNA44c7J2aZGqybLYMpQhTo4+gKA3Pnfxyj3PA43Rg6dRVdXpiSR8WVWEbWFhsTYOZIIaRKIObUh91ctIXVUjknL1C+DEvnYQCMcGq+bEFuswWbt0NPCXrI3l7lWdWrrUhaFrPQOnLdn7Z+m7XwKmAd9F0sA2G0FvfCFwC7B3JOHzIYLrbKSG7nREVF8I3LmuQUHQG0++/en3V70+7YJlbcnBmqYxCHHj8+Xz+s2rOwfbmttGaC0dQ3VknmkAy7qSFe4VLXX/bGoZ8bPV7YNPz+VtdR/NOf7A8uIVVxzqj/YwtOIvjcsn1U6bc/xDmZz71CXNu/8aSNldGfv4gz8dPmHIGyVHTnpwRmdPtffjOccsAabl8npZJuu0t3RUTUBMITQkSpJB6s962lYVNzUvHHA/cFnQG49vzuu4BZgFPIr0ujJTHTPq9o+RkgQ93ePQX3/oOyPf+Puxpcg5dwNPB73xVzfloEFvPHPOwa9deNb+8TXhPWVUc1EgHDtRiaL3k+mia9MZz4cuR6ZI08Bl79ZKi5pseQMHUvOcRMSeU41LQwR2K7JIs9Nk7FgfeBYWG8lk7VJb3q7/ZuWkYd+pKHUOd3akbTrY7G0pOoeXUbaoHS0P5A1sxlpd33uAQwe92/jo5HdCcUJ+fjvgoj01u/GX3DLnbcAz/XZS2xmBcExDVsRWFhTb90nQG18VSfguoKDL/T7jXn4sk7XdbhjaIF03MAxshqRVfMllysJiZyYa8n8QCMdORNwDByETvHIkEjAFaRDejEQPZqo+WeOAGdGQPx8Ix3ZDHAf/gtTiWKyNDSjGYY9myz3HeJo6qmxZYx4SeZmLOLVdjkQPNzdtSMTnh4iBw2wk5X0aIp6PQiL/a33GRhI+x771w8+dMe/Ikdmc3ZxA24FvNbePYNb8b+tjh77HoKq59KRK8nOX7uceOXD64W5nR3vdoI8+KS1aXTdtzrHt7V2DHu5OVTQcttef9qgoWeGvKV9o7DryzR5dz4yw23IjTjnkpkOAe1xFqZWnXPdoJVLj03rAbk+0aBJh+xVGvsimZ/Vid2cKWA5Uq/GPAr5nGLhmvzux5r3Hjzzzsn/ctEck4bsj6I0vYtulDXnO5yO9vFxI2t2JiDvfaiDvcGUG73rYNCObdpjzCw8SObpnUw8cCMeKkRrld6Mh/xOISDoWSd1/HrB90HDKMZA/4MDd/6nbdENzOtJOpyNtIAJvKiLUFyAmIGYdVh6JyHkQkbhTYAksC4uNZ1+y+UurEwuLu4aXdzs70lnAbgPKFskc3plZ0wLG/PDLJ3G9+wpH/3YxI5ZfrW7Uj+74+zsLDtvTkcn/ok5r+G+jUd+5Vc9k++UQxCr6BsTVbL0EvfFl6/w/F0n4hgDz8nlGaZLX6YokfKMt4wuL7Q214FCOpOvNiYb8m63uSVms26Ih/4PAg4FwLITUhyyIhvyzEae2wm2vRybmP0WaoC5HCvdnBsIxTzTk79lcY9sReH/yiYvIG0+ja59+6+rn65AV/rFImh7KafajLXT4U9MZ5xi7LaPruvEoEqXMr2wd+VZTa93Fo4d82ONxdr7Qx+OyGkwrcrcfbrdldUDL5xmRN+yay97GyIEf52rKF2t2W5b27lJbS+cQW035AqO0aFV2711ebsjk7OMctvTz3amK96Ih/4pI4qZnEHv/oU5HcjYi5GuRifkSRJjPRRYhx85Zsn9oVfvwG3Yb+frgsuLmuZAfa7Ol6xBzDY+6hl2IONEGjVncMXb/Wa8hUcByCuzet0HGI+YwCWSsE5B5hI7UMo0CPJpObsy+X2hIPZYOOHI5ve2e2Fk/rilf9EjQG9+UvlgOpCfWAIBoyJ8KhGMXqOMCnLzbqP+dvapjaA29cxszSrUKqcFyIkIrU7BfG+JK+Ow3sJPf7rAEloXFxnOTDmUaULaorUSTL4F8HjRt7bKrwuiV3kTtMd224m+NOWH57wPhRXdFQ/6eFR8Oq+5MlZNtdwwEHq7TGs5uNOrTW/uEtkMWAx8iX76bRNAbNyIJ3+mZDG85nbg0eaamRBK+e5G+Jl+Vi25hsa1xOtIstgMxmHis8M5AOFYG/AB4JhryL96QHSrRNhwRSgMC4dgPVLPaCPLe66uVwjgkHfcLpF7EdLL7TSAcOwG4PRCOXamEmYUwAl37AVB6tXHf/YgofX9LHjCS8A0AJnUny76fTJe47LbUuLLiVdcgkYdrP/j8pNZc3rFo/vJJV/7j8pPi6jE1iDtELXBHTfmin9eULzoLiWgmDUNzdXRXagtX7MXoIVNtbmeXAeQrS5bZ9hv/nOFydGWQCMwNDlv2vt+dEyr8fJ2ICIVypOasB3mNrUCcCQcjkZUQoM9sPKLd7ewo6kpWdtnsHTmPI2UHcrpOFpnXFiNmK6WaRn7YrotKhu26aG/g9KA3vq0vYs5HXIarkZRHJ3JtDKA8m9PdhqHhsOd0eoUXgCuddZ+8qGnC8T2psnlsgjHK1KuH53Vn/jUjr71MSG6LhvyFvb0+ripb9teqsmVX0mskYlKHRKkmIqmm5rkMVWN8C2nfsNNgCSwLi43ggvMfPbNi/5FjBk5ZUKikuoEimwgtM+ccCsSW5skxetJnmjE9V7F64LhAiorngOlvNxx56Z58fPbn1H+AfIDuNA4734RoyD8P+Plm2FVSyzuW5/OZEZoGmsabSGPE6agJooXFdsAKpPh9CHBpIBw7BjGhuDMa8ueBXyMOZIFAOHZeNOTfkD5z+yOT2hlI/YddGV6sz8iiAfg/c/t17mtCUo229Qnu1mY2cAWbqT3FBnIzcJrDnkytaKlbXexZ/VkZq1zI6+cnx+x37wv/fv/qQN2QxHzpVQxIZGgXZMJfrBaoXgSOWNq8y+ISz+pDU+niXHv3AL0nVaqVFq0G0DQN3M6uDiSy+WTQG1/LOj6S8NmAlUj0JIsYbSxDoni7I6+bEuCQoDf+FMAzb8W0kYM+CQyqmv2yYTAynXXlnfb0ajCqkO/R1Uja6lh6v4+P2A7EFUFvPBlJ+B5BUmpt6mazoXHpvKX79bR0DPZ4JzynadqaOQdAxqZnHKMGfZL/fNEhm2R0AYzJp/XTkXqpeQCRhG93pL7ywVWLq71ltW1/criyP6ZAYGWyDqbP8+sDKuZ/e/iAWVk1Jg2JKBrI8z13Z4pegSWwLCw2mMnapfXa2fve0TmictiAxAIwQEWszNWyDnqdcwqjVzgm9FB63grG/T1P+2OrWuzJbHby1c8PfMu473k46vmtfzYWihlOT+Zkw+BuTcODTDD+jNjuW1hs86iap8+QOqdbkfSo/ZDPn90C4Vgr4vqXQ+oijlTOmo6gN/7penadQwTT76Mh/7xAOPZdZFHjWqQx6ZeIhvw54M1AOHY8cFkgHPu/aMj/ibpvi0dmtkdUDen6noctwSfA0Q57urRu8CeNSMShErgdaNL1/BcnHHjndcj32vXqMaVI2tebwH+V8UUMqO/oqZrS3D5yWEV+zoBdB8bcVRVNDoB8njyArlOEvDYXAs/VaQ3aRH/ixCH1C5KHnM31iHhbiUzEa5C0uP3V8RJIA1wX6nUXDfmNSOKmJcCHhsFqDe11XTc6kehtDnHZO4De72ADyEYSvlOD3vjTm/1qbn40RFR2I+l5HUCtkad4cPUXRRXuBSlNow0RYOVqm26nI1PldKyYe88PLt+U9ECQ+qifAPMiCd9RyGJICTC28aNdLvCUdZ7ZubrkwsrBrT8B7kOeK/KGnUy2OJvJuler7c1zyKqxvwDctolj2m6xXAQtLDaAydql9ozb/oyrpWtoqsKjtY2uRlX9msVWFcAQA/kkN9ZOFSQ9o4iOBwaR/LBEo9Pw5bLaVGDKZO3SiVvxNCy+TB1wlKYxFPkyvxt4MeiN59b/MAuLbYbLgb8hk6E0vavHGtL09cdIilUpIphiSP3irZGEr885QCAcq0T6II1DIgEgE90HkeiuuV1RIBy7IxCOnbLOLhoRo4HqQDhWgsU2g2rs3obUyUSQSMWDiPg5E6mVeRKJArUVPHQ+MgH3I7VMZchrq3T88CkLazIN33/uhrOanvz5D/UPnz+4sa2p/Nmnbjgv+d4Th7ciUagPAJcSZiV7+D+4b/Q+n/8BSSGrR16jE+mN2uiII92LiNvhzYXnEfTGO4Pe+Gm6zkkuZ3JvRFQtQcxX1q3zS6t9BTb1um1Ngt64GXXuQa7zLMCNBsXuFqO6arkdETfVyPWaiogZHRgVSfjKN/aYdVqDGzgZ6Lhlyo9ywGWI4P4HUFQ+cHWx05OiqKJrOPIaMSNYeZejh/0nPKXXDf6oGiii1/mwUY3/sO0heri5sQSWhcXXoCzU99azua7SBS3kXTaWHjme7mpPCvkSMgC3ASVfaWeX1kl/VEJnj4d3+RadFLuyTlt1stLj2lrnYdEn+yAuSe3AkqA3/oTqbG9hsc2jXL8mAvsibn5z6LXFNpudm32IdCCv0mvvAm5SE7m+OAwYhkxWOwCiIf+KaMj/SDTkby3YzgUcCVwTCMfW9JKLhvwzEavpG4EHAuHYuM1ywhabg9ORWqrjgfOQSbUPWVx6GBioXhd/Bx43H6Qawt+M1ERNRyb0zyCvkfMHj1v8+2+d9sYLKxcM+ujF353xlKs45XG4syvszuxHSDTpY6QOsLTRqO9ob674dbLLc3NrZ+1rudyaGiM7YnBRpg7bjHw+1wW98dVfcT6LkcjqZKQGqBaJCC5DjC6SwEPI6/SKb3DdtjYNyLizSNq6XaWxazbbmlKELPIePwhZ5DUQMdaxCccbh9RbHhP0xjPAVYgjYSdQnS32VNkqck1Od2Y1ci1Nx10DyGkauqataSScRoRfLSK0Ht2E8Wz3aIaxXodjC4udmsnapRN6Kjz3OLpSh2mZfKcNPmvac/CeaJq7+uOlhk1W/0YA9owNzZZDK7TWWZdWvYSXjeMY5Wo0dq9uMGZe5nvoqZ8d86Otd0YWJmr1/kIkDeO/QGY9X+Kb65gudZyvmthaWGwwgXDsAKQflQup5bkLmUTugdS05JB6inLkI+l64Pb1tTYIhGMDkKiBEzgyGvKv/Jox3ILYfP8RuEnVfKEE10+RHk5/jYb8f9r0M7XYHEQSvtHIpL3UMBiBpAXagbym0YKIkjsR4fQU8v12GYgp0Dr7+jXSK+t54H9IhPTfT/7qhytLqtuT37kiegkSUXIg0cyVQGXQG59VuJ/fvfL9xtryhSM0zUhp2pqvTbv66QKe/fNFVyfmfzxuOHBjo1H/lQ6ZkYRvN+AYRMjVIhGY5qA3/thXPWZbRn1fPIu4841AxKKL3gi1iaHuA/hl0Nvby2pDqdMadCS1+PNGo37NIkok4bsUuKutq9aezTr0qrKlmqaZCTyyaLPOeLqRdMAzEdG7FHh1Z8wKsWqwLCy+gsnapUOBmLuna2g+b6NzYEmufEXn7AGfLNvD6P1AGYp8gRi23Nq+pX1Rmu9kd6YbJcO6SZaX5EEbtaXPw+IrKUEmf7OC3vgWX2GLJHzHI05vrkjC939ARK0UWlhsKjORVf4u4MxoyD8zEI49j9TJ7IasHq9E+lWNBo4D5gTCsdlAu4pmrUsHMimqBjZkBfZepDntiYidtuke2B0Ix8z+fgs3+QwtNifLgWdXL64e+b+Hjj1jr6M/WFA2vme0Tl6rqVjs1DRGIBGqZkQwNyHppx8Bd0cSvnFISuojwHeQr7ojEFGzFDj09N88nEXSCZ9Vvx9CapM/RqJda7F45cTTPK6Ok8uKVk/K5zm4u81j95T12G2SKFgMfKe0ttVMO3MiE/g+CXrjM4GZkYSvC0k5vH97/owNeuOpSML3AyS69Ld8XnMbhjFE00Ffe5Jh9qGaDPxhU47VaNTnkc+JNVz7+K/L5iz9RdP+9U9fUFW28DKPM1usadS3Nxfn21dULx6228KR9PZAMz8rbMD5SD3d4UFv/PubMp4dAStF0MLiq3lEH9Mz1H1oK7ayFKUrOiuA05AcYwDyEibXAEOH3LrLSutiA8YUzSW3V8nn8wO7TzLs+hlb9AwsvpKgN94OXIzk928NrgCGGwYD2rvL/5bKONORhO8EtUppYbEpdAPvIhPRIwGiIX8bkvb1LCK87MhrL4W4qp0H3I+4/a0hEI5pgXBsMPB9JOL1M+CUQDh2QyAcW99ibAXwBhItmBsIx2oC4di3A+GYPRryG9GQv2Fz9uWy2HRUmt+f9ZL87hXf0qptlfoAlyNpOJ1JA3GifB957p9HzA4akCjomZGEbwayQHQOkjo2CvlKcyKivAxJDfscSS/9LfLauFzt5wrEFXAtwmdf82FZ0eoH2ruqXEuaJzinvXyQ3tFcASr1DHCf/uu/1Iw/+JPvF0ZW1sczb10/7dm3f9GQyniqNuEybVOolPVSoKUzWdmTN3TTXMtAFlDySK1WCvgo6I1vlj5zkYSvaFDVnKc8zvY73//sdLfb2XOS05HxAqe+GA52vfrnEwcgz7fZi6sHSSc0Db9mIZkhOy1WBMvC4ivIwSEsdpJa5IS0DqAbvXnHwBoXQegtzP1abBha9+DKUVmHZ1w05N/a7lEWBQS98Q3qCbQpBMKx3ZFV3gdPOeSmY3pStm+7HDl6OopwunvIZNxoGM87HZnfIfnuFhYbRTTkzwbCsXOBA5HeUyYpJJXoeuA5pJ9VFvgPYoP9gvq7kOMQp7ddkEnbSYi7nBN4G0kD6wsvEsGagog3B5IedAniVmexDRBJ+KqRSFJEd9qGe4bkNGcV7oqSJpDJ+qh8nvJs3pF22DIuTWN/xJXvESQyWYFM6JuRaNRgxFiiB0kFbEEcJvNIrZYBLAp6462RhG8R8vr8UI1FQ+q+lgS98TlA2mbLLZq3zPvumIPfrvWUdu1qGGirOwbbOroHOEcN+uTRl986Y4NMEiIJn0PXrv3eiAGf+By21IWRhO9yJH22KOiNt3zjC9k/fA4867R3G5qev86AIq23rYsNmYcsRp6XzUW2unzhR7sMe//dT+Ye+5RakAR44dmSP5/gO/eVW5D3vVnn+SkioG3Aj4Ap66aV7mxYNVgWFn0QuOPlb5c1rHh18NuNtvI5zWuWiwqjU4X+r/Rx+1eR18h//PMjn09XFxcD34uG/Nvrh77FelAT37OBy/z73H/nBw0nfXfYwGkMdM4mZS/DrmfJoVNV2oRNz+8d9MY355ejxQ5OIBwbg0yYXzbrnta53wFkzXqrQDhWgbjCeYCewjosZULxF2SiVIm4yMWQ6O5nSCTsyWjIP62P47iQiMW3kdf7jcA1wOxoyH/lZjpdi29AJOEbiKTqlSGGKKTSRU6no1tXdU8GkE2mixZ+0HBi+dihUxYNrp63G/J1tgx4u7Wp5JDm+YOqRuyxUHN60inkdeRAXjM2td0piCjTkdTCp/uaZKumxf8EpgW98f9TjY9PR9JJDwFuy+cZtLBporu5fWTjvuP+dSVSx/O1kdBIwndTd6pkUi7n/KC0aHURkjZ3IeJ8eM72KrKUKJ2M1JdVqps7kEXfGcCVQW+8z/YJm3AsO7LQMht5fscCjYXplpGE73zEqt2OpJD+CDgLaRNxY9Abf2dzjGV7xopgWVisQyAc0/Rk9r7uEVW2lftmsbd1425LkiyxU7I6vWY7Y53f6wquPoSW0TG4rG3JsROaM+Vu0x1og1IeLLZLIkj3+saWjiHvdadKvztn0cHkB7tSw2undXw054QaAyejByUYNmD21EjC59wZC4EtNpkzgcORleNFhXeolL5rEZOCxwAK3P/6mqQ2IRGq56Mh/xqhHwjHjkKahd6POBR+SWABI5EIbA6p24kjLoQb1FRUCb89gPeiIb/VaH3L0IFEnhYiotzmcnZDb0ZGCuh2O7ur9x33r1V2e2YU8vx9ikSpDvGUpIYOHLNc624rSjk96TJk4r0AsQsvVdtdhLxWOr9mgp1TYzEFwQREYM0OeuNPRBK+J3WdswZXz951aE3DCqTRbStf0X9tHWYXuTrHImL/fSStP63Gekgk4XtpO63LsiGOimX0rvea6eVuNm/0yofYxP8aSfu7Hfgd8Lz6nkojvSKdyOvkcZWa+Ekk4dN29siViSWwLCy+zBFlDStqkoPL6BhVRdnIKoqmLcHdnl2z1AeQLLYZjrSh6Zn810atEDG178zQYR4kJ31uNORv2GJnsJOinAH3BRoKUhq2OmqCewXwneQK/RctSfc5E+tfzi3t2L11ZdvYe+tHvv8vt6PjfY+rx5HKeEBWfOdEEr7vBr3xGf01bovtir8gUaa+0lzt9DoJbghjgUfXNb1QUa45gXDsPMQsYy0C4dhEpKfWcGTSPE095sZ1ttOAo4GpfbgSnoBMzC9H+vlsEsqyPmWJtD6pByYhkcZy5Lkq/DpzI6+ZnMfdWar+nwYGICmlHldRRnd6MjlNI4ukA2aRiGg1Msl2ACVBb/yVvgYQSfjc0mXJXQAAIABJREFU6v5m9ZihiCgDSUG9CImYEPTGjUjC91+Xo+dy5LU1Gemj9bUEvfFHIgnfG4gL4kR1nFGIQNlHHeOzDdnXtkTQG89GEr5bkPrKD4CDv3hvXFXD25MmHvWTZ2rdxWkf8G9z+zqtwYFEoD9vNOof2sjDzUQs+mcir5P3EfF0CPDzSMJ3bdAbn04fhhqWuOrFMrmwsFgHLZuvGP6/LyrHP/w+uzySoOrTpRiGgT2bB+jJQ9oAw9WVy+dcdnJrv4sM9Y/ZhyaPfBnterVx3yfRkP99ddsdatJhsXnZA/kyPrWfxzECWZEtXfW2e9xbjxw9buX0Cn185euPHDLxsd8AN4wd9sGHXanSfDZno6O7ynxMXX8O2mL7IRryL4+G/FP6slyPhvxJ4FzgN1+3n0A4VoqYFkxez7GWAiWBcOzwQDjmLLhrAGKw8VOkZuvdQDh2SR/NhY9CIrr39LH7V4A7kMncJqHO4VHWMe6wgEjCNwiZaJfTa8qk0Vu7YyB9AFcgNVY1arsyJE2slF4r93b1dyniFIf626n254skfJ6vGMpVwF8jCV+1qrs6B3gukvANQfo4NQW98UJxvBKp2aoFjtoY84agN74Q6Qt3AyKw5iPRuDuReqbtkqA3/o+gNx5AzqO7pKqH6a/u947dkZuDfN8UYkME9cCNOYZKJ50I/CnojS9DUjYPRF4X7YgTZdc3O5OdAyuCZWGhCIRjI4HRRYtbrm/dpdZhy+ao+Xgphk1n7ukTjUEvfTazZ0xtTdapL6ieuWK/TKnLsKeyOQwjn8nml6XK3U1Fq7pfteWMVzTJSbYjne5fvNq4b7Y6xhCkViGDuBFaH1Sbly+QvPD3+nkcjUj/mHnJRtpH77fgjoa3JtZUDlw945ITnzMiCd/1FSWrDtttZHzStDnfdaWzlewx+jUNSemysPjGrM+5LxCOVSKRox8g5hTFwMRAOFaG9LSaj0Sc7o2G/GbfomOBS4GfBsKxZmQC+1fgtGjI36n2exIQAF5HXMRMpiK9tSJ9jHMFYrqx7hg1JM2xLRry3/8V5zEAiZ6kkPeOUbBwNQBoWl/Pr52EVsRpUkdEkymGzOu0GqnPOhv5zjIbVWeQqJQpwlYgzoIOJDXtNeSa76G2ySKRjq+aV05BImedIO54kYTvZmQC71Rj+Iu5sYpiNaljO7+8u/UT9MYbIwnfEcAIw2CEYWhZMGbpOm9FEr6W7TkdW12bFwbvsjjd01r6tN2ZOw15D6zhlik/OiybsX3YEN/zXvj9xuz+RET8rkAaSn+GpBDngt74J/QKa4uvwRJYFha9/BD4CXmjutk7Qqv8ZAkr9xhsLD1iHJnaElr2Hr4rmqaBUbN6v1Yt73Fkk7UlHxYtbV+Zd9pCj91+cmMf+1zXArwKmXy/EA35LXG1mYgkfBOBrqA3Phcp0O9X1KROeoqEYN/h2X8W1edPndp8xt6BcMwVDcWnP/DaackST9NBu456I+B2dmhIwfcmr+Jb7DwEwrEbAD0a8t+wibu4QP14kFSwqcAgJHXrGKRofhSqmF7Ztx+LWL9PRxaJjgRej4b8hYsZeyET6M/V44qBZDTkb0bs37/uvFzIhH2GGtt4pJ/OuttMRKy//4TMY5Yjq/UViDviuYi74bWBcGwp0BkN+VcEwrGxSKrcs9GQf3usw9logt54MpLwPQccikyazXRAGyJeShHhZEeEUgYRSyCCyKX+fzbwemEKWCThuwD4M/J8nIQIuMFIzde64/gfyokykvDtq47/ASLY4sii5BoiCd8opO5vFtIGYFN4GrgrnbVVYNhcup67TNdzJyMprNtl82GToDf+D4CzDID4E31scpDdkZu4+xEf/Rl5TjeU5z59dZ/8Uzf+8JLrMg233TIFHfkc2IW+azAtvgJLYFlYIBOIXNr4pe7Q9O66auwtPbSPqTE6R1WDXQdNy2HTNCAN2uLukVULgNuiIf9XWRf3STTknxEIx45HQu0Wm4FIwudEUibSkYTvFCQVxlyh/zzojb/Yn+Or0xoOQB8QHHjmMsNWrB0MOAPhWB5+dCMw/5RDbnoWmbBe15/jtNiuqOCbpfgPQN4njyNW7mnAppoDX4ikZ+kFUTCX2n5f4GrEzv0zvtxA+EOkxsUeCMeCSOrfU0g94oZwuBrPR4h5xo+BFSoqFUAia7XAXYjxwb+R9CU/Mp9pB/ZHivRNIfBXYGYgHHsY6c1kU/ufs4Fj2hGYgUQempHrNxmJUHYjVvo+JHV9BRKVSiHi1RSurUFv/LV1dxr0xluRFM9XIgnfPcCgoDe+4OsGk8vZrgNcNlvuVWA/etPRCvkBIgof4qtbBHwdrcBLNj13uk3P2QwDVz7PEF1fq6XBjsqtiDX9l8Tu+gh64011+zfMRRwhy5DXx/mIFb/FRmAJLIudnkA4NsY1v71h5Guf6Yv84+gaWknWoXd0jh+4Ck0rQXK370JW8V79piknqhGoxWYi6I2n/zHF9/7SVeOuMfKZnsHVjTZdJ6tpvIUIrX4VWECLprMyn9aSwEvRkL8jEI7pwKxBVV/kkbTGW1lnBdfCYj1cxdd3hFgffwP2RFa2NaR+43/IhLsSifKsSduLhvzzA+HYqYiTWIWyhV8Aa6JUw4AvoiH/w+q2HyBRkQ5gUSAcuwr4LBryv9TXYALh2G5I5GsqIoiSSPTkWuBexAL8PCSaVYuIrwuApeocytR5dCAi67+IVXxUndMLiHjbQz1mXCAcW4K4Hc4G5uzIqYQq6mQKn65Iwnc24tzXgdToPIk4UZ6PCGdjY80Kgt54quAYfaKsxl1zl35rSGeyMr3XmFf+rev55cCiPtznTGH19qYaJ6hUulvsNo7M56nVNDDyeJBasJPVNtut2IokfAcgkeHfBL3x5YX3KUv7jWrwXac1FCOLIu8DFzca9QuVceP8zTLgnQxLYFns1ATCMY1U+j+6G3veppNLalkjRz2l7kaUFWpfPWYsth1u//dPqt3O2p+tbB3pdjs60fVGkLQTJyJc+oU6rUFDJi+fz8tM2CMQXmxHVolRr6mbI4mbQkg6078s9yWLDUWJgU1+vURD/pmBcOwYtY9vIQ5u3UhK1dlIytdbSH2O+ZhMIBy7vHA/KrL0E0TsOALh2O3RkP9PSHpsBfCw2vSfwMmBcMyIhvwvr/P4q5BoRR5ZDDkQEVu3IKlcByKpSV+ocToBDIOxhoHfyDLV5mQRMA5xnLsRSVG0IWJsNTJLHIFEreqRSNatiANiFrggEI7NBhzRkH+LNR/fVgh640lUupdK11sF3LsVUpQDwAXDB3xa2tlTM1vX82MRg5RTk+niRVf8/e6r9p/w9EAkcnUEEPqmn4tBb3xmJOE7MdXleM1VlHFrEvetR9JdnZGE74dBb/ytb3RW/cdA5HVdvJn2Z0Oi24cB36/TGq5qNOqttPVNxBJYFjs1Dlvn3/Hkd+mxFzHnO3u2zn1pcPW0P04qFFTWpHcbp8ix8vCqspXuypI3KXy6DIODNI3vRRK+v/WTePED19dN+vyPtz35u4tG1wx76c5zr79tnW3cyMrx7K0/PIudmWjInw+EYz4kWnQf8Ka667dAdTTkX93XY9a56WCkKH4BYvVeGgjH/EAiGvKvqawPhGNXItGv8cDLBY8vR5z/zJTHIUg64mKklme6elwSKFH3GUCnkaNW03FlM9TbnNiQ+UwWcTsz3Th7gATSm2mC2o8TiXYtQURlDqkdexRoCIRj31WPHQnM25EjW4qnEVOgddM9twTNQKPH1fVHj6vrSsTUorqju8r+wecne4fVzvw28hxPR14T50YSvsc2Q3PgaZ7SzDTgAPV/nd76vuHfcN/9yfPAKxvjsLg+Go369jqtIQjsikS1mzbHfndWNMPY0T87LCz6JhCOPQ3GyWBoJZ6mxZ09g0bsBF+mOxSRhE/L5Zin64wCMAzI5TBsNjAMtEzOZTjsqYxN50Hg2qA3vtWMReq0hvHAC5XDmpp2u3r5gXn0tNvVXfXQxees+TIMPXbXEF3LjrnrrGu31xVUi20YFSGaCCxRRhMEwrFDEGH/BVLovxoROo9OvXr4y0Bno1HfFgjHRiPRqXujIX+fqV+BcGwcUid1L5JGdDBSD3l7NOR/bp1tyxAjhL2R6NSpSB+vPYEzEDe5bmTCa/ZZMtRvN7Jan0UiXTcYBtcYBlWahq5JsmQeEU5p9Xer+r8HMXLQEOE2ABFercCrSPp3PRJJeQxJm/Or266OhvyJDbzcFl/Dg2+cfFxr56CLh9R8PtamZ0cjz9XS5YsGrfi44bi6vfeJlQ+sWmhHnm8DaZ77028aYYokfOMQ18MyRKgXptfOACZtp82HLbZhrD5YFjslgXDsLOAU0DTQmy1xtf0QSfjckYSvVP3XCxjpHlt++dwBxkezjsk998QVi1c2DvpFd6q89YtFB2uLmnZztnbUXNbUMmir1r41GvWfo+V/oduNh7VcuqnptaLcezfseUHhNgtW7HV+4/J9f6lc2iwsNjdDgDAigkwuROzWL0KiPpOBv3bMdSWAPwK/VNudprb9ofnAOq3hhDqt4bY6rcGjmgz/GnFEnYdEhmqRiNOyQDj2z0A4tqf52GjI366OeQawG3Am0v/oJUQA6YiT4XIkilGDRLEGAqs75jo7OuY602rMV2oaH+k6TZpGEhFOSXotvd2II2KlOnwKqdUqAeaqbUYgVvP/QsRUTl2nR9S5fwgcFAjHrrJ6Fm4elq4ad+7sxQf4V7cPrcvnaU+lbY2GwWCXPVk7pvTtATWlC91ImhrIc3kLBS03IgmfHkn4DokkfLUbeWg38hqZgoi6QnYFpkcSvvJNOysLi76xUgQtdlYeUL97gFGWuNquuAUYEUn4zgPKbTZe+/h/+y9OPHPo/w05pbNJH1b67JVnPnn7mXc//9eyohWzcnmtqry4mWSmyhZJ+I4KeuP/3WoDff/iNuCiqZ8eddfHs3b7mYG+P7Lazz2xs8cevneyevq8Y/7d3DZy5dYak8WOTyAcK0JS794B/sDaTXx/idQo6ki/qrejIf9bdVqDjqQczVXbPYAYRtQFwrEwcDUMHwtM2P/U12v2Gjfj6I/nHnNoNuceFAjH3kQmqteo4yWRuhBHwZj2RybLKSSFL4RMfM9D0mRLkbQts/fRx+q2ZuA/y18vu1W3G0WlY1Z9gZhqHKR27UCiUnb1uJz6bSDRiV3U32aa4diCcTmBMYj5xXx6e0HlEaOM4xCRZvaCsvgGdPbUhAZXf95VUbL05LxhK3PYc9WAUTGotbp8YKtN17HRG4lcAjwX9MYL01LHIZ//T6E+RzeEoDc+XZlarEDSYc9DRLyGvA9GIa/XHdqAKpLwabMW+OjortXuPveKPmvLlQHTt4DPoyH/qr62sdgwLIFlYX4Zv4H0FflRNORP9u+ItizK9UpHPsgvs/pRbXdMRZzALgQubVh4UH7VgN2f3Os77/3a7UpGG23+RoB/XnHiiisf+e1wp61zXkdX2cDy4oXQ299la7EYaN579/+e3HNV6dJlrRPKAuHlRUCR23ny30cPnlrrm/jIj4Le+NYel8UOioq2jEPsr1sLa6EAoiF/Ya3Nmtq/RqM+T0Gj12jI3wH8IRCO3YTUw2jA3cCD489c8P3q8oXf26Puf7+fNue4qaqey6xtWomk/b2HvFfN75g/ICIsiUSm7qV3oluOmBq8iUSTpiGRq9GIk+B9g49o39tenD8biXKVIO/l7yEpfsORz/OVyCS5ColgTUIEVyuSHmamD2bVcdOIyDoYEWNpREh5kNTJe4AWy+ho8xA++5pFkYTvViCZydiPzWr2Goct69A0alSaZweSpnk88nyYgstkDnAb4uy7UZg28JGE7xIkjfVqJAVWAz4OeuNLN/nE+hmV0TESmLluvXEk4QsiCxAvAXd6nG3Ola11u17x998fcve5P+2r7m48cDviLHnfFh76Do0lsCwAFoNRCcZ+Tnt3I7CpzSu3FxyIBWw10gfGYjsi6I1HAuHY2CMnPTC6rHjVM4ubdxvfma4d0TlowLnrToR+d85V3cCgSMJXhjzf87fyWBsiCd/hGAzcfdybRr7RM6u5rS4N2JLp0inLW8bMrB/xdnxrjslih2cikvb3COLeZ65KG5sYqf8VKOfCEATCMc+s+YcFFjXt9sWR+/z5/mlzjusOhGOTEHGSAK5E+mV1A08GwrHTEIOJOkTsTAeeQMwxahFRZAAPImJnKdJc9mxEBJ0EuEvq0sfCGjOLRepnASLWbEhkrFzto5zeVDNd7adJPWZP5DtAQyJ0AxGBNlJtn0YE2CFIOuNy5Y747iZcOwuFacMe9MZnB8KxJ+221Lxjvb+foWncglzvp4AHgt54VyThm46kt+YK96EWovq0+t9QlACZD1waSfiuQxwsH/wm+9wGOAepabwYqa2kTmuwAdotU/ACQ5Hr1qVr+YyuZ3t0Lf9Vi3pzkEWLj7f8sHdsLJMLCwLhWBIMF4Dd1p3L5ord0ZB/h1tRVyu7f0S+NK+Lhvz39/OQLDaBe2NnHN+dLHmwpnyJzePufvyZt67/GWKnv81EXgPh2E+QCdt1Jx10U8oAHQN0nWvO2j8+ub/HZ7HjEgjHapCJVjQa8jcEwrFSZAL5bjTk/8Yr0upz9FhgfjTknxUIx+qQBauXoyH/nYFwLIFM6P4D7I6k2E1HBE41cBQyme5BRM536BVfSUSYVSEiKIdYrjuAcfkshpEHm5NnEdE0BkkX9CBiqYLeSFY5IsZMMZVDxJamtmlDxFgPIsbMKF232i6DiL1Wtb87gHOTK20fz7xjyIHAy41G/aPf9HruDEQSvuORdhQ/DXrjSwLh2A3AfmOGTPnJnmP+ewjw+vYcQepvlInHgcDjQW88Wac1jALj1YFjFnde+ujNt+g245WgN97ex+OqgHYrg2LLYEWwLEBSA/eEtJHNFWtID4pN7Zy+LaMj3euLkTQTi+2Mx6b4Tityu54ocrdpdj2VBAZFQ/5Uf4+rD2qBwQfs+kRZMuXG7UqSM3QSU4/zP/t27F1rNdxiSzH16uGrkUhSmhAgwqIdWG8qtBJOpwNd0ZD/3+vZ1IaIKTNavAhJJTLTto5GBM8eSApfMSKcfoU0Ee5ARNETSHSqHYlEvY7MSY5BRNUwxE0wh9TI5FOtWs5VaTgRUeZWxzPHYdZg5ZH3nw1JgXQgIs9ARJSB9NkybemHIBE381pl1XjdSDSrVR3/AWA3e0n+U92ZmzHshNZjA+HYZ9GQ/8P1XCsLwbyu5or+XUDZHWf+cjn88h/9N6wdA9UsubBhch7N6By22/wKTePIoDf+1LqPiSR8I5AF52coSA222HxYAmsnJxCOTQA6QbsfXP9Bihvf6N9RbRlyaX7d2egaaHMZXSWj0iX9PR6LjSOS8GnAE25nSktnnLz7xKG/OPTs1za40HkrcxOgv/9Z4KRD9/ztIrvdNjKTdZLSy79dWbKkDbAElsUWYdSZq05Y9HzF9blu23OBcOxm4BJgRjTk/+vXPFRDovutgXDsfeByRPQcC/wjGvJ/EgjH7Ei0aiFwvRJluWjI/4y5k2jI3wK0qLTEKYjQegpJE6xBoltDgSPVMecifaoeRZr/ViICLYNElyag5iqaZqw2DGphTWPhwhQcTW3vLPi7FRFq85B6LpMi4NuI+LPTG+HqVtuWIt+Dh6njm2YZebvHWL73rUs/Aa5T230Xi69EpQa+REFqXzTk70autcU3RDW03xWY32jUdwE0GvULgb0iiYsHIO+jvmgFPgI+2yoD3QmxUgR3cgLh2L+QL9C/R0P+8/t7PFuK4655/SBsuVe7F7tdzYniBeMvap6kJgIW2wmRhO9J4LR8HrI5reMHB71Z1t9jWh+BcMwDPOmwd3UNq5l+mq4ZemdPZW7SuJfiHlfXUVZahsXmRKUCYhi80TnPNUKzG5NLRqY/Rcwl4kiK1q1Ic9UVwCnAz6Mhf0PBPgYhkYZa4PfIQsARwO+iIf+zgXDMhhg/DACuQhzdpkZD/ruVoLoDEV8PIumBv0NqnK5D3PuORKJVp9JrJKEj4uZFRNCMRiaFryKiplv91pFUvcFI6t4IMFJ2vaspmy92g/YBYq3+c0R4udRptSDpiePUPgwkMmW6BRbWapmYroOmGVIXknKYQ1wNs0jD5JXq/J//2idoJySS8I1BolX3BL3xN/p5ODskdVrDbkgE+bFGo/6hr9l2IhAA7mk06s2+eHZkEeSzbSnNfkfAimBZ2JEvxOv6eyBbEntp7o5cj+YqHpbO53q0X1niavtCRa+OANB1Uk7d2Ng+KFudaMjfEwjHrshki7uS6dKlmZznp057t+F2dk0E/hZJ+OYiRd0r+nusFtsfStCgHPzcwN+A+ZrGP0rHpI5FREwnEiG6G4nSVCMiYTBic340kiKO2tdy9WdzIBz7PnA9Egl6OxCO7YIUwL+MOLCNQYSH2Ti7DrgMWRn3IiJsF+Q75ueI2Aog/a++r8ZTggihHsRook7drgGvIELoWTWOZkTkaOp3Tidpdzu7aruSrlYDxyi1jzQi3EzTinKkpiuvflar26vVvv6LNBYuXG3OqGtXqR5TQ68Yq0CE11FIuuOQQDj2BpL98bblSguBcMwF/Ka8+ILlR0z6i9kc2mLLMB9xXnzza7YDicbuj7w3m9VtXsRVMYy81yw2E5bAsrgU0Au+WHdIHMXGyzanUaLZuC1++0FP9Pd4LDacSMJnR+o3zEnSPkFvfFusu/oSqgErv3n25caFSydmanLzut99/PCqUZO+OGvwLosNXacRmRhbWHwtgXCsHBFLU4F9gHQgHJuMWJkvRFLvDgSuoLf26SygPhryfxgIxy5ChISO2DB/VrDvs5BapF8APiRK8zjSFPgviCA5FxFYXwCNiPDQAuHYBYjYalbHLEVc+L4AXkPqen+MRLzcagwtiHV6Bok8TUWs5dNqv79BUvnGIHVaeyP1VF3qt5HHZetKVroMjFIkIlaizs2DRLkG0xu1MhAhV61O2UAE1kQkUjdB3Wc2LDbbedjsetJwOjrS3alaJxL9y6hzcyBW8G8iqYg/DYRj/42G/E3rfyZ3eOzA6LauwauD3vjp/T2YHRmVFrjeyFUBUeCNRqO+cFFvlnr8lM09tp0dS2Dt5ERD/rlfv9UOwa26gz9ZX3zbJVchEdYc0jh1Vv8OZ+Op+mTpM9pc7WKtvH3ksvnjtOF7zEPT0DJZ7sYSWBbrIRCOnQk4oiH/I4ggcCARILMp79vIivRDQAQRJV8gwmEwUg+YULs7ALFrfxd4f51DDUAEmhf4LdAcDfkPDYRjABcgfa2aoiF/JhCOrQIORyJLZUjaoenMtxIRZajfdYjwMd37ZqptPkGiPiOQPlTDkeiQ6SZXhoipNNIby0z5K0JMMAaAXm7gdCKCKktv9MuOfF40qfNC3V7M2k2DNcTwqBaJbKWQFMYMylgDDD2fN7Tq0oWO7lRtRt3XpcZXqs5RQ3renQycEAjHztiZm9dHQ/6uQDh2Nl9d/2PRDzQa9TlkgWAN0ZC/FTGfsdjMWDVYFhYW2yw33n/Ox9VD2iZWDGzRbHbagZFBb7y1v8e1KUzWLh026M1P3+ju9owpruiWKRnkzto/bi10WXwJlfaXQaJHbuAapIfTf5H0v+sQQ4dXkehVDokSpYB/ITVWHsTd7y7Eee97iLi5GejJZ7TpX/yhtrn+8qYUIiiKgT8jkZzXoiH/xcrIYgQSofkFIuT2Qmp3nYgdewRJ4b0NFV2iNyWvVe1PRwRYF72RJbPRr6Zuz6m/ZyORK7OhsJ1el0CQKJNpZFGqjuOgt17Ko/av0+teZ6M3mpUvuD9f8Duj/l6qzlfepeRVtEvvQVIHq9R+XlXXZiBS69ahnqv/RUP+9/p4Wnc6Igmfjgj2NPDeOzO+99aKll16rFRKix0dS2BZWFhsk0QSvrNWLal8rGJgK8kuJ8XlqcuC3vh23Vn+b+98e5bDnp9g/l/TSAEe1fzSwsK0S78MScd7DmkWPBBJnzsLeAG4EBEtTyC1Ey+qbZKIkJiHRGLmIKmEoxFRY6bFtQI9K94saWmd4Rk04tSWFz2DsqMQc4qrEOvyVYigOh1Jz7tdjakaiSIfjphnPIxE0wJIdCmHRMJGIIJlkRpXDSJ8ihHjijQijkxh1INEqPdDmpzurh5jkqc3vdE0obDTa0BhRq2SahymkLKxRiitlSq47jZ59Xgna4utFWq8FYjgM9+rZtTsNsRxsRmJgrkRgXZiNOTf6SM4SmDdB4w0DErf/OS8rtUdw+ZFQ/5L+ntsOwJ1WoMT+BHwUaNRb7nTbkNYK6cWFhbbJNmc48ZWYyTO7hya1pWhoBh/eyQQju0yoOKEQQft/hwAhoGhaRxniaudl0A4NhSZzC9H0uAWItGRcxDXvTIghKTQ2RAhcRKS0pZEaqX+igiqgfRGf1arx9yLRL2G0GtbXoOIkocd5bmjXLXZsbai/GhESHyCTIavV4+5GDFzsAG/RBoGVyLmGBWIM99vkWiTE4me6UhNlem4N0zttwkRe6YI7EbS/swIlhsxm+hB3ATNlELTZdCGiD7TnMIUO05E6JhmCqYroJkqWIgptIoLbjMjY12I6MojUSqP2tcARATOU9tXqPMy3Qir1RgGAtOQWq4XLXElBL3xfCThuwT4NVDZ0jn4M6T+zuIbEgjHtLEXuA9u/EfVCbkeWzlW+49tCv3rN7GwsLDY+jQu2+fGJav3MJZ37JJxFzOU7b/59dXpTHlJ49KJpDMukmk9Q6/TmcVOhopUPYC4641HekGdj0SoypBojhtJ66tAhIoLET6dSDraSOARpIfUM4hw2E1t34BEmnRE1HQi0ZYTEVOM6qq9enYbdXrLFGdZfgySItgdDfkfRaJYy5FeUPerscxHRMdwJHIVVY/ZRe3bFDcOpP6rFBFLVUivqHrnxrcqAAAgAElEQVQ1DnMSWKW2RY3RrI0yXQlz6reZ3meKKg0RNvmCx5oi7VYkDdGsxTJJFezPNLEo/Mmr8Zo9sVKIGDVFmoGI31VIfdsqdf2d9IrEnDqn+cBngXDsgUA4diwWIK/j4zWNUU9ddcwD0ZD/n/09oB2EXcvrk7+qv2LFq0hbBYttCCuCZWFhsU3yaaM/Anzc3DZq/g0n/267z9evKl38+ISRr57e2VNbkcwUU+pZrSOubQ/399gs+oVqJKKzColY/Qd4CxFAZUgU50Bk4t+F1F4tBc5DBEaR2s9gxPSiHhEnGaTxqA4chwgikKjXQeq+AFI/tAJx67sKccObEgjH7kKMLsYAP0AEXwUScXoJERFzkHqvHsQGfhwyn/gAEUEOJEVwHySyZFPna/bcaaE3ZdEUSqsQkTJQnV9W/TaFlZ1eo4u0OkaW3loqXZ1DBb3pfYWpgSaOgtvyhoENA13T14zTtLM3jTvMhWizXmw44hpYgwjLU9U2RUaeGiOPTbfzE3Uc07FwZ+dkxOykzwhLJOGrQBYHlloR/Y1iHvCQuyb3jtlk2GLbwRJYFhYW2yTKhWtmf49jcxAIxxw2fUB1TfmSeTVlS/YG0HVWI+lVFjsRqrHnHYhoyiARqasRM4rlSP3RgWpzj/ptQ8TKCfTWIpn3LUHqloqQ6MlHiLCyAzciQiGpth+DpAy61P4cSPQrjQiTJYiBxSnq8Q5E/JlOfBchAuRSNaYbkXQ5c5FgDJIK+AUiBoci9WE/QURIChEd0xHB51Tn0omyfFf7cdBrSmFGpwrNKRxIpMq0U29BhN9x9EavNESAtajt3WrfmYJ9G9kecul2UkUDcWoaGfU402reQESfHbGdn6eukZfeNEMzddGdbGaPdJsj76zM1Xlq8r8F/sFOTCThMx0vj0Su17DC+wPhmFZdtvBXY4eOP31ozedVyOvrha0/0u2TaMjfg7x/LbZBLIFlYWFhsYWIJHwjm1pGngNnXJ3LO0v/99H57xy170MHIy5uI5HJ66/6d5QWW4NAOLYnMtn8PSI8VtI78bcjk8+LkUjRMkQQdSHiphgRUGYK2zwkAuRBbMbTiDDTkHS8z5CJ6n5IdMmDiJta1k7LK0eiQ/MR4ZBEUuFWIkKoG0kz3EeNYZba32rgj4hQGq3GWaH2+yHiWGie23gkUjaU3h5YdYhwydFrdmGm65nOfzZECJqmFqjjVrJ27ZZBbwPlQkML8xwr8jkygKHpoGlrbaPZPeh2z5pooIl5jTL0pghq6hhXItb3Q5EInkddp2p3DZrNnTXUMU4F9gyEYx9GQ/6b2TmpAc5ARP9zwEuRhO/nSDrr1SccaD/4vZlnDGtqGa0Prfk8w9qRRovNQCThq0beg+8/89b1g4GKaMi/QyxcbutYLoIWFhYWW4hIwve3Ve2DT3nzk/NKQcdpb0u7nT2L9hn37/crS5e/Dbwb9Man9/c4LbYcgXCsCDGsOBiJXM1FJu0jkMhHufpZATyNCKQuZHJqNtadj4gl6DV1SCPRmcrOeXajY6ErO+DArm6bk0FIzdS1SE3Srkh0B7UvU7TMoTcaZkciUfsiIuJfSM+sCvWYJ5FmwR2I0BqOpDOOQYTbOHUOqWwP7xh5hjuKGY4IKjMqZKbsNav9DEDEHYBW5G6honiZsXTVeA1sZoof6neT+qlX/8/Qmy4IvULLFE4ZpDlxJYBhoJv3atqa7czoVKs6T61gX+vWcKG2syGicldE8DrUsdroTQdsQoSsaQ//OdJg+V/RkH+7aJC+OYkkfGOBAwyDuw2DjK6vsfEvB/SeVBE2Wy7ntKfsyGvq6KA33rO+fVpsOMpg5Azgomfeuv5i5L16ejTk7+jfke34WBEsCwsLiy3Hg6l0yYcuR1swlanYP5d32Xcd+VKdrqcdhsFATcOFTNgsdlx+iphXDEEEx0RkYq8jAsvs25RFolK1iPgyez3lkLQ+087cTJNbgURBdffgbM4zJKcZOdqQqMp44DGkbqtWHbdI/cTU46cgAudcJMIwn966oT0RAWS69JlNY6cCP1TjOlGNMU9v+p0zn2OSkcdjGLiUmDFTBbNqX9XquEn1mDygVZUs1obWzGJV+4hsKlOSobfGLKv+NuvKCsWU6f5XXHBdNEQIVRVsQz5PXretVZdlGlhUs7aYMh0JXQX7TKrrsxewv9pnil6nwypE8C5Q9w1UY1iBRBMvQ9KdP2MnI+iNz4kkfO5k2tOay9trit0dHhXhywA2j6sbesX0QcAHkYTvsKA3vrK/xrwjoOzxD0Oiznfdftyd7trjsv+xefLTZz9YG6q7uuEVZDFgcaNRv9MJ/62BJbAsLCx2aFQB9XBgxtYuoA564+8B7wXCsYOBiYZh/0/e0PzLV48dtrRZ/3zRyr2OfOat2HvRkD+xNcdlsflRroC/QibdkWjI/0UgHKtCDCUG0dtfybQD70Am98X0pu+dTG/Ex4w0mQ51hRbkaUQgacAouwfNyBuGptOJ1HFNRL7f90Ym9mZ9UwfSN+v/EFH3W8SYohtplJtHomJLEPHmAN5G3j82RAA2q3Hb1NjN1L5sPstDdjcB9DWpjC3qpxLp2XWhOicPvQImBbgXN08wVncOyacyxfaC8zTfr6Xq9ypEnBVeC3MMZh3WuhEoTdPAZv+Sa3KeL0epKLj2XYhwzKrrk0Qs8AfSKxBnISmSVbmMGG/YHGsEWqc67+FIo+eaQDimqdrSnY0jnfZUcTLtbM/n9bjNlt+XtQ1ACiOKtcgCgSWwvhlDcln9tpZl1YPeevToxzqaK37Q8XfS9NYUjkLaLdwdCMfuA5LRkD/bj+Pd4bAEloWFxY7O+UhNyAVAYz+NIQw8f9Du/3yyuW1YckDFfL0n5dmnJ1X+Mb0GAhbbNwcDxyNi4KJAOPYQUtxfgkzUTYFjWo2XsnZ9kYPeaImOTOpL6RULZs3SaiS977CC+22aJMENodfFrxmp1doFEWRz1ThGgTFAIzvBwHEQIrKeVfvdRW3zNiKyjkYmu5WI0+BxSGpXjl779DzQnc9iWzXddUr1nqkKXacbET52JPKUQequTFGzQo1zJSKK7GB3dCer152TmMcwqVD/X4ZExvY2r6GRpwTQDTB0HcMw0Iw8hqaLwOoDs9ar8Dkwb3Oq8SeRiGAOcT+chaR7ggjDpUB9Pkdq5QeeUldpzl25R/oztZ9itZ9BSLrlfkAQiRrsFEQSviOQ1NK/2Wz5ZcWetoVIjV4za88/C1M7S4BjkNegxaaz5J3Hj3hl1ht7Xbx89tATkdeh+b61I4s5bltR7uKuhc4fFw1L31unNQAsbDTqn+y/Ye84WALLwsJiR+dFZFV/cX8NIBryfwB8AH5+8cSvfpbKFN02pHZOSXnJn2+NTb3ktf4al8XmQUWvzkAiH88ClwPXIJP1XMGmRfSmlUFvZMqsS8nQK5pKWTtFzTReuBuprTIjMma6nIYIEFMspIDHgcuMPCOMLFW6kxLgYrute8guw97nswWHnAfOCqRh8TI9nR1t2HSHYdN/CPwbSXn7FOlBNwmZ/LYiE+Rx6jgrgUrNht1Tm201DLqR6E838D5wrLouR6rtP0SaGf9ePXY4En0qUdt9gaRIOpE6qgp6665M18Fa9WMKNsMAZ9tnTqNoSLrbVYkjl8GBgaY7yGkabYgwLBRrJqbIWo1E+uYjQlNX4zHTOYsRQVdorNENuDQdvXJ8Et1tmMYeHyL9yJLAy0iN1m3sfFGZXZFrdn/QG38CIJLwHYhEX/en17jFfM0WIf3V7t76Q90+qdMaLkQcLi9pNOo76rSGAcBB8KcMvenH5nu1cM5fBuQcZdnhyf9n78zj5Kyq9P+9b629Zw8JWSjWYhMBCaAQRWxAVEBsUaPCqIPb6DjaKqg4zvxcxlEaHQUddVBQjKgtKKIoJYpRQCKL7MVakJ2snd5re+/vj3NPv7cqHVkEQsL7fD796e6qd7nvVnWe+5zznI0Bm27v0mbmj+ZM8cqSzceNsv9BxCYXMWLEiPEcY+nyxb+wlteGlhEDR7ztqGX37+gxxXj66OkrfAgxMliH1NS9AiFICRqtvv3UthAhKq1unQXILLOuo2hObVuHkAtDZLAwBSFgajE+jpCTYaBer7B/dSgRpLvq9SDJo4lgLMymhhgpz3gYghLwuqBcm7fX5bcFo3M67eoT8hYhVrORAO1nSE8sVdBuB7rd2DRlUGfHtVYMHAFxx+4Twi1IgFd2268jwWA7EZHy6638dD7b9J7+T1inZgyYgIS1GFvHmgSPGcNUtz+87agiOOzGcTuisq1FVBe1eVeL+xRR3VXSnYcHEDLWhqQvznTHu9UdS9ktuwH4FKJijQBX9/d27/IKzdLli5NA+5JFywa8196F1P1pa4BDECVwOnLtj1uyaNnfdsBwdzo4MvVGhKyCZEO8jKhP3ijRBIXe72l/G7u/ZsC27l4xpaXTH68NJ65Fsj0eBE4u2fyW5+RAdlHEClaMGDF2WfT0FeYCQ6cf+9kWYHjJomWjO3pMDpcBpwSGDmu5fenyxQcvWbTs4R09qBhPG3citU5fR4hHp/feOBLEz0ICHa3tUVIxBUk/C5DAXOuI1HbcJ1sWCda7iKzKZ7rXB5BAP4Oktc3FqQO1UYMJLK42alo9bEmNlFuuRWzj3wB022RQH5vdYcent6k7YQ4hT1uRGXIQR7z5yIx43Y1zBhLQaX8qJX5phGRqip2qdKq0qYMhRKRHa858UqnkKkBIo/af8o0uakAiSEzENNYYMPLfXBqbC+s6tyLkSFW/vdyx5K11y1iGTTDhFugrhWW3rAavxl0TrbHDjek2xJlxNqJgJZB0y1N6+gqL+3u7N7ILY8miZTXkvvRxCaLyDiL1a29GCPx+wL8QN2d+UsiZYivwHST19w/At4jaFNSRz5gM0QRFFXmu1Z3UArX1N7ST6qinasPBTOAtbv3DEfX7xOfymHY1xAQrRowYuyQuvO7Nr5/e+aqLu9oeLyM1K3/gedJzasmiZT/9wV8W1wJD0hhagEuRGp4YOxl6+goJYCUSnLwVsVnXoFsDzGkI0VBjh2GYSKUDIQ0GUUqUbCQR4rIGIR0JJIhfwLamFxA5DFqgA6p1SAQQmMwUG4I1bntdbnynIs/EXkCXTQSbVp20f6fbb9aNZQ+kFmuTO47QHcdUItICjZbpuONrddtRh8GtNKpISXfcNeBuhGQqOVvrjhMgSAajphZmAdPhTo9fVVUmqnHzocRMzUUskRpVdMft27Pv7c4Ltk44ttFURx7NfH/WUeNHIiRpHULWQjdmiGzrlRgq2t1rexFduwRwDtJUeilyTl9wWLJomaqgIArg/wNYunzxvUh7gRdMjdo/CHXwfAvwISAA6+55o33lcL+vQRww5yDPYQaXnlkdSFaqA0lNRfYnNvLPyVHswogJVowYMXZJdLU9fsK8mXdP7WjdCGJu8acdPKQGJAJOtpZrAep1jt7R44nx1NDTV8gi6TT7IOlzc5Dv1BaEyKSQoHo3hOCPIe5+rUhw/QiwDHgPMpu/kEbDBZAg6jqwrwKbBpMBo4TFV2OGEOIEsBFs5oCF19uuto2Fm+59yzqkPiwkStfD7eslCEEJ3FivJ3IMVJdAdSVURWoYUbJ2J2pcrGl6GqApufIt0bu8/zW1Ty3W9yeafU8jTZcnUgprYdImTNnUbXbCdp0oDTFNI7ny31cTDlWeMu53c/Dopx1iEtA622ay08ZfjhAvtX3XY2xFrlnZ/axF6o1UwasT2b0H3liSSC+0H78Q3AR7+gpvAI4CPtPf2/13swecw+vjz8nAdgGUbD7MmeJfgBO8l93zYStgViAE9iDk+VJlPEHUAkGfieZ7sQZUcqZ4RMnm//osHsYujebc7hgxYsTYJZAI6l/fa+5tzOxaQVhj0ZJFywo7ekw+lixaVqhWkaDDECxdvvgFOaO9E+Ng4BuIKrovkeqjphYa1AeIcUKRKNifidTj5JBA/VxEKdJ0Htx2xoC3g50Pdrp4OUxASYpv6e7Wsxsfe/ywzrtKrzwcsYlvI2oybL2fBHARQvamI8Ha3sgstzoZ/tyNA4RATAUWud8Ztw0NnnX8SgL1PPjv6XGNEClBSpJqbpkmh7kUdZsJwdS97fk9r3wjEWNDbFhrsHrXdZTEKVHyz6fWkGEMxhjCRJo5RMYk+ttCGIDtQMjzLIRkXY+ob0rWEkSq1oBb91+Bi4FP9PQVOp05yq6MBUQqXoxnCDlTnJ0zxTOAFgIbgtUaqxATbgFjkUmKfZGJkBBRwmtMLqz4ExcWea5nAK/ImeJ7c6Y4+9k9ol0TMcGKESPGLokli5bdG4YuYEqQWLp88fOl/moC6TTt1Roh1rDm/rnTvvLL7k0f/sH5F+7occV4UuhGgusW5Lt0FeJYqUYPEAXci4Ez3N+3IIpTErGjno/U5O3u1qkQpdClZVtGnQeVnKhxRtW9rgHsONAFgR0Zn87w2GwlXgFCBkaQdLwRIlLyakSl8snO4e53FiGHl9MYgA3RWB+mfaoeRsjZo95xKHHxiVEWIX1dCDnT7STdOMYQNcMjqyYwjI65fYfR6xPb1LER1glsiLHhBJnSZZuVLt9EI+G9XyMiwn4aprue1m1Ldoeoj/sTXQc95lG3TgtS/3KXG8/7gd8h535Xxv8AS/p7u5vrsF5wyJlikDPFbVwsc6bYkTPFT+RM8fAnuZ0pyKTHJUEqPC/3lk2ts48bAveMYBMtyPOdIFKjpyP1bn5bAr33h5F7FOTzQ5/FTuATwJeQWq8YTxExwYoRI8YuCwP/p3+HIS2X/WXxPTtyPM1YsmiZTad46/qH59AxY4iWID1t391/dcrS5Yuba1piPP+QQQKTYSQV52Ek0FY1Zq17v4KoU+okOBcxqlCzB588QURcdCY51FZXEIwhM9GqAK10f1e8bWQR4ncGkpo4DAxkUiNmj91uDdLJ0TxCbv6GBFT7ueWU1CnUbOKdSJqXxgs1t29dVvcbImYfXYgylyUiL76ZhZ/Cp8oQ3utq3jGLiPRZCENLps2N/TfAEIRWfhoynOr1MjaMtC49vz5BbFYCJ9IDHSru3GhAbBrX8TkbIUJetT5LzT4SwE1EKttMGTNfdL87kftil0V/b3fY39s9vqPH8TzBBcBFOVPcPWeKL/Zen4G4Vj4pgoWk/OWBtA2NrY0E5fpIoBM5IOnHbQiZfzfwC6L0eIukyw8TmdT4ynHC/T1E9Hnk14rGeAqICVaMGDF2WZiADxjD56yFDQN7sGL9wQdcdvPLX7Ojx+VjyaJll0+bt2GLCULSbWVaM+F8xHEsxvMb/4soOzcgwUw30vvIIqTnRiT9bwtCila596baOnvYOsYprGUaFSHfIONMoubYWvuj5MUgM9StRClpag2eBf4DCcamAGNdbevswtl3ZDvb1un3/iFE6XUv9vZfI6ol2oAEczOJiuq1TuoKIiVMicgpNJIVJRx4r1lvW7o9n1Tq+YMJRchUITDyQ4A0WW43kplonKKkGE63Y1ItYExDGuX20vEme28TcDaNqYcBQiofBDMKpowQ6HEi8uwrFBYJmv3jfAvSX6wAXNff233HdsYUYydFzhSTOVNcnDPFhTlT9O+rTUha7aeAb+RM8fScKQbIM/wZ4HtPchddGFsDi60bu+Z3nefMOHbo25kZlfuRe3EWMklRQpoJ742kI69Enqd9EXdQVVlbEcVZn9Uk0v9OnUCXIm6jMZ4i4j5YMWLE2OVxyQ2LV9372Im7j45PYb/p19hUtnbpe0/8zTt29Lh8XPzHxZV0mlRowUB45kuXTdYUNcYORk9fIQ18GlF+TkXqa5YhtU4atNSB7yOOgjXgXqRXTRcQhlV49Gcdw7ufNNyamWI1bUdJjUL7ZKl5hTbZrdJYmO6rQ6NIcLQ/UeBvgYIx9UPbMptbh8enz4KgTKSyqNlEK0LOLJLSZoHfI06Cs9wyaSKHvIfcfqBxNlzTAm8Cjnb70EbJqmj5ZhBKwIbYtvFsM/HxAxYj6pVxy5jm2jL/PEKjAlchqhPznQD9fQ678+CnDur2QuA+hHwm2ZZQWrePtNvfOvfaPETB6kDuh6/393ZfRoxdBjlTPBhxhe0iajb+RkSx3Bd5jg5B7pUfIqr3X0s2f+4TbPf1bVO3vrFrzua9R9MzD6sMBmZsZTZMtIWXty8cP2bwwWwyHE1WEbOdO5Ha0MuI0m7vRNotpJB7Uz8bEkTECqIeekl3DH8t2fxLn4FT84JDTLBi7NTo6SvsC6T6e7ufV6lfMZ5fWLp8ced4pe1Hw1vTJ0+bvoWwbtg4tMcXPnLSpZ/a0WPz8f0bF4eJhHMyM3xkyaJlX9nRY4rRiJ6+whTgDqKmwHUkKFHioSlz65G0MSv2yWYER2qsJT38aKLSNq+eDlITdToYKl1BYE09zFgkfWwqEvyrCqMW7x3eaxZsHeojkKwiQVGaKFBqQ1S0mUigpTUaENVOaSCgjXfboW6EvARl9946JJ1JyUjZ7WcUmZ2f75YLgRVIQHc8QpomIzGatuf37lFSMtnkgl/H5ZMpVbX89/1j0vQpJaXu+BpIkRLDkG1JnZImv15Lt60NhVPe+yDq3hFI8Axy/mtILcxaJAB/N2Iu8h/A6cCn+nu7Y4vynRg5U3wRUkv5JiRFdx1i9KGfE8339SZE3f5zyebf7xSvY4ANJZsvetudCdyXSFWmHfeuX4788UcnZ1rmhiSyhGPrUmvG1yYtmN3BfBpR1kNEuT4S6TOWBf4ZcbB8KTI5cALyGbMnkz8/NaQO8mbgrSWbj1M9nyLiFMEYOy16+gpzwH4J+K+evkJ8L8fYLpYsWjb4zmOueU02PXpdpZyyQcIShoklO3pcPj507nn/fPX5SwbvuPZwNq6YBfLFF+N5gp6+wj49fYXpCGl5HCEpf0OCbDWbAAlSUgjRGYby/eLyFao9e8IYBjty9VVBasK0AiCz/8Jl5tC9fw0S7OzmtqPbRbZjO5yb4Ji1jIV1COs2CUEXWLVNBwmqZiLf89Pd6y1IgK/EZpDIOKOKzFp3QliT960GXr9G1LRrkdQjXd8CV9EYS4wixPK1RK6KbURKjy77M4SI6fGqG+IIUdG9h9A48cgv0K9suxzQqKilifp0aUoU3v/+2DU1c8JREFEo/botNQ3RpsFah6fGGDrZtzuRIUkrkW17B3AaQoDLwNfc/8fF32M7Lxw5ugD4CUKw0oj6q6SqwYjFYTqiAr8tZ4q/QBpSnwe8z20zmzPFPZAGzO3JdKXy6O37rq1uzdrBYutoeWNiXXljcjMEtzkF91SEoL0P6S92FqKiLUcU5SuJ0nj1M0qfJf8e1+c7g9yn8X35NBD3wYqxU+KzP/9wCl61GpLGMDz+097TwydeK8YLHe/uLrxq6fLFbfevOOqf1m7e7y87ejw+umZtyRb/dPDYTZd3d8zdfwWnfnzpJTt6TDEEjlh9DbiNCac+HkQUCm0i7Js+aL3UaCIwgzYcHw3JpCGcC0ECCbK1qFyNLszw2Ew7PD4jREhGi9vmvUR9oqwTWWpgAiBVH8eYZGCDRG0MEpr2F9CoBvnGEtrPSgOqEKkleyUy226BcsKMjNVtxxS3rYUI4T+YiPzorPzx7nz4zY+TREFZ4O1rlMhpbzqR9TneMup8WKWxmbFbdIJg1cGmZFnjB4dKrPS1ZgKlCqNeL297E42Os0RqZBeNwfG4t24SUbDGABPKt9CBxpAzZsI0RFMiU0QNmPdz2wGx778fCaKLiDoaYydBzhQ7gc8hkw8XICmgB7GtElp3P5oqq/ep9mo7GqnZ+zQymQFiMPNPSG1nujzSPvTQzQdtNAn7f7YeLBpdmb0JUazmIkrTFcCPwe6RaA0/Uh9NDCI1WK/BtWoo2fz9OVO8EklvXUD0rAREfen0ebgWeRZUqY7xFBATrBg7JdqyW2aJq5bBksn09BXMC6FxY4x/HEsWLRthERft6HE0I3/MXRflj7nrW3f97vBLZ+255uVI4B3j+YEtSD3DCqSPUc69rt+hbUQWyBqYGyCoh+n5kK5DLQlB4GIrQ+SyN5Emt2L9IVq/1YYE+nUkEHIBjwkgUCKTNQaSrRrIJetIEDSVKH1Pg3ztk6N1TqE33nE3lq8DhwJ7QnBI3XZq2ptBCIGaafgOl+qOp+lPNXeO9nPr6baV2ICoeNOQYLLTvaYBXoiog/NpJDUGglBUrIn2VemEGQ0xSephwkLSV7b8VMRm4pUkIoP+MhoQK7nyDT/8ZZPea7rOANCJpSWsYRNp2pverxKlEg673+PAN5Eg92J3zh4gxs6GLHINd0NSP89lW9dMJVLjiII8BWgxgU1OedGoGVuTbh1fn7oWSen7JjCwT8c9787MDDeWN6Q0nXUAuH7KQaNT93jzlkX18eBtV332lUrSHwTOB8iZYu+CN2w5a8ZRIz0PfHPm1cOPZM9DnrNVwPBxb7mh9cVfGPv1I5dNP3jw3uz7wYTIBEGWRvK3GnmW25lUUY7xRIgJVoydEh856ZLVf3ug7x2j1YO/B2mDFHT+544eV4wYTxdLFi2zQHUpi9+ONOd8aAcPKUaE/RAVaU+EXKnCUkaUJogUF1UttiDBVA5o92L9ulNcVJ1p/h5WM4sEkRoVMmHMYCyeSYPnlJdGUv7GkYBphtueprtp8+AfI+6EaTf+CpJWtC9CbNRkQk02Otyya5C0N9/EQYnGOEIKM0iRvQFsWOMuDIcHiQlyo6mCY0T1XxU3Nh2vru/DkZXARKmCCerWBNnUsK1XOitu30126hNoNs3wDS/0HPvr+mYcun+/SbG/DZDgugTYIMUCYya2qaYkZXe8I8A1iCnKG4D3IAHsJf293ZdPMu4Yz3OUbH59zhTfhNzHcxBHTr9espPofsoAvwXeCphUV93OOHIkUd48Ztb8pus1taHkMWD+CgzPOWFrX+vu1XUPfFpLy2UAACAASURBVHPWb2zNtCDNvffKzq4VElk7lsjWa9uOBko2v6ynr9BqLa8rb0ouQiZdPgi8CPhseXMiWPPbzv2xti3IhMmwnDDIZ5iv/I4h9/sxwNklm49t2p8GYpOLGDs1evoK+iW/rL+3++U7ejwxYsTYddDTVzgSCYzmA19GGnweiKTKjY2sCYJEhjmZaWHSGNYhbnspxODiD0gdUpvb3CgSyIwRqVSavlf3/tZAp0IU/MO2qXS+WmMBW6/w3epg0J2ZGs42iQalyS+wvxhRqg4hSgl6DJk5H0KsxBcTkcgh9zOCEE3fRl7dBzWAVFXOAGF1iMfHN5FtX0CXCSaOI/TORRUhVw8gxfc+AWo2tXBBax2XHmmdmqeE0K+RwltPX9eUye3Vk+iyupzue537f3rT8uqA2Gys4QdVqmIpQdPzfQ1SF7M/khq2Bujr7+3+2XbGFmMngKuX+iJyTXcjSkXVfngdyMRLJ7AJ7Hjr/IoNa+w2vj6VwXAhteAcYJ8ZRw1fnWytt6/7facB8yPkXpmCOHtWgVtKNn/F3xlLAnEp3FSy+X/JmeJU4EN7vHlTy/Cj6SNGV6cro6vSHVizAEmDzRB9Bl2J9OZajUwGnFOy+fLke4qxPcQKVoydHe9CAoM4tSJGjBjPGHr6CgZJ96kDfQjJeC0SVE8DkumO0Dx2ZdfwXm/bGmBIIcQhiwRAZ9BgUDFRb6QpaL5ioiTBD9b91D59vfl3A0zAu5JtobFgTbT9ureORXraWCJyFCAz7ycAX0LqOVQ124QEWX9zry8g6veUojGtSI91ovYp2c5ura1YTJTX58ahaYoBolxNIyKAWn9WIzLG0Ik0JDUciMiLX6DvE7MQIbqziYxHmsmVGnvoNpU0DSCpliBBbXNKoEVSGWfQaOOuDoO6f922mqC0uX0cDFxCRHIXAK9CjD9i7ITImeI+wDeQZ6YTIeXqjJkiqi0sIff2BjBTR1dm7gW7CuyREHS7ZT+w8S/tdyOq01+Ab+7fu3bayKOZ9276a9vVIysyH26dX+7s6SvsAXxjO82cW4FzZD/gxrH50cun75meWvvrnmdt3PLwJdNtdSA1BrwXyZrQOslTkPtaidenEBU2xlNATLBi7Oy4HPnQunVHDyRGjBi7Dvp7u21PX+GTSNDxaWQGuQoYG5KqlwmSbbDg9VsfMQEHIMH2nYjaNc/blKoXCoPMYms9kzrt+WqWH/RrETx4aszI2sSDLTPrBwTJiNyYBIGxWBtiSUwQmIS1QkSMmagpgogYrUUI1tFIw9N5YiRR2wKpaWBaiMw89Hj+6pZPs22z4LI7LozBuLYDOkb/PPgKnU+MNOUyWa+ArRtTHQ4Gu+YOtlSqLW2u2bCeF6x1+7fUTdBAQI035onlm6DnwnrLGCRA9khdQ2qgpn/O9o5ZoeRQCZkqVmnEkn0ukjY2D7gIIW9bgZcgpC7Gswzn9rcPUCrZfLXpvaOAWsnmb3kam16LuG2WkfrCLiR1dgMRSU8g90MBcRq8BvgsmGvALERIURYhamuBlSWbfx9AT9/Kt7fOqU2defTIyls/On/Jfv+y/gNIc/MfuWX94zgCaRC8Gjg1Z4p9yOfS64CaNfbhFT+fckBtJFF34/tX5H5UlTVHNGGxAHgF8KuncU5e0IhTBGPEiBEjRoy/g56+wumIyQVAujqEWfHzrvLCN24NktkJ4wOQYvO5RA6AsK0y5VuI0/QeSAphS9N6FilS7wQ6auPU77tgt7sXvmnTwZ17VX1VRk0rVGGyNsRYCyNrGe6YN9Gvyle2lOBUkZn1LNTJpraOjVe7RiAxwxtvDZnhtkQWz0qwVrr39iQKznR5JTpKWrR/lCp0PpGZQHkL9bE1GdM5a8Ae84orE2s27WMfXXdEgymFtWAt1hhwBNK/Hj7J0WPFe8+Ns0YyGK/Xwrakq3HTa6RujNpDS493e2mGjgjbAEKbSQ1uGq9MzRjDEDIJeBKRC6EGXx8ErgZG+nu741qXZwGOVH0Cea7mIimw9wNfAa5nol8dFwFjJZt/89PcTwK4Dpl80HvwD4jL4JUIeXoA+CrwcWSi4lJkAmd3JG10M9K76uq5J2/ZkEjz6g1/aTs1SLI21VV78dZ7Wu8r2fzAAXPv3HN8U/I7thIMA58q2fzd3jgOdvsoIcT9QLffGcC9BOGBybbw1NpQoiTkjn9HGqPPR2zmP07U4iEEvluy+bOfzjl5ISNWsGLEiBEjRoxJ0NNX6ACWIPUIai8eJNsxu792ayVI0Urj9+g+NBpg1IhqGyAK0H3lxAI4lQmcOyCNaWchEvAkAZtIU5+xaOjWltk1LahvrkFS1atsLRkMpm0ubQjZ2YjUEw0jRKIVeBg4IBqXqY5XuwCjZE1TnXB/a/qcP0M7BQnQDFFvrxRROqAabbQhdtYfQgJePx1QU5TaADJTw8eyHZtvManUGzcN7s7A0O4+AdN+WV3OEh2sTRKGVRIJHVtAZEmv8P+eILrpVDlZL6dCS0bPn+5Lr+E4UZqhXwM3RpT+5a5VLQmhsfWgPayRMgYbJBl2Y9Z9qnHIVUitzN+QlK4YzzwywL8h960+G+PAt5FJiyxyLdqAK3Om+K/AcQjx2B04EbnXzinZ/N9TJT6DEJrHiUjbNORZuw5RhpYjilMZUZRei9z7Jbe/PELEV7TMqh1mkuxTG0pcXBtJzGFVeosb3xpIfwg4DLmX5uZM8W7gs8gEx1cQdSwAXua2f3HJ5vtBCFhtKNgTUa673Dh/B3wS+ALiHrgVsXgvAxc+lZMdQxA3D4sRI0aMGDEmx0+Q/lc9SKA9DgwYg81OpStIbKNS6XequglOZlLhqxdmfBO1kdXbBP3W2okfTRnMuu2NmgDmvGrkzFS7VYdA3/2u6o2lHiQoBwE2kFG0IAGgQQhRFgk+VyFpb45EBXVI3A3BenfMxi2XQpwKt3rjVcKhduwKrW9q8ZaZiRCL84iaH+s5ASFvD0YnIszNmL7q5NCmzP0rX24HRub656gGtDvlSozvQ2sZLAeEVgmd/9OsFE6obMlEmY7WDdaSrHnb17RGVfp8q3a/ZktNDHTdJKQCSJtKvTNtAoxJ0E7UfDZNdC2nIwrHUcAiV/cX4xlCzhRnuJ5PfyAyYAG57nsghGcGMgmwASEc7wHOBo5F6uQuRtKDn8y1mQkcQeS2WXC/L0HSQG90yxyKuMRuRZ6rdjcegxjljAMz1t/Q/q6VV075ZG0kMejGeB1CEi9ByJUa5swETkYmOI5GHEHfhpC1DyIq3d+WLl98wnGHXvU5t8ydQSbctOD0zXclWsL/QYjYJ5G6xfOR/nt6zE396GI8GcQKVowYMWLEiNEElxb4YiSIuRsJYvYhqruxyEzvHkTKixpYQGTRrKoVRP2XUiCpbcl2EpmpBDZ0lutmgmAZp8xoqh8msEDYYahXLOkAIUYziJqDQqP64vdwgihFSuuhWt3vV7nxb0ACvDTiGHgTMrP+MJJW1YoEj76lM+6Y097/KbdeHiFt6qCmx4O3nB+4BkhwKKoeAVuHZwUQliGhCqLuo8M710KgAhPQlTV6Dr3tNqf11Yks2hO1ehuPb8nrePx9BGGd9voYJFtpNcEEgU25Y0oS1aH5DZUBYzEEQWKCqA0jatXriOruQMwF6gjhnYqkiMV4isiZYhswrWTzK12KXCdyrk+mMdatwUQbhCqRGcXuiPLUjhhVZMG2znn1wKoZLxm946r/d/yTURfnIyQNos+Er7p9fABJ1bsZce38A6KOH4QQuGmI8vQyN/a2oQdbTiZq4n0Lonbt7/4fBR5Fnv/z3Db/7LZ/NzKRUkLSDRenWspTbv/NEYsHN3bNAG4APjXrmKF3zThq5O2VrYkb113XVUTqtn7rjv8B5N6cA7wvZ4pfRGrCJjPUiDEJYoIVI0aMGDFibIu3I7PLaxGioCQmywRpqc+BwDrb8AyRC5028fUb/iqRUKIQAiaRlsA/iML/OlAxRvpc4chWdZRKuj0UYkZSt7EQSTvbiARoBgnQq0hxesLb/1qElGh9kUXUKIsElQ8A9yH9mdJIwJ9HArg/E6li6o6nZCMgiiUmSAtRg2TfuAIiZUgVJLxzExD15AECKvWpWWRG33rr+iQyMgaZkLIa9uU7NirZrXjr+799tcsCdnhFqlwdDLLTDirrsag5yJA750pgy25cvkKpGHfjOAwJlPdBCNcqJDB+PZKO1YOkrcV46vgw8KacKd6MkIoQOddK4n3HSL2Oeh+CPC8bkWfhEKAdgw1S3JfI2PGcKZ4LXFOy+Tsm27mr8/Kf7TJwKuIYeR3yPPUjDb2vcuPZB5m4UWKvKXv6zEwheqYOAe5w69wJvAMxseh2f7+nZPM2Z4q7ISmNf0AmRl4HlKtjmdk///yZU2qVlBq4zNtwY8fLa6PBuvpY8L8lmy+6MWx0v6/PmeLRSHuK4WkvGe5Pd9R/jdSyxXgSiAlWjBgxYsSIsS0+CnwEOA0Jevw+VfpnGsbr0KIBvwZtAULImpUT/zt3i7W0GsgS1VxZZOa51ZgJZ0FjLSRbSVmbMMZYnBHDOFHvqVa3TYMQIiV3GlyGbplWIlIEMlN9M5HRxT2IRbNuq939vNP9P0TkhOaOt16FsAap7aVE+soWRIREiYim2floMvqwLVAeglQIibamdZrPsV4jn2k1uzgOu/XSk7zn/23bF1Sz1SG5BqbRat5vzlohItdKApsVszriPLiv2+cYkpa5xduO37ssxlPDdYj6ow54A0RqaPOzCY33SOAtM46Q3n27DhzFBLZ8x3/OuRoxqtiQM8VB4POIIcZjSN+rDNIyZi1inlFC0vKOddt7C/IZ8m9unHMRcr4O6Zdl3HgPQO6TdYiaNkZUm2XdsR2LTIy8BEkzPAJJ+TstZ4q/QtToE5C007cAv0QI5zW1SubXbluDwPL6WPDzjTd1dANzcqb4GDDu15i5BsPvPyR/+9Tdjht6dbIt3OepXJAXOuIarBgxYsSIEaMJ/b3dDyM9aKYTqVKqcNTB3C2/U0Z+N6gfqvb4NVr+D8D0IKDFBGJn7l4L3P4aFB9jsEEgv11MqAqMWj/rGEJg80SGnYw5REhVu9ueKlpbEbvwU5CZ8tciaUyPIwFYjYgEaACqpMgRlNBGJLPh/PjH6QeyqiipEqZpVD4BGyRSArTWyiSD8QBMJ1Ftm5+eiPe/v/9K0+u6zVlEjWAnq9fCvR4GCUxmCgTJiQbJal6iaogaJiTdz7qmMZXdMbXQ2DcrA3ytv7f7XKRe5m7ifo5PGyWbvwlRAi8kOt9p5JrpPTaC3NP6XOhzo6puO3INMwBja1Kr1l3XeQjW/AIhUje799V6/0CE4HS4YbwTmYQ4BlGu/gD8kxvTjUiT4GMRIrYFuB3sMInQJrtqU4JMqPWKLUjq4ONuu+ch6YefRJ752cCZCOF6tTvejwN/At4q2+Xf3/WN8x9/x9cv+Inb/2vc8Q0gtus5pM3Nmfl/W5ff++z1D7buXnnXZOf2juKhW1pm116Xag8/+MRXIoYiVrBixIgRI0YMDz19hSSSWvMJGp3jdAY8BPaV9MDA/x6dzMLbTznz3/fJmG/drstqIK7rGCRQVMvwgEihqiGkZyqE0yE0YBKQWInMsr/CG7cqLRrMJdz7NSRwUwfEe5EZdT32svvdgRCXTWCmgjXQnnHva/qe2qN3uWPdXqzxGJKS9CIihSHjtp9FgtUBMAtqYVerd9zN8FMAA5hwZUwDRojpxDlXZVCxEZhmQxLWEgaJiffK9XGTqpWxma4J/uebcahpQogErkoiZ3t/q9KWIFKnNiIELwlc2NNXOBwJxD+OpLTR01eYD6T6e7sf2c55i9EEl6J3EUJEVK11bQe0pgqQOsMuZFJBVVmdfNDnYQ/g8dZ51d2zs6qprcWWzNiqzFeRtNfvA2eVbH4gZ4p7AH8Ezi/Z/KAbRxoh3Cci6ncO+DHSRHox4tI36vZ1GgmbSE+p1rFBMjm1Whtfl1Hzk1EkBXgqch/NQ1IIK4gT4Tp3TANEqtsxQSZcP/+Ugeu7Dhy7bt4Bj34+3VLZa8kXv3nm0nPftxmpM1Mzlh8hNVxLEtmwYqtmNNESalPibdDf2/1o0/k+Bee0WLL5oe2t90JGrGA9x+jpK7yip6/wyZ6+wtwnXjpGjBgxYuwAvAn4LlLvMERUU5VAApmvAPd0tK7dOnvq/daJHqoUKXzFBO/v5tof2Cb9sKE2pJmQKYlpfn2K/G1SUhMWjiOqyF5E6hJIMDZMpLANIcHaQ265NiR9qROZGd9ERPiUYCSADVLwFABGCVWGiFBkvb99+EpRB1HKnJo+ZIhUtkD2A84r0FeXVKEaazp3k51jVd1g2zqcGdaSwGDCakMd1nXrb2ir1waTpl6e2Ja6AGa87frXrvkabnDvz3L7t0T9wqw7z/8G/Cfwp/7e7lG37ueAr/T0FSZrjhxj+6gitUptyH1RdK8NEKXTKoEPiZ7r9Ugq3laiusTZA3e3tGy+ta08tjq9gii19hVEjcRnI7bo8wFypphBUvRehjTm3eKW/TXyeXKKG8NUZILjHgzJ6nAiWR0O7Pi6NEQtEarAbYiT6aMI+b6fKD3xZcCRwCuRlge7AT/t3Gf8RV37j/VuuLH9sz88573Hr7w7d/sVnzuzBXgvcK4b8zcQo4sVwPn3fHHuL++/aPbB+753w8M9fYVe155iu8iZYiuiur4SWNjTV5jd01fY9wmuzQsOsYL1HKKnr/A6pNlcHXlw1uzYEcWIESNGjEkwQmRmoGl4qlbMAz4GBMOj00xYTwO1cUhp8LY9NKfK1ZDvgox7vbkWSBUnfyI0672vKU4gwVgmeisBJFoR0rUMeCNRTdYoUbC4CQkC5yKz20lvu1p/pUGoQerBKq6h795NYxtBCIOaX7QSpV+p0x5EipimQ+r58I9Lj7sVSZVS5WkzEpz6RM+v8ZogOMaADQmdo6BByJyOQYPrtNtP3YbYRGYiBdICufZcuRxWTCJITqQ14u1HzQf0hG9Fro/eN48QOdTVEGKVQwiv3k+q1O0NHNjTVzgKaTp8MdDa39tdJ8YTImeKr0bu4X9DVCOD2P0fiJzrmW7RCkIq9geC1gXjhtB0ja7KaG2iqptCwsPAVLYEWSTd717k/vsBcFjOFMeQRsFvBQZyptiH1D09gqQIqsK8P1E94W5uHGXkee2gFlhbC7Yg9+QM976S/z2Re+da4ArkeXwr8FOg1+3LIATrMGDj0MPZ4ceXdTyy8eb2M+tjB3U9dPNBxyE1WkcT1R6eALRjwoNIhWF2Wrj/+OOp25EUyJOQe/D+Sc6zfg7MRUxBpiLErQrs19NXeHN/b/fgE16wFwhigvUcwZGrK4hmHkcnW27p8sUGSC5ZtKza/F5PX2Eh0j/hV/293du8HyNGjBgxnhFUEIeuNiIS4JMgA2BJM1KeYiHhEyslA82kyVdu/PopX/XQ5ZRgBE2vTaYG+ev6ywQI4dEgs4aQgGlENWWPIgrSeiQdSWui1KAjTUQYgrCGHV2TSGRn1W0yO6EE6fnoRFzxZjWNb4wo1riPxp5CGtD6x9F83KpmJRGStBJJ4Wo2slAFaWKbJpjYju+cqOvpdq0xGJNoMD+wwMyOPaudTWNT8umTW702qmz5xh1VAGtJ2xp7mCRZY8i411e7ZecggeoPgSGwq08/9nMB8KgYxMV4EjgJuSd+iBi1vAS5b8sIgVVnvjGE5GbBmt1fPRjWx4195NKZSbf8GNhs0BLaVFu9Wt6Y9p/3Gciz8wFkYqIdUbOPc68vAlKJ1vrvg3R4SG040WJrgZINnUz4vft9G6I+XU6URngrUs+lrR7uQ9IPTyaycv8pQo5GESXqb4gN/N7u2PapjwWzHr++cy6ino4gxCuJPOvr3bIHgp2FNQkqJqyNm4NJ2AV3fGbub3Y7fvC8VVdNnZ37aPEhgJLN+yT/AuQefz/isno1cHbpR9O+Ne91Az9ItYdxqqCHmGA9d3gJ0RffOJJzvqx5IWs5ox6m3vmRH/z3l1asP+wgRMa9wxGqUxAb12JPX2EV8uDcjsxK7AM81t/bXX72DyVGjBgxdmlci5g+zEaahW6H4Jg6JFSN8YP95u9WP4UMIhVH/9Zl6kCiOmSS62/oqE8/fJTszJqqJH9Evkd0NtzvJ6X1QH6PLuNeV8c/7dmkalcaCQoDop5SdaL+NxuQwFFVNkyAGVmVDhIt5WQyO9FeS/fVSlSLlPL2qeMF6a3lK0E6XrXQHqfREVEt4XU/WSYnVwpVzCZLrdNz3pw+qDVpzQRtljdu3b8af3S618ZptPvWJs9azzahctXGCYM05WSGCnAXok5MJ7qvVgE/O/qAy+8Avod8t587yXHE2BafReKjG5F7OYvEWFpzaJA02BIySV1PtoeYRGiqA2m9/gAZkwrrbfPLZmxtWo1W1BBjGjLxkkY+E16MpB7aks0/7hoaH7Dfv6x/ODOjNjT4QOZHD188a79kV/Xk7PTaPcOPZB8D81bkfnmV2867EFJ4HGKQsQxRkh8ABpNt9bNI2HJtMPkgcI37ea3b9x+Bu1wt2DuRdL0jkBq/RxAy9q/Aa8DuS8LmsHYfwkQNqSObG2TD/WzIgtrWZAZLslYL9lx11dSD3HhWI4rdh7zz/DiRAU2rO/4tm29te/vmW9u2+g6EMWKC9VziFO/vLPKQXKgvuELXU3afcdrL5kx76LB1m/f7X+TDtwC09/QVPoV84J4KfBp5kHLIh8dv3d+/cstlEEn79ljpitHTVzAA/b3dtun1AJk9XdP8XowYL2T093bXgAd7+gpDSFr3iUhQ7Ssu0BiYN5tYKLQxLTQqG+uJUoZ8ZcVWtqTMSCkTtM2vDGdn1rQA/0gipzrd5wgS6DSPQ8ewNzKLX3GvZdmWnIRIuvpu7tj2RWamX+3en1CYTICd/dIxPzVOC+aN91pzHdlmIGlsfeRge/3sNextNgYLfdWqTtTwtZVtiaqSJiVtmuoYIGSn1Vvevza+0qQpkZOZjPjpnzoOvO34BiTppvfrQAtUbSpZTlVrLRYSOn4leTVjINnCPSbB4UTpk+NEhiCqSLzrpnvf8sfTj/3sOUQOcjH+DnKmOB/4X8SdTy389Vmogf3lbscPHlIdSmQ2LW9vRa5fuTYc2M23tZnR1WlfiV1rq4npQw+0aErrMDLRMB+Jq5TwH4DUYvnrdgKdq66acue0Q0e/ufIXU98D1OtjwbTy1mQaIWijbrkASc29BCE7y5HJidluW1VgY3pmpVrekJwK9howb0fqrqYjfek+W7L5IVcPdX/J5u+e5NzMBPY12XrSjgdJqWG0gOkGMxZWzTipEDL1IcYT5yOpqUcjz/4I4nao29Jn8W7Xb6sdIXNfQtIV1+dMMVWy+TjmdIgJ1nOHq5GHUruH39vTV5iFPFRbkQdt39UbDw5XbzxYvzQ6gePd3zexbcNKkGt4MvKl8z6EyOksywXIAxPjBQpHrr4LzOrpK3wMSRf6AVLIO4x8oI/29BXKwEn9vd3bfEjHiPFCQE9fYSYSLPysv7f7avfyZ5AUnoeIAuYUkY16AzFCAie17FZlYwMykWFpbNCrDnvQmB5H6/yKzZ25wSZbaPPe17Q90/Ra1b2eQr5LpnjjskQNkBOIoYWOXZWsiltHiUMaIZRufLW6G1YZAq1ZUiix8+um8MZpkIDQZhibnuMuWhmyG1no10wliFzclCD6fa40ZVCPR9U6PQY9F3WkbmQ2URA7QXLc+/cBh3vnUBsAax2Xb16h+/PTxHwFzLpzaQ22Pmfag/XR8Y5w4+DeSv6MO7cDwD1BkjlEtvcGmTwtAmcjZPtlSN3Qo0sWLYtrr548ysh9DdG9MkGyTYLV7blyd2VLMrtpOVkm7jdjN/6lQy32W5B7dg4QYKBl98p9Y6vT38eaT7ntjSP30QhiWmGBpcDMnCl+G3gpULijeOhAzhTvQJSpdmNYWB1IdAK7te05vnp8XZL6aFL76r0cUazORgiiqmnzgPLYmswdtmKOAnOaO87rEHv2PQDjHAu/hyhzDWqnc1X8EXCyHQ86SNEWGBuEhEkqSUn9rZsK1pQJg3Ywn3DHNwNRmv8P6M+ZonHKVMYd4x45U3wv0Ae8vmTzlZwpHon011oB/OJpXcVdELGL4HOAnr5CDsm5vQN5gMaRYsjLkZmIlUjgm0a+PKYgD7radbYQ5eVOlhaRDOu0Da9MdNo6ByAuOvsA5/X0Fd7f01eYPsk6MXZR9PQVUj19haN6+goXI1/uZyIzUnci91wGKfrNIeR9BvIhf31PX+FVO2bUMWLscCQRUtECEwrvLESBWYB8PnfS2KOqDFRtSGjDiXQkddKDyFzBeD9qkd5GYyqbKijGGEyqjcDVEGmPLYiCR1+lUaMHQ1RroqTHdwAsEylgug5EzYNVSaq47TjlKEiIZUSl7I1/sjEMEREtdezDvZcaN+32OnNm+VZzotq9V5G6ELztWW8diNQIXWYiL9E7t/65UcfEMRq/K9Xi/RD3W8fgq1Faf1Z342/ehu6nRuTKGAA1S+qG8Urn5ZuHFvppmmqd34EQyFmIHfsvkdSwKvAOJGDPESlyqmLEeBIo2fx64GEiJdNK42sLQbjR1s2mlVdO/Y/Vv5pyA0Ju1fFS74Fh5D5cgyPqs1+5dd2C07aEbbnyAJJuV0MUxVuAnpLNX+D2pXXx5yL1UNfmTHEW8GX3+uawnKhTD1oxdiQcN7OnHjoCco8tRyZFupF742BkguAXyETAFbYSfBDMbcDvkKbBlwE/d8cxHfkeX4WQrYl71VmoX4OYUQwQYDNTq5UZLx2yJjT6DHVibGb28YO0LijXOvOjm4Ns/ZOIUciXkZj0F8BVOVM8HfkM/Bxyfx6B3MOqYD+MpDWuzJniu3KmqMr8CxrG2jgz6NlET19hN+CDSO3UZqRx5UnIA9VJlFbxD8FaVGXqVwAAIABJREFU+RnbCG1RibFe3IeRHgzf6O/tXvGP7ivG8xNOrWoHLiVyUnqqsMis6vf6e7u//AwOL0aM5z16+gpBf2932NNXOAVxClyPOIK1IsRDXfLU9rnDWpI2xBgDJpioyVGlajKo2qJkBRob1Sr8FDrfgKFMlGbWXL+lznjjbtkMUWrcRrfMbO81ixTXq6ozRkQSXb2WHUUIVwWMpukpuWg2gNB0SN2WqjhdbJtC6de16XHils9474WIgrgX2xpgQER4NP3RNSYeMyYITbWWNR6PrbtxrUR6GvmqlEVIoqaCPo5MdKr659fQ+fvXNE3dtxpZKckFCeI3IQFxK6I0qgL2iBuLfvZ+ub+3+/vEeEI4UjEPqUn6PnLtQggHCehqnVf50eiK7AcRArR/68Ly96a/ZPgLq341pWbHEx1EkwpzkOtyN3BDoq0+J0ja1yc6qqPjq7K3gLkLqcu8xjd9yJniQUhLhxOBy0o2/7WcKSYQMnQ00vi4C7kf7kpPqe1bL5u19bHEbkgmyfeQzKbLge8gky667gOHn7/y1LH1ydz9F876r4dGDhzOmeJH3L7+Gak760Lq+V6KxJkvde8fBMxIdtbWpLpqM5NtYXHB6wd2Ky2dumD0sRZtazAT7FB6RrXakSuPznvt4KbHfjp1L2toGXog++GwHKx349gHIVsVN96PEj0n5yKTElciZS8nImYcXyzZ/FXOtj4o2fzYP3ald07EBOtZRk9f4VJk1uyPROkc732m96OX0YYy3zjZIsgXy2lIgeWd/b3dP3ymxxFjx6CnrzAFOAd4Y3WMPYIkiSAp884ONSJr2AnLZRoDpGZsBo7r7+2+89kce4wYzyf09BXmIErDbGR2eC9k5vpWRHEIkWwENYjI6uevMYRhDZwjnTVmIsAep/HZ82uH/ADfV2v8wJ6mZf3/FfciM+p+Q97Jnm1NdWpBSNeRSIr6kbj6FCICpql+fu2Wv0/fhGLMrauGGfcjM+haX1Qjcij0CaOOUVUhdR2seNsqI+d8PpLhoYYdfsqkbs8CJp0c4tC9f8VDa460GwZyxn0WVoxhAAkOszTCJ7J+CqOSVsWI+63npEJk5DGMWL63Yai5BseagjjirbMeScHqduv/CUkPfACZjB3o7+3+HDH+LnKm+Fqkh9h3EOVlLnKv3Jxory2lFlz90NgBg65OqeXw81e2Axfe/+0Zg8MPtJzmNvMwck8lkGfoL8D1XS8a/k7LnOojj1835UxbM0cCSxDTiLOQ+/k2JA3wSuTan1Gy+Yo3tqmI8nQAQpxvQ8wjLkLShv9csvlHvOW18fFWdzxbDj9/pWY0ndnf2x3mTPFwhER9x43leOR7X50SP+/G8kdgr5a55eMrg4m2+mhi4/zTtnx05ZVTv4A1NaQP1nHAS4NMmJhyyNBwdrca1U2pWVMPGeOxn06z5Q2pZUiz9Vbg3YjT5eeRNOq8G/YfgNe4/b0IIXZ3AQ+WbP79OVP8CjKp8E8lm1e1qwFLly8OgNSSRct2OYO2uAbrWYRTFG5BCNbJyEyL2rj+w6rVZNgOucLtrxWRfi0w2NNXOBLY2N/b/fCzMZYYzw1cU8CPAB8GgkSaYJK58wQygzqKFLN3APWwTrDpljZa55eTbXO3+fybBtzW01c4AQkybopNU2K8AJBEFI4bkZrWDDJD/hKi+p/FeCn23kRGYAKoV7AJ0XF+jRC0/ZBnaBOS2jPZJ3UzedG0Pb83VfOTbQAbhqyul3ko1cL+NCpDCj+dL4MQRw38L3HHutgtc7sbbxdRHZlPtjYjacWGxhhis/t/EFEE9iVSrLYiDmkJxGZaSWaKyAZex6vW6JqKqeYSs9wyLd65qNHopjhxniq1NnvbQ6cE9XoaW4cwhITQpJlEaYCt3rbWIqnSSppSbpkE0fd21Z07g9TBqI39I26bC21Isl4mGFmVpGuf2gbvXOm4/+SWfRA4w53nhUha2JuJ6oF2Obi6832BG/t7u5+JY+xGzt+xiNIygBCSk+rDyVGgJWeKxwD9wKtv/ej8LwTp8M9hxdyOXIODEMJfQ74XDwb+mJ5ae+PsY0emZWfUfnPTb45ekzPFFHL/aWPsQ9zvXwCfAOpKrnr6Cp3ASYefz423fmzef2PNt5Dn63LE1v1/SzavJH0CJZsfgIleU1k3po8CgZ6rks3fikz0kDPFB4E3IAr7Rcg9+nPk+bocuHvaESMHbLih40P1YXPzyium/Q653x5EFLAEMNy2R3l6597V3UiG9Q1/6hwaXZUZL29OzMrOLh9d2ZK6NKwEC4D3uHM7gKQ2tiFCgUUyss5C7utW5F5uz5niue7vh4jU6W0wPpz9cLq1fMzS5YvfuWTRsi3bW25nREywnl28E/gP4Hrg1da6GTMLZtLqN/87MELoEjn+Dnma+IL3ZlK3t4MXub9nIf0c/kqjw2GMnQ/vRWazMkBoArAhVWO26VMzn8bAa2Nlc/KWTbe0vqI2bJJtc4e32bBb9jrkw/7fe/oKP+jv7V71bB5MjBg7GMNIb5mTiIJ3NZtQZcn/BK8js+YtgLEWawKMCagj1skbicjMLIRkqcoEUYCvNVwhkdnCKqT2ARod7kbdMgm3gXlYdrMWnGoCjcYTfhofSAA3260/Bwna1Cr9KKJUN20yDEKI2t0YliOkU1Ug644t6daputfUECRAJhn9VLsWZCY/icyON6dD+jBunEp09HNMUzGbe1gNQ9BarYlIaBJYI5Vkmu6YdNtahwTWmmqmgaBOgna4/7WuK40E8koItf5sPnIPpDHYkdVJm2q3Bgn2NZVSXdj+4n7v787TMGLZPg9JKfvAJMe/q+CtSBbN2T19hfXAzP7e7gdhYkJ6CTLp+1t/pZwpzgXOBy49/PyV1wJJN9l3PqIiJZC6IJ0U0VTYdxClei4EZoWVYDGQKtn8mTlT/DpCOq5FyPWPSzb/rZy579sb/txezcyo3eyGsAm59nsh37ddQFvJ5gcR1UvHmc69raW7Kz/+GYytzzp26JH1yzoNomKdg6je44gKNOHMp/bmOVPcF5ksXQCU+nu7x93r85D7bwxRjP6MGKd9GSF4PynZ/PqcKV6GPFMPlmx+HFHNzvLGdzryDL8amejJDj+SYeSxNGGVgHqirbqFanp6td51wHhma5HXja/NBEj63/FTDxn92p5v3zQzrHLOFedKQ+HFr7/pu5WBxPzHr+/sQj7X5iOpllOQZ6sTOC5nijeUbL5BpcqZ4pRFp59+xoHH3VbZe1Gxwi6GmGA9S+jpKywGvoU8+K/Hm3kMQ0jI18EkSlbz96GgPgambbvEaRu4L9rmffgzoHPc74N7+gqt/b3dkzY+jrFT4FpkFusgYKYxpE2CdL0qRN4j5n7NB8Cs7MzaYXu8cUs92VFfjQQ8U5g8wEki6QHv7ukr7ONsrGPE2BVxLPBJJFgbQYLsECksbyeqsYGIlGRxtUNBAkhMfO62IUGHn+43w9uXKi5l9wORKmOQoFA/t5Vc6T4nrM5tiLU16lhGMLQQqUJ+yqHWBNWJCI3+nOWtUyMiPjOQgDDrjr3ujukwRKkqIwGUIWrsqipTc71SBSETM71jUnVHj7tClDrZrNo1Txjpdif7vPLr02pBMKG2+c6Nur0RGl0M1QBEz5nWlfnb1DHo2CaUOGOga++aRUwGpiKEVc/rfUgK1Uakv1AGOX+/dsd71y7eMqMfOQePIM/Yy3r6Cmf193avQ875GYjZxG+b1guIzGPeDZzQ01c4u2S7VyLGCjkkvdIgSuQmRM25A5ngtsDSks2Pup5R6jp4EfJ834P0fSrKy+Z9W+5o+yKRCcsNwMeBW51StcH9NOPM0mXT39yx79j49CNGZ42vT7YjDn/jCCm6042HnCl2IGmiNwBfc+vvhhiefBsoOMOMqQiRWoukBn4ESJds/oc5U/wY8gxtBCjZ/KNI3InbxxxgWsnm73HvV4EtOVP8ljue6bYaWFudmHgJgBn1kQSDD2VItoap9n3HwuEHWjYAv2tbWF5gLScEKW5CVH5mHTP8SiD1+PUdg2B0smkT8AV3rOe6vy/JmeJvgK6Szd/h9je2/IpX/Hb5Fa+4dTJVzx2DcWPf6Z6LuAbrWUBPX6Edefgacrwn6qQsBJMqWFpzHGCtaVCltr9OIyZRsJ4oHbEGvLu/t/t7PX2FPYFUf2/3/U+8pxjPN/T0Ffaol7nApDgxCGgZ24DNTCcwhtCYhuab/p2kHwB1JMUnS5Sis737poZ8ER0aE60Yuxpcyu1LkPSbfyZSSepIukueyI5dDRxAAnElXxZ5ntqIejZpipkSJSU8+p5vbY63THONVtX9ZN06Q8jEiLoN6nNeRZ7VNoQMWSJFZsS91+7GV3PHo3161GRCx+jXguk+q27bzZb1PpmzSCB2P6Lm+WYUeOv556O5Rs3fNm6MaRpTIH2SphPHSowq3vYSSNr+bGSS0Sdtk21Lr/MYjW6+k41LSaReXz1+EGK5FQmOz3fLrUKc5ixwM6JipRHi9ftdfdKzp69wKJJR80P9HunpK8zddFtr8OjS6ZtLNt9w/GoX3tNXeANyL53b39s95N6bhdiE/xQhVd9FrvVp2wvMc6bYgtwjn0RqmRLItf40cI9Tp540nBp1BnIdf4eodScihOPdwF9c/6h3IBMU/47Ujy0//PyVS4G9//bvc1vro4kZJZv/ndvmRYiadY875m8jlu6XIs/wZWq6kTPFxW6ZLytZyZni+Uja45tcQ+JXun0XEUfAg4jSXnNEz5RNtNVMuqtOsqMeDt3f+gfg1xj7utkvH/rCvNduvU7TFnv6CnsB5taPzh9AFMMPIKmEby7Z/H2uFu1spC3Bu5FJox5HdqcA2zQodsTwxW7ZFyHP/Dm+wcjOgJhgPQvo6Sv8EJG6t4ftBq9PkOL3bKCMKBOfA36FfCGd1N/bvVPdyDHgFWfc+O7hFakL5p0y0JpIY6rDECSxiSyVsM5gEDADSSGcLNlUZ8/V7vnJ4BvAl/p7ux97JsYfI8bzBc6i/ccIcZmCBCVq/ABRMK3GFf4XqU8UdFlf7fDd9/yJD59gafNZJTopoma7lm37N0HkIphCyF0GJtSsxxBl4DC3jls2TIJJgtF9bUVmzP3j83tSGSJrZr9Br99/yNJ4TnTyZgYSFGq6ZbPToZ4D34zHJ2t6PtfTqIIpedJ1lfDpudBtaF3VKkStOJJtr5H+7dd3jRD1CmtW0MKm/zchtau+wumfo35ECR1H1IhFSBlBEjEM2Mft81P9vd0FnmdwNVT7Abf093aPnW8+MBsIP2ovnEzNeVrImeL/Iefgn56MapEzxf2ApKo07rVDEPe627ezjrrt3oZ8j+0GvB+pQ9wEPFqy+Xc+xXEfAXwFuKhk8z/KmWIb8HWkTuyCks1/xS33YSTj5Cqkhmt8/w+vO7Z19+p5j/5kanXT8vZxhBjWHGmajaTcnYOYeGxBJvD3RtIEb0Dqwc5Enu9XOmLThmRPfQxRyr4B/D/gVHe8I27baWQCQJV4CCx7nrnRjK1Jsu76roqtBJcgEwPHApeXbH60p6+wD5Du7+2+J2eKi5DUzP9BCN16hKTanCnu78b1E2RyakbJ5v+QM8UXIbbzNwHv9dIk34A4IuaQz4whoD7vwEe+dtDxt555528X/eaG4om9Ttl6E7C+ZPO/fyrX6rlCnCL4DMP1ETqDv68AbDeA/Tu1U/8o5dreNlJIrdiHkS++LcB9PX2Ft/b3dv/1H9xnjOcQq3/dVU1Pr60Jy+wRpEglMoSJtKTumIAZmIl6AT+o02DCn7F+sng/MvOXf6IFY8TYGdDTV0gjs8p3I0Xbi5BA3IcfXA8iig9EbnM+wWiuD2reRoAE776SouRK09rU0r3sLeeTEojqnZJIsNTu/h51680Oa3ZhWDMEaQgCElAPoZZygosqM21ELn7q5Ffz9juGkAY1Yqi4fUzxjkvVPiWLSvzWue2oiuYrVAqfwFrv7xCZFf8JUs+iqXWGyKgCGnuUjRK5EGoNFEitzVz3t1/zpTVruHNYI+pDmaExXTCk8XprzZrfl0uP4X+QmfsOxLktgxiLXIikT/0Vuc+07+UvkBq35yM+hKgRPznffOC/x6a13pscq46ebz6w4KP2wmfKBW45kHgKKWGfAablTPEBpBbpz14KWgOcgcSlyHW8D3ikZPNbcqY4hNQQ/hmpR9ymHshZjn8MuL1k87+aZPMGUWn+6P4fQ56nW4Dv50xRa/++iqQznoc4Ar6oeOGszfNP3TIwuiK9Ffi6Ou6VbH6Z27dBCPk5bvxzEGX4dYhidiwygXItsCZnilmEVI0j9/JZSNrlfyGk7KOI9foKhMik3XhFXQ6pb723JRhdlzK2EqSAtwH/QqTygUwMdPT0FU6D+QcgrR7afbLj+mFdgKTJvhyphzvApUf+DanXmgl8JGeKCxGDDnUZHURSNPcCBvd48UOz5u3/6P4P3nRQPmeKRyGptouQFNFl23Mp3JGIGw0/g+jpK7wHuYknSzv4R/BMbGt729Cu4VOQL47pyCzalc5lMMZOgtpI4vujKzIvTbXzyrDMskSaCuIwZkzj9dfmpdBYl7W9z4O/p2bu3dNXiBtjxthVkEJmhhcis7DHImRFiUKFSFFppdHRLonMCm8kmqyYLB23GRUkMFO1Qyc8/GaoOrb1RERAl9X9KJFpQ4ItDfinArWMHTD1LdaEEgYHYOrtLZsHA8Yf8baj6ZAD7n9Nd9tIRC60Jk3dzrSey1fogrBGLaxLy1ei9Mlpbl0N0nQdvwZkiEhRV9SRGhQNqnwyWvPOh59iqTVjlqjvVHMKpgaMrqnyRG1bF5FiFRBNSPlpnXqufMMLrUNb6+2v152zYXeupiCTsK1IKuphSMD8Uvda0N/bvZXnJ34IXJ19fOj2MOA6U623Ye1MJMh/RlCy+W+XbP6bAD19hWN6+goveYJV+pC6oz2JassbkDPFhY50BEiwfwxCEH7iFKXXII1ze0s2/92SzV82yWZaECONQ9w20zlT/K+cKb7VvX82cm9ucscRIsT6LOSe+G8kxU+Noz6PKGY5Ww0eWtE//aCxdemjgbGcKTZPWh6NEKY7EEJ5HkKQPoA8EyngGyWbPwt4H0Lg70CcO9+IELMRhNBvQe6/G5F7cYs7L1oaYMHYTcvbGVuRgaiH3mvceT7ajekrwBddquAPgLeVbL7knfMZwMUIEbwIeaZOJpqoGHfbeL8bxxsRM40rkVTaDQg5C4DKLVe97KtXfO6s8JFb9jPuGp6NfE7PB/b3Gy0/XxATrGcWr2KSc1oZwY6sSVCvwfa/Y5916Kyiwv87QFI4at6yc4Hr4uB550HJ5uslm9/4s491/znZwhnAV60lqA5jxjcFWNvgVLYJ72bUOj+XoqpBg3a8X/13dptAPhBjxNjp0d/bPQK8HQmGCsiMtgbvQ0Q9mZRAqaudpsi1IZNUk6WbKQn5/+ydeZhkZXX/P++9tfS+TvdszFIzLCW7gAgiCGqDBqKgFWPUaBbXgEbtUYioMf6MC6GMMRgVd1yCpkWNiGCLC4JiYAZElmJgpoZZmKWn96X2+/7+OO/pe7unBxEGGKTO8/TT3VV3ee973+Us3/M9UdpzNYhewFyjLI4oHZuYS/LQ647xkQhNdEOpECaYd7hr79B72IYG27N8Wy3WUKsAY+DFpwpLWwIaH0JypDRq1shskWE2uHapsaGU8dEIlRodKlUgCAI8W8XUpLBDN6JgqaFSQPJl9LyogbSIML9Mr11D9qR+5kav5hs/RP5WI6hEWOxV34kySVnCfC6FE6rMN44rhHBB/WwEyWe5z30+7q7X7Y6PQgobkcjF9e6YoxCFcsidB0KIcCUHqQz099070N/3d8f/28/yXsCqxGTJxIpVgyjCB1QcTPe9SH2r/Ureptfnbfp7CFxsYP73KZNbi0R/tBbTGQiMropET84BTs3b9FTepospk2uar6y7yNf5iKFwufs46owh3l5tSnRWujqOndHcpNYTL9/W6877b2QuXQcEjuHvR0hk6M8Ic6tehRhIWVe7i5TJXez6YQlihDQh4/++vE0/gEQ8LyaMnO1G5v3NCGyvLW/TNyJwwOciEaFeZCxPI3N/C3PnoDokLDK+hSETvouMYQb6+25fv25FPmVybwY68zY9PK/rx5E19Ft5m36/e8aZRFfl9tZDC8vdOzkfgSx+CjFwL0ciah9F8hPXIrDGDxUnm986sqO3YK03f16tRQz/sZTJfZCDSOoG1gGQTHbQz2QHv4hgk/cRW8E09tbmwP/mKbQHSuZfzQLW3cdYO+d9z2dDaiWEmOhnzcC9mezgew5oK+vyhMtAf99u4EoswdCvW7FVj6CEqUjasEIgoknYUdE8iwfdcQ37Oc6JTb3jq/+x4oA+QF3q8tRJAwK9OQPxWENoSCi9t9K3z4exqVMiKnpudA5NEUZXdGeIkl/o/4cRRqf086K7j9KfTyPK/E4kwvUL990YIckD1VqjnQhWljG+MgEqXHil+1F69Rn304BQiRcRL/eDhMZJlC4d5sLsxgA8j0IQMBRrIIFEk5KRY5oQquj/c/csR/oiCjG0LjAXuPZEE/FnEJiRRuCie5oSW0AI71PDOGrc2sjfNQSWFFU0cX9fh3jx73R9rFGzFiT/ZZnrazXk1FgrIQaUKqvDiIG1ybXvBHfsEGIgfEJpyw9yOQ1IOCs1QOCOB1RcZOQSBLJLyuS8lMmdcljLPT2Z7ODpJz//tuekTO7SlMktB8jb9PR+YIW7EEbHX7vjduZtWkuNvBd5L19Omdyx7lpXI04W9L6IIfYXiGGmBnYLYth8AqBxWfmsxuWVY0rDsYvcdS4sDsW+4CWD85GxehNC2PDplMmdClyKGC/vQKJNH0DGlzp1ut19zkLG2DASmX4HYlTd5Z5nJG/TNzijjbxNfyVv0/2EUVntk/uR91RADBIPWTNOAFYjc2bYnaN5hFVkfdkNrMjb9KfzNh3NtzsOyf06en6n5226krfpT+Vt+heZ7GDzsR/a8XvgB7WCd2lhT+wzYE9A5sd57vm6CWsGGmTO3YoU3v44Eq27C6HFV+hyG6K7HoOsaaekTC6WMrmGlMn9Q8rkjp/fridT6jlYB0aWIoOgdaEvkx1i5TwJxBUGoDIJ5QmfxsW1wIvtY0hF/45ywuvmHoWdgEAaLspkB9WLMQ1MDvT33fXEPUZdDoQM9PflM9nBm9vTpVMLu3wv0Vn1qzMGP26X+QmWENbcCWyNDcbnucbMjpdYYXesvTzmt7cdXjLGzHpttfbMrMRjM4s7W3fcdcVPX336RS+++u4n7wnrUpcnRF6BwHD2IMr5FLK2a66VRqQUCh6FnlnEuIkSQ0TJJ3TOad6Qzrco8YzmSNp519B7aBFS3PmNiLe4i7CmVIAobz9FFJMRJAqlRo6u7zHmFj6edvdVEgklyfhPxDD4GuIJVyMlmsv5GUTZ6gCMF6PJizFJyKynMMtJd99mQrifXkeT7bWNnu9V/CAwTRY/cM3WiNatiLKohYtt5LwKc3Ozou9IqeR1z4u549WLrtHBISQ6oXWAjOuXWOR6gTu3Xe9V3BOzu37Rag7589FErHHW8AsQKNev3LFrEebAI909akjk4uhMdnBmoL/vFxykcrm5yCAwMe2D6XX2ioVykh63DPT33Rv591Dg435DcBNwRsuaYjzRXl3rNdgJZHzOSsrkTkcMh8/mbXo6ZXKDwDEu2vLDvE0rDfz1iML+WgSi+XYkihhFbbwdiTBfgqMmd/JRZO5kMtnBpfG2zq+VhmKrCzsS/Ui//HjygWRLUDKnuuM+gEQ6fSSS9DZCaKnn7v8PCBQvAbS7XKUeZC16LjKmvwxsyNv0j1ImtxoheZjPuHi2a/e7gcmUyV2FOCJeShi1XYQYkysJoZVJhLHwGPe/h0TOLkSiUfPlRsTou3eB76LyvXhLsDjZXfl/pTF/nKLfA6YFmWd6v+8AX0GMqbVIBGsUuAbJfWtE5m6ccD2J5moaxCn2fuAqJDqWRIzWp0TqEazHKZns4Cqk8Nz8GhmE795izCya4kDErCxzB9acz4OqYeg3zdZ4c2AQsG/Ewsw7Vz+bb5StQCbiALJh3/gocNF1OTjkb1pXl/+1+9mlbcYnbytmNwFNiHfo84iyY70Yx0QcAAHA+L0NTTsH24vVSU+TTRUGNUdifhnPqxUbk5PzIQJ1qcvTUW5A8iNySGTC4OaJ+35+Hk/0f10/o99XCaMoHvuZRwjZwTCiTEXhvFEYnEZbtjNX0W9HDK/7EI/9bmR+vwVR0KIGGu57bW8DIXFFG2IQRWF3PqKsXU1I+a4wnSjEr+DaNkOYc9VLSBGvxoZG3qJ7zSZ3XAMhDNEDqAV+1RKzrunfRpL0K0h08cWENcP02aLJ7lGHoRqXze4eDxHmepUQQ1Qp97VgKhBYCIxD1atRrffSQsGzG3ytZKhM+NTKRpEAajQ+G1HS17n7/BphULsVUep/4p77bzm4xSDPscf9/4Q41TLZQeOKD6vkgU/G22tXA99tTxenFp0yHes+aaZngdPPQKB/Sr7yOmQurEXmAymTOxqB5N2EKPdXI1HaLFByxX1B5tpmYFPepsci97jaxIJvrMyMvGLsnoYrV//F6NGFXfHPAcVYa7Xnro8s+fTWazorYLa7e6YRY+J7hAyhL0MMJy0J8A4k73MXAqE9G4kI97pzYu5arY498Uokz2u+TBIyWmou5ib3nA+4Zz4RgXZ2Adb41hjfRgl1cG3qdW2PwoCB2SjVXVGCiUx2sC2THVwR+V8RUg2H/8OeH1LzTk/2VC9vWF4sYWwamQv3Af/kDMVxwsjUWxFDdKV7jjXIfC4yN/9Ufzch86vXnTtbE+ypkDpN++OUTHbww4jXoZMFDSwVE/1wwVjWgaJot5qm6M2BXIwxl2Z2dgPb32UWaKcuAgYZ4P9dTSpHAAAgAElEQVSJ1KKoD6KDWDLZwRjCZJVA6Pg/giy01yKeo9cj+O8kEc9QrczOqXxyS9vhpTXG0M0jk7eUgaOfJvCWutTlESWTHfxHBP7TiyheNQTCpcq8RrB0nVRqcY3S+ITrZRD5vRC1OcyNCKnxEo3kqOJTJGTwixpyymI3itB9vwxRsNS42oHM7WWEJBMxQha87yNe+k4kihVHFJ1ewmicQvcm3bNqnUePubW5ooaXtitK415212iJ3H/U3VuJRArIfqX9odGmhxAI1QWIMhWNTEVZALW/FMq5v3VL964NSN2d2eLNzDokAx8sHgUb0GJwgBS3z5Y9nyKiDBrX9mJQpdmLze69OxGF+QR3L2V2uwmBHR6DRLI+j0QIywP9fZv3096nVFz06grkvb4Gicxcs85ekTmQ93H5Vx9DILJ/N9DfNxb57gLgPQ8NdFw3uTn5SmrmktzeY+YUJnb5Sx15m344ZXLPRogmfo3A5PYQUo5bhEH5TAS+9+/u7/MQg+41eZveu0D7Tkair3cUdse+u+fmlqqXDD6/56a26wnMp5pWF2vFnYlTghIj4E0Q5iBqXmEJyIFdHmuvLqpO+ZaaN0k457Rw9W/csacTjml1QGxFxsv/5m16TpTGRfBeiZCoFBHn+FXInn8ckiZwOLOFvm1s1atGEkHVBNuu6dJyELh7VhAnwN15m377/t/abN/8KzKXXjfQ3zeayQ7GkTyqk4G/Gejv25bJDv59Zcq89YEre9oKDydXI9HiHyAR3k3AZ901PGQdUwRAknBtrSFrRNTxpQ6VnyG5Yj8Cfrm/IsZPtNQhgo9fvoJMisv2/cpYsPMX9v0aV7OGke/+D8BbqGLRwjKXettgrATOdJOfhTBE2hE1suYbXAu1Uzd0hY68B/jzTHbwuIH+vsqjbmldnlQZ6O+rZrKDO5AFa2igv++vI1/flckO3ox4exKRz8t+gkXtR5SSiCLzh5gx44ij4V0HtvV1qctTIjMIhOswRNnSnCWYq+Tomql5ilEjStdLNRIU8jcfOaDG2iw8F1G8ewmjScqutxdR1lrcudNI5Ec91Umk7o4hZOzLI4rIbgQKZAnzk9rc/doRx8sbCeGQyg44TRjZ2YUoQ0cTGkxqRCmtuz6jGlXGBhKNs5ZRz2crYVFdCOGNuicZ194ofF3zmZYhSfGa4xQlrtC+1/cTZRBU5QxC5VHPs4jxEzVWo5EqC4EJaNFoYi0I8KlhajN4iQ721Co0TG6Kx9sOrXhejFZnXOn4WI4oslX3rlpc356HREr73TOtBSoHq3EVkRHknWiNsEuegHscijgJxogQcqVM7sJ429L2Yz+485MjdzYtD4r+zYhSDsAF//rT1+y9rfml0HlF3qZ/6z5+KWJsHE9YM01roI0Br0YIRzYgxvAiBKlzNyH5yHw5CzHEflwreJdP3N/4ivJI7FDEcHlr29pStTLpfTgoxRch83iPu5/CjKeA472mmpdoCzAeM5VRrw1ZR5TgJXDtjK4LEI7zbqQm1cYF2vc2ZK7/BonyXow4WX4I5L1kcFK8rbaiNBTfg4zFeHFPPAiqs3O2gMy3EcRIeRhZEx+NXI+sOVqo+U1ItOxHwMcz2cGLgatt1buz8HDiS+6YXkKY5iHMLUbeibynaLkEZVS1hI6WqMPnxcj7eSPwrZTJfRu4fSFj+YmUuoH1+OVv3M98A8WCJeFPUq5F9+Z9JRq5UkOrNAa1GY/m5ftEZedHlvR/P/KJCWpgawSxhtk2zbAvjDEazYpi1aNKgnoHvMjf6lE0SBL0nZns4JeBuw7G4oh1AQTXHB/o71uoVsn1CKPPBUgoHmQh0yRSzVFQ7z3sOw4NsqjVpS5Pexno7/tCJjt4LXAHYlxpHlY050jXUvWcRhTyfaJVI4RwGz1Hf8fnXUdJHzRypNdVBexhd86EO26Z+6wLUQyHEOW917VhFRIteb67/pD7Ocb9P4nM9Xcjyr+PKDMV5kaFQBSdYwgjctE2gqwbu3BKm7tuKajSYGLEjBQ7jyHkFi9256mxqMqvGkkaydN+HkEici3u/2hkC0KGR1XCtP87XTvUcFKJRqqia5ne330/G2jUKN6IMXSbODErJtsiY4hPbWo0yQ5balxSVer+aN/oufch0EY1qC9E0AX3I1CvBxAP/kEp6+wVFvjA5eaisxED6Kp19ooHD+Q9Uia3rC29aOdhb9z7TuCe9etWmNS63AUI215vZSLWsX7dii8h5BedhJT/WMvLGhdXzgJbTpnc3S5y8Upk3PwvQgBzIdL/VcSQfzkyB05F5kwViYD8DHhlyuR+DWybR6BxBfDfA/19W4HLj772d9e1P6vgDd3SejgwuuvG9lbCAsGnIHPhFmTutOD0qaDgTzWnpm4e+lXbiYS16HTsGrA21lqlWvAN1VnVTcdtE7A4ZXJbIsQbKu9z1+h3fdSKGBtHAy29p002tx5aMtv+t3N9cVf8hWDs7l+0zdcHW4DP5236jzKgB/r7fkXE6EVyD3sQmOXpgL9+3YoXITlSv0X6/lhkbWlmrmMnjuiu6mRSJ8dOBMIr78TUKlgPOW+OH7iAGG1HAp9GyD2eNKlDBB+HZLKDhyIDRAfEHPH9Iq0Ne5iYWUJgE7AfWF70FSg8cGanoWGRxYvPP3pBAwsiG7sN8Kxct+b7j8qI1nBzD2EINhpu1Y1JN9vEAteoIcmEpwz09x10Bd/q8oclkx08AVfkEXnvewiT3aOJ6vuT6YH+vpZH+L4udXlaSCY7uARxOmxH8hVPQ3I6jiGcH0pYETUw5jsgolTjAWHkRYkvVNHT6Eo5ch11hjQTGlk1xJjajsDqTkeMobchOVwXIEQNrYQFO6eQ9V3ZucYQz/ZLEFaurmqRCT/BsdZStjXusVWO9huwxmMHYrhpG6JQvxkkuf0k9t2H9JlHABMElKyl3fNocqiKqNGhfRhErl1BjMBFrs+UIl3RGGVEYV2ERIiUBTdKvKH9mZh37SiEcaEcN2VOrbBvAWiDRDUCQljgVFCjvbjXsw2LgqLnk3B9HoVRFl07phADai2iWO5BxtUwEi19aD9OsINKLjcXdSJF5n+6zl4x9oeOf7SSMrnFCCroV0iuko8gI16MOBRuQqJHlyHG0AuBv83b9ChAJjvYec/lS/66uCv+d4iB9GPEEBtCIrQ/ICx1EJ2zmktXQYgb2pH5l0Le3fvyNv3z/bU7kx38mK1x9O/+eXm8VvTyiBH3caSPjnDXUJRPBzIWZpDc5ncjiv8i5q4flcZl5biXCEx12qM0lNDPi4QlCG53z/PveZueQ/Th+vMaJI/r4ch9k41Li7ZhaSU+emfzxOLTp9qSPVW2XtMZIzCq76137X3JfAKNxyOZ7KAZ6O+zKZP7K4SV8SMIXPNUwnJB+n50bio80CBzpom5ayZeUyUIZjwDnhpYuh6pDvsfwEd0nDxZUie5eHxyOnOr0c+xVmu1BL5fI5mYhX8u2N9qVEVzr5qWzhpX8y3ghSCH0U3NMx7G8zCP0rjSdq1i7iYJgmEtEA7uKGlGtF3qzT0WuCGTHVz+KO9bl4NIBvr7NiC4d00g1VozEHqFH8kj05DJDtYNrLr8Kcg0YV2oW5Hxfy3ijR0n9DLPjz5B6JTSdVF/+4QKvELqSsgaq5C9BALl+ylzIW828tNIWLj2Iff9JxF2sVMI8yZyhLDEpYgS/1WETOF57tqHAKuMRyqoUMNSrBXMEbt/1Rwr7PWVnrrBtSlg7nMmEaUQ97y3E0adtH96gC7Po9f3SRozG7FTw3IbC9exiiPsZbqHVVwfVRAighhi9B6DQKV0j5oPFVF4ob4XNV4M4gWPMjZOuntcR0jSAfK+Su6+GvVoI1QAmz0f07Q4MJ4/W6hYo43aN1FoU8qd+wUkqngccMZAf9/Gp4Nx5WQVEjmdHzl5vHIGMiaXIbWjbgDOx9htXlPtAbCnuONWI5HSKaA3ZXIfSJncLx/+SduH17x+byue/RFCKnI28l5vQfKwtGj4fEe1jzjKe5CI14nIe3s/YqhtXaixKZMzKZP72NbvdRy188bWllrFfAeZh9Y9wzRisG0gLORrkHF2AzKOv4OMKX336rzYVNwdrxnf2uq0H9W5pgmJVY5HjMFl++nPm9x5SwhZNWOF4Vh84sEGQ2AkamaJmXDdqgF78zZ9xoE0rlImd/z6dSsudAWfr0bex/sQvVHXw6jzPooAUFEUVvQ4G8zEcMZVVIfVnwICrT5gjoBHK3UD6/HJXxDWGgAJ6RL+eAxPpGyh1LngydFaWI9AbPFIEYOFjtPFQ/HnOkB1kEY3m8q885OEHoMCokxECxKPsm8xRj1XPZJnAddlsoOpR9nuuhxEMtDf9wEEXqQbio4rVRgfaTxO8ehx2nWpy0ErA/19kwicazXiAT8bYac6jrBmE+wLFYR9IdbROVGInAeiMCQJWQoNEjF6AaKQKdxMz7WIcTWO5BisQtbxZgT+cwbQZi3d1rLGnZckJJB4GRINaHXP8SVgpxen0YvjGZ+2eJttXPLCaRp7anHCelob3XONMhdS1+GZqlm9ZH2itWnoRMKIWVQhNMj6cTMCgdPvlNBClToQI3Aocp7m4KgBNOT6QZWshdAcEGXMtdZgrWct1EpUgmD2OGUzxN2jEYkMnkZYiF33tRsRx5Ma1DFCSJcazjAXRg37tk/hTh1I3k8RYS9cl8kOfi2THTyNp4FUWhIvLLckX04IeX3cIsV97TtMLFgb7yy/hDDyuOPwt+357RFv3XNy54lT3fjWQyjDv48YF+tw9cgSndXzq1PeO2LNwUOI0+FcBKr3eqS23UPIOIJwjFhkHGotuV1IROnNeZv+Ud6mL83b9BzYZsrk/JTJaT5VcuiWVm/nTzqOpWae1bS68P6uE6fuW/ZnYxcDL+09Y/JKjD3K3e8+wlp6axHDp4Cgf9YTOlyKwEpbM/dPbWr8TW3G11p3OhfUIPsUMjf3F127wj33xxBnhuh8NbMxmPE3gfn27l+2DW39btdvbGA+hEQEX4iwGR5oORPsy1a8fPTM1Ov2Nrm2nemeZysCk72T0Jmj+mm0lpcS5FQIa9EZ0aBnI1fz9dMYEiU8hCdZ6jlYj09+gAyQR6KiWMhbso/sx8j6g+cReuVmkE1UvRBKu6uboebQKHuTekb1GmVCOMVOwsiV5uJoIvSD7vMuQm8QhJCXBJKXdVUmO3jOQH9fXeF++slNiMftLEIFz8MpGNYSw4LZ1z3ThuQX/Hafb+pSl6effAIpOPoviMKtynXUEx2FkemM0N8aqdIcI5A51DXvPsNIFKaAGEqzNObWEiCMddbzmSSkIj6JkDXwGve/5jf9zAZcgBEDwEHyQIyyZyHe3Pcj+ZZLQKqITD8cD8bubvB6T5sej7cELYTOw07EeFP2LgiNyKbG5ESwouce63tVJmd6Skgk63nI3qGGRwOyV0ahenFEQc8hpBdxwiR/hfF1IdCwNgRCp30Q3bv091YkL0P7pYK1i7HWYAy2BpUpWry4sYk2q7D4ZZF+swizmtasmjUeghonGcMux8yr71aLLatTUtulUEaFMGlkIklI5mGQ6Ns17n7fR4zfKCLmoJTLzUXH7z1l1V/tOn1tUFrcuusAXtr3GoLmrhOnTWFHIlUZDaO6lTF/VXJNdfWSM6ZseW98YPqhhn4ksnQ9slf1Am/e/sOOX/sNdrQ66b8RiUT2IO/jF4ihkUTe7bMReOxxyBj/AOKsyCHv6HwEbvsVbVzK5E7Ds96Jl20vNPQueXlxT/zVCKV+PzJvLwMerBX9V5VGTO/KV4x+xBj7k5a1pctMorZl9087DkfGl47dLe5e7cgYf7b7XMeHljv4EbAV7MtJ1DZQjvUiY3YKMa5ei0SyfjK/Qx19+i9SJrcemcfNwIuo+R+0YtCVkfykrXmb/vCjflOPTa5cmRl9qOeU6fdNb49/2bW5DZmrX0EcDi9A5m8OmZOrCPNBPcIaWLqG7EYMaEuYpwVz1wU11l6K0No/aVKPYD0++TqhN2R/8qiMq/2kwj2a6JVGnrS4JMwWsrQewmKo3r4q4kGMMqlE6WxVmd5DuJnqhqGwEw3LfxIxxKLeSD9y/EnAhkx28BWP4hnqchCJY4T8d2TBVu+Res9kzTAQ1PY5tcZTEIavS12eCBno7ysQrncG2cxLwOjeDQ2bRu5MbrF2NqJfImQK1PVcFW51cnmI8aBJ+RrlakXWZ/XYgygdxeJeb/f0dr/mCoAvRhQS9eCCKGivBD6HrPndjYmRFySmdhUjwBrNm3oWYSS6DVHqTkNyt2aCsim3HV4o+41BAhjD2hrWTrk2aYmPm5AogEa4Y9PFzrHfbXrJti27nm0QpfcSQkhOiXkQdnee/t3mnkELGkcLHt/v7t2MKFyea4fma6kjMUAiW0sI0Rqd7roVxosTWGuNh/HihliTrSFG7VrpM6YJoxmq3B6JKJ+BtXg2YNHkg4mi+0yVX10Bo7u3YV/6+GbEkIJwjFQRJfk0hCnuL4BXP01Ios7rvW3rcYd947YjT7r0RwcsHeCET2x7wYrzRzsLDyf2TO+Mq0N4M/CjrT/sWL7lfzqnd97YdkNhZ+JS5H2dgRg4RyLvemNtxp8pj8S0WPYQISPeXcg7a0fgt7e5v1XveRAx4E9AyBCuQMZyVN7e0FO91FouW/ZnY0cj8/VtQDpv04reeEVl1H+hidnPjNzZ1Fccip0/dHPrjobu2qeRaNolCF38xa6NtyL5kM8hdNzECUsf3AosAeuZhhqxRntE66HFi1sPKw5hrDJljiOsi/uVvE1P5m36vXmbvhA4Mm/T383b9Ja8TT+MIFYuenRv6bFL3qZnek6Z/hnwtc1f716ErGe6Nn0IKfCuhBwPI/NfmQgV/qsQ6yryflU3hX0RWaqbNiEOrAtSJvfnT9DjLSj1CNZjFFdb6GrmJiaygE0U+WDuOqysgZ6n48Dw6GyqfURPmmGWylJ21+oM+I2mFvFiHsu+bHB6DYMM1pWEDEhTyIY+JtclhSwEimcuIAPfRzZTjXQoy9SJmezg9wb6+2wmO+gN9PftQ4tYl4NPBvr7bs5kB29B3vk5CBvSLuDwWoXVXozkAhEsHT91qcufivwQWQPfiURZykCX32A7483VmA3wjC9rp7V4xrj6SIANMMabjXJpUnoU8mcqkwSFvbG2piVVE2ucVQqUkn060R5Mz0x6CQvtJqxfVUS88LuBDARxsOtA8jQq1Vj7IYdtmtiyZ4mSRLQgSpjCAhchCtVnkT3jx8C5LavLvg3Y5fn0Ym0jBCSZtiXalL0rhiiJ6hV2ifOmaWKmVw22kxAjTKM7CuPRa6inOQppbyRkuZ0mhKoro6n+rXuH6i1jyF6knu0iYsyU3Dtrw5gknU1VoGIgSLRZJXHS3DFV2vYQesI1AtXs2m+9GF7L2nKa0GDWiFdpXrtUYVTafn3GlyMEQke7z8bdc9+OkCFUnUH/dJAb/Zr9UMvOyQYEQntAWA93/7J16a5ftPbUisYn8GZg1qlwQW0y1jJ5f2zU/f/fSDTqPISo5R+Rd7AWiRDFgMmmlaXbbdVcWng4YZBCwx4SNTodIVNIEY7Ldnedgss7+sYCTby0NuPFjSHdni7eg5CrvB95p/chcL2eoOTvmnqg8cjpzcmCDcxGrHndyIbm5yERyhakaO8e16bTEWe1Qeb0V1y7jkCgvBcACTBFW/T9apFEb2b0dfG2oGPLt7oqxd2JGaQe6aNmcszbdDDv/yeFujyTHWy3NT42fl/DxvJw/EjCyH57vL3q9Txvyh++vTkoDcW3I7mJBnmuPPKOjkbWBs1n7CYsn2GRIs5HEK4faqSqpvJsxID+4ZPxvFCPYD0myWQHm5GJcC6Prw8j3s5o7vAjH/8I3zUQgSvGRkeC/DcWmfF7EwpVqRJiXacJE1SHkM2yRFhFuwcxxtqQzW0rsiE0EzI8/QrZGLUPYojBpcnJPoKP/kEmO3gucE0mO7jqDz1kXf54yWQH05ns4H9lsoOHHahrugLSXcg7/g4OGhpLEPO8BSGtNyFGWF3q8ichbg50Imvr5Ygy43c8q5RsXlHzPF/W5CCghsXYAGstNWvn5MEmCNflKqJoW8B6cYwfM95UPh4g668q7a1Au59geeuaoNPzKCNr8AgSfdnhrjPW1bKltLL3d12emQnAVKpBy8yWPSdtRvIO9iJRgP9C8oi+gyiZkwj8ZjPiTX/YGGY8MRbvd+2gTCJJyOL3c2TfULKJRYQKTwzZGxa7/4vuHlFjqoYoQbrZKSICd/6Iu4bC5OKE+W7NkWupcdNCaGy1IegMn9C4USOvwb2DqNEzo++AMOqkUcgg0gZjDHuMIfB89hDmoEXJR9TJqJEHzZXTviohe2cnQjJyPxKJa0YgUZ8D4pns4GPyrj4FchGhUnvbgbrojus6flyb8X9L4AUQJEwsaPESNZB3pxGtFAIluwhBWXQhBC6aD1dCoL2vXPqiiStWv3r4ncd+cMdXkblSQoyZZsTYOQRxUnwZiRQdA/SlTC6ZMrmjUyY3R7fL2/SmjRNH5Qb6+75/zcV9DyBQ+GvX/v2eYzPZwU8leyqx5X8++hdHvGPX5xqWlD9pa94LsOZDyDj9FwTKdxpiMDyMENKcjMyjGmKwHQq8yn3+M6Q48CgwBOZWMJ9oWlEeSXZXNrUeWjwZMQyPQcbUwS7JyqR33OTm5IWIkfNFpP8n4u21oHll2Sa7K+OEjJsxRPd8FmIY6TyOOjSaCR0+u5FIZRQmqA7fOOJ0+fQT+oTzpG5gPTZJEL5YFVurgA0WhPtZgQEabKTusA0wQRUm87r3evuN7lgLwb7XtpEfLfboNgzPzLB4T+OSkm3umUlCoDDBDgQrPIJA/ErIQFV2ll5CjOv9yIY8gQz6jyJRuxjCsvMSRBG4j3DTUdamBLKBx5H6FW9AJsob9/eMdXlc0oVsPgszqjwGcRv+Q4hC91FkI/vBI5zyxYH+vqkDdf+61OUgETVu4ohSt8cYasabjc4UCBgpTzBaLfBbzyPrecwYb1YJH0YMiY2IgaF15QK/AdO0vFLtOLJSQ2puFd2x3yYkXtD81l8ghbyfjSTtfxJYPDa1vKFSTW4IbIMzNrwqmDWIF7gVIZf4SyQHqtk9SwHZC45ElJgu4FvueXMYsxPj16xpUMW1GYExdSB7RQshpE+jOrXI3/cQ5ugqRFLzc3XP0hyrNkJ4T9y1bZTQONJNU6muFS4ULR0SINEzPVb3IYUiRpPfNY8tSsE+RpjfoX2k7e5291tOGDHzCWHxnYTIjZprsxIVxBDDD2R9/jPX51pMuub66VL3/UEtf//Gb5w1dPzyM5waMwN843JzUc8BuvxEYkl5j5es7Ul01sqrXz0StB9ZGHbfbSSsAVdGDNP7wJY6jptpaFhSGkeU6w1I8eba2N2N1anNyYfibUEJcS4UkPnwPdf2UcTZ8FV37fcgUaVzEYfERSmTO2p+I1Mm15EyuUVI9O7l4/c0HQN0Vqe8vvKo/8rtP+h4SXk49nbC8XcYIXPk9cCL8zZ9I454wySCG1sOn1lv/MAgMLluxCA8F8kNu8ZdIw3csGuw/Wu7f9b+r0O3tG0H3o5EudZwkMtAf9+ezd9YlNnzq9bvI453dWJ4M1sTpfw3u2sTuUZ1wFSQdzS/SPgOZJ3cjrxPdXKUEKP0BObq5dGyF/c+2TTtdYjgY5CB/r7RTHbwU0gINyR6iKbVzfVFzflPjaygBsaHpkNmSZmilvc+5Bi2Cmbfulh6/UlgMgjoNYbGICBIdNZqh7xsQq9ZJNwMP4ZsbA8i0K9jkAGsTDrK7vRN4HfIhn4m4l35GbJIKezDR0L2b0U2IBCv6CSymRaQzetMZLy93kHPtgOb6yQYj08y2cEVNuBt269ta5remvzs3fnj/+8AXv44xEje635vRKKTCv+cj2l9DaGSVpe6/KnImYSMfZrHBBKhaAEavBiNyQ4sonRdjUR7zkLWwTLiLY8jECYI8xlrDkYYQ5LOT3Tf3cdcCvEmJN/k79z5uxBlvyUg6e8cOarbfa4Rn/UIZGqPa9Ooa8t6RGk5H4E0/SOiuDUjiIWtSFL9qYTwuQKigLYy15lXIyzp8CCyzj8X2UuOcM9Xdp93EeYCa17W/ALF6qRTYqUozNAiHuhoGQjtm48guS0KUdYcsxJhsWO9X5m5uV4qSjmuBmUv4hj1axX8mJBCNxDue0n3XBq50s+Vet4iKJA4MmYUxrjE3a+AMOH9N+J5X8p+qMAPJplZ2vr+7eekl7du3jvSMFF6D6ITHKg9PNm0uPIS21U14xuT39r507YXF3fHO5C+/S4y9ieR/p0EOv1GW1t08pTZ/r8dRyLv+EZEX3nT8G0tq4Zvo2Hb97vuRhA3KSTi82FknFYQfeaNSNSoGTGGf48YNS9357xrXjsvQ4ygvwTeMfq7pi0jG5qWBmXvXUO3tF7qxa0NKt67EOjnTxDn8seQffS7eZu2KZNbDvbOpuWlnpa15cNaDyu2bNmebKzNzOYT3kSYm3kNMh9XAP8xdEvroci8vhSZ2zcyt7DvQSvTW5ItyLOsJXTAz4DZXZ3yj0AifNuQyN2fEeY7quNlEWJE70IcSDpfZ4015rJDqj4bA9akTK7zyTSy6gbWY5BMdtBHJvswUYYoz7EBLhAX1HyrOZ/54Hng7Vu2N4pPnzWyvNg+CVr6vW4aHbUKQ16MFaURak09tICv3r0yMmGVrnev+/zDyAb7Uff5dmRzakK8I62EyaDLgYy7dwHZZGaQia+J2xZRJpRWtEAtKHfes2vJ1KpOKu2NPhIeNog3+D379lZd/gh5B4Z3LnrutJ3ZnjwSqVZ/oORBxMh+N6KsHM0jrxn3HsB716UuB4v8M5L0fhoC51Iqco2kqCEEoph/jDDnSqFqHmHejzomlG5cnVsGWUc7EDjiZkTBUqNCSTCgA34AACAASURBVDOqiCPjBMSQ+TZiHLUjSn83okDeidT/Odqd+wlkbZ9Ccn9ehKAQpt11j3LP8SbXVoXYqNKqrHpJxCjodt9PIQpTq2t/lKZe886KzCWASFCtVfxSNRbUrLVtDRol075cwVyjyrjra6RLd9ME4unfg+xPauQo3O82wrxh3at/hRg9WtAUwvwp7edGAGux5RGs30vgDGGNhlWZWycwGlFbg0QnjkfWTv18GGExe7N7Vwa4fqC/72mTt2o983O/WDmx1py8ct149suP9TopkzsC8PI2fZ9+lrfpqXTP779hA5ZQ9buLu/xlCHpmPUKCMNR6aGF110kzi7b/oGNHreDfUit4S7b/oLOrMuk50hN7EjH7Karepch4OBOBrW4ExsG+bvELJ3raDy99fuPnejecePm22KavdVEaiT+3+8TpZcPrm79e2JF4MG/Tn0qZ3C+B4ZTJefPyln4MtC89e7y27OyJNNC7ft2KGWAN1nw9KJttyNy9HjHWtiBGwQZnXPUCn/KS9qilZ0+0Ni0rNZfGY0HL2uJN479vPhXR0T6AOCwbgCDRVUlhjalMeR224k0gkEY1EvfkbfoP5pc81SI0/LO5VZsQnVIJKFbjinM3LK4sjjXVnjeVb0ggAYAzlvSN+i1ryi35ry0q1Ir+Ge48jVLrfJ8mdABrEfjbENp5ECfGv/EkoqjqEMHHLksqUyyxNjSG/JjF8y3GLDzWjQFjQqjCvBwWTY2281KtDGCNAeMRuPP1eDW+4sjm0RxLsMzzMI2LiCMbwAziEfkBojBXkE34KwhM8DtIJO52hM3mXYRwjzLiZdiKeBQ2EXoKGghrEqwhrB+hMJEOd/1Nyb3Ty5fetMlre3Cvh2ySmp914v67ty6PUj5pDAMmbq/qPX3qkgN8bc27GCGE0IwwN3k7KsPUpS5/YjLQ3zc10N+XR9bQXyKMdkr6o4Q+NUJmOc31UcKGqFNigpDIQvMMtL6UltZQh1YTzOb93IGstVvc/xcikLgHkUjzNKL4fQ6JoE0jUaNzEOMnhigxr0E8w88nhN1U3XW/u/X77Z/Yfl3bXUhR5QHE4bbIPdtWxGhTA0gLHkfZuxT6p4WEK4g3foQwAmSAmj9TiZla4PlBoMaoGjnaFwGhEaP7nCpWP9brIArpbM6UO+8u9/dxiGLVQmjMHuX6OMoCOOXuqY5C/cY2LsZ3xpX+KFGHjXw2hUQ/Ztz9v4ygOjQvSCOL/a69E0gk8ZuZ7GArTxNpy4989Mgrf3Nd886J9sd5qY8AH3NK96yU9sbXlUfi1yJjVJXvTci72hkEZoffUKv5jcEpiKHaUdwTH68V/EngPSv/YvS3i54z9RzjB59E3sMmJFJ4NOCbGMtbU6W3VGe8K41nf1ge93Kp14x8oKG3sqp5RTmZvmj39Wqs5G36DmSMfy9lcmdoG/M2/b28TX912dkTcQRS+Cr31c3I/DgUWOk31fYcdfHO0xqXlY9DGKfXpUyuCYk8nWsDElNbk51juaZm45mGxsXVYxHHxa/dtUaR8fjjppXl8eXnjU02ryz/GnGmvN893w95YmpWHTDJZAfPzGQHv776NcNHAa9GctCWIuvDvYQpKbuBeFC1yURP9XA8O4LAl0uta8u2sbfqe0lbJMyXVLbIAmFpC2UX1ajV2YSGWAOwJGVy0Sj4Eyr1CNZjkPXrVpil54z/R2nUe1HvaVM0L6+6+FSpALYRYgbiQRBAeQITb8H4YU+b/RcVnmW7mC/RiNYksunMP9YCvjH7sAMmEW/n25GB+U9IgmcMGeinIYvP9Qik5QTCmiy9yEBuQRYRZWYhcu0ewuKbe5HBvAHxGiwFlpcWNXtbzz0ymFnSVnXHNbu2B5nsYPdAf19dMX+MMtDftxP4qyfo8m9HxsT7EYWyCYl0Fgk9uCoWyQmpS13+JGWgv28ykx18NRKhqiD06D3IWlZGvK3HE0LQWggNk1XuM83d9SI/VUQRfB1hRGwGybm6GlmztXZhG2LUKJRuK+K1PQYxhn6GMB5qfZ0GQkPndAQx8GZ3D81X2Az8A7B5760tn+4+efpFri7jVxBDII046Y5GjLQossISsu8FyPqvpEp67bMI4X6a81uqtTU0xccLVBoT0TwtCEuOKNRnhDDPCXd9jYgFrm+jxlIMUUIrrt+CyLEVxPkX3cuM67vDEENpG2KUWs+nFOnDsrteuTJl8iMbmpLdz51OxZKz0bUfIrV2VIFTxVGffRFhXlYHAhd9KNL2g14O//rtCj8t/qFj/4BcBvgLRF5ORfYdLfLtIflWPwaeN725oXVzPlnAmkZ3vBbtnQIumXwwmQCbtNacQOgArrr29tiqqQ7d2kJ5zI/ZgKaZh+O74q214dHfNa6d2ZoYLA3Hn5P6p1zOMQnizp0izOWZlYH+vnImO3gRMqZfg9R++zLihHnDMZfujHsJe+nyPxu798Ev9q5AcqV2IvBcYyveit03tu8FZrzm2uKg4FWQMXME4uA4FBmHxYl7G99Z2h2/oLAzfjzwFsK8yFHC4uUHqySBtu4TZma2fKv7EmQt+VtEb1mKOGbvQCJylIfjjIz6EBglgrkl/43uY0zcLq6Mx4Zx9fcI1x91znQQkq5p+QwivxNIHunfIIiEJ1zqEaw/Ui6/6nVpL1b95sj6xrPajyhVMbORY+N7tvHo1TeZzpZdAJ6xeIlmCEp/mB6QfY2l/R2jOHgdXLo4e5HPot/HEI/deQgV7CWIV/NjyEJ2HWJcLUEiUe9BBnA7MrlLCJxC7+Ejm+7DhMyDVcRYW4Z4FQaRRcYCk/heZSrVbYLGeNy150h3/ybC4np1OfjkNgROcyey4Z2AeM1rhN5vlc+6+ll1qcufsiQQ77oW9lWGsxHE0aSA7yjxgxoDNcTg0LwAjWb5iJHSgShLmrx9AWLE3UoY8VJyiRqiPAbA/yARpbcicMHlCMOZuvVKiDJ/mrvGcchcfo77/h5EUTv16Et2Ti998fh9xvARJGJ3ArJGP5dwr9lFGLVSA0LznJS+fY/7XiNXWsYj79oQB6i0N0IyprWh1KAqIc66O5B1xyeMDhbc7z9314hHrq37po94tEcQ5XiI0CDQqOF83Ufhf22IYq8GriIybifcU8fH7m66o1r0Vti5tcZegzgn1Tg0rg06DnB9gbvOMsQgX5rJDjbwNJB19gq7zl6RXWev+MzjuU7epm/L2/StC3y1AcldemnepnsRwgofgYHKfLOmiTA/UA3fHmDp6B3NHaN3tAQEBuRdNyL6RgvQHG+rJhafMRl0HldoAHNZy8rKS2sz/pqOYwojpeH4XYgjWcsDkLfpTXmbfu1+2sr6dSuOWL9uxdkIUcbNQGfepoO8TT/kJ+0mY3j3lm93dyEGv4/kPO4iTOtYAqwNpv1WArMKMTiWICkoJ7tnODooe+cWdiaOd+Q1H0V0t42I8fnSlMktnJ1/EMhAf98NwAUD/X2b8zZ9R+/pk6d0HDf9VoxdjvTJGCFzZwVMkcCPI2tiAji+OhW7uTIan0AcLYoc0ELouwmhxXsJUQO6HihJUAVxWL0gZXJPit5Zj2D9EfKt/ztjTe+a2BWrj3/ABkcs6uw6rqibqQVjakGC3aMpiuVmAGt8DB4mHqdIyN0/K9GcLIlqzX4939iK/B8ETclRU6o0B7WgQQ2e2S8JNznfBlAap2IrptrYa9+LDL5fDfT3bYuc8zKATHZQEwxbI9csEXoKdBMF8Spowcv7EKjfJoThZS3w/9y9Cu5YTWYuIsmq9yOY6mMRKEddDkIZ6O/7ZSY7+CsE3jCO5OsdgiTtNkYOtYgCUpe6/KnLCDIflOwihhhNw8haWUSUAOM+60YUNo1aLUYYwk7ERTpsABZWGYPnIOC/QfKGYsBfu/v+EolSqcH2EJLf8VpkbVZ4YSsC+15BGKVRaF0ZQRasQNb4SXe95yNOtvZER3Av8AUk9/J5iAGgUTU1VDQio8YHhEVSN2Hts6jWClhKJGInzOu/Q5m7v6kxMs1cZEan69/LkLwJvZ8SVbREjsW1S2FC+pnWydEC6EsIjSQl6tBrHEKYV6x08scT6kgnIcqeAXq6T5o+M6hQ8ZNUXT92RI5V8pMqAuE8Gtkb1XHZ5f7WwsjXAzdlsoNvdGUBnrGSt+kJBJoKwOKzJjbNbE10T25KGjBa36yREBJaIkTE6LtVUgQ1WkcQ+ObiypTP0G9a4sVd8STw7o1XLlqbet1wfNUrR7xDzhvP3/2vyy7kj6M8Px+Z3+uRfK9RJJKtJR7uTK3L3Y04OG5GqNlXufZHiaJ0POr4M2DjXmPg26rxbMV7PuI40chwC1JoGSRn8q9TJvf6vE3vE2k7GCRa/7Tn1KkXVAte6/SWZEtlPNaCGJUeMm92InNE18tJxFn0BWRtez7ieHkO8o5LyNp4vjvPZ+4aoL+HEYP0C0guascT9axRqUew/jjZtXPsiKGjLtz6++5nFw8npJMty98+Q+OHUyh3KS5U86yibEmPJLP5WZH/551X9cF6nQ1bg6C6zzUtsgkqVXY10UIs2W2fR4hr30cy2cFuRHn+DlLnRUW9AJoErf/rAG5FNh6QwX0ZYTHM5YTFH9VDmQC+hnjujkAmxyGZ7OCiTHbQuJ/6mDy4xCIL3J3IGFlKmLSvUqLOHliXZ4A4pekyRKlS+NAo4iXfjKxvysaq9N6qhOeRtfRk9/k0ULKy6sexswqXwptWEFKHn0kY+UoiCtsaRJl7kJBpbwPi/f4UooiUECbYryNr7n8hOVE73P0XIwpfN2FR4G8hkbBrCWvOFBGF9VeIMy1aM6rkjjHASqw18clSA8Y0E9LMW8L8KQiNlYK7ntKf19x3+vMsZN/RaFUMUZYU9rUFiQT67pn0XkouchOS56Hsffqd1hPT51MlveKudysh2ZNnLUlrZ4tIWy9GZ6yRBuMRIHtf1Fmtyn8VgR0m3P1/R5jrpVD5JkTx+/0z3biaLymTa25JlS5Z/KKJNr8p8IFErLnWggmUcXKK0OAg8rsFbLJpeTFoWFIC6WPJOQxMw+idzX5hV6IJWFvYkezffFW3nXoo0Zhor/3ViZdvu/+PJIz4Z+BtjqzjLUi+1sdTJve8lMkdkjK5DyCOkQpiEP0Wqf+kOXk6bx9AxrPTJSmYZDDZdnjBNK0sGURnikZGmwlr0J2CrAdPC6jplm933bXj2o6dlXFfiSjUEZREosfdyNzcgDzTUcCXkGfehjhDVBfVdTRLWJtPUVUqmvO/GDHMXpG36Z8/oQ/pxNgFijbVZWFxdYE+i5BC7EW8XF9CoAQuRGsjRpGBuQbT/AiWdSaU5mUtYFAtJDUmNyVtcSRmep5TjN5jmjAx8jBkEzGEEI2tiFfy9YiX54vIxNyDwEyOw7FfVYs0TD7QSFu6sNWP08Fci3/+oFEj7F0Ivn8VobGnuQAeITHGVxEPreYuNCDK+4sQKMBtwDsG+vuik6QuT6FksoPPQRjJdEOLRk4HB/r7zn5KGlaXujwFkskOngR8Boly5JFNfy+y3jUhBpVCznS9vN19dpz7fy9wRVDjgxjinifwM0IK8F5CZ5ZBFIe7EYWjinjE17hzViCKya1ILuR5hBGYD+ByWFx7LnR/6xzegexraii0Am9DDIOqa8OIa/8y1xY1HCAspCtkEYFt9cZmCkFHk49ntIBwK6F3XpXECXfPKmEBUWUghBB6uYQwd0kjWArzUwOpiuSfvdCdp4gRhegtIdxfA6BUq1DwfLpqFazLo1JlT3PHul37gvIE1osT85LgebPQpKjjVKMnClubQcbAYkQ32O2eYZU7ZqvrzxOBNw30980qfJns4CqgcaC/L8czUDLZwSbg77Zf1zY19vumf0x2VWcm7m/o8hJ2beq1w7HdN7XWpjY16JxQqnyNfgaAb2KBXfPXw2ZmW8Lu/Gl7dEzpGBhGjPNOoNX4gd92RPEv77znhJ/up02rEHjflQP9fblMdjCJECj8bqC/b5ZeP2Vyq5F14TvIHLsAec9rkDlSQgjGPoiMi98jzuhtwLFuqfAkWhfcSdI+l8BAxYNwrOk41VzN7wGDeZv+5h/d2U+BpEyuAXGc/Ivx7bk2IMAaJftRMq0GxOjciMCTtyJw0b9BjGWF4ioyahshsmo34gBTJw7IGnk7YsD9Gnn3P3BEJk+Y1KMFj1JcZOU/EQPmN8BVA/19f4+8OK3cbiGw8wjZo0xDBWRzqAEYgzHePsaVLtILiQWw1ifWUaX9iDl5pgYJGy9HjKEA2dRGCPHrrQiDzbsQmNcrge8jzEZ3IQxUW4BJLF5hr++VRnwtlDg9717R51GPyvPc/02IF3Qn4rWDcMNLILUhJhGGus8hrFyvQhYgpS6uy8El9xOOUQ+IFtW++qlrVl3q8uTLQH/f7chmfxeiFCh1ujJYPeB+NOoEksvUSuix7QRe4vnUnHE1REhrruQZ0fwkEG+4MvS9CFEY9iJQwlsQBf4q17aVyH7wJcTT/j+ElO5D7rqfAd7nznsnkseVRtbuCuJ8G3Zt2YxE6tKuLQVCdMMEWg/JMzboam7AM3H3XRdza1opG6LC0ZU5UKnOdY1JEEIFxwj3Bb1u9KdKyBimrGGB68cuQjjkDCEDWdIC1QKBtVAtMk1o5KUICyCbWDMxW5vNEVvICapwSa2L1YoYo/fhIGPMhbI9gJCX/BLZo6PyQeDyTHZw3wIuzwxZCrx86Ysn20tD8f6J+xs/BSYWVMzMxMakNTGrxrWmLkT7aRKo2aoXPPQ/nXbnz1sVcQNzIzyd7j7NwJtszfuX8Xubfv0IbepFHBtL3f9HInPn1kx2cDafJ2/TWxDY7teBzyM57f+A6I7/A/wLEkX+OeL0WEcYlXn42LNvnW7uHK9CcBd4MUq+oeKpo0Bhuqp3lRBkydufRsbVKsQJkjex4LjVfzXMsnPGS8j6oYiAbxFGeWeQebgeMbrOQea0R8iObRGDawhBEHyHEGWjc3US0WE6EdTVi3GkGk+k1HOw/jipIAvz6yPh/KXIINgBGPDWgjUOHKiFfSHE2Opn0YU6GuLWBVgX82ikwCCU7aaxe84iH72OGlZbkA22E7gB2YRPdMf+EDhx5uH4YcmuSo/fwJsIWZDe7ZnK9cuX5el+aYKR8dW75JltI9TwTcXWbKPeT2uagCwSmxHP6V4Ed34HUpm8CVkcSoQ5Cmcgm/pl7tzLgc8P9PddvN/er8tTKYcRepG96oxhenuCWsHYruOKX3+K21aXujzpMtDfd18mO/hiZK1tQ+bIXkShvh2JFClMWhn2DiXMOWpEnFK6nqpjrIrk5TwfcZpp3pXuIS2E+Ub3InmtNyMK28WEkR2NeK1w//8EMa66CSNGb0YS5kvuGQLXpo16/q6ft36u/cjCmsbF1XMJldUxJJesB4HdJNw50dpSuLYPu+MgVIajEYUa2DawjijDi8K+VNFSEg0I862Ye405TIQl1zdHEbL5jSIO0aVAwovRZC022Y4NagRjv0/OdD27lPBis8y5s3ux54PXMhu1soTFhbcT0sRrtC/avsOQyNqz3HkVd+7zCSGPL8c5Ih1K5gtA80B/X9l91gv0DPT33cMzQAb6+zZlsoNv8RN2Z96mCymTuxxYgTXloVvaomkUhhBCW0XG4zHABNjmatHzqXmqS7moj3W6l6khNSN7EINo67M/ur0zk932ZuDqgf6+XfPadNuJJ61/58j6pjek1uWqXSd0rU+9ZuQGROFfiug6AORtesz9ucH9gBhjs5IyOa0nqud2AnsWrdo9s/SwHZvvvOGU1+x+8JBvunZvI6zN9iASmQ6QyPkS4OqUyb0yb9OPl9nxCZWUybUizp7jgQkbmEPKI7FKZcKPzpclCMKq6v7W1JYMso7sIYwKNyN65D3IPDsS0SmVmVXTWqaQ/n0OQjDye+C9iEH2hErdwHqU4pL05lf0BrGKhxELuRtMBcwAwnLUQbjga1+rp2sYGSCNLCzqxdNwsJ7rIAyz9lkF2Uw0QqSDSjc0D2G9UqjKXuBVNuCE+6/ovXjR8yY3rThv4gcIle4NwOHW1mJD46tI+AX8JCfKswUlg20MbGyhvK8yomSciyRfLwO2VSa986szxo+31x6sTvm2YVEt6Z77667dZdevd2Wyg68nZJ6qy8EndyPJtO8FjjMesfF7G2ldWywg463OIFiXZ6JMIknTFyIb+DSi1J+MrMuaOxRVCi3C3roF8W4nEQ/1Q+7cCkIU9BnCWoEKN9RIlhoqDyORkBchhpZ+N40gLd7pvn85QkA0484pI/tTu7uf0sdvQgyELwHnVKbN+OhdjVfN7IjvXvO6ESVlULauxUgE70TXvsPcM0fJlnQfGyPcDxPumfVZylCb6WzZzujU8lbwJglJLJoJIYhFf2xmV9vmkS2jz15+CsY0IjktOcSYOhvxTAfuOTsIjSFVxLtcewKjblBh/LXJ7lre+JxMmOfVhRhkv0Ry4Lpcm7QPlPmsiZD9UWFJuk82ILlvuv+PunYl3TvZzdwI1huQhP23AGSyg2cgqBOTyQ6+ZqC/by/PABno79sc+VfzBUdgNl1hmJDUBcTQNSZmk8azfuvaoleZ9int9anNxFRnKsbaq4Ex5Ctj8bsRw8pvWVsYTnbVasa3RyIO4TsRRXyOjKxvbkh0Vc/oOWXqteP3NX4VcU70AEMpk2tDaOdH9XhX8+p4YH3epkspk1uM0Pj/PG/TD7nDNgFvTJlcM3Dmz75wfisSZQaBDHpeMmi21hRt2Su1Hz013n1SITZxf7JQGok3eB4rx+9r3Ar4KZM7E7gtb9NRtNHBJB1If0mdwMCM77iuoxWZV9FIo6aGLELW0SQSUb8fWTNrSGS+EZnXhyFr407EkGpB1oxtiEOrlTCX6x+Au/I2vf0JfM5ZeUYbWI4adRuwaaC/75THeJkdSNj3k4hhMYZsbOe77+dDCqIsMJoAHYscq1GoKMMRzPHOVQLx8nkWzCzlLWKpG2TgJZAk5d8h8BAIN+648Vi+7NyxjYm22sMItvWTyOT/L0sDNrAUg4TevwnMqO+XvWotrpCOqNFXRjyjO5HF6VnAq/CCyq6fdd4XVEypNuOfvvrVIzsSHbVR98wvAdKZ7OAMomysBI7MZAdfhngYbh/o71N4YV2eYhno7ysB38lkB68DxvwGy8rzxwDuGujvO9jrcNSlLk+IOAfRxzPZweMQJfxmBHrSAPwUcTqpgVUmdJqd647V6P5OBAazDNkbzkEUjynC3CVddzUHaBwxQJYhhsUpkeM1z+EshPSigBiDuxD4232IodWA7BdJd70hd52jgUSs0b6i59SppkRnLVpTSinMX4sYD9cjCs+z3LUmEaVGjasuwgiTRnl0n0vKdX0zXez+LfiHIUiI5xDuh1V3DRsrVVcR81aamr3TxsyRrv0/AS5CcncPR6JoNdefmrel9OxKKqD76ziAF6etdU0VQpKLKlCwlnhQMT1+wjZHztUcsABRAiGk628hpM8vIPrFUkJ42h53z40IvGmLW1tV9iI6hX52puvTL7IvlPCZIl9EdIIhREH/d+Q9G0JkzGZg+pDzRv14R6348A3ttbbDC62lkZYJZnPQ7YPpC4fueOALPcuQSNdvgPVr3zB8ZKzJNiFj+iEkSrSP5G36jnPedtO/xTur/xZrqY2XR/3TRu5s2nDztafY1LpcFmhPmdyr3eFvQMbGK4D3pUzuNuC/kVysALgqZXLnIFGaq5xR9KMI3fpJwJaGZeX7lr5wfNX09ob79/yi7daV549vNDF7qpew1yZap1eUx2MrJjY23G1r5hREj/sXJJJ9MMoOBP66FoFEnoLMtRcyN+9S2RQnkXer+acVJFd0HfLeVxLmW0GY91pAovSL3edRZ8clQN8T8XALyTPWwMpkB5sRz9QiQs/UHy0D/X1BJjv4f8iiuRjZWO5DJtO5yECaL0o8odCGKDa4hHi5ljKXUjZS5yDmBqMrBxkep4abYvef6360DscwMhjfBPzN4tOmS+5+lyHG2JFzmzgrPnid1VrzDDIhfo8oAEoTr5tpK7LR/A54bqyJ6oqXjf/H8B2N3X4i6PYba19GBvebEa/DYcA1SAJjgpAB6lTghZns4KUD/X1bFui/ujx1MsPcwbH8qWpIXepyMIhLym9DlLMexJu+FFEcJpG1rQlhtDsUMYKOQhxaAaJUx5GSGZqo/V5kj4gq37sRD63mKk0jjq0EolB0IYqm7useYgxsdfcsIAn270SiXTuQ3K2XIHCjjyLKC8g+Mmw8ti46eWbatU/zhzvdfW9FokZnIw66wwihO1GYe5kw4jaN7CEbEGSFq19jSuVqq+ZSHcHciN8se1q5s8mrJWPWxjxlJDwOyeNtQ7zdu12/LQX+ktCY0ut5ri90z7rfvZs1CKwz7fq8F/CKu3123NB+8iHnTgw3LKoWEAiSUmFrlM26Pm8n3Ac1P+jDSC5OGYm0tSEG8W8QWPxvXL8DMNDfdy2yF6tkgS8N9Pft4BkqeZueRJwVpExuDeLEPQJ5r6MIxO54YGJmR8LEJ2u14s74cHFnohGJbF4BnAde190fXaYKfkvepv8XIJPdlgJiro7jxkdqy4NfXnTzUZfsPKfz+Jl0/huLrikN+79A8sd/iowHi4yDDJJnl0XGegmBil5HGGk+D9HHvg0UUyZ3MVJ890pEh1xc3JkIHh5spzwa/xVwz+8/svx4CO4B74cnXr7tlsq032lrJocY4R9BxtPBKs8H3o0YgjFk7iqEUgMImkKjdQI3IBBA1ZHfiuggH0feqyKzNFLd7o5VZ8f8VJrDkGj/k5LW8Iw1sBAmO4VfHIhaTOPIRrkIWfC72D/Xvg4m9UqqgaSMer3IhNSEWfWAOty+cecY9QRG6ElnadLfjXgLlkW+70Dw9kch7z6HwFvORxas+fA/TaQMXLt2uN+n4epKMJe952FkU1mK5IolYs1Bs8xzvwAAIABJREFUevHzp1+KeDc/jGz2h7r+0Xoh3UjdlZPddwPu74sz2cF3Iu/pZKSYbVThqMuTLM6h8CVEeZlGxnpd6vJMlgLiUBtFFLT3IHlMDYgyr7WQTmNu7pEWv2xB9g01JsYRpqvFyNpXQxSoCWSPaSKsSxjNFVLY33ZE6fSQhPo97vjFyFr7FkRB2Y4oPSB7zhvcsb2IcXgv4hH/CiGN8pB7rrXIHqJ72D8S5jrps00h+4M6E8cIWb/OIiSrwD1bkztfc3s1Z+oqJC9juU3EJquJWDMShSsQOh4D1x6NFmm0L2BumZBxgmA3EgGp4HmdSBShEXi160+tHVaLtwVe4+JK0osH44R7pLavRAgJVKi+QhFx7fki4f57uDvudoTwII0YCwtKJjvYijhDb0D23mecOLbO6YH+vvvcR+8iNFBXIONmN+IgWDyea9jYfdL02nh77Z7KeOwjCLvewymT+xbQmLfpGcQRMisD/X35R7j/2sqk9+qpzcmhvf/XfHdQbvxu/ptdDxz+1r2/tjX7y+qU/1uAvE1/O3LaWMrk3gZM5G16zBE7fAC4Mm/Tswaclww+GG+pNef2HqO5U2lkzAwhc3kz1qwp7U52Io6PO4FDwHsf8NOB/j7J/f8sII6cqGF+MIpGbe8lhM2qfloknFdqZFWRdWIl4ghpRYiC/s/9XEtYjFnrrY4j87eZuWuRRpwNcEHK5L7xR9LxPyZ5JhtY0QXr7x7ntV6LRH9GCaNPOlii7IC6mZSZS4ARIAuFFots5v+zd+ZhklXl/f/cW9X7PjszzFIMS7HIrqKgAlruGpVCVECMiXuM0TYmxt1EzU8p45ZokEQFd8sYFDFYKiqL7AgMTLFNzb73TE/vXcu9vz++5517u6ZnGGBgZph6n6ef7r7Luecu55z3+y7fN7K6DSCLYYg+0KVEoCZe6HGnJysMaZ/YzruauxlPJKeAue1A28iq5rsrQ/5ZvcdP9Ho+z0dWk7iECID+p+vTB5B19afI+tnhfjYTLSgJtJBaQbc21+8uFLoxiUBWL1I0fKSUvMH9/WX3+9XIwlBEVql+ooH2Q6aJjW7IUyv5/szbs7nCO+PFAxvy+CTlFRehReKmUphev7/705DHLo7w6Lv2fzZXuBvNm5vQ3HUckZfDQmBWuf1HoXnSyBGa0Bx5DpFi7rw8VFDI2fVoHvaAAvK+LETz+0r0PS0hYh7b4Np8EHmbFrvjLXQvRAyEf3DbFrv+fRx50jqQAptCYKSKK+La2jT8YEfr9tTA8OGtClnfueZZUd24AbCbKFTxaHfPo2FIq+ftjCIxlsUaLq8GRUsYOcQMZMFucf9vdu0d6c43g6WBqipT6/Z1ELrokCAM8Vnn7iue/2H5aCuT7WFqwUuHffdMxojKjRij2yzXvtXqslDIe5CFvs2dN0BUzHgJ8Ff5/syr2LMsQF6+QWTFP6Qkmys0IYC/BbFigvIS56AxACI5OA9F1fwy/b5Nj/jNYW7u80e2NHUFV+b7M0HKK74Q5Uh+AI2PxyInb76x8x3DD7f0znr26BeHHmgLWmdXjylvSzS1za++5u7iqZumO6kUplfH/p2HPL7ziXnITvnMuvOAl2VzG9+e788MoNIIoLH/CURYY+Gxc9E3+UFgwgHFg0pKYXoj8EWXm3YMurf5aNxY6gluexk9tzIySJyOnuFtiPX0i8ho9WkUMmqGDstvNDFOAo8oR/IlyOsYB8VPihyydNj5/szfoRj4zUwtSvZ4Zdi1lQQeSIxXVs26bfX25PDkdCjZ0PQ4LgYcLUZ9RO5Qs0gag9QWZOHoiLVjiYFxOkoAr7mLk3x/J2PVAFowfw38ePD+1mDH8tZaZdjvQJNTG5rELaQjRABnFIWxPIKe1emuzxvd9WYj0GODYwOyfP4SMRLucP0/Ey3gN6F4/QHkLv+Ge2brkQfrC2gROhzFLp+KLFZ/RgtSA1wdINIAV/tG2o8YuzbZW/4J1NYtnX3XXa5GSEMObvkmMlr9NVIGbYGvINBiheALwH+jOXCUKCfXgFYFGaHG0Pz/ceR9egQpEYvQHDuI5uRWpPBvcO3MIop+2Irm7w0ogqGDyHBXRuvOu9G824UY1r6MFLvVCLxZToStESNzZzy86IQjftPc07HJwtDrDX6WO4HbP+j6GQCEAe0TWxLByKpkiJTfde54HwGfZ6BcjcVIibY1zwyWc4nycWwdH3HtPIjWKwvDHwLA98bxqJDwdiAj3gRRiJEB4ICofmOI1sh2tA6agXSO+ztei8sIBk4i8lYa81ktdg9bHGPgnuQB4JPAmr049mknLmTvk0gvAOC0S9eUTrt0zWmnXbrmec4DsQ3pRVeXwvSnWnqD7zW1h99u6griukLci7lHSXlFL+UVj015RYs++nl1OPG+RGv47ZYZtcuBy7fe2nnPfbl5vZuu6z627ty5Ka94Ssor1r+rW9F88Ie67YPA5i1/6kimvOLrgVmOCXAl8kBvKoXpe0th+g+lMP1jZOC+BOlOB7O8B6WJGBOqjR/NK17oQ9gCYWvL7Ep362GT56EQzNXoe7gLeZrnI31zjjv/rWjsGztnXEI0521x13v5k3NrU+WQLTTsCsd9GLkcL8r3Zx56gu31uD+vBga7H97yzIW/Wj5n/QuOZPuJ8+MeLPMoVYnY/+xjGHTb2tHkXEKLqIVnGPmFHW+uz7h3rBYE+GENvASe7++k+TzMHTcSVHigOu6Xm7qCJZ63szDb4WjhuwktCB93z+c8tGh8Hy12C1GtgU8QUd/OJFpM/4AsMBZPuwqFE16CPGInoA99PQql+Td3X92uvWEUPnEGejcTwOvy/Zm7srnC8cCOfH9mjwwwrghgrVGouCEHsmRzhaMfvrLnzh13d8lo0lyFctPPgDeWwnQjFPYgl2yu8EWkaC9D818Csd4tRZbUH6BCpGU017+YKNR6O1Ig1qK5cT0yaBmQOc6dM8Ptz6FwsjYEuja4Y+e46xdR/sK/Ila6BAJQixChxL+hXKABRKxwck/HhitbmkYmNw8u7QLfvEpFtFaMArNamkbu7enYfPyWHYtHwjAxy7VrIMwsydbnlSg64XPuGr1hQPv2+5sm2ufX2lpnBBWiQsJb3D08M/Z/B7JQl4mKCZuxMu/a7iSqz2U1r5YQkU90uef2MPJ+jLpj5hHlrDWhELTlSGkLEThLEiXg25pujIc+ArlziZTGAOWQGJHJN937uQStyefn+zN7ZHzL5gqfRgbKi/P9mUPew+0o678F3JDvz3wumyt0bvx915Hrru69pxSmA3eMBzs9y49JUl5xITJ6/LYUpv81tn3JovMH3rX5+q7XTmxsqoG3APgS8PVSmN7gjvk0yh+/2Hlr9vaap6CIoRuAv3kqQtf2hzjgeQmag2ajMbGtIzXRPPO0sb4NhW5qE364+PxtjJRagi03d452HTHRmWgNvcF7261MhYV4/gzpjz9BRqE2BLYXERHo+D61WjdD1UF6Q0fNvxoRZfysFKaf9LDbQzlEMINC1t62D8DVPAQGrkSLwPeGUzPbVr3qBMYO646Dqrj1zfKPDCAZw5KF/zUThZYYyYWBMmvTj20za0DCl08rHq6x2LUZAKHfxJzmpmADWpzXoI/dYlaPQ+7bG9ECZ5a7i4gsm+cTWWWtdksZaA9DXgx4nrfTKlFCE9EQWuzN1T0X5QfchTxUW5HFt4Zi7i3s8B3A+7O5wjvcfTyCGKN29y6aEMXwOuRib0hDDkipjPrnNPfWmiAg0RHQd8IEww/z2snBxMaje5e9obIj+ZtSmK49eksNOUDldmS82ohCnG4hKooZoPwqC41rJQqDW4jmupCIVnwSKRcLEYlQGnmgHkHz9/kIjFnyfzMCCRe5c7rQWnE78ladiABGG5qjxxB4moHCAs+d01tqWzhn2fDN98+4d2xy5rGu3V60Rg0Bt05WOv9v82Bn2vV7kIjxsAuBjxbX/kq37XsI8MwDQs/Hm3FC5WcoVNxyskIUYncOUW6V1e7C3ec2orqOW9GcfzoCk5MIxN7lfmwdMJBouR9DbttaBGiPJlrXQGuU/d1BlJNswNEjSrKHyIgZp6c/DL1bD/goCkvKAN2PBq6cXI48nZPZXCHtnt3vrEbWoSb5/szmbK7w1+hbA/jAvLOHz5h39vAlkN7ijnkiAGUjCve9I76x7bDyXxFy4dyzh9av/lnf/eFk4mikr7wj5RUfREr+D1AI22MtN3MP8lYegb6VcdgJSBJobNxXCtN37baFA1jcfbwTeB4ifutEY+nPwMnJtqDa1FML/KbQr41DbdynNknQ0le9Myh7l4+ubvkGEcnOLDQXfBd53K9DY/1sNF7jkTVjSyi1nMg9rbfy7AfXs+BmNJbOQmP9b5/sez+UAdaP0Qte9mgH7k6cMv8CZDl7FVrwPOBlYcJvGlmyk5zQ2ALjtbAsLjwOvmYyFYjZuUYBGxJN1pZgPIIAUFzMozXszptBGAQQ+uDNxPNnoI+yhhbquAesm8hL1osUhDnIk5RAwPCXbp8VfLNCmoSCdCE+nucRojC/yxFzzFWu/8MoNGMLctVmiYpohmgQ/CUKpzgHTWTbEQPPBdlcIQDet5uJtIYmuJdkc4Wb8v2Zq6Y5Zr9LNlfwG2F2h7aEFe875YHmru7jxj43tq41WR5O0HvCGG0Ly72V7clfrftl3/eRgtyQg1N+i+a+O4kUrxPQ3D+EvDMg4DULzY0PIiu4zf9FZAhMI4bAAsohSKD581+R52mx+38rMlxV0Zq0GUUVbEEhb5cj0JFCeUs3ojn2TCJ66kuAHSs3nfK7OX0r/nFscua70NpWQ9bjPiKQ8g60Pk0SkUNYtMVatEbMQCBvBRGIMUKIeFj6MPLU3YoU1la03ljIY8qdMxMpo3GGwR8Q1UXa7s77FsrZ8RHjoQGgucgLZ7ltLURA0Gi0RxADL0Trbdn9bUWiDfx5sbbqQyQXEoUN1hCwxV1rb2QcvaMfEBWMXs++IeY6aMTpWqcB9+b7Mxtiu36Dvu3BaU98jFIK05VsrnAP8Ipsbs2qOz64cBZwydHv3v5qAq99zc97PuL73j/5fZVKZXtylnvdxyEFP0QRTM9JecU7S2F6r8qXlMJ0LeUV34O83X+R8or5llmV81IX7vjnMODLK38w69XIWHJQAixknPhbBKzuQpFQrWhOa9lxf5s/uqqFGaeNhjuWtw2v/umM1u6jx4eOfOvW1Nqre98TTPpVNN66kT57L9JBn4d0+AQRy+BDaF5LAG2bmVNdwRFsY0YKjcMvoDH+lMihnIM1lO/P3P4EldzjUEzoEELSFlt/GJqwDQAYYDHwZIQQHoRVqJUhHI61W08taZYM8+pYzlWSCBDVnxOixaYP2OERbG5lexWC+AJgNOtWH8RqgNgz8ZFLdQItSN0oOfC9KAxlNcrruh5ZUgPPwxN5fK3qmulBysOHEC3pepR7tco9MwuFaiFiTTzSXeN0pJj8GCkOL0Fg8pXAd7K5wkuzucIF2VzBLJuWGzQbKSXnZ3OFN2RzBauHcEBINle4Dqhmc4WnhCq0IQem/Pyfz53YsazjR5XBJs/zQoZWNFMe8akOJxhZ1ewBb0x5xc5HbaghB6qMoTCzRWhuOxwpYM9EHh2jYb8FseRtQEDpZndu6H6PEhnTZiCl20fz5EUItDQh63sCGQ0fQABoPTJylYnWpDORgayZyChoxr52d81Kpdr2jRuXXRQgUNiD1oEfIe/RShyVOVo/tkJ1GQRJCCpIkTLaZIuQMHA5jsCWrWUZpBi1uWdk66bV3GpBHq/tKIQxXuIkdPvMwzXTnVNG9PPzXbtWzHQukUFygmht9ZHiZTnNz3XnWK70CJGS10QUTmhgzNbpwPWz7H5GY8dajtbV7CEKw8TVVvsuUgoXoXznjyIWNrK5wsezucLHH62dg1GyucJh2VwhzsL8bERs8Mr4cfn+zE35/szXXb7W47lOMuUvPyvlFT+U8optbvMziXJ7Tgdev/3etj/seLjlqvH1Lc8PE7Vjg4r3TPxwEhkDzkQA6xJkrLjU/f9Y5KOI0OOdwLxkZ9DW3Fdr70yVO5Au9OXHc38HgpTC9AhKMfkjApGms7qyBt5wU0/N6zluwutYMtHZecR4cnKbX952d9v8sfVNp6FxB26OmsHAA0fy0CLk3Gh3+83A00csp2uE7pvv4eRggrZRNH4eKIXpT5fC9Kefins/lD1Y+0LuR7lI96ABdR5TufdXIiag5rrzLAcrAWFS8e1lH1rC2H6YWoS4PoHYpB4kh+wKtrpDL9kxEfZVwbciiRa7btccQhbEcQR+jMLewjAsGdpz9zgXAacj0aJxJ6gqVyIJCcbLIYlkQGsCvJI79gSkRFyEFoz3IZBmC7H1P0RV7n+FFJRPo4kMFG64Hk20L0GesDvRAm3yBxSyeTTKF9uazRU+gyy01z3eyXgfyvPRczwP1aFpyCEoKa/YArSMr2/5CfAqv7vSvv2uDm/wnpCwkgCN7XejOnUNOcgk358ZBj6UzRUyKP+mF1miP4nIez6K1ojTESiYjebJXqZSFRuF+SxEUBEgr0YzUspsbr0XhTYFyFPVjMiGjkDz4QxkqDKAkkTeshB5m5YSkWK0oGT0U1DkweFo3r8JAYoUytM9BiWsLwH/SN+rBEHojUHzGgR2LCrB8odxbbcT5TvNcfdgERoXEkU0GM27FbRfjpRfAzzmBTOAU0IGzh7Xd7vmYhQaeQuady1n2EijAve8Arf9YXdvbUTAyYCthTKaxzFF5MHCtWnHWOkWq4X2PCKCkkeTzWhta0NA8eZ8f+bW2H57Zk8ryeYKrURG2L/N5govQGyb/wu8LZsrbMr3Z36/D67TAVzed9JY+/Y/dzShFI9xlA91Tb4/szb1weJJwMiW67t/iN7FqmAkOdtrDoeQ7nIv8ix+Cr3fQZQPeQtAyivOQnl8+VKYXuG2zQHmlcJ03Au5Dek184E3jK5secHqfN9Lxzc1XYjAVTLlFV/lSDBIecW3ofH8+QM5Z8uFB85ExvsXEeUrWohrCIyOr2+asTrfNzLz2cOJGSeNt67+eVfHhmt7xCQQGScmPYKfPYcbXrWKJa9EY6qE9DybZ9qJdOYQeTjN2/08VDvrKdP/GgDrCYhT1H+bzRWWIApQ85QYyFlAxEiUqDvdgFFNgXXNZjkzFiNbYEMIPQiaYLIK7dO9sziomo5tSN4uL5GY5njctWYSUeQGsWPizFbtsfNnx461CvM7F7oaHe363/PQALBJYAtSBmxhHET5Us9hKglIC1rU/w4NjGG0KL0V5Y39AvgqWtw2ZnOF84iUifcSFYvcgSbNf0UD9d+zucJD+f7MTlrl/SB3Izf5OfuxDw3Zj5LNFU7sWDTn46OrW54PXpPfFHR4gQ+BRxjEo4O959EAWAe15PszhWyu8E00l70PzU33IgX+KDSHvZAo1KyC5sm1KLTmSjQ/25w7gpT4d+T7M/dkc4V/QOHTL0JUz//o2rG6hBn3ew0Km/sACq05Es3D5hmaF+v2uOtDJ5qbZyKDVw8RJfpbidaMzeB1BGEy6ZSiDJFhDmSEPBkYD0OagCWetzNawox9o669JFFYfCuR563XXd8iHSBap4wpcK47t8pU6vsaUTHmovu/A4UaPQetNbYmDqLIiYVE3q0udl1jE0ghtpysqmvXrr/dHTPi7mkFykF7D9CVzRUOQ57LH+b7M6tiz55srpAENuX7Mx/O5grPctdJOtKLTWid+wTwlmyu8Fp33evz/ZntHPwyiUgMLBTwNeib+wc0Vgb20XVqwJZFrxu8d/ufO35eCtObAPL9mXLKK25MfbB4OBpXK9H3+zJgE3gdYdlbhr6bY4E3IbKa20thegjIp7ziuSmveBzKeXwZ+uZWuOv+DQojvLgUpjc6Yo0vIiPAZ5Fx+cHxjc0jyONzJvqmPoGIx3DXnUeknx2o8iJERvEplCZieZBGqx4C88ELJrc2DW/9U3d3oq0aeJ7XDMF3/WbvBUHZX4eeTU+I95w7OW1ogFmW27gIGV1SRCkx7UxNeZmHdNQf4/LbnippAKzHKdlcYQaypF2DBsFh6AVvR5YlK3xmC2YbU+tiObILP16s0SNaDOw496H4QHM9SIPpLVhxAFUPvoLY3/Whhc1Elk1jfrKLO0tfGEK1At4EJM0ascT9mNRizcZj063NIfecrJZJEoGOWYiK9B1u3wloQvkN0UJuFt7l+f7MIHBXNlf4BFIaPowW0eNQuMwtCIStRUpKF65+TDZXeCmQy/dn9kdc86fQwL/10Q5syNNLUl6xY9azR4LF5/PZhdltix++bE6iOpLo7jpqIqxMeN7YSMwG4oUk50y+POUVe0phesceG27IgS7/D3kkPoUW+zej+egGFNZiOU6bkUK+DPg9mq/WImXfCvluR2GER2ZzhU0ImPwEAYMPEDF0pZESshoBuQRas2YyFbjUEHCxMPYJ1+bvUU2the6YZSg31mozHomUYWf88m5wdsQmNHebh2cNWh/GgWvK2zgp0Ukq0USL57MGeeMuRHNi3Bs1itZOC/tJEhUjtr6PxbZViMg1SrE+BLF2Z7u+1JCX5ItI8SojMPsh5L06kwjM4d7BLKJ1PM4GbB5GM6SagXKdu6fZRKVPjkHr20PIY/gytIatBmbk+zMDDlxd7p7bx9x5/+b6/CYEBn13zWPcdY5CkR7XcJCLy63+dmzTvwAd+f7MaqJaUfviOhMY0cHHdnpbnoue57MQK+f7S2H6Trfvr5CyPkYU2ZNA7+H/gM0pr7jQeZReibzN33XnxetimTdss/Nw/Sf6rn6BIm5eB/xDKUxvTXnFjyNWw8XIUG3yYcAvhekDGVyBvuE/o3ltEo3H+qgrS1G5sTriP2v2WWMzxzc1tbX01Y4+4s0Da1f/T9+2bbd3vgDpld4GDt+O3lFcJ9yAxnVcfw5QZFkNuKUUpp/ylIxDlqb9iUo2V/gcskR8Hi00b0Ihg2bt8pEHxbxRBnSGYvvjgCsOdAIgCAOStQp4SUgk6p1OO6U+rHC6/d40v6n72xaFTUShBxbaaOeFUB0Hr01hjbu75JT7MnesF9YYLw/5lebeoOwKS46iSaWFiFHLim7GZRtRAWeQRenSfH/mWwDZXOEsFKf+94gQ40I0sM8xy2A2V7AY59i9cBcu3OQJMg81pCGPKi7G/0pg+WmXrrluclvi8sFlbYvW/rwv0dxbqYTJMKxsbXJjLsTvqtI6q8ZYqe20Upi+c3/2vSH7RrK5gjGyfoRofbA6SR5SHDYjRTnrjmlGa0aZKH/pSyjcpYWIKvyfkZJxEpESOIlyH+ag+dNHyppRqRtpkilqE+540FrQjujjb3R9fZHbvp4ItA2haA2jRt+MlMEWIkINKzq8vjpJu99ErwcVz2cLAiAGmKwGVoIo/+oBBO5egQx9xrZoRjvzfuGu89uJbV6lqTt8ke/jeT4eERmULVoWfVEkytP6CfIerHM/5xKt0/eg9enwoEKlNumNNHWGvbH24sbLUaJ8tp+ikiOr3D0tBZ6Z78+sc3TiS9y+c9H69Q0U2dACPJTvz1yazRUuQ2GNH0WewV73zHtQHvNXEXnG7XvJTNiQacSF7n0feZ2uQEDn66UwPeD2n+4lgovaF0++eXRVSy8133QJY3QOUaTMUWiMPYgMKyejsXxlKUz/NHa9VyO9cRy9807kfe5CBuKr0XfwXvRdbkAGmX872AoNp7zi2eh+4mNwEI3jdvTsdrTNL7PkgoGeye0Jb+vNnbf3nTR+9JYbusbH1jXPIjKmDCLDhRmEAmT0qSHddTt6lm1oPlsLPHN/GCkPWZKLfSDHo5f3J6JE4VEEBi5DLzpJ5Jmy391MBTo2MGMS+jAaBFXCLTe3MbYqzhwbHURkRZtO6gFDPciqF9t+GFMLtdXleyUq4FXAs+ubtaC+rfhCVgGC0XVNO7bd0VELyjsHwSBasI9Bz8ueY5yaOnDbj3DHV1ChvaFsrjDTHWPFLI0ZahwtQB0A2VzhDAS8qkx97otRTZarXI2thjTkyZQKsuYtv+fT8/+08Xddo9vvbguaeqrhEW8ZGJ1/7lCSRACJEJoD/OaQsVLrMC6pvSEHv+T7M2NEtalWEYVDF9HctAB5jN7pfve67b9DHqVr3LmPoHkuRABkhztnJZq/b0FAaQMiezCvx8Pu3F8SzZMGrpJIubsJgY2XI/DTi8DNS9wxC4kMZ23ux3KiEojx7h4ikNFMNKfPSrbQ6/v4nk8zUUifrSO22I25+0igeTqLlCbcfVi/jVXP5vsacEpLb/hSzyMRVHcSTxnoiYfij7t+PuKu9Ua0Ph+LPBmhe66j7j66w5Dh8c3JcPD+1k6iEio1pkZ8jCHgmkDhbcZ6eA8CxhMgT02+P1NyxExr3f4QrYdDwPNd3aerEJHUDqA3qPLjjde3by0P0+Ha/xoCZ6fQkCciW5AX8LJSmL6/FKb/xcCVk38I4W3lbcnuREfNjAEh4HuJsNp15MSg3xq8F5EvbEDGhcuRl+w04KS6IsSvQ8aLc9E46wQ+BuFHvWTweS8ZnI/Wi39C39cpyKN9UL3nlFdMorH2a6IxEkK1GaoJCAP0LDvG1zclt9zYNZjsCIIw8E5e9aOZHWPrms0QZeHD7UQGlklkeJpJFApoZHAWEbAcjcmnXA4pD5YL63sbqiNx2xNopwMtarPQgvYdNEh+iCboX7mfGURFg+u9RyY26cdE83UYJgmqHn7SZTI9ukznpYpfIx4eGAda07U+3b64tQb00baxK1CPX9+SlSmPMj5aam7tSZc9P0nZnTdKxGRowG6CKLH5PpSvBBpMFsY4imKStyB3/hx3rbchC/F7kBfL2AfnExGFmNXTFts/I+VkLVrE2oDhQ7XWSEOefEl5xdcCeQhZ/BcbdnSeEHStu7o3ObisDb8lgNAjGE8A3g/RN/+WUpg9RcKdAAAgAElEQVQe3nOrDTkYxFFOH47yceYjJeAKpNyZQQlkhDL2Ph95jr6EPFavc8deiQxPX0YhgVsQILL8q9uR16QLgauVSEHbgqzm25AH5N1IQakga7Bd+xcIoM1Fc7KBmi1ExXmNgbaDqUrPOJrPjcGv4u6ly7Wxw/XVPGhVphbxHSNiUbQwRbve9UhpNUp4I5faCjSHoepJeh6jKPx8Aq3RFgL5TaSUfRYpfhYdYWJlVdYg0Dvh/veq45QJ8ZLtO8mizLhqTLhlojysLUjBfpX72QB8Pd+f+Q+mEefV6kLv92zgPeaVcvveENb4aFDjaC9Jwvd3huBvQYWL/1jX3nz0/m9oRGk8fkl5xaOAKyGc7zUHfXhsCScTVuTb61g8yYJXbg82Xte9aej+9i+icTWBwNEaZBS5GI31l5XC9LqUV1yKWJnfRxRaurZl/sTMuc8ZbR0ptdy07c7OZ6NxNoGMGAPA60th+nGXF3qqJeUVz0fzlkfEPIpTR50xxV+OPO8rvET4vrZ5lXePrWt6LnitRLn/VTS27kGeX/NiNaPn0kFk1KkSGX12AEftj/XzkPFgZXOFe9EC9T6kkD8ROQ5Nui0oFGJNvj+TQRP4J1HRwz8Q1braHZAJmfYdiPnW8yDRNAVcxVn/9iSx64R0ta0PW5sHQ7Tg7aSwRAvedqK8qEdruz6fqz6eNg7gTAxces0dtPWdUPb85E5ijFG04G9Cz8q8ZgaiQODKAKEtvqCJ6uNIsXg9ipmfTVRUeQVagFPI6mvWJg9Zjq34pIe8kNcgMLcGWZ4ue5Rn0ZCGPGZJecWzU17xs2jh9fxE1T/uuJt6J0vVxOjqZg570RALX72DYNwDvEnEnPUQEetSQw5yceRIa1BNq63IQ/kcZJR7N5qfBokS2A20VNBc9wVk+baft6PvYxgZiQbRXF9015lPVLpiIfJQneLa7UPz5Eb3sxYpQWn3ux0BlN+h73AVym+aSQSCthNRlVtoeScCTzuLQbq+bCdaezbF9m0mmvMtL6wDgQ0jj7rRPYfbiGjpraixrRE9wCbPY9TzqLp+pInWfLvGOxB50mfROmYSNzwOEwEoC23yk200Jdtpid1zjYi9LB6tYtEU7yVia/wpAq1TJJsrPDubK9wNXJDvzwyhdzIPSGRzhZnZXMFYfZd6CXo8n82EDCPl0Z7XkdlcoV6feAsC7nvDWtiQ3csO4E/gnR+WE28JJxNDRF4URtc2heuu7WFoeVsvet7/gXSTMQQc3oDG0wlAf8orvhgZ5j+NxtJW9G1vqGxremiw2No8eH/b84mMFsYiegS71j090OV5aIwaSQ/ANqfZVsH3IExDmAAWhjXvO2PrmueBN0jkXbdorQrS8VqJCGwgMkIZE2gCPbvNwBf2l3HykCC5yOYKb0EfNmhBueIJNlkiUtYHgDOyucIgCuF4DRpQp6KPoUaUGFsPsKbLhZrmsJ0yTTjhLhKniwVC2ppHEx2JITZu621z+6vow2zCrKVB2EUQeCR8Q3SP5jMLiRQAA1G7A1vxPLMADYa5bvsJTFUk4u1Y7hqx3wYyl8batj4sQNaNBGIhWoVi6Y9BBSff5fZVEE3y9WjhOR+BvVlo8uoFXpvNFb6E4qhPctcZQFaTSeCBA4DuvSEHsDga9s8C95TC9Hfc5tcAl0DQDXhBLcG9V5/gVWa0c8x7tlAZ9hi4WdwxrQsm/n352pN/hGoPNeRpJPn+TBX4RDZX+ApS1L+LFJCPIEBktOADaE66FxmFfuq2P4Lo3V+H5rhb0LzVgsDW4a7NL6LQN5Cn/uPomzQPfg0pQOvRfHkqU+fjZyGvSx8wRi34Vceq7e2jqRnH4nlz0Fy4FQG2MPZj0QEQRQz0EIXIj6J1dDK2b9hde4P720gqqmheT6E16yyiul7WtnnRtjC1npXlmT3sjj8SGKuMet0jjzTP6jlu0vOTO8OJNqA1wDxrVjQ5TtRkbW5DOSX2bMvu+s0IpK5225YihXC2a+/wfH9mjfNGPRPYkO/PrHF9no/WMNB6Nds9h68jYP19pKhW/CQ9KAR0qbu/PkSWcHE2Vzg/35/Z6tr5HiJTWkNDHreUwvRmpDOQ8oog4GQpB1Dzg9GH23yiAtQgBX8rysnqRnrPNmQUucTtN13NCCwWBBOJcOi+jhB9j/E6iAYgXsXBRZC1DBnwzZAxAawGr8cpm+aIAMJm8Fai+aAP3b/NJ2VkVNmE5iWbX6x+ax96/jWmprmcl/KKvy6F6XuftDvcjRwqHqx4Mb4BV5/kicgAetkhWviORxaqAeT5sIm5g6iORn2oXVx2lxe1u1ypqQeFEASEgQjfbcA6IOKzdWgRG7cdBdFHGs+vin6CabFbGLVFGVlHPVBB4TDcabWrl3hIYhwk2SQBUwswV6Y5Pt4H60eZXUMum2J/G63uXyDv19no3bzdHTeBFrM2tAjOdX0Yd/tXIeDVAfwWheBYDPxylGT6W0RR3JCG7EmSSGGaE9t2KbABPN+G3dp7j2HzbX34LQGbb+hi6y2d0BKMLsiMXL5fet2Qp0zy/ZmBfH9mElm8Z6DvJUnk0e9Ec+4SBJh6kKf9t0jZaENz17eIDFcdaD68HxmXNiOFsAMpiT8HLkDMZzcQ5aM+g2iO/blrfyGK2EgBz/Amq29e9Mv73t20eXgx+q43Iia8MaKcsCoCNPGIi0EUVm9K6Qx33iLEiFhEgKLZXfNYBL4m3TnHuGdgZBjx3OZtCIi0EoUcWl/M+n+su38f6AnKXnJ4RVtLdSSxABnjTEELiTxXTe6cSXZdw2cipjifaF25yT2Po9Aa80JUl+w37pluIlKMZyNChCuzucIpyIhyKgLE5PszxXx/5nr0/p/l+nG+u967kN6xHXm5mokUyrNQTvEXsrlCX74/syrfn/mNy/NqyL6RNSistB99N1YI3L6PCpFecjbN1eSsM4a9RGfVaqwNo+/YInXiKRsh7MwbHEFj13RN3HGvSHnFv38yb3BfSilMX4Zy7W9A4/JeRMpiBnRFFyWCUTR+WpGxodUdv5XIIHQ4US05K/VzBSL4seduOZy/Qu9lAVFd16dUnvYeLGcpWo8m5wBYns0VvCcSj5zvz4SOHnchqjB+Fwo56ETuf5v890b2FD44ndcrvn/nPg+80FNbYbgzrNADgiBsiQPpltj59pPA90KSiXoQWD/w1+JCPsLQ7Q8J8Xabw1VPlGG/4zVMYNc8tN0BNiB0Fgov7umyRX0zGkzWb8vjGicKZzQrSh9SVn6DFsdXuTY+ieLxz0JWp1e44xOxdjuBC10u3iYUYtgL3NWIc2+ISSlMj6a84sW4xN6UV+xBRSiXRkeFgF+jnEiMrU3i+QEQbmcycccj3541wLf2Q8cb8pRKNld4JfKs5BCLnI+MdXcgBrLFRMatjyCw89dAHhUtn4O8Uovcub9EIer/ghSVBBH5xWuRt+tkREbxGeSxaULekZq7zrEImGxEXpALapMsopactfmMJYnKrM6j0Xy5DimCq9HcmEKKo4EZC1/rQ4ZImBoa34NyZq1+0ySR98nIKEoIYJkn6WaUW3YRmouN7CgeyhePfjBl1/I/guaeoDL/xTv+N9kenom8B21IWV7p7t0MlUbAETf+2freSWQgTCBygy533BJ33ikIBLWjdciMu1sQ/fY7gFNduZDpvEwjyGj7IXfPP0W5PFegd9dJVBfN+nUq0k3+PfZcG7LvZCMyFqxF7+QINEZND5lqQC774bblrQQjCSsYHc9n3Ir0DfPqJpHn6gi0ToRoPM4m0ouOQ9/aF57Uu9xH4og9PuT+DZHn3QzwjjHUq1FLfBbpW/ej73oRmvduJSLYsXE5QZQneTEqH2HPx9o+yZ3/N8iY9JTL0x5gEVFAeoRhglrt3M77NnwLFeibgzwcv3ocXq2vINrUB9HEaQxKf0EUCldv9ZpOdrffYrjjIMKr2z/lv50bQsIQfM/beX58oYlXuY57mRQKG4ldLw5+jnB9qnke1aBGwvN3+w1N57GbDsBZP5qIikPadqubYKe4voQeU2k/zM08ihYbH3nYOsKAHqDqJ6b00ybClxLV/EogS+ivkMLxdjRp2nkPowGfRO/6LPcDWpjbULz8JuC59YUjG3JoSilMx8NIa+gbi9WJ82rAyrDKgpE1LWNdR5Xv3nZb4qVA8mCj4m3I45a/JKI/tzn3auQlfxuqgRSg8LVzkZL9V0jp2ISU+esQsDoDKV/nojnJvCsziMBKFoXfVRE42YwswO9Hyp+xC/7aXfMRYL6XoK3m+cHW0xdt9DzKiFL6RjQPHo++b1MSW9x1J5CClGZqUd4Rd51JZJya5f5uYSqrYZIoHHwIKa+nI4BoFPPxNcoMYVbINB4dYt4vPB8/2R6egxS5MUSM0QOc6EUGQ2v7l+65nEUUGh+69kZcG8NE5B1WmuUR9w7K7hoBLv/KGeK+m80VfouU7CniyClOB64lCqPcgr6F7a5PVXfdO1GUBkS10uYB73nNv/zm5rs/dvjVpTA9WX+Nhjxu6UTjcg36vm9DhoJm9K7s+3ffuu8FOzxi4XBx3XAeUXSOeWWOIUpJKCGPaJxZuYryIg8m2Yg8upY/ZmNyB3p+t6L54SqivEvTV5/P1LJGq9A6ejx65uXY/nH0HNuR0WEH8Lu6dfgpk6c9wMr3Z0azucKb0WTnkUj4IycsuCSbK+SQ1fDvkBXuxsfY9GWIyvVoNEFavYud1d8dQWPo7erhqV8UdifTFRa2HKohtCh5MajhhYItXsyDZXvLxJIyYZc+mcSBnA1862c8rt73p+vd7qnjp3sG8d+2KJkYE5OFJjZF1/amYy2EiDJ4DXB3UOE1kwMJr6m3lvDbdh63hYhlJl6PrISe5/9DIRmzkfIy013bvA5Vpo4bjyivADRJLMvmCu9Gi99VDa9WQ5xkUJhp3GhyA9DftqA6sODc0fcCy37/vXSZBqnFISHZXKEZKWfjRLTtkyj5HeSt+UdUw28SeYja0dy0ESljbSgE5n+R8n0bUrjPQsrgj5D3Zi7KGZqBwptOReE67UhhvIeILGi1a3MuRtDgU060stzzuA8BuEGUF5QmCtexb7uVyJB1FlqvjOmrk0gBWkGklA4ThUhOumfSQwTWepgaAmSMYWZILBNFq3hESfB/RIZUM+BZH42AIxEEVMMqvt+008A3CdWkR9kLaTvPGUJ816YxHra5e7EQSyNvmunaONq9u+chr0Mu358ZAcjmCgmkcC7fTS7vSxHwnonWlzchfeX1iK34h64vn0CA2gyFFoI5EYacObKy5dUIvO+zAr2HupTC9FDKKw6g526Fhi00tYJ0ykuJDASgsjZxD0t9BM4m938fGgc1pJcuR7rpG5GRtwPpRX968u5w30opTIcpr2gMzb1ozBgo6kFjvgMZlHYQsXE+xFSiHANQXUjPBn3zpifGx/YIGgufK4Xp/VYb7pChac/mCkcB9xGGTYRhiO//PzRJPRu9iG8/1jhlR7f7N4ghpg19KO1Aq8tPCj1vCgCq90JN59l5NOBVn8+0O9lAVMgy7i2aCnRqAU1D41Tbmglbm6a5flALQy8R1jyCgCDZvPN8I8qwZxZPaI5fa7rQx70Nn7Tj42Qhtq3eAxaiBcfc8w8BS4KAFg9wxSbj1zfmRJ9okbS+xye+sO7/ESLr6Aga7BbyGJdJBPRuQAyF9+f7M/ft6UazuUIryhe8I9+f+emejm3IwSUpr5hARphTiSihrwQ+VArTu1iwG3LoSDZXOA7NF+cgQoOtyNDzCAojewgpHbcQRUqYB2cl8nSMonmp4o5bgrxUYwg4LXL7P4C+w2sQycL9aK1IoflqqbveWqRAzkAh8IOIUKOViH32g8Dn3W1YmJOFY5uy+TACGiNozi0jwGCRCZaP0uLOLSHAFg+zN6Bm3jgLFdqA5l/Lc64iIo9nuvOvR8aygKgmpbXnub60A4QhQ7VJ8Jtp830SwPbUvNvu7mgdOHrZyhfPA99HoDPhrrfC9fm4WLu+ex/z3bMeQaQ2sxHQ+ki+P3MNQDZXWIre7c9RKF9Pvj+zk1UxmyvMRuHqF7l7/gv0Xn/h7vmvEcDOu+stJjIurgO+Vqtww/JL510yOdA0VArTX6Mh+0xSXvEVaK1ehub0IaLv7moUilslAgSmo5iH03K24tsNdKxFRoFNSL+sohzFc9z+M4FbSmF66Mm/030jKa84F3mcPk3EqGmAq4zGzVFoHjOPu43vONHFFmR08NGcWUJzXRMac1sxQh55+V6xP5/T096DZZLvzzwENGdzhR4875lowhtFL+9ElOj6WOOVq2hhscmtA0hYftJuPFf1/z8WhFsPKkwsmdgGrsWxjxOx0MTZBXeG/3kTFY8Qr/vBLew4cX5IlAxtC6QfBiFhzcPdj3nfNhMtqnZfcXAWD5OMg6q9BVfxezXGw/i+enAaEi3aIVrUK76/k22K2PH2/Lpix9skVyOig4/fWw0XSoKeSxOaHOJ5BmZFtBCVdmRxfAXQns0V/oQm5A/n+zMTsDNH8LXIytiBwn6OQUpQQw4CcTHmhwEbSmF6l/Gc8oovQQxwX0FK0dmoDs+/l8L0xFPY1YYcgJLvz9xvNY6IlKy/RvmhVuPpeASqrGBuiIBKB/JCJVACeAvyfhjZzyCaCzsQ4EgAH0bz0F1IWTsZGYKeg+bQoxEwGUR5YP+GvFlvJFIQVwBXpRf+8WUzu9esv/G+C5ejkPlFbt8SNB8e7s5pQx4sy6sK0PzbSxQS2ILW0gF3vNGvm/J5FVFubJzQIoHWozkoiqSCci6MobZCNE/btS1UKUBRJr9ItlIFznP7uksbTz8bymsRCDrJPfvlaPxaztompDdYftkclDawxF37l4j84jPIG2lScs9rA1Kmm7O5wleB6/L9mRUIzF6EAOgPgFtd7vd/ufd3oXtnz0Tr/cPuuS1FNRx/lM0VDjvhwxsv0/Y0Ddl3UgrTv0x5xZsQiD4dld0w5f5k9F7LiJjmXDS2zGhQRt/IMUR6VhJ906NoDF2MjCBr0De1ObZWFJ78O9znMgHhiXh0EnqmK1lR89PRc9tBVJ4BotIHG5GRwsayRSLdjeapm9AzOxI91zuQh88jynncL3LIeLDi4hazk5GloIqsRysfRzvdaGKbSbTwGLseXn2mUCTTebKmAykQhvSxkSFmUvOaTbEndmw8ztwWIwtjsz7FrzG1R2EIQQjVAFqSAVocjyCi8PUhrIWhlwTwvCketPr2DGjgrl+fxzUdKDKxPtc/sXqPXtyaGe9H/G+j7rVwylamevLibVkf4h64eB/rvYvT9Y/YsTZ52N9Gd9xcd84OpBQ8hNi0rH+jyAL2pkZo4cEhKa94Fgrh+pdSmL7OAa7Po3Chf0WhW89Hib4PoyLYC4D3lsL07fun1w05kCSbK7QgRXoHUjZ8tK4cjZSK2Wg+GUYK9VyiArMgEGYGJgMUVeDH6Nv7FfoW/xYBj1XunD4UbvMQ8rbYNfrQ/NTrrvcQstSHCHjdCnyws3XT/02Uu3qrQftPkKHyJGTN34DAUjwXqkwU2m79rCEv2lZkoTdiIjvHCr+3oLFTQYqUhQVZSFyNyHNWRSyLpxOBMA/NrV9GBq0jECD5FlJ0T0LArdNdaxIpekbk0U1UouN3SGG2cC6LnvCBGVCtNSWq5Uqt1YofrwZelO/PPEydOB3iJ0SK5f+gws/nuX5ehUKnbkGK49uR5/v37j2c457R+5FOc7draysCeUuBH+b7M5+sv3ZD9o2kvGIn8DU0Js5F72QVEelMNxGFP0zVLSyUzYwEfURe2s+g77MFGN+foW5PVFJe8ZWJ9tq3Z5w62r3lpo6AIGH1qr6AKOuH8YM1Tb21IyvbksvBSyBjSQeaN1YQRQ9ZyaVBorw1Dz2zMsoxPR0ZQi4phen9VqLgkARY+0qyucLhKAb9eGLeDPdIPWQZs79NHi1EbgqgmBGu47TwWh7mVEr+yXsTXhcPa4v/XU+6UQ92dheaWO/KtnOMPtesDZYrFWd6ma6t6fbFY8dbptkfv7f6/m9GioglNxvAq/fwxenr6wFb/X47v/7YWuwcmArarF9GI7+7Z7Cne6og6t8/7YNSAg3Zx5LyiiehEKtFCBwtc9sXIo/Dt0thupTyihegHM3bEHj+X6TcXeDi0XuRQnfT/kq+bciBJ9lcYQZR2JoPfA4p2QGRkvYgAl0e8p5YvaYbkZdpgqi+jtE9B8jqfQIC9vcTFfEdBVaGAT8MA76HT8L3udcdW0MKjeX83oeo3FvZyXgXLtExnoVnD7l7iDP7GZOrKZndaN5bizy/VdfXYaIcZpsXR5lKyQwReITpQ+CNXKPXHTOBog+SKNRqIQKsIHKQY4hyOkLXj/tQjnXFnWuRIOaFMC+b5/p3n3svyeMWX1fr61o3eevy85ZXam1zECD6FAJH2/L9mR8TE2fwBb2/R1zfvomian4LXI7A0pcQaF3m+nNZvj+zzbVxJfJ6gb6LB10fTwW+me/PfJGGPCmS8opdCCQ/hIDwZxHovRaFuh2PvhELfY0Tjf0P8F/IUPIMZAC4Gq0zm6eLiDgYJeUVX988q/zfQdlPVIcSk+B1oHE5jjxUSyDs7DpubHJsVcvdtdHkqWhsrXe/56FnOoCMSz1obK5GQKvbHbMNGRr6kHHhulKY3m8lCg6ZEMF9LdlcoQ3FUENUSNhCA+u9IHGJLwTTAaYp/w8xi9u8lzGiPL942F19eGEcKBmoMiAU955Md7wVZptO4kDBrhfPv4rnNO3qIZsq9cAmnpjYFNu2uzbs/q0tC4ucIFqE47H70w2sONjcXU7cdGGO8XubBEYSfnlOECQJp9Iv7q4u2HQSIKvsV9Bk8Yt8f2bDXp7bkKdIUl7xWOAN7UeNfiwM8MYfaQe4K+Uvv4HQOx8VjrwB2JDyit2IenkM+GEpTFdSXvFdwKQtlqUwPQj8IeUVZ6e84qeBH5fC9HX7494acuCIKcvASDZXOBnVPTIvCmiOXeh+bwLeizylfShM5tkINK1F4P61yAB1O8rhGUBKSg+ad/4DkSccVR311wyXmte3zKo2dcyvzkDz2AiR52kGkaGtBYGAcfA2oxC5OxBQaUdg7w1I6bG5/R6kAFnOxR9ReFst1v5M9398XTOvlOW/NqH1dhQpoV9Da4B5yuycgCiUu40od+wZdW2e4vppofUGPJ/lnvvNyBpu927nWeL9FiICjm3AiuHxGXN8v9pcC5qqru03A98hYoqcArBikQq3A2RzhWGkpN+PlMR/QrkrtxIVDu5Diub7XRsXZ3OFc1y/tiEAZiD5G67dDmDSFbluyL6TEeRt2loK08tTXnEYfY+3Ih1xAwr3PQ4ZUC3H6pvAYClMDwCkvGIRjZ+rS2F60y5XOUgl5RV94I3lrc0Q6aOfITL4PIDyCMOxlS3rauOJ+4jySi3NxeraDRERyhjN/U/Qc50JfKQUpnegeW7lU3OHu5cGwHr8cjYKiZjJVAYfAwx7UrLrAUBimn0AVL0WBnfmxAN7Vy8qDh6amB5AVVx/y0TkEXGJg0C7lgEOi+83YGlAKx5yF28DIiZA2z+JlNC+unuZ4lmrlWHgtg76ThwLm7rCeO6ZeZf6YudN54WbjnEwnuyMux87t/6Z1j/vKuAlE5Mdpx19FYPDh3kPrH1e/D73yAwZhlAZpRaW+WHLDD7SoHM/KOQVwMfGHmrf+W14zUFyxknjZ4+saN48OdBs4yQkKur6mVKYvhygFKZX76bdVrTAzN7N/oYcunIf8J8oD6cLKSVmKGtDXtRvICXiJzhyJaJ58HR33BKikLwPun1LUaHhj7tzbm7qCiZLV8x+3tyzh1o65u/4NhFRw4A77qNIsRlGSk2IvtvfozC2j7p+Vlx/XoPmyl8joHcykUHMQ4Cky20bd/0LEVizUhhJd61hBBxGYr995DG60R3zWqay5DYhEqtZCCyZN2slCg80A6ERHJniN+D6f4G7/glERjNbL5a759qO8mMsb7cGdK7ZfNJ9a6JcuQRRbaS3EYHlnZLNFXrcO7kr358JHQC61tVH+w6RAjmIyEgedM9+dTZXuAAp8l3Au107NyEjzx+AP+f7M2PZXKHLtXW/e1cN2UfiDGfXx/6/CiDlFZOIwOSvkRfrByh88DulMP2f07QzioweTzfpQREbFn48H+VStqCw+UuAEfA21MaSt6Kcx8UoPPLtyBgUorF4GFEuVsVtOwoBr9+iefOAkQbAevyyjGjBgF3BSL3szltl4CS+bTpwU98Wu9lXnztkIGu6vpjXZzL226s7x/pouUX1Hi3rZ/wa8ZoNFsJR70VrJlpsd3cvlAcT4fCKFq9j8aTX1FUJwRsmovHtQu7lBfXnOZkOXNWHRO7pndV7t0JkGekKgoQ/NDo7HB6fuYvXLh5163LWdu4Pqgzc97n5y4PJxHWlMN0AVweHPIe6d9w6t0zP8WOMrGmKjzMPKVxbqbNSTyelML0m5RXPp0HJ3pA6cdTd/dlc4UsojG0WUS7pkPt7PlJYTkbejM8ha/B2FFZmYXE+Cj37DwQoPuS2PYS8Kq8GTj7t0jXvQoQWRyLlZZ3rziACTJei0MCFyEsyH4Uz3Ye+4R5krf88GgerkbL0IqJwOgNUVvYigbw8MNVAsdidM8PdmyW3e2jtaEIerFtcP/4DefQ818Ys5J2L54x1ImVtAVH+2tcQMJnrnslqVMT3BQjA2fP+EcqvMW/XKuSxs9pdbQg8HYkUPgN7DyBPxRACgT9jV7kIeSvfiUIYTe5GYOlq92xPQJ7ys919XYE8WzvQu7ofAe8f5vszb627xqTrwyuzucL/5vszjdzPJ1lKYboK/E/KK/4OfTMTROUQDglJecVW5GVtRd7hJjTGrOzBEWh9rSHjQSsKhc24805y5z1EVOLB9M0BNO/UUI5pGpH2HDA1whoA63GIi5l+KxHLEfid4IYAACAASURBVEQT/3Qxs9OFBBqhQVfdcbBncLW7bdNcMgRCvy4dyLxmRkJhoMnIGMwSF28EIrCyBS16FmpXZWqlcY+pwMbueTpCjnqwFg8R9ABaZ9e8xedtD5PtQRz4DRKBI7NM1ueYTSfxff4026yd+PZ6BXomEAZh0l+++pzd1fyqv6bd0y+G7m97bTCZOAlNGA05wCXlFZuRsmXgvhPwJze1UB4cJ6x5dC6doGVmlYHb2mmeW/YOO2d02e+/f+bmvWm/UQC0IXuSfH9mTTZXuBYp813u9zIEIl6BFO9no+/y28hrYxb1PqSAfxdZfi2f6kg0Xy9Hij0IlP0LCttrRsDjJ0T5Xg+isKbTkaL4E6TwX0LEFJhE4GuB68PmMOATeGzyPGa7baZcLSAKNY+vC4uQIuUjoGXz64j7v0oUnteE8qSqbt9aND9vd/fnI2PHHUhhCxGQ6nLH3ITYPbuQsha6Z/Nxd85RRAVgUyg8r0REkmG5XZuJckIGkZI4RJTr9Wa370JgRTZXGED1r8yb9SsEWEvExL37v3R9+yUKbzzTvct/RN7Ds917/AXyWPUgo6PpKX+HQLiFOf6eaQobN+TJExcSbnLDfuvIUywpr7gIzT/PIOIoaEXGiHPRXDAT1XK7HbGUvgaFEpvnfhMakw+7Y05GxoI5yCP4WuRNLgE5d/wBIw2AVSfOXd8ar0lRt99HietvYfpwsHqvx+48NB4RQcSjHTud7ClXaXfNTKLJ+rC6nfVerqmED7VA9+l74Hk9TAVdQ2ghNxD1EFqY6gFM/bOKd8ysGSERjW4r4HseJNvDeD9biGLoLb7fzjd5tGdTD17rPYbxPK16iYM46/Oux4VQLbOqqZUrULjJJJD97RVn1biCO/fQt4YcWHI8UsZ+h8gq+oENQdl/xYZre6mVPWY/awcdCydpXzDJxt93eUGZOSmv+B/AslKYfjqGfDTkqZWbgWPRXHsHUpJfhTwXRTSftyJ2yg8iUoU/AR9BCksTUrxXoflzHVLIf43WsuciwLDUtTOBPDUL0Rz3QRRmdxrSGX6OAFcSAb4z0fxmCekrgePLQ/7S1T/tnTnjtLHJGSdObEZzezsCQkuIANCI+z8kslCvd39vQ4qWFXNf7bYPISDY5s6fSwSSrJRGmzsv455jBa1V4wikWRhjiNbi7yIQdBzOkIZA2HPdz7tRnbJu90yH3O+taM2cS8QC1+Xa63X/f9e1Nde18QkU0oSjZV9BTJyecRKqh7YMKZRFd625aM0zUoWl7tkM5/sz97jzPaSo/p175iPuHl/0eBiTG9KQxyHd6Lu9BqXTmEwQMY2+EoGiZ7njlyND0e1EnmwQSOtE46qGjBpfR+PmaEQw9VjLLD3p0mARrJNsrvAV9FIvsFpFsX2dyGr4T0RUsQZSjWrz0RT7vQBFe33e7lj5Qghjx+/806zl8YTgPV1TJ49NKu6trRl8f7pr1nvmBolyo+oLEYfoWVWJcrgqaNBtRs81dH2N17WyG6mgRdcKy00SxdGXmQpa42KhHLu71+k8jI/2LivUMQbacPI8JlDx6nftoY2GHOCS8ortiCDgBvTOn48AVw4pl77fEtCdHmPWs8ZYf003Y+uaA/D/CPyiFKYb7F0NeUKSzRVejAx6v0f1cb6CwgG70fpTQsDgMuSBsrlrB8phmECA7GJ3/AACWTcAz0PejSoR4cR/o1CmDgRG1iBlyHK//hLRiC9FXq8PIIt0iMDTNmBZZcg/efXP+lJznjfU3XVEpYw8KX9GwMHCuitISTqKaE4to7Vgo/v9GqKyIVuIagzG84stj+p6d49L3TU2I0ASz30y8GVhW+3uea13fb8WgdUEAqUrUKjfVuQ5us5tuwABqC1IkUy4vv/M9SmBFMd7gNfl+zMT2Vxhrrufn+X7M9N6ubO5QjvKv3vY9bPi7ued+f7MUN2xL0QhhOPu2V7jfi4C3oW+gWHXH2M9vgv4RqMMSEOebHHr5yJk8OlBY3QbAlL3oDE0A+mMCRTKfDr65u9Hxg4jrdnujg2B35bC9Eueynt5PNLwYO0q/4diuqcL3TkTxa8bm5FNUEacEMb+nk72FbiaLsTOJABGwWuP7Q+RNcAqZMfbiIOX+kLB2teUDJisVvE8u+Z04X7E2oqHPdaH7Zm3LN43I5c4PNaeLfZVNCCNOaqDiBTAwjd2F25I7H7iHrp6sFUfDlj/93RiCoy5vqM8qwq16oj3Dy0zwisepY2GHMCS8optaD4YRIyAIfD9lFdMIErkc4GvBpOeP3h3O8MPtFKbSEBUw+jf9lPXG/I0knx/5tfAr7O5wkI0D96GPEhrkNJyHAJY30eK/lFo/tyGQMoJ6HtdibwmL0aFzP8BKS3r0Td7LREtPETGo40IRJyE5s1PIPDyLTQnr3PtFN3PK4BZTd3Bp5deMjAP5US8CyW2PwsBmk1IiTqcqOCqXdPyNbaj0Dxi+ywfy3J6DVjZer0YhdwlkAenj8hLBQIjp7jnOMvdz2bXn1muP89x27e5dr/r/n6na/c57l6sEOq8WB+vQZ6+FvRuSsCFZqx1kTG7EBzUSYCz8Of7M5c5b5SX789Mx4r7RxQqlUHGn2cir5V5C/8M/A3w72FAz9BDLc9pm1s9vLm39t9Mr+M0pCH7TEpheizlFWciA4CVj5iH9FAjiduO8hTfgOYumMoIbdFKW9F33Q0clfKKM42B8UCVhgfrMUg2V3g7SqYdR5PXJJEXxiQOOvaURwW7rz0VFyvAuzdi14sDoOkoyetBT8iuLHrx42t7OHfK9cMw2rebIssmRsdrJBh2fQOqk+5vW+SrRJbH+D1CVH/LAFg9ecV0OWDw6CBqd2LPxhaoFlzi+eR21m/4Tc8vFrx0+L+u+uQLD5hky4Y8dkl5xVnIyj+AaHb/u56YJOUVj4HAWLw8mkOXjp/4n1KYPm837S5GSuVv9meNjoYcnJLNFY5E4ObHiEnwh0gxGUPhgc9FFuP3onC+S1EozmbkeXk2yt05G4W+DiCPxw6k+KwF/B0PNvtrrupbW9mevPCUz677NQrTMeW/jELe5qHcnzEEwO5H3rIlCHh1uH1F5C2bhebnAbSGWjifRxSxYN6qG901LdfZCKEsasHWJcuNBFnGmxHQGkXG0E60PrSjEMMm9yxSRKyESSJCEDOcDbv+DyOA1evasugLI36KG1ZHEJC91V3vmnx/5ru7vMR9LI7a3cIrQe//FPS+rweOHl3TFK768YxT2heUb1ryhu1twOcdeG9IQ54USXnFj6IQ4zIaVxahZEaP7SiP8wzkzbIwwIdRfbCPEtUWzQMvRGvnCuD5BzrAaniw9lKyucJbUUy7TxRSYAuCB9DZtpkl8/7MI+vO8MbL3Tu370F27g9DCALw/V2AyWN5RwZULIzPRx/wTPYMjMwDFCeZiJNT1NOX13vqdgUsIQHetEDOJF4gMk5vb8dZfpX9P0LkGbPrV9BCaTllZaYWhIyL5VVZP+N9ma4I83TPKk7AYUndoMX3AuDWlj6Gl5y/43/Rt/L6adpoyEEgKa/4EsR+9iIUsnAZet8fcfutkOEd4M/zW6urgoo3y08ElaDc9CAKsZqu3SRSfE9DXoiVT/a9NORpJ8eiuehOBACORp6ay4CXIkXkfkQP/TpEzvB7BMYWo3nzCqR4vxnlWv0crRvt6Nu8tzqSeElLT+3wyU3NCeT5moOMi2W0ruxAHpufoDn4VARaFhOFc1tOluUJ3YEs17OJQvSGmAp02tB8fTICSzWUhzTXXSdef3ElAhMXum33IyXMaO0th2sdAnrr3H1/D/iCe14Glmwtandtdbnzv+ra7yEKZU8RrWHD7m9jOTsagbvLnyi4cvXQTge+k+/PVLK5wmJgJN+fGXD7ffTcr0JerB73DFe4ZzyM3u2O8mDyvjnPH76u68jJl4ch3kip6daXve8PxY5F5cnd5Zw3pCF7I+67fB8qar08tqsdeZ42IbbRZjSHLEaG6d5SmL425RXnuP83ojX25Wj+OgzNM68gKjIcAC8/0MEVNADWY5H3oJe9nYhWdkqI3mS5g+HRmYTS4/cm3yoKK3McfmFI6Hm7Jcqob3O6a+wgIp2ACFxNB56mI9YI67Yb+KgndAiJ2AaNZCIRBIS+j4+3C1HEdH2161nYoR1j4CoO7nrRgm3nGbV1N9F7sHovuwNP8dyu+P3viYDDpIa8Gc8gsmqucv3I5vsz6+3AbK7waaYvctyQg0dSyFJWQRb6Aq4QqJOLkfL69lKYftB5pa4JxhOjwKv34Jl6FwJf30HfT0MOUcnmCgkUbtcOfM6U5r2Qa5ECvRx9SyMoLOxDKHTtcPTNXgz8PSKwmI1AzjwEaE5D4OEUFAJbQevbagRabph56vipM04ev9nzeSUKlbX8iGegdXAOAifXIDA0RhSZsArlLb0UgZ5/QmGJHSi0cSnyan0JJatbTu1K1/+kOzZEitU4Mkh0Ea1FVsh3PpHn6Ax3rhkZl7jjAtfe6ciK/nZ3z7aGTSIQdywCKR7KVWpBNaUGidaKZa5dkyYEdo8iIgH5T2B5NldIPsHCvi9FtZP+mM0V+oCPIXKL97v9x6F80O8h0PwV9D3dDXwRWOYo/wHI5gp/D1wbBngjq5JvmnnKxAvRt/TGJ9DHhhwiks0VjkXzylfv+ODC7cg7fudpl3IPIoSaj+Ylk68jcpgUBItIkqfqP4jIW1qAIOUVj0C5nPORbvYa9F3PQWP0AaRvfRbpeP+N5q4DXhoAa+/ljYh5aRERGDCa2QRApdbOqs0n4R7rYwo/83woj0JL17QgZG/EQEUvUW0Aqz1S3059zlR92FwcoMRDIOJeHgvjsGM8wEskdgKLema/6e7Dwh89tDhvQ9ZU82AZSPOJiiJb/az7gBPDgG48PFdvqr6P9WAxQZR7tTuwOR0QXIesmN9GVuJ5wH/l+zOXTXNP5Pszd0y3vSEHlVwGXOmKP4Is2HH5BVLEzk95xRzKx1qKlMcTUe7DdPJCpCS+2OV0NeTQlSQK50ujvKof7c1J+f5MGVdQM5srXIPyo25A+U1zkEfpXKS09KLwvxXA36I8otVojrsKga/TETCbQGDmDAQWNng+p7h970f5RBcja/ThKGfite6anUTFP62w7svRt/5mpIgl3HmWPzWEwIsZ2Hag3C6ryWgetRlu/0dc/89ARsRZRDloVyDgd4xrN0TrRYjCKd/k+lJGRkeLjhhH62QXyl/6vntePlGCPQigrkF06ZcQrUm2VvS5Z3ALApK3ur6+LpsrXPQEPET/jt7ni1FExDXuGiYPIyX2T+jZfwt52I5GZBtvQqGcJr8D3h2G/OOcs8Znej6963/X3vrc3/zpW/NfNPL1fH/m1sfZz4YcGnIEGieHEXmeu/P9mduzucIbkCEiLscDAYRV8OZTDd+DgP+Dbt9XkRGoj6ikwlw0DwTAvcB5pTC9PuUVvwB4pTB985N9k/tKGjlYj0GyucLdaDE01rsZ7JucHpO98XrFZbocrj3lf8WL69aDiemuXWFq6F58fxkNpllMHyq4Ay1+9X2r7z9EHqgKU8MC48fEr2FWzdmTOwhrY37QOico+4mdxCM+kfHAQgnriy2b7CBKvqynerfK45fn+zMfAsjmCk1AkO/P1OobasjTQ8565c1nlAcTH2jurf3jDVefsWJ3x6W8Yj9SZN+KWN0ySCFbAWRKYXrIHZdGnopbkeX8cOBzpTD92dj+3oNp4WjIE5dsrnAi8EkUTnYX8KXdEBnsbXsemnM/jICL1ZG6Gq1Vb0FK+juQp2YYFQV+AyLCSCLF5wQEPP6AKOKLyKAw250/hIgTPg58BnmPWhAYeiX6vk9GylgHmkcNcCxG60YzMlwZEcQWFGYdB18+UVHhDchrdgyamy3f9v+zd95hklRl2/+dTpN2djbnZbdZQpODRJUojYKKIiUi+gqC8qEElUbRF/VVFBChVRBFEAUMgNqIAQQskoQFFpC0QLOBXlg255080931/XGfmuqZnZmdXTbO1n1dc81M1amqU/Gc+wn3M8Pu7wC7rANN/qIVPxEUejQK5Zf5YiEQ5Hf4xjx/TKsMcVxGkHT/JAob9r1Wu9t9+cfy+/Zj5H3aH/hALpNe77ttw/3OAt7MZdIP91i3CyLjf+6pbtzHvr6OQpHPyGXSj/RYty8SxqhufCs+euG/htWPOqzRG3lA21vAHgPZf4gdE/YbMxp5wBc3zql6rXFuVcf0fx/q2fU7o+f+D7lMem3S5D8P3AieNaJ4bRBpQ4YfP8x5T/TtuSVSU9qp3Bo5Eoxf9+3agpe6eYuf6CbC+gQWQljYB2dndM2q0Ae2MmRtU4SEbShB6619T49VJalZVyGwb8+V3z7So63/00JQd8RfV0k6KpUEe/bNv1Y9SV2MoHix394vOOn/7+eLVQGlSIKyVzILUI5AjGAgrDymX8yych+GwMtWSSLLKGl8Yi6TjiPlyB/5O8tl0p0huRrcaF8eO6npzarj21fGjum5LmnyNUmTPzlp8uNQMdbPF7zUEmSJewwNHAchK5+PKJpIfhVZ5l5BFn0ftwD3JE1+8mY5oRDbHJysOwmpTCbRt/IoAm/JRsHKbg9BXpYrEEkZiuSQP5/LpBsRifFzm5qR3PrryECQRUaCh5AXZj7wQURC9kMepD8jFb16RNJ8xcwa224q8t7ejDxYH0Xf34uQV6zOnvNE5OlNINGNUwkKBs8kKIjrC0+MR8TPjxgZQlBo+Hi7vojI0Qh73i8iErkWee0iyMtcQoa107GCHgRjUhVBhErMHmsMgbfsRDTuL7Tn+qzdLkp3Y+AlBMR5/jo3q3eMRPfjhF7WzUMCJgNS/stl0lcD03qSK4tWdE1GV48s1dbv0kbthKIhKBIdIsQ6cLLu7ugdqEahqufW79J++4QPrj2/otnhwKnNbyf2S5r8hXSJxZgymE6IVBq7o8gwMh+9u4eOfE/z4YmRnYB3GQp5vmeLnNxmQhgiuB5Yxj4VJQj7Agol9CH2w+f8Sf9AMBAvVV/har3lRlW27Rki199x+lrn16iq7rG/nkRsGAFx8b1ELQReq57heZX78slMB4ElsZKo9awn5uc8Da1Y3gAU4zXE4pNKO6HBwXgeeGVMpNvdKBt76J7XMoomCu0ov+B1lN/wLLCbk3WPAe7pWXskxOBGsSVyec34zuktbyfu72X1XqjY8A0FL/VHFI4DdsJCkPtxRdLkT7TrEmjCeCzyfN8EvDdp8p1owuYPOP73JcTgx0qkTPkICveryWXSLf1vMiD4ddc+jb6ZX7Q/ewHnRls6vDHPvFVes+uojpZJwzsRAXgUhcB+Gz2rv0CE73jkcRqNvtUTkSf2JOSlPR4RlYWIxB2IwqiXIIK3Ar0P70fffN8z4ie6L0Hh1n5O7HI0hixDSe1RgnGlmu6GsyiamNUT5GXtavvp5+dORRZyXyhiJcpNOta2/wbyuvmS9tUEOWSV0SG+we8tez3qkJHkIPvjE7Tn7DWII0/Yn3KZ9G8YOL6PxqIrKhfa2lk3otDAw5ys+3+5TLqvEOQu+LlXNnfrCjSW3ZvLpGc7WfdY4Pr4kPKBE9KNw9C1iqD7+7kN6HOIHQBO1p2IjIiPEtRVu8vzuHDB/fWnJS/O1wNX7vHV+F+LrZE5s389GuAzkZpiVbk1Ugsmqh+vjWh5JaXICjBJNIceiYwRixrnVI/xOiJLwLQWvNR9W+VkNyFCgrV+7IlinJMEH9pX0Ee70nPVk7D0SqQGKGPenxfKR29heb31obe+QTCQVOY6+Va7GN0JT2/7Bg1OfhhhGz2K/JaLUGqHYlOEmrFlfz+VA1dVL32srLNVeV7+5LPyuvbMD6NckhKj5/nXN2geLFuHlBZQfsJzKA/iW/Zc3kaD6pO9nHuIQYakyUcACt6BzfRtOXsRWadfqVxY8FJzkyZ/BgrDyiADxCQkjvNBZAVvRER+MQoh6kSW8WbggoKXqkwODjEI4WTdoYjMHI8V87HhY42b6BC/Achl0iUn6/4KEYxbgU86WffBmsb2acNfW+KZkrewZdLwm5EX6SJEhgwK8TsNhQbWojHPD4VLEtSxegl5gO4FrkW1oqrQd3oIevZPR2GGNQTy5n+0596Aci0qPT9D7U8DwRiRQN95f+wtEdTBGo9I2VzkYfPNav62I1Ae0nsQeZqIyJhn+7QXImmvIoLRhEIRI4hINdj1I+05V4a872r7XU13RdwliBB+MpdJ91mmw0bEXAZcV5H39LQ9Rs88lqLdb6M99wHndThZ94uI9E0gqIFJLpNuBc62bcag3Lop6D6GCNETS9G7+0KxOdK0dnbV0Ka3Em9MOH7NIZGoN9JUFacaL/Lb13827jT0PYkDQ8ut0QR4EeVgESXiGUqRqeC12HlZBL3z7YBpW5x4Bbi5ZzmU7RUhwVo/DDDR85hYbIqY2JBy2RhSdCc/vYVa9uV52tg+bOp1PT1ulZ6xCN2LOdK1zvOCdqaLIvqWyEpvlzxOHsZEu5G1It3j1f19rCWQuu2ZC9XTK+exLgHTP6YbkapoDnh4GNagF7qIEpofAO72486drHspioMfhyYOlQnFIQYpbPHgG9GzfGlf7QpeqkgF4U6a/HuAlQUvVSh4qUVJk78RhQq+gwj639EEbzRKRH8T5cB0Ak21O7W/MWzP1kOWPlF/btLkHyx4qWWb5wxDbCP4X0Q88igUbt6m2rGTdavRJHk6dIUMPuNk3T+gUL1i69j69rmnHbC2s77qDVTccy/0rft7LpP2nKy7GJGNUUjG/FwUwjccEaxlSCVvKnrGD0BGBFvgng5kXKi25zrSnmMrIjA+2fGJie/xrRSMeAkRsn3oribrG95WFFvMsMY51dX1u7ZFYjXeo7ZtFBEnf1/VSKXQr1VZonsIeclerysQkdoPefs6CIyF4wkMeb66YBGRlVaC2lNrCKJankS5bP0hgcahrrDQXCb9u94aWnXJ/2f/vX49++0JP7Txs+j697b/pSg8dFDBybq7IqGXn+Qy6XAcfxew3tDbAJImvzfKs1o+8oCWy1e+UHfZkCkd5cY5Nb9DInAT6XpfvTIYqCoWKUWaKXZN25qQcSSC3re5iMD9E31HBgVCgtUPnKw7FU26JjfPS5iFDzQwPr3W1E9rrywuvL48tl7J1XqK8PaHXoQt/JSggUYpIhbSvTM+E+kafEoliBgwkW5S5+p5qVwiFu1Alj2/3kaD/e0B8yMx9ooMgfiQMnRXXWojSAyGwKL4FhrQKj1h/eWZrYNIL5fA8wyeR5kyq02EL6KB7clcJj27Z9tcJv0c3eW4Q+wYaED5GQOWf02a/FCU5D8bJZRT8FIr6U7A7kfhxVVIbnkE8G9EtO4ddUjz9+p2at+z1GaKSx5tmIAmsCEGIZysm0A5NuOB/+Yy6Ys28SEmAJ9E39KZ9pgRlFP1y1wmvcrJuke0j6w7CuUTfxNJIO+JxpV/oNC8LyDikEAGqEVITOIJZAhbhSb6MRSa3YCk2PdE4gl3IxW709A3/p8olK7BLm8lCKvvtP+/gkjcNBRSuAB5coYQhBAa235s89tV0aa34l5iZOfw2MTihXQnTT4h8qMrWuyxmgnyfOfYvrShcMi9CQrWr7HHbCEIS/eJIBW//fBCfzwbjgjle+iej7wOcpl03sm6HwcanKwbr5RSr4QN7zsP+Gd/HrF+cBUQeZdS8dsr/FztcJ67aTELpVGMyV837od1U9tn1E3pLDXOqr0GzQX9yKBFEBkOXg3tUY8oCyDye/Rd+QNB+YVjgZcLXmrR1jmdzYfwwesfy1Cc/P7xhlK8dqd2EsMCCj7AfXRrFxCrnl5+0xvp6UKFV6YHuSpSFV9NVayDta0Tum1SeezuXp114Lft2rdXJhEJ2gfHNMbD8wwRU6R7GIefR2IQQdrbP671HBljKNt2vtqfbw30jzENDao1rJ+4rg9ttl8rjeHPxvADIiyxVt0QIXriTpSg21tSeF9oRGIVC3qucLLuwUCx4KVfSJr8t1ARxunIoj7Pl2jffeQr9w3ZOf7F2kkdDE217oGs9yEGJ85CE/C5SFTiXcPmCPveqg70nFWG16RQOOodNmRwFBrXYogQnWX7Uykj/jgyADycy6TvcbLuR5E3/1u5TPoFJ+umgSPQ5NUvPDwefbcPRnWmDkDf9ipE2F62x64mCHPz82BjyHv0USTkMNq2a0OTuYm23XLkbRs7ZOc2aid1mFhNOSErOYvBjLHnMRyRpBvRO3opCg28B03mRqAJ3u4olPEYuivZusDHCMhVc8V6A5hyEa+zyZhIjNb4EO+vqCbecnutFwBHOFl3hhUW6QsjUU2fR7C5c07WPcTu62rruRprr/U8NuLbYFUpd8iajJbEnhyO+ZsWBS/VkTT5OPJ+R2YW9nsKIGny/0RjXBE9s3cAnWAuAHMHJV4Anip4qZ7vxANbsPtbFCHB6ge5TLrZybrfA5ZWjSj9bNKJa9d3vQYYCtjb++6B8UPZ1iVX/u91SVKU9s4RFIvtPdd3hTB6ZUxns8HEPOI1Pbfv1jZYECFa6sQz0W6H1CBuTBxjEpXHoHuh4G5hexXd6qnmpxMIcrP8+PoOZCkdQy9EsdShPKtIvOvY/vqVdrsYGjQ/iAbqn9vaMSFCrIOkyb8PTbreQJOygWxTg3JbUkgdsCt/yk56v1NqN21Jk78deKjgpXrNbXhjxT73H/eFx79WPbL49cbZNR9OmvyawZDcG6I7rPfqbDThPzmXSb/rfDv7nP0YiDhZ9xcoV/iuXCb9WkWzOUit0K/NNxOp1O2FPFuvIpXURwBymfTvbUghNmTwQOTBuguRJNB3NYEIkK/qNxp9d11EYjyCb3E1Ml78AqkY1qBvfQl964fa/fo5QLXI2HYQImXPIM/fROy3PpqgHE2UbS1Grxw1rcNLXi1gxiHj3ArkgWpGJPBZVD9rHYQyawAAIABJREFUMhpr9rDHPJiAgBRREv942+cOu6/Vtk8FFALVVu4ww5rnxyNDd+2IItn2avQtmGnP/SoUevgP+kaT7VdlPueuiGwOA1ZYknAm1rNt86Xej8jch4Ff5TLpQj/H2GFglTk/jsRFljlZNwaUbb7bslCsapPiEiBa8FJdntqCl7q6Z6Okyd+MDA4/K3ipASlgDiaEdbAGACfrPgIc3WOxFzAhz4NIr7lY9vJ6xvTMySoDGD+lyZgS8WgHnaUaSxlMz33Yduv2z6uQebAFd7u1apwXJVZbpmqER+TdUWo/f6pnDpQfQugPmr43K+L33Ri/k1DpKKO72IS/vR+XXhkOWfb35xWhXMKLVTMbWV+PQha+DJoE1OYy6Tnv6kxDDHpYUYtL0WQmBZxf8FIDmqwkTf4Y4K/IUn5ywUu9ULneybr7zrpx1PsaZ9d8HvhlwUvd2te+nKwbWfrkkF3n3z3sajBPFrzUVRt5SlsVTtZNAovCOjrrwoabnwu8lcukb9hE+/wJIi7zUAhfA5qg53KZ9N19bHMq8rquQN7UuxChWIM8Os8iI8NMJLV+JyIqU9H39UTbdr49Hz+Mb2eCsDxf8XUcwXjRgmrAfdsuh8DgVmd/tyPSl0AEsMFu34S8PZXjaxtKvB8PxTYoxqDKk1IZ2H20II/WFJT/OAmRuxEESfsn2mvmGwyb7PLPohzd/yLp+o8SeNwAjFcGE+kqVVKDxq1rUNjwUcD0XCbdU7Ci5/2oQqTz2Vwm/boN6WzIZdKr+mj/GSSOUW+P+UvkWdwhvVSVcLLulUgB8UxElG9G939flBLwg63WuR0USZOfBtQVvNTL6208CBF6sAaGN5HLsytnyCtjwGAiZQxF45FY5wPXRYy8Lt+UT5AMGDwPWhYbasZAItFKTVUznS016/iTBpSvta7nqgv1U/sMB/dJTV/Pged5XcqHXiRChO7eqco4d79eVoKKkIRe+t4zh6yMBsKY3bbSyhHp+bcxQIxSNMqbyIPwIzSAvh0OMiEGioMOfv7EMUeUfrJ8Rt1u5fZoa+1O7dP2uHCpEc8aEJ5BoU/Te4sdf/7iya8ja/woNNj3iVwmXSbDG0mT/xzbaYLvyVe692M4LhrnJ0j+OoSFk3XrkMTxnE1FrixaESGJIC/KFShfJ4nyoHrDKBS29wbybr2EDGd3IiPieYggtSLydBOqBXgAIij16Bn9E5q8PoPyvs5B48J+BIXd/fynHAob9BUI6xBJewaFwI1F3qkOJKjxALJ6fxqND8PtOl/wwq93GFc/Y/MgtgwJyqxFBNEQqBp69prUIWJVjwji++x1mIIIm69qe6Lt51nI69hJIMThHx+j0ekV5BWbZa/Xr3KZdDPwrz6uf09MRER1HPC6HcN6JVcW99ptvqRz5/PAk07WfSqXSe/o+ZuL0P3ww0LXICGWN5D6a4gtjIKXmru1+7A1ERYaHhj+7nk0WbIBYMol2c0ogyfe1fe17IcgVVvh1M5SHWtbRnnr3SBAl0vImOCHwE3U8+++euYTo17bemXaLKOqTDL2Krb3C0H2TAKu9EwBlO3hTI/tIwQJ1dBd+KInysgbWDIRvp7LpK/MZdJeLpOeF5KrEBsCr1S+fdg+rbtXjykaKEcmHbHkm8At1tOwXhS8VAtwP7B70uSrAZys+wEn6x56+4wjT7v8mXOuStS03o8G/FOSJn9Z0uT7NWgVvNTqgpfqNdl9W0TS5McnTf77ydhrxzQvih6/6sWa6PL/xr+4vvPcAdGCCMnfNuVOc5n0pYhM7IW8sGchknVOP5vdgPJ+ooj0TEIT9n+hmlifA76Wy6SvR8TjQBRGdxXKr/gyKjZ8DvBELpNeiUSB/mn7sBQpJC5HY8JM4Hvoe36W3WcNCik8HHm+WgjELkai4sEvEORnxQjIz2xE8F5FohsJu24Xe37ViDQuQqRsJiI/9cgjtjPy0n0GeZv+a/fzV3ucZgIjnx/2XlnvcY3tK/b/+bav9cgb+HHrlRoo6uxxBxS+Zj1iP0G5djcicZEfAW87WfeDG3DcwYjbESmfgsjzTblM+jpkiO3cwPsSIsS7RjgQrgdO1o2i+O96f5nnQSSm1FrPRHw6VCkfriC/igDCdWEwBqJVUFEA3gxcO6PPhqafNr3liPWsp9Xt70iUREX/fULVkyRB77LvxuaFeV32vu7b9taPmop2lSijQXAeGuj7i20PEaJPJE2+lmjN0Kb5VWA8pr73jbUnf+6WQ/9975kzmztG9Wc99rePonpBuwIO8C0n6z6OcrGWr1wwsrBo1uT3Dx2z+vLlb9V8FFW9nwU0J01+MMWiTwG+EK0qf2PJQ0NN81vVVE1o62QDPmI7AmyS/eaqL7QPEqsYi1Tx3sxl0jP764uTdf2iwC2ITBSRiEUMeY6iTtadhiamJVRu4FnkhfsDkpk/DJjlZN0alL91GfJozUOesM+h7/SFSGL8UAJJZv+YdSgkb5JdNxp5gTqgfAWYuPpofLGMVYhIJRBZOgyNO7uiZ+4SRND+gUImP4fI2tUon/djKFdyOvKy5lHNwxrk9fCl4H+L3tmV9ph72utgEIn0Kn4+i0iXnw+3CnnsFvZ2/W3e3AhgpX0uhtvjL+mtfW+wioD/AP7hZN2HkBCXsf3YIWFLFNyOntXz0PPlz9kORvfmp/Tt2Q0RYpMjJFjrRxoNGCUqvFTGQKyqT1GLPpQD+8K7no9U9qM/oY3+DtQboYkCkR7974/AVfaHcgnK7WoVq1lnfW/97dn3yv+/lcukf9xP/0OEGCgOphShY2WEEQc34lXHhi9+feKa136TfH97c81R3LBe8u4Av0Pfz+uA53KZdPETV7mXL32q9uZrv3/ZScXG6FCI3E1gUKgDjkSTt8ESyvMMlIaaWLm6ZUEV5Q5Dx6r4fduTF24Q4N/Ig/RJND6d4WTdv/dHspAH6mH0HE4ATkaqr2+i3KzJSMgijrw1DiJe/0Ukbo79KQJfB1pzmfR9yDuGk3WfQ2F+eUS+drZ9nIeI4OGIgEyw/T8SRTE0IAIzhK483lIjxApI9OIulK8UI/CCdSLCVmXPv4A8YY+hvKlJwEkoJ6zDrv+S3f+VyOPloXpJx9m+ZOx+ZxJI2MfQHGAZInEQzAcaEHFLIZKzuJ9rfxSqD/YdFCK5DOUNb7DRxcm6I1Ao3NvAxbZg9Q4DKxzjG2unIKK9JyKf44BhVqQlj77Xz26NfobYcRGGCK4feeDlPkhSJTGo/B8/nHBgGiLrCO/1hb72NhDS09s+1tmf54FXxpTLVHqc/LZ++076l37VyXh40WqoKDTs7yfSvXHRHrlbWKEfi/4WskAOukKIIbYavhIb3WEoe6ycUc9bD0+L3vbVrwxrb66JoxyV9WFnNFkrAQkT9Xb+yDcfGdf8dvyU+mTHXnVT20eMOLg5VjupzaiJZ9DE9IuDoZBw0uRjSZPfDcoLIDKkuDZOqRPPw6NqSGnS1u7fDoZpSDnNL7JrEHHpE7lMupTLpJfkMulyLpO+FpGdEvAbRK4mArcgD9D3UTje32xI4k7II7AvGgeeA/Zxsu5XfMl4NNldncukF6KQu5Ldzhfh8OtMLUVhtrcgUtKCcsruBdMBXgli/0HkbySSL2+37V5AYXVFlF/TgYjVr5D64M32PObYazQf5eN8yh57DgpjbPU8aF9lvlJsMbsisvImCul00Vj1ij1OCZE/38vte2sj9pyfQwbZMdaj0htaEclbbO/F6yic8QIn6w7rY5u+ELP9us5exx0GTtb1PZW/QqG3zyAvZS0iyQuBp4FSLpNencukb85l0u9srf6G2DERerDWg1wmPc/Jul8GLjGGL9E7Kd1SITHdCFzXwr6PXklsesqjV/7dzXPkCxL2WGcs+aLlnXi8dWHcG3VYS7/euUisS1K313MAqIqvpjbRSMmLmbUt4/38rmUo/GN3VJBzvWFbIUJsACZGjEdieImO1X4KITORlXOdZGgbEng9Cu15fsSkpd/Go2blgjEe8OnaSc3HNS1k/IK/jRhSl+zwqoaXTP20dpasNow5to1IFBa7w2LA/KTJ54BPF7zUdpkzmDT5PVD4VV3ll8driRoMXuvixO+3Xu+2HVxjzh+OCs4+frF3/eYMCd0JiJZLtLQuYUHL/LpLRh/a/LCTdceB6v45WXc3pBT4WB81gXwScwjypvzHtnvVybq1wMdzmfQ82/ZCRFgORd6oTyFSshh4ysm6n0eCUOOcrHsukhQ/CoV3H4U8TR0ofGs2Epd4A3l4V6LcmU4wLWBeRCTpTbvuALudL8KxFJHJaSjUMIHykRKIfL1BkFc1BZHQp22/pyC1v7VeiVKxOdLQNC/+6sgD2rDXYQ9E9oYiYnUlUqcbb88BAoXEtSjssAN5pyajsL3ejIKn23NaDmCv72zkHRxQiJ+TdU9CUTXfBU5FJGJAptxBhA8ikp8ieKYWo/u1CpGt83wVR6vO+D5gVi6THnA4ZogQ7wYhwRoAcpn0fCfr/hip/WwQBqQA2CtKGIp4XeqxA0alN22gG/pS6xHWlZQ3UFGLC7z40KJpXx6p9Db5Qhl9Ha9PT2mxVNNZ9DpipVIUNJD+EHjGWptCi1OIzYFHKEYO6Vjli5IRQR6m3VCR1Z6oBk6Ccv3OB736ieP+3z2xphUN3P7NLxsoDmteUjW8+fe1AKx5OQ7REiueryt5eNGW+TWYuAfRMrG6UqS4NnYqmNuR4tl2haTJ74TCbOq0pEcqpkex3BbrLzRth0GJyPFRyl8FLkIS4ZsLbwFrTYSRiQamJhpafoMmmguRJPrTSJBiX+SJWelv6GTdFHA88iDdgcLWaivWD0Pf49dRfiHo3p+FCgI3IYNEHfJO/dK2mYVk3b+BvAv3I1GG8+zyhO3HUhRWGEUkaAzyUBlEpHZCRKhoz6cevatLUH7YF5E3uRYZ5UYhwtaBPFQ1KGfrb0gZMI4I2FB7rgng0UiM98TqvL/UmNKvUB5P0Z7HkbZvDyKv03/LJfZtWxYdFYl4xeox5UYUgliNvhsX2P7djIwQveEuRLBarMfvk0ip8HsbQJIm2/OuymXSTQPcZlDAybpfQs/yVcBtKNz0IERYd0bP5L0oR3YPJ+t+Fz0LdwI/QGIsYTRMiC2CMERw4FiIBrOBBf2ZjSJXXvd/4n2163acCvgDQ8/QRYtS16IeIYx+HSuMkThHpSJhuRQQrEgEkxjmMeLAdiwRG8gz1NNa74cAUipXNTe2jHmlpX3kHODeXCb9h1wmPXsA+wwRYmNxRcfKuC083fX4Ho4SofdKmvz1SZPfGyBp8ik0UXwyGu8sV9e3Rp7OHcXTdx3lAUWIGNoqRTMBTHustnxquTl6c7S2VC61GKpHFzERqJnQQdW4tj8nTb5uy53uu0PS5BNJk/8YsdJL4HXvd7TkgQeRMmBeQhOcHRpJkz/pd5xxxgIm/AKFsm1OLEZh7F60mmKs1vNrSj1NYKC6HhGKGifrPudk3cvs8sOQ8MNOtgjrJcAFFRP9Nchz86uK4w1FHqOJNrztQ4ig+blbq1Bo4S8QoTMoEuHziAgtRySohMhJ1B7Hz6eZS6BMOwWRsDKSWS8hAubnXzWj520GInFtaNI9F3nkxtl974NIUMSe417IA+YhElddNbz80dqJncejGlj/QaRxGCJlp6LcyZOAiYmGcn3jmwm/8LBfTPkzqK7Y88DPcpl0b4Yacpn0Y7lM+g57jfcFvmnPa0OMiTcQiGvsaKhCz9FkbK0xAvVJD4VtLkDPwheA0xc/XP/9ObeM/H25yJUo/zBEiC2C0IM1QOQy6aINf/gNsor16bEZYPheb6ho3bOeLxRbIFINkQi0Lox70eqyqRrZrcaVfz/XyQnrXst3ndveF0ny7F669tPP+fS1psszRjALbUSDQz2yKP3WrguLA4fY7Ch4qbVJk18ETAneCxNDE6rdUCjJcyhscF/gBGCPUmeibtaT+5RjidLStqYhUWB416Nd01keumfr4lgkcueE4xrfVzWytDyXSX8xafIXAr8pNvGpumRLxEQMrYsSCeh8GU1Ut2kkTd4A50D5WopdSqD+u1zWB8EYyqYEXFnwUvmt1ddtCM0t1C39G594uOClNmvR5VwmvdbJuhcbwwUmymsox+mCXCbdWNFmHjDPybp7EYTUgbxWq4FPO1k32zMU25KAR3ssm+Vk3U8BMSfr7o3yh/ZDXqNngO/mMukm6zk4CBGUtxEB60QepElIWXBve/xHUThlM/JO7Q1dNRdfR5Pn/dBY4o8bk+3+axD52t3ufzKwPxroGmy3OxARiiCCNgEpIu5l9zXeHiNj21fbY/ulR7okmiJRYl7U80Ye0pZA34nHkedkPCJ5vwcOcbLuo7lMuoX+MQSRhbg95w0pxnqdPaevrq/hIMO19ncC5QeuQiIswxFJbUZiJSPQs/m7zubI6ZTNaDzGAFOcrPvrHTCkMsRWgPEGpsIQwsLJutdDn7lYwEYTrP4U9FSUeEEMz/OoHVdi7m0jGTK1g/HHNa6zo95R9myyPRDt6uNA+rchbSs3s787CWpcecji+Auk8nQYCqt4M5dJH7RBew8R4l0gafKXA/8bOFP9+RzYBatRCNKpaPIzxS6vQxOvl5BlPIqspXcXvNQZVtkqBbxm5ZT94x0J3j9MrNzgFSMAHpjqgpfq2Ownu5FImvwZqNBsInidPQ8irVSEklm8VPBS+2/RDobYYDhZdziwxq8b6GTdz6HaQefnMum5Fe1iSHq9BoXufT2XSS+0YW3vQ96cA1EY4qUoZ6kNkZsEytG6AuUXTUfk6yNINfCXiGTFkYDGWORZuBC9Xx12H4ZA5OgDKO9oOHr/cigPpxZ5MOoJHtIoMuL5xYhfQoaTWiR20Wb302x/dkNWR//82xFpe8b24Si7Tz8ExKBokU4CFcJmZCy8B/gakMll0tN7XPsqJAv/EnAfUjmsRZ6+V3OZ9IBy9ew9uBRozmXSPx3INoMVTtYdQ5Ardx0izfuh5/FuYITnMRGPaSZCDIV7ng2Qy6T7U3sMEeJdIyRYGwgbl34vCimCXjw378KD1Sc8j3L7KiLlMtSOgs5mQyTudROQKHcAETwTgaA814D71ZsgxqaEh8JDjkMDCqjQ43uBq3OZ9F83wzFDhOgTSZMvVY3uiIw5upElDw5FOVld8FCY1cEEE7bfAlNqxnccMPaoxsiiB4eubl8eT6EJ28cKXurR9RzvEPAuBO/TYBaC2XlbkzSXOiDfAP6HwDBSicq8y0rcXPBSX9zM3dumcY05f8q8j+71gcVHTjseY77TX6izra/obe0C6bYfw3OZ9PIey6tQ6J9vVPgHMjL8EYUc+gqAf0bk5ErkPZuNVA1Pte0/iUjN1eh9moc8PglE3tqR53gu8jgNRc+WL1yw0i57HU2afWI/17YZj0L1htp1Q+z2TQRhgAm73qAxaB4ihxHbxpejNygVwA9JXIu82E8jBcNptk8RFPr5OMori9n9/B6FYu4HTO9JmJysOxQJ6cxAMvZ/sP37n1wm/fy6dyfE+uBk3XpEpCYiQ0Ee+DW6pwcSRBv5Py+je+2h5zv0ZoXYbAhDBDcQuUx6tZN1b0RKQ71evwry0msYYbmMZ2zg3UAJmDFEqkcE/8fr1lVYLxXxInFNfPosUGU/MyZKEU0ce2Sq993v3nbXR7sSCuUYZv/+C/CLSouetcLtjQatC52s+3wuk35rAMcMEWJT4ZLhBzb9uG5ih4nXl3oSLIOs8LNQnkYRGVXumHjimrsSI4rfH3Fgyy8X/bthL+DpvsiVk3X3Q7WCLi946Rkod+Kzm++UNg5Jk4+gkKvfQnlPm0Va2aRLTZR181A7UcjVjo7zGmYvO3rJe5MrvHi0Z+H1Lthv3y+Q3PhFW6x3vSCXSZewinY9lrc7WfcM9P0+HLgREY8HkHrdct/j5WTdIgrvfhVNaEtIpn2Gk3X/QUDS5hEIUky27TrQMzUNeAIRpD3s8mfRMxkjcDUvR96nOugaw8YhstZJ4P1KICLkE7MSut4PIo9ZhMALVWP3g/1/ml3/jl33IgpDKyKv1jS7/08Q5IuBFBPP6MurZMM5/wcZZDzgeyi0caPGPauMNzyXSa/YmO0HCXZBQixj0Xzi2ygkdWd0n2yuLCsQ6Z6A7mkBCZ/cip6ZECE2OUKCtXGoR7HiI9A1rJiZdeMqvZKUHip9mwomVoNBKee9ER/PkjmDgWIbsWgcTDTobM/2fSzvCx5KDt4HJe5ORSEdn+/Nkmvlg7+MwkLGEAh0hAixRVDwUtckI6+/uvyJ8l+KzZHeRCd8dcHpSKHsCuChubeOmhKtKc8vNkWbC17qwvUcpgpZ5nvzBm0TSJr8cOA0RJKifdhZeivv4KOl4KU2JH9ksOLXw/NL/zXhkdn/ufaBC3q1iluL+/8g78jK3tpsK8hl0p0ATtZ9FXmgXkdk68XKcEL0PLwPGdW+hCTZL3Wy7mUV7VY7WddB4XFRRNROQe/YtVjVN+RBqkIkZAyBdymGxt1622Yp8mLVIS9YGQl+jEcPbrVdNpEgh2oIKqq8DHmkfa+Vf6/a7X6HI+OgPyH/td3fLPQ9KKPxzf9mFAnCCncfwKWNI6PNI7lM+k8DaN8Xvgx80sm6X9gRhaFsGOsP0Hv0L+DDKFTzcuAM9ExF0LPTgUi9P1fbCYm3XORk3VuASC6TXrRFTyDEoEcYIrgRsC/2BCRX24KUmA4DryJspmdZqWC2sjlCCAkSSfpv1Knjm6hqWkXjIOOeqfjp1m8fHXS31lXCt0SOB9Za8lQP1NtikyFCbJOwIg7HoPj93dF7FJAhU6Z+9zaa51a3lzsjjxS81Al2m0nA4oGE+DlZN7K1Q8H6gvVc/Q0ZQ+rXdU4Z0EQ0TvdvGRV/f7vgpa7YEv3d3uFk3a+h5Pyf5TLp727t/mwIbHHX3wFP5zLpH9hlUxGxKqJ3J46epfOR8cwP10uiIrxx234ZyvFqQKTmRygc72WUI/MiGk++gdQ9v4nez0Y0qd4LPZfH2XYt6FmsI6hTtdru5yD0TkftsdpRvtQRiNRV2b77XrIWROTW2v5F7M9Se0xfiCNm//YIwoiPr6gZ1vP6xVCI5SoUPvlKLpP+Rv9XfV3cPuPI2pb2+qr7Z3z1N8igeeSOSA6crPsZdP+vQ+GZY4B9c5n0YifrTkJCRaPQd6oNkWQQCYuhecs85JUcDZw60Dy4ECEGgpBgbQI4WfcsZOWK9EhRKCPi0VdV94GiF1XAdbBeguV5sOihIZgIjDumSenqfr5990T/DQkRbEVx6P/JZdKFAWwTIsQ2h6TJJ9GE79doMngzEDNVJcZ/YC2rXqwrti5M/BeFR81B3qzrCl7qyY05ns1xOQp4fmuF+NgCyg1oUjtRSz2qx3Yy4sBmlj5e7xWbovY70Gu4oP/32QUvdcuW6fX2ByfrDkFiPmvQxP584Ffb4/fSybrj0eR0PCrkezsSaPiGk3UPQ+SxiHKenkJhtnmUs/QR4BwrlnEBepd8o93rwE+R5+h5+3MtCs27COXtfhiFgLWhUMFJaOAaQqD6V0aEyZfsXmPXL0ReC1/R9g6kXLgrGsPutPvzaypNQ2Sw3fYxBqyOx5oi44bPmj9/2b5jITacILwsYvv10Vwm/USPa3Y8qjd2GZKrX4lI3Ju5TPoFS1JPAO7wC+P2xO0zjhyBFAP/WiwlPv/0a84nlq6e+ipEb9oR85edrLsryv9bgQzc+6Lv9onoHVuKDEdRgnuYICD3u6H7+ywK1TwWhajOz2XS/92CpxJiECMMEdw0iHtlOj2oMn4hqYCxlAjkXmEdj9aAtCUGQnbW573yjMGMPrSZzsZojzpd65Ds/o7nN34OhVJUA/eF1dFDbOcoopDfaQUvdUvS5JuB73glM3n1zNq1rYviRTQp2hMVxPYt4huL/dHA/nM02dsa+CwK6RrvL4jWlJnwodVEazziL5ZMsSnidfeze9D1fTCgXIZbt1SHtzVceOIvhtYsbfyHB7PGPP/OeRd713fzaDpZd3cUfnkO8tDclMukL9kafd0UyGXSi5ys+0Mks34FIiZ+GOBS4DU0oT0TkZTlaCL8KCIQfu2m3yCBo90QAZqLRI+KdpuXECEbiWpX+ZLvP0RjzlL78w7yRBn0fvqeVr8eyQiC/KvViMAtRwIIuxJ4MvZF3o7PISIzCU3Gy3Z5CYiXy7G6Zaun7QlRA6zyytR4HlXGUDQRVtj9diNYiNjtBtTlMulrWReHoGfkWTSu9obRwKEt7UPnLVy2K02tw6rAHDOsbkHiJ/efteqiD/32kT62G6yoQ/doYS6TbgaecrLubKRu+UNEdp9CuVmNKIS1Bc2TUgQeyyXo2TkZ3YOXnax7ci6TDvOyQrxrhB6sTQAn60aa58fuS4wopmO1GGPWsfD68Av6lukit5tEvG+dMMRNCA8NTA123+0ovjmHPkr7AleErvUQ2zuSJt8ANBa8VNn+X42e+1EohGkNcFbBS3UkTd4UvNRGfzytB+sI5MFatb72mxpJkz8JkbvJVHwzaqe0Mfbotbzz92Fe5+q4IVGCjko73Drfq/sLXuqELdLpbRDf2u/yPRIrW54uJ2Km4c0V6Yu965+xynxXozydw1BI2M+Afw4GaWgn6x6EyNX1uUz6H1ZswVjBDJyseyAaH2ajvNyxyFv0AFLXe8i2OwLlaw1BxXM/hTxfLYgYlZFi71HAI4iU/Q6RJFCo193IazEEeaz+hFT8fJXAMkEIe8m2ayMQwvAIwt/bkNfsG/Z/Py/YDxF8A7wJ2s4AzC6XmOqVqSl3mBfjdd5s9L34uJ+/Zs8zAtRYIuAvOwkRhF8hUrgz8EZ/ocS3zzhyzN+fvGTnUjnxcyxhG1K91IwZXlj849O/PbGv7QYDrDDMuege3oK+Ww8RGHgmIrGQuejdK6F3bpRtcxu63q3ovlaje1xABrNovxBzAAAgAElEQVRj0XPwAeQBfSuXSb+5RU4uxKBFSLA2EU6+/MEPtq2IXlk9trh7NNaVVFume8XgSoJl4/G6TVj6CQXsc5Ufk2j6asDGE68OVK/qFJRQey6qY3LutppTEiLEpkDS5EegCfILBS/lJU3+YjTpuwlNBH9X8FIbnfeQNPkJKKRl+rshaht4zDjyOn8cTWh783p7qpfneZHasokN7aRjsV931a4G///5wMEFL7VDeq+vMecPA24rRc245gkNDflz3juvXB2bjjH/QaFzvgDDrduz16o/OFn3vUhU4B2UG3UqCq8dh0KxlqIxZLz9/z6UM+MLfNyIPFn/RR6rOuTF2cP+/W9EsJahkK9j0KR5FSokewgSsWhE5GkOgQBFZTmBVhSGmEB5Ny+jUM247ftURNBWovwrPw/zNdv2QCSr/hUCj28H0Frq4OZogkuBA5BYwjMDuG4/QaTqtFwmPaBi1E7WPcWe79FQ2h0iDVCkrmpN223nf7pmPZtv13Cy7gEo9G8BUiU+ChGoDxM8E6Dn6OP27z8iYh9B93kiek5qkGDJ3uj9nIny+/J2375wxoFhZE6Id4P1iiKEGBjuvvS4B+77yTEHRmPcSPe6C9Cd/Bi6hRZ1E5fopjLRO3oN5/OTp/xtK8mP/3cRWXUa0YepsWJnawmKKPqKTJ3ICnkC8L9IgvYwQnIVYsfAmShkaWcn6+4x6WOr9os3FFcBu48/fs0XUhcu/ra1qq4XSZM3SZMfnzT5VMXis9FAPmmT97xvfBtNLG6k72+/lWc3xkTL5XKxN2LVlaf5sx2VXFkUS4noPqt2Gz120RHJqTvd++px8ZUtFxGEfH4POGQQkyuDFBGnoPFkV+QJmJLLpF/MZdILrCfnE0AWPTOvoUnsKXYcuRlNbA8hELv4E4qOeBtNpJejMeqfBB6ul+02yxFBOhl5ZEcTeK4q389qRID8osIT7foEIjpRu10dEj1oQ7LzF9jtkijMc5w9j3eQ5PuQaIKP23P6KQN/n7+LvgFHOFn3NpvbhpN193Gy7gmV3xYn60asOMpFwKegfKDIFUCc5vZRAyJo2yucrDsNiZz8Hd2Pl+3PKESunkN12maiXHAPiX/cgEj4ONu2HT0/1eiez0LPrS9s5BfO9pUHr3ay7mnWAxkixAYjfHA2Pa5GYQtrCOpd+ISkP6njXuF5vuqgP8Hp09jtE6QWRKba7N8l+/9q2688cpefiCyLS1H1+ekorj6FQjAmoAKIRVRQ8m/A0pBchdhB8A80QL8N7N66KP7+cqf5CPD06MObptdO7hyQxThp8seiicHjwK+TJj/Urvo9CrNasOm73u34kaTJvz9p8uPRhHRoH02tKIDH8Pc0MnTvZmg3keLyeB/NaQEe3gxd3p7QsPQ9k4cvSO8+JdpRqjIdxQjlcjvykDwK/H6Q1/arQ56fh3OZ9JeRd+oMVEi3C1bh7s/A/yFRginABFt493t22V1osusU2/jqwgeHHE5QbHg8yuc6GuUzjUUerYfs8Sei8fZcAml330joj8EdyIsVRWqGpyFPRsSue8v+/D+7TRyFjx1mtynZPvi1I4toYv8w8pwdjybx632fnawbR8aV020fGoCYVRk8E3nJRjhZ92on634ThUZ+FxGAnZQT6XngdSJp+hesau9gRQLVT/tTLpOegbyXZ6BcuUbgB7lM+jzkAf2pJUSXo/zS81Ce4GLklepAz8VbwGeQV3Mv9KxFCApdF1Hh4tuAvztZd8oWOdMQgwphiOBmgrVIHY5e9BHoww/rCdfzb4efVm4Jlme1M7ptW9G2Mgcrb1cPR5a43ZFlsRl9VB5G8cstSML2S2jwew240xKqECF2eFgJ80+ZeHnWlE+uWvPWX4Y3vtmx5xI7QTL9JUInTX4f9A5+DU3S6jFebORBzW/XTu64aP5fR0wveKmVNmxvb2DmQCTfBwon61YDo5+/ePIQlLPwLJo89saYSmhSfACRcvXYo9dS6jCseKIOr5uDvAse8KmCl/rLpurv9oZrzPm7AF8uw/nFmli8MxHhtfOOLJVG1C0EjgTe3hGMUU7WTSFyswR5qk4FLstl0jP7aP9+FCL4HVS76HhEXL6FRAmaFj5Qv2fr0tiQnT+7andjWENAhL6NvEi72N2tssvXINLlFxcGkSS/VuVQRLhWIO9FDZKOb0bvQyMyKnrIa/1e5AGJIENjEyJW9WiC3lFxnLtQCH0ttsDt+u67k3UTKNR4Vi6TvsISguuRGt71KO9sFvLSRJCC4lD0Dn8IGWwW2+v2hj33qyrzvgYbnKwbrcjxew8K//szuhb/tV6ryvaHI2/i35BAyofRc3cb8rKCvJDXAVchguXXWyvZfX8UkeeV6N4MQ2IZP8xl0i2b61xDDB6EBGszw8m6xwJfRwTmQ8iKVoWsJt3ibzyvoqqn/vI8BTqYimVdKJdFvKDrN2gg8QcEA7joQ1FCH+vpaODZCXgaWQB/mcukH9hU5xwixGBA0uRHobyL5wpe6tt22a7IevoHoKPgpdZJhLb5VY+jidsK+/OhWH2pfsppK2h8o8pb+lhDKzK6HI6krb9X8FJuf/2xwhgdPScTFev3QCpoBk0U9nnjhtGPNc2t/j/7f2+GnSL6JkXQxLMUG97RWu409eWmiEnQ5nVQa3ps+jxwaMFLlfrr72DENeb8s5HHZG8kUjKyDNHVU4Y1Ljhxr2eap43KAm5f92iwwsm6+yCxgSgqLv9CH+383JlVwIW5THqmk3V3QUSlGZgz/56G68a8f+25VcO8z9plK9CY+TIKCfOLCK9ERoMGe2zfyOjnUUVR6N4H0OTY9zz5IWCrkBGkMpy/aNfNtttPRsTtTRSmmEU5Ph+262ej52EImsx/GWjoKZBg313PN8r4wheIWD5mr8kEFIJ/p21zCCJP99ttQ+OnhZN1jf+OWc/fe4CZvpCIk3UvRmGF1yDl1yuR1/IDaO4zDhGqW9Az5KEQw90QoR1LIAC0HIWuGuRFPWtrldYIsX0hlGnfzMhl0g87WfdJq7KXAXCy7tHAb9ELHCVQOkoAeCVKJiaCZLoHcXbzYFWQKghizj00uBTR4BRB4YEJFNIxJZdJH+lk3WFYRR3fMhQiRIgABS+1PGnyF6IJno9dUejIAUA0afJnFrxUT2U4fwI3FhlWMsCsYmOUt/4ynOLamEEW7ytQzsgryBLdJ5ysOxn4BRJPuLOPZruhMJkOz+P+1TOrV7a8nbiYLq9VZaSy8cOmGgms+i1AdXFVYoi/ww7qepKy2cDROyK5svggCrVciR+VYFg2ZEVLe8ObK3ZtnjaqdUcjVxZzUQ7Skz3re9l8ohHI03MBGvfGAO93sm51LpN+zsm6X0GKep+Y/JE1RyECcxwiLuPQ+5QmKPQ7E0VjPIBCDn2ytAaRrZPtduNQ6OLpBFLuCYLc5SY09tba7q6x+xmLQhDrbN+H2TZHIMPkGpTTM8duNwI9E1lgjJN1P+Org9rz/yXyqp0HkMuky7Y+2n7IG3UK0F5JzGw43A4N6yGdCjxQ+V71eMfeg677z4E/WRXP1YgUG+BgFHYZQ1E9S5Fh6zWUEvEYNmwV3eNDK/btF68+BpW1uDmXSTdu6vMMMTgREqwtgJ4S5rlM+lEbKnEHcjk3A1U21G+tiTGkorlPnNbJNq/waHl2HwkCS3UMTayOt/t4FbnEf2f70GtBw+0J1gr4PUQmf7C+iY2fOLyDToBCbAQKXmqW/3fS5KuQ6MtIZAW9n+7ky8di9L7tBfyr4KUWJU0+A/y0uDruF7z0kFV+F/t/EoUY9YVWlN+xrJ82zwFjy52sev2n425qWxp/jWDiSGJ8J7XJFlY/XQ8mAqVIyZ6L/yWps79783SVULFYp+Clmvrpw6DFNeb84QztPIrW6AivMzLJWONVxOOaciL61Jo9xlahyfcOBxsy9YfKZU7W3Qt5e8cgz8FiROSXIwJzPrDYybozUJjWZ5Bs+2jk1X0ThXcZlNc2AZGateg9+BgSXyqhyfKuyPjwAdvWQyIZEIQIeohA+TWxDDI0tBAIW7zXtveVCEu2T53IG/I+9N56th+/QobSi1E+8x+BkpN1f45y8R5CY1Q3r1Yuk17iZN3PAs2hd6pPnIlyz553su5IYEEvBOdVZHx6zP7/UeStOheVkVlklw1Fz8da9O2djchZPQonfB4R5Rh6JuIEc6kLQ2IVYkMREqytBFvN/mPIhf08smC1Imv2122zZuiSfG9EH4UYGtjno49+GQ0uOyOJ0v0IVAofR4PZHmhy5is3bdewYSZ7I7J4Gpqg3kA/k08bbvEvYKWTdffLZdKtW6KvIQYV/IE7jiZY+/eWN1XwUsWkyS9Gk7HH7bKbkiY/C1lKZ6CwphloQN+fHsIAPZHLpJejfMluSJp8ZNrZS09JjOy8tWN19J1ya2TN6tfqdmlbGp1lol6idmIHLQvjeMWI17EoYToWxUl9ZQmRBOa1a8bW4EV6Fd4ZyhritLKCsVhv168LXmqd4+8ouMacPwG4e8i5S0dHqstm5VUTV8XaI03AIRd712/3ta02Nazy2/5oPEogguEbE/xwu7sQSTkVeRV2IghtPwF9zzuRF8tF3/ifIQ/iJ+yykchD4SsHjkOe3CIaL/2wwDa7rzfRRPsl5I3yRTBWoPC+P1X08Qj79xr7ey3wE+BCJLzhIaL4Wbu+CeVCzXOy7ggUOjgaeT33ReFo3VBReDlE7/glIuhDUX7aP4Frnaw7GnmfnkRzohfRvV+E5kP/QQblV+y6m5FoSQsS+FqMDFIHoOesFhG5uN1HFUG9NQ9IOVm3aPvRb72yECF8hARrK8J6kb4A4GTde5BVbTSykI0EfowmYAejZMwoGnjOhNK9o4bOe8/ytVOmQWw6+vDPAD6JCFsBmJfLpItO1h0LxAbRR+FYZNm8A4WCjAIabeHK53smoFqFpSfQx3MYoXpmiI3D2eh9bEKqXg/10/ZBAks7AAUv9Wgv7ZbbthuLzze9FfvZ2CkdtZ2rY7u1Lkl4TW9WGzCJuqntTDxhDQvvH0rjnBpffp3OxiiRhAder54qAOpo4mCe5UHStFD3JPIo7LBor43eVWXaDym+Uk0pHiuXqquvi7V3PI/C1UJUwCqu3Y3Gq3NQ/stD9udINHlNoBC/Z5Hh4hwCg2EnMgjeBFxKYHjsRB7CQ9E3/EhEjPxQPxDxuRl5JIYTKMRVIaPGSNt+f4ICwm+isfdsROZakDc5hYyct6LwxH2Rp+0pZMw83/ZtGZqUPwcc72TdNlQY+VOI3I1A34IXN/qi7oCoUFuch56hO1EOOcjQ9UmUP/UtNB9ocrLuyblMepbdDifrno4IlV/j6mwUzppDBDiOSPWewOfR8+eLnvjh09W2/Tt2/Rcr+hEiRJ8ICdY2glwmPR+6Bqcm4P9ymfSjPds5Wfcp4K2Jo/K1B+76z/2efs15cNmaXU4DluQy6TtQMmbPfQ+2ScDVaLDcDw3E30HWqzJwnZN1b8tl0p1WSe1gRGL9Z/0+PxE2RIiBwhYePo4gd+Oegpea21f7gpe6HeVLbW68veyZuseKTbEPtS2JFZvnVceJeWUw7a0LEtWLHqo3jW9Wddtg7m/H9NzHOlXMlzOKGRxKC7X3FrzURzbvKWzbuMacf0NVTfEwOg3laLRUeqr2hqo1Hd+/2Ls+DDXuHUtQgeAxiDwdANTnMukvAHc4WbcBeYtWoRCwakSUrkeE5SJEjo6065rRBHsGynN6EoW+D7PrCiif8SuIBE1GIYJrCTxVjbbtLmhS7Yd/JZDHqx0RIT/P+WgCgYM/2X7sjbxsMUTAEmjMeR+afB9sz308cHQukz7LXo8VKA8sxIahjIjrMluM+eaKdXej/Lc5KFS7E4V2ppysOxGN+fPQs/QW8iKuQAT6FBQ5FLP7Pwfd+zYUDuqT8DqC8jdV6Jl6lfXky4YI4SNUEdwG4WTdqp55Wz1x+4wjY8BB/372S9VNbaN+h+KJT0Lx53lrxRm0sCEov0XWxHMJPoaLgW+g8JHjUI5WK7Jk3pbLpM/dGv0NsX0jafK7IOlkkDf0FuCugpdau/V61R1Jkz8E1e+qQu9Dn0WsBggP2LvgpV57t33bnnGNOX8FQzqHx/Zsa285eOjH/vf6n/97a/dpe4GTda9DHoQ/5TLpS22UwYnII/Qo8vSk0fd5DiJSf0Eh72vQJHsJ8m4tQpPodkSy3otktz8OLEQEyCMQrhhjf1chL1PZ7tP3XHTYtiPRZHs1CgF7x7aJIZKWt/u/CYXv72/btSHiNhpN/p+w/TrY7v/OQRQ1sk3CyboTEOmdhSJbHkRh2RNQTuDZyPt5MkqV+AG6t+MJvM9VqB7W44i4H0gQqurXFvVJ+U/tNnfmMunnNu/ZhdjeEYZKbYNYH7kCOP2Qx4qnH/LY07ee9+lHkTzse9HAcD1wj5N1P7R5e7nVUUDkaQr6GL6JrIpz0AfxdvRR9ZB7fyUioSFCbBCSJv8RNBlchSZ3SRQiuPtW7FY33D7jyMTlz5zTBOWX0OTv3UQnFNHE8+gdnVxZnEhT/IKvPnNbTUiuNhgXAXtYcjUMCUL8P+RNOA7VcvoaKkJ8LYpIuAPlGM9ChoxGFJLlovpETxIUBx6HJsKjUb5yDXp+V6Fvf9S2HYGMDtfb5XG6qwca5KWaTZAzdhfK54kgw+V3CcQyWlCY4FW2zcHAylwmPQ95U85FuWIhNi++gLyXtblMOocIdCd6dr6GyPsliBx/C80DfonmCtNR7t99KMTwEvs7hgjXCkTCfOn+DvTMfgn4kZN1fVGgECF6RRgiODhwEEGscAOy9A3q5Fkrc/soqnURR5NekOVzH/RxnI1yR05AISt/2vI9DbE9I2nyxyEruR8+BLKOPm9/thWcAGTqhjfu1ryqoc/cqgFgBcpReBtJGu/wuNi7/hnCa7FR8NXxnKz7BZSzNAR5DO5CCn8nIRL0jv29BqnwXYfkud9G799vkGrg/yCPxO/tNu9BE9//oG98CwoT3A95npKIrKUQwfohgfBFDSJMZQKjwkREsNqRp+3f9vh/RGRwuu3jaNu/D9v9XYoVtEHfhTsIQ8k2OXqpefUX9D3+npN170LXfCVwt/UePmW3eRR5OhcQeB390NEh6J6OIcjP2gsJmDyPnp2hiIz5JTYOs+27lSQIEaISIcEaHDgNVX8fj8hVJpdJP7V1u7TF0ICe42jFsjH2J4kGRwcNpo/Ri5JTiBCVSJp8EllGH0WDbb1dZQgmZG8UvNS2FP7zX+CO2obmluZVDddjJdfrR62irbmKztaaiqZ98q8WNFH9G3B3wUut15MeIsQAkUS5UaAJ8CuIZJ0PfAiRrjeQYFE9Ik87o292K8qFqkHCETsjz9VryKv0EyTx/qpdFiUIC/Tzny5Awhd1iDz9C42Xa2x/TrLHNshr0YwI1Aft77jd3x7Io7UWTewnokn6k764ks2nvuHdX7IQveAQlIP9E4Li1LsT1BN8Dd3zvJN1z0Qkey665/eikMHdgf9FhasvRve+ze5rKPJQtaJ73oqeXb+W2wT0/Z8NnOVk3VtzmXSfubghdmyEBGsQIJdJv428NjsaYohgAVCZTmhrinXmMunnnax7JRrE3S3dwRDbJSYj66YTSZTrRx7UbBrnVNO2NO4hi2UDkgveZnD6IY/NB264dF6+Gln6TwCq64Y1UlPfzNLCxP4270STxwLwmYKXatvsHQ6xo+H/EKk5ARGaS1BY93dQHtYYJFKxGwrHm4oMHEOQSMaXUX7M+wlKJUxABGkEmgDvicjXTFSzbRF6F3yp+Eb0nA9FwhRLEOn6NQrx8z0YEZRTdTaBuEUEebn2RJ7sToL6lH/OZdI7ZG24rYBXUJjnE6C5j5N1z0LGoUZUaH0aIlxftH8/iIjwWPRtHw18obPJrG1ZFD9o6C4dCWOIo/s7BN3rVYhw7UeQSlNLEMWwGyo+/AR6ZkKEWAchwQqx3cIqBR6GBtqbPI/3GdNlnjdAwsm6d6NcrYUEUr4hQvSHx4GvEytN96KeKZfKJEZ20rY03o5ySP7ANirPXfBSbUmTPx3lZLrFjnjERPp0tLUhD8FlyPqfCMlViM0BGyr4JPCkLRA/HIlL+EaLleid2g99q69EHqXPoonz5whU3xYjBcHpyNv1a6Q4uAvycg1FE+nhdptaFEaYRxPnehQCNs2u/w7ycMTt8VtRvk41Khy8zLbfGb0zHXZdEXkz4k7W/TFwSVjEfvPCFvu9o8fiBYjwfhP4NIHAiUHGsEaU9xdHJOti4GAT9Vo6V0bobGJRop5hyOvp2Z8FiGAdTaAsaZBRoB49W0+gUgMhQvSKkGCF2K5hCzWuAY445cfu+zA8jo2B8jyqSu18OFbNkSg0ZC2Sdw0Roj8cB/yOqBfxWqOseKYe+0itAKYVvNQ2XVjWkqSHkyZ/zPK3x1+DJgq7Ikv/K2jC+DPg4W0szDHEDgCbPzsfeaMOQaToj4joHIM8AqsRqZqCvE+Ho5fwMeDSXCbtOVn3RhSZcA16tlvQOzoUSXSPJyg27OfdPIpyat5AJGyI3f9jKJd5lO0PiEAdhSbYDciTUbLrfS9WFTJmPESgOhdiC8HJupOAX6Dn51XkkYwjslWD8u9WAWchctSJ8qei0SqeGnFwWzoSIYq+iaPRs9CKvJYlpFL8L+RdnYDCBTtQGOqn7HFf2RLnGmL7Q0iwQgwamCjT0YD4kOdJojpaRRxZmwD+4GTdXXKZ9KKt1ccQ2zaSJh9HUufVtEet5dI3XvIVFMe/XaDgpR4jVDILsW3ij8gDtQCRoYXIG/Qg8G1EmBpROOAENAE+DCnD/tHJunshQ0gdCgmMIc/CbORpakCGhSKaNA+x+zwe5W99C03G/RpIo1DR47Td1yt22wMJJNyHoFywcYhIxWyf5wLnh96rrYJ2dO/ORM9LBJGqReg+zUWezTpEhGeiCIRTTYT5Rs/bsejelhB5SqAPfgf6ft6ERFXOJigDMNX+nAlkNvM5hthOEdbBCjHo4GTdBHBs20purhrGRBPpklkF+Fwuk/79VuxeiG0USZP/EHAbeGN8JyjdFSH2L3ipl7ZK50KEGMRwsm4VypsaifK01gKXozC+7wIPoAKxBeA8+3cZhertj2TR04iITUE5Ny22/c52f3642FgUyfABNPEuo9C/FYjMVdF9ol1GxKoVecDejybvtXZ/zwDn5TLpsAzIFoKTdaOI/KwGLkSezwZ0795CYaTN6Bm4HOX8nYTmAUV0P/+OniX/freiMi+TEFEfZv++HOUQRmybkQSiWjNRmOCiXCZ91WY85RDbIcI6WCEGHXKZdEcuk77/nsvTk0yEfZBikG9JuNXmbYUI0YWkyVcBv4okymOmnraSUYc39rQ8ldGgHSJEiE0MW/sxjrxJlyECtD9wP/A0CvH6LPJOvIRCvcrADOCrKHSrCYX6xQmUBMfa5VFEmF5CXo/3IlJVsstr0WT6RYKadyXbvXb7U41CEmtRWHoJeb3eBn7uZN0xm/aqbDtwsm6VlTvfVrAPyod1kRd0FYoueAcJUIxHcu4no6iW+9CztAbdv4dQKKlf8yyBnqka5Ak9DBHzK5EXsxo9Qw0ERYiNPdb+BFEyIUJ0ISRYIQY1cpn0TBTyNQfwPI9Iucz0U65xf+tk3fhW7l6IbQBJk48nRhR/Gx/RPqncAR1NESibrrhAi/8teKltUtgiRIhBgp+jArGr0ST4ceA7uUx6JfJKzEYeiWsR6RqKFAbPQqqAUTQxHobI1yI0cR5p20bs+mpEvOrQO+6XI+hE4WQHo5pHRbssgsjXKLvf/9h9rLDHH4KI3vlO1vVLOgwaWO/irciTuNH7cLLu55ys+66KsztZt9bJuh9GOXk+kT4bkaHnUF7Ud4GfEpCgI5EQysdQ7p0vgjEcSbV32t3XIZVIg+pfJey+T0P3O2GP6VEhcIII2Y/ezXmFGJzYJgiWk3V/6GTdvzpZ98it3ZcQgw+5TPoNFD5yjufhGYPxPM5ESa0hQpwUrSof3bk6HgXDwnuGs/yZIZXr30DW0hAhQmwm5DLp61Bx378BfwW+ZcUsDJrETrbLT0GCBh0o5Ot5lMf1ICom+4P/z959x8lVlX8c/5yZne3pfVM3dSkh1AD+QgR1pBhEZKQEFext9SdOEBuCgAqSEeQXxYYVI2UoYkDDWCCGFjqhLBCyCUk2bZPN7ibbptzfH88ddo0hBDPZ2fJ9v17zyu7MnZkz9072nuc+5zwHWyB4qP/SaWxol4dlKHZiGY8SrIOM/9gaLDjLdrQ7sM52k/8aDgvyvocNC7sUWxB5MXAsNtysLy6XksYuUK7dj9cYj81XOmU/23I4tqjzCVgw/hiWrSrB1reqxoKrq7Fhoj/Hhp62Y8NHK7HhgTuxuVeHYYHSauy4B7GhhQ9j340qOkv0b8H2RRLLhGULBI0EZkZiiXAklpi1n59P+pC8p3wjscRzdP5RWoml+EVyKh4NrwV+ecaVieTWhwb8JtkUdONO33EvdgVS+qlKVzMUWNi6MTTaH0W6+7yrNuCgWq9Kk1VFDrxq4INYB/jnfof1Bazq5WgsUBqNZbs+AVyE9RvOwqoO/sn/dwhWOGMj1kGe7f8bxAKyv2IL0E/HOtRD/Z/B/v8HsPk1M/zXyK65eCgWBB6MDTP8NTYHaB0W/D2S6x2ST35wWxiPhi/dw/2fBLbGo+G79/LcKmwI32vYfnp9P5u0Epv3lq3c+BI2Z68QC+AKsUWIf4wFWEuw79OjWFA0DCuvnq0ymMG+X6VYdjTlt3EwFkxlM1at/uPBLrdsgqIVO/4/oXOOoEj+Ayzs6gPYl/cH+WyI9H1/ujT82+PDj94EBHHcl+/2SH5UupqjsBPkRqDdH02ye3CVBMoUXIl0m79gWYo0Fsxcgs3Juh3LTg3Csk8TsYzCIdjQvS1YgLQUC55m+k0YxC4AACAASURBVPcNxbIX27HMxXT/OdOxTnUh1lnOVgVs9u8PYgUvhmBzuQL+80dg83I6/Daegc0XmwWs6oOVBE8ELovEEpfHo+EHutwfwgLN9VjG8Q2RWGIYduFyHbYocANwbTwafsx/3GHHYGM8Gt7W5XmF2P58MB4Nb+ly/6HYML8UFkCXYcfqaGx45j+wjNPXu9z/HqwwxYewAOsFLOPYgg0nTGLD/jJYIZVyLFNZhH13SvxttmPZu53YKJgA9n1qw747jdjc3GrgO9j5RATIU4B1ortv9BE8vWgIO5ax8L2zgXHxaPhf+WiL9D9jT2kehV2ZfDTfbZG8+RrWSXgJWyOq63BpD7gDOF/rRIl0n3g0/I9ILPEM1oluxDrEbVgG6i6s4EAcC3Q+hHVoi7AhaNkhgCdg83GOwwKka7BO+Eysk70Vm4OVXbcqjQ1/G45lLsA61Mv91xpF59+HpN+m7VgmbQKWSbsHKy/f17Rgw+o+ihX/yCrCsnUJAH8+85lYdnAuNozve1hAFMECnscqXU3FlI8Vlww+pO16YGsklrgsHg2/4r/m+Vj26Rbgc/7rDsPWOTveb8vz2Dy5IizgPRY75sf4j+/CCpW8BHwRO04hLKD+NhZI/R0Ljjr854B9J4J0rpdZCryIDf8b6b/ueqAC0u1DB9SFmltG3JJMF8/CLghkgJiWgJGuuj3AOueqe6dMOGzL7cmVoaqdXlm7P+66trvbIf2Xf9Xs4Xy3Q/Lq20BgxDsb/7B9RXkg3RrAT161AdNqvar1eW2dSD/lF7XY7v+61K9e92fs/6bD/u+uxarGzcM6yWOwDrOHBUnvwjINKSyDcRHWCS4E0gGXaioMtd7d1jHgaKyDPAmbXzXIf48QFlg8Ree8oRAWuJ2HZWXuwgK/ELA+Hg235n5v5N0KbJ2n3f8eTsT2yxZsjtNELCi6BxsyNxL4QDwafn8klrgb6Djx3OVfJjDsovV/Hrx88CGbEsAHG2uKflbpXnp+1hV1vywo5UPYPLpfd3mf7djQu3uwoYaPYsP+xmH7P4oFVNOx4/ZL7HvwN6wYSju2UPBkbM5WGtiMZTmPoXNxYc/fNo19BwZi37EJdM7NGgmEhg7YEJo5+W/eaxuOOWl9/czFWObssXg03Phf7mPpo7otwJr/zbtdwfadHZkh5cGO9w/b1lSfXryqbvqXuuv9RaT/8suwXwQ8XutV/R07IZ+X3uUm+ksBpoGP1XpVWiNNpIeIxBJjsaFeQ7BO7g+wjNGhWJZhLVbo4DDs/3ATFvCUYFmro7DOdQjroLcCFYdP/cuqgaVbznjy1fdf1twy4nRsHk8L1skOYZ3wmVjG43k65+scigVdv/Dv+wPWgX8mEksEsGGFm+PRcLYyXa/25ILxg4cetevPledt333o4wvY2mPZOVWvYcFOLRbgjgY2RmKJOVh26OeDZrbOHv56c9Ggg9re6z9nWdHwjkjFaY3BQGHmRCww+n48Gn6qy/uMxQLfP/uv+zgW0H0IOwbjsSF6/4tlN7cAN2GBYS12zCuxuVfrsKzWKGwoaHY9q+zQ8C3Yd2AoFkwB3Op/1u/iZzGbWkYlX6ub7W1tnDQS+96sUnAle9ItAdaP3zffHfbyoFRbqjyw+n1HkhxSemvLVyZ+8a99b7yyiPRMA7AFQr1KV9OIVZkq2P7EgI1AKbjnsXkeItJzlGLzde7GgpdrsCGCh2Kd4zFYtqoOm1PTinWgl2KZjSDW2W/HhnylgKeadg0PtraXz2xtHzgZGw74T+BmrHjGIKzTnl0TaSoWvC3D+kyTgbOxIKwJW4frISzQuwzoiMQSX4lHwysO1E450CKxxHfbthSUwJhpLuCFgB2RWOLCeDTcVulqhsP4Y6Z8bOuzBeWZkyoDLx2ON/6eWq/qqUgsMQ7bP9/FAtyDsCxUcssD5d6O50tLBh/WGgTeCwSLhnmp0Sc2t3XsCH5l498GVD/x2DFPRWKJY7HgbRt27IZjQdAgLCiqoDOIS2FDvH+IBVJrseziacBTT39z7AOlYzvOn/aprSMDoTeCpF10zrX9M7ZO1kAsGBvuv24GG4Y4B/u+1fvbZFLpop3rtx7ahAVes7Egq2vWTQTopgCr9aHyZEFjIFDOLoo27Ej+7sfnVXfH+4qIANR6VfWVruZbY0/b8UtXmJm3/u4ho8AFwdVhFaduqvWq2vLdThH5N7XYuliHYvOhKrDKdM9iGYlzsezJDCy4WoN1fK/G1m7KFtEq8B+fDRSuqjv+fqwwwSNdnhvDsihBrAOfwAobbMA60Pf5jy3F5hYFsazJ0/57XoLN0dpE59ye3qq0oDwVGnrUzlEDprQ3YPs4BVA8MnmGl+Zzyabg34uGJ08ffGhLQeNLpXWRWKIOyxAdh1XSyw7ZLALaMqnAo8HC9EnFw1MbsaD2YOdItTcGHqn7y8BjOhoLPoYFPPOwYhdl2FC/XdhwxI1YwFaOZamWYft/ADZPazI2j2sU8Hvg+mBR5hOtm0KZ5M7g5UVD0h/Hgr0bsXlh78aGjzrsuN2OBWtT/X1wLDYPcIT/HgEskG/Clu44ATvuV+Zqp0vf0i0BVrItlA7hBRnflvzF3Z8r7I73lPzpUiVoBvBaPBp+Js9Nkn6u0tUUAN/YuS506Mjjd60un9b27p2vlkzBTtpPKrgS6Xni0XAqEkt8FRuydSqdRQzGYp3ss7AO9WY6C1Z8Gxu69gKWdRiEDfNrxTr9xdicol1YUDQDm4dTiQVHGf8578I637uwoOpwrOOfxCrLLcECibOB92HzuBqBH/oL3PdmlxaU8rPK8xrSwNJ4NPzDSCwxORJLbKz6kpuZ6XCpVHvg5uLh6fdOOm/7hF1rd44GvoFlg+7C9vsMLKt3N/CV8fOa7hp3WtNKF+CL2LDB24HNRYMz76w4tfG+tXcMXeCvhboYO3bXYYHq37EqjT+OR8OtkVhiAjZ/aiSdhU3mYoUt8N9zKvDumd/aeIGXpiMQIo4dzxKsnP5x2HfiYDqrHf4ey2AWYFmwof7tt1gwncaqXB6FBZxzsSBweA73u/Qh3RJghdrd2FQoOPxrr/+qpjveT/LHH4f+vkyaP7gAZc7xGLZmhUheVLoaVzqu7byW9UXhxpVlL5aOTmYmnd3wk6Ih9WdgHaPZkdi66/tgiWWRXi8eDb8QiSW2YZmDMiyQmYxlp7KlsyuwIgs3AP/COs4nYEHVfViHvRULlNZgazj91L+9ggVaDguuNmEXXmb5v9djxRNmYIFXAfBVLPuRLRv/NPAzLMDqCwWUsms//Qr4ZSSWmIx9vruDxd6dwWLv2RCZF4BLMh3uzlRr4HOZFMsDBYzEMlCDsP1aimWCioCBLsC5WKByHbbvM0CsaGjmqOmfqj8Iq8S4HDvGu7Ds0e+wYGpBJJYYiVUoXAB8BQuYt2IB3dnY3KrDsSzXOBeg1AVY7b/GdDozVZ/FjlVZPBreEYklPoJ9RwZgGdObsXW7smuiOSwoT2GFNoLY3LBsaXeR/9AtAdYCb1E99kdK+r53eh73uM6i1/fnsS0iEPC+l24NfnbI4S2rG54pu6ijseC76ZbAdoak27AhJsOwk2g6vw0VkTexGRvydwE2N+d2LOt0LnAxNkRsANbPOB0LeBqxjvdmLNPUggVFV2LZjOxcoDuwzvURWObD81/rWaxjP9d/3ew8L7DgzmGly4+lc42uC+LR8K4D8Pm7lf8ZPgkQiSWmAFdhwdDD8Wj46Ugs8SK2/+sCIa+ucFB6WaCA57FS7RmsXPtGLBCegQUs9cBzWPGQ/8XmaX0CC9wiWFAVxIb+jcKGCz6IzaeqxoZtZoMosGMQxL4TN2J/x0P+Y7djQdUV/mtNxAKx7Dy82VgAdWMklrgHG3EzGusT78TW0nod+y5chmXnnvXbfrT/2bcC0+PRcNN/uZulj+sJCw1L37IKvyqP5+HdsSB8eZ7bI/1YpatxwPHJnYG2oUe0XPPU00ctq3Q1c5947GgPIBJLXAwE49GwgiuRHioeDXuRWOJvWFZpJ1Y+exbWKV6MBUfjsHlTrViH+Cz/sXOwTnkZdhFlMtY5XuX//CH/dXbSOYRwnf8eQ7BAqhSbD/QENjxtir/tkf7rbsUCsSuxzEpfcgi2DlUt8EoklhiMBbsr2uuD121fWdKc2hU4qXxiciO27wNY0Ps0lhlchgWhp2DHY4vn8SGgJJPi28EQW7ES/Nk5bJXA57Fgtx3LTJZhQdsRWOB3FbAhk2Z6e33wm0XD00MCQcACrg1Y4NaB9XEvw4LtNJbNvBPLco7FhgAehGW0CrDs5gwsc5bEMlQfx74PJ/vbFGN9nNX+5xXZo8BbbyKy7+LR8DrnONI5ng8EGJHv9kj/488BJBJLDD5q4bpfTv5o/d2Z9uDHX/v1iEmVruZqOitIEY+G2/rCFWeRvi4eDT8aj4Y/hGU1JgDfwTJX9ViBip1YB7gd+L5/24EFR2OwAgdVWEZjKJ1zr6ZiJbpPx+bWBLEAqg7rpDdgWZkh/v1rsYzXJv+9glgmqxiYE4kl3nXAdkJ+3IdVWdyJFfuYh+2Hbc9fXbEz0xZYXza+owDL+l2DDbv+8ZblZdsaXyl6OJOmDQtc1vuPjcl08PLOtYFGL8UYbEhfFbaPb8D2eRQbKvgkFijXArdhx2IkFvQc7gIMLBqWPs5LvbHGWRM2B+tmbFmOZ7AKj/dghU9u81+/CMtkNWHZtIHYcbzZ/zeIZcOasaGFh2Dztwr8dnpYSfk+UY5fDgxlsCTn/KIWM/Pdjr2JxBLvxCpT/SweDafy3R7JjUgsMRW4NhJL/Aib+3diqiUQwoYG1bhQpvDwqzZ8OBJbd/+TC8Zvxk7sFwBLa72qfwJUupopwMZar6q3VwIT6VMisUQplpFqwCrGjcEyEPVYMPV1LIPxRSzjUI4FT8OxDEYtllE5ActAHIV1pIuB99O5dlYBFnjt8p/XjA1NHINlcxwQ8DyCeCRdgHdgwUAJNqTtHwdwN3Qrv9DIs1iFvXFYoPUwkKj1qrxILNGIBVCnYMHNwng0/MKRi5+4uXxK+9RMB1sCJTyEzec6CZgbCHF70TDv7EAho7F9Ntx/7nrgQv+9hmBB0Cbs+HwNyyxtx7KXQSCDYxcBrvCfNxCrDvgk8BksmL4ZOBFbswxsbavhdFZ7zC40vAH7brRjxz+ABeI/Az5C54W5+/3nP7qfu1b6OAVY0l+dhA31+CP2B1t6sUgsMQwbG78WO0FOyKT5atvWYEH9o2WLsfH7dxeO6Fj40qLh1znc37CT9QjsJDwI+Gelq6nEJl//GbuaKiI9RDwabonEEt/BOuAfwv6vfgLrAG8AXoxHw/dHYom/YiXYp2BZqcHALVggdgcWeFViAVQpFkxdjHXSV9K5kHGIzmFnJ2CV5g7G/s78Nd3BZwNBijyYEAjwEH5m4wDvhm4Xj4avhzdGBzhseN3sSCxxAxYYfRubX3U2Vqhi3ZiTmn5SODx9XDDEg1hBiCIsAD7KBVhTOMA7AguiirBz8Dhs3amLsLXFTvYfG4AFTmXY/l2Fzakqc46UC1JPkHOxQO0V/zWmYkHQ0cBH/ee+igVFhf5nGIl9Dxqw78ckbMhoHfa9ONR/3y/422Ww78LMeDQ8Lke7VvowBVjSX10LDIpHwwqu+oYwNtRjAXBlcmdgBi5T1LIhVNCyvqgSC6jD7XVFF2JXJj+AXR1twU6csytdzWZsDsWj2JAYEelh4tHwo8CjkVjiD/FoOBOJJcYA/4N1+s+LxBLPYZ3mXwOHYf/PX8eGh92Ddb6zC8c+iJUWL8SKYZyKdaJbsM59GusnDcf+bpyGBV4DgRnBQorT7aSDhWzyXze7TlJfNQbbX6diWb0jsc+7DQtgU9iixNNLxtCM7cOTsWGAa4C749Hwo5FY4uvY3+A1WLZxLTbsbwo28mAyloU6GctMdvjvvw07VmXYHCkPGwZ4NBagPQfci82ZOshva7G/7bV0VomMYSMXBmCFMM7B5oENwAKqYmyBYYcFVw77Xnj+64u8JQVY0i/5824096bvWNr0alFqx8qS94ydtyOy4S/lzc2vlRSmmoOufErbhTtfK3kAKACXHeZR6N9K/N9DWMfpe9gwlR909wcQkX0Xj4Yz/o+rsI5zBpubdROWjToT61jXYxmoUixIasGGBx5GZ9GMYix4OA8rWHE/FgAUYx3yIdg6S6X++4wEgs5BgdUVfAxbfPhPfbxgznuwohPfxSoxbsKCqsVYIPJbLGCJYJnDb2JB1lRsCOEMbD8OpzPYuTMeDScjsUQllkW6CgugEthohCIscG2kMxiaQWcJ9ZD//p/H5ng57Bjv9J/f7m+TrTo43n/NDPb9+CEWxL2MZR9b/Nd9Hfs+Bfxt8f/dsp/7UPoJFbkQkb6gecdzpYduf7r09Kaako5d64pmdtQXuEy7o3hUquHIH6wrG/2eHTPtAuS/yQ55KcSGl3jYlUwF3yK9wxP+7Waso/0PrPM/HKv8NxjLZIwHLseq203BKsmtwebZZLD+UBIrL/4u7G+BR2cH/jlsaGL2Is1fsc74OizgOr6PB1dggWcS2z+TsezVAOzv5+vAp7H51wH8CozxaPgXWPA1GhgViSWKsXXEKrB5UldHYol3YBe13okFRmv85/8FC5AqsH1/KxZEdWBB0SNYEFSBBdEh/73LsGPS4m+TLV4Swo7fY9jFtZf9bTZi35da/7mjsCGkXYOrNdj6XRsisUTZfu5H6QcUYIlIr9e8quh9o9/VdMGId+x8pfaWoQ8FCvgWzrWDa6l/pGxC8+rC91W8t3lq0YjUK/5Tdo+0yrAT9YvAmbVeVQYR6Q02A3/HSoFfjGWsIlgn/BBs+OBz2Nyqz2IBwED//qVYdiTbF2rHhpaVY5mZJ7EA7GNYRiu7XUfrlsCwTIq12BpMF2LZlb5uK3A3VpnvfOCk134z7HOb/lF+I3YMfo4FKsVY0PPVSCxxCzYyYA52XFZjwdTpWBbxXKzs+wgsO3QSVvL+NqwI0VbsmLZimadGbB5dNnOZrQgZAmqwYZ//wi6UDcKqEWbnTL2OZchOwo71GOyYrsWGmE7AjnsHneeIlP/zKGAFNrTwqP3ZidI/aIig9EuVrqYAKK/1qnbkuy2y/16/c0hh+dS2tuZXi3d4HYFJLa8X34td0a7Cc17DM2W/ds6VdGwvGEvnpGV/5ZQ0ZBx4gUHAcdi4/5/n6aOIyNszEqvsV4cNMXsn1rkvwjrMBVgn/EtYgYqdWIf5KSzzch02fG0aNudquv94K5ZFSfvbDfTfz6XaWLn5nwO+mNpZ8OTUT2xzWLbj0EgscU88Gu6zQ8j8DN0Ps79HYok5w47ZeeP2p8smJZsDm0MDMtk1wBwWiB2FZZyOxQKgUdjx+BKWOVqDBU+TsH2+FQvG0tjCwdOxOVYTsIzTr7HA6uPYMV2OrZ/1EjY8sQwLzrLrcWWP2/1YQDcSK7QxwH+vFJ3z7Jqw700QC8TG+m3ahg0NbcIyXpdgGTCRvVKAJf2KXwXpC0OOGHpMw9Nlwypdzadrvaq6fLdL9k/bltCf2raEXsKuYJ4EfKygPD1s2qe3/mrLsvJHH3/k6Jcq3UsfwHEhdqLOTlh2xcPTZFLQsa0w+3KP5+MziMjbF4+GX4/EEp/EOuTXYB1pR2cWxWF9nRL/5y3+tluxqnflWAZjG5YxyRbFWOi/XgGdwwXbgXXBIgonnNVY4mU4CguuRmIXZ9Zic4f6vEgsMQi4pnxyxwgvzTPBkszPsb+d9VgBjHosiNqBZZC6ZoWyQcwubJ9vx4Zoj8eq903EMo63YxmvNDaPazXwpfbtwSHt2wrWDJjSnnABPoUNBU1i65N1YAHZBiy7eQx2bLLFLtr8dgzxH/+W/9jJXT7eRP89wb4nFVgVwhe6zP0T2SsNEZT+xgFfGTZ719kF5ela7EQgvVytV9VR61U9j12x/ArglY7tKKtbOuCm1vqCx6eWvrBh4MEt35pywdaDKEoF/ac58MgkHXhvrD3cXOtVPZ2PzyAi/514NLwmHg0/iVW3uwxYROd8nCew6qDZjvxr2NCx7MK4IaxDPhPLer2GdahPxTIkf8eKMWSwuToB56gMFFAXLCTov9YM4BvASL+qYX8QAl4rKPEuGXJY+9xAAW3A77D1rgJYpvBZbKHi57FAtdW/pbFgdA22vy/BAt9HsKArhWW1zgBmY9nJR7HAqKnur4NWv/rzEXWrbx52L1a98BZs/tTBWGYy4D//MGzoYRKrLPkAtoZXtv1Vfjsn0ZlwyAZg2SIaKWy+3bkKruTtUIAl/Yr/B3LSoGntxbMur/tcrVelAKsXicQSIT8L+WZasTVZ7ml6paiq8cVS17ImVJJu9SqSrRzVvC5U5iDgX0hthkxHx/YgHdsLwO689MB/ChE5QIZi2aljsI5yE/ALbA0rsKIJv8BKbd+IrZE0EOuEZ0u1l2Cd75OxoWHnY4GDo3O+VinwY2wNrpuw8u8dWIW9OQf2I/YYlVjGqQAbUv0jLFO0HZuj9WFsbtos4CwswEliwcpT2AK+p2DZv//B5mcdggVkLVgQ1oIdw/OxuVoPAqnR72oqGPf+HatKRiXXAnf5rzEem1N1OnYsgtixPQgbOghWAOUk/+cOv03ZCoRpsosXW7C3GSuK8m7gY/FoeOv+7zLpTzREUPqjJuyqYyASS4Ti0XAy3w2SfxeJJYYDJfFoeB3ArIOefG/BgPR3Bh1UPGjoYW3fxE6qe3IRdtIuxQtcDZwLwaPAo7W2lNbaEucXDswA6wm50STJjg1sr/WqfnSAP5qIHDjXY3Ow/ohlPsqxwhcjsOF9w7BO/DQs63QwlpUqxaoNDsc62tnsxRwskCryn+/RWbr7SKxoxhgsGHuidVPBpat/P9xVLqgpqPWqUvva6EgsUeS/1uPxaPjht9q+h3gC+BQW1J6PBbTbsWD2o/79bVihkJ9jAewz2HC8yViAcxsWzNyDBViHYfO2gv7j9cCqeDTcDhCJJbYBQ0pGpwaXjN5ZCfwEW3C6xX+dVv+527DjWOT/HsSGKn7B/7kN6/+msGNf6j+3Gfu+POnf93Q8Gv6P0rMi+0IBlvQ78Wh4cCSWyFYZ2ueToHSrK4DxH7jqb/965cYRZwWK3TEjZ7W5+hWlmaGHte2tRG4zNvRkJbZQ5K+BR8FNfWPURxBcQTrgtbsZJF0KyOAyjXjBDxzwTyUiB9KD2Fyeq7Cs0xjgvVhnfwqW5djg3w7FAoLV2PDADDY8rQy7CFeCnR+m+M+vx0q8T8AyIxOwuTpgmZn0S9ePrvBS7nPYhZ63nMvpB1YjsWDiZP89e0WA5QceqwAiscSHsYteI4BqLIhKYVmghViVwDT2WeuA3wN/i0fDSyOxxJnAd7BgbR42L+5FbPhgJfAHfz952Dyo67Gs1KH+ttnS+qOw45TGFhUegQVtI7CAeDJ2fANY37fQb896LBAbBFTHo+E7c7unpL9SgCX9UjwaPuGtt5I8ir90/YgrKMxc1tEYLHYtzqXanDdgWttqbOLzHtV6Vb/AhgC9odLVTCfYuot0cQnAwBktdDQUkG53gWRDQQFe4Bm84Gm1XtXmA/uRRORAikfDP+3y67P+7a9+QYb7sUzHMiyTNRHrZI/DhrddTOfCtcXYxZq7sflYg7DO+DYsQ/5j4AgsQLs+Hg0/FoklvjHrOxuOfP57Y25I7Qq+sKf2ZYc3x6NhLxJLHA/Mx+YBfQYLMBpytCu6m8MCHLtgZfsxW9zi61jQ044N0XwQOwa/icQSD2MXw1ZhWaO/YMM3L8QWbv4acDj2N30TViL9V1jANhbbX0uxgPkKvx0pLAsWxgKn7FSYZiwrFfTb04YF489hc7AOxrKYIjnhPE/ZTxHpWSpdTQBYD94oCjIBUo5AaaYm01JwD3B5rVfVurfnR2KJ44BgPBp+qNLVFA4bt+n5bRtGTMMLUDAwSUGphxfwvPbNRa2k3RG1XtUre3s9EendIrHENGwNpcuxgGkVtr5VI5bt+iSWrXLYcLHnsE74U8CZWMZqBXA18H9YFmstltmqwbIjhcAlexp2HoklgljxjU3Ad7Fqg4dghRc+Fo+Ge/Xi5pFYYiIW8JyPZZCynyeEZZnasTlQS4EPYhmlycDSeDR8bpfX+SxWlGQ1ts//hA0DXIdlqLZiFQUHYMdyIxYcnYMd10bs2DgskGrAAqeBdBavaMMCu+exBZDPxKoKbu0Hi0VLN1GAJSI9UqWrmU0w/TDpgF/1z9ViZXg/UetV7XWtmUgscRvW2TkzHg17Bw984qzW5pLbIBAYdVITbfUFNK4s9cC9q9areuAAfxQR6SEisUQxlqH6EzaPajlwNFalrgrLbDVh6z2txoYbZrBsSLW/zZlYNuQZLFgoxLJlF8ej4WW7vV8BNi9pG7b+01BsqOFKbJ7Y4ng0/PwB+8B5EIklLsGychls2OMQLMgKYgHWEdh8tw5s/tWPsCxSCTas7x1YILoem0+3CFvr6jDsHFCFBVg/x4Koc7AAajsW0A313zu7HEej34YWOitLtvttWRaPhrWuleSchgiKSA/lXUPGBTsvOvJ1bJhPeaWrqXiL9cu+CQTi0bBX6WrGQLnnSlLbvFR6REF5mrKiDI0rS9NYp0pE+ol4NNwWiSXagV9ixRiewjrjS7E5WkmsM34xNudnIjZv6FWsOMa5WOW5JDZkrRYb8nY+cGMkljgZW1x3KZbhmoEFEKX+c0Zgc4sej0fD3zjwnzgvYtgwPUfncMrLsADqNCyr1IJlkg7HhvxNxQKhHVjAOgQb1teAzbs6GDs+m7DjsB14FzY0cZD/vkP992zHAi2PzjlX2YrBjVhp+HbgRQVXcqAogyXSB5z9nSWTB6xrKL/plx95Lt9tyZVKV9MOnl/hz7Vh4/MjwB3Arlqv6sP7+Do/xcb0v1Y4pL2y4rTmkobnijONK8t/RrCEDAAAIABJREFUWutVfeGANF5EerxILBHAsiHXYVmrk7GsRwmWGUlinfQg1mFv8m9DsCxYm397DZsbWot1+C/y778c68hfhw1F68A6+B/GAqw+OxwtEksMwfbZTdi+mobttxC2Dy7FMlKfoHMYYSEWCG3G1qeqw8q878T2+yxsjlWAzgqBzr9lO7PtWMD7DjrLs7diwW4Qy3Y9H4+GVeBKDihlsER6uUgsEahY9urzI55cX3LZHc+0f6chVpzvNu2vSCwxODRiZDC5NWR3FGfW0xZcXOtVZSpdzU1YR2Vvzz8R+HTTK0VXwciZ2Im1uKOh6J07VyUrd75aeh422VpE+ql4NJyJxBL1WBYriWVaSrEOfCH2dyNAZxp9oH9/of/7M9i8ralYADAJW/OpAutfnYQFE2OxwOFWbIjbk305uAKIR8MNAJFY4rtYUHQS8AHs8x8GHI/Ng8sGsNl9PRArr7/Df2xMl226rt3aTmcfNknnMM5C7Jg8ja2v5bB9f1M8Go4dkA8rsgfKYIn0AV+d8Z3WEa9sLQZoLSlIfrvl+sK3ek5PFoklPrDqt0PubFxZ5gCKxrU11Kw7YujbeP57Ui3uazU3jKpsrw+Nx+ZZfKTWq9pwoNosIr2bPz/rnViRhSP9u5N0Fkpoxob4eViHPoUVc8gGDa8BL2Pzi+ZgVep+gWVpFsSj4X9212fpafxS69Ow4dvHYnPWHsX26cexi2ajsMzfMKwy4CFYdutlLNOYrVDo+dtlqwTuwIKybCbsYeBUbNFhhwVnt2vNS+lOymCJ9AHlDS1laUgFwRVkCOW7PTnwz46dwU3Y1Uva1xcOrnQv3QjuZuDhWq/qTa8MVbqaATB+JHhbbf0rngM+o+BKRPYmHg23AUsjsUQNFhiFsEBpJPAQNg8oW52uDpsT5GFZrwH+fW1YQPVBLDO21r//oe78LD2Nv1jw85FY4kEsYPoeFqCeh82hGo9lpTZjBTIGYvtxEPBbbCHmIFZ50KNzQegUFvxmsKIkXwX+5A8BvK2bPp7If1AGS6SPuLrky669pCAV6Ei3X7rzutJ8t2d/WXEKNoDn7J50CxQ8g2WiVvvbFGLj8p/DLhidjs3TCmMlga+p9apUyEJE9lkklpiCVRH8DfBlbKjZZiyzUgC8jnX4o9hwwB8Ax2FrOX0A+GI8Gl7b7Q3vhfy1wcYBP8MC1SYsg3gtlo16DfgItt+XAGdgAVYrVqJ9CBaE1QLHxKPhZkR6AAVYItJjVbqa+eD9ofMetwWYUOtVtfuPn4Jd3awFfootTNmBDdn5v1qv6uHubrOI9H6RWCLgz9EqwwpYLMMu5ByGZVcux7InZdlOvR8shOLR8F7niMp/8guOHIxVHbwfW9B5JLavswVFTgS+iGUER2Hl1l/2f6+LR8OZbm+4yJtQgCUiPZa/4HAKv047duXyG9iCn1uwq8wnYiV/z8SG6bwGvLi3YYQiIvvKD5yIR8Oe/3NxPBre62Ln8vb5QdYx2Ny1Q7CCFef6/z4N3BCPhj1/26OBG4CfxqPh3+WnxSJvTgGWiPRYla4mhK0nc9JuDyWx8frlWAB2Ra1XdWU3N09E+plILHEeMB/4fDwaXpfv9vRXftGME4Gn4tHw1jw3R+Q/qMiFiPRkKeAnWEWp8wHnL3cSspuDzknmIiIH2iascIUyWHnkF81Ymu92iLwZZbBEpFeodDWHA0+C12UtFAcWhC2o9ap+lJ+WiYiIiHQKvPUmIiL5V+tVPYNl3V/d7aEM8Mfub5GIiIjIf1IGS0R6nUpXswxbn8YDTqv1qjRURERERHoEBVgi0itVupqxwIBar6om320RERERyVKAJSIiIiIikiOagyUiIiIiIpIjCrBERERERERyRAGWiIiIiIhIjijAEhERERERyREFWCIiIiIiIjmiAEtERERERCRHFGCJiIiIiIjkiAIsERERERGRHCnIdwNEREREJL8WuuozgVOBSxZ4ixry3R6R3kwZLBERERGZDbwbGJnvhoj0dgqwREREROQ5oBk4KN8NEentFGCJiIiIyP3ATuC4fDdEpLdTgCUiIiIi44GxwLiFrvqqha56VL4bJNJbKcASERERkS1AITALy2JNWeiqx+e3SSK9k6oIioiIiMhkoAwoAn4PnAEcs9BVLwFWAB0LvEUr8tg+kV7DeZ6X7zaIiIiISJ4sdNUFwHuBW4BSIOPfWv3bs0AS+MACb1EmX+0U6S2UwRIRERHpZxa6age8A3gdmA98DAh2uXnARqz4xXVAoYIrkX2jAEtERESk/xkDXAE8BBwKDMUCqxSd/cNxwMn+/Z/LQxtFeiUFWCIiIiL9xEJXXez/uBH4LrAWOAU4AhjmP5bBCqEFgQnAPOBioKFbGyvSS6mKoMgeLHTVQ75XdtEnvnDGjaF8t0VERCQXFrrqwcCNwA0LvEXeAm/RP7Dg6QdYIOUBHdh8qx3Anf7PA4GReWm0SC+kDJbIHnSEgvet+eCs44o3N/9ioasuXeAtast3m0RERP5b/rpWv8SGAP59oas+GTgE+ALQBLQDj2OLDR8KfB94FHgXFmRNAl7u9oaL9ELKYInsiZd5vbh+J0UNLS4Fj0ZiCZfvJomIiPw3FrrqcRnH/IbpI6ZvnTnmaCyQ+gA2r2oSkEwH3VPJ0tBqrG+4ECvVfgEwAEj724vIPlCAJbIH30j+3zlDV6z92fAn1hGEWQd9P6EMloiI9Eqec7Oapg6/ACgv29RUjpVe/xbwVaAF2LZ19sShG+dMPsGDOVj26h7gMOBF4BLgsvy0XqT3UYAl8iYu33btZ7Hx6JTtaAktdNV3LnTV78lzs0RERPbJQlc9eKGrPqz+iIpRjdNGVrYPKgmWbN11JTAK+BDwAvAMcOea9x962cjHX29zNhxwAHAM8E5gS6okFNp0/KS7PnTN0o/k7cOI9CKagyWyF0EYnoaHC9I8BrwfWAf8Lc/NEhEReUuZYODjW48c+8mBL28NDH5+U7GDYgezsczUNGATMBo47LivL+nA+oW3AwcBldh8rOUulTmidHPThEAqPSRPH0WkV1GAJbIXC7xF24Gqha46ALwHeGRv2y901eVA0QJv0bbuaJ+IiMib2T5zzNPbZo0rKF7dPN41pTuKSTZjRSu2A6XY0L/xQDE2xyoNVAHfBt4LDAdODCbTn3Vp79J0cagxLx9EpJdxnufluw0ifcJCV30RtmjjM8B7FniL2vPcJBER6ccWuuqfNE0cOvnRhqM3/k/TA/NK6RgGpJ3NwSrB1rvKXmwPAO0eFGQCLAlmGIctRjwE+NICb9Ev8/IhRHohzcES2U8LXfXkha76PmxScDkwHRvDLiIikk8PDVy7/c/vbbq/tijYMTgTwO0aURYA1gPbk0Ha2kO8iq191e7BXa0jyrZ7gcCp2NDBNLYgccVCV61quiL7SAGWyP4bC5wCFGFFMeqBs/LaIhER6fcWeIv+ANwFXODSBF0GSrfu8jJQlYHhwTTlu8YPm751VkWorTTU4KDCpTLbXcbzsEIXTwIjgGr/dxHZBwqwRPbfcvxqg4ADDk7BrVcM+kp6/v/eenoe2yUiIrIRSOFwLaPKyEDAgXP2byAAruHgUcFQa3J0Bg4raWgtCWa8VMYR8qyKYIF/m5DfjyHSeyjAEtlPC7xFHjYpOJ29LwiupKkjMP1n/7r77O//ZVT+WiciIv3ZAm+R50GSoKNgV5L0gELaBxV1pKHRA8rWNSTH3/tSm/PoaBteem866B5OhQK7Ns6dnGyaOGQQVgDjVayku4jsAwVYIjmwwFv06gJvUQFwB5DKprPK2gmMvPnhxyOxRDCPzRMRkf5tRLogCIEA2w6r8FZHDk9vnjvlhkzQteNoK2lqu2L9qQe967mvhcdsO3xcYXJQyZ9bxg7ZEkhnOoC1wJX+xUQR2Qcq0y6SWx8Djk3D/UEbgsHoVxrGbbL5WS15bpuIiPRDDm4vbEtdkG5LeUOf2xjYWTm8YcvxkxJjl71WTtqbAbxe9+7pTwC/WX3ekY/Ho+Gaha66ABjoL1ciIm+DyrSLHAALXfUZSbjLFTi34cSpbD5x2qTbvnna2ny3S0RE+p+Frnp++8Dis1pGlh1dsq3lseKG1h8s8BY9ke92ifRVCrBEDpCFrvrotSdXfav+mAlDkoNLzopHw/X5bpOIiPRPkVjiSDLe5c7zvn37V09+Jt/tEenLFGCJHGCRWMLFo2H9RxMRkbzS+UikeyjAEhERERERyRFVERQREREREckRBVgiIiIiIiI5ogBLREREREQkRxRgiYiIiIiI5IgCLBERERERkRxRgCUiIiIiIpIjCrBERERERERyRAGWiIiIiIhIjijAEhERERERyREFWCIiIiIiIjmiAEtERERERCRHFGCJiIiIiIjkiAIsERERERGRHFGAJSIiIiIikiMKsERERERERHJEAZaIiIiIiEiOKMASERERERHJEQVYIiIiIiIiOaIAS0REREREJEcUYImIiIiIiOSIAiwREREREZEcUYAlIiIiIiKSIwqwREREREREckQBloiIiIiISI4owBIREREREckRBVgiIiIiIiI5ogBLREREREQkRxRgiYiIiIiI5IgCLDngnHNrnHPvyXc7uoNz7gXn3Ik5fs3fOOeu8n8+wTn3ci5fX0Skv9N5ar9fU+cpkS4UYEmP55wrcs7d5Jxb65xrds4945w7dbdt3u2cq3HOtTjn/umcm7jb83/lnGtyzm1yzn3lQLXV87xDPM974AC+/r88z5vxVts55y53zt3837zH29lfzrlDnXNLnXP1zjnvv3k/EZHeTuepf3v9nnaeutA5l3bO7exyO/G/eV+RfaUAS3qDAmAd8E5gEPAt4Dbn3CQA59xw4E7gUmAo8ARwa5fnXw5MAyYCJwFfdc6d0j1N75UuZ9/3VxK4DfhE9zRNRKRH0nmqe13O29tfj3ieV97l9kA3tFH6MQVY0q2ccwc552qdc+ft63M8z9vled7lnuet8Twv43neEqAWOMrf5IPAC57n3e55Xhv2h3eWc67Kf/wC4ErP8xo8z3sJ+AVw4T629zfOuZ845/7iX/V6yDk32jl3vXOuwb8aeUSX7d8YZuJfnbvNOfc7/4rmC865o/fhPY9wzj3lP+dWoLjLYyc659Z3+f0S59wGf9uX/SukpwDfAM7x2/zsvnzWLvZ5f3me97LneTcBL7zN9xAR6ZF0nupb5ymRfFCAJd3GOXcksBT4oud5f/TvW+Kc2/EmtyVv8jqjgOl0duoPAd744+x53i7gNeAQ59wQYEzXx/2fD3kbTT8buxo5HGgHHgGe8n+PAz/cy3PfD9wCDAbuARbt7Y2cc4XA3cDvsauctwNnvcm2M4Bq4BjP8wYAJwNrPM/7K/A94Fb/St0sf/uf7GVfP+dvk4v9JSLSK+k81WfPU0c4G8r+inPuUudcwd4+o8j+0hdMussJ2DCyD3dNzXueN+/tvIhzLgT8Afit53k1/t3lwNbdNm0EBviPZX/f/bF9dZfneU/6738X8HnP837n/34rdvJ4M8s9z7vP3/b3wJff4r2OA0LA9Z7neUDcvfnY8jRQBBzsnNvqed6avb2w53mfBz7/Fu+fi/0lItIb6TzVN89Ty4BDgbVYEHYrkAK+/xbvI/JfUwZLustngYf3Z9yzcy6AXTHr4N9PFjuBgbttPhBo9h9jt8ezj+2rzV1+bt3D7+W8uU1dfm4Bit/iylkFsME/aWWt3dOGnuetwk6ElwNbnHO3OOcq9vLa+yIX+0tEpDfSeaoPnqc8z1vteV6tP3RzJXAFENnPNojslQIs6S6fBSY4567remeXMeN7uv2ly3YOuAkYBZzleV6yy8u8AMzqsm0ZMAUb794AbOz6uP9zT50ztBEY63/erAlvtrHneYs9z5uDTfT1gGuyD+2+rXPup3vZ1y/4r9fb9peISK7oPLVvevt5ygPcW24lsh8UYEl3aQZOAeY6567O3ul53qm7Vfbpeuta4vZG4CDgdM/zWnd77buAQ51zZznnioFvA891GZrxO+BbzrkhziYUfwr4TfbJzjnP9ZySrY9gQxe+5JwLOec+CMze04bOuRnOuXc554qANuwqZcZ/eDMwyb+aCoDneZ/dy77uOnZ9r/trtzY4f58X+r8X++0REeltdJ7aN73tPHWqPycOf9tLgT/tx+cXeUsKsKTbeJ63AwgDpzrnrtzX5zlbK+QzwOHApi5Xs873X3crNsH2u0ADcCxwbpeXuAybTLwWeBC41p9gi3NuPHZSXbmfHy8nPM/rwKpNXQhsB87BSvvuSRFwNVCPDfEYCXzdf+x2/99tzrmn3mYz9ra/Jvj7Pnu1ciJ2wsxeOWwFtMCkiPRKOk+9tV54nno38Jxzbhdwn9/W773N9xN5W9y/D6EV6V+ccx8GDvE87+tvubGIiEg303lKpPdRgCUiIiIiIpIjKtMu0s38YQsvvsnDB3ue93p3tkdERKQrnadE9o8yWCIiIiIiIjmiIhciIiIiIiI5ogBLREREREQkRxRgiYiIiIiI5IgCLBERERERkRxRgCUiIiIiIpIjCrBERERERERyRAGWiIiIiIhIjijAEhERERERyREFWCIiIiIiIjmiAEtERERERCRHFGCJiIiIiIjkiAIsERERERGRHFGAJSIiIiIikiMKsERERERERHJEAZaIiIiIiEiOKMASERERERHJEQVYIiIiIiIiOVKQ7wZI73dpqHpHeYoBzQU0XpVcNDTf7RERyaUlcyoqgZHzltc9tmROxanAscBCYBpQCxQDm+ctr/Py2EwREekhlMGS/VbmuQEBCAzw3OB8t0VE5AD4EnDFkjkVg4ETgYuAGmA58CrwAHB0vhonIiI9iwIs2W9NIW9jOui8XQXelny3RUQkl5bMqSgHhgKvA+cAZwMDgbFAKTDcg2kPHP2ZT0ViiWn5a6mIiPQUzvM0okFERGRPlsypGALcCbwDG1bv/NsbPKC1oCSzY+D4pRXbX1kJPAf8cd7yukx3t1dERPJPAZaIiMgeLJlTEQQmA5XAvfznvGUPcF3OohkHaaAemDNved3q7mmpiIj0JCpyISIisme/Ac4ECne7PxtTZYCUs8c9bNi9A+4B1nZTG0VEpIfRHCwREZE92wYUYRcjg8BLQAuwBstS7QQagHVY5opkIOTqB014X1vhgJlL5lSMz0ejRUQkv5TBEhER2c2SORWTgFZgMzAay0y9jgVXfwCGAc1ACLgAGAO0rRlzdOHW4dMyR79w2zXAK8AXu73xIiKSV8pgiYiIdLFkTsU7gMeAi4FRQArYBVQBxwBPzVtedwMWZH0Iy26lgVVTNjzy+ohtq0oePvxjbcAv89F+ERHJLxW5EBERAZbMqSgFLgSqgYP8u70u/24DOoDtWPB1A+BSuJb2UNn0ouTOAK4g1FQ20q0dd8zDzx70wRPi0bBOsiIi/YyGCIqISL+3ZE6FA64CPgcU+3dn6BzpsdX/OQhsBE4BKoDiAF6gJLkz4ICtgyZ42weNa08FQvcBJdicLRER6UcUYImIiMAU4DN0BldgAZbDhgh+AFtkuAT4rv/vTOCkAOD52w7ctdkLptuTdaNmnQ88ADzcbZ9ARER6BAVYIiIiVsyiHhhH52LCG7BFhlPAE/OW1z2y23Pes2ROxXPAdAdNQHFxctfvXh9z1LXrxhxRCTzRfc0XEZGeQnOwREREgCVzKqYDRwKrgU8Bv5m3vO6ht3hOFXAHNj/rDiA+b3ndhgPdVhER6bkUYImIiOyHJXMqjgfa5i2vezp7XySWcMAngeZ4NHxL3honIiLdTkMERURE9sMehg6CFcQIYxUHFWCJiPQjWgdLREQkx+LRcBr4NFbOXURE+hENERQREREREckRZbBERERERERyRAGWiIiIiIhIjqjIhYiIiEiOLF4x91hgJ7ALmAD8a/7sZZqPIdKPKMASERERyYHFK+YWAj8AZgHF2ILVHwFuy2e7RKR7aYigiIiISA7Mn72sA7t4PQgo8n/+YF4bJSLdTgGWiIiISA4sXjF3JjC8y10BYGKemiMieaIy7SIiIiL7afGKuUHgeWAyEPLvdkAa+CZwD1Cj+VgifZ8yWCIiIiL7YfGKucOBs4Gh2LBA59/A+lrfBJ4Ers5LA0WkWynAEhEREdk/lwA/weZeOSCbpfL834v8n09ZvGLu6XlpoYh0GwVYIiIiIvvnLqANKOTfs1dZhUApNh/r4O5tmoh0N83BEhEREdlPi1fMnQrcjwVRATqzWLsHWy8D758/e9kr3dg8EelGymCJiIiI7Kf5s5etAqYDX/Lvyg4V3P1K9jRgbjc2TUS6mQIsERERkRyYP3tZCrgJqxwIFmSl+PcgKw3MXbxi7u6ZLRHpIwry3YC3a8mcCgesA9qBQ+ctr2vNc5NEREREsgYCDcAwLMAK0Dk/K4iVcD8HqFm8Yu73VbZdpO/pVRksP7hKAWOBSmBkflskIiIi0mn+7GVbsIAqCaQ9D6+jI1gLbOiyWQj4NjZcUET6mF4TYHUJrrJt3jlved3aPDZJREREZE/OBU4FbvI8HI4Znsdo/r3wRQi4d/GKuaflq5EicmD0+CGCS+ZUFACf8+BHrrMST8e85XUD89kuERERkT2ZP3vZQwCLV8wd7RyfCBWknXNszmQY45z1vVznDKzm7A+RWKIIOBx4Jh4Nt3dzs0UkR3pDBuvDabgBcP5ln+S85XVFeW2RiIiIyFsrdY4dzpEBKnAksw9sbhj36IqaM+fPn73sX122nwtch6oMivRqPTrAisQSbkfhkO9kG5m04Kowr40SERER2Tc3AT8DtgOZgJ+98jy8VRuOO7i+cUJkt+2fBP7P/1dEeqmePkTwkKUnfavxpAd/kAkk21PD0ztK8t0gERERkX1UAHzY/3e75zEU8JyDWZPvX7m9eeIdcBZ+yfaieHTZduCP+WywiOy/Hp3BAta2lg7/2X2n/uCgCx98sWje8rpMvhskIiIisi/mz16WBD6fzhQUv7T2+JFPvvy+YEPzCFLpULK8tMmbMGrlAH/Ts4HbF6+YOyGPze2VFq+YG1i8Yu6kxSvm9vQ+rfQjPTqDFY+Gm4Ef57sdIiIiIv+luuaWocVNu0a4zQ1T3dbGyakRg2s3Hz1jyZeAlf42zUAd0JK/ZvY+i1fMLQI+imUJv7N4xdx/Au8BtsyfvezZvDZO+jVF+yIiIiIHyPzZy55ev/XQ+o3bqzznMgwo3fTysIEb1gAb5s9ellm8Ym4BcCGQmj97WX1eG9v7fBT4Aba483nAPOBm4Bp/2KVIXvToDJaIiIhIb7Z4xdyCscPH/Lo9WfjFiaNWFpeX1G9c+vhF53/zjB9lg6k08ACwI3+t7LVew9YTm4WVt58PFAMzgaOAJ/LXNOnPnOd5b72ViIiIiLxti1fMnY9lqG4Fznr61ZMbazfNHgBcGI+GlbHaD4tXzD0GuA8YvttDTcBf589edk73t0pEGSwRERGRA+k54F/AXfNnL7vpzn8lTgKOxIKAXmP5tZPKgGOBaUA7cO+ci9dszW+reAF4HRieTkMgAM7hAUXAxvw2TfozZbBEREREDrBILFEBHAb8PR4NJyOxRACoBNbEo+F0flu3d8uvnTQR+AW2AHIhVpTjZuAi4HPA/XMuXvNSPtq2eMXcJ1LJwFGBYAbPg2AQ/PZ9ZP7sZX/abdvDgc8D/5g/e9kt3d9a6S+UwZIeaaGrHgY0LPAWqTS/iIj0BWcC5wBrgZeA/wGuBL4H3J/Hdr1h+bWThmCFI+6dc/GaVcuvnRQEvokFJcPo7Dc2A78CPo21/+Ll1056EPj8nIvXNHZzs9c6lzkSsNyVzWU7av7sZau7brR4xdyJwHXAUOBp/75JwJexuVyzgVvmz152b7e1XPosBVjS4yx01R/GrpTtWOiqTwWeX+AtSuW5WSIiIvvjNqws+yv+76uAJViwlTfLr53kgHLgC8B24Axg0/JrJ60FLge+CJQBnn9zwGgsUDkB60uOxQpM1AP/211tX7xibjlwQrAAqxhotbFLgc8vXjH36t2qMm4C4sBTwKOLV8w9CCvvfiI2R24gEF68Yu7Y+bOX9eiMovR8CrCkJxqPVQEajV1lSi501Scs8BY9lt9miYiIvD2RWKIM68j/Mx4NL8veH4+GNwIL89awTrcAp2PhSQfwe+BeLLj6LBAE/gycAoTS4NUz1qtj2nFTebZ0AA1dl/yZ1J0NB6YCA+gM/jLY/LD5wIrFK+Y2YRUFr8c+2/FYtcFngfcCFwAjsUqEAIOwYPH17vsI0hdpHSzpia7DytbiATsHF4eSJaFHFrrqdf7QQRa66sJ8NlBERGQfTcCGBr4z3w15E6cBJVhhiAHYsL8XgU8Cy7HhgKdhc69SSUqoY2r6JY4/4k735ap6N+a1jqJArQeNWCDTneqBl7GgKjulYJP/GZYCszOZwEnJVGGZ/9hDWAbxDmzIZtfgCv/nT3dDu6WPU4AlPc4Cb1Eb8Jvs76U72gi2Jh0wDthy5cCvbGwdWrry6tKLjsxXG0VERPZRDTaH6Q/5bsju/OGBdUDXimcF2Pl2GBasDPfvywCbimltn8myxrncdswU75kJ6TGZkq2Hj04ly0LrgG3d1fZILOHu/NelJ/7jqY/fgAVazcDPsIu0SeDnL6094eG/Pv6lgj8/csmc+bOXefNnL7sR+B2W+ZrEf/aD64C7u+szSN+lIYLSU/0oA59w2GBvxxt//QNFzR2jM4H/Z+/Mw6Oqzj/+OffOPtlXEgIk7LuAOqLCuMa1VsW01rG2tba1tnQN1Ko/W1trN0hrK9alarW149KIWnGN64Ao4wKo7FvYQ/ZkMvvMvb8/zkQidUEFEvF8nmeehJk7d8693Mw93/O+7/dNFIdGFev+oDd7xVOesxbVfaUj2p21A8jeYo5VqYQKhUKhGBDU11abSJE1EJmKFFOa210bAAAgAElEQVS99EaBOoGFQDXyFpxGipEcYJEFc3oRu3Nz6AzZQ6EXmy0lzwrDXImsMTtU6KX5Gy4bNzRQBbyaGXMr0qTjCUCz6InOWCJ7PbAHwB/0ZgNHISNzZ2eOrZcE8BufJ6CaEys+NcqmXTHgmC9mHwv8Iw2jyXz5vV+oVcBfsy9vci1tPf7CNxqObwx357wD5AGztphjY4dwyJ9p7l7svVboXGe10A1M83kCW/p7TAqFQqE4+CyZV1mETJsbikwRBAgDzwK3A/cg0wfdSPG1EFgAZC0S3/1NN4WDw2bWg2Gt6G/1tdWHzKzDH/QK4LjuSOEIt6Pz+7qWfgEpsM4GIsAPfJ7A+j7bX4m0xAeZ7piLTIfsFVgpYIHPE/jJoToGxeGNimApBiJhIKqzdymtdxmgN5KV+Ub8Vvj+ovZRozc6s2PxYTGcdz7H6UuVuNp//EFvWSzp/HUsUozT3pmX7ep+AhjX3+NSKBQKxcFlybzKLwLDkHVIOjKC05s00oWMurUh0+lAipAGpCCbVGpuHhIm2x4WBV9D1kDNOYTDLwSuy3G1LQc6kC6ACeBy4GWfJ7BvE+cK4BSkUAQZieudWhjAi8BNB3fIis8TSmApBhxzzAVvzRezTzbhxdAgV2V2UyRbZDRVwqZhSRoI+bXoMLosz4jX9JPK2DOkiUFDtphjX+vXwX/2KO6JFVKQ3YqBwEi/eyNVKBQKxeHNLGSmSBR5j10ETADGAF9ENkUWSAHTjXT4/SMQ7qZgWwel2Z0UpRDa7ZnnDyVtwK+QNVMXAiOQ6YGresWVP+idiRRVDyKdEV8GfggcndmHibTK14Fv+zyBxkM4fsVhjhJYioHKV4FhWXsiOYlsOy1HDyV7Szs5W9oyy2sm+rio1rkr/1JXdzyVNM0VZTTd29+D/gxypVVPsWbb8QwqXIvuCOv+oHeQzxNo6u+BKRQKheKgMhe4CNnnSkf2g+pBCq5tSPGVzDwqkHNGN3C8gX56j8jXSs1tEdPcteZ88y+/WjKPrcC8GXMbD3oPKZ8nYCLrqPAHvTdv3Hn05lWNJ81KG/Zsn+fdzSYjxdQYoBi4Apkm2Ft1EEdG5WK81+RDofjUKBdBxUAlBKSFiSmAWGk2aPKCFYAoTpL9gyaKztkhhInVgXGUjeST88Xskv4d9meHTA77qQ5bjHCskEQyF5sTAfy1v8emUCgUioPLjLmNLch+UCFgM3ujVZuRzXpdyFS6ImR9lkCKrS85CHtcdBshUWSON5fONTThC5Vl/bhjeN6gQzV+f9Br9we9Fp8n0L2t+Yg9NkusMtfdlNNnk9uRaYuDkZGuRUApciphIu3a9yDdHVXfK8UBRUWwFAOVRyLF7ottHeGTrKG4PvK+N4G99VdGm5Xo/YUkNzowgbQG3WPzBxUU7/gicEc/jvuzxPFAXra7k+njHzJ1LSmELG47zx/0Wn2eQLJ/h6dQKBSKg0wbUI6MTPUAzUhr9nOR0R2xhHMtoJszWAjgAFIOIrsnmUv+9jruH2TTmpMwLM6o2xaPDc6yf8DnfCL8Qe8o4GTg3z5PoKfP81bgLmA78POTp97hNE1MIXDBJQD4PIGkP+jdiuzfdT57669iyObK84BNQCITEVMoDhhKYCkGKre4WsKnmCB6LX76mFsgDEEskAtAJ/ms0iZQ7m4lMjH7LyiBtb+0bdszsT0czysaU7EkIgS9K3+6ElcKxeePKrHWCRRuMcfumC9mC0CfYy5I9fe4FAeVUUiTC5BugUcjsxh2AAWAOJIGVnJiGBn5sWR+Vg1m47fK2ViWRrO+gE+3bY5uP2HjQw487/MpHwN/0Hsi8Aek1fpUZKqiBbg583pvOuMuYLM/6NWAgBD8HNi3TUsamR7oYq9P1jM+T+DSTzdKheLDUSmCioFKs0CmB/bS+7vBXrElgBQ6nUYuXbkFtK/Jc9TUNVhr6hrUtf0h1NQ1TFy4+Npzd7aO3d4RKieZtv+LvTnoWmZ1UKFQfL64QmjGnVVi7WDgauCN+WL2lH03mi9mXzxfzP7GIR+d4oCSaTI8hL2GvRpSjPwEWb9kBXQ7ETw8YQL/SmEZEcN1F9LFr0hABwhrFwUkDMcRyLquT8ulSKF3HXAaUhyd7A96hT/o/S4y0vY00rBiCPAoMNXnCbzo8wSiAFVi7fgjSpdemkxYrkLWmP0Umf64Dfj9ARijQvGhqAiWYsDxpd8/NWToMcOqBi3b2ldfvdvp0LQI9JT5rhoooZWzjcdJL7USMWysPWPY3WjaeqTDkGIfauoaBHAFpI8JRYpaivMaX7Rb47ebJuXA+YaJqWsc9CJlhUIxsDj5W/9NphKWiiNODxYYFxe5kOYAV3/zivue6x5Z1D59zqM7kfU6pwKl88Xs5chU421zzAWL+nHoik9GLtJJMIpMo7Mhb7O3IiNEM00Qhgz8uNPoZ67glNBScf7Ii8zfPCPgrDjW+GC2iAksMV1EdAMuWjKv8ocz5jaGPu5gauoa7C57x/emDB96fmnhNgEgxLs9uLYi66WykWmKIBcF85D9r2J99mOpOjV9gy3ROQ2THOBu4Hmk7fxVPk/glY87NoXi46IElmLAURLcNqPw7V2n8d4O60Am5JqS0qo31t/7ux5JkkNSs3bHnck85+5DNNzPIm6gyu3obM12t82wWuJ24Opkynrihp3H4LR0U5zT+DV/0HuPyktXKA5v5ovZg4C2OeaC5CnfXrTMNDiixTf6ekMKpxgwufI/y882dS2MnNymkBGEHuB6pCvbkPli9pXA40jx1QQE55gL2vvjmBT7x4y5jZ1L5lXeADzE3vmgFfguexsLYyIwsJgmelkZG8emdyULQnZXaVFBq2nDyBbAGN7EBFJYLDZS30XWN31cymLJrPPbwxXu0sL3eE64kYVVBbx3XrAQ+J3PE9i1z36OG3xOaHqZbVWHxZZ6CrgXKdD+BCz/BONSKD42SmApBhyDn133SLNn6A8sPfHpXePLRP7yHdibe8jZEzJTICz8r59qryVQ0q53pLLs1xzKjvKfNeprq3tq6hq+57J3uyoKV51TXrR6KhCKJ7PNxl1H4HJ1Cpez+R/ZdM9C9kJRKBSHIfPF7DnIdLA754vZL8Pk54FfIAv/bcDrwJGOtgjIr1gd6SbnQoqvPcBJQBayPuYnyMa1EeCXwIJDeTyKT8QypGvgKGQNVgIZFbKGyTJ2MPbB0bx+fAKLpZWKV9/gtF86tfCiRNLlyjI6ntc089g0vLOVcSWbmDI6JZzG6ebd5Z9wLFtNQ7+iJH/TY0CVEJA2MLp6io0cd1u7RTccyGvPBFqQ6YwL/EFvjc8T6E1z5JRpt43Z3jxh5YjyN94Ugo0+T+CNzEv1n3BcCsXHRtWpKAYcb/7yjNiOsycU7ThjnEi6rBg6CKsG0Oty97+hLflc3BZPP/afK0//3Imrm5467Sd3LT4pedtz3sB+vqWppavqot3tI38cT2bNisXtl5mmkV+Yu4PucBnNneMBTvcHvWoRRqE4DDlP3H9SCnEZUkidgWwUexdSMNmQX7NTAE2TDycyFctEzh0KgW90D8vTOkcWmWbG+AAZAXEAX58vZv9wvpg94lAfm2L/mTG3sRsZedyKjDy+DoSBFa9wTvIdMfPCKNkb7SQ2V7DxJptIhGMlhc8/nvODr9+sL7j9n+KX1jc59ehlfHFkmHwcZlhrobz7k4ylvrbaNNGmrthwri0azzIBM5F0pq2WuF0IhiFt1V9AWssngSOBlwD8QW9h735y3c2nT6x6YZrT3jMNmP7Jz45C8clRAmsAsWRe5ZQl8yofWjKv8pL+Hkt/Ul9bbQ5ZtMpfsHKXkb2xxSxesZvsHV0A6O+fsGYiV+GifA6vaX/Qa3VnJ+fbbWmL283Me5d5z/qw7WvqGgqBe4VIf6mybLnbMLEKkRQWLSmSaTc5zhbKC1eBTA+xHYpjUCgUh4b5YnbJ78UPf9hG/q9XMK0N+B5yQu0CfEhzCzPzsLDXT0gDGpETWyOzvd42tcLe5B0uDIvW+32hIQXWVKAOuPIQHp7ik3EdcBTy/+w6Exb9W1zzWph84yjzCVKYk0Pk/Rv4+ZnmHRc99LPqbz7yi1OedJudo3LMjqxyNjmr+ad+FE+mK1llvsFpJ147/7rlN877zrWfYCwrrZbYc3ZrJGWaYLdGrQ5bD5owNgE3+zyBU4ENyHTBFPAU8AqwxR/0+jL7+D3STfgKZFRVoTjkqNXpAcKSeZU6sBS5SjhrybzKfGR6xZBUyuKKxFxbc7K6ZwIrZsxt3NOfYz0UDH5pU02kyC1aJw0SAoiUuHE0h9+jnvo4CbYBO4HVwD/7Ybj9Snek6Px0MpK2ZUc0BKTTlsf8Qe8rgLdv2kQfsoEc09Q3rm48YdORox8906IbIp1IUpCznRxXMy5nyABO8nkCkUN7NAqF4kAyX8y2AmXA9jnmAhP4uQXjW6fx1PYI7peBjcCFwGLkV+oIZJpYb9NZF/I7IwmsQVpmgxRTRsWz6420RTf1lNE7nzD7PHTgC/PF7Lw55oLOQ3C4ik/AjLmNSSC5ZF6lBpwXxzUsjXVyFLejjE2JrUyIBsRXXrzMvCpFnxqmk7nvjWzaI3sYEupkUHwyz3UYdIybTGTUKnOm+y28vpq6hnvqa6s/ThPfd0ZVvPIAwhzVE80d3th0pGtY6Vubctytp/k8gZbMNn9Divi/IEV/GzItFX/Qm4N0DAz5PIEtn/rkKBSfECWwBg6LkeKqlz8DP0iYWl5HPLfQ4UrRnXKIZkvZW8yrPHLG3MbDtjfJfDE7N2UVlS1Ty0Xpy5sJDckDXcOwRtGS7+oFMyOu3kGukK6bYy7Y3G+D7kd0LRlcv/ukRy1vNp81aPROZ3HZbg1ZoH4j0sb2PdTXVjfW1DV8G7g9HCvIeXPDecmRRUs6rMme7BFlr4nmzhE6pvVenyfw6qE+FoVCccDxIU0LFs4Xs/+MXIzCSbLSSedYZHrg2ew1au3NwNaRNS4gI1Ya0sAiO/O7AYSsPYlcq4wkmEAc+bsbaEXWZg1CLh6OP7iHqTgAHAMc4yBy++nmP6520Z1tJWWr5O21l855dC1ctLbvxiEKX09i+1VQfHFWlyhxDN+8ckWOo21clqvFPcX1nDNkLRjRTenRSGv0/eW8V1d/5dbCnMa3xg8LJOIpt7F8w1nz20LDTl+4uOG+WTOv15G1wcOA/wNu83kCZ/mDXhdwHzJtcDEyo0Wh6Dc+d+lUA5El8yqd9MkTziz9aSaM7BG5RdnuiHBpPSJkKWOXmDT5TU75lG38BjZtw/Ou6RhR4CpcuZNUvpvdJwwn5bISGpJHUscUEBIwSMgb/RFzzAVPfl7FFcC3vY81/ulrV36p8dUxRzitkWd1zUwiJ0rD9t22pq7h7Jq6hlPqa6u3ArWl+RsdVj0uwkbJr4qGtu3Z1nKEY8PO6dYtu48YX1PXYD/kB6NQKA4YmWbBxwCDkWlgjyMnp9bMowdYBHwdaAeeM8FqSCMLPXMv0k0pttLIKEGv/bbG3kXB3tcdSFElMs8FM68PmS9mZx28I1UcIF4BrgLuL2Zr2EUHgGbFOD4T3XoPz2tfPf8J7Yozu0RJizDTD+26d0TJlscmJpe1nmYN7DpTb2aI4OOnme8Gtmc529cW523ddtTox65uCw0dD+bFFj2eh7zvjwECQDOg+4PefGRUazfwnM8TuMXnCdz9SU+CQnEgUBGsgUFvQTEmkECjgxJKaSKLbtLY6Cafp/k6wmKi6cbHWQ36TFEz7+nThhVlzcnb0CaSbhvWnjjZW7sgmTZt3bFFtjTXABvnmAvU6tQ+nHfVvd3IFeS7kH0/VgBkVvZ+b5jidfi/WUC3P+h1nnus3h188bTfjPEsndL6dl7x2j1HDmmMThWplN3cmpg8GZiGvOEqFIrPGL9z/yR7w7emn14e2HSea1e3w9YTdyKbtqawpXVAkBRpTO0C5KSVJOJpDRMNhAkpQ5AWJpqQ9ycbMn0wiTRDKOC9DWo3I5u+2tlbvzUd+Z0kgFKkoFMMUGbMbYwsmVfZCVyQwlpsIallUvGtyGa9f+ndtqauoRg4H5miN+ccc8F9WT/uHPXmuurfrKmcfqHb0hJKa7YEBqtr6hpEfW31frX8qK+tfrWmruGK0rzNJyCvp6dz3U2Ljp/ov8lhi3zP5wlc7w96LwO6gZjPEzD9QW81cA7wgs8TeGJ/j7dKrM0DbgIatphjP3flBYqDixJYA4N4F1lRNz1OHbBh0EMhJTSxlinGKk7RnPQQEgXmRF5Y9ssf/HlHfw/4YOFc3/xgT0WBKH5zJ3oswebTRjOsYf3fnZH0L+eYC1Rvqw/B5wk0f/+uWy5p6azMevCnZ757jWzdM9lVmLN1smFaWoD5mkhd29Y9+LZot9NwGa1m0/KiP6x9bdLPYx0Oy1E1LxPRCsXO8NTlwLr+OxqFQvFJmS9mF6WK3A8XvL3rGJE29KZjK7uGNqxLAXoaIkHX0c4x0Y2WAjpselkiP91k3Y0prBbM3voqYvlOo/GCI6wVT63Z497R1SxgO+BFiqlcZCSrExieeUsW0jo7L/Nvd+ZnCzJd6xM5yykODZkG9EU/kkLl1BWcpLdSbp5AvXBLXTx4n7dYkVHKp+trqztumue3pLJESeNRE34mhBaMULoLg7eQwnxhTV3DNfW11av3ZyyaSF2VSDmPMk22CoH9lGl3NCNbAsQAfJ7Anpq6hjzgZwsXN/x31kyeR6bCvrm/x+sPesV5V80c9Mjvvsr7+xIrFJ8OJbAGAOsZ+7VdqVHONkslZ3I7duIU0GgaaGJ4zipy9Pb06o6TXjeFs+Xt9Fn+/h7vweKLv26YbRtZnBs1QY8nSEfj4c6Zo2/rnDl6zv6ufh3OXHTjYzlHjHzyt6X571wciuRHsxyRFoc9PsvnCWwC8Ae9M8YOqbgpHM3vqKlr+HJ9bXUrwBvrzx0HRiWIcwqyNp/V3lPheX3deYYm4ub4EQ3N2QVd08om7y43k2nyi1oxDEH7W8NHJsJZxwOP9ecxKxSKT0Qk3GqPrNZHakMqmyhbuS0bGW2KA4lRnVtybKSwHhUl+5t7rD33FocSS3NMAeOQBhfr005rpb0jalrDybAGE5C1VO2ZfXQCJcAzSPF0OtL8wpJ5v45M34oCtwJ1c8wF72e4oxg4HANcf7+4+i+z4vPHaSJ1dIetQms1y3GzPgFMWjKv8kvAwzPmNqbqa6t31dQ11CBTQ4njPnujbdIrsVTWZEFqSLG5/QQBU/aIqqWZbfb7Hj55xNMPF+VsGyMEDUhhPwlZX7XDH/RW+DyBHcj6wOMsenwb0OLzBJ7+sH36g95cYCKwzOcJpIAZR5+3+FdHn7f4Vz5P4KWPe7IUio9CCax+5sF5J2nWJnHr7qzRtDqHsVGflprMK2IHk1vCZBfZutOW4fbXOZEHH1jPsdOQBcSHHTV1T44Z8sbWm+L5TjrHD6L5yIooOe5j6murV/X32AYKhqld1NZV/v2KoncoyO7I6+opLLPb4o/4g94pPk8gDWS5nR1hl6PzhZ5YYVfv+6aOXLRx+cYz60GcmeduGtLeM1QLx3IMpz2c3pmcersn/5G7WloGfcPl6EQI0HWTcUMDyVfXXLS0Hw9XoVB8TOaL2YVIc5tj23OL8jo68vWx6XfS7o6QBiIJaDoURnCzlrHmcdtfStuCWXpqkyMHeBJZt2kD8rN2dSezHlppRfa2kimF0hL7S8AWZKTqrMxrzsxPE3mP0oB/I82a2pS4+kywC1jXzLDC+574VrfNFg8dc9oj+Ym4xvqOMfZRg9fNFIK8Vzi75ca6hjOAG+trq5t63/yqdm5i/OBnnSW2zR153W2zm5rG3Op2tW04/qh7i32eQM3+DKCmrqEE6J41880Hke6VVcj01blADrIGUPMHvV+ZNZNtCxdf+7UvTJ//BeAuf9A7O7ObxszPlM8TMDKugpOR4uqrwI+Rvb62AUuQ/b/6jqEU6K6vrVZlCIpPhTDNz31goF9ZMq+ycE9oUOsLmo92RwVFlqYpXzb/WBnDtXyR+M7aNDbnmeZt5NC1YMbcxh/093gPBjV1DXaS8Z7yFxst9rYw6884Yos1T38cuK6+trqtv8c3UPjKjYuGHTnygc2DirZqAkgkLabdluoQgkE+TyAJ4A96j0P2/rgeabFcAtzS1j3o7XWNx89c+1jVyMEnhezJhL59+tT/tmU5251vba6+YIgl+FbRkCaLbjXZ3jKRdNraPbx8+V+BBp8nsL/Ni9+DP+gVhinujsZzTnXZu+cIYT7bx2ZXoVAcQOaL2UOAfwAzE3ab5e3vzNBsWyOMWbfMsG1OpUnqMaR4skaxEcadKqDDCqQ06SyYi1x07TWvEMioVzLzvmXAJmQkazRQmnRabJikrLFUEmlwYSCjWg8AD80xF4QP4SlQfEpq6hp+VGpuueTs6N/cazdN6Foz5pSc8JL0mLa1+XzptHvqxg5/e/xGpuQt0r4/Cbi6vrb65r7v//Ztd59cnNeY/9sLr3toybxKse2EofcihdIsnyfwoZPN7/79jvJoIvvJ8qK1OZOqnnsI2VT4u8i6r2XIa7AMmYp6FLIe8NLMz7OQTpW/Bu5EGrkEkdfrRUj3y93AKmA20Px+48nUld0NLJk18/qbgZ8Bz3zSe6Di842KYPUzM+Y2tl0z75olLVQemxb2Z3eJUW/NmNO4csm8Su0os+EuK7HLc+gSZLqVH6a8g8Vm2XXKKKypzp2PXnXG8I9+y+eP+3/8ha13L/1jVBOytsFhT+0GTu0VVxlKY1HLxTZ76qs9UZuxq23KhqEla1yFOU2jj534kG1UQZGBw5GyOaJ54XheYXt3mS21OfKo9aikiCazyLKFME1BKFrUhTS5aEK6NX0kc/2/K9iye9pk0IL1tdWRtMG65o4ho+y2KKbp9jstPTHe24pAoVAcOLqQ4khPxIXoeM1Oha01IbaBmdTDaYvm0FJGTIN2J4kSJ4keIB85DxjK3kIUI/PoYm+djQCOQKZl9dbiJJqmV4qilTutllhKEzJ10AT+NMdc8NyhOmjFAeWBIeaacLaj8/tHT3hZ62TE9YwOX1GVZx5dOXT9ScCyErbuARYC9+775r9f/o3na+oatJq6hqlof183i+uvBPSPElcAR49dOHHrnskjC7K3u5q3FdfqwuwoHNK6B/iWzxO4J7PZW/6g94/I1gIbAc3nCbyVeT4fea96CxmpugA4OvM+gYy4jkUacyz2B73n+jyBfesCu8F8rrxwbTPyWp8KNPuD3uU+TyCEQvExUAJrALBOO9GLXD3s6q01mjG30ZgBs5fMq1yAvGmt788xHixq6hruBEYiBAix7L6rLpz+kW/6HGOzEGVv8fgO/teIYqLFkhJCQJYzoVWVvj4GYaSASDzlaNOy9bJo3JFyWTr1AtuupBBme2Sks2xLxzGa2WZh0vBnKM1bT4F1W0Xr9qJ7ioa03r4/4/IHvcNK80cva++uyE0b+kX+oPdR0xSDdS2B0xZFCAPNguNfr3hvuOTYwDUH8JQoFJ9rMlbsZyJ733mApJu445hXX8BNzJpxBLRqKaN3kttroZ6f+Xe6z/NxZCRKR05IBbLeyoZ0BywHhAGtAsyiFTsa7R2xY4W8R2mZfU0BlMD6DFJfW920ZN63NSAM/Pk0/rnwb4NvHJxT3nr0WtonTuHFu3LoWP5j49vLZ8xtjPe+r6auQQOy6muru5F9qOqAW3yewH0f9ZkZl9vvOe1UjR26VBhpiAoXnbuL8pHzokp/0DsX+BNSIK1EXutDgC/5g97nAWIJpy+RdH8jmbKXFubuNJCLeb3XZV8XCx0pnDzAs5kxDAGGzJrJ6UAlcnExF5ma+GVkiuG3Pv4ZVXyeUQJrAJARVe/b5X7G3Ma17/f84UBNXcMw4JLMP9PAcf04nM8K5wK3IdN2GpA3s9f6vP5od6fjOqczqTncaWw2A8BimuRoIpljs0WM3KxWYRpGg9Vq2lJpa8Hu2OQhupYQFcWr2dU6EtEZZfvK4WLw2C3/Z3NEdiP7i3wU0aLcbenxQ5+PlxRs+Q7g6ghVnJWfteOpaMLmcNpl6WAqzVx/0DsfOXnTfZ7AxgN3ahSKzyV5wNXI3kAOMk3Ys6XhWhqwZGaXval/KWSESiAnoHuQFuoa0pTCJNMHK/N7hL3OgVFDE8mWo4ZEcze2Cmd7JI5sKJxCRjR8me0UnzFq6hqygCtKxdVLLjJ/+z3g7RlzG80b6xqO6ETvaGZo0zN8/UQD/dpOih69sa7h+vra6l2Zt18CfKmmruF7wFrgDmR90/5QAFQDlYaBQ2hgsSewuyO9r9uBXwGrgXnIeWs3ctH5BuC/wBsWPXHe7tYxMas1Mg1p1vIsUizl9Pms3ms+G7jDH/Se4fME1gKXI1sKvINsJRBG1mzdj/wbumI/j0WheBclsBT9yR+QX157gB/V11arQugPIVOsOxq5Wt2FrHPIyrgjNfk8gXeAHQXFsYY97VWTIulQYWFuq723zFLX0jhtISw67eg8Gku4/2K3hrWRg5cKmyVOfvZuTNieKHCUVozcarNYkwjBzf6gdyPwrM8T+MD/H58n0OwPeseWFW0qAq4FIsV52wPA5RY9fmMqSU7KFCJtOP4LUT8yCtfhD3rP/7D9foJzlAXk+zyB7QdqnwrFQGaOuaBjvpj9G2SD2OOQ9/VeswkyPw0B1rZxpaSdFnfRmzvNTG8rhBRXOlJc6ciamd76qzSwLWnT45ZE+lhDozNcnvvwnmOryhx7ut2O9sho9poPrEP23rvzUB274tOTWeiMIQ1OrtgjqrJnzGn8RZ9NHkkJxzOrmem3m+HfW4m3hsmZjLwX9QqsLcAaIFRfWx0C9runlM8T2OEPeq8DbhJCXpM2R4qioa2ZFlzv7v86ZCprbyT2DGQK+8PAWxY9/fr6nTM2Vh9582ikwC1160oAACAASURBVLMh67V695OgT89RoMJIcuHfzrvoaJFT+lLu7D2/RxpfNAKv9bEn0IXgXdMohWJ/UQJL0Z+8hrRf/VZ9bbVqaPvRjAMuQ66+ZSMLeluABcAG4IdAKBQpfCE3q/mvDlt4M3C/YZAtNAbrOrbMfm5p66648K3Np+hVg14Tgwoaae6oMgtzd69MpvRxDkfMZph770LpNE/rOkuRaRkfSCZHPQR8E8Af9A4GfiI0osmY88c3nPYX/w3LvlMAzEEWG+84kOLq5mdOCVmtTrcmYsIf9A72eQK7PvpdCsVhwanINCaN9xpT9KIBiaydXdZEnkMIMBJOPazH07rFIIaMOjmR4srs++iuKijaUT12RPkLG6JaPDlk56ljLp10U2CBMEwz85krgCuRiyYRDlOn28ORmroGG7LRbhPSTGIb8Hbfbeprqx/qs/2Vcdz/RgqXpX22CbCftbofwKvAVYYh6oQwRwuBSBvYtMxNSAgqAGcyabG0dlb0DCpqHJkRYx3IqOk9wNzTj16wAhnp+i3SzKI3YiXYG4ntrSnUSXNVarctbaxxdjN7z2qkwYUTeV1jmpCMW0y7M6UEluJjowSWoj/5E3KlSTWg/AD8QW8eMg3iKeAJpIg6GahBuhtt8ge9VwHtNXUNLrj2e8iVPVGUu+XbFcXvDB9etmI94AeuTqVECsHP3I52a3Feo8h1tRCNO01gPvBPXUsvME2mC0js2VKcXTq8BU0D4Fh/0Cv2p1i5D03Av4DJrtzos1vMsSkINCOdmT7NOSlE2ve+sXDxtfbywjVFnjH1W3e0n6yFokVMrnwYSHZ8ms9QKD5j/BuZxtQrsDYB4zOvpZGTSqu9O9Zp744BOGzRdG8tpxUZEc9lb+RLZB4WW2fU7WzqTlt74ruEYZSY0GjAJF2mUd2G/G7pznyGHfV9/lkiiRQV7fW11atq6houQIqWD8KJFC+b62urPzQFsKauwQpcA6yur61+8IO28we9GjIVr0XXzSOBBzt7Sqs27Txy3JghS2wOe3fCokvRnkw7zKaOsZZBRY0gr+sK4GxkdOoYYBQy1e9Y5HVtAPHmzmGiO1yUNaz0rbDVknSSccvUHJBbu+tvZo92P9K+/TWkuIoLgS0VFz2aMNcAagFY8bFRAkvRb2Rqz9TK0PvgD3rLgVnAV5A3jTjSMn25P+i9Almz9w6AzxNYAbBwccMYpF1tB7AuFs/6WmHODidQhMxhP1bTzBOFQNe1iDlh2EuYJmga675/ysO9oudEf9C7EDAGjWj5ajxKl9WOTQhM5ARqv1enM725/vTpz8b/8E1kmuS3wPhq2tB/lTJ1CnO34rCF2LqrdOsNF/tVDxPF54mj2RuxEkjrathb5E/mZxIZaXJkggNpZCPXjYA3s/12YIgpF/Btto5ocdV/39GAx9Z805PO3dR6hjBND1JIVSLd1iYim5J/DRnFfuGgHanigJG5By/s8++PaosSAf6KTOv/KOxIsfKeLIWauoYhZjR1yXET72sfVNb4phC8g6wlbnv81Z+UTx//gJnlaE10R0pSr62bJY4c/dCTOe6QF+iwWmJtE6pebBWCo5DXqhNZw5VA3i8fQaa8diIXJasAaziWP3FPx2hbaf6WVVZLe+/fRhHQZa2M/xJpelGEjOK9BSwCTrA6zGch/cP3cRtUKD4SJbAUioHJA9EoM+x2EAJMk69rGo/7g95W5A1xt88TiAH4g96zgChc+yKyb8gOYEpPrKiluWP4I7nu1meRIs1pmmhCzqxEOgW6hYRhiLdn/bHhZE2nBHjg7GNc13SFS8tL8recZXcyCCmqLD5PYKCk/jyMXKHfNnbo4mxdJNGFSWneJqPHuslzxazAG/09QIXiUDFfzHYi3dV2Ii3Ue50Ae9P8ets4WJBR5cl93p5EurGVIOtwbJl9hBIu64ZErnOasyVk1VKmAZw89q7gTmC8kEYATyMnti8gowXPIBd9ggfxcBUHiSXzKq3AScDKGXMb31dAZQTZU/uzv/ra6p6auoZLkNcI/qBXB46xGT+uCoWyrunuzLfZsxI7Xlx52ahZM6//JtART2ad+dLKy4qsluhlMyf962wTcWqOO/R/wF3AU1ZLajWkTGSWxqvIpto25LXbhqz9mg68CPwOKZqOG1a6YkdF0ZoZVkv8H8i/iWhmn1mA3ecJvOwPei/weQKRzFj/APwHuF/Zsys+KUpgKRQDE6NXXAF09hRYVjWe2TNz8r9/DcR9nsCX4d30isuRFv8v1NQ1bELWar0B4jtvbzl98y9nzU/6g957gWS024Y7LwFAKq2RiFgj3S2FXqMpNkob7NgBPPL4stpp5YVrflyQs8Nq0ZNRnyfw5KE//A8m4zy4EcCPtxV5c20HKq449WOlMCoUhwMlwHlIoaSxN2LVW3vStxZrXOZn2gQzXJrVmcpyFOZtag0Zmgil7Xq5NZrSAdMaST6fzHW0aAbTkX9jTiE/y8js+yxkBOHWOeaCH2f2q1xBP7uMB36BTBm840DssL62OgzgD3rPAb4PeKqn3/ynf10394Fd1qGe9V0nWYGjfJ7ASwALFzdsB1YkU85VeVl7xje1D48seuWnZwwpeeeUI0Y80wP8EpgJXIysP74UabJSBFStbDhyfvno7WPzy1ptFpsRQEa3ujTBDZolPgbY4PMEXvIHvd8ncx3vbB2TW1PX0FNf+664siBT8dt8nsDfD8R5UHw+UQJLoRiYnG6kCGsWNMPE7AgN3drSNXw7MtWvt28NCxdfa5826tFf5LpbQn98/HKtJG/qmc2dI34G/LG+tnpRn/39EbAbafGUaVIgBMTTubidnfaCwa324RuXLm53Tr7y7u9dFJvr/13L4KLVJFO2v1v05EBvcP0ycDPw249ZH6ZQHBbMMRdsnS9mLwXKkJGrXoFlINP43Owt7O+955tAJFaSrVkiCWFCye4Zw92dEwelR/rf7LF3Rgs1+F7W7lBvP6FUZt/NyLqal5ENW7+AFF+Kzz6rkA6wb3/UhjV1DQLZFmB7r4j6IP75yiklnT0V97ns7S6nPSLs9uR3v/W737Z1h4uzl2/MiVWVPf0Vf/D6YcBzs2YyEVljvBWYFY3npEGcOax0RRh4EFiOXEzwICOlKeTCgh3A7ogPabj1vPD5V9+z3mKLT0QKrGzgRDLXfKbv1heAh1q7Kq5euvzCv7psnY3+oPdBYJnPE0j5g947kFFaheITowSWQjEA8XkCsZq6p785qnzpnAlVL5aNGLziz9ee/9ddIPuO+INe7bW15zlg0j1vbjh3MvD68LLXRnjGPpR+c8M5Dbvaxi3fZ39v+YNeR05R/Oq0wS+6I3ll2c7OtCbM1zU9eYduJr8zSnv2/+Cib04d+eQu5Orght6UiYGKzxNYwv73W1EoDld+jqy7ms7eyJVA9qjK5r2NVk0gJiCSu6GlwBJLWQHyVu12pZ2WJks4oSFTCXciG66mkJNNPemy2t+89vRjTKu+c/qcR68ErptjLhjQ3xGK/WPG3MYU+187Nxq5sFUP3PJhG3aFS6at336sc/KIBgERDINyIXDluFsix0/0R3QtfQ6y1vgnSEF0JHACcMqQkrdfcDm678jLav4BUnS9hUw5HI6s+Ws2TMxINHejy9mVHn3cqjsqp25MOrLiobbuis4cV8sIqyV+PHIxYAsQ83kCEX/Qey3QFXjr0t0b7yyqOO6Lr08yTaYJweXAWp8n8NjHO3sKxf+iffQmCoWif9BW72ib9EQ0kXNFc2flQ/985eQT72g4Y9mPrrrm2dfXnN2R425+FdKrkCuOm0KRog7DtLyR3bzxjjfmDCmrEmv3XUAZDlyka/zG3RL5lpmwrF+9dWZuT0/+d3avHzL8xX+cVQyQ6af1pYeXXL2rpq5h9L6j8ge9M/1Bb60/6LUf9FOgUCj2h2HICFYi828TiJgw3JT3+d7obpq9Nu7F1ljKBmAIzPbJ5bH8d3a/oSfTm5AmASPYa2yTD9hMXQsDqXG3vjwLqAWOmS9m64fmEBUDiO3AAxMqn2/xB70/8ge9jg/acE/HiKfLCtf/qzucnYrGnQiBME1yjTSbLXp6eNrQktG4K39787iRSLv1ryHbjqyw6OlQaf7mhchrLYC8JouQ5hVfATa3dg7btGn3kWWdobKIppu3OLJizlCk4FerG0/4emPTETPYO8+NAXX+oHe4zxN43ecJbKivrU71bHH8fd1LE+4Rgjog5A96r/IHvSNQKD4lwjRVVo1CMVDxB72FpknNiyu+Mau0YP2kyvzlg8JdWWJd68lmfl7z1glVL4za13yiSqydBfwU+NkWc+zSPvsSyAL3qYkN1j8mu6252wrHba8sW7ehcdnwhnvnzr5vizl2N0BNXYMGPAp019dWX7zPmH6GTLm41OcJ7I+blEKhOEjMF7MtyP54P0Sa2ehIJwAd0DM2gmav40UKfY2NtIF0+5sK2ONuazI8rEB3be+IOEKJ9cimxX9FLspoyGyXBDJqcSVyIjwcmTZ44RxzwbpDd8SKA01NXYOOdJFcXV9b/T/f6TV1DXlAT31t9XvuNXctOX2hYejTd7WNP/sX589fvu/7+nLrC+cFXY7OozVhYALdze5U/qCw6Owpmbdtz6RZDluPNnrIsjTwEnDk7rZR2et3HNfV1l3RUl647qbp4+vHABciFwceRfa/Mrt6il+2WOKn2q2Rl7527PPf9we9ZcmU7dptzZO+W5izXeRlNYO8dm9Fpsz2IOu2rkUKtanAoz5PIOkPeo9BmmP8yecJLEKh+BSoFEGFYmBzHjB/xuS7XRZNrsTZnVGOLfvPg2msl3+As98ryP407/R9MlOjtNIf9BqUG27XyIhtLG+MEIIvXD9nwdrr5+zdtr622qipa7iBvSviffkr8E8lrhSKAUEBcAFyZV9H6iiLAC2zfNqbMpgpwkqPQoqmNNLQINcaTZ6VvaU9bokmG5Ei7crM/uLItKyjkJPTScDtwN+Rgm4Q0tFT8dlmNLK+90Fk4/p3qalrGIQ0vXgSKbABuWAXiQ+qiCXclrc2ny4y2wrP2IdOGFy0ZrQQ5j0+TyDeu32Ou/1y0+TXjbvHn6hbzKzS/E0a0JSX1fxMa1fo8aqyN67b3jxuSmtX5QUTq57rCMdyK2IJd4fdFsoaOXjZrw1De0gI4zkhuAkpkpYCZblZLe8AdyLrA/F5Arv9QW9p1aA3SKZtmCYIgRXZyuA2ZO3YIOSfw7nIeqzlyOs4iDTO2HGgT7Di84cSWArFwGarEFis+t50Xt1KCIyvX+JpiL/fGzJRqH990A59nsDbdy4+bYOV9BEARoorkTeV91BfW/3qB7w/hqzRUCgU/cwcc0HzfDH7O8iGqz8ERpp90v8FiF6FZYKhgWaAJkAX8u/e0AysWjTpBEYiLddHA10mDDUFRwuTmIA8pAFBEHAhG4bb5pgLBkr7BsUnZwNwPfssymUIAW8Ca/o+uXDxtXZNS7ZpIr0GKVAAZqxqPOl2XUt2lhVueBKZSkhNXYMFrq0CaqeMfCLbNOOPrNvhbSkt2DyjNH/zLSMHB/OBc1ZuOusHLkfndw1Tzxle9mY8EssztzVP7rFaYtFX19T8KJF0PX7ilLtbfZ6A4Q96NyJF4Zs+T+AqAH/QeyKyt+Zj7aHy8zSBiGqpUF5W80pgNtLlMor8cyhB3idfBDbDu4uQWw/IGVV87lECS6EYwPg8gWf9QW9fm+UEkOfzBIwPes/+cNnMZ6b84yVvWOjYbLYPL1JWKBQDmznmgkbg5vlitgv4g5ERUumMuupVW5l/irjblrIkkro1aaaQKVcJ5HwgCxnxuh8w0jpXpK0WizWWSgt41oSZAh6fYy54vB8OU3GQyKT+NXzAa2Gkffu+JAzDusjAuj3THwtgZyyR/VhHqPzR2jPv3N5n28HpmPhZdI91xZ1zLr9i2rztdwvBqMqyFRXIyFiLzxOILlzcMP+kcXestFujDwJ6WeH69duaj6gHc3m2s/WegtLt5wAvZ6zU/4lM+5MtO4Le8cjG9iuAB5z2nmgqpZlZzq7NwDxgKLJ/ZNgf9I5BZmL8y+cJ3PtR58cf9B4HHAcs6O0/2Z/4g94SpI18+iM3VvQbSmApFAOYjKVsLwbgOFB25JeeEHAfiP0oFIoBQymQzggqTZiYpjQVQEjh1AqkrbFEYcJpiwmRjuuJtC4gDOQifz6PTBMcq6dJ6+mUS4DFgOxIWU7n7pNHKnMbBfW11QZ9UgYzz20GauEs/EHvxdF4VtGTwR8PBrF+51O5rZ1vOUcBdct/XmFOuHL313NcrbcAxcCsTN0vbkfXM8B3ga/nuptvTKRc4XTalpo0/PmtwAzgB0iX2x3A8ex1kT0VOadd5vMEnvYHveOQUbcjkJGqBPAff9B7P9KSfgn7YUmfYToyevtvYPfHP1ufDn/Qq3WHC79qt4aH222x4Uhr+jsy41EMUJSLoEIxsEkib2I3ABbV60mhUHwIMWT6nybAFICwaBhWDUOmRRULKLGksQqbxbnha0fnd44rjaZheeaLZRcwx4RKE0YKyM34u5sCjnS09hTlrW2u6p9DU3zGOF4T6fNz3HumAd8ffHqXNdmjX1l0bKh0xDdaT7C4DBfS4v3PPk8giUxRvG3h4muFzxO4Dzh70atzO4E/2KzRvwKFSHHzLHAFcjHhC8j6QIAOpPC4+JYXLjhh4eL/O6+1a/BK5MKkHWnI4kHeSz3IWuW3egfrD3pt/qD3gxYd/wZc5vME+kNcXW8YvByKFtyaTNt/gWyyfCRwkXLyHdioCJZCMYDJ3Hh+2N/jUCgUnwmWI80onIBNA2JOq7l7xnAx9Ik1fftjpaxdEW3QSxux9iToqcyvcrSE07ZwojI8OPcLu7zDSysfeRtLNNWjyZqUfAHbLEljdfEbO/7Wf4enGGj4g95y4LfAPT5PoG8frV/ZbdF7T5py16ZHX776V7rTFFvS416vqWt4BtmbzenzBF7rs30n7130z/NOvrst8NY3/hCJ5/ZkOTt60mm99dGlV3fW11a3ZbI7diOFF8gUvjSwRxPpR132zt1t3UPdRbk7BVJgWTM/85E9t44AbvIHvV9H1phtBSb4g95bgZeuOeb2EYBzizl2aSYt8JCLqwzjUmnb1BxXU8pmiYE8R9nAacAs4L5+GpfiI1ACS6FQKBSKw4NnMo/RSFOKYY7uuKh6Yg2863OBaYJFS2O6d3aZlkjSBKqEnA8Iw265omt0iRkpyUrlbu10IyMFIeB1ZJ1OVz8cl6Kf8Ae9RwERnyewOlP7kw1MBBYj+1K1IkXLe6I/Dy++ZmLloDc2hqLFbsBZX1v9TOalfwL/qa+tfk+D6vra6nm9v9fUNZx37PhRvy7N37Rz1szrZ/k8gag/6L1oZ9vYv1n0WKymrmH2rJmsQPbMavIHvROR1/zVwFiLHu8cUb5sTWXp8pMzLoLJls6K7UKYo4pyd1qQaYk2ZDsCV+Y4ehcgzgTS47xvvrgmMC1WJdaet8Uce8hrnfxBrxU4PRzL8qzYeLZtUP4mW2XJ62nDQNOko7AOXIMSWAMWJbAUCoVCoTgMmGMuCGUcBXOB84E/93m5txWWBhAucXXqCSPHGknmC/ncnpRdt+45Zlhp2YsbWpu9I/Njm1qNQUsbAZ5DzheuBh4B/njojkrRH/iD3mKk2LgNMP1B74VIJ74WoAKYhnSuvAy4YN+WISbaCVuajh6BjCpV9HnpTOALNXUNv0S2IWmur61+dJ+PL9raPLkzx938sNvRFfMHvTOALzptoTdARIHmTLr89sxYJyDrpHqAI7sjxSVFudvLQrFcbHo8leXqMTft8gxNGzYKc+5PCEECKQjtmVawQghEn8+3nPWT++xrAtN+0h/iKsONwBSLHi/PcbUIt6OdruZ8y+Y3x3LUOa+YyPIBVeYzgFECS6E4TMk0Fh4K7FBuQwrF54M55oIuoGu+mN2CTH0ajoxmvacfVnZzJJH5tQPoMQRbQ6XZJWbaGOXojA2Pl6Q0kTZ7U6NeTdstl4YH5y7K2dx2dz8cluIQ4g96zwHqkVGeXn6LFCWbkX3SLjBNYRPCHAJ8wx/0zvN5Arv6bP8HpCtlCGmNTk1dw0RkDVQc2ez3LKRZxb4C686JlS8873Z0zQWmIKOoWcV52665/8df2OwPesv9weu/AryUqYtqRkZWVwPFOa5Wdzptyda1qGG1xTWAScOfTaRSNgOZPmtnb0S3r7AiI7gQmjH2hmXfaYLAJzmFB4KtQJVVT2oTq57HMGDr2yNMZ3ZEIEXrA8DL/TU4xUejBJZCcfhyJHKl+Ubgv/08FoVCcYiYL2bnA3XI1K1rkZNdDcAAM1zqToUr8mwlb+yMa7ANKE/bLLZYee44026x5GxqXVu0clenAY9uvmByVe7qPefuPG1MKFKRdxJC/LP/jkxxiLiajLgy99oqjRECEymKfrp22/H5jXumnlJesG7a5BENY5HX2rsCK2PvHt5nv51IxXJvfW31ppq6hm8DiZuf//LIWDx7etqw+H929m1GfW216Q9e7wSGIdP5FgJPAFZ/0DsZ+IZh8IN0Ut/lD3rPAk7MfH4e8Fu7redBw0DTNXQh5VO3y9bdGEtaxpommhDvHZQJHe07820WS8qZXRwyTQPdYk07kH83l33qs/kxyCyMTkCad5yiaWAYkDZ0KiY2YrOkTWAn0hVx14ftS9G/KIGlUBy+bEM6Lq35qA0PNv6g1xWJ5TbEk66peVlNl198zEsf2AhZoVB8ajqRTVjLkX2CEoDdBAwdYetOWJ0rduZn5pnTgA2WeGqUHk0allgKLW0OBYyusaU7W44cckm00J0XLc5KIMQTyJobxWFKZoI/6H1esiId+f4DpFZvPXk1sGrjLs8Lk0c03OjzBLo/at+zZl6/E5liOAOqX6uvrW4F+NNT/rscttApO1on7AEaMiLqm0iDp02ZdMCoP+j9JlADvNaxu8CMdLmzB4/dfqOmMQgZlboUqDEMXcSTNtNpiwohMCNdDmF3xyfanCkwMZCLDSYyNTAl4L542HlxOGExckpCMROijqx4HdLO/VAzBbgL+X/QCeRrGlab9m4SigDKkAYXaWBRP4xRsR8ogaVQHIbU1DUMh2u762urf3uoP9sf9I5GNkFs630uEtW7E2mb3tQ+HLst/AtkXxKFQnEQmGMuMJFNWJkvZpcCQWSTYISBaY0mBRkbdwPiAnIjxe5Y8cpd1pKVu3qA61MOS2jnSSPH6vFUVqw05/emw9oIvFRfW93vjVYVB50moLLvE5no1WafJzAHwOdhG9JkAjj9Q3dWU9dwJvBlXbvyynOP/0MVMm1w74e1j7olmbbT1D5qdab2axgwFnD5PAHTH/R6kNGppzPvPceVHYlm5YeimkYJMBIpmGxAkSbSKV1LtwOFqaTGf+f53COPXqeNPW11emvTEc0jBweL7LaIhqzZuh54wpUbPiqrIHSkEIR0C6fplvSqfmqLshV4CBn9G4qsewNIsXfObkGmRKoarAGMElgKxWFGTV1DtiD9t2xXc/yKO26/Uwhj1d8u++6mQ/HZ/qC3CPgr8Lo/6I2m01p2Om381GLRdIuli+LcTaZVj51xKMaiUChgjrlgz3wx+xZkpMqum2hk6k4yBShuA2yOtnDvZO014PHuykL/0MdWjU7m2COOztjKX+68od+KURSHjoyguQZpZpIlBOuREVABzPvQN+9DTV2DFWlw4QLy0obNgmwibPa+nuVsG3f8hJd/2to1bGpPNP9M4Pz2XYWrd7xT9cjk015fndnVt5FiI46sARvizIltQqbIjUJG10gltKaNr43PySnu0MpH73QBmIZJ+ZjtFFXuTnf1lCQ2Nx0pwvHc9VNGPPG4zZrMRaYe5uSVdrmR4u0rPk/gnU94+j41Pk+g/V9Pn/RnoZtPCJfhFhb0zEt95+u7gV8A6w79CBX7i1K/CsXhR09x3saXRw9ZPCOZtt3rdnQ89iENFA807cA9wH+TKb7X0VN+ZSxVYLVaDDrD+aZVj5z89eOePSRiT6FQvMtm5CTZRKZ5ARhmZqIbLs8VoeFFaWAJsLJ9wqBxLZ6hw9JOiy133Z6Ye1eXPl/MVvOFzwk+T+B5YCpwN3AJUsik+Pgpc+cCdwJbkCYZJy1cfK3Rx3Hw9MrS15902kOnDSl5J//4Cf+6BBi1Y/Wwi197ZOafw51ZbZm+VLcCvwGqMuPRgEnA8UjjjTQgEjFrqTM77NL0tNM0ccYS7qSmm6b3kgaz8ojNbWWFGzYOKlj3/OiKVwrTpn2xzxP4LjKN9s/INgR/8HkC7y4k+IPeLH/Q+1t/0Fv9cc/hx8Ef9Bb5g97rMnbzdFwzdELon8WV6R5G7rOpifw7bgcW7tNHTDHAUBEsheIwo7622rz+4f8+P7hw3a/zXFvMpFHyIvJL+aDj8wSMmrqG548Z65+Tl7W9fO22GQwq2Ijb3m4suvZLk55d9uXVH70XhUJxgHkT2AQMRjqyGZlHc/uIQnvzzBEF6Jqe09g+TqSMKY6Wnqn/z955h7lRXn37fkZd23fd665t8GIbjCmiWpQgeoAQEYiSkEAIBELyhsik835JII3XSggY0iFAohAQvUdUmRJEwDY2Zo3Lutvr7V115vvjzHjXxphme22Y+7p8WWU084yk0T6/55zzO5kRpRuKPrffWeT3SL+gaiR1yeYTQCSQWonUQRFPB78FZCKB1NqdvcaMWB0DrE1EQ6uARcATSD3wNUivqifj6WAGmFnqu3yJ19td0t03zNHdX9U/bvjyYwGj9uC3c4WsW/OV95YBX0RS+YKI4YXL3N+BQCvSksAFKH95Vo2fvppiEdq6xxYXrwrl9hv38ltjhy0bC3Rqmn7e4VMf6TYMvqrUVrH4OnDTvT/78uLXHz3mcz+iYVKjUb/KfK7EPM56pAfc7mIU0ij50Hg6uKTql1ozDwFWIgAAIABJREFUhjFCq9jqdmiRR1IHX4sEUn072tG+QJ1qcDYa9QXzdj3yHt9vPfZxQRnGUKSY2tjY7E5ufzGYdzplAUUpPhcJpO7ZU8cOx5LXj6p685Ijp91X1dU3nBJPO91byhdUj2077r751xwIBIDfJ6Kh7J4ak43NJ5m56koP8A9k5fvTwAhAGaAvjxzqyJe6GfvEm0ZpWVfR2Zfpzm0q2WR4XCd7OjMnAt9GJqE3zjHmLR66s7AZasKx5KcBEtHQw+/y/Czg74h4/2IiGlpkPn4K4kz4EjDq5MNuer7U13EpcPXLS8MTxg178w6vu8dR4mvB7+m37NP7kCDAMqTeKINMxL3AFxAL+PFIw93rMNMETfS+TLm+YsMR2RJf+1cnj/nvb5Co17RIINW+o7HXqYajkXqsXzQa9U9bj8fTwXKgd3e3Oomng6MREedGbPDvga3pgVYPu6eB7wLLIoFU/+4cz+6iTjXMRD6v6xqN+lfqVMN1SM++O1FGCMUKdPV/jUb9iqEd6UfHjmDZ2HwMcZp527qO8aWj9py4AnA6sndNr33my7m8G6+jlRf+HjJG77ehpnps237AbOA4pPt8054cl43NJ5U5xrzsXHXlxUgkewLiSmAAxbFPLdPQlHI3dyv3lJxTL9MquvzDKjwd/Ys9nZnFSNTrEltc2SDCRgE7FFhIH6obgZls6za5AngWqR3af13zga/Wj5/f9dgr3zkpmy/Jel0917uc/ReV9JdrdaPfrAJchQIuTaOoadQCywFdN3Bv2DJt6esrPt139jG/fjmfdf4TyLo8Eviw4gWGgeb3dnHQ5OQi4N9Iu5L1kUCqPRxLegBXIhrqga2uifU/fKJ00S9O/c2XzDFu5f24I+4KIoHUpng6OAcYhljDb5+SqxDTj4V7Yjy7kSzijji5TjXkkJq8ajB+jGF4URwDStWphn+a2z/XaNTvk5EgO4JlY/MxJJ4OfhmxrL04Ekiteq/td/Gxy5Gc+0qgt2Nz9XVlNZ1POlzFxffNv8YLVCaiIbt/h43NEDBXXXk98B0G+mKhJJqFMaKwwuhyl+b93pGrzzlQr3twySJva+9rc4x5lw7lmG32DsKx5GiARDS0ybzvBbKJaOh9TyTDsaSWiIb0cCypgFvAKJw7+7qRhsHRQI9S1Bq6tBRQaqt7YS+QKRSdby1uPKk6ly9pOeKAe7+e+OmXbwhe+OQRw2s3lyqFo7PZly2pzHqV0tEcrFKKsyKB1JvmcT8FXIoIv5FIhC0TTwcPAG4G7ooEUn/a0Zjj6eBxSKri/5qNjXdKPB2cAuyfzfucHlf/wp2lVoZjST/Qb72H8XSwEmgwxzgYA/hbJJC6+L2OvzdTpxoUcCYixN1IeqQSeWwo81O3akULSET00kajXn/Xne6l2BEsG5uPIZFA6nbEbGKPEo4l3c0v/Wr2ccf+45f1xyweBSysHNX2D8vuNhKgD0n9sLGxGRq+j0xw6gE0UDlN6brXQdOBUyYoA1d5Y2vO1ZXpNxzqHmSCY2OzVVgBhGPJicA85O9M4gPsQwc4d/a1Fe3do3+8uDHUAdygFOuQ9L8uFMONIhTRcGiG0jTDDyxzOgpjZtQ+4wM6XlxywdXFumHOlnUjOobXbs4CJSWVmc5lr8zwl4/peXr8pMaLIoFUz6BDKwZSDtch9UwwYItuxNPBqndJIaxABI/3vc4vng6OAl5q7RpX0rB2tnfaxOc64+ng5ZFA6l/bbxuOJScDv0OMQO6Pp4MOxKmxytzENPpER67Dy9/r+HsrdapBAz4PXAXMwKybM582TGFVQM7VxUAE73jgs3Wq4bVBtXH7BLbAsrGx2WV43R0njatZ/s+q0S0YBguU4mDgeeQPmo2NzdDjAp5DHNk8ABvOmq61149i/1tfdm05slZ17jfc7ejL6s6O/m8ihgLxoRuuzV5KP5LmvcOapp1hpuXdXFHSVOzuq3m6pXPcwcMq1r8G/B54VilQGvT0DqdQdDO8cp2GuBricuZ0YHW+4D2vfGrOP2nWiuyyF2csXvzU4blPXfJAfWlFT/HJ68+9+pnXz+6Jp4MqX3DHNrXtNwPOvRoIbx9tiwRSffF0cAnwa6Akng7+NRJIdW87Yh4GnogEUu/HLMoJaH5Pu6/E26qcjmwVEq15h8BCzDtWI33HQBY9DkXs4quQVDqXuc31kUBqn6pbrlMNlknJ80jU/EQkYuVG0pUdyPvVjwhdP5KSrJCoZQvi8ngX8Chw1p49g4+GLbBsbGx2Cbe9cGrJsdM9f/cc3FtmFOlSigORXirvmVJhY2Ozx3Ag6bstSI8iHN1ZQysUlLe9X41//C1jyRXH5rw9OZ/udXr1fPG6uerK+XOMefYiic1gMoir31GI+cL7xuy19WRHz6jSbL70C68u+8zC0wI33YsILAMknqHrGktWn4DHmeGo6fdoSslTwOSJoxb0NLfX+YDu+f84Jb369alTettLr/RX9I1qXDDV+q6qou4ao+vOKcCBlumGRTwdPAGZ8N8P/AVJq+9A7Om3GS/v4sQbTwfHAJ8D/mWmD5YApT5Przqo7t+Yaq4mng7OBN4Y3Lw4EQ01xdPBewBPPH3tuUit2g+RBckzEPE6HXiNfajnlZkGWAOMRt4bBZyEGJMUkc/YimDlgfnAsUibiHLk90lD6kWtSNYhdarhS8AjjUb9Bxb1Q4EtsGxsbHYJTR0Tv1Zdtq4qV/CxtmWGa/rEF1NAclDPExsbmyFmjjGvb6668tfIBC4POMc9vVx5N3cUVVF3OHTUuMff1DefVG90T6ii/cDRo3PlvmA4llyciIbeGOLh2+w9ZJCm1O/4Tpjpgz3nzr7WA1wB3BEJpN4evE0kkLoD4LlFyef7s5VNQFmh6Br/9vqjqoZXrM4Pr1zrqCzbouXzJRSL3q25ckgK2aN1oxY9Ujty0dXphnNyVef7J7T2ZC7798vnrTOPXx6OrRsP1yw9d/a1kUUrTxsJNO/gHM4FapGFwDgSSXnhA74PB5j7WYQsJv7AMHADFPIaHZtrGFHbXER6bd0P3LTd67+JRJK9wIuRQOon5uNvAsTTwW8jjZZfAd5m3yCAOAX+ArgP6EcZd2EY3wDlA5VGInU68KqjtDCj2OdwoquDkAWgNcjHPdK870IiWTHg/9WphvMbjfrX9vxpfTBsgWVjY/ORiaeDY8fWENM00A1Fua+9F/jCnnJgsrGx+UAsB+YX4TgD0KCgl/ndRbcTLVOg5u1Wb+e0ruzqT89we3qyHb1jK44FvhyOJb+UiIZs908bEtFQDplEb4Np2jAPMTq6EzgSiVC8DVtNkK4F/h0JpB5NRENmP6pQy9zHvvbtLe2T/pLpdFU5M11dVaM6KqaMeUn1ZKsHN4NqACYCz+uG4/UNLTN+pTk4Yv9Lm8cwkIr+VaTO8NIfHfnH2Q6vMbLYr11P9B2n8VPEmc+yPP/Hh3grnjOPuzIcS6oTZg4bXlHaopSCYkEzyoZ16uZ+ewA9ng5WRgKpjkGv/675fxk7zva4C1iARLf2SkzDkrHAxtfmjPeXTMzmMs3O5cU+xwYwvo1mjB9/Tvsf1z1c6SGvFJJ2fCjQrzx6NQajyvfvX9PV4PcgEcAliLjSkIiXZcE/zPx3TZ1qSAG3Nhr1Heyl2ALLxsZmV/ATh0NC+Q5lFMePWHq5La5sbPZOzChWsuXwCbVt00aOmPDEW5nKpZtrVL6gAV4FypHJKcOlUfr2lobeidV3ISk/O4oC2NgMph+JBm0E/ovU4GwxmxD/36iqC0qOnnHXSUhd0aODXzjn9D8/+MXr4gtaXxv3X/cZfeXeqjf0/nyVI5OrwDAUShndSO+rccA1Xzry2asefDF5BVLfM3bQrh5HmhCvH3t65xdK67IHjJu0/J54+tLVgC8SSPUCRAKpwVby78CsFdN21gPLfO5tgPvmJz+3fOMxJ8ya8jAOTcdXWrBMG44APoWYWUxkQFTxXi6/kUBqMwM1WnsrJwN/1IvcOO7s9gv9Y3O+skm5t4HNS387Mpfv0urbFvp/Pv7THcWW1/x5T3VxeMeCkiKonJFVY3VDFfrWubuQdE0dmIQILIX85ixCBJkDqft7CRHQi+tUw1N7q427LbBsbGw+NPF0UGvb7OouqdD8DqeOU9aZUkgqhI2Nzd7LH2uWbLrX09lfl6/0+5pmjz5v4n2LZgGH6Ao8XRltRHqN6p5YNXvYa+tG/yF+8V1DPWCbvR/TRMLsYRQCUxzcNz/p1FRhuG5oDqSe6Ox4OjghEkhdZb32+ke/XnL6yUuv+tfiry9Z0XT0MTmjpH3WQU+XGoZya5puNSD2IWl8j5gv60RS0k4Nx5LPI/U73wZuSkRD2XP6nrq5qqrp0mMP+dd8w+C/XX3Dpl76p1v//qdLL35H9G0HXAyc+vW//DHucuSLN1105UM723jy6P+MG165SoFuaBqW1fhKREzqiNviyvdx3L2WcCxZAnwWqan6OSKkO4Cc0igrn5px6Xk2Ip9P74ije37VusA32lWqz/AOL+oUNUdmkzYLhYZBNaiMkXP0F3JMZcDpcRgSudKRz3ssYnpRijRJ/xaSevh9pE7tv3vwLXjf2ALLxsbmo1DXnxntd5b14Mz343T1bwbO391d721sbD4ac4x5GWA9sD4cSzpd3ZkXSle37efZ1Hl3odI3xdPSp6uCTmWmoI18efWkoR6vzb5HPB0sRWy2Xzh3Nk7DUGVI9GES8CVgv3g6GI0EUno4lpxV7j/2hhGVKw88MXL/kucWXrKlPTuhUSnj30oZX0AiPxuRSfbLkUDqaRDb93AseTfwFXMbEHOEKoAHrjnpgXg62JTJldz6yttn1pb7t7j6s+WDo107o9swaB9Ts+wX1WUbK+Lpu1+OBFLvGsWdOSX5HQas3It9mfI73lob/Ouh+z/SEAmkOoHb3u97txcTAuYgKY9nIZ/vt4CAUpT5RhTGFjL89bU54zcjAqkJOBXNOLhvvXtKrt15kebRl2Ko6YgzZA9ggFGGRKjyoLqQ97EAvGXuow04GzHBGIGknj4F+OpUw+GNRv2re+oNeL/YAsvGxuaj4MrlPby1aiajq9/ITvCv2Yz8KO5WzF4jhwBP72vWtTY2eyE/zJd5D1p75vTHp/7pxcmbj5mkPM09hVxNidORK2jNRSP/3ruwsXkHhwM/RmquXlXKWI+kfF3FQArhtfF0cPEZR/i+07Du6L6+bPltNaO2FI845MHD/d5ON5JG2IVExZ5FTClu3O44twPPAKsT0ZARjiU/m4iGrLoqIoHUy+FY8izgd9l8yfOfOuRPi+LpG5Xl6BdPB08DOiKB1MuDdxoJpO4G7o49/tXPu12ZGeY4thJPB6uRmqEqJErlAwxdJ7N27dR/L9pwdmlR91y7pmnW+ZHAR3sj9yJeAH6CfBZTEfOJrkQ01BGOJccB4zc8UnkycALwB+AS4B+NxQNuB16oUw3/KvY58kid3IGIEyVgoHxF3eh3OpG+Y22IIE0ALyPiq4BEMR1o+mG+CZkZ2Q3eGXpec9SphnMajfq96nfKFlg2NjYfhRXDxrbc0rq+eFDGGHUDrPkW8Bng1t1xsHg66EdSB05DfqA3IQXANjY2H54VgLNQ6nkoW1P6+eEvNwaUUiWqoUnpfvcGLV/sCseS5YloyK6rtPkgvAz8AHEb/BowDbi9tWucAWRqytevRiboB3nc/QfOnPz0ckQsnTVh5BIdSRX7AWKb/gpiSrECmDv4IIloqICYalj3+9mORDS0PBxLfj540B0/V4rjgfnxdPAgYAPidLjZHO87iJ72139at+PpoAYchtRd3Yy4CE4CViFCS+k63nVNB51cKHhWK403kTTGjwWJaKglHEt2AocloqEk8J9Bzy0Jx5Kfa321dDgihl4HnlWasSAcS5YloqHuRqO+v041zEBE1F+By4ASULrR78iDcgJbgD5gf+B65G++YmuECye6Upkmj09JKuZSJG1xr0pjtgWWjY3Nh8a0YP8GQDwdLAGqkaaCu5x4OugsFp23d/UNc7++/IzbakcuvGPy2Nd88XTwGaQPyy8G9xixsRlK5qorywyljl769aMX3HbL57cMfi4cS44BuhPR0PYNTfc4pgPYA4loqA/gqkeX/jbndf6u/cDRtQqou3dRVXdt9c+Aa8Kx5ImJaGhfsYq2GWIigVQGiXQQTwdXA9WGwRlvrZldoSnde/SMf52KCJMmJFXsCURQhREHOYVM1O9D/rYsQCbTX0Am5x+IRDTUGU9f+wBSWzUTqSFKIiYZ7xBl5rj9iEHFy6YpxjQkgpZAzDQm6zql2bz/oELRp8r8rTgcqJn1T/UmF9VfDyxIREP6e43NNNQYBrTsA3/HLkM+n+QOnutfVThgFfBL8/4vw7HkycCvw7Hk98xWD0ciqYbfQDJRgtJaWrkQAVWF9NDKI2YXILVWWcRspwA4jH6t1UDNBE4H+utUw+hGo/63u+F8PxTae29iY2Nj895EAqneSCD150ggtbuKeIvt3WO6V2089OCu3uF3bG6b8NvWjuoX+/r9JyB2wdN303FtbD4MT3dOrn6iZF37+u/P/PmvrAfDsWQl8Cfge0M3tG04A7gnHEvuD7DujGlPNB03ebWh0N1tfapl5phc8+ET7kJW+meHY8mThnS0Nvsqi4CFSnHG9NpnOw6YmFoNdCOT5rVI1CIInIpEKix8wDlIquAFwGTg6PdzwHg6WBdPB2u3e1hHxJoHibA8GQmklkcCqfXvspuZiOg70by/Epk7BxChd1hHz3DeXn+UWrjiZDJZL0qxsbyi/Tv3zgn9bfvmxjvhVMTOfdb73H4omQPvNL03f0PuD8eSn9ruqc1I5NFqEHwnkmb4BJJG6kA+FwNxf9xgbjcciXS5kdq74ea2CpQhoowRiBBzAZ/fNae3a7AjWDY2NvsE982/5vfA/h5X17yaslU/Kxbd3s7+cfRn+3C51uBy5lcP9RhtbAbh8m3uomxVq8uA785VVx4OnMncs3uQyeJbQzw+iy1IY88e876O0/G0qy83E6c2rOh1VeaGlfYCRwH3mts9NURjtdnHiKeD3kUrTv5hecnML9aOWlSpFA1VZZvjiFi6BXgYuAPws7WXMHlkIm0gtTgHA9+IBFI3xNPBK8zH3+14lQyk5N1iGPQiETHi6aAbcaOLAMchQu3J9ziF1xC3uoUA982/xn/czL+srSnfNAmpIVIedzfjhr1Jp7+Z1q5x+phhKzqVYqf26zug0TzW3m7JTiIaWvsuT+UQgdS73fZvIA2VAWg06rN1qqECsdvvRFwIq5HPfSXSNy0MPARciRheeGCrM6OVKliCCCsdiW7+oU413A78qtGoH/LfV1tg2djYDCnhWLIMyCWioXc1qwjHknXAWYaB1+Pqvb9GX+Zt1mayqfUApox5Aacj3xUJpHre7fU2NkPADzw9+ccMmTQqHU7snFTzzxk3PBf+ybqf/36oB2eRiIbSQHrQfSMcS97QP6byiLJlTae3Tx+lkF5GvwP+B6mHsLF5v8zWtOLVmVypxzBAKQ5DmvM2IX2PHgDqkAl2M3K9uMz/DSRysRSJgLGz3/l4Ongo8AukbmdFLu+ubWqf5LzlmfB3K0u3FJGGwL9H6rymIHVUz+1s8JFAKoe0HrG4qqNn1DGVJZt9DofhBijxZijxbqbM10x7zzilFKMRQfC+iQRSDUikbJ8lEQ2tRtwc3w9jECG2Evn9uRwRTgciYqkRiR4aSEpgK5I6aJjbOZBIojbodXVICqFvF5zOR8ZOEbSxsRkywrGkH/lj97/vsekm4Kf5hR0vNt+r5m5cOYn9R7/IqKollPs3FJXih7t/tDY27585xrwngOqiGRnK+xw0H117tu52PjBXXane4+VDzSGGyzGqa8aYP+FxgdRBHJGIhtYmoqEN7/FaG5vB/Hfq+BdvrixtyufyfoVETNsQgVWFRDCslMBhiA03yEQ6h4ii1yKB1LPv41gtiBgbC/yxP1v+3zVNs1Z53b0HAIf3ZcqXdPfVNBgGGjADiWC1/S114tV1quF/61TDKTvbeTiWHAU4MrnKfyllPKwbA4sNhgEuV5Gykqbmxs0HXYFZe2azU/yIgK5AolBuoAxJ9TsJqX3zImmkI5Dvg4akG1qRLKu+zYlE2TcgDpVDji2wbGzehb//5/jgvKcu+MOVt82ze8B8COLpYEk8Hfx+PB08fCeb5ZDVq23y1MOxZG04lpwYTwdVPB10ArXL/zSsbbh/+Snesj6OPuVxKsvWGJPHvvmprwZTzkggdfPuPBcbmw/DHGNehxPOU/CgQhUnPLJUL1nffgDSDHVv5jXgR8hqfz8STZgbjiW/PKSj2ouJp4Nj4ung0aZZgc0AX3C7MqFl645ZsWJjoN8w8AA/RGqYLjb/LyDfM7O+hgKStroE+DJw9/s5UCSQWmM2Lp4JHFpR2vKPW756+Sled+/lwOVPvPrNg55bdKHW2jXuMGC8YTDBMCgxMH7m9vcHkSjIzpgBHLts3bEPt3ROLFPgMAwRVwCGgdHSUeuvLNlyLXtZPdBeyOvKpWdwFT1IhNC6bgqI4CrKbT2LVjTMrFAP8j35BQP6xbJmV0hPrkuAv9SphiG/Du0UQRubHRCOJdXsA8de7XN3n1rqbZuCrKbYfDCGIT94XYhNL/F0UA12SDLtdX+1/Qv1In9pf8M7/ZHXzmv61FcfKVU9PRt8YyvGlY3pKgQvfMLIFEpvLPNnvrsPuC3ZfMIxI1lPzFVXHkl/4Vwk9WXI3QN3RiIaygBPh2PJqYZhtOl5fZyec0xSbs4Mx5IjgVsT0VDLUI9zVxCOJY9GGtTelYiGPsrvySXI792XkaiLjfANYOrRM/5ZwEApRQ0SdViHRC86kejE4PloGxBDUlNPjwRSH7QVxx8QQwWrDqoKePCUw25sLRquw/zuLgdAJusr9GXLjPKSNu937v3hlluvuPp3UL+z/T6HREfebukaX1pdvuZUTeFAoSFRse6K0pY1xaKr8ukFX/3sffOTLyaioTUfcOyfCA6du27B238e1tGzwlNrSEqnZXJhpYcCFHFgoKsSREgVkH5nMxGh5WegsbOVLggi2ssZYnt8W2DZ2OwATeUDa5pmHjGqennvmGENTwz1ePZFIoHUmjtePvGiR/8TPe6++cngubOvXQHcGE8H74wEUg9uv304lpwM9CSioSYFq8rH9B6z4pEDq/tjpZnGt8cu6mvzD18245RbiivGXnvTV775sekrYvPJYI4x7z8M6hmztxOOJX3AzykY/myTQzMMB64KfTJuowZpi/CxEFhIMf1U4BHeQ/iaLmlHI2Iss93Tf0f6KNkplNtyNfCA25lzMbhRLIxHeh29hfSRAolaaEgNzQKkTqp3+x2+Dw5E0s6viqeDZyEuszNLfN0ZBuzf8Xr6vW5XP0phlFUXTvmfu35yIZz4rvbv5oLgUoB4+tpacz8Z5KRKdB3K/C0j1jbNfLGzZ3Q5UmO2jcAKx5K1wBarLcInlUQ0ZEz77aLr0YzzKRJA0gNhoPYOoB8dLwZF0zHQh7g5diMppnWIMNv6mSLfqYeA79Sphj82GvVDli5oCywbm+247v6LK8fWTH1p4shFWrm/ecVFxybnvverbHbEQy/9oBOxsl2D/JHLIH9kt8GsxbpRoa+Jp4Pf93ii35l9/L9K9EPy52T0Ck8+Pca97nHXTQ03jf7j48YJ79lTxMZmbyccS1YDPwMeSkRD/x7q8bwLXQbqTaczO1N5nA7N57gNWA68Ho4lDwNciWhoh81Z93bCsWQpUtvxC+AY4IpwLHkLMi86HElb+pJ5O4Octw/pubMwHEtuSkRDTdb+IoHUKvjAznGfBB5D0sAPZ9s5p4bU24QwJ8dLnj1YbVk51jj+osce1RzGG5FA6sUPecxPA4ZhUF3UtSc0pWuahlYoOl392QpKfa2GUiilwCHVXwXks1u3fZbFTjjcHLcbKOoGHUWdMqeibOLIRW++vvysGxFjhq2EY8kJwB8R58LffMhz+9jQv8FzNBK9XINEkZ2IyLacAUswNM28PTjCNQJxHswg16QlsjCfPxOJYD7KENZj2QLLxmYQd74cdE0cRdu44SvUxrbp+L0d74i02Lx/EtFQXziW/CbQFwmktiApHzuiH7hrv/EvXNzVW/3atNoHXikpabvAV92nrWuu0b3jjaP2u7TlxvfTsNHGZh/Bi6zij3yvDYeCRDTUH44l12gudbZ7tGexlsmXUihcqztczTi0TyPNWUvDseQ5+9p1GY4lSxBr8CWIwPp/yARvIdLg9HvAdxHXxAoGHO5WIZ/bhcD+4Vjyi8jkrs+Mblj7V4AzEQ3lw7Gk+oiph/ss8XTQB9yKvH+arivausdRXrIFtzNrRRxc5v/6mkVT+ts2jMh3NVf++IqzHvxQabSDGvYuKBS0Q4u65vO4dQfA2qaDHOtbpqlZUx6nzL+N9ikidZGXAtfH08GfArVAPBJINbFj/gcRjj8ChmkKrxKxVlSKxe+SQrsFsaX/sMLx40YzEqUEuY5yiIHFVOQzcSPidwNyXWI+Zn3GVnSzaFbCaYAL1AjEwn1IrdptkwsbG5N4OjgdyGoayuWCjq7K3NeCj80Z6nHt65jWrdXhWPL/Ir+775u/eeIi9/bbnDv72kj9hOe/3N1bOb29q3ySx8kXHA5dK+hOnFr2MXBcit17x+ZjRCIa2gicj6SW7a0sRyZBqapFGxrK327pc/Rk/Ui90U+A7++D4koBvwYmAIcBP0Umb+WIqcfPzfuzkYWfZiSlrR84ABHF+yNNasuR5rCXbXeYrwN3hWPJzwL3hmPJibv3rPZadCSSsK5YxGjuHM/iVSfR0jHBet6aFAMUTrjo8XPLh3WMv+KsBz90JNCMPn0TeMDh1B0ed8EBsKG5Hk3LqoNq/43P0zH4JQYyYW9GanrciNg+B/mc3+1pQEu+AAAgAElEQVQ4rUjD3F5AVwo0jYKmsRARUe8gEQ1lEtHQvEQ09EFryj6u+JDPX0cCPj6kCM6JGFpYous28/+C+c8S5woRYk7AMag9mmX1P6R27cowPpELKzY22xBPB0O6zpNKyYWby5G7aHbqA/WxsNkxZm3Vg2DUQ1GrKVu7rLV70i8BEtHQHXPVlfWuS7tuX3vU9OH9BX/LxBELDi8v7QR0dD2fryrv8thmFjY2Q4MZ7fkNhvFVlS30GV6XB1ieiIZmDPXY3o1wLKkhls0NiWho+zStccBfkDSj4cgErwKZXOcY6LFTRCZq65GGqAXgFcR2vx6JZK0GlgHxRDT01KBjXIYU4j+GNLX9biIaWr97znbvJ54O3qIXubxoOGjrGk91+UacjhzNneOLZb42h8/TawB/iARSV+zCY45F0jzrANIN5yhd1zjigPtQA/5yBuKY2YFELN9EJvXVSBTlxUggpW+3X4U0Kc6ar7sISUncjAjuO5Hv1B8igdQnus5qZ9Sphi8hZQPDkPe8lIFGwpi3O5BrcyFyPWmIcUX1O/eow9ZPVhlIjeg84NZGo35Irj07RdDGRnjCEldA9qLZKe9Ot7b5IGwB3gCmgsLpyI0r9W28rqpss/fK25a9vf9h3ScXJjv2a+0Yb7iLm0e4q3tY17w/mf7SvsOmPTnMFlc2NkNHIhrqDceS81DqK4bX5UVqRx4Y6nG9BwcAvwTiwB/MeqsRiIPjs8ik2zJYyANHIBPmNkRolSPiSkOMCqympmVIhENHUjsrzdcsCMeSZyM1IUuBc4HHE9HQI4h5xiedjYDhdBTViMrVKA2KRc1Ytm62o7psA9Nrn+9mB26yH4VIILUhng7+A7GE1w6e/BiGsVVZWX9TNiGNcZebDYUBiKeD/4tEsa6Kp4Mrrb9Bl1x6/eTaQ/a7ecKMlTVOt64jk/0Y0rDY6qtVg4j7+xABbrNjJiCiqgVZ6LCElRXRNJBFDAXGgWi40BUMNBsuIFEqsxeZ1otcu5auqQHmIFGsIWngbAssGxshA/iKOpkLj0r5h3owHzOKQCmojT53tzpw0tO9qzcfOHZ9y8xSlyN3f+XP1/xW8+kVs3o2qdTvz1UvzT+JsVMbex68/rOn/8b4Uf9QD97GZldgRlXqgZWJaCg71OP5gBw06HYdMDscS44GosDcRDS0VXCFY0kP8FtgaSIamrdnh7mV5cD/IcJHAVcgboErkCa0BuA2dKZg9iRTGl2ICFPAWvPxSmQSl0ME2EEMRLYsN7wjkZSwKXqe3pW31/yg9vz211xlemM4lixLREN7tSX/7sbsY3i45mAjMBolKWGapjfNqH1mhNfdA9LK41hEEO9KbkYm5Oc4HblxSj61fnMMRWBOJJB6cwevuw84G7F7/w6yQEh3a/kRDod+xPqldQ/WHrzyLmQS/7S5r8eQdMFpwOZIILV6F5/Lx42/I3VsowEXGChf0XC4jP5Cl8tAhFEBMMqm9rs9I/JGy4tloGsgiyNNmkufWDIxt6VntUcZBTWSbcWZgZhn3LanT8zCrsGysREqlMJni6vdQg5YosgVC7qr0NY9pntk1UrH9NqnmDn54VGqTP+VcqMNq+5TJ136EAee/AqTA0v/DLww1AO3sdmFHIrU+Jw11AP5EGwENmMY+WGvrj25etGGELAfMBk4OBxLnhmOJc8ctL0PqaEYEhLRUA5J7fMCNwEnIylfnzMfc5iblqPQDBFMwxlYGV+JTPrzyHNOJHqlkEm0ZQvtMl9zKFBWzKrqfLfjJ51v+Y5GzDPuDMeSZQDhWLIyHEt+PxxLTt/d57+XMQ1Jsfw3YiqSAf6sFH+sKtu8yufpSSMmIw9ZLwjHkp5wLHlBOJactMM9vg/i6eAI4E9I2t9kTeMRpehQGiuB55HI1v07em0kkHoGEVfPMsh2P33f8Xen7jz17Kf+eM5l5uPHmudnpa69hYix6ng6OP7Djv0TwgTkPWsHDDAMV0lRFfodHgZ6WlUCTj2nFYycVjAjWAoxsPBUHdz3cu3nW3MlE7PWwoiFdXtjo1H/9p46oe2xI1g2NkAkkLKKJ212MYloqBCOJX85a8qjZ/o8XVXV5Run5QtuzzDXeqXJEs/WH8by4V2UDevSNY3bG416OzXQ5uPEcuAuzKbb+wpmpGo5cIgjU/jTsAXrzwQq22aOnYvUm7QgRg868EgiGsqGY8mLh27EEI4lvYiRRSVSG3Mokh5Yj6T4GYBTaTgMA5SiwEDtlYEYXLgG7VIzH29D6kIqGTBHaEXEl0NzGvnhx3Rv8U/I/R6YhYixeeFYchqyWn8SIvx2FDX5uPIWkqLVCvwZqWGbiXxvLkSiDJu3SwWvQ4xCapAo1IchgwjltZFAKhdPB89DXAJfBBZsX1u1PZFA6i3E8GQrjUZ9AepT8XRwP+B2RJTfHwmk3oing19DIlnHICmCw7AbTu+MF4DvI6m8VaC5ci1uAAfoDtC2fj69jV56G72LEGfHSiT1Mt/xpu+QQkbR0+ix7NqtuYT1XSqrUw0jG436d3OC3K3YJhc2NjZ7hG/+7Ybn9xvz6myvu2uT39tZ2tNf7ivxdrkKRSe67kDTsobLRZemuAe41K69srEZesKx5J1IrcRngcc9rb2H+7b0nN9xwMgFQL9ZozUGtjojDinmWC5HJlnnA3cj7SGcDKQHKtgqqmBbVzIYmKAVkfqaiUjaYAUDdVhWLVYHIjAvN49hpSe9hBhgHG8eJ4QIh+cT0VB+l570PoCZKvhZZII8ExE5f97RtmZa58HAqkQ0tNc0lQ/Hki6g6tzZ156AGDR0AOcNTgc0TTAqIoFUx473YgNQpxpKkCjiFWyNDhuDsuqUdR1Z16pCUgN9iNnMsYOeN5AUXjcDroQG4vz5H+D0RqN+j19ztsCysbHZ7cTTwccWrTjh1Ey+TPVlKrsrS5s7Z+33+FrA35/zr3pu4VcL/dnyWxPRU54c6rHa2NgMEI4lTwa8iWjooXAsOQxJC5yC9Ip6DvhJIhpqG8IhbiUcSx4A/BWZnHchwrAHmWiNQQTQciRlzbKBziITPGuyBpIaCCKwtiCiwEpdwtyPNbEDic74kCL7wfvoRuq+cuY2M4GXgQeBuz+p/bH2VcKx5DeAM045/Kb6Em/HGCSScmEkkEoP7cj2PepUwxVIGq2GXFsutl3gsCzYrWukD7le/Qw0HvaY22WAJmQBpIoBUWY5e36u0ajf47WQdoqgjY3NbiWeDl4DnDatNkVHd03uv29f8PDqpnFrZ+33+DNAxOfu+8GdV563eajHaWNjs0OWAweFY8k5SFRmOfBtZIJzCHADkuq1RxncwDccSx6ORKkCmE1tEYG1ERFbBlAsZlRx/SOVpZUz+lwV9VmHeQ4+ZCLXYr7WShW0JnDjkAmbNfmzUsktS2kHksrWyYCzmYHMr6qQVEEnAxPEkxAHwjXhWHJhIhrK7PI3x+YjY0aiTgZaI4HUf82HF5T5muvdzl6r7moiMAlIx9PBCsSy/fFIILVsSAa9j1CnGsqR9gUG29qza0habYm5aQuyQDLRvD2GgTTezciiRRZJy5zIwKJHDlngMICfDIW4Altg2djY7Ebi6aDSdX6qFLicRWN41Zalpx1x41uRQOo6OBUgOdRjtLGx2SnnI31+LBvrANKA9UVgASIs9ijhWHIW8LNwLPn/EtHQ64ho+RxS99KEiJwy8/Yy8/5EPae0XKdjTLbVWZR52dY0vwxiTtANnI4IKyvdb7AZmHXfWiG3arNgoAake9B+Xcgk0JowdgM/QiaN1yPi9L5d9b7Y7FI8wLeQmrn/AiSioZfi6WAASTUtIp/jd+Lp4Bjkb9mnkc/bFlg7JwM8gVwnBwPtYFSwNYqlih76HeV06c2MNMztM8g1V0Suo/FITVaXuU/rWlTmfovIdTfY/GKPYrsI2tjY7Bbi6eC43n7yg/qLbQYeR4qabWxs9g1uQ/rJRJDGnQ8jzVkPBl5KREP3DMGY8sjk1oomZZDflccRwbcOETxHIBGouYByletM+kKrPvyoXgcDEy8NSTs6ERFXHQykJuUYqKky++1sFVeWm6D1mNv8v5SBKJbVy6cTWGVu24ekLt0B2KlleymRQCqDfO+v2+4pyyTFjfRLOxj4TOOmg7uyee91D7109ahwLHnOrhpHPB30xNPBE80I2T5NOJY8LRxL/vbQuet8jUb9dYgQXYKk0YJcP37AOYvXCfDKSI3CWORaG2s+p7kqCsOqZvU6NbeukLRAq+bKcv20HD5XA2fXqYYhCSbZESwbG5tdTjwdrGzbXLZKL7gc3tFtKIWhFNPswl8bm32LRDTUhESCAAjHkl8HzkFSdfYDlpqmBCOBpt1ZV2T2EjsSeDsRDX0+HEseH44l5yHNj59DBNIBSGQtyECz4LPNx+odXsNqxWG5lFkTMquZsGX5XADuAT7Ptg6DVv8rHwMr5jrwOpIOaN33m8+7kMhaN2KYcQEi+p4D8qZxgpGIhmwX270M00lwK/F0sBKJQFrC2gG81tRe++tVmw57ti9b0QeqEUk1/cjNuOPp4EjgSuAUxBXz3o+6zyFmLJJS6a1TDf1Ial8l0F5O5xYDxnVTpsDBfzlcVdCJjtONRKOtSHNL2X7ZyuFH9JRmm13FvvVuy+3Tii5b17UDOAMRXfOQdOE9ii2wbGxsdinxdNADtPpK8ppeLNK6oYYRE1pPssWVjc2+hylqPIloyGr6fSwSlckxkPoWBK4BfsLu7V+3P2LrfE84lnwGyTO2ai/OZqA24wTEnGI10qdoFrAJMbuwzC0MJM3xWGRi1sFA/Za1An4B70wRtASZZu7PaohawjsbnfYi0asxwP6tr/nr+je5nGNO7TxLc9KMpJWVIgL28l3zFtnsDuLp4BnIgoIfmbwXkc+/pLpswyGaKr6iG45GpMH1h6qrC8eSTuDHwOpENPS37r6aT21um3zJiKrGdEVJ81vxdHA6Mm+fhXyH79zH3Hb/CvwjEQ31181pUMCNSATqf6ewXBvPOuMZTjK6KVOUK6O1vyZLfqsJhnU9PdG/0fW3zc+UX9230TUaEWzdyAKPE8n9ta7FJkTU1WMLLBsbm32d3oxnod+T1ZQqkun1UVLT+hSwMp4OTo8EUp+k/i82Nh8HvgUcH44lL0pEQ+1IeqADmchYfX7WIXbIH6rvj9m3qvg+7MtXIul+SxEnvilIfUwtksKYQyZbzcjEqhuZZL0ITEdMJzRANww8wPFKYSDphTW808WsG1lht7DElbVabtVagUziBtu9dyJ9sx4Cvglo/rFZT98GZ79RRMPJcCTi5kea8NrspZiGF19D6g+tZtRdSErqnS5n/qoTZt32UiSQ+tlHOIaz3H9pZVffyFPL/U3r4+lgNFcYW7mhZVq125UJlvqanwat36Hpm5D+W28hLQj2GZMUM7ptLdScjPQ6ux14e3193dSmzaNVd2eJobmLhnIYaA7D0PMsR9ob6EgE2OGuLB485pTOf3Y2eD+N9LgbhlxzeSRtU0c+n6nI59XFEGALLBsbm11GPB2cguHf3zDA7c/j9LS/4XRxAbLqPDaeDn7WzG23sbHZN1iLTGyy5v2rgYOQlfqDgPmJaGgVsvIOQDiW9ADfQCY+nUBscApcOJYchUQDXkTE2t8QkfM98/ljkYhYB3DVoP5aBXMsm5G6jXrgMGRlfARiVJFHhF6N+W8L0n9JwzSuMHSxWTd0NOUkj4ixwTbRloh6EPjyoPfCsoR2DNreimZZr9XN2xXme3YhGAoMfKP04tgzut7UHIwGRiOTwR6kHstmLyKeDiorOhQJpIx4OngncCYDDpM+5PPLIzV1Sz7KsYCbjpr+r6onX/1W/tD97x0NTK8u26AO3f9Bw+/tqlRgGIZOsUjO4SAL/Ba4LJ4OdkcCqVs/2tkOCRuR6+Qw4NYtK6t+TUGBAXpWM/ScAwylIdfbM8BxSL3brJIJ2bDmNtr9Y/MP9613Z5EoMAxckxqyMFJEFoSW7tlTE2yBZWNjsysZV+Lr6y0UtXx/l6/+slMebwaIp4N/AGpscWVjs2+RiIbuY5DTXSIa6g/Hkq8jK88bw7HkaGDzdrVXlQzYkY9HIku/HfT8F5HJ6leBRmRyuikcSx6C2MCXIOLMh0yaLOrN495t3l+LRJmmIFGrVcBZSH2TNb8ZzUBdlBhVKJRhYCgHCgoumTOr7ZsMKwbE1eBzs6J3g8dl7d+qz7JSBF0M9OUBcGgOKs3xnYqkJFYAvwvHkksBlYiGtqn7sdnzxNPBi4FPxdPBOHA4Iuq/ybaukXnks50YCaTmfsRDlgLr/Z7OnjOP/OU0l7MwA1BKQamvfavoN7+hVlTmHmTxIgnscwKr0ahfXKcafgP8HKgh77Ds2WVxw8AAPM6KQn3Nob3DW9OlWwo9jjpAb3mldE1Po0frW++6GFmg8DGwyDG4SThI9GsY8luxR7FdBG1sbHYlzwPTnA59uCWuACKB1OuRQMq2ZLex2YcJx5IVZk1WAUnNuR6YD3xq8HamMcZlSGQpxzutktchom11IhrSgd8jwuq3wOcS0dCTyMpzJ1I7ZVGORJsuRiZObzPQ0Pc5pGi+Ham9Uuax2xHnPoVMxnJKgaahK2XgUL04Hf0bGIjQZRlwJxw87sHuZCVs25h4R9ta4+1AVuGL5sMaks5Ybm5TNG9HQf/z3Mcv+eLvnw1PxmYoKSDfnauBKHALkh6YQ7679yB1UGcAt8fTwYp4OnhVPB2c+kEPFE8Hv4h81z+jFJe7XYUDLeddQ0QGxaJGruBDKVkGKOpKIaJhIvt29HMyci1NR+qsLCzTGcM7KufV3Po4R0lxOHIt/yLX7hzdtcxXB8qNOBEW2HYRBPP1BeAXjUb9HhdXYEewbGxsdiFmSsX6oR6HjY3NriUcSx6BrDY/jIiGeuAVxN58ZjiW7E9EQy8OesmZwLmICLszHEs6kEasq4AvAO2JaMhaZf4dUs/0JwZMMp5BolEjGPhNmYiILgOJjJUik9MKZLJbZW53IjIZq0IEUxFZ5S5DakDMSEQ+VzRKnRS1EeZ2lq065u1WRPwMdiqDd++t4+CdE700qNmgNHMc4wFNL+Ds26S1+8foFZqDInC439M1yevuvrm5o3azWfP20rscx2b38nfku3UBA4YufYgb3UtIxHUdoEUCKT2eDo5Hvu9b+OA9sA4Bqtu7R43o7qvxjx+x1ACDTK7EaO8eUWzpnOTqy1SgNJ3D6x9AL7r4z1ufoczfxtTxLzm87r5EPB18C7gyEkg17JrT32M8gETkDmFwDaS3AAUFBY2eZT69Z7nXia5KkSjUeAaMZZzm/VVIpNpqWmxdrwo4pk41vNFo1D+7505LsAWWjY2NjY2NzbsSjiWHI/2wyhFDCcup6yHgWsQN7DCkpsoyrTgWETn/RKIACxF798VIfyGrcTFGkYf6NrpGNPxu1D8bjXo9HEtaPX/GAFPCseQGZJX7aUQcjUPSC6uR6FkpEsnSEZFjjdGPTL5agKPNbayxK3BbPY1cbDsfslL8arZ7rMg7U5FgILrFoMet7U4wx4R5jCJAPkOPu8KoNIpoOOgAJvdly/UV6496pb1n9Ajz/bQF1h7G7De1DrOuJ9PrMnpaKx3+8p6N/sr+RiRV8GHEvn91PB28EHgTuJQPt7i4GDA2tEzztHWPzXrdvSqb97jWbpmutXVPVLruNCaPSastHXU0rJ3NuOFLcTnzZHN+snm/8rr7xiDRrMr3OM5eR6NR/1KdavgMA83KdUAjow1awFBOJGKnEDH1JWSBx4uI3glI/eZ3gD8g15hVB5kFDkRqt2yBZWNjY2NjY7NXMRYRDo8gosOyqR6FRKV+wIA7GIhgcSONdA3EDKMVSRvsTkRD27h6vf698VuAKxBntKeRyVIAuApYBPwKEWc3I5GzI4H/RVatT0ZEjBWZsswsrPHMMMewGkkr9CMpe1akCnYckdq+hMJgYM40OJKlIxM576DHBptlWE2Hc+Z7kgc0Tyl+QzcwXz/GPGTP+pbpfwZSyCTSZg9i9p36MxLpBCQlr7u13JHJ+PM9hnHmiKq1ExEBXw5sAIpm5sbKD3nY2UDTlLGvbOjtr1q1atMhp2dypYWJIxc05vIVE92uvt79xv6nqj9XoXyeTqPc36oF6u8nm/PjcfdZ+8gi6bH/+ZBjGEpGMVDbZi5cKMQYZitFBhYpisg1vBYRtH1sez2rQfvKI4swoTrVcEOjUb9HLe1tgWVjY2NjY7MLMSM+lwL3JKKht4d6PLuARYjAaUfSp1zIpKgd6U11UyIaarU2TkRD3eFY8otAPhEN6eFY8gtA//bNdM33aZZ31Khlmc2ux0Ye39UZjiXvQKzXE8ik6jwkYtWOpByOQmo3foTUcZUjkytrPmPVb3wfcSWcZD5Wx4C9et+gc7DMKazXbc/g3lfWfes41v6sYxe325ZBty1L9y2I+YdSmiW+DDOSpgC+jQjag8Kx5JuJaCi2gzHZ7B6ub14z8sxMn5vhE5rwluRwefOFEbWbc8uaZ+vu9uzxwyvXLlKKm4HrIoFU+wfZeTwdLAEuMgz13P0v/HgD0HHubL4HOL3u3oLX3XuJ05md3NNf8+zYYQ0/GlXd+GOnM/ftTa37tSlVzA2vWOsHKpUCr6dv8K6dbGu6sk9QpxrOQHrn9SFR5jHIdZAD5QOKYDhUScEgqxlGwZFHriMX8hswBbloikgfPGtRxGqd0GZuOx5ZaFm8R07MxDa5sLGxsbGx2bVMQOqApg31QHYFiWjISERDy5Eo1HxkIrQZaEAmMBN28JqsaWBBIhrqtsRVOJYcHY4lzzSt3M8E/t/0OZurG43668ed2ZkHTgFOS0RDf0UE1UWIjXszYsO+gIE+V1cjgqsLifh0M1BL9SUGCuct6+a8ebsSMbywRJWTQU2C9QJZwwBDpJQ1WbPSBgffV4BhGDjN7a2V+CLijqgPum+tqLeaxxpkC28AugKjAklpiiHW8lYjZ5vdSDiWrAzHkv9YseEQh17UVMWwDlzeHJtXjjCWPHPInSVVvcNau2s/W17S8i2luAHYeN/8awyzMfD7Ip4O1iGOhOe0do37EnAXcGwkkGpGvh+XAWc7tfy/DEO7NhJI9bpdmV9qSr+vqnTT3+tGLXzT7+ksLRQodreW8dK/jqNY2DqF9yIiYl/DMpZ5DbgTM+KrnIYPzQDTEl/pyoHaulBhLYpkkOvWMq/ZhPwOWIseOWRRpRq53vf4tWRHsGxsbGxsbHYBc9WVXsDB3LNfR4TBhiEe0i4hHEv6kdXiJcBNSCH/s8ikpRlx83u/nAF8BalzeRhJ9bF6CL0BXA4Y4VjyO4iwuh0paneaxz2MgSL3EiRC9XfgMwz0w1FIGuHg/lRW1ArzccvFb3sUGu5B97cKL/O2ZWQxOA0QQ8dQ2jY27X5EUFnHzCGC72Dzvs5AapQBxuBUQwO4F6lvs9kzFJetm50648jfna/rOA0do6ymWy8f/tZJb64+7vTuvuFvThi5WAO+vaGlvg1JY30auMHaQTiWLANOA543nTQBiKeDk5GmxCuAyxY3htxIhNva5gTgjM7eYfc+t/CSUFF3dXAGt0QCqS7EEIZ4OjhS13l+1eZZdQ33HeBobaxh1un/we3L4pCZ/Ojd/P7schqN+qfqVMMzSFSqFviKchhjai9oVdlWp7Hx8UoNUHq/BigDuZ7cyG9GpXnfutYakEj3anNfbuQ3oh251s5kD6dQ2gLLxsbGxsZm1/AYcMyRcx78PbJCvY5oaIiHtEv4DDIhvCoRDb2OpO+BRGPuHLyhaeMeBpYnoqEF5v1jzfubkLS+ZUBD7cjXrm3rGfPpnr7h84CbzV5a94VjybMQB8G15r5eQcTH2cDrwAHIarULWbkOIZOswX2KXINuw4Bw0RkQN9un8m1NBzR00/dvIPLk3G7bwRhKo2jaa1vHHMZAuuD2BhoWRXlOAzRrZd5qrrpwu95iNruJRDTUAVwYTwcPAzo1DScaa/0V/e3AYf6+tl8jdXGLgMyG5vrpyGS+abtdHQj8D/K53jvo8TAScX0pEkitjAQAqS+0eBzYuHTNCW8UdRfI9317ckpRqB25WB970TJdz2matzRrRVkNxOFwn6PRqNeBTJ1q+DVQZujkMk1Od7bdaV5jygBVQK4jv/l/rflynYEFjCOQa6eMgetzDbIAdD9DYBhjCywbGxsbG5tdwyTA3T2h6n+ylb6v1LyxMTFXXfmNOca87Hu+cu/mBUSgeMOxZFUiGtpZ7Uk10qdqgflvEhKJeQiIJaKhNmB+OJbcb0vHpM9pWqHM4cjr4VjyL4glewQRqkuRidQJwOmIU9uhSGTKiUxw1yPW7VYqoJWO189ANMsSVf0MOIw1IWmOgyNaFoYCZUhMSVcOVDGHE4WhOTCUtu22sLVFsVW/ZYksBwNuZoPp551pifqg1xYQQVcTjiXvQiaHjyaioZ53vtU2u5gaJPV1LvCqUnQAP+rPVqWANwyD0za27HdYd9+wg5QqvDGisnGJaPutvIo4Zm5f63Mz8p17aEcHNSNV803h9X+DnwvHkp8FxsE1N507+9rL3K7CX92uwvB8wV3V1TeMQoehUref9ubCJ458K5L5yOe/x6lTDROQ6O4GYCmGqt+UrBxcTzbYtCaPXOuWoYUDWYzAfDyDRKqthZIJSKQ61WjUp3fzqbwDZRj2AomNjY2Njc1HZa66Mgg821lbpamCjndTV0+x0ne+r7VvJPDPOca8fXAKJIRjyUnAX5AarArgH4lo6NV32bYemayeh/S4moI4BE4EGhPR0MZwLHmkQ8tNHVW94vkNLdMcwIPIJOko4ItIEfuhSI2VH1mZ9iGTMSvSsxExjPAxUOy+DPQyYAxo25tW9AC9iJCz6qJgW3MLA6T+Ss/J/0pDaeaUT20rl7afQG0vpgbbuueQiWIvIhaPYECMFSt5AqMAACAASURBVIF2vYhPz+PWXOiag/lIRGQ5MrkMAz12VGv3YVq0Hwc8HwmkOrd//sJ593iyOe9CHWfl0dP++abTWah6ftFF5ySioXXvtk+zTusXiICYDyx4r8/QNIhxJaKh28KxZAypJfr8ubOvjSK1eZm31szeb0tHXXlZ66otD/7kgrWFrDvaaNTvKPK111KnGmqQCPg6ZBFlOAOCymq5AAPX2RrkWq82n7N63K1FfmOy5vMrzX1VIKLs841G/QO7/4y2xY5g2djY2NjY7Bo2A91la9orlEwJSlVX/0NI7v98pAZjn8KsvzKQSdALiJnE4UjK06sAdarhdKSpb/zQuev8SNreWMRSvcrc7gIkOvXvcCz5a+Caou5u2tAy7Q4k5fAtJG3qPKShccY8psPch8Pcb6U5njbEecyLTKpAJmd1gLfMt7G1u3/MOtCmAc4Sb5vD7ez3tfeMdZqvz7OtVbuFDjgMXaqiNPfWVEGU2qb2Crbth7Wj25phmDVaBi5NQ0fqxmZutw8nUILCrblwKNnuACSa8V3z3O8BbgXu3sGYbXYBpqh6R5QpHEseBJQkoue9HI4lzwB8BtoX122ZfjZwSjiW7APu3t4l08QBjHG7emd4XT2Rrr6RF4djyXYk5fbORDS0I3v3Tyl0/4/v+fHf4LhrAHciGsrG09c6kRRZ35ia/8/emcfJUZXr/3uqepl9SSb72glLQyAgy7APogygoF6xBR0U198V16sObhdBUQEX2hXFq153R8UR9QoitIiEfWRfQgdIOiH7OvvSS9X5/fHWma7u9ASQJJPEej6f/kx3LadO1VR1n+c87/u8K+ZGwiN6/pLHhx87+th3P/fAkmd3z1XYq+hHQobXIDlTF1I0rvArxeZ5mo8oVDuQZz6LKNWGcNV6+y3ytjPhtpv2/KnsjMBFMECAAAECBNg9aAbWa812xxtsh/LaLtjqpNWvjS+d5L69ZCSSKQVcjyTyOwgxOA4ZCP0pkUw1J5KpGUi+1JuXfm79kYgJxg+BO4ELvZytMxDzipuBX3Z3tueBy4GvIoTqSUQdOw9RrwYRorOE4kAJROGyEBVoBmIR30+p1XoElBocndkA6iD5jHXQnPs5eO49NhRsr40qdrZUN4oTlo22xOdPK7WTcuWHaSPnW2aIlju+kRp3LFQUFTeNhATmgBqlCCkLpRQ2Mmg8HSG0P/H2iU3YiwB7Eh8DLkskU+HuzvZV3Z3tT9371EWXrdrY+lrEve8dSM7dTujubM8C73zV0T9afvKS31SdftRPhhfOfPhV0fDg+eC+JpFMVXX1tE31jDAAWLro1s+/5oRv1h2+YNn7ujvbR4C2RDKVvOn+jyeBjwAzG+u2ZGbXLb/rmbuWjD73wJIIEI6p9ETGLfskMjpeyOj4jxCl6TzkmRhAct2MayeUmsrUIOp4NegWuy4fibTkhr11/qe0HplEeTewLabSe11QChSsAAECBAgQYPdgE3CbLYrNg8BjLlQRsi1rNPe7a9WHHgLeeqm+7l8tSjoZeBAhHq9Dwv22IWF8b0byG6I187Lvqz8o2xKud7+OzBg/iFjUm1yUIxAS8XtgUyKZuhBxW/s28F4kDPB5hDj9A3iFd8wCEgZo8qxC3muq1+464FCKzn3eS2kIGRt0Daj086fqkJ01y/3w7VdKuCwZIfkHbf6wJXz7+IsQ+80yyh0Hy9eP4FPSPBJniqqGkSK0pm5XE3AyASYD1wDV3sQAXT1tTeefxpHAfTfedfm3gNvnTXv85P/5x/curK/ZflVH67LHzY7Xqg/954lwVKR77OehebnlNVUD66fUrzt64cxZzj1PdLw979SsRIj00q6etos6Wpf1HjSnZweidhvlZR6weGnzHz+y8sFDF84/ctW7wtH84Fde99VYPhu5xg7n5x58wvKrNj47b0ZMpc/K6HhJIe/9AHciobCtyH1v6luNPytTjh0ClLXjoVrzrOm6xWNq9jkD9vDa8KL1/zfFbKsp5laGgUuQiaGPsJfV3yAHK0CAAAECBNgDuFZ9KKRh/ZYjZ02v2Tbs1m4ZdNafcfDz68857PXdne3LJ7t/Lwae9fRnkByJLBKidDQykFkF/A2xUleI22AIMbUYBt7Q3dn+fCKZOhwZ5PwB6PLeT0GK7v4COAsZVA0jrl8neMcqIGGCjYiq1YwoBUbR2u61Y/Kv1lJUeVxE/Wmm1Gyi3KRCUVSarLLlu4LPNtpfX1iV72tWWhTJk9mmzzs3P+nyOxY6yCD7aYRsberubA9UrElGV0/bO5GJgQ93tC57JJFM/UCpwnnNdRvskw7/3dujkZE+4KEb77rcPfx7d19Zn9m+UGkuuVRfN+Ltf9JYrua9azYfNbhw5qOD0fDoHcikxb1I/byvIhMSUy474QejKP2GSLOTjloDN4Kqsyx99qObT74fIKbSTZ+++dJs+q6ld6x5/CDnkb+cfEZGx3OV+r0vI6bSVyPXtAYhRiZUEMBd2LHNQiu1+tdTzbOsa+Zk1bTTBtl0ewPZreOVFVzkuRnz/nYhkzAfzuh4eu+dUaBgBQgQIECAAHsEl+rrCteqD80fWTjlDQOHTovYefeV24+bfz4SZldCsBLJlA1oU5x3H8IcZGb5PiS34SHv/WsQE4YruzvbdSKZmuMtiyBq0w3AhkQy1YiMNX7jbX80UrNGA68E3oMQIwshVP3ItWlElK2ZyIBrqrfeOAQasmJUJRvJ0TDXz0UUNmPLbupY+a3c8S33XfdxvmXMNAYp5neYfbxwRHkfsvp0wW322hqPbNJgGWfACMUBo9mgpqwfA975mWR/GyFhx3n7PpFIplRgdDHp+CtC7p/2Pv9E69DzvYOz+7b2L1g6d9rTbwA+ClQtf/8pp6H1Nd2fOHvE7Nw7OOuw4bHGcxfNeuj2cCh3HDKhcDxSvmA+UtYgBVzwitfel3z01tZLcjtC38nRdLOydF679jhRyOh4H9xEV0vbece/8e6xG29+935Hrjw8BHwIefhKCn8D1tobpxgzGLPcGlkfZc1vojuQ59yE3/YBVwLHIEXLcxkdn5RaGYGCFSBAgAABAuwFJJKpKDJYfqS7s33Ecxh7FTKw+hywsbuz/bLJ7GM5vD63ISYdbndney6RTDUgSlYVQphCiLX6YmTQ+TSSU3Uv8LUNt9ZfYNe6a6afPDyqLGYgqtfbkNC3DEKmZiMkbAuihI0gg84hZGA11TvOGEJMtHbRTgFth7GUYsxb9zvgPyjmxBhrZ2PXbhSrShPMWkgRFjja22QCJWt8Il1LcJ/WZuynGMtH7NGwst3cWG6qIWJmJ//A0fxViGI34v1tomjAYYwTBpEiqkngdyZcLcDeR1dPWxXgdLQuK/kfJJKpb9tWbsmZx37/57VV/b+78a7LG5GC47/q7mxf85nffOGoDdsPPXpa46oz+odnvn3x7Pv/OX/G0xbFZ2gUIQhRpEBuYyEX+s2axxcfHYoU3vWZ9/5kpXecQwCru7N9ryoyuxOJZOpQZMLlj92d7YWYSp+G5Bq6SEhklNIJCWUMPuW9Anm2+5HnfggJJTaTEsb44r6Mjp+yl06rBIGCFSBAgAABAuwFeAnv9/gWxYDLgD8joWDbJqNfL4AE8GHgw92d7f9MJFOzEGe7XyFk4dNI/sQahARoJJ/q80ho4ZucnJrRuCQ3A0U/QqDejpAFGyFPCxFV6lRk5GSKAjve+3uRgdcZFHOgXBSWAq0dCipEH0Kq3oKoTaY4qamRhW9fQ3IKyDjIZ6euAFd7A7hyZ0AYJ0nmo/L2GbfB0JqIU3BV2HXGc0EMiSoPTzTqmTnnKoohToYIGvXMKHKXAo8jxiAB9iISyZR15jHX2w21/BRY09XT9ukb77ocoK67s30Q+MWCGY+9uSY6kOtoXTYCbTkkv6ivq+eL76yOHvdZpdy6Tb2HvG/21OWvmtm8cgmizvYDLdl8dbXjWk5NdDiKEIQNoUghtPi4FSsQxczgKiCUSKbO34/VzPOBM4Ee5PtiDhLOW8/OCrMXiguUTnhYyDMxlVJ1q8W3r7/g815F4CIYIECAAAECTA5WAp9F8pC+AKxIJFPTJ7dLO+FIZNAz6H2ehZCYdyBuX+cjduJHIMrSGmTA+E/EHGDu3HMHqJmVzyrFj5E8qybEAdC4EmaRQdRcZPZ5MRKWWAeFakW+ERmMVSFhdGOAVgplR8haIUIUQwnrkLFNBC8ZntKcKv/gzUwy2771yovO832uaMHuqV3KhCDi2z/v6KiriZaTKigNVzT7+R3TTGjgGJKHM+gt24qQ8JuQEMsAexGeanv7Y6vOug65B9YD6XNav/5QTbT3nrd9+8YTzj/ti48ffdBf5yil39TV06aQ+/ubwFeGRhs+a6lcS0vj6t+O5RpuPjL29+fC4Vy1U1BRvKLYT685XT363Ln1jmNngduBjNYc17tpyqbLTviBvy7XtcBX92NyBfADZLLA1BB7EJk02ESRTBlMZExjamZBMWdrADHKeQ55Zu5hkhAoWAECBAgQIMAkwMu3ugsgkUwdh6hZ30cI176C3yMzzKu8z48AXwc+gfR3NTKQySGuga9G3O5uBnSuX7l9T1Tb9QfnVlfPKNyLVzAXUet2IOGB/ciMfQuSj+KpTnrwFQf9Zci2CjMffOZ8o/Q0Ucy/gmINLINyQuMnRwbGHr1cYSrfR5Xt43MZLHd4L4HJ/aqEEYq5V/5Efv8xepHZfI2QTgu5vjnghxPUWwqwZxECDp5av+64bK56RTQy+iXggyEr39xcv4EjF6XePzzWOCccGrkvEsq3IjlYA8ADwPHR8NiAbTG8etNx/9Pd2V7o6vniyqfuOurgf/zq9XPP+8gvwwsOz+hZU1Ywlq/DspyNwEVAb+r6/3j3g/936kWIunsXQHdn+35VULgSujvbt+NT5TI6/lxMpS8GPojY4pvnHUqeuRKUT34UkGfrNOT5M4XIJwWBghUgQIAAAQJMPh4D/htRKPYJeEWG563/a8NzT39z+l9bT/nnWchs+z2IZbsJ75uCJJUvRgaim5AZ/ldbIcLRaY7KD9jTkJn3RQgpuhjJ7XoKGQRZCLF4ClG5XCDsOJHnxvI19yMDqQI+oqM12vUKAiP5K7rsZUZkBa+vUByU+SeYy63YK8EoTqbtYUrtA8tJnFW23G9sYVGZoZmwwGqEfG5FCJmLkM8jgMWJZKpSgeQAexDdne07gPdkNh3bs2LdKd9H1JZ4JJy78ITDbrykd3B2bv22+GGrNhyvEPXxfO/1Q+Cj4VCuY8HMxz9QU7U9ctkNV/zQcazFOhJydFXUHXPrBtPPn6yqo/1qwYwnXC/Y9HsAd/7stbcO9zb8DclVPCARU2kVU+nXIU6K/0FFcgWA9iJoVdnjZrbJIqQqhKhg30DCrycFgclFgAABAgQIEGAnJJKpVuAbOx6rXr7lrvrEnHP7NtXHclGEAKxDwvqORZQgBxngPIOEFWrA1Zp+J4dlR9BKMcVregfFIsK3I+ThTsT1qwEZYA0hxGgWogQUEPVqvA6VdkFZ2htsuTlfjSs/sclT6h6Y8/o/kRV7peW+HK2dtvXXxvLvWx7+FypbbmzbzUS3v46Ped/nra+n6LK4Gri6u7P9pxX6HmAPoKunLQq8Z2i06YHbHvzwaxCTmpsTyVTteSd+7SdK6Yab7/9Y6LhD/rRkVsvTQ1qH3LFs7bSa6oEBS+kcUp5gKXD0ph0Le6Y0bHgNmgHbzhW29C6+ZWrD84du7Y8d31i7yeodnLe8JtrXMrVx/QNAoqN12YRqZSKZmgeMdne274u5my8aMZWuRcxpMsBGJHezUiFwSomVMk4zZvLE1IyzEaL1PPC+jI4/u+d6PzGCEMEAAQIECBAgQCU8Atwz5ajRTP3i0S3hOt6MKDDTgOkIAdhKUYEKe+vMwCinFA2hKFHEWXAqMrs/BfgjcB6S3D4TGRQVEEMHvOOMeu8bkAGTGV0JqVHmswIsf2FhP/HxkyvTRz9KRmxUJl0FhDzWV1jvjwQqT8A3oYh+a3j/McpVLn9oonJdVloWOYTEGrOOaUgY5k8r9DPAnsFbgCsty/0FotIel0imjgT0/cvfHGs97IZNRy66bfq0plUR22Lx4HDtyMqNJ1TNaVnuTGta14SE+/UBVVPqN/xUa2ssHBr7h6VYOmvqym8DzXNannkql6+64pl1J188ODJ1LBwau/7e/zr8mMtInw98LaPjfpML4+75XUQpfv/evBi7GxkdH46p9McRTvIR4BbgFEQtrwN0uMFRTUeO0Pt4lXbA1YOh8cJzFM1y8qBRYTev87aN5HWtn4xzgiBEMECAAAECBAhQGWchuSTfDtdxKUJ06igShFpEYer1Pj+D1MgyoXT9yCyyRgjZ3wrD6mrtkkcGRBcgRMoFNlMkbOsokiTjJtiEkJzx/CulUKAcUFmvZpVRhxyKIYOmpo4hM2Yw9mKIlWkvaql8Q2ONn+MBuEoxosXSnWzZPqbdMEW1qvw4lY5pAVprtFIcr11O9Po8ADwKPKbI88v7X3lCV09bMEm+d5AGltdEB1KnL/3fHzXVrjtK4XRObVi9tSo6uDJs5+cvnv3wo9FItjefQ9dW99ceMuuOsaa69bjuuBHDNEBHwrmmaHjsYEtxOuLAuR5x4XxzJDz25MIZD99aW9U3fNTiWz/VOH3HEkT5aqrQpxzi5Nm9V67AHkZGx9N2OD986CmPH9s4Y8eZyPdFGG/CoWpmnuajRqia7jh6MKxB+RXlQcRZM4yta+a+rj/cEB/9KXBFRsdHKh1vbyAIEQwQIECAACVIJFPnIQPG+z374QD/hvDugx8iipNRV2zk3jCKikLCeqYgBYIPpajCVCGkaSMQd7L0PvO96f2Nh40umn3O4KOIemUKEd8JxL02ZyKEZNhrw7gB+hUov+qTp6hWlYfd4dveQQhQhMqq0YRYOLOHsWwdm3rjvub9UYC239nMf0xV9tcQROVrwBQiHg8f9A3NtJeT0+vt85xtZecfNv8fA3mn5sqrLvjCr3bV7wAvH109bS2IM90soG4sW3XPlr5FDfOmL39OKT6BGL78CHjD0I6ajtqmkbjr4lj2+ESACfMzLndRZPLBGJ78AMnXurSjddltXT1t7wCWrH0ydsX33/OZ6oyO97ILxFS6BrgCqfn0p918+nsEMZU+EXgf8PmMjq8B6Oppm9+3qfnWh/58yoK//+j1KxCV+3LAxtK6qqXA2NYQaBQhFywK5Gwbea62AtPBLUSmFSx3zPr+swNHfGiyzg+CEMEAAQIECOBDIpkKAz9yxlTjur80OIdc/cQD+R3hTmDNC/3QB9h/kUimlLF9TiRTCrFP/wYy8w6SNxVF3P9GkRwqELK1Hgn/OxohGKYm1hEI8aoGbCvE1IbDxsKRqc52ry3j8JVHQoJyXnvGUKIZISQ57/hzvO3LCVGobLmqsJ0xtqhgt75rcgWwbusRWrvjBU4NvCLDljlWObkqh1HQDMz24bJtTFWtLUh+Wtbrex1QA+4tKze2HjGWbXhbIpn6tedGGWDPYRS5V5uBXFV07LvzZyy/ELFhPxUpmn0OMFo3ZaQlN2bfEo46Z2vQPvs7C7nPG12XfMGxGyzbCYcsLOAVyETE3QAdrct+BkArfOrdjL2I/kWRyYktu+uE9wLqkHt7vE7d5ad8b95Bxy+/2SmE1gN/R4xzNgJzcZUa2xKWXCvl2lZYY1W7urDDNs9vHzAdrFBua2QMcTSdVAQhggECBAgQwA8N3DC6JWSNrIvWhpvzr7JrCo+A3hFTy/MxlW6d7A4G2L1IJFNx4A+JZOoUb9HxQBIhSQMIyWlEBkULEYVpA0K2sgjxMaF42lt/GMUioTuAgrJRc84ZaGw5fuRJdiY+VUiOUzVFdce/bi6lxKlA0R2wPL/JrC938DMKEmXbvyBBKTg1OLoGL7Hea8syBYnLyRXFbXYiWrsic17YU8lu9wEPIwPI9cAsx63+42i2+eMa+1sBudorOBoJjwVYgYTGphFiHEVyfUaQyYjBSJVzl1LkLYX21CtDqsOA5ThELeXWatdqRIwdGoGrpThxETGVviKm0lfGVHr8nunqaYt29bRd0dXTdq5Z5k18dSDOnvsFMjr+N+CNGR1/BiCm0me7hdBX1jx+0InnfuyGgaNfc9+zSO5aEzLBYsJwXbTS2lUUdoRDyHPeD3R560eRsOY/7v2zKkVAsAIECBAggB/HAseoiPvzSEvOCUU0Oj8ecRUC7o2p9NRJ7F+A3Q8XGcQUPJJ1NJBCCNVWhCCZfIg8QoKmIzP6DUjolKnppJBBp9/WPAr8GKl7M4QUVb4JGRCZ8CnXLeA6hfHwvUFfv7IUWYeDDGYN+bIpJVbmfbmaZHKghil1GOzzbcME7/0qQnn4YaV6Wf71Bd9y17feH19YZudeIBIacMGZhtQUOwmYj1znNNCDqCaXeIpzgD2Arp625q6etrOR+/YxPKMKxIjheYQA3NDRuszkKj4H/CcSzuki982A19y4shoKgW1rQrYbRshBnsrFo5u9lx81yHf0kf6FGR0fyei4w36EjI77HRIPBQYv/NIPH5mxeMPHXvWeP98UrR1dioTOjiLXcwAYBDWms/YQKDOxoZBC5McCszI6/oOMjm/eu2ezMwKCFSBAgAAB/FgOfK92pvO5lmNHL7Pr8o7rlkzC28CKmEq/YXK6F2B3o7uz/ZnuzvYLvAKmbwY+CbwdOMh75SiGtw0gxMYoVmMUZ5ihNPHcRgZIU4H3eH9BrNkv8bbJAjknx7ODmYit80S9NrdRzPmqorQGlX+m369O+W9U//jG0lqMIxCVTPu2aaRoC68BtIvK9qK9+lrKO4dKqDSG8tu0+0MC/aGLfgJoyFW+2K8Q0fBIpTpABeBgJF9tALG2DooO7zm8GvgioiIei6gpByGE69cdrcse8KlOdyJFtP+ODPafBL6D3HsF794DJPzTe2ngDGRC41NdPW3zy47fCXw0o+Pj+3a0LutFasgld/vZThI8m/angM890H1GF3BjbfPgUCiSvx25v9cjz2cD8rxGke+NXyGTPxoJMf5KRscHKhxiUhDkYAUIECBAgHF4phZdAK+/4vY5DQepkd5/qvriFhpQU5Gk7v0ioTrAC8MrXjsT+BIyM9+CkIQRJDzPqEAtvt0KyMz7wRSVGEMs6rxtRrz9anx/bYqW5xrACjO3bmFOqSKdmI8oBrUIwbIpWq/XeNu4lNqhZxF1zUFIkcTvuWitsVyHXCgyXsi3PF/LFO/VQ5kI6//SpGed1acbD82Vq2FQqlpVCvnz1c3yp+GMh4uZ8/bX0DLKXR+o5sHRWf7jOL52FTKj/2B3Z/tvKxw7wO7D35BJgL9QzKEqUAybBUyI7eWqu7P96a6eNoWQq7uBp7TmrblCuMGiQDi8U1qeeV42AIcjz9HzZmVGxyuGf3a0LuvfTec3KYipdAg4EXgyo+N9SMHxzwGjz/UcVr/puRmfu+7izy3WTmjjK86955pIVW7JA79/ZT2oJsCqjY3pmacP2Dserb2z99HaDorhwPaEB50EBApWgAABAgSoiFU/b/nyuj9MfQosccMuRUtMpc+bjH4F2H1IJFPTE8nUD5BZ+nuA6xE7cBN+Y9SrUWTg6FeSbMTIIk9p6F2O4gC0OhraUVcX3WiDDgM2Wuertgy6quBqbz+lFHV2GKzQOAEyqlGYYm6FIUS1FImVISEhiuStQJHoaRSuUig7NB7mWE6uXO84ClDVM/P56a8coHZ+DkDhui6ljssvlPfkKzzsAjlteKSv38otoF2nJCes4PUh5332OzeCkMBq75r1e7WQAuwhdLQu67vxrsvvKThktQbXHVckz0XCBA2+BFztmcOcC7QCtyLOg7eF7bwdCml/GKgfLlIj7h3AHXvubPYpHAV8GXi99/k+5Br+MxTJLfjTVy7+eW3TYB54NnHFz2a0nn/nCdGasWY8ldeKuKp6Tr5myjEjSYkSRCNKVmrvn8rECAhWgAABAgSoiCdXH7XBGbNkEKf9KTYGujum0gsnoWsBdh/ejiTIL0FCoF4NLEAG+S4SkgMSlrOEoh26uRFsSutjGSv1cfOI5vqNlqMjFsJSnNp1fXas+7FQ81MbLYpqEhTrXIEQKtv33rSJ768hJOV5UDW+z65SWMpCKWs83C/PxCqUDtXqyJQjx6xQNRZSy8ZPxPD21VrLGRnu5Tpo18VUv/H6pYFQOVHSThZG1tm2dkss223vf+C3b8/6rquxmI8DXwd+nkimjifAHsNRi29+l9ZWBEApQhQdHx/3bfYV4BrPhXMjsBLo72hdlleKr1kWtyrFRor5WAYmB9HpaF22uqN12b+LYclTwFeB2wAyOt6X0fGbgOss2/3VxmfmDQztaLgb+PUvOj/40B+veXtvdqT6KaC//tCRvgWJPmfHo9XWyNpILZrlwGmIE+N3JuuEKiEIEQwQIECAABURU+kwuEeUegjge6+j4SmFR2IqfVBGx7dPWkcD/EtIJFOvQGbOjVlEGCEn9d5nv9JjPpuwPCgqVYbQGNXIb3Axtrnv4LzWdiNYFmCNTa3VO46YpYfnNpl9zFgk4tuvDhmQaiT/whA9f66VqWtlVCF/3wwq2baX38xmvb+GlrdWldu5a9NfrdHaxTX1jrTDDmAKNijbKICh8T7nBy21/aEaph47YoXqXGrmOf6QSAvAddFARCnGPAe6Gt9xDdmKInlAZyAD+n8SYLejq6ftmNgsrlZge4WfFfI/uBFReenqaVPnn8ZrgAFo7+loXfYQ8JCvmR3Ic/IdJL/1+0gu4hiiXK0AfrnXTmofQEbHx4D/A/AcEi1gNvD+0YH6w5Hr9WXgo+m7j3oGyXu7CniLLqhwqMbJjW0Ord3xYEML8GvgIX+e2r6CgGAFCBAgQICJcAUob8a2sslafofdBHwY+Pxe7FeA3YNTEFJjU8zvgaLSdCfoTwAAIABJREFUYvJODAkxdaQMmQlTdA+EYi5UxLdPtdYRkzPSBCinJmJtblts+mDykkyOVwEJAdTAI0idoTleW1kk5+sw77PJm/KjPPzPv9zy/fWTMbOPhbi6GUtuaadIsvL4Ch0rhauKdExbYTm/8kAwJwdOFmtodYT+5dXULcgRrs9pZY8fY5y8eYN4pTUoxTBFhc9c2wbv/S+RfLgfV7gGAV4munraQsDPLUXtaLaa0VyD01S7OeupWE91tC7b5G2qkNIFvb59T0bMYq5GwjmfAB7taF12S1dP280IYbgGIVrv7WhdluPfCF09bU2IC+L98IP3I/lY1UjNvLuR570emURoBlYjNfl+P7Sy+ohHL5t3B/Ba5Ddn2r5IriAgWAECBAgQYGIUJABKlQ9WKRvDdsZU+rqMjm/bi30L8DKQSKYs4A/AfyDJ9YpSAmEIjUIG9uVEyuRnjSs6FPOHDGkzJGYHYhbQ5G035h3H5Fv5Q+RMnlQBye9yfO1HEXv4AuBoTTVgKTVODssNJAyRegQJq6tlZxIJpWSs3vdZlW1jjChAtC3bt4V8Lu41vsYds7STVcqOFoh1bHPDDdofYum/biNAnXbps2ymUkp+M0idJXNOl3jXo4sAuxVdPW1hxKVvFuBqraywPWpUXg1EunraLgRu8HZ5H6UzUPOAQ4D6jtZlW4HLzIqO1mUFIN3V0/ZZoG5/JVcxlVYvg9icB7x/aLT5E6FaJ18YtncgOWvZjI7fFlPpVcDHgE8hIcsnIoWeVwHtdrXzx3lv7F2/rafuq0PPVfXshtPZI1Ba75PEL0CAAAECTDJiKj0fWFNcUu7EXYLbgfZ9dTYxQCkSydR7kTwIY11u1Cm/eYTJKZmGDOah9CYw4Xy2Z0OtlBrPH/KrQ3dHQsOHRsND0wZHp2ukWK9NkdD5wgtdwB2GUB2l6hYI0TA5YFq7qPwobrgGrVRJXpiDKAcNFGtpRaicdz7RTZ2lqMSV52HtKn/dH0poXAzJbrcJNzmuHZ6wDwVE6XhF8VqMX5dhirXAplEktjZiC35+d2f7yM7NBvhX0NXTdirikGpqUPnvjXVIDbd5wFuR4r5rOlqXXenbfyHQ3NG67JHythPJVANwDHBfd2d7do+cwB5GTKVbgP9BJi42AX/L6PjqF7t/V0/bDOCEP9z9mTO0Ds0DLurubM/GVHoWcr+fgNzvKWTy53AkpNACbrGrnSPm/UfvhqnHjn60u7N92e48t92JQMEKECBAgAAT4arKxlflyxRIPkgIGTQH2PdxDjKAdCiGB/rzm0AGNH6C40/CH7cv15oxoEo7OCrEvUhYn3+7Q1oaM1MdN6pGxpq0o6MDiJpl1Bi/8qRqIxtGh3PzakCNIAqXUZvMmEUUKoUdri668vnWGaMNQ5LKyVW5wYVbtr8Jh1QV9pmonfIERfAIorJQVdMcKCWd/jZMf5dSzLWyKCpYDUho5IPABYwXXGUKMrMfjOV2Lx6nQpip1hSGRqdYtVU7DrUs/gtRHHews3nFlUBzV0/bGztal5Xbr54FfBy4FLh3j/R+NyKm0sch9+F9wCIkHPww5Bl/HRIa+bGYSl+S0fG7XkybHa3LNgP/d+NdqRrtsrb3iep8TKVbEav2QSR08gHEDOP9wI+9IspOTKXf4eaValoyNopc+30WwUMZIECAAAF2Qkylm4g4byK3qxJAJZP5JlF5DQH2SSSSqRlIras08CpvsVF+yu3LC8igfwagXBetHZSywbJ2Unsi2sVRNhYy8PKrOC4wbeP2g9SsKc894OjwkYhhhUbcAbMIUfCgGM7NjnoqVwgp4HoUQjKMdXsBSCvFYagS0mXCGqMIMbsXCQ2solg7q7xvJtQxSumg2hA3KlwffMsRjcrsq/zHsNmZuJWTsj7v/LVv+2pvOd6yELAYL2QNeA74IWKmsL27s32fKa56gKADuTeb/QuHRxtCW/oWzJrTMjqtKjI6A8mBux/4fdn+1wM1FcgViBX7KJ5Jxn6AryOFkLcgSnfU+2u++FuQ74nXx1T6UGBtRsdvfTENP3TpvMcQFfBZ4KPAViS3MIeofDHgUCQn63GAjI6vl70Pf9kntqcREKyXCK/OQSuwvruzfd1k9ydAgAAB9hC0FXEibgnBKo4/rdo82Bp3WIGjwBtIExCsfRkfBM5E/ol1FAmGQlQsE7Jn6jUNIzPUszFqlYOLtZMxg+W55pnl5SqPpaliw44jcgh5MKj1XibPygsfDG1CiN0oMtufBNp97YWRHJccpbleUHQitBBSYuoMHU0p4YGiiUeUnUMF/UWAQYhbnmKtLT8qqVpGFfQTWH/7Bs1URo4iEfWTxwhwHHJ9aoFsIpk6obuzfdME7QR46ViDhL7N9i+sqRokGh4tWMrtQ9ST/4cM/v+M/G8A6Ghddv9EDXd3tm8Hbt4Tnd7diKn0CRTv4wV4OYKU3u8Woka/F3k+NsVUegNCtPq8dhYBbwN+lNHxDb59HSBff+iIjk5xHu9/uvq3z/QecX9Mpc9FTCzuBi5EVK39DgHB2gUSyZT5oVFAAnnojka+8H8LfHryehcgQIAAexTz3VG7Qr6ITNa7IxZWGEOuDCrN2AbYd/BrYD3wWeQfacwmTCiar1aT9mpUKQmv01AYZijSiP93sTyMDkqJiSkObNo+mWLelkVRJTP5XwohdSbB/WCk+HF53SpDsiK+9v0w9+10b9uplLn1+d4bU41BhLCUXYdxRBCHwVpznlqLyuU5/xmY9yFKQy8NKsXcDiMqm9/N0fR9jCKp04iz2kxvv3qvP19OJFPXdne2P1mh7QAvEZ7b37HIeM8C2Lp6JjvWT9WHnPyUVopq4A2I2rKuo3XZfplLtSvEVHoGcD5CngaQ+6wG37NjV7m4BdAFSyGTATnkmf8pQiKv8JpbDLwSyanyE6wqoG76ycNnRqYUOkbWRc6KqfTngdOBnwD3ZnTc9frTgeQnXu7ZvO/zCAoN7xpfA36A3CQ/RqpyfwNh8TdMvFuAAAEC7Pc4C8fW4GAxgoyFNXaDS6TFU69yJYKABbxnsjobYNdIJFNRRC2pQwY5eUrrVfnhWipbANcCHQawbIg2UacUUUpJUaW8Ldd7baMYaucPmTN27JsQYjOKmAcMyzr3Z5CPUyR+zV4bxnGtUqgdvmX+l9/8wr9f+bmHfe/NuiHMjV+cqbcA7RZw3AKErSEioUFf8yX98ZM0/3Xwq1nGyMP27esPU6zxbT8NGZQ6SGjWgHcNFwDXeM6QAV4munraliKKyzgevvkk7vl1uxrpq4sgKuwrtOYa11VndfW0RSo2tH/jAkQ9ioGeCVrZtXmramZurOGwEeZfsC278KJt7qyzxqNTFaIEz0W+Gx6NqfThXp2r24GLkXBKP3YAz6+5YcrSDX9pun9kXSQMfAaJ/8sbcuVhDmKHH2Y/QfAw7hrbEK/+/6T4w2AB13Z3tj88mR0LECBAgD2MakCHcajGpZpRQDP9hCEO/cAWGg7ayV3YReq+BNg3cSFS7PSdyMx8FBm8GxMJE1bnAFprO6S8t4Au41LGiMH7HdSAVhXEmTnecfwkxxg6hBCS0IcQqbnILHkDqJaQGq0HN4eQQY0QCXPTledR+UmeeT88wXUw+5miwuZ3vYaio6HpZy1Flc4B7yEApWxCVgiaajfR0rgKKJjBoEaUrnJDkEokEIpKnEGInc/FOCFuQ/5fT3vLa4FuoBMpxHpxIpn6aiKZOhAH/HsT5rkYv6FPuuDvnP2hG6ltHnKRukxfXLH2lOYHn3nDFY5rz5mkfu5J/B74C76yBaFarWe9eoBwY4HaeTl7ZG3EGV67E9+xEOX5fxEl65CMjrsZHV8HLIip9HtjKl0P4C37WWHYPrL/6WqFVhsQd8bvA78qazcJvC2j4/tNuOC/bYhgIpk6HSn29pPuzna3wvrjkB+iRRS/XJ9DvuzPSiRvtcC6t7uz/em91+sAAQIE2GuoAVQtAyxmJcs5AizN8MYQ9dvCujBoQ2l4WIGJB7UBJh8PAD9Hwn5MnhKUOgQaq+8qTdhTXFzAdUHZQraUGfB/C9xacBaB3QhKgc7LdiUFfSshj4w/qoH5vuMDrINsS0FHQ6CmI0YVMcTgwTj+WR6h06Asn7EEvmOaUL5yGEMK2/e53FXQv52BhRhtKAATFrhtaLFiSGuwhiiGGJpiwAbmmplj5iiSOROeaAay5e51oxQdEYcpFng2yt/NZsI3kUydgVzPEEUyGuAloqN12f1dPW3nI9bgswEapvXTMM3UyyYCPLVm81G3gKpfsfaU1Zw4SZ3dQ8jo+IaYSv8B6ADVC8zO99u5zcvqttYuHm0aXFn1zMbbmkaQcL7y5zxK0aDmed/yU4C3IxMzRqS4F3iNt50x4fl7ebkPT83ar0Ix/63qYCWSqdMQUvUzZKb1COSfPR2JSc8AjyE5VsdSlO39X4I90fBQw6Hz7lqSXd+w7jWjP70J+PKpn1gdFNgMECDAAYOYSh8D3AtutCQSK6xRroV2VLmjmgs0Z3Q8cDTbR5FIpj6KWCFrJNTM5FHZyOAl673/A/B6oMFHZPCc/Qxx6gW3UbY3hahVH0USolzHoz+ytlL+k99kw6hOK5FQvLmA2piq3xxpzo9OOXZsthee6MHRwukjFihj/mDgvy/L3RErIU+R/JSH8pkJ1vKcLNM2vm1GEKJUSa0yxZrHT8D7W6CYS1bett98JOe1bf5Hm5EaZZ8Clnd3tm/2QgTt7s72oFTCbkBXT9u5wI1AxAyVvXu5F/guYqt/cUfrsv7KLezfiKn0GYjIsBFxrbwBue9ejYyZr0ImPhbgTciVNfE8cn/+PqPj+ZhKVyPmNAVgRUbHC3vjPCYL/24K1rlIyF8GKZBWhxT2a0BujvLq6n4ob7tXZ/NO9pm1J0YX5p9c3MuM16bUO6q+mUxdjlSb3tTd2b5TcbkAAQIE2M+wGs92OtxUIN/nzTXlLTN6LP+utIDbYip9ilezJMC+hzHvVYuoIMYKHYqhahZwESUK1zg3MTPVCmguEiuzCDNg0tqF3iciNC3JKTtc0djBb37hJyRh5Pe4H4iObg0NqbBbgyaLGldwWmT/8SGMv1hxpboCBYSoVJct9xtSlO9bHoZozl8Buk7vQOGoQTXNbO+wc36I3+rdnKs55hAyBvErWfjW+/+GKf6/zMRvtXed3gPMTiRTV3R3tt9NaWhigJeBjtZlN3f1tP0JeLPWnkar0UphI8ThN8jzdMAhptIfQvKh1gL/hZirnIFMzAwg3gTfQ57Fb3m7lU+6zUIEjdcA78jo+GhMpZuBLwBfi6l0Fng38OmJChV7pOxEoCej4/tVhMS/G8H6MnA28Dd2Sab8RdR3goLGqrE8pPUpoZXq2Hhe1RwKnIfcTGsTyVQK+ER3Z/vQ7j+FAAECBNgrGAD+ieKUKceMMLw+xNAKk29f8btRI/V8jBNagH0IiWSqBTgNUUCmU8FK3ffZ936cRBkCYNQcq+w+MDlBFohyVb8op/P9lmO3uP4f1HJXPf/nGmQ23OQ9hWMX9s5GUa2scdfBrGxfEqJqiIzra0/5PvvVrYnULKtsH7+65s+LAlDH6VuoYUDfwnuVVmEodVT098VPSs0yB1+4IZWJoWkHiupZHnjG234WUvB1EInM+XwimXptd2d7DiCRTC1Fiklf393Zvt/kreyDeCvwyMBI0xVV4eGqaCSvECURoHCAOggqZEybQ4woTkdUrFsR848ZiGtgAngSmQyZBtoCzbRThikMWfQ+Vgtyj9fEVLrKc/9bjrhwJ5AczUEqq8MGJyNK2ReQnLD9Bgc0wfISPS9F/oFHINLmO9nFP9Ok85rfFKWyhOwcBaeKnSanlEWeGvNFbeLIFyL2nTMTydR/A+nuzvZ/nzjMAAEC7PdIJFPHveIaOpd/febi7NYwm/9uyvHALghWDjgmo+NB7sc+hEQyFUIGRdXAGykW1IUiWXIoKh+GjLjechOWZ/YpL56L66Ky26xsVYsbUT6aEGkAcI2y4ydSDyCWy1XecUwIXC9CstYjRCuq7J2MJ2qQ3KNehGT4CZM5lzwSahhn4tA+PxGj7K9fXfOTn/Eb/ylOpYYBNKFywuYgqoZRy/y5V5WUKn/75c6I5e/DSMiWhUyArEUGus9TdHBb5e3TCrwOsBLJ1Le7O9tNfl2Al4Zq4OSmuj5/OJtyXQZWPhh/5BeFV7bbIfdvHa3LDphxXkbHdUyl/x+iWH2r6ciRmkUXb79p3Z+b/nfLsvrliOp0GlIEeBNiw/49wu7FOFC/OEuu36L3sVrDMU4BDoup9FbP2OK7MZU25QeuK8+38uPIz244vvfJ6m29j9T07MFT3iM40F0Em7TD2drlWKS44mcRh5Jdw/8LQZiCU8eL4KL+L+WZyMzR34GPejOHAQIECLC/oEY7arqytc8owHzF+ceCJb+Loxyg4TL7OZoQB8FzKM4SGlc6Q2yGKYYH+pWqSuF2xkFwXCUqDCq399HayMiGkGfPPu5+bgjCKKI+mZvoaCRfacD7O4IMZEeB3yHEq9r0QWuhaa4zbv8eQsiVUZhy3suoTcaV0PS5UthcJXLlJ15muZ8IjWOrtYA11pGqJEpSYEw+/A6F5ccznw0cXz9dQI9tteh9MqrdfEn/8kiKwxhiXd+L1AkLIf8Tv3L8c+CviFKwqML5B3gR6GhdNgS8D7gWuU814GxbM/2pkd76T2WHq65ESO5+i0QyNSWRTF2XSKbONMsyOr4WqAX93Oxz+tNunnOGVkbfgbgDrkJq6j2CkPiDgR2Nh40OWhFY++cGNqYaANfc443A54HrYypdG1PphQjpuh9YElPpxRP1LdLk6BmnDvXGP7zlmkQyde5uP/k9iAOaYLkFTl7/l8YjMr+cshSRIlX5d63ryqvc62Pn78yJUrMqQiE/EDOQWloPJpKpI19q/wMECBBgMtDd2b7sqa/OvGRsU2RL5S1cph4/RP3BJXzqyQM9aXl/RHdn+zYkh6KJIhkxVulGraqlqLSYQb6/+LCBIQx+4mHZ1TofnZFTVdMLXhHiEoHLRohBwbewmmJNriqkBpaFkKJLkJlxU28ohBhmaGWNh+EZMmjai/jORXvHNOdbrgS5ZZ/9qPRDb9oYo3Lo604zDeysWJWv96coaN/25m9/bsDW0RZH5YcsE1JotptBsU7XQmTw+ifgPd2d7WvMAbxQwT7vONMTydTUCn0P8CLQ0bpsA3JPZpFn6fRvveVLHT03nv6dgW1Nn+xoXdY3uT182ahCxIeZZcsfq5qZf9KudpZkfj3lqJH1kf9GIsGeRsw/TkZyCY8BLup/vO5pd8xenh+0tRXRNC8dVVbY7QfSSEjsnxGSWoXUdGsGrgEu30Xfvgp8wrf9foMDlmAlkqlGZfH/prcNNrWcPLCE8TAHf5iLoCRNV3kvCiil0dpCa+URME3l78pSaO293HFFaz5wV0CyAgQIsC8jptL1MZW+PqbS72s8bGzeggu2D9gNpZxJhV2q5uZoOmKUxsNG/av2qx+/fzOsp+gSaNQnKJpKhCgO2kfZOVyuBFoT9n7jNKDsCNGpR2WVHTFtag15F7QhbY2UFiY24YeGzC2kGAKnEULm+I9tyd7luWLmczlhMhKa7VtviJbltW/Or1zdciiqQX4L92ok16Rcpa10rfxkT1G0vzfLzDXYXra/RghwqD6WL0SbC26kyfWTQgVMRQhxL0VS+hDyPx5HIpmqQXKzxoBvAF2JZGoBAf5VXAl8Gri+o3XZPRkdX33HI6//ysff+pu7J7tjLxfdne0bEJW7pPZURsfvXXLp5i9MmbK53x4ebgCnHrgZSCHPSCNyb74NIfPrgEUUbOoX5fS004ZU1cy8IW+tgJPRcZ3R8TRwfkbHb5tzbt+fl3xqo5NIpir+fnR3tuvuzvZNwJvL+7ev44DMwUokU7OBvyqLIyKNroo0ur4E11KFXshVucpf4MhFt9E3OJPntx4jW/4r0bVqfD8FNCrFbxLJ1NGBhWqAAAH2UdjIYHdp31PVoSnHDM+NNDiMDhR/KiKNDg2Lc2xINZLbNp6XqoF/7O3OBnhx6O5sH04kU18BfoKoRlmELEz3NjED+CGKhKRAqfNdSdiccVXz7e/LU7I0RJYjoWnVlBpF+I0obIquhpqiUYSxO98GTFUKyzvWWoRQmGMVKFXU/L/kDsUxjj+0EN85gZCtjchEqAntM9b1Zltz3k2+9v25V9r3uZKlu89eHgu59nXIpISf/A1LWQSnSlmWtqPjzdgI8fXndQ0jA9oo8AOgPZFMVQN93Z3tK7zrdzDwuLdtHhkEB/gX0NG6bAWwYrL78VIRU+lGYNCrIzUhujvbxycOYio9CzEs+gDM677qgS9+r6X3xOqbrn3L2uxwzbnAU8DdofrCVjdrNbs5ayVwHNBk1zjZ5iNHwoMrq1R2W5MaWRcJIc/zIJCNqXQUIVp5gJlnDGaRcF8T8jpR//a7cfMBVwcrkUxNR6pHn8nOlqllKI98KC63rTEioRFGc6KqFy+TRvl+VfyXzyye6JIqhYtIq38FPtXd2R44bQUIEGCfQkylb0TqH/URdk8gb80FUFYB7YaIzsxRMztP78O1+L43XSD8Qj/iASYP3uD7e4grmosMtk1IkFFyjFPfTq55IBbVlP5YFjzL6vFt3AJSahi0UuMkp5ykmZC3MKUExihG65Cw/keAoyiaZJgCxYYAmlDGcoxQDCM0+1kUiY/pVyESGg5Nb15Z2N4/LzSaG59E35WVsFHWypMRJ8q18qtgZTlYWhVP3Vwf17Eo2EPrI1TNRFnS4xwSZnWkt91KhJQ6SKmZh4FvI5bYq7s72y8BSCRTzflhldV59ctwg3uGsvgV8CPgscB868CHl9v0HSQf77dAyKtH1YLcr3OAR8tNJmIq/WskpM9FVKM/IPfdd4BzsN0/NC0ZbWs8YvSwfG8ou+GWJi/MQUdrY2P5uef1V29ZVr/RM7moQZ7fVyPP81+B3oyOJwASyZQNNHZ3tu8o738imQoD5/Ytr1q18sfTQsAjuzLE2NdwwIQIdvW0zejqaVuAzNo08hKTpkxYn0DhuNXj5Ap8oYM7J2ftFDFotq0ACzgc+CASRx0gQIAA+xq+B4RAnUHe3grKWXTcU7zp8p8RqRpj6itGmHrsCHaNGSNrgHRArvZdeAVoZwEdiOIRdbJs1u648QQUDS78ttPlv2TjoXi+MHiDwtjWECt/1sLAiqjZdqIoGUPmTDjgKEWSYgGzETK0mNKJUr8bYS+iBPlD7vxkZwwhWtsoqmX+elYAKhoZ1v3DM0Mhe9T0a5Rdj41MHwsUf/392/tn2g2x8pNQ76Wt2qptCgplZhuWyvfjFEZCuLnxNmyEXJmHbiGiMIwCtwF3IyrWOuA3iWTqxEQypbo723tX/azlcGfMOlO7RIELkPCu3yWSqapdnGOAAwP9iC36KqRe2m9jKv0qJBfKTPb/d0yl3xFT6YtjKq26etrU6e+86Znm2VscJLT098AtQA/wWmBG9Yzcq2vn5eZtuauOrffWhpAxdz3ocO2CXPXGf9Rl+56s8XwPGEPUKzMhMAU4KKbSNkB3Z7tTiVx5WAh81M2qK4CvI3lw+w0OCIIVU2lr88rZX98+MPPHoO9EEu92Ef7oz6MqnVQyJKuUcL0AJiZUxU2K640Bxg8SydRLIoEBAgQIsBfwMJIbMoAMyvNbVs3i9h++jlw2Qu+jNWy7vwZnVH4+rLqCC7xq8rob4EXgUqSGjQ0UQFto92iTS0Vp7lKEiR32DJQC3EKJfXlI2ZpQtautiNZKMUYx10qV7e+ZYYwrQVGKJhhm2/XI7LdLkcxoRKVZiwzU/O5t/h9zU6w4gky6hpGBnp9MAtiDI9PUyFg9g6MzTBfz3sufq2bO3/Ut84cbmr9GlSvvU4FS4gVo6qq3u1WRoVGg4Btz2HZjhPrFBWVXjbeB18aA75qFkSKvH0cmRTRwB1Lr80uIscWMQ9639ZpQfaFfuzyD/C8ySChogAMcGR3fltHxS4F/IjlQxwL/6b1vQvL5voDcP98EPnztG6/6w1Fn/fMCO+S0Ax9BwojrAJTlPFrd2J+3c6MLN/2jIeRkGWs5aYipxw+NIOY2evD5cHbgiZqcdpRCamhtQUws7vEm4d4L3E7R5XNXeA745MCK6quB64GxmEq/P6bS015gv30CB0SIYEylX1M7dfDaBR3ba6oXWC2gatgleSyNfCgP86sU9vev4AXacYGrgav8sa8BAgQIMNmIqbSyQtnH3EJ4CTLbrkLRHK4LtS0DTDtbMbw6Qv9zUW2H1A1PZo5+y2T3OUBlJJKpNsSpq5VxJcVRuKCxtLLGw9M8lES4GYXJXxtLuS7kR9HhapRl7UTAchSVJhP2588z8m/vD60z+5n3Jhdqu9eGCWvKeueRQyYry00v1ATvTVhgeYif79QALMe3wE+WTH8d3/KJjmdgcsQ0QmpMceHy/QrOGCFslLLRlsUziHpn+ltA/gfDFPPV8K5FBBnEfhNRs0aQ0K9/ACcANyFqRR44Hvif7s72SwlwQCOm0jXI//xI4F3A+ynNqcRb7026GLVJV5960W1XP//Eoh3PP37wTYhg8Z/AZ6cvWrf91Itu/csd/3ve1IH+qX91C+rMhRf0NmS3htwNf20yobgAI6CrrGr3JnfUvhxYYUL7Yip9GmIY8sWMjt9Rod8WQKWIiJhKn42UW/psRsfv3B3XaU/igFCwlK2fbTkr60Zn29NAjVeSn2wUwworrraATwLfTiRTTRW3CBAgQIBJQEbHdTiaDwOWHS2w+JQ0i854lvopw4wN11MzN0/z0aOMraveMby66uOT3d8Au8SRFK29PVju7GlP66UH3QYURvFc7qLhfn1C/AaaajeaDf3hbXhtKMtCRWuxPHJVHg5iFBvzy2fC7kBIkSkEbPYdpljDyoQnhrwJTt2JAAAgAElEQVTXeorkwkxEGvv4CDupQuNt+hUv/8ucg7/elzeQ03h1e/y/2FlENTJtmgFppZnpcuLoP6ZNkVyZbV3f+7AVQSnplTHkMNcKisWho966ft+6UUSB2ITkyEzp7my/w8ux6gG+jBBCU9j5BC/vJcABiJhKKy/H6tfAtxBV8/2U3vMGZiIk7L2qQFl3/+rsi59//OAcUq/uHUio4D1bVs2deuMX30PvhhmrneHQGp21/7Lmt1Me3Zhq3IDch2uAJ4EdVq0zuCCxY96x165937HXru30HXOb16cLPRJYjiRwXUylz4mpdLlt/N+RMg77hXPjAeEieMxX1q0GBpw8A7l+opHGic/LqEq7UqZejmr1EhFBYqK3ApfttaMGCBAgwATwfpyvhpoupfQ77TrWW4sbT15/19T88ObqESKF6h2PVFdXz8oXml8x9MXeR+o2vmCjASYTfRQVKG9QrkI10QGqwoMo3H7thdoVnGqVzdeVj8JeaMKy/BfT1L0yZCWEkKPViLJirNvxtvsfpLaQUZUMSXKR8CSPtBUshYMmatb7TSVc376mT36lyRC/YSR00B/WNyjb2TmKJMiQMBDyaZwB7bLzLQ99NOtM0e0pvv6VX6eSy6yK1cMsxF7d745oyJox+Gj0+hVGwrBWAm9E1L6nTZvdne1uIpl60GvvPiSH5uHAYOvAhKf+XOd9nAl6CcqtRVu2d3dpQFXPzRJtcuh7spqyEa8pazAfeD1wJ1L36u6MjhdiKv0UQqJ2IMrWNDdnfQn4DPKMPIQUD57VeHB23db7a06xbN5UGLUGYir9KPAYorSCEK1Ko+1e5Hvis0BvTKX/mNHx/wXwnAefetkXai/hgCBY3Z3thUQylRhdH7knN2DZjfExrPDORMlvYlGJaGkX435UAZW+QyfYUnttWTsfowwu8AxwVCKZiniFAQMECBBgMhEBpoP1iNb8uHp67o/hGfbW4Uz1GcC7yIUyg89WzW48fGxk0UW93d0PH7f/x5kf2HgIIRZmoL4NmLV8xUk4Q1pHplqz7IgM4B03oh5dea4hBOXFcl/s1KOfmFRTzBeaTqldOd7nD1BKjgx8+Uw6H7KzKhIacUey00z7fqc///5jlIYOmvvT2M6Pef0y+xtSZezk/coTlBYsrnQdKuWoVZeda6Vt8r7l5STWH8LoJ5QaIW82RVXLFGu9H7iju7O9v6ytiyiGDD6D5L8EOHBRA7wFMyGgS0agCqDh0FHqF+YZeKYKN1dyO/vvueOBNmBVRse3xFS6Hvgu8Dxw2dL2nlnNs7dd/eCfTvvg8EDdX+waZ150SuG0aScNvXZsc3jd5n80bgcOGXquphl5Ht8AnAX8AnG+fKJSGGBGx78QU+kQcAaiVi0EiKl0GAlVfDSj4/dMdPIxlVZeO5P+u3RAECwP28KNhZrCaEjteKSapqWjhKtfeKdxaBh63iZUA9EpDvYLGLy/UFuFYbDCYO/ap6eA1Ak5CpFhf/gyjhogQIAALxsZHd8QU+k3A4WMjutEMnUdMDtU63y9MGyvAK56fMUxWya5mwFePJ6DccOJAqJwTAvVqJBdpbQdrmhHXmmq0R8K6P9s3vtDBKsoJSNRSkOU/ITFn89klCLj0ucpSSpccGqyBadqPbCAopLk3wdfe2ay0hRQ3oaE+pkivaYP/vMu75tfdTJK1xiSC2YIUGW3rKLzn789KFXaTCFj425o3pcTRnzvzbqQd45RhMyFgK90d7Y/ws74LjIwfR2iYmUSydTS7s72xytsG2A/RkbH3ZhK3w68nZ1UU/M4arbdX0ffYy5uziqfMPDfzw3IPdofU+mTgB+Drj/01McfeNu11897Zu1Jb5w95en4psyc/NpV8fdXzcluDtc54ei0fFXt/Pyqzf9o/BIwE61iyCTPicBJwJcyOj7wAudRAFIxlV5GMRS2CTgXmBlT6RUZHd8GQqjKyNSngYUxlf6g186kYZ/IVXq5SCRTU4Hzo81ufe3cAtGpeezyeTImzolSSoPS4NqMPB8t/jv/NWgnB6EaKvahDBHkC98GIp6VboAAAQJMKjI6nvf9aN0B3HrUlRvuyej4f2V0PCBX+xeiwN+Q8LEwsBTAstF2uNQgwnOyU1qTY+ewDT+J8LvpmeUlvv0Vtg2z82DOrDPGE8a8whg7bECUnoL8ctuzKM1dMrlbfnXJkBPXdXG0RrkFpmiXeQjJ+qmvjfJzdBHlLcvOIYeNiApn1vtJk//9uPpUyOJsua+G/IDlV9oMainWCHu+rC0/ySuH2WeUIknbDnw8kUwlEslUrGz7AuIe9wDwReAK4AuBi/GBh5hK1wKdVtS1VMgNlXmpePeWwhm2yUqR+Er3gF9VtZF75w9AUyiS//sbPv3L+lv+dsmPn86cevwTq89+YvOWBV+qWzw2MLo2mt98e+Pos9+f4Sz/+ox5SN5nDPhhRsfvAu5C6ZuWfm79wkQyVTI6jql0bUylXxtT6Q8b+3YfFnokaisyUdAI/Cim0rNjKv0R4IGYSl/htXMwcARFR9JJxV5xEXzTV1Mn5EdIROr55K6K2yWSqVB3Z/uLvjDeF8SZiEXwBxArSaVdlFugYphgZejxP66rsF5++ucLOQtV2nYYmVl8fXdne5DTECBAgAABXjYSydQHkEF1PaVhcDuF5GntLdNoZU34G2YMGPx27hMV2TXbV1J4CpS6Bpr21iODM6NkmTYGESKyxFu2GgkfMkqSXx1yAcuE6wMURslG6vgGkvP1G+A4ioWLjXGFvzCycf4rILP5JuIn7+v3EEKUKl0rle1FZ7pamN0+6DYckvXnl5ltTd/Ne7dsGz8hK6/h5Xdd3IwQqGFk0vZdZhyRSKa+g4SM/bW7s/3tiWTqaMDq7mx/mAAHFGIq/Upl658ueOv2+dmtttp4axOStKKQJ8EyhjETfgeUrTOEP42EmQ4tOv7Jvy+6cNtbNqdn7chGm3rqY7nfPXTpPI3kYQ0gz+gqpObebKQ+2zzgaLvaaVzyyU1r7Sr3qhs/3f4br88nAp9DnDCbgIsyOj6USKZq193U+LNtD9Qe54zaNyLlGbZ5f6ciuaWnIM/uOuTZXYs8H+9Gvh/0ZKpYe0UxcQrc3fdE7aU7Hqt2zv9K6kuVHGwSydQC4PeJZOr1L6HpZqS+x5kUQyBUYQyd67XIlUcivwA0vGxyZWYAfYteiOKZ9TXAMcCdiWTqQArdDBAgQIAAkwAvKmI5MhiZ6LdoXHXS2hvYl25ZKefI5P+Uh7KZwYx/IrU8F0ojA7FR37KIb/vpiCIzSqmiEwEO9u3jr6PjN70YLzisFK6y/j975x0nSVWu/++p6jx5Nid2e5fQZESiRIG+iqggtHoBRUXQexXDdfaKAX9iTjQiF8WcMOKgAuZGJCcJEhZ6E82yOU6ejlXn98dbZ6q6d2Z3SRuwn89nd2a6cnXVOW943udFK4UOJ4gg9KE8voiFhZ89A5+iZ66tHRGqCGbfghL0RrV4vFowIp2o+RdsqrTtXQ42JVZao1wX5d3vIGcmeN+CjZODDpdxTo1T6AJrgbchAgK/RQxRg98i5Qc/y2RzrwQebTpXL1vcp10+1PeveG3jPW3eR0oDI50Hltz4jMoGAhngtv2KtMwrjScwYH5WkOfsACQ40z2wdvJRM3hcl5eU1tlRPVdr5gE3IoIYAFcgDtODSK0ViILlEpT+4Ya7W0PLfjhlr8Ax+xBn6Erg3QWdGgZ46htTXx+dWn1l8oJN3eGO2ju8Y3wS9CmE3EPBPRZ0CFwFeg6SLTsOybidhYhh/PV53scXBTvFkK9WKQ7mY22RLkd1HVL8JHB+JptL9faky14WqgP5IjcjUao6ZLK5LoR3/WgwA9bbk96SyeY+jjTaOxSJ3nSEYig76popA6ihcNFjgapGyLP0YqgHOjWwQ17MQNVFprYHM0Av8P4tfuFn00QTTTTRxL8xXgt8AYk+e46K9ozyMUknI/qAtfVMNREDY6LsVtj/2PyizPpBR8E0CA5mw0xPpwi4XbKvOlK/qetah0SwTSQe6uuhzJzrAFWliKLGnJUQ4hwe5m0bpj4zVKXeyQmqB46HRuexzkBVCjfSVpcxEMdI190/u2G7oLMWlIUP1pkZeqRp2DwXOKK3J30b0lR2DL096X9ksrn7kMzdbCSzsG6C62liD0ZBp0pJlX964IkWk5kFKKmwqzoOHq1Fu9wHllw79USgjKVnTT1umNqIpUeeiZWhFgHbanjdg6IXUaCyedX0va5beMkQqCfVve4B2lHHAxcAfV2Hjqyc//YtRy793uTlg4vjk5F6xfuRrNPvnNFQed0tHT8h4PgUdGoxIiNfh9GV0VcPz6xsnj63MmLH9MbqAM9MPXHw0dqw5VpxR42siD5eXB9+RaitZtuW0uVNEZB3JA58BXBiMyvlN3zy73cMF2JX/uMXx/3+RbvRO4idksH646fT7dNP6//RjNcMmEFonutw79lfzW3UmjISbfk1QvObl8nmLmrYxRcQTf8Fjfv2IjEdyMPUAWhliSilNeY+OqT2up1oaJt1dRPCdFjfETalVd8hI0iL2FEupgXcl8nm9trumk000UQTTTQxMRYDjyDzY0xmMQdwgjQzUw/RWD8VdJ6ME+IgDlmjQ9GIRoeh0Wkxx3MRx8pQ85TW4NasqOtggQ7WIhW98zWqf/3edpXA+Zk6LuVf89jvJvOkkUCuMQiC9EDj1Jj6kzC+CEWJref0YP3VeMbCAFKzZe5lyDtLreRfo5BG0Ems4It1lAI/N3nnasQtaggDZlvV48cgtXd3IFSsJl6+iCPPHcizNairKrT+tvYn+p+IfxlRkgzhKmfVzZ3rN9zR9qNE54A7df46piRXmtpLTX2DcYOQ7F+1A0/qqlXFVUnkGfx512HFdrfKOfMv2Gwj6pWfBU4CPohXL1nQqS8XdOrRxpNOqnwoqfLTkyp/idf/6lNb7m8756krpy8pbwwtQel1pfXhL3UfPmJZthqwY/paBf0zTh3Sc87qx4q6jZm4UHxm+QC3pk4obrJ/nFT5/V6Uu/scsNNEFf7yjVMuDCf0fyiFro0qyltCr1AWk/EHsBOAHCILeXJDAWYEGaQ2T7D7q5E0Yx3XsnX5RpK/eRRVslm88iTKtbbxt37u8MNzGqpDaEOtsCxQlqOVGlulMeU67n4a0Iof4WuiiSaaaKKJ54zenvRyhD5mFPnwmukGqXnBOqDGTJOBWSdIwzOCEDrwWSPdbcwRcx1wamMOUCMUXtPgyhC674moHlwSrgWmTRdxlmL4AhRrvc8j+A6YEdogcC6j1NsGCqn1aGFrEYmgM9h4LasCv5vrD9IDvfq1MaIlyDw+TP38rzznCuozZhXv71LgGk0dmqlPG8WnLJpzCOPbSBPhAeATwFW9PenxhDOaePngQ0gNowkaJEBtLK2L3L/hzrbHgP8F/gwsKm8MryyujVwPau1Bpz401No9sg55VyqII2YCCKYEx9QJlpCGwh9C5P8vB5asuL67NX/1tFX/+uTsB5D6yJ8i76UCHla2XpPJ5uY3CqwkVf404G4kE/ZW4MCCTm2cf8HGfVIfXvfUgZeumRudUn3X4OJY6+o/ddY6DyrGOg8sXairNlseaB3e9EArbllhhR2mp/uwEvK6F1dH3HW3tulNd7a1A9cnVb7RYXxJsVNV63p70rcA7etvb3NX/q6T6rAc3gv4aKQQ7n+A9zWIYSxExB/6GveZyeaOBj6MDLZ1RbTlya04UVsEAnWUF5EROXac6qDi2d5uVdlie58Zx/8FwQZ6MtncxS90R0000UQTTfxb4xrECHJAVSDiQChoZBvHKkgHCmRVXMeUNHmfxwPrxgPrBgQmFA30vjFiovaPbIrovwNsBLa0xjZVEx0lYt2ujk93Rrz6EYVQ2ozDVkHKAfbHn9SHA+cVzJDVkAydyUIFnScV+Nyo8f0F3zA1GTOzfHpg38o7ZtC5NEd1UZiaKwsJmJpjBmupLMRhNIjgNw62vWszjpXJynUHjh8M3taQTOW46O1Jj/T2pP/U25Pun2idJvYsZLK5mZls7tJx2E73IFlO8wwOAz9ERF1OQZye45HSmtuAZ0f7299/6/fOPLHw0P5vRNo6PAj0AtfjU2uf8PY17K1zHyIm8XqkRuvvTtF6T3Ft5EnkuW0B3oz0vkoBXzno42tzwA+Ao83Jes2ROxH7fyaiFEhS5T9R3hz6hq6pizc9mGitbAl1g3JqI9aKlb/vtNfd2rYAaBlZEWvrfyyhQRGeVNWtCyrEp4oNPvO0oVDXISXHKVkjSNZ6pwpe7BQVwUYccdSD57TOL/9m0uGjCgu0i9Y1qnaU85FCtuOQwtyNgL0tZcFMNncQ8H1EmtEU3voDj9bKG+Mn4pIbbG/5uKiOKD2wOEr3IUUllEQFVDXYajv+67YoFsFBewXw4d6e9M3P9dyaaKKJJppoIpPNfQmh6cQI0PG8xUGnI+QFO00NEZ5r5K077hTZSCUMzsGmhgjXoVYtsiXayrTAev34WZnEfrPv0BUn7hTWHlaEcAKPMoiXQQvUSRcRWlwjLShIFTTnZiZip2E941RqjPBEjaoVIqRU3TrmuoIqgMFMl/k7+FkjLVLhS88bSl9jHVswAxgGcCpop4KKtNbd2xJiwI4gypAgjuBFwM+3pdTcxMsHmWzuBER2//Ne8gKApMq/CfgacBeiuBlHeqHNQfpIrUACFjcgNL4O4BxgBhIMuAJ5PsvI83Wkt/47vG1fB7RNe/XgnW17l3658Z7WoYFFibcVdOrhpMqbMp5VBZ0qJ1U+hWSk7gNyr7xi5VwgA/yktye9wTvf/0ZqRa8BHivo1Pqkyl8KnBDuqP3RjrsXl9ZFDkTGioHk2zZ12lHdufzHk5/QjmoF9sZkNkJOyIo7yh0KWWARnVZB2TilNRETEPkH4ih+s6BTL7la9y7pu/TgA0fcMPnI0XOV7cW4LKiV+SPwN0Ss4gokkvRe4HeZbO6k8fbjpRkXAw8jA8966qNXwQDa9mqhtudcmXR8HcItujL58BFt1SXHwgqsMjIAjpeO394AaAZrG1FGOXY76zfRRBNNNNHERPgD8HvqZcCDzoBfH6RR3hJj8NcxQxp+NqKG0PVNtsY4ctqyCUVbmer93YfPOpmJOAy1wvrDylsGZzsQNvS/ddUhBrVb58SZgntNfVYItqbsGUqTcbSMQ2UyV6sDy7WyxjJipjaszNZOY7AuzPxtziPoaBlqldnO8a4zgW9PaFeyeuYagpLxbPxnXA8ujbmuwyjSxkUjBrON71wpb78/AL48znfSxMsTdwHvBG5t+PyfCL3veOTZ+AViIzvAXsBNwL+AnyPiNz/xpMyvQmzvuxFhig8jz5WDOD8XAZchNL4PjjwTTStFq2UTArqSKn8oUubzZ8BNqvw5wOnA5wo69deCTrkPLZzzzEML59QeWjjni0mV398735MQ5dD7POfqDXj9s9C8qTZiHYD/vM/feG/rpI33tbjaUXO8e2B60lnUbO0ORVzj2pTXRyitiZj3vg1x5DKIquFLjl3W2La3J/1r5IvWSkG0nWOQC+8GrkMyWINIunwibvG5SBrzfCQy93vqucvjOU3POUvF1lExAw2Efe0lZegLQ97xq4xfeFrP2d56n43rfrgpetFEE3sekiq//z7ti4447vT75yVVvnX7WzTRxEuC+7x/Rer7S21dbyUzk8kWWeNMV41sj8ZsTRf182/Q+TAOz3pkrm7x1rGAoUq1PTYwMiOMCFqFgRnhVjp0jRqaVfhiDy4SkQ4hc+5jE1y3yTyZ8w46PGGkViXkrTeiNU8qRQlY6i03jlxQZn28jFWQZRMUzAg6gsFzt5A+XQqNqg6N3UezzyIwOOXIojvp0JJl2SQQSqSpeQs6l+a6bCTb0MS/AXp70rq3J/1MY01dQadWAe8EvSzUWY1aMeeNwBuQdkbPInTBm0KttVsmHT30VLijtiyp8iHkGZoCvBo4E3mu7kSexUnAfkj250rgc8OFaHjpd6ds6HssEQO+hdR2JRCtBBuhAZ5IvVBGG/BG4DWIswMSaLm7oFNDSZX/AEJhfB3w0xn/MZiem+mLYmkTVFHDy2NqYFGiD8m2HYS8B8P4DbeD7yrUB0bC3jXcwk7Aru639B0kZWkhg+0A0qzsVuQL+yTwqcaeDV7m6v8Qz7cfuWka4W5OJKc63g3fUWzlDLkuWimUUAnGFC2CTQtdRO3IRLqCVIVgNi3IyZ6IphhDeoSd0NuTLo2zvIk9HIGizwQSeepGKLJ55Lnq7+1Jl3fR6TXxPJBU+feC/nrr/NFYdGpZhbrKA0n1VC+oFuC7BZ36x64+xyZe/shkczOQ+fRUxCgJOghBGhwwRvrY1hwZpP8FbQgjuGD2a5ypPqTGwjgPFaTGoxu/lsrGny+DGSatLFw7yiBSY3QAfm8q47wUqRfqCNL5gqIctncuMwLnbBr1lpXCtUNjCoVzA9sYZ1EHPsPbt1FUDPa/UoF1FGLb2Piy8nW9skbWWDrcOmYjm2tIADU7MlZfZbKB5vszgVuzYRUxVL+dyeZOAR7o7UkP08S/JQo6tSGTzb0L+PHwM5HHF18z7UmEEbYBaC/o1KpMNvdA/xPx87c83HIUYmt/B3FYTkXUJpcAX0UCJv/p/Vxd0KlfAyRV/v2g2hDnywQQ+oCCJxd/ORAp6FTQZj0XeRc+BCxJqvxHEUn19d7y9Uhw49vAf/c/Ft/firstuHVtGBxv+x8hPa9KSNZsAfLemrrFIeqzvCDv3psQZ/NPz+/u7jh2WQbLw5+BDwC/Qb7MDKJG8mMke7UWiGeyub3H2bYDaAd9GLh2gCcelJAl4MMEB78dVNGRuaAttrpusvH6YiunVsfpVsggZ2RZjbyqucdGhcXFj3ZVkIdlAF9Wczwo5EF6146ddxN7CjLZ3I2ZbK6Ep52MRGL+jKT1c0jEaQ1wbyabO3iXnWgTzwlJlT+jbf7o1Xu9ZXN8dHVUDSxK4I6EOrDctyFR89j29tFEEy8SwkiD3sY5MOgYjEf5a8zSGJhJNdywXuPcarI4U/AdMRNFfhsSpd4n8LnZZgiZG29A2rMMIk5VGqE0deBLqIM4bwcGjjuAzMWNohY1xLky5+qzSyq1OFq3IH2ivF5cY5ktg6C9ZPYb9fYTblgWpBBGveXmnR+r63Id3NqQXQ0l6qiY5l4Z5cfGFGIFoXcZW8dQCt+OiBt8EckcNPFvjN6e9FrgfbFp1c8CZyPNfK8o6JQROvnB8LPhT+uq2gKogk7dUdCpbyF90t5f0Cld0Kl1SA+rLYgdck/gEN9Csk03Ig2FpwMdoRbnkMMPe/iQgk6NAt1JlX9zUuXN+3ELQmX9M/B5hIa4T0GnNEBBp64v6NRRBZ36IfCXwSXxWv+jLcOgGhMLR+ErcC4B5iN1Yr34zLFWtk6OmDHvgud+R587donIxXjIZHOvB2q9Pem/NHz+XWRQzAQj+F7E/xugLwGUaBNZpmGid0O3SlqZyJYZWEv4xaZbZY+0C+XNEAsNabrafH5EYLeBZsJm+bB3jOB+DY/bDNrm5MwEFBy4TWPD8bJZDpDq7Ukvo4k9FplsbibwS9flaDRRZbGjTa4dpJbiD0iavx0YaBY1715IqrwNDIDbEupwcB2wwi7OaAhdZRjXfgwpTr7/lVesHO7tSVcD205FJi2NRPdLwNfG6xvSRBM7gkw214mMF7OQQF0FP+tjMjaNI1Bj3VEQ2xJoMk5BY/A2KORQw6cNBfdVxBeq6kMK8z/u7esxJNNlIQyXNOOzPlxETj2KBDLMMRpFLTQSCG3FdfWs3GL6955SHlkwOdpw7Wbf0iC4/rqN8uAmfCeqDd+xCh4zSFUEsQl+5Tqcq10iVgg1zhwwni0QvI/BfQ4jDuujiHN1T1MxsAmDpMpfiAQ1SsDZJqvk9Yb6BlKH9UuAXzxw4lTgPc+sO+yWB/515hOPfnrWqxG79bGCTi1PqvyBiDrgNcCl+DWUR4G+c79LNhyRmFl5+LcfT5+XVPkPIRmjiws6tbThnD6K1F59tKBTrvdZBFEdfBjJqL2e+jGlFp1csbtfOTq67pYOSzsq7l3Th4GPAa0o7aBpBWVUORtRBd5uMnEvJXZ1BmsMvT3pPxjnKpPNxTLZnIn2/Ai50SYjZAzUc5GMV5CHHFTmaUQjRc/IoJpl426nIlANtwUHN/NbY4GwOY8EPmXRFPyWvc+r+NKu5hyME+Z4yzdQLxsbhAX8Vyabi09wjU3sxkiqfCyp8odpl4uBE5QS52ocbF0XIbARbvR3EB709Yih0cRuAi9S9yTKbQGoDYRwh22mnzCs2xaUBnCtEiKXe2m43fljdci6KZPNpQK7mA2cBvq1WM45KuKcD9yUVPmunX81Tbwc4Bna5yKU48UIY6QN3xkYr7a4MVsz3ng0nrqvofuPJ4YRbNZr9jmEn2kKNtaNIUZcyFu2F37PzFfjOxkbG45VRdTSJjccu5FiNIrpg6Vxa/EwtuOYBr5m3jXUPEPPC2b0zDmXEVGALiRi7iAZtyfxjcJgxhB8UYrXWjZhOyx9sRrO12SmzPrBczffj2kybPZ3ENDZlGNvohFeRui/gU8HKXsFnVqMOF5BZ2N2pRY99alnT7hahfTlSOnN6wo6tdxbvj+i9D0DofN1IuIXD4E6ZfNDCWfk2eijSZU/BJFzv9r72Yg/eD9nBj5LIU7bGQhdsIYoF5r3Q3cdVnTi06tRK+L+CrGvbeBigFCLY+93yYbilOOGN1NnP2vC3WMt5wzt9yXHbuNgNeAa7x+9Pel7PecrOPi8BvgMMOCNeWr8S1FIfZTyMlZaB+YKjc+JHpcioSyIdmiEHz22uGaUD5WqG/TM5GUGPhd4CIkq/RwZiKPIoO4EtisjfHRTpDrJWxbcdxBvoJ5D3sSeg9OB/1t5Y2deu6xyXVztgnbArTy5OkwAACAASURBVHelxyKn5S0Wo6tDOJW65RYiuXwsgcBDE7sHlKX3nvvWLcxID8rf4RF9zJE3LhpcEimDchGVp+tCLc6/tjwcjzzxlelvTKq8MXKfBF0DmHzsCPFpNayoOwcpMG6iiecFj/3xP4jx/yZ8w9/UOgVHoCCV0MxDW6ifI01t0Xg1wdHA78YhGEKoe6ZG2VDiWrxzGfK2iyJOkok8jyBGVgf1CoA1RAHQnJcJUhpHyAKtpB3V2HWUqKcViSCVbTnrT9y7NrjvtKJ3H0L483jQOQoaAjX8VjKnB+6JQgxHYzSa+9po0FmIExhkz4znxAZLCswxGpUTXeTevgt4eyabOzWTzbUAZLK5cGNT1yZe/kiqfDip8pcnVf4t5rOCTi0u6NQ/G9ct6NQmk0Hy8Mifr8x8atOSyTfaUf13JDv06cDyW5Cm1dOR8eRR5D2eCbRturdt9ZJvTz0OKXP4OmKr3LR3/MlXZ7K5z2ayuXclVT4KHOz9mxLY95Pevm/wjnsaUh9mWB7h2qiynYoaiU2tfQEJMjvefv6Mxd+La8NzqkPWVPzgDFbUZa+z+4hOroG8e+/1zuElxa4WuRhDJpsLAe9HnJRx6TDeQPElJIXYhXjOJoUeLD41CGaKNLhVGVMjNtiNHtl4KXgrkNwyvwQjYcGJKITwQI1CklcjxkGIt1/y1nEQLf6TgeWI4XRE/TGpIDL1hyJFe+bYConknZfJ5m5Amrxd3duTXjne/Wpit8P9wA+7XzHarx2lR56xaZ0vc+dEFMHRVVIzHmpxsSN1XphCIqbfy2RzC3t70je+pGfexI5ifw1WbcSmVpQvtW3SMLVRezquWoO8zyuB1cW1kVeuurl7Nkp/dNYbtlx42MEPzmxdYH9ORbQeWhymOmhRHbJwK+o51I020cSEOBOpXWik/o2XwRoAOrULTg1lh+hWVt18B2JUbcLPLDXS4EyGJ47vSJl1DMOjjDgpFafGk0pxkmXT5S1r8c7DQqhEwX0+ENYj33Wxv+wQLaLUJsQmaPfWq86b9kho+qSl1sNLXk+l1qLwGxFr/Jq0Dm/9GkKhDMq0m3Vd6gvmzblvQjLOLd7fFSRoe3xgn4bNQsO2IfxAbGPpgnEUTaYqeE9LyPixb2DdjUhG7pfAMcBC4OpMNvd1pN7lTiSL8JyRyeZORVTfLu/tSfc9n300sUsQAQ4B3Ew2ty/wKeDK3p70uM2okyp/APAq4GfwXRcRkWtD+kX1B9azgCzi0CwAHi3o1ClJlf8qMgb8AHGs7kAov9ciSYM2K+o6wL7lPrsbyZop4H0FncoHTmW+d94rCjr1LHB7UuWPRxTCjwc6LJvWcIveUlofvgDp3/UEUh92X23IPuLZ3m4vCKGrWLoFV2FFtRp4Iq6rA7Z5145DMmWffd53eAewO2WwEoh6yfHIwNIRjLxksrkEEt06AvkCgh3kTeFsIxqiPKoSstxNbG2smPVMhAvG7k2wxrTug2Ahq2keaCMRtwTycG1EHuwDvL9Nh/bDkIFxdmB/Zt8ms3YSMln040tQglAnLkOk6Q/Bn3ia2M1R0Kk1BZ36Ueu8yhorrG9PzK7dpr3XXSkvv9oQw+w8sEx8Ro1wx4T29QLg+kw2d+VLe/ZNbA9nffbv0zsOGv0cLu7qm7vYeFcHoBhcN139/ktvr+KGTgD+CynG/S5SDxPGoqO4Jrxv9+GjbdWiunxoedTGtRh4MkF12AatwDfummji+WIlMg81wlDSg6NPAkBrlCUzVDAAGfwXZ+uAYyMVTjX8M8tuQOb0bq2Z75Q4fXWuZTm+4E8cUQvsbDhHFzi8SuzHth6d4dZ0YsPdLXNGVoYTgeVWsdKmRksduNoe79yC9dCGJhmmfn432bEQMhcHKX8JxFkdxrcZYt6+nkKcnWD5wDcDxwkB2qky6rpjGTrzuQMUAt9DkMliVAHnB64n5N3DvRDFt32A24Cbvf2uQRzB54tZ3vFatrdiE7sPCjo1goiefGnp9yann/l11zGlDaGObWySRpQCZyHP8y1IlhcQxyqp8jFETOYViPjW34Ga10z4/4D3IVnyfyJZqIuQ92Y+UKgOhCqPXDZrxaKvzFiFBAgKCP3PHONY5Nn9InBeUuUvSqr8VUimrILY07/bcGfbnct+MPkzTsk609vHeuR9/DZia1eBGOhEqLVGeHLVrQ2GGFoWc6yINgmZKnD787i1zwm7jcgFQCabm4oMIp9Dbuo7e3vSVc/R+glStFpDqFHmYWnMXI0XjQvCFNLa4Bu0XgbBKA8ZZaCJMmLmp0nde0XtuMgga2qkrvP2dTbBolpBKHC8CD6F0EjfGi74eGnMEvJgnmi6YTex5yGTze1XHeURO0pce+aKsr1Z+bmHPjSS9XxDb096vN5rTbzEOPsruUtLG8ILF187qcsthhvHjiIyIX0NiTK/C5lMjgJ9WLjdsdpTRTY/1AKODDEq7hLprlBendBAmzdpNtHE84JHG/sDIoJgsi0wPiUQQLkOaBdt2RDIYAUxUf1y8PPgeOQ5KJqwPfK3qhMH7BO1JqZddN+iSP+kQyrGmQG/4W8JCTKY/pJhtLZAa6ds6TW3tGtlu9bs04fBzxA1novJzBmq4kxMUFNrhUaPVRzU9+4yDYKDTBIzdwcl4SPe36Zvp8lsPVsrU7LDHIj0F1urXWLVYdpDCWwrVCeGMYx8Nx3UB19L1PcTMp9v9u6LyYa93esx+qLAs73ivT3piXqRNrGbQ4Qm9Kmg3lXQqc0TrNOGOFeLjaKf93kc+CiScXoEP1O6yFv/08BnCjp1f1LlTwf6Czp1b2Db4xG7/ZsIM20K4rwdhlAAH0ccsApi634eYXatRGTlB4E/Ij2zbERl/C4ksPwlxFdYgyQd3oEEL2cBZXCjib0qg6VNIY1jdcx/+ya7tD7Mqpu7NgOHF3TqJWd+7VYOlkEmm5sGfB9RC1qK0AbfhWjfGw51J1tHzsbQoPTnf+5CrQK4EIr763qtgoNpAlMTFXRwGrnSJt0fDmwDftTuToTm1xlYf9Rb3tinw4hcGKpFAYlGBTnyduDnCHB4b096LMrQxJ6HTDb3SeDSyqDV1r8oRkeqRKTDfT4OlsHq3p707BfvDJvYUWSyubmVAfuIxz8344uggrLYIBSjFu9nDbg+MqU6p/Og4QM33h3dS1ditlk9NqdEe7KCCruE2xxW/X7yX5Ei4yZNsIkXhEw29z/Al5H6pcn4AT+DYADRoDGwuC0EqXVBGBELr67JcSOhona1Xas58RoSlLQRKelk4HimdiqKiHOc7a1X8vYX0S6j5S10hdux7QiN2Oitn8BvsKwRMSkHcbJCOK5cm0J7xkDjNY9652ActzJ+PZm5NtOXC+qVz6qlQVQ4TlhZaMtmg9ZMc11cpbAsi7K3f1Pz1RK49mCgpqF8ARcRJ0gi3+WPenvSXwxefCabuwzJIlyKBH/fAtzV25NeThP/FvBUbWPbC9B59L/9gBOAmws6tTap8j9EGgYbddtnEPbFWYjS3xmIjfsPhG53ByLl3olkuS5HVD0PjU6pPlvpt2O6an0cP3t1KsLycoD/LejUrUmV/yJC/bsC+B1wOHAJ4jg9iQhavBspkfl/CJPrnQg1USMU2YeiU6rHz3vrlsrIyvBdq27sSnceUlSVPluProyOAkcWdOqp53lLdxi7E0UwiCHEs92CpBzfiiiXJJDBpwTVmoR6XRhfzWhCKNBW1He8Ag6YFfiXYGsp2UbUEE97QHY75oxVEc+7g7E0qyYW6dPgfsZbxwz2QfwYcZyiyMC/nvrar2ANWAJ4MJPNnZ7J5naKIkoTLz56e9JfAC60Inp1ebOlixuscR82Qx/0AgfbMrRnZrK5WS/FuTaxbfT2pFfc9NlTbgA1Vg+nwg6JeaNg6Sjg2i1O6+RjhtrD7bUDnRGVmtK1qrVjbtUCsFtqhNocagMhYlNrDDzRwrpc1xbg403nqokXCRVk/pjD1n1izDPWSKczvzdmuIJUwSAMna8aWGZodF5mSFmWqpRqTqiMBFGXeevuFdjHUkShzygKvw5fOa8I3Ad8U1m0RSdhW+G66zDH6/a2N72tjILYNMRoFMENo+InYunB/pbmZ4JA0Tx+UNWIDJkMkrm/weBpONpKCJeqshgBupUC2/bol3Je7a5DvNxntWo9tm3QUQ3e5+B5HYtE8PdCbIcxeIygU5A68A8ijvWngdMy2Vx7U/zi3wMFnXJ2wLkKAT9DHKUs8IGkymeQusYY8s79FbgXyWb9N6Iu+B6knuks5B3uRRyjy5Hs1XTgeCvmtHYfMXzI5CNH9kHs+csRWuFSJBHxT3xKolEMvaegU6YFwmQk+3Uc8iyfhTzvEe8cR5Ax4Q4kMbOo0hdi4z0t0TV/6TgWlNv/WMIdXRk179YXnvudfO7YbUQuGqCRYrmnEY91DeLRmuzNtERkszNamQb1A9kYTE1Lo3iAssAe37miXrG2bkHjh2aisZGHwdAFViFfdgsSOYswFiF0SU5/WK3ccMjJw6UpChmIww37fTcSmRvxPit6v4cxHG3HVYmVfYzO6VLYVhvS5O31wN8a70ETewZ6e9K9mWxuztTjRj9rRdyIlu5owcacYw+j4+BWNobL4RaHcLs7nly/Ah7NZHOp3p70C+HeN/H8cSUysZyfmF21WmZVidSGI045jOqO6SmvGhnUrlq9+YGW7qdvmzd5+gl9A+XRSGfnoaPaGQ3R96+4fra3C1D3gjp3Z1AZmnj5I5PNWQil7BbgSMRgCRrtjX2ljFFvBf4er84qCJNZMY6MyeSYYKWXfbFUqdpdRSg9o4jDZ+rDTKZoATL3mUa+EST4OoQEIDuRyHbEm8fNBG6YHjX8eq5gKYG5JtNzS2NZ5nqCgU9zvX3eZ234CodmHRP4NDXYNaRuehJ+tmlQWbTa0THqv7lnwdorNbgs7Kz/eyd7vXlzKT7FjbD1eQTvec3b93REMAskKxeEqdXagFCs1iCG7DIkG3g5YjDjBWlPQJhCq7zrPAX4HvC73p70ME3sEfAUadWOBuW89SMIY6oPSWycjdRk1RDK3lNIZupKxGG/DKnt2wc/e5vx9rMCeUdmA2WUftSK6EOHlkVCpbVhG8mi/hpJQpgWTIPAWu+U3o08+yXv3F6F2NMPI0GWVcCn2/crfmvqiUPXOkXrjMLPJm9A7OBcQafOSqr8Fbqm+rY83GrscSMy5yLsOCMR/5Jit3GwMtnceQg3/H8RD/kcxEvuAn6LDKhjxuZoZZp37gomyMSNr8ymUQo1PtNB+SttHdmrP4Z2ymDFABulSsgDMoJEzCpIod5jSDpzQcgqqdFylxoudZ2KrxgUHDTNwN7qbb8J35M3E4eODJaY+sCzak17jEp3C97yqzLZ3NG9Pemh8a64iT0CV63+Y+f3E7Mqp087efgc5PufhKS/9x8TwShZ1qqbOmOJ2RU9+4yBifbVjfCbL9s5p95EEHtfuLF15R865pQ3RKojhWjY2VQd3u9Vy2PKroWfuuNwveL67oHimvAsUCuKW9o2VJzaVzsPKv1w833tUyKdjlUdtN3o1Oot5Q2Ry4FLkyr/hYJOrd3ecZtoYjuYjdQ13IHUUPTgz2umbimYjRpPadcYbaZQ3Dgp42HMeQhsM4yfTYojWaQZSJTcUO4eRNQ2pyLz/hbEDhjwfprzbG3Y/xNIpHsG/hwe885xiHq6XdC5MYZXoxNj/m7DZJRc16LmVoEakVCC+vnb1EybTJlZZs5xM359lY3M3UPedVgts6uljv1LtXDbWIFDkG5pMmvmO9mAn4Fzvb/TmWzuAODW3p70PxDV2ncjxmk7wohRCOVrDbApk82djNgo9yOiCHsjBmnZO69jvHv4PZrYIxDprn080lV77+t6bvvAn7In39S43MtWvQX5rr+MONWXIe/zIiTwMhd4AHkOXoE45b9FHLA44ryPer//CrjFqA0mVf4B4Ghwp4JlhdqcV856XV9s3W3t1IYjIOrce3e+YiQ1vDQWqw2Hfoo8x99NqvyD+M/aD5F3+gnkPbkOeU5vBoZVRI8qRdQKoRDK8zCwMpPNHRGfNXVWaW04bsXckjMasvDFeAaAnxd06v4X415vD7uNg4UMAF3Ijb4DGUg2IYXhW9WUaO2P6RNJXJtVGRuAd7TeTDVyzcdx4Kwg27sVeRBnIynPUeAjiMd/ntaoqtPCivWHgz/BmME4OPkEO7dfjhQXtiMP1JlArdIZn7HhuPk40dCwt72FvBC7K92ziR1Ab09a08NQJptrRb7zj/X2pAuZbO43yGCXUApCCZfJxw7VopMcYwCY6JGHKvJGhC7MZHOf6e1JV7c+WhMvJaJTax+PdDjHVza7SjvW+tJQy082PDPtoiPPurO7Y1q/e/uP3hBG6E23Akd2HVr8YLjNeXTd3zsfrmwJXwjcXt5gX4zw05OIgdd0sJp4oViJ9I/8MBK8MXOGcTSCGC9rYhT6zLpBEQZDCwwh0eJYYD/g0+7swDJDwzf1TeaYbfiCTyCZrb8hlKMp3jZV73eT4VLI/Gtoj8YJM8c3c23QUbH963IqYHnrKGM4mhrrCMYhG61ANBy1yjXlRkImKh7MaFUD25rraUVrcHUHlgp5BotZNuCdRyLcQmL6q4cMNbBRQCPobGnEVip7x+72jrsAaWGzBfiH1zv0ae+8hjLZ3PFInYqLZAOWIYHg1yNZhz8i1KsD8DNkRWScamIPgRXWo6G4W7Mi8ugnVf5A5FmxgFMJuZ+Oddcmx6bVlvQ/nrgKSQ7UEIe9HXkvb0Der6lIpvit3j7uROh1FyKK3ucjz1syqfLfBb4B/N2OO2eGO5w5bkXdWdkSOuXZ33ajlQ7a1QdXt4SiTslSiGNvbJkzkQz7s8izVyno1M+96wgjGddhIDrweGJ04PH4P0A9hNSCvcqKuKcDb5v75i3T+h5NRMIdtfCq33fj2fSPIBTIFS/uHZ8Yu5NR/h3Ei7aRF/9eJJI1m4BnNJ6U9XbwHHjGY0mrie5LcMC2PNI2Hmc6hgzuSYQXvQCY5y0DDW2J9foVe9/UEg312/iFrEEEe198Cclg9SNFhdcD96GUHp3VgdMSjSCFgkuQB3ImTbwc8FNECarg/W3kTl0Qimv3IaVwy6yqiYoG2hO42JZLR2IDyMDYlNbdSUiq/GFJlT8aINJV+1rnwcWPaUd9EegHa5+1i+e13X/DyaPL7jvQNCh9CCkKfueKX3fNGloa+9XT1f0vQwqGR5Ex5CbgLQWdagrZNPGC4RnctyOBv98gRtW4JZ8T/B10xCrUz62Gmgf1TteAFvdFuQ4KX2HXwEbmzRKSYckhymC3IAadixh9aaRuyjh6FpKtAjHEFOJ0GMZHH74DZVqdBCn+AecKBSoCNS0yWCTwa6CCzqRLSzSE42o3HjZ1XqY8wKwTrNveiF+HptVI2aLmjCIG6ZC3Xad3/sZZC56j5d0DT5ZrrEGzEQVpwTdMXaROfTFwZyabOxIgk83FMtncVzLZ3FuRrMVs776tRZyxRxD61829PenLAWNwb0ayGec0BTH2LDy17uCrFrxz896LvjzjTq8/1e1IKclfgK/jMDXU6dh2wo0h79T1QB55T4xiZhJ5vu7CF175C/I8P43QBg9BnPFrkeBHDHl+PtI6v3zZnLP6cgd+dN1HQRd11VLo4HihYiMrokrXlCm1eRax928Alhd0qoz0xV2SVPmDvI3OQLJbRxR0qgS8D9QHPdXDE0F3uhXrvf356NxKfyi56YEWvfqWDhd0FXTNu6Ybkfdyp2B3ymClkJt3CH5UZ8a2NlAqOA/siB/1gms669Jm2kVrB1XpVzrSqZUdpoKorExFokrHKoVyqlSUTSQWHtXx6FA4Eq6ocm1s0gpGqcCPXoWQwXU68gKsRlK1ZlIYRPoOHId4/T/OZHNv31FVwUw29xpgtLcnfefzvhtNvOjo7UmbBpbm76WZbG45EmU8l60f4uCgRUt0E2G7BPIcnQH8/CU+5Zc1PGWliDegT7TOaQivuy2p8l8s6HQ2qfJTEErDLCvsrgu1OX9ev2zuGUA43Fnbx7L1VeXN4W7gsaFl8S8PLYvfzPcByVaeARQLOpWjoXC9iSZeIM5BshTtiGHeQj0FPvj7WDAxsP1E4U0zL5msipnH2gHtlJVrR7TGHmtVYpyxImLQOYihP4xEyO9BnIiSt98oYgCaOqwt+DLrecRQnBM4ly78DJTCb0As519zRlDEsG3vPJTyzKFg1s78be6BRimIhWuBfTWyXczxgtkyTdX5jY7YJxOypyOGqDlGGd9xMiwWU0IQbEAclJ5/FMkemADbKOIoLUMoX58EpmWyubO85XsD5doob7aiOJbNfcB5SM3MOkT44j5v3/chQb5feesMMgEy2dxrEbrhN3p70v0TrdfEzkVS5dthzoFITd2H8N9HacyoLYaXxBiGeYh98C584TRDJ93L+/y9SM3TAkTNbxryjByKvG8zEftzKhIgOQlQA4sSd//ricO/lVT594FuF51Nq8F2UeY/G3lfJyN9tu5GnsF2ROTuKe/nCu88OwEKOrUkqfLxpMpfGZteLro1q6xdHl5xfddbXZeIO2IrUJaKOpay3apbtEfR6ivI+3Hbi3Gvt4fdycEawE/xm8iMwdggNpF4xa6AUijXhUiHVqXNOC3TsZAHz1ApXMCxw2wBpm4cSLJpYI7ShCZSazIFqUZSNoEMcJPw1ZUc5F79CqF6XIg4dRchDul2HaxMNjcJuAmc8Du++atPjZQmfam3J91UKdtN4X0352eyub8g4i+Nkc6xXweLswh8/n6aDtbzgtdU8YOIvHFnUuWXIYbc74EfF3RKJ1V+FjJx9SCTSxTJPOGtexuwcd9L1ocHl8T+c82fOsNoxdQThmbHp1cry38w5X7tqusKOvXbwKGXI9mF3+2M62zi3w5HIYaSi0Si5+OPJyZrEsjsjLUOoWGZyRSB36pEIw5QCV8evUUpwuEWXUTRisxdS5Em2w6SpT0IMda6kIh4F2JEPeYd/xWIM3gXIs4RR6hJZyCR828hlHrwnRJDp7MRw3I5/hw6HO4vJmptsZCuVR2iYQeUEZIytVhBSmSwnmoEv3dlAnH6LPwmw8E5PTp2nyKhIwPnE5RZ7/bW3xg4ZrA9jBECMWUStncPzDFcxF46xju3QaTXXotHD69msrnzgVeVt4Q+FO12HCuh/4kY12Ek+/B3L7tJb086D3wkk80tQCS790FqccbDe5G6+QJiEO80ZLK5ecgzc9cxC28EcTjXLtTXrNqZ57Gb4kzgA8h3ZzKqJuDgPWNjNNWZwFcQx6kNv6XQVOS9GkSyyu/3Pv8Q8swvAj6O1ETNQ+qkNiKZ51FvObJfVQGKoDrxgw6N7QfM+fV761DQqYGkyl+qQnpzJps7bebr2kJr/tRZ8s7NoBOl91chfUDLzPLK/scTh+qq1S7H0C7osNa44ZheXxnlcUTcYhE7CbuFg5XJ5qYgBsV076NhZBBtVNmTP7Zyrl5Ub6sxItW4WMcj/apY6UQpRSgqDl9i2phqUlB1yQymk7yftiYUjJCNFw3sxJ/wViDG2xeQKJRRgYohCi8u4mitQvj1B7Nj3am3AFtsVZk+Umr/PFQ+iLxgTeze+BkyOP0En+KyLczPZHOR3p50ZTvrNRFAUuUPAf4RanW6I50Oo6vCgNrXaqntO/O0wePX/LXjzKTKm8L8ZYhxdxaSZbw7qfIfQCLzc4DBp38y+ZDW+eUOvLFl7a1t1qTDRv+qXfX2RvncV16xchj4xPYEa7ymsUc98+uuxZv/2bofYoD9LNgksokmxsEvEKP+HsRQfwCpgRhPFdAY+8Han6ADUfb+Ns6AmffM3G2cLpT0eqpBtSUeHagWy5NXIvP9m/AdBVObaKLZcUTK+QzEKHoj4kQ8jRh030bmvOPw6fUu4uANevs3861xUDTQWU2Eq1bN0TocMlkgJec3ZnsY4YvGnl7a2/9qxICNI4yD2dTXUIPQFEcR2tN7vWUVpKZptre9Oe8u6uvbzLEqiP1g9mucQHO/zT41QqN8v0f1C2IOcHK4vZZXNnMQauAsZPz6b6Avk81db5wsgN6e9HJPeGxCJSXEiD8FoY7tbFyEOJVvQ+7BtxDbZ+EuOJfdDesQZ6ljnGVV/DpJC3GwpiLPfbAmM4pkrapI0uNS5D7/EXmv9gYGCjpVQ7KmACRV/ikkiBNOqryDBHNC+LWR5vhBmDGljIiv3J1U+WkFnVoPPLnPRRvbgJ4Zpww9He1yPtb3ePwJ71hzgW+27V1aNf20gWNW3th1n65aSWBlpLt6c3x26ZKBx1sVFXtNZYv9eqQJ8rPP8V6+IOzyGizPubodoQYah6qNutqSiTBWM/WiQWvUtmq82uJr1D6z7iYW8TPnSrqzB08KfKqA1fAT/AibWb+Ry26yXyHgR8CDpY3q0eFnwsopE0LuzyT8CFkbW9XjTAxvID3A0RHvEPaUTDa3dYvGJnYreN/bjUg/iq2gXa+CwMd4A2wT24BHCbwK6AaXSccOEe6Q+nx3xKK4IdTtOpyJTCIHI7Kx1yBG6gbE+T0TyS5bwOLKlrC75cHWm9CqKPsJuRvvbj8Z6TzfiB7gukw2N+53l1T545Mqv7A6bJ3mVNTXI521v01P9/8iOqV6IRJRb6KJCdHbk/5Xb0/6IiTrsB8SCBjF6yXpOmjt1qnoBuejxgyPcaJUebPSxfUhQ3MzkuXgz20JwG6Nry3WarFjwJ0d2D6MOEw/RQy3OQgbYxaiJHYO8AYkk7U/4hjsS724kzlnk/0ZRAw287nJrC0BXBLRiNsShYgdPMcS9dLy4FP3zHptiPNnlADjyHi82dum4u2n6l3HJETJL1gzeyq+UWv2b+qozHGDQbHGfmXGnvAyAzj4oiB/9GwqYEya/wpg70g7b7Sj/AtxUA318gDEOTk3k82ZmjaDnH/5OQAAIABJREFU44ALJ+qX1duTXtXbk/5pb0+6UR5+Z+D7OO5nj/jUn0YQ5/UQJIv1b42kyp8MZK2Iu2+43WmkrjrIOzGCBCduQZ7BJchzHAyeGJGTKqIeeCNC/zsfedbeUdCpJ8Y5hZOBzyPPznsRJ80EAiAQGG6ZW2Lef24m3FEz40sUKRX6EfAdT9TimiXfnvo54GPA8u5XjF654ILN+3r7GgIWV4fsP6y6ueuh0prIgUBXx0Gj93QdUmwZfTbWb0XdJcBFBZ16bGc7V7ALM1jeS3sCwvPdH7954ItZmD9eVG7ilXcg9jtUnM5jhdMJWcXtrWqiBCWEnnDYOOdkenWEveNrwFIKC+GdRpCJ5rDR1ZFKuMNVTkVZdlSvxeenh5AJcmlvT/rrO3KdHlKgvIibqrIbONtNbB+9PekRL7L4Z3w6mtLaCzE7YPvfpI1M8E0Fuh3HGchEQm04xJpcK86Abytuvq8F6mo/CeO/288gmfgOfOW/NyBZgsdAnwMaLK1w7S5v2beSKh8Bql72KY//bo/B6wfyVSQiWHvya9NP7D58ZGlkcvWEyUeMTo1Pq66+9boTmvVaTewojkZqHN6MzEGrgbmDS0JEpzi1aLcOW1adUT+EzEeGGmgk1R2gWu4Lh+IzKyGnhmuHxuwKHfgHEBouzp7tcVA04pTEEcrghUjk/dUIfXBq4NgO4ngZpcKZ3nYfAK5GAhtJZB40TIwfI2yQD3jbGFGIg6lv1B7MCMWoD3yaazViIIYaaHn7NvTJdyGGq4nO2w37Cc75xgm8DSknMBTBRGBdkzkEv1zC1GYFv5MQfr06iIN6PjDPowXOQjLs38AXAvkkMrYUEAM4gWTPrkOUGk8P3Js0Qv/6mbcNSZV/h3ftV++qbPkV6hJ1DGwYntH2RcdWZwBttjxUx12hLpmxUF/zbznfJVV+b2T+aZ920pDVsleFwi+7cUZtjdTttSH1TWcj/eOeQZ7pedRTgoMOvELmsRRCme8FrjeS7OPgDm8f/cA7gceR9/sMGoRcVEgrpwa1oklPaO/dUH1I1tf04NK9PelHM9lcDRkbsumL7/w2THkT8KPSusi/kPHjbiA15VVD86KdbmXjva2Xu2Xrl9s415ccu8TB8pyrM5GX3QycQ4hnbQaaFwM7up+tHDHXxVUKa2s6ooxlNbfVbDfRMRQyYMfwDTATpQJ/MDcRqBgarTUWFkop9gf6tMtAeYvau23f8qBTtlaFW3UXPgfVRSIE09m6yeD28CRYA0AHqAt6e9ITFvE3sdvhVci7O4hM+DFAocGy69arIBmWH+zsE9wTkVT5NuDH4FrmtXb6o/Ur6bqEczAzXUaM1irCaV+FDBbHIoGk/UKtzt2dhwwfsuXhtrhbogrcmlT5fZBI4l3SA2TOCKKidl5yYX5RQaceChz9VGSSvLg2Yp+34a7W6YnZ5da2ZIXRVdFIUuXPbqjnaqKJreDNv2chxr2h1A0BRCe5KtKpI4F5z8xxrdSzMMwygFDLXhVlhaS4w1POHVCKloZt3EAcTwO/RKLjGSQIsRwJCE0JrPNXZH6bjrwXNyKO2AFI89wPIlnfTuTdcBBn8X8QAy3hXeMWxFmJU99byjiXc1yXqq5Rs0LYyhrrj2Ve+IeRAnwjUBHGt1fC+BmAEjKnD3ufb0ZsHJMlbEEohcfiZ7DMqG2ELRp7hwW/hyBMRm0VosDWj1A/ZyBBoksR+vJTiA1yOzI+1RBH09wTs9+9MtmcClAFPwXEGmyDI5Dv55vefnYFMsA7rJo+0aq5bWNuvpzPpm1t+HJFUuW7ECcqATC4LEptxMItWSYwMB+RWH8GmavakGc2DjocmVJ1q/2hmq5atyM2pckw34vYD79FlL6nsQ3hJc+Z+bPHBNmIP26YQMXYs11cE2HqCcNMPnpEb7yz3QLX9YIvh4P6qOfAf9Xsu7cnvSiTzS0CPhCfWTkHGb9aCzo15Dn+VeDEgcdbntnrnL6B5aUD1rzA2/qCofRz1Dx/MZDJ5n6CX0MUpp63DM/DwQpexgsUwNBaU9IuMSWOznPalq3P3UUe6DhbF9CanyOA49ToULLUtSxtgapoF9epEsZlOBRnHRKVGkEGxlHEiF6BcNfXAP+3o72PMtncEuRFOrgpxbrnIJPNfQRfHehZJKozXrCkD8j09qSbfUy2g6TKT0eMhqOxnJm4lgLXk9MZe2W1ijuOLtohUC5+veUI0qzzU4iRk0Pe9ZORPn7vQd7/Z0APg9oXMQ5fizjL+yKRv1bE0P0MEo1+GDEsH0DENRYitMKngN/HZ5Y+175PKTGwJFqujYZqtSJqwdv6vr2it/vKpQMHrX5p71gTezIy2VwGody9EXlmj6FeHAIC85TWoF36LJuuwG4ajX/lVHC0i7YjDCprrMmvMeqXI46RoQmN4md0wohjUkWMxE1IffEvgP9F5tDLgDdqzdml9aEHYtNqFyhFBHGQJiMOy0xvPx3Ui3KsQgyydnwqXhFfjMKqlVCl9SFVK6l/dO5XPQrfkdKI4MYQEskPUhOL1AeFN3vHNoqAJcT5CziZ1JDIfh/iCJmxewg/MBu0jcDv9RWsC+vDl4YfQGyC7yHjRzeSqTjeuzem2XIMmS9Ash0Z77MasGjT/S2fW/Gb7nUFnbqbcZBU+QRgF3RqmzWiLwU+8MZrT+5+aOX/tq4ZbFOwT7Ez1q6VStjFMpGSi4LVC/U1W/VM/XdAUuV/CFzA1uIRZSQDdRViJ54HvAZ5Dx4FOrDcuVbM1V0HlK7d/GDrk8g885C33Q1IreZPEaruu5Cs6CDwzQloguac0kgW9DjkuT0Iv2xBWWFXz0gPMvxMhIEnE8qz4hWwHNSpBZ1a0bjPfVoXtcx8Xf8JU44efeShhXM2ezVguy12FS3MKOYEU/IwQVGVokw0/NJk+VxX/gXgKMUTzzOHptAaHCf4WaCLtLa8ELgxzswE1QK02SHJQIhz5QI6pCyUHcG1Y6xDMldPIfLsRmWwBVFHeitiyO2QWEUmmwt521doSkHvafg/JCL5MeS7N5HYRrQhNLQmtg8XMfi+iWstF7KlhQqbACCAWquLoXtAFRGKhXnRVyKF3schBewHAL9GIn6vx6dWtYM6ADECr8WvE7kQyeZfDfQUdGoDQm26Bn9MvAwxgp9FDMqLo1OqodKAzdSThqKxKdUWXJVY/sMpH6kNquWvPPKf53vveBNNjIcbkNqLfyJjRy/ihJg5yTgCgEdfV3Rqd6uaJJD3QLIvFiU7goviacThWY4Y9nciLQhMBNs4EDF8el0NaQQ6jGSy/gPpAbkf4gzeA/w/t8ZJpU3WB4trQ8uQbNbeiME4Ez9ifjHinH3Nu6553jLwjdC4d25FwLUjqMTsmu7Yp3oy4pC0eud4KzI2POWdrxHFUMj8+U9vuYsvkGFqy7rwbRwjjlHx7suf8WtdDH2qFb8uqx+//s2g6m1fQeZvozjc4R3vrUgQdhViQD/tHeeLyFxwubePIhLgMbXXISC58b5EBrgsqfLjlmoUdGp0VzhXV6hLVHjT8FUtawZfhzACpsX7S3air1iMltxhJQ7B63f2ee1GmEM9HVUj37P5fpcj9cEnIKIUBeQZmIuraqG4vjM6yfkMMsfMQZ6r/0GeqzcggiynILb7TEQ98phtnZDXZuQnyHvrUQ991rBbtVj9p07PufKUDRUj+/esWzT7zL5cUuV/llT5TFLl53lMD2oj9nue/c2kjzy0cE5sd3euYCdTBDPZXBsyUC6lvmBzm7CtGvHwZsrVju2u7mWctqME6EM7oKw66fcQcITHPx93k4Z9B5V9ZNC16nforda4v8bi3MA+6g4xqNRYH4wu5OE+FnlxFJK5Wo5EGZYAV2ayuV/19qS3RxU6BXHOlvX2pNdtZ90mdiN4Gcq+TDaXQwzv6dRPwgYuAYWfJiaG59S8PanyZ4BqlUIrV1stjnb6w+bdPBPJOCUQ6uWfEOPv/YbnnVT5zwChgk6Vkirfj9REzESi2AuQqLNG6i8eRt7lJCJBfTQSYVyJOFLnAl8t6NRTSZU/BomiX4BEBSf1P5Gg+xWjxGc4tM6rMOW4YVb8pgu3qqJ9S0PX2Te2HUgPn3jJb14TeyJiSPZiJZIh/S/EOSjiBz7HDDZvKjPZJqift0zmq2KHxpbPx6fthRF63Si+IJOJXGzCV+EbRYIUFn62aBjJ2r4def9GLIta6/yKssNjmZiqd8424nB9F3HoDkOCjmHqqXhBmfQRvIyUGiMGj2WmzTUu8M7tPYHtzATdidRFGoEpk80yNVPgZ5/6kABoGKl9ez1+42WFBFxMWYGDL139BDLOGMcuSn3tWBFxuHJIUGapd58/hhjSw8CzvT3pSiabG0HGrd8D2cD9cIFY8txNq62o6ox0uMdC6hZvuRHLOBKp9d7CzofdlV+/SsNBSr7HIYRavRxRmPzFQn3NDjF3XqY4nK371/0NeZ9agO+D7iDitLbNrfYPLY2fhmSINajHK5vDj675a8dChMVxD3Aakgn9DPAk4qifhDzD1yHB/jUASZWfCXwC+ElBp/7pfWYhc2QMyaLOQ57TNvx3y9T+Gz9EAYnqoD0TTUSOpw9vmVtZX1wbHk2q/JuQYEcZaYuy22NnZ7BORV7qq9lajnRCh6jmxukfnbfNHStV58/scP7JComD1bi7bR1qgr9lQtIanPFs3bHNgoN34/68Sc3SYLneok7EmZqJX2R7MTIAGw74AmSAfRXCv45nsrnLM9lcsGC1EechA9VWadgm9gz09qTXIkb5j5BnoZHv+2RvT3qPGIh2I6QRw6bY+cpRZ945/RW71fktcGRBpx5EosHfR96/q71/S83GBZ2qmabEnszsuYiTuxJ5oScB/aAToM9Rtr4led6mxzv2L56MGF/GeJmJOFNGEn4F8h0vwBTGOxb9jyVYf2sb1aKivMnGHQ1B1SLSotXmhxKXJlW+oYisiSagtyddRJgQ/w9xsEzz36BzNQalUErhBgKYBpupV+qLInN7J/WqeF0wVpNl5sAYQl/bG4mM9yPvxzCSyXIQKl0NiaqvBVYrm+FwgqusMI8BixEH5FHvHPLefu5C3j2jwBekThkHz4hfBDNxBkFBif0QZ81kA6rUz/uRhnsygmSQ1uOzQxQyNxvnyDBbDOKIoxcUGTDqawu87cyx8O6JUQM052Kk3jOIA3IZkpl8DaISeAaSUTgboZivpL5sYSg2VR8X6XCPAd6ayebel8nmTP34vkg9zHnsAizU19TifaXzbfgPJRS1q5Dnp3WhvuYnQO0KdcmpV6hL5u+K89uVSKp8HF9BNmhXloHrEfroWsDFsaxyv/0NJADxBPKc7odQ/96EzEHfRxzXVyGBjQJSe3dNQae+XdCpkYJOFQo6VfaO1Y4ECYPsqemgPwv6UwjLxrzrBma8qBeD0Uot+96UyKo/dSSwXKtlXqWWPG/znLlv3bKooFOVgk49WtCpb5k5dnfHznaw7kE84AjA4NIoq/7YgVPaXqWTGWeeH29vWzCOmVJBxl49tIhPbE9lUJZaFoTsCQrBFNupEDPUQTMhmIHW9CJwkIe03fv8aXyaxQJkUtsXedkORzivE+EbCAf7vm2s08Rujt6edB8SddIgdNfSJtsoUv5pl57cnomrkTFqpP/hRKXws0krnOHQxwo69Yi3/DNIX7prkAj7ZUg2eFx4k9G1CB3nHmApttMZm1WyY3NHZ9pt1aNi02rp9lRxKmIYjiZVfgoyOa5AHOhrkej01xGakifhrHArNlZE07pXlbW3tWszfJTXxHFHQxbw0Rf17jTxskFvT3plb096E+Kk3I9kaTRQ05qK61DUeuwzI9kM9Q5GF/XOi6G3BTM4FmKkRQJ/E/jdKOlORaLcTyIiDDYyp30EMe6PRt6hCnDH0ftf//BRqRtCSjmHIk7Zz71ln0fmy2sR8aeQd4xlyHxn5ljjCI54y2tI5tgYAyazE5z5t+CrChonzIgruEi9WtR1mOG6hBBKoLlXA8h7XcGnX5plRjnQOGuWU8GtldCuU9d+wcKXk5+POF62d9/WIX2h3onYAMuRupm9ERW313j34w7EmVuMZNGXAo8gtW4XIb2xvo1kDF/tHfdpxKm5mV2EhfqagYX6mlsX6mv2ue+KM7/8yMdOW/LA58/Y66J3X3cCEpD6BPCOK9QlL76huJsiqfIhJEPU+DwZ0aVnEWGL+4BPWFH37srGsGlwfQ7yvQ4izvYg8FRBpxzgs8izMgWhCF6JCOOQVPlQUuUXJlX+DQAFncoDb0F6ZAFwwMK1G/d68+ZQ2z7FWYjDdi2wHtR6ULWGhIOhI5sAx0G4dEenVsqldeHY6KrILd2HFr/94tyxnYud5mB5ykU3EjBGShtDFNeGcSsmoDSxk/M88Tx2Nu4m/gurNdQcqNYaVw681HXX0XhRQSdqvOM01qFVkJfBQV6A4ORwMGLk7Y8YZ0UkErgYiUhcBZDJ5joy2dxXM9ncGEe5tyf9iLf+qzPZXLN/zp4Nr8ZP/t/8YIzyphA0HaznjO4jhqdhu2sBjbbudMv/n70zj5OrKtP/99zaeu8k3dlDoAIkJWHfREBApXBFHCjcB2Hgh6IZRq2MjjouiCKOKQTNuKAICqJoKeq4AIUo+yKbrEUCKUL2vfeurqp7z++P95y+tzvd2SBJJ7nP59Ofrq66W92+957zvO/zPm9kLWKl/vqkKn4QkUf8HRm4bI3JA1vbbkmnliCR4466yVWnvKKO8tJ6FalzD3vphgnNK/8yrobfYb6ATHgfM9v+BfIMsJKrOzGDkYp6TH5jD83JCok2V6k6ebyohEesvQLwRTMIhwgxGlqQoMJGrJxNE+tfEYm65UGCpfEncU7g7wp+LWHQAc/K2aJm+TkMlQJZGZ2NgtsMvDWWqEeIwxwkq3Ucck/Umc+uW9cx8/ym+vW94N2LTCY/hNRt1SGE7lCkHikBXi+4M0DHzPe04/DdiNTJkhbrRKiR+/t5c3y2VqoZeBLjuog/QZxo1leei9IuCbdGM1K7YpexttcbkeeG7fViz0ew3MCrdFKrdDi4fUPInu3RFeyfNYCM/3ORCfNV+Wy6F5GO21KAxxACcpX5+2/mfP5bPpuek8+mj8tn09fns+lnEflgG1JbdiNAPpuu5LPpW/PZ9FhRvOw30N54knaYM+W+JQuQvoRfQrIll+3eQ9ul+DQyr7ZkvozvcTAR+Z8fD5wG6g1eX3QaqDhC5NciJPoCJPvUjVyXlHSqD8lexZBr5u/INQNyf73RbBezfG9Jp3RSFSNJVVT1U2ru+MPL/1ftii5Hrrf/Ru7hdyOBHPAlglYm2A14oGp4lAdWJ+rdsjNtyY3tT+ez6SWv5UnbVdjVA28LAfLQ/vpeJhzZR6T+NSVVQWw1khGP9jCuaRXrO2fi6SG9djcjQUrh4WrV9tgyFe0ZYM1b5gyvxWKEWqvhx6EAV2s598PyWcFoln3oWl18F5K9Gr4tO5i9hN8E8Ubgf/LZ9P+a5SYgcomzM7nCPfls2nZJ/j7Qls+m+wixJ+OzyOQbNLSkKrY57uuQ3hAhtgFJVWyZ8e7KHzqfq2tz+xyNREXHI5ONa5FJ4KUlncqY5RcDvy3p1IgmMUaeNxN40VjOXgMcVl6dWAR6NigGVtehIt4E7TrjgVaUdx9aTQG1GrixpFOdZlsTkWjyk0jtxieA83TNOarjubq6WIunyyujDjUHp96j7Zhe6qdXeOWWuhhyHTy9s85biD0b+Wz6gUyucDYyGb8BaEehEpNc5cToQcYUB8mK2MxNJzKeJ5DJ+xz8nk42qxMMFipk8meJUx8MugyWEQlgI76JQ7BOowWJ0k/BV2V0LVn1emfJquM9cNYjE0YPn+wNIFI5BQwkYp1rBqqtc0AfAcqqQzYghGQiMrnrNvuy/a9azPcCP0tna61sJmoxMjlVyLMCxyGiFUTUoARKm+/YYvYxDsmw2cCY9qq45Q3RaP3kGqYPJnVtVLX2HOWgzXrWYj5h1us2+5+GZLNqSPnA7xD30neYfT6KPIdORcaDScAfEeXDGZlc4d+R7MSdiBLmx+Zca6Apkyt808jRxxIWA+fNuOMFd9yidR8xfz+HBLHGvPnBa4hZyDVZBqahPFSUCbrqlJFreB0SKOhB/A/WI2S8hFxHP0Huw0cROeG7kqp4KKKW+BBS1lNGxo8PJ1XxOyWd6kmq4vn4AQIAkqpYh1w7z+az6W8AX0h+sXgl0uPRA1YlVVHjPzuChjdWrQUQByeOBBx/ifRh2yOxywhWPpvWmVzhbZ7HUksqlMMwcmWf3b4c71Varm8VsUgv5UoDnt5Mgjj4h1IBwhNx1Iaj9wNxcRZJjjIK9WHwXPk04gsogvI/eWO4F8bQfbtARHvEtUe7Ex28oF9ABrNp5u84Igm8BxkMxwePP59NlzK5wq+QVO8hGFlgPpv+51ZPUIg9AS8gk27lRKH5gCpsXncQYgvI5Artk09ruWPVX1taXHfwQTQXmUTVI9nivwBLkqqoSjqlDWnakgNnBrgQuDGpim9C7JFX4qmPA7eDjow/qhenTtO7PEZ5WX2TSniHK62i3kBkATJZJJMrqGMW8HvgB4/N368NmZz++ZgFy/604R/151TLzuediJ6kHE9rHKUcGZ7W3tMCMhF0Rzq4ECEs8tn0QCZXeBGZaCWU4sho3aBkzZo0gDxTKgytnzjKvN9plrMW6bbvjYuMTfsjtRqWHDSa10uQazqOjF3TzesqQgTuRWRvReSe+AJwHqh+UIeZde1cpoYEROrxI+R6oNo6HXSfmbjZ79KKGFc8hUw6rSQx6MRmuyxZshhBjANqyKTV1kfZCS3KIar8Y7GZuqjZ3gTzO2HerwFu90uJ2Nr7mvSs8zYQieMAm5RDTcl5ehwxHQjWbmPO3zHmvBPYl13mPmTMf9Sco5VAZz6b/hFAJle4EiHNP0eUMOcjbqjWht5DCNuRmVzhUuDpQI+s3QpzHEWyaZDsiEV29xzRrkdSFY9CguobkOt3NlFN00HlcvcL9cvxHA+pr0wAh0fq3bbYeHd5eWX8GqTeyrpqxhG5YBmRirYjLqPnIRnYC5Dr4xRgUVIVHxvJQh259tYi2XB7fB9B5L0rAUo6tTqpiichDp9vwCdWHkObbSskmPKIDTLuidjVGSzbd8IXYA4SjOHKuOGfD74TeD10ee2BZx5pkRhbgwZU74Dt2btFJhf8cCOxyDjAQWvFZqZ/avCvwf7U/ncIRvPQGq3UFncs/cEUEe3CQLfzcmK8N4CQpOEFgg3A4eb3auBfjcnFGuAbiC79z8gNFGLvwueQq/DDSCG1xUG753D2PHhVLmlMlues+XtTFCIOQKShGmk6eKC+85kGcB2QKN7NiPT2tm3Y7D+QyV4GMc9Yj/yvHgSVA5V1K06kpxQn0uiqaEs1XutyatrBAf1RUIuB35fXRY/b+HjDzyIN3o9LOrUgkyvctP7hxp9tejae6lmaaOl8IVHxXIWuyLzO7Y2w5q5mkGvi+JJOPffanq0QeyPy2fRi08jzAKRe5yFsZpzBvkvjkXlDUO6RQIi8XdaOada9z9Z87IdPNCbiZ3bGI8RpEzJ+tSMTx36EgDwBfNKs24sEO5YhdZCfAt6OjIV9SB3ISYgr4QZkgumAM9cc4/7m2FebY0ggBPEu87uBoYSy22x3Ir4EUib3EoAJug12MNTcw5JTG6m3kxc7bnuYnleNMyvuhKP6qqavlzbHPsu8fjO+5NLWwQUj/jEki3ijWf5JgHw2/TDSi49MrjAByQRsyOQKl+az6U35bPqRTK7QiGR/Xjb7UUhz2YPM/yQKHO9WuX3ZreO/kJxfvHlPMRjYB/BG5J66D7nOmqON2tEDzkY8xwYXDkWuy8qEo3pLU8/oji69ZcITnc/XK6TG1yojxpd0allSFa9DxqsaUmt8NpLt+jsiEfw2kgX90vCDKelUFZEsWsxAMsCtGIJlUEGu7+eQe8jWd1pDJisVvhUJfuyx2NUmF6eYbBAgpMNxti1LtQ0mE7LNiBEAbMOi/q9tSpPZvU/Anjd74M5mmS8NQ7Jwmx25+e5bamQ82P/CraK7l0V1pN6bg0QUogzrU2KOaRJyMc9CSNjBCOk6CSlyvcdEKg/J5Apfy+QK29QzK8TYRj6bruWz6c8gTSZBrr8HkQlIiK0gqYotK29rdTY8Xa/liSRw+6KUV8dwYh4NyX4Vn1BtRGoytqmJb0mnFiGuXfVIoOObQN70kvksMLPr2UYXNHrAwRtwAKcBT8UbDyxPBhYkVfGs1Xc1lzc+2RBZ/2CTdWEar6L6OCfG5NZU/wrlqK9OOLb3YdAapaHOxWmquSrGxwmlgSG2D9aZ79MI4Qn2bGxkqPTP1gbZnozg1xptRJzLupCxaQWS8bHbqyKEJ4GMW1MQOVIrQqhWmX28B5GyJ80yc5DakLORyPhvkMlbzRzvFETZYQngWcjY141km+qRjM/vkTrKPsDRmjd7HuPMmG3lLFaib3tr2YB0GT9r5CGTW1s3FozI23Pk4hMjb9h6AAPRBv1o2zH9ZSeGNufFZhdsbzK7rU1mW8F6rRgyL/kykqH6B5ujD5nUHgscl8kV5mRyhXOBgXw2fTHwa+T/e6LZ92/N99xfa+q6X4qN63w+cfWUMzYtOjVz/2EjbD/ELkBSFVVSFd9i+kJdC3wUIUA/B46sdUT/s2dJfQ++tP0R5H6aPNARvbFnSfyOzmLdvyFywG6z3nnAQFIV48h4sQ7JWF0PnKscfeExC5ZVzGeXAddt4+H+EXhfSadsHSNJVVRIIKMT+B5yrSuGkivbPuHy3dFz7bXEriZYJymFM8xSfavQ5pG0NYJl+1i8SlnhaE4bIzOxkXemwDYN9v/eTgzaxEbjqHEH1YjW2WalgwRruJ1uDLk5epHogIdcuBchDQY/n8kVbkQifq9HBqMQew++hzwZt+NrAAAgAElEQVS4lgCnjqGC5LGOi9bc3XJB56Mtdf48Rn4G1kXxXEV5dZxaWZ0LfLekU9tDWiJI5urKkk5dE6zXKunUSuDwakf8JwMbI1oIliDaXF2cmFitizS4hz/6yLFPDayLpctrY18FyGfTxQlH9b2/cf/KueMOHTjjhfWH/e+6u8a9G5zbVEJ/u26C+3OvJ37YksohPzAyxhAhtgn5bPomJJvzNoQoWYLQj4w76xja38lmcHrMe1YaaNsYxJB74B3IhG8Vfk+qDmSSp5BotyU0ByOEytZy2XFKIQGOtWYbrUimqwUhWQrJwthapWAgMolfP1WHEK/98Sd2SikiWg8xowg2Q7ZmH3b9Y/DdBK2kuM58PmDOl60Js9JCW4dma8RsViqiPV6v9WCPoDqEMBURiRX4hMrWw63GJ2h2++sZVn+UyRVUJlc4Hsl01CEqlnsQidhHgemmP+khSMPzr5nlTkUyCHmgo2FqrTbl9O6mtmP692tO9d2RyRXGMcaxQM0bt0DN+8kCNe9tu/tYXkNMRPqb/ZvJJK5HnKOnl3RqHTLPOxLJaPYg9YG/Bn7U9VzD4iU/m3g6Wr3LbCOLyPROReTvtyJk64NIlrjFqXNLM9+34Z1elf+HBC1eGEUeuBmMjL5r2NsHmH0/V9KpH2DkhPjXchVx+3xPSade2PbTMjaxqyWC9qG5VQTldYO5pkEp4SjQOtDTaodYVjBStKXPt3ebNso3EmymaqT/hdrC65H629gH8NVI2nc/RKYUR6JS+yOD0mOIXODgTK5wbD6bfnRbv0yIsYt8Nr0mkyvMAVzTjDjEtmEtUN08hOOAp8HTeFUFqHZENvGBLW0sqYrHApcifUbmInUMwwcaiwZgOl7kHKTO4lRQ7znwQ51HV7t6PqE9/gRzKenUy8GVfvOf6TuCf5d0am1SFT+sy5Hu51fMDf/3IV4N7kGu2SiS/XkUWICvkrAOgjH8sbIHGWesa2A7MlHrx88AKSSTm0CyJWuRsej9GKkcQo5m4ZtqdCCZprWIbM32g3oYkT99w+z3Rfwmvq7ZT7M5zojZXwUhiBMR0uaPuZoaiqiZdwSt2e3renwHP/tdrPRPIRPdNvM6hj8+j8M3xKgLbNd+D601HooIGo0azHANNNat0eVK629cr66Ozcf/dmwJgUxKJwNnjJC9mgpcYc7XOuDJfDZdzuQKP0Im1a8gMsLPIe6lXzC/E4gl++VKcUW8VWcnndirzLmajEzG38TYxrnIs/qQBWreQ/P1wo7dfUCvAdYhNWe2pmldUhU/gBB3EEJ8BhKwWAQsKunUP5KqOBu5DkCyvp9BMrpPIdLSduRePAe5dxoAR3vqsMqGKKsKLQcjxkqnJ1XxwpJOjWh6YjJUF5njuxMZ/54q6ZQl/q8g96wlT/+FZKS/jfTlqwfOLenUJvYC7GqC9V3kn9+4IytLryrYQfK0TbvYyg62vuOR3TlGyjZZ2Af2tmBbCF4UKdyNIDfdN5Fo2KnIg/77iL3mo0ivg/pMrnBSPpseqTtyiD0MYWPhHcIv207qPDAx3v1ytQ9v3d2t4Il3V2xcjWiDR61PgdI63qIXbcP2JpmfGGYStYVM0gakBqJY0qlb7ZuZ3LKX463uQ0ityTahpFMbt75UiBBbRj6bdoHfZnKFExFJ3nQkY2LbBFjY7I2LXO8VhFBZ4jXeLG/lci7wMyQL9WckSr4p8NlqhPgE65qWISQrjhCUo5BxTCOTwDhCfI4zx2THsX7kvmpDCFu9WXYqQy3OEyDGFIHvZYmQY46vhkxAbT8tGzANqlrqzfc6CN+F0MoFGwLnpQ6/j9gmoFspPK2ZqaFi48pAQ/9A8+s8HbX7cBFlSgL/udIrESDVCWoScF4mV9AIibIT2gRwO/C7fDY92BA9n01vQJ49ZHKFBxA3udsR0nQM8Il8Nv2U+XwhcLBS9AMfQurGx5qr4CAWqHkfQGpei8g5PhpYvkDN+9x8vfC7u/XgXiXMOPLwsPf6Aq//K6mKvwE+hgQ2GpKq+GaENNeQbG8nMid8HiGhbaAfB/UQkjG1mVV0xYmsumNcGSGqFyP30mjBQpDr7V2IikYjNvGfR4I2mB5btwcPH7k/Ygjpm4jc73sFdinBymfTfzNORYeznZmskZJKEdWLq23ga+uuFo16Ix4O/WonZbdtoVi5WqUhYQ/IRrTsA3erW9nCciqwzEjvg9+roxuJqr0LIVrdSGThJMS2+cNIBKMZIb6f2IZjCxFir8MxC5ZNW/anpg+MO6Ss1v+jXkXqPNcdcKCmcCsw7a09rP5bM437D0Qnndg3P5Mr3BScrIyAc5D77YqtSfRKOlUC/n34+2aC9Mqr+mIhQrw6HIIEQ+9FSMGb8YlH0GLZBgljSETcNrtvQ+6DiWb9jchE7XGEMFSQLFQCmQivQyaF6xDCYg17pIWBP+49hU/qXKRFycGIrfNEJHh4mjmGm8zv85EJZgO+zbklYzV8x8EOhEyVETL0N0RGReC7V813SJr9Rc13vwGRXVkXvn785sHWPdDuV5v1pgJaKfqUGjTTcIGIp+s1aCslBJkc2+xhNB7tih88/eGBpWsPm9zTP8VDnjtvQLLntjff+YgZws8BMrlCDAmwPpXPprtBHCSRJsPWDOMFpMQA8/kKIJPJFRJIprIOmUuMVSSQoMCh+GS4EbhigZqXn68Xjlly+BrhKGTeV0WCDK8HPlrSqVuSqjgJuRYeQUjXReOP6Ltvv/dsOvGl69uv6H1F4g2BbTnIffo8cr8dgdzfI5qllXSqnFTF/4ffkuFHbLkO+CHken3OSB6f38Kyexx2RwPK5xCC9aoh5CpgpjNCPZTlPI4Dh+u7eUkdyYBuxFNbJ2RbweZEyGav6mKRET4PNmfc0s63hYRtbRmFEKcGhFAdhEQH/4FEISvIzfIgMojOzeQKU8x6q8eKFWuIEDsbppbg++MO61+z8em62esfaFFa64itZPD6oiy9Rdr7OAmX3qXxRY37VUfMEiZV8Szz8jagPqx/CrGH4wbE7KANkfuUEdLQjpAAF5+s1MzPXHyZYD2+BL4HGY+ORwroY0gmqoZkZp5ExqrlyOT4LqSG60tm2fsQyXsMmeBF8PtIzTbHssm8PhUZ51oRwmObCNsaKY1E4W2tFuZ7eOa7RvDH6KDixo7dNqo7IbB+HLjcfE9bZlA1+2k223wKMZmwVvFNgXPXENiPLYrQoJYgci8rd3QxGQhNRK3YcEjdQKUlavb1FOKi+Aw+rkP+d5ZUHIOYg1yDGB0MIpMrHIq4Mn4zkAELYoI5TzeZLOeYxHy98IYFal4eIfNfxCfmTcBjC9S8HwI3zdcLX9pdx7iTcRNSDnKy+X0f8NmkKj6DZJK+iB8MuXZgY+Q/+lbGZ3hVFeyzqpG6Q4UEMCYiEvafAS8YojaxpFPPDt+5kat/AAnkX27cBUeEkQ4+/hp85zGJ3UGwWpAJPoxcRzQKgpwiKBUcWr4UrKIY7LelAMos5mg87eCpnfS1fQOyoMtS0JrVZrOC2JG6rmGrA749fFBeEUVkGBp5uExH0ub1iGZ7ifn8cCRS14losP+648cTIsQehS4gH43zHifhVlXcjXkVBUQ2uyf7l8dYtqx+0bLfTegepdvKBwGvpFNbrNEKEWJPgJlkb8zkCi4yJrwPGb8fQAhMFckuvYD0yLHZAivb8xDSYMfDjyG1XOPwDS1sfdPJZns/RCbGKWRiZ6OoR+CPqQmGzh368O3WH8UP4GokI+Wa7UQY6gRo92+P2wZGwZcAWndE6xRonwvHM9Sq3tZF2ywX5lxZ4wqNEETbG8zCZrJqSAZuTuC7VRBSY5d3zHG0A+VqrdHp6GlcixDVFcBHhkvE89n0K8ArmVxhXCZX2A/JJuQYuQF9MyLRHK2EI4YQ0B0q8dhVWKDm7YeYPRQZSoJBvt98YP0CNe+H8/XCWiZXaBj/zKopc254JAM8MF8vvG+XH/RrCCMZ/MKhB/yzqXdp/M+gzkL+d8ciNU8/RBRLa4Cf9C1LHP7ijyaOVsJSQe7XJxF3zqfNukcC7UlV/FBJp9aPsN7BSLAlDlSTqtiAuIQ+ZMw49gkovS3e568hMrnCVLesri5vVGc3TPWiI5nwjUSSRvedGDmJZNf1XHAiGkeV8fTwWtEdRtA5aCT0IQ9ve4DBg7d/B3+/ykMJbnrwzeHbrSAP6rXmdxu+m5N1TrISiZPz2fSLr+64QoTYc3D2/xTe2P1i/I9OnJaXftbmut1Rc29XKypCVGvl4EVcUN3Ad0o69ZXh20iq4hSk3iqsgwuxVyGTKygkev0OxJXseGRSvx7pmdWIX2/Vg1/zZAeoAUQuZKXoDkJyViDEo4xkmy5GJm8KX67XhygwEubvVUgB/+uRieOfgHeb168gk8HT8euQG81nnYHjshgw24/h98Cyn6/AJ4Oeed9+pgO/rd28Xc6SJivvqw7bJ/gSxHXm+KJIkDPN0P6WI80d7HGvR5QoHeZ83GTOxXuBm/PZ9Gq7YiZX+E/EGfLCfDb9MqMgkyvE89l0ZQufR0fJbo0ZLFDzDkYyd02IQidIxm3N4DPADfP1wmsy37rjCw0rOy9+3Y8fjMd6KlfP1wu/ueuP+tXDqDFsDeNq7XHFpn/Wzyr9vH1/hBz9DamxewX/npuAb8AWSAh4SiV0px5Q9aY59waz7lrEvn0Rkhm7taRTQ+r3k6q4P5JJfsC65iZV8QTgKuDqkk4NyZzuzdjlBAvguDc8emmirXrV5NN6IjtGsILPmZFlgb4hhl03GJjaYQwnRkEiE+x7sQvt77U2h6FsXSJbJm0bkciajdZZjW1w4Pg1cJHVaIcIsS/glHPuv9UdUO9cedu4flznb0jDxXPN7zIitZ2OSKeuL+nU3ioxCRFiM2RyhQb8Pkk/QqR970JI03uRbJZGMjFWBgg+CenHb8TbgxCbCjIW2Z5ZbzR/22akbWY/NyCuduMRc6b9zDITzT7Wm9cVRHKUQiSC1kre9u+yxCeKX+zfg9zftvlxJXDstqdVUCpjx1hrPmG3A/48IFif9iekfs1GeLU5F445lnr8LFYEv87Ljs/d+CTMohuRe70FP/u2DDEKuBT4Uj6bvtsunMkVUog88Fdmu+8Dns1n00+yF2KBmncWYkN/H2I9H5S+dSBznDzw6KaDJz7bP61l3JT7S/c6Ne/s+Xph74gbHePI5AqfQ1rwaOCvnYvjr+t8quGN6x5svgwJjFyD3EfWQdNmrYJzRg1QN3lAz3h3h+ooxgfWP9CyGjfSh9yfl04+tet1DftVHr/rxpODUtRBJFXx80ig4HwkAJBCzvUJwGMjWLfvtdgdEkHWP9R0/fQzNx6pPc5Xke3N4ASfbVtYSsvnQrReBd8Zyva25DLojPDZiJkus0mt1KhZrCCBG21/gwc17OPh+x/yodaMB5TnUnEi9ClFK77rk13+XUhPpX8dfb8hQuxdWPbbtscQqdOLwHqjD/8F8AtjP3s8YnV7CvD2pCq+F+gt6dTq0bYZIsTegnw23QfcmckV6hCyMxu4EiEywQDdRHyJoK03AiFHAwg5iCA1U1PNe39ETCNWmHUPYGg5wYWIKdMX8R0Frf27Z173I9mKY7XGcQfQTgztRAYbHfchkXyFRPGnIURmOSLNq5lj6UUk9MGobHAMt5JC2xDVkitL5iy5sjgMX8Zoz1HCnIeWwLZttmstkqnrRsjijUg9Wiv+vKAJsdC3WI5MoP+A1NYMCf7ks+kiIpkjkytMRvodPYhk+/ZG/AH463y9sGeBmvdJYKKJRKPk+jkByXp2jF+8bvz4xeuiyP/8tAVq3qPz9cI9UYXwRyRY8CbgmRd/ONlKd2OIrf5HkeuwH7nuRsOGWm9kyZp7mw/ofTneiOtMR67zlIp5/zewKTIpNt5ZkVTFmSWd0klVfAvQWNKpP5j1f4oY47yCmKEci0h8JyGEd5/Brm40DEBJp7qnnNr7SSfCbUgfCDwPwzw0thHx5k7nsBMt2kfH1rN8W1pgVEdAs9mRPg8SuVG/sPa2emjBqIQGtHah1g+1LmJaD8of7EO7G7n54kDa2PSGCLGv4HbgZiS6e1VSFW1RvLXHfR4ZuDch0pMCcF1SFZuSqnhiUhXHdG1CiBCvBfLZdBlxvjwP+D0ycRps2IuQA5vx6UWIx2JkjIkgWanxCEEDmex9GnG2bcFvZrwGv84rDtyC3HfnIHVEa/AblMYRGWEf0Kc9GNgY1av/3rAcyYTtj5CQTQhROcQcp0IIkm2k3Gg+70VkUAo/+wVCwFYjWSvr9GlHYSuds0FVO65ORQihi4yxG8xymxAiZM0+bBPiPvxGym9GpJcbkQyCbegcrBlT5n+QAdL5bHqxbbuSyRWcTK7QTgCmTuvfEUK2V2K+Xqjn64X2XJ0BDGjQXkS5nvxvZyOZnDm1uBOpxiMOYibyOUSmuschn00/jdRKWknsPci1+kHke70buc5WMTT4bq/TGnLtxWs90cO6iw0Pe+Xol0AVMVxBV5XqfrFOrbu/qQ0/AH8ecGFSFSMAJZ1aUdKpe82YeRUwD7FjX8bQwMNej90iEYRBTfci4KBqj6g8VRQcZ0sKt21I6AA2S6+1siRti7I5z6imnZHUgyPrFYM7soNGEFV8iUGQGaqtb3LboDUMrFcMbIzSOmerfUXtHjtq/UTQtKiYfF/p9jOIlcjgZ/uArEd0uz/KZ9N37tiRhgixZyCTK7wOOOGpy6eqamf0K8B3Szr1reHLJVXxh8CHkHv7biTifCjwzZJO/XbXHXGIELsfmVzheqTmaR0io7VNfesQQmBl6a1mFTtu2mh6E349yCOIhOt0YCkyBs1BJEZTEIfAmxEpVD1CcOYiE0mFkJA27XGv53KaE6FOOYP9ta5DSNx4s29rZGHNKeyxgGR7WpAsV7DGaiMigZyL78hrmxvb7dk5gWv2Y2ubW8z2bf+tGnCtWf5j+NJA23emZj57Asm4VAPb3oAvj7T7fhY4K59NlwL/m7ORzMVzwCv5bPrbm/0D9wEsUPPOrNRHv6Vq7oxoVceVnMc4oCv1MeUmItDpVRO6stCB78/XC7fUhmNMwhhJ9B+zYNmkp7461al2RaPxCdVLaj2Rd3gV5wD8rFU/vnmLNXmxBipWTvsEEoRYg2S/zkfu3x7kuneRe+R85N6LlXRq6c7+jnsadksGC8BYgd8A9GpP6TX3N+IOjLystVoX2d82kCsNtQEVzCuNngXSJqPTM8oiI6fTLBxGLuqKDlsmeODeljc59PBGeS3rRxS1HmtQhMvm0YEgI1XAuGg9LdEGdCQGyhnyGciAGA+s045ExUJXtBB7NTK5wqcRB7LLW2b374dM1P45yuK3mZ8HECnE/kiB+T4lfwgRwuAW4FuIxbc1tliBEKR6hNw8hR8l7zK/exCCVIeMmdZm/SCERDlIk9iHkCDGfmabzyIkpRVxDHSRqHwP4mjYqxxmR2JsVA4Js14DMmFsRkjPRiQLtQ7f2c86HmrEBW282a4lTr1muSPwnfQS+H3Bgg6+lghZItWBkCIrD7S1Ws1IlsrOxdbjT3JjZhtHm++7wpzfCJIRsJkyO/bvD1xiJJwWLwKPIRmuWcP/cTsDmVzhpEyuMHdX7Gs78Ei8vzYxVtWNyvQtM7IeXYviRXsr2om5sQrRs4CXd++hbj+SqnggUl/3L/lsek21K/oN4GfTzui8MDGxmgxMHxW+AZv929ba22xsN/B+QKP0fpPf1PVv7Sd0PwVchtyrfcj1dgjSQ3VVSaeWJlUxllTFuqQqfj6pit9PquJuKUEaS9jdJ+AbwLnxFn1449QaXlkN/dfvEBSep9j0z3oiCc2EI8pbXcPtB2oKWl+TbJ59wAazWEEq5QxbdvjnI21vxGUSE1wSE4a0owhu2xbMBjG8hiw4GGgke6UDn9nPz83kCqci9p4rgDOBhflseiSr1xAh9kSsrfaq2rqHGhpq/c5iJGJ+ZCZXuA+4Gnj4sfn7/Qz4OjIArUGeVj8Bfho6B4bYV5HPpm8DbsvkCtcgKogyIouzfaYUQjDuREwyepEoeDvi5lZGiIM1c6ghksJpSMZnBlL/2IhYbJ+NkLGJSDQeJDrfixTzg5CKybJd9/kZ7U8PrN40+4ia2xBBIu9z8d397LhZY6jxhP2x42U9MvlOmr+78TNeVtL4EFIDE3x/rTmWgvku9pyUEVI0O7CPNvyaaHveogiJmoGfZTjeLK/N59ebfc5BJsFl8795CnjKNBje6TDk7guIHOyju2Kf24gNQEFLwHjQktx1UPG+qoqYaZQeatU/5pFUxQRSi1dCrs1VmVxhXOpTscb1DzcuWnNP8yHlDZEmE04fFrDXKt5W025Z1dze6MuI/P0S5Dq6AHjSiekDW1P98d5l8TcifeoSyDW4Brl/jwT+lFTFyxHJqb2Pt9RceJ/B7iZYDqKLTrWmBoLFsEOglHUG3JZNKrQH0XqPRPvW3USVgvp22BFpqLYxKLMdo/2z7hrBh6SFTcX6BxvYHCMTn+FZxsByg6v34zdRtBip2fEo2xnR693+riED20zgm2bZlcDfM7nCw2PdsjVEiG3E7xf/uP2T7cf1HZNor14H/BkJJihE/teO0lPQ6hIkAt8OXDdKD5AQIfYpZHKFf0Uk/59BGtnaiRjImHEMvoW0ixAAW//0baROxAHuMJ9/Dpms3YdIkGYgY9qViAPcr5CsjEKCHn9Hsjwps96h+HVPXn+lsbHmxtuRsfIlZGJoyZMNJtoxs2qWswFHq62JIYQIs54dcysIefwd8El8cmWju9PMts/GH881QkCuxDfMsBjAJ3kxJFMXDNwG3QzLZpkjkIntlflsuoNhyGfTW60jeC2Qz6bLmVzhv4FuU/u1ECGJn8pn0/1bXnvnYb5eWFug5j2EuMIOmospD4WCWgTcmOPFK15+vrtwT5rTHIxccz8v6dQ8gExu2YzG6dX6TQnv+P6VCXttW/Qj12uFiF41/W0dMzc91aA6no7egNRKgVyPFwGveBVn8ks/bXd0VQWVTlXk+vwecj9MRu6zieaz75V06tqd+J33GOw2iSAMNjI82/z8iC2wnO2pVXIi0HrIAPWTR202vnXXCu3/jAbXKqKD2xUrieGOHFZzvqXzrUZ4PVq2yw5Q9uaxBcbB86e2Y39B5yOrybXLWAclK5NwEKvq75ifECH2aGRyhUTX4vhvvao6ODGxYicyNwOfzmfTvUhUuuLEdA9yH0xFJnHnhzKIEPs6TD31mcBxiJwtgUz6nwTuQsaraWZxjU8Y4kg2aIL5/AAkw9CDkLWNSKH+eLMNSxJWI3OGCkJqLkJqmVYj5CaGkKgo4EFk+oaug5dB1BpJHIJE2D184wk75kXMfr5v/nbM9qw0T5n92km4Mvs5AWmiPNd8t034dWhWlxPFH1/L5riDzY5dRCIYx+8p1oIvrbTZKut2SGDbnjmPr2c3I59NP57Pphcj/+cjkOuiOZMrzM3kCu/L5ArTMrlCNJMrnJDJFZp34aHNYphbmgLtaPBiDsvefbiz8ZApJy9Q83awMn634DkkqHGLfSOfTS93KzzWPKvyPJKRegHJCL8MulI3ZWAAx2vAdQ5cfXfLbzqfr38JqXmcgJyfJ4FWFXOPRnm4fRG8qmPnhhY9yD33d7NOF5LhOiUkVz52++TANLX7M/DnTK6wCkkvR0dOqmwjtl7ftL030GC2J5hJi8Q2y6wpc9vaCInd12iZqh2FYqj9bTDbtaPbDx6n7XIPvg3u8MxbDHhnJlfIA58HVuez6X2mv0GIPR9JVZwda6m983Wfpmn9I00TKhujDS/+cKIClQDvfHB+n1RFp+3147+26cmGH3kV5wr8XjYg9+D7kqp4s3FMChFin0M+m9aZXOESfLv05cB/I4GIl5Fs03sQItOGzDvsfTQOKZQHISQbgX9ByEy3eZ0w7y9HMlvfRiLnLpLxOgJpgmyJ0gASFLFkZI3ZXi+SEXuz+d0JHIWQGRepV0oiEsKnkQz2dLONYNNkB5nYHorvMmizUHYs/jtCOu3fUbO9BEIY6xALbRAJYQzf7MOO5cEx3s7VXLNvjd9rzEFIzCak3mxMIJ9Nv5TJFU4BdD6bXp/JFa5G5GwbEOv504H/RYJZOw2ZXOEEoOkEeIuCyLCZpQIYaErUIn0V1bSsoxHJtK7amcf0WsE0+X1o+PvltbGT3QF1CDA10lh1vIHIDF1T64BK/dRKhaheVV5ed2j/8sQRwJeB/2dWNdebrtdV5TQdOOD1vFRvaxvngG526j3VMKNyWc/i+nbQc0FdDbxo2pqQVMVDEROaNSWd+tlOPgVjGrvNRXA0ZHKF7wEfBS0Teg0atV0ZrO136bMr2LrSkbcx/D2b4RrFsMJGpYYTrC1J9oYvM9p7w7cxPGO2rWdrR8hYUL5o17WOMncAP8ln0yM2oAsRYiwhqYoXNCXLl0xJd81e9rvWRKUjGteO51AeVOB0qJhWTr33oNsVewsQG1YS+TQygTy3pFOjWPSECLFvIZMrRJAJ/2eRmqBNiMy8GZm8bkQi6ici2aLVyMDbhpCefyJk5K1IBuwNCCH5VuD1KYgpTQtCipqQ7E8EMcK4DHH4/EbfKuftdRO9FIo1ToSbETOOxQh5akSi8bbnVjCbZA0BgmqQoErEuvdZJckiJDtG4D37erDux/xtjSkUYrRRNudmeeC47LJWGuiaYywhBNO6wtnx+PvAZflseh1jEJlc4R2Ivf4Bse5yYtrtxa76dT1XjXtpwwzgmvl64bM7ab/XA+1zvn/fVc0vbfhjBOprUaUrzfFKrLeqoxWvT4mHWqejuRy4ab5eOKr8aU9Aqv3pA6vdkTd7FaepMdn3gcqG+By37HzJqzjH4+gJTty9zInxLa/s/NirOh8HDgVPA160yWuolZWi5ijqXE05aq/TDuK1JuWqWPPsymfjrbV3jK/CXaIAACAASURBVD+8/9CVt7Uu6H0l8ThyP16BZFGnAveXdOrc3XYSxgB2q0RwFPw3ktGqWnK1vRjBpW8rLNI2avc9IYZvQwcflcP2BaYea+gSQXndEKIV6F8V3KpiczLGsPeGEyL7oB4pS7Yt2NFM1/B9RBBpxKeAuzK5wrTN1goRYuzhF30r4ufFx7l3NM2qdKFYH2upBa/rcbqqWt2uyNsYQq40yKTwdODfQ3IVIoSPfDbt5rPph5DM1GWIicMMfHvnGGJC0YXvlGf7QU0E3oLcW79GFC0dSNZmGSJBvAWRRU1FiNVGpEfd/yITvOcQYvRvwDtiTToxsNHpVoo2xBH3SSRDUUPcDe/Hj656ZltPmOOxGSo7SVCB9/qR8d0aY9TwpXyW9HQj5gF/Q4ictWH/J1LzUkXqOa38cBJDa6zsNqxVfAKY4Lk0eEIBLHHrA74yVskVwJR7l4xvenljW93yTRFqLuuPmNa6/sjpX+mb0HBRLNW38OZHTjlmJ+36K0DWbUzMIiKZyJWnHbRyzWmzvY7XTX7egecU/MDRfHW+XvjTPZ1cARTXH/bSSwOH/Ojoby57aurpPYdGGl28inM3MBlPrY3E+XBigjvn4EvWtgEuUa9u6ts7GvZ//4am+pkDKK0kuVeOGgmsG4k19bc2H1DViUm1FV3P10UqndFT3AGnvtobeRPStqQRyaA+hzgSPrLbTsAYwZjLYAFkcoUMcIXWHIh5oG09E7XjkkKttSFKWy6TsqYWw4+l2gf9ax2a9vOCvbRGMq0AYO0j9Xg1zaQTyttj1z4SgdpSVmt3wQV+iaStf4REJXtC+WCIsYpMrjAdmPPMlVMeGVgf+x3wZtDD7ic7X9LQUHPpi59V0qk/7fqjDRFiz4Gpz3ov8BEkSzMLkbbdiWSijkYIlK0rPhC50YqIQcJPEXJyNJIJm4ZkfO5EJnPdiG37CYgV+4NIr6yy+Xs/rXlRu9zvRLkAyYydiJhLvA9pzHqM9mioDdAcSeA4zqAzoXUY7ESIj4Wtgw72zQr2EFKIpOog/Joua4gxgASQj0bGRpupes58dxDylzSvh6tgtPbo0i6tbgVijYO108uAuaZmdExigZp3lBuLfL08oaH5uUtOmtm6aO1MrWHyAyUOuPiJlfWndj2jYpz5wePvqeyM/X9LzbsZaVL98pJzDn/fujck5xzxjcJT9Rv66ubrhU/sjH3uTiRV8azm2f3XzHhXx4SlvxsX6V+W+JauOqcBsyKN7nXjD+0/fspbur7+zBXT/kfF3bkHXbS2df1Dzap7cULXum1uAACPSEWNn7qpa+bF1R+8cuu4N296oimCZKcfBC4EqiWdWp1UxWZEinsU0FvSqeIu/+JjCGOSYIF0IAe+iDyAPo7/IBqEPXSfHFlsL8EKbme7oXtXoKKNEGsepVnxMEJU7QUnJj/bud+R5HkaGNBaHuCjbCu4/PB6qh3BSMcR/AxEyvBHJHJ5Tz6b/syr3GeIEDsdSVVsRYp3rdvUcLyIFNg/a/TvIUKEGAWZXCGKBNxORCSCEYQgdSG1UceZ9y00UqOTMMveibiUHYc/blWQbJCLEKV7kX48zyIEJ4M0R30ZIXfjgNuRdgtfQsjM+5BaqLuBl9c+0BAff3j/pZF6HTFjuM0e9SE1UrPM3xsRYmRRwa8ts2THQ7JvcxAyGazbxHzv8fgKl6BNfDWwHdtjKzh/8LRHv67RoEFF4oOfVYFz89n0/zHGsUDNUw8tOOuwafnH/zLlyZXTYmUXZ2a/W//6nlLf79vup+L893y9cPlO2O+BiFvlD+frhWtf6+2PNSRVcT6QbUn1Xjv5tJ6Laj2R7tJN7d8DskCupFPfMcuNBz5M1P0MWk3HVQqUlb92AFo5tdUHHvfCJS8+PPdepA7yPci9eUVJp7oD+5yBZK6PRTKzZ5R0qm/Xfeuxhd1ucjEa8tm0h8gLyOQK9yJuQSNJ6EwdlMz3dSDwvDXi4rng1SCa0FLrNbid7YJqnD7qZ0HpH54nxxQNdtYYstjmf4y0nWGfa88jZoYDraIjTgqD720ruRotKxYkaFvaVwKJFg0Aj5lIpgO05rPpjdt4DCFC7FKUdKoTMa74IjKQvB95Ti4Fzirp1E6pEwgRYi+Fi0S5T0cyShqZeE0I/G0zQhVk3IgjE7tJiCHF24FfIME6myGyduXNwOOI5fmLSP+eoxCTiVuBMxAr67ORwEkzQnpazTYPAC4Yd2i5p2tR4qVxc8v7ERm0mI+Z/TThj7/j8Pt1ETh22xtoEyKXPxdfdqiQjFrZ7P8fiG19F/JceRN+o+KlSObLlhgEg5lVwFMOTSpODclabTT7iyP1aWOeYM3XCzXw1IL5vz8AWKNhvF4Ti1SWxZNOi/uit96J76Rdn4Rch/+HkOa9HdcAv+x6oeGQplmVjyXa3bbGWX2n9i6pn+zUuVec9PaHiitvGxdHJLHvoRZpxp+/2WuvD9igvegrhYfOudt89vWkKl4HdJR0anij2Q8Dn8bvh7f1RrR7McZiDdZmMFGZT+Fbow63jDTYPnLU9VIMTD2URuHVFN4oMWnPEzI22uejYMgBKWXqr7xgjddmxV2Kzb/blr6YUoqIBv0a/zdH2+d27UVrEp7LxxE5RQdwRyZX2P/VHlyIEDsTJZ1aVNKpC5BJ4NSSTh0UkqsQIbYbzYihxI2ICcTDSIS7AyFZdqJro+Xg25V/BQnSHYT03HkMWIKM/+OROo/3IHbnLwHvQmqsJiH37X8iTn/WTfBcRIZXh9RQvsv8JOIt3uNtR5dXRRKsQowmrEywEclO9eMTqQb82ieFkMLlSE3YIfiugXX4GagyUju2GiF9U5HatNMDx6cQ4jc8iGnJpyWg9nw9gC9D9LAOXXsI5uuFVcQYAQYiVJ5o0t762Jnz9cIlO2mX9wA3INfjXo+STlVLOrV8//du/Me6+5sXdj5T19c0s9qCo7vrJtV6Kh2RNyA95L6FSFI78DO3HhKweAy5t74/bNurg+QqqYrRpCp+GLnHlyDX6rx9XeUxZjNYI+BxxFHI/vOPVEpPQB5iQ83StxHNM6uomDAUzxAfZ5QzohS4HkRGlgBuM9SW6Ym9uLf1u2hM0i0S8W3kYZukgq8ptuS6aL6zLdCdiGjaQ4QY8zD262Ez4RAhdgwnIkYVXwD+C5l47YdksRL4/Z4chGytBX6DZIP+AyFjVp73A2TOchaSKcohJOwTSN3SpxHTiunAycDZ5Q2R/mid+4toI/0IsalDarRaEeJSQ2SCn0Z6Bj2HZIMuRghgMzJmrUSIm7VFBz8YOs5sz75nZYK2qNtBsmAXmfcsEdKIwsP22NqI1K9EEIfAKH5mK2rOVzAA+yZ8g4tvIQYfexourjr8OuqpY/RAZMV8vfOa/M7XC18Grt9Z2x+LyOQKifbjuLZvWbwlMbFajY9z21HqF7EmfcX6h5q/g8hvG5B7cgDJkpaRLO1Pkev2dYjcdbOa46QqKiRrZX/fg7hcvpF9PHsFY7gGa2vI5Ap1Z5749dPufvKCm7v7JmzS1O2Hr2kOYqRsUI2h2udtgt4hGvfaYFut57dxuS3VUL2qYwvsV2sN2gW3jBtrGrx535LPph9/tfsMESJEiBBjG5lcoQkxoHjQGjD4rVgGxx/bu8q+VkifqpMRQrYUqcuabdZZgziUHYUQkT4kM+aaZU4DVlU2RWaUfjlBOXGdP/jC9dbSvBUhcWch84W/Io6DZSRTNQ4hUp3IRPEYhNy8iBCtcfhjZj++jTvDvk/VHNe4Ye/fgxC3ScjkNWK+zwwku/UPhJROQuYpUfO9uvD7YjUOO29RpM759G34l4xJLFDzzgJWzdcL93nnudcSpmXCZ70ar7j9zrSuxYlLlt7Stka7agESlHgTQrI+blbpRe6Fz5d06sdJVXQQs7KOkk5lh28/qYoxpHxnAAmirEbuixORe7cVuN32yNrXsMcSLICbHzll/3uf+vDPN3TPGPC82ItIVOw54FLk4RUsELXRJA/pXzMLiU7ZjNirzE29WlRxlIunbZBqKHaMYBkveK221K/rVcPuU2s8x+EV4CZkoLgnn013bGHVECFChAixDyGTKxyHZKOeR2R7DmJaYcdjB4mgJ5BJ29kIUZmGjNM9CLGpRzJZloCtAg7HOAV6NSZt+md9v9vvXDbp5N7vIYX3H0Amfh812yogk8xNSEPks5FJYR1+Hcp6hBSBLw30EFXNsfgDdhXJTq1DyJpj3uvBJ2aPIRkBa09P4DsHFSy2/qVqlokEjsn+XTHHPc6cgxPC8TbEaMjkCurxz8w4XHvqB8h1dWZJpzqTqpgFvooEEW5CZLnZkk7dDyL/AxiJJCVVMYK0VroA+ExJp35lslr3IfLcxUim+ff7otnFniQR3AwfPP6epb+9t3AGEMtn04Oys0yu8HXkQfp1hGjFkIdcHfLQPgxfYw2iG53NbiRZjvJoby2xtmMOIxGswcbGHqgtHKVPoIaqDXeAWNkNVJAImi3qtdk/q0d3leJXwLeUog94JZ9N77msPUSIECFC7DTks+l/AMcY46MqcCQysTsVyRjZrE8Z6WV1IEI2OpDaKyvZnWLeb0FI0SzzfhfwrBNlQtsx/auAhflsui+TKxyLZL1KZluNwG3IRPAuxGHwJYS0fA+RTlWQeUMZmS9Z84n1SLNf259KIwSvCT8bB0L8Jpr1XYSo1Zvla+a9KP54a1+7Zr/Wen4yUm+FOWcR/HqsbnyZYkiwQowIrVmQ+tTqWc/npv4dOAK5bzqR6+cWRMLbhlyjJbveVrJPWSRA0QW8P6mKOWS+bdsaTAW+g1z/t76W32dPwB5NsADy2fRmrDifTZcRre31mVxhEhJlOhOxc/0Q4kwUQR6Iy5AH4jrEXcjap1qM5NwXxLbI7LZqja4U9Fcm1EwV2Ig9tJQCFcHF7/1xFH5/jaANu80pKUAbZ8WI3QbyXV1kgLJOL1cjGvH1SCHoXWaZZWbdq5GH/c1Ij5IDkIaN3UB/SKpChAgRIsR2oA0prm9Bap7q8cfKvyJ1IU34jX5vQMa7l5EWLi8j2arjkHGqCQmW1szrR8224plcoQ4xtKggbnv/QAjdO82xzEGkjPWIa2EMnzyZOu9Bm/Z1COFpQiR/JyNj5ExznDZb5ZrtRcxPJzLhdJG5x58RNc3/IKSpFyFjUbP/DWYfxyDyQNsrzBIrzOunEXONg5FMVogQm8GrqoHelxOHIXLUZ4AVSVVMIHPj1SWdsjWQnxtp/UyukELmzj8N9FsrIQGAnyANu9vN+zYLOx5JbuyTBlF7tERwR5DJFRJI08OTkYuiHbgc+AZi7aqRFP7pCJO/E/gaEu2aiO/oY5sMbkIenm3Iw7fTrDcBX37YgzwgrSzAShXXIA/TVmRQeDtwntnGh5FolI1MfRJJ4SaRgeNphGAtRR66XzH7+Sry0D0BiVKsQIjQhYgkA8R9aBG+zW01n00P9jIIESJEiBAhdjYyucJRyJj4WYSMFBFCcwYSBX8SIUQKGb8UMqb1mE3MRTI7NyPyvjrE8ewtiDHFn5FMUwHJUBWRTNczSC+sNYipxotI/6wTzTbqzT5stshaWNeQMoRDkPHcuv/ZAKqrPSKAUo72JMipqxDpxpcZumbbvQhJtLKVXmTsriJGHf34QXBLsKyUsBshpnYe0QG8L59NP7ONpz7EPgBzf52C1FH1l26e8OaNTzScgVYRRNLnJVVxNtBf0qllRt6XBJaVdKo6bFsXIcqwHPBYPpteY95X+WxaJ1XxMiRQMg7fkAXEVfDMkk49t/O/8djCPkewhiOTK0xELGTvzmfT3xjh8wOBeYhkYBXy0GtEHpAaechFkCja3ciF9VPz3rVmuTcAeSQi9x9I9Kwd+LbZzbuAtwL/ns+mXx62/9chA8gfwyxRiBAhQoTY25DJFRzgY0iw00MIwxQkaDnJvFc0r19CxmHbcPgcfPIBMg6fji8zbEBISCeixJiCBFknINmoDUiAdDJ+U+B+hECtBhYitWLHM1RZUkNI3HR8MwpW/a0Jt99xpr+901VKGzWMcw9C7LoQWeNRZvkBs8/7ETL4LrPvN+LXjzv4MsVg82F7LF0IQV2OzEOuNCqeEPs4MrnCpUiW9mIkUH81Mo+9Jp9NDxqKJFXxdOQeWIQQqB+UdOpXw7ZV77kc4g2o76D55+++dPrH/+Xrd76/1q/O63q+/j9e+e2Es5FkRAT/2nSRdgnXlnSql30M+zzBAsjkClOAznw23b/Vhbdte2p7yVAmV4jks2n3tdh/iBAhQoQIsafA1GP9DDGyaEMmaYsQgmVtoO9FJEydCEHqBn6JKD/GI0TjTETmnkNIim0S3IuQs98jkqaL8e3fa+bzCfjuwtY+vR/Jbk0261vjiwrw2JTxxT+u3nTQ5dLARXkAa+9rcryq0pNP69ZKaQ26E5zzETJ4KkKGaogKpR0hfEuRepaFCCGMIFm05UgQ10oH+/HNQIIE61NIRi4CvDefTW/ckf9DiL0LmVyhHpiUz6aXZnKFNuAz1R7nlP6Vsb+0zB74up1zJlXxB4h89cvAdcCtJZ362vDtHRh/bkpLqv8ur6oe6So23Fo/feACIF1eF31eVyJz8eWr9trsRQIjnynp1F0799uOPYQEK0SIECFChAixW2Es3TVwNBJlH0Dkcx9CSNFfEfncscBnVM2dkdjYd095UvNJiGxwGiLf60DI2FvN9iYjZO39CDFajO9I6CKSpo0IoQF4AiFCEeCPSFZoEn4d1isICWoEV4GXgIgDTrCpsJX0KaQ+bBNi5mFt6K81v9+JZBYakBKFwxAiNR4/U1Uz56LLHJM9Fg/fyn2pOf5sPpu+ZUfOf4h9Awc1PvsdJ6bfMeu89UvrJ9fOufULp3ckVbEdqfWbj1xTt5R06obh65rlbkMktgfjN+COIhleB7n2bUb3FSSg8KfQRTBEiBAhQoQIEWIXI59N27qqe42V+5XAX4C3IVmcg5DaLIADpt790qr2x5efsOgjx1XLk5pbEYLRjmR4Po1kuw7Hr33+HCLvfzuS2VqN1CM/i0wK55v3D8eXG55ifvci86W42U+z/I544PSDasQnV8sRE6h1SPnBWxBJoDXPGjB/H4bUp7SZ90/XGs+riVtwNDFY3+WZz6ea113mfSsXjCF1Zr9kH3RqCzE6MrnC/oCXz6aX2ffcvsg9XsL9QOei+PHldbELkWxvNyK9fQb4cUmnRjOl6AD+FwlkXIJc5zPMj70e7b3jmG3+tqRT+6Q6KyRYIUKECBEiRIgxAVOPdSESSb8TeA9i2PQ4IuNrAuq7DmyLR/oq9w601k9CyFQWIUKnIfLAXwF/QrJBzyJSv48gGSAXqZ0aj2S6JiHkSSOEp4qQGTsx7ESITB1C9joQyd8EUNb4yjoPWiL0S6Tf1nizvTVm/WYkW6DMvuL4dStKKSivi6yNznDvR8jZAH7WCmTetgIxI1hhtrEa+EhYpx3Cwshur0Zq/j4U+Oj/9IAzvXVO5dL6ybU3n/jWh6+C1u8BtZJOfXRL2zSW7dcnVbEBsXZPI3WDVeQeTAYW18DP91VyBVuxDg8RIkSIECFChNiFsFHwB/PZ9JPApYgRxTmIGcQq4LGeA9o2LTvz0EN1IppEoudHIDVMh+Sz6c+bdZ9GJnp5RFpYj0iWvotI996KEDLb/NfWjsTxnX+bEIOqyea9PiRT1m6Wj+I7DraYdVyk7Ukrfr3UOIRceYi0ap1ZrxNxS/yDUrysInQ3THN7kIxdo9mfta/XCEmbbY5lf7Ov7wGnZHKFI3f4rIfYq2DI9g+AnyRVMZFUxYuSqngYkJxyeld744zqukhC3/jA7a/XSJ1Uf1IVG7e4USCpijHEuOLLyD05E7lOp+LLaDUSGLhxZ3y3PQUhwQoRIkSIECFCjAnks+kakvm5zPz9OPBxhLhMQzJBy5CsVB++dfRXkQi6AsjkCs1Iv6wsUlc1E7gun03/FrFtLyMSpocRolMyy1mzK9vLagWSTbNEqYGh1uwKyVLVzHs1hPy04JtzrDE/dttRhLBNRrJf9YgcsUkposrhAESqGEVIlAPgubDh8TpHe4N9hqxE8BxEAjlv+852iL0Z+Wz69nw2/VfESOV9SMbpzE3/bHhTtdt5BJieyRUOQFr/nIDUHg4iqYqTk6r4maQqHhB4+ygkE9yC3zrBZneV+XkRuHBfzl5BKBEcFZlc4WLkIfk74ArgJ/ls+u+79aBChAgRIkSIvRwjSN0mI8X1tyDzlisRsuUixfW/Qfpf/TWfTa8y6/wP0tfqU4gL3ywgkskVxiESxFlmmSsQKdVbkZoSbbbrIhLF/0Jqm6YjGSkQEmWt4euR7JU1yRiU+5llbB2YzVjV4xtgNCMT1UlmXUvcKkjW6kUgZd5HKZxEW80d2OSsrmvzXkT6eW4EHgN+bl6HCDGIpCq+DfgX4PNIpioysC52+8rbW89sO677E6vubP0I6MWglgDPJFXxaOQ++DNyj7zDvB9BZLfd+CYx+yPXdhAayTT/Yed/u7GNMIM1Oo5FLF8/gkgIpu3WowkRIkSIECH2TXwVkdz1Ilmn7yPSOg/4V4RgfQ043NSeADyCzHHeAJwFHAi8G8l87Y9kq36PSP1s41+FkJv7kObF9Ugz43X4DoC2x5TdD+bzfmRyGTPb+CciWVxt3mtHHAyXIpk3a/fuIaRRIbIqbf72gF8ElqkoB7dxZi1S1+bZPl0rEVONGrAsn00v2e4zG2JvxwQkg7XimAXLascsWDZ3/BF9azY80vi2l3/RPrX7hbpDESOZ04CrkKDCFxBi/yjwHcTh8nTECfNEJPlgM1fDoREzml8mVXGfTuKEBGt0XAZM1JrLtWY/rfl5JlcISVaIECFChAixa7EKmbhdh9R1PAR8EJHdHYdYu08zn38JIJ9NX4/UikxBskhtyCRxA0J8foJIBfOIfOovwDeRSeNxCIGbjbimvQHJNpWRrFI58AO+sYWVEroIoUqb7WhEWngcQu7akUyAizgJgszHGhDy5SB1ZRfgu7NtBCpKoUDPikc6Dwe32XyvjwMLjdV9iH0QSVWcmlTF7yZV8bhMrrBfJleYm8kV3oCQ9ExJp1YiMtQFTcmB80DVVzoj63G0C6oeyfLORswqvovUL/4nck99DrkHV+IbvwQDA0FoJDAxm6GmF/scQoI1DJlcIZbJFc5C9KR/YOjFc/buOaoQIUKECBFin8W/AAuAciLW/dRZJ37js2e/8fI5iFSwG5noLUP6Xc3M5ArtZr2zEfJ0CNJL60XEbS+PZKwiiDnGdESx8gDSqHgDYjKRRCR8L5vtPYJE9e9BJpvgu/vF8OdUTyBziIo5NisbtMtHEGMAF3jKLLcembRagqYR0mj7YHWbbfZDrVJf1xtvrl/jIbLF9WbZy7brrIbYm9AKHDju8L4jgB8jgYEvJ9qqbwJ+k1TFucAji65tX7T8T61HAD+YcHTvhxLTKjWiNUBbY4oe0BcdMn/VddPe0fEx5Dr9PPB1JEvrINfpSNmrKiK5fRsSCFm3M7/wWEdIsDbHacCvEfZ+MLDGc+kBfojov0OECBEiRIgQuwjG+OJK4G3vPOHqBZFIbRqSmboLqZMGITB/RRwB35bJFd6BuPMVkUzTFxGy9WQmVzgZaTz8diSLVUWke3OQieNqJIp/NSI/bDbL3IrIpE5BJp6eWdZmruycaibSU2gtQuCsKUXMrOMhUsdrEBv6GL6de73ZTofZRo9ZfxZC1urAqfT0jat090/chEx670JkW/tlcoWtOsGF2PtQ0qki8N6ZZ2/6BXA7Ivf7ysCGWBeSFa3ls+lN3Yvqn9VVpwL8bMppXWdWN8Tq6qe4qv3ErijSm+1QFMf3LI1/EHHPtFmqueAdCDoO2vZ0CyYgXITYfbmkU4+UdOprJZ3q2CVffoxin9ZHjoJVyMMuBtytFNMiUX6ez6Z/vJuPK0SIECFChNgnkc+mPaAC6bU3P3LK+5Fo+9uRmpAB4A7gWiQL5SAGFlXgG4hM75F8Nl01fbbeizRH3Qj8Fr9h6jREOlhGar72x882KWQCOhMhVJjPJuO7+VkHtVaEiFlTjD6zzXHIvMtmq+abbdjMVRSfpDUh5MzaswekWZEGVzdEkAzbBnxJYz0iM+zd3vMbYs9HSad6pHSKqwbfzAISTCCpiich18w04ENKOVc5Ce9iFfVaOp5uBCHpoBWv/HqC3YJ1zwwGEIajF+k5918lnQp7sRmEBGtz/P/27jxKjrpc4/i3u2efyb6QkAAJSwgR2TdxQcHCFREtV7wq7gqKWqDi7lVRkULFXNxluyp4SxQXhFuiiIKArLJFAgyQkHWSTNbZerrvH0+VPUQuENIzk5Dnc86cmem1qk449DPv+3t/rxzyc75PxsAoHYuZmZkN8ebDru0FuOwv6V1ozPoatLHv19FkveVoCtpLUSD6RxIF+f/HG9AHzOtQ1eidqLWuB63Rug0NxNgdBakBtA6qBQ28KGXPG0Rtgo1o0MS87HFrs+eWssdUUAhqRhWw49HggUbgnygcHkBt8mAuD2wD1KpfA9kx5e2Im9DnlJ2AC4BrkyjYoduy7AnNQv82FwGP/PZrL1o6+6wFu5S7WYbCeR6mGPJPcch2BJsvtwL0b3w5+m+iFVg3PIe+/XHA+nc/QW2CdwNXZX81MzMzs21LPjJ6HPBFYAUMVqHQAcUSCiCvAY4N4/Q6YDCJgv4wTt+BwkoHClr3oCrVQcCLsufdisLbe1ClayoKXk3Ze5dQ0LkfLejvRR8wD0UfVCvog2cDWuNVRFMMb0aBaixqSczHuw9k57MKBaZ8z60NqEWxP7stf3wVVQ5mZcdxSvb9pqd9Ne2Z7mfAbzqrc4eGoLno31YL+jebt//ln32HBP9CXkkdmh36gQuBSzurc5cP14FvjwrVqqt5ZmZmtn3IhlgcihbRfxkFpdsmGa66tgAAIABJREFUjln89SPm/fy8fy46su2BJUd8CFWLQvQH0z+iqX5pEgU/yl7nwuy2d6OK1zRUpZqMKlGnAn9GHyq/gEJSiVq4+SIaE/8sFISa0AfTAfShdCW1NsN8j6t83Pum7D0mZa//T/Rht7TZ+6xFa2qaUACckv28ujJAtTJIsdTEhkKRLuD1SRQ8XJeLbDuE2YUFY9AAmeNRkG/b7CEVHtsamG8vUEX/DjehtY63dFbnfnjYD3g74gqWmZmZbU9OBt6LglU+1GHS6vUz7lq6as6di1fu+0pUVfo5mg74fBTIOqmtnwIFqjGoAvZ6VKHqAA5H7YZFNBJ+NQpygyjwtKNq0eFoBHwVVQDyD52ggJSvvWpHgayDWkWgNfsqoMrV7tQmDA5QazEcRIM79hxy7EVgamWAyiOXj+vc7bVrV5WauBqtITd7yjqrc9cD751dWPA+4Aj07/5Q9O97SMvgvypa+b/RPjSE5WzUKrtsBA97u+CAZWZmZtuTBIWTj1IbArEHFE647f5XvhF98GtD60KWUdvE96PAPWGcvgQFtKtQdWsd8A7gQDRN8Ieo+nQQGjv9brS/1nQ0/KIRTQj8FvDc7OfJ2fs0ocBWRBWpZvThNA+CedtQftx52FqZPRYU4qZk368AXoE+r+WVgyJQKLVSGjen7+JiI29BLYK3oqmHZlskG07xt9mFBX9Cw1z6UVV1P/SHgutRm+xi1Np6LnBJZ3XumtE54m2fWwTNzMxsuxLG6fPRiPbxaCrfRFT5uR6tyxqPwtXpKPyckX1/Nxr5vhdqbWpCgWURaufbCY1nj1Fb4MtQ8FmIRr4fh0JRF/B3tK4K4K/AfpUK+wIbi0XG8di//vegkJQPqKiiilQJVbnWZsfdi/bG2jd7TiOPrVy1UquSDaBgVkGVrrOSKPjO07icZgDMLiwooMEv/ajiezz6g0ULWkv4hc7q3PL//wqWcwXLzMzMtjeDaF3VNagC9TkUOPZHI9vnoCrQgUkUnB3G6WGo9elmFJzas+fPRH+Zvx1NIPwqClXfQm2Bc1B73xi0j9YkFOIORyPiy9mxNFYGaS+vLzRUyoW+lsmVv6JKQAsKaG3ZY4eOYtuQ3f8gClfd2e8HocDVkh3nOjQpsZgdy9jsuV0oKN6HQtkOve+Qbb2skjV0W6K/jNaxbO9cwTIzM7PtVrZx8Imo5W8ual9qz76WJlHQt9njCwBJFFQ3u/1uVMX6IFqPMit7vbVo09Vvo8rTTDQCflL2+2rgkWqFvSqDTOjvLnS1Tql+HrUhTkWthVW01uoWFORKqEqQ7e/FmOwxq7L7xqE/gucbEwNchILdNBS2elEIuwx4z+bnY2ajxwHLzMzMdhhhnH4WhZQPJlEwOOT2y1FVagGqKN0FPITGqo9H7XjjUWVsJ1Q52htVjjZkzzsaVbT6USvfelS9aqS2xirfuDUfeJFPaqtm79GFKmcN2Wutz+6/GXgOClb3ojbCbwOfT6LAbVtm25D/b1dmMzMzs2eifAPhiWGcviaM0zHZ7a9G60zyNVjfR22Ec9Ear2dl938NOAlVtUqoUjYVBaXfo7VfTdn3L6H9h4rZVwEFsrXZe1bREIF8fPsU/n35xli09uoIFNK60ETC+1Eb4u1ZaBxRYZw2hnE6caTf12x74AqWmZmZ7XDCOH0VGn7xcWAecDDwoSQKesI4/S4KL0U0bGJC9vsqNBBjZaXMJYO9hYMaO6rLUMXrGFTRyv943QNciaYAFlF1agUKbE3UqldlHrv31U3ZYzqoha2NKIhtBH6NphfOQeGuANyRRMGB9b1CTyyM0w8DLwbemUTB8s3u2wm1Uf46iQJPmrMdjitYZmZmtiO6GjgNuAdVoxqpDaH4IvBjtD/VrcAHgJ8Cl6LBEkcs+PZOYx68ePK7+tcWP43Gt89FAagbhatydlv+ugvRGq9Gamuryihs5eutBlC16q+oPbEPVbuKqNVwEvAWtDdWR3asm4BNYZyO9OCyO1AY7A/jdPMNag8G3oPGfJvtcFzBMjMzsx1WGKdXoXVWL0QVqpVJFFTDON0DbTS8EDgKKGe3/xYYt2lpw4NQPbFpwmC5oYVu1CZ4NwpEZTTMog+FpjEoZK1G67jy9VdDrUKj5EFrr/IAVkChrC97/kTUKlhEQWxm9vr7J1GwpI6X5gmFcToWrVk7AVXS3ppEQSW7rxm1VN6ZRMFAGKfj0IbPf06iYP1IHaPZaHEFy8zMzHZkV6ENhl+IqlRHACRR8ABq8RtAewI9FMbpTLTpMG3Tyye2TR8sNLRwA/BbFH4mAIegEJVXpEooMIGCUF69yv/Cne+JdTsKUYPZczYB16EwlW8wvJFaOOsD/gR8Cg26GLFwlXke8PmNjzSO7Vne8Eh2jGTB9DxgMImCgeyxRwKfRlMQzZ7xvA+WmZmZ7cguBl6C9p9qIasihXFaQhP8FqPQ1Yja3hajENWLQs5CFJy6sttaUWBqzF6vIXtcAYWnQdTulwerR9Eo9/1RsMqnCjZmr7kp+5qA1np1on2vno/WQF2QREFn/S/Lk/pL78qGLy349k4folrozvZQAlWz5gBHhXHag1omO1GQ7Q3jtAiQV7vMnolcwTIzM7MdVhIFK9FarB+gQJVPxrsAeCsKOr8HbkMTBj+AwtInUIXqRODlaG3WQuAjaH1UH7XR7Euz5zSgcDWAglQJrf+qomDXQG1ke0N2PHdkr1cBrsh+n4daEg8Gjqvj5dgSgy1Tyr+jWvgUcM6Q2+9BY/BPB+YDHwOORRs1n4OmNV4exumRT/YGYZzuGcbpoXU/crNh5jVYZmZmZkAYp1NQqBmP2u+a0Ua+D6IAdhDa8LcNBZ4Z2fcEeB2qON0ABGh4Rd4e2EttM+EqsC573rjsrbtRhSqvcpWy2/Px7evQ4IyJ1CYOFlHoe14SBZvqfCmeUBins9EeXBcmUfA/m91XQO2AoLC1B6q2/QKdz+0onP4OuCqJgq4neJ/voA2fwyQKNtb5NMyGjVsEzczMzGQN8CO0nup2tIHwS7LfbwLOQsMovoOqMotRMDoahaA/oE2AdwYORC2A30VVrwqqauVthPm+WA9mr/0KNDlwFrWhGJuA5SjIjc2OcW32c95iOAWtIRtJG9GxLgWYXViwF6qk/bizGnSjKYwAhHH6CjTw4orsee0oeJ0DXBnG6WIURn+ZRMH1m73PucBEhyvb3riCZWZmZgZk64O+gELNOuCr2V39KBSVgH8CnwFOBW4E3ozWWg0C30P7VF2C2uR6UOvf2Oz+QvYa/Si0TQMeyG7fHU0hXI1C18uohbAGagErH3KRTxm8MomC4+t8KbbI7MKCNwHvBj7SWZ17R357GKdTUfvlz1Co+iqw57JrOv7etnP/q8fs1b+kUGAfNAmxkr3GZSNdkTOrN6/BMjMzM0ODF5Io+AwKSjNQxeV6VHlaBtyF2gdb0NqsBShkgYZVPA9VclpQG1w3Wj+1Fq3nWofCUVN238rsdduz19gHTdzbA63lakJtgWOy1+jJHpe3Gq5i2+hG+gUKR//Ib5hdWHDcwh9NfhXaC2t3VNV6DTDQdWP7Hkv/MG6X6gBz0Tncia7N94ElYZweHcZpIYzTxpE+EbN6cMAyMzMze6zJwIvQB//5aC1WC7AIhaIBFIqOBI5HGwi3oDHkX0KhaAOqbm1CoewIagMsCqh69TVU2SmiClf+/bmo8jOWWhWrHYW49dlr5OuwdgnjdN4wXYenpLM6t7+zOveBfJLg0W/9a7F50sBne5aUQuBCdB0nous6b+4pK3pnvWn1zcUmbkObMS9A7ZZ5pe57wP8Cfw7jdOdROCWzrbIt/NXDzMzMbJuRRMHSME7fj8LUQahtbzb68P8BYAlqa+sGTkZthWU0tKIZVWV6Ufvfl1FYmoxCVzV7nVbgzOz3fG+s4pCv3bLDyW9vQpUgqP2B/FHgmuz4RlW28fA7gd9P3J81bdMH1hVbyiU0DONyFFSPAHob2quHNLQPXofCaStwGHAzsC8632YUcCtok+efjfT5mG0NV7DMzMzMNpNtNFwCPonaAQdQC9u9aLDEIBqTPhOI0LALUODpAz6P9s16L1pzdRyqWjVSmxSYr8nKq1ors58HgUdQ21w5e+/+Ic/pze6bhjYZXjYMl2BL7Qq8ErVCnt8ytRw3tvOf1SpXojVtH0LVvH3Q9MMzUEvhWlSdewUKVGvRORZRIaA1jNNJI3wuZlvFQy7MzMzMHkc2cvxI4NnAu4CPoyrUfFRVOQVVo25Fla4JaN1WCQ2qWI+mCeaDKlajSYVTqE0VzNdktaPwVUBrrW5BVbN8AEQle4212WvsnD1+vyQK7h62i/AEwjjdBXgVGuqxGlXYXg+cBLz1ltNnvrRx3ODhc9634q8tkwcPQuvNZqG9w1rRNWpC16wFuDr7/SB0XfvRNWwCjk2i4KaROzuzp88VLDMzM7PHkURBNYmC65Io+C6qznwcbaA7DVVsxqJ2wCmoGlOhtpZqGrA/Ch6/yB63nNrgi3HATihAjMnesid7fjewNzA9u70XVbbWAL9F7YYPoT23lg/HuT9FB6ENhPdOoqCKrskbUJBa0jShPKN9t74Xrn+g+UgUoF6L1qOty74WoqrWYmoj7A9DwTHfAyzfgHliFnjNtnkOWGZmZmZPrheYBMxB7X/PQ4Mb/pndPwcNc6igMDQJrSVqQntpTUShqQu1/OWj1mehQNGEqjqgCYZTs9fpQxWxZcD/oFa6VhRMZqBQM1quQC2QN2S/dwK/Ak5MouCRqc/deOq4fXovLm8s/BBV5CYBk+48c/qei345/rXVKovQHl4NaD3aVHRdNqDq1lp0DUAVwxPDOP2PME7Hj9D5mT0tbhE0MzMzewrCON0VhZrbUGCYgib+/QyFp3OBPYFjUKDKP2QNXT91PapsjaVWpYHaMItBFLjK1PbLmj7kMX0oYK1E67SO3lY34g3jtAW4FIWlq4EfA5d2/b318ObJg3u3TutvbmilAVXmfgG8EbULTqY2kr45+wJVCQvAyUkU/HXkzsRsy7iCZWZmZvYUJFHwSBIFf0MBqA+FoVehatJ5wLVoEt7NwN+z26uo+nUJmj54GApOA9lrLOKxGxDnQy8aUKC6IXt+Pl2wF1XNbkRVrfO24da5fjSCfQkaBLIKOGbC/j37NU8a6Cy18DAKkG3A21CoKqKq1pWoqreO2qTFeSjgviyM0+eO8LmYPWUOWGZmZmZbIImC9cD7UHB4LVoXle/lNBYNxsiHOBTRmqvjUAtcM2oT7EIBogsFtf7s9woKGtXsNV6OAkgZVXfGocEXL0NhrgRcHsbpgcN71lsmjNM5QIDWZL0Xhc9zgeZSE+WmsdXbCgXGo+PflH2fioLlrqh18rfA51DFsJS9dBOaSHjGiJ2M2RZywDIzMzPbQtlEu3ejfZ5egzYYXob2bboGtQ/ujipOq1C1qgEFp9tQUNqUfW2k1hKXjycHhYomamPLy9ntD6M9sMagvaUGs69tQhinTUCMWv6uQaHyLuAHwAeB/dDQixY05OM31Foo16FzmZd9/zHwR2pVvzYUZi/Yhit3toPzGiwzMzOzLRTG6Szgv9D6q52APYBzUHCaj8JBAVWZ2oCrgOejwLQMbSQ8dN3VL4FDUXWqjNoQS9nPRTRxbxKqkD2CAsec7Pu7kijY6s14wzhtyM5jYRIFlaf5GkcAu6DguCo7vsNQNe/j6Fqdm91/MgpfLwd+jQLjDHSNGlH4eiOqWAXUQlgFhdJLgc9sI/uAmf1Lw5M/xMzMzMw2sx5tOtyZRMF/h3F6LKrW/A34AqowtaFR5u9CQeNOVNVahvax6kP7X5WA41G74NLsvjxcrUWBYo8h77kHWrvVi9ZoXV2nc3oJ8DG0ufJ1T/M13oBCZT8KTW9DYWo6avtbnb3+EjTMYgBNZdwPVfFK6HzXoXVsH0Dn240CZj4YZAxqz5yPrqfZNsMBy8zMzGwLJVGwCjhtyE0LgXvQh/3TUPvbRDRt8FEUoPKKVSsKRxUUqMZlPzdlt+etbwX41zqlKgpja4CzstsC4AtJFKyo02n9A7gMuG8rXuMrKEx9H3gzsA8KVcVqhbWFIndXBphMgUnFBmag830hCo/3oOrXFOAO4FPZ7eXsNX+KrtEg2mesA7gijNPvJlHwxa04ZrO6csAyMzMz23oPAacAF6P2vzwUzEXtbEtRO9wmVI2Zk93+e+B8FG7eBnya2obDy1D1pkptv6h5aJ+oF2e/nwTUZWR5EgWLgG9t6fPCOJ2JQlQPsDKJghVhnP43ave7Gejqurn1xU3jKge0TBmYMLC+OLnUUh7XPJmuQoH/QdfsZOD1KJg2oHB5AAppX0mi4H/DOO1EI+7zfcQaUdB6EeCAZdsMByyz7VgYp4X2O+65cq9H7z2mb2nlgnH3Nr3ntOr8p9U3b2ZmW+VlqF1tFgpHK1FVqoRaBa8EbkIVmxh9BtsEvBSFpU8Cf6FWQToLBbIlqFKzM6peFYFjs9d5lNrmxKMijNNpwI9QK2MZVfI+l0TBt49937WvbmivvLNt54EjGzoG5635R1t1+ov792yZVqY6SB9al1VF7X4vQ9WpTWhy4EnAd1GQ2iuM0/9C1axuVPHLFYDGME5fADyQRMGjI3HeZk/EActs+zZ+3h/uPba0tETLGN65dvdJp6P/AZuZ2QgJ43QX1BbYhaoqf0LBKd+/qQAcjMaV7wMsR2EknyB4MFq31Y1GlC9FIW1i9vzG7K3Wo5bBHiAF3g/sGcbpV4BzkyhYOsyn+njWoHVnr8yO767ZhQVTgTObJ0/Yu6G9MmePk7p+2TJ1cHDc3t0TC4WsdbKRDuA5aAriOhRClwPfRGFtHxQmB1Cgej8a4w6P/fxaQOvcfgHcFcbpG5MoWD68p2z2xDym3Wz71t0ztr0CVfo7GqrjHlzVN9oHZGa2A1qJpgC+DzgVTcWbhIZY3ItCyFi0piifLjgJtdCtRUFrPWr/2wm1wbWg0NGIKllNqDqWh67d0NTC41FA22m4T/LxJFHQh8Lh24Hjkyg4MzvWKX1dDVdO2G/T/RsXN75y9a1th/StLq1GbZA/QyGpAZ3bqahqNxn4OvDZ7L68EtiNNlfOJy7mkwRBASzfkPg64ACPb7fR5jHtZtu5ME4Lc350w/4TFizvO70y/97RPh4zsx1ZGKcTgUvQuqABtKbqEjT84XgUtnZFIWEQ7en0G+AdqBUw31S4H4WsnlKxv2fi2MXtq9fNXDdYaZqcvcYVaPPiFcDHkij4zYid5FMwu7CgESgffPait2x8tOGQUmP13Q0dg7c2tPGC+743+TNj5/a8edpRG/8XXacedE06UeXqNuABNNSjisJrFe05lg/9WJbd34FCaA9wIbAncGIdB3+YbTG3CJpt55IoqBIFt4/2cZiZ7eiyYQ/PB96CqjKzULjqQUMtOlBQuAqFg0PQZ7Fno2C1Bq1hmotCwyagOn3SfZ17zbx+/wUPH1VZunrvCqr0vBw4E3gFCicjLozTZqAtiYJ/a00/+OxFx6Kq2gXtM8o/B34CvLda5coZL1u7X/eCloeAZwHnoRbD36LrsQBVxH6NKlbfR5Wp8WjN1kZU6ZuKAmwpe8sWNGjkAlT1Mhs1bhE0MzMzq48XobVCM4Hvof2p3ggkqCWwC4WiE9A+VgXUJvhiFL52Qm2FF6MKVgfQsbJ71k33PnxUceXa3ZrRBMEKChTTUBj53gid3+bOAC4I43Ts49z3OuDLm5aWftW/tngtMAHoKBTYs3X6wNJpL1zfj9ZgfRTtv/UPFD4H0EbKRRQ+T0EB60OonfJgdO6bUEjNP8sWgN4kCm5MosDtWTaqHLDMzMzM6uNXKDDcDnwJOBdVssaidUL/RGPQzwOORkGpikacb0JVq0PRgIye7L5C30DHS5at3pvyYMsgChX5GqMXoYl7o1LBQhsBX4+OdXOfBi5bt6B15qZHmyajyYe7AP9VbORNpSaa0PlXUGgKUDWqEVXlTkdrtdZWK0xbf3/zG/vXll6IJg62oiDWn71XFbVbjgvj9FnDcqZmW8BrsMzMzMzqLIzTQ4C3oqrU4Wg4RQGtM9oVhYlmFBTKKFgUUWhoR4FrIRp4QXZfN2qVG8h+X4+CzPeTKBgYifPaUrMLC2YC1YPPXjQIfAc4DO1tNRM4G12TJnReg+halFGr32HAyr5VpRd3/mxiadw+vaXpx6yvoPO+D+0ztgsKWOtRxWsh8PIkCpaM2EmabcYVLDMzM7M6S6Lg5iQKPoQqWZNR1aWE2tuuAr4K3IjWFOUVmXyqXjX7akaf1Yqo0lOmVq0pofHlt2+r4Qqgszp3cWd17qMoSP0G7esVorDVhqp330VtgYNof682tA/Ws4AjmiYMVqYfs37lhGf33IiCVBm1Vvah6YEDqLLVgvYLe92InaDZ4/CQCzMzM7Phswi4CNgbDboYh0LGOhQgWlFoyv/onQeqEqrQDA65bwIKKnkIAw2GuG64T6IOvoxGy9+C1lw1A/cDD6N1at0oXG0E9kVhqQBQKLJw3D69K7PnF1G1rwVdjxYeWzAYh9a7mY0aV7DMzMzMhkkSBd1JFHwAtQs+glrhXoj2g8o3EV6PJuDdhz6b5SPcx/HYIQ5VYKBapVCtUkFh5J6ROpettA4Fnz8Bv0Nj1lvRNMCpaHLirtnj8spUXqk7D/g9arMcRFWv9dQ2ZB663mUDGutuNmocsMzMzMyG3/loTVEJTf/bK7v9HtQquBRVZNZTm5C3uYZqVd1Hg31sBL6TREE6zMddLz9E0w4vR+vSHkWTA+9CgamANmX+JZqe2I6qWxW0N9bJaFhIA7pOY4GfUht0MZh9bwFWDfvZmD0BBywzMzOz4Xc5cCuqOuWVqYkobE1A7W95RSpvAVyGAsYgGlE+CJTLG9lYLRfWVwaYe8JX/jBtpE/k6Uii4K4kCr6TREEvcBpwLVpH9VPgbmpr0Ragc29C+4g1ozbIzdemtaJNhU9C4bWEQlor2rTZbNQ4YJmZmZkNsyQK/gt4JXAbamNbiKov+Yj22dnXoShU3YaqMdXs9zFAsVCgsbGDga6b2r/0wIWTn7fmtrbPjvjJPA1hnB4dxul5YZxOzib8taHx7ocBz0ODK1ai8fWDqE1wDfqsOofaEIx2VO1bjoLXJdQCVTV7zQlhnDaO0KmZ/RsHLDMzM7ORUUWtgWNRFetTKFhMAy5FQasHVatmoxBSpBY48ja49okHbjp4sK9w68ZFTb8Y0TN4+qai0eyt2e/fROFoA6o+TQD2Az6A2gAr2eM2oGtEdnsRTQqcgEa053uCQe36vB14SxinJ4Rx2jwM52L2hBywzMzMzEZAEgVdqCUuD1WnoTa4mahFrh+1v5VQpSafKNiKWuaKZBWt5gmVN889eeX4v//tkKtH+DSerkuB1ydRsCj7/QjgzOyrjM6tAa1BI/t9MlqP1YImC+bDPx4BVlAb/JHL9xYrAM8BPoKmN5qNKI9pNzMzMxs5P0BTBA9GYWAV8HP0mexXwAtQ4FqBwkUHChGdaJ1Wa/a8QRTKtgtJFFSB3jBOC2jtVN4W2I7OtQXoQuc5sVphPVApFJmKzrmLWovgHihE5fuD5SFrEA3GuAu4GO2PdddInJ/ZUIVqtfrkjzIzMzOzugjjdDfgL6jisga1u20EXgp8AzgWBa4CtSpNBVW4KihwrEeb9Z6VRMHaET6FpyWM0zPQ3l/5+YeoDfIy4DjU8reqMkgVmLJpUePtHbMGmoEUBayPofbKCrXWwAIKUqXs99XAl5Mo+OaInJTZ43CLoJmZmdkISqLgYSBAo8r7gTvQuqIbUGtbvvFwEYWHvH1uBVpf9HNUqXkFsH8Yp21DXz+M013COL0ojNOjRuJ8ttBE4AC0ufBMVKF7AJ3nINBXKDBxYG1xXe/Khr+hADoe+AJqk8zXZpWz5+R7ieUVrXHAW8M4nThiZ2S2GQcsMzMzs5G3CK3HehdwIgoLk7KvfB1RAfg7CmFltCbpyyiQTQBuQRP2LgnjdMaQ125EQaNlJE5kC3wTHdMgClrLgW50vJ1oRPv5hSKPNE+s9E4+tGcGCpKT0QCQFShQrUMVq6HtgWQ/d6Fq4OSROCGzx+MWQTMzM7NRFsbpdFTV+iyaINiLQtVCNBAjr8gsRQFlBgpe5wBHoaDyjiQK1mWvV0yioMI2JIzTLwLvQe2NlwFLgPejdsHfARFwNXAg8LLscR9DofMtwBR0HQapVffyClcbCmEvRUMwDgRuTKJg+cicnVmNK1hmZmZmo28ZChdvA25G1ZgxqJ1uZ1T56UEBYwZql2tD+0b9BA3L+Feg2tbCVeY2NHzielStOhW1Cf4F+BA6108MrC8Gq+9o7RnYUPwG8Hu0f9gu6NxXohbJZ6EgtTO6DqC1aecDnwY+jgKr2YjzFEEzMzOz0XcA8HVgPpo0+F0UsvJhDmU04KEb+G80CKMDOD+JgouA82cXFuw7+7QFXZ3VuctG6qDDOP0PtG7s9CQKnnCqYRIFl4VxugAFyePQ8Zeznw8g2/9q7b0tq1Zc1zGpobXy+sY5fS3APBQoq+gaPIjaKG8A5mYvn1e1ZqGK3yeAO+t4qmZPmQOWmZmZ2eh7FLgWuB+1CF4BvBytNaqidUcTUCXrP1Cr4Hjg8DBOr7jltF2qQIzWZ31sBI97LGrhKz3ZAzNHZY9djgJWExrW8WtU4TqyY1b/pQ1t6z7QsUffPsCzgRtRlWoOqmDNRCFtLLo2gzx24uK7kyhYUZezM3savAbLzMzMbBsRxulk4EIUKp6DAkmB2qS8fMJgFVWzrgMaK2Xef9sndtkPeKizOvcfI3i8BaCYRMHgkzzudWj/q6PROT2A9rQqoKD0APAtFKR+jwZaRGjtVQmFqEbUJrgCDbLYGwW0AeAhNP69CbUgnpNEwa/qd6ZmT50rWGZmZmbbiCQKusI4fSch8rJKAAAM/UlEQVSqWI0HXoLWZyXU1hpVs69b0FqmPYoNdHdW5/56FI43ryA9mf3Quqmb0IS/C9DkwBuBD6Pq1G7AJ1F734eADwBT0bCLu9GmwScA01EwW4KqWS2oVTA/lnZgX7Rxs9mIcwXLzMzMbBsXxum5wMnU1mRVqO2R1YP2iXoE2AuNgP9dEgXdo3CojyuM051RJeoaNKTjROCXaGriiSgorUfthlW0Dq0TeDcqCHSg6tUGdJ5zqG3S3ECtqndn9n1+EgU/HJGTM9uMK1hmZmZm274PoyrOTqjC049GuY9H0wa/jFoL90dB5FVhnJ4OLB7JiYJZy+BzgEVJFCzKbmsHvpMde16Fexaqzp2QncsNaArgdaiF8Eg0av1U4Meo6lVB1amdqK356kUVrEYUrPZGIXTfYTxNsyfkMe1mZmZm276xwPGoYnMG2idqNQoYveiP5n8GXoMGRkxBI8vDET7OnYD/BN475LYeVHnKR62vRkHrP9Bn0QeAP6Dz+DnQhwLSdDRl8HeoZTIfRZ9PV8wrW01D3quApgyePxwnZ/ZUOGCZmZmZbeOydr9TgTOTKPhWEgV/Q+uUTkKBaiNwCnARChwrUbDpHeFDXQ6cSRZwwjidgSpMv0VrpkooKLWisNSNWgEfQmGqDQWsfiAFXgccQS1g/Q2daxWFtbxlkuwxD2bvP2KDPsw25zVYZmZmZtuxME5nAR9BrXnz0B5Rm9BwjB8lUXDWML//QcCGJAru2+z2qWiYxUoUmi5CmyIX0ICOZtTyNwlVsMagUPhGFKDGozC1Bg3AGKTWEpjve9U45C0HgPuAY5MoWFL/MzV7ahywzMzMzJ4BwjhtAz4PvBq16i1BY8470BS/nwGfA25KouDiOr1nM3AZsDSJgndtdt844KuoKvUsNJzi2SgsdaMpgedQa/O7GngxClCL0QTBfIBFAYWthuz+fG+wcUPesoqC3CXAO7IJh2YjzgHLzMzM7BkkjNMGNJnvQbTp8FhU3TkdDci4H1WHYlRd2rA1YSSM0xei6tk1WetifvtngY+iIFQB/hfthfV8FJh+jtaVFVGV6tfAG1BoGkRhqRUFp7XAw2hd1k7URtXnwy56UIvho6hqd/bTPR+zreUpgmZmZmbPIEkUlIELs4l+E1A4WYwC14koDH0SeAFqx/sKGpDxL2GcHoP2lvpe9nqPK4zTJrT26wBgQhinNwIdSRSsA25G0wH/jMLRL1DF6rkoHN2V/TwGharXoUpWHpxWoQDYhqpZO2ff83AFCmIlFMauAd6eRMHaLbtiZvXlCpaZmZnZDiQLXu2oGvRFYBpwRhIFfwvjdDyaUHgI2mvqJ2hk+qez0LT5a5WA7wNdwFlonPxz0P5V69H0wI+jQRVvRSPY80Ecj6CgtBQFtHycfPuQt6igALUJhcS90DqsAlrDVRzyGr3AAUkUrN+a62O2tTxF0MzMzGwHkrUDTkSh6LPAQtTGBwo6HwT+CrwLVZd2JRuFHsZpMYzTg8I47cgePyG7v4DaDl9BrUPqfNSi2AocDnwhiYIEeB8KTP0oZO2KpgP+Nrutgtr98vB0e3bbXtltbShcDa0SlNF+WZu28vKYbTW3CJqZmZntQMI4HYs2/r07iYKPoWpT7no0kfAOYAbwU+CiJAr6svv3A76JwswFqI3v82igxiMoUC1A49qvRWurFqN2xMPDOH0FsAgFsg60DuvTwEHA2ahNsIFaNasA/B6Isp+rqC2wBw27KKLK1X8D30iiYHCrL5DZVnKLoJmZmdkOJIzTItrktxs4FPhZEgV3b/aYWcAP0LqtW1CYGkCbHM8D/nPoWPas7TAAHkyi4P4h73MO2jT4GuD1aB3VWmAWUK1WGSgUKKFQtQCYjapTZTSCvQzciaYPDqBKWiU7rt1Q0Do6iYLb63N1zLaeWwTNzMzMdiBJFFSSKLgQbe57GJrst7llwC9REDobte91oHHrhwJnhXE6ZsjjD0brtb4+5LavAfug1sFXoUEXTcAUYKAySLEyQEul/K9JgGOBe1GQuhOFq4bsNe5vaVrfP2+3PxbbW7oaUbtgH2o3fE4Ypyc87QtiVmduETQzMzPbASVRcEcYp28BusI4nYxaA/8I3JtEwZIwTq8DTgA6UTjaF/gH8EpgBVovRRinLcAxwG3AHWGcXoLa9uZlb7UUtQXeiILbIuDVhQIH9q4uDDRPqC6igRtReJuXvy4KWkUUynZvadpQnDBmSWFJ15y8/aoXjXWfkB3XL+t+kcyeBrcImpmZme3gwjh9NnAFGmqxGq2regewP5oC2AG8HbgbeC2wOomCadlz9wIS4HK0R9Vr0TqpDSiIjQcmodbAMWit1sLscQXgXBTCvkZtqMUh2X15FasM1VJz48ZC30DbOiiWUAvjHOCHQJpEwYZhuThmW8gBy8zMzMwI4/SdwElor6kCGiIxFq2fOhh4ALgQjVpfB3wtiYLFYZzuC/wIjV+/FLUDTgJ+g4ZcHIuqYx0oYH0ReAnwchSgNmXvtZjaRMLG7LAKQw4xH3LxVuA4NNgiTaKgt64XwmwrOWCZ2b85u3BK44pnT7ugd0p7adrfF5906+de2puN9TUzs2ewbFjFcWhU+67A7mioRBX4HdoTqw14FLga+Gj22LcAp2X3LURBaxBtYnwN2iPruajC1YwCVAXtlTUxe/u8WpVvJLwCrdcqZo/Nx7Z3Zo8/LYmCX9X7GphtLa/BMrPHM3XVvKlv3uvSf1At8IbG1RvPBD412gdlZmbDK/tj2q/DON0TbRpcoVZNGoc2AW5Bwy/ejjYVnodC009QWLo2+z4RDaF4LlortQHtidWIpv81o4pWecj7VNEarCIKY3kFq5TdtwoNtxib3Wa2zXEFy8we1ydnfKI8YcmGEsAgVBug8bTqfO8vYma2A8hGrB+D1jdNR+GnDw2eWI3CTwnoL/cUGkrN1YFCkQIKUANovdUEahv/tqBx7/ujYPVd4P0oQPWgUDU+e+x9qHrWstlh9aBWwy7g8iQKyvU9a7P68Jh2M3tcZz761Yay/lpISf8DHDi7cMrXn+RpZmb2DJBEQQW1AJ6PpvWBglE7ClxFoKF/XbG18yeTmlbd3NaCAtcAClVTst8rqErVgypaG1BAej61z6H9KMANoJbB6dTCVb6xMKh6NRVVxXaq+0mb1YkDlpn9vxph9wIMZP0ZBSA6u3BK86gelJmZjYgsZH0BDaP4A9kf3RgyhKLYWCm0Th+oNE8ul6m17C0d8jLtqAWwG9gbmIzC17Oz1yug1sPW7Pl9aCx7rgpsRFWtALgMVdWW1/FUzerKLYJm9qTOLpzSjf4HWAFmnFadvyy7vXhadX5lVA/OzMyGXRinDWigxd7A69BEwHx9VIXHTvtbj0JS45DbB6gFp3xoRpHa4AqGPHZddltzdtvtwNFJFOTthmbbNAcsM3tKzi6cMgeYeFp1/g3Z73OrBc5ZdsRu01se7upvXbHxqE8PzPeoXDOzZ6gwTj8DPA+4C00NnEjWQo7CEKhatQqFqfGbvUQepgrUpgKCWgQH0ATCCvAgqlrtg9oNP5xEwUXDclJmw8Atgmb2lJxWnX9fHq4y/Zumj21fedDMAzbtOe2wpjI9X204ZdZoHZ+ZmQ27bwMnJ1EQoXa9FAWqpWjwRV6VakHrroooTHXx2CpVFQWpMgpW+dTAanYbaMz7Q2jM+8XDe1pm9eUKllkdhXHaCJxZ6C+XOxZ1f/L88970jP4P7Iz9z2zsq5SXTnpk3aSmdb0A555enX/qaB+XmZkNvzBOdwVOQOuzPgW8CK2l6kCDKUpouMUaYJfsaVW0fiqvelWB+ahS9RoUtC4CLgEOBG5PomDNyJyRWX04YJnVURinHcVla5Yccva1Y6pQ6d5v58av3PHJZ/wapbMKp3yiAEcV4F2nVec/OtrHY2ZmIyuM0yYUqI4CzkCB6lTgeOBQYA4aYJHvqfUQ8OPs9g8nUdAzwodsNmwcsMzqLJr7mUen/XPNzgADbQ0/+OTGb75ntI/JzMxspIRxejJwEnAWGoxRAn4KXIXa/UrA25IouGPUDtJsGHkNllmdxQu+OKO3udC3qZly06byR0b7eMzMzEbYFcC3gN+gFsIjkij4BnAvaiU80eHKnslcwTIzMzMzM6sTV7DMzMzMzMzqxAHLzMzMzMysThywzMzMzMzM6sQBy8zMzMzMrE4csMzMzMzMzOrEAcvMzMzMzKxOHLDMzMzMzMzqxAHLzMzMzMysThywzMzMzMzM6sQBy8zMzMzMrE4csMzMzMzMzOrEAcvMzMzMzKxOHLDMzMzMzMzqxAHLzMzMzMysThywzMzMzMzM6sQBy8zMzMzMrE4csMzMzMzMzOrEAcvMzMzMzKxOHLDMzMzMzMzqxAHLzMzMzMysThywzMzMzMzM6sQBy8zMzMzMrE4csMzMzMzMzOrk/wCQZBvj5BpysAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(12, 12))\n", "plot(embeddings[0][2], title=f\"k={embeddings[0][0]}, min_dist={embeddings[0][1]}\", ax=ax[0, 0], draw_legend=False)\n", "plot(embeddings[1][2], title=f\"k={embeddings[1][0]}, min_dist={embeddings[1][1]}\", ax=ax[0, 1], draw_legend=False)\n", "plot(embeddings[2][2], title=f\"k={embeddings[2][0]}, min_dist={embeddings[2][1]}\", ax=ax[1, 0], draw_legend=False)\n", "plot(embeddings[3][2], title=f\"k={embeddings[3][0]}, min_dist={embeddings[3][1]}\", ax=ax[1, 1], draw_legend=False)\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 } openTSNE-0.6.1/examples/04_large_data_sets.ipynb000066400000000000000000132074041413546205200214730ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Embedding large data sets\n", "\n", "Embedding large data sets typically requires more care. Using various tricks described in *preserving_global_structure* can become quite slow to run. Instead, we can take a smaller, manageable sample of our data set, obtain a good visualization of that. Then, we can add the remaining points to the embedding and use that as our initialization.\n", "\n", "Remember that the initialization largely affects the structure of the embedding. This way, our initialization provides the global structure for the embedding, and the subsequent optimization can focus on preserving local strucutre." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import gzip\n", "import pickle\n", "\n", "import numpy as np\n", "import openTSNE\n", "from examples import utils\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 4.73 s, sys: 980 ms, total: 5.71 s\n", "Wall time: 5.71 s\n" ] } ], "source": [ "%%time\n", "with gzip.open(\"data/10x_mouse_zheng.pkl.gz\", \"rb\") as f:\n", " data = pickle.load(f)\n", "\n", "x = data[\"pca_50\"]\n", "y = data[\"CellType1\"]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data set contains 1306127 samples with 50 features\n" ] } ], "source": [ "print(\"Data set contains %d samples with %d features\" % x.shape)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def plot(x, y, **kwargs):\n", " fig, ax = plt.subplots(ncols=2, figsize=(16, 8))\n", " alpha = kwargs.pop(\"alpha\", 0.1)\n", " utils.plot(\n", " x,\n", " np.zeros_like(y),\n", " ax=ax[0],\n", " colors={0: \"k\"},\n", " alpha=alpha,\n", " draw_legend=False,\n", " **kwargs,\n", " )\n", " utils.plot(\n", " x,\n", " y,\n", " ax=ax[1],\n", " colors=utils.MOUSE_10X_COLORS,\n", " alpha=alpha,\n", " draw_legend=False,\n", " **kwargs,\n", " )" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def rotate(degrees):\n", " phi = degrees * np.pi / 180\n", " return np.array([\n", " [np.cos(phi), -np.sin(phi)],\n", " [np.sin(phi), np.cos(phi)],\n", " ])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll also precompute the full affinities, since we'll be needing it in several places throughout the notebook, and can take a long time to run." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 46min 53s, sys: 1min 39s, total: 48min 33s\n", "Wall time: 5min 35s\n" ] } ], "source": [ "%%time\n", "aff50 = openTSNE.affinity.PerplexityBasedNN(\n", " x,\n", " perplexity=50,\n", " n_jobs=32,\n", " random_state=0,\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 4h 54min 10s, sys: 4min 44s, total: 4h 58min 55s\n", "Wall time: 22min 23s\n" ] } ], "source": [ "%%time\n", "aff500 = openTSNE.affinity.PerplexityBasedNN(\n", " x,\n", " perplexity=500,\n", " n_jobs=32,\n", " random_state=0,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Standard t-SNE\n", "\n", "First, let's see what standard t-SNE does." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Because we're given the data representation as the top 50 principal components\n", "# we can just use the top 2 components as the initilization. There is no sense in\n", "# calculating PCA on a PCA representation\n", "init = openTSNE.initialization.rescale(x[:, :2])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------------------------\n", "TSNE(n_jobs=32, verbose=True)\n", "--------------------------------------------------------------------------------\n", "===> Running optimization with exaggeration=12.00, lr=108843.92 for 250 iterations...\n", "Iteration 50, KL divergence 8.9036, 50 iterations in 43.6444 sec\n", "Iteration 100, KL divergence 8.1739, 50 iterations in 45.7009 sec\n", "Iteration 150, KL divergence 7.9832, 50 iterations in 45.4050 sec\n", "Iteration 200, KL divergence 7.8977, 50 iterations in 43.9690 sec\n", "Iteration 250, KL divergence 7.8511, 50 iterations in 44.4052 sec\n", " --> Time elapsed: 223.13 seconds\n", "===> Running optimization with exaggeration=1.00, lr=108843.92 for 500 iterations...\n", "Iteration 50, KL divergence 6.4946, 50 iterations in 43.9199 sec\n", "Iteration 100, KL divergence 5.9617, 50 iterations in 43.6204 sec\n", "Iteration 150, KL divergence 5.6756, 50 iterations in 44.2530 sec\n", "Iteration 200, KL divergence 5.4932, 50 iterations in 45.1531 sec\n", "Iteration 250, KL divergence 5.3658, 50 iterations in 47.1845 sec\n", "Iteration 300, KL divergence 5.2714, 50 iterations in 47.4659 sec\n", "Iteration 350, KL divergence 5.1981, 50 iterations in 49.2679 sec\n", "Iteration 400, KL divergence 5.1394, 50 iterations in 49.6450 sec\n", "Iteration 450, KL divergence 5.0913, 50 iterations in 51.5995 sec\n", "Iteration 500, KL divergence 5.0511, 50 iterations in 53.0170 sec\n", " --> Time elapsed: 475.13 seconds\n", "CPU times: user 3h 21min 43s, sys: 5min 53s, total: 3h 27min 37s\n", "Wall time: 11min 41s\n" ] } ], "source": [ "%%time\n", "embedding_standard = openTSNE.TSNE(\n", " n_jobs=32,\n", " verbose=True,\n", ").fit(affinities=aff50, initialization=init)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding_standard, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This doesn't look too great. The cluster separation is quite poor and the visualization is visually not very appealing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using larger exaggeration\n", "\n", "Exaggeration can be used in order to get better separation between clusters. Let's see if that helps." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------------------------\n", "TSNE(exaggeration=4, n_jobs=32, verbose=True)\n", "--------------------------------------------------------------------------------\n", "===> Running optimization with exaggeration=12.00, lr=108843.92 for 250 iterations...\n", "Iteration 50, KL divergence 8.9036, 50 iterations in 41.3583 sec\n", "Iteration 100, KL divergence 8.1739, 50 iterations in 44.0357 sec\n", "Iteration 150, KL divergence 7.9831, 50 iterations in 44.8030 sec\n", "Iteration 200, KL divergence 7.8978, 50 iterations in 44.5963 sec\n", "Iteration 250, KL divergence 7.8511, 50 iterations in 44.0719 sec\n", " --> Time elapsed: 218.87 seconds\n", "===> Running optimization with exaggeration=4.00, lr=108843.92 for 500 iterations...\n", "Iteration 50, KL divergence 7.0117, 50 iterations in 44.1787 sec\n", "Iteration 100, KL divergence 6.8478, 50 iterations in 44.2544 sec\n", "Iteration 150, KL divergence 6.7850, 50 iterations in 43.0467 sec\n", "Iteration 200, KL divergence 6.7506, 50 iterations in 43.1292 sec\n", "Iteration 250, KL divergence 6.7289, 50 iterations in 42.3653 sec\n", "Iteration 300, KL divergence 6.7142, 50 iterations in 43.3017 sec\n", "Iteration 350, KL divergence 6.7036, 50 iterations in 43.1021 sec\n", "Iteration 400, KL divergence 6.6955, 50 iterations in 42.4524 sec\n", "Iteration 450, KL divergence 6.6884, 50 iterations in 42.3116 sec\n", "Iteration 500, KL divergence 6.6812, 50 iterations in 42.7694 sec\n", " --> Time elapsed: 430.92 seconds\n", "CPU times: user 3h 23min 13s, sys: 5min 47s, total: 3h 29min\n", "Wall time: 10min 53s\n" ] } ], "source": [ "%%time\n", "embedding_exag = openTSNE.TSNE(\n", " exaggeration=4,\n", " n_jobs=32,\n", " verbose=True,\n", ").fit(affinities=aff50, initialization=init)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding_exag, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The separation has improved quite a bit, but many clusters are still intertwined with others." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using a larger perplexity" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------------------------\n", "TSNE(n_jobs=32, verbose=True)\n", "--------------------------------------------------------------------------------\n", "===> Running optimization with exaggeration=12.00, lr=108843.92 for 250 iterations...\n", "Iteration 50, KL divergence 6.6121, 50 iterations in 155.4301 sec\n", "Iteration 100, KL divergence 6.0752, 50 iterations in 155.6532 sec\n", "Iteration 150, KL divergence 5.9787, 50 iterations in 155.2036 sec\n", "Iteration 200, KL divergence 5.9415, 50 iterations in 158.4592 sec\n", "Iteration 250, KL divergence 5.9224, 50 iterations in 164.1987 sec\n", " --> Time elapsed: 788.95 seconds\n", "===> Running optimization with exaggeration=1.00, lr=108843.92 for 500 iterations...\n", "Iteration 50, KL divergence 4.4697, 50 iterations in 156.9712 sec\n", "Iteration 100, KL divergence 4.0495, 50 iterations in 157.9296 sec\n", "Iteration 150, KL divergence 3.8464, 50 iterations in 168.0550 sec\n", "Iteration 200, KL divergence 3.7248, 50 iterations in 166.4940 sec\n", "Iteration 250, KL divergence 3.6438, 50 iterations in 166.7832 sec\n", "Iteration 300, KL divergence 3.5860, 50 iterations in 174.2202 sec\n", "Iteration 350, KL divergence 3.5434, 50 iterations in 172.8181 sec\n", "Iteration 400, KL divergence 3.5106, 50 iterations in 167.7604 sec\n", "Iteration 450, KL divergence 3.4848, 50 iterations in 163.7755 sec\n", "Iteration 500, KL divergence 3.4639, 50 iterations in 169.2613 sec\n", " --> Time elapsed: 1664.07 seconds\n", "CPU times: user 19h 11min 10s, sys: 6min 32s, total: 19h 17min 43s\n", "Wall time: 41min 18s\n" ] } ], "source": [ "%%time\n", "embedding_aff500 = openTSNE.TSNE(\n", " n_jobs=32,\n", " verbose=True,\n", ").fit(affinities=aff500, initialization=init)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding_aff500, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ... with higher exaggeration" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------------------------\n", "TSNE(exaggeration=4, n_jobs=32, verbose=True)\n", "--------------------------------------------------------------------------------\n", "===> Running optimization with exaggeration=12.00, lr=108843.92 for 250 iterations...\n", "Iteration 50, KL divergence 6.6121, 50 iterations in 165.1051 sec\n", "Iteration 100, KL divergence 6.0752, 50 iterations in 170.2804 sec\n", "Iteration 150, KL divergence 5.9787, 50 iterations in 167.2433 sec\n", "Iteration 200, KL divergence 5.9415, 50 iterations in 167.1109 sec\n", "Iteration 250, KL divergence 5.9224, 50 iterations in 166.6234 sec\n", " --> Time elapsed: 836.37 seconds\n", "===> Running optimization with exaggeration=4.00, lr=108843.92 for 500 iterations...\n", "Iteration 50, KL divergence 5.0955, 50 iterations in 165.1969 sec\n", "Iteration 100, KL divergence 4.9934, 50 iterations in 167.7396 sec\n", "Iteration 150, KL divergence 4.9625, 50 iterations in 166.0314 sec\n", "Iteration 200, KL divergence 4.9504, 50 iterations in 165.1204 sec\n", "Iteration 250, KL divergence 4.9438, 50 iterations in 164.4031 sec\n", "Iteration 300, KL divergence 4.9396, 50 iterations in 165.8241 sec\n", "Iteration 350, KL divergence 4.9365, 50 iterations in 164.0402 sec\n", "Iteration 400, KL divergence 4.9342, 50 iterations in 163.1385 sec\n", "Iteration 450, KL divergence 4.9322, 50 iterations in 162.8973 sec\n", "Iteration 500, KL divergence 4.9307, 50 iterations in 163.9869 sec\n", " --> Time elapsed: 1648.38 seconds\n", "CPU times: user 19h 55min 34s, sys: 6min 25s, total: 20h 1min 59s\n", "Wall time: 41min 57s\n" ] } ], "source": [ "%%time\n", "embedding_aff500_exag4 = openTSNE.TSNE(\n", " exaggeration=4,\n", " n_jobs=32,\n", " verbose=True,\n", ").fit(affinities=aff500, initialization=init)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding_aff500_exag4, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialize via downsampling\n", "\n", "We now perform the sample-transform trick we described above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create train/test split" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "np.random.seed(0)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "indices = np.random.permutation(list(range(x.shape[0])))\n", "reverse = np.argsort(indices)\n", "\n", "x_sample, x_rest = x[indices[:25000]], x[indices[25000:]]\n", "y_sample, y_rest = y[indices[:25000]], y[indices[25000:]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create sample embedding" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===> Finding 1500 nearest neighbors using Annoy approximate search using euclidean distance...\n", " --> Time elapsed: 14.00 seconds\n", "===> Calculating affinity matrix...\n", " --> Time elapsed: 5.66 seconds\n", "CPU times: user 4min 17s, sys: 3.09 s, total: 4min 20s\n", "Wall time: 19.7 s\n" ] } ], "source": [ "%%time\n", "sample_affinities = openTSNE.affinity.PerplexityBasedNN(\n", " x_sample,\n", " perplexity=500,\n", " n_jobs=32,\n", " random_state=0,\n", " verbose=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.66 s, sys: 96 ms, total: 1.76 s\n", "Wall time: 86.1 ms\n" ] } ], "source": [ "%time sample_init = openTSNE.initialization.pca(x_sample, random_state=42)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------------------------\n", "TSNE(n_jobs=32, verbose=True)\n", "--------------------------------------------------------------------------------\n", "===> Running optimization with exaggeration=12.00, lr=2083.33 for 250 iterations...\n", "Iteration 50, KL divergence 3.2514, 50 iterations in 2.8158 sec\n", "Iteration 100, KL divergence 3.0818, 50 iterations in 2.8074 sec\n", "Iteration 150, KL divergence 3.0695, 50 iterations in 2.8865 sec\n", "Iteration 200, KL divergence 3.0668, 50 iterations in 2.7726 sec\n", "Iteration 250, KL divergence 3.0662, 50 iterations in 2.6979 sec\n", " --> Time elapsed: 13.98 seconds\n", "===> Running optimization with exaggeration=1.00, lr=2083.33 for 500 iterations...\n", "Iteration 50, KL divergence 1.4430, 50 iterations in 2.8882 sec\n", "Iteration 100, KL divergence 1.2700, 50 iterations in 2.7344 sec\n", "Iteration 150, KL divergence 1.2087, 50 iterations in 2.7087 sec\n", "Iteration 200, KL divergence 1.1795, 50 iterations in 2.8707 sec\n", "Iteration 250, KL divergence 1.1639, 50 iterations in 2.9316 sec\n", "Iteration 300, KL divergence 1.1553, 50 iterations in 3.0808 sec\n", "Iteration 350, KL divergence 1.1490, 50 iterations in 3.0691 sec\n", "Iteration 400, KL divergence 1.1456, 50 iterations in 3.2036 sec\n", "Iteration 450, KL divergence 1.1433, 50 iterations in 3.2834 sec\n", "Iteration 500, KL divergence 1.1413, 50 iterations in 3.2604 sec\n", " --> Time elapsed: 30.04 seconds\n", "CPU times: user 22min 52s, sys: 35.3 s, total: 23min 27s\n", "Wall time: 44.5 s\n" ] } ], "source": [ "%time sample_embedding = openTSNE.TSNE(n_jobs=32, verbose=True).fit(affinities=sample_affinities, initialization=sample_init)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(sample_embedding, y[indices[:25000]], alpha=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Learn the full embedding" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===> Finding 1 nearest neighbors in existing embedding using Annoy approximate search...\n", " --> Time elapsed: 253.76 seconds\n", "===> Calculating affinity matrix...\n", " --> Time elapsed: 0.93 seconds\n", "CPU times: user 6min 35s, sys: 41.5 s, total: 7min 16s\n", "Wall time: 4min 14s\n" ] } ], "source": [ "%time rest_init = sample_embedding.prepare_partial(x_rest, k=1, perplexity=1/3)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "init_full = np.vstack((sample_embedding, rest_init))[reverse]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4sAAAHBCAYAAADATy5AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdd7wcVf3/8deZtvXubem9QRJ6EQKSUAWBoNhAgoJKlCKKAQSlwxflZxcBlaoiYBBEQOkI0pTeQwiQ3tvtW6ed3x9nlntJqIqQkM/z8biPvXd2ZnZmYTm893OK0lojhBBCCCGEEEL0ZX3YFyCEEEIIIYQQYsMjYVEIIYQQQgghxHokLAohhBBCCCGEWI+ERSGEEEIIIYQQ65GwKIQQQgghhBBiPRIWhRBCCCGEEEKsR8KiEEIIIYQQQoj1SFgUQgghhBBCCLEeCYtCCCGEEEIIIdYjYVEIIYQQQgghxHokLAohhBBCCCGEWI+ERSGEEEIIIYQQ65GwKIQQQgghhBBiPRIWhRBCCCGEEEKsR8KiEEIIIYQQQoj1SFgUQgghhBBCCLEeCYtCCCGEEEIIIdYjYVEIIYQQQgghxHokLAohhBBCCCGEWI+ERSGEEEIIIYQQ65GwKIQQQgghhBBiPRIWhRBCCCGEEEKsR8KiEEIIIYQQQoj1SFgUQgghhBBCCLEeCYtCvM+UUtOUUurDvg4hhBBCGFMnf2Ha1MlfkLZZiPdIwqIQ7yOl1DTgEuDaD/tahBBCCAFTJ3/hG0jbLMR/RMKiEO+vu4FHgD9/2BcihBBCCABuAh5D2mYh3jOltf6wr0EIIYQQQgghxAZGKotC/BeUUmcqpY57H86zrVLqSBnrKIQQQvx3fnhr5cwf3lo+4r89jzdj5rb2jJlfej+uSYiNlVQWhfgPJcFuFVDSWo/+D89xA/BJwMF8ebMEeAkYAuwF7AfcquWDKoQQQryjH91abQXmaHT7aQdnxv8n51Az/ngD2J8cgHIylm0tiuPFwBxg0JbVRXsFqP0q6YZbF1/4TWmbxUeeVBaF+A8lAe4c4GR4PTy+a8n++wAevZ/F4ZiAuA1wKnAGcPD7dMlCCCHER9r3D063RcQ/jtCnAXzn7B+8p7b5Mz+6XKGDffBrXjnyrY6wCn5lBPAJYJtXIn2qIj4jU+3Z/39x/UJsaKSyKMT7QCmVAn4ALAb+BizTWofr7KP6VgiVUqOAl9c5VQTcC8TA0cCBwE1a6/L/7OKFEEKIj6Cjz/l/Ka35QSm2lswsjrgVWKovnBb13Ud9509K/+rw3rZ5xjWj8KsvE9n1LRBEEbnUPdiOnlBZfHSkONBD3zTr0u9L2yw+8iQsCvFfUEoNALqBGqYSeAJwB3AcZmbUArAzkAVWJ4e1a62HJ8cXARsTDn0gnezzgtZ60gd0G0IIIcRHhrr8jwMIVfcJKxfVVhatU2+Kmk+IVNMdwLHAPfRtm8vl1WitcXIr9W+njQNQJ/yhRBBbRF5MEIV4kYdtgZd6Ql84bY8P8daE+MA5H/YFCLGxqHczrVcHlVKnAJ8FJmA+SzamO+nBmLENuwIKWABMxQRCB2hVSj0GuJgq4q5AI72fxxho/0BuSgghhNiI5c+bqQCK50wzbfOJV5yBtqeCPfEihtvg2ajX2+YFrNs2x3GMbTtEDFEzZr4I+Fiph1Dhjpa2GmI7tlBAHMbEztoP5y6F+PBIZVGIdyEJikcBHVrrvyZ/r8WMN1z3S5d699MqkE9+nwOMA15NjhmDCZYLgKHJtnagJdl/LXAk8IBMbiOEEEKsL3/eTJUtcBSKjtUzpv1VzfiTgvJaUClwkn6kHoQBVGshjq1JZ3wgZ54LF0FtKDr1KtrJYjESEyRnW3SPA9uLa5V2onQLygZLryCVna4vnPbPD+mWhfjAyQQ3QrwLSWDzAT8JilsDFUyjUl1n93p4fAZTJQQYlGwfCfwY6AECoB+9n8OWZFuM6SJzK/Di/+B2hBBCiI1e8Zxp2q8SBTUqasZMBWpr8HziLGivmsYjo4EgBtt2iCMFPM3rbbNuBstBRSOwOBczrCQAhlrkbAcFXraFWAfEcUwtbCWo3apmzJz1Id2yEB84CYtCrEMpdZZSatA62zzgNODPwFLgAaAV0/U0ve45MAFyF0yYbAMaku054FBgCvAIJoA6mIltqsm5LUyl0QVGKaWa3r+7E0IIITY+6uGrz1IPX/3GtnnGTK9rVXhq5wr/LxCZtlmHTRmrYqOCtEWEpwJStk3GtiFlv942O/id4OZMk6vywOcwbfNjw91cNHXoNrZPJkJlq+QbluJaFg3KI45ddGnkXZdOl7ZZbBIkLArRh1LqROD7wF/6bPsupvtoDvOZqQe/N+seWq8k5umtMDZiQmVdE3AZsCemgqj7PD8CeBKYDazEhMgnlVKLlFJ/Wudah7/X5TqEEEKIjY16+OqzMF/Yznx924yZSduscqAtyuU8pRW4Vk88wfLxCCgT0EVIWuk4pSKNJg81B0I0doMFNqRImutmTNs8eUmlVHihbXkMyk5GlozA636S2tqniLpXbqNmx0u6eFLNuGKRmjHzDW3zrF+eN1zfcJG0zeIjQ8KiEG80E3gC+F2fbZOBgcAATLBzgQy9wbCvvgGy/vlygL7Ta7cCO2GqjlbyGGEqlDamO+qY5DUtYHny2rvVT6CU2gkTYJe891sUQgghNipXYIZ2XNNnm2mbYzWAWqTB9bAKmUBH+rnYwteaepPcbft0RqgU0KojCwIiQjsmStrmCPCHUm+b/YpaUF1hUeyJqMVpwtAmcnNeumWbXHNhYDEsWIsjbznkBwAfq1/QU78497OpbPrVWcvapW0WHxkSFoXoQ2u9Umu9F7BUKXV8sjbiZ4DpmK8XFSYkatb//PiYsFef4Eb32T+bPK+BYfRWHe11HgHG9vn9Z1rrPYDDgVF9th+UPMqMxkIIIT7S9JSvrNRTvjKZm7yVasbMY9WMmUpfOO0zwHQnWwtJ2QodxYSWhgGWjjyoxCYDRmGgLcvCrYS2E2MrrfFRYMdDQpXF9310rKlWhhGUHSoVQJs2OePYxBEEAfiZLXxMwXA+Q392/um/rbfN4+vXaeWbtrPdFJGTste7CSE2UhIWhXhzw4FPAyWl1CvAVfQGM4veINh3cV8veazvV68yKsxkOE8nv3uYAfT1Y2PMuEb6nk9rndVan5f8eSuQ7rN8xznAT4Dt/6u7FEIIITYSWZtRhHzWq1JyTpo5e8DY8Kp0g23aXNexaNAaYgVRlK4WobsEOuPix2BlnDIZVpPVeBrw1VinvYTrziZWCu15BFFAFEV0KeixY6yoAwArNm1zRxQVf3xUVl/yjfMAPt52s2mbZ5jlO3Y4esY5xdj+iZ3L7/AhvD1C/E/I0hlC8PrSGIcAB2AmoHkU6KR3fcT6FytB8ugmj/V+Ln2/eKkHSTCT1qQxVcUQOAG4kN4ZT/tWH0Pg5uT1g+TvW4GTgaMxM6qeobXu26VVCCGE+EhSM2aq0U3OIUdutt8BkVKHvtr2yqM3vDK3c8AgpmYGoYs9gSqWYmoLgwC0IuM42EAQaFav1LQMtUwTq8CNNE5Wmea6o5ImnQkJ/LEEYTf5k1ZE8c+wUjHaKrC2pmmIFZk4hlyIrl1LpfI1sgRU4jBX7L51R/XE9+JYT3/Z23ZgyR18euWKYysf8tslxP+EVBaFMH4FXIkZr2BhKoHfAM5Knq9X/KqYoOgnf9fHHdJnG/SOZ/T6PGaB32Amysn22U8D84DNgeuS1/Iw4yIHYkLjTpjuLif8V3cphBBCbCSGN9qXryiHVzyx6pVdU5ZrDcz1X/uZLdPfSI3krFVLIt22KI6CTg1OxcfWDmEYEAGhpWgZboEG1wvIWGbZRR0nFRLXq6IJcb1XaM2uiNVFlk0OZWWoAdlU7OhGTeDOtmpsTiV1C3Y2IpPyyGYyoFq1JlxtD/l4VLMOB33sh/k+CfG/JGFRbJKUUt9USj2tlBqdbLodeAlTwctqrT+FqSweTe/nJMbMcgq9IbAeIkPMuMOA3gBZrxbWjwXTRVVhJrxpS47XwLaYcYhjgA7MbKiTtNZTtdY9wImYSXeuez/uXwghhNjQDLl15kmjbr7h8WG3/Xk0gKPUvTmL2bO7Fx6VS6ezFx016rA7FlY7l71QPLbaZYaExHEcQz4HribV4GJnIIxNm5vK+jiOReybtjkKFHEUO4SRY5rq2MkDVuhkiRSUK8S0ERFFltKO3bDzceXCQTtVmUjsdVDlReLqpOJ13z/4oetu7lic2ur4Sq7f79D6L295U0Js5KQbqtgkKaUuw3QxPVprfcebPN+NqSAGyeMdmPGBFibwNWHCYTemO2lffbuhAizDhL9tgf70jlesB04bMwvrbcDVQKi1bv6vb1IIIYTYiAy79c9/yIfOJzpT/uErD5r20LrPDz5zZndV43aWlof9hzc6a5ZYj0NmBGHVBmyU1USlaKP8blItBYj6zCKgNVWlcCxwQw2V5W663+xIs62qlfunKAZl7AhyHsUU2MrOb8bM4t3xbQwv/5HA9vU1R7V8sO+IEB8+CYtik6WUGq21XrDOtiwwDniM3sDnYwJj/e+I3iUv+uo76Y21zvGLgV8CxwMT6y9Hb2VR9fmJgR8ClwDFZN8ttdaz+lynAn4PXKW1fvi9370QQgix4Rl2259HLz3oi29omyed9EC2FgfjKqPXPt65qog3t8SyrPZ1ocUDBZYLxCHda2zcgkKFkM5ANYB0rMFSEEdoz4ZQo9IKtL8HlcV7DbZ/ee4s9V0KpVFmNoKMohZHKqX0sFyslnQpRQ1FpKOGEfkLikUu1AFVtIZV3VvqPx/7etv8w92VCkr530fauur8Z7qkbRYfCRIWxSZLKdUAfAW4X2s9O9l2AWY9w63oHVe4rjJmPGHf8FgB7sYsizEp2R7TGwDf7LiA3m6pdS9gJtdpAPYBasCQZL9faq1PV0rdjQmcLcDjWut9+tzTg8BmgK+1HvXu3w0hhBDiw3fyCTc1aBV9JW1x/wW/PHQ2wA4z/nGBBbu8oFdsR1DOZHtCurIZ0nGEFSuqA13icqVMSWdwUgoPUNUITQXl3wHOyAyZSRZQqs9UHjnK0jUuHdBd/mt7KnNXNacII/B0gOc4TUqr1rxiRQ+UX3JeJrYfSm9t5fxqvL/WFHVbZTiuZZN2fql/f+Tp++1+ysNW7eXR+5TvaO5S2Yd+8ELxgPo97Xj8xQ/OrVXHeWmvuuaS74x90xsXYgMlYxbFpmwQpmvpmD7b7gBWAysx4Q7MuMMiveMTbUxjU+nzN5hJauqL80b0jl8EExz7BsP6DKo1esczdgPbAF8HlmMmtxlK7wysuyaPkzHdWdcAtyul+q61mAWagYFKqV+881sghBBCbDg8KxoUamv7mo6H17fF6PuiftX2QpBeFXSly13pLI6yw8ihWHGJLK2glrXJNWlSTsW0tGl7ZxVrGLgQWnaq4FJCRxD6oBS+x7j5c+IHZs91XyBn2madBxos2q1a5zwvnjdXUV6e6aTDnUi7Nb32QhQ25nW/tB0Np+ybSe1K4c4AKzdr3GHllL1aH8ruuuy2sYfeW19OA6ASxfmSnW/pVpkhI2f89LQP7t0U4r8nlUWxSVNKtQLtOvkgKKXSwAOYIDcGEwTT9C6fUQ929bUW30yECZcFTDgMMd1Y1wL9MCEzQ2/lsa4nOaY+TrKa/DQkr3eS1vpSpdSKZL9rgQu01ovWuacewNJa597zGyKEEEJ8yE6dcWPrqz2N7bdctZ8GGHfN9emGAfqhNWs0K54rj4mXFO1BuVS6ls7FHbZlQTXOVdcQWimrplOayIGsk3yVm1UQaIgjsMug8gpf9acWxqu7XbfUtnbFyC37USyVcZuzUI29IFZ+mw0pDzQ9eHGB5XZIqB2noVKNYruki3EzreCp4Ou1W46Zufm+Z65yBqfzak147ezx4y/QF057Q9vcevwve2JtWx2/OUHaZrFRkbAoxDqUUgcBJ2EqefWQWMZU7dadvKYuxoxNTK3zfIRprqJkH5veoFkPng1aa62UKtJbpay/bhU4D/gDcLLW+iyEEEKITczI311/0JKX2k/qF3i7FpbU4lo6tjqiuKiGDMj7UU37XUXl2hGB5YGdAssGJ4yxUj6Wb9rmWIPl0IAfjcG3n8eLIBtD1UErpXyN5YVRVFaaTL6gLzpcOwdfVYzijM0yBVYthtiiUq0SN5+Hyx8ocrKeP03aZvGR5bzzLkJsGpRSf8JU9eYBj2PCYj34pd7isHqoszAVSDCVRAe4BfgEZrkNu8+5VmHGG7rJvm1JhbOUnC+LWbajNTnnGJLZWJVSeyTn2gYzk+vM//a+hRBCiA1V47Ez/6Q0PRP9lmWLvfjZLZd271ouBWpus4JsS6YYhOBG4LkESoGuoFw7doOMFShl6chPm0FXQUjNdajWbuppDg94nkwWUjZYCp3S6HK7l1KNNq5TTsUhla42td3MixlZrbAyDq1SOhvrsJMs/Sik03TMH07XmAewiuNzrRdNKm8f50h729DtH60fmiFts/jIkDGLYpOQrKv4iFJq6NvstgNmDOPewHfqhyaP9epg30lt4M0/Q/OBPwMHYoJimd5ZT89MJp65GzM20saExnZM11ILEyBfondsZAfwV0yQHQJMwITRz72rmxdCCCE2QLnP73ti7gv7PdJwyP5v2TZr1A7Abivdnr2x9HEvZENe659VcTZH2rFsbBVDVuG5kMpFkIfQtjJuDRWFfVpt7yUs6880pT8NzVmgTNAVUezUxPpMfdHRw2qxf1et3L4ax3Ns23W9rSon0K8lT5i34jVOyGvBHBZbFrN1zGtuEZtrG7q7gtZS5whiZyL5tIOtpG0WHykSFsWmYgTQiAlbb2UXYHfMbKQK0wU0wFT7ypjw5tMbGmPefNzicGBHzDqKQfJjJz+nJGs4nqu1HgmcmZwrnbxeAVNZPAQz7lEBJ2MCpI2ZlMcFbgY6lFKzlFI3vud3QwghhPiQadRAtG7UZmz+m3KUvYtlOZMXZoP5pCzVPjhV7SzmonTgxG1RR9l2bQtCH9eNcByFbcVao7uURVyfaq4CxGos1dK2VH0XdABBgGPb2JZNWDxNfWNmN6rl0PDy745k7cIzUlWt0kqnWRZWWU6BLFl2bfocHeUivrLItpzMAsLGfVvs5fHQYdmn8pa6cenN1gPlDuVdMkvlfnXFB/ZGCvE/JGMWxSZDKaX0u/gXXinVDCzDBDU/+f0G4ERMaGzqs3uMmTk1jwl8Hm8cb1jvmroAeAizVAfALVrracnrfRkTDvel9zvQBZiZUL3k74DesY4OvWMhSX7fGzgX+I7W+rV3ukchhBBiQ9Dy+V1V+02PvnPbPGNms7V89TK3Ia9qcZdP4+BloG+A8olQq0Ku8HrnnGocE6o2PFKEUQbPdsGNcVyrPz1+aDV4fhxzUGHNgkXd+oFV5L62zHOJwvQt4S+mTQNwjrr6y/3aFx2y6tlh+5LNKDqANAtYxVAKFY81gA4DhjVYmY5YN/eLnDUrS1EQRjZxANqKoHHvnF0711N8pz04RtpmsVGSyqLYZLxTUFRKDVBK/QDYlt6KoYtZ0uIUTDhrSp7zMcHRApZgKoL1YNedPFefRXU1Jsj9HhMgwazHWL+ua4E9eePEOPclrxFjvhN16Q2MdWHyaAHTMEtqXKCUerMJeIQQQogNzjsFxZYfXz+g389m/sDOsG2m4muvUiW/vMtVUVACdUqBjO1SKFCNNUHg2z4VK7YtbOYTqQIpx8VRoOgmjsrlmvbCuBiXrdLSOX7z2ddu4dz+aa9WzTeCndID6q8b/u4r167KDtiTrWLVZ9aC+4ijkIoXoaMqOC5Le4JquWKvWREShBZEQWhmBLGsLOUvWxGTU9nymdZ1P5K2WWyUJCyKTY4yhr/JU/2AcZgq4QnAC5iwNhLzWan/h97HBLd61XDSOud5GvgtZtxhNybsOcBlwIPJ87uve1mYEPoocDFwOrAQM0lOa/JcvWpZ79YaAs8CdwKLgOuBnYBL3t07IYQQQmwYxk0dp4Z+44j12mYd6362zbhCs8qXxg47oVzIPJ8b1BBnapXhVLC6q1rZcQBti3xiPxgeN6SbVBYy2UnEsWmxKwGo6Glq/s8DX79aGOh37PYZ23+h7Hrjnm694J+1xgejdvfFLfdwPvGGF3dSFq1KU1z4BP17Lt7rsLbTJ3+7fcHux7TdCLkWQIPtap2JAj/lExVsGBQQuXM2bw3v3H+Hrrn54Z23FI/t2aN5SSBts9goSTdUsclRSn0d+CRwGqYqOAETDMcCE7TWtyX7TQCewYSz+nhC6O1mCr1LaejkZ90vYJ7GjMUYggmh9ZlSv4uZUKdBa31o8no7A0djZmP9EXATMAvYAzPxjoupWHrJOW4GPgX8E7PURw34DXCN1vqG/+pNEkIIIT5AQ4465DgrquytveaTppSO6YxhzA1/mvy8mjFzHIpx+pfT7gJQM2ZOIKw9g+UEFO2ql+4uDLB7WKr6xVgpK+lzE+NgEVY0kdZ4yiKEbdNlJuQsvawYPzm71NrUXmEIHnkCHaZd5QQZThw5d+FuAPPuPO0IADXpvN0a3PBrgz43eF5lfupHo9PdNz18h5rlzc3uFwV6i4bmZrdSjcq1SjVpm73bwNo/k6r+83OTVp1070FxLYzj36Qsdc3yU86QtllsdGTpDLEpegoT3CYAW2PGC/4A2Af4tlJqW+BPwM70VvTsPsf3DYQKUznMJL+H9I51zCTHjaV3wpwwee0mzDjDFIBSysWEyqnJc3thupUekJzLSa4lnbx+GyZITgVatdZzk+v51H/31gghhBAfPGVnH401qbHDxm6rZ5NJoT57yJce+YHVyr55Xx0/4Ngbrjpw3NqbtnKtHWbhaog9PNtK13pwox7IZy0sBY4FWBbEJZxUDkcp0CF2rJZGsd/TbWeyWOX2iI/hoImKVWIvTIVevlUTac/ZHZQLMP7zp7ljh9tpb+pmU3HspjV3tn18+ezMPuj0AVFU9S1wKtVI1yq1NMQWBEshehly+1Vq6dZrH/y+tM1ioydhUWxSlFKfwoQ3D7gSuBwT9h4GfomZbfRUTBD7BL2TylSTY9zkVH0riplkW5zs5wPNybbR9AbNdPLcbpilMsrAbUqpz2Cql9tigmQEvAJMSX7vxARID7NGYx74IvBvYDFwz9vcbwMmFM/SWlfe/TslhBBCfDC+cdavP3XAoElj52W6vNDyr2yb8OTlXcuGqTVW+PBQrX/Zjj+oLV06fc5S/7XlOF8GzyLWMdWg1u1m6E5nHQILgoom4ynwYnByVCsQ+THVqEpjwW+r5ZvbAJzMx4hqFlkH3LxNuew3O95uW6xasKonrOVXWPnbGqZfeaiXH1pqKa3YdvuLn8+XBuWjuUv7LdSVUGGFURSzNqKhHxU8UEnbHH8VUv8GXuVt2+aZjcDmwCytp0nbLDZoEhbFpuZ7mMlljgaexKx3eBQmyNXHHnrApbxxwpkMvV1NVZ/nFCbQWcmPB+SS7auBEqb7aj1wLgcOB47ABMQFwDcxAbK+HqOrtf62UuowTDCsD7iPgYFa62yf67r2He43TW/QlAZJCCHEBqcYxacHWAPtwD42cqwn41Tt7q58OFQVrXRs+dtV0mUGFSrey5XaJd3BCA0+VG2F72csVYjjXKiJtSJG4QOeZaF1hNYWlmfh+A5BYNrmIFyNS4Ab5bDCmKKu0RksX5bOHr7QSh8xNqr6Q9K1l+a2qWNZlJ7UXhxVHr/g8Xj8nB7bL//ftxtG/Oqw6mqdD9FDko5GSds8Xdpm8ZEkYVFsas4GRmit7wHuUUplMSGuiqkCljDrMTbSu5Zi38lt6upVREVvkARYiwl3DiY0voBZd1El5xmHmTDHxYwxfBIzEc4UTOPh0Ntw3AIcyhuX69BKqcO01te/m5vVWq9RSt2vtY4AlFL7AwcBp2utu9/NOYQQQoj/pbxtn6HRI/5yzqmmbb74mryzemGVvFcNyU1wWFWqBm5jdzCg2TSRQWx7ruVl8iqNsjpKMTga4jCmUqvSklFUKxlLZ7UbQ83RS3GdUfjaQaUzBD0vYKuBTput4rKy4oI9LlD+SHLN7rx0quZlsi/xbNSB5VpkVPr2KR93bl9pVX4OlKjdYnn6UGpRGawmQIGtlZqptJ72riYC0XraKqVm3q/1tAhg/88ds39Yiw+ytHX6PXdcJm2z2KBIWBSbFK31/fXflVL7AftjKn1pYDAm0N2H6YIKbxyfWG8E6t1P+36LWA+WNUy30zh5vu/MavXuqGHy8wAwh97xjAuAiX32uwS4C/jjOtfyeczMp+/2nqM+fx6EGed4W3JuIYQQ4kN1xfnffL1t7n/HTw5gs4H7pBe4X86mbS+TWjm4NavUM8sG/w0yB0M7g3NlK8JidSVHRcUxVmSqitUymVwpWylVwGqBGJULVFwrpGrYtk1MjCKPH++DspQdQORgEwM6CslkQpakH5jjVed4g3zLL4UlujOLGW5NpFnZALpjwCUR+i4aOq+hxwViBUrB4t8Cx77be64HRYDQjw+Nw3gPL2fdgFmTWYgNhiydITZJSqkrgVsxs4zegqne/Q0zpvAT9K6jGPY5rF5JhN5AF/d53gJGYIJkF2Z8YV1X8niv1rpFa92CqWI6yblcYEbyuvW1GO/BdGWph9Ry8rifUuq3Sqn+69zTFKXU79/h1k8HjtNaS1AUQgixQZl+3ZFXfj185K9Ti4/tWIzVTcVqVF7VUbh5wdqGADgYaj60FDuqRB2lFGQicHp8LBQ2kMlbmWAk/YKhMR7Ebg/t6bJFLZgAaDy/h7h9LcpSWBY1HXTpbBqK6Zv1dUe16AuntRBTGqyrzpieitXSL0w7+c4Z/X0ryMdl0zaXontAX0slX1+Xo2xZNWDAYUrN/K1SM9/QNj99ySVTHv/Vb962bbZia4aXc46/46+XSVAUGxypLIpN1QpMFXA+sBITCsdixhdGwOOY7qOj+hyTYn31L1yKye/1amN9gpsgOXcjZjKag+sHaq2PUErlk+uYBpyLGb9QP+fK5HpsTL+bbHKuhcBXMeMZj+hzLZcDo5RSe6NsMwkAACAASURBVGHWjLxca31S34tNup5KUBRCCLHBiVErgljVdBDNH7bYWhlpwhXD0iOrtVQBogjsx2H15tWoMU+pAS8E302lyMaAgsglF6TQjrZQPhTLPdi2QyGTTEQXNuJlsMuEUVALGNbYCLWFn57/9y+ZTkag75p2RMP+l+etFlUb+a/V014c7NzQ2H+IayllvixWwUpsr4ClbIgrkMvGcRBAeUna6/lq1fc8YHr9npZ2q6ueb7OHt/3w/01ZFgSDxmW5fK9Tz35D25x0PZW2WWyQpLIoNmpKqb2UUl98r8dprc/CVOoOBfbEBK8lmDC2FDgfMxEO9FYY6zOjvn6aPn/n6Q2KcZ/tFnBxcu4uoKyUeiy59jsxIXAaJhQuwDQWv1BKjQa2S87ZiRkXWQKuwHQhjVk/vN6UnG8gJmDurZRKKaXkSyEhhBAfnF89sRe/euI9t82//9LVZ/288XPlOwbtdmhjpbznsO5KbsDK6mrQoaXspWmr63zI9gMHR9cq+ZAQP1RU0xpfAQ5LUn681CtpwggymQZSXgY/hkpZU1O6oZJB2xlFJn8RlJeMq73WXWjJlI845CuPAYzf+093DvYbKkv7D5r27H4TC+EWm92+cP7su9YuWPSLs7/91Ogjz5i4nWVX9sTPdEIhA2EJvMs2zy2+fUx2RTzKW53ue08TW+ObpgzxKyNb7KE9dsquxXpvbrw4xY0XS9ssNgoSFsXGbjwwXtW/8Xtv5mDWKzwas87ibzHVvJHA7fSubVikt9tp/XXq3U/7jgck2b+GqSj6yXGHYGZB/UeyXSfXuxum0lgCtgK+AFyF6R47HRirta6/vsZ0a70GOA8zW+vZ67z2HZjAWb+mfsBngb3+w/dHCCGE+E+MB8bzqyf+s7ZZ06a37D46dOwfrh466Le2UqvGtK4aWciWbofIhliH+VRPe7Zi41mgLUWUhpgYjcJ1IrSGIIJaBVQVVa1UcNNBjw782KrablfHoUNXzl8xwS7d9/QaO3xwQX5t7sA/Kh3rKctT5YMJKiV0uBU2Xwi32vyqs/b85K1xSk8f2ZEbFbUd/zhYAdgaGlZB+tpB/Ytn27X0pakwfX7fm/Hj2t+H58NFI7OZePpIj+0GtDRg2uYpHb87V9pmscFT5v9Fhdg4JSHI1Vr77/G4JszkNr/BTG4TYWZD9TFdVKv0VgqryT71NRTrXVXrAbK+nEYl2acR80VMCDwPbIMZk/hvYBfM5y6rlPo58DVMhfAYzNqOzwM9mDC6DzATaAEexVQ7S8DcN1szMalYbktvFfQ6TNfUPTBheJHWesJ7eZ+EEEKI98yERJfv7Pye2uax5/yjqW1Q2/5dwZLfbLWwlm4pqOiL+x024sy//y1K2eGKUkC1p9acNU2y5ROFHkpXiVVI5OTRcYS2bFJVnLaMHuzaam3YU86XOv0xeIWXmjyr2F0MG3EezWRKu4zsXuo2tK/69z+Cz++KBv3EtOyoz8z8+aJM9St4OkOcOQqbVw66L//83Hyxp8OP42J/a5/SSn8mxUoLbTwKqfMhnbTN66+Z+PhFP36mx9cTN2/MhP08y2qLuW54nsvbu9p2C6PgR6BeG3DMD7Z5n955Id53UlkUGzVt+EqpCUqp3ymlDnyrfZVSbp8K2/Tk51V6l8e4Ezge+DamEtiBCYfpZJ80bwyKfvJTrzJmMGMV658rB9gSU8EE2JneZTqgd23F04BFmKDYnhzvYqqIQ5Lz7p38rOGNk+70NTS5lnqI3TF5fA5T0VzzVu+NEEII8b75zs6a7+zsf/OFcyYcc9+Mq46/5Wv7vdWu5/38Svfcn1+pAA5/5pXphz1UnE5H5VUvFceZdMo65Zan7+sspY8PVPasxtqA5ZDtAMsHPGwnzrh2elyjzttuT0S6ahNXfULl2xZxWqdxtZftaSg0rYgDq6ojKDQ5XQVvpzULn1na2r4KD3ZSQaVCLa4C1Pq5k/vncmWizCnAvIZ2Rv3zY8X2Srpm0Vh0S+Gy35PRQ2j0MljsBcEumHWVgze7vxY71X9S/1TcP2XZ2gNb6Z0efR6iKJqF1oHqnQBPiA2ShEXxUTEGs4bhMDAVR6XUOKWUm/ydAXbHVA8BGjD/Yf8kZg3D+ZiuoGdjKnoZzDhEDxMO+657VEweveSnHkDLvHF2VDABs5Icv4jeMYgkr5nGrLN4A6YLbJo3dnWtJb9rTKjcVWv9pg1Scj/1NSEtTDegvwOPa60LwPVKqfukS6oQQogPQmxZYwLL2QzXHQrwlfPvV5877tJxh5x4jQtw3s+vzGQtvXvW0iMADpuzuHDESwuDh26OPjl4dHfT/WsGLyhX105sjEtnWz2dWy+lkkGTB9+DUkQUdQdVTTEAu8frIXbASntEsRc6JTVfLaPbiSsq9mOvGtM/aALHAstLR9bYlzQU/Yg5u1n3Zvd1bm4H2OabwdJJJ0fZ6dd6r4wuBn9vIbos1+im1zQqVVQRLCjWsq+kaiyvYVklDep80Ku1nvamX+RmXFsr5ViAqgVYpU42X72Gv3d0DHh8wLEXFNYWC9e/euFP74xvuFjaZrFBkrAoPiruBD6vtb48+Xs8ZsbQLZK/zwF2BTZPlpz4EXCM1rpLax0CX8eEOjDjCIdiqntgwlcOWIYJcB5mkpmA3qokmIDZQe/ENy9jxhBmMctzrE32q8+U+rHkvD/CBNY8pptpvW94f0x4BFNNdIDJb/MePJU89u1b3gSsSgLiDODjwDff5hxCCCHE+2LektY77YaGT/966uW/B+he/ur4KN3w1ahW3hzAG/T5c93GPXYtTLhn4u8fm9L/p0dW73pg155jpjx3btdtR/80DCI1naBYzZR6WBs5h4A7FOW7EEGMolTJhiu65q3scGPfTaUIvNsICYjjeECjbTU3ueBZ6b1fWNzhBTVUHGp6mM3qtQsOjOeOchz7xk5vVHntiI8zr//HWgHSyvpYQz+Vu+es2vlO1mpqcsh3zA0q0ZqI0rw0rbXxIzyXNKlG4tiKLIqOYs4Fb/UePLaaZ59YDIu60NUQsKvxxC38piGDyqv0DReplKtmNGbtPecsqX7pA/mHIsR7JDMxiY+EZCKYvt0s5wF/BV5VSm0GfAcT7F4CngGOxEw08zAm1A3ChD3o7crZhhlPmMcEwqGYgJgB9ku2WZiqYSF5rorpUmJj1ki8GvgMcCEm8FWBx5RSHZgwej9mSY0JwCuYbqs+ppr5ALAZZqKa04ELkmtdTxIGP1b/M3lcd1Kew4Ejtda/fss3UgghhHif3HvAtzS9vWmwHXdRFNb+atnO/B9fX95M2ekTarmB4ZCB819euVI/WcxYX3tmuBVfc/+Wj6CDlzerHDfar6r0zs8+wo3bfDJWTY6tdWcboZ1SQSGvXcdrgrF+QLh7rV+ms1TcL07HVhy51rM9y7pVKl+Y5KeC7gHbVmePqnVbUTUaW6xeParj2euAz+QKuQv7R53+stRWVT+Tf2zi5/7c1RnF9uj9uLd9rlpWc9m6uZtXtix5W75Cp4/rRW1dPODAZoSq2YufOt+leG4Bd96bvgE3XqxmvRZu3dPtsNVw1D7bx+RT2nG919ttgpjD24vR57Y4+ZRrP5B/KEK8R1JZFBs9pVRGKdXQd5vWOtBaP6O1rmitX8MEsXswk8jcTu8kMHtgZkPdJzm0iglxGmjFBEWNCXB9n/PoDWMFTGD0MIGyMdnv98CDmMBXTV6vBVNNrHc3naG1Pg4zNnE34ErgE1rrFuBJzFqPjZigeDzwLaXUvevc/6cxYyxvoLdRrlcXQ2B5MrbzSeAapdTRSbdcIYQQ4n/inDOfy5x95rNvaJv/cvH0yt8uPuqZv1w8vfK9w7KvoeMFxWLlH8+/uOOsmup3U7DUCYOlGbtnTesekd94dDlyP96dbuRf4yb5ZJrc5nJGF8L+remMlc+pWA/pUf7mqlD96sphzpbFBt3PtryIih05Gs/xCnuu8boHRg1esb87FCvV0Ewms8PCnmteHjjuwbubtm16Ps5UVSbrvDz/8JZ91zY3dueVl8o5lGdlTi7OnHbcqEH5Iaf8YsfdakpdOTI98BN6zfSW/J6dL8bNPSMbG3tatLfbWQ14x6+wdv+Wsi9+Q9vMjRdPA5YfMtG6oSEVdTYMqTG7W8VYaao1J1geZparQ0/Q42ec8uQt+YU3/7/Lv3X0Dy8/XtpmscGR2VDFRk8ptRMmsN2v3+ZfaKXUlzEzihYwwW0FZk3CCqa7Zj38FTHVQxsTuvpOGhMk26rJ9iA51seEQDDdVR/CLIVRD5cRppLfhQl/9Srk9zHVxKnA77XWP+5zvY9jZlIl2bcFMy5SAzmttVZKbYmpKJ6GWfpjNGZm1XXVxzl2Az8E/vBmM6oKIYQQ74fvnfvULpGlM+FU94ELd9zuLdvmL5136ddbg+wZhVF3NWpWq57ZA1ZUxq0duNnwzs6f333QwNWOY6Oz5Dobi66TzuSr2IUsrF1EtP3QJtsppSmsKgfdntbPeEU/cMNad7qDhu5Kw1YvrwzCzSfkllsOazxvyXl3zHokHDLgC3cMdd3yqHG6Gq2Kh3f90x7tfKFrh8r4RrvbDxY0v1JecuCcU73VZ+5u95syecxel1520vZb/LJ+vf1HXfp4R3dmm/75gJ7OqFrqaWhhcLVMQcfsksnrPxyu//GTX205Os9OY/tbZwCXrCxVt51fDKe5qTx+UZFJBTTkHbLlahD6sbabwp5rqiv+L0Zfc8bRv5a2WWxQpLIoPgrmYgLahUqpg95mvzn0LoORwnRbzWImu3mhz355emc7VZigVcMErQBTQSwkPzlMCKwHxRBTXZyMCYr12VJD4J+YsFqvUDZgusfuixmfuKVSql7hBDP+EUyYbUmCcJRc0x7Jc9cCpyVLYlwEDKZ3bGRfdnKdrYAlQVEIIcT/UiqyX/nYTkPXnDB2uwvnd7LPW+33XNl65V/lmp9qXp0qNeZTNznbdl/56mHZ7903vXV1nH6tgTz9fEWD6s5XUxV7ZMr28015Rg+1w8eCnupjak13pxeGCwaF3oDGXH4rr1/zgf96IbPHsrVupiWfW9wvT9SYCUbkM8Of2n705Miz3O1WxdUqYVwMHT9XLv2zhn/+Ur2muoPV393OHtgYzCudXFurdq6ufXHAC1c/vMXUvR94/fo7Gxo7R+zWiDU0Wz7gC6kWrQ/XDI8ihior9+qaPQDisnPjvNXOqRzy7c2eL9lXrNTZZifOtLdEkPU1TU5Etw8px7Ybchk3VWtoPa15vCtBUWyIZMyi2OhprTuUUhZmfcLXp6BOxgM8CqzRWn8KM9nM08AITODaDNM9dRSwHSYQxpjPhVkjylTyspiQWEier2DCl5c8F2HCYISpSMaYquVfgDPoDaVTgJ201s1KqWOS1z0CE+BuS/bp6XNr9d8zwBNKqZcx4xt/rbV+IHnuenrHWpJc89zkWr1kW99JeDRwtlLqb8BRwBVa6+Vv9/4KIYQQ79X/nb99x/xO6m1zub6930n/T23VGT/hRtbCe68+7ZDZtYbZ6PDJhnlDhhUZoFq9tUOX+82vEPWMKqQy491MuVYKdZydj1MdZ6nHo4y7RWe5/FoQZitKBU6TKtyVLkc7lxoqS8KSaom9dH74gdlVpZeias4JW+N0FPt+Nkxb8cNDCs/vtLL20Gtja9/Y6cWX13an3czEhu9OqXX7O5390G7Nt37qlWPuPPCFUa0zVxxB7qjWauXe28L0n1u0Orvj9RtzSGdVzPBGlR362itPHLbVyYsmVdXiWQz7dfHxGQ8AxF54dX33m9tSlS82hzqTcl5d3uHvOL/dcrewXbw0cbYhtoq1KmitO2vqTH3dz/62xk8fVYzUFTt8/Xhpm8UGQcKi+KjYGVPde2ad7SmS0KS1blNK/RzYExOqHMwsqX/ChMNUcswzmAlnspigFSX7F5PXqAfJmN5lKlLAT4ClwKeBitb6dDDrOyb7OfXX0Fpfljw3OXmdq5LrnKqU6q+1vl1rfYhSahUm+G0OTMSE1MFJEG7DVFSfUEpN0FrPUUodi+nWehMmGNavL+rzuw38AhNes0qpHTGzpN6ttf7se3zfhRBCiLdSb5v79t5BW3E6jlUBQF84rU198/LfPLJ81z0bWOZ20ppKEx5XrTT+Y+iYFSrt+KlZT3tMGrH5M3fYHRPCQiU7e2UmHpKPolFhg/tKsVbMdOvcmnxR5VtI5Tqy8bb5waq7cbj6d1NbagjO4AWtlUPGPtf96f52oz5x8bSvnwh8f7ufO+X+w1Ql8p3+cTYFcPDfx192MOP5/nVHTY5qKpMpjbyqMKRmDbH/efC3jqgNvuSaT94ePDttD6V+s8q2UoWH4m02mziwc+K4bcr2Xqm1g7/a71H1XNeatnI8bNnfv8UTrx7/twnn/nr6HK7+0/FYakKqYfVfo6qyB0WWZcdYlYqKgrRSllJWHARe4LoXvdplTRqQ1fbkI9kX2Ba4+5E/Im2z+NBIWBQblCQE3Q6s1lp/9T0cOgXYBVM1fBlenyF1+3X2czAVxNbk719gApSPmRwmD+yAWQNxUPJ3XX2yGyd5tDBdSuu+m5w7g5lpdXyyvRszo+nzwHeVUh/TWj+llLoRuA94FjM20QFOTN6Gz2it76N36QyP3jUcv4SZ1TWDqU6Ow4x5HKS1Xg2sTrrjngVMonf8ZV0GU2F9AhMqr8MEyTdMRCCEEEIAXP3QZapSKd+ulFp9zH4zvvoeDl2vbV77i9M0sHXfnaZNnOW8uiwMdtuqvfW19m7umz/yepRnLVqQ9gflrM5PNWzeMDjO7XBA2Vq0cmWtfzqTyceBzWbVZj67piU/wW/Uf2x7zh7SPFDPSbdby/2OaqdV1XZ7VfXzGpelH/tXLbvlzplAB/qaL70y3tKqrZAdUR4bNnqL1rz0/POZFWf85LhF2xyzatyLw1bFt4zQ37ivevh3X3CceEL5+b2aFxR3PFE3OOqbX37w47+5do8XoTkdxZCBVE+civWwbrRKfemx6tKXBnsNmXzIKN1eGBd4PVOBQXzl8BXAitarLp7qNauz5rUxqbpGZXac4Nl5ImYvt3jx1XRqr62LTw4r6H+HmpswQ1Rk7UXxoZOwKDY0hwJ7ASilLtVaP/YujzsDmKC1fqulJe4EfgaMxPzHN8R0La3Q201zAGY9xAOBV4Ex9E5i8xqmuuclxzRgAmaa9ddaBBMi/wmEWuvhSqktMLOb3gvcrZT6BKbaNx74G9AO/Cq5rgWYJTXALAEyJnnNeqDdLpncZhAmbN4J3JXc51zgcswak6OT6x6eXHf9Gi3gzORaLtZa7/qO764QQohNVrlcPtS2rb1A8ceHL7/wyClHP/cuDz0DGDGmiUVv9uQ1j3zszq1Gth2I/tS4QV4n+XQlGuUu8XMM6yk0tPf7ZPsYy+50BnREXfd2rC3tsWbtqpcH9yuMLAe1eHGc84evVfNHl1LjmrC9VqimtNMwspSt7hkOT69y/fjJruWWm62hcplMj18ig1cJIv2gja6c/u9DBp8z9dEtpiw966n5+YG3zR068W63J/hEZ1pHLdXMcMvSd9x37fZFVdz7h6FTCQePY0HD3noWgKtmzWvw+o8p6oGVZWvs0ja6VvI70tvNKR6ityz8ZdCgtHuf9jrudPPxXQAp55JFRPrXr13oHt0TpYd6qmdeymOop3zP9j1rmAsLc9jL45bTt8sGMxtdveyRPyJts9ggyGyoYoOjlHoVU8Ubr7XueZv9NgO21Frf8g7nuwfz7WaACWKbY7pwdmMCFZgupmnMwva7YIJZvYJo07uWYjX5vX/ynO6zj0qeJxmXeDOm0rgvvWMZ61XMVHI9n9ZaP5BUVEuYcFnoc+3dmC6vszEzp76UXN8jWuvVSilVnwFWKbUIE3irwE8xS22AmUX1zRSBwVrr8O3ePyGEEOKyey6c57pu2nHccUdOOfotJ2IZduwtm9n+qnGLfnfMnW97vj/s/li2f2XbHkL/4/2rawelrSHzOr22fW4+rCfS6VEF12fyimHlBsf1XlELnqowapf+hH5u5Qp3VG14NDS1uZMPK93bRMMLD9de8n9bu6VjSvPBA8bHrXrzak53Rb6OB3q2jmPVs3Kev3zZS9Ely09rufqw2TcrrNqrK5fuu6qVzMRnb4gr2QHWq4M+XhviDUw3F62gn3Y/cdTDWz1x4Q5z1EPe8lIt8sPbn9z/9bZ5z1EX9mRTtrOkY+3sPXYKvr/j/QvnuFV2Ah75sv7T6hv/73p1yNmHaTBBsSmbH1CsBNW5Pwt/6qOOH4CDpbyWVaykn86iHItVKuI1K8/gWrV7qyHhUA75trTNYoMgYVFstJRSKzHLUOyrtX6kz/b+QFVr3dMnhNWXv1iA6boZ0rushcJUHBsxYdGldx1Gm97lMjxgNaZLqepzjouB6Zhw+bDW+qBkHcOtMd1u5tPbhTWmd/mLdsz4xjOAB7TWf+hzD6cCB2CW9hiYHJcHbgTuxqzfeDrwNaCote6fHHNucl0jtNadSqmXkvt9DtO9tq/ntda7vOs3XAghhHgHW392xuoYGrxcw5Rnrz3/qdefmP5wfyKnyB92rdxw2g+3by+l/q0aO6LKTn+3d+tXXjA8lx510XMTwj/O28Vtctt0ykX11JpGsjzbaJeGPoUK3UyqGg75x/0Wo/exm73BQS1u163Z/l7nqtXLdn/81sGBV1DLt54arhhecDXBxVM6Bn0dLHu12/PwGfdOOmjSUXdnQuKtx1TTL45bES5dma5kJ6wMdeB4cdV2y62Ro7oprx40JPOZZcPuPsNW9u3nXPKjG+u38MXCP071rNQBzzU8NNCppQdaW0XxFhk7v/2/n75uQCcPAv/4526fPmtws/21tR21nksf+fKAZnXJqYHbcO5WA2P/sV8UR3HItzv59RUvEbujVlV6nnOK9g7dg2M0mvkrLQqp8lM7n37KlA/jn50Qb0a6oYqNWb1r6L/qG5LJZI7ETPxyfbI5wgS+fydBbgGmMuhjJqQZjgl0YMJhPQTWxwi69K5T2IipFtbXYAQ4Qmvdmrz+wUqpozDhsYAJoS5wT3I9F2DGQoLpyvpFYH9gZ6XUI5ixi38Hzk5e4xda6zOUUs9gqqB/xVQWL8MExhjTNRWt9U+UUvcCPwD+opTaV2u9ZXJdXfQG4zr5/AshhHhf5aKO+ZHyRvZfuPTp1zd++SkX1zoSK1wGXH/H3OC5KUOcUIdOMDLc8t87fXzmQQf936mL53bkWipE/pB8aVVrqjzQCmvzVwfD1ORcZNVWZcMpa7ZwH9oiGz9UqNCVXeMOqhJ4qQ48p6tlJ/Db/Jr1tGrXpFKEYXj4Nx7YqgXgwcnzD35o8ryjvhoO/foL0fKGLzhjR85hrTsg23HPykx0fZ5BF2zmpQZ5GvqhcotXvXikMzozNYW34xaf/d6LqWJ5fJhL3fH/2fvu+Cqq9P3nnXZ7bm6Sm57Qu3RBRayIqKy9grr2rujaV3exrLp2XHXdta66CNZduyvYxYaoIL1JgIT0dvud9v7+ODMkAVS2Kt/ffT6ffHJn5syZM2du8s5znrccopwwM0/1yPHOcfe+HDv4+kNmPLBMr8/Qyg7/S1dj0uEhwp8GrDG+KR5Ntm5xEwC088V3Nj/4p/kdr/W+ZeUdxc/2vqXjUN+Sc4Rtfu5PnY0NMofSNmUsA2U+Cz5PjwznOeTwkyP3spjDLgciKoBQymYx8yxnnweAz1HT3odTo9CJ7cuDiOHLOkpjHgQRexHARgBXO13b6FL8ohD1CoshyOa9ECS0DKI8R9g5hwFYjqp3BYT658YGptGlUq5wjkUg3D/d0htnAtji7J8LkaxmBgQBDAOoJ6LhEIT2WQBPoCsuMp+Z3fqOcO73G0dZLYcg0qude3bdZHVn7vIBvLKTU55DDjnkkEMOP4h+599fYNrS4lTlhFnND503CwCefmp3j2WR74zZX3bg7A/fB1E7ADz5wo2Mccj723m/Pdfu7Ft/zOO/8hiyElJYkXtbDS+0JlZtGjuw6OqiAgPZLUVWQm7hQrukjdmObvFxu+lRCjXArAtkbh7S0H7hkMa2sjXRXrF+Aw7KG6+n8JVps0XAw5MWX23HG6/ox1WhtpBOHr+CfdqrUn5VUys5bDaY1rLSSDjok+0CWjkvEc/vpWjeQWpILzs5GWutKwxJ4TFNW2anqbPfwX185y3x1sYpXRaUS+X6yYUvDm9LFZc02PzCElQ+A8CbYdCGNkX6/dsn9rDN0Ysv+Kb1z0uiqqGU1xdsKO6L0Y38wgMUb5HkVHOE5HRc10Ly4qq+RqHXR8/uYHpzyOEnQ84NNYddDo5rZV8Ahhvf5xSzr4IgXDp3+2ITUSEEOaqBKJvxIERmNkAQO7cEhpvhdCMzDyaitLPvCmb+U7eYQEAQQDemsRFdaqELC10ZSN+HiCF8DILEuXBjGM8EcDiAI539qyBcR6+AUBqPda6rQxBMA6Ju1ckAPmFmo1ufIKIIRLxiFCJd+Q3OOeUQRPUaZ/tzZl677fzmkEMOOeSQwz+L3ufevi6QV1HRmU7qtX88PwwAc54aNVmWrNKs7Xn+V8tn6K13/rLrpXMcCtNqOuwzfBvPu3L6Qetqg/et2dy3ejA0Qks+qUVr7ZidR/WtQR7QXCvtEa7feNMLTw+m82anYWe5IO2/onX2tD+d3GtmbaxYLow2x9A/OsksSmWUcIq4vNduja9H2kqlrI0K04eslkRh0mv1SxTIy6gDX3DNB/uWDLizVFL+Eu+dLUl+MBNq/QLQ0C/sLCxpvbby3NKg/9DOps6jGvkLvuCs5WurRzZWLX973PnrXzh474fXGye0GJ5I0spmNpnsBVgHrES5qp28xbAXME/rEXO4etiKSN7eGy4qGxaPtnvbvo2ZmRt8ppRONASrPYFsomKQvdU24/hLcrY5h58NcspiDrsiLgbwPIDZkM9YLAAAIABJREFU3fYFITKHVgG4yFEfv4HIEnoDRGmJdghSGN2mP1cJdOMQK4moHkIZ9AG4hYhmoquMRhoibnCw8xOBIHKAIHN1EAokIMjkDRCxkF5nexOEC2rA2TYhVM6DIcjc3wDsDkHuvM61joAgm1dA/N0WAHjTuXYPMHM7EX0IUTtxJESM5RsQiuJhAK5j5v7bT2sOOeSQQw45/GvQOHVOvLPt+SyU29x9a60B/gI01zxTe1LVgPyWGXe9MTV0wquXLOv19SFvvHL3Xjf4ZZr6t02l7ZVqLe01wYg+/U45eq8dhIp0BMvT7VKjUQlSVaovUs1YtLpy2gm/r48yZ9qMtHdqx8ibTxz32MzeZcOD2da10Dal097ml+csHrbfQQtHhnqNr1sdGd7eW1+XbKZNJaza7bzhED3aKyt7MIBle1OBOXOjZi+qavB4vF6wHMvbZKbinvwt70fMxne42nw+8+3Q5S9WqkUHT4jIiWIj/oK1QRtZMfDlAe19+2iVm3q9siKROToLfgygKwBS+8NTsFeg6M2/djTmbzs/g5YPbccDS74E83AmHpk0zaKA3/9S3zHpZRC2+Wocf8ng/+UzyyGHnUGOLOawS8CJRRwAYDMzv49uhI+IigE8CZF85mAIIrUXxD/fgwAsg4gzLIZQ8+oBLAUwFl0uqRKE62ccgnDmQ6zwWQD8zg8giN0DAKZBJJ6xIP6OZIgspA0QZPY5COLIAOYAWOCMy4Co8ahCZEQFhGvpUcxc5GY3dVxH9wLwOroyte6HLsXSJacFRNTA27gIMPPHAD525uctZn7L+fwVBMn8l0BE5wAYysy/+lf7yCGHHHLI4f8G9n/pEdUkDFAYm9c8enMP23zZE+cOW9cy6Ml8b0UTQAeXRZrDetI34bn2xBHZgn/sMywvtWpjSs+2S2OKk1bE2jvyXn3UV7O0OBgYWyL3yXtZ9cibDZKsQGOHGkynQnK43KJ0/vTGPsZGtcMamwoEWSrEykAaRTzAXNa84YFzBtw6rZOt4i+yi6wBZoUa8fqk0XJJZlRHcROTfHkWyecMhuazDZzZWj1nqZFeYKpSfmRD0PBFby1sbU9qsi+gxUoqYGpXP3ZuZXRq6OVoUXZCLXnOf5gbPiime8+++cZ0c79pK2Otba1gbwHJE7xs2xVQpUEeTa+2TEzAqsIRNKPhW76/p/veJdPeBvB2AQA8+se3Cs656C0ASM+9f4lPoVf/1efQJ3j/OQCGbkjMyNnmHP7jkH68SQ45/CzgBVAJQeK2RTOEu+U8Zt4IkRzmYQiSJQE42/kcA9DCzH0B3AehALpE0a2Z2D0BjOrsjznbbjmNSyCIqetm+la384sB/BUiA6urGlZC1I/MOOe4CmUHBHlVAPytexkM5/fTEMpm2Dlvd6dPw7kfADgGwBwiutUhmNuhG1F8A8BQiAyq/yquAXAGEQ35N/rIIYcccsjh/wAY8NtAlU7I2/bYn5buuyLq71gRlNPzPr/+vI36M2tvbFpj/PlVtS7+TfmXnrV/Of7spc+eFE+3tyayyXTrgYM39J3w6qD7ViTiscd8GTlukzQ271P99/7XvFKqzH7j6y2oaynmliJWuW+n/l0gmYgZCVTEzUyJv5dSOuqIi5oo1b5Qa1YObYpC1o1FyFh6pRT2Js1sWVO29SkAetiWuAx+M4pg5QGx4hP6peRMNXxyFfKDRQUzrLqWso4txm52IHqqtqiz5o21h9eQ59MjGABK92/iTGzI6QF/nndMXkFRVFLkYd78vUKSbCskG0Bnoi7YbKfIPqaGhj0z0vvwracf/uoObbNLFBc+dsc77UZmYGtW/5dts2bq14Q5c0bv4P0525zDfxw5ZTGHXQJOGYyPAWSI6C8QmU0fdY4xhOrm4i4IInY3gHMg3D0DEMTMR0SPQ9RdzIcgigkIUqZBqIVbL+vsd//RKxCk0AeRPdUtRVEIoSQeD0EYvRAKYBZd6qFbhmMRBGELOMfYaUsQMZW9AMDJqHoVBKntRFe5DwWCLLoEchqEQsoALkUXEd0RVkLEbIaJaBIzv/sDbb8PMwCMYeaV/8K5OeSQQw45/B/Ch8ee27n/S498BCDTWef9C5v0aX6v9KMAkJ11CgOn7O22tQeW3r1mWbb5juSYO5aXLj8rX+dAgWz4LS5hq5k8D798xOMd/Xc/MJTQQgXwSHG/njjNt8Qnq7I2rP6r0qWlfVGwvpWMos1cMiDlawml0xWv5cPDXq3V49H/kbcm8G1J6+IDksOGy4aCUWbEKtGDz3Uifjwh4A1TwFfA3rQV4Gw6qXtNmPBAk3wgeTmaVuXBW/1hMhAAqT6/XsShrGqxbrAcN9YAGLB/6B3K2PYZAF82KmA9ACvVOYw8VbVGytzfX6isSjQZpUVKmGSWN3PFMQVKaMKwYsJem7M/aJsNSVqcosReAQ/CeOGBSTj+kn/aNmcU7bc60K8mMSNnm3P4jyOX4CaHXQaOK+opEG6gbcxcvYM2ZQDWQNQyVCGIXQsEEQxBEEYbXe6j7oJJE7qS12TQRfgsCNVydwii+AlEch0JgmyGnHPmQhA3dq7dAkH8XFXSXXU1mTlERClnm51+XVL5awCfQSiRK5j5CaeeZBrAR85+wxnHeIh4xHkAxgHoZOZePzKHTc6Yr2TmP/5Q258KjkI6EEIFbv2px5NDDjnkkMMP4OGb1NjQ109RRyx6UM+gOVzCvbdtcsxV15Y1GnlrNWRi16yf7FtVvsG70LO0JRls0Kg8Eiq1M/Y+hYa9cpNqnfnN4XKwo786K7Ic8zr/3nToqKLi8Jet+MRbnh7zXdjXCFgdv1Cs2CeRDSUh/6A9NrTo7+S1f/JZVO470TdC2r22qHCQFPFnNQmBNL/aG6VHqLDRjmSCYbcOQWmvGBKxDmRkH7wBDR4YYLPXl+HQlWWL0qrNfGS0jPv6gnp9YpMnbZhQQsFf6/78Lx/9bv0xK2PxFXf3HvGXleHPmmo7uPMo77iVZGoHyWTpR3z7/iJZxqgaSyoKSOl5v6xS9hgWUtvPXzrtB20zXprZBMgh2AWX4PhLHvtvPap/B3TZXNc2N/J90zp+6vHk8L9DTlnMYVeCH6LMxDsQSWhuh0jwsh7AuQAuBDAfgnytA/Cus381RLIXcj7LEIRPgSCDErqIogWRCKfUaSdDKHeAIJwJCPXR6+yzIUpt7O5sEwRBLUZXXcPuK4pEInX4lxBqoZvl1FUxL3d+XwXgaYdUun0c7vTRBFE2o9DZnryT8wdn7KXMXO8Mpg+ABmZO/xN9/LcRBLA3hBL62U88lhxyyCGHHH4ITP68r05sqLPT8+tWD6xMNfxqlqzK/qfM1tpO2X9aMadO+/uGyZ8GNjdlwwqt7Sxr/XA9d5zrL4itjlanJ6xNRGjllvqvsk2DwulEVd/agu/UqlTYinznkaoG7Vvc1FaIVeX11rDGvIRZHPX1SRmyvsqWy7QBA1UjjrBsasfVtjefqZy89wvBWm1lZDMqWlN2f3toUx4wugNZGFoWGgx/VA+rDGYJEknI+EsyRYh7TTCYGod1tqu6uXC4P9A7GvQXZyw9lZEDbBkJXxa4vD1V6zm1V9U1k774+OmT172T3H+4TgeUkPe3Hy4s8IcL4U+mmjcj4TfN8/552ywFSmCiFMdfUg8ATWc/1YckqSH6yKk/J9scBrAvxAL6Fz/xWHL4HyIXs5jDTw4iGrAz7Zi5E0JdkwEMA3AqgCnoqmfYD0J19EOUnLjC2XcBRJzhCgiCVu20Z3TVH7ScfpZCZDJlCCLoQneOuxlK3ZqMrotqmdO/DvF35bqvBiBiKi2nnQShIo4HUAtgCjOXAHgNwAYIpTAE4B6IZDu202fW6dOCSJQziogW78y8EZFMRIVuTGQ3ohiFKL+x/072EyWi/3rwPDPHIRL7fP3fvlYOOeSQQw47xl77jNsp24zzZ3bCl/po3bzD/bx272Eq+09UiKYQKJu1FXUFV4+QlrY8kOzM8+dH5THPVi67oiDa3G9coOOCkW3mJVvq6laYeQN3a27vXR2WQurTVh7q2v3SgSXl1DtO1tpMR0pehW/LgpXRkCLbXkuxK1tLUZr1wZNgPeDLS/UecGif3axC7dTN1VawczU30Fqq4qA3H95IPlkryM7oiqVJFjRPKxLkhRJoY7sjLesWjCyCGU3yKKrnurKR4w8r7r0uYPGU6i+LK8o83teCIe8GmfSikOYLe7OZe1YMrIpPiuTbRfWF+h6dY/Xf9hnriyfT1lNGa6GKvFEKPbxTi5yzabo8m6YXzqbphKOvYRx/jUsUo5KmnEyEfXamnzOvnjXkzKtn/fdts1ATX4HIrJ7D/0fIuaHm8JOCiI6CUNHuYuaXd/KcUgh3zfsgXEEXQxC1SnSVv1AgiFk/iJWwTRBlNDSIuohF6JntFMzsJ6JFAIagK2GN+wdiQxDJUc72MghX0BMgVtskiDjGaqf/CggV8kYAv4AgZG49R5dgxiDcU5dDkF8457hKpEsw3UQ6DOBxiCyv5QB+zcz37cR8VUCoo590d+t03D1HQKi1LfgR10+nHMdoCBL+JjPX/di1c8ghhxxy2PWw1z7jTrBhXypBuuWzj798a2fO+fjGG0vB9q8VxbyPPGb+YfEJixW/seJc7dlKtphe+HK0lV+UlSMBVRrfe22/tmJpXxDWf70y/1016NP2+sbbeGbD8UUbtRrD0xSyVo1o9r9Sug5v3nqtf/Yhj3zFWmYwWWX6G96w158Gn1tXhW+1NrskLK8cjOphHPdSk92yOGl898Vu6m4n+LRAWCKSlCzVqdBK6tDUCIQqkkjGK+C71LD4nDwzuFfGY1smG1IwqxEYSCrJWFAO5rWbHctlxTvML2lY1rk50eGzg72VIhRR2k6ZceasT26JtsMr+bmjJjP78o2fHJxCUUmpjCvfMaf9aJjHbJru2uaPT+E57e7+xLVzKNNpj7ANa8uiPgtaWq36gRbMltNn/mOHtvmky2Z9mCB1dImmn5dvez+6564Lc7Y5h/8ocmQxh58Ujqp4HoCH3QLxRHQRgCwz77TfvpMQ5n4IIvYGgD9DqGb5EMSuGILYuRlPXcLmqodPADgUgoQRBFlUneOSs8/97JJRHV11FaPOdhAiZtEttbEtUhAqYQSCGPrRRUzd8UjdfmchSKrltJsC4Vo7BcAhAAxmDv/AvESdMQLC3dT6nnZ+ACcCWM/MHzn7pgB4AaJcyTAimgaRxOcZALcDeP0/VULDqWsJZi77sbY7OHcvCIP7x21LiOSQQw455PDPY699xo+wYP9ShnTfZx8vrAUA/GHhRQCyuHT8Ttvm0rufv/BC7Zk7JQJZvaw32jukP59ftOXk6iI1/9pN/ZftvXRU2fqVkePeq1rrubt1OFXX7E11aMA7coPVEgR9UrZ2RrSleuaB+Up0g9SE8StGG58Xd6qLMlvsW9b3kr8oVbFnqsLui7CUgGkEIat+KLoGTWtCU10Nm9G+FNJLEA4yEIvL8bzG4hoUtlUinA4jJukwYaERqVRvuyDrA0VMMuMyS4Gv25t1j9/nHaAFkNZa7YwnJUXbKmyZFak9W5td5WtfmpGbMkXkZ3vzhinvSRvOLW4qP6bQl9gnmd6UPYX/uF0dZBeTH/pbWcnaZumQ+z4EgIZTeM4ObfNTNx8aUslzvMXGmlNnvrEAAG4+45EpIVl5Ic7GypmPn7fHWdfcOy2j44hMPb0YKqU7wrL04h/umXHtP/O8vw/n0IWNFkz7CX7kn7fNHz+1D4DhAP7E+5yWs827MHIxizn8pHAI4pXb7D4LIsnMThskZn4CgvBtBRFdBaEE/hJCPdO6HdYBnATgWWf/2ejKSrq1CwhVL+20kdCl9sHZzqCLYKoQhDUDkTXUgy4XVzepDkEokYAggicBeBBCibQhiKjf6ZudPjdCqK+3ALgJgtTlu307CmElM2/ewdRMgyDeD+945gSYOeWU1ujstvtK5/qVTpu5RPQsgK8gXGUX/VCf/yS2S7v+T2AYhBochHDdzSGHHHLI4d/AZx8v/BbdbfMfFhKEbU7hn7DNDVee8BBwwkPd9y179sir6lI8yrSVX3qMzVvGh3ppabsAjSMXYUvBCn3hB5tOWjb0gGeLjah2wcrDH1wVarYUPUb5HRXIZgm7xcM0IBOV9ZCR6a1/rlZnq6S43GYqSlACPGhFUioEMiEpWGEUfYP17dVqlVF8j47MIGIcZNmSR2KJLMmyFEi2BbZ65eeTp4PDsBmSnkmrijatyOd7rDNjFNqGbkuKZYezEa/sVSVOMfu1YnUYFa1uaNz8O7YzM00qmHmwWjp9fcQoVChFCqk8ruRF2i+xtvLu5K972Oaxc9+jCGFaw8Di5Ck85wdt82kz34o/efMhr8tQumyzxDersqZ6TWMgADx+x+VzieY86/HHvxq3dza4xyhp6c4+nx+DAgopUP9VojcIwj7nbPMujlzMYg4/R5wF4KIfakBEPiIKENEZRNRMRH/aQbMJEKTqMwiy5xK9Dmf/PRBkCBBETYYgawBwGwTpA0SsINBFHo1u2ya6VMdmiFi7C9GzZmMDxMKMii732AyEuvio8zPG2V6DnoRVAjAVwEQAHwLYE8LVNeHcx+EAZgN4n4iGYXvMg0j00wNENImIZnTfx8xNzJwlgXJmngzgVfSsbfkphGvvS8z8zA6u1/0ag4io6EfauPcaxDapxZ1xXEhE3u3P7IHHAVzrxDru6BqnE9FLRPS9CmwOOeSQQw4/gEvHM4RtvuSHmk2ffpHvgsfOCCw74ZVrlxz+SXPd1K9+v22bhMR72xzKP7F28/zhfWzNHrDQWqHmYUvWbp3UeUj+bmeveLBv/nPq4MJFIIVtX8IjlzbI1v6WjaaCNRcVxjW9ysqDx1viHZAdB0lLUn6IFCCp+6GiAHnUAMP0QEZpJmhXJK1mAO/pMPf3KwFv/8YhFEqEEESwrhAhpQxhVe/QFQss6XY6I7FaJJP30cH+krv2iFSO0TxWxLB4Y8qyaS3XIavqpJEmLdFqj778ouR4gBZIincvRfVUV/tCncgv7/BFxxw+IbZmdkL2vn+N/5YetvmraQeyxPwm2Txv27lZOfm1SSsnv97DNp8+8x9Np858PXvK6OvpgANuK5/5+Hl7tOipV9eEPQVumwFlm5YNjrb2++Jd/aW777z4B23zFLpp0GS66Qdt81+kUwgA+iEb6Idsz7Ifo24ic9TMCzHqph3Wj+wGYZv3OW2Htrn64ItPrzjowpeqDr4kZ5t/5sgpi7sYiCifmXeYspiITgNwK4A7mPmB/+3I/nNg5m9+6LhDMOZAZCC1IUjGwU6JiTUA+kMUtHdj/V4B8CsIstgKQX6Szo/7z851/XTrGO6BLtdQE10lN2QI8tfH+Rzsdn4U2xNQCV2q4VQAb6ErMY4JkRjndxBZ006AyOY6EEJdjEMoeF4AdzJzH4gMqZshYi4nMfMiIhru3FPtDuZy1fdM44sAvET0OjN/t82xyQCmENH9zHwisLUkyREABkMQ718QUTVEPOiBzPyV084HUedyEYSrahpAHyJqgSDd7wL4jpkvI6KjAcwhIt2ZizsB3NFtHJ9CKMPXQhDkHnCJpuN6+kOrlpMhYi2HOMTzbIjkSAcC6PPPuDvnkEMOOewII04ozv/2+aYd2uZzL5JPy9r2rR5JuuORP1q7rG3GpeN/0DbfdtuR5C0snAPWd19T0MDFzUVBLbD62Ejznmds+HjfNanf/6G/lZ94+s3pXtmvAUE180zAyozwmhnjQGVlq51NRf467Jnkx1+MS1YkT6DGwsVYesiDUufS3ey9Vx8qMyWN4qJXzikvtrRAYz+krd5moxq1N3tSdj+yZR/8bRqowoQkl0INWraGfu0jJQZHk+i4PYYmNV+vAmdtm2xJSqfT1Vnbtlu88anlpv8tkmVeb+iQkikzXCSXeWDc2Qn78JDeNh2UeXVJqrVXf+rjNWUjvhE1ofx0yn/cJyV3jGo4pG/9iO+efFf+oi4seYoOxz774qvokg+9n4zzmHpeVvFuZ5vnXXjsDm0zk/Eik+RddfAbrw+eN7WHbd4cCR4i2fZBBxz4+/vff+/XJwLAxjNml81XPz+iJFRWbnpI611i7n/kjOs/SCd59JeeAfntD53BAHDKPjf4LAv3ZjPWFxmotxM4CaAfnfFwB0hRIy1tCyJMK9e/duVlv6VTz9S8gT9er/xSr5JlI2vzXehmmzPILmTYu2VgXxEUi8c9cOSkacI2vzv3B22zZfNkJmU0wRpyZujMqAT7xL0TmVP/eun+B4Koz3uzzsvZ5p8JcsriLgQiug3AYiI6+HualEOQi6HbnDeZiNYTUYyIxv23x/nvgoiWEFGCiFqd7aOJqIaILgG2koPVEOoaQ2QSfRzCvXMchEJ3IoSbJwOYDkHwNIjYRQ1dKqGL7plPVQAHQ6h/btZTzdmfwvbExc2cShD++W7NRFeZdMtzvAZBRMlp4/YnQbhRfgmRtGcFujKpmhAusw8SUZyIvnLuT4OIzQQz/xnCPXUZEZ1CRFcSUYMzb0cQ0Xb/zAF87IzvAyKq3ObYagALALhZU/0Q8Zx3QxDXOgjDcZAzjr8513WJdxGAQgBL0JVeW3XuczKA0519h3S7pheCOHfHV868rt3B+AHhdjxr251EdDwRfUFE+zq7TgEwnZk/B/BbAEdDuFddDuAKIiojoggR3UxEI7/nWjnkkEMOO8SQYyK3Waa1ePhxhTu2zcQDLCAEcA/bXDmZJldOpvWVkylWOfnnb5unTjxuydSJxyWmTjyuGQCI5h5NNLeGaO4lAHDdda+wadmry1PBZKVmWRV27O8lF97wpG56wz7dN04OJiKxQMvxoc6CzJZshpd5+l5a2xZsjcR39xYaJcVqukwtSlTTwLw8qdIglHcMwn62zUcNXo+FvkbM4hXq31oGjE20axnLn7QNmGz5NG1zQFWTdjgRQqTUBhCHCQsSVIBTctI2NJMI6vA8lHsIQIeUyUAB0kbGsiVIhSn/a6YHBhQmn6F5JdWnqjBSCiRZttqHGP7Ax5aG+/KsshVRzUs++AKG5jXJYr1qnvnCoqoP4g3rly0KqXY4r6JRW2Z88w8AuDdz/X3anoN/VzCsctmsMU+eeMOEW353/Z43Nty0121HTznxjCMOOvGs7WxzvDP9cczXlO6sqP1w2TlzethmCVhhS9ICxbLrAWDjmc+EmPELIxW8++AR7A2Z9oZHbnr/j6NLNu0BWdd+VbBg7fmDbjnl6L1vI9uClwgRTZPyJJhLZFhfOt0SmaaUVdV9k171dABoh7WHYTM8lmlnrawPoFD3cViwFzHYhnhX6YEjJ00jT9DzhCfovXvbY3fdfNbxD9x61hd33XzWvgCQKO19ikLW4Zvm/fFzychcFtH1oz8K+s4nHdcqab5syoUPh04YflTZccOOvPnE3Y7J2eafEDllcdfCBojYrueIyK0BmIV4EX+KmS8moicA7EtEt0IQjSMg/MbdF/nTIEjJzxnVEPemOQlakhAKlUu+wMzXEtE9EIrhX5l5paManQNgLkSdvlIIIueBIFhwtmMQKperAAKC0DEEoXFdRV1l0V1UcVVMFxlsnzCnOw50xtIHXclwXGJpOtsusYw615mHrmyssjNGCyIDqQLxLF1X2FoiWuGMKQZB0u6CyPwaRlfpkGaIrKcAACKa6YxNdsY/A8DV3UprbISIk3TnOkVE70MkuzkUIvvsERAkrgKCgP/R6evvAM5xXELv6TYXkyDcaBki9hIQrsZTIYilzsyXdZ88Zr4YwMU7mFc4yXaOxY7jJkdAfId2A/CRs7jwuXNsBkQM690Q9Tr7M3M9EY2HKGeyCoLk5pBDDjnsFBRJ3tDe2RoB8Fzl5K222U2S9ljtfL7s7EuUB95cZe07/jD11gbDhC3+h/bDrmmbvVMnHhcFjt3ONj/90JPXNl30zJ/YxnlU0frXgj3XrqxbVuD19V1zzsY9X3mhft+lexgdarkeCzGptse7dr+iDglo9qY4q62LFeZJvn33/LuWbpgCG2G0d/Q1I6FN/HnnSulEjFSqQ4oUKej01iSDKPWulHpneqFv2muX2mqeCi906LCQycgIegEQQSEDNhRIkJ314WI1dKAKbW7WRJ/NRpveXy3QOMu27WG7yKOaLdmMFoDfIzFBMbRootQsaeL4a1bDhlFrmjU939db1oKaamvFVu+o59Qs0kppxcjBpZ1Z2shfGWbct/LvpeevkGUpGB99VCKgo4hl/ywlXddoB5SwmqQr9JTdn2R7C0QICgCgec/amX3tfQ5s8S+SE3a7D7bk2OYZxHw/v//er3vY5l5PnByvOWP2O5I/OT8gJfY6c1rbikzKf8Sowua1JZJSGdLkChrX+CC+LMGCzf638vz6OZ+2/DYOkVsBAFCRtSZxJv2RkjFtTZFvAYA9gYtq9cTUCFCYAvRf2X/5TfcvQWDx7RdAlCTbDt8NH3uSnkgdW2gktrfNEkZ4FVRnLfQF8FHn7KsYIhs8bE2d0W7TL/dNpP88W6MlFnPFu384L37i8LeH2LIxHhYtRc42/2TIZUPdheAUc3dr/G2rClvMHCSijyDUte5gCHJxLzPf9N8f6b8HR6H6GsKddCYzz3L2FwL4BsIAH8vMn37P+YUQboZXQpCoQgjF1SWDrgrIEETMgEgyUwpBXgrQM1uq654KCHLucc7LQriLtqKLjNoAmiBICBPRxxDGwO3PcMZvome21a8h3E+zzni7wy2n4SqU3fuSnJ83IIicDGEIogB+D1FiZAkz/6Hb/CwH0NcZwzpmHk1E+c69fwpBtqYBuB4iMdBwAEuZOe08m1YAMWbuTUT3Q7zkvA0RyO4H8BSEIQkCeJmZTyaiCyBcpFPMXNltLBJE2Y4NzDwWOwkiegwi2+1SiOdVBmAMM692jvdh5g0uAe523ksAzt0IiXzEAAAgAElEQVSmhMgUCHfXvznzkfunmEMOOew0Kif/oG02a+dzqHLyD9vm2vk/f9s8deJxrm3uB+CKNxa8+CgAvH/+g4UXv6l/Y4bi8uAB5oGvvPy71Ts6ny6bW/jk0b8/9YVP1185sdfIWCboK9znH+eHDMvir4NfSycM6LT9lkLLOupZ3jDR2qKUGmuVdQ9OSCws7bX20qm9M3sXpKUErfVvQYMp2714ndWrsERVGwZARyKrIerJQLcUUNYDrz+F9iaGt8iGTDbSVhD+piDy+uNLMMbh49pUx5gMpakC+UjpKcMX9qkJpEwThqpAZg981IqWb1MVdl85bhudq5ZGNA5gUGQCDLLQ6GlJVEulwfa2FkuCJaeNZqSShN4DBxlfbfmDrCNJSemgN6Sgelg065WyfuueNelvy6Nm3sw/jJp3s0eVlvzj2Se32ub149asDMPfuyW60uj0N6/Z48XpuxPNLATMQwBawCO10RC2+brbhiabSMEQy7CX/mbu3enX7j0jCuLVxIj94vIne79891n3xy2ctvYz5e0lr2rDGiniT3h8fyncu+GiVKg4WJyof/mNeQ+ePGnAry7wSdat3rq2+IuJ2b3csTxN06U2oKUZ2HArz9lp27zbZfc82wz/kUFOLFLNbFmKPWVFmjHm6/uuWw0A99x6Vp8rrn98w8PeU+m8zF+32tqT6ZSXOlF87ut871bbfFz/iVOy7XWjVG/kb9nKqnVvfPFKzjb/RMgpi7sWXBXKNUYWulwpJSJKQazyYZvj/2DmY/43Q/z34ZCsgwEcxcyPdju0J0RsngrgCCK6FkLZ2h2CpEyCIEwTnO1CdBEvG0KZHQ6R8VOFIF2S83mic97WYaCLlL0LYB8IZW0igBJ0Jb0x0UXY4PwuB/ABgP0gYvraul3HzciqdmsPACMBnA+RPCABQT7dDKHNEM9ehnD7GIAuxfERp4/T0fVduJiZA87nM7E9+ji/dQBRIhoNYJ1zXQOC3A6DIOsfQczlRIgVwD0BzHTmBMw8A8AMh0SmnDE1dZuTEudaM50504goBkH6RwGYx8wFAEBEKsSLyBfMfPYOxr0VzHw2Eb3rZGitgXhWZxDRQRBxlTYRGQD8RNTJzKVEdDNEjcqNcBRiIpoNoVACwkU1tO21csghhxx+BNt6oXS3zXLlZEpD/H/ENsdfrZ3PJ/1vhvjv440FL/LUiccdDOAolygCgJwOThhY3JxfY1uqmfaf8cr+7w8dFQ4UVW0Zu7f0pRL88ysTDhjZ67tDl+9XPSGmpIJHDt+7sJcdKmzABpinXWdFkgU17RsGDffGtc5Ew1C1onk/Cveql/rbbWrlG2UTIwVHT6iWByEubcSLYxZyn/YCGrwlhNUZ9T2poWKfDix7qT9CEzQUlvpAPgMW2bBMBQpr8EpNUhY6JCVP18tjVucHeQjvx8z7brZa28JekiDnq6pP0wCgvdNQdagoljWqkVMISYGhfevyLlAgX7LKG+8I6Z5C0zTydE4htry1KTMq5PH4PHI2G19a37FqsMcbUBYta7b6VEx+IM3prGSVXJk0TUnyqCgqDv5qv3kTAwBwFE7ZzjYHfaFeuhJHTenbhpxtLv3kvEWjgeA6gBKArEPE3g8F0Nccr37MWdPnW6rsBWDVcwuK++/et2Vm/zL7XQA46srHZwCYEaXHCEikDNatvX2d9TFoSiplSczCNksa39gZKfJ9q/bSPEMfjF248tNvVMijEsC8h3hOAQD8pvq36ugt/DWYvjjW+t0P2uZl911x0vDL7p629A9XzR1w4S11kIiG+5Nn3HnZ6Qf945u2wR0pw44/coyh9mH/9RMnNt+6YEH1KTT9BQN5h3kQ32qbz6Z9Zwc9hccm0YZ02+rflmxZkbPNPyFyyuIuAiLaCOHuB/QkMtvCdX2pA3AMRGzZNcz8H0ul/FPBiZ0LQhS4L4QoeJ+Gk7wEwJvoItQu3HIVrhF/F4LgTdnBse5w1cfu85yEIJoFznXceosEMd9l6Jp/GcI1x4Ptn5W7+szd+nAVx23bMoBvATwH8ULyBICVzuckRPzdcxCup8MgCF8jM4/ewT0BAIhoAwRxWwNB2M5m5ue7HScIQnkVxHdIhlAqAWfOmfn2bu1j6CKHDOGKej8EYTYhSHofdCmxDPHcghAKZYnjbvwUgAPgqOQ7GDdBlDpZxcw3OPtugnj+w517799tfjsgCLf7nPMgnl/WuSfFmdvuq6a/cmJAc8ghhxx+FJWTqRZdi5I7a5vPhwgTuKZ2/q5vm3HV3/xvZtYGH6lN7B8Nhkr3jPe6/sDM4JTqt8Z8evElw5a3R14fWxD39N88CFSxDvPj7dwvWW2P//AESg9dKH03cDlCgU3vJt+cYgaTw6d86EtbBUmLDlv6C8mfCcHo/TkaOpsRa4rgmwOa7ME1A6jPhlJKG4SwrxcypCe90Dp1mEVeYtXLqilDVrLIUjta6yIoLUvZcT0Zt9UKrVC2vLquZTTVRJZMjwmvFMAWbEEQeXZ73JKSJjhkw2rRLDlKZFT6wypnmSBLIIuQRQYtyXVc0/z+1zrH54wuPznsUfxPfFvzzspQIOhNWsHOvoVlJ7bFm+e26bSuqrxqtOmnuB4ym4a/N+x7bfO6PVbWmZRVavo/sFpDdpTXX3r2hEfu3mqbMeom+tRc3+eDQN1V5iHDj0FRQG58uzlqWHlI++RbiLn96b/fuTVWcM5dp8S21LB090PjJQOWffmI9X//Njj8gcGp4AdtUtZI0Kf1iYxcHrN82Xlmf7+synz1yi+yNjQ/IRP7Pf+1ZGb1DWWR5szjwz3lB1hWxpoSv2Y72/z8hFMoqSnP2oSlZ73/5C0AMOv+i2/zcHZwUAqNa0vYbck1TQNb6zJc69No4BIj5g0bwYTaavnhI98GO29hW/9OE9mscaDhYYZZ8X7dCksKjG3kddC5AVHoZz3DmTn/4W9uDjuJnLK46+DHygcAwNvMfJRDBhocgnjYf3lc/xKIKADh7vgVM+vOvuEQ6t2LzPybbdorzJyCWJ19nojaIAjPOojyDvtDJLGpck7JQLiiPouesYn7oyuexI0hdFd53d8MoB2ChKxBl3tRAILsuC8D7ouBAaFwAoKkuH24z6z7KrMOQWi+Q8+YxH3RFVcH5z5dIjkUQiFUnTl7FsIVhSFcOwucnwQzlzvzdSNEPOc8Zj6++1w6WVXdeS1j5vptjjMRTXLm7wtmnuS0jUC4UT2HnnDv+XUIdfcg51y3tuRAp90dECVJZjjz+mcAvZ1jGee+9oBwHf4+BNCzJmORM6Z853quy26amcuccccg5vFBAE8y8wwiSjrnj+nWl42e35Uccsghhx/DtguUO8LbtfP5qMrJtAHAmtr5/C52UNLo5wC69bIu23z9fToAzPd32ebJqW1t81yFeVrqMCB1GPD8A28d15kJf609v6Bz7boP+z4/8q+HHmJNfq+BAxur2qo7sCGZyQyq8J469OnDnvV19lKVlYyxq/bA2mPv3W9F1qcE+Tu9yBimFXCG09hiKyiVdGqxOBOTCzCM8x75pt0ztOTROEUvN2RNU5GybWgBC4ZfQYqaPFtQlimCDC8ZgJGPggofK8gwYZUdl6u9hSxD06ABqXTG8pkeGZqNOFal+/JhhQg0xUqzfiVjseK3LCNYLu+LVv6cSAYsIKOlU2abYaYzAaXvkNNGELSwZUJN29KI/Xc78/l1bd+dJJsJuWlz7e+QnyqI+oLj89VoovDLwgoAWFk+/8amjo2/qk2vnncy39XDNvf/YkgFAAzG4/jovIvLJjx8dw/bjMU38IfjDzjSVAtPxVvLPpm58J0puAR48KqzIuk0j9ah9rDNxJI0pLRAvmTvVS9UHab/wrQx+du38IAJi81E2vbEvb2sAgWF+cbF+xVv+mtFPDOjfouxLNJpPlEKf28AYCDREtWe7dhSt4cK7w5tc1aV/GAOA+R390lAOML2XkqiPT8PiIQSSFSkAsHy/EzqypqXSgDgprGT4oqPlJShPXgwEk+ew3NmTD14epIzFrWjcwzZWfjRCAm6nfEGtw3PyeF/iBxZ3HWQQk+3RBldq5kpCNJyrfMS7JZx2ApHlakB0MrM3V+QfyqcCEF0HoVwUQTEi3sZBGHYCiIKAqggok3M7LrZLodQhEZCzEUzesYX+iFi7q6GIClu0hpX3VLRRRhlCIKSh67kM4UQ7oomRJ3GvdzhdBtaAsJtcS0EoTPQRUAJXSSxu3KpOP1270cG8J7z2YYgqsMAPADANSZuqY5KCBXR6/xUQTx/H4AgETVCxBS6WV+n4HvgxCl+HzF7zLmnvzlt3YQ5YyASKD3AzO738WYA50GQyL0hiF9vCJfSMc58pCCyrE4EcAacxDhufKCTEOdp52fbcRJEqY/5AA7vHlPIzBcR0VoIEtrdHdjrzEUpRLKf0yDIouxk2Q06/XV3PZYA3E5Em5n55e+btxxyyCGHbshA/D9hbG+bEwBkiaS/TDxrUDIYCEuJZGcP21y+v0yZmF3DNpraF/PPISPqNIhyTvdDxL0D32ub54YAlBPN3cg8LQMAITv2bbWdGLshFR4VqYasDJ/fYnw80PYoNsd6tcgtZqk/kqq/LjJ8wdULF7feMTo2XlEsSapdFsjorAVblax6ZKetb1FXaYsG/IPK1uwWsyw1L1ASsj9d/LDpCfUrHIiDr0kqaSPrSX/mtdS9TEggMPkQhCcTxGY0xPqgKpgFb8ljfx+TTCNb2OndVy5h2CDd1G3N0iSf1y8prMBGEqX2GE9DJpaELVEKKSgIIF9T1Lnfff3eSSVDYcO0U7rVHgoWDGNZf6A/8o63ATQZnX2TJklp26r0Z1AnyT6PV9U8ZpFckcquSxfl9fJKATO4JPxa/ZZorN1OtZVFvF4tpPu/1zZ/14H8yjse3KFt9vDA+9hYN5DYmgMAE096JDw0VHrFCP/mMb6PeL8/lJ33wKX1D+cBQESuuUVLm+fsvXv0xVqPfrDCSJ0+4d3m1rvVrzqBMTU+BZ52JMdlzHSfosTEJX3UMxENlX++R7/iT9++mgHgd5tu+l7brF42m7zQX7RH7z8/xd5D+Q/Tt9rmS2c8eNHcu89aS7p1Wz5LcqxIRss6D7yryPv0bofXfzV0VLlX35ynJnufVhD3PhinTvl+OnHdsZIUXGKvXNSO3kPqAHixAUn4pfxM++0nk3fzM5zJ2eafADmyuOvgBYjMkIQud1QXbimIr53tDESikm1B+Pk888UQpGeZu4OZnyKiWnQRJxchiMyZzxPRuQCOBPC+c6wOQl1yV7QSECRgEoRr4iwAjeiaM9cNktCVwU1DVyzhtjEnDKFadUCQjiwESWuBqB/kh3Bt7EBX8XqXCMrbbLsJdrrDHYu/2/FmCOUtD8Jd09etDwtd9QjzIBYFFOdcOPvyIFS6IwG0EdE6AJ8y8y/dizoErNCZg6QTL3gUxFy/xszNEMqki30gyPksAD1WliHIvgzgFxAvGlOddm4WXoIg5xkIItsHYr4/cvrdCiLqDUHgpgC4hZldIj4SopzJvY4qHWJm15iWO9ewIVb53Sy1QWcuiZkLnWQ8MoR77WnoKtXR3XVMAnADgJxByiGHHHYGc/H9ttkLgGy254KZPJonk0hub5uJIDH1KOX0U+JrAJsBrHd3TE7xU/P929vmC+duKvBG+CJ/QH/mqaX7XdS0iY7slzA+9tYEEShoq2tVjaLi3i3+pvcnYvXnWufA0nfeG+bvmDQmKY+Q9/pqVkZKNT/4cknRUVEbgXWHBROBZvbpWXyTujHTkTkMhxkXaLrdoXVuGg0vVGlwcRlkvdz2QZVkTwyZqpp8taayw2dWhHXowjbLUkuhVZ6qRTzQjMYGH/sKg+TNyzMACyYhC2RMSyKY0GWbkoaOsBrgACDFZAPwK4hYGmyTuV1KUKbQ9mdSFsNPUoeppKmjY3WeJxTWoWd02N4SNQRbtRCPtVkdStPvk7bnDmOQlFfaWVraN9FX3uJrgo40wuXRfCXpCb9Vsvy5PuuMI7N2qm02nboOsD49hedstc3fdUCCsM1pAEk8fJPamUkdtfjTjkBTffLly7+c3YFumUhtiQ5cXFs9VqrkOwY3NN0MZtM9Fo60/0bKi8mNGeUYKT7ixEhDYqphSrOShEE1hQqRLSGaNlUAekWL2fphYbi3CtbGf72yh22eTdOJoPZiGAdAeGfdcgrPWesjYyiYR+rx7BSvkbmXLp0TeCr299Av//JCDAA21WBIn94qSZJty0nJ5w2nMgFT8sp2IGRHy5uTpVfiwfvPLbpHmnp/ltJygmtvW2p7L++UCkvG2a0gxDmJNElQ4BO2+TrkbPNPglydxV0EzHw1RMZLC0Lt6g4F4iWYINzvIsw8dZvzmZmrmXkEfgZg5q+ZeY8dxMotdpUjIrreSUoCdL3QD4cgGgdCKHk1EAqcW48wBUEWu8f/uZlK0a0vC2LltHs5Cxfdz5Ug3ChdIihDGNIiCNLjhVAdw93ON9BF3rq7kbiExui2LwBgIYTq5l5zMLpUZDeRjnueBuAhZ+yt6CK87vXc7K4nQRBbH4SKdwwRfbh1AsQcb4Rw3QWEG+shEDGQ/bE93oEgqXcycwjAXCJqIaLTIYhtBsAJEHGjiyBeKlz3LNsZ918AfOJ81iFcb8XEEBU7rsXLIWpm9oUzp8y8HIL4Njv3dAGARU4dyQEA3kJXpli3HMgKiJedLLqKAochvicyxHfCcsbhJufJONuP7OD+c8ghhxy2Q+38HrbZ6nEwAyWQVmQpC6rZvDax5PnGSO38nrZ5ywcWt37FVe3f/Cw8fsDX3/c1X3/feL7+vq22+fbH7qTVj12/eHJK2OY99vjN9ePH/+bmymFZRMoM9oVsLZGyxq75Jq5915zcp3Z4ixGa1FwTU5OPfbbAa1488wH98Msfysa1sfOHF5CqQCFkNARjnnC2JAhTJYyc/DUOLdX5SHu4XVM+6f6WeJ6WtTX2mnvYtupBM2qQjmfVXkWVSCGGjAEp1Vg6UDaz+QyGDEle699U19GnvsijKNUh5Hn7aNG9PKqeF0cM2aQfDNuAF+zz+bhDi9XXJDNoNg2kExaRxHaHp80IsQdkSthstwS+qGlYRjHfakXykY8DKPN5K/M4L0wgtCHmTZYYSEmmzUk28tSIFs+nP8bL/NdJibxWPW1k2xFDc3HSbvFnEU+2WWkzbezXMeKkaHhIVkO/kFfKryD4jpkdvHirbe6bDxvCNjcCAIavLtA9yUMsj3kURG6Gns8rjre85fKdb7zW777L6h8LJb301r0Vp7WcWTzj9CT0jS0JPWOZw0/0pDNvtqxUFiXW82JieCVVhmyaNljS6mR6bKUifdJWEtXag0F94ZghW23zq76rinXYrQaM5Wnx7rHVNsdmnbEcJB1JsWybbJjZuzpevwAGL5p9/glH/+PPZw2wMsaznTHbTrEtQUvLpbuzN5NqWtG6rPU7zhhZi9EJAKomhz22ZIWRJ+dDCisgqwNxvRxEXpipPKQykrDND/wXvvI57ARyCW52UTjEahHE6lMVeq5ovsbMJ/wkA/s3QES/gCArt0MQsvkAfMy8u6MmFUG4lZ6OroWONESSkl4QLoc2RGKVsRCkxCVbiyBiEN2SGa6bqKu2ugTPVRg70OXqCYh/VBshSOMKiEynrvq4bbr0SyFqDCoQRGUzhFum0q1/bHOeG9/nuq26x1wy4yaEcZU6Nz5vGoDXtpmPVRBKnFvL0R2/5py3gJl7uMA436eBEOT1m26E/SYAl0GQ09u6lc/4BkI5hDPutyBiFb0APmDmQ4nIJdWui6+bxMcAcDcz39zt+kMBfNVtSHUQMZijurUpduZgFIR7VBPE4oG7WOJCh1CeowBmMfOL3fqYB/E9GeDMRb3T7imI5zaemb9ADjnkkMO/gMrJJEHUS0xTClU+jYpTsvOeRXitdv6uZ5vvf+a6oxl0qCzxrRffe1vjHjzzPZugfvnlzeNeeH+/gO7Rimom7HtNwcN/Py3gkyQ52I5IPztFTZVriss+Ka2slEt1w7LPWPDsxxdUzBk9+faLNLV+lLet9FugdNU3tdKKUeW7x7E5btjffX0Q9WmMmlaLJBVTHyWjN2YK1b7SCn2BFqkIItVaGlfS+WqRGvGWKgoSdkavhG8j4M9fj00rMnmZ/apiVRZBkmOI2w0elvpm86AjAw3SpSp898iQlRhS8W/WtbUUR/JK+ileRQtrGkwgaxhIGxm2VItWmFkUKZo9wAxZK7SYnLQtqdDns6sQlBLo1O0Ak5FMdRanlYIOn0lb+umUWrvU9tZTomDY7tNsiV7btHtSstlExu5Mlz3csExSjbGKL2IiZGl16z9FIrNFz/pszcxathbCvNM75hzdY/I/P4dSesvAhbM93rZ27dtjXniaAeBPNP0mGdZl/kjs/oqJqdsOePXD9GyaTrVlynIy5T73NveFDZ/Vgs63nr1y/cGZT6A1f475V9pzjjin/wVbsuyLFHzXEIsGvXlkmEZh1lSXF2h6TVXxPa8uvnOrbX7Ve8XQxmzdVz7nFaMTsToJcuwCfmXr4sZsml4CIICzzVEG8++DUa1RCcvDk20pVU6z3GF7oQTS8MPWa5/xv58XRrRkN/nuY57/69/dPmbR1M/iSHhaIQ8BDDuAznoVWnSd0ryvLsW+1fT0+Gc4k7PNPxFyyuIuCudl/kMIl5He6Kk2Tiai14hoo/Niv6vgOwgS0ghBgC8DMI2INAh/+dkQyWzqIQgBIIjCGAgC4NYdHOXsd+fEhiivQdv8qOj6G3Czl7qrJ3H0TFygQ7gtRtEVv6ijK07S3U5AxOW5hC/kXKcdgqx1V4bda7vX7D4e93cGXZk83T7d4woEKXIJcKfzeRgEWX0IQjH7BMKNOeWcV0JEg4joMyI6A9j6fZrjKL7dV5DOccZ9LoAhRHQ0RImJ8RBuxO7c9QWwG0SZkP2J6LcAXoKo0zgOwOUQq5E1AG7ehigGIJ5drTM3SQg1tP8239+UM7+3QRSHHoWez8itWUYAJkMov370xBSIv5vuGVz3hXBLXZkjijnkkMO/g9r5bE858OgPD5l09NfsR++Uwla3vNoHVU6m1yon08bKybuObZYkrCbgW388rwNA1UnS+Is1jY+4csZUra0Bcziemh1qr382YVN9KqXrffrVIOJtUSiYHuaRi0qTnaYpSSSd5X9keNSqVzxjP7H00qVIXPgbm6rWjx6+5HQuevFiDFw2idI1MerTOlGroNGSpUUQUau1WGJjpo8yjvOTAxGSY4nNBVs8NUoN1PxaGNpGvVFKlaVhR2H7J2QTRYghq8tQbdn22flZPzLQdQCdEUQnmkhJBlJ2Hvyhkf0jqb6FWlwLa5oPqm2QYW721SIQ9FKhLx+F5GVFk5AtsNV8kqVwUIWSL0sA4AGlipIhqkR5kSZHpTCCVnltBMGYR2KZlUEdvd/Pl/OobEXIXk8NncXvdpJnct7I4Niie1g2HoqZbSmi4KeW7H3BzlopYkh2En1e93gHvaYqn73h8YhFhT0f5Vv3883Z/5Fnl7hEEQAk4BwNloasfXZipX/IbJp+NIDrSjPWqMZmY9NAZOCFIhOob+2DytD4Z+gMMyY/StN/mw37n4fP2JIY/f/Y++54Oaq6/eec6Vvv3ru335ubm0oSUggJCAmEYqQERFBB6R1RkaZYQXjxFRVU5OVVVBSRKk1ApBgg1FACJCGkl5vc3ra32Snn/P44M+wm8ur7e195Ad3n89nP7s6cOXPmzOx+55nnW1oW6gXzsnw8Es0BOzuS1ueqieKd5OTgWpK2bZB3bbMKpV0GnbrHJVICkAtMDv+0blZkAovI+xgBSZckB8xmiA5xrr3BnfIqhzU4WKrEMJHE2W5JG+OIHNiO+MsUZtaATeNooWuNoYNzcvkFm6vrakTxg0VNWfwIgxByLgTBWA5BsnxX1Gr8jnN+4Z7bftjhEYi7IJSkKwHcCFGCQUclaUsBQgkrQfyRBVBR+jhELMmhEMSjOiOpD8tbtudyX3ncM1FNNfx9SBAkJoBKanQ/rtFXB/Pe9+rzU+2ypKISZ8dQcR/dAKHeSd4x9kEQpDQqCmTO297/411T1eY6zvlN1YP2i9QTQi6CSDC0CqIcxn0Qqmmec95ICBmGIGJzvHl8EiKG8VUId9AvQiS/OQgifvMoiPPzCARZvoJzfjPeAz4B9MYRhYhRfBAVIpyGiMmczTk//z22vx2iNmIG4vpwUVGQ90QZQvHcF4L4OhAPVzZ6254DYJ33Ws05P83L+noWhBJ9Oq/9SdZQQw3/Hzj92uPOBYC1q3/8XCL30Frg63uWYQKAH/cv53vGf3/4sRChM2accw+IazQER66ETm/s2rvYk+mH3tFlH3HEoRvRn5bzEYuEiJI25Qi3w7oV2Oy0S9uePdCdrplcq199b3ik6VBlQaKdvjTbbbjtG1RWOwi30iBhBSPpslViRJJJROK8hEa3AeOT1sEs5ThJBEiuXudtuThhUtlN1itSYyaIQDYM4shI0iJrJBrXeEjKpktFpimBfLlgNcaYQkjQViGrCYyxBjTTIop5CmrIUKgGhSwvbQWMsjur1OnGDA0KVDUdKliuQdTRvjEm62EzFFIDvVJiwz6ZlukaNMmEWVJdZVtACu4/xsaydrZMFUJJfyCf7VIatMxkU89pNmhq+5rsFMyLbZfTwXXGtV079v959bTeQU4mp/G7+Z817SKmha4z9cBrv1987tX7/2nzA4YkRUqum/2OfXfzE1OuHCcg0uj2bXMK4PcEYT/pUuUyO05fyE2Npu05sfPY73qeV0uYuxp8fFkr+TgbwkAJeFgGFjvAFefxu9/TNt+55JRQY1Nb4Yj7r+d3kpNjZeBjw4bykOsyt8lyQIC0A+uHDJh9EX/wr2zzozecc3vKwafH0izVFTMjxTJ11YhiuBn0JlQAACAASURBVE9YQAagAWBosws9KKH/wM4SX/vgMm4H5rdtnnJW2KEOB5/owFlbQKGNQTrlNfX1dTZX13GXvfKIO3beKUSPbZt92FnD3XP2sSXl9MGHrq3Z5v8j1JTFjzA457dyzn8BYDIq9f6qwSDcJT9y4JwXIDKvzYOIq3sDwGsQxKwAQRD8wvMqBAn4FgRx8cnUCaiUtHiv5AEqBJnw4buC+vDns1C1zK+N+GtUyE3A285PNJRARXX09+MnFhpFhXyqVe1SEGSQolK+oRvAVyHcXy+DIDb+ePw6kBGvT58wHwih5lGIQPTdUEV8boaIK1wLEd+4BUKV89XCMCo1Ig9CRY2dDOHC+S0IdY5DELWLvEy1R3LOAwDuIoQECSGXenGNYkJFZtuXANxKCGmHiNX8PnYn7g0AvgfgNEJInhCyiRCyrUplbPbmgHtzZnjz4cdvVkMD8J8ALoJQXOdAEN5OCCX7KgCf4JzvzTk/zdvmexCZdD8F4G8WIK6hhhpq2BO/v/KRW39/5SO3MlgHAK9ToP69bPMJH8TY/tdYhXx0Um5BdFJpnkXiY9l0/A1O+Mp4m1VwNDvfm9HdkVxTKG0HoSqqOqHDsnYMNfwg0aONt7SszISnrZNjmw87Ia7MbY8XO9Awvr8kHfsowaKH4TYPApEcVJmoSkPGQtMAmhAHkTKsHBp2OTVRkHsRdEZJzI0jl1Vz+XQWo4UUtrKtXJIdx8mnf2cW0oybJsKGEdBBWJNhqKaZJolS/1gCSUuFwQFAgaLq0CUKwosojeoBsHYtLoVUTSUgFgdj9Xl5rJhK9hkaaFAjatGwuUWK3avR91ULyV0BBL/Bw8oXHz1vI97YentOCutuQSqTkbId3ewOJnO9YybfkXIm9+17oPn0wODO/oRUaCR/dV92Gr+bA8D4lffe3HfEUU+vPP0La7gkfy7lWP0JYtsFx+kHgICrG7qraxQgYZCDhkJETmgc77DotI2joanzUs98d+rCjf2ts8b59LYh4/ny8MWn8ruLBnDkWfzuAG9bcM8v238cfPC6M6546Adnnunv/84lp4QYY08N9u36+W+DJ7enAuWNaZSvcbhbVkOuJIFIFLyBQfqeDHraRbFz82c3nrL5FnLMpoeUvQkAqOlw98ArWmHzfVFScKTRYINmOLbD0Gqb8iFAuQ3IEglWPghkmvTyRvIzx9UvkDVzlgMyJ4XEqyMYmTgQCm3oawx9s23aIZ942BrZ+xF37DxvmD+ihcx3ZNf9lCzJNdv8f4gPS2bMGv53ONZ7tyDUoSTEuZUBdHulAuIfZoWEELIfRIH2TVWLn4SIeWuFUH4SEMd2FCrxfCoEyamDUMpkCLKYhCAPAeyu2vmkx58L303RVwH9NiYEaZOwu2plQ5CMM1CpkeiTHL8Pn6D6+/BJoQpBhPy2fkwh9Y7RLwvik0wDgrgshCCnTRDutkFvjL4r7oSq8T0N4SqrAJhMCGkDUOScp6va+KreSV4f0yHKXigQZBvwXG05570AQAjZAaEazoMoJXI5KsorAbCREPJNAI2EkK9DqKIUXjkSQshTXj3HCyEIqV/aJOq9LFQSDdkQiW7meXPVCuGi6sM3tHtDuJTCG3cIFVdiG5UkQXMg3LX97KkBVOpXtgL4GCHkjxBk3IRw280AeJhz/mvsAUJIF4BGzvkbe66roYYaavCRys36pPfxPW1zx1IyBqCpf/mH1zbjJzccCFcexdcu2eYvmnrQ6JOSxOPYGGvatUtZN3FmMhess5/c2dS9bMxRaAPvs9qCphoN5IrFEuq7JySucMajyszGTMra1Jock3ud5PjegQaeM1uHpuqMlxkJZokcSxJsauDBaRtQMmDwUhTFkTGm6i5t3T7DzZQsAqlc7smUpF65KO8lxYNTk+0oshxshVl9kf7tPaPklPmRkmJSyZXK9ZJKFQoqsSBvpVFdbwcADs4YylCgqTZsS4GiypAb9pHa6LiWZ+lkyWkOBlQKSjnU9kanobgxl4Za5ixW1mUjVm+U49lro+PtB1uwb37JWfWl0PJQ6/T2zwUdw8Foe8kkw+NUqou0GWYUhUgB90947ulyymxOLqDSy1O2T0ndOdoZHI+lr79kUa56us/4zvH86p/nT+rui1jb3TXTNy3EYXAdBYqwzaZhHgAAp/K7e6/6/NUk8OyurWP17BE3Q+ZZg/yV0CHFy41JhI+mKSnDcV9HYcP1zSd+E03A04d+54aWyal3Xt7u0j/+riGycNooC9xyzp+O/MJvEmXZvUoq8V6U3DpVil+ZxVi0b3r97FKuYM12Ze7AYQywKdjLY3rwkGRLVCLjvBXI9AHAE784m4wVtYUtHeDjr1uzqSU9rzgKEqniUHiyEkOemdlnKd8RpHYgWYjMfOFFokanzt60edeb41zKBWHyXRiINiKmMkXdm1LWLKWHF51rdL9omWPHu3BMAunltp7N6fHJC+7pfey7f2WbDzj3512EscaVv/1yzTb/g1FzQ/0nACHkKIiYtFxVIfItqBSoBwSp8evc/R7A1OrkIR8kvLINFwEY55z/nhDyCQD9nPMNhJBWCBWsBaJ8xloIpTEC4fbou6tWl3mwIMiCnxnTR3WJhD3hoJLoxs+qCQgC53r99HrjgNfWJ4B+DcdqVBNZBRUi6QDYBlHuIQRRP3Cx9716Ox8liOQyF0CQRz9Gzx+vf0z+dj7p0rw5KUCQ8Ef+i+MG8O41dB1EXUd/fgc9UnkKRNKheu8YAhDqpQ3gCM75Rk/1K3hj8RPa+EQ6CUG+UhDxpf2oZCa9G8AXIBRexXs95c3LDIhz7nDO41VjzXv9bkYlyY5fYsSHr9g6EOeOo6K+W5zzqHdtrYIg8P71YnpzN8Q5n+rtTwPgci8lOSHkRoiYzRM559UktoYaaqjhXXQsrdjm/uXCNncsJVsh6uUKcLgg2AzhLv8rAFP7l384bDP+/VcKZOsiUGcQX7vk3h88ffQxqsR3XHboExvuXb6sNdlPbx4flhoz44GvHPuFNWs31neOtgdoKD1Y7DnBeDuYK6d/FWnCd2wbKCZUlMejVlN6pmOzLC32Ttdj+70Kd/3+YCsP5SSYJjRcAg0UMF7OgSx9EeZIBK/fcI0DNyLvVS7b9UGVgnPJzagY6NhZmjmy0NWZodsY2Fxw5W4rlMQoy8ntpYlWhEY0YkYkiVBKVZHHzpUAB5alQVUdOLbMZcUlLpMg0TGWdWzH3dahNrUzOAEX7h0pZI+sR6SlBBe/7dtsLol26KZqIzMng5wzUjpqx3432ShdsCU2HsiGM2prshnBNLeTE8vK6kOGoI2U0fB2yFq7LK3KeafYtW5MzkyvV3Y0F7+aMRtyQUSSP/vykj//rVOw7IDPnAGGS0Hw2e/3njTssHJEpvrg3MHP8J+1nn92sU69xg449QbjtHFTPtC4dOMg57CLAy1HnPjW4xvvJJ8npeZSQWIcbCyYa13UHHk7wd3HxnXpQLc3OV9tzdh7D6UowXyboV/Pk6hU1J1dQ9G7Nk8JXRh+bVt5GqDIgOICT4QIeZhzPv1Zii81MDg38Lvftc23zz85z4uQymVsVedqU10zDLlz3ErSgGonOA+9VCI7ZMUidpkFE9sdnSp6joR50KVywC0TgJqX8qdjXwof1pqN599qa2+sy/T32bld2+UE3FI81qYR1xq6I9M/FQBOIXoAgHUXNx0A6Lr60Zsd3ZivWdayHVctS72fP41/NdTcUP8JwDl/gnMe8omihyXYvUSDBKGkbIGohzedEHKtv9JLdrKhul/i4X0cOgCAc25DlFW434thOw/ASd7qYQii8l2IJDUXQyiL10KUP2jwllXDj+H7KirqHlBxOXUhiISv5PlEwoUoY+Erf8z7vNMrF7E3BFEKePvQvD7Hq/oCKiTlfgA/QYWAut5+pnntKETsna+o+S62jvca8Pr+OgS50lBJ8vJeXgFFiBpEmyEI2ZsQLph/FRdDCLmAEHKB95kAONt7eNABQTL3RoVoPwehKj4FUb/zNohENfsC2EQIyXrj9K+VCISyuB6COIYgXGqbveNq9ZaFvXH68ZGKN49HQNRqfNs7ziAhJEsIednr/2cQrqR+unpg9xhQE5Xamb/3xuPHi7oA1hBCLoeo8ekXz/YJv59ZtYEQ8nNvbo5HJU4WEPGO6yEIcA011FDDe6J/OX+ifzkP+UTRw8FgsFGC+Lfjnm12sRqusM0dSyu2ueMg8krH4t1tc/vHCWn/+P9Bgpxvn2/nisZtIOSRHz97VJNly2eYNvkMALS0J4bDOn4KV/23hefggAFrn4sv3OYc98m19jXzWU9svNzSEIziW5QDVhEopxownmhTewKvq0Z8+KuxZ8/leHEZJDkN3r3clhtSgMtdt7OHbZUHihLVoCGAeWf/RO6b+Qd3i/LO6wHqSrRYYrY1xiaNxKRC/fMbQ282hWMN2oU7TTPQH9gemD4xrxJ5XBtpX2OWA2bK1d0yJBmQXBAw14TtDGHoLhX0J2WStVN0HS0g6RZzkgxbnpbmCRfgksOs0xjjbhImyyNvfqI9ag4VMs4aM+VkB4Z2ze5vHx+NuF+XEYl2p+KqLkeLds7O02BIWSNtwfh0ixf2DmLlUWnwklacuC78J6l3cHv9ixsG5v0luGvODvs/p68auXrPKb9ePvWC6+VTLwBEfcPPv6oc8+fXHph3xhv1HTvHXr+0bBVnwXugzCieUrLWI5zRp8qy83xs6uhtY3/aa+foE/vsu9foOZvuD52alak5XsqDOAkQDkTGd4y8gW2J9ceP92bnqoGQG0h048VALDcC59X+uW3rN8VCG7cq4XKsacchr21LzIOsBaWokqVSOT+n9ahynbqMAG8vAeS5FMGbycnZH5KTXwYAZzV+xjbjVR5S/1ReWXbJM+NI3Qqz0A+WWA844zBbRmy1aZSqnWTqf7bbEyJU1WSZ60SF4fZj8O25+55++Woj+fbo8Pa6/p5tyOzaIlNIpB5BGaWSxAqF+pOJ/vNTiE4AfBIiLwUAIFsXXp+PhtePTJ1SfN9/G/9iqCmL/6QghBwO4DH8dbIWHyVUSinYEMSAAghUlU0YhCBkMznnPe/jWA+AcN18mHPuEELmQcSSuRCul9cDeAaCJNgQrpkLIModVNchrHZNhbd9DoKUSFXLKCpz4f8A/Fp75D36tCHKU/zQG0cWguC8V10s3+XV7/tvGfQh7F4Y3ienADDMOe8mhKyFIJdFiCyxB0LEMFa7tgIVZa3oFaAf8I49DpG4ZWn1jgkhvgp4PASpVSDIz2aIc3E+RNKZNES9wgMgyOJbEO6j8OblpxDlTBhEDaSTIebbzzYah4ip3LNYNbB7nKHvDrwe4qFGmXMeI4SMoUL8GIDPQMRvzoZ4gHAyhMrnl++4DILM+ed4GIKk7nke/Lmr/gP03Y+1qu8Jr92lnPO7vMy8VwPo5ZzfUjWfX/Lm8hzO+a73ONYaaqihBnQs9WyzC/8xpbATFvx/rRKnUJkFBgpbcj3brCHQ/7SwzR2LpWGARxQ1OKvn2dz7Zpv/cOTOAwgnTU2O/ughzzTzn6w4at5IqOGqmRsml8/442faEM5d/+gdP12R26ne5BYl87NjhR8Yenre1pbtt+vlJiPctBqu67AN61poe/OY5ViSWhcIgbCyu+G+zxUWhI0gnb9OspWdKPbNcYO7mqncnSau9gTsu37JrWnbubXfvVQNSPncs5+QnDgx2l45DG4m7CIxTSqjx2aLtv1pZH38h2xcWpHkmbFJWhdrc2d1u8SB9O7zRwoL3FS5rruOC8opd1WX2CihgCHUoROWQ6BRHRIFLBRH3RJv4pSAaYwPdmRZbDgmRR0dDqy+hlV109Yf2bPOCulT9F5SNLg7Yo9tXqI0ztgGQ1GfO3CDVdyLqPFsAGleKPKhcsB9Zyj/xcdOb7yn66cDruzk8ksnxaUMW3XePZ89qnrOv9vwlSJaNAwfFzz+oOu2PADxAHMUwM6w1tKSd7NnD9dnH8tBGWsfTSY5QrMlJfdUfKb9VmaT9m1SrgMDbBm9T4Uic46xnRwz5f7/yGVbTg40SGF1kpZNripRFYgb0fAoeK7JyQKDLY1I23XgVhrNNuOU26WmcpYCRHdBUYC7nlPMDDCUz+J3x35GT06AQA4wqC7AlCC+aDXjHK2FTMtuZd8lY+TkALCfBLgq8GPajN+PFaS1xHSp6oBYKIxLMBs4CJGoAgdAmuWx3XCtTZPj6l7jO1hheBNxAB4I19FSJFAyBgYN71dDODBqAoYMnHQfN1dMvfg/VGJZVxPX3rn5V994t1by8HWPXSZBPpoT95ymbyyr2eb/IWrK4j8pOOfPQCgnvQBeRKUQPbxlwxDERIG4Of4FgCf3iGschVC6dv6jx0cIOZsQ8i3vqwmhlvnjWwuRsbITgmxMglCi3oC4Id8BccNuoJKUxa89qFb1I0HEiRAIRctPogJUSIKffRQQhMUnitVPpihE3Nt93vo2CEJTXQbDVyt9l8fqfaDquwVBQAoQyWIY/hoOgOu82L8p3rIjOeczIVyV/NgRFeIcMVSS4miEkPMhXEY7AXwMwC2EkDAAeIXs/Tg/AuHuqXj7jEIYpqsBfNk7lhYId1Dqjfcyr63rbfcFVFTaMyHiJXUIcvgaBNn0VVVAuKT6oBDXngoRa/ldCLWQA+CEkAcg3Iz9bLFFVNx2oxAZcn0XXwkie+la7J511jcOJa/fwaq5y0M8UPHLbWQhVOrqhEYaBPk9kBByqDfm5yCS9FQji/dOsFNDDTXU8C76l3u2WcIuyFiBatvM0QsJSQASY1C4Cw3C7jzpE0UBMgzQokXzO//R4zt98RVfPP2gr4uwDk78/10AwNrtM9dyqk8EV6aWudyYCqOVAIGpfR2v7fdUx/EjanrzruHktR2duwwp+qY9OCD32slmEjUKrKGVqbGYxXSiQJU0KfiJNyLZ5jeIG9vhFk1qRZ0midZRwt6ZwLH6AsjpCSyxJm4N5pvgDHWGrHjOKDUPYCi2pbQzmyU6dITkODVUNieannBfE2nVFtGlXXE2pcUlzAWoY0CDC9tlcJgKXeeE85JTBKecy1yGwcOIs2mQmM4VAitLc4k88rkI6icSVeKaoiOEEAgk8EaGMvKOQqUb7l2w9eq3leSkTS07sW2/nXMmrmqf+WqTddg6srb/xSXroE0MqD27xkZ7rHH2grVB5cggQMr67cYF569dLNWvPdjoTI70f2ykvPXWH1xzcRgAftN29im/mnr+8512iHfGJ5JZ66UHSu+GxvAmAMx2S9cm67PXyJ2yTqNKpy3p7YBCOSVt3ft+5rKpxyywI21Bd699Jyl1k2cf7JRdbpp2yW3vPqtlwZzGuq4WPaSEm8w28odsHdJ94Zw7WAbbZQCRkbFMd2IrOnNjiFkJokbyavFAQwb40wY1vhsGegIMXALYf5KTHsiHVGNXp64Og5slOEW3gF8YaSzgSR7lCXYjBQcBmAxIADnFHCGrzDAkI6qT+qgKFc52Dg4HTokzl4+woUEbDiaVXHXJOzw/w5j0ZxWwNYDK86dkw4u6L6m+Fm1xvxa2gaWnEP3Q/W76GiW29RzhbGX19UyAHMBNgNbUxv8FagluPqIghKQAUM559L9qwzlfSAiZA+AOVJK39HPOp3t93A/hLnj1eyW/eZ9jGs8DUEcIuY5zvnqP/XJCyCEQitXpEORqHIKI1HnNZnrvCioJXqrLUlSTQgpxI0/3aAdUCIsfW+hnO1Wr2lMIN0q/T58U+slw8qi4UfplNPz+/PYM4vfmJ7nJQRB20+tX88ZNvHY3QJB9CkFEMoSQRwB8wttfyWv7I4hSFqdCEOs5EK6v/rGv9dr9J4CvQZDwiaiU56De3Ba8+Q1BJG85gRCyE+JBwfchCPm1ECqn6+0/BOHi6cco+upo3uv7s6j8uRe9dTGvHff2G/Pm6TCIeod+LUgNwNGonCsFFcU27e1bhiCIeYgHCYu8dn68og7gCc75IVVKan3VeQNE2ZlDvL5u8Y7jaIiHEj0QSZMSEC699wD4I+f8QkLIu/+dhJAZ3r6/VlMVa6jhXxvZeVekAE4ja67/L21z/3K+cK9PNswhhN5RKKYpg8Ogor9/ubDNHUvJ/bKGDSC4eneS6G3/kvO+2WYua6cBpO6gz/zHjS8+ddFutvn2c3/Mv7n2/EM+dsS/XbHlqN9+7mfN1+duxf1juRvXd5qLHoyRdQsQfH3JdLbtL8ic/TulVTMm6KoEXe4mKO6CZiqOQolq6zl0qVkudTVSe82skvaHRbBmGSg0JREanEmU4ZkY+dpXqFsqacG+DjvSwhWZUJ57eI5DY66Crk00t3Myt2evp/ms3T2Bt0uQXHBKmOrqhk3KjCIHYHsemKxTSJKJdEmCYei6blNQxSQORnmKNfAAD9KgxChRgzIaQo6Read7x3CWpQtTdk3UIggo4ZzOWNglBVKWN7ambmjfpW8KJ9tpqZDKdgWHgzMuv2cFn8c/FmAkfRFvLTlWiWBf/qPe4dyYcnj8rJ0rktHxc9w5+zz4iR9Hesvo01O8eyiyVnbDJGEP3gTgG1xCE2sgkwMtBYuFTJ2lOLEpxjWWL1CgMTqH1zuZYuNFOx/++L+3fH5X0bV7Qq7xfQbsnHDYxH/X0TCXhHVXnxQocVYO2alypOSaOWNfFi6YRW6jwBjPF1nB1pQZ8a8Fdo59iQ6jLldG3u6W0bhXIJJ/IsspwFMM4w0LA/XKIJFBcZii6weXlxaLgXpGy09Dj/ZJR9NcnmSYDlsnimaaOoMBKymlKJPCgZgs04S9H4A8B1aWYB4G1MnaaMGy3SKsoKYacuTeM+x7DrmRLC1wcGID9arqsqgVJgEkWa6nuBzAIRo02Xn+jVugBiJ9hx96pLJt5/Etu3p6FOD7MpCgwM6SptxjhwP3bb71m185hegyfi10iNS/PTFDhbwvwL8Wu+qosffrN/OvgBpZ/OhChRdW+HeynPpPWV4BcAfn/DZ/Bef8s+/nAP8OzgLQtOfYvWQi0yFu1L8CcfN/tOcC+H0IMvFVCGXJjzWsrnkIVNTFaoXJd7P14W9DsHu5imqVrjrbZ3W20z1rL4ZQeTr8JgRxqE4+4xd/t6s+G1Xj87Oa+jUafeLZ7vUR8fp9EhUy1uT1cwOA+znnn/Ti63JV47sJoixGDMCXCCEncs67CCGbIFS5DojYv69DqGWSt90VhJAShKoXhcgO+gIEQTch1ME277OvjD4NoUCGAWzinC8hhPwcFRfUAIQiOskbd8k7riEI9VLxzlsRleQ98LZf4fWxv9eP71bsZz0NQZBkn+z7rrkOgFe8mpFAxQWZVm13o9eOQjycACqZcPu9fQGCNBYB7O+5TU8hItA/B1GSYxkEafb7qKGGGv4FIYNofzv6QCBfSK4EJRwcK0FwZ//yim3uX/7B2WaZ2WcBLPziAxftZpv7Lv2yRog0/UJb71GvsS9WtV3yF0cuWozmT98rpRuuGpdTSRoZvkJ1nXAy4vBx3i0XcmOs2bB4RM9Iat80uIER1Q0XXctWpVDEJsXQWqgvXKhL8+uJtX4ucql+qOMhzub+hW1JZcmUOKd1+2xVht+cheDqg1i71KY4k59Ac6KVF/pk4igMLMwkl+SZBJc6lkYo4Y4iqTIQhx1Ih2gxxVzE3Ew0/UajrS8ioDIvcq6AkoCr0ISdB1dhqxIoc1wKIEiGJGl2aSrJmQkLCKC5GJF6CkNWOBrVYw5Vsnxr80BEgy6HI72a8wYNlJ9yicbKGkvVqYGugY4SbecNN7QNGvef2L3/UZfvuoVIIS2fmwgseb7dLTqxm950Nn1u+FS9bvSk/b9yzI5ffP6xvt92/fb4izZx276x3L+6Q+1xV0wK7HNNKvjyc0RzpMwuuCjIV9wZOCFRXyoGAoBOUf9y8ADlBaWxYeZAabVJ1GJSb3Xadr1hmmCmM/lznXxgdNML7l+cyWW6OUwkZdOp1t1L7iQnPz4SQiJWQEMDEKrvdbc1HB6eZHcWaLnPLhigkeEd+cGWDGm160NynDaq5upC0W7PO1afJesw4EIZjZTZCt1xG8rN9OCmVkl1xsoRkg9K4OAUxOZAaBxsfh24XIDFdZeqBAxmoezourbhrq6ThyVh6t2AGpC4y2kZZTuNRISA3AhIDgejOgJXWJYJZcOGMrFtxQHGZCBEhA2/xqGkyErWohOJvlgBuk4h+qN3cTNHgfmKpi3jLq/Z5v8lam6oH134KtTLf68hxI11ezVR/KDBOd/EOX+heplXkmAeRFxeG4ArAWzhnJ/iNYlCxDLWe99lCNXHV00BQRY2Y/e6ig4qbqr+q4BKYXsbFVdKCRXyVl0kfg6E+6sf11gdA5mvWtbmbe9g9wRDflvJ27eMiruj31cZgtT6alwjdiem27xtflG1nKNSSiIOkTn2Xu/7Eoi4Tp+UhgghQW99B4SaNw/CTflZCJfNYQil8lqIP+IpXs3L6agQMQ5BjHTvWCMQLq/t3ud9CSFDEGpnE4R6vRYiHvECCAKY97ZvR6U+ZRqC7PpEcY23LgJByHwXX5+Y+27HDJVrwr9LK3r93QZx3fjz7KJCBk3sTnir3YcpgC5UUOe9JkG4Ag+jcp086O3nadRQQw3/0tChOzp0gnnXrPw7TQnAZRDeUU0UP2jc9sIPNt32wo9WVS9LrziyS1owsC+V3UWSQptdS/pZ0aRvz28e/DIAOCfdEQ3G1auUTtTdeNHb5Ddzu2WnNJDomjxAXWRcdeckwFR5PjC4WWoel6BZSKQpEmO6Q9buA5ZRGT/5WhacfatrH3dL3vz4HdKWLU20WDZtl5jsj2vt0jvlnJSxM1Z+NIfMjimMR4OIvDUt5eYLc7Lo6S24hWLaLpEkhuVSOIkizbvjRSdP0EAlSFIo09DGXOq4ZdsxtZRtNQ0jqCpoVmPQJM1WEZTyjBRMV5Zbc+GScQfayQAAIABJREFUYiqIsDoZAPJG2Wp3mvViKpXv6FUw053V2DXGYCQyrqNHpSMOMrYuvpQVDjiF/+7P5+4kGyeOOLEtLs9t3LwEAAxJije95d6cee7NR5+Y+AT+cNaqJdsPHbp98gpLbn7WJGY/Nz517OXB+i3df5AHOzp4zDgsHCWLEGAvWmONzxbG1V3FUngYBh/JzHB/rOjBUCOUmafyuwvF1eXpme0FntrUr/bdv4qXk1q2uTRLdwtBp/e1gUjhVafeBG0vSlYEGva9kxw/BDgHtVhyA4ANNrCWuHz9wK19F1imPVoGyiqgxwfRTnWiZGMOGbZ3pu1Axiy/YsoaHIwjucFBRp7oIFIPaaacD8isCxxTwGnGcWnSYTlYagE2C4HGGSSoKBAJFlw4RZlKaaVOuo2YdlSRZcIASizL0VyYOnSFQjL70WcCjLmwwQDOwNAyNMLqxpNEFnkIUAJQBupoyYpppdJkRyaHQdxfWAAQjTbcL1HptmAwVLPN/0vUlMWPLhyIG97uv9WIcx4ihDwIoR592LEe4rimQRCWBwBMJIS8gAohSkKQIv/a9bNZ+mpgGUKJmooKsfDb+ol+XFQUozwEsYijEq/ol3Q40lt+D+d8q1dE3o+TtCFULgm7J5kZhTgnfhwcUFEYfTfKkPcewO71Hf1j8Ets+O6zJ0AQlUcgXFPvhiDSrrdvP3lMGsL1s8kb1xxUkvDYEPF6xar9XIBKYplFEJlyD0WFnLoA+rxSFVbVMQRRIcL+PE5HxaVW8cbrk686CDLXDaHASaiQaQXiXG/zxv8yxAOBNARBVCDImT9mP4EQx+7kzp/HDRA1Ka+FOHc+fHJdXV4D2F1x5t78BKvWVyvImjeu0wB8z1fFvferUEMNNfzLg4L+t2wz6Lulkz70tpkamfXBiRkn80rXNK17cIxnleMcWZ3YWyYvTGjDEjdwVVRL1+V7oiWqxQbkuFlE/a4JDW89+km4U99Q6/ftB2yzHNLqVtBEYmqgtwmjoXGmayGZBV3ImkWU+S/zoHMwU187Jkzvm4fjWu7MrwYtDtO6+NHuPka/ZWGj/qzVFjb/IOX7l1Ir0tDDVt/7sZsv2dq/8NkWnbh6Rk/YilQuG3E5VC5o0g5lu9ppOwijyYwzI11K0+7x3KgV7rSoqeXAmMQMidNCLBnQEl0YtnioFMyh0VL1YngXC6ggbjHO+uJFRXVktKYDks1Mx+AhGk43mXXz1BOG3Vw4u1R+zF1XXi/NDt6NAq6Stw26gV+vl5hS1wQArkHTHY86J/Ye1hWnPUWJNutztckT5kfHg5h9X9LauDjb/7v0d4pvBB5WZJR4KlG8MGRMNIJWFMF456KdxfwWhRUPLdqaS/eRJTvGXPWZ/v6byecKBLDIy1sASdFJ0AiOrTHtmG0h0JILy0oZ0kmpvZWnJptOOSihfVRBr1TnZGUenNiE8ZGReEB169RxaVJAiiyzx4uS2urYtgZbsaBgmCXiDtsemBJNFKXsFoBfyMHTOtRpEiQZcD7uQFb0ApB9BKUoNAPg3ALj6CyjPE64UVIYA6WrUVqpTiP3T9si/ztX3TiRXTjDEiSHoYACCyFgFFCACY44WjGOYZ0gQLLIwUKRA3IRcIL+zVYRcAggMwAWoOoMaZ3xEwFcexc3xb3BxftZWs02/0NQUxY/ujgVQt1p+3sNOeef5pxv+3vtPgTYCZFlctS7Ae+EIHoLIdSbB7x3P57QREXdAQRRHIJItOLXSqx+IOKTtuplIVRcOv2SFTqAUyDcI2UA53tKmU9SslXtAaGA+USkhIq7qe8OS6raApVkL75LK4dQSEuo1IhMAujz+nyIc/4wgK0AbvXeywBOhCAvCeDdEiSPo/K79l10AS+205vXpRCESINwT7YgEsvMwe7q5yAqsZoGhBvHVyHcg8dRIWn+PlTs7s7rk+U2b+78OEPfNdcfXxwi2+pREO7JEyFcaP3EQ9WkW0OFuFJUVMC812YChMrn2xR7jz4cbxtfXfT7QNXnPZMOsar1fuKgHxFCOr0aoTXUUEMNPv5btrl/Oef9y/mn+5d/+G2zbcoDTlHbNuHmG0ebL7+Pu1TtVEOOHApgYW713n+sm/Dyb7bV5e57fVZEra8r0IbxXeZwWrMUhNFA2sCN7ZYDDFkj8TOd8SmUDB5C1R5XNjdNhpt/FPklPwV78wCi9+wv04GpQK4JdQOfDTVMYE3KzO20PLjWmeHIzl65TwXCW2Z8PjI50bK94QHld8m2838w5Yn+BszUOTW4SfqzBZPITg91CXMx0d57HUXAdcKDOmg2RRTJDjUpsmK4Ku0LuTE00yJUu1CUQQlFSJVdJ8CRkfJUChJCNPCQYo9RUy4zm1lElmynPZy8Irdy4BJ9s3HMs688tOCe2X86c3Fo6xmnBW8996y/bLrq+jvNI7+nnpiMaOlU2EwAwHULzrEjPe5jpX3iNLO0HpEnUlJpEtiqpkGsnDKgBvdpnFz3YownejZ/fLH++SB1g8rIroGVCXfUKhQTDzQdQOYYUc0liiLh6XLe2NE3CNrd3TAxR+sasnoB+plwR7966tHXfy0yURrX2x2uRwizOGGNnS70pibVoPUKHAdozbnyVBDLziLUQpukBFRQNahF2mUSkQgbgqTWQ8YssEA36jVF2c/a6hyVf0c+huxdX0cRukmBapZhIA8QTZhYHgK0ImABhFA4lPYHuUuC1ADyhqJgESJz5m8xbnNRkm3TQXIkZ9mOA6rJJEwiPI+ik0SSl0HNIiylDd20BV0gsS4AlDpweCEWYb4xDoiMqAwAdM82E5GE70dXdy/o/HbH7Jpt/geipix+RME5fxyeFP/PAK/UQwSV5CyAIA+PQhCNFyEStMyFKPEwDqFSvQFxHV8H4GxUnuaWIQquz4BQtvyC6zIqhAaoEIFq11KfiPiQIMiVX14jhor7ah6iJqEfQ/ckhButUrWtn63Tj5mrdpFlEISvsXo6IIhVn/dd9uIR494+mr3j+AznvI0QEiGEtECU1mhFhfRwr2/fRfM1r597vfk5HcArXkKhBRCqpIOKurYCwk3Un4s2iNqKB6ESy6l48+BnR/VrHPpqqf+UHVX9VJf8qF4PCDfPtyAUYz/TqU+ovwehYJ6I3eHPF/Pe01X7GYAgn777aRAVtdl3Oa4ei6/6Jr02j0PEffrwr5d+AOdCkOzXUUMNNdQAAGu++09lm1Mbmwa0qeXIumz2JwcAAAhpaKZH5Sz2qFIHTRvqerNUDJzf/vbcufXtm9Jy52gi9Jy8bP+nb36jjrfKGP7Ldal575zvDDZ0J+wMInyXGY91vBUqzpk59MLEsHP0kPTSzrFiR0pVZpqOUpr+e0hjp0JzWrj+9nSeadtIC+etlpptCu35ekJXfZrIRMaBXEFB65VaAlIY4RKsQqmgqXIszlopnAbbDG8u1vOWmclUQCpZDRzh2J91mItdt0G27CIMxCQOzuqgSSGzy1G4KrWASaWEBarq4JkIy8jFXDIkN09ME+gwkEKWMIcFJs2QBhraA5hcUOU7D7iXTHU7Gpv2YlzlS9vqnG3yjCPuO+ioy59pu5OcHL2TnNwCYOSuy/Zr/dTTcVpi4AVX5ePZcrrv9IZ6atSBEfLaRQP3kvA36v6wfeRms64vfnqmMf/K6c99l2PeNQtezpmn97TnbfPtohW2SRCm/Rwo2hXHpbZEEDxiy7zpc5v237rqpQOmHJ8iZPqrJDR9reIUbevNx46l5QNH5NxLKMnxhNa2ny6NP55mfD0cGoTiMMCmOi00xuDSuE3zAwqxAZ6BLZuqWiwX4LgFKCCTkgl3WwtKjQqlSq5BZ8qYSSS43AD+OAZIjcBxDIALDsIpwpwROg9h9DJmJt0Qg5s2YKCMskVtOmABE6Wy7lhIWTHEgjFEWVnRZGY7Ugl5C+AqTeVhg9kcCHHHAhcPxxUH/HEC8jlfA6AAE4kr5H6zXDyXUvoIxL1EDf8A1JTFGj4QEIEjvPcvQxAaGcAXvfUJiJqGt3HOwxBPjHSIm/ig9/1RVIqpz4FQ3KqVtEVevz6R0732fpmD6pqLf/aW+bGN1cH9NioKZgAVoliCULp8V04XgrT67qYpVJRFn4Du2TeqtgcqpTwsCKI76h3XpwF8CoLQFCBI0EmEkLerxvB9CALtK28OKkQREAliLocgnc0A6qsSDPV463z3WgA4HIJo+8d+CYQqqXvHtRIiTi+Kigr4NoQ7q38sKnZXVQFRdsJXUPf8D/KJ3DavjR+XSSAeBnyqqu/q9+r59eMZn4M4B5bXp/9wzJ/fzagQRb8PQMzvOET20xP2GJ+/j+cgrpmtAEAI+S0hxCCE6Kihhhpq+IjCYZQ8uGbfIxxGCUC+rEXseqkuL09rErY5+eTCcXfF0c9gjXZbGDyS2zmr2+yZH3SbNyePmfbD0NEtf5k84xtb7iNn/lQuHfQQ7Qsn55RZYKPRnGYTlvRAk/dX3TcPP4BsmhZt/sTzEuKD/MDmGUZk5Ai51PBI1pqxviyrAYamLWSakiELW/SnNYWVpaBNSov+yAAGiVNQaDjc6LS7W8ZdxLdBUtxQIBChTGO2KZulQGmC7qpZw4iMIKLqLsr2DYCqM06RKDhpApUREMJAqUJUKe2MM41RHnMN6KCQQOA6rmZly8jpLhgYs0J2memm3Xhh04xZE8Ojc/YOb40cM+s4ZQK5JFyvOkwLFd7i6wd/Zi/+yrdvuPJd2/zVrx32/S31fNnL9tvW1uZhsvPisENagvWGDoR0CfF4cP/IvTu+L7VGG6y2QDPRZOOi52561zYvKrVfTvoSTqTLNBrnuWTaGcFDS22Buvxo1HLHA8DW5kuyWu+ha/pe0t95xrQ5K68c2m4/3bIAddgZd+uLYYQmSOsC4cJ20CiQNRgAVSnAlWDyNEsjNbYROzcOrCy6sIpDsMw3IeXzFooSIIcB3QDXMq5cJmQgB2o1jJksCHBAIi7cffQQOwqHAXQmZ0RWUJwo89IBARCDAGFQGQ5jUF0JEosg8pwBPWVDNm1wpQgiU4C5YFbeHrZsjb1FwNVd2AQXo5wCVAEQzJl5GUio0H/lgHx6t+vWs80Mzv2USn9WFK0HAMiLt/+WvHi7QV68XUMN/2PUyGINHxROg3A1PAfAGRDEwQQQ9Ep6+O6Ke3vtV0HENG5BRaXzCSQDcAyEolftMuiTNFR9JhAKpu71MwjgIYiSFPoe2/oulgp2j2MDKtk7q9V5n7j6bqYx7J7x1Cc0ft9pVFw8faQgiO9hHpG7BSIL6e2c8+cA7AWhnr7s7X8SBLFxAHwLglj5ilz12GwAyznnN0Aohm/Ai3UkhNwBYBNE4hx447chFNyTIQigBUEMX0BFPbwdwK+9cfrqYAkiTvItAE+gQv4u32NeChBJYnzV0AeHl1gHlWQ6GVRqcarYnWT6xLDaVdVPXPRxby4ZKrGOfhKhAP766b+v+AYh4mbPwe5k0u9fBnAT5/x1znmKEPI6gM9DEPgW1FBDDTV8RPHn9XPO6aofuelP78w97c3V9Oye3oCdfOIo03z0C8HX7z/sGploMnVCBAo0ACjW9T2fmbhyfXTvxzcZm/eW+B3HuM2/+swsdeGrOj/jBuZc9Otj8/1zD7TMIKMSQdBtomTiZhI59iVo0wehxAskMr4PmRCaRJTkKZFYfrFmn305TX7ym4PWIfc/JEv6wbOtmRrLPQQrb9EEfZw40BiDzGSDKgoJG+ifBy01DcbY3jDsVoWBqTLCspTtgEGC0MDlEiRaVEdcWS6ivY3USdPWUIATDmo5IMSgdRJjjBQkk9koZQFHiiFmkIACu5VhI908qivuHLlcOvysTy3gQYv+XmqRZ5pR+875Dyy6zWLBvTRZ7n420vkSGQuqgU2xSQDGHr9/oWOECt+av0jh8gLIxC5xZLOSRF0EQxwqJBvA8n+//FvfNJ3SCmXzplXBzRu7AGDZ4s/csSy0btOy0LpfmB1llCOO+/T2kP3Sjrrcku8t/3x41oZtYLBgFZ6mTcUXrCZYtizpWy767O25s2/5NZE4x0gDMdw6RIZb86z7kLnptPyWs5M+DJ1xdFqSEklcVN/J3HBzChMaEYmcSEp1x0sjqgTCGLghA7AAXgKPFRHJcaO9IGkqEXY1CcBkAcp0StTyRmahn1AVEiJjtiMbJTb6Zp7wEkA1yoMykThUaoJ+nEOdY4BwFxkrDFUrIW1mMaDLcAOp8o5J4xiECxsAJwAk70l+CMC0EsyLCaCI2l/vZh7mRNjm336/f93r1/S8mSIv3i5sM+f9+CdS+z8I1MhiDR8UpkC4OC6BIIQKxFM4QBA3FcDPOOdHe8u+AUFCDkCFdPk39378oH8j75MSv94esHtsXXVc2n0APuntr7pgfAmVMgvVqI6R1CAIj9+nBaE0+e6N1fuWq9r5aKsaq68o1kFkKn2QEJIB8B1v/RAAcM4HIWIGj0clqcv+EG66BVQS61SP3U8ks4wQsgzARRBxd74753EQf8KPee38JDXNAK4GsMCbn1kQJLAEETu6BMBZnmvrCggVdAkEuQxCuIz6BP1KiIyoeYgMrDHv+P0amiVUkvroqMRvqhBZaDsgSGMfREyQrzj6xNDPIFvt6kq99zFU1E0dFfKuYff4RP+6KHnf98x/X01K6wkh/vX3G1QI7DdRQw011PARxfxUeEL9O3M798sFjpNy6oxWuV1RjYxGglleGI5esmZXq2pPue/6xoPNiwEguPDx70Rmrpqv9E1bHOrdl4TXLaM01SlBt8AjQ1R96gBH2dkMc82n5cSK6azEXNgzH7F4OMnd/i64t17C+MZZjJcjyEbf4gUmMTLWieZlW35Kkvt9sly/VR0t9CXDvZeiZfgg1NfNLMkIUw6XWiiC2tsBlsKI1mfpTJRMtvguLY1diSKKSNoZXkTOyqGvjyhEspAx2UjMRaqBayzBJHCZEYDKhLmqiXpVRpzHW4ZRchkYonmdsf6CZWuscSNL0wcy6x98/NI1mcAwv7xlYwj53swAAHT98mOD3/1R5ivSlLrj52002bS3KO07N7h/aMB98dpt9YXp7wTtSEKlTsYkaKiT4EhwbebkAKVUcpediwcXxrcOf6trR2YCUd5Vzd61zYqpquZwUNphtyqrxqY2lcvWNU0fc2caU9Oq2Vqc9c5dB7w9tMUoJLcYD9axjsMVSTsNAPSjn3w2JQ2Mq4fisIBcfLWunqi4PDQP00HgcELmNH9PiipriYY0a4zuE5ysR8170QaLJMxxZ5yVYTplOLJnm6NAKWRbFkBVSVM2AejQ6mmCKKRP0slpsJjLHc6lqVxSpzAKyypi1IJbdigcoAxYBKAmUCYojzC4FgUQgmJYKKOEgllEWc8g4dlmkUdOF/a3GEE9JEDyb6Q8I870im1uOYXoFdvMXBtmUUa5+O3371fzz48aWazhg4JPapZVLfPdDX1F6SxCyPe8dSPeuoD3eSsqhd79uDc/fs5PYPJH/HV9Q2D3mMQzUVHF6qrGYqBC7HzX0eqSDX4/rRAkrQ+C+Ez11lXvj0LECe7A7mQmUbU/BuDLAE7inK+CUKjUqva9Xn0/ANgPleQsjwDYCI9MVsHx+rRQIT8MwvgcC6GEneMRvV9BkN4zUSFdpne8AQgFlEEomud5c3AChJvmZRDxhYdDEEAOQRS7IFRPv68YgNkQrqQFCFdgCcJNNgIx3z7R92NLiTf2P3njngZgOuf8UVTiMjd72/iqKiAIra+AngzhdlutDPslQHxF91m8d81N3/j4yXP8c+EAOJNz7gIA5/yXEO65I6jEmdZQQw01fOQQ4bItQeJc3XhYZ5whp60DnbeSx469m9a15VWZSLz85tkX4HdnfQ8AjJCVkv7yRcLXHGu4HWtGjIaenbR5fZ7EdrH0hgluQdNlKTVBxrwVdnhOL/j8Z0xEs48QTim58wrbGD6UEhaizNgC5/AC4UaRksF9UHi+7tuG26VoZgcC/V11JFQEYEKvzxlc28q4vgPOfn9h6tgR4LLO7eY/OmBgGXc7imQN0aDGi9zO24Y2SOvVMSDaiUIMHLpM8w1SaixChuoSVEfqN4ynByVYVJUdZPThMiHZRIAqhIKCSZz1Tx/6kqUFTjr06bkDXW1aiyarasyMkHBG5q7GB77+5p9827wYXbKidFs0uf6ZR+x46wAGx4fKYQlSKISC4cBd3Ohg/ThDomSZeVKUZYAxl8WuXX2e9ErhECcnf/7pqTOunHHhve/a5oWwz0zFioX8DIJQe6k0b8FaFPvUwNBbcrrAbDY6r9StHrzm9BY97dZHdpyQCK24w3XLV7z9yPR427zC4dHZfXV8HLx0nxK1fjppijxYnEj2piCzmszQtPaYtldgVut40/mqnc/nrnPzas6QKNR6vV6KSg3QaStkhOC6gFUEkV1YKNNyqWmB8occ+ElmP5smpzH9zB33PFoyy7SEEl98Qkvv4tYuN3Z49FsmUigigyGYz6eRfbkYhGmGzVNyyLQqYRpkQQcEdYQiyCVoWgc69Lhw0vmzgQhk6L5t1kyv4hgHgwPkvYQQVCyDA+Ccu7gpbPNBZ/wSTnkpQIZBRMhIDf8zkL9dz72GGt4fEEJ+A0E4TFSKrO+EUJt8d06gohDlUYmNcyFixo5GJTbPz6w5BkECIlW7K2H3xDa+IudjHIK0ZCFi5eoglE+fQFTDV798NdN3iV0FQYaC2N0l0kcOwqX0UlTcRP0+fNWxF4J85iHIn+qNw4efwGUIFVX11wAu9j4XUUn04sfWvQrhnuuj3zvOBd4745xHAYAQciWAkyDcU0eq5mAFhBoY8o4j7I1tE4Sr5y8hkhFRCAIcQWWus953DuBNAPtUjd1XB2VUspNWJ/9B1bH6rsMlVFxRgUrWWZ8c++sUr//SHnPi79cvO1L2XiGvDxW7z99/9UDN5JzHvHnrhMg0+9+peVpDDTXU8OHFpb/5jZW1Tkgd+0OT7jUUkXWLuiEMKjLizihk643ptC5xAtRCzip9+kFaTDl5uXdxlO2cVY6f8KBrpfhyKg8fJ7eA9D9zsM0LXYrW8QqLvfjJYUUrhkpX/CCCDAFWL4X58FEled0Rslw3omBCP6wJG5isWpTVJeFO6kfhncnjdZpVXyiG0sPafTsUNqVuIg6cwkcbuPnqxwlf9CSUZ4+AwqIoSSOuZhnYYb0hGSTgtLDDpTGr5Cb0xKvdiM0zEAsSUOIoOZRtxvJKhoA6CJSjuT6evKVME5dNUpvlYLmeaYjRHMpu1shI/4+9Nw+TpCqzh8+9sWXkXpW1770vdDc0NDR0Q9MsAgICIiqCAqIyjiigjOg4I+A2KiouqOOAisOOCCoiqMjarL1C72t1V1XXnvsW+72/P27EZFaPv/me5xs/HObL008+2RXLjRv3ZmbEifO+5y22UFRSk3u2PxGbbQ/YZfNjk5OJNz1Z7QrPDTENL1wxBdfjHuvlMWydnKYVhbc/d4C1uot+FssZNzBwvvFjejWqtkVz5hhDSqZ4cafHZ8173VzWtApgULmDZWvWjVLOCz854fQVaqdTpDJjW75wZQIA/vnkd30xluTv7zpTbU3Yg5m+0Ngslg0zMxd+evjF2PHlpYVoz3MXlqo0HVO7Dqpx84Tde6pTxx7g+iPX/f7BcwsvzCGTjy/M5P/QE2dqRXHaq1A+MlEke/riTkXjHiY3sdfYsVyhNJ7thFUxvKqX5zzCZVknJmwosCF5BHANQIOF2KrI8OqL5qoP37SzSQfROGBoIKoN0wOoc/qt/QqxZfXluw87k+NTnAIqVOIQlSiM6C4rlWwFSpjBYw5sasJGDlMuhcITSChZlMwqCnYJ2SjAZ1yby+BMBigHoNfdovk3XNUHuJkCgHtaTu4l4H0fSr/cuDb/N9FQFhv4W+FiiJv/JATB8CBISqCmBWrYVggyEUYtfDAMQRQ9APdBkMwA7RBkxkMtZDRQrYI268NQg3DRn0FENLShRtCCX6GDECSyXn3yIFS9x1FzXY366wPzm6C0R5AneSRRBGpEsQwRlitDqHCzINQ5A4Jw2RCk5qMQpDkgNwFRBGphvPWhsitQC3U1IHJFr6g7P0IICeoR/grA9wH8wh+DYQgV8mt+/xmEiihDKLsL/HE5zz+GC+EGGxC5+tqRWYi5pphZziIIPw3qHR5pXBPzX8HcaRDkMYSZpTpkCLJcn7P5e/xn8yBgZm1MzT83ipoqWK8IH1lGI1CzDULIOkLIIES5j08TQtrQQAMNNPB2hssv8WhnKLzpumR5w5q02gJGKZIq09VwdiVV5hsm5q+Hc+yfBqV4VdbCalhauJ2ws35kIpILuyMLL7T3nOlgsHzfltkLxw6fth8RYweUdqcLSjwqbVruuUXNxuZTEUrG9PBnrpDNk54yYDRDlmxwmbFxaxrp9XPsPYcltp7LP0sfUnUu9bb3LSnMtXo2wFvwMlE6RzBlPLvTlaxBN2ozTQoTGglLnbF53reX7Hr4MDn4+AH5wBtz3Y4lYXRHCXSSw7hjONOmgqqt5/rddqudxNAdn0W6bhj2uDzuVpmsadQDQZhqUkvUgTtRKrasmzO3tUVRplqNpsNbvf79CusvyK4xFMkW7Qq32RQM6XDhy+hNReQ5qWjp4hPc0dXy9dNzwjw7K8ELzNFyXhGsmLZxqAwsnkPcNnUFXMuT4EGyqcE69Q+6HfoVAOAyRkqGh3c/9FNxbfbIr6xR+fvxx4ce7bFLs4oHIgc4lAsis0q36VIoHrpvrhuL9qZa5KVy3Dl5X1SZu2Cv2ZUtK/HzHv7Mee7I88e5lhT5Q/j9O9y2Dw8hcekYo5Qq2rJxcPpmVtox0sbLFoVuO+VqGjYrEe9obhLGXYQQ4kXQSZmzg7KHquxxp09GOek2/+m7uxMEqHrgcCjRCk2QckRWKzGiv/y1Xcor3x8FG3eUKA2XwojyiB5WpAhjmdLYkyMY0vMM3aq8AAAgAElEQVTIgkKCApWJp/6eTADFhAnACXlw4xHEZ1ybLQAREKqAMJnQ/3jyC6DiyE2sEu43LiThde8moUHbtT+sUPnT97Sc3Lg2/zfRUBYb+JuAELIawMMQ5KMewXd/CKLkQdFfvgPAORAkpRuCECkQClgTauGrQC3XzUEtnDFouwiRb/fRumMGoatBXUSprp1A5SwCOADgaYh6gzhi24Cc6pipTEUhiFJgnlNf3y8glhYEaQlKTwRuo8E28Pu30N/uW/76vf55BA6tQC03M+hXkMupANjPOV8GAISQ7wO4BoDHOQ/KRYAQsg8iP7AEEeJ5CIJwVv334Fx+AqGi9kKU0ggUvKJ/TkG5kgIEuQ3G898hQlkrqIWGcv94uwFshyBfgaupCjHHzRDmPzJqDq/1eajwxyPr9zNUN5aBGilhpgpd9PvpQYT7/B3+6wdowcMCF2LOAofd8wFQzvlz/8W+DTTQQAP/8/Hpn64+HMo8ED9hcxvvWQ913mFgugMsnvaqY0mvmprc3d/HF2HTwoJ9+9eJZGa3SkvoeUhs2Yf3Pt5d2tPmSGFH9jw6HZt1MJEpykgcWKXRLatAy7KNpbtVnPS44+3tkUtbBgiNdcM0bLfttatL9sD9j1p/d+dH2YbzoO5dC/dAv5Nf/ii07acqI12PuR1LJ2RpqhdYst0Jx4lEnATN0k3FUg6707fe+vyKBUffRD0Fky7zmtLtUt4a4VEvwhTvWIeAhCQUGVVDlEk2C1d7okXyelbGQCjbOo3hSsm08pZiVztp2nDJms6UE1E868n9mRBVQvISPWT+InkolFZznkYoPeuCLmLGHVRaTb79nELEinpzkal+K1IKF5LrD06OLkl+tC2X0nSm04PJaVRbOIPsseigKfGIhJLLXTPk8Lb+pAKX7P1p7PJjAGDBFx78vkK8a8A8d/vXr4wF03KvdM1gRCGdxuoDpQi19hz3+Tf2VzLWicahzqpkxFYUnjwzL/XmVRPsJ+2Z0yN37nmjzw2Rk09aWKi2hptjUnw6r8bzXqjVSWaNiqTI04Xxbx6TQAIAq9qhU8hDaJOuSG9GqeVMNTYldcAuL+Tmd58vJYEX1yl25QQuvS+qSix0FpgaglLdzsexh7QQF7nfJQDjuFDbqg0mdM9lkYrnAS6RQSFB2yyB2h68E2xiq+P8sDmK4ZAHz1iBE1UOImVQdV1U5GlMsDwmymFE4x3o9PLgd2axf8a12QMgqwq8RAJ2oQBmO9zSVQJHd1VJs5g1xWTw0MrovBO6tETnh9IvN67N/000yGIDbzkIIWcAMDjnrxBC0phJGiYhwkJ7MDOHMAg/DWOmEjmJmS5XJoDvQYRHfgqCoGyqWx8QyYAIBmSq3lY5MDKpL+QuH/H/oKxDQPwohBLXg1o+HQGwBiKMs56EHFlO4wUIk5qAUBb9MQlqNAZjswjAFggCts7fp9dvI+v3Jea3G6iJAeEK+k4hVNTrIXIRq5zzTkLIUQB2AngEwqQm6h/zWQgyGISA7IBwZyUQyuLLEPNQRU3FK0K4zC70jxnUsHQhSGEEIjT2FcwMOfY451FCyNf8do+GKNdh+scIyPcL/rr9fjtTqD0cCNTcQAk9cl6L/jgfWeIiyEmtn6f6vgf7B+s3QMy3B+B2zvmbaKCBBhp4G2Niw7zzAZ7tOH7/KyO/WZNpWbkuDIWCTfTwqW39U9rokil17vM9yROHmuTJXuAfHgfa91hoOcgwd1LHdCQzeel3vHh7sa28PzWV2HN6m7XpXLDINriLd5up1dt/gHT76dDJpyot6+eUHl12n1I8GUpzFfFtS22uNqsT2g473DwhY8dqd3LYJskz3lB49xhGN7cgEmestccmJMJICQYS7Qec8pSjyFNnQt660mXb5sogBVtuLXq23aGoTKdSSaaS0jwyTSe7m6x5VIXJEXWJWWFr1LbSc9SlNNdegmFLUPZ3eHAlsqNcIgPNUfrSlp2/3xRWz3AlErqqeR4O6Oniq62DkbARksxLs6xnbhhN001MfnHbYv3eV9/Y++B78nKUrHOi2ZWFUKiXNenUWL8/q+zIKaX3zYlVWxWSNDwPKRnTuwtuKqJpiiZhsiq76m5Cw6b0ow0vyZ/jTTxvuage+s4HOn/5nvccteFjZ+xccu7GJ6xI+4qJnr54S7zElhz19HNdJ42s7mjrDZm7F7NDzqb9xv72OVO/64BLQwuc1PZX2rXFbeSi5wzbCOnun7tQhFNMJVIFe7iltzx3k8vXdRL0UAkrPLdQlgxN51qok62iLl4pFDpld5hQ8sY4mAvvusoD0XvJZV8jUfwB5/GTkcUN1aeZGT1JaoMN78nPL4h137DvtXmjlYXrmsnu9Gy65slbT52CoqjLH9rjfObuyaoL1zVQiO/FPh5BTI0hiQjC4ODeNJ0uHWAjIaAaAlyEEEErOjCGceahCgOgOoJQMOISiZJILCpVSmV4nseJf7+mgm+wmslBFBjCHr3tfm7u+Nt+q/53QP5/3qSBBv568A1VbodQllZBFKMvQtyE5yCMXdpQU+Ys1PLSDkKQsRQEKQCEeUlAAIPtPwfxw/EixI2+A0GmgjBXHPEeKI71eY7BUxQCQU5tCGIWEEwZgvgEIYwmgD5/n8BYJgShRP4ZgjQGJC4ouwH/79chCBqHIGMyhDlMkDe4EYJ4bUWtjMclmJnfl6z72/CPFTh/Bn0Kvu/nQYR1vAFAJYTcCEEeW/z+nAxB1ioAvuMfu80/9u0QiuQAhEIYEMTgnftjEjwAIBAlOf7eP37c72cfRD7liRB5phSARQgp+f+/DOKz4KI2LxbEgwTN7+diCGOe+nzHMGaGp9bnnXJ/nAICbqLm/Ar855zFeqIIzMx5PBUin3ICgmQ30EADDbxt8Z27QuTchS1fU0DzAE7t6aEtFae1SD1TysgT2dbwce18yfpWuZtQe8cy5u6dbfEP3kiRO1Ydn5APpib0nsSOU1L6oX4bt12NcHFJi5lttqnMVdbKGV/4ZxO/uv4mFGPAO+57MbLr3RJxI3Yp9adibOVICg/foJJkEcmTt6hw+8AGplXacshJL3i6OKudx62Ro6GYsyHzAq/KGRhgxHTtrPrMNa6a0Lr1/BzZcLmjaEfJ8vREicpyXE1GqcltQ+Ho1eIu2LjJLDBHyzVpqmL/MT85/mQVmTNb0USmMrbXASIlZD00N6lh3E57P3ayfDHtCiUkmWcj2R33K4dklXlzSDQ/OfBJb27rN9Ibq/NGFvadUHlzE29Xq8vVLqXALznmzR7p9bk5uDEOkk8n5BZZbn1qDMXT4gaZ3y5ZClNjixKkWGSIyIRJHpHjFR2cswtPvrBYdRdEt8otmvxv1330Rvdg5frYukzL0Ce7ve6XW/8uZNp3yk6maOxt/k7p6MGFyUy0XRmfI614+ftff2jPJ64hhA84xz5yzRzjqqhMZIRZs17sGMUk7+Y9LSfEXU+K2O1joKSLOGfyf1fnOldKVJL5ITUC6CS0H32T6wuvydLYicl8eTrN45RZqNxHP1ACQD24lzPJa9fbFJfKbtw85ELSqXXKe7elCTx1GmDlNrIMDsZDB3MWj4WUC+47LJWJFwU3PB2K0ofZmMYElzVClIgMI2fCVGmSmJxpCMGCaZpwpHEMKR5cYgIuwGgFYBoIpQBhniexfAkKOAhAHAAa4BGCU9UsH6cgQ6jVfG7gv4kGWWzgLQXnnBNCHgBwFSFkDoDvokbAgry0IMS0vgZhGjWiBQgiE6h6HCJs1YWovQjUagUGxdjb/e0CU5rABCUgBPW5bQH5I6iVkOB1r+B7k6j7P697r3dC1QCchho5DEIi68Mzb4UIZRyEIGV5CLJ4EEK9W4Qa+QtyF4OxmvaP0V/XfxUihHRxXT+CdwvAzQB+6I/FrRDK6zkQ4Z0MoqRGQLC+yjmfCwCEkKMhSPXrANYC+AaE0hfkaF4O4BmI/Mt6E5lPQNQsPBXASxDOpR9GzSinHcBxEKRY9sdwGwRZDD4bFoCfopajGRgL7YWY+5V+e3n/fE6HcFF9v7+954/JLNTmIlS3LqiHWT9WR5rt1G9bgPhsRDnnDiFEgyC+eznnRzrTNtBAAw38j8aNHzP5yKbEA1LcvQK55jlGJP8jyeuRXc4cXTPsUtOvoSVabOLEFNlgEkkoOgamYYyum5jsDXsdBArKsyANtpZYhiZDpkLtBftopvXFw3bnHrPzwOlzjUgZ2q6FHn59rUOOeVMJ7z9GCefe32IffpDnLOKZWZeqkXFnolxVQnoHiSYKilJeRlDtRqIzCz6cpMWj7q9Gvv8vUlvcVQ5PvqM9Tzy2ZME09zp+z6m8SlFGjoKhlRMqKjJ3AV4BtyUHsVwrtxCiineIuIoFBkvXlc6zY/GIqnRMo99tMRirhraVprMpeWGzjGZsWXHp+47f+KuNMtzBtjVDbcvsvkr/vNbZS0rS5tP+TlrwJg8vSpN1aJXCemVZi/3SmGWFCClvWj4tuc2l0RJaYnqEDEgrFyCapTCipuruGd2jzk4s5nqYyhoHdJmmYhz8FGa4Gef6iqc+KGUsO5rUvtw0mt+USUjnwGDNCXXCPfm6nx5V3KUR+bUPUKd84leXX/+w8Fc4HscCyBUv3P46B9Z25HtvG/P2hRNmjzO1U63O+5D7wfaujz8zpK8vO8q4U85lPSSgqs3mVRHN/ghR40uiirs/IfPPyqzy4eRSs4nnVBWd8baWLUiQDqcIm8qce7yYq2yJedI5yqKQHEpx1x53rLzCvxOGd7MMihYAcw467o5j1H3X3LVjhBn2ivGVqfiUy6aPew1biyifvgGv/K4VLe+HQsHjzMvli3s1mwxQyVDDpAkhVw/lwWAi40WhS4CFMhgkgPoXakmoBUJrdIUNgicBkslJgQAKBY56hJvOY+/8e11VQycQQnaf95vbJ/823663PxphqA285fDrB6oQoYpdEDfenRCEUMfMHMPA7KYFoh7jsxAEZROEQyf89RZE7uKROWcGaoSzXmXKoGZIo9atL0OQjin4RevrUJ+z5tS1G6De3TP4uz6nkfjtdqBWjsPzt78dogxFvUNqoJ7WG9bEIUI+VX+bHAS5+zYEsdQgCGQrZuYu1vf/FxCqZYVz/i0AIISoEMY3d/jbFvxjEc65TgjphFATN0CQ3GMhCOPHUZuzMAT5/KT//ymIOZEh1LdWiHkkqJHqQMW7B8ClqCm9kxAK8gHU8iUBMT+BgY2OWlhpMIdP+tu2oZazGIzhkwB+A+DnmPk5CZTGv+SAGsxhfShyMJdPA3icc/5znyyugMgLbVyQGmiggbcdcnubClrMU92KOxy3B/oMa28h5J3cWW7emDYOtOixY9MEOQ16R9Zz9i2klmMakWUTLX9cd/qSs6M7nq2GaHQ34utnu/y4ptQgR5NTmdzb68rDA03hfWspsh3A8BygdwjGgqeqzY/cFLZbi3BX3c3Li18nedtAdOMlmWyVRKy2Cbpodkkl4Qp4OsQMjopyaEVMOuGxSfrwV9rBAGs6Am+sCD22nBMlTypzfudKGz7gSPNf0M1oBaUtnXDVITDS7CWK53IkmMwsCyF5wguxLkkKEYyW0kzXLITs4Skya1aHW2lmu6ZUHtUmvFipV6ay+43neh/6fGH4BJroqDptaxLKH39TNne+wflnPlh1CM0A86fxXGs+rh8tlxMLqqE06ZZzoXg69UvjAwUydUdkcd9sWepV0srIdHi/1FptkpjRS6RKlUJJSNBC4FqVE1vBndorRg4ckz89/5p/BYA753xUHTh/x8f7jhv/ZpPUDzSN5w/fck2CQiLLN3xa33/pz3sIJb3M8za8kri72bHp0ZHK6BovHf0ENebqEkJEiYbC0YvW3WpUl31KVcI6yahT+epkUyIly4f/WRkmYUnv/KreknvcIoDDcaJCsYMzjBGa34l7kt3OB+BwxStxt5K1p7wWNPHjtYP6U2pVBVZIAEwUy4AqOwDkuBKSXJJxZCRKx/bKEysXYCvMx07+1/tPUb1wKzNdYyw0qIflKMIkbMeU/idV7t63T930S03R6NRwGi53YIKzGFS4cKgLxm2AaCAiDAnwKCTJgOcqgOybEngGgALwqgY8+CQ3f/7YO/9ek6i8QlaUvef95vbpv+HX622NhrLYwFsGQshtEPXyAvXouwA+D+BFX3F8BUKdkfxtKgD6Oec2IWQbhCq0C0J1O85vlmOm02bgPhrkq9UbztSXwUihFi5aRk3FlCHMTt6DmlNqYDgDv43ANKc+VBX+NmMQZDBUtz4IkwUE0arPjQvI3JUQhDdw6gzKgHRCOMeeAxE+GpSQCNS2HIRhT7SuzST+cwhlfT9Xcc6PPmLdSgjFD37bmyCUziQhZNI/JoMwoAnGPjB5CcYiMLfR/ff1/vKfAHiibrt6Ekv9dWeilksa1K8kECGwAYnzIAjobNScT20IY5zl/nZn++0G5jOHIea6AuAiiM9fxV8fGPsE6jRQKz+CunUBgm0kiHDhsyDU0p9DENhp1AyZGmiggQbeHnjkjh8AOJsuTspexeV2cfwOSNLndXn2i1j+PI849JVI3/CJpdcWyaGiIWFuNqss3z1fAben90v753Y815FmxutqInTMbOKcQId7ADfKYZSzsuWmSg6hEk3bJGQQedkuhvMe0/SwG65OPcVsbYjipC0kYiahD3YjRjtTHcT2Svff4BnHPlImp/9aalq5S9cmdcnecPqPPCd8qdH9pO04IRZt7QpJhRGPl1YTTsDVREJ+/tx/IscOnsd1RUH76VOk+OpRoFz2bHJwrFRu6kiXyqFOuY9HFRu6nrM9TVfTdgJ/LuYSymaJ9/VGaVcyj2bFlsYtA0pC/qiXqFqMlaQO11R7Dkre1o32tCyr7bdvzby/59qDZy5ejfNSxIoro8eFtWe5686ZtNxMpjiVoo+2zGuKhkIZOhaJAV40WS25BC2yxOMciRDALAVk0iXEMOCO5Nfee95nZ1ybuYTV2zcuvLEgp7CkmXjtHQc3EJcOcXBsWP6Faa/vkGrP3cSq08fs7NYHlk+84PBcptPsP+kA5Uqe5NxWvqM3UuzPzHNKIDqflJxOz9m46/VlnurgJ63Y/wSpEpL+isWlJs7QrkhwAN5KaOl3eCypls/EiKQgotlSlpI4tE6UQeSsvrBCDeowMAfce3ltc+7YEbd3emKomo95nJpeZX5u1qBp0KO79g9KC/fuurBaTKGilrgRKuq0EjqsLk2kyDZUqhi6aCcOXOhpXgWyzDW5I+o5OYRQogwu5/AggVY0eFEGCQSABlDAgwQOC4T7NxTSRGt4/+Zv3bja0vUVAH7+6B/uDq7NpfNw+1v8xfrfg0bpjAbeShwNceP+DgCnc85/yDnv4Zxf5q9fA3GjHpC2PwP4R1/16oC4Se+EcOgMHDRNiFzCwDVMQS28FJhJZgIcRC1PMVR3zIAYftTvp4KZoYoByQvUJqvuOEGNv4G6Y9U/jLFRIzfBPn+AUM6CENyKv24CwvH0x5zzGITi+jhEGGVAfGXUwk/jqCmeQT+AmjIZgPvH2QoAhJAcISQgN7shypDk/bZPgciXPBu1cNEQZhas1/yxNFFTVAMSWPT3WQ3gQdTUu4DEMv+Yv4Uo2ZH1+2b747KlbhwN1MKMZ/v7BuYzSYiw2oDwB69gziMQ8xU4qAYI6mEGcxGMXxh/GcEDAsNvtylYTgh5JwQ5LvltNNBAAw28nbAAQCqS7v+sSsnpLce6P8TR23qwdO9lAEAVfiqVeTThJEJq/jiZbws9DODzACFVjyUMh0iuigUhKh9KUIMn3ugpo2wYTlN5QD32UIwf9yrIkl2qeu4zirv2V4SuOx1cLnJc+8+keNYznJoL4GldkMjQQejTHrWNkqaE1LA1P6q0Ovrkj29mh6bn6NbFd/69tGV1ysnNV9wYD3l0L5zzJjzjsluZ9Y6H6Fge3q6ty6Q/TWdsbiSJ5CSQaEqxBFmgNivxgWyR8R65H5qnyNzVUCo5VPJMe9DN8TfGQ/ZgwSMbDo5jz9T+e3flivsnwxU39/fUfD5/ZmUyFwEpaaO/M7Z9+7pfvfnj582zE2c/uWuCePR3XOV7QMJMkmTqcEWJTic0D6Q/JrfHzUKKTJgtDswQQ1lW0QEwpWgSQuBqBBojUKMeg1Mx3ZK7FQAeOef8wqPvfFcRAJgqbXfjsX/33owXNt0Tk379vlVrX8tvnfvGoQ2XOqYUIUZSkRU35JW4MvrMITR1jdLuOWO6Qd1Bxeo3aVucl7WYPHawSUoPRbF3Ey3k9YKOsLqqnDAf3j6QYnkQIAcCiUjyiZy5d/Ld8iN4OHVy/g9Sp5IlhBvIwoJI7dgCGdxKV6lFWbkAZ8gAcdefkuodXNzEDlXS9mN9r5NfzdvY9cCKN2/x0rsrzTv2SDqVaRiKpNgSiVbaeTeWO3P2DHidaG/m4DBhwrFsaAaNcO4RCoWEkEQcHQSgrgI1DEgizIoGlbYACi5rdddmxWSyNpFD/+9ekC8nofprs4cG/l+jEYbawF8NhJCzAVzAOb/WL1Se5pwbR2yzEoJAPMI5Hzli3ScAfBW1MgcAcD+EkcoUaiUiOMSTohYIghWEkwZgEKTnSIIQ5EKSur+D/MR6NclBTUE8EvUqYeAAyjAzx/EwhBFPoKDWE1cbgvy1QYTC/sR/3w7gOgDLIMxfqD8Gh/zzvJlz/q+EkGG/r4ExS0CKgtDYeqOZZyFy9wJjHcnvcwi1MFMKIML9HwJCyEcg8hkdf5sUaj/ED0LkDBJ/HUOtZEZwzEA1HIYwtfkshPpGIAxh2jEz3HMQ4od8AELNDJ6qBkrxkWpkMP5F/1xlCPK7BDXyVx9KWl/+pH7u/hLq5+ovLfcgPm+PQszfIIQZ0I845/f/F+020EADDfzN8JXPPXo2OLngi7ddfO33foteAOkbLsSMazO2nLUSy5/2H+4dmXdNbgPwUf7afNUZWiHRvq2Q5+65H63Ox7JZpOXB2SHFaCU4OB/SnD9Pq51Oi7eFV7xzMlEnmyJWPgfdWQCTlVnlT6fmm0fObcalX4d8zDa42xejnOu2macqYa1A4oePRcEpme76RTI9+xdmpFOKpv/0SRRWP4dwx6tOx/d/TBzKZGnWTvB0KwyrAmZFQDsP23p6vnq4dSfi+pSRaM/plS1rWHWyywiPHCVJSEg7qsOjEbe1Jyprsk4ID4UpUaiGbc07+Ev6vmpk8+y8J7O27jibSJWj/zbYa2aO+eqS3V+5/ulrm1jhuBj1eprfb1P1HF1qTR/cba4p93DQz1yP7957ycN3jOlhHgotUaTUVLNiKCapcJtDIS6aqINYIgwNAAdjMnvOtM3TiUSIXrI8KJoEwyijqzkMoPDuc+5PgIH0qWsiS5+4gQPAfYuv+wjy7IcmrzqslCiEm6RUW8sk8Uypqpz0ykMTd6+4vue3D36xZbz7JtdhXvm1m4wm0hW1My6GV7zEQ0Tm4TCnjvHUcGKue9EvHz/rDrskr8pWm8gybXi85WmjXVpOaOh0yh7dGaEDi839p07tr2R+M3eAO3gT3DiKexJIv5qEBMmzAEJc5pY8LmdUaXCVavc6jqoWneIdyafCoYgqD9BZ20/e0XeU6lE4U4xwEvKIRCTmVlGEYycQkQkcOoJBexiDajf6oSIEBw6GMQQZHBYsRJGADQs2TDggIJoOyTFBGDvy2vwYFK0tvuTEA4UtL5xMgO/fz82H/7/+fv3/AQ1lsYG/Ju4DcDUh5PMQxd/P/AvbjEF8qf+SAnM3aiGYQeH2X0LkuwWEMChroUJ8fusLtgdlMFwII5V6cAB/RI0M7EYt76+eFAYK4ojfBwc1VRCoKWsOaiYrQb6e7i9r8fs2ilqYa6BImQBeRc04pxeCJA1BKHlzALzpj1EQ9jkNYJ9vDNSKWo4e8dsLzFYCohioeGdgJlGsr/Go+2NXTxRLEDmLwRO4ehL4O875tZxzF0L5DMjbtD/eo3X9sSEI7z0QeY0mgKs557P9cc+j9tszyx/rBATxSvmvesMZoJY3GIz/mxDKLgDMw8zw48AMCZhJDlWIufi/oT6c+C8tlyAI9och1PGjIYj+4H/RZgMNNNDA3xSTTdr9+/q1q79602/+DuLavPY/bbT86Sn8X67NB4dxi3tIUUstEx5Smx0argCKc395L92nSwgrrRnCGOFgGqmGtZCdaaf2S3fEKh9+yKBbT4J6MOWCSJxy1TW2XfiS1XwAZi6B/OEQJBrhSjz/TGXbO0i+OAfjzY/v5kv/HApd+CdZYq2SunA/Yhd9AS2bO73m317JNT40QhLDlml5tplxLGnONh5etgf64DEKSQ2jM/KcY/e+SEutk54+uYC05hdGPIRDNinIC6NOcqDNJTyUHotoir2xuqe809trbEps5RWt6hx9zvirruYoQybrCS/+9awfHty399NX/XbHzbdsWnDtl6ZnxZenN1rLrKpLLeah+iwDpgnI3osn7n0AnDcZQ4WQktRUSQsTTERN3u4UEJcU2CQkHBEsznJpWijmzyASIQrjnhfTJJQyBjyqweAUQKhPXhOtJ4oTH/lJ6YT5C+9gqbBnNMnESxlROUtR3B3lGW/ioTN/vvfaD/IH3MV7b/lkuD/t9Z41KdmJw9OWXHWnki+PapQQzm3DUift1oXlvpc3HPNMs+Su1uSSsXrhyNU3/une2cqKsSEcmMyXnxihrTZgv6jPyWyOZflZSKAdJ5BOLUVnq+LazAGJA06LQ7xuSzJhu11DFQVpD2NK6s1retZaHz7qDJzcsnA2ypIFWyIsDJbtmMiUecFz4SJDDqsFMg0Ghk7MUtvRWTbgwoQBGYofumWB+P9c2HABzwGHRDnAGHDEtZkDV9mOdabSM/toEmvOQERuNfBXQCNnsYG/Jp6CKIfxTQiFbOrIDXw18d+OXO4rkZehps6VAbwXIh8syC+0AFwL4McQRDA2hFsAACAASURBVCaDWrgoIAjIzQC+BJHfB8wsUn8mBKl5GsDXIMI6KWo5iQw1F9Y+/GU3TOL3rV6JDAhsQMokCKfQRQDeDfjPE4F7Ib5zvaiRz0sgLtx7ATwGQZ4mINQrlXN+GyHk/RC1EV/0j53x24lAKHBBAfqg7/U5eKg7jyCklgP49zqSuBkiDDRQ8sYgyNds1Ax41hJCiL9PQKgBQZ7KALr9vwOzm911YyQB+CAh5GYIh9YSamohg8j9A2p1GOvLgHh1bWQhyDIgciyvgJjruyEUTA/CgCdQMoGayhiQyShq9SYDwnqkonhknmI9iQz6RSDcW+MQJUAaaKCBBv5Hoq3gPKlW2Yp7TrHv/ATDcogojyPAD+IvXJv3HSSzZIL321KUKglDMulkOd5afa+7c9H9UtJuYlMGc+Njlty393K7f9NDcosTLt79vky4GE9RVlCQS4BaF1O1sO5mLp/4pc4Lf3mul1fBundy2mZ6LLpZMvLeaV64cniKDfx5pxm6rWe4suOE3ADRB5fpvDjlRRZNMESYwi1Xqsw93EcXHJZkNYvIrDysUB+gVoDtZVLeu7QYjS6Pq9kNMDpHEEkNs0rnCBnfRtyI1iSHo7LSltvwzQQ9e1m5TM6XpYQ2jSKbTi55YFNxKX939enFS48a5ZZB8KPfNF/iatJar5tv/9VT2mMv2Mn+7Nzk+Hln5h93wNVbFn73tl/97Pr1niT9UrtUeznVFy07Os/YxVF5Up2K4LTWJIEeQtEz4FHVy02AdyaIY8g8pGsEABwqSYRR0ESzXoULlq5wPVu+PSCJd37i5s1lwvLLqCunRyx41uRBSkKklS2czaqjnglTpotyZ//hs+8n53zrYQ4A1vhsbkRHoSmJeEEdKlvqpm4+vAJSe0jvaJkgxeTk7uiwGU2qxab2sEVPvnjv1fufmf/Px90RP+r5k1rLqsX50uEsk5MOw0ntq7UYYK01WOmXxUwu2ZQkk5B62lXAYp7HHLAeT+LdhUmMp7qrjCK8wF3JtoRWKmHpITlO71bPVT/rrrM9R69uKBija1vjHZBzKcRSGquwLC3lPLudN5N+LIruxS53GEOUQqKMuAAF5x4nBeRBARQBkpQoiKYClsXB2Ixrc/C0uPDS7zeilA2MEBv4K6ARhtrAWw5CyCwAH+Scf6VuWRNEftzXIcJHpyEI07WoqXt/hgjbfAq1vLfgxj4oiZGFIJABGAT56sLMm30bIi9uZd3f9bUP61XDoJB8fY3F+lIaQZtVvz/fhch7nIJQvVQIMnczhBPpjyFIYlDbkUMQ16cgQjdf4py/ixByq9/ujf4xpyCIyQV151fvqFp/HoEhjIL/XFw+wPcgckEvxcycwgMQpK/b7/dWACdAzMOdAL7ob3sBBFGrJ1rB2Dn+OHzKXxbx+78RQpWrNwoCaiVFgj4GhC7IEw1Uy8DcJzjfPETY7zx/u+DBASA+Q811+x/pbBrMX339RAni89Lu7+NAEPmAOAdGP8v9/iic8yE00EADDbzNsevKrbMc2Tpv2c+O/2Gw7MAh0sSBs5sPDnxdssPNeTc73X/w8kexZdW1hYu/43jz3zSlvsqzkhb6CamYT1EKqVCVWGb3B2jvb08D2ra6cTkijT9/alY794mUsvAN0OfPBvadyEazhYm+H1zeZRqmS7U2mYYqmNhcsZ94fumBDxdvWqRKMUhznrO98+9RUW0D29vjSRsukLxVf/BI8yGETbtc2rk6wueNyPS+Gz3JbZaQyHHH8rilFqB0jdDxSgQHRvPVk5tmU2k8/a2YPvkJWPTwyNS5iwvQlf3Nu83USemb/3Hwul/84NC999IBfvbv9rnlUV2JDq0P8R0m3zrGS6/GFpKrYmv1l4Z/9IF33fXbE28FgGZ3/j8YMU2Sx6LD3soFb2xLxC5INzHAcQDP5IDCYEclyDaQdWx0aio8xl2quI7MFM9hbiTtEq9sSpPJEOQqQ09IhzpW+l7n17d2RJp6L1UlmUXOfAnVe+JE3aHsLLSR5tBEqrM5Ixnm8c/taj9NPpYx15ZfTt3FHemL5EMEyLML3KFVd6uRaRIZeAq5ieMRby96vUdvkEoed+77/trvHqumrwu39TC5OaZ75cGxbZuad0VfLK8hcEhkqSITTyJShwzWBQM7mcbzLt05DRAXzmJLlcDAsdyR5DkKvCGYlY085MncYX2eYo26UJudfPhM/XCkVZlX2pKj3qCrODKBHKXIj2emYEqpdCxDqB6h2v64G+FULiPtUigyA+ej0UHuuR71TA+A5kURkragMD5P3LOQHOA0AfsgHrB7FHKIwZ1xbb6fm41r818JjTDUBv4WeBnAFwgh3wgWcM5zEOUjOiBIQzeAqyFu3jcDuAVCgXsQInzyYdScJ4Ob/DIEUXQhfiyC0htRiFDWTF0fbABL67atL58hoRbaSv126+soAjUiYaMWtjPlt/VZCLK10F9nQ6h134BQNgPVM17Xlumv/ySAOCHkMICb/Fc9cT0eIswyIHdBTh5HraRGoIbVO44GbVQhCBb8ZQP+siwE4brQH8Nu1BxFW/zz8CBKZQSmNqtQC+t0/HZ2o6bk3uifbzC2QxDqbgbCxTXIR8xDhAgH/a0vPxKotvD74mCmapiECN+9HCIctx4V1MJZg3kw6tomqP0GBvmXNmoEHhDkM+IfO8iRVAE8yjkfaxDFBhpo4H8LOHNf04n8rW03/ew/rs1zBnhu7kO33q4Nn9KhVhapbdYp3ZmMdDWMdinxx+s3K/PYlyRgLYf5YDiMmFXCE1q1q9gSOgx67k89+eIHpcK+pJlk3Snl0SucfOt+09i4yKMEFNUWXY2aO7MxuUjUEqSYBdoG86prhvslmnbtOc+b8jteVAlJgBd1TovHSt7s3QZv2grSUpScVz+YQ6jKCVHA3vc9hnAFsFWULvuUazFiT07Mc+ymaRy3amrKbh5h7ITsP04v3KRlexcuUfoPOSG5YLOjXzw0b/7L33js7EtGW0L9a/ryA5itqdFWjcOkDnEZqUgUt1T24pPf038Q/+X1pxyePeDc1DWXf1ZOZKlStbzfntalvRo5uDqd3ZmG4zIoKrBj2ul8abeEyjSH4xhuXFXNog0oOqdUkbjHQWxOeUqWnA7N5RWz2jxl5jVFAgWtKp464BkVi6rV7IKLD81b8+CzF7rLCz2tltUZUSa4hbJub56fAjzXG9NZ82UHPt5728aD8u9LZvxAaRXs8Qp3JmEXo07eKxv5wuTuyYLKdr2ySDrm+Dk3pruPOUCprBIKVHJtYzFZOkU535juec9ojhkqqU7JPDPsZaYGI3+sFCgleYZ+l/DFbaqCXnC3izGkFbApDnMaISjEhsspK1rg4Cw8P5qUDTbXGHIvN0ZYKTTJoFQBXvKQmTRzih6SYlqYwLRci+ccCTAGsV+24MKFR+yy7RNFMMAyyija82pu8oj4ZoYptIV0hMMM7n9cm+/n5liDKP510SCLDfwtsA/iCz8HAAghCiHkGgiHySDE0YVQGb8NQTD2QKhEKQiysgvCmWsXagXgg1BSAlGnr56MLIIwJXEhFKcoauY0QW5iBjUiUq9ydaOmWCmoEYnbIHIjA9fRPgjyImOmCc1c1MJDKUTB+YBA3gaRN3iS385LEKUpUqgRIw5RSzEK4QabglDoqhAhrkAtbxJ1+wQErz6uX4dQWd8L4UJ6PoThUAzAQ37/NH+/AgTBSwO4CkLVlCB+sAERzrvLH+scarUhLdQIajeALwM4h3N+KgSRbq2bCwJBxK6DUPAComv77WQxE4HraVCL0fa3uQ/i81FP6o8MxQ2h5nobvAIEDxVUzDRSWu+3fWT9xYWEkKr/uW2ggQYaePsjObxfiu7nSvfrc8QCouCWb15jZvqbcsPtXq51I/cSQ64z+66vo3ns21iy40xrf2oPxnqa+VgsxQzsyb/5xY18cOUTTVsHdtGK64UjJU+/4Du2QfZ55IpvUc1VJtdd/BJ5hm7l7BenlkZDZNHh5KzmLXKbu2+kOS119Mcnx3vC7j/9I1NX/Myh6phTGTUyZHwuwUQb50NHqdIb10jYeBJYduGAlIwocsoA5owojlTmvBIlkdyib+JDX96cOu9Bi5cNHM4lelnToTDVXblkvjvq5VKS2uwqty025rKObDxHytQgDp2QytvSlmFH9amR15+Wb/vcbc9Exth7z7Ccj5pP3//IS5nBtuMKe7tS2ariTDQP0KFVvXzrivaPucloXGtJpNq7pWaU9j8JdriqHBVRSyd2AO0JwsOSXpmkcDIAOIHneja3ZJdGJGKpFAhrcmuS6eSE1q5yt/Le0qrUH6FK5x9/7QtXzr9kXSz3YvdD6q4Q9XoGlYqje17azcLODlmGM6m1eR9Ktsiv0dApkn3gksScxd8DlfE8ZH0nvPjkoZyaIyFXysoD5uCOU6wIidJ4eExq6sz0/dMdc2/9py91rb3k7p+tSVdCn+2QJttDqttml/Ouo4OoY1aEKOqXS1N8f9a1udlkEcZdB6ZlV+FUmQTYUxzaMQBdLuf1M8JS87kRL7UWWb3btrODxWzphfy9sdNCTdIVOo9oUYSdKI+fRaKxU0PoXNCOrnyH1IZUKCLF9QSa2RgOsREcYBQSNHF741+beXBtJjqiXBVpOQ9XUeU2nBnX5stJqHo5Cb33rf3y/O9GI2exgbccnPNTCSEfgwgzBcRNfH3dQQ6gyjm/nRCyHCJs8x7U6iL+C4Q66QJ4w9/vdgiydon/d1tdm72YSZiCgvUUNZMbG7Xw1SJqpTgc1EI5GWrksxki3OH0uj7TI/aBf9x/hSB58I/zOgRhXQWhJL6PELLYP+aP/W0AURZjvz8+D0GQ673+38dBKGeAUMRCmBkOGpTXqC/1EfTnVr+tcc75ekLIeyAI4iKIXNMcRPjoY36f+gHcBUG0RiDCcsdQMx2qdykN6h2afpsT/vFeJoSkOOd3EUKCYkebIdTdL0OokHPrzmEZRPhrO8RF4UTUiKXtn1Pef2VRCzcNwCDCUwIE6QzBw4j6ENh6BOSaQoTYXg0x1qxueb0767kQobkNNNBAA29rLL7jotVDd1/1MV1xgmuzbqz9s149rKCSGJOUQhOP9o6WO87I3o4fYBVef++P1YJ8j/uuOxmTqS2F8W3CpNf5r95rehOtb5BqCoWj/nB74rbP9YW3r7+E903A9njrmnVnUzkzC9LED3qltkNE5Qe4M7pAytmpFsd0mOJSqqmaylpVubj+nW7YaEnhqJehDw+X2OSJMSdnAvNGPe3k30pledpl9hRz3YOssPLRQtIba9Yq1nJZalmpRzwpx8OcMVuyZ23D/qc+7nRUj1eU5BCk1oP8lnfc/K8HHv5Ii0aHoFWTduHKLRu8w4XpHtNedcFpWz71xmDfu/906VeO/uQX5sa2v7bkJ7Rw2I6F0rBWze610pVDdMJUwyz6b520Micv5faqk00hxJPv5IZpeJKMMrWqIEQnEiWRHg9wOQe1qCdxlaoh6nmKK0mmzCkHSyaJti1zK45vX8AY9t/4m++s3/zG6lPzYyHNssj8N3957YruCs9VuzN3hYq4tVitJDSY/YNfJ3ev+cCAlhvKjsjLpkvf2vvPY57aH72qL0U52qisjti26qmShOVyRKco5kzIhpbQx9P3PPvyrVbZ/fNDt75wzxdfe/KuJ85ZebvnytCwe7M0snApYvKn5vD1V1YvmDubsRixnjO5ciKWTm5NbLNyim4VMy+mVskn8hAndNyMUV2x5QjxeIs6boBNq3ulLKNKCgYhaFbhxBgUhbB58+Z3Z/5YAc0pxC6ZYISzUbXqFo0sAE22dJ10sm60WCpGsA8GqoB/bVahEQPl3xvA+Qqw2kDZCyFKNGikgjJI7X7nYgCP/G2+Sf/70CCLDbylCExSOOd31S2WIcpjBKoSgahfdxEEqTkLQuFyIMjBXRBkJYHaD8N+ADf47TEIYhNgGjUlK/jMByQqUCMDE5p6IlGEUK8GIMhfHkI5bPfbOB+CqARlHlwI5Svp99X1+/FO1IhMCMKldC4EuXL99l+FII/PQhDCvX5b/f65xyBcN2XUiErMP7eA/AaE1fZfUb+NTRCmL8HYrIWob9hKCOmCUM+uhgiD/Uf/eD9BjVwFZDBQJcdQy+HM+OcbgEKE47b6Y/hjiJqRCwE8QAiZhgjzfAbACn+fr6KW58j8sdiFWp7hasw0H6IQYcWL646bRm0Ogz4ziDlMQqiUBdScaoPQ4frfwGD8boHIlb0KtbDkPRB5iwpqdR51CAfXBhpooIG3NW5fdRr5zCvP8f4P/6J2bb7nMxJNTFzB52/ivX2HibNnIYnvOUbF0eQiSJk58JrP0H71iUm+83iHnPFQBh/8xZ39/5ydBEi00r7Zo7YiSZb2FIA/hk58Hq/tPYOxptPCc9LTUPJtyHcfmk4CbaEcY3pyUopUZbhKiRSp51Wp7ckdCvPoHk1xWjh7cz63DqyS0lMxMrHiX4qLVHkS8cGBqKwoSO4pGruvjau9XkhSAZg951ferJai1tLI7NxiWpWG3cz2VeU9mzuTvDfvJKJvutqHfqYnHemdy7OnU2n8TZCQGULRO61YNOdZSllRi7obJ4Pz1qydenX2sv2rFc17gWih7c2qu9deU64+n7qyt6/w6jBnchTIDM5ViFxsK7JprVmSKpGIIqezobjcXOAu8yBzWaKUm66TS5ftZCoa9VyzrLrSJi7hVIcRMNdh0K21xSn2lCsrsRv+8MOu5Oh5T5116tOHNn/jqBvl6sYb4/O9oYF3rv9E8UsnM8SZq6mSTDq6rYjcHpKmm7uGHjtu7ISQFHnJPSSXSpWMrDvJ1lUHoEgaRos7qZeZN4VkSlybWfrB9I4VXyinR5e6YA888NV3TocPtMbTLXufmTpp4LhMxCQvb936bx+bnCdpPcSlBJ7SVt3iTTTvimVdQizXCxelk3FIceUlkuJ1ZSS5xaNW2dlsWtqxTg4oJgz0vT+UlzRZLdxZYYohM3RGZZ7m3vjhsUq8ORmXTmbW/o3TeSVUbT3GOJ5mSNHdp4yiRNIysygMGGhDJ1ToVAa95RAy13FY5wHgGiSJge1x4cxl4BIF5VzYpAaGhg38ldAIQ23gLYNvYnMZIWRp/XI/X3EbhFIWuF8qAP4dIlzyQxDqUhAumoBQroI8ORuCEExiZrhgxd9uZ127wEyH1KDYfEBUg7ISQamLnQAe8PdLHNFOkBMZEE/mt+dAkJBDwalDEM0KhKqoQoTQpiHIIQAs4Zwzzvl3IPL3boQgYnsgQjcZakSxXt0K3EHr3T2DcA1AEMb5R/T3eAhFL+Gv/zWAH0EQah0idzFQZqf983kTtbDNdoj5GYIgjfV5gS9BzMUYRL7iDXX7AAD1HVWX+m0EBBAARjjnMQgCVp+XGITz2qiR4mcxMwe1CbX5k/3+3oBaGCtHLbzUQ63ER4D63MibUKsHGeSuzoJQLyW//UA5jvgqeQMNNNDA2xLfW3V603HzcNmhm0+dcW3GFd/JWf27tpWie8r5gusx0waipgLgHvyh471GdPSqyvFP/QnhkuNpRQ8gSYBVAdfNXXKvcXDlHtvLrjm+vEedNMhhFrY0Gi5ypL/846r2hVuNyHBkp+FRz1LAUUnAyPR7b/S08XAiIulhqKoXD0nHbOcYX0Ck9FLK7vhAOPeJSypmlWHHntwLdz2y4b7h6gFk7OYoO9zCJJYBU9PwPMeNp00utU3TaGLEixX7mb7lUvX4PsuZFZuSD554x6HK82vg/ejrZGT+Y/mK/HJ5Sn391bYq1BYp+ax3cE7anbBfPnXlJgws2L4E4N41X7/9Gys+v+mVYmL0c/M/PDfz0fvu2HNM0/Z/aO6aYl2RUZk5Jpul7KJzs5t5/8Ee5g63NDseB6hMbUopqA6omlqZtsKEc8gOjxolMt8BwME9XVMoumIrYpXyvKRSjgNFyUlP/vqx3y25JcxLbZIW1u2EPS+5VGnr/8GvqTNvsogux3GbX31i8MVpPrrFhKfp7X1yq3Le4uJQ/7VfjoXPfl2SKIVRMm2kJ9YnOq0kMH4IhcxBWl15JRtPgA/P6kaGQDO4c9G+J7h2FD1a0om6QEooNM6p2STBHR09cOn9D8RpZ/PxtIkQzXHRZNuePlui2tGeStpt242Du/A45/wVeG7ecyyY+QpCLaG4W1UJSSuSW+VyqTnzRnZn9tP2vHLaaM7DVMo83GtGJApqwfTiXKmGULZ1pYqSNg0TnE1hHIcxiL3Y8w+UooXEWkkYkhFHMyToA3MwL9msLpASGJAoKAUAFVr0PnJZ49r8V0LDDbWBtwyEkASEKjjBOb/uiHWXA/g+BDkZgAiFHIZwQD0FIvft3RBhplGIMhQjECGMH4IIWTzOb64EQSIjEAQmUL4CEhcQkAD16lkJIhy13hH1KQgVMQiDBWp1HutDTm0IktLm97c+9DPAkxCE8UcQZErz338KkUN4AEK9242a6uVAKGQJf1n9MYGZ5R2C/wfbBLUdA6XV8MfuHgjF81j/3avbPzhuUI7jIQgDmaAvt0OM6QMQYR6puv2CcFQTQq28zB877q/7hX/sdaiNcQ6C7G32t9/rH4tBEMICBFmT6pZXIdxsZ0GoysMQKvRC/9jd/rh+zX811Y1XQJqD3M56p1SOmnFRUGZDhvhcJlAr7RKMFQOwinO+FQ000EADb0N8d9XpiSsvdH8eiWBE++SLN9Sv80A+ZBX071ZLRiESQZfiIiu3YgLAU7teW3wK96Suj3vvOvPSOeuuvrzttWhi5zH3It03svONi74m751/WVvb4zu0xc8cgy6KgqEV32jrNVpaXomwdNWbIt2JOa0HwAuKU56Ypxywe93yQiIvkn+PpXIBrql64agjOdMpG5vjhnL+YMLLw5OkhFQcc/4Pe+8dJkWVto3fp2Lnnp6cYGaAIUdJIiKogBEjigETuiZc85rXuOYcV12VNaKYRQyoBEUJgmQYBpicZ7pnplNVVzq/P06VPbrub9/v/dzXd/36vq65hkqnzjlVzOm77+e5H3NnDbemoHfMdC/xW8FJO1PhLY0kNLJA8lTN4ZKmrnkKI5LSXIioZ69m1A3ZIu05vCCi6yUf93+Zv6B5Lvwj94E7eDXpbjHhDVcs7QgWbW3fNeixNz7VmyeO2iQfMGlrc3Zl/IXFm0a9k0xK+4+bua/Ys+z8qiw1JLSO/RrGsd/o7Zov2ukvy5J4gdfqGvQ6eZaI7nFoKbKgeWOWkbA4ORQEpxlWT2s3x+e6dL9XEGFaNKULKcvFuQALbhMKupKPZJXrn3oRP8p1u2cikYXZ3Zpp9jZsRv8mYOjQdn7s8evR3WyqciHE/Inhd6x677zY+n5oXFGiDTpx7/1tay7Nn3TrQ2/U93S/3bZ5UE5XzVRoMnjOHdey8rolJdarTJyx//xF90w7p6AkcIRgxWlIiWlCgD6pKe7qTiP5rLHHZfmDPC+7NkS48OiQEtO/1poLLySVajXvd8EQYfVs6Q6bSau39JxQhRLmeLcf0PdRy+jQk3W5lVWm2ZhvJXr3DB3It6kt0pCuJZ0DRWhm8NpQvvqW3izw3H1tlW13a3oqEBSzkOpQEUWv2VTfzLl5F9EMnSpRQnhqmDzAmwClgK4QqpnTc32kOWWE9iYFE2ZrKcpzVAhEQbvoghfd6KIUptUfFQfdS3dn1uZfARllMYP/MVBKe8EMXV78hcOzwchdsb39HaX0QErpLWCq424wtWozGClYBuAKAHPAPrz3/UZ0P5gy5MNPQyRFpHPs+hqhOMXdJfzUFRX2ucfa/3ZqMspgZSGiSIclxu1rJ4DlJ0bASNAcMNIC+z6DwMIqNTAiRMGIzSVgZOl8Smkz0o6lACMsfQ17ju9zTMdP4Sh8jhIHMGLljHEFmEL2iH3O0D7jfBzMPAhIl4lwwl1VpNXN48AI5INgxP5TpI2Ckn2ufQMsjxRIz/MGAFchTdCcchf3Im3cgz7HQvZPzN4fR1r9nQb2vowHq7k4Dux5l9jXl4A9pw6kQ44d9CXHzvidbUeZ3QOm/gLs3XSUUEdtdN4lZzuDDDLI4D8OV323ojc7QO6VObLo58fa15fPNFvLvFaHmJvQJWpa0lqAjgfoLYRY2yGYVd+QwcnWVHDb7q2HlK9sGbOsti2wIEfZcAzvCYvRuoOHm99ei9jqOVhZ6qqxeH9BYVvQJ8tKMOBKQfKIICIR40IIOSgVsMGDMbm9EIOgxKelIFGIZV2SYrYKqC2DRmFZrl6o24fzUz1jppcbY1HgKeFcbQPdJXnHykZT5RNuuTtKFF1KNcsWUJcsKDalwNjvJ7YUrSnqdu3rIFYw8uagLyZ3Hfh2i0G6EY7vTgljvh1SOfWD8dOiuRondoanjeimWcRbvP66xy6pePnmH8Z8Ove8Aw7e2txZs6anO7cFpr4fRo1k9u/Z3l4T9ZKWyADru5bcp4RsA2HvPhhIGbLSRbiABI4mAV7n+GxRdwfdIo1BQ69MEDFiiSouZUZlwCe/GdfJkIYtvkf2NxaLWW6+sr83jJCvi1cPdz/YfzBpsioK0dBabBh60CVSF++y4DaJroUOarf6n/qVWHR8w5yyS+9+c+P7uQ/WPzKtnC+LfuqZ+JlpGFE90irHEm3E8nksN+8PvzFszJZiIhkQg4RiQLbK5QV288n22SINcO2TC4zggd1EHFDMR0bk3atFk5Uo696YNUmEUKqbUik4msdlmwGEGqu1mBk3EX9Xi+tfg9MiossyuAnuRrG0X1KYLMe0s0QtNRQkLysGd37XQ3FTaPSUkDrX/VX11ZFoOGH2tvWYhmUBGkeFHsHUDAM0RamHfcXNA0DCF6I875U4StzwDNjj35fs4SGBgA964BdFGKKGlBJFD3gQAoBvQG1mbf6VkFEWM/hNQQjJBQvJLMdPiZ0OoJpSOuEXrnkLjFyuQTou3SEXjblGvgAAIABJREFUACNpWX22w2Af8p1SFUkwMtC3DIaz3zGKIWCKluP86YSZOjlsfgCHguX+9c1765sv1wDgWvv3cWBhqK1g5GUfgJ12v+L2fQUAtZTS4YSQPDCicxTSJjyOCrYMjIQN73Ovn3/xoyEdktkXzwNwHDxNMLXWMTMQ7Hteac+F225fBSOYX4LlVe4Am+NpYHmURUi7jDpqq5P3eQZYHcdhYPmXr4GFFnt/1ue+xjKNSJM9EcyF9VUAd9n9cPDz51dv38PphwZGZB1y/Q2AMXabbvy0/mTfObQAfEopnUsIuRXp8iXAP871OwDOppk/pBlkkMHvCFVtg3N1g1vqaioqL5V6s9ogQxECcG0o0Ik7VF1x5uP/sDav/ujyt7xR72yei6834gXTs7fOhqUkzEIhyXWEmvDAH9u7J/SaWUfHV3CqmQQNhXv47HYulJIDiphCQ+3gZI877nGhgx5QrEKQDAIFoAkkFZW43PkuQgRKYq+NjUmr7/ALE9YAvhZTCygWH+gyA7xbJEVRP4Z9Nzv27knvdDXIAiUhFPRbg/2Sz1RaB/Gu9nKLm/B5U16/7qufr5vVcPzmN48jXfHI6IeFCHiz/ZrT3tx/GGnf1b9iryCXrItveO3PLjOVJQSDVbUnbJo3fMEZU/Mum9v4aI7MHaVPT8iUl/iXqxdAVicjmq+9U9jzzpDdobkjTJ/Myd27TSPo5eF3gaZAk/UpeEmvzsmFvJDl4zWrCQiEYHAc9JbUMygOXApYME3VnPZw/Ykjo7Pebe1tIo3bvuSzHtz/XPEYXKio8Tj32RZv/oggnyhwq4d2DM5u6ty0OjAwNlHK17dsfWqCTiFNdBV21RSf0lKsmrmuzm9Fs/bRWTz1KnrF/O+FwVP3EM70z1z2ydTLRaIOCyLQvzjXfM74oXPBJ71DPLuGj+am9O6B3JNAT1g0c6oihO/hOLG0o5EGCgx5ICk1TSImG8h36CDv0WnCn/VqIxgqIpD9ANyg0eo41D2UZA3gENnK1au73GXuQQnLDGucEqaahvjXbaU1M91hF0qUohU40JrQvKdVjHan3FH0mKJPADEJrymalQC4lhGD4G0LW/nh7k/fourcBST3Vo7Pu97PF3F5mgEFnEUI5dr9taBJBYqReMaCde3rVM2szb8CMspiBr81xgMYApYbGEXa4VO09/8SLgIjYVGk8+hMsJIcBIzgOfURG8FCPuN9rnfCCB2TkyTSJjdOeKJmt6OBEaMwGAFyDFZ6wIxZ+ua99XUdNcEI8Etgato1YA6jS8DyLxfb/VAopXkA5gGoopQOBwBKaSeldL59Lyf8dQeA1WAOqkfZYwPShi19VUaHYDpjAxi5Ov9n2zcgrbjqYCG9BlgdSwey3c4g+/6zwUI7nfqMy+z5fcoet27/NgAsoJTOA1Bq73sWTF1eZB+PIO1u6qiNETDS54TRFoA9Qz/S5UCcZ+fMzSuU0qFgarPTDgVwOtKuqZPBSKoTkisgrTD2fY7NNlEkYM9sF4DvwN4BJ1/UwaFIf6GQQQYZZPC7AE3Kh8HFDU0UNG7rjBdHLbleFcxuZHMh0RP2/eLaPD3OXVQYMK5deYQYXnwEtdYc9wXeX/i2ueTGF2pW3r6JuAPuYKowwa0IZMWz6+XGRFvOIx6OxmXJC0mg8Hh3iMWBbhiKYQqGD0kVcd30WLobMheinCUqJKGpauCcdX6j7AMtFtrZFT3xpbBr7hsCP3u5uGn0N9z3vk09qpi6LS5UaTTHhMXJ+Cpmman8DbzOd6J90H2GVNTWf2NYWAQS3dDlPehaWR509bbret4+9YJzvj5m1Ntvl7iDQrJ2WnLwjV/m1cur5gn+9VUnbJo3HABeeuPbzgNOapjfEZzAJfaM01UtpSaLW7Z35EVXW1T804BxVxwDXW2QelNQggU8fKJhqYaudhqECwhkTNs6aWDrCl5LIomAbFEO0HpVqsrGRZapWVRTwHWnqIngDcFYsVjEDaKWgFTH1cWn7z52iOFy8W8XBIai2MiGkRoqV3lSyY4irSLZ6FptJnC0pfF/5gevT5YdVBehnckPZbMzvvdL/mVLs0yj29TlnlFm46qZWkeb/4azLvlonjRMLVWHdxjZAddrCR//RIlQv+hQ8WuzTNzelWyjWqKVmIbbIkYApprwJKxyUp7gBVHhCa2LcEXWJvJQ4h3TbZV5DC5omSZnGZRS8D3tltS6RYvyvlcu2f7+UH2QcoV8kJeoowKmdCyhg+8qOU7n9J4Ur4UTU5MHE6vXO6BMckfRDQkir8d1U1M0ANC8AIr21IDT1Pq3qDp39vCpRJG5Pyas5t3dFdkrRPBhyAHOkL1Ui8aQMkxQCCeKpYdk1uZfCRllMYPfHISQIwAsp5RS2yzkATClDWAf8qeD5botsksvFAFYBUZAHBXQQJqoRZDOOzTBiFMUjMS4wdSt48DCGy0Aj4ER0L6qlZOr1ldJcgiN89MXfYlir30fCWk1MAbgDjB3UKd4fDuAUymlm/vMRROYG+hFYMTqGKSdWeMALgZwBJhC+DAYkTukTz8c4uUQIhVMzQwgrbYuAyv5wAFoAlPwvqCUHk8IceoYVoMRNmcOHKWwym5zBNIhmTn2mCiYOc+cPvNGwb4AuBbA/XY/HGIKsNDUfLASJEv7jEFG+hnodlt1YCT7ajDi7LL7ATAy6eSwOgY0pj1n34IZAU3ss8+D9JcGCtJfFOgAbgEjwwbS5kdxMLLq5GT2xVBKaaYAcAYZZPC7wu6akUd0l+QuP0heRd/eOPmiKZUbHsiqmijpr54PsWZyzwPXvDhd4ujfVZU++Zejnl4cQajitfbjlhfysZItXCnKxSiZ98B4g6cy/8jZjTBpc2cF154/3F9rllrVZn1npcQJsa7+uR1kR4fX7Yv3rDSMggllg/is3GCLpet4rGEfLho4BNmEAhYHUAqD5yGkdsKSEodwZNj3sLyKCYDsaiwk8aSPFPVrQT8uCdrFI9ZWatRosuDxxUG0lt6U6Hd3R/IkqXo+7c0mpJ/ZGO23P/fO+c8e9szldzzaq5saLe8Y3er74dzTB3829se1ufWLKU0daF6bP5a/QSOpe63aA44l4PHO8PwuzVuoeRvaLg6bw47oloc9r/jXP0xREeRgTJKEJFIIAYasoecTc3SPyw0KfF94iCJ3vdiG0JxAxMzKEniO8zdYy3gzfjSf0DiD6p0uPi9XrY289bcLrjnvmakL4y7B4lvPbt4zVO4qr/RE+eryIB3uUwU5v5W0tolV6tvnqX5Pz4gh5z4uUg4K1Lyc/dtKe62wj3o56fOKI76a01E9yGyrKePFgmoq+pREzfaKO8WefjdzxAiQnAQxOUkPKHUQNVkN944tqGkmh5WW60spNRD9XDV6+vFiXOJNXZD5j0tH6ie9uJXz5JA64XB5cUERruYIdgjxmEtf9NVwQ9FhtcUe3nvxgi5fSr+z92NN0JMJUXCpZk4XjQuWtLz3lPZyQeMmQldNpTYWi6iGJ1mbkDRosGD+WBbMK+friVTnLQC9xyCcruSVuDyRCB1hDInlo39AR0w1YboSiKDH5Yaux9EzdNzARTteaPnN/gP9jpBRFjP4zUEp/dwmiueA5c1FwFQrgBGc18FCLg8jhFSAEYVisPd3P9JKk0PispEmbxLSahXsa88HUGgf48HyBfuGwKpI5zUaSCtJvWBGL44a1feYU8evDSwcUgIjFivAzFFSAEaC1YgUAfCU0koA2wghMwghJxBCdoERryPAFL45fdp2g6lcl4MRyS8AzALLf6xCWjnVwMimY9JSY6uVz/WZH6eUh2nfry/xdYjsQDASpYGpic5c9LPH4ZCxJFgoqmiPeTLSJSicvEufPRbnWz6KtJrpBqvDeDrSJFK2z1sClov4LoC7KaUjwUinB0yRHg2m+DnjHWW3twXpcOGgPZ9OGZQUGHnejTRhpWCkOWn3awHSDqxxpJ+nMx8G0u9nNEMUM8ggg98jhg3Y8flB8iq6aPuoc5BvPrq+vbIrUbQvKdUeCJ5IWSM2Fr0uhvzDOR6HRRCqsP6waOMBHw8rlC2J5DZva/b0dpuGbhmKyZORGy1yDNeZx2XnW+FgES9sOkoa9ci9ZvLDo5Qm5GCykNgxOlR85Ig8sYAzdZkQ8AKPhf3lYIgAMDVAU6ByKfA0CouEAoa6vx9N1RTA3Fvcq7TJZ0t7B5o72g9CbypktraD8vFyBP0KN27cXqs8P9Y2pIKrHz0uJg0fUqNlt49YMbgTHRPiF2kFBScP7l/xzQtFkl8MkVzhgHseG/oG2rZdOnvZjNMmLT5+/yfjdmUlBuRUJEfONjljrupxzakqrKev988lfKw7mN+47Y9GlFydiiQuVq1tK73wzeLUlgMSam9VqhtxWDpAohq0I9Z7zEZdRJMmi6h9YfiXw1N86XMhl48E/B6iDcVR1jgfR+OdptGk+xQ5BrGAywaA3KNiidFzCzE0efjATbkHKsu8c/RBQs1qNbCbJjnBaA5q/eKQRyJcKJpJ0VQTfLJuTVm9C6Lo9hJJsLInKFEO3pxa3l3a0BMqMEnQz/tk4hpuRvhgr+pHr5JHe/hisdkaR5rJGI8Sab7dHZT/kFIEkkqJujhGEPMGUOQL1q0lfteE1qLAu8/fMv3Gq75/bWRREa4NiPAQgvE02z+aP2XaSqs9th4AeEqmyLrp1iam1scTKerNk3gefFCDduLqZEN3VFM6UkROtfbEW7s7enapLlW3YAKeLBNAY7xihFJYPFEkEBYA4Hhqia7erjhvqK11qJZVGMqegNtoQrOxD7uTutoNYsa7M0Tx10OGLGbwvwlfgX1o/xyskL1TcH0HmJp0BliheA8YMduMdF6dk6PmgAf7oO84n5aDEaPRSLuc7gYzanmjz3WOmkQAgFIaBCN7ACMezWDKlaN6ETACEkWagD0B4COwXDsRLNfPDaae/RGMaOwlhGwEcCmY2vgkWK3JpD0HT9j3sMByHLfa459otxnsM7ahYOGVADAWTG1zyG6Jvf9MpP+/O2Y3PNIK7tOEkNlgbrOaPU6/3e8DkVbdZPzUpfUm+94rwcx9jgIjzAoYAacAwpTSuWAk3HEb7UE6rPc6sAK6XyOdmwkA4yilb1NKz6OU3ksIeRpp5dRRcSeDEf9PKKUXgSmm0+3nAaRLoBwGpu4m7PPXg70fOtLvk1NuZCPYlxA8mIJdYo+7DSxHUegzBz5kkEEGGfyOwYv8VylFb43prs/lQPZfOzzNPTGSiIy79M0dBhe9iU4feWHyiC3veasne4a9OtcKx4ObW7nDqRUbzz9/ZpgLryvTZr15BMTduchuVLnW5vy4+4PTKd9VyRfnZPfjA9k57ZI8mrM8PE9UM66HdzV3kFe1FF5zd/WnqKkEOkTTJYguIoEQN0hwYG8wOfv1TlnwQfjkvIDQMK4lh48Lc5Kthm/1PD7WW0GqOvlUzGdFicBxWiLLNO577wkjjI8Kn3pl3eCYReJbslfV79vn/uzT+iPjNdPnvfq3icmln4d++PbTfhunLLj5Uqsk/9ns0pLHww3+cxVxf7JTam0uzm5+oLbWZ6jtnFWod3UIcWWrrCXOSPmMcQVDu4QsocOboH7L5QkIAZEMpWrAm7PNwLiP1KGEE0f8kH0+v6H4WEoVtRgAhETveZoITgcFLEOjFofAvhxerhNc7d+0ItVOn77iw+en+ivyT6gXA1qPpNUFfYGAGPC69+8vmkrqsrhwJ9F4bbS8ZXyH0Hb6amygk/RU1dSb+pfCFTOsr0jKNaFTbTt14/uTOzdVD1Qa5OFZbTHe6kzQ8FELvr6EBLtUV0GCCqloqljcHeU1l8Bp4HlfwXVevms2RPK1JBgC8RHqCcgorfTPv/CB53e2LLj4vPoFFz75GjnjabVJ0RIqTM4En4oB3SaZFr1kToHr9tOW//npF86M+N1Hr1wamp03jY9mlQro4BuS9diPfhFpVvHQgjDJNRIDD6ooIkn6vb8im63NyR4fgJTWss9dX/stXVsxaC0F9hNXNi/KrlXguTzVZcnr8HFtbdGed+qwV+iBLhQiG0MwNBOC+isiE4aawW8GQgj5Z8YghJCrAaQopU//bH8ZmGtoI5j61QwWLmmAkREffmpE0wKmwp0Apg7tAHPN7AVTIAkYGXMjbbriFHZ3cv4cEkHBiGMOfuqoGgEjVrvBDFQARmTHIG2845Abx/ylBWnXzrV2n11gitmfKaWP9gkJ9YCR3CuRLiPy8/IZXWAF553yH9sBzLDbTyGdw4g+1zqhoovBcu8omCr3CKV0DSHkbbDSGnlgpEu0x15n77PAyNQc+/6OW6xl39Mh3TF7bh1y+yAYydMBTAJ7Zo5C+QmAU8FKYmy0+3M2mPp4u93/rwAcbvfHIcA7KaUT7DBeHkx5jvWZdw0/LXvxHJjC7MzJ13ZfXGBK7QEAXqKUnkcIcVxvv6GUziaEzLH7/Czw4xcKGWSQQQa/C2xbQMjol355baYrb74aBAky4+7n+u7/5tJtg0pbfev35bU31NyxrFKNplp0L+nXFS80rlswR3N7BU+E6zL9596NmMjxqY6u+vyP7ltV97cZJ+i+gJRLwjtoh3hAPCfUE1C6c2IxH2S5u6OsZaAvkch2dQ38nsvPAtUsGG4/RCWGpFvJ8kDSQH84mHqkeGcrCecEzWI+0lVCq3LWYoCaEw64+MAe1/Zt/R5YNyErGYJ37Gs/aF55nLI/ZLR6ejgi5/AejWB9taBnnfq4UDB8VzhQbuV2tvBGZ/3CTV0doWTRHtnj6g1MMIK9fz78ucseXVM9JC5wHD9sQKtnSc200WZW8ZWq2jpNyC4piceJsVOaLLgkDUlNAN+rdLlJKDcl9OiEuKIkTvbrpcIkyzRNl+ZNmUZcphx4wyvA0AxdkPwi/TZupSyd01v2L861Sg91a7w1Z0P+VilCHznsg5PX3Lvkj2/3J54DDpWUvMcjvBI3fcK8+Wtzl31d0Th6UG92XrOP9ttdvjI3PnCO9+L5Xa17c3g1FpJ79klWl9I/pQbyXHllH5OBpbHe+n2hgNI80eIMk4sk9z5YMi00VU/QeMtXw6a6NOoB1KTklevVarLUM0I6HhyGEpCNkSXqFlCcDYLbsR63g4BGbxHXpRTtwByvIakxg0v0pMC7pW2XP/XB5NduO7GJcISXV3uLN2lb4h3fdnD5oRCCE/2ad5CbC2YFBUEQsGHr9895w7nn162pctbmdwm4Y7wIuJKIb/aVlE+NRsMvvR5tPe9qMjYq84LYaTZ/8wJtnX0mcc0BhJLxOPgBHwL0Qrokszb/Ssgoixn8JrBJ392EkHF99hFCyAZCyOlg9fveI4T4CCGXEUKcou73g+W2ucAIoqPsCGDhhbVgJMF5t4vBcvRkMEI4xD7XCb+0wHLm/H2usZA2UHGDES7FPt8hijrSYYzZ+MeyGyPs9tQ+7epIh8sWIh3uOgWMbJXZ2wcSQraCEcRjwQjZOjBXUSev0um7g1x7W7L7OAVMOXPIpvOHtxbM8bQKzBBoHZiaKIIR3FkAbiKEDAULeV0GRmx99lyUgoVp1oCR8PV2u30NZxyTIAc99rajxGaDqY4BpB1q8+2fC8GU1r+BhY2eA/bs5tjtyHYfZQDXg5W3UMCU2uv6tA0wB1WnTxKYYttjb19kz007mHGNF4ycVgE4gDKcZ1+fAnsHHrJNb06z2yrMEMUMMsjg94RtC8ggAHdvW5BemyklpKmNbGjsIKfDTd+GCx81d5CC1i5yWVM78SmTdFJQlfeXNZPalr71dJNrq6dQb/YmfdmeVri6w8IjhzzZEFY6al2HvhXvJAqhQQU5g4Qy/e+nfjLScrvHReH2bjhgWF6qku/XixxZNuHxKFZhIfK18fvdGwM7uB+2l2H/zhKrW4QBTUCsE+5Ye6Gp1HqTHjNIEoHmnKwfzuDNXeN0OadaF2UN+QFPjjcgSDk/nCH6VAFUNynJbxspldeQnkCn0qtx3G4xirib6oXlhlFYPZ6UPbY0xN3/UKoCRcLh0x8df/rMJd1ckpSL1MvDIgc+MuGbrfuuu310QV5yxPr2glXDs5rXFdLu0xShf3a7KaPGMwAphbO6FReMqAjOHcqVA3us4lCnKGVZOVylfwwUJQle4Ik75hHUOO+JdSHV0tEs9PCP8ilUWcN8O90Fxrb8aUNPMYZ6BH+XtmWz1Dx7E191w52HXzL0xlXnXnmEIixLucNNnJDlE0nQs25FeekBAfmc7siA+nh3zueAsSb3qvnQ434jnnRREMPyFPVyMiF8MBlDrodCUY24LkigmsSbpp8I5sRsgTcDvdulkNst+yy/i3pLg/m9TyMn9SW9MLI48WL4+cR9nIe0CzNxDtyQNSM1Rz00nJBvhMtK6AdbKnGFU+7rddPa06Pxyv4Gbu8xB8+9zrLEbJcgZZmSCNHFv1J2XCH6H1NslYwvkjiZazSjZk88Sa3kNu6iujVVPNLO8eUmrI0q4lWv0vhBf23aQV+Ptp4HAByoLpse0wC942SSQ9pQcloUg3euRl1Bhij+uvi5rX4GGfxPoQCM8PWtg7MYLO/sabA8sblgBi/3ALiKEHIfWBmEOvv3Y2DqmUNQKsEIkhdpstINVtfRUdIccqKBkYUImErWt7A9j7Qi5RS05+xznXxI0T7WYJ/rBlM7h9n32WBffyuYAno2WNkJN9gfwHykcx/bwXLxNDBydrg9hu/tczqQJrCOcU0PGEHsa8TjQAcjnx+ChbnCPo8DcAWl9AsAt9nEJ2KPxQdG/I4EI9R3gjmW3mX32zHDORosbHcepXQnABBCRDCCXmL3IwagwM5DPRTMjIfY7QsA7rPH+CTY82qwzy2wz3uozzOS7L7fZV+Xaz8rJ+/xJLAvFrLBQnydENcYGAnsACPPsOf8LAB/AiPTxN5HnXMopc78/gjbrRb2WOvA3t0ZYCG4L//8/AwyyCCD/2Dkg/3ddWrborkdi12cOKphR84zh5z7UU1do+fUlk4cWktz7tnnG3vVYVz8PlERjj/4vcp9q29sPUFJJh7z6tkzdKvHKvA0codf89VQzzVPJl1rTnaDk4k2YCfiXalIVq/6QtKn8J7WAsvdeoAPdA+0IfWanG3xkqjGAGTFEwCRNAx3mShIyTyfkt1I5UKP8GpXZz+3RcGLXVXdKNRDtN8uU8juFN0cVUevPbOeL9kiCQaRlaaydj6YQsrTbElTPtsgtAzBzlPG3VpQ8F25/taMC75Tsg8MIlssr+vX4eIC+agfavJbT0TK//eOLF5YWDy+je9B6xex9WNmlsm5nliv/P3KmussU/q6d0DOVqvXTBixZMidlXKhjZLeVIzk8D7TcPsgQE1YKW8BBCuFXtOvax3oyip2f0jj8UvzXN2IQDDi1EO4KLng9XFnrQJw22n0K+LOR48oQeA91Ct2t1R1N5lHEiExzFuQe+e7vsiik8++565YLOfQoxo3cjubhjVfMvvDIz95fv6b3ADtjJOOe34zAGyqSrpbzS6l27ACpLDLGujf3evtcRVNm/ce7Qlzh+6pGfV8QZFJtlZ1rXepJYLpCt1XXT/2cFrHPWnxZpzkWQ2nXPwKfePlMwqIACIFUw/xxEBPO3QjLohGNYxEa89d3An8w1wIQdeTuiWroGKHIR4fefCsaHfv36vWfpXz+qvrrlj9g2ZMGkm73eMSsbLWsp7qV/Z0e4/UA/7+PIyoETIlY8HGde13EksaKXl9REvEi2B/mc8B9GUa9/z8RX2IbnXWdswg5c08kJ1C+wwZ2X/CT9OLMvi/REZZzOC3wkKwPMI9ffadDubCadnHJoHlFDphhS/Y21dSSvcg7YRZDUbcgPQXIE1goZiLwVRDHmkyaNjt7AMjH0C63EIrWEikk1vnuHwKSKt6mn2OBKYGloKRjdlI5wMeDEZIvgQjSweAkT+AEb7PADwCpo79yb7XFqSdWzWwkFcBjGxG7b44OYwa0jmAsPuj2vtFMAI8yz4eBsuB3AHgNZskAqysx41ghHoMWHjoDDBCNRqs3EcIjKRFwRS7jwDcDBbC6WAs0qRrOaU0H4CbEHIPgAvAnp0K4CNK6cFg+ZfP2WP1AviYEBJD2jAoZo/DQjoceDGl9EC7Pw5ZvAUsB7EFLDw3C8xJ13neuXa/dPt8D5hZzlFIk+9Un3lLEkJCfdXuX8Ayey5eAAtdzSCDDDL4PeFiAAPA8rYBACUFOF1PuPf31udYIT8ZAGCSaeLNH3xHC43u8cWl60J/Eyj3psAL176Ak/dkeXxj8uQcbNeGVhdLzXquqwO8BI6MXA09f1lT3DC2q0bzmxwN+4mo80ogSazCnTDLNhq+gsSbgWhOgxQuC6ZUoH6/j2g6Zwyt6GwlQlyRq8upriu6FWiI+gdsp0P7aYKw5baQ8NSL0NGiiUUrkrySI7tmfFGOo1YWq+ExOWVBY3ai4kuNjP0QanzQwbH82JTJiwq/DD169ktjOGnUyPpvthR2dWJfv5VSe+EPnzXf+t4jSn7qxizP4D9FpDrBdeTDW9wBAzSvLaHSbi3i76inSIhCnrtkgzK9Z3+8PC4qutXakdWkhEpShq6psstHZUmCaurJzlRQrdH6aZzIi3wWcnpqo0fwLoH2au6w4fI90dYZ2hVXA2+N+/NbBADyv0++RJO43jDQfcznGLcwNfSWgKTPUGj4LMtLxjVz+65Su8qzCZXuqFqTH932jssP4GPLMu9w7YvPc56bT1k5UtiQyC5vb6fYxb02ZXRtYdckj3tpzSn33FX/1MJ3a04o2bLyAFVA6K8nX/nmwZ0l3p2RptLnSIFX4nK8nmyNfLz00fNjmg9Gsp6nPVsRU3VNk0OqqbwMU++E4OEKFi98f/l4skoIudpBXXFChcOtW9a/8mJHKCfYEm/YO/2w8QH/1lrtfpcg+k3N5Pd9tydkCDTU8GmzFmmL0Ja9LUFFSb0FxOH1AAAgAElEQVRbLMvTiWhZfLbbSSty1uYE+eblEPnm5X+6NvMQP6DQoxzkFyjEtf+e/xr/7yKTs5jBbwJCyHQAfkrpx79wbBSAmQB6KaUvEUJuACNjDwCYD0aeODASdC6Y+ngsWK3CDwBcBUaYdoMpfRSMqE1COh/xETAlysmrc9w+ndp+BCwfMgRG7lJgpEWztx3C5ZRdANKKo7PfyZFzis07iNr30iml5faYnby4Hkppkb3vErByGyqAjymll9r7bwcjmE4Nxr5trwQzeDGQDou9jFL6IiGk197XYf/OAgtFjYMpulfbpUkImGp2nX3vQqTz/6aCOaIeCOABSmk3ISQApvbKYCUn5oORfQJGUEfa/VDBSPo8+/4JMCLn5I46uZTPgJHfF8BCZmcgTdyn2+d/Z7ex3r52PJii6AF7V5aAvRNO/qRkz1M72AehIWCmPyKAzymlK+25XQlgMJgCXUUpnUgI4QG4KKVODdAMMsggg98lti0gMwC4Rr9EP/v5se8/qRg16Zi8kwA0Urrhpcex8AYK5Fx52oUPKBadv25i3cB4SZJ8dlRPRBLImeBxwkmR1cfWZ3WfNlahb+nLD7/BihQKzfTNrYee9tkYwQO6Zyf5fkiw3yRVTVE/8ZmCq+4RI1pxuYakTIpbSKQzYJGSlBn4Yo7I+3QqCyoxszZ29iLoNXxxt9VNdM+XfyDW5pE6eeBit1QYI2pMBk0mFbmQk4wl8wjfONIU+jWIxJdAkiiKZ8RaEn/8MakHxXAN2sz5uDb0fHMwwjLpLRqx3gzKuua75a6KSy46m9xzw0dRVygl9CS1cFGxWQoAFyycf0l7m3DHmMt5lSvI+vjOoQ9fCgCXKi/cpyj0j3zUzYmK2xIpuA5PEpyPQO4Mr9RI7gwtXqebWWWS4INVZhlnPVh87nuzvnm9l6Q4yWhDR5EkSYOGeLJ6utS9q4+OJgeXYMQfxrrnznrv1M+PnjKYyJ4JN+WUDLjOiijqSx8/Unjh3IVxUee4gbzlGXl0+3EFuYmJJdnGw7mHLO/es2lcYPum4fUu0ZSqU967S4E/iEgVpASZ/JBfvHtQa3hYARdDt0HU5rwRb7qs2Dx3gEiu7S0JS6W66EplFwgu0rsBOjZkiboWvit2fo27iW98YdCtUx5NATPcs2EIwwORvUuHTR09alOhVq5+J/gEKdZtrS8a6fXH43SImjB7/B7TI/mknNq1jUtatzUfm9ifsuS4W7MOMETLpBzyUu3TT5o1oOVv3w7xacqZrWUHC4ZOll+//BW2Nn/z8ursVZsGzX1xWUBLJKoWdbZNPH+awINCenGNofz8Xc3g10VGWczg/wqEkKdtBen/CJTS1Q5RJIRUEkKm9Tm2HSx88wmbuNwPRiwOBiOKc8GUsCVgH+xPBCMQpWDqpBNCOtj+bYERS+ffAlg+nkP6HHLoED1nXwkY+SBgJTAWI03+nBBSJ0R1CdKhrkn7HJd97DmkQ0mdMNlsAJQQkiCE1IMpmhaAbntfK5jS57f3P0MI2UEISYIphqq938mbhH2PPPv3rWBE8K9gCiLAXGaTYAQ4y57Tl8DIkwBgICHkWjDFdjYYwfLYJkSX2G3ttcc1FcBMQkgLmLLn1ClsxE9zFuv79LHVvm4fgCJKaQEYKXVU4S677yeDheVuBAuLdebMC2CWHf76GRhxfx/MIZYHI3huABsppafYY+2bh+rMwXiwENvL7HNX9pnDpWBfLGhIq8SVAA4ihMjIIIMMMvhfDvLUvYRcccez5PLb//x/eu3ol+gqhyjO3L+4ctb+xVOcYxOPrt0OFnHyBACod2y83/vGmsQXE5+abtVylWXfjpjLdbjG+JX8JaaZPyLm0U+sLnUVJYWSEikZPccyXZxhBnD4q6+OFFbMgbn2UCtnRdbmMN8BnRKLkxKCpfmuBPW7iOgn1Myn2cUqFyKiaMoapf4uEi1ZReOFUp5uDPZ0tRSTrmxxufeWv7zpvvkeEbEi0tnhNhua3ZBL4RZcFvd14eIlmics0kTI6tiyI6l2Z5Pu2koXufuPXM0DDz7nkeu6RVm1xLza+PbiJv+2VEH2jk6BfHDvnxInHrZjb5fR22WkVKu+dUTv4rW3JZ57+Y6m+F7fQ63t/f3VX2RZLiH4zCXRR3dcknw2SVu1iXzKVHXLsIzusNa2ogXxpe3QupMt4dIh+a5CtAUlcqupaHs5F3nuwW9j7wNAXKGfcx4rmReQQu7ByGqsTiYMXXipQbMG/RCGcM+21PTKoleubWq+ZUjWtIrZ1qigxA3N8gCUohgXG2X468yxq3yjS8KBkrzkIQA37bNlU1v2by5s4eKy1JMKcLEhIxsBpCzIhIo8hrVU1xT7tuu+UAfCXH2N2Vs7zZSE3e4u07/gTy8W7NWjt1OrTtP0Fpip5i7TjHWQ+dqFhcF+1w4zp/zguYIcKc/Xe7jxnKdtR2HQ1HHMSe+/slMx6WcmZ2jBcvI1gGE+H+E9Xj1oCZbbMqxVl77yxSmhipAy8rQh3JDTykxIHES3iCknHfF5CuJY3+bdi4RddQuLPnltlUMUbSx3RxMbqWlq1KIxAKAGhlAdB58/Tciszf9mZHIWM/hvwyZyuf/yxH+N4wEMI4RsoZQ6eRLfgZG9ifb2XDDF6GiwshFF9v4OsPf4A7D8vIVgJQ6GgBE9p8zCNDCjmOn2dS4wkvJjmQwbMbD8PdLn2law0EUDLPTwUHu/EwbqBqsP+T5YyGaF3ZYTynohgE1g5O8iMHMYA6z+Hwcgm1LqJ4R8BZYfCKSL0QMsR24tGPECGCneaR93jGwke/wEgJtS+rbdpx9BKT0VAOySHWUA8u1cwZvBVDk/GCFfBaYMAulyJOeBEfTNYArfk3a/nNIlsOfjOTAyuxSMPJ9i97UAaZKqAWglhGwC8BClNJsQMg9METaRdpGFPZ4g0jUTHyaEPIu0OnoJ0qTZyWOstq/9i/2zH0xVDoER4d1g+Qw3AriPELIBwA1g79j5lNJH8FO0233WkEEGGWTwnwCOhmD9WA/2vwWeWCcRjg6etf+NHV8MPCMGAL6QvuawOYnKVZg5KW+47jKSZG594Sb3NJQfWdIsBkOv5+Smim/AzqGDO9oREtZmjf9ggfbapUWJ2MKBFe+31g6rH9q29y6P+f05xsDtBwi+Qd+ejsqzvoLSdpgCwGqXJbfQoMKnuQihxBQ0ynEa2XzQ3jj1eH3jpRTRWqipWzFOz9vVVjZAO0ZNwiBuZV2SqlOTVornfLJiGABH4D5qQujxk9554f3L5un3FXgnV1TGCqF0gKo33aSPkFMX+v60YO2bFz31WY4//5xA6YZmzZ+KtQfVo1NilBM5rqCykvqXrxn5VWvHIQfJ/XnovCl6XG4tdJAoFh1SXKBE49/p+WgjFkGW5hospMjWLd4gpUXylEhyfxyqWwrW+y/P6s+TXuS73xl9ZnptPoXN89rZ808FgCOfeGujFRXKgmcg/2luDn1PvvdmcbDcFZ2Tn6ctNw7pbSVfiRNzB1KfBKsmpQFApX/ieTx1jdkKbvOkvct7coZXPu5KDOkPY7OXM1w8ISa0MFVDK6qe7RHJ7A2DRiz1iLIYrxhwypH++3Zart6CVdf7SoaMCweF7Qm1NeGJvd0+a+es/uQvMy/4Nuujyw6bp+wouoo/LGq6vPqYWDtvclFiUVUmbp+Qy4sWH9y4zq8a3oeHvac8e1BZbuccLipRix4PtjZrBjUgQhASuloPADkFoWdll+sKw6XvnHnIITNMC6EE6AgCWg0ifihQ/QpZxtNvlgcPezci36Dz5smnmLhgSTR8Nx5Mv5+ERyulP4aqZvBvRIYsZvDfhq04zfuXJ/5rvA5GXGJ99v0JLCfteLCcv2vACMsOMIJ3G9iHew6MeLWBKWRtYOGHDghY2GcALNRSQzpsVMZPwzgtpIniHjBiNA+M6DihpFPs305IpxvpUh1LwcIw+9ZgdJTISWBExQtm/pJvk6VKACIh5G4wI5/3wUJYj6WUfk8IORdsYYmDETzL7lMzgG1Iu6JKYETnJACwzWLivxQ6aZeYeANADSGkwR6zQ9LGATiUUlpGCHmPUnoSIaTUHjMHRnQH2eO93J5PF1i+5RC7P58i7X56EKV0uN2nF8HIaLHd1iT72RaBhZ46NSsd8ukGU/k4sLxI3n5mxB6vAuAwSmmL3X6nPRdb7XE+SghZClb/0UcpjQD4xm77SULIILs/lfaYvEgT/b7z1Q2W15lBBhlk8L8e9LIbf5W12bK4VyhF/pcDT/9xbV4ayb7WbYm3cqme4yccT57//j35mlHW4AWNYz7aUbl1vkuG++56d2Cup0HnSW7CGJZVXduoDMn/25+z2/a1eY897bxycsgh31GqZJNOIxItnH9VAJ3cCItYusZDkvR8cESX+eywZXLg+OZCYoZD5uDBTT5e0omqYh9f0LZRy07OywEpcHMgJgdu2fct41e9dQI3ZtbmnrkzBVnYWumGYFrSwDa6dFHL0rYm/92d9a1GrXuPkOrmUcgFRUAlamfZweVj5HHBmNsbrT4l/5AlAwr3vLdgUxPaKr2xfHHHg1ffLUyN3lPsX/7+l38tUZT4xCNf+ujRzXMWP3CuKHY+nmjsjrdtVErMOG8GZ5edYril5ugudTuRDCIX5sHQqUQsnPm2POuM18gZ5DUsKgIQnU/f+Ie1+bPL5004+O5X3qi7Vq8Z8vhtkVCo2MdHUu5kpz7KewA/KqAaM58/5qrSC967670Xz/vzSc8tfqzUolkyD8I1RAbVbFtHBt1V7qd6PHR5mAhqLJ6Uglbyhx5iDPdKBmeY+LQgGufiJVm82NN+0KmHrBoOAM9F5r5Yu1U/MJgfKgtlp7iC0uRoMyo9B2Bp3eLCZ3TFItkemuJ4jhdMAs4k7qRofM2LegG/xxhk9ui8GNQlj2URFzUJtWhSEMWjjrz4xRYAeO+xM8MALJHjdwHAOc9+fNO2p+9/jQM32rJM9+iFN6TX5kv+et+HHlKi8Tioe1jlQOvrvcMtUfZR8x/X5hfXGJm1+X8ImZzFDP7Xwv4wfwmAdyila20FrJlS+ndCyBgw4jABLDyyEUw5ioEpUO32Ng9G3mQwcuEGI169YKTFIVsK0gXqHQLK2/92itQ7f6wcgukoj44pyxIwQmuBkUQTrLzEkX2uI/bP12Cq7Odgi7qHUlpkq1wjAWynlE7uMw/HghFRDuyPY8C+twkWbrkPjPR8AWY+cwiARkrprj7zORvMIdVEmvzeBhayCvseCwEspJR22NdkgZWK2EwpXU8IeQ2MwN8PRswf6zPHOhiZn2jP50mU0lU/e6b3gSmQrWA5o6+BEbUz7VPa7eNl9rZp/9SDEenb7XtOActHPQMsn/Of1eusADPr+YJS+g/fshNCisBIdYwQUkopbfqFc5YAWE8pffiX7pFBBhlk8P8SftDGDRIoLrHAvTNW3rT2+yULbrYomifPe+nvkQ8mjWmwgvLnvokTlCb/Xwct2N468o2J/s4vhNgL1anghAP2tx18+Obsoa/cw5OOfCPrqdPkrqxGReK9brM3rKSoFgsUIA8SiGISS6qtUNppnjs4YAP1+SgHE5ZFgRgFb5qCJqtGszcLFW2NHF7/y2zr5NNruIJs0XTHsjjqVRHN6Y1ZVmCJqhvnqZFas1cdKumKZMp3XbyiXJ450wjVkdCiabTj0q0g4Qqy0b/3a7d7Z663bNv63f2NI6pzg55HTr2l6LMr//xDTSw+tFqxtj32xuMHAsCdD504vF5yzW5Olt0d8Hu55Mh+4aTCZ0GnvNJCzVCpJxnZpe6LbE1Ucpz14S2vrvkjWA5+7Xz6RpUzn3lTFs9O9OJDg4eR2w1OKja5shOt22LPdN6qw6L0qJwzDErOHjjfd+qy6adQAHj2jcdzNA93Cm9aGxeefMXGhYdd8UGQVw9vFY37Z40vaPvh6NzHDMHDZ2/aZJQWIeXeY+xPKvLoqCYkdk2eMPdvx527qu8zfeK76+/r3taoHjui9Y7u3dgOTly8+a7cyTIvzGrngZaY0D7h2B41W+XLTBeBNNRvgtN1AqGh9y/hD5q7d91efs24x7KLPAcbHD9YV7gz6iL1H1/30PJfXJu3P/VABQhGA/SzUQuvT/38+LN/mFlKCOm96PkvYnN9OaXvxMP/sDYfM3nuEgDrl61/J7M2/5vxH5uzaLsWhn7rfmTw7wOldB9YWOc6e/tuSunf7cMXgqmNzh8Zp9yGE46QA0bYePv3O0iHdfrA8hGdUFMOTAXrwU+NbhwDGcdd1AlFdMJWe/u06QYjhc79kva/x9rHdaQJpgoWYjscrPRDAYAsW+UbZbffl9jsB1MdLfvaENKEtsueh0X2uIZTShWw0h3fEEJihJBFfRxQnT/cjqHM7fb29QA+Acvn6ySEnEUIGW2P8XMADunca/ftOrBQVGf+KYBbKKWHgBG/JWCk/Oc4C+zZLaGUjrTHe7p9zLLn0QNGNi9C+tnsAvvSgFJKrwAwGcxpNQRgBSHkL79wL1BKaymlH4KFr663S3n0Pd7qKNr/hCgSsBqPd/5S+xlkkMHP8PiGEB7fkFmbf8c4QNq8j4K7L4cPrwOAiae+dPfkeS/9feuNE4muKReVJaxrrq/yxG9fcCMVYD3uibfUD/cJxr2TJmCmeGhOdG9EdFU08mJ5q6CkhJdJKGoaUjt0UXPDT/JVDqSuaaDZWn8Ul8xK8FrAiKAnjxoaDBDwHAfevbufFfr6SJGLinkaoOXkW9rVz3xGysqrkcre363nbTFUXwTQJLeZ4o6AkceXDeWkHF9LvGTtDD5bLBsvmV4iRPvpBucmiUmfGHVeRbX0ZUMSUtbwTzdGDqnSfYVFCSPr9ecvqB95UGTYQLhJ8ea8H9e1hpOmlZBjpt4TOjTHdA8q1byykEMgcASWYe1u6Yp8Hr0msTv+HuF4H6H86Pn0DQXAugWY852bvBqbP+XaRU5bxPZOoGGQ3m3Q960ltzeNzKWdQvCGxNeptzq/0sZ/MsMAIYvPImRxZVOgKCIr5pecRvYAwKgZrdvHHrMnmSgpuP7dZt8TRImlfGoMmq5bptt16+nXLZpck192ZstBue94h4j/sDZzMM8q8LsvqH+gX9bMC1eObKkqnNzvRv3w4AIOwgjLCkST7u5Fbk/sO0FJtQoXeV2UkygnWbq5O3hr9su30b30nIeWXJHg8yZs/PbACxL7BucNFfNWvHvN/F/Mmx112XW1oxZe9+F9l9322Jkkd/3tvpk/WZsv/tuXTRc9/0UMAH6RKE6aeyQMzIGGO/57b3EG/yf4Tw5DfQxAKSHkVkrpt791ZzIACCEzAcQopev/5cn/RVBKO+2228EcKYP2B/gXwFTHOWD5fHlIO3ACaQWvFSzMcS5YWGklWAijBEZmfPb5Ts6bABa26oRROq6iXqRLNvQlmY4pjgVWs7AJTBn02ef6wFS/xQCuAFMEnTw8gJV22AvmOPo1gBPAzHhuI4RMAXMZfRLpUg9jwQjeArut8ZTSFYQQvz2eFkLITfbcOAY7p4GRyMmEEC9YKKkCZiBzkn3OvXZ/BDACdzGA1ZTSbQBq7WewGkzJTSBdt3G0PT6eUvqM3cbpYLmlpwDIsZ9XD1gpkMVg6uVyMAVyrN0ORbo8B6WUegghjinRKntuG+1t2LmWETCSLAEYa7uyXgFAoJT2Nf4BgHPsef+gz9z//6IPwVbwUyOhDDLI4J/jMQDFeHzDzbhi0oZ/eXYG/3bM/G7xSSBo/nLK6b/a2jxG3tQJAHNnH9fOcZZryWcfB8ccOA1h4/sX3KnBf6wl3pPe/OL+jWd9cGJezrhvy2leF1F7OtE8uZ7j884ke1/d0VZZ+nKh1e07h+/FZpfHVdmBlGTFvVJcIVFZ9QV4gwcf44xSXzNPs1KCGiNRWeCIYZoCdMmQDEkSONErUh26BYsQEI3C9Ps5F5El0ZDqoKGfEYn5OrP0Va2qbg7vVwJfckIdbe+MCVX8a/tM3f137+0zbtznneH99OoG7sj3Jwc3jyzAjD2HFy/nVu6qVrJKBT61w8jqnu6tSLTlfT7yzvuH3DSFgtRVntd+UeTwkEVDJWZ+ORlLiHp7kzllQfIPy0thyJWbPzt/xUljLvJHA4HmUQPzW65a8MhNj+HoSwVAGljaxR8/t+q0m8+4YkDn2senC8Lr3ix3sqZd1RSK4Hq3RE8JVnC8Iut3c1U/6jqjwIzZyv9y7Kl3ga29OPyvz22SKuYN1ROtCVFr112wiPdTYap/dNtmfhgv/GHmk88AwNYXGq4Q3eUTDWPHyViHvBsuOP+OwZW4Uo+i01+BxQEDC41jxA8ATHd5jYGqLnMkl6flYxLdDR/6cyxQemHNIs+rF54RbnkvBXcOXZU1Rg4LQa7W6eBpVz5IK3tfiAzsxz1W3ZHN72xOjn3+qvPz+g9ha/ORF7/4C2tznGip+H95bV4uvE2uwLzPHx/9lgouk6/4P4H/ZLK4EsAfAJxACJkF4Ak7JymDXxGEEPLPQvx+fh6AV8GUr3+ILf8V4LNvQ8DKNtwBRuA6wIjNGWBkQAFTvjxgZGkBmOpYDJaP1xdeMDWtECwE01lIA33OcfIRgTRZBJhqmEDasGUPWHjoFLtPTu6jz77Hn5AOc7XAakA6KuJgMMJ0jL29BMArYER0PVgI7Q+U0kPs53EeIWQZgDZKqVPv8C/2vbvBcgc1MKK5DCw01ksIWQ7gB3u8ACNgbWAqqwagxFHZbKVu+8/mK8/uJw9WxqQTQBUYuV3e510ZgZ86ou4EI3R5YHkJ58E266GUjiCEKPa/iwgh3wCIE0LuBHuGFMBhYM/1W7D8VgcbwcJWt4K9E5vBQlMLCCHzwUpiLLHPVe1+b8N/AfZ7NhWsfEv2vzo/gwwy+BHfAjinPtV1SvkDhx0N4Al63YrM2vwrg3zzMqHTzvmXa/Pwj94kwXw8y4Mm8G9YmyXJ8BGOsr/17f0OFHnz7rjJC38bp/UYVLs45jHm+2IyEQt3xoWl56Qk0uNzlQS4nllV54VDeNzXWFwmR+aNS4xaD293BWQhCdMd9av9qz+R1F2F7f0rD85uqNoj8u6QbOQEeFBYUhikosXSy+poUvj/2LvuMCmq7Htepa6Ok/MMQxhgyBJEJRhQEXPCgMsKmFaFFVf9IesGw7rmhKKyKqY1AuoaMCMiUUByTsMwMDn0dO6ucH9/vCq6GXB1d111d/t833w9XfUq9qu677x777nZoCbR9HqDBAMQ3RD9WiSSkxPxhFf1Svi7HdiamR1pY3UZI1vrxUYxL+qprT+BUaHgK/TNmp2BE37vDw9Uw9qdCJdM0DPj2oaj15v9tjn3CB5tX5/4nlpm7Bs47EuhmDWe9NmT9OaWlzNmnCw6osLXWqLRk/2Jf/X9v3/wxDt6bWR3bO03yZfx6nyIefWB1vFXAkBcCj7kCcQHbt/maiNB6AnJp+u6u7rc17Js1678876oKcgp6jfr00xVrWa5ZmF5DwV71yZaW8NafZbLXRJ3C7rL4Sjbve0Kyza/fgc62GaRwUmCLGRBlZ7ol317RjHLDYbNVTuj7qa13UOfvdQwhU0omEk5mUKeboosykgEgLIcXORVoIQykKfqWEzAJAL2AUC8qKRvNKpHBM3E1XfOLr3rrvGLAQq9//DEp525cDkMRnohjWJZLApeT/od+3wG94iuhqnU5mcUbFtTu3egpMhrgUSvtpZowd0Tzh4vS/Int85++xDbrDod38s2fyrNPWib56+Zl45g+JHwn0wWl4J7ZM4HcCN4ONrUn/SM/svAGBMAlDPGAkTU8vfaWp6e15Asg/BDoxpWGQeLXNSAe6LmgROkxQAestraBeIZeBF5GUnlUjvPkIGTvQpwsrYJXGV0J3hYKMCfD1tQxSaKUso+ipAUz+kPTlwkaz/fgId0ArzuoF3zMGqTD4uQbAYnawqSYjjnWecQB1dH3Q1OoCIATMaYYZ17iDHmtK6tGzh5OxY8lNMNTrYuABePmQYuAPQCkvUMeyDpWXUC2MEY+wicOP4xJW/xQvDZzFHgM3/LwQmfkXKPFoKXA2khot5WXxjKGAtb9/YAONG7wjrWSYyx263vACedIKKR1jHbreWt1jEXElEqUYT12y0H0EREV1hqsh9Y9+xCcG/jEiKq/UcJX4rn8l9SE0wjjf81bFh/zMLi4ts2D/StTdvmfxPY4pdEAJ3Y4pfaaeSEv0vEt5xzKZ249LV5xEsc/eBo9zv3w7JvG6WmaEZCqvJEXQFRj80LuY22tlq2wr36wnvdwVK4WE5+YW1n0uMtLDp63fvxmFvKXt8namZsItMfN+WsOkF3tLKwIQc9LldFFFqPjLq2/VLz8RX12t6NrfsbC3q1ngTnUaskqJpDJ5UxBhI9CTgdkEImTEXIEjxre2bXVZ0clnac4i4879FBkYGLGnR/ucwMsbTZHVjn0WuOcvgaEBi0Z5y55izFa5bhGEWMTOw+NgezgO1NJ7DEpr5bty3Qi9rqyxUxATnHYTBEvNdrLKsgq8gZr/5KGdGw4YK9Qy+rpwemfRzxH1dnTrzlMyMET0SNSoGBrMhZPHx4zFTMsihLNG1a3WOYLDHHCWq+29u1yeWP9rngi62F9YYm3OKEo6sWcb2QkyleKnhNiQnhXvGPVeeWT3SAVKVGkncw9vqX4OOgR4jGNQLACVn3X2hoxhRJFUb1uZtl/K49vtwkXKeIguJ1kFRJDufTn/Zd8Oo1udkTCC0vLXiy99SLr3ptwTsYelz2deE1ISx5/P+gVDXg1dqvjStCLnJKujFml3HlnV0UeQIpDkhmeD0A/JFeGQkA8x+c1C7myZBzpFbFjQwCFo65dvY7qX3i3pU7KkRRW57piTdd+/gLv/i/Cy5ZsHsfPizON4YhEhurKdT3mT/et/Cau6Y3vUqxf2Yytg18DJTGj4T/SIEbS9zkOeu3QA4AACAASURBVPBcqkfARToeJ6Ld1gB8DIB1RFT3E57mfzwssmgLgLR/V/sfC4yx7eAErdga1Cvg5NEHHoqpgnuiPgVwCjghakSy3IaN/QBmg/efjsXtCUmvYsdJlQSSxK4anBwK4MTRBU4w7HAKm9gBnCTFkCRo28BneglJUpJjHfd18NnDe63vNeCiL7a37gPw8NQMcKEYL3hIp9863moAb1j7SVjXmgDPcRxh3Se77IWJpKqpjWes3EBYHsmR4GUxHgIvjbEdnKh9Zn1/FjwM+AbwMNNfWtelgJPTDOu3Wgbu4bW9vzHrXs4G8AgRVVvHvAvcY+wCsJKIRnX4DWCF2063tm8C91xGrVIc08F/Nx+Ae4jI33H7NNJI44dFt98fdfREf81T50Za3x9QedJM2LZ52he72QOjbNu8gqZ9kVYw/BfAFr8kgEfLBGnkhJ+Nbf74rzdtFyTNW5y3q6TvKR8RwJSGkZsOyN6YJ9GkBDzo4jbk6ohYVv/ptt/ffYoay/GYu/Y3dh9cX6THZPh9gMdZh52uTtXl/voXHV7jdn1PFyNc1VU4UOxhorMGldRE4rZic3Pvr0kUs6TMMj8yWTvcMhAMQnO3lUjm5gpW09inWtpzSqk89n5By1kZjNd1Uc2wnMgrD3hcu7ojGso1hLy6YE2zRyradaKjKXd9LDdf8NR90x8kudZsqSrrq4uasaP8uag3owH9jDNzyCWau/cuez2yrLB1e3zQFJFE84wxGTX7A6WdFzbXgTlCaKnWHmta9NEnKoULwq6rr/blfaMW94sPXruwmx/hLuEegmtZK21pOGDIU9wyJeReyv6oISXMrdkhrygMaUQiENKCbkAUAd0EVF0SFSVLEtAcN0HAM0TjpgLACM89CxNMPMbpwgzn0NLHXzyhcQxAywrL5R0APiPCqdlXq8+NOrau61N3fzn5zYd7T86WzF/OfauNyJev7GkTtE2JR30AMP6oK5cb+eyo8koj0r9UcLU5EZVkr+KMBZ/vK3d+eNDkP1QDwEczrriLCFcxB3yMsQVjrp19bsd+cN8t196Z6078JhpPyHW1WmuRK5q9v1WO3j/nrez7rrhguuIuUD1Z5S4A91xz1/S0bf4PwM/Gs8gYE8FzvZqJ6EDK8o/BPSBPAtCJaAaAWeChbguIqJUxVgVgPWPMDy7scSp4SOT1jLGMnxPR+U8CEZngHqF/C6x6eUMAXGqJ2Xxf1AFIWORDBRc7+RjcI/cVOCFqBi+98RW4Z2uCta1d1sIAJ4+VAJaAEyjgUAEYWG1XW+1s759NFEPgRDEIHo6agaTiKpD0QPqs49oeTHv/peAEjVK22Q5OOseBC8VEwYlcJ3BSZyuPngZOhDUkhW9M8P5fCf4MnGrfMCLqAxz0Ztq5mvZnHIcSxTB4bp+Ny8FDvjdYx3wRPHzVA06WW8HDUILWPe1q3aON4CI+MnhoSxl4qO0O67xTa11eDmArY+wN8HDa4da9Og7cg3gk3At+3y8EsCjlE0R0H2Os0to2/fynkcY/iTtmzhSjmtZfN43ah2++pcFevvQK9kkE6D668iRum6d9MWOPL/svDxOrfNdd+ClN+6LVdc+IuoRhrGf3neCHKW8CmcMhcducf8exGY13rEg/m/8EaOQEE3wC8N+DGZ/PAsT+gPtyTB36vW1zVsnmBgFCou+ojwhgTgBD4W75MBHOPK89r21J831XNmc4qHnRhFt+3/93d3zV8uCMTZnbT58QjFehrt/XeiJDlLzLzzJK6jNL/MMXVjrJv0Rqk0bkSgwxtoViLg1a9m5m5m5GeZYibfM3rskx45VRQZE2tqhyxp6g5POEGB29KJSnLipt12YFTTUjU9QcvtKy+mjdFkMJs1IEu9XqZc0xibUUZ1bKG/Tdl7b1ULflbjJj2TBkiTXuj/fIrc136EKcdl98ukuUmxFf17zFCOS3OD9pGecwm1igvDCW62tOPPHC8Z00aa9RV+ES9TaP7ljvvS6ROfr6SLw9npWoM83M42IaraCBQ9evjCweUCmJwplbol54QfBrCYTWT+4DACd6P2crQs2hOOADEiFA9QB6HFCcugGKGCYjmGFAOGibJVUYJxi42jGoYIOeiJ0yaYH0ohTaXd+js+jZsDdRrWfntFb2Z5+fc9JevwDkyALrrcpM1hyhjbXt+b0FR4bsYI/WxOk3ZQWTBl6Y27ZzI/RQPMGgugjx2L6os6lV+qXeV9uy5sk/vbFJaz4uZDqGFzkSNU6G/jg8tQcA0LeLdkd1A5wOh3ChJJiLGtrkC01dWgQA059/+75n/nhf2jb/h+Fn41lkjMng4WMHUokDYywAPtj0I+kJkcAH0LZICYF7VmB9d4Mn2e8DD7+7K0VF095vJYA4EVUhjR8djLFR4N4xBmC6NQlwpHbjARxDRL9OWfYb8Lp5f7LKSlwArpS5hzFWDj5REAEnkSqSpTEkcE/YmfauwAldIawwVyT7FaxtCDxU8kVrXwHw/mV75GxxFoYkEbVhL7exEJz8vAJeLuMrcEKnpGy7B9yD2GKdSy24EIy9nxj4CzYHSdK7BZxADwYPCY1Y52nXQ4yBPzsRcLEYh7XdCOu4qTmaUXDy6AX3JN5i56xaZTSqrOt24VAEAHQiorjVtoKIdjHGgtZ5fkBEF1nrokiK2NjkVQBQSUTVjLHTwZ/b2wCcSET34nuAMdYLwLbvk2PbYbv54KVGriCiuf/Itmmk8d+O22fOlMPx+DBBYAce+M1NB23z8itYeKsoClfl9mmjjBw3CCIEiNCRgAYFMgIwIQBRD2QHYAghGLIbivZIQSjcHBfpBtXA9Lq7V85JPR57//5KMMTprFvTtvmnwIyVo4DoB4DJAHE6ph5/RNs88z3/dYDZeco52bcml3LbDNCfAFYJLkD3FkB7Pl9xcXl25zWv5Gb7A8LwpSNiMJUC5JqtA1+Nxou3SAdOe/az3MqeZzrX9UFOk8zah640qt64wVuZu6g10+tVW9z1iXVClVLRqR4RbzSa7QW1iUOuKMDqlyVJVMJtnkBWfYFbcZribrHVzCjRBUe0jfx6KXKoznBnQIrWGlCznYjHDJL3d2NiU1e0VaynL3aM/EQ2jj2BtbU95/5m8BVK1uqPaprkczQ5oRwYLhiqLyAqn7I97s67yiPv72qCpOklVydqNy8bdPSCuWNAJrGGglisfVNeW6arNre07365pcZruBtbt4gn/LJu+YItg2Vzn2PI4EDkm296BpzwVsRBFEMgDrhUQA8D2iLA53ABRgSBEQDpgNeHg+MIMwqENEByA/oN5RdmvbB33qUEAGPOfjWnvqpmjxmPJ7oXBTyVPUw0B8NoF7LQQgX+Of/3SeeswZ/EAWDWZVMqrn1t5q5jO/8x5JFMcXGV+XzcuGfyh09dyarrWMQ0GE2+51nXc49cdXxTTfZHpikIp/dwVw6a/Ifqlx+benrITExzC8ptBWroxDHXzv5etnnWxHG9jhq0f9uxNyz+h2xz3VF3LhAgDQXoioJ1v0/b5p8QPxvPIhFpVn6R0WHVFvBi4Qr4gNkepNsDeptA2h4Kp9XuRqtdI6yE3Q44A9yD8hdrwLoTQDURnfxDXlca34qpSBKgPR1XMsZOAxd3uQ+AlzE2i4g2A7zYekrTGgBvg5eeOBk8r64feL8JgPdxCbxfCABGgxd6D4JPTqwFDwd1WPuLWO1tb18VeKij3d9sYhVDknTFkQypdCBZnzGVKOrg+YQKeMkHADjd+oxax+kBLkDTFbwGowDufTTAJ0tut/Y5H7ycSJ51HX3ACaZ9bi5wMglrW9t76Uo5ptu6VjvUVkOyZqRq/X8muHc+QUS219T2QOoAFljtj7aO1wgr/DZlwscH4LdEdE/KvVgJIJZCKD8CkG8RxWpw1dsCK2dQQVKp9SCs0OPhADYRURNjbCh4Hcq5AJ7p2D5lOwauILs6ZXEW+G+e923bpZHG/yrunDJFu2PmzCV3TJlyiG0+N7tyS9Dj7ksun8OqIMsnzgQoEAApobs9piH6FTUOBgdEckLSGBhuanDIpiw5GhMGNafuk332JAPDGeATYrNvf+oJ1qYH9xDY9idu+O0YpPFj4A8AmCUovrXjysde8Z/2N/bp6vM8w28XIDqf/KDl4cln5TTytYfY5ioA72yf+UFmPLRy5PbzPWdLTf36ivWtm4/v0hh0VRVnMDAJWV63M5+hzzPvjt4xcOWqA8cvCmZ22ThCjnm+drrvrfHHL5LVhjKIlB08ZvyWLF1zUru33FnXXLIvg5zTUUayEXOSQ3X4omIU1JgZVXoWqMF42KgPliX0jExnS3Ms1jkQckRDhliYyYQEI9YaV5GTFYI/Uqn3GPr+CZEdiiMDGb8OZwqQjvVfWEF+aqtqjexZc8leCfk99sSWbyiozelKg6N5gYZeYqSmrpQ8EeP8yW/6Rdl9+66qs5h+rPxe4/7tawLN7uxgqxSU9VF9GhZtKBVgZmkoRFtYcybgzEkgAUAwALcDSBAQdwPq6UAUEahuQI4ApgnECVAs28wIUBVAFQHt5kg0/qT76Odi4VVXZQ0SEVqqiorgUZT1y1V90eK2vw0+rbiT7KRu1w9akbNpp9w4cjC3zde+NnMXANS2ir9gxPrEjbvvAYAzrp9Nv7vympWaJsQYe72C6Lmvbh0z/iMIyB8045Xq3908rTqeENSHnni64MHTf+1vkrzimGsPt80vFU5SCDScMWHThLrnm/4y6dKhDjn+5/VrCl84Fnjt2zrdHU8+zrSYNvjPN9980DYzwCmASQRK2+afGD86WWSMjQQflL3T0QOQShQZrzmXA2ArkgNYO6dMAx+Y+1KW2YNYe+Br18krAhdByU4RFHkFXOhDTzm8B4d6WNL49+I8cNnnzUT0fuoKxtgt4OUhVoF7H8M2UezQbiA44XsRwEvgYY5fgf/+A8AJTzO48Iqdo6eAi9HYNRj3guf6idZubRVTE5xs1lvbxcEFbOxi8TYZ3JWyzK7JaFj/x5Esr2GTVuBQkR3NyrFrstZ3B+/bCST7owge0nkneI7uOCIqZ4zdCP4s3WQdx95Ota7TACd4MpLPB7Pa2WIHpnXcXeBKok7w2eDZ1vENJGtXPmBd03YAb4F7R5eBk7QvwQV3TgVXbm0BuPsQvEbkQRDRCdZvV8QYexTci2obkVwAAmPsUusaBFtl1Xp+S8FJbh54ORSAe2x3gocif5c0fBsAB2PsSiJ6zTqfYd+xTRpp/Ndj86dfnkCgHEmU3qk8eeQhtjmVKLIHRnHbnF+0E7ZtFiw7TJQAMxNQRY+aMAWnbiCokMMwCbKuk6YoGsBkOBRBk7OLNC3xJoA8K5+R2+Y+px20zX49nMuInMQOTn6l8e/HKMBZDWAlpg79NHXFY6+03wnGbj5VP2HJTn/9O519eeH/O9cmiklchbeGAPOGAPjr9Tv7vqSLrETRMhdrHmgejzZw/V8eEz6N9QnktOZ4eju/Do2ZNdrpbO+sFNSE+q5RmCM/6hBLxf37HeoJQ/SMOjFkGMiCM0P3NzG4s41ITAjnOTfUygmHurbGjOXF2psqcsUyMTeOvc69irltMGRv267MksbyoCkDHtmsCp8tF7q36g2u3agOu+Kx+tPEAYmYRF2r5JjcU17UkkDPwEajtIDERCwG1RvTz536Xs6a337epPmjUvdB8S7h2mi8cUtldPOWXplqXRYSjW6xx6lz6zymfKeiPP5Ij9Ls8c/MWlRaUfDgjXqkKM+pOm+SY6JsIhYDjNi2bSVugGRAN3xKOOpyhOT6YJ7KzXOcARQFlFpusskAdBlI7PJkCL2J4NxQPensyy+aN3v9N8g0gjHTiOk6AFxyVOShccey+Lu7a7dfdU2ntxyK6zIB4uJvCpbd7GsQvmwXzdYza5efGhJ9axYV9GkBgH2B298F8G7q7/bn2c+cwNjrgwEU3vX7Gx7teqKjtTwTjwEAfGquQ/UKt2x8gRVJkiQKgvDcZbexq167hz6edaVtm8NgLB8mxhJMHUCTANqZMKSPCdLav9fptLjWZsB0/O7hh0fahLFw3e1p2/wzwU/hWcxGUm3yMKTI70vgA9s+SA6ubShInrvZYZ2EZM08W8XSyRh7H5w4dAFwQ6qnwxqIFv2j4Wtp/POw7nW3b1m9AFzcZD54Affgt7TLA0/yl5Gs+/drcPL2KXi/cFnL4+BEYqT12Qfco7QXPMdvB/gb2/Yq2uIog619O8H7re0ttD2NPcFz6/oiSe5sImnnIQK8nwLJ0Fdb1GaoFWo7AMA663M8+GQGrDZt1nmL4CUywBg7F8BQa7/vIukVPBPJCRQRPJ/Qzne0a0bGwHP9Us+nKzgJtHMkJ4Krys5NeS4uBfec9gQnrjqAGstL57GI4oPgqqcPMsayAQwgooU4HBvAf+NTrOP/3lq+FEBnInqDMfYmcLCv2GDWsm2MsSdgeaWJqA3Aw6kHYIyVgk8grCIie2LI/t1Owt+Z5Uwjjf81ECiHsb9jmx8YxWjaF3bEBQN/Dxxqmw1NgW5IcKgIybIZliTRQSYQ16CbpgRZToAxbpu1FhOAhz0wai74xBy3zWfeetA2z7hhetPtTz1RdOf1v07b5h8LU4cSuM04AmgZYP7CLaqf3zmr4Dpi5Mf5R2yYDf7ulR01eaYDkPq8es0NtRPv6+qvK/7IcPlF3ZMpRfJUIbZ+cPBT7xerRnaKDK8++vmv1eAZ/bITapbTkbnXfyAxM1PMvMHBnKoouKQdy3pRwcRdkhTP9YnVnQa1xaOmXuJSi4HctpZM5lIJEacgSl3jkHRvz4C5bWd3tb67H27fbrSgPiGKXQxivX0xZ8OAj+BcOhKNumIGYg50y9/Ouh5brbUsyiA1nog0nqgOfbb6xvG+iuEDvJlvrfcW1A4oO6lx/Fdfqy/ESMPHb/eNZDuFttxBg3ttrc4QTz1RvDumS3RU5bRTqPXok+KImo2ene+6Ou8Qi9uNTS0tfafFE6IAyGAQxc6uA621MXcngExA0IFMBYjGAT2Tm9kok8U2QzNcXaNhcTPgSnTLej1y3c2VE7/+clt+aLE6l2gSAUCXstjFsuxwTMkpr3RI5p2BmK47VGHXqFPfWw3Ac+26uWcjgIdEb/wVAA/Oeff+7HZFGnD16TcfyTav+8Nvl9+XK2inhAOCgUzzdwCgt4eXim3o/FC/P9Bz3tu8AHDVa/ekajwwAIxFjO1wiU+AuG2++oU3j2CbXy8HH7utIBpHACBIosJ0BkmVJ4HrRKTxM8KPnrNohQq+C+6l2QTgvJScqJPBB41zAPwJfKDPOuyiYx6Y7TmRkByQ23labiS9jA3g+WHPAHiMiA6ZUUnj5wnGC7Pr4AMTnYhCHda/AU7oTgEQIKI2xgvUzwSwjIj+whgbDh6e6AYnCy1IirFo4G/mMHh/sz2DEvgAxoMk+TKP8H/HPMWOqqo2CDz8ciiS/TcATqyuAyeFDwAYTkTnWCUnmHXOp4N78nTrfJ3W+TutY9skE0iSP9vLaYfG2jUoHR3Oz87RjIJ7ZevBX+IqgMlEtDPlXjNrfSM4uWwmovKU9R4AVwL4kIh2MsaWgOdNntUh7BMp27SC14vsfaT13wUrUmGTRRY7rhsC7i19i4gi1rKxAC6x8yfTSCMNji0rlp/sk5zv7g01120K124RFOG8a04dz23zA6POAnCzMx55PSqK90iSw6Xb7zEi/icIBNNkIACiAABRyTDkMn+91KD4zIjbzSAItvKyE99mm6d9kbbNP3PQ0cTaleZmAxT/67T83gD0G8/FIbZ529YxbziZdpS0v+LY4BN/YZXvom3e6suzKVryuDeU9eWY06c99/T8J4YP3yvNdR1wOfNVTW2Qm1uEzmuCxWX7iprz9mp6ccSRTcHwq3O7ujJrL5W7DlpnmEetkqoJgaOrTvDs7qwLUXeUegT2UNQdFAo8EcT8YTPo7iG0x1VDM0PiCN9OhKMi1VAenIrIXPFGBOVC7Nzvw4BoIdx52/GR/5ivRnT7ciQEmS1tH4YWZAeCc0vfiPnd18iZocgpp735gK5Jw0/qu/Gc9+dXhE1DwOLFR3t2VXc6I6iG57kTYa1b8W6jZl9X1Uy0tGz5+KyMqMykolLt69xhu4fGExItXl4qxKuYAXhNQJYFtEdNuFVAYoA/CjgdgGnZZieAtgQQUwA5CqhfCXDWmxALFcDlVDHZHx130DZj7hPMJNTXt8UDGQ69OGKgOe/K6Qdt83vPXOt5dcT5V/ozsz/8pPjonXPn3LPMq1GXhkzHiAln3rK74+/74VNXstU1aJEUT9Vtd84Y3HH998HMyvNGCrqw6fpdbx/BNr9uC9e9RTQuAgB3Pj1zPIBzb79uSto2/wzxU+UsCuC5TccBOBHAQmsgeiF4iGAX8MGqDm5MUgfgHcmjai2zDQ/AB/522J293fMAdhHRST/wtaTx70Wu9dkFfJAR6rA+YC0fDl7Koc1qs4mI/mK1GQreH3aDezNzwGc+d4OXe5DABW6eBA/ztEtb2J7CVAEb2zOV+r8JTvYoZVubRNqfGrjnrhDcq/eitf9xSOYTXgCgnyXqpFvHrAfP25wEHir6PrglcSApcJOBQxVN7RBXSllm4FBPp73MDlfdA/482iGsJjgB3wkAVk3E04joWz0PFpFPFUOYC54juo4xdjm41zEOLlpjewSPWGPJeh90A9BAREf0LFsldO4C0JMxFiai7h2aLAC/R9vBiTqIaB54WHoaaaSRAgYhXxAEsVH35ySYcZyP5BMBLLRCRM8A0D+haV0QS6i6y6FBtt4TRkKAZgKKg1kk0Yaqaxrbl1WYMJiggDEAiAGyCiYBFLUbPwlgF037Im2b/0PAVjHKODr379pmyZ8ZlMSwESp7+wzjgWeWA9QW3rlB0mnL+osuW/0cAAzos+KkmNhFbdeyquiAu1th35ocM16YtapA25Ofu6uzW3JI4VBwx8lZfZ6qCqtPL/N3d66qOwFlnlZfRfFbYCyX8sUgU3TSJDOqiGQiBlUP66JSm/BTl1gPczOjyDd6V5KkXE+e1oBSM6bHtTxpr1potIqdxb5aVaLQWb3H7492iUQ1n3NXyxu7jEJZK3FdrJqMDMXtem/bKWcd51o8eH7DSe0C6brkABs2bEV9bmvJfX/7qs+kWL94c8OKzL9GQi61vb3EG3a3S4moYuzb0e4d3YfF80syVMNUAdEQZUMWNIBMKE4r6MkAHM6kfAADEDEApwLIBuDYCxjDTMQcgEtJgJkqYSgs29xF/f29kbhnSANNLyi27r27w+91zjWzQuek2OaIJLwbFTC0U6NRfdWX99xPpE5k22PxHBkn3n/lbXvOuH42ncHHSIfhpfKrGKlSNwANE7fPOqJtnll53gAxJNwlFGb3eOakKZFrFs48xDb38X7+se5wSzUnjHnFXnb7dVNeAU8RS+NniCN5QP6tIKIFROQCF/O4KiVErTN4/pkGHh4owgrc/o5d2uTRJoom+LNiexkF8IH8r8FVMpMbMvYHqx5bGj9TkAVwkaIj1c2cBp7/mErUNgG4nTF2h/U9BN4H/moJtQTABRQMAH8ED5HYAJ57dw046dSs9cCh+X027HBVwfrzgCuI2v0xYu3DnpBRwEthlFntpoKHm2Yi6Q38wtqnbJ2zXXbjF+Bhqe9b7VNJngj+7MSsv1QwJOs3ijgc9jIBPCzX9rzakzOXM8ZGMsZeB3ALgIGMscc67oRxNDDGIoyxl1JWPQke6jkSwKPWteSB5xh+FzLAyeohHkfG2HjGmK2+twG8vmPkW64POHxy6V8CY+w5xlg1Y+xbwrTSSOM/E72OPeb14kH9nS1mrMIrOq6aOHpcR9ucECUpW1EkESIU6CAe/C8BogToDDCJR9UBQDTKQARDEG2iaNlmzQRMQIcAkyIgTIN5qG3uPOXtP3T59Ts3/1jXnsY/DraKEVvFvtU2V9zwxrSMJ2841zT9RNZE5Z71+kIt0Hb7c68NuQMATIYGZAYi7Pw1T3d/8NqM9ogZajEpEIBiVNWfeFt02fCH2x+cs9e56PoHixp7X9pcWr1cUgz9nNz3jZ7FuyCIO5vMBMNKd47BlDj8cQMLhNHSNrPSLJRKheyoITQEu3lM0en1Cq2sJuzDnkC/SFPU1EoyHKI75wBYtqCo+ZFfNhgVpfvbenh75eq/uiR79y/zK43Mwad9xnJLmwwm5izJzwlQeXGtsj92aVs4KEnhOHwrTnH9YuTqyAtinvKiKwOZipowsnKiSkJnRiiuioJQ0OuLD0piSz/QY3qLBhgCNIB4fqIQsSQDUmyXba6YyIN9RAEwewG6CxAUIGoCUSEQD09xCK+PZOz11/fGS37diJwRjM26FR0gSq9KTvbbRi/7U6SQTX7EXn59/PzHrtPOf/ss9bxhLCvjanizfWZlUV6L1/GdtpmcUjY08xQYZs/U5eccP3b8OcePvRUAfHucG3SH+SxzyvFv7T8wflDbfPzIS14bfvy46mPHXPuD7jcNjp9F6QzGWBm4mEkT+GxGHHzAnIO/P9hL9TguAx/kT0VygH4NuFcmA3wwebA4uHXcueDlM8annIudM5nGDwTG2FMAlhPRX79H2yPef8vTdDt4Dt2RxG5sEZSvwNU57UL0J4KHVboAnAuehP1LcBVVH3i/G2a1F8FJ0z5wD1srOHmzvYO28I2Q8kkdlneEntKuI6Gxt20AcDsRvWSF3brA++pe8HBPOx+x3TpPDZx0KUgWtw+AG+wi67rs8NLU84rg8FIZNux2NgFNgHvmAgAuAifMGQAKOv4+Fnmbbt2zOUQ0wVrOwEOEmwBcD16CxANuBccAWEtE2hHOxd5vKYBWO4TUWha2ztP1UzynjLEd4IT/MiJ658c+fhpp/JhgD4xKtc05IAqCTDcEMRupttl+S1ICMA3A4TARi/P3jtP5Fbji9BWwbHN2BNdEgfuiAjKhLVal9AAAIABJREFUQgBBA0OGlROJ8hveeAcQ/NWPXzzJPoQ862GmXXtz2jb/gHj0tehTAC3/zWWu77TNA1kRW0t1h93/F2cNz9AN4zeKjLmXX7PiMNu8cw9j3bsSTb952JKKzpmDGwKtiUgM+4u+6nXiXzd+VnzlntGuNnfBuURisSm4Jpz3gb82HBU9H1bWrhyg5w9zBhsTWRG36G0r6OMa99K+7cEuqjext9WrtNFXMZc2WM2Wm3WwAe5qU2Rgd7XfSz3lzUIPtonkAJjLWWNmqglBlePYGB2CWJuGwtB+JApUvQpdhBjLMXvIW6QcoQ3bAzmoqHWiKZRByld9Geu/piH/hAO3D8z/5qWXG45v0fZmufZvY7Gi4JaaNxKPdks4ioTWDV9R49K89v7DmhN7P8rVDKExr6ndrUSDrRGYBS4gFADMOoAKAF8mgAQnfiBensQEEAwBsgm4fcnhBMA5dtgEBAEwDE4sE5Zt9sUBnAMEawEzA/AVEF3WwTa/cKuIuukOkOrFgTn19NQEAHC/vv2gbR5X9Nb1ZKhXSHHVYySiUXVuZIygmWsfn3P7t9rmFyuvLQVR08TtfzlIBi86ZWxYZBC8XtH17Ntv/ujP6fDjx1WbopTv0BOXfbn4zbRt/oHxo3sWvwVDwb0txeCDyWzwgaltgmz/PJBUZrS9Ovb/v0KyBiPAB72PIxmmGgMvHG4LewC8UPs19hdLoXEsY8yBNH4QMMaOAVfN/M5ZYsZYCYAPGGOTOiy/Hzy0+FZ8iyhJCnFYi2TOYRfwEMRXwL3KZeB96ytw4iWBE0WA9x0RwMvg6qJbwfsMw6EKp3XgCq2xlPXAoYSMwHMK7fOwiaJpLVuEpNfSPo9NjLGzkezrcfD+22K1ZeB9V7SuYSJ4OQ2XtY0PPAfAB07GDiBJAO1nxoUkUbQNAeHQZ8n+VMBzJc8Hr1nag4jyj0AUW8GVa3dZ+7+FMTaaMaZYTuHVRFQNTiZttdIx4GFtjYyxVdZ+yhhjExljdtgsiGh/Sq7hPRb5rAYQ/AkndHoiTRTT+N/BUPB3RikANxgrgCDa75CkbeZvSQ2GAZgiIUYCHA7A6SQAkwFEFECyXi56q5R4PGpqTsgQQAdtc4Z9UCbgF63ZX15hf3c8+8IQJmaOVZ597mdT7us/HQ+/FjlG0tmlzGA3fFfb413lPRSRfTDMUXpJ6vLJk667f8XS3nUrl3a6takh/4i2uXtX/q7OoIz1gWDQSBDko7QBXU4Z0m/7o5VPvtr37vNelJhejrhekPW5sTRgUCuZWeJpOyuH+cImjEyS6o7bLX7RY/5DjVrebUJg/9av/IXxTbGebICnTPEZDWYw7tM31uXX7W0OzG+JSvGlkf6xz+InMT1Dgik5hX1aLmoixSEyW6iBOUONUpaxm5VJaKtAvLa/9Hz0V8b82OnG7kD2+5BKjGzVDYE0f1g7WXql5qmNOOrO8WZYNFkuI09Jmz6wJfhsS1Rp0mIhY/tHBaylUchc8pUs1spGdkOLY2JOmT9Qdk2OCx7ZBHJ8gLc7XAWZYEoUcBwQ4DABBwO0GDe5igfw+CxzbNlmhYAIcaIIAALj6xUFME5nqDtDAR4DvD2Irs0/nCjObgWUOwzkr42gq6vvgjNvOfnL90YPX7JQCY/rSeFxPVeHx/Ws/mZ2eMY3L7V+rLW2fY0lLWeFo3VnRPoZjb/+812rAODpEadUzDz+9ImPnnrRQds8cdus/TZRnNOv9J6Pxx3D4hrqogm0/RREEQAMV0bnNFH89+HnQhbvQ9JDoiCZjyWkfNo5WKl5Yjai4CFpNyKpnCmCE0UV3CvyHDhhOeilIKJIqtcCfIBuD/LT+AFARF+Dixb96ns07w6ep9b5COtSi9LzBZw8IOX76eBlMKaDhzmvBs/5ywMnWjPBidogcBKm4dDyKQAwEJyUFoCTxO1IkjwBQAk4iXIh6fGLgoeNNqScq5iyPpVQiuD1Fu0+nQHuQf8SwJvgoaD29k0ArgYPj92PZDkOJzipDeBQQmh7Lk3w8MwqJD2SNgG1YYdt2ypmSNk2NSFdAq9ZeWrqTWKMtTPGQinbrrUIXCW4B7crY2w4Y6y/tf5l8FIXIXBCnw3+bFZa64cgmbN8CBhjzwP4DbiqaW8iyu/Y5seCRYDTxiiN/xXcD9MMQ0+oMA+xzQyH22YZsgSQQkACiMcBLRFCPLIBhnZjAgiZAEAkQZBVOJgDIvaD4UGPpNwM/h4FAOx97NJI8I6nkoNOxiKA8G2q2Gn8E7j5MtfXmmT+zhBpyne1ZSLrxgTqZpLZIS+cMokJzDQVROPKwbHUmSPGHhoRNrr29JvXvNz3nLpp06Go3btHu6wXEkJ9N+dRebnbu2VnvCo9nrnCtdLT3mvArtDQWr1d1WIRTa/r0Yb67BgFIwGMZp7TnDtaf+3Nchb0yoiJnYy123SNzIih6zlxp6RK3Uo+Clw9xrN5h7P//rVCV1RRKBaLBmGGDiSyGjbrRUKb2JvVuYYIjQVdxN6OWiozDjCvGcTR2CFuCfcR5iu/Gu0ABHdcYVpRKDMUCOXU169atLxT8NmKuV1chb4Ayyx3CBsaB+0b0brumpHdN/zZLUQPSBpMKaJqsltwdusdezk/t8rvMkzTU2EKLDuRAEwBsgiIhgkIz6qI1VlBPhEeuy3G+aOlgpfWAKxSGiwp0q4jW2ryAzHIEKBAFhVPbIvHEzvENmey6wM5bEoIkK1amc5dROMIotFXk9Rz3Ylg16m1Lwy/se6F/gBAYDOCDs/YNZ9Em1z7HKscfXN6SqKgRhn1BABDkAclJPeFaqS1Eh0wb0CnOQ5F+E1o3d6l7y2aV/HuonnFHdv8WFjx8SxKE8V/H37SWTrGWG/wwWMxksQ1Zv2vdGieOlvhx6EeQlvKG0gOpu18MQKfFT0ZvFj7HvCQuMNCHoloC/jAOI0fEET09Pdsuh+8nMKiDtvfCuBWxtgz4DPQYIzlAXAzxvYR2Uky8IITtiIADxLR8VbbIeDhycPBFUcngNf0Kgb3iB2HJAn1IVmKw657aE9a2BDByaNNuGzvtQdc0W8UuGdvBJJiM6kQkMyJtAdbqf19JXj4bHvKuYjW93wkQ0w7AfgGvF/bJM8DTjjvBp/4MMHDaV3gz00RksJRHc8rhGS90V3gz5EXnKy2gZcjSb0H9n15hoimWsu/Bve+7gYnq21IEvAs8HBbDcANjLF+4AQZAP4GoIqI1uFwvGTdj5ePsC6NNNL4gcEeGNUb3HYWw4gz620YBUEEJRSIMpCcq0vaZkn2Q6RMJAReU1yUPIDEhW80IwpTVCGzBJMgE3+llorAxbqRGAb+Tr7VOr5dpgMAEL9qYto2/xtw02Wu72WbA6H4TocsXrsisf/L1OVPvjDrVwB+9corM57xerI/B4AzR4zNA+A6c8TYffOXzCMA2HHsOm/Zyn4NHzV/XRSMlDx41OzpxwHApkkrhizs9uaNMdp+XBkb/EjUbLjQ5WnYFR2zuMvmVmzO1vNOjAmhWH6zCaco+VxeZDTozCijNsmz45Ieim+DMEscJ+e0b0fnrGacWbZHWi0NN/OlmBKS3GAs26GE24TmrczjLHHO2JeTd6bPCDcVKjVDRWaIm/M9xPQm5Emt8BnFLIROwrJGQ8+CqQUURVzjkFnC7VXCZaXovaCT8N7ohpXSO1ecuIKZ7brRJLMD1frZ93sEgYJtKz/rkavFQgmxTlX2B3t1alrKvikWQv1CUYcpMvg9iYA7oDM3Qbk7AjkMQAfMRnAXfBtgFANRHRCsh8uexw0BiIc8vpAnGJW8Q4ojO1fXSi7A6UW4/V7JITcixTaLYIIMkzlKZEkI4JlI4LKpABAVvUvdiWDNkIoDu+MJVJEoNAMYwkBTvfGQk5F+3+Nz7tYAjJt8z+0LBd18EwA0h+c9Z6R1x3VLFxxmm03Qk7pmDIGivvGP9bw0/tPwk+UsWnmKj4LXO3OlrFoEPnhPHTzbg2Pb4xcFH9Sm5lfZ7VM9ObbXpQU8tPAu8Bp8L4LnUEwHL6Nx/w93ZWn8s2CM3QQgnKJimrpuIIDF4ITnfvByE7cC+MDKVXSAK4vWgJOLHABjiWg+Y8wON20A7yuLwcMZbZJlq7h5wfuRraxr9yWb1MFq68aheYv25IX9MNmhnXbZCtvDmIA1dQju4d5rneud1jG/sc7RDr+yyaJN4lL7+QFwL6etxGoLoDGrnWm1TY0esElnx9qkNjQkSaB9rR7wMN4vwb2+W8Gfo3fBS9wMBzDzW/JM77H2+ZKtfpqyLgO8buOSI+Wgfh+k1FFckc4zTiONHwbsgVEVILoPWvxkMEGFKABxDZCFRQCGwTBlKA6ACYBpJGAaCiTFts1xcHvObTPPReSTatEoICmALAIEAuP52pmiOs1vxP4MoBSegqcQatgHbpvvo2lfzDj8DNP4sTGQFd0EILyW6g6zzX979+WBsbboYiaz1pef/vRBcNs8FcDn85fMo9lVUx1MNMYxk6q3/KHpVZHErLjKxj763GvzASZ9/nXvYa0BT4MonOQKx9T3Xc66bYWVW4cmNvckRq7gpqJStGe0+oqxWR5cF9Dq433k3PKPyL1zDCPXXuOFLoOFIeYCFBg7AnE517Meo0SHIZhF4m6hNdHkb95XlJm/r4x6djbgKF9Gu1l3ep11izUy2dFF6Cl6EDe3bNtr9Mjur/SVtlOwMRaNNezdtnn+L98YOf6BO6MsQ7v+08J1rCmv6rMZtddFHjoT6/yhoCHVimsiLJRRRB5WJicOrMpVijq1YsknZQ0kZhSY1S3m2Z62yHu7sjxKwqDCLJ3VthmJmOYiwJQBEvhjYgCAxmBIDBqZyBKSTnYuus5YLO7ytMsOzSFcdryCzzaxwPbaTB9D8HkB5i5T0Lxk5qbY5rVzoPYeTtGJTxzp95xa8+xMTZda3Hr5Sw92H3WIbX51zoKM5Z9/cWnVlu1L5i+Z90/Z5nkP31gKoEw35BWXTnswbZv/C/BTehbH4nCi2ISkNyaKpNS/PUC2B9226mUreIihXcbADrXrWDYjG8AscG8JAJwGXoQ9BC5mksZPDMZYV/BcwuojrSeitZYg0WZwFdFCcHEistbHGWN/A+830wGcRETzrc0FcG9XAxEFrOLxOeD9bY31/8FDIen5s8u2AEmCJePQWosBJIliHMmahgKS9T91AE+Ah1LaBNQNHoL5gLUvB7i4ztFIhkpfC17KI7UkxgrwPr/Vuge2EmsE/HlJDdW2vYNbwJ8r2wMqIkkE7euwz7fVOmcnuBppBRG9xRirBw+ZrbeOOxXAFGtfEQCz0QFEdBtjzA/gZsZYYYcamePAhXNawX/Tg7DCixeDezj/RERzO+7bQiF4DuH6lHuWRhpp/Gs4G4ZxEgxSAYMTPKfUCmAEiASIFAUTuG3WEgqIAdJB22zb8wCIsqAnNAiKDDNBEJQ4dKjWqINBiwE6FfjN9llwl1TBlwNkdTsLoYZZ4O+nxh//0tPoiIGsqAJ/xzafd+7la99566X3ibFvoFm2WQazvYpXdpkRf2HPlL+ZTIrqijzNFNgxjz77im2bxVOO2VKHG+fU47GLgkv+9tvTh4bEjB2+kiZZNtZorCWn1Iij2VCw2TmS5EYISlNdwtm7QGns86mxWRhNgqlSToYqBMKZihpyi3nhnVjrHGjmeMSAufaWTI9HN/OdWxMCGhwwM1gnT71wkR6R59beJhlVNXpO26aHJmeWTA/p+6ihPJcF27q6KDBxgHCUMai9ujviBYMc948oqdxSawzqvsQ3oaXPBnKbuG611OeR/XOYamw2oTUx0XnclhXFZT5fbLu5B87A6f3VhFBaIHgKG/XI/jrReSAim3GNFF6UlO0CTBcgVAE0GDBVESZzOgUWjAZDAPNY87YG4BCJHI5w0Ns6sk+LMLhr3D24XBw96VlUmHTNW4L8SKMgR71G1NkAuAsAmgp0nYyYKWUIsyLt5rWH2eYZZVdPuXfOJ35djd98Zac53tmOiw8SuvyS4nFHDT9ubKDFX4sOtnn8W3eyL2R5Wd/GgOPCBP74q+vv++Bbuk0ZgApJ0tK2+b8EPyVZvBjJWUgbdskM4PCacEDSY2PnSORZyxPWfhiSA2sbZsryAHh5gpXgoYLX2KU7rHy3ywBcnvZS/CQoBpAgoqaOK6ww0iYimmR9vwn89/8stR0R+a31WQDeZ4xlEFE7uKe6BMCjjLFq8H7XCbwf1IGTIA+StQ1tb7VNDBcCOMY6jAvJ/NoEuDfS7pMSkpMVtmcvDE4Mr0PypZmceU+KRCjgpNOe9AB4jUK7re1V7AtO/gaCE1r7WA4cSmxlcIEfEfy5qre+2/Ck/J+q5mrXVoqAP6NByzPbbh3rOvA6hYSk8E4jY2w9OHmLAOia8gzZ9/B4AB+mHPNzcDK7AIfjOHDizMDrTs77lmfyGwBbOuQdp5FGGv8axoEJMQiqC6IVDQfyIRrjttnptCelGBxO+211qG3WE1kwDEAUE9DjLigOBsbUQ+J+mBWxIUgqEGpE9zPqUbttJSR1FATxGrrx/YUA4HnvgdNF4LLLqpZd/vTUv6Vt84+IgayIgb/Xo2up7jDbjBkrhwBoOn/qhHEAMPvP790EBnP+1/NSUxYwqetMPwBEdjVmCZc+ugBgXoCCAI7FlLd64+sz/lQzumbH6sm9NAeayhfH6ld1jwfrdFXK2Obs7hmkfaNvlxnDUaK40wgnchiDouaKiZixfDdKBziC/dGI01xloR2RllirS8/LS2xAmbdYIoq7EsJGt1fKCJXEC1YdpxYM/avZ27tXCR3YEGkvLHJVaRVTs8zqiF+NIFOLu/Kqj9G9OpMCRRF9o3muWegqUtRgQ6aZEJRQew6RGcNej2e0tyzD1XOaaaw7N5xwZ7sUub64V2Krf1OWHBgabhETtVK7c4dPNJWeLocvD2bUEIDNDg1gMmNGVyKIgJ4FKPWA3kUHEIyaAATLNjNwcZuoCTgFgGV/vNmLjzebEQ+MSasqNzUUOp+WyEi0GaQrAN0JmE9wURzdECAJAGsc2OuhbWW3OLPyT/GFUOSseE4ZSwCQs9+jxN06E1rqTkdx0jYrqvp5eUXFrpaGhsNs8yeKepwX1KvZrQChSP8zR4ydb08KpEI35BWSpK0fe9Njadv8X4KfMgy1BFzM4k0k1SZtr05H0Y1UE3OkUgU6eIHSCiQH+3YRdQnAWwDOARfBWQs+CHYC6ENENdb5rAQPS7zDDktljD0Bnt+2mIjOTDn3dHmNHwCMMQGcSKWKDhkd2vQHD9P8mojuY4ydAK7Qtwo8/+458NDVfKv9BQCeBydSCniZjLPBc1WvAs+l6w4gF8mi9ja5ioKTLlvF1O5zNuEDkiQvgKSy6AFrvS3QZIJ7rEut83Ai2RcPXioO7eOGtU0X6350ARfrmQtOSL8Ez1eUrXPPACe7Arh3LRM8BLcneCjKLR2OY3sSv63EB8C9lRXWMTQAzeDk0UByUmcQ+IRLJjgxPgbcS2+rwDYDKLdCgy8H9z7uJaKLGWMKuPDQ5tTnhzF2PLhiYoiIrrSWDbPu8W4AV1rH2kREy7/l3NNII40fAOyBUZUAKmHiZQiWbY4nTBi6CDDA5Ux9fyTfY0QEIgYiE1pCAGOALGsQxF2wbXMcIhwHQ/tFAPMBjIaJ9YjH1s0acOXFWyNNzhkNSyvoN+83AkDuO/euz9SiPRpU9x3Bc27ltnnhc3+Bpo+DHvuQzrjx0oPn/vbLjC64PG2b/0V8Ks09aJtvNbhY6lqqO8Q2z5u0oP/wLo4/5XnkxdJNxzx09dgzTmhuoKFG3LPqnPdcA7OV5rvrDEd4ct7b+QAQOTp6gSDozy87Y0ni1V82OXxS4y8vdb1zdvnH/XYWznjkV1uLarfNmbC7X26kOYsN2i41OlzGyXMbzGh2pry7Z1PU68117JFajGw3SbnqepalG4i2FwZXZPb3rgweD1lsNVirLlaU1wTC8V4+PeyEP6O26hhzSdac2mL3GJTLuXHD2OfaVxPJKy3dKPbUXQ6X6t0c1npJW+VelV+jVN2Nff5cc83uXwtNvfxorusE1w7JkPNX7TMjOV3irbXBxqLRFaQ6j2rZu3SueKDA619V96W0TcrPPMYvxyMslwWRoYX0lfWLNTWrl69bZoHiq62imng16wmEHhREZbIzI4pwq0xWbmIEkF1AIsU227IItl9E2wo4KwBdBphWikDzG32qs28+4DOO8sG1X6gFlbS6Plw6phYQMr2IxWaUxY9+xOHd7RBDkcyLFLPzVd5GFDm7PqeMpbNOm3y5Oy93Sv4F3fc8ccH4S2fvq1Nk3ehhun2bJxb4Dj4/bc8tO74utvsGA7GmflOuvg4A8t6/f9io+kBV6KXtfgBXnn5UD/3sgd3Xl185KW2b/4vxk6mhEtEB8GLq9gDaLnuRAM8BO6S59Wk/PcCh5y4B6IVk2J+WsnwreMjcJeA16yaAD5rlDvu4HbwkwgMpy2yxkx6MsTMBgDEmA5hseSLT+NfgBPfwuYjI6EgULUQAvAfgTcvDdRt4rttGJMMw3Snt3wGfHJDAidsTsNRHiagbeAjr9eDKuRqsqrg4NF/PnrAAeN9L9drZy7ch2S9LkPQKApwYdrb2a3v+UnME7ZzC1EFN1LoXsLaZBeB1cKLIwEO2u4F79rKRVFUdYh3bVkM9FzznsRnJmow2tA7HRMp3q2A2PkGyzmkBkl5U+xz94F7gFvCcRdEifuvBw3rzwENIgSSZ/pP1eQw4eTykmC+AJ8EVZssPnhTRMiLaBH4vm8FzTsd3VMBNI400fljQtC+2ATgHgmWbTYgQFBmyKw6Ho/1bNjOg6wzxOGCaAmQn4FABJspItc0SNEt5QIRpbgVXyb4E8Wg3j9M1oUELeDJUVR7lKzv4noqJ4vQmh/uD8VXLk7aZTBWAjEjjQPbMBG6b573shEmT2dyXTv7Bb8r/Hmzb7FxLdUZHoggAe1r0yI4m/W3NNOcu+9N1juIc8w9lnYxLssuCGzUyijxCRPCAuZM7VN8hU3pr/kkJRwt6KIzwyLrY0cfOGZZpYpWr20MvbpwUqEj8ynBn3RiUDS3GVHOLI1sy93dNlG0eLPrqs4RooKuwriHGSjxx5LSWYORun7opUWHGRQ8iRjPrVrQLjc37tpXsYhSrz0eDVNmF3IWZ57lJrs3PR5VTNFhOUWen6pAUs8WhA6TBKQWLfNgrlGB9vIumO3VzW0Eb6RGBStT9aOqpheSy7HKtwAHk+VzxcPOsxo0LX+vcGvYqwV2shyCcVNEv3k0Pemcare7seMwlesfkHpt9tK9/aZ7ga6oxE/FmUYSEy6Ri3zjTMJoThjMBOCzbnDCBiMZLZUjgZlwBAAJkeJhgAuQGjE8AigOyvB/u/HtaiiIh3ecqdhg4xceiHy69sABwFQN6SychOpcRKRt3XkUGzPW6FG4LNjQXtu1vW8x3LJa2Vbdg/4ur/gwAgq4PTwjsOhYOHGKbd8b2ztqP4KlhUzsYldR09q3L3rz6z3WwbPNx3YqOERldijT+q/GTeBatAV8v8FnFLPDBeKqYiB1e1xGpXpHU9gYOFcRJzXcEeN7XSPA46kLwTr4fQO/v8hAyxvqAl2Awichtnfv5APYT0cq0l/Gfh3UvXQCiKYqm39X+AvB+O89adhx4iOquDm3PAfewDQcv3XEFOBnaCB6SeQO4Qu5DSIYw20glSAAnRjkd/tfASZUHybqKKg5X8TUB/M46ntdaZoe8pnoaV4J7va8C7+NtSHr1bM+nTkRexlgVOGnrjuQUpB/c23qqtSwO/mzV4NCcTPucOk4UxcEnSqYiWaux1bq2kv9n773jpaqu9vFnnTZ95vYG99KLIM0uoihYUCJGkRhLRMFEbEmMhmhiJbYQExsqUVCxdzEWigEbGrsI0jsXbm/TZ05bvz/2OcwAV8375ht/eZNZn8/93HtnTtlnn33O2s9+1noWRPmZoyCe1XqHORwPYLk7/oloBQTz+AWARRA1MbfnfV8Ewd5fBpEPeS4zLyGihyFA74+YeRO6MSLqBQBOzcaCFaxg/wKj2eNUiGf4OYhFKA8MmQGJIBkWZJgQ7wBhueBTG2xLME1AVRniXW06P949S2Nynm9OpwFf4M+AfTQBvUb6aisq7JBeHA7serb10wPz1VC7besz1wyFmf4MlmHzBQ8E6MXHVTBPBrCRp0z9gt5fQHz01IJv/l+Ywyz6AKRPNKd8p29eNe9M+nKdcsbuqG78dt7LfwWAOQ0XHCnD03pJzV/28s0/33nXJIvVWYfRl0fRVwfcdPKss873eRFYMrr+w8Uzd8dsUi4tk9adUrJOunMz1/rPe7eCwi0lkNtsfDBuXeqNQZv8MwZ9iGwygg0dI1tWVfWv0CQZHmtNl0mDiop4i1HU0WR0os6/Vu7F/Wln3GcHva2BiGY1t8GQfNCqA9iEsBXZFb6PwuZZZXpbZD37bVarg1FdMXsUp5VEwo+yrt1oV9o+OT2c/PK1v5ddZMYVCQPtTkKmRF2Tsjo6PBxp9ivUbhgfLzwnXHTkI9sOr/PFNxZbfYvrZRlsWSu/lLtYlj6VGSf6i3ySHktnsrvsnkBmA5AqFTp47rp32mEbNYi1XgKQyQLWLCBwLWAHASkNeBpnnLBB+3B1pMdBdrS1T5AOv3HrQA1APfPZPOHqOeNn+uPLx826lgFg8taHVygdqYOyQXpvwP1N7xPZTy96Qd++uvHPDACPbd9dZNv2sC3b3r9MkZTjLck495ax5y55+vpZC5J213DbtE69+A/37Oru3u985PFeAFA37fyCb/4Ptv+/wOLJEEIeB0Hkg92GvUVDhFsRv2PIK9YLMYm1ICb+btgqo3uFxwTEhPwZiAn6eIin70Vm/s5CtET0IURe1dEQk/FTIIBEgpktIhoA4B4AzzDzE99wjGoATfuE3ZUDOJ6Zn/lNnTKVAAAgAElEQVSuNhTs280BEVMAvNAdkCCikwA8hRz7uB6C2doI8YZ2x1YKYhK0ECLMtQWC6SpBLixaB/AXCHbPzeOLIMc6umMxH3h2Osf17/Nd/mKHG9Y6GCLs0gfBpmkQINEH4VHug2BGq51juCqt7tg3kWNGdQjgeTpECLact0/+3+61GRAg7zMA50PUaXwazoSRmd8moqsBRJj5ekeQaA2A+L51D4noJggxn2eZeb/6mo4Q0bEAznLAIgEoYeb2fbctWMEK9v0ZzR53KsTCzQiIfOI7YMgSGQa8dtxKewOAouR8s+28P8VbJA0Gg+GDtI9vtiGDHAVU4QoTME0FqvoEgDCBThjl6y2fVnPQ8zdMvuy7ffPfHvwMEq3Fhr8fDdvuwvDjJ0C8IxN89FSL3l+Q881HT+3WN1e++kS1D76m7aeducc391ryarlqWsdvnnhGwTf/k7bo4Fm9bJunSBK9cPLnN+znm6/+5OaTrpoz7unwlj5+SVKx09y4vu64TYN8mfS6Ty5eWtfZckiQd4ewVm5I91BkrdfGUY/ed4LvJDvU2RIKbu9rojpSjzKSqJXK4dc9xqePVnDwov5f95Z3VsWiLSWlEcvK2sE2DQMCzdKu4iy3xALUyb2QCgE9dm3rTCtHebaWW/4aXxs3tocpLlUhlra4JCIRZZqtMjMjl4ZaO1qkIw/csHTn1kCpzyOjtdPfpik7u7yaVFLpNbdtz5w0Tb+v4b3EeZXDqVrbaGLXA3GzpbZCznpgb14XlsP+mJFel1T0WIAgQ/eP8U5KLYpeHA4rk2zOyom4B4BsAWnHN3sswCTAIgVZQ5KlL3Qr+Blgng/QwyfQtqf7n5It0orgufvJ297uXfuXq4kQ2bbz4usnXD1n/EBkXm+zKfb0n6+qzO/zGZfffJPHSP2SVH7i7jmzr9j3nvx22VMLPbIy1iDzx7eMPXfJi3+eRhu2Ucnv7ptf8M3/5fa9CtwQUSVEuNouiHDCJgB3Y+9J776Mh9rN/67iZC5VPsewyMiFF8oQwhrDAbwGwbIoAOYRkcLM+xZkz29rbwgRkWEQE+8khBMFgJXOsT522vLQNxyjGiIMtgUiV8u1NwEMIKIoM7/Z3b4F+4etHMAQAOVEdAHEivgQCNb4FggBG5eJa4QoVfEaBCsXR0711C1xcQbEePoKYtzsG+58MYCvIfIJXaCZz3a7485d9Ch2vnMFarLO7/UQ7Lo7XgGRs+jWeyxzfndCgEUvhDpqC3LlMtw2u2PffZ5dwZwFznZu+06DCNN1Lb+dGkRY6wcQ4/sdCOBoAGh1cox1ABoR3YBcSOpeq8aObYYY93tNuJw8xNcBPAjg9DwlW4ZgbLHP9jMh+vtkZt7YzXkKVrCC/T8wmj2uEiKnvx6iJNFuuL5ZtSR/JmF7wXJaksTbwgRArEJ2XLD4pYIgOSUx8mRsbBmwLUCSQZKBdJoJTOwP7PHNg3qN2pWRSH5l8UcPvfn6Z8pHjz76zb552dyxsGkomAfhwHGubz4C4l32BYDdSCc/BpMHfn+3vrny1SeqJYPWpSjZCPEeBgBYVnZpWqE+A958ZdemU05/v7t9C/YPW41p44C0kiifNPbHFxh+87kj/jhjiGRhyPUjjr+lTutsbK9q3qZu93Wm0mWNg8bUL0pFtfmJaNVg+ZV+8ZKWUcaAXdVqn171ni+ueF16ujg6vcrO0Cap94ZGlIc88ElZMwozfRxiZlKp0IIXHrbjozX923h4U7kcMTyMKEqkcDqKRBT8FA+3vdpg2edP2Acn3uOaYipOJHcgbXgMn99QW5WGTLot7UF5xTqvrQ4ZoXwlb9YV4/PNx3o6JGVnoH+xHoyYlPm6ukTvCMLONnbJTW1exVfp/fLF3ReVhrMd2axqN+pIbQ/6PDueyRJkj4yeSbNkVLvaHvFCb4INGVpqUWYR4En5g17JsgIoTbWdtsMK5vnmrAzYdk1QZ5/Xo9W38RH+0fTJoAnayoY72t5ZKVd99sEyNZvtp7Y/2PeZmnLA9kCSq8oeuWHkCGtt6zDqipG0n2+u95a1RvTk+hIptVddxHuuu3S0jOzrVSY92HfQwaf/4MJLGADO/NUj3frmxXOn/wGiokH/CTPmF9j7/wL7XplFByx+ATF5fwwih+kc5OrP5bMerrkMzLcJhOT/3d0xss7v9yBC8tYBmAgBGv7CzJ877aN9GMAXIFjFiHPcFAQwvBUCOC5yztUEYKxzLe8x84fu8SCEd7Yx8wl5x70SwAQAEwohrP+8EVEIgnWbAgFy+kDk2x0BYDKACyEA0KXM3E5EH0GsnLtjxYIAZV7kwkqTeX+7ixjuGNMhxmL3Qg85y1dXdc3NjTwfAjR5kBPFkSDY8NkQoZphiBDqKyEA48sQuXtB53/3edCdfb9p8ceGAMYdEGzpvgJSW5w2eZBTNPUh99zthMj3XeSc5zwIZnDDvuG/AOAI2ZQxc8M+n38FsWiSgRAmmvlt45+I6iFA8yvMfI7zWTnEYsurzmJAwQpWsH/SHLD4BYCNUAMvwEiOQr5vNk0LzDJUlWGDkAXAWYbf892+OW2QqD9uWwgEZGSyjnyYAhBlAcDvr/ko7AmE+tfzOu7nm+STsfBvTWv+wjOXC9/89JXE59yV883vzFsMkkdCkiKwLRN6Ng1Zng3Vcz8S2UNA5mJIkgRDrkdIG4R2/AaE9/iHUz8EgN6vvkhpM7lNJ6zpPGPqHvG6/m++fGWWzXGNoewk85ifFHzzP2lvHDQr9Ky9db6uJiYmPLx0/O+vGORPeCrbalNHnBiZ+2Nzc8/ztAenfKk0V1028oO+7Z3j1q+UQ4lBm32tli4NkfvE01Zraabzigt93hEcDyp2ks26Vcl3A0cEQ8jygKZWtq1S+lyuoZ5mEmv8tn6O/aFCoaRk+wy0mpWIZmvhsbO8LSnTbqUOXp8fFUa9Md6zTLIlRW6XgKRcipXJIw1VDklDvZ9PXbbtiAcRNjwoLdH01R22td2SPL3smNTy1Z1Dyry/ilqqZ927pX+2P22+MlBb5DOa2xZ0jOw9STZk+YDwhoCytU1ZvToAa7dPR6UsqQFZMTbquUJTaQBgG0gTCF1gKwVI1Y7gjfPsMID4OoXgC3AscEZlvGxRojoRrywKeOMm6SmvmeqD3XJKrQvZ0eVBk+LnG8bFKYuOkQgbZrdctp9vPvGqBRp5KbLk1vP3Ura998ZL1vslu5chSRm199h5VWbnTBcwdmeL5053ffPjE2bMvxgAFs66rtzSraWk4aUzbri94Jv/w+x7Fbhh5maIGoe/hAhhm4S968K5JQLyzXU6bugLkCuHse827nZ7Tpl3fA8EG9QLIjfMBzEBNoE9uW8xIlpDREc4+7sCHy0Q4CHktL0VwL3IlVioghD1uAZCrMO9Xmbm/vsAxZOZ+S5mPqkAFLs3IjqWiF7bV8yEiH5GRHfnf05EZ0GwZZ9CTHbugMhRvMvJf3sVIk9xBARAAzMfAVGC4g4IABWCAPwWxH22IMbFMxDgLX9Mufmx7thqgQjXTCNXSRfOd/mKqsDe5Tgeds67b46jBpFrWQYxRj9l5lJm9jPzeRCKqCHk2EgLgslzQa/bBhuCAY055/NAgE83NMxEDgj3A/An59gukJQgxnk/iHDxRQAWMvPpzJyEqPdYn99wItpARK84/XxRN2I0IyHCf++FWED5rvE/HnlA0bEbIe7dVQWxm4IV7P+N8czlzQB+AODXMLJfYl/frCg6FFWHziSC7nWAbIJuArYtfLNuAlZ3vtkGDAMwDBmWBXg9gOKxIcuMbEaTs1lPKt04pKlrS6+i2vCXKdPQWuPxXnB98zvzjkRZ3xi9dd8aevNe4ZstKwA9nYKRaUG8KwWmEGL6TWhPt0DiOQIoGjpUqxZt1nbouAYG7nUbtf20M7l58tTe+UCR3l1w8uZTzrirfuKPTi0AxW+wkTcfi5E3v4aRN+/17r0IL116EV66+yK8tOfze7quOqvh7eZXyo71v6sY/k81KLfEy83rWuuSd103cvwmxfS8YSwdtXZd3Bh18+C3pgBA8fLBI4NBBFU1fEeREu+ofG1wcPPA11onfP2FqaXbUqVmgx0xdbOf0bbgMLyTrFGapWDMQqdchjVKreWVyrS2Eg93SUUcM/QWaXvX01ElnW7zJTLFwRiK5FJEMgmuDrSqiXgZFa2uAiersE4fqsuhYpUlyMuyVfMQioSglWsAQKUyiiJtkFKWV6s6YlayvxbyHhHwHXR1+tn4O6eXNj1xnL996ZRLvGWhiP+ASOTrZTC3tQYw+ErFKpmKzVQXlA2oFob5MjS+FugrWwC+BjIpgAhMPsAKiDIZYMA0AZuBrA14D1CKiqYpklbyLtdSk+KVZa8sdaXVhnhS7Wd9rY6oORKLA6Mjz2+vv/icWU2Xx30yf+ST9k7J+fjO2za8f+/vXzmrZ/X0H5dVXbLvba2JWAckbWzMRGofYMK2bwOKjo0H8IoLFAGgKRS7USIMTqTTvzp34q0F3/wfZt93GKoPIqwsA+AW5NQS8+vIdaeICeQckMvAuNt/6ymREyuxkKvLuAzA7RCT48VE9DZE6I0OASaXE1EAwMHIAWoXxLqgdTD2Vmk1IMocfOjUBdzs1v3Lu/4lAI4hoveY+aTvaPt/sz0AwQ6+BBEW6lq58+MFkCaioyCKu3sghFbuz9v2Zef3NRDM8DvAntDgHhBhVs9BjAM4x9wMYDGE2udlEAyyDTFpmQ8xdmUIlm4ehMJuCwTL5kcudzF/Zd2EGKcZ7C3a5ENOwdTNM7Sc7VxJNAD4NRE94+y7DILlK0Nu/MtOe++EYCDdsG2GAFXus5V1ftZBhF2549oVx3kVwFUQKq4+CHbzLohw8b4Q+bpjADwBAPvmFxLRzyDU83pALMbslSvh7MMQoP1bjYjWQzDyuyBCivNtg3NtXqfNk77lOG9AiPIcy8wrv+u8BSvYf6vR7HE+iBznJGDeiZxvdkVqfJJlmtXpGBplFbY/ICLlLB2wQPB4AE3Z2ze73tHncZJMbEDes+4moihULei3TEsxrfJORcbr0ZXL4Cm/HdloAL7qxXTnqW/j4FN/A5IMZI1e0LzL6b3HAkh1HYSsLiFUBpAqfLOkyrCZIckDQcxQNcDULURjBsLFL0Cy35r41JxDTMLmJedcvrdvfnfBEtg4ht5d8B6PnVrwzd9s3+Sbi5Hnm0e/dc9RQ8q95x1Ul5F73xxeflfkAdc3fw7HNyu3P/VrKftccNbQX7+9uTyl4FD0BFAl4ZCGYcCzAG67YcYv6ageXnVk51cbn+lz/IpWrijuX7XhsrHRF2evX3+mwelis7Pvhici6cMvlIjkTntX7MgHxrygZXwnv3C80sBlDcfKHaqvNt1sZyt3oVnKcs90Ew2MNcJeOczssaVWW6S3phsOGuVDyoZhE1Qr6EWqRUc6IBdzVDJDaSk5zGdhq6wrlFW8Qa8ctfxINBfNrd2x8yAplfD6Arwwszu5k9s8ZaBiPTTEVCPlJPMg1MSf77wzsjt5ZdvQIgVVDERtoFQ+EBmfjWRSB7wpwJMF4u1AqJ8AjRYAxaSA2RLSuza32lp9ZybUG0nZG9vAs2F55wD4TclY1HlKcEqqC6Ph+ObfN12+l29++4FZP+thmHVW0ttjcxhfgqlsxS8/ozF3H7IHEJ75q4f+Id/81r2XrmfJ/rQrioQko1/+d4kwNsjxFo4mNd/xh+34Vt981SEj3oi1th+V2tlW/BRnCgsz/wfsewWLzvnegZiUL3E+a4PIk3IHlgvGuluZcNkcoHug6Dqr/NxHd5Lufh4D8CHEpLrW+W4igHnMXEpE7c6xd0EUfR8NweTIEDlv1cgVv3ELlWsQk+QTIUDwTIjcrEUAQESnQgijrEVOSKRg32zlEPfKDecNAtCZ+dZ9QoWLIYDbMwBOckBGGIJ9ep+ZX4FgDn0QwO+HEGBoEETdxRREqNVfIIDUcEflswSiMPxxECBQgchPHQcBnDwQtf/CEEAtX+AmDcFSulLT7nh2FX/d0FcTudzDbRBF6N1wVDeHFxCg6QiIMTcKwERm/srplxcg2IAQhIjPlciVn3GfjxhE7uE4CNDsiuPAac/bEMzhTgjF1vMhBJxudJi7v0PUrXTDULs1Zn6IiG4GoDDzrG5Cuoudvlr7HaGn50Ms2PR0+89RS50A0d9xpy9sCJb/22yM0w/XQ4QjF6xgBeveFIj6qWshShW5uUorIHKdYUNSdMnLNiwnK5EciTkbkKT9fTNBfGQT4PHs7ZttBiTSoShsApKHHd+shP+OHgeqaN06FOGIF1FpImJt8/i035bQm/d1wrIUJOK74A+/BSk1GkQhSJIMRW5CRKsCQJDASHZEYcKLtKbBr/VAuu3Ek8nzB4OkK6V83/z+gvMAzAZhNajgm/8Bq4S4h8scdjEAQJ+38sZbL8JLNA+ThfI1o3htY2lX44PtT3h3N5y0NDpl/Rvhh8OweBZAb2Bx0VIAXRGPiYd7Ft/aq9j7QwC9d9au79M4cdWmw+f+KPnRwLXnntd03V8SjZnswuo7hz82+HiubURJJhE/8jBuGaN2VQc86WKldnXgvfSoV47evNPXZ5gK75ZDIz+SUjUhNZup0bra7fYSHyKckXYmSlL9Y5+0HrOrtFdKOwAvj/DJm+qivLVHTy/MrI1M1pY9isxa1Czyx2wpuz5VpFRv25ysOtAyFFUZ0GH2M/5qVWlFUgdVoKx296Yl20YfSYpK8Vb9qNLRtZPazqr+ChgB76nPvhseVHdYupFCylnqUx1/3XYltpsK72rR1WGlqqVYKCrxRhMbop/pq1PjAUhAxHl+GIGiNGeTxjIlaR9G0HcG4JmWjPGlgJlgnnYj0TME4INEA+ZZYat/cUj+Rt983KU3PHT/E3NvbpNZGv9Jye8BIB8oLp47vRhAzYQZ89d8241fOufi832p0l5Jvaun4jchS5L8xO3TZ3uDOJuASG2KY53NYfIqbP/tk4rTLvyWY0Vb2sdYXUmt4qB+8yGU6gv2b27fdxhqHCLfqAU5IY84xITXVYYE9geKCQgmKF/Ixt02X9ZZyvvtMoElEJPxec7nMgRQTEFMlBMQ4XQvEtFKCHZFcfaLQ7Azbp5ZPXLhigQx+c5niz6BmHTPA/B3Igo7E+6eEIBlHTOHmfna7+ys/25zwdL1Tl3LHnCEYvKBBjO/DgFuqiHAWwJidS0CIEBEtRBA5ywI1vc2CFbtPojFgJchikXDOd95RLTM+TwE4ALnWFMhwlNdAKNBABcDuXxB13wQABXOd+44ZeRYQ3di5XXO0wAxJhmCPTTz9jec814OEWrrIWGDnGsCxFi8ALnFH5el7IQYi+V55wdyIawEUWrDBbKnA+jJzCe7fe0A7jecftsIiFxcIppBRHtUionI7/RJ8BvKyYyG6P+xRFRLRL/sLoyUmR+HCBt2QXUHxHOlQvR7McQz+Dkzb913/31sLIA3WcjpF6xgBfsG45nLXd/sMm7k/D0Jrm+2JbQG/IRgCIAEeCXA50vAE9iJvX2z8MkW2wC5b0cJzACzhHhGD8WiUDPZEgCb04ryWJuqSCrbUpmZlVUznUJc/RtamxJI27shKS/Ru498ATN7DWRSoKglAJoRKj0QmZQBMyXD0jdBQhMkWEinCeSJQPN5EZEBzQZs/ydLayNbFLbn2eD3+j7wh/CIS39HEO9fHwgb+dipYR47teCbv8UsWFYSSakdsVsg/E1PCMVsuEARAD448Revd27Zer53d6om0xWfDk4mzur62dMgCoK4qLYRtRNv5TkTb+Vzhlr+F8KfHXxb889uGfjRrQvmvj/ig8YbL3n05YxJ/QFANgmzPr/79Jeee/Tj2e8teLluzcgQJUvPLon3LJW02NTB4+fQ2ZULe0zw+THY7qFlJE+otWadASmtW5Aktd6LD+p6obG0yjcqZcUM04PNpOjpMtVaOyCELHkYWTMLX1iRWGZkizgaGObt9A0JdRg9G6RAWFGCPiZv0X3hplIrur0GyR2UrUjAGJ+m6lTMc3mqPfwFAFyEl+givDSodlqveztbTJieLNJm6hc4pVZBsQSYtmQ3ZGz+ONaZbep6uGd7fenfflVvj6hozzhTBV1WGF6NiNkzwYbi0aFwCvwTQK9gnub45rOZ+exXdJ2WRDd4D2hfrWwEgAnLn6Fzn79nxpkvzc1VELjnE//OUEUkHqwKj7n7EM4HigCQUpRjGkqLb/nj8j8cu3ju9NrFc6f/cvHc6fv55hMv/8vjmUDn6rQ3Y8myIkfTUoc3jE2w2CeblqooXCSR0qRZ8udPvfG7b2ULg6XhsZF+1W/e9fmaAlD8P2LfN7MIiJWpYRAT8D8AOBW5iaEbo+KuQroMowIRMupafg7ZvoDX3ddlVhIQbNJ053/FOd/zEBNsN2fLhgAdO519OuDkuCEHFkdh7zy0fWvz/QBAlJnDRBSGyMv0Q4QNzi3kKP7D9jKEKI3EzAYR7cTexeX3mMME3gMBevoDKGbmSQBAREMgHJkEAchGQDC/bq5eEcTigJtf6CrnMYSS6M+ZeQMRzYUAVJ9ALB5ozo8JYC4EOxlGrrbncOxdq9GVkXf3STjnBsS4OhkCjAIitMfM+24ahKDMURATt1KIcLFrIEJo086+P3L2cVVRl0OwiZc77cqvj7avwrAE0b/HOf22G0JEp8gZs65iqguCp0KwtwOc/gNEnqgE5AA9EZVBsOyzIYBm0rmGgyHY3QaI5xBEtBXAn5j5fmY+zAGSSwH8nZkfJqJ5Tp8yM+8pEOyas/0IAF/lqayuhMMofgOALVjBCpazSogIhykA/oicb5bAtixEalQLEskw0gzFQ5AkBSJiwLWcb2aS9pK+yZo2VFUij6bZKR0sSQno6AtNhLMRoKreoC23Njxv9OpxAVBmobiuD9hiSHI1yNyEaEcCitaG0ooZaNkoQ/JbIElGRj8SZpcM2SN8Mkk530xWSq4K/cCK211vnnt5pO8DfwibVmZt5+FBHxhlIMwt1GP8xywJ80WCfaECkrHyRgMjb96Bb/DN6y6fwxOfm3IPkDoBUPonqCuMRUUiR7QRQyEW5FGvFDUYveUR+OS4W/ruTFpbxr0p+erDwV5NpVdZnhQq4n4tieRTNQ0SdoeK+ciV6QUdfSZf8suflW0/6Zr35y6q/7GnzzFbP+qlx4cnWdPMogoNWTbXHNrv4UQ0c1bPTj3UkC7327ZNy7Pq4K7QxmzNgL4eP8kgtpmQkjzehJa1qkx07YxSz/6lyBIQKJejtnoyQU9TmO0QpOs+rznKRMKPdipWPksp07hIP/f0/h+NMRVMUg3UfPVacLxHTV02pGbrjV8HD0yTzh3eVdokrS6A9I6UpX3apiR3JJYjQOOSK9JXNEg+35bWrPGVr9SDAIAka5Zpo71FAWDDjwxJkA2fD8el0hpIeXI3+eSgOqy8KPvh8Yw06yBmW+EWAChtbZ0aJd9NfiPTC4C78DHL54lQl2btGeOvPvDrMkPCTNXG7FaftLazuizt3bz9BxAl7QbC8c2LH5xOYGwB408TLpt//wlXPHjYogenUbOBR6nEv3PKJfc9/OLsCz6Dyh8yM3425879fHPFIREqCvpHdCVSX7V8FmUAuPvLtXt8c7jfZIpteanw/P2b2/eds3gfRM7RMwA6mPnHTohZEiIM8EKIsIZ9Q0ldpdR8pdPucha7K70RdH7nq1oeCVG64FzklC13AzjQAR9LICa1V0HIh8+AYKIWY29l1vz2uHmRqrO/y7RoAB5i5p/+D7rqv9qY+VKHdLrM+T/d3XZO/mF/ACuYeWI+IHDAgxcilNWGAGhu/qAKAcAmYv+cOLeUhg/AAiLqRC6vsISZy52SDtdBjIWLATzqtNVlRFXkQk115GosuiGpLlDMD1/1QYR/ne5s04Vc+LPi7FMCoc47GUJU6SBmLsm73ganLQqc0DGIMO8y5PKB3TBVHblnww3TXgsRblrifL7J6d+DAASZudH5/BUIsPekk5/bCcGIXoAc6AWAGyDA7oXOuZohWE4JwE1wmFEieg9ioeY2pyzHe8w8BWIBwBWfaoBgJ1c6n70Acf/eY+YJzrY/hxDqedttgNMvcaeNtShYwQq2n62aRvetAD4bM/i413nm8nYAP6bZ4x6G8MsZkHQhFMUHsAyTGJZEsLKAx+OFZVmwLAsej+wIOeqwnJxFywYkBiTZBjmLSTIhWRQGgKCTSc3wwNYlmRoz7UciJK+HQueiLaVDimkoKaoHcCCfeg3Tm3ctgSSfCtsUvtlOzwDRFpBnGWRVgZEyIfsVsGVBlmVABbyq37JtG3Zao0d/v6ykrFJTjc6wEQyqQ7uaH/p60syCb/4HLbzytktjI3+LMLTLAAArb+zWNz+49O+9mFB36Lyfrbhp0AkTJ46ZTG+seIMBYNjJL9KPRjV7nr/ishW/O2Y1VhwSSA88Jmv26eyvVq/sqda9ffR5KUpOKc56hu+q6kJTZRsdtGEwSt85ymjv71HjZPjaU6VPXfnq1tPSXp+SLQ5KzU0Dyl6ceEz5xL99MpMM7bqKhEqnLK+/qCW169nUgNppHZ2qlZH90sY+/VXT7mXLRquZtVp0yVb9AbbYp3pky5OwQ8FIadrqQAalNtgGIEtsezxa+wuvW6XHn2YVh7hrl6cLVVJIlmXZJFXdplcUwWouCbdJR3mLE2dVehJVdSs+G/nqleeWAEB5upm6OtY0mG3tjxu7LEKxfgaCQSCVas2Qv/ziN/waLNiwIAOkA7oOUBCiYKlXkiWpI53dBujVnrBSrKkyJVfuXgNgCFpwGGB6E3x2IwBkFe0Vv5EZpMr05J9X3HUIAZ1X4qhF/s6u8zq86bh7f0wJNxjR9LRVVdXT+2Zj/vJVG+urJPR0+I+b4UYtMT6HjWrLtm974v7pNwRsvHfGFY9MgfD1WHzJNUcGUdWQ7LKsnhQAACAASURBVNM4OhCSVwLA83f8ZLGimcdYuvLGlGuemBIJ+idmdWNGJOi/HSLlDABwSNX1tDWqxm17eAeE3kHB/o3tewOLRDQUYuIYY+Y5RBQiousgcrsmQeQDuiqMwP4iNvls4m7sLaBhQ4C/fSf+MYjQ0XLkmEkPxKrpLRBJ1Oc6nx8I4BEiaoaYCKsAjmLmO4noRYjQNxckZiAmv/lMaBQ5UYAjnf23AWgsAMX/uTHzpf/AZhMAHA/BBu/Yhzn6DcQK+a8hAKMBkbt2D4BfQIy5/sixeArEeKmCuNcZCNA0Je+YKhHNAnAJxP11GbpTnN/5Y9R2juke34CoYzYqb7suCBBbBzEp+yEEUFSRA4rrIMDPMue7p53vCIIxd60eAlBOZ+ZKInoAQvRmCkTukavca0A8U/nPvhsCvhUiz281xDNzI7AnfHyPo2HmKIBrHSBWD5HjOJiIFgM4gYjiAJ6EeE6bkWMews7/KjP/Ie/8YyFAbRYizPREIipi5i4iKgXwUwCrmfmuvH3cPgg4/68E8CL2zzka4Fx7EAUrWMH2s1XThG8OAWfwzOULJv/xiqKXec21ELnDP4R4HxIkh62TQICcgU1eEAk11NyrdzdMVEIBYJiAbdmAKnyzRwVMG1AkQPjLzfCgFq5vJp8HnP4j4i23IN7yLGych86vGMaIA1HW90n62/318AaGwcxoAEbymbeZ9PSVz8PXswmWZMJmBbI3g0w8CM0rAzJgZ22k/K09Lbs8SxbivuJD1HCFpsetrTUUbyoAxf+5hVfe9p2+mYlPsAhHV+4I7sQg7HhjRY45GntA82/a2n1nnDVnzi+2+3p9yWuR2bHOuP5Otd8927FuZmf6k0k9cEItJI+xtVcbJUOWEti0LXZg6+CqpN7c1NBuGdkL7XDljvopZ9YBm5StWKkcpB7+wiezstxySc84qXJZsRovURBpbjxio7cIekCTVUtHVpEBM22nveWKktlkD46q6GqV9ZXe1Gq5b+koWwKbRprQ0NQZKVWS0XisjvwdWVMdfaqiS5wxixRvSSbY279J3t1lbLa10IdWl28Z7Dpu2dn2dNkh6aCqE7UeVrWnrrZUnGko7XFQkTUrdkHrsXWVVPLYA0gn79QOrpmif93aDkuSkbF16LDhhQbJryCVBuBDi+wDrKQJhF4AtDvMeHY1yfHyYMRzKwAwn92Z3+8vTp4RBXDt2iVz6APTqjdBUfzisKHao8++WZfVTrjj6Tnx6sS2e3ttUluWHDi4o6G6Z7m/yURZ884yOYRmAMqEGfNzvplwMAhtcZKyQRnFWRsnLp47vWjCjPldiy/9TTW8xgyY0qeTZy6Yk9eMABERCx8NZv5EkaUy27K+zm/rxvaKAX6tS7YLvvn/hH2fzKLLXoSd/KbJECF1YQC/R475cGsiKsgBxX1rKlZi77ZLyBXWzd9WgWB13PjtjRDA4giICbibe2hADNhhEJLAnQDOZ2Y3J+wx5ICgey1ArlSBir2Lr2+GmKT+mZnnfUe/FOx/b3+FABE7iKgCAhguZOYPALwLcc9WM3OGiC4C9oStDoNgpQyIMbIAAlSFIRjkMAQIGQohDuOGj/ZxzgHk8g4lCNGmcoix5i5yuIsFvZEbkyORy+kxAexwPnMBXH5Ysxuu2hMiPNRltOdChDcvATCGiG6CEGxwx6RJRNdCMJ3FEPnBa51rUbD/M+86cRmCQfwbgAFOPxU5DN5CZn7K3YGIvBCA+1mIUNEW56tlECBcgQibfR0iFPYjiEWa1cw8dp/zu/fkXYhwXMPpo9OJ6IcQ4bYPANhNROOd7Zc5bGL+MVogntN9j72RiAZC3NeCFaxg+5vrmyOn//EKvwz8MAi5JAErBFHnVUVOVZlhWQpswwtZA4gYmuaEfhIDqALrMrKW83aUJEiS8M1mlveEicqqCssqAWw3ymIjWNkK4Bgw3gbBgiUBsm2irG8QbB0AKMdA9XRAks7gYy8Sgh6BHo/B5wuLmCEJiMW8sJIASRZS6SQ8AS8CVBlKl4HiViaqY2vClxoQMgJ3fT3lkoJv/hcZQXpFBq+85MQjdzzyu+cqbNZ/TaQunH7rjz/w+vhdm9Phzs7gqvmrTs3MHbD6ohmbhjEARA/NDgzIw06SLCJZUdTi3crCntnKE3sGysPb4x2Ng9uKwwNgmVujwQOlSP1ngbIOVctUGfD36NOXY78u394fK31tHPV0cJUUlj4ZffBKOy3VaVbGr3UlJH1tvV51WJnaaMjbUHVc7+ZYgrmPJvGmTSPNVLEVlSQbhmGgIrUtGvQeDL/HQDLoCWopyeYUZPJTUpaN9nhXurwtXWn5U0eRx0dZUs2avvpcNjs6Msn4Y6x4jhnz/t/uIFl6OJ6t8Yd8fihdGXPM3fOvxcxDfu4rDoTT69pb8HnLWuj2UHhiihQOKXacANsVLrdtcIAAjwLweUD6eMv0DjCNC1jr/XIR1b30gkpYqO+YvMc3jzkfjm++/NnJ0/78LsOqBwCJeZms+ie1+6qV0lTTiSrzWwqkIyIN67/0RXepUQ2rL5oxfz/fPOGS+bx47vR3Q2SfHDMkwy8h8wUPmPTxg3Mm48Dqnx++uuk+EO9efM/54wFgwi8eX/aja544Ov8Ym99t6tY3x4wrNh417J6B25qKCr75/4DR95nGQ6LIdgfEBHkkBJNTDTFJvRWC0eiNHPD6NnMn4CbERNeGeMJ8yE3k85meTohJ/ccQ9fXyzYYAi0GI8L2BzHxDXrtrIRgLGwJEdFfLERBiKj93Jr89mbnwEHxPRkT9IUIb/8rMz3fzPUHc/4XOdhpEDtwjzPxrIpoBIcDiLlqoEGP1z872X0HkIrr1NndBhEY2Q7CbWQiQqUKMExliIUFG98q9FoAtEIsK7jjugGAzbec4rupuJwRreDlEXt6VECB2m3PutyBqIp7htGmwc243ZzLqtHnfEO18ZVYTgoGsBnAmMy9ySpO8CLHYsouZBzp9eTOAKyBA5F4J6kT0mXPeOud4B0AwuEsgFG175d2P95j5aCJ6wunbgU47djn34lKIZ+pZZo4TkVvz8mRmfifvnKXM3E5Ebzn9dzyALuc5PAKiRMqnEGHk9czsKjEXrGAFA7BqGjUCqP/5AdMOL0dgZAPH4h9iZxVE6Pf1EOVw+gMohp5h2F4RVKoykMkAigKoe1KhGUkmsGHCIylQFRdoegEwSLLhCSlIZ8TEWN7jmz+DmAcAgUrATAPZmPDNI08Pwhs8CyTV8dFTc7556b21SJlfwDJtBIIBSJKspDIg24ahx0XqSWlvUlN038V0/LX3TarhfssW99wyfkLBN39P9sh1z/VnM3sTy8qzF916zuv7fv/VjF3lJpsv+ix1obIhcJPXS1o6257SJeORYe8M+vU7F+6YobWrt71TndaO2BIyVJ/sYc1q3VDb8MK6EeGLv2zpWKcNrxvkja2PDUuUF3V4rF2LhntbMqGK3eVK7PiMrqea3mosrlZZTcXYrjyhj9xhSykpFJVD0YgnpXiRsi0g6BBcpmVB79gFv1QHKiEYlhGMbehMyJESWy6yJUlWoZhU1fw1l1ld0XAZh2x4frJWKhpb3qvkysz6LZ8FMm0bohh0imol3vh6fX/LSqeOL01sbEwV9T+gI2OkfZ6w39geAzp2x8pXJIN6Mim1x0wgXIwQGNTUYgGqHINsAqoBMTdwfPOFi7ReL51oSsaC3v3NiEfN7lr35vSBADDmfLi++fkVj2NGfj9f8+gjazT9CzLLh9cVm1b7iQccdsCDj33c/8iKz5cEItDPnDG/FwC8edf04yBh1im/mH/0/HumPyHJGFKtYAjEfKH+g2yP2xQ5cmlpNvlImRV/9scz/xh/8/azUiYAVEROmTT9oXfcc147dHTp7Ws+bH/olklvpRK+kvf+8uHxieq6rqVrP+CTx9w3LpnCY4FA5u8lK65/C0D9U5wp+OZ/U/temEUiuh1ABTPn5wx96fzeQkSjkROQcYuFm9i7FpyKHAOTzx66RcSNff63sXcYazFEPtgkCJAaRm6SfIkTwuiGz+1rCkQh8QuRYxjd3DY3DzKwj1LnLiefq4uZN39nJxXsG83pxyEQ+XQ3dydUwsybiegiZs44+xwPwdw9CRHueQ9ydYS+cP72A5juME+HQgixXI8cUPNAhCu74ikqBChUIBRae0OAsosgQlHPhQB2MwD8BKKUigYxDrsgxqAKEeJ6FYBZ2JsFr0BubLuhoZud3/c47QtC1JYMQ4TMzoV4lgZC1HucCQHQXDGbbRCMqJsf6QLE/DIezczcn4gOBnAoMy9y+vQDIvoDBHCrIaIBzLwJQnG2HCKf11VCHeK0ZahzfTIEwJUBvI/cc+taBwAvEX0JEfZtOvejP8Sk8SGIHMTVeW3vgohGOBe5upl3AbiQiG5xrrsYIiR9C8REdx1EiGs7gJ9BgPuCQypYwSByFQH4hz/C1YDzUOV882aaPe44iHfToRDvC4asmTAhg8FQ83wzswYihg2CRICtkROobyD/vcY2I9ZqwGIVPg8ArRiGfjp0nAovuiBxEYy4CVM3AJzDM5e7vnl/JtAX8cCHOYjGLoBthsESTImEb7ZtGyxL8CCgn3D+nnfPlvETdpV/+fiRADW0jvrJjv2OWbB/2EafMO8QiTDkqDMzfdWgfPMt5+xfzH3aLWdtfuw3z1x0wa1nZwBg/ksfHw+bdqwevOtJWNz1M/3wx7JhHpFOGyjW019s6t82Quqd9Yc/KZu++NLmgb0ROvTV41quL94cmt1cZ3mqLJWw0/YHK6oujasR8owIDS7zZFVjZ9TbGqhWmv2RHhWhlt7eRFc6Hu55UXG68zTvCdU/Wr1yW6fWZcxAUfonhyifn2iwqm2ODtXNTKYLgRLhm3U9A79y1diVO25bNbyKOiMWYElqomRwBWSJEUuTBYkkxWc11fTd7DdXWiWG8dwqpfIhKVQUbKy3zjZ7jA53JLqqKpq3zaVdnR8eEXtrxBdvV93WevLQq2WmIdKOjMcwW2G27to24JyBfZLeHba0klEUJCsRC8lyW4y74LEBSQZ89cznDSF69GCAD2WetggA9B2Tl9YcuuAPFcUrbisvWlVz+ZW/GDDnrsR+vvnwtTv9iqUPOeqzd+cWmaG+7eaQjG3qcmXHusjGL3agpqT1/XAEGhu5qgJs4WUb8L725+lfSoRBtoUsFOGbm2LyZ/18bX/JtrdMDHvDaZBIs7E0tUMnlKpEe3zzLUccN5+ymHz9QcfcEhxbM6yzUwnqsn99oGnnegBjCdImrzfTqurbWiAWhnei4Jv/be37CkO9FIDmMBUH7FN/7UMI1iMJMRHugKCsZyIHxFwmzwV++QXP3b9V5xg2gO0Q+Rbu9hmIybMr7x12zvMxxER1KRH9BsBOZn7GFUohUXPRC+BqCNDwN4hC4H6I+nPHOMf7AsBIIiph5mXOdQUA/Bgi9LUAFv85OwzA7yBYo5MgckK7M5WI3oEI3zwYAsgVA7CZ+SQiet851rkQIi2AEHE5xNmuFDlxJQtiPMYhxlYjBFsWcvZzGWUPBCBdBOBrCBD5GHIKpOy0o9zZz11h/xNyQjNufUUJggUcAqES+hEEwAlDgMuEc1zVOXYEYiyOca5pMjOPJKLHINg0P5x4FufYbs1RN/zagmBXBwAAM38OUTQ53+6BUF2VIeoaXuYsfuTnrVwKsRDzBAQL6+ZpahDP4jYIMMtEdDEEoPzQafevIHIWj3buhRuGajtt+RICdC+BEKi5GsCdRHQlhNqb32mbz2nHb51rXuW04QyIZ7y308a9cjwKVrD/ZjOBCyVAXjVN+Obhj+R8c/C2MR8SEGBFc98b7QA+gSz9AEjbkHwybMjw+QAjq0HPAJqHIEmADBOq45t1S4WdTUL12JBlEZqvyCpYB8Q7SQMgIQAFjCIQtUFPrYRYHFxCL90ofPPkm5+hd+cTj53O9Nc/dUAmD8KlVyPaNAuWuhTMr4HYi2j0PURC4xGOyEgbK5CKjwy/8URJbOJPlgFAxZdPFBFhMtheCxG5ULD/pUmEw+qGZa6HjCJKW0dDpPHsZ/XpE9Qhi+59x2tIicu7Rh2cseJpzaMWM7M95JGeJ319dv1UVuVR1Fs9d8NZmxeGLcaHNVs3aKv7H655ApGar3f2rqroKzWUGfbLA2zbG08F+37m6TpwlcebqGlrbBlSWmcX9wiWZgNQsx62NQ/rAc2TyWhPZs3yRVZn5uueA0fW+HqbC9ItmncthluH9/icw+GO9C67tEKW0gyddGiaF8nWP+4O62aSSIcNDRJsQJega1Fpp3VQ3/Dux5Ue2Y/Yl5jRah8Q3J20r7RinQmPqvtSZrmCTMYcGF4XUUNZf+Kj5KmJ6orJh/25c/KiAwaNDFz25ePcGn3Oivj80jav1bw6axu1vSRDac+gg7ymHbXqsp2SL9BsNib7GYA8FACYL9zPN59xwgX3IOu5HR6vDN0aDWDTisexl28m07xKV7UJTQF5HrV33h5QYzbiGeyqGuZpje6q76mhIQ29F6sGFj84/WIAQ0nFh2RgjCTjV7aEsWyaR+uGtEkiyZJg2ZYh2R7Vim6oj8XIxjEAluiVkVo1a1xtasqdd17+2ytJka/teUx1QG+OSa0bUomdG/r/zGjY8lvZSmZh26sAoE9p+/FKQBukm7W9Y58UfPO/u/3L6ywSUTlyoK0OuUm6G4o2EoJNcPMiKiAmjW157esuVtYtd+DWdnJBoAUxqXQn/G49OxdUuhNRA4KJiECEvlwH4B4ieh5AGxGdjlwOWiWEKM5NEBNbHWICmoBwdpUQk9/XHaYDzJyEYFr2C4ks2P/Y5iM3+f+4uw2IyAcBIodCjKkOiHv/EIAbHRA5HiKMOA4B8NuRy2sFBDCLAXsJvisQ48dl56y8z11BJlds5kAI0OnL26YTOTGWDHIsmRdivLY5/7tjvci5xpEQYafu2A9DgOVLneO7iyjjIZRdfQAGOQJNpznXtBkC8OaDN4YAUxIE0F0BIEFEXxDR7UTUK79fnYWdNQA2MfNl6N6WQozzh5nZrYPoquR5mdnNy0xBgOSfQjAVbzPz207fjIYA2rXOtSSY+QuI8NqFRHSS05aXINSUr0UutNaEEB26C4IBNpn5Cuf8pzj9pDLze8y8+huuoWAF+6+yr6ZRuQnITkjO3r559jiybGsk2dZA5CJ1KiAWSzuh+iR4AMjEYBswrfyKxwwNChTIsC0bli4r2UzYk06aznlsSLIJzev4Zl1y3n4WaE9E0EDYdkQGHoCeuR66fhctunMh4tE2WvDzyZDhA0j4ZtX7J3joJmjqIQiV6ujdfzAMTiGVzSDkHwBWPozL9uuhN568CwBaRv2kC0xzAenlf3EX/8cbg+bXVGOjIpY9u3233vLzNl/Ua4yuyYSHyh5rRNpu6zTQxUqnPkfJmpde8dbL73x+TMMxqwfv9AGIJ1LGhnYOtv/g/aO0PiAtWZNBOFT506ZsR6K+SjYr0wHZCqlQij2eMVs1bzhS08fWvdxZVmXZGYBIVpraq6mhrVJKs0Exq7WSSzwHRlguqYuFvT7Ji5RUJ79tV3U02v3CRQEfNLstA3+7DY0BUv27vN6wrsRaZdJR6tsu+eQsoKpF6GO/2+iVRyUkXL5pR4clbfEh2FhaZJbUlCR3N/407Q/5wT6pyG/LYIzfPnbklNjwHj6lomzQ+G2vNBf13npCpCLjV6h9FZexN/Z1o0myBgRKVNgKI2kYPtLpqN7Qjgxu+apOaUjUHXTdB2MuvPrWyy7+bXV+v865jRlqdg2s6MY5d6YXdNf3MtmLFFN/fsWRZ//lD5dcXBy1M21sNqQDegphI+Wd8ev5B7CmGLbki4HxJ8vGT1MeHJoKYtEpV85/u8S0h4YoPTprZXtkEtwjpLK3XDM6L/jtw19kDPPThGEtvOnyGf0nn3c/q52hl/wt3mfC6PytpGRDHcFykvpUmGUH+26oS6y7++BI3YjD+o1JvBJtuAIAdNZPUSTIbEN5ijPvPcWZgm/+N7bvi1mMQTicEJxabo494HyeRQ7MJZj5SUeE5KcQE1q3Dlx+fTh3ezc/zDVXTTL/e7dkAJBTZ8xXUz0YYtXGBZ5ZiBC/jRBhpDc5Yhs6RIjbRojwtp9DAMankct5nEpEP4UAJP0d0Fiwf8KYOQvgZDc3DQCIqB+ArXksdQbinkyCuCe7IMDHSojw5t/nbbcWIm/1dxDgkpx9XIXbNHI5jZdBhD/JEIsYqyDEcPYtWjsYArQkIRYg3L9dtU5XHdVdFHHzbGsgVENLIEBWOYDXIEpyyBDj3g3rTMERe4FgQZuc9vR2zmFCgEp3X1cd2J3G6RBM32CI52QAxARRgpgIjoCYzO0VmsXMh6AbI6K1EM/JDudYFxPRWOfcruCOTkTnMnPEWRxqcfo2BGCcU5okn1l1VVvdmpebIBiGpc7/LzjX1Y4cK5uEAOkaxCLBS900d68FJyIqhgCyjd1sW7CC/ccbASoBMRlABggb+/jmjOoliHehAvH8JHjm8idp9rgEVDyCPYuwZEBVVTC73jjne01TAgBT1mAqKrB3nWIbtm1DkmTHg9fDsnpDkmpABMgyrER8BCW6PmM9LkNW0/CHs0i3+9HpaUBpz5V89NSbaOm9PwJJGXhDO0CS8M0VVcI3J+KvA+zF+s5gYpc+lbrm/xSq0lVXWjpox/gfFHzzP2kfLJ2eBTD25gfnlN54yeXtAHBh5bv9koa89fmOMaKMFZDxmPL6RFb+QTJAm9NSl60Zao+znj1y5f0/XDoKRXR4JmhgQ2lUX/7CorVjG6del9aivzPIGBrulKlLzib7JHoFqjVWdiY2ZzvKK7XGhJk8MKZe/upp2+Zt71sklzT4Nx5QvWaVpI6aUpLKkqR6Ac7Aam8AqgaOylgxKys1xdvDJUWZVMqAJCfBBwdBEiwraxs61P6BV5XNyR9Z0LJypma4DU3pYdktLXHSQqZW3AR4yvHV16+VjQxfXKO1y3FfXyOVTqcVn6pw0kxRj0MeVtpbd+vsK/+kpW5zn78/t1EfeUjvaIbR4E2bmVVW2JDjV+plPWRLzxziO7gSdkq3U6s6ACOqa2l5cyAUHLZR68WHh5IDd+4w37RJkxSyq0NsjmL29IeIcNpjc+7o3jePW/63tbbBlUq0dSdX1A7o27Lx4ou2YXwpLKs3W75IxxrsbkH29HHXnvvK8kcji+ZOJxNoy8hQZCDEwClvPjC90wY0SLLNpiQpHpYNA7rPR48BgMTWelmWa9du6NgCAAk9uzCdVQaEvWZnk8fUraykNW1JJVNrm4vNTDbWLxLpsI3sG24bK2v9KPdnYexTpXPl7S8UA+wdee2PCr7538j+5WCRmVvhADNnYkhEVOGoF34AwSjA2aYRwCwiikJM/FwGxBX8yK9rmA8e8+vV7SsmIjk/DDFRLs/7zp1sHggxwYwBuJ+ZzyYiBcCTTtkAMPNCCHEUADiCiEIQiptfMfPZzvXtzDt+EGLC65bmKFieEVEHALh1Av8RywOKBJHLlgFQkldfsd75cc9xGARg/CtEDluzs89IiLDiIRAhkushxtZoCDAzyDnEduQWHiyI0i8vQwCYXsiFmrrj00YuV1CBAKCvOtsOggBWOnILHJJz3B7IhYa+C8GSued8yWnnLuRYMhNiolYLwQy+AsEwPgSR3J6CAJNu2yyIsTvRacurEGz+eojQ0kedNlcxcyMRjQKw8puK2BPRSRBhoz0gnp++TrtKIZjDauREe7wA5hKRB8Ac5/MU9q5H6T7jBsTk9G4A7U75jCOJ6GkA7xJRFjmGNwMRnnofRBgxnLb8lpldoAmIhYGTkWNXXTsVQDkR3cvM3RaVLljB/pNt+CPcAMc3fzCNumwA71/kqTh6Xlb4ZqElQBALSU0ArqPZ4/J9s2AciVSQbMGUZMdDu36VSfPYzkvE9c3iX9uGN5OSVCLEFR9DlbarqVi5lyXEPR5AUXUU9dbAu0dwx8bnQJRGsul+nrn8bJo9TkGscSGf90fhm0/8+fPIRfAcQe8vqIbrm0++fCIA0Ft37AT85SAbUBG22C745m+wp8u1LjDs/4+97w6Xq6raf9c+bc7022t6g0BIKAlCQhHpINVID2AQBRRUNCDwUQSkqCjSIiAIgoASkSqghBYM0kNJbzc3N7eXmTv1tPX7Y5+TmYQSPr4fBONdz3Ofe++cPWf22bPPWfvd613vOrHH+tS+uRwoxqC+pyluDkB1iB6kAh/PAFquRik/9N3z2vcAo97Z3XyS30qtesn+d6u4eb1HCE8phPqvS7bTuFd3Wr6mzjNWOBWOlf+XtacnnK7u+vyEbG49mvsbW/IRW1t52DJRWYQrwpNP/tu6+BN1k8wVKVo2wugVppNOObareBipaV5GOGpeD/UkCariaq7SkzWo8bGCl5iQy9Y1ImQOW5k9wYJqA1yrIKQIeK47ItRV2+dobm1uBTXq9ov9w42zNgwU0VMxxolw5qFBNb8T3MH1pA0/FIquqJblOEZCd52mCWKH8W9o3X+dl4sffcDqt5Tb1b709wd7zAxcpYYo5LiWy65VtM1ofn5hrbevbeXHjMp5j0bWtGzXK/JLKZ44pbs9+dhx0xxEve76W353dfuBi17feUXVTu+saTY+0jcf9NzzBzWuWriXNuorTV6yUo9xdmSnGlYU5KvSYf26UQo3ESXwppWynNVJs7bKmjtv7uxqAVwD2yMrJ/K6BjccgUYERQghNC/sOcxWiFAImdI3Pz13dvLnt9+9x9NzZ/9pj4nR5x795Wx3TbdotpJJzxt0c6ImsT+6e27SWjunWdk88u195nLtrR/c8cE7G2nfnsBiz1MPsW3JRw9MAR1DJJIbfvnMDY0/PuiLU+Acsk+0L7J0BiBzhrb3gSIglRa/B6mEGIh6LEEJxOmQi/egRmKwIxlEWoLShfpTVgAAIABJREFUBeVWDijLjSAX6+XRyQDIFiEpezcBeIaInmbm/0FZbbmPsAxk1OjIMrAyAjJPMQdJcU0T0SEAXvi4wvLbivkALgUgx8z1n+ItxpabfKJZAGxflGU+Eb3PzNM3a3MiJEC6m5l39IVyjoLMewz48VHIeoTPQgKV0ZBRq/WQAO9cSMrkrQDmQ9I7GyGjkidAqqQGi47yjYqATvVLyLy7YL6Wt8mhpMRrQ0YIh/ntAkB5AuRY7QYZNQuEntog5+CxkKD1YP9aTf9nDWTU9LeQ8/Q4lO6VXSAjmcG98ALkfN3BF4o53/+MjeVi/LEr+D/BpokDucFyGGRe4xMAZkHmLp7kj1WQF3mgf122/zr7r58G4Da/L/WQIj2VkIWBD4X8vqb5x8rv6wQzL/ZVcDWUIsO/8QWzTmdpl8GvF7mZPQ8gPgQUh2zIAI+0owhoniGBIiDZDZv75tVgWGDo8KBDRS8YVVIJQJAsrGEV4elyc9ZzFRYKhNi4mxuwBwAAisfIqQQ4RHBRZ7ueoQCAbQOq1o3MQB1gFUG0DMAd8LxnjGv2e5p/On9LvrkDks5/JL18D/FepzJWNo1QJ609XkQSuZpwchdXsdc3vPvHQ4jwwoZJp2zTvhk3vhb45gzOm9a4peYqSGfxkak/W7Ssrax22bYHrJxdYTxwiAnlwRg98P4gn7CJb/7zCQuO64/3H6Z3RO6eu+eZO37t14fvbgyLf7323EMPWvFGW9+wvhhWjMiayyZmr+uv8f4RCr+Xa+6oHR3q7e+Nc7i71+kc1a9FzhnoaNgt0pa/bZFhLQiNrQsPdq5rdOqjF7nCOZHH7LMI+fxJnHIAVdP1SoI16AAdeQ9R3SqMafwlqPcdPeQJC4IVoerkCDiKAtj5HBT24KWiDU63RcX6kT05fZimCq7wQrxea1LzypqTQtW1piLs3bLxfA8AjaosW/QPtPGKnszw6crMsUql6SRfPfjfC/Y4tgMNZpjyZv+qjsWJePNuw4cP3qktHKC3zNBx1GQKU2H0rmvfzVnEFfmop+UdroqNsBf+4+XkSBGqmbT0xYVXZQo4v37Dqzaa99nom3df3LKbIMrEN6wa0a+G/5bMh8GrVlg8fFT6nG77sJ/TqmuiI6tfgGMd0wHlnrAzeFKFqAyviTpue2s6pwLTGUAxJyxTh2kz2LaKuZBhnGYYuA2A8vp7xcZYROxbVyVqE3H1SjCmAzgVwB7ZjKSze7WArYZha07smgsuW3zxLgdNsNJFxS24mVitEU0vW3bzebtNmd5ZNfOMB5+5mH9+00Uf6ZsdePMVFuEhoPjlsi+0dMaHPlzmBx4AP6fIfzmNkohHYOXqpx9nm7cJ1B6BEii2IYFhpKytA0lBvR2SXvos5KJ89yCS9Qn9T/v9fJ6ZD/2I45MhIxj3M/OaLfT/P9qI6FwA10Hmi8U+RXsCNubEfZbP2wuS7tkOWRLhTchcPSs4JxG9AgmM3oUEbY9A5jVeBQnyToUEb+9DRpeDXMUnIOfkeMgyFYdAgq2gXucqSOrlASgXaPiwQm5Ah93Of18Ocm4Hcy/YFLEgAdZsyHnbjdJ90ITS3A7ap/3PmAyZe9nJzLsS0WuQUUgBSfM8GHJDBmV9ciGB2LcBVENGA/8CScX+NaTgTC3kVxPxx5H8MeuHpHvORkkwyoLccLkWEmQvhVxo9qGs9iNklDYEGXUMIv1g5ggRZf3X9oUEiP/wfxhS2GY1JKgM+2MY9q8nKD1SbhvFfPz8ySEbsiH7Xxpdv9/fIAXcAuo8A0jDQxie75slOVU+mzwbsCxZPkPogOcBhRygKEDI9ECbbOoyXHZltjepsAHYRQtk23C9MMwIQVGAkm++BUBPlUvzqhSjtzYU/8rL5z74yb755XvSArbmsTeP9z7z5M2PN737x11YPmvu2bDTKa0fcYptx258baNvxnnTtuibnw1L33xg7rP65gf2ApCoMEQ7F/G0C+/Nb7J+JADrThzLAPDddfe9omVTu1gi9u64v5hXv7buH8+Ej5g8IzZu5GWFNbk33V7tWz1TDT2UySzSK8Xk5OKwu/17Y5Sl4b4nWhoUw/a8UTutrf3RPd9MHqX2dR6pd61JcuVocKezyqwZOZipFLuiUMhzumg4AxBkFOzKBlXL5BzWetNUjBgpNFT1Gmbn6EZXRzv1Z7x0dURhh/KRZkCkbWgJDUXbQscdf/xaVXK2qQ7yImenvvVeY3/CSpkDsXFNWibFpmZRMRkquEiEnGwubSpFTw+5kxNY/1ZFYUPrE6NO3CN+x+LXvMbwRCUPEWGKKeH+g0UaD3vvpdAVqfbI80jJpJxkBldu+MPi7wCoRC5aVTm88JhiYsdRPxz+P13R/qtqakfWao7tvXLInlEAGLcuR4ls71UE9NeueLMir4a/bS9doBnDdiYrXpN/6aAZDd976o6bHFVt7Rw3YWksNPD4Xo/OG2CYek+0Do7r2dun1nUoLiwuYBQEiCkHyll87OXzIo/fPDsHBmWY921tsUYpQMvoZmM+CN7axIhnPXir69paTwuZIpzLepl3ekZFHUf1duzqXNWd6RiX6eyCo1pQ1DSybspLDJ8mdC4WLp336JBv/g+zLzqyuLlNhVxAFlACh8GOyQbIxWxQrDxQPg0iNOULbuDD9FOCvD6v7LUs5EIzWOQS5GL+LQAP+AqosyAjG32fov9XQAqOnP0xx98FsMGn4m7Txsy/9am5n6rQ8acFiUT0VUgBl8uZeaNTZ+aXy5rV+OqzYyDllzN+m+lENBPyO/8+JJCaDqnw2YRSbc6fQRaZXw9gEuRmwlchgdV4AC9BAiRAiu0shKR9Bvmxb0CCUoYESwE4hP93kAdbDQn0VEhAWI0SAJyNEhW1FnIO1kMKK42EjK7lUFJAVSE3NwxIKu5f/L678Mu4+HgcZZ8v/OvpgQRtfZD300y//SRI0HgoSgI18I990//sKEr3Xg6yTEgNZJ5kEcBufvuxkGquzf64jPXfU177USOiWyGBtw4Z7fMgqb5pSErxwf5rAY00D3kfM2Qe6l3+dVn+75X+NTpE9Gdm/iaG7GPN3whoZOa2rd2XIftS2WTIzarANxOAhA/5Sr6ZfN+cywmomoAH1sihOruAAfasDKuAC31jMggAMBMUUsHwJEneAwTlYETD/vkC37wa0jc/zHPm84Rffn12tWLGDMPcsm/Odl3hFeyzsfyfl2DvMz902GO8TYTWDTudss37Zpw37be48bVP7Zs/LUjUYjseSML7JrvKFXbmvTLffMImvvkMzItC+uYWyGc35g4/efp3Wv40c8wftQqX8OOdDv/6DqIusu+Abf2mexI3OWGXXZeLuVjVFdGevvML6eLKVdHC1Py4rtgwtWKfAbPPeXWXnhGif8w/Rr85/+S8qnjhDdV/OPat5lduPs7dXxR4lwlPbtAHV698Y/1+k3f1bIWLg05rTRLDwHF0NVrEdmR0sTjcXa+uLRrqjrW6+/agoqlKfkOuG6Mn1CCTLUA1Qlx/9uznRMGdnr+b2qixStHMRJdaV9/sdP0iY7eNTKvN05BCBoIEUzxcZKE62TUrPKNe7/ZGVib//K+/KNHqSUjnnNg+20fWNxKH77IQcT2w4jkeeZbQFXJfc8ZoPxk3MDnT22C3ZbtmLF+uNBi5Iy5fej0fet6LO0dGVr440PrqYY5tZXGIzN5aMTzMO7zb8k1b0xKN7QPRmNdN5i57YrAwkEtVVJ7z/b/fUTuom6eIglUc/25+t+uOPJQvKz4yWqP8v+sz65s8OFAFhsEFwjGEyRVIp4opJaxpT8+dfasisNZl6CHQC2NG6a4OTHddpPuBDcaAdbAnXI80z2UWsB0UVi3POAosq9Lo+bXF+d9yQhPdHR1WddwVzTvVr7QGrQme4zlXHHf0FZc99MhHMX6GzLcZs0AAGhfciy+Fb97aYHEG/EL2kFS98vqIjf7vABiW97U8ghiAxKBdEB0qj/gEx5Kbfb4CufDfDcAlRPQ+gPv9YzHIHc1PshsAPIiPAZY+INr2nZFvzHz153XqT9GmiFJOYumNzH/xxUy6IOfT9pD5jish8/1u8t9zOzP/GdgYkQzsCEgHF8yzMyGpkQmUcuya/POvgsyVuQBygRVBGaXaB1HB5kVQ0iI4b0A7LUICQBWykPww/3XyzxfQrHP+T0Dn3cH/LcqA+CrICN2OkIBuDiRoW+ufrwvyHnyZiFZBArg7IKN1hbISMr+BBJcKJGV1F0jQaDHzX/0xS0ECvIOIaCokHTWgeAaKpSvKor63QQLN9cw8kYi6UdrEeQJy0ygQ2DAhayzWQt7DgQLt8ShR5EL+GH4fwJN+/w7GkG00Hxhe7lNzg/9vAHCYX6P0pa3awSH7MtnH+WYbAo2Qz0zpV3M5FUJBCB4arSy1hZPIagaKIUWHLQAbDH1jDrYCoryksrqyViNb0jcX8oCpAawAzAqI3oR8llxy9aO3vH/WuK/f314YwHaRhi375nf//om+uX3yKf9VvhnnTfs8fPOnpfAXIH1zsfzF34048S+/EH+pEC5a7RodTLxdwtJf8zxUKgX75bCDa+NOVOFY3e13Hr/XnwHgzH+vfiWZclHUPYoKHL9vYUna7u3Ws7X16Nk3/u2/Tlx0aKKurib5TqF46CMDudf2jDel9lU62cku1+zMy07X8AtjeVMTnhpzLauAkB6y9XHa6oYQj8wOcwmegsZ4EZ7jwoyEYBcAEh48obzfP6VYa64x9LoKtX/dukVGiOr6YqOUiGlSLpOLsFBcMkjxBgdzrESKilLUPY8QrU7u4OoaCl3dyvpGYgCIxCJr1ULuH3Yz7xjJOjVazJrTs75vu5bvvPTPlmaFxjaKzuLldfuu7rKePgPzumuOV7pzd0bviDckx7lxtXD8wpfowT325mGL1/wG0boGWJZi1cdfiKfbd9GL6ajrZKzXp038KzARxz/+x4y7cmCgcljj1+6dO/uQKQaOWAmyI2C4gHAVOAUFHxx91u8ZAJ6eO/s+yDSS9Yee/fuJT8+d3e04gO2SFVLxBAvE6xQsX+em86pdNKGKnvSgU+tmopVHTe0mFjatbckfbq8tUjgaovrR24VMs9udMGncRevWOQ/1re6LKh5+iI9OD/mvtBmnuMTInvbKH+N3AxuB4k0ADpwxCyctuBevb90ebmUaKrCxtIYLSZMYBumkglzEgNrnolQfTpQdBz6skhpcUB6lKAawaS7j5nmNr0FS8AglsPhPZj6irJ83QxZa35+Zt/oXN2SbGhGNgsydmweZs/IWZFRvAACYueET3vs4JODbm5kz/mvPQFJc34Ckq66CpLBW+3+PhAR0h/t/j4QEfkdDKooGIAcobVYMhyyL0YDSpkYwf8trIPailKcLSABVPscLkIugJv/YOZA0agKwhJl38a/hAsiIZSNKKqP/hrzHyq0Lkqa7hJmfI6KzAFwKmT/8OGQOYxVkPmqVL05kAPgJZJ7ud/1rL/jjcD3kvZKGBG0BuGtj5rFE9C1IQP0YM//E72sn5AbNEkh14R/4fboWsk5mhIjeQUl8yPXbL/XHNRjn70Hezy9DgukHmfl8DFkA6HVIhWAFMhJdgNwA+N5QdHHIyo2u3y/wzb/zXzoUJd9Z8s2WHYJVdHXyRFyorCdrRdq1kXGKNvJ+tUUdgGsBjsPQQ3kQwij6GjkZ24XOCnQN0FQXtlPyzYbxPIS4/aoJx1KM9PuXda3CmsWvz3vqhudOCZpc8KtrHwiL0KGWZ+1/9flzhnzzl8wevvu9UfmwfZiZ1+bpl9zWsXbYy0vq2zpGvHDpyWnrq9O8O8ec8LG++ZTW+Y97Kjcp0Pa+t2HvDAAcvPzRZwbN5pWj071LNDtx6rIWdznpg/s4Yxsr9K4NqypRM4qhieRjPXu8sl3L7hW7bz/MDGX0HjQfPag0DCc7nfOoMkwGAQ4Ynk0w48Ot9vZ/a8lkA6mOA0dVoRRs6LoGZ6DQ2N0e6nQNp0LtT6Wrm6v0VC+a7EGsCI2xhBnXHIthKyG4xXxBzSl9TqXRBMsqhNo3/ESrrbrJDpkodne/1fWV0dMbXsuQsy51YWVv/+luT0tTn9YgBto7LH4p9Rw6tIPBLvRGgUPuVEjkMu2Ve0ZuALDkThz73PiX3zmrFgOXxlX9vfr3Xl3yz2nHHEtVlVXeoJ1pnVhV89NHf9YPRPXcund+3D3x8Im9SMzSjZCu9W7Ij3WSo3bofvj6GJxT1iLZT8gkGgBhAJQG1p/63d+Pu/vZ886m/tx5tNZ57NQL7v4JADx167c6haAYgNc9xmPs4Qeux790He9a14X7jR//ITr3JyctFVbdiFDFIGx4TjLhxde30NrBdq51hImOjMON7qpvJ8c0L0r35l/OeaF0tKrxgYtuufbCL2YWfrltz5M6UwK67pH2juB0EqgYCcUMfPOpC+5Feit38fOvs/gpzARgMvO3UaKUASVq2QmQi05GqdxA0MZBGV0OpUU5YVOgCJTAYXmpjUAt8kLIRfHfmDkQ39h/s/fPhHR5t32WixyyL8zuggQJoyC/xySABBFdQESLSdqlRHQtEfX7+XLvQAq0tBFRn68GOgMygngGZGTuSMjNlQgz7wSpvrcakq7VCgmaDkOJRh0ARaCkbNqPUskWFZKWE+QDhcra34FSzu0HKEUWAzBYCSm240JSbr8NGf1cC2AsEV1TNhbPY9M6i7tCLvICRdFOSEB8iw8Uz4SMNkUg8wWvhqTE/g1AtQ/0TL9/v4SMtAZgdzWk8E9Q8ykGCdwDwZ77/NdnQAK8c4go6askxyAjnwdD3t8/hgTAqwH8kYimQ9JLgVKtSEACZqBER/81M7/v97kXwNlE9CL+i82f8yMgNwUAOZ/jkN9JCMBXIKnYQzZkAGSdRchnQIjnzJ8JmdccPIME5P1+GoBB6BojpCuWbng9alj0FvPIu5bvmz2APaBYZDguACJYxTCYfC/OQDSiIB4DQiEPdlH6ZseRzyjXvRLA45csm/e3c4/4dviBh67Dv9+bf2x5X00yDkrG4rpLfMMXMjhD9pns4dY3/vDP7bfLvVG9V32LWcvqLs1xJPXE6YvmXXDa0nmLAeABPX7pA3r82pmpef0n5uZl027/O1TUr/FanLaTFz7Ts0/nG7t4tbEZYbXj5C7bPKO9a9yOvSN3PiZVu4/36rhJkZemH7RTY7/753jeXvXm6ZP3Efni2oKhbT+geYfVhNfHw6FBMIfD7BZkUEGAwMIF0C8SiTqybYBUFXp/CzxHA7Iesj0hTegwDYV76uvmWl49Z4zt3UGu+UC1WHFyHoVcizS7ZUn3xKpKxRbPK47jel1daRSdWW5qMIO2jlVGODJp2Kst17RPizJU7c4D1q5fiN5ex4oSdKdaN7er3AeWbcPyPHvQKfTO7+lofTHzKoBb7sSxzw1/Y+2Zas3wG9KxYRGXxHNhG98+fMG8Si/d97fWiVU1lz7x018pcRFq4yKvHHvyr1pC9bO6KhIa0r0FcsWalbe9efE//jHsntWhkcjDSaqK0dEHcDvgEYXuBwAnl9+txusfHlVw9tNzZyf+8ovTUsWiFysUeI776A7fSFnjtL7Q2B9vF6u7S1PFaiMk7rvm19cePdg4YbhbAbBQWIduPb3ifXjRyiq9phIdWctSCmkUBu1bzr3pnverRk2pjFTXpPWkdt7VF174xFabkF8CmzELNGMWRoCULkYIBHVHsEcy6Xujb750K3cTwJcDLLZC5kAAJepZQG94HnIRU4vSgrNcAn8N5CIzsPL3lltATdxEjQ2lHdIByKjRHCL6HuRCdBPKBGT+2AsfV3NuyP7vRkTfJ6L/+Szv9QWE7oeMbDFk5Ish58haABdBAsi/Qs6HIIePICOIj0GCKQMSYC71/x4PSVfqAfC0v/B+EXLupCEjYHMggeVY/70u5CZGILDkQlI8g/xb2+9bFSQYJL9PXZDzuw6lkhen+n3P+O/ZzgdO90IqtP4KElSth4zMawB+QETz/VzZyX5f8pAKqtWQ0c8uSDAYAlBZRl29w+9DEVJwJnjvVADjmfku//9Bv98GSuI1JmT+7gP+9RAkmCv617zYjyB+ExIELoW8fz3/2vIAvgF5rwEyKnkvJOgNLAe5iN0ACbbJf++gP146ET3rX0+wYz3JL/exTRsRbUdEDxPR5sDvz5D06GMhNwYuh6QZBc/CJIATiajLV70dsv9y4znzGfL+Cmqd1WLTZ9eLkKJXNQAYqu5CNVw4QNFx4TK3AIhBDZjlHoFhQ/cJElYBcF0gZBZQUQWQ6qJQEIAihXKMsIqQKRBOrgKwJ/SGi+mW737PBgrWZr7ZZvugDemuF6790QV7ff4j899pfws3ff+RUM2cz/Leb5w+ac3C0cvvV8aJkcUqi1fsvJ3x4JGncy7RsAYDxbVOCBdxtRh1+vvz7gYABookBZHIUtFOTugxtyqie8m4qUb0hN1vLSdP06myZ5zu5bv1zGBPzC0+NQe30Q9x+4vegYvEosbmgqqo5xf33H5OxjAPmio2jB3nLEoqnHZh9+e4kGcIMDuuy6qSAqAzQC5gwWJGXqsw3NyyGrdfwBhWaIlVdmViNR5SZjMcZniee8RA1SxryasXYaAnw0yMYmG7CW+9N71SEw/G+91ba6zQr9RY9c3hPns9oI9EyNRghH/Q/PbA/O6jaruHNzVOHDZ6+1yhW+SLsNc4i7zqr1xKR485Al3jDrYeX/eMbXa+LOoDUSChaB+45LruYK6Idzbsm3GtwouamQcrU3da1l79s8OvOZ8zbj4X3T5bjCWpaFRqTZ3r105Z+3ao6fmFSpr0s3urI/fZtlMIKWEaqBjbJLRYvqVhmv1Bw86L7//1rO6K1dlvDq6NrreLYhmAGAS5zGBBsKmpd6bWq24sDTc2Wnvv2Ejt7SB0OaSjX9VzhqGMX5Pu6xxhjmip0B2qrWHeZUrVIK1+2822bDB+MH7KH8+6+iKmynC9awiQ7u1x+6XXbvO+ecYsbDdjFh6eMetDm7J/BvCiENXHkmK+QSJ0OZT6H0KJlPvmc2bMQueMWdiqvnmr01DLzacD7g+5+BwOuWgtoBStKaePpiEXmoEEf9R/vRcyqlCe11iuUuliU0ofIMVMegDM9T9r98+q0jlkn92I6F3I8R/1f1BJPQRy/jwOYA0zH+C/vhoSPDzDzEf5r10Fqe75PT83L6jP90PIUg93Qs7B1ZCUvT9BAq0gstYCCbr28P8v+MeyAPaDnJdjAFwCmednQYK6Jr9dDnLe1fj/B/M7hZLyalCXMYRS5DyY+0sh8wuDeoaABFxBxHAcJBj9GTM/SUQL/HPsDBkp+BZkpHE1gH5/DII8xdX+2EyArCupQ9JGjysb69X+6zl/bC+FzFVUIcHzXyABtApJlf2l/7cOCVzHAwAze/75Zvnn+W1QaoaITGbO+9HeBf5YtEIC/0AQiP1ruQoSgK/1++35Y/kBZF1Iz/+5AMCjzLxNFf0loosgNxYUlH1XRDQHct4+zsw/L2u/EqXIbGAWgOTQ82/Iyo2u3+/T+2ZGPwhJAAz2crC9KFwbIKULulIDyyKQAhADBcWimiqd7ayLQs6D62pQTCguABVwNXwVwAAiI34HoZjId+zOP3piaG5+wfZIqGEJwObRhY6Rn/UcZ7364CHEvHb9KysfL9i85tmfXnoAAByx+K6WeH2iVlfzz9wVP/koAPhR7/1XgVCRi+rfm6vP5DOfeeJ2eOoHy2cMv7O+PXdgv97xe9iFYl2W1grFmuSa+NOLxk5Hh1XDHIs/cbp3j/d7wg252GDFnlDAtdGnipXFzlA37ZDOF6YdsLKmSfrmnjU/h1m9HbKuBS2/XK1wx2tQQvlBNQMofeDOGhgjTTiuC4MUpDvSiNbEwSrgWkXYRNB1HYUCW6EQhdNd6VgoFOs3IkvB6vh8Lm/rGTuE2hjsfD5nFNklK9+G6tg4DGQ+2PO9/M9e2XvkU8qrK17ec8FzXGfzzh26ceujj1ZfULNr366VrcvfnHmGjovPuYoTE/9KqcXH8DH3zFunqogZI5KVL9RMHYCm6ejr+EvrbqNmBWM9+Z+vrHCbRps7rXu50NC/oS65Ysl5r/HEH6jjdJoaWn+gaOt+MhpJjjWtAfVtds+ZJIxfRb2iEgb0NxhL9yLsCgAHf/f3HgA8PXf2qcirdTR/5E0HPf7TPAAs+8NPzQmnXZO/+mff2dmuqlyQCOU8r5u6ugqRRq2Qd8ZHsoKZeSCrHbvu4ZdvyhayVcbo4Wt1pzDBrG/09LG1KaMY+SCRrN4rA3gMODHgpwD+eubPLuzafP78J9seJ9oXMXuXqJpBAO5ecK8UxZwxa2Og4YkF92Kjb54xC/56tRdyeVMNAMUF935Id+ULsy8bWGyAVBCNQo4QQ9LarkFpoRzkLfbCH0FsCgbbUVoAbR5J3Px/oGxxRESjIZUk3/uY/rVBLqAnM/OKz36lQ/ZR5kfMIsz87Bba0adZzJYBnxMgo1MuZEH6JGTEToMUnmnx28+ApJ3uA1lvEZCg40nIvL+VkAulQJTGhqRL/hqbUqAtyEjgjvBrT5Z1K9iwUCEXXMHGRVASYgByAbb5vPVQEnkKFmbrIdVGgVLkvRcSOAa5ggGILfrXHeRSMoAPmHkqEbWjRNvWIIFyP4AaZn7BjwaG/fOo8CORvlLtPEiw+S9mPtcXTWmFBLuqf12Wf75RKFG/j2fmv5ddI4jIhB9dDcpeENFdkLXeXoAEnMG1BptBLkoUdWBTUZzAiigpOnqQ4P2R4HvfVqwM/DkAzmXmu4lomH94CeQYTWXm9/1c8eD6Ny9LNMDMDUTUDKCPmXMYsv9qo+v32xmyrFTJN3veDXCKP4ZQGar20b6ZPRskpG92nXZYogmqC1iWCyEU4TgQxQKccMQDkUDILL+zLf7p/AQA0O/Olr75O7d+pG++54H72hQGmKfZAAAgAElEQVRVrSBg8kkzjx/yzf+f7W+RxukAIkdlN3yib77uhsl0wY8WbdE3PztpDzrwvYV82BM3f5vrY78VVZ5bb+QfteKxpFDVU2syngZA/0X1SS0A8P2Bp77W2ZI8JW0N2ye806rRI57aAyHYEzp2eOJJFLmxXzdWLolPGqGTro3j51Hvkr1KmfDjdX1Vvw5rOQ7HOyhWDFFvQRSViJEeY2PyC6FpKfS35aAnAUcAluOiZtCLgNRsPuoAqgZsAOxkDuFwGIPpAejROFQhwAwwAbl+D1rU8zxWi4aOxnyfZxgR0ZHqWW1XNI32PIbVkWFRH/Hs/v5eK6+EotAiRpWqWN02e1k3bxqDlqfEkur89/O7Tegw81GX+c3EB89cedTUMzr/1A6RN5+fpVA+Z2pe0Ttw0smxftdFzfzzDn9h2Ae9ncLLhMetfKkwwnPUMztXVe5+9lV852XnHmfZxds6hjWsLiYr/nXdCeede/Qz99Os1MOtubbKRMSBusFEZqTtWSvDWsoMVYziwqDTxUWvGBlz/JWnXLSJb3567mzzle1GdDlFdq456LIKAJh78el3EXmTc4r+vFtfdU7IK8DtSPMGu54aFAthInewH+ySrWh5B15fL3V88HZOH1Yfjoweg1CyChssK//PMfVG8wqir4Tg1Zp8CYBHzvzZhduUb97jxFyLolKtbZGj6aGvLbgXr82YhQ/55gX34v0Zs1Dmm3Mk3fnGspoDkGvRJgDdC+79EAPyc7MvAw213DogSxN4KOVGXOEfY8gFeJC/VYVNy2LAb9+02f+BbS6KE9hv/YVvEhLhv/8J/Svi0ylzDtlnMGZ+5VMAxXMBPOgrnH5SuxEABomoFVIV70XI2orHQEb9ToCcb5cTUZaIXoece8dB0iC7WNpSZh4DKZzSgxKlMqC41qBEZw1AjAEZ3V7nH38JpfzaACAG8xuQ8zioHZj0zxPk4AGbqgF7KOXp1kIK+fRAPkQWokQJFQAW++2C3E1gU9GdDj8alYAvQeF/1kjI6OpTRNTNzHX+61H/3Gf5oHCGf74dAMz2I8MpyHszGKMw5JMuAMWqf203EtEgET1Ydp3vwq+56p8fkIqnO0Cq2C5Bid5aDhJVlIB6cI2ZsvMaKH1HCwHcuK0BRd8eggTbhwH4la8w+xiAf6GUA/uA33YGPgwSA0sQ0XaQ4k0TP9ceD9l/ir0DOb+kb7ZtAcs+159CAbtH+mYPVSgWPQzackEtTcB1moAi4DiAEAo8D56qwgmZAJF8FhZhb0waUXAlAND1+yXRu/QopJZ+vG9mtkCeV5eoXPk5XPt/vR2V3fDKloDi3Q998/zaxrEP3vPwcZ/om+8+6egRfWNrB++ZeURrxe71d1Im/6JhFxchETtGZ3c/2PbXi9l0R2928PIzOn6fPaPtd6/3ZPJzO+PiOK2ioz7yFjp+fbTB1xwdXXr3+OPH5JD+3nLeuyeXjxZU5xW7NmRznrpWsN43MVnbLSKxLqEws2OrxNHakNsfrWnpDa+bZC2tgRF6CbrngHJ5JNhFsULLIkHgLjkflQZGPByG43kciie5mGfN6i3UqatBXATMJEHTVGHoMFOD3kARuS5hAJGaRsVV39IU0VM9Jj/QGOlamNQr1xrQTHZJcVraFnPBhWpl4RjxpK2rSLV55vC3VqB50QZWTVo7e97vroHiJBAyjNzXa1w+ZXviHbSRa/Ya/UNnz+anxr/5bmfrDlV1X+l6X8SrmqI6qaGnyTlryspuAvH4IjtmLKLsIFTMPuuvf3z7sBXvpagnWmUpsJmACkJUsJusZjfChQFoXFRHwMvvlF1568NzZw8+NXf2Rt+8oQfvxtvSSk2hqP39ttkEAGHdOz6s0cRqK7u9aO9YoncWCi8tHEEt78WdMIjhmkqFXqEmzDj1URVlYtujduoh4Q4Ryyyrr0JG0zAskTD3bYdoZBBn+Z8AbtzWgCIAuK53n+twl0fuYTnOPb3jSX3dAJ5xYL9cRIYBu9w3H4iNvjlYPm20BGQA4+v4gn3zlw0sXgGp3hj0K4igBIvwKDYtpxG0C6KKgZWXTwhofB7kAjKD0sL1dZSSR6dBinkMflznmHm0L3AytHO59awHMoewsIV26/w2Kci77TpIaiogFzbjICPWIyHn0UhI8BfE/aNEtBcRxYjoBcjoYSOkCmkAPFRImqlX9pqFUp0wDRKgToOcxzo2rQcaCDYFebiBwqCCTed0sOJyIKObwTkCBdVqSDD4FcjcQh2SqlrnCzZ1QgLMIOLJkNG7r6NE5W5D6b5Io5RfKYhoX8j7giGjhpcAWMrMV0LePwN+21H+OVzITZeCf66LIXM7AxAc9cdSBXAoEf2YiDTIKCn51x/I2uf9930VMiczKJGR9dv2oUS9Ddo7KNHSAws2ebZZiiUzX8zMIyBrgJqQYzAcpbqcCqQAUhtKZYaCjbPyMSHI3MYrsWlO+JD999o1kBts0uc6rgf2BDTD86OKYQS+mUHIscCgAJyNzwNANQBVLUD4bpvYArMHRTgImRnkvSwYGtgDPPclgvgVAMDFHgCuhvPxvvnUE08ZdfI3Tozuf+CB2+S9/Z9h3Mugdt6Cb256t2OdS17BY+pPWn3x0SP5uooK43EQA7aTS6zv3ZmyhStJxY4gIWyB4bm82GAWu3vG9xa52aP4148/ba/Dj50VG7mu5a03xIxf9z/2cOM4s61vV91Soq5KCiVUy/LGag48V5jURXG0RhJWn2CrnwzqjTRpi6hmkQd1GhiqgKbDVXW/D0AoJgDLQzZrI58FdCHIsW0mS7ERMXrTiseeABPJWhiO4yAslnEkpntWEbAsiwYzoaLrVBfXKclUtzINUUxNNEc0VHlpo7Gpuvfg+nAqz53KYGfeWd5lGXoh467R2X2laD150ZEzASD/boGzrfkV6vSpurpnM/Tjp75MEM02MxVhisMX/HPf7nwhrQ50cKGQXvd8YpdLEn1t755x+U1XfjBlv0uXa7UDq2OjRUNX65iQq2q9iLsrhfd+j1HMF4WXBouLVcbZMc9hG2zXoJgII1ej2LbqOM6hT982+weP/OZMzdTQ3Li2n0at7lQcR/pm2+ICMzNi4a9G1ehw16oKTR4nPFNwzvZAbqazN1XIFTa0dVpOoR9spfKuW3AaQmY01pWCIghUIDSpFu+cYNRVo+nMn124Td6/rz0UvXjhn8IjhKYdzEXHDNlm1HWcahdu3GBFheso8NJj95zlrEFJ7+KTfPPPsGlFh8/dvjQ0VCJ6FdLZtEMi6xyAv0NGgcqllV3IxW8d5GLHwIfLZwSWhxQf+Q3kgKsoAc0ByAXtDL/dMP+z3YACN2QlI6I9APwTMg9wx63dn/+NkSwofxqksIcN+d0fCRklvBYSGPVBzq3LIaNZzZCRroMg55bit0miFMVaCZmTl4EUXQmii+VR7M2pz0HJjPK6oJ2Q0czg5u9FKXIuNvuNss8RKEXYSnXQSpFFDxLMXeW/L6BnrgRwJjMv3GycCMAUZn7bj0qZZX163R+vaf54rYLM4zwFwBTICOrRfvsXIam8gLyPl0DmG57un1NDCbS6/piuhSy5cYt/LfMh6zBFINVVa/2xCjaJvgv5bNAhGQHl4yNQeuBG/d9ZyE0DBnAzgAu2VdBIRIEKLUFSqBdDlj8pdy7BPC2PyH6UrWTmSZ9TV4fsP8B6plz05muh/vxhxyxrg/TNWbju0xDiYBBVbSzs48KFim54qEEx50CYBnTYkBtBm1sWDl8IohvhWB4UocK2GRAEzx5AOPp+nKpmuJ6bz4qBYSigHQIuXzJ/yDdvZs+qfwl889IDnZm7bu3+/G/srLbfncaa+g0tV7wkP7IuDqA1urh1JhMGeyvqr/M0oRWKbh8L3TVeaT09PmH8BU6/Fcvd9ruWr/3aOOiP/EMtTTXKwMr8wP7T18aLGZd0z8Vqq2Klp9SOGxjoymQ5GYlHwlT04uxAsIWscPL1YBgeQELYNjShwXUs1w3rCoTtAZoA5RykuBOMeoQjCvr7Ac/rQlVVLaTvIHZtD6ou4LkeLMujkKrA8xjCEHBdN2cTuwNpSoqc5yaSriNUXTV04Tiel77tuZ+aZ+x/HTsu7OdbCmSYIWf1ypXVa/nMlocO3sQ3D9vAhK6uKa1T6t4e+/bS7qKaMGGYStXaV9Ag8FwziobW37O7YQitLRNdETrenLx02S6zQZi449Lna3bqePcYw42KtNf3FCVrDk32rIKuu7lqRV+iAs92M58TIjJjyGseU2Z9V5pVEeLGeDw+mMeKgbz4VU3Cu80gkKthvqrikoEBJe6yew8J1GYLms1utQCEKJI7R7C3k2Lr0Zw1cHSm3yOnYHk5rw8FoyBy7en8uBE7uFUVFVGEYi6AbB6IM+BpwNUacM22ChpnzEKDie6VNgyyvegvWDDg5s9nmxVPU+EoQAgGE0T5GvLjbPGCe/GF3e9bNbJIRCOIqMFfpE6EpJrNAvAI5ALmEJRULQNTIBeAQRQH/t+7QkYxAjSeh1ww9aMUnQxeB+SitdF/3YQci3mQRb2H7MM2GnKMtlqC7ZaMiH5ERMuIaOxmh16AVA59nZnfBvAjAN9BSXUzKKdSAwlWJkFGuQUk8AqUT3eBLP/QgtI8syABTRAdLKdIBjd8EN3u8s/lQM5N+MeDgvfd/rFA3CaIiAuU1FtfRwkwBeBoNST4ZchlW/l9/TO/72lIwZk0ZBmM+T4NNEVElwf5nT5QPAyleyzoxxLIqCX865sHGWW8GhKIHONfQx5ScTYYjzCkoM55KNFRXQC/9dsNosQWOMr/nAFI6unXATwNGTkNgHVQiiPnv/cY/3yvQorcBLbQvwbH71dAvyXIe/wjc5+2BWPmQciIug2pPLsbPkzZL2dmfJLVEtH1/397OGRfdmubcfaItulnVfXtenGTq3oTdrWSYyHzuR+DLDhwqM8x5bKtKwWMCAgEM2xBo4CGej029c05AF0A9UOBgNBZU6OAYeahEBAOh+GiSUeYTZE0xyo7iEioYl4klDzzix+J/wjbEfJerttSw61lyUmJH0UnJd5PTKncxDerHj+l5e25+ebqRXfi2JfQN3BhpiF2eldPpN5wmVWPPVfV9QJrNQPbNz+xPtQ3Kbp962+OuiiVaA5lQ2NppTLgmen9d137FRRwmxpRWlhTnLiTRbbQaRlmVcQ0LbvbiyGNFOXyna6DZoZJTMYSweqg5Qp4Vk93l2tHBSzdQdHq8WzmQtEUMEwJFPP9vTB0B8lkJQDA81x2HA+6LuDkLGiKoIr4WxjMEuycsIqWa6czEEKsrlayRxp6kTUnpWhxTyCfgtrTj9p9x19T6B7I2O0daa0y/2fFKaRD7YX54Zrs/GNWPpA5tvue1OHnX3L5Lrc+Qq2NxK1T6t4e98qSOVWgaJSzCHkOhB738qC2l2p2ntqpx6AVc25lY3hyJq2sb65fdFFRM88fUew8RmMWiVw2W1FMPKNk005UCCQcLZwBds4L/acVoLgKaCmYrqDQFStb6h/t7YmnXAcWBLi6wjuGCUvyOQx4KXW7/CCOVDT3addBlUKCQsJ24nNv6qi+9Uauvf22DsXWi3Yxf4zm6U7CtP9eF8K3mqpH0vDYdnAaI88Z0YpoxiGXpW8OAQABQgX+B9j6Beg/L1twL9pVRc+aKnIQdLmN/G4iZ3uClY3RLIIIfPOWAPOwGbMkVf+LsK0GFn2AOAfAOf4O/wkADmXmImSx7+sgF80RSBXFYOEMlARAYpARAxWyCPc+2HSxPgoSJEyEBI1BvteNkItICyXa6euQi+Hpn9c1/ycbM98PIMzMzVtsvPXsbEja3ctEtCHIe2PmLmZ+oiySpEMKFf0Ecp78DZLmGFAfYwAOgIwqMiSXvB4SjOwIqULqQUYVy/MVg/lZHjXs8z9PQILRgL66BhJsCZREITT/WJCfGDw08pC5ieVqwIEpAJZD5mW+jhJ9tbfs3G/7/b7bH6M3UIpwapDqoP8ioiP98XoSksb7b/9cKwG8Cblxs56Zo8x8GSQg6fPH9UX/PY9AgsfFKFG/X/P7ujHy51NYb4SM5Cb98T0AksJk+P3aAHl/B4tMEzLiqkBGBw9FKW9xCoA/oASiR6EkChTkiXahRPt9koiOKsuN3KaMmRshx/VWyAhsOa25/PfmIJI3awPI73nI/kusbcZZBnveBQCfU/nm1W2Gqx2nkdiV58wfBPAj2IVbYFkJOLYJ4Ea4DsPyPH95EwNDgBGDQBYCGmS+9z4ABUwIABiVpNCto1E5UdHUfg0hj0ikoes3gIQNFcWBbOdgv9eGlc4HbwiiqfCw31Yaki+1HejMvANA+EBn5pfWN7usnCfc2Bijxn2lfubYDc33zSUAuGnYd7tuGvHdJ+5Uvxk8d4xil1tRSMfmRFpMbvSq/yz0yN7ump7ucKIAL6RHU0rFAT9R/7DnVT3n8FIn9sDahlF18MSpcJVphS536co+03MMZVzOzVFVwaFmu4AxeNsLoQgotS7ABNe1FBHvg2PoDpNAxKzRNIvgKCpsbaUYaO8MCRK2IAXWYA8SNUA0okJRpG8uZojYEsgX8mTwYlJcFx2rCbEkYFRA8ZiIPYUKueWFqpoH82rFM7Zd1MhxPRScXq5MiGJVBSXSuTcF1u+oNqbu3n/mirPjOX2RUQPHrCJF0YU66OkXNG5Y9MKpG544EgBWTN/++oyjrsv1Zt8qpPo81VOXDsRGrHCNmLG4abeW62eeG5277xHMzIPMPLhoTDXboDfW6ZUrexL4e7aGzk8v7lvc1k5ej6BMFnirP1SBQUc+7wVAB3/39zfncg03rNzQdPljrw5LGiaGKwIHgGA5ggwXFCpm9baefs51ZeEMZFWYYRG2onq1m0sr6bHjbnZVb3+haKyGQsLs79uPVO0PWV0jWxXepNgOO5AQnuPqysJ34ADQTaDXBDxfzeWvN5x7wVG3zLlsm/TNf787UTtox2osZG9lT+wHUjVyHU9XVGgw/ze+mSE31r8Q26o0VCLaC0CamRd9xLE9IaOMiyAXOtehtFhOo5T1aaGUw/UA5E56sIAPrJ+ZG4koCkmHy0AuLgG5aB/tv34egGWflp7mLzT3BdDKzEOJ9VvZiGhvAE+gREmewszLPqbtZMgo4puQwCMCSXW8CDIPMQOZx+gB+Bozv0pEKZRqc5bPr3J1ziJkvcfhkCIxlZBUwGhZO8amSqjl5vo/5bmNef9/BXJXPsjrC9oESqpBpL2AUjQtAJuBMJQGGYlcBQnKXoOk6K6CBJR/Y+Yz/DFqALDMf99DkBHa+yGVTB/w2xCAk5j5Pv//9yBBdKCouRczv0xEV0DeK1NRyst8yR/33fxxC8p/BEAb2FQB9krIfMlysZ9ymu7HWfn3A0jK662QUdF/B+VVtiXzFW6TkBsgKci5HcKm8+rT2v3BnBiy/w5rnf7dvRQS6cYFt37YN1+7755w7FlQxCIomqZDXFdbiLrrlbQCtcw30ya++feQzxkBgFQAw7kCPU6mP3XxM410/X7SN7tuBq4yxedIBL75sYiSvMKFvSz/k8c/nW++fj8VslzOWp4zf83/aTCG7P9s9VPpEAg8RElD84wYqnaaOmXxVU99pG8+/tk7J9u2uKWyKfFmxfDkzYvP+EMFF53k4K2nXrRe335MItNrjaxvb+xyot66gclfax2VfPWU4ryUbUMhgmgdAKkukFGBBtQyiTx1cCX6UVvQVfNPtlOsz6P5AgCV7GWf5KKICmExqS65lsFsu5ZiwIDnu2fbAQwNUIousuxCN3QU+gHyAL0iBzcbcoQQqqLlkCoImKaAndUTAkg5SoajsSisgqVwF9y8UXAKVSppxZCXZYFUNp8fXaFMqlgrJoQH1eVLnbZcMbY40rDW0N5pfc17ve+0hsOHL60aXbFrlYu/XjH62DMA4BsLnt85tKrnJXWVifYpo+/rbaJ3Yo5ze+HZZccvvOIbDwDAjQ9dUat59oFnn3DVfQBw4Y1nvN9sKKMjcPpO/+7vm0fW37DXIQ+MWTBt9Us/j+UH9hQqprqALXJQdAMvEbmRYr6wm6oYBc/WTQzq6DYKVKHDzmdAqVRc7YwpCJHnjo9mrvx2KH7JbVf8Vs0dcASK4ye4iqsqQvE8FFLCpRj6DA0KhKwBlgdyeR25PHhE0ya+eUWmv/8Oz/N+LoBXf3TLL7Y533zAYV2ded2Mw013F5KJlOZStQItDEX/TL55wb34QnyzuuUmn58x88ufcLgFwNXM3OrnywULYkAuMAG5SByAjBJpkFTUVsh8s+Da8gDuIqKdfYrds5AUxCAisZOvhhqIboRQoqpuyXRI6kcBMvoyZFvXwpDfaRFAz8cBRQDwNyhmBP8T0bWQNOjjIMVYQpC0qwwAhYgOx0cDxSA62IhS3uBhkFHxhZCbEadA0leLKNUA1SCj3YH6aQB2gs8oL5dRXgbCgqRizoLM6a3yz2WjtDAL6KCBsEyvf23BQmu0/1OELL/xLwC/giwvMoqIroac1+eX9aUHkk56GwCViGZDqnIl/P8PZOZZKKkRJ4moD4Dp33Nf9fsZiPIAsj5lAZtSLoKxLQeEBcio4Y6QUdSx/rlSkN+5Uda2vIxGYOV02oBK/YI/Zl+Y9PQXbD2Qi/ZWZp7uq5u+iQ8D509jJ/nlZx5k5tn/n/s5ZF9CG/bK3I/3zUK0Qjeu5jnzW+n6/fZQHSUfgW7qDsHSuNw3d0NuUmiQwK0NvrCVA2A9pfKOY51A1++3M8+Z/zZdve+zcPk7cIqAogqQshNfMJ/p+v2iWR5ohLzPtyRsFpjif9Yg5DN4yLaihZJGzIVQPCdfFF6x5+OAIgA8eOAZm/jmw6w7f2FGjXHLuscf5zUlf9BP74s6VzmHrNRg3cBKc7e3+fAJk6AIFyLjGhQVJuqUHizJ661rtC4jqlTXZtiGhRRHrc6vhzQllncTrwL2ahKVp5CZvc9LFR3WzIRHghRiA8V8P1Q7Cc9geBDwPEBRFaiewKDlQRUeQhWqC4QVNQay8xCpvMVGaD3H499SN6T+bkX0KpieQY5lI8I6ulUojiVclXJM6kVKOv3DwSo3Z5j6yJ5CDQtPhZqwmkyLm7K0fa57n2M7sA9eGes8eoNqufcsba8ZtcP6lVfFIm59Ph97qzhlDImmtDe6UEztEFk7/t3f59MUD6mH3X3f7H2xfOLquB61KyrVH/zuJwf+5ju/mDUe3FSRd8g2UbXvTj/tM8MN5vzZzz64+5zCN7Iq1DpGAYyQpQKqgj0cFwUhhHCFC6sAhBxBSRNQVWi6CSSNlNeXjRciYfGHgUx4x0Jd/fL3vjJ17LBohdq1vn1QhRWuax6hQ0tAgeeF2WUiVgxf/k+YeURNkL+8CXxz5dKW6OKBtLAGc0r/jz7vibkVjMnOkatHyXNa37g3On3GLGwHyRyTvtnzANdjqAphy8Snk2bMwgkA7lpw7+ebQrdVweInGTO3lf29EEAVEf0bsuZaoCLpQi5W+yEXzdWQi3GgFHHUIXPUTiaiUZB0Nx0yIfyIsiiiDelYgojGp+ljkYgeRolaM2Rb0Zj5aSKaCZmb2L35cT8KVg8Zzc5udng1JMjbG8BcyGj0D4noSUjAVYCkp94PSX8MAEo9No0QLoGMWgfgbxJk5PJoADdBzk8LwJ2QNRozftsuSBATlNUg/1ggyhLQq5P+a3f4feiCXJDZKD1wswBmQoKpekhhmeD9QaSxyz9PFySF+3uQ0dAlkPUMdcioYnCvfcc/RwEySjodmwruHEhEJ0LmAu6I0kZOo/93MEbt/uesg4zchiAXlUFeYtBHQkmQR/jXUwMJboMoabnYxUdRdDc3UfY74/f1X1t4z3+kBaI0RHQZEV0GGRkuF7PZUjT2o2yo3uKQgefMby37eyGASvWKr73lmrw9Ss8LD/KeDnxzpf870BrQLXgGyPk7WOmi6/cbBU3UQ1N0OOo/oeAIvmD+Z/fNc+YX6fr9HsKQb/5S2Np/FP487oB4RigVry97tu9Dvnmvcd8g2zHqNbWYfnnFw5v45n1P3HlNtEI/eO/iK/v8AqfchshhA08nccHI91Y+6Ra6z64xQ4WQgpgz2DsPXs1+eS9i9BVVtBvFeohxGnsJGEhh0B14z9RDu2XcmEDRAxyxI/pX3IDmccd5rnWzbdkJTRQsaO6dKbv9xwllfAaKECC3i9lJ2g5pOlxStSI5qpkGkHSZ2XUc0gtFt6anP5nT9HiqP32Xo6uGk7N7Q0m9quAUbHim51VUC9bdnCqix8Kx+2MRb6SVcU+ECxroFW5OSYikO5AnL9/V74TizKln/h97Xx4mV1Vtv/Y5d6i5ep4yJ0BCSCAMMiYIAQUJykMIgmJUZpUICgbk/RABQQZneMyoREAEVFBABAkiAcM8hsxz0t3p9FDdXdOdzv79ce6lCoSH+GQIqfV99XXVHc+9fav2XXfvvRZls9ZDPbucbsbNEa7rLkZrau6QMIyGgXzBt+OSG8uBp3pOHcwziRbTlaPrjFSmsJ8asJSwJCyP6JkF6U/QaTd8/gfn4GUeiSkxQL24sjlXX08d+050UgOmbQQBw3TcLsvB6AJjWb2JHVnJWGDFt3DRb7KSioJYUZkGaDgHUopKbROy8VihjP4e+pxCsvGqVV0bxe57SRGzRNzlOnu4ACAAAhVAxGSSbAABCpIBacBwS7CIgFIJgW0J13UhDJLPLBu5+sXlySXQbTAfOfz1vhHjAGD6MbELph9TvgCx2G/DWTo2e55CEAj2w9KMmAUiD1GHEjPjTd0z70t56IeWLFYjFCz5BHRP4mbogNSDikpqrOrvm5Ujo5vov4UZxEugb2LPANBARDlmDsJeya53GMeB0OV1L0TTmPlfDmA1vPdg5gf+l9kxaCK3DhUPwgjPQmdeVkFnm74V2jnMhL7h2DG8fn6LSi/jmx/7uNCWHI9Bk2DQsFQAACAASURBVM6IvIyAVhFdBV3iWoRW8/wqNLFLQRO+yF80uoYj24JIvTdb9XlquEwkADWAyAgbaAvH+jFoktWNin1CAC0scyA02TqVmRvC74UJYF9UfAsLAE6GJnfnh+v+Cvqp7wgAV0JbijwPYBKA66FFasDMjxDRNGjhnTOhyVnUKyqhbxxL4fTIagR4I+mLfp8saKIIaDJ7L3SmdEI4jqgs91/NmvVDE+iDmPmjfjN5DgAw84VE1Aot7nMC9PX2bsgiQwsk1VDD67jyh1dOdpS/73mpA/e9OFiwGfqa2gIdm6v7r+MmYhxDgnzhqqTIorfUqT1Tif7C8xYw/fBQHZstPuP0XY5tOOOvv8r97OAvBzxvwTvG5onXHPNJEG1Z9tXfVmLzvAW12PwhwoqHh/632JxgYJofmGugNQFeR+uoukUA9oQqvQKwlyhu+dbo5/tidlP9TIpJX5YGJt9MR/GXYnfdIqR1YKbQz6Y/TA1yJ/hsICkG4LLvYqhpopPKPTJAiZkwDAkMk9FgjfO4ME0o57VYPDUSw8PDgSFOS5ojToP0iwhECiRbCKYyLZNQLijfSgqQyAKAFYBQ4BwKhWyvKaFsg6ipbicoJii2yz4znOEepDPtAECItW1oB09Zmt/fl+Iz6ZTfk1/bk+oYv94orm8KhsqJ3wfNDQeSYdRjYOjqDZOyDdv32VdKguk3W/soOyEhhOg3E8taO9ednM04o9v9wfONrnzgSf6V1SYOaO3MtQ4hOW9k2b/10ds6X130u+3GA+r6aWMxG4B36Gk3P7L5jl9OS++z8Ii6MTjz775fHASLVh+NUkKKMjoKZZQSFvJEID8AgQF24XslWHk2IMg3VNmFGTgxz4wnegWwPGEv2W148N4MYUdD1k20yGwpFEgqVjKdCBhwCQB8YYHCu3PJJgy44EIBMExYhVLu7MNXf6GQzMz4qCqivg6icwBg4XxcOH0OOqD1JL4C02yFEML3XSgIgBnwGVAePOXBBxCL2a9b0+J9is0fNp/Ft8PeAM4FMImZs9A2F9XKX1EPlA1dfhWJeSsAf2XmRgAXhqp+gpm/AX1jvR/0TdM7oqo/cX8iWkVEb28QXMOHEsxcglbNXPUW855n5i8z8/PQZuQnQ19DDoAuZo6epu8G3fN2B3QPTkRsoj7DBIAvQWesIkElQJeEToe+eUqgkuWJHnQ8Bd2fGxHGAJWn8A50RtFBxTuyulcv8hu8EFrE5nwi6oO+tm9Fxb8w8pDcFC7nAIiMlr8R/q2Htqpg6D7h66Gf6D8J/T07GcBkZm6HJpa3h+dkEMCLzPxISBQJOnP6NVSsPIzwOCJLizw0+WxFRT22uswsInJRmWoAYBfozOcBCEtgq/4H/wpRLEETz0nbAFEE9EO1nvC9hH5QchD0ufsjKoI/73QuJLQFUQ01vA5TBXtORt35abbH87wF2aSo2wEVlWkAYPiQUIgJyIEyim7Avumxo4QpHubvPtkA4Kd0xcwroFzBZz/4jdN3+VxTKSjvF7D6l2LzLjd8wTKk/Lgg2t+4bOYqednMWmzeyvD4irsLpnQXGdL7p5LhOTNOeX7OjFO+PGfGKUuy+U2H+9I6mbNxgULRyZac7md23m3DF/wHqBCTu5Fyl69VuMOT6c9YCKSBHAwUA0HSSkiR6ilOOhFD8h/gQKEsSj4nwIH7HUHefiSlQYlEOmF7ZBuBgC/jEAageCGAl4hIIZ4ixCiASQTXdTHQ66LOqkNjYzlYvbLIyfggmSZBCmUJQ8SVQUi2b8DrsZm+O6oLfYPZ+nTBLdzpDKkRRiqmtpTa+4pj2pja8k86z/U84PcXPWL/IQAox1LnuEGAet+rx8rl98jOtby7WHLZ7mLx9W5p2HcHnSflva43PuOdkmikHX7+xbntP/vxmNmXXjLh9od/O2WKCoJBQL146Gk3P3joaTc/8o0Zl1FJqbPjCXzNAGIf50AtZWEFcWQUI0hYyHiE/JbN+JUqoq5AFvrNGJcVyn4KKDVnMJzM+FueYRTWQVggLgYcjN2kdpH9ya8Xh1IHZDwrJmKmSSSEFBIAEcoBHJ9heyUYbgkCEkISHACO42DZclF8+LFUi5dzJ33kiSIAMPeAuadqyjoAB0OILEzzj2Y8qWKxmE/CUPABKIAoJG1lB3AjiQoY0DZj7ym2iswitLBFC3QZ3wzo7FARunfRg+4bi0pc6qBvKD8JTTKjFO846BLWsdDZiX7oTFIvEV0DrfJ46dsNIMzU/CTc32V4n1K/NfxnwcwDbzWdiHaCVvbsAnAn9PV2CzRh6g+XeQT62ktAZ7Yug74eDGiBpOnQ5VKvQZOhSOwhgCZqA9AZQUATpVXQ5ZgC+lptQkVVVeKNQjcqfN8LfXOfRsWOYwSAHaD7ZpdCE7A4NJFTqBDSdDjWedAkdBDALCK6C1rsJ8rqTYImVREROw66f3NPAJfijR6SdeH5eQbABaEozojw/FwLrWT8RLitTaj0+GbC83gA3ljGGwlX+eG4XVT8VAX09xyo2Jr0oKKQGv0vCqj0Nb+ZQKpwv09jGwAzj616nyOi26PSeyKaDn0eB6Ezjr9ApZ/2nzYVzq+hhtfxFex2MSvZ9BnJ1wOYUVD5Sah8/7y4F9sQp/rGIbefnFghCWAXMA5zg/JeAOaHm2mHjs3tAJYQiRyBnhMkeumKmfMBLOJ5C655uzG8dMpt7sRrj7lSkPBMhcvAtdi8NWLhyrveMjb/5e7nd7KKsS0Hzpnc41ux3ya84ea4sG/Zw+68fEl8Qj8ADObMv0ou7bLB3yFpwpjyopx0aRNe80DC2JArvZjJZHYtJkvDrIzXyI+lZC4/FCTTGSUQkCwOIBMvwUcWgPJjogjfeRW53gFkxvkw7OmQsg5OicGuCmBLLwgCy7Is1dAIIwgCsLQwa1YfisM+hgfToCDwZH3ONbgFFOwpc/mrrFRqacnAr7xSKS6GvduFAHM8aXO+BNgNaSLTaCTrf2K7WW5BGDnUJWeNeuypu0TruP9J+uUgRr60s2m79OKfHWtyszA5oMLmwqGlTMO3h2n0nuZ2Ky8zXf2slRh+Y2O54cqbn+0f9PD3Rc+fedlnT1vffvie14+Y+hXecXh+67WbN+GH22fwREz51jO77LXJWf5Uq/RQTlnIGAESHnC4nzRMoQyQC5hxZJQHWJTz7SARl0bSK+aUw0kvvh1AuXhdoygLwIAAykkpVE+CZGPJZzlQLHsilTK8QqFQn0ilYII9AjkQEMqGqRRWd8ewtDMb31CoW/jl9+2q++Cw8M7Y2Nffz0du+hzcvnC+/u2aPsefDrAgEoOAPB62/QcAZAhhGkoBjlPNQBS0Cv17ig9UDfVfRSg+ciq0zcbtYdYkgYrK6VXA64pAA6FsfKTm2A0tz38AtIpj15u2PRaaPDrM/C95CBLRIgBg5r3/b0dWw4cBRNQIbQK/lpmPCE3N25h5RdUyzdA9fFH/nQTwKDSRmYGKP2JE9gJoj8ODoIljH3Qp6XpoQnkh9E1VGloVdP+3GV5EgCIV1ahMdRk0oT0NOuPTFC7nQZO3jnB9NxzLK9AkMCKvJWiyZUAT0BR0xnQsKv2Y1aI5rzDznkS0LNz2Mcz8ZyL6NHQf53pokvwPaAGaKQC+AODlcD8+M2eI6BfQisWLoEvLp0MrJdaHY3DDcytRKb2tJnxRNrW6tzFC9fmJxIGqsR66/PaLzHz5W5zrbQZENBX6Qdq4cFIfdAn1NdCZ6L9BP1SIHjbcCeDMt3vYUsO2Cf9Hf72RlTiahPq6cdbBt4srDu2ToEQANaDm/aVDXHbw9ePE1Dk59KEfGwd43gIdm6+Y2Q6gO46x30vDOKAHK4/leQveGJuvmPlxaJ/VIs9b0PgWu/8nhFnFfHDuglps/gjg/t8932gUjQVSYfXBX9r5yEk9uUaAGpa2ZF+PzZ/p/1uzFO6yQHlqjTPKMkFifEPno139baJYHNx7wMv4qWx9Jo+AG9RmFQuCYEOh7SLUtR4KpHXfv/J6UXY224lg2AEuwmDnYN2mV7O5pmmPw27ZH5YFcBEwHbBfr02PBwe9gIRJmTRiALNtEYOVEMZi6Bh5GvL5n4qHH27i+qxpT93ZGe4plbys3SBjCRh+4ApTBSiuf0XFRk5GIp6qH9oUOEai4EgrCTsljYGNvW3YnNySz65LmWo0Chw3DYHhtUs8r7XZGmGYSAWFpx/41KEfP/4vv1tmC+rwhHXM/IMO//OlN//3pzMo3r5JNq3e8Nxhyxzffvygve6Y5AprStIvfSHhdr9sM2KJku8d+q1bsvf89MTb4OGIwjCeaOzAYQD2W5XDTRyI+v4uL5W22R3dYRhEgRgakH1GDA1FxyTfy2LITMIgS23hohgfuD5J0wCYMZAkVxA824GIWVBeWSXNuAJgOPDBvkLMsABg3Q9uapzJCL64tvO0bTo2T5+DqYD3W4DHhbdCUWy+HvqB2uNgPgtEAXRS4E4A31g4/721uNoqMovMfC50GWqEo6FP0BfC+XOJKDLk/lLVel0AQERnQZ/106HLAau3vZaIFgD4l0tXaiRx6wMRZaF9It+q96UfWll0Sfj5JAA7EtFZocE5mHlL6EO4FFo59LPQAWEd9FP0qEc2ssQQ0DYcErp08wBUpOPdqr8udDnnW4FRUeysNpgX0H2ESegbfgf6u+xCZzNfhiaSm6Fv9Buh7Skir9GVzDyNiDrD9V6CJqsKup/x2HD/1VLOOxHRYmgScQD0TRyY+U9E9GPo0tCR4bn8PjTp3L5qGz8O/24MXz8IP/8alX7NTdBk8+hwXgBNYMehIlgVCfRECrEROW5FhdwWw/MVIVJUvZOZO6GzxdssiOhY6DJkD5okNkCLPpWgezkjXBKWEx8H/f143zydatg6YJx18MnQv28AAAZfEIAvYPAXAMCSsdMU43gTMdhIVmJzSAzbf3TyWZKEtP0JX4e2xUHVMo/RFTPfVWwOzl0w5f94SDW837jrqiyABGbP/afYvHivTf27PTrqzxSIJQDAjK8w1A6TenJnLW2pGwaAPzYcsOXTPY8dQYa1dGri5R+RKT7ruHZHccnKNWbWSuyTywTeMosXTVgStI4kA+SLLrPuPMARfh//N6cTO0mV+zJMKRw0uCZ6yTQ4yDXs6XG6cV96vcvBBvyEbpBnZjOZ8rhcdFipFJTyA8kSTEIgP5bZTgPBb8nzHNXWYpARcxAYQ4YInvX6SocYieJmWWcmUZ+qgxyzhyw5Q2CooQGxIjahfjcM5zYht0HWrTCWFscl95LptK+G1t4rPfNzSZ/h1mfNNo/Q4PdhqDm1xz7/eGTxWOa/OQoHjAgGHgSA80685E+nrLv1anf58MmTnPvGZHy3HyS+byh3bGdbYvK49bCZgL64cT4AlGjLavbNjWai7goAKjeI37RbqB8wlWr2xaaMQf8YHMDRVkHClZ7qHwjWphpaxuWHii5lYAmTRAO8kkd23MIwAMcfzKT6bUJrXNha1MqMFRmlFMGADRMwEEDH5jvWdJ68zcfm6XMQxmazCAR9gGwAMLRwPkrQCvgh6KLpc/B6bH6viSKwlWQW/68goi3QmcjdmXnpOy1fw0cPoa9iC7TQkfe/LLcXNCGLSjZvAZBn5rGhfYYLLY40E8DPoW+U4qh4IUaZRQ+atG0PnfXrQqU3LyrjrM6KVWcQI1QLtnRBZyqPh+61rfYwAypk9QToPsHopskKtzsE4AZoInwT9I9MB/RDmIgEnAtgTLi+BLBTuHyU6XMBnMXMN73pnO0NrS4soclHCbpvaQN0f7ER7mML9AOef4SrXg1NTqLSUw+aaG+BzihGfY5RNtGv2lbU+1iCJoZvp4bK0NL5KQAXMfPNb7HMNgEimghdbvoINEnvYebxH+yoatiWYV955JYYpRKfHDVq97s+d2ktNm+LuOuqXaFj5N8we+7bxuZzVv15r5eskXKNOTI9ZsNio09Zt7jKyr+yz7SxUzZ3HwlwYefEgnOEQfsGjrp45aL0t92GTGxKd1NpQ6rOWp30eLv4chU3i86Kur1edQbEZCuoy7rsbRSxUodil+OiSTSnliHBg7SkuAdImEDc91CiSmxmhgewSUTwfGBzdxfaWy5S8fgcEZT2QbDGBSZYAUvIYhFwXIVkHcDqhHRyydcG+0dMGdPyCiVkwSygzehYEQz+1/P2jY8Pi5MKSbr6xZmZryQDp2PQbTw5/mCLNPYbxqbR3rlM1hgz8I8Vy55rzhaNMTtbPWf6cWn0t7SLeDFXHuWUzrj6k7PnV5+zr224de+B+zc+spPIC1L9vRNsu7w5lmp7KZPeINvsUR1rNxm7benrUw761xSf+2LPmu2ekFYSu46RV6uAvmLayAxaQKII1zRg5QbRLRkNq1dJQdI0MqPSynACsaVvwOvMNJs7ZDJ9NvzGgN2AKekYQEJBBR4MGYcKn+IW4ZYC5EqD3JhuWiPTVgqCzv/6ud+Z/xb/9m0C0+dgIrQv9xpoMcGNC+dj0gc7qjdiq8gs/gfQD30z+bbePjVsfSCiPwDYjZnH/AuLLwWwmpm90FPzRuhsya8BHMHM94TlqH8C0M3MOxPRGujsXSIsQ/0NNCH5PrQIzs+gs3oOdKbtcGhSoqDtNZ6EznJFfXLVglJ5VPrqooxYRIYi4lNNHNvD/XkAOsP9ROSUql5XoqIcGs37FbRn4ncBrIDuFToyXOZE6H7HO6FLVRuh+wxTANxQUCoi0R8DsIyIZoXrfC0cTwa6xHNc+H4JKuIzEdmrC19/D/crodVgLeiM52vQfZsKuoeuAW8slQ3Cv33QvZgno2Iyz9Df8Qa8URXVhX5qeQF0Ce4QtlEQUVSd4QJoYObUO6xSQw3vOUbLUf0+c/DAhtdqsfkjBLHHYX8AsJt69oF/JTa/BsDC7Lne3E8/nmLHuznGuH/D/9vl15m8c8SNszru+dam+xrdjPunieWVnQ+2TZ02s8/dEJNBkgqlxEn43ZgmmZpPZMt1m7PfnzTBOfjR3MwbsGOhbK96tfz3KWMeUEMD/yVgJjZ2Z9WsDB/2arbjydLGVUWVJJXOxNNtPoRrKFAOyInWfFdsbBJCgL28R/G45eaLPklhmLalIIQwo9hsGsDIke3I538ifC9AndcpgkxWKbKET0AyCSSSBAKh/NpPhs3mesrWwxXtqs1aK8pe168+mbXPEqN3vGDvwFtxTZt5B4DjBQRPKr00zz6QdiDl3bkJ+7xC7DeKdet/0awaUkGTKt/6yZlZADj0qb/NGOWUdgFhxQW3XDhrkTVhh1I59jWjyaXsHvHYNPT3Wol0i1OsywhVWD00lMt25EtGz4jtjZ4Rrcpb390o42Zju9j90b5EAEoIGSh8NW3DUoQet0DrEuDdmKGKJQwGymx2nKKTzEgMSmm0G0PBiA7b9ITqGdrS/9tsY/Zki+KxGGAywAFUvw+/gcpCJqTLhhnQqsH17vrCunyL6Dh3wi677iZ8lX/PLsYPOabPQXVsvm/hfHwoY/PWoob6b4OIbGj5/iOqhB3aiehhIjrpf1+7hg85DgbQQkQr32lBZnaiklLoDOME6MzZowBuI6LvMHMftFDLn8LlLkKFeOyHSubvFOg+v4PDz9+BJi0HQxOSInQW8dqqIVwcztsM/aOQQiUL6YbrRMSq2kcUqPiLWdAEswMVohmZykdqofmq5cvQolCfD8d7HIDfQZedjoUmehPCdeuhM4/HV22v+vehHzqb2gbg7vB4RkITxEZo6eZN0H1tnwjHNyocX1QGGo1xejiuiEhnoImoDMc+Pnwf2X240KW/V0GLAs1FpUS3HP4PIoGrSE0W4XY+zcx3Q9f734dtFy9AX3s3Q/e5vi2I6HAiujLs3a2hhvcEJ9660E6Z2SNSRuyIwtn3MACcfceK9rNuX/7wWb9ZXovNWzc+AaBF7HHYmy2q/hmz5zqYPXcYAJIut7iGGBcIsZNVGlg4kOm97cQFr50+1OH0y4J4Bi7dDwBG0f5uunmIO8b0AMDeKbNkSENSp8KJT6zatTdw+vczrRgVdjzgAsQzMdE6ZgZWPl0YufzZ/GPJya0jzvz1tcPX/hqdl/wQbdl1F/fbTqE/2bl5Q/2gN2RnUy7SDOUyQZSVUyoGUhheEAQwhIBJldjsODoTmkrFEE8moewOJcfGYKZAnleG7wMkgSAIRq8byIHIB4DugbTT9bx39fDyyZ//vjWj979H2sf+gkq/62/KvFIwYiPHf+M8v+meBROUH/BaM2kbmcYbxpR7jxcikCyJhxLl1xM9caO1e1l8wszunN/SRM7dO9mDFwcqMdLooRGizmxcfcp+Y2V+yyYj6PvmMSf+7OOZWDLZYlujmhc97Wz3zGvKdVH2GNznwxnRYpw6Mimu+ttuO5SXx0h1+0FjAnIaw5DM8GMJbB+LCzlux6zdPipppHmgXJKlH+VF7KpYl9+bjZlz/dygSoqSJ6hUEACZMBpSAYiUUrYZEKHAT/U+Of61gVeOvObnd/zJYL4Glvzzf/Tq27qwGpXYfOb/tuD0OTh8+hxcOX0O3vfY/JHMLBJRCzP3hP02/wV9c9QCTQ4AfcPbAt33VcPWiyFo0vX5d7MSM68mopnQAjM/hVYojSS7DwdwNBE9DN1z+NVwnXvCdeZAB8IEtOrnSmgbjX9AW2y0EtEZAD4FXQq6BJrQTIb+MTgEFdsXgYpiar5qWvSXUenPi7KN1QIvQIVkRnYb10NnPhGuPwaVclUJrUwaBTsRvjxoojsGWtSEwjFXl3U2QBPLaeHyPjQRFAAUM1+AN8o3R9t3wnOSD7ermPlFIpqHisIph/sqhdu4PNx+JOpjQdtwBKhogEnoklUDlQxkRBJ7AXw3JP99gO45xTYMZv4LgInvYpVtwVakhvcZJ9y6kH5x/HQ+/TcvkkHWkQek5pxisKiDtt8BgdqY0QLiWmzeupGHjltffjcrXfaX/VfP/fTjM3/ywIz8SU8/dVOQHJwkYm7njXQU/7H74cP/2rfui59Z9j8PJ3z3vLrdR381DJd3+qVgffdQ3xzG7p8ypZtodEojva7yiuLYpvnQvsedG44+qmnKlt6zzS3FQzpGdey6dvnKpcXPHlwKiosnuyVcX4jNOjQm0KrKLtxkQsBMDEH4GQFjyDA9kA0JXwFKCB9gNTRUkplMXCgVkBASvlJQceEV+gMVi8HozxVlU4MB2xAIVLB+fPJ6DDV8n0wAZiaYlkvu8FKHYQ2zgieBdVM6noDvK0hSpWxGxJ9bJl498aveyIHhz0z463MNwWT60dh6KMvZUO4YCF6vOvIdv8EbKk7YOBTs3pls9IyePh+ccYq2j2TOCm5pP4px2hHbRcub7mBAEGK0I5xTz7ix9e6fnpQnH9QaY579zZtuu/XyE2ftu3SFlQQEDMkbbVsaA26JSrggbuKKIFABIFixz2kkY1D2WfBtJTJFNi1GMRCGEIGpFGc5jM1SClIJwIXbm5L47hO/X/V6bD7juHO36di8cD7uhK7s+lfxgcTmrbpnkYgkdE9YFzMPhtNuhBbIOI+ZryWiXQE8Dn1zeRIz3xYuR7w1H3wN/xGEDxRmA1jDzM8Q0R7Q6rlToMWQHgKQq+5zDDMuy6HLKovM3EhEJrTiJxNREppMZgBcAp2VlNDE5mRo70MDug+xPdxstWgLUOnfi1Ddv/h2/XklVAhUtNzF0Jm4LDQRPgtarbQj3F5kPWGH4zoLulQ1A/37kAjPUS90qW0btCBP1DNZADCfmb8Vnpv9ofs810NbbZShM3zPhOuuh35oUwzHGGVVbVR8J9dBE9cgnP/m87Ia+iFBc7jdywHcG65/RUhca6ihhg8IdMXMSmyet2AQAE6d/8yNgQqOtoR13jVzdrv2lFsX7ZFQdY8RIKwgts/lXxn7IgCcf1cXXTy7vRabt3Gc5N1F5KrPcUKuuImOfu57fx3cp+T/7fxVsm9yUBr67y+17vQQMXJH7P2J12PzqC6kx/a8vNzMiDprGPkHd57SPKoLJgB/Qzt4Sm9fHQBsf92ixp6DWy7Z0jRhn54ti81ADRp14z9+ssyrWyFgBInsOpTKYwAGSqWisu2E8BUwOAhk016glBlYFkgIGMxMkkhrcKvAVUoGhgHV349kQwNgCsDwi+DAQtk0IAFY64O6wuDFzV09c7vb09n2BB2+nPc9Sznu9p5Cu10aosbOVT2ltjGNrblB23ip85bm7dSFJVYv9vV1ZdobGujuw/8rccxTC6mxZ1XvcN59sNAysm1T0/b7tm58qTyl3B3rTmQKRiI2/8aPz/4WAPzhpm/v78j4Lfnipn4fmcnslsvDrTPHj+178EUp0MQSK90h3Bl4+H+pBOC6yi2XRXE4ZtkN8KSVIGsF1FonMXGsyvv+qL71HqumOAolmJkifJfdgt+2JmZb9UJwHYCnAb4UUPcDBgHiilMuOrcWm7dSbO1lqDHo7GC1rPYmaGuCjQDAzC9AK0Y+HxHFcHotGNUQXQcvo2IGPxm6v/FTAB4AsA+qTKZD4mRDl5gWAbihHcRUAIvChxCFMJO1GposngHgHOis2Y2oZMMmoCJME1S9BzTpi6YxdB9f1HMnUSFb1YgyfpHBvQz3m0RFrOcg6O/MP6C/IyvDfdwMXUq6CpoEOwDuD493BTT5PRzaT7I/3D+H4zyMiB4MS76/GS77EIDPhfs6BcBcZk4x8+TwnEck8Q/M3M7MDeHYFSpiON+GFthxq+aJ8LxFnpVLwve90IS/FoxqqOGDRxSb66MJguQmEhgGaCMA3HD83s+agX2H4ZvPR0QRAGpEsQYAuMmczZw0XgaRCwAFZHeUxiGLY178kC+17vSAa6h9HJNbouVHffFean/hr/bQyNZfBHm3tDjY4h+36jfXJbFuahLrF+3Wv4lebWrMvdrUmNth7DNrpy+755K6fM8ZXjB4jmJcgLK8OZCWESSyRQB7Br6nsOLGVAAAIABJREFU/CAAOPDF0DCzIYBUGrAsU5pmYALKYGYqDQ8gEP3aCVlIw/dLEkAym9UD8xSUp1xQ4MFUDLPAScRlLhh/jjc+k27IZo3lhZbrzfzAQaaXH2WXCk8jaXX1jZ24stiYVWvGjb6Zdo79Ph8zX7ZTZl3dyFFOsrvw0+suv5zquldsSMBPJGRweNOqRS809K3qt/q6y8Q+l8uO6Q+XDzv3zuse/MZDf6C/P87fXLHSSdRbTb9PcP5zw60zxws3OGVd3SGnHfX1m5NHn3bzLp+fd/MlnoJbLCKfzYqffOmCm9tPP+faBgF22WWVHcLm2Kb8FjsXzCOMPYlF4LJZ9gKnqLhUlHYsmKCE3wXIqYBcSlDDgOoF1OoaUdy6sVVnFgGAiOIAyv9J8hcKm2xk5hn/qW1uSyCieCjDv9Uh9A20mfnukCg9Cd2XN4OZV4Q+g1kA10ETsUhtsz58dUM/mDgqXP9WaEJmQEvDD0ITrBJ0yefl0JmzKGtYhr7RAiq2BlE5ZjQ/XvX5zabz0TKo2s4mVLKHhIoFRw46W/ipcHyRCE8ZmkB/kpkdIvopdL9jJCiTD495KjSh7Q/PiQlNWIeh+xJfhSZyX4YWF4pKfaNz/Ry08EwvNDkdGa6/FlpAZ7BqzBYq/ZxRZrUHupw8CI8hEZZb1lBDDR8w6IqZOjbPW/Afi80jfnLumiAINnaffWUtNv87mHVIHPf/ZauMzUfevu7Tksm++wuj77530cP089d6nwwExiVWiBkPXPK5Fbvc/fAyUUa6pR83bD549DmQChPNV1Y8Hd+9KSOoPueJjYbX9vKqcbGjRhxE5IvUrRgxbYX1g8dNAP/PyC0dFPG46faipDKN86CcK4UZt5mI/OE8TNsqw7JiDEAN5/pkuk7HZmYGEVAsltGYjHNRsQIgwhuAoFiE2twHY1Q7SA2WMRQQGptskAC6Xt6IxrFNkNIGCwKkBx+GlUDOlcaD8L1PZdctNTLlvoRQjohZqXJQFH868tXFJ3zjvDP8w3/755/2LOs8blTMje3e3m8VC0P5TaN2WVOCmOo65WC0KQezfetTzf6QufJx3/XKVj533R7TuaxelcXi5p3/kTiFpVg977ij3hCbbzjvxNdScWNM0fF7E0mwbaPFc+EN5bEu5jRvXzZoAGY2loivlETC7OuOKeaYLJoIkuW4FH6+T8UTjQ6SvmXhsI757fnZXV964QO4bGr4D2FrzyyCmUtvRxSJ6F0HlPD73Yawh6KGdwciqgdwABG1vuPCHyBIY8e3mPUFhD5/4XXVBk2EHgz9Ol+AJlVRI7ICMANafCUPTV4OI6LvQ5OgIwB8EboU9DZUvnNx6HLXJuhSyqgOvdoOoxGVLGGkdhqVplaXpAIVARlAk7qIKAK6tzIWLhupivoAPg3gQmiLixQq/YMvh8e5hoh+AuBUaCuRiLA50NnGSNn0WeiSWkBnFC+ELiOVAJqY+RFmXkNE54XfLxDRY9DlqeJNY7VQKUGNFBLfbCkSHXMzKgTyCgC/I6IfooYaavjAwfMWlN6OKJ5w9sXvOjZPuOb8yUFStMmkVYvN/w5mHdII4ADMOuRDHZtv/9w5dMHFv/2n2CyJjwcFRwPAEXt/ggNGG3xkqUwPHHjGA2eZpnptgl1KTRjtnRkzBWKSfSnoABPmVUOulzeH0h2xsn/Y2K78SZRo6hPwj6D9jzsewNxgcOi2dEyJuCgCsVic+vrOF9nGBgwNPlMuFANfSviGaQEAKQcyFW8EMyDB0DGNYNsWygqUIJKJBFE8HkBKoDBcJstkJQSUa8UgS7ZhdoNLm4Hs+JGw0jH4RgDPDWBYJgT8WDk4tqUwcCVK3oYxwUAqxWXh2y0cs80XeseO6f/l4YevnTf/9p/YdXWn1tWl006QtNYW65WkeLGRvBVud+/C5mEPGzfwY+3lTV2GIDSPDB6uawnOEgpj4sOutAe9jm8fP/uReccdteZnF3/1vBv/+3gCgD/94MuPNcSCNmEYQkjEmAEiwDBgwcHoskBAQryyxeuhLaU2mS+myZcZwIrD9OzAlm1Ap99gLl+uAh9G6s7MpTC8x+9su+XC9/M6quE/i62eLL4diOhSAA8R0ePvZr2QIPwAWlCjhnePInTP2YddCrkPwPNEdOSbpn8HwHeIKB2q5R4A4ChoMjQBuufQQKVfbxEz90OL3kSEqwhNOhPQROZG6AxjJvwc9VjY0IRyj/DzEP45UyigCVH0qhalIlQURGNvWtcN/3LVZyNc/mnoctvjAcyCLi19HJqERdnQncPxlsLtD4Wvhcw8Mlx3z3B7H2fmSQAOhVY1vQLAgQD+Fu4LRHQ5tH9QjoiWANg9HHNvuJ9WaNsPDs+LhH5gI1Cxw4h+ryK7DC8c3yRoldf+aH/vBkSUCnsta6ihhvcYJ579vUvhBg/NmfvddxWbV33t4tcSDt1geTznnZeu4S2Qx1YQm1dO3HMgEZjPv5kwemV1rufwdxo/8+fGusP+fNL6ZxMH2BvEUQNWpmf1yJET1g5PnbNjS8poTFmW+8JLxcKa5U/dPnZ2v7Mh/wmraKSEHBJDVrlICP4fdpgWl20jJXWtOQXArTI/lMnnG+XgUNZTdgLxLCVQ6ukzk/E9mrw+GOXykCAQh49uTZYAEcFTDEsqKBVASgkmHWlLDhCUJFzXl41NMaOtmaAUfB+o71BuU9wBNTBzIgW4Xhm2bSDTLCH8p2Hy5JivZm+/xToQfnHRCmvs8yvbpgZD7eNpwMz+hhg7g0R2eVt7SZWK/upscui11tH5lzLTFl546vfGPPvFvuPtDcYe3O/JprRzyGmnXTOprrDl0PbRpY9tv1P/tYfc+8SBYkX/042bnacB4OcXffUao3/4fH6qI3fP2GuWmIL3SNkqvmqg3Ock05mCh+y6fnVlVz4GL9thF21TssS+aTMmbbAsOXEmFRCDAeFb5SDPhix6tsclO4ZJ6VLyXvKpH0RPvttrYdb0o1Ozph9di80fAnwk1VBDPAd9M/+OtgpvBjNf9J8fzrYBZnagfZM+7NgAncG6J5pARLuGPa4gol2gM4ZroLODu0GXV26CLjVdDS1csx8RXQdNVCJiFofOzr0GTYIug1YRvSucPwxNfOreNKYkKiWnUX9eRAgjJdCIMFWL3BhV8wPo7KMJHbYegiaEAbS4zAhocvpCuO04KoqohfDzZeH6N0GLVGSgSV0iPA5AZxujbOWPw2l7h8cbQJfifq/q2K6FFhLKhOe9B5pUjwvnW9C9jdUZ0+i4gDcS4SjTytDZxSwz/xDaH/LfwXkAPkNEX2Pmv7/j0jXUUMO/D4XnFKhg2PLFd174jVg995Iz3oshbRO4/y9bRWz2ldsDWC0Tlzy/VLe9A3WtV++a23z6CwDQ8IeluygWM/rXmmvip5ePF/mBXU0vl8vsnNn08pDTveL5/Dq7NbN3etSI/fZfu/A64qZ+MDELAuLxmK/yL+PYi5ah3200pu7/KICBNsn3mMU8ek0xEEjfqk87df2OAUUC0rJhWqkkE5EKFEuQ8iwQhntEYmBAFCdMCyCJwaFjlacCCEj9eFYacNwA5TIL0w7IMszBYt7MJISLwsBDHDTOUkqxhFgPtzwCkHvA8Z/2FVlr681YkkgWMqZreuVCon8gnip2XdaS32yWtmy6eaLksZcfc3J6p3+82h/zKV5e2TsNAFZI8YMtv9wSm3p8g2/Eyz8GgH4zc1AxkW0VXsFvIv/5G2fN/V50vslXV7iGcWiyuyNtbzc0Jt4tNy82qDRQrB8nYgbqYcR8Sp0igiIC5QIwAw+GEZRUIA2BmBEQDJDaHCCRjQENeULDGGfP7IjWYc5n9+n+0v8lNp8P4FOzph99+v0L767F5g8QW33PYg01/CdARBdDi6lcDE1s1kL7ER4BXT56JDS5SUCTMIGKL2IkYrMUOit3GXQ2bhia0O0PnYk7DLoXLyr1nIE3WmFI6IznRgA7QhOoqP/wD9BZuwnQZaARUaNwfR+Vhz8crvcitPJoJpwW2VFUq6VGhDMaR/X0LeH0L4bnoDmcnwhVX1dDk8i9ws9Rj+dE6CR9tfAUiOja8Bg2QHs2RucwskC5E8AxVeOICGxEqqvHyNC+ieeH5/deZu7Bv4Ewq3gogEuZ+UP91L2GGmqoYVtCoumai8k0T7KU853Zvz76lqDp8bXDjnzpvvMyR3zy6r7b8mu9I/O9/nDL7BFpyV65tz9j+kFcku+qDeXNl6QKbWeowF1aHLfnreTmLotR0S95mTyERSa7H/fiTV8xBvsOloXSRMcDY+Pa5bHdJ32sHEgFIyZQdgMbQjrFfF9rV1fn5u3HTkLaNinvldlnwIrfiURsX0BNwJDnwKYYfC8AEYFIIAh8SGkkN/SiUHY40RQvswqeLjWo3c1EIuUFAUO2+8gPE6ShY7NbCAxhSJ8V4Ctu6e+k7TrXQtRxEEcge/q6u/ciRVm/8MWnMk23tQ0ONq9bnsfd3/9uHACmnXb96sw0s/fvp52wJwB89dF7Kf7EY0+Oo+LExpTHnz/j5jfE5t9//4Rrc1J+zOv0NybqkzNLkBSohJFjb6hRJlL5gB8wA3GYadpiOO+i7HpuNsVFhqyL2wWUC0IJNylICniJgBNG4uqd0+mbCWJ/Rf69+5w999+KzWFW8VAAl96/8O5abP4Asc2SxVC5cTfoMsJt8yTU8DqIaDa0kufXoTNvP4cWTqoPSVDkE7gCupTnIFRI2gboDJkHXXo5AxVPxL9AK41ugu5RnBVux4O234i8PyNEHojVthnV2TUCcDU0sY0sJcrh8m+206guXY3IZA6aPAIV4vVH6DLOW8J9rQMwFsDd4f7SAD6Oii/i7dD9jn8GcAczv26oS0SFcBlm5kTV9DvDdYCKLUg16S5Di/msgCalUe9mNSGOjil6n4DuJ50I4MUwq11DDTVsxcj+cLZtCnu3nN+zyJ/3UC02b+Oob716tgv5TRPB1496dMSn5IB9vrKKxZs+dnTjiXw3Lb+7mPedONGUySvazAVrRMuoT9hFKfIl1+vzjY2byjuOU06fW79Dx2MbnnFmkENS7pJUViz+l91e6PlVT2Omy6uz/3szyVkwLTJ7+j2Pgo1obh6HwAVxCc2FAD3JrAIrn7hs2XYaBoC8HwQQAswgIibE41fD7T8ZfiwGIQHXdSGlZCklDTtA2gYcB3VuUeUSZU6llCwG3UGQ30mC/T6yEvUAEYgIuS6wiP1RDq4qjI9ZxwVeEGzMD6xrjTeMzeaH7jj0T79JJaeOMJRUB66x4/YLYoLoMILrdixv/EIp7f55wzFT77gJR70em7/99fMKbQ2O6GgcUMed+YtkNP03F5/y0LDyZ2STFvLK98xCyiyVAlcmYmIVNxj5TlWYNkI1+8OdK5Csb1662rS8QGFiOwe2wQQ2BAB4SihlsYBk2BRPjOfmhmS2PE4QXtnr7K/XYvNWjo9yGeo7YTp02dtPoW0EthqEXoBpZn70gx7LewEiOhU6YzTq/SLyzHwXKmWiLxDR3tAkCgDmoEIMtw//RoRFolJK6UJbORwATYaWQJdoHghtTj+biB6C7td7GppURkqi1X151SWXOejMWrXP4tyq/Vnh6839x9VZw/7wczNC/8RwmoTOoP4dmhACFULcAa12Wget8mpAZyoL0N6QaehezmVE9DMAv4ZWiC1BkzifiNYBOISZl4bnqLp8NMrSApoAxqAzjFGmsYxKj2c1ov/DFoQEvpqURiCiK6CJ/0DYYwkiGgNgkJlzb16+hhpq+HCAmA4oB8Wv1BktHrR41laDj/3wm3sRYD599k8WftBjeU/ws6e/Ce3FOwpn7Pm+xOaBzae/ITafuPj3E0jIPwJA3+LSnLbpQhQ2x/zhsrN9d3+TP3l0XnjSgmmXjGbY44aeWwkF1yFuXxLkjAPgkGks2bwkPWHEjwMhDmzuGx7+497ts0ct3vIIfDXNK/U+gdHbHwTf70UpV8+lvOzxfIVsgwALU/aXUZYJoFDIIZOpY2aG0MJtGO6fKzICyi26cE0LlmnAkIJcH5y2QBwAphnkjISEyKpicdPmoK9PoEk0k0rUgwTBy/X7jmEKFax2nN7HE4OF4aFM3XGOIWiyG1/bH491xHt6di4PlxodcD3ZtiHdvhfrkSlMkYNTB12ZLnDLUeK6nh9/f+qVy9k0fn3sWX+4LjlxqlN2KV7KU3DVWSeui6Uyh5x84U+WshlrKAd5DrIxUp4XLH++EIwYnY0ZXoDSsK+mGMUkDwfDvt0kVcnIpeOum02bKVuyhOsAUkAJgi+UIDfwIUobQfH8at6CU759bvLN/8/DZxx9i2XIo10v6L5v4d0TAOD4444dA8LgrbffUYvNH1J8ZAVu/gU8DeAGAM9/0AP5N5AGkImUJT+CuBJaCXT9BzUAZj6Rme8NP94J4CnoPkWCLhFdB01yIvLjQBObc6EJXh7AAmZ+CsD1qCiHzoe+7vLQ378MKt/DMrSQTrWNRrZqWJEwTpQxtKBJYPX3OI+KxyLC+TtBq65GIOjzWwdNCJ+B7mF8Ftru4uPQnoW7AVgATeQMhBYi0L2JCPdxNjR53A9aaOa30ETUhFaGPY6ITGj7jOfC9UxUiKIL3c8QoCLKk4EuS41KcKN+zAgGKgTax1ujNTwvcQAgoiQ0eTz2bZavoYYaPgQQQjwZk/EbPeW88kGP5d2CQBkGNb7zklstLoSOHcveacH3Cjfv9NkTb9rxyHsBoGlK4ve+I58yGgfXDP19GfU9jEl9m4fX9Q3mlVKKpKE43jbsNow1jF5/zLmxqUYulu3Oj+ou3v/apJanDATX97TVPQEAhvB+aVDxFxjZVgJAUKiDlRFoGQ+2Gxz88pdd/Oyz5JfLgO+XkclkwYzGsodEyfUQBAD5SvkxBSNtYXgwwJrVAuVBIJ8D9XYPwXUABNIKHKA46Dea/rRRI1qaWo3lgFcGVJkgU40ilcp42fapkPLpYuuoF0tm9tV8sfxqYMf2q8uVVo+75zd74mNjFjRaHLMNw2hW5rjDRd8MObR5117RAjIs6Qe0gCTag8DfUxKmnLD81dsnmrm+NMOUbLQEAR+Hnz1tSogvOR6e82wT1GvbY+s6Yp5jwHXJnRis/KHfpIKeRtstlxR8P6gb2Z5OJIXSsVlQADAHICjlQwppxMqi1QCA4PWKqDejgUgIIv2A+vjjjk0y+OtgHPNeXzs1/PvYZjOLzDwM4JEPehz/Jv4GvK7c+lFELzS5OfOdFnyfYEAT2LEALg2njYYmXS40afo+NKn6IXQGzwXwCwBg5oHQqqMduifxM9B9jbtCW3OocJvxcL2o1FKG+4i8FyMSGZHBABXrCAcV/0YFTbhUOK2zapvVxJKhg/5BAL6FSpZyHXTvIcJ1I3GcpQAOCfcVD6f/DVqwZjp0eerx0OqvC6CJ71UAHg73u0d4LD40GYyCyU+h/9cRgSyF25fhOPZGhZRTuF4cgMvMWQAgorsAdDDzfuE5/xIRfTn6jjBzgYj+AJ2RrKGGGj6k6D/rjq02Npdj9l8/6DG8xxiEfoD5nQ96IBEax1o/ALDdSse9VKSA9Ij0aAHQYMF2RUzIWFPjxQAAv/8mM9nUjClptztdPx8A7vnUpIFRXTBGdaFZsFVQ0jxMJFMXqYHO3WGm25AbVDAtUFtbHJ842AdBcUNGIPAlwQZ8tywtETOJUEoYAFkMDiTKFECajJILbOr3KOHJNJMTG7bzW3r7UmpMVsFOWn7ncKfTAeX6JAEhUr39iBkCvfX1bDr5ZbuWg894LE5dYbMVtE2wXl309Eoq+/944LY7+JRVV3d2DRQD+/n1a9sJywO2DkGC3FUqnsiWye+QuTtLLxb2jAt54F8O32nnMavKc9Zmxl/T0Ld2oUKwrl5krrqn6/qHiyowRiSyu5dW9Zd9n3wW6dTSdelguCAxcx/rxyum1p9ppeLx/NJ+ZF/qKSbTiYRIJATAT7oO701EJMhnoSQZTjmw4hz3eL17yo+vyQLA/1x2xYMMpE8/d95+AHDf43d/es7hX6bf3fcbBoBbf3NH4fjjjq3F5g85ttmexX8HRLQTgK8BMJj51A96PDW89wizt3MB5Jh5PhEtBzAKFc/CKLuXhiZI46AV5wrQfY8roYmehLbXuCxcZxGAKdCkKSonBTSpGwq3J6GJ0xZoFdNu6PLQalXUCBHB8qu2U+3LWIIu/WxEpURVAXgg3N/R4ThS4fJXQpfh3gd9c7CJmberOiedANYz815ENFS1nwXQiqkN4eeZ4TSFSnZwaXg8WVR8GyPhoMegS33H4o39iRGiLG4MgMfMmXBMPdA9lXUf4YcoNdRQw1tgxP137CR862sxI2asmHVYLTZvAzgJvzOhY3PnTTjqjhP6b13lxNERBIFvKqVMN2P1lRAk2+PpZ/t2eZyAkUXHWgOZ6YWwfw5gLV5aASWchJg6ZSa6Nl1GyaTFTuEBkLk/kukU3IKLdIOlAg9QSnE+XxQcxJHKSorF80DQD3gjAGsYEFlgiMGuCMoJiFIZtHkzkI2X0JiJo0QupJRiOB9kCwMEIc2BdBKI15WQ7ysi29zY2NsdJD1frk+klVGXvLd55eKSmcgctYlML2jtSHGggA0brjyENz001Fr4Q3O8O6Ukrb5u/Fd3AoBPP/ccGf19m23XWXHHrE/vd8avfjGUkSWzbfMyDAX2IybLXTP9/Q1QJpJmeWKd6y/jElS/LbjQ3gIgWOwss0e/uiWb9VwV7LfnkNufzCZ6x5Lb+dyri3bIt47raGocBUABPjmOR4LCWw3TZLNvfTkpGuNlv+CdeN1VGQD4+Y9+uIUFrL5pmbqLDjylFpu3UmzLZajvCuEN8mMAvgJgzke4BLSGKoTE41lUSiijslAJTd788H0vNEFKQV8jJzDz49BZxMXQ6qczoRVSFXRpavQkTUBbdEQksC78W4Amdlugex9boDNz1d9bH1o99eFwfSN8RfYZEfpR6f+JSlQJOqv4WWiyWkSFBM+FLpftCsfbQkQbw/UmQdt82OHnh8LjykET6YgoAjpjWUQlU1qEzmbeh0qm9I/QRPFnzPwZaFsOhMcTnZcIFB7zAHSfZIRPATi2RhRrqGHbgvH3X5NL/JhpmV8xhKx5MG4juAlHedDtRK8AAHE5JQBItgzPSXYOlhHASsjieq8X4HoCUhPqe2ZC2CdsaMfjAA5DKVgmisbzbm/vflTK582NG5WRG7oLTS06NmebBIRYE+TycIpl4SZSqbKdFF5vZ8EaXgcMrtyCsvNjDA4mgUIZvhAoxaBACPzAw/gJG9E84l6UiGHHLICkSibNIFNvDigXiGWAcq533Ma1z7Rs7kRfIiPXN7cDnktiIPdJL5v9rCEtuyU3WBy1bmXQsvG14ADniblpa8PVxdyInkVdrZx5xB711Rtu3QgAqeVLJtVt2RyzujcmAaCu1P+QGuzPty3dlBu5Zs34EaXeBiIJZTRhMEicOOiYRZYJ0eIJqYYKQ0Yei0cJed/k5mHab3KeAPy9YWjY3eGl4W/efsX8Qzqasj8Kn/uyW3I7PSeAKQRMiyEGhoiNJqcsCkMBlV+PzUZZHWoW1cwaUdy6sU2RRSJqJqJ9wx6qd4XwJnQxtKrl72s3pdsOmPlJZl4cvm+FVjgdQKUUlaEzY1dDl6CeBeBX4ep/RKXXcCa0+M2foH9xd4XOQv4MurwzH073UPFATEEL4nwTmvxFCqhF6GzhIICR4XYjf8Y8NMlzUSFaI8Lxvpl4xaFLY13okk9Z9epj5l2hBW9MVERwrgs/jw/PydHQD1LqoK09EB5DAO1juTn8XIImzY9CW41E5PQEZs4y83fDdaOmeBkeG6FSqls9PxZNYObnqlVZa6ihhq0HB910Q/sBN/zPvttddea7fgjr7/9FFuwvLnmD63ylfv9ejK+GDyduwlELb8JRiwHg5saTWu3B8l8pbg3KtBwr4oBisEz68f39+37alsg1+ydcd/mY07/+q3D1BxQH/5+964ySqlq6u27qOD15mIEh54wJBRUTiIIZc86K+kSfguk9VMwJwZwwx2dAzIo5gQiIIDkzOfZ07r6pvh/nNjMEFQQU+HqvNaunbzj33NR19qmqXYYtywRVOULTvMNkWXlfdvl0AHvA41lvmwlGRGLTtk3TQDxBOTC9LknxQyvcC3r8KhCpSEgeKArgNmJKMplQ1HgYeriU4/UjoLkZtm1D16NIpWrCHq+O0q7MqgbA3XZ1fcib21THcHsAVQVUlXLBvti68rpIPKL7oP+YZcbkjpEqGWa2pKl23fwuA/t0WJJYlm3Zqs9KZAFAx8aKJ7o0RtQ20VQnALh19LUnHvbjjK9LXTk5ua7s9oYpgeO6IRkJS0JkWTJINWYyZYTMaLwg5F7kj7t+zGLXmLZ6RSK/vsoAZ51w8R23Z198523PikumrHM0+ORUyipSbRnQbdttuSGBYatytp0jWyXtpPW2+bKbxs257KZx6cn2DHZR/L8KQyWiAyAG1VOYueof7k4GuzAcz3IIgvBoEKTLgFBxvRPiF/VdZj6NiNZACK48B5F/1xZiNnQ/CJJ2AUQ46Co0C7ekyZYCQZbkFt/TuXvpXMY00jmACyFCXCVsWj8xHYIqO8eYC0FaNQhPaTFEWOpQZl5ARI8BOMfp76D0JAkRVUGQ3j4QIavvQngW0/mEUwDc7BxnBQS5jDn9vRZCUOdL5zxWQqiy9oTwnn4KQUQ7QXhEi51zlZ028gDUAjiKmXc5EYwMMshgQxz05KOHGUgNcsu+J7+88JJM7lIGfxkX8NuUNIVtTpUHtehyt6d+FYyu/fSL10yaNwW5qpR/da93P+hxwWmHT5+2xvD4WjVx9nP1xT32g2W1tdyeNbJp90ZDTRJ5hecXxcOf1pK0Bv5AwFbBHNONvEi1bauyFPEVy5BlCcmEBUlWYFus5DJZsGPeeMr0HkBBAAAgAElEQVQXIz8sViExLIIMREML4XL1gcsl2YZhW4mUJDfUQlIUCz6fBSMpIy9PRiqlt0ol57jrKvbKlyQtmArV5srJotz6lU1Jf/vDXz/xpAVnPnXv8+yTTzKbPAuKWncf9PDxhzEA/PfxB6tcDQ3vH7N0SR/b6x37+V7Dfhz8/XtNQVg25+S6s2KYMivbdTMY8oBgfKVHJmW1SbFiye+yJfuStW1KV3YuW/ElAbSiqO1KAGuourqnnBMoMuvKHuQ2rqPIVdjJbGyKKFp+PiQzCVuTbbZi0Fx5kideU+CJHn3CDY9nbPNuhP9XnkUIRcvdmig6QiqeP9/yT9t53Ck/sMuBiF7f0WHCLBBg5mIIgRcLIjzybgivowXgOCLqC0F+khAEpxuEt7AbBFFyQdQ3rIMgigYEGWQIYiRBENK0BzOtDJqCIFwNLbqVDl3th+Z3uwLN3saWXsN0Pcd5TluAILSAyEc83hGNqXLWfwhgNBHdS0QaRO7ksRBEc6xDIr+DIIpJiJDXXGauBfCOc14+5xp94Ox3P4RwTmcIotgaglQOhPAouiGEjiTnOrkATIOo/1gE4AunHAaI6FAimkZEPX/vnmWQQQY7J3Qkv/XI/im7M1Fc1QRa1YRtss2vDO9N/9uj9PE3+7eesL369Xdi5fjX3+dJP+1Q2zyFRvEr6qjAK+qo4thyV/va38iKR8kot7Of8+5RHCw8q4Nd2DbnuPMb3+4Lcv9g6NlJl9muFiR1g+byy6lUKSSmrLbwKFT1cqOk1KmRUAB63CAw24m43QBfpMlXLMOyhHKqLLMrGbNg22w2xVJsy/OTIbsBiRSYJICIYMYTCAT6weWSYBiQamsrUFZue1UtVuJWZY1tqdRMyf5k3M42UnZBQ/28fMOysmIx9Fq5uDA3VIO2VUuqUyV5xx/228+vWsS1km2nVCn8qmpVjr5+yr333v3MI1oyr0frDmHjWFVP7KHa1h3XXnwWP1nNv3xtFrkXVSRjIV/u7B6WL3fsg4/VRgzp+4riHNPVsdDXVEhGvS/wA4A9VscS969IWjXlLrPzom5KBw4EWiskqaRmn0FaXikkj1vOzc2GGpBAbhdUySW5fc9IHs+nQF6r+kSbL56dcEt7ADh0wCNHHrL3w+8c3P/RjG3ehfH/iiwys7E9iSIRdSKiC3ey/MWOAA50SgVsC1JoLl2wy4CI3oUgMdG/6XgE4Xm7EIIUWhAhqOlBzwiIovFuAOMgch/LnNp/n2PD8EoTgix9AeAiNNcZDDjr017BNMncF8K7mUYlNlQZBYQXU0Jz6GYKzSUqNIj8wAoI0pl+jrsBGAtgEIBpzJwH4GMAd0HkMq6AIL4BCCJ7KhE9C1E+oxbALRD34ADn+hwCEcpqAfiemcshJNgvhiB9BBEmGwHQyMzt0SyIk0SzVxEQtR3vhiDFQQjRIECE/7YDcAQyyCCDXQozLrnG+OLCi7ebbZ40DZ0mTcPZk6ZhZ7LNnQEcuC2E8YxPFzIYFpon+HYZrPrv/74q8hYODccjkb/jeBfibWpzuPecPf/tPrvvYP0zVWar8ODi+7TurWsjJhAJ+0cY3bxd6ySXOyTVjLNWL1yKVKqsrHugBHW1PwRCjXZ+NEmmKsMoam1C8ypUtuwLXlXxkBIIZMuyLEPT/ACQlYhaxVZC9kWbCGqeCwnXQFvxxgEFiq5Dql5QRUbEC123kEzA21iHTqS09bZrI8Hr8oII+aE6nVIp/YpvnkHblb9ode6s0bH62EJ3uLGpPYh6NhAirQ7uiiSN01kZ0jTo0JdfOmtsXm5JxwXBrMDd7Mv/V1RJLGq7bEF9ZevOgR+77BUH6Ox555/+7EkF1D/cWF+d8nhvMiXpWGY+YNnVJ1EXVPSK1taVmWRbltv68YyH7lzbdfVvt/rjxsVcVJSdo/moOCp3WZobiiSjDQ0XvfB4FyAfMJmRqI+AjCQkSYakAi71UgCvAJwEzCDABwEAAwUFBjruX+/N2OZdGLttGCoRPQ4xSD9sR+UXOp63YyE8KszMF+2I42wNiCgbou7dcmb+vTo3uy2I6BOIOoFJZs79m455JEQ+HgGY5ZRp2BvAdRB1F39zNmXn/8chCOVsCLEYi5kDRNQIQeBkAIshvG0GmoVqohCDhBkADocgX99AEKR0aCogCKQBIBcbFrVvuQ1DkLMANoTlLNecz8kAukDkZA6CIHdAczisBkFyCaLkxjHMvJKIekGE1ZZBENiwk+8JIpoDoFeLc0oT2eXOMaogSOhlAF5Bc84nAPzAzCOcdkZBlN34ipnPJqIvIcjxAGaOtTwpIhoEUWNxIjOvRQYZZPCPwH/vCY9LoE4G6Uckxr6/Q2zzpGl4EMAIdeYn38n1ZXzp0xf947Z5VROyIaI3VnbK+d0adLstau/87EvFtgexLSXzxx++w23zhXibAByh2pTMswIAYdYdytDYKWs+PcSQrMtS5HrW7Uu+s3hRLth028bypb91zyl8ZOmQwS8jWTu7aG2oB8hj/Xhgt0DbRcFGqJKWnVwlh/WcNZxb3AGWaUBWVFgpIFoXdUlaykjpM+yc4uFgNkHSDLB9KCyb21GQJDuKNVagHvVBQDdzW+WXyI1uDRZiCKTI9huGpFg2ar56jaW+h0cOjtYFGmUNkeJiDJg/HTmmba1xF0fcXkXTVq9NFKjaAwdVr+leHwlj+pC9h6vJePbQeUsRjSSUVb335qZISDtp6S+mR1UooZhr5UDuMX2efmnl+1eO7WUq8qqeNYvK/Gz7Y2SGu7/ySSsAmHb8GXPaeKVeCdvGYk2LhtuX+ggqiquqlsfzCgcxp6ogNTZZUrur9GTyFU88bsDtQYyzwEn69t9PXn80ADw/dOIo6efAJNmWvjojcv7Z9xQ99qWuGG07J4sHnN54yga2+e1j7htkSXSqwjzxhGljM7Z5J8Xu7lnc0XUknwBwB8TA/UQiKvqT7TcBEd1GRMuIqN326BAzh5h5yY4iikR0NRFdvSPa3lYQUR0EUazAhoqcOxSOsEoUwCkQBA/MPBvApRDk6nMALzt96gURflnh/B9Kl3+AUP6UIchXKwiy9ouzLSBIVwDAUDSTPQVCcXQtmr2U09Ecbpr2Iuot1qdFdNY5340W28UhwkAlpw+3OsuPgwi3TedntiSKinOsE5l5pXP+i9BcGsQG8C0RLSWi6RB5nU1OuxqaSawG4XnVIMR03m1xHNXp0z4tPPm/YUPhnZTTTjqctiUM7IKe8gwy2P1AbgZvc6rEn+BhAHe4Vs07XKtZe+LTFz+z1bb5wNvOv+2gm89dduDt520X29wpB6FOOVi2o4jidC9dPd1L/zgp3hwab/6kIUtyDYqwsjo/O+dvsc3PYBQ/g1Ef59mBCINPIVA3AHijw/Cv+KvApfK33qak7v28tIv+SKlJ+cXd+/UJF7Wa5Amvq/Bqdq9w50D9jwd2E7bZ51oNSZLjckBnzRWAaUSganNQU7kakgX4sv2SRFlWTvHQvrnfmB2kmZG8eL1dZNb9G+HGtTFTtZtsPwKK9lE7yXShbSe5hi3dkAF/Iqk3qRqXSwqqasrtvsXtDH/9unUpSFDiMSN/3UpdXvE96tf9Gi826gYe+OtcpX0gPz9fzrpVj0ctzpVO2bu+rrh3XUj1ql5PG4l45M9faYMq1pjZiqJosiplm+4T+zz90koAOPqh+xbJ4UifyqTkb0qZdkPK/u69O05a+s7Y86fr7Uv/G5IpHPX6UWBaSnE4xSVNYXJJkmZxeA7YrcEszAMAThmsaS5NMw0FCVWSk8p+U7u+SADg/iX3N7IlFU5JsK/b/mx8V7qAJvd53b+5ewUgCVo/BslgJ8SOJlP/GJh59La2QUQyAD8zh5zvJRDFxz9g5gQzrwKwioiCAEqd/KytxdEQ3pCLIAbRfxuIaAiA77bS8/pfCO/TgzumV38NDoFIP8+hf0Ctdg4EYVxKRI9CCMI0QdQvXA3gRWZOEtFnEKSrFwR5qSeithAKqr0giNWvAPpDELAeEAQuXYsxXdtRggi9PAyCPKZhQ4Rmpq9F2rMotfh/LUTIZh8IMqYC+A8EER0HodCaJpcqRLmQFwGcBhECOgZCstyGEOnJhwhT7UZEr0LkHtYzc28imguhgPo2gPS6sdhQmKcKwru4LwQBjgHrFYh9TskOy9luRvreMvNSCPGbNC4H0NV5LzeAQ95nb7w8gwwy+HsRHff2edvaxqRpkAH4rzpWhOFPejZYAts+APm5b111vMRXHYtVAFY9/mWHINlW6aUPn7H1ttm0TvDD3TbMxt9um8e1pyHLc/Hd1HlbbZt1AE/voG79NUyeRZCFPYrq4TqMGfq32uYE6XO8cEXr7Yalt/z20KMkSz+z3dd2NYSOKfvcWJ1ySW/8ctLByQO++OVbdzKe026tNmBla29KW91QXpxs3Ra2eY3ql3oBkmXEPPOgeveEKcmFTZFeI2d/FH99n8P0ZFEHzQxG7SHR6Vos3yXp/oC7MeU6DEZkaK5XQTwS5mROKxuJ5MjimkYl19RgEisu1UJ9Ik7w5MuSR4KdX7LCFQl3HkTcJ1JZwUVkq0Z+7vFmab9BaNttnJmILgrprHdtjCiseNUar6wlA+4XKRY6PZ5XdFeysu7fqUh4Ri/LtHNCtfsDyFmTSnw847y+3VYtf+zVC+74pNRdb9Qe88HHvd8aPWZu0DR/VXvEP7dqwk8ocam1LZljV9q6uzVk1HRzw6yNVnlZ9RVJ8r6N7uK+tpGYTrKE0bf/dyoA39OXXF3FbOhNRWYt2fh+zPKzGQBObThvA9sc9DaOJnDXn759r2bj+zPqvbEzIKKlMtiJsduSxT8DEeVCqDt+9AebtQbQj4i+dwjjpQCuBPA1gOPTGzHz+9vQlT0AXMPM929DG1sNIjoXYuD/JkQe2pbith3Sob8IhyTeB5H/pgB4g5nP/bv7wcxMRKsgDPaxEB7GkyBCQgcAGExEJwDoDUH+DAjC1AUi5JQgQlndzvYxiLzEgPPHEAOBpRBEsqXKafr/BITXzwPhjeyIZhXVqNOeB4Kspg02AdCZ+QEiuhDN3sdHIUJC94V41j9m5iIAcMJl92bmK5yJEhnAURD5mek+ZRHRGRAkcQxELcdKiHzIaojajCMhiG8bpw9hIspxtv+lxeU9EqIsyWsQ9Sk3ABG9B+BMAPnM/OnG6zPIIINdBzn3nZzLhEGha//3R7a5lGH3mTRN+v6qYxFaF152qT+74MrwurJRwIBT0xuNnnzqX7bNh7r36fOFOfea7//77N9qm69rT+dGZdzdoXGrbfNEOBNtOwUmz6JwIvxIJBo9y+NyKUkk3+h92wnn/t3dmCiP4HPKnl+lmqmbky75OJdhdSNJPjWU569LynUDegdCgy//32end0jFu5psRryA6YsXu9b6fHt0iKxeXKHmEYJSAj7DDV9gb1hmVK4JuYJuI/Bd+y6BbtVreb63UM8xZ632S9xzYVM3u5FKJHi9ALGddLOUlCkRsGyvKVFWPJqqKEmVdWwykxJ5s63hDVUxLFrssgPZnqrsnC7ZqST72EZOop50KPqksaM/ueHSs0rdBSW2FEvgi6NPmXjMh28Mt2XsYYVTp3epqXxn37c+LgSAdSOHN8rA3lkff3pF9THDQ/C4ZdNrHJUfjB1ZXlYpZ+kKJBe805688IxfThv4zlHPfXll3rfRUUFTqSR3tJvqDlVH2rWZsa6174CyQQVudWa4TX55SB9698Twe6++t6S6smoMbGu9ba71yMfKpN7nT4ZfzfYGntr42k/pdN97ycbUmWdiL+2KpvEZ27wLY3cPQ10PIsohopud3CVAlDGY4njXNrd9WnTjOAB7E9E8CELihvDmbBc4qprb1Rg5iqjPEdFVf7DZNwCWAFjobP/Klgj1MPODzLxTeBWJaBqEJ+pyCA8YQ4QF/5NIe13PYeYGJ0S1A4Toy/kQs20eiPIUaQEXGeJd9ECQN4LwtKVz9Wxn2TIA5c6yNDlsAPCs005aRbUe4tkFmlVQvU77lrPvBvfaIYATnXVhiEmRwWjOlzzM8fABgkheQET7APjZWWZCeFIfhiCDaXXXryEUTQMQpTAIggxfCyHkU+b0Of1b9A1EKRGjhUhTLoC+AG7d2GPslPAYBlF+4xUi6oEMMshgl8E+j99d2P/R8bfu8ehtgwDAZOt109an5N5/6mZt86RpIENNliZ8keNiWmOfSdMwT/HkXOx3F7qzs3OO3F79uvWGK3h7E8XPvETve+mV97106e9tYxG+IQtLSMHCz7xE0730ynTvn9vmYXG+fVh857DNmDxrWjQajahQz8/z56q6YXCuJ/cfs81sWqouK3acpEdkls6fes4BDZ+dedDHV1eV9fTlGrekCuzTJVkugERqaUNduFVTNXeIrdRjhiVDcUlqIOHJdSUIegxg02u5bVNze1Hbpoe9rLQ35enx+XXK3tUf5w1Fo9FKQoLiCJY3IFr/bAqJBMdXSCE5xW3XLqlLdepUWrbH3pDcaqJrZVQO6uztGKzx+JMRq21jg5Sdiklh00RHllCYsuyzn5/aWO/u9WCXikapXWNjbO9Fn4/1h2v2ah2LKU3BJszp3eWop277dzkAuIBHPcAFPHI4rQ5Ivza4PWDZa+TXRevCh+7xcMywqyOqVeli9oLxnTduF6R8gUCtJXeKGB6KBo0eyWTkolAq/EU8qNcqa+OcZJkA4NMeld/NOQRnt6+qMb6++Kr1tY5nFeX0fa199s0XT7h+A9v8UsfJVXku77CcQPZym62pj+RMyNjmXRi7JVl0yI+PiDY+vxSaB+DvQpTS+O4PmvoPhLrj/hBiOX6IgfRr27fHWw4ies0ZpP/e+sMgSiccCOG5+T3UQpCY2yE8XScAiBDR4dtBSXWHgYgOJKJPiKgBIlc0HVqpA3iCmZf/U31jZh3Ancz8ADOXEdHTRDQJwKEQ5OkmAAcycw9HDTUGQeA+hwjrTMfsr4IIFZUhiJTp7H8imtU+bQhPYT4z/8s5RjqXsDWEx65lWQ3V2adlWGr6XSBn3/Txspy2U84+H0FMkmQ7EwqrIMjpB8x8uPN9NYA+zDyOmTsC6MDMjwI4xjlOLYCpEM9aL2YuA3AcM3dz+iATUQwiPLYTRD7w7c51/dbpT4CINn6mf25xfb6H8LxmkEEGOyEmTQNNmgbfpGnNYw8GGxKUBMAGACiS8hpB/qHJrv1d2yxHEv9BzKxSdfeJiXhTd5fq9seDleFQTcXDf8d5bA7Ptyt57blOJb9rm18qpcOaNPSDiNY4+ve26+ZFbU4pOudX4/YUENehnAAgMt1LB0737ry2GZNnHYjJsz7F5FkNCT1xuCzLMgAYbOhen3cCxgz8x2zzix0viCm53tse6jL6rvG9ryg7ZcJVTz92/riHj3w7NHjgd5E6Hwevbq1kDXn9zFF9Jlw9uk1NIJnqhJCrnV72OczaWYae0OMmgMqGVQWxYEUgWi/FV63myJp1ptvkGi2uHw3NPQSqClk1bJl0j+LNySvr2+9f49786rDW0WJXx9og676cNsVN9aq/srqhe1OIC+KMzvW2qimabZu2XBKsQff6GipsqDMaCVjetp0i+wOePrmS1HnVMm5bU+nNs1KumKLpreuqbXenktnugN+tKZT97b1XkQFU6IBeCVQf/vLHQ7SGyCpvlb5M/bHXgGcwalz/N9/tWHVwcbcjLp3y9IGLfxqRorBJkVAtFP/U+mR9NBHO23P0g1PKCuOx4yYPu659IOClRP9WyrUfTYxJkFq7LLubOxx8wgTdDgA3Tbr/2/cHlLh/7V6cS6/fv4Ft1kn/2dBtsykZKWeLZ1iylbHNuzB21zBUD4QwxwqIQScg8ruehvCIXAfgZGZ+/g/aWAgRxncIBEm8HuJ6BSBUFS/Z3p128r2OA/AAgPEQg/0UM5c461901o/EpiqWIKJ+EAXhP4AIhax1wm1DECSgGKI0QcpR7NxYLVMG8D8AKhFNYuabtvc5bguI6FIIr11LVU8TgoCMYOY5f6HNrttCMB1F3KEATneEhdjxbn0DQbCqIZ7Bz9IF5B2l0DRRBMTExTcQAjj3AZgAUXvRhCBaCyCe5Y8hnokiNE/0JJzPIRBeQwWCyJkQuYYKmvMdN56dVjf6lJw2VDR7PMk5P8NZ/yjEeyEB0Jxz6QRBKhUiIsdbnp5l/ADCQ9kLgji6IbyAnVpsk0CzgE1vZ/u7sCHxizvnsEFoGjMf46jRXsvM5yODDDLYmeGFsM1LADwDABdXUSGgPf1l+34XnPHsF9fd0Pn5k6873vP877aQjC22VXd7Ag9R4fFLkbpL8yWXAjsZyPEWXA4xybtd8byXXlVV73EpQ3rg6HefGP/15Wc1pGKcOrNS2OZn2ua+mCoMHKeF4yMgfnc3wEultDcYdyb8+MATxLG/2Fm1x9DAHAChzzw/A45tHhbnVIc1iLvXIGAAsgxA1TTYBmSJzQ8ByNO9dNWwOO9seYnXQUTP2AAkj+ZBNBE1WUY8oAX2wJiBlVvb5LAp87tOv6DfX7bNnjuufpCAg4rV3NNXjRu/5Nms03jEcS/0qDfpm6w8t/ubkmjFZXN9j1/4UvwzXHPaAgD437sre9lEsXnLbK062w3v9wsmvv3ssrJvDmib99CYbvcVF8gTqinrhXwPmd3qyo1VTesWSMXtVlRL0o8w0eCvCxZ5zYRUm1MIcCoBAIasHuxm23KDlVWNTcmOlqUPqF+e3T/cqJQnQ7onZWuaBEqYhGR9EGQZ6BuLqFFfFr5csVY5prYM/Rb+IhXHQlY+oK5bV5MMgCUfbBpY19CvQjWNla1zqODbxS8HgNa6sKPKbyec2CvH5e5kWrLdYEIJHX8sZU+dxkdcOmW9bW48uM3gRALdQ1U4xpvo7q6Ixn8F0O/C8c8yAKiRVNzQPRpZRJJEvdpMr99rWSBroke319tmTkVjtqpqfOq1H7a8/hesGnvM/5RXjixC7rUnmWdmbPMujt3Sswgx0M2FI/PveEMegQizGwZgHwgPBpz1U4ho5kZtLEivhhiIJyF+CJMAphPRIicMcntimHO8yyESfj0Q3px0UvBTEAP/X39n/wUQ5zmNmRdCDPxbQQzEAwD2BlBCRD2JKARHqWojpPPfNqco+Y+BiAYDuDH91flkiDDLAEQ5iq1tczSAmUT02DZ0rTvEs7RXi2Vj0VyD8DzH05gmigSRg3cPRNmHORCEcm8Ib/BQiLIa+RADKw0i//FkiFzDlqp+FoBbiOgbCM/eIAAfQhAtDYIopgVx0qTS3Kj/BoRXM/1/2ruoMnM2RI6gy2lrPoTiqwzxHpzg9PdxCPGYOQDiRHRlunFHWGa4cz4axPP775YdYOZ8Zg4ws5+ZKyEmRFpDEMf0NVMhyqFsHIbqAfAWRE7odqvTlkEGGewQJCBsc2sAeGr83V44tlmV3CM9snefeaHv1tvmB+96a8qke6ZtYJtNWapJwUYKJiU4NiRlI8lG3IZhJWFF3nz+ovsWPXvuPdvVNiuqfzh7c4hkvqx84bxZ6FrsYR9lf+wVttmSQ8/Ikagum9L8ze0fD2AOA4/IwOtHx3jhzckeNja1zcU3jaCe64CmHEDLA9gEkNLjSLLJJsOEsM0dtue5bTMmzxoM4ArnW3pMyX6PP5ql+QMQughbhaFTfh3dwTRmDntq7gN/tVsM7gSgreQolAOAIlljQ668QDzUJe4q8F2IeTc/gHk3LwCAiZ9X0FwzOvKphop7vCzdm7O8ak7HmlqFGYMP/Lb8gMsX/Tq0S9kvL8ouNT8/WO09qWyudkCWt2fESp6M/Nz2yM8vsuNR5CR1IJEyvOS95ZALrvvm+f0OXTkgXHGwNPujTy4um3drjsxuj0tVanKyrNrS9lqdhOTa7Dz0rVhpXj/3a+y5cglimgZPJKT3WvL9F3Gb8XXvvnqNYckWwFmyorT/8ONsN/CcopGrdUNE6bqucUGiMHBk0g05DiQSwAkuj+vwiCY97tVD83vz1/NiNsfXnTzitPS1OOLSKbMZGA4ZRYFi1pJs6rZsjm95DS+/9878O84dl3Xf0df4Jw+4qDInpZ7CWVmt4/nZHQEgHFxO37lL1e/tggQ2wgttHvMQ4m9JWanBL7gfKt94fQa7FnZLzyIz245YR/o7kyjWXgWRQ9Vuo1prR0KEuO3DzOkcrHQ4CUGUPbiRmZ8AACIqhRAlydlefXaUVqshvDSeFm0TmpUjrwIwDcAoIprJzPu1bMMZSL/UYlETgBgzp4ioGGKwXwkhqhJHc3mFljgJgqT8tJ1ObXvhA2CTYsbp3L6ZEER6azEXQghmwZ9t+Htg5mM3em7AzBcQUROEt4uB9YTnbAiCKAP4FMB7zv9vQJCo3hDPgBeC5KXvjcv5bDm58z4zn0xEayEI5BsAzoMgbwdBPD8LIQZm2c55dsam77wKIbIENJftkABIDokeAHGdDYi8wfT+yyEIog/ADwBGO322IEJCW14jJqIKAHXMPAgbgYjOhvCmv8jM1wA4CyKn8f4W+/8MkYu5MdxOnzVsfvIjgwwy2Elw1bGwJ03Dett88YTr40+Nv/sjAGtJpq+jVrDd6+eNWG+bScsaSbLin3Tfe22uGntMBQCElWRHVTeR0BMUS6ReSCXW3fDQ1Yc9AQCvXPhAqaF6l0pIbTfb/NjAniVZRqxBjsCdReytf+XDQHZIh1rF6XI/cFnKlXo89LGLtaNfyVZnnhEyNrDNlyzaxDYHAUSZZ+nf9aeSVBnmchK1UgJdbAXxqAnZByh28/ZUE8DINhEUwOXa2Wzzx9j0t5ds2/YmEZ/plfxbbZstYG4jUQXL8ry/2ikTOK6Dlrf3irH/XW+b33vn/AuOHzWl6ev+g6+dMfZCBoAL8bYK4PSqm56fnCBNKjvtkk887dpO83cIeqPBPlPv2YNSo97RekzfK79alvze3muXWPGadbB9Y3kAACAASURBVHJZXiFirhxN83iQHw1L1QDiXjcvN+UPyvYuPXn4BdeVZ3fvmh9YsvCNOQ3hs0tb73HIj8z7L99zvxya89PCmmgs1yVp2WYoXJ5tyV2q8wuUmV37IBYKo31tGXcPBrU9FfVAedEcmPGoEpNgzQJIdSny7OvGPtbB7RlguNzkDgUNv1/tZwY8ci3HkPK75ufWe2dLgK8wkvzunvk9R/vVlH1p34pNSrYceekUnjrxggoJVHf2I5M2sc03ffLoRTbjTimSevyOk/89HhvZ5kBuV05GZ85n067YeF9bttyWS7bhY0lP7vBSORnsYOyWZBFYT5xARLcA6M3MJ7VY3bTR5scC6M7MPzuD+kaIQWg63LEA4uV4wmm7nIiGorn+3fbAoxC5YkGIAXiaGNQA8BPRnRDhp2mBkr7pHZ0+r3D6yADczDyJmW0AKSLKgsiXm8/Mjzi5nE8COANAaYs+pAViugOoJKKHmHkT9cl/CDMgQoIJzWGVgCA4c9EstLLFYOafIPI7twktiWKLZddsZlMFzeGW+zLzLBJ1B2Vn3QKI0Mt+aPbwxSDCiPMhSCMBsJj5ZKfNtNopQQjdpCCen3yI8NaTIHIK98CGobst3/20OFA6jzEOMdu9L8S17Q8R/pqud3glgGxmbgDwDgA42kgSM3tbnrDzbFYCmM3Mv5un4xzb5Vw7BnAfEb3ohJieyczDNrcTMweJaDUEEd5EljuDDDLYuXDVsUKJedRTr99DpLR5a8L1ZwJCPQ4b22YrfrRtoPvV1x9fMWkaaIGxLJir+12BhnI7L5aUwgFPK0uR19vmM565pvyV8+8/FBJtN9tsMp6oB2t5djQIye9rXLTY5RifGsXt8r/ZxnenlesfKYVilhY1JDLM9bb554KDqTY+a4UG45gEzEGQ4D8mypOYZ9kAUt/vSfmQcJOrA+YfOJcfgZc0AI9GgXOiMkoNN5AVA2SAW0XwEEBdkTLWfeSjR0fEdhrbPAtC12ED2xw1w8oS88e5JowfB2PgVjX41QX9t9k2mzdNYmxmXDD17Qs2b5ttS0l06ixRgbbv/NMOmnfh1wu/8UaSsuyz1aW31y2mGl5n+X1ds3SWG/M78EuVtTEPqXFU1wairUtciIdJU9z2yn3bngwAqUAOuZJJiyNhSiyY90LuPnvqHrL9fb+fnm/Kse9q1fjQDotqVrld2h5Ry4SlZPGCvNbWyPpqRQ+GqUIBGJqWb+hcBEiVspwin8/Iysv1a/U1+/6cMud2Vdz9YcvlRllTQG6VLVcq1tWy4sve65WXGwC8Q/Qq5UpNsBl0x+rHN8h3/eTx8wlRq9JlKN+MuHHKqZu5JmBABbHEYkwBR8TmvqfG3/72k+NvH0KgQRdPuGmzQlTnrftX8CXXE+UUlkr9pjdjm3dx0N9fju7vhVPOIBeCDNY6y3Ihasb1APATM7/SYvt5EGSpJXQAZczcZwf2cyGEVzENq8WnBuERbQ0xeHdB5H3ZEMqRDEFokxACPEUAFjLz3k7bBBHat4qZfyWi4yDCIIuxeW9MhXPcdhAEc9/tdqLbACL6HiKk5G2IGS4LIsfyUwixlZ1HNhzrr7ufmSMtlr0MEW5cw8z9iOh1CM/fQAiPIkPk84yDGAC1adGk7ayXAUyGCP2RIDxufggitxTNYTfp/ENAPMMmBHl8B5vmMLYk4JtblvZ0RtCsrvo6M5/nnNcnTt8ea1lKhogeBnAhAIOZN8mz/SMQ0Qrn/Cem82eJqCeAEmb+cjPbAUDBzvYcZJBBBpvilCnT1kmSmgWkur923vG1AHDuC1/nqrLrtG6ePXqoivunq47Fets8aRrmhVPR7mxZ0BrWASCEydITRkPZpKsO3WG2+TUvrfAAbQwAbr8GCYqlR+OwAEsFtKjP93VKMVorCuYGGnSXD1i5DrAl4NwcuNxZapuAadYmQ65UONU6UKRUR2afXp86EABm7E9kxXAcyVi1/xz+dbqXTjSBB2JAThagGWj+0Xdm4dbogGyqaCsz5h8V3jlsMybPmgmgK4RY2zEArCaz/n+LUj9+qkjKBwOvv2Wn+k2+/K0PqGcq5L/ijDPW2+aRI058uWzYqOFmnz5li4b13fPcb9/+MNQUlXplle/bIWZ4Vqk+W1qcs2RRbul1ik9/cmm7fq07BOughEOIy5q9sENXhj9L7jn7s5tL4B5vRMNSY1Gb6uhnX+Tq3Xsq++d5Vzaw3dX056JOCtqtkqyoZXXYa8kCXUmaZtmgQScdPPfrqd2aglqNL4vrDVDYZOTHa/S2hSWaZRoINTVgAICfNa8+t99Bmqt2HkooabsCbaSctWXhSLatkNvjVSR+4tCJL18NAB2ViZ+4c0we+0LjQ+ePvPvj9Pl+cufox+GJn4uErR9x40ub5Nn+EZ4cf9taBhVJwMSLJ/znJgB46Pq79gQj58p7blhvm6dqz6+wvdTGTpoIDUXBhR9csFM9BxlsOXbXnMWWOB/Av9JE0cEECKXFiwA8SUQ/OQqqEyEIVFol0oQgXz8B6EhEv+3AflZC2IQEmr08MsRg3YDIm6xyBudfON//BeFBSouaTIUoqJ5uB8B6T005gK+c/LYXIYhgusj7xqiHCJmxIDyMZUR07XY+360GMx/g5LddDFFywYAID37j7yAIRLRxyO6f4V4AvxDRES2WLQUwhZnTs6b9Ie6lC+KeEQTZfxOCAKXvj+78nQcR9nMSmr2K7SHCohUIgw00v9tx51ODGG88ihYeSuevpafRbHFMDc3lPeogxi2zIZRadYgwWgAAMx8BISBV6pBkEFE2xPvUgK0ouusIGcE5l4s3Elq6FcAdLRV7mbkLmutD7hyDpwwyyOAPYUvG2SbH/pUmigAApgkxPXJHY6TyIpTNefKx8Xf/5CioPmEiVaKmDMNPGiRXlhUjPfxcwbzFz3Yu61j0vxd2mG1WgDpbSLUmNJfMWiIhyYBMgFYLGBPOP3TI2HGnLTu7LHVeGPjiN2BIFvCvfKAoipSRMuqTFqemmhKtg7nh7PygH5jXVaF81Vp89YKXpoeB56JAkQRoScBKz+a5mncJEuFFtmBpgUDNzAMGlc0ePnT0jjr3LcaYgfthzMB8jBl4CsRks56jFNw4+Mbb3/g7iOKqpk3Saf4Q3rVrJv+0sOyXEZf899D0si69uy09pHLBQ4uG9d0TAGw9p5M2v2ZI4sVlWoUpJ+cucknzNW+r3k3lb49cvLh1q/oyq1ZRscyUDPJ4dNk2ziuorfiEQ/Zo2TBleL1WqGOXzj1OOHm5r3MHZZ7X39HFTPnxkLT/qkpus3hhYti6ZejuJm1h797eeLj+0WUFrbRqjx9mU8QqsG2rYzxi7EGkeKIhhOJxU3VLlgnAbZOmsmlrwUjSDS2o6LodcvvnQvKutW1dJ8K36fNabf77iLEvBl9giTo8MPtuAoAZL07O9hZ3L0BtdhC/WN9v6XW74sZWjm1WO0jAxWmiCACplH59PJW6475rJqy3zcfr53bRbdM2/Ayq3PYorgz+Oey2YahpMPPXm1n8LYBz0eyl6Q6Rz5iN5mLlgAgB7AvgK4jB93bLg9gMnmfmYQ4pm4ANvT6S81dIRPtB1LIjCGLYCcBTzHzzFhxDhgg7pY2WpWFBkIBBDsG8nYjOgggxcWPnQnegOdx4R4OIvAAOJKIkM3+zhbsthZhsiKYXMPNtLdq8GkBbNOfcuSFyD0+GuEcmRGmXJRBF798D8BDEs5D20r0NoSq4p/O95QSAjGbPIiAIVYcW/0tonpigFvuk/58N4YWcBzFeeRLCk7sUwG0AQkS0rxPOC4jJivX3hJlDTumQG5h5xZ9eLXFNvgKwHxHdycx52DDPBxBquO1+Z3LAaulxzCCDDHZevHneiV9vvKzA1XZ2Qo+eL1lgNC2Qda2wuw27ilnP1tmGW5GYjCRcUKOu0j372qkFX9kyJNAm+ezbDRLwyAlxfuVdLz1iN5jnpxSZ3HkBIGEjZEUkRYUkEXea7qX9coCHcwBaAyxqDXTSgSkj46E/tc1kQJYc0T0F642yrGO9sEDaNu8/IiZs8+zhQ0cb8eh+wE5XTkNMWI4Z+LfY5lVN8AE4aFUTYp1ysEW2OZ7S16biqXDKtNLlqjD5vjvX2+Z9Djzpaj9R23wXzC4dirWsnyLSt/33+Z8nK+ukioYg3KZt0NrGp3JKSZILC44tW7Z0ar+m8EPLdFPuBzOr+7IV+LJt57f9ZWXPfBoM9j0ox4991i7RSufNtX7JaiV7sjU5r3GtylHGoj79UVHY2jrzlw86eGJJmHHd7mTF5exYnJNythz0aBJkCTZIlgNuWhQJsl5YMme/uiXveF158/xr19kyal5cHpVfbxrRd5kuybenNITu+OLerjcdNm45AASLst8AgGv2FrUQB509JvTji5MfC/TodMPgux7aItt8xY35XwHh/a64ke545E7Ox0a22aVpTzBQOOaeG9bb5s+UN0lTVNgGm6fPPWeLJ4wz2Pnw/yEMNRfA6wBeS5fKcMRuhkLkR5wBIZ6iYMOSDDaE52omBMHquzliQkRuiPp3bSByxrK3dGDcoo0fIAb7r0MQ1PMhvDkuCMKwAoKwXubskh7gh51+HgTgvxACKrekiSMRvQERjjrB+X4ngKv/oCtRZi7cmr5v5lwIQHtmXrMt7exsICINwhPtAfDA9iCpRPQZhAKqBSFsE4EgwQRxjxdBhJRuHAGQgBg4uCDKbSyC8OBJaFYcVZzv6VDSODYVzkkTRrT4ni6Z8QmEiqkOQUwTzrknAYyACGNWITyjNkSNy9/1PhPRQAiBm9V/ck0uhci9XczM+zjLCMDAFqQ0gwwy2MVxw2sVHS1YT8isPHPX6a3fBIBJ0/B+3IwcarMZ9M9/5Yxk5zM/4ByXIqeYTSlFpLrgipuW5A2Y9U31MyWSSm/r8F5fHnLuJr/Hk6bBPfOH908EgL7ehV8A7L7plhv+8PdnY0z10mwZ6G0Ar8uObdYVOeHKynKbCV0PJ+KrFnUr+erA5VWXEQAbYAKkoKyGbduyvbJ9UJ6J/359EM5GE265Y4awzR976Q0GFo6IC9v8gZfuTAJXt8wDSBfYtQHkAJFhcS7auH9bgzGXEtnBwvYPv1G7Zlva2dmwqgkuiFJmCoAHO+Vgm23zPgedMrOVZfZvSylr70451Y0D9o1M73dQDyMZQ9fGKu65ZOEC3efuY0KWVnfuDVM3ESksgBGLJ4rqaxIlwXrv2J+/yDvl8lsWhWS5uMf8GdIxSxZo9dGkvqZLV8WdWyAljJTuNSxNbWyK5rnhd0XqrKJFi+Vck5GlkV0ESEkAMgNJhp0lIbUAoKIs+tTU8oeHkjE94OGAYXO8WzjlLXchsWyvAScaknzLqlLV7YvE+xz+8QI7T+cnCj789Hdt83+GnzSQwXV3fPrWH74bV9xYeDcQugLwL37kzsZ9AODac8ZQzDQHPv7KoxnbvJtjtw5DJaIhELMfQwBc6ISa+iA8NBEAMxwvUSOaRULSPzRRiLzAfSHEMz4koufSIXZO+wMgyhw8BeBmCFXVUUTU0puzJZgDMRj/nJmvQHNYYggiF28vNCtBpiAG8K9CDOJzIJK4T3S2GUdE3zv9PBrAdU5fb4YIBzSAzf6YMkRo41aBiHo41zmNlQAWE9FBW9vWzgxm1iEmA57cXt5MFgXtV0KQsyyI+10L4V0cDHGf0+9oFOIZSUIokVZAeCIfQ3PocVocouV7nY4eSAs2pYniWmzoYU4LJ3md4zzn7ONrsZ4BVDLzDGYeAuHVTJPPE4hosxMNTnmL8QA2Jyyw8TV5wimh0bK49TMAPnK83BlkkMEujr0evWNIRWjp0wpLQ5j1y4569lU66tlXCcBUr5IV9qu5M66acMU3lONrtGFbsk3kMVysGgxyeyIAVkqKPFjVtM4TFg758O4nlz/34NT1UZuYNA0DATyfndvlKX+gw1Mku4dBzTr5rrue2irbbAM/2o5tPi7OV8QJQzTTcqeCTSEb0kfnJXiv/ZdXpW2z4QWSFvAqfN5AliLl2KawzdI6NMCNcTcNok8+8xIZwDGWY5tf9tLN9UADtbDN6dlgFYAmvp68ae/+GO/7qcf7/mbbrNf5yk2KLB5zWpvdyjZ3ykEKIgXi6e1BFAHg52/e2O+wLKw8s0tbud0vs7MHTHkkZBPXeIje6NlYP7ihqckOaz4pmZsPfzgcnReNJyLReDIUs2a6/FI1u1m7v0PvRw76deaqYYt/sLuvWpyoCeTB6/FKPasqkddUD2P1GqVdvAkdjISXbJ2zLVPODwchE5encvPJVjRoAGxdt3JTCcnL7OkJJCXD/Vwor8Bt5uX4NAuIWil7RaGHl/bpUHXcdfd8edLYO4ckc1zv5dUErUCKZQKOSo0cvlnbfMOwE3Mtw7rVNnnMn12TR+6su/6RO3V/migCQCiZfMMyzI8uPePyjG3ezbFbhaE6YX0zmTnt7n4YIrTDAPA9RD22bhAD4ByI8Eowc3siOh6iREba4PwKkWe1DsAoiHIT3SG8K3Ei6uq0mUTzIP1jCM9iOudx4/69ACE08yQzj0svZ+YrIRQm0+gNYSt8EIPwJ5n5EgB3b9TecAjCuAZACYTqZR+IQf5a57wVIvrVOW/LWd4OG957HcIg9oYQ0tkaPAmgiIh6OSRqNQTxWfvHu205iCgGEd3o315t/hUw8ya1hP4KiCgAUQblIoi8xrvRrD76ETOfS0SfQzxzgLiPKQgvYgJicuAUZ92ZznoPmkNO017y9JgDzudKiIkPE+J5ST/rDRACOdUQtb/8EO/C0xDPVhkzv0lE1JIoM/P9AO4norcBHAHgPojw7g3AzAkiehQiL/evYDbEu7rDRCwyyCCDHYdbH3/yaoBm3jz64hkAYNnmk0t5bnHrZHvDq3m/rImXva3K7q7xVNzndXlz4OQeX3es2n7SNPV4cPxlEJFiEgB7HiR8Y1tmna5bI7JVub8lKd2MUJUHKI2PGPNlV8mKfZWTXRAjpJpMwzDZm5wOi9033HLDZm3zf4fKLygkH2fAfPL26fZ62zwqvqFttlnYZgnwaKZ5wpt5WU+eFN/UNj/mpREa4IsAVQVAnqWgOm6h0ywTvvf6Ye0t86F7AOV9L/3qArq5AcsCVklAJwmQE1ivQKa7/qpttvEcgJzPvNTr8DgzbLkcJjQUVH77p/tuKSbPigFgjBn4j9rmTjnYLrb56vc+D1RUzLg+VdN4lm/ovnetXKXed3j5y34uLNp3YNmsN+8856JzXxv/+Vet3NS5IpUExQ2j788LU2dpK/Pq/NnxDzv2WN473rRPvp2UDODshsoKI9i50KOxYkZ82WgXalRUi+GqquLBsaC0Jh6EzooUWLJi9UFAx2ROrmkARdUWUbmpIx9oyFVVfzmp1UVErRYrmh8Gv1xctvY9ifmntR28ZcMmTnvzrNQH9JLrqPW2+eYjb7wdR+L2yMjhH6jAIbZIbdokt/Wu6W8F/zP8xIcY9Jdss6IoXyXC4QHFWbkZ27ybY7cIQyUiBaKsxOsQA+fRaB6APwTh1aiBGEyvgwgrNSFEQQqZOV38+zMA+0EMssMQKqq/QRQ77wcxCJ/rHLYLBKEMMnNLxco/6mctBJFqYObSP9k2LXSzEiJnTANwKIDRzPyWs03YOaeJEKT1KIgQvmshSEGV089qCKKRzmeT0CxqYgI4x7lunzvn+vWWes+I6HwAxcx855Zsv7VwPKRxADYz72y5GX8JjshQewiP3DiIgcBlECR/AcQzGoaYWPBi03DRBghhI2xmHSAmL1SIQUZaJddAs5dw4+3jEJ5sCeL53qtFO0GId2ACM//mlGHJhwh7zmJR67EQ4t1rAHAJMwe37ooIENE+EHmPMwCc3KL8zWyIaxQFUPR35almkEEG24abb/pQodLykQTtdUkq0bOKj7js1NU/3vuDVe2foPz20NntbxofbgrVl6UWStW8du3hJWe2hW2Z8Uh4cU2qInvyRX32AIArX1/xmQLXftma38qWc+OVkbXZkq39VpxdMgjVK/YxYMeUVl0XAcD0r77qIhuhXw1bDX7yxFFbZJtvPdxb65alrJhlVk74LNn597Z7L5coYSLOAGuWsnJR78J5qeoGrXeDfmgEGH1RXNjmF7MoImtQ4glMXHKwz8MW72/pRQ99Z64Za6dQOuY3VJUK27y2CShhgqYxLA9A1YDlAhQFMH0SzlFsGOXdvJ/b2dmDpLr6r89bpG/R79/7XmGbj47vGNuMybOGQURpWf80WdxeuObtT75Hw+p2KyqXkVrivbwktedBA+++/Dy7R093eNgRcwobm6rigfyId+XS46O5uR6KS3axGZU6N5WDNDe+79CzoSErO79L+UqkLMNe1qOfpCKJovIKqLIPqK9q4qKAWrysPLGnHi1I2UYiSsStAXdMASQTki6piKluFKciSALR6qyAx9A8Ul51zdymVq32Kok3Iq5bDa1tOxxtqv9x9fLlk/arDf82aRqyAeTY40aPZ3DWNUufODk1cnihBbyhAfUKcA4+/PQvkeqPtdf3WehaPtVIpD7ZE/0vGm6exABw5iFnLrBk7pLlc0eSptXqxQ+fz9jm3RS7fBhqi7IQcYicgvT3AMSAebzzlwUxGD8UIsyPIWYvOxFR1Clp0B/rIz/ghhjM94coB/Bri2M0MfNyAL4tJYoOWkGEmv4hUXTwCYB3mLkvhGfzGAiPz+MttjkCwBuOWmRPiEF8WhxlEYQYSC2AQjTX7TPQ7AkliAmDqcz8AUQe5zgAB/9Z54jo30QUBXDCjiKKwHqxlBsg7ukuDScMuhGCwH3CzG2Z+TVm/o8j5jIcwuN9FMQ9T4vgtXxPdWADMYe08AEgiH8HiEkQH0Su63yIEGUZzUJJwIahyC6nDQIwAOIZB8RkQkeIcOYnnLzNVhBk7jQARzvvXyPEoIGwbUJIH0I8w0dBkOU0EhCkeH6GKGaQwa6BCbd/Q7aePM4qd8cBMw4lQOVNS49MWLr/QKWNOg9HjJdJnuh2e7xds/d2H15y5mEAPoCpM2vKvrlZJT1ueKUyesNLlW/YSezp0fwqkeyOGiHNpwTkLC2rP4CJV13SZVbI57fjyWg8GGsIfjT50OWH91zj21KiCAA5gexWUTPx+R8RRQA4JsjMJGzzCXGjr1ZQPKptIP9oVYbfJ5SmAQCWjeHBJN64KM43AdyWZOR3zg0cd0ZWR32EiiVVe+BBHaiNAaUKILsYnAL0JNDgB+ABSALoyChPHRbnD6xA1pGWqY+zCwsG/9m5fOil/7zvpZgEjNhhRBEAxgycDhFZdPwOO8bfhP0OPYFGHHBKY90X37iSRZ0/mnbLg23fuuSO9x6+ctQ1Z1VW55UM3P/ogspQ0bQ2Bx691A6M6qiHtG7BGiiaJcluBQmXF55wUC/67WfvgLIVaN8QBFIw+8z7Ndl/5Vp0bGwwehs1Hdpyw9qBFeW+6j2VlfVszkdJm6P8pEAGpJTXJSVdKly2gTapCDQAScCTtGxDMi3yqejvCtYkwCYU23al4ui4Ti8+o1Hr/NCMoiwNQEn42gtn+hE5LUcyjgaA30o6NMnAR4pTf/uvXp+1duWHfo8/v1RrfUashW2WmJtgwtZDie8zRHH3xi5PFp3BYwUEKToewKUQAhwqxIC5GKJshAXhmZgEUX4gADG4TguOHessS4t8zHbWxyGIF5x2bQgPylYrcbLAHxUmb7nt8cx8pvN1CZpVNUcD60ny8RAD9kUQ3qCnAdwF8aOwD4QISYFzfumSTW4Ij2m6FIJBRLcS0RwIr9Kj2LIyBzc57Q7akvPZFjDzJGaevqOP8zfgSwiiV8rMl25m/bkQz5YFQZBmQhC3dFkIRnMJDAPiuU4/pwxRy7AG4t4DIvS4F4B3W7SR/ky3B4jfAY+zTnb+r4Eo4RF2+vOTk7d5FgShTdeDvAZC0XUFgPOZuWprLggRnUxE6Xycf0O8jwbEO5jGfOdzTyL6dGvazyCDDP4ZjP/PQQzYFUh5q0HKcSBj9G+xn46/TFunFoJ8kzsMLAaw0IZtRWKNEdNMPJZIJk6G6gpYlm1G9JDulwOyDGVEQPX4EtGwnYhEEhIps2VLNS3LjkOkXmBl6rcREavJMu1UIwD8619Xb5VtHvNWFd823doi23xqmI8/NSJsc41RveRzORiJWojGgMsBYORwogmDccLdg3F06TBa/oYR76cb8acVyZ7Y2Ljal1uDvRK1uGd1HgpIhJwaazywieBJAYVhoC5IsOa0hXHTnnTrTYfRnNVc85OsaI/atjHnz/onAdcQINnAIVtzDf4Sxgy86v/YO+8wu6qq/3/WabfMnTstM+mNhBZ6FSSA1EAiRaqAICrKq4LCiwgakBpBEYiioBKEly49aoIBEZFe9IUgIUAa6ZnJ9FtP278/9jm5N0MgwPvj0YT7fZ555pZ9ztnnnn3Od6+91vquyGjcpDEG569BOpnqnLdkq18dfeh7uLl7+dvf68u3r24Me4OmXEcx1d//wvCO1fm9Fs0Nd1i2kGyuXxWzjY4zZHTKy+W8nlJ+kZ1x/pHtWh4WDFP1+8rb/frfrBmSN7ezlcXnXugel7CdCX6+MMcjCH2gnHcDKXskVKDiFeAWMLYs5JKDe9eGabDGKZJSVquHBEbb0n76VpaMIKgb/L97t/e7HSedelKLGTQGCUP1tVnz1xw26XtmGIye3zbyjXfrGs5g1pyPFPHzmHX/6Y9Z90f3hHfZ6v4O1/dtz6riZpUx5vqGS9uwEQdMPfNHNW7ejLFZhKECiMhB6Mnzu+jcqLhMxHfRE9pIrGxduN0yKmUL1rB+8XPQMvwZEVmK9sy5aGLKK6UmDjh2G+AqpXr4CIgMvr2jfn0fvUq3N7ro+JlV7eKi453ocLw+YFp0Tl+Mznc02vN4M9ozuD0VdddVaKO5WtBkCdoYSFe1K0V98ZVS038PTAAAIABJREFUgzfS99OBS4Gxn1ZvT3T95qE9zzdspO39aM/hMuAGpdSvRWQLdAjxreh8lNVR8xywOzr8c6BKL1REZWagc/nGohcBArRwUzd6zD6C9jQ7aG9ylsoC0QJ0qHJ8XxhU6j+baO/kuGjbd5VS21Sdc4GKl9oBfo7OD15ZFTr6KPA7pdT9G/ldFqCN3G0/oM1dwDHoe/BRpdQXP2ifNdRQw38Odjh+m4NGjTx0/iH7/2Lt40t/v8qVnDll5NdGAecXi/3fau9dJO8uflgNHXxAYtiQ3YxCoW9l66BhwwB/+drlXQ112TbP90lZKQI8/rXm5eDe7x6U+d7PXlnuNDW0mJIsvVF8Y3kqba256ysHH1x97GNPHdQG4j54R8dH4uapk9dxcxI4C5jeX8gcCNT/4m/964S6tpnUsKAuzA1vIN151Pjh273cu6bP7ur98T8CRVfEzYNg5BSbLO3cPGoBh4XC+DVDUSqHWGtZMSjLsJ48kspAsgytwsJ/jmAYTaSQ6NnvUsIhxMGfNueDufnxtJwewqUGjD2k8Cnl5qf/J+bmS9W+X/79B7W9fZud5kjC2vslp3HZ2kzLDfc8ed+vZ13ygy3w/euU5dyUeuOFeYPslrfnmW28WTey/whr/h7SWfxX1jSsZE+nckBsYGkiiZvJBvWuay6pT89wjeQ+ud6eMX2jt05ZSgVHvfFCfx90J6B1EDzSkc6c0F/f6NR3ru6r8/3MMjAG+yGtljEvD9smQEIIDW34hwqUAWYJ3uzoYVxKLCcf+PO2fX7ObgALp0z6qlGQX3W3ptTqLRuDfV5aaRVNfr5m6MgbfNNcuduM3yiAOece/qg5dvDkg7/zwZ7Ax6z7FwCFQ/3j37c+4pemnPqHYqF8yOgho9xkNv3oj39zeY2bN1NsFgI3IrIL8EvgDfREOYWeOM9Gh7XF7vfq/K6WqE0JPbGOEd9ApojMj76LPTpjgN1iRVSllIryJSejPXWzov48ji6FsbdS6u0P6Hoj2rCTaN8j0cInjoi4wMloz+A09GQ8jc5niz2Ez6DrM14mIuOA89HlM7Ksb2AMHXD+pejYsfJliooSWzo6h2lq/WLo6yEqQ3LbB5zbpwEz0dftIuCGaNFgCPB6NDYEbRwuRXv4bKCslPp1tP0IYBgwMqpJuDzaPoH22kGl9EVIFDZcdfyvUCmfAtrIbIz+ArShuAJtTIZKqToR6Uff92OoGJ1G1T5MdJ7stWjPdAMwIurbbUqpi0RkMVp5txM9ofoiMEsptUJEnkGHsppoRdcPNBaBH27ke5RSpwwU16mhhhr+87H9sVvuEip+uWLls28A5UNGnRhz8wOBCoaDkapPNZJ0WoK6ZIthJUMcP9UE+Is73ioVKWabacSyUiilVD7sY773gnnKVd3ztzJGtLhBSqWzzcntjB2HrzKXHHX9I0oAzj1a1AlfGmRZYThZSYWbT/zB9o8bEu7qOHV7/8+lL38QNw9Cc3OIXjTbKgi8/xZR1q+axEnanOy1Mqil0b5mlGNda4a59HNL1ixN0QfZrLNLf+8zbcJt18xWl02drLl5+3mc5gv1SwajTEFG9UAKhhcExCIspTCW25SWNdJClhQuJWyShHgEhETcPHWynDlttvrN+3X8kEKNm9H6C2PQPPb7/CWXDcV1Ww23/Hrq2p+p6Y8oobdrGUtfnV+fHbxHurDa3jPf2XPqy0/8GkCJjLBRo7zQH3nQA0/M+c23v9ORy2eb6nvWJLfPrZzfDayyU+7zZcPZxQjDcTYytlyShZkGPNOhuavvK3mV6zmo2KvyLz+hugvFXKalpbEeGssQFMLwBKOQWzmqXByjgqDUMGvO4P6DDs5ZoTLzhmxlGRIAVuDUGzmjPrRKK7HBLMPbLXDViiLX19f7DUlh68KBk5b7wm3jnphz0aIpk74X+uW1qf6wc3XW+uzfDx3zxbeeWD3rZ/c9uOKF84/5h9Ff3Lqupd4sL8n3oLn9g3AlURTd++HOWXccOWXicXLdPT+rcfNmjk06DFVEEiLyDbSXZBRwCDrfqYCerO6O9siV0UZkbByCnoCbaG+cRWWy7KG9jT7aeAM9Qc8BL6En/XkgJyKvKaV8dA3El6u6th36wb7PgP6eJyIvxe8jIZBb0Sqq5yilZqBLYsxGh/ulgVeVUrdSyUWMDQcfPVG/MArjexTtKUxSMSxixKG08Q2dpBJK6ETnbKONxrVoA2TGe37wGgbiKLSHNlbLG4cOB45zCh9DL0psh/6tX1BK7RpvrJT6OzqHNs51aWD9kOEClettULl+Lvr6x3UV41DVhuh/HFrtoBc7foU2+OZTWSAyo9fxPdFF5XkwCG0A2lEbQd8n46N+T0DX9lwMvIXOY5wTGccjo327Uf8/EEqp+5RS932IdjUyqqGGTQTTZ5KYPpNvHH7In3Y47KA7Rk0++KFDe93S/mGoCoAZqGBi0S0ML6lyWRTdu211athcP7TUj+BkLLuv0GsVjZ5MTtodlBGW3TJ5v89btfYtf42/1h+S3mlkdyZLXTYrhUIxt7pj9YMtKj0h17ssH772RO7uI85/7b471/qBZT4ZGOYLcb8Mw9jOMrw0yHrcfO4+jed9d5/mddw8bbbqAG4dvvDIx1qW7XP2tNlqRmPBvLO+5DzqwJdNk3RyJa8++6z7my1z1gkqlZUgbTmBnXWtYtlvCvmsCRd+a7Kc0Kd4lCW0LW0huWAYymtDvGZY1gwdJrl3POb1BagwD2ooSZpoxEBhYRPgYWCTIh0InZj0oXmlhg/GwWhu1l5gzx1HqHYN6zKam3OrH8OxWxg0co/y+IPDfKL5hVPnvbpuTHz+0h//HTH3u60zeQtA49KlmcDNmSNyb5XL2sNXMJMpZ7RtqdE+Ri+oENiic7m7sqfdR2wjbzvJAPKJVFo1tLRkSxD2gZsEs84wnCQ0F4LgF1kYM2/KpIVDHMNM2SZKiWmGWAH4Zd91i353d1or8AK09SGnDR+KYwiWYyAiZETpyLgtZs2ZsEW5bv/gzdbFL2213fw3/rRqsLWqMOeRgz8vXrE81AqUuWC56z72VmGj3Hyof/xth/rH/3Fj7WY980CNmz8F2KTDUEXkfPTDoB09kY0NpDjny4n+FqG9bg9S8bhVlxYYGOYHFQMqiF7fjjY4V6DzHuOJ9Qil1HqrL9GkebJSataAz2Ovzgyl1NlVn98EZJRSp4rIQ+hcy0VoUZflwDtKqZNF5HKivAh0zuAp6NUzg0pZhWrxkxx6kh+fy8YQhzF2ADvVJugfDZGXOVMdjiwii9ALAHcppd4c0P5JtNd3NNrwGoI25OOxWIrer46+gw2HpMZj2ECr4Z6GXjhQaEMygb5HhlW1C6Nt7arjQEXoZg3a8wlaVOm8qhDTd9BjaxV6EeJr0X7qI4/qnuj8TBO4vTqkuoYaatj8MX0mP0CngKwCti6WiuIrD8H0M6m6UsHNJQ0sy1fefLPsX5myE3eGSik/mRBLTFXyiioMPMP1XZW2MhISIhj4vk/olz3bSdrdfW8FmXSrFxY670oVF6/tzwxbmswnbmhcvtBILJ3HO3uc0nLN+ePWmxSfcHdSkm9vP/n2S19Zj5v/68Bt+0WFVtJdPeP6Z3vXcfOtw789wwhJfHnVr069fVDdHyE0e5uCZcmCd+TiLKtlLK9Nm61OP/ZwubxR6r/t0E8znBbCpQJD+sB0oCm1kmJjSKoAWAoKKXKsIUMZLzkC2yuDGYDbgH6SKirLdPopHqgm+sWkfdpstdMne/U2P+Qv+pGFYWbqLr+kB2D6I0ro61xIX/7uxnl/vOv0G89aj5vP+uIZz5dEmnoT2dFGXWrxwe++PXRF6y6Jz6x9U3YN2ymjSkayLtlbyq8eqxjSI4IVKGxXqUSqKK/YbbzdMiSYvGCu4aSTyjYMY0kfP23JcLrr223dfkJ1JfP97bsNSXmdufa6LduGfe7vC1Umh9GZIExDkALbhZITcXMRSgkw+2FNCkY4IfhwU0Y4T2bPUQCvnjFpiV+mN7dmwgpfma8+XHz92ynBnJiw64/+y5/UCxces+d1z6i/mmD2we2znnm4xs01fChsssZiJOG/I3AhWsjjt+hJ61j0BDlWNY2FbQroCTToSWwcghdjYAmC+P1ipdQEEbkq2uYnwIvoSf7rwP4focxEJ3riXl+9TTS5tpVSz0a5l/VKqUdEpBEdavpmdA5HUCl/sARtAOyKntjH4bbt6LDHHal4DeNQQ6h4nWJ11IFGsotWbD32w5xTDR8fVeVRYmGZDuAWdBmNLPqap9HXv0SlpEXsVcxHf0PQ3mALvWCwPTqUNMX6hmeCirEYl06ByjiYifbMx95NG3hNKbWXiBwQ7e9RolImaK9lcUPjPxJdalJKDR34XQ011LD54uL7VtYnJLNjxslehObjG15b+ty5TtoeHgZGbrA51LFtw7GsTGAlrJ6ElXSBNvwigImVCorFogkhmAbKVyGCIQhBEGCLr5SRECMoLvj+Kc07/PSuNdNVfkWO8uqfrPGSL2Z9NdR7adHrPTt9fv9fXTx0o9z805v+Jp3PXbvWLRQTRu/z9df+Jb9um1tHn7EnwFfenfHSbUMaDgc4fXXvo1MnSyNagOx1IOfDER7URaEYsSDdrl4nyxo6GN7UjbOynnZ6WMtwxqsGnMwa3JJg+c0YBGDmcYOhWBgYlFAYCFl0XJRWPHFp4rFps9Xx/z+vVw3r455DjpQX080FDwlXNTYof489zEwpWJPs5tbtn3vmrHFhMeOq3tzezZJOeKU3OxXlbhK7dhXLjFV4Y+p8swerb5kfuI5y2jzT6x/badgZd0LeFHv7B7fuW7zMaEz0h+/INlu0sHCbpmLnICN5we1zVVO/b7Sn0kHoJMzW3m5EezHFjbjZBHMleMO1Ifla06w5ezFl0qEAhZTx+Kp6oxCaKly5fELrDua7xeZZ979n/B8x8QvzDIymmc88WOPmGj40NmVj8SxgIvqh/LJS6mYR6aYi9FFtCA58Dxs2lDb0/Wvoifb2aOn+A6uO04GeyE+LQgo31ueJ0b7+ARymlHp0I+3r0RP6R6Ljp6Ov8lQ8UYOiz9yobamq3YbOR6Hpx+T9vY0ecCj6dw02dl41fDyIyDR0zc3rROQJtBHXAmwZNYnrH76FDqm+Du3tOzz6/qfo1fs0eow/DqxQSp1Vtf+zqVxnH33dfeBZdE5O9b0RG4jxQ2E2WlHPQOcDb49WG94L7e3+VXScdTm8A87PjPq/QYOyhhpq2Pxw8X2rzrLF2SdlZnts0375nKP43RkzXuzLOo22GZpBg9NmmoZ+JCk7DNJWnUnoQjkApfANwRvwtAhwCd0ytm3jBaiCKsqQVPa1UskLxDS2NAzjtfNPrD/ksl++0OvUD3E6Xn9upfvuXQtsm4uvv3vWSxvo5nq49IwDJwJ9fufctYTuTlfO7PtAbl7w+a813BM+ZJQoPYJf2tG1SAZAqpc8Jv8iwTb4NFCApkWUsgr73UG4WKSoAxEYvRCCAJZthVIpRARFCQ8T8QxsIwAzgX5ag35KJ3HR6Tb/mDa7xs2fFL7/tXOnAZ0/veX668644qYn9ly9xDht8avNd/ZYW5UTw+nsXZ1rr3dSTeK9dUVdaXfguu/3mq0/MoOjyUAODs/DI++0bJHu9s0gfHP1k8epse8mXr3mLIAjrz5vmvH2grP33WKUPe+o7Ri9YoX/vRteMgPwX4VnB4nxueGlILAsMcWEfkLPCQxbyoFykgZ9hsx24EAjCEWUzHcs2TaA/1owxNkLR97Z6aY//ArgD9dfaNXnS8EBF01f/46aMmkdNzNrTo2ba9goNrmcxahW3SVo4ZB90Ll910YF72NPyUAjcKChuKE2IRU1yPh7hc5D+wz6t4rDU35CRRhnL+A8EfkwBeN3RJcHmA/cLyKHx+dUdX4/jvIwQee6LQf2pBISC9q7uA069NRHJyGv5r2GYnXemFAJMUxSMSCqyzIQ7eN9k+dr2DhE5B4RKYjI6x/ULhIQelpEsmhl2R2p5Nj6VGp9DkOXyDgNHVZM9PnZ6Oudi94fqpQ6S0Qei7zVW1G5zi56DM9SSmWBV3nvIkrcNh6P46M+JNGiNYmon7sCK0RkjYjsh86n3Ss699EiMjbaPosOk47rRdZQQw2bKW4+8UW58idvTatb5a1KSN1+Af7pq/tWTT9zxivtY1LbSaszkuFN48U0bMIwoKyKiGeYYeCCCsD38UUQtb7yXhiGIQplGoFS5SKhEUqQD1S+2DtWmYldFabVVc59FaCrve+q3EtXuKkVPxuGmdzLzo688Ke/fuaDFoUB8FY+v4e38rkL1Tzn73QMuf+ioxt2A/hzXYWbpx7W+uOpk5q/ATCj73cTOlXPcp/SniUwwiJBqggY1KHYlYAsIZ7tsrZnHO3Lx1CmRRuKhgIF7tIGCsu2AhKIeLgoBAsHhW2HYJqRrI2Fog5IUkRHUG1y87b/FBx3zNfv+e6UUwpfPOqMD+Tmn95y/dRMb/fTZx13hlzywiOXHr/k1Z1cYcTXGv1y39KcnygOTg8r5cx+xx7coXgjB6f9tCFokQyYYNrw0DBIj+hclnt5xQLz7rryAYlXrznrvx/70V/u/cU39xy8x5Ctzm1psE/952IOe/BF7+hb5xp5jFvrZ83JNmzV+GpWhaGtQlOZEOJRh287JhRDTzxDlILhiVAlLbeczJXKOyhIeHDpFuUhuxaXlhfdeOYpa248Zsp+KuTYnrr0XgBzvypjHz1srxo31/CxsCk+dI5F5+0dj/bQgeaW2AsHFRGbDa2YvN8qSlywPDYSfSrCHgodcvKKiHQqpa6mYlw66GTqYz5E3w8EdkFP/k1gnIhMB9pF5N7IaDwXuF5EhqPzJONrZFOZ2Mdhi31oz2cW7WWM6+/FMFnfeHTe57yhYiBYaAN8y5pX8aMjUqWNCyd/4INYRPYA7kHnwD5TtU0sLFMdOjwBfS0/R8XAj+tbxXmqZRFZCewLPIUOKV2ANgp7ov0dJiK9aM/iciqCONVYiR4P49HeRRcd3vp3tJjPDsCd6HE3Ex363Btt+wQwLwrb7ker0q0Vkd0/6LeooYYaNm0sPKjh5CD0vu4XC0dZlv0aHliGWNlEcz2msgJ8Sm5/CSDv96lCmAMLDDfQT6iMpXxbMGMWjmAYhiHKFMPrExKNCpeguaFZKOeyxc65qr2n/Y2Uk3r74ltXrP755YdeTc9qHysdNrSOchIt4w4pvXP3lzbc42qofSDcZVzBHTx+TdEc+lj/3nfVy/TOFKv/p1Vuv/wgSywVnmsouX7qZBkjSe41BKMMlAKcZAqTFGARYmFi0IvBa16GBlEMSZikJcDDgbABsDDDQaRJAQaENs66okUG4KEwIr4Po1+jG5tnOZUOtqx5FT86pkw8blyxveuzge9tdPHg8uNO38+3zHuaxbtlBDxjg/JCEn0htmViNZihcaCtOMfuD3xhLCpM+/A5D1SUzP+3u9MTWGC3ps40FZ8Rt3zMn65cG2Ya9yk71lPf+Mvrny/PW7NAlb1gj//t6EukmoxiInHa/C9M6t367Z5nh0N7MpUOQwzXQDCiqW1XJrlKCYYNOxQMmbXWN90SZj6Av0ui8QSve+lOhfGD729rzWaDvbeaqQzaQXUBvL3sxMdzxo7zHj7o9O8B/VN6M3+Z0ptZO2XicTVurmGj2BSNxT+jjah30AXql/FeAzA2kDb0UPigB0VY1aZ6cVPQ3r0QCEXkWvSqTAbtuTOAfUVkkohsM8BT+KSI3BG9PR5tWMZqlocAMZG1R6F6RXSY6WtU1FgHIr5ujWjjMzYkFRXvUJyrCOv/PnGO3MDPY1hoVc3fbswzVsMGcSB6/N2ulNpqI23fQhtgT0TXfhIVwZpiVbuB3uI4VPQIKuHFIdpojBVRif6vQnuyt0V7n2OV1PvR+TZxOY44fxK0EbgEeBr4llKqQSnVihZmeizqX5z3u0op9SRwnYhcjQ6PDoH5kVJwXdR2u438FjXUUMMmDD9rzXTbMrfbI4bON8X8mqK0rE4lpdkeioGJiU0QkE6lUqSsemk0B5GyU1XLlY4oT1EMwY+eRIHSMZhJkTBwWnVkkWWaCGCmSThNYrXP3c4PCmGft1JOvvGua1tPmDXKaRqdIWEVKeascufiPS/+0q6Tph6/5TZR/UQALjtlypOXfunIm/W78vGY9sF1PQU32dXji6hDjJAvKYUhip4fPeErPyyVQpXPA68YJm3JYiRHHTFuCBRTGCULIaCRgF2xsMMEZskCLGwrBMoRNyfAKqLMaD+EKHzCKNxUrXsax4xtY9njaCDJb6dOltc+ocu4OeNAkPTifPjbe2fO2OEDW65Ov0rg/91Sxh9l1hzlwCQr4uZDx3YWtxmxnPSYLKtF0ivtHAutHnrAJfQsFSrV0hdMejQ1QT2dHM/gFGHwnc+mh/qSCtf2h21r+jFcN9zZYM1geDOTTG2V6e1aXQiLZl0Sp9/gfuAV10yIhUkKqxj64JYC6rywvidkicDTg+Bbo//+ZMMWT/11sA0zGh76/VwbpKm7x+zKJoKuIFh09LlXP5nqXfLzpy4/6epQGYvNMAwF1cGsOT5IHUiNm2v4UNjkchajELctgD2APyql3hCRLirelbguXYyN5SbGiAVt3Ogvg344rEaHBsZYG+2/pJQaLSK3ACeiVVJBl/AoKKVaqgqYh8Bz6DDBuP7ec+gJ/JvoHLQbo+PegPY07R5t14/2mm7MsB8o0FONatrZ2H68qjb9wLBavtlHQyQG87eN/W4i0gH4sQhMVfF5hS7FEtcr3Bv4G5XcxHgRoIgOT30JvbhwC5WFjleA/dCquZ8BTldKdYrIw8Bh6PGyIxWDMUa8/zDa347oMhpvocf9crSXcrZS6hIR2RL4AzqXslspNWoD51mrkVhDDZs5LrqvY2zKSI+qs+v2Bh455yjmT3+40BVIOSVk8PMFz3Ic23DWCXavx81KKUqlEiEhhaCXhNSRsR0IVGgYYnSXi+6Knn+V25rH1KPsoNixanXa7RhuSD/Kz7MiWLO2Obl90rbThavP+dzoi7975i3OoJ1P9Ne8tkJ1zQ2DntfGQKZ32uw1Q646dfKQcqFrsef3BHhvPT9ttjoE4IeTRYrwXKLAtq2reXOLNRz+yt7MQOsTzACmFwN2DwNC26LfhXoTjEQZyhaUbZ1DMLjIuqQW0yd0XIwSoOr05+KCSgI+ynLBN1GkMBQgCh1fMjDeo4yHi0EbAvRNm10TD/uomDLxuAOAv22s3MMFW53ZYRt2+cr5vxwBoI6efBfZ7DEq5ai5yzvedAnHO6mEOciWvYu97U85pplq9K2gHCozFxg0Ok7xjuSIV4YV1jz6uYS3/Kqv7/O7vsDif9eKdeir8145r7+w36CO3KXolI4TmTWn8MaUSQ83w2EmhG2wY7HE6xhIPJvNl1RQtMQMhbDe5BbPMHawLDkz5QavSIidN1huQk8/avaoWY9d8q9T7C09sf7U07bvcK+hde2hl9w1ZgO/h9RKX9TwYbApehYvR6tFdgH/E3ntYvYpUTEUB+YexggHvK5uB9pTGRuKJpWC9jGaou/bROQNtCfpYuAaYBraY7g2apujYqDtGbUFHaY4El2K4wK0yuUh0d9stKEYb1cX/S8P6Edciy9G3P84hHYghPXDGmME0V9cSiT2UppoY/xT+yCJ8mO/9hG3WQXc8T4KoQeJyPXR61vR42idyJBS6hS0ga7QaqYuWtn2X+gyKnGgUjtalXQntPDMs2hD8ma0V7CIHkOvKKWuBI4EHooWKVqiwy1G594K7w1dJjrWRLSheQ6VMTMELXTziohsj87pbQHmAt+u9qpXndendgzVUMOnBUqpqwpB4Ww0N985fSZ3YFgpU5IYYpbsdJ0dCqBC/TxQvuZmFYJbRIJ8APqBlJaG0HQsZTgJDAdBwC51vTNa7PpkT0eQ8VyztWnoUDtVj+U0glugZfkLTY2qr67OXd124QlHvBGUcgeWxLzYDXquCcv5uyDlg9l74tkT5Y1GY+E7Q1oVfrsB7Dl1suwAUDSszxowyk3T0TWcqSuHsxtlPoeO4piNy+5hHmUEGAFkMgZGUiiVEoClbbw8uISEcfyFEiQQwEbh45OKJh2G3sZPIqR00Kko3HVxGw4BIQEBChtFIzZt6wJVN1r/bnPGlInHyZSJx30kbp488bhVCu7YkHF0+pSzDzpjytnXA3xv32N/X0r1ZYpWfp3TQR6ZfQqe269MUznp1Gt/6FNuuqO8dMQ9M18fLi1HtYaiHCM0s4bdPsRxHv3N6O12G1lvvrFb0pr/gzu/nO773Pjf3us0Zec1Ubz54O12H3LAnq8wa84lwJFX9vqP/uKAI2U7GDoUaIPFvs9NKMQP8ZRSrC0WaVcFs0EUaUGFcEA6DPdylXEiCpcATBja29S2vdfQOpspk7Y3fGdcIqAp0/m/r+WsurN/O/Pq93BzzVCs4cPC2niT/zgsR0+c90NPtrelskKZrGon7/PaeJ/XcZs4ATieNMeiMbHxGNem66FS43B67DFUSt1etU8VtRfgOqXUE9Hne6An648BZwAHUTFy/0UkFhLBQk/mq3MUQtY3/qr7v6GairHBXF1GIT5eXCZhQyJApwDf2MDnmySifMIngR8ope76EJv0AI6IjFFKXfwh9j8YHRqsRCQJoJQqVTW5B6gXkRCdTxiilW7XQSnVJiJ7A7PQiyAZtHH4FXReYFwjcRul1DJ0jisisgQteJSmcm2bReRutIdyLFoIaSo6zPkVKt7r6hIa8Tgx0TmLoPMln0AvZryCFpb6AtqAvAt9D24TvT4S+PrGfqsaaqhh80LKSK8xDXMSsD/ruNnRzxMVJt2yp40kvyz6geODYwkh4HsgJROjnrQywMbA0mnbniviA3bd0G19y0S5/SaBS3e+q2T2tKfTzUOf/kMEAAAgAElEQVQCp2UHs2flG2HoloxUdoseqzk5RnDliou+cT18g+sfUXLu0XJl3NdTzj5CGdiBDNpDVOe866b9adnrAOnQn+jCIgceE5+vrsxyACVsTAIs3kDYO21rMrZymCTwxCZMx0u5FuFYGwMj4uYAQkHcNIQBvkpgmaCXnF3wU4hVQBFEHqSAAmb0/DYxVR1eGczqiU18CmxG3MzOl8Xc/DVeveTxjTVHc6E9ZeJxzHrmgVs21vj8Uw8YLKq5UYmoKROPSwFq1jMPrOPmeiNx3/Cm+vSdN/4unO+rg5NOKTAt997qfcg9D7X1nH7sgfMN45EdEqadTahM+fOT1vj9fafW1ad7Fcp42zVDYOwPbrpuMRE3U7h7OUL2xdufTH5zz9HOv7ZoZcwyGm/+2eN3r+71d24Re2yfEXbOT2Yu2aaU6wZeN012cC2UqbCUgu4QVVaojIEkBbMMY1ygxfP2EOEF32bv5wZNnLds7y/sYpf7zhz32GXuhNzEu95o/Nu2/9rt27sZoT/DWpSvcXMNHxubomdxa/SEPL7RB6TDbxTvt5LSX/W9hw4fVVTyxYzomD8GJkShgycAxwG/EJEX0cIe5647kFIZYBF64j038lSNRhuHQ9FqloehvUEOmmAnRJuXqHg+bSoGa9yXgYZ+7B1yBpzjQM8p0fFilNGhEO1V78tVrzcnvIRWsP3lh2y/Fu2lnbWxhiKyBXq1dy1aIGY4WsioGn9AG1iTqXiM1wzcl1LqefS4yqHHQAa4DO1JPAV9LdddGxH5LjAH7Vn8DBXDfwh6YeWb0XFL6IncFdF+9mb9+ydegJiHHrfxMfZDG7dptAE7C208NqLDT3dGL+K8ixZt+q+N/V411FDD5oWMkxmXslINbJCbBUuiWvMWYAeAVcneF4A6lQrBNXS7YtlHeSWCoKcfwFNKJZKNnlvMFfywpJLpdDo9fFvKvhhuoqFYt+Wkae5b0+qmfWe/oSo1+IQwNfj4S6/47a0X//jeV7xi97xr7l7zrbivd93wx8zoXGmpJNKmDNt17qXfOiZx6bePHwrs50TcrOo4FJMsDg4eWQJ2wAMRSlZIiI0ixA7KpLEjbg4xRGFhoFk7AUGI6wOFFLZnRnxsAEmUVQIMhBDqzDoMx2xQPgQuEFIS2NWpcHOBCs9XL0JuDoi5+fcfsn0veuT8bmMNzzn5wB3SlvtH/PxadC7+MDQ3rkPohA9vvd+WZUn5RyqMVNEyzZzxXs2Gltse/OtY1NEjXS9Xp1ToKrI+XFXIFXeysE95z8GnTPrunnPmP0TAL4Z25fc9eeEaM2+k+FbPO8OOeOqGowR+2a1Cd/D4Uf2vj9lin07hivZicNwqYW90hRUxRNiyLiXbZOoMEZkrsMgF1wF6FfsFWvQuvWvfm6o3lZm1xfy/Pt0X0Li8rzhCLd59Z1HhW24gi1cObx73g9m/rXFzDR8Lm6KxeD86BK8O/bAYaBhVv4/DMXVJW41qo6k6jLM++l9Ae4DS0fdxKCloz+UFwLsikkaHCs5Gi9TsGG3bG4fiRf/noYVBHkbnfv0ILdLzCHAHlWLqoD0/8SKiSUVhMqz6LD6fIOpbHk0c/VE7LeBd2a7aIxmXZKjGk0qpBUqp0UBaKdWIXvf8PRXxnU0aInJVVW1MF/jZh9lOKTVOKVWvlHrhQzRvR4+H85RS49E5rCsH7O8MdE5ilsjbG5XPQEReicpt/Dlq+1d0aGcsdvOQUqodrT7ahjbw4zF2JrC7Uuo8tIBSV3RIDx3G2gwMRntK90cbem3osRNPQKrvhW3Rnsjq3FUXPVb/oJQ6Ee2hPBI4HxijlHoXPV7GoBWLa6ihhk8Jps9EgHvRfFhH6PmooMLFqqRMq6xSFmALWI5HMgGEIeK5GDZgSIiuPVjwwsBUUAoVodFQD2AWCjm/0P14qq4hTVAKfc/LmV6oLEeoc7KpZKr1gqFjTuHqa25PJ5z0woZtTpot8AUryG0XeoWevgWzei/9xmECMHWyiN/x6Fx/+cxfXfnbmQ8rZZyoAv9KrOY/EnHzcujrqsfChGKKDnycaJnOpI7e0IJiSGgCCKZKQT5B4AoBCfqKFnlsSm49hYJF6OcoJvswKAGKMMwRUCJm5VK+K++HfYGUipFF6DF72my14KrZmpunzVYtaAGz36MVyzd5dOz8zat6d/5ud45cspuii14U3ShmPfPAyFnPPFD/YcIoM4lgcdk1Fm4zrnDe7GcemIDm5lXVbX714HVnHHDc7sfe/bvH6jEQBDXr2QduBlj9xSmv5L9+UqHjlCP/DLDL7x/869b15txQMBb1UvxN2Xqo/qnn2nvLzBwrDJ6Q1NzMlEkC/Nc3Zry414zMyReZpvW5q3cd35VEeKfe8ZJecclJDclVP8zSeMDKJfk95s/d38F8wqrPtuVd+nJQ6FV4YcTNRhm6C+4ONow1+jwxvBDKQY8Jbgj9ttv58AVfHnNipvPZC95oGXTkmua680VkxOnfvfzdd7cb9aVcY/0YDKlxcw0fC5uiwM1QdDmAD2PoVovdxGGkAwVwoCIO0wf8EC06Mwc92f828D/oCXcRXRh9AVpk52L0c/37wCil1A9FpA+9pvgP4BK0cfCOUmq6iMxFGwo3AlPQeYxxv3zW9xb2UfEAxn0usX6o7UdFbGQ66DBYB3hEKfW9/8M+/6MhIl8Gfl31kauUaviEjiVoz2ISOFMpdef7tFmFHm8rlVI7R5/HtRLbI8O9un0evWCxE/DP6PXRaA/pdPSYNNGiOE1KqR2r9ncFOkd2B7SBWC2QsxjtKRzC+qqqA++tX0TH+SZ6PF8SfdaMvkfGR2HYJ6CNxwsiY7eGGmr4lGD6TIYCC1G+UPKixJBITsArgl2GcgISSbTEy/rcXOjpLkkiuY7fUoDnqNB3xUh4uT5RwQ9LhcKbgd89p4S3si7V8O3AD+7w8msalWEVA7d4XTpcvqDw4pwt7N0nXRz2tbuSbblAFKMuvejMH049dqc+bOywL/yL4l83eAm+oGzeuu4+Nf2CyTLXIJE1CG4G/1BgzxwEhoFJSCBg+kDKA8ukD5dsFPPjBj5OYFN0AlJxzM8KR59aa8nFiZb7zA4IGoi9iuuyxb0QbAhYSdjgYPcuZzGfwcHioWmz1fc/8Qv3b0LXllO/majzrjMQAooEJN2GV3/6iXDzlInHydgdR3Q6ppXo8ktn3nbTL9/DzVMmHhdzswUsn/XMAzsD9PzXabmEiNnpe+0jbr57HTefeMAJMsYO80aImltij2n0v9AK6oc4R+dRv6xDpv86616vQoxFHi/bIU1bPzlnR/nLzTkSjnnV7x+/5rgFnbs2ef72aRUODlJWIOKYKpFmValvcUOhN2NIsjWdspWA9BYKYV7KRquTxDZT4IWkbOO/PXhkJfJNX9Qxhpk6d2Vj9rbQSjRLUF65xbvLxg978kk1fSanFsOl3wlU8YKLjtm6xs01fGRsip7FP/Dh+10tdhOXlqg2FEO0ARbngWXRoh+fRT8w+tET8Wa0B+aB6PVvqAiOCPAQYIrIW1Q8dxOAC9Hrho9Gn41He3jORYfuQcXjOTBnMEvFo6mzGdY3FKtFSUK0amu1d2hDiI/RpZSagDZy7/iA9psDzon+h8COn6Ch+AR6jCTQ42mDctSR2Mv2wODYUIywFD0WfrKB9h56HC1E57juhjbwmtECM3Fe7M7AliKyEB1meg16/F4FXIpe7LgWHTIKely1VR1uoKHoob3db6DzG3eIjnce2oN4AzqMOhZuWgm8Cry9wR+phhpq2JzxB0B0/iER20SR7D7gORFDCVRzs5szcXMYychQNCCVsEIcyraZNpKoMLRT2RD/akXfoUHhHctc8WhX2S/faCYbGx2TQoPhz6ZzbrYY1v2G1Mj9lTKUsgzx377/aW/eTebUI9qWUOz1cbswWLWPouV8ysmc38bMqB/joTzYwz/bi7i5DG4YQtLFiEN0XBuKAVnCiJs9nGKSIDBJFR0o6uVer08pRC/Ea24OCANHl8MoZkAFYHdDol07WjEwaSAY/C5d03rVBCy+j84B32zRW2d818AgRIUeasdPylA87Wvf/FsimyjkOnsdwZeQYIPcHHkptwdaY0MRoNtOL3VNyw3rGtbj5t8/eZ8K3JLnlopl4M2p1J/xDep3A8aHSEsIwwvgi0ICl10WB7Ll63tNWnj8n+adMnRN1zUnv9NxrZVMXm6aXOYZUiz6wdmdqmuFV+ohS9FMOmZLEh8phqQgFFsZCdPExESBa9hGJ9Bpw+4KtUNCGFdIOpeZyewI5RduuGtR32GXew0xNy9OGaNezZhb17i5ho+FTdGzOBM4NHr7QWUx4pXLuEpRnI8Vb6PQTBaXtuhBl71YjA5TvAZ4GF0bMRPtM1YNtaL996OVIKsFY6oVRx10aYT6qO+ron2ZA/qwMW9htTcIKl7IpWgDog1dJP2S6PuB+4uNyBVx7T8RaUXXaHxZKdW9keNvkhCRMehQThsoKqWaP8FjPYf+PV2g8f+iACoin0GHeF5UvR8R2Qads3sqOu/1v5VSt4rIjugFi9fRYaYptBDN88DzUQh09f53Q9dQ7EPXeTwcPZ6qjcXF6AWLZlgXLBULPNUD9ymlvioiZ6FDYs+PSnPk0TZuhhpqqOFTg+kzmYkKD6VUBkMpHBFdxi2iRrcITgrcHpcg4ZCwFYYVkl9ugI1Kt1IOXTFElGMkSkCx6PUrv1TsrgvNUaFRfNOqH3FDfvlLP1Ne9x2hWfdVsZvq6korKXe/FeTcfpLZYabvBa6/8MUi+Rcb8IMSstYh0QZm2sArgt/ghtZqx/ct/5qH3qgHuGCy5mZrfbGvEkWttB4C5UjFVDxIxRJyLkHgYAaApyBUeBnBFuEttJdqEHAbcCUhipBU2QK7BIMXAiFhxyCglRXTHtPcPHWyDEZHkbw8bfbmyc3sfNmYfphrUbRLeMWmV6/9xLj59K+f9Y/eld0T3Hy5vOVe2zZNv/qKj83Nb3/51M8YKjxy/O13Ta3+3J0yaRsDtn7KsL42yDH2fa1kfPW0WTMf7pw8accCPJQv83qxwH5tCdIryunn9mwu/AN4nllz1uPmvikH72bD0yHSmw/9p9O+cbhpWlboExo2hmlAARYXIdvo0RxFapetUNkdScNbnWmtJwzu2uP+u8/85sSjzwW1vRjy/Rv//kjn9JnkgfCco9alXNVQw4fGpqiGenD0v4hmoQ2peFL1eexVjHP3FqPDEl8G/oLOfUygH+r3oOvV/Qudh1UWkSI6/C427nqi9g8Cf0LnUDpow7IdPZnfn0pJgqKI3KuU+iI6Vn47KgZqHetfgw2FpMb9D6PtHCqG6T3oMiJ16JDYb6E9T5+t2mcReBGYppR6purzAlpcZXNLlAdARH6NFmaJaf09yer/n7EPsL1S6mMfR0SuQCfgn4AuW7GViPwAuDrafz0V9VoLiMNN+9H5gjehPYEj0JONNjYgAKCU+oeItKO93J+nMrarvYpjq16baDGbFegxs2t0DNBj8K/A4yKyNe/NG66hhho+HTgYMRSOWcIwE5RcgaBS2Cqur1gsawEYRwDLJD0kQClTxFiUNJO/RquE/1OFvuBbScuqbzE6X7nZaNt1IfBa3Yg9x5xzFOXLrnmgyVv1ty+Vi10os0XRv7ajZKUzFNc8SOGdP2EU78cxHILGh8l3L8Wq34khE/bn3WfFwHcNCoWpk+XeabPVFy0jvQK7fjvKnQUfPxSo8xSWMrUWTwmCRBETE99SFW4OtaEYOlAIFQkFdgiY2iv4fSCNz2VYfAuDr2OwtwmENnS2UBCHl2nm8mmz1XNVv+Nmzc3sfNmvgT0ThHZAigzJT7QMyKgdxu/e1dSx/S9/Ou1jc3PXkpeuENQSe237CYNas/usOGbKVsOGDLmYd5dfCezjQ1ZAtgt93y9hfRb2Y8qkOwKf/guH7fWltX/0b9p7ZHv/JKsjbVmF3Zd2MGJU63u5OTvrL//onTypHWFwRpzPiyCqrJSRFgPWhRCNzaLHkNeLadfRGASy7K1+1TEv6NvVz5fH7gGIwe0KGbzVN+/4+/R7C2OwLbAGZmDVUMOHw6ZoLObQxlFsRMH6HpHYc9iNTrg/GXgOmBR9vgXaY/IyOq8Q9OTXRRt7o9CT7lBEJqI9NgdFxz0Wnbt1FDqP7AX0BHqBUupLACKSQBuXV0V9yQBHiUgBnQsJ2th8Ax2qalX1OT6fFWiPZYZK3USbiocTNJF8j8okP40uou6hDeIx6GdLC3piv17orlIqjzaMN1ecSuWcc0qp/T/Jg0UewI9MRiIyFi149DfgNPRYXovu+6HosXAo+voX0NfZQ4d6bo0eM0mishzosRJG7YcppeZv4JiPoD2GULl3Bnro43ItSfTiRLy4IWiP5AgROUkpdY+InA9sGe0nFsCpoYYaPl0oAxamozlJAFQIvqEfJ44KAlP8RLYrEfTfi9mouVmsw7UeKGPQpuUC4FWU8qwg2K3bXuq2j36sNDrYd3RQ6PydIvCgbR8rmbk/SDTuEXr5XLp15LGl/nm/sbzUIX5hUV4GNb0QLPVWoTqfmzZ73qkAV150acLrfvM7vuVfThAqDBo8aT3qwtP2KfhhaUmi7JHHTygdlbGbKdg4KA/EDjCNEpgOi1yTYQFkxCN0bZQFlgF1CUFswBRK6CgfRRkDRR0ht+DgAUssGI2JHw5hEHq+sR6mzVb9fAq42dH0nOPVS7/8SR7s8u+c87G4+c933za2oan+z4X+vr9tuc8ep4khYXLsyLVWa7PZsmLVwRSLK9DlpBwTigKpIZp/47QNUwUkR/1t7X2+2Zx+a3nGn7h1ezjBxHFTDGPWnPdw88IjDnnEqU8215cDQjcITR9D0lowMQBMI00YFtwyhF0lkm0JHMqEdXXUD/UoFwql/lEpxqw86siTbvz7H+758czij4oBo5IehrjlP5xzrFPj5ho+FjY5Y1EpNTgS/dgPmIZWIa2uOShow+95tIfPRBuOf0J7D/cGjlFKTYu8Njui864uQ9eKuxR9o8eT6QvQHppdIoPgJBE5QSl1X/T9lgP6VxaRLwKXo1cWk1TEarZFT7rN6HX8++dZ3xAcTcU7Y1Sdmxe9jg0E0AZEF9oojAv23gNcWRXCuOz9fs/NCSIyVCkVq5z5aCOniBYp+rdDRHZHG/g/U0q9MuDr2JMXG/5F9IThQfR1bVNKnRjV8lTR/vZE59HegfZ4g/YyNkWvH4/aXYb2Oieiz+NyLCFaJn5g+Q436odNJVR6HvAU8N3ouyxwqYispqIs14tW/K2hhho+ZTjnKJojVdT9gEtVIrlbPshLIvQMWzOYlEq4kHrOTqX2NzT/deB7f1DlUjZU+f2xE8edd0LTT6c/nD8/ZS2ZkB7xwgHz/JnXKxVu2Vj85w/qGLknShqufyQUzMxZmZYtW8Z23r/L8d+/Sv3s1l2/0PvS5cdfcdN9MTePq+7fRVdeWp46WY4FuVyZQy9MDNrF6e9+N5fGzJjUjyHsDQ0D0wjYRcAKTLAhb0EGE71UBltZChWEkDAwLQWegAmeCKYZYBKSjJg4B/go6jHWpZ7cDVw5bfY6bn73E74s/xnY+bKhvHrJfyw3P7fXybv7Sn3PEvnZZ1+4ex03C2LayzrGpt6aj2pqMvydxzvms3OLdn/hNRrr13FzYtacE5kySZg1R1/XKZP2BLafnR91R7+d6XFSJQ5w5vV/JkGTa0Beye8A3j580mUtId+i0JuwBJyGrCplHdsvhuHQ7sJoH5ZpD0IRM0S5OeU1ZcToB6vTRhVylHds4G3gTy0GF7alMZKGqu/wuy5bdsK+y9Upjy3CBF/MLttI1bi5ho+NTc5YhHVenKeAiSJSjxbxUOgH70XR65uBGei8vuvQk9tT0d68OMn38+h6dn3olcAhSqnFwP7xpFxEdkFPvicCT4vIPsA5IrJCKfXswL6JyOXoEMDt0BP9o9AKlvtRUZt0qRh7sfcRKl6eDSlSxh7GFDrUsAXoVEqNiIzn+6N2ZymlVn/InxIReQqdy3jyh93mPxEisi/wQxG5CR2SGYefvqKUui+qb9mglJr7b+uklj1vjv4DEI23rUVkEjrUtIAeD0X09ORO9GJFWUS+rpS6uWrbl9D1qRCR5ehV6qjcM/3Ai1HJEIUeN3Hu6jTgJHRu7v/yXu+iHb0uRf+TaC/lD0VkP/TY7kEbmscppb4tIpOBloG5miJyDTrvd+z/JY+zhhpq+M/HOUcRc/MB37jzzfq0kfp51mpwmxONa8D4fipF6HnMMAxmoPPtfzLIL43M4Z3Y7+d3FoKF0IRjrfy8bS47zijWFwzHvEwk1XjJEbsuBiZe/0go5x5tqJ/+Mr+LVe4b9M52104Enl6buOtotc+Qc6be9d8vTDvluqUD+zZ1svwK2AnUtmE6+VTQtXTflLfin6xdOxFCA8PxPFyVLZCwAvCTqCAZmYjRM1KFhEYZIxUF7luAlURFnlEHibhZ0Tltthox9XARfO6naRhG2jrrilvf/dDcfNotbU+hwhW3n7F2k+Zmdr5sX2AqO1/2c+ArVHEzr15yn9r/qtEA8tQP/m2Gs4q4WVVx82Enn74YGP/SH8+btLQ3v4+VtQsjdtm6sWebrQqttz9SR0/PnQG0uY0NZXXmV75eN2vOOm5m1pyXgJe+ArzUeuaqY4f2Dt+jwWzqKgWu51IY06pWMmVSb2tIkPRIFYMwVAYkQplWWJ07KWEYP0vCK6T0uCuWEEAaMmKXQUIo7mRiqAaSIbQas+ZcUZo86ZgMjFlQpvdZJYNVwT5h6lGps6fPZDIGLdG9uQ6X37r4GrNcOn7MM/eOPeXOy2rcXMMHYlNUQ10PSql+tDrp7ejwy0VoL6EPvIOuezcfPSm+Dl1uYM9ocn4rejL+vFKqGE3c4/3GN88o4FCl1NPR+2loIZMfisj+IhJnZMTb/Qidd3Y7+sGYRBuKa9ETb4v1JcOrFUzjyXq56rO4H0+hFU+Jtl8CNIrIDLTi55To74Hq/sQ1HzeE6LvdiASDRONbIpJ9v23+g7Ecff1HoQvTx3l4X4i+n4U23v9tUEo9AUyK/g/8bg66Rlk6+iiFVmZrQxuQAkwTkekAInJxVBYkxuFVr+MSKeegvYkJdBj13egQr33Raqq/iPYb/0FFDAr02I29kc0i0oUeL/3ocjKr0DUbQYd77xH17QsiEv/uJ6FzI3+70R+ohhpq2Gzw2y9N6G92Wm5ssLN3gPEWsMgwuCiRWMfN559zFO+KyIuNhnWdlR31S7Nu6J7TZzLJILjbhtst4flbDny4e8aBv1/HzecebSiAsW//eFQpM/bQH565+9MAhvK/m7a8Xcz+8vW3bCX73rzt+twMnIXmzTvNXPclSC4pdsNEKPbg5QhKrmOA7aXAtwhUAhWCBCWgiFEqQ7lfq5cr/YTU3Cz8tQSrCoBn4GCyBJfGqQfJDHzOwWYKuFMkmbmnujOLLrnpfbn5S3cYYpjeboalDgGYcsJe2c+dtPe3ppyw16bKzYvQ0TMxN4fo1CBA/QXNf/827PPC3U8E2JP2eeHu93DznjdeO6dxbPPssacfnU6OG0n9mNHp4iETt6eluU3ZZqHdLsqCnpVXLpsy6UIApky6mCmT1nHzRTstPXSrVAFbAoBg/GAC2+DsNeC8kyaxMkWunG68z7aaFg0K2TcbyjDf5xfzc4q8hyx3kXfDJEEiGfogIQ4JSAUBicCFTmhZM2VSV326bpuS0P1mWW7r8P8fe+cdZkV59uH7mZlT92ylV2mKYK9f7F0+RY1G0USNMRbUBGONMaIUezBGo6gRo0YTjSWJJaKisWH7YsWGDVA6LGXr6TPzfH+857DLiooRaXnv6+LaszPv1B3OM7/3aalFs4OK53611+GSvu77xy783eE7ATydlCOeThrbHMlkjk+mm7rN3O/ECWvlJls2aDZIz2JHyiF9JS/jPNryAVsoCS9VLQKfiUgdxvuyWekF/dKv2bdiKkeWOQEYA/waE7a6qm2GlD+LyHuY8NdPMHmS+2DEXjmctD1l45GgLY+xvGxv2oSjYgqOuJh2EKeIyJYYobSip6CIeMDhInIy8ISq3tDx2kTkdEyYLhhxe1Fp31eu8oasp6jqZyJyE6ZoUXl28PVSbiaYognrPLv7a7xrw2kzpCFt+awhRpydDJwmIh9g/k5aEvYTMc9k2UMYw3jU38e0eCk/Zw9h8oKmYjzlHtCzwzmUJ5B8TNhzAXM/y+1bQszzcYuqjgMoeW1/ghG7b5Suo3y8/YDxqnrq198di8WyMTHmqKo3AK5/hHcx+dfvYibBWii1fzru6FQR+Oz6R+hJyTaffujmU2Dz979q3yNueGEl2ywEJxTC6jF9/vXkKm1zKfRzhW0ePbz2PbS1FvxPELJu1Nhm38NxU7hOUEr+jpv6ePEiFKPEfdBiDIkq4ppyXvtFAzTvQigEaMk2hwy+4hk9ZfTBzpa4sb6eIyts84ejr/Uc3COmdK85Kdvc9MThmZVt819+HOqpf+p1uqp8DJB25fjabO6ixlh8g7PNTBv7GduOv0mVf+WKVHsORDzeYNpYYwuD8DstcLO67PPvu7/UNncauukhXkWSgu8HTnNrGPNiEfb8nnr/904hk553T01r5pQUibFLDjpgWRfHuQgIGT6sCphY8Lisi+TDlIcTCNHPCg1zCoRvzKnu8qN5XWudxnw2PObNzpMbNewN9c+GVeHuURWvsxd2R8AvKC2BTz70HFxwKfhAsdmlEBeqC0I0DUG1ahiDzodXc0ty8hPjAK7c+/ubtAScmDOT5WXbnAceCioq9m5JJseP+Wn/X373d9eyobPBtc5YE4hIZ1Vduob3eQOwRFUv67C8E+aluRNGxO5Hm/dodSl7H8sv8vMxL+/VtGvTICJDgDllgVTyHF6G6Yv3ta0jSsJjFHCXqgHUUEoAACAASURBVM7/hue4zhCRrpgiRfdgvGHl+5QFjlPVJ75s2/UJEZlNW+4ptP3d86paJyLPYV52JgLnYsRbBUZIepiQ6ihm1v4xTMGmdGl8ASMMh2AKO72KmVAphwSVaR8Cvapw6JmYUO/nMRMffy6NWQK8papHlP4eqGr9f3YnLBbLfyPXP0Lns7/PGrXNo4/ocwOqS654eN5Ktnn0wdIVY5trgOnk2a8ICT9mjIj4rKgYECrkcxBXyLlQiJkv2iYIBeiWxtE4iFuyzUo1Qk05N3H0wcY2X/G4sc0fjr7WW/jIPeNbZ719bgiZwzPa6auuYfjR36vKiIxyHe76119f3WBsM4fc1JV5S7cH7glC4oW8KXyUiBvbzLSxG4RtXvDGk/MqO9fVBJ/NcSO9e+POnh2mp03HX9yQ73bNDXVLhg97DhhSCRNjcK60s80fqedVNPpH1USJLsjxaXPV8sfyhPObuncvbL/Mn9gDCrNnb95ThM3y+eS02k3fes1vZXC1a2xzkRCJCnmH0Ik5Tgj4sXgYyxWcoBhQ8CK0egK5YGY3govv32Gf5w995+V9Zi4v/PlhdZyPiS0OcV+d/NLfj306KV2B/AEZbVqHt9OyAfJfKRa/ChEZBATtQ1JXYxuh1A5DVStWsf55TPje3cAPMMapfXuM1aXcPqMrJs/SA85R1T+IyK2Ylgv3q+rpHY7/IiZ375xveLz1nlIO6TmYfL+aVQxZpqq9V7F8vUFEzsLkWf6w9O/i0qr2fUQVMys/GtP82sGERB8CjFbVZ0rP4SJMVdR/lva1FONpdIEdVHV66Rl/HSMUV1UJtf1xc6zcvmUsJux7KCZ3cVJp7GvAR5h8y/2Bkar6CBaLxbIGeOjqvwwCp3jEhceudm7bFWMul9z8FzLq58PL75r6Bds8+mB5EVPk7i8EjECoxKFIu0m0XAE0aNvGBfxEW7y+AqkcITEyCF0xXlMXOPCKx/XFP/aVWxujHN2wGXdf8bie1f74/0jKiw48c3jGRGlsVAyfuA/Iz5m/dA+gShXTgtOFmLm7S5g29gvVYNcnFn365q9cV4/y3Miwxjvu/E3NwE2P97YbQqxbF11w+98kHo8RDUOt//izf2cK+ovtmuv/D3EccvkrpGSbmTzlmXeGHSTTu2xX71fWViTU+edRc58q2+by5PYOTJ4yneHDBjX7/htO4HhacJwmDSThOCQrIJML0YSrrohka5K4mXy2Iu9TD67jEwYuY9Pd+kz6vKbzkM0XzNpi0cKmSY+GjnxC7LW+NRUf7bRJ11QMf38Pd+RRN11vbbPlG7FRhKGuYXagrf3EalEK51yG8eKsiknA6ZjqlD8qLWvfMgPabI+0Wx5gPELRdutSmII5DwELVLUc2nIU5oun2yrOb4/VvZYNkMWYKpwdhWKA8SzeuNbP6JuzM9AH2KJUpfdgYBDmmViMaffiYcK3HsFcm2JCkL/Xbj8jMM9HuUXMHbR5sZuBehG5H/OcxmkLK20fDt3xmYxgPJUFzEvVbzAhpycB15X2UQW8jAnRLrNd6VwtFovlW6Pi7KhhmOUbVBAdfenFevGRf1mmfn75qkfEboH8yTjRJ6FwLAoUiVBEUYQEhD6hgIiHxFxUMkjiUwK/E/l0nJgTBz+O40JKSL0l5P6o+LkrHjd1DmKtHNUlTtyZQa+OR/9BZmO2zTKXfKEVYx8QgXgMRPAxk5A3rdPTWy1066jr9k4Xgm36j7/+1M/HnDk0udWgQalCvljtsDTf2jowjHhurLl1p94tja+2QpAy3uYtmDxlhW3erIIffxSNJNWNSyZa1RN4mDbbvByoZ/iw+xuC4nK36MfyoQbPkgx3dVw36QaECAvCHNXNvnSJVeA1pMMYxSgE04O051EZG+JFIr9xl8z7yT6NS056oPvA69zu/YO9hKrezZmX845zQj4UKp0IoYTWNlu+Mdaz2AERSQKoamYN77cTbXmKZwFn8sWcRSi10yl9XlUYYBMm/DTACMN+mLYGJ2DEwtFr+tzXd0qFVzoWMyhzsapeuzbP55tS8ggOUNWZHZa/gfHegZm8+Ji2fqHPAD9X1dntxqeA0zB9Px8pLXsfIw6/h2kDMxqTW7g/RkyXc1+DDj/b04gphnMhRrR+iCmK9DlGTF5TOqefYsTm4tL+q9ufn8Visfyn/P2K2ytBgiNHn7RG7dvog9vZ5oDfUeAwLeJKBIiZYjZiOh2b78aZQAUh1Tg+ECTMl6JAU58uO1ans81+a/rz7su7FfoFXZm+2x2cIA4jPtieI694XPNffiYbIduOb6Ct8ntHkivyFtdTtHWGLK5vGdB9wHYr2WbNznq3ec68Tb3mDPOmffpB5uHH5m5uWrCRMHUTRjF5ygrblx5xcEqC8LS/7HHRuyPP2fNpAIYPW5VtfmhBPj+8JRbPvt+iNVsLbk8hcBLqZiUbur44US9Rms71cSg2LsxGbihURC+Mup7nF4sf9kH3+1OX/vOK4nixuHtNPi/iq/7UrUppSHTx/KGL9lfV6mtOn2Rts2W1sWJxDSMihwJTVdtiwkVkMKZa5GGY9hw9ROQSTJESWPULepmO4aoZjGB8GVM4Z3dgiqoeVTqWaYW8kf9hS8WMtsWEPy5i1QZplWHBGwoicgFwCebZeA4T1tKMmZVNYkRfL0xO4z6r2H5oacwLGKE3HSPurgWOxPQhq6ctT7LsyW4vvHNAXbu82P7AK5hCUluWxiRL3vXdMR7Sv2EqAG8C3PffNnlhsVjWP0Yf3f1QguLUK/6+bIVtHrNHzWBEjg3ijYfh0eWKx7X36P1kIhFOVkCcL07YFkKI5oACfqYSL+oaI02CdE2hW7NK+HKrLunV2o2dgih/v/VmPRHgsoM8AbjkCX+jts1sO769ba5n1YXlAqaNTa1i+QaBts68oGnOvDGRQtH5/K33nhv68WeHpGfNbS5kMoVYVSpebG79bQ0cAOSZPOULtvmBPYYNDZXHe3nBQ3tUuFc3pfOfeLGY66l/7dsVyR82VMT7btOcrq/OBJ0cD7dYwC/GKcaURItC0gEn72dScbezTH5KAT4ecUz/Fr/xFSmw4M0mZ+gnwG9feiIB8PFxP96r4Lrd8tHYQ/fvqHsr9HZd797fnHrzf9fkheU/ZoNvnbE+URKKt2A8Le25AdOMfVNMu4ubgcuBKZiQmj9hWnusyoh0DBVOYnLQTsCEo9ZjXt7LQnEhppn7xs7OmGI8o/hikZYyjohsvfZOac2iqhMwITwVmJ6GT2Cehxjm/25XzPNQ+yW7uA7jfd4PUzXQw4jCfYBpmCq4TbQV0nExQtEv/R5gWm3Ui8g8EcliijTVYArlpDHhNK0i0orpL1pXqjz8GqYC7SMi8oGIfNk5WiwWy3fK6KO7H0q+9RZCXck2BzT9IaDxfBw2AzqNPliOI8aZOLy4wOGTepjkw9tFQItADtw85hsywEsWIAjNF2uQpeLzzotjb5y/7AQn5HXxqRflXYBr9oh0iTazONIUvrW2r30dsCvGLv+CL58Ed9n72g3WJkhq4ITqIZtWLslS0WvwZj2WZBY976biXqQiEXFcx1GT05/kS4oZhsp1rVGv20Lf/cHDRV5riMccx8NNK0Ob6zZ7vWtjumFuOr8sEyMghEgCLykkCoLvAVklUEc+bECWzhs+bPbzPzk+e9/Avh/kKrvXVLru4N0Skk4QPnzb949I3zHiqNYF6dYtawvZ2h3/eGtRRF4XkSePburx2Mtjf/3Bk+eevsH+HSxrDysW1yxTMZ6cjqWgd2flVhnHARMweV2dMOWMt2PlnovFLzlGDvOy/g5wCrC/qv6u3frlfIOcjg2Yl4DrMXmgz2GEzSrHlVo7bJCUPHp3YcTZlphqpNWY1hjzMDmKvUs9MueLyMulkGcwYTFTMSGjPTHvNFlMIZohwAUYMVgoH670rzxBIaVjplhZkJY94XFML0fFPN+XYHIZAf5ROvZumPzJlcJ4LBaLZa0RFKfiRB8nErtvpeUV7EwCB8e8CyncnIHrF8Bms7t07bY4GhsRwHYu+DkPclHIxSg6Ygq1hAIFB1orYVkFuWW7UNP5b+E72T6c6KWc/f9wi7HNuZQuDTVcGnZINdhIeQVjm6cA71GyzUpInuyKmUgaW2ez7fgN1jaL9NQ6P3vX0tbiEGf4iIFhncxM9e9b7TQ0z6lJRhcq1Clsclr6fjn9nVvmj3rt+pffPGVkLcATUvG/qYI/FeEgJ5Qun7oJ7/UiaTeiDVsueH1IMihe0CDB5+kcAQ4UM4E2Z9FYgJdyoNpBIhF3hwQkq6CTA1RKkdbGZUHEjbhVFW58y8/fONuNulrwxJlb2fmSfzuJkQBHTG148MiprS9HFtTv1nnJsgG9cv6MdXojLRsENgz1O6ZUrfNftIWTlgvWLMWIvhDzhdqPtvwxaCs00r7gSAHT9qAnxpvWE/ilqk5cC5fyH1PyuF4DXN+uIM+a3P++GIHuYO6Rx8oTIQosX9+rogKIyOaq+lGHZYKZhNgV8+y8jxFxAebZcDDhqVWY5+wdTCuRy1X1ytI+bscUQcqWdpvEPEPlYk6b0fashbT1d4yUjpPH3NcoJpy1L2ZSYgvaCua4mPtfA7SWzmsZZkKkqKobYkNpi8WyETL6kOhuhMWSbfa8EL8gEM2WbHMzBC6820kZ5ARU4eEWAS2lMUaa0Gy1sc2FOAWnlVfzPegbjZKMCN0CjxN/f7Pev04v8uu4auqhCNegejW/3utPa3z/u121L+nCY4AoYaFAznMRx2vLdAiBpUwbu96Lxs+WTNm8f5dhK9nmqZddLVvtMujxZNTdLVqVivDSO9N46dVt8X0/m805BXDej0vzK6ftXdUcC4u/uPpf76iGOziOc3mXx5+9EuDxfQ64vavjHJXJk+sfyZOKh4mExiKFWLzQkM3O7+TS382rNqonuYCwSyUaQBhVIgUIGvwgLxHXC2q86McNwYy40D2/eOHSloVz+8lmm6el/8CYF6vxEvlifvum+tppNV1aVRynqkqWdYrHO6lDcdPLf2dts+UrsZ7F7xhVfRnTbuBkjNdlBDBTVftgXq6jmIIgj2CEYEeKGCHwCSYReggmV3EQcPD6LhRLpDHiI/cd7X9LzLNcbmRffq7L3lkBOonIku/o+GsEEZkOvC0iKxXkKXkX+2KeFcUUnPkU88y8VBpWDsVdjnmuBOgmIieIyDBgl9L2lZieiCaQymy3WfkUSj/LXvCyUEzTJi7/VRqfwoTalI+dwcwmj8MI248xYa59MOGz5UI9FovFss654rHCyzjelcDREo+e5MAIgXeue1z7JMEVcWJvViSWFPpGH8p7vAXmS7NYALcFaKHoKs0R+CSZ43uVRXZMzqFTLE//wOPg9V4oltFVpr+sGdKFLSlNQAqOFyNZForlXDmTTrHt+PW6J+/MhY9PV8d9e9aix1eyzXtecqHG5i/qG9bWRIJiqGzS4yMKuTlIeGcLTPs8kWKwOJFjH5vOD298qV7QlAOSh27vHTHihBd/dNKwg5POXoM9op0TpGISrU9JzC/G4kFY2yNa6UT6u3kfPE+iHqTiOG6AG1UigRBERBu7uYVkZwre55nkv/LdevWvrqpJ9evec5NE156korFIrV/MZrPF22ZX9xx/784jHo3nWz6szKcbep92Yp9G13siEquwttnytVjP4jpERJ7GhKiGmFzDzhgv446lIe29ZEswL/oO5qX/AVX9ydo+5/UVERmHuXc7Y4qrzFHVY0SkCSNmBHM/LwKeUtVP19W5fhkiMhnYGzhbVW/rsO5W4CBMYZofAi+p6kOldf2BB4HBmImHHpgJhnI7kbSqdhaRBZhw0nIhmwmYKmxxVi6kVPYw+hhv5b4YYepgQl+XAlthBGU5BPVPmJDgPTGhp51Kx0+XJkYsFotlg+Dkg+WVp7beZrsW0fCEJe8tLDpat3AeM+t6sGXlUgiK5EKIxkGy0BhComkbnLAbUZnBtfdP1Iu//ij/JWw7fhzGNu+AyZefw7Sxx7Dt+I62+dfA00wbu97Z5k8XPPac58V2DoPC2QN7DF/JNqf/MOFWSaUOdD/57NjoosXH4Lr/klvufAxg5g8OHdhP9X4nEd/s8+aW+kjE6VFfoHlB74E1wdJmIkU3ffCjd3Z+7/vDFgRRqR2Q0SDvUFiGO6FaYr+qzuTihWwYaBQ3GoOsi7ogtW22+bBWwnvneglZWt15XpArLu2brt+6NVHhzo2mwpaqOidE72rt2uOpvFu3ZyzbvOvAOW912uywfWpcN9rSc8RP+q2D22nZALFicR0iIvsAj2FCA38O3I55Ee/MF3vflauylcMEf43pd7c5pspqV0y1zOM39kqo35RSGOc4TH7eOIxYPGNdntN/SulaDgA+LrelEJHtMMJsKiY0tBNtYaktGC9gFvN8nYF5jhyMN1AwYjFPW0XZgLZn7RPgPszzNg/j4RRM79BtMRVVO5eOUwW8iynIMxDzAvCaqh7wXdwLi8Vi+S64+PDEfvf22+TRRZFI+oT09DOzFeHtTY0sqQjpUrUQN+6ZfMWwiPoOioOk+yH5TQhlBr/uIclTgloGppZHD9txnzMGuDh79xm8+fHbb3O8tc3t2XZ82TbPwbSOmMK0sRukbdaTfxwlFtkbkY/lpttnA3wyuP92qlrjee5UZ+Cg2XHP7bTUl+ZF3XtVVS5e2NKjsSm1aOZH2cVucPvu2+z0M8dRyVTFHbe1kInkQ9GAeCZHwY8Q6xwBz8HPgptQlTThB53F/Tvw65fEW7i5+r0jQDVMeg/Z5vWKLoPj3brX5vKZlmScqpbWzDuDCsW6RFNrn+Ru2xa79OsxtccPTztk3d41y4aCDUNdt9RjQvwWqOr9wDGYYjcfY17Yy0VbcpiX/3nAAuAEVb2Btlk5MD3uDsR4piztUMNY4C/A7zBCZ0OlF+a52EdEPhKRH2Gei8+A0zF9FpNAd4ywa5+jeBom1Dmgrf1GAvMMxWirxpun7bkahCmE4wFdaPMmnoTx4s7GPMPlnIetS+MWYryMB5fbuVgsFsuGwHs1yUWZuJd2U+6iP9wc3B/PcEyftBzVPRKbWdM5EjZ7BC0pWOCRXTiMpoXbM09mML/rG5xw3y16A4CK+Q6NSORHyUTygNkfTd97nV7U+si0scq0sWOBOzAtnX73NVustzS7XteGJcuDqz6s32f47kd9NHz3o36kqgtEZNaA/oNO7xdyWvdCkNwi9Lvvsnh+38F+Iev5aQr4yWRr42mE4Ztpn8BryWcThTCZEBJxD4knicZdVIFMVguJbCDkQxLqDmny9cJsPu9VqV/ng9MUoJNjNSfVd+nzP/HWZS9kGhvTFS5VqkJVonqbWclO1Y01qYXLPpyxRzydOKLh9j9b22xZLaxncR0jIvcAn6rquHbLhgN3s3LZ5ZA2748AJ6nqX0XkTaA3JpdxmKr+dW2du2XtIyJXYrzQ5d5Vb6jqHiLSC+M5XamQTOn5GNpuFz7Go/gGJgc2zspFlN7CFMf5uomksqe7fVGchZiiS+V91WGK8nymqrO+8cVaLBbLOqJi/Pb3AJ+mx741rrzsl+OqjvBb839szBSSLmbGzXEJc73JVcwjEdQgyR41P/jDzxqeOPwBeZM4vU/OX7Srr7rzEUdfaW3zRkzjqSf+tnn50pHv54jc2hTgU/HG5Jf+tocOH7YTJuqnIJOnVJfHF4YPe7M+CIYuiSiJYkG7+xrkvXg64rpvLofv1UIiCWEWJFqEf2eY3iVWGNojVCfiecSiLuRMRsn8eBGhigzo7EilJmpqnednz9XunbpKXUUszAbhwmhtbU+JIumKzuyw4LO6gdvtuLsKn9T+7NT/hur5lm+J9SyuY1T1uA5CsT8mhO9pVm6lAW3hqOUiJ2CKjVQBT5eFoogsEZFWERn/HZ/+RoOIDBKRt0Tkx+v6XL6G+zChyllMMZtbAVR1PsZT/XF5YOnv314ozsN4CCswXr8k5lkqe7F92oRi+1mksoe7/bJJpe1DTLjpRxgR2oKphFpfWr4I08vRYrFYNhjSY986rr1Q/OmNyf5Laop9Fkjh2Xw9motDfjl489HuL4AWCUO/3XdkNZtRQdXteuUDZaF4X1KWPJSU1vuS1javLo3bnjOoadtz31q27VnrtW124G5XdcmgsJj9aSxcPr6zXg0gZmK2HmMjAcgMHzY+gKHVrku1REhI9HM3dLyKMEz5GfZc0EIy1koYon6IHxAJ/YGwxQDfdYJ4i8YiDkEBitoU5ONpPKlTJYYbTUn3YuukF52K5Ht+ZfjG4nShNZv/aKfGXLZnS3M6lS5kgPoBu+xewJEFEgTN6+h2WTYwrGdxPUNE7sX0x4uy6oa2V6nqpe3Gvwn0VtVu7ZY1lbZ/TVX3+o5PeYNHRM4ELsfcszmqOrgUOtmIETyjSmHC5fEzMEVkGoAjVPX1dXDOtwPHAs+r6kGlZQIm7FZEFtKWBO9iBF9vYC5GzFXS5p1s71nkK5ZBm4fbxYjDGkzV2QgwizbP4l6q+o6IzAcaVHXLNXDZFovFsk447ibv4SAR7ukXNRomcF2F+AcQmw+RBqjsmjp3wp9abimPL3sWHz6szTb/IynNHkRa4bFjMzpi3VzJhkPrthec6eNf4eFFAvyZ1dN+t+Woi1a2zROvbLPNP79IZgTQw4WlAkdNvHLt22b9yQ9vp6n5WKpSz8vdDxwEoAcfKADy+FPa9NsnF+q7H+bcZU928kFcCKugX2sYzkZk+dR8sbZxcTY6rC5KrDKGSx7Uw2+N4CVAPUIBx1QWyFIUhyaSpAnCBiRXg3hLXafQ0rlPal5VskikMsKnM2aFXbv3KCaTDuJufuZ14xZN+uWY+YgsHjlh/PZr+x5ZNjy8rx9iWVuISB9MX7pycZEcJpesfZ/F90XkQWB/4FWM5+bMDruqAcaV8vQsq0cOkw96dun3RzFiKgr8SUTu5It9LzsB9wKbrs0TFZFPMC0pGoGD261qARwRaQCqMefahKmA+jwmr9XHPDflxPbPMM/c9pjrKl9bx2stzyoVMMLQo83jCEYw9qCtSM5TIjIAE4pag8VisWyg/GSiDFJhbiGNoxFEo+TCIrGgF1JIgfM5+Vim9dPDz5cXUj5bt9bwqrsri3A5tv1+UlC9HMYdm7G2eXVx8bJ58vNdnMNLi14lW7LNcf406iK5k7CUCiE4KqDQVeBOTFuttUbjh8/Pjt58Rdf8W+8sq7n61hW2eXmQaXHUdeYOH9bQfdvjquNbDqnKvPBkuhIql0Z5mDw/XhxNFpb4+de7BeEhqXA5b3/W8sk2W22drZDY1qgilcYW++BEQHERSJAHVQIECjVINBJPehW5XFDQECcbUhS/6A/s1yMoRhINjRE6V+fennTJZYPwpA5Va5stq4UNQ12/GAL0w3huFBPq19678xHwAqaITRxTinpPTCsFAETkA+BtKxRXH1W9ESOY+tEmbLbrMKy9eCrSFrp5/do5yy+gQNCh8m35vOowwvcUTMuLFkw10/MxIaiHYMJiZmFaicRK23X0JEqHz+XKqV7p+FngHoww7UJbr0cwhXMmAZdgiuFYLBbLBok6DJAEveNORb5au4Sk5AESSLgp5LeE7EI+dgPeroyyZWWaeCxkJy/Dnpj+ygBce8aBH713wrA3f2iF4mqTmjbhxkoqlnWirl8NNTuUFm+C0r5zs5BDyeOIUvACAkcJgFu+ZLffGRIEeQWVznWBTJ6ywjYX8ItBJOJ3jlbUNU97fG4YkVPycZ5pgRYv5MolTuRXLamqiq6ee0g+Hq33PffjF6v7jb2xGSdoCf3QcaVQuuSSkS0LR1yQCpDOEK8h9MJcq87Az7TUz73nlFuvrxr01ve6FF2JSEKprfSRRDSJRCaR8y4niFjbbFktrGdxPUBEdgGaVPUpEZmlqjParbsc4z10MLNkZ2DaZLRiXvIvAia2210vvpjraPl65mDuXbk58Kpm3BTTSuIujDCbqapPrJ3TM5RCTW9V1WtLv8cwrVNeUtVaETkDOBc4uNRL8q+YZwYR2QRzfSlMGOlTwHWY3MOO3wXlAjZ+h3XtW7jUYUJhzwGuwFTsrcTYswjwfVU9bk1ev8VisawtZMK+uwBNP9iOp1MfM+uuUa0rbPPhz8g4GplJFOE0tlg+nTP6vETnTw6g1Y/iuZVcVPc6E9nPjI849EQDfx1dyoZMPSa9oWybqxDaXB2m0ZOiTMfhHjFLPph4pT6zNk/y35edKkOLiVsrLr3hWoYM5MnLj64MXPlfRF4aPuX/ahk+7PzFhfRp1RR2rTln2LKac4atKHj0zM/P7j1o7qylybyf/CDtOH5V35d+meC6xrwmfa8YiRQdnIiDEBKGhE4+7wSxWOA5jlvEeBciFMI4UWe5k5Sqbj06NbbmfvTU3v88p+gUrkwtTjXlu2WqJBWLRChEge+PvO4Sa5stq43NWVzHlF7+jwIaVfXpLxnTTJvXJl8afxeAqvbqMLZPafnc7+qc/xto18C+Iz8BHgSTG7gWz+c+YBim4mh/IFcSh9sANwM3qeq97caPAj4AxgJ3qOrdpeWTgX1Lw0wpNeMF/Cra910sUxaNzZg2HW9iPOPtacQI17SqPvqNLthisVjWITJh37JtXqoXPPvcqsYceZ+0uC5eoRK898hWFjmooS8PE1J8+ATt237sjb84pA/AmTc8Zm3zt2DUCFlEb6pXWC2TDKGEnEjE2OaJV6492/zZccMeTfjOHkv7dptbt1nPTbUhl+11we/qHrt0xDaxTPiHXNK78dAx96+wzXOHDxul8EFVnrERlzsqpky5G2DxwcMmp3Ls++8ivKwS/LJaCwVISL5ALh6FCKQKQBBCIQ+RCHnPC7LghI4rTlgggfCpeOGCml5OQ+OS5l4tp/Rd3Gv+q5m6hiFhKoSIi+RySNRrdH3/XGDRyb8Zt8pn22Jpj/UsbvuhqAAAIABJREFUrmNKxUgeYuX8r478GBPu52JCCh8Cpqrq8PIAEZFSP0FriL4l7QoEtScEUmtTIHZgc0wIaDnUtFBavhmwCe3yJksTENfQVnhmp1LLjU6YcNtymPOnwF4Yz3XH621POQQ1wDyDPqbq6mAgWnqGz8RUZu2G8dK2Yvo6Plf6bMWixWLZYNALnlWZsO9X2uawiuND4V6i4vg9dHks0edfXdKFqbedsGiFbX702fPksH2vVSsSvz3N257X9KPkwdG/9nrcWDGAmLHNE3+zbmyzG9Iv5hPP5LJSWwxDryVdBPCc1FaaL/bxYk6/8tj5w4f1r4Rrij5+LIYXwk4Lhg+7MubRKS8sSgjFvBJ8Dp8q7FWExcWYFwkVEoVS4QDXQWJxVIWWIlIRQfJhECiu64O/SMOPa/384GRFInrAY4fmp4046sx/0+92dao6OYXcHHGcVorF0wKkbJvXas0Fy4aJzVlcD1BV/6tEiKo+gvHSKCb0z6Nd4raIvA9kRORBETlXRG74rs95I6fAyi8ICjy5DoUiqrotsIWqboMJ9dyxJAr/iAmZndRurGLE3CuYaynCiiyPAOOdjAMfAlvRVnW34/V1/N0t7W8upr2LB8RE5C1MuGtvTIhrbakK7yjMc5v9lpdvsVgsax294FlfL3j2S7/3HzpYH1GHBlClh/SQJF7oBZuX1487Z4f3P3n5+czVv9r7wecee/TcZyc/am3zt8DH9+szS4MVTcQCQuDJtelJ7Ejfv07ZuqU6vsXON96/1adv1qVa395kx+dPniCbNqVu2pTKmj75mjvLY3tNnvJZWvk4J7xcVAq5kuRV0BAkFWfxQdXEb6/WDxOwYxycahyqAzSeB/FBchCIqAhUumgcqAE3CYUmmJuFgbmY54Wxytikw4a/lSgW7u/WvLRHIpNN4cZqR/5m/F4aT4zCcxtxndZ1dd8sGxZWLG4gqGpvYD4wG1PQpl+71fMwX52NwGXAqeU2CpZvjqp2wdznMmlVPXJdnU+Zdo3th2KK1wzG/B92gRdF5IXShMEVwJWYAkjXY8RbH2A8pirqhxjROJM2b2HHVhlhh9/LzMCE58bbjRmC6f34BkaYjhORWzDFHfoCm5bajVgsFstGxcPDtHdOWZRT/SyYX/jh7ccuGVheF42nGoKChCrhzJrKmsu61NWd2vDRR9Y2/4fUTft9p2cOfm3RisZicTITr1z3trnvHQ/NAui92B+aj3BKn0X+YPJ5x/GLboHic60X/uLN1ot/MV4fuuuKjHCl47LDQuH6OujUc/KU3sVefcZH+/Z+wTFF5wLg/4BMBbgJ0IQguIAD6hK6amxz1EEUY3QTMGNOj85duw3oFW/2NcQV6d6l/5AFRa++slh8vXOmuVjR2jDupjFX3+LDyepF+kay+c3vPfGXH3/ZdVksZWwY6gaEqq4yXEBV/7f8WUQOB7x16QXbSHgc4xkLgH+u43PpyBzgaYyNuAI4EePt2x1zvofQllN4ZrvKuGdi+i5uiqmqezzwy9K6gJW/DxxWDjsFk+O8g4h8Vtr/+8AATIGc+zBtRLZS1SdE5BelMZnS9u0rpVosFstGw5P76YBVLb/oqhf2KH9+6YnJJzt5z6vdZXdrm78djxPhVNZD26zCHDfg6cYdJJtbUrgiVZH4aXVNp7luobh74MpgbWxwa5OJsCKTdVwYJZOnjAXIuN6ZBaSSHt028xYufsGDc2oqkz3zLRmAwJXQwzO+HXFwAp/AdXF98EvhQlI1ecoONUcePK/ZTyQaM5F33Uh0s0xYjDuVNfdFU7F7I+jg42+49pk/XHLlr0OREKQ11lwMK3VFuyuL5UuxYnEjo+QVs3x7LsB44ADWasXTr0NVW0oFeO4DXlbVzUXkCGBnTDHxClZU1ebGdpvejCk48y5GNJYFZbG0vvx7mUZM2HM5nzEQkblAZ0w4alkong4cAMxX1XkiMh3j+RZgoapuveau3mKxWDY8dj9ouLXNa4azgGmlz4+tyxPpyKb/vKjl9RuvWuD74YNep0Evb3PWxYM/uXL8EYUgvXOh1c8le1MRy2SDFnBa4Izupe1qW5pv9uqXndcYhm8mIl6naBgGxXjcnb28tdjipxkYiYUJL+qAUchpl8ZKoS4C0RzgQlA/fNiczaDT0lxrYVGyy2bpZEXcC8I9OrcUz67unJ0/5Ko/zbt6zNXT4+Jv4hVDvEj882PvmPA/6+xmWTYorFi0WFZBqWjLPzC5oU3f9fFKYcP9MS1Ulq3GJvUYMft26fdDMaJvhKpO/ZJtbgNG0BbC3IzJmUhgBGFH71+n0s8CRvhFMEIxLI0vi8hrML0Wf4DJWYS2ENYBpWvbU1VfWI3rslgsFotllUy8UnXURfIIJv3hO7fN42+ZuMI2jz1j1Nfa5sBx6sXhQ9XgVYDW1pYjxIsUm6ORQ7r85OzXK39yNpWY/mdl6pYs+xPCiBu2HtQvkfHZpn5+yy5Llhdcj3gyJCpeNFLOE3EVkiGdRCDnUPBAXIhEoUsawsCLR/t7QfSjfJF807LJb3jUzHnD/8EESClEhYgEEaEVtpg05moB9hx56YXWNlu+EisWLZYvQVUbgZfW0uE8jEGqx1Qs/UpUtRXTxqPMzhiP4lXAbl+yzRJgOxH5LTAS42H8n9Ix+6xik3IYavtKqUswwjBKmyeysrSuLBC3x4QH7U5bLm1URM5T1Zu/7tosFovFYvkyJl6py1j7tnkRq2Gbv/fzX61km2sl3LG6UKxwPe9qKHfd7MDkKQuA7f5092/vDH058uLXCu/ull70P/Fcvr6Hun0C2ooK5BQiIaHv4MSidVEJcxT9DHlY8ooXq45W1kTDxmyYq/Ec6dK9qnHhXFqDogJ0gh0akX/FYMuise+NQHTSmKvPG3nphdY2W74U22fRYllPEJEY4KvqV7VRQUQmYLx4w4GDSotnYoTikaq6yoR1EYmoarHDsoOBBzCisCwOQ0w4axJjo3zachijmPzInhiPY6S07nxVvaW0zwuBbTGFlx7AhAolS+fbDRiqquNW555YLBaLxbIuGX/LxARQGHvGqK+0zW/+9c4bKBSHReLx3eY8O/UsEZZtWls1M+rIVb7IkYMu/+0qbfOiR86PdP/+b1eyzYX9hx+8uHXZA52TUTf0YoETcdwQwkJIPq4k0hAWcALPDZ0EhB5EnhXv85a6uj7ZxuZ8sc+mUXHF8RoXnP/Tu++5BWDSmKsvBLbNwLyksc2TMZFFwwdt27+bqDN0nyNHjFsT98yycWHFosWygVHqy7kHcBjwDCZqNvkV408B9gfuAP6NCT9txAjCZ4HDS0PLfRnBCEYfIw4V0/6igBGG3YE05sBfOK6I7Aw8gglJnY3Jbfyrqp4sIvdjZmkPVdXF/9kdsFgsFotl/eKNv9w+mZy/Y6Su6ohPXvy/Z5ww1CN/f+OX2uaxx595ytKG+gPGnjn49rCQ/Xe3P7zT3CJhY6EY5jq39Hy+KVl/WDSqFBynGC3Z5hyEPvhJiOZBG5FsAi1UgFMJ3Z/qPiCNA8Nuu+ULx5005uoVtjnnZxdoUfu4UW/iqCvGXPD8Qw/+Qzynt+Dsu+ehR9iWGpaVsGGoFssGhqoeISJSyqt8ZVVjROTW0sfTgQmY2cOtge0wM4pRjPD7Jybf0eWL1VBX7A7jGUwCbwHfw+RLRkSkHhgHXIIRl17pX7T0sxMr8wugqxWKFovFYtmY2PH4k4e/MP4y2euU03Tm5ClvqZmUXYkxh/3oVlRbFnlVv5y/ZPmESEQSV902c9CvTuixa1b1AQeicddxiLQ+1uoXhycjgesVYy4RY5JDcDyMgY6AOG4k6UYk2VTkrffF+15s0aKZeZzg+WN/XP9hp+7juuWWX8LSUMNsXdQf6GhYkYpG4zEvk03XxL0klG20yBmBH3bd94gjrVC0fAHrWbRYNgJEZD9Vfabd71mMR7Ae4wXsB7ykqsNK61/A9FycDUxsv6svOUQG45E8BPgpcBzGXlWxco/G8mcFFgC9gKyq1n3ba7RYLBaLZUPixwccu9+fn753hW2+7NAR2QhpPbjLE/XPN+6S3r56UL8FDfmXjn74/mEAmYMOeEHgec9xZi/z0xMbyWtfqRVBBAdCMSFAMYyRbYLWokNrBO+QvO//NFCOmxWtcHokY1VvVlRqKl4h+UBZEsaV6pgUfdXQSS+IOale2Ww2c+HECR0ndC2WL+B8/RCLxfJtEJHDReTc73D/ZwIPich97RbfjQkz7YYJ+3Qw4g0AVd0LU9zmJky+Y46VhWJY+jmr9LkAXIYJYz0VqMEk/M8orS+Hqk7HeBgDzIxlALSssYu1WCwWi2UNMKuRw2c18p3Z5p8ceNyva2uqHjrhf49fYZuLuP/YKvpsPgvd9tnC6TdkYK1zYLf4jPL65BNP71VIpv5nCdxUdCum9fXq8uIYoejj0wphAZgL7wXG9uZT6l4mjvvvUBgZd6nxiulFCyOxGYVkdZgJ0MzyxmyC9HQ3yAXJqOs7Gu0SBEEgEd96ES2rhRWLFst3zzHAESJSsSZ3KiIXlD5Ox3gQ3ymvU9XTMH0VA+BjYLSq/lREhorIn0VkH0wPRYBBmDDVMiFtwtFV1QpV7YGpBOdjhCUYb2VnTDXUCuBojIB0MCGo8dJ+DhGR+0u5jBaLxWKxrA8cCxw+q5E1apsnjbn6AoBQ9bVMLl9PqC+X1136z/uO61Odv8ODIJPPTJ+RXDa65ta7fv7qQXsMfeOg3f786kF77DYtX+zRHEKtsF0CEogPFHC0GKSKWUn5eTaDysrJUyp6TJ7SG2WZhup7QtZxo/R0pJ8SraW6tjLXuUtFuPnmR+fruiyKEhEvDCNVqepoKpWiu9NvxKTTJtx/13k3WNts+UpszqLF8t3zC6CPqqZLPQd/pqo3fZsdishioEpERqlqX4zgWwlVvRi4uMPiAtCKySk8D9N7sUd5t7RVN81gBGSTiDwCLMRUXo3QVjG13GtxGW3CMFvaPgQeBXbCFLjZvPTvtW9z3RaLxWKxrCF+CSQH1JCe1YgAPxtQw7eyzZPGXb04EvOqJo29etSfn753lbZ56zv0PIz9bU8B0daafv1Ssyq6jPKnf3BbT+jxaDOSwePwOBEnwG0Jsq0VTlDxTjhv4fAD5JFXB569vMXhgIhDZFm0xqWQC5dGqyKRuNvJWb58WVBREfcRKfrFTMzzHMIwAP0n6E4ahv2CQjhEw9DaZstXYnMWLZa1iIikaWtDMURV5/6H+3kTGAy8rqr7rGL9Yoy3b46qDl3F+ssxnsFTMD2hri+tKrfPACMsM5iQ0+bS/srrfGApxstYi8ldBCMY60v7bsSIyfnAbraojcVisVjWN0pCMY2xXwEwZEAN/5FtnjTm6jcdTwaHvk4ZeemFR3ZcP3z3IxdDWAH6+eSXHt6y43p94R/XEGoPQj3l3bueOr/+89mjKwW2jBPg4+bjkIDCHsGLmYYwqKlRt/HWygMqY17ULUSj5Arif9S5ZlFNEX95vKIrsVjCTySREMkuaqhP9azphwSNEEQhMt9riu120u/Ps7bZ8pVYz6LFsnYpz864QG/4coMkIg0YT16ldpjVUdUdvuY4KYworW23v1uBrYDdMF6/XqqaE5FLaBOJLsY76GLEXqfSOT8K7IUJO70ROBsjRPcSkT9iQm3L3yddSr+DycW/xwpFi8VisayPDKhBZzUS0mYDv9I2P+U92FQaV3mgP2Il2zzy0gu/zjZXYkTpCtt8+6UTbnVUNs/Hw71P22Xww0Sold0Pz3X/3W0/HxAjcMEVDzfrkE2Aq7DkTmfn7tdF5+ns7MJ/eHjDGrPp2kK6eeIsOLuqKrHgqD/ettcdF132Rz+XP4ZM0QvVJZWq6YKGxxDg4MWKEN5jhaJldbCeRYtlLSMi7wI3q+ofvmZcK8YgnaWqk77B/p8GlgPdVHVvEZkBdMUUq6nBFKqZp6pTSuOXs3LOIhjvYBzjMSyHpD5Y+rcFpl1GM7AY2BQjMGO05UGXxaethGqxWCyW9ZqSd/Ed4KwBNTz3VWOf8h4s2+Z+B/ojVltsDd/9qKeBCCiTX/r73s+dePwMoOvigjc7N3iL1LD62Zf3KGTncdvtUwCahg9riEJ8RRUBgeVQXwNJB1INkF2S6pxIFdN39M4ve+Tlrpvt74ie2eC6jUtrezR19rxNluSK2UyxGCuqOpF4jMqkF3i4bjYdZE+/doy1zZbVwha4sVjWMqq69dcJxRK7AD/6hkJxMbA7sDfw95LgrMN4KGsxfRZPg5UqwB2J8QBS+hkCTaraDdNeYzEm7HQZsC0mz+NujMdx09J2Ieb7JFs+ldJPG71gsVgslvWaATXogBq2/jqhWGIX4EffUCiWbfOQHv26//3UM89vbQjCulgkEunqFRK9C5nte2h4Gq57Vnmb6uraH+SUYk6hJaSoEBahqWLylC4uvOjAomghX2z04kte6LP1zhVhcFIhDO/6hKREGhs3KTQ3QKY1jGrckSDMFDSgueDJskyeBj9wv/xsLZaVsWLRYllPUdX3VPXhb7jZ/RjBNw/4GWb2M44JGd0EOB/4ELik3TbPA5+0+30QMExE3sd4Ft8B/qGqZYGpwBsYAQpGKFYAhZIXcQFtRW6WicimWCwWi8WyEXCgP+K9A/0R39Q2P4+xzUvrFyz9meO47pTm1vj8pa2f73v3vYNe7lZ1/t2pig8JgnErtmhufD4nfBJzIB6iCwJn0AeDNhv2wsiRHyzt1r0iDp9FCy237PDAfeMQIR+JKsmKN5e35iJzWgtkA9VoMlGRjxTz599yTSfxUgsKYdTJBE5ILlg68RdXWdtsWS2sWLRYNiJU9ReYKmtDgT4Yj+CHGJEIJtfwaOD1dtsoMKT0awQjHEcDAzGVTA8DjhERUdXLVbWLqt4G7IcpglOPKXRT9pb+vvSziOnzuKKlh8VisVgs/21Mfulvx2Bs84CgEPSZN21GMZtPvjO1qrNpgVUsnvNZ965Hc9vtbVVJH3tSq2CICEQSkWjPzjWfeDNmjI4QDGiOxAbkYO9q+EU4fJgceMvEy/f846QuB958021Nmdb9WkUKrc2tSwoquVhFxa0AES3+2QkzRDRfqE7Ge4gTTlsX98Ky4WFDxCyWjQxVvU1EJmDCR49R1fZirRFIlAvmiMjDmNDUchipj/le2AR4HBiDmRH1SuMHqWq5gfD3MdVOO6lquRoqqnqDiDyG8VDeDzR9R5dqsVgsFsuGwh+BCcAcAjnh7ifva7PNIQ2oRsu/Th8+7OGI527XHUIHnLCyOkjGY94wlm/yztJlj0crKk72YWYAzvP/Pi3m9H6k997zvj8DYPuthp5CvhDFc2pHXjV2hW0+e8KYcTdcMP7u+PvVg+jNg7mUv2wtXrtlA8aKRYtlI0NErsSEn6aBSMkjqACq2qvD8P0w3wMzMJ5ED+Mp3L+07C7gM+BWTIuNQ0RkjKpOV9XxItKlNAYR+QD4FDgCIyTPx4TANn+Hl2uxWCwWy3rPXZ1yV8T8oruAuH9uUyQyfPejZPJLf1OAsb+8YCXbXAP7Jf3AW1hdO23TpoZtpWGpE0JDCPvnKlMf1XvRf3XLMyuD3CBR+WGYCw55vvcjF+497/szRl4+etSki68ANbZ50pirPwA+Xdg9dYTTudP3E07u/ORnsajnFIJ1cBssGyBWLFosGx+HYsJJhwJ3AD1EpCtwFpDrUFxnMabwzSuYkFOftpLeAzDexvuAhcBNmIqqs8obq+ooABHZojS+H7CIth6NLqZyqsVisVgs/7W4Iod6yVSkb0Vy6O+33/yvdZv1r+Gxp7oecctDF7gRt+lvD09cYZvzkchiRWprHXf6kkh8a7+YC7pAygeqly4ZnO7TXx7tUXffHgcckt6+59Brlz63fNbsWxfOK28/8vLRowAmjbl6i3w+P8D3/X7e+8sW+f37FhdtUsx0m+u7kVzsuHVwGywbIDZn0WLZgBCRChHZt/T51+2W3ykiaRG5TlW3Aloxgm1TTM/FV4BRlHIXReT1UsuMAzBtMY5j5bYX5bDURcBw4CSMp3I+MFFEytVOAVDVDzAi0sF4E6OYXlUxVb1zTd8Hi8VisVjWF/583nkVvzvj7H0BHj7v5yts8/SfHP+32SN+kH7vp8dfV/vwP7dSibSGbqTQa7/9eyd6DUgtb2h8xRM5tcrTcwDmn3HStLkn/3h5omfvA6p6bpJwHTmmsrLCqYzF8SAIIewOsqyl8fMXd93ukFvJ/EhrgnR2p+b5n576/sSO5zXy0gs/yOb8WY7jONGIF/cWLI1U1eR6Nw1uiv30+XNe6zjeYlkVVixaLBsAInK3iDRjQkQni8jnwBgRaSgNKXsBNwcoFaGpxojGZmBX4E0gJiJHAF0wIrEf8Gegpd3h4qWfbwPbY3IX78CIvxEYYfmFXAdV3QKTnzgP03gYjEfSYrFYLJaNjkfPGnn3Myf/sKXK1fpNu1dNvu+cUbP7t+bHTB11RgOAi/ZxwHFD+gAkH7ivS8Xtd1TnZ89qzcyb01xXW7PrgT3cN4f1cpJTzzv3fyUMaiQoJpx4tF/Q0vznYui0FDp1R+q6EnTrGY9G4hqFt2/e+3u7ZsV7WmYtu/ORRx/s88orL4yI+IXjJp1/0fKO53jB7y/bwvW9lmq3el7KM7a5kFdrmy2rjRWLlv9aROQ6ETlnXZ/H1yEim2BEWoS2/7PdMB7Agohsoap7AUlVPajD5icAJ5ZyFosYL+MzqtoPSAIfAb0wYjEAyuLzY1XdVVUbVPVE4CogVtqH325c+/MUoBojQD/E5Do2i8il3/YeWCwWi+W/hBkL/sCMBeu9bb756sv2qI8lRizs2tPzoxGnPvDJIF1zjhO0ZNO5z886a4vBd93zP6kwSA696y9Htd92Yr7p1EnF5hM55EBNJsIiaGqJ6Cs9b72rX+XSZcl/Dhjw0QNbD+k1XeKZmaEETUvqG4oiZIq5T5KTp+z63KlnN0w9/dxjD3Mjv0t6iWgsXlHwISAS+YJtnnTJ1RL1IpUIfTV0PvTz+pnrSfNvfj7a2mbLamHFouW/kpKw2R84fF2fy2owByPQ2hMAp2G8g6+WBKOuYtszgZ+JyKmYa40Dc0VkWWn8xZgiN10w+YVdgeXASx32cy0mrPUOVa1U1YGrONZTpTG/VdUdSvvtWFDHYrFYLJZVM2OBALth0h/Wa+oyuZfmd6nxZ3eqZX7CZZ7r8VkkEizaYrvrqrv3TYVB8OrnZ521Re3fH/mCbVbVkb5fPPX+n//s1GQme3hlLhePeJF5D1w+dln1Y09rEAZXVlSn9tsy5dVt1rDIrdukb9cgXVju7rLvSrbZ94t/zvthwRN3wshrLk+NvGr8F22z8DwxCsQZd/a1Y3fo/M/O+0WnRq1ttqw2sur3S4tl40dENgdaVHX+uj6XL0P+n73zDo+iXNv4/czMzpZsekggoYsIqKiICIgUEUVABeXYj4J6kEiwF/SoiBXFbjRW7GI7iAWODZSuIgJSVAgQICGF1O077fn+eGfJgsBBD55PdH7XlWt3Z96Z951JeXLP04i8AHoB+AQij9CCEHXfA2gJIcZ0AOnMzLYInsjMT9jHd7dPlQPgdgBHQQhMk5n99vigfV4FAEOU9z4OQCsATcx89H9YYy8AqyF6M46G8IJ+DSFwVQCNzNzqv78bDg4ODg5/ekq3dwGwHZ3y/7CVtKeUFHuhaWdYpvmy5JEtVNdwXThEnrTMFQPzWucboUirrJpy/YS4mU6PPcb877m0lVwT2w3t/wQA3P/i890B4NCVq3L0cPT2WJr/qPz0dK8Wi5qnPzDNz9+tofC8T4K8ZbOlhEKKpUXZdVi3F8ysrOP0NT/kKxYafS+8sE/b/MTDj/TSLGt1WtD4J4DRHr97gGZqm9R5HiP9y0wViNWdaYxr/T+4XQ4HOU41VIe/LMz80289loiOAzAAwMN78egdKCyInMEYgCshGt77AJwI4QE0AfRIWkMAgEJEsAXjTHv7m/bYhIdSJqJKAPkQEQZy0pxzAIyz525pi8GRAO5j5lDy4ux9DwB4mZlvA3CbLUAJQiga9vkcHBwcHBz+M53yf5NtnlJSTAB6AugH4LHJhUW/r21W1elSLBZFMDYhGAkVu0l2P3jH3f0eu+vuhhapqWbc7+9BD0xhAAionkCORErFF0vWFpzcd27rxshHMA09UpD/niWRWReNmW0tRqZuyN//8/bKY0admy97fJLhUmWORYFQEywjNsf8YdE48uUySa7cDS++0Z8tc6hLUe7rMPaCXWzzEw8/cmLcNO9WSH5u3F2TbgNw2ytTHyUryhRrH1Vl1aWna+qnv+P9cfgT4YShOjj8Nt4FcDeAIBGt+k+D/ws0COHlYuYZzJzLzH5bHCoQv8Ntk8bHIDyNTxLRvyE8j/kQxWlyAUwDsN0esxbARRCtNqL28RaAewF8ZV/jGADDAJwBERq0EyLKhBCsMwAsSGxPyo+0ABzPzJcdgPvg4ODg4OCwV2xx+C8A9wEITikpXv47TjcBAMHjcU2++dYZD00rzp427XE/AHhcqmLKJBk5rTYmBhvgmMGs5+vReXdMuml+hmTlZriUfAa1ZVBuRHXd2xQzt0uKogXisbVz7rz/omhV5ekhHbHKvgMQ6H2iZanue6W8Dl9JEr8jBwNj2TKHusBnGBb3SF7Yc3dMzUQgtt1F8puSREsT2y+ZdC0blqnHszWr8Yr63iOMyx3b7LBfOGGoDg6/EiI6CsC/0dyPEAA0u/ro7mMJIizz3d/qgUy0qdjT8UREe9k+Cs3exEZmbr37WCJqgvD+RQHsQLPojALIhqikakIUq8mDqGzaN3EOu3CND8AtzKwTUaG9zpKkc8eYOfk+OTg4ODg4HHCmlBQfBZE7n5a0OT65sChj97F3Pv0kcTz4N3KnvnvnlRN/k222PZnYkwdzRskzdH5OWQQeAAAgAElEQVTh+F9s/+fN149KdfvebOFym6ovte7v11/d7qHix+mGoqt3jg3MX97EgSY11FQXlXZU1UTcKe2kUACZZEXSTxqaY5ZtCsUtmDsM7cdVZWV5FTA3p7YpOOnvF13AAFBy+313AeSTiW4ed9ck87k7phYCwLi7JpU898+pTdCgwkRs3COTHNvssF84YagOBy1EdAhEbtxCZj7rfzDfDwCyIIRUci9CAFCJqBHANgAXMvMP9vbLANwEYbxe+C3z7kUMjoSodHoVRKuK3Y95317vEmbRv2kP5yGIHEUvhFC0IARhLppbabgAJPIe8wBU2/sBu2cjM99gfz4JooBOCYAMe+zPv+5qHRwcHBwOZloPabbN5Z///rZ5SknxDxB2OQu/tM3uWx8vblQUbJQljJ1cWPQDAHAseBlIvoljwd9sm/ckEp987fVzLOBvclratdiDbb73gYffv/3Wm38IatEl995+27UAkCwUAWBNxTbJiGpWnurytszLb+fx+Kx4WaTSalWQF6qvDcqSBMmtujysdg9aJgry8/NccVRC1DEAk3QDAyi8a1LCNp8CkRJSAgkZMFEN2bHNDvuPIxYdDmYmQbSCOOn3noiIpkA0uE8gA4hAiKOdRglAJwCLiehBAF8A+AiincRHB3hJKsR1XwFRuCZ5rR0AlDHz8Xs6kIhaQFSCVSAEYwjiPkrYtXppHOKawhD9HTtACMsEscR8zLwZwHj78zIAXSG8kD/AwcHBweGvxO0QNmXA7z3RlJLidyFsc0Jw/cI2E8MNoBuAxXc89cTdsXh8oZfwEcNIJ6IDbZvdvLn+5K2lZeNw4QV3JO+IP/xtB1VG2d33PbBH23zNRdd1taKho7Nbtpbj8SiN7loQcfm9PsrIlDyqUmBtXg/O6YtqzdAQjqiWboQzU1JqMv3pHSiupSTOw2zETIvx6XP/6nDquLM3A7gcAB6fdOdqk60OKX5f3yvumuTYZof9xslZdDhoYeZ/AHgbonLn783ApPea/ZosFJORANwIYB6AjgAeYeZqIiogopeJ6GciehUQYaREdD0RHbqH8+yL+yCE3FV72LcaQOM+jt0EYDqEcdUg+iFaSfvjAJ5Cs3cxBcDnAFZAtNoAADBzFoRR/pqITmTmOmauA9AZwmCf9yuvycHBwcHhIKf8cx4DYZt7/w+mS0S6EESuPLCbbXZJgCzKu1EsHrtNAuZF2dVR9qQ/MuXaW6unlBQXTHm6+OUpTxX/POWp4lcBEWI6paT4+iklxb/KNjfO3zi5cavqaZvV4erk7fzYt8Qm1sa0X/YoTtCuT8dvDz352OkWLDZA2qGt25azJFtqejrQtj340M7RrTCfisOMNUbDWBcMprCsfg5NXxEzjJ22ecLdt2Vt394YKdu6fsknz7/fa9xdk+p6dO9eTy53W1kimURbKweH/cYRiw4HNcw8hpk37O94IrqWiM7/DfMMQFIlUftVQvPTTDNpuARhtEwAc9Esut4EcC5EyOcwe9twiET58b9ySYdCePb21Paj1v7aCRHV23mEgBCGDOB6AI8BiDJzCoB19rqbABwL0W4jcV2XolmgJs7ZHiL0xweg1N52JITX0wRwEhGV/srrcnBwcHA4yCn/nMeUf77/tnnqkJOvnTpk8K+2zZMLiwZA2BtG8/+0EpofgJokQ0hJQIYs6xaz6fN45kJUDwcsvGkaOFcnbqvFtIRtHg2gCCKVZL+pa6p7MTtFj9UGanaxzXRNL7aAGguoSt7+UMnTDY+WPC1ss0QWILHaOfv6ouMPf6yW9caUf4xJqa2pXmcoil7rz6iPbts8KDszJy2i+syAYSLO0pgNW2tu3FzZuNM2jznj0vZxLZpdXRfwwzK2AcDSdeuOcEuKKru8ZuWGunMnX3C9Y5sd9hsnDNXhLwMRrYMIpbQgKnj+WoIQIaUE4Y3rAFENlABsBXAYmsM0PQDWQ4i6xNPOBRB9Dt0AUojoc4hcgpYAZttrrASQCqAYwG3MnBCou2DnH+4xOZ2Z2+9hsxu2IzO5EA8RBQC4bFHXEkIIZwCYD1GCvMm+vhQAEwF0J6LFzLyYmcuI6GIAR8A2gMy8mojGQlRa/RqAREQ1APJ+5xYjDg4ODg4HIVPvuHZdSvdWHeKrKn+rbQ4ASDdNEEnYLBE6AGiAsF0/QkIPSMI2p8DtgbrTNhsAkGeqCypYO1rXYyrAKdfde88H6VkZIyHs/WwAmFL8ZKVpcWq4MVqMdeHbHn7rrj3a5sfefeJ+APfvaZ/vpl7td9/mUlyqBSYAuLZwwk7bHHxzZkABu1a+/napnu5r2calyn7Fnb1xe9W/2rTu0LlAlmv1zCxvmzatPV8uWXanrLiOePH+ZxdfdssVi1/+cHrZxSMuubh1q1bnlG0vrQKAiXfcsPrJO6eNhaysTdX9X8uA9FjmlOrWaNNydMOljm122CeOZ9HhrwZj3yGaez+QOR8ibFMC0AbCQC1g5gJm7oPmCqKwx3Sx34ft1xcAJHIYdABvsuAFZq60t7shBNtJAFoAIl+SiB7d19qI6Jyk9/cQ0Ro7xPVmIhoD4CEA9+xBsMUh7km+Pa8G4GgAgyAeJmVAeA7dAA4H8CgzL046vg1EAZ8IEZ1p36e3mHk1hLHWIMTvkn2t38HBwcHhr4k7xQst1c9Rr6/+txw/ubAo37RgaBokTUdrCNv8yeTCooLJhUUnQ9jmiD1cAtDFtCxophEGgOMpf7oXuJUMwDRNnSw8N7mwiCcXFr0wubBI2GYij6bpcjAQPcnrVlsAwEsPPjPl1WnP7tM2v9L1+XMA4OxR4+n2J59/7uMlC9Z8tuYH+mF8zs1rrsof02jEHwoa2j27H6el+uOckcW+NDVfi4fkqmhYI9PoMfiw7r1zPT6lfUGrnM6HdHRnZ2a6B5zYuzOT8uhlt1yx0zb3PaZLm9R01wjJLUWevvfBMwFg4p03vjXxjhtWR3yBhmg8qqW7MtICUuCz33LPHf5aOJ5Fhz8le2opwczdDsCpsyC8iQFmbrfb+ZmIFkF4CxMPYmQA2US0nJmPJaK1EEJxFTO/tPvJmTmLiEYAWI7mcJUu2LWwzC4Q0UMAConoAghvYLa9698QBXcSYan7anQchRCFKkTvxechqqDWAVgMETbbZg9i81EAd9rXWbPbtbQmosshwl377mNuBwcHB4e/ALq1lVxS213syLU33/df22aJkCErqJckBCYXFu1imycXFvGUkuJvAfSHbZs1TZPBnH3HU0/Mv2viVQM+LCn+yat6dQCrJt9UNHv380+eUJR5++OPjcjdoSwvr6mvAgBVcnUhifZqm6d3ee6hpkio8MWuz4yiofmDtu5oSM9tDKBjuOY9sHZUzFBrj8xtXcvgdcnH8RNPEXqeAEr1o02DJ1qgxXwuX7qqKq7hS7Wq5zvHot2zTVRWhRuWtSkoGHpIQUH7Q285ftf/dxQ8ahm4U2KWSVZ2sc23vfpw62fTHrzcUun6dCv1lF95qx3+gjh9Fh3+dNi5eS4AlzPzm//juUNozmlMwAAGMPOy3cYOADCMmW/+D+fca59Fe/+pAJ4EMA3AGIhQ1yqIEJvWEGKuFsBGZl6YdFwdhECMQYjERA5mCoBzIHIWlzDzit3WciJEnuPu17MCIrTnMmZ+e1/X5ODg4ODw12LZ+hkBj8+rxJvCl/c8/ML/qW2eUlIchi0ULd2AAYYFsMflGjC5sGgXWzblgfsGABg2+eZb92mb6xavIQDIPuGIPdrmV7o8d2pTPPxkmts/7YMhm8e4fb6j+h3XpfLwOX+TsoE8HbizbPjMWpKkjWeNOnOnbW5asKxOjcd8MSDmURQVbrdkenzsT0tLWbp+6YUWW+nFT3y4ZManr+20zXM/nE3pEdeJIER7nnvKLtdzRr/zVngkz6EGqPfMBS+t+bX3zsHB8Sw6HHQQUTZEldEfmDm+hyEmxM/2RCKa8T/OlauBqM6WEIwWRBhqNrBTbNVBFKbJApBFRFuY+em9nfA/rZ+ZP4WoQAoIjyDsuZrsdfydmY/aw6FLIaq8eiDEZA6a8ytftF9lInrLvq61EAn/WRDhtkfvdr7Edefa818CoC0z372v9Ts4ODg4HPw8cv0j2bpldoxZxg+TH7/lF7Y5bkZMRVeUDq26ToAo+Pa/pJqZ83TdkEzTgCLJlsethmFH7Ux5+kmCrtdBlisgyzmQpIwpD9z33eSbb313byfcm0hMcMlP43ba5jFJtvmHOWgCILuAs84+e9Qvom5YN9aaLuXYmsZGT15aRr07NT0Luq6DGX0693nmwy/mIxCOyjecfflT11w93vx83YoNJNE4w5eZ7ZHUIHazzQpc+bIiyZoWHwRgzY3jJ1wCUNtpzxQ7ttlhv3ByFh0ORlSIHk57fNhht3S42t5fbnvQdmLn8tHvsTBm7ggR0plAAuBi5k/szx9CGKd2EOIrDhFy+l9DRA8T0UpbTAOieukW/FLUJdY6AsAH9sdEpVQVQtzG0FzR9UyIFh3XQeQhLgOQZ1eWLSWiJiJ6F8B7AD4F0JGIroUIUZ2UuNd2f0cHBwcHhz8hFrMqk5To2fsL+nW9LPPoDv2uilRHMkp/WlixZcuqHcn719d8SetrvvxdbPPkwqKOmmlETNMAQYKiuCQwlMmFRQvE4q1FME0vDKOdJWmrNTUaN9KkrQdi7ucn3fbwMzffvnL6rXfubptP2NP4jMF9+lfGo19lt2wJ+FLqYJmQZVmNRKMhIxyOqiC9c8fW5uWXX3Spz+e96oROXYsArnWR/G1memre2k+/vHb4gNFbRg8/r2l4v9HvxhF9K6AF3tAR7zi83+hrPV7Po6kp3kkfv/wanXvecVR08SmObXbYJ45YdDjosIvBfMXMYSIaRkQriGgYABDR/bZH7RCIgixZAHxE9KCtEUshvGKhvU7w3zMOQDma2054iKiPvfbTIbx0n0DkProBvHKA5j0ColBNlj3XA8zcxc6lvJaIPieiobsJ5Wft1xZIlBEXFeQW2V+JXEcDwLPMfCqARyCEbpl9nApRkOcfEPmal0CEvroAfGzPPwnATCLqeoCu1cHBwcHhD8QNj1xfGTK1ryY/fkv03qXXnH/LgrEr7lt6zYkAsOynD+//rvT9pmU/fZuRk5PeMT83Kys7J9W/efOyBxtrVrerqVpZur2sLlS+sfp3s82GZd1qgctdqssik4AYvFMeLu4BAJOLrj6hsbapJhSMfhKE3rA1VOveWFV3QGwzs3QEQHmSJGxz9+n8QPfp3KX7dOYdM4uvrZv92lyUbh+K0u07bXNuetYDLpcbXp8vJxQIBqBrIMuSzHBw8dCT+y959I6batP9qVt1TTd8qufZMeP+MSwvO/0Rt6r8yIwyb6qcwTpUWcZACNt8AYAxJnDnprI6V22w4eMRY/7OLdTM2+KmPvOqS4Y5ttlhrzhi0eGgJCk0cwiEMEw8oRsIIV7GQISAJgTbRIjwz4LE9t/LuwhgFkQifTJzgJ3tO3IBnArhVQSEl/RAMBTAS7B7HiYgogkA7gHQD0CP5H3MvADiCacHQJq9eT0zjwRwJYCJzNyXmVOZucQ+ZhkznwxxrwlCZBZDeCTDAO6GyH1U0Bxq9BOAjdit/6ODg4ODw5+HOx+/hQHAYKN/xIoeolvWUAAA0UC3R1FbZbS83pAlMxLXTZAFX5o6cduOuu81WS5QZMWSyWV+u/Tfv4tt9rs9L6R4U/rLkmT//xAFWw1fAMCEf95RGtet3Eg8fqq0Ixazol6YhpRyIOaNkG/oCQNOfHXQ6afvYpunlzwzYSPa3FMa8/YNbP9pF9uc3vvoBU1arDKgxVP8GRlpcHsggde6+/YYCdGXeWKrE489Oq/v0amt+/csAYDDhw5adtiQfic988b75+akZBAxBUwTT6PZNt8lK7AaI5qycXNI2Gam1TK5SmNW9IB4UR3+nDgFbhwOeohoGDMnxNgzAM6HEIleAAshGsz77OFlANrb72t2r2h6gNbzKZpDTXPtzYnCMYDwKNZBCN32EF7S//oX0W6vMR5CvF0KAMw8m4jKIXImtUSPRVsoz4IIG70fQmADwoM4C0IEXs3Mu4TwJs21HEBXe1yAmfPs7cMhBKsfIhy3JzNv/m+vzcHBwcHh4OKepVcPu63P43MAoKFh/TOBSON56f40VlXVE9SbZiuqOiheb/jKt+3AjvrK8u5HHtM61NSIprramt79Rx5w2/z4jLc+BbO3samunQktV7NMkMXsVrwptRurwB6pnogqe7jlM5bF421K7r7vywMxb9nibx8NxyPjvYraoOv6PwDgsMEDZq/496eVikUZgXBYO+GcUekAwBsq6JvSn2YFjPinPfM73O9VVRUA2DT1qKZ90BgM0uatlVefPPasPdrm4f1GL09LcXft0rkjhcKhwLTpj+YBwL0DbxseTYm+Em1h+LbXVkUtlnq+PXuGY5sd9gtHLDr8qSCiDhBhkFMA3AUhHAsg+v0ZaBaNDCGqtjDz8Qdw/sMhisB4IFpPJEqCmxDCyoIQih8y81UHal577q4QbS7mATgZwrNXCOAxiHDXagCbIEJFZ0J4IhO5ian2aQw054Kez8yz9jJXFOIexux2H1shGhhr2NVTGrA/v8PMYw/MlTo4ODg4HEz8662Xe5w89IQzUtNTptSEqi71eTx3KIYnNxqNadXVdYa3heVzIQ16QOd/3H9/IB7Ttyx87eMDZpsnllxweOf0M4pA5Klv3NE9img3ADB103TDTfF43FJcar3Hrb49ubDopgM1LwA8+M97uh7R7fDF3doXzKsPNA6JRqLy4Yd0uUMB/ROW5S7fsb16dVnp+p7djhomycpMWVGGljc0RDtn58LjdnvBDMswdNnlcgHANyvWnD/w4jN/YZuH9xtNBEQYYEWWIh/MfydnxKDRW1lHqtdUrdaU5uNWMqoidejYviDQ6bBW/pgRf2fCDf90bLPDPnHCUB3+NBBRdwBfAiiwm93fDtHKoRHAZxDVPhNhqTqEuDn8AM7/OYDvAHS3hVFyyKUF8fuWaHTf5kDNm8TXEB5NgvAUygAeghDIVfbaDHvsqxAezn9B5CYmUCA8gmVoLn6zJzQAll1MCBB5konCQxEAiZLeaRDX3em3X5aDg4ODw8HKhAvHnbDs48UzbyucmiNRPrdM7fGiX8loH5fDjd4012fePO12rQFmiuyFJEFr1IPpmmwcsBy6iSXnfS4j+7v1TbM6TTz/3LEMUyML4LgFF5NpGIYky7JimWYGxP8MB5T581d+/VTJG94JNz+Y9vXS1a5VqzbJm7dVXC/Lsg8upbqyofZ7y7L09i1bw+dyvdoUDNZrsfAb7JaX6aYBIkKKorhUVY3G4/Gylrk5e7TNsxe9xwzoBFgfzH8nBwBIQTYkeKKk+Uq5NmT6QhuO6tQWGV41raE+KAHU8kBfr8OfD0csOhy0ENHg3fIOmyBE0YbEBls0tmLmv9n7Ey0tVAhRdMwBXFIVhCjcZH/ulbTPBeGJS4jGU/aVM2kX44kQUc3exuyBmP06xJ5LA7DS/kqD8DYeDyHmPMxcwMyX2+tMiGgLwHJm7grgvqRKpkuIaK69riWw76P9eaZ93NcQ97QRIg8ycc56AG8SUZCIXv0V1+Pg4ODgcJAxbXzW4BWnd9lp32RCuc6oZWBnXpxE+ZypHtnKpxz6t6uefGfqxz9+KzExLAvuG88bG26RmXfsgVqPBavKQtyy4lQFAAai3VgCPG4Vqsujkg4gxlZTIEgVVdWn9L+6cK+2edRlJ9MFY8+PjL7onIpfsYRYRkYqhvY/vncoGOZQNBLLSkvdFIxFllgmUrt36Dqwd5dj+mlxPZKTnh07fHC/gv5nDp9oRPXDZZCpMMMCLCJpeUbPbl1zMtPui6zZSACw4YtF35TOWzx3eL/RmcP7jV6SlupGi5xUXH/JVXTPjTfMPKxDlglgAcmIpmdSQGNzbUSPmYFgGBu2bar9ft3aDwsvvTh46dgLn/8P1+DwF8YJQ3U46CCioyEKpjRACJIQhEDrDuBtZr5sL8dVQ+QNlkEUxWEIgfU8RAGcODNn7nYMAWi/v3l3RLQK4smkCSGoEkaH7fcRCOEoA/DvLVfRnjcIoImZC/Zz7kSxGQVCMB9lz2fZcycbwIsgKqQdC3FPEvs0+7gXAfQFsJmZuxFRxL6mhPf2ZwBD7Uqn2yC8tiE0h6BGIDyaMftaGyEqp9Yy8+/hVXVwcHBw+H9k5FLp6D6vpmdn6vg4QDFTa9E6tLbsqE2utFbda0KBJ+a8/sqtezzu9iurWY6njD9hcFmnDh0OiekaBzUjEgmZL6anuCZGIrH4gL6n7WKbz5pPRGFq/69h1n7Z5skP3LDO9OS11xXD8JJPsXSNJAbI7WUrZpIW1yMNsaBqWSz91LTDv+Dxkj3a5lGXnUweo0VQ142G9958d/9sWel2mvn5l4Gvln6vbN6y7ccZT957qUzSUma2iEiKGTpq6+qQmZ7GpmGdXtPUdKPi8xzVNj0zXZIkAgBd17V3/z2v+8gh/acbzH1lwk85vY48ZvuSFVHLMo0rJk1N2OaVsxe9NxIAJl9z/TZZlnPKGirCAFLS4IYBMwpI3ig0jQECIyARsi1gx/Tpb7Tdr+tx+Muxxz51Dg5/VGyh+BVEJVEdQoSlQ7SNkO3XPZJUhGVd4nQQYZlrE0P2cNg2ANlENIaZ37aPJwjxowBYzMynJI1vk3ReOWk7QYRtZmP/WAOgnpnb2nM2AYgy875CRiQIEX0EgDJbyG0CUAGRs9gdIpdSg2jXkVifBXEvVQiBugDAEnufx369BACYeTMRHQOgMUnotgXwNEQuZEIs+ux5Ese3gBCTOXa47mwA7zPzlv28Hw4ODg4Of1BGLpV6IcSfLx3QGBo0P82ItUu1wi0z0zNi2Ye74JHro+HBezt21t1P5wHAhtUL16X5UqBqLqrdUWVCVsEMmHt4qGptw3ZVQcaoj6Ux74+w3gaASY8/QtXq8kaPoihBw1z8euHrO22zSe5WhmyRqlsmuWVFhgxICvySQpZHsm689ur9ss2albdGk2nH7FdmdACAwSef0wSLg3Pnvdt6b8ecc/21UrjOLAXQBUC5v/uhKwOr1m+Kx2IV0biZzZbeMcXr80Ricd3r9nyQnuKTG/UYNMu0FMsy3B6PqzEQCl545tCFmqEvkU0TLlVNBYBoNHIxAMxe9N7m4f1GHwPxvwkAwOtyt22IBxO2OSWCOHJ8md7qSIPGiaJ2jCwTHCKiFpdePvaL0ReP//j7BZ+9f9tttzu22WEnjmfR4aCCiDwQYY1gZh8RzQIwCCIfLwOi+uYvfqiJyAXASOwjoi0A1kFU7nwXwFIA1zHzEiJ6DMDlEGGleRCisAaicMxTAN6CKJoDCNHUyq4Oeqg9LgzgC4jWE0CzV/Ff9nHHA7gCwsN5JICS3ddsh596IIQw7HNazLzXNhv2NZZDhJzWQFRaPd9e80/M3IeIfoRoYZEw3DqEpzN5nQDwJIT39VsAucx8fdI8jwDoDeA0AOshiuPMA/AMRBXWwWiurron6u3965l5j02JHRwcHBwOHkYulTxSE9cD4JlDOaVk+sAPa2ONAzrETv3WYxlZQ0cN6+k/ZPAvbDOj0g1AI7RiAPhp9aItNQ3b1ykRz7OHHNf5g583li1l0HUDep26ZNQMeszFuFwjVPkoJY8UUrzV3orTqy9bqMjKU1+63W9FMsoKZJWhxbW6Zwtfa317yUPLNUQPJdMqd4fjupSWvx7AiJ3TA5CAlyzRVuooAFfk+dPKGHSYV3G9OOb883ZZ84hLL6ohtlS5xsrcliEjq84Ms8HWF1+8vVfbPL5noWubp6bc7zHT4ppUoVl0aN++Pbv37dfjq4Zw6KeRPXr1ra2vXxezjNVZqamnw2QwoLsUZRfbrKpuyLL0JICuuq5/EY5E2mYc23WnbZ5SUvwURGrNaQtnfLQ+JSU1NRIz5x3Xq+MzVTuqxsLCEEhwwQCiWhxejxswAVYYdsZJfafOR6qhuup19017dMBv+kFw+FPieBYdDiqYOUZEYwE8b3vb7ofwhD0O4T2rJSIVQFqSMHRB9BishhCISG6ZYYeOdgbwMUShltEQXreEIGQIQXQegLPszzqAVcx8oi0UE1VPswB8yszXE9FpEF43GcITehaE6CMAwyAa2S8F8BoRWQAiSaIxL+myGyCK5exSStzO/xsM0WPyI4giMjUQYrEFhJdQso8vIKJhEAVwEvt8aP4bkMilBIB1zHyTPcc6AB2I6O8QYajbIbyXGfZYl309nSDac5yMZvGZOG8iBDZRETZoX/cPcHBwcHA46JnVx4qN/ILGQqIXRi6Qmt6/tGIqgC/qFs55yqiocHNmy9pwaIPqS0lJI8pnAGBUJmxzBYRdQZcj+7XrYp9z4bdzf/L7/O2CNds/BpDFFp1vqZChoMATy0G8KY2rqD5zbtsN53XVup4lcdCMV2fpoezqVW8XvXbilJLi5YDZTXM1QZXSWkpp+TMmFxZNmFJSvA5JttkSfZljXkmhqGWcFI1Fh7vd7iWaibfvf/BR65abrg0nrlOpNvIA4OE7bkZ5fW2DFtdrJ737xi62eXi/0Ttts6fJ9ZFf93XqkO+vMyiIAMz8UEwOLVr6PeVke5s2bapqdfZxfU7La9HCW1Nf1y4WiYZ8Xp9XkiQFACzLsuK6LoWjGgKNZesOP33wTQAQ/f6ndY3hxg4v33PPBUOOPh4SSwnbnAoAWTk5qt+jks+KFFRVVWVbZA3R44bLTSosE4jrpuUikySJKBqLmSSr5FHkuqqtG5e7XC7HNjvsguNZdDgosXPoGMAFAEYCcDPzBUQUgBArpzHzV/ZYAnAYgDpm3pF0jrkQTxPfhAizlAFcB+BhCFETtYd6sauYAoRYTIRx3gNgEoQQzITI0/sJ4inl3hLlAxCi7isAlwG4BgnI0AcAACAASURBVMAXzPzZbtdJECI3wMydkrZvhcgTTLTj+N6+lsQaE15C3f7sgvAStoQQi032/gx7rMe+Xi9EuOhqCGN3i/2VEJUmM/uJiOww11chPIwXQXhOZfvcCpr7Su6OZt+f4cxcu5cxDg4ODg4HESOXSAQNYTCsc1rccmFv/6jRpmnJhx7S64JwaENQkmWF6+tP8xX0/AoAGJU7bTOh1U7b/NnE8XMl0zzKc0iHl7K8amFIkuWcdi2vuy527cPw1xB0RFMqhsBtZHmD0harna+tdEy8O0r1MCqDrPtlv5LqS2MAcxjWKZrUGHJZqVkSXLG4oW+SSOoqk0R2OiBgAS4JyPWnY3soEPKQ7I+x8YknyhNi0fg1ikv+4tabrt3FNm/8ZgVtq6/doWt67ZAzhyX0LQ7rd0qlC0hrhzQJgKUGle81w+gBLyTymGBIDBBiMA2P+BfC5VOkz9569uFudcFAtgk0RcNRmXUtLSczk12q6inbVh6J66ZPkaRQ+zYFK/wpvlObAoGH3p03Z1xtY5MyZsRZiGtxo/2A41OnlBTT5MIifunxx19VVddpely76OvVP/xLViQ5GA3WIo4URVW8mqHBtDNvvLIbbAIM1iQvrQYwcvr0Nxzb7LATRyw6/OGxQzK9zJyatI0AvADhzToJQgy9A/GEcEBCKP6H8zZBhEOug/AMboHwjL0IEWZZCuHhywSwGSJ5fDCE0OuGZg/aq8x8hb2mGgixmINdwzpDEGIsxx6Ta+/7AuLJagwinPYqAN8w8yt7WfN9EDmWz6NZDEq2gGtAc45gAra/DABjmXkmEV1t77sbu3oBGULomRDhr4OY+TsiugrA7WjuEXkcgFbMXLrb2urQ3BqkCkIsJudt7s4HAP7OzPo+xjg4ODg4/AH5+9IWtQTJ/Wqf6p22eeQSidLR7tXDlCP9uQ1pJ+e40pRuBWe+c2jnI8dEt28fkBCK++LLfxY1qSarflKWZ/l8x8ZzsjactXLt6flHz33X06HqMGw3S/3xYXmZlJ9ZaZZuzpZSvjwXAwfPhxzQTeNwt8yKeO6JVycXFl0xpaSYANQwsxmLxdItZksikggyPF41BAsaCJkgbGe28omknbbZjGqhVLiGRLx0FYBvJhcW7dE2nz4+ep9LlpaVlv7tDTCoTUTVAUizF73nH95vtP2/hviXIAIDPig7bfMZwwaNveLWCTO3f7fmZgD1sz78/NHePbrLHdvkw62qME2TN5Vta8pIT7WysjJT2bJOSj3msO/e7Xf5VW3CqbdnTznDYrdStznS2BeMnFPPOn0X23zp3y+sk0hWLMXMMA2uImZ/TItLJElgBnzkbh7sBQDMADBu+vQ3DDg4wGmd4fAHxm7L8AhEuKSc3GrCDtd8AsB9ED/HDOBQu1XGV/s5RcJbdiGECGoL0f7hTgCfQySjZ9rnbwWRx9jOHnOlfUwMwPikNb0IEWYJ7OpV9EIIW80+J9nHlkMIvFQA3wAYC+DBvdyPdgAuBjAOIu+P0SwUE0V7auw5TIgcwsTTIAXA67ZH8HEIYRrFrhCEtzPbHp8wimMhCtdsZ+ZuEPkehUTUImltCaEcArDKPs++hKJln8cxRg4ODg4HESOXSjRyqfSIAtWjQN3l7/ysvhYfRf0e8pjp90e1WmqM7OBN1VsOIcrn/RGKAFB/WHcl3KUTckccceIN3Z6pu+HQWzp17Dt7cdWqk6/W153zefv4jV0y0CmzQOou9fOf06JL6rDLP9LD7WqsjXPdsqewoaGxrinaFGPweACYXFjEAF4kojpFVaG4XBIAMBimZXkhIQMELRoLtYjEAmRZZrNtdiuZdbL2jWXyWAAP7Gm9o66Md/e6cDHDukYJG/WHUC6nts6m2Yve8w/qN3qdAVgGuCaKiKZBNysR2Wmb33nqfuXi04e+zhsqKL/nEQ+E64NnZbjlcMvsTCiyjFg8Dt00qVPHdmktsrOzZElyrd9c9goADAm1GHsUu/05d3y2qfOpJ3ZjyxoBcOGnMz/aaZubVv1MfbsfUXNc164BS7d+lk1KgwWJJMDSAViAmVAC4j8iC8A5aG595eDgeBYd/rgkef40CGGxaC/Fa0ogPIqAqATmhxB4lczciYieAXA2RKGWRB7jlRB/+BWI6qGLIDxiUQBzAQy0z5PwDmr2WAmib+FWAGfYc74I0XpjC0RLid722GTPYgITwkvZAcDREMVmwknjAhDimBLhnvb4MmYeSESj7GMGAZhqz5ECIR49EOJLSdqeoA6AzMzp9jkTPSf39cAoDpGLWAeRzznCDj1tByAfwNdJ93M9RCVYJF13on3I7qyFEN0xp42Gg4ODw8HFyKVSkxaFGtehpaYhA8yY1feXtnnut4+X6BqN0dSWsCJmY58WBX6/oUsB4i2tup/crXLG0894fClnZ8xdnEtPPMcAUPrFB1deP/OoaZJvjVShVx2RP2jCIvg5A9U50R6BW+caqBsYgeG3YHG23IpUWdJiiCkNwagUU5oWytUutzc9rZckSZA95u0q+R8GsEUzjZ+1aKw3A0qq38+mZZEWjwMAvF4vAJiVVRXlMb2xdUHLjkerimcjiMJxQ4epG6S4XAFVUXwuEG4tnJB6V0kxpcquzaZplt1QeOXAsybEL2TGamvFxYMyc1pMjaeYHFf0lPqyxkY/QXW73UYsFlcgniunJJ59v/HkPXUSQ07r0SV985fLWqX7U9ZHI2EpIyODdF2n6h31aNu6FQCwaVmk6zre+ujzeOnW8k4PrUipa4D2cSbUEbTyTv505oftLIvzs/3+r3sNFcWEPnjh5fXhaLhNRIvh23U/cDgcpbhumS4X5MR3TJIAkAyXi9bJstwOQHT69Dcc2+ywE0csOvwhIaL5ENU40yHET0JgRZLbTxDRmRBtG1QIcZdc3bMOQsA02dvqmLk1EZVDeM8SLGTmU4ioEzOX2uGUPjSLHQ3AHIgqqJMh8hsfhSgsA4j2GvdCePISYg32cS405w3CXmdCTC1i5iFEdK593CMQ4akmxO9mChF1gAiTDUOEyI4F8KS9zi0Q+YxZEOIN9nkrIETvtwBG2dcQhBCPLgB9IMTzTfb4PYnaHRDVSk/GPkjy9tZDCE8VzWG2e2MlgApmHr2vczs4ODg4/LEY+QbNN7PQWVfI73FLitVorZP83A1RRGYNb7bNJ31+0pkhQ396iHy4qte7/S3kAm1ct16qqhsImnpdTo+j2oR+3NDk2lbpCocjdTlnXdp6w9yZ5U8uWJy9uKoc4awyZBRUz/26qGzE2GfO6PTS+A9Lpz7/dL1pmt5SWmjWWmXyYRisZUsFczQL78et8CS3lHJJ5bYdj6oKnSB7FMQJ6/MyMh4B8ExU0wyYpqJpGtLS0mIuIrduQTfYhEuWAUDdsHElL9vwEVm6vmjDnPohtz72yLkuSTEsiR+RiXIlSbLcJOPm8YUpT7/8UgfDNNfBssLzZ31+MjPGEvDkv+a8W3pKv1FbCFK1YiELRqxAcns5PS2NGpoCFQCsy88689s2BZnD7il+WdYZwRlP3JMqyZJsWtwnFIqMycpIKwRANXX1cKse+H1uyGKNqKyuqVi3aUvpiHHnDd3X92l4v9E07rIR2LhpU31Ii0iBaFitqauvCZhmLkyGmwHJRfC43YhrGhS3CoWwCkD59OlvOLbZYRecMFSHPwxElEpE5xHRAgC9IASdZe9W7feSXdUzwTgIsZTwok2FCJ98D0IoDoIQSBaANnZeXwaEILMgcut6E1GZLcCOh2gonyigUwfgRABPMfMHzNyDmVdDiK0dEILwTIiQ2BiEUEyEdyYqhSZEVKk9ZjtEddPHbLE1AsIb2d5ekwy7XyEzb4aonHqcfY1/s+8NmLkdM/eCCJFNVBytgRCOnSAK/3jsudPQ7BmdA9HncLu9fj3pPifyKLLt+9KKiJYTUSAR6kpEJUQUJKIbIERswJ4n4dF8IOk8SHpNvD8KwAlEdCkcHBwcHP7QjFwqZY5cROeNfIsWoBV6yW5k+F2p3C2lHyQfpYJhgSGNXCrttM0mcI0sF2TVyzUpYc9GxFD5AFoXPMuq/F7usae1nr9g/p16bpbLPLSjle31tGna+l1TbtuWGW1atjS1iGGZFK4M/Nyl/2GXnVz20vgPS294/P7j44YV3qZ+E1nFH1uVRmldKgpO1Cw8Nbmw6J37JtzcY3Jh0eroDuvscDRaywSNCOcFg8EnG4KBqFdVFchyxO/3A4DKFojBksGWCvGANeZzqeVaPFxrmnjsxkcf8uhsjQjqsd6KLLcnSWIGpBibfgC4cszYzZIkD1NcruPcXndmfqu8v2VkZfoB4LNF77f7dNG/eoHEQ1xLj9HGptrtFRTKb0K4zexFC077bu1Gj6woKoA0SZIkWZKlF2d8MGfcLfc+X1NTs72xqUlPT0vVNC1mBQIhSJLMBiTOK2jX6pT+fU6Ir9vcaumbHyyf+cyLgTmvvLEOADYv+Lpk6/xvg1+89t4NkoTwquUbAyyTJxrXlXA8xopbecDtVlhxy0wywTLBpmnA41EhExiiF3OfSy+98ML/6Q+Ywx8ep3WGwx+JDADTIHL6EhgQwu4tiFDRVwC8Q0STmflhZh5ORCEIgcUQomoD7N6FRPSufZ6A/VnGrqGR30CIvcSccyC8igkPYV+I9hwtiMgEMJuZi5m5iYiy7HNdjmZv5ncQQvBUe92K/cXMfOyeLpqIbgUQt9f3E5LafkAc+KU9bqa9znsgKrgmPHuJlhQE0buxBYQI/A5Af4g8QhnN4rUeorhMBoTYS+4PRUmvcYgqrV0gRGainUc3+5puhhCZCpq9sAF7e7KnMtl7Sfa9zYDoB+Xg4ODg8Ecmzn7E8DDSkZ6wjKFYUC9z/2jAxy+CaBVkfhUxfmfkXLpx1mAumT9k3qBXf5wW/rF6hdRIFt824qqTINH36NblIQBQMzKLftxSA0Mxdgw87QKWNi4liSS5f/8e+KRyLbZWFXzi9nrGaq5oJgCwZc5xq7JvS3StQapbCTf4+yIDDwLI+tt1haYuY/asaSXFrzx/d9OUkuJMALJuGmcZmq4owkZ951XVWgCnxvW4RS63FI/GFWbLmjzx2m7NF/v0zndXTZs6hQiByYVF/PRLr24KxwLuG0X+IwCgaOzYLwGgYsXmmV6PJ1PXjZkAOgLAGw+9SCAyoHoVABREuNwFakVQtcqahq8/mrfwJAAxA7oZi8fdKT4fpbnMwBN3XjevRYsWGdFoNOx2ufxtW4sOXtFolBQAZjxEQV3XJYkmuTP9XfWGaqJYPA8AJIu6xXRNCYYbb2zbOtfqeughSl20yuB0KPktWgRUxT1pS812qqqvQUSLgwEy2WLZYiKJErY5C+Lh9Ru/w0+Sw0GK41l0+MPAzNsgwk4Twg8QhWFUANcD+AeECGqCCL9MUGe/xgEMhSg+M5+IEoVmIgA8drsNF4R3LyFCi+0xiQI6PvvzDgghdClEe4veEHmMU4monoiuQXN4aUt7bgA4lplHQXgBK+ztUQBVdsGeW4hoZw9Fu9LoCgBX2JvWAWhp5wAmxtTaFWETJcuSG97fh2YRaEKE7sbsMcfZYwz7Pir2+6n2fsKuQlHDrij2mEp7XTkAwMwDILySVRD9EjWI75lh34vE9y+SdC4r6X1CXH4DBwcHB4c/NLMG8jYAaXBDRioxvARPOjwR7FBBuBPAWJgwADTBQmPiuPqm0lp218Fw7YjXRUInLFy//MrXF7y/4MEPHgqW79gOUvRIt3YF6eHIxkjYm+KqiEfjlQ11RjQeNrvmt3qzU04bHNG6vVR4/925YMVnWSZ+XJBX/f1Hh1o/LW17boNeu6pS29CnZV6rgbnpuVNvfvzh+qLif54RQJOpcRwuWemmE8XtR5c9JhcWjQKQEolEqgKBQNxyyVHJ46meUlJMU5584pbHpr+48yHnjQ9OvcorSV+rlrDN0VhghUdxt3y0pPi7xJiVny2oXf7pV9vlHE9qSrtsqHkpO23z2x//+74Urzthm412SOmYzynxNLhUiFQQAAgPH9g/xWQopmkah/Q+6h7VJbuJiHw+307bXL1jhxaz8ytlWYZEkixZUrxbq47bc7Mz122uLs8BgHYDjx/wU+nPlRFTq/G09S6tjFdqP27dpGzZXmEEY5GWskyKRGAQIqpHgaIAugHL0Bn28+mEbf7kgP3wOPwpcMSiw/8LRNSLiF4hoo7257dsMZcIZQSECEmEZQLCW5cD4E5mfj3pdCdAVBDNghAoMoSwkiHEn4bmlhJhAJuSzns3mnsmzoNox/ElhEiMAzgdou+iC0IMJXoRTrHn+JaZL4TwtiW8cYnKqOdDVFSrBTAB4kndHRC5jQla22s8wv48yL4H2fZ9GWPvT0FzdbKmxMHMfAuAlyEK+8j2PdhdVHrs/REALzDzDAjvY7KA2xNxiOqrBfb1bbDXtBzCw9kdIhcyMU+i3PhyiO+hD83fy3IIDycg7vfq3b6HDg4ODg7/z1yyLK//2G9avjJ2WcuOAHD2LHpr9AcUhgoZLP6eW9bO1AUZJgBTHg4fZcaYrpw1hGckzuXLocGWFHrmpCP7ZG2rrYmE0CQffUhntV1etrzVrPKNf+0Jzad6POKcVnhVaOUPeZLfev7iifLQQ3rclKrIulwtKbEN5W83hSKvb6qu+XJLNHS5V86ItYRnhGUat1guXQlaTTpDipNpedHgei1k7XA1Ufm3kwuLLkzzeLp5Pd5EkTpMLixiS7IuU91qeYriqvVI0gSY1hseRbmjKRKZmVg7AV2IyEfiASwUKIOgyEpcFGfDzHdmjvF4vT5JUdJdbm+MFQme9LSaxPEffvXeLTE9/rIL1Ahh03PQbCtVr8eNk/ockfJz6dbGqGFEtlRUvDDo9GEzWue3zlE9KlssBJxpWZBIIo/bjbgtGC3TjLnd7jHltRUFtYHGbumWawMArPxk/o99jjku44JTzuzeUBbSli9dpx7TuRs6FrSm77aUfvH1upXLwpEoUlwen6LI7PG4AZdULskI2+vSAHwzffobsw/oD5XDQY8Thurw/0V3iDDEIyHEmw9CbAQh2kjoaC4G8yCAGyDE0EoADxPRRbaHCwAWQ4i5YyAEidc+V8jenqhyGob4mS9A8x/t4yH+QDKALsw8CNhZYMcL0TA4UeRmO0R7DdjHRyFyFwFgPUQoaXIIbTsACyEE4gf2+rajuSUFmPkmInqCmcvtTQnPZk8iWg2Re9gIIRZXATjDzmOEvc5ce50Z9qbkAj8yhMD0APAwszepIE1nND8ssuz3mn1dJprF3kY051CmE9FaiEJDBFHY51gIT+UI+9i+2JUYgAUAJtnjjoQQoevg4ODg4PDHwkJ3lugYhnUkgE1EyCaChDDC8FOKoUPXDbhcKixFxsc+OXNYmpIpV9RtWqkzXhs4S1rx1UhrAAC083Sed0jbbvLC5eWHSDk/hgYfNsDndntAAYTGPf2025TMjN6Tro6OOnZguCArS+mdd2TnSDysxgJRRHRlSErYp1dZcSaXcejsO+4aBABzzli8nNMsn9ty95A1r+Iyc2LVO2qrSLXaRsw43AxVDaZFOaf2bAAIh8MbmBCbdv3NzbaZXR1C8fhCj9tzB4CPSZa6x0yzDEn9jR+8adKVkx6adt+DN00qB4C8jMzUaCyKdvntct9/b+Zqv9fT6aufVjZGGSk68TfLPvvm9BnvvbDTNg8+c2Tu5X//hze4YWPGvGVfwyTSmC1XNK4zAPmckYPNNI/fs7liobt9v2N8vKGCAEBjvU8gGiaFJaikWA319ZKuG3EALk3TzPT0dAZJfiNu/ChJUptWWbn+/NTc9NtuvuHnYT37t/G0bEnrN2/a5oV6LLnVqT9uKR0RjEVVN0l9W+e0RE19PVyKAo9E0UajaaHflXYLxP9YR0DY5rLf88fL4eDEEYsO/y8w8wtENI+ZN9mfz7CFzNcQQjI5560FRCgkA5gPW+jYxWhiEJ60dACDsau3PNU+Lgfij2COvb0UouBLDLvm0vns/Ecraf6EVzNsH5M4/xoAvew8wzvRXDwmmakQnsNPIbyLGoCbmHnWbveiHACI6AEIwVXJzJuJaA2EMPvKvsaHkoWizZcQBW0SlVcTAvtjAO8DeM4eFyGisH3f1gHoCeFpTPb+eZOuORGS2sG+riia8y8TYa4yM1fb69TtrxEQwjYh1vsz8xY7v/NDABPtcGMHBwcHhz8YrxxfXTx2Wcs5Lx9XvQkA3juTh4xcRASiHxBHJ8kCSTIgCQtZEdEbK1k3uaIOCzIy0DnDnSK9X/pQv7nrlwSvOXqiPz+jnReHLRyc6c+SvJ4UhCIRwO9O7dGxY903G9an1VXHtIaQkXNE+3QsLF2z5ehWh6XFtGgMpsX10CklK81laXrqMVdcEmLdMof07Cmtql2LSBOUxkgU2dnuJklR02JxTWJmsM5rNjUZvb6a9DSfVFR457Ht2skSSbtU+9ZN43bdNPLqgoH3slPTmiRAY5luu71w4i62eeoNN5YDwMyZ7z8gkSQDVHnGmafzh++9v0azzA6pqmc+QGrbQ1o+fNeN1+1im02TF77871dyj/C01sJxzZWXk6qmp2Xwxk3bZuvA+yf17vlcPKyhun5HrPG7dWGX2y3Vff71uvqcaM/caGpUcXu9LIMhyYjGwx4lHIHq9siBQCCmqqpCCg4raFkgtW/bPkqA/PXGH6T6YKNWuWOH8v36tXjqrSerl338SalpWO9Ne/9VyzL0YTVN9akSEZlshI7ofOQJV950c/mll16YBfG/wpXTp7/h2GaHPeKIRYf/NxJCMYkMiDy41hDhlIAQbuejOZTyaogcxQUQOYwDIYQisOew6kw0eysBIZByIcSMx369HCJ3cR5E5VHJPsZjv78RwL8BXATgNvs87W2h+B2AwyG8cQ/tNvckAKfa4nAW/jNRe33TAICZzwcAIuoEUWSnD4CVRJRjf05UVe0AIYbjAJ6FEI2X2eOjEF7BhMcy4UWsStomQQhGOWl/cjgwIITkDxCe1YSo9Nh5oTKA1wEUQXhqcyEKAY2210/2sRuZefp+3AcHBwcHh/8nXjqualfbLFMGgE1QkS1JyHQL6cWhBlwie9hTHQg0ZuXgqlAYdQ+f9MKCL37+8u8nHdJ3iE9O9wBAvrujZJomLGZsrW1ARX0NXh8/yUeKGqyorUudMXchFvywLnJ+j6MzTUujuRs3eVq0SMeAUO4/lgYbirdt2jKPU9VhpgwJJgVbKa09Fdwg7aivvbF9myP+ffLhLf62duWau6ssQgR6wVePPsVnXnfVd5pkHr5s2yazd6cuu9hml6LcoMjyiU/cdOtciL7K+8RijsqKHElNS7sDAM4YPep8APg/9r4zTI7q6PrU7TDTM7OzeVernJEQSUQJMMkiiCBEkAnixSCwMRhMBhswIAyYbDK2CTYZRBIiCwEig4wASaCcw642h0k9ner7cbt3ZlcSfoMN/uw+z7PPznTfDvfOao5OV9WpT+fMHW477k2KIvYHsPiAfSdVeeDf//rcM5V9dtipy807Aw7fda/8bmN3yv/kF5f9qbyiOnHqiUed0r+2enzOzOVi0Vhinx1HG8wMj9mLx0r0GrW6yY17hhACABRdU7lPbY1q5nKermvC9Tzd/78HXNfG2nVrjJJ4yZf/NeHoobGYEWlsbYURicSmX35ZynQtZffB2z8xcZc9z1m8evnfNFXto6naa9ff+ofjAWDatKkEmeWz7JFHngy5OcQ2EYrFEP8SICIDMuXRgIxIfQUZQYyjIGKC6F4XpKCqhIyq9UXBeRT+2Lx/LgEpGIMni0H/RPjbLMg02AxkTeQX/nkHodBC40+Q4hEouH46/vtAqOaZ+VoimgLgCACnByKRiF6GTM8cw8xN/nwJUrB9wMxTAICZrwVwbe+18Vt6AMBtfhTzWQBTUXAWBaRAVCCFdY2/BpWQIvjnkJHADGSbjbsAnOAfl/OPCyKSAgXBCPR0S62CFPSOvx4JFAriz/IJ7GJIIXuJn7JaCWm0UwYpav9XIKI4pKj/gJm/+t+eJ0SIECFC/Pcx+VNRDsmRUSjoAvAlZHlGXOhQmOE5JjKejiSb6NJF5NamVEfdblVVA1L5VB87bTv9q/qoqXQH2tPtXnlSy1eWDTbiUYN0XS/X1Cp69IOP0ZazYpN33NGtKivBguVrSTGEtXRD++rFmzdlGDgUKXyxz6AhlSnLHRTVErm9RtZmXp43b+lH69Z+WxrVeXy/gW5pPK50mTKKqOtahcEecvl87qZfXXTtMVdcMsXzcAQpdPrMG26dCWDmL2++/mXXzO9tGMaYP1x2RRMATH/gXkIemz3H+uP0Sy66BgCOP+7Ya7EVbh4/4YCVzz/6HFdUld725osvX+uBn/3p5IlT1bhGB40f56xctAZVtTXa2s1NihrRTlq3eX3N8UMO80bU9qlk8L0t2ezPvUhM/2rR0q6D+ozp31zJ97ekUsdUlpQgl8vlVE1TkyUlWi6X40hEF7Zje7qqCQBIpdIZl9yEUFXkM2YfNkTZ6k3rnTWLNqTKK6sSNckytSWdci3FPevMcy/iqy698GJ4/Hv2vEu+fOXdb0FK2ZCaQePWNK1LoFBe8z/G6Vd9GGfmM0H8wV+v3z/k5n9ThAY3If5VYAJYDyk8EpC9+KJF+x+GNFt5CrKP35mQbR2CNNJid1QBKRTzKLh+AlIYmgC+hRQ8DmR08nzIyKMCaXtt+ucIUlDvRkGwtvq/2wGAmUcAGM3c3Yz4NEhH1olF93OAP6f5RDSaiB6HFFtJyNTZbhDRMiJqIKJOIlpZtKsGhX6N16FQV9iOnuY31UVr4EFGGFX/9asALoJsgeH5P4HbLPz1KE7BDT6LIMJY5/9W/e3FtZEdRHQQpJjbm5nXotDSYyOA0fCNAf6XiPlrUPb3BoYIESJEiH8YOgCsA+B6jGTWgpYgJgAAIABJREFUxi62K7NLYnEgFsPD40aOOjVZJp6qG4Sbr/x2ypn7Dhx1Tk15BVdVlVJeZBtMNw3WAUVRxBcrFxuK5eahGFEG6OkPX8SIPtUmAHPNssUrGja1OPq6jU5mQ+vP2HUu4gI3D0kITVUFBNgTADAskfxlMlmiqKrqpYXamrLyHjy3HgCeu+n2of0qKkZ/dP+DkhNdPlMIHCbAPwom5lnOATZRwvSceefcfMOPLrz59w8CSCGCpCD93OJF2H7fCcvG7Dth4/GTTu2cfNTUbm5WNG2o47nCYmhVleXXReNRV7XB5HL7wQfvE2dVdz07h6F1VdU1tX2xYuNGMfP9j723ly08wxCkEXve3Y88/cbmPt5FeStfEVE19jzPixqGoSqKlspmkMvlHEGCk4kSsm0bjuO4JSWJBDGhsqQC1dU1dfFIBLqqqomKeKLfoHLt0H0OxEF7jFdqkpVtaz/8/KBrz7zwg+tvv3PvG26/a21XpkvrTHfSKcecEHDz0P/tH4cgjjGUGgKF3PxvDCpq5xYixPcKIpoOGSF7BsD9zFxPRG0opDkGUTwX0kBmHaQQCwQi/NdBJMzzxwbiJwtZ6yeKxmeLhF0Q4QtSKU1IMfgVJEEeB9myY3dId1Szl4FN7/kQgMH++DsBJJi51I8sjoN8Gvuyvz94UNPEzIOKjs8U3avFzKX+vvv8e0hDuqH288d8DBnJvBqyZYiGgkgLWl8ExjXLIIXiawCOAfAAgPP8fR2QQszzXyeK1jEQkMF5BaS7671F80hDpsX+mZnvI6JyyDoICzIV9//8RUNE9I84T4gQIUKE2DYOfURMV2OYuuijPZ9RVHH/mjs/qZ/8qWjzGEbeBlQFnqZAQHLCt2BqgccHwQUP1HekM0dOh2OBhyVHMgQok0szKYrz2ZKl+sMfvYqM1ZV94Zw7I55ni7uee5xGjhyJMs3IHrXvid3cfNhlF9BbS7/p5mYF6PKAeafusmdHZTJ5nOl5FzN5u+dTuWnRqJ6774prKrY1n+nPPE+LFs0b7LK3e99oyZ0RXU3c8esrS3958/Uv2/n8uLgR2y7vOK/rirpzSTwuVCHgmG7T7y48fxAA3PnIw3TXEzMyEUWl7dUKQGHzxVefKAeA7U8/5b7KeHzagNKydHU0lq3rVGoWLl+Fzo7UG689cvdJK9evv7ojnTnni/nf6A++84qz29DtxW7bbaegXwk9O+cda3Ba18886ZhlA4b2r83lcq9pjjgGrv1APB77VUlJidbR0dFaVlZWadu2m0wmOzo7O0t0XdcBIG/nvWzepJJo3CVFJc9zRSqV+aXr2feWxBNiycY1aG1rS+86aocGy7H+NGS/ve5b9MnK8rffm/FKV6o9s6ll82EPPvT4/5lTf3rlJ/qjN+zdu/VWiH8jhJHFED8kLoAUPRcDeN0Xj8W9+YLIngdgHmSaqgNppJJFQby4KBisBG6e8F8rKAgmANCJqDm4QJH4YMhIZj9IQbrQv+4dkLV4MQBTiWgFEWWIqKt4IkR0NmTLiD7M/BxkeqruC5yjmbkWMhX1K8jUz+C61/Vajw5//wYAdxLRi0Q0FcBP/DEJyLTbNgDNzDwBUqAa/v0zpPh1/THFrrK2vxZH+fM5D4VekQ2Q6b1B2m5xL8dAbAcRSoJMNRWQn4cF2Vbjeci6TwCYAhnxfO4fJfBCoRgiRIgQ/3x4lbjAKkW/aP+NF1v02atHfySmA8gLAqIa4AtFAPDSHbRg3SJeAhcOXNhN+Y0duptw+8YGUEdXl7NkbYPtORF7zjef6ze//Vd3Q0srNGiarmtKOpWnDe2N1vuLl+P652ZHhp14VHfriTdvubMHNyej0Zq4qh0aTyRWfbFupXf/R+/e/sCHc8+trqqMfVm/8azRp09dcd6tv89ccsvve3Dzvheff/bXixfMZ6GUvnTjbc+VJeJlpfGE/tenn6b7Lr/q6D9f/bvabzZ8u3dz5+YF1SUlZlwIBgAtqnRHFjVFvWynIYM6astLc5ESpaG0pmKHa2+998XZL78+dc/BQ05oTXfB0KMJoUWqhg0Z2Ok6zubXPnj+2E2bm4YuWLw4amfy0X59apz7zj4vVVta4ZRXl3Y2tbXY5qYOvbGl3Xv3m4XKmx9+QorjTWrJpWPtZv6XRixmA0CO8w3pdDqjaZrCzBW6ruvMjLaOTghSqDxRSrqmq45teZ7lUjaV/n1Hukukshk7EY1Z/WpqV+Tt/HNgltzMfNKeux6o7Th6r6f+EUIRAEKh+O+PsGYxxA+JZhScTkdAGsnQNsZeCSmgPEgho0FGsoIIWyCIgILIDP6+c/7+IMJmAAARBSmll0H2UrT86zcBeAHADf51NkOKtG9QiHr2vs9ayDTL/SEb1R8AWaNY/GV8P6SIDNp7MICPiva7kBHCEgAzIOsyJ0KmiGiQAnk1pLX1acycIaIRkKmmx6MQHQyezir+cZsgU163R88HRBoKRjajURDU2/oMgnPCn0cgHHUAOzHz3kB3hPRHkBHQvQA8+B3nCxEiRIgQ/0LQc2jOe6jOmfUN2x+B7VwLowggEoCIAJ7DsGxAEWAzi/drRuChtAXXtYTexZ1a1IvVs/D6pWwLtsO6mbc446SRsjqVP592HYaU1WmCBPL5bC67ok1PDEuanelcNJ4oiQFAbNKEVhuwAfa5mcyMaSo20DR73sLHVlotVwDQdEXZ/NeP3k1k8/mP6sqrDUlHPfVPXTwxpCIRGxQTysEAvt6cTu9v2fkx15x3fvdAEur9XbaZdADTBaKm43gxVV0e7I8qinX4fvtcvclqK/GIZyht+q2mY05sz5jDzxl/qGrbbrahvX11Om6unXL8qadN+eWpmQMuOHtEf9s4e5fS2qPXLNnU4cSsst3G7FLZv7IcSxeuUC8961S17Yt1mzY2NSfzGXPoHntvL6KxOFZtXIftKvtEXMflrlwng70dIpGIZRgGcvkcAUAml0NLewfyjkt1NVXImCZSZlap1EpQV9OntL69wXNdVwysqlXyrrNj7V477Q0Aj7/5OG3elBufndeYymc6fnTcT49/FCFC/DcQpqGG+EFBRA2QAsfC1ttPAFKUvAcpxAAZvdoI4GT0rGvcFhoA/A7AK5CplzWQ/QED4dMKKdCCaFoXZFTsc8g+hyWQ4m4xgN19I5erIFMxr2TmR3yBdAqAN5m5O3LZa64XQpr2HO7fQ3daa1EKqsfMCX9bPwD3QEbxdoZ0GG3odc4M5Jr9DsBv0DMa+xmAXSHbjezrz8GCFKVG0VigkNrropDSG+wPRDZ6ve+CFKEeZISy3F+blyCjs4AMCMYQIkSIECH+v8Hk96kRLpKeA9sTpJCA8AA43WKMoAv2hE7v5TPYXyiAm8W70LFxkH3QKUcPnKbvM2xXuC6geIzV9euwvn4zDtl7PwBAV85EWaK84ZzzLv8dQK+83bDpgY0+NxuKoqiKgpRltUJyjNbHSIDy3BXzIvEmp+0TW8UAqGptpWEY6XR6saWqu2dfeIN/e82V08eMGvbzRCJy2ZFHTX18+gP3EoBTMrncm7dcdOlWufng8//rQtd1Rw6rHnZ4ZUmyxjTN3J1X/LYCAJzlG2jBxjUZM287+0z8cRIArrrl3n4q4Z6B5RXnTTvggIlzli56bcKRh/bg5kMvOjcjoIjSjdnfre5quaIkooqjRu9O2bTpXnLu6Z+5nrvr/a/Ner80q+zbkOqMlpTF7P86eILblEoZpRFDiRpRGBEdtmOzpmpkGIbreq7I5XKuIhTVdhwwYOmqqmesPLrMLKqMhKVpmp7Nu11RjZILli71bNeyl29eW/6ziy7m2/7y4EtWJnuYWJ8HO473mzuuiP9T/nhC/NshFIshfjAQ0X4A/gjpxBXUDEbQM7JV3GQ+Bylympl5YJFQ6o3NkJG+4DzXQTqafomCM6rmn28hZPQLkKJnMWQ65aWQQjTPzJW96+V8p8+h6FlXeAZkHV8bZPuPFAC7d50jEa3w97czc19/W6t/T+3w23L8nbU72H95B4ABKLiSKv6PDRmBHNZrjWYCmIRCqwzb//0QpKDsBymUVwAYVXxJ/7eLnnWhAWwA65l5ByI6ELK/YwWk+K3Gd4CIjoF0mjuRmZd819gQIUKECPHPxXbn0H61++KRihr0QRQKFDKZEfEcJsv33zY0ssHQHABKHjlKwMh3ofmNQ72Bhz8/IHPiwAvFMWOOYNt1KapFkbcdbG7qbKssjZaxooiXPvkcf377/es+vPmaP3nMX0688vryj1euD9o2ZQlYxN3cbHmAvniwXvZ8Z2fHxU5ZJGY5bt586c3Kgy4+l969/d5uvnxuxoMrFE3trwiYRx99ejkAnH/LDWcIxr0Manxv6ZIpQqH3zEwmu+TpF2qK533ujdetYFB/Ym6/98qr+wLApTfc2JqoSGpjK+pa5jVtGHb9eRd8Jzev/OTLgwFg2lN/uqNfvLTvcWP3iNw3Y4bbalnK+KqBSk3UsE86/JB1NXV1Q+euXCzG9B2IgWUViMfjszc0NUyoiCchhBCO49hCCCWVtx7qU1G+L4ABrucazFhhW9YoZkYub5LnAplMBuWV5a7w4KqaokeUCNJmBi+/PRepzox16Nj9NgybsvsOtzz85wNVIf7c/OnKCsd0vVsfu/U7ufkV9cljAL5Wh77Loc5PQrHwH4wwDTXE9w4iOhlSVNmQwuplyDq6IErooCB+tKJDDf+YxX5EcmtfXgygT9FrhowMtkNG7gJyyABYAJle2uSf90IAj0LWDgZtN1RAhseIqARS2C4GsAOk4c7zRdf+yL/nzf5P4KDaG1Mh+zZ+7K8HQdYQepDpnYsBjPYFpMXMdVs5x1OQou5DSAFciUIbDMNftxH+2EDgZSEjfsWmNIHb6Sj/fdyf8+iiawXrSCgIbRPy87L8nyiAYUQ0l5kPKLr2FtiKUc1tkHWYf0QhehwiRIgQIb5HTH5NnGyqfB+VwGl4D40V06ibm4kARSMnorAqBLmwoUEAtgXYCgyRhW0Bi/d8RDRUViR4TL9hYAJFtQjyto1NbR1cmohUJBJxtKZS3JBu5SPGDi4BAGZkljU29eBmllzdBMDef9QOF76/dPmja62OC8hAvEyowhGeCgDv3n4v33LhA+XE6LsSzYt3HR0b2ae2cp2mRAvczNzEDJeYW9m1X88LVYkmEpHe87cte6qqiDfWbd48BwCuvf8eUgwtlslkvA/aF1c4nvcNgDHX3HNXK4is6ef+agtuzuRzTzV3dcaPG73Dh0Or+5e3W7nYhSefnBleVqMJFkY8GtGsfG64ZdsoMaJuc3u7O6SqpnPJxg375V1bxKMxqKR0JSOxJGyP40kj4GbDylsqA6McxyZN1WBEorxh02YwgVQiiifjWjZnmnk3H/U814oLJbvbLnsk+gwqG7Z21rzXLzvj54cDGIHTt/75v6k+S4c5J3RzswfvNoD7WrDnIuTm/2iEBjchfgj8BlLQqJDN3L+FFCCBWUrwd9k7TRL+vl0hU1OK+x0Wj2uFFEaBc+fRLDEGwFzIGr5rIF1Jd4KMgNX57w1IcSkAvB9EDX3sDGk0098/30BIcx55YeZlzJxg5t0A3AQp3q4horTfvD7Ay5Di8MdE1Anga0iBCMjI6mDfQCcGoMw/foMvKgO8CVmzmYHsYzgfwJ6QIjVINQ2g+POJoSDILf99UKe5nz+/oKWGXXR8IBKD9e1EwUynzd83H1JAnoLvgD/fDBGNL9p8BWQUdOJWDwoRIkSIEN8HrotqiFaOgWDC09gKNwvZxlBa3NiAwvAiOhCJQZQksTN0JPNKVmlrb3JU0tDe2YXHP5iDTC7tLdlU3/rBwgXZ3z79FKkKi0R5/NB4dEhbMjZ0TEvK/AiSm2+C5OLh8KwKeGbd+0uWdnMzA6I9l3k/+8Ib3dzMHnbNw/nJcKrpc9ZZl/DRk08feOOMk7q5+a7Lr3rlzl9flfjDb67aTTf0G+1cJqcIvrZ00iHpkkkTug1xcq77smYYsfKKsiNOuvrXnWs2bPjYyuaXxymC0vLySHll5dDpD9zbVRaPx2KqXvb4Q4+mn//Lkxs+eeGtbm6OqPqbubxZzx5ljtp1r3E18fIvJ+2y17iI621ORnVLU4Q7+4O/4drb/4iRpXVKZ2ebeOK9t0o7zHQ0rkWQM/PW8o0bEss3b7BywiEo1M3NTOyZuZwLInieByKi4cMG06B+dWBmXtewKeWxG2VmjscTXccec7gWcSILOxs6zVgk+vPv+uAfU+/t7BSdmb8m7+rmZgFxhYBYoUM77LuODfHvjzANNcQ/HUR0KaQ4a2XmQb5z6E2QAqYFMjVyGgqpjUE7jKBtRiAaA/fTLsgUzz7oGXksbqkR1NO5kBHEfYNoli+6noRsH+EUnf8oyP6Nn0MKujMgo4yvATgRwCzI9NRl/rUDIj2bmZ/4jvkH7TDaIEXRbv6uwODH9u8zWvQ6SMcNUm8BmSb6nn/Ptu+wCiKKQQro30EKRgEpIvOQ9aDdznVFr4vTe4PXOf91sG7BWPK3C3+Ng4yEIDIJyP9IKEG95XesRZs/t+D8NgpR5JXMvPt3HR8iRIgQIf4xmPQY3QfCT1nHxldO4FGTPxWXIsu/hQnygBYqo5kkcAYKXMFgeLChsALPtSFUyVQuAGFl0NmwImYePGpi5ZkjL9b6JCvQkc3ii9VLua68jrK5LO5/9y/pzelsIpeGt3hV5ouaUY0HrL9xseTmSROKudmFZwnABUg/ikg5k7fCzQrRiQbzrGuGHX/pCm5a5rrrdlnTtuM7ljhcZRo59aPH8MrW5r7XpJ/QUmrPQCi8X3X/djtvrc0a+m5qRIdw3YaK8rI6AlvDyqo9z7GjRjIZ9CHWyOfmIYgaES2C+g0tw95+5+OlABwjqtnPz3m6FgCwsj4GYNdlG1tvISe785rVq0RpsiQz9/MF+Q//9nXZX2+7Wsxbvhiex97wAQOptryS2LatxnSHDiYMqKy2VVXVTNPMbexss6tKy5MxRXE1VVOIiAGQ4zg2EQnHc3jhyqXq0LqBXFaStAzDiCiKAvjcjOF9v5ObH1PvbVdJ0Tz2mAHSoVklKNFUqC6AlYc4U0Ju/g9GGFkM8X1gIqQgqySieyBdRjdBioRKAL9AIUKYRqGeDkW/g20CUsz0R0+hCPSsdVxZdMxOADqJ6HUiGg7gcshIXiBIAzF0G4DJAC6BTIvcxd+/D6TQmwDgGV90/giFHo4H/J35B9G7SgA7opCaSpDk9xkKET/Nfx2MedX/7QE4399voCDSwMxZyLTRoH9jC2Q6aSmkQ2pgR97bCRUopJjCP29Q8B5EbRtQEOmKf+4cCinC9ZBrrQAQfhT0re9Yi7MhU4KDdhxRyL+DCGR9ZYgQIUKE+D6gYS9V0ZS+YmD/Y+fo98DFb2DTRhikeqAKM49fWGZ3lkkaDgATClyALAibAcvs5lly8kjuNmK72vN3/rWWNGKwHRsupWHmM/TNpq/RlF+FIaOqFvaNjOS2VkPYMMemOqkrevJOL9GkCdtDcnMHJCcJCJ1AUQuk/JF9bj5hlz37QnKyAmCcx9zhCDHh8lXP//lPd17NAHZntyG6OTND2dBx6dHbmvrns2awJRQ7EokIRGOVdkTfcVNHm5fJm1i1fi0bLn9brcc/Z02N5gGkMmk1l8vpALzSSBQ686tLs21Y1NXkvf3OxzcB0FRBRjweLYiy4X2zpz103+hlLS07WVqpKCstbd1h1Hbx8047ofTBm6/qaO1KtQwo7YM9R44RNSVlMDQNqqrqdWVV6FNWwV3pDGdzOUSjUWNzZ3t8XXszNE1XYrGY3ZnKNuYsxyUiUhRFAVNLv9o+OYWEu66hAa1t7Y3wudl2HZFdtCqd+WbVNrk5AuMXUTY6iARFoFMUUcOEqeaQj0D6M4T4D0YoFkP808HMB0HW1rmQLR4MyLRPoNC6IYhWBbWCQRqlhUKj+py/rQtSpPRGFjJFci1kpI0hUyMtSGGzP+SX3nmQaZemf24PMo0S/jEugGchhdIVAI6FTG21IVtbgJlnQJqyOACOJaIp37EEK4vur9iUpxKyd+T2RWMDYg6ieMf490kAjvNfm5DCbB0RrfXHX4RCjWSxGK2AFHgBAmHoFb3XUFjvYL8JGZnsi4IRTod/vuDcEch02J38iOJSf+w4IjqieAGIaCQRfQjgLH/eX0CKxqBdhwe53iFChAgR4nvArJN491ik8sM+pSNckcfxUGAgijrdMAAdGjOYqPvBogEGIXiwq8M0gEzciHZzc0VZbfqwgZPry41ylETjmNf2Nj7rfAMb83/LPvjN/Z1PLJlR/8S7r+/9Vfot1itW5zzP8ay0puZzZQcDGKpLbh7qn88E4IHoAxQeIrvZvDnjmJ3GrgRwVV0yOUUHmm3Psz35sBcP3fPwg+3i22sa3TccA18dc8ykM7bJzew6azuyWSzesDZbb6a7Up4tmlubEEsm6/7WsHFBzra3T6e6YLkOXMe1ASCbzYr6jnbO5J1jFBYWqYKOPvXwY4879hBzz3HDzT332V7c9ac/rnvoL4+sBYAc25dlUg3CtjZ7JaUJU1VVEBHV1VSX3//Yi6m5n32B1Rs2IhrRwWD2PM/TNA1RXed4LKaZlu0CwJ6DR/Comr6wLcvsyGXzuib6GDFDsV3Xbuno6Pj463kVTW0prG+qV+rb6iNvz//4GWbeCcP7JizbXkqAoqrqOKys78HNb455buRbY577UBDOUiAqdY58lEOuPYusbUPAhetBlreE+A9GKBZDfF9ohkwdXQYpPDL+9qBXXyAWg7/JPAqum0G0S4ckjRIU+isWI2gNcRskoQWCJmjdICDbUFQA2NvfrvnbR0NG4EZC1gd8BWAuM/+Bmecx83BIIfR0cDFmvhWS3OZDRkoBAEQ0nYhmEVHcH7cHZG1mle9+uqbonnX/HgHpQFrqr0+QUqv690j+WkVREHdVAGr9tNrzIQlWQBoDBK9dbPlU0AVwF6Tozhf9BCmh8K8TrJvnr22Zf7+qfz9pf63aieg4Zt4VhT6YzxBR36JrPgkZ+SwBcCOAAyFFbJB6LPy5RInoGCLq4VIXIkSIECH+8Uj3bWxeaLyXMjVnGbpgcwfSlpmDS/B0AmkMxX+0KKACiMKCARcuojAQT2dMeJ2Sm+8Y+lrJcSPP7AcARIS6+BAYKMeBQ442dqne0VreuPJBV+l0WLHJFUpUEZGI3V6FuJJQAPzekty8LyT3qABEEtj12omTm66dOHnklJ13208R9GVEUV/nWXNur3/ixXnmrDnDCVDUIm7+6qlXbx3FNedtLwZ9SVTg5qlTf3Pjf53ym1lnnHp1HADMWXN2jkUiT6x2s1VLHntmQAVozYh+g1CWKAURdEUgAjWCXCb7ZWkyWeo4jg0ABEEePBWqpth5h7J2lkcO7BftP3SYxuS5g8orq+rKq2obv1pCMVJ/Obi6KpeIxERNRWW/9o6uXCqTFal02jv+0P3q2jpTIEXBkqZ6/mDVcs/zlLuaWltT+Xw+T+B8ZUV53jAMR9U0NZ83wYKimscxIxqFmc16uqYZKzetLtMjut7e0agOHzgE5cmq9BHbH3Cgmc23Zz5fcVx8h2G7RqIR9pj1LjP7zNp5C7qzsIjwpFNNuye3S5aYsG+c4kw7rA51nVRiaCoICkgAqJ6tPlc6W33umNnqc8U+DiH+QxC6oYb4vnAkpDBIoBBFqkAhzTSoX9MghVLv3nyBeNobMq31JvQ0XQleV0OKxbEA5kAKp3dQSCkNBEwQhYv4x5dBprbkmLkDsn6xN9ytbLsR0o10KoBP/G0R/z76QEYOwcw/KzpmJoB9mLnbXcwXfBMg22DMgUxtVf01CZxSgz6ICmQUbwCARX5a7Gwi+tpfn+AegnkWp/IG6aRTIdN5g+3FtZsBOv0xQZQTKHQ9Dp4md0EK9MAkwIH8nHufayqkYJzKzMv9Od8M4GEUPjuCrOf8mT/vVxEiRIgQIf6ZOJJV1tUSxOC7ebseKklAkV42EAgcyqmIm/0CBpfBLoGyG+N7142vm+i47s3Bifto/bkFnbS6cx3q043VdnvFZQO1oWM3bmicWxovr/Cc9DvDK0p2KSlJ4Iu1DVWSCRwFUD34/xfwgMq84+wTUdW2Gb+7OYutcLMLeNTLHf1vSN38Ny+VhGwV9QkAmF5edLhm9VKrMQr/gXXHc692c3OHnZ9pZNL7fPXgo93cfOBFv6SOlDWhxbEH1MRjc/pX1RxARKqTTjtkqAZZ7G3sbKZc1nFZY8XKq99WdmQHe6X4vHbsaP7L2Btnr/98wYoSoe6sqiryrhMRCrB2/UZl3tffKgkjis0Nm7ixHSLvONiluu/UimSyJKLrSGUzvGZNvV1XUSVe/eZLrE514LCho1EaL8kMq6mJmZYpLFuwZdpwBFE8GePKsnIqiyTT6bZMKzy2IM3yIEg48DzNZVZqEsk6+NlZzJiqtvCTzJh6gnPGcn/a1+sp7WEFIE1SeWAsGHLzfyjCyGKI7wsrIcXFrwCM96NtgbhyUUiHBKRoCNJMcwAa/dcCwJfMfDdkJAxF42XKikQUwHxmHuwf/2NI0fYoCrV+ecg0yAAeZIeMVgAgojv9+rtX/PdDAezCzIHACgRe4Jx6cNG5noeMHg7exlpcAGAPIqojoiH+tmMBvAAZ0ZyIQpuQwNxGoKfwGwoZcR1FRE8RUSOkUyuKxvdGIMpMSIEbpNwG7TAEerb6KAWwuujY4uMBQGPmw5m5hpnf9rcNhzQCIgDLAgdXZl7OzHsEQtHfFjwJDkg+Cik0b4AUzCFChAgR4p+LTflOzi17gi9oW8N7v3yot4cSwwKhAhBwEQH7MUVAWtnUA0BnDqn0OjRBAKYJoSmZLwl1d2cymaC0APM3zaF7m04z0/kUD64eiUFiR2NM5ej5jY983n/FOifHiP54WVtq1RfrGp4DIQE4H18HAAAgAElEQVT2ALAJcLdDKQFeRFX5mrPPzQLAgCkT7xxxyrHpAT858hUAKD35R0NLEvmd3VlzCtwsjXJikJx2bLD9xczCl981l6+p9zqLSz+6EU0kL2w3M3tcecetQ66849YhANCesU7POuYLbans1wopEx3PM2wrn3cUJeo6DjwiUVNWrQysrFSqk2XIdrSNWFvfFO9s7xz7zpMznvrqrXcb21s6j7cdB47jQlNVAY+hGgp23HEkfrzvHtht551pbFUf7FpdZ37T3FDmMXPWshwjGqXK8jJdRHSh67pnew50XYOuqfHr7vnz2nXrN6EkHqeh/ftTwoigtrzGBADFUKKl+4863Bg7rCa+5/CAm4cZkeissmiMYnpk6Z0PPUwAcNi3U5Yf+s2UPQ77dko3Nx/iTHk6AQ0GNM9GHhnkgv+HhNz8H4pQLIb4vvAXSHF2EGT6IiAFVgoFIRJstwDU+q9z/utAqAgiakbBEAaQTz0DJ1HAr4Ekoj394wMB9CsUxFYEsnZuJDPHIPsmEhGt9c9/gD92byJ6HNJR9RuSeMF3OH0aMg3zE8gnbgEOgWzRsZqIZhLR4b3WIgNZ/3cTgFv8bR9DCuQ5RfOw0FMUF0NaDciI6JGQEcCVwFb7OgKFXovBWgWprb3rHIPvhCB6OML/bfljXvFdWM/aWv9HZm6GdI4N5pAhogHbuCf48yuOLB7GzB8zs/kdx4QIESJEiH8MHkYEHaUjcFDjHHT4285XNaSEDgL14mZFcnMyBlOpRY1CIFUFXAHKW+tadU0LSguw95BJzkv7bIq+0n6ns9F4HeV92jgfWaeOvqtyT4isBWQ935bgLDAEyATgRIFsGeAM5llzYilgh2vfmKnQpAmraNKE5qa8fUjGcpWNprk3TZpwDym8QFG8RTRpAulHH/LCgCnTM9sp1z6tC2VnAJ/oQjkkuJ+jqfLA/bhk7pHx5MYTfnrCzJNOO6kHN6ey2XRXJtsBdq9j9m4CAAa9rgh1M6vig1g04jiWhYaW1lzUMPKaooIFI6LocDRCQz6DSFXSMWK6ud2QoeXltTVHJeOJpKHSAg/whCIQUVQYkShqKqoxetQIcERxS+Ixt19VrTug/8DodrV1enu6HYoi1Jxpeg3ptNfQ2YbxQ4aJ3fsNQd/SCq4uKcH5p580bPSo4ZQ22yyH015FJHHT6IP2qwVwFob33bI38/C+zQBOrN+00X7rvXeQaM1nHv3tY9/FzYEDOgRADpydDnGmfHyIMyXk5v9AhK0zQnwv8CNzZwK4j5k3+dsIUizmIb+UFkKmOgRPMW3Inoy3QJKVgBQ5StGYQNQEgicQngLARkjX1KB+MTBqCVxVLchaykcg0yo+RqHmzvSPH+4fv9m/ZlXRta1efRhBRKMgo4MegF/69/4sM5+3lTWZCBmdm9Vr+5EA/gTpkvoopCjd2oMdE1L4bYJMee2d+olea/JdsFCoH/yubR5kymlfyPTeRcx8fu+T+f0UdUiBWsJb+aIhorshRbYHucazmfm7jIJChAgRIsQ/EJM/FTsCOMls5/vePFxy8+RPRTE3E4BF8LnZ8wAhenJzx2aI04Zdok8YeBxVRKo1x3OgkMKWbZGmat7hs0ayHiO0trCw0xWUN+1VkYQ+LNupOms+G0yAUADHBjRFaLbwHLJKONGcAu6DLNtYiMKDzmJutg0j20IkKJuNVgNQqpR+MN1SKzXr7h7cfPJxU0dFPf0rh23XLnMuy7vedF3QM888+uwW3HzlHbdMBKDdcNFlPbj5oj/cemQuZz4kVGXOgGTZa22Z9EMARGkshgrNQMbOIQPg6IFjrCED+umNzS2bSuLxPhFFVRx2EI/EWAhBHnswLcsTIMrbDqlCcCSqU2c6hZgeQzSioSmdQmN7K/oly61ILKbHNQ1EhGw2i2w+Z5m2rZcnkzDtFOqbNqG6bICXNTPtY4aPHg3ZXmQRhvfdgpvnPzur08pn9eZUxl0dQckFZ56xBTfPVp/r5mYZD8Xso5yTQm7+D0YYWQzxvYCZVzPzFYFQ9PE5pMDR/J890VMozgCwmZnjzFzLzNUotLsApFAMUl6a/GMVSIfOdZBmNQ5kRLO4NrI4etYPwG8BHAEpbBohhWkjJEEt8e+lP4CvUWjxAUD2UCSi/yqa51LICOE8AKf759qJiI7Zypq8AeCVIFWzaPurkK6hp/lCckbR/Rb/1v3XQUrpF0X7gzSeQCgypBtr77rL4H0gkL2i7ToKZjuWf7yAXOe3/OtOJqJDsCWClBZra0LRn+evAKwH8DAzl4ZCMUSIECG+X8wc7y2aOd67IhCKAJDtxOJcDoqZhgZAh4094Uhu9oXiDACbZ4734jPHe7Vzj/GqR5Rv5zbn1ikA4AiLLcuyInoEpu02QiQU04oohos3PC2/Np5I7JTtVJ3N67UOQPW5WSFFFcIoyUNTVS/jmf3A3o1Dykun1JUluVwTmyH5dE11NLYQsv2Vk8vF6rLZ6OfwuazT3QQPi6FPOjijTZqwazCnp154cmmGrfoM899W6tFTu2LxTkMoO02bNnULbr7hosveuHHu7Ff8dNZu3HHhpa+WlZadGYtEz/71L855Oh6LzYjrBhjglGMh43NzJBFTicGRmFGhRXQ+5YGpy+564y7M+uJVZHNmOu8wYpGoiEYiZJom3/fok7l1DY3us/PmoTnTBSKCaWZcoagoLy3Vc7lMDrKvIjRdd0uTSd11bLutpYMTerkVU8pzET0iVFYitz3x0HNtHe2VLR3tRzd89vUW3JzqaF4ei8QQBWe3JhQB4BBnSjc3H+6cVBoKxRChWAzxvYGIfkpEOSL6mog+guw5CGxpwmIBuAfAHgCeIKLip3tp9IySBQY1MchWFi8DuAPAKQCGM3MJCqItMIexIFt5BOdxAHyEQj/CKIBBAPbwHT4DATsSMk20C9LJM3AFne7XNy4hoinMPIyZD4Q0uSmDFMEP+GvQO8L3MoBnitZoExE1MvOrzJwCAGY+HTIN9oVgmD+X2QAuRcE57s8oRBKTRWM9yKfEMWwZYSyORloofCcE24N01cC0Zq0/97GQaa8qpHlQD/g1qXMBRImok4jiRPRksbD2x23ni8YQIUKECPEDYPJcccnkuSI3ea74evKnYj7pst7eJSjwoMABYINh9+TmyZ+KZ4NzfLbx1VyOc8KCCbgCTByxLMtzLdMYlNv32qqu3V9+4KAPpnvzL/qZ+eXP+66+oqHkiKrjTwMiWcBwYcVUN69aZlfJF3ZeCF/7OZvbOz/VFWVzu+1FIbl5dLOZ3YFnzdkNgCYARBSxIyQfdtkuWmxA3VHXiYHnaNKEdP9JE5ZMmzZ1yrMznxz2wstPHqirSllU1Sr9UpX7AGDatKm9ufEVFHHz+FNP2DT+pyc1/v6X57166/kXpQDgmrPPPR0qLXCB2Tl2oQiFALjvL/nq8abG5kvjesQQDHWn/ju9XFVa6+0ydFeKGdGEqihgAJ7neWY2kzLzuViHa1K7m0VXTnYIW9nerqRcC47nIa4bLkUNcpgBgiKgYEBtP62mpIrYgz5yxEi7NFGyNu1abel8ercPF/xtkW1bGoDten/WB5x1xh7z530y94O3Zyaumnxk5wt1f61+ru6RZ5+vfaQHNx/iTNnOF40hQoRiMcT3DoY0Z1mELevkgIIwORaFyODrAEBED0DWGQYIXFUZUhxdDmAnSNfNOwBc5Y+7FtLYJogafgvpGqpDCiSCJJojIKNlqr/9bCIqRaFeYzAzT/Fr9hIoNJavhBRSgyFrMwMc6d9fPYB7ieh6yDYT1xeNWQ7pbBqgDNI0p+eiMY8DcFjRJgeyt+IwAB9ARiLL0NO5NIiCiqJzFu8H5BoG0dkkto7gPCqkY+v2kFHG3QFMZOZbtnFcIG51AI9Bpq4O38bYECFChAjxw6AZAIMwFMCHwoNn5wEhIIrYmRrboG9Y04Ob3wOA+Q3PPHDaDr8tG1gyBhpHoEBxLXYaVFVFLBEvG1O3x+VX/uiqXUpjyVdKjeiNrsfXAMDn9WtuKo9FYwDqoSItFPFtDRtjIXTdg2HBFeRaeKuxqf0ISH4KuPkimjRhAIDsgMoK1FVUDOZZc6bwrDmSm4mERURJQpUAlDpFG4wibh6cTh8ebWyod5nrAdw/bdrU6wG0+78DrEARN2sRo1yLRrbg5mvOPndcqRb9UWU0hv7lFSjVdavTdG5euGLFsJVr189NW+bpl0263DnjwNNF/7I+DADsWE7eNCGEELW1NSVXX3Aedhs4VJy8xzjeof8AWJ6DEeVVXlNXmwXXRcyIJtjMwcrnISDgeB6EIuCQ6yi6gGWasXgsNmFD06ZRba6VmbNs4TgQTawbt8s9W/uwV6/6JugrrX816Pk/A1TDtNV2ZCFCAAjFYojvEcz8KKT5SSNkFK8J8ou/d3qli4KTKDHzH/3XU1H4m3UhUyQvh0z5DCJ9G1FoBfGgP/Ymf//1kO6iYyDFXQZSJOYAtEKmpDb7x6eY+R1m7kQhFfOtoukETqoeZIStw/+ZT0Q/9p9YPgIpjGv9OTf6820EAN/BdBqzJE4fP0dPs5xi7OlfYx1kGtACSJOgnP/6x5C1lUHaaRARDWo2gW7D824IbFmXiF5jVcj1bmXmTX5a6SzIGsOF/lzaiShVHDll5ocgW4swpMNrS6+5hggRIkSIHxgzD/AeRQQnQkdjDQ3sjMTRlCyDZRg+VxgADHDrqoTbvLRucFsL4HnAzPHeHwFAqNophlomKtQKdOXTbku+wWl31l3e3tk5b11bfYvt2GrWya+/4Ok7vEb7fUcR/CgACIj7FXJbAPweQnLzwPIyRQEyEGI2CDlLoM1U0A+ScyU3z5rzDs+aswGA2dDZyQ0dHS8HcylVqZOZsTxvenUQX42G0pEAdQD48LTTT/7xCT89cU8X/IgWiQ61XK8Gkk97cvOkCY0Afsqz5nTzle06P7NtZ6vcnLbN4x3L7ujsaF/X1NL5dIdnLtggsoeOGDAgM7CqdklFWfleZibXpKoqMTNrmqZGo1FYrutEI1FiMFzX5UFlNdyU7kJrKgOoQgxOVOiqqgIkfYZi0ShypgnTNBkA4mVxNWvlXZO9Ngzvu2nytNOZSHlFEL1TN26XhQAw/Zo726dPvztVfL/Xz3z1KkhuBvrg0AU7vtAyZfO0m/4vf0Mh/r0RGtyE+EFARH8FcELRpiBFNIAHKWQ6mLnOdyg1UHDvDERjA4DbAdwP2aPvJsh+iR4zx/1rKZBPJTsgBaIHKQqjKPQRzEEayhwAKaw6ArdPkn2KNACvAXgCwL0oRDjnQ6Z6JCDbZVwDKWCHQ6bIBq0pjmPmN3utQca/dmxbdX3bAhEthHQqbQBwCeQT3rsho6aXo6dbbHDu4nrP4n20jffF/RQbIA1tbmfmdVu5n8CAKN57LkTU7t+Py8yJ3seGCBEiRIh/Dbyy/tbnHt50+ZHBe2a4YCgkgKb1QLbd8GpH5oTahY7XJnt1R7whmk8bcrVxxKBTlXQu7TXmNgsjLpB1MhsH6HveWVqC+4kwbv6Kpb/76/sv7rOsud57+zcvxAFAu+IOBcxJ59s3Am52Ae4APBVQAm7OAvgM4AMBJkC08qw5/QFAmTQhBdmU8SXIB6jd3BwVysfHlJWPjQolBp+bTde93GJvZExRtbznuoKBiKoe/NdHnvy0eA2MSQdnQCTy7MW8WXP+R9z823vuWmTb1nBD0eq37zvw0iljx32atfK3mJb1pcLe1bqqd3MzM3N7ezu7AFdXVCgA4HkebNfF2uYm3pDtoN37DUFZIgmXmRXPDVpRMRGRaZq5t1Z90+C1e+9UnafefogzZQtunn7d3WmoQqAM8WvOObfHXK6afGS3Ed31M18NuTnENhFGFkP8UHgcUowEqajFgiWNQuSu1BdriaIxmyG//LsgXUBvgyzGfguFHP3ilhN9IWsU05DirgVABQppm8H4eNE1SohovP/6dwBuhqz5exKy/yBDRkanF12rGtJc524URFng9Fqcngoi0iDTaxdvSygS0WgiemArdY4A8GvI3pF1AP7AzO3+/Z8J6Y4K9FzbwA22t8FNcO5ge3AvdtGYCGR67o4ABvvtQz4kohuKxpRgK0LRxzRI85z5W9kXIkSIECH+RfBlx7t/RBE3m1mQmQeYgZqBSMcrc+0AoFYi+dP3hncp0Uii020GACxsWdBwzqxfrPm8/sMu10I/XcVtFz5y65qT77jypUHltdudsOuhuPjHJxe42bH6O9++8QUolwbc+QBaATsJWGUAB9xsAV4fwCTAAdgrpaP2Gw8ABFznSW6uBPAUirj5QKHe3NnWEpRYSG4mulsjQQKATiIPsOIxv1g8f5o0QcuDXyf2PtuWUHzqmRmjn3hmxgN/fPyxLbhZVZXLDcNYBV3tu7il/hYM79uwsiEVX90ofumAmh148DzPc9jjhs4OUuKGqK6oUDzPcwEgm8sjncmCFUE1sQRHhHDZdeCYOQaAXC5nBf8l0HU94sArpwyGAxi85vMFtOzDv3244uP5BW6uEiVbE4o+TkPIzSH+G1D//pAQIf4p+AYyPVKD/HIvfnCxDLIWD5D1fn17jclCCp+gxo4hW1rA//0epDHOKkgxNB5SSAnIOjugkFoafNmX+mNcf5wCYDYRuQD2gzTAKU7LfB+yAP4lfyxDtgVJEdE4AG0Aavz5AVumeu4BGQW86DvW6GoAkyDFX3EtBZj5dSJaDmkr/kjRnEogzWwAbGFmw9h6ew2gp1tsMLYeMoVWhXSuXQbgDH/7dpA9L2PMnP2uyCgzvwS5TiFChAgR4l8YX2XeaoLkQRUAqyqE6wG27LS7XI9jqKEDIqc2lBl1fc8ffDu2q9hBAYBhyUHZS/e9UH157TPJXL88RpbtyAMrK2vzThZ3fHlV7NI9bnxv3wt/+8SJNzywLlKiumURGt8CW4CLuVm1AdGLm2kTQC4YApxXAXf26GMm2EdUl+01q7ljvn+/3dz8k7KqzzSil/JWXslms6xHo/c99tenU9OmTR2X9bw2MNfkPFfVbAcOaAtuZuDbHPDwttbIcuwbhKIcmtCiW3DzNWef+/r0B+7twc23vDrQAUT8wkPXtY6sJaxoa6LOrizFYhHYnoukHmUhhOIxgxQgrkaRdm3UxuKkKKqSz+cRjUQEAKiqCsMw1qYtcyBDKCNKqj4esf/AVfwpn9LhZE0PvJ3r2GL6A/ca15x9bm4bIhEAcP3MV0NuDvHfQigWQ/wgYOZGInoRwHHY8u9wN8inmjFI981mSOEFyKdgQ9FTXNqQYqzJFy0HFKWOArKlxMeQ0cR9IKN9cyBr/IL2EBpkewwFUmypkBE1FcCn/vU8FCJv5QB+j57ia38/XbY4ncP1z/1Nrzmu8M+5eesrBAC4DjJ19oat7WTmlUR0PIC5vkBdBplG+zbkGlm97mVbvRZ7p6IGvSb7QKbm7gUZuZ0EOe9jIVOGxgL4kIh2/5+m0YYIESJEiH89zBzvLZr8qXgBwPEAFC0CODnA9QDTxK6aAo8FYvVpZ8OOSdE2omz7SiKCDTLXOt8Oj0cqaFKf0zGoTw1s27KPHjtB/2L5t00nHjyNARywsq09ZYNVdDkA7HlA+kMgWQUoPwJgAeJtQPwYkjcdACpA/YGoArBMmyQlKgjqsuaOr1DomwwASADVquteLCK6ooJgEdBhWfuffvrJrabnxciykFcUMHseALj/C27WVe1KD2hM2+ZWufmas89dOf2Be48HMHf6A/eO+bpVX1VibNrvkc+qXv39MYeeLRy2hlRWJ8qNONSoCgDksocNba1QCagrr8KgyqrCCVX5XyTP8zxN03TTyg/IW9b8VC67a6ud3zg6qhy3aeOm+Ixv55/EisiYXstYgei7kA/KQ4T4PyMUiyF+SNRiSwET1C66kIKk934dhToGBzLiNQoyDWVw0bivIaN3FmS0cQIKUUMD0hgm6p9Hh/y30OZf72DI1NYuAIf4Y2KQaacapOjcqdc9A8A4yChgwt+2HjICeU6xmCKiLyHrDScwc3G6Zw8w8xIAZxcdR/51Fxad70XIqOh+kEY7Sci0zxoAv4KsoSwWtMFxxevae40FCp/DXpB9EC8got0h02nLUKiJtEOhGCJEiBD/VqgFpEgMCt1tD25Ug+J4cHMZZPqUgkYmd4eiSHpR2dPHlOxN7W5HNpKIZ8oTpV9/smzBqD6llZU/mXDY4ODEZYa3NuXkhpu2agJeDSAOARQCHAHYUUD7MaDG0JObpXEb0SEg/U4Azboem8g6sshkYiDauJ2iJncvLSvThTI6l8sR5/PwPNdd51iIKeq4lKo2wqQYEHFTDfkNsaZ+sxeOWXvBt0+/2M1f0cmHfGmo2gjXdSbkX357m9x88ok/6cHNWFnfzc0Y3pcB4MHZb71oxCOlOyTKJ243oPzpSiNeGjEyZ7TmszW6oF+ZrnUNaQnFdh14ngfXcb2oqlJ5vIQUIliui1Q2jYpEEkHaqRBCmKbpClVRy+OJXRVFffjw8bte2LFg+ZMzli96FEKUs2VHoRKw7YfDIUL8jxHWLIb4IbE/tkyLVCAjf2kUGskHCIxtXEjx9iGAiyHFSwuAFiIKrKJPh4xCroYUjK9ACqtWf//DkGJQ9+/jU0g31BIAnQBOAjAFwB/8a2n+/hps+ZAlB5ky+jVkD6ogZfYryKezKSI6q2j8EP8cl/y9BSKiw4veHgbgLgBHFW0L7K7TkE2KN0OuX5t//uDfeND+IqhfBAo1jUBPExygINhVAAcT0ZOQkUQDwAZmLmXmmN/SI0SIECFC/PtgfwBKQAqqAkQFFPaQNyLIxhLgitig7IT+U2FZFj5tmu0t7vhSmJzyIqUUY92e9+aijy7OWnbZovXrW+5/dXbLrx+5/R4AqNsxc9GIHdgEulYBjgXoswG8BGSbEW8BqPUvKHBzEpKbh0A+hF0Eycsnfp3N/mHQ8L7xmnhEG8gYMr6sslJAUZhBBgw4eQ+rculMRSR6dZWmf+0x3+eSYFcRwm1SF8yqWnLK4lwupR48oYibaZiuKqoi///wnaBJE4q5+ShIbu5ub9XOTr92y8Lc+jUdR40Zu6RfZeXm0ljCfWz+R20NuewlpuuKVN6EaZpORyYDoeuipqSUdFWF47pe2swiD4bjugwArut6ABCNRhV47AkhVM5b+9378ENPPvq3uWNhIQJHXT398ktLb7r4d7EbL74q5OYQ/zCEkcUQPyReBjDZf+2nmwCQ6Z8MaYLz06J9QY/AICX0CMho3lGQEbBrAUwgouuZ+SrIlEnQ/2vvzsPkqMrFj39PVfU+e/ZJQhZCJEgMQQxBRIHfhEXIGHaRSwQURQEjIKJcFkFUfrJcguGCwBVRRLxBkQERYQBlJ2gkSMCQjYTsmb1neq2qc/84VenOzCQmLGZ7P88zz/RUVVdXN8O8eeuc875KvYYZbVuISQazmAQybGb/DKXm87my49KUktdwVLL8bl04olgBTNJaHxK83q3AlZhEc0VwjhOUUj/CJHR1wC3AN8s/DKXU34CBWutRwSjiYmB4UGn0F5jRzkeAV8ue5mLafIwLzvErTG+oOkyg9YNrLtD3//fe/S17szEVYK/HFOMZhRmtFUIIsft6GJgRSYDv41oWjpeBWIRY0cW/e/Kbv6yI2F/UPm7ezjpvdbxmDYzX60GJAeqtdSu82X968LiFizqmXj/znOntne7BTiT6vVQ8e8SiNfOvW3DFK1cAtQecd7basGHD3zfq/JmOx8IcVdUUrUy8yivkOt1Eyoo5+F5bLBqLeT6qoL1CbSyenjBir8qn3n7TxOauznwiYquoMoscPVBaQ8TDW1To4nWoortrkm5qPgRgv1On37ihJ3vl4Wrif/mJtpVY2J7DCbUnH/ejfNF9ZWhNTc27HW23+FpvHpsbG0xsbmoepRobFGZ5zGDV2NA1Du5bfPMvwtj8WvicjF9087li2m1q3g9g9j0/+5+OXGZJjR2rHlRTVVUoFPyeYl7VRGKF6spKx/dcVCTK6o5WUrG4VZusIFssEnGiCnziiQTFgqnXE4tFLeAPv3l93vV2xHlMue4wLInN4sMjrTPEDqWUWoWZQprDJGw9mCQnbMWQ7OdpGzCjiVHMyGMCk9DZlNYpzsNMSx0YnCfH5u0kujHrGKcF+8NkatOsG0oFeJ4EDsAU2ilv27EYk5RVA8dj1j5cjKlIWgXciZmashQzffOJ4DkXYoJx2E6iMvgsuoPXfhvYK9jvB9cWxySnvwYWaK3nKKVOBe4NjqnA9DI8DNOPMkYpWQTT+mIofRPecmGRAJu+7UMagYVa66VbeK4QQojdxIyXrDA2FwA720rOiZPqydF97IAZ1jn7X5NMOSkAuvPt2JE43e3dGxasX1r7xoaFkXVdGzMZ653Ehs72/LvLUo6Vr3LOP/ZYffXPn1q4tqtjcGU0OrAmkbDaMp25aNyKJ7woqzM5fCvb5Sj1uu/FD/VBDbdihXgy5mR9Xzu2Y61MtxdBhbNe5p49cMjhQH1B4yuN5RYK6Ij9z9+0tQxCU4XF9BrLesMuWhdnbffcCFQWND+q6UgdsdbtWVozNPIHIs59haK7OOMVL8e0x4pHwCs0NZvY3NgQtrnaLDbHwFUQzYFXbUd/PaF+xIKXbrtzjmpsCGOz95m9961c3d567C0zTj8u67rTF25cEx2UqKie/pHJvm0rlm1cv3b/oSOGVkSjVhFUOpvBtm2S8bhZKJp3iVQmPcdxdDGbtV1QKpdpjX9i4giAn/7s3kYX//nzzzm77d/46yH2MJIsih1OKRXHNIidjKks9jSlZBFKvQrDkUUwASxKqQF9eQIU7iv/Xl7sJUwMWygVznEpJUm9E0sPM72zPtjvY5KvfSiNPi7DJKcWJiEciQk6X8YksoswydvzwOWYtYx7Bc89Qmv9slJqBSY5jmGS1PD6s5iEGG9oe64AABsISURBVEyxnxbg2KBI0DzgreC1DwL+GFxjOyaBrcRM6alm60mijfnMw9f2gTla68vKD1RKWZiqsVmttdv7RMExQ7XWWyvcI4QQYic34yUrDvxwsn385NNHX3vdpUsOfNrP0TO59jPqsn3vxnM9r+AWlO+4VsJO4vvolatWFocMHBx5bf0rat7aF/WCZWvVvNfyrFyV4xNjhhfWtHjRlnS64EI0bkcK+MVCPEpFTx5yUDAtM1QLMDQGDKqocYclE/arK5YpHC9HpGKz2PyFugHr0Loe11fKdnzLtlbZsO/DLRu6tcLq0CyJ+GpsyrEsrb0VPgyhjd87Tvzz7X4uS5TlKJIkSrE5aTl7JR3H9hQHtc19dKFqbFiB+TdBEnBsyyoMqq6Ltqc7M3m3mASojcbW7zN4WFsk4hz7/Ow71qvGhnnAWwOwRnfhH/Spsfs8OuvwY1Vcq9yYAcOOGphKVha039WTzVbX19Rtis22hmgyCPcdnR6RqE0k0u07VtyyLMd3857lxG5jXP1msfma2+eEsTlz9dcu6N0iyxxz45yhV3/rAonNYrvJmkWxw2mtc8CDmJ6BpwcFU3Jlh4TVzkzxbpOghdNGfUqjZznMiODlmAQr/IMZwfyhLwbbMsH+1uCcYJLF8DxxTKEaHex/BjMqF07nbAMeDK5zdXDOkZSSsTHBtmGUErCJmITuC5iKpRFMr0QPeFoppbTWo7TWg4DzgvOEiXDY5iOsdjob+IpSaoDWegomWdwr+Iz2BsZhCv+sx/RR+hymqukLbF7hLVwT+kjweVRQGpntBKqCnorXKKXCxfzx4LX6jPgGPSEXAYuUUtN67xdCCLHr+P0hfu6Gvec/+LkRlw4rePmTH/6kr+0EuY9FDwM0juPYyXjSKhaLhVwh42Uz2XRtTY1TkapQk0d+wp9Yd6g/LDWaYfGa3BH7f7S4cFXXlzem09k8eB4QU0QKmnhrPlvMmU0ZUFmgHfxCHljV3eHO37A2iM32ptgchUIMnirkc0O9gqvS6XRhbS7T5ij10D333K+VYu2wmiqvUjGqaGvVoT06YXgGvHQN0fZIrpsaEiSZgB3EZpfDyRPRqDNbCjmvPZ97VTU2KN3UPEo3NQ8Bvgrg+b4CtNYUAG3DGxOGjTxybWfH7BeWL/mKamwYoJuapwBLWvFHFcFq60yPXdXa/pF9Bw1/bnzdwPV1seRpz76z+HPPLH7z5eXtbS8sXb50fWtXK56CbDYNkMGJPwJWNquLFfl83skVc1hOrAuIPnDbg+qO23533R23/bZ3bE71/u94+U9m3371DXOW4vPWNTfMmfQh/bqI3Zgki2KnoLV+ETgfuEgpVUn/o2BRTOKYpPS7G/ZEDPdHMKOUieDLL/uKUOrP+F3gdkrJYjw4VwdwASaJK2JGIBsxlU27gEeDfV9TSrVgpp6G01831QPA/ME+uOw6i5jErAvTe2kEplJpmMCeUfY+f01pbeYRwfUq4Fqt9bLg/McAE4LjB2Lag5yLmX47AFiktZ6ktZ4bnGNvTEGgZkpJtAvsB9wNPB5s9zFrIk/WWodBqBYzQgkmqVyOSYI3CVqGnIUJVmAKEQghhNiF7TP4gBeTTuX5GvVtzdrK+w9arD479ixU2T8fqxO10UQsaVsJkjm/aP1h3nxa2nusSmuIffBeh3DkfodGj/7YgRFfW3cXyCXASwB+3i36RfAhEQE7jM0/AG6HQgGyxCA+IJFSJBIdRBIXECwTKYCThxmuba3ssYpdT+E90ZLL6Xtb1n9NNTa0tMOCjkzWzkGEfCk2O5DC4tOJxKY1/EW8IDYr7kEzIol9ByYebik2q3XtLUcUvGINZk7slBd+cseyd9MdvWNzLebG7bmzjjxmft4rDnhp9bJF7D96EvuPfjTlRI/JaG/0qq7WZ8btPe7JAdV1Pvi0ptcVgf2piN1NKva4spSntfZ9338VOIJx9RcBWOgq/S9i86wfXNvqFwtn+baupweFYv37+oUQeySZhip2OkqpxZhkanuERXDC4jVVwfb1mERvNKX1i8sxo3/Z4OcYJklcRWkqaYxSEjoFk0TNDfaHiZymtMaxP+uAi4Cbgbla68uUUocBfyp73n2Y0Ualtd40WheMNGql1GTM1FK01kPDfcBErfXrvY/v9dxzgSMxld0OCd7/IZiy6AcDj2utLwjO92Xgx8DDWuuztvZB9xY8v4vSNF6Ai7XWt2/PeYQQQuy8NGuXAMMLRfDcLACe56Eshaddin6ejnSBl95ayoSRwxk1sI6VG1qL5995b2R1x7rMqpZ80a5oq/ZyUY1bsVJF3+xRscRAPz1mECZ2hLE5A64NxRgkWpOWtchS9scivmsrraNtLnaQ6k2ZXlnt47pz38lmR3fj+44dsdd4Rb+ntP6+1Em5ZMmwRNX312a7rgdu103NN0ycecZhb7Su/1PMxh9ZVWsPrqu678W33joDG/TjL5Zisxlp1Kqx4SDMzWN0U/PQcB8wUTc1v977eIBDv3GeeuHWO3TlKced67vekYv+88azX35n8SEnd40cyJjUpxhZve/qDSv3Llr5x0ZP/cw3gpYc38RUW3+AcfWldh3bYMKX/qNu0pD6lSPqBliVVkrRBskOLr50zgUSm8V2kWRR7HSCPoQTKK2l8yn/w18STh11MHfT0sCLwEmYKZtLtdanlBWO8Xqdo7xYzaWYhPGusu1BgTUKmMqmJ2Ga3TtlzwvXFfZeNwmmeun5lEY4vwncgBnhuwLT2uJJ4KdAQWtdvYXPY1MiuD2UUq8C4zFTex8LknC01vv0c+xhmCI8t2mt/+s9vNbDmOmwn8XcSf2K9F8UQojdh2bt3zF9jb2F76ywWzq6/BHVKV0/dJit8VnduZG2rjaGVw5yuwqd/qhBY5xUMtU99rzz0wdEUi/uN+mAk+5/+U+L3u3ILHUffP6U2ll7dzuWbbcsrfKgpjw2l8fqS2NKdYwaMOiuTDbrtfWk7YyPxjKx2YbbGiurZ7yU7hy9DuwEWDnLB+UVtOdE0crE5s2j83cxCVgC8I/a56PfjDr2Des3bmjO2er7dZXJb7295I3n12aLt4Au6Cdf7T82lyWC26PqxGNfVbY93tf69PSDf3iM2fOWAEVmTZnQ5+AlazbFZsbVb3ds3vuszz8c9f23Pj/i0zOSaZ7LjuMrV826QGKz2C7SOkPsjD6OSapex/QtDJOxchlMldGJwE1a6yuUUq9QGhl8Q2s9Mzh2HWbNoU3f4jWh72ASv3DUMOQEX5dg2l4Ug+sJE8pocFx/02b3pTS1M4MpbrMceFprvQA4E0Ap5QF/3tKH8T6SrpnAMVrrx4Kf79rKazynlDoeuEop9Wmt9bPb80Ja688FD7/z3i5VCCHETu5AYA7w92f/8cZNBZfoX1o26ItPP4VMNovjOix4e0XmkvT0pfFqJua64ze9cGrmijUbul7Zb+KI0S2trdbZhx3/xlVf/MZMgEyeVm+jMzBIFMsLuZW7NK+1t7xlg/bA9ktNthzA8eCCh9Kd/4goNTwGkaz2/UjUtSKJQjTTbrOpo/3mhoPvgeUDGQ3PF4ru8rqaAS88fvPsTbE5edT4bs9yHtnSh/FeEkUAbHumhmPSD/4hjM13Bu+/r3H1zy3r4HjgOjr429gatis2L/35AxKbxfsmI4tip6aUCu9k3oNZezcauFFrfaVS6nTMFMujgd8DR7F5BdF9MSOMUUxi13tkEczoZIbStFXYvPpqqL+Rw1DvSS5hInlhsO8OYIXWet9tfNv/dkqpMZhGzFcDt76X0UUhhBB7hpl3fuXvDRMP2reju/uem377x6N6soX6Mz459abZX/3/Vx76m8TpOPmzBw7j6CduPPL3OTd7FNgFPCeKbWU/Vlu97+vtyxeD7YCyIOmB019s7kZTUxZ5+84y0mgUVCtLeQq6/SIoH1tbeJ5dmrBaer4FnAtFG9w7wFqim56b+KF9UO/Tsg7GAA2Ywn23jq1BYrP4t5NkUezUlFJTMNM3L8SMlDUCR/YebVNKtWKSuRcw6/QszHqCoyiN/kEp6SsPOuWJYD54bqTX9h5Ko4xhoHLZvG+holSwJg2cjElWu/obHVRKLcQ0uq/ckVM2lVLDMFXensOsPfyrTCEVQgixJR/91jFTpk+d/I3x9cNnXfjTn83MFzja66k9Vjc9tSl2nHbVReqshk+2fO9/m9S8lateAPtIB6x9KpKPvtW9/mhwI6W6dRENdhCbc0Fsjr/n2GyD5QUVxS2lLN/3CijLryWSHpiMH71fJLLm4c7WrvLrDX31+99fErOtoSvSHZUP/+iGHRYLl3UQxuZmID+2hld31LWIPZski2KXp5S6FVMJ1NVaVyqlBmMqfN4M/I7S1JZw1BHMyONLwCRM9dBwcosOHocBKZwaEyaGOjjOxUxpTVAKUMXgNVKYiqJvBdd0/hauuzO4nplB1dIdIihQMxl4V2u9cUddhxBCiN3H/Def+c2alvbGZDTmHjn1uMpJXzxpsFss3h1xIjcv6Gh9KBVJxJWGbjdbADsarDbJQPFl8A+ARB0aD7Xppm55bO6JQqrC1cXu4tt2gUpNoj6MzQWCInUDUtVki/liJtvuYkcTYL/aWFX7NtDz8H1zZ/V33Wdfc1VnKhKL+vgH/fflVy78d3xW/VnWQRibl4+toX1HXYcQsmZR7NKUUkngIcyIYxOA1nqDUmp/TBXRE4FfYHoIhkmdh0nyDgfuxUxlDYvZWJQCkir77lDqexhWMq0IHhcwI4lgEk+Cn5/aynVfGrzeazsyUYRNayLn78hrEEIIsfs44YeXJW3LuvNbR3/m4Hg02gSw4N7fbkiedMz+Md9/LObETprxkf3ue6etM/nCmndsBcRs1ysoP+m7kcPBMrHZx8OGYKRws9jsUVBZdESjFejy2JwkKE7X1tOZToE9yknVvKt9fO2vUIo/g+rp77ovueHH18xfscx7o2XdfK+peYcligBja5DYLHYKkiyKXV0Cs46xQWu9pGx7DyZwQGk00S7bl8T8/h8ItGFGA8M7lglMAhgmiwTbVwAjy85TAG4BlmDaTtQEx63RWh/X+0KDEbybMQHvq8G5t2uxuhBCCLGzsy1VodF73fzksw3/++0flmKz1j1oM4RYm6h01sWyVEcjdl1NBenurp6WTCYJysEUumtDk6KIHbPQGbs8NnsxmzQ5J6Z1cdQKEol+Y7OGG3ugaojWejK889dHnj6z97X+8PY7KgoF9/sLli509xky7GunfXyK3f3cn5//kD8iIXYZMg1V7PKUUjGtdX4r+5cAwzGFbHLAZcD1mOTOxyRv84GpbF4ApwtT+KZ3YZwlwNvAf2OqpB5BqdG9BzyImf76Y0x57nswFVnPxBTi6QKqMVNcP6W1Xt3PNT8KHARM01pLg3shhBC7lBk/+Hbs9//54y3G5k+cd9aKf65bN7jbdzN1iWgO2z++rdt9hPLYrJmPx1Sc3rFZV0XIe75ybK0dfNB0s5QKE5sVfFfDoZTHZp8HR9bU/K4z3fXjLu2fArwMrDzv4MPPx3cfSLe2diSj0dqGyQdnxg0dcdiBR3yyT2xm9rxHMVNDD2TWFGlwL/YIkiyK3YZSqgH4JXC/1vqSsu0K+CemKmkzZq3iUuAAzCijxtyJjGN6LVZRWp8IJqlLUlpU/1pwbAIYEWxz2byQTgcm4IVrIV3MNNkTg/3Tgae2VEhGKfUkZtTzcEkWhRBC7KpUY8OJwG3A/bqp+ZKy7Zti88BYZG1nvni3Bwt9mAp+BrLBMo9UHGgFauknNsdBp4Bsmn9kKnGAJC4j8VE4uBSJYgM22lakHajKm4I5MaDYMHj44/WDhk6vdmx6Vq+YPnzy1KeuvfDC/v9xPHvek5hk8QhmTZHYLPYIkiyK3UaQLP4PcF/w9RfgFa319LJjZmFGFQvABkzSFxa4uRKT0P2dzSur9W6NAfB0cOwPMYkklHo4hoV0wjUU3wGmACdQKqLTjgl6x2ut//ZBvH8hhBBiZ6MaGxowM2x+OiCe/EttVeVv05nss+vu/93J4TGDTvnsdd35wiUFyPkU1oKuBr8W3AJUXo1ZsvGMAluXllAVgUgSk/kFQ4gmNhf4wcAoqSrQy/Lk0cSJURhbUxv1fMil09563O80wJShQ4afOGTUeNuJR3Vl1m3/yNB6VCTReNJZp0psFgJZsyh2I1rrZkwrCpRSE9i88Ex4zGylVBS4FjMq6GNGF6PARZheg7OBbwdPKU8Ufcwoogd8EliAiVFFTOJ3M2baabTseBf4UnBdDuZOaBtmWizAN4AvfgBvXwghhNjp6KbmZsx6fwZ//nMTXM8r+Jru8mM2zn3sCtXY0An+teCNAd+DRDckYgfCxZFo4oBXCtl7bMs6T/s+XhibNWQULubmbhibf41DoR2KtZZlE/NvxuMqFNFiJkNW+17EdorxrPulT0w6YFTDxw+1I/FYxldWe76rZ/igumpWrt8osVmIgIwsit2eUupo4GLgSq31vGDb/cAMzDrGAmZ6Sx5YjJmmOg2oo1QIJ5xWWi5s8NsdHGdhgpUKHkMp2Sw/huA1I5jk8U1gndb6lA/wbQshhBA7LdXYUIrNTc3zgm33Q3YGqAzEC0DteMhHYPFCeEnBMdqs+U9hEsStxmYHkj5YtmV59VW1qljIW0XfZWM658Y95Xx8zPDuK488IVlwlFURi2Ll3YKTbY/EopHsynT6Tb9o//Pk884599/3qQix85GRRbEnGIJJBmvDDVrrL4SPgzWN72Kmo44H9sckcw6l5K4i+F7eq9EKfg73hdtU2c/hqGQFpZ6NPqVWHFFMIRsfIYQQYs8RxuZ4uEE3NZdis1nTuPptc8x4YH+9HbG5AirC4Uvf961EJKJae9KoYoGRlRVOrRNh7wH1FffPezY765gZCUfhDxtU46QS41VrW5uzKv3Pg7DdAzF9nIXYY1n/+hAhdm1a618AB2ut/7SF/RozouhRCjZRNk/6/OCrfIppeFyx7OfwOW4/L1UeyMJRyIeAvwKPbfs7EkIIIXZtuqnZxOam5n5bSOmmZg0sGgi+KovNFqgU5s4rW4nN3VAkX/DJuQCqvb2TiOsXK2JJIrEoR+z/cYZUVTFpzLhYTSrFwFSl5aEt38cbOWKvu7TvvulrW2Kz2OPJNFQhAkqpVZipp2HCV36nUpdtDx+HLTXK9/XWu+0GmEI4szHTb+7VWl/4QVy/EEIIsbsZ09iwfiNU9pTibDEGER8o/qvYXOhWRCuosm26PQ8fqAROPOBgb2ztYLsnk6YqmtQXNBynbMvKqVTqTgVf03Bvaly9xGYhkJFFsQdRSkWUUncppW7bwiF3YNY/rGbzUUbYPBkMH4dJ4JbuuPj0TRQ1ZtTxb8Ba4JVtu3ohhBBi9zNimooMnKburpum5vS3/x24u8fE5o2YuBoJK8vhZ7cemyMpLGCfuiEMr6xCARr8imjcHlpRxYzJUzll6mGqu5DTFalUAXgeWKslNguxiYwsij2GUmoYZropWuuKrRwXttfoT38jhVviYRJDBazEVF9tlkI2QgghhDFimhqWhWWA1/rkVmJzY8NlwPdMWC0CGnQclAWlnsZ9OJbFyEQlWe0xMFnBso4W97QJB3j7Vg5UwwYMXDl46LARLenOh874wqnnfPDvTohdnxS4EXuSArAQc3eyX0Gxm18Cs4Ch9J1eWj6aGNykNE8NvvuYxHB0cOxLWutpH8C1CyGEELujggOve6b3cb+aD/6mmpRK3LWgInseMASKCiJhogilf89uFptjjqNG1w1iXVeH35XLrhwST42ui8SdNztaX/7ZD66X2CzENpBkUewxtNatSqmzgfVbOexKTA/EvTHtLra0FlGVfQ9HG8PvrUA9Zprpyf0++19QSjUC92N6QA7VMgVACCHEbmjVk7p1xDR1dmQrsdn1vSuvT48e7vTYY6cNeaMbqrYpNlueZ3dlM17edW0L1jvR6PAhqDVKWe8pNj/hzG0E7tfoNFB/tHuqxGax25M1i2KPorV+U2vdupVDlgHLg8eXYhJAH3OXsg14GdNm44+YQjWtQDvwILAGeBE4E/gJMFNr3QmglDpNKbVN5beVUp2YRNEGqoA/b/s7FEIIIXYtq57Ub656cquxeaVGL2145RY9Y+q0y5N21MfEZ11jRiQ3xebqZGVuUGVN64BEor2g9QM96c41Edd95mP1o852IpFbE1WVM1+a81MTmxsbTlONDdsUm59w5nZiEkU7h1dTwP3z+3vXQuwaZGRRiDJa6/vCx0opDTwOjML0g6oDpgKu1np8cMww4OuY/5dODo55B7gPWFF26hiQUEqprY0SBtNgI5i7ol3APIJ1lkIIIcSe6JhXf/Lz8PFNX7/Mh8seu+7iM/bqymaHe6478PF8ZnAOXN3UPB7gmtvmDFu4ZtXX31i9YsDGTM9IR6uatmz3O3vV1N1X8Lzy2FzWhWPLnnDmbhabLfQ8THwWYrcnBW6E2AKlVByYD4zBrHecCzQCS7XWh/Rz/APAr7TWj7zP1/0o8GWt9UXv5zxCCCHE7mZZB/HrLj5jPjCmM5crrMp2z53neTOABbqp+f/1Pn7czFMfUJb1q8U/f+B9xeYnnLkfBb58lHuKxGaxR5FkUYitUEqdhemJ2AHcADyitV6x1ScJIYQQ4kNzzjlnnAXMbisWOh7ubDexualZYrMQHwJJFoXYBkopB1MddXXvaaRKqcuAq4EerfWgHXF9QgghxJ5GNTaUYnNT82ax+VHngdtz5L9oY/ec4P6HxGYh3iNZsyjENtBau8CqLewejVnHsK39F4UQQgjxPumm5q3FZkApLcUchXhfZGRRCCGEEEIIIUQfcrdFCCGEEEIIIUQfkiwKIYQQQgghhOhDkkUhhBBCCCGEEH1IsiiEEEIIIYQQog9JFoUQQgghhBBC9CHJohBCCCGEEEKIPiRZFEIIIYQQQgjRhySLQgghhBBCCCH6kGRRCCGEEEIIIUQfkiwKIYQQQgghhOhDkkUhhBBCCCGEEH1IsiiEEEIIIYQQog9JFoUQQgghhBBC9CHJohBCCCGEEEKIPiRZFEIIIYQQQgjRhySLQgghhBBCCCH6kGRRCCGEEEIIIUQfkiwKIYQQQgghhOhDkkUhhBBCCCGEEH1IsiiEEEIIIYQQog9JFoUQQgghhBBC9CHJohBCCCGEEEKIPiRZFEIIIYQQQgjRhySLQgghhBBCCCH6kGRRCCGEEEIIIUQf/wcZ/8WTEhM0iwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(init_full, y)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.00000000e-04, 1.15557926e-04])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "init_full = init_full / (np.std(init_full[:, 0]) * 10000)\n", "np.std(init_full, axis=0)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "embedding = openTSNE.TSNEEmbedding(\n", " init_full,\n", " aff50,\n", " n_jobs=32,\n", " verbose=True,\n", " random_state=42,\n", ")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===> Running optimization with exaggeration=12.00, lr=108843.92 for 500 iterations...\n", "Iteration 50, KL divergence 8.6100, 50 iterations in 43.9514 sec\n", "Iteration 100, KL divergence 8.0667, 50 iterations in 46.7819 sec\n", "Iteration 150, KL divergence 7.9223, 50 iterations in 45.6121 sec\n", "Iteration 200, KL divergence 7.8557, 50 iterations in 45.4719 sec\n", "Iteration 250, KL divergence 7.8177, 50 iterations in 45.1488 sec\n", "Iteration 300, KL divergence 7.7932, 50 iterations in 45.0411 sec\n", "Iteration 350, KL divergence 7.7764, 50 iterations in 44.9336 sec\n", "Iteration 400, KL divergence 7.7640, 50 iterations in 44.5941 sec\n", "Iteration 450, KL divergence 7.7548, 50 iterations in 44.5967 sec\n", "Iteration 500, KL divergence 7.7478, 50 iterations in 44.8961 sec\n", " --> Time elapsed: 451.03 seconds\n", "CPU times: user 2h 21min 31s, sys: 3min 51s, total: 2h 25min 23s\n", "Wall time: 7min 33s\n" ] } ], "source": [ "%time embedding1 = embedding.optimize(n_iter=500, exaggeration=12, momentum=0.5)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding1, y)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===> Running optimization with exaggeration=4.00, lr=108843.92 for 250 iterations...\n", "Iteration 50, KL divergence 6.9774, 50 iterations in 45.0601 sec\n", "Iteration 100, KL divergence 6.8239, 50 iterations in 44.5858 sec\n", "Iteration 150, KL divergence 6.7657, 50 iterations in 43.4053 sec\n", "Iteration 200, KL divergence 6.7341, 50 iterations in 43.8213 sec\n", "Iteration 250, KL divergence 6.7131, 50 iterations in 43.4168 sec\n", " --> Time elapsed: 220.29 seconds\n", "CPU times: user 1h 10min 33s, sys: 1min 56s, total: 1h 12min 30s\n", "Wall time: 3min 42s\n" ] } ], "source": [ "%time embedding2 = embedding1.optimize(n_iter=250, exaggeration=4, momentum=0.8)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding2, y)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===> Running optimization with exaggeration=4.00, lr=108843.92 for 250 iterations...\n", "Iteration 50, KL divergence 6.6988, 50 iterations in 42.4042 sec\n", "Iteration 100, KL divergence 6.6849, 50 iterations in 41.8349 sec\n", "Iteration 150, KL divergence 6.6753, 50 iterations in 42.8223 sec\n", "Iteration 200, KL divergence 6.6687, 50 iterations in 41.5115 sec\n", "Iteration 250, KL divergence 6.6634, 50 iterations in 41.6096 sec\n", " --> Time elapsed: 210.19 seconds\n", "CPU times: user 1h 6min 54s, sys: 1min 54s, total: 1h 8min 49s\n", "Wall time: 3min 32s\n" ] } ], "source": [ "%time embedding3 = embedding2.optimize(n_iter=250, exaggeration=4, momentum=0.8)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding3, y)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===> Running optimization with exaggeration=4.00, lr=108843.92 for 250 iterations...\n", "Iteration 50, KL divergence 6.6576, 50 iterations in 41.6053 sec\n", "Iteration 100, KL divergence 6.6519, 50 iterations in 41.5500 sec\n", "Iteration 150, KL divergence 6.6474, 50 iterations in 41.7626 sec\n", "Iteration 200, KL divergence 6.6439, 50 iterations in 42.2903 sec\n", "Iteration 250, KL divergence 6.6410, 50 iterations in 41.6484 sec\n", " --> Time elapsed: 208.86 seconds\n", "CPU times: user 1h 7min 27s, sys: 1min 55s, total: 1h 9min 23s\n", "Wall time: 3min 30s\n" ] } ], "source": [ "%time embedding4 = embedding3.optimize(n_iter=250, exaggeration=4, momentum=0.8)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding4, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison to UMAP" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "from umap import UMAP" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "umap = UMAP(n_neighbors=15, min_dist=0.1, random_state=1)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ppolicar/local/miniconda3/envs/tsne/lib/python3.7/site-packages/numba/typed_passes.py:293: NumbaPerformanceWarning: \n", "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n", "\n", "To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.\n", "\n", "File \"../../../local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/rp_tree.py\", line 135:\n", "@numba.njit(fastmath=True, nogil=True, parallel=True)\n", "def euclidean_random_projection_split(data, indices, rng_state):\n", "^\n", "\n", " state.func_ir.loc))\n", "/home/ppolicar/local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/nndescent.py:92: NumbaPerformanceWarning: \n", "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n", "\n", "To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.\n", "\n", "File \"../../../local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/utils.py\", line 409:\n", "@numba.njit(parallel=True)\n", "def build_candidates(current_graph, n_vertices, n_neighbors, max_candidates, rng_state):\n", "^\n", "\n", " current_graph, n_vertices, n_neighbors, max_candidates, rng_state\n", "/home/ppolicar/local/miniconda3/envs/tsne/lib/python3.7/site-packages/numba/typed_passes.py:293: NumbaPerformanceWarning: \n", "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n", "\n", "To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.\n", "\n", "File \"../../../local/miniconda3/envs/tsne/lib/python3.7/site-packages/umap/nndescent.py\", line 47:\n", " @numba.njit(parallel=True)\n", " def nn_descent(\n", " ^\n", "\n", " state.func_ir.loc))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6h 30min 53s, sys: 9min 34s, total: 6h 40min 27s\n", "Wall time: 1h 6min 49s\n" ] } ], "source": [ "%time embedding_umap = umap.fit_transform(x)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding_umap, y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 } openTSNE-0.6.1/examples/05_animation.ipynb000066400000000000000000000157041413546205200203260ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# t-SNE Animations\n", "\n", "*openTSNE* includes a callback system, with can be triggered every *n* iterations and can also be used to control optimization and when to stop.\n", "\n", "In this notebook, we'll look at an example and use callbacks to generate an animation of the optimization. In practice, this serves no real purpose other than being fun to look at." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import openTSNE\n", "from examples import utils\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.animation as animation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import gzip\n", "import pickle\n", "\n", "with gzip.open(\"data/macosko_2015.pkl.gz\", \"rb\") as f:\n", " data = pickle.load(f)\n", "\n", "x = data[\"pca_50\"]\n", "y = data[\"CellType1\"].astype(str)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data set contains 44808 samples with 50 features\n" ] } ], "source": [ "print(\"Data set contains %d samples with %d features\" % x.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We pass a callback that will take the current embedding, make a copy (this is important because the embedding is changed inplace during optimization) and add it to a list. We can also specify how often the callbacks should be called. In this instance, we'll call it at every iteration." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "embeddings = []\n", "\n", "tsne = openTSNE.TSNE(\n", " perplexity=50, metric=\"cosine\", n_jobs=32, verbose=True,\n", " # The embedding will be appended to the list we defined above, make sure we copy the\n", " # embedding, otherwise the same object reference will be stored for every iteration\n", " callbacks=lambda it, err, emb: embeddings.append(np.array(emb)),\n", " # This should be done on every iteration\n", " callbacks_every_iters=1,\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------------------------\n", "TSNE(callbacks= at 0x7f1980309d40>, callbacks_every_iters=1,\n", " metric='cosine', n_jobs=32, perplexity=50, verbose=True)\n", "--------------------------------------------------------------------------------\n", "===> Finding 150 nearest neighbors using Annoy approximate search using cosine distance...\n", " --> Time elapsed: 11.95 seconds\n", "===> Calculating affinity matrix...\n", " --> Time elapsed: 1.28 seconds\n", "===> Calculating PCA-based initialization...\n", " --> Time elapsed: 0.18 seconds\n", "===> Running optimization with exaggeration=12.00, lr=3734.00 for 250 iterations...\n", "Iteration 50, KL divergence 5.6240, 50 iterations in 2.5803 sec\n", "Iteration 100, KL divergence 5.0628, 50 iterations in 2.5743 sec\n", "Iteration 150, KL divergence 4.9531, 50 iterations in 2.6205 sec\n", "Iteration 200, KL divergence 4.9087, 50 iterations in 2.5746 sec\n", "Iteration 250, KL divergence 4.8851, 50 iterations in 2.5237 sec\n", " --> Time elapsed: 12.88 seconds\n", "===> Running optimization with exaggeration=1.00, lr=3734.00 for 500 iterations...\n", "Iteration 50, KL divergence 3.4490, 50 iterations in 2.6531 sec\n", "Iteration 100, KL divergence 3.0860, 50 iterations in 2.4883 sec\n", "Iteration 150, KL divergence 2.9101, 50 iterations in 2.6124 sec\n", "Iteration 200, KL divergence 2.8046, 50 iterations in 2.9374 sec\n", "Iteration 250, KL divergence 2.7349, 50 iterations in 3.5985 sec\n", "Iteration 300, KL divergence 2.6854, 50 iterations in 4.1135 sec\n", "Iteration 350, KL divergence 2.6501, 50 iterations in 4.5806 sec\n", "Iteration 400, KL divergence 2.6238, 50 iterations in 5.2910 sec\n", "Iteration 450, KL divergence 2.6031, 50 iterations in 5.9040 sec\n", "Iteration 500, KL divergence 2.5878, 50 iterations in 6.7577 sec\n", " --> Time elapsed: 40.94 seconds\n", "CPU times: user 29min 8s, sys: 1min 10s, total: 30min 19s\n", "Wall time: 1min 7s\n" ] } ], "source": [ "%time tsne_embedding = tsne.fit(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have all the iterations in our list, we need to create the animation. We do this here using matplotlib, which is relatively straightforward. Generating the animation can take a long time, so we will save it as a gif so we can come back to it whenever we want, without having to wait again." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 8min 10s, sys: 22.3 s, total: 8min 33s\n", "Wall time: 8min 4s\n" ] } ], "source": [ "%%time\n", "fig, ax = plt.subplots(figsize=(7, 7))\n", "ax.set_xticks([]), ax.set_yticks([])\n", "\n", "colors = list(map(utils.MACOSKO_COLORS.get, y))\n", "pathcol = ax.scatter(embeddings[0][:, 0], embeddings[0][:, 1], c=colors, s=1, rasterized=True)\n", "\n", "def update(embedding, ax, pathcol):\n", " # Update point positions\n", " pathcol.set_offsets(embedding)\n", " \n", " # Adjust x/y limits so all the points are visible\n", " ax.set_xlim(np.min(embedding[:, 0]), np.max(embedding[:, 0]))\n", " ax.set_ylim(np.min(embedding[:, 1]), np.max(embedding[:, 1]))\n", " \n", " return [pathcol]\n", "\n", "anim = animation.FuncAnimation(\n", " fig, update, fargs=(ax, pathcol), interval=20,\n", " frames=embeddings, blit=True,\n", ")\n", "\n", "anim.save(\"macosko.mp4\", dpi=150, writer=\"ffmpeg\")\n", "plt.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 } openTSNE-0.6.1/examples/__init__.py000066400000000000000000000000001413546205200170700ustar00rootroot00000000000000openTSNE-0.6.1/examples/data/000077500000000000000000000000001413546205200157025ustar00rootroot00000000000000openTSNE-0.6.1/examples/data/.gitignore000066400000000000000000000000161413546205200176670ustar00rootroot00000000000000* !.gitignore openTSNE-0.6.1/examples/docs/000077500000000000000000000000001413546205200157215ustar00rootroot00000000000000openTSNE-0.6.1/examples/docs/.gitignore000066400000000000000000000000261413546205200177070ustar00rootroot00000000000000* !.gitignore !*.ipynbopenTSNE-0.6.1/examples/docs/figures.ipynb000066400000000000000000003406401413546205200204370ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.random.seed(5)\n", "points = np.vstack((np.random.normal([-10, 0], 1, (30, 2)), np.random.normal([10, 0], 1, (30, 2))))\n", "\n", "_, ax = plt.subplots(figsize=(8, 4))\n", "ax.scatter(points[:, 0], points[:, 1], s=10)\n", "ax.set_ylim([-5, 5])\n", "ax.set_xticks([]), ax.set_yticks([]), ax.axis('off')\n", "ax.text(-10, -3.5, \"A\", fontsize=14)\n", "ax.text(10, -3.5, \"B\", fontsize=14)\n", "\n", "x = points[6]\n", "ax.text(x[0] - 0.9, x[1] - 0.14, \"$\\\\mathbf{x}$\", fontsize=14)\n", "ax.scatter(x[0], x[1], s=30, c=\"r\")\n", "\n", "y = points[41]\n", "ax.scatter(y[0], y[1], s=30, c=\"r\")\n", "ax.text(y[0] + 0.4, y[1] - 0.14, \"$\\\\mathbf{y}$\", fontsize=14)\n", "\n", "z = points[38]\n", "ax.scatter(z[0], z[1], s=30, c=\"r\")\n", "ax.text(z[0] + 0.4, z[1] - 0.14, \"$\\\\mathbf{z}$\", fontsize=14)\n", "\n", "plt.plot([x[0], y[0]], [x[1], y[1]], \"r\", linestyle=\"dashed\")\n", "plt.plot([x[0], z[0]], [x[1], z[1]], \"r\", linestyle=\"dashed\")\n", "\n", "plt.savefig(\"two_clusters.png\", dpi=80, rasterize=True, transparent=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdMAAAHICAYAAAAV7wD1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFBdJREFUeJzt3bGP41Z+B/Cf7nzr7AF3ruwqhgSkSRfjALtIm7jJTBEECGC3Bu7KpJwB8g/M/As+YJFU6z9gJs3FQOozEFx3KTlI522yduC5c2IohahdDofUivpRIkV9PoDhEUXpUcuZ99V7fO9xtlwuAwDY3Y+GPgAAOHbCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQ9NaQhc9ms+WQ5QMwPcvlcnboMgcN04hhPjTtZrPZ0jkZH+dlfJyTcRqqkaabFwCShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACSZsvlcrjCZ7PhCgdgkpbL5ezQZb516ALrhvjQ+zCbzZZT+CxT+RxT47yMz5TOydQ+yxDl6uYFgCRhCgBJwhQAkoQpACQJUwBIEqYAkCRMASBJmAJAkjAFgCRhCgBJwhQAkoQpACQJUwBIEqYAkCRMASBJmAJAkjAFgCRhCgBJwhQAkoQpACQJUwBIEqYAkCRMASBJmAJAkjAFgCRhCgBJwhQAkoQpACQJUwBIEqYAkCRMASBJmAJAkjAFgCRhCgBJwhQAkoQpACQJUwBIEqYAkCRMASBJmAJAkjAFgCRhCgBJwhQAkoQpACQJUwBIEqYAkCRMASDpraEPYDabLYc+hr5M6bMAbzalv/kpfZYhDB6my+Vyts/3n81my32Xcchy9s0fFGxvKnXLLmV0fc0h6+J9l9FENy8AJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQNPjavAAMb5c1ba3l/ZowBSDmFzcfRURRXJ292Gb/XRa63/ngjoBuXoATtri8/aT88TcRcVd5TAfCFOBELS5v342IZ+XDdyLiaUQ8K7fTgTAFOF2LiPi+tu37cjsdCFOA01VExJPatifldjo4iQFIZZfFIjpcXAeYuuLq7MXi8vaziHgeES9jFaSf9VVPVuvePt5vzE6lZXoXLq4DPFJcnX1R/vhxRMwrj1PKuvZV3dvHe47ZpMO0chH9abi4DtCquDr7qucW6bN4WPfGlOveSYdpNF9Ed3EdYL8W8Xhg03r7JE09TIuGbS6uA+xXEY8HNq23T9Kkw7TSZXEfq4vr99HjxXUAHivr2M/iYd0bU657Z8vlcCs8TW15qS5La41V1yXCOAznZXymVn9NyRB/K4NPjen6oXdZD7Jt/7bn3lRGOUrtWayuCTyJiM/urs+fb3tMwDQcotLus84b8jWHPK4u+/dl8DA9NrVRak/Lzc/aXwHA1E36mumeLKJ5+S0ATpQw7a6I5uW3ADhRJ9HN27CkVfXnTirLbz24Zhqr5bgAOEGDj+Y9xEXv7kf25oEF9fV+pzLaciqfY2qcl/E51DnZVE7TuuMGIA3ztzLpMF1c3r57d33+9fzi5tW2u+vzWD8uf36vPvfpmE5g36byOabGeRmfocO0aVZBcXX2xVhDa6zH1ZepXzNd9LQPwGi0rH1r3fEBTf2aaRGxaoFW1R5/e7jDAejFIlYt0qeVbdYdH9CkW6br7tv5xc39/OLm5fzi5vvy8XeVrt/fLS5vfznUMQLsoAg39R6VSYdpxTxW9+r70/LxjyvPvR0RnwtU4Fi0rH1r3fEBTWIAUtOItrb9Z7PZcn5x801E/Lw6GCki/hgR7+86MncqA0Sm8jmmxnkZn6EHIEUYzdvXa/oweJgOVvgeTKGyU2mPk/MyPocM032XMTUWut9C9Re4/FZ2FxFPK63M+4iYb/qWVnbpfl5761evO6bFlYHpG6oFuMuNQurWree76/PftkzxWU9h7G2aYpf9+3Ls10wX0bxO7mLTi4qrs19HxK9i1bX7bbjeANBocXn77uLy9sNdpt2Uc2HvIuI3lcdNz0dE3NWfPybHHqZF7DiirQzU9yPir2LVIv2i74MDmIB1GHYKu4a5sBGVubC15yOOfK7sUYdpbURbRMcWZnF19qK4OvtKixTgoUqo7bowxCI29xy+6fmjctRhGhFRtijn5UMtTOAoZLpP9608pr9peKpL2BXR3HP47eLy9sNYXWKbzFzZwUfzHuLicp9Dq9um4UxltOVUPsfUOC/jkzknbevq9lHOhrV8N04h7HD4r7zpuKqf8+76/J1N++5aRt3JTo1ZLpezTSe57TXZ7buUsekPYCqV3VQ+x9Q4L+Oz6zmpzkKobH4wCyFTTssMho3hXauLXx1bZZbENxHxk9ihzquO5p1f3Nw3fO5f3F2f//7YbzoyeDdvbbRXejTXvrpOLCwN9GQRPV8r3FTvday7mo7t24j4h9jxMtp6bEr5sOlz/2y9X9f3HpPBwzR6DKi+g7n+9jGhi+XAYIro8Vphvd5r2iW2r7uaju2tiPjXnsJuMtdI68YQpn0G1KNg3v2wHini8S/Cn4S7zgAd9LmubkurM2oNkiK2DLGGY4tdj219HLUW82TXEx5DmPb5TaUpmHtR+yX7rtz8Q0T8xzFPNAYOrzIL4ePIzUJYRHM9t6iU1Sm8a8cWux5bU4u5x889OoMPQBqscAAmqWkw074NHqZ9jeaNaB6xdnd9/ryylu8nd9fnz+cXNy8j4snd9fnTriOD5xc3H8Xqm1Z1mPfLu+vzd8Z4B4WujBodJ+dlfMZw15iIbtNsdi2jy2va6slyZPBHlcFIW73Xtse1/ne4uz5/Wo4a7vzvkDGGbt7eViLa1IWwuLz984j45/Jh27WFrYqJ5q5pgIMbaddpEc31YrGPwsawNOHgd43pWxnIGyck312fVx8u6vu/6f0Xl7efRe2bYEQ879LCBuhLU703pJZ68um6XtxDXbkoy6nOYV0PZj3Iv8soWqb7Nr+4uZ9f3Ly6EXj152j5prRp3taGb4L7mpYDcFTq9eR6+56mMBYx8LSbybVMW9S/sUSsbr/29oZvRXfl654sLm8f9b1XvwnWFoRel/NscXn7pRYqcKqq9eTs+lF37Ku6so9yKi3hpzHAtJtRDEDa52sOOWK40tqNWA1B/7jpYnuEAUh047yMz1gGILXpa2Dnptd0PaZddDmuTTca37eTCNP5xc2nUVlseX5x82lxdfZFyxqWH5ZrSFY3v4yIj7e4U3x1c+tam7t8jl1f05VKe5ycl/EZc5h2HeHb92je6vaGUbY/joddsvfrmRX1LwBjrSebTPKaaf16Z/lL9IuI+Kdyly83vLxo2Lax770SmJNc2QM4HmNaR7xllG1Era4s993ncrB7N7lrpg3fyJpG8349u25+fXF19qJ87j4efqt70faainkYzQsMaxEDj2x9w7HcR8TfR8R/x7oFeh3PYw/XUg9ptGG6y9DplovbMb+4uS+7GKq3FLq/uz6vD0qq6hyMfQxPN70GSCpix5Gtlfrn21jdzSVbD7Udy+/WRS4ub9fbm74AbKqjR2WU3byJ5v4iGtapXIdmbX7pD5veqK+FJLo49m4OYHi7LqRfqX/+PSJ+X/4/VQ/VjiXidbfu30bEf0XEv8XrO90c9WI4gw9AGqzwI2UA0mlyXsZnzAOQIrqP5t3x8EZpiL+VwcO0y2ja4ursq8q6jw9+QSrr/D64Zlp2734aEf9SPo6I1TSWssv3PiJ+HRG/rL7mEKOMayPePoyGNX+jMr3GaN7T5byMz9jDtGsZ5Zrl71Quha3ryMZpfl3X0y3LeC9WLdK3G+rij2LVLbyILUbztn1ZMJr3taJh25OIKCrdDa3doJWRu/9Y/n+97S9itVBDRDzo8n0aq7vIb7of4CEUMeEb5wKj19atuvG+zZtWi2vaPSr1cMSDurjY9vLaGC+Jja5lut5ethira99+GRF364FEpVdzOTe1TDcdQ60FHBGb727Q9bN02f9Nc8O0TE+X8zI+U2uZtj03v7j5LiJm0VAfVefwR8OdurYtI2LVNbvNPNNyn7t4PEJ4/qbW7D6NsWW6Vl/7dhER/1vbZz3cOyLa7zo/v7h5r/zvvnzcGKI1RfoTdDTSuz8AJ2C5XM7KbtiYX9z85fziZt2C/Gm0z1V9NJ91izI+rdTFf5xf3PwqolNrcxGPB5o+yIIhjHZqTMM0kw8i4ue13erdoItoHk69KK7OvirXbnweq28x1cnDD66Zxn7vbrDR2O7+AJyOynz6/4uIP0TE25Wnm+aqdp7OUlydfbG4vP0yIr6OiPfLMj+P7eeZFjHCS2KDdfOul+AbpPAtNHUZx4Ylufro5t3Ha7rSnThOzsv4nEo379BalnBtrZuH+lsZJEyb7oh+d33+fN3/Xr9TenWka2Wk2TcR8df1ka5N10zb/mG3GHXW2i+/7Xu1EaZ04byMz9TCtGGcSpT18suoBdbi8vaT8rnvYtUNvJ5HuvGa6aa6uCz/QX27qf4e22jeg3fztt0Rvfy56U7pX0Zzs/4n0dCsr3QhLMrnd239LmI8S3IB7EXbLSTj9T1IP44ysMp9P4jXdfZPy///KCI+KK7O/rNcGnAX9ZuJry/LNRrbJbEhrpkuohJS5bDo9c/1vvZ1eP1ZrO40UN3euqJH/R56OypihP3yAD1bNGx7NaBn3ftX6fX7IR5fF/1DrJYf3Fm9IVRZs/coDBGmRVRCqrpO7rqZX5l39CRW85uexcNg+yE23/klf5APbzb7YMH7fZYLcGBFw7YHDYeGHsWN++98ICNrbXZx8KkxG9ZqjJbtP4uBhkGbqgJM3Za3kFxEw7rnEfE/LfufnEHmmVZCKqISUi3bixiwu3WIBe8BBrCp4VBE8wpJf9ey/8kZfAWkwQqnlVGj42M07/iov8brJEbz1m37oXddPGFqw9dPeb4ZjM2+/x7X8/HnFzfvbVvv7XPaXWYRm0PWxfsuo7HcoVumUwigQ5UzlTLoznkZn32fk6b5+Nt0p576HHZr8wIQEe3z8Qe4mxVbGrybF4BHFrGHRWMOvdb4KdEyBRifInqexTDGe4BOiTAFGJm2+fi7tiZbbk+p27hHwhRghNrm4+9oESO8B+iUCFOAkVq3RHu4vlmEtcb3SpgCTFyt27htyUASzDM9onKmUgbdOS/jM9a6ZdP+Q94DdKz/Xr2VK0yPp5yplEF3zsv4jLVusWjDidwcHIBuui6RZ1nQwxOmACN3iJZp96OiygAkAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQNPhC94MVTisLqo+Phe7HR/01Xie50P1UKoipVHYqCNjeFP7mI6ZTf0UMV4fp5gWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkDSbLlcDlf4bDZc4QBM0nK5nB26zLcOXWDdEB+adrPZbOmcjI/zMj7OyTgN1UjTzQsAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoASbPlcjlc4bPZcIUDMEnL5XJ26DIHDVMAmALdvACQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWApP8Ha4K2gsL0TWcAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pyximport; pyximport.install()\n", "from tests import quad_tree_debug\n", "from openTSNE.quad_tree import QuadTree\n", "\n", "tree = QuadTree(points)\n", "quad_tree_debug.plot_tree(tree, points)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n_boxes = 5\n", "n_points = 3\n", "\n", "xs = np.linspace(-13, 13, n_boxes * n_points)\n", "ys = np.linspace(-13, 13, n_boxes * n_points)\n", "xv, yv = np.meshgrid(xs, ys)\n", "\n", "diff = (xs[1] - xs[0]) / 2\n", "\n", "_, ax = plt.subplots(figsize=(8, 8))\n", "ax.scatter(xv.ravel(), yv.ravel(), c=\"r\", s=1)\n", "\n", "xs1 = np.linspace(-13 - diff, 13 + diff, n_boxes + 1)\n", "ys1 = np.linspace(-13 - diff, 13 + diff, n_boxes + 1)\n", "\n", "ax.set_ylim(-13.1 - diff, 13.1 + diff)\n", "ax.set_xlim(-13.1 - diff, 13.1 + diff)\n", "\n", "for y in ys1:\n", " ax.plot([-13.1 - diff, 13.1 + diff], [y, y], \"k\", linestyle=\"dashed\", linewidth=1)\n", "\n", "for x in xs1:\n", " ax.plot([x, x], [-13.1 - diff, 13.1 + diff], \"k\", linestyle=\"dashed\", linewidth=1)\n", " \n", "ax.scatter(points[:, 0], points[:, 1], s=20)\n", "\n", "ax.set_xticks([]), ax.set_yticks([]), ax.axis('off')\n", "\n", "plt.savefig(\"interpolation_grid.png\", dpi=80, rasterize=True, transparent=True)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "225" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(xv.ravel())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.interpolate import lagrange\n", "\n", "def cauchy(x):\n", " return np.array(1 + x ** 2, dtype=float) ** -1\n", "\n", "x_ = np.linspace(-1.5, 1.5, 100)\n", "\n", "_, ax = plt.subplots(nrows=1, ncols=3, figsize=(8, 2))\n", "\n", "ax[0].set_title(\"True function\")\n", "ax[0].plot(x_, cauchy(x_))\n", "\n", "x = np.linspace(-1, 1, 3)\n", "y = cauchy(x)\n", "poly = lagrange(x, y)\n", "ax[1].set_title(\"3 interpolation points\")\n", "ax[1].scatter(x, y, c=\"r\", s=30)\n", "ax[1].plot(x_, poly(x_))\n", "\n", "x = np.linspace(-1, 1, 5)\n", "y = cauchy(x)\n", "poly = lagrange(x, y)\n", "ax[2].set_title(\"5 interpolation points\")\n", "ax[2].scatter(x, y, c=\"r\", s=30)\n", "ax[2].plot(x_, poly(x_))\n", "\n", "for i in range(3):\n", " ax[i].set_xticks([]), ax[i].set_yticks([]), ax[i].axis('off')\n", "\n", "plt.savefig(\"runge.png\", dpi=80, rasterize=True, transparent=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from mpl_toolkits.mplot3d import Axes3D\n", "\n", "np.random.seed(1)\n", "\n", "centers = np.array([\n", " [np.sqrt(8 / 9), 0, -1 / 3],\n", " [-np.sqrt(2 / 9), np.sqrt(2 / 3), -1 / 3],\n", " [-np.sqrt(2 / 9), -np.sqrt(2 / 3), -1 / 3],\n", " [0, 0, 1],\n", "])\n", "\n", "coords = np.vstack((np.random.normal(c, 0.1, (40, 3)) for c in centers))\n", "\n", "fig = plt.figure(figsize=(12, 12))\n", "ax = fig.add_subplot(111, projection='3d')\n", "\n", "ax.scatter(coords[:, 0], coords[:, 1], coords[:, 2])\n", "ax.scatter(centers[:, 0], centers[:, 1], centers[:, 2], c=\"r\", alpha=1, s=60)\n", "ax.plot(centers[[0, 1], 0], centers[[0, 1], 1], centers[[0, 1], 2], \"k\", linestyle=\"solid\")\n", "ax.plot(centers[[0, 2], 0], centers[[0, 2], 1], centers[[0, 2], 2], \"k\", linestyle=\"solid\")\n", "ax.plot(centers[[0, 3], 0], centers[[0, 3], 1], centers[[0, 3], 2], \"k\", linestyle=\"solid\")\n", "ax.plot(centers[[1, 2], 0], centers[[1, 2], 1], centers[[1, 2], 2], \"k\", linestyle=\"dashed\")\n", "ax.plot(centers[[1, 3], 0], centers[[1, 3], 1], centers[[1, 3], 2], \"k\", linestyle=\"solid\")\n", "ax.plot(centers[[2, 3], 0], centers[[2, 3], 1], centers[[2, 3], 2], \"k\", linestyle=\"solid\")\n", "\n", "ax.set_xticks([]), ax.set_yticks([]), ax.axis('off')\n", "\n", "ax.view_init(20, 0)\n", "\n", "plt.savefig(\"tetrahedron.png\", dpi=80, rasterize=True, transparent=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdMAAAHICAYAAAAV7wD1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFUxJREFUeJzt3b9uHFl2B+Az3gEETDKZY/oFFn6FAVYRYYULKBVgRTOBsw480SZ0NIEUzQsImFDYTsx9AAeO9gWWr6BkAMG7oAMWh81SdXdVn/pzb9X3AYLAFtldIqr61/fcc299dX9/HwDA5f5p6QMAgNoJUwBIEqYAkCRMASBJmAJAkjAFgCRhCgBJwhQAkoQpACQJUwBIEqYAkCRMASBJmAJAkjAFgCRhCgBJwhQAkoQpACQJUwBIEqYAkCRMASBJmAJAkjAFgCRhCgBJwhQAkoQpACQJUwBIEqYAkCRMASDp66UPgMtd7favIuJlRNze3Vx/XPp4ALbqq/v7+6WPgQs0QfohIr6JiF8j4rVABViGMm+9XsZDkEbz98sFjwVg04RpvW7jYUQazd+3Cx4LwKYp81bMnClAGYQpACQp8wJAkjAFgCRhCgBJwhQAkoQpACQJUwBIEqYAkCRMASBJmAJAkjAFgCRhCgBJbg6+EJvUA6yHje4X4MbeAOuizLsMN/YGWBFhugw39gZYEWXehZgzBVgPYQoAScq8AJAkTAEgSZgCQJJNG2ai4QhgvTQgzaCmTRqEPsBwyrzzGHWThqvd/tXVbv+uCb7RHIT+9xHxYeznB1grYTqhx9CLiE8x0iYNEweenZkALmDOdCIdpd2fIuLbyJdPuwJvrHLsbUS8iadjtjMTQA9GptNph96/xTjzkJNtRdgc2+uIeB8Fz+sClEYD0kRaI9NHozQfaRICKIswnVATen+KiN8fPPz+7ub6h4UOCYAJKPNOqBk1/hjuEAOwakamE2iXYU+VZZVsAeq3+jCdO6yGbNBQ02YOABy36jJv3zWZI2+CMGStpnWdACuw6jCNHmF16SYIJwJ4yNKVyZa5ADCftYdpn7AaPDo8FcBD1mrWtq5zqm0MAWq3+TnTS+Ytmy0Cvz94aPXLXczvAhy3+u0Emzf8k6PDq93+dQxrUqp+270LGrOm3MYQoGprL/OedUm3b23l2bYL54nN7wIcsfoy7ymlly4Pgv5TjLNJ/uPzXlSmtiYWoNvqy7xnFFu6PLK375ur3X6MwL+oTH2uZA6wVVsv85ZcujwM+kejrEWtvUwNUJpNh2nhofKp47FRAl+5FmBcm54zLVnHvOZfI+LHc3v99njeoueJAWq06ZFp4dol6MMgHbxj0wFbGAKMTJgW6kQJOhuGJc8TA1RJmXdiY89PXnBXmi9e25wpwLiE6YSmmp9snvdt8+XPY22TCMBltr7OdLCBo7ovSrJXu/3j49lR4XfNc353ZO1psWtoAdbGnOkAFzT/tOcnP3X9/AV3Y+kzb2puFGAmwnSYQc0/7SaieNgSsD1SnWSf3MLX0AKsijnTAbLzkF0/Hw+BbJ9cgIoJ04GyIdb++T4BLTgByiZMC3AqLHXlApRPmB4ocQTYdbu0eJgjLeI4S/ydAcxNA1JjhG36ptKrI3gJBf/OAGYlTJ8UuWdtn47ghQ4totDfGcDcbNrw5KIbZs/h8KbczaYPpRxnsb8zgDmZMz0w9fzfWM9f0jxlSccCsBRhOhNduQDrZc50PuYXAVZKmM7HXrkAK6XMOyPziwDrJEwBIEmZFwCSrDM9Q2kWgHOUeU+wnAWAPoxMT+tazvJbmPYdtRrdAqybOdPTji5n6bvJu83gAdZPmJ7Q3mS+Nars3IThard/dbXbvzsITZs1AKycOdMLdc2nNv909jGlXoB1MTK90JFR6xej0DOjWwBWwMh0RLp/AbZJmI5M5y7A9ghTAEiyzvQCRp8AHDIyHci8KABtmx2ZJkaXJ3dFAmB7Nrk0JrkrkZt8A/DMJsM0ErsSbWXdaMdOTgAcsck5U/Oepx37/Wi8Aui2yTCN0JF7ytVu/y4eSuCP3sdDOdsHEIAOm21AaoJAGHS7jYg38RSct6HxCuCorc6ZcsKReWGNVwBHbLbMy3BK4wDdhCkAJCnzAkCSMAWAJGEKAEnCFACSNrvOdCo6XgG2RzfviGxTCLBNyrzjungDfQDqJUzHZZcggA1S5h2ZOVOA7RGmAJCkzAsAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEgSZgCQJIwBYAkYQoAScIUAJKEKQAkCVMASBKmAJAkTAEg6eulDwBqc7Xbv4qIlxFxe3dz/XHp4wGW99X9/f3SxwApc4Zb81ofIuKbiPg1Il4LVECZl6odhNv3EfGh+XpKL+MhSKP5++XErwdUQJhSu7nD7TYeRqTR/H078esBFRCm1G7WcGtKuq8j4n0o8QINc6ZUT0MQsDRhCgBJlsZQhDWMLhfoKq769wVrYmTK4taw3GTO/8Mafl8sxwexaWhAogRrWG4y5/9hDb8vFrDAUrLNEKaUYLaO3Kvd/tXVbv9ugjeRObuKLc/hUj6ITUSYsri5lptM+al8ziUzlueQ4IPYRMyZshlXu/27eAjSR+/vbq5/WOp4YAk1zJnWcIxtwpTN0LgDwywRarVep8q8bIbyKPS3YLNSlfO61pmyKU2AClE4ryvU5rh2biPiTTyNTKuY1zUyBaDLIs1KtVaQzJnCGTU2Q8AxQ85n535/wpRVy74Z1NoMAV2cz9NR5mWVms0Z/hwRv0SugaI9b/Qnu8YwtxE3G6myuacGGpBYhcMRaPPQ46fvR5c2UBw2Q0RE/D4egtknembRGk2+SZ57VTb31MDIlOq1W/gj4m08D9KIC984Dpoh/nrwsE/0zGm00WStzT01MDJlDdpvNhEP4flNRHyOiL9ExM+XvnHc3Vx/vNrtI57PNflEz1xGHU1aHjYNDUhMbuqOwI6mip8i4l+bf744RI+8js5GZre1c6/G/68wZVJzdQ8eXHyfIuI/pn49KNlSN6pvHkq9bq0dx+ZMmdos3YN3N9cfm03rv53j9aBUc24D2HqtXyLfPR9RacexMGVqc++i4hZTbN1SN6p/0fzJvm6V17AwZVKt7sGfIuLlWJ+Uu9be6VaEWcPo05HHL37dWq9hc6bMYux5kFrnVWAOc82ZdtwjOOJhGdmPW7seLY1hLmPfgWKpO1oMUmNXIvUbc/lL1zl88Ng/R8Q/IuJ3zbd/jogfm+95Fxs675V5mcvYpafi51UWvB8kjKLrHG499sd4CNJ/RMT/Nl9H+2dmP/AFCFNmMfY8SCXzKlV2JULEb0H6p/jyHD48rx/9LiL+p7kON3neK/Mym3Olp6El0Wwpq3m9t82Xo23ucMA+qBTr1PXW6kl4dHgOvznxb5s87zUgUYSpG4rabxzN17/EUyv/54j449iBas6UEp273joai541FbU2Sfk2Wuf3Fs97I1NKMXpD0ZFdkd5c7favm8dfHHz7izFes80+qBTq3PXWHl0+6849d15v8bwXppRi1NJQ65P33+PpXH9847iNiH+P5yPTScpRW/yUTvFOXm9N9ebxQ+fUy2tWcX0o81KMMS+qjjLVY6D+VtKaYc7Uelhm1/c6KiHE1nR9GJlSjJFLQ+1P3j9Fa25nplJUFethWYeRbyR++JxThe7R66OEsB9CmLJKc5apzthkZyOL6fXhrW/oDvy+S+4c03l9TPGhYGrWmbJaj3eSWfIirGQ9LOvRdzOTvmtBz35f5s4xJ66P6taqGpnCCWOUmrbY2cgyBlRkTlZMWtsFHvYbdIVz+84xj3pNaxy5Pqqr6GhAgiPW1BwBbcc+KB7ZsOHvEfFfdzfX/3nkeR6//3Pz8ItIXjPmTGE9BjUP1Xbxs20nKiZd2wV+HQ8NfJ3PczgaPniO1HVQW0VHmMJxvUtNNTZMwBGH5/2jk+d/R/Bt7txX5mWz+owke+xf+vhp/GU8X9f6/u7m+odJDhxOGKNCcm67wKWPr0TClE0aMh964n6Ohz//UzxtWWh+lUV0ndfNPxURXmvuQ7A0hq3q1Xp/4p6k7Z//NiyB4ULNfULfjXDvz/Z5+TbKurdodUte+hKmrMqAN6Xserwvfr6Eda3UZ+SbyLfPy4iywqvvdVcdZV5WY2gJacCcaedznij/jtrVyLp17COdmm/vOAe/OH/PnftTzmuaM4XCdb0pxVNz0BjNGH02Dh99vR3rttC9fE/dy3Sy41lrkEZYGsO6tJeyfIoRlqsMWO+W2gmGbZp6H+mO8/fc+ulJbs6w9uVj5kxZjfY+n/HQFDTnfNGn1tf/1/y9qrkhxjfzfPu5ecup5jVX23wUYWTKyhx+Cr/a7SPm3d+zvUPMf0fE32KFJS3qdW4kPOFIefT9dksqG5szZdXmvNjWvIaO+pUQPOea9oYcV2nXmzCFEZXwhgVtpQXPo8xxjd0FnaXMCyOqbXNuNmOSpqIRZI7ri4bDJmAX+SCrAQlg/UrdLOHi42o1HD5u57nYTk/KvAAbUOoUxEgb8y9e8hWmbE6pbyrAZUqYExambEoJFx0wvqU/JGtAYmtSjRhLX7BAt6Wb/zQgsTUXNzyMfHcPYEWMTJndkqO75O4upS4vgFGpwAxnzpRZ1TxnWfOxQ1/O88so8zK3aje7bm+k7w2Glar2Gl2SMi9zG32z6zkt3eRwSCmOiaSu0a2el8q8zG6rF9uYlOKY0lo2n5+TkSmzK2l0VzHNUEwmcY1u9rwUplCnqsvl5BVa4dnseanMC5Uq9M2UGZRcTt3qeSlMIbb7BkCdStjYnecsjWHz7GxEhUq9pdpmCVOwro7KWPNcHg1IsOGmCeqlK74s5kwZbI3zi2v8PwHzEaYMUnIXIcBSzJkylPlFgBZhylC6CAFalHkZzPwiwHPCFACSlHkBIMk6U2goXwOXUuaFsOQHyFHmhQeW/AAXE6bwwJIf4GLKvNAwZwpcSpgCQJIyLwAkCVMASBKmAJAkTAEgSZgCQJIwBYAke/PCANaiAl2sM4We7N8LHKPMC/3ZvxfoJEyhP/v3Ap2UeWEAc6ZAF2EKAEnKvACQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQJEwBIEmYAkCSMAWAJGEKAEnCFACShCkAJAlTAEgSpgCQ9PXSB1Ciq93+VUS8jIjbu5vrj0sfDwBl++r+/n7pYyhKE6QfIuKbiPg1Il4LVABOWXRkWugI8GU8BGk0f7+MiFKODYACLTZnejAC/D4iPjRfl+A2Hkak0fx9u+CxAFCBJRuQukaAi2tGyK8j4n0o8QLQw5Jl3tuIeBNPc5PFjACbABWiAPSyaANSoXOmADCIbl4ASLJpAwAkCVMASLIDUoXMNQOUxZxpZezQBFAeI9MZjDyStEMTQGHMmU5sgp2e7NAEUBhhOr1Rd3qyQxNAeZR5pzf6Tk92aAIoy+YakJbohO16TR25AOuxqTAtpRO2lOMAYBxbK/OO1gmbHFmePA6jVoC6bK0BaZRO2BE6dI8eR8H3eQXgiE2F6YidsKkO3TPHUeR9XgE4blNzpmOZcs6z67mbf1L2BSiUML1QZl7z3M8e/nvzkGYlgIIJ05kNHdVe7fbv4mH+9NH7u5vrH6Y9SgCG2Ew3b0EdskM7ikff9AGAcW2iAamwDtlBHcW2DwQo3ybKvKWVSgsaJQMwgq2UeYsqlWb31hXGAGXZxMg0Yj0BZCtCgPJsZWS6pjutuDk4QGE2E6ZLG3FkXFTJGoANlXmXNHZpdi0la4C1MDKdx6il2RWVrAFWQZiO5MxoUWkWYMWUeXs6FZZ9yrg9fl7ZFqBSwrSHc2GZ2RTCUheA+m1iO8ERnLvHaOam4+5fClA5YdrPybBM7p+bCWIACqDMe2CpeU1zpgB1E6aNpeYuBSlA/ZR5n8w+d1nYreEAuJAwfbLE3KXmI4AVEKaNOW7CfbXbv7ra7d8djEA1HwGsgDnTmRybk83MmZpvBSiDMD1jrMDKbOxw4rg6G6aELMC8lHlP6NMg1FG6PfY8/xIRn5uHxijpds63amoCmN+mw7RHEJ5sEOobts33XDcP7WOcOdlj862amgBmVmWY9hkN9nmOOD+CO9cg1Ce4Dr/nRUT87ZK50fb/90TDlKYmgJlVF6YjljHPBmGPDt+TwTVGeffU//fu5vrj3c31D4fHNUdXMgDP1Xg/07FutN3rHqPtG3G3m3uudvvX0dHs02oQ+hwP5d2fLwi3wf9fNw8HmFeNYTrKjbZPBeExrYB8c7Xbvz4RXOnybsONxQEKV12YXhKCp54rho3ghowSLw7BvqNfAMpgnekAQzfD7wjFs+s/p9hw37pTgGkJ04EuDaa+ITnn5g4AjKO6Mu/SEs09fUvEY8+Rtl/37ZHXBeBC1S2NGUNmnWriZ3ut/5xgacttPC3NiYj4g12RAMa1uTJvpuyZLZmOOXc55Lmudvs/x9MOTBHJ0jEAz22xzJtZp5pa4zrG+s8mRN9GxB/iYcnN4RKdY36OiO/C8hqASWyxzJvZbm/Rrfpa+/y+aB4+u/+uXZEAprW5Mm9Erty65DKTjk7fCB26AIvbZJjWqmOLwr/EZVsUAjAiYVqgU6NfGzAAlEeYFsYmCwD12WIDUunc3BugMsK0PG7uDVAZZd4CmRcFqIswBYCkLe6AxABGyQDnGZlylM5igH40IHGKzmKAHoQpp+gsBuhBmZeTzJkCnCdMASBJmRcAkoQpACQJUwBIqnrTBs0xAJSg2gYkGwoAUIqay7w2FACgCDWHqQ0FAChCtWXeCHOmAJSh6jAFgBLUXOYFgCIIUwBIEqYAkCRMASBJmAJAkjAFgCRhCgBJwhQAkoQpACQJUwBIEqYAkCRMASBJmAJAkjAFgCRhCgBJwhQAkoQpACQJUwBIEqYAkCRMASBJmAJAkjAFgCRhCgBJwhQAkoQpACQJUwBIEqYAkPT/5C2acsPd1DcAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_, ax = plt.subplots(figsize=(8, 8))\n", "ax.scatter(coords[:, 0], coords[:, 1], s=10)\n", "\n", "ax.set_xticks([]), ax.set_yticks([]), ax.axis('off')\n", "\n", "plt.savefig(\"tetrahedron_2d.png\", dpi=80, rasterize=True, transparent=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from openTSNE import TSNE\n", "\n", "embedding = TSNE(neighbors=\"exact\", negative_gradient_method=\"bh\").fit(coords)\n", "\n", "_, ax = plt.subplots(figsize=(8, 8))\n", "ax.scatter(embedding[:, 0], embedding[:, 1], s=10)\n", "\n", "ax.set_xticks([]), ax.set_yticks([]), ax.axis('off')\n", "\n", "plt.savefig(\"tetrahedron_tsne.png\", dpi=80, rasterize=True, transparent=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 } openTSNE-0.6.1/examples/figures/000077500000000000000000000000001413546205200164355ustar00rootroot00000000000000openTSNE-0.6.1/examples/figures/.gitignore000066400000000000000000000000271413546205200204240ustar00rootroot00000000000000* !.gitignore !*.ipynb openTSNE-0.6.1/examples/figures/figures_10x.ipynb000066400000000000000000122363221413546205200216470ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from fastTSNE import TSNE, TSNEEmbedding\n", "from fastTSNE.callbacks import ErrorLogger\n", "from fastTSNE import affinity, initialization\n", "\n", "from examples import utils\n", "\n", "import numpy as np\n", "import scipy.sparse as sp\n", "from sklearn.decomposition import PCA\n", "from sklearn.model_selection import train_test_split\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import gzip\n", "import pickle\n", "\n", "with gzip.open(\"../data/10x_mouse_zheng.pkl.gz\", \"rb\") as f:\n", " data = pickle.load(f)\n", "\n", "x = data[\"pca_50\"]\n", "y = data[\"CellType1\"]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data set contains 1306127 samples with 50 features\n" ] } ], "source": [ "print(\"Data set contains %d samples with %d features\" % x.shape)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def plot(x, y, **kwargs):\n", " utils.plot(\n", " x,\n", " y,\n", " colors=utils.MOUSE_10X_COLORS,\n", " alpha=kwargs.pop(\"alpha\", 0.1),\n", " draw_legend=False,\n", " **kwargs,\n", " )" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def rotate(degrees):\n", " phi = degrees * np.pi / 180\n", " return np.array([\n", " [np.cos(phi), -np.sin(phi)],\n", " [np.sin(phi), np.cos(phi)],\n", " ])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Precompute the full affinities." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 38min 39s, sys: 13.6 s, total: 38min 52s\n", "Wall time: 24min 56s\n" ] } ], "source": [ "%%time\n", "affinities = affinity.PerplexityBasedNN(\n", " x,\n", " perplexity=30,\n", " n_jobs=8,\n", " random_state=0,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create train/test split" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "np.random.seed(0)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "indices = np.random.permutation(list(range(x.shape[0])))\n", "reverse = np.argsort(indices)\n", "\n", "x_sample, x_rest = x[indices[:25000]], x[indices[25000:]]\n", "y_sample, y_rest = y[indices[:25000]], y[indices[25000:]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make sample embedding" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 5min 5s, sys: 1.5 s, total: 5min 6s\n", "Wall time: 1min 20s\n" ] } ], "source": [ "%%time\n", "sample_affinities = affinity.PerplexityBasedNN(\n", " x_sample,\n", " perplexity=500,\n", " method=\"approx\",\n", " n_jobs=8,\n", " random_state=0,\n", ")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 216 ms, sys: 24 ms, total: 240 ms\n", "Wall time: 39.9 ms\n" ] } ], "source": [ "%time sample_init = initialization.pca(x_sample, random_state=42)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "sample_embedding = TSNEEmbedding(\n", " sample_init,\n", " sample_affinities,\n", " negative_gradient_method=\"fft\",\n", " n_jobs=8,\n", " callbacks=ErrorLogger(),\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 3.1707, 50 iterations in 5.8968 sec\n", "Iteration 100, KL divergence 3.0522, 50 iterations in 6.0476 sec\n", "Iteration 150, KL divergence 3.0464, 50 iterations in 5.8998 sec\n", "Iteration 200, KL divergence 3.0449, 50 iterations in 5.9616 sec\n", "Iteration 250, KL divergence 3.0443, 50 iterations in 6.2344 sec\n", "CPU times: user 4min 2s, sys: 760 ms, total: 4min 3s\n", "Wall time: 30.5 s\n" ] } ], "source": [ "%time sample_embedding1 = sample_embedding.optimize(n_iter=250, exaggeration=12, momentum=0.5)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAHICAYAAABd6mKEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xd8VfX9x/HX95w7s/eAEDayRJagEtxacGHdCTFo0RbU2mh/HbbV2mGXVuNoS1uxNYbEVScqbpGAOED2CitA9k7uzZ3nfH9/fC+K1IEVRPT7fDx4CMnJuefeG+Gdz/l8P18hpUTTNE3TNE3TjkTG4b4ATdM0TdM0Tftf6TCraZqmaZqmHbF0mNU0TdM0TdOOWDrMapqmaZqmaUcsHWY1TdM0TdO0I5YOs5qmaZqmadoRS4dZTdM0TdM07Yilw6ymaZqmaZp2xNJhVtM0TdM0TTti6TCraZqmaZqmHbF0mNU0TdM0TdOOWDrMapqmaZqmaUcsHWY1TdM0TdO0I5YOs5qmaZqmadoRS4dZTdM0TdM07Yilw6ymaZqmaZp2xNJhVtM0TdM0TTti6TCraZqmaZqmHbF0mNU0TdM0TdOOWDrMapqmaZqmaUcsHWY1TdM0TdO0I5YOs5qmaZqmadoRS4dZTdM0TdM07Yilw6ymaZqmaZp2xNJhVtM0TdM0TTti6TCraZqmaZqmHbF0mNU0TdM0TdOOWDrMapqmaZqmaUcsx+G+AE3TNE3TtIISBPBT4Hjg0upyAof5krQjhA6zmqZpmqYdVrEg2wPExz60GJh0+K5IO5LoMKtpmqZp2mFRUEIcUAGcD4h9PhU+PFekHYl0mNU0TdM07UtTUEIiYAFHAcsB136HhICpX/Z1aUcuHWY1TdM0TTukCkowAIlaeH4HcBIwlP9eiL4KOLW6HPnlXqF2JNNhVtM0TdO0QyZWif0N8DaQD1wFH4TbvSRQh2o56Pyyr1E7sukwq2mapmnaoSSB/kAJkMSH1VgJ2IAfFWRzgYuAOw/DNWpHMD1nVtM0TdO0Q6KghKuAx1A9sCmAieqXrQX2AJOry0kGRgPnAVfqFgPt8xJS6u8ZTdM0TdMOnoISzgRSgbnA0UAyalpBBFgN1ANDgKuBldXlenqB9r/TYVbTNE3TtIOmoAQ38C/UtIINqLFbXtSUghDgBl4FbgNuBB4E3oodv6K6nMhhuGztCKZ7ZjVN0zRN+9wKSkgDItXl9BSU4ALSgBnARNRCrzigO3b4VmAjKsi2AS+jgu5TwBrgZOBy4Oex4zTtgOnKrKZpmqZpn0tBCU7gXlQw/QswGyhCjdaqBYLACKAa1V6wBNVqcBXwx+py1u53vjRgOPCursxqn5euzGqapmma9nlFgReBbwF/A7JQldgk4Gng96jq61+qy7ELSjgWFXB/ALTvf7LqctqBZV/OpWtfN3qagaZpmqZpn0t1ObK6nCeB3bEPvYUar7UHFXDzgbXV5dixz89EjeZq19MKtINNtxlomqZpmvaZCkroC1yIGrUVRY3ZuhPIAS4B+gCDUK0EcajND5YCdwPZgKguZ8+Xf+Xa151uM9A0TdO0r6CCEhyoBVU11eWsOczXIoDjUfNiq4FLUXNjVwPvVZfTCrQWlJACNAGLgB8C4wBndTl1h+XCtW8EHWY1TdM07aspETgHeB8Ob5hF9cReAKxAXY8HSAferC6nq6CELGAUKlekANtQ7QZWdTmBw3PJ2jeFDrOapmmaFjNTeAxgGjAZeAnYsUAG6w/HtVSX01FQwi+ArsPx+PtpRU0lGAAMRvXI3g0UAD8BTgQuA24F/gB0Vpd/Ja5b+wbQPbOapmmHiCitEsAdqJFEu4EwaoxRD6q3MAvYIssK22LHm6jewgZZVihFaVUKYMiywvbY5z2oPe5rZFmhvf/jaZ/fTOFxocLrGsAPXAH8FPWeRVEr8gsXyKDvcF3jV0VBCVegwqsH+BFqnmy4upwXC0pIRoXc1cD1wDHADdXldBymy9W+QXSY1TRNO0REadVo4HXUMHkAH1CKGkH0R9Q//iuA78qywrAorboQ+A3wNmrbzxGoXZF+D2xC9SlOjv03CiDLCjd8Wc/n62im8JyFei+iqO1VvagK5N5pPxJ4AThngQx+I/7BLChhMGoL2uf2n/laUMIQ4HTUtIK/ftyCroISRgIDgUXV5Vif43Gdesas9r/QbQaapmmHTgtg82EwSkRVZhejFtG0APGold9hVDUrL3b8TlSQTQN+CR+EhgBwEWphTViUVs0Dtsmywu2H/un8b0Rp1XDAL8sKd3/mwXxwq18eSHicKTxJwBlA9QIZbIp97AYgFfUaPrVABj8y17RCFBmA5yWevgj4Lar30y3BFP/9EBLo/qYE2ZiTgNOA9+CjYbW6nK2xDQ6ORX3f/pfqcjYUlNAI3F1QwqLqchZ+1gMWlHAccFVBCb+vLmfbF34G2jeKnjOraZp2iMiywiY++g++AJyoVeFXoNoMLOBGUVp1Nqr14F3grtjxW1GrwtejehbbAVfsa8eiQtivY3/+3ERpVVysFeKQEaVVbuDHwHcP5PiZwpMMlAHfPsCHGAQUAuNnCs+cmcIzCfUa5wBnAv32/wKJPD9I4MmRjC1z4s4GXN3Z/dh44Vz86bn7h1YBjJkpPAkHeD1fB4+ifoD6YAJBQQmZBSXEA1SX8w5wTXU5Wz7lHBaqnSZ8gI/Zg/rhLvg/XbH2jaYrs5qmaYeIKK1K8Yb8eEK9dCSkgyEkCAdql6S9q75tYDNwDaoN4WnULM80VCh7HlUpW4fq7fQA96GKEZtQC3Ke2e9xBTAS1Xv7X7stxY4ZCNwMVACvHcSn/RGyrDAkSqv29gkDsGjebAEkTJszvyd2LSmAOxb+bdTrENjn+D5A3LQ587d+zEOsBX4+iamje+i+chub7DCh21GvTx8X7oRZImF6lGi9C1c9iFPceGakkzXBxJFkYxs2hG2Xx7DjUkxX5CN3xSUqlCWhquZLD+JL84kKSkhFVejXHcoNBmLjtr6F+sGoDZhfXY5dXY4P9YPU3uMSUK0uG1BzZakuJ/Rp544t/rrpQK+lupz1n+d4TduXDrOapmmHTrfTigYjDleCkDbyw7vYAlVh9cf+nIaq4PpR+9PnoaqzP0AtCBuMCnnbUMHzr7KsMChKq2ahqrxP7fe4/YCfo9oZ/v4J1+YDdqCqYYeULCtcsd+HjgPmLJo3+w/T5szfiHqefUVp1XVSBnuAn+13/Fygz6J5s6+ZNmf+R0LUAhm0gM0VomhyMqkbkkl5IkTwMgtregZZTwXo/WsT9e7tbNkCIjWJ5MT6yWPi0nY7EvfUV/vDyRkus6tZpOyuYeJDTwHpYivNe08vYr928uWOxvo2qqr8Y2DXwTxxQQluwI71phqoObbDgI2oTPBxldQAaiFc7cG8Fk07WHSY1TRNO4S645I9WBGJYcSCrASERFVd91Zok1Gh8h+oxUYOVNh9FlUNPAa1oMaJGoEkRWlVK/AqKuAGAERp1SDgOiA39vFFn3RdsqywBbXY7KCZKTyZqBD95AIZXPwph7YAW1A7RIEagZUOH7/4x2qxnwittib1/ifqYo6qCM4UnqOA2fEk/tPAiHfhOnkU43amkHZuI3WTnDiPNzGHuEVChl+GAGdWhEhfP/5eujs8jm43gxns3dm1VfRiCYBatkubqCD2JsX+6wMeWqBC9pflJVSAPqgjwQpKMIHfoaqwv6suxyoo4RbUcx0F3FtQwm/2X9QVW8T1yMG8Fk07mHSY1TRNO0RkWaEtSqtex3SeC2BgM9IdZV3IaYMIocKSAxVGg8B5qBX1/0aF0v/IssKFRumCM1KM8Cy/7QiEcTSj2hPmAkNRATdVlFZdi1qJn4Hqr31DlhV+KZU0UVqVBNySedFNq854/PcmKhx9rApRFAfsKZaVt+39mCwrfOvTzt9xS8gJnIVqx3gj9uH0NDKGjWXSH/z4tgInhwg2dRkd1gax+oEMKzuYRe6FTZlmZ1rvwGRvOKVvQtgpJNGa7o0dYxLJti13shkvUqzeoF8AIvLhnfO91y9R7837n/tF+QJiYfILbftaUMJAoCe2M9deNqoC273PY7XEju+PujNwwNMHNO2rQodZTdO0Q+tnqF2cRAI245JDcnebQZflMFFhqQtVYX0etf1nJmpveydqZid/zF85HzhvtT9tw4K2QVehqoVdqJ7ZHiBOlhVawHpRWjVblhV+qeONKocsvnRNb2rB38zhS4HrP27lf4UoEqhxTjcCHajh+gdqDWp81hqAf4gZeeOYdF8nHZvceHa68TxnY3dudq/NGnJ1/yljG8dc2vm4P0Ei3X2arZ5uzECLaE0PYdKP/v1z6Bs0cXnbpE8mhOLMllh4lSDFR4N4BFVFnoZamPeVUFCCUV3OJ84ZjvW43oLqe70t9rG97QX373fsBNQYrsrqct4tKCGroITpwBt65y7tSKHDrKZp2qE1HBWKnD0YYl3AlJYUJkjDQEob4QMxBLVgywtsR91mlsDporTqYifjhp+W3PBej+V8GLW7UjNqbNLFwG9lWeEHo4z2DbKxhWBZqEVj/YBlB2OzhdgCrmSga9qc+TLVEQmelNT86klJzc9P++QRVoNRkxeaiIX0A1UsK4MVoqgemFYhip7zEneRE2c/A3MXavLB9w2M4r6RAU2bXt0cGJQzZFUaecUCYVlYzVFv8L1u4bvcCApvqu3zCIwWEyM9Kezxuuhr1Is2u1d2mAZy3yArARPVrlF5oNdaUMJlqC1cH/s8z/FznH86cE5BCbdUl9P2CYf5UQv7mmJfs7e9oAX1/bOvCage5mdR7QeTgCtRm3ysO+hPQNMOAR1mNU3TDq3jUX/XComgJujGZxu2EzvkAQeG7Ew0sJqiRrLXkDdNTYp8a1mP8/Yuy9gJjAccEUxjUVdeDxBCLQ47GXX7eyuxW8aitKoPqu9xiSwr3DveaAxqEVEnau7qVqDxIDyn8ajNH/4MrJo2Z/5DB/A1jage3iXFsnJHhShyArJYVkb3PSg2A3YKsKtYVu7bJjEZOB9YKRDLnLiWZ5HrAu4UqZzpHG7KjrfaFoY2Rr/budE/PopBMikOINsX8E2MuMMyOXmYkAkTImL31txNrPHkMkjWsCESlH7XvinWEoJQcnwkrtMXAB5ZIIMHNPc0Nh1gPGoDhoMeZgtKmAxMR7WkfNoPJfGoH6KaYn92oF7/j2s7KQee3CcYv4ZadLbxU65DADOBlupyXvw8z0HTDgUdZjVN0w6txajV+oDAFzENgAiGOwKhJNt2dNqMtmBwusMenGDKgnhDOrosylB/RxvAm6hWhenAwtg5LVTV0C1KqzJQbQomcJkordqOmo5wIyr4/Bu1Sn1vuPmiWvhw9u0BKZaVPlRw2htYf4tqlfjdfoemA3NQu6Ddw2P3JgGXTf/90UtfuGnte6jXJBtVGU4AhmPitFrtcH8GBVJJc/rxeeOIRyKFhWVHiPQOCPVr3nHiBXFrB/X1Hjt/ias92iZ66CFC1AFgqEosEkTDcaOlsCwR986GZuDCmcLz1gIZ/Mz+4+pyZEEJt8IhG6c1DBVUf/IZ28Smoiqsjajq6rdRP9g8vP+B1eUE2We2a3U5vXz25AYncAKqr1eHWe2w02FW0zTtICl7GhNIKp3xYdCQZYXPxMLl0H0OlSAE0NmNkQXSBhy1YXNSSq98u9dmJyr4XY+aTPBdVLi7GTXtoAl1G9mPus3+nDonm1Bh515UFXQtsFyWFR7Ufs/pm06PoCrDUgIVosiL6g1eWSwrnzyAU0hUlbBPhSjKLpaV+4bsVtStcM8zP5g1YdS4vNTB8Tknpg9OqC+WlQsrjKJ7zTyRF+gIrHP4XIMMjPWylROtVpkG/F+UaKSH7oZEknzg7ddNpyeFNE8GWf3S1uzuHbphu9sdTTQaQVpEATNom2actCJi78q15J0NAWmIVtRrngafeDv/v8TC4KFSBTwTm+H6adewu6CEH/LhtIj3Ua0mn/nDR0EJk1Djuu7ab/HYvucPF5TwUz5h+oSmfdl0mNU0TTt4zgfOKXuaX5TO+HD3JFQA3Rtm9459AjBBREA8j6o0Tl4dcGYCF6Cqjwmo28kSeAu1WcJiVEXzV6jK23JZVtgsSqumo279X4dqJ9glywof/7SL3bv7lywrlPt8bELs8e+OndeNGgtW88LwV1KAy85NOWrns539JseuqQ5VPY4H3HvPUyGKRLGs/LQKZR2q0nwqKqQBUCwrZYUoagbuDLwaPWnFO7V71u6oX+lvDU2Xl7z1smu88bJjsvhnw/pmp+/N4PJBDDsm9lr1Bgl2gcjvxwDDjScHcCSQKEEYESLRlKZGl59aZz0N/lTS28KE+wQIeFOtRGkTxU+PAGRiQ2sk9h4lAsv5cB7wYVVdThQ+Pcjuc2zrPr/fjJoEcSASUYsQ3Z92UHX5hxMRNO1w02FW0zTt4NkKrGKfwCFKq5zARD4MsR+0Z8YRTXYgersxVoD4BSoUJqLaBWpRvbE3ArNkWeEv2Sf0oWbSfkCWFTaK0qqHgSRZVvjmPo/vAC4Htu338STUgqw3gSf2OVUiatHY3jAzFZhzfELz31Gr+gfPyd58z7Od/UqJbbhQLCv9FaLo/2LPkQpRVAQcUyGKbi2WlR+3Ij4NuAQ1a/YFgEXzZp8KFKACexvwWGSnzKJXHhWWoVGoKuDa8Fo7xzaQwbrwHgvrP6j+2l7AUcu2/F58u0cwZrCNbUhkCDDDBIOb2VCXS9+MBJJkDz0+sOu66OgnkSKHPhETw9zEWjN2fSZqAdQY4IGPm87wNfYa8GZsUwVNOyLoMKtpmnaQlM5gNfut1JdlhRFRWrVPNVbiQhJG2r2Ybg92FJiF6oHdG3YvQVVdnwBqgD6itMqzz8KujyXLCleJ0qpUUVr1Y2CRLCtcg7q9fHxmlz+7QhSNBRYVy8otqIpvgP12fJJlhW+I0qql+0xFWAtUXZBW60eF3HnpzvjFckpr3L5fVywr912QZKEWQX0kBC6aN9vo+HUwHxVM7wQai2Vl56J5s4ehNlsIAQ8Uy8p24KkKURTA4A6ypDvSFpXOqPNXGDwnG2jKbxhiA4Wodorz1HXKtb343t/A6itcuKzhjNkWoHf8HmqfzKHvCQ3s8USJ+uKIz2ynJROwJNLcww6fhRXPhwurmoBlqJD86qe95l83se1zdZDVjig6zGqaph16FiokGSBj6VEIQAYxALwC2SQRfVBB4gLU5gdLUFXdXCBflFZtPYDRWomolezrgDUP3f1sZvXwvD8PbO40gJtQ1eMtsqzQF/vzf9l3vJcsK2xyli5Y9Zu6Y347Lq69/Kd91704OXnoGNRM3Lv4mA0FimXlI4vmzX46di0fBHBpy+8YaeJ6q0UuJcr1wA0Vomh7xt+8rwFPS0u+2nZdsI45UCGKMmLP3Wxqa3C1yWZ7QPqQNwYcl5PtTXaOqH28I0iYAajpDm7A58J9/wCOuibZldzjstzJwhKDt7LJ0UbLJSkkG8mkGA6c4Sbq7RBBUyIdgAgTSnbhDqNGnqWiFjatQf1AcViqsgUleFFtIyv1xABN+3TG4b4ATdO0r7NYX6oTFVKl+mtXoMKsCILo7Ou0Bw71RId6kAuBp1EB8FnUwTZqgsGlwFuitGqaKK26VpRWDf+4x5NlhbtQ0xMWxcZf/XxwY8dPfnr5KWe+PTT3F/wPq8/PS9udPSG+bcqM1F2/AvqiNj3YArR/ypddAfxx0bzZaQAVoig5Wmf3SEM2IngdNcJqIpDZOjdwauvcQF7bdcGLgR/GNli4E5hj5NOSNC6+qa83P84T8J6TJhKO75eVYbk9jvYwIVnPrmgNGz0Rwhl22sCLOoeOPblzer8cV7orEcjoQ7704G2ro0648dp+fG0G5ssGZp2HuOhAhloGptXjpTXkdfex1RSIYagg+RvU/N/DwYmaDZx1mB5f044YujKraZp2EMXCqynLCvfOTx2KCiSSvVU+GWs5EHQAUZdhy3iD7qg6Lhe1pe3vYn2we7epXYYKN3egKogNqOkF6lQitphLFkpZVtgDQFmhXSGKHnh+/OATgGPuO2viI/eWFVqitCoTdWv+OVlW+JlzZ6/OqlmB6mXNBXq5+PvtqNFaHxHbTCGHDzd16G2/JZhaMbfoPAxOjGy0Uw2HgIh0AX+JfdmbqIVg3aiWBxcw3MwVk4xsYVqNdndqelpcyGN1yC4ya15pEjvebA2GuqPBPdRmN9MwOJ7EiHCL/PxEb0JVwYQd57+0pC/N0g2QRbaRTMranWw9pYk6y49vYDKpg3Po2x4kWJ9IUrYLV+uewVktMi7BE7+r/vHExtbU2Ht2F6rN4ktXXU53QQmlqHYNTdM+hQ6zmqZpB5NLzmSwVSKeKb9ZnlfyNirY3Q38EtW/CuEI+XFCXtc3lH5XQ5xjR8gpUeH0aNRUgM1AxFFaebZLsD0sRTeqN3Q0apj9v1ALzfY1B8gSouq3UhZaAKK0Ks1x7VknpfgCTjNq/Tp6X/He4DoAOBPYwAFsojBtzvwQ8KeP+1xsZuwkoDbjb95k1Piw8mlz5r8IvFcxt2gGcI7wEHAdbca5J5jLw++HngDm4CCRKEOAQahFc7WoRWAXO0cafZwDDeF7zh4SetNaKpPo9JwjRgZflDus7uhYoN2Z3n/NIH/mtDR3Yq8r19zpj7SnFz767E0Ov/NnNnJMhLDPwLAdOLJTyajpomNAPwZEIkRoprnXJmK10iyBhH7r6tOGGqMb2m3PsAb1w0MGqoXC+qzX51CpLv9oP7OmaR9Ph1lN07SDKVlKsmXW+a++eOHLl806K/uyss1NqZkT8Egbv5BIBE4HbTYiwbTdbkPawLuAL8dpx4+Ki6ZUdzt7Q1Is/E520DktJbLt2u0JtzVGjKGorUeTUCvtU4GXK0TRJUACnGOBsLiOS2ItCPcC+YmB8KWuqJ17Wn3TJmBn7CpXAD+Cj4wP+1/lom7Jvw48gqq0btnn84sAIXu5omd+eIEM0AoUilTqjAROtX2cKDtoRAVaTw/dQ5tp6DNo2dBweJ1puEYZKeGV9hRayAi+JgW9jAM68dK68ztDjj76gSaXkRTpbKttNWoCG5Jynf1+kmVmj3BabqcLdyogOmhL76AtMIxR1QZGyvvGyjiXM3FIKNrt77LaQ378TgeO7np7h9lF58TYddehthW+H1Wh1TTtK0r3zGqaph1Ed42e+fivjYtfnx53rhPI8UaCgmSZj5MEILbNAPgt+P6ORLkzZApii7xOSg6n/Srf7/h5nt8phEwO2oLmiEgSEj+b+TM2C1FzT8cBOaK0yvPgyaNnvjM459SHWHi/lIW/xcSFqu7+BjjntDU75/6qZ8+ChRc5zuOxe4+OXeak2OeHgpoJWyGKhlaIIs9nPb8KUeSNbZJAhSiajqqk3gF0tc4NXNg6N3D/tDnzd+w9vlhWhlC7k91q1Um33S6/C/xY+gjHneP6BwZbgXrU3NxuA+EMEXBaATtiN8mV1k5Wyx7CWGyki+dIYrvIJ2Dk8ubYjo7N0i0b2SFfbw+03uvAuchndffbxHpn2AhuihJd7KN78y6xPZzmSJd90gasWhX3XnLE7s3pGj/2dVtEA378LcA/okRXddBm2VhrTUxvIsm7UIvlav7X7wVN074cOsxqmqYdZInSG+qddMlK4LodD/1kASdGS4iwCwBDxNaACSwQUxOCcpArYriQXa92upbXh8y1U3Kt8PSrHTVL+3mb525NfKvhZcPDIr7DY1QT4QIko4CngIRXjx5g3HfWhPTLf3Du3oVC5aiq65vAO4+9++u3Ljsjad6ON5rlhmfqvLFjxgInnrJm560VouinqOkHvwXOOICndzNwa2yR1vGoMPs+amHYBNTCJeCDkGwUy8owauzWSvb2/Eaod48xN3hPcKTjxEBVdsu9xP9sWPKoNx0JDoC8aJ3djME1wLcAgxAe4aCTENclPeHLT62PPG9AawuN0zpoI83OWJFmZTS7bM+yd1hy5VJeq++ioydVpu8a9J1JI4+ZfpJtOo3/ON5a9LSIhp9ELcw7ETgO6CMwXsslr3k043YskMEZC2Rw4ed7948MBSVkFZTou7Pa14P+RtY0TTuISmcQKnuam0q/bUh6Y1OdnnLZQA9ICywHCBAGAltOTowaS7uEFEY0Z1vQubCkJnF2UrzMHD2SuoCPUdRxLGtxAO8RIBE/3pNqazO+tXbHjf3aen59+Q/O/RuIC4iFyH128/pgg4Wnvv9+FDW+atiZjaI2FyOjJ+uS/7touXsQarTCLuA/xMZsxSqvof1mx+61GjBjO3X9CTCKZaVVIYrui13DpApR1AUciwq4uRWiaB1wLqov+JeoFoljgFccg8RK4WC9jBAplpULF82bPcy27blWg+z1PRJuSLrcnWxmGvnT5sxf+oAoHu4I2U7quc1M5zYctEi1c9pFEzjB30n7rjSyUgywBGKUC/fdQJYpzar4jLizG4Pvjjrm/MEb3ck9YskDS6+IpmQsdXS11QgpT0AtPOuV2K8kk1axkuVTZgpP1gIZbD4I3xZfKQUlDERtmPE4anqGph3RhJTfpI1NNE3Tvlxjb6s21rXuudSSciLYyQL7OxJTgMCwbExpE3GYsb+IRTNqasFuVJUzhx628wCvAPnASvN4KzC7ffWdR9W3b3+kZ+TJb8sfRkRSVSY9FAHPSVm4df9riC3SOhqo/enpVRmoDQr+vedluTj2efHE5GGOJ487Kv83lYu7BrR034OqH99YLCvrP+m5VYiiMajdvBbHwm0CamGagZqR60P11A5AjfGyUBXY1bHPZaNGcO0ErimWlesWJBVdjZcbRBx+wyOuSr0+7myvTFi96NooAYyH0oZG6kcM6TGDL1mLsHgDB5cQ5VQjS5jmaaa5+51wSupOIu44oy3YE7A7aCsfcdPge+yIrDQzhMNMNl5//g8vHNuyq+MY38jjNnnqtm1xdTZPRE1h2ANcHXut5wF3LZDBss//rn+1FZSQBMwEXottdatpRzRdmdU0TdtP2dM4ULe1N5bOYPsXOdfFRx17xbis/Bsf2fzO24FoZKJU3V0SYNr720WaL0DV1FHSEnCnFk1jAAAgAElEQVTM9sa0hqykkzqT4vxZTjvTK+wdNVud1agtRs+P84QDg7I7z3K02kakR/AOff4qRNVtwEXAtfQhRZRW/QE1wqtLlhXu3W7WZu/OZIKM+oSOf9xz3KLl+1zm7BM27Tn5+fGDvXedO+kvdz/wShtq1moaqp/1k1yI2rRgOWo2bhA1scEB/AK1k5YHVZW1UNvquotl5QsVoigP1d7QhKoKJz08suhoPNxhCOG1dkp/xogE88zE404NdySeXZ/00qI93VZPUkokThrSg2RE0JRLXBebuGttZ2SP1etIxw6kEHVmh7w7k2tyUtrSe4Jmb7gr0jkofXDaduDx1rmB3hDWBMMgztu8M8XsbstALYg7DdVrvBEVZk3UVIODqqCE4UBrdTmtB3i8FwhXlx+8qQrV5XQDfztY59O0w02HWU3TtP+WBRQBb8AXC7PxTnfnwJTMFw1hbHBYnBk1kYDIS26R3TmmqCGXhBwjMHg0gRHvtqelWdGELakuX4JBdAC2XbOLq8mnP7u4Pt/ovtYVsk92HpvpSx6e8zrP4kTNZn2SHOI4Tx4zdf3uW1cOzhnq97jeqxBFd6DaC4YCCcWyshqY1ceXmveHVwpXovpYAbriQ5EdppTb2hO961DTFb4HiApR5CmWlZ+0je5fgLi9ny+WldEKUXQeECmWlbvhg5aFGahK7LOJOZ66JzOvGFs7JPH65lxv1rB1nScld4RvNLPFj40Ew3T0F/HOAcLnq4w81bbNd8UzP106YcTpwztkIDjMQ8ujNSu2TRppj0l34hpV09cqkm9bI0c1miG710rt+ntX78gbUu/vWGD3abaty3oTus2m1qYb4xbEX/xK26tPWxHbOJPzLj86fmzanmHbE/zt/tz2Fus94DZUsB4c++UA5gOVX+S9319BCemoqvgK4DMrvgUlJKB6jFcA/zyY16JpXyc6zGqapv23hnbDd5tHOveoLPi/K53BE+B94vo/n5NccPRLv9vm9omwkMJpRAlOjsiaujTh7sVrO4XnxSlDQlbY2NBjmbvqLHHyZniHqYzGSRsPMGhzb/rkc5ZteyRldmJK/5M8Cx969tn3moj7Q7XoO+bt9PxbU5v9oW+/veW6UbtbN86bNv4p4ErUpAQ/kFghipajRk31Qy18okIUjQM2XPO9aS7UIqjHgXTUTNtsILlCFN1ULCvb9j6nClF0c+y3vwNaK0RRYuxraotl5cr9XoJJwFHAOtNlnDPm4rxx3fUB57ycuG2rp2SHfvzDFX8z+tHlHGJMdoxyJQR3emV4mwvZ3TjYQvZ01PeaD767tXuobf0lgYTrvbYr3G68b2TaY2r7tbt37UmzE2UvzQaGNz49rtl+y5weZ8SHjz1ror/H6N7oesS9KXNAenZmasawug31m3vo+mNLXMPwroau+9xxTofhMC61o3Y88AJqVu804Keoymz/mcJz7QIZ9H+hb4IPdQAPQGwx4GcLozbGqD1Ij69pX0s6zGqapu3nhrQHc1CzU5/4GbMWfZFzidKqDGDqGUflLLmu4ajnX0iqK34pvdGxoyNbtLbm2pi2HfLbxprXkEQdrS5TuKPSOBZIkjCeNC5AbWwQ/EHSmiey47rnraiOiF9ZI3ZOPGvihsufX5c09pSufhed8d4PkloC13T1hO3MzXUbpmyu2wz0QW3P+hR8UDlNQIU2s0IULQGuQe2+9TrQcse/X50IFKJ6WVOBC4DLK0TRP4plZW+FKHID56OquglAK2qiQQ6qmrv/Zg5rUVvyHmdF7IbWrT7D3xpynrGs/cncPYGmzO7AceYI4/TwRktKojvdJziHBzY4tqBCX0pzrueENSdkjUzyRU/ts0s+6zbqT89xBE8U0QxXcmDQnrV1b6zYRLp3NOOm0WgacqK1Ww6Jntz5cqCjtW/zy+ub1naO+tFRzinREwLP/e6F71e3vTplQXPwe7efP7ml//j8nyx98C1H6/bWTUAKsAPVKrF3LJcfFWoPiupy7NjrfKDHhzmACu6RoKBE7eEcew007aDSYVbTNO2/hVAr7zsOwrlGAsUvpzbufnjzlJfuGvnSzKTuTHIDXnyOiCFEwHLF+zhl4PrIlOjAPktaUvOeb3Y1BlaZIXJkGn1lBYbxHNfSXPNk3KzNQxLdm/qn+YEp7+XnvriS7IW/GLZmeVpyd7pIMXtRC6/KgHNQC7heE6VVYwF3sarwNaPaCHYVy0q7QhSVAekP3f3sOlR1ciyqJaC1WFauqRBFuajwmlYhikxUNfZHqJCbgQqqK1GbOQyvEEXdwCjncKM78Xuu0+IucGztfSIaBbYj+dPmFxq7gVNz4Oqcp/csFcl8z26zbxVusSayNtpEb+sNbLZ6gWLAG++Lvjm+umlgzq7eQcAVefaJi2W0ebNw5Trjz3Z+v29Nn56e9X7hmW6Go2vt7NCW8F87JjQ5m7qa/p7iST392EsmFr501yvb+o3LS0nJTT4qGoqunyk8ycAVm9/Yssa27AeANlSrwROokWaTUZtOrFsgg4dtB7Cvme8DuQUl/OJg9v9qGugwq2naN4hY8mAcKqislFNndX3ScXLqrHbUCKmDYTnQAtQkfuc25vZZ53vnpYtSN0addIgQIcthZjqkvOSENY7EncKe6PKaWeFw5oIdot2wTVdPH+/k+GCorzsQffelAYO6+4rulx1J7kxXwKrKfNTfXEfidf+cN3h3Js2D60gYO4rAblQ18UxgDPA2UJwQdaReUlz97qOysgW1vS4AdWkJk9N6Apd7I1YYeA+1GOtnsc0OQLUdTEbNYl0LVKMqxfWoXbsmoG7NO4A/AmcBR9ndcnVkrXWR1ShXo6qdo1E9tq/GztsErJJd/MDq4iKQxwFXhVqtYtSs3AQgPak7+lJSd08Ytd3tXSZxK7AH/IIEcY2RKmYPSRpuBkw708oy3d5vGRs9luOJzIlDRww7cehgK2ItC/WG+tSurLV2vl07ctiJQzfmHZ13/dtV7whgVNAXnIFgeuz5bIxd2y5gzgIZjB6k919TeviiPTua9gn0aC5N074xxJIHJ6JW2d8jp8567TBcQdyL64555fuPzTquuSuNODMqGqJxCGEzoE+HTLbMhuEhV846vxAz/73cXjMke4/nlL5ZRy/dbiWvaXli9qVnXvPk+8v/dNT0nAvvfT1prb9mT3p6S/Dh3dHEd/vim7fYm//0qu9mV5+6Zse2Pu2+ognbGkPZWaFq86zjjznxnctO37b10Z4d7dW/LZaV6wAqRNEJ5SeOeiCrqzf1jNU7NpoqkPYAL6NC7RhUCH0f1WPrBP6Nqv7+HLU4qQkVYi8A3kEF2+OBpTg5kQhdqHFjPahpBnuAKJBg9GGHbOddGWRy7AW6PfY441wTzfHCw0Wht6ylWJyOmoZwbrGs3BS79nNwcRIRNtTKxDu3JaaFj7/JXO23s8w+69c+L1cHPHaH/FPqLZ7bpJTpG1/d9HaWI7eGR9xvvMATZvbQrBEZgzLub9vd1qdxQ1Mvqs2gBbh/gQyWzRQeY4EM6lvimnYE0JVZTdO+Sdagbidv+KInEkseFMB5QEBOnfXSgX2V7B2+8O5ytyNy7BUnvuKYnNTB8w/+kDe9nXJHPcIViWZFWoMdF7TluHIc6c4HhvUxrs4z/UeP9JCWmRGHh1tXhpwjcrZ0B2syErK3ZfTLPW/11pFnbdv20nc451pOJhG44rUxA58Y0Nw5buzO5jgRx5XunpUZNf7I+kDYF+Cj1TH3mNrmmu44d5upqqHDgV7gbNRcWFAjumYBy9L+6L6p43ehabKL9SGn92ZHNDzWlNYzwJ9Qi8X8qGptJjCYCOehqsA5wB9Q82fPAgbjxHIOMfsnFrqqps2Z/y+AClH0V+C8FppqXYPtzn4D+vdGNtlBr3D90ZviTGqt8dUumjfbHdlu/9A5wkiMbLR/D/jDmGNFjxVu7h2a4HKGJjhT7TN7t9t7ZBAP8G8hRP6Q/GE/8D8aSdvO1j9J5PTtjU1Vp157yjP1GxvObN7SUmtH7amx57B6pvAMA340U3juXSCDaz7/d4emaV8mHWY1TfvGkFNnhVH9ogeDAE5HVRwPMMxCyqw7Bt+xfRh3vHGO3GNERFHzIAa7O6w0y2WsCLwjz2jxJp/d5xTR+R07GMzannB+KDfRGZoUWLPy3Q7y2LlyeJy/w24LD9gUHTEiGDVG7mk7xok8Q8rC20Vp1fhsp9W+cERXxq+2uG9+x5dTkN0dmjhlbcPI2pp3a+Ivcv4mY6q3Zu+1FMvK1xFFi1GjyC4BTkJVXJej2gh6gGeAf5j9xG4Z4V+JV7le7f5zeMvjZ9+Wnl+3KnDqpopjhRSbwj4rFZiCquIWoP59uQg4FVVVXYSq3vaTkBO1aXYF5YPE5t/GRniNB8J11Fa3P9vS15OU0u2RXmPc7XlX5npT692G8++L2lYJq9U+zu6UucAjQMZQOouGGp1d8aHG1UEr6nNmuHKSrnfdcu4f/90DbFg0b/ZJIo5tgY7A2q6k9hzp8Qzckx26ZWNbw2uD+qftNExD2FG7EdX/+wZwHdCf2MQHTdO+2nSY1TRN+xhiyYPxqF2Slsqps9bv/3k5dZYtljx4M3y+1dnJuXvuP+mtwhkVvvyhmzsd+IhwYVu+MSKcYix3O0RZ/hL5aM7iyG8b48IXGy46u9N9ebVj5aQ+owNyyrN/5eLfTHvgramnVT42XZ7U3tQbVxXdjhq3BZCbYsr+XlOk/+SttTtfsSLXv7Erf8WkXXUFqb9y32ZmGc/0vhj5c8XcolbUhIOjUJXXUajWAIGqTr5ZLCslfBAyJ1p75LG+50NmZA1HAwWZbTtfjTo9Xcl94q5KzPHYOxa39qAWirlQkwD+ETvXNtSWs+moNoWWrcOTEl6+sH98Yk/k+cvnbjq3Ym5RBnCfcPNPM1vYo3eNa430Rqa4e71pMonjti9qSYs7w/NIVpLTP23OfGvh7isvtzrkOODbqJ7eR3Cwp66+4xTzNcdEBoiu5Mvc2Xtfczskc63dcuKG3jUb/TndJ40bMyFO1DaYrQ/0vnD0L/q9Hw1FTdSiuX6ojRMSUf3BWz7Pe6tp2uGhe2Y1TdM+hljyYB6qJeE5OXXWowf15CsuGLLniYs3NmZtc2zcPYDHW3L5XtNguer450Rg6zEQNO1vdbR3t5mvuZ/J7t/wk6P25CSmb4gmJU0tvrpySbjfMfH9KgKn/zKjK+j2PhR6c2Zkfff0YT9bnBjXb9jEuoWdM0/oqh7/yhvr/PZR147LKxyQc0znvcsT77nRzBMTe+ZFttgt0ocaz3UVH67cz0Et5jJRhY6bUS0JtwJzURMOamOfy0RVW58XJhck53nL/S2hAVJybjRs78EiAXgFGBE77glgAfAzIHv1aFf29iFxGcN3iKtHrO6YhOrLfcE7w7zAmWce73s4stNuk+motoQaIAcP98ed7WjofSYaJMKSuPMcVwTfsortFrkbeLhYVj5beWbRwmiNfWzKTM+7rjyzAdXLm999f2h5+H37hla7efeeEdtap5xc4PTfb3UTobpYVkYAZgpPPpC6QAZXzxQeJ2AskMG9i+A0TfsK02FW0zRtP2LJg/1Q4a4W6JBTZx3U2815c59Ke3L0yqY/O/0OV08iW2rG4hbN8uFoPvbWIbQ46lixbU10u7XLypiW02DMMfqevPUd34Plxz+yYmn9GZdGd7ueuXBU6taaFGfiDjfTvLur+4UvTRjiSfEW73hrc0s09G8pC1+Ijg+Nj8YFb1hy0c8zu57q8gQXW7tQs2P/irrtPwQ1h9YNrHSNMxxWi7zTqpPHIrk39vznoBZ2vQP8HgggiEdyFCa1npPMPla3/b57j2uXO9Exra3Gn4bawtYH1CHoMPOEz2qSDxGmAfjJJtYWePDa/Rj4nIlZAlwLjPecYb5qZhgzep+MBGWQLFTI3QlkEUfYPdowQqtszCzx/dSfe06MNtn5PQ+Ee+O/7cQ13HxFRuWf7ebQ247n3txJ3/Q18qwJWajWhdJpc+Z/sOnDTOHJBawFMth8MN9XTdMOD91moGma9t8uRlUprz/YQRbg0c0F4byjV9jfzWlkQ90Q/NLkunePFz0JdXJZ33fFm9FGrJ/ucs6YuMzROW9GEq922y/U5tnlr6QMnxCO5vQa3ZHLvDtbu3IStg8fmjl2pHXUmu5VWacul+9tu9hY9oib6JsV4tk84GLhMJpSpjvHxV/grAkutq5BtRTUA+OAH9WRsGM3iVMG0nVt347ASSJenEO89OPjStTIqjzU7fezgB6zv3jPM808r/fp6B56sdzHm4HQCjnF917I6u0JvYiDs4jiQ7UbPOM90+FzTTQu7rk//H92E03AA9n0TfXg8RgYW4DLcZHtOdnxj+Ar0QiGdS42D6B2vhqBuuWfZSSKNitonyRMxiTNcc0Flr3wzxcetqP2ZadnnxrnIj5DOITXjLNyhD/YTwbCyajJFS9MmzO/bdG82RnApVbEWoja4cv/8C0X/rG7ueeq1h2tj6x/acOOBTKoqztfgoIS0lDfU2uqy9GvufaF6TCraZr23x5G7dTU/lkH/i9OeC3DJ+/0bs/4+63Dh/uTSXX52JZtcfRxqzh3YB2DnpkpF5vbxIDUdtE2cmdcJGWrI9CT6bzVNbq1I2FCT6SjPX3RP3NtT3TXo7kJG9wr7IbtIy9LGBTPsrNGeztPSHy3t1xg9hmdO8Mb78oYv+IvDybZRqgeNW3gJlT/6jXAm+WMdrc44x2TxrVmXbZ69SuOfuQR5kRcOAjzT1z8E5N4otxMhOVCMM3eQ55hirddU8y3zExjunCJx4A/xV3mvKz3lcgFdiObCJEENIXetU5wDBKP2R1MwMNWI42RqfVp64BcVKAJeKaaQ+NnOH7g6CseiuywC4jIvNBS+5RiWbl3Ji3PlF4xR4Y5J9Abuc9IEz5ga2ddpwMIbFu2/c9jzj56HfA0KfFD7KvOPBuvq3fanPmtqB3KQFXaT1j/8oZU1MSGJzsbuk43HMaN297e/gPgV8DfDvQ9LCjhBMCsLmfJF/pm+GY6HzUH+ccc+Na+mvaJdJjVNE3bj5w6qx5VvTxkRHLXekda2/C29F2kNQ5iXlodE3r6iZRoB8PNfiJl82Wyb9824d/Q5U45qouGDq+V5eoce+rZG8MbNiWba9Y5UtNzQ3cuTsjeNmRPx3vRtse21/dJS/J2yHiBGHrasJ95MxOGNkSs3kXvrS0fbNkyGTXntdc5ykiWQX4d3Wb/bQOZy2+5YOXxY/u2fc97pXfmtDnzb1p4y5W3G8miT7TWftf/THiX3cNIGcBDhI7oTvlEdGd0F7A8mmydKkyHL366q+HCp+e3P3/3d1Y5hxi7IzX2X30PRLb43DKa0E5Sz98ijwD/8pxp3izcnBTosDYT4GTUgrOnrEa5OLzJSgi+FQ3LHqSIowf22yXKpk44eN+Razw3fe4Dm2cKz2hgxkmnn1KdNzX3L6ie3N29XYHSza9vfnDc+WOr9nvJ1wM3rnlu7eWof/veCXT2rmjc0jwo2BW8COidKTyeBTIY/LT3raAEE7gRNaVhR0EJ1bq6+Lm9CGznEP8/pn1z6DCraZp2OCR2v9lW8vsLbq8+UxwtHPzc6+f9mhG82NiHawbuhv7dxN1zlR056p8iM7eDxGcyUwLWKmdex/cspysa6Er7t2dnmtezaMzwjGzP7ktHWWnTh6zNuXPd1udedDuSvD6v6/9W7f73uS+H1z113PTIc9HnmAzMFW7eSJztIrzeGuzbZjdLWdhdlbuwyx+QgzxZCY+9++wNZqAtKr2nOd5wjTR7exca58qAfQYBSlG3/CNGGp6ka90XJGzxTk3Y4KluHdP1QoUoGpl8k7uPM99w9gx3ux+cFElwOSOzTtzg2NS3w7gJeCi42BruO9X7eub19otsiYzp3hKfIrPj+8vXGtMi6+1+qAVprcBux1HGXU/NmtXheyTyZHGw8v3ue8MnxD63rSpvpjnuxPEzdtbsnChWOYt7iSQnXOjyAau2LN4crKmuOWXdi+srp82Zv+8rngsEpC3vHv2tUd5x54+V0+bMD6IW+d02U3jOAVbMFJ6bF8jgE5/yzglUZXsJ8C8dZD+/6nLqgLrDfR3a14cOs5qmaYeIKK1yoRZQrZNlhWrHsWM5GxgGf1uSVHyrVbhnuKO/P5URZ1eSuWcggTdOl8PHPSe87x7H2xs2R/LG+p350xvFC0tOs/N2/zB+z2Y62lpeDaXGOdxxXZY4beNEf37PqLnp3Wku25F672vfTn/0zsWtd0xwlDf1b+1Of8EcWpz0ftQ32N2+mBCtjkHGK6G3o8cFXrdSUQu1sBrlnRZc3hjovaGxI2h54uzmzjtCzxLmFVTv6ztACBXkhGui2d/MEpMTXC5331Cqa9Pcxi7g9p77w1uTfum9b86vjz/e5WibdOzIZY0Oix7gOKDBb4i4+p3Oy/1+O3/4dLG+K5Qw0sqO63YKvEiSAD8edggvJ2JKV2hlNE4IBj1zwxXfA54G4nBwnt0hS4YMHzJ+8JhB1f6/2E2hldbTF708vwLgobme7wLmvrt3LZo32wPc1barPXv4KUd9f9z5Y69Chak/7fN2+YCA6TKji+bNNqfNmf/RynBMdTnRghJ+Dtg6yGraV4MOs5qmaYeOG7XgKgDs3T43HxjMufOrXNIlp6d00dMVZPnvU8i/YiPjTg8Ke/k0Iq44kX/sOmfn9vFG66Zd9ptvTDJG9PYaOWZKKi7Hs5P6/d/UznCDu72+fUvIsAfU7eoIGyLR6WnhnIgtuwMrXfk2os4SxquGgxpCLAVGOwaK81yjzPzgUuthYCNAsazc9KsZP3gwuMq6MK+jO8cdJ9uxCQA5EhkP7BaI21EbItwTWWU7mcEf6h/uzt1e2z4OuA/4j90i32m/rjd7LA05m0lvLFzmOg0YiQrBl3iCMjV3t98TbHfuMgvFr7AEnoe2/1pI5gDdgM8zyexyTzVDlt9+20AMEdONJOcg4/piWfl7gMfPKLndbreHRrfaj5pZ5kNI25LtH1b5Fshgw97fL5o320CNEWsO+UMbVi9cc4Kv1XcdamHZR4LoAhl84+9Xnz6p/4T+t8Xes99/0ptaXc7HBt0jWUEJAlV579EhXTvS6DCraZp2iMiywh5RWnUjqqq51z8AJ7fMDtKUe59cOq1Ubr9EiJQO1qR1sju1k7rwmyQtP8b2bBwsOtOHsuqvI43e0W3kvbPM6mzqMMYOvODsZbN7jJ35wdpN018ZeIIIBobGDzA7AruFx9m7LP7HI93pv/NP7Wf5PLefsywr/kzX5gceGpl05rJNP+Z5KzXwtv3CowkjGyZc0508QtwbPN1Ze8/kC31D3d8z3gr8yz69NSt+960XTtn2u3uevbPT47spMc47Ovf4pD+HVlrLrD1yiNUsd7ddG3we1RaQHPv1PlBrwE+vYk03sBSYAXhRc2q9puT7iT4rIdFn3dE6l4iLQD6xsAk0AOuES9zuzDMHOzHnonYgex7VXwlAwoXOR1GLhuYDLu9JDrt1biC1QhRdB5QXy8ravccGgnGXmma0VNpGacOm2t8EuwPLpZSNPa2+4jXPrx3y0FzPqAUy+MGGGP0n9JfAbtRCuW+ascANwF2o91LTjhg6zGqaph1Csqxwn2Ak0nmXQcBKkJDd+ENf7fArEo+pS500rQa/DT3WOpKufAjHzr+K3TtzRZOjnmO2pePdtYW+eIzECQFhnPiW2ef9E+hfNSpuch9X5qu7HxcT4wbIQYNHNS7f+tzNR69p/dnZnixPbny2DPZuy+p8v+fU/u+0/HApfevOpHlLrzQGHn18V1UwaK49wVn3owkyPNP1XnqvEWrcnDTFWPNq/36jTL+87aVT+v86v3Hpxsxg4mBHfyMhstWeYgm5I+la5z/9T0TPserlUOCPwkOCDLK1WFZaFaLoNjNH/FzEiXui2+0G4L1iWVkNUCGKADKTXdVdtnTe1xsdeq4l0+4AKoGJ8P/s3Wd8FFX7N/Dfme2b3nsvEEroEErogaUZQSyESNRVWazYsdwoVuyot7KiqwIhqIAaaQuhJ/QWICEhvfe62V7mPC9m8YkIiv6tt/N9k09mZndnz6Rce+Y614VhthLHPEDUAWArgEngOpNtuDyCCpXm5Kxx808pkuUkLkL0OgC9eJjgkPW8ox9s8AJXGxcAYDC6i0UiC2Ozi1nVJ3tYcAuPsJBIDQAeAzdjjF7PTcHV4P03agdwyfmVx/tH4YNZHo/H+wWrsuEFrsHAmaWp13+LmeSuDQLQTpMzrM5NMwHcAOApABUApaIbBp6Fb91ksvtGuGSr4BpYC1gy0Zn2KGlyj8Vrugw8Ex5DIwuSiCfbA/3o/bZyT0bQvacDY1oDxI0yL4NDTN0sAw+yLqpjQTF5dKT2nQf3lYhk4atIU+ixvMTgM8dMuXOE+SPDPVndTVH/LTk7eH2SOPJck8Um2Nff0aETMz6t3np/tq2g/g6Lnn44+ImuA77UHJc8qitBGjqgx1bmuFUYxjTqPmLHiOKZ0aK+gns9HhZs63jK/KLPB1JKhOQJcJ26zgLwEA8RzBHFMNS01/6UrYgtyiRpBFy6wQQAflaHf7aItPgTLg0jDNxs9SEAy4SxjBu4HNs1zn129KpsMGvcfBcXGfmgrNraFBch2gsA7neLb2G7aUHHMvO53uNvNLtlwuy2G1y93B9soOayhUR63wZqplq1kgCQKVQa40Ii9QQwFcCBDdTchn+RvHWoAbcYjsf7x+GDWR6Px/tlUwHcCq4da8nPHZgjJ2IAw555+VEdRiQuA7dwaTMAmCw+u9t0id2EsNWhvgBAJNJN71kQUANITEBADTBfDVodi67TsaAn/ZCR7IOmJBuxRrohYUc4cQQcEp08K6R+VWeR15gtMOl1PeFexPO7Srtj0lnaY92dcNPbo+us21mxdJ5SZ2sAACAASURBVLOcHi847OPtMOomBIfQHlNnV9zBjrfzbfIGP0Gtbe/jmnGf/ZfNSehku4pOsoJ+AyCwC032kLiOziMhu+0Zxko2zpHENNNOtM1564uWLdsX1Qgjyb2OVvqdMJD5HkCP+aBjsGyKsAX//9Z8k63I8bEwiJz3eEiyXaHS0EySNgzA1wDyRAOYetkdic9YtjQ9ZT/q/hmA6QA+T6dZFwG8pFUrBeAaNdSCmyVsUqg0xl5DTH29BKGxesGE7o8skI4SPCcZJrQyHqQ2nWb9KNfz3heXOcClL/xErwYJYwDcrVUrX40YGt5P19rzotRFsvzyNfsjjFuEgQDmA/gobx2a/6jX4fH+Lfh2tjwej/cLVmXDD0AfAMeXpuJnO4LlyMlgAC80BPl/fscXb/gAyAO3ct74bkdGEoCHAbyxNBVnABKFdp93sHlJKjvxO2Jv84VQYgc5loKmE3KUFpbDXTYbXv1j4VLrDTcbwc6xX7H/rQ1hlOXBNGnocUfJl82WS51y0WdWX4sHurteHW7zi0l1Ee63VVmEVv8Gv09C8kta93jKYQ8DEAHgNONDgrcYYmtyzFH1H2K3OF/o35IbEamYH1TCBOd1gUiQzviRYEcdHQhgDIT4XNyPmSmIZ3wlAwSj7LXsshs/XftxJkm7FcAcAM+CIBwU90gEgo9vHhDrBaAA+c9XZZK0+QBmg0sZ2M34kr5uGaI6UazggbYlpkBw7XWPAhABSAaQn06zWrVqpRzAO+BmVWXg2u/OAEAsxbYzJq1jFWyot1fQpHSaZQUArVrZH1xr3GPgFtzVO1MHrmohkYomLZkwODQxdCaANWa9ObinpedZF2+Xl+Yt2/iH5Y2OW4TxADIAvJy3DpV/1OvweP8W/Mwsj8fj/YKlqWgF0Hq1fWTpRimAWwCcoKsWFIOrEPBmcGNLAU3O6Ca5a8MAfABuZvI8gAMA6pwPr8Ke2z5Gh8+QNkF9uDCiingUTwb1bIY8/QR83pqJMKkbXNl6NEd3obvLF6KII2RQlz/tromi1iSHUDgS5MOdPoRhrcI7pWdJI0k0de+oklu7epj+EbN9bUOkw+mkcx0hjSZZOOtzJn9z48PtXaJZwWaDJRJdBQSwDvRquxRLe8yux0wDAIioBeWOOuoF4DAIJklTBA9JRwoj7dWsjRpomWSw4GutWimQzRTqzfvtB+RzRUJK6ToiImHis0yfFr3REx7YeEatPA+Cu0ARAC61Qsu202k9X9iOsO1WCQA5gMO+q2VBxm22ccbt9lsBrAXwPbiyYVXOcR8HLtCdCCBOGCIok00hNks++2Z6+frLKRwAlwPrDm4W3R1Aglat/Fqh0hy88rpp1cq+0aOiHjrw8SEZZekTzrSCRq1auVCh0ph+w4/Jr5EL4Hjeuh8tDOTxeL8RH8zyeDze/40vgBRweZ3FKUZqAbeS/zIDgHIAjUtT0QBA/f93UYq3EGuatL1aJPUI83DtJETkAD04H25D/JAQMR5EZAIdvROuBWMd1CBjYryttC3Fl7iU15OKbx0sO91oGB4mI54V3RLGDGI8cb6l20f8lSQaMy5d+DKhJ9Lfcv5CWNCWUsahvsNa9OUNS8XzWzruuLX9gnhU7ubDjA8Y10Qil+ksfRwmnGIbscHh42V28e54VOra3d1j8WZE08QxDinOWLX21+hxWm4rYu2ez0iGuMwR3ekyR/SFvdEx3tEMd+IHi7nJfuHMsVYvJom0ONrZO8BgABwQATgCwB0Ud7DtdEEnJOcvwXtsEAzPuhww3iadJgiylbP/tRWzJzNJGvF+Q7qEuCKOEKIGcArA885xNTNysobxJHOEwSQ5y3/hlrSWDVYAUKg0BekCWXbowJAxI28dkSP3kkfjyk5iALRqpTeAhyKGRcQ1l7bsM3QYflik9ycEsnCWvuIDWR7vd8L81SfA4/F4/2R01YI6AMsAbLnq/uSMDpqc8QpNvqMYIKMA4vLDzhGIBDCqp3poXoc4mCWyHiC6ECS8GCAs2EsDYLK4o9TdG/Vu7kxOfwcKsp4nBYH9yTcDgonFt8PhkRvasBi+LRMjJFbi5UYZhomPsPZdMrRj/p2WTsPdvkXxkj4HRrm2VHudOGUqdZsi/WikPOqAq2V0vue5CP94vask3tFME1xuFBV7PSs1CIJIAfXweDZ2tT16+KYq2fGp/m9vDg6reTWs//fW445cWxErAvBi97uWBQA+Y/X0lO59q16faTvX847tJvls0Tr2VarXNVqb7TX0RTjwIYB14GZY68DNUJu3I6ZlHQaKHMDtpr32mY562uDxsORAOs3SAxihW2NNshawZ8CV57oI4HUALwOYP+P+zzKtZ1mr5agjme2gUb3H28XbJcZuc4y0GK1EodI8qlBp8q5yWWIBBIUODPloTXv7axuo+Q8PYHk83h+Hn5nl8Xi8/yO6asH1tOZMBLeAbBWA/c5tOgCF/s2yna7dltn21r79BYxIQG75EMQiBTmdDIs+GuetYzF8xmek5eOpOHYsChXbutG+X47xsVahu+V4vK3R37ZFMcIW6R8jHXkgD2ZTobfJ7cjdSWc3PPVi1c47w221sSv6HHlXJLDNcOwoSml0nKVSL4nYd4hf33xRWOGoc5X7mK22MFE/QZt0piDVsbkurnhb8q7ys3Lvju760IPhnq/XjvTJAVdl4BkAh6gJ7m1LTCLxUGalMI6xWE+yKz3jzxeRzj5etna3NnOe4w7rOcc3khGCzy0nHeZ0mmUCgJ3v37ULwODbDfVnkzKbp/SLN061noUQYggACLRq5XjZTKHetMN+sKfMujmVZjnAza4e7z2Y5gP2+6kFsd5vSuVatTJKodJUAsCUx2dqzxTb5h8qZucuuHbjg9MAngR+nK+6kEiDABg3UHP3QiL1AeC9gZpLr+Pa8ni8vxAfzPJ4PN6foxDA2wDyf9hyEh0A+QZGl6fEX98vtF4aTG3ebZDdvhLQu4NxM8JNZ0Rf5ji8Krvh7VcIGg5a+r6IuIkpiibGEjdxIeQlbZbOS6whwiGTSkUCm8/YUtLmZZn21a4yryUhk/pmkZ0H3qMxd3h8YRkyx1gcWEDrLkrrxLC6RrRLI+39vO8SzexeYdZbi1kv9/vFNskAe62EPUN0Znto4lihiu4rWBe5tklaHy5/MrjGGEsAK7iyWQGmIfK4rm5xuF9N+0gyNcqTHCzcpds39CHGj8xyv190s/UCuxAnkQ/gcQAgYiIEIHMtNIb3d2f7iHwZfzJEsE0YyGQBCAGw2GWOaK/LHNEHCpXmR7finekBdgCP+LwjOw7gIIDV4HJrX9CqlS6UEZguVhiPyKWE0aqV48AtBhsBoEah0tQDgLNVbalWrZRr1UqiUGlsC4lUDuAlAGUAVoJboDV4IZEu3UDNHX/ITwSPx/td8GkGPB6P9zvIkRP/HDl5L0dOkq+2f1U2Na/KprkA1f1kp9xgYfffWmUtmuyQSHpAD88AOv2BfsfBiAyIrAbkkw7ihgey8HZaOdk8JAZLR4TCYGBg1nrZjOUy4dBvm9rnOjZ1N/hamUefncuseDQ9WmcvHVJqaZO3uummJPh33drYKPZuDnH7IETvPWvCY3HZkxMb6JQhTfsYOy0nApR0G0W7Tn7qKtKXkkRbKTtY0AiXKFdhxfjhsm2fP95v+heP9RtY3s8jH1zlgXYAXz3xTdL3q74f0NxhkbHUKmzUFUfJANzrrhJvE3gw+yynHE3gOnYBABQqTS2AJT1rbHWW444i/Xe27yQDhDIiIEMACMB19hoCZ/B7mVatDAb3YWAeuK5hHs781ncAfOE87CYPN+ble292b1uU6kYAPABgAICHANx4xfPJAbwFQOXcZAK38GzvuEWIMvsnHwSXHtH1y1efx+P9lfiZWR6Px/t9EHATBOTKHauy4QFu1u8AgG9+vJeWALhP3FUei8mZC5jZn6/AhscINcthTc2Eo34gHFHVaJV6QdAdiPOuYTjyVAc8c4QYJHbAe4G/wJ4fys4sTekbe+ZmS7fPIUuwd5NwmrGGJsiamKaogN03ygs8paGmlCmWfC8vqS6y/mRoP/Nmw22ifsJhQhGrM+YzpVSP8w8j5fP5zcUp3RA3mo+J30nuqiHdDdIj966fGImn7OeY6fYVZL7kmEupaI2tyLHTms/uae2UlbVCdtqzj/eZrk/NXYDsFQCRXS9Z7AC2QoAMeKAtNIW4ggso8+pyaFXmkrTDoDi3sDWrR6tWPgiu9e0d2rzA8h6j2GPCsJYfcl21aqULgCngFtKdBZAJ58IuhUpzPpOkDchckpbi84H0MBEST6GQJDnHeRuAcwDeANBwxWWxUpYW5H9/TrJ+iTR6AzVXAPh+3CJ4AVjVPD0nP28d3v3tPw48Hu/PwgezPB6P9ztIMdLmHDl5KMV41eLdLLiFTNdeaJQd6yoGhuK1VZdoyeC+1CYFWzMIlouj0RGhwy5TLAZvHotmfwJpxznEl5gQHjYd5f5NjL3xvLTcdpr18YiFZcwuW7xALhZk21suUFf3pp49Nw/QDc4OrRB20ISOmhfKxPP7TOpwnXShmYT4eNrlrSJ7qV/VAckYwelNfXLcL30ueexZjL/b0SmY+jFd+aiIZIa7wPoyfZ1s3RKZt4X60g/tIGHUyshvOZtJ04FKbPpACkS8adYFf7T27vwAgDQvpmtpJkljBIHEQzSAGRHj4R9X3tEyC1yecFU6zaKZJM0lKyDtHjelhBWFM30BCC+Uen9z6oT3oNAz7d1aH+UYcJUMwsDVqt2gUGnOXTl0RIa58ptEGdSOk0SIZQDaAOxwjrdModKcBQCtWikEsAhAmUKlObSQSDcCeB/c/8I3AeCpUarQ7NJ79hd3DDt+5esAQGgKkQDwqsuhTVfbz+Px/nx8MMvj8Xi/k2sEsliaih4Az/7Cwy9dxMBNDZ6jJk0R6vsyPS4Ql8XCKHJA3xaPxP31GLJ9EAaDoH7gBRQGs3TvgErcXOpO8ltsbGcjUww/WeixPUOleRfcu9yk9R9FRnfM9AzU+QaObBvienGu7Kuy0D414U1uobGmG+nJmBYUj9YFdnrnV2W8e8Susy43H7ELQ2ArvwGlWdsRWwwAClRERg439O2f0GPXf209KIxk4qiJfmHMtmszSVoSgO6A/u63THyqbzcrEuhewlgXFsRvMfeewqmZnhD3EezpR4PzyztansaPZ0ij5ArR445uB0uL2QuiPgKXh9OLv+w4YXR3CWMepqzISBjyHwAXnONXp1UrRQqV5keNK8TDBGslg5npjJR4w4HRAJkPAT1DWRqnb9cvXjFh4MrnD17IA1e7NgmAS2gKacAUjB56QrTKrYepAwCtWikVCfDo/L4f1SpUmi+ucZ1uAzA5NIU8xQe0PN7fAx/M8ng83t/BCEovqQoG5c6rCpp41M8huOsZgaOmH0xiV/Rz6MC8uxI0oBzWqCrqndxMjGYDCRA1oDUwAcnCCcyxmktRR8oKSUrzzZJ5ieW1wWN1N7SZHH2swSfsQYm1bnub7exx0+iWRB8mf2xJlWm1Pjq+3Fha/NZI6aMiK/sCY7dGRk6qMNV2hZjnNZcumofSDxNI16WQOMnTzQLYpsc0F3R97WjFfsdOAMHgbuN3AniruVAX/dWiE8tvx5wacO17LzcyGMC2w1/3vvXAanqWruYWavV2XNSf2Wuvc4zVf23vcb+HeVMcxNZLAvEF20a3EoYwAIqdXbzKtGrlSACLtWrlR+AC3EcAlLgtFJ8EN+NaQ16YcGunf1vYLknhdMUNurNNJc1B9QUN0wDkrV+ywXj76oVPADADSAXB9DOjbLl1ObQZABQqjVmrVr4Hbvb4Wk6DSyXhc2mv07hFEABg8tb9fPc8Hu+34oNZHo/Hu4ZV2QgCsBTAl0tT8ce0Nx2BGHCzfTtvyDP0Gdt/gxiMqpNW9vE9aZ2KookFmFuXD6+Zn4OZtBWVZbeRzsYRkPuWQ+BhpzlhcjL9CCg1lMkmTxJRt9IRtFvC7Cw5VXm/ZDDDsLpotuwdYimZHF8c9oJPXcLeyYLamjkVpv2b3KZLLgpMlY1vxC+++F13jciQcG9xHwcRNtRsj+rZUhfr7kEt9y2eeGkgYRwO2dHqiUO+LfkgZEbDM5kkLQVc964gcDOdT/mulkXuxJ5ZAD5TqDRm57sTggscBVd76+k0i9WqlQKmL/Fj7hAcnb3i831atXKM1/PSu8DVox0OoBRc2gDApWqIATwN4EVwQXUHuO5iDIAHSKesollg74afOd78oui7E/TkAgDdzrJbL69fsqEGwBpMRTa4Nrp1vc9JodLk42fU5dACAAXXeXV5nIcBhIxbhKfy1sH+V58M738PH8zyeDzetYkAeAFwyZGTYAAzAWSnGOlVW9tej7uxxQOA6VPcZF2VDcnseAyKKUE8Ab4VZLz2kb9v6d0oGWHCS8k0atFawjguwM27EhZZJ6RNwYhoYOByMQLl80UIRzPSGrZTdr6dlenqiczmQtp8jdS6Y0Fo9T39LZM8KqWfbh5P932j75gW1vZ1zK4CX+8PT90VBQ86KdwaWe3S7sOwLI0cXiQqOxUlqtkadto8MqLFK1Ha79yrwf2bayWxsSPOnnI0CoJYAUR+Y9oGAsSQTmkOgJxMkjYNQAAASilNIIQMBfAluAAW4GZvT3i/KzVq1UopgJHgKhV8DMAFXCD6HmGITBRO9jofowPQDMATQBwANziDWYVKU6RVK1cAmAugEcBj4PKRhwEgCpWGQoWjB+9Jmz3tAPnAwZD/zHDMU6XTLPuKCQOjCEF4zdnaWJPOfLwuh2oB1F6+LguJNBiAaQM1d/7Wa8u7pgZwJdXYv/pEeP+b+GCWx+PxrmFpKmpWZWPJ0lQ4coCxAKYCOAOum9Wv5gxk3wJwEsAaADO23Yybw6rwxk0b0IInvhuGvqcfgU0+AMmbVgREa0cGboskzPBK0IgK4PQEuIzToriPnoZdGktKHUbyXZ039YVOEOjpylZunkqVbBJzpl1wY0JnOaw++5F3OMR81BjJBE91S3mwWRNXdzbAzeIndtsfHlkXMdEGX9axli2YWmU0GJZF33qxv7myvaKjXrKupUO6vgdityH35N8OwO30zskOg8nrIztMb8o9sAYA0mnW7u2DZ8wkInaXb2X7K+3Rgx5XqDQ/3H73XS1rA5dy8JbpsE0n9bZYSYKLD0CGUxtd0vWWpcNRQ/em06y5lx+jUGkKADyrVSsJgB0KlUZ/eV8mSRMBkAB4L51mXU5lgHM8f5A8TDaYjadRluP2zXM3r7MDwKgFI30ppeaYsbH7z289f7D38QuJVApuprdmIZFuBTAKwM4N1Fz9W64z78fy1uHLv/oceP/b+GCWx+PxfsbSVK4EFLji+9UArqfb17WYwAVeF53fnwGBtDYKJQD6A7gBxcPK8XW/PNgEUSZ3KxF++Awk3m1AQwRI4SiwMUXwNZiJ61E5LvWLQbXegJ6uA/R8ZwFJjB5OL7baaGZrO1k28CjxCGkx3xWSUz0rbkLxDZcsyUE3lJCIzPKtR87OkES6lcsiw3RC4TmPeHpu4vjAvi3fdlva2/2jqu9zCWy4d8HS2ck3KrJ7ACzd5D83WJIq39zTLvDYfyYqYMU7gFatnAnAFqxo7LJbhRKzXvZOj8b8AFTI7vV+7wMQSa30hP2IKc01qT5AFOB9qKs5oB+14hzbQfvKRMIYDF7xMoDvtKqafAAOhUpDnXmyevzYMABPCiOJWqtWmgGcdNaavVIp40Y+kU0V7e217SwhZI9PmHfKpPsmumrVyrUKleaSc58F3CxyG4CJAFQ+ET5JWrVyP4AtvQNqHo/398MHszwej3cdUozUAWd+ZY6cpAKwphjpzut5LMld6w9gBoAdNDljzeXtS1NRg8sNBV7+ocVqDW65SDH5K6Nbtxdb0zWYBJ2aSmjfMxDN2Ahm5B64LP4CtNsV/uOOQpXgTvQ+Mhw4MogNjYxgmJpKOi6m0xrQhxHBFZKRN3S2SpiTUagf4H56z6CGlWuGiwpKwmslbnqqeevbd+vf85xdldgR/lxpomttZ/91VcXPBm/LGZzsQw8lhzOFtksfxkVaWqVhls/Ys29umlp/TB8SPij8Hh+5FDMBGJojh22RiTu7HPu6g/yH14dp1UqxQqW5PGvaBaCNiMnnVYHiGnaYQOne6iUjwEDGhWioHu/O7h8ZAOB5g4ctGMBdAPZp1cpvAISDuz0tUag0Pc7nKwbwhWuG2AVcoPwSuAVZP6JQadoBfNV72/olG7zCh4QVjL87uZ0wRAEgGsAlANhAzRRcKS8sJNIiAMWD5iTGA5gGIA8/Dap5PN7fCKFXryTD4/F4vKvIkRMCYJVNKLTN2v7pDgDnaXJG2889huSuTQLwKIC3aHLGiet6oREYAti269xsQR33vwHPEVshqIqGfMBenHn4C5QYrKhbLIDQ2wU9Uh1aL9azkUlyZnhJgy1K0iIIHPgNYVq9KPPue3V5u3IKCxuaRxwKDqtPMDZF7yAxDeMWFBqXL/7+XGuu76j7lmdsPdwRNTwqTBc6f0bpfS9+kKR7zOPIxGS/qoEiT2uh3Si82VAtzyky+J81ZXjapyQ1zQPwEbgZ5n4iiVkVLDjXWNuc2M66ycIBLGtbYloMIBTAM+k0yyTze/wWsWfR094Sv+WrHxDcCKBJodI8CwBFIQ9LGx43Cm0y9vFuPXv68GlTWPJwWYqbC1MIIBDAMoVK88MYa9VKV3Az2flXaXkbBi4ALuu9fSGR3gtgMoDHb1+90Agu0GYUKs1VFyRp1UoxAH8A9c5ZYh6P9zfFz8zyeDzeVazKRiQA0dJUlF7eliMn3gDuALDu6VcfE4JbRf8ZuE5TP+ckLs+6Xq+TOItx1vvc7fbPZUW+HkURYwnj1QMvEgxD3004uqUYXvppCEyS0pCzcnZy0kEmwbUA//nuNpDwSPsb0YxQ6hDB0e+cv0tk67ur3xmWUFHtEREco2dvGX/Kb/ysC51Fb/bxcU/oAeNtr0uOanZ96IFdwZPnHH3wmdc/LtFXuOXuu2da9eQN36V0FbrLjt45Ojre0Fnlm2TeBSASQLlCpenSqpUFra/ikcayxHdl0wR+LnNR6uhk7Sww0g5mmA6SLwCc8+z3cSUhtMYqsDSzpttfMmy0RWYuSfMAV7HgVjyKFwC8VOPjeL0l2jGpwMO6b/Rg6TEAsQCMvYfGedv/qk0NANwPwFerVqquCFS3eQR6lM16dkYMuNncuQCmaNXKZxUqzU8WfTlnl+uu3M7j8f5++GCWx+Pxru5BAO6rsrF4aeoPq7C9AQwGUHB+UMI+AK8AKLnWE5DctQJwpb3qaXLGr18EY3Fpt8p1bcL98zwC2yrhiCyBa0oJ+lpHYnqcHwLLA2hE9FactysEVa7BNMpxEX5e7cKmch0+WTWZpi0otrnd8mHLmmdSXvEIM0m89RKrrkFm6WcnIl8pER7fHO3l0DFn9yCh39B+bTc7rNKSrnIvOQRMRutx3yGODtqvoye4TJpgPGltk34riCfH3c5H3+XJhB/v/9/bm7VqpQ+A1z0ekRxqv9+82rTb0XzTLk0FAPR55oP/CkCfr4CX31IAArH+lNzhvTjGkiJsfcwkEVByH4BvAVSCy1W1eb0oCaP/NbuiAodOwfL8CvUG47UH55rWAXC5csZ1AzU3aNXKcHBlvd4CVzWhFeBLRfF4/3R8MMvj8XhXtwHcDOQPCjfSKGn5yW0xz43cQ5MzWFwlX/MKDLhaqOZfOO6nRkAIYFlDjM4/0PM0xAIxMGMNXLwr4ZL2CWIq52K3fxgZcHwSxp0chUq6G/YgKRVLLPaq0iB6qCJBfPpctO2tVVnVs284FTpsWKV57ctzHzrf6LtMVyHzk+X67LwQ5zKn2egydEFRwZzcjkjxI/+ZXbz+9q5R8BWbheXxp6yjXIYcqEod5F7X4wqmx1MUwowMOjLuARc370tatfJzAO0A8glDSnxXy5rBVRoAAJTANx9cq9hzAFCXQ+n0pNsm6i3yh1/3D3vtWUPpF+L+gsqbj60vAnDMeVv/U/ebxJbw1TZreLvglkySNhjAynSadd2dthQqzcWf2Z0P4DUA5xUqjRFAzq+8Kjwe72+ID2Z5PB7v6kIBLAC36OhygJRijhkhK9xIN6VcxxPQ5AwbyV37FH5bfU0WQI13jU8HKqdKLk0pE1fVP0pStzVAcMMnsPY9D8ZDAL10J86LLsDo60+Go5sqzt0h9J+c4/BvGuBodGsjR7Z2u92QZLIzfnrBhVHd/1m3x89lakwrfO364FkvHRF8lTnG+sTGb2KHjFlVSwS20TH3nffx8e0x2RouRjcsv6Gtxh4ULKrWd8jnCjdKxwozGredPhTSMXIdgHEADOCC1Xxwpa28L9/ep3RBD4DvCdkoIGRjIIDmoX39KnR6T3ttR0SQz/t1bgBu1KqVSwG4D9jvnVTVvyfI5sEeBDdjqgPXIIFcHhCtWkk6qEdsOSJvYcHsf37JA0euHLSFRMoAGAqgZgM1t/Te52zmcOxaA76QSMVwplBsoGbHtY7j8Xh/L3wwy+PxeFd3eaFWZa9trwJglqbiuhcE0eSMX3Ube1U2JA472/dr9xNVx/cmLXEfIbsZwCyp3XDjyIZ6d1F9KLHm3IbEmnh43vQdHDJAMKQEBaIF6KkVo71bBMPNPoJuexO6btLJm3a6u02CzMBSs2DirVvDQ/r5WbxyfHs6jYIJBuLVVtzo4zhxoe+d2c80tbfrhAKh3fMtIjAEyaKax6e8tv6e70fMm8caaRI7QjA0r9D/yOZSeXtpbe032zrZrYwruY2IyBwARwB8CkB6lQVV4wHcA+DV00UfnCBk4zxwgWoS/n9zhFddOkUhQ3f71Ry+pXHlHJrVDgCZJO3bdJrVe6xjPdDzmi/aXVvgV3R5o1atFAKYA6A2PjnO3FTSvDxkQHCRngIZuwAAIABJREFUVq18RqHS/JqgdCy4nNtXwdUT/lsYtwg+AKx569DziwfzeP9CfDDL4/H+cUjuWjcALwA4RJMzsn/h8N9kaSqaAHx/xbauaxz+exre3W58x2S0Nfe776vH7/wwcNz0zPhKj7KEjLDcuM2m+EtCHOsL++hsBEQdgEPUjuZvZ2G+iwgX6n1gvvtreEQK6MC0zQ6Hm5egfWhX5K0NKgxTeJ/3OdwSXVrhb0+M9m4OipH1MxxIvjBq1B63ocPLEsQBR4/FBF1KyPl+3Ibudq9HF7/wUbBLqPnNfveev6N4S2IS40/SirbIds3uurQgXtpW0fkfhxsRYazrQtEbkuHCOm7FPxEDn/X/JjTVYqyXLwLwGTCnAkBuf9fWti8jFt6+HvRoOs1qBRYczCRpxOtliVjgw+xvjDM0SPXCToeYPqpVKzcpVJr8dJpFnYGq2JkW0Cwg7M4A2noiHA2NvcbME8CNAPxGLhhR3VzaXOgT7pMArt3uD4u4tGqlCMCdAEoVKs3+q4z9RXBtdMv/mEv7641bBDG44Loe3Ow3j8e7Ah/M8ni8fyoWuP4Z0stI7loRuEVZFTQ5Y8vvflZXf81gAG300YwwAB04iZ9rmVpYXdK+sbaswytRRdiYQp8BgbVughPTanPaZWdK+305oS9jcyXk3EhI2nxQOPxt9IveB2NLIE5GDiGsWwc67vRCQFKQoLZ/B7N3UzAV6VjHh4+E+Eocsy0iEfWenFG4PbhsbDzVCYNmPbftPlZifA6x3+iP5PVhjXab3CxtmuOwQMeIURKs7Ky4tINZbMtn54zoatgaGdTmMuKt8y9Vn+mj3/FuLF2nGTix7VPleee5JwFYOuCZws0n7h8RDsB95+o9dQDW2Mocc6lNtMKcZ9cAeMXZzeuFzucsVek06xMA0KqVEeDKYbn3Go97AAwaEPvRqh5D0qKn7iowRofpBwN4XKtW/keh0tQoVJo2rVq5HMBAQogsMD6wG1yFiUatWkl6ldbyAdfFTQzgJ8HsBmpuBhfM/p3YAOwCl5/M4/Gugg9meTzePw5NzugB8MRvfLgAQAi4W91/OJK7NhLAy0NrvXMATAG3aOy9ax2/NBVdSI1YhbciAACHXq6c69Us89p6d5F89O5wU4KrnhWEnxAwnq0gSXsQXjMHbbN3YNtwKZhDdRjkdg7Fvv3IN+JEtBV5YMIElqbILYJXJDTw+0w3e8Bo4iiJ9JXr2YNlVolv9kMpK3QDkvJznnxya9ncm5ZuffyJbR+M61s2LG/BWJtXhqO5obHfC2HDL37jEtgjm5FW3eAep39B3+n+SVNzjE5O7ZETokpH2iyS1woPjfkubrh7F0uJl8tUtg3AEt/VMl8A7wDINmrt0eJ+TLPLfJFBq1YulU4SfGTe77CiVzUBhUpTrVUr71OoNLZeQ1IOQFhR5+Yf4m+c7mBJAIC14NJAOns/FkC1Vq1MBLACwJfgFqDlAPjOedgkcNd/z5XjvpBIo8DVod20gZr/lJ+N65G3DhRcdzIej3cNfDDL4/H+VWhyhpnkrn0CwJ+1wKcVwN5SP91RAC34lbewxz8XpcNz0H08Yp7M8ezdbrivTd/j0e4mswoYUWsYpCdTUEoMiBx9Gm2HZRiYWoz+04X43jCN9Fj17FhDleOAPpCm3baJCXcNFV8cOlPQHBwwp353O/Hx0C02GANu+eqDMfrbos+17Ppig23H3kEvXvhG0hZiNCZaazzLIYKr/pL8Nn2J7Ba3GH2ee5y+3NVL1xgVd2F/PPWOv1W170JdcdzCvcaZY3faprk+6PpavKHbXem7WiYHsBdc7mmprZAV2wrZHaIEgZ+jnQ1xvUVM5+/TrOj9XrVqpQeAh7Vq5V6FSnMYABQqzW4Au01LNpL4SN38mLCeyQD2ty0xVQLol7kkrTadZvUOPovBld6qATAQ3CK1yw6BC4ArrjLU/cB92MjDn/RBh8fj/T74YJbH4/3r0OQM2y8f9bu9lgHA5wCAlGvXpL0OVkF5fyMsMqvF22Im4z6XM3V9UXjjq/joo9G4SeyBcaMEkMk8sGaHH7xMJWCGSNFiMwjN9eW4OCKR2kdJoHuH0LJwg6Hpos8FYiWWLjMZNGXMxfawiXXjxd61hohqr92vrJr61EyUtznGJ7YaBnnoUl7M+lrmbu4++eDwE5WOMQxhWAsoahfRLDVASO6X8w5WxEY/aBaQm9qJ99m6ovgaRws7t/Nlixg2fJ5Os3qgQgEAZArTXgUB3O4Se2jVykEAjjubIABcaa9QAL4/GUe6gIJbjKcBgMwladEAlgPIBpB1+TiFSmPVqpUycJUoXlGoNKZe++rB5Z7+iFatlN769s2C89svvFW0r/j/co14PN5fgA9meTwe72fkyImgcCONBDASwHdLU2H6hYf834yADIAcJ/9/juTd2EJccsQBz0xSbSo29sQFTd45odtzcES41ZXEjy9B2sMEEUwfeBpjsCt3GqJmb0Nf2VF0nU4lxwpcqF9EG7W5uyM20sYmLl5tnTrtQk9ISOdtB1YP0nS5i1wj+rT6Nxjc2y06kdl6zLZolG9NhLWN2BwB4tkAdNYW6Qlbo2QogIFWk6wAQIetxuGWSdK80yntSL4NFR7W4U+wDT0FLrT9lNXoWqlbbZ4KG9IAXAJw7jbFqwMvVbsvj/Xtk5nafKlSMkTQ16Fjnzftsu/PXJL2UjrNsitUmhatWvkgAOu1hmfB/SVJ7aag20Wpb7+zIPuxzwAUXt6nVSsJABG4hV/R4HJjr+d6RYrl4tuH3zxsw3N78/82VQx4PN714YNZHo/3P29VNgQA3Jem/uzCq5/IkZOxADIk1efyLRGDxgI4CqDqKsfFA5CmGOn5K/f9BosBDMAIPIKTP5RiGm7wtD46Iiw7v3Ub8d032K3BaJGEHj0+UBg9PBz9J7ajL/M59u+fhuxjM7DAfQAt+Wwuybc30FodQ5+47WTPIMbsIpEOsLGza7/skZjJHXcsfu2+Jbsne0ut7Kr3Zto8PU3vBzVZF/VvMPafNrC0++XqcSUZ1YdFNYHJJ4mD5oOrHnFJodKYd7xzV65rY+2bUa83ZgBkjsvGYk+28/Ml1rcqEtlKo4XSBecyl6RtB1BYNNirjOSuJQ94mweX17kN18W4bE9vysr+qn96JONKzlryHWEAZFq10gRgBoAihUpTdnkwtGplFICHAXyiUGkKx4Vufe1cy7jBrLtgbTrN2nXF2N0EboHXswC2A5ikVSuPKVSaJudzBQEYBGC/QqWx9HpcifP9Vf0O14/H4/3J+GCWx+P9G8wDMHNVNp5bmvrT28w/wwigK2jtwzuqlh/YDi4P82ruBuCTIyeLU4z0V9SVJTIAUoD2DrKPg2vv2ntGsQ5Aa/InwpmV73edPF9cOXtm7kJ86mikT4EhMeUSMNYhGD3oNB54W4wax3hyU/ZUDPbKYwo8m5EoCPI4XtzkGDzwsKSkyn3+1PEf2HxFRhNY1vbgwzmsXG6zTp164b6Qs46IFl2YlZ1uV8/XHR+hjN4l9MKnG4XUZoOzkxcAEAnaQlKb2gITa+mxLTNHvv3pe28f8ugv3dSv7yG20lgOAOk0ywLgEsld+zQAZtb4+jfCAg1nY8N6Kne8c9cjoBhvOeE4BuDTdJrV4ww0bz1Z4NM8g2xsBfD+ztV7zAAEbZ2S0AulnuEKFQpjvC5+E+FxqVLIOAqdGQfQqpWR4GZz28GlEVjAVay4HcCT4FIRAK6O7C0AarRq5QwAFQqV5luFSsOi1wwvj8f7Z+GDWR6P929QCuAsgO5f86AUIz3rfNwvWQ1uZvZXNUgAsARAX4A8AlBuodJJHAU3A/yDT3FT493YslMWQCamzArqZ95jFXrVDC1fG9QS62iMFJCGcDg6/bHnxlacDgqAZ5cEklvUiPY8DffSPtRKHCQush3l9RLTxUuBVRHuHSF+Ogs9sCPx1DPLvmu7M/nUqN15sSKf5DpbQKjsGxrY8+r81M2y5mb35e3NsvSoqC92mkySUkoXdGrVSkIE5ET8jRfHA5B37gzo42Pt8ZzRcUZ3roHJvwjPuwjZWE7pgsvdtwwAiEKlsSkGr6jq9pOoTs1oHWMZIagu0yWe3jn5yY507rgmAM+v/qrPJC83y+hPVbnrHZ2sR7nO4/6n3h5ui4+yPBqzfGVZeDDWCBnHIADDnAvGdoCbie1QqDRPwVlyS6tWlgLQ4scluLTgFohVAogHMEWrVjYpVJofjTeP928ybhHkAJi8ddD/4sF/U3wwy+Pxfhckd60QgMxZNusvR3LXugB4HMCJd5HhC25h0a8NNq9LipFW/vJRV3UK3Cys+ZcO/HTETTXd3ubVJhdrkdhn0pi24Pa5PosfbJVob/I1jjkotN+yHrFiP5yy3oXZgq/gmtQJlhjhCGpC97GROF/sKigwOkSz5xf2f069tXX9GzPl7fkeo05+P9kWesHNgzZJcmzxXv5mYhs3cuT+2+x2Mm779kGDpFIHY7GIBgLYBOArAGMAKLXqu15VqDRlM+7H0Z3sXfe7CumCoLXG9ovwrJg79wTLrb8CaHLG+1q1MlpbeOhx/2ky/ZDdvkNG7PD/7uaIJ0sMkz0ekgr1Pd+9/4jtzueSg7p6JMUAPtMsyz0iFrN7KAWJj+ixjUw0bhg/tOdOgA0Dl7ucBqC5Ra/zfydXa75/9NSNYZ7eHb3HS6HSfOU8397b9AAKFhIp4x/rV5C0cFRo7bm6QeuXSO0Azm+g5t6pBzzev8UyAO7jFuHRvHW/qfX2X44PZnk83v8ZyV0rBnAXgGEkd+2TNDnjV+Wm/sxzSmlyxm8tkyQAtyreA1wQa8ZvaLLwx6KHwJWLuh5DPTqkSR4d0uy7T26pSDjlOe0WN0FDmHuzxz7XQUKrwIC+hlPwFLfikqc/4N6JposTKGMwdydM+87TryiIpg9qF7h7mgVdje7+M6YXQWwIsQYKolwGzu2h9Yer4y/mePi4JRmNMnHnA2DhYYoJ3v/2+tm7WJZpuWFidb1WrRQD6BCIrPWJkw+FAaQaoLYZD362T6tWHtpz7DE7QOoBvAGsegOgxc5zHw0grSXaVNYUbXw2qMKlyhDs4Q/g9NSITV46vehBidghAfA9pQveBxZc3P7anR8zctIHgO7gqXu3rVm+8nBYUIU7gPsAnAew+v0jOct1FtMTKw9sa7M47E/VqTTXHDytWunWXt0evGOl1gdAUUtZa8L2V3YccdjZeABTRDIRVs4a/u3gOYM21jTEAVzA3Hrvi8t2//rryuP9o5yi1CEnRPDD38fQFHIHADW4v6N963Lo36Yr3tXwwSyPx/sJZ7vYhwEcpMkZudfxECW4xTu5uL7V49dDCWAIyV37+G8JaGlyho7krn0EgGNp6t8tiP1NvgaQc/fJLe4AHkk8EH6pu3r1OEvAxfM+HYcHBxwKlnz/7rOov7cZ7jODYTC44pzWDSN94129i1rR9L2c9F9TLTDaXPDWzttQ8rU7Ar1rpe/tJ+TZoXnW8Xe2BT18TxrjatBfHGqKl9r8JCdLpyUP7P+M1PT28J0rAGwljMM0et7WNHffjt0AHgWXU3zcWUUgTqtW1ipU6AGXt9q7vuu34G7v9zk3rd0UpHrDlscd845WneficIAN8Td1N7fLf1j4JfBiVgJwV6g0bQBw74vLOrVqpRxAAYAshUpTszznm+W39h/7aWlnfeCx2vJfukWaZuoxpwpEAsZhc7wOYJnDztrAfeCJCIjzXxE2KOxmcAvHTODya+sA8MHsHyg0hSgACOty6La/+lz+bKEpJBKAoy6H1v7Jr0sAhIH7mx0FYBSAPgCeDk1BBABXcEHsZeec2/62+GCWx+NdjQRAOLjWotejEFx72c9+xxquBeAaG/zm4JgmZ/whaQV/iZOwgMsrdQPgyLnr/KaFFXkjitqTQjrkiYZhG10kip4IVEa+AZFJhz5Hg1FW7UW6iENoHTAarSPbyFdZKfaN2/uzVsZqr2+WCAOCjGx1vavw1TcVRJVkaTjdFOwxb/jpmsJXBnzWf1v9M5MOHxywbN2MQsyHEaAVXoFNA129Op8H8B9wq6+KnGcXBeB5ANkA3ejcD4AsBCBWqPCFVn1XO4AbAIi0auUWcGWz7ABuFQjQ8cpDZwMAnAQWXC6x5QkuBaO3BAD9AcgAILB1ClNlIEyAR9D2upyyH90NcD4HerWy3e8d6tVBGNIM4OwGar6cDlO7kEjrvMO8i/TteuIT7q2798VldM3ylU+DayXL+2NNASAKTSHb63Lo/8KHzuviDCifBveB8JE/6DU8ATwA7vdFC8AbwMsAIsGVsBPix0FrkPMr7fWVAnj7jzi/3xMfzPJ4vJ+gyRltJHftz9b7vOL4X3O7/HrPIRfcTO81rcqGCwD70lT8m3IdhQDqIryPlwySfnJeVtUSUnMiY4BD3sVGz97GBAsL0NbhApEhASTAjnwvCeqHeOK8NYLuf0rKhMlqLZoPt1ndrQmihWvHkKqzQrQ1+TgWHZUa/CNbPS9WhAzaEeR3frfn54flrebge4aer3L16mQHpeyzyt11BYyA7nRWX9gGAFq1MgJACrjZ1yNatVIAQKRQaczgFllJneddDe4faS24f94hAA6Da3xwGkAzgM3gPrzEgwuI1wPoXX7rkE7PNqz/vmfIB5nzWQDVOj3V6vT2fd9/sMQPoD5NrZEhAArCgjDfwQr6bnjtmccXPv2qRaHSlAAombfsqmMqbyxqHK1v1+sjhoQTAPTeF5f9qsWCvN/sZQDkfzmQDU0hDLiZz5q6HHr5joUfgAMApoemkPC6HFrjPFYIgNbl0J/tUBiaQkLATTg0g/v92wLgVudrbHUe9hCAZ8D9TqWA62yXAC6AtYILpKXgAlYLuEmMIgB3OM9vzz/luvDBLI/HuyqanPG3CRCd+bO3ArhAkzPyAWBVNsQAXgcXJL35F54eVmXDH0AEgNNLU//wBRTBAPqexSgJjlQ9+cXAuZqRKbu82VG5EFAbTFkvozrBhpCQE9B+HY/iFjuG1+vgg0p6Z1o77egJNu/eOdln1ighlXowOv9gnX34wFJTS5eHW0FdnJtD7G1pasIr738xOPT2ORWHvtoZ5WVgzTf0GS1PsRhluae2zawGlESh+swLwCCRJE3e1eUy5aGVoxpHDWyNu3d+6UQAFVq1cqlChVcAEIBSheWEBEAsgB6t5OM8AD7ggtloABsBtPTq1tUC4AiAJq1a6QrgKQCnFCpN9qxx89vA/WOWb8/bfAHAqjXLV7pZrM07GMYhBdgGgMm02iQ+Vpt0vNHsOgG/kCqwgZoNSwL9bxZ2mw3rl2yYtX7JhjgAH2ygZn5m9g9Wl0N/9kNDaAqRgPtwU1SX8+NqIaEpxAXcHYFjdTn0m996DqEpRARAWpdDe5zfhwDQ9frezXkO5648h+sUA66O8SYAm0NTSCCAleBaW9vA3W1BaApJBHAPuAD15V94zhsAjAdXdm48uAooCfhxXLcOXDpBFYAR4ALYveAWnn4I7vfxEXBpBPvBBbKt/5QAtjc+mOXxeP8E7gAmg7tdlu/cZgdXNqvprzqpXuYAmAauesIv5r/lyImUpeLxBntC7TxbftHPHUvIRk8APZQucACAesyNdW1Bxr2BNW5NwE0fquK6KjPf6T416FxgbEhuoo9DRgURrlq4BebhwSdP47PNgzCUqcbijH1E5C5gPrQ/4V2t7KabS3tofOKlCwnzOofJhHqxn76H1LcEIG30CflFGhVqtggDlr2eLDxR4CebMP2ExNVLV1p/KU4H4DkArzVXhg+2WcT3jkzdvvS95+99001uexMU1QB68MM/afpDMNhucIQ26ezLKjpsu0RDsRvAeYVK0whgxZXvWaHSdDpTEV4DcBBcvp4UAB5M94htarOXllTaNmvVSiGAKUF+YkOnzvesVGxoBZfzWt7cFp4D7oPORa1ayQCYBaBaodL80NjC+XhXhUrTtbqppQIAFhJpMLgAWwQ+zeDvYAyABwG8Ci4I+yPcCWB4aAp5HNys5cvg/ra879w/0XnMcnDpT79WLYANAC53l+sGdzfiMIBkAK+EppBXwAW8YQD+ex3PuQVcTepz4H5HmgA8gV4VW+pyaBW44BihKeRyzqu8Loe2OLe1gXuvKgAj6nLowd/w3v4W+GCWx+P97TnTHpaBu00GAHDOgF57+fqfazu4vOHrbcgQx1LJqywVdWeStIXpNOsnAfnd2CI7/4ZjALjV+9l3Y0sTANmntpuiA2vcJgA4CaBveKlnSZ8do/7bmb443C+//wP1+oRgptDOOALaMGpQO0qbi6C84xACfHSkxyajyKujOh8vOG6LYUKPnR558aKL0OrPCvU9Qtt/Ht3oiKo3ScYFNnRWD2Nd9uwZ2AHgabmrqUrmZjhQVxwnBpDO6uhzecqxYtfJ1CGJdHQM799OunqEEoOZTPWLLv9q2LSDqy4HspkkzQvAgEzJsMKuwKiqJoPZqh6KewCU4ednn/Tg/lEXAFjf9rDJL3NJWqjvall8oK8wMtBXSADcTSkeJsRx3GxxuWvRsy9dOSu+FgC0aqU7gHmUonDN8pU6AC3hwaVeAN4DwGrVygcUKs3lurifAhB2jZ2NWePmPwKgcHveZn4R2F+nH7i6wJeu3OG8Zf/k7/AaJeA+vJjB5elvBffzedkRcD+PZT996C+ry6FmAN/32iQGkAgu8JwMYAi43NYCcB8GN17Hc7aAu4MBAI3Orybgh1QF9J5Frsuh+tAU4oVeeeh1OdQK4HRoCnkS+GenavHBLI/H+1tYlQ05AL+lqai+2n6anNH8J5/SdVuaiib8uhniYivr/6aVDfIErtli9+Z+DzEzKzezxW0naQWAhQA8K/p3PB5d6J0DbqX9iwCS52j6vn2kce3qCr+azV6dJtWeIZ5SvTWdFkX0kDtVb8PdxQ47Bcod8ajcLmCPHvVmZo6uNOovQtTR5enoNIltNXW+Lcue3B4oswtFrS2S4cm+hiXDn1xT+2rO9KYVK+ZHpKcflkcPOW+4mDu2v6OTupqPwWxvEXzl8YC8GoDjyNmAiyNHX+obNyx/Emsng7NEaT3OcxwN4J50y+kXbq8OXgIQE7iUjJ+tUKFQaQzggk2sWb4ySBxe+aW41dRC95N0Mol+o1BpdFq1ssxilZ5u7/L3AdAXwMXLj1+zfCUBN6Ovu/dFjU6rVi5v6wxyAxdA7wI3yycFV2Xhh9rIG6jZAcAxa9x8N3B5jn+Lusn/Rs5FUsEAjJdv+f8R6nLoAQAHnLmtIwAcrsuhzc5zcAfQUZdD9//MU/yIcxbU9DN5ryZwH0aLwN3ejweXv5qH/8feeYdHVaV//HOmpfceklCD9CYKKLEA0ShqVLCXrA4bY9k1/nZVLBsUy8ZV1+jqOkaixlXBghrrSOxBkCK9J9Q0SEJ6Jsm08/vjTDQgJYFQ1Pt5nn1WZu6ce+bOncx73vO+3y/49MB7fRDQoTLJHXMagKpDf5P9ym7KCuVRSymeaLRgVkPjD44oyh8GjAXmyaS0w4r3d2G8Sahg5XWZlHbIJob9uBKYnFPAzG5azh4Vhb5CACTbjl+dWLJNOthP0P8ALDF4C2fqUuP8OUxr92RmDf1eC23ilwDrM06jrbxfY+qe0zcmjXrXd8JK22ouyD+X+qFbRMKpr7POZxy1MpZzHJ+zfOUAccH4ZYa7rn3fnZH5Z73+3KGG/uZWBpYt0RXPjg74293X/vDqnNyx7j5+0Z+mn5rTKkXk980D94aHN2xzuYTxm+URtpdzh9seMK95HEFJ4C2mTOCelIy8x19694nbJwytn1+5JfG80vwIA+qH9DPgQ5TN7EYpr/E0FF7zqyzbYQjymRLCWSMThoaF+F5muuqueQApGXlf5mZlr0cZKezoOHjqxOlnhQT6TZo45pQBOp3uGeCnlIy80tysbC9UhmxFSkZeidVivgqwdVI8+JlPF77X5MnMdqkJUqPnKSuUMi5Z/KoM5RjSCyU5twDI8wSAD6JqT7/uygBxySIUVcu/Iy5ZvH4g2S1PRjS300ObOv33Uf8NRi0ixX6P1aJ2Og6YLPitowWzGhoaY4BzUdmqysMcC4AoytcBw4BE4JP9msVGAilAnCjKf0ImpXU1GFiEylhUd3XiB5jXeFR92wvdcCK7FYgp9BUPr58rTxpN2jlM20ynrdU5TKs60HEzls1fDRR5u9p/GrrB0WvAd6f0j7jpXnd0U6BuwOoq1sRJ/GM20NzkRc1SF+Mn1MnIOBvBqeHNe9a4gvpOLhG3XPqxfunboUELFow6y1o4rPWDD8YbXbV+fn3jqxsCdXZCfaXptTlTfvphSV/DD+tjym657K+xgVNbdz9l/etWVEaJD766t81qMddtXz0iyj1AOqDtVWDD9fKtRlRtH1aLORkl4/XKBbdOkVJe4/KoIUQByw4UVAKkz5656RvLHfkOX6+76hxuQ9S+z1WimmDwnCMiJkJ/1sA+7ksC/KpLWlqjqjsd247S6wV+zv4elE8XvtdTmsm/eeKSxaXAcOBJz7Z5d18fBzjLCmW3atw9gV/ncQagapq/OlzHfxfnNQSYDryEKhN6ml8WRg2oQLOiG0O2osoRzvX8+7GjnWN3KSuUcw7wWC3w745/exrrnJeexiCg4fnHZdlxnGKPowWzGhoabwMLZFJad35kLgb+hqq/Wgbs6vTci6jGmQHsq2F4SDJT2YKqXTsaYlGdwz4cYns4pwBDZurPjRLtQNuW/+yMAB7MKeC9zFS6vKV4pHgUEEIyU39dC3gE2Nr0XqP/O0O317zX2HvvIJ2pj2kpga/cxtCJhbTH1bDHEMGll65ExgSLuS03y8uuXe29w+hsK3/V4FcwbpRr8+ZYh6+/3fTS2xcsW7cwdkJMTJ3ps2WP1e8uSYyadNHtUTn/SR7w72de/3hcQXzIqpq9M6/buXpXEd2gAAAgAElEQVTTXx4+94Liyus7X+clwN90/qLyevnWgQLTIcDg2x4dt06nc5uFmPvE5y+SiloA/dVqMdehGmJqUjLyNnR+YTutczfYyoqAElRDVzSqOcjqsant4K7Lk/2inS4WmYyNG66775nO96bGkROMUqDQHewAjwpAH2BxWaF0d3pcj8rU13P0Na4XoDLxKzmKhW8nIlBz9veUFizteKKsUFYD2XHJIi4uWVwMLCgrlIesLS0rlK1xyeJfwJeoHYmTjrhk4QP8y6CnGJXM2Ao8cmJndXRowayGxh8cT1a1u4oAO4HPUdvI+2yjyaS0NlGU/wKgPwGmBR8CVpmUZjvYATkFxAFZOQW8mZnKd8k2mQewvoAI1Bbf8ZrzzcDgnAL+mplKtzRNZzC/D3A18PocplUAd81gfn+7n2vI59OrsnuvvaV/bNhLRkfkbp0tooYWRwCtAQF89+0AuaFgEOPNlboxrQv9fL3HulsTRojVyxNdERF1e6OuDooaMjpm7CDhcv9QrDdkv525d7jLx9u7ylnvlm6/M84o7jPQ+wNHhW3KygprdfiOKt2tb4hrn0S5ZdVcL9/a5Mm+Blst5u+BvY/8MCfIVl8fuPLjkB8yPngNo16/Of3y4Js+/ij2vOad+uX8UsNXh6phnYG6px6wWsz9gfFAh+zSBFQgcxZK1zjV7RZbcrOyS9Jnz+z4zAt0OtHXpGM4UHiEn81JTVyy6Ac0lBXK4xks5QP/O0w2dCqqoWlHXLKo7AhoywqlKy5Z/I+e2UJ/E/jCE2gSlyxigPqyQtnlLLonK3kZKiD+Hlh6mNdP9By/ARX4HRKPtNVPXZ3PCcAJbHe62I6S9Ko9wfM5arRgVkNDo9t4tF5XHeJ5yfELCjuf141n2xsgp4AYIA5Y3ql8wI7KKO+zxZyZSjUq23y8mI/KJHfbqhflzDYUZcVaATCH6a17iGLW0Ofe61McUOtX55exdXBdb/uAFu8ovUsMEpv457LpYvuuUEaGr3DP2Xa2+PLL03T+bpO86KbNTaurAnXOVukaa3K1n3N3ZdDHzr3SsdEnuEHfYrhdv2PVi60jP3n33XFfTvi0MfD8VQ9M/gdnNTgJqt5GsLkf9X9Clak8gsqYTkKVb3zW2tgU395iG6nTL7gkdpI7SEoph8fUDY7wrg1ZROTlKRl5L3a8B6vFbPe8t457ZyxwIaoExYXSGnYDxaggZFnZ7v4TgDtys7LvSZ89sz4lI2/JR8/d5kLIDKfT+D3PLu0PtHLn6QeVHTqAW1iPEZcswgGvskLZI3XgnoakLFRg9a+eGLMreAK0w23rf4DaqekLzIpLFo901IyWFcqu2GJ3ZR61eIKvuGQRidJr/Z7uKZuEowJvU1mh3MzhXQY/QdWbbvMEzzOAt8sK5dHuJJ0Qygqlg04lB78HtGBWQ0Pj98zlqKzKXXiyz5mpVKFccU4omakUo4KywyAEKoirAtnRhLIMpb3ZADCD+ePjefLR3myN7lvuXjL0VceglpHDXCv6T9j96qjr+kzULeAu71lMPvtbvv92MJ/NHakbc2WLw2t8gPBzuJzbTjkz0ObYYTCVt/LlVin7nL1SN8SvzmvTusDa5xdesGTIA70/WvvA7Z9CJo8ueOia4Ni9KfdErHzsn5Wn3fdRzQDfv7iX7dAjXvHM7SnUVu2UrBdGlq0t/rKXt7/f59Ltbmjcdvlj5yatkLpA1rX39Ypp3Oz15X5vNghVItIRVBYAP6LKWHqh6hfDgH+mZORVA+RmZUd7Hvt5+3d3TUKLEO5qL2N7n9rAij8FyLClRqXF+SusFrMfShViGTDv8J9Ht7kTiI5LFrfvX/95hDQDb9EFPePjjSdbWh2XLMai7s1jvaCtR2XfD7qwPhBlhbI8Llncz6/tkg92fDMe62bPYqI/6p47KHHJ4nQgBFWacFLU4f+e0YJZDY0/IKIoPxRwy6S0+qMcRwCpQL1MSvu2J+bWkxj2ln3Y66WbQ/zWfdWHFtfJYK5wJJhQTW2leDqq5zBNon7IOzi/lIS+jQSuSNzcu6FdFzy5OH6tcfnAaL+2xhFIl8u1xTRYf8WUn/A7NYLodavkl9tGGfyXbBbptyzQf7PiXJqmpErjmu1y2XNVgV/+c6orNLDWXlEWdqYINE1sihQDhZgbC/Sauvji+GaHb9ilFR/1ue6LbX7rNrav+kA6AhwG7NcDKRl5LpTc0DcX3Dr3Qmgvb25vfwT4v6Zt0xtuvTs4F7iwX6pjXb/U8oFWi7lmiRwdCvzF1jwmN9G26Y64KFslaqw2PN3XVov5SpR3/INAvdVi1qdk5LnSZ888kJXyJil1M0NDKvut0dXvDHZH/W8USQe7vhKVmfs50MzNyg4GwtNnzzwiXdH9KEDVm/aIAYNn6/646N7GJYtRqKatbhkFlBXK5Rw7g4PO57EDbxzha49oMVBWKDfHJYvb6bQDdBCmonZevqUbGq4eN7IxKMezI9m1+UOiBbMaGn8wPAHow6g/xvce5XAGlHLBbtQf7e7Ow6sn5MAOxil3xDeimjvGozJ8x42cAkajxNDfzEw9GkFy2Q7iPg4tETUP9KKBsFE/TapYWDy2eNQZ9hHRQ1a2SN3ude5Kw1T/cp9lOp1eiu0ticggb7F12JnywovfkaePK8G5zp/NPueLrTFjW5t2l3zhWrEnctiYyv4NdX4GnZfNNMy5JtJr8IAYHD6DUze+v6c+KGLj9Wc/s9N1ute6Z5/pa129InzYKnu771lXXB5z/+SQ3R3b9VJe8xnwmRBzBVAbFVre3zKv4ZYZVwSuMOiFA1U33OxPc50/LUPf+yLqln99dYYA7pYZB3qPfOu5Dk8A3laL+dWUjLxl1wlvX8DnTdm2F6BfkF7E+ulH+xoHLd/kWH//bn3zuVaLuTQlI28frWKrxRyLUuSwoTRlO7geGJeblf1/6bNnHlWTkSew+83h0Xi9DXVtMk/wdE4qPGYNh+M5VHlJd7/7g4GZqLKJT7o7tz8qB+1K1NDQ+H3iqWe1orptf0YU5YeKovzzRVG+XzfGcgAPcGT1VxcB/xFF+dFH8NoukWyTTcB9eDQdC32FKPQVNxX6ikuOdmxRlO8vivLPE0X5wQc5ZBSqMz/waM8Fsg72/QEt9BXGQl/l9OOR8coBXpV6lkeHbPtpdPHWvaN3Rm3cekqo8dvw3vrXymayjLOojuqPcaCTMxNXiYazh4l6Y3jThFFrWx7oO8s9PHiNLcZdOab/+c4JN938Q+SEM0qaI01l7TH6nT7zn/qxNu+uytCEtj1vDK9ae0t49B6/ujZHf5Nh8OALUnyfbxT6f1VVhqzcuDXw328/mbHPPSTlNVLKa545dfCP6xxOEl6c2/gaqqHoIWDxUFFsT6Bi76DIqnpU6cWvtqdTMvIqPI8/icpWD0SpagCkA09cJ7wDALz14swgL/FvpLgX6CUlFzW1BE3Izcre/zfvfFTAZgRcVot5htViHoSq/53L76AxpjvEJYu+ccniT3HJIsCzNf4E6r7q6fMM9DSx/W4pK5R7ywpldyS9OtiMkgf7oYen9LtGy8xqaPwBkUlpnx7g4TEoj+5qfvEQ78pYR9pRXYXaQu5SZtaTyR0H1MiktENu/3qMEBKA3ck22TmzpkcFmXvY117ySBiCCoTaOXA95lvAJ57Gsh7F8/6yUMHdwwBzmFYPFHAaw2CaheAqR8TYpTefJ75qD6oqaQosPTXQ2cvbN9FrHSGyShQ6Urky4r+s2DvcRCvSlRgqRsrvav1C3P0qTjlNt7u9/9aJT+leWfroOaWvzE08a1hzzKQbx3vLzE93nDbna+PkiKzTnxvev/GWS88pv3VAuH2pqT1sUYJ/RJJL2m94Z0Gf0qvuPuAC52XUIibN4ZAlRqOYjgpoNwnB7KlnlW/9z1uz2wBys7LjAd/02TM3A1gt5gRUA9JClN2oi1+2b5cANSOmDm+zWsznuQ1eZZUt/Z8z6sRX6LjU1upvqmuIuA5Ve9v53i5A1VuuRtXkPgY0ps+e+Q5dqmc+tngkrSKAPcep7nIocB6q4a6prFAetnO/u3hctu5G1f7e1dPj/9bxZHIXnuh5/NbQglkNDY0OfkA1RKw/HieTSWlL8IjpdxE/VLBdgrIkPRT9UU0976I6rAFItklnoa+4j8N3ZXeFVZ5zbDzQk57Sgi4FskLMNaLqYjdIec1hu96TbVJ+HuZVqm937FuHeRoGVIBQ88gLKwv6hS0e0re04pXh35ju2TYqdMj2Xf39rujzFN96X0SL0Y9FhmSqIhNMw79aKvq7Nrufe3Hajruvekts7R2l37Ne7790Y9wDfU9tdq96x8v38cIGx+pd1cW+DRv+7yyvkG03VK/vvS4gJttodPcCtuxdlbfwtGH/+bjga9c967cGH1CW6C/XB+k++bblir11LlObXW40GkUjSp1gFHDnsHOnbdixaUn5gre+WQSYgdi8hx+5tVfUjpHAX4FdKRl5v2ree1O2LQYWWy3mSOBGnaH9x7EZf3sKoNJijjPonRWoRVNtblb2hah7vS8kVqfPnrnSM0yp1WJ+DRj+ztP/F1rfEHk76jP+BiUF9mP67JnHO1M7CfgTSiVi06EP7RG+QAX2x0xAv6xQuuOSxXOcALUTjd8vWjCroaEBgExKa6UbXcGiKL83MAL4ohsuX0eMTEprFkX5T0CXNFkrURq4v3o/yTZ5uMaNrs7HTjcy2IchHiX3U4DK6FLoK3QAybZfxOc7mMH8YPa+NQr49rzOTyzDWX5Vg2XRRbuidg5sdu9kTEPjuxOusee6R868bLhf8PCn2W4PQD/ETqJ+E+UNUXJkTaHute3T6Fc93OH76PjgnLua3TF/PrXPzq8G6fVVte6o81qreke2ufXejqZPV4iAc3QG0zhR+bGEYZ9u6z1ygGvLZuCJD5/987+Wr5+0+fMXv7wLiLFaikRnqSuPasBfUpJ8A+12+fXld736ndVi7hBr/0oIXWlAWPSwHz5bfwkqsP834NcrakeQw2m8r6EppJ9B7zxcDWG1Z7wqgDkPPXpBoH9Qqp9P87/TZ9+3ITcr+0zgz6hmrBuBtewrbxUGjGpt9a0ErvXMIxFVLtIKx95MYz9KgCLUTsIxxyPZ1GMqCXHJwgQ49s8qlxXKtT11Dg0N0IJZDQ2NIycJ1bG7ll/sH48pMintgFnQ/Um2yRbgjUJfYSj0FVcAG5Nt3evIPs7sQGWbf/FNFzwgdEgOnIV2ogK2fdQoZjD/TN5mAspcYFZFScAqMbhxVrLfC01FvlfpNvW92afv3mgx0fE1E0zf8bDrEbE79hRqxg0jPD7eK7StYVzsmPC20/uVGqxjnaJ/SZlY8nXkx9JoGu4VqhtV1hZU/3GFoaIBnw0gewNP5LzZ662QsNYhO/Z454w8ZW81KuCb8v32zf+eoeSJPvJ0jvsBgw16YTX4iFetFvMAVLd3E7D9/FtefkdSGdRa9/6lIYG2PgH+DSUpGXkOq8UsnE7j99KtP1Xq3PvUV3tKEaKB5emzZ0pP8Pyzo5pB7xhjMtrPQsjFKF3WZcAsVJazznMNO/MB8FW73T/K89/hqObGhzqPe7woK5TbAcvxPm9PEJcsgoB/orLgb57g6Wj8ztGCWQ2N3yGe+lLhMRE4VnyICg52Hu7AQ5FTgAAuAaozU1nUExPrRDAq4I4AjkkwW+grxqDqDC2orPFgoCrZJg+oYTmD+dGoLfR35zBtC4CU17hRi4KfGZ7PaIM/3iB0sG92dg7TmlEB1v5char9fAvYurE4MYIt1bWjzksU7gE7nWXhZ7a17TDLNev+7Pt/CbfgHa7n7Y1XyaC6XeKafi+zdFEM8z8Yaeo11oTTv9kVk7TZFXjT6TcWL+tfWVPnbLE9vT7QhFGexzbTAvqVe0W66pIeC77uy3/v1DX6t+9N7tc7HOUM9+Hnm9cIHSJp8oAhOqvF/Jbn2jgAv5SMvFarxZyB+nxu8ch5IYhp8POqHIgX5wP/A7alZORJq8U8x2Rsq9Dr3ftnRq9EKUb8FU+zltViDgGaUjLynHaHz5PAfL3OvdNqMZ+TEEsYsHhXRWIUkAZYc7OyS9Nnz5QAKRl5Ts84tcD9noYx2fG8RrdwoMoVerxmXENjf7RgVkPjN4ing75JJqUdrPbzT8AwUZT/wLGSvpJJac30TLbKhAo4y6Bng9lkm6wp9BUPorJwx4oOeSc/1Hu5H2Ua8NxBjg/0HB8JHNRBKHo6ViCAXwwEusJrqMaw4DlMa+UCvokf1fehD3X9/zy5enXIyKV6e8lIe+20RS0JsX226c/w/prioaeIAWU/MSxwpWhIdMmWhLOYWxHZ0OeHLY4B/2kP+6Zgj0H6D4t1VzS4jSGGnae1brdvIfQxIOPSFQZMvgFZl+6I+yHvw0XZt90+ti1yzF8w+AQvrZ7/irzp1KQXx0f3u6t9pasVt3zF61TDYqCjJvildmnyXcXQtCUvPr9+1q13dNRPL0Q5SLkBrBazDrBNvf3ltw/wfuehtHfrPMeNAe4APgXeTZ89046n1tRqMU8FzgVWmIy2p6TUjXA4vQaCKOEg93H67JnHcjH4u6asUNqAx0/0PDT+GGjBrIbGbwxRlB+D6rq2Au8c5LAW1Bb0cf8xzingQlQw8lJm6uGbPDJTac8p4EG6ISzeHZJt8oibWURRfhTgPIxiw6fAN8k22eKpc83jEGUXc5i2ZQbz7wBaCn1FANCcbDtQp7rMPYIpr0bVmnbuQn/30oXcJ3sb/BdFyRq7CG4ujrDKJz5/Bb8zQgjvVY332TFyc/NgMa/0euFPEy2GgPItu3vHPfTjFLlrh5fOWVnZ3l5U5eOzpy6+8bSgxrr4oSVede5a/157X96Q5/JdMy/4+pDgsHu+rY3fOaaybsigfsFpZYVyh9Vi/qHhP+1ntGwWFz3tOi12w5yIfCmv2QmQkpFX/PCLz4egsqomfmkGtALfp2TkdUiR/RWItlrMDwBRbreYWFMXs7Kt3a8kIbZ4Oiq7uw6VHb8ZVfP5sxKB1WK+yDN+NkqOzhkdUR7hdouW+qbwH5tbgg/YcJeblX0F4IXaIh+OkvHKTZ89sys12xoaGscRLZjV0Pjt0YQKWg4qTyWT0g4W5B4P+qO22o10sWPZYzF7UiGK8nUo2asGDmEu4QlEWzz/7aYLTUJzmNZc6CsGoNysXqObhhMdzGB+PCrb+OEcpjXOYZqL/dywSmNoLfze//HaUxIu2DI6aMG5nwfc/MnQyS1htqAgZ0sAoqaZZV6TxOK949i9tkl6B+2Svr29Bu3RRzXq9vpt9jkrMrE9f6232FvnHiy2+F3SvNxn2WXDyidFDXBlck6T34Iffzz3rKrbJ/ULGl/0zdzEF9/V6xJnJk9pGDT5zX9Cu7tOfrDNFeyq9PKfcN8NqwdYLV8+npKRtwlg1q131D384vP3jGZdnNVizgGeTcnI295xPT3UoYLKeGC4y63/q5S6OuAup1N/k9C59XqdXIRqoFsJzAFSrBbz6SgpsDNRFrkf7KpILARIiC0O0+nk66FB1QtctRud1wnvwDdl2/5uS8NQ2fY3gd4oKbZAutaAqPEHIC5Z+AKuIzBG0OhhNNMEDY2TBFGUP0EU5Q8+3HEyKa1ZJqU9K5PSeqqTvqexAH/PTKW1qy/IKcCQU4BfTgFhOQX4HOB5kVNAhKe+9rjgqTeej9I0PRY0ora3j8ZmdwhwISrQ+xWjRYwYLWIG3/Ncn4Krbutz7Xt/Dfl6+FZdqLfNWN1gtLnjvv5KbrX3wi58iYxrxHtEgBiZ7tKN1C0xeOntIU1L63s711Xr8PUyOnVGr7XOwXLp3jErMmRge//GthSA0Jzxi9vTzy20R4Rv31TRWOXqG1hZ1/+c+03FVY+9u6DFuvuxyKfDFkS+9sjfVn+aNKb6bFSG82dm3XrHXpNwSJ009DG6vZ+zWsz3WS3mgI7nUzLy8lGNWo8A69va/Ga1270/BBy19ZGVdfWRhSh5tO+B5z3mCnGdrsljQJanOewe4MFdFYk1KRl584ApRm/DB74hvpbrhHfCfpfvn8A/PPWynwJ3ps+e2WOd/hq/bTy2s08Ad57ouWhomVkNjZMCUZTvhZIMKgf+cYRjCPjZ4avHyCnAG7XNuu5QAarn/OGEstfjDNYdbgQmokwNlgP/2e/50cDfgWdQTWfHBZmUVnisxk62ySpUwHQ0fI0KiA/WhJcAZIVnTNw4g/lvz1k2bfNTZVtuqf6u+MPtr6HT+W3DZ1AsrUN6UbonSoaMaKEmaKQI3bZJGi6Mk06HXtgW10q/a/vhG+BV1fhRhf6zxnHOdaMHDmku2p3edM43dWc9Gxnav3L9gBeqYlvKa5zhPt51L0zcs2PwxWveH7NiZFjQtilD/ZwRfuNTt+58H1X/u49ZhafWdUusK9GwW1+SLKQuTAr3XNQORAc/oLK1ZVf8/d/bAHKzskWb3f8BYA/siUBZFv9ktZi3oO6hb1FSW767KhI7Mv8NqB2DDnYLIYqly70Htbj4mfTZMzvMGyKBs4Fjdi9o/CZxoaT/TrpdpT8iWjCroXESIJPS2kVR/j/Zd3u1y3gsaB9BNR7N68m5oZpq7kXpcf4AMOSrL2MusY2V2RcHd84qDkY1P+XRfT3ObUAQKtjYcIDnd6NqKisBcgqIAvwyU9nWzfP8phFF+ZeirvPTMinNPodpDg4ti1ZpjAmcF//MtMeB5BnMPysyjnqviWyrbo0PMMX00Rsie4noiC365tBAsaslkQFVn7PuLbuwldaK0BuiTHpvWV/z2S5pGhfVZqp2BTrLmkvri3b7uZ5fa/PuG3Cnqb3FsOGR/ND2ipat+nAZ1l5uu8P/heduiBodcF7tBVeVNLeazuj/yYZ1KRl5tah7Y3+udTjFOT7ugCdDRK8fanQ730zJyNvV+YCUjLwt/LpZzgCsTJ89U1ot5laUikIJqqzgatQ9dVVru884g77d+9XZD9+bPnvWi/uN+z3w/VUPHfKyD0apRGwDDmgGcbTEJQsD4FVWKI/o+69x/CkrlG4OfD9rnAC0YFZD4yRBJqUdjX1mR93mr2q3RFG+QSalHbZ2VRTlDwLqZFLa/gLta1D+7Gs8x0X30oW+t85U6sgpCJ6WmUpHc1QVRyjVlZnKtxyibjQzlQrg2U4P3Qok5BRwe3fKGY4Xnkz7jcAKmZTWIwHQDOb76HWEu9xEozLYh2WlrLQDBTOYPxQllQQQGNQbOfbayh3Br8bHfGMvDWiM6U278MGgc3Buv6Vw40h3/efBwl7pMvqPjA8MnRy2pGH+rvWOn2ouHTY9us9Da5fV12/9quq5p+8btya6l6vdEWxtPXvscNPWT0zS4TS9/pNv8WsvvbP8rtV1D15524sXJCwurmY6j3Wem9Ving6cW1HtXbxiQ9hw8xfG8Krafxw0U221mAVKYq1pV0WiHpXVXg7kp2Tk1U6dOP3NTxe+57BazF+gGsC2AAUC6R8cWHOj0diebrWY16dk5HXJNGNbPedWFW++QKfXb3S7XO9wbBUxbgAmxCWLe8oKZf1hj9YgLlmEAYayQnlcDCU0Tm60YFZD43eATEqzcYDyBFGU3wuYJYry35RJad8d7PWiKD8IuA9Ve5jd+bnMVJrZN9PaqJe67/3cXq102gqWSWk17BtwHkveRQnaHxPZsR4gAGVc4KQHsnkzmO8PPJV2ht9Pryxs+dvhFic31r83sWGLnLLjffdTq7Ovap7DtJ8lkmYwfwlwg5+ufWpt4vI7vZ+p8q6tPN8kT08krNcePnRew+7EGPz7BQpXdatsa3M4hY+XlyyuPx9fXXS4q8xQ3tJcOigo4Lnd39QG2quM1T4Fb84yut0Pur7o9b8bNnxWkzhm5CWXLl/25sOr3qg0TmytcjQ493Fis1rM/VHWxAmB/q1eeV/afnTYWg4XlJyBau5agNopqAS1kJo6cfpEIO3em+56vX984kQg3yOrtcpTdhCMKpW5CXihi5d9jMHLa4rQ6UbichlRgfM9XXxtd9mOai47We/nk5G7geC4ZHF7WaHsCXtqjd8wWjCrofH7xoH6wT9cNqoRFSgcthnJEzjfd/RTO3IyU1l/Is9/OGRSWo0oyr+bfes+mcH8BCAZeH8O07qT6bMDG4UQO7qSZd+7Wl5kb5BX71kol8xg/gKPygEAc5jmBorfzNlU88NIQ0ToNbVrNo5v6783oCkYnU60Cl90OnT2kkbZ/OIm4XV2tJcusGWE7zX9DD7Ly51t59SF58vQ4M3LfS7XvbzWPyF+6abVN2ZUiz53fidC26403j6u7p1hl04OaG/yDXQ2X0I0m33Mpk37TTEFdW/eW91aM+Xq6UsnFJasL4C/HOptVQHrgeXpYZMuAXahGrM4fYTXwLoG16nRYcbvUGUBIUAFQEpGns1qMd+KMlj4WU82Nyt7DHAZqj47ICSwamqAf0PNror+RaC70zswaJ5Op2tzORxmVO1tl62eu0tZofyWI1S0+APzCeCrBbIaoAWzGhq/O0RR/nWo7djnZFJaFV0IPD1NY0VdGT+nAK/M1GOjCft74iDatInAFOBHurFtPYdpdrqY9X7gQpGUQO8f3t2dvTIpT+8ALDOY//gXldP2Ai2lMbhAxE5Nuto/+o3/lfUt72+79PTGZU02Jrd8UqH3mRwDISapM7rwu7If+lCTwVXdrmt5arVrch+b/qu/gdd5wXrdaVGXh64uk2OTHb1eeePBu71GnXqN3xVDznutrv0x42afZdOci1tQ9bzrgUetFvMGYC5KA7dPm49p6+Lzxqw6Y37R2WN69S4uLFm/pvP7iEsWAcBQYEVZobSnZOQVA9MBeHbpIyi5rTcBRg0KLrM7TDt1tC3e28BH6bNn7tPM5XH2emu/SxWIssL1Bm5uaQu4cEOkftPbo8OG3r66LpzGBlAKCYNRi4lnunL9NY4PZfm/lt4AACAASURBVIVy4Ymeg8bJgybNpaHx+yMC5UrV49/vnAL6A//NKeCMnh77ZKPQV0QW+orkQl/h1YPDfofaHj1Qk1tPMT2UnefVrLj67bDRuj1AXUnT4BBUMHaZ55ix+lEfJpc3v/PaRxPcX9UFBoyXvn6tQRdFOAkJAJ2X0PfyFabBQRgj9FLuaXDLEC+5eq0b/dgYt2n6YGm6bqQ7MNG0bmBETVVFYK+EkIevXGgaNvQTQ7+oy6pemXeGYdHiSYA/qo66GLXNfzkQ1O5tfGj1hMFxQ5du+djLYPwpMTz63jnTbjZZLeZ4UAoHOiHOBGaiAtr9eQz4B3eeLgH21kWh17lH6A2uGfsHsvtjtZgjrRbzdXHRJWuB29Nnz9wFzHG79DPyIgcUuoW4fGuQcRHKOex6lFPbU5osl4bGyYuWmdXQOAkQRfkXAsiktM96YLjnAF1XtqMPR06BCogzU392EmtFbd/+EZpUzgSuQ9nsbuyJAecwzekZ71jyGCg93jxhr9F780zwxeWx3s8MXj88ZHnIDHZNf4qQT69//dFzfgzsfa/P+N2b9CLMILBv1PsZRgspkbjR6Yzgp0dubaNv8E4pXog3bp4bXec9NmKRY0djlGNtff+910/0996z+Utrvym9DGG+5xqc9o+uLHp91CdeW3XvbGy1njs0bBXqOoYCr6Pk57am/skyf8uKJ+b5NbVejlIKcAOnAn5WizkDuC875Qqvez5/52kOdO3vPH2f2lIf75bFBr39Q4PB8WMXrk8icKnD6VUOjMzNyv46ffbMtcD2HU/+qyTc1To1nNaQZoJHAyOA/+FR0dDQ0Dg50YJZDY2Tg8moAOSog1mP2H9P2djeC5hyCpidmYr0qAockQ5uVyn0FSHAFcAXyTa5s9PjBpSW6OZkm6zuxngTUFnB15Jt0t6NqXyJaszZXxLqpOaxz2QVgBBzA4HHQkeTOOqGuuiq/75zea/HDKnA2PPqVn1dmxIU6nOWXgSFtyTapDQIqYvylbUuV4NdOIxeQholAjuif5CI7iXYvqDS7RUUq/f1bTuvsd7hdNXbWxuXNfa++/3TN0RdlPiDo6TxT6Kl+ZahoXbjKReHr3/NWv4ekAX0Q9nNfgcMAKZZLeYVd2fkzbMuNb+D0hDejqpn9QNOB1pDff0bygrlPqUvuVnZSssYajxmBgCEBNX0R5UNdCXjvQS4t7o2xgX8CdiVm5XtAMJGhVQu9vZrqWpt8x/abOM1lAFIO0o94qgXhxoaGscGLZjV0Dg5mA3Hz92qG9Rz/P9OxADnoAKczjJffYFMlJLB290YbxgqQHoHVfvYJZJtsolj2PTT01y6WOcPTAIWfzjBXY2Savs0ZIjobwoSI3pNEeXAs7Xt4X6VbQn34E1vvHm8f9DKwHp72OnbWgY591YHRbswCpO3mwj9Vqrojb+hnjqkvvw73H7TTEFBG1e52sefLfQxvi319y9/Vxdg+qz9p5qLbdbSCaYAdGsvHhCGXtgeaLWdZWrSX+7wcjndJm5KychzWS3mz1GNX7UAKRl5bn5Re6i1WsynoSx+c1Iy8vbRKrZazAODAkLHNzSFng/ihdys7BCgLiG2+EfgWpRBxAHNOqwW8wRUoPxdSkaeA6UZS25W9l2o5scHgL41dTErTcbWd+wOr7+j7pk7UDW16wGtzEDjyBn1sBfK6nsLq2ZpC6MeRgtmNTROAmRSWrf93kVRfhxwCvBdT5QUHIjMVH4Wmc8p4DZUDeSTmal02WVMFOWHoIKsbw/SFLU/G1F1pfsrK2xDGTd0V483H3g32fa71+8cAKShSkEKzdLkhQrg3p/DtH91HBRfiwvVPPUx8FyEd5UuwrvKf1vLEElIQJlO6AxCb5c2Q5Twki5pl17CGdBLd/Yzpbr63Tvk7h2Rttbv92wRTrct+ssLQ5pe23IaOnGr97hI215zUZbl88i/jI6qiLxsovFPYz6JKGsNdi4K+eKfrvhKdIOu/UvgmXuK/Htt2RN8c+aKWyYlvHdeuO+e14H/eexm1wNPc+BFxFW+Pk1DG5tDlkspdnve6x5UM107YE3JyNtxkGtzBUpFIh94quPB9Nkz66wW87ktrb4jGhojFjhdJpfd4WNAZWRNKEvcNUBdblb286imvX+mz57ZJa1aDY1OnIFaHD3OMTLf+COjBbMaGscAUZQ/EiUF9B+ZlHZYuasjZDJwIcr1qNtGBUeAF+CdFfy2z11FbRnAjzIpbVEXXpeIypztAQ7bgZxskxJl67v/4y6Uw1m38JQWdKe84LfKWtS2/lbPvyOB81DapSUdB5XGYIuv5O+AqzQG9wxl7nCOt655YZvBf7OAEU5MujanSw4NWgoItjcNkE4/H4y9fURteR8/3/MCWmqu/e7v6MXtxoFBubb3tlV4T4qdFbv68m/3XPhF/80XTBj38sTB/311/vcm7z2GEqvFrL8s8cK7lvQ77c/6UhEQ2NAUZHd59zbq2gcBlwLvA80eQ4PvAawWswG1CCpOycjbDswxGhyBf374/s0AuVnZWYA9JSPPbbWYZ8GvF1hWi1mP0oZtQSkf/HwdcrOy+wFXRYZ5b2ltC4h1uQ3noIKMWz3X8mKUq9hy4AJUOURfQJ+blf1c+uyZx+p7rfH7ZC2q/vpozHE0DoIWzGpoHBsCgSjUFuWxogBYgdLbPB7kADTp2kJRckW7ga4EsytQ27hbD3egxpHz4QS3C/bR392JkmX7lXd8aYzajo+vxHtgwMDGeN+tS9vcvjeilDAcgJcBOxVN8bSIIJrdoaK+MRpAGoYIZKN9kGPl3nj3ZQkX6SK9A0WUzw/ek2KfxCUviPrygucjZN3F3qWlZ7BqVg4AFnPs2PJlqVK6A/3aWl71b26ZU9USt6exPSwsyLtel5KR13yAtxSByr5+B+SmZORV0qkRK332zM4mC1OAc6wWc3ZKRl6j1WL221UxYKqPV0x8eGjl1UJQBqR6JLo6jz+4am/8RyiVBX+UlNgyVFa2EfgQFchOQ5VG7AQuAeJzs7JvTp8984BlDRoav2LVrBrU/aRxDBBSdnm3UENDAxBF+f6AUyalHdKtRxTlG2VSmkMU5euAQcAOj+HASYcoytcDFwHbZFLa2i4c7we0yaS0bgmW5xSQAAy6d8uFwXbZlgDkyHu+1urHThDxlVwGXI3K6IJSG7gZ3P5x3lt0lW3xepf0RUqJcLsAAQY9YZQ7jfMW29d/EmL0+fNQe+uXZctCHxxT1fLJruHuve3umf0+Wb3lA1to2/C478T5/ZtsJt8XM5ZbLkVtsz6bkpFX8PrrM68Kt9VM1CHnAWtSMvKarBZzLGqh9P2X086UYXvqpgbVNC297ZyHDqkmYLWYU4HkhqaQJ9vt3pPDQvZMqqqJa3c4jfXxMVs/XLU5pPL+Z089E/hBymtKrBazt9st/Mt2D7CjMtcB6bNn7s3NyjagFm01wKso5YkhqEaxMSipsAGe077VuQlNQ0PjxKFlZjU0uoEoyjei7F7LUd7wB0UmpXVkbU4BHkZtcx7VylwU5Y8GRgFvyaS0njQuCEBpkK5GbYcdEpmU1nKE55kCJPfyTly7vXVtBFqX+HFj6sTpI0ynR48Lfeqc0UInnp3DtM2oLXQDqrmpPcRYtcXm8PlfO/6xVe29hrgwDEZKIaRTghQ02SVBXsJgcupPiXF5n+Mud80bNF5vOmXIWLeUr5iGhJzpbrL7vdx++dzdn/0wbmpgffrA+m31r4++6XWWW75COXOtia9keMQZ/2e+cu28xlF7Vv8d9d14D0hFBbz31UcE7ZSCW32a2xKtFvPTnprag/ER8HFDU/hEgfvKtja/neEhu5+trO69/YJb81ovFHP7ompmm1GlBvfpdPKShNji63ZVJI4Fzs/Nyi4B3gDuB1zps2e25GZlj0OVKTyPqs1dlz575pIe/3A0NDSOCi2Y1dDoHi7UNmSXpaFQW5f/87zuaDkVpdv5MfScC5dMSqsXRflZQLcb0brJ+8CSQEPoRkAv7/la26Y9flzoLK47zd3QXqsPUdUvpTGUAqXxlUSAfM9H3xJj1DnsVe0BC+0ywOVTZwvsU7WlV91QX2EXfkQG7hJV2/3cxS+1ui82+VcNjLSb5m+rkXJ0r0bhbThflxi42bWndWntV/UbWtsNo0uKfR/zqxMbRlwx3Zay8D0XkAcwYF1xYklYYtH8oVe8M2rP6t78soBaBFxVU2/0KfvCve2s9o1loXvqzwBWWC3mIo8SQUe96wxgTvrsmds8ga7MzcpeJtHdr9O5So1GuzN99sxWz7g7UFnVjpKLbfxiNbwO1ZxjBkifPXNWp2tmQHWgp6Bk4cpzs7LrUb67e4HHOp1DQ0PjBKEFsxoa3cCj4Zrfzde0ojJHPcEbwEcyKa2mJwbLKVByYJmpSJmUdsxrbzNTqQfqM5kFPaCFW+grpqKE7Z9JtslDln1okOuua5/7ash1+zQLxlcSDDwIok+zM2CFQbh3AM1Imdbmbwp2R7hFlN9uhJDUtobIpqAYoatfZXhpxoQQnxhTu73e4d38RkmT6axoH0OM/2nS5Z7oPbnXjrazytaWb1izJvbFhemogPANAKvFPPpeuHNdSfDLdz89tuo9eeF2IDRFTcfgchFXvCNw1hf3OfWZL9bej6pXvQO1gOwIev1QVrR+uVnZl6G0bJ/zBJYrrBbzk4DJajH/HzD4s/9yphC8BQRaLV96pWTk5Vst5nkpGXntALlZ2ZnA18C3+12zK1CGD8NR5QWxQDowFVXLq7loamicBGhfRA2N3xAyKa1NJqX9qqHnKLgRyM4p4KgtW0VRvkEU5R9vrdwIoBfawvywfLrwvdpPF753INWLAM//8hud4R/WOiILgHG43YsNFeV7dtWFu7c1DpQ7GvvJanus0Bn0IuZyL9leuM2Evs3fHenvtG9qjGydvzPEVWXzli7pMET5tEXMmxTgW3RTHEpqbVun8zUAO156Z+BNqFrdy4EnZjC/F7De7RbftbYbNgIlKRl5VShN4Rw6mVd4HLsyPP8fjdKY1Xc6RyHwBXBbu930Ynu7z98bm4OHA/Ok5LPcrMf+tKsiMa7TeG0JscUfJcQWx1ktZlNuVrbIzco+FVWaIFE7IQbgv6iyodeA9PTZM4+03EZDQ6MH0X4ANDSOElGU74vKDq72ZGE7HtcB44CdMimt4kTN7zA4UA0wR4WnIexxYCXqh/548TrwZrJN/qpcodBXGAGfZJtsPI7zOSHMYH4mSjnjiTlM61ZTUmkMpRNK2jPrTPrrW4yG6zwP90evf7nfZx+YqxKHlAin29+rfBd+Fw3HbtPREhclEvtU60Kit4qylTZfm9tpFwP6tDje3+SMujzI0SL7bXLWuHcTHrR7zbvvfQSc0muH617Z4nz+FTX+aw6XbjRgA3aZ2uyh53y4uD9QdfFf5pgvBv71Klgt5kHAucAbHVnUDtJnz+yQW3sJ0O+nLPA1YAS8nS796obG8I1Ol/fOQP/6hS6Xvi+IjvrZzgobpwF/Q9XC70TVzhb4+9WudbsNpbbWwJ0oY5MlqGyt9vupoXGSoH0ZNTS6gSjKnwIEA/NlUlpH0HAqSuT/cVGUH4hqcnkH5WR1J/AN6gf3pCMzlbd6aCgnSke2K6YIPUayTR7KuvcGYEKhr7gn2SbrjuO0fnMs/nT1ZW06Meq884flbg/wvtzf0FBhFG1rRl4yeMraHXZnpdsPgnwpb+uHIdSAt5ed6kX1snZju9B98AU+gaOMuqlnhxpbAtqD2nfQfFfB4PpFTj/fJ89e5zM59ml3uzPRWdx4lnvexn624brBBp072PKPHz9Kych7EMD6mvlvKHe3q1FSbh0MBiagbJ6b9p+31WKOTYjFnZKRt7/m6wzUAvN/fj6tg/18Sp9LycgrBx7yWOLGo+7XjnF6oex0/1tZlRAMzAL8dDrnAl/vlgebbQFtqOC4GqW3XAGE52ZlO9Nnz6w9+k9AQ0PjaNCCWQ2NLiCK8uNRDSAjgDCUxmtHJmgl8CSqnu9vqG3P91E/eP9Cyft05Rx6wNDDKgUHxFNWcAuwJjP1V3WC3cYz58ePdpweZhPK6eqklEP7FVPPD0GZS3zBp1+UHO7wzsxhWs5Rnn2Pt1tu+/7ztd/c9NfydQ636dUN9SP+817MlKCAiJb66KBtbabq9qi2phpR59tPNhuDhZgQqAsX26nYmSJEQGS7zoipaY3LuCffJoyV7oHGaYlhXmdGZQLB5+z8tipg6669i9c5t2wKDL7I7aR+7NDa7wDiKxl99vDrfKZveLfQ22Xf2DEhq8XcH2VU8MSji/L2PLoI3cLX1cLFajHHocw4rgTac7OyvwGuQzWQPZcQyw74uXQmFBhktZj7ud3iy4RYucXz2G7PWPHA423tPkuq9sa9AVwPJAFr3W59UX1jaLLd4T0aZAzIYSDOBfEqSuVgK/DIUV57DQ2No0QLZjU0usZYVCPKEyi92J+3NGVSWjNQBCCK8p9GBaQdz6/Yf6BDYAZGiKL8e46DHq03MAwV6H17jM91Qki2yUV0zdThpGDlmhVTgwKCruud0Ge7vpNT1XHhztOtgBVAz/zyPe2ReXX28Olunf7UBlPg8jChf8dmipzrigjRYRCAwMvfTYC3ndirh9FYKrz2rqh3CZ0OEeHvdEf5tXmNiPy+d/OuPmfOff/83bVeZZcOWNk68h/R8u01wypt2+S8BzP+VOg5++Dv+k2yf9dv0iOlMXRWBrgYuLmqpVcZaofjK1T9LCh1gcmoWlYbyqQkGuXQpUvJyLNaLeZy4CbUrkllW7vPY7UNESlBAbWv+/k0X4GS2PsRaHQ69ctr6qLPBXxRuygjgSoQpXaH35vAWJBeIDpqwm8A5gGf9uwHoaGhcSRowayGRtf4HJV53dqpvOBXHIX+Kijt2gCOg+5qZioNOQX8Hz1QL/tHo9BXCNTipjrZJnf01Lg1e2vC6+rrSqr3VheN66lBu0F8JbGAT2nMtK34Mje+gfUo5Y5xpa2JUW6Ty+3CG3BgEi20uUxyS/NgEe2SQiboEXV2g762DVnfvszrqn4f+EzpdWpY8Y+rDba26aVbg7YxgE/tYT4rB9zv38y+6h7vAtaXC8x1VtXEdSZqsTUPqHzlk+Fr3DH26BHRP/laLXPOQTVgfYSyNj4LuDM+pvjF0srEyQDps2d2fH9MKFevbSkZedtef+wfjzqdputstoD1fj7NNcB2q8XsA1yv17uG63Wut91uw2ZUOUMrKmAdjApsg1Qg+7PLUCOq8fBXpQ/dIS5Z6AFRVig1rWUNjaNAcwDT0OgBRFF+GNAgk9K6/KMkivKDUNmkLZ0DZI8iwJ1Aq0xKOylrbX9reK71VcBXMintqGx1C31FMPAfYH2yTWb3xPw843oD3sk2Wd9TY3aH+EoeRdV531YaQ3t8Jc+iMpKBILeZsBnt+CSDTg8uB+iMbjfoVJBH+9pqd/vn2522r+oWRMw7118f5t0H+I49jc3Bf30msnq9b79Th8pb/3Lt5rFAQUpGXoXVYtZlTXpEP/vrf5yJch97E7gXlSG97YJbp2wAno2OtNXkzl7c3yhlDRAHLEBl3W9CSWYVpmTkPb3/e7JazANRpRsve2pmOx4XqNKgVlQ97iDgjV0ViW7P+AXAUJSSgQ4VvPoDds/c3MAalM7s/CO95nHJYiYQBDxQViiPWqpOQ+OPipaZ1dDYD08weTvglklp/+3C8b2Ax1Bbju9241RXAuegtkH3VzsIRjWcdD6PAQiTSWl70OgukagsXjn7drAfCQ2oQKhHm908OrknMlP+FhBUGqPMOITT+b8E/YalfaN2pQJPfFt10VXgHA/2QNxGI4AQQrillEhwLK6SorRJhOef2a4P804Gdroa7C/XnPb+lQkprUmG8SZjMK4BqM9hZe7ch+2rBl78plPovQr7Jb+ZvK1wJEpF4FFUVnVjwXNfn3bH46cvabxNF3Zf7xEJF9VWPH9OY/VklAteNep7cgewwxOgTvY8vjYlI8/teb43EJCblR2MCk5nwoCvE2JLVgD2lIy8xVaLeQVwnsnYOtju8L4BRCkqA9yEqolvQkmIGTxjgGrs/Pgor/luz9haVklD4yjQglkNjQMTSte3+xtQ7l6bunmOL1HNYfvoxsqkNCmK8mfz6x+484FrRFH+QzIprSSngD7ABcC8zFSOSbe+Z0tdl2yTrmMx/nGkBPg7cNRmE8k2KVFZuZOOnAKiAd/M1H10XbtEaQwbOv97+DVXJxpPCb3I+NykGp2f0QYs1iFHeom2kFaMYwEEEqETwlVdh2G0l87ZFi5ctY6LkS16EeLl3J30ia90em0N/fuolXH9jLx7qntoVa3Pc1vey4zwdbScGtjWOMCpM1THNpX3Al4Avk7JyNtstZhPAW40Gd3n5s768ZOcmMQqaSeoX1vzEFQjZikq49qXjWV79S8v6Oe67YJyBsRcjwpgd1st5n+gvpdrd1UkxgPPoixzI0BM2FWR+GQn96544MYg/5paKXS+Lbagpa1t/iM852lF6c26UVlaHeq7OQV4kaOoby4rlK8d6Ws1NDR+QQtmNf5wiKJ8cZi6VymK8h/t6nieBrAXujsPmZS2Hdh+kOcOtOW4GVjIL1a6A4CJqAauYyU9dS0wrtBX3J9sk83H6BzHHM/nvb9802+S3Kxsb5Tj1eb02TP3X2TcCsTlFHBbZupR2x2XOTbXriif/sV/V97x5DB9WHODobf/960yyIzLIY27K4SjVy+J0Au32429uFy0/+QWLi+T0bWzRZiSokLHTbE9cflpxWvfibthT9OG9v6GCfWXb7x+vOHsnUXDSsIG7KoIirv2wi2f7B1avSEH0DcF+X3wf7Wv3ZfkY7rap9XegGrSWpxZWdwEFLvc7qmtLqfDx2jyR30fgsT6XddKP6/LcLkFSl/2QlRZgDslI09aLebBMZE7bqqtj3S12303o0oI/gSMz83KXujRp90GPOzj0+blculxGNsXtrb556Nk9+5FZYo7vpMCFdBGoMoONDQ0TjBaMKtx0uORrAqUSWlHHbCJovxQYJYoyv9MJqV9cbDjDhJMnlBkUloJ+2aBvkH5yh+07EAU5fcDmo/CNawBlc38OWjyWOBeDNRlpioVh+5Q6Ct0gP5ARgcaXeIslKXqI8Dq/Z57D6VzfNRB1qcL3ysCimIWNcU1/2vNW/ooH2PwQ2MMXrrWCL2ryRX47Te6PampOhkYjCEiFP/pZ0p9WKWQbW6kG6nzMpjqZp4XtuBlxtZ/WxMsh8YQ8swpS3u3lQ67ZEmBl0QsC25vWIrSdC0DhNOo92v3NvWuDw8q9ymtfhX43BOQ+gFDvy7ZcPaaPWUBk/oPvmN0bO+lwLMyaUg0el0JceEpKLWDfwHfpGTkNVotZgNwp9HgGBEVXv4xsGtXRWI5KrP6ZyAsNyvbDolL0mfP3ACQm5VdJqV8FORSIZgOIhqVnXWjgtqOzKwbuHnYRaFv1bfXJQHvlhX+/g06NDRORrRgVuO3QCpwsSjK/4dMSvuVZqsoyg9ByVp9JpPSNvzq1fsiUfqwv/katcxUXBwi2+hx5foHqkb0oJnmnAL8UNusmzNT970u6+fKTzNT+WS/lxhQ3vS7ofvBLCorNrrQV9ybbJOHlSAr9BXXAkOARz11pX9YcgoIYOSdF+vrNm702rXgV7W/mams7elz2tfVVUine74u1CsWkP38N55r8rEZt44avV36+cUbaYnxNzaKFuEvjF717pq5u93Gywbu9pkU69VaUacr2SQiXL4tLZwd7tD5GPqVevfe+uHgy77cGdynbEjV+oDpG967ElV/vCSkpvGGsz/68Tm/hpazAb9O00gALjIZDMtiA4Im9g4O65uSkfet1WJ+gKjgVnnZeCdqd2S9p1YWRj2sTyFh/OrJNW9VJtpaUNbNKxJii7/aVZH4HRAFtKD+djhys7K3ogLc7ba29ui99fXTEmLCI0EnQNeIuu/DUEFsK6pZ7tZov9hd9e11k4DvURlhjd8Cox4WQCCrZjWc6KloHD1aMKvxW6AEWIXKEh6IYJRm6nrgkMGsJ7v79x6d3SHwBJRTgUUHCcQnAWOA52VSWk8HajaU3evhGsYuAi4DHqBTc1ROASHAwzkFFGam/tLokpmKI6eABzny7F81KhPnAij0FRNQtr8vJdtk6wGOF/xG/1Z5VBRmAl/LpLTCwx3flSEx+DhdEWN+Sp8x5riUfexNT3CTnnBPfCV9gUGVrfH99MIV3Dw8eh3gMmD7f/bOMzCqKv3Dz7lTMplUUiCBhBqkSAcbMIJoMIJuLNjWXWd3ozG6lui6iLKGsupm0V1j+WuMsjrWdcUSFQwEYxmwoCJKkV6SkEIKqZNkMjPn/+EMiKEFCCV4ny+QuXfuPbm5M/c973nf369bjLVYq92hSeOKAs3QvbtWP/DsYOn27jCZZJczZkf76uOjNm83B70OTJJCu3BZr/N7A01FYT2f9AezFpQMlje0pvEvQD/gm0c++SC+ouHq+6KDQ9cC7gl9B5YCbwDj87JT3ktKm7/visPqvOyU3+dlp4wECkKmmf4w5NOIgVGFFmdpf9dgvHJr9K7m0RUxljk9u296Lilt/jOLnk25xusxltc1dlnW6ArvDXR5Z2nYp6VV8rsRZ9S1DOprMjU2hceiFAy6oe7ZWpSyQSuQX9tS8zJKo7f0eP8tdDqU84A0Rsz5J6tmrT3Zg9E5NjrlA0Ln14W02X/kEA030mbfJpyOdKBOOB0aqkFkh7TZd7bdVzgdA1G1ph9Jm/1ENDX1RJktNHFgJ7B4/3gC6OBOdn+d6Cft2PULDjw+Hyog3q/2Mj15b93uEZFvFXejSgz+uc/LCSgZJKt/HL8g0SVfy7cK4W+86mwYUNrBlo44WHoydVm53NM2g368iS/FAvwNqIsvt67406ayuGcGNpVtCgsMHlPlagAAIABJREFU0TTv2SbhoTpsoGaYYsLbvYc0EBDq2Vrfp+LT8vqmS0avNZrDH0HJX5VruJsNwhvgkaZvJcZ1wMQWs/GPr1987ea1ASMKHlvw1xdQGdONxbXVf9lYVT7eYjL/FBJgeR6ldhAK1Pj3aculqCz+F/URrWLjObVFQrIAiAxyeVKjy5subQk0tNSFm1sAvF7TH1vcluFBgQ3/d8OMzNU5GZm3LP4y/AoIb/pxY++/OT64vjAnI3MWymb3A+Bqfp5c7QTeuHnY7UGpc2e0VSPROfUpQ5VpnVALbp3jg64zq3NaIZyO7sC/gM+kzZ69z+t76txuRwne3yNt9uP+JeaX+UoACg9kU+uvBzZLm/1AGcnTjnyr+CtKHeGf+7xmAKyJLnlMAvSnKsLp0E7FGuwjIb4UAVwATJy8s9qW+e32plvG9o/8Jjq4V5ixIjLasNO4vSEOrylISozC50JKhNdb1Fhs6h/2g9DEp6gViOVDTd8HJXzjuq9CRJUtGz7gz9f+8NrV8b5tSV9dcFZwsbt3gTNswO9v4u1uQE1e4rR4lKzal8X5h35Y5WWnXIZSHfgEeA8VdIYkpc2vzctOiTC1eGd2L2osLewT3E8atCXAovLKHr/3eE03asJ3zx8zZn0LIMQbJiBCyuvLczIyBT9LgG1HZWc11CRvLWry9Wrq3BmvdegF19HROSL0zKxOhyGcjgTUcvE7RxucCafDgnporpI2+9Es25WiGkD2ZmX9AeNDqJn4S8AHJyKQhb3Z0U2H2O7lANnI0418q+iByqQ91nabX/bruAWyfnmxSKDqZGR3O3sgC1AUi4wvZRcwpCA2/OVgj2/RhSW7zwtr2jmnYmB5xM7W3tJgtgohfcKHVArJBqM0DAiPRxmD9AU2AlPKK/qY7l74vm97VH3lsuEDNrw57LfB4b6qJWMsnyckWNZ9eRNrYoDMqh98q3cufb2Suzwfc5UvNC87BQpDg7V5Y7t/+/Cu+NZQX/wI1uUFCHdJUtr8epS6QF+UMkFvYGdS2vw9pUn1rQGG73ckhLagFB+06trI6xDeAeGhu7dZLa4LgW/zslNGf/Qs3QpLEsw5GZkDgCygGhUYr0Xdw2fwc7bd7T+nzqnKiDkmlMLFWlbNOrE20TonDD2Y1elIxqB0T5ehlgOPhl7An1BWlkfsrOMPHr9v+zJqKalG2uy1HLz2Vuc4kG8VQcBcYEuiSz5yEoYwGrgb+Dfw3Uk4/+nCJuBxj6atDbx9TPWN//fteX2tvvjHpKfB3cdsllJaWzEJ0ECTILwCJL4Gr89T1uQ0J4Q1AqZd4eFnTb/tis96NGzpddHmJRcvTZj8cb0xbJ1ByAiQm1AlBJ+tz/aFgpzMADkJNRFtJKRlhM/sNTb1CpbekCZhLPScC3wMvICaqP4GuBNIAvLyslNeSkqbX4MKPieh/v6PA8taWqwZHo8pttngWRoU6PrC/zsmAX00zbvS5zOOQd0zV6K+l24E/qLOIf8ADAGxlv0VJXROLaJQ5SHRHIMmsM6pjR7M6nQk76HqL4uO4RibgDnADuF0jAa2H2sW1Z8Z2y8jCCCcDjPK6We9tNl1qajjgwt4l6NskPHbxwYluuR+NdDtpAwlnq836BwDfmewveoVIR7fv5u2bHmvqO+ofr4tzZe3RgROIcwSaKAZr8Eq8GHwuX00fV1hxu2b5NlQt8TYK7jBPKTLP6rDrH8eXVESMa5o+filCZNDfT5D0LTsRVuBsKS0+UXAiyL7DTOId2LOFROHxAcML6uL+jamsXZdU4yoq79Quye82h1mKPJ9itJZJiltvi8vO2UWqmZ+NMphb3xedsoD2u1JyOhGrxxRvp7kjTcBza0ey/XAmEZXl7U3zPhnTb5VWHnkd89gMVt9PmMpygQlGWVqsg5V034myHKTsckCNLR6rHmwn9qHzqlFGZABR1fnr9M50INZnQ7D341feIzH8AFrhNPRGyVWvhBwHPPgDs7ZqGxLJvD1cTzPr458qwgA3P6l/WN54N8KJORbxZ2JLnmgpp9DkuiSxajl4uOO33J4PGpydFqYNByMqW/NiwJ2Nf35+tERH354ceuECYHSK6TXECSQSJAIoyY0gzD63N7Yxje3XW0eFdlqHtKlp08z5K/reqan2RT4GlD50NIHBgKzgSXAmrzslN4fPctZwIcLRk7ZDFz27YRhtZcWFRRcsfXa5t0f3R8ZsavmVpQL1zf4M25JafN/AH7Iy06JASagSgBqgEhREST5uHeJTN74MLAzde6MFmB5XnbKkMVXjBokAyzp7me/eN9898QPenbfZE5Km1+Qk5HZDfgn8CRK+ssC3G0yej7TDN6NrR7r7NS5M/TGk1OZVbMkHLkjnk7nQg9mdU4J/EHALcBWabN/hMruPsnxXxZaC/yHI7ei1TkE+VYRBTyCkix65xgPtxilZnFYTdpTgJ7AbSiXqU7TFCScjn4o3dTle9zxsnJJAGoPpFwxdfw0I3CPuU+v3uPWrtz+9ZgxDVpzU0Bc12LDbndX6jwRAiGQUkotJtDn3lAbGJx+ptfUMxhgBJBQFN4rvCi81+6iWH7Kc1UkAjsrLRGvx5difF7V3iejygJWaF7vX6e89smfUWUAD/7pkn+8kZed8g2qPv6PedkpXyWlzd+7spKUNr8MeDMvO0UDpvqezqtmY8StnFEdBvwRWJSXnVIDXCIlt3rGjShtKRa9XGeOuDLSp02WsCUnI9MSYK6fZDDIHq6m0K9RDWDbQHzgag79FFU6cyn8LFuno6NzctCDWZ2TjnA6pqLqmc7c85q/MWrZ8T63X3e2wx9GfhWDOwGXtNmf7+jjt5esXC5A1TI/nZ58QhvNWlBZ+mNe2kt0yZXHPpwTxnbgH/5/OxNXobSaVwO1WbmEoKS4NsSXkolS5CgpilVyWAuXLfBMHT8tK2Li2Plai+vcxEZv+sf9rEMMwmP3SGMMqgUMoQkhTAbNEGLSNES4MGpVvsbWtd4S15gIs+hRfdeX/xArqu0jNmV+D0RWBkXfjrpnHkdlXLdNy14kUCsozajJJwBJafM352Wn/A0IWpA25ewFvH0d8MgLXFWal51iRjWBDUbJavXljOos4EtgqP84O71ecZO7NaBLjaurRV7auzk0pGp0g0trkdJQClxrMrXWWy2NW11NIVNBhPmPF4X6THXnALJ1Ojo6Jx49mNU5KQinoycwEpW5G4WS35nOAbRWhdMRAHAgaatTnEiUfuxhycrlQuByYHZ68uF1D4XTcTbKUvYJabNXHmLXHvysY3vCglm/zNZBXcdOV/xlMm0bEE8ZhNMxAFUjvrCNzrIDiPA3SAI0+F8rRWWb5wCLgFf3vGHhsgWrlzww6yojF97x716hj4UZG5sbPGE7mnxhXaX0FguhxQGY+gQT6ir2ya2bPO4hI0vr39m+rvfbW3o8eHW3gC8TAkIeWwGVQdGRQA+N1vJAQ+MlD/92+mfO0AHLAfKyUyxAopQUr2Dk2q+ffTpy1q23V+Vlp3RFBZSuSQuWXb9uTP/Ist7dyMtOEahA/AKUVnIWKtO7C4hAmaZUF5b0N5tNzV6vz7DO6zV9bDY1jRNC9nc1hWjuVutgYEejK+Q9V1NoIoivUZniENTn6Xr/NfokLlEMAWqL8+Wx9Aro6OgcA3owq3OyGIsK3tagmrOM0mbfz9HIn+Gci8qAZHTUyYXTYZY2+zH71x8MabNL4XTMPoK3DAUuRAUL7Wl4C0VpXu4NloXTkYjKHD27z+/2OvB2e7OyWbl0B4YBn6Qn61mn05CLUIHdF8DeSZC02cvZxynOb8rwCcC/SgkA3kK58P2CyY/MKfpHQUl+idWc1OgVhb0C139T5Y4ZjGY2oFREBALRMrSvoduonXLz39fENhe5p5TcPLjyo66sXfObwXVxjwUYUNnOnDjrjq4NnpAbAhoaLiKU5QBJafOb8rJT3m8h4A8GPGd5Io3b3nrjzp9ClHLKeOCHiMq6svF5372ZlDa/NI9FoahSBh/gQRlXxKNqaIcAc5LS5lcCzTkZmc+jAtR3//DgnDefn/XwY1IarkHV2VZIafpYSr7wHy8AlXEegrLafXvul/ebUcHxNlTAr6OjcxLQg1mdo8JvQnAbUCFt9jeP4hAfoDJYW/fU6B2CdSjryA7B31z2N+F0OKTN7jzM7kfN4fRFs3KxoDJL21BZryD8kmZZuUSj5KRy05MP2Jj2McoYYt/r0gdlCWrGbzWbnrzXR769jAemoWxtD6qPq/NL8q3Cggp4Vh9Nk9oJ5BVg0WGy+b/Ar2LwLuw1T9CKYtmb1b1/Uvf8Z0oZ3du644wwc+0rvYO2NG9tHFSJMhqwAEL6jLKspY+BvkSZd++yhnqbfKaFyz8oHvOnIOAe4CzgvNrWLjdfnvXgk/0Wfnp2/oaf4hNde7Odm824fwyIatEaRlr/VLIreuyAHxu/AnajVneeRWVKSUqbX5eXnXIfSht2KPAV6rO1FvikuKzP6JyMzL6pc2e8g/rM/BbYkZedssoSEBPT1BzyaurcGWUAedkp8cCwwpK+A8HQz//7JAIPopqKXMBT/nHo6OicJPRgVudo0VAPgnYto7dF2uyNwukoAu4WTscKabMfsD7WH+h2tJpBMyoLdbL1Zi8DrgAeTE9mI/DwPttMqGxVl6xc+gDb97Uw9V+XtgH+f4BXpc1+LI1SH6EmD1v2fTErl96AJz35gJa8OiqbfT+q1vPTkzuUgyNt9jqg7hgOYQeGxZcyc9jV0zyoCe2momULFvUsTYisbe0iWryWb1GfsQH+s0rjoo+EZ+QIab0k1qed02Xd6OVfh5zbrfqHD5SRQhUqc6rVtkYalp37O+ty240DryxceJE3O+UNIBgI1YT858C6LTeUboteb/T4nkNNAP8H/JiUNr/u9cwZw9594s9/tZibi4Ugw98Qtg4gLzslH5jV6jHl+nzGe1FGCO+gJn9nAA81t1iWBlvrbvF4zAv/M3fuB8HW2uWhwSQAF2qap9rnM2zlZ3OPq4G/+13JdO1iHZ2TjB7M6hwV0mb3CKfjftRS3tESiHqY7OIENHvtwS+ZNPNEne8QfINaAt3P1z09mZKsXG5DPTQfQo13+6EOJm12D2pZ9ahJT6YeVfqxl6xcNOAB1IP8L8dy/NOYNSgb5f2W4k8z6lFZSB9qwnUG4IkvRQNt+W539GKUyUAcPp/ZXFgo3DFd8QwcBMIgwgLqtF07ZG/nVZOjnIbJN2uQjQp8LcCiolha40ePfX/Uzm+iexZWXgasQGW8LwdeNrV6L+25pewJYIP/9QhgSl52yscQdY/PJ5J9Pu0jg+HnryUh3oh8N0vrYgnwBRkNrbWoe7gqJyMzAjWpdQKbpBQrhfDl+iQ1Bs37gtHYOh/VzLfJ5zOfj2qOexvVRHY2MJ+jN4fR0dHpQIQ88e6OOjp7EU5HENDcphmlPe/rBnSRNvtpLamVlUtf1BJsbnry/s1xJ3Ac5wAt6cn7B2t+h684YOPJsIvVOXlMHT/NsvmhhwNdAwbMAT5DTcymW+Bs46b1XSOWfmQsS06W7u59BIB7TTWut7f5gtMGSUNUYIUwaFuBN/zv3VYUq0oF8rJTglCNVmtQAeue/49FZZbTUQG1xf9vVos7INBoaJ2jab7HhFC2tpfcetFaYGZG2qri84ZXJgD3JaXN37rw/26OKa/q/hGIPoEB9Y/WN0Y/lTp3Rh2A46FZ3ayBdX8JCmxYOOW25z9r+zvnZGRegdKvfTx17oxOb1Wso3M6oAezOodEOB0mVN3ZBmmznzK1gMLpmIHK6t65Twf2rwrhdBhQEkFbjqQGsu0x9p1I5FvFH1E1tzntDUzzreI6VOZsZqJLbjnc/jqnF/GlBKNKLJajspa1iRofLm9pSfLWVNISFQHCCNIoZLNb9mjdQG1ovGikixspK0Kbayp61hb9b03MsMeKYlXpzE28LTbVD57iLQ+YdE/B480x1TWv1YWbTaiVjI0ofde1qFKhgD/2P6t5l8kS/e5Py2rNUlaiDBg2XnLrRW8Bv7/wnJLP7/3DuquAHWUV8RvDQnbd3uIOHOPzGSJ8PrG+qSXsL/GxW74HGpLS5u+973MyMoMB156gNScjMxYVSK8FXtWDWR2dUwO9zEDncJyJanbIRonXHxN+dYKzgRJpsx+LlM3bQAwHqf/zZ26jgHXtaDDrrPRCdVK/j2rsOSKE09EdmCWcjv9Km/0T/8vdUc0yR8IXqCazo7Wb1enE+LOpM+NLmYKSwZrdTzBnXWvNmSXW4EghTBaJ14uUZhFgEiLEKIO0Om+ju4tASktEjatf0O6A9Fit9uV4wqIBy8Wx7OwRuP0vzcEhoV2r60N6ba3vvXpkRBVCmIFbk9Lm/xcgLzvlJmCQ0eerBwYkD7bdIW322rzslOmA96Nnl0aiynPOBLxSijh3a8B5NXVdQ6VP7ECTrwghC+JittyCWl2Yib/kKScjsyvK+GMx8FZORuZw4FFUk+Q6lDvhMVlt6+jodAx6MKtzODYAz9BxTQ6RKDOBbziIxag/4BWHUgOQNvsmDt1t/ztUTd1dqGaPTkFWLgKwATvTkzlclnMH8G+O3iXNg8ps7at28E+AIykXSHTJQo7RxlincxFfqjSUi2IpmTp+mgnw8NaCbVpT07rAzZsbnu/Vq7+5rNYcXLvN2HTWCGmg2WUyNpgbWsMoa+yL1Vqn9Q1ep+1o7B8UVOtrTVy2tTnrht5XAqMB905XrzsmbPrk+7hNZX0awoP7fmeLCfT6ap8MbHJ7982cou5fb0bhuqE7LEEt59VXjsFmz9vjBpaXnfJblGnCduBuIWQFiDNaPZZmYAo+LteE1+zzaQ0Gg297XUM4L86dfaPXZ5wIxlmoCdqQnIzMQiAN1ajWCDyaOneGHsjq6Jwi6MGszn4IpyMe1djxmbTZm4D8DjhmT1TjSBUqu1F+iN1vB7oLp+NBf1MTwumIQwmVvyFt9vZ01L8LfE3nk8wJB25CNZs8eqgd/eUBXx7tiaTNvgtlVLGXRJc8pgYynSMjJyMzHqUv/F7q3Bk1J3s8R8CdQPfzb/v7/SFKB/qzYVdPew9lsHDjT888+3TwmtVPNo8ddrcHU1SUpSwoylwuN1cOotkUJNwyCCF3EdLSYtjSL7Z8Xp+Yp9wBpglnbN81fPjGnTvueW0Zb98sfTXRYfFrzu3qbQy1bp7yxmd3AwUoKTsAktLmv5WXnbIovrXphvjWpgnAOXnZKV+jtGBXoFYt1gJr/Ra3AD8B5GRkvgb4fNJw4bot/Z+a/27UyJTLK+/vFlUbZjR6urS4A/r6fKbRqAB7EPB34GLgRg79/aWjo3OC0U72AHSOHuF0aP4sZkeTCNyM8mo/ZoTTEYXqCr4XQNrsq6TNXnqIt9ShgtB9MzDdgOH+fw+LtNm3Spt9WWcrMUhPZjfqWnW0HJnOCSLfKs7It4rh7dx9AJCEWuLuTCwCFgRu29oElKE+rx5ge8Ogwa2t0dH3Vlx51X/DPii40VBaVrurKVbb0jCYZnOwQNNo8QXKLfUDfK0+s+hZ1/yFO8CUBETGVtZtji+vabll5tWD/jXu3j6fnmvbWhkXvXzsku8fqqs3tXyzJmLy24+nXrTvQJLS5jeigtx1wIvAeSjt2gFJafO3JaXNX5qUNr80JyMzICcjM2rP+/yTh2zgrrnPdV+3oyRg1KqN1jIpxYsCOd/nM63z/57/Av6ROnfGav//L0Q1rJ3WxCWK/nGJIuJkj0NHpz3omdlOinA6zCjJpk3A8x18+HdRZQCHzYAKp6MvYJU2+5pD7FaDkt86C+WXvu0Q+yJt9gMFcitR2aDOlmk9YtKTVeboVCDfKoxAl0SXrDjZY+lE/AmIzreKtESXPJzZx6eohqZOZYVaFOs38vjoRVCZWaaOnyaqJk+e2DBocBSqPCnKsLtmQ9yLL5UO7HGm1dC3P4vGDzYgBIBAM9AYILSNZpMNVd++7fNRfd4r7BH6+LbYroOAuJU1Yz+exiuP/nvY9J7rl5bGxS1dMSb1yg0asLTNkHqiJgaBqJrXKqCt0smNAt+ED55K+9Fkan0rKW3+tp7dNwUDFfWNM9x9e+Tcd96wXcNa3EFDQZwFvIcyY7ADfXIyMn3ALcC81LkzTuvPQ1yiiEI5Ln4LPHGSh6Ojc1j0YLbzIlH1Yh2uMCBt9t20P2i8GegmnI5b2rhR7Xs8j3A6/o3KrB5Vk5A/w9ppal+PFeF0mA52PU8wU4Gr8q0iI9Elt5/swXQSngMs7QhkSZ07w8NpUm+88K4JjIgev93VJcqFqoc3lt3wuz92XfC/rt1KS1sLu3V1Wdat05p79zYRFGQBIRD4QIZ4G9w7RLO7aFi/NcmbjEMvw+3pZq6qQnYJffm8/O9v69ql7jdP3nZLfGtk8zNdQ37IzsnIDAcMqXNnVOVlpwjUpGBVUtr8Pd8RB3LNW2k0ugcJIScA3+Vlp+xCWWmvBp5+/L5VU3w+LaOpOWBdU3M4Xp/pZuBvKEOM3SgjkTOBu3MyMqenzp1xOts9V6OaSg+ZeNDROVXQpblOI/wlB8HSZq8/7M4dd85+qMzs6hN1ztMd4XSMRNUNP3qydXTzrWIgcAHwaqJLnrD7Smd//Ja5Laeslu9bT41tEYbbNlm7ZF0y8vorUOYJjwd4dl9g8GmTfRX1RsvGTUGu/v21ltjuIBDS45OaSRNaQVFtedb6L0YuTEjY6e3ZN/i7Hxpi/vfmjomjTXW7Bw1JCGl1LXtlhP0Dj8H0VlEsTTkZmf8AQoE7enbfdDNK2eNN1L36YlLa/F/I9eVlp1iB9Mrqrme3uAObLQHN9sgu5V6vT0ut2h2T0NwStNhqqesXZK0bbtA84Y1NoTvrGyPyUufOWJKTkRmI0rJ1+88xDLg2de6MH07cxdXR0TkUemb29OJi4FrhdMyWNvsJcaaRNvt+HffC6YhANU0sP0Zr1V8rLlRZRtPhduwI8q3iapQI/L/bZhMTXXI9+y/X7nlfX+AS4I1El/zVZM1PBvlW0RO17PsG8PFJHs4B+dOgqTHjaooaUkp/LI5+662urV27dq0ZPz62xRgejc8XGGty1TRYA+uNZWUxTWHRUpgNRjQhQOAdGh0S/DfLeYFluz1DPavdO111t3it1sEF51xx6a5uvT31geFrimJ5GSAvO6VfRHhIQ21d5IqU2Q/68rJTmlErVH1QCiZhQO1NvC1Q6inV08Do8Rj7uT2W7l6fMT+yS/lo4MedZf3eQil4zHA1h4a5mkNnaJrH0jWy5LMuYVUT8rJTYlLn7m0cIycj805Uc9kpUwqko6OjN4CdbuxE1arVw94GsSHC6Qg+weMYg/JsP+N4nUA4HTcIp+Oa43X8k4m02TdIm/3+EzUhQTksdePIvw/6oZptunb4iHTa0oSqYT8qc4wTQX5k3/Nm95tA/Pg7dkV89unu0G+/LcPrrQTRCsIka2utDcOGhbtGjUILNgthMkhh1EAIzCFGzgmot9a/u3mlT9P+UnX+pAFbZ88Zu6X3CKvbanqpm6Vo1OCK3b/Ny04ZAjwfbK2/oEfM9vX+jGs8qrbzA+DupLT5e8o2BgCPAxckpc2va2qx3u71mrJCg3evAe4DxvhrXx9HqYhUAgk+n/Ead6t5hJTY6xvCrvJnZsnJyIwG5gA3oIxFdHR0ThH0zGwnRjgdAdJm31u35V/q33e5vz8qm/MmymTgRLEc9WBYu++L/jKIi4EmabMfcTewcDpigVZUc8dwoAX43zGP9jTFbxXsaoeiw/OAtkeWK98qBCASXfJw7kYFwI+oLLLOccTfgDf7ZI9D/OYiDaW+sEO+v3Rtm82PAoaiWHyjEhNnV12c1AWzOTpi4YfZTX37jRPLl3mDhw1f6zp7+BgvRjNCk0hA+mTP8hoxZnup8fNrB4xeE97/bZR97cfAyFhr8SaXJ/hag/AOAj4HnCijjlJUDevvgMiktPmL87JTWvOyU5TxQdqUNaggdztASFBdREhQ3UWoBtd5qHsXlMxWPcosZBzwb3drwPdutyWiwRVmRzXZLkHJcw32n9vdwZf29GfEHDMQxqpZp3XznM7JQQ9mOynC6RgA3Cecjmelzf7NQXbbAbwEfH/CBgb4bW9XHmCTBiSj1A2OKJj1W7c+AtRJm/0u4XTM4pfSXceM37BgIFDml8g63P4RqCz08vRkGrNyiQKa05OVv/zJxK/LOwdYAHx0qH39Qeu+ges1wLh8q5h5oDrZfKsIB8ISXXIHJ0lvM98qzkRlhhcmuqT3cPt34HkjUdJ1SxNd8pTNkh5HQlH3x4+0mawWxf48qVmZdrkrvpQHAHfcSy9181iD3t49YcKOoI0bXgmxRb3ZWm8eWGGMDURoAonoWruzefWAKNOWsD7hwK0otYJ/AtblESsndDnX/Lzl2gFZSXfMbwVm5WWnxKAMQwagJASr87JTQlD3xEBg8wtc9RnwhBBvmDeOzDnvzt9ZJwdbXcOAV5LS5i/fZ+gtqMn3GFTtbV1DY5ciVPPTf/n5+7MOlRh4LnXujFOhObOzcR0wiRFz7mPVLF2nV6dD0YPZzksjUMJB7FwBpM3ejNJJPGqE0zECJRL+b2mzFwunwwikAtulzX5Ex5Y2u1c4HRkoPcojwv9eExAnnA6D38yho4lBdS87UfqTh2Mk6sFblZXLOlSwvQW/i9ZJphE1lrLD7XiQ99bwywB3X1KAoflWcVeiS9YeZJ/jzYX467I5sZai/YFpqIniryKYnTp+mgFg4bIFXvn+0hrxm4tmo+6Pw5EADHzhxpnfvdY3+s0nzoz7EzDhvB9WRd5Q8IH5maRrxLreCQjNw8beMTSawzxSGCUqIF1aFIvMy05pfWrswEtWrImylty5WuOOIXuOXY9SGNgJ5KH+Hlmo5f9Sfrlic358rDurtKJbXd+4wncMBu+X/t9rFJB40XlDN4b75NwxAAAgAElEQVQGaQaDwXO/uzWwCpXptaHcA8tQ31cLUDa2Y4DX+RUpq3QgPwImlAqPjk6HogezpwjC6TgPpUTQLrctf2A5h6MIDA9yfrO02Q+0dGZGLb8Z/D+bUA0QpqM5j7TZj2WJ6T9AMAcPso6VXahMdnvtYb9APdTWoLLOK1DC7Scdv7zaI0fz3kSX/BD48EDb8q0iCnU/fIy/Nvsk8RKQm+iSxxTI+nV0pwPbE13y9Xa85Vvgfn4lkkVTx08TqPKGZuBhAPn+0u3teW/E4sVXBf/448VV3UevGVEV2Nt/jO/dRtPCHdGx17ssgeGat9Xn04yGqrBuAaD5RKunWQgR7DMa/hhfyoyzRqdeFzGkftKcHz/JvO22h/eVwnKjPm9hKIORK1CTty+AqjaWt6vrt1n+29gjcNVOU9+FqXNnyJyMTEu/+G6/Kyqr6lmyq/rjnsMJNxrd15ZX9roT8KImtIUoRYZ7cjIyP/CfJzd17gw9kD0aVs1aBaw62cPQOT3Rg9kTgHA6AlBZig177FkPwG+ACOF0FPhtSg93zG4osfL3OMwycjuONRZIEU5HJio4LtwzBmmzrxBOxzd76i6lzd4knI57UbWr7T/HvEkDUaYHT8jpBRuOYGwCVadWLG32giM555GSnowXyM/K5YKsXOzAv9OTqcvKxQJEpyf/Utg+PZkm/EuQWbncgMrcLDyeY+xI8q1iMmABPjgCuaeeqDrFj9tRU3vcSHTJOg6xKnEEaKjmn5B2nteDqqE8rRFOx3DgCsOcS/7v4lkflXCEn3eAuDde/8jn8UT8Z9CYpSUDYmNRwWHhdwmD479PSAjS8NT7MLaCtIAIBLSQxiazx6DREmAKvfrL/LT3z5noM0m3pyS4e0yv524ebZC+VSjVghnAC8Dd/p8noGq/d9Km9GWxwehjY1g/NobtnOy5WuZkZBqAsWcmxPXp3ytmZ4DZFFhT1xpiNLSGoz7DjwHemOgdvqrdXd2tnsB6lBHDM6lzZ7x4dFf01CIuUYSgrp2zOF9+cpLHoqH0rIuL8+UJLYnTOX3Qg9kTw1jgDlRm47uD7PMvwNieQNaPG5VJ7Iglm1rU0twZwB/8Y/lqz8a2DUTHILd1NNa7PYEHUQH7ibJ4DUeVHOzJPl8FJGXl8kDbgHYffkS5D7Vn+fVUYSKqBvJDDlF/nG8VYp9gdxXKlrgk3yoCUROUlYku2a4VhVONRJd051vFDDq4/vo0IBKI95qN1oXLFjx7NAdYaCwb6TNQ0veS88/xGgyBqFWVGYDVLFpMbhlgtIrGGo80SjeawCupCw40GqRE87h93/cd+M/A5pYtPrOMuvKnt6fXBIReYZDy5jeHXjfg96tebrR6muqS0ubXWwJejegS2lJ33SXbKy4eWzIb1Zg4f5+h1KEyravyslN6RkVYH6qsjrUJoRUFmE1uIMrrM231+kxLgD+jlD02SSl2x3YtdhWV9FkkMU4DhuRkZI5JnTvj26O+qqcOJqA76u98sglCZdZ/4gT3d+icPujB7IlhNWpZdOOBNvpLDGJRXbYH2h4AXAp8L232rbB3GfnBjhictNnXAn8TTkc0SqZpP+3YYz7H9IL1qPrSI6UE5bd+Ipfv3wM+TE/em43a02B30BKJ9GTaKkl0BjIBw6EyrPlWMQW4ON8qZie65G7/vkX+bSZUVqzkhIz2OHEyM8ynMJ8AX/jr7o+WGk3gMfi8L3oNBoOxpibVazC0yJCQlmYZVA/S45VijZvAceATSClwufCazBjwmd2C+qrQ8Njoxl0VO8J79/hvz3Hxu4K79nSZg2+otnRZdP+yzE0ALW6DsazS2vju0p4lF48teZN9GtOmjp8Wxrl0BV5MnjRG9IQ+AaZmnya8q31S+wx432RqGilgqMdjjPVJ05kgzcFBVQFer2GBqyl4gcT4AmpyOxoIycnIXJk6d0anvmeK82V1XKK4k6PIuB+HsdTHJYoMTm7Zkk4nR3cAO0kIp0ND+ZFXoLIVfVHLPt62zU3C6egF/ANYJG32V0/0WDsKMW/SeOBsIFtOLziq7K6YN8kMmOT0gg638dXZH38pwmTgoUSX3C/rnG8VAagHYnegOtEldZMMHcXUi6eiVlZygLjXms1PPHH9Ld1Lp0ztjRDlqOxgCBCh4RI+nyUAKUEI8LpdCEMlRtOqAeVrm8Nbasb9GDO8TlrEJYlVi6Zfsjz/0s96Tvz2sz4XbO1ftfGRN8aW1Hz07NJRqFWDn4Cnk9LmV08dP+3WoEBx2fjRXf9nMcdPBl7t2X1TXlLa/L3B6KJnb4prcQc8VrU7ppvPZ+oJXi04aHdXn9f0o6s5LBmlaBAPvAo4UufO2H5Cr6OOjs5h0TOzJ49xQBoqO/Y0aonaDpwpnI572yzlF6KysAfsTBdOR39Ul/Xig5UpCKdDtENvdN/9h6IyqY/tyQZ3AL1RWo2BKJero+FWYICYN+kvcnrBCXHI+jWT6JJLUBqbB9vekm8VMahms09RTXo6vyLiS9FQn8vKolje3GdTAup7yQSMnGJ2W1bs2PBe48rwpIi6Gt8bE6YEoWlBQKsPaxNC7nHsEjG1NQFlkV1jg5prQ4rCe20NqHZf12QOdl0YndtgMTRENwUH1DSarHXX/vTaDXT1DrLKhCvIxo1qWB0J3JCXndIjcWzgCoOB4K5dzNfUNnAmEFRY0v/znIxMV1hIlQwLqe6nCcoCA5qv9/lMRmCwEGS6WwLDvD6ThioxMqOC5EW6JJeOzqmJHsyePApRdall0mavB+qF07EV9QD4rXA6XtrTLOYPQg+19D8ZOBf4RjgdVv//P9gTEPvLB2YLp+O99qolHCfeAN49xqzqepQb0nERLc/KpS8g05NP3271mVOECQh9eJFSAvBnVxOBHxJd8mA1wYeiGiUBp3vV/zrRUCtLgW1efwYwsXBxM1Mv/qiLxg9PrFyynZVLxs6+5uYxQsowKaWGEAJo8nmlRYAGPmJ2Vxqi6nZjMVQH7ArtbvkxduSaokvmGIC+0z86464l10+sucDxha+2Z2B2adfo1m6FFSLpg+Jwr8aSL87v9mFjiGks0CehV2BKqzsgenddeB6qN+B9IB3o3tRsLQ8LqT4DWJKUNt+Rk5EZBJwhpeG+kOCaf5lNrbWlFb2GgegC7NYDWR2dUxc9mD0G/KUCccDOdioQ7HVk8luVPrXvdmmzfyScjn7AOShx7vbWEL0G5EmbvUI4HVcBl6MazfZITPlQmdB2B4B+N7Hb2rt/u445vcCL0jA9YrJyCQeSHx9QsDQ9mcUdOa59ziGAv6IUHe44Huc4RbgWuHDmFDHj4UWyHOiB0tYMQU04johEl3SjlmI7FX6ljLOBKmmzt1eOTacNRbF44ku5n7aSeQsXt7KnJnPh4hZgW3wpcaM3r30hoXj7IinEP42eVrNHSIkwRMmaFgg042t0U28NoSkgwFAe0ttg0ORXRbHU+AQ3aJLLA9/r+9ziy8/Z/oL9qs29d3pe/t2ql++2FS3PAEqqLSE264bAD8a/MHuB19O67N0nn7oG2XpHTJeyga0e65/La6LjgEuAqlaPeSdqArbHCawf6rPxVJDVldHoCh4JIhHVHPv7nIzMsNS5Mz44AZdUR0fnCNGD2WNjNPAA8ArwzqF2FE5HAjAT1Qh2KPer54FAf7a2XUibvYafu+gXojpCt+2zvQoVpHVmegJTUP70O4/0zf6Ma1N6MqUH2yc9GZmVy7Oc/p3ta1Gd5Xukrbah3MKOJiu7l5lTRBQqOPz84UXypLugtYMg1PL4ZuChoz3IEuNbAqVHunuy5+rjKh93qlIU2+6J8rTvEs687bveA4RWW2vsV1/l8hqkZXNMH81SW+Xz/VjtC2ktZfu4cZrBZMZrDjB6hTg7vpTRsY70tN98urrgpd+cdR1qUnwXkLw+auBZ44qWawsGjXmu1mI936sZLh0Fy4RmeNAU3GdobXlZXESI56o478Bl5ZS8jfrOLr95zsxWgLzsFENORqaGCnINQGFhSf8rgS4oWcAI1IpZWE5G5g+pc2cUduzV09HROVb0YPbY2I6yQrxWOB2f+BUGDkYd6qG51/JROB0hwPXAp9Jm3yicjlSgK6rZ66jwdx93VI3rqcRqlLj90QSyAaiJRAltFCCycjEAFwBb0pPZlp68N0uzZ7sGmNKT2VewvVOSbxXXAt6HXXIB+0jg+GW3ftrz88wpQqBsQouPMCgdAdyEymSd8hI70mZvEE7HPI5d3s6IKtMoQ8lC6RycNz07Gvoa12+7LKr4+8p56/I/WTwx6fzCEZa4QK/L5w1uaRF1xiCfZsRrMhJWt7vZZzC21AeHZpdGh/V77urxW0b8VPj07TPfv+vJQu8dE98c/MRZO1esFPBVtTWkFLWyUPrao/+7csIV49dJadpqNvjGmTD12dy0ewJqsgWwMicj8/3u3bb2Mhr4Q6Cl4Zum5uCpqPrYy4AbUN/ZpcAG1PfPAOAsVImYjo7OKYQezB4D/mX92Sh5okM+EKXNvgu/g84+RKNsE3ehZLsM/Oy0hZg36XxUZuA/cnrBcakR7Qz4l/8HAmXpyUfleOZGNSYdSAM2GvgjShT9QHqa1wO2rFxmpCefuhqy+VYRD1QmuuQBm+LyrUKgHsRulDXnoeiDcn16jyMrH1iOupePSEYtK5eJqCVex1H+fY8aabMfs+TbZM/VrUuMbz3AKSBzdCoz59mnA2+Cptm3RWYHBjSG9x26+cuW8892X+/85ME3Cflv/ZlDBrnHnFVrqKutuGDjD73XxvcTVcGhBo/RNBxVwrAeKYOvLPnPre83RPzmu2briNud75iiurhH7q715qfOneEBcnMyMq931Tfd8tHL+S8DD1ot8v92eesMLebmfDxddqNkEEcBt+2q7PFx926Fu91u8ySUzvZTqHu4ySdl4eoNhVVGo6HwzIS4s1E9Dp3GFEVH59eEHswegvE30h1oWfbyIbzfS9fVUr6xG021GeJLx4tyesGO9h5f2uxbhdPxV/we79Jmf7aN6sBg1Jfu6xynhqdOQgzwN+Bz4Ln2vskfBF+BCoKXH2S3ctQSc/lBtpeiMvCn7PXPt4oeKDWBJaiSl/1IdEmZbxUZ7TzkTlQd9hFZTz68SDbBLzPb7WQ46l7/Lx1kz3w8WGJ8KxZIBt6b7Ln6F8oikz1Xd0qLU0lpKNAoiG2vWcuxcDuQ8PKcn+5OXv7dXV9PuuOGrts+++eCicmrq6ZcVgu4QVZ3j67sOmnJF3Jw4eaW7KRrPIDpjMIt67fGxL/jMRrrjJaGhEmTq1yT17e2xDbWfbK4WHi++qHl70u/THlmxPhB6zakdP/R+Mj3HzZW1K89a0jCyKYWq1UzeILDQ6p7RIRV/SSE/KCwpP9EwOfxBhQUlvQvQTnh9QN6oUwTfFLK8UajFmTQRDCq6fQXyQYdHZ1TBz2YPQjjbyQAVUdYAsyCvQ0jw4BqabP76wvFHOBSED+AfA9odzALIG32sjY/71uv+R/gdTm9oCNsOzszu1C1xkfapBMAJKGC0S8OtEN6Mr9YYj/A9gJO/aXjKpRD2sHc5QBorwbsw4tkC3AiG12yAUt68tE1B55A4lGuad9xEJm8zoSktDtqIvchh6n57yC+A8puWLHiUs0oL+mz46snv0kY+t7qqNiQqY8+OfjrSy5aXTlkYKQloMmwtq6uyeUJt5hb3caEkkJ5/1svDLzn5vtmVYWG1T8w7rH3H9Ie+nocG0aElrQ0frVRfGzQtL6D+vb4qyHQt9WocUnDOZHmmC8NvYEmKbXHjIbWyEZX6NzAwIbJJqPnutyCb38AtiRPGhOFKj16CuV8+C9gKVBt0LT4fvExnxkMYjhqsruOtk1unYS4RBEKRBbny9NWpUXn140ezLZBOB23AmGRF/aaN/DjiQuk8FUL5ytWv8xVCPAXVPCj6lqDo97A7dpO66bn8bRUCqcjEqg5hN7rSOBq4Mm2gWxb/KUFv8gI+hUURgE7pM1esc/rXYEQabN3uHvXySY9GS9wxJJi6ck0Z+UyEzgWF6NTknyriED5yH+W6JK1qOz98TiPhmp0LEp0yeMSwPnrkTtDTfK3wD1w8CbCTkY9qhlw+4k42axbb1eTwg8/GA7EJOz4clPCM7Nviv/rK7d+XFySeOXSxTU5gwb02V7WR4vdRVNsbxl4zbIl2lVfLfWVh0fRs6KEBkugW2rajrOXbenapapxdWRlo2vhssXFORmZfxXC+6cLvl+VHE9cty2eyB2WbhF17iBtyfbLu+064zVhMGitBo/PtLShMTRwzJk1L9c3Nlkqd9f/MapLyBP8XOMdinL8m6PVaJ8G+SzrvBHeYuD+1LkzWnIyMjtrZvb3wDlxieIvxfnyoE6GOjqdFT2Y3R8TEFDVdwfL7CwWzldGAc8Ip+Mx1Bf/E/jLAgDklHs/EU7HDnqNtqKWqOYAb6P0DA9EMKpO03yU4+uBUiZYwi/9x28B+gqn489tDBd+1aQnH9yCtpMzGPgDKmP09YF28Aei9wEViS75wlGeJwY1gfuMA9cU/2qY7LnaRye37t0XQWw9KhN5fI7vdJiBUGmz7/2+5IkV/Zj894hxU4a9VBhsaSwCxsUN2JnQJaL8r2Ni4zWPpzCs2TXoeosngKoib31zjXFNjz5idd+B2lkbVtf/2OuMZ20bflg+aEPJ74GlLFxcnJedEt4zVo5CiA21gq79tuzY4OtV21RtjqlpjLCmNsQFTqgYEZIRvar+/xoawxcC58VGhw+PiQo3a5r4bercGXMBcjIyr0Z9v76PWwQaXaYrLNssferG1X4KBOdkZNqAG3IyMp9LnTvjq+N13Y4TH6PUSg7VpKyj02nRg9n9eQp+sdxfg5IuqpM2uxROx1rALOZNCgCG0HPUNnoMVdqcrc3JVGzdDbLeL8Vlkjb7L5awpc3uFE7HV9JmP9pmkRIgC/YT9V+AUkLQXbF+HaxALY9uBMi3iv6oTu23E11yTyZaAFZUPeDRUgb8m2OU7dI5/RBOhxHVLLj1ICtRvwXOF07HfdJmr5g6ftoZKcMm3j1x8Dnd3JrWNKWoeiH/2xxUXLV6TcDWzxtTK7rGYL58Z/mZluZXr7hUrg/utbaqZWdvxxv/Db/07S8CykLDrR/GjLp0eWS/nSjFlhHVV0/709mm5mvqwk2jNB9/XzciYrEnyjihtjY6VKK9D5RJje+3XRlTGL2qfj5+XWFN0z5FqRLs2+BoQcm11WKUz2suLdob7C0A/of6DHyBKjW5Oycj87bUuTMO3ktxilGcL9ejDGd0dE5LfnXBrH+Z3tI2eymcjniU9eJn+34xS5t9q5g36e/EDj5HrHx7Ln3OKadLXBhm6+u4XfdQ+P2/6TH0XcDI9+8a8Lq7AWfQ/czLgFDhdKRKm/0XdVbHEMjiH9uXB3j9Jw5R+3k64G/ouhpoSE9m0QG2JwIXAw+nJ/8yA5GVS5B/21fpyZ0/u+Y3Kli7z0tjUDqZTvzSQYku6T2Cpq+DnceHCpx1dH6B2SPPcxu4CyHmceB75EfUStcezezLXlu7PKKxf/9nyqzmMVd9UxZMc/11K/oNqOldvXpAl8a6llaLIahbaOmX6/sNjalpCu/Z2iqjhhRvF6EtTchaaXjxhYdGFjYH3b2+Z6/Uul1VI5oiumWMqigOb/BEe8xeb1fg4frG8E+bW4LPBt4OKWpuHf2PrSFAH5CPapqnpybkVx6v+frUuTO8ORmZvXMyMlOAt4BXUY2PiWhozQlNO1AlNt+hAsGPgU/UsfQMp47OqcSvKpgVTkd3VAPHtcLpWICaaV+Pq+YVmmpSCI4+l8qtdajaOPWeeZPuA1JxN68jMGwgzXVPAj9htKzC7XoK5A/SZv8cQHzpEEAmqhs8ApWZ7ZQNAyeCrFwsgC89ud1KARpKymw37B/M+rebURnJtvQCrkPJJ3X6YPYAvIuSxvpFBtWvIcvMKeIyoDvwwsOL5InoXNc5nRkxx7wm2nj1U+eHVj01MXTTgXaRNvtKYOWen/uEdX0tLqTLst/fffHXv4elvD7jLOLPvvVC49jol2zTXhr/+ZsTRv64OfbNHVFLZIwr6KmP/3Xh54NGhD56ZYqc/cYz1hZzgLa5V4KMaDT2Lgnz3PHp5oqK1VZj+dmTrnjZHRKcNPXbZVNHfuze3m2T9TVWzcgHyMnIvBdVkvMFMCzA7GoMDa4tT77zmT2fgUHAhcCy1Lkz6nIyMs0oqb6RqJKwNcDm1LkzPtznV9vYwVdTR0fnGDntg1nhdMQBI9n4eSM9htxHUEQxajlpEirgHMX2FXXUlduxhDbTVJMo5k0KAiJRXa3DgSgswWGYo/LZtkKy/ZuX5fQCH2qmvhc5vUDGJYpdwHXkf/JOcb4s8Y/BBEhps3vEvEkClSGsk9MLvhDzJgUCzXJ6gQTIyiUCGAd8lp7McVEx8Js1pKHMGr45Huc4HH6zgkdQnfht9Xfb7hcHFKUn4/U3dO0XjGXlEoIqvbjLr1DQlvWoZfkjUps4EGLepDCUwkWBnF7w4cH285sP8PAieSIcxTyJLnkoMff+QF/UZ75Dg9m4RNEDaCnOl5WH3VnndEH2r/BUPPl29dYnH7zjkFnKOc8+3Quoe3ryH8YByTyxYhfgITB8LaU/fBhWtzPxrpW+gubG0lHbgiOKJ+38ylZ0yZTdI7dtXBHeWP/V+6NtFy9PGKqN3fyjNa5+l/xy2Hhpz//vpHMszZ+kFhf96Zmrnlg38385ZWEttTeV9AmY7fM1bo/9+bs5H6gGkkB8ZDZ5Xgswt+y7gpWPMkTYCeBv8noTVU7wEcpW/JqcjMyVbQJaHR2dU4jTIpj1lw7Alw4DECSnF+wrbv9nfN4b0LQSilb1IWZQAs11P1G5dRjxI8/FZFlKbennwBiaar5DNVY9ghLQ9qCWni7DtTuOZuNK1Ez+HjFv0gI5vWD7AYbTAxWMfi3mTWoE8TeGTu1NcOR3qKytEaV9ukvMm1SG0k918LPF7RDgT6jGnuO1vBuEMiHYgurcPSj+4Nsopxd0tCC8D5X1OKBtb1YukcA5qMDrcVTd2uxDBPhXoiYJ93GA+s70ZHwoJ5+OwsfhZXrSgeCZU8RDxzOg9evMzsq3iv8muuTBZMSeAkx+2a0OIy5RmFAGC2W0cVc70cycIgxAFLDrBE0gTkn8jnd/AX5KT+bd43KSVbNagb8fbrc5zz4dhro/1qHk3iJRy/R3Mvb2SjYtWcE2p4F+F95hieyz4/LVn3zcO6bnDf/XJXzkTbdlTH/muYf/PLRwS3RJ125aQ7UVLbhF+6p3H21o34GLz/9p1ZrXQhvXc9PF4Z8kxr7p6uJrtjQ13dzFV3g9Uy/elTP6AheqzOEV8EohiKutj9x87V8frQXIycg0orKvZ6EUTypzMjJ7ofoOTMBUYLjB0LwrMKCp92v/eCD/hvsf6QyqGzo6vzo6bzD7xAptREvWgF6B8aNEv24XSumtQQVGfxDzLqyg24AwWupfw+ttwBwUwu6iUfjcrdRXhhHevTetLg+l67wERd2KNWIcrup1wDTUw3A9alnai/Lk3sbuov8Cb2KyXEFry10ge4t5k+6S0wvaZrm+QwUxu4AwkEZaXRUQWQwgpxe0inmTZqEkt0yo+sZ9M1pfAxkcx2J9abOXCafjLqA9qge/Bc4T8yY9cCR6t1m5DAM86ckHdoPyZ0//4983ApU13AG0+p22hqNUG7ahpIN+kfXLyuUMoDI9mT1i9Z+jyg/K/ccT6cmHMLv45bHMgJae3D4JLzm9oBa4tx27uukAyal8qzgbNcl51V8ruydw+z1QPBF+QF2fg1rPPrxItnJ8HKo8qCaaQzrgCacjALgR+EHa7MdrkpYI/A6Yy697KVhDNYMelZSacDr6oDKTyzqgTKoeNREt4q6zVRPSEytigS+QvuE+d/M5pV6xxur1NXZpqTYaBZeNKS9c/NjLWcLS3FScffE0S4/Kcrk1Jt77+qXjDH1rd4p7cl82DNq5LQplnbxRSm6NyA8Jr4oKDIqIKwxxm4WwuAlGTXD7ALdFhZfZG1zhQ1o95nxU0GpGySv6UN89lTkZmduAx1BlSI2oz1MYUuvl9RkjkSzgwOVNOjo6J5lTPpj1ZwZvBlLwtI4AoQEiQno1l2ZgdVOZkGuQ+PzfuZomQPakfAMgZyF9/rcAYMDTpFG5RQIGWlwGmmoEPl8FamaeAJyJqrkMQmVoo1ByW98AjxAcPZH6yig8TVehHpq7snIxoRpwfirOlzX87CZVI+ZNukf+5m+/yBLJ6QX7PmRm7/O7nglUy+kFqzvi2h0KabMfNPBpQxXq92m3M5O/Uet2VLCcfoj9IlDBXhIqW90CFGXl8hwQjgrQBgFXpCf//GDOymWP0Pm3KKk00pPZhl/hISuXBwBLVi53HKTkoC33AlFZufzVr2m7HzOniN5P3v740IaQLmHA6+15yD+8SP5fO87dHkagstTvwd7g3QycC2xOdMmlwAP5VqHlW8VlKE3Y/dy78q3iYpSV58v+xq5Dkm8VQYBIdMmD3ivF+VLSptzmIAT7xys5fisOm1DNkbuO0/E7BenJNGXlci9HX05yOarxaS20b0J4MGbdersPtVzPZ+m3J2yPjHpaCl7YERn12JUl7rCYom8fW+4LGhvZ2BR4oavUS3DM/5oq6y8w19M7IMg7fVu3uN1xFWXNBumzVNd1l7Vad3FWQ65RSHE+6vk1F+geUt9qKpR9y6xn7Lo5yLU7EaXFPR8IS/3uk+Yvz40KdxkbK5vCTevzslMu7hZlWVFeGV+CClz/8+FnKwcL6H7ZxKHRIcE1F3u9htcbXBFNgMHrM3R3uy0hPp+27FiuhY6OzvHjlA9mUZaawwAwqOEG+LyMbqxhbWAIJZoBpE/sDVj3rC4K1GMToV5T5Yt7oloBGJA+H831PlRZQCNKMLsSdV0MqOc6jwkAACAASURBVMYBCTwJTAc+wOv9EcGtQMU+xxvg3/4f/M5JYt6kHqjl+XbVaIp5k0JRS+Q/GzKcAsjpBXlA3pG8Jz0ZmZXLoxziYepv/voHSmLnRVSGugvq7zABVTKwBZVha2s1W40qzdh+kMN/BBjbGcjiP8cuDlI2MHOK6A78vVv5jsCGkC67UJmmE2lv6wDe/nQiNZ9OETFA+cOLZNPMKWJ6m3EEoSYFP3FgK9rhQG/ab488EwjIt4p79zSSHS3SZq/yWze3dxJ1xDy8SG4BOmoC0alJTz4mW+DXUOVWHWrRG13fEFcaFtYv0N3ac0ck17/T3WzThoy8P2in6YpJsQn/z96Zh0dVXn/8897ZkslGAoSEhH0ThIKKVBQUUwOaqOkC1tZWWm1xl7gU0WgQbGwaqwarVlPrr9RqVdyiJhqiEQVlVRbZ9yUkhITsmWTW9/fHmUBkkyUI6P08Dw+ZmTt3S+57zz3v93zPH4gaeTaaSZaybXsqStbvSgzdwMOvP/8fm9e7LWxx868bHSGW7bFdqQ7XuiU61Ba3vbHC6ectpbi85Zzm8JIpnasD4ZdZL/jipZ6AmjRjailQmveQHtezYs/2UTs2fPhZStzZwC0Oe0vDpBlTH//wuRsdgNXvD0wE7KEhDZ/YrJ4RFouKwEUSEAaWUH/A4kcKw2a25zkxMTFpH86EYHY3MITWCnWtcSuD9Y4w6gwr6ABWvx+fRbUGrG341vuvARhorCicSPAVH3xfihQkuIoz4NoA/Ir6sj1IFnd4mw1sQKan1qicpBAkw3gPEKZykm4JFosdEpWTdC6izXoGeBYONvkPZjpjZkS94auxNJ0FLNOjJ57cYGrY9FDAzfJpxzXNmJ7GISuc2+AB5gJl6WnsQTLcTkTLuwzJsG0KrmvfLzKYlfWmp/HREbZ9NJnCtsu/8S2LVAH5F3/29vrNfYftaM9zH8x+3g0sTHbpQ3Y5S3ZpN+Cem6LGILMUjwKrswp1/QHLNQStuA6pQ0YeymytUoWjYAlgPdFAthU9emK7Bkffd4qdyjiaDHp7o0dP3APsUfNmJah5s34KvHmkboVq3qxQwP1tsxWDdpd/2mtv1aWhXm/58u7dLwW2B3onNN6dOGIdWtt0xWoCu1eGb+/Wb26vxPUvWqz+P0c11N1vhQIFTaEtzY6o+nrDiGh21IXbLRiqk1epQXZf4JFOjS5PQ0Ko5bK3V65DHtj2S6iU6tKtvqaLLRA4u/Pu5n9XxoVOB9bkZWYb8bG2R6wWb3ifbtaMzTt93hZ3xAhXc8S1GrUaqEdqIM5FxqsBeZnZatKMqT9YPbaJyenKaR/M6ikl41ROkg2IQanBKJWIDozaYbGlYVj8Dh2whyoiazEU2i8RrQpGtQcFtwevngAKBQRQGMHgVuiEBKsRHYzQlgB6YH2gZQaiXXwKySpaVU7Sq8CbekrJbJWT1A14xW5YZ3sCvpcA+5EC2SCxiD7XqaeULFQ5SUrlJA0Obn+DnlJShgR4D1zkHrDyfedXFyLShBVHfxaPkWHTo5FitfnAS0f7tWAw2gtYd7jp+lbS0wjk5uMDLs3NZ2GwpelFwK3AI+lp+y192qzfhvSSr8jN5/+Q30VRetpJ0YLuI6tQe5CmFCLyPQ6KnUoB1wA1yS49p81HNuQBqkuxU4UfaUofkVF8wRG0kMkuXXqEz1o4hta+yS592OKhYqeKBIxkl6493DImh2aOdbYCVLCj2CEpdqofAzcWO1VWskufsAPHcZKItExeyGH+5tS8Wa1jxWdIRvfwFBTpUCgFmCZOMfJAOnPxYpqrbw2Ur/iHq6lswOdGqL1fVMenvC21HfB7IzX6DgVNBnhaHI6ohLIa1bWsGfzabUUPAPrF7q5/8oXUvN5AFZpifKHyMJ06TnHepf/tU7PHZ9OBsOW+szv4yyyXIvKJyPqG6JGhIU1LUi6h7vKb/xXIy8weBoGeEJgGgW1gzEQs725ArP02IE1rTExMTiNO+2AWpGgKmWpunW6ehWSoYFOZAdjo29UdzIr2Rgbg3w2L6NZhdUNpFy+6gxO74cKjOdCDVAJZUGgkkA0E3239568PNDsC4FSy3iYt2tqLkGnuEcBmlZP0BhDRM7RT//v6XnmPqmj53617XluscpJUq+0WgMpJuhAw9JSSVv1VEfCpnlLS2rmrE2L71AMx8n4MyQwui/dHf4zcWE52J5cWJCt6rF2fkpAWqw8jTgXfRiiipWx9gPgSyU4f7vh8iISgJrity5Cg/kzoTmVF5BPlxU71EXKeKpNd+r1ip5qMFLv9pdip7k526UMG51mFejunzzT6/UBYsVOln4rs4RnObUDXOdbZmWN9Ew4nB/AgGfZT6Qm8GLiLg2U+bXEjY8WuY1rzHdkWyr6aRkvdNgqKXgS+sKTm3t3odAwZv9d6hd8ZE2cxbNrnqvLid6sWizVEaR0Ib3FpbbFgeHwg3e10AGLqI6zvhzX5+ivNg9bG2Foauv+ESyZWB8IZd8XGFf+yv1f4CoA/M3soogf+Aljd1Bz196bmqPmT7pra+jdcaLe6Uw3De47b4wxojFSL4QtD6Wi/39bEGXLPNDH5oaH0D8DB5tGC54Zc331Uds7693f9fcucS5CA1Rf8XyPZURsQxf7ByodUf4cAfgXGvf2uDFgUX2dveN+BFCiFIhrZxUhwMv3H0X2evLP7ZTGLN66teN71xeIWfH9EtLi3IZWwvw1u6/a2QW4rKifJQILmaGD50WpuTwdy8+mC2NyUpKd9u1NCUD6hgrZZx7qtSCSjueEYtLEnjaC7wGBgS1ahPuQ0f7FTRSF/Vx6kPWZpskv/NfjZlYht0X/aa1r/ZFLsVGOBkGSXfvdU78uZxhzr7N8jY87fxvomnPENLDTlXYEbgVcV8d8mLxKe+Pwi1r6bR0PFh7z673sAuPKKm0k497cuf03PRrsvNNrSo9G2c6kfr8v20qhxsV1qqiy9Vn65PS7KZl95bmLnzhUNxO+s3RLu8kW6rURY/TjLuzoXJe50t6jGLjsJL+/uMRi4oFu/VetjE26YNGPqzrzMbIVcZ3sPJxf48LkbjUDASCzb06MxELD2j46qeMFQgV7VdV12aG1cMWnG1G3tcuJMTEzajR/EU+YDA65eD/znqWHXr/z7ljn3IUFqE6KHqgAuRoLM8xBz+ZVIRmRocBVuDb4VtdvCt7oquyOZwzgk+P0RUnQTAly+qGZz429qNodeTv/sFnyVwD+RLOWY4LayAXWoQBYgKEv4rL3PwXfEHuS8pAH/A8jNpxfiEDEXkSCsbS1OCQahxxu4DUKC/udoU1SUl5ltIC1vd02aMfW7rD7uhzgsvAK8dagFkl16n31VsVPdR5usW7JLn5AhezCYHosE0+3pp3tIDpBKmBwDY30T/u+72E6xU52DjGGvHINO+uiZubgv4GRyNy/QB2lCc3RYbFsZ9NOn8Dbvl7L0uvgdelw4wVG/M9BYUTTXsv2zlTtiYpMdXlvssE2r/ZaAtkSAxx1qbxywanfXiPoWZfHpAQq01cdeBSEhzV633xa41xpZVgZkBgyL9lssS4FagGAAW5WXmR2Wl5k9ClgyacbUb2i5L7/5XwFgR15m9h+A25tckX6bxTNfa7WJI2epTUxMThE/iGCWvl09SCcX9JRveMq36gs/VjlJ85CAcwAy1Z2ASBm6AyOBTnMqV1Ug0gIPIkFoBLSCEKfV2dzkc/UCojX4P2CDgWRuRyBtRCOBFOADPaXkTJgWPyqCnbdikUH+dqRYr22HnTGI/2c4ohn9Mxyshz3Cur2H8X/tgfyuQvlmhXwI0p5yI6L5/a7YCuRxFFrmjBQ1ljGMBB5Pbgcf2iBRiKZvGe3bHMLkzGU4cCHwPgf4NLcTNwJd1MydN+nJ3W7j6HyrhckjyoDnv/He3+/fzczFL1hi+j0aGxZnIWTZF0U9wu85Z+s6a1hTIz2rdusQA5+qbKoLbssJKLfdwrKRPaIGLS+r71zd0owU0cYDPUIC/o+Tnsl9NCm4iQ+fuzFYF9FvANIF0U9Quxt8ENZtMrY9gA4eb2gHjzfUAMqRWbVWSZiJiclpwg8jmD0K9JQSD2JH05px2gE8pHKSLEjmNQbx+hyJaDw3I1mPAQM79m2JckQE1lZt6lfraYgIfp6FBHeXB9f3cySTeJPKSfoL4NBTSo6rujtYCDUEWJ+eRtPxrKMVlZM0Fhm0XzxEA4hDbdsCWNLT9lk7XQNcimSc+yA3zrbuAO8gzRBWItXBG4LrMWBfZy7UvFlDEWnFp3r0RB207noM0eP97RC7Mht4v+3x5+ZjpM+Y6srLzL6fYyh0ag+CnbWONlvZAegCWBOTVRjSCvYg7WSxU/UB9iS7Di1bOGD71RkpajqiJzYxASnezE92nbQ2w3mAk8kjvAq8at4sCzLebXyyeuJWwJGedpRWbDMX24Ax1O0KIbRDCGXLr6ClIemCnQ3bdodHze3udV1DRDj1tPTc6/In1rt8zUOsOA0FuxKi2DQwzhZe7/548LJdTUiQe2Xw/62kjpsMfEpB0XLgFqB3h8jKh2rrO08n+OCXl5kdjjxoLwRezcvMHgg4kJmfW5FZp9c4idZyJiYmx48ZzH4LwQDvDQCVkzQLmULvj8gHioFYj8+ztl9cz8+XlK8sRwzuL0We3vvrKSXvqJyk7kjB2MDgaicD56ucpBv0lJKa4LojgM56SsmW1m0HC9puBL7UU0oW5ubTHbgaMTO/A8lsFJ3gIQ5C3BL+yxEyDsEs6UWITU3n3Hz+iRSofIpUBm9AMqF7DnAXGIZkQB5NT5PuOUGt7MPB7z8WXO4XSAZ7AZKt9CDd0A5ZWBIMgtsGspcA1+bmMyN9xtTyoz/89qHYqWKA5mSXbs5IUaHATcDSrEJ9qOzwx8D6V7340DzRs5HdxU71WFsng2KnikPO0TwkaPhWsgr1lm9fyuSHwrG6Vxwzk0dsPeCdGGR2YBGiDT8nN597jjKgTQD+QH1ZI9vmb6NjHwehMbZGV3Oca82CEXpIXKTPpvTnQ5xq3Zo6R/26eksf3ajDFKr71mpi/7MEp8v7E2Rc3gq8gIwhvwjuS2leZnZdVET0xWGhDRsiw2s919z7+PK8zGxHXmb2CKQJST37s8thwePpGHyveNKMqR+cyOkyMTE5eZjB7DGgp5S4EcN8AFRO0qVAzKa6HZs23vRvPSv1DlRO0pVI1i0TGK5yku5DgpE1SNBoRwbZboiWdL7KSXIgVcMDVE7SPcDeTrYE9XCf12/+rPqtq0pqXm1CMgbdkWnDGiTAXtIOh/UsYiH2bVNnA5EA7Ssk03ovUJGexv3ApmA/+JHBzz4GyM2nKyI/2MbBrVSrCd44cvMx7NHWpz3KF6pHT3TDvmD1IG1hMKOrD1H01aqD/s4LaoJesdmIlvoJRPpwNnKMhwpmrwFGj7MydWk9e8aWMQbJXL/ZZpkqJPu89hDfNzE57dCjJ1aqebMeRv52hyONZ45WRrMdeAR7eAg+959o2HMOfS6xuEs/bD7fXdV/7dfNW2MUTefUV0T2tVp77xwahW9riy/Q5LNYwAiv8zZixe6xcM2Kkb0SE7ftLY7fVd8JGW9fQaQHMXUNnVrqGjq9PmnGVF9eZvbZiB94OLB70oypd7buzKQZU5fmZWbfDfw9uG+evMzs0Ekzpp65EoNh0xUyy9jI8mmm5t3ke4UZzJ4AekrJXg5u97gGmW7eiQyEHZGn/F8jXo2dgp/dAyxXOUlhiMXUqO6Os9ZcHXtzy7M777kvoAO9Ii0xdZd1/PX8kppXW/0bv0B0W9O01uvv3vCTGvJLDEQmUNqaEXX+7erIeEdv/+Y7cr9VghCUVxxNccgypK3vOiQzvYo2WdP0NNy5+dzfuq5gq9rHEF1ydySjuzq4+FnBc/JKbj7hwJ//WnPdwvQ0Xj1wo0FJxY+C29XIVOAyDvC/TU/ji+D5ORW0IBnUbbBvyv8uDp8VKwDWRit2ZS3jz0gm/xta22SX9gGmU4DJGYUePXFz8MePg/9IHTX+HCCkYP4bCw77xckjNDMX76BT35kYllqaqjTNddZRsd1DqVmxvKuvcSfwGVXM7mjwUq/KpmSrXysljjQGNuyA22s1mnts3JMa1uAejIzFq5DCyBYkYXD7pBlTW2U7AxGN/VMc0E0wLzO7K+Kzm4o8JPcDAnOss58+gp3a6Y6BWBrWcPSSKBOTMwIzmG1ngjKBpwFUTtLvkcIvjQR+O5Ap9/V6Ssny4DIZQP+BYT8uvi7+/jFhlsjh/Z3n2rqHDDz33T3Pvze/Nj8SeFDlJD2ip5T4cvPZ0uxv/Pvzpff9FviDO9Cy+NPqN/KWN3yy9a6crX8GNnZ19Hkj1t4tLDeflPQ06oIBYT9gY1sJQG4+oYi/6VfI4BaSnsY3ukq1wYpkNxIR7djc9DRuCVpk3QXUpKfxVJvlf4E4GyxDspP1ufkkpadRgmSu+wIRiAat7fTegQwGHgT+gbg81MGJ6YSPh6CtVsOhPFWTXdrPAcF1VuE3JAMGonHenuzStVmFegewIyNFWeaOIREozirUZ7xFk4nJYbgOiEwdNX5Rwfw3Dm/D9/nfDXSgN32ShtGxjwN3I1arNYyRt61lwdOtba2r7QH+A3oYIh/oioyvNsDm9ATi7FVNVdYAtcAbH16V+KWtxXfP2V/XLpv05SfnAj8l9ZM3g/rZ4cjYsxoIz8vMdiJj0l72y6D+D3kgPy/8q4iNwHNzrLP/NdY34fCB+enK8ml+hk1/kFPrXWxiclIwg9mTiJ5SsqnNy2UqJ2kg8DXfbFn7BbDpmi5354dZIhc8uf02Kt07Lzg7/MKCgso3L/Lh6YXoYseonKTbkKA4HbgC8G9t/rpsWUOJe49n5yDEWmxNJ1vXlV0dvePYP2idB0wBHke62bRiATojlfB3AL/Jzec1JJvsBf7F/gB8HFK49BniXjA4uI7bkGKLA+2oFiOODxchN5oIoFNuPvMQne2SNsVbmUc4jeuQDPeyoKvB9CMse1IodqoeyM1tNuIVfKRl4xGXhfnB7CpIe82MhjAWZqSoNewPXs9HMvSPIzISkx8YxU4VjsxUrDhcs4zvAU8A1iMGsgAdug2mamNnts33+AeP9wdCo62W0A7K0IGf02PUwuo9Xw6uCQub0K26eqE9EGj1a1aArwUPlbZqm2rpaCRim6vhq8uvGdPPG2Pp9sDWNdEdajzjkQfKDs1WW+eXMrN7ITNH0Ur57zFUYKg/YJkHxlnINfkq8tC8DBl/WsLWRqwFRnMmF4Etn2Z27DP5XmIGs98hekrJWiTL2Pa9TwCCrgl9kSn5nm/umfkCMh19ESItSEeyohY9pcQHzGhdh/PViLt92ntlja/ig5d+e1kAuDc3nzikAGPBszvu8SWE9F05JmbCOlE5QG4+diSAnYsEovcg+rYEJKMaQIrDKhD9Z6/ge8OBMvZPgc9FKoLfDm7zQqA4PY0VwIrcfD5Csh8KsLfJDB9VdjU9jWag5FsXPLnUIdOVO45i2Z8gRXrbkUIUEFnJ8xsGMBzxM16OSE42IW4Pmw6xntZA517g82SXLj6RAzA5bRmFaNEf5mS2qD6FFMx/o+yIC8xc/HugF4Ou/hxX9UZ2Lnrkkw1fXN4YFnf94E4J9HU6YyAwqtbp3F4VHu4zAoH3elVXr0YSAZO9FhXTotzYDbcqV56GRGw7r0/o8e+tmxreOt8TVj9iafzdISHlscj0evlLQ0elIW4zDwITlfKPCguti/L6HZXNLZFlQMOkGVNLAPIys22IprZnxXVlxqQZU6fOsc5Wc6yzLd+HhhcmJt8XzGC2PdhUppDAr5S+Xeu+bfHDEIIEgpuQgGdFsOBsgcpJGgD8EvgLEvx8g+6hZ8Xtdm8726c9HRELGZBMaipQurF52cUbm5f1nlszW0fll6hg8ZQFme6vRzS7P0aCrtGIM8EiZHqwMuBvrlJGyGalVDxid/Mu8K/cfEYBS9PTWJCbz0hEbhCFuAo8l55GSXoaZ3yFfbJL1wKPFzvVqGKnehl4KNl1WOeAAmTacnub73uBj+emqC+Rc14BkFWoqzhyP3sbkgWPOvGjMDlNWYxkGDec6h05hUiLcWV8SlindZyVumNQQ824+dvX+DqGdbDicCo69l9oxCbcS+XSxF7V1dcjzispAXhpV0LUpK47dEikL0xFG4YbODdnx/ZfPrS6qemB5u59IwIhV7O86Ll9W8vMXoqMf+VACdooa3R1GB/QlphJM6Y+3nbHJs2Y6s3LzL4RkTO0PpyOBy6eY5394FjfhOMd701MTNoRM5htH7oj3Z8+AGYdzwr0lJImlZM0BWjRU0oOLBwqR6bmNwYD3H3k5mMv3vvy1uK9//V7tXv0s5S0Vr+/h2QTtwP/BmKeHFDiBZ7IzWd+ehpv5uZzL/B7YCaSfUxAgtU4RBbQ2+9rTAf/VQF/oM5qDYtAJBK1wDnAncCM3HzKkWxkC/AfJCBuF0ugoN7XSE9rt+YCx0SxUw0CbMkuvQI5rguRArgtABkpykBkBTuyCnVTskvXcBiv16xCXUuwE9HRkOzSNcVOdSemxu17S7JLV3PqZx5ODanjegKVFBS9uO+9mYsv2FpTMeXZZR8N+uPQpL3RDmcXvM0aZXTqGd5vWM8ls0cis0MewGbAoOjalo9dcHG4NqL8mt7rhsav6VDtutC72ntnB20ZgDRQ2cekGVO3AlvzMrN7AK6Atr6ANLRplU4xxzrbGOubEMjLzJ6EjIdZbZopNCDXuHldmpicJpjBbPtQhhQKrDmRlegpJYcMdPSUknpEV4nKSeoM/A54R08p2QhMGBM9YdyX9R99UuHZ/jlAm+n+OelpBMgv6Qj8Cmkx27LbvT3s+pc2p5wbmbQM6X4zBvFU7IIM0LMQnWyEYXH28PsasFhCIhGbm47IVPpziJfjZcHv3oAUQX0MfJmeRnt5vf4J6Jibz5T0tFNy8/iDhsgHL1fbLBdRNepzxiM6ulb6IlPEr/NNa612oY3u1sTk+0PquK7AI8AngASzMxcnArbIpt1DOzdX6kZvy3rqSjvSUKHxNjaze/k9oMJAL0Uq8+uBC6PqW3pW+fHWKQgYyvj8J/1+smp490dnRf+2HA49Dt3+gBqcYPzs2Q5qyF6LCr0RKdpVeZnZquejfUY39W+c/MJdOeuJIJ79s10AjPVN+BD48CSdGRMTk+PADGbbg75dvZx484KjJRrJICxFMg6lNsPePLXX/73bJoAchliBNQebHRhAb8Cansb9sU8+9NiELnf/3BtwP2szHG8BE5C/BS8ytX0nInsIKGXsttqiOiHWU8VIVrIb8BtEZ9uA3JB2I9PnvwIuzs3nvvS0duljvgnJBh+5eOQkEHQweNZnIcbi51c+G3uTXfrLAxbbgUgFDnzfxOQHiabcDlwFrFDEH6QHV/NmJTw0asiAGfO/tiPSJpi5OAFP099YOdvWsXrzgikdE+qX+L3vVASsOZ1iB/Sx2MI0a975GLQDeQB/Ehlv5gO9OhiohgD+aK2Ny95dZT3r691r+d+KfnRcdwMW7+cUFL3fuv28zOy4cNWvk1+3GCjbIKQW4QsgF5mdWu2Jc4cEQgLDkRmmZyfNmOqfY53tQIrIVo/1TThz/WZNTL6HmMHsGYaeUrJB5SRNRmQBrcQgmq62WQg/ki09Fxnwn2301fV47O3QB25NfHyQVVndVmW/GbgP+TtQSOYVJJAFmcpzIYFqIlKE9hXiatCqn12JTMO9g9xgBiBB8b6p9tx8UpAisqfaygWCXrSXAx8fLvBNT9vfpOK75JXuKq6xB6+GNvNx3B7CL/oce2Vnni92ql7IOYkF9ma5dNtiOBMTE7k2foYUTh2quPGmT3p0Odv7xaomW0C3zkbtpaV+Lo27L8UevpgLbh4XXrXrnr8uL7HdfX7Kl4khlq4M+UUnhvziDuY81AKMAH5V7cdpVXhDFBHRFtHe9thSbemxsboO//AH8Ib9CEvtNlLHhQOXNNoc222DRtx3tr5ur8tqe1kp43akCO86xPUlZKxvwr8KOr3yR09Hz9XNA10jEGsuEGnV/Uh77Xkn6dyZmJgcB2YwewZygBzhC8Sua1duPtMRLecoJBsbg3SjuhBItShrNFoTae2oAoGAarvKQ20G6U3eH8hHrMBsSCD7MhLIzkeysz0RqUVfoDI97Zueq0iw2yP4/bba197ITc+bm8+7p0oXeygCBj+qjyQx1MW5wCIFG2MrGYt076pAgvuvgZxTuZ8mJqchuxC7vcPNzLw8v1tsB1tAr6SgqBlgun2xm0402cdePTfRcXHc1XXln0Y6Qrf9cuDIkfER0b8BQggEetBS9xmGdTkB36fA0zu1EWHRum5vYlTFOXX13SKbAw7AhoVX6bDxC/zWT5Bs6gNAQpjX/dXwsi22MK+7Z4Sn5d5KZ+TrlWERv1rfMT4Kpf5MsMjLE+vOCIQG0pRbLdKOfcPjKqS+4HvpOmFiciZjBrNnOOlpeINtY3+HBJrnIVnTWkSSEItkXZUNO4ayKKUUFoul7WpaA1sd/PnA/y9ECslCEYnDHkTvtgDJWnRFCsDOQzK1BwZ4/wZePkSwugxphvBHREbQ7prT46XLHhIj6vkqxE0TEtRfAPQKKJ7QUGrRhHD4m7WJyQ8WRbyGQ7uYpI4aH5kixaVfMr/oQD34EANLd4en6YKdOxf9d/ATd7+UMHPxImAQHldnylck0lLXGx1oRMa639Vro6EqoN52XdH/nsT5W6yRG6tikYxwB0LrzkIexDsjOv/HFHw5pHLXfK9hRL41cPifzinbFn3x9vU9hpdtnRb2znvrWnfEF+17U9sDDm3Vj06aMbUcYKxvQiM/1GI9E5PTHONU74BJu9ABGbD/DCxBXBUMRDtrCf6srBabMgwDpdQ3vqwDPvz+ZgL+5tYPfAG/uyYQ8Pp1wF+Bt0rxswAAIABJREFUdDFzIIVi1wHPIw9CXwRtvgYBQxGj8cMFpAdpXoMFXYsQ54WvjvPY24Vip4q8yRY3tY9x9s1vR1jO9xm842xhsqGZDDzrN7Bv74510flcPe9irpk7hiXJLr3tGNbfr9ipbi92qg4n7yhMTE4v1LxZZ6l5s8arebNswbeSkKn6/m2Xm3bL7TqiuTnzlo/fLh61+MUPztpYsphfXj+XrZ9/Vucq/SpQuaYzmz72sv3zHejAIuShuctomy/8Zx+9v/JX/178Ue+NVZ8iEieNSKYGIA/z7yLFuQFE1rS80R5SMXr7+oluq62zhllvDjw/Oi8zu0/r/kz4/I8lnq6e17BwX15mdtxJPk0mJiYniBnMfj94D7g9PY3d6WlMT0/jQcQ3Vh3uC0qpfUGtBgK+Gvy+Wl/wZaOnecOagK+mIbiGOkQD60pPowiRMfwHmJ6bj0I6Y3mAHulpbD5oY6Ld/Vv2Yy9FZKSo4RkpKgL2NW6IBv6XnrbPw/FU0bta267tZmm+3hsaeL0sganJLl2e7NL1wHiPnfA9sVg6V3FZx720nqdjoR/yO+l0ojta7FQdip3qF8VOdch1FTuVpdipHih2qt+f6LZMTE6Q0cisTcfg6/lIR62DtLR3f/yRN7K5aVjXijVua8C7HYhotqqO/w7d+ZP5ncOb6TGyBCm0rEHcQ172ae4FMDSvKJE8bWuzSo3IoQIUFN2NOLQ8CYyObnGVR3sblnSh6tzPz0+obbHZbwJuy8vM7jLHOvusItvr99p2OJzVdY3dyvbUnH0SzouJiUk7YsoMvgcEs6MHtsJcBoxs81ojlcMdgj8HENmAXSmLYRihfsMaUYlMy9U5wgbfig4opSx+ROsaBfwtN5/ViFb0DsQx4a9Ixe9qgh2ycvMxkMzLtmAL2iqgrGnhlH5ABvBPpC3slcAvgIcyUtR2wJZVqNvFnzYjRamsQn0sAWfLz6wVT37iiFq5twt31keypPWDHYns7FjN+/HlrIvdw3C7l5eSXce0bhC3iyXJLl35rUt+O/2Qc1+JtBc+EAWEsb+Qz8TkVPEKUKRHT9wNUDD/jSqkayCkjuuIdDicS0FRPQVFflLHLUKcEBqIHTS8pcuAYYnWmj9GWqKa6HtWKVvmPo6MN2fVB7As8VpSh11xeYeOBhnIDJJCxjZL8OdUwEHquAeQcW8z8BUFRfVNE69egFLDI/zNoHgSKYa93dW3abu1zvpTv8VXHwjowT6//4+I5SA33HBdB8TtZc6LL75stqA2MTlNUPqY78kmZxLBwJL0NJnmz82nP+InuxwpyhqBSASuRQKgLcjNoi8SxK5Gbg4twPlArbdq+SXa5ym0dhraxbA6nkSyLkuRDAxIJreDr27j7JpX+xdkFepGgIwUFYIE2CuyCnV1bj59kCzvG5XPq18hzgtTWpc/XjJS1AAkSzMzq1CvPvDzYqeyIVOQG5EitNsRmcZlwPvJLn19sVN1BvTcMXiRTNKqrEJ9WhR7FTuVFfn9bEl2ac9hllEAxxF0m5h8N6SOuwSYDPyFgqIlwfd6IzMY+RQUuZi5+H1gIHA5u1dtZuVro5GHuLXTmkJH9zJ8d453eJs/9tpW2MFzhcP7IKKZDUGCWTfSEvoLZHzbSkFRKanjnECz3nXBZIXB7DG2Z2oijQuBEAL8ztJoKfNF+PZs3rmnosXjWfLUK8+sBrjhhuvikHqBd1988eX3vrNzZWJickTMzOz3nNYgts3rDexvnbkKWJWbz4+QG0QxUoXcA5ERfIhMC0YHl18ORAZcuzr49q6usHboG4nV0ZoR7onchFxAVcDnmlX/wVWpKqTz4Nx8Hk5Pwx/Mun4CkJu/r/nCB+lpNGU8z04ks7Ivw5yRovZ138kq1MfSMMGPtLs83HfOAaYivpJ/RAL5vUBoAEa/Ead6RsGU4DruBfI4jPn6kUhMVjbEw3dtabFe9m3LHy3BRgrrvmUZM4g1Od1ZiFx3+/+WC4q20KZ4LIB+xo8eZMPYysrXQpBC1ypg6vSfPbCc+rLZ/qX/Hr/WbdynraEVV1DVqpfdi8w8+ZGH1YuB8Loo24vbH7i22yCLGmb161kKw+E3cNREqIVI5jYZA8Mf6V9z04z797WkbuWCxCF2r983n+A4ZmJicnpgBrMm3LU+aSMy/Vb55ICSdOAZROv2YnoajUBjMMO7Dejk6J4aY09I9qCMAiSIHYz4zNYi8oHnFYZP2SO8Ha54bwJwTW4+bwC9gtvpi/jOXooE1jvHzOUL4Otkl27reGBwBN3v4cgq1JuQjM9BZKSoK+wjuWz4El62+9gJ/C8AqW4HUTY3q7b3JGRPLA+PXMwQJIOskYD+eIgAkhGJRrsFsyYmpwNq3qx4YBzwrh49sfrAz3PzuQG5Bp4KSqG+SUGRG3mgPiyP2JfUIwWmPaYVFG0mddznyAPmIup3NeHs9HtL5wHbfh4X9Ym9c187q199Fk/Tg8jDeSKSpY1DvLJrvVZ1W7PT0r8xwra6Q62nGcjJH2WzoFQqYmXYadKMqR8A5GVmJwC1k2ZMbWqzS0NtFusVSOHqER8oTUxMvjvMYNYEZLC/Egikp/Fabj4TkWm6ba0LpKcRyM1nKRKE3qgs9kQkKHUgGRCQIrCRQL2yhvwo+udLnkAyGDsRa6vJiMb2V8hN7l7kJoM2LH9o6THsmn/8/eunb7ljyMsAWYX6uWM9kIwUlQK0ZBXqw1noXO9xMLw8nqLEnXymwOG2E9HswNJiI7IiDhUw6KvBrY4jkC52KjvgTXZpXVqsqxOT1f1I200Tk+8b/RD/6RXAQcEsIj/qwH6bv+OhFvGwdgVfbwCaABuLXriF6B6VDP/d5P5gx90QjadJIzrZCERCZEMKxhKBv1v9urTn1sbrIus83QDN8mn65+CbkHzdL0MdtnMdNtvmSUBeZnZnIAvJHrcdh4qQAHzbcR6PiYnJScAMZk1ABuYMgoFlm7a4B3IL8F/Ed3YzcqOIRgq/mpBp+xuQrMg6ZCr/mfQ05ge9cD2I/u0Zf/0WZ/X/+tS1ygc8Hbt/vnfsrVe54/vHH+9BZKQoA0gD7Bkpan5W4cF6UhXgocQdXBqzh7u0tK4MVEfha4ygMX4P1R2qid7TFV2ayFXdSjlsMVpislKIFMFdWqxzAYqdKgF4GHF3mANQWqx3H+/xmJic5nyB+E/vPMznjwPqQKnTsTDtlttXAw+1eUvXGcpT6Ay9+dLmloFx1ZurmfPQeUiwey7y0NyABNqtnQ07BPd1Uoda7ws+5X0PTR1tZktcze5xrmb3GCR4LUNqBOYhGdh9TJox1QOn3HnFxMTkAMxg1gQ9pURzFJmGYHb2H8ggfyUy4A9ACr4eQm5sIYj2tRDpljUeqQSuRoJle3oamzNS+kwGBmakqLuzCrVr01NbQDIoi49mnzNSlBUIzyrc1w6TrEIdyEhRRcCNiPThqzbLd1QB4rpt45lOVYyyekTDEACfoWkJcbNq3UDcXisG8OXmvtTdsEH7gt8NA1yHcEdQfDN760a0tYfKUn0rxU7lBDokS4tcE5PTGj16og+55g/JiQSxR+CcWsOwfe2wezv5Ax/ENbf0RRqvNCEuK7uCrzsBjwED/fLQfa4FAgH4dW2MfYxWanXnWe+OarPeN5HAtft1F147/sJLBlttNusg2N9Oe451dggQPdY34Zj18yYmJicXM5g1OSbS05iXm8/nSOAWhnQByyeofQt4Gh437BG709Moz83n6uByVD6vvChrtSWqb0guax+y9/lltWfza8uAvhkpamvnm/Rm4C2OXoc2AUjOSFH3I1mZnojzwifI1OS6dWV142vL1l376bO/fRl42NlAbEIpsX4LyqJBg2oMwW/3Ut15L3t2J/CG18EmYMUsLyNnJasxYyy83sNgSnDfClo3XlqsNZDddoeSXboKKaA7Xn4DXFjsVH9qJwsvE6DYqeKRwqHXk136UD7IJicBTXlXxMbqFUX8yuNeUeq4TohudiEwu7vPP+rOFmNr+Mi78yh5tAty3fRBZmV23tPofNyKvmxKaHNCBwuGl4BhRfcIYKnS8FJTiKWDsqhvuJwELcOq7hj5m8xobf1t782eD3aeZe2LdDdcH1zsl0DSHOvsqWZAa2JyemEGsybHTDDj8uKB72ekqK627ikPho141P34Kx3vqf5vtyZgd8bzKHvva0bae1x5tb92wyKtA6GhA27Y4ej18/jGzyc/oZt3/zM9jdeAl1vXFWzGMAyoSU87ZNZ4A1JcNQIxRv8F8ECw+KsE4KPCVwe5XXWXtjRUdTY89ApxEdbkRDVEQEwNam8M3oYItnaqYbdVU/zgXP2/1pXPSladgMRKjbeHSCq+i5vXAmSKtPbbFjQ5JmIQeUtnOGRTD5OTgwPRzYbl5mNDZEeb09N45cAFNeVOwK2IP9iBJKzzOGzOW2mqmoy36UsFf44774ZfYA19iMiENypqy1Y94QpZNMXZsqujoZv2BFQ3C+rylX5r4GKLz6NptvoAK6HOPX7L7+9YY9nSLSH2laGZ2R0mzZj6jWttkjv+xY7axmaHfQXSrezRvMzsp4D5Pemz1B3fEr/3isoovpvxwMTE5Cgxg1mT9sRnhMS2GPaosPIFt4+1KjXOovUjQLN354f3KEd0F6VUoObNcx/3166f7jxvesfQQTetc305fZ9bQEaKGgJ0j7l202eWqD7piBfsnw+xrRWIzOFcxG3gfwSbNrTyydO/ehalekZU6bPP2kqY3YPhCofaGNgdj68lhPXaRkxtDB09Dv4R1MGORjRz7wCFRUXaDTx6Es7VQSS79NfIVOkPgmKn6gVUJbt0w8ncTrJLry52qtuRBwWTwxDUfHcDFie79AlLBBTxWzXltyjifYhdVgTy4PkNNOVRQA6Sef2/g1bUa3RHdi3vQ1jHMbz81tLlD8wesnzNgpEj43u/PaChvO9mv2XMWr/Fcn1DeE5BVMP8WRFNz5cGjM6xRmC9BZwOQs8N4MdAhVrRA/pbfbprQuc7ERnSN7yjh3z1WCkw4/3M7O6IEmkgIqGauu2BzX2QDHFH4E8nen5MTEzaDzOYNWk3sgr1HuC6s5/JCN8TXfbCyE6dug2prBwBTNfe+gXutS8sBH/MkoSELmcr36eWLW+st8Wet7St7hXpnT6o+tW+8zrfpJ9wlK52FTsHTwJWJrv0wjbb8mWkqAeQm+9DwJttC76CxWBZuPSAuN2McDaDRYPLi1YB/M1OdmElB5jod3DW1j7U4iUK+AOwqrRY5xCUSLQ3xU7VESmi+zTYLvcHR7FTxQEzkA5m/zzZ2/uhnudj5KdIE5O7CBaDnijBQJb0NDy5+Uzl0K4GbkRedBj9rXoTi7UjTdXvA/jcjWNKy9aNWFu62N3HEcis7xjzunJ7414Pq+sD3Gcozu5uCdiRALrZEzDcC31Wx1BrgFgLnR4La/FU71gV9kW3/guOsOu7gv86IEHtEMQlYTamzZ6JyWmHGcyatDtrmha0nF9dXTFg794xfvh9o8MRGW1PCLeEJ3ZqLp9nqwgPf7AyLGxZyoYlE/1VSzZnpDx3lzMs5s6kK+4JG/SjKxatWfnBa1mFuh5YUewcHL0jkWsqY7l2bor6OWJzFZpVqMWeR7SQXwALg4VaUcD1wG7l5Xqbj5DyRPA6IKQR7YrAWx1LExqf3c2oqDrGVsbSBdhdWqx1YrJ6lOMs4DoGBgO/RwKGJd+y7PeVvYgO+aAObSanjDcRqUvFyVj54QrCFPEtwJMApI5TQCgFRa59C/x10i5gWuvL/hGRdy3HU7bQa70h1qr/tGj0BZXXz10wfE+dCvSy6jokCC0PHsuE8gA7I6CvVYo16xWEd2xuWnfVhmXLSR2XgjiyvJJ33qVOIHrSjKmlQN+A1nH1DS7Lxh273zx/cJ/XgM+BzZNmTD2WBi7HRGKyGgpYSov1V9+6sImJyT7MYNak3dFTSnwZKeo+YMVHvXv3XNE1Ye+tPZ/ZlqCjDU/xz3O8RlP/80t3hiKauh5Al8gOXYeW7dpwYUXlzlHA5xkp6iJgYZZL1zw4TtVqg8HIFOXFQFpGinoIabMbBqwEfh1clw34taUF1XcDjrpoqIyFXQmQsBMd4mU7igVhdXTtu5Wu0XXEJy/Vu1r3vbRYH7EArdipYoGYZNeRl/sWDu58dCYxc/FPgACTRxx3F6Rkl/YCb7ffTpmcKMkuvZt2ysieAD8GbiJ1XDYFRevbfpCbz/lN1btGL5z7VreAj8JuhvLuUrYiBRcuqfP9ZLPb1jTF6rEg2d9ioBRwRCt8XawBbZfxYjdSMFqEZF3PA7osXXrFe523RP276ezG0LzM7N8Dfq31F+u3lUXsrqrbhWR5rweK51hnz0dcWraO9U04KveVY+AGwJGYrG4JFpmamJgcBWYwa3JSCLauffHRnKQBwHBtWBZbQhPDQ8J7rvjZ2tm/BmIRbWo1sLeyoWmee0/lUF/Xn5ZQvuo5xC/yoYwUtRILa4E9SJOGXYg29mLE8utPWYW6IZiVTUVzjsOFI6oGZdUQWQOVnSQzuz0BjY0twHJvCN4AfLlsGO7kYzu0icCQYqe6M9mlj6tQK9jl7LCdj4qdSgHdgfJk18FeuaeUmYsVcDUSGJgtPU3am2rEJvCb+ubUcZec++M/Pvhl7zEuW2h4rbvBXf/cZ2+3aumXpL7y6etPhfmSEKlEDlK8dTVQ6TI4KxyU1rQAmxALrieAtTsiY+5ZHZsQpkribQ53wO/t4mly0fQIkHnLnx+4KXXUeAvyt94Rsftq1f0mA2s4SivBY+BxwDADWROTY0Np85ox+Y7JSFGjAC9yY6kJ+sNGOvpPPNsWP2Z546e/fw3pNFaEZFo7A0ORG93lQFKN3T6pPiT0rPjGhmZ7IDAeqVh/jwDO0CawBMBrAbcNmVy0oi0eGiwBSj1OnnVpLlob4KIYePeVIn3HYfYzBPBkFe4vhil2qla7nnnJrpNz8RQ7VT9gOmIl9c7J2MYJMXNxZ0AzeUTVqd4Vk+8pMxf/EsmG/pfJIzSp4y712EInfDXk50898sm7GwqiGqzAz4DlFBStJ3WcAdwN+ICZyENnFFA9v0t0+oiKmqE2MIKm0I1IhnbOi8NGL/NZrJcC9/V8tM/eups3XRhf678pxEvG8Nce3NR2l/Iys62Af6Xv/kEhDTFXJq4ePSu95J1Tnck2MTHBzMyanAKyCvX8Q7xXn5GiNrs3zOqCtMf1IhKCOYifa1ekY1cG0MPu9/eIcTV18Ys27hfAPWhCUNDsgI57oTGWtm0NfEpR47PSAlzvhuW7NRV1h7FqykhRUUjr3S+A/7S+n+zSm5Ag/GRSDnyIODacfkweYXrgmgip4xKRB8mvKShqz4e7Qcg4YDBzMRQUfWJPHTf3gkeu1wVcD6njpiJe00+SOq4RkQx0AspvbXCGPh3ushmKHcAl5++pidHS2CUaGQ3CkPGlS7jHvaA21FoLVI71TfAwbHot8gDdi+B1npeZHQeETJoxdRvA7Q/cP6AlonrQpgvybe14vCYmJieAGcyanBZkpCiFNBxwA+nINOEYxAJnB3KjsiBVxS+E+f11GsKVtNK8hQAhKAlbbV7wt95m1L7/fT4HNchNbUC0Yn29Jqkq2PM9MVndGVx/bnCKz4MEuqUn+9gPJNmlG2kTQJuYnMb8FrGvuh0pzjxhip3KMKx2t1LG7qQZn04BnM8+868ZlVdeFcc/ni6ddsvtGrnu84HXkLbS5wN3bfWrm2KNwNcfe62VyXbf28AzDk0DkqmNYP/jbQLwyjWPPbIGWJOXmR2al5n946hLbRWJlYFPtVLL27QHu8Pm1T12XzrjrrhPMiuBd4HPnn5UmzMTJianCWYwa3JakFWodUaKegvwZxXq1cDqjBRViBQJ9UYKphTQH7gZWKxEW6dp7cGuZQlvKNQ6g0trAkhguhfxoq1GNHDzthTrxja7EIJMa7buTzPSDtPExOTw/A950GxXD9+Az9N6PVYCjirVnIQ4lzwCrKGgaL8fbeq4YkRjf2mowhVvaKywFJEbzEBmdUqRjGx08FtOII7UcX8AUq61OSxvDxweVhdh/6guwhgBzB8l2ybErd+68GtfZlyNvh2Y9vSj2geYgayJyWmEqZk1OW3JSFE/BvKALsiU3xfApUhxlAW5OVmQaUGh9c9Z7XvV2j/eClwJrM8q1L4Dt5WYrNRQA4ZZOBsoyyrUJ9uey8TE5DAEiyBJdmlN6rjf1IaGDvjXRReVt1htjT6rtQeQM+2W28UHWuy8hgK7KCiqJHVcKOAH8Gteqwlwzka/8d5Ie+BHSFMUhTzgvtYY4Cy/pofDZrF90G9oWWV4VBHS3XD9pBlTfQAvZPylR2Jl4E8/2ux/r+vHmUXf8akwMTE5CszMrMnpzHgkg/ISUjl/oYb3NPzJkEBWwQHelZKNbfvKimj6diOVzokZKWrymLlEI16vbyS79KbSYq0zUlSrJvcTJIg2MTE5BRxQXBnTobk56p6PPor/qlu3wHtDhzUiD7GCaHWXt3nd3Prjn8ek3bg3oGb93OEehxSSBpAmCBZgwKde6wcLvNahP07oaq0Mj9qBuBPUTnrXDe9OHwRsClzt6LsjzhKzI85SOukkHrOJicnxYwazJqczbsRDNga4AxhV5XBYo91uO6ANWUYH/6l939qflW19ZSDm/BORqctcQDWHcOn2Hgycm6K+BJ5GikLmI0VnJiYmpwdPI8Vgfxi2c+eS94YOWzTtltsPalww/R9Pd0EkBcun3XK7vuGG6+JDzxk8pN8FU8v964uWseifSUj765CAxqgMMOhH9sBHYx0tTqNi6yf/SujxNPAPYFhA8ZahuX9rnPESImd6HGmfDUDqqPGhgKdg/hvt1kAhMVn1AC4D3igt1nXttV4Tkx8CZjBrcjozAxiGFIM1A+eGezyR7A9gLbTN0HwT1ebnKKTtrS/4ryHZpcum/0Rt8dgZjGRtFXIjuRSxBNtHsVMZwG+AimSXPuZpxmKnsp92frEmJqc5mvIOQIMqKPIDTcBMgzatwFLH9QD+UO8IeenJ5OSdwPgQr/eS2z4p2cT7780ZGB05YvSOVX/cE/nOeodhX85+Xa/2BFAN2ghzY9wVhs1dXBVvKflwzZtJlw9qAPr/3xX2P/Yt9W/Y2M1yBxCJdBMLzcvMvjm/ZGkooqf/kvZtxXwWMgYtQApVTUxMjhIzmDU5bckq1B5k2m9xRoqyAxtDte6E+ETejmRh1BFWAfsztOchjRdCgD9mpKjVOHgRcUrYi2Rv5wBbgK0HrMOGFJjs4IBA96cLjO5INXfJOyMD3gM3XuxU5wO3FDtVdrJLbziqAzcx+R5R7FRhwC+B+d92DWjK7UgXrHJEFlQIzD7M4hHAuAW9e49G6zUo9URcbe3GMI/nOqDLJa6WT/p4fRecv/S/VRZI8Wo6GWA3QO3RqCU+IzBC+bw6gKOfc/dF53ns1yOODGf7rUqv72l9n6D2FngFCJ00Y6ovv2S8G+net+3EzsxBfIzMIO36tgVNTEy+iRnMmpwRBAPbmbDPxmsXkrntyX6lbNvAtu1rhQSkcUiGZzxwGxK8TkQaNEwGPkLcEtYB+7p7Jbu0u9ippiJFIwdyKXBF8DvbM1KUASQCu7IKtR/JBpUTtAAzMfkB0hm5TuqBbwazqeMmIt21cikoCiBygvMQedFyYGNwufHIQ2PO9CuvMoCwadKB66vwluZYQ+tNMY2N2ycuWuhErvUdI956b0nwe78E/lDmx0i0YAAqzgJjlM+IsRBhtfhcjYb1zV794hdVSxZ2MHK9bgPGIQ/BeyfNmLoboGD+Gx5EqtSulBZrH6fACtDE5PuAGcyanHEEbbz+h9z0hiFtbm2Icbod0b62YqNtSZgEtn2C/48Bng++byBFZQmAAyAjRdkItt3Ncul9HprB9/sAG3lIvYPYAO0IfjwMuA/4O5KJWocUlZmY/CBJdultxU41BZkBOZBoJNhVAIr4Ok35vUCzIr7tw2M4EH3l+Eu6dLRYpvRo8od90bv3wyO2bYu0+wNdf7twwRU9q6vrgPe9mi+zXSEDF44aH2UjfNQT4a663pbAfxq1CmnW+j6nIsqq6NDFgvIBAUNtXNW3/zJ3SNgjaF2BUpXALKT4dCRQAxQgciQTE5PTEDOYNTkjySrU/owUdR9SnXwOooWdANyKZG1bdXaDkIxqFPL3Ho4Euy4k+3pR8L35wXVkAn8IetwmAL8CZmekqMuAnKxCvQO5wU0G/vLOyMBSvqlv245IEQ7ZWczkzKDYqbohf1dFyS7tPtX7c6aT7NLlh/noKUAhulhAAtpDLDcLMAr6JlzVr947yB7QRcWDzt41eNeuR5Z363ZVyqpV/YF6Cooqfjpq/OvAX23onXZ03xpLWLcNif0rtkTYlsVuWzU9rLlxug52A6uNjfQ3O0P7nbdry5ilCb2dTVZbdwPcAYslFBk/diMyh/3So2HTQwHF8mnmbIuJyWmC6TNr8r0hI0UlIn6TYcADwCqkM1AtkmXphkwj+pCp/0gkGA5FAuBi4DW/nX+ieMbi5l3gKsTjNoVgMJuRojohWd2Psgp1LSZnPNP/8bQKdpYCoNiprgF+DmQku7T5YHKK0JR3AeKBFYp4rebNigD67BxxcUWCI6RREX9Qs4aV41IsoejnexiBAXktjpf29rv41l8H9sQ7Wiq9IS31b3dubuwdgLMN6O4H/FYLbmUp+bj34H/237s7uWdtVcRbg84PNNvsc5HrfjCSnX0UeH/Su+5pyOzNn1g+7bhvoInJyoo8TG8sLdZlAAybPhKRKb3J8mmBI3zdxMSkDcap3gETk/Yiq1CXZhXqAqTN5e+BG4HrgbVIYUUZYq9jIFnXAFCBSBO6Auv9dlLKLydm90/4RXD5AUjg+jegNCNFdQGMrEL9hhnIfj+Y/o+nzwaenf6Pp/vfqNI9AAAgAElEQVS1ebsAeBgpCDT5DtGUh2jKZ2jKJyB61z8hUgQeXtUwLGeTe2i8zf4I0gnwIH5k9Rv9rAHsBq7L7N7PV+7cdFuop+nvcTXla5rsjkvm9jjrx26LtUKBNjTK4/Hjb/FcMG7tV2vCK8qXhfg8kTGuhlXI2KAQ14IK4BdIA5dlwNITCWSDJCAzSWPbvHdx8HXICa7bxOQHhSkzMPnekVWo3cgNB2BRRoraiLTEvRu5MQ1BblLZSBZkBqKtvdPw4gktw2+r50fITeUDxDInBwlof4tIGNK/uyMyOclo5He6LzhJdukmpKjP5CSjKQ8DQhTxrZpahcykOJB21ksItpYFLnS1ePo0NbtLIsJCN+1bSeq4nwDJyDXdggS6gbOKPgi8KZ//DNi4vlPXFxvtIdfu7DKornfZyu5WiDM02BRhG/3GvYUee/9bQ1vCUjet/CjvvEv9iIToI8TN5N/Aj/OudnQGnl/5gLIClqcfPW4Zyk7gL+zX2wM8A4SaEgYTk2PDDGZNvvcEW9NWZ6So2xCtbAJQlVWo6zNSVAdEcvBXIE5pXB2XMg+5kVYg04y/C37vLuAtzEKQ7xXTbrl9DWL1ZnJquB3ooymfrIhvVsQ3a8r/BKCI10jQ18rTQEjkNbdUI5KinhQUbUOu1wjE8eARII+CotfbfC8LUJdv/rppe+J5Pdb0vezmxLK1TpvyEiLzk3W7A7w01Oq9166IQK7xcqRhy1XILM12pB1uAuJvfavVbz3r1evOv/fal5c0HetBlxbrAG07lwEsn9aIWA+amJgcA6Zm1sQEyEhRI5Ab3h7gWSQbFMP/s3ff4VFV6QPHv/dOT28QJgmEUEKHIKAOgqKiUWIZFVx7Lxs3rmv5uQhKDAprbxs39tW1sK5tVBKJlSIMIEKQKr0P6b1Nuff3x5lowISagOV8nocHMnNumZuEeefc97yv6Ag0GqjUwaaI2bqbgwvBWm+vAj0Qs3uhMwr0tQc4lglQguXGDnROyowC+Qsq/T7l5OUqgGFa5iUORJrPewr2Q8sTzUgPRfyebkLMxo4sNTeMnhe3I/m8vX3OtWrGR8gvfDU4NhaR9+oGIgLRKS8Va43DYqv3pjSarRg0TbEG/LpJ1+6oNVmiqy22y5cl9soqCYtcimht3QhceMv0yRUvTXvECBhumT65ecpd4ZeP2DP4LxdsGP+maflDL7Z1mpIkHRsymJWkoKkTlGjAN6NAr5s6QTEgZmuvBnb6wniq4gQeDN9IeYiHVcD7Mwr0z4PjRiBmhW5F1JU1AJkzCvTGdo4zBbGaenKwFm1bYy5DrKbPmVGgy1uO0m+CjicGMCjYSw82Nicv9wpEZZAp2ZlZv1jIdVAZ6cMQizqdwKYtIVUjnk9eaR1dkrz6nB1DvzIafadbLI1PAWMQ9aQfQDV66Drwy42VO5I219XG9beZMFgsSlRzA0ZNc9WaLVGbo7vFbuxif6HJZB6JuDMThqhJ/S3wz1umTxal/9JyzMDNwFqKsr857POXJKnDyDQDSQqaUaBXtvoyEtFk4XtEDl5NuYPZtX1Z0+dVhrQa1wu4F/Fm92N9AgNKT6e8vpcSgpjRASCYzvAXRMWErYiamweahdJ0CHygDomaecH4Rv2TL+WnTum34F4gUsfzl0OYZa1G5MK2+YHuoPILV5KR7gXKgHcjmkLz6n44ddZWv+mS7dsHXa4FTNZ+/Re/ZDJ5v0FUK6lF87/A3h+SjZppy1afQUtVmkJ7+ptCAGVLWPSASltY9NDSnWHVNtufd0R1MaIoZYhmD6cj0gvy+LmOdRhican7iM5fkqQOI4NZSWrDjAK9YuoE5QFESR6jqY6KgY+iIGZ4ls8o0D8PDt1a25d39p7BCcn/ZaQWwkDNQGX3/+nnTX1I+WhGwU/NFmxAChA/o0CfdQjH/59ywfgdiIVnjyE6IknSr91niMVcB00XyM7Mytfx9AVu1/E8FsyPPTz5heuAyQDP5+WarO61K62Vnq4RyevtgVL71998fc2Os/2TdGAZGen3NhhNA+qN5i3JTfUbb7XpdnS9BlQroCXVV3eJqq8NMep+4+C9O+ONuv7olpj4/yIqmryM+BDaOvA+GTEzW4b8/ZSk40qW5pKkdswo0LfOKNCrZhToZYhc2DMQ6QStyzWFodNXt5BaOZw3wrYwvcd7lET8yLWI1dUt+/IAtwOzD+MU9gIrIrvVVDvd6k1Otzri6F+VJHUeBfs8BXthuwMy0g1kpCeQkd661bTS3vAvQpTQL0IU8yEevmv0yQOGVfVNMnijarcl91z7+tnprygA2oT0M/cGGFQU021Tk9EUo4GnqrLi073FntKKAIZSTTGZdS0sQvObiv14ujbWLWowmcuBiFumT/4GETCHsW8ZrfnAw4jZWUmSjiM5MytJhyDYQjcH0GcU6K07FPUN28SJ3Wcxq7krGRUj+DD2O24FeiKaNrQWgwiGD6nkk/7Jl9uAJ51uNQ6xCE1DpD0QzNVNBnbMKND9R/HSJOlYGgvcgihJtUrB/nR7A78IUSyIvPWtwJMH2mlOXm4PoLeiKN8E+vYbWl3f8Cy7dt0GmMlIL/DB9SpKRuPOPYsSLYFqFeIrqyvfspitiR97zUNrdEXJsjUbjQpaDyOvv3TCaWXoTMeL/43rn55Gd8Yhyvet/OmgovLA90d5PSRJ6gAymJWkQ7R/k4TggrEEBf7R2B3VXMY4cyV2xKKxTcDQqROUua2CzZuB5KkTlNvaWxzWFpdDK3O61XsBnG61L7BpkFgc9nfgGWDh0b86STomtgDzEKWuDsaHKF11KGPPQeS1Tgb+PXzXLj/wI7Cb/EKNCen3fO0zzV7iN6x/16s4Ugza4vtS+lwJDD/F79eadEU1KKg6sDM0Uge20sRbhjrDTYGQwHRgA/DSLdMn72zvBA5LWo6CyMkvlp2+JOnoyWoGknSEpk5QxgB3A4/MKNCX3HmjcpnNw59qBvB6U1fGdVlEgupjKlA7o0AvnjpBSQUuQ7TRfWBGgd7odKunAbHARy6HdsBfRqdbvRGR6nDvoIf0JuBczcgX6+5TuiNShoYimkA87HJocrZW+l3JGDMxCjgRWJT/7fv71GLNycuNRZT3Wt26LTEZ6SlAGfmFtcF99EpUA08268qHb0TUP6jrdNOgyqAQAYT4Qd0TEhFY2637ml3hXc6MyY8fXjm24pRAjN8OPNNzZu+9wAmA+2z/pF9WGUnLMSG6hO0+YIewtJyRiP87nqYoe+nRXBdJkuTMrCQdjaXANIJpAyEe3gOW7XYyEp0URefFrvM5Bzhh6gTl7hkF+oapE5T1iA5k4o1O02+yFHNW0odkTH1I2Qw8doC0gW8QjRxK1jyghCHSDG4GLgKWBf/IPHjpmHO61SggxuXQOrP97zBE+9dKMtIDetqgUGb833jg4+zMrOWICiE/y0jvBkwP6Cz49xkXqFGqtsUV4fuxSQejItrj1mo0bNdUY7KqLdyqGc42oDOwocYQvWNj9MbYOpZdqBQBVwBLbpk+ed1/3S/cadtqO9+2LeRufu4y2Nq5iBa8DwIbScsxID6slu4X3O4CFrBvQwhJko6QDGYl6QgFmx6savV1ANjkdKvFwOrS01jbdb4+HKgAap1uVeEBZTGiRafidKsnGRp4O24RA40NwTe8AyyGCQYKWwCcbjUEURasFLEQJdfl0Da3jJ06QQkHzgMWzCjQd3XsK5ekX7gGGOl0q3e6HFr5QUcfmaXA9CEG/xrgn2zZAbX1ZnZ5emTcfPtW4H6gMP/b91sqjZQDH67xq540o/9NBbabFC4wKbiC+1ryitfa1de7953nNFf0WbCrAguQamrWAooSvjMqZjaiisGPfr1xwP1TT3gxftSZPUJTu+1IerHH/vnwLbohWvG2pCSNR6QdtaQqCEXZexFNHyRJ6gAymJWkDuZyaLW0LP4q4HuCi0ScbrUfkA28g5iZuS8QprwQtVY/BfAD2owC/YD5c063Gqs26zdH/8CSylHK7UBjO+kJ3YELETUyZTArdbZCRJ541cEGHqn8b99vBJYDkJH+qFJT69OvvuNqfP5zTzFaFy/0m1T2/TAYBvw41KSdvdBruL+fIRANjCG/8I3gPq692do05PMw8257Ze3Qy8xeRQHqzBbWxSWGhzY32U+o3nLe+i4Jc2pN6qUxysioUub9yxJ78Vtn+yf5aFsDsCP4N4hr4kZ86JQkqZPInFlJOkacbjUcuBixAMaD6Pk+DshzObQ17WwTCaQCK1wOze90q6mWYv312EXUR6/mmmDJr1+YOkFREHVtdx2sba4k/ZboeBTAqGD36dddcQ4VVTFKIPANUEN+YT3Qkiv7FFDVYDSFfzD8hKZz1642x9XX7ya/8M7gmMyFzeqUXkbNFqkQ+NarxEUpKKm2gOI3mwhg2evukWpsNJpzam2hC316jdVI6K7NO0vtazbtWpf/7fu/7FomFnapFGUfWSMISZKOiJyZlaRjJDhj+0bL1063WgSkI0r+tOd0xC3c+4G1Loe2Ietu5fbwH+mJKNbephkFus6+9XAl6bcvI91M3ozr6ZE4WMfj4vUnr2OXZy633jcDUTbr2eDIBxCLxf777uCTSmqN1nNdQ9Om3+Re+FWrvTWlGjWzTcGswvYfNcOm0Ua/w6T5CWlq1r1osaN3bvLuiogZcsJrL+cC/GvKjBdDQ6ynAi8iKonsS+TFykBWko4xGcxK0nHicmibnG71VpdDO1BqwQJEzu2mlgdyn9R/Sl2QpD+Yq3j4n5fw4J3fkRB/HRC3qqq+jITE0tTivbutGelmRJmuSuA54COfwdTH4g1s0jB/SH5h69+1FV0M/BuYCDTfHuJ/FnCAGa+GYlR0NbaxriK2se6nlAJFUV4ur6zdBHzd5tml5VwGpAGzgDqKsje3OU6SpA4l0wwk6TfE6VaHIKohvOdyaO3l7UnS71NG+omIYPENPf/1VKBxet4HJ4c2NV119ZLFSnxt7f+AUKAvcA/5hc3t7Cc8oAVe3xYwbDMpymnx4bZvLc7LuvHxu5N89fXUaqgGBUJVtI0RbHoxZfE3fmoeHWr8x55bpk9ue58AaTl/Cp5fN6CSouy7OvgKSJLUBjkzK0m/ck63mgTcC7yJaJYwGviCVotKpk5Qhgefe3tGgd7+m20bpk5QTMFt188o0Gs66rwlqcPlFy5FVCJA+bmSSHF0Y8P6rrW1kxCLHu8DXtwnkM1IHwX03+RXNm/XDDfV1Db971S1/oxyxbbnn2qXNTnJ8Tt62WyXkNJ7p7L6B3tAwRSuoqigVprDelmJV81K/w+B+1+a9sgsRAm8pbdMn7zvgrei7HeBd0nLGYxo+iBJ0jEgg1lJ+vVTAEPw77eAT10OrRTA6VYVID1hKCdG/0AKMBsoOcz99yPYOQn49FA3crrVVMDrcmjbDvN4knTEdDynIz7QPaNgr8/OzKoFlpGRvrtwwMDrvk9OHuMzGtdn77vZicAJYQpjhxv9k+aHhGzfun7VKxXN3vkXDz/Nk7Jry1289cp6/L7pqsLnMQqKDmigD61sUiK0UTsWpQw5Bx2focaQHIgMnA80IxZzApA1RRmEKMOVm1ukt1e6S5KkTiCDWUn6lXM5tJ1Ot5rVqgRX685DIcClngy2Rv/AvTMK9EMqAeR0qypicdmWQbAReBpos6JCO9sbELPFlcD/Hep2ktQBugI9AAtQ3/JgznnnJwF2xOzsfDLSdwAjAS/wGvC/OFWPqddRz7dqpxqGj6oHngb/Q4AVb/Mct8VcMqrZazAAftBVUBRdD6gYqoCoyAXRRZGLo04vP6fstfqhtft37gpFJ27i2nNHkZajUpS9o9OvhCRJgMyZlaTfjGAAeiKwDRiAqDG72OlWuwP1LodW0cY21wORwHOtF5o53Wo8onTRIpdDe/4IzycNaHY5tHVHsr0kHQkdj7KruNzy2odzbwRKszOz/rf7ij/1WpmU9NLKpKTtXpMptEdF+cvXL1r0J6APUER+4c+5q6IzWB6wleRe97F9yzggAzC/Gh763JW19SsNeA1edKVJtehRGv49IREzujbVh3k2D/zyh7phJ5VM8jRpodobt0yfXNz63LaddnN8z+qkJ4G9wCeI7oA+oCcARdltdQ2TJOkoyZlZSfrtSATuBL4CRiAK1C92ObQDtcSMQbTT3F8JMAPRHveIuBxa0ZFuK0lHSsm4LrWrwRDOuRN6AzaAxOqqtOiGep+qa1991zNl9SXLl29ALJQsBL4FICNd1IDNL9xLRvqlPPlyX1Ys+YDo2FkUffcR4Lixtr6bH7YqqH2tgE3TFF1XTUYtcKOKPl8duLBhab+vu9vV9GQTkV+w3+9Pz+qkEuAJRDm96xF3TnogAtp1wK3H4BJJ0h+ODGYl6bdjN+KNcjsiN3afBSbB/Fmry6E1tnr4aUDZv/xXMGVhbUeenNOtjgfGAE8Ga+pK0iHT8ZgRCxHXIn62rwSWK9j3n828zhIIdB29edPti3r3aWkIMsegadtKwsLf6FZdvf3p8WddkZ2ZtU8d2Eqb7Z7KkNAJu+74y5RTYRXoPpYtTqrcsf3CPX512SCj1gvoZ4Q4MOqaDpqCUmO1YfF5166LS7j5JfucCHRF9wWq/tPbeMsvP8yJOrNFpOV4EDWkHwO6ALXAlA68XJIktaIe7xOQJOnQuBya5nJo37kcWonLoe1pWQTWyjjgeadbTdlvm2NVxD0Skc8oPyT/RjndaqLTrZ7jdKvm43D4AYiFiKcAEYgPRsPaGPcC8Pj4J/5unZZ5ifiQll/YYAkE1o/ctnWxt8nnAx7PycsNbb1RRWioUmcxd+tdXPw0cEPGTXfrU5Zt/sf/FWsl99WH9F/tN9yNyK2tBOpUBTRF0avNFr3RaByzJLH3Xbkz9WLQb+9luHENcP1L0x6Ja/OVFGUXB19HF8T7rA2oDnYIkySpg8lgVpJ+P8oQ+bT1BxnXWT4E/upyaJX7P+F0q7c53Wp2MO9XOgpTJyj2qROUS6dOUCI6YfdjEbfHu7c7YtMeE5v2RHXCsdcj8rgXK9hLEAsMv9DxDNHxKDqeNB3P3/X81+P0/Nc1RAeuM37aOr+w4f82Nn765dIt1tqSqk3XuBcZyEifREZ6IsBbJzse71le/nKCEm5i5A0DuoVGXrwyYDzPo6n/CaA8NbiwoBpYBNwDVGs6mknTleTaKiUs4LcNLNl1/kvTHrEMNf4jWlEMM4HbgN4HeD1TgGXBf5sR5fR6duQFkyRJkG8skvQ74XJoq1wObbrLoR20NJfTraYE69celqkTlPFTJyhnt3N83eXQ/O1s2ultPp1uVXW61cjOPMavxGBgEp0TGOUD04GtBxhzJfAkm/a0lYt9xBTszQr2BQr2quDXpYjuXJOBgYjgcQKQg5jxLAL2rxiwzVvX9OW6L4peSikvTwpufwJAdmaWHtHc/G+l95mvEt3TduOw0+cADyFy0U/LGDPRkFEdnnBPXcgor45RB1XTRT28cL9PHVq8wxscW4koY7cMGNTuCyrKLgcuQPzsA8RxFDnqkiS1T94OlKQ/mGBZrfuAGsQs1OGYABimTlC+mFFw6KVQXA4t7zCP8xOnW+0F3A68dJDKCWcDlzndao7Loe0TjDndajgid/j30BRiHmIGvsNbpQZznQ9Wom0dostWh98B0PGkACcDHyvYG9iw5TM+nBPLRekX0a93LPBs8Nh1CvandDxhOp4xwPLpeR/0OPHKcRcFz6+G2Z/W6kP6z6S4bL0C5OTlWjjv/Kpsb+oLwDuj17xZkx+JemF12Hlnmnxjr7R6F15bG2q6wOy9sE7HX6lCstJyXug7ImNXAP8C3gHeRnT6Cj/ISzK0+reKfM+VpE4hf7Ek6Q/G5dACTrf6KtB0BJs/DCiHE8geTDD14Cxgm8uh/djGEAMigDG12iYMUY6s9XnsQARi1W3s437A7HSrd+23TYfIycsNA7KABdmZWQs7ev+tzSjQvYjawMdHn4QlwJJO2vtI4HxEl6/NhNgmMnLo5azbVEm/3s8AHwF3AXfreP7GijVnktrrVkJtUxEzsBchZqw/1PNfTwL+BryTk5e7AXgAeJs7TvwGqOJzbgP6vBpe/2y0op9qUMh7sK/pie/CoqZEbtlz4beh1mv+VN9giA3Wm7X4fOcbAr6KgMF0FjAL8TMV/tK0Rwy3TJ/8y7sOaTkpwINAI6KqgYIIxq/vpGsnSX9YMpiVpD8gl0M7omBkRoFe4XSr4U63ajxASsHhigKuQNw2/kUw63JoG51u9c8tFRmcbrUPIpB4HZjbatxa2q/Q8C1g6IxANsgG9AI2ddL+/yg+Rdy+3wZAQrcSTKbZNDW9D6xUsOs6nk+AFUAVj/7rVIb0r+NK5yrE934esAewPvHvT68658SBvsH9U/YiGo3svmzC6ME6nu44b/pGEQvMtsap+m5gJnD+TRNOcqwLD0s59a0luTeEWyp+iKr/c0jAGzKwtIZtMV17RjQ1hlSGGF9FUaIQebAPISqLfNjGaxmKqMrwESI1A+BSZDArSR1OBrOSJB0yp1uNBR5FBA1vdsQ+XQ6twulWpyNyEdsb07q0WA2wAVGY/lCP8VObXqdbvRAYBcx0ObSG9rf6WbDs2USgzuXQPtv/+ezMrNKcvNw7EC1OpSOkYG+iVb6uoia8QnzC/mM20jIzXVv/ot41LoWe3a+eltn9Pwr2tQA5ebndlOratLq8t+KUrVsjsvMLVwHZOp77gVTM5tX4/H7gA/IL/cBSMtLts96ff81zMV1GXVK8Oy/O0E0rDqmx1RsCSpk1RDf5/UqdYgjRdD1DVZSNiNnjVbSVXyyqFoxHlOR6AfFhTSFYF1eSpI4lg1lJkg5HA+INvENnIF0ObXNwAZcT8Bxo5ji4wG3mURzOirjtezgLYFXgVETA/YtgFiA7M6uxrceltul4BgPJwGcKdu1g49uUX7gafc9EGhpvZ868PktT+y6Zv2zdAOCFM9evmzxk165LEQ1CWjwBGJT6hgZEWkhtxpiJ1klXnWW+duTQHtalq5Our9wZv9xv3D2iZI+1t7HJ36xhthGiVNtCNG94jNrs9a+0Wc1jgYuB7FumT26rhbQZyAVqdXRnA407Q7D1UFAgLcdIUXZH3dWQJAlZzUCSpMMQbMjwFpDkdKsxHbx7K3AercstdY7/Afe4HFrdoW4QrNV7P6IIvtQx0hFVGQ6rxFiwRNcNOh5LTl5u/DNvFiQvK1xUyo+bw0Ktltsjw0MuSU3ulpL2wkOTDK8/tQI4k4z0QSBmfhXs9eQX6uQX1pBfqANTy8uq8/T/+/NZpsGp87f61TVr/IbSt5vMU8M1622RmlUL0TWGlXnUiZtXmiKMdFcDepqi6XVA4HPje+rnxvf2fy+9AlGF4S86+j8CaImtnht7xFdMkqQ2yZlZSZIOV3/gMsSCqw5bCORyaA1Ot3o/Yva30wTzZg+YO+t0q3agtnXA63JobS0sk47cq0BkSymuwzAcERDOjokMvc7rC1y4rNHvGgl39ktJuLZHQtyNYSHWJCA5UNtQO9sywWnCZ8toVaXhc+N7PVTVPz4mxvMxfVizYe32nTvfnZO03mu6tm+K/e3Rnj3D7QQC5Bf+25SRfjVwGkC0t8l0ytYtoXptr76hjSz7rrh7FaJUWBXwJABpOfGIfNxdiA9o9c0GnxIW0MMUFAXIBr45mgsnSdK+ZDArSdLhWoooCN8ZpaEOOQ+2szjdajQijWE58M/jfDq/W8Eg9nADWRClsWYr2EuHpPYojI0MS1u1cac+vbpb7l8bmv4dFR7SjEgjueO2N9Rue+MmXe3VrHEZrXagq/pgS3TVvf6eK88CfduuHcWvZX9QmzWqd0JT+aknjwn/eq7JWFY6mox0+8/nCz6d+rXRsbub440Z6Pqd1ScXfxK6PqwSkcfdwoSoqfwlkKyi6l0C0XWIfFkjMIK0HCXY+laSpA4gg1lJkg6J062eDJwJ5LZTQutw99cXqGqjLS9OtzoUUWJp9n6LvzqV063GIeKWzzie5a+kdinYm4FSgHEjzyzKycu9GigAoj76cumG6y8aNxlYp2BvToz/YGxkeLRX0w0rxM0EQb9mR49tIWqYqVStZacSD9hKaxqqy0Ks1rNGD+tiW75sHmWlAxGL/nSgUdexfuVVQso2bro5eoi5pDws0gdYt03ZPBsxCysUZe8iLec2irL9pOVYEC15M4GWn2MrEIZYHCZJUgeQwawkSQfldKs9EIt1etLSZ34/UycoMcAQYPGMAv2Aq/qdbjUCmIpoYfpIG0POQnS6msu+s16dJlixIAdRv/beY3HM3zunW80AvC6H9sWBxul4+iNKWbkU7N7Wz+Xk5VqBAcDa7Mys5laPGxEduXb2KSnJ2hIXF79zb3m5gr11nV9nWEilguhs9pNBO5uVLUMj9/S59gZ9YFXFtKGXXbjr6ssv91Q3l44tLPjq+6xtO17DbJmmK+pwmhtDdAgooIwz6cp3aObI8uLPysMiPwemIRZ7vYaoPSu0LPAqym4mLecJIAE4F1Hb+TmKsmUgK0kdSC4AkyTpgJxutSuiWUIU8NcDpAKMRhSpTz2E3dYCbyBqcLblFWDasezYFcyl/Rgxy3dUnG51ktOtXnz0Z/XbFfxwcC6iRNXBnIJY/LfPokJlwRvWfLtlZp1BeRaRK7v/No+dsH37+WeuW/tq3+LiBwFHTl6uudWYpYjSWabWG3Yv9r/Uf1TP9/wmrqgu2f7AzMcevWpwSq9/VFdVfL5i5arZl1fYdjT3Tn2ucVx6uTcqVgNUXUHXDSqJEbZAZHP994if91XA22Wa+8esKcqANl9ZUbYHka9rRnQMu+IQrockSYdBzsxKknQwlYggb22wmkF75iF6z68/2A6DgeMvFsE43epNQDzwSFsLrpxuNRGIDDZIOCrBzmMml0P7abbP5dA+75ezslEAACAASURBVID9KsBJgJ+2i+n/Ibgcmu50q9P4+fb6gcwCvgBO0vHsVLAvDz4esjbCGBvq15eOK/Xu32Z3I1DQpa52Q6jXu1jV9VJEua13c/JyFwE3ImZkFwAX5+TlLsrOzNoAQFF2YGFO9myDyXSWXlvzEQHDXU0W66mX9hrx+HOfL14HyjPvLv/RdtnG9RvX26K/GAjX7giJUNckpqDomrFfqefviOD0B2DGHu2Tm4ETsqYof82dqVe08frciAYKViCWtBx7MMiVJKkDyGBWkqQDcjk0H/DBwcbNKNBrge8Od/9Ot2pD3C7ejAgQrAcYfgPQ2+lWsw6ntFY7rgJGO93qvR0xA+x0q1bErfJViBXrfzg5ebl9EIH8h9mZWY0uh9ZWYPcLCvYGHU8FcAGiC9xyAH3stRXKgjdunxtvafh64vlddDyRCvZdANmZWXsQs/vk5OWWAUnA94h6wFcivhfDEHVmRwPn5uTl3padmVUOsKprFwdQSXTU2rgVm5+1JsadZOnR4xRY/CjwY4ohYDX7A2lDa0t+9CvK/xottnRV06ITqsv9O6LiQoCdiK5jGqLD3HlAZtYU5SWgMnem3rqWbDjiTqgfEYSXH8HlPSRjrkEFwr79z7FJz5GkXwMZzEqSdLxNQNQbvR/Ig59mbtvyDuJWdH0HHHcvsAXwHmzgIRqOqC36hMuhLeigff7WjEB8PxfS0pL2ECnY63Q8D7Df91Yfe20NgI7nHiBMx5PZRpOF0YgKBu9rVTvvsTQ3Rpoie0Q0GY1JmtEYgfgwVgRUkZFuBEJH9++/qsFkPiHE6238wh5Xqm3dtWLZms1rgH8AhsFG7QdgzYaYeFdJSPilA0p2YdP8ildRlAGlu2sim+ofHPnaKwUAWVPu24xYKHY6MAiRPvNeq/Obj2jVexFQRVF2R/3MtSUDuHjMNUz79j/s7MTjSNKvhgxmJUk63lpmc3cfIIgFRKcwOqgkWDCl4KjTClpZBTyNCJr+qD5G3FI/oiBKwb6n9dc6HgUIUbDXA/8FLO10C/svogrFAv+iZy7u0WPgOWOG3+Wbu3F34V5j2G7gg+zMLJG2MvvTy4Hx8bW1G5Z373HBnqio8tDIyIGV/sCsHfNWr0SUZWt4oN6WnRve8M7clIF9Y+pr44bt2doQV1cTYQv4jCbom9BQO4lgfnXuTL08a4pyNaIGcz9g3T5nV5RdQVrOS0Ao8PiRXJvDsB2RoytnZqU/DEXXZak7SZKOjeDq9mHAUy6H1nS8z0f6JadbtQC+Y1kSrT06ngyCbWNb0gsO5l+XDx6TMCrj+fjhp9g+X7/DH1Nb6+1RUb6zqEfy5yjKi9O++mI4Pv/JS7p3f39VQuJFeyMjCzVVHQYszM7M8mSMmRgLtNSFHXrGSYNO6mI2WCatXnyrQQskKqAHdOp3BZRTE6oczSHNlFOUXZI1RbkGuBqYmTtT/2VThLScKYhFlJMpyj7u11aSfk/kzKwkScdSLKJMkfy/51fG6VYHIT5onIYoiTbrgBsc2TFSgUaXQ9tn5jYnL9cONGdnZu2fY+sBNnEYaSXnmoZesqTamLxx7tKP6dYt0GgyTaq2hSQBFQaF15veevZ8q9XScDL23SdDbnCzjQA6nm6z339Ou/m26ed7SsouBmzmysqQcY2lMQYtUOcJiyKuvkb/oRn9iy4pt41KNp5mL9MWLp32yOYQes5rYFsiMD1rinJZ7kx9936ntg1RX1bOIElSB5NvKJIkHUtvArOCi8qkX5fxwEhEKavijt65062agb8H9z2l5fFgKa3pwB72WzgXrGqwvPVjZKSHAJHkF/5UDUDHEw9YFezbk8vLv0OH/zgc8UBGo8Vi3Bob60NRwrcvXzb68dJdp1966Xmu/v3skJFuCO4iXo+KuI3pd/cnqnvUfddO9Pz18bwVoHw7cPdWf3ej95Z6o6lrrdXaO7K5MTDA2Ozfa9EMnljFUBxS6df12N7d1PGztmivNAN9gXMQ7Xp/VpT9TkdcR0mSfkkGs5IkHTPBnFgZyP46/Rv4yOXQdnTGzl0Ozet0q3mIW/ittVTLqDzEXV0HjJp75+3T56X26wd8Oy3zktt1Xe/2yKsvfOI973wToo7xCei6Oay+rrLRYg0EDIaTopNTqrdt3vD1Y4/mvf7aa2NABNc24D1q6lJY8+PqOt/OyJf/9e4VEei9Hg+rr0gy6IuATL/RMHtjQjwBs1o5bM8eny0spKnYGAjbHbpgSH1g671eykt0nVKgN3BD1hTlo9yZegVpOT2AsUAZsJKi7OPeslmSfm9kMCtJkiQRLE/WqYuGXA5t2f6PZWdm6cCcQ9l+3dZlJ/c6b/x2y+wvKxb27tMLuHXQ7t3w1kebGy88e67X558BRADNQISqaautAW1YYnHxrk12+97BAa1317j4KUtSerXUMC5BlINbpWjaX3jxnep6Temu+awOHRUDpNdqGC6rDR+cqzbZQhsbA35NX+oJi6zYHRmbouN7s1bfMM9PzYrcmXrdVVel5hlDapOtERWa0ey1AxVAGnA50BV4C3juaK+jJEn7ksGsJEmSBIDTrRqACJdDO9RZ0mMmd9brfdNHD3uz9OIJK5My/2+iX7S5LXOuLBqlrFh+Fh989nfOTl8IOIAuBk2Lsfl8i8rDw1Mqw8PrEqoqY8ds2mhfa094GtiRk5c7OTu/8JWW/WeMmaggFpt9E9yH2W7Qu9xRFxICZD9TbVzTvWibeW3A9Ery4NTLu0KcQbF+MNB434Yej6f0+Pyx93o2jhi5pMnc+HJU0oa50SlrtwZ3XYiYdb6fjisDJ0lSK7KdrSRJktRiAvC0060mH+8T2d81F5x6YliotbqkomYWQHZmVlN2ZtZKo6Z9BDwS6vXuAO4GHgA2nbJxw6IrlywOGWSPWR8VHR5dFhr25vLuPd7cEB+/BrHgKzInL1dpdYh+wJWq0TDwxCvHpY+87NTEjOpwb1/HiIbRw1O31kZELfOjlD8aWv+YcdfOgV5P8dzIpnoVeLvqpMopzfamBy9OHB2bMtb1THTKWiewKWuK8jeKspsRTRXeBQ6pIoMkSYdHzsxKkvSb4nSrExGNE14+WF3a3zunWzUCusuhBTpol1toaS7Q9vFU4Axgu8uhbeygYx6S8FBbRXiorbBbXNS+tYHzC8uB8py8XBXRaWt5QpdoyxrrkBN+jO9WVlNZqzR6Az2wWE5Zk5Q0CJGzeyfwLPAKoqEBwLK4Xt2eShiSvAd4KFDbtBewb/eUFQ7vFnnynwzVN2LxEqZQ9Sdv+dyUvWUX7G6I6T8ndVhi3dDar5v6NYR6471h+OkBnAVEAn0AKMrWScsZFjzO7E69UJL0BySDWUmSfmt6A/GAAdEetMN11O12p1vtCvQCvjvSgNPpVsOA+pbA3elWo4E4RPOIhxGB5yNHc57B/aYggrC3XQ6tup1hUcA1wApEg4jW2xtdDq1Tvh8ACvbPgM9aPxasYnAV8CGQpirKBRefdeL3fn9gZGllbcjCJp8XbyC3uaH5WrPVPFZVFZMO3RCL3ZYAP5XPOvHKcV2Am4Cvzu0/bE6MYr2ofMWONeu37v7q7/U7LTG+hqmagXlGhQf6qHoiMCS0sdhw+oa5G/N6ft0UEX7ilATOW49o3nA/kAPYsqYoau5MXQter+6k5aiyzqwkdSyZZiBJ0jHndKu9nG71Cadb7XcEmz8FTDnUwMnpViOcbnWm062efRjHOB9xu737EZxfa+cCdwFHtJ/g8Z8L7qfFVcA0RM3enYiSVh2hO6I0V9f2BrgcWgUigP7PfueZBPzL6VZPPdqT0PFcquN5VcfzTx1P7P7P1zdtC6tr2NorJy/3pHVbdvdBLLBKALZFR4Zt7ZnQZeyyNZsb3Ss3KIDV7/XH7VyxOa62tDpEF5ULLMHxKvDnnLzcc3Pycnsggtz5QNGJ3Xsv7pOUuDhvTP93Pg2vOSWuse49VSHNqDADUXYrr87g7bsxrOQ0H3VnxnPateX60rBbpk/Wg4HrCsQsd0TweCBa/f4VUbpLkqQOJGdmJUk6HsyIWT7z4W4YrFF7OOW9DIigwnYY22xG3G5vb4byUBUA6znC9q6IW+Ib2DdgnRN8rMLl0J4/utPbx7fAWqD8QINcDm1DGw/7ECv3GzrgPCIR36/9S3iRk5drO8sx5P2o8NA4k9Hgea9w8TvTMi+5A6gMVkVYoePpVVxe/X+apt8KmIxm446QqNB7VIN6CnABIhVBBZKBXSZvIEFXGO83GWKBzOzMrMbg4R6j8NOxwCREs4MRiOvjABpCAqYUBfYuSkxcFG7qn5Cs9QzPmqKcAywArkQEvVbgz4hZ2UWI5g/bOuAaSZLUimxnK0nSceF0q4YOzPU82LFURG6p/A/vV07HowCqgv0XPxs5ebnWs08Z+n6/FHviZ/NX3r+npOK7e64/3wDsVbD/9L19+j+vmsNCLEv3llYN1MRt/2WItILLER+iyoC7rI2+j41ebbbRr5VWxdoez87M+rlBQ0Z6TGlI2IDvLNYuCWNOu2lgQ6PZ/FXBLYjUlrOB+4AYT2hE4af9ThjtpWb3+sAjxYgGEBOCz4cCASA2d6beqWXPJOmPTM7MSpJ0XByrQDZ4LJmj+CsXDGIvQgSmi/Z/PkeU4ho0LDX5nzaredwVGacsBAYDfwEezcnLXQeYgCbgnpr6xhCDqgZSu8erxRXVA6prG9YguokNA0wJXaJvDGiB04tLq4cqur68dSCrZaSfWWexPOwzGOJLuyZtjumWmFjbLWFR7FcFvRCB8X8RM/7/jK+vybD5mqd5TXrLwrk1wCjETHUY4n12Kmk504KVDSRJ6mAyZ1aSJEkCwOlWQ5xudUSw9eyxPK7jiqX9rvdrvgnAuLbGaIpvVFnM4ty7Cm6LVbDfBwRq6hril/ywMe61D78ZDNyBWAiXhghwu4eHWtWh/XroKYldA4iUlibEbGlkdW39qZU19VehKqG6Qf2ajPSee79y37mlQrveZzTGloeGxv6QkLjVawt/fum6onOinn/ijeAx0oL7GlJptgXWhcVuLtpdUTZ/8c6qrfMvqcmdqfsR6QSlgJgt9vM34AXScgZ12kWUpD8wOTMrSdJvntOttszqlbkc2vyDjT8eggvQ7MCbv+KZ4pMRi5QeRsxAdrrg9+6kxkDdoLllH8wY3/Wysn0GZKRfCURVX6N/UhxfVOM31vUMPnNKiM163eadJbt3FVfUGA1qldVibvL6/HcYVKW5sdnnraptMM9ftq68urZBRwShOrDc2tjYS/F5zV6TuRqR1/oFcKWhsvwSS1PjavPjL3oatm+6dcmm1SuzM7MqgudRDDyEmHkdVG80nTKvz2DzhiYG1Xj2PBHdZ0kTnrgFiGoP3yFyZE8AwIBpV4inMqnBXt+Z11KS/qhkMCtJ0u+BGUhHFKVvM5gNBk0nA7tdDm1HZ52I061eiQhan94vlSIN6Im4Rf1rvd28HPgnsM7pVi8FkoBnXQ4t4HSrA4Fal0M75MVsTrd6ElDjcmjr2nneisgxXQ9MPqvrnb9YfKZ3iRlIszf6mf/UveC8v+wWfl6Ut8RoUBuvyDhliYK9ScdzbW19459e/2huWHxcVKCxqfnh7Z7yiSUVNb2BuUAPROOCFRPWrX24KDEppa5Ll1BUdR5w56dDhr58zuz3CrURJ3oVVb1jUEpqzaCzxlX8dCL5hc20BPgZ6SsMmjYZtf7t7tHNISdGdC37VouIizSFlQJsnX+Jah/+pdcaXh0AjCgoj4x78YTcmfq2Q712kiQdOhnMSpL0m+dyaM1OtzqVAweJcUAWsBRRMP8Xgk0IegObj6JmajSivJWKWPzT4jnA7HJov9ZAFpdDqwK+AnC61W6IYNbgdKsW4F7ESvwHD2VfwW1uATyIuqvtaUbU0f0pkA125hplNhnLJr/yWFcCWi2W5IALtreMUbBXAvOC41MmpZ8c2j8lIfL8M0bWVFXXvfXJ3OUjEDOxNYjZ1L2IoPnBOQMHNVv9fheqegrig0fp8uRkz/mPPL4JgE17/kyfBB+b9vQFNPokbM4YMzEM8bORFhudtOg/0/8vtvuL12+qaS7r47COTejV/4ZFc8rrXp58/Z2RwN89RafvTRnr+huQGzzlsYdy3SRJOnwymJUk6XfB5dDKDjKkDHgMEdS0ZwQiaHsScZv4SPwLUPZf4OZyaE2InM12Od2qCZEusdrl0NYe4fE7Si5gcDk0n9Ot+oJft9kZrC3BDxiPcIByXcFr8kDL1zoeA6AhSnNleX3+dRiNr2M88HUD7nqvcLFx6q0X3dwzoctFn6zfXoz4QNEXUYprHOKDzP1Alwar9d0G8CK+H89kZ2Z92npnOV986Fe+4Oo/O8ZfYTOZy57Ky30dhUR0LgN6RlnNs7XGhhHagOv4rnBOWHhyWHOvZYufJ2XwtF7d42NLK2uf3FtWtR1RQu1RRJ6u1qqBgiRJHUgGs5Ik/SEEy3KtPMiwjYg0gB+P4jhHE6xEAxmIWqvHNZgNvg4t+G+dI8ihPZyWtzoeCzATEQC+BDwOVCjYf+rSlZOX2y85IW76uWOHl3aNifi7wk85qC8BilFN/EHHszUqIuRxq8VkbWr2bQb6Ia7rq4gGBvMR1/d6RAewHTl5uYmIJhSrgvVqDTpcNWvFIkuXsIg9wLXRSXFPVe4sAzg3umRP2Q+5z9m/LG9IPtOoK7rNWh4Sn3qt1WgOafJ6q6MH5qs29sYhguUvEKW6qhCNGTqqyYUkSUEymJUkSQoKdrh67zgevySYLnFUbXTbE0wdqHA5NG9n7P8oabX1jdWfLSgaBczPzsxatf+AxK7RffwBrU9jU3Nty2M6HvO0zEtsiFQCgKgxJ/TXKurq3i5au+M8VVGMiFSCEERwuRPYAUwEuiAWvAWAGMQCr5XZmVn+nLzceVVNDXFVTQ1PANFvzXphA1A0cfwV795grNeq66prJ6Sn35HaP3WPpbZqmxpvH3dFWMQ9//r4tR+aKG75GfoISAU+Dh4rAhnMSlKHk8GsJEnSr4jLoe3qjP0GW87ORHQl+29nHONoXOROHB5al7Kl79a/9VZQBgD7LBrLycuNGtQn6U8jB/UuTU6Ie6jVrOwQYAoiDeJLBfv6G5/LmfaJ+/tcR0qfxJOSe/2gi0YJfQH69bSnbd5Z/JI/oC1GpB10UxSluFtcZE+v1/94Tl7uhOzMLD8iPUDNzszyIcps8dK0Ry49e/TQkwth2i3XXJMINAJJ6Lpt07ad/53zUX6t1dAlJ5YTi8pZ0hsxw5+NyPVNoFXOryRJHUcGs5IkSX8M5YhV/QdLtTguTIr5woaw7fH1IVvvC2voVdLGkK4btnls6MxJTohrXVFhLfAhMFDH41aw17/x9YIyi8G4ZFBKUn1il5gTdxVXRAKKyaiyp6TiPH9AG4Bo33up0aA03HTJGW/UNTZvKauo3XDSfQ8PYPan/bPhI/IL92mbrIM/gO6rqa5XEC2PFwJD/ZoW5/X7/9I1ruvQkqqVi73UnAWMRATZy4Ca3Jn6nR1/1SRJAhnMSpIkHRfBFrvGY3XL3+XQGoHXjsWxDpeOx5o7fEF8ta90a6pjQnu34Tf6/IGsNZt3lU3E+VNesoK9UcfTjAgePwTq9U++rAce/rdr1kNllTUnIXJlm31+zeTzN/uB7sClgF9VVS0uOiKsa6x6Z6+keA+QCYz6JjV15fy83DBgRTCPlr32sJiavZWnblq9eYrvr9e/0GxQLcU3/HWdNa5rdmVFaVifnkkn+zeXjq2pGGCrY1tApzEUEfAu7aRLJ0kSMpiVJEk6Xm4Chjjd6r3BQPPXZdOedOAU4HH6JNQebHhbnG7VAAwCtrgcWt0Bhvq7WpJWdrUkbWtvQDCgLN7/cR1PX2BhdW3D/Gff+qwqOzPrp+d2eMpDEbmwJkRebBOgIGanewCq1xeoe+6tOa/U1DfagX4jBw/5JGP1qo/n9U2d4PU2nF9dV3oHsCW4yz1+r3+XrmnFDePOurY0oN3xbdEP3si4rkp4iM1rsYVa/X7DHl3xfmHUzXO8WpOzsaqLUrx67BokSeo0MpiVJEk6PjyIslFHWs+2s4UhAkHDUewjFZgGvAW42hukYPcjmjUcFh1PKOJWfvPH3yyrBKJz8nL/LzszqyX4vg+RXjEO+G90RMjNXl9gWH1j8zZEtYRmIKqmvvF+xPth87KePfMyHn2icOlt43+oriuZuHPv2pHPT318C0B2ZlYBIucY/9ptXzeUFls8Hxf4d1Zsi7gm/dTqvfUNMYbQkmpPzWwDULBt4UUWdHUSsABRpUGSpE6gHu8TkCRJ+iNyObRPgZeBE5xu1Xy8z6cNHwJ30CfhkGvLtmEromyWu2NO6RcaEKkTlsF9ug9FLLjyAuh4jNMyLzlnyi3OD4GrgdXxsVGDhvXr4QU+QKQPFCKqICQgusg9jUgL4IeNX63c7ln1qqYHvmt9wIwxE8dkjJl4a21jU86nyxZ8Mmxwv4bLzjm9LHrNCn99VaVi1roBuC547H/RPSsSVwLvICooSJLUSRRd14/3OUiSJP1hBBsj/AmxcKkbcDPwoMuh/SoXZv0W6HjsgEHBvgtEB7EBvRJPu+D0kbfpurbwsdc+zQXuN6jKZFVV6nx+bT7waXZm1us5ebkqYmFcCnAB8ANitrckOzPrxZZj3HjT386zRYVGb138ow0YBdwzfGj3UHt0t9fHjBwWN0zVDQ32pMq9NbVZ/oS0tD1XLrzRu6rqs7P9kx47tldDkv545MysJEnSsRUOnAGcjJixfArRZvWION2qwelWYzvo3H4TcvJy++bk5ca1fK1g97QEskHx67bsvmVW/rfrnnqjYABwG/BcQNNf9Pm1z4EZBNMesjOzNESKQSyiscIgxHvjT++PGWMmKgaL6TlrRMg/hzlPng/ck//6c3WnjRo7eeWP27pXNfhqtAFD1JCY2D29evbcY+hq7RsyoVuTZtGXtOwja4rSJWuKEtN5V0WS/rhkzqwkSdIx5HJoFU63eh9QE1z4teBg2zjdqoKYwfW5HNq/93v6XOBSp1vNBqqBymDHrmPG6VaNQBqwweXQajrzWO/O+TC+a0zE67quu4F7cvJyRyA6en3TUnUAKAH+tWNv+S7gQmB7dmZWZU5e7nTgVuAFYF5OXm4uMBjYBAxENFeoB74CBuTk5ZqAESdeOW5sxc7SR4wW00BLqHVX/rfv17Fpj7FnD3tEv/5JpoUrv19ns1pOHdCnF+GhIT0sG1fnr7J+2T1wtzcVmJc1RVGBB4E64O+deX0k6Y9IBrOSJEnHmMuh/WJV/iHoiSjSv79NiFqm/YArgX9w7FvhpiIWW73JARZ6HSkdj4J4bd7zxp3w7cbte+uMBkPLNXQi0jWKcvJyTdmZWcXZmVnaP155YbjfHxj6wJ//8ixATl6uGVFB4kIgDrgseN51iNJd2xEtZ68BHIgFcGdrfv8WX1PjqY1VJTmb5pd+nP/t+3U5ebkpQC/frl1/9ezddY6pMcy3ocuOvh9/9k3k/2Ve90q41RKjB/zfEexKljtT17KmKB8jFpxJktTBZDArSZL0K+dyaHpw5nWfGVenWw1BdMD6CFF+qggo22+MAqQDtS6HtrAjz8vpVhOBscAXiGoEqw91W+WC8amI3NMP9E++bGpn/2HANQbFNO/9k7elAQ2h1p7/G9bPcgti8RfAc4AVMXPdd+vulZ8tXrlxyDmnDHNGRYR4dTzfTs/7YB2aPgOFDBTlXeApq8V0+bB+ySNCrOb8b5auvdhqNK0+pWeqZd6WdZf5NS0UcS176ejGfj0HqIZK08zdlK7IGDPxjhOvHDcBcJiSklbvnbdpVcYZI59LG9Rv9KihA81NPm9tiNVSFxsdvemiO25e1PJacmfqnx/m5ZUk6RDJYFaSJKkTBYNJo8uh+Q46+ADa2b47MBHwuhzax8CTbYwxABchWrJ2aDALDAvue5XLoc09zG1PBNJDzeYFwI52xkQDJwV0XzHwAD8H85cCvXU8f8vOzCoFyMnLnRsbGVbSJTryz2eePCStoqbu++SELiHAqcBwJaBdbwxoFhSlccrf/la7dsuyRSmJXc6ub2yO/Wbp2pxQs/kGi9E4wmwwbfNrzXOC57TRYDT1ieve8yajGlG+Y+dWT/cTeocAs4CvEekMt+peLaVqb4UWHhvp7dk93NLY2GiyWa03v/mPZ9+7+r47ig7zukiSdJhkNQNJkqRO5HSr4xC3yKe7HFqHlmgKBsq9gV0uh9bm7GZwXDwi37ai1XZnAhUuh7b8KI5vQQTUW1wOTTvY+NaUC8ZbHSl9Bqf3H3wz8HZ2Ztb8do4RB1S5HJpfx2NEBLSnV9c2pP7bNfedO6++8afSYToeRdP0LJ/ff7qqKC+ZTMYb/f7A1zNfdp1k9Pp7htX5lnktxqcbQk02YFpEmK1bQ6N3hT8QGIjIPS5HzPJ+Fvz7eURAfQ6ixFYm8Gqw3ixs2hNdVVN7n8VoGm8zmQYQ0Ct0iyFyx+7dxh/WrPGpqnpORua1Hf0BQpKk/ciZWUmSpM5Vi8jJHEYH1xsNLvTadAjj9s/RtSByRncBRxzMuhxa86Ecvy36J1825eTl7kQsWtsnEHe61cHAjcAzLoe23elWR1y2JNw466QNE4Hd0/M+eN5oUP9kMZuSEGW0WoSrqjLSZDQse37W56U3TTxjjoKyFIj2m41f3jH1b2/l5OX2QeTMbqupa5yPaAzRFdEhbAtiMdkrQA3ie/cgMGfH90sabZHRG82hoYtbHc9mNhlPNptNfXRV9WFkd219vc3eNd7QNS7uQdvgXj8FsllTlJYPHrtzZ+q/vo5vkvQbJoNZSZKkTuRyaN873Wo1cDYw+3ifD4DLoTU53WoObS8oO2ayM7OKgXvbeMoEhPLze9R1zVqDQdMDG1TFUAKMHty3e49xowYO1PFcCIwG8hTsIO5haQAAIABJREFU23Q8/3B9vaxbZU39lMdf+/SV7MysVYhGCkk5ebmDEbOrPRGLvZKB4YiFWlWIxWvjgmPuDh5bAWy2yKhLGqsruzRUlistJzkp896Gybddl9Cta5dAVESYNRAI2C1ms01HD9is1vf3e029gYcRDRveO4rLJknSfmQwK0mS1Pke7qgdBVMEYhApAkecJ3aglIfgMfoAOw+UvnCknG7VBjS1d/4uh7bC6Vb/3Cp14TEdHYOS1HLO9+8urtRtVosBUbM3CvirjselYJ+/euPOHYiasctaDgmcjqj48CXwDNALEeRWIfKOP0J0ZEsCJiFKjS0ArgMei+vVt7uvqTF7xt1/L285z4bGpsb5RWs+veL89LGLvv8h/uuFS5Xbrrl0c3d7fHNlVdX0D7LffL77Iz1LlIC6l3vZjQhi9+koJknS0ZPBrCRJUic7wlJc7RkB3IlotvB9B+63tVRgOiJP9OOO3LHTrfZELOZ6G7GIqk2tc3BbB946HmNUeMhzpZU1eau377RPefO9hBlXTdo4vHfy1Gavr4fNAtmZWQ1AQavdhSHSB1YA7yPa3m5D5MnuQcwEWwENWAJsRCxsOzP4eKOiqg3mkNDtOXm55wf39fazH7+bpOv66Eb0+ObNO96sq2/o5fV6twBXNDQ1XW5UjGd547zfWYqtb+XO1N9DzMoy5hpMwA3Axm//0/41kCTp0MhgVpIk6bdlLyKI3duJx9gJ/A/4PjhL2wMoCTZ5OFr1iJquZQcbqFwwPh4xQzpP/+TLJh2PAXj4r1edW6pgf1K5YPwg4C/XnTm2qr6myTZ36drT/r+9+w6Pqsz+AP69d3pLZiZ10glJqKG3EUSR0ETDiF2wYWWNXXStIf7E3o1mcRXsqyIYgvTQAwOhtxBI773OZPrc+/vjHVyWRUXXdY2ez/NgHmfuvPfOnT/mPGfOe44oihsy52c0n7WUSxCE5CZ7t7GwqlwxK3XEk2DlAzIAG8AC9j5gHRbmAlgNFmyHAFgAoACshvZBsA4GKgBl25b948rEISMHRvZJ8AycNtW0Yv3HNyz6/G3lkideKfV5vQsNen0j5+NW4p8Z4tMUAEaAfQdTMEvIf4iCWUII+Z2zWHkdgP4ADuWahVqwrOx/Ta5ZcCCQRbRY+Tiw8a9rwQK809fEgWUtf7Bc4AfWbgHL+p4Tl55mBDAAwGEAr4CVA1SLaFCB9ZJtAAsoAeAgx+G1iYP6D6ysbo4URdENwCmiIQVsLO13HExeAP/wC0KaKIqJapk8CsBSsBKDCrAgtQ3AQ2BB6tsA6gC8DJa1XQGg/IL45Ogme1dah8OR2e60dwN4tc7eou1Yu6Fo0tzrTSqFPDQxKu7Ohtam0YdPnBTkMrkmfeb0WO4y7iSSoirOfI8Fn8A+4SY8AhqiQMivgoJZQgj5/ZsINr0qE8CRn/NCi5XnwUa2VuWaha5fcO5msI1rZ2cXJ4P1tV2GQE2wxcqngnUK2Pof1PNeBOBGsADaCRZEnwLwAICZAB7gYFoBAGJevk1EwwEAQSajfot5WEoHB5Nt0+6Nz4vA6FGDE/fotagHIMqk0nkGlSZmUsrA+sz5GWUAIKJhMIDxy9bvXn+ivK4WrDODMja6T/+ausrhgDgGrFzh5SkpqXcAKAPQhqQoR1ZO9vOiRuaZfXvGfQBuUzTWx1+cOuGaDTt2Rezcd1jR0NTqefD2OVK9RpeaL11WPNV3dduZb7LgE9h+4f0hhJyFgllCCPn9s4K1iTr5QwdYrLw01yz4zvFUAoAnAeThjMzq+QpsAPvyHE+1gwW6ZwZpVwTOtxu/vFPCFrCM6GEA91Z88IY6Pjx0GgA1WFY2ftfmrYa+hojpEbrgrUjiIgHcClbvahfRUFhUXtftcntO7Tx4si1zfgbCDLpMW4/LB0AOQJuVk30vgIdC9Nqpt82edOjqaeMKns1ZXgxgHIDXfH6v+qIJl1q3FayuA5DocbrDFy796Gt1n9BxTq/HnJmUsSlzfkYFAJR34qUgp33d0g8/frmmvimss9tWfcGw4fHDBvRrMgQHdR97vEQK4M0N0mXPTfVd/YOfHyHkl6OhCYQQ0stZrHwygMcAvHf2EASLlZeBZTuLcs1C/X/xGjgA9wIQc83CO7/Wun6x9n6/6LtXxiuKwepnOz9bscMq9fOTp/cf+qg+VZsLVoIRAbZxbarP52/vtPV8EGoIOgZAZ+tx/q3L7jywZMWWZgBXgnUzuJHjkHzHpEueMEn05VkbV0gALJJJJYNdNpvC1tzs1kVFPyeRyawHlu+crDHo7k2aOKhJIpUcBGsnNgrAqcwps4MAvHDkxKm+uw8cLVyVv/2dMFPInpHDB6kuHTOqvLO1O0Z+XLGj7vmWLwAUTvVdferXujeEEIYys4QQ0vu5wTKl/5YNDYzBzT/XiwLTtfhcs3D2hqnTz8cCSAUbrtCVaxaqfuQaOLC2Vvafd+n/JKKB42D6lwzLX4/NknDg6q6Imv/kuJBLJQCuGz4kYUR7q32rU+ndbIDJB+AYgGMiGrYAKNxSePytoyXV71w1dVxZnCl0s8frW7PzYLEJrEWXC6xjwRpRhMwk0b8GgAfwUGJM+LdTzKmhK7/bHHW80e/gJJJ7AdRCRJlUIS31e30fS6SSArCesW8BWAfgVQDbhwxIeWXIgJTDKzduCxIlvHRY/36y4ODgBL/fz8nGyyQiL07nBE4DVjJBCPkVUWaWEEL+QCxWXgMgCkDpT9WtWqz862A/vd97rmMtVn4ugHSwQPVUrll4MvC4JNcs+M9xvAIsM+v5udctokENIAvAAQ6mf5yx5lSwLgOv55oFh4iGSwFcBiAawD/AyhoWczB1iGgwAYh97m/L53j9/qk+JY7vrSwvK6luVN08ZvwYnud1YGURD2fOz2D9Xkvr+wPgkBR14uv13ypbOro3O5yeWIfLvQDAXwFsBPBY5vwMISsnewhY7W62KIpP21u7609sOLhg9Udvh4DVM/8DSVGb1n+64r4LRwx7Ta1Q8vVNTR3VNTUN5bk1T4V+E755qu9qqpUl5FdGmVlCCPljSQ/8ewJst/6PWQlA8iNB70qwNmBKBDKuFis/DMA9Fiv/cq5ZKDnreCmAVIuVP/QLhi2IYB0G/qXuN9csbABrn3XaeLC+sW+A9XtNAaAD0AFgtiiKF95+1eQFIx946osOh+PxML0uxePziR6//wUlz48Ba4V18PuTJnEXADC99dmSxV02hw6sdZgKgAmsdddEsBZdLYHnGgF02Zu6ttQcKJ/N8/zsrDcXl99x/eyOqIiwDgCYMnaMwef3CV6fzxdqNMohiiHSq6TzRj03bPXPvCeEkPNAwSwhhPQyFit/E1h5wEfneHoXWLnBT9bH5pqFbWesqQCruy3ONQtfB563AThx1svcALoAeM+x5CiwFlfPg/2Uf7pmdxxYzW7bOV4DAOBgcoJtVPs3IhqkYH1ZTwF4D4CSg6lMRIMRQAEHUxsAfLG6oJXneWlFbbP7tnETJUqVPHXkgD7CtgMnHBJwA3yC/0YA+v+75/5NWTnZfOb8DAGA0uX2GrptjhVgGej5gftwmSgICkdnR0VbRakIZCDQBeGJTXs29j1ysmGWMSi8j9QnFa2b1z1z76JFx5ev+foAABw8dXx735j4x/YeOsY1dnVzk82jFIP69x/0waovx+46ut+6ZMnnwr+/S0LIL0XBLCGE9D5JACQWK8+dnVUN1LX+WG3rv7BYeQOAmwBsAhAE1jXgB+WahRMAHvmBpw+A1ZAePeOxvmA/zX+BQO/aXyAJwP+BZYmf5mDyi2gIBusFezArJ/s9sO+zelEUyz6wbr9uXHzioOFx8Y5TdY0tq48fRqQm+ERJa1NZsFYltxTtekoqlcRm5WQ/88z8K9fVNbVxIgtke8Cyvl8CGObzuO/oaWsZL9doL87KyS4CcL3JNGzZ3EtTH4uLirDvPdHWiCWtERFqnd6pc4acvtjosMgdZVWVy7bt3n9lDwSlIT6iMlkXdHDX0f0PgGWQi37hfSCEnAMFs4QQ0vs8BwD/QS/XM4WAjcg9CbZLX7RY+aBcs9D9cxcKZHJ3nPVwKYCX8As2Polo4AD8BawE4RhYUGsE+8nfCzZFrBTALADTPyncubK8raUzwRg6KlSrG+nzC8++sXrdugv7ptySYAydd7Cuesel44YakuIiLgor0pX6fAIAPNE3LrI1c37Gw1k52ZFg5QsFmfMzFj3z9ut5ar1xvMfpWAPWSSHa5o2fs79EE52k02zqajrUFLtBPuAS3nx8w6XbnkZpvRTApHBDSCQncLEDE2PtQ/ony/X64AiFWiUFy2jLf+59IIT8OApmCSGkl/klG6x+ZK1Si5V/CEBHrlkQLFb+QgC3W6z887lm4T/uixrofVv4U8eJaIgC8ERNa/uy+Hn3dwI4IeR96gUQBlZL+yxYlvcdEQ17Aaxts9lH1rd1nARQ3uV0uhq6ux4E0N3pdDysVShNAIrKP3pjVH1zx8TPVxcMyJg4efUzd1/5it3him1s6YwSAcWHyzfb3B6fqrXTpgUrk/gGwKisnOz+z9730NGsnOwaAGGZ8zN2ZeVk7/d5W2edPFE7dMzgqLHzzWlfbejemVk9B5HRo0e5waaVveIXBEVFTb20b2yUVqlUyaz7DgdPMI+WaR2aZA/vnQzg0H96Xwkh/0TBLCGE/MnlmoVWALBYeQuA4WCTrqSBx/oDGArg258Koi1WXg2WSd2daxYKznouCkDrT6whPP/1ygEApgF4kYNpj4iGZwGAg0lot5d6bU7n6JjQEN2MzJeXSCUS97GqmvjqJV8v5dLTasAyy0s7vsr7vtRCRINMFMUgjuMAIIqDyfb6x9ndAJ4GcKCuuUMAqzFOAXAbWBszk8cri55288EbBqToEgxGm5CVk/1A5vyMzi1HSq/w+ZTJNV0dTyaFhG+1vtPk8Dgcb3s7HSHPLnlr+jPz7n+B5/nrTeGhUa3tnaYDRacEj8ctq69ruHxc13B7dWlz9JVjb/h8+Z4vGs/z4yGE/AT+f30BhBBCfjf6ANAj0L7KYuVHgbXBug1A7Hm8XgWgH4BYi5XnLFY+GAAsVj4BrNQg/YdeyMFUz8F03+J1m78C8HcENp5xMAkcTAIArNl3+I0H//55ZMHxk+KahQuuKq6tf/2emVP2imiQCHmfSquWvPmAmJe/R0SDKbA5DBxM6z9csaXA4XQXgm0eA4A6AG8DGA3gQrBa2SMAtoFtVnv5wMFRh0WRv7a+3tgHLFvbDQBd3R1ae0+3/IuDO+OQFNUIwO+ydW13dnYUVzbW+pAU9ZWE52/3eH1XhxgNJ2NMJp7jeHgEr7Qn2q5u52yaYJd24HncS0LIeaI+s4QQQgCwkbhgGdmBYEGdGWzDUj+wzVt9wPq9tvzIGhqwwQQTAdwCVt9bD+BaAAW5ZuEXDw2Y8sz9i/Vq9Zjsu2+RhOuDggPrxguC8HR1S9tNe06Vrbr2wnFvA3gXQD2ffuNLUp43PjUtfS4AX+b8jFdPr5WVky0BcDkAhUTC733qzr+UZ+VkDwBwMYDPMudn2Gbf8uEoKTravv7okYozXhcGln2uB/AtWB3v3sz5Gf/su8tqZ80+v/+l4ydLx1bU1goe3snvOFTYZNwZdtu+4JJ1qwu+oS9fQn4lFMwSQsifUGD6VyqAXblmwX2O5yUApoJttLoJbGPXKABZ5zMW12LlU8Ambi09M/i1WHkewFgAVeezjoiGeJ/fP7zL4dz0l/eW5seFGd2vzJvzJliHhGQASqfbnfX0Z8sv3VF0qnLtwke+NOq0erBgNhXAhAWXzGhUy+Wtoij+g+f5usz5Gd9/8fmEuvHljc0Pfbhx28sv3fzAnh+7lvJODALQmahHHQBk5WTPCbzHVQAalVLZlHFxSWUX9R2wHcBSQRAS3/9ihWTs8MHa8HCD9M2vlrjauttnLFny+bYfOQ0h5GeiMgNCCPmDCfzEL/uJwyYAyADbVPVvcs2CP9csrAWrPfWAtcW654cC0LPPmWsWTuWahZfPkcU1AbgfrHzhJx0sr5x67cvZb85e9OaNH9x3+8oXb7neC1Ya8CGAGgAulUIxNypE/zZE4ZoDpRWrbD1ODwfTbgA7AaxZv3/3M41FR4bYW5rWA3gpKyc76PT6RyqqNYcrqgduPHh0xo9dR3kntGA1ubee8fAGANmiKEwTRfGlWH3IlKGmuFtfW/zJI+9//U3J5gPWysvSLjze1NpuW79lN+wNXqXWpVkxb96chPN574SQ80PBLCGE/PHMBfCGxcrrfuSYzWDDDX70Z//AlK+7c83CgUBngh8yFUC2xcrH/UQgXQ/WHzb3x8572u1v/b2yvr1D8fxN19ypU6kGSnj+jWUFez6Z/fybjX5BqATrixt557RLxlc2tnR8vKGg55sNe5oBQMzLLxLz8r8aKZP7/F5fPS+VtQCYBGDI6fVHPvj0xmteeufqY1W1OVk52dFZOdn/9r142cSrE566447bGmpqloKN0AUAZM7PaKnaa91fe+iAt7O+5mSzreuj1h7bW/uPFzdt3rfTsWLr2iq3z7Xz0Mnj4o7CA6Ldawev44MmjxhvPp/3Tgg5P9TNgBBC/njawILGHww+A31k95/PYqeD2EBHggEAtueahbMngHWD9X39K4BqAC+efiIQVPtzzYIj0Bv3wPm+kTULHxU5jmsICdKaXG5PyIsrvqv7eNOOhqrmVnNZQ9P+lGhTPIDXtSrlyeqP3o4srW4sWr6xcB8AcOlp4QDuArB84QxLK9j9MAG4AkABAIh5+SKXnhZmCgq+1icIKVKefxOA9fT5s3Kyxw2eOeolDtzgZV9+eO+bixZuPfP6giKj5O4eu1trDOvT5XbO/vzQrhVfrPno8VmTr+6XljZxSmJ03BTL1ElNmnStfsOeLaLL4/VdMmjCzA3SZSoAh6b6rj7ve0EIOTcKZgkh5A8m1yysAbDmfI+3WHkOAJdrFoSzHufBhiq0BoLQSWADCioAlJ91TqvFyu8GcAfYprHTa0jANoF1Asg832vi0tOGPnzFpSGv3Hr9E2CB8sMPf/JV6qTUgQ9c+ujQbeMeWZgtk0qLwYLSQgCGho7O5Oe/zRv0VcGeI5nzM9YDMAC4CECpSiH3qpXyqG6744TXL6zLysm+CEBh5vwMJ4DUxu7ueKfHXaBTqr6fniazTI2/ecz4V6KC9GpREPbpdSG6rJzsULBs8F0APjbExk8AmxrWBcAr4fjiozt2L/3LnGtHXjxu1EC3x+MpOXXCl9pvEOeqq+UluiD3qe1HHUq5cL3XK5PhZwT2hJBzo2CWEELI7QCSLVb+qbP6wF4MYB6ARWCtsr4Dm8RVGeh8MBFA8ek62kDA+z7wfWcEGQCXxyUtPLS6/xjuhbSxYl7+nsDzPFgA7cdZuPQ0OYD7vys8yL1y6/V2AKsBHJs7aUJ9W7cNQ/rENQp5n1YBGFTR1DJ37MPPpA+MjVmz9IE7VGWNzf0jDcH3eXw17QaNurGjx9EKQLlg3uUH3R5v7ft5mw9sOVj0Qkp4pFItl98I4CCAxR6/76NXH1zwL1PPfIKQuPLowZY+IaHvzBw0rAfAYwCOg2WgTWBB7B6w7g0aURSNUo8wVSWRWSITTOhxu1q37d/lO7R7u7aqolSwe7u4tppq5d7OkltvHhJ1cFxXiBWEkP8YBbOEEEJ6wDKnZ7e3qQTLejYDQK5Z6AJwGAAsVj4OLDu5BsDH51jzDrBuCY/UHDHttbWpb5EpvGPBgj8AeBBAqMXKPxWpTBh+R5/nJjY6K4/9vfKp42KeUMelp71a0dTiANDCwdQjoiFzVFKf4QAMoii6RFE0cxy31ef3GwRBUG8/XiyPMhrmPHrlZVNNRn2wTCp9ok9E2Lcd5VXHAKy8YEFW1OcP/2XRG9+t/ajD4YhweT0rrZVl7oVrc98eHdcnd+agofuycrJvArAzc35GWeAa21t77MWtPfYjMol05Ji4xA69Wl2bOT+jMisn++7M+RmnA//jAHDZxGsukavln3ttrs7k/n0OLD+2p67uaNEV3m6/OnVcP37HkW2cCyquxy3w21I80arqtmOj/7PPjRACCmYJIeRPL9csfPEDj5eDDRc4l2qwDWTVP/B8K1g5grfvmJqE6AFNDr+P33XW8z4A8m5v+6MFrSv7G2UR7WAdCJ4U8/KLTh8ookEGQCWIQrvDbx9S3FVYnt/yhfSxfh/2SY6KjK37OPtQTWv7tr/c9bgZQM3fP3hZA4CvbG6NARAJYOTuk6V39r3zoedNQcGPckDouuJjXwAw6RTK6NSomAUAPgObPOYAm4CGwGsjAWibbd13dbkcA/VqdVRWTvZdALYDWDtv3hwZgIu1YeEN/SYPub/uSKXWFB6iHGqKS6lob86SSKWhYpD8sr01JRJ5cKQ6WRcORZJC7JccJ5Fptf0QGA5BCPnlKJglhBDyswXqaw+f67lAicFUsO8YCYDtSp2nBmd0Tsg1Cx+fcfwiKSdXbWtdIQXQftZaoXpZ2Oic4dZEr+Aa8X7Fk7X72jaV35jwuAlAMIB8hUwmTTJFJLX5vJdreInhlW9WbTxWU99Xr1F/2G7vsQHQAVgBoOKu8ZPaANwAIChzfkbx42+/cZtSJrsMwBawALX5jNNvAFAg5uX3SGdNfVwplWvijaFTwYZBhABYCyAGwO1+r7c0JFw/XDd5aAs0ikafICgmJw2+N2XMpH37TxxBQ1v7jNi+fdSJwRE4fPIojleWhoYbwp4bVlp/NZKi/qVWmRDy89DQBEIIIT+LxcrPBRANQANgRa5ZOHTW8xyAJwHMCPxNAxsVOxJAR65ZWBw4bhxYNvStXLPQ+QPnugXA1f838JvmgUFjrznesf9YeZlXnxgWowwJ0rVHGQ0rAcz2+Hw92qvmlevlyspWl7MWwMMANgJ4NXCOMjEvf31WTvYIsBKH1zPnZxz8Oe87KydbBuAdAHIAj2bOz2jdu2ot/9n6b4fa3a7yuBFjRgIIi9QF3zY2NsmcFBrhUUgkUqfbrd1xaC80aq1YUV/FHys7wdlcDqRNmu0bttc0IOX1MaU/5zoIIf+KMrOEEEL+RaADwT0AmnLNwlfnOCQGbOiBAqxfLSxWXgngarBs7VEAbwWe6wAQAbZZShH4d1poYC3lGed+HGyc7gKwLO04QYD9tudW7XlzXohjaOKQsDZlWfDbqzZ0JUdFKPyCeOdNl0xYG6kPHrX5uSeHHCiqSNx7ovwvn++3LgbbwDYXrFRABWD9glsvL+nucS6VSSXHz/XeufQ0Diz47j69We20hWtzw0fGJrx9+eBh1QCMWTnZYXOGX/BMxty7xxxvrJ1hsaRvzsrJ1nY4HZObe7o6+oebPrHZbZP2nTw6OTI0TNfdY4tq62pXDYxJQU1TA46cOMTvq96U8DIomCXkP0HBLCGEkLPxAOLBSgTO5VUAbrAWVadHs56e7CUCeCbXLKwNlBt8AxaUzse/D0pYDWDjWeN0xwDoBzZYYRuAj7ubtbq9J2ue/nzbLuPI5MTDo5MTL6tqbpvhcLmnbTh07MLJQwYOjg4xGsb1T5L1iza5XHZPGfbjEID1gXNPA3Azl56m3PXcM/5+8VGX/235xrZ1R14/tqO8ZBmAU2Jefk9WTrb+oUnTEl7fsv5qsHKDs8fbPn6otlp6+eBhW8FalJXWdLaHAkis7Gi5DKX1yJwye1vWxhXzd5afvHDnyWPX9TPFzpIIgkrqcqxrbm+9bHTKULGpqoXr7rSB89j5oDD9O2C9ewkhvxCVGRBCCPk3gSlewrlaZ/3A8ZEA/gYgCEALgE8BXAKW5dwCVrM6GMBuAOkA3gUwHkBbrlnYfsY6usAalwE4nGsWdgMAl542T62QD9zy/JPvjE5O7HOksvqz0vomQ0JE2I6+keHjXF6vJ0IfvA2sHdi9HEw1p9fk0tMmAHiJ5zjDI1OmtyeZIqIq6lv0h2pr7PtqKuztjp4nfbkbvs3Kyb4FwPQdZade3XSqqGLhDItt0fpVeqmEnyOI4jduny9qUGRUzNXDx1wNtnErD2yz2KypKalVLq/n1Yr2lpKarvZ5Pq/nmE/0JmqkUrvXw0l4cK4hMkVXkEyVcLisli9trURomAFet7/1hTffCPuFHxMhBBTMEkII+ZVYrHwYWF3sq2C//D0MoDTXLJy0WPk7AIwGCwCvAPACWClBXa5ZePan1g7NGDkrSjIo88WbbiiaMXKov76tY5RCLot3utwlwRr10I83bXev3nd446cP3S016nSrJXzUu2e+nktPCwJw2VXDRqlLm5sW6lSqoi6Ho9Pl98W02m1Xtjt6bBG6oOun9BvUmRQW8S1YpvmZfdUVZUfra++MN4a8u+2N917LysnmAPQFUAcgvG9IeNTcERMkAPYuKdz618SQ8MFGqeLxY61137V1VCeOjRvYvP7wSbchVB8fZbejsqQEbTKlRKPUwC9xo6K+xqHpUae8s+zvdb/W50DInw2VGRBCCPlV5JqFFouVXw/AA9aD9kYADouVXwygBsCXAOwA8nPNgsti5f8GoOmn1rVYeemQKZprIpuGyicMSBkF4ERUiKEEQOnq4pKK1ITYVJ/f7y5taBp///ufee+fNa1wbEoUuPQ0KYBEAOViXn43gC+ueeZx3uZx81UdbYc7nA4LgFIxL7+BS09LbbJ1X1Zmb3GMH9O/uWjzvk65WqMYGhldGB1sUIZotBsm3IRIIMNe8AlKASArJ/tWu9t9yYd7tsknJQ3ImDfm4uXNHa1T11i3vj1q+Jh6ISzGWF5frTUEa8W+4RGe1NQR8hKP1zU+vo+6f1g0l7t9IyBCzUsxH8BTv/4nQsifAwWzhBBCfjW5ZkG0WPmtYPWm34DV3aYDCAOwCay04NLAhjEzgOUWK//2WZPHzl7TZ7HyH741PeMlnUI1HEAs2M/7cVql0nQ3BapsAAAb/ElEQVTpwld3NXd1bQkP1t2VFBUhSYmKBJeeFgYgVSGVPhoVbHgPLCOMr599QUBgShmXnnZma7HjoUHajxdcMfO12LAQf+HXa7YAkKG6omTJks83T7gJwQDeANvg9haXnmaO1AV1h2uDt7T02MYrpFJ7xVOvn8rfW/BOYdGhfvVySRDAOVwdbeMH9knRerxuZZBKww1OSVI1dXdxXE0jikorEBJqgNalfuwb1QdrrnLe/n0f3sFXLOSCNA8O5Png+oJP/jkemBDy7yiYJYQQ8qsJjKnNBtsc9jxYDescAMX45zjYEABOAF6wkbhesO4HZ6+lBxCcaxaqcs3CZhENH4LV4I4BkABAccGAZMm2F5/yf7q5oOb5ZXnLBsXHrIy/7YFiAG9LOa7ywsQU+UBT9EUIBLNcepoKrINCBVgwfQpAnZiXL3Dpad/aXK7W2LCQKgDdYAF5LZeeFhKruLE7TnXzOrmsq3L8/Y/dIuX55BZbd0ybrfvlVLWmJFUmj5c+dFPHwKgYryV1RG1iaGTDRbFJsV09Nnlbd2dhcWnxmFZjhMrvdHE2Rw/aWlsR3KLDDNtEyKZJJcX6iikAvg9mne7Ndynllzwokw1+FzD+0OAKQggomCWEEPLrCgYwAqzt1itg3zODwAJWDmyT2CqwLOdwsAlidouV5wODGGCx8mYA08HaeEVbrHxGYJTuth6vbYpaqo0WBMHdYXe0hAbrfCE6bdQDs6b32RZ+24OvFe8aaXMO/gzgIn2iWFnb2dFh0Gi+O+P6LgVrIfYeWCnEBgBLAEDMyxfBBidgyZLPAaCDS0/rA2BhjfvTr6uX3fz1tXc/naXXRzwkujyrO7o7Jkfog9NDlZojfFdnsUahHOjz+5+w99i7io4drErRGWLs9m7Z7uIjyY2nitsaTxUZ7Wq1TCJXyCJDI7gLx4yB7KAcnU0dHD+VfwBA1umL9Hj31fW4vtmt5RQbgLG//qdEyB8IBbOEEEJ+NblmocNi5Z8Am5I1BKzEoBpsgMEGsJZbKWDtt5QAjoCNsH3KYuVLAHwN4FYAfcAC4HoANgCwe7vuXFm7ePJI4yX29jqdyy+Ksgu0yf0kEkkzgKaxhunTCxLyb1Prnd2OTnUbgMltPbb2viHht2flZAfzPDfsvTtvrnll5Zp1FU0txwPnHsKlpynFvHzXD7ylDrB63y4AKK+pj43ocTqdovM7USKZwEnlYmhYxE5PV+dbMwYM1V7Ut/+VetFfs3b7xiEftS+Lm33xDHeb0/H5oJHmiUPj+0gam5tlxQf36HwhoQr18GDONCkcfaqi0RXSHbx+/Dfh03Ze1QwANRsdq8CCfkLIT+D/1xdACCHkjyXXLGwGC0gLATQAKACwDoAPLM04BMA+sBrazwH0B5sopgSgBzDR4+JVx7ck+hpKQtYAUFisvNotOBtLnQdVb5U8wnW5u9qSoiLdEomkxevz73nq02WPBldc9/4T/T72u+0KD4BSAGHdbpdHIZXWt/bYQ5Vy2dD0sSOvWTB7ZhCA18F64toAiFMW3Bf6yJuv3nDz/z2TyKWn6c54OyFgmdw7AeAI1/X45vaqqw51t/wj3hQ9Ra3WWA831G1f2trU/NWhPdMX796ywqVQhCrU6mibw+7bXHLCu6SsWLqto/WkNixSPWH0Be5pUy3e8YNHYmB0DLwS4MCeY1i/v4CzTi7ZmZWTTd/LhPxMlJklhBDyqwv0py2zWPkdYDWz+WB1s5UAksF60C4HcAfYhK47wTKlCgCPN1cYQ7RGx90GU/dVAMareG3Qtp07BqjdSW5ZV3/Z2+Wr+r1/1z1VALwixCscbre00+5sDRVTj/p9kiqwcoGRbp9PmrVu5VYA18YYDC89cuvlwtr9h3sAGAB8KeblVwBAym1zXo03hE4vaWkqBNDOpac9ALZZzQRWP9vDpadx4nf5TQCannznzdT6rs4cpU+SOPhQROMj2yfsvGrKx+ElzY0+l9cb4wsJlxRXljg2HdgpXJwyILXD1fPu7orS6ebYPmGy4BBx/f6tXLw+BBcPGYuWS3SIaDaip6Szr1TORQGo/e9/QoT8cVAwSwgh5L9pJ4DrAEzMNQtFFiv/GIBUAOEAJgGIAvuV8CqwMbiLADhiBrTGiCKSOQ5+AFKnYA/7Kv8feaawKMP8Kx9MPhbXgCCNMri7x9GuVSltL996QzDHocZ4/V2DANwAtplKCWAU2BAGU21Hh/3ZnOUPjgyJnzVyRvwXmfMzKk5fZHlrywa3zxfU0N0ZApYxvh7AX8HKC6QAhMB1+gFga2lxSGVba8gljcnrLFuHmzhww28OCa+QSKVXFFWXq/wuhz0lyPC31FC5PcIY1lfX45je09muaVCqJSFavW1ov1SZVAS/u6acq2lpgdGjxLAv4rjKGfzQCTehruATUBN4Qs4TBbOEEEL+mzrAamWLLFaeA3A32JjaFLDsaCWAOLB+sCKARrDM7UyOAwBoAQRz4Ipm33XhPoVE9YQxRC6ZZRor2pwuFcf5B+89VVbRbndsmTFqaLVGqSi0OV3tAIxgm9AEsKA2HCzTGgZWx6sCUHL6In0rN6zi0tN8AC4Aq9u9CMBasMllAHBQzMv3z5s3JxKAYldr004AGYedtWU3YkQEgKM8x00V/f6Q2vKS/VKOS+yfNDAkPDL6w+7yk4tkctXcvgeNXuOEYKchVt/URx2tsbldko0nj6Clxw6pQ4nQxGCIMjEbPtwJVmNMCDkPFMwSQgj5r8k1C14AHwJAIJjdCzZUoRrAPLBRt1vBameDwDaOtQJwA2gOLBN6c/zTyyaFXX3P++VPFfTTjh0Xpog2ymVScOA8JQ2Npq8LChW7ik+1N3Z0DQZr81UD1k3BAjaooQTA22CTu546Y21k5WSHAHjgiSkzi3ZWlMrKWptDazs7toCVRdwFVld7EABEUbyvwu0aJee4v7pXbtz0wGsv9Xv3okJvSGO9CDbC91kD4I1KHXn7lopTWUM41AU7e0pcPe4eb6HAnzpSy396yXdNf7vq1uTwoGBx9pCxXE9fB9r3dKH1+h4krmyLHzO4085Khwkh54OCWUIIIb+JXLMggk0Bg8XKrwYLEJ8D6xlrAHAbgENg2VgFWA9YI4CWDm9z7ZqGpSknbXu1V0XfawDAdfc4FZ2uLum769a0OR2IHRATdee0Uf29O08dvcveLVsr5fmPBVF8ThDFTLBMcEvm/AwRLBsMLj1NAcCzcIZFASCi0+kMrWhrmdHtcroAxAMoB3AhgNIrDSGz5s2b4/WKoqPC4x4o5bibs3KyD4vAl3qlSi+ThrfUOhp8EXLJDpcg7PviUOGWMXGJ3vsumDzV4+hp+Ljgq8eOT6p4qsMjK+1yOja0tXQP04hyjUonlyjVQVxDWCdCDXpUNdeg34DvZmXldB7NnJ9h/20+GUJ6N04UqSyHEELIbyswAcwHYCaAh8E2Wn0B4C9gZQEasNZcYqQivu2WhGc61zd+uruyp3jyX/t9GJekHcK5vF7xSOMR//I9u8p6WoNin7lulqymo0mc+X8vdjp6pIrF98xrShs6+BXTzRnXBNZ/Eax/7Ttgm6yyAHQCaEowhi6d2m9wz7ay4gknmxttAB4B64VbB0AxTKl+rVsQBo/Vao90+f1Hi13OD70q9UUzB6S+4Hd7G8oqGoIb7R1NM3wmZ7Gu2+U1GbyTkwc9pOlse7KivibuYGtBpUylunLayOueGVM4YIysiZspu1Ntt6s9UaOi+0j3nChFS08rnD4n9MHGfQebax8GUJ45P4M2gxHyEyQLFy78X18DIYSQP5FAIPsKgL65ZuGTL2uz9oANW2gH63JQBZap5QFwGmlwcF/NEENJzyGxr2ZY7LSIOXKel3ACPH6DVuebMehC/QUD+kl1ahWf9c3nvuAQNJRV2aXlDS1RX+ywjmto7+wB0AIWOI8AsB+AHKx+9iIAF3U6HYX7ays723rsPAAd2JSxbWBB7/PtPp8o4zh5qcslL3G7hGafV9rtcg6O9HojVfbuYo/X+WK/4rhdXJjxmgEjk8PumTQ14vJBw0O/2bz69Q5bV7EuOnZnUoI5srLbidSI2OvlLl7WluBUO+CVJYVEIMyg4xLCwmHQ6BBvjAgvaq5L9IuC5uLRYwp+y8+GkN6IygwIIYT81nxgJQSVgf9XgAWVFWC1ssrAXzWApiZ3ddh75QvsANRV/AmpzfeEGCQ1QMYrJI2uKm+1/6RrcJDZ4/X7DdMnxPPxwROjDx35ZPWB8sqZYCUL/rShg/bNHD3c/sqK7z6ob++cDhYsbwYLoJ1gZQUDwTaJPQbgcQAVkc7tSofE5BwUlOw1KLS6AltXKERxkNorG6f1KVy7W5sLImXyhtFa3U0b+xbJugROPbQ62lPaUhEeYzRM7T9qwmt33HjDuqyc7DgOqAnXKcs3Ok78/c67J+2JlkgW6BTKoTUdrYoITTBkMjki9KHQyOXSEXtMo4oVzU/9Nh8HIb0bBbOEEEJ+U7lmwQf2U/9p9QBOgAW4KQAkYJlUIwAHWLmBHoBaLdG5nYJdyflELlgeht3ta5THWo7wxW5/8JQhI+onxk/YUnCsXNLc2T0AbHPZhwDGnqitn3j52OH2YYnxh+rbO68BG9qQAFZGcCGAHLBM7GawQLpOzMsXYqZwGp2/Wu50CUed3Ah/jyCMEQDDDZWpqiFtUd7XRmwKO+53uAap1Yu9ohhs83l276kql5ZKZQtcgHHp3CHvobR+2cK1ud9kTrUUPjB5+rUSns8G25jWxy8IHR6/EF5YXcaNiU3kg9QqOD0u9LsuVo0ZnjK891/+MAj5A6CaWUIIIb8bFivfHyzRMheBgA9smld/AF4pJ/9oXnzmlMnh1/WR8Qqu3dOE3eVH/FxPuCRSH+y3LHpjlcPtWdJu7/kYLLP7GFhA/DSAeinPP2oK0j/YZOs+5vH7dgVO+ylYaUEXABlYpjYPrKXYymjnJjUAR51qMgfWKSFteEtUzeUdSaXHRx5P3eOyOZyC8FDrt+uOculpHIABAAxyieTudXcuCJqUPHDVW9vX99PIFTNjgg1LY/Qhdp/f/3piaDj/9aE9ZZ/sKwgzKLXqVy67Rp0cYYIoiihtaRS3lZ2YfcdNN+b+JjeekF6MgllCCCG/KxYrHwo2uKA/WG2rHaz/bCKAZoMsouLZAV+nRKkSjaIooM7W4PS6JMoQXRB33wdLjr4wd27FBY8sHFjT1hEMFpB+ASAbgD9Wb2xJTx0ur21rXV5ZUXKzDNyH+5w9/QAMBxu9ezFEv4+DUKfk5O4hGu2NTsF/eZvPt7vO63FFyWSvqXhJRJnbVTdYqSpLVqhUDV7P0G7B7zrhcm4XWe3vFAArAch5jivyr9z4ZvPB4rdUMvmVjbbOvBs/Xzypx+1MGhoVZ9tTXe6p7mgTXr78OsOUlMHKAZFREEURLq8H7T222pgxQ2N/8w+AkF6GZkATQgj5vWkD6yTQBjb0QAEgBuzn/9AOb5P20aOXNx5s3gU/fJxaKVHp9TwafMXcwLEd/W1OV7smSHBK5J5gTuKbBla6oAYgb7HbHBVtLdsO1laKlS6XwSUKEWDZ3+jAOZdoxe4aLefVm+TyuCCen9Xp9z/g9Ps/l4HbbNYEJfdVKN1qjotr8nqvbvJ6p0o4LqnN50sWWd/cgRKAu0gb1G+OMcz+9yvmLn/tgw/fXXvi0GiZhF9V29nulfBcgijC5/B4WwZGRitvGT3B6xcF6dLC7fD4vCLHcVDJFTCqtFEorZf8D+4/Ib0KBbOEEEJ+V3LNgphrFj4C6yjwJVjZgQKAC2xKmKa1w9Nn075q1HY0Ymvzt6h3lXM6mVGckXC5cKi8aktZXYdKwsnlHDg1gCsAFAHY4vJ5DWuKjsyt7u66p0sUWo+5nHqwQFcNYGhfuWK7OSjeGCLVnmj2eMq32briVBBtfkHQcxCVLsFvdPh9cQBCnYJfKucglwGdTT5vGQAkd4TsGyrTHY2RK6Iig43hs4aNe6eg/OS1RxpqU2QS6fR9NeWpyaGRnsxpsz3ReoPG4/chSKluSQwJ79xcdsL7bP4a/+lfTFVKJQ/g+G968wnphag1FyGEkN+l62Izu7+szSoFcDlYsKkAC2yDJTKfJ0GZKp2SlMaHKqMRo0zigmVGTsEFSxxud9+jlfXiojnXGqIMRndVS6t0dFKiobatPUEQxViwDWflAJ5UcdyNPmAoWK1su1TEF0qpNLzC4+12QGwYqtakyzguuNbTrhEEt6/JzwthMrnNKJE2V3k9+mqvR6Lk+W9a/b5oCGhfvPMax5jG+IaChFM7br9izrg4Q9gFTbau+tSo2KbBphhti607/KUtq9Xl7c3OqSmph+u7OhxL92xr2V9b6TjRWG9Xh4TaBxjDgk3Bep7jOPiAEIlRl/W/+gwI6Q2omwEhhJDfs6NgNaizwEbLxgHgJRJounWFQoe3GR7BybmEHqi4ILGls8t53/ufSWQSSaJl3CikJsT2eH1+2dSRQ6Lq2tvdJfVNUrCWYH9TclzJWI228YDdntwNsUwCOLpE4em9PfaHwdqHjZeDS5ZwnFPmd40LkSlVHYJgr/d67pZy3GAReEwEZCfcrpEANoFHuUvi9b4fs/eOgu6uG65vb1VrFGrJLWMuNIaotVWtdtuBeGPoBYnG0NadFSWv3Xfh1M/eufKmEc9MtdwdEaRHW4/t9Unvv6q7pbbyocIHFw6USmW8HxwnK62PRVJUzf/o/hPyu0fBLCGEkN+twAjcYouVDwFwHVhnAi0A/qitgHu37BH/3LjHJGqlVpTyUi45xiT94N7b9jS4KspEmavfO6s2Hvhu78EJR6tqXRWNLX6w8rppAGa4RLF4j93+pQtiJIASGXBdiESKVJXmk2KXc0Gl151W6LCLGo7f4pGFOYxKtTqK47hCZ48riOcPgdXztoO191oDQLos4VBGi8rRTxTB/W3Xlprbx10UDg7tz21cGXKwttLd7ujxFrc0bgWQfO0n7yaKr3+SHK4Nkn+2f+eWbaXFZQ3tLZXqkPCqHpfTE6qV5csg+sG6LBBCfgB1MyCEEPK7Z7HyMwA8B9bRIBgABwAqPsh7Uehs2WWR8xChioPH7xJ4XuJ86eQddY2uytWLFybWA1gUWIYD0AxWexsO1iVBC+AQADcPTDBKpJ5+StUSryjeeNxh1xslMiFGJu+q8nl2Kzk+xeb3qVsEv1MOcGqej7MLQp2PdVzolvESk0IiaTG0qd668rJxwt0XTO5OCg3/yzNrl+eXtDZdcrf5kga/KAx537ql5Jsje1sBvCO+/snRvGMHwmYteXMRgFoxL//p7990aT0HAEiKoi9qQn4EBbOEEEJ+9yxWngMbphAM1mbrWrDgVHw8Zak40jCZ5zm2p1kQBLGgdZXnyc++Wr9rm/STwPFqAEKM0fDx8fdeCrr4iecnHSyvDAXgDeN4hxuioVsUjwDoAKvNHQlAJwO8CkCql0j8tX6/VAl4XIAHgEYONHrY1LIBAFw3jRzvVslkqqV7d8AUZNBsnv/Ym4mhEZsB7AL7JdTgE/y39Hjca/VP3H1CzMt3nX5/XHraCAA2MS+/5Le4n4T8kVCZASGEkN+9QLmBD0Cbxco/BWAc2AhaHO7cjhGGSSLAcwDgE31iinqkR2zZZwCq9ABqAAwG0CSVSnbuOVU2s7KpWQaWUVW0i4JOB84JVoIwEmzamASA4AVkXgB2v18G9gKnF3D5Aa2HjcTlwIJfSbfbtbikrSnS4/fP6XI6/JtKisIFUXx1SeGOvOdnXr0RwD5pSuzzwQDEvPx/eX9iXv6B/+oNJOQPjDKzhBBCeh2LldcCeA/AbB5S/s3UfKlKqpF1elsRJDOi2lHsf/a9A8XfFR61gg1DmMdL/CZdqN3e1aRzA3xoYCkPAp0MwAYztAIYyQOaUF7ibxb8MrB2YP7APykAQQZIhMADgTVaABQCWAc2Hlc1KDJ689j4vhcnGEJkT0+9AgCeRFJU8W9wewj5U6E+s4QQQnqdXLNgB3AzgDki/EsbPOUbre2rxfVNn6DFWSfml2xrr2ntUk0dPrgBrLVXm0ThdRqiuk5neEUAXrBBDA6wdl1hADRKYK0cHGeUSKUGdhzAMrWnwDKxUi8AP+CCCB4CZBBhBDCDAx4OUiinGVWanuONdYuX7Nl+/9GGulcAvAag7Le7Q4T8eVBmlhBCyB+Cy19+yCu6B0k52dbIufflDolPeOXyMcNbH/voq6MAOEDwgMNAiHwPgGT2GLxg5QZuAC1ScMsS5PIJNR73eCMv4QxSGYo8Lh4sqHWAbRrzgpUiaCCCgxj4Lwe7lOPapw0Y6ldIpMuXL3rxMS49LQOsrdjjYl7+vv/BbSHkD48ys4QQQv4QFBLVaK1UH6OUaKY9cPnM93x+wfrZll2VYAMS9gD8Ioj8LgAa/LO8ABKgSMfxAgfIRIi3eAVhaDDHC3ZR4Ko8Lg4skOXANpGFgnVAkAHgwMGn9kpbIMIJAD5RLApVa+9Yfut9TwYu62Dg2NG/3Z0g5M+FMrOEEEL+kLj0tCiw4PPdwENvAHgRQDdYhjUeLLAtTVWq9Ca5onZjdydE9ngCWMYWMgGI42ViK+fnukVBFFlg6w8GRBvACYANgA4sQfSkmJf/whnXwAHoD6BRzMvv+C3eNyF/NtTNgBBCyB+SmJdfD6CeS0+7Aiyb6gGwF8B3YJ0IHgULWKVVbpeMA9fFAbzIAtO9AMZAhFQiQvT6AadE4ALpHxGApIv9PZ21rQIQEjjPmdcgAjjx33+3hPx5UTBLCCHkD03My28F61IALj3tJjEv38+lp2UC6AELQlO6RVF/xOXwBY4zgGVbXeCgdfHgqzmvqPpn4Pr90oE1ygEsBlAE1neWEPIbojIDQgghfzpcepoOgAqs5GA2gGvAygsaAIyUATove/57CgBJMgWOe90AC2SdANoAHAVwu5iX3/DbvQNCyGkUzBJCCPnTCwS3UQDeBJAsAZr9gPnfjsP3vbpaACwDsA/AqkD2lxDyP0DBLCGEEBLApacZwdpuDQELVs9VjucH8DiA18S8fOE3vDxCyDlQMEsIIYSchUtPU4N1P5gNwAjWqUAE0AXgVgD5Yl6+/X93hYSQ0yiYJYQQQn4Al54mjQ6NWHTvrLmX6lTa/Huysx6mbCwhvy8UzBJCCCE/obwTCQA6E/Xo/F9fCyHkX1EwSwghhBBCei0aZ0sIIYQQQnotCmYJIYQQQkivRcEsIYQQQgjptSiYJYQQQgghvRYFs4QQQgghpNeiYJYQQgghhPRaFMwSQgghhJBei4JZQgghhBDSa1EwSwghhBBCei0KZgkhhBBCSK9FwSwhhBBCCOm1KJglhBBCCCG9FgWzhBBCCCGk16JglhBCCCGE9FoUzBJCCCGEkF6LgllCCCGEENJrUTBLCCGEEEJ6LQpmCSGEEEJIr0XBLCGEEEII6bUomCWEEEIIIb0WBbOEEEIIIaTXomCWEEIIIYT0WhTMEkIIIYSQXouCWUIIIYQQ0mtRMEsIIYQQQnotCmYJIYQQQkivRcEsIYQQQgjptSiYJYQQQgghvdb/Ax3m0jzsqmzEAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(sample_embedding1, y[indices[:25000]], alpha=0.5)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 1.5281, 50 iterations in 6.1395 sec\n", "Iteration 100, KL divergence 1.3389, 50 iterations in 6.0844 sec\n", "Iteration 150, KL divergence 1.2583, 50 iterations in 6.3079 sec\n", "Iteration 200, KL divergence 1.2150, 50 iterations in 6.4004 sec\n", "Iteration 250, KL divergence 1.1948, 50 iterations in 6.5539 sec\n", "Iteration 300, KL divergence 1.1771, 50 iterations in 6.4223 sec\n", "Iteration 350, KL divergence 1.1670, 50 iterations in 6.3885 sec\n", "Iteration 400, KL divergence 1.1595, 50 iterations in 6.5070 sec\n", "Iteration 450, KL divergence 1.1536, 50 iterations in 6.5603 sec\n", "Iteration 500, KL divergence 1.1494, 50 iterations in 7.0096 sec\n", "Iteration 550, KL divergence 1.1456, 50 iterations in 7.1817 sec\n", "Iteration 600, KL divergence 1.1432, 50 iterations in 7.6505 sec\n", "Iteration 650, KL divergence 1.1411, 50 iterations in 6.5691 sec\n", "Iteration 700, KL divergence 1.1397, 50 iterations in 6.7194 sec\n", "Iteration 750, KL divergence 1.1383, 50 iterations in 6.6007 sec\n", "CPU times: user 13min 9s, sys: 5.99 s, total: 13min 15s\n", "Wall time: 1min 39s\n" ] } ], "source": [ "%time sample_embedding2 = sample_embedding1.optimize(n_iter=750, exaggeration=1, momentum=0.8)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(sample_embedding2, y[indices[:25000]], alpha=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learn the full embedding" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 48.7 s, sys: 268 ms, total: 48.9 s\n", "Wall time: 35.5 s\n" ] } ], "source": [ "%time rest_init = sample_embedding2.prepare_partial(x_rest, k=1, perplexity=1/3)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "init_full = np.vstack((sample_embedding2, rest_init))[reverse]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(11, 10))\n", "plot(init_full, y, ax=ax)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.0001 , 0.00011403])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "init_full = init_full / (np.std(init_full[:, 0]) * 10000)\n", "np.std(init_full, axis=0)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "embedding = TSNEEmbedding(\n", " init_full,\n", " affinities,\n", " learning_rate=1000,\n", " negative_gradient_method=\"fft\",\n", " n_jobs=8,\n", " callbacks=ErrorLogger(),\n", " random_state=42,\n", ")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 10.2843, 50 iterations in 56.9785 sec\n", "Iteration 100, KL divergence 10.2803, 50 iterations in 56.5474 sec\n", "Iteration 150, KL divergence 9.3190, 50 iterations in 57.3148 sec\n", "Iteration 200, KL divergence 8.7463, 50 iterations in 57.1166 sec\n", "Iteration 250, KL divergence 8.5086, 50 iterations in 57.1942 sec\n", "Iteration 300, KL divergence 8.3858, 50 iterations in 56.8681 sec\n", "Iteration 350, KL divergence 8.3118, 50 iterations in 57.3070 sec\n", "Iteration 400, KL divergence 8.2644, 50 iterations in 56.9948 sec\n", "Iteration 450, KL divergence 8.2318, 50 iterations in 56.6485 sec\n", "Iteration 500, KL divergence 8.2095, 50 iterations in 56.6715 sec\n", "CPU times: user 1h 13min 12s, sys: 1min 4s, total: 1h 14min 17s\n", "Wall time: 9min 32s\n" ] } ], "source": [ "%time embedding1 = embedding.optimize(n_iter=500, exaggeration=12, momentum=0.5)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(11, 10))\n", "plot(embedding1 @ rotate(90), y, ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### No exaggeration" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 7.4090, 50 iterations in 56.1328 sec\n", "Iteration 100, KL divergence 7.0913, 50 iterations in 56.1467 sec\n", "Iteration 150, KL divergence 6.8774, 50 iterations in 56.8166 sec\n", "Iteration 200, KL divergence 6.7154, 50 iterations in 58.3645 sec\n", "Iteration 250, KL divergence 6.5846, 50 iterations in 59.4526 sec\n", "Iteration 300, KL divergence 6.4756, 50 iterations in 59.7037 sec\n", "Iteration 350, KL divergence 6.3825, 50 iterations in 60.7804 sec\n", "Iteration 400, KL divergence 6.3017, 50 iterations in 61.0211 sec\n", "Iteration 450, KL divergence 6.2302, 50 iterations in 60.4779 sec\n", "Iteration 500, KL divergence 6.1663, 50 iterations in 61.2701 sec\n", "Iteration 550, KL divergence 6.1089, 50 iterations in 61.9637 sec\n", "Iteration 600, KL divergence 6.0568, 50 iterations in 61.3257 sec\n", "Iteration 650, KL divergence 6.0090, 50 iterations in 61.6352 sec\n", "Iteration 700, KL divergence 5.9651, 50 iterations in 61.6618 sec\n", "Iteration 750, KL divergence 5.9246, 50 iterations in 62.9434 sec\n", "CPU times: user 1h 49min 46s, sys: 1min 35s, total: 1h 51min 22s\n", "Wall time: 15min 2s\n" ] } ], "source": [ "%time embedding2 = embedding1.optimize(n_iter=750, exaggeration=1, momentum=0.8)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnsAAAI1CAYAAACuZjyLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsvXmUXWd15v0759yp7q1JpXm2LXk2xhhsY8BTETPIBAUCSUgC+T7SSa/+kv7Ql07U6Sir03TaJKuSTgQZm6SdhAykQyYZEINIYTCTbbCN51GWJVlDaarxzuec74/97jq7riVbkksqq3iftWpV1b1neM97btX7nGfv/ewgTVM8PDw8PDw8PDzmJ8K5HoCHh4eHh4eHh8eZgyd7Hh4eHh4eHh7zGJ7seXh4eHh4eHjMY3iy5+Hh4eHh4eExj+HJnoeHh4eHh4fHPIYnex4eHh4eHh4e8xie7Hl4eHh4eHh4zGN4sufh4eHh4eHhMY/hyZ6Hh4eHh4eHxzyGJ3seHh4eHh4eHvMYnux5eHh4eHh4eMxjeLLn4eHh4eHh4TGP4cmeh4eHh4eHh8c8hid7Hh4eHh4eHh7zGJ7seXh4eHh4eHjMY3iy5+Hh4eHh4eExj+HJnoeHh4eHh4fHPIYnex4eHh4eHh4e8xie7Hl4eHh4eHh4zGN4sufh4eHh4eHhMY/hyZ6Hh4eHh4eHxzyGJ3seHh4eHh4eHvMYnux5eHh4eHh4eMxjeLLn4eHh4eHh4TGP4cmeh4eHh4eHh8c8hid7Hh4eHh4eHh7zGJ7seXh4eHh4eHjMY3iy5+Hh4eHh4eExj+HJnoeHh4eHh4fHPIYnex4eHh4eHh4e8xie7Hl4eHh4eHh4zGN4sufh4eHh4eHhMY/hyZ6Hh4eHh4eHxzxGbq4H4OHh4eFxegiGBkvAQuBQunm4Odfj8fDweHUiSNN0rsfg4eHh4XGSCIYGFwJloAf4O2AN8FXgp4EEWAfsTTcPj8/ZID08PF5V8GTPw8PD41WOYGgwAvJAF/Ae4ALgp4BlZrN7geeAPuArwJ+km4eTszxUDw+PVyF8GNfDw2NOEAwNbgCWpZuH75jrsZwDWOS+3gDcCvS73y1eBxSArwOPAP5J3sPDA/AFGh4eHnOAd/7uR68O0uCfgD8KhgYvnuvxnAMYRR7OFwHfBMaABnAA2AfUgHGE5G0Fvp5uHvZkz8PDA/DKnoeHx1nG7/9z808uXvDO9z9w5MnWQQ40gF1zPaZzAE1EtQO4FCgCDwCHgEeBpe7rGHAlUAqGBg8BjXTzcOvsD9fDw+PVBK/seXh4nFUEUWFqQWn10Tf1vuPnkDBuY67HdLoIhgbzweab1wVDg5UzeR6n0j2GFGKMA/8E/DHwSaAOtIH7kJy964EbgAuRcK+Hh8cPOHyBhoeHx1nF1m0EQLhpI/Fcj+Ul8fF7u4AcH7l24kSbBKveuJhe3kFveFf6nW/tOd1TBUODYbp5OHGVtjlA1bhJxFrlIoTIXQP8KLAfuAdR964Hzgd2IyppBXgCCf0uBHanm4drpzs2Dw+Pcx+e7Hl4ePxAIRgaDE4qn+3j965ELE6e5SPXHreqNQiurQCXAU+k6b0T7vghUhFbVdXSVdMuBgYQy5SH3PcmsBpYDyxAcvIWA3uACaTq9iq37RhSdDHmtvsaMAI87ratuO0OIipgAQntHkw3DzeUUNp5AKJ083D7ZefCw8PjnIYnex4eHj8wcKTrNsSH7v6X3Pjj9+Z+u31X6b/EX6yerIVJMDSYAy5HlLbngaeQ4okehNDdiKh2TbfdCBJqXQascIdZCESIwjcFnEeWr5e6/fW9CLgfCe3uccf5OnAncBgg3TwcB0ODBXf+Q8DhdPNwGgwNrnDjvM8bMnt4zG/4Ag0PD48fJITAKvfzS5K9oPGrARI27Q+GBr8AxEDswq0RUELUtDxCrBYgKt2bgSXAmxAC9iXgx5D/tzchFbMLgYvd/jmEtEXm9Avc7y0gcD+HCNnT/9sVxET5GrfdQeAoQviWubG0g6FBVfmuRnL7voQUcuSBtQghPfTyU+fh4XGuwit7Hh4eP1BwLcaSl1OzXJjz54BrEaVsH0LexoBeRHHrQ8KuTwNVpFL2NUgXi0sREnUIKZaoIUoaCCnrQhS/EKmuzbufY4TQ2QI6/UcduJ9jhOjV3HaHgSPAM0g+Xw/QjRDApxFiuBDJ+/tnpKgjcNuN+lCuh8f8hid7Hh4eHidAMDS4Bgl/FhDStgi4AiFyuxHPuyKSM1cF3gZchxC3y9xhmogyFyCkq4WQrW6E8OG2j933SSRX8KXcEtpIGLeFkL7HkbZpFYTc9bjztRAS2OP2aQHPAnvd178g5LHhu214eMxfeLLn4eHhYeBCtD2IanYR0rWiiIRfr0dI1XUIAWwgalkNCb0WkPBszv3cBl5ACjMG3CnaCMEqIUQtdNsmCEFrkpHATthtqm7fNqI29rljjiCVuIE77qT7eaE7r+YDFhCy+lngG8AB78nn4TE/4cmeh8c8gLMzKSPKTWvTRt8q6+XgvPF6gBGXhxcAy4G3I/lu9yEq2NsQoqbFFweQXLeLyEhZASF8E0i+XhG5F1OIsrYIUd00fNvltqkhKl8/WV5eC1H4OqHh2xQhb1WE3IXuvLhjxu6YebfNMYQIKoHtJsv9ayKq4NPAx5GikinffcPDY37Bkz0Pj3Mcjuj9ArAFWfT/CPijTRvxeVgnQDA02IVU5XYDf48QpBD4j4h6931ERWsjOXgxopC9hcy/DoRslRCiVnT7TCHEbtwdX4kfCAkrkxG2mttnAVm+XsLxyR4IOWu791O3j/ryBW5fm9PXNq8V3TG0IKTt9hlHPjf3IXmJf5JuHt7r5qmAKJktxK/PLxgeHucgfDWuh8c5DEf0rgM2I+pQP/ARYGDrNu4E7j9VlW/rNtYBH0UW+Z/YtJF9szvqs49gaLAItExeWoqoWAlCZG5AKliPIuHaUeDdSJ5eF1KQ0UCI2kL3Wo3sf2iVjIAp+VKypwSthhDDlvu57L7aCAlU4naiXD29j0XzWoQQO0vsC+4YTYTMhe74Xe74uu0YmcJYAl4LvA5oBEODn3DX2UJUycMnGJOHh8c5AK/seXico9i6jQISbvwYQlb6yao16whx+SnguyfTrcIdbzPwK2S+bo/tqz/3E1889Jevebx6T7lN8+508/Dzs381ZwZOmboVITL/CDyt6pRT94rItf5npLp2goxAXUlW2boEmc8lZMpZj9s3T0aiUjIypYraFBIGTtyXErs+5H6NmuMkZKFhVecCMrKo49Vwb+pez5OpgXYsNTKfv5z7ihHiGiMh3gAhdM+TfW6+444xAjyIhLMTfCGHh8c5Ca/seXicg9i6jW5EebsVIXwHkcV9wH3PI0rV7wDf2bqN/7VpI8++zGFzCNGYQIhIDkjjtPX2ydaxmyC9IiA3HAwN/vJJtd+SdmMtPnLtXIaT+5Dq2fXAO4CVwdDgdxCy8zaEBK4BBhHS1Y2QqSPI9V9KRpI0D65BFgpNydRBNTsuIASvTVaEARnR0nPg9tfKWQ3LaohXiy8CMnKZmO00PBya4zXcGHVsZfdeiyx0e9RtW0bCzfrEn7hzxG5ODgA3u5+fd3PyXDA0+IQP53p4nFvwZM/D4xzD1m1ESH7e65EeqN9GQnJvRxbqEcT7rYAoUTcCj2/dxp5NG3kpb7ka8BfA3yHhvMPAYwsLy5LuQt+OvtaSHy9F5b0vxE/P+L+xdRsL6o2JjxyYeqT2wuG/+6tPFz5YzUlu2/uBvwaGZ+/qTxnHgD9GrudihBiXEHLcROxRbnbbKpECIYdK5PQ9DclCVgxTRuZcQ7PdCPlqkKl1fW6/bve7KnK4bTVsqyRPiZ/m26mpcmD20+1DMhLYdK81EWKqpsuqFirZ60WI3B7kc7LQjb1JpkBe47ZPkDy+FW67MeQzdfA4c+3h4fEqhSd7Hh7nELZuo4j0Sv0gErZdgZA7DbmBEMAisiAfRAhPilSanjAEu2kj6dZt7HS/Pr5poxKdXn6N32TrNj52z9gXo09/6B0NM54QGAyD/M+UC8uOVgornn2yWIkvbkytz0ke2/7ZuvZOuNZkK4Aj6ebhqWBoUMOp3cC460urhRPrEZITIoTlEoQsLyZT4bSSVcOmkJGv0H3vd9trj1rNs9N9E7NdQBZa1deVrGlVbdH8rufQMQTuPSWP2mVDiZ3dPm+OrWSyRUZE88wkjT3u3BUyUrmazNz5iJvHBkKSi+56XwAePu4N8fDweNXC5+x5eJxD2LqN9wJ/QObZBhJ+VL+3PPAZxDC3G3gMIXuvBx7YtJGnzsCYukjiH9o1+chzjdbzI7cV37zgrUd2jXal8ZEzEcJ1FilKUlYi1xcjKl0/QoS+ixRIrEfy0ZYgVilXIJW2b0W6WWgf2iYZ8QoQQpd3r7XJwttK2HrIWo5BVmhRIDMzbpARJvXR0+N3Pmh3dshQaOVtnSw8XyULEasZs5JAJYltdw5VJ/NmGyWBqTmH9tkN3HiLCDk+6K67COwCdgJfAL4H1NLNw1N4eHi86uHJnofHOQJXJfsvSOstC7X72I8szH+NqDCLETsNVa0e2rRx2o/tnEAwNBjY/LBgaDBEiNd1CMFaSUZKepFw5OPA/enm4XowNLgc2IiQw2sqhMUqSZJKrp5WoXYhBCclU9sSMhKlodcDSEgWt4+SpBiZa1XOrOWJKn5a1KFFGNZaRc/b2R5NQ7lWkYOMsOl9VX8+HS9k+YPaYcPmD+qxlRQ2ycLUSkg1P1FVxcMI0YuQsO59bv7/EiF93ozZw+NVDE/2PDzOAWzdxlKk2OI9HD/9QglAFfga8A+I4vcgsnC3zjXfPUfsLgCOpZuHjzj7lCsRhe5diP9dGSEhD7jv30f88EYRAnghUmW7HsnbW+S+qzeeEjAlS9qVokJWtaqGyQFCrlS5U+uVkhuyhlK1Glrz5Kzdi4aRtejDqom6rxI2EDI2iSiWkPnpxWbseTJ7FyWqShA1nKvflWgG7rgxQmCnELKHOV5kzqWk8KAb/w5glfveRMj1GQvZe3h4vDJ4sufh8RKYGvrysmNJe+U/XrrhgSyH7exi6zYqwK8D70Ny1F6qZ2oN8Yn7NBn5+MqmjTx+psc5m3BEby3wATLvu4uRitoLkBBshJCqZ4C/AZ5ElL63IfmLjyLk5hAyd1ch5GUZMjdapaqETithYaZFSeLOpQRRtx8nI2xK5CJmVtQqOVOypeHemhvXajJvPVXxNEdQCbySLiWnVkXLmdeVJNrPaURWsQtZEUfLXYNe9xSZ2bN+6XE0jByQ+e0p0d3j5v9p4Kvu9b0nVa3t4eFx1uALNDw8ToCvLV1yyboP/Pq/hv1Loquf/cqt8EO75mgovQi56ePERE8W+7hRIkkuJN/1fiSn7DmYNl+OgPgcaaV2NfBLiHqXIuHpbiTHTrtSgBCuSYS09CNq0+MImVmOFBq8G2lzVkSI2iSZQqc5ampyrIqeEh01QW64n22Yt8BMcqjEUI2VISvg0P604+61PJkiCJmip0qa9sxV8qjj1G30+ErwtHuGhncLZhtLIItkoVmraNqcwiYzVUe9jtDNUYB8JjWfcBkSFr8AUf62B0ODTwLNdPPwy/o7enh4nHl4sufhcQK0m9WouvuZx8rdA0fuXbR2341zN5QlSEGALsy6+FsICWwmASS9RK3XEeZHkUKNHmQhvhxRYM4FlW8hQtxyZFXHRWb+z6oj1aEl4A2Ikvcokru4CLEPqSD5dCWE+PabrwYyj9pfNjBflmypCTJkyphtVaYed9qvVm1WcPtXyRQ0S8DU404VvRwz253p79ZYWcmlJX6QKX5afQyZwqjHgZmfm9jsZ8ke5jwavi2798vud7WV0ZB3CQmPfxsplFmOVO3uxcPDY87hw7geHi+BHeUguLU6d38kW7eRQ3zifozMyqPTfDdD0oAkgVyXFm3sRlSxJYiq9FX3dd+mjYyepcs4ZQRDg+cBv430qe3nxW3EEiR37ChC+DTUu5Y0bdNsQhT1k8tVgaVklboaAtW+tJpDN4Dk2Gm+mua1NZFwaz+Z6qf5bDY/TsmhKnQaltVKX1UBlaRpoYaGS3PuOHVmqnWxOZ5aruTNPpbw1chIpRLI1GzfMvvYPEINV6dkeYZKNhtkps2p+V3JpxaoVNx+zyK5k08ADyFVu/vSzcNNV0Vdwit+Hh5nHZ7seXi8SuE87JYAdwC3kHm/nawir8REfz6AEJunEL+9TwNPv9rCuo4UvBv4XSQHr1PF1OsaQa6nhhDau4Eirdb5TFVTukrrKBbLCAlZjhCNGlmf2yfI7FQuRQhLgYykKaE6RJbPpv1lIcvfs8UQSkhVhbVKbGi2sWTL+ulZdU3JXpWM7Crht+fVBwANv0Yc70EgI4BapKEVx0oSlVTqsbUlm5I0bfWmx7CFKlrVnCLFMUcQEg5i1fIthDAnCBkcdT6IHh4eZwE+jOvhMctw+XF/joSwPrFpI0dO81D9wI8jRGOcmaG/zoX8eIjIyMsYWX7bFNICS3Osxk5zfLMOV5hxA2IafTyiB3INRxASNEVGLKQoIJ8/QKW8kihajKhvVYSwaV/ZNpnh8ypgKQQ1SJXUdJGFT7WHreanQUaCWmQ+fEoQIVO9dH8lbUr29AtmErPEbKvbaNWuzREMzHlsUYYqdBVznJx5Ty1kNNyrSqGGhxOECGunjao7ll6jzT+sk9nQtN38L0dSBrrJ2q4tQlTTa5HK8KdlvumGl23f5+HhMUvwZM/DY/bxP4CfRBbQJxAF7XRwOXATQso04f6lKnGPByUDFYTEhEhC/Yg77ghw/2mO70ygBPwq8CZeTPRUzdtFpmB9GjGMXomQjtcDKfn8GJn9SReZ+pQgylMBCREfA4qQ9pJ1rrCtzAJm5tvZDhdWAdPt9WdLppTIdRZ1KIHS9yGrlLWh2xJCvPR9LaiwpFBDrSVmVhfb0G1qvlfNGCtk4WGb56chbchUPFUrVWUukIXAm2Qha/3cVZGK4yVkXUUeQAjjyePj95aAK/nItfee0n4eHh7AqS8cHh4eL49PIeTiBSRh/ZTh1MEEKUzoQxbx03k4UzKgFatlZNHdh2uztnUbC05njLONYGiwBPwKUmxR6Hhb1TOQ63kQKbhYgZCbRUh3DJDK5csRAqhdJpYgalIfMo9l93u/OW6X+66kZQIhK7aIw9qSqGmxFmlYwmaLLvS7hnZx29rqXSWKSh4jZpJLtXfRUKoqcmHH+TTMq6RVc/qaZFW+BbK2aFrMoSpp3X1pRW9IRlhr5hp1vwT5nBcR0qd5feNujvcjhHoKsdL5v4BNzPQSfFkcSap//3Ry6F8Pbh2+/lT28/DwEHiy5+Exy9i0kScR8nHRpo3sOs3DLAfeSWb3cbp5dRpKLCLEaLX7fiuinl0KvGfrNpZs3cbC0zzHbCFCKjorHa+3kTDs9xEvvT1IQcZDiJJ3NUIkLkHC02WydnJ5pLK3REZiiu7nMfe+rXTWnrORe92qXUrIlGR1kYWF1TtPq3WVzKi6ZnPcIDNjbpORPngxMbQVwqr0jSOh64Y5nn4+lIRaA229LlXkxskURNx7PW4/NZW27dlUqSu5OVNie9Adby2ZZ6GaPEPWgi0kK3BZBnwI+PlgaFArll8aH793WUL8RJvkoW8nz/u+vB4epwFfoOHhMYcIbt+kSkz87mUb87csukUT7j8M/BCSU6YGwieuwj1xHp/6qdlwYp2s1+m/IArYnUjIeddcFWwEQ4OXA9sRFU7RQAjIHvc1hRSYaI7cO5A50lwzDUFqjlxojtNCQrhlhJxoQYGqYOqlp2TGhmhtta1CQ6pK0DRPT0mSqnhWmbP7KrSPrebK2XHrOXBjV6USMhVPf1blzva3Dcxx9Phjbr40xw+ySuNDZD6GVknWn5Ukp4g6fDEyny1EwVtIliM4hvRmXo/c033uOIsRov4J4Cvp5uGXDul+/N4lQMRHrt0P03mdkW/R5uFx8vA5ex4ec4sKsKov1xeFhBfGafx8FEQaomshi2uNmQs3vJjYnahgQ0ONSlTUMy2HLMwVJLS5BpfEv3Ub+zZtPMWcqlcIV4F7C5k/HW7cR3UThEx8EbHz+EXER28BMztBaG6cFjZoSDFECI6+r2Hx2Hx1mghbtazT9sXm2YHcJ82rtCHTTmsWhZJMVRH1Nd3ebqPXUSEjgkpsdW5s1W9wnNfUT2/a/oTMOFnVyBD5TFiLHz2+zql+dhpIzqcSyykyK5q8OfZ6pCBDybmaOl+GFB9NBEOD33jJytyPXDtif13RrC2uw8rgYzdcQcq7abc3p7/x7Z0n3N/Dw8OHcT085hhN4Eh3rvtAT65nZxREe5EF9AlEbasiC+UxMsVEe52eDDSMpoRIQ5YthDxcj7TAWo9UwGpI7mwjAn4BIRMg1/0w8E0kdPsgYt/xNPBGt00fEi63uW46PxNk3S5UqbIeeVqBquFJJYdW6TuRimq7WqiSh9lOyZvmvZWYCa2iTcwX7jgF856SPyXrOj4N82oBiCWSmr+nhEs98zD7qXqp25TJwq/6HmSkUq9XUUDmV0lyDcnNU7Ks+1QQRc/22lVCXEGqrn8cWO/UupPC/3zhkb6bx0ZeTzv+Pdrt20jT3zrZfT08flDhlT0Pj5fBlg3BLwHXAT9z+/a0PpvHTrdsVdNekDwstm6bLh6wlY7al1W7HcDJWbCoIqPbqgWHkoXXIHl730QMmG2rr1lDMDSYQ3rWvhP4PPDldPOwVcbKwHeRnK4EseV40L1+2L3WheQarkPIw16EYCxlpn+cVqpqT1slOUo2lEgp2VMFTKtbY/O6Km2R+dnmUGr4NjTv2UpVax5s1UBV7JScqr1JZ+GCnlsJnL3fGvJX6Pg1hKvjzZn39HiWyGpo39rB2PMqebPnKTOzQnmFuVY7H6omalW0HjdAHjxucPP0G4j348vissbkwcn+lQ8Rhl8nCJbTbP7ayezn4fGDDE/2POYUWzYEarSqbZnS27enrzZ3/fcC5yFEZNdZON9hJPT4VoTk2aR99YCzFiEvBVWXLDkBWXA1Zy0FLkTI1XXA5NZtQog2beTI1m0Es5DHFwBXATcipPbLHe/3IK3OehHVKEFyu3a58deR3MWLkNy1Y4iqp+3MNHSpZE7DnEpitKpU1agKWeGEVfMgI8J1MjKj+1vlTUOhMFPhK5BZlkTmu55HCZvmUdr2ZkrULNECIXXtjv07w8JK6JQAqnqpljMJGQlsuO2tqqlVuzbcr9diQ82QVfOqSlxAFD5VopXo6jzYsSrpLiCFSNcB/z4YGvzNdPNwjZfBlXekY18eGrwP+IA3ZvbwODn4Ag2POcOWDcENwL+StZTaB3wJ+GdkkZ8AGnNN/rZsCFYBq27fnn7nbJxv6zZWAD+BhFVXIIuqJWY2xHi8PDD7u5IbG/rrLDyIEVXleSSpfh9S+XovQqou3Xfg2/ueeuafp25+y+8cOF3iFwwNFpDq2afTzcOHO95bjSiMSuqucuduIPmE3e71XjLbkcVkYd8ymXdbL5mapX5yJXe8UWROcwg5seFbJUU6N1qZGplt2ryYuEA2x6oK2uINJWD6UKNkzBYYKJnMmW1VXbRdN+wDettso0bO1t5FCaM1atZiCj2+LSixx9bCHt1PVeVO1U/JZ4LkV6q1i1b45pHPVA9ZHqreH8V+RO3dkm4eHsfDw2PW4ZU9j7nEW8gW6yJS2bcOeA+ibh0EdmzZECjJ2gNMzHYo9UQIhgbDdPNwcvv2dC9nt6F7HvGMW85Maw8laZa0WRzvd2uoCy/ui6okY8D9/DCZslNFwmyrjo4+s2R07NnzP//lDz585xfqXx7+08/YEOxJId083OQ4voPB0GAeeJcbw10ICZt0Y1mEfCbehJCEUbLuDWPI50ZNhzW8aKtaVQlNyXz1lKzZatxOexPI8tuUHOn/S1v8oKRbu1PATD9A68enJKkzh86SdD2/VdLU8NiGbNXg2Bo6q0KohLLzPltVcIqZuZw25w+yz51+hvQ1fThQ4qvXFiH3p+b2qZD5D/Yx0ycwZCap7kfI4Cl573l4eJw8PNnzmEscQsKVvea1HOIFtxpZHG5BFpUYUf3+acuGYC9CBnffvv0MSNMfvzf8TPzQ4tuCi1cGQ4NPpJuHqy+/06xDq281aV+JiC0egJmWIIH5rguxzRFTEtGZDK+hvDHgSrevJtdPABPnrXlHXJs6MprPF1cuW3ZNN3LfZgXp5uFWMDT4RaQ45GJ37auQvL3vIlW32iauipADJT+jCGlrI8QQMmJlCUxnrl1nPpoSEFWxVOnTUKSSlbDj2Dacawmg9pvNMfN+qfKm98cSMN3WfqZVedRCjJis8ldJp6ptqiZaEmlz52z4v8zM4hDb2k3nTK/JEntdMywp1pCtkkdVJ/U6bV6kbSmHOVYNiPUBCw8Pj1mFJ3sec4nPIdWgP3mC9zWfSRfatyML/xMIEbhzy4bgm0Aya6Tv4/cWgDVvCtb27gvHc5+Pn5y1EPKn7v5k8KEbfv5kxnmUzGajTqZ6aLcGJW62AhVevOhDpjrZ/DKFVZAgCxdf4t5b7cZS7i4vXnHN6zctRNS23NZtQog2bWS2FuYRRL3LIw8B+9y1vRvJ6VKCqdeTRxSjKWYWYOSROVNyoYRFr28CIYtNMksbzTmz+W5WOVVjayVWWg2thEoJWWi20/60apqsCqOtqlVSaZVEVdMgU/QsIdRCFds5Q9VDG161RSZKdvUabT5havZVWAKrJFXTAXSfznHrfqqETpnzWQ9CLdLAjEsfLt4EfA3tcezh4TFr8GTPYy4xguSF3YYs3J0WFhlaBEzRTYEiZboQP7Aiogo+gLRsYsuGQP3jDnN6JDAGqivDvoMfCd9S/ch/+qVXTPb+4q6/j5Jk6sNhyOFP3f3Jz33ohp9/OTPYAbJWXap6di7Kdq6UsFnlRhdaXYjVG82SDlthGiI5cG2EWF2JFEPsIcvluwbJq+tzr38feOYUpuJFCIYGNZ/uRiRPbzdSIdx28/CTSGFMSKY2Trl9DrvtSu5riqxoICArsGiRETYN3WqxghJnJTIK7aShPnQ2LKr/N62qBjO++7MiAAAgAElEQVSLPJSg2UIYu+3x8v6URCkBstW8eu9tmzLdt1OVC8wxdFvNo+sMEWu4VkmuKnl2biwJ1gKOSeQzam1W9Lqsyhmb19WTr9PsuYwo+N8D7sGTPQ+PWYcnex5ziR6KlXU0pnRBOB6REYimpQvk88A3EMXrNcDIlg2BJtenSL7bfmBsy4ZgN9BLTJMoa/l0+/b0+KHZj1wbI6rSrCFJWu+I4/i/JUnhBQi/9am7PzkFNF6C9I0hOYI58wUzCYklJhoqszlRdsHXedOF3RoIW/PgAFnUexGCMkqWz3ULUpGsJOMbiMJ6ynCeahWk8GItQhpWIIreI4iSdyVCKvvIQoYaouxx468B6SKKSUoaHKFprWXGyYoBlPTqPGhbL6tM1ckMnVWh0oICJWlWUbVqm1VSIQth2kpdPY9ub/P0OkOpLTJSjjmXJYL2+ErstVDDtkGrkKmRqkgqMWuZY9TJVDcllqnZpkVGgJUQVsjCtraopYuZ+YWqZKo6qMfVedXrvAy4JRgavDPdPGzbvXl4eLxCeLLnMZfI02r0uZ9t7plVMwSqrxSnF/N+4DmEcISIh9t5wP0ICbgcyf+6iAYDwFIixpG8rvu3bAj+vlhj+L9+NT3j1X9BEN4dBIU/S9OeJ4OgOoZUlY7gfPUstm4jQMKo+xEVxObjWYJn1ajjmf92Wq3YZPvO4+h+mtelHRYWIepaHzDRbDcPpmmaL+aL6xAVcMnWbXwKUfe6gSObNjJdPOO89UrAVLp5ODUq3pUISQ+QLgwN5J6tQRb8CqIg9iJkQnvHalWsqlmLgMZhGrFjGjGZJ6H2qlVVTYmQLW6xZC37hGWExpIxS9g0fGpz4iJE7dI8P3s+Ld6wxSCdxQpK2PX78SxhlCRaRTY2246TqZlqJK3bBO41W7RhTZB1/jSXUF8vkiYQhAFZf2EtfLF5ehr67SR69po13KwhXT2XfvUhhN/n7Hl4zDI82fOYS1RJ2ocRNWcdRAvyhZ5SqzkKnSGx0C0aCQVCLkcWlnsQUlRCQornI6a9dSTf7FLU/iFwRsRtAmJWAj/cSHh4y9uCLeT4xu3b0zPm1/XhW35qHPhv+vun7v7k82R5U9NwRO86sjZgXe4ta7liQ3DHCwnqe0nH6/ZvXfezlh3WCLiFhNa0GKAMHGonzSPtuJ3motzSKIwWAj+MdLP4EtJjd8xcS/nWBT/1xpHm3jWv7bnprg/+1b9NLg0v+NmDyc7bEE+/vcg960ZI2THgauR+aZ9bm+/Wmd/2gtm2K5Gxqj+fPhCoGqW+bjbkqd9js31sXu9sR9YyY9H51PnTuS3z4vtqC0PsPdH8PG1LpsRLq2QtKT+ewqf3SYmeqqVTZMqnnteqgDoHVlWzKQI2HAxJDK0m5AsQRrqvjlv/ZvRzqS3SLKGEmX6BSvjsnGvO3kVmHjw8PGYRnux5zCXayOL0dfk1vq7VHFUSYw1s2yTExSo0Q8K0RJ4cR8nzNUQhug0hHA8i7ZeuRMiSLOZFpzwkRDQQ6hhAGnJlo83flnL88ZYNwR8Do7dvT894+OhDN/z8iap7c8BNwCAZ+bLhNYUtDrBfdpvOjgfRcX5OO15TRcxaenQhJHo0F+YP5sLCyiiM1JOthkus31N/qr2r9thn2fYjT7v9N97Q/6M3NJPaQCEov/5wc/+NF+WvWl5tjBcmOFxEVLCV7viH3PcjiGKroUclTkog9BpriAHzQuC1ZGqRVde0WlXf0+uEmZYpnQUTtkLXKoCaC2hDojaHTsOd2sO204cuIEnbhIGSO+0lm5r9O3PfYnOczu4ZtrLa2rUMkOX6WWVQj2UVdEu8FLqNvBcEEIYQBKpkamu0HrIQbpXMugdmkljMGPRcam6tIWut3l0KvB9pizcXFfAeHvMWnYqAh8fZRBtR53Yhi2TTfa+7n1vI4lIPAtpRymi+xf00+H5xnLuRfq4FpF/qbUi17hq0lVNMRDytHLSjOnGk6eWCoJTSxyT/qTTFH1RGueHPLwn6Z+viHvpw0PfQh4P3PvThoLM36olQQtSNpYj6BXL942SEBTICYXPzbFhSFR+r+uhVd+boYbZL3LkqyBxWyLo59BZyxWohV3gK+AJSpVsDHkjT5NlmUicgKJMRmGd6Cv33dOcGnmmmtfXjjSOLC7lyros+kIW+Gym2UEK31I37GFm+3Zg7h81DrLttFiDEUPPqjrr3bDWoki39TDWYmfdm50dDsUUylUu36TQ2BiEnGvJVoqg+f1bJkzFMJXmOJkXi6YIhrSjWY+uDt55bFUdbga3HtMqshk/181A272lunBJHzbmzIWlriWIfAmQ8QQj5IgRhhJBxPUbV3ZtO0pp0fLdkWEln3p1r3Iw5D3STcmOhufjSRVs+6tcmD49ZhP+D8pgz3L49TRAftYAs36hOZrsBbiFPQ3ZVBxht9kJPnWPFFmuI+Tng5xHz4UVk7bMmgHGaNGi4SsqUfD4hDEwqeQC6/BTaTQYbOX5uosIls3FtD304CGLyn6tHfX8dB8X/fpK7DQI3I8SnQba4aguqJjI/lvjpPNlKzJDjk8EWM0mh7YagC76qe6qKqaK0GCHXyxEV7h43lv4gCL95Xtdln3/Lgo17gZrL2/vuQxN33/OVY3/7uXo69YmlXau/trK0fiQlViLUQ1ag0XDH05yvGDjAzIR+69mmRssVt6+GnfvI1GLNq5vg+AqeVaFshwyFkjwlRzUz7zpvWt2auHukx7ZfAE0iEvJB4mZeFUFrZ2IJnKpoNt/Sjk1bnek91QITvWa9p7aQQ8/RqW5a8ne88L++rmOeQkh6L3IPbQcS/cx1tpSzSqoqrVrha8cUkbA8jtPfIwmW4uHhMWvwYVyPucY4stBrrtGlyOdyP1nS/HNAk5B15Fk8UeERIg4QkUNUnhAhiG8EFpBSo8lucqwhcmGxgLRRJk4lfy8mnQ7pQgHa0EuRtx+uUHWVvV93ZPR0sSKJuppxYWAqTZr3vdzGzrduBbJ4V8mqYhNE4erlxR0UrK8eZIu0baFlw7mWPOgCrCFKJVVafWs91UBUtNWIz94lZOpZF3BtFOS6Ee/DEHhu00bSYOg39gLVtw58oLfWnhzpypefvaR8zb7R6siCFtXVyL17BMm/04pSJUkaNtW+t5AR0ByZqldw86VhxTGyIg0lEjof1mJESU6dF9uZWOKiKqmGJa1SansOawWtzdFTU+uIUtiiNK0kahGHEmwLm0un59bz6LEtUaybOdPiCVXONKysrc8sMcbMjeYPWgXRzkNKpoyWzFyoOtlipgm4JY316TnI5mmK7B4r+ZPPa0guzh2+6EjPN1Yj/wM8PDxmAZ7secw11By3iOTqgORy7UcWjSXu5ymgQcQiuqlVJumaalKnQC+iNoGqNzEVYmJCGo7+BECahoSI0lMmALpISF3ph2xXrtV5eyMOzusppZsQInK6GMnH47841nvj/sHf+uzoSWxfIEv+17CWqjNHkb/VBWRkTL3f9PpUdVJCo3/buvjCTJXM5rJZ1UdzJfXYuphXESXtQjeepUhF8VHkfjUnWseO/PX+21f97z/s4mdX/2b8+xcPj+yv7+JIc193Ieya3Nd65l8u6L1s4li8/9JHGt+4BbF2eRTpl9uL5AZqN4wCGamxaiQI6dBWXEp668iDwwQSIlZlSZVCe706LzrXx8t5VGIZk5Eta2NjyaASOCXP1nRZc/w6cyX1ZyVZKTMJl56n05eus9hBcxOV3NnqXGv0DDOJZWeFcieUsKnCOY7MpVbPK7Er8WILFZsTOIYosUr+JhCSr60AlbSW3VFD8pShvSUYGvzAHHWv8fCYd/Bkz2OuEQM/Avw0UmBxAFlQ1gF3Iwuldk64D4iDhCSNuYGEFaQEBDQQVWwxADkSApYSThOmOkIsykg4ruzOHBCQEGaLXQr9YZBekohydNq48o60BTx+CrsUkArV58lIjy7EqoKof5qSCktQdMG1ifw2H8wSHiUiGtbVhVjJjOZVQUYkymQ5Vovdey+416aAyYCwq5k0Klf3Dd4y1Z5oV3I9j4WEV+2pPzl1Wff1T11cecP37jr6mddW48myu7617rirc1TCNlPqc2d97aw9io61aF4bw3knIsUZPe67+ujZjhg2nKgEWQmHVQH182ArWC1B07CjkpsCGTordHUOU4QwW7NrVRztvVPC12mnY9VbzQXUkKlaxqjipqqgVSjb5vh6HFUebYhb1Tu97gZCzmwIVue2s5LZhsK1wEYfUDTMHCDFHCD3qUzmb6goICqy9jz28PB4hfA5ex5zitu3pzGyoCxGctauJWsVth74a6TLxkFc1V8acUu1jzeQZ3muSiFX4wAxPcTTi06RiJjAJMhDkRYBdQq0aZCgAS5bpEC5RK63TDkf8cNbNgRrz9Y8uGsbJyNPRbIiiQuZ6Ylm850UNtSpC7GSudi8prlXtpgDMosSVRetQqRhQiWZVbJw4SFc27HufN/q/7D6dw6uKKw7f6o9dimwcKCwbPKS7mvGa/Hkqon2+EUL8yu620Fz9WLWRkW6I0S5zbWZUtUHZnqvWbsRJTFqFdMwr3Uj5FHzNq0PXafqpbDtvDpz2mwFqZ07Vbu01EfnWUOqqhJqCFavR1+3qqvm29mKWcy827C63i+tZHX3MIrc4RKzf8PMT8PdLx2DHhsywmmvzxpvK5FTG6AxRM3VBIgSGcm04XGFfs40lKvzUUUeavYiRLLTXHwK+NV08/BRPDw8ZgVe2fN4NeD3SflRUi4k5HwkbNtGCOD7EAVpKbKQHAVWEzBOSHccsSKNWE2bflJSoun8IDXqTRHCJKG9lIQWkaM8uvgogdAKywZSKDG+ZUPwW68wd+9kUXLjHGVmuFYVk1E3rqXMDOMd74FNyYPNy1PYUJ4m0isZ0u00fKrEUhXCLrL/GU+7sZwHDCN9jruLUam6sLD8oTCIFgPL8mFhVz4cKBxJDrK8uOam5YW1D4+2Dh9oBc1ob/PJ/SM0FkFLw4Pqh6fXZg2GlYxq71u9Ps3rzLnXK+5LldFO7zzN3cNcs5IuJYidVidWqdNjWhKor6uypsRYx6xqpHbFULXUqoV6zTZ/ErK+xjoee94Q4jYEGsrWKlkdq83htPl+tqAi6NhW50EJqyqE+5G/vYVmvtrmuEqE7WctNNvZnMwRpAK/gpBzzL6HgF8GdjBP8dCHg0uPwKebsGIA7r/mjvQdcz0mj/kPr+x5zDlu355Odk/w/0Q1Rt3SOUBGfNRnroSQi6sQMlQkoJiW6CdPj1u2QrKcr5DMQkRUizwRRQJyhI7mREQz8thGkI4cI2SLmy2KOJNQQtNNtuBrqPWYG9MkM3OsrBIFMys2dWFVYmCrQy1sL1m7ryU3Gi60BR51pGfthcAqZNGuAb2VfG/Ulav0IN0xVgHrFxaXLu4rLAoIWLq666JWV7F7ZxqmoxV69yE9bvXeKQGx3RWUSFi1y/a4tfl1U27O6swkH0nH8fS6lTTqa/pl59Lmy9mwesjM0KmeJ+34rsfIiGGSJMRxZ1hZO1HoPNuqW/2MFs3x3D6pzc/sgkDHl7q5VYVXr8GOU49lH2g0XDvpvsaRv7k1CNnTMHO+Y9/O3Ep9TTuy6D1diDwkNN3xq8jf2kMI0fundPNwyjxFA76Zh4t7oacNNz304eB4f5ceHrMKT/Y8Thk7ykG4oxws2lEOgh3lILejHERbNryyf1iVKt8n4P8QTDezryFqz1XABmAlKTkSlpKRvwm0WrNAQnF6cVVio+E1XcwTQnLTy7RAiEOLiDbPIAUDdWA036TYO8pVWzYEZ/TvxHXOuBG4lcxvzp6zRGZ7YnOlbOGFDb3BzA4MJ7o3NnkfZi74enzN8VNyoIUT5yEL9U6kOvf/JeuAsRuZw7uQRXwRMNlsN/bsqz1bWhAtbqcp0cLSikMB6R53joNk9jLaRQKyClKYqSI1yNQxDTN2ufFpBwlrr6LXZkPSdHy3hS6dpAUzH5qzpiRYyaYlT/Y9Jd2ZWXOzmVCvJ8SxDWHaIg6QB5XOamq9x5ZMFjte13tvFTgNKyvp1BCv9RrsvNY6Uil/Hxlh1DZsBbLiDN2+84HB5knq3B5GwsFLkQeBSeRv7hPAB4F/TDcPnw0lfc4QwfYYWu4mNK+8I41fdicPj1cIT/Y8Tgk7ykEO+GIb9kzAPU0YrOXYuGwfb3CWJaeFg8toxWXKhNMhyxBJ0j4fKZZYQZ21NFhCSi9CIFaQhbVsBaS+pgutVcMsSRLI3jna04bGTwKrci1uLTa4OteiZ8uG4HiL/6xg00ZShOzsQxa/TisQW8mohMiGHG0VJ8xcdO1ifDx0ht2s0mVzrdTcWJWaVQj5rAOr0jS9qJ003we8F7gBUYEWxUn8ulbSWp6kSa2e1Ea7ou54QWHZyuXFtfFY48hYkcpSCBZBoEbKebL2aBqm7QzLq+F2kaxFmBI67c2rxExft9epSpjOb2y2saktnXNmPeWUXGmI0vry2SIKvXcBSZIE9XpKHCfk8/nuJI6KrZaG6i3hmvmAMvPzq6FtVfyUjKt61nDDdvl2hYVkoVu9n1bJtT8r6WgjatsuYA9SKf048neox9L7o58Pq+qpebWt5lWy2I/c373Ig8Ao8DXgH4Bn57Oip7j6jvSnb7oj7e2Fys13pH0vv4eHxyuHz9nzOFUkwGQL0rbkzl1carN6xV6+d2AF9+tGO8rBDyEL/y7gb2+tpi+cxHF3kqkbuoDqYtRPjpqrvtUQkrWhsPllushqpaJadKjKMZOUyjKVJwsfLwH6al0caxWI23mWICTsTD6B70QWwSvIyGqCEJkjbmw1smIEa69hFc2TIXWduX6dvnuQFRNAtlir6lZFiHYTmSuq8cTh0fahcFFhxWCaJnuCINxVDLuuq7YnwnbaCuvx5Jp20BrIR6V/O1B/fmdPfuAtvbmBfBQfqyzkvPgp9nZDS8+vCp6OQ0O8nfda76lWKtsx2zwyVdQ68xAxx7Ohccwx0o73bMWuLa6wx9J7pyRV57cVpGkuTZKIfD6dLHWFhKHej9QcyxK7Rse57JcNPyvxs9eXg6b1F1QTalVFlbAqaYPs85YiObPr3L4PI8VT42Qt4WwupO5vu45Ykq3EHaCdgyeLFPZP0bwT2JVuHj7GDxiuvOOs5AJ7eACe7HkY7CgHAZKHNXlrNZ043ja3VtNkRzn40RBuinKsLbV5IA/r81UeuH17akNSaxGT45XAP5/E6VWRU88tLRpQtMkTIOpWDzZRPiGl5ehaOL1oqpJRRZQJzXfScGcWHgsICNwim9BFyABQJ+T5tmT37QPYsiEIbt+eninloQBcRlbBqCqbLqwpQvhyZAUJ+t3m0qmaqSqZDa91+qDp65Zc2vy+BllifZnMBmcKua9VN4auQlhq5oPCowHBwAuN56qlsNxcUbogSojvi2kvqye1lc20GhxpHTyvGJQW5tNcvi9amuxmZ6tC5XCF3hVTHAsh6Qxl6hjVIsWSHh27Xrv1xLOh1M5cRht27SRrFpYE2WPquIKO7VShs/mSuen3w7CQlEoJIhIH5PO2Gjcw+6tqqSFaVac1LUHnQ8PqVlmzhF0/CzYUHyIPDepFOIr8faRkXUJCJNw6hahvDyMPSDvJPhdrzLGXkJHNcTLlT8nnLuSzcjmSf3pXGz5ZIH0h3Tw8eYK59/DwmEV4sjdfcNvb5R/85790SmTEhWV7gXel8DMxnN+A7/zPnrVDvzz5299P0w+86Hi3VtMUycdSPHScQ/8FkuszdWs1ffYkhpIifWF7mGkvoudPyJz/86TT1ioJMSGJ+02WYmvKqwbBmuwuKomYRzQoOLWhQEybFiFlJNTUQgoM3gF8B1nESls2BE/cvj3Vfq6vCD/y7TAAgptHkhSp/l3NzObyqkAeIlMc1ZPMVmAqbN6YLbCwCqnOge0mATMJi5JB7XmqLdpySOhWScZCJLy3Kx8Wli4pru4B/nVBfslkMSx3Aev6CgsnW0nj756q3v/mJYWV51Winv5K1F8YbR/Y3x32rizkF3c/23o8ylGeqNC/oMpELqVlSZh+1/HWzWvaISJwY1WCpPNiySFkn6vOUKadI3ix8knHtrbIQY+rKqx27tDzqIomiuzMbAAberdFMUrOOitc1dhaFT9biGFDyNYnUQmjPkzBzPuuua8NRL3udV9TSG5dN0L4LkEeerrdGPJIEVOA5NaqJUyApCQsceceAb4h5w/HCHNfJGn+I7B/avOXfK6ah8dZgid78wG3vT2sVqs/fGzs2JJn+0t/deNo/aTIiCN6PQ34UApbgG6XyX3LRCne8/6Lf+cofGD36Qzp1mqacHwSeCIsQ576m8gCVEcWD+3VmhUTSKuzkJCAglP08jMqAktkC3EBaNAiR0RKSIGUlGQ6HCxh3pAcBbe41pyqWOQ8QirA+4FtiJoxghg/v2L0tK5bU2lfuvhY/q4HFrRubrg5sKpIE1Hz1HNPPdB0Ue7Mt4qYSUosbLJ83PG+Va2sqmjVIiVbaidyjMyoegFQStJkcqx1eE0+LDxXjrqXIorO0XxYrF/Rc/3jhbB0YH9t55XNpLFgvD36mcXl5efvbVWuBS5qUy2HpIdTWsuR+6cKmZI3e+1dzDRWViJjve+sSqn/51QJtUTSmhjbudDwqSVGNqyrY4vNe/pQoZY2ncUySqgtwVMbF3s/Apx3ITM7bagheJGZ3nSZyi3Q67YGygpr4KzzVUPmvBt5qNnrXouQh7oppOhGW9Hp51JVxQPA/W5Mr0Ee8lSl/zrwXYKoyPobuhhYtS+96cOz8vfj4eFx8vBkb34gHR0fZc/ePZc3mo137CgHOxFSUj26gHCym+5Ve+gNM2WgD1mU1gKVGP4DUNGVr5mjvLD8QmXNwOFp9/otGwLN+bkGedJ/YJb95yaRcO81SLhHF1UtSsiT0E2L2C2LKYHStjAkSQJCt4gLJZSFteVqeCVdXnz4UhKK5AmmF33tL9p2Ry4QE1EgQpTGW5ActaeAe7dsCHqAntu3p/teyQX3tN6QVNqvyZXi83JIiGwKURW196sSnS5kIR1DFntrfWFz0pSMaPN59ayzKpFVtmxyvS3MUNVQqzZzZBYZWUWpjKeO2K9U22k7nya8vS2Fhjsb7dq3vjX22eje8S82//P5dxw61hrp3ld/dklA/sIDzV3X5IP8/jWlSw/tbzx/SZXJsEXcQO635lse7/9TGW2dJz8rMdKQqJIqJfyqCtrw8PGKNSw0HGyruXU/67Gnc669Z7Uq1lZJd4ZWFTrfeg8s2bQdKpRwa7s8JYiqFiZAsICu8Bg1yHIXNaQPWXWzHtd2BtHqWMgI9VeBzwKvQ9S7a937jwC1CN4Qi3rXjeT33YWEeieQz8IhRAV8ATiWbh6OAYK7/0rnzcPD4yzDk735gM9/KX20HCwFPgT838g/6G8AQdcEUT3HAzF0h6JMNRFVqAsXdsrBAl3BQqDYJq4cYuHiVqNozrIS+PfATyJP8u9F/pnPCm7fnh7bsiFouPHZnCKtNExoE09n1pWmu0yEkIewkSlampIeEdJ2qmAii27YJiak4aihLs5KLkTtCGgTA00iUnKE9FBgLSE7EeXjtcBVWzYEf3H79nTqdK/5osk/2Avs37SR9tZtTCG3wDaZh4wMtBCSrrlr+poSLyUOOl8WtnBDCZG16dD8PoUSmcTst8SMaxxZ0BeR2XeUCmEh6c71LUuCJD/VHi/n0tyBrrC8++aB9/cA9b21pw4+Uv3OM1eW33zhpeU3rP78yF9Wm2n9/AkO90SUD0eQbwuJuMRdg+Zm2oIHbb01RubddgQhx1UkzNzZBkxVyeO5D3RahsBMRc7a0+h8qULYObeab6nzliJ/Z7bgQu9Bp3Jqx2LtVNQ2RVMSGmZ7vbagKUKfzTGUHtAC266sSUxISkyOwwjZK7t9nwJahKV+yr2LmRz5mnt9N1kl78qIkJhk0r33VWC7G9fziFVLPxLiryvRA0hv+Bnbm/isYNWtQQjk9+5IG2f73B4eryZ4sjd/IEbD8g94AXAdkCu22TNwmEcjsVBYhah5SqQawHku07sOlAKoJZBrwJvKo/z3HeVgE1Dl5um2WJNkJrizjbVkypaG0rIQV4EmLUJyM3LVWtCYWa2YIyEipEXiln0JNwWQhI4wBtPKh6gyMTEBDUIiQgJypKTE+SbNVp6UhBIh64B3Iov9GiTsejL5iMeFs1zRBfA5RN1b07FZyEzfObX0KCH3wxrb2mKDTrXoeO+pcmWLFWyBQ2Tes2FPHfMY8uAwgRC/UiFXnJpoj64Yax0p9+cXLX/jgnc9BbwZqF/WfX15ZeHC0vcm/u2ex6rfOVpLJ7ogLQ2w/FhMktaYXAZNWy2teYuJGYf1iGsgn/UlyOe3h5mEyZIirTzt9BxU9dp2lsgdZzudA1XObFhcw7eYY2i429qppMi9tObRNudOc+10vBFZLpyGXDV8q+kNbaA1JbdE0x8aSIh9rdtWVeuJAl2Hm3HzCK3y0+QmnnLHXkpWXVsmqXczGa9B/mcscmN+Kt08XAuGBne3SB4GLgXehKh6h4CG8cc7GgwNjs6pX95VHy0Bi9YOLGg8Hx0bWHVr8NzeHbOTa+vhcS7Ck735g3EymxENKeVCWFZKeQ2yUKxFTIpLyD/oENeGzMWMajkhewnwfBLSEyfcFsHIm7/OC9+7mnvq3TyF+NDNmqoH4IyLR8lao+lCqJ5fFaDocvMCsh6udbKQpeZfaQO0kICQhJjAkbmUSbRLRZvQBcraJBQIyVMgpEibIlVSSq2UNGrRE+foBnKODH4TUTFmszJ3KbKAWiRkOVTL3fwsYmbenVWIIPubtmTchgOtkmT94TrVRN1WVapxsuKDihlzgCjJDYSo1ypRT7uVtI7trj85uqq4/vLe/MDKVtJ4/qmJ+6PFhbUDF5RfU26m9a6I4oHdtcee7ouwuRcAACAASURBVMkvrKRxfCSKwxuPUl0NaR25g71mHLbKdbGbh6PuWtR3Tw2ptcJUr0X3tZ0mdL6UeKmCpsSoM7RtcyGtqbJ+V/KlxCwmKybRamob6rXhWZ1nVQytSqsdPTR/Vcm47RGs++xD0jcWIvmyY26fXiDJU3xuZXH9vbviZwoprcNIusTDwGfcfpciDzA1aLWA1ywp9Ky+pHtF91X9a0vn/+GPPZhuHh5z9/obwdDgt4HkeN54rwJj5AGWH3vDJ5Zed++fP9c7/qu1j6VcQ8B9s/o36+FxzsCTvXmAHeUgQha+AzEsDiAM5XdVgXqBD6SwOsj6my7HhOpCV9mXysIykeajB470xbsWH+baIjyaTxhZfpDvjdXoLdcZWHBsOpwFwK/e2Lui/3Dw+lVHG/f89IH6yKlew+3b02TLhuAAsSNdwXSSewqOaGVKCsxUVhqkpIUGxTgijvOk5By9a5OQElIiT4EWdXrCNl1JncjV7+ZJRMVzATAlPgUSEgK64oh9BAS5BuvbedZRoIosrD+2ZUPwJ7dvT8d45bgICdN2QhdrW+GYQ9S0zkpc/T1qJQ3aSUIpKqVBMN0BxM6dEidLLKzCpMe0eX1KSmrmu1aEthGiMBKncS6Excvya86r5HpvBO4Lg2hiWemCC4pRsbkkt+K7K0rnTXz16D+90JPve3NCXK/Gk/mexsBVSatdG+WQqltWsdQxqnfiJFnbtBghNlpBDBmp1f1VqdPPjV30lXRpUYy+d6LOI6n53iB78FByqP16NddSHzB0vjUv0haZFMhC9C2ynEElc0rwVO3DbJupfm0aJIyR42FC9iIV3kuAfS0aj+5qPNZKo3gdEQvdPtchDy5thCQ/iDxcLQBuKoZ5bl54cXDzksuv+OLIw7vcPBMMDS5FwrSz8dmfNfyvN95ROBoca/+XD35rlDWHD7++e2Hh9f/1/6xGOtC8wDW0EKUSYII/vTnh9V9L4IxZKnl4vCpwJkJxHmcfCbAzhu834HBTFqBdSA7OEuDNLbiklqMnyQiUdmFIgSCANIIohXwC+a6BRfuOLeK+w0t5vJ6nNtlFeHgRA5c8zuIVL5DPtaeT//lcJbjwqu9O3Llo//jHi/XWe1z7tNypdp3INakzSYuGI3pN2jSnF+JOpUCT5dWtPwwT4kD+ZTcISSkRk3PUMUdIiyItepKUHCkBCW0CWi5rrQq0SKarE0dIeZSACbqIyHOwLX11C8APIQrkcuCNs9RO7Sng6RO8p+rbYTIVLUeWI6bzPF0l2k4SiCGm3anmdVbl6n5WEdT8MP1d1eKc+7nbHfMZ4E6k3dUoQmqerMbj35xKJsa68wsGAsJlk+3JZQHhxb25/mVjrWPjn94/tBOCeLI9esVo68iSctjz7PuX/n/fy+fyj0B4NE9Z89LqyMKsqpeqb2q1UnHj6QMuJiOe+qBg50/RWWyhx1XFLs+Jzalt5a0ex3Y7mTTn0vnqQsi62sLYog+rqiq0WlaPIw8zmTnzMeRvu+WOX0YeCKRKN6VMmq8S0wTOc+fehxC4W1LidyJFFGXgASQf7yrEZmgZov7rOXeP1MePLi729b+2b23jf1z2vr0AwdBghPSw/blgaPCU/sbPJP7ujf+Q76Jw2wWFhT/x1Uff+oba0gXd1Sfe9d5kxbM3wOS/g9rvEoz8GWvu/0cu++YfUTz06/zhR/8d33rbirkeu4fHmYZX9uYBbq2m6Y5yUI/gWA52R/JP/iaySs4cQBBDkC0ktgOFLm5BHRoRJBw8eNOKKtVim1ahTfVoN8sKdVZWy9T3r+TgweWsf2BD8NTNd5EW4WfbsD5qEPeQvH6iwheRxe0AEv47KazdRftAmbiaI5emhMQUXMUsZGqIzd8CXXgD4rqko2fdN2JyxEjP3ICUNiE5Wu0C+VyThBYRCUUiquTJ0SAiJiRHSJE6OZYRkSegQpt1ZomPEcXkbUhP2/ds2RDcBXz2FRRsHAS+SZY/pQUFbUSl1bZgMZnSqYn1qsrhfo+LUbGVRHEhF+Q1v8z67ul3zduz9h66baeNi+3SUHLbXEzWp/gw8BjwTG9u4E3lqDfMh4UF1XY1GamPtJaVlvXESWvpzur3a9V44qJqe2Llsvya86rticZ4++iaXbVHnn2wdtd9kF8BrYvcMaNCUIogiJtpTcdYIyOilshqaFQLT3S8nV6EFhEzVb/UvCbzlqZtgsDarehxFUrWtPDJdpNQgqfhVs2BtKFXayukx43MfkcR5a2ChGdryENBCVHfQP7O5O844ihB6xA5ViIeiFe6Y13hxtPjjnUAKYR5HRIWvwpJz3jcna/ntX1rmu9bfs3UlQvWHhsodX/v/Te/x5Ld7wOH5qa9WaBpHHWuScUK5j5a7/urj3bf9yc/cmPv2Jo3DiyJjzQefePEaKF0xZJlT6blam0Rze6I/v0lzn8wYWAsonfnTTx77b186lee4U2zm5bi4fFqgyd78wePA3cU4N3IP/F+2xsqAIrp9GrSmYifAkELkgiiSOwVKv0T3JB3liD9aXepeCQ+Wp6qXbjqeZaVpni6q87Kpy/g+Qt38pU8vC4PvRGMFKcIK5NMTXVzSgnRq/fRnFrJk2kX19dTuhPRkSyp07GrorcfWbi0A0AW8ksJpmtpg2lFJiIlClsQ1gkSoYwhIUVS8uQInLKnif45AlcIEVMgnS4NaQK3uvOuQPr3/jCwcsuG4OO3b09Pp+qwDNyLFGpcCVyP5KVFCAE8hCzWNp9RQ3o2dywEwjAIk5DweKqLfU3Ji82HwxzPmvsq0VPLEyVRi924RhES+togCJfmg0IILMwH+XsXFhYeygf5Z4MwPRgFheCS0rWL9zWeW96XX1S9vviu/Qdbu6K/2ffbF+YoXt2mkctRmmjTrkC7Uon6ciFRcKRds22/lOzrPBcRIpw3r2t+4UtBiZ6SM50D+Z4k9NTrTOXzY0k+X3HHtObF+rejYVf9DKotio5P8wCVWOu21lTYFspYglhG/p73kxHKtYjCVwAC8pVnSNNHaVevd1mqB91cPOX2ucXdm6cQwrgeya+sIw8ZjyPeeE+4rypQ+7WL3z2xrrK0b1VpQRX5XMokST7e37zM3J5JlIHz+Nbb9zH4D5fTVT3Ig3/2QuHfPvy2SyZXd9UrB1nwuu8sLY5cNhAei6LC2scqvG5HhXpQpZ0LCVo5qktDrt/exU1f6KaUXMenP/d9fm/rKEKgxTDa5/d5zCN4svcqw45y8IvAbwF33lpNf+pk97u1mrZ3lAOAHwcWa4zWrjy6MuUgcJn4SpxUYQgiaEcQRlCKJEzZC6xtLFuwf2Rl7lDx4efKXZMs6D3K8kKT8ZIYEO8L4MvVEpfVY3r3Lu96a+Fw4fO/9vXROqeGgyX436MFliYhG8hRJJxeVG0rMF0oH0B8+QJkIR4HytRdmFZ8+Jq0SGlRJCQmFrLX1SCoQSspkSdPFy3SICTNh7SaebcIx9TzDcJW0Z0v+9c/Qdj3TcrLXmDyyR6yxPZnOYX+uVu3iRqzaeN0Qv84sgAfRaxu1IIjQExsR8lUKw0f2gKCTsNenbMT/Z13VufqcbTKV4teIJt7JTyqgGlodzFCvPcgVihTwNJ8lB/ri/r6gQN58k9c3n3twIH6rtdNxmMLm2ljd280sO784hVLimG566Li6y7MB8UHn64+NDDGkeXA+Gj7UCUlKTKz560lRlr1qrYkBTJTZjsXx/u5cx6U+E0/I02FYSEJw4VmfuwDjNrYaH6dVQntnKulkLbws6TaPsQomVbCdwgpnOhBlLgFiKK1EgnlSpFQoevL1MZWuX2rZOkZb0c+U223/VE3j48jOZ9fRqxSJpFCC6tKp8HQYPOWxZft/e3Lf3zX0q7+k1bozzg+9qcN8o0aYdJiw6cW1bqnrshd8Fg9v/O8Nyw8sPiidmUyzK17pId8UOktHz7CBU9WSHMpR5ZF1IqjSTu/sLFgJ8X1D6ThioOr2HvZOv7g91dz82cWsvuii9n52i7g88i8eHjMC3iy9+rDRuS+3LCjHPTdWj255H/X13Yx8uQ+Y9Vyq1SSHj+cNZ3DlBe14BCyWB4jqwIsFp7ac1H5KJUoLIathblrW1NTo70hj/dOcnS8yLp6nnW1CrUgDidHekqvreWLD+4oBwdca7WTRVd3jQX1IocpTA+/RrZAa25Z7Mb3RkT9OowoXlIRGZMPG9STLorOGLlF05VbIEeY6pv2hxM1L08tkNBxSkKVFjVi2r2TpBMB+WaekDZNGjTo4t9yK667p3zFf9w1+Y1feC6Z3H21m/tvnWLv3NcAt2zdxheQENo9iJ3FFPBdhDSdj9ix5JEFGjcPVWb2xbVN7ZVE2IIWiyZZmNgqp5Y42Zw+23VCzx8gapFaffSRJfavRO5Po5U03xknbYoQBLnyed25/l3LSusHDjV3j31r7M7qsdbB/bkgqi3ILbrisp43l/bVnryiSWMVtMZCoicT4ovd3NoiBZhJqsbJHljUQkWhRQyd+Y0WNqcxU0nDkKRU0nNpuNYSNVUbg473bKWuHt969tmik5abQ70GzY9UhVmrjde7ud2HhFCb09fy/7P35lGTXWd572/vfaaavqpvHnruVndLamuy1JJsDNhtZIMYjAGzMIEFl9wsFpAEww26KxEXVrKiG1ByE5mwsC/kJjFJTIAYkAPClkCebVmDLVmtsdXz8M1TzWfa+/6xz/6qui3bssFDxPeu1au66jt16px9dtV+zvO+z/N2Vtzxu+4artvKInYuncIyd1PFPh/C1uqdxQKaMaAs7j3mfAqfK4QX/keWnw1v++ivO+W+AXxz18PfXAuTP/1ZhUyreX21bcbPHjP15Tck4yea/ms+njKyOuIt1/tEyifoB1SbktqqT5jE+N2UsCPjTqWfjs9H+di89LLyrOqE1/n3vfWt7Dk5xeLMOX7rt1dZ3Vvm6DU9rF3VJo+x8U095+3Yjr9hbIO9b714C3AX8H74qtKgXmz4e7khEkBJfpGUUMJlua1hNmeYLXFeenNYANUHlkLN2tQS43HJjPano2dV1nlGZdQBZQy7M4XfqfLxWkefOXKmfbCcNcEyPV/N3XHn0g6W8JlnUIPVZ+C79nLmtA70bGAoAYYAJQVGe9gOGiFqq2ma9RA0ocKQ4KPJMGgy1rVPOYEKKXUMKR79jQZntUcDyRgZAYYFYDm78Fe95040N9/30d9o/rPv+fFPAnwNPXN3YNPuHlbs0CoeHTF7pthuLwNGx4lVhgEKDGov4XKbERfDPnwOGDkA5chfx2gNW4skXA70hs2WU+zNgY8F3dXi9QkKhjJL0sNx54KnjbnV1ObWQ788WQ8aY71s84nnup+t9HXnuZ/b+W/WL/XP3Yw2k+v99cM9NlRdTVDzxpLl+NITZVG5fd0slhiAIydOcWlsB/KcJ6EDn17x/wovz+ZdKWxxrw1bowyPrxtHV2c3/NqwPUyOvZaOkXTj6MQm7rjcPjawaVXXQcSB67AYxzKD+r2L2Dm/XuyrVvx9BdvS7yiD+fM09obiSPFegU0BX8ICPg8LpEtYYLineN4X9x47Ye56uCPuPXYSO+9msKncUXHvsbPmroe/Wtb+bx5HiYD9vOGD3kYQ/5CuL72pMn5mv7zq83XhbaR0xwOu/pTkujimM5Jw9KJica7K5mSEd0EzuqTwUuU1FlJGOgaZSuqbQtQ+uo+a+YcEtOiHf8rNH76Rq47PcPrqj/DQj7V4+g3bQG87/pePbbD3LRYFE/abX8NbKwqO6ssBgKbw0EshVyCK1exKoOCAno9lDzrYRWUKm8atAXMCfK+XqPDM6nkJXgRdAScDzSfpc6PY4HVL4zR29NLjlQ6nsMDxFcdH3wjFcV3H5TVLLnUp6ZIjiPDZjaZHQBu7sEuR0gv75H1Jlkk8UkShSgRDBR/VUwgMkgSPmJiQWCb4KsFPLdfSRxIT8CwetdzW560AJSJa2AV0PMnkj8St5oWDU4+9954HzNfa67OLBUi3F+fQxjI4LWza7gB2Ie4Wj069uYFdfIfByXAnDcHlYGY4HCPlAIXbxjGmLtw+HMs0bGUyXDs3V3y2Y5XaDHqoNtr9c3G/9UIwOv66Q5mOE5EJMZ8uvNQ37dae8NrS+fiF8INL70nqclK1s83+anoxBLLY9Bc93V6N6VaMEeeKY3CpzDkGdZUvIlQFo8vWQWhLAevYvC9Vuzec1oXL7WXkl3iPq4wY/u5cWe/omDtXs+fU3S6FG2HB2OeL16pYMOo6pFSxwOy54vk8ll3qY9k617VknoFVz1zx99PF+0ZQkSGPd4MZLY6jhAWUC8BfM+jscam4XlPFdp8qxjgAOuauhzNx77EAyy6vF8c23Jf3GxNHUcA1LT9/Zzpx8VB5ufQ6E52LZOVC4JdXoNzxkRn4MTTHQvpKIZVm+vQoXscHH4JOQtDNfe1pvxtljPXIPaRSpoSmTE+NsFn6Lm54cIRq+1o2a0f40X//ca579APwy9/wU96O7fjbjG2w9+qJyBOc9gQzDFJOEpApqERgpMJElhNzzI5LT2Xa2q6kygINBx6WsQvRBDDmFRYYAiZzyDPwcpjYrPL56gYjJc2uqgedCl0/53i9Tfmhsujc0X3FPXQdY7PAwD7DHaNNSRo0AklGjkahqSNQhAgjaPcFgoxGIezoI1ESQi8jSwJkydbv6S1TFtDCw0hDXxpyrbFN1EKmSWmScZqAMewC90xxXL6S2XqjvLR0/Y6Pd+++U+wK9nx/Xv/uDy4u/78C1bi6Xv/eByNV3bX2rrfx5RiQzwD/lkGd3luAXwD+CvgwA4/BEjbN5xgqB9S2fPW4HNwN1+7BYC4424+QL64Vc0DJgZWEgZrUsVVOTQqDBb/OwN8uwrJIC9g+qoc9LzBRNN6RUu2QudHobsMzcj+Y9ZtqbzxXk41DY+FMZ8rfda6Zro33ZPtznvaf6ev2hb5u3wq8OaF1AfgcMBtSPhvTXSqOxypJVVQhiyM7jbfsU1oMGOHhvrkMna8bPwd+XTixhytjGGZNh9PkjkEbVts6dtHdLC1ggfAIFlTNM0h9B8X4l7AgOWUAmq9i0A+5i73+h7Fsew68oTieTxX72VvsZxrYD6INIgMzzoD91tg5vIqdV7PY77aH9Xl0foDPmbseXh8aj7g4nwBoDbdA+4bEUSR2Xk0lO86Fvm7ebAhHmDmHTwBnroNrHgMyg1aCXgUSqehO+b0dJ0yuhS4vziRy3/OG8XZIjk8uJZ6tT0bg4QOdXPYnLx4UaZCEa6M+0dq12chGeH45qa393y998uZ/9jtPfEPPezu2428xtsHeqyusV9xgYTdA7EO3ahAqY1QOAJVb1Dsa8sRuX1J2sXB3+q72Z6u3qGf/hTFM5TCfSqa9Dm9SEPgpjCyT9Ma4pVViotzhOd/wCSwj8BXjngdMcved4hTwCeCdDEDHoIasvMVMaQw5CSGmAC4+NRR9ElJ8crokGEa0RmeQE6PJwethMp8YDw8JeUAvr7BOzCQCgUebHENMBYkm4Aw2xXUKa7lyVkleGKuufHKsulJFld+BDGX70V99EEh01i/lzZNGVXe14EuDvXe9jf599/Pfi7Zp3Hc/c1im5cPYBfZUcS1aDACIq1esc7kNyDC4u7JOb3gbBzCG07jODmS4bi264v1OkOE6ljgA1GXQg5Xi+Byj2x2tHjBUD3T7veaSbp67vmVahyI5+mJtZFdSlWPXCaPEdLR7tZVvmFHhn4rpPpWRrGDBb73Y92FsL+ZUoNZCSt2YnquB88g6/eIzN7E1jhUGNwjDHTFcCnjYXmVYVOFukhwb6MbL1UO6cXLPe8XxDbc4c63t3GtTWCY2wTJwjgU8gxVfuHZk61jA1cd+Pw8U73H1fNNYoOZsWFx/6x8s9uNhGTmr1s57HZCjxXYxlgk+W5zb3diuLDXsHKN4/xGsBcsHsCDSRacYh8NALO49ds7c9fDXv9fsUQSwF5GNM7I8yuRFU/nffnV3cvoabbo1Lc4fkBx4EtZmYGUO9p3P0UhGzkuaoWazazISHW+Udak7FmGUJkWgCIg0lJD0MCjIih9A3VgMZK4UFw5U8PMp9hyfHF1tjI1lM2MgToLZAFHGXoOVbTPm7fhfJbbB3qsnXHqpj12AnRGrL2E+LNgZAzM5SGlzXnkOMrbb9z2b5skZ9Gd19h49BkX9YF8sebAz0RipUX3QAURlzWy2wlpe4zZt+CTw6ENlEQHmju4rakYeYFOlvwO8i4GXnGW0BF0cqyVoEpAimCreu4YkRRKR4ePjFUDQaGOVxrkhy0okKAIymmiqaAweERFRUYnWISdsViPfQ1Mmea44rtNYEcUEFojYusa8+xPJub+a8UwWqvEb/lu++tRZ4VfTd71tq7bwS8YQ0JPAx7AMlmMQM+BPsYv9jQyYl2kG7cvcd3g4vejSvQ7UODEADIyEo6FthxW7w4vXMPs37AE3/Dd3DCYzqVF4ZWF96RaBHYnuX/REsBaVRqaSlqfE5kV/VZy4pixe05TVyYcl5uxCcrqW5XlnPbsU9nSrQwEgPQI/I3myTP1gTj4S036pT2u1GAe/GKsm1ppkf3FsC8W49bFgyAHiYQB8JUC+UmABA3XtlbV4Lm3uau9cR4ZycdwdBn52mwy88JaL/zsg6MQwe4p9jBXHm2GBhPsuO6X2NHbOLWJNkQMsQ+fAnLueZaCF8EOEGkf3BfYG5QAWQM9TiLiKz3RM4kSx7z/BCkAQ9x6LgDpafz9J8o/I85na2sozc2n6QXHvsfd8AwCfAo5w08PXr+07c8PSNZdKO2/87HVm95mkO3tyPpRmSguJ3BjNGR2TqPM5ufPKjGWWBsLTFS/YOZ+r8XXD6LqHltDXtjIxR9DyDUGqCZEY8D2kj8oZPycpp8Kr6ZnGjgt1stIBjt90nqtK/yP2ZC30dIgF6F+LzdJ2bMc3PLbB3qsnEmzqbBb7Ix5iUy8VLLvgA20NcQJB4ZeRC1ACJovqdmWKBU8MWI/hgnIDW1SVNFAtcl2dQpkQAqUyVE2LpoBK12NSwq4oY4Mv3SFiOErYY/hD4B3YhQ0GAoLhuqox5BZYybCLYadYmi0TU8LPNInQRCgEMSkpF2njU6YGdNFbrb/aZPTpUEEhTElezFBPQdIDHukq9Zn333hT7Yeefnp6LInHi3F5C7CXfDPKW+dmGm9+/8V/8tPXflU1TffdzzgWYAdueN/1Nnr33c8prGpyFMu69LALtcjytKWNEYEXDNftuZBXPLp04zAjpwFpjBGa3CjhDYs+HOvnFNEuHAvowI5LCZPqWLTSjVzmnvSTzp6eMq3Ez+rSBHNVr/F42Stv+n6lmwtZjYJpKfzKnovrn7n+8eTRz1b8hpoO9l33dPszY5rsPIiJSJYnrinfFp7rPa/W88UXPMInsEA3w7JSB7GsVAlrfRMz8EjrMugh7NqWDauOh1nQ4TpHGDCcrrctDHz0HLhzdYx98FLIYzDd4j0jWCa2xUBY4VK9jnVvYkHpFJfXQLr2b0541MMC2WUG7FoDCyR18RkjDEDkWHG8MSb1MKmbo53iOg4Dyr3YOXdDsa9u8TkLQE/ce2wECxJnSdIOcTqFr0b8LN9db23W7mguff3r9h4j4yhPnjm80lj3G9/tZQtTaa46XnleZNen4/1+Wft5LKJjfwFhosjxaEWCpCFoLOmsnGPSKDVZ2WduwVAGmrqw6wZAUE8VPVIvQygFolWF+aslE2dgdgU6ZExcKlFZmiWv/5ONmDedXJx9rhLl7716esGVQUd2/LZZvu341o1tsPfqiSZWjVcBfhi7GJ3DLnoploWakCACW5sXA5EEVRpSacYWAGbhIO01nOpKsB58/RQibYGiDq0nn6/AFGZioYBRA99Fxq5uyGa7zIf/9etE8iufMWe/wnksFp83zeXWIGAXLefrdmWnBJfy9VFkZIQExEJWsiTvGJ0gqpbD6KNpSEWgQYcpqdb006hIbyrKviIVsF6newb4a5kzO7NALezq6ujhXrUZha2xJFbYxXIXNp0TmdXPifU/OnLb3X/EE/c8YL4acco7sIvu/wOcdGxf8Zjedz+fxoL4Y8V5qzzPy8aQxyJWoQqH1bfD9iiOkXLp32GWLwPCft4ny9I0CqLAl4F7rwN0L7d4OQbMAUcBoISHL0LIk24/WfXWPX21EmF7xC+daOerL873T904JSprKmj4fnkqO5+d857rPXmNUVyXmfQLs9E+Jvtzl3q6bQ6Vb9o56e02i+mZrJmvhZq8m9D9KBbwjjEAfE4N/MLWceV5Ha1zfN8ZF/ex3wkY1CG+HGPp0r5ufJwiucnlNjUGyyzGwAZRRZL2cvLEMXpOke3q9Ax2noTFZ5SKfzcVx9/FWQZdDq6durhcnHMX+93YzRa42Dp25y3orussA+9JGNxAObuc4evnQLuHtf2ZA/4dVhW+BGiUrCNFn053sS/VP65vNP/qwX95/JXW4X7tcRRFY3HMj8UdrfFs51zpYhppb4xqk0a/g64tpVHf6zKRjBSVvRqFQCtFSaBmFhI6ofQ2RzWBFPjYmZBgr54bBQ+fDC0aSHQfaiuG+qbBR5Aj2SxDlElGLs75IdVdjYWD9XLeBT5BPzqlP/vWIwvP/OCpuZ/nwtd9TLZjO77G2AZ7r5K4o2v0Q2XxHDbVuIhlufZijX73ULS1EtDx7I94nQGLsKVIFOCbQVrPpbKGF/7YQGQkUahBgZAQFRMpziw7iACvSJaUDLQzzdmRVR7/vWvEuX/w3Je+Ay586gwwf/ed4t3A/4Fd4JxQwKU3h4vxwf5894AaAh9BgCE3uqN8SDUkpKyR06VEqn12YZsIl/AokdOXmjXtk6Y+AkmMTdOEQDODQ4Rm9B1PPrOhA7rYFFoX+E/Y1NcMdoHs81V8r+67nwpwC9Yy4zEsW7N4xWaL2NRaiL1uk0JKZYyOPamGTQhyZAAAIABJREFUGbvhlOOVIGY4/erEBMKTUqOUUcIRulvj7N5/pbjBsWXDnodCCiVrQcNLVVISXpBMQySllF3dHK15Y9e14la4LpILY/U9ax3TrF/onO4cz473dnBg11Q4+YbnNx9VI2L88R+d/KWk6jemEpOUHlv4cDWlvwwshpR3xnSfL45rFgtaOlj16XXY7hABeV5D6y5SglLOc84ZGWcM6j9BhZDHw0pjN3buHFO0FmQZ+H4LIZxdiqtljeg3V8FEWNDfLT6jgZ2LjkNy9jUVBmB5Ymg8g+K6uzk3VWw/xoBhdYy9A6gO1FW53M/PfXcdqHXH65TEL1ef6eZEA3uTlWJvFNdw6ess/4jq9c4Em+2PP/gXX7XF0CsIUdRgFvs+ygiwk+s+nVZu/LOxqDpRV2sq1cdfa6LZC0rteaFHmCSsXGvYNE3GNmsoDFN9n/yiRJH7bZQvtGTsJciNoIkhQBffFEVbGPrllImOQiHpQZ5lqNFFD6EtzG9PSMptGO2Dwq8E1CpRrrOUnWnCAT8uzT/Se8uOp24enX6CD/zP/8APf+MtabZjO15BbIO9V1Hc0TWth8riP2IXkh/BMiFXYRehNQbp3PHEShdFALr41fcAwsEC4dK2rnbILRS+BwqN8SyzNxxhia0CP9O37cfKIiYvx7ym0uXn5/fx/rvvFJ8uxBjiS5kQ332nENi07yaDNJRjKFyazS1UAigXd/Y+abGoK/uz7msyfAISRoA6OZ8DdpGQIIjISekQeSn1pMJmGFPHsBkLNAEHtWJhaYJmpUdNiy3vwBCbPlvFtph65p4HzNm77xTyngdesfqY4hr9WHFeNwBP3Xc/S47dK6KCBQEfBW4D/MAL2sV4jDMQJDjA59KODsQMp2K3bGyMMWR5LhAykEINgzoHIM3QcxiAyeHU+TBYkL4KdKpSic6NkqqlCLzIq1w9F+3ZPNt73jNKeNqobmyajyS0RaiiCxIxLZWfVmRjIye72MrXF3dGV+3Y4e8vB7o01TXNgztK+yf6dF441XvasWfKJ9pbl2Ppir7kQN1zSLkJ9FDqOuxUnGLQUaPLAOxJpMrJLwN6DJ2vxoJHTZ5LpFzH81w/YmfInYDRWKAXYOeDs1kRDG5G1NBrcHk3EMcmBtg55dj0LVEUg7IM1xNYY4GZE8w45jYutnOgdFjA485vuBZx+Bi8Yny6wLPmrodXBlOGF4Gf4esbM0BUiCDMuYPrurbm3SzWD5RS6a0dXF9ZzWvdUn/ujJbNRkXghZFIOvnsyUB4sSd7UKhqFX2pCbTAI2e1Ljl9UDK6BKW2YO8i9COgnxIaH7/rY6TGaJF2Q7KuB3EFf2MUff5qgtIaves/b1vZ+Giv+A08f5qgmwYHx4+oaOot/6bx+nOvbf3Ue27fyc/x0td5nLZjO76m2AZ7r75IsCzQrVhmr4pdJBawC0KALS5ZB0a0FWs4Vaa72x+ub3IpQlejExRUxJWAZosNKoERQGLIMKSeRKGpCENUW+H1B9uc/u3rhRKzVO++U6TYovESsHjPA0bffadwBsKXsKKFOjYl9nLgxUaMwFAiRRdGCoacFImHjwDaGHwyAnpchSAgw8PDw1g7FaGplno0/Q5SZfS1R5J67AL2E/KFTkgPC+5c4/mUgbltDvBVAj2A/b24p8gNMlDfE3rhs8CL991PHcvAOsD1DJb9eQ3WdiMpxqXGoJbNMZ+OoXNpQVfP51SpAwWqEcOL/nDTlWGGcFi56sCimy9uny5NLAPPzzWeH8iwGpqo3096Qov8dCSrJs37ad9049nwQNroT23OhPvGd5YPPPTQ8n99YTVf/BGB7txQfeNiapJnv3PyHZuPrP/F97XTzVmjGVlN548W498CPnxd9dumJ7zZyYc23n/WoI8Ch/G8CpaVAgvMVhl01QgY9PCNSLtbaWjyXKO1wPcvT+d6XoAQGqWcLUoHO1/3Y5lr56O3WTx34ijH5LkxvVIsM+xz6Ng4g/0eONsht93wTU1ezIsGAwbPHbM7T1ffN3zd3HfHCT6GbY28Ykw/B3yQV1Zb+7cbL16/xvQFxa+/r87k7x7yfvDS7vUn3/htau8zFbm4//rZ8kuVtRsey/L6BmESb7ZXIlE6t2vE33e+JDz8UoqkWcnxdUK3HpF6OUZ4+F3bQSWphew+aWVdm1WBpxSjHfCNYWVKUWlqOdKTphQjkoxUZdCpw9gqeSkxmUF4GVImkCoajXH5plavtnRxPVqf0Gvz5dVLF9bPzY6UjyK2e+pux7dibIO9V1nc0TXxQ2XxAeyi8VNY5mfYNFkBMoRGDloN6t/coh0yKNB3jv9dvthzzLETVxa4O0pQ1HJE7GNyhU+fmjHUSj2+O87QeRnl9XlBK8LcL6CapH/3neICMCNSxiR8Ovd5EFtLNM5wg3lTLIIDPWVOisAQIDAoUhKU8cizHOVb5W6DGKKEsX5IjxISjZAZKswwuY9IFX6Y0ENQSz1uCDucygXdrMpubDr848W578AyfBPAS/c88Mra2r1M/HneW49VUA516s/ihbdiF+QF4CPF+F4qXitj7TNuLj63xqAnbBMLiB3ocqlW5+fmrpEAZG4yk+apCDwfaVO4Dmy4GGbuhhk9ObR/N/ounevnJpOxTpC50l26OvD9mhAiEUZO7atdvd9AWwr56x9f/ZNbxtXsaF93Fj18Twu9L6O/UGX04lPtj5lnu4+k2uQXtNF/tS88ck2gSjdUdeO6Tb3yMBas7Z9+/v6nar2O7+0Za6bV0WdI2i0sQ1TCMl6uu4RjyepYsCcAHaYpwhjTDwKj0lT4WtP3PIEQDiiDEB6e12EgipAMlLbT2Hm5UIxHiwGocmyfY8i3lOxFuBur4TIJXRy7Yw6Hb8Lc33Vx/k79O2z74pi+4ZrLHoNyDScScWpliv2dBX4d+PQ3pRXaUSQ8NcPtHzL8zD2jfOidjVLMjt5b37dRnXh+d35+b7Vr0m4oN7omjcfTFC/w4xLGeFKhCTBoEkw5Q3Ry6gukRnh5rlTQH8vF1LzfUwvITqCjkSQl2FBsTkpMJJhdNZQ7KVHiqR5ZWaLoVkXP9zJufkyShFKVUrHcUvSFj5iv5q1WMDY75verI2fbG4uht3b++uaTycz4wZ/6VzM7/tVPjIFZBVF4X37VN3/bsR1fl9gGe6/CuKNrzENl8Si2oL+OVeXOYKVjytj0q1EDla3rsTpcuO2Mep3limKQOhqu6Rq2tMgBVaxIWkFHazIjKBf+faFJqHgJP1xfpBJs8oXeKN1ehK7F/PfFaeYQ3BAkPL33DGtSU3/+WjzgPIadKCLAxxDTJ0VSJixSloE1XFZt0AZtymgkSWqP0gfGyFBIVD8kRKHRxPjERuKbLnEWYbSh2q3RlrCEoJRKZqVgBUOTnLXqBt8xssFSc5LVdn0LXL1sYbYQfxACxph3fskF9F1vo333P7z3tKrPHlZzb8zU6MHDQWUsxdYBSuDbgDPvehsn77ufmeJ6XIIto2fXYsuBOsf0JFhmabgl2haTlOYZJsekIhOR9IavowP0w0wvQ+91vWaHRR8Un0uSp0bnmY8RPSVkOU1TaYxJJCprxc3cU2rzXO9E20C2p3zEe7T1QFMpuVlR9bVAhN411dsPl1X1Lz+48l6nVj31Qvz4+YacPFH2RlaL85wEmuf8trezs9lSYvx0Kn0feBMDKxGBBcAphSedsIy28yc0QZ5LpbXs+77IgyDJwccSnQ4kh8W5+di5nzJIDbuboBoW8D2KZdJdmjdmcBPlBCJu4b9ybN33zFmuDNdfwqBG1QHtEgMltGNZHasbMGCa3U1em0EK2AG+VWxnmE8B7zV3PbzMNyOObln3tNgc19z0yTaZd6J2ad9oZexzN6FilUzqTX9kNZHrM4kOe3NmbEn5VaPV9IUm4JETECGYXNZsjGoWp8mnTxvTq+WJjEVY1iSbgrA7K0ikYu5MRmNeIhEoRNdvk/ZIqhqtcqXw2rpklKRdNvqlI5r9L7CRS9bTkrh653xS2ih5nq6WRzUV3Q+eqJXVxc3RS6uHd54BKGEn0R4soL70TRnX7diOK2Ib7L1640WsuOHNwPdgWy75GrxcYHyzld51CwVc7s/m2IjhbYbTT1eGYx1MIQvMNeS+IdG5rT1KoOZDKYADqotvusyIlL6pMe+n1PZucqI1Sn9mnplqh36/xLePrnBmvb4FLg0SgcFD0kUQM2AoQhQqj8BLUZkhIUQFIGKFn0Og+rQxZNiWaT4JeZSiswAZSlTWI/EMpSxkSVc4TcpNJkH4CbrrsScLMJGg3vZ5Nst5FsuIDFt0bMUP3XS73DfxpqtX23NNrGjmS8eZPxE5icjPPxiHr/vNZSpjDwOPMwBX3n33M1k834UFHs5st4MFfnUGLI5m0DvWiTFgAM6Ur3yRkppA+gxdT52ZVKV5RqQiIYRjJ7aurwMpwdDrw4yVjlSUZVIGvgwjIGknrbibt7tlWakbnedxskFVVv73DUpiJty3Xmk3zh1vfeqFtt44+4NTP//YVeXXHpwJd6/f/zM/2hb3HusVn7tzQy/PbyTLJ7FincNA+5npGf3M9MxngD799VuwYKtWHItjgZ/ApiVvLg7YGUD77TBMgTZChAgxrD73h95/paq1w6D9mGPRGliLlNFijCcZ1FEOe/O547rS9PpKQcVw67o+A7bQbTtsk+P+766JY2RdGtfNzQ5W6LOIZSUvYNX7Z75pQM/GGHasTvGT/1ryX375EFosiMWde3S7VPKPPNqsPnn7elLa3Ds//dne9FXpehSSIlkjo8eL1+/tm35FzVwKfa+dExtYmMxVbVPnF65uyv3Pef3KyggqEFkvyZLWpPHSwEuzuE/mBb4sKdMV0vdTj3IvpVeFzEjqTUhEJhvrgOBqL1TS+LlXN0b7Xa/V6WbRmUNje0rhm+W1T37kxjDT7bh84elLu3j6/Qf3veU7Px9PHF3vfBPHdTu247LYBnuv0ih67K49VBZ/if3h3401WDbCXKZiHWYQhuu73KLn6ruEtkbMW3+8gp6QWCmdwAo/jIIsV+Q9n/m8R1UbRjV4BmIJgRBEWuCHfYzMmCj12ZUJntuoU2pWiDzDbOxxEsNxrFpVEhesSUiA2PI+09akBJCYLMSIBN9YgUZuJCLNaCvwi2RWjiQlRyAYMYZks4JGEJGhZMIeJD4C30/oBBkNnRMBOzs1grVR1n1B3QdBwhqCybvvFM9j1bgNoHfNLIxXLvrrvek2/KMvf7H0+QXggPRuWfFGDzwIPAvMzp04sX9xYuIX8tHR88W+P1pcy0tYYOcK8a9l0Ps0wzJ9Y1xuJ5LFuo/JMZEfoYTKlKcGNWvFXIj7CYnp5T3VVCPBGJ7YwnLDgENggWGQ6ZxIbTXbMEII6QtHiJF6yjtl8qyhPKFKeaWUGLVnNT/tnUieb47W9p3fV72xO+3PXdfRzewDS7+1EzjhWnW5tlzi3mOXAGHuetiIe4+tE9YWyPMJsu5Vxed8HAuo54tZ4ABvHwtsagzYLVfbFg/KFUnJ85A8T/D9LkI4exJXb+cX4+3qV53Nj6vPG8OC8LT4W2VoW8euOsDnvl+Sy1PkCqM1iLwAno5p10P7Sra2fXnbGOdFOczmBsD54lhPAB/C9uW9aO56+FsBjCwDa1z36RKV5iyLO783O33tS6t/7z/4rWR6acfynrFSWluNdaY6nfJkb+SlR0KJAW4k9fb3UyobXb0u1vanUambhN2x0fQL3yZlc7ZdOXC6QzUe6xnirDVOotNAjp/3cxMb88gdFWF80d33SCqRotxQKalAZyJNV2c9mRnht+f6BLGWSuYm7JaCVOZ5B2IwrW5ZaeHV17zVciPPv+djT7723PueuXXm6omF0et2L1xz9iNzfzZxdO3Rb/bgbsd2uNgGe6/+6GCBQh1IhRVPXFlvB4OF48oOCQBag0qLbUIg9mxz3LJ94xZYLFYml3bqhilVlbKU2P1GGZhuQC4TdBwRGIWnEoIwRa7VaUifQ1qxHFdI4oD13iht2QMtaJLQACJSVNEhQxeJtgBBSoCHQGDQRqPyFK0MhgiFIiQrUtK2r4ZBQT/EIJFFlV8Pie9nRLLNeCbpBYKp1iirYcAFrTmkBOWy4AiCThOkCfys0UlPGH+rtm4KCywuTo1ceHZq5EL/y6mOi6gCuYmXnjTNlz6x8sff367c8s83qs2b7lxsjN5At1ulXH4ECyw+gmWafhyb4s2xgGaRgSBgvtgmYMDOCp0ahSHPvbynhHKpThj0TvWkkohMmoSuSfJYeJ4/bMcy3KFDpmmWkuNnUepAoQSI817Wy9siUBEYsXtEjSmT080UPjoo60zocVNd2qF2LB6afMvxQIb1Dy6+xx+RY9c29drZKwdnCPRZU+Hq+BiGLmtnlrD1nNMUnTMqop6GspSs5QuuFMEKV7LsAkJ4KDXK4AbGFGMvyTKF1h6e10cIx64FOAuWgeWPE8MkWJDnGMBW8R5nWuzG3o2fA4JOYevG0T7qXJIkGk8JvMAd25X1sOqK5+CsUQbfueH6XJeydeD3RWwHmAVz18PfGhYhj5FylJDDn/9OJi9qps8/0H7o79/Ye0iUxv7Br7T86MIO9j0b1AwnDng9grp+DHtTczOlLA/2nFmqnJ3dNOdmZ83Th/RycFF1u8tM9TWVscV+vl69xNK+mSrdcrvrJ61YbJbivKTTNFQjG0SzaxpNn7gWsDEu081q15y7SuelHfi+J9h/PA0n1rz+xTndO7M7Vyr2QuK08fwtwWZSFnr0kuyHWTVamBxptUs7XlKTH3rr4WfuP3TzuTWaYyO8ec25GcBjjmXdrufbjm98bIO9V3kU9XvPA3+M7aN5gMHiMJz6cenb4bTfViuuL8o7ZVueLAbL5GWF10Qqra2LklAqQOK+TFBqV+hECaUoQfQUa6lHGpepV1eJtUD6horqkQcZ/aWdiCimxCZzccBuBBtodiLwpYfR5jJLEACDGjS0Xw98Y3JhxvqJjDQ9DM1CBBKhaaGRIqdsEjqUUISEWNCjYoVWXbpaUk8DJoAJ0SdXihxDCmzgc0ZCNSE7mYU0gz6lmSXG+iWmmlWuT0s8i2UtesCuu+8UyT0PmIUrr09hMXMfcMisP/b/bfzZrS1grrf6ZPzioe+9LdR5P8/zFNsx4hB2wZbY1LzE1mPCoM4ywoo3XOG+S/Npz1fGGIwSSuQ61/24p6RSlILSFlNUCkqiFJS8zNRShecYpuHL7/aH7ymReXqY/QNINMbTaLWZrJQ38zWm5V5d8iqRgTQn1yUddnck4XKneSrdrE/6Fa9+vpar19wmb6gcbty6Mbwzce+xSew9xTms4e8DrJ7xgT/DdtCYL45pD6A0upaZJMLWoyXAOMbEpOlOpBwrVLUpg3o8Wy8XBBJjBFI6kOyAkzNXdtu6NHCXAevnBkAyqLsb9qh0fxv2KnQpY3vdhJQoIZHe8Jy+UnU+nFYfDifgcL10Heh0LeMa2Bu+zwCXhkUY4t5jzvNv828DAHaJot5isGd8unXiK4IZp1x9z5sMtTWPscWgdfSxk0sf7c3kcwvVhg6P5CdvbraRC9Xa+TwI8g3Wq2M0x7+dXWcSlOnKJNysjcSbTLX8/noimqWz6B3NSrORUVkfnQtOHVlWZ/bngkzWDj3lr80+siFLSbq689Fyqd6t+f2K9IzZ6Ge+lLor0tVZ38uCvmd0j73Ha1l9rZynpLq6vJT4o340Pz2q6hfN6vmDLCzP4lU2ZLZwNj8t89rto6f3NUbEbau1yu/WDnQinrh5BHtzYPt7H+UUj4kce4OyhlXYb8d2fENiG+z9HYg7umbzobL4LeCvgV/A9lndxaCY3wEFKDy+cvCFTdtusSDhkMWGV2zXB5mD7tuFJVCSTNbw0z6iHFPvC/xuRExKpsHkmg0FKsgp+ZIKhmfHU/L1ETpZSFxfI8jKyKqq7CVNOv56WilFVDdGMFqhEaS6EGMgtpiSHJC5scyinyIrpKkwVpABBJnBFyG+AonGI+EcITvxaBRsYEpOgkIQYXJBjT4RBoUotvFIMaSFtUu3mjFGZhYJqPspO2PFjiynktuxPQx8AfgEQ10b7r5TREDiLFruecCYu+8UfwoY99rdd4qT+c7vaKwlzzVKwdzTpfDgw1gA8nYs6NuDZZU6WBuOEKsyXcfWi61i2a6IgVBA+TLY8svLTJplJD6Z1KWg5ICPmwt4wndpxitj6zVPBp43mDMurRiWVNlEskRLbJJkaeopL4iCSACdtkkutGiKvDrVyeh6C/HZay60Xnx6feOR8gS1kdbq//DEvb85BfSLWjJ3Y+Ja0+0rPv7HgN/HtghsFfNgvWda/Z5hCrvIWrAjhEIpl8J1IC5RCDnHiFyiGcZJDEIYgiBiAMyG0+AO+Drxi2ag9HU3To7NG44rAZq44tGOuRAGPxIvsx1c7nt45evuBmc4TbyBvQkwxePjwF8AXzB3PXwlAPOw81VigfPfIIQyXY5HYTzdeuTGf1174Y//DT/1ju7LbnqUSWCOo1zisY+O065M8D9/pq3T8d39o4/McfWz5fb8/ov+s/uryY61mhk/OYFRI1zYLzl3aI6RxYCROKbTmCaWdS4eXIlmzo7sz6c3unPLK33VzNO8MZaJHuHUGVSj2cql6Iy88PpQTM9XRkcuer7wddrOddavhKzvjr0oNd5oK/DNyroKhUfQa2dev0Er8vMTu5WYnylh6sbsFYxNXxJRd5Tu8gyeUqU9U+eC62fP+McXd7xhZS1c+vMn9hz/wuePd3/wp3/i9P7f+522r6JQycB5WW4ALSvk+NpbrIl3/YEElLnvnV//1nXb8b98bIO9vyNxR9f0HyqLzwG/imWFfhxruFxnYFIsUhAJyD525aoNdjG8SDkWIY/s7XsSg8oBNIlqkgorAMk9Q1xOECqnE7QxCfi5z5rKqQcJlXLG55XkUq3P2GifFT/hSDNCRKr6zNpEd7XbS6eDPhWVMaIrbODaTYliMc6KFkkCkRi7GCrQgSZFIfEgyyD1qAGmBKqwm702SCH1UNrqGVvoYvFUGKCuNHkU0+yEKCIkwv1Ic5GMJVIIMhqJoLde4dq24vVexgU85kkoI9mNx2uL9yz83Nuj6wOl5qp5/gSW9QPgngfMcF9bBwB7Zvrbfq8fjp8ofc/9LwHXY3vAPgZ8JzZ1G2NB5TLGXELnfZTn0q2rWMZmuNZOaaMTMIQqEtoziSflMKhzj4752RKIFM+H0/5XxhZQBIQQgqo3IsNyGCrhgQWmF5K8++lL2clcC33+2fZnnx3xG2dO9Z6+uKd6lR8FB5fZfXXCpX8+Dvji3mObQPtA6YbNi/GJvX3dnS/OuwS8OMbcC+ssrBp0GXhtcc4xyIOgxxmocSOCoF8cg+uZiwDta6Q0RqO1RAhX6zasNB8+Z8cmD3vbOU8899xZngzfPA3vw321hn97h6+/25dj2V1cmb4dfs1dpz6W0fx0cd5ngAeB57+MpUrGoKfu3wDsiQD4AaUYN0mo/BfufDvhpeewGYWXixr2pqXFb98DP/Kex7j28SPlqUtvnLr/ruVx73Hp96K57pHP6Oq+xzf8fiWgOT3B2HxEs1pmY3SdkYtPo+P9tHZ4eHFMdVOV+mFcOnA+oWpGW6flYq98qqr2dTaUEF11/qAXdMcqcbMlSvmOnmk3fO2vdNTGnlx3atLkOtbdch7056a4+okuX3h9ooL2hok2S9knXltrtdsbkzsbZb9bKYvRxdibOZ2WLt1OOPcsk/6JNMt7pVI1u8aPkl+Ie8nHJiqBeXzkobOfLv/AF6LP7GiBefInMB0QC8mTt+37j6cO7vmDi7924mP/6F98EeP/CmMUGBfv+oPT24BvO75SbIO9v0NRiDYuPVQW/xVbu/MW4PsYFPjLrGAiFOhiclxZHySNbZmWGVu2txrAeAihhkTBujC0FUzHdgHqqpyqhBEBsQAjcrSBk2VDWu1yen2Us4mPMrAv9niuWyNS3cXe+ghLhNTjHhUMo2iqyK2ap4ysSNzmCEJ0IMgNSGlNJtwi3PU8wlygvEENUwdJGJdRyKKfqGCVgASYICDGo5/75J0EiaFKRhufGJhHc5aYa0jpZjlvlYJyCr6XUcIjQhCSMoXCw+M4NuX3SCrEdLNSWao2m1/xbv6eB0z/7jvFBzTkRTeNT953P48W10nopJ2brN1U5Zm3ALOkyV6MmUFKg5BzXG6K7Ar7ZZIkymBEEASi5JeGgdwwyFHGGAEgLABy8XIqbPe6Aow2OVIoARgpZB6qkkslq9xkwUa22mx4U+eUVCsj3ujjT7c/uWnuejgT9x578qX42VrS/MNxbEcSJ3LYtys6NKFN/vbT/WduAPMeoDnt7XnxR2Z+8ex/W/iNFYOezjJ9xJDNd2nOgnb3KKO4mryBKbiH1glaB5nn+Yv9eYHWAqU0vv+lANVwSvdKAOhi2KR4OMzQe4b99tKhsR82Wx5O97rWZl9uvrj6wTUs2HVCnjI2TfjSl/POM3c9nIt7j30Oe+PwN4k9wFwU8u9JJw1ypkO1+RTvflTxi7deOSYA56hsSjp1w/v+meb8oUv85jvE8xxpfP7Hzh26KWouXjN/vpm8eOPPyvYbaxz89CobEwHLU4Lnr7tAb7TDhSOSzUbC4S+EHHxyk16jQupdpGomSTHR2t4xvzdNuNRsEugeEwuBX984JbPMl3nQTUY3J/2kIuWTb140r/vTGTnSmcyfvG3dxKHi4p4MEaW+lxnGz8TiqmUjUtBT54xuTrfaqBX/0PFapVtrZGt7KioZy5TZCK6ZvejVo66/EiUTNRP8yacenn7N5J70Zw619ny2tHT4OAjJ0lxD/uE/vvk7yhvVzRs+sYytN/1awrGm2Zfdaju2g22w93cy7uiaGDj+UFmcwt75/ya22L8t7UITN+zi43qAOlWiAFbjQvEnbM/YtgIT8bqJAAAgAElEQVTpQV/ahewsVqSQYXMUCyH0hE2/tTJBQ4eIdoWTY22e0YrHTlxNp9rGG13iGpXT7tZoedbiuQ08TYCPYRPJXmzdmu0UYHkUXQC2XIktYNNj2B5EkIWDGjaryhT0UVuWF84o1xoRGzIEAQqPCIPGx8O15AqIOUjOBB6+Vsyg8UZSTO4R4XEIj91oJIoStkYyBl7f6Pf/spYkTwL77r5TdO95wLx8iquIex4wl/2Iv+ttJPfdzxzQT+c/2tTdxVp06CfPCxX0kaqB0WMgqthzdUrRDMiSPCkZTIBACyM8O1SXsUgO5Bgg76YdYcipBnWXQnWAxaUlvwj49bO+MJnBCyS+DF3ak17Wy3OTtj3pNdez5c5Sdq69nJz/+Bd+4V+sAoh7jzWA8cT0u5P+ruov7/mdOFKVtV964RjA8p7StZune888A+ZmbGeYv1jMzn7iqsqN7Y1saWZncPDbR8sz/tzI9Q9+ePMPD9Bb2Y1tQ9fAmJAkkWjt43k5vp+SJD7G+KSp7CBBZxlhxfui5n8vL1oaZkGd3ZBjBIcZPYZeuzItPGxxNKzIHba5GWYTxcu8nmPT9hGWOf48tt1ehu06467/V6zDu6I12tca54EPAEvsuJDxk/+4ztqea1k5HPBuzvCLt7b/L+7zj/Lp62//z0fbUzfdsoObPgqf+d41XvfhM/zcr6WAnmWhdfpS7ez47/9b6alT50Kl58Wu400Of2qdhblRtPAw/hoqr4PZR20Nwu4yXu8cKwcPs7jrECpb6Y/PP2Fmz02Wdp5ucO7gChcOrHL7h0IW9/dUJa+y80TF75b+k26s3uSF4kRIegO1pki+78/b8VKtG62NV/LZ+Zfys/tCeo2rotmFJOqoMV3qhqYfx/31yYlkYrEW3fbXm+n83mQy9zdL69PC5KXs9IuejHecuP7bD7/0Qm+x8eeVpWtvu2bmexcnb9MVoMn5g6e8x449f60yo9e+eOslfuBrG3Bz3zt72N+67diOrxjbYO9vK979qMSmQw5iU00ngYRfvPVbtnXOHV3TxYK+nwB+Nbdpwp3SpoKcwvASNlVYwrIHH5HwDgNjPpz2oKZse7YnsT5nm8BuDXUCb8JLsoawStGnEqhmgrAnGQ+6XCMMf3T6AJd2L3P9WMLEOpT7HlcLzSfChIU4w88k15OzC8UpbOP3NpaxcSYaw24wOXZBbGONdB3gcwyJY6Ac6OsyALQdPKbI8MgpATVSND4pIUnx/jJwI4pO4Ww2Cta7H0NNRUCBogi2GJxCsMzuEL47z/RkZnjas+fzZcHel4g1oOrPfsdItvzEg0IFz5o8uQnBO4UXnC+uVYVBH+FNINdZHhpQpbAElwO74fFz7JOWUngY/0rj7JcDeg60aCmRuQSNNv24J0Do0FeS1PhSqGrkl+W+0rVRT2+Q+enwIuXMoZd+ee97kkiWp7Ap1xRov+/SP/9ejHk7QlzHwAj8/ne9jeYv3ct8ppNzm9ny9Lx+8hxK7sLasOxA6xJxHAhjMMZIsqw4TyOQMvMz6dfwxZoSGb4YpLON0YUidzguq2llGACnaY4xOUEwXFfnOlaIoedujJ0h8nAMp2Ozof24LiY9Bi0DBbYW7wUsqD2B/b2R2BTusrnr4W9wWs/0GYiFABHhb8xyzX9+C/WFz37ovTc82PyZ7/DyC6+Zlks79jJ1YZRdLz3LG39WM3d2FlvWkE2wuvQDv/b2NfY+91am53ulcwf/iIMv3snUZkxtdQzDLkqdeZLKBgeeOEvU3ODzb+igbpxAS8365AiTS0E+stnu9xtxoNNQJUqT6V08dqzHzKUWzckwjX1Slb7RR49RXW7yqbfsQ2qVveGBeLPW7Pth3G93aiMq7EdeLz7XX9mxTJbfLr15P1svLWar072oH+F/+4M95i7G0svj8tTCU/04qF383Ov8/V7ekJF5zdtuTB/6/V/5p09/au6x+Nf/7c+NARvc/DHBz/6LrPOp73r+kb0fy9/MLa9gfIXgQ29/e3xp7gi15DfCd/zudtp2O76q2AZ7fxvx7kc94P1YlVUJu4ifBh7n3Y/+Gr94a/ubeXhfKe7omvWHyuJXBNycC671DONYYPUCtjPAYSzIOALsCawSsoZdkPdjAY0P/Ets39aWgCmZZG0JBw3clsJ84lMNDJHXgX6JyqVZDiejTJgYah2EjthIPMRIm8eqHWZaNW4DXkdOH8NJ1BbI6DFg5Fw4IOLUiGABXVQ8OuNZ+zfDGGLLlwxghpgQjQBeIOQqwEdtebM5FiYioMPA/Lbjlm0j7IonYOA8NzDl3QCCJGXnBb1jqVUvh6/AkuXlogX8uQxGrgp2vGkZmE7Xnzsk/LFJb2RHIoQsFZ9n09PG5BhTRgpPDo5nOGU4XODvGCZR8ipwOWt1mbAgMylpkiGUkJEXaYBARmg/j3vtVZ+khSxNyFwHYJI8zTY3lrKV80v5xcfOJS+Vni2vHmz85/9zpN7Lny+EGB2A++5nHWj90gvHsgry1k6W/CD9/k8jRINSCaQE2y7OIre7HjY/8B//4NHEpO3Prj1QIVs6Oa527GzmqzekaacW5WFUai56rVq1lEmZk+erIMoEQRmhddrvJfgItE6RchOtc+K4gVIBQXAl2HURMkgL2zQwKPI8RqlhBW+M/Z40sfOnxOVp3WGza8WgWw3YlKzrzuK+a3uwN2KjwCPY+sNF4HexQqAESL/xQO9l4qiJ+ad/f4UDx0eyj7318NVPvZZrfz58sevd+amJF9UI97yjzvrUDp67eQcv3Bjwff9lHi/v8GvvO8mNH38nr/3oNF94fYkTN52k+u8WyL2buP7xEbqVTbLK06xMHiEJx5ho30JjaYkTN8bMnHuS1/35gV4vHDUr1axBXyvt5wS5z/Jej6e+45rkO/9kl77lIzVTP39BrO7yzPFbyvQrR5nf18PLJvpPvr52/sjnN0ekOVvZnInkvqfHxC7/UnLiNZmpbS6yNrrgLx9cDuYPLFQayyOqXTf9zE/y2nqYi2ydSjZ9+y2frNdH1uphKTnQrse798x1XzzZHL3EyHob1zf5He957ol0uZQ2R3Z+6L1///9n783j5LrOMv/vOXetW2tX76tau2RZsmRZsh0vSUScxSFxQjYcMgmTgTAJMPYwjBh+JgSGBGZEAIvADAHyAQMzIQEyCUmcRYkcL3Fs2ZYsW7KttaXeu6u7q7r2u57fH7fK3TKOEwcIs+j9uNzdqqq7nnvPc5/3fZ/n3Ov/7adeOhVbzjknpvt+XSz0r5OPOVd87NRH3/1Xv/Ir/9sSCZfjf7+4DPb+sREDvVcCr2FFz8ohtlDaDfRx8Mid3LF3/gXf6wDeTjwR/B137P0Xrbu4pa4i4LFDjniCePtzxBOMQQwwLhCDpmliCYd86zPLwHZiMHgFcQp3VsB6I57UpkPYE0Kf9AmBmSBBJdLJyIC3ZpsUXMnxuU4edhys0MUKS+yZ6SLQGmT9OPm6jIVBPOmp1jpzrDgdrO4oXg1K6qwwI+0aMIVC4GIiSGAR0K6zsjAIiDDIItBbiWLFyuTenvTNVetpN4v4qgUA1YvXfXUAjtLAtIr+iD/37xDa//qNX/z5sp7fulR96GfLAB+7V32vtIxFPOEHrfMxKIzsRWHYE0LI9ayAjLgNJQw6CENlG6ZCyjbLuVpnbrWmWzva7FVbQ64NbOM3lSKMAohUqMKmVEKEQrN8wFSeF4jID6KwaSCEqQgjD19MNscTmm7tLkfFxVek38S6qPDOSMda27lm4e4v8DuAX/XLe0rB3Jk572KjW3a8zlGNf6NklK1LmSKKPIRQxED/UaAmDuzT1P7D4RcX/mTelslGM6ppm5yrZ690bth8pPh1b1I+pzKChq1bJ8tKJVGqgWE8CSzhee/wPXcw0KRPJApIebZ1XrMIMYIQ7XPeueq8t4FYkxUm2UXXfaIoRNPmiBueHEAm4FwjfhhqO1m0O6gLxF3TmdY5bD9IPE7caNIglpp5mvi6WyZOk+4gTtemW5/Nt9b1+It02f5Lh8MfHpjg9X/5W+Jr77q2d/2xV2rFoevM8e7PIH346nt6GTh/HdnZ2UBZne5MRz45vDDGez/6fr703h0sZ+Y4fuMixc4mh25fomfWovesQbZYZaH/CoK+DEvdPZTyORb6u+icE/TOTCpBpBaHAtlIZbS+xRqjT7sU1mVJVqYZGGsgfS+UlUG/aCfDmXQxefLqR7BFBxuPN1jumU4Kfe1oqtlMLPZkpOn7LK4JefbatYncYhSsefLs9GKfp1/zkN89sXCaxb6hupeUhYtrM83NxzMDrmM8+sxVytz27frVnfNLi/Xs0a8dual/8y0Pb/z1E6/4KnvUs+QKds+mB7G6pzn6wf8sSheHFjbuPbrq3i9aD2pqVZ2lSJBhY9B17tn6E1v6Fpby+c+ava/+H3d++pi6+/biD/3MXo7/I+My2PvHx/XAb7HyNC+Jn7DbaZ49wI9z8MgnWanZeRvw28Q3+8XW6xs/9C1/kWiBvmrrBcAhR4hVfrtHiK3XSsSsQj/wv4hTuVuJJ7Z1wCAxwMqoGKCYGkQC8lGDqg6hVBjKRToBpj7M9YsC3/M5lYgITY/tIqI/FDzpG3QTF9pbrXV2EU94baHb9jiWQIKQJIIQSRKXEoqOFj8XASYRPiuF9G0xWoWOQicili1ps1+ri+bb6bUGPO/p2T7vkQTthbm5VSEA29EZ7Keu6Ypu0E4F5XO5cOmpchjJh+t+0ui69ubzS2GqHj1+7z+YvO/+AiZxXVaWuKi7BHhGdlRTQbM/bC7b0kqHQsi2fIiLlAIiWp6vL6wna6cJYUVWZLVrQzu1GSqlIkUkpdBohg2iKIokCoIwjKSnNM0SKmiGfhSa0kx5QrMEoU+gJbAsJ+iuO4SNUr0iot1pI9URqrCWNfJGp9lzPfHDwhmh2BSq8P4Oo/eLjtaxJMPmY4aU4+j6k1vN3GPzwq8sKr8G2Dg5Hc3aKX7n9fPA5mZUywP3fXD444una0e/1VBVExlt9KP6VDWb/HpoWXZrn2aBwq2nTx1xI/VWX9OGywn7sScTax5Gyn1IaWPbT7XGWg8xm92u52w/MJSJAfc48Ndomomm1YivdZv4WnD7yT98kaW3hnBb69imgKPEkjz9xNf/d1rnM2y9N8pKWnuytd6p1uubav/h1WzOv4zv6h7k/etm1x3vXUz+9KObvUSkd9I7luCWv3mcv9pfJt6vFOXOkKOv2qVteVwLRfOZWu+J0Wypa71huzuoWikKXXlGj2+pFBLVs7/9Y8e2/8wDPXY9+S4SS0km10rWH9W59Z48ux4+RWZKYzFns9Cn8+iPOmx7VJBeMDi73cAu2XQvLhNpTXH0VTPWUudQuPU7fmN845D0gx6rYVXpmZ1h8zNzpmJR+cmGu9i5kZmNXVz3NZvmwJMkl2/CPJO16gnVU0mfo/tCNxk7F1S7pqejeSutGuWOyGl2VLs2oHTM9Seq08PPzFaf3b1TzQyFk0HiidNbj3/lZKX3Da8JcxvChTXHzpzeMT+y9anRHqt5kmK/f+rN374mFYS9P/5s5okviAgrWwk37j22vHJghSC+t3ogCoBibLNTeuInh5zuhcLON3/p/f/z3n+V/+NspsMXtR4uz9+X42XE5cHyj4mDRxxiRm+ES1klWAF8XcQg6ANAH/GE8U5ipscnfmL/R+pb/fNGq4v3+Z+HHDHZ/vuQIz53Sz1uJDjkiOPEbGaZmOm7EkjpLUbEiLt9UzpoCopag1AFeJ7GYrNINrvIkx0Nnjmxg6lEnXqgMehbVImbHFLELEu7YWQ1GHJps20KoTfRglheWLW+Y7MC7nwiPMBq6fTZrBjcV1mxuGrX9rW1y6SGE4V4AoLVWmywAori/7WEaTwNQv0f5JtDM2YlKxBeEUx/cxkVZZaqna9ebHZFo+ZSd0Y2H7vrVtEEerJvvh+z/+YEcU1WQMwKtfdnD/GYG46ai3qIgYqUUEIaaJqnS6OBpmWEbl2yjS+Idg1c+/fwBZ9VgGyGDeVGzTBtZKQQQogwds4TkRdp9aobqdB3wzBDFPiREEhoqNCPokAEhjAnOpNri3U59XCqPqFVTz/ce1/l2HTflk3+rYl/NShiIPOILo18RPDIOmf79J3r/vQx4O+Jj9WFO28jFAf2idFkhz3frHbUkd3kh9M0ljOtMfcsULnzNiK4evy/H5j6HJqmFdPpRS5tNDIAtXN+/mEXzk5lsjcU0+lTSDnZGgebiK/J4db+f711DPqIm4xsYvmbQ8Qs3XirozVJ3DBxAZhpgzJxYN9nia+FDuCviR1QvNZY6wJm6d+WR2o2U09ZxMxduX1OXgDufsghRGsbPVDLq98JRDj8+pMD79Iidxit5BAEM5zb2EV+uoDujbLQP0oU7EFUArrGHw9UIx1d841uf82YaXhON0YQ8Z3X1AjloJNZSG1cX9mgh3aTmp1A84ZYdzTFlqc1ks0SuYmQpa4efGsZ23XJzjdZ6k1TGNSoO4L+M2NsO57BNQSl7Fm/2Lk58NIJ/NDhwqYAp+qy6clO+mYtntvWqY/tcp2ic0Yr9WwwEs9uoOEGVMwBRsYMCldmqDkbCZnEKabV2pNPRfpEirSXRrft9O7Dx5hem0cIze8ohVEgoqE1Z5Y3vOorU4X5wdsSw2OTV65/pt+164MddlMfTC8/mRo99wDXPpCSavO6ZiWdumOXmvrEPT/XyNhNCSQufnZEPviuG3mPUhGIaeJxOggoH+9NwjPeX/jLO745+Itf3P/uxxh/N4yLOz+tqbtvf7Eu58txOV40LoO9HzQOHkkR17LdzKUaWIoVxsknvrHfBNxCfBEvEt/oPWKJiV8B5jh4ZDNtY/s79pZ/eDvy8qMN+lq/r04/B8RyD3PEE1wXMeBLtgGfAhWA9CwmDZdPIbiukuLaksXjnSW2Cck2s8lYw2E98UTvEE+wbeeCNjhbbq0vx4oVFQhkYLdcb2n10Cr8VqrVBOotHb0CEnvV903iVFvb+aCdwtWIJ14vpG5xqWfqdw91qcnwqnCIz3Mn8BNE3qNAMW0vpKVUR/Jmzc/qnk7LBUGYuRQx0D1/522Ed3+BmdY2W8RpvQGloqvQ00KgyyBsSBWGoRs1heG5CU0KzFS3kJrV7vZsM5VtsLx6P9qMpwUQqVAoFJrQA8JIqXrRD0xh2skO2ZQNIj+IAlwV1hf0wC97BpEXycSUippdmp4ISPQ2JCrZdKuRbXcuusFosNZJPTd3Yf8JP1we++bAI9M/2v+T4611W5aeUAffdmXz7i88z3rNArVM5n9pf/FgYeTNA1eoDmH+dSjUptlm+T3f6Fp7HxefsIkZtqnVDhBq/+ElAHFgXw8x+z7fWp4Elj52r/LuulUsrCsvn1hXXp5+6vdPF8WBfcXWuGoSs3ozrXFxtnXe+oldOybacibiwD6rtY4i8divAcmW88e02n94URzY97etc1hu+fuKFogbF//tnTZEO/E9gZCPoyJFDDgdYoaw0VqPDQRq/+EfZrmHID4OGRBPgWp76YqbZ+0epbx+M1FYixH0UspdyXduuE7RrKpczZJ9T0IplaGYq/HU7iDdtSCD+1Npqzawi92Hyni2w+AZaBhli86nrLf8vc/a451UrxF0TpXpP59lzSmTEI1K2qKSt7i4YZny4Azrn+vBdXR8I4uvDJzyGqpmg3oqYvuDbzQ2H+kQ1Y6aOLVdisFJj50PTdBdcWikBAtdXdrF9bYW6lOc2X2G7mKOodOD9E5myJbmSRVnSS+Z3rEbB8kt1cy+r0Wjm8pjlI11QdlJ18LEmLnunPnchQ2PZR780emRE3t947ovbZ4vJ9an5gf9Vw1fCKPOUpaaVNbpK7GWRraz+Zgkuzw7vP7UYRJKt3u8qtr1sCSu8R7Rk0Gp58b5sb8S7z75HtXu0BdTgDKStUNBsrxJ9Uw/ySpZn8tA73K83LgM9n6QOHhEI063vIc4BdX2oWzXQ7msdN0JYpunBjG4myGeTNsddAliMHg9sWTCE8Rg4KXW3fb3LLXWkydmICrcsffmf8I9fVlxS13VDznivtY2nQU+Q1yb9ErgvcTp3UjEGn2naxl+JFBIT8O0PPT5Dmwj5CYZ4BM3h+jErE27FtInrsNbJp6E6sQsXAjPN01YLfjStrLyEc+DtrjGSmIiSCFosnIDbaftGqycQ6317+2Gh3YTw0sDPRF/y46lZ17Y1qmxUntoEbNzz9im+rptLlxsvT9IDJQfNTp3RIBx522rmMn4YSFFPIaCoFnZErq1hBLSRLNMdF1pflUipKX85SBwU5HpWO0aNBGFgSaEkEJqTUAopXQhhAiUr5SKzJZsCjW/RoRH2sgLQ2kB0jajyI/C5hJoiUjowg98At8whO7XTM1wQl13EmF5KghUULR0/URk5dc0gkbj6WdOnV8ozIxeNUSh97WfmH3DjN48/fHPTt45T3T3F9Ba59lvaQkuAuU7b6MGLP/Fg4VOYNvmTPe55UZN91QkbWlanz9m9p8Tr9/0H9RXzvLdtcZqreM0Q+u6anvtfuK663nF+PjcI7fdaf/mg/cMcf37ptRN7zsJIA7sG2+dc7s1JjTih5irgRvEgX1jrLCsHUijRuS3ywsk8EbgAXFg358DQu0/vCwO7NPFgX3GTtE/+ve/++f+m7Urxthyg0ZtsUbhXBUVmcTX8qbW+k60tsUgbs4oAvPiwL52Q1Sg9h9uA7B/0vh73in+nr81DvAz5/IsjgCjICIgwzsOFo1vve6d0NhJ3UqTmdfxcwYdhaRbEzT1hnAiIrO7AEYzjd3Ia52FujZ4IuLRdXBqu83Nn0/Tc75JqSfLkzdv5L43zjM+IJld10t6oUKYsGkYknq6EyECBk67OOVBThk5yjmNwbESuueyvNPh/FadSoegeylJz0JTM5olbbnSTbIW4Ot1ZJhg2ZaBCnsi5a+LXLOiN5IX9FB2MjugmBuE7Y8L0t9pnE8u1zNnrlxvjq/vnpkbms4K48m+vQ9Jntq9MdBqQxPVbKlX6kI27LXu7JrQueabCTdTHFjykuKMdJ/aOXi2z53P+OZyt+ir91Tner3hXLa6J+WLhyIv6nLKWZ9z236S7NJptjy9ADip9ZXezmsXz/2Rf6P63Z/6auKPz+71r6mp+AH2MZ7pvE3cyW2fCfnN915uyLgcP3BcBnsvNw4eEcTgY4iVG3CG+KYPl0p9RKzIL0TEIGUDMUPQ3/p9hhjYPEScwpn7HltgEIOBIeBaYoboFtppp4NH3sEde7+bav0/aRxyRLvzs7Qq1duedNuyImXg3CFH3ANsMGCjAa/QXB6rwi1ukoym0KwKV+kej9Q6cFXMuI0SkUW0DMziY94Wom2/TFbAWVvqwiUGUW0Wrt1hWqSdynPRCLExCQmpYLCEJK6BUQSEaGgYCF7IWr7QCuu7h4j/E6u8ZFf91FFkcSlhoKOxHr3jJ4UIH1R++XhrH8pAcOdtz/udtiNJDGAcYCAK/fPB0tkzItW/JyidlZEQgZldq4vqjJJOn8DMuprQjCh0S1F1RmDlnQgzQillJhJaI2jghfUwZWaF7wUGCmG0RrKhGSilI4VEmLoVkRehW1RRdQ4zu6EUqsi1ZTJrykYYUA2EUioKastKUZJCzRTciU+Xq4vXPP3M+VtL4/q6vqysVs58bapSNIuHD0XDFDpcIT596vc+f3tI/HAQtASkPQ4e8Sd/debRtNmx7fXG6KNffcWFH7uyo79CB9c+Wrho3DW+93U7ZO+JfeA/y9zP/SlHO4kbNy6JFhh65pJTc2BfDLZte+RrmzZ5RA2f84+uZe65Ije9r9b6XrujtSYO7BMMXGkxd2YTofsTxNfdInH6+SwY67Czp6kv1Ii70R3ih8GIuBY3Lw7sO0FcC6r/iNiQ6SU5CpRYvLBIsvMYutket1lAyzc0++ZC9nX/+mduf4D1LBHX53niwL4h4C7ih8z/Kg7s+8o/Fdv3xhvfLm8Mr+eXbw9Gr35DLffUxtPM0lfOszhEyDZO7H2Mnsn1JOc1Nh5JsTSYJqkHZAt+sNAXhl5NF7ma1rzuibBRzWq9T28OpelLhk5J8iWDjqrJVfeFTGywmFxvsOloQLlbp/eiRWbGYH7UhMgiW4SGEVDoMnhup8TNCDKLNp0FDXvZZMPZGtnCMmFCsumkQ6FPoRkBfdM96LjM9YDZmOeW/6kRGWkW+9f5pY5k2P9sRDNps9ilM3jmOvovNNnxUFkFVq9Y7C0zNZoktTQoZdilNh4Nfd9Mu/e/pc9Nz+80zo6u14OEs6ZnYrMZWEoky6NTI0/dWMnMrUsY1UZfbj7syCxsmimlhqqFTdFI98JEYuvjU0aWDmnNuGcqtj39nVe/dde6E3OZobNXIiMPeISxTcUsicLujz957t4fuzP973PPvad7S+jzxBst4IvARVD/8t3Vl+P/+LgM9l5+mMRP9z9BrKmXZ4XxgUs7HUMu9c+ElXqwROv7/564SPsQMQM4xz9U4V+JO/Y2OXhkBri1tR3V1ve7iQHPtRw8ci937P1neeJ/QWRarwrfQ8X9lroKiUHtqUOOOGSA1+HyhBnx04tpttRN1ms+Q00Y8wx2ELGLBjoCGytOo+KjodGBQQowlKIZQlmPU7Tthokq8aQZm6CtpNhTAERIAiQSjYgsIU00+pEIoEhEBy4aBv4qrbzVjQttou67uUmsjtWesasFcwMUGgqntWRJousqooaNX/4OMZi7xNGg1Zwx2NovOwrcbr98vlOa2QUtO7A2VKYu7Kwpgzph4HmYCQ1NU1JaUlq2FnquCKpTTU3oWmTkTEK36TdVqPCbYeQHKvTTQhdSeUqGzYbU7ESgVKS16rY0IQ1M24i8SEn0pBIIpQiTQgWeCJue5fSfiZrFISnDnEz2ZYXELMq54WOTjz8bXOjZNSLN4q69V9dzqc3h73/k5NaKmgYAACAASURBVLenJoLO1jFpO4S80EVAJLT0FlO3tUC5u9570weerxl7L4TN33tgVCklSlEj+Lo62wFk2ulRcWCfALSXAEHdSN1GMxz8xhyn7ssQs7rZlsBzWe0/XBEH9hkI2QEMEoWbGdyWZvyoJAZk8HwTln8l9eIAcarYJr4ntFPBbyIGhReJSxvEH0WPmH8RPbGxHNQrTPKu1vn+A7X/8DPiwL5ngYu7F5Lv6azJn922YH7wTVrHH35xtPgFtf9wJA7sSxHbHfYAb1T7D3/xew3C7xnHb3W44r6UnXitc9Z9TjLxhq6ucyd7b934N/MbZmq3U9uaJlJzfOquYTY9sZtS+jo2PSXpvvdpLmzIcX7HGn/HMYPpjdIfvFA11jwltfFh2+10ooSmFH5CMptyKAxK8tOCvrGAhuOjhU26L1iYDYMrHsnQ6EgAOk5NZ7bT4+G3hFRykCjrBJYkXZX0n3fJzLvMbfAYutAkPzeIVddYMyZZ6spT6KwzP1xj5GyN/opJMSGwS0m6501RGJVRJIS44QuRPnLGYeyqRORZKbdj2tLsYmA2c8XRUucUZm3B713SR03f0IzEkKrkNoULvVmta4agaa1Tk5ssQ0TLeafoq2qme6n3bC356Ku7/Zk+w9n6ROT2TPsNpaaauVmma1k9rObOP/zMrup1fmK9n+rvo2/6CPVsADM3cPSmi/zNz9e8P/iRXf91x1deFRy95Q0iaZ4mLk2I2IPgsZd0ULkcl+P7istg7+VHnrhWKiJOwzrEKbcXsj46PK/j1pZW2E4MRnxWJC2yxDfvrcBvEtfpfHewFzOL3cSTySeJ2YwCd+xVHDwySAxq3JbIcw9Q5o69P4h47/cTBeIJTD/kCHlLfbVcwHePloMHhxwxbQd8MhmwoeHwM7OdXOenuQoXi9jASuohygUNQWy7FbdL2Ag81+AZH4QFGVPQdo5ov9qSFnYIkQ++BpqhMBAoDCIUIRYGGpryaUQhndLEFBZRC9K1HTnaNXovZqf1YrEa5LX/bjN8PqCQLbFmhUaDMtGEVJoP8PNRhJgqrX3g/MLuz911q7hYNHoy+e0/G3bt+dW240ddqSgnrPz2KGimvdL4WpkesNAS0p054pmZsqXlNobR8jkvjAKhwg013UxUtMw6qYKKh6KhpBmG7nJdRI26k8g7BMK3LFP6NEWIkBpIlEBd0gdDoOtCF8ISSkVEQSAjM1HWjOF5XXdOilTPc6ggK6R5FXqiY03V6S49/KtHq8/e/Gvv/te5XbOdW94e1Rvmhz4w/ej/9yt3vDSDfcfeqPLhz+6rpOqf+0y395ZfesHbtrT/e1U1n74x+OTMOGUDOL2qmSED9IoD+y6q/YcvAc2t2rcC2291CEOTwK3w3Dd8YrAZsiKK/jRg0bV2J3piM83lBez0EpgmeMXWebSImfVFCFOt7x8BdrKim7eHuLFjmrim79oa/jtqsUxftfVZr7WsdkNG7bff8ZNfu3Yi+Usbm4ntVyylql8cLX6+tQungN8jlm3665c8ht9PfOv9Izz99jcwsyH5/jdOfnltw15D6viwfXrLxqs7xx+h2h+wMNCgkhxh+7fXMtubZDmvk1tI+dW8G01uVSbK1ZUXBtlJIfqnKsmN306q4PpIrzo+0tbInm1C0lKP3qzE2e0J8hM6ux6SeFaaNWd1uh+0SDYSVLINfM3DKZks75I4S+DpMpJBFCaXMMyahl4TeF4Oe3YLM10m0wMJuqfriEjRRFFMCTqLDolqJ3UhKeQDlJeQQkbR/LDGUnfApkcU0tEZmvBE2FBicRDRMyFYGsjKYm+eRsq3NDVnWXUR7T2kB4Wcrm9vonLVqLHYXZIdsx2qmdRyqRLMrPeE65hBOafp2WXDqWdUvl7VVHJh59K5zXqz0K+UHi6uEWJdqm9soShd1zp+/VAqvXQNA6clr/+Lk+huXRT611+3rjKo3b+t1jOz7dTj7swZM0xs3uHkcq2xeGnsoYN4zjjOY9TYE5eWND+x48byYvYva3r0ybWv+/bH/tHj43L8XxOXwd7LiVhT77XAu4hTsD4tT9kX+XRcH7ZyI28LpjZYAXPF1nsdxGnY3wA2c/DIf/kufpLtqACfBaYvcei4Y++Kgv3BI13AN4EBDh65wB17d73Mvf2uccgRSVZkKCrE3bK1Q46YA5qrGzheLO66VUhAfSxO+U4eeIWYMZsU/Sw/hc9b8HEwIDTwVYRGQISNi0YCgSKGlK4myKGw9BVRZ1jRNGvQ9pFtac5FEKFRIYFJiIFPiIklIpRWI+Fp+AiClsaevWp5bZDXrtVbXb/3YvHCf1+9jLZIsYnEJiJCoOE3m0j6gEEEImPND2wbeCDjGt33jdtbEmNn779wk/MnShvc9wqh2d0K1R9oia3u3NMj4YW/Swiru6kZGUNNPRC5Hesj0ms9rWPLvBa6rp4ZyfvNpi6ELjWZCIS7VMHsMFToengLgdQdodnOshCy0zANU0WRrgJP2XrihfthSjMR/xa6KtJNT6kwrxCE9UI6qo2N1/MD3witvkoHqjuhOdNBORuciNY05ra92V8q+4/o1dJTjmZ/X9pgo7/xzifEg/dsBNQLwR537PVScHj8wK9pQPSCrlWXuJ41ABAP3tNmfutAH0LqSH0Gp2MS6CTTn6M80wWMoRknCX1HHNi3Fpigb8uzGA6UZ6GyuJtk3qY2226wUSg8fK0fI7QQ7QcNqwbuCCugvy1O7QNbiMs//uquzW/+9kR5oePw4rPFSa/47Ord+49n1k408f9HSPj2q2raJ9r71/p5sPV6yRiewfhY8B9f/5ZvyDdkus8dZevxgKXhz7DncAOE4MTNOZ582weY7osK49efXH/W3rxGH7gpsANLr+Sf4cgtswyfMdl+uBPTvYG5zgVOvuIEC10VBLtDozSiaokwHH2uanTO5I1iL1WrHrHQF4nJDX64MNg0LC+Nt9ZuDI770ei4Lp++LkgYvmjKKBJ607ZyswqpAlAaqVLsfN3EI10x0AIYGA/dWkejmSKRQmiGLnRkFNG54PDQDsXYbsFVD5iEU4pkzaOWi2joGQpdEqsW0HXBoJoOxcyQQAlk55KyUr6ONR5gB0rMjGCLRZeka7H+iYC5jT7PXqex9ViO+fWaHNuWMLsuNFh/Xoq5AasjO5cK1p0y9OQciWw56R6/vo5nMr7pQSPdMyvsRocltUA0UovCXs6s39KxoCECa01oOlEkyuOF4eUoMyM2dRW6aOqniDKvZc/hbJBcnuze6xb7kn98TFPioe8cU/3G5z8odrCn0j6fd38hvq+0mPB2w1l77hkGev1y7qN60N1dCss/8zOf/Te/+8l3fuqyndrlAC6DvZcbCeKUzALxTXv1JN6OgBjEta2PZoiBSEgs35BrLcNpLW+GeHKK00XwU8DVHDzyy8C5fwD6YnA3zveOUutz64AtLWHnu1/2Hr94ZIEfI2b2DrMCeB3gwiFHpIDyLXV1iS/nXbcKQcy6bCFmOiYA9j+sQuD4XbeKX3DKfD3w+S0VMugnMSIAgwSKAA8QSCyaCDwDDEMQssKyRMRAzySe3N3464gIhNbu2hVMotGHid5OuIcGgW+h2QEG0fNgrk1rtTtzVzN2LwRCq9/nRd5v/91u5FFAEEiUb8fHLhEf17oUeNlkbQBq1+PrrxlUZz6X6NqdcWcf+XGpZ/oMp1+EQaPuRo2kFpQHMbO6KhzVg+bFiKApqD4X0nejIXVDRNIw6lP3m5qeTmqpXonT72J3adJICeXVl5U0IkFoIFUBsIVmREHQSEQRetSsKzRDJBJJIr8ZoJDStBXgCc2yDJ0wcMvL1frEWLny5MUB2XHqnFTnlCGPZX3nTabTM3Dj/r9/8BVRVIkUpcLF2dBOj4zM7OgbEQ/eM6Zuet/3FAI+9nV9gxv5e7iJe17s/XazBUArxRm0unJXj70UcY3reazkArmR65k/u4Xu9ccJw15CbwNQpntDgrV7kxz/koZbjutxU11TzJ+dZ2lqL0JKcv1zJLIpSvMmUcWiLhyK6RT5sosVdAKvBFcQN2qExN273yDW0AuATxFfM+c/uu2d7qNLZ/9s4bl6aXK2eAkDyZMfCW34MDt//SPJJz/yAwkmX5c8PJooV+8ImwMbWU68iflcmuRSN+88eTc//fYfJT+tM7HhNCev9qrh+ULp+k8NaqVNCz3VrSJbvPLRwC9ZtY7aK63igBCB4WkXtmb1ascuLHfZTZctzzdNI132gpFxP6jZLoXBCn7WZ6knr4q90k+VhGU2A6EFkVKRH25+XIU1oYQlrGA5q+Tp3QIDjXQdAtPHaGgMjoEXgtQVymzQdWFZv+oRy0zVpLbQa+FZgqOvlmw5JthxRJGtSoTSOD8MIxcS5MqCQhdoKiIyfTKhxLORGpY1eA4Wu+Dcdp2dD4fUHYPZkQTldBP5nCSzaJKoL7PuGXCaIT3nHbKTPXQUFolESBgmCxPr8uZUzunZOu8yNYrVTBpWsXtpeNtk1vVyppubC/x6SpZ9Tbe6zjklL1vtKw6M9HZNdo/VM7Uc6jvDW06WaSTyuM4I1ezGplW1nppOaJFW6XS3fKmzMxu4P3WTP1h6/70P/s2//bOJd4qS9nufvz0ifkhoArM8xgJ7uH9VireM9HNWUvuDOeZ+/ZmFwa+9+aonrPi2p/63dnC6HD+cuAz2vt84eCQDvJ64GWKUFZanSDyZOMTsnSQGFVlisDFKzH61WTybuEan3S2aIgZLsSxIDPpuJe5O/GsOHvnPP1AaNnbkeAMHj/wVsfzJ92QCXkaYxPqCEXGjSQo4TVy0rhF3E04dcsQFoHFL/XmNLkuErFUaWV5EW/Bj9yof+OLvXCMuNnV+ITB5rTLJoogQSLwWYBIYRORazRsesbbZMHF6e5nYXWArKy4burXSKFMCQgTn0ckDeSWwVBLXBNWyR2s/Obf9SNu2YqvFsleb3MNLM31tQNLuCNZpgdLQJ3R9PCfxfKo60XoZwB4I6l3h/G49mLk+Klf7g5N/WvfNzKg9dIPCdxOqUS3L+pIZBeUstQJgTiE4T3WyO0j1WioI8hgpM1SREpW+itn/ioZh5zuEiqoi9Cel3aEpIVKBHzaImnqoML3QVVJIVOQqXYv9cf0gRDUKi1a23xKxjEtB1/Q5RFQMwslnfP/CmLnu7Ue3m4liM/DuNzG2AQ1Nl1kNuRV4zcYdfdkSta/8TfI7OSD14/fc2/zM3Mf3A5Nq/+E/f+FBe9Vr/5v4wx2dj2nClPf94p+cevXHf/qR73J8EQf2tQXMG8RWYrRq92DvexSoeZ77homV2UV9+dV4jSHs3JsxLIHTs4CVDbAzN1G4mKFrdJLi5H30b1HAOqoL3Ti5G9DMzTSW+3ByBpqZp7Jko1c1wrrCCN3WOWs/xEXETTYJ4rKP9cC9av/h47S6bAGuZeDCl1+qgf5lAz0hQUUA+dTcYt12f99KLawhvbyfSCm6ZuZ46x9LsoU1PHN1nrMbP49evWrt4tW7+zl+7uLeL3dfPDdrbVXJa/2JnaqysDbrP/Wq7mjts35Syb6oZiZMIS9y+grNxS5rmTlLFnJw8coo8C0HzStgN6pGMWHpqYWkWxj0sSuWvu2YlDWnqbxOS9kFX3dcFRhFGcyOaHryyYjcrEBpgsU+DTctqKQ8kguCQk/eyC4kDFsFVBxINQOKgwZLkz6dC4JNz9Uo9DaYH8kzMhOSWdZpaFDXJZptIUqCRkqRX/BZHIDlXotcEWQk0FxF78UQOaojhER3BXN9FlsmBEm3QdatUSdBzVmDFubpu+h1N9IR06OhrKYlz+3WKHcJeic6k+Vsk7Xn3bqx7JXDhKkiJZcbXVlXhobdUdDddAnPqmnW4IyR6CpXeW5wknJqXTRyQq8EhPnmwPBC0698/cja3LqEVXj1FdX+xaM7ex5dcsOP/tbnFn7+wlT1kclfDB67kRW9w9W1fI+xxB6jZN7wrdN/+Ef7l8eKXcFn3vm5TDwmRS0WBrgc/y/HZbD3/URc/3YDcW1dHytp2ZAVp4kGK1IgTWKQ0AYLDjGTNUkMjvqIQcIsMQiZbn22gxVg0Av8PJDj4JH9P7D23h173/MDfe+lY1cI1wRx/eK+FpCaBebr8HSAyDioUzp8ibhoHYDrH0Z4Jmo5w6NnN313eZmlHp4G7gD2AR+mLUXRyiASoeEikFSwqBEf73PE4GuSFTP59jlJsNKhaxLrIzaJm2EUEAgoClr1fCuMrcNKY0VsP3Zpyl6teh8uBXurU3htsKhay3BRVPDpanpYro9tG0QYBK22O9nKSduAhwo3BcWxQcy8SeA1lBSpBqEPepOgaii3ZFBfbCKsJql8gsp5QeV8RfTf0CmNKFmsX9ASnosR+SpqFKwwavhh9aIvMmtTUW0ibaTXJj1dDojQWNK1dEJp6IgoSlh5KWKLMqUhZYjUVRgEHlQaQeVoykgV00nf3rFmw7iZe+3FE2N2UYZssZURAN8iZtNuaJ8Py9bnOkieDwjPfGTu1iuONQ79voaxPcR3xYF9f+lb/yUamzv/8YuLpaXisv83H94qD9iWJ5sNGTaznUdebKy0avA6W+fyJKsbhYTsARLMnx7Cq2sk8lsR2itxq90EnoY00gTBCAObNZS08JqKeqGJko8zvDtJIpWmUSphZ16FkdyCYRv4TYWUo0TSwB9QND2BN6ZINiEUFSp2k2SzjKHKxE4ZO4kbr04RM/r/9LEHgyu/E/KJV/0cCyMpPve7v88v/EL1k/L2pZ8yzS++b9dHr+bCmjNEZg+RvcDvHgz40vBf8Mcf2cGrPncD1dRo9Znru7lw9a5E7/wOb6gULE1NrcnYPVF2eigK6r0DGk07urjWjKoZA7SKmNY7TTOYVQNn0+aJVypVyQnl1GQp72VFddTssAOhOkqw8bgUUxt9d7Y/DJSbTZh1IdcfD6I1Y6HXaKIlG6FezCuaaYOuScnpzZCb91h/QnLy+gxzgxE7j0hm+xTNLJi+zt4v+yQrkkfebNAx47LzkQTd8xJPDzm2y2dyk8HAWRiektQSHnMjGtUUeJakcw7ySxAqQT70yU9E9C5ZNETAwtoIXY9g2aGYtMnUIpaTTU5d42C6EqcsEmNXCFTkU82G7H5Q5+S2gGTT1CJTyzQSUcYou0W7tCi0Zr+MNGpekqB/fG5suT8SupbflFnscKvajJsf60r0uPmK1JKnzw2pjbmiSGSr5vmunJM1+nrcU7suNsIwt/FDX9p8xS1jG3jaOXbDV6efveG3B1vNYip8bHx95/rOuWw+WbsIKuQxIlD1u5/79BEgtCavaXDxeotX/u5loHc5LoO97xmxrt3bgA8Ss3Tj8HwzAKx4tbbr8ZLErJ5PDN6s1ue3EgNDm5gB84gBkiBm+nQuBXsQg4T3AW/l4JH3c8feL/8z7eX3HYccoZX7cm9qiijrzJSFBpZSWAjWR7BeKPZIoZajeII7CowdcoQOSAtCy2MxXaU59BJGTx+7Vylg+a5bxeeJj8t/IV5eHIIIQYT+vLtFkRgQ2sAwAT4SE0mBOOWeI55020xc2yUiRQzG2jpqbeeItkPG81ZorNRdgSKEFtv44vWa6gW/t5nANvsjiMgRItIaDd1AWXpcfxO2XCyMFXFnBcInwkEYFpZuUZ31WDqhUKIc+rMVwkY3hGngIaqeDl4af9qPzv1tQ1hdyVR62BdBYFI8GwbC0kXf1Y2oMnFe8ypeWC8Yqmt7mdxGiUAKR00ktURfGOlpoUKkToSmh3oi0dTMvmWhmbbym2E5WJiwdSfQ7SE928cDZyfRwpAriVPz7TqhiFiKpIe48egBE/384qveVv5vf3h4aFvTHR6ne/lJpr+q9h8On/vV//mocLUd67MDNLT6L3d3DWnCU8ieE7/zhv/0E9+N4dKJQbmm9h+eBxAP3iNolB2Gdu7CrXpUi2sJ3Cvp3XIDmtkJkQ2hiZFOEIagW4IwkggF6d4UhrMdy9JBmUwcXUCYP4Jl6HjNGUwnRE84JB2FZ5Ypzy9hhR1gNPATj1Dp0zHHMxhNE9hFfM+otsbAG8SBfX/1cl0xXn3qiR//kaXP3f2a2Qdnt109/qH0mouPggrZ8/wDyXuQbgPD+0ly8x7vOvg1+A9PA5k/hQob7Xkm1v8BZ25ay8H31oGb+ONfmeQnPr6BYtceNj51zLeEb2vV13RXbT89sV1TteymYMFKuwMTQlhVks9sF6JpNnVcAZFjaq4nKulAO76rJpNukYbhufZSasmQQXLNRKTWzuiNSk4kvWRk2qWGl6slte2HsbadCMNSh/IKvXpifki3Nh5TlHOCWj6kY32Ia4NuacgxDavm0T2jo3Qo9OoklyIqHRoylPQXFdd93WdyFMpdJmevdKkmITGvoTeg57xPx6TCTQZ0nxHoQwblVNgsdoSRQtmDCSkXbMn4iMdi3sJyBZYmycw6NKWOZ0mMWshSRxJCjWRZUDc17JKgd1rRNyWCSC/W6l0ZvetMqDdStaDQpyWVZvV2z9ilqqPChb5g0k9ZzcBM9eaqy4ZrsXDk2rWO3Vgnr3hMhZZaMgnUQN+UTNh+04gwr33lKbfulkQQnatWl9P6np7ySEdf8NTSyJeHu9/4F22Xn4vizk/X33zlW4Zft+W48aEbv3nxkpvP3bcvAXBwfedE/4NX3PUnv2T+5cmdZ9Tdt38/5T+X4//SuAz2vndsJZ6wssRpmSorUhoOMbvV1tarsMLktMVV2+LKCVaYolLr8wlWDOdPt35u4FKJD4jZwT/n4JEPA38CmNyx91+s8FYv1/s0W9dCCTKCprhkg3VN0WEK+jzEm0xUknjynwaeuaWuJg85wgbWH3LE9C119VIpakEMGD9InIYeBHQEEhtBzJCWW8vvI7ZL03HJoaNj4ROneK9jBdSVWJFlMYmBd4mYcYVLwfY/bMRQhDQRSDSsS9K5L9zudrTHgMFKk4eBFieMpcRIxnp+Xuu9lpktHs+7eqg1qCUf3wI96UNpgUZJCLtnGWfUV5UTDdAEyGlUaQqzfw9eoYfm2aRyiyWpmWl0O+GXpxyjdq4cJHOzds/uutG57aI7/9hUGAVDplJrNBWVVH36icDM36aEafrKiIRhJwy97krTmRCaWQtUsGjqxpNdcujbtuZcV21y4ZETTBAD6gQx2NOIr5urWHGkmCF2DNCAx09N/dnjQ9ros59Nve2Bjb/01l8DqBj+N9KacWUkZKR1ZyRaElRE58CpJwB+ir9LAO6f8rbngZ/af7gqDuw7p/YfXgGD5x7uJtP7KszUKxEGaMaN+K4E1YEKFFYqg2nqeC4IEWGYEWFDInWwE2AmcsBmQr9KonMv1WICt+KS7Uuhm5vxfY8FL2K5GaL3PsVwNEd5woLFRXr9JTLWVfj6zYT1XogUEYvoqY0QvJmo+besgOHvGcMziG1G+bcmqiPZyTObstvXTB9gDR8A8RyoVve9v8jaZwUWf4bWXCA3HhJLQ60BnsFqHqFz8SSL/bvYft87KHXO0XBmmRvo4dSuOeYH0x2zXSHbTp3H1wvWcmqj39HcEAxMhMnOhaQeJHwj2CDBcKBhoUXDiDAwzKpgaaATtwyRcDURBQPKV1pvGBavqC2Z42lblXqMpl2ph4lA2hM7XOFMS62eNbXZ4VCfHQ2lXRFYniJTEGheRGLZx15WLFsGRl0yOuZTTliYywF9czpa4OEKxXLSxKlqRFbIkzcJrHKDdCUNCcnwcyFSwcmbDGQosZcFa8YkWl2pC1tE2DUpglJemtKLmBl1KPQKdn9NMVQIWEqaGIHEqSqUJnE7FDIK6Z6MqCd0uhYVmclGWO0Rs8Uew+woRUa6TKPUY1QjTUTejO94lqVla2IpnDd6pzWvMdur53tnsymCVL1uhEldGXpgl+1sXXoeUW+HbwiBrUOY0dwO0xq/xrWsoa5cNmNohmMFja1KazLhpr/8oaff/6XdfRdr/+ZuM/nYF/cWfvY3r6l86N5vtO5ZQie+jy2wRzU3bx7v6pTmvvn+3hrgijs/PaHuvv0yy/f/aFwGey8VsczJFcTSCYJ44soTgwOTFb28tsWUIp7Y2o4L7VeTFaaoDSZC4joeq/X7HCu1XBErTQ9tjbcUseTCG4BPcvDIN7lj76VF3T+EuKWuwkOO+G2z7u1sQIeAWIqtXRKiUJogdGF0Otk1rAXNLSNupQTcC0wfckSRFTDzkvVIH7tXRXfdKi4AY8DtwL8jlpxoA6e2dVqBNoATCAxyrIByjxiEL+Pj4dPEZgpJkpWavlOsuBa0a/La22izAvhi8BfzeW3R7NVNG6t9Zdup4HZtZ/tz7brOJtrz40YjBq0Ze2WZtLa7SpymdPErPn5FQzg+qh5Kp285CrUUMA7hMkaiiK86IDoCYgMkBlBuhaAk0XKmnu6vs/xcTU09uOSWzvqN6W/1aMmhPU2vMB91bJ5OpzdccE99alEkRiYiIzmvJYZ6pbneEbpuASqIfLfsL0Ve1Jy0dHv7ojdzYTix8e/uvI3m3V/AI66ZHG6dk43E18h4a7+1SEVby26z/3V3n3v668n8R2H5+v3BtwqKtwKw58Pvu+uV77vyw/ffcyJ64BPfeHcx8n4ln6w8NvWa6vQvLk31b8+qjjPzflmcvWdO3fS+54Vm1f7DkXjwnpZvCWnya7ownR9BRTsJog4CrwszE2HZEYFnEAU6oQ2GCZa20lSjWaAZtP5OoxlXkB9WpLqnCZpZDLMfM+EjBHRrEXotolmSOCkHZ2uD4vj11CoRet82VDoP0xrBBPh6BX3HCYR7Hzzxsq7ZiYc+IT90dcd/3ZN+5DeuGh2vO0Pz3yZ+kDRb48MjWXmCd/1RFjiJziTxvcL0fNyqL/dZuuYkNxz3MZc3Y5fWUOo+R2Z+mrXP6JzdblHKXcm6Yzl6JpYwgyVpL2+0jr8iZ+05qTi1R6fULVvDtkmyFqJ7UE25SvKmaAAAIABJREFUpMo2Wh1qjsSoJjWzUTaDyJYTw9Iq9vmlKOMG1oKW7h9Pad3P1u2zV5skEwlRTwuBL0KFMkJDIwgCnLpizVmDMzviKtypjYpSp2S0GvP3tbTPmU0R659WYMD0EPhORHrOIJGHroJk9Lwgkgq9CcW0ZCFSKDR65qDUB6WsMqoWIpWRJiEMFjR23B8ys0XQV9Kw6hBlQnRPEjqCsW2QWvQxjIiOIEJfVtStiJRCOHMl6To5LXd0obqcDmX/U33OyLxWaWYTWiWr3FD3y4vd0UC2HPb0F1KRGXl1vCh9zbc1y837ZtIXQMI0qYVhXFesFKFjY1UrumX5bkr4nmp4CghGm1WnVAizPzoUzMubcrNf+Srzg5uG+tX5425NZD99Ui3fXiHOKm3nO6+7ALj1UM9OzHWXt5/L3XduT/HkZaD3/3ZcBnsvHTliQJYnvoGOtv69DeLaKb92ZFlh9BQr3rgRK0bsHi1JEFaARYKVeq6IOPWoETN6bXHmNK5nIMSrMY2bgc9x8MgHvodEyz9XfFuHzybhAwIUkYoCRagLGpHkYQ2+5cO7IexwIj8LbCO2hHs3sYXas8SuBuqQIwSg31JX/iFHSGJgU2l38n7sXtXevyfvulX8J+KU4CuIj1lbfqBtP9YEBCZV4lq9LHAN8fF8CMX2Fic7R3yce4kBwhPE4GSktf62Hl5bXqbdVCERKKznmdx2529b7qUN9NoTetsjud19295GWJHiabOHBhEBARESgY5gxfs3XoSeDlC+i5W0qF8shUsnC8i0CcyiOSf04R/Nho15l7ARqGKQRZoFXG+Z8rlFOfDqcZEelqFXKKiFhzeH/lrZrC1eaTkDWWVkJ5brE+e0kXccC6fv980tH5xAeU2vfubJZG7Na4T8/9l78yjJjuu88xcRb8mXe2XtVb1Ub+jGvnaDIAESpAmSpmQDEiUdU9YYR5JlWbZlwGPPjDzQjOQjUxqNxxJoyz5nPPbRULJJWdRIhGVSC7gABLE1iH3tvZbu2isr93xbRMwfL7OrAIoU5CNZXvqekyerMt8SLyLyxfe+e+93gxbwLxC8eSk8lx/1p6OtePWm7WTVjLgTzsOPVIaMgiELSbgJuCFKE7vRbxdGgvwp6aBjEx3fbGzu2Zt2z4HogQjJSghetsc/85oBeP9PfvizfPrkv8e37vfIfzLZiwvuwlZaf2EhmQPGxBOfedPedX8EIJ74TB74nsF47mNk9iDW3E6vIcFacmWBcgIkBikdkKBjUK5GKkkaG3Ro8IsZuzes1GLtNEJE5AqGyPQw6TrW9DF42DSkWEwpxNcS91sURreQsotbvxlRKJMEAiYtjbbFDYpsV79Cd/ZR+/A/+ZMlXLi98X85/8YZ3XU/oj7+XJ1ie1C2zYY8R8hxRunW9vLkx+YZWS/g9g4xf02OPedeMOWNB3uUb1lMZrevdy5cUMH2DMf/sMb1j1mKYYnW6AQ3PXaOVuUARo9wce4gSXoj+88VmbxgKTYlhe2U12+OCLTg0BNddC5Pv2Jo1gr0C5Lp02HUHXPdVs3RYcVvH31NBWNnnMLzH6ymotJXs31p3AQ1vlWwrUUjRhdAl7DtkjB5Tb+ySjBSV6zPdBm/aJg9pcj1Na5JEdbFtQ6eJ3GEIi4aFo84OEYSFSEqGUbXU258vouKYrxuiCMCViYdGlXY+2aOsCQYq1sKoUVMCacgpZMEICPL4lEoNB1CF167SbDvTc3mXkXQSDl7k2JrSnDTmmLirS5n9+TZHLNI7bDdUnLfudJMQKpdOXKxm0Tu0rVUool+Z/IcHZP6JRmb0ZGtHvl+ElQ6xnVJ45Q0H3oyWfedsOD7qU502HH6Y+W01QenXhee8pHGc5E2jSZKDYz0xJub077ecPfv37eS/Ojer2xcm99ojq+k6p9+6YHtI1eHMy8WCpF48HNv2odpAU/ym39bA7WfbV330n5b3PhwPOXw5HfQbr1i/13YFbD3na1EBlL2kS26u/tr+JQ0dAmmu16GDGwMA/qHLODw/yFjM6ybO6y4ochYw6FG2DoZ0Hgea6tE8UdwVQ1clww4fR+fPvkKmctm6W2ae3+Gdk/Pxo/mxW/LrA1FbVHCElnBpoKvAV9xYfRAt14n67fvj+GwhTs1nHDhjAufIWP76sDVj+bFq/0cUeSx10nZfDQv6vf03i4Z8Kkv2fWHPi5+AvhbwI+QgTGXjFFKB//H7LhAh7V0+8Czbi7XSk0aWNJhEH+JbCyOMoyXtGzJhJxRyAEnNwTlQ3A/ZOb0rs+G7wE7IC5lJ2FnuM0QJMbEpBgsPs5An02hkYOtQpzL1zbQMkwd0pU80Ke3OSjDpwuY7hmgj+6Nmait/bn75m37rB81598k2mxBcgSYNGtfjcTW6PMoIePizMaCWd12ZVgrt09tFHRU6m6bW8Xy42cL7Qu9xAm+1K7u2dPJFbz96fG/Y9fevKCKexecwvTI9ZX3FoC1yPRbk/6+btGphA/eS/rwI5wlK3kG8BwwYa29y1p7eEt0qh3Vq++x+fMVk7il1naIzz8A/rZ96OFvXwoqe5DRrZVMauixUxFkQHrIpg/tFjLmNyCJpjF2FMcJcPwEg0QKD4HE7GJnQaAThRAGq0H5EpxsNuTxcTAYLYkThSMnyZWbwOmsoAgax68gHUk/0uQrHl5uP3ZEoIIqqdHECJxZQW4E0tRQck5x5o8thfitdvBr8Nb1D6qtyRy/+Ks/zM99Yic+6zg5nN5hgq7iD3+gy9fuK/HeL97I2r4j5Nuncn/9pzccT15KhX9R5Xo3k2vV0H6AUCNUNlO07nMknGZj1mG7nHL62hy1hkuxFcXTl4xpVI13/AlH5uMQ0oTNMYf5a0VktTE3PKG8jQMyMTlpXWvj2lbqTS7Y/FrVuramo7u/kKxNyf7kQuD45w/6SXlBRGv7PffSIeWUNyMXx0lO/KGk58H52wT57YBeBYLQkjqCvrBMLmpm1oBUMb0kOTcN29OSesVQ6lgOPGcp9FLOX+dTXQjQM03W90fEnoeO+8yGDv28w+qUYGIV8quCkhWEkxAVUvqeg6cMfUcTJC6RAwqXfi1lc9QQtCVLc4o0zrExpej5UGkpNicM3aLPxMKGGtsqTmxd6xPoMKg0XXeso5ZbJRm4ke+Ph2m97TQ9wZLvUshFVE0s06hYt16uk+/3vHLSzxXCYgskWgjX6fecuDBmPaHxQicXOTYU0+mqf2p1T7KWVLxKuXXncjTRa7w87u5Jn1w5+r+ZMx8wskKjy8C90uSXAej8KIfhOEvseJ0uW+94ekwhmv5z6ltUEa7Yf5t2Bex9Z2uzk3Qx7KthDN7QJTtcyLuD1zC4vzTYZlhBY7hfSuYWHlZlGFqFHXduf7D/PPB8lEa0ks5sbKPNvA0qbt+onO/jSMeLDLfFEaekx+8VPn3yp3jgxOk/o754pz0JPAL8ZWPxrWFbG5qgPiLwbhEyfdlVyRng5QEC/lEBExa8GK5N4RcVfL8HP6VhbLvKR/2Q9U6OWqXNdYli/Z/fLL7xky9+C+Bbeujj4p+QsZ7fP+inObLYu4QMKIdkVU2qZDe6l10KlanCVZP9dNts9ueHyTU+GdicYyidY8GPEbGHrxURO+M+jNPcLbUyHNMhgNgtoi3ZAYm79fcA2hhyGNJBe0tAHhcXiFGXHxpy7IBYOzguvG0OJr1BX6Tm0qMXw8ZrG4SbGt2dJAO8WU1g3Tto/Yll4s4plYZ+seC2y+N3fNVdfmbM0d1ry0ac9qMLa4CXnv+NJ5onHtx2S7Nj4SN3NXqd+VlAjv+4XQNkBu6C874MRhkA2wfvJXr4EabJQHOuRzipXHlx1hmRddE6sG30aLid/Jq6dGGkMHMo+OXZh9MH7+VbgN5QLmV3EsPS9GChmr6fwfVsiyc+4wxct0PB5BlgAkQBY3qkaQfHT5Eqj3ItUS/GCheExVPZWCaJwMYSP0hxLKQWdA+sC7gSKS2OECjXIIQzmCceuVwJow392MENcni5Ko5n0cbBcaAfgucYhNfDCwQVz0eJuv3b/wlutD0vCBanz3H+mhb7TrfpF6YIelt84tUaU+JvtrR3j+v30kD1vsG+U1v87l8rUWgGuOH1/N8/+/jU/b+8OlV+eZrVqQOoRLI5tknQMWzszbN0dZHxpYDamiAMHK59IURahRvmzcRCl9kFpWsbXaniCywfylOsl8A1ZmNCpaVIqnSx7yxe56epUGpqScjKarfSmnIIIt2cXFOFUjjJzZHSZ/YJGY1Ik9s2qXJwQoFTbGmnU4JcS5BvS7bmFGtTHQ6d7jO6UMIGRSYupQiTafHJdp9yWRLG0PQc6mOGfSJmY4+inlfkRhR+O8/qREptpUe1Ixith3RbHq3pgJ6EN94PjobRsynXvWpojUBlPaW62oNAMb/XY3las1aFnJGs7ZVUG7C0RxLYPqNnNNrm8buKx+5THHxrgg/8gQ3iQONFktV9QhfbdiLX7uQ6BT8q9v1cY6ra7Qrrj3YuRbLtgxK5NCe2u0RFHDfvGZHzcCOQ+THBpN/TUZdUS1RAGOiUuDQRlTAX106+dszMedp9fWP2g0pGzfdc98322C2mPlrqbl3FKQO/9kfNIIcsK36FLFSE8+/rBMrhAWBhP8X/4088J6/Yf5V2Bex9Z7uHzDX0TtHkIVgYxlp1yeLKKmQ/LJ+sb9Nd+w5ZneGiPXTxDQHgUJ9vPxnga5Exio120ll6ZvNFbztsup5wtjtJJ3jv2G3pNeNHcwJcLZGu4D3AA3z65P/CAyf+zEU07+lZ/eyJm38+bjaOsHzxBpOkqp+OzGtdnlGyPS0Eb6KT/YLwTqGSioJzElwNI4MVT2g4HsIXLbzmN7AeLE+GfNOCqBfZl0D0yKh46d4t+7aKC5/6kq0/9HHxC8TcAhzDu1xAfoPMzbtCxpwOE2Pel9DdWG9fMCm9HpnL1iUD8ytkDNxhoI5ko5/nOsTlmsa7WVfYGf/dwGsI5vpkwEORAc6UbEyHDN0QLI7io4AEcTkkIAI07mUGeCjtU2cnflOC60MagH2JTKxXkgGgSzjeOMK5F+tvQPcQGZAczEt5EyPXVlTaill74vXpmLbo9Y7ZeDvALX0tQHyOpJUO2r39b372lzXQeegP/oZDlnQRPnjv5bayq28nHn6ElQfvJR604+qU9C9GpFMaszQmyh+UTVVNlsXzL72x6d12x20rKmfe23r5q1Pc+6Hz4onPZGXCBi7Z948ePXRVaTr4gX/3U69//tJJx/7PX/2WEnziic94ZL+TdDCWRxgmPQgE1lQxQpMkGpP0s7FRLjppE+Q1yskjhMFRKRYPcDNSJAU3HjjeHRBS4OYgexDoDM5Xw9qEOOwhGMFxs74QQiFyKaanUEA+n2DMFnHvJaLwn5KvnHvndfyxdhzBx35pJr7hsec5PXfG09dclea2yvr3/tr5UDh/yxtZ/l6xNF2xjZpkZKVAod4iaPboezG9/F7mr6n3QnHIdmamC8gJJldHIF5l/noXI1qkSUplI491FN4CHHhD0ncszZnImVhJbXUTd3OPR2V7D6tphX0LEb7p5g5bV/vGiSpaShxFJEU6vqmdqXlH7F/wcUJkWySjUcE1+5cSXb9Zqkv7UnnNc8IbXYZOzrBvyWdrQuN0Ddd/HZ7+HsHEqsLKItLxmLgE3UBiXEutkWlrjmxqYhnTKrgEPUMhNHBJUllWqLylUfMZ3ZDgWOJSB2yBaguEp1mesWgctLKEo5L6rKRdMBjtcPIvFYiUAFfTqgiWDypmT8G+NzRXvSV4/VbFzKU8KjScfo+LjgyRK+jnLU5L4jRBV71Ew9rFyV5ppFTSUWCks5bo1b1OEuXGGs3NYnffQivoVEr5rRmHRDU5eFEUJKoTkbb7npuUKtaEdVXyUqQmTlOkBaUU1CpRcPzoprzYL/q9xBQnp+eDPWPNQ/4yzfNv7Zn/pZ/9H/f81s3/enOrXDD24U92AP6t+EH5Q7d9NgHqa2xEP3LnT8gvfuO3zK9NXghvb9b+fSLs5v5ByfAr9t++XQF739mGOnq7g+Z3lzwblulqkC3wFXaqY8ywE581BAAxO5IaanDsVbKYMsFOnd1Fshqbk8BWySu9PupXX3aUc+2/W/zt9cc2n721cqH4O69//CtPexLr+cyQyTwM9fr+s9jtn/3iyst//8c/F62vHpA6LQXO9kQs7O8LEscR3RNWcBfQU3BRZf31VRfm+plgNAA665tbXUj8LNt2JALlxVTG15n1YlqP5sVL9/Qux+7x0MeF+tSX7MV/fEL8s36en8NlYiBIXCNjQw8NNl0lA4BlwItofIkM1FmyGMzrycDhi2QLeRGYRJIjG8cimYt4qNUHGbAfZlHDTiKGxw6At+y4lLvsZOMOpRMKA9ftEDyp/gBUBtk2w+O1yOZCYdCOGBILeRd6y4M2Hhq0T27nK7NOZW6iJIsTtPo96G9zOZnFtNh66bSR9jSDAH8bbZ8VubGF0gd/fcGb+UDz730i2B7276N54QOzd8PKPT3bAnj4kYzVfPBezIDJWyKr0DL68COsDhI1rIPj5fF7Ihvbqo60ML3owjdfmX/m/R87NNl/64ul/tlv3PjwIx9ao8bkoB/PA3xi9oSaK4znfvaN/68GjIr/80PzgKS2V3D0Q8Pkp2PZ1GEWuG/QN9mqJaRASIlA4DiWOLYIsYaSIyAERiuSKCGXdxEqN9BVlEStAVfqgxWQ6QsOH9TkYG5p4HWscdHJXqQSoDQmcLCk5ISBgsXmFUJYkuRVPP/XeeHz3/yTyq0Mrf37926d/v3rRIXKjHP3Ui30x2ZHexNXdUfW76zKpOimeRzHmEi2OBs8uT13a79a2JzzKLQk17zwvfr8NWOtyYsp/cJ6wUtPU1gfTaYviDDoTKt8W/rNyUjJvmFk06WS+lgk8/3I8YwlySVs1RKueqnA7FkF1mNjXogInMaMiuNDni62kEWLdULZ3zjgOdWl1CtZpYSn2JjGL61JEt+IXqUrK12V9Eo2qTR1MHnBUcG6y4WbYl6+rUdx01Dol3DrmoUjoLXGwyIElBotUumhEkmtA7UXeqieIMp5OJEgMJa+1Mie4ManJK2xLbZrRXJdhW8iVD0izQfkn0l581bFyl6obCqkhe0pCLpQ6oB1Q9xmysVDAe2yIacsQc9h+rTk1HsEqg9jlyyrU4Zjb0pkX/P1v2SpbvuMrAm3W7UVYwt9p8uLXfREO/Anx9diu7bfi8obvhc6QbhVaOfbuU6leaOilEja49YkYUv6raDmr+dDQdiKsvrZSZ/AcZECdD/JeXGnrPL50JscaRFIXdKLR29a0VGpUbIHX7qjerqfqt8ba8dr3Ru3Ri72TrNZCor/9ps/OP9D9rMbP3LnT4wDpe+68/sWvviN39JkOphX7L8juwL2vrM9CXw32WI6FMUdMhuVwbsiC+ofgqzdsWJtdly9lgzU9dlx7y4MXltkosA3kLF5L7ID+Oq+8sR7J267CHz9Yn+l/9jms7/TNJ0OD5wYtuXC4PWf1w7PhI2XTv6ajcIPAx+UkqtzsnGSrG7vprW8n2wR3iaTllkgAzQHIktNCojAppnEu9eHWgVucqGfi1mNCjTqFUSnQuVXj4jOnkt4T74Xi8eehz4uVm64yJOLU/y/jRyfRFxejOtki3+ZLGlAkiUBhGQSLLsfZZ8lAwwTZMBpL1AhJkJSx8EnW/CHCTS7k2kUO6B997yQu95z7LDAQ7d9nre79dsWKiFYlfXNkOHNMkszYDN0Ew9K4PU6ZHPpFrL59g3gOq9fH23bl79W7KVTojDt49dK1J9XYC8B5wk3O9bxhgzVOPBo6c5/2fP3faxCNl956OPCA/a9eh2t6y/XebhsewfvCwAP3kv/4UfYIHtA2X74kcul64SPOwV0oiTMG4yOamHlu3587uaL//rvNic++LFL1dvvLQ/a8LaYob97+KNnAOfep38pc7XuubHE6qkbUd4UUfcZxylMp2HjQ2AvUhjZQxYS4WCMhzU+yhGIxGAR6CTF6DxJ7IMEg4vVFnk54WbA3GqDRiIVeLl04LLdzeYPf/tbQB2pcuTLk2hjSQYYLsbFR2fRgCJLuHHdANx3B/SO45LdU3xgdfFX/0p+81fmqtf93I+XD67tP+LjVsSb0a395olLwUbByQm/JY4uXXJEKL1rnhvp3/TlWHMhiGaeC7yxdt71YkuYb8rlKYmf5ATdPGk+x/bRvJk5FQuSEuDgx1ss7SuS7xfJn0uQ0lLoK964MWTtoEsSKGbmNU4K+a6lU3PQvmRrFtUeR0xfSOSxl3rmzI2lxagsSxtH1YxZEWL/otHajYVKpJYIu+eSX7S6FeUi32rPN6/dplW561o3ltH2Xl9MvLjlF5MUVJXuGPSKGqcPha7EFwWKRtHPGxqzlsqCYxYPRpGXWCfXMe5z73OpLmX5rBYIkiJBr0DOKFKZ0imHRDltl+dk2pwWzt7zUowsS5S1lDoGaTTr+yXG5nCjGCeV5GLD5NluKtIgmlqX6lykHE8rp7aqSZUmqUqwMY5OKWyBH3norky3Z8VGezaePfyCmomqQhRbbsN5s2NlW/ZW5lxbbhcLo6dE75U7GnJtzI+iA4Vl2w0KjUSWA+OK0ma62Bx3kGks6G/5iagWtFfsbHq9qLzOynY5eWl5Tk4WOvKucj0oF3sH6z0/uf8Tf1B6fOHa+e9/JNKPy31Tv36j7pzvXm1nGp3Fgap+xOWQIyF+5Yl78r/10u3hYz/5c1cSN/47sStg7zvbMTIWapidOZRU2f3Ur9jR2lsffL6PHdcs7GRqQsbWnCYDAecG++TIXGUb7Ij7vgKc4oEThk+fzAL+Hzhhf4wT/Bi//J9WTePPwD6wsNZ/NC9+BPi0zdjFG4EVAb8uBKfIFmNLBlzt4P+uJ6iEWeFGbcF1AC1wsaQS+jn4ahrzGBa1PsXRoEcDqO25yKsXDiKBvRsTXHQFX0TwXrJYsYgMIPXIAEk0aM8lsjHYSwYCh3IsLllc35Ch7ZPyBhHzkUs/cdiXS0gcBxdxOcN2qJU4jOEbjvMQ9O3WVnR2bQcZMHPYSS7wgIqAtJK11R+0Y/i7lOzU/B1WKSkMjlkl07JbBWZwqu8rFPbqQlzfVJM3p7q7IuldSiE3jTBNSE+6hz5x0HRXr9WtcxNEmw1/7nukf+j77YP3sttNroHu1hidj/Q/uwWYXUilCfDwI/hkvwdLBq4TMhDtk2U/HyYD0Bhh8QJXFeLiXRPSPZPWZpfdiVs7wdSBCOjYu+5/OxN9eGaYsV6mMAq58tWUJkcZPXA7ve6Nyi8cSIV/HUmvSdz3cLwJksRHaxetJQoH4YJJNV4gQCjSEIQbIoWHcgyeP3TPZ4k12gqUD66yA0ZvaMPEG8WOSLoLlBCygCPtoI6LBgQOEgdJxt6eBj5r77r/3SZljIaKvX94B8FIJ65e//XbbvUOv3k344uTha1KRdn0ZVVo+oFNfCpLo4wtPKUnLu5V++av4j2/90Rw8K35q2xSiqR3t1Y9150/bElc3zvw1mh5e7TjlXI5qnWH1BN+fboqc13rjm3F0Ao4cE7ipJZWoKlPG6bXIq46GzG2neP1WzwuHRYUOyn5BYmTSlxXcOuXW77J5Tl31BCErunWxGiC9saXBG5kxMiG8MvbyvYc07nhRScZaQtJdywtr9rowmzqLF8t3KmlxOx/05p9bziyujrK6LamVzT4rYhK01CfSVk67JnRhiedVpfYVYwsOoy2RZQsi0Z3TOQkdqQ+afHagtGWYaTex/gl+o5lu6JZngMnzVNd0+lMV/R1nZwex2uNG4K25Y33WtxUUNzaJr89gprNMXk+wQrDyj4bLx6V7bGlvti3YHKtar6Sq8PRdUl9X8LGVIuDp0v9i/sE7cQmE4uJWrrddUXI7LEXKZIQ5dtyYiTKn7s0ZevLe5yx8qtxL9h2lspbs07otievelVrEdHtKU8nOdWqT+S6WqUjQU+0ksCt90tu8voN4aUzM0nu/U+qPWOrkVhV6vnFva4on3ePHGxVip6+uhe1+tOl1ZHHP3TVk19/Ze3g6znbS93ZRwAjHvzcJLd9IrQPf3IN4LdeOlE+vzVxV85N3uDPgyS4Yn8udgXsfTvLANbfI4sHgrdnXw7/NuzEZOXJ4vXWyRbmOhmwu0i2QB8lW9zLZMDjqcH+XycDe8cH5/mDwff9y8zdAye+fcbifwF2T8+2H82LH7Wl3PutNn9T9uL3AIv39OzvAo8PRJRTMhYNC+uxYBRL3gFciCV4riWSGeD9fQH/qtRlqV1ENEYIDlzIslP3L9K/cJDzQHFlhjYZqPsm2UI8rFLxAju1SYdZsUOJFjn4fHvwmWaHbfVx6OOzjceeGOX5WieAwpIBCXWZpd0ter376XgIBId1cO2uz+HtsjyQMYWh3ImBc3btZ8nmSzJo4/jg+k6SsXkjg+s4TtqR9LdaRGv3am80j+5/g2j7VVE9WLXC7bD98lTaOHfRn75zy997z4wsHRa5g/fdBjz3D+7xrmmFtb9wfuPmf3P7QTaA+Onz3811M0+cqAQbW/DJ0wAP3kvj4UfwyAC7Zqeu81BiqEjm1nXJ5nzJFR59Ygq5oFIpVP3e4VsW0m4zPxiDYZjDH2UVulsuSVimMPF3sVRwvc1IRFchXA/p1IhjSaoVAonWEp0IrEwoFly0EBhrkcIgfYMQ5WxcDFijEHIoeyRQDgQODCVwduydsbrD32527SkCayxYCWq3nmKPLJ7yse9wfe+0euQTTa3xfYcuJncU63dP5NpUksqlQjxTDjw32etOnn6Vm07m8MNrzI1PifDCe6r+xFtrwbWvvcLqnnGv679XrM6W7Pr0CvleQrE+qfadMo4TGTm6UcEhJe120LbougmAQ16nqF6MJsHGjs6ty1R0cs5Mx6iRdUPshnQra1GxXRXFjYrXDnJVJMJdAAAgAElEQVQYaZk5H9Ifi3nmI4r2tCcnzpixi9dIjrxs2HfO5goNjFAq2pzE7/iRU694Is1bU1sxbiV0zNmKbYs1kV+6AU/1lUxKklYaMr7hMVM0hCOKQCfR2Hlpgg4yJvG9toNrRBxK68w0/ErUMO7GlOL6py17zvcoSIu2Rc7tcRA6YnRL09oTEKw6zGxGjt+WQW9UqKQlmFxN2ZqyvHqXg99IuOHrHq981KG2bnGs6K3MCbO8v5SLa6Jy9ZNhes3L+Tj00/Dp+7zcB38L5s4KvL7fr0/RO3cUyk21ESqdxiJZEp4UFw6L2dnFNHGNFVJ4oZUiHNk0oU1Up1XNF4OWV4t87axEtljqu16+a1utibQRualbimOltd6Y35MoRzoyKbjTc6u5k/PTGgc7GjQ7zzf2y68v3VBeiVdcHcjySr8c1HJh6WtbtZFtZcWc1RfPwhyoIfFQFg9+rs0nYjH15fvz+yqbvW4UNP4E8/OK/VduV8Det7cymaDy0IaM2/Bmn5CxcS0ysFYa7DPBTpWMOpmbymNY4SEDPB8nW1Q+S8aW7B3s9xzQ4IET3T/bS/vTt3t61v7Bh489Jc6vdunFHwX2PJoXR8hA1wUy0eL7gI9ELhupoueH+A5oAbEAZZQQRtu+gTUHwmGc3vdC+9G86ACb9/Rsek92ygjgoY+LNvDPyOIVv5ssRm4JuB04Q8bqGQaSMGTgfZwMsNQGzV8jG08PKOBxgw9jrtGXpERimcJQARTqskuVXe/OO/43u/6Xuz4fsoDDxJyhKzg3aPfmoE0DDPw2KZ8cWVLC8OGiQDZnEmAF0hbR8jXAKN3VPqZ7KzYs2O7yMsp7FThoN58dDTefLSPcrqzdNB1f+rKr8lNaCn2DsMymxg0Gxx6/dvqptdft+/rV/No7HzSEbs2XrPI7TmF6lB02MiSLH3Q1aSpRoUCUHaWSwPfdPTlXSld9rHjsvVM6bK+TPeR8e7vhXovy/iZJ8ldJwxpCpTjOGDYugBQoP4HYRYeSXNGAlEghkI4gTTTaWEyicB2Bo1y0kRhrsLHJEi/8kCFLqwdjtXukMtvN4A/rYVdhIJvTB0wKvsn0/BxnWOf6V4FH2VUX+jtaVvbML9r6x69aUn9D0S+H+Xbqnj8S9cca7Y1PfKG+Z35yf7dRm9Prhz3/6qcM/ULXvflLSi8dXIualb/sL8zV5MpURYbFUtqYKoTXvbCcmz4XSm17gSyU0F0HoQMMJSoNBxdNK28RsaBTdJHaRSmR1hqJ9ZC6Q6q6rmVmUaTl3lKETJz1sdhbvGaK6qqi0AuIkw63PaEZPw/jG4YLR4rEOcHYcqSXZ4zwtWsbYz1/a9xxer6nhdQ0albf9bvCht+0jG5Ym4uE4zRhcz80Rm1U2bbx/kXrNsvGL81bnFDQKFnbmiiZ/Jrt2SLdzliiDr/h5sMUaxMZH1ww3njDB/r0CgLrJWyUDJNbPjd/HboWHDzhJMYdv6TZmDNsjaR0i1A7b4grlovHyixcL+ie1Wwcltq6Opo4J9yoL2IT+ioU2uuWHTqjlt/7K5pyx2Vsuej2Rh3X0bRWZ7Q7uuyNzZ02ta09sZsq2Zw/al5bm1VX7X+dorLUjpw2lVZORrqizPQWSTcO1pcOGykdp3DoNHpixYbdsrHGufD6hQOmuTwXjNZW3C0bUfJCUU27Yq1ZFKlyzYapdpcbo3piWQVv4QfKT8RYRaRXb00VmmOLTnFmYeb79q78xUdeO15d7Yw+vt0vj+DbbWV0dfXqUrT6TO1FoCce/FxxmNBxxf7btitg79tbgSwWaWhDl92b7IC7NvAaGaAYYBBGyEDfGBnY20O2QAzjt4aaR7eRMTRHyUDKE2RCw3/qCRb3PS13L2H5wbXV2anUMcwIhkHM2Bfu2O29e3f20S+/lQDffDQvXiJLUPkkGZD9j2T9MQuUvARUStuBkoANAyqGhvScOI4T2y5S9eTbRUDv6Q3TJd9udz+WAajnbmG1W+Y8GcCDLO7xa2Ru+B4Z2A7IAN0MGXAazv8c2bg0yOIvN4CulDh4OFiKpLiDrXe7ab8dqBv23RDQDeM2Y3ZYv6EY83CfTEMvcwG+M0Uut+uYiiwW8ZZsO7cNyZ1kbsMKUMfEp9Hd7LhJo0DCXyMDkyNAFZsUzdZzE/HWc08BPdfhH46W1qZuL//+x9bCW777y+nHorW5ubT/tR976Z39rdvz9F//lVKy/mxr5N4nTpHFQdbI4tn2RDLy1/JrtUpYkZW0YgARWOg7AoPZK0bKeN1c0FtdOub47jmYev2d5xBPfMYB+Rt0W7dSHoFUkZWCEpVBPJzFsQItQXoCx3NwhCEKNVHfAWEwkcGkDm5e4dYsMgGExDoG5QwrsIh3nvtyhysI9S721g60M8VAEF2iCbAkg21cuQY8DvwO8DqwbO+6/1vm69vsOAIY70o+uDrGxMj0S3f0Gvv21YtRzzu6Vva3S6bYLU5W/YvNsJa2o/rMtB+5Qm1XtHSnJnLLs3SXrr2mrbxQ5jrSLfb6VojNHlu6cNPXahS6kVWUbbrmS89AikUMZrIhpl3xcPseUV4QyxTKxp2MRdqz0m2UPSprhkLDdwRHclt7HFq1hFLTIAqKfhDid6HQCsiFKc1yHu0bgoa1E+sy9LtKLu8Xbpj3tCGn5xal6OY9980TOAdeQ0xcTNXCVY6tNG0YHkA0JqVfz7u2vB2bKHS0RoT5nrAdL00FOMWW3ChK1qO9urhdcaurbScemdf9Uge3EuFpIWn7PomVdKRle8LhDRnzvq85uCHEgcPifsWlvRB0YPOYz/iyobYa0+sJEjchv23xkxS/6+c6ZUF1Ie1Wt0WzUS6NrE2Z4tiqcQ6/mnLhWI8L15ZR1nPGz0S2cXMu352gUltNgkKL0I21nD5n+9YNvDDQzYUDZrQ9rXLHXnSUcazXztPNN21/fVz0zxxXuZGVSNXarso1kjGvp188eyQIdXDx5tuewq6OxvblcY+JuohIVdqzyZlwwst1auJqVTFxa08CkdjK4b64dG3SdyKxGDlib98fO2RsMSf68naja80w/+xtd/3mXLvl3v2br97Z6+F+lizEJRUPfu41+/APDhII7Z9A9FsEZPfN5k4JpSv2X6pdAXvf3tbIANEMO4u2IQNoj5FN8jkyALePHVkV2HG7DTMwFwbfl8hAR3FwrO8hW3AmgDoPnPhTFbi872lZBn4OeB8Zu/ZlssX5AJkA7uODttbJAKoiy1BN7ntaNoGPkRWw/3mg9W4B4D09mz6aF0uD8+0j679hGS1fgistC8AeA1MJ9DW8JfvJspVUjSRwY75FcuPb2eoEU4Uea90y/8iNaee6XN8uU0GxRNbfrw3O/1cH13merN9vZScOMyFjp5xBm3cAgSCL2htkvQ62GcbSwdvFlXdLrHiD74fAwn/HdruTO4b6jLsrqyTsxP0N40aHmb0zILfAiSGZHFzLIfCajN28zvZrv0O8cfXgnEfJ2MwqjIyC6UNziwyg+UDFCkQ3HZs61b79wxudvUvhaPFN8akH5wFtH3r4Mmj5+z80Fz308X96ctCOE8DdZHPrIHC91MIfXWpNe0KWKAY+iQZrLUlE28NW3GACKb1EOXfH7eaj//ifN/+nn/7Jo/9u93jau+5PxaO/8gS+uQltFCjwfZCDh5Y0TkutllW4bqNatoNyfRopRNZLWtDDoRBojJ/1r3IVSR80AUJa4kggBDiKgXzK2+xtS1dWQ0UhGBuUuGsDCoc2wmth+UMcniX7Tb0JaHvX/e/mtzKXiuTDsZPeHXSdqlidu1jSqiU71hH6YF77qQxn37SN5ZsKM69dQzy6GXn5nu7WDyrK/WJUW9qO7SWTSt1wIuiPL7ocaVVKXZXz8t0iORpxCNaayLU4Kqv8UgJAIe34iuj2ENJt2PzWrANGy2KovUj10YlPgSyEIaTs+VuSg9sS9xXZ3xxPkqARFT1VltqxFBsep24yNCf7jJiiuXjYkUSas7eqdHNGiOkzhskFbfJtZUdWrZhYTynE6AUXWayHxu3GIi6W2iv7lG7Ponqxa4ubqXDjNB2JcqHOk7ih8UNH5NO2KRY6VjiR7rue9VJrVQTJZkm69X0FWmVo12DvmymFyNAzIQEl+ljaFSj0U2aX3RhX6siTAofcxiwsXGs5+Irh6uc9VudcxzjCXTkmnRUVM/eKyXfGrXPV4z3GVwWTF9rMnFH0RwNO3aK87b2ke94yhUoDsbpXp1uesut3eM5ER1yluipq10R3Y8Z22gXKflP40pL0Y718Zm/y+mLFTHibkZcI0V8c8TbSPMuvH4vuOPTqyLT1vC8sHJabwbaM4yh66rVb/JFyKmuH1ravmpmvtZf3qYVOLT0hRHqyp5M3co7N+Y1cs5nv5dsV5zdO3taP+7X8lM3fNivbo9uN/AcK+V45sNGpHoUaWQhI3T78g5J+/lj6wh3Hkvm/s+n92j98WtVnc8A2z33HkpblwWuYiHjF/gu2K2Dv29kDJyI+ffKjwA+QMWH3kvXX+8kWyBqZm3cISnrsLMzDrNthXFOJjFUZY6ec1lkypmsDeIAMRP5p2xeAOwZ/Hx60ozVo/w1kC/aLg2s5RAb0auwIRs8AN0n8Y4bo8/c9LV8A3nw3oG/AxD0PPP9oXjhkjNtfIGPPasB1ZKpmWAhdeN6DBQsblZALhT7vypX92N0gDPM2A7QfO3CG85UWt8/PEm9McxCXFTJ3mkfW5yFZHOX7YLCY7cT2ZbGSOzqIQ+ZzmIwwLH02ZHx2x+cN/x5W7xjGcA2TK4afDV24u1nAoQzL8Jy7geDwncG5c2Tz5yKYJejPkTF9AVCEeIFo6/PEGw0yV6kkY5fvzrbZFqAiNP8Bxb8lu+FfI+Hnz1ff/69O3na8ElflGrnNxU9uVX7iVln54d/9xX/5U+eO/a2vPnhv5jr/1JdsdyDDMkIGJO8iA5NdV3i+a0eruDmHZt2SppJSDaMhipLA0b4WQtLAKN/1RoJ28+/90m+Gj0o/t/XgvbsWjFzpIdJwkoRPZr2YglGgNSjp5JDIDAJnY2GNg7YCd5DPHFrwhCQXDMWvdwTMjdYIFNaKAawbJtFcZvqi4SjspGgM75UOCZaEFjlexuBfk+55/A3n4jeB/h/L5g3t7kYpLKfv0yq9s1cI83FSqpi4MFmtnu8UtBmXl8aknl6iVahLZfOmM7li2je9Kj1nnZ4u6Fqtme/VulW3+pa0Y0tOQ4Sj6tw1NuwU6tUjzzkEWVKYUhQ0pCIspmhPUakPH0aMUSgrsgwpxhpgI4s0PnkjydezudlH4eChZc70czISXWLVMWkrKNjRSCANuInD7IWUmcUgXTqgkksTia3UpRhfNDbXDuUNJ7Uev+i0Vo+q4p5V41nRDVuVHBMXU7+6LeXqTMnOnJXbRcTWhFAjtpOO9HMmVHmnZ2Ntcp4b9wNUuyhGnKaT7NukkZcqMFHq9TD20l7dXd+betPn3bzbAe1qal3Y+6amX8zFl0ZTLWOVrB+W0qQ6P3NWI3oeW3OKypZAHraUmi5JOWV7vMPBN61IlZ9bOQRjFyO5b17Y5cOCQtdh7YgkkdNsHHGoz4RpoSPFpbz24qorN/YYmpOOUzsdNxvNlKAlZW/cdro5Gxc3RZG+DdtV0XYiFry9znpBp0w+s+0dbIgo78uV9X2dZxb31PbI4vSB3nRy5ptXhStrx1S/b3Wi4sD1S9B209qCap6a3lZLudbEgfqoWN3aG76VbzlbSeIFoWe7RIU0FXqpP1ooICuvi9DttfXs9OL0menJ1lKzF3yTbD24ALS//1cf4PNhzTdrU38x7RSaSyn1emEzeU93LKu9PLSb/tHIYL9XeelnQmCTb97d4Ce+NgX0eY76u5r7V+zPxa6Ave9kD5w4C/w8nz7pk7F5P0xW9/MqsieaKXYW8xZZrNg6GTh4hkwnb4xscR+FQdxXtnwcJZNX+U3gG3/apc7ue1q+j8zVB9nN3SOLY3uSTHKkSRZPWB207XWyxc4jy/I8wiBj1RDdOGjvJvAP73taPvuFO8y7Zt7u6dkUmH80L353cI4fUyOj1+skwem0Ug2nfXhmeZLn5ufwUw9jFJV7Mubpbfbyi4+JVxae+Qth2j/ReubLnwdGrGR+8PUPnT7A6Ykt5ntFLIIWGcC9e3DeV9hx16Zk4K9K5jr1yVypcvD9kEEb1sj1yID7bhftkIlL2GHshiXxhjb8zOzadjdAHNJKw7q6Q52+d+q8mUEbs0zSlINoangkCL+Im5vHRFvo8Ms0XnmTbG5uAx8hA3s5QBACsd4oJOqJ7mR+mrQ9ZK3/L33V5mL8UTnBdqfMq+d+YUUW/rJPfjpN4//BxO3zP/PDH72Urj3d/dSXrB0c70Nkc2woUVMBJLXprNXRskBIEIIAn0nHwxHSs9rERa+INW2s43XXbf2Hq4w98fAj3rNDwGfvut8UH/+N120apVZYJ5ICIxKLNRGpKzdKIwJps4oYFoFQGt81RImDQjDqWHLeEOS5GJ31nO+CdByUTjFW4Tjf1pUL7Cx1Q042i/HbpMcrKB5RjgwdKZ+0d93/7jPkj6O47qX7W3Hpu7b0xEhO1ificntsfW4rbh58Qo49fa2XS/JSWBk7rbLs7V229urnZaBC5Tbyqc4Jt92uKa9yyZG5dloobY5LL3JQb3T01rE+EcsoAvJMOC7KcVGsVaBXcPDaMUGiABPHWOUiczkEsgt0h96LoZeiQGfEglB4oRN1ZJoEOK50KASuUvm+g60bNmYFI+ugEkcuzyUYLzWtGSfZs6Lz4oLwaytFvz2SmK1RaZuzbli+5IQq77bQ4eyrRxSlVUx5VfrXftVM1I8itqrEzRE3HGkKkqJdHcG6xYZoMW7HVSzSOJ92VBXhLyrfGoNKnc7eRdct10lsbD3PapUo4SZ4YmPS6H4AE0sptSVhv/5RFfb7Ondgw4jY77ihK6lul6iuWfqBpjHj0vc09dk+YaA58mqaHny9y+EXPb9b0SzuU2xNWqKSoLhuKIXazp2SCEV3bTpNi33p7dtKGe2mJi17PSfR/bFTshD0Kbx6u1159gPm9JGnzOmJPKurAbfm32huvVyTZ8IDk8ff95z9yNFXmo2FsvPy8nih3XCDa3IrnLRzdsvapOzoxMNbbVy61nujt+HOm1yy6CQk5Za/7fR8XHRHO6lSqdtOfC9P4nfxY21Cmxak7Dcn9i90J9ZS/CNk61Whlm/L//DaLbZ/8ntXvL1v/bourl98z9VPyZHYP/Abp9/XPn589BLPXU4m88nWvQFusJqfYBL4BaDFcX5xcH9a+2MYwSv252BXwN67sYzl+yoZOLiGjEmZImMzVskAw1DPbSjpMcy83QKu5e0AYOii+zTwG38GQE+SMZFDkDE8p0fmetsHfBU4ReZ+C8nYomfJ3LbXAz9EBvAmdh1nDPhp4F/d97R89At3mD+RBMw9PbsO/Osvl93n3JGRv+rlC43+ay9vOBmIjKSlGQfkB5v/kRnIuUsrNZsmv2B871juhjvukOdf+gMT9lbIgHaPgA+u78Eg2AT+d7Kn14+Qua6XyVzvA5cmQ5HePBlQKQ/6YXjuhIEAMTsxdsPM3t2SK0OmbrhYDgHdbhkVy06cn8cOgBuCwN1SLcMya7vL6cEOGAxJUWgMLlsI0xOlg1Kk7Q2xdXbb0Ygox+HBNYXslOJrEfIfxlvYZM+Rm4OZG4v9xc9vg+lVt1n8B7/0FT78la8s/j/H7/nAmcrBTzxOGJ249Nwz7dab/163F1rp2tP7yUSru7vaL/+IdmZWrmUa0ARIL0Q4VsfWup5QNi8EnahEv+Z7ib99ttZ9K8mbNtm0zUx1m62+8aJABMa1YqMl00f87a6OSs5HcZ1ZpNAAQpOzCoV0DMrEYBSeZwfMXAaOxWAc7GDcpJLIAZjeHQ367XJxs5HTOHRw9Ms5kX4u9P1HNab0ilqMeBf2XXd+n7rtPavqf/rATx82Z4/c4VZPTVWLi1WnfqDanjkr46tf9vpBKs3sujDFety+9Zk47hzw07lLwh953c0vTEvdKcvazAtGhEW3t3YsDcKk5/urhVxzNg6V0oVLo7PR+vHQTF/Sri8vqupWhep2hbHVgE7eY3vUYNfBOgJSjTCpTnClf7knhqEDWSiCkwqEFkR5mxOBxQXflqws9BQeKQhouw7SsUQKOXteB1K68ZljqSwvG6/QdFCkSXOiH1U7jqyd9Nxewcn3EwIpA1svW8rLSap0bFcOSY+eFaOh155a1G55U/Y2D2hak6KRk9Kv9mQv6JNuH3InVyObHvbFhusKZ7ovRNdHhxPa9hwpnLaIK+3E2MDLaScRqXJpTPrlXC9NCpGTrl+XiJEXtbtysEBuW7N3QVOqpxgh6NY8zl8lqK3C2FpMWFJKihWUU6V+YBwdCG56MqRf7FKf8p29b4RytiDjp75LhlGQNKYW5KjSuWJFmXZTabfVY+/6Ua1UIMPVGUUvJ9ZWR5N91QtJbnRKFcbS6ubJQ4TdPUF3sukGB87xbGevqbYm7cFg0+Zn1xP77C1ur5fzJ686LetxVFvtF9OkXxwRjpLrUspaoSmmnNAabbhzzwrzoRsuNfOi4ndcnboipJBMVbdapWK/uNyYOlpwwrnIeGc9lTx3z5GXqgtLk/zA6Od++3f/xf86Xx3Z7G8/+Nn24X45fyrfmlGItVuoZffDl35mlZv+0Tov/cxuINckCwOqkMWtL5LdQ+scx2On+k9nF2i8Yn8OdgXsvVvLirJf4NMn58mWgXF26tfmyRIuhi5Dl4zuXiMDSIfJFsY+O8H33wD+4+C4f2o2AHr3k/3whuzQEGgaduRHxoC/RPZjPUMGQGIycLoO/D47VT0q7CzqB4G/Dozd97T83OC4nS/cYd71k5xN01fD82cfuqdnk0fzYgS4KcpzMC84/Kkv2We/077VybmmXp7vIoTjjo4frN3xsSc3v/bbl4CLnvF+2tfeNyIR52MnniOLY9sgc5dXBtdVHHxeY8ctO3RXDEprXb5BxezE7oXslLobgpvd5e+G0OCd+nrD+D2x6/Phdpqdsmu7NfuG3w3rKrOrPTH/P3tvGiXHdV4J3rfEnntWZu1AYd9JcAMoLtosSmpoo9SWZKpt05JnNO52tyTPTM/YTeu0LVt9xt32WGq3l+6xZUteZMmWWpQtWha1UIJIiCAIAgRBAAWgABRqzazcMzLW9978iAxWkVrdlsc+c/CdA2RlZmTkixcRGTfu9937ARQGAih0QSGBSKr2+QklxXy1gbxv4L7AxGYkwNRFAmyfBvAfDAPOII+ZfK8/KM7V6sXTsnfyNqwdPI0MgCkq0Xzj7NHiY+PxIw2n9HWy8uhKH7iOZ//vGgB371mEj9okgz8ONoPra0iOnd0v2bYkNBPQMBSuSkgIqoMTBZ0y14cSkddkg+cPtz/9rSn/ypBVfUsC4I5+nOg6W1KB3+uyKAumX4DGnxqV6i7WXOtdqVZCGEyDQqDSmxGpXMRiAEpTkJ32tA5AGYVlpYB8o/dhui/Yi4BeujeSDsQSMXwtRBCZOGp1wzOHHq9rjifMRx7YMorkPP8ugC8pKMQdsN5L332gfv2bN5858uWd1fZWXvFyNBpdsHvoEHrXI+F0vsvFWtUfjDFBHMOIy4GtdWRM9UGktQoa1ZsI7z5DI1vQTBAI/dId3PLsotkpicBzTH9+DzeXNylMXrEiaVFZXCvZqt9ChivwOIbtalCSIGACK1uoVWhqfqEeCYmYSVA6lJpg/TfDR7Gnw4cA8wQx15hlQEDpEWRAsZolMFyOTZcBHwSdsoDV1JALuO7aTG8XFLSWgs6jINtxRGOXpumhULWZiFzdyu2bH6fRrY8TaCGlK9NBpp83/MqypJtPiJwhlQBIGFWYFlphnxJuWIINYAhlxBgwlxk+QdufUUKnKA8kKG2TrFmXXI8l6ZY1rE4r365RQg2qXdsBHy5hTie2ps7H0AOG0rKG2NAQkwjtskSuA8yckYDUZWBQavQBM47ls/fmEegOPD5AtuZgYs4KQqcv9AEnmXaWWkuBeeiLCoIzboTQvKwkvi4z9Sm1VqPs0slRasgp8LWdcqrSYXfv8miQddXh8sVmNHtncMHM5JxyW3E/w5eevUvLtWxpDgZi0ZyIp5p7tNFrO1kw9jzVfdVfnt+dNQeGP6n7UidKp6zp755YNRT35NL8nqgf66agTOYyTI3mO4O4YfuRHLiv3f+sfbE2ZdZ6ZHwsWx+0g3zvjunL7Z2lWrj21eqosbQw8y/+6h3xmw+cbAPoPZVtXnm/ebLa1MJJfAB19ZEHOgDwEqAHPEVCfO3Nv4yH/kxD5BQA5ATi+9QheZpDL2DkehGCTuOmJ87hxO8cx+1f+4FujG7EDz9ugL2/ayQsnELyA58Ypn70eA8JSxbi/Yca+OhxAqA1ZAQ5EqVtmk7735HcPb8P7z/U+QcY4W4AP4GEyUqBA7AuLACSi+D9SABMB0la+jgSILgdSS3WTUju2J5AAvjS9UVI6vtuGa7HAPAn9x+jqwDI514mv+/JfN9Abeyx2gfw9Ny9GBMcM4/a5Piw3u87xugdL4v9z3zxHSpSv6eZzvnyna8783P/9jMSAD56X/VSh3aWI4RTSOrynkVyp3kUCVs5gySl6yABdInCMtmuAZL07SasK6ZTYLCxH3La73bdlHcdNKbznAoqNgo4gBcbcbOXLJeyeWmf3I2AcSPzlx2uqQfyAlAcQHrzAJ6wB/ir0HgBIJaG2/2XAB7VA8xPLWK7a+NMvr3w+GhtodrXLfofdv4BXvnYu9zhXMWblsPWkf7xL/g2/RZLuoik8+EX48lC+55X7r/+xGnf2rG7NjKefQ7JDcBGBvNFoE9BgYJAAkQCgOczPYpQ7XVjdyH+2tqWB1en8Evk9T/zBy/c+at7H1SVL/7+mb7AtVinm6ARAE2Z9acAACAASURBVHhDaGnlg6uDgHjaQl3T2z34IRR2goCAEB+cC1D6LIB7QJAdzkTKvmLDflyfWwKyEegxmVTyyRDY3GRyqSIuR0S5ipEBI/jdvc93Tt359VW6/+mm+8gDW67gu7DQiYIYm+DZDWAwunVmxx5junZvZmmpKG/7Sti/NC4aZtkU2+vaWH0kGsxcR8VYtoh7IBpgGixSMVuYEeZYi1g7T0u9PKBicR/F/idDrq3BHDtFed6kELZUjHDOQpNOXe0xRQIMNJ3ve97E2ngF81uALZcYpIyRdQUCcBTqQH4gtAChIpRTS26sD01vDg0AAgQURHFElCCQClNXTQAh+kPZkkSIpQmK5S0c07MKtK5Qmo+wuJOgMaOBCWpd2Y+4V1Zy8jI1i4sMs/tZtLpdIvekF3LJRbnjuKNdFfgljEoNkAFxDUhZWKEBmL4NXSIURwwQh7eJ2xtDyx/HGsugFK0qqlxi5zrKyLmMSBBoNUk8SykiTcUgo/KqUHYXMisjTJ+PsLJdR341BhUM2aZEb9JGbaoPKojnKl+ubnKoLg1r5iJVq+Mga2MxICPseQYIHaUiaSt7jSEmIdyswkhdItNW9Ppom9S3Up5tmdIMMvYII7m91+XgyU0D6NDmK9eMLtp4vrknnEbdmumyuDJy0S+X2wZZMbRn+9zvRhk9W/T1p66Nq6dlPjYql+MDO44akhaLod7HpsoKaUTQZ3ujZCA5Ddy89HRDTBFJY9+QTr7BsiQK69ezvB6b0VS+w1a7+Uyvn+EHSkvkat/peiGVC+2S9uqdzx09q8Z35zZHndmTu7U/O3mPGp7PC00t7CHJYKnvcFxXAQgsbDHhOzN44Leexr/5+Vp4T89qVGc3U8mmRhcPlDFxaRtivYB2aSr+8/c6/Hbypb+b4vdG/LDiBtj7u0QC4vBtadfk+eJLngfDv2MkjOB/w/sPKXz0+LNIlLf1H/bw7j9GORImJxUEvNQPbmOkrb9S0DEzHHMPCQPWQ2JOvB0JkO0iYTPLSABaDon4I4+E0bwEQL//GH0SgP+5l8kfiLG8b6AiANHsPyOPjc5hExLmrfe9PvMv//kv1z5x9L89CID+5L3vfaEg/v2P1qKHjpC7kFjZGEiAKpBc6HdhHbilIotFrHsiFpAAqdTmRL5kHEl7PAlJQgyUDhv0RSxceoHcqNx+qaBj44/cRoD4UssWB98OmlKuKRV59ABEAC0DRAfEIoDK1a0viE3Sdm0hgD/88CPqCgA8apNmL4P4bTXlP2qT62/7qT8Y31a/siNgZPnUraogwwy7vPlORzeaXqmwUlz7hX9PrceOrjl/+XARwMjc4X2L+u0/XTv6twNeXr566XXvPPAarKe2te8w7qEPNQEHB/EESCwppPRD3egWpwtvgp09+voH/uDaRx4G+cBb1i8se0+cX3384NY5ELU3D+zrAHMr+Wz+6xrvQ1NnFRFnKWCAYkYmN14D6NoUEiY3QMJSu0iOVYXkJkZCKQohJTgTw/lhkJINGcFQAZxHIGEMERLmm32/Xlhy253NznKY0bynXzHWP/GL/2cMAD/+XRm9Dfv51347h7sentk6efXw1PWd+lptOruab1bxuqMZECEmRZ3ZT9+qi2dvhuBrsPLznDmu0rYdR6yvxaOdKZstV2I901aU1n1fCR0GY9QOY83NKSBmZr4m5CZFqCQMtSnCNl+QyPp9sDUTGtMQK4bWiAKTPljHgN3kMQePu3nbYATw2woEVDBQJQGuQyEiMTQlYECHBgUqN14xODIAPAg0MwY8h0BzQ0hQhAjhBASVeQmlSwxylC1sFiZRSsyYhM+cJYNs24uIRcMCjLzwI1Z9JnBXb9IiobE1b1Tmwj6xSJP2hBZloj6KtEWj2ECdjMCNbZRJi/QKMRyuEaPfJ2F+AGgB5V0Ohjg2qBDSbnLaq1Bk6oKQKkF1mcBwCRa3yvDaroHs5yyMzcdhriN4rxrb5W6MVtUiKzuUWtoeo3qNiEGWRyoO6U3HGfpOgJXJASpLmhlRLiavBog1gkA3ZHGZRFQyYdCCWii5g1WTGHvnlKlH4biZEWevn2OXmrZGPUnnL+di72Ats0pj8ZRaNceaLtmaFVHWdqNctBrNd2YwvnyPtc0f0xcPPgajW9QiMdkVysrv0KA4D8ms2cIOyeRXQ5Ou1XPebqtitYgnB81JcTHUXI1SK3JHDaYRa8Elzf7l7av7bDlR8ZhSvZyjNi/mL3VGx3/36Cvf/OY3HSs33GztPx16fOHn/vtPRnON0RiAUh95V/yRh1Ud+DZnBIrkd9pGY6yLT7+viefuVPgEyCVL0vrhv/j6cr8sf2zx4Kvx/G1GBHc7djxzrxh9/j39j73zZwvvwcPf55y5Ef8AcQPs/aCRAL3NSC7Y83/nz6cA8f2H/uKHOq5h3H+MZpHU470cwF1YFwOkjBDw4j69KQNlIAFxKcAZRwL8WkgAXlp4/1kktYe7kTCUB7AOkEoAPoMECN4C4M/vP0YDAI0f1K5l59dR744h+8R7UfrtY9RDwia6L4DGS0uF4fddx/YJ8ZP3vvc7AsIPP6K6Dx0hZ5AweIeH234GwB8jVRxL2En684UuG3kkwLcyXE2AdeDiDl9PRBgKESQsqBcAXAo2N7J2aaTr2JhOT02S032ykfXbaN+TMn5kw3s+kh/eCMmc24BMX3ORtN8bvg4PCQC7CKyr5O4bqN6Gv9U7XnuEvv7ksTuVVIenLmMeZDDHMnrZZHuXRk6s6p/oTmvxgXeF9/zlwz4Auex/yX3fsb+9ML7VLT93n5b6R4Z4sbBEA4BADiiUhE5MWCEgKYECSzKbOiFys1VvkvAUaDP6jw+Xx/XEA+WFc2vb3HJ8ecvYl0KX/rO+pZnIOwYYy3lZpwmoyxDBc2D05SSOv7XjwvV4pVra2itOmEC4CSo6j9BTYLwDrh0AeAlR3IaMtbzr6tlanSyMVQjyOQU/CBBFBJalwPmylOAhBXOAVjcXtm7B+dVD7eVec7bwn/7o7YfWfmDFbRK1uFy/6+yO8OWTM1/OjQR/Mzp/+Y3Fp7eMWvtH6mRzuOzR6qWoQeNMJggFnbpI5FqVWnO7hLyyLSLlVVDFlNke5UQqqHzT6C0fpLXqopzUmoQaAxhWr4tin1PZjTG/O0AvbyHbZSBQ6BkKuiJxDkGc7YYajyiNFBMEVMSQwurwQDIYMnEwDGWye0jfBKttBsauCdi+BAWD/aJj2UcMAk830KoSZBsK+TYDsWKwvAfasZDtKvQqFIENGCE3y0tKKRsRmJKRzoOgKKNJlxDT1ZmCV+nrslijWjub4X7FU4xDMuKrKU/oKnKEBkJKhNHVLAHxQxRWOXihpjQeq550qBnF8CWRhBMNayJmrgYayTjQQ+HAp+S52whvVyjyTSZu/yqLaQAKc0AhrHCtJfSxRcp7WWn6Ju8QGfd8GpSIy3mWSTYxpwcBtaV3p0Fmb+qjNe6b3ac7HpMTobHCZMdSrGxARsroF75J+aDKea1CNUoVya/xzTc/R7XHb6OeWwK1+5oKvbCnQhKUWojmdB99XXNDw/CXKoS7pnq83ffzej9aaZfWrKBQjd1+61q+xu/Y863gmWsHnes0gm2G+rWgqGZ4qDd7TlzncXuzMFihuS3uUkEmQ6cozGbO5v1OPbafe7pnMCsyM46gZrQ2WmpH9r0j0nebXSsUirzq6OVdq0LS07uqC7WfvffRW75y4Y7RzdX/vuoY7SXg3dfXD2kVAuQKABs3H+vjuTsVnoL8tfufr7o0uufE+R85s5hrWQdzy/6WbKvvhdGUueW0rSormdhW//q9/8cnztWWZ8LP/fHLrwKExgM2zm3RA9Q/mTag/3+MG2DvB48CEq+2a//YA/kuwQG8AomRcVo838W6h9tGi4n0X1qTlj5P/MMSZmkSwBsAHEMC/gpI2JNnkQC8zcDQYDZR7v4rJABDIbFVEQB+5/5j9FkA4vuBvs//KsZXdmMsKpIWEuZwDMlc9//9a0ilvHnP4ft/7NfOffz3fnzS97prH35EDb7H6q4jAbE/NlzPo0hq1i7DRwiCIjT4oC+IQVI2LmXEUlY0fU6RdrRg0JSFeFjw/1ILFuDF6V1seF1CgoKAbeC90jlJ7VrEhs9sBJNp+jytDUzVvzYEQoRQMHAJFGNIAG59uMzzoM5ucONNDx0hn/vwI+pFTvkPHSHO7gF5tU+Nt7oE+/UubFLQz1XLnQ9OPvo47Ss5dvE/X11tlIr4+iMqwJDF6r8KpZ0lZ9fCoLFNrC2eiid3T4BSE4RmIYQBIQU4F7zf1JnXA1gOUBKBnYUVc1TZVdQd3WsQvnJOX82dMhZf5yhr6S3+HeeGLXUBAB/72J+KnZ/+rWO8P1BNneUQRptgsgGAIpTaZ6/2ZgJH2wvHuCg4KwhGt0J5US4mbkDiWkDIAiibARBQZq1K6TvQMOpJgyFjSz2O1kKIfuKkyGxocR8KSyOMDwbAWdfG50uaf/rndnzl8MTNbfYlefMa5vG9jrsk7qsX0a6Yl3/mv/5I5/YTpczdn+8FT79jvD/7Mtt6028Wx+2u2Jvf7U+5K4WJVcus8xHqEJdmm2Po5Vv9cLzmMHmOkUxPqYklaU1fCPuNgtZmI6wsY5IxLsfQI95vjSkITRkIHKxsAqZWe9j0ZYI2c1HwkvR7pkMQO72YIA6cMAvA4DFEFIJzE6BcV1FgCl32KWESnIJKQDAziJFvAFqUijZSFwEJlxIMCgrdooIcAIWlGF6BQGQYJq4AurCwMCrgUx1a5KM5wlBsMGhK+X6WCk6UWexT01WaGnSIahTj3ggMpzIfUrMnLKMjw+VJPaxXSL7PRWBTSKNFLMMnUsXSCX3FeUi5nxemdNmcM05atIR98ik5ZgwoU0Csxs1eOC7D0jxxin1ddCeVvraJxNkW9afOa7Q9SWlkkLiwREweUGYHnF2+1cDIVYlMT/lNj3Y1R+WcAWFBVvfbBSAkROuW5IDVBcvNEUZEBnP7uCrriJb2U0wFhNZGCL/9U8zYfp4Mzm+COLefe4NcUOt5nlY4Z2RKAx1mI3JDFoXnN6s7p6B7eWV12kE8OjYbyU2GYYZ7iNVu+TVviTpUFS5TU51omvnbxvwwv2lp0OlU25cXqsU2ivlJqynyg0y0xAZ0iTNuoe8pkc9XAoOWFZVEqMiOM7yNcItpxqIJfb7FiMwq3rpv+pLxatOz/nZtojYvFQTItKSiSnL20pzYVB6L+u49u35lMWcv5YD36Mlx8EKJTR7Xdm7Bl3/0HO7+a+Y13lO8+3+7NfzQw+8SW/Y9Pf7G5Zm9f0W+zt41cb0ied039j+pPDeHGl/bf9++b/4vj9cnHycf+OT8qfx+LXb5dr0YXjvwEG6AvX/AuAH2fpBIWL2dSMDNyj/yaL4thh0y3oYkfcmRsCNpC6uUGWJYb8OVdulIQWCI9bSji9SmI/m3EwkIGUFS37eCddAYbFhvGevszmuRAMcsEqXtsXefGPtMK6oBSYr3RcDvj15GjOBB3E8kJgH8FyRMlAfA+/DPEGYHeEdz9twD//XX3nwKejKOh46QX0cCBh0A3Q8/ol5IG1//5+iWv4o+b+E1ejLuVSSCGAsEKwBqoGgPt3V8OP481mvmwg1zlfarXRdskBeUsmla9aU+bRuV10mkswwQmC9SPKYsa8q0xgCEB2gEoOa6QjJlBVOVbwigBwHBFUZ8hUMUaNF15tAGNRrQc28EdxSh7JsPHSHOhx9Rqw8dIdXhvhOVutqa7/sHQ52OdA2dhix/4KosvLNr5r64r3Vdf1Pri9uO3npYPHzzXcGVDz1hA8g576iNGu3GNJ87d5brhh0zEiLwYxTGCZyygFIxYoDpuYgIwmngE7J2CXopB1HYK5vUiNcoG3hQ3U32Mj1Hu+41uI3/rP9N7aN48EXT5qzUa0TJPxQFfj8ITdnXMQAzg6wuQAnA6Oa5rdPPgZCntDg6tSvwtOcN/d5AN/cAeIICpzNh447xSO66YGaaoWUa4VRpoMMLxmi94JJc1zK7FyrcG9Tisn1r/srsM+5MPIgLpSZM921jp75216lf1I/1tm/++am/6gM/1QI23GzcAQv5lSPY/XThzL2/8ZTz1q1vFXNvuHSa7pzRtPntu1rN0i7/ynaq2fUlf4ySvYu5rc2uaa1MW4FXQM8z1ciAgV3ZTm2p2QNyQfBWhbNQ1+TkhagfVbGKUXRQAJM9jMcdasf1qBuP0V59My2yvolICcQ+A1UKjaqEuWIg1nRYfYVK2x7eJeh6WovKAcaguBESaUScchUhsZceMvxKoby2rg73hh6SFjginSA0TSAI0RqjyHaA3CpF7EVYLVMwZeDqlhhumeHgN4Csr1DsxHF+kYJqUIz5eq5B4RlUuVbkB6bkPmVuwdVUaHtqfrdFumOyG4wTwwuoKvSkmJmNiXlV4zGPTXuFEvQVyweMCjt2hCcwqDHNy7JYMkRGAy2H0FCjVJkFZPQeSGFRie0AEILGTIfwhckUQi9ny2gkVkYn9tmAGizitLrA8qNzypE6YYHjBdOX+eVGRStf3q0qWy6HGmubTAtJozuK2FqlNL9CFK8Le+cikYUx6HO30miWUHfWxqBDlSe6umbqWqfrUNUjoZsRqmzFqNJuUGxPuPrkJUOd3U5ECHlNbwZ137ANTtggU1BzSnDPVSTnB1q354hHzu111lzLEsg6k5KSO43+YFUaTseDcEjse1QninsRiQyyoPXpDqmieaBAobI2c33mFWozkd1eMNu1vN0vrepd1GSOMkKqpxY2baIW4aGTO/GJE/eQz5b2Bf/+8GfFe/atLiKx4zIB8jSgYlzd5eO5wwdRWtnq3XTUOv2N1zvlE6+++Mm3/FnZvvOr2//0i28Kjj35I8b+gV6a2HO9azhr7dPnbym7smqWDpwtvOknfv3yR179xUl/Tbe+9T8fqi18brp+4KFv+9W8ET/EuAH2frD4CSTdFz6LpNj/n1psxXpf2DNIumTYWBchuEgokxTQDbDOEqUAZRXJ8ZCaCGP4XgFJTRtBwhSuDr9DQ8L+AWCkhJFiCw0oxHL43RqSlK5GQHeW9PE7M6x04bp//jPDzh5iOK7WTy5iLF/DcmMHTgFYHoLBAQD87JeIUWnTAVvjnYiFr0AMCxwxEhX040jSlo8NxwgA6O4lxC+pr0z/P9iCJLX9Fx9+RJ196Ai5AwaOIxGj7EFiBpy2xEud1DYyoBzrLF8KuMzh8i7WrVXS+rsU+H2noBv+T5dL07SJYvTFLeuUWm9jl/bDTT+TqnJr0BB6Ws5dNWTOEN6TFSG+jISJ7UKGDyLwboMz8SHVv3Q7gJGHjpDPIqm1nHvlY1haquKcBtQ9ZVUb1gSY17PcC5039alpCZBHxptrjdf99WPFTmXmp9j1szkxvS/0dD+wT/5+Z/KJT/lnPvgbJ5Q//25dOJRqpgBjPUhlgFKiJAN0B7GRUcqyiRQdtG0lFgnvFWH3Q7ayRozmsfdEU1/40L2v/44ih2fe9yHvPe/5Fx+cvXXHnyCx/XkbpABANGRMhcAL0W/EMCwLuvN8xNnoKduqRIROIrlpoCUHNNPg8Z5O/yKYWLygaQRStfdGmbEymbultGoENzFJ/Zma+lq87cmf/tqhL//KljhbK8rVpBPGg+pY7+NhhnlrPz12tDrcfxtLOWzo3l3oFXJaVuiRc+4eV2e5zfKOhR3ek4oNxorerefzfSsky/29Til3yshJSeN8g5OJs5FtGsq0DRmXL1MSM56322Jw+4U4HBRip5/VB6JCC6ubMLblFPjIMqRrEdqaECOVZyWJOhxaSFHsUAyymVDvMpl3Yw5JeSST5m6AIoAYtMy4ExFrpOxJLiHTBoCMKgyPLSElaBQmzoWG8cKxmDLVyd9ZXyC3xKEoQb4dwBkY4ADquoZWDogMoFsgyDQD1EsMlTkgGwx4K2fG+gAaXMXcPPV6FepnW5qhE2I4PnooBhpzNEP3pKrMqZFBJ6bjc8SnehAJrpTuqnyupnuDnIoGDty4ooKwzHPZq2FeE4r3LY30SopmI+I4q9IhHlFenqzJUbVQIGonXSHMszn6JRDHJSSOInDG1dgVI9SEIuXGgLh56J2qrsYXiFSKLc/v0209pGPXdjO2NoXGzKxstgp8OiNURLgaVJek8rqEr2yCg4NC1A2q5ndSFlmC1pUysldpnG0rcyQro7BDV573hRGbEW9LJpjIuTu/0Ks18qwZ93isRXEnW9ClZxr5zoi659A3o3HW9y5fnh7kc60119Ir5xuT+XKsFIFgjLflXG8UNYSDscklOdYc6WfqlclYxpzFvH2Ox5EwhLMamETqXBtZ04gQ3SjrqDjuOzvq5/aEW6cXltdC5/pUuZ1vB3R2uZ4zNY/3PJdsLpvB2EotTz6vbpm70hjR+fLm8YOPPrCTzK2dv+trF9zm8vSZlezapjyPIv/P//W16olXt+If/V376VMHd9tro5k3F9e0ou+YPWldckzeu+z07o54S2y5am2+c0L9uNso/Jludt09P3c+vPdTj5eBT0pA/dDbhd6IJG6Ave8XHz3++wDeieSC+1+QsGX/ZOL+Y3QbktSthUQkkRoA+1jvBAEkYCItos8iAVMpqGFIumVQJIxcWquXsnNpXZaBdaVuqh7tAcJpoxkoxFL40AgBaPLzzwEIBVXphY0oQtQG8FNICuYzSIDac1/9X/EoGOaFTc4AcO4/RlPA2Nj3JJxwOjNO7ptoDp6cLdmXZaIY5NiHdaD2/ENHyGA4HrIPUGc/SC6ZUL+BdQscIOk9fAhJmnkfErCasmWpd176yJGArbTuMU3Ppj55afo7xDq7mYIy4KVCBQIF89uc2zamjjcyhLGRvJ/uu5SltdL3h8tuB0Eo0X3GMwtnNZ+dghBNvKAqVq+BaoMMlk6rhLkcxdAIXPOxKCjGtAh7CMdSPhrsIM0FneQYzXvzuchkB/u6vXDrUxd2dLJTLBrP5uiJTzfbhZ/+WDY/2jdXLl3tbxk1B7sOvJJfcWOi9Eh3smuE6YmFShgCoFrEGAhhRHFD8rpHw36tv5rtdnUwP29f2ztSeGL25+uvsH/l6MdLABbUvQ9+G+j72Mf+VP7h0Y+fB/BHUOoIwtAEJIFmJiymom2AmUgsdbZHlOWGc3oaQGMQ4WRRm3lsRbaLRyb+0riHGte/0nkoumlh9G2a/Gz28MXK4DVxtvaF6gq51Tl78u2P/2T/DZ/add75tT99QS2v7n1QAmgA/7KHl6gT/6enPtP9nQ84H55lT4+uTK6+df/UopjAM5H8y/ER7tEKm3x+wR9byXbMQm7F3QzNz/Zn/LUoLq9UeallZJhGtFIku/Eo6NIEqqwPP9f21UhTIx2H5vUVIu05wQ0hpenxZVYkXMR0JCN0c/SKhG9LQCNY3q6UfUZB6lQGBpDvx8NfeA+A7Xk8DHyNlMseURyMrB+n6Y0NlIRSgEZUwuQFAFUANxOuX6LtcLg5ifwyYEgDI/3184NLoNRQkFQi0yeQREd9LIDUCZy+hvktxBy/wmC1lXA6UbTjtAYmaMezI0oINVhDs8YazLXzUsY5JvgVmTNaSoszunltDx3MH4DYdIlwt0jgl9RqEdFo1GRZumZEJmGcD0RXy1MKG1rYjTW4xNZczg1KBsKSSoulXBqX1AwY0yLQK/sVM/ryqlqmxBSYyvdtObBIMDYXE8Up2gXiFmvc6Y9oMtTFxYgqXNvB4etedvoUHdl3ijizu1gYF4nilDW+eTi2t16h2HYijhb2if61aVa1D9KB2jy46nfD3YULdljy6Zw23XO9amGqqdGLsx3namdcJ14/kAU3XPVz5mjYC4tbrkf1TEu1LxX0Eut6W7fWqSKUzWRdPrFmki/JSLn5gTtWaEf+ytTadKE5supaLJtbFt1e2fJUXHYE986EtNE326WSYkaAknR5OHpX3xmbivjoSuV6PXN94srOovbl86b5djfPMgza/MQOjHcb7CTtkKU/P363fmZ5ZpILbLpb8olg6vzMplibwB3qG3e/7ddnd+TWxqabo/Z0N39iX3l2/Iu/d+eEvKMTUbfE9e3PrRYqizh2ffLOR0/eQ1VmdenK6qSxTbPH+trym1fbjn30uVuPzj86oX60dFSNTHa8J6687Os/duuxG+ncf4C4Afa+V3z0+BeQdAkAEhPiv/5hGyD/feL+Y9RGYhVyGImydAxJLV0B62pShgRcAessVGKpkDz6WAcrKbOUAhsD62ldR0llyBh7mE4MJHV1i8PvFxJRTyloSoFDvQB0zGT9yqhFC2UkhtTd4Trbw+8sLxwmY0iASFrzlxl+tjH7XlzVFrvX4rL/CnOvbBuLcGkMe3ih8pEwfB0k4C01Le7u+xXVvS2LOMdx+WstpBftAInSuIT14rCU0dtoipyKIFKwm85lBut+ezbWa5lSYLgRzL2U4dvor5c+Tz+TsibpewFdN7JOASjHOrBMBREaAKYB05YUza7jsAvj4+Y3t2y99u8e+1oNian09V/9zFIXQPehI6QL4GUkgtwxi+3NDHYZHWxmEiaDisZjj0VNIN/sM1/TZ4ilvzEOmSypkY62isbSo7/tWqc+FWUrt83lnv68feqPz04SM3uYVyYe0wPtEGHGJNJ2b7rOSeDHnOs6CWNEQYBYz8mpgPGiNW71+aDb9scyzbV79heImol16veD735qqXsfVOTox58EIU+D87sReEDY0UBAwVgPInwWMJ9BAuhDJMfZFwCoQQh/9pWvmgcIveKNbKrq3dbu2se8E86VU7cvZnMFWr5eamUfPanG53714H/cgt/+44F9ZKmz3t926JWXjORFysSfw+8X94kr93z1I6cvjj71LX1GFrdZ/cr4Faqy3u5z+vZs1zav7LiYs5orWyIvP+Y/ZWcq5xidFn1OQRbD0RgB1TVRp/l6RrBAMRTDqLiww5Zjfu58EwAAIABJREFUl1UEnaqVKkimwWR1jUTKAFUSGbuuh4qD52LBbZdDIkb+eJ9pUpP9gqnrMYcBCf2Fmzk6Vuh7yqeWbDkkFDrRyy3KUqvp4bnPOEAIJKFJ5xGZdAyBCiEIB4UeUcg2RQygXQIqrg4jEEm/jRZQkAQ6ALeRrHHqqkI3q0ELGLp5idYYAT2nMTPWqB4qKiB93WeaiGFGoJEGERMudSWF1CRzlaHABVhxQbX6O0nLnxZTcY9qnkKx1NHtPpPojdFook1o1APjPalZktmhrfF6WUqrCVt346wKuKgdgLx6UGmbnxUR/DjeeYIrIgiJpTIMwUAJSLkDRBqXEYdklGzTm5K2dwrNcOWIXe/0lxtarwjWXNzJR6KC5wc2NUq2JXadFHxpP6Vrk3AjB97VEX1l26U4ru1WdeFxTC/IczJwnXHfmfLYaDHM01CFNN8rqq3MIc5USEp+Ne8ZPjolRlaiMW5FnmcrvTfery7bz86MF3ef1641qzH1mTpANTrIxVZ+dNVu9p3cUjvn9fRodCbfCUruePyc7hPL6HjMjLT9uRUV91lYs3yh5aJy3+2G+zuj+oBE9opnHl5Zcqik/V17+kFnR4N3rpepHhxA/IHP37/gLvbwwZ3fUtuWdj/3vnbQD/TnSjcNbi34LKj2PGN19eKrurd42dcUyLx+TG/uCp/aa5kXgxPeG9vXtnQqu+c1P/+FdtU4u7A53O+Ul7tE7D8e2LTvNvXd1L13186rlrRk7ePP3/Xc4vHKyFcv7rvpgU988pj6yAM3eu3+kOMG2PvekR0+tvD+Q2/5nkv+fxxDoHcH1usILyK5+E9j2AoH6ymYNNITKL2TD7Huv5fWhqUgJmXX0i4TzSiCLkOYUilXM8glJIAvrctThMBkBsogSFWaaZoybRnnITFrxvC7dyNJOaa2KJPDbTCH498TjZLXS6HKshOyeAuc628BCidxlXdRpz5mLB8GSdaxhPW0p0aASabwaqUwWuD49ENHyOXhdt2EpMWXg3XA9FIT5BRolbAO5FKbGoH1tHaa5gW+s1gDG15L1c4b2dTUtjdVTKfgL13nRoPmtG9uH+up5Y4PkACAFQZ9RZBvZIvTAFaHYoxHXjKOAYCTlEAPLGQxIAUpaS4PsUsBTgTAopaC9BiPQsNX4b5eyfFk89wcZcast8M+zcdv3eteOTHpVsv1/NE/6Xive+1JUdx7jDQKdSS9jytIVM8aDJMQABg0oElBZXYCMqhRm2u2BTHjGM/WvfyZ4F3hO7drk+r8R8bujsjRj5N1kPVt0cv4+N0+cAfAdBCgFEdkYLDIV/ETAE4gsQmKh/sr2DDfAJTcYiWt9a6PA78Y/9LT2/bXov6+Peo3nenRRwe/cG2283a6g59r/eFTJD0OOYACQK4Bw7rQO0B+/xc+ZC+ubFPbf7T25v317rtnzIWFqYON2uIgs7zYL3mDRjsXlM53ke3Q5ZGpvGPzEdvu5gkhtM0taZt9SRt56i3fppNsHWZuRVrFJSKIlD2LUK1VokRA6bmVkGKgoVcg/a6t9OqATFkrKu4IQhhgMlBQKBBoyMk8BxjKKxJSk2gUCAptAQsUQJNKdEBlVvUKBvFtoNhRgExTtH0k53sK9CgUAVcEKpQRSY5MDXaYLNXVFIISgS8AIyAIqEJzlKC4HEFAQULAAocODbxHEUPB8Ri4haiT1/x+iSJSIlob9zN7TprEt4lkYRS7WSkNYuqekKRZgrbtIjUjGkdmL+Y9ofP+diVjwM7Pq0oQkYFTpEHdBuoDUCqZk1WgBtCar4j88ji0TZQEoasCkUdPm4Qz2lLVyCRob+dgIMTPsOnbviZpYYD4zG5JMiGRVhex5RKXKEJIrOnV+chamlITuabdqnatbDaSupBKC8pZ0TZkPCiENX+NOiMNRT3IcK5IB1FPlXu67C+Fqm0v6Nv7nlxtb5ZZL8OqO5dVZB5XnIyFejbSSnqP5DOG4TueZGZHmHUpDBSUb/bE0lTIb9m7sqU7P+WdWKx6rjSNO/Zd0Ha2MvR0qJFvzU+Jds+Rmwzaa4UqP0f6Wq40zyYCfTCRGWjPd4oFY2W6GzChtGzfOjgx27zUmKaXByNk2vKd/tRcr3rlNstpU5brZIsWie7d1b62+OVn8rf+lnZ6/5vHL137d9knGrLi/tUn1cjenW55Z2niin9s66z18m3nup9vFrYcjqP93IurhfrmhlbaTK/deuoOlJaOZef35KXr9ELSe37b2NWX3TUxVx1QyddqhvWNnqldeO4W9fZbju+Y2lvftscdJf/2937iFI2UBf5dS2FuxN8jboC97xXvP/RyfPR45R/CE+/vE/cfowyJCOIWJCzZItbVcmmXiFRYkDJP6d17WhORdImQCghAoEGBk3T51FzVwLpwAZSCKQ0aAclGLlxuYEA4TiFRum4HcDehL6QkU4CTsmUeEjC3CcAZ87qa15pY7u3DW8DJu5Gwj5eQpFznkbCUADAQGjw4WDBDIC5i79JrMTdyCnlexyj6cKwm3koUvohEOTxOgdzBLB6gCq+CwqOxQgMJqxgMx2FiPVUbYL1WLxVepM+xYewbAZo5/DtV0G60VkkB9kaT2o1CjnQdGH5/um/S8gB9w3o2AseNrcm6iGBDICtMzIaa3iry0SuVzHY6hu2lN3zr0/pDR4idKpYfOkIYEsBtA7i293lkyk0EIZQlIG5SQDbdWFd6hCdsIWQMgr6rB4DuiN4l+9DPzJqDxlu9q0E21u1u/tTFs/lTx58oHf/KtbN/6D4K046H81zGoFcG4RpMAxAREPbgW0L0s5xasQ3lUTODnbrkemnNy4yFgf80e/4TeWgYIUc/Pv+d0rm/2XzQCK49HH9w+jpKIGgRAnD2DRjm74HxxwB4Gz73fVWzvzr9iVUARz/Suz0/Qy9XbtWO4WT0suCa2B4jOR5HkHj1DddJuHj4fXn2Y5v3TqBf3Hf3x4s5beU1/c6UvfhEuaLfs6vzbOW2TIHX6dbq0kBKhEIpPjJzdBN6RUfm3ch0uc6eu1nTClezWmjQ7ewKmFWTlqN8KE+HSTQtKGmYWhTC7JPAgLAyHY4Vg9hMKiVAqBJKUCgkhigKnqlApEQ/lDAh0c/FiBhHqUtB4MPTMkCkgcKACcWzK4JLKqBJoiQwPGet4bSIIABRCkCjSsx+RpFNVwAutSFLmNSu5iLAusLBBABQGNJHcQ2I8gB6FEySoZCJgg0V43tPmAgNGS9XFTEkUfUpoUWa3vM3QXXHqFU436On73acXEdkxi4gpj0lmhnErSKL83VZJs0oK5c0w+hLFFeI5puKW2tKGqCxTaQKszSUIQwXIp7fqtbKCg6MGNfH+XKnLMOxgGqMQrkFqvQBZHku5jaD0gZK9C3AzwrUJjSab0G3XND8VQilK/g2iZkfhdmmbgVcWVfGiGKW6lhUmrm2Ty/uIPzZd5koeMQbf5bT1ogQPdITZs8cxAIVx4mqUgtGMpC1aNYPxueMdms7Ucs+a+Tq0hmtqUrrJtaZuSCMfEOyCyOqYi6T4wNDmyKBnt90jTWthrnj3G4qsh3U9BrRt1ygJ07dq65c2c8lYMzb8ZghICrOauTTPt25chOXDZDIHJBVmbEmYlvTfJd0nUBd7ZabuWyDv7qXjc/1N+l+yA4fKK9GTcvDJZdhQo/zM5lBZdurztlu5B+9cPXAtZNzxq1BZoRdEIXMeYQrr4hpdOXa1ptjwkfOgNSpmb9s7T/7cM0gN9Ntpzcd2HWy+rWI5E81Rr0ITB4YX8icbJf1gtG3NeFru0dXmCt18vRzUzvyWaGimBV/5tVfuP3C8uZvDYr2KvkALqqPPHCjfu+HGDfA3veLf2JAbxgOEpA3j4R92ot1IFAcLpMqZdNerRq+vYdpynrIJFnzolSuhherSi2uEZNrkKEHpQj2xbGiVGIb08kMEmaxgYRlGwzXZWNdILJl+FoOQI4GWLSXoXo78VlwHEbi3TeDJF1aQML87QDQQYUcQ6zGPILOcJS51s14ypnFwLiAAhJF7aHhtpcJ8MgIQ9kAJgSF0RcoImHjJJLU3uuG696osk3boL3UOiWt0QuxnvpOWUqFBPilStmNaeCUiUvnMo3wJc+BdWC9ESRuFM9gw3hMAFoKMR2FaTsKq4QhRtz/htNf64d+Jweg/NARcvnDjyiJhG3bByDOdiBYgDf0TWRZhBEl0KNAUQy3ebgBJFWDmCGYkQD0B5YfefyQe+vo9DZOqEnL8437/tUS7S6R0vGvbNnyy/c0Vh/4v+YG2162G4auQ8QcnCfZz/Ik4LWg1y5QXq7yIGOHK1q7Z8eq03FL1fN2bXDzta3FLZ8Zn7h883Ibr/yuHVRihxculwaLc0wXu5VlXGjmjberex/sf5flv08oCRD/A9lfnQag/5Tz22J6WV35Qvn2dD/WAYyhWyiDR3lB5MFo9/FXivIsu33LxacaWXmwq8zbQ+eas3WyMqG1K72qANkaDyw7z1kHJKOVamaWSD1uTBv+cjVggjB+eRvDlKQQOjA961uFhkKzqqFpE1ZZg9UdIXKkToM8QsMDRwQgvxZSJnX0IMOICqFJwlJWvuQDIZQQECxGjGKLIUKAHHR0YcO1GPKRHN6OBGCCgwkz9IEogrRsEMrAQ4lQhtCVAoVEDO5TpRFKoOhQlpQqxxU8UEAIaEO2O6QBrm9liLIE25/nUAOFAGx4S0TRqMSKRnFgDxSBQZnREOBdhkGR630iQZqhYXQNMv0sJfUx6XdK8JsjEdt2yVpu20IuTrFJSwp7aiFkGd+IdNCA+LFQOteFARn5xIvyoL186C5mlOuOGmp81s22C6ZRq8Du6apa7ETa6DUanrxTQuWY0OqIc0sRaUxrhu2DVOpALoKKBMjlg7DtiiRWCF8yEUtomL1D7yz01GDHGUobE3GmuUO4ekx8V2ijhscvLqwEhPdFUYsMV2tmuoumqNJRpbyl+LlVYVFeVFXaI/4lU2iDHvXHJYUbx9qlLepsjYmrfZNO7yrJBiv5eqVmZluZ0KYTeqbhst2ZFpOHz6ilZhUXgxw7PFLDfZUOMTt1XO9l5OralOogUvmFbbHDEciBHV0yWqxFIRZYRIkKSSbWCJm/KXeHtzSYKKwZOSMDGjDnktFVqq7HWWh2wLhqLY4gtBtVT6rBXG/f1JlyLtfXO7ccKtbK/shaPtL83Zm1GWnVJrcGbHD5aB5rgorcSsZ979bS3PZXknPBxdlR0aJyohUz69XbLjdr/axxqT6Cmyausl1TqxjJdchuZ2DG9RIJljYzq3o5Y/uBOTrvzV1dc147FbTrWCcmbsQPIdgv/dIv/WOP4Ub8HWJoszKNBMzlkNSrpR0wYiSMWAtJDVwKODZ6vqWsVABAgBAJjUgwkl7ggHXRwHpD9HUrEqYkOCGQQoLKEDrTMEkIyQ3f15H43NWQALiNIoO0MfZoVCJ1dwtK0EmIJAV7LxKQNzl8/AKSDh5JwT8l20CJCUYICPEkRx0UGdqBI2OEmTa86nXs6dsYVRxXuxGcERNMKDy7EoJGCgGSFmppr9/KcP50rIM0jggEAnIIi32ss6NputVWQOwmrb8Mvm7AvJGx+24dTFKmbuM+SZnBjYzit1u3rC+bgHoGBQ4NBIwAAeKujAZLiw1v6RlLBHMA+h9+REUA8NU//eUiBPR8C97u0/gREuHfSGCnpJjUkqYITsBhSApqyWTgKc05HC+PgWykk1F/EK4V+p3zdoTZxTuP9GbDswVr8dy5yvzFfc7pv9nf3XRLTRbG7oCUGTCdQNcBSgFughp5QTQnNtqX1CDXbxcz0ae61bNPfUsffXpntK979TMLfueT/qL62I9/RxHUnbshD90xU/ubZ49/omfpn20UjV9R9z74P9xrkxz9OPlGZ2elcKb+spXLFnlX9pm5ESzeX1RL77zFOLGb1sea3bY+Nnjm1jd6rngnrM67euLaQd+Z34KRaHyVlffRmFVGW6FTEI4x2Dpf6lT7ubw5n7OahYJ5faqoFVp2xzS0LkoyG/a4btQ4c0BRnZeoXgfNDyTiuAdfs6gTUEQ2EHNBysuupgASgUNyAkEJAgoQqSglVOlK6hoYBQAD8DiIS0BAoWkKDBwCHBoYNOiBggVAGzKBw+NTKRBIEK5BEAISRUn3DArAtEG5HVCa7wsYLxyX62x1PCxJICCIkIytkzVRqnHkOwGWKhyeBlhBBEYEGqM8UkxF2VaMXi4g7RJHP6NBj6GPzRPkWiEjghlK0dj0WccbUSs5ScOgSm23EJV1oYniMvUroY6YJtWErTJVg5zkLJAGCQgLFJGEE3ZxPwlqm+RI+dTA0QaUDRzYvenQXh2LbTfLeWuExdKPUGh7xETMGxOGauRUWB8Bc8u054eQliBaZOFyw1Irs7frvd6EiAY9GXaqdNnPoX4hS+XKiurbXW3QoSTs+aper4nANaTgjMWBqepVhJtMHVmnry33La11PUszso8w7jO9x2kuNy69gSECErEOaauxbSENAxL7hQHTxhpyV+WS8ioNtiXvkmi1TD53fSIoUKFW22N0vjZJJjxbtWOFihOw1y7PiBXC+VpE/UqQ15YMj13LNzWfS3P3Wk6RXJtmJPO8/5e99462JT+rxPYvVThVdfK58b17X36v+4XOLbVaCQXUCBhJMJZBjD0mjicZFgwO47VsaXnJs2zAAzY2s0yYwUMGMWhAGoQQCrSkVqtb6vRCv3xzOveeWPkX/Eedevd2g0ASRmu0/L617jr3nFOn4q+qdu3v+/bWzBAMgtqA0RXd2KzpxFHDDZ+ut7xFd8FRrcTeUAa9RAyXVluffLB1KdnoRqdeTDt+wzY8t2M8ude48NTAn5oZNmf3uJ6JBDqWobVGr3VkfeDPbCXMW499fnMwEy42tqmV2/ekoeMc76w6Dx+5xWue4nJQNYPIMYdae7JtOO90VmSyw9MnL57xutv+tLdmlqIP/+/WCz/9u+zBH35v+PWe43djP+6CvW+y+K3VDxxCIZ58DIV8yKQOj8yhAHKlu0JZs1d2bh70Li39V8comKYSBJaCwgddHA6+AkBKOQjjyKGxRhg0E8RP+5Bx33Dh4DlCSdnFehSvlBkpn9RsAEug5M9QMII7KPT8jk2+a6JgojYm2xehALFzk3ktQ5BrKoDRFYAGWD/6HC5MLeG1AxeO9CEjg+XNFDspMKYE3VAhwX4H7KPYZ/GCyTKLfZMjhwYDhwG5Y4VVgmAXgBUDbEApEcYQa78O79U1eyVA3GdDXpnqffX7Mg4yeqWWIT3wWdGkUYgzlzWYQ/DGxd2ph7ZuVgLtymTTlYn4s1//QPLW73s//uzXP3C4soe3HH4ZP8k13k2AaaLRhEaQA4YBbg44XIE4B1aqRLkaQFKxGEnGqO5GXd8KVlmatCXny71GdePo0rpOzj+B/uPvqyQL5+8Hs2zANEAZUyrTKcmhGAGxKv2QKjk2cW9ldmrj1szCZl03r0yNjo6vH2144fcfuzn46fN/LUv39y88Kn/0zENb71+8/2/ULPWB5ectS1eOtXd1/Xz7un7DwlPHw7j6j07t7r25lQ2nrv/xgzP/+sNz/2QtGT945vTuKSuvVXPFxXYUk7qd+g2pHDZsJE5Ut935S4S2d7hrD5TlDQQRsWI0U4PawETDOSb8XlKvrTDm5hYTmREiNWa3reFHGBEHRgph1/oUsQ9UYmrqISEhtxHVDOKWROpzyEzDy5WpGyUsEFo8lDAwQBbtPoaOBBHQObzJeUNhQxSgTEkgS8EZK9wyKAPlFgwhRUmDkoXEkENhwMGKgktQxDjoDk0mI1MjAsOworFyhMAJJTpdBj9UkJAw0oFINFyjwMFQHRDmj6CVIZbXh7FSo6sDQvwxNVZidN4QPGdGS5egsQkuCezqls7GR6g/8IzdXKNue5uKkQfy4gPEqg4JoMiubhAmYoVUpMAItJeDXztLA+mB5NSCMDwFU6JuBPV7uV6dYeb5N7Ls1iyceqI5Mbap7FE5tDLBPI7tQ6C37jXi1EVCmSHDK4/T5NIjWM93+UyzR7Q+ROm1k7DCiNAhV8wOmcg1oYmFel1atXjGVKJDvCkqqlYJheutGeKvY64+JocaCUhzyzCaG6YsTXZrwC7EgK7CrVErMMco8yV5zf3PEnpjWmykxyobzZDWCNhO2iJ/sj5LLQESJZ56OazLzVzg2voxGqeuWcyquguTbSpiMU3tNRGz3JbybOSQx7otavw1vS48lueKvaF/SLScPB6NvfA60841l/i9Kqwh01hrOjQcW1E74y8nivL2wFvclExtZNXuXuqP18beA2PwM7asdNp5BRt2Mky4YQpphyCzu1z4iW0zakPvpFUR58I96Q9oNejV/UrqzvorJswC2q6EWmqe30r8vSMLLw83lMWujGeDrZbb2fAr84OO5ezMtC78+eyJEz/8keXn3//E+Vdbtt2NrzHugr1vonj35ykF8J7J330oAJ0NQJzyHnw44M2FvtxxUAgcl2nGkmUCCtAU45XA4SDbdzCFWL4v2aQcBdOVYlLvRzlRjJMXAaztXjFcS8xSBzeETUqpkzYKMEWw76+YTdabAfg3k/U7PNmOppaY1hqgFAEKvcB7UAK8Yppgsi6HsEdazaskFBx58wZSewDenUEcejhkOKa0Ah9IbA2BflbH48TApgrrKOoCByhqHjvYZ+ZyUGTgiEHv6Aomk/UFJuyfAhQ1hloAlYBiACevTOGWDTAHO29fze69Woal/OzVQFscOAYHO3jLlHyReg9O/ppl+YKPl7enoh2FguntPvN7v3Co4x1ZTNZ2nM4uHuVAhwAcls+gJBEwFgArNxClngwOrED5ly12DLjIneHQWASUapU3X35qdOKLn7ocPfBdb9r9jp88ntz3jmVo4wPYE6Q/Q+U4gKIspyynQkSGis2Q4fnVZutyr9rYiSvpn85Vd798/FKbmK3V4cpCa/fHA/yNANzXEu9fvF819z7efHyxf/zkiZc6VrJn0I2dzt5u/drSnP/nT1pv085q+/7XXatMOZzFITXGiUwYiq68dU/SgrPuN1e21VS3Kmo9sSvmhUUT6bLMQSUDav1BYmwR2H3dsnazlMJVhhqeNiTsxGSK0dRLdeQRVlEZs6wsB49M7IYYJa1cWBGjTprDzQ3cBKhGyC0goWAKoILDTPxYCMuhWUqoLRSFhQQcNMugpAQ4xwiAzDNwowHKwGnRhEGMAUkTECknzJ0BmA1KCAw0iElBUglNKAwVd54DinEdWQR7UxTDukKnK2HnxdgnYBg3OMIq4CcSUjMMbQ6VC65A6KhOeU5TURsaIbQlRKitsUctZEr7Y2ZcpbWTaW4n3LL6WnZ2GGvcVjTxCBm2SLI3rWMLsLbaBPPrRCAiQ1mFE4RWriu52axCUE15MDJ8OMvD3gk6CgUhy5VcD4Wg3QWtrZ1UjQPO+kedayNbsWqSBX5KB90Gl9fPE8fKofhYm5VF0r1+nPQjg+MEhO14yIfGwCwpKpt8J5+lbDAtm9OcmuYYKyaQ7tyY+keeJxYJKV+6lyc0gf2aF2AZpjaWD6l8cNKwSii8tMNGvSGa4ynT8BrYMVSze1d4/Z4XyNauTdaYS/qE0a31WQkaQt7wpHzpzWJjMCd36+ukkvh22K9rMWyPj1gqOzGuy/PdRUtpgmt2PzkS19PUKKfPkzRSNNvV0Edk2Pu27uGK2s3N7UpPRP6a0wxG1iLZE4QlbCmv0tmoaQ5l0+K6PfSvyDrbsmqO1qx9OHLvnWXZqT4NNc0td51HtYFlAgAVwNw+lnqxpAyJMC9pKa8PYjvwhKx/y5HnslCQ1u4ooK0gpvVKCPCEBF6iR5mrNaFWLCt+qAXZCevk0vYhtR7X6+NkurqtqqfGht/6wB+/dOv9T5z/j0YJ45sx7tbsfZPEuz9PBQoQ990oAJAAcBGFH+pjyshZFCCslOVIURxfG/t6cCUdXqZk7Mk0Jas3mkxTdp4mKGrd2OSzBEXKdQ4FAKqhqOf6kjeL28kQN7gDCwVzlk7mFWK/q7RkDcv5fQeAz6EAoEsAPhCN8b8xjkXHKwQvUIC7NwK4jIIF7ANgUmJWO3B2TuHezga6z70PTzmX0PWvo541ELIUdSvDJQM0DMdDeRUt5WC1soF7ULCI51DgmGiyvyowkxo+eke642CXLJssWzjFjuMJoPoA84C4ug/GSiBWsqkHBZkP1kyqA+8Pgr9Xp3APrsNB4M0OTOtj8CITI/s35nR6YvLd8wCS6eA4bzqH3OHi5rjf3/3vaj38MDN4gma5YNCgKNBemYOOsa9Fg0m6mQLwb2/pCE5sfD4aqaGRFaxNJ9hNF+7b1kGja62+eCaeOXYZzdk/A/BmlfuPm0wZCUqEsJiAbfUEvJ5rP2UEti2D6gatf/L29/yL+DZl90KrxQs/hx08+Xt3UjYv/AA5CmDrwq/8ldZ4X388AvbOf/d77kYg67/4/He2X9o8cl8/8g7PJte9rG8bLq9Y98wPMT0zRHdEDCQ3Wd/N0Um5bGX+Rl7r01RhNnhZgvK4l3jKs3Y9i0EYYCQ0SxobTUt0ui1dgysTpHxUF+AZSZujXAcje2zaNERTdqwbOSTR4ITDACK0OeF2CJ1Q8MgHABAYqrFLM9SJ9YoHBE0TChoFBo1BUSMH2IMes5OEoTOdjRwHjmVjmCYguUSNMuR3mLtCLMVYFmAMJCGT/t4YxGSA4aCqeJoxE26+GLck0xCpwukvUzRhIcFoAtUdEJVj814KZVUwvZpi0FCQOYE/JsgrBitzBGbZyTvbRnFQhi1JCYxlQcYSOuodNk4OXR0RObI8Z+x5rG5tESOZIXPLdCTGploJ0Eik0YM5Mtxu0nxBy/H6YuZtzrmJvynJALm7eYRawZJU1jVGdh9x0/UzNGl2U+vkpxO5fMLiq/fDc8bEunzUypGSHJpQnev8+r2EiGPit7w0AAAgAElEQVRmb72m4+oqFbHW6U5Akv6QaDGgg8gxpJGosQooUjDanyc3Nkg2cteyOg9NNrtlRG1xBDLVqFRjQcRNZCpGHYl2g9wih/qKV1umHgz5+LNnWNWuYvFNf9KXUO7GZ95sT6fbxGq9jBeGi1mdOYIngjr9B+iJwUl6XxK5T9K9tK+a2EsqdCatWGuNLVLXvSzrVU2XReiAWe8YNKyE1fQzh6/ysUQ6TpCvu7G+4V9PX9ShutkeGNtYNNsWo+PRVk6N8QI/M625XfIFmdW7mq9YHrnturgnS1G97YSMGL2ZodKK7aKQxM4QGAons4QXUdHLYcwg9TFI7RDInO2xqP6b597YeMvJL1GXxLCgtNKGXFw7RiyRC5tr36YqyyTb+tL6Yv7C6kJwa68dJLDmuvBdFNe8+wF8Gv+Radx+s8VdZu+bIN79ecpRuB78DICHsV9n5qPQ1TvZz3foQO4cBFJlc0QpEgzsd+ZS7AO9g6lGhlfc60FQgMp1lDV+BXhLUAAOgqLurWb5JKu0yJgxcniy3PZk/XIUGGIHr+wKfhJlR3BRpzcEsEQpPcGYWWT8DlDCZJopFDWAVwH4lKJlKHaph17YApMKr7Vu45Q1hqU5rrgDjGmxD95MFSo0x0d4iGeowniyz6qTdWnc2e4UBBJ8ksK1UIBZYN+xIgJgEkBNkFpKATkBTDH2s5/APqgt2b2Dosll128ZB1m+g8zewfQwnez3kqktmUSDAqR/AkY9iX0rtfUPftTo+9/zg7t7n3p6dfTFT5pWTHMcadukG93LtKKUwAggIoCrAF7m9p3CceGgVIxONXimperNsJ3YJvNEmzmLspvjd/zgRb61ftTZvGZHp14XG7sCACcNs++B5c517ZR12YgCxLiafd4w+nMVia1ND8lyHdvP/Oh7k9/45d8uH0763/cD71UAcOPH/tf/RZAzP2tZc2nznd/zFF6XEHxoPcD31f5G6ZyP4V3+v4Su/VxXNxfP/Pbbr0XnvvsjvUMnbu7NHGWV86euL3xLOxpYrrczW5tjvjN0PSLITj6STKEhiWgjJl41TKpMO0i23NBd86SpZh9/V3V866GosfAF7lrKtQGaSVA1bBBWiX1uZTACoZDcoSOfS+lacEM4qVINNcidzHOQVDX2prWY6kvbHue0H3BkwgXNNKKAod8hdOtQJkQouCsFyAHtRWIodKqgISc1dppQA0BJYcEVAg4h8KUqGrYogUUpLEKQcw7GOSghIPSO7AoMxiAkcjWrSsPsySAdwWC7pcBiCRca1ZDAgQSBTiV0klOuudHET0ye5BhjSIm0CScEkiUkSRzIyi4yNybKjoSOaxJ2lJvIEnL9CNVEapXZGauMcicWPA9nncSOdaZazCMR6NgmtBLDZtu5NZzXYumEMbkDh49j3l1EsjHSIbstMgytbLPN7JohaeOy8uZCYqk2wdr9Oo4o7NiPHd1ijs2ku3SSyGfexNlejdvLZ7SGkSOTS8tKSBRVETgWToiAukv3mpCtoVJRkubHiCUYrzsD5boBNyQio2jdnGjv5tX4sHbSTuYmc9BSK+rf5vF6GupEMOvwMhfNgXCnExPXUzV2HUU6tyKl+9B6j1ZuH+UNqimOhfqFW1PoLNdJYrFsh2ki/TibWnxZZpURNTYVi82u7sZqmI9b1hezzKhNDT9y7IfHbfbwaEpMGYf7hlJjZXBj165nNhFiORftDbISq7BuEbvV4EHlRE/fzu6NdtIGfEfJQ0cT9cKYM2Vcd34ReXcTscpwUxN4RFHfUWyQCwhhYBq5pYaWCkEzzZ3NqQEhdSNJB4yHAO9orZydTMiNvZbJUkEiYrHeuI4vrS4QW2hikLPNUYtd3Zmj/aTGT3W21JX1DlJ4HLBtgEgALwHY/sAfv7T3/ifO6690bt+NvzruMnvf4Dj0dvIhFMzSfwXgU6sf/4qdhweDAngCRdqRGINcKzjGIJcSi4KjlklNIJG4wR1bLWBfS2+M4sbtY5+hqqMABhIFmJvGvjZeWVNWeuaWHb0Bijq8gyCjgqIbto+i1k5NPj8ymY9AAZoUiht6uQ6HUAAtgqJx4gaA44Tq38xiawokO8zFHb2+EogGAN6NgtFsc4EpKLOCEEQSTCcdbHOOq7XrmOdFk8ptFJZZ/55H+D0UbOQ8Ctu7NoAfw37TCAHHJHl1BwCX2xlN1nsKBRAipuhctav7XrQC+4C4BGgx9nX8yuMs8RfZu4PxV2lMlQC+nBedLHM02Y7qBz9qrv737yTkgx/dH1eP/cxPheNf+OlLxK02Bz01KwGXU2gNCF0RTck4zCi+4wWnATKhDjmtAMoWBL1cOYDweW02c2kvuLV9lT10dLN+/yZdn/6ONUPQyireeUnRdOKoCW06oMyyBEHFMKRmmHkY/NvZcH75Zx6FQdHEkwLAR578vR6K4wUASP6Pzzc1O/uPmegwl52Mvv/P/4T+o7NHVhb7aWXq8d79+Gxj6fAGvEPstvz81FFSzKfY3s+nb2bPZq+Z/T73X802+PDMHz499eWP/cF2ev3+H791z7deaPyn1Z3H78NTUyHzVm57/rds7t7zhs/MWCNu7e6cyubM3K7aznXLnkni6onKo/bzRyp880g/nI+uD9dzl9lJq8+SxTZ4hnn+/KDdr+f5br1lNHVaUy9yrUmaD/xMsZBmQGAWb4fOqKGwOU+dxl4DsWuQSuPkAZE5I7xf56jtUIxbBlMrBI424CYFBzHKyRKSWcSGdthYIXMUptYNKnHhyLI/liIoWIhtCk4tVOMEgKkQENuDIvad6wCxBHJjoBi/M5bKtGzpsFMw0RQWKgBCj9AsN7BlIfUybCjsLHKkAUVrV4GFBr0pjc1DlmEZpJXH7OTLWUR1q6v6Yqcf6Ol5xefbeyTXCjKj0ClXFMShmU3ABMkBzddOgFx+hMozXyKqvWmxTOhU1py8sa7cSs8E2SrYzXNInQSjCtfOkJMsYowFuwRHXjYwqZ0sJbvZmBkqQua3KZemS3JFaa5CK+u7SuVpWj3+HOoqUez6g74OW7Y+fREybkDSPpQ9Nkm0I8PUooxVGQ3ifAZNpTenCRV9sTpzjWz7ypxrau1urbCNWwNUjuzQZMNO1abHZx1PV7JjLt2t7eXCjKsnVl1OZt1wuZ2R6R7VNqXjz50T9Xt2tD0d8Xwvy4WXkUP/4LOVbNkjO1+YhV9btzHbMztuaOzN15lWShFYL1qXwyPq2OopIZsbZIMl+krsk0XC9czecdeJGmZxxDMVrssvuhuOoBX52nBWqxzWsp2qm7skOybr4jRANkaLNrH6+jtzWrHsqPuFUWrRxZHbsOVgOp0Zr8hh7fM7/fopskLC0WynLmtW6A8/RiI57Cb1jy2mwcm6FONnrf7bfcVZAw7dJVlKoPxFOqocGfir/XH45ZWANhOLxdpFVuGmEkknvTbq8NvXWpj3u/rm3jSafsI3Bg12dXeer/VbxKFJeGZmlQ5RD/ZNg5zSCWoOhTTXX2qreDf++rgL9r5BYbBBX/u+R74HwDsnH51E4en61cQ0CjFgAUCP+/CVholHmHEDuKYCYiQSyu74qAIFwDMoatMYCqDTnXxXWp1JFDeBBvZBS+keUZlMtzv5rYcCC4wmfx4KgBejAHOHsZ9qzFGcoKWVUhfFzTxHAZrqk3n7KG4uKYpu3JRFnT/avub94lTDrY8ry0ernfB1lGGEQnh5evK7C5N1oADaSPCCswxNFVxp4QFT3LT+fLLM6wA+M1nWeRRNHxEKXcAyNV34CO+7CZSNLRmK+r6y85kCoPY+6ybxypR4yaIe7HF4da1eWT/51cRBnb3y/1LipfwsR6Fz+GEUxxwHgV4Zb49M9kv0zTwZP3O8QeA7FIoAoJkkkuo7hZtxMcMS4PMBqaFfadBKsqSrseHuUtce3Xvmkphy/8jT5Euhe7I/fuRdOwC8PZ676zV19f6VlQRarqF99ESQClLTCiTZ/Uylkn854vNHfuJpLP3Yu76yDp6jWah95znki3SWT/3KOvO/7UvHWVOt5Zj6O//cP7zxCwzA/AXxjO6phttgvZsAQjwCyn/xwmO9w+23XeWnm9PJ5uxz5j+/P5pfuvSOyz989PlFNrdy7HOjm/FDZ/v9M49fTMZVEtX8CzuELoxz3xllVZ/0eTYI1l4+w6HYDRq8JpVd8UjQSXzcfrFtR7Fjzcz6/ePDrTjvn5LXNy405bFn4/bR277n9vLh+oXUNglHbdWWeUXz6VV/bPfArbHxlCBx6jDR3kAiI53ceNi40yvwbGWgQ6AectBQIYMDC0LPbxgJGKKRY9DIEHs2hAYEICWUUqC2PRljNhQIB1hGoMDAMAZQZQQuCh09JAmMMaCMIWH7zHD5cFg+4BW1uTEYFBjcrgGflA5QAM0ehaEGvRmKzSpF80aKvY6XbE9rSTWIYUE/WDGSRUxkLo7MxZQ7WhmiEG67xhYeFZoSvdWM3eYui0EtMxQ26axrmj2j4I2IHlaRtLp8NEdgj8CszRMMnVvI6itaZdNEDQ7BXJsXe9jD9NnLBptTyuSMIjSV2uIodHfOSrXeIKPqJaIGDWjJzfJnHO60M2LVOBUxl7YzplRxI1eOGVLbZJ4kmi2dQ1Zb4zbJlB9awK1ZrRImmKRsoxcl5PyYd2pasNunhN2ThmS30b9W1TkErfp27qT3ZSwUlVnPafQ2bvo7t26ms4f7VnBsW40caaV8oN3MVeLcgCEwRqhbnFiUjZ+epaw6MrTSV7h3PadNIxrd03zOiDyKppha36GRn2dDpvhw6SQdiZz42mhdG+bKifNNycM5WbW5VRHK2Yvk0Mpu6gFRhIldkSKgvguhcC14bvX89rwT4EIllEbHK9udy61reju4l1aIDKyFNWQbJKps5LpGUytMkuz2irDdSvjg2fbwqedW/dO3K6FNi3tC3Bfyxph3qaSmAbj2tWjx5sNp5xM77lZbqLDeCXbbqUAAIlzHTlKVsUwp29KM66EM2EvL00i0ZXbTQKaohKl2ql9YrziAZQEqBagFpA3AfhjA58zPfu/fTjnH/0/iLtj7xsXs2x9/x4O/+ZFfv5bm6bMAfvmrYfUmAsrvA3A6S0HzDJVoRDDc5hgOGqg3+2b2eKaURMooxiiASakJV3ra5ihq4kpA10DBejVRgBgbwBUUHa9V7Pu8ljZhF1G4IviTzxIUN4gW9ps+SjBYgpKy9s/BviPGTRTAqkwNX5m8Pg7AdhDQt594z3z1xPwXD/OHkt/91LNPvpD/30+582stSvFGAGdgzGkn96zMJFUtFJAisxIYJ8eSu4O2JhiDoA2DNoqnwYuTZZ5Dwep1UIhQCxRA8DAK8FnW0B2UnAlR1CSW1mnbk3mUmnhlmrbs5gX+oqPGX5V2OCjBUsbBMVGu08GavjIVbCb7fRfFMR+WUitfKeatz71+j6BKCJYMoAVAmTSzNpQ/UZvW5YImjRpKJCHz+jmENBQAZRquTonMuN1WX7698Mz3P0FbUOmh9Yu/cv/aZx+8ONu6aII3ht7eytsGeUqYcAAldUOT/7HnXuhjP33+leNHH01PA68v37Jnn/mzDz02/0t954Ubr33Nv9pdwS+owxtYWVeH3X82+OXje7pD/l3hkuw2f+d9rvxPtnb+9cyPS7Nz+OaJW371YUYvPLpRvXB86cmFS/qJNB2erp1rX2ka1gzqvYZ75lKVgxOX5oKF9dxpta/7D1qXdMCpd+qWye3Q3YkG7x45na1O0FnOp+NIz16c0tnLb3qdHbeIXrxEvWMvmWT7aH5btDA3jHfasWi7/q6rcrBIe3HmSDbKLCHjBbhVCtAeISKhFMqgs0eU2ksVgUsjm/HNwxHmbijmGu4BjBAoMMm1FEZFdU8MI0immLEylqZIbBs2KBg6oZ7IBhEU52ht4tGSTxTvKAE41CvGr4njIhXsupMHnQQVZEWzBnzkE7nlYoxXYQAVgwwoyFDDOAbdBWMcqaXYJXF1GyQYwwMyi+RZxViCqgxRRJJ4XOGOVeGesVLM9H1QQ2lEYA3rFH5GdCWibneGmJ0FZW9vEXX0RUNIjSSuhC2znAWJsPS2sa+3TLhFwWSg09GFWCwOOetOK6sHljprbVXLGFlY0rX5bRrvhlpsMvjxEV0fdQz11ozJq8TqRNI63FcIG7ZSYMoMhnyU+yY+wmmwTIkZmdHwkGVqe7rh55SZQ3a6Ndbz7vM5S22GIGTzTej+WpuMpGLEzqNFMxUN8ht8LLlFK5kMr5xhw5EFtbBl9/2UBrUWt2cIlNk2mQIBVyLvC50OxMi9X7rucc33lj2WS22aOcu7cy8R71xf+oyxRjhjcvXZ7PP9N7DaeIo2pGW6bsyuszgbNPtR2l9gNW1Vh9zkPR5uCy1aN71czcCmjrbJ2I4g2NjpO3t848wNgbCd6S43rUoS+b0TatXfc7TdT7xavpdVuLs1Oo5hzeKEdGl36LX/dNicNrByUEyr4sG7boi6ZvNRnit3AQAbCNL4xNT6PSBilqDSYCRv6kSlyhhrO2pZtdzSIAwXM1/IzJCtqEMMtDauZYC0AlgcsBkgjIsei2EroEowceWp/Je/OvOuL14f/uaz/9Nd0Pd1xF2w942L8Cf/i//6D3/wPT/0i0fnHnv5a/idbwzemUV0LpPa5BJ6sOXS3oYPndlQAY1AQYQLRkvp0/2baY7iGA9QgJV7UYCzkoGrogBwMQrgdtDpoWywmJ58Xs5LT+a3hiId3cQ+Q3dQI87GfmqzVOhfRAGGRihAVmMy3zkAOQHs5fHVSk10jywEC7fe+fjxy9Mrf7/7FP2pdY18BUDHNu7WlHtobjfZYGE6BGI0yQ7ehh4+QRQ2OIXPKGahMIMM5wGcA8cDoPg8irrBGgpQexsFAGxN1qG01nIP7D8X+40WGgUoPNilXNbOpQfel/v/L6u5K78r95M+8N1f1qV7sP7vINgrj1EG4HcA/CYmrN5Xio9XCCMU94O7drduP3msOz6mgEOqMLovF2LMpCFDAzkHTKCk5YWSZUBuACt0OW1xP7919gL73Z/4H9ynj63UTifh+mf/6P1DAMOzVzrRf/jWC4/lzJoHKyxUlUf63fbU5Z/4NkgUtZlfU3z0oYdjAP8U+F4Uf8DKLOLDG6+RAJZ/+5+D4SUcBbD2ydP+7bd+Up5cfeh69qEjzpf/Qfu3Tj6tv+PMUw/oaZa15/WaiG8tkFE98JyFrgpHldxLGsZd3KoKT0dYWuzGu40sOL/VkPHxm9H8KO4On/q7y7GarlQlZfdX/7Tq2nudZztt6tda7uz81Xh6+rIWV85idOWtvFnVfmPVb6q5y1K96cPE2jxG7VxbkctMFmzpoP2SYV7Ce6ZCGsdehBs7BBIsM7CNhE6Rw6v1qCWMhdAH3Z3JMLMs0Rq6YTJPRtxGZzQ/IGLgwd6hPIVACgIPAAcFR44QBBrVidaeBkEGwHccaMQwkFBIEcNGFQDJUigQGMcBITkY8kktr4sUNnKUTjEZDCIQRMwClwphVcMLCeq9gQvtsekVxu1RTOAxzw6Bbk2jtpei367q3jFmdqscc5sSrS2DpJ6jElIbgiajKW42a0ZPr2m5cA1orWQQRvh2zNKkjo2qgtg+RDq9FtiJS4TWuoTUWe5mNJFfepO08zVuFq8aVHtaVHsaRDC6cMmYUd3YskXZfFfPvr6XuI2QxWSVRSt1CVzX1dEDTNSkxvq8pmefyelUnk2Jscm6NZbfOEScc5/Ot1bnmBktQgvCsp4FOTqnXJuBJPOAcInlLedVwZg9OiUrtJYPwLbB9JRF56TnT+eWGtF4r5HV6j3fdTaR9+aRXz5C4pwoOtdlO8uzlDb61gMkNtGeYLwigTw31Zdew2bPXM743CrdvbGQ82vneOW6zw7JWbrVTmVXGBHboelyQbjTn35OBqyWWmbLyrOXrdA7Ys35jPToR/31vGcrVlNSv7Z3yumzSmU3T0XL7atrMkqT8Xxl0QQKW3viVn2GRSKfcVnCOa8qYjTP8gpr5JXKFjcPTkoHvrx/HWL+WFWmATIqrkV5yybRyRS+MrDRz+tjgI2BPANgT+d+llEe3OY2BUmSWHOLR7nJCAMc4VAkkoCmCnYaw60UIgcwSLP03rXeWpDlDy7sDLq/Rt735b9nfuNuOvdrjLtg7xsUBLP9qodnql5V/vVTvyLmjMYpmQlmtDQ6V+Mshe3WMubW+lm9nW1ShkoWw+QZQ72lIibuyHXEKBoaAhRPRxJFrVqG/eaMCAWYKOVFtlGwAmXKtPQIBQqw46G4+A8n83kZRUq6ZOxK9Y4SyBzswvUmy/RQAL+yczgBkMUYqYvRF4SHYHSiduFaELSzztHdrt7IX0CRTn19SlO+bdZWMhF3IFGBAUkBr76DJ8QAGVdYpQo+gBQSVTDMQMMFxRIKUPcUivTzLgpplzIVW9YnHgRTZTeYRgGMD7qQlGkwhr+oWHKw+eJgQ8ZBKRvglfZqBxm+g3Zqrw6B/XrJLoD+Bz9qLv8l070i3h4Z9fEK+XlZIb/lqfHrUoq3Mo2qLgxfMwIQF6AxBRHFkbNQ1CdyCoQCYCmQhccWEnL2IVJj/s7Ljz9x1dv+UvhYeor8xnt+PhVR7+V4a+Vdorvy5uHM6TaoVfjBjdnqj/3y9yh828f+utX8i/HIZB99cbJPHrlTZ0pWvogcwBZeggeA/O6PPPtw73VPfdfypaPsUJh/9r34NflScPjbPj2nT3u9tHZm0DNXmOje5qcDEzcbubcXh0f8KLx3FDx0Izan3ed0d76i9+g08XsCZ8gXd2fat9pHHvgpln3xHZXt+Cxt5FUv0Apn81E3CrrqyMwXKv4uy9jU5txetI1mnCVOw/XzzirNZUOb5dOKtG8brgUfe4QqaxXOno/OVDdzqhAQGQFDAgNOFOAsHyWccBetXQNiAKIEiLHQczPH2uKkHklGMypgJAQIcthQ0OhDowIOBwyhp8G1QBAflOcpHiTYhPEXiFE8tHmeDwsGmhAYCDBIaJC/MI4JNAw2jhvsTQEz60Q1thgTCcPcdQY7JtagTawXH2UwkHjgMx5uPMgxuwRMLxmWc1ZVRLJKbBCMA6go08bVI28P1NuTFeJj1/jx0IqdaqdncQbNetAY+aw+nFNiY1bD2kzy1XlL29sxOSyietJO5U7i6t4Uw9xNQ4+tmJAF0uw5PH92lvNAG2d6B1Slqv7ay8D0QIrlI8S1taZxYpmrmphjFzPKQiKoVRfHn1VwYo217xKEzhB9S9NObQuO2Fa9TLNxZpFRVCFe2tY8C4jRHYykbTtOqJu00zLBVsDEYAyaqTCjUI2hsO9fc4dbG9Qst0H7DKl7y6iMmmBmVqfw5KgnKCHKziu3tTddI44NmDynpn0VDbNHzF6HXVk6Z/KBr6zkIW4zSkbmBtbTfp6uNxnPCG+EC2yNk9zoEMbKzZqj7M1olK/bIVU6j872qfCMhbkht12zYZZ0RQ9OwLFP7+rRjYqb9obj2mBveJYfEz1hs2u5EsoauWPCJGJHjyQR8E0d+w+8Cnd82N0qivtMD6ExKau04dAEQAhY14rpmQAgbzoqAVQMYArcUZplcebYAlRJAIlWLgchAoRREH+/vMCYqiXVobe8sPTMsZ1BjL86W3I3vkLcBXvfwCCY/XqUwB+mDKlbSyUhMEaBV7woB+LNNGGhjPwoGaUqTTWxbKSUIUXBXPVRnCxNFKAgQKFVdxLFCUtQpFAr2Pd7vVOXhn3QUr7fRZHCLGn1Joo06MLk92UdX8nwldZrB0WDy7QyUDB/JdAsv/MlYtN2Tj9ZszqfyVR8fDtZPT/YtmPuZG/gtpnmzLCEh6soVPwX0Qep3gZoH8zkqNKi8aMPoAMHVWjk4DiJgtXcRgFSCYDnUMi+vAVFDaCFgh0rvWlfDc78yXoeZOReDdz2pWf34+C05ftXy64c9OI9+LuDF7USAJbp2wSFw8jv4KsIgw127P3/rcX+5f8VDGM8FlO0hYZMURhfcQEWAUwD2k1B2QHXkBSAIci172zZlUOfE9eu/tIw6w0Xo39iv7l7dFzlrYb1m/+Y8uMPHI9OPn5+aPvXZKWaphZm7AwMFPM/+yMfw499NSsK4IfwIYqJVuQH39qZ2jo0OvGHF/MXHv1ZL30L+ZZ7nj7D2dwumouP4LMAdnHuKYWowmbD0elNKWtXX3/Z+UI+c9a7/Xce2Zreuydmo9bxaElszpJ0iR2rzu3Q6dxWzunBKqxeq/+ZR3OzeWJEz2/mZHGbBCwP8Ix7lO2sBYuSfILOvHgyaFx5xJ2auT7snroVUmuHzw5Nw7t+mkSrb3Jl4xTZffQTYf/Ecl7NB/6YhUm30rWm6DZl1bpm9oAmjk0H1IOgHipWBEZIQomhoJRh0DBWvUeUAbFGNYIoAOZuATykcNYNaJ7CwljY/UAwrSDhT3xqGQIYKKRIYE1aowwaoZmAuoNez8X/1p1u7rIml3B+ZyxqEEgIMOy6BJlFUR0wJJUUtUjDBcP8eo76nkyCPpAJS1kgliUJVo8prJ3WaK6xlEdGP/2tOenX4ARDg6lrihphuGKGKi4wrlFYYysfOcqE0zxr39aBv6s8GVWGmWf0nqVpMMwtB3Z+9FJuX/SNt/MISRsCqVE6nV8zfKPjDFYWJD1yjfPlBYnbR8Zm6PgBqYvMXya6p7U49gKMyYiOm2RvnVGfO5bZdTPippwN6sz2AZCEG4sYuX6MmdPPE02EoLXVGDwS1upx5khQsrWYH1UUU3qY8TwQI0R57uV5M6yKQLWMCo0eNW+QjCxb3ExXrXgBXmWIwakPZ/bpFeUvnzRDts2MdNGcahI16sDEUu2OD4kjR8ekMx+hsnMPNfPLmkoqtXKs7MSaqW82tLl2xgRXzrB7jz9tXrRo/txugzi5YmM4CRuktmM8wkSQPaQ5nQ0Ts727zK87ofU5oYjFRHIio2ZkhtlRW+YkzOMAACAASURBVJNmi3B3WJVBwvXySpp1rMZmK3SmNwJ4G7XOuFIbKl5RzEmTUPVTGcOzlWVRiJyClNbJaKC4zldRPDxnKB6KNQTPAPiA4ATKtVnuJ8oRk7GWS2oI9rMrFMSywaABUV5rAQ0JyBRK+2BMgbMcjq2eOznb6dY9/R3PXKvft7xz7tfI+3YAbPw98xt3tfe+yrgrvfJ1hsEGAcacIPhbfcr4rdUPGABnCcEMIWCEwggbIhmD5hljXl2tRUMRRL0KrVT1bcdXPooTcYSCdTMoAJ6H/YLsm5PXNvZFeysoAGEJJoaT17JOr4Z9hq6s5yMoGEAP++1TJZgpn8zK+ooSQJVsoUTBLKST9ZiaTNc/JE7uXY+/7N4ev9zrJqv3ubox24/GhypUtimHBkMCYAoSDraQuesgKgUhGjQoTn0JIABBiEJKojXZjhTAi5P/hwAeBPCdk20zKDp3Sw27kkErt6MEZQdZuIMNGOU2v9p/mB74bfmb0r3joE/uq2vySvBYflZ2RGsAn4Y1dRFU/OoH/yh+EV9F7Hzk953N3/mDd8UrqxfSTP7dzIYHhb5x8YVKBk4VRkIjgISYXD3NxHs1ToFx7DAtan7upOpaZen6LSccBudsq/r6P/h5iN/+F2Pv8qcPWcMt6tz7Oiurz3WH9eCYJuyEkAaQGfnej/7kz/jv+fav6lw5jyfm3pD++397tnvze56ZnWq7IY7d++EzDyw1gwvz256/VbWbjRAXQi9mL/3Qh86OHnr6zc1jnzlXffj33eqO0Feszpsuxg+85mp+dGqFt+2hXZXVJGSB0zN7fo3EQWjttEiW7c7L3apwovae2OM1vl3l6abr0kPrPvVHRCPhrH77KPOXGun8ctWtr8xVahu+BUkHlYRygqwiR4E1llPWrdGjjqCRbpp+JT+0ktIc3FIJszt9yDDQ7m6buCRWzekVCjfXO+m0sNdmmegHOqHG9LikXTnDvPbLoFObhrjZhH1XBDYyONqBZWxk0MigkUDCvjOyxCTRSictQGbyXzn2Do5ToLQG3K/NLcdXce4y5NCSwUoZchCsHKawUwIvp6AgiClUv8YRNThnsaHb08Dn3ikAmYMpoWp9om6fAsl9GWmZ6e6h3Br7rlm7hwvNGG1sIHdiqH6TmjQglj/i1qjJsqRmxusPkTxtsYFDqMiGbLc7TYe3zpJ07TAbdaAp7Q9tOhTZ8/NcdesOFp7jlsOBpePcZD7SkaIkZMxux4m7GFNdGWmrnXNneqy1Mma4mxna6AGrR6hFq4wevaxM7ih6+GWqg67OXz4J8oUnLDbqUKUp0fW1mNk6Z+MOd8cL4HnLShhVeyYhgbaIE9vS0hUqezZNIo5W5RQTumY0EihNkHTJsEoXOTtyi0W7mpj10yrjLgLXpiIPdef8RRrM70J1T5FMU2LVt0D2pvXK0hmtMgorI/nNlZN5f0dgO9zpr4412bWYsA0XPOoqxWh2IvflYhIwYpRyTOCcootCUoNVO85jwTllSTybO1bX6Hx1uKqvsFTlg2FKdqUjKRtdqSvOpgfuFBk4w5FgocPhC0kdUVWVyM10lVDwnGlFM5KrIRgtO7ejyRgrnJsYs8FYBmA47Q9Gi60dsT3yPICNJuOsrB0vzQAO+odboLBBESPPYygDwKTgPAVwEYT8h6HnzFydqT8GQ5bu2dgbAxheeP93fzWXlLuBu2Dv6wqDDRfAjwB4DTBeBsYhQfC3sqzfWv3ADoAsS/GgUhBKgWkNnmUQKrVYtZWpSlX2KkFu3EB2aNF5V0dxIm2hYLqmUHShluBqiALwlKndAPtMTtnqXvrUlrVhLRQ26jEy7IFiAHpAqqEALRQFO1ZakwXY14cra/fKbt1JTwD8yXRisr7htly+d7W7LnaudI4/NPdQvpg1dnO3p493HmwyIgYjvVeotluEoYqM7SCtL8EIA2YVyyhSygYV5KihsBYrJVTWUcjH3IOC0TuGgrUsL1zhZP80DmxTKWR8sIv5L+uULW+ur/6ulEthB96XjR0le1jW8gH7wE5Njlm5nz8F4BNg1f+z+vZf/7iYe9OlNzx+31cFoD53/wNaRVGoB0OhDd7CFISrkTk59ryZqSmnVgMZjtoUIBRQYgJOM0BZQI1KQ2mUpnFm/EG7GbV3Nj73Np3d2rz0PFe76wTA8up7/+cdOn+atPduPr09dapBCHktU4Yji379bc/8ykfwff/ZX/sU/qZffa/7yPnwny3Km++qY2ux2lPT+sb5eRIGb2Cu7mRHlrc4IZ+dOvZJ9/kf+KP7t06uPfLirJ3dOD5q/T+Vd762p1sPGWfUuDZ6iIy5QxuiJ6gb2Zdwwqp2m3ZfUNv19izw8fh2o+EPHGVrN+LBKKBBkiTVQ1+2HRPT+pDby5glXXTcxeuuO7/uUKICsl5nPBPE9lYXHSVyyqxYb071VWJzKhKQztiQGu8qVhkT2hxbxouw6VvKZiPiR5ahlRGBo+CYkLKhR+B3pZra40RL444sElGfZNVUcaaQMxDuQoNAQHKBsZ1DSoChdL9lSEEgQFGkYA0s0AMp2FdrEhJkXIKawm6viIPZnf3mpGJkMlgA7LSwZSO5xO17JW6c5tzvGq4g6fIxYOkMhaYajW2GzKEcubFWThOa2yQdBxm/+LqKlTuWOPs0pXZI0OhRs3oMOu4wwnJFOSEyrIL6A/CrZxUlmunKkCWbVVx9+hwxO65xGSWVuS6pLs9YTupzESQpeeExqjfnnJiusvGGA7XwHItHfZpu12AyN3XbCSMiIwapNpbNKK1yURuBc6rsxW3Ozl0iBgB99q3UqaRczq5Aq5yTL72RoLdASGcFbHbbkK3DQ5MKs5tpa8zGugGPVxOPZ+ORpsyzmHGN1/CItF2pdSvLh4O8TzeVVkw6meMwwZhDO1rvHCKt7JyqVmpwEbBaxaHpmoco4RTd42CRA9E9QgfPnsfmi4/Ier9BsHMkj7fmpVkKrGND377qOJWB5ZKpjEOEIfEB+4wMaCtrkKteRJ+qJzKnBA1/VQl/J6uPLdPYSzyS2ZiLVC+SmXPJV2QeY0a08sbUVdrKyOH59YpbT7Ol8ayxkorqJHUqiYgSCjKUkaMlKMm08bKslglugxILxTWyguJaXuq2pghjk6SmOsyCRm6cOkAiFORDE8W9qMwslc2D5QO2BGCDEAuEjMD5GJSMUVyHPw1gnNoivbzQeer3n3r/xn39jLz/ifNfzeXvbuBuGvdrCoMNgQI8/TSAb0UxQL8dwD9EwZb9fx5/8Jg23/4p/tFqeuw+Foz/YS/dtJWGsVwQq5IQraG4MHssUA0UgKxMVQ5QnIgxCiCziULLbgEFG9dFwQACBQ1fplUJCqar7Ny1UJygxdOchgTFYZg7Ei8ar+xGbWMfvAD77hFyMn+OV4LBHRSgs/T0PQogzyP9tLk87Ntu8HFx+drKtzzxTnJu4Vs3l8IrrS/s/vH9L4ZPDQn4fVbFtrP53rq8jLoI7wAuG4CChpj4DDDQO+zkAoon0dnJtOsoUrqnJ+tbCkuX2oPlxays0SvBq3Xg/R0nAxzwAp7sBMpfmY7NsJ9aKwGwPPBa/r5Ui6eTY6pRsJK/AzUc/Df/9IlVfG1h8p3uEgDqAn8KjddNjsVxq9Ny1CiMYyCb2KmMciDIAEsXlvMEgG20aRKDXl1K7/9l702DLbuuMsFvT2e847tvHjLzvZykTE1GlmW5ysZtbGxk44GhwUCBq6MYmqIDNdXVXZSaaioCRVdUR4CgfgANDdGGbgNV1TYinMZYNi4oWZItW7OU8/Bevvndd+cz7r1X/zj35HuSZRp3/bRWROaNd++5595z7h6+9a21vrW0eKSW51n9+PKJl5994Rvm5U9RTeRJc/n859Spq38jXjn7/f8bgN+Dh+ShHwkG+Mf/3/l6t71nJvihH/d/bDIb/kzMHK+aSVK19dble67yF+9TxEDVB3bbD9b3b06fe9sVrzvTDi/rM+kQjVGDJvw+c8LV+cGEtrUh83cqFunElqmMJjCsLPlXbTpl2FIeuXE7tPl00rIuscglFg0Wbe4lNGy63glRR3/ZyKQ20OmNwNXC4Kv3EkJL2h8M2eYiia07EneuM8T80b/VJ/qaH3/rl8lon/IXHpCqU7Pswt0ub3wZVlpjGcQUbQvlB5y6DUaJT6hk3BNAPrMGHbmkUkHMN4w39hiHghj4XCe+pLBrE8uscomJ3iTD5jTHzE2B2sjAySOMmMSw4cJ0GermcDs9HBqTJUMtkbjAxrKD6XVCZVDO38MdYoDDbHYZuaNIoxfmWF0WoH2GuhYI+gxxaLB7hGFz0SL3OGauETqNHO2zQFRRwh9RffGmZl7WQ63diDVxVhsyL5VChhnL5FrG/UzRyAPrTrLEUOYf+2qWy1CwnRqXV0+yU71ZsOV1KwcS/lqQ8r2mMRPbArPbSh2/xu1W3erEZhW3xc3ece62+sgndnXirwVsYl8oX2K0XbM8lHB7K/BrPZFrk4vZVWKzCdlOw2L2hkyOvQSkjhSjWdiJbZBvwLxOotdmckWyZv2uDNyB1LVtra4+ICRVDTizCInbkdGx7AxtyJTfU0Ru02aJ7nvcSQX2FnZNR1aHATXj0KQLVylf97PG6E4fdcOsc43J0VG4VLMyPc3RX0A/3aSELLeOZpezitceOTlzZpw93tCttMvnui0a0NBcdZopmVhd4JnoVVZNIvJ0oj+iUGvhhVVKJWNoRawr9+Nareex/UryPNXze7bjSo0ivxuolEStOohs+xvd05kmxiZZJcr92NsQPNjxSJNNR4CoAALkSWeY2AR5BghP4UCVoMQROYBIZHlKOatH0ucQrDMeTzM4cMTLCEUZNangoMPTBIQQEGJnfM5r4399AAOuu1+du/Dzg8pP/lVDBmfm2UOfukqPfvzNzhp/D3sT7P0dRtgsPd0yBPgDAH4UwHfjYHF8AMBvEjb/EMA5AGCY+6bBxz78XgVA0mOPx9/u9/jsu3X8ib++64ko6f+YIThKQnGAUg2V55hhDFpIkDEY5hkmwVDzPEyjmGCXUACHSRQgxwB4BsD9KLytw5p65cQDXpsjVgIZCQVvvHWUoc4SBJUVvKWQcglYDlfl9nEgw3IeBfhSOGDLLIA4jtG1FM57J7t/e/OPf+vpR85RRtjk7XTrWjvb1glG/4aBPtLE/Flj/by92P/bzR82H/JXwdnX0AkHqABoQ8CFi0kU3TDKsPU7UYCmZ8b3wkcB4Es5mOMoPE+NYvGRKEBqo/wpcQBuS2BW2uGK23xMq6hxl43DLGjJEo5QPN8d/wZ7OAChuyiEh0coimwWcVD9u15+4MMPsgoA9sg5Gjz65wXz+NBH3jCB2UchdN0H8NsAPi24uJv7/lsFY+3u1tqCBiYE4BjA5IVoNBxAKhQeAwR8P0vs5My915KbL/CbucBzH/rX9fYvP7gHwDfKs1N7l786v/1K+tBHkI+v4e9t7/1v30n/8PRz9wlKlQVhO13M/4a/iz93vOHuZ92k2uuhvbK5uzK5HV6utk5Do88G2F30r59Y798+k+UT9Z16FVLu92fFDd4kaM0qlQZ2yBVDtls9QlO4SOtmQUwjxSaWqI8JZqUnOMvJKFc8m95LDRnxmWAPDf9GnkwNEVUhnl7YiyvtCXnPXx+T7ZOMdU+vsrvmn1YivJmbVjvT7SPkz72q2N4UmMozv7LvZLEVIJdkd5pM2LU4ckUwxhjWmkh8Y7UJIAa+oHjIY1vjem7LTHgjIa1BSi4yw9DZPYtKsMGdmHLu5IbkSPLVZaC275mJHe67Q1DumUSPLDEkgYDCwVgrHwu2WaUWjW0Gd3iYrbbIxu3R3FvpA0UIuQuNYaggIwvSDvyU4O5r9CcMbp5icEYCtz3FoWLCN94tcPEtBhNtgd40sHxeQ+UDFgwN4oYyXAKmKmizacG1zrwMPGowb+KK6ZpcpBmloZsSW1yt5PtTtJe1sLDyil1UkY2N5JZFYtvfkJUZm3lxYpMLy8o/8QL8+/cNukwk613SA8c0V2LuMdeqhfPEFmPGhy7VZ5QV9R6DCpGBcamFw4ZNk9/oJWzf99TyS9a4e8Ru3gez25R2ah1CrepkmA9Z90iFB3vIDU8Q+wiyCW7AjVBCCLeXWjfXLK/yfiXizHM8tKETPRDcmobIwk4ubRJL6cuYZ7W4AW5J5j0DqxjxjFF1xmPoniAmyOibC5FMW54nfbboWezf1HQeO3nARxs8iJtxTfpvi2alSOv8GRWLhdhXNyrSIslIMZMvDr1sUyrmkknamywzlSkEXsNba211d9UGhjeGjeGsiqhbc3ujKXOVUbIfjKzXUz10/DT1svldd9AwLtXZsJ2HWl5LON/WnmzAVQqMKYA5sMRA5ICxMvpQgj4C0DJhkMLqCJwJHER9yuK8YLwGbaHYn0o5ppKJPpzGMsIBQJwFsDt9+V9IAMfCvc85orrV7M/95JVvZ435TrY3wd63sCInD+9Cofd1BMVA/RG8tp0YUAzk96LobvFuAClh849QgCx+qCijBaDOPvzeK/TY499uRS526Pyrg33xabeCH/PdoOoKh2nT5YYghUQIIGCE6wBW2EFLsrIStoICZF0YP96Pgq0rw7altErJvFXG11n2x3VQVuvKW6zU4bZepS6di4O2YqVMCXCwEJQdMzIcgEBCwYp6KABJToBXa/h3Ts5N7L581+5jH32SuwDMv7v7y69cHj270/vzr69+131v/32+NNd5tX0NaNiAJHg2jbw118hpFL2Q2nwLIAuOe1CEo0MUH9bKgGO86NrBBJDz4rXe+Dvs40CKJh1/97LbQHmdh0He62VRykfLD6RMyhw9jK9xb3wPys24DOvu4aBY5hyK0MUOxt91/J7R60STWwDEo3+OGAWY6+H1IOuD72dvv+8B5+LlC7v7nf0OipB+sLCwuL6/387c9e3zc0cX+f6g/QgPmy1+ZLGfPfVc4I5SzsZ9khXAtUFulMhfNDLR7/z5bkXKu9ynPr288MSfPL7+85/cBICzj/5zAv45vl1b2gT7xAfu//5N7b/XS19gHobmvHO8v+WQXGAXwymHd7PeYvRy6+7J3RMvsiPycuUI20o7lYl6R8/N9aleXdo1Yehf7nWyify8XEw6judNY1fkEMkWTqt9NGklXYdlodgXE6KKPWPBhfG5nkpj4Q9TJsIumUHdCk2yHq5jte6jE7t2KuMyOnWNX7Yxn3NXs6mlx5iZ3OSp0+Nu7uUDM51oUZmoNQcONbYE+TEbmoBYrtKGSJmKm8phXUqHHjCcpHgY6r0KVMUdco8ylu3OgE2OhFEjkzLLOCPbad9B29kJcWx7ijkv3OWYlWcTlkaZzXwZObnK+uCopdZxU24toPEaoHd40yzGqIDAROdwqkDh4BVcoEaGHLECVO4gQJGfJ7VFSAIsZcBGBgMLLxKIPQNLAjdPA8OWgHUIE3tkJlelGNYJhsU4c2kPOp+GFZ545X7Dc56IlecFKnlgOhM2sUyTjIyL0Kfqrg6OXOeck9FOG7VGh3haj4bDOdcK44j6Gur7NfhHrzli8lUmXnwHOX5mtXHJTF3nvmTw9s5yNvUKLNvOgmNJlVVAtstzbbn0AyPQ2M7l5pRRdfiZ5CweOI6s9mCnrwE7DSClBNdOM4pDZpbO5yJZcSRvGbbykpRb05x6i2SHNWlTa4VryUErQOogz2Hqe9ZxPc50bqSBl4SyJvrSeD3bU63dWi6TVFqPKS+fjWXOFFkGoX1wX2dmt54zEUhlrIWTYZZ56a4dpFv5tDpOc9FRVGuX0Ja0N5RyIrEbcUxq0MmmuCvb0kHPsbvT3c3WTgA1DGT+StVQBmNmB7HSBimC/TDo6IykZEtprVJnSmxw1rNSmdOR2J/PRHg1EcEzFQ/VVPI9qy2CPF3sdV/sVtTCgARpogyMcXgOkOURsiyG6x5OyykdYAlHCkCWwK2Bg/SdMuebUAA9i2KtLSNMpTNcprFso9iLZgGcBfC4MP0UgFH5dqT2/xK9P/1c+m0vNt+h9ibYewMbM3pzAP4bFE2YWygW0tcDvcMbfQvAT6PYkH8IxaZ9cZRc+/3lf/JLX0Ex6DffCOiNP48Y5t4wp+mjT3Lh+Gj6NTw92PMnFcvuhYeatnA5h0oGaLghBJdY8STWcdBdopRJuYECsNRwIL0SM7hRDfVLMUYXMozuHL9WyLAeSJFUcDBOXl99muGgzVLZFqz05MShY0t5FQ8HRRuTALTRkERQUuEVB8HpDFHTccDTZD+5GQ2/y27yu1fOm0bQw/DcP/num4zw6uQxrMRPPhX/2rmN3/7YkwtMAEdQwY9oQqU/mV+qLD1w3d/Z2YlefJWxIofwBICzBLgGyLSLrvFwShBc0ceOZyGgMQSHhMSzBlgQB5XJ5e9bsmXlJorX/X2Y4QMA5R4A7sPAsCxSSXDgvXZQhCq+Nn48jYPwxS6KBfF7x8f/DYocw9JKls+Mz/NGzLGsVqoz997z1n189vPl+Bt8qSpvGGOejeLo2fjOMPJmZqKFU/cv0mb39ktk3z1irCWJSrArUyBzw5D5O1eWLWPaqU4u+Fe++oy/eSH64c9/8r+0Ku7uV0fLn1CsPUfIWZjw6Enn3dUcit1JT8VceFQ/uRFfR8XZMEfjKsWm6vRnOqZVCwZUn0naTlUqioWQXYcdmRIbTi89pXr5jHCDjGy/ziZszCJeH+QirAwcZZvYZG0cpQhc6tholUBU/Z4dCAe7QUDD+iDfwpS37gasrRi/3dklubIpFIbC7x/ReThSKnHIJJ6fhHngLl628U6LduQ0KoOjxq2viY7rQqScVTuKk+JAXCfr9RHqmHuxy6IwMc7suvWmd/iubIleZ5GrniQmYt3Mc6cWPg/HH+S0VxOsN+Niaj81uWK8PUWq3jHKQIsM3PPA2MEYYzjIKwUOr1kaDO0Godbl8AFNALfIeJEAYZEpBspTSHgwYFCWIwWDAIFDojMhAAa4KcPWJOHifRY6S+H17IjHTm8gzGTfs058dIQLxGEYx+JFopkLJLzEldZxUdtjqG7zKNUpb89qb28parZn5OBvj4GaO6Cwzdq7kmvX9SYxxXbl9cRZvCknV0TGm7vKyEjKvT2h4rqxpmGTwSJ4siSEYzG80khYjTEx6TKXJSbfPiLZcEKkM08Z0kZYQGhnn2yry6QDqP062OYp0OYsMJx31PSQVKSRMQVb3VfWk4i3Jhnfn1WRk8FPOhnzXAZTJRKZYeCMc0cFWZMyCUr9iGp5w+VGmr3kpUquOvwILWbaGWaG57EnW9LRDoMWjBEZs3FWC5DDc08adBmDlBahqZD0zuhp+np1l3RSrd8bOewi79svDDuUkTZBxrI9P8bQcpxOPf+SZ9ztWmaaqFjG4jh18ljmTv3kgMT6ek5tDbvpVWJlY6dFN2habZt4OON2J/hEFyNuVEXNM2UGbm6hZBdc8GtO/T7OtJzdG7F+nNvhRM0DYwYMFRTrQpnSUo45hWK99cf/ejggDQ53VCqZwBQHUZRCwruwsr97FcXedQlF1EWsn/2/y9zrhB79+JuVuN+GvQn23tiqAD6BIoF/DmN2A98sjzHuG/8aE3muZ5MsRW8YLe72++/4hQ9+7+9/5ulnfq8a+EPgFms4gyI/7ftQyIL8IWHz3LcAfBbAXlDVIyHYNWvMXZ2u+ZiUOGYFAjJgIseecjHCQeeKsnXXMgoB4TJ0OAVgPUSzy8F6PexNAXYRBVCooQitdnEQ8i3Fhg9XlJZh25KROixwyXDAhJVVt4dzNUoW0QLIsxQpLKQQmM941APQkAIsZrKeZZhs1O2PsgRfnn4BEQjHZraxduMYNgBohjnzmQcsPvok3wFnv4I67rxxymETO/2mPb7hBDv4SriNbHxNp1KOZXLA0ylkRmDe7cJIwINBTyVYyxUCLTHqA9YF0vBAXsDHN2vpAa8FefmhY8qQdHn/ysrH8rlSa5CjCGlsA/gqgKdRgLdvoAjh9h45R/Twg2wCRXi5BuD8ww8yH8D2I+cof+QcHXYeDoPAA/vs53N88P2lzuAte89Ad74QsC9/z9CUeV6P44Pvb1y5dmXfjfMbGeH7FHBMACdiAHHgeqNJkd85HO1easw+P3jLB18yM8cvffiP/pfXnJc98lATAKeHH33j7zO2pU2wP2p+QD0W/fA7gY//q2foXXddotNsyuxgyVzlDpETshGtYkVZ49Xu8l7o3s2e3DyV3MTiK3OPXznhua5K3napdpT2wpaaaa6PImep7iD1BlSPZ+RaVLOj2hQ2nBu64eyLBv9KrakihNwiIxdWMnAGeFircp4lsGY0slTrR1ejt9YCk/E8SJnikR2KGvPcIWfLe6BhSlvDyTzDGUcYIGIBB2VyKlzN5VJXQy/Zm8OTploFzzgTae8kbD6iutixprKX82rislFAikk0vMxKBzbTufB0L8nQQK4n3Wrruho4IwjS1s0Tk5/5GunulJBHzwu7druUGyfIPXmJ5USQOUgUNe5lZX2Og9y71xkDIBgApBZZO3O4S5lscXBIKExEAIdBz8sxaiiEW8hH4KYG4UUOcPU4AZSAQhdPv5+jPy3QnhKIq1bdWOHO3Cuas0CDQ6Ff8SEF0JvspXc8X6fupMsyycRu04jjm2KiU/OxugxM39SYXgV76W3cPPtuaRafM6yyyfjklvSGO2ZSgtvOnLatvSSPqxK9BUDmNnc7fJjvon9z2WSTW5pbQxAyqU2lHTOVhHnCWT6xBX9qH9mQuFvJGcyAGd4FbUxDJC7QmYZZX7L9i5OE1SUTLOwx555Vq/pNqepDytqZYVdODeLto41Rc5eqrR3N9hfARC+y5HgsyB0y2iruUwyTA1LkcWSU8swsmwdRKw6DJpGkkQn2Mh4NZpmZV5AMDMg0RlHCum6F5l0OxoAEAlXuJc3E93M75e8mlNS9ju+zgcgGVeHV4Z0D3gAAIABJREFUCYHpT4XZltezTMSsYV3qNKsZ1wm2ggGFGWWe5p5iiRnGEVLOhhTMV7phojJojmXY0wM1Ia5FdD3IZWemmdcMkiDt8f1KnkH4AokFiFWt44vI5nlGFELnGRzlQCoBxsrCsgCvzfecwIGzUUEBBkuHt1Qz2EZBSJQFgL3xMYf7oEuAVgDbK9KM2T4Khq85PuerKPa6N+3vaW+CvUM2ZthWAPxjAB9D0dnBfd1hZUiybEBfgsAyXCIznePmbhtfeO4lbHW7anF65o4v/tovTzcqoUPYvDJ+7yyAH0aRAxiiWKCvEDa3GeY6hz/wMw9YwpjB+eiTvJKM1H53MzTN2fzDnm/BOe7WBqGOcMEPbnlQCkWu1xIK1rEb9XGFcUz5FUQOPA2YBLBnUbBsVRxU5UY4aHNWVoyWIczDkxY4KCSwKIBkuQgcXgBKMFFO+hTFhPc4hyaOGgwsMcSMoQ5AVvxMkwfLQXd234Gn7v8yOrU9XAti2EfO0esneQzgCQCvtK4GH2DpXmMU9k55FvfkDJ/khBkC5rN5/KfuSbyFYpz1Buj4W7AaWMoVNBgCCBwRRZFCHQwuERgDBAHCFFIkr2dPDrN85bgoW8yV3Tf4oefLPKrq+Jg2CqZuDYX32h4ff+WRcxQBwMMPMj7+PS6gAK1DFA4IG7/v72ef/fwbJjG/LyLzuqeGszOzT6ytrwqR5wTgE+UBbpSCrW7vdQ2+MrH1hxd/5HN/QMBrpQ9+7Nd+WHg76pXUr1fYrz70SfrVR//pt/pKP/TEF5Z/69gjc+0F+Q6AHzWoqn2EYMJGHb/JZuOumbTbJgvrtIv53kYyv/sW5zkvVkHcn5X9db8y18y3oDCvpAjDtmgGA9QkzzWEMfC4z6eCS1HXVO2+X3OME2ESHSSoiXUs5as4RgYZAYIrZlnm1PgaTjCLHZpknWxmH+6mTO1RXGd+KNhlOWnfUv8GpuprclfXWVtO86uDBbbSJuJ8n2mH6cRKIyavqhBNP8/FYEleDszijrianDBHMcFqTiyt0zHMicEVdBsTVI3hOh7YxECCvLZMlE8ykix0hoDhDATF/T3DZM6s1WRXLrBoatMEkoyQYHanIXKZkZqLOBeIcOBQOQDqt7heH4Akg5l24agZwLU5kzGzqFAxn8W4SMg1QIQYDI52oJADWaLhzG4RLpwBhk2NtRWO7SWGmXUNU1WOrZjJrbt6OHaxAjX0ACYx/8oIuh7CH2pU9/r85koFe0eFA2kS1TPkdEApyzEMpZjeYJw4sZrGysqu9YEcS1dVEAUsvnoXj/ZP1bPqdV2tDTiSANbbB2tsklq/HQLSJDfmeWNOKClvTJAA8p7LaaRZ3ugj63ImG4oE1cEqfehBCOo2YQZVpHIHVFE83w85zV4xpBMlrt5Goj1LfO2YE1iGsNljNS/SfOOkZqbqZTxBHg2EnNhhxIJcRku6ZX2eZY7InIFRcMVsxUOGEetjA1WquVZXez3d7tXyKQdSWAHOJDjLqeqkvKtcxTjY0CJxeOR7mcytXd5amdx0oZ+pRdkVPticGCTtMEdltaXVdE17t/UF/9Jk6u7XHTa7Z2hl4HYdkdjNfFSvGcVGnp9LEnLZ3uw0dert9gOQ5NmLzmzYP9m0qVQDuEL1Tc5skgfIRQQlOjBGQbA5GOUMahPMqEzDETGADFKUQI6hWHtLKYpyTQSKNa8kHAyKtb/MgT+Kg8I9gcKJ3UAB3kpx/2B8rmWAbgAsHp+riaLQ8M2ijG/T3gR7YyNsLqLoQftTOKgcer3WWcnElJWlZU/YEuz52liWadNLczNUXNw4tTB//h23n9ppVMLvRxGSu4BicHsApLb6L/fjrYFh+bm5YPn1Gm2vsXGf3DkvzKNqU3+RK9mSKgMHqtZggXHMoQit7qHwsMqijBgAB8McWbQATHaweR1gyfh6DguvZnhtpWgJ0EqwUuYDloxfiCIHLEIBRsr+uGWVX44CoBwuGuAogLR2fVibop7HQch4BOkjZALEGARjSAE2Hdfp5PnvxUvf82hxTV8I2LX3RXTrfJ95wBoAvY8+yUf9mfUv5RXcWf86ZvgA870VvNd6WHMiXNh/O9wswDIlOD4aQVqLndolMGMwKSQWx1Wn5AOBpVvXbgngh0sXD92HDAdArrw/EsW2ehjkFZICB/clGt+TVwH8+/HfsyhCFj6AlYcfZHs4kM75CAon5N8/co7WHn6QWQD+ww+yJQA7j5yjvzNvZe7/fPYPFk30sZFO/+UrP/Oe336jYx5+kNUAqEfOUTsE2nnAOAqG8S28aCvHPUDwzMQGGLwveuO+zgu9tflZfqJhKMO6dH5iaRO/tDaHb/5+H3w/O/Pejy1Fjrv4tYV3fXF83d9bxdYch7cjEVdv+IutS4PTbCbqijzQrUm0G46OTS5Ed2O626jz3uxafneziq7kyKyGFG8RT5sRD+2zyf1sSPA0wUxhw/T9K3oDsyzFlKiilxkoK1KH1WPI/ZqF5ZntNLUAs7wJx1/2nox3Zk8NuSPTu9OvVJL0Nu8yjogOn6ZrPOf9tOUt0RYX7pDmTz4l+tJnPNj2Njem8ppO+JxcB2cslBlX+dAxy3zb5nFL5YaZsI7crYJbDeXrHucMjAy3YjjpZ8F+HrauGjsKuDtQluIa00azfmdRVvwRl06kqDJCUo/1BpbFUXtTWKO45ZZsDuLiVteXck4zZGPnxB+P3QQEgnF9MMYIwh9XlRtwWDCMMITlMTK3BrhGyFTZ2Ie8ep/A9rzF2mkPrTXgnY9bfP0dGeIqQ+6n6NWG0DWtXeMl1R0j+LbHyHV0s53GbnUUuLHU2kXCc+jNZcsMN7ax6bhCiMxLydysiPTCWVOtd41z4xhDFBh9+8swayeJPfvdI+0NQnGaFPHLxCoj8Jkb2jWKtSZ7RAOPqVO7St15zWbhvrE3ZvKQKSHqmUyBnFoj5UmHxasViPYKif4k02lAPGzDrRJTO2eYnmfgZpanNwImuh7E9WmrbAhe7TgYBORER4jrKRBZA6rVte1aszWfOAhJKd+znIi7ghyEkjEGDgwVqkzA5rHBqo2W1i2Mta45bYEFLnIuqv2K322ooW0zSgN4mMstnFSDEkdxUMr7Wb43aI1IbtaceMnWstmkE7JeMtyaiCKZNuaPMzdppJXL8zaSR+z0xBflBbVedaD7SZe7FZV40k9acW64rOYjiWOIaBBU9KYIcpjUg84krHIdVudg2geEh8ArHX5mBAO44mMsp3GwH5Qdk8r1Dfjm1IFyPyjHZDkuyyI+B8VeMnPofOWaCYBPAcxB4dhuowCB+/Tox285qeyhT9VQAM6NN0O739reBHsACJvTKORU7kUx6MoqouJljLXpi0G6jtcyNiULN+hH8cS1rZ3hsZmpp08szu3dc3L5ZRQe0BwKb6YB4B8AuA8Fdf0bO8nqY//0hXfWqrLZ+aP72m8Y9mIffu84/Pqezkd++UubAPjkkf48isKGeQA3PHkrT0KhAHCEAnzVADwOYNYPwcBuSYpsjOdleR0uiglVft8S9B3WlStD1mU+YJlgm6EAl/x17ykncx0Hml8MwIg0RmmOhiDk0kXGdGTJIgPdyhGUADhHMOkFzjuvPtAZ3vunON/YLIpMfuteVttcwOA9jyP/wz+DjVrsfkQ013wO69rFfuUynohmoNNZzFoX1Uxj3b2ChlnC1dzFW/0RWlkIJQTWcx9DPsAqBQhh0HRSBABcNr6WUsuFvXFu3gAH+SoGB4UUpXBF2V2kvJ9tFL1sLwN49pFzdPHhB1kdB8UwGQoNwDsBPIbCk/0Yiu4nz43PuTH+LVbG5998g2Fzy04g+aDwPW93e/jrt/3yb06c/19/8ZE3OKwCwHv4Qbb/yDmi90VkvxCw58DYpxiXp12Tv20spvUf8Hcwipd+4Ks3e3927HnFK2dRVP3yNzzws5+nm3/06W9cXDx2HQUj/BwAPsU77/FYX+UmT9poDmFCEae5sEFfaCj7qr3zRmr9apx7k3ew59IRBek6Fip9VNgkuoCNmEXNOk7qAcQNlEvcs3u2aXuY5BmkTVCTgMVMPMzUcFoklX3T4G3d5RlLqMYJxnFZJHx31zaR1M57p3Bj/gRPyWdz1KAgEjTb6zuL7gg7ngMbWr7AN3DTtLhu+nJSbAFamCieZqZ3xO5OuGj4m4KcfZpw1qXxoS0DcgXLYZixsLCkzdQaWatNOqq5XGrwMLFaWgzSptzrzpHRl8yUOxLoAlwHytQ4s5ZxxiNygxEx7aZYCwUmOhYhHQiAB2CgQ51bEuRgEHABxx87tDE4MhAYMkgAMRGaNyy2jpPDIws+MuhWOLbmAK+jMXcd8BKLbs3ic/9IotbNcPxigowLo5XO/HVGwmRKCW497SdBmkMxhbmtNN4JxDBqwKg+Y906ZkzAM2c0Gr58h5dfPy7kW5/UA+aLIFpSYM8aqu8zLF72G34nzoatmnji9tjOrwu7O2Uc7SlV67K8tSMQ9qHDLZ/2W1yN5skBWT61m8uW4dbWEOUZsDGH/PrtzOQOMWSgoxeYxysQwgNvRTBZjchdBS2ch7ABeLqf8v4is3kLMg+EkhCA4IDJvWpDxBlcGHBNsADjBEtx2tOuCOEqz4v4fpyxQCvltuqJM8mBjuapY8GIGbufDH0lIQPHqVRsVuE6cQQ8rhLElQE0rBd1XrF7a2fy6QneFfNt2a9vhoydQGO31m8lz0t35+19Ic9s5oMb83LxS34nWBpaIc1Itv1GEHAvWa9yezM95vtJ5AyqIkpc1/OqlTbpzDEZ5QDj4OSAkAJWwpgOcj0JBgvXTSG0LZZBVRZiHPRCN0aCQJDicLSjdHyBg2K20vEt9fQOi8kLHGiyAsXeWwNYjqJA7QyKnOU/QbFuZgBwZW+mMV/vOI74P1qZkUdxUFz3pr2BfceDvbFA8i8CeBDFgCyp5JJli/Da1lUVFNId0ygAzi6KgXpZcJYqKUhJsR24rkEB8LZRAMKrAPQo796T2LTedKbqnPGfnw9Wol87+x///H9++QfLThMYV59m4/Atxt+lAmD4mQfs4KNP8tIbugzAcxCs5cgXCfkCgCyNsGSNSDmn1Gix7dfyScYgGccIxeQZoGCRjqCYdBUUE60M45YhoJLhszgAL2XOWZmXxnAgQHw4pB1jzF7iIH/o1gS3BgNYcAYoJmBUCBDBKdi8W3l+JAHfkr3DyVj46g+yV459zr6SBZjZvgvDzhKe/0+3wVIKH8B/DYU17eOp6mXstt+BZ1MPc5lEKHNsJ5O4jXPcQQNscRc1IYDhDGy/i5a/DS6BlEU4CqDOXgtQ+K3/ygUsgwFBQyEfC0uX+oSHQ7u7QD0HehzA11EweZMAPg1gyIKFfffoh9oA8Mg56j38IHvhkXMFW/nwg+zV8fEnAfwzFID+L8f3FONq3PjhB1kM4M6HH2TJx971p2Zv8T3RB35i8psKgD7Uclc++erqegRy+5Mr/2P4xy8t/Kh/9BfulFUckmnZQiHhcsszfl9E6RcC9uXpU7d//96lV3+L69wB8NnDrOrr7TMPWMKvvf8BoOP8+vf/RPZLc9+U53rL/vU/+thgaRMxkNd8NmzG5Peu2qV1BlkHaIrABqjr3jbNTgBTfICpSwK5T5DSR7e6R9NSI3IsKqqHGqsism06IgQMn8EuX8V0/rS9PwZkwMGYhot5rJo5bLAdzNmF+nW8Wn2LnhQDOsNfYEPWpD3Tyrvw+AgV2se0OiteYKlF5vMeBxgDcSSMk5MrwCosYtOYqCL3HELXzvCa6jCVeKZ75R9QNQFl9a7NIViXagi8jmYKjh5xnrhWaoXcRAF47sTJ/hFXTV9yVKBtLlnOHc08ReCUykBsmdb8Uxa9GjPdGQhLqMcVUecdIFTWjI5CTV3PWOIyGk44FA40D/NSqNZC3XJOi3lVhYSBBWMRhlUPbpyDcoa+bwHGMZiogKUham2BcJPh8imDuJVh4zYf0SSHt53h2hnC7JrBwqrFO85p7CxyyLyO8/dz14szeeolRY2h1pvz+975UzO1d/8/1Xx/0UovYmx6T9gkMPzMBQ4CgRyj261Ann6JO6deIlvty0EzEaOv30tNb4hU3pTtnpQLx66jsnkHQeZOknokb55KVKOdi1Ho8MFZqZ0IuLBnceyyoEqcR3shE6gYijlAnhDNG5a5glvwJK3ucKJMy82jHhMTXA8cw7amtGdqQt08zox2cpGHhH4lY3E9ceCEwulwiJyQKCelkAiOARcOGCA5y/KEXIDLzHBYGRkFRp5lWkIqbeKqtdYBtyGDKzQoFlCJN5p2rDtwHD8TNrNWM+oLY7dNdW820Y43zK1rKvK4jnl2J6W11RMvhto2MHnpgdnGsOqsypsXrBwt7SjvzGL7WP6BYDOi+UuTz+bb5itxdXSGKutnd90TmdVqkw/FU01HbQehcyRsN++qjtiLa1MdbYNdBKq/L3IPXDiwVKTw0HhtsJYjs2sQJoeSp3FYyzHLAEBBeLqQZrm1ZpYOfxm+BQ5SXcZ/l2QDO9yWs2QJyxDvDIq9506MJbPo0Y+n7KFPyR+46+P3nZ1dZyemtp54ZWtxHW+Gdv9O+44Fe+MiiZ9DIa3ydrxWqqAMXZb5BmXRQYwDEKgMmTi32WVP+HdYa6dCz5s9c2QxQ8FUtFGEbCUKRm8DwB2aTC2z8ZBgNcAbAP7l2drbv+vTD6z/xj974Z72Oyc/tnhv43umv9794ksoGDrQY4+n7MPvvUqPPW4++iQ/DM4uAVidD449k+XJ5Y386k8DmDMaymqWkhCdPFWZT/ks2K3uGWUj6zKU2kIByro4qMaNUYA9OX7PYT29EriVwDgbn7NssVZqwZWTvARvZfhXAZDCxRwXSMca/QxAztg43Hww0SlDFCCBqCgsrL6Fjg81vpvFSG4+gLUsgBgwrGYuppBRDRLYfyu+r7+M/WQaWzzFvGboIcEM+YAJkWGEo4xDDlZgkGOi34VGB1oANQfgrJBK+Sb67jVmkcFAg7ADDxM40Mrj43uRAPgsMHoZOR5AjucQIAXwIQB3Axixxm1Xgrv/h5v/6qfuq8iJO/2JH1/bevTP0XvoIyAA20Zi32r8BwDvG6+gR1EsgocXtHUA2Vzl9Ef6uy/87LKceHHtdxZ+dunnbn8NwPqfPvRdo9+5Iqca/au/sT4393HY5Ae+uPnyTz2e9TcfwveeAIASaL7exuHaPoqCpb+fffbzBCD9pW/x8uL7mIN/exF8duXopLu7NciqBmTPADTDYZWF1QD1XCRByuoRmBUKfTWNfbePYDZCwCJw28fRUCPBEvZ4E3smZYz7GKDJ9liN9tllOi72sMDmcIMi1JDANRlUvoxLxMGRMB9NsW2OYFVqi/wilrkDzetIKEKd5eC0Y2a1QpzUs4RPJW3GKtL2lWvyxWsc5iJmNuoykZMYTkhMs3W0zSzbSkPRdRuSrs/kO2LI5xc/Z5tZJLQ3smkCbHZvZ83mDdNkQ2X7Tbvae6tMFVfLdAOCYnUzqIi+nWNHhlua9meY6S3wYXUPnrsj4nYLvswgkAPag9ZdRN0JQnSD/Iw8ozpIRg73q7kS6tZcKqMTxSYswEGQSBSwtcjh7OYIuhku3cOQKMDrEpjgSAIBnhIGMwr7EwYv36Ex0XMwFTP8xScYRJbjzq9JKAJq+ykGVQViLo6/aMWRl03OBPeMcZGfFNw63DWexTBkbHLdhisxkyo3tLNkafaK6MTTcPemLNMqNa+8TXblpvW2jxjVvM4oDcgZ1TgzSoHHKZY3LL9+p0s7c4FwRymEtu7MWipTN8/3jjnZzqwb9wzXvQD8jhcknCGFt29Y6XLGli9qtnjFuoMaM9tHBLvRgJl7SZut7xY8rQtcv0ty7RAHCLCjBG2e5ZbXJMugtQETRSM6UhJWEJeMCGmuYS0gU0Nwa7zOmUgN5ChhXNYlH1ji+4TBERC49QAjIDYUnHm4qKawOfUYEZTMXW0dpuWUEYIZP7E0MrebVmvL8iyvdcPGwgXHvfYB7pDjeElo3o6a82xrJ7rm9yc7uczm/Vh78UR+yQTkMFZpzK+6/trS/g0K/PXmNIfMDIjpfqKSRISh9oTAiFykaQrXLQsRa2BIipUQPjRjAFvGQdpKCfY4lKNAJMBY6eizW68V6yJwILdSgr4x+0fjRwYc9NmtHHrP4UjJXQCeByDZQ5/6KoDwMy/et/IXL92b5lYG9OjHvy1Nz+9E+44De+MijGMoPIb/DkUBQ1kpWZaTlz1Ly9ClQMHglfpwbwWgu3FHt4fb1dnq0noU52c1H9QaQUtUnGoLRd7cS+Pjj6PwTjbrTusPKlRfFkweQxGuW0CRK7j4Cyu//tKL3SeePVO7/8bXu198DR1Njz1eTrAZFEzaYPz9kkfvfunKZzZ+dfev1/+sualvvIsq8W0EPcs5rhNlm4xhMc8wBWBSOdhDEVY9ShY1reEJgSEXWEMh+cFx0A83QQFySwq+zDkrrayeKtuhxShyzErqvmQBS1ZvHwULVoRJJXZQLAi18XFlmLgsfilAM4c3DBAOPfj6NGPzl4XLMtPP6zQhLK7f/jfAzTPY7B7H8ayFezKDFC56toYcCQa5jwYEXkUFe2wSU6oHlfsg4mBpDUQS3AJUJimWq9QhwFcuYoUcjYMcMdogXBz/hiUTfF0DXgK8woT4/dDoWKTwDeFOGOQQ2Eb1xLq39P6nicuX+1/68ZC7rQ+JqbfNMKf2BID/DCB95BzRT/wOW249hv/qlmcBfHJ8X27ZI+coffhB1t4cXn5PdfvJu5eqJ8/WVONXULDJr7Frv3hXDtz1C+GnLvxHr731FwtRg+95fOFnP/mX/+B3f/IDT5THfSFgfHUJwdXjiL4VAPwvscWfedvtaMz/FP7s4S/4//3/3pxyNxZH+lQ/N96rLpJcsbQzpMrbAVYFkhxwJwHFfCSVfVT0EA0FKB+YGQIeAyBW0dAVtkNHxVUa0KTo0yTfpknTh28II1KIUIXlGrAcPPwaHmAKSbaPRZ5D8AqS1EPPuOBemxomZNdiBh4SJDpoWkKVsuQIrQzbppNyMVd/WY48ptfFAh+2BFlH8oYc8ZGZxeXhfeYu/SJzpi8j3XH4yAb5UkrKbB1HcnRNtJWDvJ4JrQysAanKtpyrfckTgzpzKbWRBdbyeRpiGj43MvabNNo6aznf47PH9qxQ2xjaKR7EKWpuGyLxuQ8NDB0n9rV1FAmpEsvEa9QDSimlg3Uuh4s4c1C5TtivO4imc8xeFOg1BeZ3JHYbCTYXLQYTEtdPMcQ1D1GLkE4BA48jrmRwM+DqnYC0ORZflWitA5OrGlO7Kto6xrL9KWILF2p64XlqiOEwP/G8Er2K9WOHI5lg2rgy2Z1n67u+sYOQwjBWdt83YtRQi/E0c0YtfvPxSa6Drlm47WqCrzwok2hBYfmJjNYXCf1pns6tKdcZIV47wZGELibWBeOp9atWoMWQdo9RJtsyXk1NZeMOkHaIwh0j85BR7jnWMaC1s5p15sE354jp0BIcEmBKgTkm9yVpyIx4xNiEb1NBsIJLzoiBWxCgGYNGTAQntYADxbjDjGPdWMHRlnITy0qi/EjmgMg0Us2gWoAjAGgBZkcIVB+p1ZWkEtqowoZMCHJjznnLSQRNYMiIJtjkCz8INVpGjoqNkCWTXmXxRDzR/oJ6deQhE1+WnrfUmeA1F2y10u89LbSDlqHW0I96IiMI5oN41I3rmyBe87QczufuwhZPguhw20spRyjz8ByVw9oInFcPjami+EKKBAfFaDg0zl4vW3VYoqoMlBCKz4zHz/s4KH4s19s9AM+iICbmxsdOAZi0xEeW+NcwJkXetL/bvuPAHopB83MoOiks4OAelBRyCWzK58qKIoFiUy8TSNvcOIOWN3vS4Y43osTdi7cHjnSUIgec85FSag0F0ItRLLgxACGYTFEM5CEOPJiVOW9lZXrmSOVi75nwE0d+JUXB3L3edgF0PvOAHX30SR4CWProk3zt0w+sB/2sa/5q64/XMx4LNm5HRoSWMXCtRd0aJJyjJiRmAEzmOVatRcUSBq7AFRQgcBYHQNfBa5O9Syvv0WFQLMf3p5zopcxKyRACRZj4cAi4goOJWgcQ5UXrBi44qiEalCDZNG5Sg4sWUsTRBG5Gy0JXUiM1sX+YZXSiv4ARVXA7JOtB0A5aCOFgFoplsLTCRkwSJw6HgSbJZhVYcBDasMJCp5MQbheGawQkgcwBUga4fVgBjAiIVLEAnQfQB4eL8FZovRSmfhnAMwaQBjADP8jC4YAZHyEsLARuAPi0d/RBL7z3V7rcm+zt/i4bOsd+4DFRPRpyp9Z96CMHYG54N+u6L9JTlWs4w4Bf/bfn6Ok3GAt45BzF/+4Hz1yeC08ypXNVgX0HfvOrj+EX3/b6KtvSXlxp3yAdjrCV2e4Tg5O/eOz3Lm1c/+mT1wBgt4XWtTs+9Nti+r67f/Rn/82ZP/ndf5F/i/P8/zPpKjiBwtwJP/z6C1u9GW9Oz6vNqtz/z8zmTt/67ykKn3HdwLm7gjYbYm5ogYlJbDcq6MZbuL0FCAegDJAMCDGi48FzRnEiTXXs2BBxfhuuyB7qLEJgFrAr62izLiZ4HxNoYMedwQ3LYM0xXIVEYqqUod0/YW20ItcnTZYJribEJgt46naqgpzqNWdzsEJDtUzWZvY6TlrlOvyt/K8s71TZVHAVy/X/y0pwDBDw8K5V8vKAq8Ek5+DkI7ctHvOK3OYiA5gCsUSZCc+iXc/RxgwbaiUnaI9Oq5cTl8HN1BbyM1fTTjLvRqMplhqTizAXUUUJHinyiCNeuWhjnhhOsa42oTz9mt7N5WOZlmGhIRAxhvV5jdaQY3bTIvcdsNiCQeLGEuHGScLaHQ68iLC7BPQaQMYtWpuE1dstkkBh4RLD/E3g+nHg6fcJzK5zzF3NwbkVqcdEtZ0LCvPoAAAgAElEQVTlMnEGNxfIcYyv+YZgRH2uax5TMamL3yUwqvC2JWFunqRqTVBQ6YtRuy56XpstsQlM14eULb9setdOs972hJ6uZ44YzkpdbTPm98lpbAH9CTK9JrfBhmCxIHb2Ovd3jhPrNq24doZR83mrwy7jW+9g1ktYxkZuPpgguX4bhDWQo4biw6lEjhYEQ0ApDAwyoyCNVFJYlWsOkhaM5bBGcg2AM8nFEAhDWOKMcw1PE4EbAgkwyzFqaQwFWAqBQWZR3faQhE2tKzlS9v+y9+bBlqdnfd/n3X7b2e6+9O1luqdn0cxoRhoNkkZCIAsJEYEWYzAIMAEXkFJFLlSkEicFRpoUssupQIQRpYCrTFnYYTEQiUUYMI6wENqQNNpmn+7p9fbdzz3rb3mX/PE7v753hFbHsRNJb1VX33vOb33vu3yf7/M830eX8XSqkKFiIgu0snjiURwk2honx4XSToukpHCyF9R8gRmZoRRgywKVHohh2BUHbk6k5XeNnzv8dLyHKNy62BrL1dXYje7cvVYOT1ycG/Y6Cy4y28V0+ZKgFRfkuzq3ZwedwR2Tzuh6kveUcnPgK2pwtcrRWl4b/TXQa5J/Go/X8NjMbrw/TWJHM96amLzjcXyzJvzsWE+9FzZVNkqOYuQb5Yqr1HtEUxCgDfwx8FR4xxu/Iaz8FbSvG7A3q2s7Ry14/Fpql2UDZhqauemP4+LJHhDWVVIr02QgPQyUnawVCGFRaz1dMdFmUghZVOXlS/1n7kl0lJ6YO/VSrXQEDHI3OScQ+7FK96gD6j9LPamWZr/fHev4Fh/0a27vPPBdt3Xu/3Rg8zdn33+EOj5Pv+dBP2bG8Hz/yf/Ot3RXu+ACsPOJ/p8/WYTxHvBearfb7WVJO3i81oykYqD0zQQOrRSaAFIQqEHwcZdro8t3nH5v6roeD7htmLpmYWhak5naUPHNZ8cnfAOg+4AvK7rOYRUYJRDSq/3gfYRBAZeJGZYn4Zm5Ut/92dbh2vUovjg3OHHjlN0sl5kAFUIYWjcFnFuRUq0o1mGky1soQwUUVCgkhoAQfbTTqMNTSFkSjEXEO0BCKAXXQhsjYh7Ruzwl6ri2J6kty5PUyTZNQokCSgU/FwkxvzIe7QKLKP4QxRh45vM08eD14YsmV7znQb/1BuR37kF1LHbzC7bvfu7PfOzSwefClcETfmX53ldrxKPRL370ScDzky981rnjN96x++r/+eM/Fu1eeKC/9IL/Yy/lBYPU35T6GbUZzkXf85pueUKE1Y/+DnUm8Jds7/7zX3kvSrz7h1/+E7/75Y7l7lc8yl+++yF1/98N7rHr3zJMTry4Wo/N2GanK8K3BvRZUBWwbMnmR6Q5RNMRyXTEmRh0t2ZEprouUOIkBBcoZBUkK+yLs+KK2g0LaCpaHIoBGzJHuXmuVME7OVZReRjWjOOAjD7PcK76VvHHe72dlcxNvNvq5HZJ3VD7cjmEkCgbKjEVkXtUPNeVXSW36ARw6gE+KPbkks+E1cXgdBCtfRHrQzNxwW+7VTFuL7Hhn8boQ99pbxWqM0JAqfpIWSzHPt7xYX/NIiNZrgzDtfwedZBYd0vytEgjoTp2LA+lCe0yS0/sVm4SV+JQG12KAcJ67/dWZKkD20tOLrZKFVV4HZCkCISwTFoRphCY6jjwU3lFCCr4aG1LqMg6Ag7pFbvLFR94reQTr45I+ill6vDSYQqBQrB/wjFc9ajJGB/aPHO3wpSSKosZdQV2u2LjMU9amXjtkosXrzHyc4MVP+6EiZG7H3mh6D7n46qaxCIyiYxa+xNx2E7W0p4exIHDQRpGnUEkzj1NlGyGg+wTVI8+zy/+xfdrsf5otRfv653rZ8v1/r3Brj0SjClC9an7ZFXOCb36mLUbF3348KtKxouyGi3KMD9SIS5UZk+HMCirUMZ4PXZ4Lc3ijUrKSOnrZxX9k8EXkQmUuSI3mtJJjIBJHpMIKyaI0DIRpgIXFFoYlKrZZ7x3IsZqYyIdJKECaYsQ5QGVGUFVqiKyYiKnzlKJcaHLLAoLm8oFl4XJUqXApcj9Ar2UYWRKGmEYKqpRwJsQq0wiJUg9RVQOL5+UAz+Myv4gUf07+kvRqamPL+u2Ma5fPLom88GCVcrrpZPTZO6yGsaPd/odK4vB+XK1MsJJIaAdtNnUg95n5GA7oEZgehxpss5zpDPaeL3csZ8bkqIp+diIxDeJe8elVb6QykQ4dr1GgLmJfd6i3muy2bEdatIkomb3JkDRmuR3Lg3zl4i3/MaHwzve+I3EjC/Tvm7AHrU18DPUcVOLs88aIHMcgIRj/4sQgiuqXIRAGgLKaNMAnjmtVEY9IEdKSpWZjjeyyJ4ePHZhTiz28mptva10Ccxdn16YK9z04nN637TIUS3Al1GDhYuVK4MQ8oNa6tuzuHWOenD/LDWw+Cx1tvC1wObTgnUH8H2nfoonrm3yjz/81lPvP/sdxf0Lf2t+OT5x7WP9P2vKnbWBJSGptOFQiJssZQIopVlW+qbg8R0cxeg1cYv7IdDGI2duoePZtsdr4zbt88fT8QWiqY87Pvb+zTW3gUhI2jIQSUmJQIFvdXTXHbLbAMYuEGiz8Nj9461uv3hsYp2uWqyhxRy1mzhjHMAzT4oqYy9LyjowXRMTkERISqQsKYxClovgb0Oll5F+DDjKbJdt32Ukc6Qs0BaWzVH28TpwJzUQz2f9vAGsaVjQIfy7t78vDH/6NWIAVJ9X3uwrbu950Jdf/ihYmzv7fZFqC6UiNYFX78ed9wqperdNDy7wBerT/snP/sBvAb81+/WTx7/7iXPfXrwrefL6Yd5fjIb+R7/YPd/9gV8VwFKe3/iEDL2lqJSvevcHfvX3fvhlP/FF3/XUJhviux+6l+9+aP97/4Tyc6fuWXzyRHEvHJ4pEadBnwChIexBtAi6EQdegF5aq+IkszgfNzMcjAOvwfmcRO4w54fhuaIiUhGFWGBP3M0n/a08LkqyZKHaxYVYfk4lbkKLjEIscCG2IepttqL8cjtNrqVtcwJtNflgm5Us+FUzIYtkcOFseVmMfeT7akUOzJy/Zbgd5qLAcOlKtRclPvHEj1x/ndyly96CpJfssGZuCJyO8fgkj5yfzAkvghsPT3nX9bj2ls2EF7fLz5lJnLtMFvTtikzERVfZNOyq+WqxOxRKWK19Sw8Pb0Fk+1S9HWuiQi8ZJ1KZu3ZKpOuQeI01kmtnBN2+Z/VaU3dUAy7U27UIxtab8qBnePR5nsFGm+2NClE6zATKxLOw67lyxiIwtPY1uxuKSMZUHUm6CbuLnoNTgs5+oNCKzedkVDG09y3ipBFVOhGr13Vy5oI8mYytTJLSlnEsN88ZEQ+RWoS2S9jr7nJxbypPUYZVEyb646+Sl6q+WWIFsX9SyJOfLOP1Kyo7uMNUw7aqiruUPv+ZKozbiqfus3G/p5LBGZdffKDPwX5KchhV0cAqq0PaX0/Lp58rkKIUwhb69k9GdvFpg/baLl7xZn8lYKWSIhLsLE6NUCm9nZHt+W759LqQoT0OOG+o0oSkqkN7hYMoBnwwpQ8I6a3xUkcaCDFOQ2mhIjISiKSftooyVxLhiigpjCyMDpQBhElJlcaMI0IFVk9xosT34iDjQ1GKksqnaOeRyRTvFn28eb407nGz381CxJVkYgsd5q1keiPhUC+ryQ2ztqymaumWopV5nYeDaN+eGceDQVJNB0llrRjvRH54LhjZQRlDvQ/cAiTgo8QL4RCqqpPQGk8NHIUzbVOvyWOOGL1GgqWJFYWjfSDw7P2i8RoFaiJmwlFIzDb1ftGnXlubxI3Ts+c07Ul1tjfO76GuPPSN9mXa1wXYC2zOA79MDa7gKJ7s8y2O4yxfEYK3+/mWD17KtunpSTUcahdNWnF7gVoKw1MDjA2gk7uxmtihf97KCz/4+JXt05OovLudpBoIJ9KzIdRBpop6ctxeuGLF2SquKG5sT688upKctL1kqU/tLtygBqhj6g35GeBGA/Tql1ifvv7nf2x//v4PffvyuN8Kwl1eSTYen537R84yCY7XIPDAQZljPJxSktREdXyGq0itR6gaEDYs2y6zbFBXEHuotKOU0U1dwQYg5xzpDDZ91/RvM4mbjCy4qWCCpV4wbnAs29ko9lCsUk/mwkTJONKxOix2G2tRU4MX7yLMQcd+Ezb0yFikKT1mg8ezgEU7i1QiQIFF4OrUECGIg8BhaRGmd1KQ0EYjxrPc4dGUwF8RWgOMKriioa8g9nCXrEuvDagzZedmf6s/p3bjPjx7zjHA298XviKw9v+0KaEe7rUXv0/UNstaWgx+ZwD/3MNPf2Hdky/R/uhPwub/krxy+SP27D2/7ip+6R990dsCCz6YBQVgrYWbtTK/WKuAk1j76kJIXxi9WMRuGextIBbqazoPxRjEIuhm7FRgNFQCxmW92ToFFGBF7faV1PrBRjpyPEGPWBCHtEWXveCVEdfCLf66Xpfn1WfEAocMyaXCSYsU7+Xvz4VskqeMpGOZG9yqOgxiw3A6Yo405NHCaOD/7uNX5HDtivvA0r3mr8y3+pdVT4gb+4nfObUpzonHk0zgTxWHYl31uSSnPvZjqfDeVokYjL2UoVBJ9yBU0hZ9e0bEaV9igg5qzGpvEEorlHnmVhdHpR+1lys3nJdXWyf1NLs6vWVXiCqxakM/Iyx9O2gtqxCVfi7KRWrxVEh7cAKNgLPXAvHYMpgfsXRjjpGPkCGgCalEBSiERBHQOAyHczmFKBBOU6SSjnScvKhYfUYwaVd88kHF6qYgnTrah4pRxzPuScq2pDCA8SSFRE8sBwvQmShWr1St7KDjxwuqkEZGizteGSdinYphuksxf8XowgRRJOTzl8X25XPhzr9+cej274pdrkPHdHLalfFRX7Se+lYz98D7pZsbq/JGN9i9E15PlzWHXa8OTml5eMrzRGmtlPOj9mE0N1yWZtILwrVyWUY+PthQQQ9Etbjni9z48qk7EEU2xQcjJp3KHCxXNuAiMs1cP0DLVAcojzCCoAVxpRGmdj3GGpyFvASBoZKeymvdAXIBiWcG2kBGCiVT1hXgTVRdC9Zt+M0zmTBuKAQVSKmRsSaUYKcguoHQgVBig1L4kTAqA50q/BTKwwi5tetF76IIeh3RnSeRE79fjHUVfef4zKkntvYuqSTeeWDQi0cqiKeTzfJEEgbr/dbeJTWQTtI6M05bkHYPme7nIVhKPyTIDM3ivCnd/bTUxWkkLqTTJhnP4MgIJLMV/QRHIshNckXD+jWGfKOs0KzfzTrQkAWNZmtzfBOvt0S9t36MmpxZo96Tbqo87M63kt25bAP4JvGW3/irb7hzv3RTb3vb2/5LP8P/q+2YtMqrOBqYx9PBG5dlCTNX4Ew6JBCue+dNbNIML/xWednZYG03mpPU0iUfpQYwC8B7QmA7Vun1VtSZ6ybp2TQyp3S9AiRaRpGR0RIgJ9VwaWonvUnZv1j4XOBFIZXa6UTzTkvTCBRvzK69A/wF8NeC9b9RHuYf/PKvjW978NLOnYvnz5/Ibp373lNvufan2//qEjCVksIHzmtFSeBEPsbtXuxtWhcGWccZIbCVJQuBDNhVmgH1BFuYXb6LYxgCl6ViR6hnCQM3rsvjpZmOT/Tjyul83nmWmrZvRJhvzN53mSM9vngaRmbk+iPqzOYmeLikdn8vAC280GhKpHDAM34q1gtE5iMkRljlKag4QHJAzD5CtHBoPIQYTYogRqEQM9sx0EbZPkW2x6O6JK7a7HvBFMd5VT//2dmzjIE/Bf534BNvf1/of9sPvu3w237wbf95hT1fvPFh9dEb93rh74BAjBQteEDCj/CRa3/AR671efHGV3y5X5Q/e/jAu/2V7/5f/1n26Uc+dPpHPv7b4Yfue/Wz3CT3nXmB/9Tljx9qaZZlsKdFEv0dpLp835kXfNF3/6kO418eDZWWgxc8siyfv7fk7yYxYkasd2ZkuqwTMGINcgb2bKgLmzQMqYzqwiYEcDEI1eOGSOlT0haegKMjwCPw4m71ObHEbvi3/tX+tLwiuozFU9zOkHm1wI1qid1wmfPaoZnjUEDpYnI5xzUBUaZIonvdJ22WW7pyoqP5bbHSuqLGImMrLPhRrGSotDqpPxGMHlWy2w/XWh3RdwtyVW2RchDK0A1PqNvUKEqCjnNwsSqFJKqmtuerqGuqUEnstMJHwmP0CFMRSRkijxLL4lqZt3xUkYrFSSSuJcvVp+RzVKYPSOVUVKNFcg+5QaK9MPFEkuWBbKTRuWCn7XGJJBTYiQxiZ12ISilkDlJ7Rh3H0w8kbJ1VPP/90D4MFJllOC/95mk4XA3CJYpQVbjIk+Ual0CeQCsHZKB3PWdts0SqCDMqWd50dAc+/8vv8CFvRd6roHr9dGomwc7tBLF9FvvZF6rphdvDjq1EUszT3T03lcN58vZeceL8M1WXVB2KyeTitBBzoiMVUvn9FRdPVio1XBDTkSqVcNK4ROCkLNgtxnlPuamq4sNTSvSXhZ4mA6EKK8496ewoLSaTKBbCpGFnXQRnqmz71MS5lnAkSiFasjU1FIkMhz3vyW1ER0WoGdBLC6gEZq82odFBkEQS3RAIBqycLYMSRHwU+ZJrJXykVUickhLhiYhiQxxBNIYwc39K7dGJwx8qRaVlEK1CG6llhLX46SSaV5lqyUT60ooJRatLbC4ko2KoXVQI551w+yen2XA+F90n7Kb9zPygdWLpmtZq+qmPCL11dtLWpybJmU098j3ii+NQFp5SrhTGeg86CTtWenexzH1w1qP1kLrqSkYgQuAQN2uie2ombkS9ZzX7bBOr13iCjmfqNnvFlHrPafRI89lxFfU+eCtHjOECtcs3BmSQohekPDf7fu+hf/vZ62/7jufeXH9Ovkqon/rh/8xr8f+H29cDs/cK4E3UVschtZXw+coaDQuVUrM2E2BfCrnWSeYyIBQht8vRRgR+l1ozb2V2/DL1gJ1PTXYX9aDda2XpPrXcyjI1A3Qwu89GCIjKV2UnWbpPeDGUQlw1etVTs2odak2hana+AH4IGAc2f0+w/qyA+fD7/87/5uX/gVuz57/iRHpLeaJ1yx+/50E/fcOH5AT4WBrre721LyksiY6J55fHkW7ZYVGwAsxJRZACqzSLs3seUk/GZaAtYwpZT8CcI9mW48CtidVrZFUaNq8BgMz6p6Hho9nx3dnPhtqF3Jt9HnOUlVvRCBJ7PHX4+Pmb5xnGGEYgyjxnvqi4VUsyKZEo4Y0GtFAkDGhYJRcEgZIJYmafBhQCiWfuZgC7rc6wV2ySSUWWn0S1niJ4KCvYM7Ubdxf434APvP194W9kv/4XaP84p3y+QpwCQ3CKrX6xEkXqo6sd8w5+8aNv5ydf+BVl177nQR/4FJZ/80vsnPrmn/2Z9srf/uDjVzZfesepW48f98Mv+wkL/OS7P/CrbwHkD7/sJ77k9e/cGayVqDdD+3l0w0pdgtgDfgo+rzdGFCQG1JR6PrRAzgqpRzPArwS4KfQFGAtOTIiFJ1Ir7ODxbBMQBLr0/bZfFDfCGmNO6l2uhTkOpMKyziYKZw5ZcOs8We6yOj4rH8m8vze6wZqsmJeHLKkBWchDIhba19wj87F/3r4Id4yftFnnQG8P73ZDlkPf9cSn09cKp/5CLdiRcAh/Tl5kWvXkZ/0qSeKqgUjDnfqS7IiBM4MTJg1BiKVpJLykcMjr5ZpH2hC1d6U6XMAVyoelbbvEWBRVkCbesTtZR6l4aidX7zB3pIWIut7HvYrQ3a0Uwe2FbjaaxuIWtSsweSDJIyyS3mjM1kblxsutKp5IGVc6NlYyWFB2smDVqKOEmgbGLUeRCKZdwSMv0M5MVbl3QpnWTqlNWZHPj5hogzUKYQNxpYnGUGm4eo8itGB+a8CVOxwqSO7+KDouRtXemmL5sri+2aJQyN7iSBoXB9ceBF1EbuhK1771UZLJHdr0Lvj24g0jy7lMzO9Ucz2pjVzX6vqSd6M1R38B7b3zIVaQphM3qkTrGe/9mlLtEM/vL1QqiNwTa03wECdUFQQlxcYz2/Ly2Tk9PCkYZcrlPsDSnEKWoI0DofZWPOgQaokUG8DUQCwVQBusp9cPHC5JqlCCCbNw5VDLiZS+DkeQQGTBzJbQAPgUnI/RLhBpiRR1khGdOiTBZIB3TCmougGFED4Pia6mWBlpNezI+cRIPVdQiltDJ95iai4xspvpwHWCnm4m5TgINQzI0ZKM99fFwtL5oizWQyeIqr12W5/RmUqvvHi82ou8G/3VwmFMWqYLssi7JGOEWNp2pn1gOUDYNoIB9Rq+gpwlaMib3puGya+oPTLHw3bGgACf1EacaJL6mvMa/LE8+7/xtG1Ty0qNgNs5CknqcLTfNAkc68BLqAGjEW/5jb8M73ijXfg7L1ql++C55dc/5xM77330G4wfX+Ngb6al9xJqYKE5Uuk+7m5sUsGbz3PqgdOUbxkDcRwleUwynl3j5Oy7DjVIacqTNWAlcDQwm1q1F6hdf63MtEJqWokUUqLozZ7hXmrwWHLEZI2pB3WbekI093pWe8Xq95ap6O4lOlsENt7wIXkZuPKL9/17/UfXfu3p91/707v2B1s7oozn00gseGFDMUFIRR6nYGLc7Dn17D16s3dotPkaC+zSrH+eO3vWJpOqCc5tFEsaq635F83+35qdd0ANHC21Fbh87HvPUdxGAwoHlBwC87MlpsnmVUDsPc46EuswIkDLINE3n2l47HoF1cyV3KNAItie2adt5AzyC0oqEj6+d54iK1iNJMZl7NkUH+2zYWoQ/ivAb/+nkCd56F3vlLM+Pwd836yff+Wtb3rz3xBI/hLt09eY/vQKyT8xiOU8n0bX+yXdVpxEHfPK3zm39OhvPzJ++LH51rUr61+hyvz3/oP96OGLr5xMd0TXrK98scNmcXpfyn0LwIK+fnJiT94SiNIaN4cYdFQjBWHrqnRBzf60ijpIfKZxnQrqMTrbTKZS4mOPKUGIiiUD+D4qCA51j23rSClJ1VjMuSvhrOpwEBwLbFPYjIE2BDTWbbPqDzhpFLa96jdLO15RA9Epetn14LFRQsK2meMZTuk7+YzUSzfk9TAXLvpT/lr3vNFh4NH7RHJEb9y37ccf0PfMbwqWB+I/JPf5TXFOrImL7k79qFrMB3YQL6rO8qCKGPtChnjTdX3H564ttqQSSFW2EVfvET6ZiO2ltuuqHZXLtSwSO/66Py0zeYE7ksecynalzfZDV+NEEsrCEi1N9oKxHUGBJ0XjCUxx9OcU7R0lk1ipoKS+dHcg3RmV22fNqH9KJNKKbG7fM7/puPT8iFI5Lt6pRdlCylxIRUxn6DF5F5NIpJOM5gO06rSkKHfM7Qh8gMn8mIt3dXj8fs1w+cDc8fBQXj4V37Cu47Zv13Ii3KOXDkJrkDEu94Mbd1V7uFE953TQMbHWqzd8e3GzLJ9csPnF59D3O2lLSStvf1KKK0bYKmM6mM8y1CQwKkNy6H1JEL7fDvGuNj1X+elyorwNBiOo2hqvSp65w4Sznz5pFveVOhR+OtUjJeNIexDElSdEmhBq1jgojSw8yht8CaqRmfIQO3ZPjEC1Si+KvBxUKpmqlpaBVi7xTjFcnSWnuXKHUqRI1UYo0DFILxBCYMQUG6AgJW4MZJlTpgLhIoxShNJjdEGItmQeNnzazmQcV7hyn6ktKYiQWFGqG6rUF1r7k3aVFt88XvEyhOhFowW9b2K3Fw4vdYdr4sK4t/7N16r+maTX77fHB0Vrur/a7R+m2WFrMEmvPpOMTlmUIGT7y1NxcarixVHEdepYOY2iISWa+dnsDU2GbBOWMyuT6VrgAqic0sd1rSFz3LXbJIFMqPfdIXXt20azdMTRXsPsviX1ntLsKxE1YfCXs2syXvyvesJNz7po+XE+T7Lq67V9TYO937zy83z7yt97cSrbOjUZHIk8NkDheKaopM6MtNSb7iwmiPjY8T3qgXlidnwzkBy13tqIGsgsV9b2vHNtIaWJjGkEjFMAIaQVR6np6ex+jqPkCEkdC/anwBPUZdwWgZcHNv/48925y/HJHeAXru8fLL7vxr/YpKa+L5/JXp6PJv/8Awq9fP2x5SU3jtort+7qbkxbaaw2aAVUJV4pnFQU1BM45miCNEAwAd7fZv7EiIPncgTyGg29wFEQuKZeEJj1W5Ot26Oe1A0Vf7xSiaMGyDeorbWm3zuAQTGto5KYUAPSxjKMpGROGUIqQdZ2p0cACUPqhagG0UXIZpKyJZmoYzaXg6QizCxVEHg0O6bLvruDZa4Ts0cxPcGuHvJJXWLDiPeLeqx8Se3lL9ceetc7F6grt7wGeMPnXe9lD73rnT/w1je9+SsDkz/5wnAn/JvqFz903VT2rVbaW1dOmuRqmtjfum3p4vvPrL76aiteBt5PXZP3K2pZZ+6X2sz9NM4VVz8a9MkXiq8CgIrW705+UL/l8F9F+HBayVNnA2YyswcURLOfEznz3iczYtiBMXAzMPx4BuBsfljpSSToUG8mQgBl7nuB0NZTUQjkKCionvZ36oAOJdYL5kcVlVL4jmYvDFgSBq+jkHMgUrXPcpKXPVGo5dhzAwNuQEvMcd0kFMxNBF4s+huZrhxGnkg/HXI6Ki5zhPT0w2lj4xbtpR2KVivIPA3zB+0QiROxiAt5MF+xxbw4K55SBcH1/Xrlpba9qC+ul2syYyw6rbH15z8pc2XEnjirfTEZd5OrSU+X9iXir1WSjap2twzj0JWpSrTQeQBUrFGxqQKtfYFGMAECgWEmuHxeTBd3ZUgKn12fr7h2UpDNCbd30su9U07c8WFDx8Mj9/jqyqmc/RVMuaQlmti3wPeh3xI+QfuiJZSaBkEia9JLOVo7BSHWRPmY9NCQtGIXjU21crESJ570zoyjRCpYnHozb0X5+O1STdr2sXxbxHc32MMAACAASURBVNklTpi2mW6uie71c9YfrsAzz5exzLlaHYidkQ23Vbep4drVMuT7zlXLqqIMhmAnui9M2cmEX9QRB1ZpUxZrB5prncDCDkFPAlu3TovRSuaHC1LsrIu4PXRME5FOFmMRzABUUjOAzoGcsc2VU7RThQGmJYRGfivMlu0MrA3SD0DMy6hIplmFmXaUFiavlxyrQUvJRJRYcnSQ4AxSCgQOGerYvmdVGaKOaxAyw4gplbKEKIHS+KBUIEaIaoKdpEHG1kk50ko6oYvz1aKyud5fdInrlMbsqdHt0gZ5slWZb+8eRuaxW1wri1prmZms6zndDoNsv2PmvmVxuPIhPRo+mndODaxZRQqD5XDOx1srpVrfduNX7GQ4FFNKm+OsQmuJ0Z5AMVu1Mo50ane4GdMtHEhD8AbvAgiFuWmsN4kbjfE5T71/NvvCidl1DTXbt8SRd6gpxzmhJkVOU8uyfFi85TcysjueoSZNjsvDfF23r2mw91tXf4FvXvjbk0g3CUI34/QagNGwUFBPtglHlkajIdcwdc1njcvykHow9TiKPUupB2zqvY9L60slw5XImFVqy8dQA6TjJcnEsfvvAXulL5WzZZxG7W+hZvxeBPw1NcjZBv7D8fcUrAfxulc+DdBbG5iX/ygjwIrXvVI99EOvXTnd6t54JvnItk8qRv39nmljWvPESVRn51UVzjmk1khtGFJPniJ4OggyIfgMtc4RIw42OBK+nNH0RNSgzHJUU7ix7japLbTG7Z1xtIkfr4/YZNsedwW72bEZ5qbuU/P3aAB4CbTSaNaHHkeJx2Dx5JTks1ScFiUxGkkiGkkAQyo8SbAMZkkmKWVc8lRrn5f1O9iixVPzH0WkO7xABT4m4GFRL06zhIIvz2gdbw+9653N2Hsj8HPUIL4JSm6ap2ZP3/DQu975B29905u/Yq0785MPfpB/+mc/XI6uv+hzt9/1kvedPfmap7uZvNBJfjco9Wlq6/krbtljH3rYnXguorPU2mpdfvQkZ277Ss+9ZM+d+FD+Lafx/vbFSX5nHrnOOIoHNXOSetAzPS5Z1YAviDo+T8FRuEDDAEyZVV8B7FF8k4ghqfvHEy/uF2ESFX6aRQK5IJ0rfUv0w1iqEEikZ9xKq0oP3TzTpBAlyseurCiKyCXz4tPyJTKfwybimrzOEhHomIldKAcutod+4Bf0E/a5cpKOpCM153jEKtcPnf1U2MWEp8ySXbxjVy8bJdKiJRZutMPZ1qfkGpvlTv9WdxifVovJUzYRRSxBjULpIh2kEtJ35RY6KH/DJiJKS2SYhrv8x9GyZt2NRnTlfgGUTqr29s5ZWmIc1pefquePheAQUwjBIsIEryQ2NdOIpesuXLjbhUt3qqBLLRa3yb2R4WC+SrYWVbL97YLznwucui7tlXsUhx0V8BiGCDWFMxdhd03YwSKOTAjXEaoGNLC4Gdz6TttePx/iyXzpxj1V9YYmdLZDWaguO4tzrlA6G26IotBBSCnU4XzoXHgg3J/s0Dt1TRTjlpjmYy/mtkV8cCKocRsvcG038tnKFaLBuDXePDnuXb9PiHLOeJWrzJmesie9b20JVj4sqkv3erV7i5UI5yY95cuWqdp7KDM1nrGEVLJ5u5PJgRciUoRUVlStkhGSXlbH4xYlOEc6lkxbM2kf2eIoE3XmcdAROBsT9eJEq7wULpTEDm01ZhazrBVgFskoqyL3LpQ+CSqHVKJcjHAS7eWR61MAOiZqxn5ZW7TWWbw8RUcbJ73TbuuiGcz5Kk/OWK1DbKrYUHzP3lr8cJxSKdcOTkYuzhbXaPv2YeKi/VXZHpzMbz/I8hLRvtwukuA79p5Boe/P1tO0WPVnRu3WByHflOVkM+b0Vnu4sTEyh5nXfVXZqFeqbF/YQwItfDDSE05PU3k5mVZeMaHeE9c5SrxYApmBFAgEIpQIMaLeAwYc7R+KnC0ChpQ14CyEmRdJrB31+bPKcBqOkv12qb1DL6T2uhXAbnjHG79grfmv1/Y1Dfbe86APlb/8c0qoVepiyp+ffXu8lFBJDdSm1KDLU2/EQ46kQhpXUgMCu9QDrylhlcyu5eMoahOKsWxKztSgZTy7bgOU2rPvGhXxIfCB/XyrNbb9zoY4v5GYtJzd9zmzZ+0GNq8L1p9qXiKwmfrf/3UnX/f3Ng9vdN17HvQ5gOCV4mOPX32q8ur62ZN3rOqF7Rc+Pri87B1jIAklTihCCMjgkNUs2FYbcu8Y5AV96Wkl7ZsxE2epQUgTM7E1e+cm+l8f+3xt9s4x8IfUKf0vgZtu66bvm818cqxfG6C9xZEL/iz1ArHHUXwlhJDisCgEQoDEEweNwzGigyAFMUHiaM0Yv6PF4ogxChSUbJIxV8xz0nn2vKLyPXy5RJJtM1BwbYHUzpHYCxx84KvJtn3oXe+MgLuBV8+e/7+mHl/Hk4WaRf4R6gXsPPXCePiV3geAf/iqGz147+C9F/74/RsLf1VodSoodQXYurJ+BE7/0ff2zmSnbzcbb/jxC18s3u7Mu/7ZzsUffXPp7ntp5CJ74at5jNfv/pXIQywIQXnh+rnxd4GbBy3BJByp5M+MHkEd/3RzTDTsbcN8NMcr6M2YdzmTGaqcCEpnVRBOZEwlDnIx15eqp52f9oL2xMGRGqOqcOAWg0VQkKqR6KmO2Le5T6sL8pyxQihFhZsmoqd3xMn4qlI7K+Win8oPnuiGOeXFCa6IisoV6DBnxnZx8SnljHRPc6d7zM/HZ4o5f7t+mnOrf1Flc9t65Nohe3LFtscFB92lMMBbKSbeWlNuczKOq35cocKBnxexnEjBwOchFYe0xLzqRx2RE9fp1toFhMOFjeiSw1VRkdf6eipAafFTg6wK5ePhfJCFYnpiy/vuloyf+J4gD85JMbcZwt4tImw8EotbH648Wk/3T0j9yIPWtEZ1f3f71g0i5TAC10IMMpLBYtCkQlLOQrfqKVhVxuejhDBNg1Ad5Z00YWnblXKaTwctmVS6E2/eqvJsv9q6um4761thuDuvk701cWevVxWP3KpLG1eKIEVAB4wEIUKZifmqI4tqMpJz+y6+7fFOMjqTq+0lj+t5UKpFKQuXEfJFL3ziyCONj4ILMtLFvKSI3ATdKihChLCSKEILHaYyQKQMsQlUVkCAvEJMBCGVTHuzhIyKetylM+movAAfzQjmxjNTKWLn8cQEPUs2FcfGsYxMbLxyw4IyBaqAl4CI0U3CWrMXNy5OAJ2gPChV4p2nqtA+SGjfWnUSS0YUSRJX+hOhLRZDUmxHO+lYlEvfNT41zPNqZx4ReWQxunprWsVK3z1ZCdeZrF+002pHTsd3PbM2ifY79rnJdHzD5GuJ6Rc9FfdLirap4tajWRnQVLFFJFYJIp3iRUCrK8KTTFQ154+8YI2cVqDeL6Jj7zIljpp1XcyOb7Jr20xZRBGREs1s+ZPADqimVr2bHd+brQGN161FXfIU6r3iFcCHqRnCb4C9Y02E/zgJsP9ftcBmAvw48D9xFEt3vDnqzTWlpqA/NvvsxYflXumdq+aSpUUhpGaW5OGD81KoWcDuTVXxptRaM9gnHGnWNZZbTO2qfHp2zElq4DeiHsB+d3r98Inhp55az04/fz25ZS/RrbF1TgH3aaU08E+Bfy1Y94FNRe22HQnWr3+h9xeve6W++85sY/nux27vlzf+ey3c/sYZuVGWk1Ypw5lWmsWVneJCUEFSSYH1gUpKxiGwl6Y8Tc3Mrc366KnZczfCyc27GWpNQA88b9YXjct5a3bOAkdl1prM3ZJnV93wzjKtcjZVRGaim5O6xZHruK7MkQdDQKCxGOFxwVIRY7FUSAwKMWNkYzFBPwu4BwAxDCEeUeVdDmiJWiTZB0dOYS5xtXWJx5c/xXvdEsNv2l7oPH904sk7//1nPvuF+vp4e+C//TH92nuet0C9AL0G+LbZO+xzNA6TzzttF/gzamDbBT4NvOutb3rzf1TcyalNetRW74Ur60ei1//j60TPGf6Jn/I9Dh5pwSu+YPzhd75aTrP26/e/83s3N37k+z/8Re6hqUsI7l9Z54nZZ5LaMPg+amt/H9y3gVrn2aWR4Ijhhb9Z+aFZ1I8LujZUvSf4WtJB1N0jvAtSDoRjYQpey6nwBBX5LATwss2uW2BLXeZ8gGIm6aKEZqecY2c0Yb6zxK66GlaDt119m36Eeb2LtN4nVVX2o3akqGSC4zobrHGlWhZ7ph12y3lGZkVeCloZqdzELya7tOi7SBR+Yucjv78udHboz3Y/NS6R+qPVy9S98qPyUrhLz8ttTBj4ke/IrsmdCWN3pTxtUlWJW8yV0Na7NtNIRR01lQdc7ClFIMEjhINE1Rz2DeZ8Oc58d1faOKqkW9iRMk9U8qGXB/n4N4Gq5PTgNIedQx+vP5pXu3fE7top1fvsAz6UHuZvYMzEu+F5zSTyeC9Z2Sa9dpYmj6qYFceJKXHpPuXKNcLOcpFOFgvb2sPd9bEqeD0an/7kwjC7bMzF+3X35LZ4un25Wl7Ky+yj3xLFJDpsPFWFz77I+2FH6uly0BNT6rylp/GAarDSioiCpToYP+/35yaLB2rl0l3D7tPfaglY0J263g0WVEQ2rcgT6X3li6hAlB2fqMPx2OmkFD6KQq/MfeX33TRdMqlLSCQIaVyVWy9UrJURopJ1FngUZktbnWGrCljct2wvWBAJGAW2rAFhe8bkOQWVrLNwZailgpJmjDeSW6mjdAppjm1DjceoWU9VHcqAn8kPKSC35NJSGVAY0mjCNK8IMseGi2KYr4bMb4nJ/ufSQXzvZEFvkNmE4vBwcet6UoUX0hlkdvPk9IbXo1x6eRBVXsji0r1ubnErPhj9i7lrS+9L9tM5GbmWk2oiUU92JpQxmooSwQBNQr12eSqmWGIkLQwFAou4SXhImtJrRVnh/R5JLBFivu6rWti/Xhc4xHFIIEbjIHTAbwAJqAboNUkfjXHcxIMfV34YUBcgeJg6Rv6Pwzve+NUZyl/D7Wua2WuaYD0PbP4a9Ub7A9QsERxZHY3kiqIGEXcCnwFuWF8WYzfo91gqRc38zY2qQzGxIz0fL6dGRg3IaTaiATMJkbKqjHPOaGOcUSrhKJh1SL1qjmb37XEUryA70fzi7Z37KovtKWmWgPJgOLqI4HC51yuog1FvBZ4UrLvA5lXAvuFDUsyu1SQnrANXw+/7Arh03y9vdKbDhX6oos/dUr7s4euTTzwwjZ5euef5HV1prQ+GA+88RBHSeSIZgVZMgftplM4rFFMSMuxs9OxQu2mfogavDdu1PXs3Mevvxqobz75rrK67Zr9PqBMfzgDGeZ70sCoD8xyBaQ03s4ZrNlAQUSIaR7DJha8UFXIWn1kGRyBKS1GkRtn9BeeRaI4AhQyGsjQUpPSAGBsUFRaHrGJa03WWtxfYWdhicnj5wF1/eHj5118j2kD+N6pizNqPv/2h2870er+wdXCwtjo/38SeNMC/0QUsOYo9aVjnLjUDuj075wTw8EPveucn3/qmN3/VC9eVdQ75Asxgsciy3+e1EXSkFi8KVu9Rj8Fntz/6E5/C//llhFva1H+38tTmTdC/SJ1JtwIsgI2gTCBTHMn2wNG8aVwyDRAXxz6HI8ag0fITBASDISgV6v0WEaS3CmEc3kAWfNpcq3BgvQUxog1BA7GsNSinYp0tpZDzh8jQoq/uFNNw1az5jujLoeuEqWgTIvQ8u2FIWu4yh8foBfZ0EWKryMISO8EKRUsO7UEVW8plUygjKieDV5Jzq5/Do+UUGW3bE9WK2BKZnoq2PWS/7AUlWzIXPWS1JbtY35Lj6t7okcjo4LVChIBygeBsPV5qfDcrQS8JBAQCP5GtUEaxWhjpolyYcu3wfnX6sPDy7KOBT71c0NnzKt0J/vJtrtpumUzHoqSo/MKOQeaohU1U0bbRpvTTzr5ksIC6djvHbTRfk60BlFDW+/TqeY/LHPiRH89tFZ96SRKfe3ytY2Ki8oz1MnNJ3o2jxWnkL58mtZkwgxVbea3DtIvZ3yASArm4WU73NpRTQpdJvzKTW6SRA9N58mWD9ALtVmqTIsh9i7UxNmicvUko9Q40Yk7IQkuRTWwoEz117UyvPS3kXF/z2N0WoUxkhAwIW0d3KGk9qXdFCCquhNDUmaNa10DOz0BfVWKsR8ceG2Z7hZrpjZYGIjzYgtJrTGIgQFFVIWhXCqmUcEbH8RSHwKFwfrb9NvNfAvjgXAgOJV0FogJnwAiQpSbpaFBQp5Q4Qpxj7b4o7eU4R+fSnAhmuahaI0GYLBItKlJv9+TnTLx33067ilCFW/SdhaGvprfm3WubyaCi0N3VYr3f79240ZbmubdP2nvn857918sX41KxpSw9XdIREpUHRsphvCB0LMI5zDjCUpAj6aCRswS6vJ7npcQ5Kyo/CVEoUSKhNuAiaoOvC+yjbiYkjuv3VoF6/2pzVHbz80X8m9aUHu1ShzxV1J6kTfGW3/hgeMcbv5pEt6/Z9nUB9gAE66PA5s9Tx5D9N9QApK7IUA+iFWbWgw/BX93ZuSOJonypt3pjPl7tWG8pbT7SMjqhhE6EEFYK2bjE3OxfQg22DKC9c6X3HldVkVGqcVU21kjDVO1QM2bd2bnDWKU+TtMTwEVqdiROtD6NFJ6aFbwNOBvYvAhIwfpk9hwpdRZx/2WLr5987ODPfO4nN6nbraeXr04H0a+sttcf+ab77/pbvWGR/elnD/SV6zeen3TL3nSopMDnSgcxPtTtJLGq12beWTQBJRRaOjSKDpbdWq2OG9RxV13wsfdI79n2niiK6HLEbjbp9U284yWOEi0ktTW2M+uTM1FEohWxVM9y5TUWXQP8FIYhjgRFSo7tTHH7GRpB0AVV2wo3SMM0Txm7yCUz6HA8drMkESOfcEi9sBgqNNUMmC7yeLHMp4qMwb/85vDpP8tE9JcvrqSy3LqyxfDPMnH1VZNnM2I/8853rCdK/0OEfGkVfAghDIUQcGSNNj8379O4KZvxtDh7lkZ36ieA33/oXe/8vbe+6c3/SYSasy0u9Nv8kHD8gZGySbL5qpv4wL+UCCUWVl76p1nn7CHwfOqx3NSR/kPg78P0pRDmIDeQNMlIcPR3PQ7oG4mG4+LcjqMSfnWMXo5CKIFSAtIZUEyrkszM4qXE0bmxJniqYPxIdF22b2RQsZ7OWQujsMOy8qSicnM8qrpugT05IRVPhOeX8+zpQ1IfUYicVKWUImPkzvBJd56H5ad5uS5Qxe3hUdsSZXa52BCHzOk1uyV3bSc82PpEPPbKR2HoV82u2bO9KFOlPq8+MdECeupQ6DDwRrjos+4UC3KTJT3Ui+z5VAYbG1woqMqcTMVIK0AEgqqj36k85D4Wc64IlcKvhk1tnQgqWRciG4n21iq+VIpqDl8kdjpcd9Vowcd3fA51kNmibCVxlQh19/9VsbKr02G74uPfolm+JvVgGYcnuom/h6AKhO9C1heMe1AtAZGYuT13NL2PJCp/AEEZDxamfX1QHA7N+slzm35lPvfbV7RYJJF61EOXiWLrREXeERTK+5VLJnQPENfWXM8bhyQuVGl0q5Dz+ysSNZVFPM5sEYWaQvPSop1AFP83e+8ZbFl2Xoetb6cTbn45dpzunp7BRORQCINEkDLMIBEFU3KglSBBNORylWhLZRBlWaYk24RZkKASYZYoBpA0SIGZIEAShAAMMJjBzGBiz3QOL7+b70k7+ce5p+8bkLQA0kWWONpVt7rfDefec84O317fWuuT2+saUCIH475v83TlAtjBqlD7y54MaVLexmKQR8lcjSAZIDgc4AX3oQgNIwDQBlAOELKskO15Cdopj1tnJOShBDkH30L5mgdA1VjmUxuV6bVizhsQvLceZEplBzcexKbCDK5NIQA470GA5866MYAahQRW7khEabSsMgCN6diRAHwA5jw41n3NqIynXTF23CBa03G3gQAMVHfweQH2jpGpW7d1bNDAHK5TEu6JTBXOj7eYEU80D4vjeVz7rt56cayo9c9Mwu0XapNVBmc7Kdy8DfVVlSVeA6dzFQHIe7wIDEN9In0OkAssUu3QcGUuW4GhDuQGcBohOS/DATg7nP72FZTAxh7K9cpPn3t2+v+7Ua6l1bhvYlYI4Wg7avMVTeeGCOWaeRUlaPMYykDwZd9eNsEeABBWtcf2b6EMNv4zAN+DGU+sWkx4YfPGuJhIx8wNRu2NSTFeHxQHiwEFlgvpoqBWRKJWHrI69FR+5b0rPKAYMSaVArROlFKVLUmFYiyg7OwGZZBpMCtjVnG0OtP3rQJIGvWaQYmgvQKl0rUKgg49tl/4nofXh29d+MuvaIiOXt65f3R37f7X/fdn/+VFAEtTjp/b+T+f6NN73/H5wSd+xe6OnvrdA3Pnje5kMH7sGedbq901a4KOCvM0qCVueKN2asCdNBt5HLcLSwwKBjxQcIwjg8SVIsd8kRBqLf8oMXfapVgqCE14rANQRYFUKRQoFcXzKBfwLZTQfX/6r5qe1y7KQPzS9NrMM37b36mNagc4mwQAwIMRIYaA9gDz1I1JogYKRjAdHeIgzMkJFAh8u1AIQHS0qIRGOeHsA7AB6nFbdvJdfwMgX6Cgx2r5nZ+YLD0/RBmc4p2JLz7/nUQnruBwbQtLKBHKyUc+/jEa1S90bl0K37VWX/jRmlKtY+15GwqRE1EyvZ8V0bhCv45y0ypkuQpwmigDvgC4Xev37R/5+Me+ML0m/sMf+OCfmIMxTdn+AYDmP/xO+lXA/uM/4aEkvF1O9j+3Gzf+RmXHsIIZknsOwHlANaf+23kpxCBghmpUqviKzwPMgr/Ku6u6NpWSLwSHg6u7aVLXoZzUB4CYx4xbS0Dh4S2QWGt5zKyEq7ORqwcX2RYWfYCumGCZkIYQlNiAH7qiZAmwBIo1YPJ5HAQjxIhRYAnXiWDkHG3pOjlzOr1GAxHJ7cO38UbtER83Upr4gM3zfaqzHvWK2NfZkCbU5k/p0/5AL7h1ulyQsOBwkrtJYbDgrGesbfftktgNmqzr+RRa0h4+91BaR8RYqo0CahKCNLCvCZGUKJy1XoANobwsmI1SwfXu6UjXL1H7xoINr50D9peQbZ8SudBW8AJpTRe1fs2Px5s+GTSw0O0Q7d2y9tYdxNXI485LkA+/w0tTI9wGwx1gQ4SkAZUDqQdcMBXMZAcAv85Z8SR/1ZcFwr317GCxwdu6yTqHgrilxouv52J/ScNrmzHtQ8otshohDwAuWEHCFfUDE4pjKiwiPWCH4nGfieWkIU54lUe9mEtY52G5X76SFo0B+Rt3Mp6zMVCbAPmyh2OEmhVpnZETglyLrNtL3Nw1x3c34MEdh5eAt9BEPiBTVr3QDvFBgKQxjWy59bCGyuCMgMygNlAYxRzWuWnpPkwvDGfgPEKcoxJceSGUYATJUJhM5XnmuSBIHhIAZqxm1lrvPRgBjgiOcaac95YhzAETTwtPSJTzQWKMth6+LYWiCJEmaK1h+00EGTf80gaiUzlgDfSKQ4qrwvausX7nqtzvR2ls7nP1tYAzvYyA90R+xxZNisfiQzPic3a1kFuvHUdPHeLg7DeiQXYoEEfA8YSKvuFwYFjeQtFzhG4uMYci8wCJQAeICMI7+IJP66FnYFABgWUWjBEYX5nOCyjH6G2RnQO8IDjmwSYAbU7nkOjIHHPEMcMDQArnFKzhkEFVeaPKlFRCP48ysxDiPwV7AF4mnL1vbh7bAqXK9UdQllCrAogCU5VQUiS7BHokUtFbetn+YC/fOrMSH5MN0bKM2FHj3yot5QH4fn7gcpdgKdy0UzSnCsoqEUjlCzRAuYhXKt2qcsft9CLKTtpAiY5VpcYw/Zye/tYhykoeP3l9fGG1qeZXfuFXPmW3D7biv/O+/+k393uNVlHQ8w+en3/Jrshjmy6Nv7H81OFjJ/6PP/gX6B66Vx9e7RyrNXF2456bvUHfnhrut+5pLHXd3FyRqSYazgAOODQaE5uKvs4EHx02+qtn9w+iBuZGh0AQ494ghjQahwCuByGIGFJTYI0LbBPDdcwKXB+gDGYFymCKAFxv88Uvx6z1X27py23AGQBvxkzYUqFjtSP3LYb3Bho1CBAYDTBGwg1aNoQAvANBIqDqc4BHBuBaSPVgPTjDd7Irg4yNW9JHJrOjHvLGVwJX+ykvi2ERdBmAK59+vbs9WD4bE5t+f/rOxPsf/sl/JMe1i2/je6c/HOn2g857z4jyKIry6XeK6X2r+kNFZq7EPZWqucDMW7FSml2Yfm4TJZ/0EyhR4C6ASx/+wAcrZPfPtG1uIwDQ/pfyfv+61rUH/27/F4YPm3edQHld1lCiuWeA4rUoPTrGpalsOPXVuz05V5ZIVTqrrFA889qqODtVoF4hgZVKXmK266+4fhXSTkDqkYJFY1BaL5+tsQMzT9t8y50hM2UoANa1scMa6GMLxwoLQTUcUITUNzDmEYaUooE1dhmcGA1tyze0MXfs9XGi/bAtRsfUk+xO11x4xtV4wawvEMDxgFJ3XLzo5qjLDrFAfdO0x+RNt6q2XOgG4Y7dQNcvFS+4e9jIzrH7gof5W4LfcYqDCHB1CZ8bsEOtKPFtCPK0wva983B7to2Y+kwwkCrgd/w6Brvn/PHJvq99+Z28CEbgKjX0lfeIRhYgWb9iqQiY0tLp1SuF3LnD2uuvqGVJ5Fg8sDJKmRxGjuU1hvm+wLXjgBWADFGkdQc2YMpV4nsHCw4Na7F2ZRIa0ct2N6+acLLv3vyzmV+8+T55+U7pIC2PEhPN3yywdyx2SWgOxg34UStbXjxkGHcEnDfYPYVMJMxaRuG47jR3PF+/GT6eFq4WF+auW3emwjTAuFXWaoHXfC6n4bJ3F+4NnDQp27gmsXWHpKzhPLw19RsG3EIM2+Q2LoK6GxFNNpjHyOv2FkX9devRIl+qKQwDeYHcUf5FowAAIABJREFUE1QIwDpIm8SHLEiVlz6a9i0ry3J9wpXp1XQasETVZmQMgMNrAWcYiDhYqAudc+ccMcahpPJ5kRnvnCXGQyIYYkTkASFmSlzMNiwcQK6NHhUi9xy8U0JabgfggYETFv45j2JHQZ4SkC2OfpQtHagvZeqqmqjBkmfrdReHdahWH3rCADtA0Xk66tO/W7wFC2y/elxX59I4/2x7j12Nc3FNprVLUcFB2EFZr3xh+lsSEA5QgIPQBENNGmgHzFmOEAQ7VwDdABYBIsyEVW46tqfP+RSAmG4A41LwQsCMz1uBIyi/1/sy6EaONOdwRiEMCVwCM7535XiRTz/7CwA+BeBp/9H3/6EKVC+n9rJC9qpGWDUe25cA/G8oA6m3oVxc0+nfl2MVP4fSxTvshIu8GXTASVQpt0pQUPnAlYcFSLKAMWKeSiZNVT5GYSYsqLzfSnRiZi9yxEPsNtrTmB63jlnAWAV5SygXU4OSL/XqY/VzGQD9/e9636XPPvyZn/+e/25lqETYMpaCL/5bvCQgIKz603Xsna7fO/zag79uP/ulm3cvnqmzZKia158w9/R6TlmjRulQFqPYBe2lwyEFviaCvGnzsL1/s6GkYqkzrL59qbkRNZwlMnsqylieI0sHUgiBNYK+RgwqGYW5UrYbt/T1GubuyTBm1hQPQNzmsu1Oz4spEabH4zu/tt+78arM5NeswbwKwafn3MYs9V5dkwxEgIKG8Q7aWwhqWkCDwUERMLNJKYU0BjlsvRWEi+aUuqc7SrrjSdEjy8yLp2+2vj4/eOhrGm978fH7f6gAQEcDPQCYpm5vX9Mf/cF/rL/nx7/32lIumWLOcGIiCIMAMySq4lF6zAK8Jv5w/chq0VCYwSl8eq9XUYo9gBLhtQA+9pGPf+yzAMyHP/DB6rh/Vu0egn3gZMO8WCB81xLf2Z0aVPRQBqP3AO4c4Kf9Oggw24FX6agqMLMor2dFZOewloHzGLNUTeW1V6Gi1fxViTiOWiodCay5B3dDLn0IBQniNPHrNPHzFqDpRo0ckLM+OhhhDhY1DiQuQYtNsMhyXDX30Qv+mp9D4prkWMBvYNXOy64/v/wkeDiiocxpBM1v6leyO4Y3Ebb3fYh9zZmTh1ihRfTYEttFJMd2VW6JDh3SBXMOV805ey99VQrSPoOgB8VT4IDrFnUKlefSTuAcrGBF3vYHoXGB8RlgCbwux97ZMjDOTMTIAHPNp1wQedDxZ1xYSGaHG/zyXT177NICl5GBHy9YKojUrXvkYNJiYiyNY85TbyH1kzRkYSrRXXRIFpDNbcELDUrm4dMawQVAZ8eg53mKDmlYCDBO42aY+2J5aAu+w29GjbNfO1Vbe0FaLZDsLdplxpJ0Msdd+8BEc443bpzxaF8JcOdTCt0lC5lnOLwi6ZlXcz5cACHmzE6s21syr1jqmvDU/gQ0GLtJdwFRwtnWRmr3TnI/XgwMywtfG8ao7wuGTWtQ5xYJZ144ERSCOj2wrdMEG3CgcARrlY4YBycCeMkJMN5CSA/lFMiVNABizAliPh4DAQeKqfdnAaBgQAtodBlyxVFEU5S6KDl13nP4gk33MUzJgLx3RMSm8ChxELNCCG+YZg7OKzBunZXWGEOc5ZLLilrhAcA7Cw4+7wE6RJJr6GgFbcnBmIdb3sZwpYAOF7Gs6iZmbH9+8oBXCyECGYALDmoAFLeg3B6ybMSMSpn2ayob60kkIxstf7G+r7/UOJgsu0Z/w0TyEiu8dFiPNIbOoxhLtECY1DWeuXvUmmuCn/larZ8ZIh5Za62D6wYYdxWakAiPjN0MFUfPOglLgCwkSJipCIOOjNtqfa02c5Xn6mxMKylgYMBExZevyp2GmHm7AsD3otx4foQ+9Mmb09f7/qPv/9b8S/8CtZclsle1aYWNVwL4X1GiJk+jDKwOUHrZXQbwFpRw8DmUHbCyWklQGjm2MUPkvrlVi1oEwFhv3FiPEIuakUwNUM4EU6+x2/YjVYAw46XNnqv86SYoO3M1CKqFsBokYwC/kuX41Fe+EVw5uW4Ojq/Z1envPiSs5tPzbwLYHOqu/Ylnf/S+p29d2fjCZ9Xx7b3xu7WhOZuqJKybL91xmq/mualN+O5qkpko79dVOgwsCCKKcy9CnhKZIsswbC0lteZCYsgLtnBsOIg72S5jYPDiiSByTCh3XKHmBsNiSVizJmt+z3GsOIOBCPBo0qevj7qNw8Z8uk/MZ9sXFjaDevpdi8cHExWhC+AvoQz6UpTKxBqmLGpiaCD1pbRCQaIgQEAjmnozOUhocEhYaHh4OQrYyu5Dy+987Jn+lzrXiwvX2qn82Ec/8XoRZ9Ge+rXf+pYFER/5+MfE7nDwbiL8M8XYcsBETSoFwXnFTcunv7k1vZcZynTF0R2pRBnwDlAG+j2USB5Q2rBEKP0OCVNuJoDfQ7kp6aNEeAnAGwG8a3qtvgDgr/5pUr5/XNvcxjpH8fYfjH/swHt+x0+nf7uVo/6dmKXcpyldQ4BXU1pgFXBX46jaPCnM/CdTZFmIPAeUChFFFTenMu2uEL7Jkf8LzMxWg6kyUpcojHJAaoEhBxo0JeCjJNjnFvC2VGwYU6ILQgIOkblhU9QILCRMChK+59YaA9OinndgbAer/Bye0gvYYgqpv4hXykMskUaEDg7wAP7AnRcvEDcDs0sn/V3iSVfnE5kj5DH6ruG7OKBj7AV7FkxrTMQqXoE/wOn4inckKMTQt5glzuC9LWtNMcBzAeNHTPg8RKoMGeHdhEXUKDSKVNHW7rxer42grKKIaeF2z2D7ypsQP3e355NGQdwmJtlRCrHyHcmUF3x848xwoRczbkiqSQC4FjI+kn75FgOTwN46WNHwgCYeDo3JFoQG+VFN+9ZEMQVTcKQqBZKk1nvBzt88Lu77fJuUc/kw2F+hZphub4S23aVarce5Fxo7xzlOPAe0t10xXM9S5BG/eYz5veNa5o0Ck05wqOGjyPto7XJOt04TM4USr3hCuJ3lxBwcF25S4xkSy3094qQ985IDMTTGbrj8fE6RRW1pW4lbxwmcWzepFaJohsxzicQY4Y55CTIOHgUoEGAkoCqhEDCjGgC3ea0pAEy9ItMpNSNiQJEBrvSY85zgEwEoBxZViPU3rxEeAOXILOAhQcZo4s46AD4Lw/joxq8AwLUtmONkrDfOAi4mxcYYUoIEuyiyJkguY5EDzHIUGSBrE9hcgDwHVxxk91Ck18TYNoxUz9T3XLs28b9vJqOara88F+7JW7VDB8j9vpDJ1VAvxh7KeSREiLrl6Lt2Ogn5SR3MvSFpj/qeX9Nk5b+v755NYDqXa2aICBLlGlWN3SqTwVBAwyJAUHgwaQE6yheu1rdq3StpLz4XcF6DSQviVUBX+Y+W6yz5EWp+hBwCmm1MP1uV/3wMwE+jpO089u4oooho5ZI2e9/459/355IZ+bNuL0tk70grIemyqoBCGSQ9h3JR3UPJIesXLt/rpXvdpppfj2QsjbU1YyyFgepjZhIMzFKMtz2WymweAYDQrvDOWjLcKjnjbnmUC3yIMtCsBodDudhXatSjsHbl21fxvKr7WAWNEsD3hwHe8NZX5x9HGSDcgVLw8RXMyPEJgBtNOXff+47/fTc3/irv3L/z3BefurSXZ3jtfK2xomLb3Fju2K54ev+Rr4vd/k35IJBVpWqY4RwMXmoTohhTa7/XLno3cyHCPM8m8mBudahqHS0IcjWsjTaMZpnxky8k4/Ahk/MX0ZcH1rqlgKu5xUV1auei5XkabsOjSEdqsT438pQtHEifeSBfn/5mAY86AO1zGekxg+W5DxtwVCY9QkiycLBTEUkEj3B6d8rrK2GQuWeCtPOrN/XFR7XSHXg89m/enO/hx99N+I3f+raCo/5kcmfIxT8MldrwzhUe3gvOKyuFqm/0MbPZUdP7UKU3qnaIklzMMNtQNFCietX9709fXwDwHShT4BrAO1GKdx46crzXfTvn8dfxS+wT+L6X7npfDYavveQ3VtYqpy3U2r9J/l7TQa1ZiPMo+6tCabNzCOA5QHRQBuiVy34+vS5VgFshmg46ZQBF4DyAlNVuv1KxY3relfVK5a5feXxVxyN4DRSeQzkPAi8FBM2pYbdGKWuoF4DJARMCZkquVwQ4B+SsIwawBTcFi5S8fgvOgfbPLYtMKRsgp+O4jiUaBft+1VhIqmPPcEyER0Dz2HK7OM4umfuxgeeso0i2bM+TcW7o5xjI0Rn5HG2yqzjOuNvmx9nANAElkem4CFnCbro1sYXI5xTTnL9ujwddfmg7PmCZbvLUxSYkw1M+ST3PLLL5wCnOC9qc25fx4UnAcp9dO+298rTet54tP0vu1unAaxIHJnC1Zp+F/XXuOfM82IplK3JiYSvDC68KMKyR52OyW6fAEQIoYIItHzhhi6xOBIes7jTmJjbItKDmlaIIjVPp/Ki4/zfp4ReS/pl9GZ9YZIrP9eJE9V1e2/Lx4sjzQZMBTGLtGlAfOsiJwc1XMFBCnqQVK1vKbZ9wfC8yzbjrkjxMezt1gb6rba6NKY9TqVsHNW6auqjtcx8WQu+tchcmxGuJN9tL1jPSWacnZRr57NZmIfvLIuTcIPHOm6bzIE/gwgJOwgsGmBCkNZhIkXICwcMzCSFEFePx3KG979FdBHzFFSuLIgGoqm8YgBhIWqCWA7xCs7+5VesEAoRTX2XPuTQlgAeKnbeaEa/GiwHgJVdl1oekBUAJEjXEiApoUQMLF9DWDEw4QDjIBoFwiDwqYLI1NJwCIwUEI2+2uiqLjo87tdZ4yb4+HDaUA9eks51wyCcM7Z7QsSA0IiNHsXb9HWl5TEwuJN5FDlLzUXh9bsL2vI9s0Vh4lenUd/UY29FoLoVx0KKsCCVfQl3RkBAQFmCcHbk21dxWoMzytKfPlzO68wzeB1PT9SpdHsJ7AlE1h1Tc+4omdRXlHFkHcB/KdfYnASxc1/r4O+N4sU7sawBeFsEe/5Ef+ZE/79/w59jGCuUi+gRKYcAmSnXp+vQxANA2zsRdvYeI1WIlQt0djoNhlpwMpBwIzlPMTJerIK3iHLl+fgBt8sJoZN4yxEGEgAeVFLwqtbaHWQBXWU50MVvYKlXSURXpUY+ho0F7lfaTKNGfN6EM9IbT7zkAxgSMC8KqA8YFgLqzUPvjw9F33P+aS9xH8pV3nFpaaNUXjDVI5PZjvN5Dd5jU0160a5ysASoACHHM/GSkrM1ZqW6DJaeF8sz7NBHdSbeRe/jBYC9eywZsOR0Fjd5utKtT5g+2m/2Dy82z48No4fBWazS4ufjYjUuxYB6uyY+JdBCvTSbe1wdveTFeGdYL2zuhNe4QXnagXQiHiecuTcaMcRMZGWoDRTlAHDksTJCDrAVHgbxU6EJiAIZfA+HnmYt/5syVv/M7/8t3fuLKJ65+6MqnX+9KIu8P/LVvqxfRe99B55ZW/jZA7wR8VI9iJoWokIGKX5YDuIGZf8XR2o69aV+rqocolBOexsycdIhZQfA+ygC+QnfPorSwOT99VP0kB3D2W1Xw/je//Mm5/Are9em9Z3vfe+zuCQB85pd+YflL59WrLvzi/O57XgH/Y2Pc+2NjnMZsw2Ac5KrPihsAIjC2j7LaSxWoVbUtc5RBq8fMe7Hqu9U1AvKMw1uHINaQ0k8DvilinTdK7zKHqerxiArydtqnvOa5t7Cee+sJjBwxwUuOlSHAFWWZNc/KII8rwDuBhBRymLIEm3YglvFFAZJEnMNHwulGmzfoEBxC12kiW2yX5mlPgwLJPGgZu7aFPnaxQBM0yYOjgT5P0aAUEU18h12lcwzQus1HLPA5Pa/voTEipz2nvqt7ZQszl+3Ka+y867smG2KBQqZZhEPXpw4Kx0w9TLlQqdhVc1RY62M54jUfejVosVrSgOlsezNpWnHzLq7SAEFv2WESE2vuk40KZHSW8zufZ0oblyJnsCDTXfSsc0B8cYeDZUz2NphAEwzMawATn3lnQ/BOjzyzGYnu5TjYH1muu4Ow793SgaHFrUH+xl9ceDS90OzpLPRWUH+7ySLfYoSAq3u/TDLShPoQeOBhAZE57J9gPEqVmtvXQtjIzW8xdutk6gMj+YOPMqGDxI/mlPMioLmdxPLCepAqhvMsi3qUL14X/cY2ZWoI0+6SnHQYJw6fMM0UhcgaQg46DnnElGsFgBIMNQaoKReAl/pmOFeUWiHh4BxBs5K3I0thkMo95vY90oaDVUf51dNMDBMll0+UfFFi/KggzDgDazU4uz1dV0ifLxHlAgzeMAHPBDwn8gCv3AsaQB6UamGXT7/DFSjYBJkvoNkcWggRpQANGCgmQAaA95A+ZR4jqd0BFdw6F7c860ycrW8gRgaLFFZeViP2cLunCw+ZcsiVTPkHkgU+p2XPKqbuGzea9wzq9g7dtNr73t2Txfp8Vlerwja73sVFxgvnGV90geuJosgdJCyCZaeAohhrkwcQgkDkQFUNbFaJtapNLDADMCJUdB3iGswTKJhxd53VyCYc1gkI6QAaoaAmHHmUWY4C5dxYWX49jXIOXu85Z787rn2twdj+W996x39wbvyL0F7uyJ5BiRhUyNohSuuI+1D67N0J4L6Ah8PjtbOMEd8EcBhKfqM/0UWu9fVAykryzfK8cB6ohYF6CmVHddrrXQIFg3FiBLEz86IWJzphUoi+5KryG6oCgKMBwvL0uJUooVI3zqwnyu+oFJ7V54Aju0aUgeiDKH2HXocSucwA/KTH9u70/F94fPLbu88Gnzn/5GP3z6817jp3bH7u2K8/vv3khe6F7ZPL+pl6dj7aUEn8A9/34KkvP/W8TDFeeXFnW+wcFgagGqDrAA9LEi3BTiJtJ7VOAbQHt5oFoENgSSIwDnn9vWUlIBcBnDEWM+FFc0f039RaSQN45P0brUvI1+DlUmu36L7dvKBqy8dXz3g+5CHrpAVlqQsnX2UyvRC39JgZtQqGkwBiGCzDI4fU18GxCeAOlMUoNRyuQuACOH7ql986GuGt5UX69Ovdt1X27Jta8Pzu9vJdS6tWEMx4PFb1ev3o6w6z1OVR+5AqxSBQbjb2UaqVWyh5mN3pvTqLEmW+OL2flYHo4fSeVl921IPKA3jgwx/44LesRDt3fUfst+pRey/jm6cQPMT/nze+8W2/9y+KydOXf3n3Qz8IzLcA/BhKwcmPKGT7zfRmo1vMnZj7xhPLk7X54+npc1tAeBmlD+T89PduoKRGVP0ynv47xMwyoeRfhtFRrmu1mCbld7pputtVhdOr1G0V5FmvNcHaggACU4AZEWzuUG9PuXnCzJTAXmBarQGQPEbfEDjLIEmhQAcGE0gCQth5hiU8xw0SEDgP0Qt6vk0N2/CnqS8e9yc8IfcPYAcT1O0eVmkZfTYxNXdZnPfcG7qR3MXfmD7iW/P79ji74Lxz/rI/S9f4eb9rF2kOe4a8ZC64GhZCaU1czlMPy/wJKO5dXoT5Cm2FTEFaA6SAZWSkCL0uaE4UY2LB7oZVTnIZjeDTpvetnvOXj1na3kQwiYFWl0GA6PJJx7cliXoXAZFzTptd6sJ3A3G2UxhkcxxgjDV2TDGa4xwuFy5KtxZfpNqoWawUbcnX9hqjxo5NLt4lO9RYtPf8mnXN3Uidvsxru5ptX9dcxiO3fPzAHzRqxel2jKA24qYIlXnyDVkQD2Pq1i16nQLDligaktlo4H0wtrzWly6JJdtZdaIIw06kC7M0okloue8tMRkYmy1dsOmkFhQmYINOgeVLG6DgFpL5awjnuoIOl7TrrlryZAViLeBjLzMGHVhAOAJnDiAHb8qKdJksUV3yApwYmJcAGaedYFIgrzHcOOPghDvSl6uNeMU//WYvuGpuJmsMeefhXa6VCqbvyaYZm9ADjANOsplXabWJmYIAvnJ9qKgQTkLZObTlGGPqoYcxEinQtB0ox8F8Alc0EdCii7PnTZdyX0QEoR0YX/SR3qLJ5KIatSVnbF8kaBpWW3dKPiISssTMiAqzr5J0STfD7+it0UKqohcao433TjaxoCNbwHYP6uNmEnRFSEJ2dOgu6X468Vo82pxAMnhpGOeFbXCZc+uVAXFemk6Tm47FSuBYeekB5VxZAR0AkSjl37c3xCWa550FIcWM99ufHm8RM0Aknj7eML1HiQW++A8+/O5vq3Tkf+ztZR3sTYUaN1F67gUoeU5nUS7C9wJ4GFPFICM+RJkGHTMplbW+KLTJUO4YbgGwN7vdg0Co+Y3FucdRLojLS9HaUwBMLPR4UPS+NND73y8paHBX3wHHAOWiWMMRqD83WeC8RyhCT0SVIqtK5QIzBLBC8So08KhFBTCbkCKUSOUyZkFEA2V1hhcA3HzTwn9u76jf17lB9tR6e+HBWhjMp27yJO/c2pWdwfx191u33nzyHYtXL3/DXO+Nf22hU08mhTUAP0nAawIZ1DONY/NRI8qdduM8zQDTAnidmNXeSQ8IhtznKOuZUsmZEt45JAZWwaM13GsOG4tS5yI52amzF+eD1ePSNMJWssrj0W4xcr3LZu7G12Le+cJE9bY9EAWydqE2Ov+GoXz2PUBSA0cfDAvwbhsmTECZQ4AYGo/KfOnzWnQfBczidz/Mxt8svPiTtOOd+UYzDCUIYZLnnKTX9Zm4opheb4EyKBtM73eMUqxT7WKr6ixfn/aF96KcuLan962LMsBZRhmgn0PZb1t/zM/6Jx/+wAevfDvn8cMf+vt7+K53fwq/8Rn9r7dx5qv2wY+NLDseSr319fV5oOyHVZm+xx+UD4fJWP/AKGdv9K1Gw9QiC9gVlJulRbyUf1rVluaYVZ0BZgarJfrJREVnqD5jgFSWnmZMTMG8iuNYKdwr+gKHcw7ec09kKGIeE0ZgjHA7yJa8fDgJJFSqgoUDAhriOMEWBIaioIhuYnN66BEA5vaxaDxiVaKCCYsxgUTh72HPu9f536Xfdn+Ffx0cS9h1zSyVe2kNfdX2y6KLFHVnRMxuiWVax3Pce0kZBPuKfY23rOZEkbrl/CpbrO36U/wpHQvNjtvLRjDwhshdxDMbM0hty4KhB27BxBgSbOZ1ItjueB47bgXr/WM0f1h3Uo4o6M2zrWLB1lxb8OYYYvukx8ExCk49ma3GVyCOXQ5pd91J73Ms7XpqHCqYhocOLSwHOttFdtdjUe/SOa0jTOYKbto+1LJhKbd9lreu8GdcX9hjY/aKlsDkcF3aYUidM8+Ortqro4m/ubBaHxaNpYDXT190Qbrk0V8NzNyO9jInf/E+77n2/sSlwFrDndAMvY5n9bHD5iWpJovcXj8JTDpN1+yOxvUXzTifiw9OTsQc3xZ8v6n52tA2XjzLJouJJSF8NFhkvfYum4wbVKheUBcdVpussgKTgYYOpa452bqZ0eB0aB0now1BMRERtyk8N/BEsDZATAzMFjrnzhrmhIUSysGJBOV8Wgmq6MijElpV/a0KTDwAYozBwYFzPkWhbRWMoPxsKeYox41x0yEhgGBaMjLMgIIAVXHHuYI0BKY8nD1ENxWo8T3oTgyOEMwaUMhhoUHJqouEQwgFkjeR5JlwtmVU1LCKD41mxISLDGNjwb32VCSyGF8PRvrOQT3/GwcnV+KU+5SZQngoa/2qgfOtKHF7zSHO7TVwI075QiLFC8KG+3zCT44jb0TGboQZQyAMRM2D0VSs4eu3lbUvdVioUqpVLeyKm3d0/ihLzDGuUWtVfMAE8CGAmwCto1znrqHcUFab4Q2UlX62AezRhz75u/6j7/8TVSb6j7G9rIO9aeMoU7krmCl4aihTnj0Az6NcVCsfuNOxUisn15YWOWMpgOuY7iYacfSI5Pw8ShfvylbEA3gyd4n9/f2fW1gM15P7Wg9NZCmxH6Hk0NVRdsKNXOvAGs8KZNI5bWtBswpGKuuOiq90FMWroO2jgQt909+Vknhh+t63oez8GwCekEytbsZnn908hxrK4HX7zJlw8mzt+uZXB7/1bgCbT+Ansva5Ox57+921x0+2z6QLu49e6Q/z/u5T5z4/OYjeu321phY6Td6bJPk4T4eQ+SnmlNo4ndnJGOxwV3jZ0Nd0P4wBtQoIHgKaOO2lbBLETY30sL3fwoLfPNlX9brJwkl8s+U7g5gat8TozOUiu/rUzsJjT33qPYn+7oeZALBgjT0/jJ/7IV7Epy1PJnDyArh/WOj2b7bGd8nD+S/vAiaDxEjLvRTTAP5bDfR+96/RdtugnStcecNP+buOvkbvfQerB8F8P23s1bj0660OxWFY7U4rwUxVOq8K6rJpv9nFjFOyjHLieyXKWb+GMqBbmB6nKgjeRtmvqhrDf1T7DEql+X+4/V+PvPQYv/EZXZY/cz88wcrG1/BOBx//2vQ3zwH4d98T/tvr3x397NLf7X2yF+Lwaq6UzE+eraGmpjzC1ALRePqZeHpeLeCbuDuzutAVCf4oYlJtaEZTxSyfXr6qH1dKbAdAe+9Kh50gkL7IAV6a21KtUaV6/Wz9EAxILcfIW8QcaWlzBmF57mACqTWIRwDTZZqXPDyc18scykNgQDGG3tvc9HjHf86+3Z1g12mMOd7zy9SzS67t9u24qDFJjgMRQlh4NQYPtiBo4nf9hu/YoQ8KRzW2y4ileHXtq7ynm/qJyWtZqBIW8axYZzfMvl0SzoDO16/zSQ5/1dxJ2kob83bMCyK+0wCU9635G46f6FNm72OJmbNCORL9QAQL+0SZdli55vHsvcZcv1PKO54x8IXG3joZmcDEE85aB8jEVpa1RnBnv8HSraZ1d7zo+/MXhM7bweHePEbXNyfi/GOtpWzZTFYvMi8N7x2ejXbUMA86+yGrXdP7N9aur4dyf+6dT21vzvfWopv3hgvpikGYCKRFEcBZ97rfDSfjRew9d9yIFzfU4vFdcpSC37qDUNQZVi9TLnMfPPtabrJWbvodcqdMjeKJS8brrl6yCdpRAAAgAElEQVSEevH6GqULN5woeL64FTuZx0CgCq7ScCIG3ISpGLW2qTt3k7hjjeXtu7hMpea6JT20s46xzFlIDxSkOQAY6DIgA6cIkSQiEFFGwgdlN46qNQJ4abBX9V1gxtk++reRQh3l75ae0OUhKu5ahdipEm12Di9FrnlZxu328RngSKLwEsqfw4nIQskWtI5AinIUYcCtgrRDZLEDcgUaOyBSjhXMeMYYx4qPMhb4Ovex30FqL4YjMwhJRIiUhOm+MulEYc7cXpCQI5YpQyNJrOXgxCDuaq4pOEdtrAzrJpY82DRNnEwyZMrhOen9UsH9hEFPFCtLrzCfAt6UujoWo6x1WG2M+ZF5YR+zdC5htnmsAurqek45ew4x3B0JRIjSb/zE9DhHswDrKHnFBUr+3n8K9l5GbYgSwfsISpi3EkyMAPwASvHGYyiRle9DmZp6XAlxBaWke4Qy7XR6qdV6I0oiegclSvMwylp95wz029bDM1FTzU3qsqEZsTmUvKsKyQkGk+Trtw669y+1Y+VFTpI3YpSL/VE12NHFuVrIqlRBRXQ/+no1CVX3OkAZOFSo4lkAb8tMwhzszzlvD613Oy0192zus7mINd6y6E8Xu/mOhsrSPr+a9L05t9V7dsUrzeIWTp143Tc+NzyMd2yw/q+HafdLVMvmMVgy973z8kO68K+P9bHXsKypmmvXUsXC7Z1b/nKRmqW0i9dmMhypRjBk44b2mXz8XIO0aGSnMqV+8Vhz/utt0TwXZK0mR3AVwOSO8K6dn/7ORH/o5/5bFi5unsxqN/4mVPZuACes1QwOW0y3H2mka7806Dx56/9+x+f/NOlZAEBs0bDl1fxDVcP8r37Ove9//h/zJJk8ILioMc59FN32A63U2PuYpTGfRbnj3EHJrxMoOZVrKCemeczMo4+KcDTKiWkZMx+qPyrYGwH4px/+wAe/pfP+ufzqm/YjdfZvudXPheUEeRNAHWAFEF0C8HmAffzGKvLNbXw1euEFf/+Ff/7qr93z2tc3O7ee3Zm0lkBFA22tkIsUkVZlelQIQKaY8fYClJuiapddEamPlkcrlYwzpG6KhoamTOGyKiiskMBq0YO1Obw1JFTNkQpmgo/yuoVAgTXccgO0MEHLAoxZNMvrx8x0uxRYck4AsrKC4FNRIMFqARgPx7HI9kGYABy0iassQSwvurNmkW7YPbNgQpcKLqwbLq7ZJvWxj1CsYYs14CA9PCMD7gMysPqkfA4h5eKceBrH1DX/jex73eFgnt0XPQIeM0GcFfv7HdSKTPcW68TCnGo0thkjFcKAmLO2iXzBTYKVgeD5zoYR3Xk3yBquISeycWPJRTdPs8DlDsKKfDwHRx5me4OHg3mXxl0atW75iDPy26dpd6hJLuw5vn7T3tJZVC9yWjh+xQ6Rqd5SmHSDMw3Z2m/KOOlf2YttfO5Fs77R66Z8cd+efmKz4YYD/+ydi+98yxOrrc2MFSk6GtsF2znvvRnIQo4nbtypsdWrcpCtC/aK56TtMUMX3wYqiLk89vziK2XhnSXpmYlHsBrOwTXEpXOs48JCsUXKEA+jwbGocXACGR9wFuhAZxi7RLjacCGva/i8tlcfLl4Ten4fYu9kNHSZI7HHMtHgQbBngrXCRjtnpHTky1ysR1kvF4yDZ4BRUoBBhHLqp1eNt6Mb6Sogm86zuQPcNBiJMB2zVeDHvunzbpq1rKgcdvYdnDA1kMNsMyRQBi4VnccAhx7hxCObJwOZE7yuQ0EnE+/IeKlrGZNkFHhk4CQH+BYmSZ8ZrcEOFl3Y7Sl9lhyLzmTC3VBK6aRh65aoEJBDyVpX48n1w0yPJ6lfXoEc57GfM8znx2zdHXQ39Y1gmA+8CRYoDC/7kXk43ndrifKNIsLIJUgD4ZR3qfSe9QUaCCiGYKak4RblPEZegbEMQlW2SuH0MZqO4Sr7VWAmyKq8bqfXjLlkRomqlP4dzOhQBcrNcg3l2vdu+tAnP+M/+v4hXgbtZR/sEVadx3aKcjG2KDtCC+WilKMMhj6PcpE+Pn3+IZSijopUuo0Z8mJQooICJZ8qB3CixpvRK+ceWiLOLiiufh+zRbuqqJHGgTq52Kr3alF8EKm5vd5weNzp5LW1OHKMyGKGPB5F88rTmD1fkd4rT75qJzRd9G6/v4ZpbVbtCjEo+kqAvS7x49jDnWzK9uHdjVfrE+H54KvyYffbz3zh6W7t61eH8bPLAN7noHcBXNY5lMqaJ9c33KMLGy/mANjNZxfHh9eW3MJm/sVaJ+0cXrkaFUmcnl3JY/Trh/HJfqtvHmikg+RmJzPYd/biwaA5DObOP75rLr3FcHt+lTX/5n9x5qFNyweP//ZXLv+DV9N5ZazLtBhkb/r5uWB9rv2a3A7/d5vhHJfgkBBgRQaGi07tf27A9m/8aVO0HtsKgAtC/Eqm8Z66+KOVrWutdqvHxR3kQYrzo6pph7Jf/SxmROEXpvenQoCXpo+qukglzvGYWQ9UxcPXMEO8/qi2D+CfodycfEvt+fn2Kw8ajQf/1uaJ/bXCX/6ZbR0C/FMAOwGwxwH8qxur5e73xiqK13ziD9Y/ufeX7um33/jAYD95wJ0/OQ/OW2BOAEwBwRhwoky7IkK5OHWm514ZJKd4aSoXqCrZZGnJVwqjitc4RfEY8FIvyqM+XJYzxR0xNvUxm14fQ8ABTYeN72GBUoRUIgrAbZ1TUOmgmFM85EA6VVcSAFUACCE0wTqAcexgw/mCM3jPR0HH15ARSNMmu87nxKFTNmOXs1PQtmGTiESEhLpYMGQmfMCWKWBDl6JuT/M+DW3d5mB+TvwentH3mkfs28Wae46N9wIax21+9/rzKoYsgqQmnt19t0iXd31LDBFhQsaS25rcWfQ8VzTI+OlW5ld07Md2gd3sHuMn3TXbocK5NGD5+jXIwwXnJGeoZWDjBjCs2+7KM9wSpNlaMMKHemui6fqYc7p6Srl7/z1bjm/57a22z5NVVSxdance+LK7mRrQ5p6M7HxC3MtsMi+WG3p0WKxO9rv19O7jF+v1+d05n6Gw+/Ny+9Jx6o2C5MTrbwRQicC1exgf1rSNbrhWva/Cw/uFrQ0z7ueE7+yQ3ep5/vQrwVa7Wic15xe2OJeOiW7kqAZeR9cH3eWRBAUAhcrWPCU6jRGQgGhej6gXChHXUucOgwteekbaazeOtpzKNlic1RxczVM+lJI4AEEE4yIE0/teBW6HFmQJft4DUZVy/aOUtVWQVgnn/BRZKsmhRgtjrWOMSMmAZu+5XTtcWmc8AM+ZqIR4pW3LbKNejflqbp/yBKMYmbcOwriyEgVCKLJx4QiJU4ieB9KFGFFQwAmNnDS8bkGaBlTUQlAzhWVLkD0OF64URl4XeXpMd+SeHfNtldOlaNz6ncY2O6brAbNucilIcCm0W8uHUdjyal4D6bO1YdYwqYGHOW3bxWGYBPWcSRuy7HIjb8KipsozK+uOa1hQXnLyPMlSI3M77Q3MAuLKo/bopveb45bpBpD4tKZutXGs5piKD195l1Zz6/cD4PShT/62/+j7+/gL3l72wR4ATMuofRrAX0WZquIoOW1jlIHfa1CqHRVK5CNDSY5/BGXQdgalkjcH8PsoU6NtlB3ufgBCCVXtNg5Q7lZWUCI690+Pe1UKsbbYbj+Jkkt3PzG2kRY6DcMgZyXP46jH2FFkpPITqjr0GGVwUHm7BdPzKFAOgEqt2wYwkEzZlpprEKNX1XzzeQAjIrYphZpriznx5o2H+Ln2Xdn19JlzFydfZ08OvsAWgo3tu5tvGPz6zk8MfJ2koZTnHmsA5pdPHz76Q+9/8OZff+MPi3919X/4dLHy4q+Oe/64aqYxNfKezJZOzQk1b48Hj2yOX+ytDc1zzw021x7C+psvjlL1NG4kpxorx+eC1rFINtb+3jvuyt584u5fFpx//k2fvHNz50rw43tBb2FxJV2Lm/C1GgpY5LCtp1Ab/lMwf+X/h0CPoayxOHrwJ/0P/H+999LB/umFOI4W6s2iFoZVIDJCKfIZogx4tlAGfE2UfLa3o0TxBtP7vTd9JCjTtFUpvaqcXKXe/eMCvesA/hGAX/5WUT0A+Mbp4z+zX6/9/vX15e8D9GuA7Digzk3ZAg5AuLmN9r1/5S+PbRTVJ//Vf31+srKy4naz1LYGb2aHh+uu07FoNjUAW6oFbZV2AWaK8qO80sr8dOqJd6SChr99jtU52NuvzWxaqns7rUiTETFveJnirURLADQpUFCU6wOlpZvDlFBfiJIz5XXJBWTV8fUUkakW6wBADjQEeMGm+A+DdYAraBSs0ARDv+q3mDaBH2COz/GbWRItRdBKMhQuwsjX0TXXs1fQHl9i5xhnp9nz1Lcd7GOV3SWeJkuSmuj6c+NHeGQOkYxjFMMV+2znAZ83BE7VX6Cmf84lrmn3Rss82N6n9fahX6h3WV6vI1E1D+TEF6+LNGI0/yKyhZ7lrDbiuO+L5ArF8ywEGjdhR6qINraMi/Jg8cxV5CqxPfdoduPiMa+vnxSn5rLM1Ces8eYLbPP0ZWxda1B3bxXzi7dYPm5QNqjRidO36mF0s/bck3eOE5P5w6t2LXj0IXqh/Y3O6fdei+qG59YK0796Mt+/uKYyQrA6t2cU48wvXXY6sqrVfN7yNISpj+DNJdKXzlqjPXOv+x0fv3Cv53mDnCAqhGIhYuZqGcv/X/beNMiS7DoP+85dcnt7vdq7qveepWfDzGAGiwACMEESJBSgSFBhgVbQYZIOBiyYou2QbElhEaDkCNtB2/hBmZRMSSRDQSBkkZJAAgKCABcQ2wyAGcw+PT3TW1V17fX2l9u99/hHZvZ7MwRDJMM/TBg3oqKrXr/Md1/mzXvO+c53vtPq2XC6iAir68XaIi0RcoiaywHHcAhz12GdIlVTRP1Fh/4SS6GkmLRHyIN6CCUi1Kw67lpAlEiSnE/JlusycuDMArJKM1brr1qP8yLeZXDnz9EGSokh5YgUK87IGpsLYyxJKVgXNoEBcJ7nYMB6mk6kkG3rrJBCznP6EgAiQ64EJCuIUqZEMVC3AiRUKQQNwISoCYYWhMEKoqSZTlfkCQw0KB14uVBW1qUV0wi63oaf3xKjQwV4K7HfaEvFgdHdw+aUHhwviI00pJfDkTdayuhJmYY3o9Q74XSDrLr6vsnaSGixpgi45Y+2J55dPFZx/LV2z9PpkRhL50CwIOhMQYBLtJ2JAR5BSq+WeUEOqMzjSvi+4uFWz7mPmQxZJZ5e3a+KE1xluOa5lNX7KurMPA1qudyDTgHo0s994p/yxz9UqWR8R47/n0uvzI9xD8CPoUBPBIrCi0MUKEuOWW/T51AsyG+hKON+MwrjHaNw+lbKc1QixhKFga9kMnwUxnsdhbG/hcKxXECxOew6dk0iamulpnGaeqNJTFHo1w/T7cCxU768IzJb6QpVG1G1wMeYPQgSs4dkhJkxrNqz1QGQEspTpIZK6KeU0D0UaOVtxLmnLTWiekOtRqcblxqPRvc13qHvb74t2qifu++diz8cXWze1+vlB5kxebigVztCQv30wz8Z3NN65B+/d/lD0QPdN//BUL22ziLb/6mL//Cp2+MbTzP94ZVF/4UXKNq/4XW23ntq6an47sPvu7WuVl4+T8v9VlD/rXeduTy40N64tt7o7jWisAvA/MI/+/I/2N3j/2Sw73ed01haMj2t5RPN6YP/pjN54P+Ka9tP/tt3pP8vdJIYAwX61gLGtwiNb+s8fuyXfym4crC3FCm9vNRqtUtRxQkKjad/Xf47Ke/BdnnfH0fh7CcAnkbhEJ5BsZ6GKAKAC5htWlVLvW/n6FV6jH8bwKf+PI4eAPyNs4vTj1LzAMWaeBSg9wDUAOQxgL8DYORfv+6d+vxX2siS75WHB5cmd126nNeiy2xsm32/zu024PtlOlbGhbaYquabld+/QjynKBzcKk1TcfgKtEMpAaUAoiqdW3GZ5o3xvDYXAXlZvEQo2lllDBgL+LBQVGa9CHea1EMUabI4BXIuJIOAOQaEnPtXAokszsnVPAmg0kcUADE9TF+jdd41fdsxViJ1MjdGKrWGG2aIBacgvMxz3NEDGqLBy2pPGCfYQyoMpFuUt/msvyUX6YCiIMOllR00fYujAfJ439paNFWBGLk9c0bQaMq0NxELi8eZbhqspydiETGtJGPSQ599G9PS0ouJ1qzk2ZeYa0OB1x4ku3uORr3ABWcO2Kxsi3TzijP3PkfQsRnRFCciDtK9U56fe8r/4G+I1/rEh/stqQUlC+evUaMxFN3uWNz70Ja6udWWzz1/ilIHy2Ka+Y3hopo06ld2GsHa3VdER2tLQep5kiMTDhE2U9lq95j668oOAiebUxaNsZsqpdgBMvcVjVqpcpocC6KD0+CdMxK9DZVbsEgjISY1p09OCTmpkwQnPmqC4ciHzHIYkWIIQoa6lV6Uk/NsmAWTRdmarOfReNnV3KI3RuIUaBogmkjoikIwxusLqkoHQQNFWrfSf5sJA7++MAOYIVEVslQF5lrAkACMloqsMCk7WGLSUqqqCEnnxgCA0EoHWZZKa61w7KSUqnIubYacc9ic4QKNCgFkAdjxCA47Xmba1lfkHIj8nBA4wCJVWWhMMO3DWQF4kdMBOZYHiFVGLtoKJgcv6yFNlK2HLK5E5IcNp8KBHe1vpqHaDbP2PUl9KoXSu35a6+ReIh1ci/yjHO64m6m1zLE38d3ezTCeTGRWY0n+LT+zPd9N4TICSwaRrbE8qLMQied8sBNwTLlSvpUgwDCIchR7aPXcz1c4V3tF1ZWjUtM4LN+3h1nxTHVvq/NUPN952asaChu8CuBzH33fA9/R6dzvInvlKCtzP4dCpgQoFsI5FAb5KopuGksoHvprKIz3B1E4f8conDxgJrJsUGwiVduzVzDTTRugMPp1zDh/FwHY2EweGmW9adNbuBGo6EBIEbAxHsAhFe19Kh7H/CZTPRDasTNl797K8RPlXKq5VZ0IKii7KmevKosfLOf3i7GZvk/fHh+oXFzw7uoueZ6/mrqDcDHq6sRmS+N0yEv+qciXDfXXN1ZF7tJRxtn1iRnYoem9r5cdXlwJN4JLjYfP/Jfn/tEKOe/JjMZv+e8e+Du937z1v143nNX30pvjsen/LCT2+m/5l5+oD+/9zOkXv/cJTDD+3Bee+/f/8MMf5PI6Xf7Uk0+uHe2rd6dJE0BGh7aeX+iufqp97+6v3Vf7nq2js398bm8yWftrXxUrAF75t2/bmfzIV08FHb1s/8WbnwbKTZuwxmX3lDMonoFrheYggPf/gOTNddH/b3/y7c7kHwvanWujrz/1jdUf/qk/rZuG/p7zlxYZeEkJMUCRmp2Ua+IHUTg3fRTI6lL5mVXUugPgKyh6/2YogoILKIKBCl1ewrdX30d5jgGKLhtf/fM6etXYWgM/cCMfNpN841bNH0BJBeD3UKDVq1apjfA9P9G470svPP/pyxHZdruVSqUoDJu8sEAQwsJaizS1CMMIhYh4VWVX8Q6rtSdQBEISVcP42T4kymPf2C2jitir6r0qOi9fDwkwGlD5TLUCY0AERc/SGACXOuaGyvQYAdorpFdiVxRtEBc6fmDApxLt46JqkMrrnbGGhKdTniAkpARJDsvRLrfECdpyTSXw/TqlnJqYL/dP8udrC1qEZNZp30U8DKZoOpNb28FYrNNNvuku4hXzmMqSmjNbQ6q3hkhDZzsLr9rei1398vWLgV1u8rI6MaEZY8E7pvV7X7aC6ioxwmpKacKZuC1WbXO4mTfHvseNXXVy//PGx9jpuMVeNPIMxmZveV/YmiQ73ODBSMbZ3nuije5UyY0XRW15D9fzqXl16qV37S4lNweoTQ4DeusjV8Tm4khev9kVSud2peHwwgsbGB11cO5MFi56QsvW7WntPZ/1H9lpjxfXe3kiTJinrFiPsXhmQNZGGImUGp1biYi0sKOuzNwCEs9H44kfgpYpuDYJ+erdCE+6nFpFLjpk2Tgm0rFMpk32j5coHK8SIMiHqglASFgCxokEK4WABHxFENBwLKGJwa5wCJtgkKtRdxCw2tNF7n59bp1V6Jya+7daY8DMWZjn61V0ARTbvE+vf90B8sTBelLAF4AhDQPy2AkX2KKy1gnrlKhcRMu5Q6HoTOzYASkB7AqpGEESVhFojv+nBWACgpPCZTJHCpeBtdYgKQ+AlsxjHfZh0wkyFwuitvOpBi9ow4+GlGXX5Th5d7y8nDu3uI04aUosvCpjMRVZu6+kzdgOrsl4KoU/teQ6PZnkK9ZPl02w8ELYW305FPn1YIrjINNHcrxzsd/3Y9cejjWOQfkpmLwDKQFP5c0pIj9Hs08Z1YzPY50LaMdgR8iyHCQVhMzg6QqMqCpxqz0lKC6srYJLYJYdWMbshgCvRwDnuezVnlOldB8A8Cv0c5/4b/jjH3oF36Hju8je68b4CoCfQrGgqsiBURjoCvatNoPzKLob3ETh6C1iRgg3KIzlQXliD4UhH6Lg/oUoDKlC4RSsl+cIHNt4ake3fRm+ooV3M/L9SbMWtQSJek01My2CgXUuF2I+7VRELrGJYTMjWFopSVVOXhXBALO+vFWKtzKeUTmXlfInzE2av3ryVNc0KWwurD4LT30NQNtw5kDk2EJ4CJSUQqc27jRUu7vkrycZJ2cEyUbupssN1e61/aUxs9PDtJe9tn9j8SQ76bSDJlmYwfvX/ovJ9vi1e6d2+EMZJ6c6dmnhXcsfoH391fSpzf/t8Fd+5NOW0HDiAz+Sfvbrz7Y+/u8++1OZie8t9Ki0CWvu8zI/9fd//2d+p39p/XTNF2H3m8dffFNm3H/lhu3Gsnm4eXHx7F0/tvazD3WDtQ9Ps9GP7sfb77oVP/XUcrDho+g48Z8W12/cztxJYzveuzy898HvzxcXf6WXHp41t66ddZ//6rUnf+SvXbnwDz76J5ypdz/2ePbFb34jEUK8CTPytEWBtkUo0reV83YJM508hQLVBQqHroWZmPcSCqS3Euv+djwhRuFEfgXAbwF44t2PPf5t3vYfH5u7iHzLjzXz/J1G8LVU6QGKAGQMoOGEePPAp7u3N6P1vYfuaeXLKwLdboLFxVOo1YYIgiHS1Mdg4MH3PUg5rwVZpWSrzbXqf1txkSotrflIviqSqhy8KjVeXYv5VFoZ7YvSEEsUfClfAGWbPDhd6D8aAlIgcYB0XAjecl5UPVoJhClgy89UVPKuMPv8xMEk1DJjasshDdAGJMNJiR63kAqJE7fM+7zkDnlNGRuISbI4PXK+jp0Y7qqV3R432xfka/IsvyYcQnT0ibnLfUu+emsdk74v6m7IwozouRt3s17yNeeZONjysNHadRfr1/PXbqzxS/sXNUkiMyI7sAvZ7lGLGwOhB3nH7R3dB2plwh20YBFr7/YpTTfOs2JiuXYga3e/TH5oZOyP8nzhhuykTeqcue2l6B/3nDV7wUES3f/s0I8bwvdlsLRwoMa5U6lRYmNzm2/eWBI3rq/jxWdPoxEyP/bgTZPtreVf+4M3944w0EfGgeppGK325cntduAmbVlvpsgmAVNtrJr1VIrxImH3NJCFMtleF8n+CmsR5/TC29keLpFTzqpJk0TzJM8WeoJICr2zmfsuAvKQA4RCoOJmSgBKpMFQGiOhXMA+acNgy5iQhK8jtJSG7xRUFiHcE5DKwCgCdQREMrfm5sW55506AFAMJwgJgD6XoF3p1KWuDAioqhcqjrWE1kEOKyVcs9RzZC0ABSKRg40Ea0EkLAASsFJopZXnGIDSKiEyJV2HpQQLBeEpePn8fA2s1BDoOk0MtuSEdhrUQ6wMXOBBhQGE6MALcnZUh4QHAQ0xBhNFuWjWWU/6yFoC1Fw2nljMA3qtHmPoMYepOAz9sPVs1E+ebUxHitViaHXLCm4fqTSNlYkP/OnoSGXbNcbKvuovXGsoD4GSkFKDpQEkQRsxyZPhiJOmYwHfKZEpEAQspBRl0Gjg2EBJAaI+ZtFb2Uc+N0WFvBNFRS9JFNX6wIwnnc8dN18AVu0j86NyBM8B+J6Pffb5Jz/22ef3P/q+B+68ofaJK+E/emL7A7/w5ev2f3zL5jH+ko7vInuvH4cAPgHgJ/AnxTKrtNN5zERhgZl+XZWuAgo4+SYKg/0wMptAiQUIOoXCiC2jcA77KPh9D5fn7vsyfHYtOuuhQBgfLT/rJooFuXrQ6zfGaZ6dXe72y3ZcldHMpSAvEan2qF4Z08o5rYxmDbjTUHq+iKA6R5VWaMnYPb4yWIi461vbFCyLquKdhu4sADi5ba+/wOxq0zy/HIpIR7Jel1Ldo6GvK6aN07V7L7S95QPH9quDtNfZH91uPLd1fbMXJy/vX7rxhat9LHBv752BObXekQed6XQY9G5de/OzJ1cfMKMzr/0PP/g7/1l5rQBAP/Hqax8F8H4gANg5cPbq42uP/NYHf+ji9wyy4+PPvfTHm5976SvjGyfd9vnTNW7gVFhv0A+eqr09O1u/72EAD0qpg65ad0vB+mkAXzqJ97NINbuBDj8A4Il+dpAM33P5R/1+49GW79UXmmeQjd0076xWae/XjY/98i8RCkf9g5i1Las6ZeyhEOU+X/5bIbg+CsmVTvnzV8s1VsPrq1Bng3lWdkMVwnSnb+5nAfy7v0jv283dO4jwmYGv+lMtPuOJgVdU4vojADfk9jZbId7ZP7/U7l9a9eD7AwTB85gVHxUpWd/P0G4raF2hd9V6mqBAKKsWaRU/zyuvRbUphwAgsnHmpCcgvUpfsroWleNVpdKqYGx+8y4jeVWlfco0sCt0WUCEVBLYAcwCFKBwBIUrUD2hS4Ms4HJG6icA6hAa8MGAk7CaewzuwRXPPxWXsIfzFJkEbhoTOSuDxphy3cTeggjbu1dkz5wO1mpHp+p2RBfxIi/IA/u0eZz7/brSLy0JUBvNYCcAACAASURBVMo9swi57uhN7W/i+FbbLYyYxsMG3bq+STWXiNpwHPa2pOFW5k7ys+lQD2kx6Adr8kBHhwFr2mO0n9fSeTSimtj/8jl3T9AivzFgs7dO/vKBUbWRSA+dHLyy6kV3HVIW9nlEw6klL13QefDue8b2ebETfv2JBxpve/tL6vT91+zufksfHERoder0zLPnUY8MGu2B1YHOR712/q1dZtq85jsvywdbF73JWmKkvKoOj5eyezpSR1K6w/B2VnfKo0lopJd5duO6o+Nl0ecB1D1PUjBqUb01UbS8Y/PD0yIPRyJMaxD7DasP10xjfI9IkErA2BQOPkRWtAVTlEHknCzKEFpoEVoAQiJPjJrUYbQQxRJSAJQAnXNwGcNJAxdKWL/w/jEskF5YIBxg1tEoAMAGOW3jlgvgpy2Evp7zMEqeXIkOV0FLDkAS+isBEJagQcULFDLLmJ2DzAOwhrCB9mSWZTI3OQwMfM8v928lAJsVa9RwwRuYiehbZ9gI5uI7k/RBwnq+iZE4HzLSYBHAWQNP58jVCkIHEFs4toAmGNVDlvZUOrkpp8lKGqgQkbGE6VsHXc8paT+5cmvaU5MpAPXwqH76nqNG40Sn6VZ9ePJCc9KPJcAE7ub6ntjB22ms+PDBYDhpSFpPHUHYU7AuYOKhzc0hakFzAijAORjr4CTD0xMITgC0CxHlO9qkcnZduWpRmhTdRQQwE62vle+vhKertO+8pBPe8Pt8B6p7APwGgF+kn/vEJ/jjH6r2/EVmfnvhXOIvLfL3XWfv9UMC+B0UBngFs2qp6iFOURjtFAXqwijy/WdRGKYtFA4cAfgRAITUbOBgbFH3bqITHaBACbsoFtkVFCmtHmaCkjkK1C9H4RzEAL6AYuN5W+j7f8Uau5gZc6SkPCyPvQAg8kSQeX5QRSoaM8PqUJYbYmY0J5ghl7b8/yqV2xW+dyFcWXF9OkHuMk9KNSzn843cZZsEbuzF27V+1o+Xvc1hV3Yj7YL2UnD2nEh0aNjEngw9Bj8+Mf36bnzNerbZvX24c+ri6t2X37v00JVe72D5FB4aRAFlaZ+88U22o17Y2Hpt/+3/5Kn/+zf/Zfjpv/lPP/F/9su53V1MmwBgrCkfLDVaH4ywkE/T5MnpwNty++dpY3Q2+tAj727ce2rtQxeWVxcgOPCUrwAIXwYCEgqFI72ihB9PzOBFQF377VeevPHY+iaW1i9tJdHoslSesfCmXzwK/8OvDS++dM9HPonvKy9c6eStoNAq/JvlPS03IDTLSfZQFPC8tbyvlVNTRZ0CKDt+zFLzwOv1E4sNyaCQrgukhYe8XBOfRaGld+PnP/yRPzexeHMXGsBPoigY+kMAJyTclhPhI3CqW85jzWmdwdoxoqiHIKi4hSGKNeejCFwYUhqEYVG5OkuPVCLfFZ2hNve9XPn6BAWqmYMdBXkic+s4D70Kba7EaefT2PO6km9EYeTc7+W1FHn5CHhQwsKdiELc1afCiGoxO9akAAvkunLACVxVPxLgCwWQAJcfTwGAFEkc4IY7Jxv2CB3/CAeIMgspAxpqG3nW9504JQ+8/pHgw7xhg9VFSsY+TeC7vteBzEhcxtfRH4d4gt6J0aQu+HjPSD8Qa419MZ2E9PTwTawfrMmL5rV0dRImvTMqGus11cg7UItbtNjLRJoNaTgQuDVZwKefOyceT9vuXXedWJ48LMLW05J2pEi+9UhePzor2p3EneiJfPZ6z22859mmXN1R8X4kk2td2R8peX2rm7lmj9iSPHfuSKRpBGaDvYMQ58/28642ajw1+ebdO7S8ehxsbPYgjlPlrezrqAH/4qVjKa/em48PNpVbfVG3rRYumKperLl5+hbnesDDk03T8Q37y1PPLt1gHgY5JQGx7rPZvduJjaskFXEKiDJNKlgdudQs+QKcWSRjwNYEJGsgB5wPpMzgGpIlAUsWvpWFpl1Z1MasQbASzuT+SUbdbY96nWA67spQaKsozQHfK58zsnDIwezBExINDbSkLUrOyxEwkJayKz4KjiejoARMsiITUVSXZyDNgBVSJM45ZXPhtFYFai3AZJnkDBlPAB2WTqgrAfBKVsgCcFIokWdGEAB4gUmRg5E5WfRq4xDOAQ4O1lq4TIJUwU+gmOG0D+Wa0HpM5lSTdehDiVfDUWKEdfdPF+k6RiPlUFMsm0vWM/sqae/7qd+TmbweZWaoSpshII9U3kXRe3wEIIJF/3Qc9HteGvQDeYIkY4DOots0IIJLnQHcoNCWtguAZmiVIU0IiS3lVshD4BsA/SJA86psR1TuHVl5raoitgzFfltx09Pyp+rUM8+zrEZV6StRgC8/DeBZ+rlPvMIf/9AUwDYp+b+wsX+pO25819l7/ZAonLcXMJO5eKPESRU5zFdIWhSGfoqZVMYFAB6U6EOJa9DqFoD7MSv+qCo0KyK+Q7Eo34tZRFkR2oEi3RuHnk4ERbUsyzuR7/emSbJPcG0rbEiQXPOipJxPbqxdzLJMEIjCMGDMqskIr+/nm6BwThooU7o5uab0IupwIHwR1MvvfR3AFYbbSmx8l7P2eGh62fnGXQNBoktwCxq6XpON5lG625nI4XKmfNvRa9lDi+8cdcxJfGP6Su3a0c0Ly5OVxn3hQtK9+HD9maMWru4Mxv2dUe69dnUwSoPzlu0DJ62DRwF8gT/1+ZH4wHv/SSeq/aJ1TliTfkbppWdfvn3y3KNHbu3wyO5eaF+K3vnDD/5Mq1Zbi8LGxSiMtK/vLO9y47xzPwlAK9K1VXCtY5lzML/QTeRDzf0xX9EHA27qUf/IO5ZpfNcPeaMffPrm9lH5/VHew0cB/N1yvZygcPQrfbzqen4VhVO4hIIXUsktXCyv56wdUDEqpGqIWWqdoMAIhIO6k3b/CoD/+ec//JFX8RcYm7uQAD6CApH8Q0n5F5f822Iv2WjBSaAINroAYl5ZOVd+r0PMdK7Wy1NVlXNVYFFpilUbarWOe+V3VbiTjrkjs9BGpY1FAtOwXQIlr0vpznNMgVkqt+I+Vs9omd7KNGCplMsA4FcIoi229FZWGGSjCoI7UKIvZTrYZYUMRCyK+g5ypaORAGTAvr+WnsiUgBOfAISAiGEp4H54iiaiwYvYl8eo2aaKM1psmYlrplcHZwzyNGzJTTVxa8JybBoqV2fWd/Da1+q4lqyDfUBE2h3eUBjtOrrn7gP01jbZtnx3V+2aevXFTm5PnXEvcouWb13DPa2UBi8+4oKgJUgf4fp4yFs3L1gV38WPR4vq4j0TtDxf78ZGjncuUme86VrjFRutDFw2aTnbGbnGfVc1hRP55WcWzMHVTXb9Jk4GnmmtHxOZUJ88c7/Izr+C1toR3vWuKzg6brtJHJjb17pYXMm52RyO6u1R2OpOQm49R9OJI7ML180gg3OHeii2rMex85RPnGtrRgvkstsqnzTd+vQi0zSHXIgNsSTki1rkDUmpNjLpELHKRfNEcX8AoMYAiFo9i+M2AYEQkA2AUl2k6wFkBsg5AytWxKRI5Bg7jUaJiqW5IHYM0g7OuSxQdLzhmamnr6cprXjC1p0KPe0CTSIGsJsgWxJAtIxTuYNQrli880EZSm09ANmc45DHQCTLWM6igJcZIJBUAfIMbIvua4IEe8ozUHdQLIlij3BZnjkiHmulgkpaJEUqHRwEJAdeUHWUUQ6Wi/wxWQLrFML5ENqD5kPEyGCDJQQsAWVhDUCui1A1citzWD5Uqdvy4tTA4nYztQd6YpZN0PSdWbQuT/oy4xe7SWYFkxFcg0PkM5BaOBtBgu84VwpA81AkPKbymddyAAcBQRMAE7hUg0hDy0qqZgC4636aWmEQxKHvQZaFhs4R4sSDEAJhUFXreigyKLXyemUAHIGHbdhRD7JWpHnvBJvVda2AjwrsqPaUihJ1BkWm5gaA6eRDd3O5//2lHt919uYGYS1l7P4+CkfoHSgetnkOhyr/r0qNVguoau90H4qHbhcF+X4dUgBrzX0U6bwmCiRljCL9da58vVb+32J5fCUcmZR/ny9/Jp7WQ2vtSAlpsjzvp1m2fDTqZxMM4zzR7vzyqaNuow4AR8bat+Qu8zzhzUcyFYo0wcx4Vi2rqvVgrHWZdY4FUS1JUx8CZwkUBF5w7IngyTO1e64u+Zs1PfY40NHdx+b2aJHPTcgTlxI75aEdumVmtTO5Klt6RTVoQa8tGu9xeynjuCk2FzY3klG2lcuk3t07mz688/AJLz7v7gp9OT4RX+hPv3Xl+PtuVpw2MPDP4ix9X+74QiP0v/pX7r07XWm3eh9869vy1YX26cun1/9xlmVLBOIwDN/Iy4gxc6YzlD1ZldAWgKeAu378/nf8IXp9OyG7WJsuBUevTmM3Os7OLNfifZm/NDm5Pa/DRCii1yvlvZyi2Axsee+fRBEQvAcztfYUhWMT4vVj3qGpeH49zKc8iQgeiXLNPAng11D00v1zj9LReysK6ZerAH59Qe9HWS7eT8g8RriBAvmsZFI2UAQkQ8xkD6q5CMycteqZmHe+qo20EimuUM0qrTLfCrC4DkK9wYjeKfDA3OsV2g7MeKdVX83C1gEEZA64s/bnSNpOFIafUcyHCgkjkzrkJCCVD2OdmBp2DWUKZMaJQlJGECiDIeFyYcWdgkANQIQEEHJIa9DnBsYkXJyf4d0932TZdrZ8vsZjJTlDNDjg7PmR2hks4PTp62jhFF6Vl5D1pau/eosvdA7E2spJPs3qOB4u6HFwSnXVSSoXfTQWrR5OYz1OV2lFPod+LRe97ALOpzWx0Brb5OEpn946kvVkPd9aOpHjYUaD3YTGmYBbnpqRv09nz+1AhmNB7Vv9k7VRVPcz3nn+gXycKdXyRLp4z4m3eu++DP2QbjyX8/6tJcjDOl+667ZgMEQ7Un5T9paWk2zjdL++0EqtcOQZYW2jDjKs4cIJO2/qloWfLsDCdY6G9vbC2O3XVqZHC6buCeU2rzKpHHmjz2bqifHWWfJut13r6uNk4bLg6qNe7jISSHLlDwynqwGOzgrhPPiSCWBYkO9gCICSEJLh5UCSMVgTPFiw1cjKam32PMA4sBAgmXPISCMTCuC8L4iMBTP71ppMKy8AAAnBgEMhb3LneZ1PCZbri1AUAjEVtABPALpcj9YCviNIQ4BQkGQ0OTAgCm3IymGDsTmDIZTSyG1GzjkiL/Wwsu/QW3XImpbBpbwRV89CmiBVDowE1kZQkkEKoDSBgAeWHjQc2ElAJ7DZBAZ9ZGKI3DVJc6bY6xrPelOEv7Z6bXToxdFqHGJt6ge3g1gLwX5PG5F6wGIGDAmcaRgkiH2BMHUgiDtZpQYIYqywCIcYQBueV/B3k9SHUhJSZWCbIDVDBF4PQAYh11I/GPtk6oh8jYLOmAKUgsS4CMIQYKbVWYEvFWjhMSA8cOgBWTaTIRuVe88CZkLLFfIXoHDsbqMIdAUKmawFFJm674jxXWfvDYOwts/Y/R0U6bmHAcgkTXMwCyIx8X2vgowTFNyrinBew6x5cx2lRhmKhXM/ZpWvldjx/Zil96r0VtVxoOokMM9ZOAYwTrOsnRpz4mmdKClPKaWWFHmmI5Y8Drlf8z0DQGa5SZhsNuYj3VEr885exaeqHL3KgQVm0ZEXBL5O03RsjAmtM1IpBSY0UfQ9XRAkY7CLut6qrOvWsnO8PRymZjhNryR6cCoz04jZeU2xpA5HB8E0tnqIXZu4HkXchR80Ve6NFwbTgYqWdX5vK1T6wLQ2wtMHI7q53Hv5eI93W4Z/ZrcJYMKf+rz1fvj7/yfD7u9axtr9p0/l59dXXjq7uhQlWfKLueFmGAQoRXXfOAIUD+0RZpqE58vf64Ps+NGJGf33q+3TE054YWfvoLV19ZhaTe/w0r0X//Bnf+HRz/9trPXmztcE8E4UiN0NzCq9jsv7tlne0wUU0jznMBO0/naj2rDr5f04g9dz0Sr6wB8B+H0AV3/+wx9xbzzJn3EIAI+huB6/C+DiYbbqALMG6DdjFgUvY+bAVVzOhfLvBor1uotizVao3jzSxnO/V2ndeTmW6rUqfTKvkVU5ihV5XgBwnMQAg+B5jqREOafqOZs3uqUx9ubnUBG1TZG2LXuizG6LBajAEYgsPAFHnoAWxetIC/I9QwLOHfoB37H5RgC2klhj58XgEc6qJMycAAWd9KWOUvYkb4PBbrKNB/39g33veC+FPxrjxlGABDnSUYJxWhfT0aK73LqStzcVvyAuC+c301V9oBdXYzs0DX/XrGJ5YYwz9Rvu2DLfuHCIm9F5Wji57fqTKE/jFUFez15pf5MydZumk674fRqiXZPZcjg059Y3RWfzhXi4uBNO2IWRGKrFKE//xrsPpWnuumglHr26/Uirjk3lzDHd+56v8/6NdTm4dmZ6/VX2Xr62ahcf9+NHvv/6/oMLL133QO/C1cv1ZDhw0YXro+nEk7KGIKhlggg9k9m6nQYBHYWKAlNbfuC673JljRVWUp/9yaZvRm2Y1jHpwzVquAZJhCwReHDIBQQLBDKhE0+rQyvMgmPJKDlUYQaSCQYuoJpT7DkDpwANv0DhnIAWBaiGsrIVuSgpd7pAcy1IJwrGTZgpklQLlFdVhOsA3nxB0XyRVhX45EBYoUZIkMCDgoAqMwlZWlD8Es8vlD8MAAQyqNZ/VcRkAbC1VjEXALdjW9TmsgrSvs86004AcYAADo4FYA3inAHNoDyBy3JwouAtaAhOkCFFljK0WkIoYpBw4PwQiYygPA3JQzm1W8FE+k7Cz8mw4K50JFrG87uZ53U5CrYpdU81j0Vfw4GQdXJvGgpSWyp1qYZqZLApw7Ri5DHgZxoeFAbIMYWARgoL5D6clWAm5DkhDARSKAhOwVwUtVhrEAY30xAKRdYkBBAhSyV8xJFWmDpOADpEUfDWxoyzFxX3icQ+VB2gIxRc8zPlPrVe3seqlWOr/Dss/34ShebpMQpJrNv4Dhrfdfa+/TgG8M8B/D0Ap8GsmdkScYVClNVWaKB42PsoFlAbs8rC6iEGCsMpUaA/p1AYvWjus8Ly+D4K/l9Qvj80LgcRsSTlAXjWMdYI9IgAiBlta6zXqdclEUyS56G1Tm7tH5wopc7UPJ0JbtPWXt9b7wihPa0CTwMzzkNlDKv0psXMUSVmDoUUMoOxmcu4puohgBXHvJrmeRyp1jc8Gd72pH89FI3eiOPO89vXvpTI6ePKnDqOpTpjhH3TrZODdu6SLkSq99Nt2bZNeTI4Jr8z7R4l23Z/fDX3xaFqXVynxsg/Rf1gJN/65lr4+HscCsfpJoBxzu5lAP8q1Hrx+a3bm7d7g6Of/v73PM7MdTDA4G+rT1LeB7+8X1XRwLXy3B1JnopE434CWeW8vKNaXnS5pp780jOtX//Vz7zZZDknU/u/f/pL/8Z+7Jd/SZT3yKJA17bLa3YKs1Y8D6JwjCpdxT/LqIqAqt+BwtgcAvg0CsfmCRTO3l+YO7K1hnxzF7+Bgpt6DOC9gFoEVB0FiifK81ddYbqYtSaa17eqxKHnOXjVvPXc6wlmjqvCTDOrOk/VsHYeqaveO58m1gWIA1GiGVWauDqmQtkZCKpzVE5killxUvnZQVn1kpTOoU+kcltXPTfCggQiB2UzwHjlVAgMhYQBAYYvZs64kIB1XACGQHOSyW4Wu9ek7wJkvCXOiH233BirRc6mWTLcIV/2ImjvJhboEAf1M3Yp2ZOb7iqeGz+KLK+LXrIosj1BfaUo1H13fn0b59u7/u6tM/Zl/aAK9VVa0d9M/7j3qDgY1sW7Fr+Jw13tbnxlk87Hjcl0uBgJ05LhhT7VWykuL4f5qZVJvnbhRmAPRnkUUnSQE51Ee7jUOsbR1bsi31K+uaaMW9ll78bJ0Wi7bfTifn26vNFYWIdc8G66z39teS85alHXbfXu27zlrTXzRyYvnxImXoVd3AMzBaNxTaYp53TU9n3OF9pRZhonpy0HOUhmUnnDLO30NKZNjTCDjgXLw7NO1HpGmkCKUVsyHBEGghuxUaNFwxy1/OQcOcQxwEwwFlHPx9QnQp0DtOBzYLLMSniOGUZIhKwK34wK1+nO7uADAVskxGBW5TpywFgCDUilinVhk4Iv52XFMkYABPNyHphbUw4AYhgzhZUWgkoJBAtIr6ALMBfFQsWqwSxAqfZgA4CJhCEwmJ3KsoQ9LyDOpEO+QqxUXoaWVkBQjIwY1hGkEBDaIqcprNct5yQgEwFnFKR0YE9AWMBJHyrVkCoEu0X2PGshuokPDaHISr40bUaRkSJxKZPvoByoz8gbDDkC/FtBZnxfGOmQ+RY6lbBIgZb1qJtReD1I3UICMfTwdO5wDuANNU0yCE6N1sWUrQN8baDVDgQtIDckk2xslVxF6PfK67KOKivArGo+a5E5N7ZitUz9Vs89MNufxij67Y5R7FMOM029GEXmZBszGahTmPXv/lUU+19V5PEdM77r7H2bUaZzvwXgiwB+IAiCWm5spNWdLhYV96oyxpXQUgUN11Hw/h7CDO2oHKmKL1c1gl9FsciqSKNC3cixc/vprdQjXy8FGw0A94e+1wl9L0SJkjTqtZyIzNbBYeacq9fC4Mg5zj2l6uMsP0lz0+yNJgNPyvFyq3khIYhA63xurtXg1MaeIGm18NJpHMs8tw0iJqGUBBsCFwhPnKSNa/uHYnNpYa1dqzGAYDSNV4lIvu3u+88labbzR9ufG/3RtVsPHfUyrG0YY+TR5GSYdbKxobus4p3dZ51ZM3KiExmNfLm0fi646+x7YY6V/fLo66nv7bZ2du2P29/8wq//wI9/b8VbHPtaP/X+x9+0+qNvfezqu++/53Kaph8JPF8AKJQ0vv3Q5TWv+HRLAF5C4ews1HVDlcltI5Ps8OT2kX/95oH4yhNX6+NR2oSd/MS990/+OYpN4CyKTeJrKKRTXiqv499DsZlEmDn0bxwOxWZTtb6bSy3eGRXy61Ckib9QzpMAPPPzH/7If7T0v6yyPV2ee3trbSY+V45xef7L5ffJUaB9lfB3VQnXKd9vymtXfacKxasM3jzfZf61+S4YlWHTmDl38/II88VQlfGs5sIAJGkJuJxRdBbA3GdV5wDiuHgtCLgUZp6vbp57v6EyO6xLtMcypErhVa3ZBDhjEBSQGoAKxFGAIQhIQEUiWhU0P19RSQukk45vx0mcsINcT8e91I8mJvDrOSixA9OKjic623dY4x22WUrPPX9Zvv0xy52FAXWePOLBOHBXaw+T90xfkYDZfHMq4jRUz2y9dXI0fgu1Lx4xJic0fm0D52vHJjNS3376IdRvrIveYEHd3mPVFD16qA46uXYpHaS+vjxYcdOLX3U7I8/VRh3e2etS7YET8jaNn7dgEytkOuioSPesHCzV77vneR698o5Jr7k8CGq9eDoK4T0c8+W4KcMFiTqPm+18rzGdwO5PrPHdODSZFM99bcO/+94tXlsjdbjT4unuohl2T6jR7kur+yR3L9pJ/3ygH/pjSKMQG82ZcSy9BHa8oE0PuTtWIvJHlKqtNJ+2FNtIBUKyB8s6qxE7ltpXapsHJpNanzJBHJDvAwgEEVsIIniCC8KXK6utHSAVkAiAySHnODX5RI1cU7ZdCE2RVK1ASCpaU+ZcvLfqsMHy9eunWs9JSQeQAvCgIeBDkC64dSVy55fBSUp31lFZre7YyjRP2VPaSKFjAIGnPQdAMLMNfZAUmiAhLOXIKPGsIq+WR5TCZVuIZR0CHVBCRdURObDOYBycsFYYT4MiDZFOkOQ5nOdDqEX4sofcZcTyjGvGcdr3btLYDnRmT/zUfqvZo04sVKIohLPZiUzdpvM0EmuuNyzSBlTKbnx+GsZBRnpLTMeZj/otkQWRw3QjF96D067aDqfumaA3ldYqo2kAFiMQLcPmjDgdoB5KCCoyWFJkVolVFOXTcXmd+wAYWi2AKM8c2UbIyXh8B53bwyzrUHWLOsaMOtJAQUGpY6Zqocq950L52ksoKFT7KOy2AyDnqnG/I8Z3dfb+1DE+jQL+9QHUpBC1OEnJ5DlrrSsDXVQdFYurSl3N6/pUHK0KxZhgJrAbluduYladWSFsHgAiIiKQDWSdtPCqdNoCZhyFlwXR14loR0k5hrMnznHNOI6ZmbdPTly31rAHgwG0ksNRmtZrWiuT57FzfKSUQvVZjp3YT24h55RrqimttZ5jR9NkAk3aaE+nUklSQsVE5GslXSMM61KIFQBnJmmyHMcZ16NQ+56eaJ94tb5yweSytzW62u3tHjaHr+56rZOhuDdZ5FfGQ/u7T309Va9lyYXwrkTWWk57y0qky+JgY735jKs1hgPfXNxsP3H+3jcfMnY7f/+vf+ByIwrOXlxdMe+4fPeGFPSfS6IHmEmW3+WNkd78qDiXFdpWOWZDzJz3PRL0mW9+49Vnv/7VK08/f5A8PxFistoZ/uv1tfjLf/RqDBSpgxSzQpyKA/cD5X05jz/Jy6scOKDYkIaYVe1WiOq8Pt8VAF8C8BkUDtn3oigO+f13P/Z4+m2+2+vG7z7z0t868YN/Ac9/0EuT3zv89Kdrn3vpWvqT3Uv8f4yxjmJjuwtFxfhbyrmfxQzxrfpKziN2VQVg1aavCmIqXUkqj60KNN543UeY6T5WiN68FEvlKFYoXaUlVqGDElkignwCowOA7iBrbu54C2OKZ1PkjMwQciNhrYRSFdeqPC6t5piXvicDSlrUHGAINiOkTkISg4jARUkmpBTQPiHPHTsLWCbSujT8MeCYOJfIqcF+OELojcYZVMCJdhMifV/75UhKpvEW0QOrt3D2/mOKjzSs1pTmGvUFx7lROB43aHrg3OJDNBltnMXV7VWKT60Y0RVq4eS62xjuuPbtU7rTY9fMp3G3adTaQY+8k6ZYDmLu5JLdXS+m3cXjaS2wojHdUDeuL4lDGXjbnqabW77GYFX67dz0Fw2Gyciq9W236IfWcQAAIABJREFULRblS1eXRYBEYBopLAynZ1evuhWRjaP6ZHrh0l57ve3C9YvXg/rCtB7v1bkVBNq09zVN6qQBXjrTF54P1YgSbjeYRC2XKujDyUxwp0dm4YCcSqy2wik5FdJJiveXxOH2Rm5fOAO7c1oqydl4SNgdOgWVQyuTKshcc40ZUJadGw7ImbipGn5oih63lqUEGcAw8lxB5wQtC36mkIU+W4H0MUDOktTQSkklZNFFRVDRg1yUsh5lAZUDEKhSV68KIMp93pRrUBIgWYDgQYuC5/e6yk8uWpvJisaQA3DGZZSbzAopUPbFrYJwQUQshLKFISAWsCZVzlgJ7VkPEmQ0yLUQuADaSQgjQaIOCZkjY+sYijUAoaFkDqMk4DFYHMJwD3GSwQURlDzUGb0ajdSK9amTePmzzZEXk8EmNzwCsGFrVM8lppSlO4ET0Mg043gtC5f3REJgriUCQWAhQiB3xDeOVHw7kzxYm/D1FOlyGuh9aH0IKUJIoSGIoVSIwh6uFMUaykGpKhBkVA0MhIhAQqcWcZyRYlCMIrPyNGY0mXG5B62isJPt8tzz1KhvoNC/rRC+4/IzvoZCymqXP/4h99H3PfAXpcn8f3Z819n7U8e4evAWAXSNtd04iemgN6R6LcyNtRkRsSBiADdt7F4iCSKiDRROXRMl9o8CEZqiMKBVBFIR2yvHoUL15mQgAF+GSgtPYmYc5w2sB+CPAbCnVfOZ124cHwxGlxnO9MeT/T945kU5zbMr20cn05rnf6PdqHekkjtH42muCLfDIPgmSn4YEUlPBBTKupOkSCmVeoURI8/zRORHqRKKUUScALOGsz4TIUmzMAz8oB6FrVL771Ntb/HWYr27tLGwdPOpr71wuHnbna2PEtSXnI2TNG+27s5OtqzMX9gJuL+Xe90L/dtHqTfmKe0ebIlXnnpeTwP7q//133r/FwmNHBivSyHOPXrh3O3PP/P8tf5k/LPLrcY7a4EfBoE/v7FWRr3alN8oSzKfLhyjeNiPUThYXyWlnvxXn0y/+XufvZE2sLNKQn7y0//htz/x7u/7Kffuxx7nP/rGkwMUaG4DhbN3BkWl7f0oUg5vTNtWPM3q3laVq/MFDSjn+TSK9Oo3AXyqPO9fRRGl/vuf//BHvo4/w/jtl2/+2GFr4WGOp9fe9slfv7EQhXc1snRCzHy7u3IGs9TFY5ilnCt5oYpDVyF3Fe+UAUzZWSAehRBKkhCVMHd1bMXJm/+pjq0086oUbnXMfPRcIR5VdXqFDhakbyGdkR5DenNO2x1jWqCHWktoA6ScFsdREYDdccgq55CAQitPlALM1VypkMwwDEcMGZRgDCxyR3AsoDVAJFxiyOYMIUVOUggwLPJCQCMDPOGsNLphzuDq8Gz+crN3LJppVBeT4UquyMd6a9va0ZQixPTwPS+b3qAG21lwS63d/HDQco1lwfdcPnDHtXO63ra4O3oNm93r4uI4JXJL4np7g4JxJGpbDVJnhnJ0k5C9dtr6K6+a/qTNdGZ/evjQoR8q9lRzX39rosVLYpiEyyk46KhJ3sLp9kQON47h0r4gm7FqCnM7XxJrjRsuCnv29MatvB64KLKZagZJLP1EyfrxNAzGW57HHU8LquvwIGoNogYir+UaTJ0hCW2IJGCUkVqCLHLavx1ANVKEUUZCIJN+LvMUfLwvafpKy6nnH5c3rq+nvVPf4sWFOAtcRwdK2rT9Qt7wbYy4ljmLE48bij2myLe24+upEgQZDRVyz6JQ9LYKUgh4Ve9wFGsuK9cMESGAVtp60oMs2uiVQUaSFFXaWhRFFVSm+e9o6FU/ZbZGMaBNKe0yX7hROYbzo0L7JuW696RQSkrFWnrSgSWBKDexyHIjpBSCSIjEZNYyWyU8o5xOPauthJAJMpXDmQBKlK1nlHQsPGiPnbNgpIJk6AlPEog9KCWhhINAhpya0KIFDxKgyGlMbc6hJR0rq6xgdJ2vdjGxtYzgC09MBE+cgDn00jQX6DkDNVCZHQneW0thLNBoON9bM1Gw42U7xoxTkee1ni/csB30ofUJpOzB2n04ew5EOZTan9sPJih41VXr0C2kGeDcOqQsCwrJ/T/svWe0bddVJvjNFXY6+Z6bw7t6+Ul6SpZlWbYcsRqDwBS2AQNdFKmrym4PQgdCd4MxXWNUjWpqAN3uNk2BaUIBXdhgbFzGdjkIy5YtK1jvSXpPL4eb80k7rdQ/9tnvnvcsqvmLh9YYd9x0zt777L3WXN/85pzfdKBo+P+rKCS5CAWJUjqbEoXDXapZjD6TfPi+2wBcQFGklgy/7wDY+7W331Xaim+r8QrYe5nhsFpudnGS5Ucvr200MpVPEZHTzlLo+2x1a4uMcZwn2Dap9ZNLsW9yd9ZryikUk7Vk/AYown1z2A/xRgAQJwn2+oNd35M+YywEwI01YEUFUqmnBuyzJiUjWAKFCezrAXYEY7XNfs/dfmD+6kSroTkjXvGClx66/ejOoZmpM/0kzc8vr67MNBvXWrXq/FDPqYphtahgMtbahYyxglMkYtYBzjpmjDGpSkWcxoCj7ZXt3ayfJgkceXGWR77g2vc8DwVjdhwFM7WT5+ruQUILVVFNj80srjw76Mf9GK3xxKfeypa5nhlmNpLdhDeu5kmf24sbLLt4HerchXTt0uVzf/iRz331L25/j37Pwn8/AECcs61vXrrSyrX97tDzjtWqFVEpwF42vNejgKM0wBz7fYrLIoASgFSG95NbY2uXLq7/9S///F/PVKL1HyXLe3WRfOxHf/IHyzAy3vzAa/LHnnqSoQBBbwfwT1FUYZeAaX8a7Ytsl+CuZMpGK07LwpzHUMihfApFl5WHAPyL4XH/GMCH3vzAa/5BRug/rHWent/Z+OzP/rtfuU4vnWnuHT7x4pWDR199cO36D6pG9S2O6P5E+K9HkaN3I20A+6zc6KZWMmxFFVyWOfR2BaSfk7gRqS7Z7FLKpwzplve//F6GT8p8vPJZlWHeUdmJ8ms/HE6MDat1R9iSm8bwHNyAnIAjDs8jeN4tQC9ngDSAsYhByGTJL5abtQExBhHlyDMFBYIIJIxWxd4PBy6YM9rAmpyFLCemJcgQGHeOBImUmWjg9wdBvhPxbpopBJ0d34t1w7b5ds5b2vaimtxb9RgzjsK2co997W5z5dSY6lUn1c7Reww1fX08PC1n4/PiaHXJNSIeK1+KvfV72bWNN8sFeZ2d2NxmHEzU+pMube1StDTvdmNP6AMXZTiB8GtLb/TVlqHxuVXrDr1I/abQrc0j7NiJr9Hh+z/HWnNXjddewYGZNdNs9VFrDOjggfX8xNS2GgNI7sxKCvY8J51VyvOMcOSHylXrbkN4GJeRJlR7HvN0wGQO7nEmG30rPGO1AjlrGUkNOChjtAsrIKtAOhMMzOr1i5yufukYZVe566faTjxwvjt1Ypmn51vUC3bIermuRi5Hq6+4kWSbl1Ula1ui0CBa1Xml0zdMR2x6S+T9ACb3U8mcIJBXALHROW2G84yxIQYcnUfDtamGOaWSDZeAHercEfYdk1KYvpyrI99tGfIv5bgM9nNxXVEhblEUl5gI4JYRoxTaauSwIOaMZc44MtyBwEymtHbOWE9IQyDJQBIAy5FbBe2EhhJMhEqnph9vkdLG8iB0iTDKZx7jYOU1MgAugYIH5opiEVK70JrBxdthRis8E+fknoql3Z7VkaxYH4tZNanB41si6V6L4t6Gp5t+DtQtYB12a4C3GmIs9zDgHnUNEOekug+uJqjsbCRbVW8nqwRVFHvVmMgVG9vr2ziUyxCyPbz/Z1AArnJPC6B0CK2mAapAiHR4/0sx9rKApmTuWijsWQ37hY6lRmcZFt5GwQImKKSyrqFg9GZQ5G+/4H7rh3v4Nh2v5Oy9/JAAJk5dujqW5rn4/Knne686crD3wOFDZqpRr2dJyrNekgaO91aW9tCMGrt+w+v4be9+ADxW8V5qBquG8l5LTjYFkxsoJlaGQp/tJICNVJvlXppmzVr1bgAqzVJrrNGCCeac00KKUPAbBqv0UMseoz3sV5g6ADvK6jmT2fMrz6586sjdi/e/5tiRORDae4PYnrm+cuTs9eX27iBeOXFg7rNSyp08z4/002wl9D2fgVym9QHJiOXGIAgCDoBZbUCMmDY6sM5m5Mj3PNmYaNY2E6Wv+5KfFJxyxrlBEZ5sogh1/hqAVq0S1d569FDH1up7Uunn4sVDjb9+6hmxsd1v2DCUm5MMl9a3Nx+d9M9GCyzRl0Vl7fn1iYoxkYL3JkzsPgvg1Pc/MXftrx5avvLAz//KkYla9Rfe/boHTtZC3zBrnTZGCc5LmZpSXsUz1kpetJUrQ5GjRthhv2y/CiAe9NPsI7/z9TeFUdyM/HiXC/oiCgNx67gNwDsAfDeKsO1ouAbYryJNse9hlsCuvM4SdK4C+CgKRm8DhQbfawG8d/jebwD40P9f39ufxsc8FOGLZPr1SJ7+6r2X/vw1j8z0J9qr5x753uT+88/PrLbGX59xMSbhBsPn1B5eTzC83tG+s8DNDBwAEHw/RGOcIL2SuSh7K5f3t7zHJYNXztsc+zl8JeNRhq5LFha4GXSW9yzHPiAt/1aCxfJYCW6Ez5mFCBjEjftehp4JiE0hfjvU4VNwgYFNi+KPYWha8iKsmzJujPBNzmNNBj7zoBxglQZX4BUC4Hmgsu+u5WBkiPmAgJVpagfKNS7yY1Pz/oV8rJXK1uAb8MdYvJTcyeNLEeUrgbmmj4nNbs1Yzdm4u57PSmPS8cXU8wIham13wObwk4Bt+alV0uFMa1KOdSRr7AiIgJswD/LN3GPWPMgmpxj6V4htrwt1R2sX93Vf5BNBCt7cQKsbublGZu6b+mLQmDulA5nqL188JqDJveHkSuwhDGJKnU/dfG+5gmjlTk+EfW9veULl1b6SXsC0ybdY09O+2z3GCL6zcCAI4vDQSBzVr1uiYm4zBhIenFEgnUGEFRhuQFkOm217NpB1Sl9o5P4OD7IBRz61oVpN5osn35bn1+dgqaGd8Kp5uyP1/EVNyUHOMMHXSZnQWBnEbWb1gDnV6GdbMkj5gEJTEZngzN9/7uV8MoBvASgF5QzSCgAECErWfeis8DJVYTi14e3XFd1wZmg4H0dzUYdTULOC6QsE9ouCkmJSGs5gMhRSP6Io4dYAQmIgo0GCgUDCd1pklsiRAIOUPASodJIAwDPGWG5YHFnhiMMmSPKe0JYT1wStBiZGzK0fQGpRtBKUAHQPSXqZ9d249aI2fBHDmnUk+SR8m3I32BOp/8b+XGgHZixhLn6sua4OIhBXeWyfqmzXBkzLVg6+HqARM2wAyJiDV9zhfK3LlOpWJWOKdl8aDza9fiXuVP05lMURWnOpzMJ8nMVz40HnuaKzTQRgBll+J5wLEEgD2ACSz8DJHEQchdNb2puy4OLOoW0oAWAfhS0rCyXLSEKp7zk+vH+ljuhpFDJTHAXQ/LYFesArYO9lB2EmdVhd/syzzx84uTi3Njc+Xg2ld7USBqfyPL/tuedeGDt9+nx3eqIdN0Vt47UP3f+NymzkUIC4idwk2VJ6bvWZpVPx7dHDJ+9fvGNWcL6OIiH0DIY5SWO16vmxWpUwbAxPRDVGzDjncq11BkIiuCh1/cqiCsK+VEspVjsNoB35QV0IWv/GkxfrIvSeP3jP3LoQ4uKT5y5NfuaZU0c6g/gbP/Ydb3hsptUSAB5X1j1xaX3j3hPzsw8a4CXj7HvIsoGU/AbbI30PRilwxnmaal4JfAgu/GatNtMEsjRNozjLM6MNg5RknVNJmu0ywPd9v8lUHoScR0jTEF9+cv3O73zj0trR438W3BW8A8qeeOFvPjd5cXPLfeP5C9GrNhqnshe35p/rJORBXMpBV2H4LoYLnDCTPH3xyt2NKHww8sWk4IL1k6R3X3R4WXBetqwLAARpmpLSmgnORRiGZXu4sgjAB4BunEASEIYhB1A1Tn/81NNPRWO13ItC85eM8MVPPf7Rm3I3PvjhD42jYC7fO3xuwM1Az6AwOiUQKc8dDb+XuXDZ8P9/Pvx6PQoj9RMoNB4jFK15fv4D733/+j9g2pZsdA1AT61vbC9bvrx7+E4O4I1PH75jHM5FYCwEUdnBhWFfSxLYB18lQCtBbKkBRkTMwQ9z3Mzm9bAvG0PY18YrUw4YijlbVsuV4fVStmWUAaSR15SFGqNi2OXoDY9Z9ocuq8uBmzt4GNxUpCEsoFiRrA+gDpMCYlhuUV6DBRRMogU0WMykg3EAh3PcktnJJVUYeFTm7rp8mMjvikT+3IAZtmN7HutmNvBRrUJtbeiD2TbGSHUafg3kVcMzZuH4ef7iymKuNoV9oHUGOU/53KwSA3+7Mj9gbHf3VclaewJ39c9oXAlbzV4Vh8Oq8WqbabZy2LsoyCxsSD7e83m6d5c1B5+yh2/zzd7VA7S0ltPCGJk9dZwF2TmXNhTJyRXZXPhCL3RGY2MmmGntCOvZlCuZZRuLIp1YC5HnTLYGqUsum0HaloNBhYET971BuhK3eRh6M+1oVzmNfh4jpJ0pL6inLq90YC0gBRyXkH4IlSZgzoJxAbI9iO3L3AVVsPRCU+V7t1lxyctn7l5D77zHwvWHehVvu7W2tmSM6Ow4ukNlewd9T1zpt5899PjObvN12vl2xnmTdcV9oJHGe4HeyNPKgaxlKgIpiHdTo6aJWO6xGwxzKXzPAR3SPvNG+8+7nOteyQYBNxwNlhfzK2WAE0UVaFDuneWctcPDORTMWzmHLQBSKs+MtTXnQWZaI+Tc8xkrr8N44NYr8vksCKRzgoMDPJiAy3JtI7PGmSyHgyPOSDLGjS99t4c47kHJlh8Kz/AQihkf1JecF0V1UBWCMRzEcm5o06a5Dym2kHgGRnWF3hAZc5EMxG02DBScPu/15YPdcaxEMXOMTIUJnkAHLRO4sYS2LlWSfubjYA+IQ5tu1aWmrUxOGCC0EvFyU4i29ddAdDeAKpSqINc7CefLL8y2MyelRBE6vQrgBJydgXMVWG3AnAWshCeD4bIPsM/s7Q7veYibu2WUgu2lXdob2pjG8KvUCb1r+FxOoUiZGaCozv22qr69dbwC9v7+0XrPm157bK8/6DKirX6a/EU3ST+Z5cr7z0+/cODK+UsPtn0/PXJ48Qtvm33TMgrA9RgAWfdb8freldXOWuPOc2L3hVcduJHPJYy1b0iz3AnGVnzfO4mCmdMAAt/zPQAs1zkERCSEcNhnXW7Vd4p2en2+NxjUFycnOpyx0xPNxmdfe+fxF4/Mzs6cvn49DHarwZPnLrL3/84fXqiHYX58fnrtwup6ODPWFAcmxicqgf/iq48e/n9QhFxfZYxZ4ZyXreAAQHhCUEaKp1lujHO8aM14g0Jf9TyftHOrXIjbjLVjvd7A2+n228bapFmvyYnQ46hGwMljze16ZebZ68tBa3qybYltXd/e/JPj83MPV4Q3s7G22ba88sSRYPfxDZhGm7kXnrPeeezU3ccfsjkA0DveFgA42ouTPFcady7O2YXxdpcTncVQBsQYO9DWtJ1zjBdabGU48dbwbSEg51z5ea6vbnaW65Ns1um4FEq+KUw4lF35rwD8zyg8Qxo55mhhQQf7DGw8vLYbLFhu84QRWxMkys4Ur0JhlA6jyKNrDn//nQ+89/2X/iGT9ffwruSn8bGLAPjWv/hs0HZT6aUP/noPRc7f68HY8eFzLcPWw0YAN1hiDzcXlpQboL7l9yEYMxrIh0LGYRnmsvv/v8FGlwAbuDmUPVqlDhRgEdjfJEsAOsosjmrwjW7I2cjv6pb3job0DeBxwCvzZwECd9rCqMwjMMNDf7hRMAfljLKWhPRckYnPJJxlrpcZkgEhouHnYgQ4U5TlwgGWO+c4MUeS52kKm6zZ+vZmdTxAZcprbCbMd12bLdYH2zgedi96plXXruFOVK6qDhoL57t3ZUvBeKciLvcOyLFNTx7wfZ0OFnl0reZMHJqpmY73qhfn2ZansJ6v4BiRqUydRjfpCcl9Phso69kQ1eNfNZ44RRgk6dj4Gb7QjLOQG5HsNfh6qr3Ds+eNSGastqirxQvwGJG24CLnUT634Sjf0OM2cv5gXJid6d7RhRdSL3COO+xkGlMw8Fm/ISA5UOkYEDQI0uQwWQJyKZwIYYwEqAYSKiJq9YgLyN7ubuaf3PH1blVjed6KqUu+Onw53dtbrPjrswG7EiZ9Fyc9sy0HVyt1TlZ3ZTK9wrb8O3IOpm0ojQwTm2NgBruCRdrj0l/LetQWflCjlMFCCyHAuaBimnQhEJEounGM5swCN1eHExCWToIFUr0v+eNKhq+cb7JwIMgUsj83CuyHXVnAwODDIocGxdqAwPo+i8qcwlIrkqxzLrWGC85BgEu1JlhddGAWAgCRYg6CSMMhcNbazKjM45yPoWICuAC+49BwzDiRsNQIYiaDchaOQi25zyWzwmapzlkMS5MIRV17FIF7KTP5EpJ1zkWzlYY74zD6SdqcXRKDrgZqHGDCcVnJWBQKYhkccd9Vplo84A7ZtmEXUIRhBYQ4td2unwXnjwLYBecdcKvB2UBJOY8iXaWPQjlgDgEfhtC5LQtpRtZ4GSXgKOxrFzenz5Th8tLB28W+YkYPhU0t8/f08P/XsB/huOh+64fLHPtvy/EK2HuZ4bBKAA4ujLcbF1c3slyb2YNTU3+y+FM/p3753d+78Ivv/a83fvU3//3vjhkn7r3v5PZQqqWs6tSMWOuR6R8NH5nGN7GvwB0DiNI0ba9s7epKGNha4Dd83ws9z5vAPqiTnvCQmExaCztsFwN8q5SHUtpcGqT5urGgfozz233/o4em0ZxsNwbs+nU2SLPKM5euHsy1OnTiwOEw8uXiC9eWn3rk7jvehgJUbGZKnSZg25NyQmm9lOV5lOeKO0Ic+b7n+35V56aSqMRFfpVC37+RHN8bJA2lVKteqzQSpbTKrRFCmPFmzUvSxOc6I7CgkJfSOqtl6jWHwspL4cyUD1Bw96mzS7PV8CNfGGvfvb3Xa3w9T85+eaa68xu9NX0PaIBPfeLWylMHwN2zMCPfcMcJOjA+ZuuVMNnY7VQnmvWYMa6SNGkwLpQgeARYzvmI543RhG2EwY2IZQ7gj7tp9sL8PQfPpt14+y//9k9ulSsBivDtf4sidHvjOWA/h7I0FgvDcw6w30tZAFDKmY62dtvBnBJC/GcUTkCZ0/MzKAylBvBZAH/2Mtfw947fw7vcm3/il0V4cXdh4xf++RwKQHMYRUVvE2mSgCDgh6XRLO9NgP2q2HKUgKls6VaGWodGVzkgM4BfFnCUjGC5eY3KnpTHKu9DCY/L9zHsy7kY3GyXRsHfaBi3AI4ZGPTAA2MKYahwcyuk8nOMgr9S8ohGz+BcQVvupxyGjtU4MWsdwemistfl5FcCcRtnxEBAYIueqMQAR7A2Q+YYCNxlyrGlmNRcK6rH62hla4c2a4uElCV5uOcFwWV4wohl3LYrFgdMm4HYGvN5cDhwnb5A9zy3+fSs2xljenZ3JV9OQrbd4H6VtUwz3mN1ZUVaHWDCMDcxlVHVcMpb686JLf3czm32/jGSebvhVDUSE3LTrl++K0wae1ZOWtl3ddrdOTDQtMFMRRnf3xasonzfwJGF0xzWMOuQE+Wpp6Oa4YIlQkzvTfs1U+arzhEgKYTw7jgHqwmetw+q+x0g2xRw1xeNG1tmjqfc5gJxv6r3LsbM25EZWefY1kGZbnCvMu64nTmnt17Mulk6CMx4zAYrlnV3WLp88e7WRaQnjp58nifMJ7l2XMXQIpSaN7hnZ22QGthsKU4aswHJhg+b0cBKwPrW8wycibPECQkRMhiAlcVtKOZbPmyMIUtGaJRZdsPXDvPAmDd8XbkmhvPdcMCxQtKHl3O/ZKS55J6VHFkhfedCj8QN0Wbs94emxGq2mieY9kLLedEVhHJy1jhSpOHLAL4fOQC8n/WYdYYkR5DBOQ6muRRWIXNJHmvmhFjWSkx5VVETAs44I4mLceNrDjAF4zLo/AAix4A2EeySFwcxcUzYkOW+8xtKNvIA1boLKt+/MZZ8bOLqwIKPwbpgIau6vtffMdqNdbosGji+aRnrDdfwNIB7wXlRAJbHm7BoQvg1CH5teN9KRYQ6gA6MHgfBwTIF4n5R8zK6fi2Gz2AGRVSlZPTK51SysgyFJMvM0E5cwT6ovzJc+z0UQFOgAJtt+rk/iwHE326SK+V4Bey9/JhCQfXuNKLgbD9JLnFOF9Nc+fceWtwMfN589KenxOvb31sNeASH1XUUHkLZZeEkgJgw83WH1RUUeV9r1tr0yuWl5WvLK+nBxfm7XKV6e6NZyzzPKz2NKoAwUwqw1hnnGOTLPqIUwEtTrca1qVbjqSyH2NiTg9NXBdvtn60dmp7cevieQ9We2rtvYbLy5CDNxCDN3nppdf3QfQcPvliLwh9Ns7QNYG9vkMxobXfqlZCTNQe1sc3Lm9u7E/WasNYa69wlazCWptQCzyIihkoQBrlSpLU5yRjJ/mDg1ve6drLZEM1aVRHRXjDYYlb6LWcMKAgApUMv8GcOedKH52+gGsU4fviOtzn3tfTK3hlKcfLLa1cnv3L5fPetDGvblu89+vC7GYZU/ace/2iZu3UuB9UyrfkgzdBNk/HNTofqUchqFdmWUgpPyDhVuZdkSo/V/TJM+C26d3xfFzcFcPnBu45svvauN3xLEcSQ0bsPRej20C3/Lo9bholH9edCDL3N3KrUOrsTcD90ZPYc6IsomOBFFGHbwyiMH1CENd73gfe+/+UAJwDg0YffXerV1Vd/5Ef05ve/0yHP8/A97znW/tJj1cGJE48Oz112uwgAIjhX5uWVrF55E0YZ0JKpHO0dOQrkCAjiIqSlFZCwERbE7L/mxoYJjG6efbB6BnQbsBA32hWNVE3eGKMefWnIg6ogAAAgAElEQVSAy1AzYCBgDaCUD0/akfeW7ynvH4188ZHjEgCQZJAyHL4uAWAsU12yjDvwioNzupBQEx6QGmIWhSgzFOBLALBJ35FRjMChU1ixsmT9pWWWhUccNZTb9ucFWECQkLmsuH4uGPfr1Z6dE9UDWcqXV7FrTO7SKcbdfGTaG5nfzLt3PjkbPZDl2KO6t8HHk+CCkXd0FOqhNFeODtiJFcdo6wh2J9ZtvOuJaKIKed+aNbVTTs52YLIq01oOonuepXMXDnoH0kW/PnVFz+1MBPy+05w4wETHswwWGgSDgWeEShKtu1dqImzrml8NHKzwUNkMsC8TJf0ADhrWbgUsC1PAACHnNoshdGJ4npHLTRf6sXtlcPxiNuj0U5MM+nalbdVXH2LRVC9YX3KJ4KKGqp8GAXHP+WxmvNuLg47Kp0/xoNrL8pXbYuXPpntJo3El6blsvZv3pDb3iXGR5am8nMZiXIpWR1nZqWRugWsnEDgVGyHJca20JkA6aw1YlQFeCQg0YPsQawJ6LAJkObfK4osRxyMoHZFRhnskj5SbAvBxYJ/VHi06GjKEzIUsHAWCpf7qDda76uAEkXHl3ySzcIY7CSQ2g8ssEQMpM4CBJnICPnFmwDgHTy1E4HEhjXOoQLgKkzqE1ODFdfhIlQMZoaU9Kuo6gBADGL7pUkwbX06l/swJ17IDqFYM5R1MKyyB4Q0rRVUxdbbR13UlWNMJz3JXcY7sbsqTwMeBkMTBgdNbw891F4AmbK6Q7U37mUuycNxC8GO42fmuwbkuErcGzqZAqlinRYcRN3K/HQgKDgYFozf6PMr7Xj7bQ9jPlw5RRFBKkL4B4ASKvOuPo1A+cCi6HdXp5/7sSQCrn/3dH+EA3COx0/Rv38oBWPcLX/hHW6n7Cth7+VHmHD19x4H5zt8+c2rlf/yDPw/tJ/7YANj7o6v/av0Tq783s6c2tm/f+87uE197pvHjP/4DDc75PIBukuUv/sHn/6761197OvrMr//iPIDja2sb6cbG1pd/7Q/+397s9MSJNw+S88cPH3hhfGKsiwJIIM/zMWPM64jIJyJeVNPfVKlYUtQZigl8DIDzPbx4cEYd6ueX6o89f3Z6slm/J+SZenHjpbs+9/yT27cfmF/9+tnz2+ON+vHPPHu6/Z333fkHR+enfpgAzThbztL82na3+9ZKEFSWtrbTpZ3t9XolzJJePrbW6aYMxHxP+p008S0ATuRABA5rpXU8JlC7XoMvhaU09WBcV47PweY5QTCACAh8htlJBi4OgtE0hNjCA/dMAgjfdHv8H5/6w42N29S4ffzy+Qs7BIVPfcbh4XfXUHhdVwAMLv/eb3rv+9Dv4erm9uXICxYtIzaI81QpN62t1YwxT3LBtFaeNtZxBt86V6II7pyDNRZc3CD3yj65fwXgNGHm71vIbQC/hCKEeysDBuwzWSVDVm6IbHh8meoUuXOagI/53L+IYkP5IRSORR3A92IotArgV/AyycKfiyg8f+jOQ18L53qmOnY8nZmZ9nd2rsuNja7c2DCVZ56pIUm+Z/s1DxxzWh9H0r+OsDqGMmTq+xH2GbgSPJXh59EiIHJ5AghPUCFgXBa0lNXOBoUMAhtWFZYetUPhLZcedwkkNfalgiwYemRRGTJ7o9Iuo0noo2weRo5XGngDDgviBn5FwGejDEzZpaNMoi+Zw3IDvgn46yQBLKyoDAk/Aze3uU65x7A+fhsHcQlePnbHAa2RZNYQNzxgHiD6ZjmR0JBymoTX3wOPe4S1TSc5E52pY9pvq56fbIdZxxcBCREwp/VWP6t0lsRUkHpZ1syCLY8PdretEE5XjpF/Wy9SBy6NVdrbLbF7tOfYlBXVaNNGsktTCcuw2tG+6nh7Y86sH9GyL0LbyOpc3X6Wd2Tf2O05F7R32Yq+rV5tPp+e2NujRtJygRaOuUwl3SrjtT5nHkCWM9cZM2FjVzrSvq2A6HACT5qMR4lDAwR2Y47cCNdnKayOI0YidSTg9MYER+rBpMs2fbGJLE+Y2YWpJJO2f9FX1vBaFFlj25v9jPV6tqVDGURq+Ypy/ExVNlupDHvTOxftZv/5rcQ1duuNjbRGY2h529VBZlqZ31j1ZOwgv7Kzqxd4BYk2Eg6cccanddWElvHdVONi1jPzUrnbvFrAOGmf8xxw4VAmBcVcYBq6UebVluCjnJejqQxlSLCc26NpC1RU78pbGcFylLl7AoDLtDbOWiGllJzYqGMFZgxrMGnhnIBiBOeUJyUp38HCwWmrHYrSI4fICnKaE7MZrNhCHHqa9BxvMuZXyDiDaQbtgWfD6xEpUu1g+il0ooWMxuEJB0frSNSmUHpcRewOVx8QI97xdCQMqenENxkp8c2xAeMs8H2d2pxyd43nuqqZH0HINT93KdCE03soqmLLlJBtkBwgHJvNhOMg10aWC/heDwVgSwGkIPJRCSsAOPI8BygdeR7DPDzG4cq8bCMAJwFRRn9Kx7Rc22VeO1Cwph6KsO3M8OdxFNG27wBwL4pijQEK4H0IQP0PX/Wuxj975mPr9Iv/dgtNfwY8K2W6/lGOV8Dey48y3LP5/NWl+hdPnZ1/52vvn1nd3tmZaY8lD4599+5frXz46p9c+zfJyU+l9eMnDt+zurK+M78w+yiANNf6eaM0f8vJ2/ny8lorDIPrX/j8V9nnP/948untje3s4qUzP/ND3ycOzs0kUoovA/gcgEaW5abX67+r3W59j+/708NruIKC6fkqAKe1eUsvSWJPiMtSiB1PirXPP/fC+UYY/MATZy88+Duf/sKFe25b7L7+ziM1O6h84533fwe/fmDne377E5+t7/b6189by+/7+V/9xMf/p5/73Vzr91V8L3ztsaMHunG/P0hT3kuS6skDB+Ty9i7OXl1KQMTqoVQzrfbW/PQED31PGGMghWSeJ71etkupi3WIFrNWAAW2qpOQWiutnLaeLyxICCAMNXLlwAWhYELnANQC3zv4iz/xjpWwEjz9R3jfpRHQFaNYoAkALE6Oj//s93/3oRevX5UP334oaleqrOKHfLpZp8j3fABIs5wba5gnhfHCwBmtkSrNg8BHmqZDsl+YIBCUpmpnMHCfGB/3/wfCzLdQ9x/88IdCFEbhfSgKKEaBXgnCLYpQ/TUUwLTs0lG26UkBpIL4mT03eLxBlWsojMlrUXiXFRQGpgRLjwH49Afe+/5vAZ4f/u0/a9nL174r//o355w2oWk01pU2u+zvvnp/bXOnkb3x4Whw7NgRtNtzEJiH0W3sSw8IEBH281bK8GpZgFL2YobLEofOlkGlalBplZV8o8zlkB0MFBCULGEZwq7i5pDKaOEDAAhECDvRjXOWjCGc0mSyzDLBDQuC0gsn7Eu5lC3b9pm6AEMZlRtAuWRyRzdji/0q4/KZYeSaHKwFsoyBGOAJt1OLlPJgASJnHQeQEUuL8ygpcgfLYJlODJAPpJiqCvT7hN66tsYYO1bjrcO+zT2jbKfDTBCGLLEcFHAdA7mb4V3Vp0EMOq1mggXNzMISX39gu9LsXO+E62k7cZgVr75cgYwNaS3Q3kjdoTxi1YlVe2WKRGOpTS5cN5W7nmO7E5PmWjqrwmbXXQ3n/YFWLNLjtq83nNfo0fEwCMeOPWOk9I2t7Yow9mA6oXJ1EFmisD8GNxjjKvFcJ9pF0MoRNrVxDr4p4K5LEzA4uCAEQwo4idw6SDiuCZIjUyzlWw4Bt2qtHrPBbOAHXdnvBWm8M4jVQA4GvUhMHWSs+Yb1ZHdt4Mj4Va+/eN08WVOd6e0Z389de2xza4zXOkt/c3yhHwfdytz4BaG6U5fzfqWbhEp0++HhoMWhTc5Emp3LOkGVOD8SNtxBHmTkh8F6nmAzSzHOPLbtBo4zS82sxr0o0cZaLlwI6xxnRKGgBrAPEkqQUTLFGQDj4ByBSgauBHSjLPioU1IyduUxyrnHAHBrrYZzpI2xXLCywIkDCIX0jDGaB1xS4hKdSUPGOqdyGClJS8GdtoZpZ8BIkiQQgQTXVmSZsrtasXo1tCEnCyLHQIlCLjINI5jY2ZWu9rzf1cxykaiuOWlavZbhfJL7WUPLug+RJFDBGk/cNR67Klhwd9q+9JVoY/bLtXVsyiSe0AEzzoTXAxVyZdNEKgwr2ddQqAscwr5TOA6ieYjAwikGYzicy5CmaliFSwj8EEAytE8Mvl+u5RJUF2kSNyp3y3A5AHBeOJ03wLRBYeMU9lOj4uH1lFJTh4fP4goK6RWJYi+6gkLrNAfwgx+96+2JYuwPkc0cQHwiR+25UgrtH+V4BezdMob5epMoFn3nf/mTv9DPX70+f2J+evEvvvLkxP1HDn3t4TseTT/+kIXDati/K46Wdnbuygrpjy6AnCwo39NU4TL83Gf/Tg6cDR556+tXPv/5x6+lf/05BaDvsFrmanQxzOur14669fUnvjE7638IRVcDiWLyCQDLACg3+tOPPX/29pMH5qoX1jfHnjp/sbO0tVtZ3d4xj585v7g7iHf+0zeeo4PTE+Ly9ax7ZH7s3JfOfNNPVUbdNKt31aDOOfn/7uOfPry0tXX+X37XI7OnLl29fWOvt3jvoYUrT7x0cfPo/Ey3EYRzQegzmw/Ob+7tLGjrFgZ5zg5PT8btWlUILnwAQgjfqSxJszjl43XJc98jzjlx5wQDtAMYESsWr3W7SNMBpJjGfuhuOkvT7wNA1tgLjLNzAM4CwKce/6jBzQzXxsnFhS8dWWj82HilGRhjsby95c+OtfqWaA1ABQzcGFsXQhrBucqynFnn4FmrCfCtc25jrTNIB4NBZ0f+pxcv4vM/8ZOzt4qflqOBosvET2K/aKUchNIr3c/XK3NAylCuRMEKPh3J6KkDhbziT6LI0ysV3kfHEoB//YH3vv9lE4W/8T3v2Q0uX/7SwWfPLMisl8le77zY2qqqRuuHXZ7NGOcSVKsTCIJpOOtANzEMCkkiAIQIwxKElmEoH6OFDdKzqFQd/EqZ+1ZuarXha8u+keVGV4ZPRpmQ0YKY8ucSjHm4uZCiYBuNYZbIknO35udh5LXlvS03gtGOKSm+tQ1gCSZHX2ddaqx2ucdJOuH7GnvrhVhypQKdcQzkGIGDA4yZLHFInRMtRoCDY76TEgzcOZPmHMKTxJxGKAXsuEKNgbgWrexF2qxMZKbRVGBBFejknonJhk10dw8T68ZmohJzY4yTvqguzwaViu0bHtfZRDZtnBZmuZpK01xjplNDeM46b2bV6r0ce3sTZqKRKjm1a9cmfNkJInas/XfxqebBaOPqW2yjAz1/4JLbqwT+MXHRTutVx3NNiHJKEhju555czIvnl3gO2xOU1TZdEuyKrfUWpqrWhg7IrWaZg40sOPrMIrSEDAb9SLgg9qG5Yf26Vjqn3nqWc87JyQGyTCeNB88H2Bq3bm3Qd43rMbym3yFfi96k3777Oq82dzDwZ7P1S/HF6y2zPOWxww2ZTqn6drNx+XXu9uDExNNK9ah56SvbL4Xp0qVolnOPR6YXggR8S5R7XX+bBm41j9AnB02I7nJNLGWx0dZopTVt8z7rZ4ofg5fX2ysOcUWonWnnHIiIAiO4dtZyxjg8LgYjc84CSAysTZG1GAhhEb63OQwMDA+cIwJ3IFGyd2V1uAFgnTOeg00LscEiN1cIAWMM+UKWTHWZUpFJYuBcVhKjnRUgNnRccmvAjYfEGuY4Od9wRyAH5zhSR0QwkywkeIElm1tFMvaYsBaWdpGornKdKuSFgIu2tHzWz51HVvAENq9yb2BgNlZFWp3UQTeC1HVj64r3jc+k66hs0MjlxolerbLXSCQAbmH2LENSMVTZJJeDbkgrTQ0/T440ZTB2DZWoBSCElApSxkizFMYMwHkFhQ3MsK9WUEYBbk3/GNoSxwDKiup3JwAqwXfZmadk8cvK29LZWx3arxsU/dCOMRRkyrtQ2OUHAawB7oiV4nQjj1NAXEPvNan7X3/zH3Uu3ytg75ZBmHEOq6cBnCXMdD/zzOneTKs5eP3tx2YXJ8fvPDY303ZYXRqyTweq1Wju9DdPTQeeFyzv7D53cGq8U4+iS4M4MwNg6z9++Ut5sDB98pxR5iMf+Q9q5DwKRS++m8bRIw9ZANccVn8fgBteT5m7Vol8f/Od//q3L+79+e9OfP70mYfWO736nz72lal+mnsoFsw9v/XJvz314U9/Lo9zNZi9fTXVOZ/sZK0xgJKpQ9s7MzPOHD527mT36/7e73/2ixjE3UniXvNrF87X9uzKpW21KOZqB9K7Fm7Ltvv59G4/350d9yNiYH/5xLO00K5lD91+XE026hWGYDAVzq7FJm7IayuebtRiO9lWNs3lYHsnqFQjizAsNu3A5wj8Lork3WJDzzJPZim3QlKSpCeSfvzQ+DTOEWbsrfcGAJsdax1L0mAyyRQNshyLkxOQjCKrTRM+oloUlQs8BXCRCOQsKkrZ7QB0KBvEtXDpcnb6by9euLRcufDuX33ThZcL337wwx/yUdD7v4ibgV4JcByKFmbjw89SGjkGwLPG+jo38ALZBdVOA/EVwNyNAkACBchv4GYwdBoFuH/ZcX0GCWYOPrXwkT94+o4f/2cTu294w8Lx7vqR9bnDdnduIUSjUUGjkcM5BmIOvl8yCkWuXp5beF5pBG8wasNRypWAGFeotMr/3WjQfsvnL0N6pdRBmXBetqMrjW4pG1QWVZhb3ovhd0eBL6QxoKILC7BvqMvNcFQWowR64sY5rI3gnEFRhX0jL8upQqmcisMOAaQbFooogJGE7xfvo8y1+R46uiUARjDKWgtYZrlJyDIQWWTwyVpf9lkWNA1y5IgVonDANfd4rlnurMyX228VmfAYJA9hCVPbgs3vnjcvHIkEXORedV6Lh7spN2FmV1eabskkfIr3qOJX0DrPeKWzaa9Ud63cqbPjXguxWta7y+OsZ6qWuBXdR1eVngzS3uC4lpcnzFffXLNPBq+WXju0De8ufo+74g43nuSaD9y6Hcs4GS92gst4QEL3UAmdIoKQxjKYBGQ4hZHG4twm/AAsu3AMDpn1jl4V2sIhF6ms5Nw6+LnNFNfgsMSdIZssB5YqvdykxHUqwBxM2jM9l8VReN+5TDGVBxPZ+PGTV+2e3ROraVKvZTZefsrXa+eyY/eeqD/klt+e5vG5y+Nvf6pLk2x5dtLd1p5PGjLw3/3JvB18HQ51o92h0MtbHu+/FHdqqrGFTrNP1asnXJLn9ITaQATmetAw2tKOFzG/77s95fTBCHawNMsIpAMLyRgAzriz1jjrrINj4Aiwz94JABGBegRyBLrBVufIcyVMKLUGsx71KbMEbmsIhmHFXACezvIczqUI/JokEgYAScYhGR/NZy1liRQAljtjda4YCUBKPqwQYdBOJ7nToecEM45M7nL4JCWHMxzK+sRtjhipIreTaVQZC2qRoKyuVB1BNdLirqrz1XQabhnYuYq1fces7UFHFlYb6M2/q2xc2hJpfcGEreVwEGZ9pY5Sc35cBNUqkzpm6C+msnYxzFopR0cxx4fh/RwFWNJDexPA2D4Yqw9tiETh4HJ40sLwIrJQRNR9FPZao7CLXRTh1nLtD58FDdc9iUILE8A+S1/m6PaHv5fg2qGINpTECkchwJyhsNlvB/AkgNuH79sCnEXTpbevnYvfev4rCz9w6lPnHondP3pZllfA3ssMwkwCIHFY9e0n/jjHPi3/NQBrhBn363/+fzV+9h3febQeRWa8JZ98aff0nb/6l189tBAdW3rr3Xf2f+mnv++SJ0Xn1z79cY0zvecv+F9S1574JQLAfmj+v7M/vPAb/8VEz1GwM/x5gP2ODJXme/757Ed+5r/58pW1zR9Jc/1a7LNMx4y1G3FuFYCZzSvtTlRPXoDhtwMY6+9U/bNr9jXXr2zeY7X4QrKVn2dSJ7qTmKWdvbnK2CDs9AfVuUrcmW42B/PtdqNZry49df5yd7LVlBdXV7bipOEdnJ4SgS+YcLjeiFMWSZmne53MSE9wZbYTZ/K15dXZxYnxCRlFBIJEGM6g0FW6jqFOUtztj3UHmXAuxfLVzSDuxL+xu/Xk+X/yUz/1ePn5/8kTjADQXz207AN4Y5ob00tTB1vkCiZJYi0QWecEK0IBAxTe2rPWiJVLFwZuvKFX52vmEf+5F75ramOn9tbj4dbspP+xucP1slr6xvjghz90BMC/RBFqbd3y75KtGhXzLA1HgmGumLUMIJYp7b4iZe8LKAoxMhQh+3ehAO+juX09AH/xgfe+P8Z/YTz68Lsrs297271X3/u++5zg95xeXDjQnZhaRLOlIKWDlHPodn0wJuH7+zlFRAFqNRrmgZZMXWnARkOmJZAbZclGw6KjRQ/APqC71aMumb/y84mRY49qYZUhVgsgI85LSZbyHKPgcNQBKFMt9v+eZUV4OgxvsHjOWK507gggGQY3KoQpEExCAEgL2ZR6aAAJCMU4BODgIKS1KRQsPOLcsw7EnbF+njuEBj4UZd0+DAvDeqWPg/K8uUZzNjcTBsrZhc1ArI8xr1dxFgST+tws1e6w6QDUUo43beDIOORJnXEr7G2DFGmvgWfqDCfW1/xj89vicM+qmb1pqyac4FRntrbkOr2dRDSZ3h7LN3dcdW8wkI2t+sb4uWzB1es76Z3zp4SfwFvOmblOh9zt9LQTnTExsCGZqQ1qRknmwXHHoEINh7TCESaCMwOlAFE0CXOod4krwUQOlzCQrebSWShmkOZ7VR62lLOJs7kxnM/HRmobdDZqXA+Yja+HjXA2VRTmNl0La7ubUyrPSIV+l9hUnOd97rrLY/1gm8eVT7z5RL9/rNYan7e7M/c+OffOxz76xGU+da755OzhcGph94WTjaNZAxexxQxMdrhZ6ylt7FaeZdHK2EQsJX91U+aLdiz9YrxdGVjDjoa1fJsrUWfCOeN0aqx9IeuJcS0FI0a3+b6TlsCddSS5ZATnCZlgn+0u7T5joEa0L76cpUglB0lfhxDO2JwMLAwnMG7hjEOm+XBKcg5rnbRFFe9NbLgdWTvDteEEoKzHWOY87hWFQI4xAgQnxQUhIJmTomBTp2KVEjoUVU1kM6uy3USyKc/z6i6G3lgf7Lldh+ZiUs2F9YKqkyEzZLXLL01re47JsNHNjUcBeRpKfp6vnWiz0PiOpnuhmsyUZzairNPPzIRveHrNH2yu84R/595kbSEPQ+1cYtEfbPhotLSkhGubcreOwoGtgMggCgWIQgA2jFPPM453aqEGYx6KjlEY3mfCPss27BzlhvmVZFG0Mh6VYSltSWkHgJs78pS5fqOV1bPYTy/B8G/jKOzyn6KInA0dVXoQXVxe41Nn2sne0rcD0ANeAXsvOxxWQxSTo459HTwC0Prkk8/uft+/+qfVhfGx2cWJ9uDHvuONZz+1/eHWxdXV1/XCwdLXz7lvjrdl95PPs8av/f4XFIDt7/vlL8QoOiMkj0z+6NSB4I784tpzzx+anmydvb6i/s1HP9n8oy8+vmY/8ccKhYdRnjMCsFQCvw/86f9J3/ua+zgKb+SHfvJ///fPo3iGL6GYrG9G4SWm9cm9U73t6HUqo3d01isGYOMAxGC7cgbAW9Kedze4XYaxDShdB9AFvOZgJ5obgGgF1/sZOrUDjcXY534U+vI6MWq0qlVvcXqitdnpMd8ZJsHHkn7shzOTvDszs1QNK9G0CL4ifYoO3HNH7DsKsbVTA2MO8yEDEOWZutTb7V1sTjRu05LfMehnYZKkOP/MefT3kghF7tvomAHgX+ifun6keveM0SpKs4ymWmMuSRLCsHFllmYkPWkE52somlt3q1X5zcVpMaguXz0O3upje+8MHvv61Urk/2+v/q3/e2P0JB/88IfGUHTFeCeAt+DmXBygMCgDFJXTvLhnN0KNOYowQQ0oJHu5EJ8kMv8H4LoAXoPSEAJN55xHhTJ8uQYvA/ibv29OPvrwu6sAdO/Ok8ed1g8PZmceENs7R/JcOTRbHFGUoACcPoKAgags6rkh7ArGRvOJytB1CbrKn0tvugRmo5/91sTzMvev/LmFm4s/RvsBlwBsNJxbAmeNfY1CNvLe8lwlOCxHCWJL0Fq8h6gE4OV5GIgUAXy4VYwCzOHv0gEqh5AG4FWAuR1MOkjPAchZoKxwegskmwAEgyZUBhpeTXbRcBqKIcmVqPR5f4+zLrWApiegHW1NJ5angZle53atBd0ZlzzxeXBk2ZDygO50ny7Jvp1fYTSuJpmK19XOAMyj0N2xXef3783bgW/45jhMzQoRW2Z7XjBovfZKv3n3hSjam2G8GnNvDkE/XzXNM1mvViXI+l5lk8bEqn+Y+TajGbZJB/gzpsYcIn9J1wLw3A3DWCkAstTdDG23U6PqscSiPSCdOmNrawwSbKBBeYJMCjhwCGOhPekYC7M0vuqnmb1erwTaMwPQYEkYv2mMN2GNzkLr0qTLmwhjRbzajNPg/2PvTYNly67ywG9PZ84573zvm4ca3qtJUlVJlCQQCAEFdKAWdChwO2jTIZqwcEN3tBt32yDRtLsj2u3GtAIZY2PMpAAxGTVIhMAulQqpqFKVVMN7r169+c5Tzpln2lP/OHnezXoW/DWK0I7IyJt5T55z8uQ+a317rW99i2pPx/Sg052v7V99AP4XTp3EeLkCKKhsRJNHXuh3vlaptEa39Ll3bfz74fb33Hf4teCBm5Pu4sDb4o6pp4d2LK+m0r9l5YbM+cGK5y7fJCOx59mtfp4sD62sSmKloDRLrKLcEsII1PODHb7MAv1kfd5IqwUnnBlrDbQmjnCpMppIJQUhlHhFh5guiqhQyVPVsLkFsdP5aDWIQywkcafzMy/67xqAGgZwwX2Oo64+ZXVoyX2d1frTRcGBZhREM8KMUVZJYgWgdMXx8p7IBZVgRBG7kcV24uTWIgRh/ljDdBn4EhiMb6Ae4JZckySZwGjAbGEAACAASURBVA7mdCCFQMUwSw/ivYODfH8rzGq/eOjxH1g2lI7i8dwVd8s5SRatoKKxACeAxhvv3F/YPZEET6bUVDf9xDiE+HMZi9bFmEtq8lCDu5rELeJURgpByuQWikXiKQCjKdCbAMiEtg49ArZ1vLWgYjJjR1jxno4BwwDuTDl55fazHX7KdG8JxGcpH2VP8jLTU9q0MutQvl4F8CMoeHxvTvdZgyHz+2KefudHfnvyDVt+e8/4Jtj7+uM8gPegmDAWRXrtVQCVtVbzdD0KxGbvYO9H/sWvDH7kX/zKkDnpEnVE46Glx6/+0He+rf1HN38Tn/3Nzzy+v9WuAnzj85988pm3fd+rD9Hxmnaqc8Nf+srvnXvjxr999Jf/7j9+7ZUr3fpKu+GFrhvfOThsLdbr3+EKvnnQH6aTPHvz45/6Q/Hv/uKLOYDwnefPPPjVG7fWPM6DyHM2D8fxezzOPMH4m6Ms81AYJwcwZ5pr/XPV+WG9s1WvJkPehuYTAAmseQCQAYhxoc07ATMAnBLEsEIgFhUgr13a2tKvXj+0Fa8W3b88J7vDsVv13F6/15lrVSvuQ/PzqDBRBUj37FxLU1dUjYAcy/Sp4V6v0241UurwGIyUHK8NAC9mSXYji/PjaZKup2M5RxhtN+cb7NQjp/Hmi9eQTrJ3/quf+T9//yM/99NlpdXkv1r9qbCpT53o9IebruC2XasSorVV1hIuGIy2hBBIY6wGww0UwpvrAJ5tVCjDmze2sfT4BsLg02Rrp4MCIN8dH//kJ+ZQ9Kb9EIoVnzfz7/J+L+dDhiKKF+GI0B1gCvQA5ITgq4D6YxTg0QPA0jx/O1fqtDXGyzPDrOdAhBTcWDDq/vbP/vhHx/g64+mnPuSg0MobJ6dOieHFi3swJnUW5uZi0BieV0q8FAas6FE8mx4u+XkW2likGSAcBoeVKdbZ71kaVjPz3kz04a5I8qwochn9K3k7Gkf6fPdW1ZWRvpLAXkY8JtPrVwLOWcmX2aKKEqyWxvpISsXzZiOWEgAhlDDh331/dj/T82E5is4F0yhiRoA8s8YMCPU8QFWpb6fX0UNBEvcAEA1QcIcwkjoYpC3TNTUGUqFiSFhzbGy3yonnE8P2Da0NqDeIYKuZJu+9YjAJxlit36Lurbau7LbQ6lHSbfl8dB8lj6437JwxJolzxXQQGGKNHUltJ4q0ZMV6w7B+8/MnncnQevedm2u71c0dLZytK0YEvaDmbKuV5g5OkBV23bwDL8CzA4ioQ4c5LE9ghjkgBAyjQMJAuCYqV7mQ6YQ6nBO/Yo3MAa2R9m9wZ7LrcH8tZnmKzF8GU4fVYPTySeuubRBTHbtuqGg2gJEjpnTO9HgzoF6+MImWzDCb27aiPZ7c91DX3VpvVvOduXx4uZKbhSTrLMxVTje9aipySCc1V++/8Wf+t3z+hYG1Fy+p6OFXr5xfcpZr/gffa72HzJxKxl271xe5609o5xCJBtqHGKQ65dlhzN2HT+jwETQGnuLZ5WQ3CSDmtVMXL/b3ZddI/4QXqV0i2ZdH++kxt8IeCGqmyl1jtBJWWzuxWnmECWVzOlaK1DwvEoSWnRvKilDfQttRkNkoBhXg1oVQCVRJL1AAkQzubMejex8l77SMeE/nLNGAE2Raw8hcWAs2gVIaylIu+W5tQLnmaMeBPhGE5I7V+U6S6PNBc+RytiWZTn2QCgVhIdH2lFPptN3aoUPpjQESfwNjdAJzfJs57rtzn9KssznI9j532Lm98B2i/i1fWgvDAaX6+8erpJK5277mB4xj447XbS0HQRBot9LhylY0V5aA9DlQ93XDhAMMEt2FERaW1kGQg969B8cAomHF1zhqaTgbYSsjcBKwFoUdPQGwtODmTYs4jgBb+dnZqvCyow9wRBspbVdZeFYuemeLZYBiAX4BRwvN5wFso+Duzdlf+PDXoxN9Q45vgr2/ZkilgjjN6p7rzLlCRAC6lwZfDvcz+9hCw9/zlm89POmG9dFh9Fc691Z07g0OermXZrJ/61LtSQ03sppVALxf5XTt2our36p7C85/xNYX7uBVcmz5vrnPXv/jzi/+u+2+0qo+ybIP/MIffW7hfRfv57/zl1/OYSn/9HN/dUwa8xCAGwAaVzY2q1zYC3CSBd8LglCKdC4I2Fipd42KFNY2c9NbwtXX8lg8CmomSc9rApTCgq5JPr9P0jMZyYdgbh2EzAF5F/AYiklfakyNAMfqRAiArLgOH0oD/tqtO77IZZUJ4bz37CmMoro0h3uT5M5m4A1GB/z8aTe3Vjx8fMVnSVpXSZLDrXpscdGwPEuQZX247q1eHO/0k9EZL3MHlloxt9JiWml4ngtKiEYRUS1XxJD/0weHx/7390fby52Fg+HohVOrCxeqjjiTpwpplua1eiU96AwPavXKtXboLwP4ExRaSlcAjEl42uLDp/sA3sTyo8D3/dcA7kbyjqGIiv4DFILJpQr77CgrugwK49XBUXuuNgqQMlsIcB3Av0chjWMArFprV63WZzkhvkpyjPYtJkGExiKFaycDHgW/c/FqR7x2vnUXfD391IdKMNOWUXR/srZW865e3T5897upb22/bvTY+o5NKC0190ogVRrHEhiVqdUM0jAYEGhFACbx1iq2u9pz0+1njaSd+ZvObAMUEU4HRy2NyrTwLHC8FxiWxyolUcKZaw28NTpYnmO5ui+PWxr88jgJjoAkxREZe/Z8NJBM3/fLc5r2MTUAcoUk5bkWocO1QzxpAEmRCgoPMeAwB6NJDusC8GBBCJTwxk6uvUjnAtxkykx8WENDElPGBImtq2MLLojbMdn1VSrclKMZL1Pfa7OTkiPKgFAukdYgQ61niJdY9sb4Nj9u1+SK8umO6pBLac8+6cx7WzsL9qV4wxnYA9beeWqCnScgjm04jZMr1QPPtIcQvEr36cS2mGOHo/u9r4oIsWMcWGqBwx5nYaTyqg9qNAyZG9ggHKRezl0vVDTb4dRpKMI8WO4QGrQsYRb04OU6rSZJpjuErH9esPa3BJbfJ5g6yBN3pFwxPEGW3n6bZHFMmDNi1iJSOqi4IUY+kTi42dDq8pPrDdqNXjpMws6A3Gw+sd3bmL+uaye6/m5aGb/2qSc+tLDYf3CQcblpq3T1zBZ5mUx2Vtxz9H2intw5cfvG7u3aaQMPIfix9tohqjoYZJuVzf6W2VNEIhPZoQz2jzWkv9gbEfNq2s9SaPlEtcUGec62TMZdxvK2cvXISGFB8mPcF1ezgejIzM7zQJ8OKncXLhmkIiCuA05AuNHIU8mMqwtwogHAL+ZSGbVTEgoaWggIw0DLhUtZQFDO2zLqRIp7R1FAEcIE25mMzXaeqPuDKqUBFRrI6gNf7ySp7CUjshb6dl3EGSZZbyWtGMbBe26+tTnJwpNOdHwHVW8jS9gDdtLc01l1yfFwyq0d+JS/Wj3pX53bbs7lqb+dyujysZUnbjpW31yAXWOJWoJVL+Qu0dut4TvDNs1qfRKf6lVCxgx5sdLdW03D7CvBoNULUKtYUCcxk1QqF4ac9IxDQTFJvbvSR8sz92P5XScoFuNl15yyP7dTSBuBUBBtwHwc8XVnbdBshqHcb9l6cdZezNqa2QBd+buV78+jsGFFoMOYM7CGgPHjH/y5f1v7g/vHTQAb+MGf+OsK+b4hxjfB3j3DYofc3t9bk0o+OEmyXc/xDghw4tNf+9P33+Cfr8GasZw/ZKbrJFqT0uFXALy0Pdx1fukvPjPJM+drgH8MRSrxqTx2Hty91rLQZvLFK5dPGhZep077ud9+qVs9HI3eBoAARv3Jq18kz914wfvqlf4DxtpnUPAJFlHwueb6ceoDcg/IFzfiLgC+E2eD4/Zup24bMEeuAvaJw/XWsucnCwBdgeXbsBgGhkjL+dlCrJKlgBIAK9t+lTdKDXfJ9sQCoN3RpDFJ83GS52kdCIjM8/1kQg5kmvc9T6TzDTY67M2F7raVlqRr8+3cdZwgyxRt5DmxVhqapkRrG/lzOLvV3Xk13xxcWayEXi7Ns1E1PCGVdKN6AOrRGxjjf/vIz/30ZOZnCf749585/r0fvXByYbVdI7D/MhlkD+ZZukBB78CSpDVXf8yCPovipn0WwD7B0l97c378k59gKLhz70CRfnwPjrSh7u3ggOl+ywKEs9PX+zhKF7Sn13EA4C9RFN9cQgFifth3nSolRA/206TihPbYfBZspkA2UNIL+f+xO7d6H7XmcG0HVzeWCgdyIdo8NWbRY3fE2lXl12+oRuNc3p6zQqtry9cvZ4ePvv0wqbc+hLdWqJayAzUcReQYyv62HteghE7p6bOAcCa69ZauE+X/yshdCSpnpU1KoFdGATHzf+CIy6dm9l3y/8p9z4pRzxZvzDrHWaNdnpcFYFtJDgIEh75jUQDysk9yCQRnuhpIAJwARqBwxBY2schBluklQgQlO+oh1wAWxtHIQEAUCm0vQx3k9bwoKGZK59o6lBlBOTxBoBMFLyMJZwwSmVXGDpWitkJQz6Vzcuy4i3vcJIwTdJexejixwh0SzoSd74XEdAncWCJ3gCqvON04l9QXNm5y2sgNIW5MpR3JLg5UCI9lZDm6FjVVtHS936OBjfcixhZGdA7bqJND1EnH3dOr2uYiFzbnkRiSSlXRqg/P80C0AhESOs+ahjS6UBOqB3su9T1FWQ7hL0nraWnHb/rUOvNsRDQhcS+jYebkXQ+47VpRGYjkwE8JaQjSuEXdVqyYgZ9uR9XsEqNslUnvPPTjj9yWG7WEBadltLB5Tj73q2J0bYsmT57YaKrrEZH9U9Jr7JDN3RoLVmxeQyySv0rFX65nt55sju3pdG7Ok8HJylq/tSp0/Pqbczf2Rt6dXHn9J6vN5Qt+jfW0tISq9VdH+f3xCDjd2soXGT88g6WsZ9WJ2zohj7ZFPu/lWdXJhMPjvGJWGAGFjq3WxrLAEkSMMUGo0jDaQLEpY4EWE5LoysizApRkULAwTEFrApqHhcg209BEw+gMCYhDvUruGWMtlVISOJAcnKfICQVFUASbGMBsAqVNklNpjYmtYoSBglg70BnhQ2YmsZLbMpODiaSakTvnaNRT1lzYi9PqgUzfzFN4bSNcbTWl3PKNYBL0+/JUJDmZd6OFJWPnLuuu+t1887P/i3j4+ADOQ69PBnUvJCuvxv21h0l+6WQ6JraxduYv2b53Kok6Z/vBaOAnc7o5HB7qg3Snmm5bV/H7B3Rxk2bYRd6DwyrgVKQUOSg4lM5hDIPgBaXEWgoLDkokpNyFVA6MyeD7QzBaAsAhAPeEMNxhlr2ZclFw9uw0Wkfi6f1cmdoU4GixNxvNu7d4rCwgK7Mcpa0s7RCb7nMRwNBLRvUwj91ede7kTy7GKwCeRkGxuYJv4PFNsPefDnpnf/9tI9lvv7r7+vU3bowuf987nnz0xevXzt9MRnfOPNT/XHMtSW6+/OBxv5IM3Gh8OhtH50Hs7tqFvfWwGfPX//xcAqDJHHXH9dNLypAHGNc7nMustxfeb3Sl8dzwsIJi4j0BaL++3P9SjxySw67TMqhtTXkKZQp5BQUPog6IM4BYwtRp2sK5pwB2AMLyUdhA0Ws1yCe0CklDMJwCReWqKw0I4mlBowKc0smWz2WKspTHSAEkxlqR5LkHwO8DHNroawf9g3ZrMIx8X1YXF+YzJYJ8kPih745fu7l+OYq85vmlJZ6mcjVTyjqUWglb39y5E1zTf+r9IB6Nza2NvU5Uf6m91HpQW/MuR7h48v3vWN68tr0++4P8X7/2D4PDzvADniGLIalXtLRfcULnj65v7j02SuLFBwQ5f2ptEULwSyhC8N2/CehNRxsFL+/x4rrejSzdO8ro1/b0eQUFZ2+Igu8xQhEZbKH4La6h4N/5xW8CBeD/Czw3ilx3a/NWvlDzzLt9rs4uLKjEOs7fB/inh1G9MQyqCsCZtR30N5awt+oerr4Rzb8rWzxByGb3Ja/X8x6pimbzl/8f14v3N37n4cfeA0rLit7yEaBI55c8uCIqaQwDnUrgOIWsHY6id8AROJutEizBXJk2OeLCvXWlXaZYZiVXzMwxSp7MbBXwbNSxPNejlGwxyvOYPU6ZHipfhwAyz5jYFDpoFEXVXxlhLA17GcEzgE8KPhBVd/eVwjUAOt0Wa4m+OTlW9rDp2oEZp77q+db3aAruAtSO0aKF47GKBcKBJgyuTyENKc5cGFCt4EL4nTgNw8SppJQeO7Q4rBucvRlRYQhWDxN7/x2X3Fhh6JzJyQOvFPi6jxRGKPtwuEImWoktlqHDOLbcwA6bE/TVLUKl5YwTvSU2dxtnvrBx/+Lz/cHr5+Y6wUgM6znG4DjuH+gxbZpX9OPkBL0Ue9wEp+kVoXiFEDWiOk8oE0arHJQFuREujDXG+EsxpQ6gMxDKQYwFvOVER31J5W5LjLsiC+YZSe7UhOzEaHxrZi2xVK29ruLdquGJl7GTA8LS49zlCWG867pNDIxJWXW1H+1tNemp2o3Rd3+PbjuShl6e5MQi+sM/XK08dnI/ePDB28H1W8t0fzciX7t6KuzeWb7YWWzaE+3t9MziJfGhJ/eXV174b+xhc/6lPtHPv/D6IL1tOvRqrHYk5eylcbeZW1apQE9qy4NqPY8oPWDDjMldP0rr2fxGvKguiBqomyzuHQwGAWW26vkDppdY2BuYXHTz3FVGc2ksZ4JIX4iJtCpUSrlUEF8A3MKaGIlh4FRDQoD7hcHQxsKAgXICyAw6tgCXWrmWGhiAKuTKAoLAYqKl1kaziLvEKhAFS1ackK2JgBDGTZ5q4hs4mph0aIzeyZPJQOchGGsTj40k9OtfS3rna5k4c9qpVPt5RnJi+5La3AVzHqmEpoJAAvCIwrH5DT/8gPIf3RGTphJ6vOZ6rz4bHCQ3smGj6lfj8GzNSQ72lysY6TPb7esB8Zcm1WGw1lf6A2mjduuko5yxobTXS2tNnfZNpfmGyXIYkwPIIBMHsBVQymCthesIZBkKqUKag6ABEM60yXUuDXyXAdgFyBggZzclyVwNpwg22Kl4MhKA9HEkmVLam93pPV/HUQq3tFllar1cwJZ6oLOZBaCw5R6KNpc25U4VBJmh9Na25OtA/jK+gcWUy/FNsDczLHYCAGdqoT/+w8v/hq7TL70zW8Plf/rpnb94c3vn4WRSfebS8+0KiuvWVMas6oydBwyBpaPhQbje2ay6IPYxQs3ZsBF/tzHY55qcNZJua0t6cT+MASx4UdpTOc1U7twAyJ7Rlva3/fcid5PpHFwDMAHyKqDfBohbVGhulD0B65VRkBbeSjg/BGgZ0fEAJwRHH2QajSF3NdKS6WOCo9ZZwBE4KKMqBkcci6njtA5g5CDP6we94dLrW7dHZ481Oqf847TmeiFgKzrXx+IkeyHN1MHLb1x66vjq3Pnj821mpKLb28O53USdWD8v2PnKuavnGtU7jLFPJEl6vjoftbJEknd//7s8ALnFTgjAep477sfp9v5Axs2w2Qfsl16/vlG5vXPge4GIhpNkP5fq94Xgf/U3dMEAAHz8k5+oouBjfBuAtwGYw9fvilF+/yEK4xCiAG9XUIC9h3DEkyubca8D+BUUuokcRXRpAOCnf+KHf0T+nQ//xBKPggtxq85T4TYRJ//yn/73/0PZ//ZgbQdNAP8llfnO3/v5T7zy0LB2RTmhcVLb33/6e95fv3XDPHblq17n3Pkf+sK3/thw0GzPuVkqaqOh7TSaolAzKOYmjkBThiQJYa0BpRN4Xg/FCnZGsPQtUbiSZzRbUJGiSLnQme2AIwNaFmCUIG8WQJZjViS1BHb+zHaz2nrAW2Vhyu1LIFr+PcQ0Rb0VesE9+ykLQMrzVdPfUAJiNh1cnBPh2iQZiU2TZrxho7ahko+1sNJXlgupqhoKGq4BMqtAiIRHQbil4KQAniLPoTQHJEUOzWOeBxm49aypmEMd5T7ZrlX41eO5CrXGAp2ge42TeoczIczdE120FQz4iBzUxyApgbUjiGEPFeGgMSBGgrFH6Hk0RV3vnPjywSNLn3vmDn3fwVatcuZabS7O5dB5wHb4jnPKzNkD9W73c3SJ74YT0nCsAWE2s3LMbNdtmnp4aAWHVsGYCwtGssi6fko0UUhHkH4DVDCw3AG1wZZBPzXBihXeQwfa9BzjzGvjt/I8Pc+TSR7ysM8nWe7w4e00Zcf6nqlQ3t9f0tXDIbeaaZs2DaTmwwO3fWHBLLSbu+Z2l7OB49kzG7sgBuzmzXn2J59+JFhYG5AzZ/bhPbAbHW5foE3q8MF+Zf9wL6ZzZ15Cnd7/wJqjP8gy/NLLr8TZK5q0t9G6HjpsvPiQ/tx3JPXT/d3w/K5xRqHfpwsL4fZTdt7moh5FdkHTsZ9ef3PN36MTdjF0R/OOJ7WF7uak4rscWmuaE0M9w4UFgSLSMcqSRElifGJ9MOaCuwREwdIUkvDYkWTiToSbCQgI5VqH81zRHJmiHEqj7CnEqQsODatvpX0WS42zEQMhlBpYSBgScm597mhJkoklxvGIyE7Iyui0X3Eq3DW30kGeQ9f7Vu8tCj8OQBfHJvestWrFCdqh5Xr9cCItJ+75SpADsC5z2P28uawd3U6gHcNIfzn0njUCO/eJ6OZ3pCtb/7D1/ON1mkerS/HAnMgWWi/U7r8ykkxV/L35Bk9xPon6h8ZZ2azoeepFVwRxqfWSntxniWeCQcUaKwkFrAUlxT1NCIcFAWwACx+cZZrzDA7R0wg7RRHQoAokVQYegD2ARIAtF2+lTYtxJJg89XV3tULL4q/ZorPShpSp29K+WBT2ebZwqwnXr6Xw7wCoffha9QP/4Ja+HTAMbv8gvqHHN8HezJikWbDb6z3VG4+f2rhtVze1s6ecQXtzcuuJZss5sSfNoso0AdhlAFSOwxXAnnTC7KuwRPV2qh8xitaD2uhxQ00UD1zo3ImtoZyALFvg3QAoYDPhK5dQllN38rLrZ++yBpwQfc7CasCems7RNwoOA7kPwJJw01gLhCpmdqozVJLggQL4lQ45R9kihsBHcQNE02cXJi+qoKhTfr50iuXNUrbVKh13SSLOAcOBnG8Ph2Jy/U3iUuUv1kVrT/UH3cTdW63XZXLQpd3J5NF4nF8Z5ulhOAoeWG40/EmiZNOvj06oRxp/cuuVG6PVhdfe3m50AdzZuL3tdg66/+Sge3Dz9cs39M///D8HiupS+xuf+eI2gK/5Lm89fvFMXyqdU8qiC+fWnqWcbpw+tvDuMPAGfxPQ+/gnPxGhiOb9NIDvxltXgl9vzPLTysIMiYJP2JxuE6NIAQcojManAPzO9H8ugHjaCUMCwKd+9/FEMLn59NMv/VYlkNddqH91zzFXAAxElu6PWvOn7jz8HZ0fnfTlG6p79su9rfCsHtnembMP82r0oJnSy+b7h3yhd0D6rpPpSr1oD2YNQ5ZpUEphbSFpQEDAmDc935LoXAK6koA+Kzg6K3VQchJneUblKDkys1y5kgM4C9jKazrbAaNMFZevZ3h1d8cs0JuNFgoUc3o2alce4979UhzxCUs9r1I7sYgUeAzEqNQ6Prc+w0iyWF3rO2i5VNTqmQU4oUqCcgbkuhCE5XRKdZhGESyDLxXSjImhbzxolwqlusKLe+GK7+0Z/cTLAo4xxHFi6VvY7RXfhzaoTCwqRKNuBWxuYOZ3EK+MIa6fxcnxAs4uZqC5xo14ohx9Di5ynYSd7tzSHslq0dIud+rZqdeDHO+RFQXRIh11it3gj9IXvSYfmQk849sDW2EgksfWqxPCHEusArEWdGDbTLMxKtGYcAOoDJYycClhHA+wBCZa1gbtrsjH1vqrWsd1F8kGpUFjyCEWk83xgqeirepce9eqfZfZbuzqPUfxnIjxumdGQ6//Zjofu+1h2BuCNZv7XspisbF1XOyOQzMa8vxwdJwuL++yU9WRvXmzQZbnh/j+p79av/RinNkdP9246gxeeL4x5P/FF4SVl+nupn7brU7tZ10+XHuQ+OYHww//wdsv3L/Z/dFLTvP5Ttr/D/cdBjoACVSjY/KwgYqpJoJV4Te5z3DS1nXIUo2GlnuVPqUdR04iPbbwAyKoXzEOIZRQCWksjGaEkcQYEYESDgoFo2hGKQX1uAXTUBlxmEFGbZyk1FBGiGutgnGYpYZaCkJBXXCjrJUyy2jN4XA5hdEKzAIaGl2Z5wweMakZb5t4JBjDMdcRD1aaSyOVQ2ndv+g165eSrnuQxd0mc+80mVOJjQyuTgbsC6MDZ4E45nxUY3XGHCkl9xhLKCHCBTUTSDCA1OA2cugfqKR8H9TbuMXGG7fSPL7IwoNjaWeULFxevt2KZdaWK9Ih1evRcCfLe80o3YzlSiCSoUMrHUwuuDR/XWSx5ZlfZXaSHrL2QeRRW2hbMriugjEKWjswVhXtiTEBTATAB6wESNlNJMFRsdYeQD0U9tUtXoPiiAs4wJEWX+mvSvs1y9crF7Gz+qIS08wAjvT3xPQ4CyjsfOtAMQKFTXwdXdxvpPFNsDczPv3c8/7vf/GFC4fZ/rH1bre5s9cU4K2PViqGrZwdjZ35rjMZsXiw2TjlR8bp7zWuA2Tihep78szUBNONbOQ7WlFuDIhKXQNQwgghS1HNH+YJhlnGABJNOmGNU6pyO6kQUCeLfQZNIgA5ROJB+gDIOwD3AEVazs3GQQXQY4C6KCZpGSWZ1TCbLbYoK8LKrg7T1JfmKG6o0rmWny85VCXvCZjhb/mATMDY9F6hSZZDUT66tj5xesGWOr68+OYqbwSd0XiUZbm4trlztlUJuU119wsvXqkYoze9QDhvbvbfS7HWfGb99s6f/sXVzwMwMPa1rHv4j/t72wdpHJdtaTanzwGAU0mmsltbB5cu39yKzp9Yev7Jh88t/rM//0zz1195tjs/qP4wQJ4E8MsAfu9nf/yj9uOf/ARHwXt0UACD/xmFPE2pBttxcgAAIABJREFU6WTx10f1SkAzG6V6eHp9xziqwC17aP4GgH/2sz/+0RKoqKef+hB9+reeeWC6zeWnHopO98ZN//bc2TcDd/LVL/6//+vd7iBrOyBiczOgo9Gtxc07V/+7Ky+TtXH/8Pee/Pbj3Zs3z5GkF11/8MGT26JyMgsCkhNSb3QP/J1qXXQ83+TMkUBmAKsgIWBBoQ2F4Bl0riCEgBCzhRzAWyO5s9VtJYCaVaQXeGuqtUzBAm+tcC3FTSWO0rflowSZs1zBDEer9HtX3eW5YPr58cwxylTOvcAOM/uxAJRVhtpcExqIctvZRY4oKmzHYIEUyBlVGTFMcK5TQunQWizBI4AtOazwJlJYlcu85cAhFKAMmBSpaAsC5eFY3xDtSnu7PaVlcbCbc1xduELUvMycsMt4NPT0IKLQxFguXNJNKNKBMnVB6H7kwev7cI1FDGnowTxCLnP10FAzWZc6vM2k6dF9f83bf2n5W7KVIHpg8bV6LRlaO2kQXomT8/xr3hl+ySGWUSkdk1NfK5pgYudormLTomMJCscALGCpobki0kKrgq3LeR2AAOMcljJY4WoYrUEYIPdDPvnie8zh546z9J17Nmrshly4unMCk3BehV41inTX0bW59RFx8zQNKy2dCrc5uGEHSTM6ebovIn9oNm7XxCAl5sb6Io0R8tpjcypqEbU2tPzwTUtOHu/bStpSa8NKrOubOPWe+Pzg8vmD0adXb21GX1n86np28c4d3OchgMd8DJJPfeCD2b/+hdf+rMNu7kzYxVOp+8JhRneIji4uVaPWhMeVgTch1IQTK51DklkfQpl4En+V7XsdRvjxSmhOjyIxrilGlc5dzolxLW/uhxAeiJ9wwnNWWg5G3EIETgNEWkWcgSNdcJdyaTKhMgJwAmuNstQorSPXN4QSFpOEJ57Ka8pTbVeIcZZS5So7yjQSY6g0JvUEN6u8knDKQmapHOeJvpoOCAULTjqhqlBHtyglNSEy11KR5MoeyNQe5Kk9W6tgxQkUtbAGpqxE5QAlPrjvARawqQBW2+DtdX9EX+GDw7OT8FrmXvns+qnLbzPhqqxUm+OTpu6YnbG+uXCQTm5krOY7FSFyurSvjE1G+lKdVOb6Y3tqB57dtFlsncGXVpiTeZ4LIEYuh1BKgzEfrlN2T2oC5KC4D0mxXWFv+yh8WAuwNQqTGVBvWpnbxFHbxJL+cQMF+KvjqC/2rLzUrL2b2hZDivQw86bHLReRpZ2qocje9AA8CICSn/zUH9hf+PA3bJHGN8HezPipf/1bS4M4eQLAqqCQsMyDJM6oa+L1DT23/FD/mFfhObGWRPXcH/X4IzqrdYYHQQPWVotwNOHZpGwPSgyAPWpt4gveVEYEkyyHhlWccB4J14klW5ETbWDslPeUW2gKHE3WFu4WYAgGCIkifQXM9DPFW3WGysl+r2xFMYgHHDnYiUspoTBpUmxlUKR3S2deymKEyV1g5JCK4/JIOPRwMq4djEZ5hTu05gb73d7kHAB6ZmXJtQRhrzchL19etxaYnFqbi7S02mTwHJc+rvP8BABuoBNNk/uzY93Xtt/3a9ug2gV+KyFYyqcRuRAFgODPvHj5ZM77D/Xu3Hh5OI4X4hvpj3oJeVfC0gWf+xoFIHgWxSpsFcBTAN6OohvGeRxx82ajV/eOWUJ/CYxnJQAcHGnKKQBfA/BzJdB7+qkPUQALjitO5Jn8J4BcvfBg70rk9F957qb485133f9Up1F/FcDo6ac+RDXnj9zP+Qez5eX7WBzvnafyKYR2/z8+9PiLv/ct39lvPdo5OOluj+Ohd8HvSboeBMm4Ug0Sv+LBcY3KqCLJRFsaWXik6JKR5RTCsaCUgouyu8VsEcTsPJldMJSgr5xP6fT3n8eRbl6ZSimNaFlNN1tsMUuSLgtb7q2mY9PrWo57wVo5ymhdKXg7m6Ip91nO93tBK8wg0+Ywo+R4RAmHBy55oeMVCIBOlfk5gaFGawYCqqxSBGcCJviYAiMCRAZgFAk4LNcSvgAxeopXc0ATIJJIhYec2CF1dcIyCpYbonKLpEKjsaW3TiihdqhcTMG6aymvjSVW9hUizZEuaMQRJ/TQAelUjEsM5bmxo1jTDdk3t5ztjKeSPx5lelBX+VVZrewtp1k9q+m9rI05c9WekVswtE+Y2KkOVMV08jpZ4F00vIQbTq1PQDSG1qUSAKzjFNfNw9ikBlwVcFYVFwTQI6juiMut9RqpRhPOe8p4TW0JtbqxtEf8B86pkT2XkHOvOWdYxDcXo3RcqzlxjwDndNro1ki6GVm7ODA7yakqx3r7xMo29byU5mNK/IzZSpXT+XcQWxEKbzddZhyNm2/TvXedOMgefjxfjqmKzSPZSL64QBs3OF/q3u8RfmccJ6fv7HeurFqY9hAxEh1jrL9ssqzLj73xyDOds585PcqHH1xRp87NL2jl1iwJlcNZ02ToE2EiQ0RgSG1MTZYLc8JW2DEuenpBs76NaU4t81qM8rEhCdUmgsTIWttDTmrWwVzqgXkUBMQqaENBpAFNO1lCKtRR867Pk5ZiiEG8iWAWgGU24YQ4FpYSEMv9gh2Wpim1DBCESyoN1tzQNP3AAJApspdTKc8lBvXUmM0acydVx61B27oyWjFKg0OZn3Zg2VhL2fJ8/13Vtq4xd+RwTiisoB4lFlYTEOKCI4VxAJNZwFhYJ61JR7Wz9Bebb1xOknTvv718SHt0+fH6FpPDaj6OTqjdXKY6vnHY1HmbtuaqaTK4rMabdyq+UnM+W8isP0/quU0ucWWGgSM9iyDLtAVnBAR1UGJASNmnNgdwCJBDKP0GYM+D87JPuIcjVQQWMuuM9N32mkvTez/HUQVuaZMG08+VXYrKzE0Z9SsXm3SG5kwBUnL5LAr/YKf7ngPwnTjiZf8Y+clPrQN4xv7Ch2fbeH5DDPaxj33sP/c5/K0Z/+jXf/VhAB8GstBY2wPYBsA3AWeSprrJKA09Nc+SCUOaEDc+rPgFp4CHAHMBVtAxAEz9VgwgNsChVdKkaV7JYBMASlu75zFKKl7AAOhMKwAQALNF8REpHVnJSyi5U6WDnK20LDWC7o3wlY8SEKqilRah06bTADCksAfKgttivykpihH2cKQ1d6s4N+MDRNQcjzWCwGQyGwWeK0LhWs8RuLC61vSEW715sH/ncDSco5YuUUt9AIdK6/1MKm84SXOl2Fhp1LTFGoCTsRxfyNjoSSvtyeHYJNtfeXC4faU/+NZ3PG6/8JUXlgCczel46aD5rLJE91vLmF9uz6/u78iPJFK/10AFlkAKIl5BUSAx+cJXXugC+GEUVbbvRFFB+/WARTnK4pQyKlUakA0AX0LRJPs1FNFGH0UacATg4wD+0Qu/9Uz827/6u/SH/94P2WtvXF6KKs7PtOaaH4yq7kVp7EKzWTnnRPzcm499t9m/+Ki8MFpJfuOTv3bfxLPf5Rr1P9Lx5N2811t1HEIaQGeN4+C5x999Og7DR5+a7H3vhf2DM1cqS62eCBf9TDYUtSIWXk61sZVRP1VcCOu4LjTh4EKBcw5CCIoOoHrm+8yqz5fzywLgc5OMhVLb2OFHIC5JXCjlgfNi3hylWEfT61BGjct5Wqb/y3RveU1n52XJ3ysjy7PAuwRqs9G/cjU9CyLLlOxstfG9q24CgBDBFAk4iE40GY8tXM5Ac4OMOEURJSiIa0CcjAoBKpgiSmrOiADljEjAWqMJIRqGUqSKQhNmKWd6lINQAcJDQIH6auBQYtnKFjJhKMZV6XKrab3PzH0bsMlcn9hmTI12iGM1kfUUOctJ0K3AtRbtHoitG7QGfh6anFMY4lpPhhD5ep5J3xEMMY3VkugMWN/1F2/LxvnO+l5rjh5gPtgXFeeWMydadI8ZWD3edck869HIUTYQ2loCuEIh8qwtKJ5H3E2rYYwEUSPGxh1LiAGIAzrepWTjWkXkNQfzp0bGrQFWMEoquU3ru5PBo+uGnYQTQJF9YbUfdZmJqSIyin3LxMhdqCWVkWSOdL3GhILAvP7K2XRDPWRzL2Busopd5zR1z2W5yA9MP67HNse203I4zS3p7JpMsH4Sxdyp1eaVx/zrW6Pdz26lD7raPuaPSL9is9QDFIww9iM/0/1N782HVpeq/dqJ4bnFUM7PNeacuTFLma3QVPqaXRV9Roy1foPYeCVJquNgcNo0w84Dk7B/IgkD8ImsKihuEHSFdsaMGkJMxiWE5MxjgKs5qCZEcQsGyoSl3AG1hOhYO3LEQESU+Fwniu5MEtrLc9OgDhzOBSUE3DKb+spmYczdsQOtTcY1BSHUBIJrQZnNofYypnjXJJlUNgsIz6qcKcdSv6tzIq1tVigPDSNOV6v9JhfOkohohQmzZ1LScn3musTk0JrAUgE+ASAAQgy0MUBmYPiokmPD5mKHjZeH3FzP3+6tC9a+2No9tnyrumW117UvxpMY+/tx5wm7aOe1v/yVAeubvmt8FtRHCYmXxaAbOMOBsBU/V9XVzKfKapIwKOs5DJzn01aGPQAHU9vqIcsXYW0NhESgtLz3p8CM6NwSApCyaHBWGWAqmQSBt1KPDIrCjdJv5lN7Vdqc6QKPqqmfZcDdNnk5Cv8X46h/eTi1NQ+hiPLd/PjnXt/62Hdd/Bv54X/bxjfB3sz4+Kd+fQzgHMBXAbYJsBcKwEcfhPGWx4dVlvcjHUvlDXsuhfJsUbBgaQH0/hMn6qDgHrRSY2wO20URcm5Trvtw42CcmkGmdGKPnOVs2svgaJVTLkVKB0tmHrOpxtJRz46CJ2VyAitpURFFe5gK0xrAsUdRvCGKG9FiyjsDsAXIFiA9lwsTcCe3QJqm2SvnF5bmPMcRdd/PzpysR1meq9e3ttM4k2HV8zhnSkoiZWc0+WycZC1rCbTWgZSyoYy+keZSjFJ5Vud2Uaja6bTbDNt07bTn6dOfuf5vdtaP/yaBZmRQfb21t/B5Ifnw8H767dcC0052dofns2xsUta7RHn+rGMrn0YhhzJA0YnkBwDch6LYZVZO5OuN2ZL9EY6qtjooCi9GKAzKDgoDMALwcwB+82d//KP5b//q784DWH3uPzyb/NhPfejvzM03PkypbjcaterooOveUA16exwGyc5hk4zT87q3/f6kHn6baUQr5/c3mJqM97nncpw9p0/6bP+0z4Z/9o53f+93/dWfv2954/Zia3Oz2Uls4KYTfnxr07vvYDvYaM3ZJIi4tEaZICrSF4RYEBCAMBgDxLGGlBqOUzr1mWIbAIVxswBIqDWxhCAu2k4Wc0kpAYCDczPtymFmPluulIG38uoyHHEBy8VGCcrK6zz7e8ySqGfn9ezxZqLYRhHkCuDlar90DuXxy/c1AEIYUcRhOQEErGXwvBzEo1DGgIBAGAoYA/Ay+umBKwoBZpUhNoel/b4hgz7gRwwgFGQCSjnRQ2nhCkIth0gNX5EdjF1pU8clcU2JXCTENUCjr9LVXZUyv2vafU77HszpbbBqruEPfPQ8gfbAwJfENvdclRNDsogQqWhv6CQDfdzy+lmdysU+m3SZGJ9PJt4DX4QMaH5JHc8Hbrt54M0bQ0hlSOqOz1O9rG/rU2ydgjPUvZ4lBJb5IIShaAh35AwLYhWHURNCO6+GVO82wSsTM54g9jhx5k+MaIUTkgUhNULnIUg2ief4da+Ws/YoFY4O1J0quNvHgpOJcDgvglaPSJn2c+37l/3TGM5VgQqN42SO3M6f8uCdcvsON/tUmVHokclSRY8yf7K5vxAOl+crlUa1QhNlWDpio9vMs2epb5cFR5i0yPzAT3zDvy3wzOmH7r+z1cj+Yry+s/i93+b885X02Lm9dXUxvuXvt50HHu1FB9Gd+TSoeG7uUH59sJy7V96xr9K5EW8fEjNZ6Ej4Jq1p13nu/I7qthOXRTB3wmQyEZm2E+auqwlCT9hK4kBJwrQrrSsEsdTaAY1NCIciA8ZM5ZTRQLrGEZoLGEMJN9SHoNZaSwDLYKVgnA9VRnqxYu3c0x7ByBPBRHBOiDCKUFAO3iXALWlNklsjQs6qsVSNzWTS1tYqaqmRxkYSRi/y4JWW45mQikqDC8oIoSksWm7gUEICDnAK7nIwpMigYRwAfIDM79GEZalh+cSkHScRh2bU/79fe/+ls91qpzPcueZtr09WXzfVfEzDdn0cyYtpuzvo6sqlSSSsBDfUhpSzqJbSeNGoENnk0W3pt4Un4tBzl7lPR3VrM6UtQMcoFso1GCNgrS2qdEHAWAOElD7Qhcop8kyAOxxHBRgJjri/ZZQwwBEQjFGAsxBHPXkZplQoFP6TFG6PlBmMsl1jSV8iKKKDYsaGldzsNgr/eOtj33Xx64rg/20d3wR7M+NjH/67449/6td3UaT8Oij4Xm8HcAIAASjLFKAyTqFcCogpVUOUK4T/NGV6lE5yAUSAoYDxCbVx5GW1VtQQcZwbPVsZWACNWWHIUiLi3s4Is85w1rnOOmTgrhacGcNagLAcRE7xJS3EYY8c5TSiYitH75MQxWT3litNRI7PDsZ9nhozXK7XsdJqipW5Bpe2J5JUr2c5lQa2Bksi4WYiZnuj1KYrIak6gePaSRwfG2SJawyug5EkEm6dElY1BpWqiFabUfQgYeRxmWXvy51OK/b2cimGiXS71lFNobaO7V0hf3qLrC+b7p3DL9G58UtGJCNQ8wVhopen3/d9KNrKldW2s7yNe0FfCVhmyf2jmedXAEAhlxZGUrA/BvBpAM+Vqds//cPPLiyutL5r7dj82Woj+vvNRrjsjcfeXOS6nesbpDOWdl5wQ9OMiDSuZEap/UcvDp1zp7fPbd7uVLPkFXLqlLe60Oo9YeIVI9yKmIvOXty405o73ONrd9bFcq9bnwSh240qmSHMOfRDaji1GagCJSNQxgCIue4+4XFMM+EYUMpAiINYEmSKIdMApwaUzKY9AYDFgqtYsJKnV6RZOSfgPJtGgtnMNboXuJWAbDa1WwKKcnEyO1e/3pgFf7P7LgcDQBnG8iS95Y9tSMxbKZcMb+nVawnu0qlAwTmDZwmIIQBnoIyAEgpqTFENyIC7AFYTwGpCKLPWpMRYQhzqIiVEgxPqCYI0NiSwhEYOSGqNyTkbUdfmWZ0HudKmdgAh4ryprDp5OMkOj40r+yuxUx/k8AGiAkLbk8TWxgzrp8YIlSH1AwZXCdZZIKy7MkEj93nczL0Xv/22Wn+qczhhDcdmIr+5sLtrnhztufOpt81P8B6dV0O2mFKgsmRv8wf4ZdZgXeJCEc9qUmd9FQaWOhyMGmhCwHTxq1JCC3CdJqBGg+i+B9pbhrs2VLyinCwLUWnlmUMCne4QmhqaSkaI0iltLWc8ig4494ZeYrgzQcgSs6rzuK2d+StknDa86wfvcAdr1l2yA7FwQEXWNoxHAY31KYrl2NYfPKQ4XaNuReeLya5mg7G/3+NEXe/RoLfvLlcyOTr0s2PzB8OotnvbHJMtNR86jTnuOJ0wrrEnn3/k2/5/9t4s1rbsug4bc3W7O/25/b3vvr4eq2MjFhlKDCkplgXZsinDiT/0ESWI4wA2AliAEOQjHzEDGEqAABEQIICTD8fyhwwbkGwpUmRHsKWQlNgVi0VVX/X62zen2+fsbnX52Ge/e6pEKf6kQG3g4janvfusPdeYY4455s/e/9mj5//XH0//k+1swv7GW70n4/WfOfqxkdp84a0wTWjsqnUTjAVf5Jc+c16kJ+NkHFfMBeVGUOVt8t955dIejV3Zn0tLMROxjVxckL8czBFwZq/lbcoj7U1Pk489N8KaUjlz+PzM8Tk5ljEvwJjnEM7CRJZRLksxaxfUsorNS1Nd2IpHUhFVhsN55Fr7toymGTOHFl4rCKGhXab0O6GVbxTKjEphdliGvUjK9qKyReHRBvkUjAnnHP+gSrEZR6ebKt6MhLTW2YAANgxiHnEhJDhJSCbAKUMBCy88LDEQnYoMeVsTJaA3w1l68tzkm+li9u76ph7tXpTB+Ov/WLKLozuPY8dpI7zovzB147PF7skB2mw28qHUVbamTiefTQK/r2j8hU1urnXyxM9Fe2Hppf7NsB84+6SX2WlpGCqfQpsOhHAqm+pukRZ53D6HEAcguobVcYf5woN8BC5U7UT2bBZ504EL1ABsjHrvy5axoyFEGoKkacJrunSbeLRa5ViNXfEyfs2Xjw1Ry1jWUZM3NwGcfPl333jzzxO79xdg7yPHl3/tV3uorTkak8U91BtAU/+vahaPA6AxwGnZRdSMg2lGOa2WlJqNNQSVfRYa7io1KAqWLDIbWY/EXy1KDmAG6AVQBYBwy+dvNq1mAa9uuCVqUeuqIN6tvIeayiYegQkCaQFUUS1QlY3tymL5xQCsLdvdOwBVdamazQEeaWuFYNxWxtBQsuil3Wvs2vr66KI4mVhWJCGSFllZeedZOwjWneE+pGTCrLp1uchpnuUdAnonsyk6rUDc2th43TqQ8e6a9Z61o5ALzhMH37IwG1TKj1fO/DUtzn5G2eF2b/TJk9HgG9nlxh/e3DKvvPr5/VeeXtJsVzPzt0N38z8WiA7hdR+1rcoLuGrGaI7vBzSaciEAGOd9uXAld3BngvhvAHjXwQwKfpJmR+l88nr82//zL//9hz/xmc86ADg7eyv89Gee+8/ms+xLw/Xe7s7OYIuOT1vtxUKwvGT68YF/aT5OP1dO9Zoty5jRAeu0Hu6YgmJiB5sP3j984MQ9G6iEnn9uSoK5lqvuvXTyuO2tLTpFGVTzXB/3B7Li3Czilnky3KCs1SXNBC+Uqq0LgiACEVmQ1USVE5IhDjmMBSoweEDNtbEBByRvWJ1Glxfgw4C4BndEfgn0mnJqEyybNVmvMwe+LNZysGdrlfBhLd+fdv6bo2EdVzt2m2DaZNnGA8J7YhnaBPCmnNO8lyVY0x6oavsH8PraSlOOSnMEzgHcgWQ9ux6S6uv4WSl4CfZMhYqR11aZnASLWt45771iHo4RPT0hzyyxbptgKhY5xiriHgre84KieMGZdVWsinmnSBMM0yCWnlpzbjafJpQlhQ+xoMRwmm6OKVcarfM2zfsW3mgrKm9laclURohFaSdtlT7dESYospNi98kjtXMxyCu5i140s4qN+jS+RqjkF+S/k/fwGtpsxnvsotxPHrMwdFJbmNTGHNYwa2CtBzMWMBqOeZB1QH4CeG7BNidWJIV3AyITxWXACo2wkE5btjAtctthNIs2sTaRAe9O+IQPmc9KorzDZrOXic5i7dmkEMOiNb8x94swgSq1dybxZ20lDuJNXg4WmATcL0TMKhb4INQ+gVBDO9Cl6EzTIs+CBEL4fZ6jtTAC6vT965fdxSc6sjgT5Z2JHu3NX01ejeROOby9NZA/2tIbL8U3CzX563lEW/Fdk67LR9KUEfzBVuj55LbZup7lqbr+9rtluuH3jndaAYVOVLyY9bSvdtywlUk2OA9x46JdxWfKf29tFG3KwA2yVtVNlYvmokqqwIFYNbqWOR95kOZGFxCMOx46ucgrF2RkRd6tbFhJIXNBxDzFgeCJZMwZQgVrSnLjQLERA4sZSEsIxcCZsnIqwI8Ob07LS1b0+EiniedHpUclyLcF52lMIrxw5fmZLOl6EG/FJOFgcV6lcu5sGHmmpJAEgJdek/HWenLMwvEUpTcMlG5kmA0z56xZ3O8cv3v8xfEHrJi2h59//do3fu740drvDu8G2WJP2/lEbsvt8dvzj6fvz5SbLvTucYpgoelpP/IX1zpsttm66J6UD9qnQTzeC9fUuphv8C5O2roaPLEyizwvhS3IeuuEDdk8nZXEMxclEWq2bBdXxIOAEBycM3DRxI8mTmeoqzfNvOEGlDX7YbWMGU1TYoYroNgwgHQVchzqpJCaalbz2KYBL1iJj40p878G8NafJ7D3Fw0af/JougnXUItBmxJqQxvngB0CVABsgg9veo9wJdxvNtFo5T6AV+QKt7RsEIx5Qiglz3Q1c1eAzQFujAAMJeLlcxhc2UY0dhiEWpvQxpVfXvNegD8JCJdaKl4zG1ArmxscgG8p0IsWvm3BDOAtavq7jaUmojKmtM4dt0O1ISzYw4tRxZkI2p1AGs3eESLYiwOstcKoqrSezYvShzwZFqU/ny4yIRnfQpygsJY9vRhtplnxhc1OL40VVZEUlpEQAHiAAAaRgN7pL+gUVegQlu0fKyjtmtmn3luo+B//vc/+1fkG7V9//LXRHV9OFedxz/vqM6jLrC/hSofx7/OZN+dMe/hp5ounIZQIodYAvMkg3gwf5yfVH0S3zOPZMzrp1bf+MIocXrm4nP2lMAlvMcZaZpweFI/PZEHo0aMD93J64XuSZSe5u9jotiZsrfeVL77/nXLwiF0/1f7Fb05K6vY36BOUDW5+7f95vqvLh6zdxqBadAdHh+6808PT3kZwuLFj43xmN6Zj+a3bz+O807Hbo3PB4PxoazeE8wSCLQfDxhibUM3rjzhUAGO+imIg5KvBs+lsXTUe/ejRaGQaoNYwzc15q+ChltCxAc1NQ0aTxDSZ9v8f2LMfuV8dmI3xKEsBxoAoYhMMHWBZnQiJhpVuEq2lvpXk8rKr9azacuQLi6jtIJ65wqwyk0ENEt0S+DEg9JyVDixfgAJtWSe2cFJpYxBsKM87bfLIXCxy5pE7RJzFObywjOeIPGdl4GVGl73IqzPpBqaoeGckipsjnG7EvtQLtH8/tryIGBUhPb2Vo1wrsHtMfOedkBnG6P5LhT/ver45CWV8/c0ieeXM/dj9WB6cBMff2Wc7U3S7cwy2A6TtHpuI3CmfBz1WVdKyGTGsHzpH4HnJ5Lntoic9eir31JxxqhMBWwB5ATsr12iRJ7z99FLvfG4yDsNJ35VgjIHIATguWQFl0m0lHq5lft9rbuF9sZ6gCgly8djJbqajjlGuSHS7MzbM7ooz1rOTrU1x4LeQO2Wz5+B1AAAgAElEQVSu02N2eHzLsxaM6jHjA2bGmMuwvRZ2EsNvXxvbgJcqPduquseGAh37qj28PdtQcrhvT8o+3NR0fvS5T/5xN6BifCRULhJu847+bvu9/ONFtVc+3Xnng87pnO+x/daszTlN9y6mev28/eTz6qdfu52db80u8qwcbh524t37Q7z++aMFMxRyhkXvLHjnjZfPru+cdaKdWSdXFftgWHSGJQxL42I7yUI+nRTz7nHQbpeKDPP8SBfEMy1jKD+iuflAz9zeOCme8z0qN4yUjGs38+mhyKYzo9O7UbdVtisVpnzCNRtX0lxo6ESAbwJcbn/Q+Z0WSm6YE5KpJx1y61NLuxwUJEyIF1X/1R0Wv9Rj8oYzJvXAQjIeWyAgD2G9N4xQWTICBKc52OnOFONF5Wfr2uux9ddG7XLcG3/tt8P7l8P76+NPbd+O1GHEnnxA461b6+/cSos3bTj+j/yI7emjmeSuKm4vTCzrC8WK81KVDyszF6oYvn0hHVu8c/TKlo7WHd7vPXhUdgbdlrY7ySah62Q0vhApTOXcWmudvNqFf6a/a67/OlFkPAT4apWqiSeNDk+gJiiavzVNdAZXnfpNAqhW7tfEieW1bg2AGcDjlfs1e+mqxKRpZAtg7faNiwfN4IE/F8cPHdijL/0UByD8b/5e+X1uIwC3l19DfBjoTVHP7hvKpIJ3xEweDrAydB01OGyMYhuA1wCq5eJjHmCN+BQGnpG1xRJp8PUgvDwvixIIGEp/H6Bt1ELRCnWnUoyrBZ6hzmrauNJPVbjSNzW2GSWuysliWVlrfPWaxo42gM8w8rn1OKlfgzrL88AA54LImoHozed53k2NQQTMNgdrRhu7CdtNA5FNcppdl6yjhGAMllEYxk+UVPuT6XQWyXB+bTjcEMSoF8WLWZH5QtugzNMsQXxBQWsoGYR2OKz/F7XLkSCp9sEXDBXLg6l8eKeTb6jNyx8Jf7n45dsv958vc32rF/r4qzDzs+W52kENupss7aPHamMCcKX5ygEcc2Jf7/HWvxHgP7v8+y0Ar631d/94c3L05nmIKQD81j/5R+s7d6/95Pq17ef5YUmtjda01UrKnWK2V97ZKO+/+fjSztNq16PDyXtiODrj/I3rMOazLq/ErNrlJXY3DbMiGy187jb62SLYVPhUmFzMut4JC8igMo5z0sl8jr3xhXzh4OnkjVu3lGSliBeZTNs9AhGD1QAxhiyLEQQanBt4ZuEYr0ekSYDzVbuSZ+L85e8MV6zaR01JW8t1p1fu75brT4LD1pNi/4TxcrMWm6TjzzqaBpDV7tq6bEwEGGMRBEsWvTISc6ERsaVlXmObsLxepQdkjiswGyAKCFHAYMHACwsSBgiawI76u0fNBpICGAGeoSsMIsYDsqJymgOcAg+LZI955T2QeVlwb5gkeGYdK10ojZgVMVtrHcsOm/hiGNB40rdyBi7XDRPKkAkKjAaWvvfShAoW+UQw3947JBEbGowH/sn1Y6SmcO/dSXQrEuP26eQtZy/uvRe3h0GobNwr4q2eTd/Or2dz8Du7OFUcRhWk/H19D+v+gDaDY8X4cs4dd9S6mPmCQppt5M4xZdq8Yi0FVs3g8qMQ3le0YLEeRTfYYpoI/noVJkOLau55dCvwcsdTu2vVxSKqjtSenYg1VhTcM+fYFEM36WzR+voTHYbn4WwcqfRyq7DF3Ov5gKXVpj9nlUmTvozc3I3ZuvO7bQ5HvHJexvmiUNQWfBQjLebF5n4VzGwbE9jFjVIuxIt3e3az7CF8z5Qq17OHQ4rY1vD99SJxpw8oTYePn9+4ZfjD0y9m4r1cVoXfvF1cW2NrhrobgfKyUhNRtaGj1qnZ6F7K9HJTmsvutDLW+rWT3mTvQZe1zuJZf6LmC+39ua3MFw/3Ft1MGpnLxMLqp4OpphJpqYx+T6Xujm4zm/jkXKTZ1z+YRZs6MndY16+zKHzvaE4jZ5zet36+U042jztzCTkCK940wvUDmV87feG9rs4ncfjeDcGLfcPB276urAwDiH4A8QgRxCwqZKjFbtdEj3BVhpy2oOLClWyiS84ZK31ATxic8A4tBx9XWhsS8J7ghGJElkEnoKkusir0uH0ispMqmad7O/ffm2ePXn6F34y++qnF3/3mhv7j83/58Nudxc/tnY4CUWWnpbGbs35Lsm5R8dLHLneyO5vn1ibz7qsj3357eme+lmz/B11XjD+2YUZbyfbhyEj1WXcZlay3M7XRvZcUpWUojk8CEZbETx6iYzq4aXIwuGcJZ1N1aOJJiatRkAnqkmrTOLFqmdLEHI6r/bHx4zQr91/GPmYAVq7IsJo4hJXXb+Qozypm0WLkX3j6vW3UzYt/Lo4fmjLut/+HX987/oO3r/+LszeocGb9y7/2q7N/8PO/8CF93Zd/7Vf3UXupfRL1YmoWXLr8HgKknGaZd6KEfybT4wAb17c3XUEMqIelNxtoY+oKrHQbeiCz3jdWJ2VXhGFpK+PqtvQp6gaDRkPQdDVyXFHU0+Xtq95kje5hhbGAxhUQbW5b1a95AMGS5ms28RA1eOKA71pThfPKqspBKoASJaMwCsZJRLqfxE9cyTcVZKW1TyQTgpxzgvH1wtlupNR4t9crWmHklRCvhSp4cyfuFNd6/T9m5OdgwnEpxu1WbIpSnzjnyVLedchEiQVxrxDYDrXy24rsdNQ1wWuEYPiu+mok8/5Cmf5XUXfK/s3l57eND4O979fx2fwM1EDvFMBbAI44sftEtEDjqg5847/9N/+6+mzvwv3E1/9J7nHc2ovl54THX4lL/cX1axt7e9c2wuGda65ypn16MUvNN7/31c3jw8ueRakIBz2O8T2dvXdjcmZa3u4QgYTHe61I9SVzg5vMsRvKyr4Hb2kXc+ZxGcSIygVai8xbLhCUJSNnQ0akWqUJZlGPRmHLF0qxvcklk4sU3BpeccbBuYAUBooJSEVg/KONGQXquRAOBLm8ZbmoHbsa+yuAK8atSRJWfBuXJQ5C0/QDXCWSqx58qzrSP+1oysTN51WvYcYcwtBBiGWGXZk+RsgRMCBaNWFurtcR6vXeQpOVSyJozeCch3UEyX0d7EtRi7WZWF63bsnucXjjYcCgPBkSUFKTk96jtKTyAlYQBTK31pYupy6HhzfaI+qMWcQXFPO5Y8rytphz1cp40k5ZzgeM5wFJVhA6hPE66KLLwT1j2Y1TTJPKy0KydKvCvDt28VNU92YxXW4+mJXXX5ufxMmJvvmBPx/Y7cxHUYi028ZlJ2ZVrHwB7pyvHLzLW7zlzmw3uSRWSFc5MK4MJa3c8QAgzzikAwzIeljNjSaQXfPwPd/KjRlL2dWsRIgzsU+L8AaTEP5cxv50sV3pocuROBmxgju09dZJRP3M2cWaVbNsh8/Ob7hFueEvjj7Bx1GLz/tKsNDx0kfcW4a02GPUAQBDgHCagSmeY37q7ZlWbrY/ELnvMjI6993WsHCiXY0NpdE6P+61hT5QJ2sXbKpOo8uSSss/OeyJzWRLBhe8OL5wTvl+PwlUeOP9uJrPXfB4E7K8NpxWZ7iMyrUw609122+1z0VayEy4jNsnt9J8fC87iZ4E27OyGOw97m7upElCjhKA2KJdDKb9vLN23jLgnqWDQrOY3k03Mnb/xemosy+SwVTqbxxO/a6IREhcJZC0EyQ6cOJYGSGq2G5s2vi03XMj1T8b4s631+Nbr8eEvigXG+1wLqbgJAlMWVtxbfTHnlbZxpvtEyt26LI/jn8L9QSg/nK93y6g+4dlzmImIkm8z8FaMYucsUY64zgx6RSTUEZ4pN5f7i74ho+Y3AER53Y8N2kq/a98pXP53S/R9nd+7jfu4rmiM5A8/MT3hv76uTl7OLhMF7lzN096snPZb8nBXAeGuOxkRgaZtuKgiHIumevyvtVoX+5G6cVOl+lzQyep1KkJit4Ncb52gz1iDmVFxJ9bD1gyDexRph3ch6xSmrnWzTU9xlVFS6GugjXMGsdV1esIV0TIeBkHtpe3N8bxzd5X1hU2al7no/O5gas9ezXBnRnQOzvT49//zye7Fb5xGOMbhxaf2/2BLun+UDB7P/ELf4ffDvtfaomgtxt0/vexyc/wYZd+0Jd+qg3gxwH8NdQf9Bj1grqBDzMTFuDKO8wARGAVkTBTX4UM4I3JYwC4S9TlRAKAlhRuoY3wV8xJibr1vIt6U0oBTM/L+VgynmtnU9QL/iHqknKCmlnJUVMZ0fL5V0Fb0x00XPnXGsC3Wj5rvM9Wp2M0fnoc9QXVsIJp/XgW1b5k0LtRq+y3EjY3hbi7te33hoPHelaZo8tLNui1J1VVtQ9nkygioTZb3TiOwyoQwjKwDee9KLXuBVL2F+S2YN06VHwEYMM62PEsm2pkuwUrQ8qllVG3aoHIOy2IiHGXsClNny/l9Mu96Uun3idvw/MU9UX+k6j1lgk+3EwAXF3EDR3/rKnAw0sP6xjEBLW9Sozaky9Gbbty1CqKp6hd1QuPY4F37388vhhvsv2dFEoMRRQN0WkLeB+JXp+pZHq++NHPHN77vdkb7TT9AupGnzXUwNECiAl42zPwITMH3FtqA4tNwm0iVJVEzwFhVGakifvSe8u8EzMVeceZcJW1OmeUTFP38XLh369y69otl8Vt4azz8PAoK4BBICS3guubsgZgMIN2Q8AFgHDea4f5CJARiGQjZ248+hoGrDmvJa6mrax2dTbj5brfZ92tAm78GX9fzehXS8cNS2iBVnSJdYt6pG1zv+a+CTxCeIilfrCev5trgdHEQgqLQc/VmtWqZu9Q0dXzEH/2WppZgNX9q8xWJQIFMA8+J6csEXeo4DmMQhSdAtIxFigGMiS989N820dpBZec+ijMmBOcNDuHLyRiDztljvl5Zten2o1uzoVvWYrnHOXAY9wj6hyviTYLJ2et4uLf0QDn7i+3N/erSTs/23rv6fVdkwR+Y5AqD86mPvGp74Bwj6/hqduJvgapOHeIUBjHZ9ma5a3KdHpTpc+Fa/uCGOCyOcrJLNTCrIU0YuSuXxRx+FrV2U74jHc5vPFJkpL3uSV2Tlz3fDvh4rZ71Pp69goe20/6tp/6nrE5653aNh+HXpc+YHN5f3M/vKi6mFtnx3HFctvyBReA6fDuqI0pLYzqLDxbcFYs2nqq9WI2FKwjdeJYiHjnjLd2p8PFkQMvCzcbdu1i3hNoxcOt54s7n/jK1OiXnxyN59wtjNm5vPsApRxneGPzRpmiZR92zZ3zi1Iez8noy6javjSzoFjsP7oWu7V532VSijEFUsg0J7c9qvSF6pm1C5ffUBm3yR6p7LQAI6HCCspyH3BPVnHugpJf7M+6xw8p/d7kxxYnpu+mO+fBz1WQ3euCR12m5E6QUEB81rXx+xjh0MD1y9A4Z/3icljdyXU37LzxXD6t+mFWbMi2pY6zJsqlq2L4iKz/RGby8mGWFrP70151yt5eBL2LiPEJI+qjBjILDrboSdVmFl1bkmDkGAKYgEtfMuaJvM9RQoAogfIvv70JC8fesReFLb3Yq0L7ld75TWdx/L/80uH8SRfh3+zu7ky67uIJn+b7Dj+t+vyL/dz2njvJGT/OTKgtOcBygAWHZThvQx3s9KrLmz0mz1Jl3p9dn6huMZ8RH58WAf+YfDIr+ezNf4XvdNYxCkJEi72yQz/C1pLXgTJHR2cYAPjUR+IGw9WeZFGWE2h7giiowHlTvg1XvoJlzOrgw2BtdWjA2fJxXVyxfoQryUnDDjZm8nblsQGCuPcHL/9MCSD6ZlG8+Lcvzltv/OL9+wCe+l/5+R9I0PdDAfb+YPLQHYST/9d47x+Xk5H/zd/7EKNHX/qpGMDnUC+yIa7YgebDbyji1RJWXeJ1Er4iDbAh6ixihiuhaUMbK22t91esXJNlNKL3ZrPbNIDzzgpGrOO8a7yDmrJtQyfny/eR4GrWLeFq5u2yvfzZnMDmaN5/o+Vb7UBaLbc1oLHRQCxQC6TCiFi2NejPnt/aw0U6pZDxPrfYG+ny6DibteZWb4yzNMi0bt1pD3WfBbodBheust2yyAKeBHJuKgXCUSQVcHVBWtSZ6gzwRoIHJITkXjhGa947mzrYHoPkib9J2l/ueEK3P3vuHVaXbf8eag8k4IrR+37AggAwD4cSCzgYxhAsgGokEV1yBPHy/byxPN/f/u//7n89BwD/yt0n9D/+bx7/zT98DndufAnP3VRhtzMBJwsp6+4w77vCwwbOXduT/m/xW9d+yr/+1ibV55I5j+25xZQRdgKO7USC92E7NzwUeUwNQzQXCFMVhbNWJwBgJuCVnWZs6/EHxJPI6SCBZBSgZOZ44xP2fNAHg2ftycScbrWcjWNVB0Fv4CxDLgQisVoWqb8z9MArAvMOnEpUmqNYcEB4KOHghANXjSA6xpU5crOGVxsxGjlA/KHXuMqQm3PvVn5e/X3181rR6Pgm8w5wNRpp6VrebV5z9RqqkzgLFmhQGcCDIYMxBCkJnQ6HlIBkbtlxi7rcWy1jIS3XjiWAewgmai5TOkghAipQ+oAhCbyxHopX8NozveDetZgjcIpY5nb4GbgAnbOKvFUwjFlZGtY+jVD0pD/fzmEcwBxsGOeoIk/UqrKIV61kamg+iHG2PvLVIvDSkjjdL7TZMm9FffnJ6fmtnSmOOwvboWO8rFKMqSgkbtAH2Apeqw7ong9h1QZ7bMJwQpq89y2GlkphqeJUGh+1jFsQLyNthSnlXF+2pGILceTv0OXBVnuf7uu4a7jY8L7jZjIci/TJ+OPu29efaxVMcveWs/H+A77VOfGnsyHa10fyYtuy70SfM854KcKSfCvwa+cCO+9e84e74A/2J+AspzokaUx7C0B5Uc1C1yk0gpIJ10pZ9/YsnN+PmV20/LTT874CdOxI9mOmS0HGGysKQ9ZV7T+82fO6m/R7F4Hu/dtj6r4nbbHRDdNXKrn56iMdv5a42c5fxuxOqFTXSvn4aJF24vjdL7xnt1KzlY62gvVZmwajjWsX/VLvyWCkD+Wm2PMiDiHzQLMg4rxKCk+XSsgFH20u2o5ZRBHU2u6ZPKnaNDpeH/HBo6hz6/X1vl14HnRICWIg8jYIxWMN8+5oOxs5Bb/2OP6j8/V5R2qxdtnLtvnJNtj5bhKjq3qIyIReMLiY6nangFte3A17Ye4i2/Lhzpv59L/aVmFxTSV/iDre34goEEPCKxNX8oUrixYLnGTMAFARCVagYARLgHIRlKpUjnF/4TtPW+IomRX/9/DJ7omb/RcA45/99OC3X//j6TTB5c2vBScv3Xn6VOxdTG847iXTsG2YqgL8RSzlINNQy0YsO4e8cTwLFzEzs1jxbGFC9WBSzeKh4HssLnNv7KSkfkJ3W2vqdy6f4uSD13Hwmb9Om+EG25qdu+qtryA2FU4A/CVcJYzNflcnnZ4EBNvAFdFxvowB+7gyRW6885q9vBkS0IC6ZuZ74xrQADm58ppNrGuqc8CV5GlvGbv0L11e2Dd09V8CeADgH+IHVMf3QwH2/G/+nke9ef9phwHwY6inLTQApymdMlyNZmpMF1cWDMuWeCrDctIEarAUoWY/EgCudF5c9UmQBYIZ6geOUQOeRovXBrH2ja3d+OTiTGS6PMKVZxBQL+AMNbA8Q20PE6IGmU0J9xg1i1TiqnzbLO7mf2jqc6sgsulqzFGD3nL5vFNFjCeMT3bX1v29ze3HnECtMBzmmd6e0CIqqjJRYHpW5hYeoXeWkWKsFJ6ySq8xbcJ8UfnAmNOkE7FAyBw1sxkD6HvYqkSqBWInkTgJtCHhAIwdigJgJkAkGHgnwK4pik7OIC0H316et0/jaoar93AEkKdnJtf1UaLwAISHtwalZVAZgBRwv0tgX0cdMN5CfeGmf+dLP87uv/n1e967o/69564H/+h/StU//61P07WdV/Lnb29KKYIwCq8AjbUk1np80G+33P0HJjo6WsdVCb/MCWkqwAYWgQP2GVBUgCgYqA8MNKAVEERVoca8j0hXYn828aEtQ81If3dj2959/MTfqUqctbqIYIL16QTMaFdwKXpJi4oy8JpxX7U7HEzUWWZeeHCmoYLGLZ6DIYCSBBgGMEdBXPnBbgIhDayTEM+64BrNCwB4FEUBogWCoI+rZEHgqpGokS+UuAqYwPdv0Fhh1ABcMYR2OZLYA8IDwuAqu2/AZPOcjQdiDTyrioE4Kzl3IFgUhUaeJwhDjk7k6jKtFHWpGgBEUAvBDQeYrS1bXF2yZo6BOYJzllBa5p0kX8GT4+AMFswFlWWc5ihc4NusZLEdOwgtz6quZaioHc5AzBLjgGpX8NxSIYFMgUcZPOchlQFQQkg1V6hEivdenMIWnma7xg/HkmkdJ31MLrWx72OEG+s92rwYxsVp2S09zuIkGFOXn7t9foC13NmD6YbPpl09fn5HzKjLLEVmP825YDNRqLl3Y+1kbGwgbWterdExnndd8x4rp2OLYktVm2skL0tz0lrTJuIsRhDP022bMseN0ezUjXA2u+PdxnNsKxjBcWcfxhtE3kY2Zf5Q3SARBUzNpIft08FgBrjAirTNCQxGz4FEA154+IDNAg6y0vUun2+le1NugwzIHMxc2/E5cz6IeWVCFlOub5Q27Z774HHoyycla0ePteTmosw/HeZJe9i2T3Mkrxa2ZGJR2nGef/xoOAlaoqpYtXFauNZYJe/2Rnl1VOXqYDrttD81fHL9URJPOQaT9t21p8NKw48Obs8722/1mLFYpOtVR5RMDKayHVnuAF44gMVQye20u/nOpTr3HvfyVo7uLI6qlsMoy+fbYfLkrLfoJlrtQxNzyq4zsL86OI92Ziz388SzaZSoHoulJ8uNdfDwNO5mRk9FpUrL4J1cF52Si9aRYzTTOt9pM3mGq4T9EMCWBS4Nd+rSVm9wxu857zeN96aw2jsqbSBESy1Z+KCSbFbBn8tcVdOsQIT1tXkpjRQvHH4sf/3Xs/8w/afy1W+dDeVn/tZi/zkRnE8rPr8/tWI3U1IFRkfrmSa21NZWXWFdS7LoIpfbZcX4p7uOKk6tHcnyu6FI54yiwsWVpZ41Pj1/hPcvD3C5+zyuLSYuLHNgfExjU/n/C8AfAfhnqKdX3EC9P95Fva/NECqgZjRXklcvAJwA1BAHN/EhLS4UPiwtafbDZkjBR10AGmDYxLNGu9+Av+sA9vH3P3v/q794/3sAfh31Xv4negF+UI4fCrD373HsAvg8aoDUWJY0DEVT7ltttmjmfjZM3XT5uC7qjbTREXhcZQjLDY1rQDasSI4aeH2opGq9y8fpFNqaArWOrGm+OEUNJI9Qb3A/ivpCYKgX8xquSm4BajDIcFX6rVCXfi1qH8FmFFtTmmsunmegiQFCAFvdIKC7a1uHm73uJZwPPxidXtOV6UaBkt67YLvX3enF7Q8eXZzEp/O56MWJ68QJ3+x0ZFVViillI8ep04ksRUEjnJ+i9i/qGRTzgh3lodtTAioBkAeCdGn8A8C3PKhhMBMGgQhdSWACtcbywfK5+gAiD+cKlFTPmGtkisDy/+Me3hFoIREfM/AJAzvhiNcAfBbAv0QdVNzd3M9mZ5Nfqqryb1hr189PL6fVdH5o1veTl/d3d5PSdPD0SCNUEoMelu7wQJFTKMji7DLGeFagDsZrANoMiFqAZxxc1l0yZgFkHLzlYVUI5BzIFLxonR/1fV1jrQyBXzDmptEgSYe50Qz6/bVNcawi/Mx3v+EfXL/Bvrt/F0YIFJyZtqlQGQskIQHedsaXmEWxgAo+wp5xv7QvsQAikkFd4hesyZolarBaT6uoKoL3CroaIM8VwtAgDJsZt01jRdOQRCufy6pecPX46N8bf0lRJ0W+Ydebub6NpoevPLazfM0UZdmCcxHIlQg5wRiLsqwTGiJTE9QedfMFXK3ZKUW9SdiGRWTLarEFGKHwHsKRZ55V0MRpVEl4m6PNrGsznlIRRFZ6YVkAjd3zyE1F2xQDEiHlsH7hWiInxuHzoSXNgeZTCHNOWloIAQgyYG3AdhksA3joHYuN9Y8qufmkt91j53/le7da2dl+PnhIHx8PJotiaItEpqFc4+f4WP9tpkpH7LWB2ggH2t3g6VPstm/O7wfSgqYHz1ctfu5myVMPkEiqhVN07s7S53z6sCfi9UA8pOss2+zN4tiqi0dKmutWvI/nqkW2Ked6D9xNKwonvPOFkFc+4pfY8+XBukdxybN25aVIkfOQGXRguPDH+6W/v3nudc8zQHG4yEtnYXpECBlQMQ/vC6hQeQVuzwA3Cjw2FHBWkreKcxVxaOvRD0odcH7miU+1MYWKhnZRFPzddCplEcRs2C4mCz/mWg2nUx1mOxLBRTt/cCb50QRkKzMd2EBt5dkL3+4Riem1jtjsU4Q24CKO0Ifnogrnrlgft4uT/XSqwAaj/YU5vjlbdE82hfEunCXGFMI60/GmP00QzuTe9uPW7M3nj68f3ZtGn0v33HDe5mtxQBUzG8XcObKVGS7iT83G2eZpNFaqEurB3ZEajCJ45928V7AqtmAHznhb6fnsRJasxdeoSwGFgYMzzNthwILoetAKALyN2hrsC6ilPqylgsoae/it8kgWtoQpnM4qE2yISGYwZREs9LpNVFtbrqVFMTbiMi6N7x3ORftsocr0wd4Ff/zqdsB8/uBx8vhf3PmJVz6tN1785OHp/fPo/uL85ZPLAa6fVugdPR33lyREAZgq4HPbZ0kwYdRTRT4ttdmbaF+48CDv24F3jk+sikC0Q31oNsd/2hrgn27fZje8Qyvu+kBGuMUlMquRoN6nGgkTALyLev/rAL7R6wUAtQGM2jBDB7QWEA9R+8I2kqRJ/Vy+Aryu5Uh14MOVRdqqrKfRJjexaDWxBK5cCHZRs3v3/a/8vKFf/LV/BcD/oJZwgb8Ae83xKdQfXFOybDoPm1JsMxqqsVLRuInuxkgAACAASURBVKLpCtQov+mO8rgqo1aoKeb1+ndCPd8WvMP5wnlU2jlTwh+iBl5NRpGN5zOHeqEXqIFZiTpzaDbgz6AuX4aoWT6NegEubSewAHCx/N4MiG5Kcg2rN8GV/q/pflzNXpQDwkQoy4jm6512bxCGr2WmasFSJxZVzCBFpgtzuQjMNEv7D8ejTlZV5t7aFp+VVWVAFIowZ5JXiMApCNq4yqiy5XvY4pCdwO0UAupZF63z9XtRiGJcdRoXAKIl0Jsun+cu6sDTlOLYR7oBPADt4YSHt4A5A2gC0LsM/BMG5iULe6gQ/iMA3/qFL33RDXutDeb81sGDw7snT8/uRUmgioPzLfn0YE/L+KTaTMbr7shjf68NbQIYAzBWN3UJBVSagZGAcRqReuLzakzAzQDoaBBV8Hxp565CIPSwOdX1xDKoEclZBrgZCSO96Tqg8M4G/WziL5SyD3dvqvVybj/25LEuGPOFI3HW6nkuAt41FR+tbQHOMxgDhKGZdfsc1jj4IgeRWp7i1a7t1bm3TZLTdMXly9sLKBWiLGWcZaxiZEzdHdscDTBssmtfr72rpl38SfBdA7bG1/hDMUk1LF9jMt6Yoq5M2vC+bpDiMwAWnBcwzoPDIyst0pKRtYFvKQvZdNuC1UCuGSThl930zNZsn3W1PZG33jlP1YLgOYPgBAhmMAiAwke0sDEbO7VpvbfkNJzLfIcfhgaAYjFmVeGFJ4JkICqdhxZwYLBdB4IGFX3rDSAcr/uHnSrA4SFBkIFjLFD20dpc3zSozPqlcf35o45Ldst530a2ync/4AOLdY/nxsG8HZkL2nJrG8ZvhSelGbbDSsQyTU5NcNm1/PqET4UhQ0O/1T3yMzaI8yogfX0co5PDVd7kj/u+iNd51j4s0w3GduJRoCMmLorSJc+9JReiVz2Z3U7TzjCWIpf7BwEVetMVIIZJzDJomNDCtgMwFtN0M/YoM6YWyuckUfZNBV8G9blGhonxiHwEowFdmNk1ReARIeRAh1WQPDCCeeSepOQK8FRtBN2q7wqUEKYdtYzlwozL0pmY75w5l5yWxfRjjE/9rNPWjJKD1Psqo42wleiJzLP4Ya91/44pzjUVg2wzVIeOnRd2dv0a+cTkg3LhMA7Xrr0eFYvdYh5r0fZw7u1Pn5VGu5CeKjPoBk+7uZLne4tuMJcnhdDbTvq99lAJuwGQ8VgonRhlk7XTRC9U9dmsKmRiZWByi9PNzD5+eYroWxJrBxEb72XQvgSHZ4sqlSo1vMMlRKK4LhZei6Lo8/VGj9bEdI0rUKMB9ARj1R01bPUVXR+VhSscwTvHrKB4hAx9xAweSKEhOBB4Vo6wNtmC+90NzP6Pbw/bKJkLXlOHetZqBdl5Jlo+nIgnw/tfC0t/ydmiGsoNlidmOF6kAC4hsFsx304eZrMyFsJT2A5TjYth1xxOou/OXqed5B67FqxRnE8clVPbKzR9IsvEGx982z++/YJNZWV/dvyEdazmLy+DxB2mwImhF1aoVER6kfuTymELoOuADQHWxIS7aT1NhwAa4KrRopkGFQLGLqu0AUCXy3PWzFxfrVAAHxou8KxpxC5j22IZjx4tv+pA9is//9FRjT9wxw9NN+6fdXz51361jzpDagP+GCALriVIJ/B8DFCGK/arKek27F0D7LD8uaGEG+DXAK3VUtbjQZxMHTGuuJhaa87cstQqAePqx68tH9tCDeL6qEetPUINPr9Yl51MBrAJQCf4sMagGUsjyWlHvigBUYHo9fr/RAtXtiyrJd5n7edUi29FIuT01vr2RT8OJ1V+EulytE5SfSuQ4ptZ6YYb3UHcT6K5dtpo4/yNwZAM+czDZTfWN71QIpNSllyIFHgm+N/ElRYiInAvEbZYbQkTAgidx2PUALeHKx+lhjFqBPyrAl0CQAQChwT7sGWcg3eZrHQK8helmj4sWF4olyhf62Ne45BfBdB+8fYu217v38vSxU8cvH/w+cP7R5vp6YTT+/ex9fRJ9dyjD97YfPDou7S98Q5uXEsgWQtShpCSUJYGZ2Pgd34PeP+RhzaEyrRQU1QlAYoBXNSon9XvF54AUQAiA6jyWEQEcIAp7y4uHHozgMUE2p6O+MZ8wshaRFnBksKyUdwST67doPZ05GENLroD52UAVCXBaA1iDt4Xm9MLk0spLDGly8LBOsa0sagqD2sDcMZBzxi91U64xnS5BltCQCvpnVIVomi1wacBiY0QetkMYiwwZbV5sVptxqi/57mFrgfQLkvHDevdvP5q2WW5TisPVAawVT3qzHMUxsM6HjzJRPe9kdx+/Vy++G/P+OBgyqdrEZmWkpDW1b5a3NX8GvOAEXVThpd1Sdsv3wN5IufBLEDGgnt4GA5wcvCkkNl1dlHGNC48Z3KBATNcwIbetkvvb5RntoqdbfOSROlnknkijrxbomAOnACtObTjkKAGhXrLyZEoPDFvgcpyXWI+65xNJjFZIyGun5UJnyUyHVa7a5iEz82P2fn11GdKiCfuHm2Wx9WdzrdMYqmlCtB1+bRSRUjUO5NTRYbbhLLpPVUKA31WQIUVFUkLITKKyx7TWGdHnX40zYYsi4U+ZLu+aHdYHJVi4ge8PJNiXrW86w3U+STyaUKcbZ35jeCELnkHC0qwGEnvNEi2LGgBWjtpw9vQmTXHYQuCEkAlJC5yDw2gC4HIsYAEkY+8a7vaMEZb1yoyLsccuk1UG+xKghcEw0U4PSefEBbbXeVSyORJ6bRAEFRl6JOZH/SnDmuG5/eET6YdYuMu8pmT4/TpYnYrtvm6kKwY+7bt2+1pl/IgrSYun1suWilPnW77cu2sxeO5SljqpJoGoXzHVXtHfNGq2oVWrvDwgTT8+lk3HV5/vxfuHHQQFhI6cAARWlnAhWUKAPMgK8FSlXMeLgSFU0FVXOHprQnKuELrPCLJBJvHBp2qxVI+9iELbcyTgjkRGWtaggu3jO1hrmfHWXXRVSLJiFjAnI10kaoAMuS28D2XevIZj2Tg1jmJlgvgvcMltw5gJtC8fL21GF20s//zn23R3AjuAIT/3eQnd36896PBk/FvFPm7X+8esNMX8y3RfhKLt85bUQkuvrue5r/xYLP7m6edcDDtRkG8qI4kN9kiRXu81Y6mG0m+Zn3HFWKt6Av4Fic9Mb6aat/ZEEKE7IWLJ9gY368O0kfV8+MpG7CI74gQN6M2PsYYNk0FVVlkhQazHuf13ufFEtT1AKrq+EAtgM4A/P7KHtFUGAIOxzhR5MAbkuNkufc1+2WjL37mt7qyzzR2VasA8H0A3/oHP/PyMf6cHD/UzN7Sc6+hY+eAtaqVt6t5mHFpt8Dc2GY4AvxNwM5r7RAUrpom2rhi+tTyb808vmaDWj0cahbu8iJbXHrvtblquJgDWOiaBWxYvhNcLdwFap1CWf/NM6Bos6C6cGWbALk6M/cxrka8jAjuCN5sePgB6NmUD+BqvFTDmjXvkQHIlzyVceTZbq8ru3FcGEnXvKtmzMrrmTbC86wcZ2nKBYuMcfre9s6jfhTHIBoGgXgVzve4FG0A3zSVtsUsp7jf+izj7Pry/I1wpXNoWuefx5Xj+TZqYLpq4ttcoCE+7MfWMIarR9ONZcnhUbswB0WgwgUPOoa7lgX/5yHU+8v38HkAydv3j76yniTi6NHRT5+fjl5YjHMHXRi1KAUVZcA5e5l2d/bBxZv49vcSfPolBaXqwPLBY+CPvs3w6hsltG0842Kqk31pPJwggAHl0pOuAdc1q2oRZUDcY2hVhKgCogXASyblAFouL1i+XmTkZWjvPHqDffPGXT8bbaA3G7NvfuwTdMAlwWiKywLMVmrOmAdj1VnSgxeStHau9suGAQQDHK9rvgCiqNaiFkUNsuoSbaNZucp+VVDiSoLQaGKa9dNIHJY2BxLAgJYfzUrzxcrPtZi8AY5Nxt783FxPwDPXfGvqsqsiwOUAj1EZjkWV3/vDE3H9vYmcD0Kcb4fm8EfWbd4SAOdFTaRqBvAcoAVA8fJt2Jol5CvgdVnVkQZAEADatzEFgbtl6u/ZRaZsSwX/H3lvEmNLep2Jfef8Qww37pRzvrGqXlWRrIGiJE6ipLbapgWiYUiGLRhWN+BFwxtvDK96Yy9EeGFvDDS8MXrjgTZAS5ZsQ5AgqTW0WpCookSKxWLNVW+s9/LleDPvGMM/HC/ixsuk1PbWFBhAIm/evDduxI0//vP93/nOd5ZJDwNMopZIngrW+cxVLCqGaIhrNQQ8TFVC0tqeZVWVVRI2VGq8dwBcbSASIQikIeTJsWkNJ2rEacjKsHj40mo7PTi+eW3an1+X/on+RF5U5ta7sy9f+608GRimWtUqRB8XkOj2C9465lFyWB/FDX2vt4cnT14lPajNtcQLphtRL7Z1Onnfb/vHIaZaH51cp/MXejhIU1pIT6KOkQZRP1EvcE2g0hWxjhpnjwpbD2OzbzJxrsEqfSrLkPGFXqDWpQg06cRQX59LtipIz4a42Haly6JD6XvQmUDYI9QJhkhBqkG0DGWlVgkgilEG4Jj1zgcJCq2Ct4mqThuEPd3eSYBBWUeek6TzgZkljvwOxadfy0x6NMXW71EUvslnbz6M2AQWnx1DFMnWYOay9zytbvaSZriIhM36/LM+Gb6dcnF2rhdSj472ggvbRw+SM8hUn12wXcTeavMFhbCZJWncpKTxqJKs0RvD98ZlU4Sjg2szvvP+WA8nFuwjtADjSYrV0KPZ8BhO0m6lwh4hK2LC+w8EC1uD0xTXDkYI0kje5BQRUG1e0LJeNTOzIL1gFHa0G2L0EFGVVIwY+0Q89KHh2pUjlslOlgzmUWLoKSqUMDRUWiIuA0mVGShWVsXaKYmEPWMYQuUpHH+60Ys6m4b/8PTG9neTycmD/mpxN1nwm/npvpvsvb5pm5cm6enJKO74etRMng7yb/ez5N69V3q3viVFsvfhyXxnXn5rdmt0PXs937z3BN9/uMhvlJzw88ezrbnR9YOPTI27qrqeVnnaV7yqOYsBDae4ZiP9Z/lOtjU/pSdB4nVSMS8Xqh8b6iQb+2g1998FMF5LLTodcTf/6/Z/+EW0sbLTzFsAza0k5opE360USVtM2WXqOsnSeg54Buq6TMdV145OqtLJpF6j/+Kb78k//9UV/h5sP9ZgD5eauw/Rijs3guMIYB6q5DGAWZv/rysgFu3qQdVotXBdWhT44QrdLvh1A6hjslK0K4pjALUTGaHVIYzRai8sWp+4q0CmA5dd8Otat3mADgCVR59sAHoHLdvX+e91TNgCgMQgW/BlAaMKcPEFXDJ/nfZQ4TKg0/qzXQDMJht/rb+RnC0X2TDNdyNsaKAfnsynr06r0p3Mp6XVqne2WNjNXlG/WAymmjmPJNONLJ+6gBotlT4hon8AwkJEjtCmvjVacNt1J6nWx1+un9vDJdjtbuwO7HWgtPv/1U3W51Zd2e8plPrOPM9/Aqp5XOA0y8Ko0W0BR4VW//hPABy//fHDXy/fffjvBR9fXEyW7fcRJU7ywp3HsdqOdQ8GDXL7BaRJH0+eMjIbYa0Da4WDk9pHMFtjVOO677TnBV4CjGthUzQK0O2RTwGYFAjESHrABlErDCYC3WZQI44NQbv2uogDY2ESfs4t8fPvvOH7VYm/+Ozn9dJaJK6GV9ys8p4CeowQ4nA+zae9ocBRo7RqFCgSWJCmFmVpYKFgTXusTQ3MZkvkeTfpdcCrmww75q6Py4ruK9ZEz6yFOk8r+lv+1ler3whZ1mlSr/6/a3jepa6Ay/SKA9KyXeGzrF9j0Pj43LvnGYVIq0JhNrK4/xNbbvniwCNNCzxbmavuvFTrrZcJUMoPDyMJgCOgUi0L6CpAqwVyUWD2sGqAM2W2AxEtKAlF0IpMQqsotGr6euqbxoWGbyWJljDqfRjmNHi4DPtpkyrUOlPnbhA37cn3GeFlEDarpuc5KD9I5xWMDFEiKDJGxurRuP/J8TEln3lYz3u38weKMsF2mVafmb1rz4dGT+8qOr346fnN8Qry2A9Wwwx7arP2N85RI5Flk9FTuoaUoYKeYTov3IaMRCrFP7j4jB/fCZi5Xlykm1xpqa2HDsTqcSxkbgYENDg2m+woQ/zCMHIAl82UdsanpHKRedyVY34BQKQsTqIaKiSIXDYceQRq+kqh9oyFA6Yu4LplxBDBjaDIDHTiG48YI3xKtcHMOeTaTjcafbF0JCqU5mjBYW+gMW+cfSrGW0JVDxQtQ0yfLCn0JNY3dNMrG+OHUa32MizCht/889Npuk3jWPT57BrSfvXU7R9QXJ4/KMtkHqfP7fGJ+0CFSZ4/2mnisseOHi4fju72xtnHhlyy3F6NGivZODpfxzgab3zy2irbebxYDMosNZOw/aR5PNEhm9EJm/5c23Q9ZCMBjRIkSFCjXvfkigwo5esFaQnQTYKdRQrnQZVMkVCO5z/ZDqGsImxPwXvV0FISM4irZhVK8banijQRBMP5LTZazZvTZRDBIN8Mhdk4d1I1Vg1EJ4lZlecxpJVdDU85k11Y30fUK9S9Ofm5PlgNp28+PHvu0z//eDBWC3z/d/ELv//LX/nXvHt6UX+l1MdJVcymO8/vyhySfb7U2Em//M471c6/Ozy9+8r9pVqEOGqYzy5I6qM8UYtb2UWcYlOdhK0mSCVjqlSiSGU0wYXEcNFIuZ3pXoGwc1tu8IOg+seqOm94pbebU5643bOlWcLaT+Gy2n4PwOeA+ObQZHcb17xUQk5x6W1bpsr0fQzwErt5pytO9GdeUgURgXRsX6d1/zcVaHTZjE420m3dBFGijVsf4Yfbov5Ibz/Wadxf+9X/JP7Pv/W/h3kI/7EAdwB6LMG8DfAMoAFAne/WCpAE0GcAzXFZFdjHpccYcFnx0wW+iBZszAGc58qozFpTBz9FO3i3CdhJWeUkuCiM5SaGo56x30MMW7EFAQXaAW1xmaq0ADLAPITYEqASl/5A65ZuGAFYQWIPfpYBboSgzmGy/vq4jnFpldGZTV5dxRAAbOfFaqvfX47z3knj3Zsb/X6MIaaH82kyrZZPzpfzVzKbbl0fjeN2v+8SZQqrtWKiuwSuiairBB6y4tdMZoes+BouW7B1QLNjdgq0hS4alwxRt8qyuAS/V5tYAz9sI7NCC+Afof0OS7TA+i6UIk84ipDHVkYPdBVeF+e+AmN+GS2Y5vr47M/D2eKfxjp8mojaXLA2BKP8vvb1LfiFDTHB3p7FoEgQJcHGkKGUw+mJjydnarqY65CmCssyaGrTAASo2OZsG2qNQAxRm3qugMQBKQOcUDtmfARzhGGCArWopm5FZaoXHO9ODnmaFvJw7wZXRV9N+wOqtI1LpeULDz9UjTVhXgw9ucYXTWVU3TTsvYk2m5G1iWmcjctlQNMYGEPQRlBVApEGiAJjBMZ033VnFdSxwVeNqbtr1KU9uut1tcDp6va3ikSANZPowNwtlrr9dIxhxyyuC5AoAaQEKgOQQhll8Mk8pnPv80kZj24P8OCndoLfTzkYbjtwMHdgtQOlnZP++j6PCnBgVF7ApMlz28nJrSCZYVrJiC7iCj1V8JwKniurmSU619MLpTQxoNkab3qYJd6btF6Sysw8kiJ9HK4vzpr9N174wG5KUo3KQiZC8XcjBcooPumt/EFxnlzEXnVYplQ7bx4MT9PkcLenn6Z726XOqdeowerCJ0+andXN1dxfW0163378D/Tf3L2O1XJ48dqL74RhemLHG8cqHj8flrNrzitqdM+Z1V6iGpMisKVlk6vhzhnxfiWyMdLeprKSHWnqG9HlHg1tiLODuPSFAqeRez2OImIjkxgjrmSpLXOZ69jwDgcyVJ0yGbXAWJ3FZRyQ0xuIKuEQFcZPra/6ouAdYeECCq2gCLCKwQKYNFLdcNRe64UXHNUMw2JWdZQmBA41er25pidOhTmChlLRKIkk5KNj8sHjhmBwcqbpcQMqRdLlhNn1iHuFzW8vlDk4D6u3l0J3F96dhxhibDQnUZ1klE4ebFXLMgaV+VVzUg/PKG5f7BXRu0OSZLoh2zvJmSvSc5BpjIRcZuOz3sRW7P28rlazZfOpi1uczcUzqL8sPIVYwy41kpXGIqti9Fgtew1d9OqSA7FnMBJDebDRxoSWhQMpknTK0UrKIbqYhjTkamw1Z4EUxZlx5qgok1pTHDaGBaQgohUlMSg3bkKTG2UOFNleTFwz7d1bGoxdyUlWUemTJmdNigI51KjC21n5/m8NLu7/3+OzRX0S/vpnnyThy5Y/9zPlja2vnOubNT155Xd3k5/2KvuHT8r3h2F6JKe1IX1Rf2kyxzgugv54NDj/wXM7Hy+2CutFvw7InomxqomGU21MPUhrv5eJFz5tiFBCY3BdH/XGeNQf08grKudlaHpbclY1+sH8QrLY1AcA9aAUo2XqHgHyeQ3/fMH2OkR9TkHSCPECiCI+20h7PSI8rINnAKORTk2PlC4lLGshqQRJW0VHFVp5UOcR2sXwjtVf4pKg6cAer+NKF88DgN+Tf/6rD/D3ZPuxBnsA8Ov/5/9x89w1X26A7wP4Ntq0LqNloxoAM4AdoGuAvoN2MOzjklU6R1sI0bFxV7V5AWuzZAAHSnEqIsHFWAF4rIm1Yr4mMfQccFrH0APAilApVre8xAEujSK7lFmFdlXRWcEArY7vBC1LaNCmRk8BaMRQI/gG4muoQQ6lt9ACoxJtmrcDW90qh9ACQVso7W9tbB2/sLPzMLepyWzyYG8w/DhN7Dt3z45/42y53Ogl6ReHNtH7w6EfJFm0SXJSu4ajSC9P0jtoW89to03N9omoq7S6qr3rmKC1/gL5+qcT2nbnWeESCFwtce/K4rtq0On6/38M4K/X57Vu+4YfMBQp9K+BzMBX1RfqVfMFlSabBBIIYkB8o6ji64b1Tbrq3MIcXtXidvq2pjRLUKSM+VJhVUbMy4D5XOGduwbff4eb0/NoFqWiBqx1C+iYQJoghrEyjKQmYLH2iCuBKgCGCbVZrzB9hIQInPcL/XS4wUW5YgUJHogmxsCAWuqEWDHG85l/kg3k8bUbiioXtsu5rsjE082tBD6i5z2GTz+JImLKrVGSVgu9MZnIQhuCNgZKBUio06rSXilC0TcwtqtW6/SRV7WoXTrj6kLhahq9e89V4fPV1O3lVjeAdzVEFIy5yuTqK++7mr5fL2wCt+yeMEjHOlFqei2nw+f7q/NrhbjruQqpbsGcJaBxHs5rMBxIIlCr9t5mv16fRYRQKS9WAnGmKwoQ0S6kqarUkM44p4UuMEdPLRCCik1EWMQtSpSoRJUx5VUMkrCqa4qKMeYJ6TxV52GsrQ5prhbbkszHMfO6sTRhxk0X84JdOFZWcNjL3kzVoqpqe40ardOm2pqosn92Hns+5qf6bLvHvqeov1xkzNVynrKPxP5mIr+w/7a80HufXS2YHeaLh3vXzby5Qyc7gsf93eycNpEpR0M1oSgibALFpJDZGZxDonvVkPsXmuebJNHkwUdvqwvLOonglCIuiEPUEGXJJMTasFCeMoyCNCL8XkYhtTJLx95xDq2IiELEPER7Rr4eRYOBRSxspBgjFDGUIaRZxKJhvj9XKhDQV0AtBCYJGjEgUni+z3ViRN935Det8mVAHCQKdSSkFOvnB0pm0W3cP1V803Iz5chnIah0QBGZsidnwZzLqnplI9ZE1Ng6pRnSmqKJZtkbyYiak6aqnj7y2SfB99IN7fNlcuGW1ycOG4XJ6oveudI2X6Tnjd472Xjc8/mKmmDZ63c33fB4Kwyo8stPPlZv3SpHUa2yFcnKCxOHoMWtEofDF+aqmKSJrUQCGiQhpdRZoAGltUYR+hRdySu/JEsarLSJ8KTICCsVmriMdT3jrdgjG1S48NPmaPERB+U1kj41rlYTqnqKOBL8gdudfqu/uua96K20yVKFVDMAkYBDNXXvjc+bw8RdvDc8+cO6b0/+4aT4R+e6/tR3hge3SNQXHmyX9tvb6Y00JKNc1XzUb750fD7Z8YQbn5jkzgfjzeI4T/pVIj9ZS/mZU2Prt7LB6H6vKCpteO4VTmc890s5Gu7qP914Uc+mE43FDCEtMKGI7y1XrMWqkRN6OHnKJzHK50G8D8U9sOpaohFAeYSyq+htjZi5Fug1aPuZD5rgUXnf6ehVjP7MS7zngT4QB0Bs1vd7Csi0zdQhxyVZ0803HbHSxaZunut07TnauP/u13//7Xd/7Wuv/8hW4F7dfuzB3n/76/9buYjx3EH+HO0F3sbahuLKT5e67aPtk9pfv32O1pNtgXYAZLhkIrrG8W0VI1D6GJ2LsUQLzN7aLoplkSRPpnUd1/u6DeCmFxl5iSO03RaAdjWR4LLitNtnZ3Yb0BY89NAyWlNE1yCUD0DqB0CzAfEfQhdTsBrgsrdgV2zSMYbdZ00APDAiOkuTOMx6B9O6XCimo81i8C9TY//Ex3DhXPjayKYvpYkaMlGyUwztVr9fzKoqJKz2Umu7/Q9xqbvqqib+dtB/ipZa726m7nVX/deupgs7QNDZ13SWHwdXvpsdtNYsM1xWUB2iZVWfB/A5gF8NIdqACCOGVaMSzu3ZsAz/HSC7APaJyECkHua62c71cpxZxf2ewb/zcylGhcW9hxWWZQuUL6aK7j1q0tJFcdCpvZKuvJxUlgBi23BZTTKIGKAgIPjKPYzeGcU8NIp6ikEVs41A6NVl0IByDIoMmwlo6Guxy3l9PNjQF6MNPN3cYROcnxQjlYdGLZNMHDMn3vHJYEMth2OCJhVYYmVVjKkxMJkCqwitomdmJGkEc5c2bxk1Ede21fBrBvYZCu4YvY4pu1rYcVlte/nc3wV7rgGIGdZoMHff19X9dI+79671m7xOwxoGE5AZAeQYiT5CplZIbIAxARoWCAYBjCgBDAUmArxpE+nOtPq/ALAyMbKQbUSh8R4QVh4DnEKh6xu1qAAAIABJREFUVKmqODclaxY2ccF5UpFBrcBMiapZkzBIEJhhlEBsgEcCpaJoghrwtKAsJHfDXvJJ82IRRPocYt4EKo79zu7CF/2TauflA3d90/B06Iomm5yr6uLUrswgM8bM1d74Ee2Nn2za6BTVfbW79YF/deut+ProDbtTXaTVWWgu1N7s3ZeeT2T7ME2LR3IvfCr6qvCNMj4JSwlKU4VCzuMuVoMt5UdDWuQkK7WMIc1DeeaqfHlhis3GbdgLI77GXDZBOqFyrp2Pke1gfT0aAS9jzMmQFEqcZVVfIAbXdnJRSaXqQa1gEBun2IvXugmANgZpAsxI5WcGYboKwoGRG4WBdjZapZbC/P6FirVwOc4k7iURAUF/tLKxxxF7WcDARL43W2AVfIhp4vJE9BMnrp9QeaMnm++UTt93VU8918ueMLnZkZhzEShS09sFh5EofX4Gfx4lihOR2LhBGITiGN718zNf+u20PFfGzo7LBxOZzb1bLYhBFyzhjEK1Sphezs3ofTj3/lsPf/2YQsxHuJltzK13y6lP67zUYpNknohtRFmvDC0rnziDsjrnZX1IwTcRzuG8OvaQ4DMztCCuFesgJCG1iVGcUh5NTEOqajjlnVMOCxNF6qy3tWLw4nFaLRx8uSU5VNTB8+qIUV9niZkCkVcrzAYHVCYlYtVfNHn5Ny9mqzfP6r7aquPLj7ZmF3r/wXT74KT8q51leQt53KzPZm89Lzvvars9Oj3Y2Fg8LS6y/qg29jbKi1cGi/OX+rOTsTdWzVQahW1S5dZvTOqLcdl8omP8eDSUbH8Q7h7P9V+M9vi5rX0USY436xWuz8/InR/QEsAGWAm0noBVp10/RUtkNAAsQJ2V09V2aVtRpItjOYAQIGMPydZ4bQNAsp5KEkDW2TLqsnHdYvaq1Vr3E9ESCBdX5qQUwGcBPP7677997+8D4PuxB3v/+X/0j/1/+c3/5RBtAMnRgoFTtIyUQmuyu402xbeLy5VAghZQbOEypVusd7tACzCa9f46Q9geLs2PjyrnitK7Koq8jBbs/SUu3be7ll8nuCz7LtCCoI7h6/QK6fr4jgYm+W6MsR+jOwb8OaArHeYvDmLwkYt3g1Id0OsGdQegOh0WAGQKKDXr3jBNFy9s7qyiyHdTY9/Y6Q+OABzs9AcuN+aXh0nyJSJNWhsYrVUvzZJhnhuj9YqJHqD1RtrBZaDvmJurvn6dFUwH8jr2Drhk60pcMn3dc50GsrMf6NjPAi2gu7be5wDtSuyT9fWcIcYXQfQiEfWIBUIEpQ2EhETjj6ujw98PMf6MYvWiYq5BON3XOL3d0wdZU2fqSz+Z4fkbKfoDwmhgUDuDj+553PtE4empArBSCkw/rAXpjpuwPuEBpDGAsEB8gHERwyBhmmotRJR7wOjgedhUSrXVFE1PoFmeidrIAPrReBfv3nmZD8Yb/uNr1/i0GAhFxGliSDsXX3hyoGptUA76Ah9Jz5bw2gCKGUFgnjzxarVUcXNrgZbObMF0VQHeE+pGIUaCjrE1HNbdKQB/12X+KkCrcAkI/y7QAwJ8EBAzEtuBxqta4o6x7YByN27QVuHWBJBb2zAoGMWw2sPqFExdH03VdjlUAqVLqJC1jGC2ZpLD2oIlCsAMJQTMI4MsoaaCliZAU4Uh9XmGGCsozqEUYJWiUTInQw5BCEoFZCpi0aRY+AQrFFLGBIoDknjeNEtUpdfUSGZOZc+ch4HbjPPhJG5sLENvs4gXu1Fxr4mWQvCyREHLbM+orWGEtcUwPdaUVdpR6rf9ZPnZ3luJMofuev0g8RgqHN7Agjdm8/TaMFKeTrI0BGPVwo85aSzNMQwPqlsateJRFtQs5FLbkTJlJcSe5skuB5OBlQ6NydmMhuSDUnVMEHQeTKbk7M2o/Iypdz0BYBAbAc4qvigDLWBghkyshFVmFMhEnUVAa40G3ISA2AhMEBZN5IMihRD1UqJbVp6MUkhUQMa6mKSKo6FmuYiSAHoVwBMPbKYmfqpPGFnGo2WETQRlE3WjVPa0SvSqjLZeVXzBomA4ObamjqL8hmY5PAohCd6eB5KR0fo0wtytG/E5U6VSDOcKnsU69lKtHodp804/j6uRbOw0F8ts8fjelng302Qe126R1dXU1r7csSbbFlM2HJNkk6/fHe99umj2rehJ/WEW8xQ6tUdcqulzjbJCSlVRuCQv0ZtFfRFDXElqhkqEPBPqKDU1oSQSqVZ+JpY1i2iyrFTpLuBCDR9qWbkZLxISnYycblD3OT1/rMNxyfTkeR7n7JPbPvjNw637+TyZwKUXfP/OX5gPPvVtcqp2D4z9nY9QfPuXRD39J6Oj6ReWn3r07qff+s7DvVNYf/j+c995fEI/2zvLP3N/YJ6O7zyd14MizKBYoz89hoIo1onq1RWpehnyajk3Wj3dTNkNehJ8P/vLF6eLe5Xj7HSq7uh352e9efNHs838wZMPkc3O8JX+GHurJWy1wgYiJs/mHmAPkB4QR4Cs1lNKR6pcnW+uzg2uXUcH3ersmQBatAtDlbbTZZd1INvOHc+IhY5IQHsM0kos2/iyQutt+Bh45gO4D/gvvZg1h//1H7z94T/7xZ/4kQZ8P/ZgDwC+/s1vKFz6Fh2gDRC3DbC9BZgaOIkt67aDNhA9RpsW3EALJMZo2SugHRhvoQV5XcPmGi3YW6EFgtcAfCTALIp8jFZb9kdodWWCy+KFQ7Rp5Y5u7opCenjWMgrVen8RwCMXwm6AG4H0D8DmGMqckluOlST7TqVJVM8MdEf44WbTV1c0850s99Zav9kr7m31+k+3B8MHRuuzTJucmXu1d5+yzL8yyHub20Vf95MUlhlZZiITI8Z4zMzTRbkKIYY0xOga1xhmFbglhVa49CvsAFBXYHI1Fd6ldrvfHXgKaNnAt9bXZWf9nk4vdrXrSQf+bgF4AU3zGi2XL4B5E8EzM4OtBWsCNBoivDEucQzhL6bG9AGkOtGT/ef3/2z72vjYvPjcq7S7aXnYb9P2dcM4PQUePiZM54LZsgM1Zv2gm5Q6X6yrXnQOgBfgaSVwhhmJ1o88+IYHslKAU4HLCYHbetZgAIQIftTru+N8KHm94pqZg7CYpgpbUtnEOzocb/EqKYJCNCfDDSyyzCPNNFxDRiJnZSnRgaSuZW86MU7g3XBAIEpAFFFVDsFrxBggqoJmDc1zVGLhnIFE3xav/pAur/v+gR/Wxf2/bQFGA0ZfTaU4XJqDd3KJ7vvrijYcEBdAwwgxwEUFifVa49NpQdfp/SDtZG9qEC/aSl7oVpPbGEC7tZe6tIEiWo2pAmoYOIz5hJzoyAD3zRSWIopkAcCJBEMgQq49iCOIAYlAjB45eZz4a1KGjBqfNX04Op/bZtnYpU0oLGRsBU4YKp+GkVa8Qp/nPAsZ8slR7s9Wvuptp3M/UkrJsoeFXkKrcTgMz2GhR8VZ/krvT/jl9B3ZaET6D+9k83Sgz2mcx0fQzbRh1Jv+LBmIk/TcZSyroKDAaEyqyQhJpljEwMzOhRiofC46IZf2VWJyI9IgNqanROdsLTMRNckGkO0QK0sAQFCMmChEUUgHjDRnbM1SoURIUcOYR4VJ8NCKfQM0U4gxYO8B34BghWLhgYwIPWYMNWTqaFaWFI9nlBxW4m/mwLVMg0AYamkKE9QiRP3RiujRgmU7VTFnagyjWG3G3jSLqc9dduBpdrYMzeNZ3Lh7ouoxkdvIjKp16D2odTa31lqLalMswyjaDUpq2LAIYv3ISB02zFINWJLax9hTono7+YuPmliprd5zG8vmvF+Gi/1+tm1X+l6l3eZFlmy8msX8VT9dpT3dr1y5eJsIWQiV663kbjU5kJUsRVUxKd05VWEGjVQG6X700pS9ZHPJUAhSl0bnhgCQKJo3h02iC66aGVehxNJICa3s43ji3++F6sF88nQX2QcG2aKvzWRK811gmbrexXZlmv5qcF58fOuvzCorWWcToEajDn/qyZbLn5rP/a4rb74zOHv5W+/8p//VZy6+/7nRRf+9i5c/dW/2WuGGGfvVu2+f0/a59jsNiZ4pQ4kAiS+jqsp6uHCrs90bj87Ge98y/fS/+dnDSfKlu5PHt19+7m/mX9ZV/dnibOLtXz9tkrm/YVI8nv7K5Jy2GzG3Gg92c3ziVm3xoulhGN2zattNaqe8GcAdSTG/Egss2tibtHOCz4CQQskCQrrV9lKXEVt3tlrXyYBs62oBtJ9FwDOyxVNbXsOC1t7lEG3cf4C1Bn09JamZx3OLqO7/2tc+++j/Y577/337sQd79Etf7apPrwE4kd/+o8XXv/mNOYALAV7Pgd2cuFpCPsGl+eJBBqx8y1o9QTvQutZinX7pFC1QewHt6zxa0DFZf7TtmWTQT9N3Suc+RkvU3EYL5G6s9zfFZTu0qymyjg3r7F6GaAfghsBfh9R7IPUKSG8BgHCy3ahkPxrbBcKD9WcM1vvrAi0BeKSA+0Yp1bN2pUCf3D0/mzMkmdW16SXJNLd2s6ybTwXIz1ul+gRokICYKbGamVh7H7RSatgEv+ljvK+IG0jcUVoTU9el4BmLY678vpq+6xjMjtnrVlhdG7jONHpn/V3M14+77icdc9kZYz8A8DGcewnM1yGiESOaZRVKL4gSyDmnog9JnaX/1+GDa2f7NwcJh+Ugenlz9+Wb39h4/Q4Spb6git6I5ksDEaBuBGczwXff9nhyFNHmBhu6BCnVlWPtjKAjLtvrWSJ8nDKmltEnoo2pgCygNcEToCwhTAnL094gkKupRwhPB5uKENWoWkX2nqCFYpr4IFqvdCJlmsTB4jxkTaCL8QYDQUEbIIYYIkKT5hKd97AW880tdkVPVFlZkdhWPZdVg7rySFMDJTXSTAPKIwQDEYHiCKW6a9eB9asp26tp+6vFEbjyuquaPo3LKt6uCu+qWaJcGSPSrtq1QkBoC+1goXXHDHcFPgBquz6UZTtuOFkXd1ugNgBpoGHCkoGKgYYZggBNBRpWsiJDmjbTM+qpFfIkoBU+aWqQwUsGTQ4ChZXXEkKQsS2piFOsfD8aNHGJPFRidJKFZcwGg6UMEQWeoYsJhspBuAqZmtFQixcx947UPL9meWhirssG4DjCNL6gPj65bZ8WKUu6wWdZ6udaLwvWdZLQX1hKpitf3D1RBx9vmOPhphp+NA3h6Y7h2UnUo1Wans7CMu+zAlWUkljrUcgshiQls6zc9vJMfC8BrFUoPUvjbF0JIRLgBcxQKmWl7DP2V4gIbBWZofI6J48A9KeJX4qXYJziKggUC0SUrj1hIyFrJbKA4YKQjaSICCEqpIagwHTeeH1Sknm6QhxYHweKUVi2S8fcUNBT5+nxSvmLSPK45HqUlTJg1pyo19+7TavbaXBNGQbvO6tOj9XuWQiD7HrDRqfy/qnSRw5JRAV4iq5m1wvabUaESpdSBqVFG/fStrWNiaFepWD4vto96qc7g1Wz8D6WN43JM88NF3prkPDQGjf0mRlDq4SiD0Pr0KsXFxsxNoPMjpYZ5/ewXBzOLh5yrJaNRdJnSrGoDkNAPe9nO15BB63SonQX5Kg6zc1AtOpZgCREV2ZmnEqMCswBobZ1FBxVC99TeroRkt8c2Z23v7c4f/lps9gptsuNLK1NaU7j0fbdQTU4M7OtR1QNzjBeJhCrw9+sxkcxDO/9jHKnf5LP4jf+pxvLLx4+/bwcyxLb8fZFquj05Xy1m8/DXyajTz/Ok6HPTJz2x4fLYvMDyoa/eXbthX+h9nYupsXw4yof/s3mefjz3Uezgzuxr453069OHi5f39rLHk8fJ/7M8POxakp77r9cs3oh2bQNIm4Yg0W1wLfyIR4VY6CcYtzO49QAbAEetoWSz6RLV303u3terT03BUILgPutzIO6tqddRq7TA8eWvQvrWMHVOs4oINZtRb7SAHmsu5SgNfDvCjNnAI8j+AagfuXrv//2X/3a115/iB/R7cfaeoV+6as9tMUWj9G23Grol77apTVfFODOCRA3id6EIKxfyxnwZAh6whC3bBmjrhPE59AOljO0acM30Vp6ZGg9gv5ttCArBXAjt/bbm70eT1bL19EaJh8CuIlL3d+LuBTId7qCzqai+z3uTqf9USkobQC1jRbMvQZlOnYlW//uWMhOh6gUoDQr8SL9IHFvUlfTzJrjcb9/sqPMG3e2d/Nekr6QWbs/WS4WBxfTxQubm5M0SfLFYrG5bBqGQAcBNEGUYuOr6PIkbUREK2YlIgtqgV4PVzR8jfcIwavUJrhSEBHRBv4uhd3pM7rWcwu0AKpLmXc9g7sOIF11VbcCPFpfj3NofQchXKtEmEP0tVAkEePrBr6OIJIm2dnIfudfpX/6/LWnklMc98eFFBvFry7m1by3t/uEp+f7WKwIs5ngw4eCTx7XODztgB7h0som4pKF7dLNS1xOUIQW1N5av6enCHHMWMwCEgZsVJDTNE2C+PBkMEJ/uVSCwDuLqV0lFrWiWKnUL3o5axX1c08eyifjXTTW0E8//lBNbV96y3m4yHNV255U1jSSpmr/4pSOi4GqtAJADsQSFAmUBryr07qsA5F2rvHQJkHwKzjPUBygDKO9ls/S0rgE191jDeciYmRcdtroNHm48nfXuaUr8OiuWZfWZVz2qQYuFwoKYIGxAu8jtJ7hktG9Yu9iux69PWBZACFBCATFzPCIUBEQEmhuJ34PWRcSMwSGgUo8eVfLXBtJpWarCF4IjAirZ0B0iGSEASJiWqGHwCbs8wFVU+vrvuEqFryo+8PK5lyhn7bdXCQCiVJuGpIESodFLKZPZKls7YYjO6STGuDDIc77BuVgjx/vP28/zJuLDTfQT1HFnqze2KFqmStvN+XiwyXvfXxNXv25JyF9uE3LoyPkn0Y1rQtZwpFLcsV7MJxGNZdEVKxDQV7VnEokLQgrjVUAVFAwLOIhoQSdft+Dm4D9L4B4/Mw+9NKXrA6AIoJmCwN/vLu0dQmCWGR9alPsCw9aEbJtBcB4MmBDJfkgEAtQoqS9+jGgbwUCcluJYDtVqD0ggmaYAEqCerjUUNDVK0WwFMuGhW1hOZZL/yh/T68Olo3fV/bil5RO38jjXrLlbNm3s/5EKq8b2dAIlST6uAbARKfs7byKekYRYIqqYXe+6GWyt0pyX4udbh6cf9eNszuNi5Uh5Re2/3xaZH0VFrYsmwtoSimEA29Nb8CUfFS66QsC38+SzcKLLj5ZPt2Q+j4GetOmamSdrFSIq6bINtQguUkQ8i7WGkJ16SeNJpM2oRTDKjJrpGZIEX4exG8oYjNfPMVJPAXpfb/h+4981pv+Wf5g8HgmJ4nQtc844dXwbDuaxqhsqedbH2CQL4SiprTZ8XOi0o1m7/9hc/jWP773M80fm8D3X1ztfVFNbn+xf3c6ey98UDF+wdXcu/jC1h/s/2l940ir1yRKnG4U910/P9w/PBea++Jga/AbLxxMpr/4vbfzjWV57e1b29dOVif3YsOvFSfhZvU/JD+xmaj60Wv4ndL2HpWZfg+a+zIHBY8FaRgAr66m+M5q2hUMSm9toDxE663XFYh1PrfHaGNyt0isWnCoR2i99Lo42hUhdq/r0sDLlt1TFqCwjiXvAbjT2jPpzj/UALIPYAqQQpvR0wD+GuABWgyQAfgSgD/Dj+j2Y83srdO3Kdal1PLbfxS//s1vfAbAVwF8DS2QOipFjtEOtDMAv8nAb05bA+SPgWc+clO0F/z99XPbaAfYOYDvoQUkGS5TjsXKNXvz5XIegD9AC0bc+rOBNmA9h7VTOv5uIO0CYdcvtLUmITIgFhBdtS4hXAKsJdrAOsKllQkEWDHxfNzr5SHGQYixoShPP7V3zXzlhZfmvSR9xWpdO+8/fvPJo9Cz5mdzY1+vnbMXVWnqpubDi3M6ml2Qcz7kaRrBzKkxhom2AWytgd75lWMhANI0jYIIWK1185fVtV3Xhjnam6uHS3uYAdpU+nL9d3cDdx59HUDsKqg8gH8B4CGY/y1obau6Dk2MubGaU6OQVDX0bDmbTMv/tTcsfvsfvXTiDLkUwBPWVL78Uy81461hkWb2C6xohOMTxof3I1Yrwnfe9pgvLF2ypJ13YAfmulXj1RVmd67nuExXOgAJEzZrQSRglTBS5T0yRLM7mxKUsY0E7gdHTSa+zC1XKkUavNo6n8mU0zDZ3IVrQjwdbtNoucRPPvxQJQT63Ccf4trFGS2SvB7ML1iL6CrGGPJCQ8SrulqKUoBi8kxJ1ImCTSIUA0qX2WLWJ+c4sgqQSNBaELxBXROCj9DmauUu1r1028dKXW00Dvwwo9wB46tjvKvAJbT3T3Hl++tes0ZlTOux8AM8Mx7vQHdXyBHEYJ5GiJOFcCyFKTHrOMKkUSLCgiHSp9M4oCVrqpDpeYseqSDNisSJLOuEUg0YXUGJE9agRAUyFCRECQ1yF4WlUZmsNHM/9bZeBXGHNcQaCWYQBZpQEoGjEkM1wZd5XFg5a4yBwmDQNGKiKs/zzZ3yE9koLjKsijRrFrqcjnjzrSnkQMRPe+r44DVuyl2oyXM8GV9TtJtglebhwU9nsd5zen5tEJZFT2KSNumwgkYjSjPYkPIxVeSVSC811WCoMPERlgi5JUqMgIkWDyLC3KN/m8G56a5de42aCMSGEENrKDQvFc0b4lxHpYjYKEaIHpo9tjOGYU6PwaqEhIJYrSLI6gijGBCPSK72YM1E2E4ZjAgfFbQiXDQejbA5qMiAGAUJbxnYglO9oRVWQeK8VMnDZZzdKXSGjKFC5c8mzeY9kwwPtWBZuvP/4JauriWcPlqiVxso76OqmSMRE2BJAty8ZIQFyCwaSRZquZwniCJ2pEyjzlRZTyOnvnChSpXxmC5O66fzt0eNq60LZWDSqyLZzZnUsgk+JEptakGS2FFCzF5R4gjxLNNbnNmiN60PE4keQJzMmxOSKMaFaqmIuPZzk9khazYkAh2il+PFh+Fi8UBAdXO6EyZv2SO5WQ6+8jm7+9mLa6fvpNfOsNHYbe2zDKZRrjeVkC5qRBW8t7om1On2049enF779c+KCtPmXyXfzzbtT32+4tvfO94Mh/6LLqH8heMno+aNxb03Nq+5WWI3TwZ9jtbcBjOMD9+a9rPGa/2d//5//GO5OZnfarTK9GfSyeql4qeSkuL9zY1H70xpPBtFTu9QWVX8RWj2PRMO9l/mw7qh+9Yiugq7aAvqXgVwrW2JFvotIOOWxVPIVYIGhPtpAedr7KFl+ubtPBsHgKgWlNFVguSqHVrnwxoBStsJgDo/3PtA3G476eBBO3eECrlPIXEHkUYAdRKoDJfyrw8B/LNf+9rrP7IGyz+2YG+dvjVogdcdAC9//Zvf+EkA/xRtlY0H8CcAfgNt2vNw/fcyXJrFzgDkQEiA8A6gHgN4BS3S71YQ7wL4i/XvOdqAdRttsDehZXQ+vX7t9fXjT6N9vjN8vJra7HRLVztGXG3ZdlXg3hUEdD0AF2gB7Joif3YejFawdEyANCGsRKSuYyxvDUdvXR9trJj5eFaW5btPH9967/DJaaLMv++8f9VqtlFEz+saHx0f6tRkyBPL47zgfpYpIrrqy3ZVj/eM4VFKRaUUK36W0buq3+qsNpbrY+4e+/Xffn3+fbQAe7Q+3+478mgZwA/QWusQgNcA2BjjSiSOSatERceZX8p+4xqTF79z951PPrm9Map6g3x/Y3/DhxAfffkXf3qSJ+arXJavgBTh4Cji6LRBwBwffvwxatdvImovCLrtVNKlE99AuwrtWOOuAnrtl4iIFvBptCvVHgCkDJ1yy7oyACVt/pEkOAHEAEpVwRel0/MQsSgGtDOd0JkuwtHWvl71Mu21VlurC4oiclYM5PHmTnwy3CCXsNs7P/WpjwmiC8PlgmzT+JsXx1SyUgxJ986PwzztTRACwzUJiFOfJDqyblOpAsCYgBAIITBACsb8cAWuSDtuk6TTXeJvjYF/Uzq3BRLeM5oGgDD4WWeLq/Y6XZq3q74u4B3BeweGAVVpy5wp1+r0nIowZHyqRielXtQC7hmQ8gQI4vpWsHQWClVDIjHgEaOWQi9IKQ1DAQ4aIQqxFmSmQaJaqZ6PjJXkpJg44SqsQhrKJpU0cUQktJC8SS1CTPqm5HwKKAVlLBTUBk6URaM8p+xGA8763vuLBr3jM3Pj/gmpDZXabMp6NjY3H9axVrOo7rKZPNiL++eb+uluTgvLYXw+UvdeI0xUIWWZhYPPjUzTaLIHZZVosWa0ynxdYXWQGjtgmxo48qRwUZEwKUoYAAk0E4QJApAl2J1Ig32BzQmwz8CeAuBAYCyJUSUUOQb6eM4kJLSZBHVWkz5rKKbU3tNKMZomehIJudJICRAv4AhkSYAnhguJA0CJAmswTuuAZSQMDYHh4SOiCxI0K9mwwF5mKFOA1UCh2CkOq5d6JDd7tv9xaNL3JkvzeLbiuo4NqqCqcrJISpKlk2KKcsttqRAr8hJQXdeamsiKKjK6Lq3P2Nu5ruP0KYLPfDJL8juTXI2ntDrRCYvu8d45x+2HoVyU5fnFyQaipM4tpZ9tjSJ8cjL7YF42Z34rv6GNSlOCCi4uVs4vDpbufEjKj130CG6hXKg8aVYhOEPETW5HaeVnw9JPdQxBSj+LrLJGiEIIS13Vk+B3dtPY72XjPL/fk7x3h8a41QtVL602Hc3mKmQ7kQI3diE+nVeimzOk86QezVYHxv/5z52+/K+Pb/+hcg+OX9x64M6a57f++s7v1aWh9E5y6+T/Ye9NYy3Lzuuw9e3hjHd+c716NXVXd7MHsjmLlChRShOaW7CTAGFsCMifAImNIPG/AHEi/nCCBE5ABHZi+0cM0w7oKEoidzTQYouURFGiOIndTbK7q6uqa3pDvffu8O50ztnTlx/nnnrVDpDfkpgDNPrVfXc457x99157fWut7872zvAnX0u3r977gs1ZAAAgAElEQVRsrbVZiu1JO/+assslQdyam/y3XVtugGj37np7/9ppKc37f+aF6e7ep9am95975lBdeltdtO9s0jWRVCYuzOZCaNvpcrIhzIXgOR/eUrE16EJiE4RUxFiwRxfwvRqMCa71tViQhGCGYId23MFDu2QD8Hr9Pff5ar/IEsS54NIwfK3R45XEivixeaPAeXXI4BH75zUhcAZ81ULcROZvIkGOBTYAyuqyMtRqrakA/FcA/jZ//rNz/AU+fmTB3ue++IWPA/ibqMFZIyT/MdTAbwbgywB+A8C7/Mqrd3/ts79659c++6uTz33xC0vUjNsE9WC5uH7t4SKOuV/Ok0MAH0Y9EG7gXFvQRl0qnq7+zzg3VbRQl3avoQaBMwkkXDt/Gxr6cRt4o91rSrsNA0KPPd7EZjQN7Bsw2OxG4sfeqyl5FQAi470CswXwra283V1rtZUgeqp0dvDqm28894177352UpU/P17M+5vtTtRO06KftfYjJd1ZUcbOW5nHMSKlGrDncP7laj6rWfQVACIiJ4gaw0JzPY9n6TFqrWPjnOrgXJTbRf1lba4nW72mCcXUMGahyuLYS/IkZLF6/R2llLbO7lbOJ0wCZ6M53bozUovJ8faL3dNbyR98J3tmr7/3wgef+vHepbVBa2PtMvnwnHjrNqEs55jMJpBijjdvLXD/QBFDLSzcxGOmBcaaHgVAHwHYqRhdB3QUPdKMVDjX7RFqhrgxATEDclkHPIYVBctFIhA5lhrQAfAE6JVIlKxOAvtgllkWZWZJl0ZHWDfHYW9yiLyw3Cqq8u7GjksYUZ/nchmneqpjlyKgV869I3GsvYs2JqNomLdd5ENwQubrs4naGB0Xk7TlIdUqggUaYAWlIkgpoRRB62asngM+KWv271wn87h0pGFpHy8DNz87WCPAXIFZQakG+DfjqWFyUb83zwDfgfEDcOgCoQ0ZuM7QUwowtXUUkgN7LTXBtQUoVgyUK0KWGHCkwMIFLSoQCCkcWpTIGaTUkGKBXJekZIBBjETVQ9QTaO46JJhDIi0vQ1KNyw2hFVNExfTMDsKc1yVrHaZIy2BabaWWcaDCd3BCMSrpOCIHCsIHEQ1LFb+1SJLFRK7nHbg9Ujoq4WXL6cklu3c7iE6I6C494R1BjjNCetQK2z9seT8Zh7OkEOZqT6aHikaDogyWfGKL2O5pEu2YbcgjKEBpSFpaYgaKuCO8J1aTpQALhmYCPFWyTWXQELEFSQWhxeO6SylmJJKJ8ohY+IgA5RkK8EoJroxgrYBuHKCUQlj1HpbMiIWAFLWhhaQQWggIEqAAHUVetCKGdYCDir41ZE9kXSvWYmoK7LUUF95jK1WQATi1hJgAa72UZeBEAUqy8UURCR3Mxy46I0JWhbPZ7AKV+eG0EKVvy4mBmpvSBuMJ0DQLQnoIqqci8qGSbFyC2EgUkWVvRujPvbC9dmG4KNy+mk9HPDtbet2tksUJR4rSMGhfMRzwcG6PKx/cbje7MIh1SwnEUSSkSnT3OFKZ8bbcWripF3F+1ok2hZRaSmAOJq5C1SLmzAWvBUVIol4IfqnPAlVRnGsVnDG85L4ecJS2HtxL+sen8HtXKJYbPhsoCtnk0k03yw/yee9QjTozeRtu0W+bKcVIJfOfPfODX/7NT3z/Fyh+8DH9p88frj/R+sjVf/97f/3Njw9+2fblgOz+Pt/qrm185bkPqDe2uxgnqUCku0x4m9+Rv7N1v/zo3mWT6B1x+V2ZR7d2t+Uvz56sosV853B7MRj1o96f5vHDoie7uizUzmS5pwSnRsWts6HkqZGnYSa06OAy18kWS/ZNlYPX6vKqbNh9RsAMARZAYpeN/jn0FQIHiNXcT3ZNsN+NORk5AQbbPlwoa+DYVFos6vV4vlpHOqvH5qv5a9oB/dMCYgBLT6JgWZ+HaErKQL1m/rcA/if+/Gcfb0jwF/L4kQR79PJLGwB+GsCvoAZuX0UN8gaoy6k3AfwLfuXVya999lffY6f+3Be/AACOhD974TNvt+PMmO2rZy4Z9fvtWZZPOFxGXV58DniU+bO9+u+DAF5A7b5toV5hHqz+n6Nm9ta5fk3jtF0tUu+9BLx3gQTOIyoa1kTjnDVq/v245unxEhkA6IRo1eAVoquT/tN7V9ZBYqYFvSVJtLVQl2aLMllLk/nV9c1iq9X5RjfLh1kUeyKoThQXWZI49k6zEKKdpUtZl9eaMnTNBJzrEBtdG3BuuGgWccJ5o+qwuhdT1NrHxoRh0Gg4QohhjQQjQR2smaxKe4Bz2qtpWURTqbj1NYL47tWHD79Ugd6/MOFDwThZzgsMH45ILCuRnRUXth6ePDdS7aft8Vhc/8gzNl7rD4KxH6cbNzeiB0dnULrE/tE9HB7fx3C8wGyekfMPUDtmq0QiCHoEbghAZ+6RFwzOqG6DVgWYKuBGJLCNmm2NUE82AYAJgC4AXRIEARwCMMtECIalrG2j8kwAo7yDuCrRLWYBWnGR5yopipDZikJsfdd6WYaI71/Ykot2Lq48PML1O7f4uLMOGQitYExWFPPBdLw8y/ssifVhby2o4KqFihCkUlbHbLWKWelmVwuACERAVQVYQ3U4vXh8TDZHvZt2FjBWgMhCiAbQv1ebZw2jqgDwEkoJMEdQUkBIfs/zzsveAsyEapmAQgIJBwggQgRIxRwpIhsBYSW0ZgEBYSICKV6d6+OZqSU8hHfQlMFShjNEOAvdZOqIC2cpEYpAWllEoqrpAY9gPREAl8jlwiP4s6KFRJZY2NwWvqcrltpAc1H0YicJRJoifijXeIT18kh4ikIqKrFN+1xybkE+aDlXNhioZyi0ulNhSkEHh6mNJsvJzp1O2l+wFlGHjtoty8eBr74uRNtY9eAqqsNLlZEiM56NXlyFDm1fYXgiy6QtSCZWdwTzglmlIiIPwHoKQrGQELIlASGAWBJIc1AKggNFBKi0nl6CZZQVMzsmbYmEYxi1dJRLQcQCoEDEEIBAIhmxRM0USgIxoLWEEkBRogwBPmgSDAhNqCoBX9agG1kcIIT0uQx2kIQqlYgcPAor0NMKEoSbc4+JFbiQEFIpu+2F8EqLoCIVIklbr8fDWOadxVZIJi9mkZyYNCRK85MX853iog4PT7mAi0PMgmJSKtcBSWJ5KZggJMBApfVKRja3Q9EqZjbzPOT02YMsUEHVRAaKqwTTto645wRRgHAy11tdIRJ9iFIb75KIK3FWPWSwjyu36CmVZppSq0kJwSqWpCUoxJFuLxlBEIQPSe5B0LHoUB53VZK1vEsqrYUcR9AL3W1Nbj9zb1GyUZcmG6Ld2y+KzXvCXPzhfLhxZ+wFh1n/Qed+sqA4qcpeyneFSfudoye+/vz3fsX+o413+R88/5X+RSH07vEz6mOTi8Mvrt3Z/mp2c/1wY7P/h2sf/Vb1bIuTF6rXhw/FD0C8CRkd9jbL8seu31h/cracjUzyselS5KOtdHFr997O5M3b5e8XvYvfGOTbZQZjUwmnRYczmeuAuIjEbBn0MCgxznZwKRtgp1oiBz/qL99blW+XNTPndV3MEA1xsaqO0JpGiDoACkgGZAEILpjE3FNlAKfgOQaSElKsJqjG/HiMWhffWb3n2/VjxID8wwLiDwD6OQDXAQJ8uAhnMwiJVczobwD4L/jzn23W2L/Qx4+cQYNefilCzeYdA/gD1CW9X0JtrtgH8JuozRI79PJLKWqdHviVV/2q9DsAYMG0KSXM5rXxrYO3NmIxTufL4D6rgau2HjzbqNm6Oju3BpUDTSK1tTDjLoBvr05rCzXYeR7nBopGZ9awe8A54/V4w+ZGB/V4f9HHS2LJ45f/2O8aAMmoBz+XgScg9CyQV7bqHJwcVVKI+bMbL7zywd09ujc8/Q+ub26lrShugShm7zcA7EopWXu53s6y4Zpqz8fLYjktl+m0KPR6q900nH6wugfNl+0E57EordV15I9dZ4lzrR5WzzlF7YaKwDwHUVOaNvCe4cMmfPDwTkFIIEkAIQixTjw6l0VY/oAgLYDb68vF1iyOt2IWJ0GJPIryZdmNBmb/tGMTSfs27R1GSVFRbL/wr74ef+JXPvmhFtTe5nic5SfHPdy4/TqSaI5bd3PcO7yJmundSQRaiUC2usbmenqGkRrGQhEeENU6TONBDDzpBIwiZKh3tvPVGGhLQOYAojru11JA3J15EepQuWABPhysURQkYl2FxFYyUo52hg+ZIeikuxb8iXCtYhnuX95EkaS8sZyEz3z3T/xcx4v7O1c6l47ulRQkJ+zjRZ5uTtq9lLz3eVXYs1YvhhK+IBlCVcZ7o1N5f03JoIQFCKiqBEoxhAwAC1TGQghCCLVbVmsJImC5AIKXiJJ67FmLVUQKHhu/9QYkBAYYCL4FHQG1r6Nhgh8f8wLMprWY6SJO2DMBHgaRqCANAV6FwORdKZXKiARopeVpvgpcZ/QJt1IXMDBDXJUV4giCTJLynFp65mKxtFLFWnEZIlosFCFVgBBiJcys+ih9BICE0wVMiOYBEUX62PUkMmlZV0HQknuaICiXS73wmZDHFUwixFh1Q7Yo6Ez1pjrqaB2V3OuN58mGXYYqzoQKWjrJdCIRu0y3x/3W+NmHtiwRmbtGDOb96CxfYr84w1FMuPdM7JZ7uRRL406vhzOTis7Jg0432brGqt2TUgQlguVyxIAGc7sldFqFRDmxElawh2dDmpKYSJZLmDngYwG18maw80BwxEzGdyN4lAomODAk8sQggw8mSCbvU1dJWzEQJ3WaBSJAnbuytQBZDwhVV1jcmQVcEHEULLwSUMLhYgZCiMlLX2zElPpI11kEHsiVREbAxAKpxET1PBx7xJKR+fn+X0ta1ekiViGltZONZFBJnvaGS3V53frTVIQfqAU5tLlNsekKqBPvfCCuB5swtW6LRedTB2p2qDXfXFcoTAVCLNMy8CIOToakGIKMHc8t4wzK3xVq79JpcZQY3bNnVTXfam3mgZzuJBed4BBbHtpE5yqWskNSCGcXbmpOjTFL7HSfkf388nJenqZjafOYMs5UGrTQlQi+qCwmzlVvJ+kO34uKcnOSZUUcTnY7fl0NRltR1RnJcuu1vFgMh/n0Z21rqdeLttaCD0DhLFtuFL3p7qWvbd4S/2Tz3geW0eLdSPo//3Z7cvzhZPITMzK/+Ftbo4maH8Qc33t473u7lXLB/tztg4L67Vt/5tvfbl0vcmy72a+/efmnqkIOEOki0XjfbCdLvvOBrd0jl3RSY9O0qF6YRepupER77lRL9oRQWmzDuZM4l+NyTm1rMObwKC+2hQAJQbYeLLysVUYs63KtarTbCvCRBdMIYggQFLHKBfPUU2vJIMBFDsKPQaYGeiwAaqptTXVFo16jv19/Hr4PwMYQ/3WFsCJqmGBdVtcsrIeO/hcA/9lfBkavOX7kwB5qLdxLqBfUDmpDhQDwXdS5bfdR6+u6qNm+a6vH76JeKdoAnmMW3Xe++uL/bazrWe8/DOBqC9h1NavXDKTaNFGzNrNEabmVt3G8mKnS2ZRrCniBuqR7DcAnUYOZxt4N1Fqkx1u3AOelrGr1ePPcCc6NDA3r9Xj5999kXPDY79XqPCUAXYHlYlnQh69c+zgR3N3J8Ov7Z+NLRDhOdHScRVHHhCC9c9KU5ShOkiJJkgRAVDp/HEd61EnSj6AGsc0XqmFjmnt0HzXYbMKS5zjvSzhBrYloSoCNvlLDhzkCR1AigGgdwBhC3IGUObRuI1CAq4AaEAAgUkQt5dLjtXJ+f9hqudd2L54K5t/SOrqpwR8IPiBr9/IpPfwoq1Z6Atvf2tY/nfYv7Y2nU/Pu3dOnN+MYWzv9ewhO4O3v3MbRyUX4sEc1SNtCDfAbYLLKfcJDAEsJbMcCD2LCIWoGdxErLBi4rKhutVdWthKSIq1UitoO5lf1Ul8EpGAQeXiWsFQLWczGcCYGbKQArAP0sqdldDCzJu+qw1bL9hezecLorBcLNz869lZL1XZGSe/STrWwZ+1ceSRyfzCYjVvdcGE6bGfLohoPNt6s4mx37/hhJqyZuayVXpyNEx8cnI6Lo/XtqI6uiwjOCARGbQpS52PMe4cQVO3aFXU1NXhetWFrmOvH3beMOHHwXkDKx8fruQGpPmrhtbNqMJ3QpNUpp52eB2DgDcMgQyQFEWlUJTPIUwSue98KBtiuWAIApVxlanlAECLpHCk1EHPf15NKkgk2xIhCEaUSCgJeMjMH4iUilmy8JEOSTBAkHIAJoZxLIXKD7iiXi3hq080x90CgnNMKEgXFQUgaJDAqEpFdhPXFgmZxJ9rXmzblOSXedpSsxEbbhNgXJT8Y8Pqw4CcOhzoOsZrHMdHRFuLjKcriAVrtLsJmF29cGGL7fh5lnZYfXljGmFkTWSJ1iqUZ5BxzGS/HiqIWZHsXjloEqyGBSABV7WNJFFEd/QNyAeQsSwUSKgbzCg4qIRIAq97RXitFNtYKWjRzjZSpJKTSWivq8O5aYPI4uCekSVD1p9ZzUWCkia+jEWck4UtAQkJII4lYymAghQYHAoERCY8nOxIzFzAsDHqxhKA5BFLoQJh1MmFIo+el2xCilInTP/aMlN97Y5zfcWq4PvJ+18y7d2HEqYE7U9paEXSdyWGBMgKChLJcDaMWH7YvA7Gnlpmy5DD+834JqePN5041Fj0zP67eSDfoIFS9i0U1LOdhNIMduCTw0kdCT1W8nqRKRJXrePZQUptAQWoRR0rHuvRLLjCqznBcjqJMKTMtIxeliHRcBR+WZmhOdWlmSXavK8T91nLieqBF5K7sfatz/OCV7lh88h7CFh8Z/+Kbgllzb7J7aFqjL12ZPHlxXKmLxeXvX1r29w8Mm9+4MbmaD2wevXlgftB5bf/tv/u+39W/+cHlc787UeUPtJ63kpb9YCd5+vmb7eHrN0Y/vPYwf2p0SPFoDXujZHDFnEQvOqXfjzS+DaBnDAb37sp3L8/tdK+bXXC9FmRcpJmg68sz12mTi1oIxWgYpdAiCgsfMVRkS3EqFLngVmuEhAeD4RFB+mZ98ICXqzlGoV6jl0BYrYEh7giSuzHsO4UUJVMMKLfS7BV1lAqNcW7K/BrOI8++j5r82VnN3X+rQuit1qgKoACtTmHtmxDif+bPf/ZP/l8r6V/w40eqjEsvv9RDHYXyN1AvzBdRLyRfRV26vbv69wPUGjGBGhBOPvfFL1h+5VX7uS9+YRM1SHvp6ubms4N29uJwNm8B+ISpAcwA567Rpmx5E8CXGfgKgzesqZYS9N944PeAR0L+Pwfwu6jBQVOinKM2h1R4r2OxaeR8E/WOpAmifQd1iXiIOkqGce76NAAqMKL3BFvUR6OFIkUIod5G0ZPtvthb38gHSRoLIbYTpTY7aXaaRNEmEc2FlItgzTe9rc5IqpaUMgLQjpRSeZx4KYRGDdoaN3ADWhtWZ4pz/WAToDtBDXB7OGf8Gp3fI32i8y4SSpEC0sBcQYhdSNkHEWuBoGPlmIjyohSJMRBJQt12hcHR7dsH6GZH755c0YO2kFr+W0Rk4qr8cpdxsDyc6Wg8730icfLyznbpllU3WprLnSSCOTp5Pf/an73ZOj7NdJb8ER0PrxLzOprd6Hkf5NPVtTWZcRcFoUgEUkXYWl1bTxK2FNW9j0tjgwsuRgii0tpbgKPVPbEMVbra8x+AwBKYEygAIYeHqJEKJQDSM8OWtIus5ydPDrwKVVEoTTMdLXYm4+j9794wnhDmnVbEIBH7ivvTmesVRVikGeVlRb3FfFbkuZPW5b3hwzZJlU/TvGqbSsyiOPLe60XakoiiehR5TygLQMc1k8q8ml+5MWkEaE2I4wClwiqypQEFgZkjIKziF8GrEi9wLjVo9H7Aec3VQ8qwjBMqkmwGIg8ghrE6n07ZeoBELLwlJkF1xykzEeCFg+RQu+0cCxhPYE6wMB3MdZemKJF6MHxHjkuwhUEvYRZgXwVzb1ymOjitmQLTbO5aUoogtUIlKJqTVD5WrgsIYhEvA8NM/YaIUVGBJI9RaH0289oZqHZEcGChPYIt7W55s2in0zImo/b0voqokIE0SUmaxy3z/GsboXdjGoVcYvOkn6gbZm4GKhqve6ROULG7hF/uY/SE9m5HE+4MORYtr1SqRdc6upKrLPel1OxUIinusRISUisJqYkRaUAJD9QFerGaIygwdEhYWqJKgywDWlMgrQAlASIRtBDQQoAowAaGqWqDhyQBKXjFwjfO+PcytOc/SzBAwdcfvnAekgQSAcSRosCsQlDSeDZQrEQsYS1gPKAoIJMBiarguEAucohIYhmJ3dekTimh+RMBVc7WtTOY0UOEeUWxVVrooMXdOTTShaJu7LiUCjBAJSFKBRmkyEzwgQRGXa1EpmR/nlB3oTHXCTIrPVVVcQdGrM86Irfb85Fdny/GJBMz93x8Iqzk7c7z/TTO28TeLsrDZFaetiWlSaYGEEKSEIJi3dbOGRs2qntSdx70fXeKLAeUDmZ5Mj8sbi2r4sgvwv2TTpY+v0/uyjtqfKudDBa3Nm8dfu3i2z/cnr+91PJgUmy/duAf6uHW2Qd/UOzc+0FycPXZfLz5ft5+J6ftu3eNpr/3Ww8+eNcc8/eOvlTqq93Tlx4uhtvDn1Lf+Vo3HF0edtWDSJ+svdPBlemGNvP52sONtU/e6CTvG/f5CYL/eCRMtJlNphOvU0C/L8lDJs9KuRByMI9EN9rWyymEIXDPll5e2HCy3fE8RRQqR9NQ8XpX+WU/pxM1EJeqChrhkS49hoAEvK9ZPVHzv5CNZInrMq9yALcAkGVIV5GflZRAC1kz+OTq9ZHuAfgyQA9QR6yUqEu3r+I8/3YA4KdQV9kcgO8B9DsA/SmE/IdQ+h/z//g37uEv4fEjw+zRyy/9NID/CPXiewn1H/Lt1a87AH4SNbpv2LA3cB6TQgBievmlCeoJ61sA0nlZ6EVpnsD5pLWP88y07dXrFqvfXfchbE7K4r8E8BWAS37lVaaXX8LqNSW/8moJ4Lfo5Ze+tHrtNQ18zAJ/HbXW7wz1AG1cnlOc9/K9jXPTyLdQs5EtAP8ezg0iBMYFEDZAK9OGg4dEAYIHo53HMeCCOQvWb3Z6YS3OPMBb7EOPA9+OYrUjiJ4F8ICITqMsP5EuqoSQKTNPp2WRKxJxniTsnHvXh5BqrfuCHvUgbHRXTVeR9ur+NAaMfZxnKTXsZLa6liWANyyHCyfl8rTLvpdLVU/4WmPlBHUW8K04nrFxuSXqQEoRSLgzJz9ysL3xj4SVv9+/vL7G4D8EADEe+V/41jdP943+zjjI8YX18eRFpz+6OD6lcZKK1jO7hU/1/SsP7ogLGp/GdOb8/f2BOg+7bpjUf70aC0+uHpugZm2bknyjxRQ476NsAQghhIeHAwm5mtUaM4pzDO0IHAmIWAAWUIrhJMAJoC0gqzqnoOoCkfZWLYAqaMVn/a5fXxaud/ed6Csf+qS8MD2MArLiztpuNZcxPvDgXdsq54jLirYnQzzor99fm02T+xub3S3r83HekSfrG1jIKD/urweQEHUYtV91JQr1fNpqA1IRODQGDqDZTgjRLOhN6e5xx24UfIngARXFIBLysXvUdBhpNjMaj/K04MHBWkERhGh2/haakkUWA0KUiGGi4KIoHrNDFpEmSFFqC3BLWi69LZjTolccd1ymKECYAi0kKINDtKh8LpnSNHhhRWSJgnfL0pdkkntZpttD2133FIc4FIYgvJals0EzA6mCtRHP1yVCe+T7WYAPFlIsMZDtVEpfWbc0a9zWI6TKiiBYkEYeR0yOfEw6yBTOzYoWWnNHH/+jjPfeztLTMkGEyNJelkzWjlrVBc9od+j45BiIYtDudYRPc7QQRUBLWPR97jOEueyEJGEt40jGfREEIBGYYDzjrPTIZJ0fG7xAGp2bbAQBWeRgQfAQohaWNECdz//G1AB0Ce8dTPCIvFwJ4mk13oEGqJ+/Vj16Xf0+QHv1VMsKVQgYxAaSYshIoDAgAYoFHFACzgNzB6wnApmU5EWEmBRbv0RVemhnR0/FA5MECeMKaEXHT1RK/Xg7ce9Urvv1QGKIAhJzsFTKF/MYpQeSHGCJjtHwQoRSA7okShZIeZ3cYqCrzpSSLQu31MIcRFEYRiZJenJ+4EtZOSIqpY7Ta5Hf2lmYs7M7kz933c0k7sgnWiMqqNNZo4pSmtip0K7yWrRJElGerC210a+11fp8LoYfVQaY+rPhner7y5sb2fgnFpdErz8sis6927Pyiel39kbHxxduH7SV//bfcxm9+nHufEMYce0L3a/wjdP85H3/x53jy8nmzmjj+P0LL1rr717Ifuzr83e/95Px//a//vgpAPyHP/PNKzdAn/oT9O6LX3epue573SrCjst+0pK8VoiiX8w03W4vs3kGA+2vcuKzURzPJkablcS6X0w4a09MPstjhyiy5VHQK4FbiERguRv7ZRWGMHKIJeWI3eZCqbQStKsCYraP4pWaNXU1XqSvO92AUa+DwLlZL9SFEAiH4E8ZER7tjXFarxv056u15Qy1+fLCap7uop7Hx6v5+AA1m3cTwDcB/A7qMP5m4/4eDf9fpuOvPNhb6eyeAvCfo9blNewYUAOyA9Rsmlr9vEDN8H0IdeZPE847QU33BtSMXPdgNFn9PmzErcrbIpoHL7+JemD8u6hNGnPUZbxNBjZCDR6X/MqrTa1/Fe74qPwHfuVVRy+/pAC8s+rj9AuoB1q8OufR6jPejxpUlKjBXWMMUai/BOuoB/ufra6pC4FdAJ9efZ5JPeYXWu1i6qtobC0tjdECHCTAB/PJWXfR6m7JTt+GMJ2byrSyrI1zHeCQiDKloy0AGTP352UpFQlEWqU+hGC8T4hQRjpatXJFVwAiUTpbOhujZsM6qHdUt1CzZECt6dteXdsQNRs2A7CjhFjrJmkUK7UFQEGIEQN8XJ8AACAASURBVIScru7FGYDZfFmNAZwhST4DQMBjmaosbQGZLKufLpWCtmY7LouzSzdu3N4ZjX5io9X9yLUnL293N3ckHZ784e2ZXlNbGxf7zzwZVJ45WLclirOxuHPP+oCPq1qbN0Rdwk0A/Cxq520Ty3OAGsxuPzYkG4YTOGctbaQkRSptYnOalmD1GGHYpO6iYQwgIwaSuqcBylXmAGmwshAOcFn92NJb114bLjTHibF5KkiKTGjJrel0uDUe0ujKM+1hpx+vz0clE4n1+TRLFtPtZXcg+6OJCHklTXcdfHriLwRO4Srcu3iNnDUYzIYo0y4kgGkUwXR7zfXJ7fEprNQ87PQIzFCmJA4Mr6QCEUMqQu3SJnhPQmgGbKN6bsr2j5uIAGtqHaaUTdC2hjEZ2FswltBRD0AJzwE6VogoBYIUqWMtggvW21ARyURREN6DXbEtTvykuNjOKik5qbAUigJUSGB0jBPHwUsWglkgDogMx6lOn4xLofTTD023FOyLXjS0gu0ADih824/MzqwMKurK43wzWfQmi9jNzJqqFLAWxq5QwbhIKhVxADQUeB7xdCZ6orOkXtzSU9dTI7NwOZUmFz3rsPOm87r00Y3nj+T8IS9Mq0S0NnSLNSs6wzh0R0LcfaZLS1l5007Bdk40d46f7E0nJo3tOzbJNl0WZV4Eq5WYF4G1JMojghZATKqexlZTkAl1Vbc+LAAgRoAARwEWlYugBUMLu3pOw7rXhqREr0wyQjz6+52Pd+C9UVL/5nEOIPsJI6CApDp2JzABgaEFgxgICGhrQqosJElAuLVJXzBxetwZC4qUF4hbYR1RJYqAlm6BwDBVcBdzKU6k7d1f7tubw9Hk4525PzCD/PQ407khe8S6QIpkwkxwjKgQQoCzyyWW9x6CvXWyiEzxoMXouUjETkMLqZXm7Ak7KiYHTg78Ir1EL/Brnyz7Ym9MSe/yWXGIub1hp8EmGxsXMJNHS1Mdl9tnV0Qcd6XolAvN4s3Z6KRz7G7s9ta3i4kzuFe8u4zKsd59cLwz9sfVdvSBN25E9MS1ip7528cvbL3jDr76ex/441uf/r3/ZPEnl1/v3mgNT0c/M7r/c7ffTZ76VLV1vRx/wl381hOTZLK1+cHX0qg9e+LC9VsFANB/+sXW32k99Wa6v/brTuhrHxDmw6/dt2vfm5+tXcpbLROW89d7/olj6fQ8h4CCh7aEwIBTIig9AQpR96OVSQ30NFDLgk4BPECgDc51MTyl3UAin85Ft/47K+WCS1odhozBcwKD0WTe1Z18IBr5T7qSYDTZeI28RwBU1O8nJQQTEmoiys5Qt9T8MhC6ArgdIJqqXZNFu4XzMPbvA/gnAL6ylbZvrmXtF+9OT7cX1vwpAOLPf/b/B3t/EY+VweIigL+FWn/XuFstzsN3JWoNX4LaQHALNZDaWj33GDVAewr1wBngXBsXA3AQYT1K7YC9LEwhL6AGiYer09gAUPlaHPQDAL8N4El6+aUlapDZB/A+AN+il1+ar4wgcnUuM9Q7jH8C4Mdxnuvz2uo8WjjXxDW9eo9X1/mR1ft6MFhZvD5oZRcWwWULZ0YA+l2dmE3QN676fP6uUj/jqYy9KaMkSqtEK7/V6ralEPHZsijTOF52svwSEb65ujcaNZhsFuc5EcX9LDc++MuCxDZJSGKeS6kyABw4JI2D09WOWQmgx8xLImoAa1MSbZypx6h3X00pWBNRnsdxG00XDikTANSWelF4++Byb+NG5Uzv4WK2sBz2YWwbwRuLRF9Hchri8uhWtQzKGbo+ObvwwWCfylvtZYRQ9e/dXcSnaS7WBw/Tz7zI827vrJsnYvT119Rb9+Y8XUzf2jtdrEWETl/gDS+wFREWivCR1d+nyW2KV2NmA+cZehs4z0d8PEC4MckonGcfghmCCGLlIXMWoNLXzR3SOr6FE2AZA4mvdwXzOZBLIFoAc6MiiSDlYDxSHan8R38YypPOGn743KUoDS7fHR4uGXx8Z2tPXzw66OwMD5dvre/G725dtLc3L8qN6bDYnJyqDevT5w7fdaoy8veAsHtyCC2FfNjuw7HAg80tHCcRIGPAVEJUJSiVBGZExTIMZmMqtOazJKoFAvAkhBBBqDrfQkqWcQq8t4vGSpwNgnc1gwMPpGkbDesrSCDIdCXkrpm/AA8mBXYatkBQjpYm9+y4SNgqQUYQcq4CLYRkNolKDmTuOpKEgKcYhTYcc1v4HZkE5yzIObaknNNkFl7AKLi8OpGFXiwP1TMSgdW0klH7sLgEEfzAgIRDS5VhZg06wnFCwaJAoJNcHQ8sssgjipMwmXeS+TJTvm0cKgiKFctMVh62kEEfRUJLrqpcja3U64dqLpayyHrlBNXdmMwTWZhvk9jPZ+QyYiYpi/7CpRwRBqlyWnWkAunLUqULcjaokBRQVKUy9Ex9v5Q459ElAJKMSDQxSI0ERIMrQMTAeKkwKQI2csZW0khQePWdrY1j40qpufPuQiYBsK+YjGeONQWhH8VWPA4CHz/qbhwLC7QjIBKN9IMRGPX3hgMsG9hASKRDOxVwnLCzoXBnJsTCLoaRIg+012EWqgrQIqnJJ0nwWiT3Jkb/8GzEr99/w5KtqEoXerg81V1zLAdm6Sk8ER3xJnHoUt/GABCGOcoCwVdeoONkpLyyDMZCB9kprH7yFEjGqdgwTyabkPM7+bKonJjwRDxz5blelI+mRw9G+/fLW9mGv9KPCpm3c0hjE5HGAxnpLPKB1NLcf/9MDEfzcFaYxB9KMzd0eLcdq/7wQfHusbCTlGf9/rx/UY78yN+rhH5D4el/8fAZ8S+f/aMv/fx45zWare//5oa5/M+uvFB+6t7h5pOfmh0/e/uib7VP271+BYqR/NP9Z8N/9w//WefTNx68ODv87vLe/ke/+4EXdj79c5eq7e3orc5337yylk8l3Y1974QmatnPGLH3iKWo28vCoA4ubgEyA6gFCYlctgF1sho/BuA1CLNmjzEeuWQCh8vska/GDSMoPTkOp3GCAoy6D26EExjs1XOibJIZ4tpg4VqAKlZg7/EYsjpIvZYMLFBvvI9RM3rXt4VzArR3EOgGQE0V7AZqjfzt1We8pkisX+ttjP7tpz4yCcyv356cPPj1//jJvxSO2/+v468c2FsxeQ0D9iEAv4wazHVwzqCtWqDgZPXzB1CX2JqQxQYoFagBTY466Phd1EyNRz1IDIDPIqiN2TB3COIq6h1Ck6y9v3r+FQDW1/f7WQCtSKqnrXfHDAw2O+3uExe2f/bN+/u/Ty+/9Mer8xyvPv8CagD6eQCfQr1L+ZeoWT2DGuT9CmpzhwZwokGwdS/fDgHdttBPebbXExYPSyEOV9exW1l3aWrD+v7Z6KlF4jtnyi9bATTI4kWn0yFvvY2ZXCQEUq1avTTrENFzq3vVtJd5GvVC+xqAIynFZ4i5K4iIhBJKqiKEYJz3B6fz2XUthRpkORshGxanYu91pFRqgY+hBkEHlbOzWOlLqEHedHU/Gso9EqhTNoV1kiLd8sxx4HAK4HBUzLZ84OfXs/bJw8XZDVTlIFsuUcZaRVtr+uqTu+Xoj/8vioZHUgh5x5F40RP2PEOlLjCmC4c42um+9Y7r7m49wMWL329NTsnevb1xx6NHEmnMeKH0eKL0OOtI3FYK93FeqlJ4r9asiZNpJqdiNe4a4Mer5zTlShUYWIR6NstWBa7AMCzrZNDVc9kxlA3wWuBUEcYALlaALON0/c2Le5RVnjoHc+NDCIu0xafdNVmkaXb5aJ/LOGJDkucqoW8/80I+izL0FxP95ME9SyHM9oZHatTqq2w2KcZ5NzneymWRptJFKeAMBuMx7u5cxDLJAOOAOOK8KlF6ppIBmAImScTSZTA1bGWQA2zgQIwIVhkSjDwnwBNYCBA1zE6ThcUQMga5ms2pH7MAYui4Qs2sbqKqVJ1MKC28W1Jlw/p8CK+FGCUDICUoLEeKXJqJsbRzVZi0YxSM1cqjJ6dbLrBfho6UQCW50r4UXkuSM85xVmWJ9ceIlO0Kh5B2KFUJLlUumjP8/sytrXtfbTLkBkMz2C6XLg4sQ6sfHaEgLbxI9Cj0phKi1adT8m6ppyZfZ6tlZ+4Wcv2QpkbJYWiF/iKKL8zaqCR0mYzlMjHW7EGKTRXiOz3LE2fNuNTlBWrNrgOYGqRzQloodM9icXYxWC0clBASPS9IBCsXsYVBwjmUT6MVnQdRd4zyQNo000ED9gQcGIEDwArCMcAB2xmhE9UZoUVlABYgMJQmKAkwqzpLu9bX+oAAgvAOStQF/sfTAh4v6dc/+1Br6V1ArbsCEBiovIUgQhxZ+FIgcIDjAkBpHLp2yaXr+kxH5ONSEjErxBzAQYCkRRVRMoUsU1CykNPOTB0Vua3iE1bRd86Od17a/4PdXzx4wRXEh1/beH7ybantUBMszTnoDEEINxcCKiBtOyrGqUZcOVQZw0QBHBbVOGvb07hVlpJx0sL0YGM+Xcu7h70/61/eCAG3LqtsL4zEsFPYZN5Jy/62WETEsV1OojuChAhkOlngA9USvcLPqQ9eL425aypeoi/7w+BG1xanb31gL5h7IRTf+pOfaE/kpW3QXHcO5Wz3y+Lg4M5G37/v3sm82FPj713ayq+U2dbOh79cTGcnZCcB2SZ+/XNf+nc6sXQfef+d0+iF/TFiefD83sn16Cyy0YF2mzKbnI2mnT2ZFf1wZi06C5/pMlmWGWOWCAy8BHNVFxIE1ZqOwKjX1durde86QAFeLGD5ApUwaQ/R4uzRBrfexVkhqgIxgFKkKFYk8z7qTUQTYcU1uFQrUfAjOUCTz9qUeQvUxMr/jno9JwBbwyAvpOBjgOaoCZwz1AQDoa58tQDoSKq1tbT1tPHu4d//m4N3zruL/uU+/kqBvRXQewm1vu0F1OVKoL7OxtXaHBLAVZx3dthEDcxy1Fluz6MGiC3UC0/T0koA+EXUWrj91WsXCPJdAFsauGxr5q+N8w4O+er1HsDPSKJ3tlrt9xlrZw+X8yd84LHzfn1ZmU+jHqxfR83qNROv4Vde/R/o5Zf+AWqG8hTAv1r9bgc1Y7SHmhXbSLQekHN7pgoHHCF3bMkoXB3b+dOLenD/awCj9TT7OzIO62ehiimW1aZUk8I7kadppx9nSxXJo36rdU0rJYnIOOu8jvQaznvTTlAzkwPUbmJfOacFgwNzLGvR/KKsqp7z3lfeCu+ER0Zhde6LHZ1OCtj+GVwrESpxwQnjw0ZRVZdjpaGAxNX3X67utR4IjSSKYJ1DJSIosB+xXS68K0HUH1flBkLgKJEzAZy5LLsilTzdTuKT2fFQD4Ptf7h7fS/oC/e3vv6Nb7cKJorV06J0LWz0FbbWA+bzER4+1NCxxr2j1zCbdfY0ZCJxp0cgIrwIjzYxRqgBh179rbdX46yJz2mCtR/XJwmcT1LNWGwWPY06oMISkBABZW1BM5qwTFemFVNzZFVgoKzLHl0QdSJmzoFC2GqkKVpL/ESp4DgFsmfu31brw5PQ3rviY2tZuSzqlbPBrbULh3fWt9O1ybjDNoT2dCqu0QENpqfF7smRh+P4tWu5DioWz934IXOckCcgL0tcOj7A7d09gBR2Tw9IeYezVg9rZyPkZ4FP1rbhGHT9wR0cbO1i2N9kKC0giIz3DmAFFwBecr40vMhaAlFsca7XVCAKSBKz+g7HCD4CB4bUGQAn4ENEk6os1BJJZuAKJrizk3hDRBwkMZ9JuB4Q5yXnpQizCFL6JXd6TEZVyOWpkxVgwJBRjmVhdXc4N+1eyouFgcwMiySh1Aq2zlJGiNEe0wUWC8hdnqwnljjW1kViWdggdCKmGpBIQrXsuIN0keWVDcoGZHIyjYrSF1I5Gfk885VrVcqOtWKWHLOQc8uJtCJJg59HFEyyjE5bFblcLnYeJmfpcRHfuz7vFJvQ1lpGlRASCsW2EnO0vaugVLsSwQslvHMoI6ByGlwCeSLKFCGiRy37AhKJGu1BB+bADCkFBXjUbYJBMwhZ7zF6WUxnleTSEyJBICgwCwSqEFZpJYPE+x6rlY5P6Yi9qAJUphotVvMdAN6bEFD/nKyMH9Gj8GaGCYTgZL0NsAo6qmoJl4gBPCSBElproXwXsRKxtIF9sOU4FLotulJxgHSoUhX0GMNr301vqzcefF/N7T0CWqpVvZV/Idj9f7yN2T/v9NiFm75QPQRqyXawnJfP+5tCAiLAQRSHCeBEyJ+csi4Snh7GUlx7qLU2ZnGcZJhHFtBUVVPa7BSnbhSt3Tibt8ivyb7JU1+MChrrXMYpp2t6WsmZLKMxF0XXKy3l8nQ8oxDJ1HW2RtXh8XR2My1kunE/Ki6ndpExx/JumA7SwdXB8t4I96aH/+fD65unlx5OWp+4tX9xsq2v9svFyV+7sf913IpOtz/84TK88Pb09OHJHPfdrPsC/nsArUqr9K3twWtPuGc+dPJ05+oL3RsPv36W7dw66k/uPLF8o5SF2u4MRRi1UnAWVwyCJYZBAcMRJAR2XIooRCuvDQPRYLUuBQCio2JRQsAguHCI4yUga1bwkZbTAiAonEJikLawWIxRoDYbdgD/Qs3oyQKguC7hPpJd9Vb/NRsGhxrsPUA9V3ZWa9TQQh7YmqSRq/U1Rb3OT1bnegLg0tKZ5esnD8b3psOzv4+fx1+V468M2FsBvWcB/F3UBomAc/F/016pKR00rZaOcA7bm8y3KziP0mgaITeZac+tntPo5No4L7tZAH1bv89N1OLPptQaoQZkXQBXPbO6fzaOUQ82M5zPp8N35vdQg89fQh3N8V0A8w2l9S/1BjdX5+hQl36nAPRFHbX3rWlxDTZ+e3XunxHW5sbzAAEpDMbL/4e7N42RbTuvw9a3hzPW3F19e7h9pzdPfBTfIx+piJQoUwrlKBQU2rElS4EDyAjiGEES2PCPWDAZA4EjR0YQQ0rsyFEsw6KURKIjiRFFi6RkkSIfyTeQ777xzvd29+25xjPtMT9OnVtN2kH+5ukAhaquqj5Vdc539l57fetbX4AuOD48q5dfMwE8bgC9U0ylBCoKEQfEglREnU4cBmEgbZQEBXkI65yRzvu8qhSorgKWgWyjZhQfQU2Tj1ADzZVWGDHvvVqYKTMAXcYYl0TdoexMIiExbHdyV1Y9EA9FFKw/s7HJj2ZTdzCfMuJSnqLoxmH0IK/XC2I4+G5ltHXO2W6cyPX+Cm6fHmGc5VUaBgSlpSvVGo+CTab0dDWbByXRI4boMXDuJ3FyR2iTB5EYuHlOrVlOjLG19uVHfqATRV1cuzkPLm3v48LWGNdutTGalbDuEk5GhN37T5HSZcAgthgeQj2QJGCIQ48NQfjg4twGqIH4IeoBpmnd1rAWZ1ehTcw1FcbAkukDEUyLQ6t6FiYLkKz3r6o62DgHZEgAcWRlEGKvPxCrR/dF24PF3geD7MSLLNNOICED0zGK4ulJcOn1E36YdLM3ty9PurOxDvtr4rHdu+HDB/cqB15Ko2a5TfPudHZuNBhE1zbWeZpn7vmdu9SCZSdJB14b9J3GreE6tvd3EBYlOkVmN6cTfn3jPAyAi1Xhrhztm/ur6+I47TCnjYuLjBe9AVCVHkS1QscbD2Jw/oEcpgHGTSqx6W9ZSzC05vBg8N5BBOS8LyzjEoGYgfMKFK67kAMqkefttfsU6KMbeHg2R6S7mHQE+GoRuCPGqhXpmYTXlUaIEJWI3ahinKLcBNzA5hm10phyNXWdonCJb4lxkVetJMDcek9BKKp2pbyajwNLwzIs740QBlPPN5njQou2yqa6DEoRaue5bXGbpG1XMGaAvtvTYXI04yKY5Zzabc64IKaKgPuTmJMazjkvAts7DeDCgg33knK2ZvhedDrgPAj8oOvn/dj1JpbBWVd2ChaHpUAbRksIZ4QX3EjOGCBiCZlzpNYBDqrwPHLWwsCgHTlwigC4qvLCeW/jiMmayCMJuDYiSVDQwb6fsbHuWeeYjqWDDS2kNYAHrPD93dBP1iqvTSkAcjyOnJtVzCvLfZCAGm6v3iyWPbCXW92dFjgLCANWmzLD1Xx3yBnAs8W1lsiAujIgUXvigYMzbSt/ms/QjiVsHHkFrYVXpaaj6jj+3bfm8s39rcX1+bs3ZsOvln+Y/oAZ8UmRMQzpcMc59hqEeNQ684gfRwrkGbjygncYtzEqzBgfKLhJCecEzIjLYEAatm6YDFPETpLvrgwm9ye6UHs2Ob91m3XK91U75Y4b4W63qEaiK9ajQCZeFfnoVM1CEVtV2bIsZjfbqVmxM7XTg6/41MyZMJZOmKdROxQCOtmZXGvdT3vBGMOTc4ej6++5sW8vHk0OVrPZet4S+y/80jf8//nPfvCRX+9/bu+Txb0fH397S05eWTkZvajuL+asl3bf7Pj/7QJ+yEq0Byuzq68E2Y3ED+520rJbtku979cSDLhDKcnmUsN7izllcGIVbaSYwKPtNSJyKMnB6wGgCFJ6CF5kRVVZDUKIGBHWPEHCoQP2wHO1kbicwiLIRg8aDlxGndWKaiec72qT2FqMCY3zhcfSOzZAvcjeXrx2shizX0QN7J5GDQgJ9ZzOAPwc6nn7BgCR6epupqsCf4a2PxPWK/SJjzVs2y+gTremqJmnpgK06UHaVMfuYWlkPMSDnniL9lo1g9Cki45Q++41Or6mDdl08d426kAbYJlm7KMGjMli382KtqkeSrGsCl5d3D+5uH8Mden3ZQZc+0DcIhDpv/yr/zgD0N/o9577Dz/8QrRzby89J+TzDPAz514KuPhj691vco9rpLECg57jCCAwAT1oLSY7YdTW1mzEQbgVCTHQ1l7gQDcQIlrrtMOLg1UwYtOd6Ugp69a7UcLaaZwp715nRCoMgpgx1lv87t7i96xgwWgRUcKIGmNgC2AkhDBSiGAzbomNpNXqp1251u6KUIhYMiYleMa58LGQQRSEAsZKVdcfgBML+mkr5CBaa7XRj1Joq+XpfIr9MjPSe+3hob1nBBLSu7CfzeOoLOKplLGRsql4RbsX2+2tNdZKopOWsaITy83oyuZKmkTx+M5kf3zh4T9uP33xC/jTl1q4eL4L7wIcHh1QqbhvJdfx9KNX0Gk9hfW1NZTzFSgrGSGh+jg06fum6XYfS/0ZAFjngXGd8BALN7qGKW5MshtLgQYAal6DvXKRA+aof0zggUoAhSTMBcHBWRXlJWt7N11Y1/L+fJ6GVaUDB2hAGy4o8M4qQGgpzZXDvVlXlad//J73d0eddivJ8tmj929pbqpUxYFIsqzNjRXbuzv0xMleWHKOvN0ixjkM49gaH2A1n+HffelrmLW6GPd6zEuJXAaYhik293ewXmW8lWf27nDdpNZxRoxyLjw8HALmEHABGRO4IBMLD85cbXD8oAoXZx5LOBvB2poJ4EKBMYJS0jkhEEgLgRBCKEDQxd077Be+/jtb7z/cW/nD4PwX17K3rlHSOp7b9nqQ+S1DPFbghVRVTtz5NX1PSMPZqRyyiR+mARViwI/FxK4EJeI8dcU84uVp5eN+4aLAF5ZNi56e8yScG4GVSVs5M+JBXAljQ1/N9dS3WHlAm6HnlML5JGEFW20d63PRmIZ27KpuJyHpeCfIvKOYiUxUIWM+jgsWcU9pThicCqosQ7ZqxWjT8Zzx1GZkIYS1UspuEdDACjZJK81iAzZnjs+lETtjxiwRGAMCTlJHhhxByUqS9SQ8OJjncJbBOICY9QyciITgBLIW8FoAjkEKgIOsJGlWJXd9AVTkkIEQMo2QCSiw9jxwZWKNgQ4BckwKEDECZ0QhB9FZIu+B1ur/eyMCpKgr7gU7W8DT9MnOsTShN+AsY3CdgCMISsngmYEPJ3DmwDH3le4f7nwu2iuPRj8w+Nbdn778pU9/5ftW2ec75vTjn4y+/ZHnig+d++KXH/nYrd2gJbzZGW7rCUvQ1UXr6YltrfJYurYt5hWrMmJFJthpGbJUWkacMaMkkNgY0B6kUQZFVJ4jx+6EGY+tnpensuevrHbCdZ+czwR3obCZpIztVxiN5y0t7Nzc2ijNcTwudo8dSpOEA6nsnGL4wHjPZ9JZMtZWDDSYzNj5+wfhB27l558a43KQXuhdO0y/eGVv95W/8Xd/P/7c+On2+XMHT5S/zX/u7hvrHVWI12Z/svEbn/jizeQ/ePGdtLocuzc66eOqmLmv5p2bu/vsvStrpxdWWtGlYy9yS3IVJY8wA0cEQJFCgBn6RCjJIyMgIYaQPCwc4AS8IwjBwMj4EgEcQqQIIdBDiQgVOAQMaOGhWnfOdiAnwVy4sF6sW5pZBjiWgoiDvivlny7Of6OBboo6zOL+BpYZtWuL59uo3S26qHuWr6OWRDU+nhI1GHQAbnz681fPffrzV+lTH3+mad35rt3+rDB7VwB8CnWKs1kprGCpizorBu6gZqEEvjvV2lncl6iDptFZ5Yv9N5q+NSwFySUeCEdrvcFifwlqsNlYpFRn9tmwOww1MNz6nt/SpPN+wnl88Muz6TfA8CYcZiDcbMfh+au37/04wuALE2Wmufez/+zDfy4PhVidV9V05/de/fxtWU53g/KFicCHI4+RMGCKgQvC0CgbNYIHAiUE6AqgrTgxT5877xzIjOazw0IZ3ZZ+xXEcV96pVprYQIguZ2wTS7q8MWJumKnGUiTGcrXVaK9sLCWUd2TzeZS0unqru2qPZyO/l00QgHY7aevKehQp4byfTEo478t+nPRsVcpSacU9GGdkOjKy98tR5QHVBiI/GfOMy4wxQHvvp0lERRxCOSdhKtZvtxJn/RWTqaMwad2L3SxxW5uR3uySnY2kLlRVrA7L4ptvRXjpTzo4Hndg9DbWVnKs9Lw/GoV49PIQ/94PtzCabOD2zsPYHAL/6qvN+ZJY6pz84pg0VbUPUlFuQUWQB8LloNXEQrl43BgH31fAOVH750U4U7nL0JYAjgAAIABJREFUAElAtqBDHIAyAlrCmYoBHgQrgdACIgakBawJInHQWxGbo6Mi0qpM8jkIiA8crbzvzZeltHzCrfG7w61ef3yIJ+/c0Kwyep6k4UwEpD1Dezan4XSKo/4A+90+Jmkbb61u4UPVW3j29ts4iWM7a7W55gG2psdom4qpIMRJry+fvndT31nbYsdr6wRdaTjU9A2DA5X1sZDEQMaBSYmlBqcp1KjvvQW8F2BMgIt6gidMAIrBwYDyFIj3gfj4Ize+857VybQvKxuWjyfDk4NwA7/xW4W9fEl1nniU5Z2hYl0K+2rkWVhJCiIau3jmvJYWhknmxMwl1QSDiKOKSCI+1Btd2EpylBpCxjPfSgQ3Nu6akE+yNFltgwXKe1tQxdvrMx0ox0PAz3kspzwiR0JoHoZOl3FX6no0kAWYj4QqZUt3PYMNIlivofOeCV1oYde8QW4rbjULT4Ncm5Fs789tMutW44fJj1OrEXmB/Uqk9zl3vcCXlSVVEGRfIkrId09jUfXm3gZQcKRBnlUVcTCIMHIErUQQL5Qu1gtYaXkllW27JpYV2ogwNUDOBVLYRZlavcBJ4Y+SIgdhyl0kAFjvvaSQE687oTTn9d8c8Rqjjf+3so3Ft8KS5W38+s4vrp3m+mgWCgmkTIWaq+juXOhB4M2w8wcs4DeGXxnfXfnq6PPpJ360+/J/Hw6uXhXtlPn5Xzn/yX75Rnzv8tax6H/tLx0eXPmV7+s/MfuO2HnmcHzuZjt67Pih5PL4EX06ee/9+TxCGbUwCV3lEh8BzE4DKqaWwgtzruewrkhKhDasbgDZLhVEqnSRG7SlphZPlWEnQB/sZG+EqNKSZVGX+7Er8h1WqYzP1TwHslvO8oec934kDIT2aAPu4Zkx49SiU0Tu3KyivdRtJySmaA0P9oeDh6L25Cef8QdbL/6t997ufIdecCz85Oj6xsB7SQiyD6bbWa86iFcA2Ed4We73szx96GDtONl95tV//WjbFO7gwvpBOkvMw2/vbAgwxhdURogAwBgb4OAIYUEoEKC9sEEGIDkiqeqRCAmSB8bp9fkjWNAD+636NQ0D7wW4CgAUdZiwEA4PwcJCE0eAABIpajb3ALWePUcN6Fqo59yzhUW3UZM1T6DWuzfFjSdn4ihDLfm6hCVe2EU95r6K5fz9rt/e9cwefeJjLQD/GMBzWA4kEZaTZqP9AZYTyA0sxfMMyxXBBHVwPIJ61fCHi/c2DOE2lqlbhxr8zVCDu2aVWaGmik9Ql3G/jZoFLLCc2Bu9wvf2vG2+Y705tODxGBg+BMJHUeGHTrKsvzsaDfNcPzxW9rgk97VYBtH1o8Mr37x76+QVPsk4kJ0E7gAMeWBRPqziu+uslWhu+62khTURVL4yGNlKWSBrAbTRFWEvVPsnmh1p2JWAc95NW7YXJ68lUp6LgnDAGWtYyebYNSvphhE92+mjPPObzDCIo1REYxJMpDLim+1uAGexP5vIbhjG0yyfHlSz036UqtKa8eF0HD51bsu3ZCDmquKOiCl4cVhkJg0illtjpRCh9t4q52ySpmHP+WDOSFvQGMaNTM0iiBAs7KUCw0LhSKkkVJXuC39AVTXr3LjXptGYtdVcrcwOzlEQXARDCyJYw6OXAzzzxBCPXrwIxj+Iixcu4J3bIe4fEPb2gaNToAbODYBvyv2Pzxyn5nxyRvApgcUER/RAt9eAwca7DADEIn3bWaRvsYgrD+Bkkd88qoB4wfAZ59E5dShK4kdJ1CXY0nnAZSB3nHaqtiqlFwHrlLnuef8OgM447YbeW/7I8UEwmJzw9ekxS03Fe8enfKvMBkxXgVOODYuMz4VwRbvFLuQTDMfH2L6/g2nS9lnapttr25gmKe4MN+1xbwWCg3WzOabtLq5vXYQOIworxbMoIYjAw1kfOyNUlNCiy4YHEQ9mcxvnOSkmCN4xcHG22q5mxhkncOIQEgs/vwRcSAj55qIm5jtA0AV8cf3C6slh5t1nH3rfGwcIB503Xttif/Ll99HV1y6Y115JioNpZK5c8CpNLJNuqigKLAWxZBAcJZRLCkHmfs+fOIl5kVGaGisZEDAJLRm3PA011z4gr51Mk31pZcCUjbmQFchreBb60J+YQTIK4sDyiDsoMOV5lIXcxIHQgQdnBhFLkOepLhMGJlQVcG2FlCLnq7cShBkX+bp3NuRZcpDF8bji1ZWVGZ+UqOZKoMMCupczOihZcO1Q8bkldSl21UboqQvPPamKz6pqYKUj7yB4DOu5NuDOkRYh1R6HggPaeFspz3YkSxV3agCziOIYhREoVG2kzWER8wL0AKZ51FpAENGtSjlmrQuIUcbqa7GZYL97a6Dav/1VYAnyKiyZ70YmUseAdimUSaB84k4rhpDHNCOEO8yHY0Lvun1n+Otv/ML0h899qf+lw7v/0eHf1u8tP3Ju65VzJ3/7793oKOv9UITylvo6/4//018c7Ny8KW/9Vn/78Kurh+P9/PpDf+2db/WfmjyX3WhFtpDGVOBcsq71jkTOKEDp5GZOTjlnx4H0ARgCb8VmyZyWYTgJkuTiKB08fBD5iSsms+lsrO9FVdWN36nmXM4VXFV6cKjTbIeUnUfanmaATwHftl5zEi3WQiyEr9hCDEydwgkDJk59JU5il5Uu+wpmh8W9rhhOTZSuvV3dzOf8+baunlGWRPfUsqwI/Buti//r6lF19DP+13c+/Nd/ZP5H3/zCpP/4eP5w/9Tkx73gnfvD/PLFe1z0su6909XCz1iIDPxBB5RiMVY5WAQI4EFg4ChBcPCQcAtvRgkFwgQeIeSCy7UIoEALNwvAguFtOCogXADQMSAClGjDopY71dBRL+LDnhlfm3F0jDoNO8ayv/g2gDdDxt/x3r/P1/P6APWc9SjqNPGPAnj/4r2rqLM0Z23Yrvv/4adO/q1R+S7b3tVgjz7xsW0AfwvAT2KZihVYCvodauTvzjxfYdmWq4V6lVAsbk1qoI8awA1QCzrnqIekc4vn88X/NfYqhBrcNQ2V7y0eV1iuXvZQg4ER6hXD0WL/FZYsWQ7ohbs/8w9yHuzBWihBhYuw2ITFRtviKeWAycko3JmM5MRU+0MlIgI+WsGddwxdzcBnMHdWuAymzqzNfUEhD4uHh2vZw8Nz1eXBMIyFCB4aSPPIcEUP2+fDThizUps4lIw/tLYhkjBKGVEXy16vQH2BBVVRSKN1LqRUWF54TRu35jky1pRJFKUXeqv8OJ9xCYad00O/V85i670Yqawdyyg01vprk2NOnIu1pHXNON+ZVGVotGHGW6+dg1KlyJz15OEq77wWgpj1otIqiI1iURwKW+Qycc7H84x6qhTtblpE87waFSbgebl3qd/Jo/Fsrbx1t5u/9qrmJyMRbW61EMd38NRjN7A62MBKL4XRKwBCjKYS4wnH228TXnvT4+7+gh19oMFrQG5jot0+c14bwOIZgRE9AIZNbDS3pnCFPMAdwBmgxNJkuLEUIAXkM8Zj6f08AOZEkNbjbkgMTIZtZu14FgTHJ53eKjeaEREfpR3JlS5g9V0GEFkbD4yWBig9cd2Fs3FZhC04Q0BL8YBFtvQWjK3kU7Y9n6CVzxGpCoYI+xubdGXvjp0nLXbY7cEEIV8/2qHW9MTu94ZubzD0xnlXEWfX17aMC2Jr4tCFlfGxLlnJOetWJa9k4MG4t4wxB28dUIELQHgP8IYZrsGEcwSlPaz1EKJJgS/S53QC8HMANgHqaSbztzevXDpsrVzG/cMB+4Pf3xKvvxm6N24Rv7mDMAfR7QNXbpwvMt9m1BI+RiYsxYzIUMwr6WAj78OuYUFfCsdKL3hczViss0DJkIVkPPOVr/aVx9HEDWNt16bWR72Z6cfKbZ5Y3hK54IkHPMgzkIfPJaMWIxdqELw3PvBKpMy14zz3zko/8i3uvGSJ0F4WgrwFOxkkMmdpTGSYqCxTMvbVzYyqmbGiK0bR16YotCdduZlfbzH/UMhEwjXj5OOxlK5imPWNFDp20JwhMJwiSYZxXmTMBi0BYgRTeNLWkrEV6R6Bxcyj0SYbAyhrQd4jIECK8EycN7KFFoDAWkcAYiGYpGX+VmIJ2JYjCeAXvXSa55t03FmtaxMHZzMJNdBUisFbbrWDOoYQmfAiYprATyXhxUf+p/t/f+XX3nr5v/77n5r+4N/8QXvT+Ce+MjsM7u1Ud3/7dKcPQP/QX/9no3dOq/Rx8dLG67+z2smmEQ/hXu0+OTla++H7fzHdnl9Sk+j19e8fv5VuF+bk9XRgjkMG6Th7/HR+7kPHVE5C60ahBQchNNwhkrCSoTBoPTuNk425U3sJqVMps2OKOLh0+sBRXhAxg7nc11ZX00JNEuPzB9mlCrCBVZJ5CAOL/Yh7oz3nsGRg9CgR8tb6ipim8huGvFydl8Nkbvb9RGTDU3WJHYPrXLbhAzmOkm+QDv90kJWr3/n0b5m/8dXrK1/oX/Bv7KwfP5ys2J96z/z276muuzNOyqNXW1W+l7ah0IeERAiJOQQMHNow4AiQgoHBIAYhhWchtDcgEAysI1TwUKQgYWCrCYweQYhwEQLVYoYLIBAAvA1wCYMEDgE8HAQcAnBI8IVuPMYS/DdWaq+gTtUGqOfqLy9e755P++NClR+x9WtvLeLpadSM3vtQgzx2JqbuA/hV1Mzg6M9CChd4F4M9+sTHPgzgv0Xd+WKA5eDR0PiEGsA1zEnTk7HR2TUWLQ2jcoJ6VVCiBnQNuNle7OMEtR4wwNKVqkknPCiaQA0EOeoVwtbiszqL/ZxDnQZOUXfoCFAHVJPOc7VMyzowVQJSgS18rCzVBLgEUA8lkWIYQOMHV0r/o/0KlzXD6VQ6GzmkmvlDyxCB8KhmeK5k7nq7k4YiCK8pb19fX+n7Jza23Eqaii4P7UqyllnR5WEYxr20dSMJg7wXJt1WnAyIqAHITTFKk+oWWmlLHpZxbolR0wO3AcdNKTy3gE3ATSeK356XpYX3nISQhTOi1Irl3qnc6GpaFZIDu8+ubZthtzc5mc3u9qL4XCuMgmGrS7qqfD9ssYgLGUgu0kkeRrMZZpzG6WSsB4OOtd5XyXjavgjL+6rk6TSjh8rcD7gvVgftYGOlE0Wj6ZBZ01MyUPb+/ePI+DIslUFRfh6bG89hZ+8CRtMY42kEyT1ORxwvv0Z462aBXDlaxlQzKT1gkrWHrwCS9KCCuJncmrhsmNFGQuDw3b57igFeAkbUr0+w7NIBANDEyt21TcesPa2MvXvUXzngUnwp1tXjd3r9yJIvemUWpmWerGgVZa3OvBKCD6enfBq1O46zYNWoyANBAdgETiZA6oE2AyIHsDKK0TKKZTJGGUboVsVC50DIZIj9/hqctrjfHfj+fEpbo2M8dHCbDBMwcUp5kvJ+mbEQwOr4RLfKOT+JEsw7XR+XFbXyOWtlMz9O2oAQHIyRa3c9wkhDeI66SYivPbwWCwlrXN21gxiEwJnjKBfjwPriWAlAtMFkC8ZnkFLouM3VjTszdnwacDjy5zZs5+nHJyvnu2yyts1UGJUKyRQuaREsKx2zpW8jQ2KdJ57ZvtC+60pmUMkBn9IACWUkmOciAet2C+sjQhTn1oUUoBIYTkRhAytt6hjnxAC4UECE0klnAAIoYvAdUZKPAqY5ByLJGdNoOUXhMTktLS96tnLQU7VTOF7pU+yXPBfhyf211v18qxVtRK0wGXane5dabHa+Y/WllHiXm2JOTAKkwUWhnM8zq+K8cqgMQTiugtAS1R3iuQGsdihzIjLCJRQSSxgWDRYFitLCWIuIydowWTBIDjSLZOMkKsVBDGBEQjAmBGNE1JwfjSWP1wA3Bg4PCYKyBF0BlgGCNel7jmUarVlINhmEhtmpCzQdAwUCchLo9Vn7to/871V997+Ul/g/evinvnz1Z/yvKwD4p1vfDL8zHn/kC7f3H/s/9nZvnVhFH/rQ2+XP//xnt/7oS08Gw92quMaj9+5NeykLw2vP/7XXjvLd+CmnOGeEKYF2Oi8cpvKKGqqdpATAOlvVYXGrl1SjjqYk8J5XNt0uub4dclRUq2hn3Ja3hpW/d9lG5lxKXtiiux+i0r6YFSYMOiLDsZvMD8O5nWSLgqywAtSc0PFAxGEZAc5yzplxNo8DZ8Oo7GcV753M/fZp9oSwZrNTVdXWyZT2++mTXJqL2LDXXl3b+P1Oqd+IPP3iL//Yc6NWXurzoyyRhXrhRt59fPWkeHpCZbT63Bv5nx5u+knefba4Fz+GETZwDwl2AIzBcAAPoEQKhgCEzBECzyCpFDGYYF7YHB7Keuwrg9xmGHAHpmcLM2yOQESo2y/bRUwEqOfcuh04A4dGAI0ZMnhEkLVM44FUJliMh3cXsWEX134j43oJNQDcnKiiY2stvEE9j/8Yaru1NmrgeDYDg8X89W3UhEz06c9fHX/q48+863323pVgjz7xsQDAf4O6kKHpLAEsU12NlYVG7ffTCLxbWAIroA6WRti7jvrk68X+GoF9G3UZeTPhWCzZwnDxWSW+WzTaDERNOtmiBnW3UBeErHOg6+sU8FcWtwIeDI7vISgLMN+FC2REjAWcptphBIt2GgKRBpgHWAKkAoGxSJnHxjnDxSjwvz+X3lmGjBk8C4uLYFAluTeFEP/8oeG5z+1MT/dWW53ehVa3Ewair71NIXinHUbzOIosZyxPw2i/G8Ut78FCxk3MpVDeNpqyRpvHAe+IiItAfq9Z8B3Uv6+ptpMBMQaPwaSYVfeK2bgoCm85S433ynpXAtCLVgrq0mD1nTRMrldGH60mrQvrnb4sytJa5ykJhM11ZXokscbAHzLarEm5v72/97ZcW51jNNEuy7z04H2t0nGaCJlE4lwSx1EQtNNBt92vKhHeP2rFWlN7+7y3WR7SeNbis2wDeweP+/m8R6UWcFbhxi2LvQOB47GDg6N6MGjYY46lnQQBkJlDeOpgY4Liy0KMJkXbpPOBZRFLA6BL42EUMBE1wxEB4N57KKWE977yjGcG6At4oYHpcbu7e9If2Btrm/FB3Dm3Mjtpj1sd1SuyMDE6AdCWAE+qQg2zmUgB4kZFkTU9Ceg5QBV4V8L7EIjtQlcVA9Q1io0BP+OMumX+QL3sZIis0/Otco612YgYEyw1CuePD/zqdGrvrG7bAHAKRPMwpVgpd256yjpVCaGUm3n4jlHCWGPXs4ypMCCTtmj79BBKht4IuWBJra+rKznQLNoYZyCy4Jyhrvg+e71H+G7gTAAqCFEAkGy1+0rw8X9nB1vrM7nW7egPPVv45154rcVnatQZKsikY1k4iWk+LRCGJfrCwssEVqYY6QztAJDkKeUaAgRFRKXXPqRWUJJhnheux3xoZaUYW99NWXdObLJVEIQ3kSq5B3wEzriFNwRICxMxcAtPBpyc4D6owIZ3YkhNTlXOOge0q6gsfHmCuTJmEE3QiixWGPc91y1LHrPIFdM5qqO9YNzaqfrtXIlZJO9ObrDKhjiJytJCWa4TNg4LfV9n/pTDSVVSmN8jtLkwfIdcWXqijkUqGAEEBFXNrilF0LZO0QnBQdxBMAJn7MGxVsbAW15Db66xTLOyZgzAUhLzIPa994D3joxn8M4BDJAMWGpeFZYLpiaD07Dp9cZIQ7KCBLsTc/4PHr4x/Ju33zv+lz7EOwBmf+dTP/+g1PuXH/06vX5rFh79Md4fKRYemUod7K72v/HyhezaW+vjrUcON1+8Of2rs+PsJ/ftTudXv3Tvdx578f1vM+H2rvzszbR9weXX/9WGUo7LtYdnNuzpOUNiJ9cjZ0nDz5nHREz1cbfFOOPx+dyZKGDmMNAohzZOe5TPR7py06nzkCwLrDVTllczropTRp6HHjrky2MnLBAIgCoOhB6Q1nkOlJUUpRC9JKxKUuSCeSuUynkP6/rM48KdQdra2Wodj84le5VODqa99j/95R97fneSRu95/cLa5M+9fPvoy4MLz93o9rtPub3Xt95zfO+zR8/MDq73HGZMYB9p6jHQYwAVFDJ4lBAwCBB4AUVA6fnijGYuI2N3FUelHXIhwR1HjzsEjkFYAiMDEQKccVQoYH0JggCjEmqhe2ZwMDAAKjCUcGghhAB7EA8HTWwFKeZW24PadpVaqEkXhuVc3wXwISzlNVuonTKahUJjxtxsBvU87RafEwCwn/781emnP3+Vferjz3i8S7d3a4FGhJrtOEUt0jy7eSxtGyLUJ1ZiycI1E0GI5SpRY2mWvI4lI6UWtw5qkafCsoT7ISwLO+aL5y9gKaZnqFcR8eK77qMGjBmAtq+/Swbghxf3t2DwdluTnFF6AoYC4IOVVryWa9vNrVJhRPstxtpZaJWv4FOISS+KVif5PC4JTAj3wkYpV++l2qAWnPIYuJHXLONPHedZeXz7+lc+urHdeXJ47noJf5u0+U86adqnumu9YbWpbSiIbfbjlCZVruCxa+AvLn7XWZ8sJoOguVAsAPLOeyKUC3PcpuilAJBk3oKZynghzpN1egZbQFVlKgNwFioBzKa6KjSw+tbpwc4H++3XLMPzI1X90VPnL4WtMPrwUOVtsqS3jBZpFB11RFDJt16Lx7s7vKXd0dFbN76wfXj00yvTMTxR5QUve51O3AsUk2EcROtbSHstGVRVG/0up/tHVt3bW2HntzLIKMTu3lPz6VxWXKhkPHdiH15+d0/ERgPagDeNJaDlAGzCUApgHBD0Ip6aCt0mNh+Yf5aA9HWvgIgD+8aiXXhkTCAKCHMA8N7rosiFNiZnvRUHYtMew7H2Dp977ge2V4r5O73xJPjtD3z0+z/77AfTH3/5K9cu7N87KoCrmcRaS2MgGAscsQDWdFt1PNQsEyA1rNdA2wAsWurjCIBrA2RUiRaAo94KImtdUBZsJZvTdjHzDKBudhWzKIXhnO4N1/lGfiJ2Qu6l78MA6vzubT5ud2nS7rOdtS3WVYVDVdEjJ/tiWGRIjcYrXMBZB1QlQxTr+nDFDfPexFc9XtXp26aQqUnzNZpZgXpcUFhWw3fgHFgkHg/77bfnf+kv3Cx3PzpGFIni8LiQaULh5JRVPlhDNxoypsoQpVAIHOC5R+XmNWYG4FhdE5JBQqBysevTiEJSDDy3wufcjCySUCFse5oZW3dx5YxxzwzzHlAQkGChqwWXyjtUkCQcfOCs1Zn0fha68Ghij0qosQvy7a5WlWfIWO9elM9HN29izeyLS+1H8kAomF3Znk2u+QuzCXMzLXRiNYpvlf1g1Wuro/YgTCdirPM8YqII+CBet7vyzdkMERL2ZpfZ98KZrguzTDHsstIFmLXWCkZEDNYmgAcEozq3ENTqPAN64DEgQAgEoIgQ8AcLlcW582fOV1Np/uC8VmVJ3loRJQnIRw6cmvc3adw56vG5uY4aPfAEtS76X6DWV7tfwScn6AP4EQB4Ac1G9BkBIHn4vUH6gYP1SzdOR/kAuL0qo8d+tn/xo1eLMd558amX35d0Du/83vCZuXp1a2om8VC4H3/myurf+5kbnxkD/nW8+LG9nb/zc+8dvXX9VvHTn/369geOL7CYv98dsOfmN+HzE991wiTBhuXVkXfEDbjUno6s8/CBuXRIs+6M7Mh77EQCswpC9Fgn2RSn+VsZgMAjd7KO3zYAK+KSb7SjKp+uiPt8RlHmiaClBVpBUXlZ3CcN+MM0IE6enEN4fWvVtMvcJ7NCBqVtqVby3LiNzVcurLxZRMFXn39nzz9z++Bpw/DNbwy33ukk40efLq5+6IXV61+68yf9l+6M1zaxi+dxE7aweGNxLrYB9BBgC5FhuK0ZFAjrEaGER0Y9OGjMvEaPhRCk0Y9ErenzDHMnEIoUgikYTMB8vBKafmlYnp3yKcYIMYRAGyMU4CDcRwvPIIJYpPcbuUAHNYkzVZnJIpigBL8LyBg1oLuA2lLladTsXLKIladQp2wbuVfjpHF220PNCB4tYniOGmMQANB/8Zk779aWae9WsNdHHXjNKg9YMnkNM9dMCBGWK8FGX9WAP0Kdum3sVzIsB5Mcy24HjQYrRc3yPbV4303UYC5BDSrbqEHdxcXnHqDW79UpYKumICrAJHNAC15fBngMYiMA18CgZwJDWH4RDBMA//f9WbHtgI8DkCth8AeVNTwTWPcCm8TxUo/zp5TAxkqrU60k0Z1bp0dycWzedAJv5h4dEP4r1NYu/yWAn7yTZ1gp8uNeyr/IgTdTFlQwtqWtVUxqE0oZG+9oVGZj61zogQKwE9QXSjPgNqvqB9XO3ntUZWWEYGKl3e1x6+4FMuDjYhYkMkSltecOh+0gbEvnrLZC5s5wAe6ZoNGkyFulqjrE2VGuVVlZ/ewzG+fb39m59w1jzWsb/ZVDPmWx1upCnKRP6iJ/g+7f/mYxPv2RI8Y2vjNcE5m1/od2D3c7zu3HQKytfTQ+OpYcYGE050wBARco79zliEJ4CwTwXJ6egu3uCeSF5gzErKEFgmgYCVtTHYgWsRGjHggadq8Rkc8FwYhlXADL5u9N15ECdbW4KBkK6+GER+6Bl4jhqdCDCyCwQMEBa0DshMS8F3BKOWsZj1NOOF3J55efvvHWPHb2ylcfeUpM49ZgJcuhHNaLKLntbPl1IdmzTBt7v7eylgdx5+HDnYg718S4jIDY1X+QW7YPLBexzghLo8jOfIrAWia8g9EKV7cu06XdW+g6A+QTf7+76tNibr/v2rEYPZb6QZq5kkd0Z7jhWRwyAnOVh++Vhd/vrfqW97SnlCvbHTZj3M36qwAXzWq7SXH/myzOEjT4M89/r41NG8t2fhWkZMaCphPeghOv4nzPA/oJtJNoTGuwzvQ8b3VIe+Q5m0ial2Ev0gTX0kgjDSEBSwEqWHBwEFo48l1xQm2WofCRSzkxy5WTBCZCYY5WSpu2IHgGay1clUqEnILKUy6UF8xDBJGznrxjpXfSZLwMOCIHvb8+m7VPslG8ypwcBvmp6l3ZAAAgAElEQVR4p7q4f1W259000i/NLmUnpm1yJ3SB/HIQVPqy23Cp59OXMoZLxA/vxk7M3EAce4oPDOmhb1sFlBbkCtDmdrCVt8KWI0eTnXKexcwNnhSStzlcAI+WDTXxMPB2HwSP47IbjBVTKzG8kMgYkeTwoeSAhLHKc116hCl3xB4UY5zVsn7v1kyYjlmnnbEAUVxHHCaL834PNVPjF2MqLf6+ibrw7Z8AuP8r+KT+3p0/CBT6DC2u1VQI+0mu1J+fb2SjeN3d/PD94Szg7JGWZOmxLqNVFl5JmfjXd2xvayP5yKt72ddK0Di7/FH/QWNofufqo9+ev/2ey799iGz89P3wMTtcf+UfXrjCnpy9//Lzu+XmJ/zx/E5Qjt/sDrqbxdbozU5WlJzNbiYAEceq8qzjGd82RP2IvHfeOkz5WrGKvcqnZiPM1NG01myjRD0f9UAEkfpcHFes7VRqAGcBa+pOQtYBZAnCS4lMcGJCxypi83Hc1klWqGGmN19vh/j2dr9/utL9z/uTzJ6k4bnTdrIXMKjxOHn5Mvaeu2uGl2784ubwa7uPbqOPERRGCHHkQpxihBHqitYYElMIErC+B8c8DAgnjiM3RENfec4UHIWQ1kKQREkcjCqoUIKBLRKtARjiyYQ5I9kcx/4eJm4NEQMcTWCwAo4+gAD0IFYsajDXSAEqgM1K8MsApajH6tHivRtYOiSMUM+Jl9C0YFw2WDgL3EYAvoVlelihnsMvox63996tQA94l6ZxP/2ZX5uirr59FvVJawZ/YDn5NmCvmQya1NnZ1WWTjjwLehsQWJzZR7X4m2OZ4h2inlDOLf5OF48bRs+gnuwfQr0SOYHNY9jSg2QOcsewVQn4YzBxCmAXDOvgWAdHiFp/lPqaUq4AtGfWHhTe54A7HwV0qzTuZKyr9xiBzAp/e2JNMbZ6E4RtEF4EYBcqvxXUF+owJgpJ8N9ljH/hmc3tm700vSOlbDPOaV6VvHR2M5bSEFHPeS9QexE1mjSGJVvSHG8GwHvnTFUp7Z0VnIlRO4pvWevGJyrvG+Ccc7bjQd4YY6dVkXZkKra6/dAaExS6UAB7rXT2tDR6HHL5j6zzn0mC8BUBtv/mnVtiZ+/+W2Y8e/nk4Oha4Nm3xqejuavKd6gVdffCpHMb7uUDzr7FnZjMo/jOxfnkl1Kjr3qgNw7kMLZumlh/wtuJwt6RscenTB2NDZcCbGXA+e6+xGTO4KGkRxDEkYOzWnoIWmiHaBlXIyyF4g23sfA6RoV6gDjbJL5J9TYV3M3AJA1BCQ5PDq4AdQNCGDFUOWGw3xlwVBX3FgVjktqB7EScaQHMSlXFTKlytcxWB1XVm3DeW82L9Jk7b+KH3nl1L7HmG6n1WaJdjwN9bszFRFfdRCuFesJ8UBgiASIH8gAJOtNjsn7gPEAGQOQc9Jni4UkYY6WYNz47fqPKXawq99bFR7kLGHvy7jUTKiNUmnA4kBEBESN26XAP81aXTqOUilA6IsEMJ7LEPKJIgfNmFf+gTzC+GzA06ZyzALDRR4aL+xQAk9nUpff3TxQFGt4xKhz6I9MrIvYIvO8hFN7xcBMi6BFngLDGZXOlVBTJllBtmiTEPSdvLYdiEgoWU58ip1VxSIIcwJmzLLSxy0mKkAeBdREDFw6CJEABCNISPAQcYzmFbEYJCVFlgSvuec7dfMKlOtUqp+BG1QpGXYPPtdLOfrUtqvm9IBsfttfMcdaO4pxlx1xg1SM7ZVQUcTA7cTKfW64r5+e3ILuPg51eJ2OmbJxPWVAdkJicUHD4bY78GPAO4bDP5ejE8ZN3LNRM3Tv+ttOz21qHjzPfWnHWnbiZPjKpTLyd3Rd37NtH/dZJJu3cuaiK3XhKptjDLF4hR9xNzLGZFDMmu5UwQcVKk/jDxbXQ9M5tzl/D1o1Qs3L3uAimIo4MEQnvPKtyPykn/stBSp8H8CcA/hh1NuWzAP4BgH8J4KVfwSf3PoEnH2ipfqz75dbv/5O3gz/Pvt76H3/pRf/xv3DzsZ8dXHqPg3/fgSl/0Dn/78s5eyzTalNx9Wycy9N2iz2aGfXs6yej1ZixlDzYV8rTk8eC/rzH129fSL/v6lsH71nZuXkl/I3/7q+8t5VM/nIWnf5AOL6g/+rFh7p5aYvb4WmfWvbl1gTjzkO5EO3yxHoaRBdtylMuZFspkpjET1Rz3mcs6uTcWHi2FfBo3o51UpFC5qqj0sOrJqYXC0UVQYugGrNj5xUWi0hygApQq1Y9wEuAQqURlxpUaL+a5eL88TQ4SSN+1E7iUSCQhcLqIOicOx0PQuMHf/r0xZ3/64VHr2IFPSV4MlTzO4M999br1fkDVzKNPkK8gAIr+Bz2cIQWHkWFSzBwmLEWjJdwniHjCsZRFDqz1bN2oggAZ0gpQMAlTuCxCw4BiRwexwu/PkbcaTYGox1UltPAhWghgGMxEsRIIBdAr7FeuiNTfNFpfAsP+u7SeYALgLVQ2/BEqJn9ahFfAeqsXFNt2zhH1HFojIUqZ7B2CiGuoS7I3F3sv8nK3UQN9vJPffyZB7rpd9v2/2tm7+YYhHpFN7/Sw/GZl55G3Ue1mXDP2licdWVvUDywTKG5M7dGE9H44GnUwOxsV4wU9eSYA4uVxrIabBP15N20xWo8fuLFfpp0XR+AhA8KQAxAdh8IJmDBwyDe/H/z3RugOKk/U60COl0QGY8zpMyBDUplLcALMOw7YDbTqk1aPQWH56TD7YToNyfSf2PxPQ8BfF/K5UcudFf2Hcf//uLdW6dPbGyFlwarAPCO824YSKGI8xXGWKOt6aBe1ewvHs9Qg9yzkyyqqgI8Ag5yljNhtAnLsjwu4Z4mbRIvhakADzgFgHVEINJACAePTiv1+USfjk01AXCnFcYvoqbQs6u7OxtX3d13+qMqPp7tFa/PX/YXexvjgMvTv/gTP/F2MojfV1n76Mq0/GpvPPq1i//zPxyeJEn7N5//QPbWQ1v7f/f3fvfuqNW+EQXy++PT04g8un7vOAD886FHKoGeLctz4s7tBIVpzneOQKb8/6HuTYMky87rsHO3t+daWWt3Ve/b9GzADAb7AAQHBAiIQ5oiYQ8pQpbEkMWQLMqyaUfIDhJjOuwISyHTMsOkaYkGwQUiLS4AKWJAkMRgsM3es/T0vlRX1165Z771bv7xMrtqoLD9d/AiMmrJly9f5rvvu+d+3znnm216rDcgGMXT8qu2ACfltSkm12kqAJq2RJuWe/dQrj4XJt9XinJhkEzGx7SzSxGZ0kq0AIQDy1gZjKoMkJrQNWlBqUU241LOSNnce2Qw1OAPecQWrTyLaZEnn3njhSObzptZQ2WmCXBYawD8GMpFQyWSRQRZTMfZDZSBb/ne/WFKJYRmACNvA1Y0BWwGkBrupSdNCEsfbG+ClgoKEpT7cLfI6ZgRvbCzaxf7HXpufY0+O35I/9ljH0HqeizIYlSylNbGA0ujijWWsmEYqCKqUHi+AePTkuzBBdz+lmUAAYXjlKR8TM5+P7OHyfVQyDPrdzuEF6oOxiQcx1qXs0GazFVv9SuFNSJbChfg8wC+mAB2FtvK7C4Ync1gfJCiEFZqD1nsFXkDDvdn2cAKphHQpNQHU496ZKwL5dqazo3vgZJJETL1QCFTLZQmEj7DEGCe5MyVWpAsMlIvKmZs4TmSw+TJqHbHif0w305o1qbd9EP2rcsXa/NFYqPjK+aIc5SnydFqMHw5n7eDhCWtiJJZS9mgi+oCMHc/JNMwqg3B5lCtPMDs3d+DrMzBNTFEeyzzaJZbXSQD7zxq7TcYEdyOoOhy/yUl6CHQUSpN9SzrzKxI11y1yWiLXXIPNbZT2atXtjTJZyxvpNR1ukKbVNf01XTbj3OXvqfeDsdun8UwaStV+ViHRkH7FXYMChEMRmAI4cBDqYq8CInDSEQTgaxNjDmM0VbKDl7ELF6d3Et7k3tO/Wv8TQ0AhHwx+Df44sIktkUA9L9YeujJnWJ037/4bYo/WevdlEYtb+ajjy8Stvc6cAxgrWEKWktFpbXHyJt67wdZMuNJS/zbJmEeGAJKjrYsJ4ed4LvrkvYerxyZ//aw8chf/kn0KotDf2sgrrlVcmS01j30HX1peN9//uIiu+EvvP6bJ7903I2ykR/3yRM352bPdI7JvXniy+ZcempbFHHesGOR91+ISGY4wQkBf9Ey9VZu6Rshbc4eJv3m1jgZjXWacdepwXKpY5tovwB1BfNb3CghrTQpMDIluglTgE9WkWa7JoggnFS6KZTWjCoQZYm30wxtY5y4xdgxcd36MXU+kgh6wdBJH+oZeN2Z2vOvNRe3tp7/BfNbAJb/wedDVw/rGyQ8aofkxfwJP8c1EBRIIcAxggRsOb8ZUKRcZes8XZeFQNM6IJxggAJbSFEgQAaGAhozsChQ4Co0XIxxEm2MsYctWrP3gwYrTKQD1GwfCilqcNAHwwgOMlCsyxgUwDGkOQWnGYS4jnJ+bpWhSfdCqPUY7hkAn0BZlu1Bq2MoZIkTfH9ShbG0XMvSHMa+gVKB28e+pdqpSYwcoeT63ZrEzu/L7R2Z2bvVB+ll+DkA/wxlZwzay/BmwytB1tNf/MI5AH8fbwd0B8UZU37PQc7IFODZ73mMsd/bdCp1L7BfAp5aBUxbs0xTG9POHNNSBUUJ0PoH9p3yBAEgAaV71CQNavKGpe48qHBAaDh5n7+enHcfJehYBbAG6OPB7CCrLg6XpdJNXQifERGuNGarVqtW3XdZLGWFAq3IUmZghSCojS2aR3J2s//M13Y+99RnR09/8Qv/7tH5la/8x+ff89WdfLyx2msfe21jLbi2u9W/f2n5YRByKimKl0PXucoZOwUgirPMMEodSoiLkpO4hP3uH2WtRhtopQlnjHieIxgVBTN6qAzZFJzP1aKoqoxeM9Y2KcCrRFSOzMzBpUx109FtQtmmtPqbiZLfPlxtVCLHc0dFtghtar14vHR5bzu/ubX1KvVQV4Ke7FO5Viy0TqzHg/Q9J07ecUNxIYL/xtIj5/vB538zq2Z555tnTu/Vlv6R8/wjP8O+NbPrv7S80q5m2XNLg8GQADdIOUGsayBShAQ2zhJGrIaxoxzwlDZVkheEZjkn+y28OABL9oU5HsqgIFFONkHfgPUNdEhgCcEc9rupCGVBKLnXn3kKIHenY4oBmQA2JtnDugU8L08cj1jrUBQTZe/rAF4DQZUQ2nIErwlCKnQiwmhqOfKtnY5DoKQWZNqiVZjymlFyL5udJ0CkAC5KAjgBhXXK5+8RphhgeSnYAAOw2pzDyPFIlKcQAEagRMJiDOi9qGZj16GXT59my+0dFVhpDYHpRXV2d2GFBnlsvULqUVghnlbYCyI73+vkI99XMvIsLDFQ2gWBnWtv87hQBJ5n39Z2QavSuJUzglLl+XZQmKaAUgC1FMrQ3A9IOtviCDwHgnMIWrMuiWxSuJVYu2lNuHCYANM5YtPWie2zCGNQOgOlA6aMX2N94dPEbdI+q7CeFSIeB9w4PtMqMEgUA3GZZpowHria8HICJOAAtdJSrWi6lUJnICxioD5BfEvHNrHK9UhfM9ZlDm6zQFxmnK0/FL+5Ewabs8/h9EyRkjjdyN+d3Va2s0s3d69U7nr3VZNIjGfDakGjh0kmDVXj17UKW6ZQKeTmsyjyNtGiCpauWa6GYNExOHEHlBlFiDE6Kew4EfB7LxM6+jb3EVln6XDiqe1c3XzB15I4ccG46a2Jq/3n9fCQyZ3m8/lfH8v0u8Uou3H2t0cyduh6+4Sz0x+bOwVnhQrYlRHkNwesuOBU6KW8b7NsB9+acWkyv4eoPsZqJvAt7YHJ1G6ne6bNXOpRY2gxkIfzjDjCJ5w7ZC2Yo59H2cx+dzIUPQDyy7hMn8R99umnL9YBnJj/GGpE4oNLWXjsVjpceX7Q+eDtDmldHyk/hXnID5KzkuQtk9tKDPgSqHShmLKSSMDnXHpUctMxqRLGsiZz+nO+lxxzqnMXkq4aan25rYrhV3cH3+0W9tLwTv6NK8H8StakK89cDUZUj3rvO7d9g171R/4PDla6R+XS7kPivpPv31hl2nDiZEuKp0WWhDYfuZyTmOqdGUGrHuO1TDvHO1YaQ7MoJfaBkauoZd2uBxJwGsHVOpdFQd3CE7NGECKsYdwhodv3dGCVoSlgxj6zQtk8ZwSKWDKbG7UXuabdqNjKOFagjHtKKWmYVob0RwGn7WbVFVKtfeja+qIFtgZ/5nXG46pX/6//F+fNX/yDB0eEftou6Y95OpmvreZFx1SvYIgWCObg5BpjUJSOlBaKargkBCHCjrhBj1PsIccIGXqQkzqHRgKCLihiSIwRYgwf+WQ+EaSBgnqSERcxIqQIwQDUEKJAAYtnwZEAGEFpP0xzHhXyaua7XcIwgMUHAFRbSK8eharugGmAvhdAE3mRIi+OgBoPoBRCqBKoKgFKDQgr4DhAuah4YBpJUCaVptZsLoD+5z75wLRM/H23vVMze4cA/G0AK6N0fLs/7Ivl+cMM+6WxaabpoOfSQWXj1OR4Cv6mlhcH5dNTU+AM5cpgiP3S7Hiyj0I5GU+JnNNjTHlqFpNBgP2s4FSFNkJZqggm+9YAfdhY4QLMLa3U7nG4OIBPYV8BFKHMXN4GHKLGlWu1un9WwqNDcIeAUM4oNyDN3TixAIYGcGJqhGGwioCA4AMDrW+wH3miPSO5/2P22NbPHno0OD1//NR/80NPbpJ/9nfuAFjYHA6q6/3ul0+05l6brVZlUuQndof97dDxKoDlpPQ1GmE/Q3mQI0koo9ZxHBAQowECbYRxxFwepx+RUNrCbCtYnwOocz8PHCciSmaaUjtSMvZB/6yX5wZARWp9O1fSAUC5NGfIILn+5YuvbQLALz70I4tG6dOmMDfz7eEHLm903yQ//dMvAJOrBuBX/v5XMwD1tH3hEfRe86v1+y+jvFnZo7/2fyp8+hNXUGaK1wywte35PzQiTHiht3mo7mlk8mze7lUYYAgljAnOkJeCWgtoUKphjAKgMw1uDZqcgQg66ZZiYUh57RYm13IEoJ9q1LQFF6VZBaclaJza1xz0fpx6Lq4DmCUAY0DNKTNtq5Mx6noEPTC8hv3sXBNl6UGitPYZTsblBoAtZTG0Guc0hcNpKTBRQLMnfBXI1PMB4tK3Z9HGAGLioGkL4kwyZ4oQDPwqLLXoa4nZ8RCGUqTMgbIQg6hut6tNoxVlWmoOEKqZ0KfWb6m4VnOGVNhLy0ewXl3UC902CQuplgZd0m3MsNhyDgsGWEApZZUqnfqVJBDOVPBk4XoW1nAQek+YQayhFsSCkMkizwLWGHhuuRAr9yUwKNuCMa2LpUB356yxgWCglCHNrZ90BULXDfJx3ndqA2Z0wHRuPG/Ma3rEmV9oIZALYtqWGAewSgjSDa1asAQ0cAoriLVQZcpRwyLtW+l5hDPB8nhMKXxFTEG1G9tNeYvcrKw03GFLVeOWJxMZhr6Xnczn8Oyt4twPVDiawSBp9Wt0cPY/SqqiP166eMHvJXeSw53XmKAd3qlXVK9/wZxIEu6YF2XXLeDXjsI/XQ+6q1cyvz8oonCFirhHtFzPtc0NqX+YW23EXOf3LNCDBagfr2ZkZ8QL2nAkrboiG8C7+1c0PXPHp4/POOc7/s72hW+OT9dmhll0hl4r/Fp+45uj2Re+YT527Kc8qwt2k960COftR4oRboWH7ZcJtcuHFnn/069UPk9HxXCviC+u/wgZGEN9C9uCwCPKmCtsp7/oGObLWkC1ZJJ79OLu8+bSn75fMQDs71lnBsCnzQhXRn/Onc9c/vd3TrPwvhs6/uHRHcRuzk42GcfVdNwcQa9s5XJPAy5A5jYTHY+l9vPSvodh4uWzA02blDkMHiqEFidoRKTRSUjd+QebjIlM6joRc8+qHW81Sf7CAKMlvd5udMcPXn81fGjx0cIzavHlC7/6D9ThX7n09foHX1T9oli8xYM79Bprj8JhMXN+PYCIO+PX5voU7hwNbOQ+qIWDWkFnfFbwXu4EKuC1KrpObLnUhHQZbcjc+kkAR0W1ISmstDrPil2aW8MY8QglFkxKqQGncBhhUqt+KFRc8bS2muaxpNQoc2uxztzYMyd3BzoDdM8GbCEf68FMsBHleaPRTz6wXQsuGEpfwgIeZCfNp77WOJo8sN1uriJaGLzh3HVb2eXR2LvTupDsFC32PCJ1fNTDExZqABlWoEgdAGBQTKR7HHriH5qiiWxi0wJoDEFAoVEHRQaKAgwMASwsInAM4WENBTQKKHA0MeZNGOWgB44dlEbIQ6byrxCuG7EI2gBE6xDuS8bQcRdxD05qoXsAm5nEvyYIfQicufCImSRCJ7iAJgAZgtFpk4P3oaRizaPMPL82OQZFWRrukX/yxfOTefCq/ZWn9vB9tL1Twd4GgJ8HsHTh2mV24ebltZ//8c8erJUfxT6JnBx4THlImPxdYF9WPZ1I88lzTZQXsYr92nwb+wbLNbyd9zctAR/MFk79zyK8XXEWYb88XNnfnxM4NodlADGdCc9gyjNawj6fJZw8mgAuFWmYdG6zbw2Vfh+AqrJG32zvTk+OALoBIj3DOAX4FKhWxg6qxmKRG3Jo8fzj0fXmMRkMN75bTXpd++W/LMiTTxgAtbu97tqJ1tytpaj2/t3x8JO5kr4jhBGMTdXGUwm7jxLA7JuZApyycjI1xlANrZkl2g3ckDImORDMujXUQ2/sMdHOChmNdD4mio2VQToTVXNQSm7trsvb/Z7xq7UNQukGCNzTIrz5uR/+sXHDD2eXopp/fX1jScA++t77z5nhOO6TJ58gv/RDP4bPffLHKYDaTxwR3u/faszu9tc+SMxg86xf//ovffzHJYD6v/nd3236998ffOrixTM+kMaO83rqOL12pRYm9dp1xxZbVZ7Osl6vzjWs06gDaSptXpTK7kqUoRJyu72XG21iGLjGwrX73VfiOrunZB0qYJQBx0TpipgrA0UpsgIIKTAgwO2gBGsKJbaiKPmZXZTZtqEogemknI9gMkaOAujerjb5jeZc+Njq1X4NVqMkrC+jBHork/GzCmCeE2wohholWJkcR3FAzMqU0+8pyU83AcC1aiodLl11rcWjGzeQANDcRZ5lkJ4HFxTEFDjU2SFUS9YaD7A6d4jN3L2K1njELy0eg9QG/SiA5T6rxWNFmJCMggyDUAbWVpxex/hcYBBWNMDcvcasAiEUwiGYquutKadqyvbLttbYetKBZA7GTkggKMAcoMgoVIGy3DsBsnlKnXSEghgHXgXGoQBlEoWMoQl8V6k5u62DrexoWMlqaSAY9xMaWWkCUljLQEDRFS7lWQ7CGXG4gxa1ELkCKSSIgSWUEFgGay2skRyxgaa+z6MKjZUmw/azecATOtAclbt/JFe2mdY4m7xeua9Y43awfGn9yIP2XBD6ujMWLUEcXZ1LEs2WaXusEsyO/ni4bMY55Mik299BJeuhIIu+YsYmCIDRXWLslcIZd3RknYzrPNTpLWiMKQGY6b3KCQ1A0CMov1udH4ra4/6oYkY9fzVcgtPZQzvZBjkR0kYrMHRvz7tTmzmqnLMzVy5/Qs3seJXLFz7Z5f0WSLRU9Cuz8pWffuSZb32j/fgDo/Gs+9ov2zmdwH76CTrzV/31YOU8sW/tJh9M/fF43Gih/lj1t7wKPsQFOaypaGgGpYz283XLQVD59x++R4HoffXJwmk9gtOHtysFuy4eHXSKZqqzGQNEyU1cT6APZRjMpoDkgKPKBU8LQNApmA/AYQDV+3GbGID0jMYb6ahogYpZFtJlN8BQS+937uy5p/3a4FOtWXbdpo8+Zzrjq/HoTgHn6g47crTT856ZDw6rpY9G6+52LfX8k6uf/NovJ1//1491WnvqJxdJyql210x3thPvVdr+ETW2h0YU2UxN7wgox1oeJcKlkc0GrhTxjOO7e7IYJ1TEPshCbkgzIeNrOduJHC0p3JO2B2abSo8yjG3CJ7weFRXaiQnYVj2kS71hTkBTWNDDQ83663u6Xw1DShijrnCKihhvLNZMfZwMqh3Z22rV67v1sPc//d439Z+ePLG5WUREeHo2Pl/phAO58dgbW9GzN4+cbbHixNnm9sJrrUPvDjPz0Gsy9AFioCZ8eR90woifzsFlTMnuFQgUBDg0DDg4GAooZKiDIAHBHuZgoXEaPlxQGEhk6CLFTbWLLqoFYMwZOF4XQOoXSS3MR6NxWH0YAPp7qKEU8wTain7HiCoYcpQl3FOlpx+bejaScg4jGTRxQUgDFM4kBg9QVvkCAD9AjOmEMlkYu1EPJQAcoey2YQEcIf/ki18F0LW/8tT3hQffO7KM2/CAhoeNhocr7/rHf/vKC1de3/xf/85nyzY8Tz5BUWbB3od9ZSiw3yVjqv6aiAzvlWGH2O/PN+XWTRWUFvv+Z9NSLEUJcA6qe6fqnunxD5aRp6rB6f59lINm2talACENEBbCSgfaEFBrQdhBwE1QrhqmZekIpSLLz43dAvAw9smlrgUIIwgsrAAMKxNGXE4+y54BXjhciNWCmD1SD01j5X2d7774f2V/+dJztS996Y+GryVxCGButdsef/1f/mpy8fLlkw7jpzTsKqP0+OS99ORngX1l89Q/8KCfGSGEgDFGCKOCMuoSQnq+8AShNGKU0lGeFrFVZm80Jr0ifq7qBTceOXw0A4izu731/N7G2mrYnLlCGds1nO78V7/wT7NvvPziHLX4gWpGlo9UZrLH73+g/tCZUx84fXTlwThLtv7Gfe/+yeXGzPsAoDveOXJh58Uhdi8sqdH2dXH722s/8NgH0YlH516/eeWp9u72T24Wma0SwrZarcqJNDE4deSm26zf5ocP3eommVvZ2pl3KDFKMN51fZoxprw8p8RoizCgyDKdKBOnFtLlKNxSNT01jE1QZuWmaqDDBggNUCgCKQT0TOMAACAASURBVADHAvkY2FZAzwNmKECNMYvW2ogQIlEGm6IoitNaa57n+bZSal4IMbUBSMeUXvuFp/7RRzerjYcX765W5mSmWPm60WTczdwbc0BACc5zijlG7nFUKQDOAE73Va1TJS4wucg+LEhZ7yDTaGbKsi6ELEwuJbWOh9AqVAFVM4o2kjGINebakZNEWImBJ+yw3oKXpXpUrbJY+GiMB9KTiaXGkj03UNv1JjvUbVtPyyKnjGoQAyEsykUEhdEElOXI0wJKKjA2zdZpAJxrCcmE0dIayAJ83KWGcEBrQOUArEKhKIoURuUQpARlAIOS2hLhCipjdnRwE8TadNdf6rhJLg2IZoG0C2Qvj32RcZj+TDIaEZcvUEupgFKEUyo1BCzArSGUUDjKQo1hLTEFDWzHKst0Stuizt/grthla4ntvkGu9WcP6fZeeLx/xxStqnSdunNWDLveTlE7V68UtepeMZB9e01qR3RUA2t35k4wkifFrSzsv6mV6lqPAvPBIcKiWWKOejkPfKi1FyhGPRNqWAZtiMp4gbFm0848JqO+Hh3kRVJnOBZUwiUYsG1RxVrrEWwwgbkdKvu7x3ZfuN3tmqwaqtaZVe/i10YrV/7Yj2d1vKAqnFfexa499pnhS3/3yBc+iCujR772pVMv5GdWsubKuNl59q1qQbqfmKuyd21c4HNbL8brN15yXj326WAU3B6fVIP82N4bOHLrW9SjFS7iVeD6v8Vb+R52wcBNAm94Dae3n8epuxeLY+12/q6NjXxuT9tlW8bnRQDzqgR3FbNPs5nGXoKSU0o5YNS9xQ2FBUUCkC4slTaHoIwOtSSXbeZ1VMLe1QDZGcv5q+Mkoja/8alKvXhi5pFGV8OuboH62Y1N8+B2q3vf7NLP/uSFlL/10TknGX3YfJsE2782++yymO9nhieVNEK822jlyml5nUVT7Z+1ZNEKPTukgau10UGhajGFJSTwWxZzkuZasX5W0YJ6ReAM4M/pjGViPFSKwU9YQgUooVYaC8JD2pBGWS1H662azRzWrmdy58LJpWI3ClVY5FQKOs590dWU0kpa3FcdpwuXl+cGyhEXj6z1q3nK6qxCitnd8dLKrf7qQ7c7z14+3GLt3O+6Qi63hXdkXLim6nG7ORYWgT0EQggIZSjAYe7NA1Nrrmnio0ySGFhw5JNahpqw4RVCjFGHwgjrMFgAh48COSzaKLCHEBoin4NVHJZu0kLOKHSOjxx2HE79FADfKMRG4RiAh6FxFAYRCBiIeRAg8+U5EAcgbBLKchgzQJo3YZQPIUpPXIsIBDsAbECEXRxs7pzfutLqBbUoF960KUJ9Enem7Vmjp5+5mD79zEXvc598IMU7eHtHgr2D2+ee+qz93FOfvUecevqLXyAAPoqytj69macA7KAH3NSIc5pGnporh5P/30XJgZq2oJp2QGAoy3AL2Ad5U4A1bVYfT/adOrlr7LcGmyoZp4Cw7N1Yvq9X7mc5iHVBhAdCpp6AU9DoTB7rk9dVJ5+hijLTF2AfbLkrszPEFy5giVGaksVmLXv8+Mkrph1fXvLd7BNnzlaqVNzsDm+d6Nz5+k6N0QyA/c3f/N3x01/8Qu5zsXh+4VB95+56O9dq02HsQiqLN2y5gpnDfkn62uQcpmrNg4T47yXSTzmPCYyuxCr3ldGjfpGZYZ52NWxhAb8VVba1MWuDPFXMD16+e/3yxq/8j/+8+Of/xf9sf+O//Kf2uxdePdEejw73hsPgjZfeqg3j+PYn3//elULKz4zS9EMrtebPHq41HldKFYyS3/39mzeL1WH8k+nm2j+k7e4HCcX4O6+/bGtR9Ol4NPxbu7eunqiMRvUlxi8mYdQ+1O0u6LQ4bW9vPLy31XnvhuJVo3TtjhduyEwOxiDNJMt5pCUrjB0rY4lRKqLWCgcggmKPEnQnn3kXwAUDyBg4oQFlgKopAV7XA3b9MstrLJBwgHBjKAUQJwkxWpuCijlKQChBnGVZYIyWhBJQSj3O+SpKsDfbpoKtNec+dGiwx85urIp5Lafjfx6lIm0X+3zRBOWEOBWP9LDPJ51iuP8viww9Aa5k0hfLGoAwSqkjhKkRTNW4zADGAUhY5JgbDsxKe5eQIiNRkem+8LXggi/vrCF1AnLj6Cm351e4oJZSQ2xNF0wRxurp2BauRwrX5TBWQGaWZZlc6OzSzPOMoTwHFy60JMgLCqNRuKHW3KEwiqFI6Vy8izDvaQmitcOZzXJLttsETBGXANV8iMTxARDiqpgZYkljvKPmiw7pOAui4zZveaH9k6geXzLSuxTFclNHomjKHnXztJp7XoOj4ERrDcqossRxC2NtoUg61qAFHQ02lLEKfeZgJ7mDO9ku+St3ln99jywNczPzujMyte65Y8dHs5GlPzgz8Jarwgbezk6n2kkKp9mOlrP+N4o9c0dvrnejBbExPhQN0FKLLtcuqumg0POLHl9sguCoKMiCEGPN/LtvWphdVKHB4RILwwkKKcukl2fBGYO5V4VA6WEuKSwVIA6HRgNeEYqQuQUHH95EY+c68a0k2N6gZu/y8Njdt8ITW9eieke7x+Kc10XTvZwm7vajM6+feeutE+9+/mvLd02V3xfW0qP915IHr92asUmc3cwy9uyFF2a+dP58vrrwKe+H0++kD2w8r3Ta8BdGO8wllrD4LuytP0V4/Em0Wo9gJrmGdhHjMQAVldlT4zQ7n2lTs2BTPvUM9rsgTBfz9EAcNQC0BKz6DzslAACpTG6KHZMTYmEKaNIHeDXjfpWGwXo2opxIX4nkUD10G2eafO1y8Ea/zddml9wjH27FC37VkMUPDVrHtoaHL4vNtW+iq4+uPs+jNAv/4piaO2M2wpV+mCjRY07OjKOr0iIojDK5sV3W1VqmGBvX5xHVQ8qKbkqUliSQEr6fdYe6qqHjkX+imOM0R584zKgq55pYYVNmiEGfUJ9C83EUsKTitjeadWd1qSFj3/MooaRT8ZyuKzyhrQAs2vVQUGDxTr0yx3r01s2TjefmRzHxUv3Sr3fetf0qW6p9SK+vjcZiKYNzCAyjVa+yLmMbCl/NG5AZJEwc+E6/N+Ey/b5LqKcn85ZB2R0lgY8FjOGDIkYTe2Agk3WyhESAO5iDD05HoN7lWsKuGM3fZVhxBNS8DFE7ASAA7AggTZTMiSYIZsH08ciVYaFNDjCLfbu1HMAtENKHtnMt5XFFYbSE5Ao9w1GtJLny8izt+z6TTBTtsHnUUjp7YKzdKUMhEpQCxgaAlaefuXjjndxp4x0P9r53+9xTn7VPf/ELMcos1yG83W9rCsimfnvTL34KuDyUAG4TJe8vxX4PWw8liDvYz3QKvqaAbpqi3kYJaKa2LVNhhZ4cT2DfEmaqbJ2COQnCChAOEDJtSjDl/00BJ7BvcTIFlNNsTQMHrDG0NqrQatvj7kBq3Tkz2+q/59jRasToDUjVu+/8sa3P/ugTL73+yhvtRcf1jbWLPaXEZz//G2cMcD8HeffZ+aXRkebM9n/7n/1c+uff+dbQTrp8oFQjTR3vp2lwTD731Ndweu7ke35aAJkuVaxioVIXFtbLlDyE8vg1o43whXP9sZUTl8/NH9r5mZ/7ufzTH/oJH4QcffXNy/nsicUHd0f9hzUhVlS8lgn41TdfurzVqtY+Wq+GM8oahFzwq3du/s5///l/9df/+Mn/JNOG17/VL3709fkHWqTI3mrpJD68sPQ+bskHXJCw6LYP71J+dEQwOJMkTteSBWrtPLWIB15g9/zKg92wsnwzaswWxnq1IuMs8Ej39AmncITLOz1KAZcJKjkhsbT2VgHsMSAiQGzL8u2phKE3Js5oSISNrOpLDTUo8puqkL0BeJwVcp0kwy64Mz8kglrKihGhMSW45RLMcM5vCCE8KhwtGB+SElT2AKSwurK4tXF2ZWedLGfjqy5wo6/BU4uKT+Gh7BG5i7KsO4MyQzwNdNPxCbxd3HDw+h3cNJ9YPExuJCIAGwNGEcoCgEyIh4SURFTLANLKM9vmQl87cpp5UhM3z0lhjLp6+CjalQaNkjFmeh1bOB5NPMF3anW6U2tK7bp0JmnTBMxoIQiRiaaU06goeFKpGeN6FDLVyFIOmUgwQWAMwJgJx7spyxOTC8e6uhCVbGRbWd/weKwWNteocblB6BvGiEnr9QTMzQ1TBVRmYF2x6TQGA39uFMli64Hda0eMLarBbELe+8qNXEq/grCoq6rwcuEmynKnNhprJ1V54rpAn1PdpkXvunoz34u/GrT0HW/O+0sR8meTPPju2re8zfXnxAXy2AopdtNK5lfOVL34NFGkfnRxi93HL6Xera3netVFr37aO1G0qlezrh4stfSHVEJqwcWeo8fKDIrItTSgflVkSzWMd2/rza2hU4yvm9p4yIT1Xe4EBdEhk5jzOGJlIVnZjCGiO+RQFEEXFnlqy8QuDKBHANPQzADKmlHB49vKy2+J1KamvvSuDHIgk97tyu7eXsWNE7/iVjLfFkGuEnFjeAXJxvOs+erGuZtXr82s3nqr0REbO+PRVX11MA5JoStibbNya401nqsuFe9ZqXce2gmrx7Y3vdHNb4tbnUu0mvWoMQp07Q9A4xvwnXk0ajXWXN6pvb+T5vOS4DAkOQbYAKAUoAc7cUzHbQ5A1wC/BpLG+89P2xNOx/lBUAiOexwf60Dbauljxw0o38szuoPc70A6Ub3I6677vrt5tpLVkvrm6ongVuJmb7TT1Y+7rvOpwslX4t0v/63d2e4nebdF4L7rSuGennlYrLYafl0f3Wu5c7ejgc0IYaIgHnHiXHKW+457pz7iM8xhqiL0KlWF3x5WFiq6iqaT+SYfNCuMD3PPusTPxyBuA4KnTeJZSgzL9Kbr0DjkrFMJenVprqdH+Veky2a7TsBqSTZ0jbGrc/VevxJEhlk2ZJxZz6nQQlf7tcDfPFV/Nqm5F5585ea13/3I/UlwUdXm3Pyw/5nAYTpfIzdwaUDcO52Hw9dQsGVzh0rk/MgkUzbtKDU1Y6dlf7gC5RqQAPfmTssBQu8RozIYpCBg6CECD0/DSh/baGCEWeyAQ8LSO9yIOW6Dzdy188bzBUTNLecn042Q3VfAHgdYBRQV0NJipdDKBXgI0BqmSSGNDAaFm+ehl8k8NA7JHAgDdF3gLtFFrRmnc4bQQ8PAdwdBrWcJGpDFGTA+i3060xQzeCgTTw8C6D79zMXkc598YJoMekdt33dgDwCe/uIXZgF8BmWGYurlNL2xp+VWjv3M3PS5DPucvKnadhUlR3CAMnu1iX2w56Esj1UPHGe6mpwESki8vXXW9P2n/L7pOUxLvFMLmIM/75WksM8TnPL5OMrGzBFKoDddLVkAY6n19YbvvdqJky8ra1KaFdGS79+91tlcp67Hd3q7ndfe+uvGrKoFFOx0W2Yf3ZKFv66KMwB+bDaM3nOkOfPSUr1x+aPvecx+4+UXBUrJ+jHs9wgeFXm+aoytM8Ya2HewB/7Dcq7CpJ6MfVsSmuW5jFWRT/YXAAJp9IxUcscVzncfWlrZRbNin3v1Za8yVztNW0Ev4aaXG/0YIeTHQcnxY3PzEBU/6LS7//vJxcX5ehDNyDz/yhe+8qVf+le/9htZOEx0FFZXP9fzfqgXzARbtcX/4djai88T4XQ47GcY5fxOPMQNmUfbBPZUrfpMh/LnKWN3DqXjFxNG52LOF7W1ReZ6wZgLWjg+3W60sGcIdboDEqal9p8uzDJDSEXnhbLAYQLcZsB3NDDSLm3HNW9jPXddQaxuaXlxW0MOtOb9QmVD5u41GBqiyHf2sjy2wvGqjui4BN+JCLYJweuEkJ4CuX/dYFZbrAYUjckYKQhwf13lrUWZS69cZSZjC8GAhkfRm4yzFkqO31T9e3DxMu0viQPXDpPn7ylGJgPtXml3uv/kIrPJDZRzgBrAeCW3j+yGFbQrNdsYDZDl2i6M+sQtMnp9+biGcMjR3Q059nxTTWJiHc62ag3Sr84AjBnlOKKWjUl1PEbfCRVxXG2YQ0e+J43gFnkGmqecwQhDhPZMohyZcWkpgmKIejGOezw0qVdxKnEnjmyeurk2TRPbbmt2NKo0h5qaoeb+GCJQtFDZ4e0tObu1FmlljfTcwfHtbc2NOcFkcYL6sGDkZqp9RiK7k1Nc1WNyyI0QwpBitGbi7tDZGtNaFsfBnWHbPLv2XPVSmKT/94fP3vjTD/Kvr3/R/vzS4MipUD56KkRuFynMUe2E5w6p3Xq92hO1mU7SqnRvbHhHzvY77klzV5s9NhcnPf+0sWbWHA511pip9SqBA09Y12Vx5gsqx0TuVmqRGZE6Lg4sLAgCl6hUE3K0ioZjYMcKZtHnIMpiVrjYlQxxbmGtBijcGWZ1LrgrqG1FJk0Lyi3cKiBEGW+sbx1dG+2IU0Y6xxCyo9CmZTXhDTiQBjeMxUlonN7dCR/duVKrw7Ajee6eLhL/cUs9wSpU2ZRmYtb8p3yBfKyzUdg7f+Go/su2N+zxfvct2s4vFrd2XkEj36AVKESj2wh3XrEuGVC3V8ihSbFQxhKmJvS7g5WYg+NXnKEBdZkQHSOni+KD7gvTLPaEH221hAIBIwDyw8wT1jKWQZO+VdiGtAaW+IKKhYZcvja0tp3TdRd29pgXzZ2vVI9URp0V+NczmlQvfa1YZu/j+afeO/+DD98385Hdl2O5dfaMq3gzDPPZvdnmqb4ZFPMOcSIfvuRSas1QGOYbQRZi0LHNixSx8rOE+HAkyx3hW/dQELiOLfJhCOxqR9shZx1Xmp1AyzDUrO1FpjFKVaBN53gvfu5jH7x8PV7wGjdUaxTE6sWdRoV2G9UbBPYbw8Bno1owbyjhgbF5XgmUFWwLwBvfOXuY15L85D+89UrlsXODLH/6WP/qsZnLb/5BRYwyn2EHPnr4G5C0VZZF74kgp/PZJF5kFpB5GV/4RCBpVSS0sMZKY6mL0nZKogoPKUJ2GFEQgWVDdNDGOirogJsmrE6I1oqqwZlCGArLLkHjUTDMAliyMKEBFQCr415ShXBAOAAVgCVQOUFRdKFojLxww3Rsq7kRKStGY9/pWUFesFa9utgbHxt5jk4IY9FoXKPKVN00fbiArAGkDsantllTU+ZZlDzqUyg9deeefubi6+9EP753qkDj/3UjTz4xFVccBEjTDNO0jHuvA8Dkb4NyEMxhH5HvYd9PZ6qqHaO8YNPFnjN5rxHe3tNxOmm6k/fuYN/UdZpZdLFfZp6eA8F+H9+pr9/B7CG+Z/9pgPJRZmruTM49BCB9wb/xgTMn6enDS5d/+9lvXxbAexeFu9zfuBNWvEHuSrvkJnjc8VmjWg1zIt1sJ43NAmdHDlF6eccYdyGo1N5/5NRDFzbXvk6efCK2X/7L4ulf+9XvoiwHdircOZerYjNJ5azjO9OWctMAOjVMPUjytwAK+3YzVZ7DjH2QFCB7KUwLJVgQ/SJjC5VaE8DO07/2q8nc6aWHsmFyxgm9k9IaD4BntA5UXojuYFCZa7Vu3NLtSzOP3f8TxaU7jyvHOK+/tJYAAE4uyf/up35uRH/o731cuRU+eOo9I+AHsPvyW5u7ve4ffvuV56PecBDu5sVi6JOduz/9ZOcrf/iVxYfb/c1XXXbt8WT8+rHR4K3dIKJZPPz4mhueSglZLpT1mCxUIZVsC48GMhs3291UaB1qwEqQQQJ6N/O8ZVrI0428eHUuSeobiRO7TNYc395d5iS3zHEyx7nMKdYrlIUDUjuW5mp+gaLrwc6MpD7fB1lvuswtgIICqgKoiOLk5Lu9DiBzynLwNIsNAGmLYc9YNCfjagUlSJ/eI9NJzz9wjQ7eP9PxpgGIKfmQAtrbB3sHM94m2r+2BID1J2p4DcCLR8TPUh3CsvOjPSUBB+Dm8O6ueHT1mr0+u2QTL6TGcensoG2Octekrg9LqM25I2+Hs/BEjprMnFp3kI+DyHJr6W6jHnMVB9oSTQnJWATLYtKPkr16NdkV1iAbBhGhhDJLWJ5VGm0v72x3FufqvaTCHYdrn2cF8V2Taz8wWdZwi2J43/pdPj/oFn9+/1Ks/MrM5cNBnVjDF/q7Iu56516er7AaG7zZ8JPnuv92+/VD53p7boN8OPE8feM1enjzq9JVdO8Zfry5E/OV6/6cs33KlY8Ob82+7z0Pf/uNB+l3N19lj32UK7tSH3Xuy4U4YQISd53qtWXv+ulxd8bZ9Gg0L+4uXJ45HuW549obvcZplY8aUa5fayxQ02Ay7Ay82PosiY0HSoJB2nN5gxbzpxzZK0bDpFFdILMBs5siJ4nivbVYghGGBixiCOwoipG1gFPgEByMCMmP13OSKQT9PosqlncV1SbDENq6gHQAR41uBnk5eaKJGC7gSGOcTrschx8BkEGDoT8VRuR8MpQ4ClrVXSkAeya/A6p6Km33GkOAOJSRY0bjXYCNI2iW5TaAYENIREjADTCzimSMKlIQWKSQ2K/OAPsxnx/4m1w0iSYGwu6P9ekCfApMMLknsvK11lhIDQhyVWcHRXgALElBmAhNrd8Y2zC0pKX4Ue/42OyuavTaenTkyJstcnJ39peeRfeQ964P3e9XP/lKoJof460//hGckO3LM/e/5nYaMm2Nlr55eNtT4qzIJCTTvlMzhTicFKg5IYKMaWecwQsoa6NpO9qqYWFJNdUjNaCew2rzI0+hXdiqEyNsOSaPhOGvKrPEBjnLMRCaJJvVcOWr2x+9Xq2b1tFkFO564cVrR+bWubLe+dXembW5SrsXRdsFpV6XYNOCXofEtyCQAMAnLtzamR2nNfp6vvnVrwu2ec1UwLEDhSMY4OFJeDiNsruFgZ3MBQIEPoAh5IQlMmm1fU8oyUGIseUUl8LBmB6y1uTKwAhXZyC9W+jAR4EQNWznzG1ls4xzm/jiZuoygNAhcrwbHMvQCMGIK+/hrgObVmXxXngAkYCyCqpIAFmFLoqhoGxYDVNwMQSjDICjBD+61axwqWWVSBlpRwgCs2AYJ+C0DcYPtlqddvCaR5bVQNGA4zWw3/3lHbe9I0/q/2fzUAKRLZST2pQnB+xnLaYCi4PPTdW5ZbOWEqC1UYKoa9i3q2ignFQnqxIcmRwvQRksmtjPuk05eTMogWIx+X+ASWN77K8A9lWF5eum/5+Wmr83EOWTfYcALk1+vzn5TGkjDK74rvPWja2dH76+teuenp89o2VROxnWRDUvTiE254gGAsaA3CvSohjmSKMAzKOgtfcElc2bWdYvkrj6Wy986/1XuzunAfwmgGdRGkf+OoA81epDCjgbVCNOKEa2DPAHzaqBt4O9ael6Cgh7k+d3OOWrgeeKNBl/O0+yI5Zg5Pne7//5ldeaf/DGC49ppQdFnC9aY9+jlazksaEgtlXAdhSxlV6RpTuXrv3pv/zlX0wB4P/4vT9cu72zfay62Pz0j/7gUy+pXHaaK7MP/83X/5DMnz70KnAGADBXb6zP1Ru/dP+xE1vkO19l5wrxkWautzud/t0TvaFdkur+50KfvcjZd/63vdG/axVpc9MN2dj1byKX50yePFJLRrFLadyJIhsN5G6Xu80ZneUMuhdTMXKNnOe9ditw3QWHUsEZGXTS4vevS7UyK3heE5gdJ8llbbBXi8JzAIqIs9kVynyPYscaI6yx1FhLJNh83yfLXmpXmsQUWmlqOR8QQs6jBGxN7BtwTz31BCVIJ/+LsA/kplSAg9drym86yGGa3i/W33/hQfHTwc0WJbBjmDSknLyf8soyrptqxdtAEXHBE8c3PInNTDoUQTK2hjIxl/T5kHDDiLJxxaXS2qKipD61dVd3K3Wy05gRUudUUw2Zpfmo3vQhnEARLnieOnOjTtwnlXgsQkv8YG0mGcyNXSfLvGhYz7vCOAw0YJtbZilcGrXDQngmgUPZmHiqGhV2NBK0E6u86oxuBXWzmlmvH9U9wBITRYCSdgMtBSeogpBTQ9hekVX5+Efvs4fsN389oeq7nTv8cUt2uYF2ie/a2gPetfF6+PLyx9nxN+TZTw6rD1T+aO9TR4qhd5Tp0Yd9GXcSzoJEuLWK7dkZtUf8NvHnt19J3Ln4/uAoRmI7HSVh5JA7A50nIyFP+xVApKgGzBRZCjg6d3nF5JqpM03P3RgmVFBuT9YjqwXFSGqSGm4TxeF7AlwX2EgNeiBILUW5wCXIXUMdMHNlwKyPfm9MxaBAagyJoCG8WenOzY2djZvhUGc8Cl0jrKEmkQUFSB/wqii5oKw8ZjERwDmz5Vggk0W19QGpANqC5ju6F6yjXFA3jMYdTOy02hBlTAwh0ccu9mkrVQxBUC7O5w6Mx++tjshJXILZ53FL7NNkJrHXWsDABSMUcFMQWQp44WE/NrMJNtRlRzJAjgTZ22XmxOHUXW07y9FYEMelZtHo1dbRwon04WbA59/aeN+1rnfxZP+VYG/vm+defevH1x9JxrRxZvnSA6Pxxvz6NXN73vOHr55nm+e94PgyYz3SF+uGWd2lsQis4pYcMyG1cNAXygbK6JQQYwRLaaKNHtvqrCfyiJNUB4aYGkiQWt8wsudqu13xkp3Z+vJfHz12bqU/etX15MyNmeYDAPGsknbg8rMFJUMIngPoWOAO3TJbZoNaPApTtgN7avN3yE/t/Mzf/bTF6/YTWMMDaOLL2FWyhtQfwDGA6wCowN4rAnD8P9S9ebBk53Uf9vu2u/W+vHXevDdvFsyCwQAgFgIgQUIUSNmCxGhh2YYUMQmllKNYKVOpJBUnTiSmXGWXy66Sy7RpVVlKZFOC5GgxaVKhuIQiAZDEcAE4GMxgtjdv3r703rfv+i354/ad7hlSpT9DfFWvZl73fbdv3+/cs/zOOb+TgiBFmO0Dt8ZlAlO2gXA/uetHHxbOwwQ7cRF7sQ2wNipFDYotuyKHscMkSnFq7IGSptoCY7axLA3o84B8HIoXwO4pe7p3JWOWNioBKplRWQAAIABJREFUzg0snYJ5BowPMPIPadir6TgUqDSXwRwylq1WanGGUHNjsSR1rBhAAcaQcW19PvDAQWbj5wAsA8Yd50ksAGcwoW77kVrvRGcvR+b645+8kSJPS0lk0GqeNs27Zn1kD3NhfNwFZE5cgoy24o3xeWNkHDs9AK8gy8ffQeb0HQXw6PgzGriXQLmAzCHMa9vyWrsE93bqTtcX5lHptPLKnSSGLKV8EcAXkRnwchk0brj2ndXVleawPzgv91qN0JiDGmFVQlAbBEFApayIlBJQmkhliMWsvaGMfMZRcV3OAJRUgsfOeJ61ncYvv9rpS2RpWwcAfuNXf00BaH3iU58sSqMVgB1QPGWAOSv7QnlKO0855ytHWHOUCADCku30pVYbc05h763N2x4sa01Ks4RU97au7/HmiblZp+ieVlId8bsDp1AvPqpSdaiZrhHQgpaqE4TBThQkzd565/EPf+DvXPzs//uH0Rf+8htDu+TOW561bIwxAG6mcdollORKGy+89yOs4DrzozDqfv6VP9bms1/WyIamZ+v3/uRyi+Cww96mS3SzjM9H2/yFn+gtRP4tM2Cl9XJjr5DGm2XG1rhSfStVjaIxSJRmHOYrlLHtuOAuOoNEUX/wtiDVUZ/b77YJ1GNl873vBvRNh5LnAVw7jGI/sJzHGMFaDfAYwdtlBg2gCUbdkkUOBCNVBTgGppHYQDxInCSJuwWvYDjjuaznpQv7yIz30bEstceveciClnyqR75+WE3eDywK6NzRSzSgNQylSC2KFJNieGaylK5KxoU5EcBtQLnZvE7WA6U9zknf9UglTVhKKPbqc7QYR6TUHxrqOFCOw9puTVPbsTtcaK7SyFCm6kEvnettyW6pKoaNWQ7bMkhSAsoDabviwJpTCagFQxMDpyV0ixUluLSCG1U/XNq3ZkSota1a6dxA20mnthgTw5iklCwODxOlR1t7cFNl17yNmYUQbimRhaINy94FEzUQ6sCAAtRDqpmlJSDYc+BYuUyeO2Ad+XrwO6/55i32vfTs0fXCLz+81C3zM8fbm8+ODJnrNFaPgpF5JPo4rKQCNawKLebLaKu+PWtUCKMUITfESmxHmnqv7qXB1gOyQypW0gr63HOr12sVVlyuBKpWQpoaGlkVqYeyrKS0jEHKCraVUF5qgyISbhElpsjukIiyQhwSCkEJkphgACBEiAI05hhFT1EUCeAZg5saoCBgxNMHaIBlyH3Ut8x2UqAqYgJgYhRrTQGedfTKZiYpTj5PnAJ6ATAUUAyww0wMmMjGO6caEBpIVwDTyAbRkP5Ylo5kSKChgOOjd5eRINchfCzjuRxPy/D0vzlvas6Dms9NJ9nsSUb2oLhBpACdKBREPEnp5ih3fu40o7g0bFzloKBS0tolCALbChmIaNPefLkveHN/NrUUXdRs6Kb9n59X65dLcXOnLFlv9bFvLn/tekkG8dzhcjBYeDRydHN59sr19eqXrdromTk4H2EkmZEjeKaWhMKjWqUQpE2MPayHshGL2Iq72LNvI6KzzOWV4rG+O9ygEoQYi/Z1uE9QOAJlOjWf65YsqTi1egNzaqNTP6mKRbksDg9FKIOEn1QW99cX6w6E2AKwBa035/vBK7Mt//2HNffx0mYSko+/1AWwY8wfyF/66EtHsIkarqFVPQjDD+L267dR4t/HzONppgOeRGb3ch5Mgskkqbz+vZz1baUaMONMhNCwWTs+ADEBdcCwD4IrGGG38XB6MGf3V/ddT7Rr7vWE8htgDgXoGaQQUMlpCF3MODmn3ReJifmxANsyUGkKxtOsRtDpQSYR/N7aTG94MmK0OXR4AWlEYFkpaM6mQOtwrdzHoAAGIKSKrCzGQga+5P5Hxuxh2xrZOJ/pxswfufWOq9n7xEv/zgPwCwAexJhLCRNHCeN/R5hQn+ROxz1kwJh0I95G5jA+D+A4MuRqHplBXcC4Zm187DFkUjVCFmkCmQDc7Y7FBA3JHbxpWpYBJs5qHvHcXyyfkzJLAG8C+F/H3+EpABUDYw+lnN07bJcDP7hAQPwiZ2unLeeDSOWxvf6g30uSVtHx3tCE3ORa7zNKo0jLE4IxAQNjjFGGEsDocNZ2fzfQ6o87Sl4BcO03X/zo3fbxr33nokaGfu4imy84q7L74WAyu5QYbQgAEELur+9ilJC3ml5puyLswWp91r+xtSaSND5wCq7kggyI4C4h5HSapieTKHksGAzPGGW4NihorRkhJCXGBDblAGX1OI6btYWGk3Zvb1YZrd5cO9BpnK7rVD0D4GQyim7+/h/99s3nnngSAPDjZ89WZxr1B+IkbT//4ed/sDX+F38pvvnlj/aatPOTFtQL+5/5xGtz/+FmzP7g031Xpns3qw1OtF6sJOFWOUkiD9qpKHlQVukb3JjvKErLrVJR+XMVu2KJPVeRnXUFFRO8tWyRwz7jj6277PznKnZzWdjftQyz7jAWNWGaIpPfPjJHfodRUkIWDu9ZEg5R2LUICQXjMWf8DiFkD+PxZ+P7fw1ZMDGDSRBTR0YPkAcZ0+j2D9TnYaKc8iAjr0E1AExqAKOgCQHl9K5ca4ZJnl6OC7PHzKTCzmr3pAPDHCmpG/rJXmOORgREpFJRo7FyuEMoZVibOaLXG/OJFQZkVChgpteiMyOfaU5CV0dqq7bIuUlNvbvPmYpUTEQEglDZdgKK0I6HrUf3rywQrks7pflEG3t4snNgGE/SNNDOiJXKkeNx5ZZuaWVctxDtERqvhU553SrjYuq5h8w1NWVbDUNEATLdhu28AUIr4KIGrRg0WEq4ZRIFDbIKivmV5K1l//TyGfXESc/5sePztGg/jVQ9mMj0Kd8qnAW3Z5FEHiK/5PCg5Fg+UZpiga3DJw0SmSJGxg4YZX23zv0dLPmdpJYkq0vCnrGarFnw4sMIdjs0tOkQaXNuKE+IRQRVRpm+DFFkLgRx0AosBIaoQQwy0Mw626D0eImQ/cTo0BBQSqEUAYMFj1rghiCQxkTKwgASHBIxNBRs6IQC1EARamKuATrOhDBiQHnW1KoTgLoAZQDlIHD4jLZ0AJP5/VQC1AYQAopkThMVmSOox2S2NA9wCSCrWWWASJDxTBYxQefoWActYoo26Aee43sb9e5pPFJZvRiSjDKSAYZpMHZf3JMH3WNWBaJdUCJBxtfggzHC4pARo5VZWdgYEcmLljtwzpxat1l9Bxvbbjl50x7yQ9W72bVnvY3z1htvy+Od7Z2z/ViVZsLy1il5/CAOGr0/2O1tn7eunYvkYBYhmLsSGuGkIQlqVrDlcjoswW47fhwFF6lIZ3QoPRWygikSl1TtiFhqO73GrNphoO1GNxxwo1qP2o5cLDK3YMJ0CN6uL5cuF53HdgldAiUeQBqwLApGb5Ekefvh9cPWua+23oq4SOdFYCJBTxxWi0sAtj/xhcsae5hHF4/Bx7V/OPzG5hDWhS/j2MkU9iIyVCuvSfcwQVHzWvb8npLxcKup93QLPPV1zJR3AkNoflVrl2IePinTA+mbblTX3wSnm81WPJdSXFWW8aDUBWjmQBkPoASckKwvDJoghIBSGgTwRxJSAg6PQMg2QG8hiW8XZaf4AGm/VetHe2UHx4eEO1JRAUoFBCeArgDGylQZuTP+bqOxPgUmQxCGyAKVEgAvmyx+l1Xjt81vvfjyD5HP/9/XOxHZCwB8E8D7cK9SGGJSx9RBZkgFMmM6DdNnjqFBt6hwJ6FwEgqDzImpI0th7iJDS5aRRZWvIUP43hbAQ1kNErYBPD6+pny2bj65I6+TmkbqNCY1fDmvWW58Q9xlsEAfWUPIAYBXx/9fGX9vOwEaFqMVIcSdsutZveGQLRP+MwDeU+Y8pLazJiilKSFzRspFwblNCBl5QnAAiIeaxFGalmYsxWz7z7U2r1z/vz+nyIefv2I+++XcMQYA/Mav/poE0P3Epz75PDLnehNZCj239cQYg2gUGsqosT0n/055o0akjen6QeAv1ZsmIoqIUsOWYdAn3ds69INZX86dHR72nmSUrhYWyuXUl0TFw33bc6oy0be4EBujbv9LhfnGaVYQLhHsNhiZ3bl9+CtPPn7Kf3PtsHej1a4Wwd4tsgd/5YX3fuQrAEYPVOnGmfpccoPajbWNXYeQlyxjXvyBwtkLv2v0pY+Rk8imtly99DHy+xc+bw7tF36iOx+OGh3be5Bp0LKMLjoS62P54ABmLaUO6nGQfH3lxP/xGp+1fi68fOmkUYlF0AIQMYds/o/Hmr9eMOaBKySMf93vNY6F/h6znG+A0T4yJfkxZDnnKrLg408t4P0WcAGMUTBGkQUieb1SXlP6KCaKNZe/vIknGD8LLiZdcvc749MGMv/9njpMh0LGHIxN6qPk9PEka40EALrVXCDeaAQ3HMDOrDsbdznSgVeSzqhPhDJsa3aJdQueTIRnKkFfPnPrLbYxs8hUySMrvYO01u/Jt4+vkthmBcUtSBkTRJLMdneoWxp6RcXtPa8YSsfuz6Td5tCxiuvlo1DcoUuj9XnfJbfish2tXr/W0OZIc8E/5LRSOLyhyqpS6rs9d+Z4zJ0HWKtN0XRPaxM0qZ/EtCjsVLgnkaYclNZAiCGjMKqNWmIwvzSbKtmDTErEaDaIK++yPE7h0S0IMwOjPYTdNOG2Be5ZMNp4/JBYsk97cdOAFgl4FW8pl0ISNPUOXD+2YlGuXS2df0VfcNtY6ycJpcedamFOMsJEqODt7qdBmFLDBCeclIQlAGMS7Pq78jAtV4RkukFJR9oGOyOq930Eo8SwitAmTRl7uMrVhp/CGgPuBW3wtiJIISxmSKmoeZfRuk6zkXSkQI2RjCPWQGbY8oB5DJs4Y+jE6Az9AocA5Ra0hGGADAE2dpAiCyAe4OlJ7AELk2kWEkAdsPaApAZEVcAJMRl9OcKkwSh3DoF70b28/jRH6HKnLacYcsZfJMrOw8j4+nKqrJxpIC/zyVHBJBnrcRuGxCBaKWiAqGqhRz70wpX6xYvHNQ0Yx2ZFeadaoj4Xbu0FVXuUHqZ231k42K08O2ShIoUkSku95LX+6Ghk+q3dGTxybFYOIiceBg7fHuyW9s8PlWv6WCipufqQtEiLb6SFED6JAHZeMK3MDqzIoZ5boMr2+3C6vXKqnFRztIuz4myI8ilHmX6ZgVcKvuHmlZmk6UTxckVp0ndor5DI4ahsS0CcIZGxvTD82o1i9cOtsJC+uHjrT/5UHSsvhPRny9L7uWsl/w6u4g208DoIrmmY4h68lRQsBYxtISgnYBXAKSGfRf2DzTAC0FMolw4B8dY8en9pK7PaViVVOYhLdiEla8vNTYS0HOySk9ECbTX7tx9AWN0dhTWWlvg84A9h8RHSAge1+mUW1H3QMb0fJQ2aRoyY0b7yYha2tSGkoj0e1El0cQS7S20sHGnwejtdeKqYjCzLkWWrT7mjDboFYNxswsay7iIDc3IAJxi/ZiPzE/Jmyfx75sIdIKN4+5Fc7zhnz3z2y4Z8+PmLyFKteQ1HPkYtRvYAV5FtSM6hV8YkzbUEgMNgNKcY6xsVtejd93LW9uvI6uN6mDhySwAetABXA6GaOGsJMmdNI4t28mH3eRHxdEoz5zrL8WY69X6KiTENxtddQVYIu4CM7mUbQGt5pvGYI4Q+XSi2u8qcKxvzKIA0VmrLovQBW4jUApyUUk0ISQzg2ZawkjSVw1HqEyAghAy0Npc7//VL7Ge++RLMZ/U9jl6+PvGpT1JkDvAlZMXY+Xe4Kzucc0IYyVMiFoCEA20N2BpYRCyLV26sDWeatbe7h51NnWrph2zQupNcGA1unYLCGRBY/mGf0AIL5pYXNqCwyxi5Soimo/Zg7/qwc9MrOnrWZ4t9//C5ztXNmZs3d/pdf1iLkGoG4gmwwBg8FSd4mjms31ko+d/rdm9t7UU3Bn5pHkCp5P325jD4u/e0xl/6GCEA/u5YTv4pgD/62Md+sYi5pvzd/Z1Xv7J8ereg1XNWllZvj/+stP7AYrB+ctHbrBdeMq5YjUfkmU06v3nu2vb1sQy6D2kVPhomexcL7vxSImdtx6rY3uwmDVrdlLIegLNCq5vIHOlckTw73vcHkPMbZ0GAi4mRypVSnn6iyIybjQwJyeuU2NTx+R5Nr/tTu7nDpwBQA0MIjQlACWCJ+48XgBQZd6CZ6XaMUbFIAc3HHXrxmIrl/J23tQCo7xbJDS317eZ8Wg1HwbvWblbmewfq2ysPjL7y5GNOMe5DOS7VoG611U1O9W4lG0ePiahSt6osSd7/9mVTHST22pElL/KKrl+kB7un650REwUWEhWFwk7gzwYtcvlW41E/2ThsyFu7zJ9dOEWCfiMa9QvxUSr0chF0IIt8uD6vg77W9ZlePby+JxdnO4Pk6Ly1sV7U1Yolu8pCOKSqFicg7DiYZYwQVkfPVKx4sANhH+FU1hGHREE7UCMYaicgylWEw0FsiiIkqc0QhxpQNhrRPgo8orulI5aSJE5E9VyyP0o55x39vQPhEwRmpSTd5ZIIFj03MIyq3UCoKGZxJzXMgtFdOUNmHAIdRHHTtugMo7jcAaoOxMNN6FFKKGegNqBaIYfnaBQtikGkMU8U9ozxCholbviwC5MKRshRG4YBCJXGfpw3k+XBtMxUIQTg5OwHlFSkNCMqo22KjA7F5J2ZolDwmTHcBEHFjO3oWPaUyJA96mbno/NAGFjOKE2ixVoma3eprXJ9nhPk/7CAZVqOp22Bh0nGJ3focv2bI015poZOHR8gnxcNGIDITPYFANCQ2LixVdMPnNrRPOZ0Y7esD7YfBLlzZmEkw8F+GC5QHix8b9itIkz254r85oFRC65/YF3y2uSoK8+cUxW3u388Ko/WTHG9znCtci2qbLu8HA95FBU8g1FCLbe8YM+O0O+Shioa4tLD2A7LAn5lLp4VgU6iI14ClQalxdQt3+TJdytH6nuOO5s0mCloICKJFForLpHYWkdFu3ftIKlEDV9ZTpic6B+ttNRRzL88t/Ds0+5o7qh/7G+ObnvzPpJ/s91OvoMudgB0/y0uPD2E8zRArwM4CpDjgCllfWRiml4lnwJFARVlSCojgOxRmP0Cks8JsELMeB8L+Et3mD7clU4JF2gCiYsr6+0bVT5MunLodWdWFqHta09c3r/4nSdrj2tNl2BoE8zQEayCho4hoaE0+rbYAEEXIJZqLHwdWtZBQGyk3QDEjo24tTsqnRppk6QmsfuJl2hLgmRJqXGAwMYlAERl3xEaGVsHkNngHM3Lv2M+8jIPiC4BeB0/ousd5+yN1zayGrsfx6Ttm2GSgso5f1xkDpiF7OGPkRlCDxT2mlAzhiBBhqLtI0NsCshQlhjZxrUA/FfIOP3o2EtYdQE3zD5jA5kWaCJz+vI6vHzm7bRxzZG8PK2bcxQ5mKB91vi1HjLnc2l8/hIAcsxxiX/YpRtK6tiy67OUWbFlXTXGaIfzPRCcX5ifKTBGva2tfQ1glCrJZYJwlKSHVMD3XFYEcHP11CK50Pxbj13s/j93kDkyP7B+41d/TX/iU598C5mTkyvOu7V6hBAI925ZWA5lJ2MCU8oBt+i6he5oVG3F/jAejDaE45aihL8x6iYMmXNtw4Clo0SThAbqKBzLEd9jBn/Y22i9K+5HoxmYfdZOVnxJjgAohIC4cevgKQBeA9aQZxO96nFCTJQY7njabnk1aSfxI8zahxDhVx89/WqpWd1/4oX3fukmgL3Pv/LHEgB8hX9EgJJHABLBOe4/daZWV0lT6dXXXVu9b+Nay4D8Oc0csjxFpA6E94jbDX92huijB7oUOoJ9tXe0eRXXtu+MZc/6esI3/vNN/9MXZhrX6qNBWRP9rZBucIfIY53CchgIu3q8d7gvtD6GjMhaI0PxjmMSzPjj/ZkfnzcYy+fe+J4PkSmnylhuMD42l7F8v+539HIUNl/TDmEeiDADAzMpQ7m/RpMi0+bBoopFOJXKUYDpjxG+GiAkoFrC0YRRenZnna50DljjsJUQSlnqFoSX0OSg1GCGO7rRaieFXh8lBMQl5MCNR8WiHoWEq/rhfNk0wzZ80lPtYp1VmRy5GNgmtJqLNy9HRnY1WGM19BaGfakHu42FhTQlTVZwZvTetnavvt3H5pZAmszyJHS5P+DJ8WUWM9uztR2dDqVIBpu6X51Pg9kjrM0e4IRRB1raoJCgzAFhTsIsBgZrkW9zokN6h6xYzG7D6B2j0UAcFjA0c2TO7GAwKIIPUy2gqUV8lAMfVG5TkSpygxcapAxhF9NKqJyEUp0mrlWSBxESQGhPGRMkhA5SgnYgrZNFrriojTYCEewFHP2Y4EwF7MG61pSAJIrob3c02YxgSg7FliIohEDTkejHBmcqHLWY9MIo9W8bIRUh5IhNUCQwayOC5J6MBICIZ2k4QrPauphncZw2RkkCSSzAzoOO3EHDaFSk4wmVbEpsSFZGJ51MNPgQQAOwnCQi/THCAkymByXIMi4W7lYMUDYlynnwM40qqeyc96RzcwSGTR2XPx8JMr2fOYIEAmUw9DNGhBiQYxNCAcAjoTH9AlNMWbGRZpO7JOpS6tgH1v6g5A4q1R+DRYfYoFtV2BpDU5ReEhgPyhZqZwbdfqtN58JCU8jQn73AkkEgBiYRWqqdnbeN0ce3l11tLTXdKiP1Vgp//mR3Pvq+Me5MCnZU+tXqIHDLpYdUlfBR4LSTvRke6KX6nC5a58DMneKI7BtjNsskkmn1VYlYd4xbx1axBc73B0VpK03rhYG80C27V99s1PaeoD77yKXFh0aE2uWo98w/6yRfRGbfRhvF+jU08U2kuGRvp++KYXNAsiw1zyTAckR1jJoqDiR2dosdDfBAQ35rnnfrlaZh69r5mv+c17nZ9m6ghXlQLCLCzeOXh+mHbq7t/YP5D7QR4Pk6goVyO3zOvlNfCj27BIohmoQqolOeJG05wNCK00JSd1+FJWzotEYsahkqPg/Qn9qFfRaZjrw80M4frpjdRWG6i6Eqpc0yWTlkBS4JFVld4d04QiHTvSkmgwxyR9bGJGO4Pz4mr41+GVl9/4/keqc6ewoZXLqNzMnK07AuMuGsjI/Ldy/XDB4m8GzJUGR0AtnGXhn/vBuZY/MYMqj2AJnSyefmHilkTd0WJrV88fg68i7gDgCbANV5YBQAaX9CrJxfb06tkiuZXBmtI1NUHQCfRma0FwB0CoyHz64c/VB34Jd2up2/qBK27FLmGMATtuVIY9ojKXvd4YipNAmRpX9ji3ECQNYc64uiaHmc06dWV4+2nv/gs4LO9oSvOoO/5n7XAXwQGZfQVKnW3Vqw6TU9lUFJYGg4SWZm672S5b55J1rbjaPBM+3tw48iq0O0x9/bBhAaooe9O603klF0Ow2TIYDvAXBOrjC+11Y/OfQxjwm1DVzXpjyJq1IBlKDMmFG2QEwVvR10yaatzIOW5xg3GVVce68JShadkrcqo/TPAexf+hghxODj1IAoCnADwcE/+N/0h//xC9WiKtre35SUXLdG0WfG38sAwKcjq3/le/t0cbb5xfNn5NrRb9zajvvJ5x2GEQD2p7HwnuSy/rnYYlD2XjW0Wm8McHxHa/wddB1tvE4lDhOh9YhprTExaENMSgryICYPXPK0Qq581PjYXKZyRC4PejxMjN39iAgweS5MbDJLLsg9yF1KQIQFh5KMPPW+7rq7zmKe1qfuZBxhygCvCmg6TvXGAIscJyVKxy4zPCSC3jx2ItlpzroD1/Nro96mKZD68o2bmhla94sEJBqlT13ZnCt2usYzqXPt6LJ1bamuEwcsKnPdq1fEue2bRwKvGQfKXlst+Hvx0GqkHI9s7W/Q2pWbduLWC9IfSaZD4WzuSjIYldJaRahqmdqOZKaXIuq1ZWv1wuVj6rI475tbm12+JhriSdO0WjdKtAHjFUCoAuM+gCo4YyDFGgC0gjIyT4mDgYNSTaI0RpkMk7nojk5J0QIvUmpR2DI0oamQLmEIKTUFGgsdSmVHA2lcj/AT9Ric2fJan0Bpw2oOZQaQFYuQogBZcjkcTtJrXW20JiZIDPqpQUwIF4ymm91UXh0IsxbApKmCQ1JYoFgoEHiM40ZAkPazylAKIhUBOIhpRTA7GpAS436b+zpYwQFGs8Eweoz0EQc+KIStkN5FyaZSrYJMVFuuEpxxXw9RAMkdQwoUbEy6zHMHrjklZywTKYXsetxpiqrc0cuR7Hx8YR585mU1eZ12D5luByYBi+HZTGsbBg78PC2ZaHAqITkbO7tpyQuNbfns0qVF0h14pD47srothzpeMIipXUHHKaMsNAD0oFUP9inRNukar37nyFDcMFf7z8011wYXo4WuLNiElCrto9tkty2tGw/aB6G9iHrjEZUkvOuFl+LZuVSNqrxgDmvlUqXC7YawHuBeLzGWKiFU2vEbizolwV6B65Z3gG0+0G1LMa2rhhqXu37nYDYx/a1qifM6nY0jjljzzZ1GdePUeu/hCzutzS2++NZHnZUTC9ahrbwEH4y3H/9nqHYADI150ZCPvLSNDVyhl/XZeQwPD+D4IRwn48xPx3WcIg/0QoD6AJkFUt9GquOi800E4tO0Yp89eWqw2i1/Y3WoCg8njTNfMJfr63BwEgYHL2NJvxYtDlCBRhELnabz0KtHl7bDkHFYKYfNI1DSqyKgJ+zR7tuJ9V2bRo95njoeKJ5If7iz2HDogHhkR5fG9GrmSUB+CKCt7aRYraRDECJ3O4HDAmoasIxijJTLITC0UkjB3PE4xgiZ7feR2elpFory+L1vIyu9WUHG4pGDTj9y653q7AHZDb6EDN0bR33gmPDk5RFbrgiyVvGJk3WAzIHjMEZDJTMAnge3h+NzH0eGoKyMPy9CNsJsJID1NDvPCQBPIxOErfE5GwCCFcr6rkFDEuNLbUZlmN4gm/pRxWT8GjBBXSJkkcJVZAjSl44K663NNPmlYl0gAAAgAElEQVQbGPP5aCW3CiUPXMrwmL3wU5ZtuztD/9Ly4swTKzMztV6vf/LbV258p5f2S9JgtiiEMzNT9zq9TqFerccrK0d+zLIdEYWj/unTJ7eGA//aeypP33mo8nPxX3OvHWSRdk4Xc1eD5x7H1MqLdrUxJmEK84bSl5MofOOrX/6Obu+038ML/Ak9TM8jc2CmaN0gkMAatQankT1Ij4w/9+iNO2ofDE9zm1MZyy8kWSXtdQqkSuE4gDPaYFyZCOYPrWLx+p2TqYt5mLupzeuVuVqVO+J02B/dvWyXoSeBOtfQYPijgPW/tCp5/CtDumaalVf5KPq5EOSxvWrzf28ddPiWorN/nFh7KdD6/kar/e5D//I1yZ75XGqdfkYo5XNG/iCyzv4u7C4F+KPJ4LnV3f55AXgMbGSr+VGRklMkiRfLSbyFLLjI+RrrAE6O5bWPSZCwjAkakRvDCia1TPlUk7mxDIaYOGL3ITUAJkpLKwMdanAC0Aq7SxSeK25DQMx9fzu917mRzx3NnNOSxll6l/luQRWikFxeXJZ9t8jnuoeY84cstTj989NnzUGljvlOR1d3Ny2LpdbSXlvzIOVeOCA7C7NIq4xVVUDmWh3e9xw1rBOjCjRCzPntqKqHXT0YzlY7z79+6avNzcP5/aL7yPzB26WIFMH2tq002CXpYCgSx4Gq1bkIE251+5oN+5TOeNCDSFt+y3Z2e3PBqWNfv/jMg69vseKcHS8cB6lYxTTo+owxuCWKOLBpGnNtFRgYB6RCkIz9IBGBxz2jVVEv0D3fduRrA3WEjyLnbGRXq9IWw6GmQgfE9smISsKsttJaJ8x3fcOGyiFwvIIMY849y1JBrNQo7RtOXWIMY1WLql5KkshAuZYQDxbMTK9l0u0ha2+7ULGGPggE5QJwDUPLAH6gyOkCN4oYXB0oaFD0QbFgA/3Qspsai9Rgu22QyBjZdL97Gq14lsYEMnCWpkAkskw9NYBjxuOvcrqofIY2yRq23fvRZGR/R/OAbbqRLZfXPADO3xujdTQElD1Os003H91TVjK57nt0fy7zFJmjlx+fO4XE40gHcpyiU0gBkwC6AKlJ1mjuSCDE/v4sHQwKBDCwbY1Rx0a3U+GLlcOStWwQX7UUWokNpNxDXFzEQISSDUaq+NSmx8/4qvzQI/EwfP/iW2/MNZyWv1YI0na6Z9tO7XC5uHzm8fBGKTbDoCmH8aq5PT8zOEKSGT3jLMRBJ1DpYl+0Y12KuSIF2zKlYmwMDUqiC1IUCsbt0xbqYElqqsSidjV9kEvRP0M6usCx/W2xsG7WycHNvdlOQavv/Jfdy4fNz651dv+n9V+4PDyJQVfjcNT+Bv5WtY0FMEJeKiOzWzMadHEfTqihkqxZh8UciZCAQkZtONY1pAOI5BjaV1fL4e6rq8vfcDajfb+N22/s3Hp8yP/DB4qq1FfeiZvdtVcOCXnpNWNelIS8dFQCDp4DQQ9lcLoUujaHTLs8ji1JdQjYr4Rwqlfh8GDB6pVJ91UOVSJEdxJYR/cSy4TCPQagZfndjZTzU9zmx1JCPWmXwnaVjpzeYWhaPYZKvQpLUapD6USM+9oAShJQFGA5DgiZ1ps5vU+uc2eRBQ5XkAXpr5rfenF64MCP1HqnOnsMmUFcwcQY5jV0eQq1iHGKCRPy4pwjLncCbej0CtKhAuImSKGT9UBgH9lG5t21ClnK7DYAn1BacbV+OsmQk0AA6wWQjR5MQRCyyACrxti3Hc6PbkbRoEiYcCjagZJ/LrOH5jQmEWk4vp49AF8bv/7FKmM3nyqWl9WwH+/ItCWAY2XGf6bd7fszmqzMzTYfnZ1rtrztHWd2pnFHSvlyp9MjNSFeppS8j3H6gNaqW2tUigetXbfd7TgPP3zWkVLpOGZDlaoX3njr8uJnX/qLf/FP/9W/+iudvU986pMFZI0AFUxQnbsdnhbhSI2867kWuGUilRKlzRxJZIqU9IcDP93a3n1Kc3KEC+EUPe9EpxfnHG8MEzqPePw5NdcWxShOF0y2jzepzYspTHQgpbtfrPElP3QootoojP9ofO9OKAXfACmjcFwr6lBqhjpFdSwnHwBws7/b6YBplyx1Hvnw/7nw9c/+rkkufYw8aJnCR7zAeQRc/dlW401ZjOv1I8NzXXs7+ZIEfpzAeIlS0b8J7WcjmCdTkM+Mr/sn/nFYuCFhAgX6vr9IafKllC26kO92oEshYN8GmZ2Fil+gaC1a0A3K1hRhj/iFRstKo++68eAoMgO3i4wSqIh7efDM1P3JC4EdZIqmOP7JUQxgUiwN3NeZ+MP2mBEQh2bFN1N7PN3QMU0pQH7I+1OdlXejWmoAjCwbB9U6WWkfqPluO4Y0eqdU0UJpSYxUD6zfCGe8ChlUK/T66gPlo9t3hu+9/d1owW9XBpST0LULIkrkXO8QoBaZCXvcjCTdrNVNf6lE58KNeL8xPypweuknvvWVJ940laX9cyvVo4eHdmVzHT2bgaeZB2tJCRKFYEqBAFRKALsB7Ky+yAO6jTA5dniw+PBGerS4K9N4lpdtT9ve3ny6/dODJFmmUUxEGqsht6m0bAKZABQGroMmu47lZEt1aWONcPFvb9NzXSeJnq2mw65fLdQhCNcjMiMPg5quF0NWtKrE4UyFcdD2VnatsrtMgApjUNIhEXM8aYzkOlYwPU1SHTG5EWqUqTRhqgjAfO6lcRNUfvPAoGoRwg2oITB9KJM9lNp0Qo2YUIx0VisswdGJgVEG20VVMGPyxleay91U0wPjQKyBOAW0ACTPqDRsIBtBlZPCe+O9ny73+GErrwcckxrfM90lzxrkJQX5eVhG5cI0JvV70w1v0yu/9mmC5LxWb7qbN69zNQD6A4lt3OVAlUcAXc4cGDJFMcWcOPYQxzZKpT6AITY35wFQtNsVPnRcG8AAVeKiR4MaguEcooUeqJ0YVvY7TBjmdi76x7FcioZxMO+tbYmBxbt0dinatN/FeiAkTtZUVZasQ7ZCHgyCwsPxKAqS5qYZHhgbiOyEEldqwA0TqmRJypKE4+1htmz0QHqkEoa4AOg3CB/solJuhHH7sSvdz9kLePniyqKNGvp4FeT7Jxeav/4LCy3zWy+q3/71F7/y2sm5p/vXEuxdWfokzuAoMl2jxvv1JoDNCPy/A1AHjA0QKSHIeF8ooEIgtQFrCWA3DjEzanrWFXXdDFfD/fc9i61v/s6IvlJFuCfkaA+DvRYhLy0BEIS8tI4MNAGAJuXykoYugFlVGHJe2mIZlHEA343hfQtAExSN3dTtk2jDMWy2aCtt9xIKCP4MZPLW8u03C1G5utY+enI7JXwFwCk4BRXVyALKeiF7QqhJmdS7JWqgxzKX5acYJswGOTo8jV5TZE2aHoDvImPb+JFd71Rnr4QMecvr8XJjCOAeqpMQ2cOcd9rkCJKFSarsNoiYBREM1P46MlSkicmItDlMHLQhADbUus2Bq3VCj9hGv3sEmBIhi0VjTpQoFS5lqsyt0y5jr6dM/rygdDEw+lOcWpe20+SrSUYdcxYZckWQGfEhJkTOy4lSD14NR7vvL5Xf/PpwcGWRs//igdmZpfecO1O69ta1uSAYOdevDeY7nS6/ub5zVRBylBMSA1jR2pzViaoTQmqtg06Nc5vLxOjvX7oc2pYz3NtvDdqtbuXk0ZWnCufdVwD8xV9zr5/FhPtq2hnQoZH3KHTBOBOgaU+FEoKxRrGoNUyns9erq1SmhZJTq3JXR65zMwijJUwmk1CMGw4Ix1AbXTSZQ28DuNXh8uEC4zNMwm8m4YesbHhENL6uDgAkKcraYFRwsefYpjGWkZz0+hEAFrV5gXlyXtaCbX1tduOF935kHfj5yCLyM/+6uv9SUrodDqXtJKJf8puvHP6D6z9v/kkh+M2HuFpqtQf6EG6ErC7jAJkD+UQM4gLkpwGsAsTToPYICa1AGwYTaVARA91FF5+f4XgdQD+2i9VhYfZco7v+y5jwRc5iglQAk/rNvP0/N3DjJte7U1qmDRkw6fyeJk/OX6c/5P9wyD0IS16AnP8+Xe93vxHO08c5QpKn9qkHxHYSm0Zrz2FpSpCmaLtlPh/0WRinpt+oq5FXKgiieG04QNnvR4U4LtpazowM3PVqHZ7y0RgMuSlQRoeBDmeK6M3YqKSh6I94OHsYdmszYeJFV1ffrBWW+HY7rt5Ott5w7CODurbTQaJ1ZAgH4VxKoNu9K6t5iJ5xIVHiACk21sK5f/977z71zIM95xj52W05u3e5+K7Dka6UNLFlUCxrGONAmRhRZEGnFIzFYBi0zKIhsK4uhP0/3qKra4rZHxqs9x6esaJuuRy91qflp1N/5JACC5UytmoFgbVYqkNpz4SpJy12hzvWA8LAloYY5dACUZwSQ4wiiUpe7wPdhIpzVctUbWM2IwzBQeZLEbqHFP1ImLKVqllPG0trHBUMnhAIQoJIY7w/mc4faQVASVC627unji2veZtykigFIgYkDlAlAFWAlde/TdNeTctPPu3i/jUVLBh3XMTfmzRr3GUnoLiX4WA6aJnu0M0dRXXfMT+s8YhNnUsiM858/Oahya55Jjt/yLJGkrIaxy3jw3Le/hjDoQ3LmmAMw1aRAE4EggEGGbXLHiqHMSw7BB16kDNlxMWl+eiAJ6T77WvnPgex9LC1tHX4c++/g9lTitMK291MyqgNR636onqvUfonwLzCdgGiXYzYg4uRZMqyBTeJBc0JYwjcDoyruCAa1LLUjkyNa4O9m4/YUpr2/zApvDEyzhfPXWl/98Rr/Vv//u8/qNGEwS9iFgzHKIOs/eOX3vzn/5L9XuW925/5vwpPzCIEx58A7k+h857/yMyXf14tQeHB8ZOzC7BDIC3ibiOiohlaaxyOtCPB9gF6s+RZhZNz3qLh8nNHtvo3H8H+YbD7aZ+Q970FgBjzoiHkpceQUev8O2NeHAEA+fhLwaNrt4adem3vtlPfgZT/GYwpwbZb98hVigCJXYKoDyAKfuzWz4DQfQDfALfU1sq5FJTtRsQpZA1EWAQwgu1RyFTBmBiESCgqYNtjuVVjxJlMZ0Zy/sZpFo0hMt18DpkNWiIff2lgfuvFH0mevXeqs5cbvhzJyCOPFPeloJBtRt4ZW5o6Rx/AIag4CktswJgbIGQZmRP2XWQO0HEAzyBDTvIu2xUJzEjg+7HR9RLAVpg4UyUoHSowW5s+oWSXAGs2pXGZ0ZLDeKFkzHuNVmJRWHu+0n+UaFkaAH8PmeBUkDkubQDzDHhPjfHuDBfglMVPF8sFraTLR/GJb7/2+gq04YPBqANASQMvSJPnGEhSsaw+gA8RQooAZuMkovuHMVVag1CD9a1NyxWFO5SwM0oqTW3OTqwunjDYtQkW/ip0r4XM6XJxr/L8oQLdiwM4AHEoO1TQvFxy39rvD/coo0MCeqiZGbU6vWNKqb9E5uB8BBmNThOZ3b1hJNwY6sR4L9cAKIzi00PEzQK45aqoRACSAH4CPORle+xZFgoAikqbuSROtbC44oy2ARQ1sBhG7sMI6Vqz5H4h3a28iXahiqwesp8YXv+V7pHB5//TN/V/+96PrI5lpQ2g9A9H7vy7uGxdkjzvdi0D+DgypXMcwAcAU8nkLrOVAhpL0OQEhZ416uoqJ7dBkALmLQAn3aj/7FzkL3OoK8gQvVwRjcvbIDDpvi1ikh4HJh2zJUwc5bxAnmNSRHy/wcvhG2CSfp3WAdOITG48c2T7/po/A4CEOiPOKtC76Hr+3Clk3BxKp6lJABUY7QoV81KrFd0ozytpCdqvuLo2ClnFD9jswUEilE4MNC0QyNV+myslkBqpg5rF9KxLjWUhdTmcRBpW5AjPNo5KQ4LesD46/Nvl7unL61fa11qNlp8GzUh7UZBKwFoASD4M/Z5FkZWPA8TACfeKDy9X6MFg9PQbL7885Mcf2y3MHchq8dGhXSwinwwTRyl0lNphjxTSAJ3SfBdh0K7Q/sqx4e3SL/+nb5X+7Gzw/BcuPHWB1tz6lisWNGw77UZlxManZatgOaGb3A4ttcMMbYoDzfVQ9WOHg6o0VDI5CBkpc0KKFgxMSAhxqStMdH0Q6bWBTT1h1F7ItKA2L3ONI1Y24n3NZ8ZXDC6LAcEQEYptopAYOn5ip5Ez4N5ykjwTAtyD4jJMyqCpBOxpJy+XqWlnMX//r1hmXMivWUa+bMJxB65EJu9DZEFcLn/56wwTjs9p9C7/TjZ+kGYoN9SZhzb5nvnncQDi9IywBrGe2Rmo2exveZJRtNAcXZ+yNTlHXwFhKME5gZQMGbrFCzAYWCahHiK7jBgj6F0L5GgNI9jQg3PxsNN3vVuX1BzYzNaVxXJ/6ZV9N3iy5nebFXJyr25T7wl5xxrws6JfnbdSS6a1O9yjPjMDkpKqQsni/RTGkkHsUotqYyluOpYkpoSSmyhDYlkUpjWrwtsX+OD2VytLXzkx7O/+0t//6SKyoHLzqe9vNi6dmjvBV60PPfI3yNcPPlq/XLzeWnn3znDj9f+ldJhsQXzgRW7NPk2OVE+rcu8KhI3kdQliFMQjGbm2GYMihAMkBmTfgr4uIT4LYLcTwfva7TDYHZjed8xvHkwJwfg+4xayNOjmeH8AAI/8i/8+MrWlgXz42TtYmv0+wtELIDgHuMF433YA9LELjY5omGPzF+EgQ+6yQPzHAPVqVK6dAdgCsuD3O+PPWkGaPAmtQwhhgTILHO5YjKJsv2leKpBnLRxMAlpMvb4yvm4fwP6PqqMHvHOdvSYyB6GByUM43fZdHr/mInOiciU2jUI5yDTYPAAHhBTGx5YAPIxM4eQjexgyFPBnMan9qxGgWAGSIlCMpaIa2itSkRjGSvsyXTXA8arlaGP0vtZmY0GwizVtTneofLc2zA+MvhhIdS0w+mEJbEYTbrSqVFKtLszu3Wx3z0VR+Auu0kcGqWzanGcj0IyZp4ylLqWJw7hrlGRRHB8BAeXCgqAUggukKgWjFMYYSiirEJAnAYhOt5dcvXad+oH/1IMXznymUV/Y/ivutUDWEXxPlO4QSiOjp7va7q4IUAVC9udL9Y3ZYuX3b3c6b7zrzGn10+96wv97/9s/CZEp5fnxPf4Wsk7nPA0pCYUFBmU0GBQYYCo26KEEXYhBZpFplhhZlfbfHu9JmeXEDhogFqckazwoa8DRpYrSSUqZUghbo4eAyjayB9Uga/QZAZAvvPcj+TNhI3PkqhLkgYtSdMf34VcwaVSZ5v6aEj9lCAg5RqCKGqbB8VbI6e0Y+jiAnwEwQ4BUQB0iS0Gb8b3oYFJflJce5PxOucHKnb7cscsVZO4AThvAacQ730tgyhnDD6Im06m1aUfv/rSwBECTrLHj/nRdLhM0BPSAi9iTKWOAKCgNwm3uCk0GnJGVw3VV6YfKdxr8xPaGVQ98oYplUQiGpKIU7fcV+hVON07MojAIYWRogqpltuyS9I+ULGIRcHDqyMSVS8KwG4bNfO9A+QlpxYA7ytJLM5hMt/lhTUUGQADmtmgq/6Joy+v/6H/41+kHbn7pX+6Jhf/ZjPrnwQknqaJgjgtCerAcnahCRzulERV058f1F7//6Pb2cw+1SKlctX+tEEYRSZJSXfi1xd09cc2cqqs7vtaH4S7eNWfrMHV0VQjCSF841myqVN1K4gBgxbAdcRkmIWAgIoVkf4T0tq/TOz6QpDa1ytR4Air1YbaHWo6UwUgLYlFjEmi0NdCXAiSRyNK2+Z7lCGy+n3QiZ5IAKQHc+wOA8e9iKghR40CA5WmtXI7yVNd9YnD/0hTQxqIUc2WQzR6tjo/NnbB8WkEy/gEmk4nyICRHWqZlL38mpg1u7uT1MNHdwfi4PrJgdnmUqIVYAc4KdLQBBeNqTJ6n/JnKHU4GWAqQJggqY6Jpa1y4CQ0gSmDZElRqGOs4WnQZPltAfzBE4VKv7WwNbPH1D9L1Po4l7wkZWa2mM997z8ZPIrQuiUr5zhKv7j9B/VPnve6cFbkdSxhOi32akpSOiDEOAJsoEqm+cY2rBGVUEgZCe0NVbyu/EST79Ij9SnUpuV6U23hGd2XzUy5/ZKN9/kpQKX38z76tC5302bYoz+xHrDw6p8vqfH3JYYKffdyT53/R2v0dkqj59/1/7L1plGRXdSb6nXPuGGNGZuScWZU1S6UZoQGQQAYhBmHZBmQjBtstm37GDbbcbhv7+XUDtrs9LbexYZnuNsg2k2wLMBYgJBCDxtKskkqlUs1DzlPMccczvB8nTsatUiHAj9dGa/VZK1dmZNy4ce89++yz97e/vTelAJYVsDiD+tYbcGjrV7Bt93GUQ8BdB+KyTrihAOADznwA/xiAhwDMJhJ0saVwlvqm80Y4lLrpsPknIbeRn8QH7d2ok0dyW0/WqoN1AAsu5E2xX/xJ6D35MejOUjsxjGHkwVGEgNZdLej9xMkj3O4AU3XkjgN0BmlyKXhK4HrjELIIKA88yYNKCZYTIKkNiBIUYyDUyPXGpeH0Oo/GoZHQSaEPQNd2/LEdLzljj9xwLYPedI3nZ+oxmdCB8cKyPBDghciEh36h5Qr0og/QT0TYDW3gxb3/ZWv4WQCGJLDiEitdE7wIQlAhtvQdmwZKJQxqkBCiKCHfsJkVFQhdcBh7n1JqsSr4ulBq1iH0iII6dTSOVCxEsZ7Er6CUXhFIuVi2be4XcheNxclFT7Y7E2OE2HmlrJCngBBwKUVHSsdx3bxNAK76kQYuBKRScBmDY7kbN+zAJdBhEdrthqzbDcns7OLI4uJK8J//8x+/4FkvPLnfevnUlp2Pzx1/QXaRYzmQMiWJEAZJzQ47VqJU8Pxbr9t18X3XXf9mjiMLmwBMfO3v/uoItk90rr/q7W0AfLBa+ZdWHCrejt7du7ZvKonzCXWeh0xWAdAEageBKti6EzUVOq+/5QKDXHtsp3lTjBIwEEgJGoU0p4gEJV1KB5wCcf1RVY+uIdrYbEGHZGVvji+E3gxM15T3QLcEmoR2IIZw+ubS+1sqgAtACht8eQTs6RGIyuXAwKCNNO/gaIWJx4kQNSlJJQQVHkHMCLrQ9ACzDksCKCaA3dvZsvdlwqkGucgadaYzSDYjMRu+Ne8ZNCSLfJxpqGVrq50RzttYVxtoTuGFrCxzXQI9kpXNU6YgVMfOqeeHxviFc8cwU1ujVirivE9qLFHRtqWDhbxIhhwQwgmxFKAEIBUDTXwfQ7PrKDZjBNuHSVDySadadPQ2nnKRY35KeMAO1cJu3h3MV52D3nw4OQLUiy5hq0KOrHNeA6xtAIbPvGVJQDjQVchx8dDSIKE0DyD/2Pgl0+DJtFhZp/lWyztXdLq18qBcLQyLyPcKyi/NFurrJzY5x04ON9bU9JLM7xRiU8Ox2M5wJfqJPQ8GB8kgWGddFLpU2Sciq1UaGIn31YLIhrR3VFJSUlC1gMoozSmFImMyFFFqy9XIo1OWFEqFySNrhMcSqCcMUzmBQQuqkSpViwiKDlfLLQFCfOoISmeo4i2HqZRrlLXsAesRQ7iB2hqn18hQb2451YkP0uqhGtrIyxOCspNgId5wxhwookC8FKLH5wPQRz16Fl4CgCuN2HnAafqZugBViQRdaRKpS3hYia7gA4a+gXfGNW7cg7k+hT7abe6pJ+NK5SzJA04JQIyRv0FMRL9ElwTQnW1KAFBuGV0ojELrgaz8C/QpQ1Kje4ns3S4DEqUTWKwIBAyKtiWcbgu8EsMN15BGNYxKDntpS7U796u/eX8hH/EHH71327NzVv5VJ1adSx91T/FXrU9QO41eFQm2E40BV4aEqjwHEsDxKHemYUmuakIJmjbSIhdwnBwlIAApQzk5GdCFdF157iqtqGdsF4FlxWtrQY4DeNf/Pbmv8mR38AvnnVoLOMij5y3Vn9z0fH3zjW9++GnyAJv4/L3XHT//sWDl0W/Hr2PwFj5Fkn3QevKVMXKFWRT5MAJ5AqVAASEgV4F4qGf7SAt8DfA/w0GfV+qms9ZuBQClbjprxupW3HfhKnZsLWLl2SvmnzjyudsfNHVNEwCfIbfcxgDdw5fcclsbPjZBN+cuQDvh50Lr9qAL52VdyDZAdwCYA9QuKAwCGNOF6qULwinSVgTqECQyhYQDKhXcjY4YZr0Y2TnTwahDcxm70PZCFr38sRovOWNP3XGPIDdc+zyAr0Fn4k5CT4Ah+dPMjybzKuVCxQCoAnWMQjCKQ0JDsCH0Bn8UGmFZQL/ODoEWpm3QkyoBzAAYbxC1twB0ywpWjiEKpVwmlD6xA6WL/cBadMrq27BQhDYUpggh02XLfqR3rS8DcMluypIW5+OrhLyMQ23hiZipMqe4fGKe8ChmBQVQpeA4DJYUoISAKYU8IVBK6SYxCoBlwbdthGkKqc7c6ze8EYOAGs9k9PjxWRdnGWUv506VhypPL5y0034h9BiAaKWRh9P5L1mvJyg5fnFmcPhGaA/uIIDlgysLU0Ga7KzaK4f+nz96r/+FX/1MuNqu3yjL5HroDiVdAKeIncvbhcpEUp/fBmDMAbVTWAmDCgiIS/oILKx+2P7MeyWEgDhMSkVBoXiaSCuWlrXmacN+FrqOYrc3DxcC+HlojmYTupD0a9AvgUIBRc74jhggjgUZ70JycDewtAQSMUr2TDLLJRYPNtl4wiJIOOc76s262+FKdAdGMGWhO8Cw0JOr3b178GKArQOyqglNNvobnzHszAbVy3jccGgi9GX6bOs6K/dn4zZlN0KD7qkz3s+GeSUAwcgGSpQN520k8HCAeYhSAtDhNFYvmz+qFgaHGGFOkpfq1FC9xhtDRfgNzvICTQhlS6ggYSimQH5l3MPy5hIGVruwhMTAWkdVTzRIRCkEo7CltFrTDGS5ncu1oh3KZ6Nzv3Hlvuibp/527tsLnfGrL8u/8ck9r79nPZmJIdoc5OIOyGAPRRIALEogKXBKQn2DSfkopGwAKLfz1SlL3rgAACAASURBVCqkuAvdNTHBo9dubXZzFy6c7OSfVZeEJCZfecfrmpOy0/Ga1tipwzPOmF/v7LRrEWWR5x1ZV5PNtju/xVKRYGzyuQXVfaaTpq/1O3zHSJ64zCcgRCx1XUFZShKeppGidJRYspkgfLZGrLVI2OeWmPvyqmcFnIjFSKRpSuLZLnCiQ7AcAzS2YBMGj0riCcpWpEpXAgUfFBQUC90UkmW7RWQ5mSaUT3sGmZnfnmwjRFe5ULHMzGmYQHJA5QGlAJ4ArkGBM9w/IjXniWYdll7pCrKR0BMrS1FwSN1Oy1ybkVfzvzMhQpMVaRKWssizgrYeaaWgWNIA7Z2wDL2uW+gnZhjUz+991o4XNpJMzhxnWVMW7eX69a6Rp4C1DwQSSq9dD7JWRpQsoTxcQITXYm6kmqbPut2k/pXPnjvTOp6bv805t7aFNsb+abW+MHjO8UPDrmimqWNb+TYTboC4uggOnoAgSG3YcpF4ivI2zZOyN65IKiQoAZRIZBqTJW+qULfFeJP79XWJTv329c3xbJrf+orCaq7I+IktT7fDYJM/kjsVtm9YOJA0rqmGq5WXt55sDm9q0YGierrmjFpqskDkQlNt2DVLEdzuo5jaMoDwlAs8EQE/kUNyQwQpJagkEPtmEP3NBVh46kvqw9/T0Dtt3P4xw6mu4cYPJASimcI7DmDhcyo6M/EG6qOnGZAu9J5skjWBPu/ZA5wy+pUNlmC7RVgOQEiul+ihoCwCatmgtgWbAWkiYTtmrhl4KiAkBaOAZWedZFPiygJwPnQB/HX8H2PvRz7q0IvUxO+zBR2N0jFGXG9SFUBkNhQVoJ892IVWBL1+jvCg0T5Tp6ze+1+195NCV9hMa1IMUyCnCHJCSiiQYLtjC0FkhadYcwiuAHBAKpVSQvaiV5oFum7dFADiUJoO2XahQOk0l7IoGAcjhNEkhZISM7aNWAjUeIoyZbolIACmFHiaAlKCQ6kcYwSAtK1eF1MQuJ6POAqyBHugr5QBzVl79c03v+urt976uSD7kPO7t3T9PbVHUim/A+Ac9JWx4RdWALgUpAsCVyrl9577kaFi8ami593zD3v3qIPfXLQ/9L73x3c9cNccFMjkvD3y2J7nfub5gehlTGHC8xAEwUZNo0EVBxcm3WATgAE4sAG0bU3gtgAEOc8Ngygu4fSCqi8YhAC+qzllMaCcKPJoFDUI8FFoJVCF9gZf3pv3Tegjvia5wzyvRJcNlL0Qp6QAqTlw5GYqcucodSil7NkqVCeCWu2AJj7BQxbBCIAtlNKdnuPNMFuOFcEDX3eaPa8ns0X0etu6gBoClNNPushy57IdWExCBunNh4t+qAs4ewztbOHWrAFnXhsiv0HKTfsqs/kCL9Qd5vPGAZAALI1OeoIAyQAQ0jQuLgsBRwbK78ZjhBDV8UpHIj9YIQhTS7mbOyKkHoQnbRkFZc+X1KLrYyWx97xREg7naVDJwwsSTB5egccljnZTOMtt0vYcB4PuYLKrejO7bPq6+DfxB08eOGdu5vDBI/H6slOEYpzae9dGx7/rLS7+IoDre/cxbwG/jTB8DkB7/IGbrFz6rdbDa687PJ6bW/2z+VOXb553a/9SKXx874h4x5Sai4dmF7s/c9c3vlSukJcdmZgYrKplx1+vsSefa3eP7NgSrpTtKBetDl52aq9ymlzx8ZL14K7xRHJrmUhRRdGzoAizmE0Fo5YMANBUQcFXqVLKptQ9Z9AlNmF0h0csJRHzGhXLgsg9KwAkSN6BqkUMgiscjQlXQvBVsJ5Zw4Gm1MViBwzZPOvkmZF19EykhIDAg+oZhTybZR3bOnOZSg2+IgVCAviG5tEr3WPbmVIchlOd5YyaryQSrIcAbiDZ2VIp5ndPtjgA4vWyP42MnolgMwGC+QbLIjIW+tzXFP01Y0LZIbRuNtzvLGp4xrWYiKGDXn4UB5IOYB0EMNTr4fUMRrAQtfzZA9FYftStv3K4EhcLNT4+2yiRf/qDSw89k6+Orpedrc1mbmLZTh+ur+UOEesI6wQ4aFdRTCzrAsKIDQfLpKnulY7TYJLs4AVxHsuRSUZJkK6n6/wISnQTbFYFU4rJOJVt4tiB1alcwFnyuYtKdW/yHHf4qenz7sr96aIKB5zf4z9XqU1+evZLacm6cH5z5YK1/QOnVocG65avWsFF7r6XPxE895X2/2UiBimAg4TcxuZRas2jZJCs5wO4r/QQFhXIvhiFTx5B4Y7D6td+MENPDwvaEO8ASI6o/3Xih/jsGrRx9e+gi8ofgHbSKbQuN6F40nuf6mWQSSgiDCBMyxulgOudTgsQgkL3fmeZVWN0nAMtUyl0fdzD+DEeL1Vjz/S9XYIWlG04PfRkVqOC5uNZoJ4AIadlkkIbeGUAW6BX7QH0Q3VGUNahBXsZugSJIWonEjhFgDkORKD0vK5S3TLUqALxfZfEti8EKH2jVOp1dZ4e8CndnmPWcQB3AXgLNGrkAxgnhJQ9y2JSKaSMQUoJwTkakKhHiawrkQIqWAfqedu1VnjS4IBTVljxbXcrIyrY4bjrUZJsYpQUGWMMUG4cBVlvxIwsskPRTz4JzjgOO19xaYi9e/4rgDdDo5k5nE6Ejify5We6PJ7qxtFWDsUkwJtR9N+fnD9ZO7i6aHiP6a//4s2nAOBdN/w7p9Pq1h0f03YOTrMh5xmPKO1G0yqX/0NQh8BBDK4VQO8al3rznks5N4mUAniBJy5tiyHlQidZ6iUsfB1TkkQv+v8AjdyegEaGZ6ANeqPcyr17rPdkyNeonlqC7gsazUAOeJDLRSKfzEO5myyyd5cj4jGGxuMJaoGSkzssvAo6/LuVUjpVLBaJEMJmjI325Gi8dw8b2cgMYLn+vRmeiEFkNsIKXCmbApISItGvV3bmHJ85zkRggb7iMjJPUs1Mh/dCsv6ZHM3s3yRzzIZ82QCx+22F2jZAx1prhYWxMVmMI9a2XB4z1zk0Mhk0y4VwbH6pVe1Eo7Hns9UhJ6YgNHKJe3D7CAG1aHMwj3apjNJ6B9VGpIJKjlTXArSpD573IWlCiRKDxbn1QUno73v5B/6CvXz8CZkbuu+NR54mX7v+p50TP/Emtus3fj0iQlgAWcEFg782/ok37Ms8n01luxEwJCWhyHRaiJvTCQ5fIAf33jEzsr7wpoGvX/EAna4WRZdF8diWuRNycbJSTBY6QydOJEPrUYPLRlKdkSexO1onR9IhnCCkG01vC+TDc+NhXabOleOxNZV3VMGzSDNOWMEiyNk0XmxzGabKv2SICA9MLQaWxYC0maj0WJPQARcyBdBOuFoJCA4mCgoUqSJQIYGdkxi0GDrchsgL5CnQ3RAFI2vZ1+yM1z3nGL3pQ4IECgQJBinDOu05QK4AIgKNyFmaykDNWjWlWMw5s7oZOK32I0EvfGs4caabkDH8MrpKKe23wQOYcdKz3YeyJTHMx7J6ziQRGeoPgzYyJHr0lt7/bK0qsmoy211GIp8P0e1S9EuzuAR6/5gCkIJiGS5iSHZNE6VKrEQ4MMlrrULuv91zZPtjRzG4+guvuW/cr5e2NY/kT711u/Pai62yWxq2V9M43iPz5BgNeE0pnI/A/qqaSx62t6vX2DZdFkUroQQ7LSXXeURGGFGhaKOoLKqsMnui0pp4fPTYxf7ypsdfkUg1f2Ft7ZmTyAUHJ1/d9N7lj7snWh0JPLH6c9OHR//+pBT3dt3mT4+GQaROPvlheexR9Usv6B9OyG3k3JmnttbbQ7tXahOnpLLmALwaUF4R8oAD/nfzwDdeLHR71nHjB0Lc/rGjuPED/PsffPpQH72pTm657YvQevTX0HfgH4XuZuEB+AloHTyRvR39K4ZGmZ0zneB+IW5mSRChQJlGj5UC4oiCcyCXAyjzoKNXtR/n5AwAYB/+8If/ra/hhx4fue3TdeiNWkKH4MwmY4q6Av0NTBsm2tCjmffNBppVfqa+WQUa7o96r5cckHNH86Xn22m8CG0Q6jpzwLoPcI8Q0lBqjik0FCGFLk9cC2TUYWyIAqNcwaWAZRGSJ4S8FtpAnYFWDvnetYEQAotSMErRTtPjR6Pw0P5Y+g2JbsTQWgRkTam4oxRqgFMH0iohyyXLfjZQ6kSkpOMR2kylDLhSXYfSuHefUillEfICGyAZHx/5/U6nu/ZTP/W2F8DmAHDNZZeH9z7+6JPQfMUjAOaoDnkvAfg2oXiQc94JlbxA6ft4spvGn33/L/xi697HH20CyN/7+KPONZddHgPA2276afHZT/7jSSkxYTsOXxcpeBjvYoK7zLIpbEtzauiG154twGpLqQx/JtshAj0ZUBYhqVDKZIYS9NouER2GJQAJevM3DwszkBiCVgimcXoCKAqoJqDmAHKQgjujkO0B4Os+6D3nQtgSrH61K+/9OV8e2Grj1AAjNitNiEnevmDGUq+yCIahjcqrevMcUkrLvev20U++MDwlU6E9Sw43ITcTxo1TqWQSS1tIRWyLmvBaFlE7W/Ys0OebvJjxphJdFh9Mw0EZHtTGyP7PzJFZV0HmWPM+B7SNogCak8pBKqJ6aTCp+8X2wamZQSVlqdJqqWfZSCFvCXtxx5A9N51XgjJWCLjLCSHtAR/10QI6JSbyzaDVLvkrbd9LktR15s6ZVA6UyqUq7DhWq9QIJNajTlSLz9lzyWUnD1/9zsU//+4Xye6w1Xns4WdKE2srv5oCrgC5e+QTr7+DFpxcT77tgOfa7Tg3IpW81EXrsmNTtbWdr/jt+61z7579a/pbU9Rxvbc//0DUZVblufJIqZyGoxPNFYw9fqy2sOSMpIuRs5Uv0l2lDokSEj1obWneO3FBq4ZyPl6TfuNkmDKbSJp3CStaktmESIBAgqhOSmjOokop8P11mjy2BrQlkn1rRJ1oQR5rAfMdYKIscKgt0VaWJSKq6xq7BFJSNJqAChRgUaR2dt4MYmXWRjYkZdZKgtON/N5xykEEGyBK90IF0w4Ql5rrZ3H0izEbPWy4dUa+sj3BjRwZ9DrB6ZUUsiHdXqN5wrRByaB9CBD0OabZ6I5B9LKcP/S+O4Sm6WTXglmHvXMKCkRC359l1l+GLsJImjoKcHrLAxwaYfpCtVq7OwjocShrGS2MQ+jkKy6Ivb6YO/pcbfTUAsr08OFbhpuPDZz71EPb1AovH7jcym3eVitYhamlA2oi3EcoWYCFQ34wilw0vEKrmIZLC7DFnG3hgGVhP+FWUTosTQfth9USHm8Q1pCjzgOul7SF3zkelleOCBEVokfSTfaXlfQH3uOxkZ3zc2/tfPevLn3v/qve9Fqx/3dvXx9Y7p7Il+SJ/P+cmn3ywz/7AocfAP7ovz0+U8w1r6YsrcZBbj9VZEyAvYlCjG5G57ZZDN0tQZsf/vAFL/zw7R8bxnOPFnDeFd0XvgngvCv+1UbSh994gfrIXc/uhaZfXQydjFWABnJM7dI90MCKmcteog/vcS6tDJ3hNPshBaUxmGWB0n4EiXOAhwqUEjDLAiHLAL704Tde0PzX3sf/jvGSRPbUHfckAI72kjXeAc2zMgvSlJ4w9XCMJ2c2VZPRZRCTEKbUlvYKLGghMZ7fdgDTCVSy0m3p2L9O4d4F3R17pwTmpVSdCmOLA5QJH2SorVSHKjVKAJsQ4hQZOycQggVS0jxjZ2YGn3Z7AO6nhPzRfXHwZFOqC0DxW7AxHOnNqBIqqSiQUqDIgeFlJb+ulPxaSZHrHKAZQj0jpFysWNZ2aMOUc6V2c55uIZRZrv5+M/gb3nDNzNWvvnwdZ0H2zPjQ+96/5yOf+PjD0AJfzjnua6YHhuRKuzm/HnaHABwiwHlKG033fOh97zcLW6Lf9ihbdLKuBO4qO+WLAtuercdiU+r6lzOyIZJd9A0REzo3G4OHvpLPDgmAJ0IaTpsEkAqpIgJYlMoWwDrQSnsXABccF/deh1DqgCXSAc5sCiLzg+CHK8BDR+HwEWDvFkhuE5y8xJGPHBPWv1zA+KsusaWoSczUJPI7PG/J5vIKStQWCYy1QSoEKvWBrQIoWJoFb+TOIA0mVGyayyPzO1P+YmNzJpTAogQAPc1yN5uj4TCdbZjsXfMMjeIzHTAIdNwvUj0NiH7GcRbmyCIv5vPm/MYA55ljHaUTT+ImUK8AYSVJAtKs+eNxUBjoNFyXCFIO1NHGiApnp0farbFCfsfqyXNaRUd0beYLx6EL44OIRgYIS1PV2FEJ664TCstO0ZY0dnweDDjkYK06t+P4MvhIzOtjVZcL6Qz57dWFtXT2+I68c8vbf9NrfndPafTI4afzSjx3kSX/cnY0zwHYK9H40NH2rpEWz70XqP9kBUu+69R9J1qR//W5W27f89HyX5T+fLk6xFZ2ffq6tzz461/87GNvvv/rLdv3ijTsDq3Swo6FsQFsDdbklFNTzCmqmnRJtw2vvcz5Whk1ubnglGTsONtzNsZyjEnFbRozEVseXw2VCIViIWeyFoAkAJnxkagYONwCmiEAqdDhCk+sK9QVACU4LAZQBTAwdGSRBKypXDiIVQxL9oBaUwPRbFrGMTAyk6LPBc1muvaMJiKhVKqB8kTqxAyb6CxdRaC7bGQdBxPW5+gXpzP6FxmZM/KVLXZv5N6UD6L944l9xv+zaOLZ0GwDAhhj0IV25k1o14AAmQxlIgHKekvAGMDG2eotC6sfCmQQ9iikreItP/2z+xrbNs0v/fHvv3m02RxYh402gBxSp+OC1yoIaYP4anCwte2X//Tu5ic+c+23a/DZ3zTDu8rnHBLnrywd9LZbSW9lNamy4tTpXummpY6FhAbu8rLqIE4X+aO5XFmsjMvXdBw5tiVp7aUD9Ct5V7QUwitbuePPA/gGEdgsFtUoX3NzCAI3XxsPP4m3tc3Debf6vPoseefg9q+cYP/r52fo7u7HigfeVZ7DsKoAaKurfyEGgCT1SvOrM0d8t3vAFeS1NsLLmpCPxLC/GsN7KFHvPrshp0cW6f2RD/XRm2IA/0Juue0p6JJYF0PP1To0cvtlAPcBeDeAa/Kgogu5ArAhnSykT4NeJAh9pyhG317QMkII4PsAA1yRIJaOArNa0ODHj/V4SRp7AEBuuNa08OpAh90I9OSaxWuUTLZWjpnATEsfcPTDuWZBN6DDe4asDgArXHuEc9AK4wB0U8BNeUIariKRRdmyazErzyzqKWW5ti0pITWphBNxPkG4sIlt6CgvGAl0GZL/BODZv11bpgC2geIkKO4AcB2011KC5nQRCsgAaCxK4S/G4eXnELa+q1gkhJDUsSzYlJ4EkOdSopOmAVVq3VEoxQCTSvmuZYnxytCx1nq7x7N58fGh973fIEH1vfftuW9ndax6eG1p7sv7n0gB7Cy43h8M5YrHTtRX17Of+cgnPt4FMPCRT3zc+tD73s8B4GsPfEFdf9XbH15YXl0D0CnB/gYhuAh9zoxRwB1oA6KOfmIGQY/jlhkS/WKsZlMhSqkgjpM6AQl8n9UAUQHYKKDaRUjaBvW1ckdoiaRdiZrVhl9wU+Y2bLAHU0r3uTlfdRjdVw4bK6+z01EONrNfkk2XOmrMo2Rgj3RmGI+HtoqwYIfhCICDCXCyzpytJRFflGoXMg9g2tH3M9CTPxt9pONMlM1stkZ+N0JfjBDle8yga0Cf0we8CIcRfe6U+dtshGaDpQAk62/+BkFF5jrMdxglaD5HMuc0z98gsmoxV1Bdy24PtuoLCtiZCE6rQUcJQPpKUEE4VVbobrPY+NOlgbr0yENjJ9ei9ubqpY2KH3NJ7aFOYs93Uk4iwtOK7ShmDxBGKbHi1GVB2vFy62EtTU955fKETJI0ZUKUvIVmOCpu/MYnKk9vGpwcVIuLT7/monq9efDP/nnu0NIk55tvOeLiaKn6+pbIXQIsXJMP6ZCDxBktzaLMOJSQTAixuxjWBs9ben7vq54/UKiH5cLYymptRHG3G3SGbWDHpGi7V6jjWPB8binP22+Nxl/DFOoRqHtsnU9ckd9Ht1ZGOpUC5ERuPLUwRCicBK7NO5FKn65LNeDQtMshOgLCgnLHiiQJUsiqq2BBYq7LUANBLHqbJ+Xa0NMGkA2wsmurIKIiB8FicNUrkWJQtOxcmdImhTPkwMiiMfp6OpD0PidsDf6a/rTEOAPInAPYoEBsGP7GsDSOedA71uheib7jbWQ0i/Jly2EY1M447L3wK2L09X4PETwtozfNfD6EXovZ93u19XxzvVkDNWuwUAAxBqGsItrMwt6JV7lHaxfsxFt+aq70X3J37vvN973zzt3/AdcqCe/AX9FvduCPdOEnSrHKBbv/fOGP3r7y7CH1/iYht9ELL23IXTfNj2EoN3DnZZd3Lr3s2wELRZL+7qkD9iY2hBQzIFAqgB89xrvR/TxJt6/d6f18/lHCaM6bYo6nL7oNXZv0PACrhJFT9iZ2ON7fEGpiPLrlp/CCEC109IFcemRp02gz2LT9K2v1r9y8tYq/pi559W2hUjfVtk4ePBYnnpxf3ZyWEK6MoXvXINLHD6gP/CBGzsIPcMz/56E+etMpcstt/wk6imJQNgt6P98PbeSfE0J2ANwJ2JeiTwOahl4HFfS7cmWpLiH6ABBgu4RTBlAmAdyR+b4f2/GSNfZ6YxKaSA/0SbYR+gbcCE5H/MwwCsCk45tSGyE03CvR64OLvgFhFOBu6MzNdQB3UJCFkLFOgdkHcoyGa5wXBqh13oBtjzmMNQCsh2k4kCbpZCDayGMAvlXCGSMC8F8AfMokSdwKCHLDtXO9e7kdwCMA/j2AGwGwCBsFRqvQ5UHecFyJQ14UHnUp4wx4WYXJZyOOkwMeQSjFJpeQlkPIqFIqJVJ2KbB/1+Ztf1US+X0AuMJiAUBIMP59eRcXT2wOBSHh9Pbz1Yde/Yr0I5/4+PF2HB3/jzf/8tnQwVrvWW6c9/qr3l6A5kC6ABYVUIsgqQWiHFA7MycegNXMPAD9jSGLEkj0myJsJB4QQqjFmGSWRQBsA1QJ4GyAWWrccYIjIV9Le5mHw4zwQS+3tIVi4RTkY6tgy6Vi/sCO3VMTQ76TfOCJhyUmLkvSiYt+fiBs71XP/OPsMuyRou/HW0icupo7IgGUHWBqRMSDBCAdkMiBct1+x4si+rKX3SSz4aIsQoLMMdnkGrOxmmdxJlE9a/SZY7IhE7MmjNFmzm8MtWy4LUuRyKJ9Bpk0YWhzPYQDRFoENldKcXAniVGQGOIUlAIDoe5sj9hWgjssn1p091ypKu7fdFmxPlKl9HzyjGW3fDsfI7FZku8GcuZAN6qNDJFQ2W5jsAJliSgXBpKmSa4YKJ+tOO4CKVI0B1fXFit1OeGNbFtdvaGF4olv+Zc0NzeOFS4/n41Ev3Ie+cfuVOGih45duv2+/b/emrp0anWmClqMkfMDSLiwQNFNGWbb53bPJc/e8Zcf+4cTN9vfnX/dnc/cVd+/+lPry+n1Az4bjAgZLwnu2QxqRgRJu1yQrToTzmonWUt8e3F6UOQOrFDvVG1Hp6EoLhpZQ0FEUoQBBnNl6VCKWCoMe1A+JTLiIC6VVEChIwmeb0l6KpByPk3RVh5iY+SEvcQGdyMxJkKBnIpyQkGxBFz22j8aeTKGuzGiPPRRN4OOGaQsG4I1TnKvy4VjzpECgvTCq1lqQDaMS6A3W4V+IV2DLifoO+UGrc+GYmnmtZFHg65loyO5jGxnE4uyzom5PpPIBAABHCRITnsu0OeSSiOWDHjhujFroFu9EAsWxfLSd3HPYoIj1JlYfPaXruq84VfuWFW/cpP8ZXzxWQB47i/ftv5bY0+l7xjcPHHjkQfS9qq989u3XVL9+ucf3ANg9b7Hf2P9rv/xS3J2fGzXYFjfFrxrMPC+1SDxoyKwN7E6T8RuGagRVqBBcXqETrxj4ur22KmlQtIWsNCE3u/OgTagD0AbsQGAo2/94qdftPf5u9XnEwBYHvjlk5VOtOw8J9tfuXnrMXyRjQAof5y9R155QTjZKKA9t/I7p6BRsh983PiBs9KDzPgseaeJxgXv/ufd2yCJg7f+3nM/1Hf0Ro83d+ps75FbbvsygOek3jMFgGfQ674ErZdfCeBn0S9ub+QrmxXeOxmFsBxA72vf/HHn6wEvYWNP3XFPRG64dhZ6QtbRh+qNB2gWew1a6MfRh/pNeCFBr4gxtAKqoq8YDkOH+oah0ZgG+l7gkgssVYulZL7deibkfLEpxKOvKBRpIORQ0bYLvmVdDe0tlFzLbTTiZthK1ooUDGW/hMw1rAL4NIBbz8yGVXfcE9x887uceql81XPNxisPra80obkhm2HCdToQU4ENGhN4R5OYlYFdDkh9LaVDY8rd4rnsqIBIXEpty7JgEWIB6E5Pj9+fn8jt+bs7v9C++u1Xer3zzvfu9cXH9onwZAMRgKlGA8c/9L73v1gIOO49X5AbriUAnOtQti1d/+o5AEuqV6K/R4LpoF+VvNSbszVoI8lUQ88mAxhkMotamLqIvuNYY71LcQBwCkkLBGwNyKfaiFSlnKeCCI0xgjvfm48PPsqJ//WEqJtr3XdVHt5/0aZ8fCcYnkd70baty51NA6XwS6m9OsFTOSkatRLDcAIcd1nuAERwtVQYp0DZImjnoDyiDRtTw9EokayxlVUswOnKReKFRp8Z3ys8cqahZxANg6xkEbns+Y38mw0ue57spmfWmDEKGAChlO6OQAhR3KWIGLE8LvhQ1BFKoUAp8gRIegvViwFSCZrrKhIDkjn5sNOId63MhoXj+0vba0evaVStnOCq1Sq5dX9OjW2qr6nDqR0EY5NlN+m2PLvVLLXT7YnL7PJ6m60PleAOBqLBy27jmG1BFIfXVCTlxO6ZlULly+7qvgtHbHqRmyR23fPio1sHr5o8VJua76xhLq7Ad2I4nRBJkWL1FMHa3WgvfbNx575n/O3/dMnv3jj8J+tf2/ML0/XuZ5aP7MXQlfORu6Pqd9muEy5MtgAAIABJREFUpZqoMDDLhdq9vOIeblurDgvKV1onrMcFtVaiFO2nmrnCVIJwzC57I2iLdYJ6Otx2hvMDnSSNxcnAI5tyEI0UyiaCtiIBDsuqJSlvCYqV2FAXlC6CDAIoMycmWgEF2pMrZpC17FrJFkHOGkNmnrPZq2ZNeb3z9BLfWFF/VsRAwgDSAbxsdxdzTuNkD2bkz6DFAnodG6fBXJ8xNE3YV6HvSJhhjE9jvJlB0S+jYnivBk005zXyrzlaCbr9+00UIDngsV7FozRj7HWh9dGJ3ut1NgTYgj03IsaW8uemzx7dv7L/0CdV9Ht/8zsK+B1zTcfNH382fUkMIPl8VS3evvjE1HJ9bvJOMnwBQOcJuc36q98pPj84EiwO5pxN5Fp/sj1kL1cW1xtOu7RTiFZHNdNVIcUm23ZfB6GGWeo8LLilYPFHoPeRFHrfWuz9br3xVz71ooZedvxm45MbRazfQ25LAYTnYG0pDz4z0iB+o6AWf9Bz/ZCjPOBNz7zynZc0kR56AlAE3/hgBdf9yQ+X7PF9hvroTSk0wgdyy21GDvLQe8wCtPH3AIAboFthFqHlroq+rstWO0igQ8Tff7/8MRgvWWOvN5rQVryppeNDh/vMZuX2fhrQRtIUtAIwqAqDNuAMV8VA/yasO907bgga3uXQC35fDMi1IDhiA/dcXShvXubp5BCz57cW/U3QxuV+aC6hb1FbDDpl5sOG65WMgq33jnsQwP+49dbPvYDzQG64lvxEsexdWN5amYuj86AFbr533hIAgRTU40DEUAdFqR1jVHlwBhj1EnS9LtRcS1gVl1GXMdaRQizHSkWubf+F47rfvfuu+84HUPqTP/nrwx/84K+eRD8j9QcZJpNN4MiCSUMHgDa2T/TDwkcWzDy0oRfXprrFw2FuKwDh1x74grz+qrenlt6cgD4yN4Z+UesQ2uPKol4GCTPGneH+mPCoQfkMR0kBRDGwXIfHssERAE5poFL1xsdGlk4cPdzYJqOTX0td9hhni7/H0vMWI+s/csKNl9dCe+6xo0/905+eaNfPfb3F32URsJDg4SVGJx3K5sa8oQOqHZyzksqBiLFS3nOcXJqERSizURuKQZZbxwEQqRSakbRtBlJwGNA3xAyXKUuAz2Q0bozs8zsz5AScbqiZ52O4UzTz2jSaN9fp4PSQVoj+BmrWmgCg4jhOAFDX8yiJJbN7DpVH9cbMASkYqC+g1gCyNJb3fCW9wbUorpWZcni3c9HC47WBRiwXxgZGhSMS2Ya1vOzbFZszUYJVbDXZEHGRgAyoAuHbF+bIOi2o9fy4KhQSoTqquU5n1Dk4lVsOlLu8LEfbFF0Cp7qykp+cOdKsrIxVVtIS8SbstQNrm/KeNbKcv2J1NV+zysQlKfHWET1kn9tw3dZBu9v4VnDu2CVWKZe/MD78jvKe1ZL1arH0eHXzrnOeXJgYCcJa1UfZ1zUHu6cklSts0CrZibiSzzJ3LibVUockjJEABTU722YNPmSf8qp+ybbS5vFWKhpcpd1EYJ1Yaq4j1TOrVKaKYbRE5cku0JJG1nuGkmVrVI8a9CxT8Pc0Xp4Z2TA7MnNp5OJMtNjIlsp8ziDoXZ2JKyMKknhUNAIppgHG0OflVtBfh+Y7suFQU9A+i6BkEkI27iVrrJn7yaLbWW6huf7sOc/GXw17zzJlQCo0WDAAp0VAOEE82u2FrCPoCI8PfUwOwFcBfBfAvoEZTPsly3nN1ee2KtX84od/KRed+UWfxNv6BstjiAEcO0L+sXohMNVk82N0dMrbtzAy2Fb5kV/74ysW1ctvOvXEtZ9a/vr0qYHVZiEdCjcPlhqLF7LB8KSqyAf4mryI8/j8mISL8bI4RXJ8FrqOqYAO35564698Suvwy0DwKZQAhHjs+9N0zhgjAK58HtWH7zk/bTRzOK/3DGrf53P/mtF82Y63RZWLnv4I4GmjvunlcTrH+0c61EdvMtSCVuZ7mgAeJLfcdgrAl6CfwaUAroSWp2LvJwTwtwC+A+DISwHVA176xt48NM9tJ7QRYUJgEfokXGOs1dA3ZLJ8D2PhZ5VPF/06ayZEQKANxpHeOVZjwYs3VqrVOk9pKAV1KNkEIAyF2NmWYqLq+zxv234YRsR3C4HvFkyR3ATAs9DexOehvbKzjcHvtJtXPvTMo6CU/T10+ZcitLH3bgDnw0EuYiBgKEDABpDvJDgmHcFywNZFEuzsdLHk2vb8kO3O+JSsJFItVYCDR4+cmIM2qMqHDh5zCcY7P8zD3zqAcOOZriEHbRwD2vvNchiGoWvZ3Qe9sJYLnJlWdvL6q97uQM9hBMBSUIkCFAUx4RsbOnxuSqIA/U2ohX4WtQkfmc3AGFYEGTK2DyIZGAFoBSA+AZUyjLtRku5VNjnQVlA/nVf5+xcHf3KcRrkCSwKP4TFoL53w7qo9TfB6h+E8BiQHOVnzlYyrTFaRzv5MkiQjLhe+DaemOFkSUBOxTuZRieZVNZmeR7MpmWu1AJBUvoC7Z+Q1G6LO/mTDrWcaegr9loEE2snIdhMwHEiTAGOUoDEGshspMn8b5NRs6AZR74JgKAUaCkgtIFUKpYgjByCVFhgEcgEgHIBUOgELHMRdr3qsU7J3dgppsDxeLuWCtVy5HnDRJlbssHy5QHyRU6mk8apycsoWcTmycl43HRhoxc3QUZEf5iGG1us2osHqcH5+dXNrodUYKK1hUowNkHQy8sJXndq1ezmWjX3H0s1kkNZOnp+b++7x88YvCR3rZQlzfgZCDYg2WZiLqqujXXL0mtfO3/fge177yDzZckJ2krXKk3e/J36m9Qq5jHKVhgNba2s8X2sXTyrGi4Laitm5qhfQKR6VuwGXXsIxEwX88mrgco+quztbcMG+Z+3pxTT3YGWrWJZbcWBkQqzM+75qcYJ6TMhsB5iLUqxK8PmQoQ3SC91mkggMt+w0I+hMuTCyk3F2TsvczjoNgHbeTAjUOCMB+qWZGLSejQHVBsSK7zi0WGp7wRoXQNmUNMlSEgySnUW0s/KVHdlMSCO3JrEEOJ2zZ9aNOdZwRb8XXzWbVARoXV5xCdxAYRZAE0lZgMgOQJYBdxJ6H/gW+jrkOHqhP6Vu6kCHS/81Y50BXx50+K5NornpIqtwcTVXGn48bC/84YG/mbtifrJ0bHU118iHV5Addfcayn17Tbi4d+i6kenJKB0JjjXmlip0Kn2VNYV/iB9PBT+uZt9I/6RZaEwAnwKD3req0FzvfdDty36YUYPuEV9rFGFD69kXDcf+q8btH7Pf/eULgO6906DedT1gVgH4Bdz+sX/EjR/4316kWH30pllyy21zvZd3QztSKfReWUCvvp/66E0/+ufx/+N4SRt76o57EnLDtf8MHX58C/RE5KGRvBz66IQDrbDMJpctd8FwOhlzBf0SAWnvPAp9hKMD4MiEZe2rc+6vp8kVY457aMJxVyhwTUdwSykVRUKUakHoweYuJcRkWpoi0McBPAngCwCevvXWz30vz0ABmE+FwG+95o3zg7n80ge/+k9jBJj3KatahFQDwbdzq7fBM9hgUDnQkwFkPgBytlL5DmA5aXIsR6iKgdGmkpjnfNuUkrNTjvtY0OBD6ixXcKwBBl0dPAFwaOsAXgxWb0F7mOgdj4984uPsQ+97v/juni+eNza28+e3Tp/rqv/+6duxfWL9+qvePgNtvLZ6z3oRuqzLJRGRJQ6sFRSVRHc6iKER2GwoxyAQOfTDvoZcG/Ve95p0n5ZQwFsgNQomdY4LTojGSjBfX/bPoeINZarWb/aSe4+jNPH3neJUQJkcLHUiADPQtQajXRaOCYV9gqCZAJ0xqlSJQuW0XJUpow1fYQSKCxrzMhzXZUCcAp7SifvWGbucC4BQQmTZoySTZJvdwLJGq3nvTPQG3+M1BUAEQBqAlYOuUI1+sWRjIBrDzSA5BukG+o3ms0anQV1MKFd6nucAkAKIFRBZQC7hIApoC6BhCwxLC4oCOQ9gqqO4a2OoUYyYsJJSriUHaVOJ+3dcloq8FfidVhdCEeInhDoc5aVEeGo1KbGmu5IfwaZwjZWSdSoKzHGTSHR4IV0dGiFCxqJbxUyZrROXxZS1cnR2yC2tbJ1YaHcHdsZL+cnVp4t7nrQvC6468t0v7Xnr+Yu5Vmug2fG3d/j0I0E0eqoctu89wC5dnidbLgRwmBaclcsXjn01Jk2nNl287mXHlvCqzgI73nW8NrFsRWjaDKm/swhnu9eSTYGgToAZn3gWFJwUZKwYY1+zoBZPCbmyGqd2fc4+7+VCLY/uorXFEIoTKKEUOtICRwerphvAhqFkOHhW5vlnw/DmtUEBzTrJzlU2OzWLRsXotx/kQOJq3pqb6v8nCSCbgB0DfgJEO7qJirtrhUCXh9xIyDE1Tnscvw2ZzBprZzo0OON1F3pTnULfUAvQD70ZpNvopBdLTMrKq3FMKgDcQGFN3yfWAacLhe+gn31LAXwbugLDEIDZs/R4/YHHZ8k7KYCc8zIalN7r2mrJet77iEpzZOmVY8zb9Op89Q2H0taTh6JTJ3Pe2uXT1D3vvHD3wuThdIDbrau5reJSPFGtL8266epxWi6Mfqv19EkVfkP+DGo4nJzLH2mQWXJ8/SHnnJE3v9Fi9puSJFhWBFc6l/i+Y/n347GzJmi8YPTamfUMnpvS6696+/3Y4EHfZrjiy9+r7dkPNG7/WAHAe6FEXbqt85NuxQIIPN9bhHb+d+PfqCNFxpDL9mc++W9xLT+q8ZI29nrjIHRj5Nehj8INor9gTbbWIPrlOozC8dAPWXWhlYCANiyMEjGJBU30MlEdYDLgvEAJabHB6nzFcVeCRm08lrJUS9NihVmNccf9pgLeRAnZhT4PrYh+KOQzAE7ceuvnzmpAkRuu9aGRsrrFrMqRtZVz1rvtKoCLGSAoZa/r8iSZyOVPNKK42JbcEJ8bAWQZ2vB1U2A51UJakEmkPMps6jgnPcuarTHr6nKh8kRnbfHcPGVHAezNXkOa8ms4518lhEae50zibKVZjizojiDbJ0xyDADgI5/4eBHA2L13f2vpnK2XfJAS+3yp1Bev/8VfM8jCAgAyVCnnGs32DiGlBWBeQe0iCgxEhgA1ZepN9mp2mM1iIxTvOTbhnDMulYc+YmGQ2wTaoBQAiWUf1bivpbAwQNTqOBE7LSjvXXuvLm3zm2HM+aiTq0eby6LVkyHeOwdhBE4AYtULlWSsGww5sK8Goz54VLcZ1hiRQZzExyhhU05vM6OASgFqazk1SONGMgm0wQe8kEdnEiGyx2YTM4xRmB1ZPpYDjbBRBhRBNhBQc04TCjQcKAVASqVUr2izMQKNvnCh5TkbNjbIuAUdzxtmWh5yjgUpgZM2QCkQu4CMdOYAlQSUEWI7ou0UGy7tOhbGZ5dVw6tgITfu562uXR+okkIUKxXnvVO5Yc9mUTrR6RBHLLMW9SxLRRCpD1CG5uAAal5ZxswfgOWHA8kai5ltL+Um82jQ4anolLM4MLgpHLZzqQ3vINta3St2kulvtbeXi/XFx/eG7XBkCuziMWoNOpXevZpEou+8/5479zx21daHDyj6lcrB4JeDwHqFzUVuwBWk7ED4DFYzgUoExJALshAxsdh17QsGAjxRG8DRpABRyBFbhLQV+/belXFS+E5KgsqCUgGA8YLERAGI6sZYMglnxkgh6Bt8WSQ3y680c24GAbgN0FSHfgH0S62Y47NlSGz9vooBuQqtt3wNTKMF0DaAKuCl2qbPmZpmFrDBgcsacNkEoGxCEs7ytzHqrJ5MGZ1j1nsCjU4Xet8T4rQaeVk9waET4V2ifR2sQEeDTKs0G8CeEqIahcw1YI9o/iH2934kgLBXKPiH4mV9lrwz3/ZU+tVLU/61B75gnkU+Rbrz4MGDS5c+/fILc4+Ozrw8x1pr1sEjq7Q1Oswv2/SWoe3TTTb7zMnj9pah4eqWSdLdTOv5dMDaQVSOHw2HV3wRhtawv4sMnthRiezolaoVTQvCp55fvNvPOdXrkiQYbHQWqoXcyDTnYcN2Cjkp5RYAv4rLcAJAhMdekHDyouNrD3whW/SYoZeZeuittzr5CRFNfvy9Apf1HNPHwM9+FgAfeKwMKh08dMUafhsugE1I/ZwCzQGkqUBWoPfGh6Fr5/2f8SMaL3ljr9cr96sA3gmtlExmjwkTZMtNZL3bLARrPNE8+uifIQCbTc3w+nIceGUCPDtgOU+6Um1abtSmRJJERcvqlG17v0/ZNouQ10OHfI3yNIWTN7LMbr31c9+PHzcIYDgRvPWV/U+dSgTfAqDEAafDkwjA0GzQ5dBFI9cBvLr3nWMAWh5AdOFYjDIgIpS6dSnsXJJY79h98blNy5pKpBx8fGH28BDns2d+eRAkf+g4BFDSlKk5/f3njhfWg86NDmUnR4/gW9g+kfUaeRhEwXNPHbSvfMsbLpEKXrvVeEe5WDjRbHdme9cbvO1N1+585Mlntj79/OEVAbUeQjYI5AAUdRWQkH6x4+wwm0eAvpfPE546GRVmjDwTuqpBh5drgAoBUoN2FJ4GMNZQdO1B4dz/d0cu6ojW0PbXDMyz0UL32O5CqzXoIYAORU8BuALaCF/zoI5IuyLbHj00pKydYPJyiHYOCjSKoraUMu/lPVNjilOAuVrWDM0AODs6Z/5/JuJxZrKE+eyZSRpnfi6EhGcJ8BIAauk+VxSwCdlwPkyZCh9AmijpCplIANKjLieE2FA9VIhApYBn95FBZH5T6I0x6s2NxQhiJjAKBRsMVADzPQuzYivwgKogde2gWo+LOS8ho6LrUHufNd2ZL3GHSz9MSMvzYlcol1BV5cyTrTwvuGHs2B6PI1VJw8SPbM4xGSx4PEdI0xuyi/UwWClspvPuALV56kzXWoN+HG2arwww2N21rVhaKoVp96goVC9prb9G+OXC46+ZedCR1g5iq+3QtIE90D2UJYDS9Cfv7syOI73s+jccfjosP3VfbRg5mV57kb1mWwqMKVAF0LZwHDsRbMoXRCiBIAGWWkCYxqi3bTyXjJLjzYLVpYysTFkKS0H0/7L3plGWXWeV4D7Dnd8cL+aIHCNHpTI1pSXLMraMZNmWLWNjYUtWF+A2NCwwTTd0QVFAl6EbqC6KWrSqqqsoygxthG2ZwhhLnuRBtixrVmpKpZRTZGTGHPHmO5+hf9x3471MJWPxp7V01sq1Mt6LF+8O556zv+/be39YgQkWtFAAQ3pR9irEwE6EApfNzgwrV4esUBRsBESAQIAqwFHIAE9uOzIyNFcsZC+oFLB9GKmCSvSW3ZRZBVQZILkgDoDl94+xi8Gam/Obc67pcBYvN68dDlCG59CQIhbl/lySyLJ8LgYOBHmAklsY0cHfyxkFSgMkAWQC8GPI+NF5R6TT/c/LA+ge/2GcmPsadgQPYfdJAKnWf78SXT9bR+7R98mh11irpHa9cqU8aDCYt9/0oUfu+p45z4g5Bk3vrAX11d5X4grpdo+AkeM0dRLq067ncmuETm4zIkwdZQtG3D1jXwiiokigC9XJ0C1PlDDflt3l5SLarNPhvVtjK4o00dwyS0eEkO9qxYuOnzalQY8nNdUmYRqEnJm2xy3XMKNrR4tv2QvgYWSA+R81tL7LJ+TPTy983LeLc8lPE65OIRMqjAOwcRRn8eRlSr7/8ht1VOMfRuDswUfv+xqyys7XAHOSwaTM2aLKPIpsLtZw/73L/5juGm+M147/34O9/shbXfnIFohhNe5weSpfLIe9lvKyQx4d5hFoAdki911kG3wR2cJSAFBUQJelsXFmdWnW0HrVM4wnbcYeLzO+gYzQuQMACaUMALgOY/mClCCb5Ps+9rGPnr9UgTs0LABjJUlnZqQ1dpqHAIXun1/ePDrPWN2IzJrlHAYt2MaiwYY7WgHGy4D0AdctFHpX771y+3Kv1egk0WmvVnni6ZXF1xBvy2X3pl4vPMcY/c6uCl4jIKGE7jEovTlR4su4JLP0v9/6wejdv/jLR1807D133XoLoSRBo9F409Ro6Wi725vvX5+zSqmmyq7HMskye9wEDRmoQ0HycxkeeUYr5wvmohCq1EUk5Hyj7Gc+pAWgQoDnXOidPggB2GlkEfw8ABqD9B4++ISdKjJVtJM9XOGsSfEygE0FXJdQtstUstJHVtoAwsnm6TkAuxKI0KT2BWg1A2DCtMy2lDIkhIwg26Q6yDauAjIAm4Os4YzZ8BjeHI1L3ruUqzc8hr3Jss9rSAgoKAjKQZUGDzRiCbQrbGujbPSPC8iy2KMgMEUijJ4Uqee4nEpKAOjYQEdmygBlD1Saun+OJQBSAnUJUBPQEOgihQcCAgaSAnWSlbKVBJSSMIiW1a5LgtAyvUIgZCVRIN12mhIixqJ160LNk8fm9sgZv8mQiiIPYqoNyOXSHFnzxlZ3ds9as72l4oqork+xtapTSKwuKTmhJqwUh3q2tSKWKhPxGnG8SBAFkxZWy+Vt6wyTpJzuP9Ys9ULmFlXBGuNJSrkMuzGshhaKkczC2gJwk5v4z/7Rp38rvPZIVVqJfPHBh/Z8aSrYmJ8uNg+PcXmD2zef40hgczCLAi0RYyXx4NgcTBkww8zsx9cckCpFgwLdPiCS8KA4hyUJYp0LL/J5kgOanA7iWFQSAqEjpQBYNEuebs0RAkCb0GmalWN1ZohsbsKFiWCrLJ9zl4Hsy6gGtARdQmapMpOdPzH6Ct8qsrWWA4nTd2sxM3aAnT93OU0mP46cf9sFQMFRhMgD75QCNARYrgBuIQtaU2RrGkUGUA8gm2/58cqh/+e0GwEjMWGlQK8QAPJxgHcAfBYZ0BPIArxcpUp+Fs9QAOaP4vjK1/Wv/oPKtLSMKdWG+Wly9zwGokB/z9QPRhf0YwdqadtxfPZs2d52IwwwQHpmUC4n892IjBpqatuh6xCx3tnzTx1e6541PD4WNoJzJBXtGkTEEviaANRvdB3TGpGbS+fXIGIbCEsJVkm/h3YUo82CuOUp+AIgPdH2rbMyJWHgF11ZFCNObbHoFne55oFuK1xoi93Rw9tP3/CPFhZofZdK/+ReQ6WghIPj/ns58IkO3vaIgeufreJ+xMh47+cA9HBq+xQqvV8Ak2XomgOeHkaCB7A6XkKxbaASBciC77/sf+ZGDAKcN8DeP8F4vYA9A8BfI9tI8/JFzhcZXiSHics5ly/FoMNGvjDlkXERwJXIsjKByv4+UUAUA9M+MOqmiWlxY7Vq8A2bsQSZmaUHwNJaE6k1ZeQi01GOzB/wYKVSsr717f/2Vze//YMXLTDkjlsKAO4CcI+EqvpIPJWdyylk0XheglnuH/M0MtXQXyKzixk27e0YwFIKyheV0jHA1/zeru+dO3XqTbO7X7zn6E0vfPJnf27jchd1VwUSFWfm0tfJHbfQn7rxHdYnb/vAmyu2s6cjkq/8yL/7jeqzozV66gf3RfqtPyoBlN73ngMTj248MnWqcT41ez0rTkRxbtukePnM0ioyQBH/4Wf+0kEGNBIKcp0BUlaANrPLdWlpMvdSHC7b5P0xc6B+KUm7D4h1y4Wep06hMFP2OgvrzW8GUj+P/ubxwCOfbwBA653vnVNU39IFtjFO/YpS3+GAHxM64zO+r6Sk3S/LegCmLKCaIB7T0FKq8GUGYxNAgTNe5owXMShvFTEgOVMApgAkfy3p+XLl2+FxOa7ecDkuB3p5lkQMFfcEMg/4lGkoRkAw8DyrYkjMYRIqJSzSs23DkFIQQlJw5DXmMgFiMrzBZiMvrZUjIBQAI4BSDIYVg8OAloCigNkHFGm3BM4ALyVEhUynwjIojXjXinziWFYxdEvkbG2SExlRTZAagph2IOx6O04Xxmskplxt2iPu9vZiumiP0+XyuLPm1gkRUglGRQ9m6AVxz+smepxsjm4WHFpQBdWDmbQ9SwDJiOuHxUVutQVlIZOqKCl1JDOZ6kZH5avN2+n2yjFWdz4PoFqO29PPjeytqX3i6ZKMTryrtLxy3+fkb3+kqMe0iV/0LPy4DiV1TGzdijBlmhFCDrotOFTr59OaKquQIKykANWIt+ZuGxYk2ea4Oow1ziT5XLYL+0FFD2m0CAm9VSXgk2YAg8ZkPnCQXuzQ058DVHVQRHafYgMkBLScRGD3+c1k2LoEANDMwOQZDIyKe8ie1zqywCUvIyeA4gCZACQDpALsnL+ZB6K5kCfM/jRWAEQQ+XosN3btWj0UBFZjZWX0YWQeph1Av912dByF9AVkYLOCiwOlnKqRe/XlIqMNpLYHQRcA9gjAHkImhtvEgPsYaT2soLwLnyZ3P3t46ocljqLmJw19dvO79rHFz+brcgeAuEffp7v/x9eqT5361K7R958qx3b1meLdB3bM907xdR2t3P7gvsOJVM65dvcx2lFvOtioHdoZ9/xSXPwfCvbYWH1651KvsLy+/MLJa2RguwgMr3uhszNOw1DJtBJjw3yp+QVpM48yKK7gJyQLSplKV9mphY4MYVSn+lULMrjZJqANlTVBloA2unKBIABHimqATifwFyfg4+pzm2ef3lG/obajfn0Jf4/S9KfJ3QYAeo++7zUWLoYLH8Af9+9JHU9iBfc/O4kUe7A4sRvlxi1wkjVovCgri9cqW3zQYAhg9U4DvADQGGu1BZjpJhCFyPa3tf59fRxAF3d+4jUK5zfGP268XsBeD5lq5lYMbElykBfhYkXasBN7XkpwMChl5R5QuRHwmEmILBN2gjH2PCNkLkqiMxTYWSBk1KTGxkjBS7RUE6lSbzUovR3ZQ9QhhNgeYwEhZNgXzgCwE8BB13NH9u/b/S1kJYrhkSAzyNzhM5g+lRYyW4c8sk2QRUEGsvJiDiZuw6CbyPn+eZzTwJf3z2w/tNrr7DzX2kxiKa//j9//5v6HT78sx8pFFZ49tfzBD97xd0a0H/vYR9kfbaxaJuXvP7ex/EPfOH3s8J7q0HJvAAAgAElEQVTR6cpa0n5f7dD0gTWPf23y1dVzn3v6T8Jb3nbj/vLe3uE9RXKjRR2PuB42188m1frUMw/88Y+y23/s5/KS+vmp8bohi+YtK+dWmJFqJaDy+3QpTy/PzA6LFvJrOszFHB4CQAiwkwFwZqdnHzq0bTKuOPbLj51dKiOzc9m8/aYPNR8od8kzzZGPEp3euassIrNsLPfiuFUBDtlaeTyNd/LBPLIVUMgUpzzV0GBZC6VSP5KQCUA40LMvFom4ANJ+jz5iAWwodTlsgZFnMPPy3KXP6nBpbLgkNpzR5lvvMTAAEQgCAoy6bEshmQc/Ctlm3AXgREAkCOGA9A3GGBk8L9LMnpE8OMqfpy4y+gAA8H66z48Apggs6kLGCj4kLJMBmoCGDDrmkLUGFOlIXQKIFfWoMlD2NaLqRsRYSafRSCvedCvGtvW1VEhHGIlvBRY3ForT6tTIPhUapeq6MdFZLValoVJQmYqI26LljFJXaT2SRPHpqZ1FKSNs2FVKqK2hZBmJtmG5vYDRRpF3CaU0qfrrQolkolGePgCR6nKwFqpAL0eS73FEHC+6I5P3z17jfnbvu0qTCps/8fR/iX/xZ8+u3fBTX57H7bd9EoDbJfgBDVQ4JRalULtL0jwdaLSJpWuWJAUnIb0NpkwoLRBESrpAf+Ol+90ynfKE+F73FEaxG+vQAEwRglouWMygtdiaT8lK4nACh6bZIzEsRBgubWoAJsAltCEAVgFknzf6mi0gQra+tJCtKQpZhSPPsgAD8RsAMwCSECAewHlfG0IBKwKlNpTK+bldIF0rFPzFXs8RgLWaTRG2Owydr7tu+jVk3OsjAKZmZmO/WBIH58/wKAztpywm3xRLdABWABD0mRyi36c351n3AHwHoN0feOvJ77znPS+c/uVfvntR67tyUHPRGvdpcrcd7HKnkzJ/v1Pj5841nixupKeuMBU9x3TpRQDU58pWlt3bxw6OnP3w/bx0LXvvzOwOb+OF9qptjQb7oreUO4XNfZ2lntETdFakslq2p1vF1C6HvpvSwK5LxHs23XNNa6NSCBq+k7rxFToRpOOfpT7WCwzcsXnNSBFTBV9EskUAEA6PmNyhgci2BxOpoEgtgYSniJUBLgEjt1ACLrWgSrfWgxKAgxLycFct3vTq+tc/cfW2O/++zgvjAJxPk7tP36PvuzgTmJVX13D/vQyAEd9373alcNSRuBI0mkbX2QEk14LhR1KpqdbwFEHJomQMgozAVGu49uUFZJWVk8jmbR1AB3d+YvU1R3L/vQYyn9V13PmJf4hF2BsDrxOwp7/4kCZ33NLEwAcoQQaCXGQLXubMLuNMgMEsEwM+X94H71IC8GlkWZl2orXwtfA8JRoGZStVylcYARlhXFiM768QNiUZuTaQMi4SUhJax7EQTzNKx13OKbKS63BprQxgpNVsPfj44882f+iHjlx6Pgm545a/QAb43gSyld3JS5ZO/71hmwSNgQ9dHumuAeACuPOJC2etkmkVkINbDXOp07r2G68cX1xoN4of/OAdWy3OAIDccctY/7pd0L/3p+bP//4nGYCZH9t/ZetbKxcOxlLd+Mr6an12pI66U7zt5v1Xz+1h5FBpJZCy5X/t9OJmxW6WDs3pue0OCYiiFripF66Y3tkDMOvYFsIoFg888vn5/+3H/3k1KLJdtGilpBF2WFbOyRcxhuzmaYkEHBYnoLmQxsVALZ0Tri8lgiO7l2QFwNtaG03dCTsn5n1RBlgbWcmg8cAjn9e4/TZ3p7n5DkZZrah04sSx5pm55hgBXGPwvCgAjp9lp1gJWeuyFOkYAzUSMFsBkgKOHvCoehi06OP9lMlWzS2/7Lg4q3e5TOXl+H2Xgtxh4Ud2LdiW6e6lbvABBqrznAJh6KwTPHVU2jUIKYHQHgaZcxODMrqBQXCVH5+gWf3P5YArgR4nQKzgxhox44APBK06raYOQ5OkkYpBEgJICW0koDwBiAvChVJe2o7bzClMrSV0aawQ9IoFv8lrllIU1Y1VfqQ5b2odi9P1aT7RW+a711tYKM2IZW9CAMq/UB2PtOeWfDaRCm5mdjxJbFXaiyKxHRIUx2fTmHa1FrM+MXqh7TkwwREoHRUKNUGN60q9tR2VNFgKC9NTlNciwbz1FhHlz+z/wFfOjcydlMt4EX/41c3zH7/tJ4s2Y8jUhDcBuFWbOLRLB/FiZEZtJXaWqcXeNNYgo73Ev5AU4jUdttNSGf62EkUTkM11HyUsAXwWrlhFgFK0gGLMUNBbpU9oAHGkODBwE8gDgy4GrZ1yII+sTOoRbIElxMjWy7HCDpRjHzRdRzQ0J/JAalt/fvRdDTTL/tEIoBZA4iyu4QQQJqAUILpQvIgs+7eefY/aR4go9X8uAHqeQcnl5VoTIAvIhDBvA3Cq2xG/f/DgCjt9cpsGULppvzg5UlTsc4/ZRzMeXnAMlcUIndkFe9KbsMZxqP08Egh8FUDzvn/9DWO6kqpf+qW7LspefZrcbSJzb0gB/JB5NrieWriJR6AdrOtOdMZkpNskuv45BTwXlTw1Vbyqagcm8bzKUbJMbzLkTp6urL8iq6++RT139puGILdvX/YOLHdih0ONSzTeJ+WxSpwmfdqPtKPGenoW32S2MZ541SprFUPebq7wEnpMAExrSYEIqQVGBJQpAQFfChHlCQjtQXAASkJRAqKRuQnkAW8O8nPAP8zphYJiCRJQkB7Vvaf+VhHFxWMDAH8N0Bsed35iGV/7+bdESzMfV83SLDNVGeVW2PVR9CJM2nA8YocCKQxigqCYStB0JwSuwkaJwU2mUYpIf47NAfga7r934zKdN/JKx+WC+jfG3zFeF2CvPyJk4MZFBq5WkUUlea9VH9AaIHmj7xwoxRgIOHJPsRaycsNeZABy0QaudIA9445zgSk9Eks5W7TdulJSaSm10KpqEGomSkklhUcoPUpBXGQb5OUm501BEJkP/vU3v7/2zMrCT/7GL29NbHLHLduRgc7jyPiClxv5pB/myAy3FKoh29xTaOWBCNM1nONKsJVeKlOLgXPC/AdOHPv+ibWVFk4tGcj7WM5N5RYHcuHX/50FYMfPfPCfbf72//sfux//gXd5B5cWznGtVq+Ymq1PFWqoWB71onDuAMicOZ4m58Vy67kXX/zSiopvfvPOK3ncWMbxk0/LQG/7nbfdcNMSgCiMYglA337ThziAOW6wWsG2t3ezbAJwselwH+yBMmiDDEDMVoYDFytW80xYXlKqALgFAGlCvbLkiwsOoavQWHngkc8Pq6Enxm3MEC2pNsHtbP7kgp1hoCcAKKu/qwkoRYCO1JL1lJIOZQkhGbAzs3PIo223/3naR0zDfKbhYCBfWHNKwuXsKoZBn4SE6gsgdP+dHCxKAFxpTYJUEU6Ja3OadyfJQXMZA8DcA+A5QN0BDFB7tP8dLWQgIO+l6vc/U4gAAg2XAIkJxCS7QWnQL+9aQEiAkskBQmAqIDKAqNLSgWoJlhiQusAqmmipUs2VICylmlOmSSoVZhbWSWk9VRJFOtVaoid3zLInxg9SxSkrBS3MdM6pR2evFG2rXiCCskl/SbpK6K5ZUNouj4DSqimTjqCsg0Q4kEEBBDKwPXhKlYMkYhErFkEI86K2Y+okTHo0ITpWxqSbSJdRIsSeOIldJL0RxdyQarip4fEzI3M7wK2p/rV7FQ98tdG/ri/g9ttOAvi0Y8JxTB1PFONd7QgfL9nrN835yfhjfMwsR6I4HkWRabd6z5p72w2Bol6SF0AF5Tw+JxKSAmYRWmktaD6f86CmgWyO5iPf8LdEYP3Xc/P5cXgYdWbhhSdZAomg/14v6cLTKSJkge42ZHwrAgivry0yAaMHpNo0dK/sJcF6y+lrjqy8ctE/NMh+Bi7J5omsAigCltHtkp0AiwEsl2ypRwoyPrvBtwPsxuxzUllmcJS29bWPPjT9XFIyXxpPesGtJ595/oqJ9MLn8IMPA3Cw75FlUtjwgCebH33qf3xVNJIXH3wPuuuPo6P1XRpH79oOgOFJ4OP4CwZAvZ38BQHAkiK7Kpg1R4vHw1u5xi4eZQIwjYgC1JK64GjYH44d/1CtI/7rdTve8+TznT8l4oQKTD+e2mjM74rL0UvarvN45bGPgNFrsG5XgcAQfZpJLw1r/WvPFbSRQKc2tAzTZR63OReW0iZgkP51S2VXAyCGDYMKdOHnz680IygGgNpZD2TNwMHABQYepExbVGkCRiOVt8AbDhahoUFBYw3968jKpX+vcY++7+8uo95/bxWY/HE7sd6pTcmII4giYCYlTDvagoyJZcOEyki6sMFAUYLC+2F39oHhFLJq1zPZHMBeAMBRNPDP782FkwHu/ESC++8983e1X3tjXH68nsBejKzOvx1ZVi8vx44iX/iomWeBfGTAJgcGOfeNIouKLWQiixYyDtze/u9ZBa9kjTPDXGisRR70pmnbvqV1SUKPMRCeCBEoQtsu4y4ZcMxyLz/gYjCy3+H2+zpx7wvY8jQCMCj15u3bLjfyqD3/ezmZe9gjLSc896CUtdLrjoHwcwA2Gdj+McPZf1V9Zv+JtZWlOE0dyzB2I+uGcVx/8aH122/6EPmFX/m/tn3gtpvVXe9/F/vUr/wbAmCjXK4+O1kofY5T8kkFAZsYMFhCdJyoImNsz/T09O7pKXzusc6qyx0iRA+cO9Haarjr2RdP7D45f/6JBx75vAaA22/6kAWgLlJpMYfkTaktXDw3NYepGRRIlgxThMDQegus5GXwXFkYYZAF1cjUswrAqwB9yYdq+yAlvJYvF1gWIDVg0ou+f/ge5D5lsQm0A6DWBlUaOGmD8VAbBwyNUoFsgW7070EOYvMofLjh++Uyc5eOHBAOG0cPl3eNoffyTHAuTkKiYGqpWCh0nHDTtIBKXxmcdxzJs31jyEBdXl3OS0O5oW7+/AAA66MJqQEaS1DNkNpAqoA0tTGCCLqUHUdgZgQkCCAoAqCx9mOKUkqZp31JVIFwW2opGNXKJIbblShFssQElNtpMhihMtOISn1eV5oduuaMqY3yiFEIIuxZPF9LaA3NkksgJY1hktGeX4i7LR0pKubibmFhZNzr2tVeQYaGG3d1jzlUUsYMIXRKtAbVKpBCGIwZM42FwlRvmTw7cUSOJM1KAGZGdqXuKMFCTaNyp2W3e/aG2l7fTTmWkGU3b5xdxjEAXTfx9e+/bybe+OkwuEfflzWLv/22VxU1PnM2rIk9XmtSYsXcEfauf6I1kiSWva42klAtkRFA2FSpU9aMFYg18mb0lAKSXnYPCAOsCAOaSn4vcs5aDsJzLz4OoAwbPWpimz0Jw5qADlcAtLbAvZls4jiAh5Ctn+9GxuEMABECaRW2bYCBwRc6SZludrmRiSoUAMH6y5sGCOn/3xscG+n3KNctwKwgy97s70RprxPpisFVLxWsCrD7PrDnhcfXTePuU6fGk7Bk2XgH9M1fXlib7rTHv7owaQHY0PouAdyF//TtR42pVyvkDlyRoAYTj4FkAT2AbE2gfaC3g4ayoyqFI6rV222m5lVOo7JdAVdqhLlIBYBWANUalIN1y6ZWTqL1nv+Q/rqceXflQHjm/Psta+WU9PQyGR+5Bp7j6FcxAUsIuCpFQAHAcYzRVOo0SUTXBKRMoaUGye4FhVBUUN6B5AN3iK0MHGkDIWBxgBlDPpc6c1VgHVBTQ/kE8EtAh2VrnqkLzIJBoFcSOhQMMwCpgjQSJALAww6cJ+7R9/3TgKX776UvObXdxKzs259g1HIQwhV1MFAkmvBKShgxCJQCCBD2r7IpAKZBoFBACftBUUcWdNjIgpIqNkoBbnh8H5qFHko+BdOnkJV3NYDc668IYPNi/uUb428arxuwp7/4kCJ33PIMgKu2lStVm5nmqcbakww4kgK7ATggNOdsFDAg9+ft0/I+rIX+P0oAa7pcU34SuTwM9Aio9gNfolD0PNMcY5kUraYIMbVUnDKqbcOgrSRJCCELROH7IFhGRna+B/3Im4O+qKCWFLBYdYvLDrcvJcqeRpZtuQbZQzCCi/lZw/9XQ/9ysMeRZSS7fZMNBWqvItvI94FhKoBki51GwFb4DXvr46O/9dCXlt578HDl6cVzvs25wBMEX6m27JvaEp/+ywc37nr/u7ZMpQ9NzjwH4KOplFBaQaUCE8QEce3Qc92zlNLv/PnDD1f9nv1CvTz9NseQlWbknRGLr1h/eN8XZhbX1i7c94UvL/f9p4xD+3aDUfr0/Pmlg/1zvZxnXEIyjzAGQPWX9Bzs5Z1J1pEBkwBZZiK/zzTbBNRIEeidzXJqw6KZbBBSYdCMksEqjEEGOO/zuSWGiLNSJ6HQjgVypUkMgzAYdADK8gxMYfDntkDVpcA8v5/DvzOc6RtWWRq4GKgSsK3SjcTALBzo9/TkRBsribQuhAlptiWpu7x1fYm3KSE7MMgG59yn3I8yt89IMLDAyWLz7L04BjoSKIcc1CYweXZdmwxYtSPUyMUK0kUARQEQmWU1bEFhSlsa0oD2XUZ0KmJLSa+yDmUKSMnAIwraM6HCEUGqgeF6QQgqKCkxX3s6JJyEZKTbVeWoRxcmaoKuaxnWCrQSbCgvahiv1nbG64Ui65kuBWTsUw9V1fDc1DdDq2oQw9BtQrStFC2KwCz4PdjCV1RGaqSzCsK5GThVpNQynWhTR86IkRisZiPUoVIJ4mCHGYdjlOqy5uZZRfl392y+Ot98xhhLd+r2b9z4m8d+/dFfi3/hTz+q3vUXzx1/cvXIn4jewsTVq6cr0blex7PkhfNp2TwVru8/VzQblOrQFqjM+/aNECj3E8gU4BIwE0CbQNzKyrJGG1k2Lgd95/vzbQmZKe1uAE0UsUOtYzQIIVOJhChwna0xc+i7AwDxMsDeC/CwPx9qBIJqqCKiMALjTYAmgDEuJOlvtsIGJAEinU1pmh+HykyY9QhgmUCqgbiY/W1SzkAhJY6d8tFKYPoNWlPJyPXTp/nTB8aWnyzFSBO/deJMoTR/629/d2Kj6a7/+1993wSAMiF/vvClL80eetNz+ot67fzxU7/wP//wxCf+iyrsCPrPJjGCT+103NXrGgd/ZDtO/f4R/wfuXNrrVcr/IrI7+9imZ9C1EuVW0ekkZ5TSIuc1binkzVrs2qOR2TlXMEd69gH1ZtVmKxvMqW2+lRhct5+tdlIFgh5seERjLE7QsCVAjDBqMEAm8JDAB7dA3IzOQRgUOMKtdTsP4PLvVxKaxCbhugyQLriKkFoZUMw/IAFmcchEZn6WVAEWGilAt6od+bloAIKAhhSkQUCeRJbY+Kca1dGw9WsCZEIArmnD6oaQsgNRKRCPcbKlpVUZYzgbHLkWmkLDQpaQsQEczeYhNlDuzOLaJ0aRmkW8PHc/NH8JuNcCkPQBn4MsIGnj8lZEb4xLxusG7AGA/uJDF8gdtzxYsb3ijmr9QCvo7Y2krKdpnD9MF5BNrKyvbPZazm3Je7vmG21XA8zmfGxvdZq2GhubnBHpcKuWxrFZM0xbibSiGaNmRsYPAWy6tvtNGeP47ud3neyMtpb8UpD+h8/+15c+9rGPfh3ZBndd1Sl5E8X69/+X3/lk+zKnkXMQF5AZS7YA/DgyxS1wMQDIbRl6GJQtB/5zWrkQaQRKKJiV2yDsAEA5EBULhfJV27bffLq5se/Z82ceWek0p758/NkdO0fGT73niqv+XBG0vlPpzusvPrSlxCJ33FIaL5T0ym/8+0MG61ePuQFuGJpRdpJS8sXzy+srvVCP33b0wFXaMKANtzk2xhcWe2c217rhaP8ebL/9pg+dBLBR8NzF+QvLezs9v4MBuIJlMKSphOobXju2qcIoyQU1eTTcwqAJfA7EZjDga+agiAGktsvj7ed89SCAC5eUcAHHuxpBz87XJA0oY2CmTDAoX7YBcAWUNRC4IG0jU56OetndyZXew6XmS3l3l+PeDYP4YcuVYaCXg7gc1Ouh9/M5nYNMDYA1Y9V4vhlNP9IUrqElilxSU5meKDLbJESEwIYFjEBDJplHjUPI1rXMuVrDa8UWGCeA1gqmYkhNwOck+/40uzbrZnavE2Rs+mICsJiApDYQFrGZCBBlwe4WDdLhSETJpg4UcbuJtADdcQmkgraklkQoDlAkDiOppnKzNCpDQmQxMZzRMKAHVk+jXVL0qT3XsFaxDrfbCldKc1rbrtmQCgCYlfRGYm5KSQ1FpNLNgisEYwyS0yiNYWiluk6dEjZKiDeNUdkxBQF0GicgjHFQTPeWeTFoyc3SeD1NNmxXyEoxbOz17QrMJLjaovKtSRC+8PDOt5wJPmxdeOxH3907EX5FuiPezGd/8kbvVGf/+Qc33rf4U1/9k6lrxl55/IZk7fP3N97Nr37p5Iemyrq8LC3/+RMjN2pfutk9TcysjSsDoIKMq6ULgGwBRoAqfQRNdRZZ+fXT/ftVBcXPA6BQiNGFC0BAoikDVOgIQhnBQIIiIJKMYireBugZQPZFGpapYbQBIjxbFpWmViitBEi9bKqZJHsMZJIdEzUAnRXrM6VtngkmgOyvIdrLuujphBCjkSS80e1yZaXRrjE09z6r51R3pfbCm8Yu3DpVWrLDb+wYffz562+PlvH8EVwwT6L+nAu175X/+3k1+ZZ9oWisuV//bvVDi7/3Yyf2o9kg7t0jR3/+I2NuNPHOxrHrX5l56iF/bOrB3Yra03SnZHxyg/lrKBHdhq1KSCBpAqUs0AaDcAFhAbYWHVOEgrgIuDN7Nhblr67G0TI700lqk7IjTXlaWzCIARMSmikYVpNMEDMNYHAlK7BB0MQSShhBSgwS9oOpSRC0AQRbSuc86eAAoJyHujzKIIlFVAtUA3lCPGEAqWfiNVsCHss4w1kVRENDIu+9k687CQBJQL5twf4qgK/eo++77J7zDx733+uGEW4pG+o6MIyYWcBhbfaImSaalTi0JJyEPmBQwOmTcigBGEUuiVSpglIxOGWZy4TBQQHUYeB6GPARJUqML/zgeSvex0KsbnMwj/vvfWXlD8jGsXm2dtv/+dMJ7r83W+ffKO/+reN1Bfb649my5y3FWv5MzSu859zmetoPOzkyJQ8wyIhVMOC4MADwKI2VUmaYbVLk1OaaEnGYCCEsgzK7IJTVjSN4lPYMoGtV6uGBicnwlfmzGxaj8+99yzu+/ZVjx1aevLCy++jK5JH9z8/Zf239mfsR+UPPvFPcGSLz7fsbx+/85z8ofP2VF01kZYivITMCPQ3gt5GpHfMMTA4IcvATYVDCiwDUAKJAiAZoA1kUbwLwTULMt27fF0+Wy1xKJS1u4p37rtzVCv1G2S2sv7hyof74wulr5upjj578ld8deDnccQsDcCRIEyGUUnxIXmBwTgDsT9P0ZQU1uxmprmWaezg1io1u0jq1ErId+64VS0vLjyLsAgNRAH/smRdCZOXzvEG5AkATIaH7oCeBYlEURwY0Y6A5mFHZecLEwOYhL9vnPycAegCZAkj3PDGfBqIuLi3hHnulgP1X1NNnHs9b5YFlv5MrVHPxwkY+XxygYwGFBHB7me8cKWaSRGDQoYO95rsG9+3SDN7Wpcbf7r03XL7d+mwkIKHBOQPlFBL93qK9OHWk1nzKJr4NIkY5MY9UzcSk1O0B4kytgB2NXscEvFCCg0HZA/+//Fhy4JyTwiMAzAYk0bpjpVAmJc3+vSgywGTAigIKEmi3DXqGKr034trxPfRik6z6FYsLrWlCiQwdxmBQMzakQKhlyqnUiZJuV0MR8JgzloaMBJbShpCwlK9kYEmbmNxuJfHp6W1pwy0QUxuOlRJS6vmimkTqrFuWQT+9UO+tsoRyJrpCdA0jNQxqsEhIyVIyGq2xUhKhxQpEMqknuptoOSVNUi2AWIyYJBFRKgVALBGZJX/TXClOFmpBh29rLzg9wyENsyIdImzeCwpeb31390qzu1EY777l/DcWCyvN58+PlUI26vAJ/8Lm+IX12Z6H9skDhamnx8Z+bsRyV9eO75uxvnN20lpgr15xoP2theWkutZysqoEjH7QkBT6GhnVn5M2mqqFWfdBvq2wC2vRGXGyo2vVzo1OXZLFkyUAbAYaIQwtUNZ1FVFuOro7ep10Vp5iDIkqZyDM3JYtH6GDrQBHFwBhMAbOAZamkSOUmc9BPZgfugOklkETnirLAJguIoUiiqSM6EQo07HiVAjoVDJVtBJZLaqK32C1Erf9iCivpQO+oOnNXZT3lL32XNFrjR1Z3UhqwWjZCusqJe25HfoURZG2nFdOt048/J1reH3iugtlfd30bRvX7LhheTS9EO57qbO9eujA8cnijoUKOlbXWAjekXaiQnNV17DhUCgNiRRhuoQYGgKMOEj6wjadAJGIYvtFGeM7JuRo8erOTcRMK7FvV3Sb1TTlijg6ISAcAgm6ADS1UYLNx+HChoEqIswhRhtdbEIjBAeFgQAUqv/c56vWQFTjQZKUdrWUAYgU6MeOiDHgltvIgB8FUBpaOMgli0i+Bi6C4SFIfAP/HUbKF4377yVxirc3A/yCxzFSLsECUFhrQzqG1lNVEEqAbgdQBDD6TrbOJd4KoQCVKUxGIVKhOaVyVkoQZnDboChCwQNBTKrxHTUB1RRIuhIniwwb4xXNbrtKzOP+e38fOacv49m/Mf6G8boDe/2OGkHZdr4ihdQJ9KHpUsm50OnsQN/Z3SOGtk1mbcZRzn0yyrajXcO0272OKYHEoDQuG6axGUe01euyFkBKIMwqVCgrFuETxjjRG8rkzUYQVs6r9MIk5X/11cceLq5323uDugrste0jbtt+syRiFjAXkZVWBuPUEgHA1nod6+mFs3N/8cJT2FYZ8eqFQglZ6XcBQOuIN3H6pL/y2QD4GQzumUDm/+Qgm+x5a6G8LKhASABuDaslCQBlGGa6rVZ3xkrl0qbfdg+Xpuya643edmpOT/UAACAASURBVODwNxMlf/e3Hvrifgl477/imgN//sz3R37ilz/1pJ/EPQDKYvzEH33445Oc0q3sTixS1fH9rsWNhu/71SiMVo7MVp/fPjr64dgPuW279clx79auCkYrpcLx1c7aWWRZ1nEAhXq1clJI6bY63TkM8RS13joXCiBVUB4FzU02FQaWOTl4yjNhEQa9QUNki8DLAFYavejdyPiQX0C2wGbj0W+P4MTxq2TfF6z/pTnYC5Bl9ToAugkwywFOAU2za1rte04YapBxzRfbYQ5dPobFHn8TJ3N47R62YLk0S5gPCQIDOqtX91+jAMK6Y7CKSZvXAjpSOrQZdy1ODaFBLAJvptXjNtClgLQpDGPQsSEXAUVh36alX7MLkYkDNICaxYivAd/vd+DwsuDd4MC2XpaWIF2blnSiLE4J41KVpdQOTQnRFg0lBwW0R4U2Ct2EFWKSgtuRiShUULzHSZWbMIqBUm0OlHtK0rZA+6BkRCuVWjShHFrYOvHirhhrrmjNKO0U7Pjw2kvqXH3GLqahG8DULXtM2TK0R9I2hYmEacrHgmUmNYWTdhXhka5Qqi0KUopa0gB0sdUlNdUk66UZHnIeB9zG0sgOcJXG062z3X3rJ+2u7bHDF55CxxuX1WiN9Cqe7fOyu2/1xfGxcGVOE+vaTuwsqPW0QBL61nrLX5SL/sMv3HCovnftxIErNl7a85m33/FOa0fR3/0n33rJVM7LO2f9//Tl7068S0r5zlRQVyjCsktL2gDNDcRTAFcTiIJK0v3mFaWqCsRD9bFNvv3tzG9+n70azLMSGmEFtrZgMYoYqVzVrF4JeJdb2k8MC0holijK56o2BtUxE52sOEssJiAyzK/6hYR+cEaKQCqUSpD9HaUYFPGsGMRL6MamqUeN0Jio9zRTKilNCMxM9lwRK153qs5jLxnRCwtl7pSDuYJOZ5NUe70z9rYJbJCRSvNErRD7LSlW5wu4/offfOL0zsPn15f+aOKa1vLaTP29ku4YWxmpHFx6c2r5dR6FrdRpT7UYEfb0oenKDesSNOksPDA9h9O2DSQpBTUAAw4ESQFCoErZ4yvTBETGEM5KWSW7670L7qy/Dx4tITUqukMUJhlAUgJuEUgwxOiSObgwUWZFMFlGRCQi3UYZbfjQkLBhI0IRbVBSg9Ks/8RmF9lB9kwRaAe6k9F4SJYqzcVneZ/v1wSCly4GEhIpUnBQl7tml02Q5+UZXQFDgMu1vPyHD2YZqFAL4wZFKYyguAEzjCAsAzA5oGIgiRioKV/jip8PJTMyowY0pSqBljROUDbBPcOCRgITCQSzUSYUYlMQ2olI7ZCrCCVIkAlNXgHwrf71e2P8LeN1B/b6o9OOwtPvOXDkoZt37z/3u9984M0u2KGUaiqUihnVTCZpOuuVeS8OaCAEcRm3HdNkKxnCEBNegdTdgnLbPQWqeS/oVstOQZmuRTq9IJocmyjYjO9dF+nznW7Tom7hIK1Unz83f5Z4Jt9RtOVLi06rGo/hDLHUV647eeByxsUVADNa68O2Ydz+1l37Fk6uL//BE+fOxMh6/X4PwPykVZy2QPUT/tILGIhFgIstNjQGhP+cc5WDgzxS1wBQJFSeWV8NlUq7V4xPNWdHRt09IxO92Up1/F++8/3kgRPP/dXzS+cL9ULp6geOP1vyk3g7gJP6iw/Fa8dOBELIXUrrI/0+rujFkWyGYYPS8NnWeivSQj5765ErP+BH6Yh2TBSMAkwjYKsbzUQEzQjAuQce+by6/aYPhQCMn7j7g4VysSB//d/+PyNJmg7z9YhtMCRCElNTbgLRRL1GNzcbNNVbCuoUQGwwSlKplpEtZgoZ0ZwCiAHdI8D3dGYgazIo+1/YUQG33xbhga+mOLXkYGxiEtXqMarVTh36O/mAl+Yh84F6GkAtBQ6lGYlK99W6kZ25ycqEspApaWmARYAmmfQ75+8Nn9ewmvjS1y79Oc+m6aHXNQZgNh/UZtCSQjKy5RmpAXDHoIWYkCRKU1VmrGZblAQSxVCBFihQUzBiwEoItEOQDskqdZQJ6BjpB+kiQwEmsqxsrn7XBKiwTLCh4/7CS4EoMdFjmnjVnkDIsebEqMsQeqMKxtLUNQV06Jl2QRLL9MN4dE1GYDzgSbIiTJ24KSYCqTnR0mMCRHagQMASDm0nvuIqpWGt4CY6bflGyeJEGwc2XhWr1Wlj2Z5mI0GHzW6eA9EK0q76u1RMzahrVESPGD1Z6DBbmRR6uTgNTghl4IJQJQVlmlMd1zaX5cjaZrHGuuFGbVYrbjqmTo1i0kuuW3k5unbzGS+gpn+CH6rU0xa/avEl5SF0W2GBrJojutZqk4XiNkx01wuHLhzb1ShPGfPlbZWOkxq7Rv1wIXUWOqz4p83iWJBw8xbDovaUc1ZuRvuPnz4/im2T6bFa2e+cnPfGVpvFGddJaKkcPrKy4rVBQGGBI0bRmK0I7dFEcUJVM/6BU2sjPK3rk7xJ55nUFgx5q+xSA1rGEGkgIxKdetpuReDjmX0KAKQRQAVFwi1GjFDmS4apAKWJxVUcm6xfRqYUkVKAznC9UoBkMnO58oHY6IJoUydiVzXiczqVGkodsTb5nu3cYDNRxGspg50qlfZ0r+WarhPSfXsaxY3TtlcYJaygEQvfIp0I+33znFO16RNmYByWL7GR1khhKTbM3WaSzl+RLIjd187PpcI9FNEpc+rwkrf2aHVy+ZGxJnfbOzeWbIPbhkLI17KWzFE58zO3iIABDSUlLM3AJGBI00MrMYWrHbwjVXSj/Vx5QzPTQY9SytOU2IlLuVGWiWaQxEIKU1MAHXBFkKIFrVMwRJkojI5jGjWyTZ3TEYB1lFBGAIa1bI0DoCW06kGsuGDMAK1yIOZZoJn7puZ0ECAjIMu+GoYNLRoaAEkykB5rphMEOCnP6Ju1gb2kRhr/7SP/7Fc/+Jk//e8zKr7zEwL33+vbXhaAisyoh8yMwhIKdL2ZzRCfSkyUspUkEYAMAdMCWNYJitDs+DNuBmGEkFSbBAUCBSFoIgBLGzCdzDCIb3O1NKlmlMBIJQypsN/g+CgjEE92+bF//Zt/bH/+137stf58bwwAr1Ow1xdrNH7mLT/Ir5nZ6X79xIunj104SwqO43SimFcsS04WiqRge5oSortpTF7dXKPzjQ2h+8rD9V4vtjWNr5ieSltBWO9GMbMMy2GUa8KZ+t78q2zULVijxdKRl1eXpEOoNlYvfHi7pie2E5d/r9uu65kTxJtlbUmi2RuPvrTxmY99YflTn/qzYTPL6MXl81OdMNg3Vaqc31GtP/mjR29auWHHnP7Pj3yz8/SF+ZuXe+3uNxunjANjU+GIdL68GYUnkXXp6Lcw2krv53y9XMRgYGApk5etbQCxbdp6ulIKOediuds29k5MF1OVFlpBUFJEv/fp//U3T2NuqvXSI088ctu+Q9s/cPio/NAf35sAwJdeenbv2Y21A79083vMgpu1rCVac6oUO7G26jGhF7eXqjZl7E2UgScpBVcJGq31RpF0ntwxPWFtNpq5kjQGMPNnf/mgcXDvLi6lfM18lEoLrSFLnoueHzSFFMVUw+CMciGVABAYnDHOOUllnCsQV9D3SATw0iRUcNiQZ7+dGidSqJsr0MY+LkcA+Lj9NoGjb0nQ2EhgWRdM10sR+nlnlQ6yTONMKCEowbpJURRAl2TZQxeArYEgIgQ9w6SFNPVsJYgepEXynrOXy8hdCgKHx+UsVobB4TDQy3+P9Lti5IrM3JohtTixNDeVBLoSACWoKEBHBJEB6AREt2y3UY9DW2hVU5SBE6IhRZJmGQais67yMQYebAUFBBrQDCgYAPOBlgRMmqkHdaNKS6llBrX1VLuhLCaAiigUIcSlkWTaplWmtYEoURREUYoeU2CESrfhsmnVldwvUARFOy6zoGtHxGxWjKoZCWtkzU8TFqVuJ6HjFZ4wpSuBU0jaRVs2XaYdtZ4GnlsUadoskai6u3HGWa7s1FXRZU7STWPTASGpKgahiDQNDK08K0loNW0l5bDZCLlBbRvENXqJl/rkzUuPkNPFnYkEPFMoMtd8hTAQVlFJWgvWlLI0lb5GSxZIKyiiGIWqYdflYmWHGVNHm0mQFHqbCXWmNqY7qxd8RvcW/IbRTIovvLD/mkd5TD9aWFsbDd99zVO/94HfcUtup0PIfSd/7P2b/6pFqqn/kX1XvevwCwf3V9ZPf+l48YnqNeTI3pEzE26r8eIfv/I/LfsPLhy29laYeKVzHelFrLWqrikmPV8JTPU0LQOKoMcJiK6ajgij0JoHaAGAAcuOC/td9F7aJBUPVsEJUlbmhvRTubyElsHByiPUaqVmGK4HDGBkFLKXgJpN9KgNaShC04KNZHY6NKtWILuBleyd7cZLTc+uzKUtmvbi7XMdt1goSGoKyWxZ44mmLy6bZmVXrG678RSbqhDVHrd8cyotjk9cCMTZir/4ZMEzq50JZ3tpzm4HCM8KTwTmfhKnwt6W2PW5VkX6dLZ9rm4vfe8wO/e9elp2N1HfjqNJmpihNNLOvBOOHFrpxmN22HulaIMYtgpUP4LSaQpCUyhhggjqE84V2ZwcJTst19Cu2d5sv1KpJlwa5hShcWxZ0FyY/x97bx6sWXqXhz3vevZvvfvSt5fb3dPd09Oz9MxoGS0YjSQ0QSNbQg4IMJYgoYwhsYuQpFLBIjaJ4yRAZLvsFCUCNpCABASMhNgsIY220SxoRtMz09P7cvuu33q+s71b/jj3m24NIkVKpFKR8qu6VV33+757zzl9z3ue9/d7FgsKAYEhGDahYFE5A4scFCEEaYK4Xey4C6ic5zgUAli84K7gPjA093dzHARjJCjykaEeKBchPGRoTdcGC1eWUBKgVQBuUftgMggQV/d3Hbm9sS8paEXgtBDiD8HxJAhOk0PkjM5DPnqh9VHUU45vtp5lJT7rgAc5xQIX8BiFVgakl8FFHiHKElBS9yXMfryLUgDz6jWt0jDKgDqCNAkQwUqqjDXWUa01uCP7nGQKxwAzK2+vgcaAOIMgNTje9PHBLtPVh2azL/+Lj/ziz/79n/gR9Y0P+du72Ic//OH/t4/h/5H68Pf+oD7KojxX1ejJ65er64P+CaZdTIgZPnbift4vCr2TjfRau8sCz8N2OnI6z8qWF/LcKNViwr7zxD1spdlmW+mQMUrp5rCve+nYCi4KOGBY5rIdRpwSonKldmfjZjtT1dXSqBevGLXoS75EuFvTWh1aac+KEwvLV//ZT//r4sYrl/2zyyszX7ly4YFnb177/q9cvXKsl6c3f+3ZL154z+kHomOzi2975NCx45ujQev5m1dPEdCEULqbayVLY6YquyZqwHEF9QN9apirALCES9oJI6RVaVA//F8Vnhit1cHuXGCdY09dv+ROzK7Q5aQdNUKfJmE0kYwP0RsP5uIGX2y0yMn55fzDP/ZjEwD4rn/6j+atNcfee89DD/hS+gAQSC+VlH2stOr59YWlKwcXF/4WpfSo1pRZx5Hm5LIg2Q+dOb5+46H77r7+wfc/3kcnsR/44Pvdr//Sb2bDcWouXLnedM59H2qQ9mpZ5ygArbQaWiDIinIAAEJwTuBSznnWbMZkNJ5MR7sadXv/CwDOA4gCGL1GzfgRob/yPll13+OplVmO86gJ7T42ri+jv3cd/Z5BPpkq5EaohS+zBZCXDvPWYtlncBTYsEDX1SIzpwGqgUAazQNnYQAtAMjbHVaJ25Fid3bsXivU+EadPbzme1Ou4mvHvyVuCzimlis93OZW7WkgoDWfLpYE1hEYVStjhwAaxNiicIjHVIAwNslBaGUNNYQJBkdEzV7PSX1dxqi7eJ2q/pmTCgh0HcAKDWwV0p9NORfGh/FL04FyvAqBYYPwtCkxbMkwb0tBOaWlxwixRPuV9fjYylC5iHNwbkB6kayU4YGwNrKcURN4DkYZP3VuJnPMUKWIZ+OoKOle0NDDODLjmbZPPcfGNEIuPH9t75LjzonAZHatf9ksjW+BcSdXx9fM8d55LBZ7GtbaRjXEkcEFvVDuUEJJstVeopVnJagL57ItubZ7QZ8avGKCbEz2wtlIWkVTy2VsFA3zESZM6puNQ9SDtb7OVSrEMBOxYUJYajUF4dIR9FduvDgvdgaLN8MVtzlO1jxWHV3AYGlv7UTrz4+87eEr5sjm8+5s9vJP/L38z37kLZP/7t0PaP8tS9o13canfqxqb10IG8lPP+QfWdhc/vFTvzj434/8l1743vUDZNZ/qxtXLbEQnAuqzBxopP2Diz0pTSVHY04IQbBwUNGFh0iQ5vDMSFvAaHhcdQ9IOtnO0zznjK8F/aXjThyZ2bNNL0/TXG6eWJukK42B9irF0jzYOtVsP9fwLWu3b0V+zOTCnL61tFi4M0e3/RN3jVFaVvoNNWov6o3ZGSc9VqqV1TLMDStHTdEPD1Of+YNJYQMqAzs6c88NF/h6kjRxc/7Q5lzoFcI44tP5ibd4dsMkLG7NH5rMJQ/ttKOD4zBc38vNmAs2R5Js2yejC4ma3CxkEO6N/Ug3/NkiHr4UTbKeoGbH97WhgXXomE2fG+uNnONEgliAKFvfB5UACQgIV7q4KCPjcb+8YFNe8ll3nM/Ct2PqrJShKYkTKTg0IXAwMHAoUIDAoQcPHhiOIoVDFyP0AMQIEcFiQRtUxMG4/XkLASYUJA0IC6WgQIgd5K+mlFQW2DUwMUB9UWcVTwDY2gwMjhhoAuQI0UeC8zznzzLwF+k6+aPkA+K83rZX3AE8QqldI7Azn1787n/3urvwzdWph4f/8z97dufovD3citHldUIP5wy8HcPEwtJWYEEYwXgCcA4ynUloXYdnM1Z7jCYRQCkyQokdF9RpAyYYBByIc7CEwlH6KqWEAXDGwWkLsqfAGdBqefAO+q58Y0N/GeeeNDj35CzOPVni1MPm/+o0vp3qW7Kzd0ftfe3WjQNXetvpsbnFT72weWOBEHpmNk6Ob0+GEJQSn0n94sbVNBYe2QJ4VSm5EsX9pWYnHhRF6AlptLHFuCxCC7BGHI2dscWMH+QLXjOZkQGXDm7Jj25Ugi1fmYw6Nyt1HEA4qIpN7D9gr1gd7I36R+Vd7R8eh3Stl45PxX6g5pPkz0DcE5UyL7b9aADgIQCKEPKlt5+4Z6Uy+unzO5vNCzubZ+FwELWX4FXU1iprqD3kpsbNHLUXApdSKsH5VOROsM/FmRGe10xi6gghkjF7pLNYHp7tIvSle+rqxZsPHzqextJ/E2r3/y8C+DzWl15VcG2lo0uO0I/5nncPgHej7k79qziKfv+BaD0G8MOV1o9wQrgvHQC724n44/z4w6/gwoYvpTgIoIMLG9sA2p/45Y9wAHuP/dBPvB1/MR5tClScra2Cc9QL3ctlpQBAlqaaq/paoBZOTEPST6EegXcozFkD128yuvm4Xy0DeCfqSKYLqHmDDjWX0wdwFLX5cgDgM6gTMNIxMMcZ2oyhnIDmHPaQA5q2Btu7vFbHUQFEDiCT2ml2GoM27dzd4ebyKlC7E7B9I2D3WuuVb8TVm7536ql4p2XKFmovKh9A26/j3fS+D5fhgBkDZJ80xK0zqwQwga1MaJFei4JJxaMDsTUUyilQWwqDUAO8qoFfut9G9hxQFRKF0hDUMR/OtIizsjU0IGPToga2FsSCgjmGskLRChxCSUyuQY2BEtYVoaAFpRXfLWiYWpQcpWba8ZJQVWrWKhy8SbW10/G6WZe7cLsUmZA07YS0AhnCL9jB3g1dBby/GbQEipFbzy43BAe2GwvF7GiDdMwujexEh9vDSouADYOgTOzEmy+vDRmFaJohOMDbWa8amLbkbOK3Xc9KnZU+XBsA2m6S5byt4Spi4DGeZiwIFapwRsi8GDJu9W5jiRXU456eOEoEi6sJkTAlV0bdSOaWl9V13nb9g6Rt2ciPF7xJ4T3c++zWreaBdMBmT35+4e2H7xt+ZXSiPP40vMY1AMPOMG+GP3TXygPHLpFRVYnn8AD5GfU/HLPanQ7y3Y6O/bvkPe0rfDW5Ri62wsufv/roya3hc4tNtVF0tYDVB4+EtJ06J2VaCoAmCmTXb8g9mo7grCzmFya2fV/rcCPrjRb9tHHghI06iZZSupISFQ7jjl7iY949tD1jCCnmumm/mIwnM918oP2ks5iUG2uLVRhHe0ZwyvVKd3vyfFEmd7lu1JJUSNPtE0ov3wqr1TDB6eP9naS729GKj5HZLdpNnclYWY0k94MJmvOaBXEu0tEluvdCUycnnPXWyoQQEnTeNghsLr0qFZ7ZUH4o0qB9d9mtNkG9taEnrtBSD7oCFEydb7P6z54ZZsTU7DzlIDmr73VCQRSAIgDrQOdjT5lTVstANbkGZxS3fCkWUYgRDPS+ccgcciTg2AWFQYE62lKDY4PMILG7OE32EKCFiY0h3R4YDKyhyDlFAQuCEhUD2YYGR/nqJtAACBhI4kNaAjJV+TsC7BFgzpWYOkWdQ4XnYHAawAw9iyea75dgCduT/ykn4+fyYbVpJMzkOxZ/6UNrePyjV/6SteSvXAst8kIrwjlW++SdAMCLAs458FcHG9pHXtWDfgLAWjhGQUDgOAecg6oq9H0PGkCDE/ilQ+77IHkORggYIUBu4ZyGYXVOi/N4zS3ISmInuSMtAznj4c0A/iGAJ/ev3zP42D+/BiD//5W638KdPQBAb4yFpJl8x9GTk8rqDQpCtXGbDy6vHXOEJO0wwkIzpsO8MNaaa6DcpxRuNmpqxrjXkL5JPOFW2jOQQqCXT/hc2DCLSVudWF6ODrY69vTSATsXN+h7zpxtwOHyhZ1NWhi94m6rMEVDeGxzNMKt0fDo648cO/yWu07efag7u8gpe8UX3n/zXSfPPPXg2uHinSfuSUZF7n7lK09c+czFl77w3afu3XzniTMbwyLvfeql5xuFNUdRP/j/HDXYm6qLW7jtBWc4YMeqYqMiz7G/YExfm4tjfmJ2uQwZeguNGf2W9ROttfZMcysd5f/tn37y2a10cOtwd9ZvhVEPwBMAdtEbU3QSCwAf/t4frH7g7e/s8WH2J6jJsZ8B8BuoOz1qXBbrVuuz1hhaKvXyHz/xxZ/4qZ/9+Wc+8MH3W3QSPbq2mfzWJ/+0+6Vnv2YeOH3i6DAdJ5/64md7z/z5+e/njN3diCNaaQ3nXIHbymOFWhjAUYPWW6gB7hoHMXCv2qJ4AIah70lK6Q1j7PMzoEfWWvHld73nXZ/vzM9fxbXL86jtWn4PtS8f2/95M6h9DR9E7Uf4OQDHSf36MgVUSdiTlZTt2Ghma4DY4oBHak5yNOWgsH0PCnq7E3dnosFUYHKnjcqdZb7Ba1/vqfcX606rBYa6UzcVsEw9JIP9a7gJoF8CtppmaAKBqc+BFuMcIaOoGMMQPNjxE0mKXDWkmUwYybiGVwI8paRhHUgEOFtTcvwKcNZDFihXccBXxCAwjkgDailgOAitkDMKxgkRk2bAtC9AlEGcOSSDMo1TXVU+7fPKaN+BaYGKCFp5qSaN0jkHlMaBVJSzUgQ0FVKLSUU6/bRqVmXWzlK6sjMYB7rqz+p++0h5XsZ7u8yTxEpXUN+m7FrrABl7bZpUQ7rTnCe9qKP6Vcxcv/S4VVxKo6SxkkN7zJQVzwoRusoZDspLa8EoY8Rwv5zIUdiQJQWb+E2nYlcQ+Fq6cjD2u80x870SjHTEQMynmyyxQ+HpIs3QaNxaWM3TuD0ookYU00JeC4/rYTSDtf7l80uTW6Tvtx/MeXA8rkYHBl47KZkQcHY5TMY/Hp8O3sSWw0cyyPZ1c+jatl1a9HW6ao27R2vaa79y7bP3l+eWLzzD7klz6V292VIhij/9j//xxriitOx/2U6uXfIuJEG56xShMkDQ6Pjb5r6FcwgxikJXHVkv/Dl/ZGIziQNp9TAPZMacTWbMqB3ri55vZ+IF5515aHjhTSe30xWbljOEPLvyevLiYju/ttcTO0txRRd41pp1lV5pZq2ZxbSVNMqbcTTYE5UWNmOdxcVBorlEszEIHKHO645V2BrNNedHLOyOM9GunPAMRFT4lU6C7HKTjp+Tw7wvvKib0tn1nlR9JKw9brgKkhBjTZfkyfLAFtdCtvG5JRHMG2dALqInQ4BIgIwAdw68zGFdD2BPE5AmARkB1VVAt0t4DZ0JDmNvGhpKR2nHjVyAMWeYAUMOiRy1sU0LJVLwfcOlAAIOM2AYQRYJ2Rs62hJwhI1REIuAKTgSQYgZCBQYQCHbX7t2UG9mE9SCjFdtlAjIZP8en64fAoAiAC+BcQb8r55FDINDALa9s/QP/VNiDwCMtu9HgLewho69JYR8WdArL/7ep9fPPv5NmRGfmXzRSgGLOulpGYCoKnBCwGsyISW+Tx3ntScEAUAJSBAAnKNUGs45MAJQzmvOuScRhF4tBhMCTAgQSmC0AnEG3BpQa2AFhxYUYrsgFASySeFZi9gaHISDYAyvB/Cu/fWwj3NPjnDq4Xod/dg/lzj35BGce7LAqYe/bUa+39pgr5M4Mcx0O4wUAblIKNlycE98x/qJ7lKr1VpotK/mlabNMKQnF5bDNx86Iv7G+ungzPKqJxmbCC56964eCpq+R9948JiJpK8PdWeKBw+uXz3U6oq2H0XHF5bsyfkVfy5KooOduZV3nTjtlpOmOzG7uCoJ7ZTGxAfa3ZtZVVWRJ72HDx599kNv/I7rzTB6th3F/8t8o3kB60sVeuMKQPrS1i3y1Y1ry89tXNv8kR/4wI1f+J2PH/nspZfvu9jbeRj1iPPLuK1CGqC2GjmI255rvr5tXznlhNEDrY4LhNQBY+WZ5QPyroXVmBDnHjxwxMVCDJ++cfWlW+lwZIFopd3pH+rO/QHqne+jACR64y10EnfHtS3QGz8P4BmsLw3RSUbojQvrbDWp8kXG+DPnX7n8g//TL/7qDQDZBz74yWRdQQAAIABJREFUfjz2yPt4lhWv2+0Pki8+/dy1dz/6lq1/8isf8Z85/7yY7Jnn1g+tupluy/X6I8IZedlYN8DtsaRjjPrOOYZ6IVzDbTUuwW1jzkmzEW9rrcSp4+vy6Prhyw8cO4gZX6b//Reez99QDHYFfZXLdh01kNxBLTYYoQbOXdQGwBLAKgNyBmyFcMeo0SsV4GnAZvvRe2OAi9pqhBKA7ksmgdvKuSlAm57LnaKNOxfcO4HhtKbX/Bt29lxNMq0AaFqnduT75zE1gO7jtlfeCABzQFUCTAFDue9H6ADuAM0Y04yADv0QxDkbOQU0RTU7LiunYRkQl4A3AFM+HBSFbziasJBpALYXJ5ErzVbi3GDUZtwRpz21TyYKeF9S3CKFs4pB92Zk6QgEcgNFadUYmu2FHlJZ6b7UKD2LRGjExFitrMg4UDjnJjpgrTSKyV4z0dfm5orlvSHvFsqJSrM9w70yZNzjud/Wo0CHhueckdBNSMwtdRB8a0DRsCWbo7lJPa9Mo6gSML6rjMvDSHX5JLBwnAEii3geak2icUFSP6EaIdfSMeYLRouKKHAkdoKGGauuUje6+YQy6/im7DoBYr2skKEuxG60WIyaLR2PJiXfNtaDym+trG9ux3Ntv6ziS8FSOvETR4iJr7cO+bsyWVCGzAmdmdP9rxxXhHxnaYNDjthjxhQdFHSuKr0yrgZUsskZp8khamkhNsfF4d4rC41EdW99tVSNuxnYO9Yvd44yri+kP+Bn+drGwB/Ovz54SQ6q6AB1d40bUTwpTXtkJV9cyN7YOOwdGne6V8XDi5cn23b0pWvL6a1i1m6I+W3qR5tp5o13bzKStrqb0du6L93XeGUnpo2qvUA2hG+IUO2rQD5utsqFINnusmSSZjqk2so88FNCpJ1r0PFcmGnP8xSVSdXUVcDhXEoca1YV8wUfewTEc2A+bKXT6/6ezgPGAkWjbhW7vUL5vKBUNz3KlSAZEWVJTNZr2MEzCwLWSFKxQdYXV/TYMXelOYH1szrGTWwgcuf4Mddy1j0PcAeCGTDsIqz2II3gME3OVAxhmvBpjIGIkHKKChy8IOg5qD5zpYYRHCVG4OCo0ICHBAwSHCW4nSHUpU77Y3DiIcIEAtW+W4ABxQR9WAjUm8cpuJvcsZ5b1FOLqa/mdFOn9+91nQFbBFjwgLMkRJOvknP+m8X/wdp0FcDdlJIGKLlXCisdDa1rtb4oaPHp9bOPf3PdrnNPNlFPlqbHvkwZQgco36OEcUqsA6307QVN1IHgCoDjdbRj4clX17lpqtV0nSTYp6dQCmFrT09L9kfGVQXSpKAtDgIGtq1AORB6HMucYhW1gO4QaiB6GeeezMkvXmofD/TbTobmDZTgPE49fNuR4Vu8vtXHuMD6Uh9A//XrS3g93nwZFzaaL29v9NOieOrS7vbRTJfVvctrQhtTMELD1VZTlNrBEyK+tLcda2uChw8cMScWV7y3rt9VXO3vVZVzy0uNZv7CxnXMxA1prCZzSYtwyqnH6XIvy3cv7W01+TJh960eTK72du1Gb2+VgWS/97Wnb/3c3/zA76O+YV/B+pLbP84CFzbUmeUDdrHR2p57W2MMAD/3Z3/w8rDIIwL8BKd0VVk7loTFlTMvo06JmPqe+ftfBW4LNl5V4CpjrVK6P9aq/Scvv8APtDert66f6K+1uw0Qkr//vocmj528b1w4dXmtM3sDNdhZB3Av6m6XwIWNJ7G+pO+4tlNl6LRsIOTNQMgPAdh64Mwp/xO//JGTma4C1CNT/oef/WJhrb38iSc+3geArd7uTQD2E7/8kZsXr17/3X/xy79xsFJKvf7+uwdxFK0++czzG8NJlrWS2DHOj+31B9P83+nudup/RwAwB9e81RvearZC8+ibHn4gIPhS+eK51adfeqERjvuPvKTx1P2szgPdzzEFHnvHdMzdH1L2Md+afD9KbABAsP24MwMcoXUnzzlgmxJSVM6xW5EHf1JW4vYiNQWgdwK5O53tpzFW07rTTPm13nvkju/d+X4L7EuRDVxFoBp1L3DqH6lQj6bmSiB0wDiox7ikBLoUCASgqvpvpfIAZgDHOA0IAKkqxxiMlxvncui+9GhFEQxbQvNxTozwClWYiJalMbb+O+Ml+GTWg9R6edSvtsTA6MCCOuAVanHYy60UzkliEXWGKOzFMR2EPNrpNBBnJuW5b3seokQVTWb0UAMBA0RQIh6FNNluMOMZFAUhReETv9IkN7lyOw2fmkKLSrrRthcSHvIm9TVKn+tb3hI3RBFKUjNbDqlvC7suHPFVboYhShLY0bHdC0GVcTOsgKagniKGTpoRo8pY5QmPCyvLKidFSNH3Q+eXPpQaQUrnIjIigbI2rkdr0Rg8La1ucFdxWZXZ2GtIS101EbZICtq/1jy0eyy7VEog3CyyYqbYyokFWTHb9uSlzwQ73kxQ6FLOq0kjpRIvL949H6uRUoZXndH15Qc2vhJuJKsujYLMtmkno8ljUdavKuptaMd2ySxf2omOHZ70+uOVN/eH8qgdNZs3MHyyeOtL20vdJa//knfY7S0+wBvPXQvV/Nq4WgqrfPMyW/LbaiHq0JJe3ePkel9MmvPphfX7tO7YmP7xLRIueJg7VF4Iz+1ePHBSPXTXXbf6S+pQXvUWmzdHulxcyewY3Yficm1zZXbLUyiP5Og2nKVBUgRXK2fSMk6O99GIFpINTxal0YRDlJUhglBr2EyhCiItI9uXFwUYjGw5K6pJUlSh1ZdDeFEUBK09V+7wUbpDyM3PdSq2VLK4uUvKiW/VjOS+Tana5VVVuYaIS1282GXgpI0OtlHQHkbYQ67nzQYS1xdLcFgjixi4HB30vfvQtM/D2G0ktMUbVSiXK1lmkpqX6C60a+AGOAQsj0EhIVgTXTMCg0WGAMAtMGhYCEjpXFuex3UAATwAOVI4bGKCWSfB4KON9NX7fQyBEqr2Q93/MqhB3dL+vT3t/jVRrzfbAfAKag9W5ixeJhH5AmuSm6g7/CWAFpf0MmQQcscug5R/8M4f/ehfB5etBHB+Z7g1jL2kxz3/CCOk7Uky3j+2pWu7oBc3CTl72EFSYG8MNHzwIKi9SgWvs+srB2YthM9ejWacrnkCtfCsNhMDKJvy/wDD2P57LdDkIJKBeBwN7WA4gcs1TlGgyxg6nMD80Ym9j1HCuq5umHxb+fJ9a3f2vlH1xioUsu8ctv/s4kt2XFaedfbmqYXV0b0rB+JOmPgN30PExXA2aXpLSYMuNWdIEoRMMs4j4THrTBT7YSPXVUhJnfe8EDdJ7HmMMm5aMhxbZ4frMwv2zNIBbA6Hzbk46fQmk/LiYPfTl3s7X3jP6Qe2sb709S3kTuLQGw8iz7sxBVX/+fd8b/nh/+3frM1GyaMNP2hbrYcLSfOwUmrmxOwChXOLE1VFqG+KaVdouiOc7gSjtCr8XCtHLaxg1JxeXtk9PrvUZJzS2PNHLT/SVpnPPLlx5aWXtm89fffiyibqm/ko6sXlKQC9r+vuvbY6iUJvPDXu5ACSZ/tXDv38K5+Mv+v3f3rj/Ec/Uf3aR3/j2iee+Hhv+pHHH39v9fjj71XoJK5jqekNBpuvXL76VLOR3FTanKmUEuNJ9kQchhucUUyyfBpvx6UQzliboO5aDQE44XFHmp7o6fz6g6dPVfesrnyBUqK9KP7saZOtHylGC56znwLwPD7wA/W5/PqvxgCOWODwdtA4YgiSyJgZ1AKOVQCzCli2+zEBfu1VUUm4vgNMrIyOb5uaTpWqd8YgvRa4TYH4tOvn7njf9PPTMa674zOvVeZaCihHkfoEFSWIp9cG9Zh/F/upFVXdvWvp2rKhSQCiAbU/M3YU8PY9ApUDDHfObrbiiis1Di1QUh4pzhMnCKgjRurKaWNVbBwNHGwOaOmQV64qhbKGEwwzSN9QxmDNxDjMVNrxEGgRAi+vuaVtTzlCADUzzN3MqIqFqUCtHaG+AD4AaQC+54FOhC8NEXDQuVfahBYQUamDUIH3vYhbnzPiE0appUYxk/vcZKHveW6SzdpMFCmctsb4PFMtV46ocOPIFCwZT4RLC8JtJRskk4m2yuRVBeKoaoREW0NRaG6bAQVT1AoOSAuRKUdALTe24kB+Tc4k28myB02kV+X+2G8hoJXzVUpzEfmiQhUQ48UkTcdhO72RLDdKQ6MqYWGvm0R+puQgaPtDr1EZL/IU51km40Q76gyR9NjuywmMCRwcnR/3zMLwytAS1vKLyvOrijFoMlcMOtqTgXVOiJ1xZzHfut4ICK0WWzPtOTuZPRk9dbDd48cGX10fjb25xlxzbtyM9HAiqtW77WayoBMSeF5wqlktzY+8u4OXDkcnOzNnTg1HyesSl5zh8Ttmz5tTx/caZ45/bf2kf7kRFEKx2a2sPdN/WZd81cNg6LU253nU86iwjcy08mRYhdHctQAzZWLSgNFqopjQg6JoNrVqwFhKiM6JAxPSc6gmHhylTiOmVSaNLapU2yTW1spsR7JiEoXB0tgpK3llQ26G1LFAuqrFqModwZaiauxJkvlN4+VMdivPbIQeClsCbgG0WoJQEXK6BPAFYuGTEjEMmUHpfDh3AxYdlxJqhia3ufDhGMUsEYCgGDJOElAmQVzmiOqjRApZjgkz22AcMOjub/yyfcGGwxgB9mDgoGBhIGAQwGAKvK7C4vP769nUHH4H9X2wa4HzZH9zpgCua3sWzoDLrKbT0Epj66JLPv+rZ+6+8qbG9tz+mrGHmsdGKXG/T6GfWT/7+DfvS3fqYfVf/WevG++Ndma4F3l/zKtrLcoPiFJq5+BxhrBUEKUGmU3qc3QOBBTEEyBlBaMUnDGgoxLs5h5YVFuzGELAihLUEZD9MYkjDMQyEJ8C2intoAkTxDhDHSUgfs25IcqB5FWt5q0UWGEQphonGcXdxyPcfTiwn6MEv4Pv+fFvK/HGt35n77W1vmTDCxsbB7uzK68/tL69Mx5HL27d2ACQzjZbN62xJi8L3xqXJGHkdaOENTy/frBKgQDgc7RZCsboQtzSie+bZpxIq1ThHEjAmFib6R6qbDW5OeoNrDMi8rz+A6ung/fd+9CB337+yXedXTk4Rm3q+42O7+sAIHn32yiAZlrkf5b4wVc7YdILpHgrZfRwVpXW1Z5qA9Q8jzutV6ZgYgo6dATang3DvBFEvlGaXdjbCpPAn4TCm29wr9rp9w8+de3SlauDPfcf3vc6jnoE+Nz+z7qG9aW/CsdjBXXr/CYA+/4/+LkXRnvj7zw6Ci7jG0WUff25b37fT/7w5vf95A/jsUfeJ+IwuNaIo6ZzOLTbH/wUptFAAEJfZjOdDm5sbo2sdYUv+I2Fma5st5IWPFHSPKUn4ujiLEU6u7LIcfTIDvLeL2Dj4gKAq/jEH9bH8dg7xP4xTyhwaSEbnaBwq6iBbhdArGsgJFGPaKflc8ALAUVvcyIz3ObV3Anw3B3/vnPHau/4/rQj+HWWKiVALOC82sTZ3PF5DcARgs0A6A/AWpkzNHDgAYXnAYmrA1U9XnfwOvu0mcgANAWw7QViocylBDILDE3Nv6Mlw24ay3k6KeNhEPhuXNhK2XFTTcokg9N1HBrRtR0LKwAYQtmEMqeNb7nRVVjlVyzsgYkXzyZGrxhix5JDWCC8tuBjDMQHdwqKwmVraV6JWsgE41BaIKoAZ0F0AZeOAyKVH/vhxJAJpXYYtyXlHAu9HvKiwIRLNrJSb2dtwmEtE2SsqEO6x32qxqqVFMTmyLMCTrapaMNyCWR8R113Qp12AkkcQPmwRgFOASx0zhaFMjKtvFAp6MivZdCBAAiB2zFgldYgyDXYZBeGa0vDUluzqHdKAUppIYKxDDUFzPLeXi6h9/Ko4V9vHpgdyFZ+sXW47SVZd7G4QeeHm15OPaRMTkY8YDsyqHxX9dcGF4PSSZtMegFVOb/VXOUVGMn6265TVsumyZtemdvADYLTo5uz1pHMo6SfWxsv+LstqbL7q+0Z3fZiuekv7JJAdV/XPh9WVPsPvVHRW+eY3ut7yfLqbvFo43zx/OJJyx6MU6qLUnT5vcerDf9MdomJVf8gPJhe4pfRsSuL7e2yDPxxkJcuY8s32zNs2BlsLfCG2JvToY4JqVZV3hohqva2RbfPo4tLsbCdMrG0oTbTgJU674eMUaI4VdYNrNATxrxlrVlUVtRLOMkzXo7gWBJq3iWRk5LpVNv0giPN2TFVVUBsTknopcQ/MlDZ9SRoy0zQVYPRZgPoMVek1HiHcqovNC2MDQCdQLiCHbbaXCEShFZwGLgJhkBFarNqEwOmg0pKDan6Fd+JhZn1KzTAQaDgwOHQh4VGoRz8HE6EIMIbgjvA7mdeqNoQi/nYNfm+z8ACOEoAQ1T793OMkBAUTmEVXUxwE68A0CA4CYemA8itUO68eGD2s2/cuHVSjOzBVGJw44C3cfBS2UssMgBfIm186fxs1/2rR++f/PzM08f2l5AKNbf70wD+FED5zh/96DfF1buzfvaTzvzuP3jk8pH5w6cEdYel5udv9vFQJ0JCcwShhDu1BDABkhdGlYZ4DhR5DkIIOPbNAmMBVAHsKIXKCEQcgjgLpxVMUYAoAjSC/UUC0M5aOOcqaw2XHssAFJVGA4AgDExYgNI6OovWHcPpbtwB+DK+58e/bbh60/r2A3t1aoUGsPu6taNv2E2HTwdSRrvZuLU7HtO8LJJEeFHse7LFYudxTgmjsCDUORRxGPIY4RCAiMMocHDMOSg/CFVldMAIJYXRbqnZMccXlruTsuAHml1OCaHdpEGPzy/ePZs0tgCcx4WNlwForC99QwD02CPva74DzeBPOqPzpzsziw8srpSfvHLh5cv9Hl+IGzf7eX42LYuz++czRD2+A2rgIFGDPAUAHKCtJBFzcYt1Gg1zeGaBHurO7e2MRnuRDMzJhZUvrS0uPP0ueu+X33jo2EnUAOgp1GBv4bOXXqKf/uPflqgBxzHUo4VbT/7aZzwA9hNPfDwFgHSS5f3haPNf/spv3tzt9w8eGNp5JYLHG0au/dr/+NF/8oGf/NBfiSfyiSc+rh575H2f+40/+rfuh//mj568tbP7d1BzExkAUymTaqVuCMY/r5zaeuSh+zfXVhd3FmZmoplOc2W8tbV7YH7hq1DFVSj1VRxc7OIDH8rxj/7rr9y+wO+YKnDfDOB5AM6D20Dd3g8A/A0ATV1HoUkGmP2RLkUNPAt627+Qot5BT3fTd9qjvJZrx+54bVp3Wqq8yufLalsXsv8Lp2Bx+r4CNe/wsAHrpIQr6ipHODHWWqriYDEa5SGru5EOgB4BeQGYivlSG6JGAIsAXkjflcaw2CilGQ3HQWAgWbAzG3FxtVcGuSq4RZ8AtGC8sytCLy6yIues8nTpA1YUzhlIHrqqwCDm63Fm2xqGEEJ2DUFaeMGirYpcCy+R41IB2BOE+hMvCD2t/WEQTFLfKz1Z+mVGJsmk9LzATihj293dwawg8LIDs9o5F+hS2zQO9JhIoh33nQUgOLKAW1YJnxLjeKhNW03gTbQ3tqycb6FkviUCMKZE4DGsOwPpjDPUEVkApQ4ocbAuAFjqOGv2U3DnSNENMWiEsJxh4kI0Q081JlY3bGVVxUmg8hRyEuZh5LsU6S5pV0nVI0vVBpn4Xa20GQhUgRypyjYIBl67c3jrxaZvK050ibm873I/8l5srUsheEuaykYqS/yiGh3JLvEoH9Lr8SrnOiOUSKEbLbdDW9Hi4IZNiqHLvAQWTJQhmke3zjW0obRiPrGKLka9Pdukl93y5nPN1ZbrtGQVX6dkh4f98dH1TV1cjOg9R6+LwPre01eRFu14tl1uHfBkFex5bdEgOR3IFkWeuIXkleD46rNrVdZ2QebbsaoqSewBplgQdPbkZK8RWl6ea65cLapRQ+5tdMVBunVM6pCTZqlEiYpJRlDmOYWKWvP9QZ7GkRqHgpAE5Y5HrSLcMkL1KFbVDaHlXJGRhTDWPRD4zEVLlpAQxFstuBoSsJQQM9TArqN0IbOmJ2m+Y611mEQrhdSZ9E3mefWeJ7BwrrJ7nJGGDYi1sHv2ImC7QLmEAA65zAB+pACXGhiDmWVdQU+aCEQLkDdB0USBDDlyZFzDj0AEo4SjpjRMUEHhKkI4UAhTs60tOqAwKMEgQKHA0cIYSwgIxbbrYwgfLTAsgmCJzIO5FDsqx1PnDs33lKCSLWLetFG85M08tXlvsrW3XnzurZ+6NcPXyCut/8KfO2uq8aN7m+MlL38IdVfwAgBtx27Q+6mi+n73639tQG9aj7/hbw8BPHt3zf/tZl1E1jqlNQgnoADBaOzI7qT0OiGDZJ7FfswK4JxzGnDMLrepGWdgxoJwjlLXUFlkFtjVsFyibNRCjlxyGVbWUGO5thZlyNC3AqEFSARQWnv5uZYPMinqSCSfYgTgH+B7fvziX/c1+P9CfTuOcWcAfBeAQjDWbQbR7lKjdeJAe3aGUKxnZel3oth5XHicMSqlJJxzXO3votLaNYMQqB/GPiVE5GVplTHUOkP7kzHNtUY3TipPysA4RwMhaDuMw1YYO2qd6ibNhi/ETKnV6I/Of61Ya8+EfJil6CR/EfDtjQ66UMz2pVmKqfcfPLC2bv72g298YTsdl+85ff8zr+xuHxjl2WkNNxUqTL32pipWS/ZHgBaojrVmSGE0k5TlZw+s6+86eebPz6yufXq1PbPRTZLPe568cbAzu8MoLVF3qUb7RxL9u3PPHMtVtYxaHPH3ALwJwBu0Uv3J7lhdKLezr/zWp8kzz7/4+n//ha+sfe38xe+bjNN/HVr6d3nFjh6Ym5f/8Ed+4Lf4bKt67Wn+ZfWBD74fAPDus/fvfvnZ5/99fzhaR00GvmWdS8dZPjHWXiWUvPDOt77xmXc/+tbzq0sLr3TbrReXVldeZGeO7uHgigPxpqrkDJ2k5hw+9g66cevmihDyI4yxtxNCbqAGatdHnneNG3Oc1mAPFBhbgIm6izdEvahNNw3Afge1sGVLw3FeDx5e5Uvu12sB3xS03fn9aePwVZA4ncn6tz9DRsrqwjjlMzLlZ0oB40JnbnqBSLN2HNCiEsTYwDMOrOaTlc65whHCC0CXUtA5XVoPTlKAjD1fpFHgvEqrQFuaMTl0BiIaZXu+cs/M6qKRgzUzOF9Rxo0hqmIsrRzVkVNcEVYO4Z3ncKPA6EAR0rDaeIUDCazOQkAI5150zppgVEazhXa+Q9/BRWPpeyXseGO5sZtU1XBvLrmSjBX8qvJGrYjvJZEgitoh96sqZEUqY2kdYUQy2w8C0Q8TTEKPIkTlKC2zMeGcGS58R+aQaU8YwiOuAw+5ACpnHSiFZII6BIwb58pRDApJqBw6F1v4lQALYRl3joxmQqIMBzMcYVEhKK2dmYzK1ng0dAa2grB+XirJNJcu87QSk7TRGRsSvLyoR4bq0tzwuo4RMhvYYnabN9oZ96MKFFmQyNRv0NZwh8c2x9hv0XjSd61sSApHVVuNMkJJLBiRA5awVpG6RjYg7XSHLU02ydxwc9zWGfPKiYAjLFI5XRztcA5KnQgoMZaHqkf8ohISJmiXMpHE+DxQnY6YeK3YlHFARVPogBSEr/g78TztyxPVxXgdV9h80c9f8u/PskoQMfL4wt6QL5DztNgTRk8ir1K2FW6eaJnhbGSWrgvJc3jxqM9y1ZJ8siKqZsPbC3zNb7pyi42sW+RkEgU6o5xXiopYldp5VvijgijSpw1OIByIpzT1CaUNp6nkPnHKY3FJaQwTLU2sUamxL4IHNDXpdgSz4/liriBqHNPqgkR+JSCu4ELdDKjZ9etYTE00IHNYQsqMeRXh1grGaWZ9AkUBI8Apgw6kDXjgPHhEWdnoTkJeGma5oJjAsgwgE9D9MEROQCIKQuBqSxEAJTwQKBRwGMJAQ4GQOQREw2KCABYhgBgMESwCx0BRQeMaxnAYwyKFRQQJ5h3HH8st80f393baDG5sgfQLJw88/1Jz9tk/+U8ObPzO311Xv/W3jvTe+/ELXP1mGT/8wKCgIYkB3Hznj350uH728d0vP/IbMYDDz/3Mb43v+fB7b/Ou/zrq1MPAqYd3cO7J6wC2BQdzSvnUqRhyklYKbDjwCZ20HI9MFUjCOa3XTwuTWmuoc4YYa3UUMO5LUEprbz7OQUIJRBIuZMgpqcV1hBANQlW/HgHQsLZikQ4w1MLtm8trAlhtQDONzGf4pSfGbPedH3vp1t9/25m/8nPoW6W+/Tp79QN1DjXB1QI4lvjBAMBntDXf2fTDezhjc/uvkVJrxwmxy822ooROuVQJ9kPmKQilcIRSPvakNwq4aAGwZVnSfj6hc40W4CyBc8b3PGqtrcZVJQdFdvfOZPRKL0uzpWb762++CxtdAOp7HntUNZ55evnlT+819VZ6a/Gu6MpnL7+SvOnwsfHfefDNa9f6vVsf3ds6B+A+7AOQiHKbWZ05wLT8gC8324OLu9sjZfTMqCxHi41G4+zakWo2jm80w/AP2lH8JGrvuRVMrUjWlzbuOJb0T84/f20vS880pG8IwXvyqnywRb1G3xSttXuPHp85svgzvas7b7i5Neyvd+ff7kn5BgDHYl+K+W4HO6MJKq0+6UkR4cKGj/Wlnf9b/2PrS+7i1RuXAPxTzlhsnS2sdd+Nuvv2pLXu8//y3/zm5F3/0fun4Goy/egv/C6IVv6q+PN/u7zy2x+51bhy6dqj3/H2NQDrUkrh4I46OElA5gGYVMria4tLyZkrlw9GdT5xSoFZH2ia+vcVuJ1QMR2R58pZ6eBknfYoKtzmUE7rVREJ/qLg4i+tfSXH1wk3+srxwqJKhGOUkDZqsYUTBMJWumKDiTbaBQyG0Nq/tFCA05YUSsGLfQRhmTsAA1ZnW/lDwt1eGJTxeDwOLHPzwxGpgIJwQXpetPIy96ygILIomPV8R63tw2itQelXH06lAAAgAElEQVTI8N2SedXQD6sijv1TOzcLklszkCF1hIrSWYTWFGNGXZpE7WaRM1Oq83Tf1FpTUxFYfmCrf0ArUnDpNz0oVsaisdVIClXQcrMVh2LggmicD3RM5TiI2NhAOQtiGWWGEwhBPasMGVjKckNZp6rIOOIT4qRAZXNKETnnPGOJMwbENui4SgLfy1PwCqXzSBgzp0wAaA/WL0HSABh0WjQTIfzN1Bza2UPoTMU9NiZAYfJS7twc611LsTgvrsR1/FNzZnStMqDXLXD+hj/71s14qVuVo4oYI0de4kk41jKpIsOBIxDIgpiEeV8f2T2nj/Yvstxrs5c7616nHDfDbMf52pCTescFKIlFjQwkDHL48chr0G45srmSJm/Mix7rGMesmck2GYeDb6ytxhSm6ZQKBsZDhRBMQonI3WpwWRZiqH2Sjbg6cHhStTavk+UDPcJc4UyvyU8MnsksLKxdopER3ogseqpSshokDKAu5ZvOIRbY6VivkrzU6khWTgj3dWCVM9X1nrIihI28ZkCvliKhm5VEQ1Z5WGVcM1eymE0EFvppSmeEsox6wUiZ3IdlMbMFN8Qox9sTpzdYVsrSCY9FZeFV1XVLws6O8heImAwZHVyL4bYJ1HrhZGQoLrWBDetQMgBOAhMGsNzBj2wJa1u0YqhCCz0qQVNR+InHiKABFCdwZkJRDUJiDSX+GI4ANAcIGAin+D/Ze/NgS9OzPuz3vOu3nO2eu9/u29PbzGhWSQwzEkiAwGAWRfECiiIh7AIKKq5EVQmxs5gshqpU7CrAxHGKhACuchSJYimCjbCMkQSSEAJtM9KsPT299+2++9m+7V3zx3dOd2siZEFwqsjwVJ06957lO9/2Pu/zPs/z+/2UbHH9LedehEVsKymQUHAgzPXjEJBSTk0cRIZjaCz6cgkEhohDCIzvLCItgAIFptZAhhfxxJYvAj+LPsvwCZ1x++a3zVS/4kcX0BP3XZh0f+y//HxeHLoRAD/9381s8F8nX/yu/+gXm3tciUW7UB/gK+jIEn1Qo6WvOo7x3X+27N8737eHX/2fD0rfvDCN9X/SkWwJMm7t3I6+E5jYWuHWazZlPAYFUA0QB08DCzHEYIDIqqr1qaQRWQS8gyABdDgws3X0hKovkhJAEAQ1UCgkgwawzAKIm1asWSat0g8nxCOHl56u8c++U2N/bLG1wkOOe3XRXyP2Wgz2SgC/A+AbAaw756oQglVKTRtjjBDyipiX4qqmZgfF1C/n3SbTyWKyXfRNMQCUJsmi94pr0SkwlySTQu4M0nxVCZEzycg5F3yMlCXJi8XMP1/Y5rfeeubBz271l46/rIzblpn7AOof+8mfPlxbGS49rpYvfOFof++jH/1UvvLQyVvvestfMSf6S8MHVjY+Nz8OjjkNSYwB55ZWblXeXTwoZ9u1d793fmVt56G1za1UilPb/dXtdz3xDf7McPVfd5LkA2g52G6gLfuuADiBizsR57emP/Fz/zQ/0V+S3oc3Ani8MnUjwL75TH95Zbnby17YvamOY/NUVyR/b72rmfPJ/u29w1u9QadJMn2rrO3TXsjjc2dOffjzz774YbSIsQdwcQd/2oDvQ5/8Nf/2t37f55z3QHvf3kLbqzj50Cd/7as5p0zw/AfExpNrxNNfwF06lDcMl9e7iOFZRugDGAJ4OnEuBkYX5OrGt6EshiimKwDypr3IPrYZPm2AkgPE2wKNkMQQICK1YvILNPS9pMj8nv/vpVNZ2J9EmHzvewTAbyWsiQBjRBO0TmuA9r7rshgDMy4Nbem5AWAtUFUAixwWHFHO+/ZGmgZlngBHppwudTIhYxK43t8Z6Khg15pURj4O/EDmaUiXxsPycFxF8rMkFzw6b1TGS0C4o5E7YOrwAdesDppqOAqcfKqZ1SkJ75uZ906EqndxfeV0kaSzkwf7PNE+69iIxvDDTNbcK5UbL4op0xVAfCblIY/cNUr5W72hrkYx78dKxuhXa6kxaaQVnJzMuBiVIsz2CYOhZ+NaMSbLerXXSKGCt4pgfV5W8LN1VkrhIYoSxAQEr2weRAxGQHhAdooATog2wogKFBikgIjhchGKbho3jHGdUTBC42DK6AvaYFjVbCgTtRJAmUrFJGvBLkc7nC1P5PCh04e7fBhm3MTb/lZvQx8my0ibIibRhI2DijeUM6F2Ywy+hNCsVh1/mKwlvXrSnDq+Gj0Ft2kORcWEZcywbjBKtDeX54DzIhKIIMOkWnJc1KO6rrnkVdIlAydEqBij2ifdGJKsqSZIfAndTdBE5pEo5zoDHex+ZHFikmUbptdycIfjjvfjrkEasg1/1HFIpgIXQ9rb5zhaIuM0YpM6rwsSmILrcZSoYl13GaAQWIeMMZZkgezM1DY2Cz5PRbrUUNJt0mnNBbjXWTIbepdMRleWnHYHPizjAN6nfBzQ3A467R/L5igtseqjuy1i84LG0kahrFfCHPIwOFvH/iOHNP6jdVffGggQXDSei4qF3motyqRmrs8Ix6wNrQwXQMwiwJgDqZtWMEAA6YjDH0qwBB4aRwisDxlzIEbGVArCDA6tXgbFAAoEBgmBLoDjFhiPNqjqY3aXFHn+GsLlCCy1CP/5w6OBQAGH8Z3kQ0uUv4KPgwGThJ2yBY02Dv0yCnwEKR3Jh4me7ByGJzuH4w9804/b/+27fyDkhV8FMH5v/MBCG/bLEgjvjR+YvZ/e88xiX76CLaP1z7P5cfzZ7J3vCy/90o8ddlny2327eT8js7qpcid9uq8HswQycgChaaCMBaynOtUyCmoAyDQA0cM5MsEKriIB0xjQgKPvIoyP8fbc1/XQovW9oLYbUhBcYDBguIl2Tvs3AG6f6aK/meKt12t8MuHs1/9gpkZ/5uP7C2yvxWBvBe0gnADoHFdFar1fU41Ib4yPT6/m3ebE0pADYELI0EnSKIVckNUutGYXiNdFKW8hsdWg7fdKEqVGiVITtBkzCCHsnFb8M6ud7r9c7XQ/fod25V47vxVxcecKgHj5xg4u39j5rfm+8qPxZGvp1mTnoY0TxwAeXso7LV8TgIxE7Kc69tLMN8ZGTiTX886nv+7EqV/9qXe8JxWcdV+8vbN/bm19/76l1bNowRdhvg9t4HVx5xjtgN/4+O981N8/XPubr1vZeEpytfXi7o1+miSXrhzumkGWu1wms4dWtuLRbNoRQ/ZUn+mpraxf6z35B5+9dumf6LUerr98/V9cvnh95fL1nfGHPvlrHhd3CmPt+aPRuNer6j/MHjt7+BWv0MWdNjA6v+Xo3/92DSDEf/G79p6gzqCViftazGwk4frSw4/lJ3/sH48e+bWf6uB1j85we7dks/J+xPhJ+HJjfj2/ToSg33rp0rNYXfsDIORopemaOTI05W0Ht/BAGlpaljslV01i0Xe3CO5wz32CV/2/WDgs7iUGINYt6OFOw+VXMsloAQYpcZdYe8FJ1fOAmqNz7gSeEkAFhBm4Z/BtBYp4FirvHeOJsrV1XLnbw6Vlzhg894GzWO+v5KLMs3zd5ksMfCzZdBwFety6iQu+8FrnE+/XDow59QY05XK07DBNQsnYLhN6GETsjKTOqqhmQsRmOJl2JWOqMwnwqcCxTM1kKe3unh0weeSOlm+70FXp4JWzm9bWTjRVGECHDJr7UIem9oofN1xeVnmivK3uk443UeAYgjccXgDx+Lg321gJOWItQ8pDlfJdyUMBA88jqW6O5QiwMtViNuh5PRtX2cwJeJCNUNKA+QAeFMBBtDqZNv3aTJdDBSdRuES8sr90ujwxekVl4EmasPTYBV/KtOijyuDjy0Q06NTTQSmyzm6yRCvNXh4mQW2MrnturW+ynBLq0KniciziNDQiVf3ihvNKiGsr5+1Dt54ZnaqvysN0JQ0y01oIxx0n2Zhg50AhAtxU9HiPDLoM4Wa2LCxErYy53jGjDau0qohbUZQInjGyievIYGzl+4XXDDz45MRh4FUi3Tgvko5hS4Kv98u16Jv9JlYdjuFEqLTRxkYgBhFnmhWWBcnSsMcyzkCiD8+8mUbvJGs7R4yEiArR1pFRxFYwDetXVKLDkukIRqrmZgeNJKeTGWeRZazjeYTUTBBx7TxZcwTDuDMBIVNu1kCJF3QaI3d11nPNnhuJXqOaIpeHVzmquhdlWo7sjbSvVyqajaMovtiLfl+2nbUQgGfUjizi87Q7YxAKEJ4DyymEn4+hHADBowk9SEoBpuHjCAEd6HQHaAp47uZjua0jlAAiEhAsLPydPt5FNcihQcRtcCg07bILHhYRBZr5Z+cCagAKbJcJxsqHrNvHSTRY9lexFG18s78VeXgw3mIJPf1+es9R3vLI7b43fmDBr/oV7b3xA3cqHkQfTAC4GN/tWucRtrcwXf6H+PgXgXd/tc38W+2NP/QzAcAf4Wd/+Wd8dx8dac+QEBNwv452ccpixOakRDqqUGwuWUoEBGc+cs6kC7ackNs/IRU3Dg1rvScNVGLR+mENoKwtWPRYrgIolSgY4ThReAHAP0KbCNhGezBb1mPvpZq5d7zUL+PPvvs1qabxWgz2bsyfpwAGudTfaMhupFo/vo2hFkBaVRXnUkYlBFvKOsDdyfleQtwJ2tKpQDuYe2hRqNfRapLeRhtQnUHrQC4C+GcAPgPA3gn02kzeCoAC57dKAFigXj/0yV8DWvqMhS0aSytc3PnUtdGhfnRt65MXDnafXOp0+Nmltdn9a2vl/nj829srqy+c7A/Xv/X8Q9+/1Rt83oWwfW55vcqF+hha0IUE8CZc3PkUzm/V898tcXHnGoD0zfed/+7Z2tZ/oLh8AkST9U53TwihU6EudbVa2ewtdWSg4rgs3LQqD16+cn3/5u2D4vVnznaGeYf6WVoP1wby7Dc9PPm73/J2wsUdBgCXrt649cnPPi3e+uQbh6+7J9j7iZ/7p/zvfsvbkSv9pvm53D364ssH/+03fc8378zG45c+8dn779/c+F4C23Tl+Lcu/vN3/KQ9emX2+C/Fr7oK/U//Gmz14vFHrAmj3vs/0AOwif29k+h1GhD/dSz1Iy69tInWyf4NtCX6r8f+3uNoV9lJjDGQ98uKMcEY4616D7y4y/f3asDFglpFz19fgDo47oIvgK8Qzy28kP1Kb95F4S7uu+n8OhLaALgBIERLnDwmwFbAIABRAFUXmN7KekcoJyyFH6S1g5dJ9FKyaBkdn+yzo37i8pE91uMmSWfOjnqdJDGWL48O9Ga2rouVR7a6to5TlJu34/FBNjraH/AYZKqaMVPwHL9/zbHtclauuWDLE4ngUsnsqlquErj6ds+tJ0sJ3zpQ/ezWLL/uu6pu2LE4tja7ZdV+1uNWhspGrktFDLWXclRDQs+YlqhGIX5p0stv5F16qF9ntS1CDIhLy3CijwANvpGgJ0VwjIuAyoU81GvaNUEIxHmQ7uuOsEYIS4GZQeNLySFdgDINWMrBpAa4ReQjSwEQGtWuj2iCQJNyd6xHFxsNDKPCbYBOCs/zmuyyAV6mWj5wn6/ry4P7MNLOeS7YXrourPVhGCqRY0pjZ42Mx1LBO+NccDwgAIf3HV9OuKntZjg6lMAm1UcxjZHSBrwEDG/R1SwCVAEidTUfuBHzQL5eHSIGJ4p89Yxw0a3OdgvJDZN63OzVK4IzkIJtQmqqZiqVCIw8S43iYKfWy/zYop6UQs9qB9036aA3GgHMBjCyFU9iGaHXlSbZBIt9YrTGMQ1AtwKHIe+7ys84EFnCuzZGdDOaONRGKiUnAjbJZjdXROZ3C0QfGUvIVQOTJDMrktKTVOlw+XZ0Fk1dipzx3Fd7IqcscOs8rJNN3nGGQqOyHF0nEIvb3aBCJ7AMkkSl3e8J0HLKedIEuVJ5dnLMimdWCSVChCYPRgzwNgFHhE2bO5nxLtqe3KPQljNTzCB4gYnfgmwMtJyBoUYDiah7QJgE1LCBNYwUZNsvXd+ZGxaZvo5rBz9nbfk0zlG49o5fcHfIhFMsKjQVXj9jVCsZC2WgYLAG4Klmx4RwgT8v1tlh8zm/CO4m+CrZOKIPLoBdHG2069HOS8eYc809jr3R23Hx9QDe9H56zwUAR++NH/h/R01yYv957vGHPOAkZJXNjzcFkCgNKg3qWQN67pq6/ei2zTItu95hCFLpEhcVgB0lkKBdcC+OYd8YW4FRxphY8wFgbTbyEO3z/4lW0eM7ALwDbZXuS0ri597x0vBa/Nl3//n2K/4FstdesLegNrm4cwbAI1mS5BmSdQDr46qSxjsTYrAJ5wsR+8WK6472LL6cZiPD3QAQaMuBfdxFbO6iDfSOALwIYPdVGT0+/zzm2/xaj8PvTI7lSqdX3Jwc/3QMceXm+Kg6qqanZ6ZJLOHkU6fO/vGZ5bX7QLQC4OeJ0YqU8jZaGP4jaB3cGoBr83OyoAzpKS7eNuz0zgKgcTEriMgnQnafOnV2zRgz0FzwqjaH0YTA63ipyzR77L5THZ3KB+O0lI88eO7zQoi3HRUljLcpJ36bMzYeTws26HXLrbWVW7i4s/HLn/+0/P1XXtz+/jc+9c0Uwn+GNtDzAKZ9nVTveuObezuTA6lDo2pTQwsOU9c/ai3eC0B+8Yfonzz+S/G/+RPP08WdPBVYTgW7AM72AaxgOl7D2sYUwDYEewydXoksu4jDo+sQLEFT34fWwSwD8CGGjaoug1aJUUpxAoS+e70xP2cl2lXnolx7b6x27zhbcO8tFg8LJwbMARkLAcy53ZsZDPNzs8jo9u753RwteGXsWq68JALaApEDkwh8pgJO3lceb/h5kOoA9GwtQDEe5t1JnAbqmUromTtmkKuZn4XUG24gcSuhZikSaa54nq7g+moPV3rp2ubv//KVjeXh/mbZTMosO/1SEKuXd/dOA36lw5zkdVGd1DLwTFsJSk401ahp8rWSJx0zVMJWzOUzM4h7EkcuuSZcWB6nJsTD0kQub4VcbvIGHFUAOFtx26nbUEImo2rClyGMy1TSB5LGIglGlIG8VIgR0gaVNZEsyEztpJcyFOVMc3RtChDzVDIVgTqpFE/r2rlUwKU5BDiMioiI4BGwzsI4h6TfvdOH2ej23kio5TYcJ94dJMaMZ5nautkZ9BrdOWaEplON1/J6lF3OTtqhPyaTSKmMpsB1ktU1eLll6k7NGWamD59Y1OFMcfWoj5AA4DyGwNtrGLqArFqwVcMB3iTLXkcXuEM4UgPiMagl38hOeUiInjRikN4CyPSwX3uFxADaJmAOCTQ8gtauIu2U04lspkHZ4GydHYbV1RHXwvebokcCZHqpdzxpdKToZ+COIFifVTwmANCBjzoACYOtI08qJHlhyn0viXEwVsZgEwIYJ2cF52IwXD045Lnvkp3J2YVBxqR0UHWAKgK3ks+eG6acNxO1NGHm5VQOT6kmPhBLNjMc1oFTYZv9VPAZuDMaYQqESa5CLdDsa2DFIlClcs3BJIUAHSLAgoBnDo2qkdGX918TgE6YMxpQAtA6LK7DmptQAJzEXJ9bo4FH18KFiOgpDxIRCuWdfruFprcCwB1QRaBO70qgzdAq9qy/ymfcaREiYNgvYh2AsRlhXQDfTIhr+7K+cHTD33z053lVKvHGvdXu+PT+2AHA++k9y/NtHOKe9eIyvn3lEGmKNtjcRMvhVwLoEn0wBcAfBtt5EMfX0SYelgB8luiD+wB4jO/+s4IZKnB8GPxOMF35gMc5w4MxgiUKz5sJpo0n+ewNuVc2KM+s4Xs2B7zLOV9Uyi4AeA7A96GdW0/YEJh3cV9Jce2ywdPbKZ5BwNueLkSz29BL3z2075AMfxstEfWvA/h5/a733Yzv+jMexf9P7LUX7N21FwD8Itpg7W8C+FudJKHaWjosZ3xARKlSi4lW4cv7rCzac7dAeXbmn1HAHQbwav7/CwD+AMBltFmYIdrB2Nr5LTcv2/6pV1G/9EcfrwB8FMDucdtzOwCwspJ1Vl/av8XfevbBC+u9/hRALTg/PDVcaeb7t5DRmmLRrHtxRwF4qLb23MsHt2avW9t6SHLRB8A1F9sxBOeDTyOiSJVmBIROnp7XSsx8xPLG5mrRS1PfOLsxK+rdzZVhh3H+babvXjkqihUi2lvv9o98sDvbmxvZR6+8+K6t7mBpNcuzYZo9mAr5BBH1AMD7wI1pMq1159zmltga5Aiz2/BHvrLdlaqZ3bqF6eUB2vLFw1/8IWKP/1L8k/r2PNqU/gEefPQUpH4Cw+UhYlzF3u0udncEQkzA+BPwZg3+TkZuIVOkGLFZmmQ5Y2yRqVusDhfl/QUCGrg7pr6SQsa9fwcAZHAXRr340qsG5cJphxCj3a1DEIRmNeElgL4FnAWiBIIEagNcMm3rQCKBoAEZgdUZ8O0RWKpahJpTc7Q2wCbTbk+UOh+lUzOAQaHrwCZa7TvP0qXZdFqnyWTEE/ecu2FXjszsfOeBMzpxPTG1s5GQg6nopb3x7bwZTZP9wr5uN4RtXkdi5M2FKPnUW7EBxmO3u18uL5WTTA2VaKpT4/2RHma9qRyKy9czXq7oM0ubUXbULCZVMJPVvq6GQg9HkwN5KwQPnKplElQf9lyw2mQyUuFior2svfCohK8bYmmoQhFFDEg6OoWTm11biSQTTT0gCsQ9uAoROXdy/xqzMoB6XWjy4DyCS4ZAhKOaoXQezxoD6naxgTZrvzV/xACMLfiOin7Zu7w5yc9MbvnbjHI6nYfpib45PhAIZYhQZ4rLYoYO9Zs6dEPDelVJjgDbm7IlR8yBjmrwIOGzFXjH7tL5wAPBAvxQ91AxRevVQWULTLgtmdEcAfBdM9UOUYxVn1Izcx6RASCPbEqwWsMx39IIdTRESCQCpAtAWQCowaP0PgUsJutJ41WwwjVaAU4AUkkZIyCTCBFdA80DAtKqXZ1UHSBGxKwGG8gQqUPBe8kowAeqwJlESHLABms5DnfXhcSkn8eG95em6J4t2OHVDe4ue2KjLkcCXs9yJ2aVYZltOHlK2KQuLylZ7nGKvCdDOYwinSFMCeZQqjCjgC5nWI4etgG4Q6y0t6WUgSsGwEblvDmbcbpplJyyxYJrwUW6yL4zAC561HGEiIAsubs40w7Ru8anBKH4kvLa1B7CECphWwXCO1KVC+nCBoBn7fZrtIkDjbbEuGgLWkihLXSxA4DAYL2HWLUgboABA4VwflX7Js3cbHT1sJPmtwZ5XJ0UN/PG3UA7l/UAjGpg++X1QTGbpdfeUlzDdfR23oRb5f+Kr7sCzM5k2P/WcyB9BatPFxCnb6Fw/yMe+vCP44ULAJb3uqH79vLF3odOvC6nN33wZfwxyj81cOOd74sAXm7e/9O/YIz7gb1pxkzAyw9swRxNcZKAc68/jWJvFC89f5MmgmFiQ/yHicYTSkKgnVcfQisNupB93GdECoJ2iAATcenjE/GvvqHnr1Y+fudp5X7YWffOJnAeGb2/q/HTeOf7Jl9lL18z9toN9s5vHaHNtgEXd54FcCpT+rsSqSQRILkEvly66g6dCdqBu4d2kr8Pd7M4Aa10zRTArVFZ9Cd1vbLe63e1EM/Ot/H/zN7dK0H2tdrFna34M/9co+1d4/NtVABu4eLO2fk+/W0AfxXAb6KlDNnFXGsQrVM4/vlPf/T4R/Ft93vvn/Qh3EeIrx8keVI09WOpkEIrLZTWEowR5xzOe2gpEbxnQogolQIj4kmir1vnsl63u7qytHSmqKs3BONnWkqXSnUixHC5tnZ3ZXVl1knSnS9ceObscTGj77j/0Wuhdu7BtRMs1UkEoIw1blY1IKLNJEmilkOqGZvWu888C3P0KVbuXR089r1/NPrSr94GUHyVQG8hQ0cAvhtv+dY34cwDAsUkwUvPrrlEn8Fhc6Hh/PO3OMQ2cHOcpheTuj7Ri/FkDQw4kDgingpxr2NmuEtzU87PKcNdFLB4daPe3BZE15hfn4V3j3WbTFgcx70l4XuzyVQFcM0Q0ZIkyxEwnXJ9e9U3nXna6Ta1nIBbAuABoHkmT89FNcc5wAKgG5FkU0ahIj7r1ZNuzxrhKjka6zQSQjbL0jUnZFZJvROjr2wi1l8WxXTv2u/+o/hy867e6vpgItVyNSuzPQN/MKvcQW26BSRnjCIzkVYV89PI600pXeOdHyUpgbOb1piVcq+QsYumWBugWU79VlZ2cTvwyyfXSJ8Ommp0TYfH0GRlt4tutymaepn7YMGbgZQM8CpSsICxPYbZiia2izD2TNSNYP3aMKdSdRy6Pt8/9MQIsGA8xsiCb2KWkeLMyxK3OUcQAlEIbBgbc85i39mYK06PdZfY8ZxoI0c7AZ0CMLlq+vEgWco2q1va7CdZfykqJmKuj0YRsFykWJeEigNBw/EeRjGGFuSj0Ha3MRFZghgCYCfIZw6lShGXBaJogFkEdVh7DQP3jmkQOADjwXRV+Vwik4DS8I0FaGoMZyCTIbIAOAMMauRhBcaZNitYahRJW81mM0Aae0gURQzUwOc6LKNklbP5GGCqXStwDhgZY/REDV+KBG8TjlQheufDtCF0QpwhNUQ4SlANqHakV0fc24zboi9isEx0x8HvDpOw5xG2s2xWdUmoJlbHabRFj/dPX4MfiVAXPMSEIwrddePg+1tTMfu8kqMXOpk+1dSenCAhyRnmzZU0IsgIbRsMhUIdA2rUOIStK0Ex8BRaKzBEZhz0Th34LHBA3em3njtfEu347QDgsEgwRmyHDEokWAEQWY0U8DGCQUyNgI8cPFooImikmCLOxyqPiDwCWXJ3TTcKgDaSEqG8kN4jMuHhSc6LsItxHwH4iKAJRgZo2fYX4tT61YaWjYWIeGJjNBPdor6OPLzZGVwREQ2ArdB+/wm5X104DNlehHFvwbVPTqD3vh5XT98APybU5xhEP0OzbGEUEIbX0b/2A3jH50+vX9h6Cjfec5Yufen1S2z7+GjlxP0YX3o/vefie3/lGyza7AOsQAEAACAASURBVF/ztQZRH/7QJ6qHHjm72h2u32/q7IXDMV7pZXh9CFgJHv3tFdRLPXvMIu8Mu/QCgN9GmxBp0Ep1jgF0S4Nv4oQzQqiRCajrgP7DHVx9A3NTAJ9928CPJtb8F5BOF2Wsf/tC/7d+8Cf/zl8GenN77QZ799r5rRku7vwdAL/IiL5xKet073l3MWd7tAEenz9y3C3hGrQD/HNoZWlKADtHVYFRVT6y0e2O0YIhjvDnZ2vz3z8GsIqLO1dxfmuRbr+JNrtzFu1Kcoa2VDzF+S2PizsawKV/9eIzvJekT5bWPFmb5hEOeiJPs9PLWc7rxqhAgVlrIaVEotrCouRtXMuEcAAgOB8AsJzzsza4VR9DdVzMOrVtVjWXI8boSj/NAoDty4d7G5+7dPHSZ7/0/LXv+/f+6hfOrayeqcbVzvGN/aLePv1CJ8s+CmDCGeOdLHmrUuopAOeI8XHSXfqC1E8JJtT9rhydSlYeHJ96x88++29V9WgDvScaa95p0vR+/eCDThl3jOPjTj2bLsfeYDsSjkTTnC6BFx1jDwfGHqq8P32okr501lHwCgBL7wZ6i3J3mJ9fg3vKsnPVCygg8HvAPE2bDYJuSZID5mTJEaB52fZeOTV2z2+YAHBDxLYyFhMihjm1iwJs3zeHc8LlvgAeSABm2oBOzhsEqQM0EbBZ+3rHAIEH730UPreGp74RVsqDQ52VzJhMSdU04EfcuCgjMnCapdPaQiINvd6Dx55kLTXrEUIVQ6i0Bjz5oo6klUA3T8k6R4I7u9bvXeG9fKXg8gkRHesel7d7ptZV2ku1oePVUe3zxLGCVFpK7jkjJWbEkypGcb1peIP1YMGNkTN5RAwZMN3WgkrHeYc509FRjEn6XgCTIfipQO/AiJR705hY15cpHQpCs5oiR8WDLa1gle4exVs9xA7vxFwI/btC4A3WRh+8F86iMo6Y4OjBw3jEHue0gbn2dASGu00WikC91Tw7GC5P9q6nL98/yQdruYtBOETuEAPHWDIMTJsiEx6IXjEIE1zakvoztPfFViP4sXTZbYnpClBQhEoAredBfCRXQjrMGOB1DxQCbrCWaHyxCI19VAhA5CYew8UOz4JK4GsGrhhQMTRZe0tYByRVhXQt9BHlyPJu5iRlRKpH8/UCRCsegxC9B0wgdCcBSeaZTgRgGCi4CLrkwYY1kB9Di/uAMWAEoTuIIOZLGVEFF1VGnI2a5FyhOhtNqMslLsiHWGe+v/1yXNoo5ZVfPk3j5/suKB+yc40yGPDZKKA8YJxy5buPzXRoiKZfDCGMJYGEB9cOh8RwEAycIkBkTcpLQ/AabIQAjQgiJF5O0Mddn86jhHO2zcYJiVGwCKwt0bsIBNe2VmQICCAwBnJJK2ZjoCEQKMB3ORhxmDvl4C5SiKaKPAJIgJRAZACxN4Q93Ox0lpqZO3N5bLzvWkAQBBg8GCIIfTAUaOCkcGDwQCMBI4GSl1iXLbvIBRnidursRpPwQYjYBlA3rf51FsC6LOj7H8fBmefQY19A/w0GfM3ApW/H9QufxdZwD2oJ5JZXluPOrWLdDCv71Dr2k3gssudo88lDO+sNb+3KDdTIIJsp1E20AXK3vYfwNQVSv/LLH5785//93/v967dlz1SzpVmP3/e67XSQCkjGQVz6l675yW1b8ebcpL+60sN9aOfaj6FtgTrlIvYLF22HhSwEnrqIMI64OQ3YPfkD73MAHJ79npu922efczXbO4D5+E995C1/8IM/+bXs4WvDXnukyn+SDbs1jqYvAXgb2sEOfDlVxoJkeKFWcYS7jbgjtEHe/zH//MsAPrr0+P2f23T8M5zz2wAOMOz++bGXH00naAfEAkpf3NGtPZpGAH8dbbB3A20Z+QDA5Dc+/jGdcPFkN0mw2R2cObe89o8zqb9dcH6eiFIlREdyoThjNM/mecaYmZ+L4L33IUbOGFuQBs8AeM5YVwrRUVxkk2LGnffexXCjsvZyL81uAoAWstfLs8J2xXovTfsnBstb167dtP1B79T2yc1Ccv6vAVSc8yCFuE1E70er4vH3iej/YkJ9hhivuM5rkS1fAHAVR1PC0ZQw7EZc3BE4msb5awxH000APxhi+A8rYx4hUE8QA9+/dR2vvKJsMauYd3upVI9lhHWqq6Jj7WYaY4e3wIxeGnxggNKtBuW9vXiLbO8CfQvMS0LzbB3j82Bu/lm4Ngiked3IAuALHr05+TVqgCzA5v1BNdp76/Zc01Y6Ile09C+cA3UCFAlwhrX9RrkDsgD0yjZAjBYQEmBZ+7shtHyBbCpkcxzYjtMaeVPLOklxReVyn2uKiaoj2NN1khyEEAZOM8UBnoaol8qZ6R/shmJ5ud5t3CxYQwceS6PSxlFT89oE3ukvMcZEqKZFfbKrnxsMepcnkU6CszRwfiRYOOg0ZfdgaZXVSZKoEDCcTVgTiUWhQsJ5kxaGsto0vWMLYSVnDqICl2UJoQOUGntSexG8ikI2HZ7NNJO7dWyin0nBVGZqAoI1qZx0XMUSZrhGyQWPPKY8EHEvICK13RO11qJLhCEI4xDRSMGKPGUagG2cDQAaIdhCtSSPABJV8zU5tRux4JmOq4FF7SLpLm8ok0CMgGDoBkKnQCI0HCcAzse4KNtT249XEsB4aGwn2AG5MOQ8CgsEBiFUGxSWAhA0LxPWQA2Ly8HhBHxMKCIaTse1QcINNONEdcLkDDlL4YNoFxsyQkgJ18wZiHILBMkglOXBaBDLeODOUxgFRRqCmBTtz/GKfODgRAA1sLIBBUGMAsvppVqmlQbWs+ilKIKisSfrEmH2IoVZhOwLA5VYN+UIacqMUzxO4JvbzDe7HUldHkYH66zYW47NC0zECRqhbGwGnej2ybvDhMXAo6mUMWNu7M3UhZkkCBnRMzMcSI0gp4CMkNzKYYi8YSmrKMUEHneBUYtMeYBEw1cQg8OEE3LqolcDijkw1kbDlQdcYGCcQyGfM+kFJgAevMU0OkaMuAZ3AVUdAJmhD+UjvLcoASY4IB0QGKiwCS8ExbTTREob5uGFAChiFQ4JAkoQGjAENH4O/tMdKlKHJEQo34770dzvBx+xVGg2ZWUkBbwc2uM8oRC7Erbj4VaPoQYEv7aNss9QUw6zOUAxMJBsgFKt19OwYo0BwhmP8HXBy9dd9YPhK3EdK9XO55Zn1zY+hwfL38DDL/34r/xojef/eAJghkfe9DVNVdVPPE+HXI7GXZ4M6ejM6Q31ljRRHcYbCcClSlxjiC9fuZkcDiQ/189xFW0iYw/tvHqOEb5+dDQSdVWWTKtbgrNPGOB/OZng83jkTe28t/ZeD9ncYm74O2vf+QvP/Mc//Po/d7WQv8j2l5m9L7dnAfwcgH+Atv9tkVZf9FJwtH07Hm3AR2iDqM+hRTU9gba5dYK2HDDB+a0p/l0QOC6Qu629miRzwdn0MoBn0AZ8t3B+y4jn2cakLk+EOPxbvST9LrQ9hMQZax1ne2yNbvsVFzgBDSCEEJgxxhARE5wvzgcAeBe8ZEQHAGw/y/Pg/W4gfJq1qKoMwC3FuVGR2KPrp/zGYPClrkpub26sP9yQf/O4KvupVE+jDaKfm39HAriI81stL1Kbkfw34+kszIrSnNhYW/BSuad/82Pl+TOn1jpZytEG5iladO2bQwgPS6IBOKuVNRHdweN49NHr+XPhi+j2GC6/vAGdXLSA9kCi22uaLwW/Pt8HjrtN14s+n3vL+osGawbA6y8nTF58xyVtTSzOVxALfsZFBg+Y067gnjfmJmV7P7oZUO0DmQRMZ87jZdpjJQCzBrAK6OdALwKW5lnGabvtzAKuASxC4FtwS4eV3T/sLPHEmdKTU9IX/ADpGEptLzf142l03Rt6eFBFM0bjMQKzq8SE42q73xO7xrgsD6WvEx0klpLu7LrNo690nk4fSTrPnkv5uSMlBmAiMNidwFgzywYDx5QD5/tl0pVlVdNYiq4RylIMnbRpqlqlZSAvOAWlY0RNFCUgnYZvliG9gOtV0TtGitWz4Bi3uuepVzDBJSACNw2piBihECJ5F5UNQXFEMhCcyxvEMYjQmoDEWpyREmNGiLUJWd24qp+zGRfScU6GMbYoX80CsMOAkx1yngAVgCEHRA7YPJSiMWCe4JgE4nwx0EEdAxCPkYgUNfk5HU5os7OsJHiK6MQKwjMylUxLF2NIJjMHyTvI0qlvoaQTT2gscDIGvKXhSHhNyBCUFMS8hQQDg4DWiDOCtaKlC2IeUAxgBkniOePaI6YNDghYsn0QiLEkovGOKW/BWYymaFGkPmVkkCYpgCpad9Md+ILlfJv3Y+0gGw6/zRFFFqgxozqYayB2DjWIM0oc8V7hQ9Aiek5uT3PSIsb9EMl0pRoWTHeMbp6Vzu25SHms2DJjcpuErm/DOsbrtB/dmBr3x/lOI3hKHh21TBF51Ci4BOIEUA45kyjqWdyNAx4IAF9w3VX3jNPWLJJwDC8tMjCIcARwQNJ88TZHUqU8wKKBQY4a4U4vpTKwfQAidSLCWdFSLAZeOBYmXeXzymgZIA3IRATDgbh67JfhfcAMkxhkOvexBrM5S3br9wyAmQJ5BaSwkCYgzsvwAFDJ9nnKgM4uqb2sH7bScXNu3ou7EwBZIqQB4NsYhQ0oNoAJjwHsEIkqwFiCo4RQqyroFCinN9DJJBi7CV1ZZELAZy+EB2JA+EMLebWCMi358oqJ8WujL3k/vYcBGKbPjh5487cOv8OneVLsFdd7idiUCVsVCQjAyrrM6G1ncMAZVtHOw0O0c+oqgE8AuD8RdOEzf/TCR0/dt/WZhx85h07bE3ivq7XYxkVsfy179tqzv8zs3WvDbihuH7x0eX83UVxsaSkXZdqAdoJflOzGaJG1e2idxycA/Abuwt8bABWOpnsYdl/9K/9urUXUvhmt0zhE29P30v/wkd8cfPG5584+uX12cG64+iOJVH8NbTp+UTpctJktypQL5OEi5ghEBO89jwSSQizea3wInUlR8KKsleBskmhdSSkNI7ZuG5M4H1RAnNZ1c/OVqzc6J5aG8cTKypCIzvc6+b6S0mVKNYLzBahFYk6a+cxLLy3/Tz/zc8vfePrcQArxGICD3//05048//KlNaXk3spwIF565Yq/fH3nrxRFyZNOemZvOv6mQZafA/B6AGPG2P2cCyGdC+Q8h2AVkuQLGB1ZTCZruH75i2w23YnAMgFBEHsQiMP5PnDMa1n05eCMhSLG4rGQPouvOm/3vsfpLh+eaNpJeDEDBbTOnNACLhaZ04D2fmMcmEqAukBvjhRpAOw7YCkCwgC1axGbilr0ZrAtCESE+c9QC9DYYTEK3waNfMU0B9JZyqWY8iy1R92lDaM15xRU1zQ3vZAfy23tLJf9EvzYdbtfCkqf0ZwE10oukZ9EpceOJPUzdrC9nO6d6iQXVJ519klsOMZErngZZMrA2AZZ04uAsVJVUMkKlBp4ISWYMOA8RYRkBONCkCGyyAXnog1WHAOCLIB8iiAQQyCKwkaesuB409jcUiJsiCAEAeu0L3MGLwGTcHgbAUsEchawDpmPJKmVtm+fgfLg2A1v7TW9RAsjhTxIND/gnK6EgE1DGFQeuY9AxpDbCJgaIngEwcFmBYT3iAQEKVGICOEcGDGQI4ZKd5D7OnKAuIejgMgYfADqAGjhIUHESUCyEBM2rQyXAo7JZlaiFwOSskJHE4SQYIHDSsBIAS0EMS5huADTDiQd8kQEzgDnAU4BxAgscCYdY9GHyKxAUA1jBBZFjIFClNY5H1z0XlH0QtYcmIi2nMYB7IPhJZDqsoRPiYudCPqYSmbnOEyHgjomxUwEBFXUwBJPNinmw0MdQsK8Q0BwDctqJ5PGyq5GdUCQvAq85z33CvVMU7gmuakVzLEObj8RfiprTHmNCevGATeMk4gsTuKml1zJSEY6yklRDkKDGVyM7RqJNBYkxoQEd9sk2lYMhyMoWPTAqUYQEX4eDcZ5LTuwxYKsj0sA+jAtnoqBFRycMbA4h1YxgEV4RFB0SR0VAChEHkFeAt4iZhaRRKAC8Q6wD3Co5tAOgzZ7O7zjQzy4aVG9QQImAUa0CUcRa8zCqUB6nCWZ8F6kPvTm+35YJpj0OlBLNQSHpw4Cy1DzAchFWNeAxxnUzEOqBkHdQk8fQ7kjZDOLKBWKlEOtr0FtW8inALpZQ0kA5h/8g8e+Kqffwr74E78+BLA+PJv1ls/k37K00dlNuPy9o0vFRzpryZtiiFJKIQF0BMcQMdwgRn84P/7FtboF4ONZlvzh+b//U9dX3/Zd+Fqzin9pd+0vM3uvss5/9SP1j7zpbf/yv/vOv77aS7O3oUXeLUCSHG2JdxfAx9HekJ8F8Kl5L9ynAHwai0zQVyJN/v/GDnB3BbsPIKzm3cF2b/BmiuGHG2MeTnXyahq3e0vWryYBviP/lSTJ4rVFnJIzIs8599Np6bXgR1opB2DsvFsaTcYqML4ulVSr/f7Tb3j4wQe0kivzbTsAeUcna/NtXj04Hp3VSq5289wD+Mitg73NJx57+A3WuheQgAPYaExz+Y+fec59/9/4nketd2cdonz4ofPfO+jkt6/s7cuZq984zDq+l2UJ2p7GESMawzQM1o7Q6byATu86trY3cPHCFCGkAB5MFjK0/UGsTaNiWYwUkPg2PcAYEPXd4G0hmdbHXfqTexHbX41PL0GLohVoT+6irMf0XQBQPT8/BdoJ4RDAkgQ681W9n78+TIDQAPUM8BkgSuLBRR+TFgMg5sRaoQaJiMgBbBsgFEkeVN2ICB8mUN0ZF1smYNx3TV55N6qyjjpg3K/PZqcCwokrva7LuTjMrf2GytSOghg3/XxkpHQdH3buU/XriyZnXGvHJAYMpLQQ13SMJgiVgtgqvA0xeuUj9UG0BkDCWd8Wm1kO7w28UxEko/N1ZJTNz4MIAHOA77YnmBgY16WxQBQIGgY6VhkDd77xJCgxtVK8UdZTIPCSCdY0hiKi5yAoxhAEoiNCdA6VcZIQQx59VZgG3arx1VIPOwAEan9ExC+piEctRxYIwnikrkLjPHiLuIhgRAwEcN5qr4MQjYRtwJAh+BPNsfBtVEAhghoBJ4EsaVsFDkOCLiNkABSMQ+PQDUx4IiwjRvjYiLpOglahkIwyCaIW00kAQILm918r3wVE+Agk0oKpchCQj1G44HgVfJMwziVpmJVGOqe9OkIgHEcDosgTLWVIPDOiybnT02F00UD5IVF4kHdxDaArAM4yH2+i1B8JZUxdjd9GpJOo5TfQqh2A8dwzbsvxGguBR9FvYnB8Oi5PRLZB+cAdcMjG11Mdve9Fa/9v9t40xrbsuu/7rT2c6d5bt6ree/1evx7ZzSbZFjVLRKTEEC1bjiEJThBDgRQlQIAA+ZCEcaaPSmID+ZgYiA0EyIcMMCwLie0EkeEgSBQ4kCOKkgjHGjiIbJLd7O7Xb6rhVt17z7CHlQ/nnHcfScmmAioyhV5AoW7dW3XqnH32WXvt//qv/xqoT7dxH9dGUxVSo5FLhKUTwrCCLH6P5SaCFZsfiOdErHtNinypOQUiS3eLc5cmnzVXuYrcBG2JbKfnZ9zKFWxJXOLJT/yae+rv8nSckge8jD4R0heLrafnOUUEgwwGiiIjxVV+qoOOuHJ6hhPakbnNStdsZM4QZBRDgWHgi4z0Gzv53ZJxk9aWUFXje04TnYzr0IPl8ui2nCx1v+3Wp+P6pEC+PdChYwp7D9phbEOSguRuYUlc94o3sBmuOGlWHNk7tIuX8Pom1pxTFyW+fJPV0UNqB+UHvRt+HvjP+eZta5yoceYnN+/17uYHV++Vy/KXfdG/1u27FZKLbt8+57xrU8zNG7/9bnt5du3/zL/0sa8wgilXfAOC9779f7H3g72vM/3FX8r/49/+n3/z+ePT/4XxwZlbac1IjWXkTMzdJq6fpBnH4G7mh/xRWcXY01WAN/ng3Q1v3Hv93/jYx1/ph+EHuhheL4vi64OPpx+mp7s/wOjYZnRzFhCevwRwIrJfN4u+csXOObthTFtr4X27XC6NKs+5wp06a7/fWfuYcVyf41Dw8oPAkFK+K6p1jPELwBdSztUPfvf3fOTRxbn/0rvvnn/nq6982Xu/+vM/9nH753/s4yvgI32Ir1Sral3Z4nZTla++dufOP+piKJd1vQoxJmPEWWO/CLxI3VjK/Iiqep3zsx7M3yPnkrr5OEW5YHOxAcJuc3lbVQsDzaSjE2XkS80SK7OS+yytMBdorDgEy79XB425gMbBASaeAj3hIOUi09sPOSCIc1p7Tp/PLZdqHQn+roGlgb7ynjKoQbMKMRmi3VG1A9grV0iTUhE0yZUpdHN8NCxSf+NoGEoVE6Mxemu/f3hZVeePY3ohpfzyuS+PM5rwhdkVxXOar096Y4tUNRYRVOyVRz7qC7delP5isDaTUo/mVNb1DtXXKIpTVN8lyA7jjrHmLr4oAUvOkRx2FMXJKH5ig4VUGesRgUmcuoGuhH0QjoxiSkjE5IGoHk1IlSzZZWPKvg0iZHE4k1NO2V/vc3FtNZ2E1PZVlffe5lMRVDMWRUSj2WwV7029XplLb6rPW8t9ySmw13Xy5rlsjC18dIPBn13Cg/ve3DqJWhWqXRt1YRRqL8hM12RfgGZy8LBwoxh3SKC9xQVLYdMTsezayJNNg0Hc0FcNWZ21mbhcDsbZpHXdRudiQzYDLObK8BlhLoBMQUZxCDZFRJVcuNBpoKwHyd2FpoVXNXdInT0vbMriLSqG3i7sgkr30YoWXXHhu8aGtL3dX4Xkj3VjF5pU8drq8znlu+kBP+qe929kYz7NSoNschDRM468o3QFRM3ZB4gQGzVHoQr3bd5Vx1Udg2C9ibGjv24k7hsBjH/ekhMSvyod920i44yxmr0oOywfpqRC3D3bm0p8vFDlmo6aBXYag4NwsQNEhSAVvW4VEMVRmhMqrdlzTa/XT6gVCagoUAbaaf5FlFl71QKBAs9AzJAClAKhOtA95kBzvrcZOK6QgsCGKARLiglfj79bMVAwih3P7Ra30zFsxRMh5v0AvTxk40efsDEXl9Wd87PXizHF3E1+JJMp6BmAtBjR/jKxkIz4Ak0rtqmE1Z4+nIJ/vrju11XhP3tVl8qxbFjrGat0VOvi1Fhzsd89V5fxX7h94/5f5Zu0f1X/Zv835F8Z8qB/3xXyZrUqv3D/dzYLV9iPXd7fGVuD8UY2l5u+qivTLMr68YPLnp/6xFvf7P943745E9X3g+bf0964d8Sov/cTjP1nT6dPtsBvA38V+CSw/Tr+3Lfe/stf/xCjE/g0cMFf/NjvTTx9454HPsJBP2/mFf4Y8B8CH2ISDf066+CJg5yDjdlJzc6ymF5L3w8ZNJZlWU3vnTMiUIvpb9qnzsEDJuV8axiGpYp8vinLI0YuxsAomvkao+NaxxiNwrl37stt3y/6FO9ITA+MsdvC+18vy+LX3nz73Q/tu+7yT7z26qOY0nelFL/DWvcD1phaRN4AnuvDsMwpbUTMF6qyLICKlAqMcWwuhd/45JYUf4f1yS/zm5/+czy4Hzl/tAnwynlV/aBzRbHYXtUKuRpFTmcnPiMGmYOMTvPU+/P3p/meTwfP/dd9Po9/5qDDNaMCe0bu58z7g0PHjFnPURN0+2mBcDAMY4eFXMOZwjMRmgG4gnjlG21yLK5caaM1+axZdNe+CqWRs84UqyIGuXbLvRayPequbLL+pfN6cZ+mCRh3Alw1V5erIvT1ZbNKVHWFyAboyXmByA4RHfb7lESOfF3fF7hlxtSSI8UVIi3GHtLcMSREAiGssMbhy5k2MSOk/qkx0TQillmGQfPQldEXuSyrmKeUmEE76DRhNRE6T18lW0ss1dT7LZDS8dH20hqWOeNDojVCrGxm32rucigM7nJ95PYIG+AkJwoRTq0ztXPZ9ILpdvizMxMNhVsvOy0cOJdy3di5AntGgNHDZiEARYI0TAhSBf1UeDHzQ3eA1YTse7ZlgYggxqS1SIjx2omrgsfbPEmIzChU8dS8mekQRCUjRActOwbNfnFdDvcYdO0XYopzLYaADrflqsiSTSTGzd72R+WN4mHa+MtUxQV7yUb9TbsjyJmp5CI8jkd7cYvNpf38C6/ni30lL1l4YQFZs7bnxq9rwt2y3S2Msyo++tQ6Z1K6CsHlmFhK1GTJxkrbdQ+9H951Vr1JRelNPjUuPOgH/X/Y2mepEVz6MgU9Ii+icknKZyR5HquBQIPFUHBF4ouECdsMzKoJKzI3ksq7u0FD02JdzRHerohpz4bHrKZx6yiocVj2X/PszTqlM4e3AHwG7UeaRS6g5IiBa1qUGfmbMyMz37cH+gCL+eYxggen0+/NG7wtB05vYswwPL4o5KFZL+z6onvTxHgfuLmFH7HQWHikYxvHwh/kO7eM/tYOoyxUtuS0Y5cfsioNJnveHZIf5NovzP19Gzasms/wwfY+r7W2ZmHE2+3eWXHxczkv/qOU3W8D598sdw+Av/XXLEBo06sp5B/5nc9+7j/bt8PRyenifh/yJ05Pjr7yyqsvXADvvY/mfevtfWTv97dr4G8ztjf7IeDfYZQzecD4kD/zD995s/r+v/KfXABGf/GX/nAqf/7rTwvwHwMfBf5b4G/ytCjz0/bBu2GSGpkrmdJ0zj/CKNFQf/2fpJR0GIbSeq8IprAODoFex9cGfwqgmrshxjNjzN57v5gO5du+J8XQl0WZxEjvrHtpSDGHIYg1shJoRPV7mOZdzrkFvtsYM++W1Y1cwGeAwjp7v1DNvilXmrMWY3qYuq7+ROGLFrhy1v7LI6WLK0ZH+SJwYcR0iFqx5lmgot1/if3uLtb1vPfeQNcu49W117e/ete/+Mqr3Lw58Du/dewvL2TRdRtLd7saF4oSkCEjGaIXnJUn3MbiqXGCg7D20+3Rvr7z2bwgP22z6bjERAAAIABJREFUqGsxvZ51+5YcUrol45wMjAjiu4wFAys7pn+vMxxliOUk4VbC7e6QDmIFD+oUbr5dlDw4Oi2LYZ+tSCnGms66OxoGOQpDKJJ2D4rlaSdOrn1tKMoKsdfkfIkxm/1idTwMpSCyoG0j1kBZLRFZoJrI+bKKYdVibHDx1Hm3jxAFvVVomMatHqZ5cIbz12i6gYQrxEvOLEQwIgfpGcYFawmInQM/a7MYE73zwsSOH8c2CxTOYtWOdQlqk+xlv18rzoHu952pS5dVLGUYquh8N8RttLrP3h6zPVpK4zzEyLFmSlVaEazm7IdMxOCrBdwusrl83BMVrT0Uhe04pPRnnu8Y6B56FYsFrcdn9IwxQ/D0xqFn/KXTskCyUnrbFyKqUHlxWfuhVFHjnGMwT28bDqiwZZycunfCsKzj8fWefsiLNATRlaykkIVAkpUaBun6YFwO+kiG+Jny2rxaamz07XTcP2x9Pm62pqEwXr0pJaZSKk7sxgbjVyf6SiqlKceOEBHoxeSqodtoL5t8ZpesMdarCNr3sSioTRbVZDJ9DsmH6zrkxln3WrZhCyYWMojB3I5Gvz8ZKXB6hmInvt1jJFuEu5SayFxRYLFEIg+JU3hmqZFRjYaelsQlJiu2R5dl0igXoklxBG5QkBlwQENAprZms8TILJh/Pd2rGzpKG81pXs3zs3bNgJIy2YAEc2iME566NzMdY67mn/l7iqDoE631xGHNuRZ4pyr0rjNhNVRZeofJe6PlkH/TwisBTsVhTeQsjee3LA4+RgqI43UaViyrK2KK7LVCcg6FvQqP24rWXVHHJeIW7MrUJs44CaCZWFRgfnKay5/koAbxT7af+kQC8H/rr73ha/tYnPnAg4ePf/DevQef+Omf/YnffT/A+8O194O938/GlOwW+Axv3Psc8L8xcik+AHzfpt1dfuLv/PWbjIHJO0w9Br/l9sLCcG/7D2j1DTK/xEG49xtt7IIBY3DwGqOD+D7gg4wBAhy6foxBzBDiru0KG4IC0SwXpTOW6+3WgLGrZTM6yvGYBrAiYkrvb3jv65iiscYmRd2u3Z6fXV/97s3F0XMYc/vG8Qmas1VhIdY6VLN3fi7+yN3Qu5xyWjRNJSI7xvR40XbtSVZdLupGCuveZXSEnwe+stvvv2+9XEpVlafAn2Ksfp5lSuaxKb1zDuc8Y/V0wNiC7e5ieOsNG19+delfea27+uVfunZnD79/fftOweI4oKzQvFyCYGxBTnOKlpyxmiFZrLX0jIv0mq8N0OCQwpmD/3l3PdvXB3pzpXfx1M9zH8n5mHMwuGREa+fjfoZxx74qYNdCGeG9cmzn5iZO4D2BlzMcJdCLTEyJOhvstlqm1lc2GfELTZttUQ2a4lDpoFGsu26WDl/sgYahX5J1Zw1lylpE7wz90AOpCqHrrC1JyWvWda/sK+evUtasMeK883sICflSoVnBzC2ZAuPi+QgxhrJOqtY/vnBLZ3I+Pc5zSm1OW8/jG4EKa9U0y/M4wluFPPFnSUC3o3axzZBESMliM6AiakJsluQ+iaSQSBraMmZD6n1slt42ipgYWCiUIjwqCn5DhB9JIQeuukxTi3rRyqM3jjXsO66tpbL2SXA6b5LmhX6PPikUmJGa2WYpJzeNy7yJGO+5MNFF1YBia5M04uOYnjXj/FSroNbIHDzYabJlmzTX29abjDelGGNyqjqpKcSqoQMpVbW+fNeXhcubZ59JH7LOn5pCYnpVruLt0ohVUjIb19jPpGfMowv4Ll/aXdPz610prwX0Gdurx4tVQ7Ll7mJt84f3l6uSslBygn0St+isKWvt+iwikjVrMoVcRlvW4bFYxy7bzjK4fWcWZpCYtBuMDY+M1ttYmDuYDLPUsbAhyRJRj+EhmUTickK+G+AEz3tckMlEei7tcz3LPMGunXUM3KLhEkURrkkT9u6ICPnJfZp9wY3p+Swy7PqawrdjFYhAj6UnIRmVPb1aJNdUPYdgf/eUT3i6teL8jDOzLceOfWoMZsmc5ocPLRORq972Lbd2BfV2Wa2ev9x3RcYPkIm4YuTxNgrVAL4Y/34A7jFwEpHFA8qoPDaJ+wyUWrNqT1n6a7y8gMfQthVfbR6yihtWJuLEMdy16I/0VG/+Sd75h/xBgr3ZfuoTGTj/lb/ysZ/7tU/9lv957f6x/c3ft2+NvR/sfTM2Cve+A7zDG/c+BXzKGnv2ybfeGLkdfxjSKrP9+OuJ//Vz/w2Q+fHX/0k7nwC8s2n3t0V4/ahq/kVGZO9DHNIBLQeEL1VVqcbaEFMUA15V2bU7QgzWiJsRqjmtsAd8WZYNYPow+O1uF+uqTk1VudXiyNVl89Wc83a3391q2/a1QahjSgLYXdfLyWrpC+sS0BkRY6wtRGTmG10CPqsWQ0rWdt2yqqpZ5uYW8NMxxpeSyUVFuWF0knvG9PFcWTynWlrgvf3Q9aJUztij4fjoyy69/LwsjwSxX/A/8MP7Yn+94Oj4Hb765jXba4MvK0J4Hu97+rSZrv/UWnIWjDVPOD0Lxh3/CeOCvuBQ5QffoJ7ye1pidP4zl28xXUOcrisyBpSRMRiYBWG/yLgRmVO/DrghCbGWQqHdQW3HauK1gXxlK4Iztx8061RfbbrT3c7ukGTsELQuzc46bVJXXJfF8rJaGaztsNaQopAlYu3Sqcpx2243vrRBSUhOkiii84Ix76DaSI5345BM540vq6rHyDnwvIW1Mgzg7+OKNYcgd9K0lAR+LcJJ6VNnDAu+Noie0VSm9/aA76DtRxRTKrCQFfLEoVQBdWPw1x2NxdBmp2oq1Ur77DJE9abzRvI6+1pK17XG5mtruYFCztPcFxpVzknqPXkteQBKSRFXeLSuWIogKeGT0nuLF3mS/ssoC3RCeOVJwApjAVgC1jrKfiw4pIC3bpxN5RhbEEdQkNa5wTln8tSshX7IgiJFYbBW5i451oIswJM0AtkVIrmULvcUdkAyOeWol4iRoY3V/Yvy5vahrV/7QHdkG1qNdlueSlOeLpb7M7uRZnjg4Z9ZZD1K9/PnjbOfNbfcb1oJP50Jr6sUC4VI0l1SE+zSLEEFTUVKklVdTkMerNosaBlDXqRBoq3YstY6mgKn2umZiqRUuCNhv/fV7tKFwsRoE5YWy8modqyPEKkxrEjasuOcPD1Hhj2KI5FZMfrpLbCvSiFnVgY5xpK5h6FHSE8QNUUIWJT8hMaSGZ+nmQdYGSjLlmjhXGDJC9i4orKfxQiIHat1YfRT1XgfGRjXi4bRDztgwMj18sbadVf7RRw3UXmgl4zGktJOAd8loLS0jBX55Y2Bcnm+DxPVpG1GfnHDU3qCcvAnpZLvJHJSxCmtWHay4igNWPsWqjcx7TMs4wMuVyc8liuOo3LLXrC2CpKIUSjuCLuPr9l//r+Sf/1X/y39739/AOIfY//ur/76DCT8U2Py7/2C/IOj9Yu/bt3b/8F/+mf/WOn0vR/s/UFt7FLxu0tAf/GX/v/5nz/++jdb8OGB72vD8PGY03cuiur1mGJpjPHeulkS5OmFthSRoSy8LfEGkG7o0j7spfJlXjbLOQU488i66ftjoLXGGmtt8M4G4E7pvSm9vwW8FUK6kbL6q6GtVFWqZhmstRiRSb6Kviqr32bSLmMM6BpgY8X+cGE4YnSQNSOf5QNAU5Vlpar7tm1vgLi6ruYFdXbIF9P5WSDlrM9L1pyQLvvqBZ57oa5zvkGzuOOfeebv07crjLU0q8ecP/wMSW7y+OHPcP+dQLkY6HfHgPWCYJ+ksg0HR/203uC8iM9I3dPt0Z62uWhjYAzi5k3DrJfXT+NxzKGbhmFEj58FPsy8Sx/fv2BMK+0IiHj8xNPTEuwOupR6BMfp9UWhik9Z88pklzSFM1e67PxpsGG4qBdGxZiCbEOKjzSjFK7GF/tYyPXGSh/EvI3zPTFW6uXZWJYGMacgdhFjqiTntloWasUkOLEpXmJkKKI2KN/FIXAL0zXfncakAtx6pTP6PGukDYyprBNGdHNGPo0F9eiRmJTJkiegYdKvZirgpR21i8PaiC2z+mmRkQwSvA91yn0KvcVY6RorpWY8cIHwUDPPx8CPGsNeSrtS35DEjDd/vJMy1cSO934k8jtyhpQN3s1ITkC+BqmcC3uaHvKFP65Ow2UqDvNmRol1XMvn6sxcTsMzdl8AY0Q0q8YQMcaAyNfoNz7h4koid6U1RZliOeRyCJwkLKbP8eI9121zKdsWnn9l6EuruXjGdiJV1ZpK1IVTVX3RJH1nqdTDOY+GWsr1cfieogjnqcyXIeqN5KSoCnlGlVqczCoG0TiR8wtvnMq+8uAbLSofbHdZLPMmq7kSk46K3p/2pFY0D9qmRH18c7DLVQhuQ9Ilht8k8AirN8kkTH5MZokMXS4FxI9jNfI8r4gUlAxT0cpAsmupJJA1kugp6BjwyFS56gDNfdKenCw+FePzGBmDyPm1DuTFIGbf116P9mGfz/SYB+ItDIIUNeWM1uvXfc33duakZrLm2A/EGPaBEB22FmswpVpp6dCv2RTemK5vV8FlQR4EMSClHY99uR8fhONmDk6XKkPb97u0P2rZ6YKjvCKngLEej0XtmoDB5BW2ecTCWx6YF3jPKnG74lbtuDSnDLpnXb2Ne+WT3Hnp/+SVe/+2/MK7qj8zZxy+re2zJv3lE2P/4s3d1f8A/Jt/1OfzrbT3g70/LjZy9V4BfvR0sTxW1eeAoQ39mTe+9tbNAdtMeJ+dx9OVtbkqKrXG4qyLMgZmHkgp5701puGwUK+ctZvj1dF+37YuhBTKsri2xpzu+/4DReWfqX3ZVHXZZdWLwrqjRVXNqfEdh4XoC0zVu0DZ94O2/RCt6MNqsdwDxymlI+DIWmvLspSYUtl3nTEjWenpoGkk944o7AeA07ooL1B9cbPfedDQGPNe3u/Dw33rTbN4+ZkbJ0fAES84Hz72J5M8eM+6rs3cfrYnhDP63V2sHXf3Kc0psnkhndGn2cxTnym/P7KnjIvG2IT+QMx/wBjgecbA5phDYcjN6ft7jChmnI7RJOX5CDdqy30sbwK3FeQCymFs2ba4iVpS4IzgHPh6P6Qr15irG+tiX1Q91j7UqK3Ckqo6GkQUZUEIO3LqCMFSlk0Qc8PG0CfYYw2obpBhbM9qzHbnfU9RrrKRQiH4nCqGPlTWbXZFcQRiinHc4LCrnzcgMt27eVwDaBz1qM0NsNfTeB3N4+jBBMf2ulLsluQyhkDASMYnGf8+L63kwbkuhOgnHrspjPTbrLw9hKK2Lt12Ppeqft11oaur3BtDiXKXqRrYWhbOsQ/BSEo0Dsb+EofgXaxFjJkqY/se9kFZL8BZnRC9M8agtpjuKUAnmMcuBpFDsUViRGOmDnvMcz1MOsznI3BHA3RlaTRGbB6D1Bm5n6tAD10jciqKjtIIUUHxxscBigqe/4DEi671hrh0XgkdRdkfNcXyUV0UD6VdLFLs9IW8T4+LlXuneM18ytn8IkYuwV9Y/IvB4QKs2s0CSThTYUc97zpdbaO5uAxuueCoqo3VnMcE48hkXLpnNfuqb2zJFc+pcXW5jFuCiX2Sndq4wfgSTc9zxYdoJFHnR2TOSQy4tKVQUL/kgoFm4t+NKoqQGIjAw7zjLiUdW4opwDOEJ8F6Oz7Ig/WoV6wdG9o8QeTenubrabSYCKVmygjXad8PDoOMrebCU/PiacTfsaLm+gn9g+keHXdX+zIQdpGwM6gti6Izx7Jsg9YEzM5QVI6NDKgdRbmvrtAHl/bKnCS7W5nVzTHzS25HjcChHncDQtYq5ayJkAeCr4mdp+gUUQMLQ9KGaBuWt5QcXyTqY1YpsXWB4D/Mg8FCypTtbc7LBudKdj/8Bs/VLcefFPmF3wGy6s/8wfu8/1Nkod19ORTNXob+83/U5/KttvdFlf+42Pn1i8C/D/zplNNH+yGUKcXSWz9470+tMU9LwsyyACP3iSf9fROANdaKyJ4xeAp9jMX9zYXsuu6RN/aRc3ZejL8M/EpK6UOgOOeSiAwpxZsgN7x3tTXGWWPmoNFcXV3Xb757/6oui2NVVWftjjFI7YDKOXsX1WPn/f3Cuy8Dd842l/2u3e+XzWIFGGOMs86J9z61bXvZtt2XyrL864zIl2F0yD3wmhEhw1u7dpcv2u691WLxWTH20dmufdZ7X4Yh7LyzDSJlu98/FxZLSihJ0fPWl0+AhuNTx2Lh2O/sNGYz53FGp+bqSzg48CcBwPTznBLop78/Y1zMN9M4zkTvGZWYUzHzQmEZA9rfYgz4vsgYDL7YZhaaKZLgkiAywliFjNqAjR87d8QEXGSKfQa1hapRGXwlV0WFteLVWpuVDtUvk1PCFxZjPHHo0Vxh7BJrrRqbCcNtE9ONIrYPEzLgilOyJpArus64oXdifMaYa9CUnb+JmONyHL8LDkLTUwvhmMZiQSxfU0ik1dhWVhzYqa8re6b2cYCIiqraXZkliFqP0bdw+f4oO5gV5EzRlLKplFrAdpB2ijrBXhvjL3PKN62EyrqQwSTv9coYCu85EoOIjG2hRXhkc1FV0lSYATHoVEjyRHtSFUIgG2OSGAyC4Gyc7vl2mkNzQZEFvEVPG0JlD0ixm57Ji+n7TBOY0oiunTS5lRHJftcYKmuRccpj8qgJZ0SeIPlCNpqUgEBRiYkR6yxUtZiTk+SFlPu+9OtVrI2jtqZvQp/c0KkaX6SMHovRhfX2UpxcGyOVEb4E+Jzzy0H1lhHZ2StO9FpEGrIYGQBvrVAUcr1emKBoKeoi2aWcpLDeCLVivAjZ3cs9TU5WzaCPsyavCYch40xOF4rxqJhs1Ejg2bEQw3Rkr2LkSAwdnvTkuZmvfyyacjSmwakh8xwpXmqpgKnEsEDpacGowXo7duC45KBusGCklOCVXCitjRkHrkA6jysEeYjHcYybikPmCusCx8CSLXt2HCrtn2jyGUzL2CbPSJLLoWN5vhIkMATFboEy08v4QFQtana2TUv1t6q7ZWOW8kivKSq4VR0haQhVIpsUcjRIHk56X0Qny7zMgUF72lIwXoGaWjM5B/pQYbZCrM4hVZzobby9S3JL+lwg/Zr9fsXuQw1X33mLs3CDdrjEys/9pR+45NvYbv/kD/yj9Z95/b+4+ee+61f/qM/lW23vI3t/HOyNe8dDDP9aSPm7jepNEanJGTHGeeess7ZjdFIzMtVzQOhapoVKVdequjfGzOnQPXBqjVAVRdPt2tWj9sI/d+fW1hjjGResu1VVPQS+xFgMsq7KquyHIeSUKjOSjuYK1PMhxqbv+5O271lae5JyDhftrlhX9fd66xrgYV1X/zcjMnczpdyGIVJVZcFB3sXasYJXVHmw6/rPHB2tMmN6Ywd8B2Ol4wXQOmP+bo9+R9J8R5AXXNMUL79Y+/2+vXn/8VltjHmwPlpW9vkXRsRzudqxu77FnbtHxDjQdQV9ryrG9TnbAtTIk2BlHtund+/wjYUYygGBnCtrrxiRrLuMgdvRdE/mYLznUBE8MAYJdxhTuTenY/TO0KaxUTth/PxdwKAEHflHMkUF1dJgdjhsDmaRVe9pzgjR5iiSKSxaDUYimGtVXQI1SEYnuroQcO6I6Fwm971besIgpH0YdWVN8rFfNCmkrXOPk6sX+HJt0EWJpmlY5jT3NC/ClHF2MurHPklvyRjQ+DylZWduZKnjxNUMZqHoUaAFaSaUqYP08ogoxT1oA/ZkvBaXemSfKIYSW1vExZSDtdX9IeUblW0fi7Fh1yrOcqOuc+89XixJhF6VZ6xR5yTHJE+C8RnRtUAUQYxFxVro2dMFG7KNOPue90+Q6GK6x1vGIq9TDlQEOzYLCU3WotVsO2ufBIAXjEHCDQ6I+Fy4cz2dh6qOBRzWEK3Fd2gu6Qa8ERPKh85zG3B1/WRuCuDXR1CVXbIOcQ6jogQsSbOn7Wtj7UaSfAn4lEa9ylEvbGUE0Mf77UVM8fO36vWb2euPi5VS7JMmMck7MeuVK3LSewQsIka8ufYLWYkQdMCq5gqrhbFyPw/DwpTGi5hBTG7yhpw63Z8/X/vCxfL58lpaKSVdiqQ1xhbSsyVwBvRTwYtnQUlBQWbHhp41CatKT4fNV0hQTUKS0JnkOyMWKcG24xR70onoWRzHgCOOmzQRCUbZGbjtYGmwS2afmnmM8gEOqHUJKBHHY24ya+EdhJsF6AVxnsIBGxTrAnl9rsPUUadKStXDph4dT1lCdzcuL8xKX2lXw3XdFMo51nQ4LMepTOROI1YLa6ypN0uRLFJSm45ePIV3+HjN9RAYQs3iTLDqqTC44z1LmzAq7IeamCLXxlDXJfHqLmVpaO0Z5Xe/zbv5h/jcb3xc/rvL/0t/7fJnpRJAfl67P1a8t29nez/Y+3a2N+7dAZp7Vxff3Q3DDx83zcteXFkYE721S4XkrD3mwPWYUaOBA+o0q7TXXdfFGKNa7zuMKZqiMMDSGSsrX6TmyB/JiKy1jA7qxem4XYix8c7dBmJKubm4uKrLqvAn66N58bbAreP1KpeV6+qqwRm72vXd8tH1JlqkqqzbGiOmLMoPMy5m96w17zRV1RSFn1PNcxFDAIbFolktFk0HfJWD8PAR49x+E1i1Q//P1s4vlsd1Lrw/BVREPlXV1dWzz9z8UFmWzwOxKas7pPQaF+dLXnjVcfKMJbUFX/i8sLnU3nv08kL6TF/bJ6jojHLO6A4cUr1Pc6+uGBfqc0adxjvTeSrjIj2nb+drm/lsTJ/30/97lhFpmNO+XSkYLB/I0AgMHlKvdI8yaw/DicUvwO4h7BTdFZX1VvTKFXFbNYK1uKxShna/8XWj1q0oyhKRBpFMXZ/QD8oQBsQJnoQIGNOgqXn+6vxoi/jLxTEs5UYsi3ab9CrFCF1n8bYeARLrwK+m8zdoFmKIGPkS1r0MMnaOOLSMmjoZ2LmScOKvRc3GmVCJlVZdUPoCThVyhNoipcENY5OSfGPa1/Tjxz4YjCi6MmhhpFtltVeWfhMxN9thcXNR7jVrZfpekjHtW8Ada/TMOdYpcTMRTLYhMwZVbpxPMJ1vYOxJjCpJ6iIQc63GOpSXGTvaxOl+z1XdEhPWCI0xM8Ibw1jXkRurduYoVowp/PqpOfbmNJdmdHw/zSljDeV0vKBgMhFrfF8UeJEnkjwzh1YA01T4pprSnwAIdoIG6enF8GvizK8Bfyfcz386X+lHzGuyu3fR+H1sP3PrxBwPIX1HTjkUhTUOK6nNVlGxlYkiko2VO8Y6q8pmGChRKSzZipBVNWL1VjB5t7Um3zx1OW/iZbIsZQ3FsZZLjVGHbMNjUnpHM1uzcR/A5IXWWcg8pGdBIYlBb7C3p5DeBGTUE1WPGlhqwMtjVTdy/HJE55T3LHvydAtEWKJUaH4sipUT02eRSej7KR9ggFskSi5oGQPxOavSPHW8MStKGqbim2ryDTenY5wDbwkU9aQ4sBo7r4TihNvSke0SO1waHrpmt+h2u9z6bdjoC67JZaVGuEBBcqA3tjK9PzUL9664PG0Al3rkIyFm0rsLljnjyktqf8IgQvILkFvEuKPSBWnouO6u2dNxVTmGZ5fU9gbVWeC5+CIXH1T0uTdx/9xfkA/8jQZ7nUnNz0q1m+b7+c9r9760yh+hvR/sfbvaG/deAb4X+Gjjy38+DEPjsSsHimphvBMrZk4JzSjCHOzBuHhEDs7IqmpyzhVBs5iMZdzZtkBIyiAix9aaDeMiMSElrK/2O33n3QcvvvDcnXLVNHYY+mKxqJwbUT3LAUms9sPedENXeOfFFZa6KPNLJzcHI5JiiI9QcwG8Pp3vMcDx+igxpmhPp/OWrCp93w2IGeqy/DBjMLVmdKhz+7IaSCGll2rvF4uqeXs+Ztf3P7i53j26ebK+Y0VW5LzHGIO1p9y8mbhbNSCO/ZVweam8804q4kAvYC0LHUs74Rs5e3PaZk7PzHpy8y5+xYjkwbjQv8aBl/Vwen0y/fx0cL6Z7tWCsU/p5XTNG8ZFXoKxrwzGXhVxeICMxxbBXIA3YI3YxeP1iZwXVW+cj4Or7GAk4Zz0iBt80WRrNwglQ+8oikuMHUBuYyhQY8BmUhwrhXOydbfvmq5dD+VSi9zHet8vdk35MGoNQ/BIr0jZY6UHOQd6huEm6IKcd8v9Vjvnn42r9cUUXsyI3xw4zV1bFmN2OhpIYBFTlsYMKbkhk4feZic+ueoU+MLYN1jfAfdD4JyiIZtqabP0HooRYLNnWWxUkVXGP7Y2JTDrXb8Mhvx57zhNSb+iytvGMDBqXd7ISo4Rq4AIUVVwVmUKoKwqktJYSWycjTgrfkypnk33+IXp3qkqGpMZUsqNEbZFQanKRqT6KqTbmqxgUA5Cyctp7szvxQRmC86PeettysSpapyU8M4RPGLisOxzTo+szRfizcyPPAFe4iABAk+h0rEfwVjJRnEapWOvnd5nxQvmBj/EkXmxU+q2y21dFtU6x3IT83MxGprCvwnYFPIzwFJUkVKvdUBNKUtEShFSDnqdbG5EJEnLrvCdzVtXug6bbOyzpNqupNKaIIoen8d9/4ZedWXl5YQre4cul3os5wg9hjusWGD1msAjznJHT8eSwLbDpH2/qNa76+WYlRf8WMiT/IFqYZ8aA5nul8+BgltOMM5RaNK3+iA6idE3ZPZPAvJZX3FG6ufN9lxlPWlz9mliI9TTv7vNYXP47HSsmYNpBPJS2AiU2eRCY7Bkf2ezrPqjbdwdHxV9fGif0d2uQDEIudDiUSDdkU4TS9nZZ7Sxj6RLRz1y4YynuLz0Z++mlKrm+PiDuon2Mtm3B4I54eSowqhF+xP0PFMvj7Hhik2xpc/32QzAbsHDj1ja/preWp4n8NLptv7Ov0v439dV1GSQHfDFn5XqHGjfl1r5o7H3OXvfjvbGPZdz/l4R+SBwWlj7HbUvT3Z9Ww0x2NJ5WxUQfJoGAAAgAElEQVSlTGLFM09P922nV9utLcsCI5IYnc6s7N577633Hu+cFM5FGVs+CUDhXG9EVt49QdhmVKqMKecQY1EXfplz9oA1xlCWpYpIy8gp2gBqjV1YsbYqKysiTkSydy456yprrZ+EkxeMwU7LoRLzVcZgr2L0kMSUzhDe8c6tOaCMs7ZeZgyILo3IA2vsuYBLmqMRE/ddZ/f7btXU1R3b7gr222OK8ghjGqy1hFBzdSmk2CImY3BSVobdziRVa1ERa4VDB5qnizJmpKQFPjt9P59ez4joLOmgjAHqMQduz5rDwns5jd396TPHIZC9msbkDkDrfG6L4lEVQq3wkcawMkLXulIfl8v6YVEX99ZrLtbHm2x8ma3NrXWKLx+riM1DV6FqqKoFKRtRinp3XUWloCgtVgw5WFQL4pBubDfOxCFWSg0acplI3rpevNpBT1SwOGoqO2WdeAy6I+Yb4zjlXcipzsY1lFXDYTGcF9qZFznN0eQgKhhrshXfE4o8OJXkB2eMaM7O+G5sb5efg7ycih+vQ22uhmNX2C7vjM6oqFHbJRe7uA24CwOdxZ/dKCsjTneK2qTunrOpN6Ko8mpKHI/VzEga8c2cs4k5KcbQT3IrYgzGGPYTn68QQUW4X+HqSL453cdS1XQpuQ87lwvn8CmRUqIFVsaYaAwRObQm5IDGyzRv7ihspslUSuLdmLiFso1xnIvWsjWQCdlowoqhMdY00/gWHDQ4v6GgqA8JVDEjkhvMwrbmyL752Rsf7q3EI2OHZ7sgt0/t47N10e9idm3q++OytLGs3d9LQ/4eIpVJshUj12mjZymmtbV2YbwYa+mswWhSp5K9K3KdOsnaOFscs8RolS8oUiDF4FvRbON7XOhDFuZFrLEojyTFd8l6Rs2GTIlFEBKZDQWJVioyhqxRveTkChQ50FoMB6F0G6eNmkDK5DaTK4OxGlC2Gd0Nsd/vHTEXFrsHBjJHqhQKZ3LILDwdMM5fT3WFMcUYo1vzdb8z34tZVHkuolOUUkpqrbOLZ1F6tVzV9clJqVfLD9Wn4o/74l4bNKe2v9kTs7YpuqEVsfJiHLCKnmQd1rK/zMuLB717XD1TJXnm6LZ7vhYGecfsuoudxF/Jxiwv9agJdMHz8C2DZo+707BorrmwHV2xZVNE4lLg8cC2Luhu9bTPpvjFn8w5vGjhqxZZw5MCmPgX/tLP/eF2nHrffk97H9n7drE37o1k3g/e3f3l/+N/sh+59eyjP/vaR1+yxvzEsqpvFt5XbrA7i4SqqmqmtEIXhuyMxVmbYopmmJw3B76eY3wQPVOrICMiIaXaWzujB2vGalhlDEpOOKQm7KKqds3dO6uu6wwQ67p2WRVUd4ypy7lqcO2sS652M2JTc3ACfQjBdaG/vaiayMhl+jIjkvHy9PPMa2uNyHVTVV+Y3g8cKlfnzhYfZQywPu2dawB/vd8d9zmvThfL46Pl0izqem2tLclRMKZCROk7T4iZolREFOsdRyeeOy/A7bu4coHtWsx2M/roywvo5wYaTxy1chB5vju9vsNBumHBAcGaA7mWURMRDn2XE/AVDvy9y+nnk+k6v6rwOspNEbbLMGwIQ97CMxMr/e0WwlcX62ey875Xg8Nsq8QX+5ReFesXo/C0StG1MiAt3gaMDVTWaAxVEmlQNagGrN3h1CAmFcMuL3Lw167w19bur33xMHluDMYIGWvSQKrJFPY9SMux01NuIHW1DEMKph6MW2jRFDgrHCpOQfWaYQgIFUU5L8gTv08ETAYbjM6Vk1IaCussgxlT3rchVZAvwN4Esmv1gYSYTOYGc5GM5o1P/QtdGF6P1pyelOZy4evCm9y2Q/nVmO2nS7rbuZJX+56bWeUta8vftiZ+Z856ZExqxLAniqrwFsKQMy8gvG3ME57WTSL3CJzslZ4iv4djr8palbVIvut9KAUVEQzCDqgnKZdZ5qNhvK5hmuvz/AHIRmmWym+o8hFV9iL0IlwjPJNHhH4NXFpvoli9tv4JDcMBu6k7SMFB6gWAIYwgVMZqVfismcF4KQKk++uTl956/ubRBzdfuX/01qUcbx5vrbS/skNe2Ty++mC9qtU7HjtXDqagMIVcipW1SlppkiD+SbXqnsQRgzjjxQwtgwy2zI5gowvGMWgd2V0jnURz5AbCQ9mqyQu/t9a01pmVJrYcmwqTw8QrPcZgEfY4+wFOQfbpC3rDt9SeZKZrvwQSFSsqPJeEMPYttgYqYRgG+mTx0WAagzxgoI4oEtRYTA0sqBAN7CYx0taMYuevTvd/bq02b2TmIC5O+7n5s7kH+SywPNuCrxVfDrphEKyA14wtXehkq/Eyf+7ar7n+iiT9SiLr/qr9/oLaOOe+FI7Ss/pQ6tzK79qkTfVd5cnlTbu7/FQZy8K6dJSPHu7D9fWFa9b4/5e9Nw2yJDuvw853t1zeWq/Wrq7unp59BQbbgFhIcBVFQhBpmpRg0hSDkkzZlO1wOCSKcoT5gz9shq2QFZIibPqHQqEQGbYpkRQkmgQJSAQIGjtmMHvP9Ezv3VVd61sz826ff2Rmv4KssEWGLQFW34gX9epVVr737r1589zvO985sWvKx0XP0/mj2RhxHxYIPfRvM+IZRuwMsSoFZFhg1qlQcNXwTlPME4N5HoFOBJ8j0IdtLZF0IIG/AOAS7rd/K+0+2PvWaTuoL/jX1ju9+Pj6mVUBepog1pmRSkEYdftAfRMoACxCjKKs7GqipFQyK/vdbtnrdGwjqdIKA7cixC3o0kVVwjuvvFbkIrtxuZBrnR5lxgBAWdrquLRlNzFpkplEAOgQEWVZ1hZ/FGVRJAxwlqYDIUR7Y2oth45QR/tS1MClAsAu+BRAh5nnVPutPol6cUiw5LTZ5nEI4Nea5x8C8Fxz/D5qG5+voAaqH2n+V2qlcs08EkQnAF6WUj4NYAVp1oIug8iAdxKDoYM5K+F9gNICWgtUJVFZgvp9YDIFXnkepzJerb9tW7hyvenfPurISeuL2i7a5089f6N5/wRLbtZh813mkbF3EvFCJlBkhMebseNjk/WOlFnHfDocEQ0l0XGIIXPgbgDs1d5IRmW6Y5MnlRbVOOmesBQFkzgrrfVBEIFISeZ+xtx1nbxgk0YEX8IHhpLSdnoahAASNY9Qqj0AKuR9OXceU52l3phjGHMNbFdRlRLE1pl0IWI5jlArjZ6dB8gANBTMXcQooEiAoQCummBoGyYlgJNaFsOJWmsY81puRK/XWcs2AqgrAkJS93MbFVaAKIDoGMo0ZsQT7VGiBj4KwA4oFmUmSplkTok4ngWxZ+FgQ/lgUajeKMZHR7Py6Umh4rzTzRNTvmaM/WJVJZ/wIfxkYuIDUjCFEG5qjX9CwA9bj8J7ukxEBRE/mSY8o8LcEIvenXEorttu9aoxqlDC/ylmPMMAK8nXlcI5AFJJGEgkWFZ+Zs313PJDF8s+qjmDzqEbgVUCrJLYNgpjAJLoHs+TAViSxLImH9SFTMyXwryc1ZrNIpEGXSyjUlFJmpWlPDCJWiNJiiT2AFz21lcb+y9917TceLe7XB2by9PDPXfuRrzQ72f29Q1JcjG9cdQzFD8+2F5djVFwqOKaymWiOrJQkJ1YccmeU5HQSghBMrFQQhIUOU6YjJHSe4b3roQQCcVpJWxgmwmWj+nzYRpVtfAus3mwJ6HDfZdR30A+IItYcYUrIC5gsAMVJCIOOZCBjt1oaSYySBAHTghkvHdzlai7AOUKULGmPeiAyAIiA/iwDvxBV4iSgVIinUhQF4ASfZg4R9QLkBBiO4ZYtuODZfS+nrMCAvEb0uW6GdMCyznc+pSfpnEAS09tCQAKyk0Fqq+e3xQ9xAtdV1bftn8kesC2gtpdqYZ3SNFu1HwpSfX7ZFf1cCQ/IR/DAxjGd56ZnbiNjVTK58pYVU7sPl+UxmWDgG72cnXxA1VA+EDcrXroVUDnUQHVL7HgIxwejXE4KlCKJU0ZZ1FzCiHubURIFM0mLgBnJPB3DfC+U3P4fvs32O6DvW+dVpfqX769/rMf/L5nAfw3AB6OzFrU7PB2p9zaNVkpBOVZeiJJtDySnIhaArxFDULaKNSweR6kVNL54EECh7OTOLdFNCRYEmmjdQJmubC+fGX3SvrE1rYadfvlqfcFgCCEADOrRguvBZf3nA9QR7x2Uac2HYCH8yybxMh3qE4xZwA61trBdF4U3Ty9kCRJ6zjRFiz8CGrA12oICgBceffYpCyGK3lHKyEHqCuFt1KTpM13zVDzlIbN523wAAhZppCmAJFG7dNbQUpgtJbi5AS4dgUwBtjcBvZuWRSVR1WUaFIUzXfZwzJlc7n5ro1TBLpYLnYaS7cPjVpS5RyAa81rbwEIZcT+Wx7YkbiYKazOI6qZVFvMMWUSiSQxc0rmUyFWYHlxYvL9o35/40ClvSoxNM+6gJAGSs8Q4wAc50ENDTh6EMmgjR5r4yFlDqBACG/Duw4EHUGrlvPZyp5UAPaDVDuH3UECRoYkcQCfCTEqZ0ORKH2MNOtFSQGIDtA5oOx4KsZCIEMqj0BxG0pKxBCUYRE5xMhqAkCBSMFoBTgJMMGVEYQIlbbyF7dwL/VN7bz1qCM1DZgTETAXLShOgKoDDDs18KGm//cB/wBIRCHpABB7gJjZQCRJ38gyecwz/y5E2pkfej4qq8V0Xp4b9tT2ylCXQgUhBL/BjCEJPA/wyDl6pXLhoUVBT3ZSVYDSF5wr7ji450RnQl2oXSEMKpseCXN8iwjrMWCvgtiyMgYiQFoMpUIlxT3+52kB7lZK5HR6kIWAAWMoBHpCQDbz6BUhMHcemyFiqCQSIhw1877m8jrXpZnb6CySyo3kdW9CCuACFTgG4USkdLPfM0MsLRY/R8J+6ktn37+2V8iL+spcZF8/OYcyZuqB5Pd2u3mZdjZ3V3jP+nH1YRbyDIAjV4YBFK3IyJYEeQAqxJgzEAxkspgXihHVYDDwJIkCM5GhIrhKTHeLJEx8R3REFBwpMhR7q43LIzMpfwyJCVnWZMMN9ig54hw0EjAYGgyBKyghYMSZIIvj0ptoGKmyxZiho3OyCxVi6EooT8A0aWgUColisCSIDiQiCKn0RL6O/HWaa7YMdzkj0CDtdaBTzdP9kz6WYE2gBnr1bxsgjOFQgPCNkT4JqgtAUNyL8rXHtP/drnEKQJwB/vWL61nQUl8Zpifvc+X+/p5wBvHFBLhNI1oXG5iboTxx+3FfbYk5jfC26GLCLEq5KjJ6F+b+LLYPX+snR4udUR8FXsWo30Epz/tx1FikFUimyDJG7DAQEyRKQXuHSTDIhIc1EVGeAn7/yhZqQPgggJf/bw+83/4/affB3rdOu4M6tbcG4M+h9rs1gqiNprVVeBK45z6xZ6RqXTNOC3iS9Z5mVWF7aZ5pKdGcQy/KgqqyCp1ORy+8w8Lb7mreqzrGSFlLsog0SQemrGCD48I660KodA0U2oibSdO09gFdFia073/U/FxDzTfbb/7+lhRSSIEx6pRAy3d7NjG6vUG1tnSt88AqgO9HTWZeb87XiZH7Asgj83Fz/JNYkqUF6mjiKpapw7aYom51n7Y7cw2impvX6wJnz0UETxitEd734Yj0eeCFL2dADKijeW0kpo+lA0QL6BLUEc02+trKqbza9EcHdZr26s1EH01T9ciD4+LzicDupsSNocB5AHfuBDx8Q6ri4Rh/c2sxXqkQB8L5d82T/OGba2emt9J8dZ52+uTsgqRkCFGlVaGyclFN+sMqCFMBWEcIGYKzYjGhmGQllKqrCrXZgJQOUo3gXaqYo1eaUWsv7qMG6RIm6TXjuw7QBkhXXrP0JDKU1uR50hfkPKBKZuEl8TkiTiBVgETtkSwERwTPDTUULTeN2uAHLDgWTT9daEcIuJd2LLBM4Q8BpGDngdgHsTZIyj4o6hgUinkCqQTSfAVAChhRa/cF1GAPe4B/PLA4mM+tcpAnUsjh3sLv7I0P12/s+Ytbg9Q98FB6U2uxPp0n5dHx4hNpZ6vwPf9twzArSxFezaMb97v06dRUJ/Mi/5M2iDmIricq/AOtwsw6ehcj3ZfCfrGycpNAGaI9VoxOCOjGiESm9/qitf9qI3Rt86ijd1EpMJbX+KKZc23Vdp8Z61hu8BbN6wfQOhEDTH2XJjGJL6NeU9K8UIkIdGuauqKycT6Zhz8YdOXt33r8T9ueenln7+r029/oPnchXMyUuPyHyWjv4M50985e2F7wwutixdM5vZolUOoGpPptacLHiTAkQaG9DpUQgpv1Kh9kkSN7AMJFZ5gZsEqyh9daCs+V9ewp73eMTGWxuFZW1Q2v07MJgoZGLvpingj0IFDC1CtgcEiFwhUuWPAJJsKUx3Hgcu9EJVKTa4gOn8iJ1G7mOmZFtnOqLYooOUVCVnCzgkUAsd6JkGnGZBERe1V3nqqFYcxROauPsfTMbrUX67nOCJjAori3DrX6erWdmoKjLsAFl3WwWuom2q0AlIAvgCKrLW+lC0DZXZTdLVtGm6ytDY9KSsuYE1COjdpOevjU1k+KS/EApf97dhY9V4P/NLk1+bvVoXqCKr0je/aNuCblijt4O48rhyVlQC8gwxoOyguYjj3EvMSiGxH7CVIwIB180kFvv0RlLarMomj5xt/QWnJLm9JAHW7/GP6YYO+0nMs//lN/jj6dfPjdu/3e+35jgF/mv/Uf/GtX+v5HT//1dfeBjS+Lnvvf/t7f/Lmf++N8lm/Fdh/sfes0CeAvogZ878FSpqOtmgW+0Sy+9VJtjdhbDpAEIOdlsXLr5DBsdQPyJEmllJVWypTWGRcjtLWxl2XigdV1myhtQwxSSpkCiC4ELwW5ZzZ2FgE8nBaLdNTtTZxziMwyMUb7EIQUQhGRPfU5HWogN2seJ6irURl1ZWBEDYY0avC2bozpGmMCls4bfdQ3shw1t+1DqOdxm/owmTG9ROtKELVcvk0shWqX4qrLYgpx6nn7wL3j0rQmR5cl44GHBKoyIoY5+gMHSQvMxwnu3JkgSxPMphHF4g3UlmYRywraK1im5VqQp5txUU2/tCngsNfLvlgXdRZHtwb5Z37/3Mb5H3v1RsCse9JXvlyR6eE875Wr0xtqt79+q5RKqeBv3cq6jzsh1zpVkQiQKkALMBUaBAm+haVsxwqkDHAujZASgW/Cu89DKgOiLdT+xQe59x1ty+1x3hNQqvUCXm/m3Bpq6Y9bAHakUBvdXB1b53MXgqlTX0RAHBCJXqfjdMO5o2YuKoA4RnW6UKXWI6xKDWYBLRRMmqC+CdvmmBGW4tatXlnNq2fnYX0CSRFKgULopsUihZQKoehi4QWcFUiyBNowSCsgdIDQA+T7QXITJIdCxkPL2N/r6K/5WbGaUDJcGSU0GHWND/K8Ulp4J3sHY/fDkyIeZ2mvT+WJLb3vLOTsf+11HunbKj5UVloHplkMyd4c6d757TfhA2FedL8Qg9nL0pMfyMxim4E3rMWTMSJm6b2KzpZ/6p0D69qe4fQG5Z58EkfuBMtEgJCpAICzoYoniEhUQsdEdBQiNgiYN3zCDEQBmfmqAyYxsowBVgp8uuh7U5Txq+E4HleeL7zwYnVZdMSrb9y+/NMG7ocW6+sPdI6vDVbevkkHX18sDsZivprE93cmsy/0lLzLJtmNK7qTddJLAPoql3exrDAXACIlJKjZDFIUIYYYXeE5cIhEpEEsBAm3cqZvlRK+cKU2mSGlZea3rMeIcp3AMnMVi1JzKiQdm6k0QkXBge8iICfLKlQLFKPOmYwMdCFdDhxLgQO4DGIEYC5IONK0LIjoIkaOPJ9PLxskVYK0XW9Ou91UADQhkAgCFAUAZKFyreh3guVmpL3GEyzgkcDVws33fJDrh4PgfXBdGzJH7Xh4ryAuoHaD8c06eMMJpGcOpskZTyIgXsf+4V5e2jiT6bW3Lq5O1sllo8/PB+UfhBl77KXvkMeiS2u9nzVpJOrNbvKjQlerlUvekTzSTbfePGADiHdh92QIt8MIx3O4PYJ6j4VTU5z4gEjHOIKBNgKyKLAo8a8AemjUzIF629boSbEGhj9Bqfij6u/9BKUKNQ+b/gZ95I2twdlfc38W32U3E/z5UfwOAD/+r3su/Sh96poebmZR/iyA+2Dvfvsmapdvr6Im/B4C+Chqvlcr73FaXsVhqVHWWv+06UXTHGcBhERpvTMcyURqYa31IjHdw9kCVw72/c5gCCmEiDGwYNZlWca5s9xPM86ShL11OlVakTadSVkixiABhBBCFWM0ZYzd0nkjpUAvzykyK+t9MEp5UVf41qKhSx26ljNYoY7UzXEvYgSBmn+33vRBm3Zuq1JT1IDKnvo/EkQKdUo0wymnAiy9a/9lmYkGfHwDz8afOs5DUECnm0CSh48OZWUwWi/woe8KKCqLq28M8epLk2Z8hqjT5O33vNi8x7R53QGYFyT6cxJPdaJ3aW2obgAcvudgstF8rweU1Ttn97LvLhc770xPkqd6sOcuzMSRe/bstbEsN7rV/PHDbNXf6G6ctd4/kSH2BLNYmIStNgZG59MkOZzGmCDEdTA0pNQI4QQxdkUMAKTrFnOepLmHNq3Q68FCm2t9W16l4N/BShWowejZpl87qDcfc9TAawoARquBVrKRTkkKgBIATGRaYE2n5uVpoN2OTwZmAoPAsQXlLU2h4cHfk7NZrV9nIriciQBJAkKG5nQS0QdoMUKv4wDLWFQF5nODlYEqk8wqBJ/IMAiBM6QqIgqXpXpi7Xy9DIp7G8Mv8MKe14yzg04+qKqguBBRZ+RG/UxPbu3tzI8W7nCYpEGgeO9zs83cXIl3D8TwzFmxv3/rwQkTXWZGBaCntZv5mSmt67yxMtx3ROQFcWBW28SsCOFt7/EAM3paQ3oPESO0d7DK3JOjwalr+giMyI4HpKgPwEbHb/hJGMQZKrEuGV0aBo8OAK8UIAR2UQP1twE8ESO2Q8AtD762d+D0pTeLjxVl7wxzHG9t3nn5JfugSa9c+1477D9MK4k8e+1lzbv7jDM933nU3RzEZNseqe8QQ38YD9Tz+aq6LhTdAfCjqMHPpVjywySRkKZ7RFcADgEhTJlEjxMSFBlc2MLF2e0585lOyUQLHfWKpSqYbtd2O70wPjxRbCTMRZnYYxKLceXTVFlsUM4MjxuyxF0sqCtMlqYWAb353synsyzE4NkhUoJUWlS9wN5lIWcoKuFxhDk0eoDIpecF5V7GrpLUZYtA4HFt3QcPoE8QXhSqHQ/PtYTd6ZRr61DUBLdgUaEtSGojf6c1NWeAPAHSLiD7IBhwu05qBwxaqshJFjGvQP0VitWZ/bvXymohFyjLLLMfemqYfDkbihuLz8VHMMV+/uPKJU+JD3DgK5TTw8WUHj+YiodWn9GDzM71xbtjooY7fQEz5WCDB4kgszUEKTVOPCAWFsX1EsVKROiPcXTG15KS3wD2FJRnEAPOh1oBJkigkDWX+rf+mELLLXD2fQwDM1yWcezlWiRrfO2PciLj419cCfHTMdri//no//+0+2Dvm73VnrfvBfBDAN6BOmLUpgDadGPS/N56zhZohDib11sOSRvh4zxNC1FR1znnrXdBG0PTopKZlpxrI45mUzgwD7IOWWsTLUSplGqqIlkBsJHZuRBYNLwSIUSXiFgqNdUxBimEAhCrqrITV4lcm1kvzZpKtHupzhK19thN1Gm6FSwdFk5QR4/ei2VUp/3bFMsKxcetc5pjTJIkOe0U0qZJTgOMNmV7epfOWMocnJZOuWeMDuAEJhnDu3NYEJAkHllGWCwkdi5mMGrq7GyveuEFYYCRIbEFEh7RTyCVRvBt5bFBHXWVAG4sTPJZZvylhcU+wX8qrQHU5wDMPPD2EW08urLf33n2bvpAkH71KJdXnO/tJFVIX5kHfTJY3x3a/OBoZfT+fLp4X0GUTIUBCw7IOoQkSVFVCdhnTRpW1RtsnKRV4UqhFlGKI4DsJM2ehVR1pWcIdyHlr0LKpyad3lmQkKjBZ2vxVKLmJp5v+tE55w0AIq14KolUUXLXJDPn/dQ6v5qniRGC2v5ubxCyOVdbxFP3vxaEGBkqawFifmo825tFBnBEbRBiGEIBxFDGAUIjxprr1uvF2gJURfS7AZkjHN2ZSLiBRQKLPKgQ1wF3E0V4G9AbJ5PFRnAkpwsXlKJZlmYvGxGTxIiEGT6GEI6L2IXQqVTeRIfMB+EEyzOu7H40JupuujL+euesvzzev/DWfJFc+5lf/Pn4O//TX5hOZ6PL1nVWAXBls5vMcaJF+WCq/fO5Me8GsGVDSAMjMCOQAAmGkxILVYd2AurNxD0dR5LUUakAJByAIDRllGEuBA2hSZdAiBKaHFZUXRB0DfVG5CUA+0JgM0Qcv3W13PrD5+1zIeB7YpxqKeBskj40lem5pNehYXF0PP7al4bzxbFLpY8rYXE4f2uxcme0fsHKpIqd3u8Pu9PPLPbndPfSwda5D2y/LpTcZcbD0cV1AdFVmtqopQVwKFIyJkgrElI++DyEqEExdLc6PgpmGyupErkgEHtbiBgjpnvTEPpBG5OCBmRznVckRCokfDyBw2PIyYH5WAjlhAzXA4mp6GDI2lUVYyZ9rb5CPnqewSKDh6xQjYhhzSSZZOikc9itqmNNvkWL4gqzcjHRUKxhGMAhICsJMgIiZ3CywJwlZC9F1mY07nHs0ICpZs7LiBgZ0Umok2b9aVK+tFabYyCAUOPHpRanBzCJiKoL9Kf9bpgz4qorq3yz84w5ym6aSl0x+zhSq/Ed8vtJ+Dd5K0zDa+Wr8YE4QSEHoqCu2O9M0BOFW3W3sSHvQkfCa1Z4K7XcopK8gj5cC+nvHeLwWQK/N0UiZxhf7iAfVfCbHu48vsHaEABQevhdAPNabDp+AsAXUa9rUwBfxx+j/QqXAd9YyftDRD9OH3r9b+LiY1t/JLHmv/WJX/oylsJ4P2wAACAASURBVP7a/860+2Dvm78NADyL2gLsKSx18dobXmvZBHyjJVormdKargcGqCzrXVWWppwmSQlmpWRHMtGcKCZGGjWvrPcc9eFsRsF71iSDNEmhpZQAVJZlhJpkXY0PDphjSFbzvjTGEABZ2gqBo1FKRwBBagXl7CTRmrEEnq148A0AvwvgO1ADu5uoF8WzqAsb2gs5oo4qtfyle1IxAHzwPgegi6JEjLHM84yoju7pGGshmMajt+Xi1enCJbfmtORBG0Fqj/FNf65C6YBB34Kxh7J0KMt15JlHDFcYUqDfe5jzjYiV9RLzkz6uXT2HyBathImQsVJqHpzNNfNsNU2DJXxlrPWLixB+/a3uYPTGaJM+evlFHIvV1ye0sWE7dlro4qUrW/0rd/pm0jlY9GSljm+M4nxjUSZv6uyJjcLOer4IB91RyGMofYxmHNmlAMCswSzBsQelA6SYgmi/lMpCm4vBmLPO2oBIaaoFYKuvSmcXQqqBIwwhdR9STJqxeidqoD5vxqiNKGfeOwkQZKJipVHJSgaimKB2cjudRgeWqfS2grnt+/oGKZMWDp4G6wZLwGeBKAArAJUASjanJ6Cq08WWSxAJaAWQ5HvvozWweZ4CGDmgCVQCsgKks9blzpcHzsdkOrPrR9PZDnN4WIhwuLO1HlOeT0noHkk1VV5miZRhZ2Nw6/BomjG62Rgkb1xL1fpqcig/uHr5N/1Ddx6aLE4uX90louck85fC//wLvzRu51ZiihAjzYJTj0OGNQdrwRB9mVUTWXgAIw5UMPQEZFu9jpYG1UajVwEI0jRp+mcXwNeoEI+IDOshoTsl4OZJfy0LpZK7pddrtB8VXpSC7gJIRAijJPDq0aE9qKYLj1RHQLgY5cmd232RmIOnHzLjoGOUchoqIXB7u4eXRszgWVBhtWOiHa1f++rqg+n7r16kw8mkvFINpjvzf5yb7iOxq9+tunQgJXpYVp1OAQSSlMg+dWxlj2OMFEuWbu7KfJD7+fHcuLmldLs3M4mx09vTVQjuqY6Zexvl4uA4pINkluddI88JAfAcJZzqwvhDnse3cIPm9AwdSCGkhHM+kFYSCalQBkjIFEiS4rhiAxk9ghOgbkDIHKIv4dJqXp3QcX6TQRcAGjhYKyETgPKAwAoqQNTevcJLK0AyIAgBQQRq77FtBLstkHMOliREN8JnAoqw5PcyI6YMQNTp4dNSLAsAEwfbjWC9MpMn3FNXzSB7C2M8ggQ5tpDhEB/3X8U8voP/MM7xPjriFTKo9APi2TiLI3Ih6Un+QjzGBSrQDwvcjOzXJyvqTOJgemXSurG4BIN/+uZg5XNivPdtY5yYCkVXQr3YzOHWuUU3a8IXUG9Wx6LORB2jlqLZwlKN4f+V9h/yr/6RQN6/6+0+2PtmbpdvZ6g5BR9vXjldmaVRL+xtFSJwT3YCh1imOBVqlm+0QAgxpJIEQowkhSjSNE1CjLGwlpkhqxhoIzHCx8gzXbFjtplRZjQcdEOMbKsqggASovQc754bjUQI8ayq03ZHNviu817EGAQzCwDSSFWt9vp9LDlqx1jeqM6gjlIMUYPTNk21KKuqrKqqk2VZx2jdVlES6ojSWbRVbEAppFQxRgFmTUSJD0FqpRiAqqrKR2afpalofH9bT9I2MtqS4VsBU2AJMGzzvE1XlhCyQll0EAKB4x1oswD4ohmts1ldY/R6Ai50URQWK6sVnn0vY3//7Xjp5ex4+yHiYrKDyfGVZDL+jJ5OXv7yI888vx/53V7q1aNOb3xmcrwlmNdW4sHJJ5988osrxbR4+uDqa3dWV40V8kGsms8dVq5UqbwdqPsuKP3+Wy7urqjsLU6zlXkMGYrFkJ0TTmkljPF2saBESiOACkR7cG4EIY4QQ5AgaWt9MFf3AV9arYqtwiTf6YR2iM4q7y54k3QhxOmIRYZGWBuAb6qllYigjQoQWkmgMkaTMDptoxunwd5pcN22U1WK916WdQRPnJ7/ST2tSdTSLmg8WInqYklpoYJHjBq1k0wz7lE0VCIFkFU1D0oB5CNjM8aYhRheExAwibZqIU+OTortPDVnjDC7qan2SLhxojsoXeTAmK2NhvnKsJccTjnXlEZonbgS1W/91y987MWvf+XvXC8YseEWEj33GQB/DYC+cKE6/vJnj/Clf/oDnywW3aiF+8u2DGqFhrSVjEaPKohjfxTviEk5jd6mhNQDl0Mtw9RudtpokUMjH1TZuG0df5dKOAXThOrr7eHMT7WuuHKHuB1MuPTSLXdyblttnVnXojPz/cNDu/nCW34LEN8PyB6UCPCxm0lB3QhfHrjpEWO3svjKgzv61TWJc2YuN81Wdv3O/s4zVZEaiuqiodkFcyY7fvhHLs7jXVTFJX8SHpOv9wdSkI4z5/ipEKJMEsFUZ3QjAIoxBgbLYAMhouPmjk2io8p7RafbqUDoaZ0IayvujXqJMcpM1NSd7I0TuaaLblevFNchMGfEPtkyFOSycrezWNmh7ahtUU79cewZJANGZAZHjwiHqIAQI1QM0NGDgsVMW1RJD0M/DInV+9ow2EdEWmAOjxAByACvCRT1QAWpSch9CWFAhV/oNKRRQbOFZQFBCqrdRHoASkAKBtMpWlsBAAFBFVh4ASFzdNpMRGs7NgIwiIh7BIrKlyuY4gjTJLEqvkw9dHUQD2KEAIU3BWNDPYtcrol3Uo8JaRzFijd5gd0wxRqt0AYcKozY07HcyRbouppPuEA9z56SZ1Usf/r8awe/pl7KLr3+AxXKVQCfB8gCfNKspa14/G0AX0a94WhpMCdYuhq1Ke/77d9wuw/2vlnb5dsKwMeY+Yds8OtaSFkXsAH4v1blddACkfoGnKPePbdFHESAISDqNGOKsY1ydW6dHE+8c9nAJHnHJHGl1+cEpLKaUV9e3d+Lx7b020IgxCgZ8ARS3geOiANjTNnTBqVzoioXeVFURZ4kPam0AN8Dbqn3ofTeVU2VbssxHKOO1l1EveAXzeefAZBgCOsjp5FbKRPG0k+0tUYDgJjUzhtZjBHeh6CValOyHjWHT4r6O0+wBBQtn+Z0UUbbvy3x/3TasQaDQgQQKXAUqCqBu3sW3Q5DIGC0eh06KTGfrmJ926PXI1x8rIfeyl1/d1fFjnF2Ynd7k5OQgCUi3n6lu5IOh0P5be9+N87v7JzM/sp/PFcci6MkL8qEVu8kfXzsb//q3u/9wi+tA3jXPM1OxnN7/fjG7qXe1ihbGQ7+2fHcn7w6nvJGP6apVE+yTj7kQ+ySs8y1ma2IJJwQVCJyicidumYjJiAUWZreRp2SFTDp+t2B0lRXAu4jBBN8dQswq6ijr4QlSJ83fTUXQjjU0VpVA0cytbXnPQcA4B5X+x6IPp1iP11AE4HQMARCMz4pNdWJjU8uKaAVXWYALOq/6xrcKdFKrcj6/DyprYMFoTabB8BWK5c5L/SiCCqEMOh1Ou9YiDKVUuh+r7dYG0wLRpQ6MQdEnctZt+pVNm5kRtwA1B9YFx6Vgh7s9MQzPTJaQK5UEWdu3LwLsfzebbrrB5vHwU9/dPxX1s/fvJR8x/4b+58aerb5F5zF2dyY9WFXi0wDHbMpUKiBxUHSiBH2sNz0xHmJMwJIG/+3VSmhgmcfIjYCc6zKeD2s6h6kzPKFO9IOsVyP2dyFbx8N5RuJpptferE4mN21dG03eg/5MWdEN4MAPBSYYB3YFwBP0eEu1GOPyv3z2/KkKMUznYd7vc6N2QcH/tLxiRyI6JNxeLn89skNrsSQvhxL8Zzs+a3jz9z9fHis+8n1Z1fe4xG6keO2ragXSxaUwElBU621jSGe6JHuVWObVGMLAJFjzBMZtB4pkW+lVnkdq7FVwUWfDVJ/5/VdOZHjmHa6gaoITr0CBOvAK0X03z5Vx6yEmpVlYRy8UlDBZpaUkoK8FLZYVGknqVAgtbFKCTRPoVICFIMkQxoAGYFMgC8lZCCgz4DT0CwhE56h8iIgcpTIqUh1IsVdqRxciI1zXbO+tTJVPQ3d0mraqLf08M7DOwCCEU9nIu4JK0fEysEeEuQlDf1O9vwKACXO0Y4P2NAac5zNhFihHbmxSFhi3VU8FFNE1YUWW1D+LpI4xnmAAzwchjhDK1SkB7idKOzCQMBiDuB5SnDpu0d3r37i6JOTCaZrqOWkfhvgF1DTiixqOsCbWBZQjX+Fy1aKC833LnG//Vtr98HeN2O7fFsA+DMA/jMXw4PReVgZkZqkvfDbKBSwjPCdoL4RVM3PBPWNuE2RkamBikatfYfI7KflAsQwYFYHxdxvdnraZBlIKKTa8Pm1DRxMJjwpCupn2YwFKbAMzDoaGZJUGwdgxt73YuCkl+c5ESIxIodQob7AE+esqaxbhBCPtFZjY0wbWeuiXhzWUKd1ZqjBXJamyWaSGEtE0+bYHSwLN9qoZis3waht2rQxImBpZG6zLAO+MWXYLpwtn+Z00UbLbQSWxRrt31veWA9ZHgCy0LqEkA8jRmC48jwee3IdB/tDDAbHyLJLeOmFDXz2UwKrq7l59MlqsL55ELfP/fPZv/idSVbMX/v1x98rAegf/J7vud3tdFaPTk7EP3rHB/WZz74yfLRa5CJGG4VoP48EcFdJ+ZWvvXplFiM/7mx8aXNcXXps/8bauMSHj+bV+KmHz/2hl+bhYOfHuRReKLknZdaVQpwHELiqkmjSSAJroiwXAI4RYwHvZgAMTPIkpDJcL+prkPIhNqa112r7ddLMuTY6l6MGcW3xTGPBdW+D0kTfWNXVhl4DhmuxZR8AOaunaMia6BwB3EZaRV3cFxh1ukvVwA6+PpY1EALgbQ3iWnFmdnUkTwCgCqCsWfJcM9c8QEMfNAOBpHAFB+p475Nuni2YeVpVYb3TSRTIzrNkullUpmI1OJorc2XgZRwXK0ddPf9nmTp+egh+GShHc58+HYUY/vmfeP9v/A//8Au/EW/ZHwOq9wDUW1ITkWwm8ghAuph2fkyuV+8Pd7Fbzrvbu1S9ejecLM6I3iNMjDHbuSfYKAhOykHiPJgxc46qet8GihEUGZoIJssEUsbCVRBahO1go4qIsyzyW6KKI2PDu+2MXzqm8PwRief2j+y5/QlWZhAPN9F4hMiQTMCcwApuDiyowEEpcXhjL2yfJxlXBC5oWuTxituOGrcGewcHvmQdcm9I6s7iEE8dv6GPz3572Fu7sLJnZslOXHChDP2eK/EhNrjIE3SCicwBgXKkMaVeYqS0waIMZaS5iAQybmTnYew1M+tqXgVnfRCZJt01s9WHVrxMlALcBFkoKRWbXEKb1FB/R5hy18rp5TF7F2Pay2WYhkVAyKO0UNAh7ySgVFTFvNAOQRIEp+hMHQRH+DIBKwfVDRDMiDOANsYYa4DCCCOysEo6SQpqIhAgxqSV1AJAGRGJQFLWFeizZv2ZopGgkRnFEFjMbRUlaAYEE8FKQYUE6ekMTtasczMCfdEg4QhBAubQgKaQ2FU5basKI+oisC+KeB0rVuAsL5Dqs6y5CyYFgQSst2DUGThy5Avrj/mtYGjTSDmEpYAeKYrxBk98iefGu2Z/8l+Ft3/5L/3txZ/4X37xS2t7VzabNfEq6g37HQDzpvBi0jzut2+ydh/sfbO1y7cHqAHQswC2tJAxahZaSKC+wNrCg5Zj1nowbmEZJr+LGhC1wAZYVjq2LTjn+PxgtBaBEGPEYTnH5YNdesfZC+h20tq/0QekSgkFIMQorLUiBPJap7MA3aaMqyRNHxFVRQkEkGhfVlWbGr0FoCOEFEJ4mi2KKkuTqTGmh1P2Z6gXjrvNd3gSzS6QiLrNZ29JzBp1JHMfSw9dbp63VX5t+roFd8CS69VWYOVYgri2IrjVKJs3/daeU8K5CO8jBAFJWlefJknAmW2CSTycO4EQF7CxvYnbN+/g5ReNvfLW4zh7ZpOS7oSNkWSSZHH2/JFZW781/M7vu6ZeffGNg5de66RJ8sCXfvt/vP7upx57+x99+VhfevvO93/CFb9sTuLlPynEj2C5I64APH9wPLkeIz8CILu+e/Da9d2DwVsID0wgvfInZ3Y2V9/q9fJ/EYVyJYlz2vs5Rz5DidkEZBTlYiRZpzEVACEHsAlwAMcKkcf177AA+mB+EEQbWEZB66IAZ2vJH22o6cv2xtSmeVvQ3EZQy/r/WQGOAJYNmHNAcHUqlirAJQ7SE7BQCCsNBq/qdG1suGrBA04RsWZWrgneOkA5gNoxU02AtingkB5QWW2QUMu0COE6MSrBLGZAFFmqkxLQJEQAEIioTBLJgHRCCme0yADqysBvp/C7An4107MPelaf6nYW12bz9CHAiI5ynwRwoPP06l/++Hf/89/67JsHV8dv+bffmHwXQH8C0FMAP3zn+941/e/+/nf8mQtc/tm0muSV7YwFxeMAxpViNu6p7PmK/SPzWEYITCaZOcOpGanjOUcbTYgY5QmslPAxImnUISkQKU8AQFEJCknArg+4wZIO4xq9pir93t4ssJ/y+kuHzh0e4/WpNrO8b68tJgRN9L0o0Lu3nQyQvgs5TXEjm+ET5TGu5EN6SIPH7rqtcIwHF4QLbo4X5qvnh+I68+adm2MR+EzqyyvGiFxvZDlJel10sMOSzruKriZJ6KVnZOEtSkyx6mZQUTNCBOtEnZSgvJo50x12aL5XaJWqggR4fDzOvHd2/eJ6L1ahv7I1Yt1V5I6DFysIsIJC6bDwjp2teBEXhs8SspAzT7znmaJQhEoWQheyYJvaufKyE8CVgBEdmG5EgAIdWxSlQNITtW9fYvIsdwsnGOQNVGCwZ3AWEIKCEgami1oCvCnCCJbrgjYREIYCwhDuKRKwNFBBQE2togSu34fxDi4kSNAc10bBVXN9jQl0KUV2dYHw1AJhT0AYFcQO3oBCHyPaxjpG6CDACA3wJiQ6gGrZ2wChC5AXUFJSPFd5mDhVXQz8AXo8heRr/KJ4Al8cX9Gv/+4TD/gn7px0AeS/+/FfUB/7+3/trd74rvsVLmfNOny/fQu0+2Dvm6+1XLuA2vGCU3XPrrKNPLX6WiVqQHK6arQtgtBYjm9zU4V03oMEMTOHoqooUQo1q14sHlzbNLePj1mAKDJLQeSUMegLYQvvQzWfI00SEZnpZD41JHDUSUd9AKuCSCSkAmIsnXM5AEkkFOoii5gkhoiEllKOtdYj1ECvBZ85gCPn3DkhxKGU0qGO9k2avuhjmeprCerrzExE5LEUnG3BXhttOi1P0/ZPC4gZgAwhkvduIYQwWusean7TBMt0eO0MwswgUg0/rE45C+GQZnV6JkkINfDtoCw6MJLjY09keORJMGEx+eqXMhdYBmsf27906eb21tZTG8+86xAvvXb3O5/ZppPf/C9///obUNj5bz9y9fqtDzMhr5R8yvvQV0oeAMDP/OLPHzefDx/98I9eRw2A/dzkCezi/QB+0NvQvXzt9ssBdKcnEXWSPLmxuTol4q/nyXBNdNb6hFAoxgo5G8CcQGsDIR1Meoh6kwAAE2nLJwPHrC5wULq+9zTjFYICUEDXXq5YRj1bDl8bKZ03/dkHEBm0iEFqKUXSSA3qmvsTu3XkLgQLYVPyBiwYiE3qK2kKaaKswSIsM4k6coe4VGO5N7amwekeCLaO/MmiTv0iANznyC2XTzZFHpymIIBN/R7REgmXphkDuQjRQSt7hiH6gvUUQMyodJWbrE1nVBGpq4DaEeR4bWX3UGuf/h9fn/sXLt149Gji/yqge4C89cF3bv/Uf/GTF81vfuYBedFMn/QbVx6g0uRlmbKQ/BbA4xi5sxtOZqUPn1WU9AH7XH9hL7iFN/BxBkZPKSYhsIgBZAtirTk4BY61v6sWmTBcCm8E06Sk9eO0v9kv5m8kxr+AN/kg9fg2LvmgMvQEOvjeCnpvGN3iZIbfyST+vSJANQnGAI8DGcSNnpGr70lwNO3hornt17JOvsGVt+L1QAnhoaDvblAkzxWmhrAwIzzqP8Nf4Q+Hm8nj8qK7wsE8Iv/JSRnLzZ76z2eVf29l6dXBSL4vF9StnD2aH0zW4FhmeTbXW55VT3SLE1ukw6SsFtVwPlto9jHGEFkbEyDYju9MnDvxvW7WHThf7gfve1F44EwwnU4uE5WGxX7Bx1fnsg/Jbr1a8JEux2HSK6pK6iCrEdaqANGlOhodEuhBCh0IosewKYPgFh4MdjmyKkfmCSKLiCQgmMHCwUFDSQcXqpqr5ySkjmCqsDARKhikWT1xgyzGPq1QhgwGXeRTCQgJSmoeqWzX71afsN1ErVrYnQpzmSEfKajaZtFhHRZ91hBiBB0dRAyA7AAogJA3rFdRX1Eu1WaWiyo1vQHO+q5mIK4HEy1EyPF0VHhoHe71p8qTn/v15x65gVrnLnzi5Prp9Oz99i3S7oO9b762D+A/AfATWBLg23ZaTgRYVuK2IBCoF4Z/uRy+jQJ6770AAG00aaVUAKOylgNA/TSLZ3p92OBxPB5zr9MRidYEpdzewd2UOVCXOhhmnZinSaKE7DWfQYUQSSaqLMtS2spBChmkooBlBC0AsYox5jEEbQGKIWgQuRjCrta6OplMg1Y6Gw56E9Qiw61+Xisf0xrBu8WigPeeut2ObLhiLQhsgXC7rLW/A8sdctK8FnzwNsZomDlqrVvbsg0sC0VqsGhMW1DSCgJ3ACTehy44Hqiy7EPQETrdq7hwcYiNrY5MM89Eb+HO7WPbGQwPibfFwb4qIIavXLr01a+99FL8mV/8+Xjz7/zULdTcFzx1469//fYTf/W///Irb38SKX5HKXkXgPvoh39UAsD//t5/nx678ZJ5CPgAgIcdRG863Hgqmew/rcr5BoBs73j2ISXIH0R/sJmalwcrvd8ZDQcvpQqHlbMf8bR6FphLpSARSgeHBcAakkaouXgA0AkkNCJr+AAmD6iMqWHUI0nbsR2c6ttWKLt1QRmhqbhs5mSogquii2km1A0iPguECJQRAHso4aEhoaVgrwEdAe/rc9OpsVWiTg9TqNPAMa1TwPcExhvhYXKAWTQRwRIQbTWvAYRhmCZieO86a/Qe2QBhvZ5DXOFeBF1JItYAJcyyA7BmeApBT51L59abr9zez2cPnJntW292SYCfe+rJJz/3/MF2DK5/MmekSnzpp/70Ox9cLPzTHzGTNwPLTxfVyMfKfUR4QxBqkq9Mvna0kj43mZmdnisXEvE1TnGFAq9HGzoW6BBBCwEHhV7lwLZS+0XilYwxFY4yGVkCJGJwcgK5EU8Y4rCwKvVlUXLGF5PhyYP9i2dfr964kE+Ors2Cm96yeVVWUgfxpjNmTB6rXJfCKBMRHzD5ebWVrpW35+dXSrWl9kujiqO8iHmuO5TKKdaS16tII5RIIJCik25Qd7jVNfu70++NE5Zk6VNylV7ZyfT7bQijo3FwVcnz/5O9N421LLvOw761xzPc4d031Vxd3V09UmxOTVISKdG2ZMWRQyCDY8gxDCVOoNhAAiOBgUwwIdiITQgJYhj5RcBOZIMxIiuB7JiKYEvWQEokJYoim81is6fqmuvVe+++O51pTys/zr39qhknooGIaQm1gItXdaezzz77nvOdtdb3fcOSOlLqPCJ1qeFjJYTIyyITA6KUR5aFyHzjs9VsZYtxJspBKWyWpcBeVUd116xqQUxYYannhwvpr8fb9oyeLO7OSSa14oCsmlaCc3LNsjX5KhcxJgUQUdB6gDIN94didjJH47vVCAOREMqAOBQQkgHl4LjGwucYJQVFHm4soTnABwNLAd5GRA1wiEhtgDMaJlOwbUKyBKkkIFqITqPLEzrB4MahbTRAEuWcEc5FnDDDdgpbs4Aw9vDJwLCC6iJCSuCrEX5K4IsAb64PW1C4gi0YNhB8CGIP4AxSWkLoDJCmNy8UBKQlkHSgmkyW+0brB/Nk9ierbpcIx9DieUxIcxl26anvr6dX/sTHv3rzb/3MN5faNYy//efwKP7wxSOw9+6L5wD8hzjNzm1iA142/pjfCWQelq/4ztiwIPU6AcZKSKXyHI3v2HdMmiASJw0i//qDA9VGT0/u7Muzk0my2oi90ZhPqhUfVSs1ynKaFKVC34y/XFb1arlamfFo0BGJsZKKidBppSoAAx9jFrynlNJRjKwCN8nX3pRZKaWQCj19f5rnmRJCOvQX3Y3G3ia7tgFfCoAVgtp0qou3cRXYgLzN8w+7d2zkaAqclngbpVTrmSdrEeYcpxnT2Xr/NjIXGwbvppx5DOBc09SeQMUgxW2EFFDiHvLiPPJirPuMFvO5c4/nwGvh4KBdOu9qI8W8WorV8Yn8zKc+LX7qr//s0fQv/v0vAPi4r9D8zmtvvhfAL3zuCz//PwPAn/74nyEAfwHAf/Sxr/xvX3HF9rchsRQC75M+xdHsYIddE9GXwR8DkIXEAKQ57OK3tzoZ98688IERHjzTdYtu5nwk+LtEGAPagJFB0iX0QGnTz3gG2igoZqSWGpc8fItCEUEZQIhNmb5b/52uj8/Glm4jWTNcz/kCgM2kkS6FN4jEMRGSFNVeiEkDwimgcSTNQAQlEFKIKgC67XvyeE3mSOgvW4jrbF27duTYSLlsZD2At6V5hO3HwRoIa99cWePUk80DqLg//r4vn2kNiEEPGDdEHypj0pssuQGcURJc5EZJaUeJ/V8wyr8a2b48W+zXUgTLEB/84NPnnvyFOwsAgj/8wpVfAuwbndc/FiDfo/PmOM3zy+Bsf1QPiwSat0X9eR+V9RTmJbXLLrnHZUeKoQ8iu8sxQhGgpQB7AVkRBEq/RRpgkAhWKd2F6C2SExYpWZGGbfS3U5pu222n5XnqGHVuj9wnxu0FoV68+qXp3oMk5u6c/OLsbv17t4L5vVGHP9ZJqCAhykyfGSWelreaKziIO2ka74kjYfT3xYEnUIwAVRA+IkFD0mNkpePaT/jqMS+RpniAgg9kTk33u3FXnKWPoI4hkgAAIABJREFUhh2sbt1PX5GKbl0649H5aKfa5nzJZFtNgyijiFJoAZ4po7baeafACPm4CO2ic6SFapa1uvvNe3Hr4kRYq2tfdUbnPJyHSjaHpE+mC6eCjCSVFUQshuQWyxNx2BymXex0AdAaQlu2M+llmcdMOzjj4WsP1wY4RCRWUJ2EIoZcMfSwA0jBWwlOCalyaEUCkoSMGsZKOI0+8ycDwhAAJGQA0BBaD6QxAU2GfCohpwD2IyLVqB1gpYYNHnXu4UCQUJAdoJqAyAnpvICaZqBWQRmv/GUkbjXrBUkq+CaytESEhMAJRNqOHLaZxBmFtGKEQFAEGB+xlwB0nRRVSyG6ALaSxtA2Nx0Juu9t+Ib7MsziM526eP1rjwgWf4jjEdh7N0UvoPxv4tTmbBMb+v2G1bgBdf9vx29T1twotxOAlGf5hgEZAJBVhsclEYeQvXTnFmKMqYmBhkYj1ybRGkyWxi5zbeBjKK3SmyyIAJBrpaCVbglkOud8IqRhXhxLKccAYooRIaWOiI6LQV5E78+1XXtfSllkNtuJKY17xw5OKcX7OGWnAael240g8QJAzLIsZVm2ARzf6Yixdm94ez839kaEPuu0Ke3OpBAsrZ1h7auLd7p6HK+/x67/nqBnDmfogd/rQohKSvUCbHmItY0RM+8D2CEi3XXdthByV4+3zuxrE1c3b64Wh8dnYufOaK23vPdf+MynPv3Nn/p7/Cd/+d+z2f9w95N/FcAV/N/9I/88gBcL4PvyevprJPE6gOcFMCxco9ZjV2sdEsaagT0ubLGVq3+LT956aoF2CKS7GbCgfn8zE4BAMOltxio69CC7X2dEBJlDCK8R46ZzfzPPG5LMhqm8KZWX6IV7b69f3xBrSgBLo9XvATjD3E4Th0MgGwFCAUkNySlwMpGFBULRk8i963v8JIBg1kxaWmN6swZk67YF9gC7dRZvDcrevkni9bKPADXr9ygAzGARQEIAQgIEyAggtxBFh/SQMw0xTjPnEYhGShQAH2Y2jR473+4SqRIQy5jyGkBz4czOB22BjiXd/dGPXv0WIEqAnxIIFzhgBPBlUD2ojUSC2g119rHiYL4HhHZO3BUWP9g6oQVSFSRpSmx8gmCGYmfIM5NQnJFQFIxPCsGhw0LWGGZFkifbgk8eOyfL+ZyPjlUzf77MsqeN2/nSUSsnKXXvtfd9JKGOtMjNXntYHl/E4VKw1XcnXu+ccDcb57TYLdzNxcoM/IJo65D3kJEQngW/HuH3VEolIs0hl6O8C5dyk7+5CIN5KKPA82AsItAqnf5yew+/fGjz6c2zozgaz+9d3Mkz1zzY1Vpu5ULoJJSRmjJmwWChYwpCCOJs26oAj3becGhjKlHM81E+uPS+S8vg/M7tl+/Uo2EuxsqqbZu7B02TESvrOjqaXBya5c2ZqWYrq5Exw/sKy9YiV4AoXOyEO3FNg7YJcEHDVDXqPUbMCTIxOpchlxK5MdCSwEL0BIyGQL5C0zro/S1oB2ApYfMc3Dj4KYOvAOjW0ivSw5UBcRkRqog0IlBipBQQGgZXClYaZAcVVpcZiAY6aJgaPZjcImDh0JmAOC6gBPZSHtsodBINL9QBVhiihIPBgG9EEW3IqRPUOu7aVCWhJEZP5EiZUk7CyvGI8sIQZ/nA+TTVmdheQRTS4worOacr8cdfDrfGydov/sN2fgffRdDnfzYHQPxDP1l/N+9/FH/w8QjsvbtCogcRPTmij01GT/8/fehfEg/3r32HnMVGtqJvtu/aVvqUKHFM+8ORLLTRhdZRSwWbZa0g2lzEc0HUWKU3jLIWPTA4n1nDmTXChxAFESdAGqVG6LM6re29bRFSmlw/frC4NNqe7m/vmxBiwcyHaxmYrZhiF2O6GmP0UsoNuN20iT8sFl3glAyw0a7KcFqeXTt9vJ3R5IfGHNBrQP0u+szdj6AHBb3CfeIpAIagDUA5RA/yjtFnzVYPjcGWZblpUo7r78uquhYhhFVmbUgpTTxzBqXCZGtrNZhOu4tSFjGE8va9e497768DaD/zqU/f+Mc3P7mxU/vasMweVprXAH4OwId0v92PccSAIybobwweAEhEuJD4bdmYloHlYFAka2FFXN6BlG3sBU53BXCFgYHTNFyPvUPPqvPrfSvxkFVcZrUANCMlRi8BtHEh2djzHa8/Nw8hFDGlqJWaCiEKAL8B4EIAniLAyf6770kKi5i0Begs4LYJ/kxMmgB5t8/khX0gabBYwMUIyUAv1K0BGgHGrLNzmzXgAaeA4AE7BdR6nXDoQRrpHjwGDdAAb8tBJEVwRkLI5GNANScMRgmqoNQ312/W05qgwgmIGtAGcBAIhWh9CDariTAE2pESbi+kyV0A7upju1/9T/7sJ14xWv32/vbo/QB+GAhPCvhjBCx6kwQtnHE1kA4FEqXET4QwGIrCrwJ7bhp1QGO2TmYz0TkVMpBorUQjIpBaVlGlKBU7GRYDDmnfDnbvnnTSQXoo5ZSR/n3b2eCrh5Piq3UgpRt1m/Pu5eVl9avdW1vX/T8Z1Hhh3p7Yzhz96/DuPYhF8Dotdnj19QtBTDMvlSmSZyUXdptMOlB+8fWRcCsyRkHAIPkMLO51SlmOxSLlqHAcRsVKcvi6ZH8xKGyrs/yBsQ37ScXbExPGeRhtKdNyapZv8XJ1brQ/2FfGol0FAjsKKQqhEUMT2Dehi4FRTgoZQiyEJupW7aqZdzupCyj2FAar2oTp/WJ89jx3ElyhdWFVr1xWDxKSkBC5B7oZFnYCbXLkTYu6EaATh3ShRjPq0G5JkCWoNMFo6eFIQCgg2ojoMuRagGKH4DziqJW5doNchKXQbWpWAlRKqCLCKwktNLSusGoJpHPkgpFchEwMrg2skpAE4HEFeSIgBYGesMgQEZ2GltQTj24pqDGg6oRUJ/AtAPdSxTkaca6x3a6ogsc4kimNpUIZ1DH6m96pi3aJlt4QS7GDCWpJtG2k9DLwOaOl5ajYVW1ItpgsA2WHsXR7qqWyxNPVZTpbLSZ2Ndz9FnrC3e8b526snu9yJejzP/sV/qGffCR+/C6IR2Dv3RWnfKk+Ntkp4LSnCHinG8FGJw449Q0F3uk5+zBxQc/rmgEII0UipZIIjiQrGhqNBEpaKiIlEzMLEJmUUhK99VmHU8eEjX0WsM46aqVYK7Vx+NhkTSSAYI0ZIoR8Lx/cB+FOVVXF0fx4OzMZnd074wUJY7SxQURIKTfeuQuc+ktuWLQd+lKdQl86rNBnojaWcZtMX8QpiLHoL9hzvFPqZYxTi7kE5mNUPkHSAIXezNemlLxh+L6MHty9AKBMKZ2NMVmt1SbL9bSS0jrvOcaIGGPHzHY2nx+QENeOj6bN8WyWgfDZpm03rN8EQHzuCz/Pf/rjf+bn97dHlz/4/ONXPvOpT1//x//iK0P0mT6PHqh9GD2I/hhOS6VaCFoW1uqmcz5xapnxoMzUP7x0dudwMhzs981t+gk245eZ+Vzy7UUgnMB2Qpxm6dRDx3Rzc7HJmva6g0JwBDQBRvTzma2PQ4u1MHZKfN25mAC6Zo3YBdxjAG4oqN31En0CiPPIZgCIDkhln93QNwA5AvQWELq+5EoGPg3RCQ2NFRR1gF+X9u3695FEn/FT6wwfJUC0fYbPG4BtbygfDFGKzIaBqABZ9AxgzgF2AjxjxAGkXoszo2WwUBAhwGUPJZsDEGUP9hQSfCWIDwGX9e3w/nJI2Q4QvyWou5aYzMUzgxFgfxjABwFcABQD3gOkGSIBJJSMIiWxk1i9yq68HmDONrlOSK5T1r8RW/H98MkkrxOTDehg1ofNS1Gwd90Ujc6WxpjVmZF1h4bLrQiq43Tra8fjdqfQFKjcf2n5gDVNncvOF3eax+g4nk2SzqV9bjvLi62U3z8BhstCrVRYXX//9rmsyMqreN1XKiKZO07wWWRYwvM+aWYouoMUgRMCdnKkXBy1kQUHl8jpHbcQF8I+EZ0VQ0Q1EgfFoR8+/crsSghVltpbd8VVUdOWnvGxLGgsdkAQUrAM4BgqJ2IThTUWWZnj4NoBLXOpR3tj0a4655fu8Qg2rvaD1a0jj4O5mN2bjg+O46Ip89t+1Yzu125HgDQh9yXyqAQFx13uuUkSfNyBtju4wTb2ihpz7UEUQdFCQEFnBMEMxBWWQUNBQQlDKihWqkaDGGWumtQMkph7CAngREBkffaYQSBjkSkG1x601ChYwBlGMBKyWTNvpezJFgFA1NAxIhYOHQmICPB5B1cLSMngUYvmJoAzbuFYQ3678vVOOSh3MzJ5/WrjzVALZkpyS7Z+FR+YQv+GVcXz8QFu8AV6EmfwUq7pI6uAD6fEQy1ZA0iSQOdEpQqLVM2q7aZlM3nqwvUbT9pr+C7i3/4P/ho9s2ufdZnsfvNv/Fe/89185lH8wccjsPfuig1zcYnTHqQNeAFO+882F+cNEPTowdDDhtsrnFqTbdiRqNuGrh8ddEYQdsoRxoMhFVnBVdvYe/NFjGAye/tBuqS9CJ0iEZmZjTEzJeUDrBXccer1uBELDTjtr9uA0E351QOYWqVaOxwpAOO5c/XRfC7PbmkbfPi2MWZLKTlQSm7syyIAmRKTEBTX83IGp1nJzfbG623Yhz63AUFL9MBIr8d1BqdCvh95aA57Rw8iZk3nKESBOq4glYRVAJBCCAOAlFJydz0HDQBqu26QYpKJ0z1rjAVQZFk2JaLtlNJJCGHCzLZpW25jPCu0udF03a0LZ/Zvr1bLiF7I+BKA/c986tNf/vgHn0lCCG+N3jgjfN96H76CHni+gFMAb9b/LlPi0sfYGYnDnf3JfStkevLSuUme2d+sffslO9i+mttx6V37ampPtgm4zMCYThXuW/S2WwZrwLdedBuDdL9+bpl6sFcK4Gg9pxtXlG8DmEspKmPUie6PpQY4A1IO0E0gTgDxOBBHAI0B1xCokyLthCQNOkEwSZDwJTMMkBw0VWARoCkB3Vpuh7q1hAv1oC2tQamRPZMx7gFxvs78dQCYEK0UoYiJIvOmD1OlvqyrK0DMo9al16bOwFMAWwEQFmwDkuxBnuo9diGavvfPSiBmTssX2zYsM5sWQogL/b6GK4A7lkL9HiFwYvfxxIoA2/Rrz4yBLkcyQ3RSchmyxHEbyVymerCtXLuXL+aV39KjDthS3J0liIxhO9nEBUNQEsIgaaSgM5cEMRQPjkKADN6pwlalFbTo3M4b97V+ZQa5AiOqkSn8QO85h4Ld/Dx2dSbqxUn81uiQbjnKLz0Y2gcmc7RzYursXnpGnQ3PUE7CL4U1jNQuIcN5yDIDqQjuNDgVGOEAKotgAkybUEpwS8uQ8yt4OoIDSjRxQB/hIe3JJjphhcge8ydyZMawcpzv6yQMLX2IOlAYpZTUar4K1bwytszQPGhle+B0IC+PX5tGEjSwZVZBpXh051gdeW/ssktZYeQytQM3XwWVqYkcKmmC5dAEWuCk0jumoGBcfVKnBeYmIaUBxkWGMgR45v40wYxOLDGjHAOO8FmOTAqQq7CSkXMroQLBLgk2HzqVHJooIIcGZgXAEsgnsGjRRoAVA1kHzxrqIKEbB3jlkHFXmLhTx4UAcgfnCDASqvHweom5GGDAACmHdpmheCAghwBdJoibAD81x2w3Ii3KtrjTdX7ShdbUESgnBbUnXcJJinzO7C72t/heLe/82KT+ZRZ8DOCfAvjvofIPAI0BegmF42TkKkGWsuVBnuoXxq1/8d8ffFd2Z//7//Q3+OP/zd/8NZH4EWv3XRSPwN67Ka6eZ7x+94voL747eCfhYiNWuynHbsqT6/IVNm4Bmx/kAj04WLMZeyAmhejOjid15/0kJFav378jpZDh8s5eOy5zHpqcJMljTdglKZYpMUXmMoQAJeVG125tq4UTnJIjNiXXAKD1IQyEEEL2TNm76MHnGwA+AGA0KAfiyrmLtVFaSSk3pcRN2TAB2G7bNnofV1lm/TpztpFQcevH22VGACrE0IYYVGayTc/eFk69hDVOBU03Zd8Nw1YByI9OZsa1Tpwrh0wMAYo1oBoAx9OTk10X4msXz52doQdgCwA3pRAvAOSkEGL93R6Attaq6cnJEwDKtuuCD2E317rb3d7+jenx4XaeZx8q8vy366aJAH7Y+fDKy6/d8rtbw5PL53fv/drvXDuYL+sdrcS21dqv2s6A8SJOy9rA6Y0AATgwWr6xOxq7i2d2vFLyJM/sTQA3Eqcr0tWTXIuVb1c/KHqgWlMPlHl9bDaWW8V6jfGaihxNX7bW6JGVV6fCyhu3kw6nbPA3pZT3pJTN+ru+CehnAXwCSJN1L9wJYPeBqAW1ipnBEFLKOI4CnVJdmyInhskAH0F4A1bvA1wApMCpAst76EvE4373rehlVdgBzhBcxlAtYHP0RvNLRjaMMbm+cS9IQNSn++6OAewbCNsLPMP2GUN2HUTqtQDjqrdiowaQW3j7pktlITIR1BYzAUgrIIy7Dhet5gGEfE4JV48Hq+P5MvvlhLQHiI8DfA4gARELGKaYAMBNINJ725wr6HQHcSTDqhlKmCoV7suE8ALDSEAZR5noRtqqJrKHIkrSpkKTfcDRHkAlo4S63iTZdKRqnZRqEmUp0gBNOoRxhyHLGpyfXNVlkcpR9kQr80+GrP1VnnzfW3xOM0hElcLd2Y66m6RMSkUP2dVQXQKn8ySLB+zEAux3ABkgZUT0vSK2ygBmUI4aDgEG25B0nOo4wx4yDMd5eKMbtnfkzuBEKimSS88KSwvS5GITrszuzYiYRLvqpBSSk0vElPRisYjCMLyPdnl72aWUblFGKTXpAhiFgyEuTFSJoptiQjmIJHGbGuHgCDB+WIE4BuXglIExDnVY4SQBCRb5coBy1AK2wjIlRB4iIkPuErxSMEJAeA/bHWfZOG9rZOh83mtF6oQ4b9EMI6Jw4KBhlwqJEnji4VToM9ZqiYWXkD5qZVtAWsi2QDaY4SSTECShCgkpM+SuRUcrzDkhFWPQxMIOtRAjj/YVn3wKiGcJdLsJbRkRRw0a2WB5nC1zE+Yutyjl8k20Dy4+ez3v5pff/N1vvrS4eaw+9BMfbAY76n+Bwqyr5QcVhUkMXgmWSA3BFhlbhZkg3MBptej3jS/8t//1d1Xu/VeN/f/05/ZXiJ9rJP5X/tt/7r/7g9jGH9V4BPbefXETvffrk+v/b/rUNiBuI/9xWlrrX9tkMDZxDn0fFdALLjOAYI3NzhqrqlWV2gezkCshISVya+V5vedSjLL1roBUy8xYBWDSNI3vvDuSSkkt5QZQngAo2rbVJCizxgI9iEgxxui8X/gYCillO8wLAHiCmSfMvCuEWEkhdseDkRGCopTyPPospMcpeAkAaaXEDgkinNpyNeh743KcOmh4AL51rQsuBCFEAaY8hgAQyTzL0kPv3QgDb8DzhnVrlFQ6mbRAaY+RUPYkTwQAZ/Ki7EyIDn3W6xA9GJ9aa1/BqaD1fQBdiFG5rgMzD2NKfjqbNa1zuuu6we7Ozo9fuHD+pvf+A49duqRu3r79+aqun54vV+fbrnvx2huzg9975a1fAvDHAFwSQjxb5PbV1od7IcQDvBPsYT1X3XiQV+NhkU3k4NZ2HNZplH4OwEsAUOp8O4a4u1itXlzP2z302cl6/X3H6LOLI5z6BEP2a6tavz5af0ZQD2o3/YW8XrPz9bGJAL68nren++2JawDfE9QMmMX7GHpnTaSIiY0FOivQdkRpRbYjLSu0Uc8APg8ARCowRw3oXYBbUdVHsm47v3txACGWAG0DqQN4CdBdAHs9kxbrpZHaPuNHOUMS4JuemYub/e+EIqDuA+FCz/ClCNgNEO6Abq0hSCWQFugZKgEAM6MB5MpopVlFSxSqfrvifGK0VSO5LMWVkNTRfFneT7AMyMcAV6/lYywQiWQv8wwEQ6hJ2cWUsvyfOT98v4S8CLhjrYqVgJy1QQ8Bs6OZNS9cikJxGEoe1Q2nLgiKJCk5F52WhYrLWAhOZ2VLQrb2wA+yqzjg89B4OU2YhEIT0SavxFY8u0ppmCYs7V0U5Yl6Y+uCWTX3XGfmECygRIKXgMpbiPCAXVih04AQx11FIJIw236d2dcgSUCJGjk0EFZd8K9hlL+YUQR7aRQN9scXjFZXpBW/kGbY9sexElcgutq9x608srH1buWiHmiObQgkyXt0hZ8GqUsVo4s1gAu84hoCHRIygFR1HGoGOwCZnwXhEfLT375DVYta9I2XlpCGClkaYcyhL/v7EkXKwY2HJA9vZjjhEl5IWFbo4gxtlwFFMBrGpaSTNwK2jUg6Im0xEgU4uYQrM5DRCDRAIeY4budYzM/inMpRDhrUovC+TX55r4K6FFF4gAMDxSEekIB0OWzs4GSDGgSSK8wfXyIdgxBiSi8GhMrDY4TRcwFuq0Utr4u35qE1cpSqzgrpG26n85t37t/82u7qOSkrP6hGo/H4hXjMb+gdfCH6+JqQ9ElJ+MjyxG93sju/NVLWe1BIOB5aHKzPF9+T+POUncXaNWmv3P+oVdlyzLvPfe6v/ZW/Mt5S8me/2hQAHoG9f4V4BPbefeEBfBF9b89GTHMjkDxHf7G2OJUVqbH2xI0pRWZ2SsqNX+4KfXalxalMBgOAFKQsKd4bFF1FLO6fHGO7HEaplApdynMjN6VYIimVb5vyQTs1Z7d3OilEGWK03vtRjEETy2QfKjEz0CTmVUjh8YVrkRlrtZSj4+lcKiXS1nikAIyEECmGQEKITPaSKwVOS7E6y+xGXmbD8Nz0lG2ySJuSrgGQlFSdtJKYueWYNBFtsp8Px6ahf8PuBXogs9oaDTYZSwWJCYCErotIKR9ao1GqMzj1t3yw3v4RemCdAXg5pfTSqqr+IgHbQohuOp+3b924ZUJku70ztq++cf3xqqm+xcw7EviJEMLzUsqDvCja2Wr1Hu/x4+hBUiiyzCVO1XS+QohpjN5l5J1gr+9InLoQTk4WlR8X+UuS5V0IfAM9ONshoreklNeN1s/HGC8k5oyZI06ztFdx6lf8NhFo3cu3wDvsz3C0HodED27voS8xX0APDF9ar1OJnoVbDMvpG5ldJWY6brrydtPQvlLde4Vwz3ad7QiyEoouahHuReBpLVEKQSKmdBRTyBSF8623txjJI9IuB0ySsnW/FsgBOCF4z2ABZG8AeWCki71+X+R1C2l5ulaUBcTd9X3COltuhkB6Ewi7vbNHtZZfMQpAheBmCGILmS3W+9cCiSIvFh4rWEwGgowAKAdMBqTDPEvVmnlbEPj7mOwOWHYAbwHqDBCyNckEiQOIwhQwDSPNtOqyzJx8lPPFmyeLoRRwZ12w7wXUBOiOAL0giK0sgTjFZE66pCBTZA6qoLsJaeSEmrSTIPKT2piuDXrIS5bhJM09XNlxsx3z7Exx116UiV1bk2XubiShTKezZ6SnN2c1Xml27PLsmEhJThAWEALgFqDooNZaTk5BWrmuMlig6wDTANoQtGQIOHgptUyrZNJd9sxAHOO2lMi7uc8JJN2NNJBjjKniJ4URYuvimL33c601QhVK17pV6MIshXSpmTbUHL59zunF0hPs5hy3Xt+tgBhZ5KJFw4y0+e1QQuvTqV2j1LBSQ7oBBuwQswYOCiRqVK2AAAEUkKLUlDnpfN3O1RBFyhf3hIXpMpRZg0q36HQGm+UoGwC+BMUWMxVBKaITc8zSEsvJHvaMQ+AObRSQLCDOUd+76XMUbwWESxZ6wIodBRoCiRSsD/Bdg5YjQt3FVjVYPiYgKgFVaei0QhfzXZRn1KS6MVt8Yx5m90dUPCUH0o6HpnxxfPPaG19880ud6LZ3hnvXHv/QlSMAdnZ/9v3eB04ufi36aAZ5+1Q22ntPw/RmhPy5IWLz0Ln4exESwMcE1N+xshhbkc2rbtWUQ5Zq6PDvDEz6fb/hUbwjHoG9d1/U6K9C99FfQDeZLkZ/YlIhRmJmz4mtMXoEoEkpmaZpvZSClJR2/T3D9WNT8owueDJKp6woTH0GPnKg1WKVCAgD66X3Hd9bLWyZ5R5A9DG6FKM1Wm8FZu4vqEghhlFkJiGkEEoCgErMERynSqpSK5k6TwgxQgpRAbhRFnYvpeRjjG3nulKQsJFTIh8grdyMMaEv++6gB3jNer/td8xFu37/hiwiMpNR4rQ4nh6rsih3i7yYrT+7IbFsNNImeDt7CLWeq8V6zrHe9hx1vYB3+0iRQEWEUhVCFPDRQom70GqM3nFiuh7364dH0/cyIQ2LPNybTl10fnjp/HmeLhYnkkVzcHSYz+fLC6NhOdjd2T6zbU2azlY1ubB79YlLzx49mHWLZf26i+nk4z/wQhgVw3s//7lfOVqP6XUAX0cvqGzgARBYAEVm9IMyyyps0Z2dZyeL45OT9zSu9W1wH82U2cpN9gPD4fCpummypmkIPVjdiEcXD83HBgB79NIpb6K/gA7Wr83QM5m/iFMrp1vr42RwKlxdADgoR4XKiuy5UN+4Pxwsjoq8/eZKD4LRzWtNmz/XduZJotSMB8u7KdEHWsdLIVFK6QofzCuKo5Ki3ofwE0Bec426TJnbJpuOI7VbgNkB1ANGKABZAfGZvlS8uQcIDSCq9VhLgAf9WKUFMO57CZkAtQ/YtTF97NZdBb6XfMECtTNI0UKLCJlLgFrAbQsiGIpHkroTKfzZEE0OmLU1m58BXALurtbhQWIehiAeB7IcULtA/8NJLNElCQaJQlINtFMl+RwjrXwwLxClgRT6SU4wzKSAbACocYQQAAmBKBNE02Qwdct+HLtCF3Iw7mrWsw7ycA41c8qO5GDFnsR9voSJCmoSIZjP8Di2ikxGFI0+iq+G2fFsZbMcl0dnxVxp08iVaXG8LvtvSFesHKTuJ6qUUGG9aFa6b2TUnoA4hKAKSeSA0OJYbAmfbqDt6rZIxC1dzhPlNJMfTk97011qp1SLO5zZi4Iip9R6ypYFAAAgAElEQVTNOp8N7O35cftYaMJWjHGorSFwteldBk4VBjz6rPv59RgnLFmH6GN/jN9mqn+nv3bn0YkD3J1tYW9njG3j0QYPaI82ODhhUUZGk7b9pIU3ySJzQ5SDFo0ARNtgqRM4E9AuglyHNjAggFIuMEcJs/KolUcsNFT0cGmJRWCkpUVW1ah2JFQUID/H7PEONW/jzKoLrQgIHiDToFk2qFgiTSXULY9wrkWXJSTJSEcVKr1A0RQ+12U9P1Bt/X8A+FJj6w+Pi/GPhCbeeOKc/a271XzUAulP/MwP3wCwfful2/bWS7cO2qpVl957yehMP0M53xvk9Esc5D955Veu3fqtz39rpzqu5J/6S3/3ewWylgBCQvxW064G2UT/I27T74RvVRzPD/+LD/7Mn/03vkfj+CMTj8Deuy36vr2vo8+2POxJCqxZpcH7NFssWQoRB4NS5lmWRF/OQhc8+SYlTUJYa60g2hAlZNu1xIm5YyirdausuS+C3Ls4MWBmSpysYpL75YCMUiKlJKuuU0fLaRhnQ7E9GrMS0gLQRCRTijERtSOdy6pp+Kg6uZWJ7oE2248tm2YCEnGcFSSILgGgPM89ANe27Zm26RQJCkWeG631CsAhM0+ol3oZ4xT0bvpENkQTAO+4o39YoqYQJFRRFoF6W7MNmHtYb3DDLl07KcDjtCftGD3jdQCgQNf9KNrGQqu3oI1A1Y7wa9+eY1o5/GvPX0FpNSKmGOU3AIy99x9zMX6YwG8ppV+aL6rHB2VeXtzbrXVuuW3d8MrlS903X/k2v3X73u2qaa8tg69mR6udYlA+9sc/+uFoM5vuLe7v/vKXfxPfOrj27xLRrXKffqk64oiE96HPKjkAZrNnLGA7tHMT5c9fPHfhUCr9o23n7f3j2VfygVJG6ksxxp2T2azglBr0Gb9ivf8bQsymdxHrubkN4PPos55vATgwhZY608fNvHklRX4Tp2SYcwCuPffhZ/KrL1wxL3/xlXj92o2zAG6+5/ufk6t5Nb7zWnqjda/TfLG7k9vVA2PiSevot5TgiRRuT3u8yYZvixw/xInOA6ykdJeZ6WuCaAaIc5xSNV7h2zEoUw3iJR/aAhBTAF8nwrPMTEAc9+QNwYBaAmqIvsw+7teDF0AoAcOAMr2FWtCAGwY4rxBZUCgSm9T35sUKEHOUw6sco2UIFvBND+YQBWWRsJ8xp7MxkQKMTwmCiAWR3gdUDYgj7/U5wD1J8MRIg94Kq9dEFz3p3one//e2FCSlcgsCzTmFpyRVYyG04pjLvowsaoC3glCUIFgkSnEkQlrEXNSRMOTtdpzp7LgNmPlOQij9mBZkwk6+VXg2kc0w6Lwugn/Ak/QgRuymEHVQ4gq9p7hvvzW73x51cWdnd7/M8ufx2/HXkaHGi+t1gwwYrq/6m+x7zwrv1wsroI0CZSR0JiKAIDDBGB4CFlFHOQ81X0hTzKDjiTtsL4ehK8M9UcJzlLumq5YrCqtkF/fm5+VYdqRILI9X5uTuCUPCI6ElkGPwGP3NxiH6Pt0Ngx8c2Xm4txnveGeP8QYsSgWjGClfoT0ZC23H40JVJyvNIAWQr7E82cLWdo0VNLSUkHKBuRBQLoMYht6GTzNINKqpOWhOEDGgqHexoxRSJPBsgfkFQOiI5EsUiwxlVmFRtGi1AMWEZBs0tkLFwOFsgGFa4aRaYik7uKyXYimOJEStQDJHUdeojhkIAW4s4YbtfCmA+IaAOBpj6yPLblkf3z/+TQC3v/gPjnL07P7r//Rv/uKTP/af/yhe/cLrtpquQrtoZT4svvb+T77vUEgxBvDWUMW3XvmFrwzRy04dPXQe/YOOFsBvAPyLf6e7/Z3b/Pz3aAx/pOIR2Ht3xufQnzR/EKdSGBsfT8GAyq2JQmqlpNqkMGhQ5vJwNgNidB04EqHLelsru2pq7pw3LMBjrQUAMlJtz51rllW1NTBWdzEkxXBCCK6bxhBBUYqsoHWRZTBKbXx5u671qm1bM56MGUAVCRAk29yMHutiGhFJzo25My7Ls+gvEA3W9m5KqZjnmUwpHXfRcdU1MSsz5ZKPI1U2gsT+ep8T+pN2i75ECJwC34ct0TasrwDAl1m5yVRtrZ/364fGO63lZuj7yzYgyjjnfyBxspm1F+G6CebT23jymQeQ8hJOTkp8/XaG7ZIAiojs4eMIMVlIcZMZo0GRa6XkJWsNve89z97nlOL96dE4U2o8bWbdbLG65Zx7ynm/88b1O/cCp5Ukcllhfpc1/j5kfPrO4cFWCP6vNh2PpRTPKknnpeZrsXubGOKHRYZl3fbzQIitTx+uF8ujf/6bv/Pbe5OtGz504WRRv/5jH3/vzWFW/Med9x9KKe0qKe8yYGLvbbsh9mwA38OEoCWAa+gzewLADVf72xxxOUU+Qt9OcHP9uR8AYOdH86uvv/xWOb0/nW+O0XBrcOvo7vFicdKeH+T5WJB/zNr2DAL50Ze3Iq6uLoQk4a9dbuX77z2jh+FDIahC69gRdZRYfNlVxigVPqk5zdVsy/vjccXjw+XW1urbPvgHzPokMf96CHoSIv8YkEdAPwWIIfrs8EPHP61bGfxwnexNff9eggBbILRrP13f+/LyNmAmgSF8iizYs5WG+/KvbACdGBgATMwezKSqpnVKiizPbNGXmfWKQXv9nGnX6za36CViMi9EFwtBDbNgIG4xi5ePT4pjRfy00imXMmUp+qoHp74myI4xqE0KWQehGUraRWvr2gdLglWSItxrPDfLTluXKYssKuI086SCMGJLQBkpzJYljNrU1UIGkQghssl4VGzpD8CHWyODgyyHTw0ZMF+AxTYiFAIO0PduDjtAr9GTRK+hqNDTpGujYEQEJUCKBOYjaIhEGIBFpTNTojDfT7d8y+cVq8cNZzLmRBAhVrfn0ztv3Zv72p+d3poWw/MDITJKaZ4SAgi+Z/tzn7HbkNQs90KKJxAo+/69h+3z3rHOV3i7N9XckbCDiA4aIqTUdt0qjuao6hU47GCULEwhII6PcVTuYi82WIoKFQ9QcouaNXQaYNjO5TywIcqDSQqiUVggQ74fEe94+F0Dg4TkCFJpmBjgbYtGB3jyiCmiEwLcWWjp4bIajV+hknPMVEQQFjl36PYUyisRPgJpaZEfObTDiHDLAk8nxAZGqd2LkwvzN5dbDD4/xvbRbHfrm2lxd0+69iqA8uTWSfrSZ3/7t+58447/0f/sR6pr//yaeu3zr9+a3j55+aM/8ZF25/J2/FN/6e/6f/CXPyvR3/x9z9i1n+V2Q8J7FP8fxSOw926Mq+drvH73TfQkiBHeqZXHeZZxnmUZ+pPcRnKFptUy65JnI3VlpFouO9eAxMXMmJiY1aprpFDKTUqx0UczSoiSpMSsrbvKtdnQZNmoGHQnbSVLqSGExPmdnZRiSp13UUlF3nuWSsQsz9lIpRNzHGV5NsryiwBEYm6Y2UohzuC0RBjRAzYRY6yMtUYKMT5Zzjmm6CWElyQfEGjj7WvR94NtMpMlThnIDfry9KY0uynTbgDMxjN4w162OM0AbjIRWM/B1nqeT1JK8vrNO1dJ0c5Tj13uaDRqkGUNtFZt294UK5eZSztv4o8/s4fJ4AqkAFKaQord6Wz+FQJaFmLHKHUVwFhJmXvm2jkXb987aE/mq/Dlb7xUu6rhMjevntmbwEV/HwQ7HpafPzeafPMbd1793Gtv3vwQQf6kFAIAtLTiQBXpTnR4v7J4UhvZPH75zOTByQMc3GvAER13OAZwIYR4Rhr+8tZ+cfGxxyedkfqJzvtP4LS0tUenAsEDnILfDbMZ6C+Evw7gXwB4DYD/qb/+X8bPfOrTwnd+I0w9BODWz38OgL57/X66e/3+DwH4BHoQLX7l5359i4FLLPh+15RXMc125K9PKpTtGL914bny1VUIl6pWvDEZxIbHYn85V1dWQxqlnDT2ZUwfyrquQIEBlXgcL97/qqkP72RlBBHdTuyk99qs6tFISrplVLXqXJpELhiwk7VeXgL8Wghbhl6wGbHvr6MM4AbgBwJRAIVKLOdAOCuF2wdDgnwMLGqlkpJCCbDcBhEDMqFHbbFvVTUJYC+VXCLFcYxuKWU66NefNGvtvwwwDmjX43K3E6sOkLJn+wqTWD07nWejVC8xLvxbW2d0TlKcC5wksyoAsQewEUg1gYkBERtPOa9IKuH5uHOZJuWOFqBdjbRjnQpJZ0MJNWCOESIGK2qE6HMGrxxppXWD0IZlF/NBrlVRnKOFWoSXwi5ruYPnUOENEDwYEgU6sAccv8OvmwGQD3B1BBvdyRZSZVzAugrOSFBrHFPBSkvLNMYF36Bjk1bsKKipOvHz1tIOL1fTBoP9wcI/8HAjd2a4Pdxu6pZPjo8Ah259jvhOdYKcNutXYQaHHbOthZt6rM8B1fqxtV7DAUAT4SYRrgJQBTg6QDfe9Wd0gJQaTjHgGrRKgZDgRUDkAD+qsRSMKAYoJUFgiUV6EO/ocb3tZwjKw5kCZQJoHsGJwTZbqxolRAeovEOrJUxM4ESA9PAVI+Ut6qiRKwvkArSSEE4i8xKqtTAThigqrNijC0AyAPYlVJ4QO4vhsdeCOxcWAeFNCRE08hjHe+QG2a3irWv30TsBiRtfuXEBwOGv/o+/NstG2T8DkB9fP770i3/r/3zrs9y2APBZblfrc8Kj+EMc8qd/+qf//x7Do/iXxXSp0feWbeO0X23z+E4PXA1AVF2LyvnoU1jFxEeN67YH1mqtVA0iFMbGUVGyEEISkQPARv1f7L1pkGTXdR743fUtudZeXb0DjZ0EWwRJcAMtakSQYWqxTcLWkBpTSwxEL4pReEYzmAgadsAhGRpPKOSFtgSNSFESLTowoxFJa0yBkrmA+06RALob3eilqquqq3LPfOvd5sd9r7MASpQYMSFhFH0CheqszHwv8937zv3ud875DheCECO4kEpr240aNOScUcYsZ5wQQhRxjltrUSplCaCNNZYxmjXi2ORFIYs8D6y1RAihAARVYYQkvt9sDcQiY01ZFGUIoO2cZZxxFsqASS7CySy55kpLpBSglNYVv3W3ilrq42BycB2eLYqioNZaxxirRahpWhZubzoxAeeMU3awyre2g9qEFoAjhJx01hwWlIpms3GJCHkeYZhpbc7u9YexYlQ1XnlTA0vNo2CUgZAZKE0H4/GRwWh0pN1orHXbrVBrLUuliHNuNhiNv+wMelvb1xbyPG9888y5zixNA2vp11aX2h3OiYNwvTe85t4nj6ytsbMf/8/9Lafy5U73uLHmdm2Ns8ZdoYSkvOGWCIEhwm1rlm+UxrJcGUDhTNgiH7UGT8oYZ9YONwcbC8udk4ePvjrJ0lcRgpsISBtAYZ2T1jmujQm1NZQSygghNWNbF/F8GsD/DeCLDz7yUHnPG1/vAOCeN77e3fPG15f3vPH1+quf+EwJoP3VT3xGwQPJl8JL/bSR4TUE5D4wHALQVAx35EKIzqcWp2wnOMK+tXCYXY7vKJsqVCG7TEXeshead4tvLR2lAxnTo/tN0nIRBCgcNKEwxMuIHwJFiwXuOIDvzwv+WjgCIfRlB9oOZEocoZQQjLQJL1d72aplmg0BGOJ19xQQjAG24EVvRelFkkl1qwkGGOkcaTkQ6hwTIIEEmGBMCg/0tPUAjVR5fh4sE0K0s447OG2tQ5KXTWdpg1GeO7CCEN72Gn1mCpjqetsEEC1AGC8d40LOi/V2F5yJoG8JX7dwkbWCA2HXM4OuALiF0RrOKae4oQmyBg9InEolFpNU5lwiD1244lIOowNKqBElikSVzlhGpTJlUZBkmNiSFHaaWW5TLvS4pDKOgryXNstZErIFGtIN2kRaal0opWPVd5mx1ukwBJ9pFEEJzQDHCBxzsBYUhjjGqKLcKUs4UCpXApGl0XrAkFKWjVOmhDZUkXy6N+Pp+fT8+NqgZ1fMYTUq2jZxQX6xPEopadAOyQab/SDtp6zyArUPrH+PKj/h73ODGICOFiJXTlXdL1nBg3OQuRZ9Lf6+Ag8CSwNNMmgi0ZhZpDZFWth2wbU1PLCBMDAsq9i8ABEUtJQIkGACB8dDSANwkcLEGq7kQD9AuJRiEnHfyYVFiDSHTHNkhILkBroMEbcZWKGhSYpMlUiNg4OD21VQuxKBKlG0E0wjhTzUMImEHBnoHL6nswQw4KAXmGFXzbgImmg3DPT5KUZfxGi7IUd7LcyL08brt69HnfX2y5JBWhazol/d/zMA2dv++Xtww/762A1m78Vqpzaexfntj8E7pxjAD8MDv7pLRL3I1FIZdqHRspxQMsmysJdM+xZueFSsRAAOB1wwcGGTPAtHWUKXWx0ecBEAULlSRVIUhFHKhllqGyYw3WaDBEKqLM+ZtUZnpdIWDoEQjlLGKePXACRwuNURwrRxKfyuuZUrxbYnQ7EcN9N2FFsAQhtNhrPhgiDChjK0hJAEQEwIMWmRY3f/WhgGkQuCIOERn1TfjcMn/9cFAgdFog2AXBvjrLW8yvUT1XOOADRXauL8wlCLP9eMVp2z1z5wHgEgWFleAnz4YAeVeDTnLOm2m1YKsQYpFzDv5pEACLIsC4qiWLHWIsuz7Mru9iDgYhpKOXpuc/eW/rDXv7K3j4DIbhAFfcrwLUfNYHc4jBpROOs042ef+OynyNHe1trSRN580/LxTbHMHx1vTk/r0q1ZjZ5V7kzY4ndZadedI1leqsTCdoMQWnTAKKE7PMSnj6wvnmrwcGl1afGVYTt8tczkusm1cnA01bnkhLOAC1hniXGGCd91ggIgFrakoFcA/DaAP37wkYe+W35OCA/yFLyw8oYmyEuH5UYkbu80IjudJbkh5rzcZ6z17fax7mc6rJRlS5TiDQ7yJHFpwHcdMVgNlJIRYB0FmbJhZ09tDCNmXdsw3IkYaeiZxFpm5yvO4ZPG2lMAUmrZvrXU5EVIlY6eMi78JiAGAO4E8OMAXgqIBoC+AyRAOaAW/PjJ1I+/FD4KSRVA1oDIVcUZDgChlFDqW6U5oKhzRl/IGjsAUgiWEUOo0YppZYkzqjDarhDK80YserieQ6ZIRiJOHb8WAApQEZDtEqgNKfIJIJcJD09bW9BiAiqjMqPCGec41bkeaQe2v28pidt6vROzJXp4t6uSddUdmbRNVE5o6JyjWcpNORtk7Y4z0vFOMh3RMIyoGSmejFNLJcP46pQ6IYkUbSeEoJxpZJNROERiji9FrPxGas1AT1gpKA/Zarmmr9EdJIAxDtAEpLRAaGEEgI5wwhJQ7eBMhhQcjBFQ5qaE2DOEWmM4A9PYts2ZSZfsgs254k1Y0uidHZQmLrrlzIzCOOjZwC7vXx0oWzrKGbfa6IOtEFH5v9o3cMz1I+1sO6k3MrXeKANgK1mWgILOABJbGANAO2CxDBdLlERSS6mBMgpKsYlISqA7RUFjUBmgQVuIZwwiSZF1E4yJgcmXsMYnGGQGOReQTQ0aFVAxRaojxLslijjBrJsisR0sTiWCDgNVJYowwdQ2EMPAUQLIGdJBDkXb6CwQsAZgGYWzFoYWMI6BZw20/rBA1gbQFpCFgHyqL4bOSfG6bhI9Y2H/S4nykoPdoQ4SwG3wG+inAOzf8rpTLcCpoBGc/xdfeKa+32/0s/1raDfA3ovbvgzgYvXvFfjFq4Hns311WJIOkpn96qULRRwEk3YY2VYY1UUKISqAQ0ECAqoIIXUYlEVCUEIcLRSj0zw1knM5zTIMpxPLudDjNFHNODaxkEUYhrX0yQxAmwo2U2URck5qzTVFKQElRHDGrufTEUJUJKOIESbDMFSYt3bTzTg+f2R9Y6ZLfTGUksH3q9XwIK0Wi87hF3xVffccwBXOWFdTmh4o7AgBuEgG7uaVtbg6DoqioJTSRAhRh4QBQJVlKRlj3FgrjdagjOlAyjPwrGqz+gxrzUbjjwD8KDzAG8FXSmcAWssLCwklLCWMBrMsW55MZ89trK4Gl6/uLl2+unMoSdNCl4YuLzZGd5468c1GJMtr+3t3GWOfpsQuTfPZzbGInv1Krl6poV436k3/06Em/5yx+Gdw7k2Mk88yyY9zQe6GYDGAPQb2SQe7YmN3GcDnAHziUHN50Vl7v7EmvHj16mpn0JKM0DVrLJlkU8sJ5yxiuXVWMkq5IJwQQg5IrVAHn6f39IOPPJT/OXMzAXDh8HuPue2f2HzONF2YBuxmVRQv0QSbjSvRMLgYLIUX4sOiFDeF29GREup4XhZLBrbRQEBEDokADJEC7QlHGCaU0fPJF+/87PaR3tLh8MK9hVWhsFjlIZrcczgxgB1K8UfONjra2iOztBnmRQsAaYIgE3c2LrKNYJJ/fFCi0kMEcE81bhFATgHOeo07VxVKGArQGLAjgFYSHaRODahZ4YoF9gXp1X0wqeZlLVvjCCEzwV2TMwJrg8JYS0BIJgTTgH0W0KcBFBrRuavN6NpC4ZaDshwArgPwDUJcGw5TB7Hi5yp1joqUoMidFnGZJZoScAJCY6FDlY6G+/2rgncWrwlerJl8SpwxkC0qiNVkNrVRMR0sTjW3C1FHEQJeFrm11JF8krE8KZxKFRYOCzfb6ZGEUUeHKJJrKSGh0dPZYGSmhAWT2JZpWkTB8oKIw2Ypp1eKUh0lIHscvE+BlxK40oEy51xuoAIOoTl4YWEjDjgKTow0MLnhcCSyGT1csDJyiu+UaRH23XBiCvVHS2L55UaNw7SbXKYBOV1eUQuEgfgiHBTVeNSdcUh1r9brWd3dp57btR9xuK4t6SyAiEOSAGE0xSgBkM6CFT657Z6YJSmZ7F5Be5azBlpNB5tQ2GkANBwIrVqniRxFs4lWaaFZF2thEzGdYlg4KJuiKAhgEuhOF6t7G9iYDdGnBfIyxay0MOEhHNUUTDShoj6GdoAtKkETgIYMnCjkchcJ9e39IDlkUc1jGBhroW8iIEMCJg3sWYf8U9QRxkpyWwddNkbveIz4TQrmyxPJO6DylGP2GZii0fzbbz57+OWRFhTn3v1//LF6Jwk5/NoyqnLmbthfI7sB9l7MdmpjDGCM89sUwO9jLlTbxBwE1WYF59oSMju5vCpWmu3joZQN+CT6GfxYB1EYkigM64pUB4CFYchChDYvctOOImed4/vTSQ4HDWPdheF+uaG69MTKGuAXtwLVLjnggtsgEJSzxeocVjKercct6oxFqVQqhbCMMtGMmgnmRRV1lTFllDW7rXYLHjxtweu23QIPKHLMxX6vwIPeun+kBkDDIFgEkA/GE0kB1+206+INB8AqpbW1NnbOMSFE3fqLlmUZGs8MAoTAWpsD+AR89elr4XP5AOCEsfZQWRQRY3zaHwymURixbrd9AkAUBMFOsx0dztLMpUWWR1EopQyzwXRGv/7MM0WRmVVdFPud72ueuf+1r+tf2b5yajadXtsfDhwVdClk4utRFJVZJH9cheZUW0xZHB4VcoWbgOKXbzpyYv0T3/jCorPmHIC7AERSit/dWFl77sLVy+1u2CKlVkczVUwCJuw0n71Kp/aoLpVpR02T5GlbOSslc7PI2t4sSzYcIWVDRtSHcgkkF7XQdIh5QvufaQ8+8pB7gj+uAdy0/vhGMnrt8Ju4An3lyJ4UgneS56LD3QuNNTcIbnNO7BFC1hzseoQgAIAC2knCNEmFdmW6Q7h5Wgr3EddfnI2CtaWts7f84c0b53/9659/+7Jc6b3t1PGvvGq5MduBF1LdyotobzRdXZUiu1oqGQJkiS7zs3RZdthGcAR+k9QH8BH4KsICvmq4CZARQHcAe2+V2pn50CqM1+C7DiLqriIH8j2J8ZW8RlRg8KBe44HXE0cIYVEUsLJUIITMpOASwHHf89dlHGJyU5EvQIk1gKSlcuFo3FiJIxs0Gzzw05tpgFLKHBv3CmL1qOSci6ARR5QVjOhp0GjGprRqSFZ7t2Qs6XLrtsOIE844y9KUBUwV8fGVvEzzYrQzapS5JlE7cFIKFi821OYzV7UMhLRb1plSu9leYowNymx/avNkOlxsLX3jWPvk95mGYf10dGF1GLTUEOvUkNgBzsDAEroMihmMAQOPDDQcHDWwDefz1XiJ3DFIiIhop0CddtQpZR3g0iwJpBM0YHKNu2ilFXXynOXhpecubvG2yFsyzvM8l8aZenyuF6wd8GUHe4DXOahF5SdqTU5ejVfTAUGBghQoMwIwArraLjIWPf1lmps0dVZLwNIE4wEFCx0I5eAlg6MGilMImiJnFpQ1EZIphjAoZxR8KsF6CfoLHDLWMM0R9lcd9BEKajh4EaEpKXg3Q5qMMYwDSBNAzgpQzhGEBq6METGGBu2jB+N9Y6FRGgBcImyUyMkIAwpgyUEHFtoBeLPQ5E8E6CdG2D9CwbuLWLmNIbpZl1M9XOTHYGZv5sng28XZzU88d+xo0W1zBr/JE5i3gvwzwR75ud9dAsDcr/y3e3+en7hhLx67AfZejObBXQQgw6kNi1MbFue3dwHkWZZpQkghhBgzxmLMizdMN4qTv/mS06bUilzu7T+z3Gx1FpqtGTx46cCX3Nd6UwLzUDADQMIgTKrnSSOKNouyXAJBkhuVxEwc55TUII/Cy8IkAFoVeOSuCpEQQDrnSkKIhKvEiT3gFKhAnjGGEEKsMSYy1m5wxoZJnt7GKTvaiBt7mAuljeCLBA7Dh3QdPJPShu8yMoB3Tg1rDHeUWMzDOQUAZZ2tWVCFeccRQSl11lpCKBWBlNCMfZtzruE7mHy9Go2jAKIizzcm01mj3W414zg+Vxq9Dy8a3AcwLPPyJWWpbCCiPRM6IhlT7ajpjq6tD7SyeSMQV+Fs80tf+3bjzOazU0PLjjLm5dDGymbHWupCQrABB5qr8gc3ty+cCCDPHj+0cezQ4gppxM3GbDb+fQCXAWxmZY4LVy9zAC9RSsE6YyamPLnRWrxjvb0SXBxtXd7Keseb2VTFMqLOWlOWauZfJfcAACAASURBVKKdzqilAyJIlzniclM2jTN6odHpSyam8C3tapD755kBsBdsh2Tt/zy0lgbZqf1ixwQ0aDUuRqt8JDjgupSYVd91ggSAg4LbTl9ivt6+GnwkzsUlHpLMBu7sW3Z+fPIEfzyaHmut7kxv6v3wP/q15Je+CNnN9r/uRiGWGx8bHtT5euzhR9NSNQx8n+OTTrkxW5FPA9gpPjNi8H2Hr8AXm3wDPq/wJwHs+bZnbuhFj2F8v1tMq3shhGcQ0+pxDS6qln4Evkeu15jDXIKmieuME7MAU4QAQRCk1bHafu6G1dy2K9KiqSw6w4n4PmttOElEIkVCvQ6gmAHEqCxN0/5YATDOiSSfZp0iKYuwFUTFYGZkQEcrJ6S1BtHwmimby8EKK1Q6no57o61hk/LABI1mfzpIVifbO5xQyKgbQeUK6X5GXWmlgS76vT5s7sJRb0rbxw4FVlhjSxvLcXh7pvKh2SDPrawefY5eIiBF8VoERJvCdgzIERoRhoAUdGhzgE4AEQLaWVjmw/OUAJIYaJUNyjEDaxqYIEUCq2wjUgEloL2FeJHkRd4s0mycdWcj5OZYzFqsGbeRqJnBPI2lbp94UI7poNWhW1ONSy1C38FcmFwYOMbhGIdgGsoCSc7VLIp9r7w6VB9amBC+AkczMAdoQxHYAA3FIIMZ0pGE0ABoE80wwWwvxRQNdJYcXJAiFwRjcAgXI2owUGZQmE1cahBAlpAzAYkUaVCgyDpY3LRQoBCHI8SNFMnEwtQSUdRCb3Wx2JhgdAIgMwp6WUNNALQJyHEH91SB8r4A5FCCmQyBxQ6Kphlea1migxK4m33tTP6pXlo0m6X5V5/7R5ef+h/en73jX//kheq6fTerU19u2P+P7MaAvQhtMBvEH/+TJ15+dvvs2Yff/s+8Ory3WBvbEJyFIKQORdaJxyW8ztR5ODcRgs8YpRyeIduHB01n4QWLLfzCM6meb8IvWjUjNgAwC6TsAChOLCwzwCWhDCLM5VBK+ITm2skqVVXGCiCKokhbaxX1nyGHX+yWANCyVMJaQ51zyFWBUARUOzeFdQuWXmfs6pDLprU2VFrpQAYNAFfh2U0GvyALVG3ilha6tROvgV0G36fTEEIUCOlXx24AKDnnlHNetcLChHOeAbi9uib1An0MQMo4/0Yg5RKj9BQLpEtGOcmLYhIGQQTgEAgVymlBHSONMGJ7w8EuEYhO33HbaPva9uXF5W5jpbv4zM7+PvRFcn7m8tulYCzL8yss58XuVv+W0ppL8H13AeB4gfLMpb2t/mK7++RsNn4VgHur73t39aMByMTkIQURBFjYmvTsatQ9ayux6Rw6LvWsLK22AMYyYdPloHuTULx1LRmMS1NkhFBDLIatsHlZCN7jjPf+IvP0fv2Ae4I/nsJvIlpxEU1vO3Pz1xx3r5I6WiUwvqwClgHQIYJrGuqTkrN/U4piKx6KIQDz1sE77YFjZvd7QAsA+MxvoQRW+sAD8D9ze/CRhzQAPPbwo08Z8LsxtqfdZ0YX4FBvWgbwFcP6sYcfLTBnLm/yFa2y3vjUgK5THboW6pZVTZR+QU0UraYY80Ua7MDfr3e8IfByPguYt9NrV6+bAEgA2tI64M7BOItGIA2ObeS5FGYGGKbbfF+Voi9LHYhQtpgMJs7Zdbq02LdafUVyfpQelQ1iM5eN1MlkmCeumMSEysZsUEDNCrX5jUth1O0aFk2jsMGjIi/D/qV9J2NJk1ECNdGURSwPmnLKjeiUeelQGDW70iOMSt7hh2hDN2M3cUajiOiiOsWW6S4i8+3xZXlryKi1Jst5hpAXzhUo+xIIOYLIQTMAyhcZWANAzzCzKcbOgU4ChA0NQyhI7mADB8ezqdkukfA0NAu7vd0QYIfUqGSjbGAVVAZ9HYDVLN2fZjXLV4fg60KxAvOoRFgNoAaQa6hKlskZB6cAwAKOgijMOw8FBpowcFKAY4wJ4XAIIRSF5BR02sd+SyFnARoLDSwwjfyqATYonJxiLCLETCKwBqqYYjwqYaIOmp0UmQphXYqpBpBy8DjBdFaiqDvYCALSYGAJAd2j4FmIxniGyVEOEUqEvQLZIgOPQkShhLzlGnYaBubYAL0OQW8iIJvCIVDOZQGwaDD9ofLKU+TbR1768s328RLA//NBlw/+jGt60Hb+Aq+5YS8yI879ZXU/uWF/Uft7//j10WZ/8/5xMt7++v/2DSu5PAHPOvyYNua1cG6Nc84w74f7FDz9LuGZmc/Bg7Ifhg9dJfDh0R14lu+rAO6AB03H4ZmyuhozhmfQLlePT2Lej/ZY9do6F7BmPDiA0gK58+r59YJXL+JZ9VlTAFZr3dBaS+ccV8ZQzhjVWudRGO1wzpYJIYAHqFcBnNnsXbmy2d888bLjp081wsYQwH2Yt+86KL1SLwJ1WMdU161mVcbwIb2bMc9lrHOArsGHiGstvjqsier3BQBdrfViXhRX87xsU4YgCOSaA0m3dq6StMwdZzzZ3N8pu3FbbayuSavN+a986+limIwbxlo+HeW7vWn/TKKmr4nj6DOzbPLb2tlQCvG2jeW11Ys7m+/GvHPI7wF4KhLybBxFb+9Pxuvwu+rD8MAZeH6P5BqM9zDPS4yq37T6/miycJlTxkYqyQD0JGjEmRjHNBwEUlw81Fn9BU7Z+Qcfeeg71PL/3V2/JI9fPNINSjkEQL742q8uHbt4ZPXQztrd8ELgrzIwt88WVVdOSQhlISFnDOy/Lr2EfYRR+aW9P8mm8IyouV8/8P+JIv97f/G3/rbNt39WAZ9vAr9QjV/54CMPXXdwjz38aBu+5/DfBPBW+LleA4aa5a7nzQxAB9DMa/EJ4qXl6nlW7yeuY46D3V0Odnmpi4ks5vdEnZ5Qz72uP0fZB4R0Fi1CiS0JrlFncqLRV1kmdJ5qa22r2V0QVNBzk2vDT6rpoBxtbR+JFhdek0+mG3GLLenRhO9vXzVLh1dneVaulrMRgqbMg2ar29vaZdPzvYOCwvVnz4mkCFsyy/q5AlhxqHuKNUiXs2ExYwDtYXdPILyyurLGacmnJjv0CmnsemySTMOCwHEK8tkIzTsd3NEcGSUg1MIyC0coKFEodYnMKhjKwIyEFGyR7papCou8IAoFFMqtEcahQblEwMIh9usCqgPX/3qu3guVCYDrDCxw4HX1prj2mYyCGwdLnddejOB1+1wOlztf6z+NFS05xERDLcMXIlEClnOE0sJwA0UDhEmIyGSYuhIFB4AY3bFEoygxI6m/7VY4AsFAlUDEGMAszLRAOTDQkYFqwgNLHiBQFk4qFJuYC0UHIcLIwqUSwaUZJidjxKMUKSEggoBMLCyl4M0uFica6nYDRsYYT7lvCVigilJU14XVSdHnTr4izU7c8St7J+7+peR9P/s95+qRJz/gb4D73vXnMYI37K/QbjB7L0L77NnPOgC7Lzt+uq4auwzgzQBOccaWMQ9dOPhFaR8ejAB+ATtZPSbwYKcPXGc7Pg/gj+CBzgl4B1jv5i5Wf5/CO8ku5oUIKeZFEzVQqp0uhf9fPZ/qnLpK9uJ6YcUiAMs515xzAsCEzrnd/V2Tlcqtc94VgtcgslZ6F0ut5YVIxEekkIvw1WQHmtRfD58B87wcYL64Rnj+Al7rE9YJ3Bcxbys3LMtSWeuOykBySkizem0NSGJtjMmydIELOc3LEmVZTpM87VJGC2JpfziZPLXaWTpKGG8WeRFrmFNHjqyem16cbW1d3V3LZrlwTHeLMj8bB/KlraD5Y8N8vGUtio3ltW9e2tn8Lw74QfhreTMjdCVT5dsLpY9jvojVC1+NNOreyXUI+wjmC1zdUs5V163MTGGNcXVFaUvDETizNtWpczS+qI0OOPVr5U/91DtD+JD9NgC7dGLhlOLqyLVD+1fu/NYthSjlLbPVRAfgp4o9HTrreNrRayZTxKoADUQ5gA8Qjl++5yePjL7873frsOsaAPcEf3zzfv3Ad4SNH3v4UQ6A/wUKRQAALN/bVYhvMlB3FjBnAtgvw29uaoFnPPjIQ5PHHn70U/AboPsxb/1VM+N12606VEg9MQwCkOfN9Xl9wHXZGmCOAGudx1oyqF6wa1aqBT8vk+q1KAqWKU0tRbkErWgQx6kUZAGwWZJkZ/PBcIvJ4Falim80l0W3LA3KZPYyHrXOLd9yU5bsbT+dmSAg4+Oyowcj3lRkPDARgS1GW31nrGFMhMYlRZ3rVjP0AFDQKDY0oCzLihxSKjAW7E6vXl2kqQ1BV2M0A4ZwaYbZlO/zThdLsr00BU8bY5qJjkZxNUexGiI6TUBaCopYOMNAGUAsB4OFYRxCU7Cco5QOjk4x09FArhCwYoppWSKzAYLlBlpWI48KlPU8ZQwcBroGzjXoq7/PwdaJBztm1INVPybVtdcWuvBt73zXDQuMCQijcDk0QgrKGdhwGavLGdK1MYZVJa+JBahOkeSApAUSplAyC1sCIgdMk8B2CkyUg6Wo+mmHCAmDoCUKKxDzFrrNEfpyhqwCWC4A4AqvwVhXu2fVPAlz5DMOaUoU6wz8CINc41C7GupZBzcF0KEgWkMtUZBFhcAprEmGrZL4zeHB7iGW+d2LW7t2+aOLm3+yF37it0+88/0//+wHXX59g/SxX/1p/pZ3/8afCuIqkLd44pnhzZbSHPddT325YS9CuwH2XoS29XGXH3kT+fJ7/s57SMCDLvzieA98iC868NKa1YrgddF24BP4LTx7V8IL3PYABGVRHmKc/S3GWA2Wvgrv6FbgE3TPw2ulHYPvnXoR8/6pz8EzgS/FHEAl8ECrXuQamFfZWmstK8oSANIoDIvq+TonygBwRVk6Cmo6zQYPgoBWn+PW6hwjAMM4iC/HQSzhd6YtzMMqdWVaC3OG62CVMqpzMaX0jDFKKKW142pXn6GLqgVUWSqilCJ5qYaEgARBUDN8pCiLrrOOUUYjygUbJWNXlKqbqVwNhgMiOHcLjS5ZX16+RQgRPH3xXDCeTtTRtcNda/FqZ8zXwog/OUnLuweDyS2MkaWkyNYYpcJanEiyZOmTX/1CLiRfD0WwKyhtMs65FOLwYDQKtQ+DGcylPmrHXeeOAfOQZA0+6tymms3gAFoGrgBAnfPSIpbAlVY7AF+lZf6HO7N+71rSJ1/4qXeuYp4bOQNA+mtDM1wY32yl3RisTSYyaCzf+/TG1eZL4kvFlybLbopjlBASlNy3j/dM8qNvyh/oAcDwf3xcVp/l1fCh6D98gj/+0fv1A+7v3PuO8FCytHB5YSc8ffsJd3htUT728KPP1eHa72Z24dQX8/H2MLLp7QAeAfAjmEvkHLQpfAHQHwB4HTwwrq9dXVFbA2ozjwZev9YHjLzwb+bA62rASOA3OQcPVJ+LwYPRvtEqUpN0FVISDpMSUkyAoGc1CziX61yyKyxkIQ9aoEz8AbVYYGG4KOPGZjHYWmXcLYSthW+z7oomuszkJblYprnOx7PMzEpGGXUmTWqx8jb8fZv6z0ImzcOHSZnnke71BFQekaARWaZnvdnmGQYJCnqYgrYE2B0zzIZNtJr7/XHRgjwXobzbQDdCRKmEJAplZmG7DIw6OKpQ6iZaTsE6AwsBoSjIOEPWLJDGFpo30JAUEAAtRhi2GmjSCE2iMUSEli6Q6giRzJFB+/BwzUDVUYYA8w3ewXE52GqyBopeiNr3SmbcS65wA8QUKAPQemzOGBg1wXDFenHu+n5Ciinz5y0ZAGphLAGPBCJCQcsEIw04wiE5Ae8yEKeh9AzjDAChIHyGMVXI62K1HP6+kJj36o6qsarTDwqNMpKQhUSQxYiDEvmShmrB++dPA+4mDXVngDhsgGUp+k8p78+ZrBjN6npFBNgLgM8v5cN/EVjdWX7TT4fHH/zlY7/yYWz+3I/CfuxXf7oB4MjHfvWnN9/y7t94nhzLQ6/4x/Fbv6/bKTl950IvfV3REmfIbR/4prvvXX9ZvXNv2PdoN8Dei9S2Pu7qm6aP89sdAPdorSOlFKLoOt7rw7MGZ+AdwhkA5+DB0t+AZwMZgLNFUb68mKbLAZUBW4iaIGQID9D68IyPgg//fj/m4dBDAJ6AB5AJgC/BO6JbMQ+B1cxXvcsm8IustnBGWx1xymttvFoUWgEIjDHWWcuajaamjM4YpTN4QFcDFgYPxl4KDxRrh14nzgPeuWeYh5MPdhsBAGKM4VvbuwtCsujIoUN1ZWb9PgbPbFpjzSEQmkZhcDkIgiE8+9QCQApVDsq8nLVbrZuaUZyAYNeGppXmmdvZv0Z2+73u8m1LERW0GEyHQ0KApMy7W71rrBmE+dZg71hvtP8yo9BxzCkW0R0h6DRRxVnnXFzm7poI8UoAQlCKMIgJFyzrj4cjDVvL7SweuNYHOwfUTIZ9wb+vK8divggqAMJZx7RyoAwx46QG7ndkruCrbOnzB679EB4cOfgNx3NW2gRAroW+Ipgrccl1z/GLHZ7IVzZtY6ExlFAwoKBTAH8fwPSTa78fy1XxOnC8EhptAG+xsLckoD8xbeHC74kP/cFb5X1vTex0rbMWbHeuuo8M4+IPX3HmJZfwCP5U2/rlT7+hULj96eLp3/iHD7/b/OLDj/4bAP+W+By5fwngb73wPVVo+quPPfzoOQC/Bp/GEJZKl5QQxjmrk/pr1u8gQ1TbwWt7cGNRs0ovtIN+tmYC6zEyAJphSJlJ9PnCBM90V+ITRJUynWSDzMZmqcW/bcNo3VFimYwSAPdyIXba64c+Ptu7dncySD5nCnWZYb8VtuheXw+20lOdOycfPwv0JuPmaqsVtcONwaW+0dreQ+CqikuSAmiAgDLGIIOA50UeUMYCKZkqZtkhB7QdHNPICYEwDUSwsFcZyLUGGmGG8Q4Flhk4JwDR0MzCjDjEGgBCQBwFLRUUUVAwsJpDtCmYMiiZg8sNDBhEi4HPLBJpYIiC5hmGboh9SsG4haEpEm1h+YFrWl/7Os83xPPn/sGwbz0+gH9COV+VEzGvbu343EdxeB+zCyBLkbbg758YnspNPTDFtDpnCFjiUFoLRxlEnUZSaJQ6RCxzZExD1WwvTzCp78eapefwvtxgXiDF4X2QrP5WArRVouRA2SqhtEFJ4QHhXQCWNPSKxqyhYWwAbYFEGWBK/bG34TfHO/ARm3MM+B1m9bUPunzzVz58XW6qNgW/GfkOvc3NDfaK4PLmqcEdx16DDr+LbtDeO3796Xu+/5/+jfNi2Hcf/+a3R99xF9ywv1K7AfZe7HZ+mwD4RQCrSvl7LssyRFFUt/65Cs9SbMM7swnmTm0AD+Y+Rwk5xAzRFG4IhzMgOA/gNPzNvwjvhG6F1/Kr2Y36OB34RXGnOucXxsnsKGP89mYY1mxaAM/+OHinaDllZSOMhZsfp95V+geMacoYAFDiw2TXZRW0NmCMloSQGpAtYB6qTODDEkl1vgzeIVrMq+5qOQxCKWUgTo2nKVaXVCKlGMM70TqJfgfAzZRQC2KvRlFYV719Dd7RvZwR1ixLLZTSvVwVvcFkdGV9eU1lqlha7S4vBkzYZtw0/eHQPnv1SnMyntDd4b4TvG9O33JXIw5lcdPG0dsJ6OTs5oVEWSMIIeuCMFFSPQtj3qeCXASQj5LkZlZkF1pRY7vUarX6jlsAbsI89FazSMB8UVOYLxz2Bc/VIV8H+O6ilIFTRupk9QY8i7tyebL9IwD+PXwe4+R97/ug+6mfeieDTw3YA/BhAAzWDt/7H97rnvj1xw89dfuZN67xlS7TrRYACD/kHwbw1P36AfulV31szXHcxzpkzfTdjv/8jlIYSOtusYT/w0bRli0EWO6H7Y7F7aoV/c9TpuwT/EPPAO7NAB/crx+4HmJKC/ELxLITq7jjSQDPLAO/6YCfd8BJC/w3FPgnjz386G88+MhDfXynafh2cPc55yJrNHeEaM5ZAs+oHATUtKaSa1VufGdHFuD5wPCF7fnIgfEhB/4eAQBlTLdWV4KWVRfT/t4dyWiyKxePnrCGG1h9RcAxVezvJPuzSd48rqNue5EJcVpI+YblpaVQTNiXLkzy00GW6fbyas+8rPvh7I+/uqqAtVlSLsC6ODQuTIDYa8xRBWDMmq2myfNmNhr1y6JoAmCW0izv93zlPKUqanWoSBkXiogYDR0hfAkB36HQ3TE2Fyw6SYTmkRyZa6DZFuALvqVYCQJGLIzMkZYWhjLwgIFJCtpmkMMQsUoxIzmyKETsRhheVdDdEGg4OMfAJUfINBS1sAelcOp5wDGvvK/F02uQTuD9QZ2/Csx9UMABaw+MB/GvrYvDxgB+IBBNWqhZ7QsNfBl3VClrTxwQVxOB+rwJVSooW00MAmCSI9Xw0ZM6p1Zifv9yB1jnk0KXiP8utYJB/Vlz+OhMBg/aAgALGmWuASGqYyrghKgAr0bpNBQI8HLhexVfhI/cXICP3vwOgK980OXXGbuf+1HfNrJ+3PsHGQPQ/3H3H78D7H3zVYtXBlHr9tnGai+H5arJfwQvdy8JRu3HV89vnYVfk27Yi8hugL0Xv/09eIYN9botpJwCeAY+RPYcPMB7Fh6U/RC8s/oKgG9W/14TUnxUrLaPw2EDlDxTHTuDdwInD5wvhQd/JzFf7Az8TrBbnU8QStpFkWfNMOzD7wYXqveOMc8Za1F6nRSpF7facToAYSClzbLMOefqPq15UZR2liY6kEHWbMQ7mBeDoPosJzB36gsHjl2f53lVR4QQrK+sqCzPcynFGubsXx163gCwFASyzttqVNeyDw+YeRgEp1wHKWVslE6zY3lZ3pKlybMtGSu7sDTrNlsdCicaUWhCxu3lZChBrWrGHZpkCS1VqTc2jpb7gwHjlEpCaKGtMc6RDiGkywPSqsbsNON0QWc2nCKpcwmPYb6YaVSYw1knQKCrdmcHZXRqdgL4zr63FkBJCGGMkzpHTR14XwAP7BvVOD4HIHvf+z5oqn9/h92vH9j54hv+14+cutCk8OFZAT8f310XYIy+Nu03b41/iwsxMyh7AH6Tgr0zhH6zzNy0BD5FIW9vQBxDJrgL1K0FsECoozDsLgBbI+j8Cf748v36AQMAE8v+d6Vx1+/1Tpy9F17779cefvTvAngSc/HtI489/Oj4haHgBx95KHvs4Uf/M4C/Swi5lxDKCaF1i0KLeb7p9eoLN7/4B9mi+prW889kuU/0C8Prz9fP1YChzumrQ3gWfkFfY9TdH0fxuiWCCDrMqZ3MsrSxRXj4Kse75y9tklcEV851j951y8WVZPCtaHdPg8Y/rbcvvIWsHT/H+vsXo93NbHa++cq4uSB7Od2PaXGLnOVLmVMdAd5GzPccjYUDuWS0TqFVlzNKSKddoNPR2eVL18XMCeBKo4LMjskCFomFdT30ejHyfYZoNYM9VOBa/xACpqEYYDZbaB/yytOEADACgSMgPENiAGcVSm1hLQe3XSyGbXSoQnmVgnYkRIN5QDpbwXIWIuyMMQx9Sp2pr7mqxsTkzVDLWc6ZH5NeNeYdzAHgWfjCofaBceAAnAKMBmzkN7wdzNMdXCCio+urxyM4h8vbZxP4jWwTns3jZB5iFbqaI7K614ifN3UBzlI1bepevrVkjK4eEwIJIgLj1LRmKSfV3IvgfRBFpSDgRb99vrQFFrh/LoM/cASA+yog4+CVv8/D9/87C+C34P3JYQDfOgj0Xmi/Q97RuvKyw2/cfOWx3n/35Ac+7+57lyNPfkAA0O94w88Eo3/+35/cuftuglA+QYbJDyGgbQCni3jp9ObG8ofIkx/4lLvvXTf66b6I7AbYe/HbK+t/RFEIeGfzFDx7VjMyb4BnXOocuqJ6XFemPgCgD0I0CDQ8mDsBv6gvwofnLOatydYxTyg/Dp/b92H4cOoGgFFDhltWhitam1aaZSMpOA3DMIMPw9YsW+08o+p3/bc6Ud0BIGEY1k5cAig5Z44xljFGz2AeWvl09TkPV++v2ataO6+DeXistuugj3NWxFEY5UXBwyC4Wn3vGN6J56hU6avPn8EDm+eq6/R1xphoxnEbwC0LrbZrhhFPsnQl4IHLi5wbo0SWpVZZWxxbO+yyPLM7gz6Dc+Nnty7HnPPt3f7+Z8fT6TFHqS7L4kvwOZinACw45xQh5Aeq810OIn7c+JDVCHMwVjOnC9a4XGsTUUIFl6R+HpjnJR3s/HCQETm4mITV77ofZs0WX6mOcQnziuTvav/00/9y9gR//EPVw5sB/GINygDgfv2Ahl94arsEXzX7Cy881hP8cUKKEOVhvdDuu38C2NcD9F4JHn70ld+S9+OBDADOXXzvHzxnyEf+1ft/8/o4/8wjD30bwMJjDz9aA1/6XXL+9gC8H8DtQSC7mPdbPsiCEmA+oSuHeRA819cXAIhzoHCo/nu+6PmBn3qjUoPGuoAm1KVddyzYDxrhmXIyPQ4rTrJ20CCOnMtocDU9IdtyR2+KKDiHa7OTtMzvF7tXO+H2me7pYU9Trbozwu9I149vcJ3fStTsOWnykougNQOFAibI7BRuNgawCULbAEQ56DflwlLI4phmnEtobQG4Jlmm2WxKHJQcY5gx0EmKlEWI3ugoF41wY9ukuzsBguMRGg2FfGWG3BVIVYSISYSsRAECGAnJBQLDwJIECZ9g3IwQFxyiEJC9FNNugHCpg2WTYTYtkJcZsq6GCgmcciB5gDhWKGCrD6jCwJI0J8xCYF6gVV9bAe/L6uhAdOA5Zz1omzkgpyCi3VqkSTppaqOJ1Qhn0ymULix8Pp9gc7+VVmPbJh5cKQewalD34aMG9Wuvg7oD43ywuKQASgdVBmTO5NWvmVSffQ3zDdwivA+OGIC8YvJDIA/9vXpQQuvauLP2ZSOj1sr+pW8Rz+idB3Dpgy7/rlqaZmVJ4P633B6dXND/y/s/nz88nO1jASGAwXRjWa2PR53gymb/mqZ/n1oVJxtrsNF1bfG3Afi573b8G/aXbzfA1f4xYQAAIABJREFU3ovffgEerK1Uj2t2zAJoZEXxusKovB01ppSQD8EXOJyEz9fbhgcsPfhdKIcHSxF8Vesi5hWbAr4QpAO/+G/BMztNAK+AZ0skPPNxkjHWU0UeERBNGWtU79+Ed0R19WsNKGoHHME7pJqd8l+IkLqirgBgGGN73XZ7F14I9zTmYOUqvAZeiTnrUsuO1I60XqwPFi7Qqi1YQghRxtqxMSaUQizBg9pmdbw6ZybKiiLhlL1JCL5Qfa+6l64sjNJJnrI4DKklVlutTak1Ah64JJ8EQjKRlUXWiIIyK4stY0x0eHntG+PZ7Llbj55YTIo8/PKZby4CaKvSLDqHkDG6wDhSeGe8whmLjbF1npCFd/q8up7XCEFICeWEXA+51+xmHcau8x4PMp41m7SMeci7rgjNMC+KeSmAQ+973wfP4XuwCtD9zvfynj/jODV4GgB4DwD8WvM/vK3fTYp/+/lHMgB4z//0rkOhHT5Xkkg99vCjnYMSKwDw4CMPFX+BUy0A+AKAfwfgJ+DvjbrX9POsnlB/ij0vp48QWM7hCH1eykINDmsQWedaAn5MEgDGWZsms5mVQbgkG817TL6QMWJLKXEvQM4FkyJaN+lTjePtJ1pd/oMFvfUOlnw9ioaXugKuq8eDwxMZXyqlTJf3d5p72VS0stlGAEvHluyXoH8C4HY4HAewCkJOgcAGQcCLvIjK3r6AtRTGMAAZKIfgMQiQTUymDMpihkmooTeG6BeLVv5XnpZHA0SvVSjLArOAg3Xb6BAHQzmEcXBQKJiFMSEa4LAFR1AKcNtGO1AoxxrlfgOtQw7GMPB8D3vIkaON1nITMWVgCWDtDDPnYIiFKgECBrhmb6zInPU/KHBdS99IzPviBgfHIgSM8/l6Sw7O5HkWaqMogEy5rOhPrtZtGbXzYVIl/Bi2q2NSAI74H+W8cnibzPPcOvD3r4P3LcvV3KoleeSBz1MDvDrMW1d1dzAHsbXcVJ1TKKk/J4e/d+sKfArPrP9uqPKvG6BJfKrKBQD9D7r8zyyieCcJjwLAzT//wauLJ+/65NpTX/5+NyRv5/uzj2EBzwBIW9u9/Ae2rlz8RFq8c+fOU683kQitPuDSjdmC3zzesBeR3QB7L3Y7tTHA+e2fBfCv4QFfCA/CMgCb2phCGdsG3MsBcgHAa+ABjITfEXbgb7yj1RGPVT9Z9XMCc7B0GN5RrMA7pDoHkMCzanUVaKMoy7hUmimjB0udbi2cfLY6783wzrUOkzLMF9GD1cQAAOecI4TU1WgDeIDahNdDi+DzSOod66D6vQ7vSA+q49c76dqZ1Yuw5pxbzvlMa70/myWdoii6C90uEYJX4RHPuhhjnuqPJkuqLA/LQDRCIVqg9L44jMAYLQFYwUTOKZOUsGmaZcMoioKdqz1ljdaF1SfbUTNcXVgmk2KWnX3qIi2sdoUt9WJrQW/299Z393abAE4zgEOyptYuZZxw+JBNCWCtMGoEv3DVlZx1PlITQINQYrkk17ufYC4ZUi8MBxPUUzx/oauZhVr3zcAvJHVxQoHnJ2r/ldvPzP7B/3XwsS6mqQKzmkC/EOh9j7YM4OPw98qPoZLh+B7e/8LXUnG92/Dzqm8PFnQA88XcYi62LHgQpYQxAYCE3UXoIp/ls4JTEROF9onD3UYaheSL2qkCSPNwuN83rW7XykjJSfoklcEf8EbzjsZot3RLN6mx7i1eS/bKsdZPA3wMSi2c64PQVyOOVmCsK4wqQYmCUqGPbhLPvludDovLOQPPAHRKIAeShIHbBNMOAbprOGwkQt7DtSlAZBMdnoUMe62O6wwxXdazyMFlBhHh4C0K2rGwmoKaJpppIlMkZREVyNMCxRUHGyxi8TYOvuQLEYAIwUTDTELITgATp7CY+ipY/L/svXmYZVd13v3b+4x3qFtzdXX1PKglNCMEiEGYwcIgJg+AQxpj3LYVk082+RyHyE4iWUriyLGdL2AcEfmjbQNtESNGMwkhAZJA89gau6vn6prHO557hr3zx7677+3qbqkF8oBU63nuU1W3zj33DPvs9e53rfUu2U6jtGF3CWhtWDtbPWvfr2DGtX02MtGej+JmUod2vq9lxzIgck33DBt2twLdNiyvMQrdEjMnScycW6W9WKvQzm3uBP8W+HdWzuuO/doF2f7WOeZb24ZA6Le3CTs+38TMJW8o1Jd+AEsxsLBLRzM8i20XocD4icF9f7KdpW1vWV9YdfYveE7Y7+9/cv//89e3PbRw19/5wKZDf/9Q7uB1/6qr0VOcJuf1sLDUpFodo7t0E/29n9KX/mr2bN+1Yv/4tgL2fjrs65gH+FdaPy/APPTri7nc43mtvyeFXI9xVF/AOK5Wnh8XAC+n7cwHaFeSBbQEfJMkSZM0XSsgyuVyETBeazRmPcfZ5vv+cGtfdrXseZ5XqydxKqUI6lEkE5Xu784XlzDVwDZU20t78pSAkynlOlIeY06iuInOlHQcx/V9X9DOGbNVpo/Rzok6l+NDbZ2Vpva9uHWMtlrYHosTRdFgM0lC13UyIXM513U6QYIEclEzPjgxPePncsGQdtNDvus6EvpBp4COk0R4jitGhoZlnMSFOE18H1Sj0ViIsmbF94Kl6dmZzPGlWlhacsqNao8n5G0qVevQbGo26tNKZT2B421MssQHXNcVGW1QfQi4U8CbdTsf0QJwqxnn0WYqLGCwgM+yAxZYWAfQWbTRWcRir5F1ih6mMOOftUr+9X/+pSWOT7x/3nbFdVfN3Xj19bay+1MYYP3znFjRfcwiQGuNpVQ77GRVu4DRb2kNZiHalaM2rB5jxqojpCQsFKwzNztVugctXKX1JNCLdg4B884jtc95+ZkvFeam3yjLi6+unP/Kw5ULLv6R6hkYnb37rk2PP5Z0jZxzWc/c+JffVCHvxzSOABMb3/imaHHvwTc1x2YPBKLfWYwOF9CpcgeHgnR+TpBlllUCyFzhuoEuJDPU4hgvCUmPBPjDikxniDMjnIUc3gJILZG5gNxshnAzTxQbYTRfrjYLwEyJ3JoG9cxUwGpVoy4kUuuYwYSsO8RvhBTiJlEzwPUcvHSCo3FCvUsh3Dr1vCYLUxAax17nzopc7M8MUtkKi7fOpY5Z2B7EFKDZ/D0bGrXPmFXJtoUUVkYqbO3cAkrLgncKx9sUCZ/2AtfOlw3arJxVJrBhXSun1Hke9nvnMYBxgU5tUwfH8Rw3izJ7n+yzbkFkTLsD0Fsx0YLHt4tQPhurt0tHersIH8D4iNLsntt+vnJw/9l+b68/d8//vpKslvpmPm40i4V9sxds+z5wf9fY9GP1tav3pm/7raVT7XvF/ultBez9NNjWkYzR8Vsw0igucBmtIgohxJOOEHsxtP0EBmwtYcKpXZgKzl7ak4116H20739OKZUJ6G5mWZaD2SRJ+rI0HXaE6MUAwxoGROYAJYVIc44bl5OUTMcFUNsWlypuqatwl5QyD3iNOF5crFXPcx3H6+8qkaRJNU2zbgnCDwLlSCmFEGhItGC+XKt813HcuBDm+jFSAl20V+QzmGKSQUwIuoBZpXu0AGjrVQVKaZqqTKks8P3jJBgcxw3CwJeO41gAZBmzJhAU8rl3rF01WFys1mKhvHJ3qRSrRDXTJFVJkuSSLBFRHHu+7yultIjSZl5lSs9VFt16vV6qJ5ETRZFcKC82l5p1V0MuF+bLQ30DIs6SNQu1crORxkcxodLOYpVq6z65UojHVw+sypdrNb9Sr9iVumU+U0zSt53cY9phXGs2zG9/t6yB/fsEeRoM4Ehb9/hy4DaWAb4dO7YLgJ07dz0nk/bAf/u82D2/v29ww6ald175gX+2yvpXXHdVA+DGq6+fAG7GsOavoiPNoNOSKAaVIYIAzzkO2z0rG9iRw+fSFve2gGR5DuAx80NfgOxCeK/wfCYcyVeJVa9324RHzq3PfnjH/UrrC8kXLrxfNhafKB8KNn33uxJYFT1114OylJ2tg/B1lKP6a3/3341uqJz9ci/Q6cH8vfcdrT8+V9Vz3Vmj7rvFrlfLJJHx4oJ9VipO6ERO4HRVliq+IJvslkWllHYb1GcC8osRkrHwyPrxZlYPtPh+H725RWa7/ag6MzAe+A6yq0FcCAhjByfREGVk2sUVLo6MaQQeoRcgIkgLAs8T0KhRq0XUQwmlDM0SC4ByXHy3hoo8HKsZaQGXcPFkSoIG12mnMdi0hXHMAsbDzI1VzNxi5wy7vS26pvVZW0wDbfCXYVIdbKtGmzNsWTorXm/FznXruwq0F2VOa7vOAh2bEmpzm23aTYypGt+MaeVY8gIv9HKurEcNm5rhdhxDpZIr7U3Cwg/7Fibss+9jNC0f3i7Cxi4dnTTFYbsIrZZpgplbX9WMD/hLCxNkOXlmGsvXu5m6Vyp95Cszk1Vx59/8HUB5x3/8SZj1FftHshWw99NiW0dswi6Mjn8dE3KdPfYeZGwd0S2plgDjqAe11oNCiBGSLCSKIedrDKNlV8UAKggCFWepLpp+oQOe5w0opZZalZ6F1svmnyigCymjhaiWVqJGbSAs1hv1Rl66cqvrOCrNsqLveaUDczNRplR+S6bivkK+WW405mtxlO8rFOO85+cAkcvlEq21Ozkztdr1vJlCmLvLHhdwL6YgpQczCXVhQFEeswK1q2JboOEBaZZlDa112IxjGfi+CxAamRiSNPVRSjtS2lxFC4AkkO/pLul6FHlaqz6llJuqVCiltBY6coTTyPkB84sLKh/mVM4Plp4+sM8r1yr9jut0VZdqeu3QsAyDHGJpmrmlxWqUxrkjS4uLC3OTAhAOMpeh9mIArc3jmsSswOtKa6QUU/kwWFOpV+qtbZqt87c5kPb6WGfVabrjBcczH6dkoFrXwQpuX7Jjx/YfLQN2tsr68HW/+3sDUdL0J2vzkzt37jqBLdg9+eQHkvzGT8eTgj99ze8M/d7dn6ic4jtPavLiywWAeuCb/1iOZDPmGn+m9bttnXeceUKgTZJY53U8mQzLMWvRRkK0GZcI89zaohD7PZ3J+8aERuOgEa6UrIHk3crRYvFl/V/rSpKayhd+Dvh1hU6e1s07H+lygnWuc8hNs7H66EMzm3/2rV9YOLj/sebSwkNePh+NHFr/UJcoFTatPtv78tT1o6Hu9rOqe3EyvaSytCEcXARCpCRZFqnZLGlWBdovkU/mV3cX066RoeHFkLQyWZO1KVdHqa8hbOJsqyMOzzOz3idfKVGseIQ+SCVxZJ26zEjyAuIIJR2k1EgVUVcCp9Cg6TaY16AXinQVIe+lKJmi4gJxLiMjohF7hGmAX02JC4o0AFKB42q0xuTOQRuApRhQZUXXbQ/wPG2ZFZs/Z+cOm8tq2T7d8betpp1ovV5LO0/QAkurHGCfU4FZrNq5s4lh+ux32uPs1G6s09Yw7QV+lTYoXJXUkzCpJ8uVDTKgpoRsLPaujpdWbZztW5hYwJAAFYy/uAiTf7z3FEPV5kbnW5/5IfA+P44cYoi7u78oG0tP2w4b+tJfXQF5P0W2AvZ+Gm3rSI3R8X1sHTkxL2LriP6Lz/zNUVc64hfOecUmAV53vlDwMx2gFSQK3GOUhK3izIDQd1zrtAQggiDopR2ayFrb21V1I/T9p0PHbyRetmGsPN/dHYYFrdTAdHVpIWo2c+sGhubXdJdGMy3Oi9JmNj5ff7InX+jz3PxgzvMLWaZSKYWMmpFMslRkigN9+WKKaed2O+28lXngzbQlXsYwlZRFTMHGCGZS76aVgyOESLTWqSMdn3bCdi7LMjeKmmmcJFlvd0lJE1IOAJpxEruuk3ccJy7kcmkcJ57WKD/wVRzFXhQ3E+HKwxPzs96je/cMnjGyJhseGNJKZ8m5W87cH3p+7yOjT1aKhfwalUl5aPqoAo42k7i7uTB7hoJSIN1VaNGQOAuJTuYwTJotTunDAID65NzMQa0ZwEy8nSK/nUK/nQUY1iwz01klCscDPFvg0pkr1HnvS8BvALswINRaDejbsWP7LwI/X3RytwBf5CQVu71zulENQzzh8O7XvXIjsHv5NgDy4ssd2vIS9vhtSH5cXnz5O9UD33z6ZJ99gW0M44SbGCf3WkwqhO2EYRYMgSWDjruepwR6Hf+0BUQ2pSHDhOz7aC80TrIvp/MtCdnWWkO8Y8+G4aPf/lH/Tf+BuTuA/ykRo4e97NsN6fJ/klonk3q09eI9b7xh8EelBw98ePT9+x5b/MKZj1dH13l9uTHZVc8FFXd1jWhE4NkerVOgZ8jEWhcvVKTaa2aqfP4GlR6KA904sjZA4hJUG+imQHdLGn0RaV7geovUsoCmu4qRmgh9rxHVcXBcUDSJVZGS8vBicLVAugJBjOiKiJ0q5TBDK41udFNSPXR744xLQSwz0iCisUYiUK18Ok3mZe32bzYEajUNu2l3C7HzQIJZKC9hcn9tagu69VyJ9n0fw8wpIW0m8HzMnBTRzn21i+AixysCZLTbM9ptbO6yBXUB7XAzGL1LG62wqgudoM6GdDuHmANkQqugf2Fi1cD80YsUTNfz3YeL9SXdOuYDPEt6xi4dJcDh7SK0oPMjwJel9H4P6Xz5i4tT3zrVZ1fsn7+tgL2fVjsZ0GvZbK0ipRBuqjJZ9MONAhEQuhJXalyn05l05rwcE4FVWqOUUq7j2MouawITAumhNbluGBysTi4udDfjJFhamI8ORI18b3dPimby4MLsnv2z0zNnrlq9ND0zvXb06MGtl130uqGufCFJ0qScplmWJnFVu7I/bjbnDkwc+mpvd2kMGD12fqPjtnrU5uGd0TrmJzCyJd/B5CRupp2H2OP7PrRzYiIMUOmRUrpRs6nnFxazfBAKNacyuoUULqJcqbiFYjHrKuQdx3M8B5XGSezknNDREhbrZSfwA3+4f9BROptNmomfJknlzI1bH0LpcKK8eH6K9L55/711pRKnN8g3XekuasEqzwvCKEpdpbKBDF0GGWaw6Bhn5GNYM8s0ziRpao/bhqodTCi7THui78zJs3+LZa/lrNvypPDOMHAneOxtXdfOCb4MvAtTLBRUs8bjO3fuOqk0yxvuLd0y/7boXlEUzpY//ZWTAr2WraPd+m25WaD+D25XXHfVPDB/49XXH8CE6X4LeC/tvEb7vFkgutzsvTjZnGoZHSvYHGHGqoNx8FbXDo7PRRUgaOX56STRohmLYGrWOfPhp/wPrB6orPvTz/TesPdQ6RNaf0Bd0fpQq6+wf8V1V5mKA3GTcIRae8GZ/If5siq/Ze93v5rTmdg48I7NZ2wI9PDk50er1YVNaPoEXm5z7uVisTFenOKAD/pASjIE7N80K2vDD855ffPaX0rdNzbD4Z5CJGWe5uQ8C0c0hVfCXFeTuNpL3gnIZQlxKKMsF1JKM9KmAlfiOg0jNBwnxG5MRIZKBI5folSaYyZS6EYP3bJBM4yJZUKkNJlrCi+E0GZcWJkhW5xkr7UtJLOsmWz9PYYZ103M/FGlDeR7AEe1hZN90S5canXJIKFdWLOe4/OHrdlUC0UrFxPD/llZrM5cvU4pnrQ1JvppA0KbStDd+rwtCrH5gZ1jLQKqAg7lG+UA6GlId7HcPXSJFvKJrtrCDGZxcbIWgsdZR15fBHyt9Vqxn3JbAXsvQrvmI1c2r73hk/tGuvveRLs6bDnQg+PZhGPVgs2kKci0VJ6H77p2QrITWC9mwijTEjHtyuWm+uP89NjSfBdar6/U6qVykiztnR47Z7ZcblajWnNifLw8PT/dNTi4Ok2FSCfmp8f7cR5bNTAUIWW0d/TAkYOHp+59ywfev7xkvwuzKj2KAURHaAuJ2gm/0nE8TQwj42FAYoWOJGohRNrX0z1fLOSdLNUqSZO8j6+EQLueq7XK4kbUdA6MHXU9x3FzawIajUaqQeX9fG0mqlZHglxh49Aaf2xmSml0t1KqND471ddd7ObMDZsW9o4fmhibn6sq0VhfTtNilKUVqHcXBL19vq+BHCiRKOU7UmqJcIf6BoKZxflKprJuDLNWap3jOG1JkBHaUjlgHICVb9AcLz7deV+X33dbbWiBvs1ZtAxCGfg8cLf9wI4d263DG6ENzk5Z3ddXqMu+O+v/CjjMfzvVVqAe+OZBefHl/xnY0XrrMHAXhjF87LnCuGsvE5aZzapjb5CLT/2gCfA58S8dQH1Q/+2PE2rahwG5F2POt0SblQk4nqGxz80x9q9l9j7Za9+kVfWdNuPMdf3dWqhtQspNtASMMeDDhnXt34eBvACv0aC+WJYbxibDwlP7cq+vxs7rwHtj6GV/9sf/5k+eempfMPs3Xx+Uf/q7Mj96JFh/5sa/2rPnUFgDtmZafvCZg8ErcoGjlSas0ffZXHfs58PmWUcr5w3XhKrk9IOPdYvZ9U4i1gV4q1y8ICX5notXCcktlOjeX5quF1zESI3G0YVmljXRjRC3J09BawNcC0XCoofrCUSk0KpOFQchHQIEuAvMpgF+lqMrXGS+pNE6JEwcxALIbo8gKlBs5Cnk60wIiSAkr2OiBBCZaTumOT5E23kPLPAWtLvrWHAV0wqranN/becMDShhullkol012087LGxBe2dHDXvvoM2w2efKFlBYVrdBe2FtAWiTdrV953iy3YSsOHNGe6Hd+TzbPMIUw5DbRWLmqnSoZ/rQcJjF52G6Ae3ZpaOVKtmXqK2AvRepXfORK1X89KF7garvelaD6aS5RZlSUgiQQgpACy2SDOWglc1h6SyAaDQakQCKuVy4CpguBOHM/mrlmXwuN9yIoq6FhdlCEBZFbWG+O61Wxd6FhclKdWl6f2Vxcv6JhweLYS6rLs0dycbm7/Xy3QuiQH36mYkkqpy0UKyJcb4TGMC3EbPSPad1PF2YUMwI7Y4gHibnyur8QTu84wL9Sim3ETV01Ws2Bj1fRFEsqrWGEwaZXwBnoNTjhr6vhRaq1mwipHASofrqaSLiLK0JlU135wtZEASry7XqRZNzs+UDR8capUL+B8UgOFIlPTfOKPiOrAUKhlatLWcqcZPF+cXA9VejtYC0DygotJicnynRVv4fpB0SsqFFC+Zs9SC0VfmhzWgst1OFGDvlaWwOZ2dXh1swTtFaqXXtL8Ikuz8E/M0p9g2PXFPlwmv38cg1z1mcoR745h9hWgKetglxkwCKQ69Z3e/mJ/6XTlnt9d41OfR652t5T9x2xjlDL9/cvX40ue7aI0kazFxx3VV67WXC8aW/Jlbx/Nit+lQ6YJsxY8kHHm+d6wUYdqbz+ndKqXQwcces877YYpreLE2TTKtGXF5YnWV6zuvqTcJA2BB9FQM8BjD3/EuY6scNwIDjEh2ekG+8/0mvVEv8VQanBOcordbu3purffuHxVcKsuE/+MTIfCGXDXseI56r7k1S+YvA1lqUE7VIHwCROESvCaIxr4BzEL1GrCk1GwfKW/bMqO77xtKjw1C7OCXrB7lO4uwBGvPMjPYx0D/H7FkO2i/qbEHjd2VkriIbETjSxdV1Gk2FzLrx8HAdheNUqaYOjdjB8epUtEOfX6eS1qgkBUqJxK01iSchESGB6xM0Q3KlkAKKrCHQjkKJlDiLqGcgc0AXqE5GtHPxasd3N+3cvN7WfdQa/MyAaJw2MDwGzluAz4rA2zCra77zGDjszHu2oMujHaq3sik+bckUK5p8TAOUdkV2T8d+oc0Uz7X+v4Xjw7m2Ip/W+yMxTMbS359TceLBGi+LBzDz4yFOZPlX7CVkK2DvRWyff+Se+tvPOO/+3mLXm1zHsSGHY4BAaUWcpujUzDu5XE4DTek6dRJd9BzXTkSTGADiAktJktRrzUavlvS7QtbrzWZep9nm+crSwtGpie+6rnfu4sRYd7laaw4Ui16q1ez63p70XRveMnrBpm1Pu47T+MH+p7wfPf74noe/ef8Tq7YFKuyS+ovf+rsTQwxbRyJGxw+2vn8dpor1zNa53Nv6u4CZhAPMxGar02xumtX5iwGdZZkrAM/zhBfHslqrxVmSLXZ3FUqOdMLZxaVUSpmFoe9K6cjAddV4dSmJ0kRv6R1cLBUKT6Rp+q1Uq656o/7Brlxh68Xbzi3t3vdMBSnOPX/zttWTtepkyffvyjnO+NT8rDPk+2uSVGxb8IKa67lp2kzGgPM86UoJuqlS61RslXDM8V0c7CQP7dZonWHXUxVd6I6fsuNnp9nqQus4KxjHuIF2i7QFTCu0g8DHd+7cdRensHe8/r0B0EORuX+oBple14HQK+17DaL+b9OINwgBjqPOcgQ/kyrmn+qbmFrIR08MTg5/dePNxdsvurV76ez1556Xc/IbIhU9xKlFX5cwIboSRvLoLRh2J8SMLTgRQHc01zhmnRI4x3JkHdetK62VDsPVtcV0f7Mm5jyPNY48FjKsYRY2hzHsor3OTjEvou8/srp3Zt5dp7X7SyDeBOyKU+fmW+8uZUN9yWXS0dtm5oMLnVhvUioLk1TY5h8t4CL6gLMz8jc9ePTMp+49mpvrZnpqA/NHHuc9+Qx38yQT39jM7XuL+G9V9M9lLI0WiKMCxa3d9JSPcPCOgHAwCUrb6vFCL7oSSaTsoa8Q49IkimqUw156mjGxjmiIjCx08VKJSAqU3IxEN0n8Il1RjkIoEV5I0RE4OYnUKZlToVKvspjGJElAruYiZZ20y1xrZa+tfU6siPXyZ8KycXUMmAIDpssCKi05leHWZ2LdZt1sbh+0mbzOfsedGnt12iDNHsc87UINu0/LBndW7+ZpC83b77L/61xYdD7vFuipju/LgFjBXBrkXdWIBx2TDzpEK3RtCytW7KVpK2DvRWyHFmdVOY729lC8mFY4IlOpGyd1EXgFHSslLNATpmZDKK2kStIc4DlS2tCtXT0KQIa50JmtLeUmpsaDDT39kVAqXjO4alWcJut3l5+sTc7NdCdZ5m4YXjOzcWT9vY/ue3Kf6zg9Pvq+2bmFsFaxxdVhAAAgAElEQVSr58vV2mSuv3TgG3fd/OxK66PjRUwRg4dxwiOYvL2wdU4lzGRoQzVF2kLEYJgxgXGkLoB0HKmSRAW+l0RNL0VpPwj9wPf8cGZuXhwcG88Ge3tmhvr7VuXCAAiSda4TL0X1ffkwPCylvNf3/bsLQmwMPf9pRzqhEGKot6fbeeDJ3WMDfb0H3nruK/Y9vPfxu6bmZxOg9/DUmO3UkThp+u4MFZVyxcnQDy6K02hjs5ZaORW00ggp7DW3IXR7/S1LawFEZ85ep1mQaEO7J2P47DbLE8Az4PXA/R3behgQ8jkM4/VsFmDu1VLHsb+g1n/hfxdCJm/FiTarGCMDDGQpwgnoFzFM1hayrzx0a3JWYfjCGa/85oW5ZwbO7j7nc8V8V/28d/ZP7f763An5gFdcd9XUjVdfH2Ic5J20u5aMtF62QMk63+UOtLN4pjOEbqsrM8/zHM/z8tJlaxSRT2JcJzwW3osxXXB+/4rrrtp34vFRxzDc9whxUwgIrT/QEOKmIkI7USSGQASNppM1mrK47LZrl8qQS6oiSr8c0ftZ4Mw51q+eY/1+DIs51KB3yyF+1r2Ae26OCBcFT5xVY2ihRNK7yMKHi5SmEuJmohqzDV3pzxl2Oq3QqCemVkdKpJhjwemj21doJyOTITkJ1CTST2gmGZnuoS/SIBVZArKUoVSDhhbILlBlDz8ROLUGlcoi825IoRQR29y5YyLKHJ+vaqtiO2VULGvXAJoCmo5ZJNoqd4XpSGHDuXbxZa3zGbM5c7Zy1ery2Vw82wvXtq30MXOVHTcnK7Ky0i/ddhzplrinY3IIly8o7O9e6/cysBRAl9dYHHLa4dy7gKeBv14+llbspWUrYO9FbJesP2N8uNRzh+e4OUx3gDBJG2qpPuX0Ftco382JWGvpux7SSKykJidcdIb0XAzYsqtcz/e8wnDfoO/UKjXPcTzhOH2VWjXuyhVqq3oG9x6ZmozWrx7JbVy9dt/G1Wsf+d7Dd98zMrDqZw5PjZ/997feOblh1Vpncnruqas/+Ydzz3UO9SjyAs97k+M4DeDXgYuiuBn4no8UYiPt1XID017NrrSnaa/6N9NOsC66jiMdKYV0HNXT3VVPk3TGdT2nUq8641Nz0nOd2ro1qx/zff9SwFepFtS17BNdZd/zRlv7Ggg87ww87+FMqVBrPbBh1ZqZL33v2z/cc3T/0rte/7OP/PH/+HMrMmodNDt2bHcyw0psK+byfcVCcc+eI3M9jpADmVZSJ2jhCSfRSkjEomPuRUi79ZOt/LP351RyKp0g41TSIPY963w6ixByGOcPwM6du+o7dmx/DIhPQ2evsCbzzvnVaE3PpqE/fnjrpoc2Jak/U8g1ph77ws1WTDg6svoEoHRatvYyke8J+GUBH6xAKEMjUJzZzCgATTeC/GK+vurhoUWtE4ppljT2LD1yftPnPOWwm+MrjTvtcOvcY0xfaAd4O+3nwSbO27CYu+xva2LZ7wFtYFD0PfxKDVWukvT34uRCHIwsxrW0GdVTmtYf6CyOSabn/ccwLG0A4uXmxasw4+9R4ODL+hc+MJiv5u86EhyNyddbxzKHYb02AsVRes6BPHt4Sz3HrH8ZT9Ui1EiDfd/sobSuh95Mo1dVkj0zuWMV5NpPqGVA6uJ5Lm7sEaoC3apKTYTkKFIUVWqJi1cvkPdjEj+l2dUkyTRKN6m7HsFcnVpeo3skTrWfocmYbFVGkHSR9AnkbNNtNnSabqUN0uBExrozj6/Quu52fNvijQnaz5TAgMACp2bJ7fdYcNYZQrVjwy6O7ULZLkKbHA9OO59Zu02ntqOTQdIo9uiwuuj67c9AWxnhWDPa1jH0CNjitMfYbcD/v0tHjz3L+azYS8RWwN6L2C7bdm4/ZgL/Nibf48LAK5b6u9YJ1wmUEEKGnt+5InakEGkYBHXMZNXD8SEIO14q+SBgQxDkGlGUVGpVtFa91XrZF4JtL9u4KVrVP9zd29XV9eCex4LNa9aPn7v5zKDRjLwn9+8bPfesLXMv27r5WRtxW/vh/Y+cu3XDurWb1q8Zz5QaWKxUvMB10EqTC0NoV5M6GJbvEYyw9AQGIK3DVJXa0KcC4lwuJ499LghygBsnXtP3HVGpJ/XGYnM85wdNJ/QDIYjIxL31o9FfBH3+463rYjUOxxwpb3PCcH0Yhrunl+ZmAH/XLV+pv+WX33fC+ezcuSsDHtmxY/vTs+WFp3zfb/Z3974pcN0dswtzfbGnQqW1V8uynCvEdNFxlzAFJxY4WBYjwzimzl6uy0HfibptJ9/WijPb/3mYUPlmDEiwx346/WbZmoXz/yZa+85SmnvVLznZ5x+cXv2yV+dVX/8ZF3mb/+Ibh3+UX3/z707u/T537J56R3G3pNVSKiPtWZLj9bvvuO+kY2PdBEJ//ve34RT/SyOrvj5oa5wBaMfDtrWICFvhO0HQ9KqB1yRNtKjUPP1OaSDyKPAHJ/ueVvu1CODGq69fxISuH8YAp40dm3ayPTak11mwYU3TFlFOgem4UU/qDaYXK4VCuS7O9kNVz4XsBv4X8NTzbQGn9QeamHaFz7Te+vrybYS4qbD1DH/25Vtc7/a/zf8ZGnUeX3YblAZHeeN6cLYCoyBDaF6Q0LxIosURzkzyzIU5wrNLiAcEYqJKlaTdA3YYU0TkAKFP3uuiJ2t6hbFGkvaW6BqcYUZJnGaVstagPVxfQOwSyiaJjIllie5FYH6ayQ0S4fbTv1ClVklRW0B7LoFfDWppfnCdl0zPVuO4YiulbcpGp0CyDXda6SjLrNrF0pQ5VzZgQvrdHN+RpfMe2mfFsnmdMkgs+wy0dflsKzar69eZ42nDwKrjb/scuoB0wM9Xy9ppM8h2gWdBY+fCz8Ww0SXMfTkCfHMF6K2YNaH1Shj/RWsmBHoxZgLowfTNfSvtCjObTAzHa7PZMInseNliDQsQTEWb1lkjiqI4iYNyvSEOjR+UlaimckHYrDbqjz6xf/SWVX0Df/nBt/3866SUet+hI9/fdtlrTyrXcZLjzx8+OvF2x3F61wwPleI0fdfY4uyGbj8sFcN8FPh+gHGiVQxLU8OwMqOtPfS3jvs1rW020Q6DHsuDS5JENZvNci2KKosLlUIzSyprnKEJoRnxQrcWDgQf9/Le7cB4S9z6J7JWJwpn585dqf174/DaTYM9fZdGzTjcfeDpbYlSr5VC5B0hxjBVyH20RZVrrWO3XVAsM9dZFQhttu5ULF+ncn9nizXVup6/D9y/c+euI8/rBC+8VgC/l2k++r0KjV0V+hIK+ddv83B7Qy0LlW/+zHzzP2+ZSZ96R3F3iAn5DkZUNzXkQg3BLT/6wb2Lnbs8a3qpW+z7wS9XPn3Nv+TQI69oHTOO7I0ytWDPwY7lSuta1dDUaNCH7nIbhVwepsOcCer967Fb9V+dzum0pEyGgE8A7zjFZraIxlZSnowdUphcrjv23f/41279YVel7+zzy694Wa20bjhd8D0euuK6q04LUP84JsRNAXApMKr1Bw629NRe1jreJ6auuHbDhvn7Vn/xq296vCt55vwcE79Xo7Q5RHSdwYGugLF6kfm7XZpJSnKOizsDQqYkFQwQOtfF87so+S6latUXWRg3mgX83jLlXD/9qkksCxT9FJwAt5mh3DJzsUuwFFGv1KiomGRAkRZd/Bg4GlBQvXSni8wVGrKJyOe601rNQac2PcNWlC8nLzpBkpVgse8doC2dkscAJdtfdnkFuwVvy3PuOufETjkjO7fS8Z6tzrbAzrKSNkcX2gsXd9lnT2adRUEWNCaYCMf3gauBIy3tvBVbsRVm70VtW0eqn77xnXcMljZt6smvnnvD2R/+Akbi4l9hgJBQWjlKadd1HAsM7ARoc0ssc2bBkbUSkAkh3MD3HUfKRCByg/2DrlhwmlKgjy5Nub25Pj9V6ZbHRp9+dMvqjSN/9/e3nXf0L/929IY/+gMwzrnK1pHaccc9Om6P4TXr16xeBeymnuT8hebh4WJJBcVwuyNlAcMo9GBYmFHahQ1WFd+yXzbvbRTDVhVb36QA0jSNa7WG+8yBI8VMpbn1/cMVVc/CyW/OH14YXXxw/8HDN3/40G8/pz7V6ZoQYqCQOX07dmzft3PnrrQVFt2/Y8f2A63jfYMn5V5gTXehNJipZLDaaIzTZiZqmPwxe38s+2qdSidIX14pupxxWs762X08Q7ut3vMDe6atU8ERLL26xEjkyvDhKOGihRobagh6muvLZzTGufX/i3n9e7PW/mPQRzVKhap0Qh7ntvtuev1D3951LYee6KHtFONM1ew9zzrO1L5XQ7APB1/TWExl16BUDGUOwjEajadlV1x3Vdpqp7YTeCPHM0A2RGets1p6uclMIRzJtjXnbkv2frv7weq9PUvfvecjp7f4+cktwTCU9e0idDCLiK2YkHH/8HBlqy+K5//mGX/22J88ceB7bxdbR2fY+MaQ8iU5Dr07oNElSNblKC2kxJWQ4mJC1F0lmaUlyZSS6AXm4lUE/uq4SzRIsoBCs58wyEgdhVISKVJqiWe6ZIgKNRzqeYV2QSYBuTxkWpFFKXTXWAhi6o0MFQXKa2TVyIG0T+BoTdYJmCzotyCpUxC7U0g+xrDlASaE3dk5Q3ds35nmYEOnVvNS0C4Ag+OLReyz19lZo1N2hY7jsuDTAsnlof+T2XIQaNNYxjFpB3++S0fPmQawYi8tWwF7L3J7+OA3XFoT0BvO/nAGPIXJHysCM/Vmc9DRFEUQaEdKhcnv6aK9gu2cKJcXAziASpIkzFTmaK3UQKkHVwgaURytH16betIfFL6+JB+G9yKYXzM80L1+zSqBmWxtm7BaSzzZ9oPcjmHo7sHIrozxvaktwCvyXe7tvCH/KIat24EJpdZpq+SPY5idaUy17rswenD7aRcjDGIq8gQw43leEgR+MNhbCtIsi8NZ/4mjX54ZH989dd/9ax4ZPfSWMf1hfvsFuiPwC/EZG0fm04vPmnKO0lEVunPnLr1jx/Ya8D2MI355d6G4MVVpV7XRmMA4im5MiHoV8Cba8gvdtDXgOu1kOnvWnGXbWcCfYdqGfY02sHp2u/DaXozD18C7gXOBfADx63MqfE0Y4wtwGyBSv7hqMWgCfOOumzNg6R2vf2/5trtuOSVreuZ9t/Xu2fNYrmpSRwVQ1pBC3CXMNSygj43PHIIE8/6FBPQI0iCvxiczc50yoLT2MuGN3apPi/m44rqr9I1XX/9t4L8D/4HjWcQnabPnRdqdaTq7HBAnqGqDucDl0ULef6aiPzrFC2QPf2vbzbK89tIf/MHbzvydfR9bPNk2Wn9AAXOtHqgvc3Ff6xEMNahdCiypGz/z2eBtl9z7J08cWAD4lh49Anz27eKMv3Wo/m+H7Bc0BClJr0+gKizGGek2jA5mEbMIiYG8xM37BF6DRsHDTwC3ypIrkSolc/MU3Rq1LKImmtTcjMwTOJmDG6Q0lU/oSZxME5cAEpoSCBsktrpYa7LOnNVOTcPl6Qud40piFoOdYV07x9let50i5RaE2eIZG4q3x9G52LLPj/2fDflqWpIvHfvsFE4+GSt5MjkfTvFehJHo+XjrO1+wcbViLx5bAXsvcvvkH+n4yj8QB4CUrSOK0fE7MKze/5uk6QxK9SCl50hZxawOpzH5fTb52FZTHltNpipDCokUQgNKay0bUUOkWstSroAjHb9ZiEte6G+pVCtHhvqHHi51lZZ68sWDv/b+9+hWD98a7fADGEf5ZkyCeRk4ytaRjtY+YwvA41TSGbaOTDI6bgHPK1r7OAvDEK1pvX8PRlvqCxhHtAPj/KcwYM+CmBmEyNWjqGtidg5XyqcXbl36VPT1pFcgn5p7+cLenTt3vaBdHAZ0OD2QJKP5NDsBSLVYvsaOHdsPAY3D0+O3+I63VcDbtAEU+zEhqH5MPuIgRhZlgbYOl2UTTtfsvbV5SfcCX27lF56urU/gVzQo3zi2RUD6UPAFVETLo+VCQrLbIDmOvfvGXTc/a3j87um5Radn+GkWyqswnRDySpLPpJt309SRkCLI0McayRcxgHMa4wwjBwYcA8CWMGD//LWXiYfHbtWnpT92xXVXqRuvvv7jmCKK92PG2mLrNY4ZdwcxY3IIM78em2OlBFeyqDRfxIzNn8w+fp/go6/S3/7Ur4tzXqsu7zpvj7j4I8N3AOffePX1NsTpAUtXXHdVul2EYtu73tM1dO55yfTju1OfQOUo9MdE4xnZHjE5Hf/Pv/q7ue0iXAOwS0dHAb51wQcVMHb73MP/9dNj3843qL+6Qd1WJhcEcptG2b6ztZBiokiWFlnMZSQ5iXYFIslIHYH061QJyWcpaQI4BbqdMvNKkxXSFoCLiVJMXmzk4HggvIy0RJuFs4VLHmbsdxZaWLNgyzJmFqzZtAdod8ew1av2/51hWytBJTBzk+1zqzEA17Y0lIDXBKGhER6fPiFot0PzObG4woJACzRPtUBbbjFmrvs/wANAsktHz1n4tmIvPVsBey8B++Qf6TZY2TqSMjp+M/CYgPdIxEZHOoMYpziOmZDyWuvBRhS5UgoZBiG0QhhplpHEMTiSnB8AOFJK4Xm+CoSQQgh8zxOhH4TNJPUOV8aiw5XFrnWr1xZftb54mK0jSes4lucmKQwD9xTwfbaOLJfsmAFm+eir7CQtaPczXcKwSmUMmDsbk0g/jwEvr8VoxCUYJ2sTm6tALUmS0mK5Ur7rnkcnR/ce+V61Wt/9W/xi2UE2P3Hzjc8KQq694ZMS0Nd85MrTzuW75HP//hDP4exbAHMM4H/8l/882Igaxdml+aeXapUjrfMMMKBjAlOQYpmlAu0uDCer0u00G4ayjqWGYVq/+nyA3ncu/ujQAN4bNpEfcXBi3xxXf+tYHDgWY1Me6gsg/yOPXPO8BF4vmZhcfDKt1p+AemY7WijiTGfSg240zYKgHAl2Z0Zwu4Bx4ra/bYS5/xFmLC0B7wHitZeJx8duPb3k5Suuu6p549XXfwXDvjYx424tZsyVMMVQ5wK/iFmAZBg2sdt1mC8VuRszRmsn2f1p2eSffkfqRP611xSXquu+/7a3Xf3pZ/Z997xHpw8OneXMX3B5a7NVwFWgfwkS+fHfvvL21RdtyY7ufWSkNlt5CpirUzszorFFoR7FjCG7wFvOdq4F3v7m/pff9uYjX9m3XYTfxYDplwN3uLhfSojfA/wcEAiYnWFa9zIoBOQaNLSLK13cuEnqa1KmOJo1abqgYo/Qpoi4QObiNxxE0qTZBLp8QqlQYUZq2835retXx4zzAq2uEbSF1G0xRWdhVmdesh33kUbnNdSk6Qnct+waKNqC8haI2QIJy952CjqjW3p9GoQ43sfaXM6ThWqfzwLNsu9lzBx3N0bk3Or6rYC9FTvBVsDeS9EMs7bfdd3drus+iBkHX6jXG3ckWbamu6u4Gniz0up8oR0b5hCAK6VsZaMoGlFD58KcDIIAz/dVM4rQWotUZcp3JOuGhuv9pe7H7jr4zIOe4+SAlNFxZ+f3Pl28+u+ubozdqjsZsyUMo9Q8aRHER19lV9LWIuB22mHQczCT6d9gwpvTtLW2XkZ7pb4O4zA0BtwWjkxMyiPj0w9dkG65ofh4uO+2rQ/MvjV9XxuMjI5b4DDP1hF97Q2ftCDiIAZU1oHJa2/4pAfIaz5y5QuTZD86LoCRl287e2x2af7bG9J1gw8/vfuVC5UliZno92Ac3d20Wy+dDbySE6sFO4Gf/b3z/xHwV5iuDc+lo3ecvTLteneT7F+4ojbvab8I4WrMfZFZKzdekNGFtySNdl35+eyfd/xcWIrr3gKLvRmsckBnxpkqR+tEZDgIvEjjpYrzhGtkNGjnnWraYcYGBmgvAtuA3wRuwCwyTstalbI2VNoAxm+8+voHaac+HAK+0vpOCz42YsbLHBA/32rbTpNe2OdU6u91HCniZvx5Pn7fxVs+uvs1gAmgGysDh6HZ09DSxdO/WBrokXpqmoaMX5G1wv8KNYdhADNMLp/cpaPpZV85i8kFmwJoifNWtotwH+AFhGP9DN48ydEe4LwG1Qhw55j1eii5M0zpiKbMEcZdlKoVIqdOVWL65GYRNXu/NIAibShEs3VcXQ1qCQibf2sLcSzTZosd7Hv2mU84MUzayehZFk030YmS+DmFEAibs2cLLSzzZvcbcHw4V9JaNGKAfxaYsYk4Pox8spzZzkKOk2lldlpncUja+s4ZzBz4Ocyiz57ziq3YCbZSjftSttFxD1Ot2wX86K77H35DPsyde+bWDT8s5HK/qLTql0IOY+QJhtMsC5Mk1p7n6ySJASFb8ic0Gg0FqEypNFVp6kp3Qmn1cC7M/VfvrPV77Fc2ntzX//kfff4Vn73jM/tu/8wzJ4jG/gTnUsJMmEuYIowEwzz4wEcxUiJV2iKoEQYQzjy5Z9+Tt//w/i++d/XPfm/4yvNPZJxGx8/GhEhvv/bWL0WYPMDXAZ/CONUU4/Qva/195zUfufInb01kwN66ar2e/8qdt1w4v7S4NDE7dcHU4txqDMBzMK28JjCMXF/r3De2fj+bNiNhdcDg+ApC0bouXwau2rlz12lJ4gBce8Mng3w9W3fF5xd/vpg0PloJJrRMvUYuGyhmCF+jbSEMgnjSJ78g8e4BPsYj1zy7mHaHPf6212z582zvv3sgrV0+JZNUSmdSEQ8BXgpzriYPDKZN8lqj3YBIyGPnZxkc66zrGNAygdEhewWGHfn3Y7fqH5tt+8e2xT+8rex6wiuXlz438se/8Jun2u76G/78xofE1l9xD4xmL3v8dlTlEKNP7pFqLu3MbxvDCGjfB3xml45OewxsF2GwmTN7E5oXHuHgNuBXMayiD3JJ4AxpEglIBzcbYHU6xdEAlL0/9hg0oEv01crML5rPM4EJh/dhni/LjNn2ZTYU2qTNmFmGsBOo2fw9hQFlEW32L0tQUoPnIXyBsKF3m49nF0X11t9Wr88WgNmIhBXbtrmzdj+d4Gt5O8PnYt47zQK5ZhPqGg6EZoHyCeDpXTr6yeebFXtR2wqz91K2rSMJo+P3ApqtI3rT5PShMPCHCrlcE/iOFHIPJjT4KuBX0yw9C41Q6CwX5mwhhwekQgoJwnHAbTRTb6YyGxbyBe06bq1zhsv5uYojnUcrjcrSCcfzk51Lmy0aHd+PYVUKmAn66dZ5JJjJ2AIfD/j62du23HD25Zc+m6O3oWIrnzCHYTv+NfDe1n6+3/rfTS8I0DPnpIHDlXse6Yri5si+8UMHXMctEbMpaISi2R3Nc7wj24xxgLdgnPdHW+/NYPLLbNcRaDujCeB64Iunq6NnrauajTQ9sfngOudHL9uvV2Uplwolt8REoSQ8tooUyNmQ7j8AXo25F8/re65Xexf3qWq/avZ0aadeVmHFhs9cF7pb6nqu45NojRDyBO1BOFEIuYmRIXIxLfd+c+1l4hOnm7/3T21jgXN1nCXv+pErfufKZ9nuglLaPFgBlU8ODIz1/LGsx0t7Fp/8jQze2qKSBGZsPAo8wanbyJ3UdumouV2EUwW67sSAxUXgQ8DLAoJik8axnDlFJueZonULbNi1U5w4qVPtol3MIDH32bY8tEUYtvjBstn2PDqrYDvVA+yixgL+ELPoqwGBh3Q6PmPTR2yfW3sceY7vmlLFhO0XW//vAnYD52iTolrEvCqYOSfs2G9nB41OW563Zy1pnec4MNboyj++79Xnfn3bA08++LWF2TortmKnYStg76VuW0eOObc1w0N7MEUOHvAkW0fqrRDmEvBw4Pkfi3TzTMykZPPGioAKg7BXaV2q1GtZLW7Uj85M7d22ftN9+VxOLPu++ENbr5r60I6r/mHPaXS8Cvw9xpHfjplEf47WpBtFkS+EqAVBcNMJ0i8n2mFg7Npbv5RitPoOetLJJSr7CwzzACb/6y5M9fALaqsvubDyoxv/5Ac/8/JLVq/pH8p964t35LKK+lCcj27R3rEuEMOYooP7ME5MAp/GOKkK7UrlVa2fG1qvu3bu3DXzvA/qwmuLH+qRhbvPyz24Z3OuXKin/WsmV79V01ASqUJynkIhkQr4ISaX7WYeueb56X694+ecS0VfclBWa8WwypyoS2XYlSVa/Zq1KcooC0ks2nmClnmxbIx1oDlM+LaAKXSpYJjQD2KYvt3P+1r8E9gfVsqfOpymn7k/bkbPBvbevrf3dzZt/O7Xvl6cv63rsab+oP6G/jPxa3eezU3fyMEFLbTxt8DHdunopFW8z2WtsG4NqG0X4WcxId8rU5L3c1wumyYhtms/G161XTw8QKXENh9O0a6ctj1vm7Tvo4sBXDHtvtiWjROYIokEcMXx4VufdiGGXfAcY/los4I2jNspR2UBJZgFn33PChm7qUTW+7qDcLEy66fKAtNp2tX/FrTait5Os0Cy8/sPt85tFjOP3SUC7/5n3nhR7elb71jR0Fux07YVsLdibTPFE8snkGFML9qlVKlmudk8XNQ4SZIseI4743veJZj8tUgKUfAdt7Gqd2B8sLv/DinFLp4nU/ACmsA4iw3AzwAX0U6+zuI4fnTy6OQXoiiaOH/ryLPvaetIBmTXbL2Sa2/45BKQBK77QBJnRzHJ62Am9P+NqQx8Qew77hcErZDzlt/fkJucm94Qen5D9stHFr2ld+uU13qR81DSlXVh8oVMEYs51yVMld4QxjEt7dy5y4auab330E9wePHAopp+1521yviAs3Gq2wskoh6Qa2CcnwV6k8DneOSaZ70ut+7+khMlsXzXRf/iuPFX0WnpAbXwkaM0z02cpnG8uqX9J4i15p6kEW5FRiN+SJ52PpMFDbYV2FDHbiUG9C5iHL9q/f9jay8Tv74sl/R5WauvbnrFdVf9g/QE5uP3OfMyfft/ylV+m1hNXLDmzF971u3/8MP6LLj1LIB/a97ao/9qcbu46dteCw8AACAASURBVM2Yys0XlMlsAb8ntovwGo0oAe+kLUXSyWRZFq6IuWepBieGrJVcZ8c+tPrZYoB5gVa7PQxQtKAe2nmaAFJBrMBzIRPtcGxnyzILOCOOZ4It2JrBhLj7MMLfqrVt1Pquauv47YJiCM1+nQsqslzrBmU7vDiYhcXjmGfjgtZ5JK1zyrX25WLG5P209BAxkiqvxVTaesDRr89M/VigfMVe2rYC9lbsuewIBkBs8xxH9ObyZbTeWEtiP9PqSd/zxjGTYQ6IcmFonfUkRjD5+ch3vJDmYEDqOowj30ybFbgnDML/lqTJ4+e/723Py9ld85Er5wCuveGTgjawawL/E8Ne/VhAoQPYgXFYi62/NwGHP/r+HRycOBJ/+qv/x41lXBR5uSjn9Nlr5kdysY73jXdN5Vvu6gjGeR7CsK8x0Ni5c9cLwwJceG1wcLU7PH1OOPGqXf9+mguvHR6ZzUaKs81V487EeE/WvdrHRyLJkUsx1+aHz7Xbo5Ozc1rifubOG7s+dOkVx0LAu3W5npBt20rQvZdmOdW4qBZD4vBFIXhaqXDUcaPLaTv69bQdedK6ljZ0B+2WU+dhxoct5nlF63X3j3Npbrz6eg+T25m/8errJ4HJK6676gVlXxpw5aJyrl9VGgYivn70yFXv5FWn6vF7Stulo3+wTh2t/U9fLS74zX088yvAxzDM1skEp49JjSjIMkhTaBYMGMvRBoVWyNpW2S5igFOJdqWtDZVqQMs2CLRCyrZnrQ0L2wKJJdpjJ8awaQojLG6f8Utb+z2M0Sk9FwMIR1v7Ww0oV/NY95Hpw8DlmNaNX8aMszKwoJDDArVHmOIqMHNUL4ZVrmJYURs6jjGpDwMYyaUyJy7GV2zFTstWwN6KPbttHYmBmNHxB4A/9D3vfRhJiZJ0nA3ArRiwtwUzQe3GFC7c/k8I9KAdlnkNpuuBFRz+EvAxP/Cr55x3zk+SkD8AvFVrTQxeIEQNKFzzkStPO4emBfCskGsfcCFteZAHWz+PAI3qD+qq+ulGbTDsV95b/bmGVN3z0Wyht9w9RFUVpvRsUzvqS2+883XTXdXCw1993y1pC+C9YBqBX/i9P5PFS3IXBoneCOI+Lrw2zKDXgYIn1M/GpBcnIun1tE9ConPkLIPyrB0i/vKbf3F+0OUFLTdmQ1kAvD99IC5pZyrRWTWVREAPmn4EGzFV18WwuJhgcg/7MAuTHCbEa4tTjmJAQWcI0Dr3bkwYcB7DvhTWXibE6UqxLLMU45D/l1ZqGPjLG6++/oYrrrvqxx5n3/7Ur4u3/danjx1LDr4T0PwvPr6rq2nS6OldXjn7z8au04/Wt4vwRswY/hCmGGwVxwsg0/rbvqmEuW8N2sDMnn9IG6AFGOYrA5xWT7TMMXInNSAnQDhtkeQm5lmwvXxthWzc2s8YZlw0MAvV/a3jPhOzv3FMvunR1jb3tL57HFMIplrbr8aAx49jFg1LGKZuU1LcND13yQ0bkuL6w6pr8+4Nnw0FZgGSbx1feZeO9HYRhhgdxwyzEHkGqO/S0T9Wp5UVexHaCthbsdMzI9eyN07T/SrL8H2vWwr5aswK14YyrW5bgX9qCQCTt9eDCeFax/5FjNTGyeVdnodpra8DZEMlxJpMSvcJT8rnC27PwLAe/Zhwc75MbXLBbX5/bdpz2EHOvDV9XxngO+4XAmDdqpHBrtoTzfl0Y7rUP9mn58rzQ41CNOsrdzjrUv/iwQsf607z6WPA2I4d2/fY/rsvhF3+g/LQ4SB706Egrm9quqsme4LXZYLh4QXGczqnN2XruyTSycjwDYlWBv6GR6551lZzTZn9pyD2iONm/Btv/u3jmNaxW7Xe/ObSlzOhLoRoLSrzWplSGrPA2IgJ1R7AONsZTEeLy2k73jwGIHQmx9sKzv/L3pnH2VmW5//7nn32LXsmCyGEQBQGUVAZQdEJoKi4pC7YaWvr2vy0i201taRjbdRa69JU1Lp1FLfYFitgTVRQh0XWQZYECIHAZJ1kMvuc/f39cT13njNhyE4g9r0+n3wyc8573uV5nzPP9V73fV+3EcLpiPS9CqkuTxzp+LguG3vCMHxRNpcLglzu9zONjT9DCs9T8JWrPhl798c+/BRl+fqrvtQUKwf/8UQuu3D8zsFv3f/TF3/xQ/99mwjjB8/bOBfq+Jdb09Q25VfIkug5CxfWveHKIPMLpFJfjMKSMxDRtqKiMlByMVYL71qOXGVnmBy+Rd0AkMi57UsQT0DGJcJZ+7IJfLEEaD6M4gngbkT2LHew353XfHduliu4FfiJ2y6FHsZyjpzdhebPGMoHfT7Qd02YHXRt6bYDO7e/YaP1Gx/q6SakO2uFHgemuuSQRZGFea2KOEKEo0ZE9iIcEXaPDj/UkqlKFArFTDqVmobyoYaQotKE/lA2cQg15xnH5u3PA/4eqQEhespew+I5x3xef/uvn8vk8/nOeBBQiAdMhOVrGxOp21e/b+XhGxEn1v0xevrfX3mXp0CZsDYf5HeFlN8DsZ+sT6y7c3lxRXl5cUVufWLdzaVkadsZDQvftrdmcOSxwW23lnO8em7/3Obx2Fi27/TthWwqe0YhKFct2rgg3jLYpM4pxwNtXXUB5U/PKRQvGR8d35aLJ1+VDhK7S+n4WKDFrCdF6qVAOqE/K1uRivrpQ+z30vckufM/PlwfLzcmPzbVJvn4yBaK8buCYvyJhmx9Zjw1PprP5Frw+aSNaBG9Ay2Mo8iWwnJQZ1JBDtD8jLn36pmcz/VWIN7aEfxd34bwiOfKuz/24YmvXPXJUgwSxGP3MgVp/MpV/zi9VMh/6s5c8pHuVV+6pmfNeydt00Ttm4fidCxrXMjECxf+/ZOP/sdXOXCx/4uXPKNh2OMNp0ptvDLIPAR8EY35Kejh61XoHpXiundzEKEbRWTcCmwKiJwlcJ6JJVUS14aQjSk/zyxYht02dQGxZEjZFHMzQZ5w2wwikl+PFLQA2BvKm7Mq0P3bgzwS+5DiZyTWUMSrjhmcF59rS9cKZK8Js9vdtvcexliF6OElQoTjhojsRTh8LJ4Tjt+6o7exqupvk4n4WUg5mYbyVsaQ0tKAnqhr8cazJxby3PtfpJiVgE+hKtkjaSH2FLg8vVPHJsZuzCRSMYA44fbZ6er3HSHRiwGf4QCLhQDKSRLDcwtNuQSJBBrPwfWJdY8sL64IgeLCrfMzXBfEauI1zaf1L8g/MWvHWJxE08Lt82tO6Zs3fSQ5+ryJ+uK0eXtnj1WTuR0tascD+QzxIhRrFlJ7Wr5UWpii9EBNMf51tFiejldPsoh4fZ/e1U+vPLV1nQq8K1kg9Sf/MPx2elePPM2WRRKl2rBUmptNJodisdS9UHyFS8kyq41apBg9hv6umUfaoPt5GZoPAb4K0+bDCJ70TUOWLD9o7QjuPMpwbn26qmrmuz/WNWUu3a4in78xOfsNhZYMj05UvfFVZ1986c/u/cX+rgfnNC386ub+nf+cjOdTYTKe77jow78zCfkVBSElZ8r8j8APUAi+jCIFL0IK2/2IzJ2BiNRCYBPEHoDyKUAhDqeXYX4AexPath7Nh61ANk0mnSTVNMbIcEhYh/fh+y3Kx3sC5W2eiv6GtQAPlaAnJKhLEG4LVAX7G1TQ8pT5cE2YLV4ZZB7DVxf3ubds/pWuDDKz0cPEzmc6XzJChKkQkb0IR4QlLzm3DPwP8D/O9NcMSOehJ/EWfGjmxGPz9vOBn+EbkP816huZ4wj93SrhiN4S4KZYEGssl8tAMFZbVb2CI8iLc0RvIZPHpwwESZK5JMlGpFbdhaplB4Gz1ifW7XafqQ73hr1p0m1p0tNnDGZ35xL55mQ5EQQEmYZC/eyWvcnTkiTmAcn1iXXfXV5csV85ek37m4ND9aGdEr2rc7G2rr+oJnNhyMTMNIkgQWA+i/ORymbkVRW4B7MxaeuKIzXnJuDGKYne5u3BD2/7YVN1qvrl4/nxNCFPEKuaXl1Vc1rrzDMTO/Y9/sTY+K75BFSh+z0djW0ekc3taEE/A5HBDN4cdww9mFhiv6UdxFGI8UrUSu23R0r4XDh316pXXFaz9aYbJw6seF2bmXtZS6rEIE1hORj9ZWp4YFIYr+rP28PZ/3LrH+XyuS9m6qou5ASEats7qfwuBz3dx0kRPggccRpGcx2AK4PMjUw2SK5HuZn70L09DcoPobnVDySSUmOHEHGz3OEY8vYrh4S7QkKbH/3oO3Wf+38pan3XgHJmnw/0BYnUplJtwxiD/aPAXiNoVwaZAOfvV0n8rgmzRff+KL64wlIFTkMVuM2os8rjx2cEI0Q4fERkL8LRQ3lvVh32uPt/D5u3P1Lp33dCsHl7Ip/P/TCZTC0PgsAW7s8BXwFKR5Oj5/rezkaL/3dQeCmViifIh+Uwk0g8igjYTPzT/JRwJA+UPzYbLXIN7jVTAKzSMI0Iyk9RUvslwK+AnbnG0hjwWHow/gCwsGWipS1LLoiJq8TipBIFisUs2YYktQuB036S+F7T1mDnor66HS9/Ra7thZ9r+eTVf7bow9dwB0c2Jr2r99HW9atqMpeGEMQImpHNyyxE8A13A7cdVNXrXV2iresu4I4Dvffe+c4rA6A1lYrPHEttX9pc0zxjPD++lTgzsgw8lImndyaDTLo5NnvHGLvOhyBVnZr2m/F8fyNarB8BbkG5o9Px1Y2VhrwpRBDq3HtZfCi3HhX19KJw8BEXuRQG952bGav6l1Ss7qYrg8wnrgmz+xXW8XJpJCiTGp7I3zb+d1d+iI9eOfnDn789mBaP/4gPv/y/jvS4R4Pgz74bv4C3vQ09GDUCO9s7+SkiTz9G83IcmOjpPsI5c4RwxNj+dhSA7JVB5kfu9xuRejeAr7QuIDW3dE2YHXE5cvavrkBhWYFCAnVNeRzNhz5nBh2gsPCEe/0R9JCwJV7Mb/3evicLbn9VVwaZOjRXLHXlLqbOo6tDRutV6KFnLqrwrkMPNukpPhMhwjOOiOxFOP44wUSv9wfXvqepecanUql4urFpGlVVVTuAi1k85/Fj3HUKhf/+Gx/yKwFj8SA2mk6lHnOv7zmMfc1Hi+Z2VFDwb8j5zHzCRvAdA8Kgjh2J6sQ5xXzpgnBfuBQpFo+MNxeGkxOx09OD8bGQcBYE0zJUEScgJEwHBKkExUJIaJXI7QUKNc1h7ezYxByqSZMaSqzZPrD1x3NYcDRdTL4TECwMtOhVIXJVQMouiFhtoHf1ofvf9q5+OqU1nkjEzyslxv/h4b7NddtG+n6OfPXOJFEeiKXK6f7RLcN7RrdtIWBHc83CM2tq6y7JjFb9eGDsiR6kPJ/t9mWeaLsRua7skWsN5UGqtLW5AhHYNwHr0T07ItROVP9o9uyZzdVnXvi8e+6//t+oCKeP/P0fz5/qM595/ecWkY+P/uFrnxevLWfq0p+/fQsfPO8ZVdjmv+e3dYsKL3ptnty7U6QXI0I8Hd1XULqB9RO+ob2TG1HxQOxEqH+wP9/P8PgUm+wPc18TZivv6cSVQWYfIu/D14TZApP7MlejebwQuO+aMPsw3hLFkEAPXEWk9g4hde7prn0CFQjNRLmhM9wxNwLfJCq0iPAsISJ7EU5afPSKyy8JgsSXGpubZnVc9iamTWvJx2JBF4vn/Mvx2P/q963Mdl299lF8u6UtwEdGC4VTq5LJZUAiHk+MlErFoOvqtanV71t5MAVoGCgtL64or0+sG0K+WhcBL8abxvahhXYgjHNxYaR4KWmqaGYbA2SAi+q2pEYhbCkRVo1V5evDkHQqGwszJILARSKTJCq7EIzHie9NkmyqKqRyCWKF+qDu3+qrGo+8zVJbV4M739luPO5His/zmByWPhzy+7T4+tevKV77ja/e+9N7b5g9mh9No8paC9WODozu2AL0EHAWMDGWHQ2zhfHq8fyul6HF1M4tREUAG1GBzmnAa9y+9qAx2o3IgJn1Trj3a4ELgVWtHcEHjrSN2lg8dltTc/Ol8Xj9Ldfcl911qO3/+qU3xFLJ1O2FfJ6fJh76Wq68cPTX973sn75+iAKb9k5ej3JSf6+nm98eyTkCzJ44fWmJ4ttiBKfgQ9lWtQwakyR66DkV+FM0Tx9s72Q1ekjZ19N9/Cx+jiccwdv7NG9PA96CVLctT7NdCpHClwCXovl1HyJ0T3lYuSbM5pGqjCvQqEUh34jkRXhWEZG9CCcdPnrF5e9AVZ6N8XjA4MBAaWDvjp/Nmz//Delli45rmGn1+1Y+2nX12oYD+91+6htfX5hJZd5WDkuLhkeG6pHy0QewPrHOercGwNjy4opweXHFgHsvjQhdHCkkp7qfH8b3xqxnkHnABOP77UEmgKYEsVYgXqIcQjieDOKxNIl0wP7Qte1jDwoDfzVJ8v4kyXwN1aEr9DhavAp4A1Itht01Pt9dTyVR2HIMxwBg+Xkv3/PNW77CUG5vgBZj02tqCTiLGMvQmCRypf6QEiVgLgQTEBSg/AhapBej8N9NQDsKjy9CCo11QKlDi7rlrBXxlixvB75BRV7Z4eD9X/q7Nx3J9nMvf5BbfzZRXY7Hgsy9E9U7pr1sz8MTNQ34jidPgcuz+wSy7bm1vZMLerqntnmp+Exl14rFSdKXJ0nPQ4TXerA+4fZpRsbW/cIqWZei/NUXolD3ve2dFFCqw46ebk5sCsfRYxsiZkWePg0jH89kqkr5/ALK5QdQ/+AeRHIPCkf8Bo7XyUaIcCwIwqMqNosQ4cTjo1dcfikh36ZIjetWGQIPZKprOj76ne+f8IrFn/c+VHfr7TfOK5WKi4G7Vr9v5TaA9Yl1ViiQAx5eXlyRda/HUWinEYWe2oA/RoTpHrS4DqPF51K0uGYR8TClLoVyyIohYSIgSOE7B5ixa4I4WyhxO1qANx4jyYO2rjTwLdRJIIeUjWkoJ6kO3z+0BJxP7+oHjul4wLs/9Jorbrj3hi9QpAWI7e9UGsf3GnH+bEABglgmWZfKFbKxkPw48F00fgU05ptQov878PZAA3iPvRl40kfF/48Byw5WqLH0de9O1Fa9ozGTvnC0p/vobIf+4vJ33hvmkvX9+1626PEz31EHjB6MODni9mp3nQl3Peuy+V0rH+r7x8zMpsvGZzVdVgKwXLv2zv1dWfah+TcTEfgXolE1Y+gqvNoJeoiYVD3uYFY3BeA/kLXIXcjk+hnP8TtWXBlkYkA4VZUtwFeu+mTQv2njuQObH5m58567NwH914TZQ6coRIjwHEOk7EV4zuOjV1z+VuSZN58c4jfl8gjx4vkfv/a6Z82P6pVtp4/03LrhEbxBK11Xr51d86FE5qx/bnkEr5QYatDi+hjKcVuGFL3TUPFHAilNrXgCl0ELrxEpsxRJBgTWTsqQQ2rFIkqcgXLUbkVhzGPFmShcO4bCXRYGTgLJApSLEMSgmPY9iI8Jb/7cH/7PG3jHQ/dW3/2pW6b9+vS7p/2mheR+tQl8X9O4/g9vyhZGz4TEacmgrpoYbyuURr6GcvFmo7Eac+dejdSsTaigpBHl6JmBr/rwCjMPda6F4v3z8sUtL0klXzKzvTP5Y6T0Fo+E7PzLdV8/u+LXQxKKnm7K7Z3cgJTKnyG17fcD6s5dPOujj9VUZ6ZDbgekv9TeyS0u1JpF8y+P+q2m0D0cRPe3Gh8yt6KWpzNIt8KXevf7SlTtaorWx5EF0nMWh+oN7CqrN01fesYDn7n7luNlYxQhwglHpOxFeM7io1dc/gngj7Cq1XQ6Ty6/qal4zqfii+b++s++8J7+Z/UEHf438YOPBbAzIPjq468bPqVhU6q16eHMXcuLKyapjesT66rQ4jqGFLFXohy4sxEBWYLIRoAq+ZqQcpJGpM/UmxK+qq+y9dQEWrQH3WdTqMLwXcuLKx456gts64qhCsN3IXL6PHcuLe4Y07MQL0MYQKFKSf3/SO/qsvu8epEeopPGVFifWFcLtJcoBT9r/knDT2de/zeDDXvnUEUdGoe9iHQMIlPc21Pxun2N9bM+CrGafUNPPlwojw+jsNsdSM06F4UqaxAp24S89ZrRWDa6f6ZkDQIfQX6SN0+l8LV2BNXNdd2n1NW8/a+CIP5at5/bUHFHE6oKX19J/pwyNx3YfawKWHsn05HCd0EYliiFeRL7HwOqbgVW9HQrJ82Ff5vRnGpx/58GrEbErx7fSqwW3/LuwM44IVMTwaJ7bwfwD6il4p6e7v2FExEiRDjBiMhehOcUPnrF5QHwPZRIb4vtBHBD4oL29wS1tbnV71t5QqoADwfrE+vOQMpQiJS1MvBD4F/RYmeEL47yoBJIiWlBJPb3UA7UICJw1th9O1JYrKerGQXjfk4xeaHN4zsLbEEh1lpEzt67vLjiwaO+yLauOpQj+UqU/5ZGSp+1iKoGmkqQdjfsTqSSZfD9QmuBR+ldXXL7tFw8kVd7/SBYn1gXjCVGaz503vviuXTui4gc5xAxm0Cq5reBJ2rSzUvmzV62bOvOBxIT2YE0IjfbcB0X8FWWMWS4nUSEeydqPt+ED2HuQeHJFuALwA/6NoT7z7e1I0gAC5Pxy36vpf7qt2UyCxa587kbKbiz3Th8sKebX9jn2js5B+WMlYHP9HRz1aHG4GBo7yTjzu8tQEpcOABiBeBDKC/tETTvTsG3kBtCc+0f0b2sRXNuAN0jazvWgm8xV3m/zBPPfq5EFlWh/hdwT0/3U1qDRYgQ4QQgInsRnhNwJO8HKFfNwmcllCf2/o9fe91zdqKuT6y7DeV7zXYvlVHo9N+RP14TIkeNKOftDLfNXkTKZuObtCdwnmZo4ZzhXiuixbeIV2PiFcczAtyPkvrn4/PT7lteXHH0SfNtXacjErUYqVvWBP5MFOIsuJ8D9/M9SAnbikir8uB6V4+5/c1Eis8l7jp3ICLyl24cRuldfVBC39oRBIg0z3L7eSUKhf8CWAckaqqaz4oFsSUj43t+ie+QUYPI6itRA3vcOTzuzt3Me0NEDJNIMWxGJGkH8OG+DeG1rR2BhTDfCjUvirGsrr76Xb9qbPiT+1ESfxkR4k53zG/2dPtCgPZOWpDCGEN5b2/o6abnYNftPhdDc2C0p9tburjXFwLvBd7P5By7EWQjcwsqqqgFznLvjbrzXOrOpc1d1zg+pFtwP1t4O4Z/4JjAV+2Cr1434pdHJPPPUT7fcz6XL0KE3zVEZC/CswpH8r4DvA6ff1YAvvjxa6/78LN1XitXBQFazApr1xye7cb6xLqL0IL2AqSQjCN1pw+RiwIifk14o9pqtEjm8MUZtnBW4Zu2l902De684oiQ2L8iUp/MaHYH8K3lxRW/POpBUPi1GXUoeB9SeUzl6kXh51MRMVjsPlVCraj+Grh5SmPltq5PIwuPSSpQSBiWCK9NEHsfvasP6QH4uR8RB2b/6MdXlB/Z+qMeRJx3IzLq7lnsLigPINIcQ+RqEfBalDOZQuPV6/4fQ+Peh0K7NWjcE+helZDy/EFE0lPARdBQVZ15w10zmr7RcyThyvZO6tADwQIUZr70UESovZMkInUDb34T+3I5qtNpxv/s9ZQd4atG6Q8fw+d+gkjXF5DZ+ITOmzlorlSh+/kweiB5ifvsIJpz09y1lt1nLdfPegyP41XoqcK7JUQ0NwJre7o5+rSCCBEiHDGiAo0IzwocybsF9VS12soC8MmPX3vdmmftxICVq4I0arF2CQr9LT74JwRHrH65PrGuHvgq3uojwKt+SUTMRtDi24QPiZlyYgQEfM4UaDG1z8fwhQTjwM3Al9Bi2wdsWV5cceReepPRCKxAfnNV7nhJVEAyE/39eAIpQrbAW7i6dT/RU85fWEH8fgG8Ealy+zPLsmQD4A0FYi8ptq1aXte75lCEIAaks0/e9Cb8+M5FIWTXiqvcjDz3diI17PSA4LGaZE3/aGG0H5HAEiKyI6jTwlJUIR1HYx2vOE8jM01oXjQAG2Bo33j2m0M93d8oBcF5QRjevp+wORWyum9D+BSvtZ5uRto7eQHwJ0iVfVq0dxJH8+lRXNeI3f3U79nDi8fGuJ/Xs81V7462d/If6P69HRFD0LzqRIrytW4/E4jwbUHz6Aw0f/a6MSngq62t8nkvXnGeQKqlFb/UMHXVbhw9BM0ANrV3shMYO4lsWiJEOKkRkb0IJxSO5N2OVBV7+t8NfP7j1153XMyQjwZOyXsXIgr1+BDX3JWrgua1a8LD9staXlwxvD6x7u3A7wOvR+rYJkQirB1UGi2iRpICtFBW+rxZqGx/8pU7hH1vjVztBD6LxrVwHGxWLKetGrgAFWRUJu2bbYmrgiXnXreikTrg7bR1/cqd20KkUJpn3N1IIZyF8sW+B/RkKb8rRnhejPKsLbF9vYMvuPKvLrr7mi8e5ExLheJErrpubn8wNPR4KDJX594LEBG7GJHTe9y5DIeEjxSKhX5Ebs5x5zEDkb0GfPFCHt232W4s7D6dg3Itf+r2OdS3Idzb2hGkg/gLZ0KsNQjOuzsMbzfCvgOoa+0Izu3bEG468CKckvfvB7lOw0WQXQdsG9o5dPZ962eGv38VYyOjbBkZeUr17jQUQv9vlP+6xL0+AxHtO1BawE70QPMkymFchhTmTSh3sdWNSQmRuRCNs41H2b1mxNnIm1XyGspoTi1AVbsp4Pr2Th4/UZ04IkT4v4yI7EU40diDFgoQqen++LXX/emzeD6sXBXEgGuAK6Z4u4zy0Q6ZS1WJ5cUVxfWJdf+BCMQHUOhzOlook2ixM/Jm+Xpm5GsE0L6f9nuZyVYrILL0eeCWY8rLmwzLLXwnIg3WP7bgzqUWkQTL57LKWEMSqZqvQzmXWay4RKHhl6JFfwL4K3pXfwugCX7424vfUtc6MOfmdJg4dXE4/a9o69r6meob13+25pfFA6tgb+r5y9SypX/UFk47qyUcevDrSO3aitSrl+DzCE931zGI7sPcXJirQnl6O9111ldcdGmlAAAAIABJREFUWwJfcGMt8RrQvUmjMPBfAGdn4O+zUGrtCFqBC+pO2Vo3smV+njCeC4LzhqdfcMe8RIqGmLSuY/SCzO+DfHUhz2nxdOqOtsv3vba2uWkAkbVMeye1KB/O1LdNaN7+KyJ9Zu1yLrAG9b39Bro/M1C+bAoVb8xF88DUvNBdu1XaFty4WIrBdPwDyoR7zayBjPRZ5bh5+4XIm+9o2vZFiBDhCBCRvQgnGg+jRfhLH7/2ur95tk9m5aogjsKfr5/i7TJaCOcezb5da7T/RK77L0OLW7vbXwZvaVFCJMNUPyN0QUhYLlCOxwiCxGSel0OK0R8Adx4z0WvrqkYK3B604L8KqTxWCFJCylyIzxu03C0jg5VIAy9HBQ8DwDhtXQ+i6tTVbh+7UeHHfpz1i++PAGc1tK1+b5zYO4YY+5tcKb8I+FFrR9BnbctWfvZHDf177r/4jrv/uWHvvo0XuI/vRqTy31He4KVImWzAF7VsdNfwMHCe+30zIoRnIHI+gZSqbei+VOMNhkO89+FrShqvHW4/c6rn7jk71TRy4+CDi95fKpamF0pcGCtBLE5P34Zw5+HejinRXH6cgeLegExLPJNclk6nfw78nTun5yNi2t/eyX2IxO5D82sXeuD4KrJYiblrT6CQes5d2+mosMdyNavwSnNlGBuk1FrBS+jGqACT2qY5s+v91ePgVe029/Ot7Z08xCEMpCNEiHBsiMhehBOKj1973Uue7XMwrFwVVCFScyVPTSgvIJVoALhw5apgK3DP2jXhU/phHgzLiyvC9Yl1D6HQ2RmIHMzAhzzNxNc6F5QQOYm512IlyrEDuJRVKX92eXHFsbUmkwVKN1LCbkQq0FmIJIX4vxHjiNRU402TE4gMWZePymKAGCpKqcF1/EAtyxYjVQ/gxikLOIB4b9eXaOu6qZe+rnXxu98HXIYUuz0AY2M7FzQ1nb5oy+M3FoZHHupA928J6uwBnpx9BuWKvQ6Rl2akXIHIRhwpo1cj0jqMwsxPoNDm+e7nARTObUVzIw3UFKQK9rqxmZ9IESZSuZFU08gtZcZfH4tTHdOt+8ZU11mJ9s79quGmqYo0RpYES+puSw+SChozVMUDXceXgRsQuZvtxsDI2Qx867peoAPZ4kxzr7WhApt+RPhG3JgMuJ8X40laDF8VbiHZHFIr6/Cm37gxTeIfXvYh5bTSBNz6Fr8REfMeN94RIkR4BnDg03iECL/zWLkqiK1cFbwE+GfkLXYg0SujRT+LkthPRypV7dEcz6luX0a5aRvxCpGpeKaWGFmyhT4MCOIZEiT9V3Uv8Engz4+Z6AlXojzFuYjIbUaqzyy0mFd2zDgVjUUBkYAmRAq2IhJQYnI4txYRioUoX/EyRJL2oCKAfzvomfWu3vSBpms/3F+dS6M8uec7uxMefPia8radt4xNTOzZCQUbx0XAK5BynMfnnn0NkS0jMDPxhPPFKKfNztvsUDaie/5iRGa2INuQx925W6jSwp7n4IngrHTD2K/q5w+MptMQSzCA7v2hcGkYht8JyX7+Ne/fkTnwzdRj6aBIaiRBbHtAtuxOuQZVFs9z1x3D+z0OIZKVduc1gBRmC5vGUK7nCrfdb5A9ywNI+ezH5ymGbkyzaC6k0f1NIUX1SXzo1sK51vkli5/TRgDtoeb3URHJVe2dzDqMMYoQIcJRILJeifB/Ck7NOw1ZZ1yIFkHrn1qH940bQovmOkTG9qAw2f1ogd28dk2YP3D/B8P6xLoA2ZiscceqmWKzp8vPC4H7EDnbfhwqbaGtayHwS6QAjQM/QeT2NYgMPeneK6BQ4HxE8GzRL6ExseKS6e48GyvOfSfyWMu51/oQqXwCuOpwzJRbO4I3oRyvJ5ECtwNVS7chA+s9qBCjyZ1/zG1TmXN2H1Ir21AhS5/b/hSk/GXwPnFbUah5Kcpzq0Hk7iH3/l73+mVu/7vd8SwEfAcKT9+G1LY7gEv6NoQH7SDS3snKQnns08nYEEFQvTsMG/8fKgKxUOo0wuKXoDCbYmlhGE80BbH9nLAPEdEnh/v7v/P4nb33nnVZxxgi7mmknhXQ/VyMcuXsw3uRAlqP7s1C9/Mcd+2N+DDtTjR3K/s1T7jXF+GV6ji+onwAb9ZsXoyjbt9WCDPmrvVD1ukjQoQIxw9RGDfC/xmsXBVk0OK3AC1SW1B+1xL0Xfg1UrReiYoI4kgtmYdCUY+hIo4FwJ0rVwV3ofDTPiBcuyYscBC4kO73EGE8G3nXPZ/J1bWmMhpZKrtjfA74xfLiiiMKIx8CL0bqSxGRsZnuvObic6tSaKGewCuQSXwVbhIt3g+jRX4OPswJIoBF4FNoUTdfwFsOh+g5/BciMxfh1LMCnB+D5rhyFv8TnwO5DxGtMRSavgAVJJyF+sdud+e2wP0/3X3WxjXprn8hcB3q7ToHKZ64zxXcMQZRXuMut/99KFT9W6SCznCfyfP0/WUrMRgQUKaeWBifgfwn7wH+B1j3gtTNoyNjtdlt5dnl3WOJzamq+ItSVfs/O9cdZzw7NHxJqrZ6fjGffzKRSr0C3eNxRLpCd46vd9eXRGrmnyHlcovbz5mIjA2ie2/jW0nkqbiu2Wh+5PFhXFOtW9AcmMDP9TQ+59OKlS5HRPvvD2OsIkSIcASIlL0Ih40tg7wV+PmixoP7gT1XsXJVYNYPlnt1CeraYZWvG5FK9Fa0aA2hgopX4z3H7kGEcB5aDIcQebsW+NraNeHhEhjWJ9bVIcL3HkS0YLJHWYjvbXv7EV/wwdDWlUHVmGcg5ethtCibX53ZcrThOyCcii/G2Iauu4wPiY6iMK/lyxkp2Ic8Fb+DiFM/cAO9q0cO93TNqw6pcfvysCMGyQOeVstIMbwfEYkZSKE61X12K1L4Soi0ZBC5zSBVbAbe13A38i4EzYkf9W0Ib2rtCBrddk+g/L/XIZLUiQjfze74ZyHj6AQiVT8Crpmqr24l2jv5PoSvc00oipArQux2SP/TmXN+2Ve1r/jqB0dfcMFgPjOUTKUuj8Xj9Qdc/+5CLr+vVCpm01VV1UEQ5N25/ggpeGlUkRwA70Y2KDF0T36OvgMXIVUzg0LhMTQvLJxbwlfhWt4jiDCb514czYfEAdsPo3lmli1UfL6M8g/f39MdVehGiHA8ESl7EQ4LWwZ5NWrmPrBlkNegxTEBDC9qfG63PnKKXjMiGkVEAJoQcXsALfBNSMXI4vzYEKGZQCrRG1COUQh8EeUanYPG4BS06N/uwsQb164JD2mzsby4YgT4p/WJdetQWG0hWmBrUUjwapRr1vd0+zgGXITGZBgttg2ItBXd8dJogc+icRlDJOY0RPiq3GcmEJnrQ2HeLF6pMbJXiwphWhCZeBDdCwuLHhKOJI25f8zpCH4aiIRbay473gK37xLerLuMiPoCRNR+hUK5LW4MYu68XuG2NzJ4OiIfo8C81o4g6NsQDrZ2BKN9G8Jia0cwAmxAIdyvIyXxLFQFfGHFsQMUGh9q7Qh+0rchPJiv3B/AxF0QnALBKJSyAaV7wmKh8eHHZ6SCzPSNhXjTcLqKJXhSGaD7lQBmJNOpRJKU5cw9hMLfBfSAE5QpLomROM+993toLuxhsoo3gbdZsVCrpRZUWrGYybfBSJ31arbcyVr3s7VYs/66lrIQuHG+zx0vInsRIhxHRGQvwiGxZZAU3qS1Bqlh30Vqz6NbBtm8qPFYPcSOD1auCpINDYuWNjWf8eTjj12fRYt2M1LiFqAFZQ4id5vQIjcNn8Q+A4XljEC0IoKxGykX0/E5XlY9mEcE8vtoEVuJSMJhYXlxxWPrE+vejMKqg2iMbwWGjtkgeSq0ddUgQtKIwpOz8ERvEI2FKY0Wzl2GxmsIjU0dIhq70QKdROQvy1MLWazTwgJEMDZyjJWX2zeEK5zaZx075iGy+VJ8uPGV7tzNfuRUdE/tHBchRdO8BI0cJtx+l7jzXIMPxYZG1vo2hCOtHcEoGqu5KNxeg5ThWnwf2eehsbsCWNraEXyvb0M45fX3dJN99Z+U/mo4n/guhI2UEuNhPAPh8BXFQmKAePUIcea483oUb5kygOYmaC6aMfcidx7zgcfG4n3TR6oeeHHL6AWLk9RuQkUZ/+TO92yUz2fFNuN4Rc/Cs0YGzU6lwOQqXPs5wId+p+GJo7Vcq/SQNIVwABHYuvZO+iMrlggRjh8ishfhcHALWuwLSJXJosUx7f61bBlkGAhPtMq3clXQCJhf3xiQHBra8sahoS0PoxytekRCxhDBm4kWxTG0mJ2C8vg2ue1eiwjAdrR4PYKuuRVZV4AWV2tzZtWqMbSoZ4HalauCVmB47ZrwwM4GU2J5ccVulJt1IjAdVZo2owXewtgBGi9T0EyxGcUv7A8g0lTr/hXQdcfdPhJMVvVMvQnQmJ2LiPAs2roep3f1US/oTu0rItLzqHu5G6C1I0i6c5mFSE+1vYeI0nZ3LpZbOAdfJQ2emFyIFNZfmsffgefQ2hH0I3I+4o5jodEh9PAwD4WQlyH18H2tHcGdwFumCuvWVedacnliORJQCKuIld5JomYTsdZ9xKssJFqF5l2cMMwDeYLA8g5N5Yuh+znLXfNvKZXPSOWbLw6IPx/dtxFU5NLgxmCx23c1vh/uELp3cfe7mU5Xmn+b8mdt9XL4Cl7L/axh8nzIoe+LeTnWuXPqiYhehAjHFxHZi3A4uBstVElEDkKkptyFVI8nEYE4e8sgDwNbTwTpW7kqSCK/uVdVvDzhzq8ZFT/k0IJnIaqcO9+5SAXahw+zTSAlZBZalKe7bUfxuV270bVXIbVoLyrcWIgS8/e4Y7wMVWMeFtl7xtHWVY//vv8FXuWq7MM74X5uRGM4gVfBdrnPz0fjaaE30CJdh647h+8la31lA7xqtgTlQVr162GHco8EfRvCAlBo7Qi24kORfwO8GZEX8wgcRoTsFHe+4+69JL4d3ZXAfa0dwe6pyFnfhtA6VtDaEczG248Mo7GMu39DKBcujR4qHmvtCE45cJ/bBmsGgARlIBkEBPE6YDHxqjwiqb1ojp5NGDa1DA/vm0ilFo5XVZkCV7m/AiJuc4BLapg/XJOfvyc3MZIvlIYyyVT6rYlUZgF6wMmxv8NLYRjioxCzeVOZq2d5eEW83551yijgu2uY+mfEk4p9mBdgLXqwyKJUgCXAovZOeVxO5Td4MLR3yu6lp5tjr1aPEOF3CJHPXoRDYlEj70YKx178Ij8LEaRfoxy3XwB/hBbUd2wZpH7L4DM+v6oROatEDC1Mde4c5yLVYiY+Ab8GkbgHkGL3WmA5yrkKkI3HUveZl1LiZZRocZ+16tzpKH9tEaoinI1UklpEjK3jxLOLtq6Ay9aez+zm1xKPzUCFBG/Ft2YDr7QMoXMed+/X4LtHWGu0WMV2OTTOTfgwnaljOUSSD1ysy4icvxpYSlvXLNq6nrF54tS4OLpnGXf8c1A16igiGDZnKvu/jqGw4l6kdr2KQzwct3YECfQ9GcWP4YDb/xwUMh3Ez4uZeFW6AlU3QNVVxKogvt9aZQipl3e7c1qK5l8mm0w2ldSPLXTH3gNkwzDMlgrjQVguNSHF7HykZsaLhdHN2eyu4bBcjqP0Aculq4bso9XJ2+9Nxx/+AbKNyaD7a+bY9nBgJNYUfiP4Nk8qyWGlCmhE0R4oBtxrMfQw8UI0pw57XrR3kmjvZAl6eNjW3slph/vZI0Xu07+ac9/7/23aobeMEOG5g4jsRTgsLGrkjkWNtCIy8yWkiFnCdhUiPVcAfwh8pVjM79q374lf/PXHZ561clWQeprdHivMMLYSlaqCERoL4zWixeQsfKWr9Xw1FWYxIocDGFnJEaewf7E7cOGahhamJrT4Pg/4E+Aq4NaVq4I3r1wVWC/gZwM1DI2+nbHsFYThAqQ4GoOwxbu0f1tPcrLuvT3oXlchQrAHXX8VGjMLpYLP0SogsjfOU5WmNBqnV6CHA+uy8UzCwoi70T2fcOf/OJpDzfgOEs348XkAqdd17jwPZZ9ivWNNTSzgw8vWb/ZfkbXIbjTutx24k55uwp5uPo3Mrh9DeZXXImPmu5AK9yTksgSFxFh1Nbl0ai+6b1ZwEiuXcpns6I5EsTAGmquzUQpCS3XdzL31jQseTGaqB1GByalI9ZwGiS2lcsOuYthUcMezhzyzTrH+zQYjgEn8Q4EVYZjJc85tZ+qp+VtuRnYvCUR+rVhqn+vxe0i0dxKg7+138FY6lx3OZ48Yn789dfPNG+7Y/NAD9//9ohce+LcnQoTnLCKyF+GIsKiRcFEjf76okTnoD/OnkVphOTsA5XK5TG5iMFcoTFh+1zOBZUwmCmX3ewqfU2SKjYW4JvAtnl6KFsfdqDL3cbTgVCESICUoxTgJNuMUE0QCTY2wtlBptDA/gRbDOYjUfBm45oN/mfi9Ta/8g2eK9B4MY4znvszw+Jcphy9GqqUVKVgobQKNgRlLWwizGo2DdWQYxSfqW89T+/y42+ZJREasghe8ZQf4FmxWEKG509bV8gwqfFYhOoCKSXa4c57nzsOqsEuIwI6isZkFvAjdy8uAN7V2BHWHOFYeKd3mIdeHyDJIAZ6F1OTfIDX8safbUU83N6A5fj56eLgfkbVzgWth5LdQSkAuhs8XtNBoGIunw3Tt7DCRrDYlzh5QFgVB7LR4Ij3uznOO26d7aEkszZWeN1oqz9zo3rdUBCNxsYr/LVRfGaoFr/AGFZ+1tIHK0G4R3Yd6NKcCRDwbnn6IhfZO0o7oJVAXkCXurSx6ID3uuHrTz2f1Vk80b6midmCw/6C+mhEiPJcQ5exFOGq4vLyr3D8Atgwqz2nXzttK6773CrNmOGjngKPBylVBgMiaJYTbomJ5VjCZYNj7d6K8pOehRX4zCs3uxIcd82ixCYAxEowjoncXPnRbh1c78sinbBTZerwNmOEMP6qJc2EpLLV9c+kPL9yx6rufiwdsrw4L2bVrnprwf9yh3rMP0ta1EdnDmDI5hsiNVUeacfIMfK/TnNveiFKIxspsaobxbbkeQ4t0E1K2wBc9VPZTrWwTNxflQhpRfND5/+0DJo7AdPlQsOKaBnfMZUjFtSrdPe7fBCKCF6CHBjOILuDbkt2HSNdUaEbh3hCvhlWh6mMbt/egTiXjiGDHp9yTg8tZGwdo72QPsow5GxiGum2Ui0ViQS26b9ajOADyQRCUEslqq4q1PDmzTGly57sD3+t4DBHF01H+acm9t929N9+Nmyl0Fv6vDNWCJ4Pgq3ItdAu+qjeGHq7MK3AHPv9zcXsn23u6yU41Lu2dZFCe7E6UTvJH7vongHf0dHNE3W0OB6+6cnPqz5un/6K/uZYn2BF+YWBrVEQS4aRBRPYiHFc4Alhc1PhyLjorzMLUf6yPA+Jo4akMpRyYH2ZP/Tm0CKTxhRZm9zAXkYs9iABaCHgXIm+WkzYDhfWG0EKexxsvNyNCMN/tf447mywBDwFjxKgbrB6/uEzwiiAMq4dg45tXJf/yh2sKG4/XgBwCZyOSY8TbwmpxvCJkoW8LyVlFaw0ao7loLGrwyuAYfnHf7Y5RX7G/A8mMqawxFHLrQONlClMfItT9tHU9cCzVuq0dgSmsDe7fhYjIlNC9b3LXaMR9H94XLo5C9NV4UrsUOLu1I+hDBHJb34ZwX8UhrYp1EO/rNw3lkV0PvMUdbzmwChVaPH6419PTTdjeyX3oAaWpfii7azgMC9TG4yT2z+ksvurVlDKrji7hVdoUCn3afSwjdbAfqbMNKK9xjvvsY+5aTCmnYr+V99jmjpFBa6lmoV973R4iZrht8oi87XXvzQEuae/k1z3dDEwxHHk8WX49mku7gTf1dNN7uGN6KDhSGQJzy+HcYkuqJbcwE5aX1TV9lc/fHvDB857THqMRIhgishfhZEUCqU2VPWSnCgOaopFFC1gGLUjj+DBlGZG9PMofKqCFL49I0iBaoF6MbFhuQSGjCyr2afmMFlbeR5w3EiePFvRUCJfGCVfEoT2AVzRQvOu9q4KhBHxkyXDTT84I/nCgY8sDJa7/6fFVDNq6qlFhxhJ8qzMrqrAqWSMDld0OJty5m6+eKUTgK3b3uH0uZHIY0YhGZacEW/jNfy6FyFcrIlpLEam41W3f7PZ/tJiPFLXT3bXXunMdwrdxs/y8ae61GnfuZjmSxitYdSjv7Y/R3Oht7QhurrBksXBwHVIAzffu39A8ehLl7mWADwFLprJzORhM6WvvpCVTLKWLhOPjYXwEr0QbYa9H3w8j74E7v534XNY5eEK+A83tErof0921POrG7Sx07/a636uYfD+NBJbwua2VFi3gCzpy+NB+AU8G7buIG+e5wOb2TvYdWJXrrFkG2jtZhr6r4/g+zMeE9k4CR6yfj3JcbwSysUTVwFhy4cvPaRz/43Qq+bOI6EU4mRCRvQgnJdauCbMrVwX3owWtAR++qgzZVhZS1FX8bon0e9EiUYdIxhCqLgaFJJfiVZpWt20D8uV7Pp7wmPplZDOLOiqMIuUiBaSS8O2kOmW8uQifLUBLLKSxkOPq35b3FZ4of2Ws2Piib132mku6uP6nlu92bFAe3AvxRQiNB5xrAp9gb2HdPL7wZg6+A0LSvVfAF2nUu5+b8En3KXxotpHJSf3JirOzEHLS/TwNjflZ6N48QVvXF+hd/bR5bU8HVxl7OWr3thiRRrPjSOLJfR1SJ6vQ3DC10/wVZyIiOxORilmIsN2GL0iwe7UXtRyb5/4fRSTz3e69KjTHGtG4/pTJtkFHgm35VOqaUjy+j2TyBWhOTnfHMMJU6WtnFdJlpAxWo3SGOndue/GEcC+6F2e6cVrixnDAfdZy7OqZrCAO45VS65BRaaBc4Kn5fUYSa9y209x5vQw9VKWBc9o7eaSnm6na69n19qM+xkeVMtLeSTW6Fxlgd3snt+Er6nf2dFve5TmjwCen3Mnnbw/+N/1lLn3v1yISGOE5h6hAI8LJjCxSJIyAwOSKSVOWUmhxg8mO/zG0uJTQwrULLZjT0YJlIbwYWvQeRaRmvnvP8p8y+O9SAfh31GrtfGBfy3BDR2yCnxPy5bVrwnDtmnDdl9aErVUqbskVVN6RzKbGGm9q2fz734pNHM+CFquKnI4W2crCivgU25nyN4IveJmBVyxt4bZtBpGKtdu9ZkqeVWKO4xUfIx1GRsKK/VhLuxmICJ2Hku7PP9ILdkTvMmQxcyEiKgm8mlnvxsM6qczBm2/Pd79b5401qD1ePyJpZdT/dhcigOe7Th7mt3eHG6sM8M+IgNTiQ8UZPDm8oLUjOKpCgp5uytf9d+3WXHXVV1FLvXvQWJvHoVmi2HwP3fVZ0cn57rzG3XXOQGP+Enwf6N1IYVuAnzvWei4P+8mXhXMb8eTfSJw9GIC+h5V2P2a03IhXG21b5/e3P9xe1d45eb1q76QFEdC7kfr2raMxY27vJAl8AvgmssKZDoQ93TzY080PPdF7enz5/ZvTn36wZ+C395w6Vcg5QoRnHRHZi3Ay4wlkSXErCjfuxZM+mBxCMosRFV1ogYvhF5tTEdlYjNQgSyIfRgv7PXhVx/KjDlTGQ3dOw8jLrQRMNGUb9yaIF4FpK1cF+79za9eEV33xE2FjdTWvKOYYHyqTv3/Gom/9V33/8fTnawHej1f3TAEFr6xZ6G0XUn9q8Yn4lddm4TfrimCN7fcipetx95p1TzCSY5+p7GMLvmPHLLy1i1VsWo/eXx3FNc8F/gyFb6e5fZ6LFCkrKMi4Y5oBMu6Y1oViHsodnI0eKB7Dt2a71p2Xed5VzoMQzZ+/RiTxre79NEoRuB64Cfa3F/yD1o7g6qO4RgB6ujF7lO8D6yrOH3zPY1Ms7f9Ksl9ZlFOD5sspeMXTvk8lRLrORPe8iObLAD7nL4Z/YDCF1Ox9bGxCfD6n5XoGFfsoIGKdwrfAu8u9f0p7p8a6vZN64O1oXle7c5t1NGPoru0RNH+/BfxnT/ekcTwk7s42V+chWQoSqb9d87lDWfREiHDCEYRPNYSPEOE5j5WrgjhaeCbQH/y/xfc4rQwRmWGuhXArKwmtSCHEm90W3T6uR4RnL1p0trvfX4oWxWmIMJhFCYgU9CDrkZuBX65dE5Zp6wpWXfzp+HBmLLZ2TXjcqwSfFm1dAVKm3sVTvexsPMxKxlpemWIZx4c9rTPCML6FVgIpOwW8UtXsth3Bh3gt581McytD7JXnYgRy3P3+a+AD9K7uO5JLbu0IaoGPIlWwCt2TzWiOWD5hEq96mZ3IKMobbHSfy6HK7QcQCXgJCmf+BPgRyg8zotOK5sLDKDfvejTe5i+XR4RlI1INFyFicyaeVH+gb0P4jSO5VoPrGtHq9vtu5GFocxMmmyGb6bKp15WEy0h5v7u2EXxbwLn4HLxRNG/svp9S8XmrrLWUALOgMT9L8AbONifMXNnmmT203YsU8t+g1oct7rhxpEK2ufM8Dd3X7/R087OjHMMEmr/9R9q1w30+ANKL66/PfXPta6JFNcJzDlHOXoSTFXXoD/xjaBHtR4u0hekq84JMdbDwrYWH8mixSLnXxhExmI/+8Jvqt8VtN4QW/DTebw482RtHFZZ3ADfvt1bpXR2uYXWlSnaiMAe1+prKtNhCqpYYb23TbEHPIXKXxYfUrDLRPt/k3i+4fVTh7T+G8cUPFrq13Dz7PBXvGym0atbr0UJ/pLgAeIM752EUem/E5xGOu/esEMWOb3Y0DYh49OGrUl/KZL9B8C3WrC9ytdvfJuSf92J3XCPQM9w1tSLF0TpiPM9d+9rWjuDRvg3hESuZPd1MtHeyxZ3zDuAFeEsiu8dG6izUamH7PJ7s17hzmY6+W4/i/RDLSMmci88FNAJo+Z1mpm2FL1bpa901rHuGwfwareeukT/QvVjgxq2I+ikPuPGrcq/fjkjfMrf9UT9I9XRT5Bg63jiCmJULTIQIzz1EYdw0yo2nAAAgAElEQVQIJyvqmVxN2I/Ulj4m54SBzxmzSkVTJ1Jo4bgPKXdVaIHrQXYZD+JVkTxaYEKk9mx3x6zMQWtAhRk9J8RD79B4Pd5DbSoYYTXyG8fnd8XwalEtfpwttyqLJ2dmRG25kZaLZeFRM++tDKsbKsmj/V8D7HQegUcKC0cagbgAKUC1iMS04EneVKrWiDunnYgs9uM7pGTwfXVb3fZnuePd1bchtK4h/+XeM9PtcXxe2y/RHC0i8tKHH+/rWzuCU4/imunpptTTzQSay/+IV0jBF0VYdwsL39v7dt4D+MKaGtjfcswebixUmkGk0fJa84igVyrBlrNntiqmBFaqu+CrhSvn1xyUTjELFey8CIXh0+i+bEXK+Qz0YPZLRLoXOoUtQoQIByBS9iKcVFi5KliAVJMBpLiN4UnfBKqctIXMQpWVLdTAE5YYWvzPxOc3ZVFSfbX73Ea06D2EFsA9mA+cjtdUcXq1wJK1a8Lbj/d1HzHauhL4Tg5TFXxY4Upl9XIRkR3rQmKLtikmJXzLrGom226kKvZhRrq496zSciryZkSrEjl37keLGjx5qMZXHBvRqFT1KosOAkTkF6F5dR8iHDk8kc2g8ahCyt98VJm9sbUjGMHnA5rauxfle/aj+3C9e/+FiEz+Bvg9pBgngP9t7QiW9W04unC/swy5xl3LB935Vyq79rBTQPfS8i6r0QOM5euBv4cpRO4q54lZ02xDD15Nbn9mz5ND36UhfC6d3YMDUdmZw0iijaPlky5H3/OdaI6eif4OPN+ddwl9lysV/AgRIjhEyl6Ekw1VKC/qHLRItSBlqQeFevqY7OwfQ4tGpeeXFRBYaHI2ykVLo8X7fLQwzUZxmXPQwvIOFHabja/gNAJUYLK68eyid3URjcndT7PFgQqLFU804hU+8OStCl13ExpLax1nY2q5kCV8+K6yEtSKYw4Vzt6FSMo9h3WdFWjtCJJAO95o2zwWjdjYsc1n0SqBhxF5mYn3xpvurvGHKF9sNyJ3d6AOGrej+x93n88jFfUUlG+4EZ//lnX7OwN1V2lFqvEIyge8H19Y0Qr8rLUjOOq/zU7h+wZqGXYrk43NrTCjuuJfM3rQWYqvGh5COXOb0bw2L8kGt719No4euu5x41Bp2lzr/llxjvkwwmT1vdIn076nJffZECl4L3LnZBXf1pd6OsqnnI2+uy+N1L0IEZ6KSNmLcNJg5arA/PK2oKf57WgRehKFXy3Pp42nKjiVHmqmRI3jw42jiNDYgl1LSBUhNcQooTyoAaRkLEULkOVkWUizzMHDpicWvat309bVhQx8X4RXZyotV8wYN4/3IKz8u5BEC7iRtZLbxtQi60RiRCqFtzgx5bCM7ltltasZN1uoz1rSXQ187yhDuNYVArwiZOQmgw9tVlr1WLjV+vTurbjecxC5PwcRlTp8bmEjKtxpQIUds9z1DCISt8GNw0Mor3Sm22/aHescVAF+NiKiD6NcvhS6V58G/vIoxgBQDlp7J19GIeW/QZWrlQpvzJ1LP94XzxTQQff7XKR0LsbfuzQ+vy6HxrYO36XECjHsgWEcn8836sah0msR93oNk3M7TeVrdsesAy5FldB70Vz5MlJKX4osdgpIMb2T49Ci0ZHGOuDHSM1f2tO9v09whAgnFSJlL8LJhKXAB1BCti2883BGqGgR+DFe3TMiM4b++JvSl8ZXmw7jQ3k7UYL73UBzKp+YNnd41kiilBhBSp6RgGa0cGYqfjabiVSlvcrxwNeu/saCDVd/+9qff+F/jvzhrHf1LcCb0Nh9FV2fWc/Y4jqCD7FNlWtoC28lLDfPyKEpqGa/kcKrPJaYb9Ytlh83iEj6XWjRfjNw9dEQPed1926k8th1NSCiYQS/8jrMdsTCz3cgFcseJELgElTJvBzd5xZU5RtHRO4VSG26FU+GLZXA5t85aI5aW7DFqIDkfPfaAvc+SEXc4X5+Z2tHYMT1qNDTTaGnm+2IOH4ajfeBY2uegKB5YYq3eU8uxXsDDuHvqaVHNKNQ6kw0F4bxRSHmpWfKasa9D54UmvegFXYEFfu2HL5q9/8sNEeudK/9BqVc7HK/nwpchIj48UAGqfnnofv9X8dpvxEinHBEZC/CSQFHoD6PwqpnIDVgDPna9QJja9eEBeS6/9+I+BnBMCXhQNuPjNvOwq9WeXoh0FCMl+qH0kMtxaA46PYzA1/paGbBVukbd+e083gXZ5waq/7fxqbmS8LU2NOFZA+O3tUhvavHUD/WVcB6FJrswxM9q7isJAPWa3UvIsLb0DXvQwu/hdtMGZxw2z7itt+DVNc9bvsdeCVwLwpl3u7+3QbsPZZeuPiwvBVDVMKqbYfQvapFBGEUH8o/DYWBA0T89uBDj9ZpYqd7/0k0jptra+vH3/i6zplzZs1rwyu+5hvXhPr/vgipeKe5fU3D29iYSlgDfBuNaQb4n9aO4JiJS083fei7c5Wuq1iGXAHCvDt+yh3PcjVrEXGbcOc2hAiVkdkhNG/SFeNjBSAWFh9z20/Hh3wzeA9FC+9bhxsrHLEKXZsnpvy2IOJpxSPnoHu2BE/q56FcvjnHOmYOOeRd2Ouu6w3Hab8RIpxwRGHcCCcLXoNUmwD9kT8P2Lh2TbgFZ1C7clVQhypQE4iMWaVfAW/uC36BacKHtqxit4AW7IfLifDe0cREEz7EOwMtRuahZqFbM2hOAHUrVwVJRzyPD4qFb47tG/4IYf4Hx7Sf3tUTtHVdjy8s2IQUpmnouvbirTlMjbN/4EnaDjS2Z7jt9uEX4UGkjPWhQod6RJr24v0J0+7fHKSkPYlC5McyZgmk7ho5MBXPwolGHsxbzsLIpioNu/Obi+7rbhTCXIIv4LgJ+E/ga4hUdAJPplLp1jvu7Pnc9p195yDblS+4MbkJkce57hzm4U2n6/Eq8w407vNR2NdUrVPcsd50DOMCQE83ufZOfgjkofQG4AVQaoSErQFmxWJz2tRay39ciM/NNIJoSqC1DEwhpRJ8BTJ4+xkr+DGCnazYxr6r4MPuhQP+X4KI5lfQGJ6DxmwUb/cyhubUMcN14+hHf3ciRDipEZG9CCcLZuHJ2p3I3HbXAdskEHl7EqlQOxGhq0ckxKxCKhc0W2TMOuQJty/LB1yKwsbj+JwmM4mdwOd/tbjPjXHoIoQjwsv/9B2fAj51XHbWu3qctq7bUR7ZI8B1aEyWuy2WoDw1874z0meKSy261jy+r3AVIndZNM4W7t6Cxr/Z7W9mxfsFFC5/ABWS9KOF/GgxA9lzgM8hNCJhZMBUshhSaoyAgtQhUwWnu+sdc+dbVbGf81GxToAI0EMDA/0PD9Bv+70YFTbYfDLl1OxOQONmBCbhrjvvfp5X8R7ARa0dwby+DeGTRzku+9H9BcqFIjd/pCu+YfdQ+LeQeCuT8+cqO6tYxbFZ4Zh1UQHf3s7sbWJMNlmuzL8zBd6UP/Ahb3sQq/R2tOOW8cVOVhmeROMfR7mdC9BcvAEpppeg+XgWsmOJECGCQ0T2Ipws+BrKyZmByFw9MO46aTSgRWUYKSt7UIXjLEReXuu2H2Vy8rflCVWaLc9y72/DW1NYWMs8w4zMpfHhKEtsvxCIrVwV/PY54rX3VChUugsjy21dDWiBtoKDJrxB7jQ05gk0JhYGfRwt4tMQiTIz4jo0RjvwxtcZt+9GfJ/hX6M8shLQf4zhW9CifzFe1bOQY8FdW+DOyao4jdTZWBTddpX9fc/EK39G9uOIJH8fqZHLEFH9CKqAnYZCtv+JwrZ3IJXwTHxHj634nNHnoTk3iu9YUVlNWgP8dWtH8IG+Dcfc7mhOMsHs97wzccs/fJZvIhJ1MU8Ne1qhDky23jHC1eDO1fI2TQEcwXfNMEJvqt4Auv+m6Jmhs6mClkqQqfjdWvKZnYoVkbwQeBnwPff7/UjtOxffBSVChAgViMhehJMCjjh9eOWqII0WqBD9Ubf2Vn34sFIVUq7GEFHZhQ8djuDDVabOWbWgmSwnUYjIwmnW/qsaLdyDiMhY6CpEi5QliI+hcNwxVwSeIAyjHD7rF3suCsGO4kOjzWhMRtD17cSrYnvQOG1FRNz8CWejxbfk9mneczPRQp8Edh8r0WvtCGqAt+BNr0uILFhyvxWjZNF8GXHXVodIyDAKtZoCWHKvWUjbihXOBD6L7utt7rNmvTMD+BOUE5lFiuUb3TnscGNh4eSH0Rxqcr9XuX3MQgTRxsnwZvSw03ss44SU1scvOYfsP4iw/hW6z2uQYpaZ4jOW8mAwwmn5fUbMzJS7sitGHSK01oLQ/A+NTNv30CrBK21Z7PtoBTSV3nkJlNaRQzmOi9CDyHfQ34GJ9k6Srm9whAgRiAo0IpxkWLsmzKHE+F+iKkBLmjbVLosWzRmIoMxGKtQT+J6b1h4thRbxCURg7DXLwZuFFCMjdnm3vfV8LeH9yAbd+9vcuZwcXl9tXXGkcJXoXT2IFKs70FjUoms1TzgrVEihfL0iInO70NjuRord/YgYDrifh/HWHI1oLO/FV2weFVo7glhrR5BABNtCz1aUsxPf9zjr/lnFaMm9/zgiI3FEyHbg26mZEpdDyt3D6N5ucWO0q29DuL1vQ7gL3fdGvFn3LHecxWiOLsaHMHejOTgdkZ9Rd9z78FWuRqiy7viNwFdaO4JKy5wjxqJGcosa9QBy5tYnE3P37J2PSOlbEGl6El94U6kiWo6jKdswuXOMrSPmX9mCz5+ztIh6/L0wCx+rkLY8y0ozdKuiNlRa9YC+1+9A5Ni+b7fg7VqitS1ChApEX4gIJx3WrglLa9eEeZTzdRN6mm8CXgVcjjoSnIsI2B1IEfkxyg3bxWQLEGvtZQv7PqRQjeLziaxC0axWLLcrg8jPMry9RzNS+BY+U9d/nGEVxmpg37t6G1KvvoAqEfvRAv8YWrhH0FjMR4v6CN7TrhblUZ2LQpltKKx9Gd6s2TzlbgIeo3f1UakvrR1BFSJS5oVn4U8jkLPxoUhTh5Lu+AG6v6OIgBoRMUuaCTyxLaBClg2I6IWIEI61dgSVxT1mazIHkcxdwDVI9bQHCjMKXoZyzOa6z/fgC2G2obHMo/m4x22zDHj50YzVVHjVPffOv+TOe+Z33N1b6ulmCPgLVHDyn+g+WwgcJhc2mc+gEVsqfrfCJSNrJXTfmw/Yj6l9ZrZcmZ9n6mBlUVAlKglgnTvnOYi473HHvrmne7+fY4QIEYjCuBFOYqxdE47iOkSsXBVkgFei0Fk9KrC4Ga8uPICIQS0iKJYUbk3crRp1FKkulj+UxVthgFc0zCfMwr5ltLAtRIuOJas/u2jrklop65WpsA+pSvvc9jNR3tm30RidgRbx16JrLSKlLouu04yqx5FaNhvl5+1GRGoJ6rwAvh/stcCeozRONlhoeU7FMawHrP1ds84RZtRrFaZWZDAbH+KtdfsruG0t7F+DzI4fQEVBO9E9bgYGWzuCLX0bwkJrR3AnMkGeh+bPhaiDRQzlmIEnlBl33knkMXgL8EdI7bSqcSscsmKiGNDV2hFs69sQbjqGcQOgcXxiZ+P4xGjrwL4StNHTTaG9k9vdOcwH/hh4HVIprUACPJk+sM2dhWVNqbMwunXkqLRXseuyvD77nF0r+EKNSkHiQHEihpS9T6ACJjNa30GECBEmIVL2IvxOYO2aMIuq8nbiwzrnIBJwBr5ScztenbDCCltc6902lnhuvU7T+Dwk6+MJk+1JrPqyAd8X9LmAGcDzaetKPc37eXpXb6N3dZm2rjq0uO8AttC7+kFkJHsrUq6G0N+MBYhgnYXIjXWNOMP9XouIcxmNqS3glpd1I72rj6r3q8H1jn0IEdML8Z0WTI0y8mEK2W6UR2hefOcjRXCuO6chd67mN1dgcp/fFvyc2e2O/WTfhrDkzidE82/Iff7NiPxfhMb0UfRgMoRUyAE3ZnlEWMxjLoeU6C1402dTTs8APnKs4VyA5cUV25cXVzy8vLhiP+Hu6abc081ETzcPIQPuDwAfc+ezveLjB7bas30k8CTZCjlm4PsTG0y5s9Cwfa/sd/s+Hg5qkEpa4477CFMbg0eI8H8aQXjMBV4RIjx3sHJVUI2UiYuQbcpDaNG2hafBvWdtm0zdMwuVXYi4teDViEpSaITFHpQsOd0KNXYBvwI+v3ZNeHQmyMcKEbu56BqXIUL0VYz49K7O09bV6M7XesDm0HjlgEcmhVdVrbsILarvReTEqpnNQmPI/dyHFvJZiIA14olMFvgM8Al6Vx9zs/rWjqAatYJ7D7qvAd4r0HIsh9zxTbVtcNdshN+qq61gwixnLAxYRMTrIeDnqGPDBJA9sDrWdfF4vTufm91+/p8bj0+j+bccqV33oXGfj8/32+7OcwKN8TS8JYype8PA6/o2hL85lrE7HLh2YQG6l38O/CG+QrbSI8+8A42EGpmrtO2p7F5i6rjtx0i05dPadk+Xz2kPXpVixY3A14Hf9HRzzDY1ESL8riEiexF+J+GqdptQ2GwOCu82o4WrAZGgFnxSfKX5rlXXWkjq/7d37rF13+UZ//x87PgWO5eGNGlPk6ZsbVdacDvGWhpu3Q5jVbloYpvEtDD+YBojYmiT2NQhBW80k+CPjS3SJiQGhCGNrYixUVZwGTDCBIw2hpWSXtJL6qRJc7cT3+2zP57v2+/PTqLQ1Hbi4+cjHcX2ufj8fsfxefy87/u8IVSG02OW3+TKtwH1uPUjcfX15DguLD29HcDtqH9xMxITf4mEz+No4GITefXVCXQc1wN76N926hyPuxKdxzeR3bRwbEaQmHqGvKO4GZ3jGE4YBV5D/7bHX+ohJmF1PWrS/0P0etWRGIpdqx1kkR7RJuV1ZpCdpIgACQEY6+/inB1FE7j3pmPaDwyFs5eeUzvq16yQN728PV19Dzpnr0/PYR8SxmPpuV2DegPHyJO5PenrUfoMB/Ie4O/nIIrlZ2bzFlrTc7oJuaK/jl7/1WThNczMvslob4g/kELMTZLLvOVycFwfhACMx4gezNmDIaCf4Q8APwYe27XzjLVwxixp3LNnGpI0tXsQXhB+4S51oDeEVUgQxBtJG3kqMHq7wu0Ld28ICcXyftUy0+hN/Ajq8/o2F6ecew0SGyvTx63Au9AUaQwo7EfHFg5XB3K89tPTO0r/tpnB0D294eB9lTwxeSu5DNpeuhwlTy63lh7lOVTOfMkM9NXr1VrxPCqHll2j2P7xNHqNYw9yCzof69PHcXzNnPkaRYxIxIocRmIiJkgPINd0GSrpBmNI7Hakx3gV2fW9Iz3OMFrFFnmF0XfWhMrfEUC8IX0eWXQRdDwCNC+k0ANt4ACe2byFA2ig5AvoD4rb0c9YrDKLcxv9dmUxF+Ivhpsgr0yDPJEbP5fxc9aEzkuUfKO3r3zflWhY470WesacicWeWQo8goRdDxIHL0ciJURdZImF69BELlHG1OAkeZk9zIymCNEX2wJ+Dr0p/6x9R3OHRNkvoX7FECIFOubjqIR4FTreJ1HfXTsaEuhHb7Sb6Ol9etakbDtyxPah6JvvoTfcP0ZT0OvJorGSHi/cmFbkuP3WHIQnv8BAX/1otVYc5syyXxtyFstr0ULUjZPdWMi/A/eh1zjW5RVIOHaRS5ZvRH1/3073r1RrxWrgVOohrKTHuA1NOF/OzB7Pj6Hybhf6Y+AqVBpfS54E70TC5eXkfseYWm0i9ShWa8VXB/rqT1zgqbtgUnbdic1bGEU/X33pqjuQi3lZet6rmLmaLv5fRdtDiL04N/EaQf7jCvLatUrpviHgZ7P2HF83ZsljsWcanh3b65Nb7y72I1duL3Kw6shpmkJl3nD2IkIlBGA4FLHCKt6wwnEo9xUdQW/WneiNcOHiH5SX14zKa29EYjN60aJ0/TYkwr6LnL06csA60Zv0KvJ+4Nk9dd1EjmH/tiHkABaodLYGqCGHqg1Nrn6RPKAxChyYiz69s/AkWRCFi1hBrli4RBPM7BcLsReZbyPk6JRj6b7R19eKSuHHUcnyfiT2RlA5+zqguVorutDrvxv9PA0CX0YZdhVyXuEq9Dr9BHgfOvcRQLwxHU+IpOh5iz9KSMd0DVqj9lS5jLyQ7Nr5Qnbh8wCbt/AUcB9y+N6GhOx6dB7LIdHhpod4Dco5e+XJ3HA7o28xSu6z+/lGkfA8gDHmDCz2zFJhOVooP4Wa43ehN6IYZoCZrkA0+IeAiGGOuF1MSEaPUUFutN8NfHLH9vrQfBzIC0hsXZa+7w2onyqmXPcjsflour4VTSRfAbwGRauMIkEYwb4V1Mu47ywO3Ei6TS7vKjplLH2vz9DT24mme8uO4Oz9xXNNxG10kadpIfd1VZi52xV0DKfRz0QTEiM3ofNwBTovx8h5ezHgMYLE8TIkZgbS93wnKrveh6a/h9LH16THWZe+x3r0+gym7/1Iun+ELq9Ajl5Mk8fzDNc5pnU7Ue9ff7VW9F8Mwbd5C23pOQ3t2kk9lU6f27yFQyi+ZR0StjUkYrvRz+oGcjl3OB1L/N+KP1AuI69piynd8maTLvQ6j5NbLz4PfMIlXGPOjsWeaXg+8sHiLlr4HVqooLiNX0PbAjrQm8ozyJGYXQI6VzRRuBJRIozNDTHdehmRWzdfSOjdhaYkD6I3vmuBzyD36TZ0TFWySN2ASrE3I1HzACpLdqFNJCeIFW89vTMncvu3nUR9Zufm3Fl+88mz6XILEkJRNp1Aoi1WeoX7R7rdafT6h4N7AgmuEFqxKSRKuFem21yerh9FYu8oKh9eibaFXI5e+2+lx/gaEjzfAn4zfb43Xfdk+vidSDxeQV7/N0kONo52gOhNbEeZkiuQsxpbKhaEzVtoQeX/aSTsXvg52bWTaSRwj6Xb7kZ/aHSQ/yiJ1WnXpo9BfYyH0M/sK8nRLV1IPO9F4jhc2SfTvwfQ6/m9XTvPcKONMQmLPdPQ/NP7i/VH2/gQmj59ALl6G1Gz/DEkYAbRm/8GZvblzf7/UQ6WjVJSeWpzHAmC+yk7YHONolXeB/w5ejMcRG+GragvcRkSHD8lr+4qC9flaFK0GU2zHkdvsqCNIz9GYb+H5+0Y5o5J8pYJyE7YJHKOYj3aUSQaVpNfp2YkzssubvQdRrk1BNZK5Nh2o/MXeY3rkFA5hbaMPJu+bzihR9PlunT9gYG++mi1VkQc0HfS97gFleA70Gs6Sm4hiD8mopetGYmlG9NzWWimkZs7fL79s7t2MozOx3Fg/+Yt7CGXcrvJwxrD6LWL87ISCei16I+xR9NjVICmXTsXzd5pYy4JLPZMQ7N6iqkV09x7ooURFJsxiQTRe1FJ7gdI9H0F+DAaNjhf2Hg4ZeHqDSN35Vkk9L7BmT1vc0NPbzNwZ3quy0vPpZUm1jDNtUiwLEfHF67W7GNqR2KhFTlK0Vc1hUTiYloi/yDwOvJAQ0xSj5GjV6ZRb2EXEnovI0+ONpGFR7lHM8qNpK9fj/5YaEbl1ohD+QkScvvJQwRTSIDfmj5+GYpwqaTYmFayS3gauYK3kqN9QqDHNGuUNWNzCOg1PntMzjySHLT9L+G+g+nTcz3300hMP7J5C5VZjt38/RFlTANjsWcamjv/of78ndrzCsDWu4s/Qw3kz6ONBt8hZ3n9J4qSiP8X52oGn/35g6hpfy/w1XnO1rsReAdyfiIjrwPYzzSj5GlY0DGGO3club9wipmrrcLRmkDCtTxBekmTIlgeQCXSbvKxh6sX4q8duUUFco6GyBs2JshrvU5z5lRn5MStBl6LRMrXkeA5lO5fDPTVZwjkaq14BkXVHEJ9kl3pMY4N9NVPV2vF3vScjqI/FJ5EZfcoH5eJmJbu0td2c4Gia7Hg0qwxc4PXpZklw9a7i27gj9DU6FOk0OMd2+tTO7bX68DngB3MzFQLZrt5IBFwGOhFe1DvW4AQ5Q3AW5Ab+TG0Z/YhchBwNxIQnUhcfB8dT5my+xX7bUPYbkIDA4vJ2RtFLt4Y2VFdQ96EMYUEXoj48tRu3L88cBNiK6aSw11bjfrJ3oOCnK8Y6KuPIGf0quTYvUAanHgY9ekdTI9xYqCvPp2ujzVuDyKX7pPATlSyPNv5j/5CkFj9/Ys1jWuMWVzY2TNLiSEkyqrAh3dsn/lGuWN7fXTr3cXfIPfm59Ptr0LCKd7IoyQY4uA/gEd2bK8v1HDCASTUulApcQUSa5FldwwJvkdRybGKehTD1atz5k7SMXLf2zTqQVtMPVEnkTsWIi4mOGN6dYI8kDGOXs/l6DUsD3DEgE0Lcu/GkHCG/Pp3psd6A/B4tVb8W7rtxNmCjgf66mPVWjGIMgwPp/uXr69Xa8Uh5AyfRi0AN6NYly3IkY1p1BbULvB/KNS4oV09Y8zcYbFnlgzJves9z81OodVi16GokjvT1yO5fwiV5R5CPYAPLaDQA7lA/4ry296KhgBOIqHyJBIou9Ewyu1otVX5/3k4eBEfE0JxdbquE4mNb7B4MstOIIcsBmeG09fbyFl5x5DYa0WvcfTFRb/l1eR4kyl0Hlekx4mA7NiJPI6E2zLk8n0BeCKtSztjZy4S4d8kZ/7NIN0++timqrViN+rd25OezwYkZg+hyKBxoBIOoTHGnA/vxjXmLGy9u2hCmXM3oDf1W9Ab8GfRxOrYju31i1Pq7OndgMRYZML9DxImP0bC5wdIFNyQLltRaXYSiYcoT0aQbcSSFOnr/4sEzL8TO0+VqXfJUq0V7wH+Gh3fc8j5XI5E2yCa5NyQbv48cmxjD+4+1KfXQc5vG00fxwBF9DWGmJwAHku3exqds8eARwf66iertWJZesyDA331Fz1UUK0Vy4Gx6AOs1oqKS7bGmAvFYs+Y85CEX4Ga/E8nh/Di0tP728CnyWXl51Bv2DGUR7YaOXydwD1I7I2RNxEcQGKxFYmZ2GvahNzLB4GPI9fwSfq3jaZsv3a0ReOScpWqtaIF9bz9Cjr+V656YJgAAAsuSURBVCBxHqvuTpPLtrEKbQK5dqeRSFzNzG0P0X8ZQzpDqNw9jcq7I+RBmf9Ot30MZeqNov6+H6JzODV7gMMYYxYKiz1jFis9vd9CZVqQcPkeEjoH0BDKf6HevY+Qd9euIa8Fa0UOWAyjTCA3bCjd7x7UFxZlzgkkhvallWmXFNVasR6V6VehDRPlzRnTqHwbK++K9Hm4bnUk9qJcDzpH0WMXu1uHkHhrJrt9Q+m20ygTrgn1PX4RnceNwMhAX31g7o/aGGPOj3v2jFm8vAn4BOrfa0c9htcBx6eY2n+w+cjmzsn2npV0X4kGDyDHjTyBXK5fSP8eYWb8yNWoJ/BE+togKhfv5dId3jiBsuwiKzH2GE+g33UR3hvu3jg5luU4Oq4Ocs5eHHdBjkKJGJY15DSDdnR+D5H3Jq9EQvOzzF4zZ4wxC4yjV4xZrKiP7k+BtwP/jFylcaBjmnrbWPNYz0Tz+F116muQwBlPl3CnhpG7NYxEXMSXdKAy5EY0oPKO9LUVQAf92yZTSfeSIsWgfAaVUR9GAu4UeTMDSNBNo+NvI+8zjn2rEUMT+2jDFYytHGNI3LUiQRi7d1tQj9616XIlKiW/FzmGLuEaYy4aLuMas5iR6GpLl03AHUhs7DldDL+6qd70i+20rUZi5AnkSE2gva4TSMAsRwMGb0QCbxL1+y1Ln4+jgZCfoAgRkGv1MP3bLjmXr1orfhn1G1bJQi4y90bR8UaYcit5ireZvG4NNN28kpkxLVH+jUndOjm8Ofbxhqg+gF6Xx4AvobL6+EBfPaaFjTFmQXAZ15jFjNy9EWCEnt5xNKDwI+DRznpHF7mnLwKTY9dq9LNFXmC4UW3I+etEQmcNEjKvR9OsG1BA8MPM10q4l84PkTi9E7iMPFAR08gjyJ1rJgveGMKIFWnTyKkbJ+Xopc8n0u0i9y56HSPXrz3d53EUl/Ia4Lb0vQ6jTRlPz9uRG2PMWXAZ15jGYRy4D0Ww3DjJ5M1TTLel62JFWgsSJN3InepEgmgdWbgcR0MOVeRatSOhuBm4CQUA76J/2/jCHNaLI0WU3IdEXwxOxCaNyBYModpMDmEOERd9eSdRL+MJsjicRE5he+l+E6XHCqc1hjdOpu+9EYUkX1etFf69a4xZUPxLx5gGYeudH5n+0rVf23S8GHz3FFN3jDJ2+Rijx5BYOYxE3GlypEgF9eItI4cKR6TKOvLKsIhl6UBO1TWXqtAr8SO0+m43uUwbk7mQg5OHyG5d9ObFpO1aJHIvR+K3vCt5tHS/EHzRE9MK9CBhvJJcPn4F2syyolorZmzSMMaY+cRiz5jGoe1I27FXnWo9talCZQoYq1BpQyJkEgmbfWgzwyASISFeWtHvgynk9oU4ir2wQTdyri5pUqbdHuB+5HguR8+7kv5tRscb2YLRZ9eEzk3EzLSm+4bbFxO942Rx10wWxsPkTL8x8qaNK5CDegvwaiQejTFmQXDPnjGNw/BtB275yqrRFUeA5cvpXIccuk5Usl2Gyo9XkcOVC/KEKuQSZAwctDBT7E0hEXXJk/bOfh/4K+D96FzEcUdJOxhE5dmYUo6J3BZyuXZy1sct6eNxdF6G0vWdSOgdTN8rsv2a0N7laeDD83PUxhhzJnb2jGkAtt5dVFYNd6+6/sjLr15O502oXDiMeteeAX6KRFoIlHDsptHvgWXpEiJoGRIt5d8RU8Df0b9t94Ic1Bww0Fc/gDaN3Et23U4hgVtPl3DpDqBVah3ovBxE5dc4B2WXs0BieCx9HkKwPX39FHIEK+lx9qAtGytQVM4Hq7Ui1rcZY8y8YmfPmMZg7doTq993rDL45rVTq9cgobYHTdjegITGLtSDth6Jman0byU9RlnIzHbzTgKfAv5ino9jzhnoq09Xa8UXgLeg/rsV5AncChK265DoG0yXEeT0NaWPI3cvAppjcneE7PTFPt0W5I7eWLr/8+TMvia0Su2maq04cCG7c40x5sVgsWdMY9B9tG3w1mP1Exu66Ohop60VuBVtyGhCou/NZKESwwjL0v2jd4/S54MobPlf0CaIkynqZTHyFMreexcaMmknH2+Us2OSNpzP8q7gleTS9gS5HBxuaJ08oRuPF8HLHcA16DUIsXdF+vw6lF9ojDHzhsWeMY3B/s7Jtu+2TFc2Al3TTE83qfoYwwIVtMkhRBzIuQK5UiFUQC7ex4HvAg8tgsnb85Lcva8gkVZHgxJrkSAbJ4uzKO1CjmGJsnZBztGL0m45kDkGYWZP/S5L/7aVPr8cCc/j1VrxyECf0+2NMfOHN2gY00AcvHXr+vbRtttX0PUgEiYnybte16BevrVoj+sr0YToM0jIvAz1l91L/7aBi/D0551qrWhG5+BdKHR5Ezr2ZaWbTSHhdhSVw2N6N0KZI2qlPOARGYWDyMkL93SanLkXorkT5f/9FK12+9uBvvogxhgzT1jsGbNU6emNNWCji7g8e0GkYONrgY8i8beePGkbJe4QfeHiRdk2ev3Kpe9wTyPDMAY2otw7icTeKBJ/h4Fvor7KnRZ7xpj5xGLPGLNkqdaKjcA24A0oQzAmcSGHJscKtcjji5zBcPpCDI6lyzAayggBGcHL9XT9SSTyOoCHgM8Buwf66vF9jTFmTnH0ijFmKfMs8CHgk2gQI6aUh8mTyk3kHrxp5M6NIOEWGYUh1KJ8243EYDlkGfLk7hjKO3wt0GqhZ4yZT+zsGWOWPKmX7y7gA2iCuZMcrFwm1seNo5JubOGIdWkhAEEir5MctBwB1YOojHs1WmH3UeBTaaevMcbMOXb2jDFLnpR192XgT4B/BJ5Dwm2K3LsXpdxy5l58fSpdN4ycvyjltiChF9O8oMGZq9PjLAd+lTwZbYwxc46jV4wxBq1XA3ZXa8WjqNz6VlSOHSU7feWyboi/GMaIwOXO9PHl5EgWyFs3Iocv1qxdy8yMQ2OMmVMs9owxpsRAX324Wit6gcdR+PHm9G8XedtIBCnHnl2QE9iKnLtWZg5vxJaMcTSg0YqEZAWtaTs1f0dkjFnqWOwZY8wsBvrqI9Va8Wkk5PqB30C9fJOo9BpbNK5CrlwdOXrh2jWRHT3IDl95U0dzugyQxaAxxsw5FnvGGHMW0oTsWLVWPIDWrU0AVeDd5Ey+dcili5y9ciZfvXSJMm0HOcql/LUQjMYYM+d4GtcYY35GqrWiQMMV1wB3AL+HhN8IKsvGxG1EqUQ4c4i7GOSIEvBB4HcH+uoPLtQxGGOWHhZ7xhjzIkjbN5Yh0fcHSLg9DLwDOX8r0Rq6CFQeRf18DwEngI1I6O0EPj/QVx9e2CMwxiw1LPaMMeYCSU5fR7q8DrgeuBl4BdpF3ArcD+wFfogE35WoD3A4TQAbY8y8YrFnjDFzQBJ+TUjgNaF+vm7gEVLv3kBfffziPUNjzFLFYs8YY4wxpoHxBg1jjDHGmAbGYs8YY4wxpoGx2DPGGGOMaWAs9owxxhhjGhiLPWOMMcaYBsZizxhjjDGmgbHYM8YYY4xpYCz2jDHGGGMaGIs9Y4wxxpgGxmLPGGOMMaaBsdgzxhhjjGlgLPaMMcYYYxoYiz1jjDHGmAbGYs8YY4wxpoGx2DPGGGOMaWAs9owxxhhjGhiLPWOMMcaYBsZizxhjjDGmgbHYM8YYY4xpYCz2jDHGGGMaGIs9Y4wxxpgGxmLPGGOMMaaBsdgzxhhjjGlgLPaMMcYYYxoYiz1jjDHGmAbGYs8YY4wxpoGx2DPGGGOMaWAs9owxxhhjGhiLPWOMMcaYBsZizxhjjDGmgbHYM8YYY4xpYCz2jDHGGGMaGIs9Y4wxxpgG5v8BdgTZ+kICUQcAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(11, 10))\n", "plot(embedding2 @ rotate(90), y, ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### With exaggeration" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 7.6484, 50 iterations in 57.5835 sec\n", "Iteration 100, KL divergence 7.4664, 50 iterations in 57.1662 sec\n", "Iteration 150, KL divergence 7.3579, 50 iterations in 57.2441 sec\n", "Iteration 200, KL divergence 7.2860, 50 iterations in 57.1173 sec\n", "Iteration 250, KL divergence 7.2353, 50 iterations in 57.3436 sec\n", "Iteration 300, KL divergence 7.1975, 50 iterations in 56.7744 sec\n", "Iteration 350, KL divergence 7.1688, 50 iterations in 56.4604 sec\n", "Iteration 400, KL divergence 7.1451, 50 iterations in 56.8742 sec\n", "Iteration 450, KL divergence 7.1263, 50 iterations in 56.7128 sec\n", "Iteration 500, KL divergence 7.1102, 50 iterations in 57.2065 sec\n", "Iteration 550, KL divergence 7.0969, 50 iterations in 57.4542 sec\n", "Iteration 600, KL divergence 7.0852, 50 iterations in 58.1009 sec\n", "Iteration 650, KL divergence 7.0744, 50 iterations in 58.1829 sec\n", "Iteration 700, KL divergence 7.0653, 50 iterations in 58.5190 sec\n", "Iteration 750, KL divergence 7.0565, 50 iterations in 58.2187 sec\n", "CPU times: user 1h 50min 3s, sys: 1min 34s, total: 1h 51min 38s\n", "Wall time: 14min 23s\n" ] } ], "source": [ "%time embedding3 = embedding1.optimize(n_iter=750, exaggeration=4, momentum=0.8)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnsAAAI1CAYAAACuZjyLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xd8ndd95/nPeeqtuOgAexNFUr03qxkT2g5lm2tn45aixPE445Q1J5tFChPPvuKR40HiCZ28ZjbxehxrY8dJ7EnCcawopgNZlhVbkiVZXaTYCwiil9ueevaPc0GAKpZokQQI/t6v1xUB3PbcB9DFF79zzu8orTVCCCGEEGJxsub7AIQQQgghxNkjYU8IIYQQYhGTsCeEEEIIsYhJ2BNCCCGEWMQk7AkhhBBCLGIS9oQQQgghFjEJe0IIIYQQi5iEPSGEEEKIRUzCnhBCCCHEIiZhTwghhBBiEZOwJ4QQQgixiEnYE0IIIYRYxCTsCSGEEEIsYhL2hBBCCCEWMQl7QgghhBCLmIQ9IYQQQohFTMKeEEIIIcQiJmFPCCGEEGIRk7AnhBBCCLGISdgTQgghhFjEJOwJIYQQQixiEvaEEEIIIRYxCXtCCCGEEIuYhD0hhBBCiEVMwp4QQgghxCImYU8IIYQQYhGTsCeEEEIIsYhJ2BNCCCGEWMQk7AkhhBBCLGIS9oQQQgghFjEJe0IIIYQQi5iEPSGEEEKIRUzCnhBCCCHEIubM9wEIIYT48ai+HqV7+7Xq67Ew7+c20AqUgWnd25/O6wEKIRYEpbWe72MQQgjxI6i+HrvxoY8JdAGwDLgBWAo0AVcCRSADHAX2Al8FntG9/fJGL8QFTMKeEEIsUKqvRwEXAdcANWAV4ALNwF3AciCLCYAvn5YTA8eArwB/CYwDFan2CXHhkbAnhBALQCPYtQB1IAXaG1f9ErAZyAElTOhbggl97us8bApMAd8ERoB/AR4HHN3bf+IMvwQhxAIlYU8IIRYA1dfjYoZli8B1wHogD3Riwl2hcZ39Wo/xI8wEyAHgHzFVwL8GXpBKnxCLn4Q9IYSYB6qvx8HMwWsCVmPC3LWYuXjvwAzV+oDCBDz1Jp9SNy7TmGrfY5jg921gROb1CbF4SdgTQohzSPX15IAE6AI+gBm6vQ4zNy8C2phdiHE21YHnMfP5HsHM7xuX0CfE4iNhTwghzgHV1+NhQtwGYCXwvwOXYSp6M5eEc9cSKwGqwAngaeAA8A/AkzK0K8TiImFPCCHOEtXXU8AsokiADwObgCFgC7AOM3fO4UcN0cbaXGzAPeN98HXj2CrAIGYhx5eBp6XCJ8TiIU2VhRDiLGjMybsNs7hiH/AuoAOzorYVE/QSXm8uXqSB1DRSSRX4b3bq3qmHifk9UMIs/shh5hD2AfvP5BMJIeaPVPaEEOIMabRPyWPm4V0M3AJcgZkP99OYhscZTMh6Y6kt1RA03qc9BfYZDXsvl2DmDd6Hmcf3Zd3bP3o2n1AIcfZJ2BNCiDNA9fW0Axsxq2g3Yxogp5h2KhamenY+7EeuMb38poDPAP9T9/Yfn99DEkK8GRL2hBDiTWjsS3sz8FHMqtoKZguzmYbHWV6/+fFCVMcM5X4HM6w7KPP4hDg/yZw9IYT4MTWGbbuA/wJcMucqq3Fxme1vd1bHX88CHzMU3QnsBu7FVPyEEOcZqewJIcSPQfX1zOxP+8uYvWtn+uLpOf+eD8O2rycBhoE/Af677u2P5/l4hBCnaTG8EQkhxDnVWGn7LuATwPWYoDcT8mYWXyTzc3RnnI1ZRfyrwLtVX48/z8cjhDhNEvaEEOI0NIZuL8dU9FbOveplN52dp5em5nL+sjCBrxe4aZ6PRQhxmmQYVwgh3qBG0FsL7AB6eCN/MGsN9br5OJs9i0d3hmgNOgVlgXrFNEMN/CvwblmsIcT5QxZoCCHEG+cAv8sbDXpgApPjgTqNrW6nGlXApnkYfElic3Ff9ZgVcCVmUcrgOT+2M+yT25bkuoJq27XBVIuCjznw8Su+oMP5Pi4hzjQZxhVCiDcux2zfvDfOtU/vT2uL2RmA55ptm4t6zZdYwmz3dl57+sMqf9PUyP99WTD16BQ8EsAvVGDi6Q+/spwpxPlOwp4QQrwO1deTayxM+DlgxVl/woIFpXl6e1ZWoxJpMk8RKJz6q8IDfl319Syfh6M7Y/5h/W35g+0r6xrKjQ3psCC54gsyt0ksPjJnTwghXqbRKFljVqK2Apdihi8/wex2Z29eGJqH8s67nsvHgf+ge/u/Od8H8uNSD93rZeKgbfvDXxx/x/6nrk2c7OiNnxt5cb6PS4izQcKeEEI0qL6eVswq2jWYPWKXY4YsrwZWYwpdZ8bMwg2lIJM5Yw97RmkgTsx+vNYp1b0HgV/Wvf2H5ufAhBCnQxZoCCEE8I7/8UXbxrk+IV6CaTNyCdANtGO2P8uf0SdcyCFvhgKSELDAOtleLwa+C4zO12EJIU6PhD0hxAXvZ+79Ztcyd/1PXObc9taIcMVgfMAbYyCDCXtFzNZhZ34S3fmwFiCTNVXIWRXgB7q3vzxPRySEOE0S9oQQF6QdO7GA1nI8cccyd/3PpGmyss3vbjsW7C+GxCkwjpm373G23iu1higCx3n5MOlpPg5nfnO2MATLNbMWTw2lA8BDZ/CZhBBnmazGFUJccBpBbzlwZz2pfCTU9Y1BGlR9OzcVxbUjK6z1SZ62DNDC3J0wzhStTZhKEnOJ3+R2szEQYKLpjxLwxjdxs23zwHODXgqE2WTJ6HuiH+MohRDzRCp7QogLUR7oBEZydtM3tNaX7a48tju0qpevzF1SKril9mylwJPxt2x9Nra4TRQkqanm+ZlXX9urNQSBCV3u6+RNm9nHSBqfzzU3mqWvcv2rPqb9yhumQJIJ1x7/dSkUCHEekbAnhLgQ1YC9QMtAsG/wUO2F8rH4pR8cre/97k2lu5bHSXzCt7JXapLSy++osNCvW0J7HTagMmDpHz1vT2tT+Xu9sGc1LjXMu3o45+uuhrgx5y77uhktwcTGmYM69eBswBqPH76sJ4T+13ssIcQCIWFPCHHB2baVGJgAJi76s3vtotMy2emvHPv5ZZ/o3jXy5akD4TM/MZVMt/GyfSyyqkBeNTOSHn1zB6BoFM1+RNCbCYFaQ02Dq17/HdvCDOnOSDUEdfM8XgZMmIsx8xBf/uS6cX3SuH5mFuBM+AtRDKD4e+Zvfw8hxI9Bwp4Q4oK299f/JAGO79hp6latbveSp6cfnB5JjlaAKuC7ZFRRlZjQI6RnY1gXIE1NwJtb6XMcE720MkOoAeZde+7oaooZpvV5ZQSzGvdFzdzHZnY5RzLnEaYbX1ONRxpvfG03sK/xzH8LvKB7+2W+nhDnGWmqLIQQDb/wpW9bJ4LDbzkW7E2eKX/3Whv/+hTeYpG0uXhunerJcdAczVSZ5LSLXFqbYGfbp37tRzVYnnmKeuPfLKbZcZpA6pmI5s25fsbsXh96zqOEmOpe3Pi4ggl2FeAFYAp4uPFojwIV3dt/lhKuEOJckLAnhBBzqL4eBxOMLlLYl69wLt48FB+9rc50u0LlNNoGKNJKlWkSTrPQFUXm4vunBr4oMs/qOq8+jy9NIQjNdXqmjFcHlZ2NcQoTHK0UHBtsswcGpvY3hllZXMdU6/Y2HuQo0Nz4/CngiO7tH1F9PUr39ssvCCEWAQl7QgjxKlRfz0qgo6BabkRbK2KrfmvRad00Fg76CdFMSju9rshhaEKbZYHnnXpdiglzJCYIvrzvXpqa1bmAKePZQACeDyEaTYqfKoLAQidgZSKyzgQwgqncfQOzv28MfAl4DhP2NgEfAZYAfwbslEqeEIuLzNkTQpxix07Utq0yAR84DgyX9fhhYI1PLovWAehlmF018piB0jcuSUxlzvVe4wYW8BordC3LDPHqxvVaQ6BMnS4mxkGhVQ1LeUS6jpPuxlTqnsGkw13AV4Aal79zAi/bypP/cCNB7VbCUJPNvohlPS1BT4jFRyp7QoiTduzkL4DbgWu3baU638ezUKi+niagDdNk+Srg3cD6xtcyvHLZxKtLGy1bkhSUbeqCaWpasNin/be3JibumCZOU04EPsW6zWicpRlTtfsMUAJ+SGPBhe7tj1Vfj+K69+dIk7U8d/8GNT7UnRsYPGCnaf/k5w/XTvcghBALn1T2hBBzfQjzvtAH/No8H8uCoXv7p4Ap1ddzEFiHWcTwEqa6Z/HG2hRD2FgnYaVgWxDH5KoVHNdiKpN/o4Fvpn1KjEN9uIljaA6vnfajbKgeeS5brwPfwwQ+D6jPzL1TD93bSvPyZn7wtylXv2c3hbbndFBuDbq70ugPHpGgJ8QiJWFPCAHAjp04zL4nVBpf84FQhnUN3duvVV/Pi4AFatIm+86E6q2YrdcUcWyTpq+cj6eBODLNkTUQ182wrOMQ5HI4aQz6dRs1z9ygBhwGDgGPYRMBrftLQWrBAeB+3dsfzrktqq8nS+uqmA13ttC81GPi6ChP/kPUeD35uJDXmAUcQohFSMKeEGLGJ+d8HOzYyYeBAnDHjp28BPxeoxnxhW4/4IFua3FaVTX2VZXxMuCRprbZ9zYGr/H2mqZmYYbWZujWtcwCDADLIvF8pvRrzeE7KcQsspjGNIM+gtnC4gDQCjyExXBqhm1T1ddjAboR5lxgJWOHxoFDLNmU6vd9em6yPHJGzooQYsGSsCeEmPGuOR9/EDMfbQJoalxskLCne/srm37zsqebq5XmysZbVh9LD2yspkRADs/TJIk301LF1YpcXTOFRisLdGBiWzY75wF1o13KKatvZxocz7RNOYqp5j2BqSLuxwzTTqOsMbycg58/pP/DV5JG0FuNqc6e0L39kerrOQaE+ra7X/H90739b3LvNyHEQidhTwgxYxlJrbFZVjbFDAFmMYHvn+DNbgi7eLz3+ec8oHP/uqZnj2qu73CXr52Mxx4JdXUttr125nZZbdOpPKrKIUKbsEc6234FTHNky5mNd4ZucTLlKE3GojRKW+3cyHQSlfP55qt0WB8fiaYPpDAETHLDh2povRzb8TDfMw2UMbtemAfr7S+fg9MihFigJOwJcYHbsRMP6AAyzMz0SuMmLOdzwPsxW4YtAzbu2MmzMn8PMKHq0APhw00VJxrpslYXlHKahvTBZubEtlpSZ0THRI4L2gbtQhqYfnkz1T335Ntw2rhfApRjnVYTnU5dW1pZa/b8zuGovmLccQ8ljve1SjnYWUnCOpBg2TZmKDYAM68QEwSFEAKQsCeEMCs2fwkA38E05HWKwIuYFacas0fq9Lwd4QJzz306VX09FZher7QarkaTU0mavg2oEZDFwsPFiixLTTmW6a+X1EBZpkee/Yo5egkmVA/nUPuydmbPaFK7OIM14LnWSJgmK5dncpnp8vjA3vJQBbB1b38MoB66N0tY0zz+dx633f3yDdOEEELCnhCCCvAeACwXs6MWNnAFpiHvOHDftq0cn6fjW6hswHKUt3tV7tKW47X9j+bSdd/Q3ug1x6OJ69pot2v1kdaqSixSDbbtYSmz44VpmqzRehKlImAUE6Z/sK6p64lIJ02j07UX66RLHxo53JyQThWVe8S27AlMs+e5wbuDykgWszDjgO7tl8AnhDiFhD0hhAOsAkh1ikKhTBjJAw8AFwPrd+xkGMgB5W1bF+/8vcbeuHlMoNJwcmj0FLq3P1b//JmDkVJVNdF2dPTIw9/PWDl18WQ0RjQe1JvzI6UkzFtJuKpWLOXteq09tK08SvlojVOr214SR0E2O5Q4zgFMyu54ZmrQt5R6AriOTCmbWI5HdWx0WkdDJFEVuA4YU309exqLKw4zOehg5leGLz9OIYSQsCfEBWDHTpoAZ9vWV+2l9lYgk+qUIAzAgqybBbgF+GdMdW8UE4BmVoIu5ga8OcwcxYOYHTNszErYVyq010nj6cef/5tmdBCESa160Cp//+bjx57+gWONRrZVrjWVfiOx802Wpf4eld6B2WqtZLv+VbabbVVKvwDsA67BzNnLpFo72JkRCp1VykNZ0B1AJ/AYZth9BbAHQN92d4xZZSsVPSHEq5Lt0oRYxHbsJIcJB+uBSeCluQssGo2UHwCu01pTj+sopcg4GTCT/D+N+aPwWcyuDOswbUACwN+2lclz+XrOBdXXYwNrgBFmd8cYfa0WJaqvR2HCVx0z5L3USZKR6HcerDSuvwtUFvRjJMlbvTB4W2jZy8jm8+RaBggrD68PE/84wcayaZT8TWASZS/D9rqIazNj6/uBE4CP+V4+L/vYCiHeCKnsCbFI7dhJEfgpTDB7Cjj2KitpW4GrIESpkxW9GSXgSuDrmIUaNiZ0xMBG4OYdO9kL/Cum+XJ121Ziv2/z8lW02Ht6/+7QWXx5Z5PGVDBvwcyPew5Yp/p6jrzafLjGEO/cyt/LX/cDoJdhhspfaI/SlRM2h6ppXKA2MUgSDe+HaxNzXndjwlwenRwhru3FBPXLMVU/jfl+rsWEUZlHKYR4XRL2hFiEGhW9WzDh7HlMiFA7dlLCzLmbqQjdAjic/DRq3AUaH3iY4ds2zNBtBXPjo5hK1h2YVaTrgWM7dvLtJRQ3XqOWtvHZR4f5+A3Vs/tKz6zGfL0/ALZhXt+XMEPYJ5g9SafrIuBqYKjk+ON3Lt00pbR+7MuVvQWSqA14LjF98dZiehqOYOYLuphKqsIExQ5MFbbe+FqL6usZxww5H9e9/efVuRZCnDsS9oRYRHbsxAI2YAJAK/AIpoXKMkwo68TMRas29r3dZu7pYeb2xzQWmdL4z/WYoPM9zN6pFrC08e9fYwJKByacDAHpnWrt937evrbEAl8ssGMn7vPlRzZ8Z/xrenf18d2NViYOJpylmKrZnwMv6d7+6E081SHMOZzIOe3ttrt+6urmaw5+OfeDDgZ3a0b3j2JC3kvAtZh5eZcCV+GXpgjrJ9BBGXPOO4CHMMPF+cZxJiC9D4UQr03CnhCLy63AxzCT95/GVOTaMIsORjF7qc4MRV4PbDIf2syOEp6ybdcq4APAAPANzLZp48DEtq0EwAs7drIPUI3PgT+uYCqAC9L2LaoNsDp+WU9k7QKelT0ZlnRvf1319fwi4Ove/ldbzHLadG//JGaOHaqvp/yd2oHvjRY2HsSycyRhHrgMCPHyAX4xQ21iA+iNxIFPrrSPoLwWE9bBnNd/wQz1+kCie/sPnonjFEIsXhL2xIK0fYtaBxy55z69oKtDC8mOnawA7gZWYqpsDibErAee2baVSqPy5+7YSYTZP7Vo7q2Z3cDhFE7jNmsxQ4ovYYYc237yf/x/wf2jX6zr3v7z7XuUAaxtW4ng0mfNqO0s3dt/1sLqKcHvoZEnqI49gwnQy0iTZSRhCPbNlJYmTB71KY+sgWQNpjK4HziGKcO2YVL5om2BI4Q4c6zXv4kQ59b2LepyzOrPkfk+lvPFjp3kMRW4O5ldPfsYpqXH2Lpne+tPf1i1O+HYJuAngZ8BPsTJdBfzaiOBqU50lERjmFDhYoLJkpbK0Nqu6vhNmNBxvhnAzDk851RfT0n19TQD6Nvu1oRVM0mytHQp2eYuFFniWo1gqo6mQBTdDnRjAuoUpg3OwcbHDrBS9fX48/FahBDnD6nsiYVoZoWhu32L+v177tOfnNejOT9ozBDtdzGtO34IHG0e+XYS+p2Dy/Z9dvlE4Yr3ueVDE3Fr6zjwNuAts3d3MIEPtE5JkoAoAiysRCeR1vp2z/G6G4/9xPqgXnedDfpeEzrOK/fcN6/9ppoxf2RPND63gQJNXWXc7Am0PsDI/mmCyjtJakXMHL0K8CRm7l8dM2fyUlApueYK1XHZB1cI8SNJZU8sOPfcp0eYXfn4f87nsZwPduykAzMn7z5gO6ZVyos9O1V8zcNvve6m/ksvqeQ3Xpco962FycdjzOrcizDDga9QrzeCHgAptrKXxUm0fCocn8aEk669hfYffihsfVi25jptR5nTpqWxKGSMyliOqcEyQTnm4ttrxFEB0/5lEBPw1jXucjlwCbCRTCnH2psc1r3FPsevQQhxnpGwJxaqxmR//O1b1G/N65EscGkwng2PPdCVxvWrMfP1VmOG/cBUhVS28vQmv3J02o6mAD6C2QLtR3IcyPp5HNdWlXgiOFLe01QJp7qA1jEv18XHb1g0DX1VX4+t+nq2qL6en1V9Pfmz9Ty6tz95RSPkbLNHdTKPnUlIwpS/Koec8F/ChMImoAuz8vZ24N2Y+ZMvYDseY0c3cvjJJY3GzkII8apkGFcsVIc4uVKUT2zfoj5/z316dD4PaCG65/c+0RIO/lt3PPxYXHrb10a85ZtHMS1Wald8QWvg2cc+2npRGscbULortTK3ElUvwvFdlI0mISXEwju5NMOxIU4gSTUuYCs7zdiFdEl27WWO5TmYVbmDM8fQ2IWjA7Nwo3K+7ZvbCEq/A/wG5nU9gal+nv3nfujeDOtu7mZy0CFb0lTGOigeuY1asRWmDoN1BDd/EUpFhFMtwF5YdRCvsJRs/TityzVxMK5/4c+l9YoQ4jVJ2BML1WeAzzc+toBPb9+iflVW587avkVZ2Wv/U5vTdlWTrhx7avIbbxu95z59StB6+sPKDouX+0Ntmx+2oslLpwubKkTVO0iVh58lJiQGPEJsgBTcxiPEtol/lrLdgl8sJmk67tjOdzCrc9t37KTSaM68GtNceT+m3ct5E8obVTwzLGrm0f0jZsXxufHE10rkWpaQbSmjrGmmTsCyySVgL8MuRviFkYxVfK7u1jXKGsGxHyVY20I63kFc+SeKnQHFzvNu3qQQ4tySsCcWqide9vlPYaovskJ3VkvtyT8suiu3fP8PvvJC+eVX7thJznnHcNfyfX/a4gVDT6aZpqCWW3E1VrYF10zzshsLMyw8tLaIojqehtQGa3ZgUCtljTi29TjQgum9NwEs3bGTGqaqtwmzcOAA50HYU309GcxruRgzVPpXwMd0b//0OT2QoBIQVMZRjqJ5aZHm5TehPIuqupKYjH2i++t5Z3hJPXdsFcrew/LrnyUOLiFSbWSWtujb7n7xnB6vEOK8JGFPLFQRpt3HzLzSLPCxXTl1z+aqPq+GCc+G7VuUBfikYSY6+I+v+P94x0484Cdjv/2Ww+t/c/emJz/ywN4Nv/MEuVU/F6VhJo4SPDfCtlysme3RFCQ21CzMwtwEIj/AVo5KdFxzLV9HSdRlW/YTlrJeBFZgvj9PYVq+zGzpdfAcnILTtmMnWczct9KWto9cPBjsX/fD8oMPpyR7dG//fO0xGwAh44eheUkWL6toXrUURYHDNScpdlw12jTaTjkpohll3+53kl3RzMrOp2jtHp+nYxZCnGdkgYZYqLowgW9WxEePdXL1rpwqzs8hLShZTGXqAGarspMajZPXYFbcOs1jD4dWUtmcJsGvAKuSJCGbgh3Er3xQJ0vWzZ7cUCPRCZVkWo+Fg5mJYPTYw+Nf/8b3Jv7pycbw7VDjGBLgi8BfAm07dtJ69l72m9IN3BIlwftX+BtWXNV05/Nrspc9PY9BD8wuGC34TatR7q1gZUiCKykVWljZbrO8HpFxR9GlATIrVlJsvQYdNTF6qML40UWzQEYIcXZJZU8sOI2q1b+HmZJTg0vpwMX2T9ZySS3NqdiCYWBic3Ve+6bNlxpmEUv15fP0gJ8A3orpgfdAuXTV+N7L/viuKL/mJ4Fm3/EhDngtURqebHzjWC4qwYp1LvHtTOrZ7v4rire17djJGKbdy8paUs5+a/TLad4u7elp+0B749gWomPA+Hg8fOTBia/yYvXRPa9YGXsOqYfudbj+Ax57vvMUYfh2qmNriSOHbOliMrGPrxIs1jDm5mm7pIlsUeEne0imY9J4jHzrhfhzL4T4MUjYEwvK9i1KAT8PvB/Ny3fvUrGjrx7t4IVimbhlAseLmWK2J98FoxHwXm2e3iXALZjebI8DLWFmyc1klnRi2rHYSlmQzb7mY9vKIW6c0lq9hm3Zlmv5bpRG+axVKB+oPX9RkoarH5v+5uC/X/6pJ6fisabhaKCpf/xvo//14Q+cu8UNp2nbVkIghOVP/mHfo/Z8BT3V19MBZLnufSPUqysodg5xfPco1Yl2nEwrhY6gsRNajNbt5JpjcHNMK0VaqNHivsD44e/qf/ex6nwcvxDi/CNhTyw0HcCHARNXFLMd48CB9PJanvW7N/H8RXs5sGcD6ttblH/nt4k3V/UFF/rmasxJ+x1MM95hYC9mlWk3ZgGFWZWRNgqB1qvP4gii4OSuq7ayCHQ1GQqPBSsy67scvDu+NfqlJNZRNBDuC7dtZT+sHPl030OW7u0/b+ZSzmdFDzMXNeHx/6lpXbke7FU0L/PJlHz8Yh7bbaae+NTLzeRzFrY9geP7BF6MVRwian6A1V0LtXoqhFiAJOyJBWH7FuW7dTLFCjdN5+liZk+AVw5UrQA+nFp8Pj/NY5c+xaaJFiqJYmpXTh0F6purOnrFvS4M12HCHZioXMaE5+WNf026C8wQbuCDpzIoZcqntaRmZkk2znkmY67TobYKVqkTS1dW5y6ZuDi8ZqSaTO8bCPedbPlxPgW9+aZ7+0fVfX+cZ2jv9dhenmLH1WBtoFbtwPFWk+gcU66i4kBep2SyReyiS9ZymGAZQ2oTGb0b0zBbCCFel4Q9sVC0lMbZ4AXcalXZO5ljGRnsV9/Qi27KeIUyl6awNBMykFrU7IR1gNqVU89trl5Y/fgaizJuwQxpH8T0u6tg/h9fTmMId84+aKQRJF6Mg0ucRjD3jFmg0SgUlnJ0i9PuOJZbzSjP2dL5npJFYdVPd//HZ87V61t0JgcuI996C7WKg+W04HjLsL1u4myWqq1oVdDaAr5lobUicRNsrVmilqL0WylwDPj2fL8MIcT5QcKemDd/0KP8IMMyYACYdhIG3ZDhJuiPc6yrpKzi1Xb9THGx+e2HLuWry6b4+kQbpVzA0KUv0IJZpRrvyqnDm6t68py+oHnSCHrNmD53U2ESDFrKzjqWczem4XEWs9jFnjt0a7ngKLMGJkpiM2TuYuJiAkEYoBxI48RK0e5w7fDV5XR/zvJePHxF07UvxOF1eyBzoVZRf2z8cPjuAAAgAElEQVRqx7u6WHHNtYTVy4nrIcodxs10QdSEsmy0MrsWq8b3yvMV2rbxiFEsJ6JInVH10L0P69vulvMvhHhdEvbEObd9i/Iu3k162TRrDq4mfyDvlo9OFC4hqZTbSuHweBNrdEIAaALUyeqewsx2igCFTRPvO1YgbZ1goHkUH3g8hms0vN+FJ3bl1BPAsc1VvX+eXuq5cjnw79I0ScO4riOd3Gxb9gHHcpqAAlAnTbtIYnC9k4sz/LmPoAClCcI6tnZMCEyhUpukmk5TcNtGh4NDg56d2ThZLdrlLLt8nVmyYyfHt22l3GhSnOre/guqonq6VF9PHr94NbWpDVh2npbVS0iCK4k8H6UcPAWOBqUwUyxdiBqrlNTJYquPxZQEPSHEGyV99sQ5tX2LcoBV+9fSViwzdfmzvPTwvqXT9jEHFZKd7spULYu1XkLCzP6rISaMWJzaeU9jEfK+KdgSpfzEYAtrp4okdQedwo0x/GFF8YmvF9SNu3LqtZefnv+agOY4rq+LdXIxmu4oqbUlOs43ruskCBziBMJTW67UkxphGuDbPlEaoLCw55RTLcshJU10mvhrs5cXMlbxyHAQPPP90RMnMC1W4sbesssxvRHFj6bINpfxiktImzZh+yuJ9QqiWg6URZpAqCD2wPbAVeZn3wEsAjKcIMdePB6a7xcihDh/SGVPnGsJMBK71DdXdQ1gM/B3baoWBFzazFsGhzj2/GHn4BQ52oh5BzE+CrNwIIsJfHHj8xQnzrLxYBddK4dZXhznuAsFCzIJYGtusjSXAIO7cuqTwBPn+6rd7VtUE1ACjjVasBxLg6lpPX3UsnNdE5FtVetJrZ5J8wXbdiwgh21DkoAz+798PaqhY0hISQhIdIJKLZRt/gasptNUo0kSndgDyb62Zqe9vc1b8ifXOD8xuCK7vrJtK1OqryezLnuFt6/29HEuwBY4p0P19di6t7+s7v1YFZUbZtiO8LwczZkCKIhDSEJwbHOxMX/oJJh3agcf08i6F9NWRwgh3hAJe+KcWzJAfcMeWnbllN5c1XWAQsSzVkDNeeaF0uSa3InRAt0FWG0nlEnwqWB++fmYodwZLpBiUaBtMOCK0gixb1PSCUtc8BzIKjOXbRNmuPPfduXUnwGPn8erdi3ALW25P/MHv/a+yO68Kc5s/EhiKzXgOtnEdzN1P80q384sx8RjpW0FjouyZqt2+pQNNDSeyqLmzJFMtcK1PFyyHKnu1dPx5PRUecBf7q294sHaU8Of+ey3/q3DXb5uY/7Gll9buePJbVupn5uXf/5RfT0FYKnq6xnEzUKx+3FWN19PzllqbpEFJwM6gEz2ZOsbbMwfNmU0DiEF9gE/lCFcIcTpkLAnzrXSRDPLaxl0ts709i0qJKWrexVrlMUkSzNjXWtvDUePfcuiMlIgZYCEFiwsLIgVOHPDnsXJYd6wmdIzl3PlkrHieEdQLDeNDBQdUwGb+Tnv1vBeBW8HHt+VU98EvgSMbq7qV+4dtkDdc5+e+K9/MxRZ2c41Ogmr1f1fW5HBusRp2xQADwLXealaGYeTOdv1ssrJqiSMUUDsRqQpuM7clS8poMwJbqjrKqPhITy7SLPTQqvdkUykQxvUVKX3ePl7R57rnPhC+bI7ll5xpDZ8cfaaGlLVe02N+Yym9U1paVxyO0emm5a3pCpdSZJkUW6j3Y0CxzdnMjCfkgAWISGDJPwjBf4eE/+EEOINk7AnzrVaLcfx8RYm371Px982Q5K3ThdptzUvldVwkhnfM+GHtcNBnXFSugE9s1erDadWPWZ22UgxKxhdWsctx86OxMMKBpugYJtFClSAEIsCaV6l3K7gVm3xayl8bVdOfRoYOx+2Xtu+RVlNd+1abbde1RJPH7rOKa4+armZY5gQUAcsSLIanUWbPUhmNiLRGkggSuZmMwtNimrcKiWlnlbIWSUs5WJbHiWvw6oGU5lccX3XiL3PdXLKvjxZu3pz65UDv/fuzN5z+PLPC6qvx3WUt2R97qrxxpeKNHW3s+S65YWJdWk1HdvqTQ4XQs8mzQLEYGXATswq3JlpChZ1bP4S+BtSfgDY+ra7JewJIU6LhD1xRuzKKQewN1f1a266un2LKmGGFU8sPY67K6eK7s3UI5/DlWb2eGVW5kajTSemntAkTPsBqJh84JJosxhDqRyNQUxmlxelYNUaG0OkEKjx4qEiVnYtmc4BJlsD3IzGs0DZaMKZu6ZYWHQDvxpoenzFvbty6n5gYHNVv2IrsgWkefrBj97gX/p/KF0fWp6Uj40AI5hQew3QptzcKm0pVQ81Kq4xs7bTsQAFUSPSJkmAbft4nkUUQkKdMA0J0zq+lcO3s3j45OyS5SiP1a0bnbbSyvL1XkfGqeefLOiM7OSACXdArHv7Z/5YUNc3va3rxtI7mscuuenoiclnhvFyFtWmdx4bH15LS/2KpjCwnSRLOesAjYUYoQ26BlEG04Il3H/Tvw7dn4l55oG+7Y2fcCGEOD2yGlecKe3Aql05M+vrv12pmj9zo+pu7HXL9i3KAjoxKzbVgVWsPLSc69btQ1khR5YewWsfobk4zSghttbcnK8QdowznUlJSNEnF2bEmEUaITPVD3zd+Ng2/yQucZyiKs3kJxzKAQQeptRlYe4z508dlSouSeA/A38N/NzXlzflzslZO02f+dLh9uzlH3fQyYG0OjBFOOE53TffpuN6EVgD3I6Zm5gzqSNtrGNJCXVENQgIG8VLjcZKa5CERI2GKREBkU4oOR1olTIaDZASYykiz/YndoeP735p7KGn9v+vPzwef/e+0W1bX7k/74VG9fX4mHNfnPma7u0Pt3Z+7LlV2UuOD6dDRarjG6iMWbTYwywpX4Jr+aNtLZTbWzB/ucQ4IeDGjT9kFNgMZKvxpz7w+X37fumPnpM5ekKIH5tU9sSPbVdOFRIL9lyMWutSc2OyU01kt29R9YvLrM/UKY61MdwIeq1OwCq/TlwpceexbjYAQ9mAdUuP8s7IoVk7PNU+xqAXsLTm0aYTMpHDlJ0wBKzApY2Zn9lGfSNMINGQ5sEygU9jYRGTr2fIZ8tMTrUXg6Q6TbGGlwlRMyPAMxqjwFqbX7MbQ3j3wJ2XDX7uo2998KOfe2Ds3J3RH237By65zmq/5sMqGBrU6G8lw48UraV3HPWW3u5bXmEU+JmgWu7WSjmeZ0O1TBJM4OSXYMUhUTSJk2k7OVwLoJUNWjcWO6c4Ootn2VjKRtspURphOVaYU8XRfFraMxYOPn9i/3e/0/T4iyF33TJv52KBiXdc8bPebW0bm4EpOBkAlwCDXHzHKEnkk2/bjApvI68uN39x5Bo/zSkqzdA95lEONRPFKCnV0iOe7f35cHvunz4+9IXXrJYLIcQbIWFPnJZG5W7DkcS6TjuukytFL6qIVYHHsWqW8NAq1jeNEU8U0TXf2u0FqWelLGsdZd14gU2hTaddZ7BYZUUM9VqeO90Mq/0aB23IWyHLippOJ6G7ZtE5XmCs1kSbP02QDYirOZywiZMJTTWGJa2Tg2eN3OJiYaHGl5DJ51rL8Yh1IhmddLI2vm7N28VjFdcCVGpamWmFavzPYAOXd97/g1+xPW9iV049tBAWb3zyN381T/nIR9Kocg2FFV+ncqSQVI4cfb7k+d0DTRvWFNfcoeOwJa4MucotEIQOWmutLEtpDYnjQZpHNXbMAFCNiY/KchqfWziOR5rETMRjFOwCnd5SfKWILKVavc7uTn9F7Y473vtsemuSsWw7BA7MzxlZOHRvf8LegVFO3cl5pv23x/7vLWHj227B8rcSe9fhepZpURhBnOJom6bsNFOtecpOmU63MnT1muHf+5fKVffr2+6WoCeEeNMk7InXtSunVARrLfhaYrHWSXHb7FRP63SqOs5L2ZTRusu3JpoZyk8x7Ne5caTotJzwC5vWnJhQ460cyZRxs4rOwOXmJGAgcDjq11nt13gaRasXk+YnWJdJuMWNmagqJmp5OlRCm1shS8y0G2PpEPN7MgtYphTnvvKQNQlWrkZUs4nCylErVUng+RxsmiaTTgRNHhSVJpcoUluh1JxinwdFrxp1Uo0uB/YAx87NmX51f/jpz2fxWz+ultw5pYOR/8dvv+zB7G07SnZpQ+mqiedv1WF8V1gZXxJNH8vq2jFUcR2WrUjjWNm2Y4YGHQfLy2OGdZXJxCmkSQU7TcHziXVKPZhGo6mmFe3bXiVj++m00i+lYTzpe6WXtJVqwLZsewwYf51Dv3BctPT43E91b3+k+nrGgRVWcWnB0s574zS5ASvxwSYbalrGBznR2UGTFQSdTtmaLtbCQqwGuvLJH/3W2vu/dX/zn1Tm6dUIIRYZCXviFM+sXtIyODT4JKZVhK5iCmg2WAmQpCZnpaDy0JyF68NJIg3rOicIXRgObVx3Mu7sOjBRmWynmo+Ytsqo7kGC1KHmKjojl9CvciwD6xObt054eCOWmlwS6RY/JlOIGA0ScrkYN82SVn0KcUzNTUBV8cIMzBmNPJWLRUgcxmitcSM/abU1OZVxUtZ3DoRDJw4FFZocxVpA1WEqC92Y3Bhh/r/wgZVAK/MY9rZvUUVVWFly2q+rWyp9NJneO2ytfPt77OKaUcsrWrmWK9cFlZEVGp23vIxVmw4Tyodsq3IcVVyHzrThRWV04kEaoF2P2HK0SlNc29aOla1m4igNg9ibsup2uXbUzme6a8syKwOlrOdVXF2dVmN7dFwXdVN5+siJ0ezGlmWZJUuaD2/bysR8nZeFqjF82wWcwPwsTaxqufIt5Yx/5XCS+tmgSs3NEmddamkT2veZsmPdpby6pvnrUSbzjWPNW/b8fO3360ea5/e1CCEWDwl74hRT01MfZs62VzOrOGn8O/cy87UCuDVY17j9uijjZXwFmVpIfhASs65QpVnwa2BBbCU4jbUU2kpQbo20G61CiMchyMH6jkncEOphzJAX0qRi8lULPaWJCXCY2QBtpv0KQAyxA04eJ1U4KLPvQJwwGqvMyMTypnJdj8eEtVYrJHBSYjemYsMxz3Q3m0hMOz9twUDoMrR9i3LvuW+2AXNjDqK+576z26Zl+xblYfm36/rEJVqnDxZv/4v14eH736r85ppy8xmdxtfpNL5Bk2TSMCFNE227WSvRsdZ+QbleM1YmT33ixVTjxH62xbadJdNJPDVQTWvjkU4nnELn5JK0vdtD13Q4NJ7Prrg1rwraCqYGtJWZipJIZ2y3o7Mteyix0+MnqtNWvS1SwB07dvJVaaT8ChamCZDFzXfXCSrrD0yN/UdCpxPLJhtHJApC22O8qRks9NIoqG8qHP3TyD9+7/ezvz3mZVrznLoxoBBCvCmLO+x99tEPAu8B3s/Hb1jw/dMWgmqt+hBQxcSnTLYRoxrt2U5uUTvDmf3cwlyfKVTCk/0hPCAGFQGqZm6vwKl7ECqUqqMsDX4jP7qNbnkRpMpsAR9ZAVgeGaeKnVEw0Ursl3ECu/EEM1upNQ7SjyFxIDVLbxMgivPkx4JyYh9/cQJFkPFwLYd65LCnfZxpEjLAgRg6KhYDfkpzBiqjbaxdvY+rvt6snsuEHPv2nQCswgwmD57574CxfYvy8FrvIL/qGrvlEpymVZHtt7bZS269or73q5U0LNfIr7oBr6U5mj6mdFiO0mjaIxhVjlciCceIrXykdEs4Vds/kh19wbbarrFsN/NdZ/K5J5odT7/onhhqbnpHB5YXApO1cPLBZq9rTcYu/IyujXQEQexPTzlH2rqLpWLWzQCXrr6hbbSuw0KskzEH29uxkwhT/ZzatpULfn6Z7u2vqb6efdx8t3VxuGT9S+7xT+mCWmNaq3iM5YGEhIkxm0wBmvLYiRo5PLDynx+76zcGGg8jAVoIcUYt7rAHn8O8xl8B/ts8H8t5YXNVPwq0AezKqXbgT4EtCvyZH5aZ1DxT4YsxSU/NuS4Ccq4LUXSyOqgwjY2zQLZuymgpsz+EKSc3xIgtSH2wXchPg+/V8BMfK1TEXoB2E8jUYKoJdJ5TJu4ls5U+hcZH46LJJQ6lBLooMTxo0dQ8ydBEGypqoim/n8dTmLSg4licUCmVqgtWQKa5TLcXUgYGGi+xAmcv2GzfYl2O17peNa29wW27do239j3fqT39Xy+rPPcXuSQNvdT2r45xpqKD/9BMlNgk2lHRuKPiqkrLh4gLy8MgHIuzU3vHdKbpWC4Y/Dt14gdXUT00Hkcj9/ur3jFueaXRq/JLJ4C3AHng+c7n/q6unHzJuexXhlTT8vW6Fk1rFb3g+c4VjUPzm+NsOBWrcctVe1CUgWZM2AvUtq/4gKt3fHD0bJ2b84Hu7dd37fp7dyxWP59J7atqM2XnmeKwa9sUmsCyABUc9vNfPJQpvDRvByyEWPQWb9j77KMlZvdW+O/zfDTnpc1VPQJ8aFdOKeAXgP8LWA1m5ao15xIxG/RmcpcbRQRABlMVjBof23PuNzP6GmXATqEQQgjOzNcrkObAVzG6GpNoHzdrY8UB2gNVqsJEoyNeoiDKgZfMqT5GmObJGqw6XprBxqErLuCP5kE7tIy41KI1lAs1Kk01ktQhjXzG05RavcA1WvPd1CKeLnEt8Pin/vmvx4DMp9RXlNYfPCMV40Y/wkzh9r+IMlf03hiVj7+rmA4Xdbb5RPX4d5sSp7hShRPXJ7XxolaZShKUM4zsL1FsdyHRevpFK9Q2VqZ1zM4U9/p+5ul0em9l9Ng+a7I28MONG3/2EXf52zusTGchHn260+u+9fk0TdekKROOYwVAxipdlE3GnqvrJHipUnWv3vvC4O7u5S0tTc3ZInAYyCVx8q5KtTYd5HN21vVXYrajo/HtLwBZte0r43rHBy/Y5r/f+8RfqVuX/fDyLy/Z8PZalFpkgEwG6nVzltIELEVrJaGmo7SW9/5W33a3LMYQQpw1izfswQrMrgI/lCHcN6exhdhfAn+5K6fyCj7pwAcVNAHWy3+r25jA1+hxTMrsxqkzP3CqcYkbX/NiUNpkM2WGcK3G/e1GKEy0g8rXicsuhBkoZ/CiORMII0DbYIXMbqU2J1GmHuBgo/GxsLSigE1Q82FyBTeValSGpqlojxbb5iU/4jvdg9SzdbomS7SNtzIFjG298k+9F47fXMp65Wfhg6/aVLixo0Lyu99+oAOw7rlPH3+12wH8p1+8JYPffhXBSHf5B//5UPaq3ypY2i1Xyzr1Wq5b6mU7OuPKUCUtH+8iLOdUdbqg4zGPNHWoqDQIDiX16tF6pXXDUMF37muafHYf1YExwqkBN41TX/FUqeevwrQ6uDSuDi2PJ/cUSOojjz1wwDvwwkj8Ux+9rtn17DXZi38e4KnKdBAkaXJkarR+VRqNrW7vKkw7lm0BOc9Vbe3NOe1q28XM7TyCGUzXmEUJ6kIOetz1diu5/he7109dtWKk84RLFIKdh1hrUhW5uJMRUYuycTJpig6SvbX8/K72FkIsfkov/K1AT99nH80CF2HmVn2fj98wMs9HtOg0qn1LMHntphrcbMG1Dqy3TdbLAG6jFZ6ug5dCqhvLKVJILbPDmXZMzrMSsygiCBTa0ripTdVNqNRdcknCsNIUtEN+vJlD9SzLJvM0eRo/9M2zzcwrdGa64s39UybF3MBhZuw5RWGhSOsplLNMFVMiPyQlxSNiCp9HgHJpjCTMs7xW4gsqZr9bsVtHVGuSK4yMOJYebTziyKf++a/JutP52o0HY971/RXA5O9++wGL2dHu+J779CicrOTlAZW78b90hcce2BqfeHw9fmueOLqI5hU14nAEt6XN6b4pGw89FzG5eyPVAbOPVprYUAnB0pWm1SNTeuIRtzb85Y7W6zOZVXedqD3xqZCkOgLU7rlPD/3R5763NJk+9Ba7ZdMxt+3KZ4Hyk989dKXWNK+7tN05fmiypXNZ04utnQXr0J6RG21XvfXE0anmOExyN/SsixKVrrKxUtBFja6l9dreiSd2fbV+6Nl/Wv7B3x8CckBl29YLfDuvu96eL+faLrr/xvdP/vS1Ez/rToy/txDS4brZQzqb+Wx33U53+5VfDZW+1A20uzLltr3v/dhz833YQojFbfGFvc8+qoBpzCKDq/j4DQOvcw9xhjQCoIUJMR8EtmCCzgBmtW533czvsjALHPIanBSimoUVZqmX6oyXPZqCFCd0mGyq4NaK+LFD4mgOl3OUggKVqiKf+KzUYKHw8RoHETNbZ7KYHVtOmK3y2cyWGhXgQGyDo0gCTexP44QutmUTOR4K3ciRimEiRq0Ke7XN95IszanDCQeesgqrdydN198xsPfgjYMjua8+cIOeuO3EwdYb9eGjmI3dbgX2YXrT1TCV5zuV16xVblUm1ckQaXobcCtBxWXZ7QeYenGK0cdayF+6CS/XyviTCiuTYnUExAMJ6GqEO5JZ/Y7/d9TXf9O8++8rHb+sY0zwygEj27aaM/DHX9y9on7gH2/yV26ZdtsvHwSeatymc3SwvO6p7x1uXrOx46U1mzrGapXg/XGUtsX10cO+l95s5TrW1XR8scoq18HSebJuUq8nlaED+5LR4480X3brJyzX7wT2b9vKhb1X7l1vtzDntaZ++0Nu20SlrW2ysiwuNg3t2/rLBz/3iU9bn7yhrS2ylUoU08Pv+MiFfb6EEOfEwgx7n330EqAfU5X7307zvvcDd2BqOU18/Ibkde4hzoJdOZXB9K5TmEn8m4FLgOsx87z8FFQAuRRU4JBGioobUVY2o1FKvtaES4hdLXHIS9B+wv4gQ7bWxLXTJtx1BT6KBP/kRMGZceFXoS1QSWMhyct79HnMjjkHEDiQupzs7jL7II3bhCR10DpDNavYg1ZPU9y4fHxaN+vK8eeUO3lCK9VUcnXVVqzG7B38HSAPzhMQbwHeojJdLVa2K0ymjkyg/Rq+u5ygcpRc197/n733jpPrrO/938+p02e2V62atbYky93CDReMKDKJFQIEQwKB8KOEBJmbhCRXkBBulFASkELupVxCQgL4UkxsAzLGGAwuwmss27J6l1baXW3fnXrq8/vjmZXkboNstef9es1rd2fOnDJzZvZzvuXzpbTXh9r5QBdWY47YBCPpIxwIRv0amXtGrOXfbAu/vdNhcq9aNxMt75NNQH7vgQf2Ze68Sq5eJ6N/+LvP9PojD1+UmPd7GxPzf7cMHLj5RuSa29WU4P5d44lZZzU2o1Lzy4CzQ68kYn86VfQy3RNj5bMa2tOJZN62kyRMSYzne1W88sNOovBh07ZLQP+MuNRoNBrNycPJWrP3OpQgeA1r+8wXKdiGUP+Sv6eF3oljWUXWgH1wJOL3BCq1vgK4GLjCgJQLSJCpECOCTAh2IsKILLdcnPIsIExKpqazOEhygJQRhu/SCBxt553pGJmxYpnxiUFtoGarZZOBWsSrP8er55NdHwy3vg5XjVA7Qj0xKSMIbDBNiMCsq5pcBS4xQ3mxMbI1EhHStVjiOPhSysAUTMWQNNRmZ6m1Z98cU2yq1ULDlaO2GUyZZM4pYqWLwisNSDHeT2lrHiW88oBHOD5A9vw8tcMmwfReeq7flul47eqJx9aPOuGkidKlLUAJGP1f6+8wOw7/6LwbhDW1arnYF6XcwWqn2FbiO3vav4S/ep2Uaz7we82XjBwuXFV1D655713F+iu2GNWMMWi5metxM7OdqNKSSThu0nTcJK6IkcQyBAcnKtcGhG3OCU1/woqc/uNx7mg0Go3m+GI8/yIngJVLP8tRr7evvODnre1rQv1r/t/AO16SfdO8aJZVpFxWkeGyitwGfAZ4L/BB4D4DRk3VkIEFUQIst/PclsKSVxcMGAtdhoRBg+uRONyIM9pMW00gpYchJQ4CCwtVJVhv8/UiCELqBXzqMROw6wIwBKz67/WmjaPC0Fc/ZrqFkSiTFQ+iSK3SRxUiJgBX1IN9NsIysFIudsIlaQlStkFTCHOr0FaDLNAJNMFUY0DoxhFRFEUTMqr58dTGKUoHpw3H6MdwZqKhs4FEvTnZozoikcFBjNpfmsUda+xwsP+8P/zq2Op1cgCYBPYA4ciXBOO1sl81c9OxFBUgW+700ofeIOKc9foekWhe8ImV70hF7Zdfd2D2la8eyM5y7YFtC1ATQzqAJfVXYwqws4VUoqWrwUmmXVGjhh94hLUIprzIjK1q1Z0qHD5rqxUkqgvW3I6e+6DRaDQnGSen2FPciBJ819VF3AuhDWVI+iPdgXtysqwio2UVObGsIm9DvccXAb8PfA54ENgW+KXB6fGtA4aJlALhp8lYFj1ulZ5A0ubZNKd8kmaIgYlJxNEGjJpq0AhtOFLHR10LWiATSlHKev2eW3/cT3D00+Cp9Ryp66u3WFgSrLpVmnDUzQgg6SvhJ1xw7CMi0cZDxAGGITENtSlDrT0edrH8dLrbd6z8AxUo+sRdeIe6o/E9IwThZAADnlWIwbUhTAOWyPVsNtuv+jxx7R533u8+mrnkb9pQ0Tzq0zwkMBdokGtuGt/9lU/ucgiKwNzcLrrm3XfxVHv2bzJ2+1WGt/O/cjXBnqFZVz3xjUJ8lfuzzy5iaOPZwP76kafUUZMHkqZlSCGEUrox6sVzLEc2Fa6PPfvdDXvmXmn4lome/KDRaDQnHSdrGhdWLr2ftX0/AF4PvAv4p+dcfm2fBZwD3AX8/CXfP81vTN3SZRS4Dbjt7pRIAfPi0X3nheMcSMZcJGLOH2+lLZZ0VF3ysYkhIelJ1dp7hGMsjk0T3BkX52M5ZvZbVD/zDep1efVaPWzUp8LjqDngMduxJVieGhYsLRA+GBYEpIiokJgJ/VnqZygg9iGRJMI4UlHYAE4kkg74Vo8ZISRMQRjAVCOidTKWxTnSyZuhEI4VeD5QkvHUhmhg4yHgVZVHPr4vdcFHhoSVPFZcRajmpNqa20mi5O40sBUwUgOm515y2Yj72v+ujXxJtFd++Wn9hpwAACAASURBVOFh45p/8zGiBVPBDjMYf7jXbe6dFFbicP0V2Cul3B+JOC/BwvePCmCU87Up6a6JcLgxyJZMzNLNN6L94jQajeYk4+Rs0Jhhbd8s4D+BLcCfs3LpM3euqQ7cHCpKNMrKpU+8bPuoecm4OyXMiQKZQ+3MS9R493CWi31owaTbBCIbiYU4MioNZmruZmZoHDs197mJUPLGQkmkmTPN4Uhq94iBYA18AyIXkvUt1DCRRCSVaFQJZo8oiDBtiEgDBsVj9rKCkphpMMfAGYUoi93gERwmhq4YS1qYCfD2YTYOksh/i+rwDrNp8dLYn9onp7bfvXqdnDGaeRJrbqcTFZ3bM2OHIsQtNmBKeVMN4NNfuNdMJXud6D8u7fAu+8BiY9E7W638rLSHXxih6OfDVFOl5i8wnOgiN6bFCSCyXAzTgAgqBPi+Tz6T2ZoyE58AfnDzjTzj/mg0Go3mxHEyp3Fh5dJ+4BPALpR1x7ORBt5UX+bAy7BnmpeBZRUZvWVATn14g3w0V+Z/uDavzcW8zY64x5HsFAlquHDk5jCTl51p0ajB08THs17d2Mc+MrPOmfm7cMS6RSYgcuoqUkAUQ1CJCMr1ZRL1rSQxbJcAi7Ae1ZvxH5wZKlJPe0ahZ3hhTFwg9GcBcw1osDCbwRGklkSisdcXTrobIZN2x3VbrHT3s5o01zkMHPjwilsMIW6ZEbxtwCwhbjEAnM5rWw2vfHbbwdrs5ofuaUoO7O2iPJWVYBqItGFwjmtZ8wiNlrGwRpA2MBzjyCsYBxFeFFOuVltRndfumttpqXf4ajQajeYk4eRN4x7lCaAL1WX4dNb2majC8hxP7sHUnEa8fb8MUEnVh1ctF79j1EhLk4uB1QiWIJ4mMGZk2ozwm7FUnvnbPGZZHwMZgCDCOTKkF1TsTVAvylN3CXHUkiUAAgOkrI86VWlfUT8TBRIbQYCKFc7IxgglJScAIwxEZtJ2W7J4pOSEibp4McELwfOp7JiUVf4bGY8YQVCrbfyMD/HA6nUyrDdEuMDwjO3J3SnRvBjEa6rfHF94cce5hmmMA/1/bX++mqXkzm9+wl61/G1O07tLeb95dhhe9eFtItdSbrj/myWvZ1ORy998VafkCj9hNLquaAilgRUJTFEvSAwhiALSlkNgR4ijpjV51Mi0KY7GQzUajUZzgjkVxF4ZGAQEa/tyQImVS4916c+j0rfjwAZUekxzGrN6nQxQ3af3rFou+oB3Ax/BI4eFdYyMMzkqOiQeIPFJEKNECajIn0RgkSAkJETUq/vq9XcWFoZl4NtP1y8zGeR0GkwfghhqLqSi+pw3FWm0OBp7DMEWmHY6iCrTMeSdQCYKXtW2kggMYiQxBiYqIVwBLwBip0KicYK26Vy8p5KlZdVyUWx5n5yJFB6LCxjv/IsrZTlXy1VaPFvc97WDP7bWNwLnTUVkgaD6xL+MpC/66+zo73x0MaXxYs/93zQoDyw25s5rF17Cie1kR9Q6O5/I5LGkg1UDkiFF6VM0IgwjgEjU0iL3LWAd6jM4cvONuklDo9FoTiZO/nSLqtN7CBUZuQrlVaZY2zdjhWsBY8A2Vi7VEYUzCxPYBnwIm08jGIAnjexyONp+EQIxMUWe3GurXPpMXFwMPAQ1jsjE0AnxrWc4raSa6ZuO62HCegewQM3ofUqMeSbKaJNotoxklxsJqyUGN0jguAlM00ASYJklAqdChRhJlQm7xFanQmssaMmUOLxoK27DGNOoOOJhYP/NNyJXLRfJVcvFzMSS2tv/dXbrvjnrhqbKnyu87wtrsg++fsXc/oUXVtJlpoB9VuGsCaBgDe22Z33uTfPK8y7tqnUtucbdteFC+8Dm7uzYvlyiVEXUqlhBVb2C1Ugda6LMwcTm0c25vh9/ueXeL9Vfxy6enjbXaDQazQnmVIjsgfpHcg3KlLfM2r6Z4vZWVLdhrf5T/6M58xCo938vBmWU19xlwKtRdWSJug1yEqfesyuwUeeKwVHTFsnRdKS6X2DM2K549Qccr+6mXK/Lk4GK5FkoI2YbjgzzeMo+HqU0SiwnhWOGbmBQsw18jCOVfmU3wJIqoj3ghHw75bPNCehqGGNbxygR0Hb+E0wDgnuFBNx/XQB0MdsMGV1WkaN3p4TlOdiX/nD1EELICzYE2XLvggsOXvu2DbO2Pnr4sh0Ls7vfubzg7Hs0Ktz9RcNvWzCn1pg3/XQhm9q9vaEye1Z6ovfiWJoJhFUzMQwShoUVTcpUpuwXCjvHc457ywPm6C9ENjHFDiYAU0/Q0Gg0mpOPk7sbF2Bt3+3A1RydbFoFNqFSthFqYsb5wCo9B/fMZdVyIVBzd9vsxramIAwvIgzegmEuIZGxGR1AtVLgc/RcmvFOVqncozbMM9Nzj+g2DxVGSx4r9upr9Ovx8aeNVns2VKJWoqJ/M3WmqmM2ouZUmPBc+pyQJ4yA7ZHDErdKes5ebmsu0gksrsFoOcPeXJnNtuT1Mezc2cumnv00Jz2GllXk1Krlosfufm1+vn91sf3bq5b6VqLLMK1tlleKh1/34UJpyfWvMEoTtdgrz4nCaqqYqkVu0+Jex/NTMpWXky0LQiORsicc2YYwTNtOAdWqk6j+0i7sf/CO5ovvauzcONiZ7M8Ce77C73rPftAajUajOVGc3GJvbZ8Bz+rbdexoe1AisJWVS3V07wzm736/PZM+56Jro0o5V9mysdf0g2s8yj0oIXhs2YKBOofqdXRYHG3kMDga6YNjI3Pyafc87UR8VmaMYFSSOcY4Yg8zsxoPmEbyuBnxq+wUSSNkMFNCNg0zy4Ah16PJgddXwYjUZvsdSFrKkPpuAw4CEyPNDf7mxU29qYW/Zy/YsL2roe+716E8DRv9RP6s6Z4LU1EqX4ibOgd8w8lOJKL8UFMLc9tvKjmJqb21nD0eNi1o9RxzroV1oRF5oiJjPGpbMlbDRyPT3PONczl0XtePvYxddIHyV/jdk/jLRKPRaM5cTm6xB7C2b4KnF6A/G7cBb9PTM85MVi0XBjC/4cobXpPsnHtW8cGf/tzctj072ZC8QBjBBdL3mlGRvQwqxVtBmRA3oMaZgRJdIeqcO7Yv9zdjxmx5ph/XB5JP8gGsASPAr1Cp6J1ulV22z8GLHqUngNeX0+TsMs1CcCmQk5LUTChSQE1C6MFjw7mWXWZODG25fPG88+7b0NQ9NDWvXsRoOYikh5WK3RRRY9dEcfa59xudZ9f6m7ITI7mk3dnz5h8X9j3SV1m6/MoS1b+YpDS3IUq1pquhQVCJSaU/iJu77b5uZj/STjEy2NvfoVO3Go1GczJzKoi9i4AHXsCSEhXd+1vgK6xcWntJ90tzUvL37z87K+d3vNFsbZtj7B74f26+I+sXp68JJoY6q/27uqmVu1B+jFlU1NiHuuXxk9O3L0jkzRT+Pd/CcQyGz9EZvgFKpdWYmd97GMlP3Ro/lQbrfFcleW2P6qUPk41c+5XDizquy24fXlQRtUVWRCpTQsQc9ZOZ6UpRg3RhGkPmkMKUUhn7mU4Y5GdPZWVuNChVxgYvvOTQY9dd/N3ewpXnymTD/lr7bGtQjsjcr34QNp31BlFMOTcFjnGlU3MSbhxjyfIw6aZX37zC3jlrkBxg9Xcw/kJeJ41Go9GcOE7+Bo2VSzewtu9xVF3ecyFQEwM+BryGtX3/hEppjbNy6eRLvJeak4SPfnF78UPfWXRfsPOXg0bGPavLSjUX5i10o+qs4Vr/zrSEbmAnypsxgzpnZoaiZYFnGrT2ZOpLx87RQbDPVa8nAa9utnxkOZujCVyJRFIyQ4YaRzECl67RVmYT0Wn5VHbNY9vcQfNg2JjfaxmjebtCNjCZV7VxDKFGtql2YoUDTKUNSsl2QalMORakY5+mWExVuhY8GDRdM+1WokfD1vQDDYvfvLvpO58dSe7fOHngf97VbbsNWdl+zlRDdeLNRpzqjtxcXDNiIjfCqhRvRbTvAejvYPqFvicajUajObGc/GJP8XngKy9w2RywDLg+ktGQKcxvsLbvK8CAruc7M0jdsnU0TNBYu7KQwU0GibaesjCtOeab/vjQ6D3f2+gNH5BE4VUocTdTz5cl5ti2jGeozqtTr+yTEmKhRNbMdLVECKL+qZrJ0c6EmJ8W/RNAEml6TKYqbY+XG+y9GAd/VklTBqyug7QkSyxIlyjEQS2b+/kT89waBRN2VcB1YLahRvMe2d7M75lyjB9MkBIxg4lGAj/VL1LWurELr//G1Pwr7Fz3ORvcdCHdBJ3Tr3zHdG3249OA2+UnuhK5BXPsyUMm6cYBLDfvSpEKpQlBsPfmG4+djqvRaDSaU4FTRez96MU+IZKR4df8TuAvIhn9RUAQpP7p/gOu7XwIuPcpxsya04jhq2gRkJHh4Qe7z1seTG95uCOGnJHOvSZ/4dXTlQPbv1natH4SuBDoRE1nsYmwiLCgPvfi2VBGyYhnWqLe6lE9ZtGZLgx75ox7suqLETwYx2P7pWfuG+xieM5e/NEmslbAQUPiGZKLzYi9LmzxbHJmgJ2CpAFyxjcmiYoymhJiqcKT7WEVoyN9eNFw9dFqc/vj3/jEZ9a9dffm/g/f8z+Tk629xjduXFPATlTbvv4Xsrzw6vPtvb/qEA3dfvvo9taJbGsQpRr6sV1HREGb7fsCM9v2a7wdGo1GoznBmB//+MdP9D48P5d1VXno0DQwB/gZ8Fbgv4Fb67efAD8G1qNMl12JJPADIZAikjG+DM04jhpNw3i7aZh/yUOHdvDQoe1c1nWSFy1qXiz/zif8WjtT1R5R+scr75C3/82N1ahSGsrMWZhxGltsJ9+4qbj5oRoqpXsWUEBNq8igzJXVJItnS+cKdVMOyYoYNVvXrCeBZ0TYjI+LBKx6yvUpl1hlafLjwI0FIhoIY6s8bhbe2HbATXRO1CYRbDZjXAzGzZjiRDNBLDkgIi6wJDnqmtOkrjPryjIWEGeTYTz/wr3p1osPTS+8qmha5Z1v/NaXesvlcqY/EMVga99VKUMm/ULn6PT5r73WqBRT7uShnzUHxVy+NCKKhc6x0E4uQBjdxB6IeOcv92Q2XXYOuixCo9FoTiFO/gaN34S1faL/0O6GohF9fDAYuandaUrNa5ptJO0kwP8FPqIbOc4cPv+l984q73zslZUDO3dGpcn5qE7cK4Df42hzRhsvMuLtc3TYrvMMj8804sLTavsCRNNG5NingGFgIJJmIajklxUOp34xt3ow7bsk8hP4FZfO7DhzQodzRMS+RMSrbTi/rjmpcXQwtAAZSmQVESUyqYeSrUvunew6a0pO7nx48dCenG+nxx9/5xdp2HzPNT25xGB3NH3nt29c00lpPIeT3GnBKwqjuy8Zzc+6hHT+fKAVGUvg68TRFzHtR7V5skaj0Zw6nCpp3F+PlUvlLJaOAx96zTKx8mML/7Rhcfs5baiITp8WemcOK9YbyVmbZSW3i1HgEGriSg/wU5QGOwuVYJ1p2niGQRhPZkbEWRxtsgUVYQuOuX8ma2s++ekxpG4l2/QQ02MtwEbgUM+hqNwwOv6z5snxnftm0WP6vMr22DLezYhd5g8THrMtaHYgG0EpgpRdDyhKQEiQAmEK4lws9yESB6c75u2vXbj8wZbv/f1YHMdXW3HQVJ170fZkIvHVi+77jPz5FR/wgTEyjSFQDeO4Vk4WGhAyCfUonjA2JUtjn/uDW98fprzpDDfeVXzx74JGo9FoTgSnt9g7hoN3S4ka1D4ObD3Bu6N5mVi1XKTCJAZ/LtonLsHO7eIAMGG4KRFH4bmE/iRwB0rgXYvq+vY46mjyrBwr7kAJvBlzvmPvt6lH9KQat+aZMJVMT6XC2n3O9I6DqAjj0Op1Mr47JUrA2EgjWcvnothitu8y7ltMTDbw+KwhJh21iVIIRgydAsyZbdVTx6Fh45sBU3F52jP2bihFjd09g+/6V5Fc/erNYzf+ZWxWpi73Jkd2Wt//wSNP3E4nqkllDPAWbbmzvG/WRSM4mW+jxN5WYPv7vvk2ifIn1I1OGo1Gcwpxxog9zRlLs1XFtoryUGmBiEHGq9fJ4N9+8I+yuGdzPPKLOxZQKWZRTRoHUMJmQf25CZ7rM1JvhIieIgkdnpzaPRYjhtgg9Cx3h03U74TRQZS5c8Oq5SLNtRxavU4e+ORVYm7LMBW3yj2ZCiMdw9jFDPd5CTY5NVzAtWFZDF0z9XozjSAmGCIgCbQ2NuQfd4zpy6LH73zAKo5EUsY1imOV1MYfG874oca7U282uEWOAlM336im1QRf/fLen5nvu2PLwuX9wPSRlO2Nd4GyM9JoNBrNKcTpXbOnOeNZtVzYgFi9TvrH3v+V+z6Xnq4cuGbse9/K0z9YQumyNErw/SMqwveCLoZe8Lg0RQ14MMT4gkW8CRWlS9a3HQHbqc/XaBtCLtxGFRA7FpCcnJNpc2pxQ+/6Smsq5FXAQmApT57+US/bIwJ2br9iyfdrrYTz733i+5lJBurb8f3G7nz1rMv2vune71TRaDQazWnN8RkFpdGcpKxeJ4OnCj2Azfd9kv7EIzKany8DO4DDqExsb/1nwNGM7HPy/C7MqJBbxBROwz/Zs173WcswB1ARvRRKVBaBjavXyRp1/7/D7chlFSmXVWQ80EXLeb2vPK+5e8EVEwsaXhE7Tq/VO7/VyGdmyvUCVB1iSN2SpQZ5Z6oSDYvaxkSRFCpNuw847Iwf3KWFnkaj0ZwZ6DSu5oxj1XLR6BrMPdxTrYim8HCHathoQNWnzUJF1vYCzUATT8/GvlhiK6CSqvJELdc+aTQuSQbDfRN446AEWAx4q9cdCbNXgEFU7SAr1hv2OQUqY48+elbCk+m2Rec12JnxlCstJxyZCMpHRwUGgAigEIJRhcbW7ftyczdHB1DNKJ3LKrIfXXOn0Wg0ZxRa7GnORBrNmNbEluIuaTGBslsZQAXoHkH1VCwAWoCr6/f/JlHwcmTxaK2h83E/GD4sdn5jG964QE172X2MyANUNJJ6F+yK9YYAegauJZ38f0P7eneCGBJv8oOoUCuXJeWKAHwnl+/zp6csoC1UXs7JJLhOGF0APAw8Boz+Bseg0Wg0mlMULfY0ZyL7Ab/xsSNRtRQqsteM8rvbiop+1YBy/bHnR6qVCfGktG4ZuAc3t1XMvnbSnNxejEYfGa6v23yq0Hsqt10eyxXrjeHpJUI2/5fc1zzNonB6aE99/zos08qGUThi1Lxp0zB3RHHUXHXZb3pclQHHhCFgG7BjWUUX6Go0Gs2ZiG7Q0JzRrFouBDCbo3VzLsprLw18AHgtasLGc0f2JFCDyADfPWKeHKHSweuA282Gxf1RcZ9HWJ6oR+9eMCvWG21A9q1/JIeb9pEDXgW8NZfJEUfxDxoaG8V0cXr/1PRkcePrmDz7p7zX9VkKfAq4VQs9jUajOXPRYk9zxrNquWgFwtXr5Pgx92WAfwDOBeaifOieaUCGQgI++AZgg6OE3p2oUX79qHF+0fNF8p6NFeuNNMoKZvyD10sTeAuwxLbtB+Mo3pJOp5srlcrhMAqnllXkxOM9bcuKpdLrqrXKV5ZVpPaV1Gg0mjMYLfY0ZxSrlgsLFXgrr14nn7Hbtm7XkgNuAl4Zw7UCUkJF915o7d448E6gCyUUH0V1244/57NeIHenxKVAHni8vq0EUDkSwbvhtcbmrZvsxXsOecdjexqNRqM5ddE1e5ozjTRqXN4eoLpquehA1eptW71OBvWIXjeq1m0HcJkPsQDPVZG9GR+7o0hUsd5Mz27MOCG3YtCBRTvwI1Rn7fEZz3fDa9NXX3FNsVQubm5a/0ilfm/5Scv88K54cb2bV6PRaDRnNlrsac40Sqg6uhnhZaMiby7KusRD2aGUUI0am01YhLJkiVCyzn3SGoP6Iy5gEFhFNnZXbUOaNO5vDNZj8sSzRRF/TSLXdUuu62oLFY1Go9E8LzqNqzkjWbVcuChrleH6XcFT6+nq6dwPAm+RMFcKEobE4qkXSTEQQc0isAU/LhSzT+RaLm1JesUf7vMf7gttJlFNHsVnMnjWaDQajealREf2NGcU9Zq9xvqfScCoT62YedxGRfGGUV25XcB05GKFKSJnEmFIrJj6qArAVpV8kXB4wKxxWykj08Hs3vvapxs3XHXLwy0HuxC7FtCCigFqsafRaDSalxUt9jRnGp3APFQ93u5nSK8aqCjcBGqSxTygV/pUDEggScc8eZaaB5gQ2D4PZMdJxEapO/HAF++cs4kykOk+RGnXAopooafRaDSaE4AWe5ozjTHUDNlpgFXLRQ6orF4nj61/M1DdrRlgiwfzY8lsPNzoGVbog2fkeNio8ogp2NY0znmhQ/u919K/ep08CLDspT0mjUaj0WieFV2zpznjqNfrNaA6WHtQUbxp6gMwgCWoej6Acz1YEcEcoexYnoQHMujkCSvH3zds46dApXGMpmoSPnqv1OPJNBqNRnPC0ZE9zRnFquXCREXtcqj5s3uAOaheWgcl+iqoYFwNmBJQE+q+J4m9GDBgzExxX3KEPNCzep3cip5B+7Ij7vuaALJATb7ynTpdrtFoNMfwmwx312hOKd7xb8KotnN2bJID9tQbMxpRDRkSZcNyPtAGVFHRv0JkInxIyWPc9WJgMoNvG3w9OcqXEmNsQY1b07zcrO1z1vS3ZVD1mE+Lvmo0Gs2Zjo7saU57Vi0XDsD0x4QTWTKX28H4F1YfqdFbJOFqASmU4LseGEGJwAag3YlYFNikvCQ40+CloZaB8mVNO6zxysNND1WXALvrz9O81KztMx7Y982FebedcztelQY+sXKk8c77MpWv3NpQPD7G1RqNRnMaocWe5rRm1XJhAPNRTbMHy72iWu4leNOdoiV9gGp2ITusKS5PDNFjxpjUU4EosdcuYU4AKRkgPAFRGqIsVFuRucnWzdgDjZOLq2MFn/R5P6Lj7pTYf2RkmeZJiNU3nw+8GfiCXLXm0K+7nk1D95hTtZFLevIXXFyM4zdEYdghbOl997ffuPb47a1Go9GcPmixpzktWbVcpIB2VA1eEphK7ZdRmGGq8CsK9jSX2lOUEWw2KlhSMokSel14XEhMhgSWL8jGIMI0BIV6nV4ZkJSDePu20iXx/jgl/PTPZRjb2C/Hsf3nfV9OAtY7Xvnekz5tLFbfbKFm+OaAO+s/NwLffkEruOG1AiW8PX54Vwng3H/86+D+m5bcelnPW7YE5eBdruXih6XLXpID0Gg0mtMALfY0pzMS1VgxBDTO/U+mJ86jQ8YstKYJrGmKbplZwGygCbgfCIjJIGkIBE4EVmgSDTUhuRArOQVeCPF8Hqi1xttIiPuBcM81IvzsX8UvV0SvEUj+531fLr/jle89nmPYjgti9c02quHFQ/kUXoeaHDyIGlX3nRezOpTvYRk1wg6A99/yRIkbXvur0kXv/aYv8282nOgzrO1zWblUzwPWaDSap6DFnua0ZPU6WVmx3qgCSfewrBQew2juo1pcwFCyn1QYkbWKzFiwbAvB8WO6EwamAcnQJhlA7DlIrxHMDqwI8Bsh9glw+AYJMYhqcpIAK9YbLuDfdvnxFX31DmJ5jAH0YcA4GYVenRzwRmAp8BBqfxPA+4FfyVVrXvjr88O7Ym547X6Oelgf+5jM/M23/x4r/hcSDQNo02qNRqN5RrTPnua0ZMV6Q6C6bH1UGhegH5jX9HPp5DfTaZa5yPAILYnjB/QWJrjEtzFrBZqISdZMgloj8WgPZnQeCdKAAD+g32nhT5wKtdQk0eQsMQ6Mo4TjwG2Xx9PH6zhWLRcCWFA/hs2oiNklwJb6NuOnzvQ90dRTtzcANwNTwJeBxcA2YJ1cteaZvKk1Go1G8xKhrVc0pyvCKcnDH7xeRm9/hxxDCb1uoGf8PM6fXMC7KhneGlm8DrjRhguMGg2WT6cURLUkh6TJvihNiRZsWoA0YIBt8XNMMXj156ks+j5d2SFZQdmujKCaO44bdSFXRdnC2E5j228b6eyHgWuBXqD5eG7veCBXrQmBO4C/Br6OSt0OoF6fkzUaqdFoNKctWuxpTitWrDecFesNYY/LlsR+Lt+4jHluiRQqIubiy7xMs7gyizDK8v3IYkMAJSIiaWJLCETIqMwQmA6JKE0QNeAx47EXURV5QuDVU+10eimsYruIb7s8rtx2eTx22+XxcUkl1iN6AKxeJ/uBJ4BmEqmEsJJNMbyvCv/s200XrP7Yx8Wzr+mEIVC1kj8HtgPfAx5DjaD7zVnbZ7K2zz0u69JoNJrTHC32NKcFd6dE0/99legB5rVukw1z7sYzi1i//CN6vvptuoDZwpO5xAEW5h4k7Q6zdeI67qGdki1xQ0E+dondjLnJjvmsUWVXrZGRYheSDhwAHEhlnT0NYfac1idkYve1NGxewQRwXMXWquWiAMx917+K5Ir1Rs+K9UayOB8J5P2Bvd3e1HCvB1e6cJ0T+HdUNt/29uO5/eOBXLUmBvbLVWtG5Ko1sVy1pooyPb5IrL75eIi0RmAOa/telg5ojUajOZXRYk9zWhC4OK3bMUxPjiz7R1pf82UyfisPBC1iM5ZoIJa9Tj8LkwPMyxyko3tjKtHzHXFDci+LiIijFIlKE/227Q6YIa83fZqBZJgkg41FUoApiEwMGUYFe5RZQcTsWkEEwMHjfDgREIRZQKVvCwfeJnr638hOYJ2fZDDIzeRDQ6htuvk4b/+48AyNGGOodHf6OKx+GtXdGz7fghqNRnOmo7txNac0K9YbeaBB3I7llhm89VpZvvug8CsNROV5YjbKsmMMuMxrpZDZySYjbbd1mYvzh/dvOTsMys1xRMoxCRAMT1CJY5d8cT6Hh1/JHJIksQFPggGeHcRewh+f6mWvZfAzYPdtl8fHtQ5t9TpZpD56bcV6Yw+qOUNMLxbh6vfEj7zvE+Ky0GVh+hf8EFnNAr99PLf/UnHl9jXFh7TShwAAIABJREFUDXP+Iag6I02o5pJfH2Wxom1WNBqN5gWgu3E1pzQr1hstQGtiSoa9P2Fg4+8KF+XpFgOvAjaJaemaNXrDFC3Z3WxuWcdvJUPx6hiZFlAWAXZsM+Il2WrEHAxtEqOvFHNKvfJyQtqP9PJCjaRYD2wxQvfrSzav7jekU/zbD/yJtvx4Hm6dK+yNV//Juau7G38/cqc+QESMx59/a5H48lve8TndtKHRaDQvITqypznVmQCWNO7h7NZtPGp60opc4aBSq5NAm+lTSAzjRAbZ9DauTNR4SyRkKjCpYbPLNfBlzDQGZbe5sTyypHhBpS1owRbG0U+IBIsqSkhuyRbPGTak04qydtFi7zlYsd5IX/XqxjecU9ryrrlRb8Mu3xQIy8Th05OZ6FfAr070Pmo0Gs3pjK7Z05yy1L30FgLnjM0h1X8x05ErNqJShDbq/M6HDQxWOjlkTdCS2MOiyEJ6JkEMvkzTWs1THr6MsNpNzCs6Foe9mbPjZubj0shMK4EEbDFghbO+mwrPeXSq4fERYPfWc1bXVqw3svV90Twz8dgCZ+9gQ/LRRVb4zyamlxchhiODn7rOi/sOWtsnWNv3gp5zd0oYd6dE+zve/Kc3tv/Td36R/uc359fcTmHN7eguXo1Gc0ahI3uaU5m2+s33GsTQ9tcRAwFq/NlcVK1eKz7J9AEutQZ5vZ8kEBFFz8CODQ66VURlLtb0OdhxROtgw5azcIlxhEWEpWJ2EgTSOow1t9LbetHoq5I33XWv5Id/EqxY/6FmoAXYjY7wPSP/ffmhGpczNl6t/Z8/++za5lwQ7e+tBV7Jkt9N3V/ZzA0vanUtQIa1fftYufT50r8WcK7d3fGNXHOPWa588MDW2tjru5zcbrAP/7rHo9FoNKcaWuxpTklWrDeSwCKU4bBEWXE0o8yTD6Nq9vqJ5Xsoc1MxS1suTSZMEYiIndJmXDjEI3OYLRsotLsL++O8Jwbk3gAHFzAwZ7YmABnnpyh3HzxQO3tkK6jIYYhKFVeOl7/e6YigQ0oGhxuTiQjoKVqGtc0xH5h+aNcdQArVRPNC8ai/Ic+34LKK9O9OifUyVRiUptEdu0ln3cSee+Lpavzj/xO8/v4/vv4Xv94RaTQazamFFnuaUxUTJbZilNBrQokGH7ii/tjr8Lkcj3l2BaMqKNNKzdzPUBjZzsTsyJdnxRNGTNOoOxqbbuCjxnvNBhxqKBMUAFOEY11s3124aNtUfFX0tTcM1NYCt10eh2j7j+dF0FGs//qQFOLPi66zEVVv+eJE8sqlU6j36AWxrCLLH/pU3zWHp+NVJMW7076LbUnjoty+f7jmF99708+vfuPQi9q+RqPRnIJosac5ZVi1XNhAAijxMSFQ9iQJlPBLoyJ7eWAOcBFwNhYONoYoERkpxr1OJp2YsFix/ekFxqFUi29HkubAGB2tPzcLWETSrtfpgWuAL8LOUWt60f3rnQ2vfCQYTu8y4ON6xuuLpD5K7c6Xc5tBs6y9cmDn9SXT4onO2XFTonyo/exdPx/5yU1zv/rvk8G7/70w9nLuj0aj0bzcaLGnOZXIAh3TC9gDLAHOQUX2ksAvUbNXO8MQJwzoFQZp1xWQlYE/ixIWU7gMh438kqAykOykBUu0EXMAExesDrMSNkWSxJES/hjwY6gxuHhL130X31nZ6x3av+vLt0ot9E40qlHDBbznqt8zpEzMqk6WW8uTw5YjvrTh0mtv++TmWcYlsf3GH80ptfzpLYe/X77p7BPmQSU+/SoLQH7kpzpCrNFoXhK02NOcSkTVdtJDr2EeqtZrGtWQ0YOK7r0GuBjJfCAtJTFQJkFMC3sRbELSmtxLRWYYKztiCpiDSdxqzrFH/YOzrYBMJDCflJgNiFPD/KD30/vvubQiJy99mQ9a83RWLRfGsrPef/EFXTdcX0i0/hjY8GzLHko3TNw2++L/3dv34878Hd+9e+LPf29L+pbJroc6gqwlgkaUYKy9bDv/FHLpUu/Pp//kW4N/c2t7c7690/6zK4MTtS9nAj+ovsX42h/ZZqo6Yf7td3Yyz9rViKoFLaAKNxpQmYJ+oFR/WhE117nKETNvqf0hNacMWuxpThkGX4NtVojCFI0oW5XzUF/Mi4BrUF/WPVYkkxYEGBwAkUAIA5ddwEFiacgWZ26QCF6BJ/8DV0wBLWF5LMrVonjSkTEIE0so+agkgNdby/1w3pzmF1wrpnnJEfsmHgt6m6+YLCRaF7O27wLge6xcOvkMy9aKucbvTdkpmY1lpX7fFDb3hrCVEzyJY5FIF5MTct5YzTdGasOTu//HFy/9nc++f8uJ3KfTF2FsDv7XlU1/ELUtz36vuVizcn6aKxyBhWr8MVA1wLk4IvQDJhMJDgOjQCYI8A0D2zTZDEJ9p8AQqinMBx3x15yc6AkamlOCVcuFsfs9zKt1iPkokQewFPUFnUWldPOAiy/LSErYDGCIIpBDXZnHQLWN2QdHo4HGyAy+W3/sFUYg29KeebEfxG2eg7quj6DmQ8Kj8raH5v7xWx4+az1Q5Yd3jby8R3/6s+Z2koB/84284H+Wq5YL4y+v+YGbS7RcAZwLfIuVS19ww0X6lu3iRKZvZ/jy33zSPHeq8KWM2fr2jG1TqtQmehZNdRU+8J4Tvm+nF0IEsXnej6pvWCir1UuvTt59nWnLKGnTYAmaAZMqEokkhVmpEHtT+BmBbTdh+JI4DLGiGGmZBMkkBupy0EA1Df0A+CawHaS+MNScVGixpzklWLVcNPp5Ove/lchvFW0owbeMqszgczY2aVJCoro7N6DSvBOoK3I7STZTpWikyYtGq8PrD7fVUObLFVTN33k5mhdPV0bzCMCGalD3dBnh4U/d8YqPzp7M7QNK/PCu32yuq+ZJrP/aoWwE1zXXSsMTicxDl7+z68V/Ka3ts1i59NSteVvbZz5ycNM3C176t4qhL0XLdN/53S3/4mWGg9I599zddOF/6znAvzYiAbT9rLrMnGtu+9OMHJ4fRGJByqjlkqFRw4rTjiCHjaSEQQhkwQ8hLCMFiGQaIgf8GWkHJGdigSYzZkASiY/BIZTwW42KGscgdWpec0LRaVzNKcHQMqJqG51+M1eixNlWYCGCLkxCQsrAYyj7lY2o9O4w0An01PBqlrAP24bbbpvu2VboDIf4zYAnMHMSozuGBCkDFQCEZAzVCGppHmuuJIqAw4uw/dC8MMqPrJ7f2fSKP2jLnTUn79f+FrrWAWpahoq8esL7Kx8Vua3Ij/z06cLnVBZ6ACuXRpvmv+Ltcy/7t681deffmGp0LhubtBey82x/9KB9T7D6J9/Mn28+sHPvqyLUazJ23ld1zdjzMWsQ872pTzX9WWbVZZfa93ZP1/KLgqHe3lzsN4hIxrXJTEa09VtO84hg0omQFgRJo1wMMPLTiBCBAdKrazwDdTkZpcGpgCEhUoV8wkIkwCXBPAQfQEWbh4HdIL4GHAKETvVqTgRa7GlOelasNwpcJj4A3ID6uk0AfwhkcQlw2I3gIVSzRgqVhE2gpmjMAxY6hjnW7s6ZPVw9FJeiku2QSoT4FaBVYhywcXp8fBNi9c1tAxKSMfHCfZmttZFS4/DkpBHHcTz/5X8JTltWLRcWGBf2DP6y4fpFf9ra2bj4Jtb2DaKEu4makDJZv7WiIrWnZZTrnbtlyKfu+dbO8eryWi1OJKfS+VSxldZMZbmRqdqWOdSPOv4LgE3A4Ind45Ofv8p8pODF5qJQcrEZuL35PZ1zjMmWQtIObGp2VI1ji2ItomFMYhiCoblGNQyJRIloskAmN4poqhDXXT1NC0QosAc6kJlRRNPkzLUh0gQkhFWD/mqjnU9UrmlMV3zUJJ8k8HNgFMSjoFNqmpcXPRtXc9KyYr1hr1hvZFFdtktQ52saZXqsquqE2IIh9iJEiBJ6u4FfoCJ6s+MIpGQolOHualQ6UC1GZm0wmor8KAKrPUfLrByNZwtojAnUxY8NRBDEAI7orS0pHB4ZaRoYGqzs2bdHf2aOLwJieaD8aPjYgdsD/OkbgC8A7fVo3X5gTH7kpz6wD5WaP31JZEdxsw/e/Zg1vnVKBmGhOpHKjz6cuuCxjV480v7g+JwojNmK6kTXPCdCfDD7mes+mPnkcnvaMYYGL+qYjBqSsn17iQX3V+MoLJkbL6s5e86q+sNzZDVImJXJJLhT2OkKlpckqmah6lArgTeeRPgQSklx0mB6b5o4BISAsXYoFpARBDLGEgEyikyvQjLy6Ig83gf8HXAd0KGepNG8fOjInuakZMV6wwXeCFwCdACzgAWoK2QDVWu3C7gNmI8SgylgPApZJAy2I8kWx51k1s5sbWxMpyt+pb8nOyccrO1NCBFZXda8Uosxyx/y982anj6cwkCZK1gmhBFxAMT+dHNHzy8bm7xaT0fXgcy9D+gUzHFk9ToZrFouvpEgl3WM1Md2Dm9Izm5b2hUmChck/vXRka/MWerffKMajfaM6dvTjGHXrBjNqXsGB+L+swwWS9vYW9zUe7vbsPuhHcX7liw/+6OfTsUtiWxD/h9QpQyaZ0SYQDcwxxKixxztyDabg24weySUhUmT3fOkt+H6tB9YpiENh/WvM4Q7GZsdewwKI1BuRWSnCQMbuftsaulB3EKJ8GAr0q4hExUiAaKUAD9BcjRDNYDaSDukyjS3TYFRt+kMMBGkXYNzDZu5qPTuv4N4EOSpXX6gOWXQYk9z0rFivdEGvAO4HpXGs1DRvJkRaZPAduBeVCrLR03PaKqVjd7SeOIir+LsbGuz/isZ268+y7molkmUWrZXNswKzMqhrJPKTIVTRsrItzUkmpxIhIlhex+xDAEB1YiqBwkfCpEzZu109rY3NI3zw7tezAxXzQtk9ToZeP987/BocThrSIGMgmYn8v/usJt3LpocHK19fmBTIo7KzGQiVi49LecQn783SL9nbktbW8Xffn57ThbKXviobdz3ikPJew8d+k6xsfWcoZRVGLFCs+1E7+vJh0igRib6bLy8SO36BVxw33zP8S1PmqlE7NipIByJDjcl3LiSL48XyiKOXFoPRp6fNVO1pMSdgs5dEGbgwBw4ewN+NQ/TeaxyiCh3wtBsRLEJe8m92AdfgbfLIdE0jAwllYlJzMI4rjCRB9IIuwqZKVVhOd6McCsWjZUsJtejLk7/HcR/ABWd1tW81GixpzlpWLHeSKIMkt8D/Bbq/MyjRJ4BBASyhMkIhjiI6qbt8ivsiySPeBUzUx5PUplwvVo5Zdmlxte0ZjOLB/3aYCK74+DcxMJKk9vV0N08t39fZfscEYp51ahaSNlpqz03m+FgkJAqmJBwAQe/wVz4kzmlwjwYqqBTZy8Na/uEa6XibLJHWtITQoIj5RLLm/pGszf1aAI+BexBRW4nWdu357kmZpyqLJkoB5OOdej+5mxbGNe2i1pp58bu7sd3/H5y7Bx2AjzK2r7HT8dj/80QBiry34yq24XpwgqG2nNGKdNGYcKLnrjETe6fW7GvuaMxbpVitL0UpK79iZeuEUQHz8tw9oORSE3ZlHJEFJHTTRh7zpPlyBajwwtoq8wjU84TpYcxTR+5aSly54WI/CBhbgRRbMTsn4e84Kcw1oEsJxFtUxANIks+YrgT0ToAmYqoCVJAbyLBn6EyFA8ozz7dcKN56dBiT3PCWLHemKlbyaCuylcDi1Ff2mlUMbqPSt2O4ssJApoQlEhRBrrDgN2VaecKr2oyeajwq8nRVCaR9pqEGe+NpVeoFltLU6Pm1mzbRNDudKeq0XS2GE42RLG/NG02pA+V9tZMU5hhJAGpbBccYhFhEDJZc70vdezbvxvQ6ZaXgrV9FnAxcG3KNbYFsTlfIB1TCFJK5F+C8i57EPgP4M7TVex8/b4dAogebEh2jk488qqdo9WLf9tufIBr13+Iq9coEXOaHvtvhoxBHDrIrNHhsGvJ3AX7Jhu69sQcbkrZpYaOSjJO+hm/05LmhL3rXCE7BipGnLGt5qFATKUTZXc0Ek5FJC2rFo/MTnhim0zaniEMYfh7OmKxo8cgOQ1BE/HiHRiOQzzUSTFzAGHWyNsBpKu4uVGwDEmxURhViWzvg6EO5NCFiPbdkJ0mjhGygoWLrNVIxy7/n2VxnmPzVRA7VN+vRnP80WJPc9xZsd6wUQIujZpqMc7RMUMZoAVVZ9eBql85H2WV0o4SdqCu0GduFjCOyQgxIQZjwIZa2XBq04mDMeGi6eFcoTyWc6QME04y9lu7a2JWoufB+a0N3ZN7ZxUP1R4vbnU2JDLkOwZKe1rTdsE9HBw0iyN7RTLTynRi3AhFDQIkHjEmBi6HBtm2vfDdLbpO76WjBdVdGlqCg5EpBmtE50r8piSOCRBLaZdr0TWWaZyfdIzZrO3bAtz3LNMyTl1WLvVY27f7srHpeMPYjmzNXHROb0HMYaLjatb29U1MMLdhqmGczy04eKJ39cUyaxA7bU4mz3Uezbyp+pXp761s9r5d/bCxw/9W6raWx8t/2fxbLlCRa25Sn7W1fQ4zIxGfVeAKA/XdYAOBM9q4KPP4decas38yRtuORtzgcZq2tySn2+OgY0/RmibLvgWx+ehSuxs7Idr2R7WmQ57ZEzmilLYTHTuN0PTiSjknvZEGA98Jk+UFRv78R2Q6VRVOaEmZnxSMd1ALXPKTc5FtB6Dqhk6lYInWElGpQfjTbmSkpozEngWitnEpYesATuxQ2bRIhrlBYdmGK1Jh7Lk1p5qudksrchpTshZjfTfpiAFgDORpWaqgOXFosac5bqxYb5iobrMvoKxPyijBV0H5TZVRqdBmVNolX1+u3v96pDtcoGqb21BizwU6MYXAZFJKyl5R9I72p5Nj/U1LTNtPWl7bWZafs2vx+F4hg42v7XjrVIJ8a+7/Z+/Noy276vvOz95nvvPw5rHmKpWqNCIJSRaTAdmIQTZ2bEIgy8aNTew0mVfS8UridtztXu0sQ+J0HGx3x3THOMZ2lGBhMBgBAgoNaKjSUHPVm4f77jydce/+47ySQMwaAoj7WeuuV+/Wefeee+85+37Pb/j+Clsrxw+WTn9U1cOKmD425ew9fH51tTQ+XohC+rrf851BdlvGWoGPJiAk0QohQxxx7z23xiOhRzqt4jc+/pKkmWrAx0gbbfLrol/u6fAX91O8hfQ40XGCt9aOrSDGPDLlvM0xxZuBKT744B8CEe+/+eVT7/T+m0MJZ17B7Wde8Tuf/xDFi69i9abx9iD6xzv98OdiI14d/3viJ/jAge94Usj3CUeEb7zVRIXDC93GK1/doCb/MCwt9/a+6qbHn/y1zCAeRvYXW8P3DP/1v/nNuTfVbpGv2x9Du2ny6783x9GHz/L2D8U8+51VII22T5GuF7UJY02Vnip2xfk73fprxJy9OTWV9xrYkifsxcdC6vb1CAOKjXUxc86jNZWV3aJbLjcsvZMX1IsogcrsXTaHud7AeeBOO9MpCzFpxNrrmDqoShWaWmbrsWNXZeC7UmcG6FJfJC1Dmev7ZGQGStqxgd3QWnmI8QaWo9ChjFV+KJSRGGpQwDADaSdahtLI9GU0M7D7P1rvFby869//W/e99YHfuu8jm/oD7xhlE0a8aIzE3ogXhbtPyArwXuDvkEbzrsyYvOJSPEeakg1Ir9it5zyE8Zzf5e42VzzqXdJI4GQUsrexaTZ69Xwz6jteNbdXZkqRFU1sLZe9zW5lMlw/ND++ZWLNjnWvPvrU8Etzrpe3dtTqvO8PDD9o5bZXWtl4wZPJHsdQg2FY6I/b2kzwg2Yji9MPjInz+e0jf/nSvFs/WPzzN4kSMPbP3ySWfuPjL/IkgNReZX33t62l3/lzUU2cZQfrl0kbdFpCJGNZV1eUL4ZRok85pnBJR+QdB87xcq2l/JVXxXzQfaTjxvl6JriFfqJkmPTQyRQffFABOz8Iad35DSzANjrunnBQNvdOrq9c9br2LIVL+WwrsRfzG/O/VLi46Tj6lHv6xtveOb6xNzf9Xzu8+vP384k3GyyeOcQtnzZILwwc0nUkT3oBuU16MbmXYuN19rH7JpP6dGTkNmzyTOD1JAdOTxN4FSotn4KvOXvtBFp7HDwpZWcCX0Zdq9rXGHZR1ec05a0gU1/MCAth73sk8m/8oqDQkv7yPtTpa5Tc3mMxdVHp/RdR3YqScSjU+LYQjUmE25b0qjDIC2XVEj21bvSzzSRYOxjlxy9J0Z/RBpYZDQdatWfJjLVMq7qxYbXGljO1eT+au5C5enL11e9+xec+CX+zPZq8MeLFYiT2Rrwg7j4hx4FfJY3oZXhW6MWkCzM8G7UzSBfpb+pV55DFwGRA29jd/kpzhlQJgVaoROmyl4vs8UXDDSp2NafNoDIdXlp3TkdCMm7ZzNzf/kj+bVO/ZG9vL82d7Z88EFqDOpKgN2zIScr+IGjmu4NmDhdFgupGzUQPNOVarjbvOffUMoc+NrX11ksv1fv2A0ZIKrwtrhTAv0S85ld+UvPBB+ukndYhsFiPupdMV/mH8pXgy2OZdiJF99X1wb2kpQH+S7k/33Pef3PrH/BnneLT4//5ui9aD7/p0cnHKcZ1dns8ecbS9/uT+Q3ypKJ9qp9Lgktyqn6+cPOn35H7/X2GiG4LHbf42EOH4vlqY7v924v6pmNvWT1S7S5bb/vdccbqs/zKiUvEXMJEkYq9SdJSkAHpxd8U6fpyG/18mchZMBafnCj5STYxG7lgettwpPJYmZZcPpoj0xoQao0dCKxImjOrotDLe0xdUH6/qpLKhhn2PWl3ikgjQnSLhlxbBKONWWwyvPYzIsZWZq6fyHPruOeOBzqpGHSyJlHWsuKWjlolKNZEfOkI1vR66FmxFr2SbWUrxL1cN57YMta3ZsyM1zGK3UwQM3leu7pPv5itZnu5t193Yuxnb/ziPqAP4hLowffo4xvxMmIk9kY8b+4+IW8FPvNVd4WkIi8itUepkAo+xbc51lSIVhHCymr01/ZCSK1BxRAEZJQCQyLROhs5W55XbCWJOG93M/a8a+IBDwATO8HO0S/ufKKWNa3zHbt7S7bhTgeFwZCQ+Y7o5yJbG7NrFdEaS0Q/bksdxUFuM9stbxe7ravaH/0PP/np8y/iW/WDTkKads/98zeJh3/j4y+xTcT7b4754IOn0mdOEGFv0zdES7gi80S1MNU1ZTmyrf7r/8bh+ku6H98n/D5vV1zFV+ZLPPquX0DtjpGr8f6bfxBKDFzSOtz4mPeVz/+s9wfDt+T+1MjK6HHgc1Y5+NHjx1ey/qO5R3qt+T3n7wseO/rpf92E39hHKuQymEjS9WQv6QVlTCr2jgE30XBuxwk1me4pDt9fJDHnWNuTCzcO2Un3UmAe+7Iw+lrjtUR06apcZPvCytW0de64RnuC7TmT8bNw4TgyKGIf/Rxy8Tx6q6+7ycCwy2s4oQcDW1uYQlU6idUZ982lazNs7LXIBYLx9STZKCZRULIwpMBAY2OIXDvJtcelV97QgTnEcqVqD3JCNsaC0myQUUt77WiYOVw7fGrjrNmPrm7lFyq5XpB39f2ktlKjyN6IF4WR2BvxQrj2Ob/buz890sjDc/nqVK3+qp9SGgidQJgMEMYz/ycAkgh6TSkNR2E6SBDazWkTEcjQDGMJSV8NsdNi/zuB7DAYLJ2OzrtF13x1I9uYU4Rg4GPjDWUgSBAMbYXhK0LhF9ucG+iodk4nK0+fner82xfn/Xm5EAIXa7mf/oXK4iv+/U9+5D++Nfj3n9q+9wt/+tJFld5/c40PPvgQSbR3srszZOWBz/DKXxo8OJ5b7FrGjfcujpVfn46h+qFhZXo3ipfWKH7fC735Da7MNn4MaPxf1Xe25s0llzQyNwS6hqEfmptrvkpH5QOL174hb7ki4EfrJ/nb/3iTd/+fU6TR5NOkF4x50vXiGtK15I0Ms5MsHZxB9EAac8xcilFGFsc3TbtnEyvUsGS1moeEvVXGFBHiqgdRE3XBpaOAFvQzBNdcBhnjrhzEn7kMtQWMXllsx5r8UGAXBP7GvLAa4zhbfZNWJkdpU+DUJV6QUJ8wDWOorEw7StYXrKibV8b0qk4uHVXm2I5uqbUkPnnEyx8YeIOp8811U0X5XGcro1arrtfX0+3JqcQa5s6tz6yUip1lsePeMF1oXrNYafwViFUgHDVtjHghjMTeiBfC75HapWSex9+Kr/7pGBkCbwDpVbtBmp4zAcswQVgKlSAsE0wLgffMuCFj9/ld0i8EH7Bx/Oww7B4Ou95sbCZSSm1gCBsHG1C4Um1lWs1YBjkiVgvrU584O4NXmXnLydf4f/v7/ov0fyS/8XGt/+Wv/8+18Qdf+b6F9oxdXD75mS/Am+/6kZ+6dO8X/vSli/K9/+Yuv/XZP6Z52aCz1uP9N6tPbXAB2CQVCyO+j1mZFuJifFANdHbnmPVoLZ12RwC0n91KnI9Da6YXmjOZheWzXPvlhPH113L1A1fWlAdI14jbgQ362ZCt6XGmz9+GEDdTyxj0czYreyz8qsnFw4KF0wzzPaknLmF2i3by5AEEIII8xALD7GMnIRi2wAghEajzx0AaxNk+IsygqxvoQp2pvsQaG5KEkGTa0DMxgxyB4wuUwJkcCvyKOawXYGxFel5oXjZjQmUa+7L9hMqOOayuyMHqvApq1yj3+k8EM05ijjv9oD25/FhYrB+zss2iPnd941yrIFe2q2NTsfuF8fJgPtFGhrSJzQf+C7D8P+zDG/GyYyT2Rjxv7rlVJXefkPeTRtNeEAFpWYrWmHGIUgrLdhBCgpBoL4MIA4RKQEmQz8YINc9270J6TCfA2DBqqGFsxSWRaCQeYCMBG0MYUsUENn2YjDLdtT1by55b6d0k3/dASVw3qpF5DnLskNOr9B9fdbavdzK9B4o50fujT3z0pe+C/Uev6cNrnvl1ZZqYrxELI76f2WeeUzwnCvmJ332NIJxDAAAgAElEQVSPBRg/9kt/4AN+u1Zd7gbmhPU3f9u1S50VDjy5n9L21aTnclQPnNWSDPYbgp8hCbZZGztEduUwKh7nsesFYcEg29JaKCGaBamcWRhYiNosSe0QxpFH8EobUNyBqcsk9Vn8Ug17+knk9DosL4AVQ2IRiwivMUs8ex45toPcnIPmGOyMYXsJ5vQmWApltUELIrePbkzBxFniYczw0q3kGpMks2dx8qsIETDolqRY25PYKo9Vm3Nsq29HjZzoJAfmZ5ozE9bkRbnRmLEz5cvDkg7Ns1vTm7PVs92y262SmsxH/JBFsUe8+IzE3ogXyn8EXsfXd9c+L1QMoY/stU0K1Vh4WQCE7YJhQTCARIPnPfMnMokgjMCziJRGouglkLGMxPIyiUAjkMLgijA0QBMLIuzMgL5rZNa2hDEu+p6zNfO72UF8/Qb84ovxcl5O1F5bOfseLwnetfhY7RNHPvHR7e/1Do34fkcrYOm591rO8Cov31voNYsP58qUq7ObdqbQedLJDivAcSZXJ0ktm44MQmaIw/1BIHVGqUUaEwaJkaFdEojQYu4cPPIqIl0inrqEPbGJsXQQs19FZTo4RgeyWwzdLgR7YGMGMNHrhwm29uCpR9KU7YGzJD5EdofhVgkjL7AtgesMEQKYP4s91oWuB2aEeOh16DCLsfcsQWkdEoXZHwevT2b+MuGBU8T9gmHVJxC5DoVX/rVlxQ9rbSaq15gxunZP+yqyExHHLF3tPdkox1vuds8nsvZMbh86X5veLHpRrZxdWyHNWtggRmPVRjxvRmJvxAulT5pmeFHEnjTBtCFbiOVze3YNAywb5HPu16mEkzE4JPgKLGkiDJOAEBeNiYECNBIz0bvNIDF+JXJO7JSa/zWslc6MlyoHYtG5Zmhc3CQtCB+xy798369o7rpzlTTCcBV33fkw935yVEM04rsmV2kNqnNrdqbYnSMVMvu8/CADPAIcBg7QKiRoWTAj5rLt7IzINDUixt+YdoaDgXS7jvC2jkNtBopd5MI5aJUJHQ2Lp8EvIewuwdwyhgyhXgQnxNjZhxy/QDRUMHYehI92e5jr0+AOMBLNZtfGvXwMU13CVybexp50kXEexo8d9Oo+ePpG6JcIGxM413+KWJtILRD7nyBZfILszkHiwCa57ouQb2L2PMRmUXeiWBuBhZsZWJnO2EI905Jmc7If21Gh3Xb8XLHX3enN+Hfsv3Rx71jNTZRwDKlPA4eAIYjTo9q9Ec+Hkdgb8UJpkQq+/DfdQus0kWMAz5TapSQBoMFwIfTB98GySEwHrcGMw1T8XcFy+DpMG21ohBCgJZaOsOOEONEIByw0ggQJCCSohMgeEuci+eTaWPBlbbM8e6T+lB1nnibW7r97/f/x8vRte+EEpF/Igh+ABoER308ICzBBD29521/Wgebu7ceB16CIWV84QHlr1reDKbVemXDaY5N2kDOjzIoVdcZkUO2iYxMZuaj1PExfAN9BL1wknDoNw+vAd8EegtlFr8yh802U44CnwWsi44D4yNNw+lqwh+jDT+IXh/D0tYjIwxIxM0cukwRFVHcaw2tBNoRaheQLP4WePQv5FvqWT2Kcvw7DDFGZmGSYIZreYd00yD91gLF+hY7yaa1PM16VCBUSNBzZDgpuvj2lK2YgbWVXz8nBIJo5q1RtKrfanVHXzdYvz+XrBw0Zb33+/H67mumPOVa8sb+6dXPOTTr90NrO2sIH2qMo34jvhpHYG/FC8XdvXdIuO/F1W8S7W3h8/RGn0mI7kQAKhUYiMIQk0QoVJUhDf51GfC7iyv8LA1MKSCIMAdrXgATXeWa/EktRk5IuiXwq99ik6Ddyl//8185Ed5+QVs38Lwl85Pm+Fy9v7v2kAu7/Xu/GiB9IJoB9IJ4gnYXsARkiDjIQs9ga6sWr8AcT2rFsYTfdeKEmo/oYbbbprywyJSVi6GI7QDYgnF0myXWxvAE05uDgKWhPIjt52JpDWQnsLMKB09DPgt0mWtyEeglUAEri18Zh8yDIBN0tEOY6eBcOohOBmlohGQpQIf7sKnrJAJEmCESpjXKGUNzB3lgkSTRGtotc3wtnXkk0voTpKfJhDkO10F7IML9C0J/DkQPhJKZQZqKdlQV36HWNieqqOjzmFMLI3OuKOHj4zKHFyckdnShh/tljNx24be+56JV7Lj70pcsH31TN9pu3LF78GKN53SO+C0Zib8QLpUIq9ELSKRkezxV85u69z52RAUgXDIWKI6QlENojURrDAMO00DoVevq5j6liiCKw3fT6VsVpTZ8QYEgw0gigiJ+zHEqMpGpMdXx2VjOx8eCeJ2Y7nY3ilYaMMlC5+4S8cM+tauRvNWLE80JIYIFz10R89m0VZi9d5k10SVeBN5JaNulYc2cjKN6a14NJT0Quk0+6NMalc+rVQscW0bUPov0cnojJTw7JWBGYBqpXQGRO44curB4gij0wY8h1ILuDCu1UBgkFbgi1IqDRvQlUZxwj70OmCYUdaMzA1gzIePeWEHkD4r4LS4fh4dcwHJbhus/C4imIXKgZsHkYNhdQkYk/uY3nWwy3AqaGPnLxJOTrWEEeoRKSZplIdMjN+GSyPczmJoPSJm2rL9CHrInWhJXMnxt0LWk9sjKz2HnscP1G0S0fOfaVRhcvnsy1an5s0A/tfL2f39jqFi/98kd/nof/kRCj6N6I75SR2BvxvLn7hJSkhdQNUmf7Ic9OWngWIdh1wL8yOk2w668nBAkGIgnQw0CITFbrRKENCyHkM122UkUaYT4bwvN90AKEnz6QUulXjPGcI9o0v2Y5jE1l9cpGJc5EpXvq4tjH/tl6o829n7yyRRPoj4TeiJcfQvKj20U643PAOR56ESeP/MR5m1s+leOf/p0m6TnuAiZWcIh9T9zMwVOP0c03yHevJh1xJ+OEYhRxgyPaBpI8MRaxLehXkI0pmFyBIEe4Pkc2OYghQrjpM2CfJHjkVbD8Coz6DIkliSMDc6oBOxMwGEvTuFMb0HeheBmMEIIs4tE7MLSATAfqM9AtEGMi3BBDaSi2QITEvSwIDWtz4HtgBBCaEObB7EFzHlpj6Svt5MEeMtAGOlpA2D7abSNsYPwChgwYri4Qb4zhzNbwtxbZcrqEKwfYqazQ9trYfQMZOG4cqeRqp2/51boTFBvDU/VpebkxOZyvNJqfOnvt0mfPXzM1me+c/pF9Z3q/+sY/e+efPHLTmZ/58EdO6g+8Y+QeMOLbMhJ7I14ImnRYvU/qdXdlLNoVrtR1KXbHpyURkUqQlpuKOK2wokDGcaJ06EvDsBLTNHdjAwABMlGakPQRBDotvjMEAjB3a/hU/DV2LM/uoAbdBS1BumgDe7htbLS93PiE2cweOjpjnnv6ra9v6//+aXXPrSr6VEY4n0Lk3zDQ3Rf5vRox4nvHo7ct4rb/Np1KDozfBtZe2AMK56/8t+Te1/wT50t/6z/tm4zbe1g69BkWz06Qrglb7DlzDTOXr2Hg7eHSYY8DT+XJDCYBJxwyRmKUbDMxPAF0DGhXId+Aqz8PuRCjm8MFxOohMPuwvoDOd2DfSWhPYa/No0st/LGVNKrnuxBbQAtci8gwsewBGAPoe2mkr1+CS4dBCez5c7QOryCkorg6Bq0S7IyhT96GWDiPs7iC3HuZRAwJW9Nw/jgMbGhMA6D3P43wi9CGKNNEPn0Tcm4FWarDhXHiudOYB58iM5ggPHUNUpzA3TpIWfdpxzZT5VWmsl3yJuCEcq1Tlnprn1nvVtXqfDuzdOHwwtNbM+LmvSsblzena5v9Yn1vda38U9c94Bm+M/V7X/rR2BBJ+5/9xc8M/vKpa73H1/ee1x94xyi1O+IbMhJ7I14IV1K2JmkaNyCdYnGFtMBlN4oHqH4PB00sLYSKiZNQGM1tV+QqfpLJJ2aiIPnquQxXPPV2Y21XRgdINLYDQqaRPvlVTRxfTdq0YSNIwnTsksjk9cQjVsE9cap1sfb0lpwBortPSN7xczpfgUXS4eojsTfiB59f/YzJytgYx97+KrLtKTDOk56nL5T9R8wnjv609592lm+8uD6ZnFTMXjjI2eNlZs/eRDaYAvZhB3MQmtHUci0Ug0U3IWskjk7qnumvT1CavgQ6A7EN+SYqzhFFORRdcAZphM8b4EU27BwkKD0AuQE0PPz5ZVynD3YCQxt2ZmB7DLrHYXEJc6cEhoGKE8Ikwi02wAxS7yblEg4q5Ds1VK+A8l2kN4BT1yPqU5DpENgDmNyCngPOEJAkhTrGkbNwYR8iNiB0QClsIlS+jphagtiB9X2Y41tQr8L5a7DjLHprDisZYOVDMgtPc+ryLBEWmYPn2NYx7dYkceyLpcyyl4nDiZwzWPFsvxCH6o7bYnPqL07f8NcfLexrHJjcqf/dpWOP/9STN/rv/vv/WHqWf2sQGfl/+NqPZX7q//ly/w2Hn9j6xds+E4Huvwif84iXCSOxN+KFEJPqL4fU6DYknaFqwG73a3qLVcIwjFAqxolS0+QsiTQtR6EJdTBUwnR3s7RfXZ1npKlbW2vC3WtWQ4DtphvpBNSuEYF0v3Ejh5Uzw4RwjYTM0NdnfC9Z77HWta85qadXj6wvXrseAscu3oZdWcYlHbg+YsQPNjchuTl3jKF7G4/fPkecOwV8jG9p0CtMwPnGQkEYwCwbczMEBycX9pwT/6LwD+1MabAOGHTzR+mW7qJfOBgbNdePqJgmGdfQ+djeLupmzottH2NnD6bRITvRwCIDVpDW1llDgiFQWIHmApghaat+ALk2epjFJCJaPwD46OoKQ5HAeA36NjTGoRhBcQU6EiF64LWQ48uYQwPGerC+F1qTRLUSRpLFOH0Iw1XQLUC5DnIA4+tQ6AABfO7HoLIDU5dgfT/GnkfAUJDrEdar2OVmKvga81C9jBgUwTdh5gwMHdg4BP0CBC5sHiCYPYvy6vSTAJkz8VYOcv5snsDosbZ+FbJSY9UIRe/p6eKWF8m8M2ittQrV09tVZ3L/w9Gbqo2Hf+u+tyy9600frTVmTx969PzBmbF82zsysVy6fe/Tb3eMZPX2vadN4EEQj4AelaSMAEZib8QLIyKt1TtIKu6Mr7qFpGLQBhIVY4Q+Q8cldrJ4gU+UJCrMOyCr2og1whAkSuNBmn4VgmeSwoYl8L6Bk58K0534ViZ/AlRVztZ99COmZ8ltdWYoInzLZfLmn3wiZFeoPvVm2q/4Y4a8OJGPEd8lH77/QwIQ777jvS/dzN0fLo7z8DXvADzUob8CPsdD3zZiXQQmQVwA/cx5cGftsZnXO/+r/XPZD5YKl6+asJtjY0wtexl3cBD4hWBIHJpdYV79xWNeK2OJr9xYkfPnbF3pWL5GYuAZhR5RU6KKmxh2gHJ9hioNzBm+YLg1Bu058IbQK2KJkNiMcUzFsLqeGmzWF9JQvwGs70ujeVd/EQwNXgRyHaZqMMjBmevgzPXgdDHP3AKFBvQcVNch7E9jkyCVgKGA9VmYvQCHenBiHJavhmIZhkVYLkM/l6ZvvXkIcqhMD+ZWoFsGI4LGBHJ7BswExldh4Sw0ZuHCNKFbwzbyiPlzOJUmne0qq188Snx0hcX+GBM6Tzh1mnjiEmQGDLXkE/WyaPSdXN4sZyy73zHKa/bU3Nq8ZYixudKnzp0ZXzUfbR+4w+8UekW395W7jp8amy+1B+vtur3cmrx2LD9Yd8zkLIgWaVp9CLr3Uh1oI77/GYm9Ec+be25V+u4Tctcpj10fLeKv+rcChqawjCiWlhRBIVZ0LAttWfRdD8sQNL2kEtTbNVtaKJ0wi4ElBM+N8X1DhJGur9L5uqjelYaQfky8VlcbnSITEwWzaJa2xyi39cZj++t9QNxzq1J3n5DnutMiecNr36D5hn3DI+CZphzrnlvV1wjii630895X4nkJtQ9/9nceJkiOom0+fN+HfhWT3373He8ddRo+H25C/Mufp/jeMuOzTXcADIAVUj/Mb4FwSGtvA9KYugmoE8Grxc96x67ea56dOh8e9PZcc0ZPRQ92cYMJFDM64IjaLuUMQ5q62hAErmsMSrbbHLNlscNQAK0sNCYwiisQSZJuFgoRupgQ9GxExwVTo8wYGQtw2kRHHofGOGGcQ1kgrR3YW09P+o0JCEtpM8bOHJgqTfmqGNrTMPTAakFQhdo8FGvpVWHtIDI0yOg24sL1EBhQboAy4PzNUNiGsADVyzCoQKkJxRo6yCAKa0ACykZqiZmrQ6cEmR4cegg2Z2DnMNhBmr5t51D9CkyspGLy1B2oqYsM9jyC98oVsjJLbFzCNzSrbpNz3UXWTu8HoXG9AYdNKdXSIbFVXXJFqWU+vLxnqhc53Z3e2M/fd+7op8qZfnVPpRb+hy+9vnP3NV85IwRWmDhR3hpWeoHDx568On7bsYf3W4YukRoynxsZMv/wMhJ7I14omd2bzbO1ebBrfgAYGTVhGb4bt4YXDK9ABk2sEyIk/cDEj3St45Y56HlpY600kLuP9W2R9rNPGARpV65pEVgmSWrGkvQgegz0uTabpYLKtN5wes+Z/eeJHttff2aw+D23qoC77nSBI4DBXXc+xb2fHL44b9HLijIw9u/eJjaPfIoqsLp/XcfAPiC82GJpX+k7++wA7j4hXanNwlvVb14lhSEQIcTe1ZjpXNSX6DW8LJnfwATkj/9TDhoRr/uL21n7xb/gP5CKvT4PfevPZT2Zy1yO9y8cNJ+Kx43aYSCIFblbnc8FN8dfKfUvH5oPZlamMtl2vC7KjWLcq2QDdZO/OZVLomw2EInueIZdzfa9wtWf12JnD7pdgkwLHn0tBDbsM2GYgV4RffTLCJJUJG3MwcQKcmscgjwUu6AEIttHbeeR1WRXeybQnkCXBihCDDcHnQJEGQhycO4Y7EzDxl649gsQKeJP/S3EtV/CmD5HbNQxZRlh2nD8PtjeCzvzEBpweQ+85nF4RQO9PUv7fIVMIcR2ewgjJin6iM4YcqMCs+eQdkjYLGGuHkHmmuB2Ye5c2l128QaYvIR2a0RnbwA/j6018ugWlcY+hl6b9v4zdK2A7bPXcnmjwsTaDTzVzVC3AvrVZYrjdRZzbfHZpue0WqXM0elNd8Lr9R2pnJ+54UT/Yn2i+ODSgcGZ2syb/uTxmz5/254LN1/eKWXyXu7cv7j3pzdumL/wqhumLhzfN9m4si5/LBV8hCPLlh8+RmJvxPPm7hPSITVSjkjr9hRpZ26dtFFDAHKzU4u6DVtbwRxh1HejbNMfdmXGsNRGdYa6M8Z5YBYoaaXRinC38cKE5w5N++ZYBsQC35A00nuSfLoPOtrdF3eFi59Yd6KTb/6PS+E9X/8Qye5raZGmoUd8PT1ALTz0rBCb/fIfy8bs1Qc2nakPB0mUqTdlO1TN+dv3X/VtOwM9Xcn4tEraIiZWNoodsvwZcPjD93/oMtAfRfi+Y2aAyueOc+TIEqWN21j69z/L4ORhdtN3QpD6Ygak0fcu6UXVOBB8oPO7yZeDH9E/4f1x69XuX87sxJVbpo3VqxeMpbV+f/bSYOVHXlGx/9rqGwO3E+VmG605Z9G5VB4Uuka8Oa7bWWGroSsmnryeSAciHttGNKp4PZchPkyugl+A3CYcOI3I7p5ii8vg9iE/hFwPLEmYiTCQGFEMuS60ymAbUOrAwEHZLXQQQH8KbbcQ00tw+jrYnoLyFgxN6FvQK2BYIeLyflhaxJxbA68BS2OQDUAKOPpQ2qkrNCgJmQFcPobqVZFeDwYD1MY+fNfGcZeQNzyALmzCoIyZEdCwUI09yOk1WDwL7Rx4TTA10VgDd2cv6vxRor2PIcyYQafMhQtHcH2HsTBD9eQbyZaX2TI7yIKP7mU43JlhUyk2DEEgssaM1yPsla21iGw3cKc/c/pA1bBMu+s7TVDn7r94YGynn5tYaZbCv/uq+9RrDjw5fXRq6Ud2hpkjU0HjsmUiLYM7SC/WzpA2oY34IWIk9ka8ECqkMxuvpEyv3K70xg4HfaxWHSOOYnKOo3w/kiKHsDy1bbsYhmRAehx2tdaZYIAjIHFyzxgpf0fpXABpktjptjlS4TYg/UIbAzaBdSD6+E0r9nu/sZjTpGJPk45/G83HfQ676duAdQAuAvDRf2c2WytdVZ3MtrrrVOK5oj3kD/77/r/x82/lT77pWLUP3/8h8438WqVPrW2Q28SO5+h2/tAY6vOJl/OBBdL046gz+jujDuq2gadvckLjy3lnZ+Of7PnpCnw2BB2eja4y15P5vRVZ6xZlo9RThZUxo5ZMGpuvZVjcvqq/MZu4n3tVPcyd+kTwN8uHwvWqVdSXDBEvhTnrUG7qdHYgZKkdTGebgZnfrB3LFjKB6RebMuzuMeb0aeTsRWTBJV7aTxJa2Puewlc2lHagsAbKg0ERzDVYL0K1nZ6p9VnoBZAYkGvSjqaQ3QJVczNNycogPSPrU5BpYmT7sHUYvXyYwGtiRztIEaNf98fQm0NoF3ID1Oxj1IKIvLbI2BZMXIDAg61FaIylsnd9HtoFqLRSYRlqRG2SslYI5UPlMjLTIHPqdoQ1j5o/RRjaOPkOBAtoPSDxs1gyQqwvQq4N5U2S7QUSIyYsNbDnz6J6ZawTd9EvX8JTGu/sjVj5NqK8Rtdu0bAVx/oeX5KaXnZANJykmWlzPZKgk6cTFsS4id2WVuZsY1jMWFFysT1xU9kNsn999qbkL55IhEQ0NzvV6O+9+pMHo4jKcqOyudNzSwcq2wdnyp2rcx7/LzAAMQR6owjfDw8jsTfihTBFOjXjiiAbkAqlLKDDkCAO0cg4myRCb4VrXWEnUUUSOw7bdoaSkOwBCoAlhOgbUl8ZbGbuPta3E3pXFitBmjpWgBknGErh21gdjGgTyX2kjSQu36wm795Pxtx152XS0U5V7rqzszsibMQ34647bcAbfOD3HkHKx6v23mszZkZEcvjGhZrMM/4tBbMysLoFZkLgJIgelt0SSRKSCvMKowjrd4PwZLesTB2cuK6wdbvz2PB664FsqMxxsIcnwvd0L0SHquNi48Bh6+QxSyT6geFtSze7X+wtdpLJxXjlVZHR2uOK1tlHBj/efnX/8tr+8tmhFp3bHdm+fWtvzfFjKz9MCqKtbdsz1z2Zu0gltuiHA6QZYsoYKqsEmT6+F1Op1MFPoF2GxhHU1AUG4zWMQQlPeCDbqeiyQyBK/fKqa5RbgFuDfB2ddxCVHsQubE5CUIJuHvoZ/PwOTT3NxMVJpN1DLF2NdrvpUWP7KD9Lq5HFdCSZSjO1cxEJbEzD0nGorqeNF5VNKDYAH6IsDHOI+SWYWE9TBipAeApiH7F+BDfIwMFHEXtOwfm3I7sTiGgbtsagsgVKYhS72FsTtK0YO9/EpMR2K0cwM2ShOERfPMwgiZF7Hmdza4bNp/dx0DDJX/8QaqdEtpPnkioyp0y2Mk2ULZChBbbISiHjRCNAeQWndX0Qm71eOB4Z9Psb7exYNdMcZu2+tdmt2qaRzPUiW7YGVLY7zrumS8Gi5/B54LPpCx7xw8BI7I14IeRJl1XFsz57Lum1eqgUhjQQjqui1lpOhMOsU53f2lExebfA4m669Uq6dwk4aGWESRrJsUlrAfXu4xuQjrTUPDMODUBHMYFOiGwHg13BGYQy6wdGUHWdljQyDyjaDeAy8NQ9t6pvHim695Mhd925AciR0PuOyAJTU+1BuzNV+YiSzEWqn3EnMycy9m9+y5rH3a7brQ/f/yEHuBfDOEw2d38Mq+++470xo1TTd0v4yvUvP5qj81iyqC+8P/frNw2VpR8Pbzs2bmyGWd3ZqsrN/Vqr4UBlnb7K5c9Fh44/4P/Ifb9W+vv1nt0/U7U7T86bS+0n1XVv+tP4mHrLUm8uV3lqoWIvO+vGolOQdcPWfTEtfPaoAUKbmCtzZHdKsK+VfqPkdjCrNbygRNKrgDZTG5V+iFhbIJrvILfLUL4E7UzaSDHRhc48uAE4YFa3II6hV0UEeRhG4PjoI08i1qrw+J2QgHXz/eTlJYwvvhrWDkJ1B5HpkeQbSN8i9DTVxZjy47dA5fNw/11w5CyEGWhMwCCbGjmHThrha1ZSm5fZM/T9PHavgN0sQOCk1SDuAFHagV4Fzl0HuW1YO8pQBwwaZUrZTcyLh6HYA2sd0x1SGa+RrM4zbFQZZEKGCRSW5ugt76G9oOnEK4xtzmC3qwQT26AixoYeJcNipWsilctez8eWikt2Dy1tjJ2MuRpO4jk6W9aRbijDI0gSJf36VWPLE0qJ4KmNudZnLxxSbz76WOyZcutz5646Wi0Mj8CGW8kFi44ltzxbnAI9MmL+IcD4V//qX32v92HEDyB3n5AG8OPAzaS1QldGpxmkEbYzwYDN7rYrgr6BV0iGpfG+cnJ6mERYjoNyPGqkwm6ZVOzNa61tNC0hREQqHK8ILg0kcUykQEsBQpIku3N1Y02iNU3ToOFRVELarma4Fin130MdP2abahz47D23qm8/Wuid79K8810jofed8Ef/Xwh0H73zeqNU2TdwEjXvYFiGVfgvSlROlt1vP6z92sUbk8eXv3IZeAw4++473vtNU78jvjn/IE/i/bf/Xd/Qf3jwyzf9es0grjzqX/fKrXjqtevJfO2x6Oa9YWJVTkY3ff4q++nz9wU/3ng8uvFQoL3hUesrEz3hHc/Q5Zj5leuq8cothg4PWB2vaA1Mp1c74HQMZS7KCzJoTolkOC/Gxh5FekMGWpJMn8OaaKWTb2yQGoytImR7aNFHjNWhXEe4MV5mC6tdhFwjrdHz4vSWbWOiUJam1R3H37wRx2ggvA50q7A1j3D7aQNHqww708hI4GxMIow+ZMJdQRYijQAR5bDaE3hbM8hOFVrjsLUA65NpOrhXSb3wbroPrBh6RRhWiIIS3VyC2i4gCztYLmlX7sUDqZ0KFvRy0JyD81dDtk80toRoTuOVd8DW0HjPZy8AACAASURBVDWhMYMotdGVdWKvTW+YoaQdVHeMcHMvk9LE6la4vDXNUtdlDo8tI8fDQ4eKCSXDJgodEmlg5JscnNhmrxGznSj2O10GSY7poGR6w6KciGxznVB4sqN3OgX7fH3SPlufyjlS5ztDt/p/f/mO6Xq/pCwZunGiS8PAme7FznGpVXG98ztPV7P/aBRBf5kziuyN+I7ZFXgLpDNkO+x66JFG9BxSkdcHrGGfvmEw42TIiUD0BLo27ORl3NBM7W+Hhk0I7JCKvf0AWmk9HAARtlfUWkgxJBV5PdLCYsM08JXGEQYuEBqSyHDJ6JC1xGALqAxpbweDXCvSpj1rv1J5plvY4dNTpOniUdrixeTeT6pf+V9ELD/62crCwmvX/qfNV/03Da9QY5P/ufK+93zH3czvvuO9LUY1kt818xsIIPOz3u+Lt7l/VFh9x7FejX03fqF1/YIllTOMRCdn9Pwz4WHvc/Hd/aLYbN3inLgjf3aGH3t4bvXTb7jGyDsrc08Mrl4UppmJcIaZuHNIBUYuYzTdWrmvN4VnWO2ymDMahIYL7oBEdxnEHpaw6JcEZatFqMAxIU6A0MSc3QJhI0INO5PpyLPyCgymINeHwgC64wi3jTYC0Dli1QHfwQ7yxIEFCw1QoJsWupdFnr8Gxi/DgcfB6hPXZjAac4iNvVBdAzmEiQZk2unUi34OUZuCRhUoQ7YLhSHJcICaWsIKs+AXYXyT2O1g5joM61OEbkK5YmB5CZoB+vIeZH8CCGFjBoRGGxGi2Aanhz25iZreIFqawcqLNG1dGBJbAcMHX49TbGHtOclw+Tgbfg5jYgVzZZaNyGGAiZ/tshlkGO9muU1NMeX4eKagabTp65D6MEfSVLQSyZqfoSVMwqCIEXlcFL6wEqndYijnyv2yEZOc2xlPDANjKrsTbfcd63JjWo55fbHTKxgXamNmrC37tQeevvlpMVf8+Jlbvvyf3snD39MDecRLzkjsjfhu0Dw7NcMmjcZpoBQGRBqwTOwwYNBtYOSrFCszvgLOdRvWUCv/OlPJUAjMwKcuBGumRQDsAY6HPlHcpKslhltEiDS9a5EavRpALCzqRhpFrIDo7ZbsBULTUAkoyVAKGQVRdrNkzc29bc+PRZap63+4+mkDGFmpvDQkKglrly99clh8XLukdZwF3veeUWPFS48DLJ6Njhz4I/Vzt3bj3A6IY1t6MilI/6SZ9OZvF5+2rjEevi1P56GrMyfXosQ4bGeDianqCbOSHGgX2DpwJjk2vR7P9XycG1f9xfGpdmBkSufkpcGkcMsJmWqfWbHBmi6x1+ix2pti4Jc4NHECo28hmGJQ7GDqAUpA7MSYXQ9dnyaWA8xSB6E1xPn0tC33EcpD1yfRVhbGWtCaRGViFEUsu05m7yXIROjlKsNIYBdryM40xD4kVcj1MUtPkux0ESdfifQ9KDXAciHIplYsXgt8B3oe3PClNORIhLH/FPLyftT6fuTqAjpwYWMB9j1FbnOBXsNi2FogynSRhSFu30NFMWFuA9dwIOcjenkIPXRhm3gzi+EWsLaOgL0Jc2vEgzJ+O4fdnsS6eJTifoOmtIgHNrIS0C8McRtZSr5PJSqxtz/DMgMOZGwEIRcjG+0anCWhFTqsbI1TNU32BpKnlcdCZJFPBNsqUe0MEOfs8zuzkSkjc5BYyiAxttplO1GoUFvJcq1sKi3Mx9fnxW17zirHiOwTK3vyOkmubw8yp4uZwch0+WXMSOyN+LbcfULaQHzPrUqRdkdy9wm5h1SkjUdR6m+HJg41thZ42TKBYbIMhMOOFSWx0SzP+kkcEyhFMfLZLxCmaWlFmqrNIdF2iXXLYVxKkSMVe4r0OL0ylaOTuuupssTKS4QTE3R7IRkhsWzbDa/zbttnTI0Z2z1r2FMbrzvkHm9UrMnNRrQ1qk15Cfid/01r0mgv3HXnNLvd1d/LffphYH4DCSxCMBvHyZ15dio1PRk6qrdpoMwH1Gs7NxqfGa8llSrCqBRl/dprxMPlHf+g+wXjTa3hzVv73+J+6OhyNJtf1zNuka1SBsdL3EhsOoYukhfasOiRRVuSoSgyDCsQNMgIWFRtih2boJchKTcJhxmSboZeEQaOh2mvE1sDGOYxohgxtFJPvPEWeEO00pDvQHEbYgExyPY022VJULuGBb2MKNUQf/UunMOPIvY+QpzrYq4eJdnYTzK+hl1YRhAi95yBXgGOfwGKbXjiWljZl0636GehvA2b89Aeg8gCa4gYVhBBgf5cHVSGbLMIvous7DCWsTEpMZAmyb5NeGIB3IDQ7WKFeQwzAMeHgUAsLGNtTmI8cTVIBbIF6zP4pTZ9IqpXPYo4dQNJbGItHSYnbSZ6ERey6yyXN2nYQyb8CNG3GBAz0xynZhXpFrbRQYJ2YW9gMUgiLK1pEDLfhKIXs5P38ZUynGFE4NqJ0hUxUFriDL0cTb1/coOVZlVMy8gY357Vh+Yv6WhK6qe3pnXD96y9xa25jJH8sz8/efyVj62989/82/vffE5/4B0jf8uXISOxN+JbcvcJaZGKuiZfOzN2nTSy51sWsVIodFqqI0C6DpZh0dEKSyWyIrRp2A4dKRFas60SAsPUAGWXvNQoP5F9U7jak5YQu893pf7vSgOIMnCcHAW3TS1UhELtGu/m8rIsyTWmmbWnvT1xb+h2z+8sBYlsb/zk/C/u/I25v//4j039k1Fk76XkrjsFqci7yChd/pKym749ALxivzxzQ0/lruqT6zd08eA6162YRP0MzXlDh1YrqQ7GuZzTmGPrxlzW16ZYNzLWuBkVw1ibq0xnngiPe0fsx4295hJDJQmUKzQVJsxtmuSYiS5Rsrap2i2agUVWaqhsEOkEiksMHEmYFBC1EkZ9DDM7JMJCTixjWnWkSKBxAMjA5XGYuAzza6nYM+LdYpAdWLqWYn9I0ssi/MMgI/5/9t48yNLrPO/7ne3b7ta3957pnsFgZoABiIUASILgTpqKKNFW4EiRRYtyUnZSZTosm1VOUrYjh0KVbEVO/qAdVMFVriQllUqQLUaitUSRKCUWxRAkSEIECAIczGD26b377vdbzzn54+uZAQmSWiyRBNlPVVd33/t133O/+33nPOd9n/d5WbyG6u4DIXK4DKqiP9dD+VnMl9aQegwL23X7tCyBwMFX3glHL0MuYXmv7qxx9STM9iA3cPkkvr2DuO0C05UBaneVhpRw6TQ0xwRRDgYaA011bQlnJuhhlyB9AFm1Ie3B3FVoFvDi/chpCMTgWnBuHuIhzYXP02xbOHsGBktoYdkWEQkhjYlklgXyhctMJYSbK+yIlIuNHb7qYlZEg9smc6xEFbhdMlFypwkZlYLrxhI5iWilGDNkedSl0epziUQYlyhdGDKpGZjEXu4tqf40QAdTsWu8eWb7iDg/nBPTUnN5d4XTS9fj5zaSYJwHP/jixoL72w///r+Cv3ke/OFc+T2GwwKNQ3xL/Mq1x25Uw05+Yu2j1Ssev0HAKuDOqqIpBKnWCClxVaH3xr1gpiyqXtiwLmkViZDESjFxjq1iKn4ziGhIiQxFMh8Q6FEv9ZWjJSWBVEJSLwFQV/1GwK7Hns1JOwfPOWAS0MgDGl5jVUV5bTffzJ7e+8LmF64Vo6+sB7/zP77lg390uvm+wbfzvH1f4id/Cn75l84BnwcG/ORPfadH9D2JtQ1CamnDXQJ7ZkHunAbfm/fbO3N+PWusr5fb5vh86eVsQxZrDiHeMPhk72h5oVU40fwt8Z92zo8WlheqKn7Gvrl51RwLZ6pNeac+x4Y7zr5fRRQDOnsTqjBhNt5jXg/ZsIvMqz3MZgNjLL7lKR3oaQsjJzTNBLGzRGPzCE12MXGOm9vH4BDSw9wetHrw/INQzsHsFTBlvS0oY4gLOHcGs71IEA3wwRChJW72Sl1lu70MF+8kTyPk1BBtL6LzNuwsQ8PWbcrWj8OFO+DynXXrs5kdSA14hTclTgjEziwizLELm9DqkVw6RrS9gJjMQZyBS2H/CIy6iEaKGq6iRYBMu4ggq+1bpiswXYThPNX2In48jwt2wUsECipJMWpQtnsQZPg0QI4N1je4qguKMGe2OURJz3IvJipW6LqI0jt6QYERmpGHt/YWcVYwnNsmaffplYqxFhTGs+c1pTcM0VS6YCK0KERBhaYd9GnZQOzlM2UljJ5USsTRjtxybXt5tCybfkovjf0LW0f9l9ZPymevHlHPbJ2Z3Rp0luc0z81t/dqkc/RvHBZKfQ/hMLJ3iG+JTzzibqXovu7xR5+S20Vtr/sylgxFR2sEUBaZXMeJRVdRSoEUsu6Zm025XKYyiRuuNe03I6nLZhH3N8IIVNMfqTJMaWmqW6nb6uD7hFonmINvGOK0JHXA9YJpEyYZ4PrF+Ho2nWzl05kgCYs4VO0ot+VyqDamgpXDirO/bPz27+5/p4fwfYDXAW8Aph6VnLWvu2hs9tSPnP251sbCXT+gwmGrNfm/pl/q3RP4terlVc7Fva1wtHrimntGv/3Ys5M3dKNyL/hKsCqu+Vk3KhJhxSwXOM2YOXIv2CoT9sQCM6Qcz0KiNCKLLZ+0d/FQ9CKpMNxXfpq2mDKZ3Ek80fRmKoglx1euYr3FGYEXYMsItEU6i/QV3P4VyJZg0qm9fWVaF1MUqm6tJkt8cwhpg3x9BpUHyGslYm8ZO5nBzV0kLNqoPMRLh4hKYAxrO7V1yuZxOHoRe/wlZKuPSEZk144gkahsHmFjmN9FXz0Fozb4EOQElxuktTBaw+cdRHcPH4xg/zbEpANIyAsIKmAElYNyBudztBHYo1cp/Fmiaw/D6c/jhx2qZ96JYIpYXqe3OubChiUed5nJW0QuQNopc9UCBY5LZogqNQ+YJs+oEduNjFaQ0/Qe8oSNSiBzxYL0bIRQOE8xDf0J3xQ9GeGjHKggcChdUVktHCKgCvyyKX2aJfJaNqcBVGT9yLXEpa22GKeBH7tQwjTcnsRvevr5hz/wtvvOffGD/+rnPvlLf/8fH0pfvkdwSPYO8Q3h2ZCClW9qP3JQmSuqTNwttO8ozS51mvcCcMSExTWpSBBIHdAAnvOeJVeKB8BfUIa5sJF1beWSrJ8MxUzWFVidj6hCA9y6Nm9EFkNgCbgbCA6IXmc6pVLCl2HMPnDew5UsT+7F0V2d3+s1zLNPhcpc47DP6iFeozhI2XaA9OoK+cHDc9QboC4oX6rGPZ8/9uOqL2bdpcaZrZOjZ8dBkB1rhFY8597Re7r94NIw/c3jxcjMxKOrKpzRYuoscbovg2qPvXCJ7fYax8OXEdsLlIFALgRssozYWWE2vcjK8meZhiHpXMVtk7PMBCMq7djUGdo1ObYtCMUIbrvGHgnT/p0sDIbI7h55AeraPcTRBmK+DyMDYY4dNqF/HE69iEjbyLUL8PJdbFdzBK0hflwQxyGxNHhd4DaOEeiUKg3gtvPI7SXEtAVpA3oLoCTEY5jZR1w6BadfBC2RZ1+Pdl2YBlgvUI0BLGzW0cDhHHhF1swQ02XiSYggwQ8yRLECNgblsS5FSQMqh9YIdIrtNQmUoipSbDBCzw9gU2FFDMmECI/I5yk2U4IypDlytHxKGg9IqFgOZrCjEYPWhE5rh5XtRYbCcISc+UHFy3N79IRAWE3uEpyE1027YHfo2ZReJxI7NiMeCRqlpwwHlEFFrwpqvSIKXCAmYB0NgQ0ECnbzJZF6Sz+N8JRWgznZ3VhGBLP//nz3h7+487B9ure4efr//tEXPvq+X5sBeuAPN8uvYRySvUO8Cp6NGFj1bFwXrLzKl+7Rp6Skbny/aBL/grO8IZ8QVoX+chHxh3GzOiagbyvmdMgsB9WzznKxLIK7TZAf1wFdHVQNnxIWlZyPxYpZv3RNO0fTJIxRGKKD1mWeUtOcegGWcRvYAq4Bp41CecGAWleox9utP7i+3rky321MnVfNTns9+OtPHS0PIpSHOMRrEYZ6o7O7toGnJnq9RIzdGf0cl6pTs/t+sf+Z5qODjt/+zNrwhbWL4eveUS3ZBVmMxda1WNHoNH4v/YGFUFt9rX0voc45FV5k6gLa0T5R6ZDThNjsMQ5mGKqYFVeym8e05/6YtfJp+nqGyOe0/TYLuSbIwKgC72E/apAITyOwRGGGrgSxT9GDiKANwkhsGSPSozC7D0EPlwwYuSbx+p2wtIsnI+oM4eHPUoxPMNmNOdI0hI0BdHcRS+uI3TnEF9+N6e7i7/1j5NwA/6lTCClA5+Al5CF+4xhy+zj0Fxk0e0yPvMhCR2M35gi37sJPZqnibfSohWhWkDVQwT5WOKpEINM2spoBF4IYYN2Uie3RdMeQSQ7C4naO4nE4aSGqsIMObnA7sqoYZ0egrGgyz14jp2PXkOvbzMYvQ3uViXeUaYi3DXLvyQcpU1tAJbiiHNsJ3F4mbE0CRJjx5v2ISybjWpwSOc1a2aABFJVgQZQsuAaLo4LJtOTcQkAociSVnwojkND3RjMRdfeQhrKFVapI1cGlpXWF5cpwXsTG6b5rnVnvBUeAO3/h6bf/7+889cLwXae+OuGwm81rGodk7xDfCJbapuQbajY+8YhzP/7ZRn85Oj53KbuwG0jzeW+juZLR+dFufKp3TW0359Lr3ZX0K8BdwLQqGJa5ONmcySuhkNSRiq6MG5NwEpnO5Ji8dOVaO1pENxoiJ7rZpzajpIxEstsw7XKLlxew3uDposlNeLODRw6MbG5mj4TO3B+uvbM5/MGPv7z2P+9/s/dxiEO8FnB1hWJtg0vU90OH2rx81nkZRiLd0qLYlsPRCw6aspmv7Yv5dw/KxmlkJau8wvUGgZxWYX92Xg7kERAJtpiw55r0g6NYAixQYdgu30y7PeXO8WXes/kcH59v87K8h9Vwi5SEWa7jXMwlM4OzXRbMRSKVIXKHn/dgp6ChlQsu0IF4wtKwg2kMMKeeAWfrVSeLEBW0mKCOfQnvKyg9VVQgd+eZ3zIU508RJA6x76A/CzZiah022KOrPPSWYG4TF29ThRXB/ir0OvgL92I72zjbI9w4TjVjqKYKOxL0G+fovP0Z0DnVuUcQ4wZ6sgAeBt4w1T2WQwvNbSInIG2CbyBFQWASJLI2dHYJWINWkpKKkooqWyHM56hICXbm0KmhFBX7ekLoFc2VElGsEvdXiLMpEzdlGkpGgWM8MWxPZlnVHXK/y9wwYckv8Afta1xPUt7a6/Jjg2V+d26di9GQwjoiM0fHjdjVlh01IfKOwszhMotilsQipi0P5CAKiBXkijr8eaN9uaBOnkhy2yC3Sgkqs5DsN7en3Tdf3F86/t7H/9FvWQINT37Vf+wDh/Ysr1Eckr1DvAoH2rZr3+qYR+beHzRE9/WDzeQM0cTcf/LI9US0Ljy9+fRDm3v9ZhCXR4HfM8RnS8rT+VTadKznG7HckNKVFdWRkFYcELgN1kdXL6TL/W1RLS14vCUUGVUB1leUoSEYB9tHtdJySZ4otqYXlihxdNgEfpM66nEnkM8d33+jS4enh9OOs7XWr0Wd7nqV7vAQh/huxvvf9mNHqFfljauf/ngOsLbBlPpabmQknc/sP7wRnHvBR9c/vVTe9Ya37bq5Bi5ZxRcBYRjnPpIcn/U2VGKQ5jC2GH+VM92rrOcnyIaCZK7BWEckesjcniTpDBkHDf4weAt9P8dD8lMUmSYMJmgbcHH4Dk42/phAlVwWt5PkluWLsySiR3vGQgJiPMPcuCI+uoff7SCkrbtkDBrsBQuU0rIktlCbK9AoEFZC1UGnBTz7DqJkQLxzN7Yy7AYFHVFgTr5I0M2ouv8Bv7SLOP8GuPQ6smCDfvccC8IRXLoLMeogVq5SJQY/7TBbtGj0FxDbS+ilCHnH7zOaTgnXF1FE4AU2vI5SEWo/QZWLmPSgfbZQIDyCNhEtWNqAQYsyK1FKUKtLBGUBkfSEwRBfdtBpEytSAr3F7Z0NzNYqbn8Rg0eUITqskJXkWGTp64pGtcKcKFktm6gq4/nI8rTpYbykqCoGFFzRQ7ZNzsB4jtuAQlTMFwk7wYRp4NCFRDkJwjBxJZkUkPqD4J0Ep0AKIBCQefDilkImOzBSAI/U/akByiVwi5bwCLAC/Jb4yJNf9B/7wKGt0msQh2TvEH8mPPqU1EC8Etze+dHlf+CWT97T/rUr/9vkc2e/mr7p+H3izOLteat5YXFs+3fO67X0WOPuN3aDhWtf1l/43bJbnlLY4b7f2mgxW0V0k217OZxssaz02LSOklaSaJx6a5rCRBEtXGxckToJWe6mo8QnIRKNom9LrihDk7pSNwEWPCgn5ThXfuSLbrx45a/b/jQOeeQ7e94OcYg/Bxz1Jqbz/rf92Jd/+9Mfd8HGxqoYjZo2CJ73SfJ6OR6tMRosTSOV4uw9bG6vEAYRUSwxRiJbEIcCDzQN3pQUWZOL/g6mtKDKGRcJxd6EshkzKyekbp7rsSLuFnTFHovscVvrLHNcQ1aSPXUHVZHQCK8wr3Nm/T6JvI10+xTB4stMJ3MQ9GkvfxHvPM6AvH6SSntML8E3G+y1hoQJzEgBlFTTBGNK2J/BJSn5bZuEm7uU4zbTxW2iUYgZt4na+3hn8FkTH/eR7SlhWNJJVzD7R2DYBC8QYYFZ26By+0iTEW3eBuR0x7P45x8h3emh+7MI2QAcKtHEQ4XzEu+BQtYBMAmQQtCDYgnyBF80cHqCriQQIr0gKBxsLUHcQHiFKB15IwcbE2zdBQQoa6imE1xYIkSAMxpXWpJhH+NGBEmHLXGNKA9YVi0ynXF/P2Q50MRByFY08QIpTk4bnCk8n2sP2LOa6kChkoh6d5tXgKyw8sZlFIM9aHYU3VjyI1FnZQ11dC8SYOuikwqKMNIIRB0SRANvOzjwqvjIkw6Qh6TvtYVDsncIPBs3xBsxMKXe6k2powrp3/7Cg93CpcGHbv8X20YEDS2D05kb+zarVwMfnih3Fl+qyuq2/enoLW+YfWD90SMftp/a/tW4qWbvrKj8na0H+++c/fGHfvX8LzanYjPeqC4uS8RlI6KnRnvTh50XTQK/2bmfdrmPdiWKet4SyJRKQFAwSoNRx3jTbCQzk0naH5Sj+GgZpS5KECDa4L3RvDgzPfFry5N33TERY7HeE6sX0jIX/+I9u/6//3++bZVljz3xuKDuGbz+0Q99+M+kF3zsicf/J2D00Q99+J/9pQzuEK8J9N/85r1gY2Mn2NnZe+EXflEfu5QvzP3Rp45GFy8uO+8fQuvV6ZkzLw7vvnvI9Wt3Yd0yo3HA4qIkiiR5UUd0rAWpwIFQCh91GWmD0Br8kHI4pN3OGeqECypATjwOR6NsEfc1z6hHCY78CneFX6Y1iLkr/h1+w/wwqX8rd+cXmd9vYLp9rDmPKxW9oEEzmaBkjss6qCpGmByxdBVOpMxsLxOOZ5A+wO8uUpzZx8wMYCeB2OCyiNHZd1HFKa1wm1VnqWbXyfZOIgZTlDOo3hEcgvK2P8YNZ0jsDKKKIJ7C0hZsr9St0pJtxG1D7NImIhgjt1dhuMzifIVymrzvuZ5PWbYzJCrExSMsUyjbNbWRAAKKLuCg30UgCJWDmQ3oH0MRYGRMGRTAAEQHpCLOZvFOQOWxrmTKOt5qtJ0lQeGo2KfE+hyf9ujGMV630I0AaXcYiwkTY2gUio43XDZO7KsxdxUzFCLEjadEQUGgPUX9MoxveBiIGJQDm0OZHrwRB5T1TE/EwfQOztfp3cBAeTBFOgkqlAcHLgGzB1/rwHPAuvjIk2f9xz5wWK37GsEh2fs+h2fj14EfoJ4NxM2HawjAPv66T+eAjqMoe/PcD00m+UhWZdlL/eRSEVXqf/nhfx4akVRTBkuBkqNYtaY7natn04lb7W03r6Fmr54Pv3Tk/NbVT8jW3jyhuytRrf29Yjdwmm1pZkdRa3wkTopm6nylI8AzAhJnkc5T5A4xQ5OIRFlZxBPZd8Wg7bKxOxkcza9J5S8Bi0BnkpyPrpt/91IW7CR5HlR2e2mdq8e/3bq9y8ACsPPYE4//I+C3geG3In6PPfG4Bj4FPAD4x554/Oc++qEPf9OK6EN872JtgwX+4X87L6qq8FqPgXfozc37w0uXjsvJ1OUnbsurmZlicOrUaX/kyBGcP8LsbIQxmrIUGANlCXkJYVz/00hBqRDGQFUhsgwfhwRJRDcqGE8DXNqG0R5R5ZiZ2cU3M6wMaQ9KCh2zqVvMuJRj+jp71RzNviPbWmakI5LWFQbtnD3fwV+9h7bapoyHlM6TKUnGPG2uoKOCUGxix0exRUxYOQS29uH74uvRkybdZB87aUJYIa+eQooc9/rnUBtLlC/cRSU8ihJ//l7k/lHk/C4MOjC/CQtb8MK9OBXi1+/A758gX95D5glR1oaixe5E4weGWSGIZAMpapZUYrHLLxOOV5HjJWrmdMPSM7n1AZVdmB5YjYoAHYZYZcnDIeG0QVVBPyppO0OgBFWQUUYTojJBFQqHp6BkJAuSaIYyaLEuDdopFAJNzEKa0rMDzs4IFn1JGQhQhpeSMaNgSGotzQJCCeMQpnVGuR6qgHpKP/jsdXUzaleTvVd4nluPKjOs9BBFYB0oBbfWg4OcNovAj1DLCCZAQ3zkydx/7AOHBuqvAQjvD4sUv1/h2WhRV7aKP+nY0hY4LKGqJw/vHVmZM8j3kF7QCmZ9HMUuKzLrnbUO8kHef3lr0Puyi/rXn5v8v5vvXvyxLz3X+/QPf3LzlyNdzXBh78qiTba7Ybmmsmr7fhmlUju8lbQqQaEkIs8IpEJITRWGVBIdOyoJXJv29OdGw6hsdKadKHZtHZID28C/Bs5Sb2Y0MP7EIy7/Zu/tLxqPPfH47wLvOPjVAV8FrgB/SL0r3gc2qKfdFnUnhAr4W8CjB383/eiHPjz37RrzIb57cNAG7Rgg1XC4baNojsnkvwquX9etLz83SleOnJg++OA6cJx+vHK58gAAIABJREFU/z3MziZMpwFxbKgq8B7iuO5h6P2NhbuGAiqP6PfRWUq5GCBMBW4WPw1o6AnGDxkWMbPNDRI1YV6t82D1PGdGz9LrtvG7A47P77A7eAMBI2bbVwivlywHV5lZ3SUTIdHGMoFKkapAGEn+3NuYFm26R5/GxDnTS6fJ5qe0W9eRhYRKQhUi0w7eZPjRIuLzb4Y7nsU+/yZ0lsDbPwmiwp+/D1FGVBdOU+oMN3UkRwaI3RXYX4E4BSEpxgFy5y4kAnv0OWSpENu349Bs5xOcKViUc+gyRh6kbL33EGxBGVGVMUaHIGr25BE4HC4VKEDGB2TPWCibdYlYWFGTLMmUnDA3tY5OeqbhLlEzJ9sT5KpNZgwdD5QVA1J2jCJE0CRgQIbD87Jb56VmwdPNIWuuTeEL2l6jCdAehsZxIRzwR+0BEYI94w+ika9EBaGA3NazUUNwq8W5pk7felAGhPmTLs8RcA74JPB7wCX/sQ+s//mv9kN8u3AY2fs+hmBl5Nl4kdq77luiKi14SElBgDISrCOWCSDAIwqXK6WUckIQKB0bvfBQN5h9yFJyunVv1YgatilmRwv29O4k9cH56LMvXi6fzdJhq3pxkJ5NQr9EIzumMqhKXOYIqoJSKWItqfIpIyUqqUJSPLuVcxfzsblgq/g/U4uTWIdcpxaijD/xiJv8ZZ+/b4THnnj8b8LXKAQlcAe19uq91Lviy9Q741PU4w2BLrdK5AB+8dsx3kN8d2FtgxhYA0zjha/snfjZn+2c/x9++j3Z2lpbaP3He//5j6dU1TtQ6iEG/ax74athL7wvoNVWCAH6YEoPArBFHZQStrYkQdysS/etFmWrCTojkJ4sCBAhRNkeLblLP3w9uyLk3vBp5tnjS+4hLraPoirN64NPsysdiIykV9K2mu5tX0UNjjIdhCTBgKkx7MgO7b5CmhQjKxbVLnlvjvH50/QXerRnXsY6yPMGXD5FOFimOPMUYdRDvnwvZB3YPYpTQ+x0FvGZv0IqphjRwE0bBGGOXrqCMhLfm6MXJERNi5YZgR5ghicO6BlIlSPbI9zOEt43ORI2qG85AQacqCijMWbcQg6Xsa5iku8QmYSo1QFu1K3WhRkikJBq6KSQjKEfga/3lh6PAIz3eOe4wb7MpImrQsq8TxhbpBeQG6zTJFaxpGIGckyJJsEwlo5pEJPKCV0RcGbaZkOOmCjL8TLi2XjIRjClKi2FhzaasCzJw1uvWQ+8pM7GqlsxOiQ1O7X1z/pGC/Ib8HyTGEATOAn0qbMQqfjIk+awn+53Pw4je9/n8GwsUEeekm92jPOWPPtaiyUTGkpbT3zOgy/qC8njvFBSGq2pnHVSCmFzJ9AQm5g0S6l8ibceJ52bpkW1O92Tg/J6lfmR/cL4d9S16cVqc3gxm9jcFJZ+Pg2ONNp5EWimpSO0lhfzSbg7HUQLttQ7zdlJ2V3Jd4pMbLtSLw93W79x5I69L1CTvm+bN9RjTzy+SB2563yDp28kV1Jq82nNzdUGzUFy5caxH/3Qhxt/uaM9xHcjDsjeSvtzn9tb+nf/9njZnTXrH/zg7cXaWkKvN0MQzJiLF/+KT+KT1dJywKjfoD0j0AbEweIcaPCuJnk5oAvwwasNiKoK8hwajXptjz14i8ineAQq2uOo2KZlhxilsHji9Rne1fp1mvMvg6840Uu5PdtiOJdyzt1L1/U4kV3kXH4fM/EO83Kb6vp9xDshw9U9ArPP4MU3kc5abtvJiLIYtXQd7QqUoS4IvXQCginc9jI8/4a6521jQrYxx+5E0O2WuK1VAqcxy1vkZQxKMWpWGFehUkPz4ipy0GIS5qSpJ05yzPIm/qWThNUxLBWWBgEKqBt7T4NdxCgmme8hRjNkaYDUJYGZgVTezIhCCq1rMFqBolFv0UpxM0XqtUcg8KWjqiqMCG4KYypfUNqcIAkplKh1h1hc5Wi5kJ0gpUvMmJR9ckoEu3rKZ5r73DFqsqtSzgU7VFKx0bDMlyEawScWNjiShlyMc0Zxyq3BHmRY4+ig/zAcMFHQGbfS1Bz8bPhaFc83JHwldebk3wAzwFPA5/zHPnDow/ddjMPI3vc5BCs7no0/AP7aNz3Ig6UCBIq6kZkSCpR3WhphfSWGduACFSlxMFOUZQXeC2eE96IqpNcWCJBoUQnvVSUCQjmxVdANZ5lvtvS+3eD9rb+DIuS53h82Ip2I69Prra/uPW9dPHVDtzeZpNej/ljN9q51ikZ3IoPQj960+pZLm4PN7mc/P5nOrY4u6sCvUU+9+48+JXeAeeDlTzzivqFH1KNPyRDw/zHE8LEnHm8CP0G98/1GuLHVjg/GM6aO7s3yiv32AYZ/3nEc4rWLg04ZAHuj17++WSws3F8uLHStUgUXL7yZMHoQpQbV/PwJqXWLINA0WgKlb6Vqqwqy4hbxA6iCV70WcKDZd68I4og6lRkHaLtLV0wYiEUiCSOvyFzEvarPOkc5VV0mVFB2xpxtdpjzU6Lc0pucZHUvYcIqWTCLWhqRJhGdBF5Mj3HHKGH5zmfYOnsbotcliCyi10GlCagKZnbh6ho0UzhyGd8YIKI+RBPClZzF/QV0Zw8nPBvn1mhfW0IUR7GdHeS1HeztA4pVT3z5OPLMCxSNS+T9DvLy/SSXH8JmDbxW2Hgfm0oKG2KURqLReQhihJjbwI5CwqiNu3Eab9jRCYAQtMY7hbAHB9xo7OhA5AKC2kjaVK8gekFBWUwJggRVBEQx5C5DOZAlpHpC5SsmRcWFsIcRDRpeElvDUppwjCZnTY/z7QwhNEfLBvens1wORgQWnmtMCNEoG2NvFpdooKzT+fh6A4Cp276VQCwPDryR0n1l8OebqnsMcBT4cWpJSo+6YOPyYcHGdy8Oyd4hAH6eukgj+kZPZjZj4nokpoPWAUYGTMoReTWVoWwincBIowSglJFhEFC5gtJZH8iwklFcFq4waZY68LkUyjpvQQjRacUmqya2QoWmipxSQg7zfbTWwoiIhzrvlHcH75LnR181aXIlIfHlhhqsXQmvxemY/otfFnPPbzXPZ4lumMTOL93e/4rU7jPAgJu5C1rUqdJXkb1Hn5ICWKWe+q78eU7eY088HgG/TG1P8PXE7WtOJbd6/k6pCzgk9db6lX93/59nHId4bWFtA00dGRlQV24vUceJVn0YRsXy8r1qNLrDNpsX4zC4s4ijZWvMgp+Zadqy1KQppCkEIUhZF2R8I1hbL/Y3UryUBDKjUDG+EUO2B1FS94z1gCmpVMIOTXQh2bAxaeqZD3c4txQxQ8qz9k0o67i7Oksr7NEJNpAM6G2+if7ZVXTVptMcs9D5/xCdFxjlCxzdkTQqScMlHBkZ9NoFdDHPeHuFnXHO0aUpgfH4xgiRd/DXjyE6PayT+CsnYTRLUDVhfZZ09jLijZ+i3F+iXe0xvXwE5dpEX53HDFJ0fwEaOxQqR08S2vkCNm+jtYCVlyHapdo4hcokghmQDtHcpBomuEuvY7eU9NWEo0ToaYWWBiUs4kCPx+6J2pQE6nR5AagAF5R4NKoQX5sJ9aBlAIFAClX7HFegrcRZD0LiqoxdPSQKYjYj6Is+p8omlfAIB30K2lVMSEzkYGka0tM5n002mGhPy2gW85g9n9G/Sdpekc6Voo6c2gykv3kt1FPjDcIHNyN6aQo4iJKv3TzU6AJvodZ8n6OWqfxV8ZEnf8V/7ANbf7o74BDfThySvUMAfInaRPlGoYAuXUHlLJGK8KUnpo2yBmlqTmKUofIB8sDMSQlDVk3I/T4LZgUtA7REpmUa5FUqAhFOna+aqZ1YLY2cVFMfiqS0Lp+uD3ajbtIw1omi9AWJaYcLbsWfGz/P7qDHaK8jYxO5exceFpmdRuPyC9yjHzp6x+KbF74qtk935/M3zHTeeqlQ/fVL/jPVSvO2zx9t3u5+5Mg/XX/0KblAPSENvtEb/8Qjzr/542/YPfvCo0fFv31yDbjmP/aBP7W24bEnHm8DPw28nW9ClqlTuFATut2DcxxQz7RfT/TsRz/04e0/7esf4jWNZWqz2heprwnLeGz01atz1epqR+7vB+rKlZPmxPE7liib1/M0tHFiGAwE0yl0u9Bu1wtxeSN6Q/27tXWULzwggkKAgEaR0nYTBomgcBHkfRgNQQQHa7y8aSTMRFEFDsI+p3ohwgacM0ucM2doUFBahcpCHho9C40uiSx4+OIOUijueuBTTFzJVqNNOy1pzL7I/P4ZQl0hvMXPbLBd9QkaDfJ7PkP+0gpZWFIkG+TmKHMBiI3b8fIc1ckvY3ePYXsJwf4ShfOYhQt05DIjHdb2KkdyJvtd/OYacuYKtiooLpygOWeoqoTMQkAKzYJyv42QGlPElNaBmmCUQA1X0WWE1BGJLyjLEhlIAqHJywxCiT6Ifn5NStyB0yW20iivUULcEmXc6O8DZNMRaIlVCToGmYJEkSeW66QsZzF3+phCeDpZjFPQsprnghFLIkaVkuMuobG9wvl4h0lUcVWmbAYVx71jVWielwV95UFAZCGTVf25ZjdStgGECvLs1viCG4RQgK/AVbVVz8035+pK3vBVxRs3ejb/BHAf9Qb2ivjIk791qOH77sMh2TsE1NPRH1BXAAKUVWU1FlHJEhUqZFHf/KUrUComkBFBEJGWab1MAUJIlK3t8V4JjTKFK5JQRi5RMi2tq7SvyuuDdRkFRjR0S5eudEaZMk+l2OGynoyYjHtRdGy2I1S7ESy0Izmp9nhu/1m29wcs5KeEW/LhW++5zW8XG+FCu9Wdjc888L7oR39QKPffaKVeLt2VZ/+PB579nd/b/qXP/frWr838vRf/5XuNufv5f3nqB55/5fg+9+l/6KgjLDd0dX8i2XvsiccV8C7gJ4F3ckv88vWRPXfweMkt/4ZL1KncGyfrlSfsW3YuOcT3BtY2MO7S1XvdpathgXg2edcjApghDN9fLS6+iaqaq2ZmxpW1MZ2Z2c2qkAVSY22dsp2dhTDES3nr4plMQCsIowOyl4OyoKPaM1dAWWkynzAtDCiDiLv4oAPyYCF3stb7FWntNRekhKJkGEJAyVvFp1hTl9mZHAULIksQVZdr6VuYP/pZxkcv0d5ehdEq9o4X2dFtclVxovLkziIu3odZucLOFx7gejFi9Uev0hwdZ3WyiOheYorGLm5Q7Cqqao5x5QnjEnf8EgpNVQpEnkBvmSqcId6YI92OUHFEkAq8CnFFi8zkrIsxJ3sniJslqaqQUlKUBZM0JGxLjKuwVUy1sIGQHr91msAYyKHlAlqBxIkepW5jvEYXuq5qhYPCFw4KbwOsC8gpaTpza/aw1MQp1rVWrgRnC8IoAWsphCUXGTkBYypC0STMFYFyKCkISk+pKk4VCSC5wBDnNUWQUwSGTTEm154lH3P3XsjYVETJBCOhlNCsBJkxB9XEN4wJqOdroamnI1OriKOD93Nz8O7Ap89BcSALsAKKEkxQX2fkgIpBx9TylevA+4Ad8ZEnX/If+8DhpvW7CIdk7xAIVrxn4z8Af5U6tZgHxqhKlUIKyaDcJ1QxymlwkPqUWNcC4NjEVKqgzGtblpAY4SWFz7C5q305XUBZTZWXLvXWi8qXzXE2nT539aXqzOpasdxaKL3QmffODmQ/zKpUShknTXccTSUCKQubK98fmmB/vxDXNyqf+V2RBA1SLxhWI7wqcXnsj6/FOtSBTt3o3nE+urcRtj/46NG/N3rv3N9a/2x/8/K/3h3/Suszv3R19JYPDgDEH/2ChmAMfAEo/zRRvceeeDwE7gX+BnXqtnXw1CtND+p2ozWhS6nJnqLW6U2pZ9UbaeZX4jCF+z2KG5q8J574WXH1i3+kTh57z6C6eP24+Ll/+kNsbKzSbp9B63diTEJZNQh0wvLyPELIVB+QMe+h3f7a3YgQ9eNJXWPlowCEwAUKtb8HIoWVOSig8IJSBqDqqd+jQdXBPOzB/y9jzChCCksuDIVNCLo5UTbCIdkpF8hlyLzYRWcNRu2cgW5x1T/ISnSZ050X2D7SJAoHLA520ONFwrzNRmvIxA1oI7FKsLJUsOLOYs++naq3jEgTGq0lwv4qO+fOIDv7ZHGGea5N8+LdkDbIFi8RX34AfeFBfGcXMQkZdwuy111g/soc40sLuPVZTB6yvDwhEH28KlFHtvFpQHX2QYpxH+VajMp9lJ5D2UVczzGKdmmXXZTwEEsiBwWSgauYDRJw4sB4wEIkEYWAoM7VGgXamZu6vZswB0tsCJFs4q2vdX5WIhE4bWgTMes1hB7v603zLgUwYYEWOWCoaGOYKhCV5OSwiW05jNC0bU5PWzZMyoxLWMhyxqai4SJykTEKooNBHYQZ7Y0IsOJmmw1PrecTuo7qCQWmfMXzov6CmgDqG4Z+N6t42/W7JKSeD39HfOTJj/uPfSD9j7lnDvEXh0Oyd4gbeBZ4CYic99nuYFhqRaPdUKZymWiYFnHQYJj2cZQ3yR5A6b+2zM97V0cRKFGEGKNxLkRYGgpDqL1vRQ1x7+qp7X65OdoauPbRmWMizQmlTZ0UcrrYbkSdqJlOyokKTUsIJ4SfyvJO/V7/pc3flpujHaOKDi81N0QcOSsmdxUzC4kvK59EWjCtRoQyISQGQSuW8Z3vnj1x50Mz5Vt71v2Tib16sV9Vv7EWhE9d/dH8kn/7f/Eqq5af/jf/vHXp+C8ujFsv7X7iETcUP/Je9fqja+0H147/12szc+8RQqTAHnXS5oalKdyMdSKpdXo3SGAKbFJrs1q8mugVH/3Qh79tfoCH+PbhgOidAo78/F/74Mz/+fC721Fjbra8fO1uZ2TEpHyY0UgTx120DpsOxgiBEF/rmvZ12qk6GOPxRiMOCKHIxyBy8AnMztTRuhzwYHo9RFWRz82Cs7VWD26lJcsSlKSMde19R4lVmtIWKFOyaY+zN1rhjujLCD1h0G6TT1boJyGjYhkXt1lVLyA6Y3xqGH7qh7iyNM/0rt9gteohbt9BbK+xfNcLxGWTcnsZIwXVwjovzQj8guTuzyY0lje5tnqepLuJvfwQkzKi8luouR2K/esMrzUoRkOSvTmiIymNYUaWTtjoHSXLA1ajiGCU4NoVk1FEYSLaM/tIZWnImVq9WyiiWY8aWHQ8RSYTxG5dCStoEtEiSAO6eHRZn2xHQSlSgrwJXt3S5aUeYQSFHhPYpG63BvUW78DKDivqDmQGKAWlnaCshgaoSlCFU0oXoTGEaDIaDBAMSOmxA8LQkTOcLhpclXs8MOkiKrjeCDkfTui6iIuNEUdtgz1XkoW+5mquAul4VTm2t6DLOqor4eZ0JOBmBNkA1YGptLagg1vH3Wy1dlOcGFDbBjWAZ/jW+uVDfJtxSPYOcQPrwH8H/H3v/QNbk+1p6gbTBxsPqMXo2KwUMgBIwluOIP1sFy0ilKvvaXGwECmlUCiCuJawZUWJRN1ctBRatIOO7ix2uv1spXlu8HzlKKdpLtqTLPe5p7g07uVHZxeibIoylcCp3C0uibEuj4rb23fHXxld1JvZus+2E7k2P8+54VR1ot1h5W/bHdjRciRbOtGN+jVvJEqVpF2aRiI5FUp1at7IH/jc/Q/vtYz5fc/GPwMuClYqgMeeeFwKqVcak9vmx62XBm/9B3937uG12/7ObNJ8ZHc4XJyLG64Rxcepd7RwS+FccctaJTj4vUE90w65pdUT3JgMrT/IqIiX/sI+zUN8R7G2QQgkJ//JP07773hni/e9T1D7kz16aWY+7CHXh83262g01wjDDkotonV984AYh1ogRU3IbuAVOjyhRe2dZw+elwc6vaIA44AKVVJHahqyvuIqqNptRFFCMYXBNqycrjV9NyCBoKyjOEqCi5lkR5i4nNzNsNyfcN9GSdA8RqwLyoZhd2JQriJubhIZwfnth5hdfpZEZSBOcF+6j8sgdTEynDLdacPyHkX3Mvn+XSw3M4TI6TRLxt2MnTst3ZdXiJsNzPAY/Z0V4vaYnrpM/twSp6uISo4ZpJ6sFDSqBnLSRswXzDU8l8qKre55Wt2rZG1P40SF+9IdbI4LWnaPXAkazXma+QpGBkhApQly2salAXM0qQKPlx6JIIjFzcCYtAatBMLJAz89AekB2alAaI1T9cdxM/x6I7Z1g6cfBMMMUV3w4QBTILt9hsOYpOwyD2zh2KHkdhdzUh6j8oK0FFxiwIoLGVWeMYKTeYuCirRV8IbpAi8EAwZJzth4cgHIg4pcqHMJlal1eMKCrQ4quV9xDRQV2LIu6DEHTLWk/i70K0QuN1LBryznxlB32vgx4NPiI09+BRj/WXTQh/jLgfqZn/mZ7/QYDvFdgbGnTkkeA2c8+SCK9KWWmWloqfvUt3skhVJSKJz35Lb2jDIixHlPEsdo/er9Q5HnDKs9PA4jg3qCrCeGINJR1DDNYL/Y0MY4rSLrA5pBKBuJ8k3z4pWN6TRzl1pmpmzGoX76/Dk3GAojUSoxhvHI+cub++NRMVHGCNeKkqutltouXBY29YySUlrvvTPGSKO0c95KbQRGGpQQNLVOAinvAf5L4KdgfC+M3TvfePdOJ2lPzXTxys+/+9+Pnvnyc28MlPy7R9rdU0udmW4Shl0pRIebsyiv3PKOqCN+BTXBc9Tp2yvUnTNCao1gXZmb1wLoI337xofe9ch3xAz6EH9K/Or/GvPC0zGve/ib2vSsbRBRC9bfX7Xap4ok+U/KhQW3ONl6vayK+4rCHS2vX7/dR/G9zM3NEQRzlKUEFFEkcK4u5fx6D9Qirx8z5mCN9bfqJ52/RfycAhkepBPFrTizB0RNUogDaMwg1NeJ7j0IlddkT2jwBdgSIXJKqXldcZ4ltY3DU6YN8mmXzVYH39ziuDhPVK5zKZph2q44NdxjYTJhZc+gL93BzuqE4VLGpJMg9zMmF05j9+4hsgbR2kY7zWTjDq73H6axHRJ/+XZ2rswwbV+me89zRKMIvXkPc1FEzP/P3rvGSJrd532/c3mvda/q6/T03Gd29krucrmUSJuiSSlLi4ocQZEswTaCxEgQByYs2EmAIAEsfUqAAIkcIQYS60PsUCRsAVGsWAApKSZNWiK15HK55F5mZncuPdPT9+6613s95+TDWz2X5cqWIVEClH6AQk1XvVVdmDr9vs95/v//8zRQyXnChUOmaze5vt1CBEsECLpndmh2twnzgGQoUL0D0ixi8u5zxIlklO+S9mo4F9BwAYVJKYscjE9i+2gRU3qSGQlSWaR1TGdJZaPnNFJXbE4YgbMW4ckHolmhLKa0aK3m0WUOS4GI1AMXFBw4a7GexAqBLB1WaUzpo8uYFItgQqEEr/pjrHPUCAnxkTg2ZMLNeMqhZ5j6joHM2I4y9oKcUhoKZVEOJsqSH5+OnaJVeGSaKjoND6SZK46mWlOPwtjqMSErsug5eCSy/HF8nx+foKpefJrKYP7GL3762e0/7O/lBH86OFH2TnAMD3gSqJfG3LmRvOrdnV6f/XT4Xyz7yr+TF8X3+pPpfxwH/oVGHKssTQkIQYbWgZRCMJvNCKPISCGUdY7SGEpjsM5R2AyhBdH72NApqVTkNWpN3cE4gx/WzM5gr5hmpXz+zFNxr15fd06It3dvcGNra7i7X2TdRmNxrdPwxqMt/uDNm96V82vlLM/dzmSnke1tYMNpFvvhPqW3mNtZs+dWcy+qTUI/mnclE8zvj/8GQuD8/PbzwOYLT53feuGp8/9wlm58canReDFUsh0oTwZBEPBw8vbY1sXwUNkTVP55mmr69m2qieAd4EWqIY2HRsq+BEuxVRMHf5Jf6An+aFjfRt5bfazT6t+EDlDj139lys981vIPXqlI2t95qZi/VwR8hOpiZ8eXLl0mTV/CmE9NZkW7DLSm1cMuLHbQuoXvV6VT3z+Wxd/fQsU50N5DP73AhzKvbNOUekj0jvFet7NjVqgkyAARi4djQ48+LxXOzpdmAkvJBG1uMg58wrrHZtBlUOszLZcYZR2eSu/SaNwnOjzAvHsWe8qxvn6fZ/1vcTbex1sx5NcvoBs+NZej5IxoMiJ99yr1WQPvwju4mWaYLbJbRARZi5XpFP/eCvc2a2zUE64sKPQ3P03eUCw2A3SqyVdu4516h2J8Djlb4PzhZYw34eaVG6xnmoX7lzCzgv2163B4iizYpr0SURs+hVVtvleALcY86Vt61kM5jaciCqWZ4pCJwSIxlAjtQENeGmJFNZmqZfXXKwWFLPCkB9ahE4M1FsJjb0OB9HwKcrxHAnIEkp0gYaoMZyYxgazi4iyWTRIWiPGNoinHNJ0kxyAQaAd7tQys4LlJk1Ho+M36bW5EQ7RT7PiW+17JQu6zlvtc9x/uSZzPfMjCgMznJdl8rvzZajNhRGXAHIVVmffB4pgf40xFAB8ogX+oF5+gmjb/FHBG/MIX/lPgO+6Xf/4k6/vPCCdk7wTHyKmSNBZ87ZPb9P+60nzhk7kr34zhdSllcjgcvx4tdtd5tBdDIoWtMiUL6/CtRSplszSVkyTlXn/ImaU2C/W1x/r8kiR50O1R1y0iVUcJhXEGcGq1vaJym3F/fyTvHw2bZxe6ycHkXduqD9p3t7Td68Ppbi8dT40xTpitg/3pq/f2TVa7VTT7tcnTpy6+W1/plveH9/b207vNsBHrMAhrUiqXl2XRn0yWWnGchL5/XG49Pnt585/PA+fzPH9hvz9494c/eLHz1s3tjjUmH8/yMVVxxqMySXbz1wuq4Ys+D8mcmf/8BvBXgKs8HNCooAQofu/v/62/fXIi/NPCr/+KAqIXXvpPzDP59PJXf+f3Zx8f3LsJwM981vHrv+JRkfIxP/PZR7+XPUA98lgX6PAPXrmz/rMvLVBd4M4BPwelT6MWEwR1hIhmtW4Xz5MoJdzqkiRLHQIhtIL82J8jrcqq9vGlUEuHJDrAiuNT9sPoM7R+SPaOM3GTpHqv+dCNVCyCAAAgAElEQVTG4165AnIPXAEyxZcZedGsBhAkgA+FQ5eQezFWnmLxqM6eVpQi5yYrBNIQRBk22eeSvcHAP02RNHB/sMhRfwf7sWtsjro07jyF8SyHfol99YPoy7ep3TrHnduXaV/9Nmc++C/ZufYiG/sfxORNenstaqlAzBo0F0a81BLozoy9gWbrQslC/Q1q9wOWF0dMt1eYHS4Q5oJWe59U91nd6ZDswtRJvMUCeXSR4kDiqwXCZAVUgMFyZRaB8ZDelFJolKeYjUYcSoVQJQ3jsZ9NWApjPOVRt2E1lBAyH1pwoAUit8zMkDioobMQJUN0YDAiRzm/OgtkIO28V+/B1Cv0Sh9rMkrPECLxUHgozlFDAmEieZ4uBSUCSVEk3GYPh0CWmqE0jK2hk/nEnsNzJeNAkQnY9QuMc1SLpJqmHflBpdhZWxG2oqwU3uPoNI9KEQawWbUmNfNhWyAp5sEcx716fyjRexTHIsI/Av4X8Qtf+OfA2P3yz783z+UEP2CclHFPAICgAUwCKt+v+GLjmddXgjMXDvoJb2zc2z23vPhMuxkfWZWihX/W93yU1lhrcdY6a51Iy5Iw8KWS0pXGFAhh61FEp9YQga6UC+scaV58X4lKColzjsSMQEokCik8tJRCCUkt9rx3bu36Z+tnle81wzD0qMU6u9c/yFotf3JutRukZVKqKN09dyb80vn2hc3LnSuXfe2NhRCDptcOoqBeL41Jbu/umUmSUwv8d6D8x1p5HlWfXZuK6GWAzfNcpGmm++NkoRFFnTOnOvLS2RW/WQ9FmuR7QpCWpQ2oyO+x+1ZO1ceXU5VtN+bvWacqrHWoCEKdx8+W/8MnPvzS6z+QL/cEj+PXf0VSGWmfik0xen608/QPjbZOx7bMciEb5zov2b+yd6PWLdMVYMzTH3mgk613P+L+7keqn9e3kakSwW9EO+2//mK33i29nzTO/Yh1rGHKNUz5ND7L+LpObnyKIsbzFLZQYvOWFFIIggih/YqwlfNf8z4RloVUOCcqFS+K5r5pczyq7Anx8GdnH0zdPoDHvARcXch9ldCSR8zKBrWBJCgh92UVTh1J0sBH5wHrw4K9ps9Y1kA7PDWjLoeMdxT37y7CxNJYvcVh0CaNZywx4O6bKzR9n2J9l6OOz/b0PKaXcEZMaUuPdP0W42CMP1pFzxq06xsQ3qfIDFF/hXqUsrC6iw7AGy4QTCzh5S/TPHsPpw1HN68S1EuUv8/1vs/GrMba/RUyZ3CtHY70AV6+xmr/STr5Mr7zSbOCol7id7bpznrEoom1mrR2wL7dR2cxK1GDSAb4MiFWDTwvgEJUvkzHlUwzJ8a5JFQBeSHZL3PqgQeFI8lGzNwQ5TykdAyme/h+jHykP1KXkprShJl6THbxkUgkuWdp4TPDskNC3QhSURK4CKTgbX/Ajk541nU5lWgGWuLLACcNe54hC6H6kPN1ZcpqbUmJUh5+7mG0rQYwclsd9piXnq2UYKEBUX3GBzYuj88N/VsggB5VSfcZ4OiXvvjGnV/89LP/Lu9xgj8mTsjeCR7BpFIy4N1pPuYo335yOJ2lJYmJI9GYFeOzB8V2s65bbS29YDAa45xDSSkKnNsZHonYDwk8D+ucCYOAehQ6KYWaJQlZljtrDTgnkizjncMb+J4mnO8chRCUtkQJyTQt0FLSqsX0mnV8X3N+Zd2eXTmj6lEklDRieSGSDb/OeJxHk3LqG+EYHIk3Ot7q1tDsLa63T10OPNlUyp8YVSSBim9kefnN7f5wFvlKNWrewbXxNwfW0ap5jaEUMqQiZneTNHXD0dSNJqlyWFu6sljqdEqFkEVhwuefPNd98akLzcV2IyyKwms16nmSJDPjCKl0lB3gNlX5doHKfLQzv6/zeBZuCfyXn/jwSycxaT9o/Gd/3UerH2E0+RivXhMfuPbG1rMLOoxteQ8Y/uPVZ9tf7Zx5/p8uPTX47P1XN4GMpz8CwPo2LeC561/+av71b77+oXdW15/4/ZV27604+kSzbHzWT6afdM5eKpR+AWd7IGKk0CTGh3kNT0pBaSpyF7cQOqjyaeGhQidERei0fkjcpIJIVe9iyscHK48nJ52rVBtjqtKcFVDkjyRnwHuL1YaAmWiA0RQSMA4TiOo1omodzLVmEqdMGlW0Wk/f4Yr+Lo2dBOWllKEm0B61pRG5mHBFT+i/UeM7v3+K5xoDsuAOWT5hffc0Mptw4A8YzgpyEZBHFjHQdJfv04oOaPuG/ZvrbN7vEZ+5i37yO4xvfYBJ1MGYAZPdmI6MufPWFTZaDq9l2dv2uHlvid3cZ0F6LEhBUQg2uxsMVyDcPIXGVpymLJFlTJ7llHVIWwqVBHhGk+chkawR+A4b9ilVgMZDKIWQYI1FaoE1BaKQ4Imql60Q5PmMghl1L0bYSvWypsATGqEFToFPjMgFVlrSYkjpHKkGXQpKZymVQzn5QGC1c/WsgSZGMVYOX4UETvNqbcjr9T6pbzgUORMzpa9L2oXHnXDCAAPCERtJvZSEsxGlBesptIJu6EikxBZlRfwBvGA+WUKl/ClvTvR4+FhRVmVg73if+kdS944PjKiSNj4M/PYvffGNwQnh+9PDSRn3BI+iP79d9ZS/envw9tdW2+emmOT8W+Nv7S3q1cWaWDi9tTvsL7SFRgjfOSeEECoIhFhs1tCeISsKpkkS+J7n6lHkADHLizJQShuqEq51DqkVSTZzkWoKPR/WjXXV0yeC8rHSkyc8OnVPGmNYbkSI9RWajdBbqaeq12i6r15/1dwY3z4Yjmje2an/eBi33uxPxrPb2fcOrk1//+0XOz/aruvOk4Ffu7faa+Q7/WFrmkbhcnTWKCFj5xgh+CLwdFGa/cEkearM8nOBr3emSdbJSjOy1vrGUQPhhVpLKdDtZk28cPUsiwudcDSchpsHR0V/NCm39wbBNMmivHR1V6V3dKmUw8oh9XEc9/Sd4AeFz7zcBCSeXkCIqxRlh72jgOefOLaU3dnzYv1KY7X7E/vv+N9tLB9S9WMu87P/wQ7TBH71SyHQeGdx7dk3V9Z/OpEyFkVxlbDZmZVmYRZIgfZLoI6QAl87hJKY8ZzcxVU/nudBfaX6XMl8VDPPq+eCoCJtxwTumPhVBwPzmQnec/J+1P7IOaKixDpD9j4DUw8gqLYZparUqtiQB4+6ZTjIBFpaJqpWlX61w8MQDiMWRyE7qxKlHNqWTAYLyOkBg9M+8WSMWfX5Wvcs2ZsJ5ZHio6cE0xvPM1s6oD46xYV7mlvFHll8l93xJU7dNYjnf5fmM3fZFD3ue3V2bqxRLxRBGmEa0N85Sxzd41ZekNtFvF4dQ51FEbDUyaFWcM+NaYwV5+s9pij2/Jvs9hsMdM6qC7ikThHb89zRA1wv4dxRhHYxRlo8KTHxiM2FdwluXeGUkUgPZuKAgT9FTAJqQlJTDYTzEUiEcth4QpSViAIQAr8WwUxgywQQ1GQTjMDguCum6GLCgpPURIB2kh1vwsAvOZs0cAqG5HTxKREILALBbaZ0CPCEIygNF2eaO0HCN5sj6oHlbBHSNh6ZzUE54qyg5wI0mjSZImVEqSQjKznKBMZkPEbW9Py0lM0bOR9T+Y4Dfisa+vB2XNL9I+HYgeA88GvA/y5+4Qufd7/88yd2U38KOFH2TgCAY1sD/35/PPnAazfvqJVOx67HF38ySUsRml7ZjFvPWiuUNg3XHyd55Pv1Rs0PcpOWSZq7wAul9rGe9K0UUjrn8LTntFIGcL6npbXGOmtFmhXO933R1A12RwmNMBRpOSY3JWlm2OkP2O4PycqSehggpaQ0BucqOwSrExbrXaE9JZI8E86bqv1+pncP09qZ5ik6Uc8LAlvbOcjvHu57bwsT7F5ZPP/K5nZ65tbuvUU/LpuJ3N3u1dr3FqKVnVbQ9aSQQZYX7yZpLgXumeksG7YacRiGHjiCLM3v3t89GlpT+nHkSyucy4rcjMYzmeS5ydLCBIF2oe97q0sLwfqpXvf5J85dfOHpcx2JKA8Gw11riaka92s8PEM64Ff//t/621/+M/rq/3zjMy8LPv+5JarBGI21mtG0wTSZAUMunX6dbqvBNDG1g8OlKFDbHz3Y2P6pL3xu8j9+4qeWb0bt1j9fvPz826fOXjy7c7+23eo+vbm0+neGzfYVPL9NUZ4HllDSR3kevgoIIoHWAjGXSezcmiIIqlLtcU7tg4izKt6KybQiglK+/4RkNSWAKA1Oyoc7hjCsFJdHUAqHUepxW5X34rily1LFo2lXefJZwJs/4Qxndx1RWjAOU3CGSVoncy18YYnKjHjoczo54MLIR4chtWrEgbA4y72m4HzzFuu757Bhj3TqY2a7XGz36ez2SIsQt77FwlFIq4Rxp+Bo6ymsHmFrh9zdvIgXzFDqgFmUctQaMDi/S0mLxmyR5K5kI4V6d5fwzIhhEhFOPEzXEtT7FNe6mFEdx5DJ+h3ujQ/wvCYTmbA3GtDL6rRNTOlydtOUxFqEkhRWkyWKho2QBczcAUWR4pkakarjywhbFlgc0tNIpQhEDWm9B1VOZ3OEUuj37O9yZakTUxM1lKse11LiOUXDeZWAhsMrBQUJ2gkmoiRC4SEY+hmNwsdTHpvBmFUbE2eWbzQmfLsxQfgKpRRFbpmKhKMYxrGPjRT1SDI1EmdElary6LLRtlLyirxS+x6sv8rKB2xlxeIdD5ocEz/4QwmfnUeNPOYPWSqwKyA/Baz+0hff+OovfvrZP3S6/QR/MjhR9k5wDJOm2aWiLD9Zj6ONoiy/fZQPrt7sv7PcU2fvtNCjIi8PN/cPrp9bWkwW282fzO3s4mZyS29t58Ezq0/otcUF2U8PAokSrbhzbL4ksqIQntZSCumcRLTqsbi9tcv+ZIyUQgzDicmZCU/7Mps6dgZDnBVY65jUYmqBj1IKZx2TtE/KmMW4Rj6RZHbsNo62WVvsEgZPeSY6WNnZm25adPQHd757fnLYMC9dPX31NX37p9rh4tcVOvjenZ2nqO+fu5d993AtvjT4od6P/j5p9uJ4nLQnxi6sLXfT5V5raziZ5uNZcn6aZdZJUqdxnu/XmlEsBkk6ttbsBaEf5zMb393ar62dWg6tKYt2qE2vXZe1MJSeVmc//OzFM+urndlrb93a2+9PVJLbR898BXD9z+Yr//8FjvM7+1Rl9S7VFepVQHB/v1Fqz9mdgzV/pbf6qW/964K37kTv6NrVbPto9Rud7oVrH7y0OPCCqD2d+GtHu42DuL5EFJfAMrjjeAIFOHRYfbfJe4ID9Hv8ZdP04WTt8cWw3X78mCpO4ZEHjq0yyrkd3/F7za/Y1mKkrKanHnmdP8ixEspFv7L4Pn7q+JpdUFmtFIoc8IRDlAY8BaFhp2cpCoUUGm0KnBGMjWPDX+bZTcfNWcal09uENYu6dY7F2pBGe8TswhZL20t07BJhZ5/l018jeqpFeadN8/x91JlvUX/rJQ7TOsGZa2wnhu8ceohwxLPDc6Rmm/aawyYhaZayLSRHsSC990Mk1xXdjWV0bUD3hSPa2T06rUP6ly5gD5dJbq4w3Iz5vXRCbdrmRdejm+Q0o1VkEhPFmvD8dfzDkCT3mfgFKzpgzy/ABshZSeEnUAqUEoRljxoC6YdIFMaWOM9VQ2VFgafmrbtzEdaZEhUEiBIoj42UQYTQSytOncYGgUQnEOaakZexq0piPDwkVhumODwEd0iJgK6RdPMWvx7eY5sxtvS4kNQphMedcsxMlhTaR1jHgvDJshmjwJB7HpmDvUdHw9479J1aCF21KXl0zT3IhjtWj49z4iwPXaPfZ1NRFtUmRADhsTrteOjNJ0OQfwN4SfzCF/5b4A+ohjdOPPl+ADhR9k4wx8TbPOx3tg4HQgpxPfS8a795/f85NP7wcHbQDH/3tXcba+3lf/nGxuZs56hfXlld7oVeeFZKmc4SmzXCmmrVaplxJtJSCk8FDiBJUpmVpSzz3NZqsTgcjkRelmwdDchNwtpC2wxnhWpFDZOmpTPOyVBpWqHPcrdNLQhQSlIUBYUxJLOU2GsSB3UCzyfUdRHTEEluXKceiNPLNTUYZG1rZVvqIv/hS89sXt/dSIxMr3TbOpypve+8svXKbOjfaO9mdy5IT7hLwZMH04P9U9YrG73ekvY9vZMXeTMvzdn+YFwcDccoqbzI96JGHLWkkjrNMpllpmuM1c7YSVm6cJYmeWERrSi0URyVaZ4fpkVRYEWEQ7YasW7Ww9rW3lAdn83a9WDy3JWz/9Ols/UEJoV4kLx2gj8R/LW/4fj854bAFr/1pZTPf64ODKgIdshg7G+k8tx+d+HZ9mpn09zb7b3j1z/2Tz/0yY/lWXl6as3aVMh4v93rZUIuu6I4LwUiSLMg87xFilJhrZwraAIpKzWtfI/3SRB+PwEMw0o9UWqeUvAefJ+yNycNnj9XC48xV1esqQYAHnjcUv07M5AanBZQVg36DyzTCh7agc9fJksDpUBmAudJSjfG+iGukMiiYMkNEbZPOOxzutzFLU9I/SZvB0tsxmBYQex1GUwWKRY83OLr3N722E0vEg3Pom3IVjpBrwxJi3OI3FHuLLH9rY/yB987jxt1uUxAc2GHNeeo9Z9gEpXcmg6YdQxstdGJz5XpCmns05zk6K0ODX+Z/ajGdtBntlUy2WlhigSdSQ5ISMcBy/kS9dLgAsHh+TGSGnkiGCdwvzxEhT4bYhM1EywUdWJdp/ATpv6UJFHUVEhhSywFOgyRSDKTUhQJ2vcrg3RjMbpEpgqsQHgCU2ZYYVFak5YTxmFGiqKGrv6vEViTcDA9olQ+VjliPGQVzsY99omzKY3Sx5eafZGyFSfcrk0ZiJSBStkIJszUgKxpKOo+/TwnjRWldcR5TvHoevIBT1brAaqeUOeqdesfW6wcmybbip0K5ovl2Hjg+PYgMu37YczcbeCRKfIHxNBRjbyIZeBlqqjO67/46WcH7/9mJ/jj4ETZOwEAgtXip//7/+7tO3sHuy+/8NzsLzx5+SNCri/1h+XvZXr2VFbk69+7c7f24uWL59q16GOzvDg9Lcaj7WRn7dzygvXQ7s7uTtRrNIWQisSkVoCVSqvJZCIKY6wDURqjnDF06gFjjpgWQ9UKFog9T2dZaW9sb5qjwVQutRsuNc6uLfR0I46QWmPykjhucHNnl27dcnqxx0rToxc3cGj5+sY7dF1PnWn5KvK09+ylpdPdTt357f43Dor7X9tOWOwfuemH2p9WWg6OrqdfbxwlB7tHZty6NtlZzqfjwad6y6+9c/8gGs+yC9YU0Xia4nleo9eqr9zY3J3dPxjSjsKp73mx72GFlUGtXuv22nV71B9PpJTNYZKFaDmWQnB3a3eglRdNZtk0CLxWMstV4FUuBgq4eGbV/oUXrv4EVbrC1x3b/+ej38mf2YL484XjyecBsPXZv/lfP/Fj3/79T37mtX+9mCi/MNP0o5PtPl+JW2d3n3jxwtebpz74jUvPLI21l3hZGq1s3a2tZ7N8b3nd9Vvd3pnhoZ8Yy0jKisQ9imNFzfeqPjyojjGPZJKqR9S6JHl8gOKB8+NDT7ZH3rw6QDpw5UNSd3wf+JUHHPPHcgNSYlo+op+hx5ayNY/GOs6xh8eGPSSA1rT7gDAMwrnvWgHEJVYkNMINGtOCNPU56EiUNyMZNSm2pvQWNrjrn0GvHvAUY5IiY1rzidc7TNQar4xnPKMOiUdtlIwJjOMw2GfQWcKpKR9dg6JbcnuySLd3jk1uIAcbfDMa4FYHzHZW8OtTlmLwU8W+TQkGlr1ki8yzJOUK0xtnKFJHe7nOqYOIPLKkYc7hkYKVLdaLgnayiJ6cotz3cVqQrKYMhwErvYBCgXq3y45S7OdjzpuQMjLM/AGRkRwVlpYXoa1hFE6ZNgydo7D6fgvI3JRQ1zA+HOb7TGeKhgxZ0BHOQRS2mJAhHaj0YTleFYqYBjXj0SAgI0cjGLqMQX5AI1fkUYehSgnKKfFkSEM7zqUBGsXdMqfIBwg14aAVgAarJV6SEReGWduvvPSgGt45tvuxttp4pI/66h0reXP7UGmo7ESpSrxl+shmRFWpG/576IRUj/SbHsM9fM2DNQ1UivvfAn5urvL9kxN7lj9ZnCh7J3iA//wf/so4zYvxGxubZbPmn23U9NnzvdXnkrxoffjqma9+5do3Pxzr+gcurizVrbXy7e139DRLFxdqC6Xddfmbd7f8Wi3UzpS5sW5sLYWWmCTP9HSauEYtVkpIcffwgMNRQqTr5IVkpdclKUoORhOx3x8K7SlRi0KkFKWQGE8rlaRpKbRSpii4tbvPQr1OI4rQSoKQeJ5iudWgU2sL35eiFTfFuaUFf3u439rYHZzytYzD1lh2g87+X33hp4pLvQv9fnmztjV++6rxzIqnvOEov3etMfO3+qle39jbu/YvvvKK/86t+7NuPU6WF7otJWWyddRPt/YGen8wapxdWcqa9dhDMq5HUVw6W7MOYYyVd+/vl3fvH4TTNIutsUSRv+0pVd/aH4SDcSrmnVqsL3d3rpxbDaSUTwAXqKxvPgIsVQGek1WYjASN99rknuCPis9/7lQhVUv92j+ZAYv12fiF9e17fymrBz/ypasfWPq9M1d/pJeMnrnV6L7wO5eeX3emrLcnQ/8wihYiazqhMfFee8lzSsdJXPNmQSSOmu3K5FiISg2RsrqwHZM9ZyuCp0RlXitFRQakrhrftX5on3J8sZWyOjYpK6Xv+5S9OYytfqcVD1Iaqmi1+YXUzd/TUjXdS0BJbFRVmqtj3/OelipD1VmIJWnNUjZmWJmDBu2NOe29S2EEe8Vp9vstprslC50p8SxEfEszuFlwzk64mm2Cv8+eythM1igbq2RrIcHpIZE3o3O4xO7BFW7VEjZGe3znrR6H4ZijtQ2unLXc6vbB24elhPLqN1mqHfLdkSKcrBNOlpgt9pHFPstlB7meEbYGFIeGG8oS6wUu3Fmjv5+SrE9ZG0fUS4Fv6nQDzfjSXQ7CEYdHA5pDx2qxgDcuKE2CExJpBcOJ4HAi2HczVjuCmhLUZh6pzNlkj6ZqI5EU0ZjUG+PNIkpXoK2hdCUOi1ZeNQxtcu6lORaPGhUXl6UjIyUrJVtJSsNXCCc4LGcoJYljn9Ll3C/7FICXOO6FM+uKkJBQ/La+yRtqA184rPNpCsWCibjRSBjpFC1ihkZVPZgO2v0JwlpmkQfGWIQQj/suUq03TdW7mVOV9fV8vTzMm6yOL/OqRAuVYle6uTItHtnIvE8PX5lU1i3iWN173z6/CPhx4JO/9MU3fvMXP/1s+n4HneDfHSfK3gkeRY3KC0ncPLzF5afSwdqpqxu71yadV269uVi07ro39oM3n52tHyy32n/xyaVLHzY46ioOp1EurbWznf5R0ohrthUFO1HgL4IOuvW6aUaxmxW5LkpDXhRMs5TFbkQQpAhZ0qvFKCHRQoosz3h3a5/7rhSdcV0094/KZi1mudthe/+QTqOGp1VV2sVhheBUt8tSo8kr79xkPDXUasK9fu+OEUUonll+urfWa/bG9ujwjr3RudO/MVuOz+drxXK6ty3u3w3emnrh7by1U4p82n7Cf+LFaXg0ee1DT114qhU2zpdZHv/Gv3rlG6dXe30Kd9qWJdaTl6QSEQLrSeVba3VpzEwhpOer3FqbSE81Q+mLxYWm1VLW+sOpv3M0FPV6jCcF41lK6OsjrXVK1VN2H/hJqh6zEZX33xPAzLF9H/gXwFeATcHqSazaMT7zsqKiKwFQHFrhH1nOnFeuoQUb/bjufve5ly48f/OtpUv7O8sf37j+zNDTl2dx/PRT+/dPbzQWJ3txW3tKtp7evqXae3u572yYKu3tR5HoxzFJXPfLanhCJv5czbPzC5fW4L9XiXsff+zIr8pax1O3j3rrHfvjPfYW9v0HLI5j0PR8y+Dkw2GLYy80Wz7SSE+V0sL8dY/i+HoOSFPicFhrETn4gcWUBuk0vjSoXBKkiqkuIRTEFzTTdkDUT6gXbZ5uTFhtJJjOIfkkYHtwlYUoYaU2piHvEWYHmIFhXVxi4EoO3JC1s/ssp4sEccKdQxieGnO+9xaDu+sUbyww2ltko7/Ocl5wZuwoO4fsj7fZVQlvrV/j6LCGN7W0J+c5emWM/iCcfm6Ls6/7XJeWvdaYpbTk0IXUkw7tu+tsRmOKmqU4CJnmGyQu51D7nLVrHCjLTm+GVyZ0ygivN6RfWjrECFOjyCHWHmEJ/dywN45YtTDwDhilPi3n0Qi7iFyjHXRZ4IWWpTQGY1JsmWClIkxLPBEwEBlHrkBEKZ4p8ZXPtpxSzgaMxJR9TxNqXKurpB7u8qbZ4kZzSk00q7Btb8SWVHTLAya5Lfqtug6UEl45dAVaKGsZN33KcJ7VprSs1krJA+J/XK71FGQFoixxKoSsrNRiqDYSRVGpd9qvXqs8QFak8DhV5PtY5Bwmr9ThwoLI5z187vuPe/jADwPfEL/whZ8Abp4kb/zxcaLsnQAA8ZM/KqlqRwFw/uNXn3nio1c+0D/V6f2zO7sH7V4zbp46xdEkG196/eb2U6c7i8+VJe2izEfZYVKawpntbJRdXFs+atZqo2ubW0UzDHWr0dDGmMDiTH88lUejSaqEEI04lAvNBvf6u2A8uvUWAhgnUyaznHYzEr6Qrh5Han2hK0PPFwfDEf3plJV2BymlOBxPXehpoaXE15qxGbA32seWirxwIlSRWu8u6EkyU3Hk6VZQoxGFtcN9E0xG2TSd3txLct+s1F9Iru/dDAbe4exs76X2bRvnyeHBS6uojcTz9iZJanXo7QVa1wLl323V4m/X/MAejiYta+2RkjpNTSGFQwS+5/meJwR4aZbbw9FYLHVa4WSW1L9z/VaYFghPCeI4wteOtaX2O+urS/+IylcjoUpfaFGVNc5QlSCXgMtUWZP/UXWb/AxMLlCKUIgAACAASURBVMJkDyYJTDRMyj83PX+febnG5z/X4fOfm/HX/sbxY+LX7h3UPnPxh+P/eYL5uz/3suTzn1sGTt3vLIRxllyUYL9VyOahFZ9YEe4jgeRHVZHHQZG11vb3PubjImrR+aIRrwwmRac2m8h877BIhGxfmA7bTZPK/VrbxXkaLe4N9TcuP0sex3J12JejIJBOuMoXz/PmitqcRHk+pAVkyZwA+tUF1fK4QpdlD4xtq6B57/sVPKUe3o5La8f+Z+Sgyof5LGUBTs/rr9UFXJcZnWJIIv3qQjwP83PWYUYlLrfISFaf89gSvHCVabNU2FJipoYgTnDSYXKFSBzulqJ+6IjqAi8sEcKQzCAcHqLbJafO3uP2QYe7W2cJomU605idYsAUw1J2DylT4miM9ffYThOOyhqdrIWt1xHtEQvdIWPbZNAPuTdeoe8W6I9jduMDgjN9UrXAaHsZNTbcuduBwx62MNh3El7fCdBehP/JTaYf3GIQLLO1ZVhaamDDhHajgZfGfHsy40gVLJoG0ySibzwOzmkOOpLQxLzjBhyM91nZGdP0arSnmmw/Q1qNsnWck1g/pXQDitoUCp9C5ygcgagROIGLAKdxc/86bStjYwf4widUEVp6DMojlrwIXyiEJ/H8gEBqRJkR5ZaureOVBcIPRFaf2u3pNfGNYMxocZGZGTKwEyO1El4YcT0eun0/LTKVqEHdiNp4KkRduoVaZl1eCtCCAOesEvhizrPEPCoNYL5xcbK61SRoSQNDw2Ukk5mrPCLnAxfan5d256rfsS/jA7xnkyLknGDOoVWlImKrY62FbC7iyQebnhbwc8DXf+mLb2yeePL98XCi7J3gGDFV31iopGisdrq37w/vH9zt31/wtVesrjdHq/rHLr7+2u88eWf3nvla59VRL1x2UeDu90ov7zS7oda6OU1z45z83VO97hN+EBx9++btmZby8ql22/TH44Wbu3tllubtpCjiJ9fXxGK8ahdrTWud8wqLrHyrBKNJQrfd0OvdHqU15WiWcnN71yhfyyjQpZKSSWKD/mTGJEs4u7jIUXbIMB9QFnWscSz22gyTlN2jkXAqFbO0CP/C1Q+VrU5EKNWz0p5euHUvze8e3SvzjbdNbM8e3eJufdj/3uiJT/3lm+cWenp5oZc7x9e39482rt3dXjy7uqBG42R47/5eZi1JUTO3F3vNdyZpwrtbuy95Wp6yzoaTaSqUEnFpXLl9ODw66g9a0wzRrgf4nkYpwXhaENdqIfCXqC67I6rBgRoPtZpjJnB8H1GlP5ymMif9LGCttbvOuf9bqe23gN8GDt+v58+xLahIfS5Y/TdPvX3mZUFFNhN+60t/2mXkmMrnbsxnXtZAuO9FwTCuf/yjb71WTOLa71D5433qKKqNv3blWfvpb//eh9tFfusjnk03S0ZKEE2D4EcE7uITu1u3gfM5BEfDrB0MJ7Vudf7zL9lJ6wgTxkrKETb77qUXouX+kfamKZn2xMpsSGIMNkmr0i2u6serNx725eV5ZTNh3cOoswdTj0Wl4D2q5r1XxcsNmAKkPy/rztUSN1dEHkQN2kfEk3iuCDIfjrQwSylLw2EQP67s4aqLq3AgLcwE2llKv7rQ1hKPRCussqggx3MSqQq8sqAeTlnZqzFq5mThjCRsEcmc/VkNNQ6492YGYsz/++Rl8qFjYXSPsh+SpQKxGtJzB8xuXiJrF7iz77Ib+nynvMBev07ngwOiTCGDLku1jHdvGLYH6xRtQZZG9PIZdGpMtce9BUFpDNHbC6gsYdY/YPlsi0svRqyPp/Q2OrxeLHNQGqLGjI39lNme5bmn+qSJwltqkCUG7BHmaAPiC7w7DmjuSEQseUMesZeMuGRbtH3NZDBkMzIofJRtUI8L8naf6SJ4dxytcZ16rhjVZtQEZCKjnnbY1SMwR6y4VXyhMQgsCi8WWHKs9ZhkhokJ8V2BJ6aURYCvI4QzBIVDyzqllJhy6MgzUR6Eouxdtks5bpORSrV1vpyxMivdvV5kM88ve0Umawr5rieZ1n3ipLAjHSori0w7VJChR/Xjsqys1lWYV717hbXkWqIExHo+Bm4ZW1t56wkrqs0F8xYCA/5xM8qj6+z9YtTcw4ncylS/In+499x4n3vRAX4D+BngX73vmeIEfySckL0THCOlyvVcNdZln/val1/96M9ctz/75M833fbTf/Df/G+/0f305dF0VS+cG3qlX+Zys93xtlx3565NlvLffOvtjwTaP9tr1Dv3D/uXzywt5G/du3/wq7/95ScDT8c//sEPCi8q797Z2w2m0yy7ur62jDVRvRYrJVQxGE3crMhV4Ptqqa3kLJ3ZnYMjO56mNvI9Zy0+UorTC103TvPcWFM3FvHmrTv5aJr6eVlSWktDLrJ6bpGD8Zh6EPDWnU3G+UysywalFsHW4cFg5eIlIt/XZVEu1aNR+9m15aIR/dh3t6/dGvkkV+5+76aYRr/7WvQXP94tazWsEGJ35+j827fv31LD/vlz9Xrd9NpfCaLg2miW3pkm+XQ6zS9v7uy/G8XBvYVGbW2W5p1uJ1576sJasHvQn93bH1uAwSQDMi6uhabTiGS7Hs0tPDigIi/PU5G9hHnhgyp+LUmy3ENQi3zfAyhtQZGXvtRQJPZsWZZ/p9Goo7QapYXd8NTWPSXFF4G3gJtU2+gpcJYqxm06J3987+W/6TeFO39O2bv81pdm8zURUqmLm1TG0D8YfOZlH7D81pdKPvNyTHWmH8/vfwQ4XTjqm7N04anf+8relbu33vj429/+D6kIYdpIpk985M3XGEbxc2GR/1Ao2A7Abhj6e43FG+eTgTqd5p9Oj4tWotASurLSNVxXwvJgR93qLYut1nLkJVORBBGp1qymI6I0ZdrsQFyrVDjHfHrRPsyxtWY+PxFQG6cUeUEeBRX5K8uHfX3HKRfvLdGaOWm08wb6FIg0FOqh0lFaKOZqjNXgJw9D+iwVUYw02Bn44cNYtKJAy0q5EXWfWpFRZiUSSelKUCXThsGXJYWuCKZLfWaez0qwhzUhha4ju5rCD2ndHVM0LaEVNJbuc/XCLV4oh7xenOVgVVG2fUbXBN3WgNaZu6ysjPDKBuOtDzHw68yiDYq2z+lJA2Xf5rbXYPpWkwvBKZpLS5Q+HCQS62aoRh/pLaP9+6RLEw6u1jg60pyZ1Tl9pcHGpMCr+bA6ZBjssnVtk1bQIC8LooUmRcfylWnBneu7/LBMeNIsE+l1pou38LuSbt+xkEUQaA68hFqmOBMtkuWGsZW8WWxypbfMkplxkG2TDUrUbA0x1GS6xFiJP5Hsq312pyHrYUTpO8xkwoqeIaMGuTGUwqCwGGZkpWUQQM36xNZROMc0HzOTfddzNZG6BFcYClkjNRaZjxlaQVKL3YVpYL8j+y4tM9Kl0GUyENdNf1Yb7eSxkvVRvWdVWlpbGhlZT+2PBadt7u01wnRaTc5BJEFj0UKBZ3GZFMJJ5VJbKl8iPUFi572dc+LWiFGRwCRuvtZktealQMdQzngPjveqCjB4QqOlx+Pz6ILq9DZf42H4yGuPx8Q1VJu+/1X8whc+5n75539w56E/5zgheyc4xvH26hQw2e/nH/7dX1/68k/80t5bX/2marz6xtHqxWDT/PDTT+594MLF9f5w0v7q4WvJl+98y1vUpw0m3Pvwxavvturhk9+9e2+lMHby3Nn1ty+vrO6lRfqJd3e3oncn39vt+ktPfvvaUbtZr+tPPPuUsdbieTLQXiRv3topRtPMPn/5vPjAhQu2NEbc3NoTu/2BfWJ91d0/2BfXNu7btW7by11hd0b7s72DvHzm7GmVpbmaZAnTLGO9t0Td90mLgm6jxquvX+cbm6/xYx+6zAdaZxZmk2k3kEla5LnODgemLoz90Mq5J/Zns6Wjfr9z+/ylwbg4WLj2z/6Pd0S5tlW89BPj1XPhx1585lwgR5Nu5OmZsbY+yzJ1dnUh29w5EErLTyjfWwk8ve2UNoUp1O7BsL/Uce3D/sjvNGv1wWh6vF/Ntg6G/sW1JRt4wReo1LoLVOrecVT6aP7vkqp3r7Y/GK4KKfX6Ys8CXp4VzjqjygSmw4RknNI/nLCw3G32c/e0dubZji/+shQu8xAityZzxr07nWZWW/Mb7eStHXaO7CDWd3+tvuKdL2ZL+y+/3Pz43/uv7O5b70zTqDf55l/8Ubf/nbfDv/fv/XiyIF38SqH5q2E+4re+ZPnMy4Lf+tK/3ROrUghhHrVOJQUIKtJZv336xTM4OP+ZlwuqHsVy/nyN6kSfCnhnSboPXRoePN2YHLxAtTEZANse/NDqZFAfRrUR0Mwcq00IfIszO5uipUgSR2/bSm9B2USBerezKE73D5WHdfuLK3b18Ejc7Syz0eqI+ixjc+006we7rO9v8cbyOkdLy1XTuptfBLWuVL7S8mCsVYCwhk5hmEpDHinwo0csVxTYfD5B6+avO7boexQagnlMmq8eqoCFrX6/nX8GZDWscfzXK6iyTI990sr5E1LhnEXMfdFqSUa/HpCpiqgqlWH9KqdBUSIzQ4RlUgh23DJRWCB6hxRlhJMZQbtE7fng9lk0A+Kgz3ej8yRvLcCypOholpaneB1IvbO8Xc/pLt5H2rdJQsP27WVmYUB3JWMQdznSi7gVxxv7p1mIDON7E8QkJiob7M+6lFc0Wg9pmSE9l7B76QzN1pBzCz2+s+/zxX1LwzbxlSVcraMGBZ7nceZcjrfq8d27dUoJs2bJ1I3YbIJJOzRtib4Yc2NjG19pVl3CzCv5ziEMaxOuNupcCpap+zXcVLM/GDPtNNhPDwmDnFot5Nk8pll02R8X6HyPqfGp2TahWGSnSOlFIZHyOAzHBFYgZx779ZxGAC1ZoxgcodqOXmQZlKG4XqT4o1264RKusPYg75sD3/M2hHR9m+Ufs55bmWmJqomtzBV7denCbFT2pia40wrFdGayes2mRnt+L/b8YH8stHEiD9HVEI4ThLj/j703ea4rze7Efueb7vhmjARBMslkDpWVWYOkDqkkudXR1dGl7rBWDtva9B/gCDu890bltRfaeunyorxUyBHt1uC2VSpVKStLlZUjkyMIYnwA3nznb/LiPiSz1B12W9ryRCBAEMAjH3DvO7/vnN/QrlydQ9EAIVlPMZdFxsgTNJgH1xpSgEtQ1PE+W0DZcn2VMQD++hDC/iNAb30zfFkcEW9NoEv71WWDgYIHA1D9R6d7v0LTuwfgf6b//od/+MqH7x9Wr8Deq7ouh3Z6s4sWfDzKZ0n93fi/jd77/dm3VGNuffbskL3++kYx3KiX73/6sGLSvNlPo7N4NGb1PGb/9qNlyTZOD9/qf+PH33n7fr+XxGf/4ltfv3p+ebnx5Pjs20+fub2nfhyt8qq8mi/DeV7wxmgeBwFjDCiqmg16CVZFQUXdiO1B31W2kbOykPOswEWWW+aB17aHweZIYpIJtdULA06c/+2j54gTZnuJYMtiSXUDhFKCc4FEdrDdSSF0Fx8/f0aDaErv3bwXdJUQu4logv7QOcYZz4hv5Tz5/e/8Ezl861ZsF1VncXC20WwO/q+btwaH7Ozo3UfzFR/2kl8OtrdXjw6v6tdv777VSaO34dxy0InZqJvcyivtvGeDOJSq0palSZxUi4KICN573NrpO84ZdZKQV1X5xnLGi+6g+yZeGmlcAngCYB8vhRr3t3rdBoRzAAPjtXBkVhxyz3NPnOCYVKwmC+c0uAMiybCcL4grGSa9DuAoqOeTb9u8geT49YxHQJx6fzFe/Tf/5XfYnKlSRmGjr0LY/R0/GHaLaeAeBdvdP3vzwm4ugM7diNnKown/9b88AvDz+vf/ZVp7DLscl2i5hRmAF2iRTIoWlG2sr69UO2Qrh+8GwDwRKADIzvK0j0Zv1x7DgJCsrz+PlrPjATBBeLLHcYAWFN/PgMcAkhT4tnbw5NHt5PnQAlprWEOwXYE4YpCC0FEABtxJ58E1wWnGPIg8BYpAnK6E8JvLCb17EeKz2/cxTgeASnDS7yBLOkD9lYzZa45dWQIqgG3W1DwG+Ag4oaBtVxqALtcuFtTy62LRTkQ4Ryt7BADbKnmvQeG1CvJ6RRwF7aURAqjWrdA1oAbrHFz2ki9F62xdfc3j44ABLAMCXsAEChejGBAGYCswSISsAXnWzmGoQaMauJGDrzkSn0FUGn1WwDQOdZLB+QaisQgCh5WJsLh9F5dHb0B2GuwOclx6hupSQjAO9LcwMwWiwKExwPjMogmAKujj6VWD4cYtuMMt+JnFXgQMeIblOwF0btBX5yiXEfqhxurWJprPBdiPKnR4gqtbFid8gt33FB48Vjj7Ww1/wvBrXQUpCVm/gHoXaLCHjSDC741iHM4qvB/n2OwYLM7nmFuObw/vA12HzWEFiBh5WuBRfYJQKdxQETZvpnjcFKhPAvySBG69xRFIh+ePL7G56GDMezi2NYYyxAgjzBnHsS+g6gWsXWGJAXaDIRitUDGCdzFqK9HNBU7q0nMZIAlyCuIcl2rpni8Mu1GG6PgYVTljeV2zCXe+awKTc5g/7R+IqXYKUe36K1PdWjDvRcVXntlRWfqdBiADf7UbuGTTiKYU+sQEkhJGUWlsUXIGC1LCojHOIxQ+HnJTrJgt48SizQcnqJiBg1tnvSdvWmsfRqitixEwVQPzwALkXHvCWFu0QHgmQM6sr7016FuaprVr+dLnpz3AdIig4HHmDVo44r/yNcCvmkbin6Glrxz9B93rVf1/1iuw96oAAP5P/9LTH3y3BHABoADwCID/vf/hfxz+m9/57fdg3btxL/lgFR+cZc1Vnw2O+N7WyL9x4/6P5mbCPv0Y+0FY3f3x459Ffz2ZnO52toVk7L/+7MVx98OnByfGu/R373+N8rqZzlaZvFpmvcsso44U3nmvT69mzhFjRa3d4fkzfXdnV2VFYTf7KUtjyZaL0p1eXOk7O5u8bBpiVY1+lMrHlyd+p5uidtqv5iWnIPAvpudIWQIW93Bj0Mfvvfc1KCUQS4EHs4dsaRfujtltwqjD2O4gnJs8CK2q6fbNS7a5YZLtFKKhiFm6udVN4gYX9cVJMTl/qourI8Tdd9nX+0zuh++8/ennlZexSkBNOb1IhrpDTb/WzYrgLwRjuizLb1ggma2+PP5ayRkuJhl2Nvp5IEVPa9Nxzt1njDlr7RMiOmKMbaLNkPwcrVhjGoZBUxaVrOtmGgTqoMqXXSVJ5Ksircqa9UbdOKitqYpGKMG9t9Yfnkwwv1rQr//m1xDUFZrJDI0SgOrAdVLosqaBCrthIBBtb6W7o9TYVPiNh8+EVGH9XwW2G7436qfn/b2gqkZDFVo1q1YeeAElbxuPbav1vnaIZCsKnVlA1sA4DxMzqPK+bKdzEm2CxQPp8Y5g2ADwZwCGG8vTydximHncJg5ShBTtq3y4/p4BgHfWP4ebGkADfN0CTFjoxiCrLBSPEeWATwN4oVEEvLW4JSDkBB55NBMPvhSh6AGkGLy2xt+YXzLdaAQXJeZJD6P5JX7385/h/a//Oq4SAa8cojxH2e20YfHtHbMGYx6cEegrQwgfBi04/GqFAOMazrgWFaJGi2nXAovqKwsu7+HX4I0AIC+BUAKwL48D17VWBAtfwcwzoDcEHG9NccsSUNRO+ixD7eOWK6Xcmhyv4KBQOsB5DYEMjCo0swTG13AbMYS3SKoLZG4bDVkgJxQkwbsGARQKE+PRkwy7WQa/F2La9fDa4XIjxMXDEkkyRvyNFOVFgNgaDHoN8iMNH1co9iL0ZyvcyAs8ngyh98eYyQIzk4JGCvAWpBPsnQB5k+PsqIs82IGuGaTr4ZPjArfPOd5KFR7vE3CTIN636PxOhZnOsMoC7EUSN2ZdzIXDWWpwSgbBucOqJzEer/A8O8Cu3cPykmE2rZGHMZIbNRZzh1m9wkeNw6ezEqaa4/3sDBuXr+MmD+Cq2/iG3ISvHC6owVPkCK2AIYEbXGIU70CGHRxXDS65xS7r4wXPwQIL7wWsJ1z5grq18+widU3eYZk4pn3DGj+I2cmi8CmE24zv8RoX9qi+8P1ii36SLBc64nETh/OkEcv7eROeGbDDQRdcVlEWhX1wRPKyXiovVa9ojA9AlGnImLPHNiESFnuJxcFcAIyxVuRBpBspVQDXGKqiLouM8VZPucmn1zw8rwFuJRAScayNI9dchBa8MQHqjIB8+tKd5cv7Bddf/tJ6ZeLdGsq1695fneb9B/YsMb7MKHlV/3/rlRr3VX1Z3//hD3popzEfA7jFZHMv2T8YPX44PzmdTQebt7D39KBURyfNHe/43nAUNp8+mX9euIU6no+3p/X854FPX8sXyY2d3qBnvX0tDYKOiuukpjn/tTtvLeMgcC8urtKfP3umn19Mq41Ohy2Lpf3g+QM3THslB6O/+fwRl5zTvCxcL4qMYMJ+/OLUfnFylg07nfze7q7QJbdH53P2sydPcXg59SezuX97b5ftdrfIGA7BAhxPrjDopugEAZ6ejrGoC4ybAzBuXD4lvtmP2JwmCFaWAkPSx0qxmxtBZVbBYpbVlx88mAWbwwlzlCLz38q091+8OOdbN/vihMfbeZDwy08v7/7V352xrLhaRIE09wbJvnN2RZw7IeW9q/myc36+iK9fwoZpoAvtwrKskMahfev1W3/X7ST7nPMNY4zTjY688+S9f1bmJTfGWCGEJ6LTqqzS509Pxh44UUp9+PjB89dnV1fjD376mTg7mdq9/W1mrcXlxYQVy4yBRF1XllbzHJwcmmXhIZXLG8t0UWASd1BcTCB6XRT9rvNCVKNe14ZxKFiouD8e22EoXSwFp/PLLi+KWJID1doD2EYU3GZCbHj4B8q6YyK8pgXnKy73cufSmvhlxxnH21ftewBuckIUcBwKhgHaid+3AOxSa/wwZEBgXZv8ztYLI+3AOcGgBYyct9GvCbcQuTFKOzMKGY+cgJ2BVQpeVg4296CEkIQEaTy4a/WDPBeceBSjl6+osZ4aBzRc4fTGbfzN29+EFhzncQ+nvQHKOIZXAVhewkjRgj27XomK9TZaAKTXmbdSwdcFoGsgjkFctNM3cuCK4Jo1D8p7oGnTFr5cDwOthYuxIGOgGcCdb333wL8EeiTWLdCvfxoEKCph66oVcjjfAjrFAIrWPnvUpiCEev3NDkAFQgkPDkCAnIBgFaQ3CMMCUhI4OJyQ6GcBwlkXixXHKhdwWsN5i/KjJXBZwYw49E0DiisEzy2KvwxRyxW2XpvD34zBhcAiMpjOFJZlF14RyrlFJxXwPYmDhyWUDoA+QFxArzgur2qYzxjypykyXeOcSTS/YWCGNeqYsDxmWGrC+AmQMo6eVTjdAur9FJP7AS71APKjAN0mxGmygry3xPY+x1tmhFgzJJWA4ALnNoe9Mng6nSOqA3yD+pjnC8xyA8olekGKwmpwRs5JRvrKIqprzITDHO3vs8wdnqLCO6MO4orwvJlBkMZNuY8+i3DSTHE+XcIqgzNWAiVhzueYLa8oTGryQw+vJcps4v92L2PH2bLyqyrcTXruYHClP+guy4h1qxllC5iFMTI4IX8ZjAs7Cavw8jJN4wsOilUT93MTdi5KH5PSGIaUxLEvzzLjapipkt4rSYsaS3BmQREXUmRBRLbWzDPOhdPMBIIaaCLTENCGvlF7AiHUgK2E5yDWrLHeWu1B8M7DNNSyC/y1putajHHt1fdVL75rddFXS7RkwJZi+9VPfAbgf/qj7737yoblH1CvwN6r+rK+/8MfTNGOyHsAfkPG1Z3h/iL53a3fnHcUdx+cPd2/ymZ7CC43Vkt7Mm6u1IId3LhYzD+ZTMyN6YTmbw7feRIgoekysy8upisQjh+dHvcdYfWTj1/Eha7lJ8fP7KiXiO1ufyqJFVxw7cjIojD8ap5z4zTf396255Op/snDpyYSAetEgT+4uKSnZ+eDrCjcoJPidDbN9jZGLIwYnywKtzcc2kHa4UVZI44CmwQS/TSlm6MBxssFrpY57ow2cSvdZ77xvD9QbGmm1Iu3yVPsSltAo+DFbKrJCapJ0cOf/PLT07/9pZbBsNvtu57N5sXnF/NntN0PVba6Wvzsc51PLs1KOrzdj8q9jf6dRuv08GrOZrOFyItqUFv/pVytbGxZ1TpgAExtint3dj/sdZIUwBgelfd+ixilzrgvJmezian1EIQrznlQlfVdOP/jbj99sFxm0cHjw/tcXC3f+cb+z7IFKcZYnc3zwYvDMTdN0ySdKFeCyQEHZ9ohJI50u8/nMnTFizMzeT7W4+dXOi9q7oWgOIosP51aO58h7Xe8snUulMpwch7go08VanMB4AWsa9DtxOh1EzadbUG7rdpg1M5WPGucm6TwuwNnIgn8OtpV7jla2cEULcir0cYjpQCkIFxIhoG28N4j8UAgGFhjkTqHyHjUgqFBKzCpBEEohqJsdKg9EwFnSwL5mPkg9mAC4IYQB228O28cvGXgkkBdZzEqsi/Jg1dcYZp2ME17yLnEh/e+hpPNfUTwaDxBWwJf5QAxeBA6ZY6+MSjTFD6QLXepalpQBwboCqgWAAIQrYGdUHDNerJx7ThxPaLzX+ldTfNl72NKguza/ywU7TMBrh1WXpaxMA0D0l77WJ7ayZ0gKKrglYaHgfQVlFhem4AAcEhRwoIDqKAuz8HqBtSLUNsQypW4yZdoLEBqBZ1xCEEIegtEYgFZFhAj4OZrDjsF4eypATMG4oSQlhr0Bgfv9sH7wGzaw8qnqBCgzgDZdYibHHEqIDOF8ScR2GsS3GnYyqB/PEVnP8dGZ4DLoYC8q9CjGP6mhllW6CvC5rAAvy3gnnawbCwECLbvwR8JpEuFYBJi/zBF2AhMJksUaomZABIVwmUGGz0BdbePTCp8VGTYjHv47s0R5qHDBbOYvXYF0QnRBALzM4lv812aVA0eepT12wAAIABJREFU6AKzeI4v7i/Q6UbYqgO8MFeINo7wGjdwmUBDEbzlUI4h8IR5XaOUhBvdyB+WM1rq2t9KQupiCBkUaKhENlfu3zdfuGSZ0k7tkfkxosVsFesMJvCrK5+fkLAVqLSVypKgcopnXXZUV/VxhymzmfSqXlD72uhJwyfjgDIvpegthXqUxMXFMJpD8hSNb1RMsI4pgDtdwwXcy6YmaT1jMMR0jYmpcS2WknjJs71WTWh8mZVmOClQO+hz8I5c3HOk6+tJnkcICwdaM/TW5WxrRcScBXH2MoqNsRZg0jqrDTmA/xXAf/EqVeMfXq/A3qsCANAffDdCy7kK0a4PnVDVM/LkttM3rnZGe8///S9eYEPQ6fat3IqNbPP8zHHi+ODw4xvPjeGr27tRvLfV+82BHC3G00KcTSdDYuzZn33w4Pz56Syc51XXWvfFrB4vgkiEXdWpvzg+C5NA7T04umJPzy6vDi6ugl8cvMDx+MIeXlyarKxwNp1lz6+mha6b7la/Jx4enFDARc6F8LNVFmhb5q9t9/Nv3rkntbYiDkPsDQam10vFbLXCNCsQhwE4gICl4FBYFVMIy1GZEiRqxJ0hcUUicJJ3BjsOIpgfHXw0v/rRX33Gz0U/jO5HYi++qo1fbvjieEtk9+3RhZyHwYPJavonJ6+/M9425a3Im+B4UeYrJjv5Ittk4H3vLa159HUnYKYptXTOU2ls8c7rN/63Qa8jABwQo0oIseKcPxBSvK9C6b3H63XZHNdl3Q0CVSad6HB+Mf/ehz/9/DdUoKJQDTob/Z1weXL+xeXJ5eXFRXn36KTwg1FnfPvuRl3nVTqf5Lwucxp6Q0mkwIncfJLlom5o1A3Npmv0MA5YR9EsKrIG1kwDKTTzEJjMI5RliiwzKGuCth7ABTifVt4PTFaltURAhFh4BIqBRcC5bDV/+2gbQor2CL+FVnncX7+/Dger0TaTrgeWHlCcg3MCs4B1HoIzOM4Q1x6qtghAMJzgYXmPE5PaU1NzwDPwwINJjzAAOBy81yAmwAy1hMJkfc1fk1SXaRdJXUEwjjKQuEwHsORRyhjztAdXVoibAllviBtnx7BKog4i1JFsjWdh24hPzlvFq1RA0AHRWn0bCYCt0yn8utmJ63zba9TGXqp7AUAGIL7OK+WuBXokAR60yl7vv+TpqXIKGQKG1tYuUkOwBo4B3mn4xgOBghI1GIl1EJYAWYIsJWpBSPglFAmUiBDwCsQD1BDo6wyNJcBV0M0lxEaBsOvBQgltPCQxKGiMtMXsuED2RELeJPT+IEckQgythJ9OMB4HGP/SonnIcf9gArHI4Acaxlm4S44yTWFfJ0wmCienEeaJQRGnELdTbHRTiA4wPXGYLBsM92rwGxHcGRBfxOgtOlgpDeM02GuEG78NWMGgp0BqgMOmxnFfY+IbXFw5fLEoUAYMve+EECMLdmUwhoaIPIxlOC1LJBsGF/cUqBLwLyRKVWFpDFBw6MKDG46vyT3sVwEUDC7jORB5sFpANkNsdLqodjIUVKCYHiMKHeJhBLkhyHeltQW5HZXSftjx1WJFZWHqbZuSBZMpYkhT1mfC2mok8+2mE551qCyUrgYdv21u+hCn22k26dAm6+1cddHZ0VOTbYa2M671rUlRbRqt552gbCL/DK72pwGPwISEFnp7mYs4pDij8NrJW3qBwDnyaAgAmriPQFcmWEtuJV4SCFrTwJd5aq1Sw351YsdIV9fAzQGw2GUO3LO1Gnc95dP19aGHtdm5pAFarR93hnbD9McA/jsA/7v/4z98lSL0j6hXYO9VAQC+/8MfRGjVkdcnuZu2Cb5VzNPswdH4b37x4tGku7F6fbpUu3XW37WOXptNZTN+uJm4Ouq/+0bPfvc7N3a3ox3xwWfTv3h7Z2c+kHLTM/bm/f3t3zLa3b5cZr/89OBFpRv2nfHlMp6tcnM2W/QYQ/LF4Sl7enaePxuPO5GUqjEWv3h82OwOu0JxEZyMp2ySF+JkOqdpnvm8qqbDNOW+aOR8VQnrVVRqK/ZGA58GAuPljJMjdzq5Yi8mEwyiCK/vbmNZlsizAvpkjDd7Q2z2Rt4SR8WXpE1pPGDCKK11lh0XX/wyvnr6+Gk6vPvnd3/7G9Hot+6eL3yx7U+v9pZXGXWS9Op4PGs+rdgtc2KrEW++bqA7R2dXPcXRZ9ZvLbNSXgsm+4l6ChJFbW0MIgnrKOHir0a9ZMUEn3LO30erMP2O0eaNIAzeJ5Bxzn28nGVbaS8R3vnR1eVseDWebKZJtBsGYiO7XKTs4bOjZVbBFHVss5q6Q8Gq6SJVTaOaRjfVo2dsJwkYTxLivYSl3WR1I2LlDhnZk9zH/VR0+qlUk7lSSVjgyfMFjk5XkNIijCzi8BJZPkRRhgRE0IbrsjKWIJdbiRego9TYBi2eigBEjUfSOIQgeN5KQSMASQNwDUS+7TTX1AEOQHACEwwhbwOdNCc0koPzdqpHxoMZh5ITEkHwjhHnICYDhJIjDBoQsxDg7Q6IPIjWVzRfX9jXbckAmCXdNZ5yOO31cdkdYqNa4ny4hXkUoWMsasFxsy4AIiw6XZSdLmqV/Kp4MAoBadf/gADBgXkLD2pBWePXtKa1oa1bE9qVat/kV9I0gFaNW9ZtFJVbgz1r0PrsmdbjzLYDEIoNwASsVIBoAFHAcQbYAD6rgLMzIO3AiBgGBAsJggNVBmmjUQcNtOeIFMERIBRDhDkK9DHVPXDm4csa1nt4CDQXBrLOgYUBI4eaApi8QLApsbQcFQDBLcqlQ8kdilOD4kQgOzeYPxegmxx1n2Op+wglR3krht+VaE5KsGmD8DUGJjhOP1aotwDbM0iZQncRYC4c7C0JNBxFqDB55DHZKDF4O8OQarChxFYUIDt1ODioIYIlZoMx6l0JzQMEUiHUAre3A4xTDz+rcXSe4zgl6C7HZd0gSJZQN5YoBgqHh8Cnjwpc9VbYDmLc9RYrVaPuKcRdwBZAAobkjRgs7uOLMYNMIgy4QL6VwxOhWgqcrpaorXabq5iYJbuK4LeKxMcVQXrNukHqhK55z3GTVMjjqFcVplGTkQsf9oqTT+P5EzWimSwYm6m8KM5IPNbhTG2w8abw89KiV14y+/bY1fusQ43rNacD1p8KuTkOIx5lpTQOAHHprA3njjiMrSG4g2d5fyUUCLUMyWoDrSuEa4dlDziCg4Fick2p84AVLdn0ml+wvra/5Nm1BxkODw/C0hPKX+Hg0VrN61vBE5EGqALwEwA/A/AjAH8C4AsAEwDlH33v3Vcq3H9EvQJ7rwoA8Ed/+G+a7//wB0DbkN9Am9hwA9ALGZonpub/GRP2LePs7Sy3t2aXsdVVOIGX/UDwszf37qAu0XlxyG5M5vX9CHTw2qD/jia6kzd2u9Y6f3R89Lrz2Lq7s60FE/35qoxmyyw9GF9ZIWTdjSOcXkxZUTd2UlTqMsv5wfmVNauS7aSdIEhU9MXpOa90Y8CoJxkFj8eXVDonc12zstFuu9cVs1nGDo+v/IOjEzwZn5KxhharAlEQ4tHZKc4XU8zchb+7v+VLPmPWadxQN/VADZxSiZckFjFT/X6ykS4/O/ZJwjZ6bp4/evjk9Lhwl5NFHdLmZtPZ3+2d/MVPD167uZMoE7323MeztGNnPiuSLCtEre2GX4ugOMFyIfNVUTlOZLudKBKS+cDTZ6H1Mby/owJ5ZI2bFKs8W83zVb7Km2JVfp0JEX34088Op5Py6ODR0faTR0d2b2+L5VnRn5zN5OxiWS+sDZ+cLb9+vOSb21txsEMlVReLaHxyYTlQuekyVNZaDLsl4hChsZ4Zm4LxOjw6LdThC1kHgbfjSSGIe3z+4AI//vkUdR1jPOE4OT/CbKFg3Qm1U7gt2YYpUZzrVaztAbVQp4d2gjdqHEztoL0DGcBI1kIhDbgSqBigZbsOWqHFXnO0j83Qrm6ubWjW6gTMBCFkDJliiAGUovXZlTUDdwQlLTgF4GCg6ySwBu0jMgK4BUr2kkHEnEVgLI6iLn769W+jIQYjAhwNNrCRLbASMca7uyikQsYZ6s1teCHb/2HA2gdRaIcSDq0PHudgXCNUOYwQ7bP0bi2c+Hv9ytqXPnxfrSYHymptmkyt7UretI2RU6u6de2zcDaAvQ6ph2lNkxHA5h7QAPUYEEetufNqBXCBAAwQHqUkBKJCz88RsxJUadSQKPMOpNFonIBfNeDLJVjjYeYcdeOR9FqBZhhaNHmAeuwgdgWSbY8otggej9Ec1Zi7EIulQrVySHc9RvdK+B6DGDAYzyBSht2bMyRbDvMrBasdOt8kRDuEsG+hxhk2vYfvKrCZQpJxpBtAfMJw3q9xNfWwSwfihCvuYEKDwBOmLzRY6tC8U2FzR6MUBNpTkCmwSzHu7KfILjQOvrB49LRCugWE9wSiyGLxQOG4JgxjQqdO0Yw5duMA28MIs6jB5e0KJtSYkMVRpXHDhr6PkJ6PM9TKYk92ITKPZWl9reF8nrJQ9nyXdcCgTOM0/9hcCqq4vxMORcGZK9kSVldNxYp51Qv1pAu5Ny9Et+mWHyxOx4lPo2i6SjUGn72YmR17Xg6Cbpfu7nLXNZn4BUMVNNFi671Y/vUtnjyJTLpMSLiuABqRqbMamlyMrmBGcA3r6bWTaR7m1VmmVJCSCph0LooF5Y0TqF0FQQoODJUhOMfWaRvriR5zgFmfZH6Fc7e+tQghHO4FDpUXaCzagwtja0Nl4MuIQHIOYBVAn6MVB56gBX0WwB5a2kfzR99795XH3j+iXoG9VwUAoD/47nXbuglgBCABjATTv64ix0wdvmdq2vbMAPB34JiDlxyA7SYJ3tm7NfyL959tPTm6fNfB77wYX7nzrPi1z45O9h4dH3U+enEaNM73LMDgbefNGzvBIi+706oOrPNqVddsUhSytDYujFFV3ZAHuPW+TpVYGiI16KXEGShv5q7Ia+kMSDPHrxZLvL67S+/u7zhBjJ9eTJGEIT2/vKSffn5AN9MeIgN/lZX49PDQP7k48cOtUIeJZI9PnxHNCrelUu+I1aVZMnd8xsxFXvE0LUZPnhZdYrvlxRzlgwO+6g8eu+3BjrX+F71u/Hl8fvXJHzz7VO3L4vSXb975v4eoo2GqdotlteEIu9erNg9cNNpYAB0PxK4x0lsXRHHQjz1+qrV1cRqNj5+c/O6LR8dPf/YXH+Tzq+XXCLhb183t2axyn37eeO6NYaZ8rpTs20bby5MrsVgUqYEblUUjhRKrXpk1/NET2x905xt5JhQcpeSQG7s4+7vHT/qduIz2b0iRxix0tuST2Zn77NFZvsgDU1eH4Y3tAKfnIxycSMwWCpOpxyrnZJ2kFpRZaplhkoCMAxN6OeeKAKiqgXcGThCY4eACINm6e3hqRwbzoP0z18CiajWqNWuVt+uwztZnsPZAZeE9oSMIASdobXRQGAqMo5QLKN+AoMGYAq0HC963Ig9MAOSgVgTrgYukh0oIBEZjEibIowj9coVhtsJMRpj2uph3e4i9QVRVcN5B+RqyyVB0NtoGVebg3IFEA2/lOvN2/VNhDJ556OvBB8Q6VeO62REQBu1q9+8zkJo19y9bAFUGDNJ2gFJULSAUas2UEkBQtBs2WisZK73uux6oJfxFCTetwW6kIGEAb8GaCbwawAoP13gIaeApx4AW8DpHeRVDwKKuAmDRILAzUGPR7ddQ5xqV8eCNRnEl4aYabuIQJR6SVaDQIz+sUYwZVFBB3AzRuSsR9g3iG0A6cujvEAge2SWD0QKDdxgCVuPiE4lKKIhFiSpXoDBEctujObYIjARtMrz40GJVrJC8O4U50rBKwXIPPCgweCsA3RJo6gaNqlBSgN5bCkWfYCaAe2pgNoCmVEhGBJN7HK8qXHYq6EWFG99k2P9miLQRWB165JXH+WOPx88cummNb9+7gs8k3hqNQNpifjrF271dUFggVzUt0OD0bAGxUphlGkXtQDD+vV1FPufU21S0fL3x07PMVD1twALRNNqtROUOple+Xh6wmV7UD2xGP4ozddEJvLJKr2rm9qoQlE8bWc3v6DAZYVQqbutoNx6gZPWy4o497lLZEaUqUvg5r+J+vWrIuhDOezsPoR1tIGEKXaYgmYAWphTky1CNLLmkUExWVdPklbbQVqKoJOAlVMAgwSF4a9joKg94DmLrPS3z7UXvsE7P+FJR4eDgvEPhOVyF9nAivVsrlPByzesdQEcA/RQtr/cEwClaY/nrFTJ9/999uvqj773792Tur+o/tV6BvVd1DfSGAN5De/eNAbwLsLvwft/U0gPs2wC/D8v7cAqA7KJNdtg21rrz6bw3yYo7jfMbWVkrzvgbWZ1Hx4tVN2t0hPV0hgPNqtb9vCxZKKSsy4ptD/vkvRdZ3YTt+IN4ysgrwWXjfDir63CyWHprrTybzrjzjlaFw7xciWWR+by0Ig5DFochMUeOMe7JenO5GjNdO0ROUEABqdrVgyStAhY4WDKnB3NOc8fzEcOJeMZ2dId8WZAwFLrcePPRR5M0jAK1f9PNl9V5Bb45JVqFkbix66tVcGPnIVPi9Onp9O6DKFmolB/auo6yon7TOf9NMAq+8tp3DOCzzX5yu6h04GoTWM7Ia8NcZR+WxoyjUNXzy/k/nV3MB8To1nKymubL4mI6W8pilV+cHo4L3mR38/my7vXT7SAQvKnNRZY3lFWVDgWr4tDn9dF5eVEaP0jD5q3t7jAOhE/mq0WytzXZ68fhYLMfB4Gydrp8UTw66gOYyaPjQ+r3hmp/t8OT+GN8/HCJy0kOjyMMup+jqnfWk7tw/bZAK7ao0FqrlGi5dz0AlbbICUgFh4w5mjXQ+wKtJrSrLU4ADDkh1EC3BEnRMs6uQWOAFvQZC0BbBMSgLIEb54KqsbLwCKXnkgi+tlC8FXKC2sEXMbTbUIl11Ce1+Ir7GszolgpuGpzFCeZhB2HTwBuH6dYI034Pr40vMEliTHspmo7HVdABgnQtyCjbJRdUO63LixbIhbzl2GGtfjVrscS1BiMMW7C4nLdfEzFARi2Qq+t2+gYAnR5UAhAnOCdfZuhyDigDSAcyDHBy3Q41RFHCyaBN2DAeHHPIJPcujYmZBrGaQUYSmqUADGJ+hoAVcIhgkSJmOaoaSANACY26ahAmDH6ew7oAwsbQvQBkCX6lQUsPFVsEqYbjDDImLJ9ZmILDG454n4FZg+lnFp3bHBsdA54A6AtUMw+ae/hU4Py0g+OfRhiIBne+ZjB/KqAiCRoBi5LA3lSgiOHiqUVjKoS3LQzvgBkOm1a4Ey4xujMA3iKUEtiIE4yOBUgB9pYFRRyLCwejHdQIYL8JZN5D1zVIG7gkhNOEKhN4/tCj4xr0iXB8psBmwAgcdcXw+cyg3rzE4YGGmsVITIIDvgL6Bhhw/C67gzeqIQ7qKQaQMM7SSEX0hI1x3FkhGgq6MdHYjfrGem4xM1RlKymdwU6/hymP3FkSuVNVCoDMlMzDk9WsLDvm/LPG/ukvQyvHG3F2oxYvbs6y8/Np4z9s6mrVq6JLIy5441fjSqQuDRhFdGCNTxobGjcVMToVg+UMhns48ojIWCm85axW2kjbSImGWcQMIJKAJ0ilAcbBuAeYQ2009FiCPMCjNcAz1J5Yvmqb0k7uPCzawA0OcOYgPIGu/fg8vVTiMteCRvTR/uURgLP1J7top3shAHz/3316/ioj9x9Wr8Deq8L3f/iDIYC30TbsGYAR4H4D0K8BcgTwe2gnLjHAQoANAHQ50AmAqPH1nrXYT1TY99YwCYoUo9g7H5TWXTN1Odq2FwKQeVPx2te+MI6WZc1CJagXSeS1BWDQeMca96XBBGNCyCgKxWSZk64MLYxFrp1b1ZYXTeNTyWnQSWm1zCjPNH9+OYEn4P72jl7ogt+6uU1WO3DGLKRkh5Ol18aKrUHXqmFQBSOobb5XjijNaGVJCtGIIHBUl96MNi7d/buHJ8vcfbhc1EpF1XAxr48ev1h8oiIK3r6XiN0tq70jwP8z5/Fb4Gz3K0AvA/BIMNhRP/WLrFqB0AeBa3h/687OdPvG5o82h93Pynk+qXQz7G8NvFJqGfbi33n4ZN59/mJWbif2mSdz0xH1oyA4zLJydHZw7hmjkyQKynvvxn1wnejO9iC6ub2ZQ5lY8UoDh6lkynGhgl5nJyQn2NHJxWTh/2J2mo2Sk8NE6UqJO3t/x8Po52C8RiCf4+j8MTUNQ1VrAAkB76MFeTfRGj1fAvg5WrB3BODZ+vnWksNLgTPOYIiwQDtg+wDAQDt0tGunx5IhB7B0wGGAttWg7RpLtKd7JQhJ7hDUAFcMgogkeRAnDsnJaQ+59CDu4GQr+Wupba4dQUa8HUFKD0hGaNbtqQHgOEPWi0GeQXOJ6XCE88Emnt3YQyEknt+8g5TlKEPAIYFhrkWN2rVrqSgAExaeZAuyOL10mvhqQEZwfQesp4DFCuBrAFc2rQVLvgCbTeA3BwAYJHfw2sGRat0o+HqDZg0UKxAsZzDkW/4e8xCxh5AVbBMCtUZHTZCOPFWGg9dLSO4hRIqmDAFRASRQogMDAVM2SMQKvZGGDxnyxgJLC5corMYK5ZQh2CygkENKD6UsqqlFOGKQTYPq0uHyc4IeM9SXDlEfCDcIjgMXnxP8hgK7kWJxbFEcWCwOAdaXQMAQDwhCWohtieQdAXUnwPRpDXlZoJMkiOIIbsnQIYfRIEecEqQjaO+QXVbAboTgloDrNmhWQPkhQzjXWIwqwErIPQ62oRCf5miKEsE7EbZSATCG1aWBUgxhEuLpn1eYHRrcHjkkG4SF4tgKgdG9GFdWYDIFlucNomUPPerimc6xUaWQFXBjr4/dsy42vcQX+QyFLpFsc9+9FVK273zWwN/6YsN1Igtn8vrpRV4KZ02ta2Ucqm4nmi4585NBxUSYLKfH4yeLo4NlLAe3noZafjZfRauLYOvhWHQ/8RRsugv1oradkzI6L/pUl1KZIgvu20ZK1qEsW6ms4aF0EFuQ6IJJh3PRAKJmPd5w5SIH69BQarkSMKYGWYNIKljhEEoP5hl0wwHG4MjCkYGXqo3iW2c3k1sP8zhegr1r+94v/fkciNj669bj7Wv+ntOAv2pfV0jjpXjrLQDfQDt4+Bna9a4BcPGKu/cPq1dg71Xh+z/8wS20k71DtHFV94H6dcC/C1APYCnaFS//ynvyrVk7AY64Z1RYQ7YdnlDpHErrro9u14zdrxgtWdKmYdfhi5V2cNbDuGsHdePXpz4KAGqco3lekHYejf+Vx3MdxRhnHJEK7HKW4Wg2hebwN/qDerrK9axe8lVVem282U5SGo6685Ns0bCi4m/uvVazmskoDX3c6YNYL0BR1fT00IurGaleV+tBb1Kbq+TDk6uP2Fv7MiDxi88fnnx25EV/Y3fz7vRqea4lfzboJnFdV/+5dXgL7bwFaJ/w41CJsjGuWGZVAOAWGAWlIS4CwbZ6iX3j5tb5n/wvf84vxrP67GTyOmNo6rq+XTd2MD43ua7Y+6PU/x/dUe9vOLGimOeTYpEL7yiUnHkGGuaZdWfnqw/jNN3c2hn2F9lqeby01SrtrNJhR1QnF56fX9aJd8/ZoPvzdBTX4TB+nOjiY0xmhNp8hL/75CdYrErs7YyR5SXmyw44ewPO/Z8E/BjtgSBHC+6u1h8v0K7+P0d7WNhECwAv8XLyN0TLBQ05wThOCe9EmjuXMe+zALhkbVTf9e4zQnvA6HsPdu5hHWBSBq4ACjhjoSAmObgxIN0CO1LtFhXatxco44BYgy9jFAyoHTKsfzfCe6R5AVggSzqYdnoYlCUIHg/u3UJcLHF/ucDNyytwx5GFMbiz2J3N0MQJjAhaGp5n7TrVrn/zDi9XtNetyQKAaeeX1+pd5gG95tkFIXyUtP9hxmCsgGNBCxQdA6hpnQK1hm8Ijgg8IBC38JghQA5JK3jP4QoLF0poKeB5AxmEMCxG4QNAMAiUiFGDUCFmBpx5MMZA3qJ2DMIQFsk+GuPQmWdAmaHbbXBrWyPqNFgeegjn0JxbVFOPakaoZhxBl9otdVFAElA6hdkRgccMinlMftxu/aJ7EtE+obok7G03uIcY7DbB3gCqhUc19gi5gewL2JiwYjWqwKFgBLlFWF42qDjQbWosj0LETQDdJRjpkTUOl6EBBgLpWKHvBOyEoeICfE9CrxjUXyZYTAzqCIh7HPklIZ0obGxy6ExhNlGYzwn3bkdwbxeghKOTCXzrnwxAsUSTWdAd4O52igNXwpx5fHG1wrGf48ZOitTVGIaxz1Kiyxee3pxumFldZHOa0qn9K14sJ3IruFVvJNsIRTQ/inl5lV3UP5rPlvkS5s5qsHmSZ6NjV2ISVhcPp/GRcajDjt5putQ/LOLZeaeTNTsR6lzsRONq0wyZRCpiq4zEobmJsR6ArIB0RRDzmGmvHGtCrxC7heOYGwnGGCRxWKfBbADGAoATDDykU+CuZbt6b2EbBQQE5QnetnecWHv8/IqX3peMWN+iQXvtmWfhPY+bEo44PGMOsBpwy7ZPsE8AfIj2YNhDO9V7AeDfru+qY//Hf/ir8bqv6j+5XoG9V4Xv//AHA7R+aP8UwL8Aqn8F4A5A/ZZKRf8vSSutAsv+qvnl9Sf+/nt6+THHtT6SgcEDMM6DEWG7P4D3nLT1hJaKTsCXhPtrl7Jr0MgSydFoYLqcNs5q1niLjV7fh4FrDqbHoijtsvFVlqpQVY2154tleWs06EGEFBkvk+UqGj87c71e14eCKH74zMXW1YKg6LXbnUYx7X/yMUZB8ujbb70ZvHd6/kXwza913hDoJVX1uj6b0JWSV6Vuhtbp7wG48RXSsuac/Y21lnwLfPbh3MDlJZalsAv4pbDZ016UrEytlz//28/C5Tz7Ghh7fO/1mwdSsj8ddMSP//m/+tpf79zZPj345Dl3req18JydkOCrMcwaAAAgAElEQVSvCcZuE5A6jStjMMiXZaJXlYdGlgyiQsbB/unxpAgYuxRZtuqcny1FUU/w4LGRe9sSx6fH+PjhGIsswJt3/zn+H/beLFa27Lzv+31rrT3VXGe+8+3bI3tgcxBJyZJFRqRF0koIy1FgC4oBB0iCGHkJYiAQEAeRnAe/JEZgJDBiIAZiR/KDHdmQbYCRQoqkKQ7iIKqbzWYPd57OVOfUuMc15GHXubcpMTZlJC9hf0Dh1qmzq87dtdde61v/7//9/3lxnc/9/g0Oji3eX8H7a8CRwPWlR44ckggrI2TAi7TJ3YgWjeusHxPAEBFhdJ80ERrbrK9dxygV6SxZYl2D92ceuCntpL5Fq5BiABHBRpB0BckELRByR7ANYn07MApraSTQQRFCCwM0QGLapQiBuXhq5dFrZG+e9rC2ZgDcG25wf3uTGxf26C+XxIs8bK1K2VsuOekM2bl7i1piFptDuiennJsdcbe/1cKGQa9HJ+1lbwJEzdq3dv3Hz9ZAsx7RkWs7bK0CCkgDhHjdddu066SJHn9mRJvo+QgchEbwWQyJxaNQ1hIrjZaGSml6gxKJPZ4ELQHQWDQZcxKmaKsJy5zh/C5dNUewLOsus3ID6wzutMG5BK0apMyxjaZYdqjue1Z3KtKNjOJ+RXkSaGaKw9cU2yNHcQiSNMg4pgoGnQnTP4KwBLtonUF6Tyl02hby3CnEFxWZ11hTMT/w2AIkEvSukJ/A0TcquOfJnhRkJ6KKImw/IbpkSF5OkT1h6SqOHgKZ0N90LGeGsvRsXapJLnrKrYiiH6gKmLwlfPeLOctBoMg1ZqbIporRUNjaTFhcs/SfDVzcMLx4d4eVbTjfV2QnXfpNijcOY5ZMG0c9a/jZpB+G/ViOXcEzvTRk11IZlUN6eY8v3zyVeu7CubKuHtYu6oax7M+P8/t55d5yW13Xbeobq4PlKzYvXy9v9STo+LlFHGLVjVw1K4/tsrxdUHUayQrkblbWmZ/4rWoZp8Txxp5aXbBFyMsDPUTr24z1EJo+s2CkbHI6eNKGaCQajXErF9OEgFeKXGnyoIkD5EpRx1E73fsVpTrVnZAFUYalcYjXFE4oRYgt2CAIgk4B7dcbmoCoZm3eXK9hPwOqWXdlaOMav7c4co02TW2SEuTOepAX7faMbdrbt6Zt3OoCH6JN/G7+2qdeWv3wdejd+DfFu8nej3nIZz6hgE8Df4kWNv8Q2B0I3VYPQv0Ilnp/ItH7Uf868IP6sEqEvfGQxjmK+pHfjn18nF8r14rttH2PQUJwNrJudziISha6sEVwtZGTxVTKxumqUiY1SfzkhZ3Q7XV8WVBv9vqdXtSJxBhRliAnYo5Wx9Xq0qTpBl9pq4zf2ugv++kxG8mRLtRwvLnR2X7x6bv/9GuvL46WxZ99bjG7lefVG/sbw03X6xRQ/Qz4j7eyBI+Ava/6EP7F+hw/AFyiaRqZzwStp5M70cOuTmVzazD+0E++5/pqMl89uHt48+2bD2689JHnjn7+F//sa8+9/8n5eHv04eV0dVGU7Cynq1nSSbby4FLvuaLhJO3EoTfuxL1R92WtVU/HUkTahO6gezzeGlY47g8H6Wk4v3clnE5N9/hIKY8mL26yf3xTDo9vEUUxl84V7G1/Qb73dizwsXUv6Oek5RzKoUcfe66MFMNY2KZ1xthdn+sebTL3HtqEz+FJCET48AY+nO3YNwkhpqpjvJdHr7XR53FqpGlzNh0LUSSoOiC1a40kCK1mMQoyQugEJ1oEpSAKgVQJcpbs0TZsIO0Hnu0YnHdkgNYKnc841Qkv3rrJyDVy4fSI8ekJ3965yN3eNrcvXuEkG7CZ5wSJWcYpthO3n2YMeJDgMGLxVrd8PKdaUWS73qZY2kQPBaybOhZVe5spgVC3SV6q2m/eBSQqgBrVWELsW86eB1EOrXLECd4KzQp8oslUQRWgDh1cSDChIlOnaDyKQJeSuJyh5jOGI08T91j5EYmbYUtFp1MiXlMdWNKognEGnR763j5qllMfOcJxxeRVYXUQUZ04BpcCFzYC3cwRXwxsvFcxu6HIT8CdCnEH0vNgOlBNQEJgp1vh64bbXxCOHpYkuuL0loHcwsxTVRpKkIWlP27IbztcGqPi1igkHkI1b0eoSzXxptDZgTTyFAuhyRUyEDivOHnoqJeebmIYOUWzA+NnNL1LMHwCek8G5JrgQqA7Doyd4e7nLfO3pqy2azpbirv3G5oDxfl+h6eqiPpGIA5drqLljaMcP5nTswNZ7DfceLhs7tWlKhoXFsuyPKpPy9w17rwZ++Myy49lN7xZLvR37WRpfK8MRX20tByNpTnqGY5fq+XU9vSyO+6YhytfKh9Gq6hTrbx+SuFGJtN1cFqSUzcqbTAu0wNKVaF1Dx8WlPI2D3SPSoYsSO2xWrobQZgGRSQe08TEAkuBXByTuEIbTVFB8CUh0oQQqcj5sDKKoBSxDSjdihhqJ60kUCSIOOoy0HiF9Z7I2LXO0HrteFTnxYuSPE6lirKViDgQ2+LvYml92bfXN8fB+vEM8LPA88DDX/vUS6//SEvLu/En4t1k78c8fv0f/YMt4BdoE5EL4DrtzbmG73+AgHS2/v5/Ee0CGIBZnpP/oLfomscVdNuwGUShpQZJEbkwHKmTorSbSdefLhtVl2Cd10VuWZW+inRU+aBmw6RfDzrdYroo6mWZz5Ruus44OSysrbJUx/0NMV1nJ/qOLG/uGz3etvPD6cPNJtlaeNkojmejoXKdi/P55K2Tg9uv7r3BG/0nn3NJ/ykCXkR/IoRwpQztf1m3EgN/n/ZLfG+s1VUXgkGpfaJItneGozROwrnNjc9/5P2XV8Xp9OHXfu87U0G+ce7quZMLuxtpaFy6uTfedM7trBb5zvJ0ebR3ZWexfWHr/Hh7+MX9yfR3Yhu+dfnZi4w3Bwuf1dO0E82uPvUEIVC6xn/zw5/44DfO9eNRVpcH+3lTJ42Nxk9ceE1tjmMOj3ax9p5MTp+Rus64de8N+f71CXCVdld9HxiFfteh1XM9654eafpdxaX1eW2t/10Cl2k9lS/STtxDQBPQ+JDTTuRPsE7e158/AFIPulwXNt/B3IbH0nge2kSN0PZgiMZlESpShMgoiSNNFAm2aQjYoGMdIhFUy+ITty62NqptDBx4hwIKMZi6YFRWPLGYkzrL1nKJxzPfGJL6wE6ZMz49pTIpadNw0OmTRYpVZ0Q2nRFVNTZJMcqSmJImxGB0u9ypgJYapWqCNy2BkAblG5QEgums3TGkFbMxbl229YjJicjx1hPn4IInM1M68QKsIT40dI6EplsziGcoUWQ6pwopDkPHrbha3saZyBfSEYWjp0+DqEjytE/cDbgglMsMfbDE356iJdDplsyvN9DLiOuaKC2RoiHrekRB8TAiuxQwV1L8oWX0NLjSsX9oaGaBzp5idSBUJwF77CingumAzwOLex4/D/D6iup2w+yeIt5QMA1M7wGbMemTiqZS1I0hG3n6WzD9isMuPY3XVDMIRtpe58KBdURDTdAQvKI+DYgGsyEQayaveFTjiRuobxr6H3FkXcXoiUAniqgbz/ggxdxMmB2vWAKdNw2Xlp7JyjJtNOlBhjhhz8cMT8f0VBdbCTeXNWkTs02XYdRj5DOSTkffk5XckJVsJql6RqfR3dXdhcHwfR+S7Xjk53X9oEOC8SddX9/Li83t3n4nj2+cHpVfxL+12lzFLq27J1M/e+gHXe/1gKum65No32n9vSAq5Auz7UoVkbCE6A6bYQelI1APMXaLrmhmsdDEHXq2ZdDWoqi1YU5DnbStVKdWkwWNN5pGORYqY6Z8EMA1uvWhcWBE4yOPEd/uZKjQ+gjjPTbEeKsJOEx0NqWfzd+PnnmlQHCg8vXE34VH2kFm/RjQJnhXCGEXkT6Q/vpnv/vVX/vUS8s/xcLybqzjR0Bt3o3/n8fZYroDrge1aodF0qxRPQGQxBOqP+5h+P9mrN0FRHA+0I9jFnUNaxCmbdIXF0kWtvtp2OhvmFVZYJR2lzfGzckyV3ePT1QWx812d+TwIa3LRpmO8juDYUh05E9WxWpRl2MjanPYGS+VccW8maVpOjAxqTVJIr08k/2D72XzbBSaRJa+gzztQ2+7E4fVxe144mDR611Oobg1ChfMneUTaa93nhA+2ITwgo5jCCWWmkiyh7TacZcBTKSx3kdBJB5ujm504+ja4EnzVpo0v+/qPDqezC8ezVbf/0v/0af8e19+avO3/u5v96b3J1vbFzbfHm0Nr8dxtHf/xsPoQx//gHrq5WsPgKsmcPv29+8Orzxz6feeft+Ty/zVP7xi751ePtwZvYAPl/Pvvf2vxjrc0C89lfu3rx93jP9m74UnPmyunHuF77xuGI+f4j/4aRf+u7/zlpSVlZY3d8Jj4s02kNLrvsQqf5+SatppS7cd4A0ed+hGtOLIP0M7sVfr167QlnTfz2MT84rHGscKqCuIFu2g84P2GOFxUnim0yCpUDuNqjzSBEIUCKYVbDbr47SKTWh9M4SVR0dCQDBnrRsdj1qzQkPuEaUsqcDNwYiHW5s8cfcmUeNJSs007tBZzsmqilsXnyEpVwyqnDLtEBY1arjJsFihfeB+d4ANBu877Vhu1mdY5IiqEB3jBCgEJMYLJNrhp8eE7gq6WyApWkqCivEIhkBDj6A1lSHY+yV56Ep8zaKlolx6KklxqQTRTkrrSL0QS02mHFpqatHYqlHJ6lbQ4wEr6YGKMFpYTWNM39GJjzFDS0g1jEaEcMjm1ZLKC+7timVu8KZPIx2aWyesbim2dE3cnxM/bTC7MSb19HuB41cU+nXPw68LaaHYVHCwbJhaRTwILE402x/3VLZDNW09YWUDTmdCrWPkwNO5opGYltt/7Jgu4OFhzLgLXnuqhdDb1/RfBHUY0KmGpUb6gU5S0xw26EpYfdej3tdn+IYhjR3q4oKHYhjOEtQ3LJ1OhrneY/Lmiuu+wKUObM1QRXwk3eBCp8v9gyNunC556pznUpzyHtXFoMmD5dRa7pVLLqYdRhtD8Np7H+RciCQJifvG4qG14dAisfsDF2dXle7t+qYZab/c8RM5DgyH3cie7IzqNw/yC3fzLG6y0Hk+LNk4Ki+frNxuxdCByoCEAstcJTS8F2EbXNmP/d3FSTyhxwWO8EiYYNSzMoyS0EiHpY6piDgfNRS2wKuUrnYcKaHCQlD4YOj5wCgqWBDYwLLJCswY74XghCYNKGUxBBLtadQqLpulj0gsiSENgmsCOqoJdFGPkIHHpZ+yhBhBqXWbuuqv7+0eben23vrIZwFFWWaAIUkyRD5Na5u2/6dbW94NeDfZ+7GMtdTKNo+Ea/0RqNugngGdroGVtRNCiCBIqDL1b1+u/QGenecHVTjXSUX06OONyDtru7IVG2xt7RTMuW4ndKOOTo1i6n3oxJHcmpwkWacjRZ7bUXdQbo9G7s7piTJdrV+8dNlf3d49na1WwboQXNOUnU5kg3FCkzrdSFwFH+KhK4uy6ty4dxzX5ZYKRrl6Me3Hg/KpLxXL/Fr/0iTTWTR940AOj06vZtvXtnYWOxHddM9V5ZZX1TVFopVERCpBtSdzB3hh/biM9x0VOG0ai+nL05PZfB5Qr//5j35ASaS//rv/8mvHBwcnN/7MR1+e5YvcaqP355O5+sd/559FAJ1+dvj8h5+rr7149QO0wsUPLr332hPpqDeePDyJv/BbX/7Sf/Kxp07JOrtqo/+Pi7fvjj/w9a8M9dWNXT7z576p+l2bv3bTHd46SMbX735j79M/02O6KBn2D8nSu5TVES1Sdxx+7qe7/KuvZSjt2d1SaJ1SVW8gshlaYeAlLXqXSMtFHLN2XqH9eQW8RVvSdevXzgSTY1peTrJ+SAR1BnHymNf5DsLbo1YHDwQthFThS0c08ahxcLUVwSjlY4GA8iKEEGgqRyygjALbOoURA5HgJaBmkeDGfc7Pl0Qh4COHG/Wxkxkj57h24xaHm9vUieK54pjK79ELnutRRGVivK052NgkqLXmi61RUQWkeNuidOr+fVySErYvtDItTQkmAYmonIb5cQuoZzHoEh1aTekacAQUJUYsdbdHvFeIL2JENUhiQ7JdkOtEkFQWskkUKVakaKzLmkIXQXFfXyGd30eWTlw/Y3akQ29UwiJQFx2aqebCpQU68aja0xQzAoYq7pG/tsT/0ZJ6ArJlXOlFN/sN+X7Ezeuw9ZwQdRp848mrQDRWxH3P8ssOXQnOKVaNprYeHTyztyEawvxLBZcvLvEfHpJuOIbPCIeftdijOoyClenvpeRNymDXcvtbimJWsfPeAekFx2IegmvEH/6+leLziRp1NaehgQ2N2bSYzLA8Msh1izktWdzzDO8muFp487cMVWaZfV/Ivp9w+whQU+Ig1CHQT0vUQhg3gZt2TjfSjBxcLFLG85T/cPAMQ9HcUyfc85bzxvDS+Dz7zSl3ixl3GiMXXAhRnMqr+UzuN7Wyg1WU+DjXpL3fnh/UJkvqjbxcXPbLXrmqzKt+Yy6j0YPJ6XJjYRmDZLHaHM9qf/wmy60V2ZnDTMyCLYQcyxI4AN1f1OoqyHtYAks8iFYSBhfOEx1opKYwiG+41fF4Y7AusEUgQRgS8yCydFQDUlDimNNlA8EEz8p78tjRfXQHW4y1VBJhUt+xebRaOUV3TaoovacqBvQzIV6rs7yzIBSAWiCVDmsdzfVDURYZyBXSNOdMSF2JI/gz1O+sEvBu/FvEu8nej2do2mTvZ8B9FOwHIZxv7734zGZ9rY1X6dafKXGtTJ5+Z9J3hv78m+KMs3GG0MDjpM/jm3YqUC3PLUJYNG0ZNwXmjVMJqC5IbIzMVqvmcDaLKyXy4HTW7Az6MoqT8FS8qUe9bpoIq6fH29W0zKOejlVHJ8MmcpWW4GoTD0MISV26ackiG6V9ycPCzIr9laXSniTu6swPK2fMSWA/euDv6Fvm7Wa3/Mk3x6q/3H3hmWFy/1CZrJzUu9Ew6r6WHW3XZZnF6mJbVmrLtzlt9+qSFtXqKqVOAu47cRL/jDS2F82Xd5edzpv9fudONV3Zb37te/c/+ud+gv/zf/9cdufNe0Pajtdoc2/jqZ3L22ldNlKV9fDL//yrD178qednWS9b3T+dnb+7f1xx/zS+9MyF933rYHXjg6p6o/eBFy+89Bd+1rE62edbr0z5uZ96mn/341HCF95qvvAHtfruvZxf+vkln/3Cc9w/uMJ0HtHyY66GTvYtBt09nrj8Ar3eeT740hV2t4bkecTvf3PAN7+zxDFdHw/tjjxan3O2vmwx8LJrnc8nWljSJna99XVP1uPBQeuM2X/sufnO1j5Yz1Mu0Ki2YUOUoDKFBLCHacccqyRcy+dN3PozxSHgQ6BJHJldE+RM69xkZr3YN5FSyWmJ8YE7G7vooqYe9HjydMrW0YwYKPMlL+VL3jKKg60drh0+4NbGOV5/5ikshu0yZ3S05Ljfo8wGdFcrzh/eIpwz3BpewUca6go/HIPpgq4w0hCiGmNWSJNSRhk8daHlsiuFhIZGQOGJHCipQUMImn61ENOzrHpCRewTguqrEqctOBVogvjUEuFZha4YSme006rIWYVNqv4ew+WM0XYQ1whNrqmKGJETDlZbyK19RqGiDBrxDjdKKU8r7EFNdDXFztDNwxXTt6A+rUm3oLxb0+QzptKnWKYMnhbkYhe79OgKlktwGw3ZOBCUxuUBax13TqE4AckF5fD7326UfSuQFZUU+zbcOYwkecFy+pbg3w4kc0VRCbMTQ7m0Ur9Ryng3VvER3Hlj4SaidPKTkd80CyeFREeLDsMji74U0LcW3Hp1BV5RGUV12zB5RdHdLtA7lt4+sJswLwK6CIRxQ7WIGE5jviv3WEmXcdyh142Yh5rXFjlv1UsS0VzPTzgymvdmGc+bMV8sj+TACueUJqdRV0yfYZ2pe47ReepgQmKuL3P3OvSVcX7Asjwts94rb2JsK1p3HFb6eMKq2cfsNJgZqBSFw2OBnJwuLUViCWqT9r4p2zlWYoSuz1wymS6buklA8gZZNNS7CrJ2P3WshAjDBk1rp6IdCk+D5oSGjBRTJYjTSFrjdMmYQFWuVFWVXmJDlsl0GFUIQ9oGLaFuhLKWdgYwUMXrFaCCJFlbo0ErRunWaaBqEXznfCu+QMKZ1EqcJOvzM+u5YuNHWG/ejR8S7yZ7P4YRfvv/svKZT5wAm6BeAgYtVYoVbVI24NHY0Guqk69a3yd4x7A5Q13eaXoI/89JoPTTmFhrVkVB6YEQlAo1PqzBGxVhVNsuaYBhv0dtnUQ+RINOZldNrdM49iaOfIaEomn0bLnypSr8RtJRSRoHC/r8sB9HSRSt6jpy3t9NjBlWdbl1tJhFJtJ+ZzAa1k3jHzYnRVktzIX+UbdMXRD2yClUzpK62uek+LYqk4nUrt59Lpyr1cVRfT9bbKQV3Y07dcLSj81oe5SkqtXSfRzfto0tgPeayJRacbfb6RxVblEHL8XKeV9r87kg6l9cPb894fy2+19+829MLlze/YmjB5PFnTfvLQD/n/7NX13NTt54kPWy0dc++83F/esPPnJ6cNrJuik6Mlecd9/od9PlxnOXRpt74+dGdXGF23euUFUjBr1b/IVPHtv/9m+n1d2DjbjXvbNzdLixs90f8ZFPDuSv/deLkMSbUtUntM0Vm0HJKb1uxuHkGZ68+iyzxWWUnONgskmihaISomQlrtoObSm3oU3gzrrkFBCaQOUckQvMBJLYoI086o04K+PG6/Fztvcv1p93Jr8S1o/EBcyRx6SCHUm7SYgVZdk6cOidKlexhMT7EERC3Sjt7jvCwBOCwnUdjWnZgyZd1krJGjroDbE+RnnF3oOH2ODJAiziBCVCWpcM5sfcHyTUReBnX/kmSQi8tXWe0WrJ4e4F+tYRlUs6q5wL+ZRi3zMJm5yODdqXxNoBFTUGE1nKICg8Oq5b4Fw3j76OUGUkeoGKLYM8h0SxiDJyN6BGY4oGmzWAqMpl9OMjkphgdEdiV7PP0FeIh1Sv1Fh1mROtcprQJ6iI5RuO+MkEHSeovmXUXWKGmpNJoPi6Z3FgCReEOLKYnQPcSRkkxLL/qkIfV+y9AOOX4fh3IZyW1NPAxlCzsIH6NhwfQOdqyeYTwvIwoC2Mm4rmyDGtYtK8YEKPeJxyahPU64K1qObUBNGB3bGR/WUs08OI/mWwswY7F7wPzL6dO/ctx+aTSq1uKuVvrCgoKTeCzucp4c1SncbBlRPn6RWqXJYcfCNivOPRBBbHDuujtnFmAGUO4+3AC2PF/QeKO8uaKdBuNxXPYbHWo2l4tTrh9kHMN6sJb4WGrih+JhkwE8er9Zxh1uWnTMw4yrhezGSmHRXijHLhpJYgXsxdnNtSYmd4e0jZ/aqN3XP6aVfrNJxztve2W5kAISU2dAcbflWmdcud1njeIg1P0/WBXK+7e9jksduMhkbDvCZ0NaUzuawanEpa8F0JJGt0XbUNUA0Nh5QEIhKczlFuTo1ixQM61JEwxODVnBvc4L1leb5zd2MZjcxcOhZLAHuBKGwgrcGtTkSbJoeq3d9Xar08hAicJ8sryiQm6LNNnQA+Axyd7plgUULbqPXHeUMCPC//xT+S8D/+8rtae3/KeLdB48cw5DOf2KZFm54GGYDZBH0MYc3bE71ug1driRTbttCLe2wj/0OlVfghz38gYq3xAfLGtceJENAevEKUQ7RqQqAvipJAEiu09ahIi9FaV7UVjVJl09T7+dKOk9RdHm2q7f5Ql87aXhbbi/1RniodzesSY7QMu+lgnufDoqmjZVWqzMRNbKLiYDHPnDRxZ9SofoitMx2T0AVEju1UGkDFNY13qvKNWWUmzJpT90DPm3nTixvdvyR7m8ODaoJrNKIVogyUpTfl6T+Y57arlHreRFEaGd3ZHPenEsK1ZVFFjfMzlcR/92/8Z3/x+7QK8WYw7DXAtfuTGV/bP7j11/+r/7ICSLKm0FpPf+c3Pp8Xy/IEuPtn6tNOmkTPD1ZL6b198+X+pa33XPmjPxr1n33imBeePSWJKyCm15GDjc3prVn9Uwidwd7W5/jAi0c82N8DOvzKL865sPcmcXSFT3/sef7Kv/+Qn//oJpH+d3jvs88goeE7r21y+86Aw6OSvGio6pmU1WC9gryz3HpWktG1w9WW2IGKhI5WrZ4xLerX4XGCd4b6NuvnKY9aVVnRooYZoKrASglRFJwKPqjDJHXf37qs+otjGa8WKtFK1U0TvPeuiUysNNHQYCIDSUwcQ2TlkYEZFkiso1zlbJU5PR9aLX8Fk96QebcHRUGdJHSnE5TR7JQNo/mMphM4t5xz2B1QxqC8p4w6HI63WCRdrDdoqxjmBbMkIysaNsslZZZgfYxXBiV+LUO7JveFAOR4HRHE4ySw1EOauofSDaJqnIvAOHrllGhWsEpSemklpaQsSfDSXZ/hQnWxJAS6cc1wtEIqiN48oTIROtQk8YyezilLTfWNBW5W46NA93KMf5DDQuNulsJuBGkP+9bSu2PETz2ucQQ0NorxnYyeseSnEWSe3WHFEzuWu28bbAW+ipg8TFgdt5SupsxwtaIsHXbh0d6H8qYPTaGFS5GspgatA/ZEqG5Y/FRT5RH0ne8UTjUKlnNDUUuYWyPORLiVYBeBYlbT1JE0y0hWdbtnKFeOstB4v96TWiFKFdmeQGlZWcfticZj14JQPTI0ozSh9J7bIXAIFNYxxVMKXFWGTVdShRn7RGgrnISG47riJOTcahZ0okzl0shN55QjyATXrMLxcYY7djQ2pueiSNLX62J6GBrVwAhCPWYxWTV+sEiUpqNiah6AH6ikHnR92a8rMaAP1/dLy6tWKIIFdZQgleCHAYwF07Ra5Vmy7nhl/Z4aOCJmiQOGKIYoDjkhQ1C8xlLdIDILOjIh59TIdZkAACAASURBVD47Krc9ZVdpZwMJdXRaHnsVX0JMhtECSDBanI7o1BbjAlUvWgN4DrylU9dUrSS6RxnfSrKENvETNBLWIvzyw9YQB3wL+Nyvfeol/0N+/278a+JdZO/HLNZ8vZeBX6ElwQpwCTgBZ+JBEep5ttZ/wNOiLQKSrofLWZn3jFx/BrH/SLF83GV7xstSKK0JHQilJzhvlIlQCpz3ZWHVXq/PyXIefAjMqlK6ygWlpU511MSismVdLXcGfb1lMmODY5RkfYzSI8LJ9nCQlHWTTsvS901SbPeHFE0TnS4WRgdCL4tWo7RjKut1jFFRpbzSudpItrwOkTq1B2T1OWJJuZMdpMdHNgwHe51ltMhM082aSYPVPbRXKG/B5xDskZb8yoXL11K30lfyqrE17uTuw+MbIvI8sLhyfvMrl3Y3Hr7jq1HCuXqZ37z92tv3dmnLNDnAL371whhw/+xv+hm/8Mlp+Fu/GvPwIOHLf/Cl1+auXGidho2+O//KG0RHJzf4j39Z8z/8vZwPvWx4/unZ3v/8v26fPPv8t/XWxx2T4z3+3m94inKPNBlybufLXNj9CZ64/FfZHG7hXZfV6oSdrZcYjTNeen7B27caDo8dDw4KZiuw7pEG3noM1KwlUmjLLE5rGiMU4vGxQSuhsz5+uj6uvz7vQKunNV+f8xnad8btWwBOCXVPYwP4oqhd7b2qu1vJdGtXDcolu0gIwYcg0gStJQZSkCq0brXRWrffrgfumQpK4ixP2QWZtL9fZV1Osg7D6YRmpZgkKZurksjXSFiRRSVfeflZFqMeD02XRa9Dp3bMsgRtLVkRqOMus96Avm8wpYduSqMcuY8pirhlQlSOKPWto1qRganpznL6Ycb+9hhLhjVd8CXEC5JgMSpmpTXJcmX3JguzNF1W2TaFSzFJzljmnPiC3G6rc+UJO/Yhq94A3whJk2NzRTa2vN3p48gJd2tKHZBiTjJxJFcS8tdrVt+wdMY1GEd5y7F6oyDbsSRjJcF4mrlQK0WcKLIdsBNHk2l6T3gW37FMDgPdfYucRJSpQXUaqqUGE1HGMRJgFC+pY8XqACKjJIgIi4B8tSCvNGkX6irx2B5gFcqiTxs9IxEenu0R1nuNaYbG46gCRIqxFvIkiKkkrFw7VP1Z/06bQ1gfyPc90QIqDIEcMFyIemjpcLNecb0sSIHFeppqgKeThLlveDbagHrGRS3kdcJWlrIsau6HJRbPM3oQRlEq36lq6QUXKnxYUagesRXiTY/NHuKKg8RHvlYGqmsYFtg4zDFDQ2X6VdksbC9fEz1HfuX7S/SZo+DOeghPgQxPDUmG35W13k9BkAL8iHbTVLTHlx0IK0jnxPIdBsCE9+FJwkMcMfdQvmOw+WhZbSyK7NXqJXPMBsdk6kKRbpwj1AMJy8VFWW5Vi8w+yLr52i5NUMmAOBUf1cQmgNRQO3BNIOowdzZQi6JyQhZr4rXeqodhDrM0KMyfAO3OON9na1dKS495N/4U8W6y9+MX28DP0/KtzrhRMyAGrep5ZkHHvTRx5zfGi9uHx0vjbH8VHs2UhhZmrw1E9jE6806fgDOLtB8lHC0rCZSYCO02Ox1iT6CwKjMao6DwjkSU9NM0IMrldZU23iM6ne4MBuWVzU3/8OFEn+sNtpIkNsu6Wm52+5kRHRdNkw903A8Ssn6cLXpJWqyKItaSrhIf/+H0uLyWdtSe8rjKSpT4AV3Tc40Uql+9B63vY1xCQU6VFcm0Ph0l5aW4ikzbMekjMIr+copNEtx29r1huuPqSn0AZbuRU7MG/4p1/v1469F68uTl3VsffvFpL5wLtNw8ALqd9Oarb925+5//8s9ngYc7tC4UA8DxC59cAJd588aYX/rzG3zyo195oU0ID6a/9NfSTlGk4W//N4HI/Bl+7qdf5e79W/zq3wrywZdefkHbh9y8rnD+fWTpKzJfvhaM3iVNIupakehj7jzIUWrOoD+hqt7mrZtdZvMrzFYZq2rCZK6kRdlWtKVYRcsVaiGptpzbAC4WDoJiWECKp+roR+Zh78y3fGgfVrVoH+uxOKNNBs/0fgJQmVap1aksiyO03ipr+cBb1+uoOaIBp0Q0Sru1xHGIoAn4zNe11CKkSUwi8ogs2gXmAj15XEPWvmKQe7T3lP1A5D2CJx9tMR1sMsHz/pvXeeb+Q7749PtpjGYx6OAbwRQ1W8uA0Y5lFJFYS6UNKCgzTRkZMJbNcoFvIqZ6TAgFHD+AwYiusyTiGcspi2Cx1Zg0hoBQ2RGVUgHlfZym5mB7SN5oz2yuQjPzdhyrRK0YYvGmj4oaklPH3M3I/Sic+rG4JmbZj1Anc9AVShwPv5XiJp4LvRVpMyXegv39wPGBpvvTQ9ic4L/kqaYNvZdiUZVndtdCLUQ7bS5algYbG/S8gUxTFJ63bkdos4IF5KSAR9c1aitifB5Gd5YczbusQkyx0BAcIJxWMYLHrs6a8Gv0liBBUU2MPF6yzq6ioqJsXxrUAWcUs7blWW8pbG0f6fVAm7J5FCFJ0UNNufL0gkP6gp0HZk63PMn14KvW72I9Pt6oKjTw1eaQSDznJCGn5KHLaKQhjRJKF7hR57JazjgBnyHBgaQoe4hPIkwc02SXkShusuIW1dxj0t2gq0PYJoq3m6ZZashwWE2deupOwGjQFW02dNIO4XACstPO3wh0zygSaWvFElx7OzAh8wfY1YimsaDeok5uU9Eh4JlzDsOrvMwf7THdZCV7HQmdskzq6gL7OyfL6snPT+9851O7R0UdD0LZq6Zdpmnl+3hfniMvGuT8MVJiMWUwbEYrH2qdT0OTgVHURWh9d1XAOcF5qDwYS9QE0lyY6dD2CP4grvdIp49WCeDDwOf/tavKu/En4t0y7o9RyGc+kdJ2hn6Ydnc0pu2+XJfO5HTdM2GMdjpLdDTL54n12vjHZbYAuN1BT1zTuCY8Itm/s5z7x3h8Z82U1Tt/dfaibstXDVpS6ZrYXxiMlVYh1HUpl3bOsTcY0dORHC8WTiHinXO1d9oH74dRnD+9tTPa6g3EK2Ybw6EE7fteJGq8w6DiJDKyMRhmRhsa29hOnEZxZLra6MJo1VnWRa+jOmMJ2jjnxTYNIMFHTiWuyzAfECKPacYQUvE0psdFUSQo4vYsG6FxJaZr854+nZo02fanzZOVb5I6IqKqQORclOcnm6cnKvHlyZMvPPd7WudDWGawLGB5AdAf+9DHV1laD//w9VvXvvKdN4r3XLtw+Nr8q/O//KUn42BMn3/v4w0X9m7fun900u9lW0pkL50t+uRFzKc/pomiizx5+Sbvf3GTv/yZkmtXLlPVA7bGM2Jznht3j+V0pjHReX7yfSVFfY63bnSYTE+YzZ/h4cHTTOcXeHhguHn3HEcnJWX5VWq7uU72jmgTVKHNmRoe8z2XtIlaIpDWgVIJ12Phsy5gSs+mC/SNwgOrFSxXgUKD0m0edsq6FLweKJ3187h1QdPBqixOVU+r4Bj5uY4IroSyTrtx4po4aTkHZ5JCOjgrTRZYStR2HYVWr3ilNFWS0bHN43qY8wxsW85LalA+cP3CDg+ubrF5OEOFChN7pllKieFgd4uj8Rapt9B4QtdwcXGM9C3OwiLrYiONdKR1v5AG0YJC6JVHZHnFoAgUSUoZJcxHEZHCO2JxukFXNb7JvV9pQTlhkYe6K6oOCf5ES7eesbG5FL/QDBczbDagFqirBHfcoZnkpCsv+2GPyBdk+xP87RU4TzzNmfxezunrK3pNQdLz+K5hfj1QzR3l3Rp90pCMAtG2Jn9bWB1Y0jEhnCJpDE0FzQLcDUBrTCfglp70coTECruS9SU0BALSCE0pHB8rilXUikrnATJHsqdxkUCh8I8q/ZaQB3yheJzondE8z8Ce9bRTKaFRxMExDBWrhUZ5t07z2vc+wox81H5m5ajTgO8IIReqoNDBY3HQD5iR4FeBjAqL4NaJ31aU0Q3C667kHgEbwGjFvbK1ibOh4YiABx/QWhGYI96gbYLtGlw8otBTq+sFkgaUj7VsKF/6a2GiHOJm9GOQTmgtJSvQNSQFqGkEb3sYgh22DXTqjP5Qr+8fP0hduTvyy1kuBtzJXljuW+t/0+r4FYi/CuxdCtN9E7k/KNK4YIevk3A0zOoHRREtJovu/9Gs+OL7dx+uyjk75yZ550I1+9akn8Reyyrf6r+WZ2ka4mh/gHUFJmpwq2D9YjgrF9MQV9ZVuSlmidNdjQ+KBCGSIJJ7JFagwAV8JKwS2ru2tuuGpXdgUSGcVXYTYPzrn/3uP3nXI/dPF+8iez9e8QzwKVrts03aktuUFkkZro/pQJjnVTW8eXBYaJWKaVt0W0eENdpSNk2T+5DwGJE5S/LOfNBof3a6fctZGSXQzkepYo0CAQrRLgalPaGbaD8a78pGd4g4z6Is0drw5Ma2rr0N87y0eVPrUZQkCtW/v1wk3SSTJIqM9c4k2rjTxQIfQt4fpzqNE6NEQidJDN53dWxKV/ugQuhIQmcrGnRc5YOIajppRlUUkfHaKPohEkQFQ9SAaE1MtW4mUTRYIjQNFTZxZJL4uKxQx8355bYbeF+nk9mxpHQjvaqfoT+Q5iisNh/GNzbi6atxZArO9KV+458qfuUXo/UXxW/+yy/nvSx9+rknzm29cOmvf/2XL/33R/DJhOeujXnyyq7bP5wurt/94A1rV09fOfc2f/WXOvyVvzglSRLgiP/tn/TZ2brIpz8W2Nu+wfndI/qdEx4enSNL3w8csTkSlDh+54sd5osdrlxYYO2cqj7h6NTQ1Bnz5dcoqg2WRSVwnVZOZpcWyftD4On1dazWY+B4PSZKLUQjzWx9jp+24GYNobHY7ZQ0M2gD+sQTL2B+WTERIV5/fgMkIRCL1hrvFHqkTdrTbnVf2yavidruoQxso0jzxIopkaZt9RMdMFUQdJziaTB4qneIf1VJSpWkuDJ/VJNOaLPW406fpFqy6PSpJcZqj+o1RAZubmzztc1nSESx6mRYo+lWnovTJXe2tnj9wi5sGphbhnVONVAMlwt80VAFTz68Sj8/4Oqd73M4vkIjCd1ywaIzQNcdcp2oBkOsKqp0SFJnkqhJSIuC+SJVaUf5SmLlg0dvxZQuQTcW5wOj1QlZXbOkg9eeJBtxAkio0aFiXDnii5bJoQ3RQSnbQbOsYbJMKPYD8Zs1vSNPejVmcttRL4RICyoP2NKidCAsRNwJzE8DwVjwGpyimQFVTdoLdK8KyzsZLfC7LqVicKXDlaGVntEKGodQo6zCCUhXCBNZX/5m7bvqeCxaeFaAP2OQnF1NzxnaVyNYYhIaNqOK00GPYgKPmZoCZUM4k4csHa5sTY0ThCuJ4UZVUS4cdhFhsGgaeqRcUCmHvuLUW5YoFJpNwPuae7Wjg2PqNbeC91salRL0gbMBSqE1EElTbHyIhBytStX08EkKNCeJD1nwOneqbJCs9c3TBxA7iD0pF6nrtOvDYTdLmsOCFW1jVfOOubSk3XCVseHtnWG4vz/lxGdO+p0yotf5+n7UN7zJJFbNyTPJdOvWxnB50u/9T7yXc3yd9P7m8IT3syDhe//w7//z5ndfr54rdy9FN566tqk68umLzerW/VFvZw7bXqs3gWcfMDwHy9NL5L2pSw8iUXm6nG7tbTZNOK66bxb5qhgNx0QKlA+RF11LE1oNPxsgk0eriAKs/7/Ze9NYSdPrPOw57/Jttd6qu/ftbaZnenoWDteZISlRtERKIhVRmxmANCNHgmEjiIUIQSDY+RErQQBbgJ0wEexYSQQ5FiMq2qJIok1JJkWRHM6Qw2U2crp7ervdd7+39qpve5eTH1/d7h5qAlOGYgujPkABXdVVt5Z3e75znvM8QJkBcVyZJh3Tf7QClP4ggP8YwK/jXnzHcS+z91ck6EPvuw/AT2FuRo/q0KaukFcUsykrDogG0KpMOKUE9F7AmBbVDnvMsRoCaBTWiXoQBsY5gTtZmLtFNOdlX+KKW6NR5VS8B7QnCIqEYs8eTMQaUgcqYKkEmL0QJISxDpMy4ziMaKnewDTPmIRCFIWqNCUWG01u12LtHee1MGYiknEQ1hUJePa5llqGWhlJpEtjvPPOE0N474UgUgAkgUJbOpE758TchTSOYwRhAIWAiDSgQwSiCQGCQIy4aEIagBwDisFghEigZeCXQsHt6dEgs9HiOA5UAEtC1hxIUmphekM7nrW7X3j0Bx6/tPvCJbF2/jThpYsNXL5+Ab/6O5foAx8dAcC1rWu0sdIRZ08u74WBPpqN9uXhE28/q9N0UVlrJpuXlBkP2o0HHxT1JDoHKW9AKYf/5p94bKzFsM5jMMrx2PltzNIS959ew/JigMPeaTBHuLr5TfzC338Rs+wCXnrFIy/6MGUHaXGEazcy9EdXMEsDHA6aKE0XwAIBmWN4QZihOjkXUZWPGqhKzRaVgHKKCvztoLI8YgDWWdQl0FMCKlSoqcrNzGjAxQQKBZbmf8sDOCjL0heo1VTzBMhOU4RthWCBbJEK4lJSNWCsAFKMkIwTJlaejL/bqwlSEOokEYDA8+qfAxBbg1qeQc2dzHKqTkwBgokjoK5ghYRxAu1ihOX+EDaXmIgGjhZWcdhsoujUcOboJsgZaAcsTIboL3VhWaJxOMTBwhJMGGJhPMZ9O7toFR77jUV474CUQY5xWGsga7aBwqJhMyCpFPY8iJE7E08LEZSWXUxo10vUXc5exmQ0Uf1giNaggI80mm6IJju0D1KMVQ0JUnR3hjgIViESh3B4BNk3yOoa6qCg4TXGhCVEINA8LyEaDuNvAKNvAeOZgCkB2mYU2wJlVo0mDwn2ENUP6ACYeVX+WBjDWwTLhHRHodwngI/tkI8zcxXgijcIui5hxxKA9+w0+RFVl523Q9A8c8e3Te+qx+dbTACBAvwaZkDV91NAgFHZn6VlAPi7G7+PYf1xX9GxLaOCg8DUG+S3s4YBPARKKHgIeHYYwCNlg2mlN4mWEMi4IoWGzAhVgJE31JLEdSGpIQjegxihOIma3wIZD6G6SLhGys7YpoCowTiz2Gaz24z7mVcBrJoCtAuJ7fgkoqCNFWZHRc48s5RVrAZZAGIPVZV5E8DTqFxvvvzkB+zvrL/TP/fNZ8Qr6+Use1Ltvdp/Z7zXe2vSwDIm7p2yt/lES/XP1XI8gB30McJL8H/n0tf4x3cu9z/zxb8/eNPP/wT/d7/xKTFYrPPOidOXx+14Om4GV7JYb7IQQ1Qi7O8CeK2Gwq6roiAjP9/uF71pPTrhtNgrJrnsNToLCFUCYw109KojXUAGAkJqSC1Ac9JsEFZFH4uqCi1VldVz80KQ95U1IdEH/9tPv/wLP/+Dj+FefGdxL7P3VyceRrUwc1SptRoBYRDIt2e5O87OHWufTasuKdSKyiQXiRDGeM9D4OX536p55wwBU67EdeeCSvNeekUitxaMu8svoKq6B8uACyPtVhvtcPuglzd0IOIgxHA2La4WuZ9lZbjUbNKkyD0Yoq0D1MPQO+9BWtGp7pJYqCf5Yq1lDifDLFSyweydIHZSK7sgGso66wNZmTcKIQwYoWEva1prIe5QCpXQXBeCAIgsy0Bctbd9e5AJAGch58UDoRQAfZwVcl5A9nwoh0v3r44mU40yB0oD2JlASUEcx3sIZk+fGt7cmnw1vWCNW8ek/xz2DhPE4QilOe6Ww49+7w8XjN1voLIXq6e/9MnErq/+NfOWR9MokBd/zv304en2+d5/3f1xi6osfz+c07h2s4dPfy7Ez/zUDoBt+lsfXuCDxgX8zE8d4P3frVGUVxCGj+Mtj34/fvnXP4Xx7HFs7wQYzzIQCmJkAE5wBd7eimrAMgCUW1DBWIkFOJA4AnCAqsknQNVgAVRZuRqqDN8QFdjrAeDM4UdnDsGJGi6iarwIABwmFcjLLWO5dCgg8WoSNkpKFh4W2aAkoS1USFAtT7MhB1xASNj56S9RHRMQAI8jKTuprbyX6LUQwQIYtQL4iUEdDAnAKsDZO/CBATjJaIxH6AFokICpe6RxAFkKuFBAH/Xx/ekzePodb8XCUYmkTNGNgM3wLA5aK0BOOHlrH4oMXO+KK6dNmbkakkkK1QBak33ExDg6tQIuAVeXkCbn0FkSlrnsEblahKRwpNKJvO/w0O6vN4OeWIJUQ4gyRVfvIfAKS1f3MesuQOU9bPllRKcDnFzcQVrmGIwD3EqWkaoI4tWbyMIajtbPYvL0AS9+rUfsHbjbQD20cD0guw74ukC+Km63y+iWgI0IVDKCBcCM5u7Ut5d6CAQE1ARwmIICCVVjFP1ibnB3HHevpxDZrqsspJGjOtWPJRarW4IMKcLj4aXXqvMeP4+h4bGKHD0AJQLIuITWBcbjFjwMZqwBe1xskLjTX3ZMOb0DQKFLwADZ3O6wYrQcfwkFi0osku+iJJcAtr1FDIc1pTEsGQl73wTIWqZdlLysYgtAK3h3iDJwMCXAvI/SzcsE4yrVKeK9TI5Qox6cSwHTAoJFKAxkjGi2B8OjwM3XWAPASwCeAczJ6qJafQHAyw8G9R+/r1a/dObD13ZfuKlOPfTAUe2Bg8Hh4JG4+e6XthavmoWpuyrP4Qt41f+kmEHCApjxb3+EAYw+Qb+/gaOKe0M/+0mBJz8g5+v4EoCuKHn55/7vL2e/9P431waNcANSDQnFpC2YbtnWDTD+zQcuXrv+B2+9/7mbjQUtO9EjLIO/DmcNTQYZR/EUSSMEcIR0sgOSDTA02KZwahMyehjx7catDEKeQBzX4b0EWMzLuV+9J7/y54t7YO+vQMw7cLdQ7U0LqNL8CQP7gkThq5JuBiAAihyga0BwsroPIxR1nKeelHImnCt8tQgpdTZBld15zTwiQLQaCWg6m0usvMZPtxJSBliUprR5MXZcHmUln5WATdlHDIjdIiU/dJiaAkxEm/0jRHEsu3HiF2tN2puOaZaX6vxiYhpRnGwP+mFvOjFSSbMRRMILEUhSVkmJmFTAxMHEG0qLnOIwfM3RIyugpwFAQoDE6yvH0DxxcLunT97Rh2YI6TwwBYyeZBFyy4ikl0GH3Xgg9O6BFZ0FrNY5ilp7wdLpR66v03KAvcOEPvv03i+de9P277088Z8C8OyLX+jc3D1aGoxnV//2h79vACBb/tMvUrq4eCX4vp9jrC1POy8/uP2T9/3jN6PKoj0HoON6R0MeHSr18qXeSz+9/nk2aMh1fOYB+dTvBdt76/jdP7yGE6u38N3v+AasXcYrV+5DLd5AFH+RxrMRGKdxx8bsAVRHPqMq37Yt4eLQ4+mI8KGZwaISKEOJMXD7VM4BfBWVPdwUwC4qqkDdM8ZSIm9V+lolKoL1EqqDqwVgwRF8wTCKEIG00mFYaLQs0l4CqS1U4ZzrBSkblkCRUCXJcjzBlAc1+sVrMcY8GECugEkrRmQYnBqwANJGCCo8wtRgCsC0JPJIoTEo4GSCSVmgPRnBqyYGS3UMgwRjneDwRB2+WaJmJ0hdgvBghiXahztBiGb7OGwsQiHHudGOLNQIh1jHrFHH7tIS6mWBk+kWjmzbtYZOGkWYJBVrsDXp0dX4PCa+hLAK7ENZxCQsmDVSIPV0eBAiPjhCVG/ALC1DnyGY6wbIFc59cxP12T5uQvuF62ORt2OkCwb+agG8pQMih0A4MlpgOghA6wAaNSQETK+NMUkC1MsCNgPyKEQ2ARArSGUQdQCXA97c/cuWQC0GjANQgssaJlcE2GXfNgL58SqqRsMAgKtA4qxqggnhEcBijAZYCkTCIr/dXHEMxY+BVnVdVCBEH4AAVx0KyiJJCozHc014RLjDMDnW/pbz+37+GKrHzGT+f3NXP39s+gMcl5GL15lbSjgk9Ql2Jg5AgIlNfQwnlmBBQF5YGVAlQGL7gK9SV6GoumYtzYWRp4CXyAWwb1fvM72jLdRdCbWAQmxML5Ua4BQIb1bMBT+BkjuwZngS410b6sM9NF/kAmZFx5+jJ2nvnz+XBAAOzz4eiPCK296v1Q/Uvju79Gpa7FHj62CUmFsj3g2cPsa/tnXX15PdcSp69ShtDPcvnLn0TLnz0PcOTw6m0fKwP3NZeTWtL2wlrdrT3dAmvbx2I0/U9V/64NsdKp/sddtafBnABrxrcpwIBFGEqpJ0hCAazH/wHbD8I0j52SXpLhw6uYuq+nTMAXLVJV2lKcgf/4jFvfhzxT2w9wYP+tD7JID7ALwL1aIqAdxoRmGshFzeTtOBqqiw/Vgi9BRmM2vOodoE8iYgx5a1hDsQRIkHHkN1gM9Q7aLxmW5b9NMc46za0BnA/mAGfo1PzmsiRtVkSFvDkQYXHciAjPea7+rkPTQFFnQgrbE4zA032NlASOExFlmemiCI1aQsQkXCzIrchFo5wQLjsiAtpAulVFIpCSGFZ++1I2pEMZR83c8EZyyYuOKLBH82syekBJy9owjMBOTzQy2Oq6/OTDwrKCpLKsMmtBLkRjmTceOBk8/EUZz05HIn+uyXNs+N1KKLI9/75f9h2vnDr7znQzsHN/gT/2yn9a4n1hu1aHnnYHCDsNabv72NF9vPoN08AWD8jx79yoyxu4tqDT8O5zrZF/9oq4x7dDn73P8Yeaw5B9At/NC+3N5Z/tbFsdJBWw7PnkcUXsS5M5/Hb/zB+xFHM6TZBioe5x6qQtpjdCdb1wbwZgD1usQDdYl/nTqs9R1kmzHRApdFpZe/g2pjHuGOvEoCwE2t2n1u2IkebQwuLYWmg+oEXkSlL9hGBSbrIeJQ6KwhgTeh6E9RzBqQzQBSKRQ9oJjm3lsx8QgToJHMfVeOx+eYwfV6o5ujIp6ubo8gjQOZEohjRIMCMwGYdoxklMFOHZKxg2dAxRaTpRqmCeGo00J9mKLUQNDSqJkUy5deQegEnr7vEQwX2njT1et48OgqdlbXUTM5GyloVCbYW1jy+8maODOyaPX7RbHTvwAAIABJREFUfq+7RHWXkR8JSC1RyBhp1KKl6T4aIsOKPERRX0FmA1zY3MWSnRCEhhr2MFJ1JKKGiYuQcI6sIJ5c1qRHMbp2hCQrcXm8hINkQbSE4OyVjKZqxoVQrnVzpOqxQ7ndw2BGKI4U3ExBBiVOnvMIAocoAu6P+hgeSmxuL1cjepTBOoPJTMPPqswaLcwbaE0AZPOy23EThAOAANCyKkmPjrNyAe6AK1SXCAoACAyNHEDV6xqCtMO6nGBgAvQRYqEpUJQSaX4M0oBj/t4ECRRs1VQxqWMyqQOwkCC425nAY4Ra4s/IgAZBNWvKStv3Tubw7ji+Rj3OCIaISSLjEtYzpHNooORzYBrCSyO7riVBazKkS9nIpnJA7EKjoCwQcqQgpnxsIiEY8FnFUSTA+KiAsB5qC/AdoLQJzDOAOJWCttaQvVCj4qHNtW7DjOX2eZ58LvwnJ5qvPkv28p9y/IX48AbuwysAVt856rbfVzwcf/2RYfZ8spm8d28TP/bC5fSf+X88eZ1l8pog+qR+L15cfvfyMPqV9z/eKvOx3Lj2/PCxr/z+fhM/bo+CIonKrGNcw459+NwL//A/TV/z+p/95Caqte0A/DGEPIdAZqj2iA0AEyh9bKO4D8D8r/dPwnc1zFgLbD/4k//58RVDidfGPaD37xD3wN4bP5YAvBPVgmJUB3BEwEwz79eBpSlwsitEC4AZWr8H6DGq3a4xnWd6HNBwzHNXK8TzmyaACufh/Wsz6tW9b9swXQnI42ovKHMuBAkC1Slj77011ATRDOxjSMRaWk+kcmuwutDxWVEMpkVRC6VUtSjmcVG6UZbZVhS78ytryExROM8iM0YHATkhNEshAEHSGi+AiuVzV2dX9VmZYUsLIQXCKAIA5FkGpXSlyPs64UEVj4QIEBImzyt3X4ZygbQlpPMkNNKZX5UOs6V2U0r5jvZodqUzit/WcOrsqBi8NHHZ27/2h89Gy6H2F1Zq51+tNZ+4/+TKS87zlz7wXR+oEgk/9AMdACfo4pUbHEeXcNze/Pf+0TZOrl/A3/hRg97gy8Wvfy5+ceXBbnPy0hkn52dYiv6EJrkd3GgE9cXtE72FAp//yvux1H0cy50M1k5QlhGsOz0ftj3cOYZnAF4B8N049qoE7pcCIlbYUYRNQbCoOnNvoDpRX0UF4Nbnc201EP7EySith8Ifouq2vYQqo3fCeTcl0AtCiPcCmdQQioO6mpRZEukk0GZUwJXHqRxSQNgkyODbptbdsy8AUDDDew8h5Z1Mn3dQlisQQuGcSFgdtdE0A6vKuOk4VYkaISoMUmVR70WIRkA93cPhww7dnQzGl0hK8IVEkdN1lOMMaLbRGFlITOjlk2e47kvq2r6QKWNYSxCHWiyN+hwPCxfASgo08kTA1QRkauG9AJkSalKCnEcWGUxrAjzsYXDI6M9irJybQt9XNTcsj6cUcYjhQy1Myjpe3QqwPbawrxxi++sz6geRS86yFC2hjv6wj3SJEDgFvVFHdmjgDw18TLjeU9AUI44l0seWMeQSGAYVMf6wQH3FoN2x2BnG8FDg48Z6RlUTuJ3zOh4JUc2G/PixENWZPU+Pt4NKf2103GxRZTYZBCBHmjP2EIMhQSQQBjmcu6vkehvEVbap9vZRdvz+Eu72v+/OMt49ceZJYZr3kN2WeDwGp4w1GcF6h0M+bgyp/kYdAqfDBHt5ihIOo1ndFyDuQNIyAu7omrxiJsXEZj3AhHUXiQdk09Z1jufy0Y0o1F3l8uWhSwCoHuCHgI1D+K0C+mQfUWgRbrUw22sgVz3UtlswsovUn0D25QWU37g+Wjx/Id3P32a3n3zmF8KbZi9Z/u7Tw92nHz+5d+nFD2VgDDM2j66bZO0HO58b5JF2V853XvwbL7zSw3cQy7jYOcDem3f7+9ci+9jmNK5PFo5u8f/JuQWA/+RnP5kC+AKAgj/+Efftr+ePf+Ru8DcG8PW77u98+/PpZz+5MrT07tOhp7rCK9/JZ7wX33ncA3tv/DiPCuwdALipgcOmlIuDvDjtK+XkNAEm5zptV4Dk5Ki3FgOTAFhLK3dDVZIoSvZ9qjosGriLOMMAdofj70BUz9/2vj2O3POdOujcw6cEwwGIAuU6tbqcOotJllnv2GdZkZC14Uqt7gtrZ4LYtmu1YLnWqJfWuN3xqFxpNUUnqXmXmZAchDWWZBgwO0fVG82V3WS16TMzvHWwRQlIgZhCcPUsWGsglQTNgeEdMMgojQHsfPMnYs3HRB94r7TMLTtlrE+t9ZuTzPpmm7kQSU9QveucCVe79iBEMjoarx5+5tkHm+947I/33v3k4wdHabvZG1955NxGjbFr8a//ZBGd9ruoP7QArhLWbl/V0ksXwe9+exfjKfArv3ELbvbe5ix7KvCNW1CTBgCFBGNnx1aHQur1hSlefOUbEPQoTp7oohZXDPZZUcI6g0qWp4uKJ7iF6qjuowJyY1RcvIWQ8EqocBXAMwCeRAXgTufAyVLQfVKrslaYRVRl3GuB8O5cfXrsinGcfOt47+1sVuhAi/dGUdQE4DJAsYj1wGdBsxiKtuCMAZc7JCAUsYCsv06V3dzRy60mlDGAc/BhCBbi9hsbLRCAAE8gOeeAM3AUEfKaxrTTRFpXSDaPUJMaTjnMlptYuDVBMMhQ04zpOMe1bhedoyOMFmLsF11enKY06y5AScZSsYOtdAmnBvv+sNOVa5M+3jy6iG+sPwbEwH2Ht2iYLIqjZozHNzehbh7iUX0R9xcHuH56Bed2DvBN6qFoMNxSjJtiAafHu0iCnJ2v03Av4hNxSskww/2Tmxg2WvhmGWOaCrd3DTzcs7QUzER7JSfOiUe7oZOtuuTpiJkNwi78ZEJSxYD2bB96UyrTdkiXP6sQJZJHSUJln6AOprc9Jab7HnY4JyyEHt6I6mLHy/nyDVChu2PDE2agJFl4xOC5Am6A26BweCc5o6iE5cBBFAJ+vjDBmCEAoADW2O8JMFfochEz9FGDf53jS8EggcP4drm2UsurRR6zfF6evT0b5te/xXHG7th57DgieHg4wtzkQc9vQI6Ut40lCYUVhM7JqNh0eVmSLGKhwjFMRMgHAfnorSpTBYLRPgkRiEZjEf2F/RlkADEGtITEsOWmL1rwm2YIOoBKMwQ3BPjZFaQPpdAzh+Ar53FwbRuNM9/AmitYX8YYWz9GN1eWVfBWuuo3E85nP3zx1e0P7l472OPhw7X739+aJa1zGU3SS+H43QBGV9Y6L36Mf+3PALPXi2Vc7Hdx46XcvnBw9df/VVW2+dX/6vb/z0u/6f/Hy//c8WTdjN7bNpN6Nazm3/L0e/HnjHtg7w0cc129OqqDegdA+4OPP9pqaqV/46vPT7rAaQXa8OBWPprZUAo+CYIDr88AvwgUFEZ6xByUZfFQAFAAsAfk7K73kahQwBR4Xb4UgGqzJIuY9JwA/dqPWiXFqhUeVZbw6I+HQBDbWEm+NexZwT5MkrYaFnnOAr1HVzcaDa2tKUtnwLJTS4KT3Y5QEPHRrGTnLUtfET143jmnlOLMlISSobWCNx7Oeggl4eDhmWGtBzuPoiir7JCQ0FrDs4e1liEFwXnkACIhXOCsKI0hMBsfhJK0sokpiyhPOWu1yJ0JguLwMA9LcbhvMzsN1bTb7ke00lwc3GpvP7m+2Lrve960nnc7g+XD4a2V1c4xKj6NNz38FF68tMqff/Yy3QVn+KU/kQQYPPzADl69fg7Xb63P1OW0O/V57nHVBmpJCLHqVNkKufVwK7P7SgerGE/fidIsIooSnFhpoDdUyPMMFT+mhju6eTuoGj8S3Gld3EZVwp+iatLpogJ0EkDdAO6w2zwvQ0nB7rCnnW8AeMAzbwqiZ3AbFahTiBs1pIOtMJQPK6kYwI5Bs/TwjwiTTbrEAYElA6rgOSeQkVgGjK/oksfZvWw+g47PcF8NdJV1Jbot3EFC3kn8+Op4LzXwqiYMIHHfUYnQzHCkaqgnMfTEYnimjaN2AMsei9JDOANpGDZJmOoxRZmlTn+AxOYo6xq7QR3tusESD6FmSriIy6yuVU8tiprP0OmPUNYitAeHJNI6dupLsBhzEo/oyMQ4rLXRmKU4P9pBv9vCUU3hFjdBN7ewevWAlpfH9oV8xUbRNKKB4eZsREXLwV0OeMlZ3HpR+vXTJE5GA8qeqsG8XEpzcWzdPom8JWC8psNNI1oPBj5Qhchf9UqM2Dt2lKy3MYsF8XgGGQqwAuzpOtDLgKMQuSYIAdRP55A1hdl1j7J/rLgTQDQ8fImK9gsmQEGTQadRYjqel0oDhWjZId86HgiG5QQEJ0mCvT/m5h0fTRYi9IhXGNk+AYVECYKCR4n8rqlZDaqDQHlbg68q6IcoLFVtnR4g7qIgBUEDEAMOZdX166tOjsJGcCJHBMBg3wlgThVoVaQ5SsAoqaChywAs2AZUseBRZED/VFiT61JNn86Gat8775DnPZR1jdwU1A8O4hYmkeqWuUAGbQE3hTOzEaKGBE8AHS5ivLuEvHcNC60Ybt1Bjkto+VWsP5tB73iIQwD8q/j9/ttOfEy0k5PX/k58ej96vnkNAD5BH9U36cvj96c/RAMfzfr5bOMn90+s/Nbyza396Dt3n3iJ/y8z3xf+vcQPLBj+vV5ofnmfXvnnf++n75Vq/4LjnvTKGzTmXL0PouJf3XxQadMh8UB3lr/r8LD31IbDQxtBuNKWqr4gta4zh8baUBKFIaACKfXZhW6tHoXqxmQsGAgagF6LYx9bqyyAEnBBVVqjYi7VfvsDsH9NqRRECCHRbNYwK/7MRRsdm6MyqmvnmCSE84gCyVIoVhA40+5QN6mLUGt3dmlZt4OoBqksE0RAkjzYMlOphdJKKTXNU89KykApcq7aO4RW5JmB4zIfCIIApRV0oGFKAykEPDxICAghPAisjIEqcrZSstKahJCklPKJcya0VhZSWiiFgshoTSJ0zoWz1JlaPM1L+MOjSRb5LNPToas5a98R6VOzRn1zf8CXlzaWzc1GI0ta9VcfOn/qWwCuE9ZGwNSjWd+DpK/R579yGZ/6wxkA8M7zAV68+F5EwRp+/fem+MJX+ihLCSGDadS/bkT2tppvrhuydZZ2ukRv+kKj++BLesrP0N5hAKCO6ewR9AZjzNIGShPSndpY5Zlb8epOoErTHEusHOBOS2MEYGqBX80UuNTqwuT+tUl9ODVJVtyoFbYAsFh6tpenbgzmdk2JBVC4wKp+isH1PJ8p8q6hdZASIZYiiJlFTjwWRD6BjGLHvvLLJAhB8+IaV1BCz6fXcX8lzalWbj7fIOXtOXi7oOeBiRI4SgRCx9AeGCch4AXWS4dG6aD7KUpByITHtB1hIhmceRye7GJSC6GOUlgJunruLAbNDs6ODqGFxebqCZyZ7CBxub/VWUbkchSs5cHKijgMO2w8UXMwQeJmiByjMTPI4gQ2TmjBTrC6P0WjnzOPxrxeptRwFlcWFyA3GZNpBp4ZaxcbpJockM54axrYqzuh+Np+F5tbMRf7Vkz6UpmMhNzLsDWs8dZFTTa2cuHxgIQFZUbDHljKspDSQwcbaQzCBRp/y3i0iY0iEtKgnEmUB3PmJhg4OWeWDQHvFGzKsFMNtse/vge7sKJp3ObvKVgozIyCZwOAHFgzScU+c/PRIxsERm5sHLg8VcLa6jqHYKHgvQeRCICgQ7ATiVUbIAMQCe3WdY1GzhHfHmG+PR/mJVpbi3N3Icz4IIug4UsPPT2JUZlClxOEUChTB/YSiWPoIw2ftmB1DjEnprgxoKaLVeM2TzA1MWbTDfQPPGp5BtgAIvN6IjVn4wM/2yIL76kIe5zrALr4vmShYOcOc87rC2bUP2XG6CJHDpkb8KstmJaBtBHEc0/h1qd/BK/uHiI5dR3NSQ1+wBAHw/W4ME+qWzyhq0jRPBGON97hdtuww/StGx85UDJM8bfBfwu/rW/8/MP8uebD/rFvrjzekXT6tMaPPDatn290J1/8TJh/5i+rXEnj0pfbkviJ04Hff+yJt/f/Q3+eN1rcy+y9cUOjyr6cekLq79oIo/fMnJe+NFiWEiYMYbyvWC9SwloL4T1CIdBQkYiZoaxFCa/eCshRVWLDilQqDwIuSlOplsVRWZRGWGsVOJexCDlnT8xlJf5+FwAsAAzHM6zUYgyyHOVdPL+76woZgMCW6LSaKI2ViZIiCrQQYeSFVK4Rhz6UQWNoizzwTodKUbfZ7ItceClVDkFSCGpLFWhZKasxM8gzw3kv4iCEsw7eeUglUZYFnDXQUPDMLDwTiCCV9KjgglHWUs1Y7cPIWc/krAWBKI9CPyNRlNaOtVaRAlkojnxrQY1ldOi8ezU+PEyWsmyxO51MkihEuby8PgLct17JjKxHevf69qu1Rx5cXR6PH8VLw8YnbhwcXNs6CP/Bf/Z3R4zdHO982wJ/6l8Ut3/I3/83QF4wlhcX8V++Z8/90WcOjHY/VO6671F19by3s6+6g5H3ZN6DoN4rivzVdENfbn726kkCLgB4Fs5bzPKYq0weUO0Fxxp5J1GBuRQVwDselhkq94waKkL1ZQDv3t9Y7IyW2m3qNOW53f7z9Vn5TVQXGlue8aWDwvvVUPyw8dzSwo8nsGOUE6W9qRhflaLFEvsyJjgooGUAqjQ+fHBcIA+P54q4XYVHngGzeS9Ai+6ws47p9Me2LxJzaj4BR02NS2c7OLvVx6lxgY4tMBUCm1EAOtmGVQ52lCOPBaKjGU4bj53lLlKtMLx/GclCDJ161DHC4mDKzXRI+52ElZtyoUJSueKCdGrrOgzIaZ6RjUQh3rLzIpaHQzpMGrixtIGpauPs9i0exy1Kc41btUWkSUzbqk3XvEMJBm8LPr+9Sb0asPPEQ0TW+bWXXxX5ggKWWurwKmHG2rV1oSZjjZnQ7I2i/kBANkOKHvPsVeJzjmR2Y+C80TJeCyEigRlrUFoiv5mCvRatYYmCS6gTAZz0wIMSuGqrnaQbQLdKFLsGZjAvkXYB6BgYZ4B2rtbI3Gwcyqq2qx0Ry2S1RNaHQ0EWkBIewvRRVma1zID2RDYPAicjYSiHYYA5BlSA0meamIzi4gZ5NlYOoUUGy3XUpWORzf224mqYS1ODVyFyyhFzDpQb0Wy0oolupk4y+zJA4Wsw4QBlP4bdkPB0GqN8Gy6vw9wsgRFBvdlDzAL4OEaZJ4jqAsjH8H6pno8yo289vlps7++bC8/n4ZoXo/47osK1ItW7NdtVs3y9dZ6LvI7DrC1Woq5XQUnTWgNumGORTmhezM0Rp8h9jsCE8D1GWQDqnABd+3310M0bvjVLvO88gZ0rXzyzcYiHsIxz6OARHL3zc/t6IS7OqFt0ub99PcdzmH2CPqpe/FfvDvDB1dPyqIg3bm4+8kdv+dP1H7nxxFua2ZP3YeOqevjcZ36YHzj9v+M3f3GCD//MX67M2W/+onh7He7tdfwBvk1t8V78xcQ9sPfGDQ2g9gDo+9ejeK0i2FVsNAGJ0jmQqJoVcmeQGQNJAiEREgJkGEAbB8ceC2FCXUD1rYGczVRTK98hMh0hCK2GHMwy3hpPK1UqZmIIhogoBsjgta1TJQBvHTT/2Raru/XxJ2C0rYc3Fo2kjnpSk8MslSNn0KknYpKlM0egehJa4Vn10mmrtLZoMSkEgRNKmlatJoUUHgwptRZaCJZEjj2TN46YmE3pwJ4FM7xhK5QUVoeBdHnuwKAwCn1RFJwGWhqtUTDTpDdwYRBQEsceRAJClIHUUFp7Nj5DXh7BZJFnyi3EhvewihHmYdSxSl5b6TbNpinyaSheuXByvd945Mxz73zrQ28Jv3X5HL51pddeXvuexx88tT/X2VvBXCWfsevxD//piL74nAFwhR8618BPfGD7cu+T6xBn3hdOstPSm02xEu1m6f4WMruFoqiNm/0ng5a4wI1JXBvqmUawgbncAQHdQYEo83K6GrmHBeEAwK+g8k9uogJ3B6iwlkWV5dtCJeD6FgWMw1k+PHionrCWvBSrWX2CMSogOIwkrbx3KdjcyWy5ObLfeqwFEVpzzgMujKKBAEaAXgE4cCjCEkjM/PQWMHOe5WtDztOQBqi09FzlojqFh3QeIAmeo0GPO6IbI66arJvDAo9cOUB77CAIGLdCbHmBdpqBhlNfnmkJMS2wNDFYn+VwUyCalBhOQm96DRE6y+QtqcSh7TzdurCOSc3j1HRPDOotniYJnfAHtb1ogevphB/Yy7g5zn0iZjST0oOcnOk2HSUdK08V1MgKacIA15tdE7pclDqkHeqIEiUvjGZmP4vlTV9X0aoSwUEmT76w5S+216yLZnJjVchcOdUYHOHSToKZ7VBQB6uNiCGU2GgPeTgyknsztoHz9chTsCT56FLuIHWgNZCnBtACxaD02jthlIKNQ4g4AxVg1weRBcpJANqQ4KkDhgycCh1m83RaE9Ik8FAEBJFFPxcxUpw9ten6jUa5c701gxFNCJTw7LUoLIOF9aSjQtD1V1ekhWeFomjAOTREPJmIcrljA0llnqg0lFzKiztraLdTPxxQOfEp1VFaCTM5E2ZirxC8hKkcI3IKWbok0mB/FKZjthRxWfYRbwRwyJGMz9Ym0a2czMgprcDpAxiWGxBdj0w8i8XBAsx+C1mcQe5rTO43QB7Gpnuim2k9GCkaGFGayZ+EiB5+b9J84F0No7vttJU33Szbm9jAxnYkzjRJ5O3SjTMsqdULorZ9tUj8RT3I9D6MYje+gH7aRbrowOOv48T0KuoLWw91DxHRjXd+9daNH8SNM72bsd8aL3wT53GVP/4R+643/08zCujoq0+dufm//dY/yD9BHxUATj/6489kn89/LCVCdy13vTM/0r7PX7txZu+3zyttBZ4YLD8OPTkB07iB77CU++8xYlSVhJt/6YDoGyTugb03WNCH3qdRuWQ8KoDzyyTrEtVAx0ohtRaWGZVvLBDGARLjMbQGoSAQgKn3CEoDKSQ0BEysURcKIpuhX+ZQDmKBZDh0hk1/SLF1WJbCH/rYFJWAFJYAngAQghCCCN7bElAhvDsqSsiqyPaajJ6SCpm701eXmxJCSVzrHZEeDrG20OZTrS4ipcTRLA27SS1oRFFBgjDJs6xwLiqEaMTWkS2LwnjYpJ5oEESg5hoPgBylM1NOc5XEoZCBckEQ2qIoSmedIkGQzrP3XpRpiWyYeigSYRw4I6UvZxnCMOQwCoMgDg0AIZWaQGNKgGQIbWZFHgm/1GwvJKnx091a7WUVRYelh06MWUKgderc7oO9W4sru7dcHtFbigdPboS3dsqDP/5SoB5//ELtkQdLAAGe/cZpgFfxwNkR4ojxypVFVDqHg+sH/083++9/6392o8P3DbKdpg+SYXecnsUE343KDqAJj8yN9wf2G18/EMXgbUyLHhysALgO4EsAotKLx65Nm7NOMLgVSZxEhbvr8/epoZJPWEWV7fvC/LXLAE4XwIGNAh0Zt03jjI6AB7vASlR13Gaomj1sLKlZk7hVEq3nYdCpF+WtAnQWMKECmqpKzkUMyOMiv8gMWCmQ9YASiCUh+/ZOoJARovJUN0LASELkqy8wBZBGgLRAZgGfA7vtEGUscG6QwRIwjAlpZiAKxgYDlw8yWurl3ja0mC5HuNpN0Nkc8kJpSU8gyolxPpEyEgHsoEDpPE5ulrhxrkHQAmE5cRPdoPY4t830htpsneTF0Uxd2L1FX79wv++H0iWA1mXpl9RAjBoN2yhLnlGNTu/t+VsnVgutDNWv7MVFTfL6Zk+3Q0ut5Rij/Z49d/0anxZDNR0IcX+9T6Vv4mtLp3iiVhh2IjZOTIAlxdlICTsrXeCNrMcCYdtRnHo9vlnAjhPbXo988Oa6byw7MfzTEfJbDHfTieCU5FoyonF3CV7HAGbkH0pQ2BCUE8Tbmp6vjYXfz/zqikf/0CHzoqAYqqwnpXikJnFrlNZPmFbxCvvdm8szZUDLSeb6ebwrHwonHWFXZ5f80dKS6Wo/bWEq3NYgtlOQP7Fq/Gp7VloBeWM3KtKZJjPVpkTiNbT3YDEbJOokZl4q1tZauxJYkYSRm9SEyyd1ERuTB4GZxmvFojBOZoOg9HCeM2s1tD5DrUSj8LHEtR3XpCm4G6EMjlC/sYh0YxGp2kXT+oCKxTXs7W3yVyXKJ86tzVo1k+r7ZoO4R9FjQUMtvIcCLty0+acjK743Weg9YkS6xcNTnPQSaVbM9awXt5RP2r7jezpcO5WUwahlS8oXBtuj/JUHeGujj3prhHhzMUhHkcXFrau8gIBmD2D4DQn0T/jZwfl+L7/48b9bAMCXnv8v+gCePV4CH+Nf85+gjw5E4QsAafjh3dp5ceIHinMrP/EnZ4an7/+uT0N/7TyeWr16CPNojr/Apoq/wEhR7Sv5v+2J9+LfLe6BvTdeNADcD+AxD7S32fYWgEbAjJGzGDmPWVlACwGhJGRhYb0HOQcDWBIKtihpB07GANbCBJoCWFMiBKGjA4yNRckeHqBaaZCDUK/YOypGlXHpVhw+Xux2MfPO9XoDnQBMRMIzuNKqr8wnjkHfMdAjAKHWEFrBpBlm7CGtQ8OUpKRgENxCEOnAU2GdNaEM4kYYhbFjSEl5bkoFohqzL701snK6ILD3vigKYUqjVC0krZRnJmL2TippBAQkiYAEGQIxPIfeWyG9MiSF0FrlRiobBFqFcSRNWTodBMJNkUpf1Fl7oiT2ul5TmSmGq/3xtwal5RPe1Avn052prdUbivdG2RUc9XcG7e4PbAGjM1v713f/xe9YeuGb6MzS5tnPP32r+9QjE5RmA//Lv3wApenCucv4rnfs42/+9Y38M59u7mz+7sNpo3xKNjb+mptN1QKyFDarQeKDMKoBaScQCKiz6gD+j3qDZ357wz32f2iO3oMqgfon898/7wTev2NhsBJKfB1VMiyfD0MDVfX9ftyiWNf3AAAgAElEQVQRV45RlWgDCxzmwLIfp6tnv3bl02Sw5iL5dmhpYFyBivH1PIBeCNlSFNC+s20f6Jo2Zb303NZA2yJDiFAGEEJZU+kglpXNFlmBMiAYT5D2eMbMb6Wp7C90iKkXIABtSZCCUbKBIY1MCiTsERvAeocTs4JVAZoZYBAAgQ5gsgJrBZCHgNKCLh05mjqPTpOw0ZI4Wm6Q3R0hKC2SMBT79RhGkBXGiuFKXXRf3OXVG7nlKJbXu8sitF40ByVPOpQHhR9PqNW+dtaHhyfrZCdBEJYzVi7H+v6R1RhzLTO4vH7WFjUWuQmEZNKNhbxcz3tBe2+P1s2Yu9HAaYNJqzGK6MGmDi9JFoksF4thuH5qma6EDXf/7jafvZCTybW9ajryyjAu93cRd97lkAQeqg3Uz9Z4/5pETRqh1zNqLArrF4nsDaYiUuXsSNLkZEuvLXkB5/3uUpfrZDylGXIbgAXlthZHqOdcthqBNJOUenYbUnRVRIFoSFAM1Y1Tl7wr57jj3cGLIsdMZt1mWcYLKZtdSxY8MjlqebdRFiRaa41hUTd2r932Sxfeto3eKPD55BRlphRousbOTtO2lXcdUWKhPsnjIesoBs9UqNtJKdkUZZtjE3W9W1rIYx401X7pnOgbXijycIxYMcxRjKI+4pR7s2DnTFK8cmpxNPzmDk5LiEcdDmgInjxOE/PWWr95UI/O9Is4Or86Pgj2cZLHqJ93O7zofGMWBnIR9rFlu1mqmpMXAxn3VkdJbyBXMd2RvrQ6FJ5LNaLDhUTW+NBtpeg23cg0pgcjN24Nu0yDCBxmUo0v6sXhpB5HGOkCGbaRIf6XeKz7u3hg85/ij/UZjM69mT6+/Tz/7NEn6KM1VJJa2x/jXzMA8DH+tT5A8mP4ZPLZk+95cNq87/s+9cCPnri8vhHcf/mryMKvYDlI/uYSXlcX+j98fPhnGL/5ixk+/DP3XDH+f4p7DRpvoKAPva+LSgA3AvABACcjoLYmVewIorDO5d4LKQVqOkAtjpGXBjeKHCM/xdCNXQbIQjPvO9gJEdcInGW56BcFdBAgIIFICBARIhACIRCShATLYC6f4iuF0PIA4FGeKS5KssxIAVMCPgapEoAAqA5wTIR6HKFZT4iYEBGhFgYgthiYSvxhKanxerNNBHILcUM0kwRjU3hmLqMgVILIw3pLhEPHPrbsSQkiDaHGkykkCTbWkLPOBSREqDSHceTYepg898xAFMeQWgkiyrVQJCQ5nYRZVI+HbK0zRTlWYYAoCCEFoUzz0novA6lqEMqbIod1PhfgaQi2LVvuDEkM5Hi8VMvKU6PcLFn2By3iS7cM7YwhWrzU2Xrq5o2vHMzy7tfrrSvqe995tdZpri81IjN95cqb/1/23qzXtuy8DhvfbFa71+736W9/q24VWVUsdiYpNokt2oplmwkQ+EGEYgkJkocACZy8BEEAQfoBeTCCPDlPiSAiiZ0oyItMK5EiSBTNRiRLrCKrWFX33rqnP7tde3VzzS4P69wSJdlqEClkETUe9stZ66x5zpp7zm+O7/vGcK2eni82F5u7t4+TL/67A/74+IZ75bXxul0cVT69iIjNfRK/Odj7+JtmeVb51hphEzimzyChwaRCbYgZ2/Tb2W/ELhPoUsMfQ8e8vc4ZjkUXK72FP+rGTdBlP99CV6t3gC7Q2wMw3YR8r5DyoAmCoF8p1681KS5tLFgqG1VJD3F97Qagn/Yczwtv96LAH1nXbwa2jgKgp8ECQEjyLRlNnZyNMV05FwAICUMMJK47Q1w3KKGbd62svJRQREj4tWo4ETxxBEZDNAy5J98SqLRAyMi7iGHrPfUsEGiLNpCouMN8ll53ChGK2mLCLGYMoNDhaidDfjj03HuKViXqJGQnRxNS4wSyUb7KEiFK7VaDFNaAxRuNh7s3yWQuUn2J5VFkNYONF5yaQFAxHrJVr9dkqtCeRVQPE+NBMmtUK0qlOdNyago6sFtSYUyPHxw6y1w7eDTvPZruskQqm0RgPW5dEBrcVY/8nl0gfib28nHpl6de3x/k7NmdpU2nsGzV0mAs2d5dRt5aBNzQ2ofW1RZF1RMDW7Q3Bxu18zfCuA0iEjshjXcUFXxkZR9W3EysSAKKEl1RLyZ3uoFvrJVQ9WgnGEw/EGCYNNqXzWqG8+R+sPE3P6VVOPD80s8iPe0xsdkOXzj4wYQLhVZHAy9cEvty5Zbt9md/5vXVR+9tljtRG/T2N5vHJzOkNKA7O5cplHJ7B8vq1geuWJBpvHjnIggd82VOfu/WisYZsymzxcSv3AfvPvQPnl+Fs55jMq+ifB6mMXQ0QuljeKMgoj626pngShxMF3ndtB9OWXn+QG/mf4NO7DRp008PxeEtU8wE8ijO68nPTpf3ZLlzg1rqHbltMpvs9G7cqfwzfReXNk4Gg73w+fCQQrQ0IgpsGWOda1laI/VUsTqSfFdZ5iH4xVDws9SyQTtvbuht1pCgkw+M3MXhsNAm+DoULqFxBmCVQN24hdx+Biftb+Pmzjex7//rX355+cqv/PMQ1yoLL/3yv+8A4Ffpiyx7Nt9fvzL4kNvQbPziyffNbfvovNcvL/+VxAM7/vVf/Mjwf8E//M8u8MFP/NgFVOp//u/uCcKH8drXTvHBT/yFpGHex18O7zN7P1m4A+CnAXwIHSsTOaA3143vi8BGQpDR2nLnmQCo9c5V3rMeLNamdZBgKq5ZwsBmKlaKAr0xGhYIOcAuqy1ADI136HOBjHEIxtAyAgxDaxQaECQ85V0612vn7RgWYY+o0b4wjKJVDd0DkgAwgziBEFKWurEjS+Q9o2OrUFbGD6LYaYAkwL2HyxvFp0kmsiQGWecGSSKNasXj+aU96A+YZIIYY8OmVUxw4TkxoigArxu0RhPnzDHBPQDt4b2ztjbWJt7Cq7JkgosqSEIPwMRSbsGprbdlv9WmZZxdcimmRIiUbb1Xzhltl0Tw6CEeiri42DSVsD5LOZVVrYePsj4jbdozyD886stR1OfPas/vHrfqtf7t3Ve8dfN+yJpXDw5ubOYbEa/K0aNS7T4Q8lKfXi7L1n4oHg3QTkf7b1r6wu9/9dWTf+urX31r/2R9rnY/9brsDe4k6cO6OPvmc8nj1VCWh2EZLR4lvv/d2gW2NIsjvqlPEj38RugGZqB3XkBXbxcA+ORKyvpiOJzdu7pSsouhDgH8bQCvopNeeR4dg/BddPV7Ep3nLW37UWo8v7+ajr534+wqSDbV/qyp30aD7zCg8EkCVNWAgBcA/3wAxC7C3AEkzSpyHmlE6DkAsB6rmkPAIuPAH7VUCMACQ3ndeMG6XI+ygCAGxgOUHEgIEKzr/9bdr8MlAQMhUbRw33Wc9SL454whK0BvHQ2oOK99lDc2AkTVaizv7WEdMsy+c4opI8SCY2YMeGvskyTh6mBgY+/YOq9sKqW3yqB3kSNec+qXlr3hAndVcmKitWOlPBfAxazvM1/x/eWpYMphHvebwbZBpSQxwBacm6vJOD6X+5x7w/um4ZZpPmBl0NMtZ0zj+OU9v8HA6ZbbnXnL+ixvJqzH852eu3yzlJnRNDi9tHYcsqujI3HHr2AmrX5h0nAlk0BEiasLZ47G1lfD1A39sd/fMfy8GVj2TuXd4VTwSasnoeaTy3W0OhpYPqz5el63QYJw8rx2hvqmZ7bCmKppXGTpXowsGxZ7wWq9feQP1BzxbrDUy2Xiw53UHc0k7r1xJr72rQ+44nDCn/nwGd3vnwTzs4z846GXeR3eeTEPZT9tfnr/9XSZwwR9M+TgwXyVBnIRD6fjTfTMy2+xN/+vGe5ab/jNNuZpK87Pe/Z824O0xoT7mj93+zSo5z39ZNmPD24YEY25fPUPpmrv1uXF/Zf6B+hfuGDRsFHM+8r6zXrLnGoq/pxYjodG3wULZx8QcncQXCQ0iOpUVvz7G9EPDUgWuh1hIPoFO3ip76KFPjcHo4Al05jmpsqoUTQYeKdVJG5RwM16pcbxkSt7c1tT5Ot8SFng6EQc4/tZQh9WY2xoxR6nlKz70VGqV7mTYl5F/LUwVG/fbldff03uzQEQpkjvLNbhkd9+dIHo7c/h+Af/O57bAsDP+18r8Kdr7nrf+C8+Ohk8l6/mXxurwX8uXn8QzA9ul//3Gy+/eNvufWzYQ+ds82OHf/JP/unoQcT/zkupVQchvvKjHs9PKt5n9n5CQF/4/EcAfBHAZ9D5mgYASg7YvkcYebDGGSqs1nPvGDlPJ3VNV7aTozXEKZUD29rAlUpYQ4GsALYFiBHzMWdkiNMGHnN4pMTQIwbGAA2PtVZuDoCIaERkJDEm4OGAuhc4yx2JIITrpZwVpV8BkGEQBYyjvSwLWRvrjec2i2LfGEOts3TYH3s0xgDEoyBkYSCQSOnSMKQkjJ0kotZY3mjND7KRCAWXFj6QQjA4LzxIMA9Ya2GURiQFOeoEuMhTq43RVmkHQToIQ4hAMsbZwkJdKV9+D4ZNmrph9boqZRQMibEBQJKI5iDEHv4JOfp1xtgqr8odLngIospKsWyMqwMmQnE2T1xV7edgMxaGby/meVnlah72otPhrN//7FtvRieD/rOn/WE8gV33t9tpsdr81ut5c7b3zhMdSRFnF/OBK6vEL9abW8cnIjJG7pRVOVut1vFFzUQj71Hb3vbMrYxUNfPsa6kbvh2p/qNRffDmgb7XZma6JNACXQ/lOYBzxXkaN83HQ+cOecf2vYBOWHmMLrDT6Nww7qMjzQp0mo0yrvSTXqWyyaaMmNJtIeWJsParETAlzgq89KBEa0qq6jvounx7FjjwBgPn/aCtEXR6KQ5oHRg5MCIIEvDgAAkQ7/RVzHXnrcO1UZUDdMBxKQhbkgjggBYE6py5LkCYMwnrnPOA31qHnnYsdc6tq5YFtfZMG1+oTmJRB5Ly0RRloaHmFWbwfhA54tMQV0ngYDzj05iZhNFiv+/sMCGkiRjmmg3WW7CmbmGETIylZ07P9GhdKBE0bUqb5nB9LpluCQ7gFaOcS+aEcDcXl4wf5+TWCzZiinQQ2lQVTTsMOAucSOuNFYmpXMTNZH2Om/NjUW5JFhlvxL7hj2hX8lZRHNau6kWU7sa8jQKVLS9arT2vpKQ0a9Fw4vLI2hdunJrLvSPKopZ6+dK2k8yN7kR8MHDuIFmpKuzxzYqxJh5g/46wvC4Q3ekxaTULrqoga1ZUjoZstmdiOt4G/DCGvdTMZpS6fkoyEUbMpDetj3wAOxwpXd49Cn0YCl9qZJGNDm+No50ksfezb9MtXqhmlUU/9clXIpn44RbDsFpL9vYfTLLGRL0P3V/bYJDH/Sy3z+01xHwszOmU1qWwz93cukHcWptA3nhwgU9+Ys72DltZNpUPFMlJUPpnX9ykdZGh2Xp+Oh+5eZXxD6Vy+KFdnZloZZyGyIQzN+7PstlgkamtSo1l6c6LRTDs7yGQd+STMg52pw7JqA6E2NBgXHEaZ4wtYrq8bINxHmJvzEj1tyjWj12vTGQ/HvPwTiF7UlJWcDT1HFvfwoSAiguI1SmOLhRVfCzXcRsdad3Tkn8z6fmLYM8PTv7O4ByfhNvfz+9+cnNqvrPZrZZITj+F08X/5n/539i48Mqv/HNjS1GUj9Njb9iT3s/HrdVBAaB++T/8by0++IkGH/zEX+GO81eHL//et3hlyZ9p8fsf+sX/NP9Rj+cnFe8ze+9x0Bc+z9BtyP8lgL+Jp76znbl8rojikjFqrKYCYFdAFAGeO2NqQC4AMiBIFkJ7jzMHTxBiwBhVzroGECtn7cQaK0CsZd2UOXEGtbOYGEAR4bRzmvLS+/YKaIYC4ZHg8qGrmBkX5K5GlCsWlrltdkSYTZJInTY1JYMJz43TO2HMOOdagTBOUyn5gJgFe7B7KFt4U5vaJ0EolXM+L0oIDRb3EgyyHvpx5GUkyTrHvLFEIGKceQKhdcYQY0IGwoFgyHrl4IUnahhnhkWhNNo01jshrKm851+xZO43fn0rxo6QUn5F+ebMafcFzkklQdxrbMuZ4GewnoPRLyqlrDUmahpUjVKbXhiPuaCRa2s3rxpXK2cj4YtRVX2zL1ncyvjqBlkZ5/ktNeg/2IvC6nHpvnZbN8Wz5/OAe//gMss+uXGuvXhy8aCv9MXtovjmM4vFPnPu0+gKmb/LgLkWwxt5Iva4fWx4f/DaOLi/U83fqGUb/c6kvfGPGFgfXaNFi66T9g6A/8nvzd4Y1PXLflP8LusCPIeODX4C4O3rOXV8fe8NdHHWpBF0wT360nbOqNBGa2DDbXsaAgMvxUsgdoxvv8Zg7KUFlALuc+CmBEJHELztnCvgbGeZBY6Q5DUXDNTX5rZx+8elGrW75vxYx971QJ3A7rVtlzKAYQDAISAhYdhGcARWoN+U3kbErGE2aj0/DqjNBcSRhcqMZv23ngSvpzEuBeHuVNLFNHGNcuygaEUUANvTHGd7KXqb1tX9BKzY+PT00vte2FRxKv1MIt4UulESNooTI8jOLst2uZeKIpggd0JtRjOf5FsprZMN6nY0QtCTUl0QIXQWkStEtlrwg3Lts83abCapUZGjyJSORZ6uKOEszcyz4SVLzcZudgeBLSDCAAzOWKdN8I34Gcxu14QGVm5LE0A1E+RSkQj627WmYsvPXpeiPDCobgZsN9y0R/U5p31BZ3szg4e63p4jQJaFjRN2Z2zEggckZZ/ttRWNsAHlxl8GL0bZs63gdSGsac3mdS9Gzxo7M0oEqnKn2c0QRhODp/DY+PNiFyvL2fPlt9y9tKBKB9E7bckfXfS5KkO9rBL2nPSj/Y8ozOkSe2kTPDq+xXlBWNoCEU3pfu+IYfSQ9g42Pk48L08G+Oof3KXe4BG9/FNv4PAmk6/8zl0X9E2Qw4kz3dh21BPPHS3sgxvHPn00ivU6pUSqrD9QbDepkoNnAlo9ikz/bgYVb8kzxeP+AlXl6fmpA4178nJ4iISdIT6JILQF356xmxOGXrMnRNXHJPCQosHWPcHb6hiz5QCyMSirBj0JvFgzcEmw5RzbssaCQmZ62meF9WlVV33eLtlye5QZ0/vW/d39NgoYh3PP1KvFa9g5/zLulv/C/zd/ZofqtSvGD7N95vq7+2OPX7pZ7yuLo7eazon5Rz2en1S8z+y9h0Ff+HwC4OMA/hN0/qUjdJtygGttYgtw7T0HwBtiAEAtQFsQLxQ896CnrgOcGM+95xag2nsPkEiZJe0cDLSo4FGRQH2tJL/qHuQdgc6vH3rAuVeAIOdFBonCqxrCBbqRvnWMGAE7MnG6NeK0bHhdVrwyxs76qQ6E8HemM7k/nKrWGe+9Fzujqe33YpGJUASBsMY7z6TgWZpQIAQE48Q589Y5st4BDuCM6NrjzOtWW4AYY9wyzrwMJAGoBeOR1bYGITLGNCpvRLUpKxHIQvBgaXPeI+IyjKMtGPaYYAf9MGpHMtHbuuaeU0IeYVsrWW2Kyhm3IUZJI6KrnKWrwKmEWqUa1XIh5WAnlVefa7bbYRwMS86bm/PLMfcua/t9zEfj02///utf+Ufzd656zvnXd3bvxVrfTMaZifJiOikKFjtnH45GCNr2mdj7Ap30yfrXlbTnTIwG/OK1dGd/HQ1vmGZxrMNWukaOTSXENxJrH+GPnOj3AAR4cL+E85+lTd6njsl7al3w9R+6dgngFjqXjN+1gL48nHysGKZtuq6+wbqAsOTAHQn0GaAwGvSRpQx58RwBtx2w64FDeCQtELQyI8Hba2M5BmUZrixDwgiMd7NX0rUsDwd61PnXQgNcdDsYu9b6ZeQReIvaAbnt7F2k9gik9zlpWMCfGeerxuKAQGHIYDy1VjB3mqV8SYyNjGVWQgCOFguFVxFi/uwQ5eGQViTQpswGkUThUE1XNfa+n1fHBbP7i4IzSbTZybRsjdehJOEALbjTjPntZER1THYyr2RsGr2RqVVBiFSXoU4dydbaHjUmp6CGkPwgP2f3tseib0sWrsuW+6pNyxaF5mx1Hgc5IhSjKct6hpP2NpaK1zJh7bkRO5fHdqwXQq0UFUtvd+8YSslSkRMzPGHraIonD0Nxu3iH7/kt8jqgsCl8+HjpVyY1E1GYdgXmV8bHy7UsNjzGfoqe1Oz58iGj89wFU+HTQBFCrnvbkgVtQS+r7zNqG6anQ4rfmfOdeCuSCMyetOI8mLCdUcHu2HP6SD+nZnpE88Uat6sfsCj0kdz1pEYDz+uWBwpcKscnWc4NcQ4neTxbiji2fnVyBJduMXeeB5MNVFq5m3fWLEhqSi6JD/pEe8MzN7DKs23CRlMHX0tidUB3773D79w9pclQB3eSRGbUp1guEWxaMRIxt+eSVxsjVo8gwyOApQxm5Ug/dtRe5cgkRzIkDDYCaUzIWYX90QjRnkI7I7SeQQUrbK4Mcg1/xTdEaQlYB7fyUNziJBIoLGGUc4iNxFuBxdsDgb3VFW5UbZv3o/xi2K/5xi8Mob+cptG/97U3rn72D956fdDo5mBnW/5O/l/9SZWq9zzoH39p8Cu/8d34l7e/rQF8Jtf4+2uDu//Db73y1c996qM/cX/vjwPeD/bew/iVL/2PzwP4D9ClbqfoGjNC4I9Z1XIPsE59DNSDR42uJYs4SAFo2LULge9syJ9+0wggBoGQiFXgcNRZUKXURVIGQAWQAnkHUAZQQ8RL7/kScJWzpJ0QZc258txIIscBrG0rl1pzAK0nb1qClg2j1po2krxhxPg47WGUZShUwZVuedW2VjtrJ0FMg17K+0HkBDFyxhAxRk/9a4kReQsfkdDaaqFVy03deOOcjsJg0+tFsWdMWOvhAQJDyAQrRCgA7y6CNJp4D91WzUY3+kwVtdGNfQaMkMZhWdV6k5eFhIBQtUrgsW019Yh7kfaSSyujt0/Pt4lbrd+Y7qRFNsx0RPSbfWu+cZ43m0sSm1UY7YhhbzVkrPfmRf52du/m+T/4h5+bf5mCxWybD8dlNY1COUv2d/ZHV/O3ZN2oJgzPLGM0aNuF9P5kK+UugLtjq8e+ce1Ubr7u84sbdLKMEp2qwA5m7wyz5PFUlUdr02Ng9wC8WvH8FXg8z84Wh2iamqy113OGowveYgD76DpyD9Cld4OGM/f41uhFVqlZVjZIlLmizhP3HMDdbipgirq5j6LsUXfYOGDAlICxtZAwgGUtQgC+7SLKhhE2lrBxnf4KXdugPa3aM8Af+dXj2mXl2tzKa6BlgGmBnAM+42jJY83hF/3YgSQvidG8ctY40DvKu1eXjPc8qCRjqzAQK+3YAoLKu32EGwuhDB4Zjv5JYR4EzvZzZSTgt/2EB1sj/LJlxUq3+8zK5YO9YDlLiThvZk8WTVQrYvAsVdoneVn1lNcWLGl5hCrI6tRrebC+FOUwRBUOUCeB586gXxSs1NKc09Ayb5vEqLTyQVAmw7AWiaiXDFG51eOpYXXaC7CuhVSexXXttAlYhJpCoYkEYY8XLKtrX18avzzmuCEXVjotg41hR9kax3nmHq4HTdS34UzXjE637m5vKUyhWGEjtjuqZdx3LuSa7bor2K32xZvKsNYxUpYd+iuev2XILhQfjozf7TUAk2yYNojhySpDk6SkYmPd1KzpxrSmymmYqsRtfuWNDvlym+LmdMEypngPNb31ZERNEeBwV2M8WaKfNQjiBhcbiVXj6JkXz1jSqzHZWyKV1mXjmkd8TXuBAR9xTMYh3dnxFEsJ9nCf3rwUuHF7i/2dCyRXGYIqwpsnEVYBMJsocHhwtQfOIipPSnCvwD9kUCtgrTJ4ViGaccQyhikWiFOH6WiK4WIPUcnhdYCzyqDcbIC2ABmFNVY0HUWYRQME5QxyO4KxhKW4xJ7KwJ3HmZ+jEQZZC/QK404p2zhLZz5idRt4NGF4JL05/djjK3+0LOirDw5vv3pj5n/hFz77E+cm8Su/8d0dAOm9yNYTYT/c53gh4/AvpuY3ey994sezY/g9jvfTuO9R0Bc+nwH4j9HJYPTQpeD+1GVPP7oX7VGh2zCfWkN43m2qnbslQRCBeYccIAug8g4hgCETyL2Dg4f27+rjOQUwBU9PaSBnHVqCDTq9NNoAzoHrGMzJKEHeVEEDAMx7cEIMBDCookYwMMePLy/TjdF2ZzgsRkka7WYDaq3j8NZkQZxzkj3pGScAxhpSrfFZIN9N9jljYZSBDEkkIvYUMlvDN661KPJi6AlCRlEpQ5lIyApdkOIBXLg0jpq6MaZqvaraCzjcsN7tVKt2NezHrQqCUb4pvSFX2K37PdeaD1vwraroRhC6cLvafitOzfeyi+NPWefeti/NXsqiEKo23/vO8cLWJ4X4+B3ZukYdPvTBjZLwfRMEX9nbnxxu1kW8StOd/+NDLwcff/z4X33mrTdf07X+mUbxn2rj9PFb08mFYLS8leffU0QfXaS924lq/A1X1/vejufOjQ2xO6FN/iD12ZsENtwv5vPDdf0MkB2hI8O+dtE7jrm+FU/ruEpU+2V0jRh/H8BHANxE92qPr/+d8vq1htq60TZND/bKNg43zZK6utD19dRq0DGA2TVLCAMMLRBxIBS4Tr3armPIPg3eDJB4YMo8LlyMwtadSCMBTQkEonPfsgQI3/kylDyGMBoRNygdcFITRtyDGSDOLawBziJibKM8A7MHHtzB0jEIb1kCZMiOmPJt4e1ZZam0DhYWo8db3DQOd5nXS+/4aGNpo7U/FDYIG6IdwcCdI5UFctSalCLY8cW2piqA3u1FRHzJpPfccdnf6Jh0nV7cDLb5KM1tZfwsP41l7WwVcMVdFUjtcKhaGeYLKojZVObJKdtpXZrI1hZoWtnqMKLIGt9L1m7c5hRcbtj3Mk6U16a4Mmzab5nkCztJF77nW3YezCjcep9GDaHvHU9q53JlZqqQ6HFd5pyc0fbZvbWJ0FjFPTUN9zGj4Pnplp1+d+keDqd+GlfYr1eWjVJ5YTOEYhnws3n87ngAACAASURBVGM3CUPILaOZcDToBT7NhPAiRH9x4oKM/DY54LTSGOcL/C12wutzY9dsl+0nFWlloKKQXF8gWy9xejFEKoAklDjcLyA5wJMK69phTC3K+QAqT+nenQ0mvQABGCRzCPqFML6FiwKMXlw5870ZK1mBS6sRJhZ2uMZOz6InNjCXQ9imjyTYYKdXYl7NcHJlEVUl/CqHHKVAEAGqgXoi4GoPUUlEByG4dzCnBRIxhqq32J5cQK53UbUWjbkAH0q4hGGrNWbtDElVYOAtJsMRbGNgGMHXBUh6BCSwCUMsxBwCAioUgGfkBYLjYXJzO0x2Ki+Kw4cVf+Htq2/fbPOvbONo/dVnD4cnk+yPNWIQfYm8/7k/t5v2V+mL/Dq1++OKEwD0v86D3UOpnv1kD/uDAO2gW3Pex18D3g/23oO4Tt/+U3QuB3+SyfvX3EAIQVh798cUK5+uBAZP/UV9R+0QQ+Xduz9r4RB5AwIDwH7Y+eLpc33b/UK69h5nbUfSwAEyAHM9wFw1lfDdvu/6BLsGYg204FDN0LsoYnlV6Lj2NiqN4bJtxdQ7W5Slrq1VcRiFnhluLTmnLW/Je+UMkVJIgwDOAa1qoeoWURJiNkh04mMxn9uwVKXPG+W84GYkwwDaUGNNHMZhqZQKGXFXNPVAtc73JO+LUNwOIhk0ecXSjPvRIDlvqgbbqpCBDL0p6lumNY1MwjwbxAZM1mrbqLN33pnYqqY4Sw6ffP0JjFcLUvqZwcEhxUrzIBAfTByKwvmHy9Y0f0+2s9/8tS+/UBCTs2cPv9L04pV0thd4z1WjV0YmLq6r82cW829z75cAotD73+6r5pmeUi8r1MeK+ANLR7HQl5Y8ss6uHvOxCo2DfMDAfg9dUHver2+Fi3j38RLsu0mVZwA+BeBnAAwMdKWwPYvQlxxijG5BbmpgZ7mTfDZcbjRr7A94V1cj0Vmm3UF3VjiygPZAT3SlA30QkUd3uJAMCMKu1q61eNdWhVF3g3Y1It9F3aXtio2c7uoSlAcGMWA8IL1BQg6eAC+BgfDINYNJA6imwVyT22jQDM47aNLw8CTYDCAIsFwo/5AzRsaFG6sQcoE9ZyByCybIbzw4NqW5POqbhEHyqjE3Vo2YbnIKvUMFmMtp5HUirQ6812mALQuDehBPo1qZWgSVH4iUwKkRPEk1BevMXlonRADIrDUhLRqS1DbjkokrL4N1wtwhMziqLsNFtEPOMZMUBoIv5zqIo50wH1QAnDW4cfoDb4NQnirZ8mWVcMV9KWSjeETrFZmWjcK+sb4dRnI0yO3ZPJRNKGlvvGTDreUTodx5ESXFVlKUsfYg0d5OrXPwLh5CR95itFo4yY20yjh93rDdocaQVWzoV1hfJR5b8gcJkX+9RTAAJomDT3f56PwH0Be1N5woSy1GgeWX1QZNmmCnv8HV462ZHgUiN8w/fNijlx/k0J7hYHcO0hynyz3UW4X0YIFV3Yd3FpQpXJ0FCMmAJRVcKSDKCVabGE+MY89MDYZHZ1guEpjTEdhpCHbYoFqM0FYtRNqAmwLZQYvqUR/bbQBraqxKoD8wkLwFKgk8AUQNZG0DVsawVkOkMQbjXTQmxerxO0C4gtwLYS8UbrcPUKUGGALG5RiNT5CwIYLMo1ABXBVhKidgWwEVpVizGts2wUi2kNbqNnOsDiirUtnbEF9G3F4l1lw8HGa3fy+6+dOvPdr9Z/N/9h+d//Fl/EspgH2iLz3x/uf+jezXr9IXewD2f5W++OTn/a/9WIoU+0/PYwDpf38atG+VbHuTu71Djnsxwz10DV3v468Y7wd77zHQFz4/BPBrAP5t/JDv7J8F67sO2j/Pg6YBcOE9xozQA0NxHfB5MGxw7UL/w0906HK+QRcHSmGg/VPOkKMBQYAQdwEp+Y5dcg7QDZwFvDYQThKiUFJsrT+vnJ0TcBR58EGcOGONOi82Moni2DrrelEkHBm0lltlFIVhwAm+82wiD3BClIYUErd129qmtYwxIYWUDRyEkdy2rUa13upWaxv1E81AUtVl3ChtEPBCpnFhVDv2HoGxWKW9RMTjXr49uZxGaTLcXtVrZ5rIe6tEyG+CoYH3cN49U2+qO2pdiXpVvppM+mf5KK6SWfbK4HAy3ka8OPU+M4Q3Zrvjr/a3JS+L/Gbm1IO0btWHv/ntl95x/Jsf9nmhONdvjrPJqNgmXFAdavMMd16vg/BhbM2TTRTft97ng6YJDPwcYrBgfnGu0QTWJlnNy9femL7ojzbLV3ea8gRdeNWbtOl00haj6zf4YXSp2yWAxsPvEeTHAX+MLj07UkCR9+SDfNQftJJv4tXFJuzit2N064dEJ9myowHpgaBSlqQDEDH46/S6RncyaJvrrlrZjUj7LgpdKAEtDSIJFA4oPDBggGddrrgggcQbcK9hqJNfaTigCV55R8p6/KAVKA1nA6GwYhBeSru1DLUxuEmwIcCDWNBjRagB5EGI3UxgqLbOaIBljC1ZYndLjYV1/Mx4u92SE8RBgXP9aSjktvURoyJtDA8a3VvauGDzlWFtK1nreFYXIs+SrQtjLoyWPs/Jp8NpO0x9uC0otL4daSGUyMRFUmA1GPqRrSkojQMswvm5DwPLeONEGYlMiRqhdsxpRuso9S4JWfP2xhRXMbNJqCPmwVgQnRaRHaSK9+WCqYoMLbT1vZpFQQQRWlKetW0SYyMjX18xPkpKP0xLJ/qCr9msYuuG3Tgy+mh3K2bHhVgUzCGsEY4q9DONNohRMePvHpT+xozwWOzioN4ijg2qmlNw9gTrU6/bRvJ2mtLlvSPsqRWevbpEE6Tohxph2rrzE8JKjKkvV0hDieVGotnOwCXDeiHQj1ucrjOoXOJor0SzzvDm96ZYGovD3hwDYXDnQICJFPOtguotoVSEvf1LrBYGZW+CW/fOgRXh6l/2gJRwuT5EsMfRuD6qU414mqJdAStfYMw1jAkgrwBfA9uRBask+uctVNpiPfSgfgSzBcRAA0kAvi8B14IvFLZXG4BmEFGMepRjG6zhaIIWAyjdYs1qnPeAnfUZgtohaoCt7OFxMnBVEIpB1aDhslcNovjrd/Z/izR7bpNEtxejJMKfFkB+uuL+ecyeQaeN+WPH7NE//lIMICo+CZly/NQvTNvPfGeLjwjCDWcBMPwMgK/9iIf5E4n3g733EOgLnycAPw/gc/gLBnpAtzL8Zb713AMCBIVudZHolC38Dz/xqYntdVE9B5BSgMJrMBA0GngEMBCwROjKAX0ruvQwa8BDXC9IGmDH5cYzYGKA/kHaX0ROsO1iY97Wdb8yanQrjlhlLaFu/TSK/FwXPAKjXsDglQUFAIEgRQBJ8JqIZZB0ullCKmOFg+kFwgVCOle1NZRNvLZoSuWavLriHmHbNOvhzjhZny2PwzQQzvpBOk65d2AX86UsLvNKDOPI6qYxjWrGN2aDdNQbcuff2GwKU63Lm+BwAC5FL2LJIOnXjO1tzrfrqHr82uGLtxgj+sZenKpCq6Ntv/fZbzD+5fzkyf95N+GTG7vD3WJnf3L+9htF0jQjt9i8dCEDvR6k/XFV7zci+M47w2nx2Xde78/TZFQzSjOlXuv7Fmm1OvZssLZk261cZt77f2dnuyxPstG3Zrp+BlwMqW3/EB1ZlqPTYnzueh6dAggkgpVE8FTDi6OTNl4kjW723774iuPsppPyhbYxpwFwdD0TdtDV9UWss6mF7HpjQNRxweZ6/ll0vrbwgOBd56y/bryYwnSCyIKhzx0GossBl9cFp1AOzAEsBr4rQ4ypRWY8jAMNJLmccTZmFlIq7zz8KTFWt8SY8DjhjH5gHXcAVjWH4x69TMLUHo32uFJgRwauKeAxZkLvp26W126v0f4WvN9Y5wrPzaENbG2VgLUpGRRx3VZDsS57Wg9i79w2Tnkd9yIKmeBKq7QGWyWEJFey6kkvlG1bGKMjbqzYRpYqFxaV5mtvIOAHkXctAWVDfM5SZEJHs0JZvjHWIGAR1djU1OZFaI72S6YdscOd1sLBhFoJCEZkvKNEij7b+sh7rAKQWtZ2lJGvAjLFNjD9aWMw6YG7MgQzrbqw7Xg+iZvx1vLUyFnf8klkadFqnx1mfgBFlVIomPSNJ+tra3tZEUTcAppRIzL4zCHo5wiZQmgV/PESMARz5lHJLYY3GHp9BAOtMEyf+MFMIQwthT2JthU4nUu4xiJKAgRxBdMHKp3i5o5G/4jh68cOlxcZglGDeHiGWOV4LhnCKoNmHcO0I0BH2DqG8O0dHN0/Rv28xMlv7qMRKSayQGxqRCaGKocYGoCuOMRUglLAtoAPgLhvUMkSte9KDGpdANhCTDwQAtqXCG2KvLxEEg/BzgE3PoPZAHYRIpr2UF2VENpBuxjMNujPSwjtEQLYeoB7Y9N5u2VBy+PApS4IWr00+Rs3e28WW5npRnwfn/vTnbTe/1wN4J0/b/2+ZvNO/yJr/Y8A/b87VPvOo20dbjmPTz9IcDuh63qPbm16H38NeD/Ye2/hYwB+GbhOlv4F8SeN5P8seHR1epy6YK8HYAxCCY+rP3kx4d0KCwtg7RwCcEwZx9Kxd1PGtfXgRBIe3jNY35E8Ht380wAiDSwj4GxvMNjdT/qjxXrTqkp7JCweJYOCgWRelihYHQV8DGUMsmwARgwOvuuAsw7OWU9SEtUtiSjCnZ0p2yy2MI2OuffrAx6uNoHVciJmlFcOIYssZ6skTa6SvdlmtdrMmm05TIYpONjCVTpt2rZRVbOjnE7FFmfDWS93rjfujVNAW6Pyelgtts5bE0/C6NXJ/WF6qtpoW1fL3sFovz/s/e2A8ZkqmlcO9nf0WCQfKU7Wv2t69Gh5uhq2XCwudib1xUt3T5bH80/94Whycne7MUm7WS5IrG5U9aO0VVXg/clnHr9+K4T/2MvHx8Ixtgy9/8o7vTeP54kZ37m6XX+nGQ83gT37RCj/3rCp86OqvO2T+G+2g37lz6/6kXc1gIXtxJIz3r26wdP3gI6FHQPYAGgj4H5kEGbwZ1bbO422fd4xtFwDb/OuC3fqAfb0QEEBA/lrIWROCI3vDgpOA5KBcQ5lu/SsEQDjncI2CAjgQAQY3p0EOAEph9etIwhgC4JhQGQ9GgW0CcNm0mP5RYWxM9gNQP9Pk1KTcEAV5KUArMC59WCoXeYVmxlgWTNEhkHmCgvAVSD0tKfRsoRTAgG8HpGub3skF44syFJTGSkbQO4FWKc81AWpgGg8Kr1E610RcVIKPnaS1TFPiFldpN6yiPPxvHZeAoVIdSX6acJXVLKY3Glr1ZaEZrrenZL0McAj4jyyTm8884KFvi8UU77ow0Yyq2j6EiS0tWUOJzIwwxkFgaDtQ2AQWZYdeHgnPTXOZRPLF5fw9dyL4UFN6y1H1DcoFEeUe9bLQHvpMr54IxaZLEbDoLE0ga44UR4OE5fEul9qmsqGR03rlIJIQrBErQk1XNF43pQ1iYnxkYdcMrSecxatLN8smd3fbVl/VrD5cgTu4e/ut5RGOUzr6GQxxDtXDEf7GxD1IWBRLgWmh4SQGSShQhSvsBk4vNAL0DYWpASqVQoqWywFwT/eRxoW0P0phkWBSAvYlYUxIVQvRDpwyJoSxCQQORAHIt2CbjBgaeCXgBfA1gK9uDuxRKWHeprFKBxoBOAm4J903xIVKsTrGHAB0BCwAeoWyFsgqgPosYfnEuGiM6EJ9RYtOga7SgOIVgmfJoELIk9FeawFfyO1/u2cy58pbwb3Y2O/h7/EYf49hqtfulG7TODeysA9Vnh0M8Lz5Dr3wzjA937UA/xJxfvB3nsE16zeL6Gzs/prhQYQokuOFvgjHZcQXWRggC5lKv/161HpLOz1QEt0BfkbeAMPYVsQgnd9cZ+WC5YAtALydlseHDe6N1fVVUKyylhcDMJwIDkvezLs16bla1WhbpXbHwwtC4UIoqBT8mhbyDAkyTg8A/JNKe8fHHnRQD9WV1TmVbphQlpObVlV81DI1DofhlHQu7E/Oa6AHIl4PcjinSDiz8ZapJ5YXal6TcbJdJw5o03d6/cu01HG/brpLc8uHs/zsj8eDw4t6/1AvXmijXMPpkmwakiWbBItIsRPoOw7IpTPr6vyDxtXr0Y8OkoHg9d6z0azn37upat/8dq3o9/71pOD/Yfnmw8UT94+IH0j0Hp5ZPUqtiYTwGqgVYQujfNIAnM41wJ4mdGz0sjyY7XU5qB198aa/mUs6YQ5CAATVHX1qD847ifx/f2yPAdwbxUE3BElkdarKgyHO3XNWffKPDq3jAnwLrlbA7jHAQiLQnemFX0D/C2Drn3Xvqvc0n1KXDf+2K6bR2oH4wwgBGAdWmKoJYfSwJShqzK8vtcBsA7IuUQPFqF3lMRAxSSY0ziE9mhBFx6ImHCxdeyRdpgyYMngam5gnKehdmQ88xyMBlOPs0qyvUrjPgMe8tAb52ntDI4RYBAL/4LTZlcZWrXG9PvcN16slsTRFyo6tcKoyNsDAzPe1pGuiIRzSStS/7A3qB2zgbTwATQPanjZKmrGnKceKI1FHgcOhgVRbYK4LSgw2mQDb/XYGhkgfOecM7ayLC4BKSECGI8SQg+BMmCBdmhDDUOwgQN4JBCIBCZ0cO9Y4edBxsNAeROUfljW5D0Mg8e0Nnxyl3ExZMAkxF2qyiZM00Rrn3GvLYtl4QJXzS7cMNJtHEKbCqk2ne+0LK8c59pTCPQlWM0IWxUQK5UbhJ6tC3KSe6a31vYSiIN98LfmGd8+8rg/LMV4bMB7zp88IcpiTwEDdCOo8SGaViKQFkYF7sbUsvSehgWHFH20niCcRn4ywCqXYIEEDxuUhQBpBt963DRbODGCD7rOH7ux6I8XqBcZnnzjANgQ6tBAvs2gywDsrkQrgWHfgsUEGUZQNeCvABGiO+qsLWA8QmvgWgkoA37su2OPBZiWcBrQvES9ybvVkhF62oPgwKSBaBrAVlBmiep6TVQAPA+9t05ryZvGURHk8CuePrJJeJaclm9Nz+t9F7GijcVtdAfiH7s07P9X+E/PCcCn1xq3I8JH78a455/WeHTbye/+KMf3k4z3g733DgYAXvr/40EKgPbu3cKQmDFMAFTOosZTQbY/YWtwjQBdDtB6IKZrr1J06yQsmmua8WktCqEjD1foyrdubpyhQJk8ArYiFOep/n/Ze7NY27LrOmzMuZrdnf62r63mFVkstiIpiVYiWY5DxVHkEEgMBSal2DH85Q+lc+LAP46EIEGQHwcWEMP5NGQRiAwLoWDDsQlLsRPJEimJXVWxWFXvvXrt7c497W5XM/Ox731VbExR+ikWUwO4uO/ts+856+w991pzjTnnmGrcnpUDGWeRiZvMWJolA3ImE2bWLgRkiYbWFoMiR5RemsOSwup4geOz06ig0FVNcN5xGdoQHWeudYflulSJ1g9tYrvVeT15fHzc2kH6UpbaHdPIqdLUljYuEcJ5XuRXxeJsAO3O3zg9WDyYn10fT274VTte3Dub606c25udx+eestVrr700Y6l3i4GTeThr/Ea6pvl4kib7Ok+Lxvs7+TCxW9+MJqNRuHN89O9Ui9IC6sZgtPvasSofDcvlOAO6gXMnGrgFYHSe58vUuXu5c/cAPI++Evs+V+4rDeQNIf6DZ7Q8R8BGu+559P1tv0hAubdcfrBmc7UFZgnwSh1lGow2omXota6k76kc0SdHXzp5GXqW7wF6JlBLr5tXNBcRWQhQX/4jdABbIAKduphcpCc/FDEMp7ig75A1DkEpJAbgi3M6vNm2IwJgcdgmCgoKpo1wICQAGq+wTskfpTkXUyPDzkHyJJqmYQUJz+bCddsSCWOmQVtfoi2BdQM4MOaQeOxEbsSWrgJ0S3UStZSDrbc5hPJBgmaap3pZHjyOUs9ISRopoVrQqISTEhuVxqYbFHurLG+OEtON2ecgFcfCyTGjdrFgv5PrbLGNbYU2IlVBNVGi1bquJSbEuQtS56zrPd1Mt86kCVJj4ead8smUbRYdWgsRo+KkCRk0xNdQpBAtEDsLBwU1KztKj04rGMRixAlZbYxzWnuEmMVQ+8gnx3kY1g1Va1iVNuHKtDaZZf24drKqcz0ZSfQtB5ZoNxVinkR1M9lwaKDzBFBZvzFr6kR9udyjfVrGduDD+Qm4oI4VgQQIagMcVuuYDRh24KUjoLq3Iwcz0WUDrEpGYoqYpsSTNGC4J6ijYbdlWI7IswrlCkBboCaF1gYM8jVSTRAl2Lu6Rj7usLltQd6hdVv4LYFTj9lTEdm4wYPjAtVxDuMS1C8FqNIjVAAZQmBg8TCDyoBZrtH5AA/BMIdwCkIXgMhAYbDWQJgp7DQdcCLAkBCMg6sATBw6BlxgDEhBjEPRAb5dwrW9DXvqBeeHmcAGYNJ1OM/y0NWhFqeD9VX5YDLLN515dfAo3X/6dPMF7NE/+sPrN2r88QIy7yQUISK+ssXsRoL3rjyuXNVIIEDWR4m+LwtKfhDwrs7eOwS/+Om/1PzSZ//+z6HPkfqeoKEQvzmX91tLLIALFZZv/VtBz8wcsoaViI1EXDQqwLcpXoaL0lsA5iLXDwS0QPD9Wt4fUdDQICASQB4Bc3goENrD6a5khFWiNMfgMVTWrF339a2vw346WF7bndrzcssr1+bTLFNWK51rrWbjMYyxQAjYzwegCBhjcDUvwEJ4/Rt3XRSoq7Pdrm4q7Vw8LVIbl4tNUVW1ct5tDJvWaZlXq/J6s20+mhs7HWaDbafjKgL7u5PJbDjIdyvnxkWa2bZqs1Ga8u54Om0oqvPterduWqkCpZPDcaqIB3GUH338wx/4sq3Cqyerxf813p3O9vbHxd5wvK6i+8ZW/KN1VT3eNPX8aD7f84iPk2n+ijsc/Y4oSq+uFx9srNkD5DwPoW2Z67/3w5+Q33r/C+s/89qrx+ijQhbA4sgZd3ubD56OiRozms8fvtf9wxsfemGtki+9p5z/KwDDIGjOkzRPu8YHwaOjLu6d2+SL11z7/+607a7qidtF6AtndwBYEWl88CMmnhHRdQB7DCQXYXhI7EA+9qrH0QGSXHAR4SLRgHotvQtjMooQBegEIFZgXJDD1FO7LRQUBA0uS3yAEgnmaQEHFoUoPgLnbLDhhCa7ijvE2Jy1Ma+Eaw41iNG6yKtGhQfBqjw2gMDDQRjEDMTHItyKladyj1s5QVrEqhU6A9yRZaOUVm3ZsDaJvZKKmQvs3BKeCmyXZPXXr8300AVTG5Z1TGHSmCsTbZVaySonizble6N9biw7JCqJraKVtT5MrUVg4TYipDGwQaxsEmOi1ayprbLMPkfXeSFpIg0sGBmDEZ1VIFYwViFqAN4ChqEdEJyFGaewZYWYknjDUVMENEEpCxEHuXMnl/Fe5FzFWM67dZHBxhqKRWhdista7w2JswlM3UEXGbiK8KtgkGURCpDYoo61p7OH3OnEQ+2kPE42PFUx2pb1Ym15sdactJ7ScUTdgEI0nBVC0gq9+nULQxE7s0Y8xdi2gr1rDR0erJGDobdA5iJaCbLZpISTBrEGLAfsT1vY2KARhXwfSHYCGsnQPSZg68BskSW7KB8I2hcFNg9obIq0ZWhHqGqNPHcYFg4JFNSA0CJivQASA1AGwi5AjQIcAzMGOoYKgGUCdhXCGTD3jHKcIM8IoYtwQWAkgi4FQ1T/OKwZOCssqsSCth5NPxHSuGtIu7jWnv+lTrxqc3N9kSZ22Ln1ucqXOmX9xlOT1+R//XT5PU3y7zS89HvZnWOiJuInMoWPbx12RwpkFGA0PH72F/6nt3uIP6h4l9l7Z+FvAfhH3+vJh7NdnC/PUUV3GWGji/WXgIihTbHpKgG0XAThLj3DJ1xdDUEQwfLiYP2tH0IAa6AgxkYiyjedSwEgCBAI5MLSuFdNawUwAWwWwywRBmc3ptN8U6dfPzk9Xmjg+ip4riFFQhi9Xi/uXW+nj26Odn544bvZrBhS13TUuID1ZnvRU5VxQoxJkcOI8a8cHbUD0ipL807qJrl262nV1l11enqard1GNet6U27XkiZpFEptvS6/dm1vx8+369nZ2fIeBRrszHb86uRkua7bMXy46xUVrVF2aJPhMCnObz94XK/Xm1ZrPWjKamE2q+s402E1X42TAurZp8b/OBGWsywMDw5mxbXBzD2qVw99iFm7rZ/htWtC6752dr5KX/jAc/H68mzx+Hjx/B8G8r/9qP0X/+00DhNIaIh95+JHPva7v/8xo9QXtsZeW2UpX1uvjytg9DSLmppQ5Qr7Xy2mN/+P6x98/jSf7e529X30HTPeo4PvRr4V00vX0Q7h/s1qux4y3o+eybtXAe+vATsBdhRgnHMxhDAT0poU+LKaOwNQi6BBgAoCCwZS2/czY/Ty3b4FVAIioOCLmNSFUjL5nhaMDLQXAsolgFYb5D5gBKAmDkoi7YeGVR2lgZFaWS5TXXdGYz1OuvHjMuva6CDaDSaczimzOUmcubZLO68a1cWE0d7S8GUCvVcJOgY9DkptVcQ6KrzirJ6L8y7V2Gk6+yBInGaaYqeCaI1xYD9QnX8siBtNwVVVFD9slqNhrjWp6bJyYlXiK4RumEob++uTKiJeidruJ2HYsJit03FsQs1tHSXqhqZWrOHp/CSODZTWfEmSZyqFpBtApQgcIzrAdAASDwQDiIBUBEvfXY6LXrVSsh2tjVZG+9YvkgmSdmMcArmhch++tTLOQdCQ3iswXZ8zzxc2Hlz18dlpy7GBkAeHgBgZIgzuUs2VSqmJJNXCdcOx8GgM86O3luwDgG2ltAFvC+Me+LFw3dJwWsZUgSWCyrWJqxXR2FSUnHfx+VFkRb0Ke7DB54WP7p5K8j1NOgXQ+tjWmtEl0Cwo9iLKM4/WW2zIQFONBArSBRBb8FqgZwFCAaFmnKOFf8mC7yaITYQxAA0cXAQkUyAdgZqgrYAtIYTeDhMP1MdARE80cAAAIABJREFUfAwM1hfTYwWkRexVuxsGXN96SKoI6hrw071mpD0HeAi0DjDNm9FIySz2XUQaPJYXz0wD703cuiHSlQzsP//SrWv/AW+6Q3saBv9q53rzwM7GdC5jHOML3+sc/04B/ZeftQBI/m2sjlfy4VsH+FPC6A4tKFO4zEJ/R3bO+E8/8zvDieWP3R/c+csfGz/63b/1P/7Xf+/tHtN3wrvO3jsL/wx9X9RnvpeTH5wfv/mfy+wP3bN4DEaRGLQdgYCYZ4O4qLeX+soCABGIixhUhm/XAHgrIoBOvi3qQAhQ6EBgCUYHL2DtQdSrq3FMjT74kevPJD7649dOj7MmxJ1VcMkB5RsLZpHq+k5WNE0I0xMh51ar2IoXtbOLWZZTEzooZmhW6MoGLWusIlBYTSTSJalq967PNsvVevrl23e6plzb1jXTzaZd5dPhPUrU0FW1dVVn1qvtB65Od17u5uVwPp8niac61O0/OztdbALw7MHVvfdcGQ7+XLmpTznK12/fud+R0jsmTx4kud5rKtD5cXTRnWyCcyYal/0/f/DFn7mS3bz96KWjp9xxWxw8b1ehrWenD46YNdPNfHx1sDNd/9D73rOfGvOh++eLSXFevvxT0/Hy3gsfvFqevvj6Yjgrht41gUL9/HK9N6n9M6eT4baxdncLPPcwneidbmu2yjz9h9luuc3VFcduFRj/91+4/YUEfYeVaQp8fa+u/6UCPuGB4RWN30Qf5n3mwjr2AIxT4JCA2AAjMSYlVgjqm4nfJvb9yzJYiJa+nPayWMf1cimBEqgAkO7vdm9z/WmVBmLXiyRHAR77Aik3sNSiBIMg6GwSB20tXYR0nriyHDjLWarW5wmp13f28PV7Y0rrstvdae9djVvXKHpmNRsXy2Udr4xOmk8Rski2JJbMrByIoOa5pRcQW9tG+8Ar5TMtB6b1qULSDq1+GkLX2tJ5ldd7THYosl44bm603VTyTNuZbUdI0yNDHIepDJKqSarusYp+p4LhpthNKdZ6YkpyrVK+JI8EsZCA2JKVTaPPV5YXS47jK01Xm8wUuuZKExQBJPDGgJIBoiiQB9wmQKcKmglYromGAyHFoNjXt8ALRBEiw0OCB0VQ0laGICiVZsc6nU1DzBeJy7qZqg7OnQ6tQdfBr0lkCBhPYjXHyIG5RawaJck0VU9TFbIY+GwCOAtjgTgeYrtYAYgYuwGCKIEtGzGTLuqDCYbdQtotZLzvkKdAkvjoFVP5yMCVUTIFOKfNeoM4HYDuvJ7LwdORViah8ekcatDQ3qADEOEbhRgJuvPQaQJsgGo+AhwBmwZILeRGBtkQZA/gZxRwyqCmzxOVIRCe0Ugfe8ixAZjhdoG2AfIWSDtBbPoNC12qRmUAQkR9yiAAaQ5gBvA+ULwOYN2rxSMFKhi4DdAhYIQIc2Hjk7KDRa86XgPIAAFpLGVMGahYVfrHk4fNwyY1DW3xyCjZouRTOUKKu3D47Hed2t9xYO+u/vX8/jNtV6iPP6N++rTFldpjcGjx1jSgx2/fCP9k+BX6DF1/6vpPqiuT/279kd0f/lK18+mf+1/+zl/7B3/jP/+ht3ts34p3nb13EORznw/0qU9+DMC/QK+R9r2jX3AvaTeJgBytlgEQAau6rbcafYqyoA8RQvrl2zqQNgQ4ETB6o/nWLdh3cAYj/MVjLCCQyICNbEOoPdIIQLfeJ195fL+ru64rg5sNbPqeCGAp1dEQ+ekkzZv37l/LS99OhoNippzdrLbrx5rNeHdntpMqxU3ToQwtODUIJFhvVlg7UbHzAz1AVoBn++NJvViX3yhdexiDk92dnVNK+DTNsisbTeb8aPny3mj8lW98485r56vNp7Jpnj517UqVWvMTbefseDQcENtZaEIDUFOWtFZQz914an+ojL1x++goX8W2G+bRa9E5Z2nqljz6yheO1W8vj57m2GwTxfTKvfsP93em13cp2z64e/8L4w+Opker8w8VRfriSbVwS22m/uqhrZPsBWXy4RZ7L0+DK6bZ6O69rtzj8fCqraquSRJ//fw8T4C0ZCZv6aZmvjFCt8rLmP2p+aN//dydF1+kGPci4Vkm9EuT4JO1YLEMeP+I8HyhkQD4ffSFGS/kfX7envShdwYRjFbfdK8TAK1cHBGASD+ZrAV93Q55QBP63ipInuQIXLY7KwEktt/MWwEWrZJCW5olHkQM61oXug5HnGKZ57ykgSydagdGCQ/S8sC3Qz1v42xmmtjx3T3Du7WZ3WOib+w23dONj+0ApnHAlYfZQGdWsXXzqhQ1qFnDNI5c52NMc69FrGZxRSJ6SB4LH0U14nQa05KIgkkKTW0CzpLtzjC6XcvrVsm2BYmmECfjXKeNT4my2HmgpQidkN420kUd5+djlewkdFUaxqKREDMtosOhWQlgk/VkTGlo2zJJEmVUmK1LbSGRNQSEGAGbt0DT9lqWUjJHG4RSiDTwVSCsykTt7LTcFEyqDZEZeowOLUEMWxrVrZBGIOu13y3RakGhodLDKK1DNAZkncCokDgPOINAWUC+qCkGiaUlbyfa6sppD8SUMByYpq5dGrmGD2XIrksJLoC7d7dh3iUhG4NuzloMTOsMkalNArMrkLUh70LICyjDUBKBGEGiFbowIOJTpNGjrDJIGTE0DEoi2lNC3GPEVRvrO4psoqi4GeCDQ30vhV0QpBMgGMiUQMcELAjUErIrAjVmmAFDfG+usVBwTcCmJQwPBGYVACiAAZoA4hj6sUcHjbIFkhWgp0B2sXmhFYB9oFMOLlxUzakL7SH0VWcLACswes4WtDWkksgh+Jg+HI9/LCN//tzDze3fHV57LEqNi2Vzu0RaAjgk+qyIfPrb5Ffekfi1Xx785uHRze7k+M8umvd+QKXZwTYgqSL0geBNbQbgb7+t4/yTgWb3vuzjY3V38PxPf2SRD02lB9+XhTXvOnvvMMjnPt/Qpz754wD+CwB/E9+5Tdq3o6fJ37ruCkipi2Q7i57dqS5ee+uPs0SkCMqJ1AC07cVz4b77JxIsPAIEmsSL6bYxBv+m2OdUBPasLi+LNM66ru32RzMp6/I8psncptlxMh4tdV01vlr95L/7ng9uquiOA+F9rMgy0RgEFEmGzDJqinCdwAUfoKI7uv8Qhzt7PLbaSozReWDARZpnyej44cmo61xllC4m2WA4mYzen0yKwflXN+vgQrMVn3atf7+HmOViTVme6sbH17ZlF12ZPq3S5NqD8zPXnFf752fLKbw4AJu2dbPOZhZZBlf78VLvdVf0wqYpp96Fq23n6+VmhVQwau69cWhmu3cOiuEX/uArr/xHj05PakiMNxMeZclAf31ntPoh0xYc24/v+jbVTf3AE734OpnaO+EXGFdeqM7fs4bjOdw/3TPQgdXHf+713z+H4M++4pHf0ljOCIPzgGQjuLXDeDEA/oHDtb2AwUCDrMIQfX/bDABdZkhr9PFBhvTLlXiADBQsAjpAKwS8qQNEb5HiAdXwlKJDv4O41NsDeofRXloJA7fyDW3IgLyLvYgOdCvcradTXo+GMVuvX7u3ty+N2BujWOOxHiB1rcq4O892qeiMWXWG97fRpVNvZEqN6qazwZGPm1OdDq3VGzvVR6Yq9ylK+kCR+MINuO3MIAolmrNEfFV0ITdgkAEPEdqzLqhzjWQ6zFMXOTRjVoCpoLK2sIGD5k47X1jvd4IS9kVGk5PaVzShNtHCECKKFFcxOIBV8EImoSvjmvRQdJkSDcp5q9uotG1jPtReBYnCYN1CxQzBAM5m4NyAhEVSHSQqEBw4tjCmr3R2qpZk7AOiBtca0QBEDTCmxtkYNTqmxgUFs5ZuA51awOQQLVACOGiHdTpgKT0kBGM0lDSy8Q0lbRNNahwTPHSjQgui4E3mPEzTIaYaoRRQnoOeueJU5wDZyyTpQE2ZcIme/D3YEWxbH6uF9uM8RkVEORHNDqxamAaTao6YGKlrHTWBo9VoA1FbAo1KMdUOaBWZtSYeKdRtAiQaxhCiE5R3BDIwaImxf+D6aMMVIBrAbC+s2wJURww2QJsrhMSAihpY9TFxZMDpo95YxzoAnqACwy8Fmhix65lryQF9BsyyGt1WsKQcaX4Ryh0U2CxKOAATJTCJAqqA3U5oDbFiaHhzfVo93JltX+fJyu/SxO3xnW6lFgDMs1i4/xivFL9Cv7H9efnVP7I12vclfu2XGcDh575oMd/YZ//ij+3+uXY/n3zVZQxH4/cmQachwAMw/rI9N/362zvoPz5+Xn41/vf0Q//8qHv0W7e+ev6Xf+Ov/6rc+8yz35f37F1n7x0I+dznI4C/TZ/65D8B8FkAL/wJ3oZAfFlLe9l3Pseb63J7+RpJrAFOAJgUUAF/VH+2fpggCHTfOEEAeBGN/qn2F1l8bQ563ZDqVuLPOkicjUbTD16/5XSoX/rivbvXXrp/5/kPHV49e2b/6j2v+cq6CWKBY900VzjNgURDATCcQMMjG2Xwvg2LsvRN3Gy22xIg0PHR2c3YuLRLyc5PFkWm7M1WhfXN5w43rq643fory8X8YDrMX02KfHv39v10kNhtU7t0fb7U7//Q89XZfLHjfbjy1I2ZX3saPHi0qLdna4P+OSrQCxbDbtYAAy7NzfCQHtyg7FpStZNMqdPtUvNmW+btdntzIdl0UMQE1fZ/dtvtTg5jmuV5d0Dtc+e2LH7dPL/9amhe/gTqh8/l9mB2ukgSMn/1BdX5r4l5JXHdo2h1bIIariJWHw7l4lHAx7aCP79vcXhL4StDQoa+0G2pCMf3A9ZpwIcs4QZbDCLhVh1FSdf1OU1s+iTMC4NoxAEIgBACCTooCF12VnmLoycXPfMEQAo4pHAgGLwpwEeqD/nnACogdiBBkpJqayTo3DkPwqMs97uGEts0dVS63Hh9tOOVu95Vk7kxVUcMD2kTbVUXfBUHalRbk866TV5QWerObBfapKdRn+y56iQZZR9wrNKJq5OiyDJyzl6h7uTVYDDJwmCPA6rUQEdOWNpmrJSqB8NwO9jzva4bSSvZ2WSqMhM6Y+qkVRjEyMwqatEqdQ5eV5adW1JlRAo2WoUytrYgKMKOI0mrjgDr230FFwYRm5bcvo4uZTU9ccQRrJoIQ4G63HZ21ZmOwC0BicBTE3RQRFAsaQG1eAxSBnFYADFK0KoDIkKMUKpvd80RQJNoSl1nXMJQLahpiLzJRI+CVm0bXQ2BIOoOmqo2Fj5KyBLJdTSnD1OJwSeHe4FGFr4pRQWvOVowCYghPksAHUDz1uJEUp4VCgVq7GKODUC+gaheBjPKNoKp0/vZCN1EcaQt69YzStO4WZ1u7xvpKsW7bSdJERRtBDrEUNWKGkM8uebh10DnEnJXBVkucHc1DAh6DPhnADqKSLVDfoNBBjBDRlMB7pWI0BCKpq8jupz1slFAMg3gM0Kchz6v4DmCbgR+JfDegAE4OCQDxrJiJKGvUJJzIDSAUhoGgBdCtxEYAO2ihEa/C+8GCm5ikT2qoZxgBtDWScwQrTrerr+GK/u37q7Mz77x2ouJDztb2NMEIXuIwez3cGX+83/knvr7FgRgaDk+fedEj0uneZCPwnMC0VHyglo1VEDstdsBC0FPhr7j8EvypUvNePyNt3ks3w3vOnvvYMjnPv8KfeqTfwbA/w7gU/hehThFLmiYN3026o8q9BFahX4fLBm4BWK1lRgApMPeEUwB4BTfVR/gkpzvALgUUBf5ywpAC0IFgCvIFYgPAA4/8xM/PXzv9afHvF6/cbqa6+PVypoom4zouYeL1VNvHJ0d709GRzd3DtZVVeWOCEZpBGKsYwvtRZghy01J88Xaj6ajVoscnh6flK3EqS6SNk/SGKMgVN3t1GaHt0/vmEbmq/oNEw9zfsoMtGRZrjaPT87sYQFbmDwGv7o5nYVYu707R2d559q4Wi2pK8v84jqll5e16QBrARUBU1Xra9NNtVlVw6NNORikqipuXk1CF07yfMinDDpfrNJHX/zKh32MQ2NUW2cJ7pssK7Zl+sP+/AOPWxn89mKzHtiYzznXu9JOlevyoi+Sngn83lTz3iRNX4ibqhgxphC0mhBnCs+id9rXQ4UfHQKDJfBCjJhmhBkISi4GThIvmtgGGNa4jK4o0ujACNwne6YXNy9KAF3YUaUNWgCGHHJ/aVQEi36CEQUEgay0oRoKQ9/AA6S0bs99cAlUNx9M5Hg0zWvApdHd18Rlk6bewr3RaLJuvS7WPgsTXw7sFQtnk0WT6pNGZKJJJfPhtZBWKZ8OxFQhdmq9PWsC3m+80yuj2Tt3kmuMlirRfmRHKpC71jUlcspdGvzo4UqPwNEnuknI1SVpVNNB6qCiiV7FEFGWvHCDdEwmErucchWpcyV7SjzlMzOgRhwxIBlhy+RJhSI1DHZUk95acXnRrpRoUrFSlHaVgAClY8xiI5WZsFPaUnS6G9tgyfluHTUH4QBILn0EcTxCDBoBCVRsRGsgMkWESB2ILTvA6jaCIORZ1YKQMwRDLVZCxLr1cQBLGtBboVBGUXqgFUHEwwcdZBspZlprTrouCFwAQQVEAnToEIkZhpynaGwm3u0lVZAtlCs8N9FKXINXJZCmoEkm1C6ij4mgsaXIBoZmgB6ScwvfRWKj2qBRAo2RYDUIV4GuVoITRWYORBPQfRVoCsJxzDBTAZY6cCNQ9wG1C5ALkFMPY1LIDKBzIIuCLFH9VmzebzzoPQx5HRARsCHIsl+p3QGQe4XMePg0QoEQ2ghrGc1C45wSDNECsae/GYBSBjEBdvtb+YS9vlxY16UX00TKXP/6BX+IObLC2fj8R/3j39gJtSuRPLdEenAN5csPMXD/EO9TL2I/+TvvJGfv135ZY+frP4y925tHm0/e+Y1/PcDzV/z1n/5oc+3hXGY7A0r3J3jOakzrmhhgKPTVGZkih5/9he9LRuwHBe9Kr7zD8Yuf/kvtL3327/86+nyrW+idj38zRABpLpw9fqvsyuWDRm85BoGotp/XBui7XEEB3XmvMKDxTQ7mtym7XDqT7CMiCCV6HaUlgKD7gjYFoCRg81/9hb9y65kr1/VysVw+Xp+pXOvJrZ39iSIKx9tVt3g4f2Vcwe5m+cIM848EiuhEAB8BRHRl6cW78Pz1m3UXvbRVM1uWlaw2q610MRwWk7Att7aFT9vWp5nopDnvyHuzvT47OMizYvaomxuJMZ0OZnaY5eOTs7ldzNc3Hp+czowyBTy2zrn24dERh06m6Df6/UUjAIagbAqOQQDoZ5gGN9lv7tdON4rJe2mNVQ9TneyvN1VVjAe8M57kZd3y8em81TapD6Y7m0lheN1hc2Cw9yzkVoKQpVrH+6P9LzSdX85iVTxFmOwA98lo5ZS6WXh3ZZmMDyix02HoNL/J1O6gr8odMGOPNEakwPD9LTNEiJF6D5UFCZsnjJ0igiJGhT5CKwAcEZg06KLvbUCvq+hFoPlNts8DqIhRQWSuk7hQhogJZQxUmsxr37rHs/2kUUYFZWLQ5BtlqlwiO5MgELdiU0RjdylRt8lmcVd3OWusSOkWm61SdV0cBs+lKSoXfErGGuOc5Fl24IosG/pAHezq3ChaGG6WlNp2lKssuggPt+mEJ22bLCWJIR+gIuMbTXYzzAslOnVbNpTCbEI0y2C3g66rJlVtmJLEJISoHAWda2IlzBCQZbCiVjOC0ay7jhofRMVoTNsYZy2UROjWUdECKpGIRLSOgamjMqy9ikLgaeKSynlfe2NJ6YyZVADghboIakZGRc2iOyFEIkUMAXxMSK8YTtU6qq5/PllYPGtRE4UCLXUtQq6hE41OL1qR2lGnrBAhtjVLYg0fTAk7QwSSNnbBesVRbK+WozsHbCoymijAEMxExB8HmEfR7IqjxGkQE9pBQlYHYqG+MJuJu0ceUkbiMRFSECVBS27gYcUUih0Bq03CnGlptFGVKKqJxQw82WHfWDtEIMkiBlFgLPUJyBXQLSPiI0CWDG0IWAmQOJh5CjgFGkZQ7GMU7hHAxwQ5Rr8dyhhxCsTTCLUWJNwrREkg8IjgGmCAcJF+wCgh8OhgAiM4QkSfmvCWDpKIAAYRlAR5IoLQlzahrhEX5TjJxjHsRs/7JdRojsHyCsrfjMDiVcxO/iJebn/qF3/6+zL/6zvipd/791yt/q46G/9VbdP/5Eqm3nNtJJ/IlP6R43U9ujKLOoi65joZMhl+U1PfwhjcwQc+8Xff7q/wg4x3mb0fAFwUbvw3AP4HAP8EvUDudwH1ZZD8xBm7JHLwlt8OQLxQTLMAXAGYNcDPskIRxd9FbAyQPdl6yqXgHj/5FPSfJOhgkD6pCW6tUuaF/WtLnWb++Pxk/b4rTx8+ns9rSHRfe3Tn4auP3jhIle4W9fbotZOTzbXphA5vHLy2WVSfXFfNj4z2R7DGAA6ofIUMOo6ygkmrzsdgCBHZKA1XZzvLl9t2Ua7LonJtsSjL493pZPfmreuzulyHsqnKxBbF9as3zKuvvRqrx2Hkk6Z2WTkcFIUV5xsAtm1csq1LPHi8GAYPpCmMUF9o+sS9JSBRDG0A56QDJD1arfdv7gzuPpXQdj7Mu6vXD/n+nYfTq1ennFh+5XS9fq9V+g1GfDQaD8bBB1ij1HPPf8C+fOfuPUCe2tsdFbd8Q36xHM9Z3yTC9lCEUwJK0Dg6l42c288JZt+vtALYAakHkPROev7kFl1KIkpEIAciA80MTjTaiCcO3LdYCwx6py72iikc+/fu6dsYY/oWlvhiPVYKgCOWCiQbzehU4o0r1e3da1H5rpv4DIskD6Mu8GFTDoz4tRsalCbdW2vTCOtMNCsVw3i37IpclTVn9GqIcm+l+CMzw5qVetRaPYtoutpYGSA0rDWLYOJcvNsMh52JMYud3u9MmihDDYWwGpAZlCkf7dTNdNbi2rFNXZtaGYkMSgtyLUcsO2m8hh0z60xoQuluw7rV9Xma+ErK6D1IcetEZVa4owwaAQQH8mlMfSBGJM2KLQKTWHCdKMVltApwkQMtgtKK0VFGPiBpO0IyoDCcl8azoFRGkQrEgaJwjGWIhMg6qb0kgug9IhNsCIhKk64EgM6sUB35wjRZRYiPkTZdq4nIMEwUBONBGCeNeD3sZpmYMsTTueFMN12SgmPsIlsdNXtREVYYBgK2GjGkEJOwjRAEB7Wz4xFMRGwInWsJRYKRboUEpHUkil670+CwpHaRW+MWBvvKG1ZBceupqobihwb0jcbJNgaeie4cCzklA92yCFAWGuuYwqCUdONIDRiY96qQ3BIGxwaSA63vC8lEAXJmkPEM7ayGv7FBvAPEL/YPyCWU1TAJQ207SIVeBrTtJz6PCL8QWBA0CBEOHXKsobGjIoIAFL85yfkyN+YyrGHRIsCg6509J4BNEFXbSnvi0tE5UihgcRPlAMCHJuiSv4YvvQYAv0Kfuf1OydurGixCHOqQ2Z1UzM7+FO9DAOdexeIw3bLEbVv5aZEGBdGIYDBJ34wd9G+93eP/Qce7zt4PCC7y+Bb0qU/+efRafD/6HU8kAij9tqPf4UyFvm0pMaAd0EUgTgEpQLSmmJJgaQD2F+wW6JvN6fp4xq136mSzMRC0cLAwiACkC6G9t5ovdrGzd222bz/+/AcPZnmqpkm+uXHj5nPrrtIF86u3H93fSTTfm3KyqyKlLWJVxm448QIxvTazYYJJLab5SJq6PXvpzl1v81SF0GwfnJ3de//NW9fyPFm+fP/ei2MzNSn4uOnWh0fDl4tGGc7D9OBsee5D4EqpJBOjk9JH/dqjZdtWdQSgO7/NTxaOrKEIAwYaEL75OmaJRd106HzwADYAtZPDA7pTtwPKi5U1utybjPeL55JrozzbnJwvuD2Zvx7Hcp5nOV05PLDr7Xay9dImg/TxM5YeuoA7zzz71GHy+LHKjx/d+nG4nZXbFqlgVBk9CK3/mKFOa5MqF6HHEskBLlyUSnQAQgAsX2iKoV+MWghWiBhLAITBBGSi8UQ5W+HJ7MD9uom2tx4JgBOljQ1eerWQN83QAd719uJrcHQx2NaYOPYdfOvVg+HUV0mhlbIJ0IRFQ/XQWF2pSAvY40FdUzQatdJkfNtoGLfQut2aZOyJBiyyTYjWkYSWxWCoKd6pkyJChTdmrv2IbWIikA0pdb6azhpdbRQZMzXW5Bzdto2qFG3USmNxsuq2IU91mRXz2ugUrM4eMu9eieVwJJ0vUxUpV50koMBanTUJFwjaRqMWlUSX6EgQUzcenAMhJTA8NIAMwoQYAFEWQlpF8UF7jkFFEfiukagLsRYUI6IQHCvEQSqJJEDwcAwgYS/aU/QSAnzUFISJGbYDNc6pEI0mCCwRUd7LF+ZVLQpwpJGGgKiiR4Cm4KILolLvWLcxRk5hkpTESXBj59XAkA37axIgntYZTKI1swklx0HWNspwdDYoUgbMACmGwAmSDmQSj7Cn4LYsviTyHeTx3JLWAU9dbSGKgxop5kFIEh9JRe98CWobA2o6l27mKl8jhE0keQzRO4CZ5riPXZ7G49A8UqzOfUxnTk33hagkhA2hWTgUI42oCfomISyBzPVlzJ4J+tkOm/uPIHMNbPDEI3trzrE5NDADje0b3VvzUsRrIuMVakQkIAQQCAkKBKQATMpoul7a4BItnkitvJnvAgL14gQxArKFCpZjNTBiuKsWzvLXHtSThwFaDlEdjPo98TkA/05x9ABgUeJlq/UfaNajKrS8qaDTpE2SoeeUePjwMQ/mNfPzwwGyIoIHfc+czMLjZ3/hB6Py+PsY7zp7P2CQz31+Tp/65M8B+N8A/Bh6ZueP/DN8s8N3ycBJeLMgw6b9nte/Fn1bADLrGyA4Abh+sw7zCTZNBR9jP9MBGh4Mgy2AnQQY5aTeuP/4Pr1ByA72DlYffe6FcZFldrvduBCCza2emUCONr47D6tdCfHKdDqu9vdnMpuNULm49FlPAAAgAElEQVQWW++RmgTP2OG68t6XTatC3fnFauXyWV6FEO7YxOzuH+xNzrdVK4tz13ZtsdnWr+Zm572d6Qr2sklY58NBrtpujCTRotjwo6OT0ESJBqgBsvBe2IwsmQyhOv+m78oABsMMXdvBxaZlSscTDb9TZPTQ+2BNopQXdfu1+7w7nagSsrh//+gFReoBg49Nqu9+4qMf3vHeL1+9e/clJv6Amk2fv1FXg53jh0G7ZksUZzPXjgfXr99XxoyV0GE4umdUAwUwpYoAH2EAfXkzLrtXdHgzvs8AMlLYiwlMZDwRB1MQxIuIU7/EXC5jzEBIL1rXRgA+9B5t6PPUQwSZSOxTCSrpazGwRYzrbAhnM9VVW+dZqSR6GXR1jKSo9qGblvN6T/thY9QxsnR+rpJDS1QNu3rYaDNcahtj8JNVkhKipACyELoB+cCRNZfgXQ4hhW+fDr7LfURVmmwExPtIEz5XalS17nGhaG60GIhSIJ3G4LPp0MxS11DllXdswlDFZGUH+r4z1Rgt4jDjAMeqqaLkmY+UEOvWO1FRMbuy46CN1kVhxNrILZhdyKFVRN8rRKv+EkXlAwMgA3SdksjEUG7XknZB2U6i0bACiRAm6cQHDU2WaBIVCQFt53Xw4Cwj8YEEAIWoInNkRIlKKbgItgpsLUAOIgoiDtyKQew4EmE4yhGZY4kIOA+zPEcYJgBVXSw5kgAIHpSa2tRMhNYTR4J3IK7JViGGZKRUZBEX4bUSkxhCDOSdVdpZotUxY7jXSaoDrA1CZFiBgxt4VjHwhFxs6g7Hj4yLPrH7KbFJalEN81GdU0yj2RuXQe0GfXj7GP68oqgzyXeJh75FpCgyBC0eOdg0YrtpkU8z0DMKXAPogPj7gNoDkGpIQmhWgDoDzA4umjbjSTyjndfoDEHewvZ1GnR6JYM6bxFKhV2EJ/l4li7aArV9gfolmou3XMOA4XDZyDr0suNIADcEug6qpcjjUQjHWXCjUTy/tTts2y8NrvxWc2q+MfLh7OflV7e/Qp/53nKw3ya8/J/9zfTF//PRwP/Mdfehjx3+6VmBH6o7OdBCOkQdI3n4gJDkW81K0c54SG0AWmqRmYtVpfdAvvr2fpP/f4BE3jEbh3fxxwB96pMWwI8A+GUAT+Etobw/Am91/DpcKHAAfeJXfZEecwPwGVBtAC6BYQnYiCeJf5fKLJc+R8RFCtfF8e4wG8Qfvf60+p0Hd6anddkB+J2/8pM/c31/Otv/8isvvnq2Xa6e39nfH5F5PNzK/Lmnb3y4Zm8n08kb8+3y3/cE3QaPIIIhlB/ZdFm6hs+XVfe+fGdwslicDg+nX6vm63PycV9rGj/7zNNmOh6+8mgxr7/yxtcPqWp3lF3v2GRYvfHS1lXrZm+9Xh0Q+U6QtNumNcIoBoMBfNMlbXehL6fS3sW5gFKMECLQlHABCMq0aWo1AOyntlOJWUetXZFlTKzXZV3LT/7oR/Pb9x/hpVdvf+njH3zf6X/4U3/64Wg0/Hie5YPf+fKXqub02M3SfGC9u/be3VmFxdmkuX0n3fFdovZ2EluXtjo7NXR6RHn8N5fJiABd3472yU11FzeV+pVIkCJcvOghF3VxPQPyZlJN3/LYtT1rOAGg+SJitQViBVakNQ19RxGQVsBLnZCE1gFW3xuN/clwKjtdo4quLU+HYzusq3Xh2gUpdbCw6eNBlGaV6JFjwxJRLSeTEYSMrbZJ0NoHze2wc0mIYgrXNafaLtfKpENGNnZt2CRJK8psAmikIP8fe28Wa1t2XYeNOVezu9Pec7vX1qv3ikUWySqKIiVFjWPZohDZsonYioKIoRM4dmQBjgDDH4GCILaQjyQ/AaLoRwkiKEEICggNRCGcQAFoKUIskRIpSiSLYlvN69/tT7u7tdac+Ti3ipJsSiXETgjzja93zz17v4t99tlrrDnnGOO0N6ZIMVDfxqPKwliiYfL5DkQXy6Yt9lI3HjWr9KScWHFWcpJxIEd1HzSJpuGgsK7dGGpqp5OyT34QjPUe0lsIpBNNxiRrjQOhZgWjh5GcEqtS1HQ50mqC2SalpshUE3MwxEDMs5BpZz1BiUAGoJQsFAJ2oh2QfABbB05JNCalzDNUKBkDkwSRCabrCSBVtmBzOTOpBLQ5hFYWEoiZQpcbRPXwXQRzghqDuGlARBDLoHHOPoiIJDJmqJQCtGuRSKHGqOnXJJsqsxV1mFjA5IihVussISrSugPde83w6jjJe78rclJSWCZnRbQ3Uvdglig6NJCvtgIRL+NcjCTxbadmz7onD522FxnaXUP83EhHZ+di762ROUHX5aYYh8RGTFxFLO6Rjp0QX21gqhH0jEHnAK4C8hUACwAjACtATwFTKVQI2mxjR2SFbf1sDKQJYB4noI8ACAKD1XC7M0Un2OkVfLlr9WMgLjoQgIQM3eX36bL6/abd0OnlkPIGJq0wlCuYb56FmlP4lqCb6whnAMZxW3w/WxT296Zd+tSj2fD3H0wHF3/uq4/eDuD1D+tHH33TL/ifEfT3frkEIPrf/MT/qwza5r//r68tzuofW/f1Dw4PqhcHRTaISQePTmHzzGBvLGo4J0CBbLNlx41FTMV2pscogAwikKrC4Kk4418+nlb2/hWFfvwTPX3wA78F4M8D+OsAfgbAdfxpn7kqAVs/NWwXecElcTvbviMCsPcBHQMDBnhI7IcqcQ3watv2JcuG261IgXlraqBj4xNSrKNxZn84Xv3O/VenFGJ7sxytR2V18Guf/e35D7zw7tp2ga/n5fXPP7x3+txs/1PfceVtz6WU+vOm7nvorOHIGTGsKqbweHZ2EB7Vi8Vq0Wbr5To7fO4d9YvP3+b1ZvOuL626k2BSHFXFcFQVNiWddo8fLO94b8UMgTFNh9M9mQ5uxSq66stf+QK9+torIa+GGhvm1pBP0dFoOsHJ0YPtFbgkeh0SiqLA4XiC49NzuLzCzmyKVR9oPBrIg8dHNtuZ2p1h6dnYbFhWJqkQEa8O9qZrwzz/jpfedfr2O2+rbl+Zztquexz69qXvvXpwdRmb8ZJtVh+tOh6PDv3OTm68ZTo7dxJa2tx9vV8YwmSyA5yfftOPk7at2zfNtHtAloCU2Hq3RQdlAl0St4cglCDsiIKTIrmte8oGwONVglcgcwy6VPKGwKyROFQpWop9EQHpFHRfSOpkuwnBlJY1+ZJ2+9oJNF3kRWNDUDWme5RNB9E5o4aHtUgWRYbWsgbYenvrqPZl1YEoMfOFJN2pLdvaulUg6kmp7OsNqWPnlSofOruyNmkfr9rcGO9SxkJTMtLnba8tMOjZLWHsRZP5l2M13PG5fSeQKmg0LloqRMSAYIwyRp50kEdYEGnqkVqA2MKAM0uXyvVoiCxUVRTKQQkGNqFpMsAAA7tNqkUQNsaFQFCFDrlzqgjJgABisJJqhHMAE4gTbNqScLGGmeiNmTBFSCpQDiDlTStkbeKSraoQEm3LiQJwbqIqQVTAqeLl0mQDt2hKvz2NJ0ZigmTG+JQsDXwi45guqEfyIKpBxCAVSvkUljip7SAioLqGkCG0ArUJFFqEaGyWHSbuWmB+arBzXXp1xsLBOgYWS6tolAdAv5DM1McsV3eD0NByUsQxBzuxgnpFGl5pyFkxbV6rFUeGAxCRlmfeBFNg9rYOZBUuAWEhQFDImQENobgOwhzA4vJRpgINPXTpAeFtwHB9+bUY0uUWNF5mohkwEoqVQT0GUmiQjAGS35K6HKCFQXdZ6wv4higpXD40LYAACwKlC3gRdI2ijMfoModEEyRZAc/ytgpPBgg7TbzrgcV5mb0/C7HGVsj2h2qHfzJ+9Rf+Vg6g+5Gf+sU/Spy+Cw7A6P0v/eoGQ7wPwEMAr77V8/5xfOav/P2D6gX5+3Vo/50rz+4OS28tk5KzQBKVpkl9X/SLwcAde2eKJMXNvu09AFjTIKQCdQ84hvoMP/eU6P1/g6dk719h6Mc/oQBW9MEP/M8A/gmAnwTwV7GNyMr++Ue9SfTeAAMQu919qm5/ZgB+CWAAkKiIAprj0sLAWNye7Zsn8zOMfabXJzv0ytkpzZvNcJSVg+d2dnVvMB48PD9bvHDlBmZFWT3aLKa5d5+7Ndlxthx1v3P/66opXfza1/6gtp1+yWemapsuu16Nb+7e2GfnHZZtg6P1uZQu66MkO86L9pzW7je/+uXye+88Px9aF99159nppml3AdSI6W7ddWE4nOzv7lR798MiO3uyaos22Nmw2p9Vs3HpXqLjx69R285no+LQtMtTausaFAIO92Z4cnL25oXxMDBRsN7USClh/2APe7sT6Z6cxtWG8z6YdbOp5TjE5s4zV9aHu9OTu09O96eTfFMNBkuAi9nVG+/KitHs5N79JzUbX+b+xtBnRZUVOTvv98qLoWkalv1D7cDi52dq+kCG1Jd9D8poOyNUlEDXAbJtN73RpbJbErcEULWIVsBUgK0HNBCaehsIsBptP7oBtt2oeiXoO4VODSpHMAD2AaxIIUq4iMCUABOJ0bPdCJssieRqYS7a1CyygT9MvWud42PnETVJEZO2Ra7nfuA8UnUBQi6JQ5YVvXWOY2iH7XoTOD9qsixAUgXQHMZ0w249E2EV5jY5P9xEOfAqx7nlEIZDWlhTMKH07ZphTF6rwqyaMzcsQiewXgkwzkKFreFnxkWxZkYOaX3ebaTlrEHmDAwI4vsAFCZGg0RJfR6IYhZ6jJjBjoWUgBiROWvQdtDMGwq6Tbnok2hhOAulhzDDELFRgmXrJYrUrcAwUW8JxqgNLYllw10CrItiDPhNLyQGVECiQB8A6yAEUB96TmLcsHI8qLYWOUoEFcSug7CF8yskYyHsYNoOlHoZD/JGOUfbMprUYCQKMYZNp4QuxMScQgIcK0gCuOAsieuMNhBC0oqTBDbcG3Czgo0GceTRFg4lKmTjqpPcQ9gTUwUmJIqRAjNlEoHCgaiPyV2FyrHr8kKZvRp4UDpHkoiUqpQVBTTME6UCqQyOyDIVtwCew+eIUI5YNhYorI6OiPRu3LpcXgH4AEQOiEsAd3EpkWXoI93OMmTYluCItr2H+fY6Y+YB8UgXCYwED8VkQbDw0MTf8JU8BWLFQL3dRlX4hinpG/L3BYA1GA3IWFgT4OUuUrNAqdeQsuewKB/BFQ66cS48vhr05R3oZwDEZ4/nzaU11ic/rB99S2rcX/2Fv1UAuIUtkVv+sV/nAF6Yrd0P7qndPxnF//KtnPNNfOznPQCzOekyePnbey9VP4lcrxGUyzJXa/jMZv6LovLp2Y7/P1+7N//68ui17l/7vu+oATjD5iWX+ZeadfixBPdSniFQwheGBX4UP/7Tf4J711P8i8RTsvdtgEvxxgP64Af+IYCfwzYv9R8C+GeUGt/Yo24PxZY32LjdtL6xcQ24TMYyW1mGOQNSdqnUHBDT3fkZvCiR94mSKhHMwGVxbzDUcV6GK5Nxd1BWm/Nm5d+zf7iYFCW/ujjl88V5fbyaz6vM8/uv39p7spz/1VO0R3/p9otpzNng4fz8+RgDBsUAg3GOK/ko7QwHeZA0Wa6b8xuHe4u6b5tXT4665/cO03g0HKm1ncbu4utHj7KlxOLdBzcenjfNedd377q+uzMN65pLZkUK2Wxa4b0vvnv227/3JTo+OXlzenFQOOzPpjg6OXvzolSFh3cOF8s1GMDJ+UV8fHKGlJTrtocxJr9z66ZeH5Xzi/mqvzYZd1cOD9rj84ujT3328+nFF952ZZqbweriiSJ2L1jnqrLaJViTsFmtB4uLgR8MDS0viAcl7M6M+XyHUW+A/BaKxRIaeoAXwHAIGIvUt2j7Pq6AVU+UXVFtPLAKEBVIEYFuBDYAIEBHW0HFay1Q5sAVbJdDU2wlt8dm22aaMJBZgyYB5wzkS+JBYGumqd+YFLNonEnO9KeDoe9yY6xKNE1CFSONJNEw9Xblc5nzIBhNHSwr2MVaNYnLlnB2JsRYG+eTdwOktEDfZ/B+CmOSgjMv8bRhw8bbOiPprDGPjeGxA8aXH0m9UpyDZVAOUIhmYqw3peM5SSpiiptA3ArxfsbkgHgHRNKqTWh7BWUEQ2vvYYKBIFkLMkzMBMBk2RscOmnbCi03JU8GEaqqUELqtulx1lkkCMAmBWI69xbjPnAWSJqeJHe5MpMJoohqmCkasMCQgggc47brRQC6ALEMpASFMSwxwXvirpPQBXLeRogw2JP2QST2LM4CjtEaA6cMExNSShBDMFyTEdVeLZwzMBhBhITTEhx6H5OyJRhbcp8gljqGM4pADqzikJKQ5tBEFAeVJHSBKFmvhrlfqk4zVV8JrddMGWurS/EbQ9wTenSgcd45PRbSMpYHkzp0cNCWWJMychhbgdIGEBeomjhwJobXHj3JZT8Z8LskkMRdA+JGoR5Aa7dDIiMAy0sbgIqRvMCly7uaiq1BVQvgLKADtpm4ZLZbmY6gG0ByC7UMC4VZK4rCwe4w2iYAF4ASoSsY2gEmbh8R+TYTEgpgBcKrKHAKpzvoaQ9LvUDlFijHr2FmO6z1AOepxUCO4b0fSqq69YI3cTREkp0mfP6LmO6fofjuj9CHPv1h/ehbqe71AJ5gqw8BAHyEPmRvTL5r9P3P/d36lYOv/fAd1/yNk9p+8WQRz775af4YPvbzBsCPrM+aH12e1u+hgb4zDjo32xlhSP60LItf8t7/twDO8ON/T69g+xD5Q+gA/JYFfmsI/MIbL/5zFp6n+JeMp2Tv2wiXlb4z+uAHfg7AywD+bQB/AdtH3dZCiv5INsabA8IDn7lZNXSL1QLX8yr9wXpOsu2zIQdpDuUCoGCzbf8rdogJ8vr5qazs2u1WQ33PrZvMxqTXzo/6zz6434wGg8n+cGI+d/RwaonSvGtv/M7rX783tJl/dvegP+1r3Nzdu3YwHMbvv/32rx4Mhs995vVX7el6hbHxeNKutcoybJq+DqSLJobz53cPz1b1+sp6Xt/oYpy3rDWzXHzh5a/wzWdvFAej7B2P796/bZ1mZPPhdDDtT9v7vglLc3F8V9Xv0fWrt+n+kx4PL77+5oU4Ob/AYnXxZj/UWoOmDQhxuzGdjIexKso+xuAen56jzDfdeFCxdQaLprPrmPDg/PTRX/srf/nxutnYz/zeyzffezArQFpd9DXdvHrgcmPIOTJsrQyfvTOMTavy6J7i+IipLMFkQLv7SE8ega5cQ950kItT8PEj4PwMYJt4/1rDKag7frxGSk0E/BoYOnBN8A1tW7INti343QLojnPj5srDO114RYGrS3DpSezQoBKQXeZFymJc1bHX2medC3HdMu0nw6ZNJBeucEvvQyKXsjZ0rSN0Lu8fWeMbItUElAZdyw6t9bE3pjfOnyfD3oIaMfwExgLETer7EUL0rNqCTS9schjTrgfju5D0BEyTnf3pst600tb9Lrb1nAfYLvM9QLsQCpSZhwbmKoCaYL4qxlyNTo0DIuCOkoQ7IbTqvT8FGxctRk5FeRO8kjZu6C2M6QEXtud2dGk+Q4CDcwllruocETORKmAtQAZKEHSwPSNJiZollT5ThTOigXLrTBIyfSD4LElMQAApjPNQUVDbQ3NnKTMxwsJ2PdS7bXWPDRACQlFYRwYQNThbBsksae5tpwImAvoEYwTRJIAI1loIExKgtuvIxDW73idKCc46SOQiWktMSvTwTGNOWXzGl5GqmqODkphY9jtR7DqruwZKSqpEEq2SZbNaqVDXqRFDq9ql9YVhHifH1uP4sSdfJhnXtdJVoeRAHSznDG859Ezg/gRkFKBrUFe2woaNZYjkjBQS4yJhmQjICFVkzVoDjdAVe3KpR34zITwRkCekISEdKfxEwBUunTyx3bq+QXPGDJMCiBLCysAeARS2I6zwBF4DfdkBE8CwB5cMoAMWERor6BnBqIAQL+3DCQqFoscGY/DIYLzciIc3Hh4HqGmOyhh0ixXceIFqbKHxEYbpe88fVhZy/QjVD58Cv/wclhuGvheQw/8R7w5/g375NdWf+BMJ2o/81C8m/LEEiuvveulKXhU/+rHiZ3Ye/WjxE0F+4MoPmYdf/OxP/Z0/Hm3+z+JjP0+Xa8IzbdP+e6fz4+9erYM7nE6X43ywhMF/t1o0/8PO3/kHmz/1XE/xLYGnZO/bEJek7xMAPkEf/EAF4D8F8DexrZAAf4jk7bgcCYrboym899hs1tithmYSOz1vGywBTMoSPgTThV4kBuRENDQOnSbtJHGVZxgXZbq1s4s2iZ41m1QaP7m2MzXTPAtfPbVx4LPl+/JBCBKLdegm750dzF9dzs0oyy/+0jte9FDds2zNQTXyhgBOEgvreyNGdibDk6v5MD0+Pxdj7fhs0xd7o2G8cXAw6ULP/8c/+eSd4+NTM9uZNe/Yvxbvr1/jnb2BFuMB/9bnf9sfetjbt1/E45jh7msP8JgzrGNAWXrU9fa5mDuDclDgfLGGIcbhtWewXG20XZ8BQL/abKQaDCU3VL/ruWckL7O02TTnztqZ81n33PVrOh1Wb5svl3cP93df+v73vUfy9bI0RZa97dlbxjsbVEVCimmgyRCzQ54bvnoTUpZYsUXlt1Sz2D8E9q+ijT10Ot7WFNarutjdm9PZ+b3y0etXyiQBeUF92/QrIMuA8yHw+Xzb6tnDdkJpGYBQtPI4Q3qctkxmKMB8DloG6KEFvI/BUUrqgNqnZO5P9ipfb1azblWs2OlFMeybzJtVlpPvu7TKKwrWGU2ReyCCjaIolpQ0DNrNIFq7F9nUUM22yaUQbOsyc6gKiIZCUBh7Bu+3409EHYx9DirUzJdJ+u4RiG8CrsS2dTUDUMJZgUjcUq+01G3M33cqYLchLrEAWpNS8l1IwVkqAruii50xxlrKM4pJRNW4RASDkDsgAe4y/UUVUFhjMKyEVKFNkwhAynMPosgikRwHpyzBIpnR5YZAkHGeC4iCqGSsW3EzJ60kRe3bKGSz5PzQU54iRKIYBlKk0JM6ZxMpwEpIjnwSjdRGC+dJu06iCJqqRNm22DRqRsaIjBx1IBmoGg+i2pjYe6sDQNHHLGlH0RSdzfMN94FjaCxlJRqHaJYGZFxKBoiWFA3VBagVKdDHqLCJDDJ2kpDSJkh9lvEF27iqDQwMdmJNuklhp29NNuitrRwZTpqmmtRnZr4QHbje2pkjdknRJUKCuiqjMIcKwKYEZC3w1xyKghCPFQoYiQm+MjRJHWwU6FCRFgTNFc2aYGqBJbNt4RbY9lbz7Z1GGaDObOfwRNFjawaaQbbVuUwhowCKBK4Muodx2xiNALB1lh4pgJwB4xE2iogAUwHUAbPYol8KFNbcxQAFllrDxQeoYOBGGVr6DPYpMWzlNjjuB12v8es3sJrdxez7fx/76V04bgKoezvOn1nBP3mrz/aP0IcyALezH+Dh4C+O2vUXF4eaL/6Nx927rzzJbkLKwfv/1JN87OczAD/Utl354P6TAYDjz3z6i79/8uT0lfe874Vf/Nf//Hfdx4//9GLyVv+op/iWwFOy920O/fgnNvTBD/wXAL6GLeEbYSvkqAAgqIAJeLCaw1sPNYQ5CV7av0JfOHmCkiwOhxN9/eKYAMiLs0NZkfr7Z6eimcf7Jld1dzSSa6Nxd3Uysyf1wnzHtZvEScxLN55RKK1uTPeyLx091u+8cbMfZdW1J6u5fWayO37vHT85Wi1XuXXzZdeU8fT4uUTKSaKotfV79m/Op8XwhJmOVsvN2w8mk2rRtLrY1NZk1rRdP0lth9l4rLPpGEkToqTjO88+W13Z3S1HZT64WYxRL48wGE2wbEusmvv48te/iEQeO6MJxmWHxaqBzzIc7Oxgs27QhYQnjx6BkFLmLEmMNB4ONXN2uTseHH3P+95z9Sv37nq3WGZ3bl2dz8aT4c50ElVwG8CzktKEnV/obNdmg2GnbWu7vhPvfeVcIUqqPXQbZZbn6Hf2ENcr0GgA/86X0EtSJY5YL5N2PccUepP5GDfrrntyP8Vm05TTWeZiWvm2cVcVrIRzAtLa+uhjIAstWuDJMi9yI5r5vr0StyLOeQVZtcBFB8QLn8cL56eTen3XwgAJ86Dx+mIwQQbhxvrBPPNzGFeSqFDfccjLriuqzK4vZCLJrS1rBKvmrhWJVrM8h/MLqMbI/MaAqAUwhjUtiAXWXsOWWX1egJcYuE5bCxxu6t7C6QxIXsSsY0wvElPlrC3BJoDNBQAo7DBtl/jNNpbXXs6b9oWzJtpklKPuw7tgzTCFPpBSNDa3VSc2MmJGDIDEAMIioiAiJo2AsYCACETEIEpmK14mZeYEKFSMBpDEAGOZYAwBiKrqmAgiatPWniUC6A1gJPQxpUwNNGpKMNqyGmdcCqlXFbYMlxTc9ghtrWYtfb8zZDGZcym6FEIXknrawIvlJvqSetpwI72zgORMCu9TqxnF1rGv6i5aUliFsDGJql6zWb5cbdCnrp2lyhnmQXv6IG0As3cw4XzTJfSB4C1WYFSbc2i/zHsUxiVlN9HOR5CYnJIvg+jGqCVlN0mUFqIMhZ31WAWX6t70NAxlS0lzUmU1jDU0nIF0pNALApLdqmmvATQj9OcALRKoZdCJQHOAdwjZAFg5QnpokJ9uI820xLaOvYdtQzFXUNi2a20BmJbAuYINAZG3Rsp5B7cP0GOGHhMEAs4YfSQ0sPCXtTy0W7eiiMsiGAliZDgAA/R4iByKiK5gCo3zt3GBBk6PUdEKDhkSNZrbu0qHN/HkhxzkqER4sIvmPQ5YOujF9+LRk+/Fo6O38iz/CH1oAuC7+RZeTEHc4lP1KzUOH0k+/semCp/Ynaz/rY53PvgWTrWz2TQv3b/7KP/Mp7/os8x/ejlf/1ef+tQX1j/9yU+f/+mHP8W3Ip6SvaeAfvwTawC/RB/8wP8K4AMA/gMAdwgYaexH16b7fNZu8P4bt7CoN/j9R3dxZWcfVZZj6t3Hv04AACAASURBVDM8t3eF9obDNMor8/zBof3du6+k58Y78rarN2jRddYYkyaZd6uu8QDVGTM/Wa+wbFqusnxU+SIblUVliOfGcXF1NHkwKfPQJ5Ebo9mJsbiXWTd5eHGeEQHWZOnqZIoqKwxIpl8+OZ2Grm9u7ey2u8Vkent3d3Dn2ZvTzGf43L37eOfzd3Ayn8flamVsiv4dd57N2qb1IgnXr95AN9vDK6cnOF9dYL7ukNSBSTEbVVg2Fns7M8zXa/iswI2rV3Dv/gP0oY0AbB9CNxqU3cFstn7m6sGro53R0bWD/b3rh3vFV1+/PziY7Tx65trBpMjzveWqgbOmijGSSKyCsnSrxRmM3ZP12klZiq0m3MeG0TbbDArn4X0GP7YwoQOJpJg0tQ9fCfS1r5z6djONF2cL8dnw7PRk8ij0m52d2eCwGmX05MEqAe0q4BVmfMoPy1S7/BrazZHtu6+urR8lMs9FCdeZmL3KozXowoGeOGK04P0nWVUtjdF7h1eSSbJ/ZX28f1SMh3WW+UWRx6XLY/TZxqhU1WZJdTHsujxfBWM2BqZojR1HcjWsaWFtthlN3nAzG4OohsoQXVfA2tV2rFDGMGSxJWkGQKXbxI7KABmIlnAeXddNFNgYTrVI2pCSCJsixNg7ax8z0xrA9NJKjehNvYotACIiAnHvIMqGohVKHKLTCKKBdzaTaBGV4LWBCRlg0AUmayQJwTIBzFARUIwRSROyzAAQUSGJpJRgt5Y2FNH1LGxsyr30CvGqiS/teAVgQyAQibWGEZd1TCXYWKKmI/SdwMAxki7b1GcMa72P3JKNajMXQKBIvbGFMPeRNfGhXcdkEMh7n4Kco8NUoOwce0DXrFxzSIMYfAOTSmNi8gjqYbiONHLGc+Z64S4G6vqQDfkgIVjjEF2DFDt1bT4Yx6Zrzy7U5BkglsL8LIP3iiuzPrY9liehKA/225wMxFZJJSHEgDKcMco+UlnF0N3TcPo4mp2J4WoMRAmKXojrDBSBRnQrgjaEtHZA3QMTgFsFxgSqI8I9AV93yOYE10R00qE/yzEcARhia6icAEXEpo7AysP3ilQAm76GlwI5DDr0oFah9yyoN6jDBmwt7KhAbzOcZ8BkBVADAIoOG2z1SxnSentjxwnQxALlOmGGh0AQWDtOnHLjFGSxQoFMjqSgQU8YIObHyGYPUKYM9p0M2R8g/C6B/q9fwovuU7hRfnir+fim+Ah9qACwC+A5HWFsb/Bvtv+of5Wur4x7PXTdf27u/9L7vuc/e4tLwtl6XX/s4//br0ciSjGm+X/yuS+sf/ItHvwU35p4Svae4k3oxz8xvyR8vwXghedn+z/cxvADhnm2adrrX3hwz14dTfHcbB8jX+Fu18qVwQRXJxMep9IYIXlwcQ7vuH/h8Eb2/pu3sexa7XoxZ/NzujGdxVfnJzgYTqp3HN7Apm3ri8063x+M48SXzdfOj83bdP84xGQfLuYVFKsfvPP28cF058WjxXw4r9euSzGUvkje2pBZ24UY+7br9rsUp97a/mA4zt2NG86yAaWEW9cO2yt7B5idT7I+Nrq7M53Wq9o+OTvJ96YjDAZDrNYBx0/O8MU/eAWv3DuChAbloESfWdi+w6qpQWzRdB0kJvgii/2miwDsoPDuYGen61PIqmH1dm8NsSGZDAd0bX9mvM8m3ueLPsYshJCMpUGfklZFIUSU+ra72re9Y2sgUDir6BNth7OsB+m22afNpovL9XKTus1CdXr04F5vH74Wb169sVrdujM++9qXw0Bk01XFI9x5W5R1U57t7L36aL44P0ibfAo8WzR1nw/HSzXUH7P7csfmVoxx78loZ7KX4mfnsY+1y/atyG+ss/w/PPPu6nSz6cJmTb0d7qobzB6MZrrJctNk+aN1Oe7qLjzjgBvWZnUazbYaUmuDcy704+kyMVcwRrAdETjFVv8YsFX+RigitiLGDETNtl1rHLb1mIyAbluV21aasX1mbQAUUGTObR2hidmHGCXG2DPRPrPNCbjrgANsl/sCb9pHGwHEwCNueWByzKqDQUpEuQGg4MhwKlsvaTDAKDygSqbrVTInDAgRKZoQkYIgy3twlbHte2hImkKyg5IItumbULBhExiuASgXdLotNQUCoArH0AClmLyHxhZinIo1yYaYU4KqpWQlVW3UMDTSp6pPUap8lUehPuQXgHLuZXfTeA69S0TEXlKTukwYdkCoNaXYh8ALm/KuQGiUqCJQa1jNJqv8YuStO14Z20ZKhjZpQ44bTIe70QfmZBjtJAdPEutCU396Zvjh0ts7z6xqj5Qyb9OoMphWSPcXyPo1Yj9EU6y88d77ut+EpNpE1TIuYXqiES8UhRCRZ6giIFNLO6SqRFEAlxPcBugfAZIUWQD6gYMsAT8FkBvQKwnYbA1u+nkPuxNhZgAuLo3CN9tPnyoDDoK4AjrTwjuCJQOLrdrWVwyqeZvcDSBHgT4SqAEkAyQAWbMdZusQ0aFDhgwJGxgYlNdLtJuIrG7g0arHkkBT5PuOV/NWw4YpQ8QESzJw6uFlg5J+E9c8YNxLOOl3EffvYvS2HublT+FGjbe2Th8CeDeAY/0qvtzdl08CeMY9eHj6Yf3o6b//Z1kIfvyn+wPglf/4P/rZP8tRT/Etjqdk7yn+CPTjn0gAHgF4RB/8wG8A2P273/dD+/9mVvztlx/d/5uzamBvjMd63rS0MxzQ1ekO3nfrtmoSmpQDPlku5Gy9srf3D1BZi7sXZyQiGJa5jvOSFm2XJz2XG5N9EqR8Z1DR108e+7Oudu/cu7K5OZnV864+XLR1rHxRlWURuhjSol0Pp4MBjbIydDGlKKp9kpWAxi8cXuOLZkMX9XpGUcKmbzusQ9OltNzbm4lzZpCkn5V5SXmRDx4cnWK5iTjYK/Hw6AS/+7mXsd40mC/nmE0qODfCsm5wEjvsFjmO7j7UddPRILMYDcfY3ZnY524MzKZtpSiK8I5nb+n5fJFUoLnLb1ZF3qkSEQhVUYyazXI9X62r/d3dTBR53bSae89VUcSYkgtti/F4DJ9l6LsOACDOiahwe3HRN8dHcbw8b1mkWxzeuEixR5yMH9cvfOfF76T+9iDErq2G94f15vGz5STfvXprXh8fze+NJ3Tx8B7n9eb+ztkpNxB2zebX59aXX9q9eseH/jyL/SdXw50XpyQHRyrtA7W+F/2+iZVJZ1xek110470BrN1trLuADJdk7DOwro8iA5NZn1LSPoR1lfkp6zZZTUVq49wKwPHlrfWGYvYUIhdQHcGYHsxr5EWF7bPIY6uCuEyeQA1gTFv9JC6PTwDaLMvk8t8r52wAQIaJDDOM5eLyHJWIOFFdW2NGl69det4mYCvWuJwZ7CgmsUwJxpjtiB8SW6NIkkmnxJ4SiBR5TlvZ5aU/Suk9ohHAKIJVokbgybLNjd3GS1fIi0QhhFICuXEGbUzb96G3BG8NdRJVCZSUVMl5cBKrXR9XMMU4lt7auk5EpoSmOZBp36WGA0y2Ii/kegIcq+iqrnqQqwBm1XSUYloTeE/QN4C0FpaMYTAlxyY+NpyuGNas64lqa1dN40ze2jQYrEpfSLY62ZM0jkFJJNdoYoSkFsaSqDdNpNyLgtGFgcuqdVMNUj4YdroMbqB9TI8eD9Q1rLet0PreKGyKjjs1G/iYYVe53DVix4rZ0hJPoUYh5DLqOptMH9nuRdjeUtuosFfuTA8OBD5SmJqhBSCOwAcZdAOYgSL/cxlEPNqXGwgb8t4jXlqusGFkRxZuahDVwJKHLQ02PWHTOex2PWgCIClSB9hCgXmEhUGvwFSAYgjQanuzMkokKAiCHgluHdGtNzAlo2OmfhnBaUPcAzsdUCKihcUQURVeHXp8BQWvYLIIn72OweC4LJMfWPve46Mf/QHc/cf/FM/8EeHFN8EblisbtOj+3eaj+hH60N3L+/0pnuIp2XuKbw79+CcigCcf+0e/cvwrX/jM//SX3/0d++/cu/rOq9PZ3hce3nWPV3P72XuvZg7Q9z7zHMZZjj4lfuX8hGdVpSKRXj4+QtM2ONg94CzL9MZkhx+cn0kYBklJUkzJbkJXT/KiKzLvzru6Gvjs9M7u4Sh3Nq+ynFd13YuIGefF6tZsX/oU+bzeVOu2uePZ0Cgv0kE1qOfruozQrMiysL9/2J82K0/EFaDGOYcuRLPe1LDeISsqnC9W+PznX8a6a/C11++i63rMxiNk3oBIMctKDLICut/SxXKDWR503c2l9DfI5xmMtzQdjGAd8/VrByb3Wee9HS/XzVet4QOXZTweDUib4/HVsfFZWfq67QBiYiIbYrSGDSbjEQDAMiMAQEqJmdOmbUOrOIUxtNw9KNcxxIuuQ7acf00SJWnbove+HNy4odPDw/no1z9x8fhsUegffGlVToZ+SHQ0LKrSj2fTGvqV14cDsymH69MmzRdlOXn24ixfuHIzbFcvv1aOvjskeneusYt5NZsTDUEYN8OigASBzQTMUySzUaYaxEPWpMJoCuvmUDmB9wExbBDjlTbEKYx9UuRZiW/49x0ASKZr73pJaMrBDogStmra53FpbYvtwjXB1hHNYkv6FN8w+b4USwAADBz2EQAiWltrxrq1i3lsAe26MBSompyJiCzetBPiBJC/DIchVUd934GoR1lkwKVbYUwOgOMts0ww2/+WQAIo4FQwLFSEHCsl6EaVAgxlDoYkARq3sXJJGJQFNHajJAyXEYgUCWA2LFsz3xBLiPQpqRrVokL0aqN0gIt9oExAFZCF3tjOZ8KqiyxqNSQYH1JsBEVtraku3ZGQUhYUtNUJIyucxi+Iw6GhfqCUhiLcAalvOzeSWtiEVQ3nR51MmFLXDmRd1jMqhFLsj6lncaWGqWnsvEsgKcX421c3zNbYrqPiyX2br8Yi4wnCBnnf9GxxFuPJO0I3P1sXwxXng6aF3AHcWHqrHHhsS1lFSvchegCKDcL6TMxOCUhvqL0Q8CGxEUXSSxPwFQsEEAbTXCFLAs6BpNt0Ru4IshL0vYJzv60LLwB5FCGngggDmhTAGoimB3sH08kbUiENJSgMFGVpICvgfAn4AijaHjFLIHgwLARx6zAOgzXWyOYWeZnBDBjrJkLgwQLU7QZ9GqEEtGGLc4nhwliHLNPr9dIOTYPX097hGlz6OuK9/b04Q1j/NXy1/r/1Z/5UL7oP60d7fENr/MZrzTd5+1N8G8L87M/+7P/ff8NTfIvjXe98h/7YX/iLj6+K+6cvXL3xyUlVHd3ZO8xuTncn46Kyt3YOzGw4TPOmxnnb0LXpBNNyoH0ShBSxMxzSi1duUpl5akKDLx49pBSVD0fjmAC6aOr2HfvXHDOZ/cEwO14ts9P1oggiflaUvOkae7re5AOf6zAvhUQ1QXNH1olqVjjjBllFLJqc9dgdjUxRZA4p+ZOLi8Gwqvyoqoy3RJIUwzJHu1mn87MLbkOPuu3x5PgEZ2cXmIzHCDGgyDyGZZkWy1VsuiDvfP62Pn/7lirlsel6zZzjG4eHeP6Z62FnttMPqsq8+23P5FVRyLDK7f5sVs4m4zLzzlqfO+ty1/QJJ+fnyKxFUeQQEfQhwBgDiKDtg2beCwg9iOarpj9bdfGEq3LtJ5OzV86ePJkMhwfD4WiZx9Avz49lZzyRpFzyeHxB093Hfvfgc8V73/dkXZR+FGXQatpfXrtFr1nfF6JhMZgtpWv3TwO+/sCXu8jLO6LqN8Z6k2QqhLzNcwNrBGwAogrMGdgAIhaGC2T5GVLqmLiwWe596pGrZsG5Bmx2oTpMRI6JW2vNG1FrBbZkTZWoFmOSGusuf8fYtqFW2M4mCbZET7Ft5+5c/lxdvse/cS4AFQQW2xYtA0BDsBc5WZ/QOOLKMIsx5ID+8pg3fMHfaCmTJWKyxsBaBRHoG7HA2yAsC9keAQNAIHIZOyIMJRCYQTAgUoJ1l564RAAZDslYadhcJvMKvEmScwumBCHWBsZKFC10a+GRmMmyaiGA6wGcA2xAyC45rhrWoEQZIUevLvbB1c6xY87M9pr1nTWciZoR4O22dU1+E3SckqmYghJl6siGgTMbUdkP0Q6brveZ14wIhg2OTRI3HZwU7/qeT9tnb73s6ZPfZx49uW2WklOOqMcLSF516fW7Q98+UuuipJpyZx3CQWqanbYFL7hd+AKDvuNp0wXeoDUWXc9eojVWG6F0oiwXYqIB2ruqWIg5OSso1ArZSLQGvKGA7ihQxR662H6QOlalJW3TMioAKW4DayPghgbmigetCGABjQlcM7AiECtQJhAIPFa4qChbgCoAGUjPAYqAaQhtF9CoRcGADQAnhh1bAIIQAyi3KCY5uLk0MQiKonPwKpBIIDeBbwr0gx00PSDaE8PYoIgKQ0Ess6rWKPIElIAtnfTXLOR014Rfef8/+Ourf5HP86f49sRTsvcUbxmDK3tr7Azv4Xz1u8zcD/K8eXb3oM8zN6rygsosD4aIB1khRDAZW9kdlDzwBWJKuDc/RxMjztdrqkOk53b27PlmbRfdxl4ZjPjaaMLMZEe+0JgC/f7D+xhlJQpjcdosUu6yOqm4mKLz3uusrDAtCh0WJefec5XnUuY5JxHy1or3XrMsc7k1KPKcM+9xenaB89Ua602dnpyemdF4iM1yg+tXDrEzHePGlX1c2d/X8WRMXdNhUJUphZT2pmPJisq++uAJPTk5c49OTtg5K9PpFJmzvu/7bDCobB+jVcBUReGg6hUgYzMmdtt6EhEmoyGICOu6AQFw1srpxaI9X6xSnvlHxrBpms3FuCjuOmcff+X1e792MV82ddsNlot6sHr0CGY1f3hWjEeTZ27Nf/eVr/7vZ+fLr7Srenz91s3PdP/LRx5+8pX718KtZzt35dpvnLG94mZ7t+eb+uxiU7/cj6bzbGdnROvVtZDlVzZZXlIfit67s66sjrQoK4hkIH6MLBvCuhzGBBBqMH8RzPfR9zv/D3tvFmNLdl2Jrb3POTHdOefhzXw1UBRZrC5RstQltWjTktwNsA20u9Xd/DBsuPljf/jHNgELhMwPNW3Ahg0YFkD0lwHCQMNtwLRsqWGJpCmqKUsssjjX8KrqjTln3jmGM21/xM33SmxRPfhTuYBA5s2IODcy4t44K9bee20A6xQDtASu0lyDmUHUg9JKa+20Vu+hJWT3ARwC2AMgYH4kSjVoc+murX5WaCvCN9DKap3V4vDUNAMRbQupAi1RU6u/XfZw9qv38wSoLKKrmB4ycytzIUjrpqZUu6/jdhdmgISIiOhSONQAAtCs2jpzACAgCiBauSe3FxSgiPebkdOlHR8LAUJKRRI2DGKGhLYLLpxoWNEgYmIwSYySEWCImDhGcoCetVloPBEopRUyQawA1dR10FozPBKqibxSxBrKAdIFkAOUEuyJIAntOYzHQMLOapLgVWLiqY/FA0VUqKC70aOjk6oC0pmirFNnBdl+RrEkaryd7z3/w2JjSDw9vEn/7Ad7TcVN9Fb4oip0hFcsrNZjHZNugE2JCxaZjznYDjjdF9N1FXUOvYkG0HtQWIOeHCdSzAKUimHJ0YUgul6CI4Q9ETVzCEuMoy606jD8RKJdEKWaAQuaHAaUdaR8TAEgpnUAOQOB4OYBZgeA1vDnDXhEQM6QRwCIEFIHqxuosQbPFFAzLCyCjXC1QiSStE/kS4GIh9ERkmnARiSbGpJYkDaQXCOuCUwETJHAFAmkIZCOwGaEGzCiMegoIJnX6GSWUo/YQaQMjusINYRlQcAFOlRChQSRdlCqPkLz7Rs0/Z/++3948u//p//givBd4f8XrsjeFf7VsdZzuJh/H8AfMNE/yUzSV8wfNEpzL89daowPISjNSrFStKgrnC0WEBHs90fYKvoYN3NoAl3f2sJ4MVcn1VKnzPrWaAuDItdNhFrvdOX5rR2OImhc4J3+GvpZlvXzjsmNcalJOElTbUzCEqMiwDTOqUlTxdSYYJiLzBgYbZ7OxE+OL3B2fo4be3tq1O/i+u4ORutD9IoCG8MBhsMBBp0CihnTcil51iHNWpRWvDEa0vrasM6zVLvKsjaab+7txUG/52/sbSVpkoEhROA0Bs8gqIvpnCRGdLsdkAiYFbQyiDFgPl+gqauYJ0mttF4YpaTf641j9A0t5h7OlxNxhqLX3snL/W5xw4dY6iyjupt73e++XTu7OW+kf3A8f8fYamfr+vWct7dMd3Mzn8ewdzhf7ujZeFN3uotHizKrTfKCGGM2R8O+no/HFfEm0ozmQh1vEsdZ9hhABz4kENmE1gnagghqFT4eANiHUmurwosYhBoYYyCxAxBJG7JdEpGgJWrbaMOyBm1I9rLg4nIB2nw8i9Yv75K06fftY9Em2hWr7c9XY2s8UwIB4A0FnKQRWyudhdv3plFrl8KmbfoC1TI1rdpN/Ir4tZwyArBRWl+2BCBEtDw1QrFHlEvzcXrfIu0hNgLEEippbXaJFAQBiA3gBeAMIFDLSVMIIFEiA0kAFJgEIlwBeU3kGyYZAKGzsmoRSIoQjdWacwZ8AlpqRA3oU0B3WgcdpALuAv4YUDnAPSDMAbynTexHSdMo2cgG0pXHmotcMOCE6DiIKQghGsuGGllI7Ix/9N2757Pzofne23vusXPJtIr2vFQl24yWDmZr5Lm2Qz+4Nqcbew4PDnP9aNZV631Lhpw+K7uqO4jeTkPGFQibZAe3Y5F3IixQ+45Wi4lBHoIuFkHyIctwS0KnjxkJWJYgrGnppYqlgOikrYY2NREq4pAA4kU4CYQLQGUJ5F1q5blOaPn1UoAzAbqAXTqYRQod1NPrZ+FWtsgGcR+UZgDNPLRiUC8AnqA5QnIHP47QzkB5AmKA6qMl+IhA8IiZIPQEaYehzxUarjGLC6RrCqmNSEIjITGsA0tHOyyHKe1Ws/BXcJqsYUJ92NCBBFPGteE8uxh/9n+/95Hf/FtXrcWu8K+Nq5y9K/wrgz75CULrx9fIl35/gnsHn0erwPwagD3rfU4CCEDRBSwbiyYGZEZDmNDv5hjmBS6WC7ywex0v797E0WIOsKLXHr9Hu/0BbXUHan9nT2ulY6oUa82kWWeFToJSSjmRLEqMijS7VVGDiBAJoZ9kQgLEGGW2rLRWjDxtG4SsDztxc73PnSJHnpmYJAkzM5bzhaytDYiJ8OTkFI2z8fk7t1RT1zGM+mZj1EMQlJsbo7izPgq39nbj+WRKw2GXt9b7aRTEbpEG7nfYWqfPLiYwiUGiFYosh3cOIQTMywqZcWAmzOdzR+UiTGeLenNrg4uiOATkYjxdjGDDmU8o/8q3/rjXz7rp1nBtrZOP0u310bKqXf/dowfpfDp+vCztlvXpLT+enwy3i2/Unf7ao+nyhf1bPclmU312dr5b1/U0PHo0BaspgGoyXexezKaRWW3kWisFPCmKrAegK8AdACkZc4I2tMpo1bR1tCHXCYAczBkAK0RVE8J1rqqkG521JkUjqKKIylWyQ2AgoQFaJnQA4Ftoc/iuoU0oPwFwF8/uRQs8UwMd2u4uCq3id4KW4AmAUzwLDV+2Jk3QHn9EqxZeks0GQFi1h3kfO6PVWzA9Ew6BluxJ29c2DW0TrFoBQohZRKRW4RNhrLpq4M+0GUwA+ARBGIBHFLTdpSsjyD2h7bcRIR0GMQGKnrWgTl2ICoDJ2oh3qBURdMiVRVwCnIBIp6l0AWVbHTEmrelz3AUgXlTQJNL+v3QdoAwIc4hXaSI6xCwC1G2PncoIaSLQEW+0QAwgD9hxT8+T7uPjQteqGpnE8HS8OZtOhU8vOG1q0vvry+CTujw4NS7PoEfJRKUNeH5ow8WR4/GsS/fnWe6TaKlK9a2bS5UP4OMhcHGWZjkJdS5soEqS4m5I/I0k0nebuj4MSZKwwJHnPRYzJnYHUOOxgfTgin5t0hxIBxyXc4npdUa0ljEM4PVE4iQSFgB1GaQJqFRrh33kQUStFcqI4WcBMXiYeQpKCbnkEAGSBKDF6hOjGMgCPCzUVgo4QbMUqF7aPoYsLPRSUI8DIB68lgEdAp1GKCKYbQVaWpQ6IhNCogKS25rcgaaca5wwUb6eoAwDOoPlbVi8iMAMZDXClnXFRQ+bL3nMv7H6/lzhCv9auCJ7V2hx74AAaNzdc//CbduZaRPtZF2hbdPzWwD+BMB/kmh9e25tFmPsVsGZpbf0vYOH9OqdF8Q2lh6MT3A4XWC318MPnzzB/ckp+lkhv7R1l/7xd15TN4brcnutjDa4eGd9mwlA6TxidOiYRBGTmAhFzEoRX7avBREhSQ0yShOjtanripfLBbQx6BY5AGA0HHLZNKiqGtY5WlY1ptMZkiyhunFhOptjOp2pfmegUsNxd203VI2PScr+fDyZTMa+1+11+MXnbkUo1hv9QTRaK2MUhJij98wA1od95FmKNElBJIgiABBtU3Ow7NIkqeoQMVzfvLecL+jRwUm/rOq3P/TCB+b3HjxKl9Wid+PGXh1cOJs2s+eeHB0lLz334v1uZ/9IGE+eu3anOh+PP3z/yf2+4Ug/9dG7v9pX2ATFH3zg5rVbT46P3IPDI4QYp5GT6eZw5DfW1+nBo0fVbFF6rWhNsVas9OurXu4DAYqYrE0pNDuEZYM2fy6NwJJaj5BpBBUAHjPkObSEagRBrsolW0JE0a3Ix5QlEgUIKEZABbRTYwrgQ2gJ3QZaxe5NtMSP0R5HjZbgbaEt3Pg+gLcA/I3VZT5abXuyGm+CNqfPrPYv0JK7Bnjau96vlvcVdoS4UuOUPG15pVe7ARqMgLgyzTXQsgrtBgs2EcwRPhAQ3KpBatqmxYGhE4FXl4pR6xlNHCFOE3QEUhDgFUjLSk2UNr8vI0AhCsWmDqI1YEyaROhooAPQGKYYYwjCjpKYBseMCxHdEVFJ2xvCn3tbdHUqDSBLgAqAm/bRK4nE9ZCim4FULeIzIPRbsqvKALIAj4iatQPzIQAAIABJREFUB97m1w6WdvnDI+oSZ/nmIDc2wk5mOkwWEE3QD8+6vc2hq1+47cY3d5cz0+D2/DHs4WmqFucqDgZL2ly3qr9lVdpYLWfemx3U9hqZzEfIQxcuvidZYoGOFb/2oZKnvSaWSWP1Q5MlGQVsUxEKsJ9XwTQ2dm4ZwEEMCJIA3R1GEGHaFniA+NQBhQEiINO6Ne4RhaqnME8TjNYtzDJAnMBZi1h6GDKAZlSowGWKVBTCMsgClTCYk0zBUCJ8KlQtanCPwWOg1hW0KEzDHAkMGClkKsigQR2CSAM3t5BrgjUVoKgGGJCCQNsK80c1imsaEgXyqEGJHA/QQx8TOLStdwuUm0PgV54gSf4j+i9+6x/Jfz17/434U5TRF6UWXOEK/wJckb0rXKIHYBf3Dh7g7l79/hX0yU9kaCfSsXzp96N86fcDffIT97Hyr8DdPQGwxL2DP0Kbl/XhQPLRWVkWVVPvhRC6BgxDRN1OgdvYjj86OkJuDM+aGiEE6aQJJTrFS3s3YgwBUUDjuuLSO+4naRzlHWIQsjyLRukQYzTBNWjqCnXjkBgNrRKoVcsj5xwrpbC5uQGIIIQYatew95EQ220IRAfHp9LJM7xw+wbNlxU9UgwGYlZk8eLswq0N15L1UQHrnNnb3tly3sdRv6uij2DNMIlWTArMCj54JtJIMwUmQl03aLxHXTcoisxnxmC7W9RViHbeuEnd2O5YZH59d/dMkew+Pj7deXR4tLGoyvPxYloMF73kr37klfLs4iz+4J13zMlkunZ0cXEcoueXXnxhwJCN9f7GBQEmK7puXFYvb6tknxUvZ8t688HheTXoZqeslI/gfzzo9V5kpTa6RbZk5ltoQ6MnaEnQEYCUxL1Bofl2BH5NkDSArX1raPxAAS6otRsUypvc9lu3RFRnxk/QLYbQaQJWKk1Ua2fiBSvH2QnaB4OA1oKlRqvc1WjDu2+ilcd2ABzjWfFFjfZhQgO4AHAdrRT3bQA/tVrfoFX4PJ7ZTEzQEsq11ThLXPZ+BgQIBeCjIA0BCRMgqtVx9KpFOwGIKQIBgTR06/jnCVAJogAxhHYz0s8oJTwABR9Wc68wYGvAaAgHYBjRdn5rW4MATMwWTJF8yAEQiKxRCBJEvEJuAO0BnzkgAJqj1AIhbgtRKYfINoRm7fljAGGuDDkfE6WZdWvcTA1AJdA475ORIvsoiDeAWge80hQ5iJr4kFSLKhl2i8VeE0wdyBzv7cyPykonotJ9cVpfzNTwWz/sy42tRXln07u9rUl1fVRicZH1h333CHGx0Ru4zq01Y/rrVrQLoTpJeLhX2feeNGFgO9ga+bL30CYUkJfriImG40ipe+RCJipkGx3lHpDDGgdM0VQqqCpRqrsHMZmQnYvUCwlZZIQAoEORVErmXBTmERgyEH2EE+ZbGqEiqCeCNGszIEECnASYXMGWESIBy+UCFRpoVEh8v41KIFCNJUy9hsY1xD4gwCHMFQwcnHdQIKQFg6FQ6A7izMHCgsFoBhWM0sg6Keptgh4OQD5BvC/gSOjua8xY5OyhkPIBG6ix3y1RWYX3bEElUjPC2bpD2X+MXvgD7L5M9L/88d/HfwC0PaEnAPY/RdnJF6W+yum7wl+IK7J3hUs0aBU6wr2DBHf3nvozrRWd7ryu11wMc/rkJy4NcWv50u//eA5JA+BHANww7+wtq1q9c3Qmj2bnz416XTqcT+V4PlVdkzKRyNzZWDuLk8WEl97iQ7v7+PD+Pl8sS3RUKmlC/PD8JLy4e526SQo2BkzMVVVxO8MKJDgkiQFE2rwoACKhTdcSxEQpFhEJIfAyNhRDwM5gHVEEF5OZXR/1PamECMpsrg11kac47o6FWRbr/b7a3d7KltaimU2pLhuGSLQ+ot/JdVXX8NbBhQbDYR+5ydpupzHCOo/T80lMUk1M3IwnM87TpN7odR7X80UwRg963WL54OGBTozaU1q9vb+zmVdNc+t4Mtne3dic5VnKEKmbxr2V6OSjSmFxMZ7Nru9sbQQnvfPpUr/17r3NG/s7TZYXv//o4PjmwcSulVUp9fzieF4238/SbHer13/+jbff/cW33n2wCMGp9UH3FjMvAHwNrXq3C+DrBLxAbr4vwIFvM5DWGAgMEoLcAqA5nPPTax1FIcgUIgMo1YHWHs+88hiaLvPpLm1UamDVTQr4CtqL+PNolb0frdb3V+MfAPhjtETug6sxTlfLEwA30E52XwLw62jDtxatsncZqj3DpVT3TLbrAjQGuAKkR6CCAA+Ibd+LDCCmLeYQ1YZWLYGTNqUOcTXMSrljtUqRW/XqeJpqyICrwIsJYncApJenpf3KMHvEmCDGbMXdNLUDCZiFO3nCbRGIQ1tIwqE9piSHgoOCi5AcQgKoApC0PbAUhj3aY/crwhwSQlhIW486CpI0mpu0blRaNl0/6NZWEEWEXYyOvU+605lUAjW6c62u7j0YZE/OVZ5rLz6gubbdLD/209Ojl7eTfDTik4pt33ljFiGf+K2s6J8ed7syNzlT8EFTeWTGVpH0Uh5SJ6HFtF6uDcgFHTnbUE53qQ5vYoClUWYPwSsxLHDC4OpdSWSdkWwTowvBJCg3C2LmSqOjQhxLZInEwkRrhOAUMAdoXxgjA6QEXgLlNoCLCtV5QK41EBkSA2AAvxahfIJirKAiCUEj5kJ5N4c5M2DHSFwKDw9BBEDwiOigC4cGKedgx1BrBICRkoZdRNiT0BZx7DGoIASvQU61CREJkOkO1usz6u0G3DKE5sM54nsBODS4OfOY1bWbYB7GQDLAeP8X8Ud/92eS7z2AxQVasjdffUDoU5SZL0r9LxOVucJfUlyRvSu0uLvX4N7BKYAPo72B/AgAcO+A3v2N/3bw9ulR9TMff9XSJz+RdpL0+tI2x/gxXyfc3Zvj3sFrANajRPO9w4fffDQ9v3+wmGzcHm4N5k2t3jh6jH7Wl0ldxXFd10whf/PkCLc3tjFezmFIYVIu5CN3rhErxvFypjQRQASjFEJ8GrQFJIKUBhPD+4CyapDnKZgIIgJaVQiIRAki3FGZkEJsrFVeBJ1OThujQW6tDdpoLQC6RYHOzYIgMgghBhe8ZIgqFkV0tYtroyENeh1aVDWKLBcPUJoY0LP3hGIW0ohKs0zGM7uxMZohxrwOYeaV8fOygtbJm4hyeDad/JujtaFOU2WyJJlNZovl0dHZB29c39r2TTyeyXIKUkeDzuBFeMw2h2trd/aukY9x/OjBwze0Ulmv0036ef76t3/41ldfevln/67z/tqyXKBfpMssz6vHJ2d498HhB7bXhjrL1H5Z1zYXSOP87TxLHmqlPoBW2e0AeBdAycAhwR4oYJ9bNjMkoKfwNGpu0IjWFsZ3c4D5kgWlaJnQZSGGebp9u75CS+r20RKzS4Z0Ey2x+w5aAtpBq/R9f7WfAfAAwAfQdgr4P9ASyw6Afwrgl1avHwEYhBA2Q4hNkpjR6jiWaMPCOwDPAF4nuEMFDFr1zPYBfgKYTcBvA04BbFsZSCLgDBDbUs/YtEUWQHv4dJm7d5m3tzpFRkH6XXidQbePAQASiHgoziAkEAFEmC8/0yIZglhLZOKqaGRV9htVW0nsx4DpAwJF0QZRACQDYATiJEZmhm57PfAcaBiItwTqeSbrFGv4YGKEaKUlKKsxmecqROnlaTwc9gIRUX9nnRYXC9iLaRev/2hU9Htxeev2ote4ZfPyC8tqa62sl7Fxg36z19WmTLoyP7vgDhlktDmggv1CmhNkZPLNXSqzOWE4193lqNFV1l0vg3VuEdLO0Klk28wbB0EOEzIsH9/zTUeFtCOsy0rJxZOcN3eDdHXQ8cyjuV8Dg1SSgVaxCdotG+iZgebWNjFWAnsGqEcCs7TALiF7ziC4iPiuhx8AYd0BY24fFBNAjWLrQ31KVMFHihpZY2BEU4AgQYoAQCNDgIVHhC4i0jKFLANsWiIcBNhRASKDTogY1uuQGCBPItImA2x7j8AtwCY1TKaQUgE78NhZJ/j1BuelhuwSdvMZjt44VvJO1yzsOvcw62U8+WVj+ubMjP4bltAvypn+otQPP0XZDQAbn6LsvauQ7hV+Eq7I3hXeD8Iq/0neOTgiwRJ39+zg3sHyZ67fSXHvQMt/9z/r15884LPl/M837GwJ358y8QeKrHj4eDb52C/dvjuxMdKXf/T9Ytk06nh5SL0k5ygxe/P8RILAb/Z6cVzVvPDnumoaapoa26N1LJoah/MJ9vsjNHUNZUzrnEEAfPsDALwPaJxHp8gly9JL/wxxznlrg5IQJTUJQSL7GGOQyCzE3nufJIl5/79AAFwIZLTWIEKaJOikGQ+6XVGsiEjQ1Ba6UORDwKDXg0RpnfSZoJgjE8dRryPnF9N6saxyrZUMu71FliU86vetEIdep7NIdTJ+8+376y/evjY8Ojkt6sY/8LG5d3h2uKF5t/vWO+91Slulz92+Ux+enuwsFsuGNC0zY3qlawzA787m9WuKFzfqpvn417/+1dGHXrhdnZ2Pt2bL8q89OT7rHZ5NhwB+npWc3x3uL7RODq31u7Pl8uUYZDnoF99Bq7BlAF4ioKNbhW2ANqHrktk4tIymNTdOSXsDA6VFRCAxZswcVutztEpbudpvpTyhXI310wB+AOD/QUs0zWqxaBXmYzzz4KtXY95Dq2YsAXwTwIursb6GNky8B7gK0B+uGvtNa32/z/xdrbEG2OuAehegRwBvAGrYxlklBVAA3AfoAsA3APwC4HIgWfkA+j5BnIAJQaVoOgCHgMwToFaTa1REgraBCNCW8QJiMujVbdZHAx+jMApqCaFfnU5aufleFoqopDWMg0VDBi4yOopWIXFuO7OyCpIYwHIbX6YQAygEua5UHCuWtM0JTJeATCEYRjBJlIZp0TBRXzjJ8ty5sgLKhpPpTF+7vhdyrbLcBztdH5Tj7701GC4qzjbW6nS9Hyrnw6ONLVteGy2iDXogXpvGwYCxtjFajq2Nzs+M0mkA+usny0PhkCzKNI8NudgJJ5PEbXaolqS0lU7UCeti0u+Vm1N7+E6auKDTUFZuqCo5L00s12JIxBrSgrqMrjwRXU/JI6tMAhJbRFAeogoZZCqCzCtaJ0gFSAXELEKKiOwsAsrA3QkwEwaxgU8j4gOCg4NHjTQrCBCYPtjO6tg0wgKCgYGDhYdDAws9ErAPqMsSghRGEiil4EUQLWDnQAoPxLbUx74raC4m4IlCUhgQK8yPSnT3E9BzOWY6Q1EwRhsBm9LAnVo05xXYO7W9X8XqvaNmCm9dXHRp+eTfSlDuzntr/3eB2fhTlF3mTOOK6F3hL8KV9coVnmGtJ7iYl6LwESj8dUS8TWu9KS7mQJuLpwGUO/2hurO+NcNaL/y541zMLQCEGD78pw/e/ZgI1r539KR8/cmDhY3R3L84lSeTM94eDnExn7tRt8fPr+9Q2ThzMZ/KuCmpk2a4sb6Jsmkwq0rkiUGqDSRGSIxw3kMnySrHXsCK0e91kCYJWefYe09aawkhoLENiURu/S5ItFaUaUPGGGZm9eOHP50vcHR6DqM10sSAiaCUQpIYYsWYzkr0egWKPMOToxNMZzN0i8JlWTqNEIiIkqY6T+oqFv3uexFUVY1dlHX5lnPOn46nu9P5LG/qZvndt+49efud+7u9Xm/tzfsPh+/dfyhnF5Pu6fjicGM4YIqYkVK3nr91s6jKev7wybFloiERetaFOL2Yrz85OrteN016dn6uT86n6BR59ujgeO98ukwVqOO9DwKMR71inGed6F18MK+WZ9N5vaY1n/Q6+R8C+BRa0rWJNtCUxrZoI6NnLsSXxsWtdQlRBBMBiM57rmqrtVbMRG1/2ZaYzdESvBTt1XoTrZ8e0Cp1i9X6JdoQ7Wy1zx+jLcwYoM3Rex0tWTwA8A5aMrkPxDe2Nx4xkz+Non6QZ8sdrfMOkfmOCA4So86IkAAyBCQBsAvQCcA/AOIftMUjgQF5feXGawE5B/RbgPohEL+pVFwH5FyQTIBo4EmQ1mdoxECMhooMCGnlRVqGh7bKlqQNz1KMFYE1oFUEcSB+qvQprMz6qC3UEArgp8Feqr1BVTFUHqElADAhSGUtZeRjYBKAPQE6JyIVooHxNiJI0UpdhmHBmJWz5RKEEDsxVASjM4IokMQkcSpPE3QLSquKDKCDUTWil3RaJdG4aW+t76rdbTq+cWv5IN+kF7iM15LAqbG8kJQUWK9r5dYjsv54kZjzaYjdvqkKU8JUMU2v5xWKMEwyZcpjO1fT2qSbeVZOU5ssVF7PbRif6zMfoFTKSc+LVgtSXCAgCUYhgp8ENYenVJHivpNQBYnHGj2bCp9ohSVFiGLeJ/DEQ1WAm3v4WmDWc0i0WByWiAcMe+HRtrYgxCygSSsorWCsEUVEzltaYAENgwQJJhhjgQoKGUQsQhREF6FWzj/RaTASkLVI4OAhsHCwCABy+MQiNk5Sl9K5jTid1yhAqC4ufHNyzhwq9G71UB/McfKtc8h5hF0oLO0sTOQNNP49NJioCTpFgmajsNN9boudGMDBF6W++Je7yV/hLyuulL0r/DgaBHwSAc8D+EMAD9FOxGdoCZ9BSwYM7h14ABe4u/fjycEBAN3Z2Ha/fPdF+/2jg/qN40MvgvCtJ/c3BFDLpqaPXr/tXty+5rtZxrc2NuOT6Zj21rb0Wt6RQd6V8XxGhdbUW9uAuox0obVDSIhAREh1gtJX4LbtAeqmgcSIKAKpa2RZxtPlHIoYaWLen1j1E1HWNQiC2lokSQKjVWvZgJatJKmJDKG6LOPp2Xko8pyv78Jb64okMePpfO4MeNrrFMOt3oAi82Nn7aBx1lZVfU8xdR+cnhYf2NuXrDAVJYIHT46qt++/12nqZqc/7Pud/WH/8fFpMzmbYW3YP3eVne2ub/DaK6P8e2+++ZarnOv1e52q555rpvNw/9HBzHl3YTS/9v0fvePSlH91rZtfGw46SbebTpIkedw0dnR4ejYlwou39zd/NOp3x3lqagB/E23OW02MQtpmEV3f1hHM0zY/aJWI9jQf7lKaYgARSjk2MNyeqBrPFDpBS9peQEvQtgD8Dtp8u3toVcMMz9RARhvqPUdLPs/QKoOHeKYAHqElfufM4QFR3G5cthODPt67UXz1xkd+7mhycA9vv/7m27PFxh0AvweofwrE60DcA/QJgD8BTArID7Wqf50o/oAopgSsC9GFs+nU6GbU04uvzOveP7K+E0jbdR3VzyP4V9iIsXX6QREVoiZdu3xXq0oSRKDJMnBMkMXLUxQ4SQhURkApBYs22e/ylGL1L4IBDQcWgmplvi4ExkQkl76AEqlperpcKpfmsXGRsg555rAkUnWaKIM6zUGlakuEoaBI+zRpyrPSZwppqNRoXBaq1yNOExcBsVqVjVZpFrw4ieKn48Y8OkyGs5jZrfXq7Llb9r1+T/uEvA4uvA0lLxUoCo7mqDQXjiT6qJwyPmwPKoah8oKCyiOHRaIHTsdszfYjVxFcuSnP7y8ys+hFpJxKcajWbnTMzo84q7XPT2dGn1pO1tKI4BPjjEcxdcCFUJFpKbYj+SYjeVdJz2S0KKPKrIeulMZCILmAIyMyEHONqifgaYVsz6DYz4EdgTt2YDD8WQDVgEeQAKGgIvnawSMgQQKBSANL7Q3DQINgmg4YDIKGrDQ/hgZhRfjgIW1vDkySBln0SGcREaA55nBzi27CaE4s0CPtdRXrZcKLPxVXH1ilt5lpQTD3PTAPxqE2gKQatkkxBaPpEfARB9wKwEsOvPvruve/6eAOvij1lRffFf5cXJG9K/w4PLXKSdvd4N5BgWedDO6izZn6AdoJv827unfwNtqJeoG7e4K7exb3Dt4CsP9Xbz9XZsZwoblzMJmMD2dT7mcJ3jur8cbxEQ+KoviF4Z1mmHeQG03nZYlZWcYP7lxTw6KDGAVaa3SLApWzUMRQzGiaBtE7kNYrcQlwzkFEhFbMTEQ4xhi6WXGZQX8Z9SX8OJiB2N4nt9c3VophBK1IJdAWXvgYo9GK2APNssHzt+/Uo7Ueudpm80UZNtaHeb/bfTdK3FtYZ2g5V91O7/F7Dw+XWjPdvX3jW0mavDudLwZaG39r99qOrdz96WSxHyP3y7qmIffU49PH/OTosGNC9pGffvHFR2Vld4il6fW6b0OofnBwpPrzeWc6WYzHi/pge9TvQ2ir381+an9j+A0o9Waa6t9RoA+nLvmFJFHXJkFqVph28/zFIs87eWoygd4W+F0ClE5UmnWy4WK8FKych7lV1vLV+bvsVOHwrEdtBFAxM5KUp9Suu+xha9ESOUKryM3xzA9PAfgY2hzBx2gJ3gDPiN4lMb+HllgK2ny8dwDMP/25z0S0pA9f+OznJ2irc4cf//v/5cPp6Wv28Ifv7fQ6s+VssfE9AGW/ex47xTwGr97Ab3+Mi3c6Jv6Pv3d8drHdr23+eqxMIAm8vnX01exLN4d+Z6nDc/VF/p2ND+Yby7T65noHjX5j/jcP/tf1/sU31Le3ri0/NF2bGnMPUCO/6PztYkmRjBXJ6ZeemRtzAbCFsh5I4uq0pm2xhVLSEmpiEEHgIMQZEwGRVz1tCamKQMZALUCIDJ0wWUeKxXOMzOzbPESfI8YUwham4xGcgvIBinq6Q8lIF14RCUJIlGV4zzFNfCDUZ6mhrKxiKAq9gJjug/c65aPzwfpoBLp+E//v2qbqh8gg0pTDaeS+CHFc+4AHAnpZ65hpTQtfR2AQVMek/XI81tXDC3XtZn4EtezasCyaconRJsCTnnpyGim5KU0v1d3oauJkoYo8U/pxohdDE8ygoVw7HewyOk5YtlmUMOEQIKehOmAZBEjmMDsuUfS7yG0KOQKiD3BpEHVTCI1HiAHTwwUkEtJMMJ9M0MwFHdUFPMUI4RKlZJ2cdCCEJdBpWzOTUw699S7ovEQaGiTowgEQkBAU0YqwZwBqNGhgoaDBUNAhgNihSHJES2hQSo8TLJcNXaDEet3DkNeioxQXwhilRZlfy7LH336oq7LGAnWQ1q05MJAVaJ7eu5o25WZNEH9j0d38znB6fPiTb+tX+MuOK7J3hR+HBvAHAP4I7eR8F+3EfVn5VaMNte2inYDPANwCcBvA13HvwK3GCKfz2eRkPrv/+qMHLz6/teMikButbKaT+tpwoxclhJ+5dkN+9uZz6Vk5FSJSH9q5FoGoRlkOzQpTVyOJEVmWAj4gIECnacvW5P3crX32Jm57nHrvAQBN0zAAuiyg+ElQzAjxMniGVcEF/5ltGmulcU7yJGWdKxlkAxkyZcYkziodQgyVtbbpdjrzqmwePDh40hn2e68XWXE0nkyk6OQAUI36g9/9Nz760gu/+9WvPZ8Y82u393d2v3b4ehYQwsbWMKUUen5ehRBktt5Nhj94553AIPvBu7dOlNJnJyfnUxb56Pn5bHc8n9nlouwrsQe1dT1F6qcj8Ksk8tha9653cqY0f2t9sG27e2sXaX0GZvO8SjESUotgBntkzz+ogAtvAy/9cmt1Qh23pGzVFuCpGrpqPYaL1c9TAGeqrbAF2oIKft926wD+T7TKnEUbJj5crXsM4P6nP/eZEwD4wmc/PwNgP/25z7jVa4+26vbw05/7TAng9Auf/fyfp8zO0ebtVQAw2Hxl+urfeWV6ufL3/uF/nOGoEDzvSqNdze90MgApfvcD/bVB84H62vz79b3tu57yPlQ6Z0cfpde3FH9375GUcNib7uqT5MO6VqPO74y+hSfXLnBzttvruG/2fvHRN3FSmPBPbt3Ty5FxCd0/+AeP/w6A5wCygP61ld/dNkBDQJ0AYaOtoA3OgxjQTNCeJmcerBIM1mRllixAIgSrpLWBacPppBpAiRLHSucKghTk1731RCGKqqNBzGvEEKG5QMYRJEqnbABFFBHIRxVjjIBXhmnt4MxMrc3WBj1ALWt9h59sZTtzPpf1J/1epaKXNSY1tzZ2a2euJ5nJEtDFpIywoU4yVZmi188aVy8QFUV0MttMw/EBrVl/r5d1pQoWLq1TdPqFMl0Tt+ADOhdNjYWaPQ6w0/5oqwcaPGd9eQiVlxWbUQImxUuO6BYgDg0uwlIGNKCMDSJ7aBtgK0CGHkEicAEsyhq8L2ScQXLgUHdKVNqCncay8qhjDaII6nVgx5Yte/TznOLMo0FECaDXGiKiCQ24JKhoYEGw8PCokUBRikwUmDwigACwYBkXSJEjR461kEKHS0E8SIqC6qaOJRw1WMoM7IveRpCG2B1M2FvlL+6f1qUsimVo7BjnDpDO6p76lOi9X74zQE9Pj6svSv3np9Vc4Qq4ytm7wiXuHShczPcAvIRWcbmzWvMW2qrIj6BVeD6G1gqD0E7cu6vlGlrlZbhaX7x3cfKzHW2eP68W+7Omrt8+OWzun5+mg24nHeRZ56+/+DLWu92w0e2Y0+Vcn80X+ND2Ll0fbaCf5SAiaCIYbZAlKXzwIK3hXeswQETQWsMY/ZTcsVJIkuTp69VxgllBpFUJf5z4iQDWWijFq9cC6x2Y+KmqF2KACEgxc1hlZjEzOe+jUupMMUcnUZTS0Qe/cM6NRaRkpdbmTdk8f+vm2fbmeifEkBydn1yMx4sXfudrX717cHK67WxgpXknMSY5Pj03s3quvAe21kap9yGdT6s0xojK1uFiPN96fHJWRB9vmkS/DZF/1uvmcdDr2mGvu0yNLoiQJYkp13rdKs+Tl7pF9no+3PmTbqe/SGWxqTQ2GThmiCBUH2BgndrKWA95WoCRrpaAltyfrc7lHG217AStWncfwISAt+hZTt7X0JLAR6vfn0P70PA9tITsEJfV3kD3ta98ffLKx1+VVz7+qnvl468+ncde+8rXFVoSuXjl46/6FdG7BcC88vFXl5fbvfLxV/HaV76eAEhe+8rX3SsffxV/49V/j778P/zB6PXf/rqE+9huAAAgAElEQVRcs2uv4MFgD9vLd1D4Jf1fdxcAlvT2ep/GqZjnT8rkMNtIvrdWpU3sE4WfkQfDO00WlvcmejJ8d93ni2xKhTO4NRtQ0A/dtHt6+PPNxaReW/76b/6Wvf+fvT2hRp/8O7O/V772la9/C8CX0ZqM/6Dtx6BygPNW2VO9tsrIMIMVQbGFiqSJ2GgNlQTAOkFUAUoxggAxChJQ41zuDbyzLOU8IWcJJnPwPnqqY3Be6cAKi2kKw0BdarigkCRA4xms0VjhSakDa01ESgvETk45so+608EseHtMGl3dzZZkzJve4oSrGUfLYXZR7kuwVberZ91q5HRe69o2BZwrTWqODcUQnI/zxxeJjjU1tgSSGKLuM8QtE4Ybj8dmMV3ksVwaWoRMzwxJSE2pk5D0vDwph/rBScJFWodsZEAu0CzXkRNN5n6FOLWULgxQE+JM0LznkW9nyHuMcB4QcsAOLdQuMH5viqTIoPdbe6bx4xkW4woxOoSyxKJ2bfakBDjnEBAhKxG77YrrUaIkaxtoGHgEWCwwxQQJUijoVYg3QiUKPg9wtoFBsirMITSwIBAiIjlYXOBMZlhAQUDQ3jeKfe1sGqIvK9dUi1k+r2ZWgasMXV1hQdVKmHl/grFffSGztnT8v/pbv/kbJa5whZ+AK2XvCpfoos3Ju5AYHkRv/5pK8ttoJ/p/G8DLaO8t19Cqe9/Fs7BcjTbcNkI7mTcAzra7/e/Pm1pChHz78f1b716cbhGR+blrd9S4Ws5e2r/RVZqMa3zoqkzd3NvEIO2gk2RPD0qr9iNaVf988a+I/Jm/Cy7NboE0zYK1jbokdUorpKr11Q0xYlmVSLRBlmWw1iKEAGPa95ovFjBpgsS0IeMwKQUShDsJhSBULktJjAoUMYVWi8SEDEBYluWJYkYI4aLXKXYGvf5CKXowXS5Py6rp9/vdZLmcf+TtB/eb2aR5mOo0OT4/nYyn41lisjt17TjPMxVAogtNKirM5w2Phin1Bx3z8OhgR4k+290YbNY2vLO3tb51enZRiYQnvU5W5EVRbWxe2zk/PT4aTy6OOrkBMZdpkljI9BqW0y0A73Ebit8GcINbUt+sru1o9Tm4DNlaPMuPe4y2QvZIgMfUKnZfQ6vgBWVYZ53sl+pl/WZwcYw2zzNdjfUO2lSAYzwzVr7sgqFWIdl/Dp/+3GcsWjL5/kt8WdDxFF/47OcvO3Mw2kKOutPk6dZs7eeGy7UnVCTvCsdNFPYu/vD64Wr/WwA+SI8Hh/jCy0bpUJt54eIy3kCQXT1Lr4H8CXneGgt/Jyf8CU2yDXx/i+D44dnfO6aYy87qvOFX/N9+6nH26c99RtDOxTMAX/7CZz//Q7SK5waATwD0KtoHp2wVAhQFKG+6RHCR4ZWNQsJRExo0SJGCAgEKiUGlgrI6FfZatFIC4oRmtU2SVKRcJCiXrKdn8LKjYBIibyEqA2wJhIikqsK1DjmfZqYWLeWi8aNwmpdzzafq5iDvYPpkkis/s/KBD8odrRLPJ/VWtugN3CD6fCub++CrsT3cNYz90UCJC+spH8nR+cHiQzMs575sNvLcmIRjU9fWTiZl15bLjl6c1bGRRHsmqYLux2Hs7nSR9HQ0zZEev17F0XZFyE2IxsnC52QflqDtlPtZBntqIRporjvxaMifBTSpA2yGWKdI9zSUAXKfYXI2xemTM/BPMXAWsTieoTydoTcaIBl1cbo8QS6EDAXmmMJTAAtDI4MgwsGSB6++HIwcDEKEQwaVdJBaDQ+HAAWHGrn1EEfQyGBg2up8AB4WnEqsG0tzzKXGAjlIFBLKoHUHFFmU2MovxpjEJJeqcP2F9/7xHKe3AWwQnmV3YvWha3sCYQzgl9E+jF3hCj8R9BeFtq7wlwPf/Q9pSNlwz/S2u/b0rZdV/8Zi+5f+848OX/wV1knxNbQWFx20k+QO2on8y2gn3hfR5lh9A+3klqNNon/peD7Z/O0/+jK6SZa9dfzkr7z26P5Pz5bL3r/7kZdvXhv8f+y9ebRl133X+dl7n/GOb6pXo2bZcjzIiZU4DkmAMGhBAl7QQQtCJdCIpEi600kWhE4B3RVSgLtYNAtjaGiqiZpOqE4HQQADcahMjgfieLYsR5ZUUpVqeFVvvuMZ99B/7HPfe1WqUuQgJ8Z631p3vVv3nnvuOeeec/Z3f3+/3/e3GLSiMHjT8rHw6Py8y6tCHurO04qT223ibeGcwxiDkPKmAg6EIAzDqq6qkGYwnil0YRiSVxWXNtY4OjdPEux1XfHh4EmeUdUlc90+qZCuKmqclKK0PjIZBAFKqsIU5XPXB4M4TuNWmsaj7dHgyvL8YhBHsZZC3F1r/QWQH3rhpZe+QQrZvf+eY2tKqa//4osXtze2t67Eadz+xY9+eLIx2HjnjbXx3TaTraUDc4G2BVEoxpiwSuNUKYH7mgfuXc/qamM6zXKprDGluZBE8RFwz3Q66bO61t9Sa1qtxaPzCv2rxXC1G4bBZTzBagN/DO9l9yv4XMuHmr+L+KjQrH+txA8gOT6U/6vAN+Jz654pFMJIJu2aAk9gFoHr7/6+P9q99MXL37O9OnzqynNXL+IHnyU86dnAk0sBXMQTvN/SAPYn/uk/DgB+/Ad+UO99/eypMyqJp2pZbLXEP3xn342S7ct/9eJM9BicOH3Sfce3/Cnx+Cff/UdSHbqA4MOuW95f/LGLFb+51E5WEmG2E4vjgcAEK8A341XqS81x6AJaU68UiFbq1CeVEJ9ktyp58qh+7EtKhD976ozAT65beJX8TwHfiuIejK9ydmAExoKJjdXOSaRDYYiJAOlbqzVt3owErLGhrCrtEuGcMAZWVyVWQ5pAECLq2pkDdwnKKdJUECgYbkM+rfODh4OyFjqRk7GYDNrrl1Cbrbv03MJoRC3aSZWtH7lLbJvhdnta18ly/0BMT7hiYKQRzpXjYX2wf6jvwiLfHK7Zyee3IzMu5/Wx+bGcC4tOv5WPVlcCeU3Kly6uLFZB7ZStwrDbF6EVtamcc+MiDtvOdQ4nZVG4SBZStpKOnY5ia5djisVOwFpu68yJuxfnRbdYYyQKXAXuhvNB8CBGbYAkwvUqrLDIKmCQbzNlTKffIUol9WBIVThcK0H0Ybg6pWVbKCTRUkzHtNjaHpqQULXpEhMSoJjKiPrQPN3tglbuXCFCtuYTcWxrSk1BgSDCEBNTUVFREpM6X9bVpu7UJIG2xbBymZuQMdUZk6iidiGxqFtH6lhmeaimBVMog8oUdS2cKYdjhvfj76eznGOBn2C9CPwo8Gv7Zsr7eDXYV/Zep3jqcRHhCcC7gT/gisFaVQw+AWyb0UtPD58693O9+77pKlHrOvB+HjziuLAiefDI7iB3YUUxC835lmkAddNnN+rFrfr//viHf/3rjt1z97PXrz2yMtzuGK3Ta+NJfM/SofC59VVrUNx/4JBMwxjxcheUV4S1ltLUrhWkIk1TX0BRVFRGg3MR4Kx1OBxKejeQqqpQwH1LyzfNlFWgMNqrgu00RQwrdD6hnG8LnMU5XBRGAilQyEksZW3a6VJS5GtG26Kqq3En6RwMNCZNpCGKkjiORV3rP7Iw3x+nSbIdheHHJlmWjscT046TOG0l6eGlA28eDaf3BIwkqZSTfExRVUirOmlUmrnenI2UMsMsa1ltTBSoS0EgB8jgLmNtdteRw29VUn7r5vb2ZFpOR+HoxiUXyPeHYZDgvezeBTsty+5i15vuXjzxmJkgz0pHN4HzzXsfxOdvfhyfo7cohDiKFGOwN/ADTwzw/n/+gV6n176htb6MH5zuxYdyx83zVWDaqHiviihJY+9WxoX40PBeHNImmLMtI9VSJuVma3zi9Mkt8Xd+RAG9v/R3fmT67bAwiIadtl4+BKyLcVyWzy0v2Ic356Oi/7DuF5HLwmNipfNRiesKgoea45A2x+BCQNjtwEUEHz/9Zx8z+BSFwUd+6tVt/140al+ND4F/+OypMx8DfgjBn0JyN5auN99TEoRQMnI0yQaNbXNj44KsqtpZ6/utBWHphBPCykAo4aCTgFOWJJFUBcSpIC8QZeasFMI6jDDKyrij6kKa6XotozjvCoQL+mktbSE3XlifbwX5tHO4Mx5u2XUxHC24XlSLZDAcrOZyfKnutA6mSDvOptdcX+c63JhcFmVQiDKvq+KFDXvkbcdyc9EE+TNXe5krgphEyFAqLSIdBcqV01JpsL3F0CER1ogkD6WLaumIEmkXY7fQVza1kk0ViuG0FqN8FZYkyVwXd8NQjUo0FZWqEUISxiV6qjFG05UxISEhEUwsURgRhAfJimtMsjWqzHOnhJSKKXk2xVqHRCqD78ZTY9Eo8kARjHJEXiBoicTFHNyqKJgCjgBHQIxBY5tAb4ahpiJGELrEDYZbBC6UigCDEQaTl5SJRtsNtWKptuXcxI2PyHvqrBjPj9hQ+AnILGcWQFt/HZ2W8MvnXLFfkLGPV419svc6w1OPi2PAXwf+EF7BmHGeC8C/wIfB9AN/86MvT/bdS/T8/w23hNSa1x0XVj74wuZacn08WHjY3lXNpe1Lxpm3OG37l7fW9P1zCyaJ4/DBhUWRBAFJGO6ob7cijGPqsrzptTiOKYoSKaTQtqasdjzxnJJKRFGEUkr4MK8gjELqyneAUyogjUKqutoheEabnQ4YAkE6lyKtgFBBIBGNn5wxVrc6SY5vsS7m+r2F6xsb6spL14NOktwI07mgo4LVWohFbfTceDxdv3Ttara4MH/14rXLTkh5tKyr5ye5GRw8tGQeefObN6aTyfzmeDu1NYmSgqX5Q3RbbXH12roZZ1suUnHR77cuxqEaQXDPfC8ZFEWBc25jfXtr1IriqwLa7TgehoGowT0AvAN4I5rDZAxJEET08Spf1Pz2ovn9NvBKwQeA38QTws/iQ/V9fI6dAbZj7TqxdjdOnD55pVGrcnyuX2synK406xrgTZnL5nEZyE6cPmkAzp460wHcidMnd/Lu9qJZ7/K8EncjxeTsqTPhLUrgdvDFOTNMk37x7dtX/8IXfmAK8B2fONS/tpC/6crC9tO9rPXQOMoOu9yuu171DYzDI93P9Af2QmsgcvFg1K7mTC2WCKtNF7sWE/EZgfp1vNr5LLsFKCuP6seK03+ONl6pnJlE/1fhxOmT9dlTZ/4hmo8B3w58B179VL41GtHMrK+ZAgnfPcZijG7UarDGCEdAZbQvce7MQ6AkZQVRgpMSeeUC4yoS2+3D9MKJTYRzMklBBjbtVQTEMt+0ThtHVjk5HqVGLFGLiCuDbS3DUTWJB5ODK1frZJDlSY1i5bPTF7rd8Aj5tUqLyA6G66Kz0BnXvQRdu9baM1dUsa57bqjTjKkM21WtTGhaC10nQqPGa2ti7sBcMH/4kMgrjcm1S1wmApmaMIyVtWO5cSUSB9vgjBGTUUJPSExZUIw1bBfYbotYRIxG2ygiQt2iNBmOmsLmaCrmWcQZS7mRYaiwWEp0Y3qTMGGbmgqXeROVBZZJSBgzdikHRICkFo5AGqdAGLTLyYRhhEChmiyWEbmLiUlIhQodZe1EQc6YqwTTRFi0MGgSEmuwQhAQEGhNaVI9zGxlXwyYk1t2vTdmOPHnAcvsFsbdMPCx59/+Bz/86d//3R9y7/2u9f/ac3Afry/sk73XCZ56XPwF4DuBb21eKvDGtT8L/PzDT7jBa/qFDx4p3vaX/1wFdK4NtpZ7cXL47rmlqh3FrAw2zGYxiR46cDgIw4gk9MbFxlqklC/zRanLEmstVghCFeCsoawqhIA4DEEIrDXktcNhhRAgpSTLcoQQGGdxVeU98qJ4pxAjDEKssTjnsNYxHo9pdzp0O21IU4qioKwqJtMpc70+QmARMJlMqUw1EkIGaZzMLXR7EudavX7nnoW0u02oskk2/bTAPXTlxmox2p6w1F8sVm5szK8ONp4+tLDs2nGyLJx8MY6j1fvuPfbg86sXg+FG7sYjx2SwTpqMKE1txyv12vJy/xPOml9L09YDeZ1/Y1a4rwukOmCtXSrK8jfLuvhUJ24vh2FwP3BIhfLdprYJMI/jKIpjGIZ4MtFhN5duE58396+Bn27+L/BFOQav0C3giZs+cfrkpbOnznwen8s3U6sqgLOnzqzgiV1DWGgDVxolb9wsc7j5SdNm/bcle833HouMKzDuBbziCMD54El5Qp/Mfn7h/z04/Ibh0Xp5tHE+eDIDou+N7hVr/bI+tpncV9jyW0OCt8jEPeUWs9+DTbfUOI3VUB0GSleFVwP4jDsw/gj3jcc8s3iBcTphj5LyqH5sJ8flIz/F9Fv+HC985KeoeI1w4vRJffbUmU/grYx+CfhxvJozh3fyuKkcvK4rQmWIkwRnJXVdYYy/he/Uo6c+k8taS6UdsZCIu+/HjDWphsjasNRgS1XpJDTxXJfMzpVTKSsz3WonuarTw+Fa127P5xv2a6tSZ9ZGUxe18rwcJIEZj6qiyjeev243IAr7bZ12A6uUmuaTojWZVKUqzGQ8GMwBLaUSYw26zGtAK5sEdm6pE7R7sZOlE+PNIVvb205WTqSLCbo0cuNSRCVGIokjV98YE9eKRRujAunK3Ip8rF1sKhH2Q4SAORaoqNjQ16gx9OiRk6OQKCQ5GoMiZ0pKyhQFaAQWQ0aApUYSElNRU1LaiFBqxkyCGNvqExdWTIV1crkvcmPQGxepZIS2OQtNg5kOHVFTk9VTJDExEZuMsEzQVIQkgGPIlhJIOWbb+1RaokAFh8IgGObl1GpXd7RvobKZ+EmYBn4ZGfz8p3//d8/yYfexjy8J+2TvdYCnHhf/E3AGf9PI8eG5H334CXf1y/m97v2/ZP/BTz6xsTYezQnropXN7YU4kks3BltREkTyXfc+KOaTNtcGWzigl6RIKYhUQByEiB0bNx/QkoCzBtM8F8CwLEhkQJpGKCmpnUaEAdY5tLUuVFKIxnAZcMZoUWmDsw5wzDyysJZIO6QQaK191a6UhEGIkMoJKWxVVYWApDQ6ELhFJdnKimKwtrXZnuv3xXyn61QUG+BoHEVhXdVxlMYvvePtb+kI6R6cH85f3R6M9JEDS0kYyOrK6kr8uQvPvLkTpy3plIyCSGSUCCF95XAoZW8uVVqW7trGja8fjCfhXLe9rKQYd+Lkcl5W/a5uLRJyCE+ySiA3tb2r+X+fkEUUEslhvKIX4on+EB+u/f/wuZfLwPqJ0yft2VNnMnwhzkv44ooFYOHsqTP34j3udgabpkL2KJ7QzWx43oLP+xvhFbLZcvfjid6nm+VvixOnT1ZnT515Bm/DspfodYGD54MnLweEV5NryfXxNw7L5jt7rUpdvHe99aLBvr1FLCTyC650V8SNzpBcCXyR0WXgPwrk54E1sd6vHr3+veVtNuNleC2J3t59Baqzp858HPi7wON4z8AWu23qAAiCEAiQQoESKBVTlAXO4mW+cDf/1GRTtDa4ThclIGxHxBKE7dMOHJPcVZEptLTO6MLNT6ctq6d22A7zzTjJt4J4oeXq3EZmfCFdPqKvjgd6mlmRjLlRjswCUVpTF1ZG7WiyPdF2mgnVTpyp9FxV6EoKMhyBMUUAJFgUU0cxHXFjZQTgMlOIbTGAANdfbAlnKzOe5LKuOk46IUJyKUtQREzYZEuPSPQCi9EBEZsWamiZMsU1xEoiSQmISaipiIipMGhKSmq22EQDXQ7SJWCbGwRYEmCIYMyUqjG37tFHUKC0ITJtgqpmuNgTEQarCzQV1uYEKHIyDAiQLkCIkpI2EQ5JzpSCgpoaQaVbZG7CMMBPkrxXZYnQ6HA1cd2gyIUAqSGzvijqg5uLR0dPv+tPfLRoz1927/2u4rU+B/fx+sA+2Xt94BP4xPj/Anz/w0+43zGX9V945ql0Lm3plcH2pYW09fBDc4fV97zzm8OvWT5KEkbQGCTHUUgShCgpUUJgnUHd4vA2+6OEELYJuUZC+GCXgSgKiFo7p7RLokgYo1FSNiEv620SBIDFWo2SMeAtW9JOB6Sgrmu00SRxQmELFvq9yjlXllWNMbp2Ajvf7W3nVZ1feOlS1O2km0tz/W4URRNgYZJl0xsb690gkIeUtN/2wrVLVweT0TNWi83F/twb8zLvT0ujhtn4rdkkU5FUeSvotstwIIMAirwmCgVL8wtV2pKx0eXb0yiRdy8f2BaKUStK0/le7+5qbbOr0QfQhMT08apQiCf0B5vnLSQGr+ZVeAL2ReAfsVtk89Zm2RkmwIsnTp8sAc6eOpPjVaYHgMHZU2e2GlUPdk2SU3w17wiv7B1OOsnhnzz99z5jtBmeOH2yOHvqzOfweYPJidMnt86eOtNrtmtjz/oAaHz1boXGh1El0Gl/sTOQ/zxpj46V73SSun85Li1OvNi/2m2XrYtHiqWOcCIii9Jmm9aA/4wnmyGQPaof+4rwJjtx+uT07Kkzv4BXVf8g8D/jSTTG+I4wYXDr7doRBtA4EWGN9Y4uQFIWCOuocRhiXDmlJEKEXcLIEVO2QLpqMpRUU3FwOWxXG0Wms22bj8faltENEYYDW/K5wZXrR3pp1A2i9urG5o3rSRjcE6attFZqFMXhCB0lGjFKWsHctK5SBwMtsMrRFhCTpgpfNe80EJgd9VTRZDJqKZhuZirMYqTeJAs6tgiRLpowqFoIaiQIyxSqNpIONYacnApNSkyEIKSPYp6CDEFESMUITUlBQERChGPKFpqKEoOgbhyGHBUa4fthSEUWGuzcHElPMD+VlNs5eTdHVNskGGyzdI6lwzw5xmkmYsiEeSoiAgwOhcARUDFSY3b65AmFsgbTeJeHSdSKEyXbkR5Nhwm0S3jzFA5fvuftv3Dj7rdeN1FyDT9R28c+vmTsk73XAR5+wn0MP6D/juPn/sIPJR984Yv2Pb/4/mAp7aRvO3g47iQpDywdIo1C2lHsO9bC7XP2GgvdBMmkLHFhKJSUO10yluYWGE5GVmOlc846kGVZEoahQTjfZd4awjDEGOurdwGBIopjTN0IRwJ0sHtBOOttXZIkqfMiL9aH20EvaceEgamMTgeT0QFda9FJYhXH8VpZldS6nsRxYte2NuY+9czTW498zdu2eu1+7/KNlTgryrullBcPzR24Ryox+aXf+OiLy/2FZ6MwftP11c0Hb2yvu8mw1rpAAmpa1Ta7ul7P94K1+YWuNM7mW9Nht592xoUrN1Y3Nw9qpytiDIqHmyMV43Px6ub/Cr9LAZ7APQu8D/gN4MYe1eypvYe8IV571a4xvp9tBoxmxKxR62YmzKt4ogew1O61DiwcnFtYvbJxv9Hmi8CvnDh9cnL21Jnn2DVFTvHh3s09r90W54Mn23gyeQOveN0LLE+P1KpY0P3O5bC20hppZXe+6C8FVoTNfjvg5/FFJleafRGA+EohejM0x/WFs6fOjPF+l38C6GldB845lJI73WI8BLXxPpEEIUYrtIYgANOdI7AWg8BRIbQhkCUqTlBKIFQidTkmHwxACDs/X2mVupcGw7qV9PuHbV1fRbJJq31vPRwsK4Iry0cf/LmNj3/uYdmN70p6ST3dNnkxnHw+7ffjuBMko421FrktpQ9NLotZukCeO/xmSt9YOXUpc2LMTn2BmK5MQeBwhWhhqV0uqjpE25KckAjpjrEoEloMGTEhQ6AIiXBYhowJ6VMjcVIjrEaSYHfamElSWhTkaDQJMY6IggITxwSlP+WnSmFNQRHnFAfmiI1B5Bq95U1WyBytzJLQxVBQU5KRkVIiUFLiyKkoGNJDYdFkVPishZ0MFSNRLqFFTlZbjIa6cmsrAwgWgTxHHaoxmUIky9cvbD4TJR/B58LuYx+/LeyTvX18WdGOEx4+fNel73jobd/09UfvObrY6YpBNqETxSRRhJq1OrOaUOw5HYVoRjE8ETOwVhe0jWah3REzZpAXBdYhArwBclkU4By6rlFKoa3GGrtDKGeKoMHBrvGyNysNvPFyUZcYbUiihHE+DXVZ9cui0KUM9Vy/H8qiCJ9bXQkCISZGONp0ktraaTmdLBXDrcmFq5fXEW7y+Refvtjt9B6q6lrWdb3ZSpP2yvba9MratXRla/WAw06zSTF/fWtLFqaOLDhCpLAYJx3GGa1DktwUU1XJNMvKA9IgwiCYdlptAGsDGwMd6ZP65/CqQYonZi18GPWLwBN4ZTdo3rdnT51J8TYok72/2eOPHw8A98QT5wzAx65+PujF7TfcN3f0DWkYPwVsnj11poUnXDea75opirFzDKeTujJuO6yK6jJ7vPL2FGm08CRv/d73PBCff8+T0aP6sSF3RlAul2+u5+q5znOd5/xZQdhaCer4upy21sJEOrkEZEtl/z/jye6IpkjkFquUr2i/qROnT66dPXXmLwP/B/DPpFSPOGeFvGUyVCNwQYRQBqcUaKhtc1OXEqQkcA7hHGUs0VKCKygLwDnK0YhyMEBAOY3Clc6BAx9L8mrBlDpSYXRlsno9M3nRElEUSUG28fRTadxKe3Wefcrqalpn9THqeqSQUV3kbxFh+IKDDbFrszM7ziWgBFgBLhEd0U0WqrzYVNpVESCsz9RwgBhRgbGI7doGCJkyRuJEiaAgRxGhCNjkBkssoVBURM0/TctK1iPJVrVFiEAEMegChSElYIJhypRMCfTCEp11P2cqsVjjBeXsYJ/UdQnLkklP4JIMhKQzSUlln6Fd59p8Qbo9AQo2GNIiQVOSEiORlORUVNxSzyMAZTHkZNJiKkCFRJOaykqUA33YYSIlo6Gw/PWj15/7wH74dh//tdj32dvHlw3i3X8oundh6e6P/uDfeOPFjY33taPo6LG5BcqyIokiQqUIm7BTaQyBEDf75e1BEseMyozACuSeZayzTMqSXpoipMQZaxFCBmFIqJTN81yWdUUcRhjr3DSbiE67DYAUu+sRSuKM9dV6ZcV4OqHdaru8KEQcRkglbRonQuvabSQn55gAACAASURBVI9G9SAb1Qf6i8XGYGMzjOK6HSe9azeuT7qdbm8wmU6vb66Ocl0ERVFMFnpzh6tKD6aT/DfXJxvL+aRYqIwel3WVDyejNzrDwcmk6pYTkAIpYxAhrheEk9Z8Ui4lc1v9pD0FcZcW9VAJZXtpZ00IcU9elyHQS8O4wBMu2/x9Ae+B9+vAXzxx+mQBcPbUmQQIT5w+OT576szdzbIvztS697f/ZfsD7/7l+8u0GjzxxLkrjz9+XADzAnHwWO/g+GjvwBaeVBp81e4X8Pl9ErgYp9HhfFP/PrZizYG8ljEX8YTw0onTJ8vzwZNi/d2r6fStk3uAtXvf80CGJwcRs/ainhxqPCnNaarGi0P5n3HO6XS19W/xqtcqvnr4LXjieKnZjqt7iyv+W8bZU2cewRfP3Hfrezn4WVBdEUQxFkmkbmn8XNegNVZA6fykxgC2qtz4xupzQRpPhDFX20sHVgWsjq5dqYRQ686YRMbxWAXBJ+oim9NZkVz81V9eFQJZTSbJ7OuB+1WS1CpJjlbT6QBrS6x9F879aaFUGLbbVJMJWFvgcz+jQEQ2kq3VzAy74ObwHoMWKIU/BwKwEnAmTAVSIctsZ79a9InokrHBUe4hJtnpJhEgCVBsMqFkigRMZ56gdgTWUdbrTMkoGJMriZ1bJN5cp44gqMCGCttbYHj3MTAB7VwzWewih5scvDQgig4QWkc1vkLtT1FnW5FYzuawbFExJaVLwZQpY6w/2rf+dG7P3xFesbuID9sfo/FLzuETF7/2D5/67O/7sx9y7/2ur4rzeR+/e9hX9vbx2uPCSggc+8Xv/7FBUVdvm4tbP/LA4tJR6yAUgiBNiJTPZ5nB7QR5uOm1WWi3KEuk9VWzTkKQtPxNtyrpJSkqDDFVjQMpGjXDWCMbJc8BIgiVsEIynkzodro3fZfVBmMtgVLEYUTYn0NbIzZH27SiRIswcpMil9PxOH/q0hftwfmlZHl+UQynk6MXn/8icRi5Trut4iQJKl0sXV67lmVl4R66+765dtpqtyL72cFkcL0VJW9cK9eTvKoSjY4rVy1JEWgLlVMkFrAZBDE6PRhFKVEdKCmSOJp31lWhk+04jltCiKNAEMqbXKEDwDrnbjjc+6SQnwWuzYgeQPN89v/rgJgRvfPBk2ExVz7QGbcPl2m11SzTAt7icBd+4r1///rZU2fa+IreG3iiVdP0u334m99STkbZ712Va/fVL8ifdCSX6mNFgDfinmFp6T8tz+meXinvLjJ871uBJ2tfx64nYA68EV8k8hBwNFqNn8URAo+yWxRi8QVH06+0sOxrhE8Dx4F/hScCOwiBurEOctYR7z0TysK31k1iCEMkOx0XyKfT0uTZJ7pLi/8uTJOHJqurP58k9Rekqt5YL8wfDeLWhjV6s7WwuAZcPnH6pDkukgU8ERP48+aAiqLtr/mT33mtfehQaMrq+ud++v+Rzrlvjuf60+nKyr+TQbAcJMkD9XR6yHkyPwES7apUm2oOn0NZAz3BrLms76sNMgQCYTRYMzMU9rtGToc+AXOAQyLJGSIRpLQIEHRJ6BCRM2F7ch0X9LGtDmVtKfBCdmosbnO9CbDGaFniOm3KOCK4eo2g0KjOIqEr6RiN67W5MecIypIgWqA1NBAGIjvUZZWIuRfXMGivGlIhd1pEc9P273numv1dwqcowG7qwbaT4TkdJs/vE719vBbYV/b28dqjIXv4m/t/X1bV/2KNiQA2JhOiMKDXdMrITY2uNd0kvWkV/rR0N+XxlbP+tSrYmaUIKUliX2SR5/lNd1WLbW64s4UhzwusgFac7PHVA20N4+mETqtN2LRoq03NeDTm6UvPZ9MqD+85eCycZFO7urWuF7pzQkhqKYTLijy4PtgIpEGXZVlldaVWNm6sb2fjeKk3r7vtju4k7Q/d2F4/gHP99eHWsQCZaKzE0SqyKrLGYWsRGOOoK5AJ0/nFSC4GXZOocLOXdCqp1BxChCpQKg3jSArp8HlRM0NkgMmkyP7DoBqfPf3ef/CZL/Wne3LxJ9u/8c2fjreWBjWe6E3ZNUXuAlffdextZhaOBWjCwfX3nf7z7sVPX3rg4s9eCe3fF89uPro+p7vafs/P/tCMOHI+eDJt1rv1qH7MnQ+e7OFVqwg/Ml7Gnzdd4G14YvEA8E3Nfn4Rb0dh8cUMo68WFe+V0JDs7wVO448V4Fm7sw4phXe3nmHWRjCO4Gaz8q26LD9u6+qjcad7AVi99Gu/+rHJS59/6J5H7lk49A2/7+lpNlfOinMAjotE4H+jFj5kfwkQv+dHf6wVpundwKVP/p//ZH66vhYjhA7bncV6Mn4RT1r+OPBH8ZflvfiJwUwZnPVXfjOe8FTA55rnd+HP7cB3DZE7wkSfeUJiNBaBJaFNQkpJRkhCTIgipiSnJGeTDWIiHI4R27c9vgKBw+IQ1M3GtYkIUIDAJSm1Vdj5DpsHU6QLiW6sEW1tEBpBLNt0rU9WHJE3Te52f6Pb9AWq2Z3kRHiyZ/FV8iXwGeB/PeeKL9x2g/exjy8R+2RvH18eXFgJgP8O+LvWuUPD8RipFEoIVN00ClCSrcmYJIxoxfErr08paFqjObsbFhFSEIahU1LNWn1R6RpTa6yzO6Fau4fY1brGOocUEgc7oeS88mpIGIaUZcXaYJMwCnn24gXiMDZvuvt+OS6mNitz6SxmMB2yvr1ZB2EcFXUlN7a3tKkrndf1aDAe1sN8PNds5qB5HKLpOxtK1amtiYDI1EZVlTYgFdqhKweKqrsQj0NkEhGpdpCUrTTJIxFIK+m2wkRGQYiSCiGExludfAY4f3Hr6q+uZtuDJ544t/nb/fkef/z4PH7QvYQfhGJ8Mca1J544d1MSUkP2UmD73vc8kAL36V59bfKWydfpfl1O3jH+9T3q4RyeNFxvyF4HeAN+8Js2j4fwpOAQnvit4EPSl/Ck8zLgXg8kby/Onjozj1c134f3PsTgGVLMLaZ8AM763hvN//Dt7Z4GPoIv0gHfXu7acZEsq1DVP1VNb8uGjotENetw51wxK9AJ8AU2k1/7W3+z3WzGxuz95nNtmvZzeOL3bXhyc5Xd0HQPOIwnPjN7m3vx50kHr/aqkLgWEHaZdwUToTHMs0SfOTp0GTFmyhhNCQT06ZGRM2AdiUJTkzNFIBGNy54ixFCjmsJgH3b1J13a1O6aqMPgyDxu/QadUpGFNVYK4tLR0lmzO6b5BWb2Af6hiDAvd+zR+PO9Zjf1IsJfwx8Afg1vav7SOVfcrip9H/v4krFP9vbx5cGFle8E/gFwwDrHeDIhCBQSAaX1WTrhndujhXFMURTUuiYJI9LGLLYoy12yJ0E5hQokSOmquhJxFPsijVugjb+Jb+dTOmFEaWqSMGacZXTbbZIgZJxN2BhsEoQRRVHw0o1rHOwv0m61iMLIbgy3nZMCXVfu+csv5XEUBg4bdlptmUYtfWOwUVy6cjm8vnmjUjIIC1vNFJgaT/bmgMg5Z0zlVBBKiQRTWSpdC2vBVg2zCkUWJ1EmQ4IA4pjUteNkNJd2qWzdNtaGAcr2O93tOIgu4ytOfwF44cTpk/nLDkCDpjvFXUB+4vTJO7rwN7l69wDTJ54494pu/WdPnTmAVyZevPc9D5j83uyh4mjxYPuZjka5p6P1eAvQj+rHqvPBkwfwfnsjPGmb+e/NzIRnfXuXgP+EryDewJODWXHJxuuN6AGcPXUmxO//G/BG2Ide+RM7WMN7Kr6AJ8sfwNvd6Ff81GuM4yKZtedrAX8S+AN4ljRjRzfwpK+LJ3mH8dfNUaAlkNJhgyPcRU5BREJISJsOYeMcNGCDbQYIJAssIBAMGDbmK1MMu5H+gBDdFE9ExFgsUZKSFVOmGAIVEhtFHrUoe5BubBGiKAlBStrK+GZptQJGMxeZHdlVIIiIyClvJeJuz37PGOIQeAafknAe+PQ5V/yOWWTt46sf+zl7+3jt4VW9P48fwJFC0Gq10E27MmK5W2l7GzjnqMsSFSikkiRRTN6EpXKjCYQglApFgHEaWxucr+gjyzMCqZhNYrTRaGMJgoBa1+RVThyEtKLUd7W3jqLISDp9kiihnbYJlEJrwyCf0ElatFotMl3IC9decocOLIvBaOCubqykrSgpluYW7PrWVtxpFarf6raFtFOLdUEgHRVFoJTrpG05nIzmHE2XJpBBJH3kCBABonFlRYTgahABoXDMA6KGwogyT224PMlzW+lilJtK9+LW813T/gwBTwP/AVi71a/uDrht1vhtUDfLAnA+ePIe/ED87KP6sXqP9co4eSkZHfhXR3rlvJliRI1DABvReuzwrfm2zgdPfuZR/dh6o+a9Ax+e7eMVjUN4pWcWJpziQ3rP4QlCiVeAuvgK46/G/LxXRNMyrj576sxV4MeA78eHt3fgrMVZi9z15PskcAIvVo3wpti/KySiUamy4yIZ4CcnAk/ktvCR06p5vAv/+wp2+sOKKiFJKkrWWEUgidAIHCUFHdooQiQhSywzYsCYMREhmorpjivQLvZWSVSN05Auaio8oy5MTUaNrQqiDXbCsikVWEvYiTF1iW7smwR74uuAw1FS7qivM2YX73ZpmVkkbQGfAv4mnpgP9onePl5r7Ct7+3jtcWHlLfjZ6cLspcoYTPXyBgTGGZS4s8KHEKRJskP2tvMpSRiSBhHaGIyz3oy56X270yujgbaGuta+Ui9QlHVNHIYopZqWUr6Oz1lDHMU7eXzWGF5Yucza5iZhErurwy2m05FYbHfdyvVr4urmGkv9+TwvC6G1Uf1eT8VBWF1bX71ibV1GQdIZZKN565zuJK3W1mQ4G30Vt4m4FUWFc84bMTho9UKcA2ewKhRaCRkFSCpMFckgT4mm/XZ/tZek/6YTt/8tPpH+y6bUnA+eFMCD+HDq5wAxemQoJg+P7jdtc/3QuSOjzUemfyg/YLbEfcPP4dW6q0217VvZ7d7xrN9DHsGPgYfxhO8b8AUf/wHvhxcA470KXrMN8qu0EONV4eypMwfxJDnDp4L9SxrzZYAqzzFFQdzt1jIIPgscP3H65JXfna29M46LROJDwF38OXUUfy68AT8RmIWI+3iVb04RCIsJ+syjsY1Ziu8yu8gSIWmTeacpKCkY4wgoycnu0LBlL+GbXZS6+XLLrtmkwxPA23Xvnrkkz7r6lM1rUbOuWd3MLN6QgmmMLHUIE+WJ7wXg359zxbO3HKcQT3i3zrnbhCz2sY9XiX1lbx9fDnwLe4geQKQUuQCULzZTUjGZDFFKodQu2UvTFGMtVVninGMwGhNFEVEcUxnNUn8eo2tfaSgFUoQkaUpeFAgEYRxjrdkxSw6kIogVkyxjmI0xxnJkaZmiKkmimFYaouuayTTHOE8epZOkSUyv3WWUT9gcDsXWcBNdaW6srghdaYqqpNJVOs4yO8hG1eZgszTYyiEWLG7cbbksDqNqmE0WtibDKf6e3+Y2RM9vZ0DVtEKQsjGY9sZjEogsjgpjAOk5Yf18YYobLRv/R7ytyWuuBJwPnmzhQ6urDcF6/nzw5Czx/oHep/qu/YWOE1YYPVcHsjLt5V9u/0G7Nt+5eqTceuMguhevxn0SH5b9Wvxgvo6vpD2EDyuGeNPmnwMu3ilE27z+eiZ6Ch8ufxfwTrzxcoI/JgqopZQ/bZXqIsQngX9z4vTJa01unf3dUvRuh0a5GjePleMiWcOrexleLf42/PWi8WpXq0anvr61xttF+3tJQkJAhMAyJUNTNbbG2/jDYpsWavVOTh54gjadbyNLTTsrd4q7ZoPirIJ5FmMWe/7eCh+TdQSNXD8L6SpgmvZQ1hKWk1nxh5A+o9LUcEn5a+Bf43MWb8Wsz/TLpck74LhIEkCfc8XvaJh+H1/Z2Cd7+3htcWElBX7idm+lTcWt1oa6qoiD2HvmBQopBHHjIuKa+bYFWmmCNZYwDEilD5IYBFVVEgiJCAK2sylJk4hel7dvc9pptQhDxTjP2Rhs0Wl3iOKIqqqRUuKkoqw1VgqcqQh1gLOWuqwZjgf0RMTS4WX+y/oaTgg6UduURSmzLJNAWGE1oMENgGicTRFCZAvd/oG8Ktt5WRgskqLpn3RLPYoKBaFTvr1SKHwxiRK3FFL6xO7a6c845PvWsu3VtWz72hNPnPvtD+IXVmbqySoPHrmVSEX4/CpJQ7Jmqtr54MmrwEFVKANk0Vr8jgNr0duBN65bF65eiT/4BinfiGCEJ3Jz+PBsH98ObITPCbyID2E9Dbz0eszFe7U4cfqkOXvqzIt48vzd+CIGA/x74J8Bn/wf/u6PZ7BTPGGbUPs9eBJ1/Xbr/QrBOj5v7X58xPR+vJI8ATBQF0EnDXUBZMwzxyIHuMIVBmxSoRtaJ5nuqbhVCAJiDGbnvjKDBJQFZf3rMxKXkGIwFFRNLa5X5e6k7AHUsx7eDdnb277F28c49jAvp0ALuNHYIr4VT85uMhU/LpLF5umL51zxqiY5TSHN3XgS/ZX8e+/jdxj7ZG8frzW+Dj+g3xF17cO5M3PkNIxuej+QCpkkSOMoRInWNVrXBGFIGAQoJXcKNi5trjKc5Ny/uETQKIS11pR1STtp3WTdEocxZVVTuQolhCnLUlV1RZ7nCKFoJQnTuiaQAiElcRjSbbc5apdppx3SOOaRN2uurlzT07o0RZmrditxOptU1ockSzxB6gEdATYMQl3pGmjGDfGySHMDRxgHhK9clKyAoYR/E3Y6oygKy3/8v79v8oqf+K0Rbn50e+Hpv3FBljeqlb2dJh7Vjw3OB0+OZq+dD56cVQ7ubaf2Dnwo7pDwaZO/1Be0HhJcC4T4dnxhRQL8YfxgPga+Bk/yPoYngjmeDN5pLN1HgxOnT1ZnT535MPBe4H/Dd0X5S7d2Qdkb0j976swWvLwk9HcTTXhSzyp3z7nCHheJxZO7D+EJSxdvs/OQAOd0NlTYFqBy8uAaVwGDwZAzJiIiY3rT9xg0hjsLXK3h9GWvGTQavROSfSU4PIueXdSzb9qxhgLaVdasd9fxHH8dzDX7+CF8ccrLNg8Q51zxMsWvOX5R8/kEuHbOFfU5V5jjIlnhlrYd+9jHfs7ePl5bXFh5G74P6R1RaY2pa6wzSKGI4gglb5O3VxkKXeEaCiClIo5vJobjPMNoQ7ynSfw4mzLJMw7MzROo3detsVRl4aySGGdFVdZcu75Cq93h4MI8xhpK7T3/oiSmyAoG2YTN4RZBEDHX6rA+2rQf/vRv5FvTkRyMx0VtqsJByzmXCCEm+EG1jb/Pa/x40Go2IeY2hMZYTWVqIhWi5CvOv3J8fs+fP/imtzwQRLH+Wz/yV16808JNjlsLyG9pF3YTfnXp3y7WAz2HV9b0+eDJPmAf1Y+N96xL4QsoJuzmoTvgO/Ek9FP4MNQIr1TUwLub5T4KfEfz2Z+RIaWtWcOHh6fN+uUrbeM+Xo4mrGtfZVHOVwwaonIv/nxYm6lWTbXuG/HXj8GfM2/G56y18OHdHp4vSYAOPSpKKsrGK88B2IRUGqDmjoXpwG6u3a2o2J3VzJZrsZuft/cqnYVs90pvN7uG3nb1FT439YvAT9yaqwc7/obcYmUT4I/BXXiSN988Pnk/D60HRO/o0f18j7kyJhl/gJ8TQPRq8/3Ej/yMcu/9rtdtqsRXM/bJ3j5eW1xYuQefaH/7vmewU2wxw0ylexmMg0pDIDHq9q3UnHMUReH7gpoSFfl5s7WWQAUYa2ZedLi8os5LTBowLjKqvGRrMObowSVanQ55maONod/p4Zyl1CW6NgwmQwajoUPK+sVLl7KJLsNLVy6J9WwIMDHO9abWJIkQRSRVjh8LZrlUs3y93p2Oh3WGylREKkLeuVglB34Z+NEnnjj30k/8038sAV76xK+DH286eCPrF2gMW//MT/2JBE/QrjyqH3vFnJ+9ZOt88OT9eKuUy+eDJ5NK1kfysLzeLzsxfpCaecQ+xG7e3Q28cjfE5+Xdj49+HcDn690NXF58U/KpR77v4AZwiR9+57768DpEU5yxjD83Vs+5Ym3PezHwrfhzpwf8aXzhxn3NZwS7NQ8oAgICSn5rLtOOl8gijRsPdl6bjX63zsBq/ExtVnRh2O3lB7f3NbxTPt9tMPOU/HfAvwA+8Wqrb4+LpIsPb8/ac7wJfw1+pk3viET8xUAELy0eXlq/+5uOfaq+rPMvfOILRqN//Z+6G3e8B4gf+RmJn5x9N/D33Hu/6zfutOw+/tvEfhh3H681Sm4mBC+DCGaZMOC0Jq9K0ujm+GVtDNZZ4iQAIbgTBRJCIBy4wuACgTaWKAhwwjGeTtG2pt1uE6vIVc5UVUAkhROhUrbAyk4vJQh9fl5elUgnqHVFXRsfPjaGdqvFcxdfyp998Vl9cePalUPLB4Nxlc/RuKkKcMLvt8aHVbbx11YM7O7sHSCFIgleUQuo8UTvPfiwKD/+Az9oH3/8uGRXIYnxg+JLzTbMffT3fvzyN3/onZfxkaZXxC2q2mV2x0FWuutvfG7xSveZ5UtP/fDH/vQsFanCE9BLzf7dhRc03oYvIOjj/dxmeUMfBKTO7LjZ3n314HWKJmS7iudOt7K0WRcNjVeJt4BT+AnDy8arW8O0t1bG7kVZjkHvihszO5S9BRmzzyh2yd6MWc6W3Vu5uxdfQg5CgE9f+Pv4fLwvRdGe4NXAmfj4Iv66G08ZLwrohmFY9uc7C0rKY+WG6LXptjOmW8dF8gzwSIfu+OsPfmuZjOJ1kYut7/nhP26O5wfe+rPJxt/Qwi2z27ptH19F2Cd7+3itMea3GMiTJkcvnxVTmJff62aefE4Fv+VNNEpiSlGiLFjn16WkIgpDpJUuCWOttZYqClVV51U1LUQrSa0KAxcIEQ6nE7s8vxgcXjggi7pimk1BCUpbMRqP0c5sXltZ/fTdB++arwOXXbxxdVYh+BkglELMdVXwdnwOzgJ+fJg5OITsGqj+dnLSLH7w+yd4orf3YDm8QlDgzWeHeAImgOmVe1fMo7/y2O09J14BTShXNmFgrZxaPTSeLx598RtnxRztZtFvx5O5CE/2PokP4YbANXy16DPALz2qH9s4HzyZDi9Xel/R28c5V7jjIhkCDx8XyRifw3kQf8585JwrpgDHRfJx4K8Bfxnv17hwh1UCN4dTZ0USM2jKm+5Ms6e3q7Ld20vY4pCIm1S9VwPbrPM2F/068GPnXHHhVa5qB01Id29oZNI8wF9z/xx8CPhrv+3rwmsXL7UzJneVFFfwkej7wyC+q1DFUk25cvSt91z6xe7m26qu+x+/c9oN/2qV/coLgT3/pW7XPr7ysU/29vFaIwN+Gm/4+opI412zZOscsqgAAam3Wpl53v1WkFISq5DBdMzV8ZB22uZIf94kcTxJkmS9quskK4qWFIAKnx8NBjfG+UQNJqM3HF08OG9wZW11Got4KQ4jyrAkCWLSIIaSKnLxv/jar9EfJHS95zcu9fFq2nXgE83fh9j1PXtEwmISpzIr84KCHiGgkPjr7UslfBnww8Bnb626feKJc+7xx4/PSg9rQD/xxLkZAXx55nmDhsSpR/Vj+vHHj8dLawuH57f6K3/tv/ztqnlf4Ss4HXD9rtHB7bs4eBg//l1tvuuhZl8eKkV5SEhxMDLREn6cfD/waXx46QX8BIBH9WOvnEC1j9cbBH7iUIdEhxLSb8iY9A1mR8hvVK/PHRfJCeDv4A2iw9uvbma0svv8Vswk6Vne3UzNu1POSY1F42czAQLNna1XxJ7HzEhZcbPRMv5+8V3nXPFlDZM2pLDiH1HhIw2zHMCfCcJgLojVXZUtWu3D/a+N5pM/e+C+aby8lvIvr/U+/a7Tf3Q/d/arEPtkbx+vLR484riw8lfw/UxPslucMMNe2yqBFGBd0wJt9x5zu/y8V4KMAtdJu3ZRa4cMTJqmW/gQx/8VqP+fvTuPk+oqEz7+O/fe2qv3hoam2RoIhJCks0AWSUyidKKJTozhNZHIOOiojIxx1HF8XWDaiZpxmQkjvig6qChuJBo1K0TM0llYYiDsYWvoprvptXqp9W7vH+dWGsJOaKrpPt/Ph09R261T1VX3Pvc55zxHH61r2kTLdTLDCwtfT6TiPYfbW4bpaE4wEAyHwyIWCUeCQmi3hPz+oK7rmus4uLbrih5hNj/dbo0qGTGyblJDq2H4WpCzSOuXL1/ZO2/enPyCaJ49ZcyEl3Ye2Jvqivc0BQPBMte2x2OiYVOGoBz9zePAmf7mDgDN2UDP67oNAQkvsKvwHtcFFM6bN+fA8uUrLe+xAjmWL718+cojZ2MWA8WrjVV1Y24YFY30hqcUtxemgGZv7Vofslu6jL6ZspORGcS49z6yZcTGoLsxBG+kg+m1GPQEugId3uN2AJdYwu68febdh5B/9wgQe7z2YXVAuUAtW/hgAPm96vzE17/kLFv4YBRvGbvTnSwyRwSz2eDtgFHCsMBwRvbo6Ou+4D7XfcRavOVAeqWbap0jgj9Flu3JnmgcQ+f4QV7WkXuZ7I7IOclzdASOtxyMj+NHmQ5HT3XOzsISx263B/hkfwd6J+IFgCnk+NpmgA33feeVvEzBzkhd4A+hTJCpReKhXLRN6X8q2FPOvYnlDnsav4ssC3EHcBnyu2Yix3hNBC4B8oP+gN9xXRnc+fxwGpm8IzmuY2lCSwBJfyTUNioctBDiEHJn1gl0aZpmFBcWxbzbQhXDyw9rwUB+SSh/Q2Ekkp1ccB9yDdbyYCCgARnHduykP10vhJhQ/7vmPSM+Njz/o9WzI1sP7nr11jn3ZLNUcZ9ubPrgTe/Z/Pzm9cnHXvzLjoxlXuf3BTqLCoJ/FSkxqdvpvtHCHYM8cJVw/CFFJ/IEctwS8+bNCSBnK/qR43bi9JVsEN71bFBYjDzWlCK7nI8s35Dw2mBf/8L0jlhh1yuFsYL4amNVHnJQvIYMMtuQ3dEx5N9vMnLiLmbQYwAAIABJREFURXaAvQUEA1Yw7eA833V1rBdB2j3gdhpdRsboNXRTs8TasRtHjugumdAZ7O5I+81RyO7vg6f5/pWBJ4ws6hxftvDBF5EBfBT5HTvdGX/ZAtlB4LIWmnZewTXrgJSXgRqD/P4G6fvu7gD+Afga8E5kgHlGZ4Wat8HsGItTP14QOMVPNZsZdI64Ljhm0HIGWSrnuTNpb3+b/st/TQNPs3h9hCBw/ww1Y3OQUsGe0j8mlrvIkhsvsqexELnvCyEDlWuQO7+bhBDVuhAjATBOdk5+DDeVSWdcx9UNXTd9Pt8OoAMhWpBn0HXIfXolMpMVQi5J9JztOO+I+EO9PsNoo+9E3EWOpRmO/F3omWQmDTihcn8ZLoccy2p/ff+O0j+88HTZrXPuqQNYvnyljVfd/q6rp3HXx+btWv7QQ7FLKi/acuVFl3SseOKRUT3J+K79hw6+1pNKTPfafjlHLHF1Er8AFi9fvjI7XMhFjs/pwRu3s3z5yjcnX8ybN8f03mccGbT5kRm6o2bhed2pR3apxlYbqwLILGELMkjO9kYNS5VaF7suXaF2w0SWw2hEBvAJ7/X2aWjPRbdGRzlhZ0zsHTEtOSXePvqhca6e0ErGdI3wlfeY5utl+6JpfywOBG+febf+eO3DapLGhSmGnGAQ9a534U1IWrbwQfN0lu1b6aZMoM4L7LY7OLEn+X0aOXwghfyOD0cOBcgWG/YhM4F3I0uyTAQWIAPPEyX1jkreZcO2E/YDnwWB3ImcYGavgwxSvwg8d7rFkc87FeQNeqr0inJ+7WnM7hvTyH3uxcis2nT6Ml8BQLcdm+54Kz4j4EZDRSCDj2w5k/p4MpmwbGtMwOdvDwYCu5DjxHbSF7AUIMs7lAOd25rrX12ze1v39IrxM8vyCponlo5Ie/eNiqdTVwqoCgeCFyNns2a6G+P7rFbzdcdyWw6+0PDSZt/fkpExhQee2lJbt3z5ypOPP9vTqAHad1b+qMRxnVBnT1djS2f7SGRZiVnATby5yPtxZdeUNZAHvtQRQd9xzZs3Z4T3nvciDzIFyED34PLlK085KcJbHi3kPc8ADEd3/W1XJ6t8HXo4v85/qRm1q/w9+nbNEpcjM46/BJ5CfmazHJxQJj/T42qOJnzinYHWoCsQv23Ia3lt/fQtkwMRw9rf3tJgO86Bx2sfVss5na7F68Wq2Lpwpx0Xby2gnEtzRNC4/vNfHOMLh6civ8thYBMy42z/avWLiWdf+bPjlVsJAYls3bhlCx8Un/j6l1wv4Mtm6C4KFYRGaYbmxtvj45BBZQPyJCcfWYi5bo4IjkTuP+qBu5CThbL1+IqQ33sN77eD/H6W0bc2tYM84Ql61y36JvL6OXKoiXS8knzZZepcB0SaEAZJxydPZFuAp4HHgFePVxhZUc4nldlTzi+Z8csGLbLMwp7GLcgAL4gM+u4CxqXN3tEt3fv8hdHy+mioaCTyAJLNxr1s4b6wrqHOvKpiXDoYCISQS2459I2Pzp7JhLc215uPbNkwDhDP79+5YfZl10Zebzo4UkO8Om3k6K0NnR290VAwL+QPVAgh/IDhj+rBdGNmoy+gb4qUhro3rt3QfplzWevy5StPVVhfdmWDs+PAnlbkhI5ir/070fVybPsAsnbYjcgMxlutRR5IfMiSKta8eXP2nmJptHagOztmD+icN29O1xHj/XRkRrEDOeYvW1cvW2PPQQa/mrctn2aLwoKdgR69R4x3A25JptCu02zxir9b3+1t5zFkYG0Dfg3tkmB38DIbe6qLozsIu1vXDoxIFRh3vzbhBpP8/XZX3n6/4zuzQZlD2eL1ArhiZmjSpL8mdry2bOGDu3NVSDkboMGbA/7HbFi2NHjph+5dL0ZVjLMc7bKOQN4HCtO9LR22O3bi1Vcnv/YP//xj5O9xDFC3bOGD6YZXXnJKL75k/EP/9IXsSVP2JKVu4vUT8trr20fG2+MFyMBrmndZDxz0ii8bQMqb1fss8gSwGzlpCmTmuQSZhQ4jA707kb+pNHIYQRQ5bvAZ5He4zbuvkr79zHRgArDNu24g91+tyN+zD2izwuPyk5X3TPLFG17z7f/lK0DmyGLIipJrKrOnDDx7Gv1A/uGuvXlrt/7InVpxS9vlY2+7HrmDzpYxkd21Mng8LTVLl4QBa9H8BZkf/uIX4aSZGT8sktc8dUTF1VHDXzqysFjkBYL3AZenzUzYsZ12K+b8lxmx1286fPDgLbfNaq5ZuiQfefA4vGj+glO+9m+GLzOeue2FolAqmLlp9TucNe99Ni8TNK9AHigOIDObC4Hr6cscHAQWIbvLnvPeb9B73fZTBZtehi7jzbb1A8by5SsT8+bNMZAH3OwEwwP3rLgzOz6qpdqa3b3aWBUCbNvnDhM2wzVHzEYGq5VAyNHdQ5ottgJbgD96z/EhsylhYB5yEP1UIJiBTEwXqTzbzA/R2WOV+J7JRH0/Dh+IvFRtzT510KyQXL4+z+xOP6S5eqLN7PjquC/e0XXqZ517yxY+WAD2jSD2gdZqpdNT655/tqhxw/quDZ/4eGPdO+/7cDipBap6D5dOix3qLch0T9HSZlGZZm7VsJ5M9SRcbOf5lm1bp3bVHxSGP5BnBAINh7dszs7KzQAFekBv03Stw0yY1yKHXqSRgdllyKCsHlnIe58X7JUD70MGaJuQgZiDDPAOrXRT2XGAJciTrnr6AkXx1qBsjggaK92UdcR1HTBWuqn0HBEsQmYom976vJlz0WpXoCYeKQOSyuwpA8/E8gzQVkZ5271X3ZC99W3Xflo0f8Gb49sO93ZpwKHeTCrcmYynRxUUTQj6fL1Bw7fBp+uXuo7rc3D1QKnf3dl0kPUH9xa+sHRX9mw/jDfB4WSvt9pYFSymaOz/+dX7G5EHn3F3/e72g7+Z++hz9GU6upDFVbcjD3jPIjN5dlFbYbC4vXDU3sl1B5HBXtR7/AmDJC/wGoPMzLUiJ2jkzZs3pw4Z4O2nr6s2WwosibesZ7U1O7naWBVsnZEcYwfckuEvBvN8aX2q9/hezRbFyEki64CUN9ZvBnLVjDjyIPyI936m+OCNsO1eZ2HkpynKE91WoeZ3kyrQO337UocNkSgeFjPD8T+0Xpz+Ts5aYk0G54sg8kB7RPdp0fzKCa81jqtovfSqiy9udl2RMfxNhus+N9LsKY5gGv4At+hwC4ipkeLC/UF/Z4HdGSjTRWFhIMz+tt0N+cUTyq7t6TbXZ1KZaEhYnanuVJeNfQVyElcaOaEn4V0GkRnlBsCYI4IXI7+/MeRvagoy27Z3pZvam225F9i1A7EjA7njZd+OvN+7nh0+wko31YlXyuStVKCnDGQq2FOGqmGA7riuiGcyibDhFxnLMuo72vTy/MJQMBDAtK2ETzf2l+cX1V1WPib53jvea9UsXRIDehfNX3A6A60t5IEhg5yJfABILl++0vYybgHv/ggyOAJZmPg+XKYbpp7RM3osk3Ti/pBWiDzw6fPmzYkig69Gr/zKm6qt2eZqY1XDX259IfqbeY8WI7MdMWSAOgLYd8+KO03kAdFfbc1Ozps3xwUi1czOBsNacoTVGN3je2+yyByjN2utGiIIRP0jjQY75Txpdzq7kBM6su9tm9e+A8hxkyGgTEDM0LQqx7H/085Pj3ENbgo3h59fbazaWG3NVmP2TsMl//S+zp998fWa/cmyope7i3I4wF94y3zp5RaErKDz6rPvuk3f6xt10QTncHSUlf7hAb2geXbrluyEiD8Al4M7DUQ+uK9HI/Gx2pSS4sjw/LHFE/2XFlRE7OShZJXt1y7rDYWaAx1tVtfBtnWu6xZ0NsQw4+Y7LM03DIEwbHM4spzSOuRvx0SezDQhC49nV2bJA8bMEcEDyOAwsdJNOV5gp75zypCkgj1lqKpHZsoyadu0d7U2vYbALQ3lTS8JR4ThMwQuVktPl3mou+PmCSVlL7Cn0X3XxEumrTu4p6tm6ZI3ThXwvfz9wwYQP7Dh5QAQWb58ZccRd5vI7tps0f+dyFnKVwAgqGgr6/A3Rts29rRZ5QUjDMvwaXnIbqgEcuxdO8cuN0W1NbvXC/R83mtm5s2bk0ZO8kivXrEqgLeYvFeLTwPEamOVgQyCh49/JD9gBZy4I5yYkJnGLiCVf1F0uZ1yX+pc15WtS5sHvOq9Bw0QXsAJsqu9Keg4+ZbPfS1uiDH+tBCuK94nYDHqwHvaPvrty14FqMlpK/QmC/3nabgliZ58qmj61Zt9FWWWEVjdQvFjQFf83sm2LK+J5f17CXhp2cIHfSOH1wmfYY51RN7evROn3nZYJKfnF/p06/ChTF6hXjKyiN5ON2jn540e4wo35rhupu2NtmJXExf7LHd8lPxUmlSLSeZOb9svI4O8kchu3CbksoLjkOP32pAnJAfoW2VCUYYkFewpQ1U+clxZg4YoNXSjsinWWVgUjBQkTdPWNN091NWx47n9O42Lho3oaYi1WxUFxW5ZND8WDQQPf/5j/3jcQM8b06ctmr8ghsw6hDRdTzq27Z83b05nNhPnXWaXMmsEmDdvzjZk8HQIGOFqblQLuE3RYj2oG6Ir1BOsS4XTmqu7MeQ4vkL66uzhbSO7Uke9d11fvnyl7ZWISQJUW7PTq41V+6qt2W41swEavECvApiWzrMiWkKb7ktrI0AbjZy0sR9Y2vZc7OFqa7YL4NXlCyADvKOrYnuZQ2Q38Q7dtDeFOnzv08DWYA1qbdwL0eGDGaP+DaLlm8sq3cP55a+4hu9VYHX83snH7drM+sTXv2QCPPXDj+3/8bWf2H84f8Qlk7ft+fCI4srCLqt2Z7npbLiosPXF5meariwoKPQni3oSqa7U+/SAPtxNZzQXRvZimsgTooPIE50ZyMlNJvLkJzvjPIns6s1m00+5NrSiDHZqgoYy9OxpNJ7ZvXXsxoN7g2nH1oFxYcP3RcdyRkX8vrq7L7v2ykgw5P/rG1uWv9bSsNEn9FdM19aRB5kyoGPR/AXHLaVQs3TJaGSgsx+vXt+BDS9nAOEFXKc0b96cMd5zDyODuiIjoxdcvGVScXdhb+rAhIYGf9pnjGgc7r/+hel1XqCF1zU8DJmx3O/9vwzYvXz5ymPWyPWyeuKeFXdmi9de5eBG26emRkYO6NXhuL+AvlU0fuP96/C22+YFjdmZvMdYbawS1dZsd7Wxyii8Wrw7ttF9GNm99wzwyWprduPpfB5K/1i28MHgK//zkJHuio1ABkXZWa4hoG2lmzoma3zp5779QPuIys/H8kchCorvA34fv3fyGR9Epm9rLfjgjkcedg6lp/sadjzx5HMXLZi3YWNnLz3XBYJB8+Xg2rhpZT6YSZiTcbjcxBmZARFEODqix2tr9gSjFTmerwl4HXgDOXkjJxNZFGUgUpk9ZSgKvnvStGBdZ1viUFfH3cDUhGVOAPLNjG30ppJGyOdnWLTAXxqP7Ykl4iFkBq0XGcidLFPQCAhvpq4JmMxfcKbtq0cWXp6BHIje7WjOvoKugu7GipYI4N7129u7UoF05YFxDflAbN68Ob5gIjCisDN/RE9B/MDlr17i7pi2uyjjTxuTdlUe9Tu/febd+gd2vDMyedKE8d3RHtvBGa+hvQG0aIiCou2BGzXEJORYQhtZzuIPyIOq37u9E+BEgZ53XzYICO3c3fnRERTqKXCD8FuOXtFDOc+WLXzQD4zLLx/lb+2KlSO7PCuR2d0gsou0/q3PS7s9t9l2M7ilDvDY2QR6ABsuGdbFmC9V71xzc8U3Xr+hce36z9kAvxQffpkULEu2u8B2r8zKLIEYH8F3JViX90LIgGDQG4qAzIbryIBvv/f/wNm0S1EGKxXsKUNR/Mmdm5sauzqKkQEMgM+PwMINm7aF6VgpTdfq7r70msT2w4fqn9+/s33R/AUOpwhSTnPixqnoyO7PFPKgle8Ybqr2lnUtRe0FlVXrp01J+zMbX75ho91e2lnysiypMs2XMURJW/HwjG454Xgo+K6nZlrCEa26qw9fbayKHRF8jdw8fE/ZtNaxtm5qaQ0tuz68Dow00EYhM3oCWfvsIaDeC+ySq41Ve04W5B1H797itudSKevmpM9ZdX/s0788B5+RcpaWLXxQILOzRvFFk/ZYO5oPXMs7fZtYHzlMU72DXY+c1HOM8tYn3n248B9+7VrxLyTunXz8GdWyLqAPMI9ZmWHxegMIcP+MOHkxd8pd1P/irr6773N/ddTjV7qpxBwRfNxAOFcxvWgTG6sF5qeR3bYa8nu7D1nY+yByjGAjxxnLqihDmerGVYacmqVL/MhCqd3IYOpm4OMBoU2L6AGt+uLLtaSVTK0/uP/fdF3/y8euuXnfmdTze1tkjcExO+r29HznVz/KdpmGkVnFEZM3T4xP3D228oVb1iW6i3vTyBUGRgJTcSj0dfjbzG4SN267+kB+JtIcjUdEd7Rnyo5pu7s3F+zdn+xy3IDpM8q7h0WvabikoiSZP8bA6EAWeNaQZVuuRQ5yfxlYCqw/IlA8K6uNVdofZ7we/MFL/6HGT+XYsoUPlgMfAbp37W9cNeU3LaUhS5sSo6N+B6+/8QO38Zgu/xOSwVsU6OH+GbYX6I0ErqYjtInnLjrMs4H0HBH0A2LlQ8/nIb/Te7l/RuZM2+7VvCsB/g8y0NuBnCCURNbCO+VKMYoyFKlgTxmSapYuyRZxzUMenG4F/r04GPZdVz6etmQ8vr+r7VMtid6NwIHTKaB8TuxpzAPeg+w2fuFrn/pepKitMH/npbs7LZ89JRAPFIzbV9G9Z0pd2vbZ2TFLJjJDOc5KO/7S10ekRulFw+orD9W/90/vru/K755xeERbeHXpxpRtutePipU2Xdd42d8O5DdXTmsdX5hvRTXg/d52ypDdtH8BvlZtzT7jA7IysC1b+GAU27p2WHP7e3vi48bWvd7y7cod+9uB/fe5vzqzzPTi9XnIE4Qk0N6BdVMM+7bhvSIa6zHHbeyx6h8Kxu8f9V/3RAB95UPPH0SeYMXVeqyKcv6oYE9RgJqlS0YBL48OR4ddN24qLva2Z/bt+PvOVGIfsn7cKKB70fwF/TvoW64dPAyAieUtq41V7wRuA376m7mP1tG3fNMr9K3xGQRao7HIOMtnuYd7ev9WKMLlkYA/ecefZrUAEVs44dVj143t0ns+FXHC6VsOXJnWHT1PR4v78PmRJV1agTuAOuBbwOq3m9FTBqY1C/4lv8c0GoZHh/leio395Rf/90P/eFYbkpm8IuTJQn0M+z477b4/3dYaDgb9JA1/+oFk78e7H/zQ44BY6aaGdGbXy3AGgR61nJpyPqkxe4oiNQH/WxrO/1zI5/N3p82miD94w7QRo3mhbtdOvJm1/d4K2V3ccsQtB5E1+DqXf/k75td+/L2O9ljnuJSZTgGloXhwfHF7YaCzsGtjXldkalFH4a7KZn9lLNhbPqNhagg58N7VXW34VU1TrjwcbI+m/aatCX1KkMAEh1Seg53SCHbRt57wRlSgN6jNWvLf3Uv+ccF+J5GYkA7Ff3rWG5LZuY7k4rW1e7W/FY5wK4N+o8QMRsJ2Btu2rMQ+XTfWrnRTyVNua2goQHZD70P+3hTlvFCZPUXxfGvZDy8dEYg8NrqopLSus+2NuG3uuHLU+BVr921/Hpn5al40f0HuCgHvaSxqi3VcuvbVl4saHm/ac4CGWxzcDwbS/k7TMJ9JhdIVftP3tyvXXZ4p6ih0wk4wiZxkUQYU9ZAY3RBsKRuWKryimPyMhlbsYuJiHRQEJwhECHgY+HK1NbvjpG1RBgXx7VuE+8W1b/sg8NQPPxYAKsu7Lp4y0pr2jaAvWpbobv2nL337o4//NNOlChp75oigPqZw4pVXXP2uSOeNd6z75NdmqSBYOS9UZk9RPD5NLxieV+DrTsa1Q4nu4QLMAx1t7wr7/OsSZiYAaDVLl4jzNn7vWMmCaN7e4ftKIoG1wcnGKMOMh5JbrYAzLtIamu36nf22377UNmwn4PjiyGzl5CRpX9KXOhg1w3VlqaJgmMAIDc0AugS+WoGvDtl1fBBYqAK9oeNtB3qL1/saQr7wr6d/L/OtdX+raPL13tOe0fSAY7cOLxn5rAr0jvaVBYsdzTK+TzA67Gnb/mfx2V8/6T50r8q4KP1OBXuK4vnEtbfUWRnTfmHfTheIulB8uDcWTttW4b/edEfLj19ZWxJLJXw1S5fU5yTgm1ie8sGhzPfW6z7dF+8YEds1+vCYdtOxL460Bioz0fTwUCxQ6Ar3sIMT1tErgLFpLYPruuUa2vZiCkqRYxBTyO7hPOTkFBv4erU1u+XEDVCUY9yrW/a/f+Zve0OZmFloaQEaTXv/v/a0zXj12x/sBXjqhx8Lp3pSpc/96Pnulr2t3Svd1JmU7RlUpk68QvT2dvstyJQ67FWBnnK+qGBPUTz5wZCd1nSruaczAwRM1ynb1xubcvXIMdNdx+2cWFq2e2PD/pyPs6m2ZtvAIcPQheV39J6C+ArddD9ppTOjzKBzedAOkTHt3b4K/1Y63J2ReMiHww0G+pXIsioGslhzGlk2I4qcffuXnL0p5UJ1IGI6oQozU5ivhShDsD7T80Y20JsjgmJ01egxheUFI3raezuRpYKG7iSN+2c40cXrbwbsOffPUFlP5bxRY/YUJWtP43Bg++//Vpve0t5SnLQtuh1r6+TCYT+/YczEDVdWjN/IxPIBtabramNVWTwUv820zYtt4dyaDmXGheJ5+Ew6feFIXSBBA7KUymTkLMASZJmMDchu3mnIIrRfrbZmv5Gr96FcwBavHxZL9zzqQ7vccVw7HIwU6p+9xgWYI4IaMEVoott13E4goWahKsr5pzJ7itJnDhC5fsK09LqmJyzhYBT6/eM6eru2/XnHaxuvvOkdAyrQ87QGU8EXfK5vt44+yk47YRBFDlaJnrDywBiH7KL1I0u1WEDcux4E8oEfq0BPOWv3z2gthHeAtzpHAvEJrnEfvu1TAWAE4HMdt3ulm4rntqGKMnSpYE9R+lQDjCgobLdMO+LTNCOTTofjBqFiX2h4zdIlh70l0waMamu2s9pYtV9HbwJ+rqOHgZkuukBm9ELI7loNWTomDewBtgNXAl3AX3PS+AvAl7/7oGGlU4WXdHVlNpSPDy/57Geac92mgejjPBIWplMwubejKL19z5hV0/+z6fLpM3h9zDpt78EdDcgVYBRFyREt1w1QlAFksnd5QDjWARswQAujvwsZOA3I34tXD09DLh31IPBMBiedwhIuro4M+LKLxvciF4wfjVzJ4EccZ8F7RUp0tI8gnqhuaj+wo2xz7Z77/uUbV+W6TQPN0//xk+gtS2I3XfLrjk8Zwyu/MnHyDT8PjR/xeKxJv/K9N39oF9A2lCdlKMpAMCAPXoqSIzqym/OxkUXDP2zZtgsgTOsdyCWh3uzGrVm6ROSmiScURE60sIE/upoTs0OO6yDfAvK9Od7lZcjgrwFYp4onn1g63tNqu84rprADppPUU50BX67bNJAsW/ig6Mkk33NpW3TxFW3DPlsemnpXXuX0/PyLCgvSvtbAtVfcYqsxeoqSeyrYUxSAPY3ZyvYW8PvP33z79rxQqM2xbdJwiWnb85FLplGzdIkPGF+zdEleDlv8Vh3I7tnOeCBzcfewlN+M2gLtmJjUj1xuLQj8sNqavfs8t/OCsnTxD9Pf+9Z39+0fVjpuZ3715c3OF9bluk0DySe+/iU35WTWR4lsGpsO90xu7+Syzg4mGL7e3iL/Y6hVIhRlQFBj9hRFqkIemHYzsbwVQNf1l4Wuv991XQ0hLgc+XrN0yU/oW1ZswHRNedk5+y95j9hakXGVTSoUaiWpOyJ0xMMMsu8RXgZezEVbL0TLv/W/vYCaxHIc933j8weA2Zv/7WdTivW8jT6/X08HI6vf86PPNOS6bYqiSCqzpyhAKpn8+1isI5hOp1494uYV4VCIgnBYE7hGIp2+3DTNq4CJyN/OgKsX5gq3O9Sr9eY3+2OGY3S/5W4bqAUeAX5Xbc1On/8WKoPV5f/50Z3hstHrtaKy7pJI9JO5bo+iKH1UZk9RgGQqcWkykXB13XgjANQsXaIh69CZgM+0rK6UaRbh8033QczvD9ZmMqn9NUuXRIDEQJml6yRc3cbJ06EA3MBb7hbI3/xr1dbsvTlonjLIldw/45Zct0FRlGOpzJ6i7GkUPbHYKMd1zES891fe5IsAcA1yIgNBn982dO07AcNYEwxGeivKxw0LhyKjgOuBaTVLlwzL4Ts4UpeOdjBIMBQk8NZgT0f+5tXvXlEUZQhRmT1lyDvUcLC0qyuWbwT8iZX/+/1W3623FSOLwT6NHOPmF0Js/d7nvvg4wJbGnnVA6ImnDiSRv6FS4KKapUt6Fs1fkMrV+/C4QAuyy/Z4M0czyBp7iqIoyhChgj1lyFu18sfvcsAeXjpqsz59hh8Z6KWBg8BU4O+Bb2Uff2l5XgpIXTrvYwD7a5YuOQSEGQAzD6ut2fZqY9VPgc+e4CGbq63ZOW+noiiKcv6otXGVIW/R3XetEri3W5b5E9+tt90P5AGpRfMXvBkUeV27Qe/2Af2jWW2sEhns1zXEROPYHtuZ1dbsV4/3PEU5JxavF9w/Y0D/RhRlqFFjd5Qhz7YyVULThGH4nkTWoXOODPQ8o4BLkYWLB7T69/RqnZNSD9gc93hbd56bowwli9ePAEazeP3bLjr+sZLxpd/7u3+due07tUXnoGWKMqSpYE8Z0r565x0aUGZm0pZlmS8BxUB5zdIlevYxR0zY6AWSuWnp6Rv9ZLS05ECw24+27Th3d5z3BilDiWlprvl2M3u/FB8WyZKL76utb5j3w62vFJ+rxinKUKWCPWWom4ecyND9wKOPdQGtwMFF8xe8uTSa1227H9i5aP4CKzfNPD2rjVVRIGhk9DqBeADYesTdrloaTekvy7733cJVpVstcYAOAAAgAElEQVTv/kFZ7F1/v3TfWY8Hv2HS3fojU/NuGz5y9L3hYYa56cXfN5/LdirKUKSCPWWo+5h32VqzdIl/0fwF1vFm1C6av8BZlJkRZPH6gvPcvtO22lgVAiYDw4BDwGGO7nYWq41VA21NX2WQCOqOv6fLLNq9K3/s3g1jy852O+WFe/SppeEHgoHhl11thMpf2PNS/Fy2U1GGIhXsKUPdJQBEIgeQJVROqM1NXHmQ7uod//PUgJzFvv+D3XbrFclmM+JsQ44xvB7Z/awo/W7uZ7/Ysmmz/b0tuy75GZZ+1sMFfrthU8Yt76xvK+ykPpo8dC7bqChD1YA8aCnK+fDVO+/IrigB6fRPi18LdKw2VpUjV5poqrZmH7UqxgsFLaVFQv83oWkbLh6AEx2ab0pGigucMdEDvmJfXNORWb0jB7c7qhtX6S//c+PS8BXdHf900xtfeCqR7G1kxa/Oelvf/PXP7rxl/nvGr1365P5z2ERFGbJUZk8ZyuYgA7s4lvXa5OWFQWA8J5hxOyzjfsbw6Zckenr+8v3vfXcgZsx6SjcEGwMd+gTk+6ql74TOAX6Qs5Ypg1rN0iVaa2VycUfhRV+OXTrpnnOxTRXoKcq5o4I9ZSi73bt8DmgGeoDNwBtvzeoBCCvzvURbT1uG5MpAl2Wex3aelkXzF1glW4KHNMQu76YPe5d7gIurrdlfzFHTlMFPdI1KvdhZcfjlRIXx0/vcX6kMsqIMIKobVxnKbkAuK/afgOkFeL0nevA7Pv2Rx4HR56ltZ8tArv4RQI5HdIGNQGMuG6UMbovmL7Brli5ZQQU/XzD/X1SgpygDjMrsKUPSV++8YxqQj8zm7QXGf/XOOwbDyU8QGfDdDIwEmoAfVVuzB3TJGCW3li188M1Z2r8UH9Z/KT4cOtNtLJq/wBnoq8soylClgj1lqHoQWV9vKzITFkeOa7vQdSFrAl4BFACvARty2iJlQPvmZ74W2Lp+57U1cz8bEWKG/hn2fGgx9dVfEXcOxHGpiqKchcGQyVCUs3GNd7n0gUcf6+Uk3bcXEm+2rbnaWDUZ2ZVrMjiCWKWf/O5Xf73GTlu/PZBwXCDWCWM2kn6yl47HvpHrximKck6oYE8Zcr565x3/iJxxaz7w6GO/z3V7+kk3cjzic6rcinIyLb3mftMhv8dBo69UT9MO93n7ZM9TFOXCobpxlaFogXc5mAu2rgWeAlbmuiHKwNaYWlffZrr//ZabHxNihn7cJyiKcsFRwZ4ypHiFlCd5Vz+dy7b0l9XGKgOYhiwhk8x1e5QLwjeA3d7/U8hjQzB3zVEU5VxS3bjKUPMQsuCw+cCjj63NdWP6yXzgA8CWXDekPzz1nwv9z9d3zPzT7uaO7e2JBFDnbHwik+t2XchCwzvSVkexz5RztgNACEjktFGKopwzKrOnDDVzvcuNOW1F/4oi6+t15boh/WH56/XGg6/s+/T29sRLwEvAxblu04WsYpbQii/bO8a0Y0+DY8s5PabPddersZ6KMkioYE8ZMr565x0X0dc19YFctqU/VVuzvwXcCNya67b0h4d3HU4i35tAZqD25rZFFzwdKCu96uBT0PMwWnyTHk0+l+tGKYpy7qhgTxlK/uxdtj7w6GODMuuVVW3NfnWwzsJ1Nj7hAtlu2yZn4xODomxOrjSscU1gsy8vfchfmt5uhPVa4RrduW6Xoijnjgr2lKGkwrv8ZU5bobxtzsYnioFiZ+MTE3PdlsGgYY2bAnHITkW2WL15L1nxcDrXbRowFq8XLF6vxrcrFzThuoPy5F9RjvLVO+94BngHYD/w6GPRXLdHUQYiIWZogKvG6x1h8foRQBmwjftnqGUHlQuSyuwpQ0Wbd7knp61QlAHMddc7KtA7mmmZw3bt3fLVv7zwp0E5BlYZGlSwpwwJDzz62D3AxAcefawq121RFOXCsWP7a2Xbt216V/Oh+q/mui2KcrbUOARlyHjg0ccG84oZiqL0g879rS+SsRrDbnRfrtuiKGdLBXuKoiiKcgJidrcd2Dy6rTftG5vrtijK2VLduIqiKIpyAocffdR6rbdrxnY3fdW3/uH/vSfX7VGUs6GCPUVRFEU5gcIX6twivcCsjJZRUeBXa00rFyTVjasoiqIoJzDrW+sKJsfaEw6C55paVIJEuSCpYE9RFEVR3qLl+78L9Xa0ja4onNaUF8ivfaXD5/44cdvWv891wxTlLKhgT1EURVHeorb+0CcPxlPfNFtfeuWiwg9Vf6d5/BXAeKAl121TlDOlUtKKoiiK8hYNlmMmMojOTLT87x4YbyGXW7xu5lyCuW6bopwptVyaoiiKohzHnXO23NOmX9oBPAMUASXA7toVqAOnckFRwZ6iKCe12lhlAHq1NTud67Yoyvk0cy4+QKtdgfruKxc0FewpinJSq41VI4EosK/amm3nuj2KoijKmVFj9hRFOZXOMRP/lir8+PNlyxY+6Mt1YxRFUZQzo4I9RVFOqvrWnzjx6zrnxArG7yyqz+z40Q1fKcp1mxRFUZTTp4I9RVFOxeouGjvfLhnls0cUjwoHtQm5bpCiKIpy+lSwpyjKyT3+tNOpFfS2tli47bHOj+Tv2pLrJimKoiinTxVVVhTllMYaeZluF6LRET9j2VfP28zE22feXQAIIPl47cNv63VdmsS2hj3+aRU3qJmViqIMKSrYUxTllKYUj4hn7KQV8o3cRVWNHygAOtm0yOrnl84HChwc+6Ybbm6JuCWdj9c+7JzNhl7c+bdJhzpav7T14J7v33P9P7x2jtupKIoyYKluXEVRTikSKPh0UXjE94O+6O8BP7K4rL8/Xuv2mXeLI662Ao1dWoOd0DpmxEVb2dlud+++Qx+PxzP3dvb0/OTtt1JRFOXCoTJ7iqKcji7kmqABoB3Yy6ZF5rl+Ea/btnLmf6zsMaZMnzSuYlJPa8NuK6l1bRZoDojOs964y2VGADJxd9S5a3H/q5glxJTiS/J2dmzraVijCqMqinLmVLCnKMrpKAOuA9azadFLwDkP9Kiq8Y3xBack3Og/X7ErfntLbFuXLzDsU63s3l9uXZp6vPbhPW9n8912Wi9IB0BoF1SPxpXDZ5S5rnvd9eU3vogMuBVFUc6ICvYURTkde4GngQP9svWqGgGZ992fKv+vX3aGh0XjXdroUZ1ayTsPbP7pXg4/Xvvw28porXhhma7pbhE6uNaFlR3bG9s92cyk7w34g7uBFiFm6EAYiLvu+rMav6goytByQZ3hKoqSI/fPaANWAI399Aol4F4Rtp2yy12hBTtspjUlU9Fh6daznZDxVnn5wQ/amvOzSDQ46Vxs73xYtvBBkbas+QnNeZeZsW6fKb5dLMS1w6ePCVfddWlJQa7bpyjKhUFl9hRFOT33z+iXmbeNV3zZN5zAP9oEPlDk16kuhBbSZHSx+B2f+t9z8ppzb/iEjcxKzj8X2ztfRO+20tETqvJ2p5K9qYS/9I1p//PzUp2ySFh7+t36le3A2Y9hVBRlyBDuhdWjoSjKIHLf9feU3pYpnHqnNeJrOuJqcIMugk5o//jUL1755K9CQ3aMWsUs4ftw8eWVq2N1a9ucrmLAdU0hMqbAbzkdozuDv+8Kp7b0DOPnDWtcVTtQUZQTUpk9RVHOu5qlSwQgPp8aOabQdT9kkLFdRFBgE8BwR+GbMcQDPf9NoYtmbk8nPtfr9GTXIhboLprlYqfJO5yXugedicDvABXsKYpyQirYUxSlf8jiyyabFh2v+2AkUJ6ZYOQX1LW80zTt8ggF2Sm+a9m0qL/GBg54FbPEmAkE5nUnm65pJn2FheMAOgBCDrTWgvhMDYsQ30aWxVEURTkhNUFDUZRzr6omBFQC0ePcp9+wMeEr7LKskjZ7hmu7YwRaSGAQINSj4XvfeW/vAFExS4QqCd0VT4c+sCvFlc1kCizQw3hn5jY4Otg+sA3Wo/Gsqr2nKMqpqMyeoij9IQO0cfzuRf8trybDN2xuqXvDtzOpO5W6TghwAfGR89rKAaRilggDU/MQ99muUd7gmn4DbOGi44LtguuC6QeX4b0ThtV+NGhMCgCpXLddUZSBTQV7iqKce5sW2chgr09VjQ8YjlyBY98vbyvXb37Md0MARwvgR6BtA/5ygm7fQa1iltCBPOCft7mJSiuQiOsuBg5+F9xuG2H6wKeBADQyazXXHIYMplWwpyjKSalgT1GUflezdImYXB2dWNZuTeiJ6Fvf/z9fqLv1us+VC1K3QT4augPcyKZF/VLe5QIwWkCFC5dYDjYQwkVzHWyho2uGN1ZPQABSLr0P+H1TdwJD9fNSFOUMqGBPUZT+VVUjPh8S/voyY9Qbo31d7YV6mqqaqWiJbyT1tB6xQ4QIPcGmRYlcN/V8u+qmqcIuSowNBfMnGanuv+txGY2DhaDDshiGQ8DnR9fwOrnlv/1gba9dgZ3j5iuKcoFQwZ6iKP1tVDTpXlxel5gQOGxuG5sKFyYM3ltkFd2SEAkMfDbw8Vw3Mhfy7ZGlrp2+3R+MGI2p7ZWAhk4C2K5plKAxPftYAbjgCHikYY2rAj1FUU6bCvYURelvPuBzOs7IHUb8F3Xl7uYphyLXlRL0l7hBgLVAPkOwhMghY9NEp9e9KpWMFQCXIPAhEMAw3c9wZEIPAD8gZLftoRw1V1GUC5QK9hRF6W+NQBzcglDcvTPPMi8rwr3ZQAA4wN8zxAK9illCACMwGI+Dg00BGkkgH0EYGAsUc8Q+WsgLC9h6/lusKMqFTNXZUxSlf21alAb+20UkLnPyKqckQzNCiJC38/kzEGPTIieXTcyBIHAbNmJ0ZtIuTNGNiYGLQCbxAryZzDtKBug5z21VFOUCpzJ7iqL0uwxOYzdWUwS92Ic20rvZAhYMxVIryGBvjOGP3hgtKQtq7Yd8jpsoRaBxbIB3pBiw9/w0UVGUwUJl9hRF6Xd+tEAxwYYw/kQQX8C7eQ2QzGW7zqs9jb49tc+Pu+6eMVXAPcAwyzbtlsyhHkdPB9FIe2GeAUSOfOoR0fB+ZMZPURTltKnMnqIo50MgJGvpjfCum8ALDIV90J5GsfB3C8c8t+3Z8kgwcnUynWwCLeg3ggUZK723PbHfAArQDMAykRNaAGy8NXEt5Acl0NuiwU9NmzmXjbUrGGpd34qinKXBv6NVFGUgGAPMoK834UmgmZN3WQ4K9W314wTi/3Ynu8fsbdkbBx4FvdcwAnHbsjo1EZyan1cW7u2NxdJORxiIIzOeFSCzetkPTeOWHYjAKODVnLwZRVEuSCrYUxSlf8ll0qLIGaYgM1brgVcYApMNGjsa7z54+OAH2nvbLaAbuBpsLZHqrAMahkUmXFRSMGr0jp5XWpEzl/3IMX0ayGhYl5syhegpSqYfL65r+q8IuN05eDuKolyAVLCnKEp/CwJfoW9/8ypyHdiUt4bu4LWnUTTHmgOb928KO66jkaEQB/mJyNqCY7s7D6Xt7kTaobcUjRCwDShElqU5clx12nZ3pnHdfI4axqcMFDPnEgQy2S5277pWu4IhtzqMMrCoCRqKovSfqhoD+L/AhCNu7QLKGQoTDSaWu4XRwu+4rvsQ4GAiB+AlARs/UJLwdQc7tKZeNEwggfxs4nDMmLwYFJm6Nqnee5wygMycSxj4AfDzmXPxz5xLBJgPfHzmXJVYUXJLBXuKovSnycBnjrieBqYhM1dFVNUM+oDvne+/N/3an1u+oqHdSYbON+/IAC7Cr+WLovwxwke0Gxng6Rw/EN4HdWsdd89atVzagORA952QuBvMe4CRwDe8f+rvpeSUCvYURekfVTWTgGdBTsNNgpOROS0XOTbtAHJW7pBwcI29hiKux8fGN290yGSc7kAqk/Cb9BYBpcgw0ODYYTaHwT0wdkRnz8y5g39iy4VkdBPCek97MfSaZtoknUj9u21ZKe9uAzlBSVFyRgV7iqKcW1U1fqpq3oUM9KIgo5c0dKdkQeDfA//LpkWdQ62gcsMat44AcxC0AJAhAASFqwkI+JFlV7ITWt6qThMjrmiLffp98eSfis9fq5VT0WMEjF3ibsjzW46Oiz5S0/WFQK/3kHeqAF3JJRXsKYpyrl0F/Bq5tisAQXBD0ByWs3BrgThVNUP14FePn9sQtAFg4SQyXb2Q7kFmPh1g1HGeZztu2/h0+vninsT/+I5zv5IjFd8iTV0oHzADQY1AKIQQ4nZk9toGrgBKVMCn5IoaNKooyrlRVZMPLAXu5NgTyUxAZvTWAZuQ6+EOqaxeVsMa162YJXYS5BFs7kTHFwmF/YYoEh3xg0nkBI3jBXMFYCVMZ+vLZmZrumKW0LzHdjascePn9U0ob6WVZ9qTjXYglXGi+T6fjW4YxYDruq5w3MyHHMdc7zOif0RNrlFyQAV7iqKcvaoaDTmh4EPA/+P4QQrIsXm7gLVAATCGqpo6Ni2yzks7Bxgv4PsBOlcBpZZjpstLK6Md8YNNwKXHeYqLzJReAlQC24O+u7bZzt5i0968Hjl794SEmFEIDAf2ue76IfmZ9zM37Pa0hoWWSB1RFce1UgXxhKlphl7kiuRdPiPaMnMuCWT37p7aFcdfLnDMJ7eIhuD2qCuctPvQvZnz9B6UQUwFe4qinL6qmuxM0SnIFTH+GTn4/FTdinuAw97/k8h9z1Bf7msPcgxjIJVK7DvUsnekd/vxhtfYyFnMh4D9gvJMSfj2kkv0LU5IVHUcb+MVs4TRsMbNBnYTkVnAunP6DhQAalfgvP8TIZFI+RORtGvajuMDyJiu4TiOqwtdBALR2yB5I/hfAn0l0DFzLr3IwG8ssijPDGDvMMbbNlZ5Uu/aOnMuHYCoXXHygF5RTka47pDsSVEU5VRkYFcCXOP9mwjcAoQ4sxNFC3gI+BEyaGln0yKVrQAqZompwHeAkaBZ4OQh1w8OvuWhaWQGtR0C3ywrqk37/Rf/X11zTPD9EYIr65pEHaA3rHF7K2aJIuAiYHfDGrdDiBkTkUH6Dtddr3b659hXqraFdpYbPzkcHvbuIsvNTxgh0uEwAI7jOEIIzRFJMpgYaBkfeSuB15AFtBuATyB/U5chV1lxMqTTGnqBgTEGmQ2/p3aFWiZPOTsqs6coytFkIeRG5CoXb0d2BYgM8CJ9xZS7vdsUmWnbDQwD5xAysxM4zuM05P56mKFf/hncQte2M8W6VtTk3R4SBEtcUsGKWeKAd1s+MLZilvCX30JP49rpLa673hVihgCE664f6pnVM+atiCHe2v26Z0TJ8B4zfTUikN8ZEaCF3rxP0zQNwEbgYGJiGT7yrgQiyBOnFDIzPsW7Xg6M9xPIDpHITur488y5TFIZPuVsqGBPOca+GAK4HKCykE05bo5y/s3k7Qd60HeQcoHRyP3N3qE6Tu94Gta4iYpZoha4EXmQDyAzoTp93bnZTJwNaI4rgraTas0k1+5JpuPfTaYfbUibf212STlAETAeOAi8hAwo/EKjctS7NxgVs4Sj+apwbDFWiBl/c931Kug+TTPnogF/57rOR2Z85FBXfih9SCPvd8OirVfmTYtef6i5TAMBmoDjTLr1EURHQ0PXkOMuBTJjdyXybx1BFmIOIE+Ggsi/fXZjRcAq4L39+06VwUgFe8rx/BW58sHr+2L8FPhDZSHpHLdJOX8mneXzHO9fdr8ivOv7kePTioACqmoODNWZuCfQhczuTEBmdjLIYA/kwT5biDoAmI6z0W3rfl8XtPZA/GKgCjmW76/IiTBJZMBYAFzrbSeOnOCR8BX05Nlp30ftVKBciBl/AFBdu31mzkWvXSFXvJg5Fx0ZhPmADuCwizMD8vNSKRsIfPJASrdBBw0LgkcGZ0cRCPS+pK2BPAGKAS3IQL8MWV8x39uGzrFrIN80cy6X1q5gyzl8y8oQoII95Sj7YhjI8Vkgsw03Am37YvylslAtvj7oyfIpXz/LZwuO3ac4wA5kMPPPwGbgF6jlo47kIDOpUfq6a8UR99V793t9g7Yf6iqBCuQ4yr8Azcis0DXAq8ggYiSyzEcEaELWfEtohlNgp9nmOprpL2m/2h9N+YZfG6jz52UWAU80rHH/2P9veWCaOZcC4K6ZcxmGnD0eQwbf2SB6jyaMp31u5C7X1TQh0n758QL4TY7OxJ2MHxmgtyH/7nnI4Q3dyBnX2ajwrdsSwJeAOWf5FpUhSgV7ylEqC7H2xd5crinbjXQRsGtfjBaV4RvEZJHjV5Hr1p6N4x3kXGTAchFy3F4tmxapQO9ozchsTvb3pnF0sBdEZuaakUFCDzIrNAoZKAxDZoEuBsLIJdf2ITOqSWQ0shuZ7SPZXJoB56lQeUtesKztdiOUDOt+PowszfIhzv7vPxhcilzLNh/5mTcCf0DOmLWRBcNNIYy/AFUQKuTomejZ7tfTUYgM7BLIrvc4cr9re9s50brR1TPnotWuGPKz2ZUzoII95RiVhRQA7IsxDbnTa0IeXCKPPvujPc+s/pSx5Jtu6mTbUC5IQWS2KMumrzvxVE6W0Xg30A48xKZFzWffvEGrC446iTLpy+xkkMGd691uIwO8FDJIMJEzOEcjg76t3v3FyADvau8xY5GzfHeMeveGTchg8BrXoVNofN57PRfZFTyU7UUG2zpy3xdDBmH7kcfLNuARZFYuH1iAHEOnI4O+0w3AHGQwV0hfVjfivbbLyY/NQeTf+8Dpvy1lqFPLpSknVFnI1spCXkLuVF4HDj2z+lNhoHLBl0Xo5M8efIT4daEQv46c+pEXrBSyyxDkwcjy/p2OEwV6FvJg9mHgvmOWSKuqEVTVFFNVEz7z5g4aJRydHcr+vwf4E3J5uRZkQLER+Zm+AfwMGXxUIMf7FQPvQHbx3YIcyzccGRhUIceEZTNPDqAJjffRF1j+Q8Ma9wP98QYvFLUraEKWCcr6A7Jk0J+Qf4PXgGeALci/y/eRSwBmfyene0y1kb+LUuTYSpABns6pkzAGx19OT1FOSGX2lFOqLMShb0FvH7JrY0h1537wgWnffNd/T73mjU3Vn0OOOxt8Ni1yqaq5GzkGLEzf+LGzZSMPjCORB7VrgOupqnmZvsHn2ZUhejjZMlIyGBwJNLBp0WD77jXT1/Vn0ZdRbUdm56LIjJ0D/AToRGaVEsjPbR8y+9SN/LslvMfPRA78t71tbUBmkgq9bV0F3OC97qMNa9zf9uN7vGDUruBBr5DxHGT36nXAC7Urjs2kzZzLOuBh5H5xKqefCc8G3NmM4OmO9cuahpxtrSinRQV7yhlZ8k3XRHZtDBkLPis+XSj4l7z2ba5966TBvpD5FmSGLztZAPpmBJ7pe8+Oc3oMmWG6GJjnXb4E9LJp0UGqauRi8VU1QcBg06Le42zL9bY1qCYJeevb2vRld3RksHYQ+fkNRwZyryKzrg7wuPeYi5C/xQpkdnAr8vP5IzIITCOzeWXeY64BxiEndewAbqXvb/pv/fYmL0C1K1g2cy5NwD8iP6/nT/C4zMy5/AT5d/gCMN6xTTKZNH6fD804XsnErB5NxufDdfCdbpCYVX/qhyhKH7WChqKcxEc/7w8K0zys6fjTCazCEvKXfHOQ/2iqaj4C/JBju6TONPuwC/ib928KMviYgAw4/oDsGqtHrqjhUlVTjhy3tG+wT+KomCUEMkArBj6OnKkM8jPajRw2sREZaPiRn5eFLGHT6v0/H/k3eQdyybp8ZJD+JDI4zHahj0B+5uXe692KzPS927vfBIoa1riD+jM/UzPn4gNuRnbfbq5dceITjZlzKUauDV3tWOlg2swIv89AN0482kXQhUsHskf2RHMxjssGxteuoPVMnqQMbSqzpygnsODLQtNcNggDv+uAP8jCQR/oSb9Cdi19Atk1lR1DdqaZvezKAO9BBhwHgZ3e/z8C/B2wCfi2d3sLMrM3FIKOcmSGMw+4/ojbdyPH43Uiu3fbkQHgZGSX4pqGNe4a77FdFbNEBHgFOckjD9nVfb33nCbv+Y733HxkllVDdk1m/65qneLjqF2BOXMuz+DVvJs5F6N2BSeamNaJ7GKv0oxAeVD3+4Q46c/FdinQ+xK6Z+Qgsuafopw2NUFDUU6gC8aJABM0H/iDrF++2P3vXLfpPNGQ3VafQg5Wfx05TjMBJzzYHc9kYBYy+KhELgU2AxlYhJBdix8EfkhVTdGbK2tU1fTtl6pqAt4EjkGzr/Kyepcgi0zv5OjVSsLIjJ9NX2mOCHIihs4RKSBvOxXIWbhdyK7bZmSg50NGEgFkNjWADBA6kFnbR+kbdys4w9TSUFG7AscrsFwMTJk597hL2eFl/V5Gdo9zikAP3t6xd3G26LOinK5BswNVlHNp9tfyy4BnEQhdxza0IbRE0aZFJrLUxFbgB8hZnz8ClgNrkRm408kEBZAD0TVk8FGIXNGhChnoRJFBxpXAH6mq+T5wBzLrlRVFdlGe19nfFbOE8MbTnej+/IpZIno2225Y47rI7F03sgt1tHeXAxxGfl5B5OeVXTrraeRn333E6wboW9nhDWQ5kK3/v707D47zru84/n6kXZ2+zzhZRw5JnEASx4EkpOCUQll6QAiZaQmZAXdKy1AGtYXSUiqYEYaihqvQQW0Hyqm2YUoh5QopLKQkUQkhJFZD4hwEJ7bXtxVb1i2t9ukfn+fJrlYrWXYkW979vGY8a++lvazns9/f7/f9oYUYfWi4dxxV+H4a3e/1KHgvZfL8x1eeynOpIsOoAjrtex7tWfsvwNHCFNNpnerc3xHg307xtlbFHPbMirS26St53UT/zTWwOtr76y8otMmvDj3tE1Gl7SjqvXYnOpB9Bfgk8HHgB0y/KjuHVok+hObmfRENUca7EWRRSBlFR8Wr0dy1zwJfZ/O2W9i87Tx0kE2i6tXcmF2VcBWwoTjwpdJBXSod1BZdvnK2PzKVDppT6aC4enYAvR4BhSBbE/0ZR0FvIxqGvRO9no+gEPeS6L4a0ZzHo9lMOA7UNvHCWpMAABbeSURBVDQ0NTU1LYpDdAsKkvUo8B1EKeRp9JqOFz2ed8z2uVSpAVSF7TvB9X4A7Cz0RZ7zbaA/FYVKs5PiBRpmkda2YCOay/TAKHx0BF5RC/dfffjCGydqJkb//LNPV+fQyeZt61DY7UVzv5aixRdrUHuKX0etIOKGsLvQMPDno78fQcOTF6MK0goU/hqj+3oR2rnhSgpDmgMooDwVXT9EYeUpgEHG2Ftz9AVBPrh/Ayu3J6ndiY6w9ahiRvSYB+lpfzbq7xe3uLgAVb1GUDVtnJ72SZXKVDpYEj3mg9H95ClUHBPR7fuymfCER/NUOkigodS+bCY8UHLZ76Ht44h+xm3RfcdVu5+jUPir6PVril6bh1HFc182E/YBXHLj4uabXveW6/Yd2HPFI489+I1Dh/dvRPMjH0SVwhFU/WsA/gH4TTSPD2BPNhNuPNFzsRPbspVWwomPQw6CJHNYU9kFvDLqBWh2UrxAwwxobQsWA78N3AK8qx5eUAsHEmPNN7c+8JZzKcx3qkbHUYliFM0bq0FhpBYFrJ+gcPcGNG/pATS/byc97b0AbN6WQFWRz6GAkUTBJYGGpW5HfeE+MEbunGMM9jZRdywkP95AXS5JcjWq7qWA5tEgN3EsGDpnOU3nTpB/UZLa+1EVcC0KiQlUuXoAvW/r0IKIh9EQctzXrhk4zuZtT6Jg1EdP+3EUjOKdFNZF930ELZ3chKpszwKk0kEDCr4Hs5lwSqUzmwlzqXSwh6JKWjTf7gqm7nE6FL2GTag6N4FapgyioHZR9PchVKEbi284ODQQ7nh8e83Bw/tXHTq8/0NoMcZeFBTXoyHyH6GwuILJPeFWlD5uK2/LVgL0pWS0u2tqZXtF3/GuZ+uStwK1NM5Z0AvRUL53oLFT4rBnJg2oqncJ0dZRCbij84d/dQwddCutke/sqCK2Eg03rkUh4whq2/EztN/tMhQ6hlD1K4UqcMtRNRB62o+xeVsfquRdhF7vJ1E16xUUtgJ7/yCjow8ks0v3LN33yO4rHl60YmjJ3r+8/22jaL7UOLCyOazLLwkblwVBTW9DmIwbEMcBMg6lY8/9fC1e2B+dPh6dF6CQ04h+FxbvB92M5rbF244dRAf4GhRaU8ChVDo4FD2uluh1ATRsCwTZTDgAkM2EpQ2jF6Fh2uuLzouHt5ejcPZs9DM3ojD5vehxLYtOh7OZsLgimXvo4Z/uAG6I/mwA3ojevwYUWJeiz/gqJs/Z8wKN2UugLwDPbtnKkdKWLFfs2tN/d0vqCHV1awlDCAI0ghYSTD8N9ESGgY/N1P7FbCYOe1b1onl6b0YBppE8EDJKLV+Jhveq99u0+t+NoXAxgdo+nI9CUDwXbIDCCtCm6Lx7olPYvC1JYTHGCJrL14fCWIiGJxtQL77scprveO0DXxsAaG0Lmo439o9G7VjiuUrP1kdXPonn0Q/siP5VvKL4GJu31dDTnmfztmfoaY8Ppn0osF4EDGQz4XgqHQxFz3EVCoLXAV3oQLyfoiobCliJVDoYjBZklGpAr1/xXNDi/W9ri85rRGFvFK32vBBVFw+n0sHT8VByNhOOAXtT6eATwB8C10T3fzEaAvx31JPvPDTXbxGFBQeevz1LUUuWXej/QMuWrewqDmEf6bks3HLTwLdRQ2YARkdGCIH6+jpqak62fzJ54DtoRbzZKfGcPatqrW1BHZoz9k10EK9lDMhzlAbehsLLN6OdQ6qXhmEXoRB0Oao2PYWC3ir0Oi1FwWIc+HJUzatFFcFrUFB8Ojp/OQqNE2iocQSFmF7gQFHoWlCilbAr0Qriy9FuFfF2ZAFwKJsJJ6J5egEKdblsJhwuuZ9G1Mfw1uisfHT929BQdw2qJq5Br8s42nquD1VPVwNXoR1PtseBL5UO6lE43I7el7+ObteEwugwWghzPQqyy6OfP5HNhKe0urhabdnKciDZ3cWhMpe1EIaPE7VgGR7W21+XgNrkSS0sH0NfnG7u7pphO0GzE3Blz6pSa1tQgw6YHwJegwKJqMVslhwfZIKVJBhDB/Vq1owqQjkK872OoZB2PRoybEGh5yEgHw0Br0V7sG5Cc8z6o6B3CQpN69Bewz+np/2R0/h8Tkk0LDsQDd/ehYaF4/l1K1BIm4jm6dVQGPoeTqWDJAp+YTYTDqfSwY1Fd51DYW8YDd32o9ezDw2Xb0LTDHajiuAeVNxcBtREq4TXoBC+F/gqmn+6FoXDNWju5aHodBWTO/rO+bLRStfdxdEZLt5DEMTTFmioryM/MXaC7dOm6AXeA3zfQc+eL4c9qxqtbUEDmqwezzGDwkT9AjXA2L1koOmJxvGmpqaJxntP36NcgBTa6lClaYRC494xVHVahFqnXISGSn+BXuMLotudG/37cgqLI+JgeC+wf6FW8qYTVeriat1AFLaGoxYo8XXyqXSwGzgnlQ7WoYUp+1GvvAYUgmOJ6P7i/ngT6LVrQMN3D6Mg93I0lNwcXTePgnMDmke4DIXPu9AwcwK1zxmJ7vPi6DZHUUCPOUzMoe4u8lu28iSqaBPU1FJbc1IVvX7U3/L73V3VtRe5zQ+HPasmv6Cw6nBqyJvsrpGG8X/tuKd1JGoyXM1qKQzVDqN5ZC9FIW4UDRuuRjF5WXSbK4FXocCyODpdjqqDe4H/Ro1+951tQW8ay4DVqXSwM5o7FxtHFbs4YMULfdYyeVFE3F8vj16viei0Eb3eI+j39U5U2auLfuYQCpHLUCDsReGvH81xXINCdj8K1rWoonqAyY19H8fm2gfQ5/xkGygfR70sP93dNWkeqNkpc9izihctwPgtiodqi02gQ+3kX8mPjyXGB0v7r1WlnvYcm7eNokrdLhQ8FqMq0Z2oqncpCivbUbXvIrTKdi2F/Vlro+vcDXyPnvaB0/tE5lU/em6TvhhEizN2FZ8Xzef7ZMntQxSm16NAt4RCw+l+FJJb0JD33uh6tajK1xdddj1awbsfBboj0c8eRpXADSgUXoXmqeYpLMyozh6S8+tx9F6lTuI2eVQd/5yDns0lhz2rBi8HOtGBMUfx5z6PBiPjznESAo91doQOegW9KDysRhW6BKokHQReiypNwyRqNpLLr0FhcAOFFaf7gEfR6tXvVkg17zlRNW/KwTkKdkvQEG887FtLNLxXZCL6E38iL0KtaeJdRA6jOY9PoGB3FXpP4nB5Dgp4tWgI94Lodi1onl8zWm0+it6Xc5m8AvfuU3neNr3uLg5t2crbgTtO4mbPAP8J3iXD5pbDnlW01rZgHfAmNFE6R2mLiQD9L5jcDWEcpq6wq2o97X1s3rYXhYfVqAp1HVo0sCG61jBBeC7xqmYFl+OownE7cDs97XtP8yM/0xrQXLuxVDrYkc2E8dy50u33htDrtRKtUn4GBeTLUDDciXbTOIK+jHwXrQjeiMJE3H9vNwpyy9GXmwEUxFejQHgVGuYt/d1/5xw9X5vsXhS6ZzNh7xjwGeAL3V1U+9QRm2PurWQVZecx6nYeY+nOY88Nym5BFY1mCrs/FKpKATmSjJX8T/C36vKaUci7AAWWFlTVW4xe0yTjYSOaFxn35csAfwJ0VmHQA32WHmXybhd5pjYxbkKv4wspNKMeQgHvSTRUW48qfik0VHs/CnPxkO1B1ELoQHS9RhQEB1Agvw4Ntzcy9Xf/gl8JfTaKQtv/zOKqOVRd/ZaDns0HV/as0iy+9+73v7hne+fg+NjxRuC9aCgsFjJ5dl68CrLYoc6O0HOYim3eVkNhl4lD0emr0dwx0Gu6sugWw2iYt52e9qdP4yNdUKI5e/0lZ4dMDVvx7+JkdHokmwnDVDpYg17LA6jyE7d42YiGdvcAm1G7m15UDUygYLgWbccWb+fWAGV3YNhTbps3mzO3ojnDM3VT3of6IXpEweaFw55VmmMP3N/xQuCdqIdbfPDMU2hcW1P07wRTqyw7sMniHSYUXK5HFdMU5Vc154EvAR+Idr6wyRoof+APUcXuMYBUOlgM/A5axfsMqvo9jj6/SVT5ewaF7GNoPt4FaI7gYfS5TqDdPoLo+vE+wcVh8ydz99SsjIfQ8PoF01weor6Id3V3FRbKbNlKLarQHkT78JZ+KTWbNQ/jWkX5+48FHwE+iuY9JYsuije1r6Vw4AMFE5hc8SitxJi0oHYq69A8smVlrpMHPgW0OehNa7q2P+OogrcBza/Lo2HYB1GlbgiFt4Po83opCm/7KOxfHFB4X8Yp7Ik7Fv19KVN/73/p+T8lm04U4D40w1WeRKuzSyvgi4HfAG4CXrdl66TfZ2YnxZU9qzQXz3BZgObG9KODZR2FxivFlZbLW9uC+s4OD22ViLdDu4rCNlvFxoF30dP+xdP6qM4+5UKwdmTWQopG1N6mBVXukiispdCCmHvRXL5k9OeS6D6XAS9DISGFFmpMoMrgSkrXnBc8OifPymZyO/BPlF+o8dXurrJfMIeAX6Lq3gagcctWGoH+4r14zWbDlT2rNH9wgsvjtbcJNHH9eHRafABeznQ9+apbvFvDJibPe8yjfm7XOujNSrmwF3/hmEArcFvQF5M1QG82E/ahRRoh+nzGVbsADeGeB1yLehum0ee3Prp9vGvJCphSHdqB5vrZPIp65n2mzEXjwH/NcJv7gP9A2zW+HvhH9B6bnRSHPasonR3hACfeDWAJOrDGlbv4NP623IuGymyycVQ5Wlly/sPAi+lp9y4Ms7NomvMTqIIzRmFRxlEmfy7vRStvN6FQWAN8H831Oxe9R8dRUPwVCo/x6uhyVb07okUkNv8+zNQFMr3ofSqru4vh7i760JepRWg3lFu3bOV9W7aWfT/NynLYs4rS2hachyakn0g9hdWlyynMdwJIegi3rPNRcCiu6vUBb6an3Xurzt504SqHhu560DDucqKdR1LpYCkamh1AVbsA7c4wiALdvuh0ILqPEdTOJUlhj91ydj//p2Oz0d1FDi2cKfb24kUZMwij2y5GWxG2o+bLZrPisGeVZjOF1bVDTH9gTaAKVTxhvfj/QmnD26rV2hbUt7YFddtf88ZrBxlqL7k4B2ykp33ayoSVdXya82vR53UpGpY9H6163kRhq7nV6MtMXNV5GXofnkB92nagz2+SwnZr003sz6NVoHb6vKPo7yHQPcvbTaBV2n8bnR5DrVrMZsULNKzS3An8M5rIfhwNed1E+c3Ia6Mt5muppbgBS27+H+bCFO0jvAhoeMXOa1e+Mn/dlUcajw6uOrz8poDg0pKr30BP+3TBxaY3Sum2fRJEl21CFbs7ULBrRJ/OHKrc1VDYLm0n2rFkmEI7ofvQIqQLKKzCLecb2UzoBuKnV/FOJUe6u5hVRTyq/u2I/nx5Hh6XVbgg9HQNq0CtbUECuBod8F4F3EK53mYKe1JYJ3cUaOnsCKuuk31rW9CYmEhcc3X2svXX7Lqyefno0itHw5Fda3Kr/jhJcn1NoQB6iJ72ljP5WM9mqXSwG1XpSh0Fvo5632VRT8P/Q19cEmgeXw61XQmBn6Iq4KvRXNS1qJJ3GLgRtXApXQGaA76QzYTvmtMnZbOyZSs/RnPvrujuYv8ZfjhWJRz2rKK1tgXx9l63AO+m3NSFePv5AA2UJRknwZrOjnBkynUrTGtbUAPQ+b0P1gFNOXKp/fWHNy0bXfz7DTRcliQRt+wobjw9Rk/70jPxeCtFKh28Hfh0mYvyKOh9C03KXwX8CM3VilfTHgUuRP3Z+tEn+GZUkd0HvJlCv71454wJtDvDQ8C7s5nwwDw9NTNbgDyMaxWtsyMcAh5tbQs+i1pSXEZphS+Of/H3nnESJFiGqigVqbUtaEZBYF1yNHFOf+3AqsUTi65IkLhu/ei681E1qHSuV4iqQq7oPX9fAD7B1N/BNWgLuivQMO0I+rKyBw3ZJlB424vC4JXoc/oI8BIU9gbRfL8QhccRFArfjQKim12bVRmHPasKnR3hnta24NfQZvCfp3jrooBC/NOa3ABYGs1fy6MD5XiFDeuuQKFtWRAE6wfqhsPFw4t6UaioQ3PAjkd//wZaANDtVbdzI5sJc6l08Eu0BVqpdaiVShIN9S5DjXUfzGbCnlQ6iLf8g8LcvH1o0cYImtN1A/BS9Pndjd7De6LLHfbMqozDnlWNzo4wD/yktS24Cvgb4L2ULtwo1PxuQTsL9KAWGM9SWZuU70O9BPNjdeN164ZX5ygsTAnpaff8jvn3d0BXmfPrgLcCHcDPUGC7FmhIpYOabCbMAxOpdFCHKnjrUa+2n6N5e3HLlTVoRfqHs5nwO/P8XMxsAXPYs6oT9dD7YGtbcA/wbcpvSr8aDY8dQSsax07fI5x/nR3hBIUKT46OM/loqtaP0VBruZXiq4DfjS5fglptHKKolVA2E46l0kE3ms9Xh+bs9QJbUU++HPAUasliZlXMffasanV2hHehOU/lWq3UoA3oj3d2hIMVNoRrC0A2Ex5Gc+3KCVAPvWtRhW4JZYZfs5mwF83dy6M2LevRF5SHgU8B78xmQrfHMatyDntW1To7wl+hdhWlK28bOjvCoagCZjZfXo/WgJfTDLwczd/rRQFw0u/sVDpoQCFvCRrK3YWa7v4vcBuaimBmVc5hz6petGJ3BZMD35fPzKOxahK1QPnBDFdZiYZkh9Hc0UQqHSRT6WBVKh3E2/0FqCffErSDzEE0JzOXzYRV2yDczAoc9syAzo4wRIHvDuATnR2h5znZ6fJWpl8huwi4Bi20eCqbCUfR6txLUbhbgxoor6TQsqUGNWJ2w14zA9xU2czsjEulg9vQtn7lHAXeg1aE/wJV8jahXnuDaI7eS1HrlVpgezYT7prvx2xmZw9X9szMzry3MH11bznabeN9aHeMVWgHjVehXohjwC+BPlQBPDzfD9bMzi4Oe2ZmZ1g2E04AX5vhKktQT73NqDF4L1rJuwit2B1Bq8rr5/eRmtnZyGHPzGxh+CO0ndl0zkVBLw5269CQ7nG0G8obgIvxDhlmVsJhz8xsAchmwhB4E0WNk8s4Bw3nbkAtW1ajZssHgfuADBXWANzMnj+HPTOzBSKbCR8DvjTDVQI0nHs9qu4tRqvIG9H+xU9GodHM7DkOe2ZmC8ufAs/McHkNGratQ9uoXY4qguO4qmdmZTjsmZktINlMmEcBbnCGq7UAr0UVvjq0YKPfVT0zK8dhz8xsgYlW517D9PP36lHgy6IFGfehKp+Z2RQOe2ZmC1A2Ez4N/NkMV7kM7Yv7BGqs3JJKB8nT8djM7OzisGdmtkBlM+Hn0RZ+5TSgHnsDaDeN/WjRhpnZJN4uzcxsgUulg28Brylz0T7UX++xbCZ00DOzslzZMzNb4LKZ8EbgR2UuWot3zTCzE3DYMzM7O9yA5uYVq0VDud41w8ym5WFcM7OzRCodBMBuYFXR2fuymfDCM/SQzOws4MqemdlZIpsJw2wmXA88VnS2V+Ca2Ywc9szMzjLZTPhi1Hrlh8B1Z/jhmNkC52FcMzMzswrmyp6ZmZlZBXPYMzMzM6tgDntmZmZmFcxhz8zMzKyCOeyZmZmZVTCHPTMzM7MK5rBnZmZmVsEc9szMzMwqmMOemZmZWQVz2DMzMzOrYA57ZmZmZhXMYc/MzMysgjnsmZmZmVUwhz0zMzOzCuawZ2ZmZlbBHPbMzMzMKpjDnpmZmVkFc9gzMzMzq2AOe2ZmZmYVzGHPzMzMrII57JmZmZlVMIc9MzMzswrmsGdmZmZWwRz2zMzMzCqYw56ZmZlZBXPYMzMzM6tgDntmZmZmFcxhz8zMzKyCOeyZmZmZVTCHPTMzM7MK5rBnZmZmVsEc9szMzMwqmMOemZmZWQX7fwwfQSR/cVQ5AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(11, 10))\n", "plot(embedding3 @ rotate(90), y, ax=ax)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n", "\n", "plot(embedding2 @ rotate(90), y, title=\"No exaggeration\", ax=ax[0])\n", "plot(embedding3 @ rotate(90), y, title=\"Exaggeration 4\", ax=ax[1])\n", "\n", "plt.tight_layout()\n", "plt.text(0, 1.02, \"a\", transform=ax[0].transAxes, fontsize=15, fontweight=\"bold\")\n", "plt.text(0, 1.02, \"b\", transform=ax[1].transAxes, fontsize=15, fontweight=\"bold\")\n", "\n", "plt.savefig(\"10x_exaggeration.png\", dpi=100, transparent=True)\n", "plt.savefig(\"10x_exaggeration.pdf\", dpi=600, transparent=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } openTSNE-0.6.1/examples/figures/figures_macosko.ipynb000066400000000000000000074730211413546205200226760ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from os import path\n", "\n", "import openTSNE\n", "import openTSNE.callbacks\n", "\n", "from examples import utils\n", "\n", "import numpy as np\n", "import scipy.sparse as sp\n", "from sklearn.decomposition import PCA\n", "from sklearn.model_selection import train_test_split\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import gzip\n", "import pickle\n", "\n", "with gzip.open(path.join(\"..\", \"data\", \"macosko_2015.pkl.gz\"), \"rb\") as f:\n", " data = pickle.load(f)\n", "\n", "x = data[\"pca_50\"]\n", "y, cluster_ids = data[\"CellType1\"], data[\"CellType2\"]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data set contains 44808 samples with 50 features\n" ] } ], "source": [ "print(\"Data set contains %d samples with %d features\" % x.shape)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "N_THREADS = 4" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def plot(x, y, **kwargs):\n", " utils.plot(x, y, colors=utils.MACOSKO_COLORS, **kwargs)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def rotate(degrees):\n", " phi = degrees * np.pi / 180\n", " return np.array([\n", " [np.cos(phi), -np.sin(phi)],\n", " [np.sin(phi), np.cos(phi)],\n", " ])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ordinary t-SNE" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 6.9036, 50 iterations in 4.8035 sec\n", "Iteration 100, KL divergence 5.8490, 50 iterations in 4.6414 sec\n", "Iteration 150, KL divergence 5.5915, 50 iterations in 5.1917 sec\n", "Iteration 200, KL divergence 5.5157, 50 iterations in 4.6856 sec\n", "Iteration 250, KL divergence 5.4786, 50 iterations in 4.7059 sec\n", "Iteration 50, KL divergence 4.2040, 50 iterations in 5.0005 sec\n", "Iteration 100, KL divergence 3.8203, 50 iterations in 5.2200 sec\n", "Iteration 150, KL divergence 3.6045, 50 iterations in 5.1878 sec\n", "Iteration 200, KL divergence 3.4581, 50 iterations in 5.2776 sec\n", "Iteration 250, KL divergence 3.3493, 50 iterations in 5.9039 sec\n", "Iteration 300, KL divergence 3.2641, 50 iterations in 6.1645 sec\n", "Iteration 350, KL divergence 3.1953, 50 iterations in 7.0562 sec\n", "Iteration 400, KL divergence 3.1385, 50 iterations in 7.6530 sec\n", "Iteration 450, KL divergence 3.0902, 50 iterations in 7.7999 sec\n", "Iteration 500, KL divergence 3.0497, 50 iterations in 8.0094 sec\n", "Iteration 550, KL divergence 3.0152, 50 iterations in 9.5879 sec\n", "Iteration 600, KL divergence 2.9857, 50 iterations in 11.6082 sec\n", "Iteration 650, KL divergence 2.9603, 50 iterations in 9.4068 sec\n", "Iteration 700, KL divergence 2.9386, 50 iterations in 11.8334 sec\n", "Iteration 750, KL divergence 2.9200, 50 iterations in 12.0988 sec\n" ] } ], "source": [ "tsne = openTSNE.TSNE(\n", " perplexity=30,\n", " metric=\"euclidean\",\n", " initialization=\"random\",\n", " callbacks=openTSNE.callbacks.ErrorLogger(),\n", " random_state=3,\n", " n_jobs=N_THREADS,\n", ")\n", "embedding = tsne.fit(x)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding, y)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 42.3 s, sys: 208 ms, total: 42.5 s\n", "Wall time: 26.1 s\n" ] } ], "source": [ "%time affinities = openTSNE.affinity.PerplexityBasedNN(x, perplexity=30, method=\"approx\", n_jobs=N_THREADS, random_state=3)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 6.9036, 50 iterations in 5.0767 sec\n", "Iteration 100, KL divergence 5.8490, 50 iterations in 4.6882 sec\n", "Iteration 150, KL divergence 5.5915, 50 iterations in 4.7817 sec\n", "Iteration 200, KL divergence 5.5157, 50 iterations in 5.1642 sec\n", "Iteration 250, KL divergence 5.4786, 50 iterations in 4.8063 sec\n", "Iteration 50, KL divergence 4.2040, 50 iterations in 4.8813 sec\n", "Iteration 100, KL divergence 3.8203, 50 iterations in 4.9773 sec\n", "Iteration 150, KL divergence 3.6045, 50 iterations in 5.3570 sec\n", "Iteration 200, KL divergence 3.4581, 50 iterations in 5.3103 sec\n", "Iteration 250, KL divergence 3.3493, 50 iterations in 5.9283 sec\n", "Iteration 300, KL divergence 3.2641, 50 iterations in 6.3240 sec\n", "Iteration 350, KL divergence 3.1953, 50 iterations in 7.0321 sec\n", "Iteration 400, KL divergence 3.1385, 50 iterations in 7.5696 sec\n", "Iteration 450, KL divergence 3.0902, 50 iterations in 7.6971 sec\n", "Iteration 500, KL divergence 3.0497, 50 iterations in 8.0837 sec\n", "Iteration 550, KL divergence 3.0152, 50 iterations in 9.5295 sec\n", "Iteration 600, KL divergence 2.9857, 50 iterations in 11.5929 sec\n", "Iteration 650, KL divergence 2.9603, 50 iterations in 8.7586 sec\n", "Iteration 700, KL divergence 2.9386, 50 iterations in 11.0126 sec\n", "Iteration 750, KL divergence 2.9200, 50 iterations in 10.9812 sec\n", "CPU times: user 5min 29s, sys: 6min 8s, total: 11min 37s\n", "Wall time: 2min 20s\n" ] } ], "source": [ "%%time\n", "embedding = openTSNE.TSNEEmbedding(\n", " openTSNE.initialization.random(x, random_state=3),\n", " affinities,\n", " negative_gradient_method=\"fft\",\n", " n_jobs=N_THREADS,\n", " callbacks=openTSNE.callbacks.ErrorLogger(),\n", " random_state=3,\n", ")\n", "\n", "embedding.optimize(n_iter=250, exaggeration=12, momentum=0.5, inplace=True)\n", "embedding.optimize(n_iter=750, momentum=0.8, inplace=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding, y)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "ordinary_embedding_30 = embedding.view(np.ndarray)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ordinary t-SNE with perplexity" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 10min 20s, sys: 440 ms, total: 10min 20s\n", "Wall time: 3min 42s\n" ] } ], "source": [ "%time affinities = openTSNE.affinity.PerplexityBasedNN(x, perplexity=500, method=\"approx\", n_jobs=N_THREADS, random_state=3)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 4.1779, 50 iterations in 25.3916 sec\n", "Iteration 100, KL divergence 3.6062, 50 iterations in 25.0915 sec\n", "Iteration 150, KL divergence 3.4733, 50 iterations in 25.4358 sec\n", "Iteration 200, KL divergence 3.4647, 50 iterations in 25.7202 sec\n", "Iteration 250, KL divergence 3.4617, 50 iterations in 25.5012 sec\n", "Iteration 50, KL divergence 1.9847, 50 iterations in 25.1921 sec\n", "Iteration 100, KL divergence 1.7605, 50 iterations in 26.0311 sec\n", "Iteration 150, KL divergence 1.6580, 50 iterations in 25.2343 sec\n", "Iteration 200, KL divergence 1.5981, 50 iterations in 24.7610 sec\n", "Iteration 250, KL divergence 1.5583, 50 iterations in 26.1104 sec\n", "Iteration 300, KL divergence 1.5312, 50 iterations in 26.0728 sec\n", "Iteration 350, KL divergence 1.5116, 50 iterations in 25.1728 sec\n", "Iteration 400, KL divergence 1.4969, 50 iterations in 25.9328 sec\n", "Iteration 450, KL divergence 1.4862, 50 iterations in 25.5400 sec\n", "Iteration 500, KL divergence 1.4777, 50 iterations in 26.0211 sec\n", "Iteration 550, KL divergence 1.4713, 50 iterations in 26.0283 sec\n", "Iteration 600, KL divergence 1.4662, 50 iterations in 26.1143 sec\n", "Iteration 650, KL divergence 1.4618, 50 iterations in 27.5363 sec\n", "Iteration 700, KL divergence 1.4588, 50 iterations in 27.9017 sec\n", "Iteration 750, KL divergence 1.4555, 50 iterations in 28.0935 sec\n", "CPU times: user 32min 55s, sys: 6min 11s, total: 39min 7s\n", "Wall time: 8min 42s\n" ] } ], "source": [ "%%time\n", "embedding = openTSNE.TSNEEmbedding(\n", " openTSNE.initialization.random(x, random_state=3),\n", " affinities,\n", " negative_gradient_method=\"fft\",\n", " n_jobs=N_THREADS,\n", " callbacks=openTSNE.callbacks.ErrorLogger(),\n", " random_state=3,\n", ")\n", "\n", "embedding.optimize(n_iter=250, exaggeration=12, momentum=0.5, inplace=True)\n", "embedding.optimize(n_iter=750, momentum=0.8, inplace=True)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding, y)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "ordinary_embedding_500 = embedding.view(np.ndarray)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multiscale" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 11min 46s, sys: 4.46 s, total: 11min 50s\n", "Wall time: 3min 55s\n" ] } ], "source": [ "%%time\n", "affinities = openTSNE.affinity.Multiscale(\n", " x,\n", " perplexities=[50, 500],\n", " metric=\"cosine\",\n", " method=\"approx\",\n", " n_jobs=N_THREADS,\n", " random_state=3,\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 716 ms, sys: 2.09 s, total: 2.81 s\n", "Wall time: 283 ms\n" ] } ], "source": [ "%time init = openTSNE.initialization.pca(x, random_state=42)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "embedding = openTSNE.TSNEEmbedding(\n", " init,\n", " affinities,\n", " negative_gradient_method=\"fft\",\n", " n_jobs=N_THREADS,\n", " callbacks=openTSNE.callbacks.ErrorLogger(),\n", ")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 4.5452, 50 iterations in 24.1374 sec\n", "Iteration 100, KL divergence 3.9698, 50 iterations in 24.7509 sec\n", "Iteration 150, KL divergence 3.8935, 50 iterations in 24.1341 sec\n", "Iteration 200, KL divergence 3.8668, 50 iterations in 24.6704 sec\n", "Iteration 250, KL divergence 3.8539, 50 iterations in 24.6298 sec\n", "CPU times: user 7min 57s, sys: 1min 28s, total: 9min 26s\n", "Wall time: 2min 3s\n" ] } ], "source": [ "%time embedding1 = embedding.optimize(n_iter=250, exaggeration=12, momentum=0.5)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding1, y)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 2.6050, 50 iterations in 24.4708 sec\n", "Iteration 100, KL divergence 2.3301, 50 iterations in 24.5998 sec\n", "Iteration 150, KL divergence 2.1883, 50 iterations in 24.7276 sec\n", "Iteration 200, KL divergence 2.0992, 50 iterations in 25.0521 sec\n", "Iteration 250, KL divergence 2.0396, 50 iterations in 24.6656 sec\n", "Iteration 300, KL divergence 1.9954, 50 iterations in 25.1094 sec\n", "Iteration 350, KL divergence 1.9628, 50 iterations in 25.5308 sec\n", "Iteration 400, KL divergence 1.9366, 50 iterations in 24.9663 sec\n", "Iteration 450, KL divergence 1.9166, 50 iterations in 25.3492 sec\n", "Iteration 500, KL divergence 1.9000, 50 iterations in 26.8514 sec\n", "Iteration 550, KL divergence 1.8872, 50 iterations in 25.6067 sec\n", "Iteration 600, KL divergence 1.8762, 50 iterations in 27.0795 sec\n", "Iteration 650, KL divergence 1.8670, 50 iterations in 28.1401 sec\n", "Iteration 700, KL divergence 1.8591, 50 iterations in 28.4183 sec\n", "Iteration 750, KL divergence 1.8524, 50 iterations in 26.2929 sec\n", "CPU times: user 24min 3s, sys: 4min 51s, total: 28min 55s\n", "Wall time: 6min 28s\n" ] } ], "source": [ "%time embedding2 = embedding1.optimize(n_iter=750, momentum=0.8)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(embedding2, y)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "multiscale_embedding = embedding2.view(np.ndarray)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Paper figure" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n", " 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n", " 35, 36, 37, 38, 39])" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cluster_ids = np.array(data[\"CellType2\"], dtype=float).astype(int)\n", "np.unique(cluster_ids)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "cluster_cell_mapping = {\n", " 1: \"Horizontal cells\",\n", " 2: \"Retinal ganglion cells\",\n", " 24: \"Rods\",\n", " 25: \"Cones\",\n", " 34: \"Muller glia\",\n", " 35: \"Astrocytes\",\n", " 36: \"Fibroblasts\",\n", " 37: \"Vascular endothelium\",\n", " 38: \"Pericytes\",\n", " 39: \"Microglia\",\n", "}\n", "for i in range(3, 24):\n", " cluster_cell_mapping[i] = \"Amacrine cells\"\n", "for i in range(26, 34):\n", " cluster_cell_mapping[i] = \"Bipolar cells\"" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20',\n", " '21', '22', '23', '26', '27', '28', '29', '3', '30', '31', '32',\n", " '33', '4', '5', '6', '7', '8', '9', 'Astrocytes', 'Cones',\n", " 'Fibroblasts', 'Horizontal cells', 'Microglia', 'Muller glia',\n", " 'Pericytes', 'Retinal ganglion cells', 'Rods',\n", " 'Vascular endothelium'], dtype='" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(ordinary_embedding_30 @ rotate(-80), cluster_ids_, colors=colors, fontsize=11,\n", " draw_centers=True, draw_cluster_labels=True, draw_legend=False)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(multiscale_embedding, cluster_ids_, colors=colors, fontsize=11,\n", " draw_centers=True, draw_cluster_labels=True, draw_legend=False)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n", "utils.plot(ordinary_embedding_30 @ rotate(-90), cluster_ids_, colors=colors, ax=ax[0], fontsize=7,\n", " title=\"Default t-SNE\", draw_centers=True, draw_cluster_labels=True, draw_legend=False)\n", "utils.plot(multiscale_embedding, cluster_ids_, colors=colors, ax=ax[1], fontsize=7,\n", " title=\"Multiscale\", draw_centers=True, draw_cluster_labels=True, draw_legend=False)\n", "plt.tight_layout()\n", "\n", "plt.text(-52, 40, \"Amacrine\\ncells\", color=\"k\", fontsize=7, horizontalalignment=\"center\", transform=ax[0].transData)\n", "plt.text(43, 47, \"Bipolar cells\", color=\"k\", fontsize=7, horizontalalignment=\"center\", transform=ax[0].transData)\n", "\n", "plt.text(48, 6, \"Amacrine\\ncells\", color=\"k\", fontsize=7, horizontalalignment=\"center\", transform=ax[1].transData)\n", "plt.text(40, -26, \"Bipolar\\ncells\", color=\"k\", fontsize=7, horizontalalignment=\"center\", transform=ax[1].transData)\n", "\n", "plt.text(0, 1.02, \"a\", transform=ax[0].transAxes, fontsize=15, fontweight=\"bold\")\n", "plt.text(0, 1.02, \"b\", transform=ax[1].transAxes, fontsize=15, fontweight=\"bold\")\n", "\n", "plt.savefig(\"macosko.png\", dpi=100, transparent=True)\n", "plt.savefig(\"macosko.pdf\", dpi=600, transparent=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Header image" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", "utils.plot(\n", " multiscale_embedding,\n", " cluster_ids_,\n", " colors=colors,\n", " fontsize=11,\n", " # draw_centers=True,\n", " # draw_cluster_labels=True,\n", " draw_legend=False,\n", " ax=ax,\n", ")\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"macosko_header.png\", dpi=100, transparent=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exaggeration comparison" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n", "\n", "plot(ordinary_embedding_30, y, title=\"Perplexity 30\", ax=ax[0], draw_legend=False)\n", "plot(ordinary_embedding_500 @ rotate(-50), y, title=\"Perplexity 500\", ax=ax[1], draw_legend=False)\n", "plt.tight_layout()\n", "\n", "plt.text(0, 1.02, \"a\", transform=ax[0].transAxes, fontsize=15, fontweight=\"bold\")\n", "plt.text(0, 1.02, \"b\", transform=ax[1].transAxes, fontsize=15, fontweight=\"bold\")\n", "\n", "plt.savefig(\"macosko_perplexity.png\", dpi=100, transparent=True)\n", "plt.savefig(\"macosko_perplexity.pdf\", dpi=600, transparent=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } openTSNE-0.6.1/examples/figures/figures_zeisel.ipynb000066400000000000000000111223561413546205200225320ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from fastTSNE import TSNE, TSNEEmbedding\n", "from fastTSNE.callbacks import ErrorLogger\n", "from fastTSNE.affinity import PerplexityBasedNN, Multiscale\n", "from fastTSNE import initialization\n", "\n", "from examples import utils\n", "\n", "import numpy as np\n", "import scipy.sparse as sp\n", "from fbpca import pca\n", "from sklearn.model_selection import train_test_split\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import gzip\n", "import pickle\n", "\n", "with gzip.open(\"data/zeisel_2018.pkl.gz\", \"rb\") as f:\n", " data = pickle.load(f)\n", " \n", "x_log, x_raw = data[\"log_counts\"], data[\"counts\"]\n", "x_log, x_raw = x_log.T, x_raw.T\n", "\n", "y = data[\"CellType3\"]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data set contains 160796 samples with 27932 features\n" ] } ], "source": [ "print('Data set contains %d samples with %d features' % x_log.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature selection" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chosen offset: 0.18\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gene_mask = utils.select_genes(x_raw, n=3000)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "x = x_log[:, gene_mask]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data set contains 160796 samples with 3000 features\n" ] } ], "source": [ "print('Data set contains %d samples with %d features' % x.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PCA preprocessing" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 29.5 s, sys: 832 ms, total: 30.3 s\n", "Wall time: 22.4 s\n" ] } ], "source": [ "%time U, S, V = pca(x, k=50)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "x_reduced = np.dot(U, np.diag(S))\n", "x_reduced = x_reduced[:, np.argsort(S)[::-1]][:, :50]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(x_reduced[:, :2], y, ax=plt.figure(figsize=(13, 9)).gca(), colors=utils.ZEISEL_COLORS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ordinary t-SNE" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3min 18s, sys: 2.39 s, total: 3min 21s\n", "Wall time: 1min 22s\n" ] } ], "source": [ "%time affinities = PerplexityBasedNN(x_reduced, perplexity=50, method='approx', n_jobs=8)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 27min 34s, sys: 1min 2s, total: 28min 36s\n", "Wall time: 3min 44s\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABBoAAAH+CAYAAAAyMW1sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xl8XHW5+PHPObNksqdpm3SZ7gtbTQstAqEEKFgRUQRZStBCQQQF8YqCyuYCKniV6wXxwg8wslgjiCAoCmrBUCLQgm0pWwt0S0tTmmZPJrOc8/vj+R7mZJpCW9Jsfd6vV15JZs7MnHMySft9zrNYruuilFJKKaWUUkop1Rvs/t4BpZRSSimllFJKDR0aaFBKKaWUUkoppVSv0UCDUkoppZRSSimleo0GGpRSSimllFJKKdVrNNCglFJKKaWUUkqpXqOBBqWUUkoppZRSSvUaDTQopZRSSimllFKq12igQSmllFJKKaWUUr1GAw1KKaWUUkoppZTqNRpoUEoppZRSSimlVK/RQINSSimllFJKKaV6jQYalFJKKaWUUkop1Ws00KCUUkoppZRSSqleo4EGpZRSSimllFJK9RoNNCillFJKKaWUUqrXaKBBKaWUUkoppZRSvUYDDUoppZRSSimllOo1GmhQSimllFJKKaVUr9FAg1JKKaWUUkoppXqNBhqUUkoppZRSSinVazTQoJRSSimllFJKqV6jgQallFJKKaWUUkr1Gg00KKWUUkoppZRSqtdooEEppZRSSimllFK9RgMNSimllFJKKaWU6jUaaFBKKaWUUkoppVSv0UCDUkoppZRSSimleo0GGpRSSimllFJKKdVrNNCglFJKKaWUUkqpXqOBBqWUUkoppZRSSvWaYH/vgFJKqYHBra45BLgVeA14FnjCWlDR1r97pZRSSimlBhvLdd3+3gellFL9yK2uyQNOBn4GjDM3J4BfA0uAGLBEgw5KKaWUUmp3aKBBKaX2c251zWeBe4DhgOW7KwU0AkVALZLlcJu1oKK+z3dSKaWUUkoNGtqjQSmlFMi/B6758ASAYqTMrgK4GvibW11zlsmCUEoppZRSaiea0aCUUvs5EzT4M3Ds7mwOtAF/NY9pRssqlFJKKaWUj2Y0KKXUfswEGcqBh5BSiQ99CJAHnImUW9wBzNtnO6iUUkoppQYdDTQopdT+bR4yaWIK3fszeHpKe7PMRwgoAb6i5RRKKaWUUsqj4y2VUmo/48tiqAUiQA4weVeb+z73FJwOAJ8EjgFud6trbgFmArVaTqGUUkoptX/SQINSSg11y+/Kw5Q3uA3DX4ARi5DSh4eAzUiw4KBdPNofXHDM907G7S4SrPgKMhbzDOAq4LHeOwillFJKKTVYaKBBKaWGvpOBXwLNOIF7gM8DK4CzgNeBXGAUPQcRPF65hMPO5RTe9tlAYW/vvFJKKaWUGlw00KCUUkOUW11TClxM0fBDKN6Rb9nuX7FTVcBrSMnEu8D5pP8t2FWQwc+ie0DCCzxY5nlOBp4HXundo1FKKaWUUoOFNoNUSqmhaxHwXZqGz6W5aDFwFY0j2oFZwI1IuUQWsAP598BFJk980Nxjr2GkP9gQIP3vyURgAVDjVtd8SRtEKqWUUkrtfzTQoJRSQ1cV8BOwvkFDydeZc1E9knHwNWA40sSxACmfSCH/JnglEnvLNh9R4BfAFRpsUEoppZTav1iu+0EXrpRSSg12poRiEfB7JJPhDKAVyAcagS4kMBBDmjp2ezgSeMj83ONL+T7bQAeSLXGptaBCG0MqpZRSSu0ntEeDUkoNQSaLYB7Si+HTwOnAocBJSMnDMCSLodR834gECCLmKRwkoNCGNIvcnWwHCwlW2Mi/LzG0QaRSSiml1H5HAw1KKTXImaDCdcBlwDvAA8AJwGSkB0MAWA3MR7IYXgCmA8WkAwrZwBLgGCTYkGWevpCdm0R6j3HB9d1uYR6XMB/DZBtm9ObxKqWUUkqpgU1LJ5RSahByf3vrJBj9K+wRZViBO4HvkQ4GeIGAF5FshIlIICGAZDHsQIIJmb0TnkMaRA4zz+UFDHLoPmXCex0XXF+Gg+Wa+wPmhlbz2FuB64FyoNZaUNH2UY9fKaWUUkoNXNoMUimlBpmqimheU3P8btceeZKLPQZp7ujnTZCYDByCLPYtpBeDBYxEAhAvIo0gO5ASCcw27UjAIIZkvjkZzw0992rwSiuakSDDc+axY5EmlLci5RxKKaWUUmoI09IJpZQafMpfW/XygbOPPDAZDEbaA8HwhcAFwGdIBxlASiOSwHbzdQwImftjSLZCCRIMeBU4Egk+vGDuOwU42Ny/FRhlntf3GpbXINL13ZeHlGFsNM9zqnmdKDCuN0/EvhBdOGsS8CjdSz5cJBizFrgTqK67b4VmZiillFJK9UBLJ5RSapCpqojm5ReUnnzgjBMPzCsouX/C5MOnIU0dbyFd9pA0m3tlDF4Gglf+8DaS8WCZ7+uA8UAnsAXp83AkMgLTmyARMs/nZUc0mtf1Ag/etIlm4D2gCAlw+Esu/gX8mAFcQhFdOOseJHCzKwng18g5mY70sbgXDT4opZRSSgEaaFBKqUHNra6ZD9wA/AX4MhJo8I+odJGggOO73QXiSODAQjIewkjfhiDwCpJ9kG0+OpESizLzPGHzuFagAOn74AUTAubzWiQQcRBSpoG5vQo4HvgtcMtADDZkZDR4x+WVhXgBmxTpwA3m+0bkXP0PcJcGHZRSSim1v9LSCaXUkDQ3WjkJuBG5Un/L0rrF9f28S/vKSuAR4B/IxIjDkICBP4sgbL5uNvclzW3eIrmQ9L8HNvAx39cWEqA4AGhCpkp4IzALzGdvER4gndVwALL43gA8DExBghcjkRKMS4AVwGO9cA56Vd19K9YBM6MLZ81BAg4jgJeQc9gJVCBBGn+fo4DZDiRj41XgKe/O6MJZecA5wLlIMMgry3gCOEeDEkoppZQaSrQZpFJqqLoWWABcBbw5N1o5p5/3Z185ArgQ6RtwJJK94E1/8P7G2+ajifRV+ACSkeAtnr3tvKv3Dum+Cy1IYKIECUp4nIzH4XuM9zyTgPPM53+Z/c1hEAS66+5bsRyYjWRfRID/h2QtvIScuyTSu8I7563AJuBqoDbj6cqBnwHHIpkh3s/kFHOfUkoppdSQoaUTSqkhyWQ0vEX3kY/nL61bfH//7VXvc6trPgvcjQQAWsznAD2Oony/t0KWuS2JLPj9/RUS5vEx81GEBCKy6R6I8P/j4e/R4GU3QPdAxBZgLnA4stBeBTwxEEsnMplshHIkoHMGcnxrkGyS/wauBE5AykvuRc7xDORc/g5p0vl7JNDwObq/JzWjQSmllFJDjgYalFJDytxoZR4ySrEMyWYI+e52kAZ+LwH3AI8srVs8qBd4bnVNKTI28jNI1gFIoCCJLPwDSAmDdx78wQf/19733rb+kZbQPWvB/xgvWNHT83mBB8ds1wn8AbjGWlAx6EpZogtn3YwEFbxjcoA3gDFIlkYWEmSII4EZF3gaOBr4EdKf4nYki+HPwKV1960YdOdBKaWUUurDDPjUVaWU2kPlwM+RPgCZ5WHe6MVjgVlA19xoZRNQO4gDDsciV9O9fgoJ8/VGZIrERqQvghdo8Gc6ZJ4fr89CinS/ha3m8WQ83vvaawTZU4DBnykRNh8XmOe8dq+PuP/cgry/jkIyNMYgzS5dpP/FFmR8Zxz4G9IQ05/RMBO4FCnBqNUsBqWUUkoNVRpoUEoNNbXAYuAbfHAfmnykln4scAUwWEsqypDyBq9cwRtnOQI5/vE9PCazrMKhe1lEI9Kw8E2kLOMMpFdBkJ2zGlzf15nP6+2T/zUsYLbJxJjJAB5zmanuvhX10YWzzgAWAY8jpRSHI00tn0UyGn6MlE/c5AUSogtnbTe3z0bKLcqBx6MLZx1tbrOApcCnNfiglFJKqaFAAw1KqUHLlEmUs3NGQjuyWB5O+sq8lfFwG1noAvyX6elw50CfTuFW1+QB85DmhDFgPXK8KWQxPwwpUyjIeGhmWUPm116QwUYCFxuAHcDXgGLkPG4BRiP/diTNbRHSwYcuc5+XEeE9b2aN3glIdsB04Bp80xkGOlPqcBNAdOGsbUhmzF/q7lvxYHThrEOAt5Esh5GA955cBJyFlFmUI+f3K6Snd4BMDLkUuLkPDkMppZRSap/SQINSajArB24AriO9WC0HTkcWxXlI7bx/CkNmwAFksTgLWDA3Wvk4A3Qc5or/mlo6Kjr/xyPHnFlh24EcIBdJyU8iEyVGAQ1INkNm5kFmdoc/iyGzaWQ7MBGZFLHB3Bc3zxsw9+ewc1PICOn+Bd55tpARo2N92wWQPgXLkfKCAcnXBHJXZQ5V3ufowlmlwB+BqcgxT44unHULcs7ORZpfFgIvIueiHAnMeGMyLeByNNCglFJKqSFAAw1KqcEsAkwGjpwbrfSyGmqRZo+VZptm5EpyGdKgryfeIvsg5Co7c6OVjUDVQAo4FA6bfkVh8dQvNjW9vq6o6KCIbQfygTORBWwBsoh9D5gGHIAs6Htq4Oh931OWg4OUldhIAGMkct6SSP8HF3gBaXDoNY30Z434AxheWUUp6fGPIaRXQx5wHHAJ8O2PdGJ6mQkwnIaURkwHfh1dOOs2ZDSnl0myBAm4vGw+X4Gcd+8czADuQMopvOacW5EylGYkMHYAEtDxztetfXF8SimllFL7mgYalFKD2a+QK8bXAsyNVq5AFoBXI035KpDF92qkFv7DeFMXvoBkBxw5N1r5hYHSKHLclK9H7UBWMJTlTLUs28Z1HSxrGOkF/VjgSOR4vTGVmQEGf6PGnoINFnIOvDIKb6xlCCmlCCPZE01IaYrj2z7cw+s5pBtRvoz8XCb77p+6xydi3/Maio5Ajuu75sPjD6RYSBCh3fe9dw4ivq+DQNR8PRwJWozwPedryEQKpZRSSqlB74MapSml1IA1N1pZimQqpJBGfOcCPwXKTRbCF4HfIlfkxyILQe/qvjcNAbqn/nvTEcaYr08ELjO9IPpdIJh9res4T0MqBq6LG99BenQkSOaAg+z/SnbujQDpUgl/sMF/H+Y2l3QWQwfSCyJlbs8j3Y8hTDqjwTvHCfNYG+lT4JrPk5Fghfc6DvDWXp2MfasW+Cbp82pnfHi3eYGcMNIbIzM7ZFf/xtpI3wuQkpSXgAXaCFIppZRSQ4Xluj39P1QppQa2udHK7wDfRxZ7NyOZCwBLvAwEEyD4IZKeH0AWhH6ZvQQ83sjGVmRhXIX0bej3haBbXZOH23IabuJCrGGHYdm55i7/cSSRKQZHk55C0dOkCei+GI4hAYQupBQjjAQJEsA2pC/DCNKNJ73SCX9/B+/crUCaHpYivSRcZFEdIh3k2Ao8D1yPBIMG1ASK6MJZZwG/RvY5iOy3vzwE0ucxgZyvzPIR2HXAwQWeQIMMSimllBpiNNCglBqUTEbD18y3t/XUS8EEGv4FHEb3xV/mws+bktCTFHJF/9KldYsHzAhMt7rmeiSdvxnJ2sgcVekdk9dw0PNBi15/I0cv62MzMmliO3IVvsk85xjS56wdKRMI+h7XSnpxnoMEPxpIZ134J108hWQEXGctqBhwEyiiC2fNB+5B9j2O9MHYgOzzVCSzZiY9v4e889qKBFy84/YCEw1ImcYvNdiglFJKqaFCezQopQYlE1i49kM2m4csBuuRuvjMxbjH8t0O3a9GB5DmiCcB/RZo8I21BOlDcScwAWmomE+6H4B3XN6i18viSJH+m5857tNBFsIFvsd7QYdGJNAwEgkwRJDmkxuRRoaWue1t0r0XAkh5hY0EQrwgxEgk4JAkPdqxGRl1CVKyMBB5pRRlwJvINI5zkeOrRrIy/hfpZ5GZ7dCKBCZCSHBhI3AR0nj0JGQix+eR/hUDLsiilFJKKbU3NNCglBpwTLbCInZj6oPJWigHansobYggi/AcupcQeHZnBGQjskh8ek+OYR/4DpLBEAMusRZU3O9W11yNZHXkIEGC00jX/nu87ISe/t77mxcW9nC/BRxMukRiO7KYXoFcxT8DGU95ENLkMUg6kBAyz19gnstbgGf2MShEJjacMZDKJvxMpsGD5sObSrHC3L3EfI4hUypmIOcohPSlaAeuMfcDLKm7b0VbdOGsN5AA2ErgnwzcIItSSiml1B7TQINSaiBahCzOAG7a1UYmyHAFcnX5SuCxjE1iyGI2QvoK/Q5kcesfzZjJa5JoI+nu25EeBf3pMmR/cpAmlfcj6fpnIMf2ENIU8yR2LpXwZ2ykSAcB/LdnlpV00T2zw0WmJnQAhyIL6k5kkRyV/XJD5rljYLUhQR6vN0Mr6SkL/syK7UgZRjmD5Iq+CTxkvtfujy6c9QiSdfJx4Gzg90jvkCU9lEWUAz8CHkbLJpRSSik1xGigQSk1EFVlfN6VciTIkL2L+xtJlxR4DWkKkcVtKd2bGPpLCVzSKf/NwF2kr1z3l9uQrIZXgBfd6ppW5Ir5SvNxHlACtACrgFlIM0Z//wl/RoH/eHvK6Mgyn71Ag7d9F1KOEkPGaD6KBBLWEYxdRTIyA1wbrGzkPHrBnKKM4/F+HjawmCFwRd8LQEQXzloC1AC1mQEEkw1RjmSCrAIqgYOiC2f9BXhCAw5KKaWUGgq0GaRSatAyGQ1e34IXkCv83oK1HJlGMct8n0IWxxbpFP+cD3j6GFIukQ8sA27+sDKOfcn0aChHjq/OxfVKHVwL68fIonU26V4AxwOHk17QZwZSMrMZvK89PZWR2BmfU0hmxaHAWkgVEIxHSQUewg2fiZRdeEEbBymj8J/3d5Gr+sOBO60FFf12fveUCRi83zPDCxCY209DMkv+BjxiSiW8n18EuI50MCgbKT1pBn4J3KTBBqWUUkoNdhpoUEoNCXOjlfOBG5BFHObrV5AyDBtZ4HpX9L3Fsn8R3tM0iiQSlHCQpn8XD5ARl791cc9J4KRsrNYg9teRev8iZLFaiJSIlCDH1okcS7753htB6ZLup7CrQAR0P1/+cpMWpKTjJOAsc/t2pLnjAqQh5CSz7WokA+Mo0uf7LrP9N4GfWAsqfvgRT02fMZMobiVdthJHGnQeAdyHBFW8YzsFKQ+JItknk5CfxaPA10m/J3cAF9bdtyKzLEMppZRSalDR0gml1FBRiwQZvIyG64AK0sECf9nAB5UP+AVIByM+DfxkbrSyAPj+0rrF63pv1z/Y3T+5qjSWil98+thj143OHt4MXJu03TdfGN+6bnZdflcwyYHAfyENGm0ko6EIeM0cQynSY2I66f4T3gQJr59DT8GFlLnNCyx4jSM7kCvx64HrkYkJLea5HgDeAf4MnIP8PGYiYyADvue2kGDEnchUhjc+6nnqY7XAVcDRSEPOCDAF+Ady/juQHg1fRHpqgJzj8aSDPRew63GjSimllFKDlgYalFJDgsk08DcTfGputHIlcqV9mrktczEN6UBET7wr73HkCvQFyIKydG608oy+ym5oTXQsWtX01jUFodzg6WOPrcsNRi4Kn3XcDysAt7rmLOCryIL1QKTkoxopo6gDPoks8L2yhaA5Lm/sZeb58G7zT6TwtvGCLhHz9VRzfxB4CZnKMAZpzLnEvOYT5vPHzGPakNIJGzmnhUhWRH/3wNhtvrKJCDLi0zueU4CtyPvFBk5GznMLcpz+cx2k+6SP1UgZyaA5D0oppZRSu6KBBqXUkJDRr2GJLwiwBBm9GEGu4meRbpCYMtt0IVfj/b0K/NkPYWSR+AoyrnCKea0+SXHPD+VUHVty6Pdq3lthH5A/buzHiw+e5lbX1JpxkEeSLpHwHAEcYvbTy0roRCZoeCUQmWM+M6+s+8tMvPs7kfMXMLd5TTizgWOQzIQ5SCnBNGQh/UXz+l3mcfl0Hyv6JSQDYDAtsMuRso8Suh/LMOS8P48EHYqRY4uz8zn297p4FjhFezMopZRSaqjQlE2l1FBRDvzUfJSb2xYB55MeZdmGpLb/Bbm6HzPbBZBFdJe5rd587bGRBXstkuqfog996bs/rT9tbMUfvzz5s8womBJAmlx6QZVSugcZEkhzQQtpMNiMHHeebxtv+w56DjL4+UtOss1jEuY5vMyHEBKAGevb9mjknFrIFX8v2OFkfASQTBHvZzYY1JIe1Zk5yWM+UmbjBfIDpDMe/Lwgw4vA2RpkUEoppdRQooEGpdRQ4dXMX0W6T0MV8DPgT0iPglXASKSHwAvI+Euvb8BmJMgQAEYhWQxe80OQv5enmm0up4+vwBeEcq84vPigX+QEs1YhgRDPtcBG3/f5pI9pGJLJ4TWBtJAggccb/QlynB/WHdjKeH6v10IDEtD4OLAOeBMJPNyGlEW8aB7zLungjrc/IFM9Bs14SxMUeAg5/qR3ezgUYurkScFwKJQF2OZ7OxwKZZanOEjA5g/A5+ruWzFopm0opZRSSu0OnTqhlBryTFnFZcCZSC38ycgiHNJ9CHZ1Zf/923OzUqvvv3zNNSWFiSWcv7pfrkD7x1ya0gnc6pqbgW+RPg6QXgH5wBrzeTJylT1BugHkh74cOzeI9GcydCDlGN45SgCtSGPKLqRnxMPAb4EyJLMiAaxFMgImmcetA8q84xkMfOMqdyAZJu3Tpkyef+Kxx4aeWbqURDJp5+Xk8IkT5vGflSupee7fxBMJkPO2ArgJeEIzGZRSSik1FGmgQSm1z/zvp06ff8CY2G8efCnvr2vCbTfah9Udjiw+76y5Y1WfXsX19XD4OJKqn4csEseTXkDvKsvLBfjMnIbHv/25zaOA6zh/9VO72LbPudU1pcAVSHPGLUh2wTQksOJlZTQhWR1TSPejcEmPuvRnGPT4Mr77/f0FrIzbHaQpZBD4HDJNYjqSzbDN7FcOkjHiPUcbkv1w02AKNmQ6/9eXl9q2/bX2js6Djpwzu3zbe9udqZMnjWlrb+fxvz25+Y01a9ch2Rs3axaDUkoppYYybQaplNpnDhgdf+CEI7aMbO+KXrDhnfjYBByMlC7EkSu6fWZp3eK2udFKkJGKMaRs4B9ITb2/t0BPLMD5x6qi47JC7qNFOcmV55+/j3d4D1gLKuqBb/tvc6trJiHjPUcB24FrkJ4VWf6H0r3HwK4yGDLv8/ck8IIV3kSLBuAzpJtGRpHshgLzNWY7LwBiIUGfy5DSlgf37OgHjt9ccGs9cO29ax/KA8qLhw1b297efsmKVa9Es8Lha+vuW9FnI1GVUkoppfqTBhqUUvvMM8tGu+PcYdT+J4uixIay9ySl/kCkd0J/GYEsei2kUWRm6URPIzAB7M54oOAP/x5RCfz2/O6jNAcca0HFOre65nfA15EgwAjk3EM6iyFz+oQ3zrOnUhLvPnz3J0lP5LCRgMZI3+OageuRaRSzkQkTEeTfHv9zu0gJxoEMAedNO9M/avXbzDq/H/dGKaWUUqrvaaBBKbXPrG1xv/iLZ/IeaGYTY3Pbzn/4jlX9szj/zYw8oPwTZeNe+fuqYZuRsYvQvanhBqSM4sP6NTzN4GlceDNSMrEFGTuZJH2sE9l5nKfje+yuRn367w+br73yiRF0lw8cBtyIBBF+B1QigYXsjOerQSZ6KKWUUkqpQU4DDUqpfebhDXc/BZT0934A5SmHHxXnJVch++OSTt0PIAveVRbJRy3cMx2CY3tIamgH/h9w89K6xQO6j4BpGOmNv7wFKUv4EnKsLjCO7oEET2a/BX9Zhf82//cp0ufQy5Lw7ACORZo+Hgn8Bwny+IMaKeDPwMWmBEQppZRSSg1yGmhQaj/kVtfMB+4BLrQWVAzoEoBeUvtQ7YjHH3+p2OtR4F9QdyGL3RkW7scsnBJ2jjI4wANL6xZf0Wd7/NGUg3MrtlNKVuxKOvOqkD4UnwZGI+cgxc4NMP3Hndm3wQsgOL7vMwMRXq+GkPn6YaRfwxxkQkUgY/s48u9QEzDTra6ptRZUtJnmlj8APoVkY3zJWlDx6l6eC6WUUkop1cc+qPmZUmqIcatr8txHnrwMUn8GNwqp+/t7n/rE+avbfvm3MSuSXaFh04JFeeHuf/rakMXsFIdAyiHY0cMzJIElfbGrvaSWgqY4dipCXusikynwOOlMjmTGZ+ie2YC5D9K9GfyNI/0BCidjW68cJYE0omxC+jK8jozcTAKbzP1er4ZTgBuQcZEAXwO+jJSyHIlkkiillFJKqUFCAw1K7S+W35VH8Xs/IRH8X7BC4EJRQxbL7zqL5Xfl9ffu9aa50cq8udHK+WakpWfJcdnRO0/Jmdg+KVQQQ66wPw5civQNeBLsSpfAxWDdiYxlXAm8hoyOfKKPD2OvWQsq2shvrWR4w3LC8YvNzbXAUiQQ0AlsRhb6/kkS/mCDP+PN3wgycyayP7hgIYGEOJLR8GlktGUAOBw4Bpkq8QfSIzZBAhHXATvc6prnAG+fPZm9H5RSSiml1ACmpRNK7T8WUNR4CcG4TVcWZHU55LXnAD9DrjoPpRKKcuQK+XWY41pat7ittuz2G5qcrue2p2IRZMTlEl+/hZPM5+UM4hGLHuuYLy5HFvfyvZQkXGW+PQGZ8gASQNhV88v3H046IOEvr/DKKjqQYEHC3G4j5/0QJMhQhAQLwkCj2c6bfAHwH2tBxVNudc0zpLMavLGZmeUWSimllFJqgNNAg1L7j7Ms2w1S0NYKbf8C/obU66+ij6YomAwDr0nhkn3YVLEWCTJ0O67yVZe2AY+dvI9edKCzFlTUu9U1lyI/g6OBryABhyeRBpEzzKb+EZ89jfv0j7kECTKABAYi5vbPsvO/MRYS5Cile9PJBtPAsgl/TwjXNZkSzoQ9PlillFJKKdVvLNfNzIJVSg1Jy+86BPg58E3mXNSrjfVO/+dn8pAr0bV/POHxNugWVChEpg7cCcwE/g+p3X8ZWLi0bvG63twXtXvMwv4e4HSgARlFmUO6d0PI2xQJIGQGDbxsA5BgQRsy+vN4II90EMLLdvC+T5Lu8+D9A9QIfBWYjfRnyJJXduUxTmOtde7njv5IB6yUUkoppfqMBhqU2g/NjVZOAq4Fbtzbhf5lky+flGuFvv9KvGFF11Htp+ae3jXdbbf+q/F7uU8gQYciJLCRj1w1fw6pzb8GCTT/RxjRAAAgAElEQVS4yPjD04BsoHagj40catzqmrOQn9EaoAIJAMSQAEKYdDDAyzLYaean7/5OJGAxlnRQwZts4SCBCpedx2NuQt4rjwKfQ/o7jAS246aycBpehC0XWedergEppZRSSqlBQgMNSg0xc6OVhwD3AVORhd3CpXWLH/Pdnwf8FUmdd4EXgV8BjyytW9xm7i9HGiEeYR62BFn83QjUAXccGh5539FZo+cuidUlk6GUvWViI+3vWJvcmLUZWWwmkUwGf/1/DFnABkgvWhvN/d9aWrf47oz9LEcDEPuMyWooRxpDPgWMQRb6NvLe6Kk/QmY/h55KK7ztMPfFkAyJJro3dmwGXgBOBLYhAagXzfN/xVpQsXwvD00ppZRSSvUjDTQoNcTMjVY+Axzru2m7+SgGbkHGDD6AZBp4HOTqcxuycAwBNcBcJJ2+E2hFFqIuUBvGnjkhWJAfwnLn5YzjqY6N7ppkk3/kYU8L1C7kyrYXcAiTvsq9CViGjJrsQBoGnoIEPP6CTH3IRSZARIFrteyid7jVNWc5TurWRDzW5mLdFYnklJHONNlVIAHkZxpD3iP+2/xlEgEkeJHl+x7SZROrkffVn4AzgCutBRV39cqBKaWUUkqpfqHNIJUaehrMZ++KcgI40Hz9YyR74V/I6EH/aEMbGOZ7nk8gAQfLfC4gHRQ4LI4TX5tsIoxtJTpcNiRb/IvRnoIMLcgiM4IELeqAOb77x44lJ3p6/hT3kda3U3V0bEOyHc4FvoAEO2JmH21g8txo5Sc026FXHAjWsNbmrdlY9mGRyKQaYArSU8NBggQWO/9cbeTn6QUXvDKKOPLvSxwJVsSQINbryCQM875y2sAdBtYTYP8ImfbRJ41JlVJKKaXUvqOBBqX6WVVFtBRYBFQtqqmr74Wn/BayqHsWmSrxbd99NvAlpCHjDtKLdpDFYrP5Otd8vRFZbFrIotErefBGFhLHcdYmm7wFZgDJjEgi2Qo26YBHIbDW7NMoJI3eq91PAdbn86c6Y4N5gS8UHhS7p/XVR4oDkQvWJVoCcXmKXNIjGQEORdL+B8RYznvXPvR+qcd5084cbMGPO3GdY4tHTjrWsqzPIxMjAkBTRyz+QHNH7LDSovwy27a8bJSR5nFegCFBuodDgHTWQjaSxfJP4ONIwCtktg+BlU1WzKErMg942FpQ8X6JDwDL73q/lwhzLtLsFaWUUkqpQUJnkyvVj6oqonOAl4DvARf3xnMurVu8bmnd4vNMv4MvI4tzB1n8+6cJ/AwJHoAs9C9fWre4GDgfWA/8Esl6+DwSCFmLBAUc0gtJSP8daUTKHOLIVe7MbAkLmBrGzp8eLHLD2FPNcznAO0Ds4da37PdSHU7CTYY/kzOp5HM5U1qmhArfJR288DhIRsQJc6OVpXt9snpXOfAj4DITdBg0rAUV9XYgeKdt2+2WZXnBgmeBy7566+Ln7/5r7ZRYIt6JBKhqgS7XdR3XdW1TfhciPZ3Ce691mY8VyHujAAluuea27eBuBKcarI5d7Nq1yPvx2n1y4EoppZRSap/QjAal+khVRfQY4HGkN8Ja4FPAYqRxYgrpTdDbLgSqgL8D9cAFSGZBh7n9i8BBSFnCJvOYJciIwVrTHHIJ8B2kL0IM+buROTkggGRH/BwJSkxDyiMKSE8dsAF7QrCA+Tnj7Sc7NgbWJpscc/9I4JXNdBx2a8uqwKRQQdfWZMe00mBO8fpESww5Py9mEZg4IZg/en2yxYrjTMojeMX87PEn/+/0659oduO3XL/2pt7ICNlbtcDDSGDmZQZIpsUeeAL4GzLusg7pi1F21SlTQ08882Tn66utB2bP/mQucDIQi8WTLeFQYIQtgQkHyVAZRjrA5PVlmGM+AkiGg4OcqwDYh9GVl415v/WwTzdmfO4T9659qBRfL5Dzpp2p2RRKKaWUUntAm0Eq1UeqKqJrkAW4510gDwk8uMD3F9XU/bC3X3dutHI+cANytT0ClAGrkKDBDyeNy2m/8qvTiyZG85YU5IduZM5F9b7HlgI/RZoyFiAL0Im+p/cCBSlkIbkWqe23kfKNtUggI8vc5oSxY5NCBe66REt2HMcbnxhD0ulzkAVrERLQ6NaEcHqoqGt+ZHzWk50bWZts4vissc6JOePsTieZeCWx4/or19x4U2+dt70xyMsnvHGX/w/5OcSBsOs6nevW/vu2ouLoo8UjJvwemADEXNcNID9XLMtygWeQ99YI0tkNSeRnmEW6+eN6JIPnCiT49RVrQcWAWcjfu/ahzwK/Q4IiFlJCdOJ5087UCRhKKaWUUrtJAw1K9ZGqiug3kKkPft6V/i5gIVKq8A/gkUU1dXu1UDXBgUVA1dK6xfWZYyIzAg+xB247/FvjxuZ+wrZwLcvqBG4C/oc5F7XNjVZ+B1kUdiELzz8iWRAR0lkN7ciizM742gG2AsMxC1JzWyOSUTHOt9sJJNjgNYvssawrjO1OCBZYG5ItxHHII+geFRkdd1z3bysS2xc3OLEntDnk3jPjLk8GTgWOR4JLYSSIUAzMJj2hxCuf8b42gQn3/Z+dJDvsNLWiA7gECTLUWgsq+v3nZQJEpwGfQ4Jq4W4buG79ie1tl49NpZ5gzkX9vr99qeKSMu/vye+RQGltzR2r9qtzoJRSSqk9p6UTSvWdu5AFnH/0pLcoy0Lq34uQCQsTkWDA3lgEXGO+vsksvP1p/LWksxt4463Wq0uGRw7Jzg6MQa5kXwe8YB5ThSy61iHBhtlIoODP5jgmkp5GkDKP9/dmGNPD/g1HFpudSGAin3RgYoN5Tm9cYreAQxyHtckmzLZ2G0nr77FNLnLefoQEK7o3FFS7zSz6H3Sra5YB30fKaS5Ggg7ez8LrueE1+exCgktheh6DmXlbFjDJWlBxf6/u/EezAJnGslMWjTFyVTjr9pdt66kdax/6E/DEYMxY2VMVl5TNQUpoRiDlK3HgSuD2/twvpZRSSg18mtGgVB+qqojmIf0OvkV6dORWpG9CiHSjRgdJYb9yTzMbMjMadrHNfOBW8+3lSx89fi1wJzC7o6PrVxd+6z/LNm3pXOLPDjCPuQ1ZVF6GTBG4EulHcJjveLwsDY/3fQoJWExGAgy3Am8iGRQtSIAhgPSxmAzMID3lwmsE2YUsaGPmYwcSuChCUtxvBW7RrIaPxq2u+Q4SrNoCTP+ATb1sBm/qhN1DRkP3B6RSm55e9c59ddtbblt0zzf6rafGvWsfmoQ0RD0QOIDuDU49DvJvpIXrprAssKx24MvnTTvzwb7b232v4pIyL6vjJODfyGSYBUgplN/LNXesmt3Hu6eUUkqpQUYzGpTqRVUV0UOQxe5K4B7gM/jGVpqgwbVVFdEXkWyBQqAaSU0/CMliCCEL8y8Dj7CHTQVNcOEmAFM2MQ/JXogBXvCgFrjKPKTWpIPPB5ifLq2IsXMmxJXmuQqRNOrtQI051uuRcggveumY58gxX68kvWiJkJ54UYAsaiwky+F1s10N0uPhEOBgZDH7tnmuauA98/gjkXKOGuBcZKKBZjV8NFUdsXj4T/9+tfXMY8pODwYDWciY03DGdl4kIYb0G7Esy/JPBwn6g9mWZTnr6huXTRpV/PXmjq42zPu0n1yLlEp82PQlG3CxrCCWlUCmuJQBQyrQgJRX/RwJ3J2KZJ4k6B44TNBL03GUUkopNbRpoEHt90zvhJuBby+qqfufj/h0P0cW9sciV+SPAQ6qqohempGZsAS4G5kCkQCWLKqpe6yqIloL/AGph7+bnjvx74lypJljAbLgv3JutLIZuYp75y4yHtYCq4HOudHKezE9I3wTKC4DLkXSqWuBSuBqYBkyQcOfYh9GshG8jIYss10hMnWj1eyXbbb7M9KIbzQwC+kJsQTpbdGGBBUsJDDhmo/nkcyGt4DDMSUhveVP58w5pmhkya8LI9P+iWN9b9bPftGfky36hLWgov6T0crngRt+9ZcXrltat/gp07/hPOBy5P3ZhdTtHwf8AAkaHIjJckilUk22bY/IeOrVmxtav7W9pf3s1o6uqr46Ho+ZJrEICfLdiGTCHIO8ly2TvSCREctKAU24bjaWlQuA6wZw3YQFI8xzzWSQNv7swUrkvByABPamsnNm0ik1d6zSpphKKaWU+lBaOqH2e1UV0SSSNu0ALyKj9oqB2j0pW6iqiJYCP4g7gZPWt494Y3xOQ10kkDwHSen/L+SqL0hQoa2qIvpZ4A5koX0ncMPeNoDcFV9Gw5VI0KEOWVSFkAX9pf4yg4pLyvKc1aPvZFPRGaQC25EFfQPwhaV1i58y5RM/Ad5AFvcVSGr9g8BRSE8GrxmkJ2Fe7y0kSNCElFp40yq8ev+3kaviDyBXmn+LBBjmIfXzJeZ5XLP9v4Ft5nXXIMGRU4FvLq1bvNdXm015SzkSRMmNHjT1zTknnFFoNY9l1UuPLDtk2os/Gz48+ATnr+7TxWVt2e3vL5LLV126z4MdmU1EP2x7t7rmt8A5SP+NZCqVsC0rkO84qVbLsiO2bQcsy7rLWlBxyT7e9V26d+1DXknIj86bduZNpgnk75AGmK1WrKOhq2HL5NDIcVihsJtZ++HGu0i8t4nQyGiHFY48igS2HgBuGYzBBlMu4WU8zUb6w7yB/F5nXoh4HSmXKkcbQiqllFLqQ2hGg1LdmxceiaTlNyILkj0pW1gEXLgtVhB8rXnM2OxAPD4upzGMLOxvAEqRdP9LzfMuQRbXRwNfAv65h6+3J5qR44wix5kwr1s+N1pZi1k82KdQbk3aPs9tCwfZnj8KCQ68DpwwN1q5FlmQ/AlZ0DtI1sbLSOPAj5lj/DISSPCCDV7fiQnma2/kIWabFiQAcjASRLgMKckASVMH+BdwFuk0bhs4wtwXNK97JJL18FGdjNTuXwOcvX3TlsKN79QzIj+KE0jODoUit8fdz9wWqq65pa8mJpggwz+QMpIv15bdfkL5qkv36UjIHpqIfphrkZ/ti0hGygzgZNu2Vtp24CjkZzay13d0z3hZFFUmyPAd4BPIvuXGtm3Y0bl6KTkzjyc8ZkrmtAwS722ifcUSJ3fW8aHw2GkLzM1fYxCW65ggwxXI72s2EvAMItlPmZKkg34/RcquBtXxKqWUUqpvaaBB7ZdM9oE3su0l5GqehSx08/iQsgX/VW9fFsLjwI9KI83MKV4fLI00B5D08gjSzwCkB8ERVRXRK5Eshy3mNV/5oNfbE74r0StL7OwrysIjLn4+9q7VRtLFqzeX/gjvIov3nyPZAmucJw98lRHt22nMKTHbTkQyFhzkavUoZDKEN7by78A3kSu7ByJXQyebXXF8r4fva//fHQspo8gzX1vIgmcUMgJxDNKQ7gdIYGGKeY5O0pMpMLeF+YhlE+bneioSuLgE+HjxmFGMnhrmlef+zuY1y7uGRWZ0HfCxI89FSjZ6NTBUW3a7/wpzFjAJyXa5GFm4Y257qrbs9vn7OtiwJ6wFFeuA89zqmvnAwkAg9HPgebC3A2Wu6+YuW7Op5JvRytJdNSnd186bdmY9cJMvyPAN0oGwYHj0lIlWIERo5DjooXdDqGSck3voPCc0cnzId3MxcNy9ax8CWDIQMxtMUOFk5O9cIVLykYdMfMlD3m9h5Hc2l+4BFhfJUmnpw11WSiml1CCngQY1pNSW3T4faUx4YfmqS3tcBJrF5E8TTuCsjR3Fp4/P2fGxkJ26H/AWPy8BT3xIGUM5kqVwHfCUec4rAEK2QzSnEeQ/6xHkP+ref9xLkSu/YWSRugC5Cn9jL5ZNlCOjHlfNDI84rSIytjDlOs5zXe/aE4IF7oZkixXHyTbblZMuXSghESzn3cIU8B/k6rm3cLeRRo8gtduTkcyPi5GF8S+RBcyjwGtIOYPHm0QRIB108HORK6Ze+YqNLP5GIancucDpwF+RzA+v5MIru/DGa3YhPR8+Cq+/RieysA9uW7+J//zt72xbv4lUPNGwetVrNZ2xPybferPm5sSvOncsqqnrzZp1r6dGLrIIzDa3e6UqBch5moJkkZzXi6/dW2qR34taa0FFm+ntcHAimfpqVyJ5xORRw76G/A70p5OBr9M9WIUVClvhsVN3+SArlGWHx/R4/0LznJez77KS9krFJWWlyHvqFGCYudn7e/Q28l4PkS5xyhwV0owEGgCWhO3sqw4eUcG3b/hh3s3XXT/ggipKKaWUGhg00KCGmvuQxfx9yEK1J78DTqmPFXS90hQ9NMtOBqM5jWcDZ5v7K4HfVVVE48ByZMzkI/5AwEs7JqzscoKPZNnJlUiQ4TLkauFm0gtyABKObdXHCimNNLsh22lArgxOQtLL/wIsJv0f+d6wEngHOPX52NZUwnVY3rXVnhDM55PZ462nOjc4a5LNPY2fdMC1wU1a8LaLfSDSV2JXWQJTkavC60lng5yGLEw2IWUaHi+YsQO5Amz5HmOR7nDvXSn27ss1j5tIurTCu+qaecU5RLrUYm95gaE7kQkWBcl4gi1r3vHuHxXvajvr9Vee9Mo3/gBM/P5nrp9UnJ9z7Y7Wjhu///gPP0qWgTcNxCunaUGCDMuQySRRpPxgLDC6tuz2L5rHTQLu7IveDR/GlJP4F9u5wFGhYCBcftBE55AJu/q13LfuXftQaSRhX3zoK5F3O0s6zs0qCeQmt9dh+jF80EMd0otvL6iV6V/AvfRSVtLeyui5MBI4E8lYOJTuxwESqLsPmIs0xFyD/I5lBhq2IMGtWM0dq9q+fcMPY0kn8ZMtrWtOPefKs67+3X8/2O/vOaWUUkoNPBpoUEPNBiTQsCHzjqqKaF7b8Kx5rZMKTo6vDTIy3BI6dNjGzSWRlgBSk08ibN/SGM2dXLyp3QomnAjyn/CjgLFVFdGXkUX8zDWtR0Q6UlmnAS/PLt4A8HnkCv/jyILjUMyCpD5WyEuNE5g9bIMVzWksTjj2sPpYoVUaaXZCtpMPXIT0JOitq7wzkcVGcSuJume66uTEJFt4snMDG5ItNqQcsO08gszJGmUv79rqtJG0AQIkrWHhztbWRPHbB4SKnDfijWVxHJCFiRcIsIE5yGSIamRx4o3lHIYEEzxJJMAwDPmb41+oeZMjYkA7chXfS+GuR0o6AsgC2zGPiSGLnzHec2WHg8yYOMpevX5rbmc8OWl3T5QpobkYWcw/YW72ell0ZW4fDBIsKQmybVuSpAxxHDs3Wpn3uaMOvvvUIw8+/q/L34wgAYq9Ur7q0jbgsdqy20EaYj6ELBj/gQRb/OfuRKQ5X765/eLastuXAt/pz5IKk8FQjsloSCS6vhMMho8ArEDA3lGcn7PKra7J66v+FgCmVOLHxR3ByuF2uGtMKhLZuu1tt3PVM1bOrHmEx0zxj3DM9GHjL+PAo+dNO7NfehZkBBfmAecjv3NeU1YvmNdGuv+CF3T4OBJc+CMS1Do44+kbkAyxt5CeMgC129rXrdjWsf7sgqyRa+nfEaVKKaWUGqA00KCGmguQngPf7OG+8taSyE9fPWqs274jzKHvbmyekNvwJUyfhaqKaN6m6cUb1h1eMuXgZ+oYve79kuQAUiYRR5q+ZX9q9KrvP1x3+HWkr2Be43ue45Ar+ycBB5VGmg+dPWyDUxpp6QTiW2OFWS83TsgxgQeH9AJ6r3g9GWYVbViZH+yYD9NPAeslC44KWKlfJd3ARWBNTNG1ZUNyW8TBHQbhAMCcrFGckD0Oy3Ldp2ObAcdxsWPb4wWV04P5WcdGxhLEfvbl+HtNwGfMSzpIj4Y2JIDwIjhTwSoHXLC8vhT+BVoxEqjIbDTnLYS2IYv9eaRLLZKAbYIIrF6/1e6MJ13SV2u953dnTBzlfumTh9t3P7nMXram7qvIuNLdcS3wVSR9/HZkckUecAY9ZJmUlAQ5bHaEl1+OORs2OWzpLNo2PNxyXmNb52EuEArYnbv5ujsx/RlOQ1LctyHZLpOQZoMF7Fx2YpEOMoAEXs4CKmrLbj+xfNWlr+7tvuwtE2S4DCkJetOtrkl2drRPzS8IWWCRSqWyXCd5ux0INtvVNQusBRV9NSqxHJi1PTfxYIMTX7r69S0TRhxVGs2ZefzZwRFj12OaIbqJrmHxd9cFAcKjJ39YpoMnQC+PVN1dvoaOF5n98AJzWb7NvAyFOPI7FUdKRlykBMdLMbmIncdZBpFeJb/GBBpuvu76tnOuPOvqgqyRa4N2qNuIUl/QA2CJTqZQSiml9l8aaFBDillcnbSLu2vzt8WuKtrccfCkhm0X5ATiFy6qqXt2brQy755o5XyoqB3e2fobNnFMvCEYyvjtCCYcO1gfKzyqJNLyclG484ULJ9e0k24I+X6quCmxuB+4v6oimheynfJoTuNKpJnhrNJIyzmzijYWjMxqXYU0WlwK3PZRDhu4oT0Z/svGtqIrwC6wcDrLiurap+fXv/PQpo+fCFybInwLJM+JwFdmhLLyX0vEA8u7ttgWrrOsq94rVUgFSWbFsez1yWb7yY6NzqZUawdQBti+tW4xWKORxcjUbDu+JuYEkiFSL8eJ1CGp/YeTTjX3eimYJ/CvmS2QDILRpMdXAoxHggj2lz55OHc/ucxZtqbOy6rwj7lMrl6/NevuJ5exev1WkMyI3TXX7FsuktnwS6SEIx8oytjW2bYtab38Uszati1JfWyYvaJx/JjicPuXX3yzLtzRlbBe37ht6oV78OIgEyVibvLid7qaiw7KKr7Ysqxs0g07g0gdfT5yotqRRaLvXMq+kV4kjgJ+gUxT6GvlSHbPViRoEsrNK6x3sVwLLNu2CxyCjmXZI4D/Q94jfaEW+G486NZOP/vTbVfzaf6+9OnSVNaEN7o6E79vCredDVyYqN9gtb3412wrEAi7M497ITK57EAkSJaZ1eDvuxIEjgPu6qNj8Rb05UiA4wvm8xbkdyhz/0ACDF4/ihDyfnkNKXs6APlZBXzbdyHlOqOQMbKfR6bLPAVgyiV6ymRYifRvaUJK0QZUvwqllFJK9R0NNKj9hgkAPAY8xjXd/pP8fmPHhnfzH5ncsq1gzMjmm8mo99/cMYxljROtw4etP3RiXsMZyOLv83zAGEzzmt59j1VVRJeE7dTzE3J3eJkQmZMrdot/6gVU1ALX1XUOL+pMBQCs4nAbZUV1XbFUCGTixdXAohTBs8eFitxP5o6LFSbWrNvCO9F6a6s9MzBx/cqOriltBPLj5mJoHIc1ySYb+CRg+da1doiurgSh7cCIMKHg+MCoWeudpo4u7DqkKd4/kAyFEBJEiISx26eFioKu6+a8lWxCyjFcB6z3MGP1wtjuhGCBvSHZEjTlGvbq9fUdv35qeWD1+q222c7XU4IOINEZTyaXrakLgrNlZuGG26sqonm7eU4vQkpd8pADfNd8nXllFyCZTGJt226FSiZPthPvbAY2uAXBjs1v15U8u2xN3ReAB3brB9jdojD2dyeGCgIuWFa6MWbY7FMW6VT3RtJ9K7zAgxeE8KZx2JgyEDMW8/3SEFOasS/VIr8Pm4GngZEB2y5xfYte27Zt13U6LCvwlX28L+8zkyC6/Y6O21oyE1lgv/zCzNd+CbwWKBwRCUenloVGjj8zOHzMfyOL5UrzEH/vkQI3EbcS720iNDLqWqGsvu7N4P3N+hHynjsXCVbeQvr94EmYD3/jyxjSV+JQJKCVmZFhIyVRa83H2UgQwR/kqPVnLFRcUjYHKe8BycDp134VSimllOpfGmhQQ8rcaOUc5ErpV5bWLd7dtOz3u+QvrVvcVlURXYv0BxiPLPgsAMtywbVIudjr24d/a0x2046wnXqIPfgPdUbgAfb+it/7wZGldYufuubQQ9e2JiPfX9046rY4WeeMjjTeFbZTrz+xZWYkbCdudVxrcdINnFqa1fyiG2yY+LfOriPXJdpndLkjOWlYIjHNmVjWklxvr4634YIDlu27aG6n1y2OA7adIJQPdj641pRQXuyE7PHZf4fYm4mmsUiJxDwkHd1b/DbOCA9/5eTsCWUxUpHH2t9hbbKhwSUQQTIHtgPFE4MF7qdyJiRe7doRaEh1MjwQcZ/v2hp64c1N/nGC3s4kkfKNp5Fa8xcn5by3rqxoy/nI4uhDz+2imrrlVRXR+5ESilpkioN/0eVlTXgLf0omjuOwTx0Pf30aS5pEHnPGuOW/e3DTEY+xd4urKhe+lm0Hxzi4zWAlzWuFzLkbDbyOlEa8Z74PmOP3Sky8v+Vef4t88/3FyMJ/G3KVeZ9eYfYaQZoRl8O9m713kuu6DrjrbTtwdh+WTezK+7/3JhDxGMC9+cVPADXm/ldIn9s/Iwv7AsBOvLeJjhVLyJl1POExU4fv9Ox9tO9IScMbSNZR3LeNF4xah2QYebeB/H07B/ndS2U8t4O89y5DytAuRjI2SiouKXvGbHONef2nzFSLnyPBCC9A16RlE0oppdT+zXLdnqbNKTV4zI1W/hP5j/AzyCJgDrB8ad3ivUrLNtkC85BF52cx6eoJxw7VxwpJOjYvNU5snp6/9eGPFW2+elFNXd91Xf/NjDxgXkeHE/nrE22xlhZnyaKauravHHz0b99uK10QthJbm1J5o8B6Bvj3xJxtORNzG87MCXZdva595Ng5xesvsKB0c0dRzputpc2bO4uLhwW63OFWqd3g1DvtjmWNzWlsSKay87JTY8Prkh12nJQLlkt6EWEyCpxEfiCW6krlJiYEC/M3JFsScVyv50IKuRLfhVw9jWUR6Dgyq3RZkxOf+2ay8c2YmxpBegE9HXgr2wq+d1L2+DHTQkWzIwSI2EF7Sccm5x9ddS7p1G4z2tJ1s+2ul/JDsS9v6yq6AqgEt3VaXv0vK0rW3PJBGQ1VFdFJyHjIZ80+noiUUYwnIwCbcGzqY4VuaaSZkO24wXDIKpk4ztq2fhPJeAJkoX/DoguKbsHLMjl/9Ycusky2wbeBw5Cymog5t9uQBeN4s2kSKZ8YhVyV9s5DHLLhefwAACAASURBVAks+LMvWpExoJ3IJIHngflIjf0NfZDRALzfq+F+4LO4rp1yHCeRaEs1bq/79ejoId/qy0aQvcU0lDwZ+ZvwKSce25Z49x03VDrhn3Yk98bzpp3ZJ38HTEbBycCByHQUgD8hwTb/7ynm+zbzOYd0T5Qk8netmO7TXrYjAcJc5PfMmzpjI9lDm4Erze0rkf4hV/ken0R6i3yj5o5V/daQVCmllFL9TzMa1FBwHPIf4eOQBdv/AXudlu2VWFRVRF9A/jP9BpAVsp3/i+Y05iYc27EtNzYqu/l483r7pNu81+QRk2lhbi4HfpqTY/P5Mwou9xa06zpKWjudsDUqtzG3ozNrXdwJHAb2cZs6hjdPy9/mjs5uuWp0dstzSF32qIl5DZ0lkea85TsmW2+1Dbd20AyEnZDluKFAKhCJjwnNy5lsPdmxkQ3JJmtisMhdn2xx46QcafZo2WBbralsK0zn25tSsYMsIq3TQ0X5GxKtO7rkImkhchV9FLiFXcTj/+qqi4G9DPg78PEsAkdPChWMfCfRnB3Hqet0k+V/6VhfODVU6EwNFLbOyBpe1OB0NSCLnA4kNXsp8rMOdzmhI6Ohps9v6+JqIAjWUWvbRq2oemPJBwUZ8pAF2olI88Q64PdIWrx/YoUD2PWxQl7aMYHDijd0jstpzDbjLr3yjQTwiHm+97NM2L3MgUXA5UjgoNV8DiCBhhlmGy/AMh1Z3HlXm2Pmcwq5ah03z7EDmVbhTfeYaJ5jEXJF/tnd2K+PzFpQ0eZW13wRmEfq2Y/b8UfO2rS25YHnnuv8wADQQGayHh40H+KQftmV05C/cSEk0DSOdD+UzNGUKeBNpA+DF4CwkSBDDPmdyiGd/RBC3kch5H2YRHozDDev81vgBWSyzRnAd+kegDy35o5V6fOjlFJKqf2WBhrUUPAMJqPBlEv0SoM5k6nwQ3j/CvifgNyQ7XxifO6OkchV433Sbd4EGa5Aaq+vJB3MqEWuIAKsfPsHMy5b9kboogn5BXnrmodbkUAyK4BbMjzUFmhI5FtF4bbNo7ObpyYce8aWzqKDE47tBC2XMTlNm3OCyfFHjXiLgJXgrdZR7sis5nhLIje4PVawqTXZMYmOutxsAtbxWeOcwyKl8cc71revTjR4i/wIWFlhAkwMDitbn2xhUqgg6xOR8VlPuhtHrU02WWEsZ0KwYPSGZIsVx3WRQMBBYawDJobyP7U+0dZ6cLj49XmR6IjHWeeuSTQVAdlxHPe1RGP724mW+Juppo71idZWpNHe75H07GFABbjvAmzqGPbG0rrF9XOjlZfyft+K7swYy0VAVVM8+4iWRPbM0dlNqZDthJHF2NdNxopbGmm2QrbjJhzbro8VUhxudWYXb7BLI83ez9pb0LUDvwFuWFRTV89vZvjT2XdHFTIl4DCkzn4FcCNS9uEFGryFo3fVOYgEDvz7Ys4tI8yHn7cIHAn8vbbs9rnlqy7tk5IFk7XwGL/56hLLpmb6AVm103+ydlAGGQaYs5GMA28qyvG72M57X3zMfB8n3fOjkPR7yCu3CJvbC5GgwUgksPB1c7/3u1WOlJCU0j3I8GsNMiillFLKo4EGNegtrVt8Qh+8zBeRxo+bkSZp05GrhN+tqoi+uqimrrdHCZYjQYZs74a50cq8cSOmz/vBWRsj33twfOwbn9r02fEj4reUH0Zo+bs5bklXM4XBjgAQLgq3N2QHk0/PLNq4blPHsJHbOvMT77SX5KVcK5gVSKaOtN7JH5+7I+ViNYzI6tgYst99YWRWyxsvNU46tSGRPwZYlrTco0/OnpiVbQUTuXbIOSw84nerEw3FZp+6AHtisCAwP3u8/WTnBtYlWvKecjc6G5OtO4DiCcEC+5PZE3iycwNrk00W2BbAxGA+8yMTw0+5G4a9Gm+Y2e4kctcnW2xkweSEsdtnhkf8+5VEw7A3E007kCwML8hwJlIa8GOw73dgWswJ1AIsrVvcVnFJ2UrgZxWXlJ0AXFBzxyrvCv4ipK48b2uscMYbLaOL51huMJrTCNIsUzIXGicwe9gGojmNVn2skGUNExie1WHPLFrv1scKbVM+4dW5x5GsiMeqKqIrzWtU7e4V+/JVl9bXlt1+Pek0+BYk8NCMZFmM923uTe/w6umTyILRCzT8f/bePD6q+t7/f30+Z5kz+2SdLJOdnbCGzQgoEbFicUFrW2yrVC222vZeu3y9V9vb29ven9WuWlupWrC9pWrV1rWAEhUhyBKWECCQkD0h60xmnznr74/PDAmIirvS83w88sjkzDlnzpzPmZN5vz7v9+udFiTSj8d2oUhjwUfb6YHBsm7M7gMfHEWp30EAj4Ddm04fa2D0mhAwap6aAMtYsINdDxTMALVgzHY6mNj1JID7x3gtbAaAxbdMrwOwCazsJ73+/259sOEH7/eNmZiYmJiYmJw7mEKDyb8MY0oR0q0mYV+Z3CktUuaP/MwGrY/WjilROJ0mReeM/oSr3CuFvALVfg3m2D4TzAjtrVpqvlfq3DZ188SC+JqDnba/LfSt2gXg0ZLs5H9Jom4typLVOx8vcS6YEObH5SUNm2QYA6pHC8Xs8WnlMVdjZ3aOTxi8tCeWoR0L5amyQQUdnEqhccXW4ViOFH4UwMiLvTOahmXHdwA8l3rdjNT5uaBbjax/Pdk7qcZS6CUQJmZx1pkAKsDa3cUB4+J2dSS8KW64O9QRXQZwTA0YAHkGwPxONZS7Od6R3aGGdECnTGggaFfDdHO8XWOlGIY11dlCB7sfkVLBhWopf+mILovN6oieOscBMKFnP1gXjDu3dW9oAzO6G8tqsE4SFMDDYGIQwLIHAMBR4Rj4rINPcl4pqGG09Sa8UhBVGR3wSkEAQKYYhsSpems0hxqA4U/Y4BLisHBqVOBUYZKz/2m3mHgWLCh7GsACMNO9b76Lca4Ba6npBBMB7GAeAEkwUSHtx5C+V6cFhPTydHZF2rQykXp8UqAawxDeR0mRySeGG8GEtywAP8c7/x/XMdoyxgaWHZPeRsNp3XVS3L/1wYYHzrSzrQ82RBbfMl1ObTsCYOUYQc/ExMTExMTEBIApNJj8C7Byy4o5kPVHc7+hu4f+4rQaCve6MF5bqBzlEkQ0/kBEYxXv06D10W9hzMzrqS0k8WJ7JKutPlAyWaSKdVHOsTU5UuxlsBT3n76vA1xfeTKtHzc0njSUm1YS/do3lvUKmxs8eHx77sK4zNn2tjq437xQYG3osItxmfKRhIALpwxiydQgKczMROuAxXn9BYN4aAuH3S15NIPX6By7QPdGDTlmGKAQUMFnKy4E3vjS1sYnWmbNKotrYqOVk5tfG5w8COBesEA5N2aowZ3J/l+2JAN/WGorTr6e7BoBjCwKfYoOchggVEDY2qxqOkBBoRsC0Q4mDdEJYEoSxmPH1ODRYtugPc8SXHkw6OuL69ICGTp/TA0QgHIiKEp4l9GhhmiqnSXalQD9p2FwHWo4bWS3AUArgP9Z96sqqbjQvpLnyCPY89AqzLn59ABnHZgvwUUAbkovTJXB3L1usc8rUN3mswXmpva9BGxmFwLVDZ8tcLLG3S87kVB5UmYfVKe6upI75AprezybAsQJGEhqYtXi3OZ/A3AHmMhAwYwX3y0jYFkaXgDjwGac01kjttPWTYsyp7ffTB+3DSwATK+X3kYFcOlHVTZh8uGx9cGGPYtvmb4NLJPhSry5a8RYDLDrKwgmTpWmlisYFdnEM2yX/w6HsRYso2fd1gcbPjozXBMTExMTE5NPDabQYHJOs3LLCoeu4nEi0HLMoXB64gCwnMsEF3nK4odkeNQeWq+HyUa8ubZ+rLlfXSBpPR7VxElRTaL/7J2WfYVv3wq3KMsAbli32LfvfZjcrQEzVRMB/BjrKx3//tms2w51WqMWwRCXzRjB4S6HvqvFWa7KAgm0FUuaGlIBXW/stGu/35xPJxTE9AumBoWOgWztkdpctbFTogAVyjhr8jx+gpqTsUfvjNvtUcVJJuolnCcyLwEA45yDnwczRGz+6b59dy/0raoBcD5Y4LqMgxIdhp75ZOwI8qRgcw4vxLItoZocMdTUGCyWSu3903oTnrg/6ax38smRsGqdJiD5kgJLBMBPtnVvaFu32OcQtMztNZgl3TfSfChmKDcAiAE6X8J7nJdYS8jmeLt+TA2ogBHiiGw7phoA6BGwGdsgUu30xpe5HgBryecB8Dj2PDQBc24+ed5TQc8tY09uyl/jLjD/g0EAG8F8Pe4FExtI6tyfYqTn5KO6xxJHnhQweuOZexLIWDh3gg/H2jv0kEyIR4hUgBnzYcy2BesW+8pWb+0+W8f9WrA2ggcArAAra/CBlU7EMOrVMFZcSHcOSL/m6b9VsODSidEgkgfz+vj8WR6XySebH4FdJ3PwZjEqTfqacYBlP8hgAgMBEyciAA6BXSNzUuuGwbxHGt7uxVOfs7vf53swMTExMTExOYc5U12nick5gxSUa0jUyDE0QEhymKBlwV7CCdQOYl2kBCyz1Js4r36NZaFSdoayiZPmfoLmWV5ACxeJ4AmxGCCVFHtjpRTsy/pMMFHivdKk6Rh6sd5jX+hbde0/dmXecdls/xqOM+K/fqFQ/d3GAu1gpy0KwF3Cu9zLrMW0hHeJAGhc5gLbmtzRv27LpX94KU/d3eoiO5vdogBBuHKiYXQb6uCLSrvozIe43O0jOZZEZ4A7tjYoNuxMGSQ6APwGwLpUackKjAasMwyQNQARNHDoS2QoWWK0uT/hcYkcLpuR0WVtjuTbArLLLVJtUYVzkFuUe4xcU7y34Mbyrf/5uaJdc79RufAnIUVaXhT5IjKI53trcvheDtrPAW4Y4A52qCF9c7xdb1dDhsAm4m1xwyqCOeVnW6iMq327YeOSP02Nx41gAXja1O5tz/u6xT6vIODFggJ+Nc9jH5jp4v+XevonADaJVmlzwcQKnReFk9spOjUOjJTStmg23TE0Xggp0tzK8nxy0yXzMLG0mBigZFh2OsBEgj4w7w4DLA39rrMZ9LrpD8wB8BqYR8R8AN9NnftDYDXz48asfvq92sBoOjwZ8wOwmnwXWGA5drbbFJbPEVKtI68AKxM6jNHSmbH9qtPXTNrLQwDLahgGuxbsYOU0s1PrtWG0re+LH+47MDExMTExMTnXMb94mpyzXPKtq70eDdcpYcKR5TAKhzzk/I3j8fpFzTiWCBA+Ty+XD3GGMEFThWJ9eOWWFcsA1D190XMRAHikdXF6V/ar3NYrZnPFVska7NxS1lMorExy9lrZj16sBEtzP9tOA2fixb/VZU/6Y633ZpuoXRdJEIs/wjnDcV7Y1uRuBHC/jfBzpgnuqwWDZDbJAZxQI+lZ7QwAXIJQul+yQSGcDJDksgqdfnnJCTtXm5/djy5u2UX9wmuvJ7Riof05Qwhd+Eogv/noYNX3luQe9mVL8SYAR21cMhjTLDLYjDgBMEKg38fDuFEF79RAr4wlvfde7MAOheqxA6Hs8QJR9LguEpcoCxXuoQunlOvHe7qV6xUFM4KK9ZKeWIYlTwoq7a6H2pzylKaIeOSKpXnWn23qm54PYK4CbUuz6p8qQMgq5XPEDjXEyzDCADkM4HInH1/qEeOTvliy8wepjJHN2PNQJdiM7stncd5X5+TyE2bPlsje+oS7t1ddAebz4AZrZXl52cypmycvnBfd/dxLXE9Tiw0A+hNuYzBu10Si0KRhoXFNsAb6D+sPb6Joau9BniVgzM5oJ2BB2nSwWeEIgOMAfnmW4/771PYTAazCaDeR46nHR8AM9zw4tQwCqb85nMrppRKWMevoYOUZJucIKZPGJxbfMr0VrHVpDt7c3hJjlqW75KQzIEQAnwX7HqAD+K+tDzZ8aktr1tbXeME69fgA3LWmqvZss4pMTExMTExMPgRMocHknIWIxprYJGml1kM4i0cj3dYRo7agVW7dkhCibVYqVGgUgqEldws8l6cv5rLlbxoy+f5C36paJxFumyRkfKdNDQ0lDe3RF4PJuTFrJH6epVguj+SueqhtV3lrrnfd7x/b9r7rkxfeNd0OYCoA29LpgebzJ0WmPl+fRepbHQATEv67hHcKn7EVZwigVDZ0dGhho1kdIVZREyqLY8Yxh0Wnlys0+rSFlxv5ppeOk0manq/XthFxWjkhIDCOJxW9N5gdU3TvCy0R7zcNkAl1QxPCszM7Z3ql0P2XFRz42t+65q0DK+OQAPg1iEcALELKPPNqj32mS/Od93zAyh9PJFIBDCGDstNQPPnG7NnD02UFZNcxR2mWGKZzMtv1QkfQmpdHp/T0HSxrD2UcT2r8ty1EzhOo/uTSvEalbmj8EwukrK9NRUlVndxW/1o8/HMAuwHss/Py4wDGt4Rzmm+ecsEdMVV8vCOWMx7Ardu6N0QW+lY5PMLzXy53DCx18okf/XTfvtODi3XDQ1rpgQOJ5YODahyjM7i/APAdq9NxL+X5ufUv1tr6WzvTgTq8UpCWu4aUoyNeXicqimx+rdjup0cGQvoMV5xYqE5svAqwoD7dJlABEwV+tW6xb807lU8MqvHb7YR/hBA8aKVCbWpxNZjh5iQATwHYDuBSAJ0Y7TaQft1hAJk4taRCG/P49CyI0Nsdj8mnlivBRIa3ylBMl1AQMAHKgVHxgQMTLMNgJUpvyeJbpnsB8gsCrLDx9pv/+du6T0wry7X1NWVgWRgTwN7rF9bW12wE8NU1VbWmh4SJiYmJicnHgCk0mJyTLPStchCXaBcadUPM1kmJy4Oe/BG1WfW/FD9guRQggEIM++UyF/27EJO30R59QJopN3PnA7gun7evvFgq4o8oAetBZXhvvxareSM5+JsqS35XfsRZG/mzBCsnL1/1z4ureuIZigb+/m3dG97TF9oMu/LNmkr/yuWzR+hv/5k/6f4XC6SDnTYalzkKFlxqbWqo4cVYp8QZhlOFYXSoIQMAphXHtJuW9nHrtuWG65+225RmjgLoDcpG7O9HjXkAofWtTvxuE7q6erGjK5QBHfyNgJ5PoOvQtcHdQ6W2CRlD9Es1gaovlR6uv/F34+8cCgvXAfgLgHusnPzDVSVvjACoE+KLdm4On7jqYEKZIRC1XTWo3wCd5aQxNaqILkXn0BHNorv9+SizD2Gqu4cW5lHMrpIwVMdbGjp9lTMzOvULvUd1rxS6TaAav6LwQJfHGJ9TmOEgVcPdykAgXEWAqqnu3vqBhBM7h8sXWKh8fV/cfYVAtfFgvgU/ADPurE5o/K9bwt7MSk+PA8DVC32rvABWl9gGH1+ah/HJpPHdpf/XfgvASikA3APgPADBL/z3d94Y6R/69s5//JOqsnxyNligOirdPXyGEDMIMUiBdcQQqE6qMjsHu2MZWfX+EsJTHV4piP6EG14pqAtU18ACvmKw8okb327c7w3tzQALAlu2dW+I1E1/4CdgxpKDAJ4BMA9AFVggWIxTsxkoWN29ARY8chhta8lhzL3dYFUWuuqS381lafLpQcDodRAD8/dwYlSYSl83NpxqHIoxj7sA7HyH11kNGF80ABpXY38A8IkQGtbW1zjAzCknjVlMwToB3Y7RNpwmJiYmJiYmHyGm0GByzrHQt8pL7MY94iTtYiHJ0epwAaZuzkVt+XGE64acAAigx5QWLhB9SixwdcTUyZbefa/XT/aCtf+ztyshNHJ+ZZqYKfVokfX9Wswb1OWu5Y23s5Zvvu3LFJH8YqDCmac3U4okBLzHL7SXVQU811/YTyUB9PtX9ji/+rsJ8bjM2QEg1ZWB61BDMw4pwypOnZ3EwU4b//DLefrBTptTlrl0ELGQwNANUAoAcZliX6tEK0vkzw4mSDgmgweIYIBgUHWXilAGDU+G01cS+xohhr5sRuDlpdNHfvaDx0rQ4xcEn3X4PyKq6Do44ntS0fuiQ6rjZhnOr39WGv/MBD7jV4+FDwcs0uA/4uHk/MEhlFfP0qXjg0Hs7c6nbqqqWT0Wug99JBmI0ZkZnTAMQr3WESpQnVd0qvcn3OV5Fd1qUVVAffUVd1VXd9Z8SgzqswXiAPb3xt3zfFIgXuEYfGYw6fiJjUtOuzS/QWJdQRbXzc1s67Zwama+NTgutWw1gDvjmni6KIHVW7v71y323QpWi479m7ceiIfCLw+0da8Em/HPTA2LLlAdpY5hApap8ARYSnbYK4WqZ2d2WDLFMH8oWGhpj2ZjTma77rMF1gFoAbAQzP/h3XIbmEiQB1Z7PwRgG9iMdTpYTM8+O8E8GAiAjtSxSWCz0u7Ufk7OcMcLR+Jdt+5OXIDvvIfDMvmEUw/AD3ZvaAAzl70brNXq2NanPM5cWqGDdZmYD+DZt3mddQCpJMAKK2/72gd3+O+barAyqCRSHWRSEABT19bX7NcNjO9P4P6GpP0n6dI4ExMTExMTkw8XU2gwOadYf32N9zPzxQ27fNIislhD6ZEsMumZXByuGMDR7TEkopYeAiNggBpGgljlRq4zydH9u6IVALAYqfplGToOJYZoBe+iToPPt4BrTUJ7daFvlSNlGllnX6E8ylVqt+tPcJLcSCe+22NNmS8uz5Byl9pkG61Z0IWEZtDK4ph9d4szBMBWwrv4S6zF2BTvJM3qiGAVNVQWx9DYaTPiMqfGZY7b1eLsAPsspwMLaoAcBvQZALWIIPqyMiF/+YVd9KEtXsuuFpcxGn9QyLDkHuuleHZPlqV9wEJAjJUuq/bZ6omBrdv3CbbhpKOsOeyVWyO5N/FUc8z0dM2b6NqXn+f/ZrWL2Mpv8IxX/Mgdx43fmzOhPChRKtPrLo4ktj+SLyE5ns8ITUZP+Liu218HALLbXwZCDJTY/cZgPDsSDM10dbf1cZ1RP9l4NIfkSgG93DFI8qSgDcCciCrRvmSG46oZfVlfuCQ8uO6PRxJg3UAS27o3bF632LcKwHoAFldOVs0fb5vT+t0fN9xrleU/gxktMh+H9ZUOANWrv+qp+9WDSYwotntjz2/7i41X7gAARadXpbITIFAdAGhKCEG2JUwkTi0HkCNQLVBkC7g6o5mh9miWpdQ+BK8UpBHF0vx41/xOAH89g7HomagF8C2M+kx8DcysUgATCTLARIckmIiggQWU6Vr8dMnGWNPIJgBTwEo4AEA3YPSGqnrvlvMjtTA5F3kRrJXr1amfz4NlxkwAuxa2gQlu2XhzeUXaQNLAO5DqNPGlD+yo34JUhkI1gLo1VbVn8zmqA/AdABcC+DZG36MKJqDMIAByRXwPSdRiTAtjExMTExMTkw8Ps+uEyTmFAdx+TfXQhbPmhDnoEI53R8nL7nZ93+JOSOepZMokz7Wihc8CiIdYDI9YqRdFeMelSV0sA/uy+owIGp1gc+PifB83TvCQS92lKLU5isCCwmoA2Na9IWK5TLs7ulHapTTzAAsG3y01AB7IUT3jxUPV+Mkf5uF3/yxCY6cNYDPSWocawqZ4JzrUEAFAKotj5OaL+jCtOGZgNCAtBQs+gZOtGukcgIqArpfwblI2NFV/8dUyvfmExM0bF6YZdtmYOy5iWEVdF8GRC3w8OW98hAsneCorHE2qxHoiYFnml+3eDDFqWKicmOzqfX1eZlu83DF4EYDZYcuOLENIKL4yP19myV0cPDhBfWa3R2/rF7RXG518vj2sR1QJmm7RPcpkmoxPTBoGSRowMJRw6LLOtZQJLv4SawUmJpbQgu7ZZLp9WJ+fdZyU2P26QHUIVKdT3T24fGonqZkdv+ixTeLT/QmXH8DfATSvW+xbBjajvwTAXRPPq5o5rtT+vWfXn//G0rwjg6ed73S70upXByZLu4bLMgaSrusBfBmArz/h5uv9JehPuJE6r1p/wo3d/jL1SKigH8CvMCbQz7eOuOZkdmCquwcC1dEZy1qS3v/ZDP627g2Rbd0bNqdFieqGW58AcCuYaZ8CIAE2y2zBaCZLJlgmw1ho6nkdQAmYn0MahYJm5z1b6TzLoO1Tz+JbpjsW3zL98tSP4y1XXF85Fesr67G+Moj1lQrWVz6M9ZWXpwSpTw1bH2yIbH2w4VkwA9GfAFiX6krxJQCvgGVarQdGFYUxqoICIABWevBJEaKuAhPcrhq7cG19jWNtfc2ylBBxktR1fRDADLD3k0YAy/Rh3issl+P9mPaamJiYmJiYvAvMjAaTc4q/1WX7Dvba0GS30IRGoRzmjSDCxHoYxuQyO7eoo4RsntOKnuohqvQQWCboiG207Em+IaSN+zrKBBf9jLMYocII4u0KDkzrRk9/gMNhugljvqg+fdFzkYXXr1oNVo//XlLl3QAs7SfFhKgutzjjYG3nBAC8DB3N6ohhAEQH1IOdNuOhLXl8Y6ctnQKtAOBFULGEd9EONaTL0DkRlJTwLnSoIdKhhvF8ELR9WCAzxgVx09J+svWwC4snh8jDW/LoSHuRXjqYT194FWjsZCHIAxsLhva3SwEPHxnpjmWO64hmJXOt4Wilp2dAoJqfGMJcIoQ5Ja9+uGheb962reMxcKIxR+0xoounaNwXFo3YBkMWHGoZ1ltoXWcIQpHAdb/cHMx7pdAa+F5L1Jtt42TNLYQ0F9+tWdXJnEefiPMtfsPPN0dlnesbSLgqvFKQClSnenBEf2mHx3juUN7SImmQi6lioVcKijZeuQzAD1Zv7d688fMlEh3Yvqbn9ZaWHFdw9uc/7/yNqhrF0aj+59jvptS+Uht1ZWdzTYmE0Xxh7pFpQ0nHSFQV22Wd+5JItTyvFCRzczpRnA8oIxRE13mvFDTmZLbpXil0GVj7vzwwAWC3QPXZPlvAmR7McsdAfF+g6BeEEM9951V82SkkywCsXb21+914d/wVQH/q2rgarM483ZYwnf6ejhXT1wBLTWF/F4G1xkyLyH0A/ghg3bs4hk871WA+HAATB0dnsFNZLWCf4wcw2toRYJ4a1wNoxPrKXwL4O25o/NSIM6mMg7vHLLoaea0AgQAAIABJREFUwAUAloOZit4Kdm8BABiAToCHALwEoDbVxeJjJSUifA0s++LKtfU1/WDX7ktg3hOXALgTY8Y01W3iCZw6lkEwAcUGAIQAHEHYLJswMTExMTH56DCFBpNzilCcv2vXEddnwGZ+08GYpvZS2lMzQrfbmuHPH9FFt06FyYAehq7ruk2cpd6tHOHsRoLobUpIOhz1h6d2ZDi3K73xNwZ6+ehxkg7iTmFb94Y2vIPp3+mkSiaq3TZ13rTiqN0A6N5WBZwIOrc4ZmvstEGTBSMlHECGbgAgFOCjunR4dwvnBuAFCz4FEXT4AqnQMUPMtrwQ66BHVD9KeBcusRejNqdNP9IVoseSQQrAaOy0y49syTOaT1jUY702y8FOG9XUEH0hBHT4eV1OTf7VHXVlAcjWSc7WJZLv99sSveiN85f/vWt2OyXalvmWbHu1MKWwvqXzcG9czGzobwsV2/vvz0yq4ojfOdPh5C/VQPUBxcY9E0xEBJrUEClxKqr9lnF2W8cggt64Lk44FsgkvGcPpnIUFrUEERIgAEL9CVfhXn8JZmd2JItsgQ5VRb4yENamOLu53pj7QH2gtNDJJ5uu9O3dD8Dz5GXF115xpeM6jiN5qjro5Sg5n9ooBwBOF3ejLBuXCyJxHT4siwB8HlG56XCo4I+9cvZXZo9PconhYGt/zDW1xAc6f65Ad+9xafLwCBGo3lVkCzwf14To64Pl/fMyWyULpx0EsAvMjLEyNayUI/oN4539y9oj2fYRxc45haQTQNW6xb7rUq0535HqhlsjAJ6tm/7AMgBzwEQGYDSjARg1/0tDMCo6pNfTwTwnvlzdcOvrZ3dlnjPUYbRV6OgM9vrKMgAPg41ZAGz8TocHMBPAIwC+j/WVX8ANjYc+1KP98GgCMxZtAstW+LoB/BhAsQHoKnx/saD7Pz4JAsMYqsFMHdPmlo+ACWdfBhPg7sObsxJWg5WI+MHu+z1gLWa/A2BH6rkKAE9++IdvYmJiYmJiksYUGkzOKbZ1b2hbNO6LA8I4LVNp5oiRJCoAIiZ5WtjqQM/EEagWjRKDRe6cG9R5nTwNEaImdgh8/GUxICd0+lqk1zksJ1/vUMO/ixwiu8GyFmaCfRF+vzW+1RLhfvrFKYq4bEkPTcgEv3rBBwC4+aI+8tCWPAQ7isgyqRib4p1oVkcoABhA0KrptILPLCAwSJ8aVedKeWS84O7PopLVIEiIhA4ByOlQQ8L2/Bb4rukyup93IrRfBAAjLnORnc1OF4C+XS1ioQiqlvAuoUMNkZTIQAHoIqhSKrioBdzMeZbcQi9nvefRSJMlolkXAZjXT+x2hSfUMMT5RZyLi4uuQwoxpjYNZ846+IJ1H+9xaCP9saCdszYkVe5pm0WujqiWmgvcqnMeX1DhtZ0ApF7kSmFkSEH5lWCDJRhR4UaMVlqRnycFaVVmh+qVQgaYwaJLoNq2Ipv/qYGE8zVZFzYOy0Lzcz0z7lriPbKoKEO3AsggBITjCCgl7JwZ0AkB8fu1ke5e3dsTy0CmGF7YFs39UZl98OUlUwL2i+bJxZt2uPQdbRXUoK3g9siGPBIOgrXYfA7Afw0kXHJPLIP0WjPsHNGn5lmDl4pUi4OVMQgAjP6Em+uNZxRVOAc0TScJRadUoPoysBKZtzPZO8lC3ypHDpVqvuKY7FYNbXs+71hKCXHiVMEhPU4Ys4yetpyAzV5nnNUVeQ6RCpxHzzfLYqgB8D0wo04dQG7q2bHnbmyWiAAmSGzH+soVYKU5TQBe/BRlObwIYARAXeqc/HnxLdP/rhNPTdR2K3Tqrd1574WfiPeytr7mWgB/wuh1nm4VK4N10rCAZRQ5ANjX1tfYwUwve8HGciOYSeoMMCPVz6WWL8XomMY/ivdiYmJiYmJiwjCFBpNziku+c9Ui17f08Vy2gchfLZD3CZwISuYeL8S0/gy8MvU4jucO6ZAMaplm6NQJEBFQwxCkC2VqWSRnjvxeMuR2AXvlwb3bujc8AQALfav+E2zm7MDpr7lyywpv6rl1T1/03NmkydctlYqemy76vy5xLfI/D7n5lC+D/vCWPKOx06ZoSgibjE6xQw0BODllbSvlneM+ayvRDYB2KGHMsuRwBJiUMDSyI9GrdtIhfUnlyNE3jjkLvM6hoevlgQkxjdO3QaRgZoLpwLMAAC0X3NFl1mLycqxTUGEYvWoEBbwdEjh+ma2EbI512p+Ptdc3KYELwUwHTwDYaADXAOCkgh5rYXUb9+qrzkWa363t8peNEOAlWyAJryUGgerPRzXrbW7B/8J0T/cFXiki+fV+aue7YRCV2PiACoArd3bhGFSUO08AABWoDp8tQAFYwYJEBcAssBlOTHb1zD4ayvvMQNL1pdq+yf9wSIfbN75OvzJnitoxPChHMrPEqZ5MXqLQSTCoJndsU63BwHSyM2A1vLaBPr/uWTZ7XNulJOR37qkXcLA9R53ldIlLp7vQ2dWXtGR7+hL+UDNRlMUAnMXu4IsTK46Xt3YaU7b3lWsL847bqsrCZYMD6mAsydn6Ei4+SwwLMzM6yEDcFdwTKHV2xLL0OZntETsvz1y32Ff7dlkN6SwXAJKPd97rpEJWuxw3Avrww5WWbBuAr4JlsIg4tXOAnjo3ljft9F+FseUQp4sA7LnbAHwDzCw1LSakO7RoOFWcOR03WKtRK9iM+VexvvLruKGx7QzrfqJIiQubz7DsrESvj5g/4NRr2AJgWepxEqxcaRpYeUR6jP8j9ZwVTESwg43rcgATwcbVitGMnzdlpJmYmJiYmJh8eJhCg8k5w0LfKofr28YGcZLOGRoAlwZxFoxx8QxSeSILDYFhHNuWgDFFBF+sAWGNqn0GxEkG+AoDhAMIBXXfmNBHfiAoALwLfau8YIZ8MwGsALAXb85oWA1WNwycWiN9RrZ1b4ismHBrU74+KJZSjXYNWYy4zBEA2NXiTLWg0/lmdQQAa3E5kc+Al7Pye5MD2guxDpUAWp8SjWRQS6SEd7p2JwaE1xK9zgVTRtxrLj5ROK04EvNlJjM4xdAtMPoy7HLBZVUB/oX6DCMQFSnYzKHeqgTdG9GhcqDGMmsR1ygPG5ViFj2mDGkEMFToRoM8fD6A58FmR70AJh9TRiK1ia6d7a36Et3wuRvaNVJmDzSX2IY3DSYdn/UnHRdHFIn32QLNI4r9B3Y+eaDEPgwA34xxbWmxIx3oiTZexZycLuTm8hgYAFQVwGgAKCHliwDgQFLnV/fF3XdKVK6P6VYyKLt7/np8lojj8FTsGcjKkSKJRdX5R2dOrJo5cnCTkeGICJT3+Uq1BYaXdlNC+rMuntiL5Reo4eajlkefqXNeocXLc2+5LID8iWHk5uYEdXty8hu7nRObO2lIN0jXRVWJ8KQJWqWbTx6LJTv9M0siC2bN9pCtTdNznngtoScUNVKd3TISU8ThYxHvRF6XR1qUnCxZowMX5zetAPDGGa4bpK6vNWACwiUA/mcc7/6HSOhtkywZogajFCzdPRfM9HMWRmd90+dIBEszH3s/18BMAD8pBn8fDKOigh/Ab8HOy2YAVTitdj+17h1gIo0Dp2aBpFPz06JD+rn047QPBgUTGw6nnr8YwI/AvBxMPgBSvgzPA/giztwVw49REaIWwPlgokESzIOBw6ip5UtgmSfp7hNpQQl4b4a9JiYmJiYmJu8RU2gwOSdY6FvlyLArd0x9QxY7M/hwQTDboZWAdI7302F9EFv+TNDSEgUqFd1xbZKCGEhsE6EGAL5CBrXgZGghRDnMXuKQ9f3iomMjoXviFqVamKi5lTb6ByNEz+Ravu603++IeulcS5H7/xw2i86Py4/HxP3UljZzVCxaXBiv2ZRmDkaSkBLeRVfYS2EnAlFh8K8me3iwwHvHhuixo5ViVune5MDlMvQETw2XXdLJ8ll+q1WEs3NIUPoC4gvXLBi+7gsLhxwCZ2DdK3lAKgVfho4uMoxZJXHyWq+itSXjSo8WkU6o4VifnmhrUYKVAASrxF1VOclFGptCJJ7Q5iWhJY9px8IlXJ41/8QEI0tviwfV4eczheh1R0PefAqNVDj7k1PdJ4Tzc1rqVm/tjqxb7PslgP1gweF3wNrvAcARAJ15+fycmTOl7F17kth73KF7paAqEo6zqcWRGN/FGUSeC2CGharr8qxBHI/kDgNGxThntHxWoXTJK2265Wg4j7TFNKFna2LKwf5jtLvdhpwMiexuzUSe0UpUoUcLJJzi84dzOIvQ0eGl0Yc5gZw3c2pHnuQNoPM4r+86MNIxKGQF9h2XivtCWbZS0VXx3O5mfms7x8mBQElXwlcxlyuL1nfmBqdOWlJc0bab7j7WNewW4rc3BguvU3RhKoGI8byHdsY5R3es7799tpG3crtfDRYMD4OZE9YawExZ1wUr5alA6EIAtwO4Eux+faZZd4I338u3VDfc+quzvR4/Ut4uA+GdqQETGPIwKrjcCCZCrcH6yhKwz8bE1PNfBcviUXFqNgPGbH96MwYdLGV/O4BFYAFrOVhqfhjAy+/ymE3enmoA0wE0go3djNRyEezc52N0fM7DaNnLWLIBDIGNzzIw8QE4tdVl+9r6Gu+aqtp3Y85qYmJiYmJi8h4xhQaTc4XqqUWx61dXDXgOHRy3z/lG5VyN18nGZBMOK8MYGAgCFgI7R0neX7xGJxfW402sjj/u0xXrBaoIDdBGoI/XPbSmt8KJbDiUSceuadZjQ/bliltu5LHpV0+9KTBKlUu8YybDWK4t2zVxumOEAtD1LJBSt0O/RCqim8KdpL1imLOvTJLoUxYiN/JDHWoo47loO+/lrNiT7AdYICQC+EzEUD77RrLvpdRuRVmlRjRBDY9NG6EU2SU5Cvfbm46ft2mfq0k3jNmxOFXG8x5LpxruUYvlXbZL5c/Makj4vzJzKP8PLxlGpMUZbFaDMQCPNsh+gAVslspJLnrzqjI8tKENu/cHeAC8X3Uti6hJUTO6G3o1+ZeikVmQkWWzOpKGXl067M9L9FgETrt6YqVD+d7chciA6+i+QClqvIcXSZxaCBYEWBSdTmwO5+03uIG9up5YerDTgXp/CZnm6W6aIeZk58RrnAPWl5WIePTvAKSqzA4AOFzp7pGOhAoeunic9VtLzgtYig/ZtYe3W6mEOI763dLRHQRAPnDCoBx0I9d1LKmpksWvZHIA8Pz+3EkTXcZT44u1/CXTAmpvuzV6qGmw+XjINmNLX75MIVjOlwr1aWIm9kSFliPRFvVEoqKiakIpnTBrnvC3rQ3DLx+ujx/qGOgH6Bc8YrxpdkaHe2+gNDERE9VpoveGzfHO/E1908vTLSzPwLrUWDYBeHFb94bIj8ffsfaoMiJOsWQIFvD1YEF0BYDFOLPQMJYEWEvDW9/N9fiRwMwY7wbLinGDZVy8F7+TsSIDwK6jBWCB6HKw2W0JzAwznU6fNtA0xmyTRkv95jCaGSICaAfwKIDvApgM4DUAf0E6S2R95ZzU8wBwPW5o3PMe3osJM3ekAKaC+TH8HMBWMM+GHIx2VCFgIoMOZgwpgV1HACudyAbrWHH6Z0QHG8tvAYiCZQiZmJiYmJiYfMiYQoPJuUJd24D0nX2tjknkwIQZUlKYJ4uqRp06lSo0oh7TAcHQJ853oPof5XTjUBfXrI7oAIz4C5Z1RpDm6CE6yeD1vK5rRqTXLjsqgYAOj/PbnFTzya1Q1HbS/kEd7I7yvoaQ4u6fGePbXp1sm+LTO4Xdljjt3ZkkyjHOFn3KAqWZ0wFkyNDlg+owd1CFCpYenAH22U3nYUwDIAB6oL7VatvXZt9/2eyRyYTAIASgFJWXzg4FCQGZX6LquYeL9V3J/t1N17TNFCs1+xFNtK1/JddwWBX9/EnB3P3tjh3RBPcGgF+AzRBqjU0h6dG/dQw0NoVywb7IqyJ46yQxoyGha18K6MnCBXnq72+oGbJ3DIXVeeVB+2v7862J4UwU+E7canVb7L19nlhcE0hf3C1poPuKbP4ckWrZfQmPcSzsvdYlxEG0AHWSCErsw2qGGN1hVWcPUUNcTUB1sCDhTgB/B3CDS0hgflbr93d1TXmEuFy3T5nYz0/q8gLDQ7rIaeH+uMvmEuMkINuoAUqcQkz0iFHSHssEQDCi2Gm9v6SoOa4bme4ELEdnu1Tsyc4QW/wuIRbIMYomTRez+D41Jh9MRCvDyMwGCG1s7zMe+ucu2tjeXxaXVQPMt6OgO+YpV3T+zvOzm78fHp6+s0UJnuhVo8BbZLrUTX/AcU/m+TMA/DLVbQIA8MPmu/vBzEfHrvs7sFlfN95cApCepT8O4JLqhls/qf4Bd4F5exAwgeGtsjxGeXP2Qy2AH4JlxGRgND2eAzsX6ayPdCvQ070rBjA6I26AiREa2Hk1UtulSyaWgIkLBWCfg2YAtWOyMH4P1tEg/Xju2ZwEk1NZU1UbWVtfMy71pwQm7HwLTCTqB8toGEvae0EA0AWWYZIuZRnbfSX9dzdY6UwYTNAzMTExMTEx+QgwhQaTc4LUjPETAPCHST8u00k0Iliwq20wOi1yUPyM0sK5xSqlu6c0UPTqhOPOzpF4GCoLLIwIHYpvtHwXALg8vUaBfH7zuME1RIKLUBAKUGkyLJYJyW+v3LLiubM0fHxbhuzKi6/BNpJ4iUy9c9KJeVnlGv/7A0nsPJ4LI0kgN/LAaEBpA5tpVVN/xzA6k5eatgcFkBmXefJYrat7xZyRB8FqycsAEI5DhqYhWTl+KNbTXbf/M3nDy4OvuTf2hCzF0WdEVfFEjds+0y8JvEEe2pLn2rgvM5F6nUcBCPGE9uXtu4eHwVoCygC4UsHluMxamksIqfjfkT1o7rP+ZGez8/ZLZgbcPEesi6cnadOrRe4nNjkU/8AJWTWoMM3VFddBjH3DRVO7YxmW6Z6utpAstc7M6LjIKwUFAPDLTnREs7hMMXpNQNr1DYcy/usJrh8AdoKVXkh2O/l5SYmwaHBI6w0k2/CP+vKt9T0Z5x/stFom2EQs8R619yfcSqYYUWv7J1n7kx56Ip5hLM5t1ntiGWiP5dICawBuIQ5njoNOnuDHC73Hjfa+ZLFG8gejmlQY14PageQQP0fK0seJzrZ9cjITMIy4rGL3sW4DIOmA9GIAUzb3Td1NYdgFqkkvd75ZLEiTNn78tmuGJ4NafixD/z7e2aDvNbD686tPW54OrAYBLKpuuPVjSQs3Htt6UhAgX1h8avbG40unQon9GoTfAkN9DqybwE8B3I71le/UyaEawP8A+AGAzan17sb6ShFMdFLASm+mgI2FglEDwDEFUSc/S5mp5xSwQJUH8EcAl4KV8oip9bTU4++CfdaaAdwAoAGp+wxYScufwT4nXz/7s2VyBm4D8DswgcCCUYGoDWws/gGW1VOZei4tFtnBxoUbsy8dQDD1nACgE8wIVAbws7X1NSfWVNX+q7V8NfmUUF9fn8vzfLoN7+meJSYmJiafJHQAjaqq3lRVVTVwphVMocHknONrTT9sQ2qG6xqcDOxuk/cKn4tO1vYnlw6eTzhhC14RHwfr2b6inHcd/ay19GpXXCjb/KOO9Qcm9g7YPpeM85nIE5IcCts96C0bmarw2mq8yzKJM/H0Rc9FAGxW2yv/k6MsuPnmrH7d5teTzxzMiarFcCvNHGckydi2hRawD7X1tN0RsGBqBDCkjpDL99OnfO03X9QzkOUySjnKglGOg8XGwXLZ0ta5PAe+alzwop88VfKDQyFxlTNfHXf0hERmlET0pZUjXXPKw73ZTmXP31+RhB1d3hZZF2wY/XIfAZDTroSSz8baEnGbv+KCKSO3dPToe/7yera9qccqTvFF9SVTw2hQhvRBLaHEdVFtDubYKhwDexdkH8/siGZOao3kUN3AhIDi8M3LbINAdQCIeaWQUpXZYc+VQoZCtcQFB/7j2XWLH3AAqJEkMs/r5a+1SOgdN048L5FMlkc1aUEoIVDD3xObbI9wE5wniEB1zmcLiACECvtgaES2e3zWABGoThdktyYzw3ExtR4ZjHuwaYdVf6VNphZiMygl3qumden9/Uo0nOiRuuwuY+aM+Pz2+gwSiPKpoJUEMSr2QCJcbxmfsaRVCWkJXUsAp3SSqDutdKIGwD2PhA8/Wcq70K9FsfydL5kZhmEsTI81IYSABVAZYAHx/3xcIkOKUUFgfWUd2HsEWJbFdrBztRQsaGwHcAtYC0L2j2l95XQwQS0G4H7c0Jh+LwfAMliasb7y8jH7vASjs9aVGM1kGFtSAYym3I/1ZdDHPG9JbV+E0c8RwIwDNTABgwMLcnkA92B95QIA94NlPCTBBL1MrK90fIpaX36iWFNV+/Da+prHwMoabgIznt0LJiYVAqhbU1UbAYC19TVTATwG1gVHwKlZDH4A14GJWYvAsr1qwUqJSsDuo0+Cle+YmHzi4Hn+4by8vMk5OTkBSqnxzluYmJiYfDzouk4GBwen9PX1PQzg8jOtYwoNJp8q3iZ4e0u2dW+ILPSt+q0RJ3uj/7B4eZ8+RWnipgFYC+CXAN64zFqypoB3XEEALFF9t2futz/aoraEx39++JbgoM83f+N4vu6C1u62qoGzNnw8GzQdTRzFBQBgEUBvWTZomT4/suceV0F19EkLkRv5dA/5tBN+I0Y7D6S/YKd+U0/q8YLi7OSGTKdRQMnJGZEkWMAU5SgMAG6PA47bP9tz57ETNvussqhOiWZYRWB8QWRpTBaEfLeyODiP0GJfX31vyC4YBkhSpXRvq9OryQJKeZelWRkpX1AQvPUby3o9T9TaLnmqwaVta3Lqh7qsRFaIbLW1h/7tYlV59CVPoDMk65Nd3RUi1XKKbX70xT1GriXck28N1Xul4HIwf4G1AtUu9NkCBwG8gNGuCdUA7snJ5exz5krWrk65MBjS+NwcOrEiO4jyonYkAyGB6DrGnhdFp8TKK9bzclr6im3+bACw8aowM6Pr5BgUWPy6FrTQmZYy7Avb6OemjRiXXRim3d2yfuToMDd5Tg43qVimbsGiP3zCRaPHDd1IkjYAkwGDA3RtAp81/kp7uXBQHv5jm/jKzj9eUn3ttYuWX/bC7pbKaEL+D5zqRSABsEUM5WijMvwtnE0JAVB3QB5aJ4FbM05wCzzhABbYpxXkj7u7RB2AH0Be1wzgNwAuAHuf6bKENBxYlk2640MILPNjIkZnpc/D+sqbwILEdLcXEcDNYP+zdIym06ezFdIlD8BbzwKmOxCQMa/FA7gotUzFqFBhAwtM06JEuq1oCVhHg0vAxIkIWEnLNQDOJjPF5C1IlVD8EMBGjBEWABw6bb1Da+trzgPwNJh4lf68awDKUvtZBpZpZAfrGiSN2UXmh/g2TEzeL5WmyGBiYvJpgFJq5OTkBPv6+irfah1TaDD5tHFqKvVZkhIlNi/0rXLIQzSYWly3rXtD5LbybzVbwM0lqa+rFokvmCpk/JvTToRLWkP00e1+9WVnO3/8/xIdm7///ssmxvLaYffQhVOCusCDEgJwHGi1O35+VWN065Zm21ywQEYD+8L8BtiMHY/R2vzT0QF0PPVGFlkyzd9ZmKEVgQVQQTBjtU2vHHI3Z9nlf59WGrflZ6rO/IwQFA0qIYDAg3dKUByScjySJOVTSuJFc8bFpxtGUDJgkHCcx69e4DDSXoRltmLycrw9tPt40v3bTfkOkZDkTLs7eTgac47LjxjVkyLiE697sn/zLE3uavfkxXVO3RMo/9ul+Y3dBbagME4ZiCV08f4qTwcA3APmKP8GUoHG6q3dEQC4d/6kRTLJ/MPcimgiGlF+OzCgListEy+UJEoB5PACTXo8Kt23jyo93Scnq6MA0JdwY99IiVhsG/5ThWNoCliAmDaXS0OceoF+gaWCllh7jVn2bBL29yKvkHMS3ouJxSHaN2iD1D6DLp4Yw1b0IXIY48GCUipCEUst6htWys0c5z5WlGntuMDivvAXi6om5gJ47InXD58UElJC2fTUMVSmxuUdqW64NfJ936q7c6i0a40wTXKBSwCoHevt8HGSKpfYjPXf+CGAVWAp70V4c4YBwN77+NTjiWBjcRDM0G8umOnlRoxe5yfAsh8ksCCRgl3TCbDPRXqfb0VaYEiPu37a+unHaTNIOmbZ2BaJxpj9TB6zvS91HGODWZP3QEpceMf7ekpMWAPgPjAhwQXgtjHiRB2Y8DMPwH+O2VQH8Mja+hrHmHVNTD5JUFNkMDEx+bSQul+95XcwU2gw+di45FtXe4ndWG1EybpN9z11tgE8mzk9u1ngN5ESHE6ZdXRS8S6OUp9CNFAQyHaFHio8YT28QKa99bnKgaO2VyKJaDVGHeY/MHr9lvvrjjmFhRPDS3geVQAox4HeMaVvet2zWb0xcE8Qp24TyvUvKa10pxGmX8XorCwA6FZR0yqLY0Jjp02Ly9xzAP45FBZ/uO+460nf3MBXATiRcmtPynAGIrwwzhuNchQ2XYeu6VA6BkUtHOOMypK4YRh4xSLiap4zXKEY/7pMjMMir92oaFTa3OAhjZ02aGoIm2Ptep8alIr4LIemxHH9xUNC5/Zx5NHDPI52auTFV53Y1QZwgmq5dsGgXn+E2xeO0rtWb+1+k1nhusW+W5HKVEkLDKnljqia9VdkZhdOnabj0AHtv9/YEe+qnOaIjx/H2U+cSGL/vrjF7eaTg4PaiwBmg10bLwP4RZ4Uck5197waVqRfggUwAgDwPPTcXJ4MDKiGqoLE+W7it25FafEAKVtggQwd2/f5ILZNow7HaxpnlWnU04HLiU4yJJU+LWrWuCxQAJBhsdRGg4Hx7tptQfHAtQBmJob27t+1U1/gDLW+DJRjoW/VstRxLQdzxucArAHzAIgu9K360rbuDW87G36ma/cTwVjDRqAXTBgrx6jfwZkY+09JAesyoICN3zBYWnwjmPnpQrDzNVYg4MEM/tJdIt6O9DZhJMwsAAAgAElEQVRkzN+niw3p5coZXmvscabfU1p0cIKl6mtgnhMWsFIPO1jr0nVjykBMPkDWVNW2gWW7nOm5CIBn19bX1IJlxaSrk/4BlhFWjffW8cTExMTExMTkLDGNZkw+NojdWC1M1O4kdmP12W6zrXtDZFv3hs1nWzbxdqzcssKxcsuKyzu/07FvW0X7juetbYefXdw8sm16Z8P27oH/698iHKx73d0VSfDrAVwLVhf8gXLDo7X9F9674//xNzXOA/BFsBIHXeSgrbm4748A7s78cexV183xgDRP9YLN4moYTemmlcUx4eaL+jCtOEbBjPFCAPCn17z7AHxN0xE0DMAwQEUBn1k0NfBvGQ7VA8CglAkb4/Nl64zyuJXnwIFgjq7DGooBj9Vntd2Vl//6S80Z9//q+cLw49tzjbjMQYaOY2qQyxPcdJm1lIZP5Ot/fiX3xOutnHFCjeglvFOba8kxSninfllVANddMEyXzgxPsfHy51Oz+qewemt3ZPXW7s1jRYYUNfnWEVGK9Ov790XR369aFAWlu3aU2Bq2zEck7Mas2VY9FFL7ZdkottvRVzXHsnzceP5/JYmsFaj29KyixJM/+/VV8y12247UedMLC4XmufOsEa+XlwEoBlH0mNCGoUAULa1RNByI6vFIH4y8XXp2RpjjlDgKy47Kk+c14ivLj+G8CWGaytQ3ACBiaFfURvrdsqEDwDSihi6Q+7caiHdjkrP3Dp6oj0g0uRzME8QJNoPvBLsHOwE88oFcUB8P6SyjarBMBQmjmQxvNTOnj/lNAXwBLJDvAwsAVQATYhrm7hyBHmMNKM/0/yrdKUJPbX/6/t/q79M7d6QRTns+rdSnyyqMM2yX7m4xH8CvwUpHvgnmL3DW9zaTD56U4PAFAFemfj8OZkT6noRqE5N/Ff785z97CCFV+/bte8dMrTvuuCPvozim98vtt99e8MMf/vBD9Wex2Wyz3u8+7rvvvqyvfOUrxcBHc8wmJh8mZkaDyceGESXrlKMcjCg56XuwcsuKMveg9edzXi0r3XlxiyPikYcA3AHgfADr3qrjw3vxbkitf098QsLW5Ovkgo9muYP+Gl4rKPprw7YL736P+3zv3ND4BNZXvgZgDaVounrB8ItXP/haZOWWFbWxl4V1iTrhc2Cu9zUY9WwgjZ029aEteVxjp00Hmw2eBCB+YkRMLrxr+sjNF/X87Mo5wz922MBTCsNj1cMWgbnvEwJwBJxhgPAkZRpJkQ0AHjvw1UV9129A9qUvnWf/90NPeh4Eq0+3WEXNmFYcM5o6wW+Od+rtajwqH/dlXWz1khP2SHLZwkZ90mTZminn0RfqMwAAG/c6I0MR69VgJm/vOJtYN/2BqeW49b86HU8QURjEwImTs8g85VuA7AimzYiCEJEaGi1q2JZTPGP2oFFWzhNZNiAnY9/t7FQDmYV5i3lRiF6y5ks/fOb+dTFbTsaMzhh+NrAL/y/eH5up6NTen3BzXimYLMiivK9I4EZaeCw9Pyl3d3VrkmSRnE6euD1JmRBYJMHAFXP8ON5F9Y6gJxWQkqzWRP6KudlRhYT8WjAhxg4H820Wqn4t2xIu74lnZGeL4eltsdwGsLahAkZrx3WwFPBPK+ksowMAbsRoxk0Sb24vmWZsoK+CdQSoT/3ciVTmQGMY+kOdRP9asYF5npPrp0UDP0ZLKYBTzRzPlK3wVoztTJH+XI1dTsD+V6bLKjicmlWUPiYKNp5fAfMXuRfAc1hfeQfMzIaPjTHZDcswWnaH1N91ZgmFyaeZwHF/5tCRwUItoYqcxMvZk3N6Mioy/e93v4899ljm7NmzI3/6058yZ82a1ft269533335d999d9/py3Vdh2EY4DjuTJuZmJj8C2BmNJh8qKzcssK7csuKupVbVmgrt6x4duWWFSdnszfd91S/banyW/sV8vyVW1Zcu3LLipsAvDRxf95VU/cUzJ6yu3ACWKD/MFjwsWbllhXLxu5jDGNnVU9SN/0BR930B5bVTX/gTNuk63i/O9I299WQfRHVsnNfB7AO+P/Zu/K4qOr1/Zwz+8Cwr7IoKIg44oZoiEvg0iKWmi3UTSzNa2q3n7aXrdpq3tt27yU1Mcss025qlppYpqSGJogLIAoIArLDzDDrOb8/3nOYARGX1MrO8/nwmeHMmXO+55zvzJz3eZ/3ea+seqIrDJj1v4mD0ladHTDrfxORnl+N9PyXkZ7/hehgvyFlk8GwWv0+38o8DZLbrwcFWVsBFLdaZfjlhA6tVplceP02kER4BoA3lu0IOfbQst5/O1ah+ZLn0ahWgmUY8AwDzsGhxWpDCw/YGAac8NcGbzVkDxlqPV+3nAGAPW4e9qYbxjRyg6NbmAdTqtiY8Bau0F7PW2E3nLI1K3a0nj47aMCpT5MHGcxqrc3kAGdvMMr5T3b5tdQatP8AmE0AvDpTNXSCt+XQDQpsTfZEB18FmcyCktJSHD1i5kpKrObCA/7NUf4xtpBgNRwOnuc4HjwPNQCPmtLyNUX7c9fk/5D9i1s3f9/4/kyi0aF5bc3+0MTTLR7qCpO3Yk9NL2txS8CB2hrH5u175aZV2aHM1mxF0/HjlrN5uZbdp8ts9Qw485la1JytsTviurfi4VEnmUh1Az9UreJGe7L81GENbPINnCo4WC5nfXwDi1uDdfnNoTeYOcXuEE3DW6dbfQHgSZCR4RgA/wG1rXwHwIeXPYGuFDL1I5CpL0CmfsQlvS8934D0/G0g4mQ4qGPASQAtOFdJ0BEsSKGzF+RdEQdqM2kBYNLr0JgayH+t16EFQC1ItSASAlp08NoAEQNixwgRrioEVzWQ+Jq4PQ7tlRbi+MRtiyRDx226QiusdyOoFWsq6LvrVaHERMJVRMaB5MCMA8lPZRxI7iwD6Fp2lwihxWrGgeSJGQeSpWsj4U+HhuJ6n7O5Vd0dZrsSABxmu/JsblX3huL632R42tTUxP7yyy/uK1euLPnqq6/atlVaWqqIj4/vHRMTExsVFdX3u+++c3/44YdDLBYLGxMTEztx4sSIgoICZY8ePfSTJk3qER0d3be4uFiZkZHhEx0dHRsVFdV39uzZIeL2NmzY4DFgwICY2NjYPjfffHNkU1MTCwAhISH9/v73v4dGR0fH9uvXr09+fr4KAM6cOSMfP358T71e30ev1/fZtm2bG0BZ/6lTp/ZISEjoHRoa2m/RokVihyw8+eSTQT169NAPHjy4d1FRkQoAjhw5ooqNjW3z2Tl8+HDb/yEhIf3mzJkTEhMTE6vX6/vs3r1bm5SUFBUWFqZ/8803/cXzc8MNN0THxsb2iY6Ojv3kk0+cNPh58P777/tGR0fH9u7dO/b222+P6Op4zodFixYF9OzZs290dHTshAkTIi/mWkqQ8HtDUjRIuNqYDjL5A4BbAcyfvCN1qdDeEaDa2fdBAYQbAI/8oRU2lmf4/KEVDIAToFZ4E0BO9ncBqJ+8I9UNZDbnDWC17zvMt42vaRc7qtiOkthbACwB1cJ/4fqCMIaNABD33Q8/IgCHAazMe2v0Nc08si1Ny5X5B/2t+kHL4Wwh2Q6imSUAJIWmPQhnTbybipFtC5fp4krtzbCCE032wkHZ3kMADlfUq/4+PzMyeXpy9ceTBzUftRncx2p86mG0YtCvp3Reib1beEZGBITwfo4HwLJgFTzkas7h/9HDhTX/q/Guuq1ffcDHP/vxnxYFcuaaIE4Jk9wKzt8KB1PC1PjF2BDx723d6jw0jl8PnnQf6Sk3ck12dy+QEeMIUOu5uehwPTrBAgYMDPKCTaCWompQEGkHoDCbocrJsRgAzNPYYJH7Wp/lbGp91Wn18Yq6mpbKSnssgCyzwWj7ZeO2VADgZbIx+4w6TtFQ36OfZ2tOoLrZp7LVsw8YMGq5NcFq5a3W6pb8XuqK/sbqJh8Fi4ojRyw+RUXMoaBgeY+z1fZNQcGyuUOGaGRWg4W51d+IQFNPzjOyggkZVICKakAmA6aMMMDiKOd2F/rwGsZ6MKcl8lc3lePNUdGNtr2FunijRfYZaN6LBFl/ANsEQiwRQHZnRo9XWWXzCWjebEem/jsA/4f0/HP8NC4AI4AdoDp4APAVHs9nXgpQ8H6L8LccwHEA5QBu1Mrgc1sgvEFdHbxApqH+oIBeCVJCiKoJUVUg+iiIfguuhDoH5++eWHYhejI44CQSOioWLmTOJq5rBbXotAN4HEAhiHS5EaRE+uN5bFwnEMiCpaC50k8wiwSoZGI6SEVUDeBmUCeLMgALQNdsDrr4PhK2nQhJASHhD4TaYzUhPMe3SxjyHM/WHqsJ+S2qhjVr1niNHj26KS4uzuLt7W3/6aeftCNGjDB99NFHPikpKU1vvPFGld1uR0tLC3vTTTcZMjMzA44fP34UAAoKCpRlZWWqFStWnEpJSSkpKSlRvPjiiyEHDhw45u/vbx8xYkT06tWrvcaMGWN49dVXg3ft2lXo4eHBPfvss0GvvPJK4JIlSyoBwNPT015YWHj0/fff9503b17Yzp07T8yaNSts/vz51ePHjzcUFRUpx48fH3Xy5MkjAHDixAl1dnZ2QWNjo6xPnz76xx9/vGb//v2ar776yufw4cNHbTYbBgwYEDtw4EBT3759LTqdzpGdna1JTExszcjI8Lv33nvrxOMPDw+3Hj9+/OiDDz4Y9sADD/TYt2/f8dbWVrZfv359n3jiiRqtVst98803J3x8fLjKykr50KFDY9LS0hpZtvPcbU5OjnrJkiXBP//88/Hg4GB7dXW1DAC6Op7O8O677waVlpYe1mg0fG1trSQTkfCngEQ0SLjaWAmSEvcBBYn3Atg7eUeqaIw3D0QWHAcRAvEmD2v23nEngwEUCO/pDWAKqC1eFag0wPUb/X6ZF3+D7xvG5wFkTt6ROkFjUCxLe2dYnsJN9rCbURXAgInpapACufD6lTvsiwen85xh1Q9azuk8Z1xo3ck7Ut0DVtEN72P/dxMA9C+xNb2nYGQfbDGVKovsjeJ58QMFTwNAgex4k1Xm+8F33QK775/6HYAeau+GxaGT13+6LttveEur7G+j+ja26jTwA6BhGPzCAPE8DyXLQGWz4+EgL2vQ0GaD4qv93jaGYWTmHr7s6CM9WbOxjC+0N5wFeP/RvaqtD6XUdd922Hf5PzeHnFQx1ht7uDd6ezrMP5WZ/BcBeBHkd3FrUmjalq6C5cS8OUcA3ATMwcqRoatAN/pqUIZ4sXBs9wJoCjDfqK4vUvg7YPnpRP2RFY2t1m2g4GI6aO6tBHCScTg4a22jTQF49tKdTQDA+akMNTHeNcah0cbu7mq51stL1r+pqYWxWlnuzBkuwm7HaauV71l4kv+5vCW8u6xVzv9srWipqrZrHPLyVoOi+D92bd2ECDUXE94NbF0FcOhQK1dcFYDwMFZeVh2YBiBzVN+mqjtvqBnhp7Ot/Tzb/32h7Wo2gIX3u8cUZcd9sAg0x/uAyoU6Ky9J1qoUb00fF/8pv3bXUqHbw5WCyuXxNlAZzoOX8P4s0Oc5F8CroOt8FkSeubaOdIUY+DMASkDXtD9I1ZAHIBbAZ8I2/w0iz5pA86AYwDqQSkQsZwCc6gSZyz7Ez4WiwzLX97AdXnNVNXQkHjreUTIgokIp/HEgdUei8Lyhk2OXcGWxDURqcyBygQd52qSjvRKFA3XzEc08eQAfZhxITgbwwqzBWZ0RzZMAvAdAl3EgOXvW4KxLU/1IkHAVICoZLnb5xeKLL77weeSRR84CwJQpU+pXr17tM2LECNOwYcOMs2bN6mGz2dg77rijITExsbWz9wcHB1tTUlKMALB79263YcOGtXTr1s0OAHfddVf9jz/+6C6Xy/ni4mJ1QkJCDADYbDZm8ODBbb9n06ZNqweAmTNn1j/33HNhALBnzx6PoqIijbiOwWCQiSqIcePGNWo0Gl6j0dh9fHxs5eXl8p07d7rfcsstjTqdjhPXEd+bnp5eu2zZMr+EhITTX3/9tfcvv/xyTHztzjvvbASAfv36mYxGI+vt7c15e3tzSqWSq62tlel0Ou7RRx8N3bt3rzvLsjh79qyyvLxcHh4ebu/sfGzdutUjNTW1ITg42A4AgYGBjgsdT2fo3bt366RJkyImTpzYeO+99zaebz0JEv5IkIgGCVcVG1I2VU/ekZoAulGbAAomKkDmdxNAAYPY6u85AD+BzAzTQYqFZNDNoAeIdNgEYBroZt4MCopWg5zehwOYDIAJKPeYqzDLbKpWhbzRy3TEu9Et41oc7+XgUMbtG3EeJUNHOOqZZEbJvw0NTOsfyuk15ou+Td1Mbh+dMRrnnHa0zAK59ru25gMo4DsJCtKqIUiIN58x5G57q8/QWWMrbUl9WlqaWxXvNrfyk33dHblqJf8rgJfMVmhZFv4ODuEaJacd0buF9TplMatUMvn2fE9831LOl9oNtQDbDeDxc7HXqVFHazf2VFesBEKMDIMPS0wB0+SM43MA84Xx7QSVDtyCC6saAJBZJIBtK0eGuoOuezYosN0LILvc7Yv5akegV/HpM6Uca34SFOi6AfgWwNcAtgibMoPmyT8gBNYNVjdlQKDOb3hSMKNkizil0qay2TjY7cDen1sVJ0s5XbXZk7FyMp9Gg97oie7NrZbc1Xb1Hj0YW2KN9vugUM/enkzQ8JaS/QfVNTWNyuBgue2B8Q0WD0+DLqfYTfP1PmVq9jFtpE7l9aVO63hGJFhEpUp23AdPgVQ3CgBHARS5qhcGeJW6yVQ+8x65beyw5AFREV5umhdAn4HnLub8XRCZ+kCQYkAEB6AWmfrArrwF2iss8p2tCTP1zwAoApVPLAYpEFy3DVBXCTmISORA16sPnN0degD4EqSQWAoy9BODQ4C+HxjQZ38yKGAUFQwOdG5I6drq0lXB0Bk683noWFLhSmK4EhsiMWEDtfosAjAcmfp915NXw6qidQWgDiEnpkVN7f17jUNQHAwV/hWvx71wXh8eRHadBJHafUG/JTNA88kT1A3mnowDyWYQUbsGwGyQguYFYR0ASMw4kJwJ4MnzkBISJFwTyNRya2ekgkwtt17uNqurq2V79+7VFRQUaObOnQuHw8EwDMNzHFd+8803G3bt2lWwfv16zwceeCBi7ty51XPnzq3ruA2tVnuhkjnwPI+kpKTmTZs2daqac1UHMAzDi+85ePDgMa1We47CTKVStS2TyWSw2+3nU9ABAKZNm9bwxhtvdFu7dm1Lv379TEFBQW3ldmq1mhfHoFQq27bLsixsNhuTkZHhU1dXJz98+PAxlUrFh4SE9Gttbb3kUvSujqcz7Ny5s+jbb7/Vff31155LliwJLigoOKJQdMbfS5Dwx4Hk0SDhqkMoUUgAMBXkifArKPgV65lF+IAyqS+DbgiHg4iIL0EZzcdAN3wPgWTe+g0pm+QbUjZNB7n4j4OQdTwT0Yis248r9o0pZtY8uE+55J/fGa/6gV4DNL6hhb2C9WdYxDV5tGptrDVQbmWf6KHw1N2XXDFh1tjK19zVjh2gIEr88VKAgjkWwJwn6vdUP1G/58FtrWXjAHzw5c++D+w7oSutblSM9XF33JhX6haYuTNg2/q9vh/WtCifc3Co5HgY5TKwDIDYcKtyx+4expCjeofdxjsscHgrwTJRcm/WZHGLXr7D776de5mhABLNnPI9k0P1YLNd2w10064HZa39ASxPCk178iL9GgC0706x4uRI/xUnR9674uTISQVG7Wqj4uTLHGt+UTjeUJBS5m4AqxleWaqz9snS2iKKQQqPf4MCDwSqm2yRnu7gZbcyRY39+cNHbSgssMJuB8AA1WZP/kBDdxnHMTqLoqQs134y6zSbe+Pe2oj8o03B+8wOeWt10Sl2/7YjKMytVPWOUfHDbtAq43pxHg1GGTMgwhg1KaF2YYLPSS1fXvI9f6qwv0CYuGIlqFyABWXxxfKYVwAkWjj5dHefXo9PSIi90cddq2AZhrU5+EfjHv/ht9eWk3fAu2hv3NgKkpPPQqZ+XBf+Aud4o6wcGeq+8qPG/is/anwf9Nn9GpTR7xikZ4FIAtF08QiICDKAfB72gTwO/g4qmxLXE6EGfZ8kgAL6EmG5qGbouL64b1fDyM5uiDvrSOE69vP9boqfOZFkEMcRBSLV/g/Am8jURyBT/5RA7vzZ0Qt0Pnr9zuNIBJXtdAQL8rP5HqSuYQB8BJrbzwPY7bIuAyK0A4T3pQnbfQ6kNHKdA38D+eFcFDIOJPfNOJB8OuNAsiPjQPLqiz8sCRLOD78+/hUMy7T7DmNYhvPr419xudtcvXq196RJk+rPnDlzuKKi4nBVVVVeaGiodevWre6FhYXK0NBQ24IFC2rvv//+moMHD2oBQC6X8xaLpdPAfsSIEcZ9+/bpKisr5Xa7HevWrfMZPXq0YfTo0cacnBx30X+hubmZzcvLa/sN+vjjj30AYMWKFd4DBw40AkBSUlLza6+91paUyc7O1nTcnyuSk5MNW7Zs8TIYDExDQwO7ffv2NjJdq9Xyo0aNapo/f354enp67aWco6amJpmfn59NpVLxmzZt0p05c6ZLBcn48eObN23a5F1VVSUDiMy51ONxOBwoLi5WpqamtnzwwQcVgvpBKp+Q8IeHpGiQcK2QBKf0+EIUbA9QrXgdaI4OAd3IJgA4DeCrDSmb2m7WJu9IDQSwGRSgNQDwtKkcKBhURQEBjxDmOumb7qhis1p/UMyT97Q8OOSHiOG+9R5y0I3vS5N6yms9w8+MHT+gcfHkt/p8CFKD3AIgBM7gSJR2TxX+1/QKNmvCfS0pn+/xtfxywsb/dMyjV0pc0/yM7UG3r93tv+i5O8q26sNMY3gePCN0pqiytyrNfkfY+hYZGyX3ggYyjNOGY4vpFFtosDafMATdC2Bwd5lu40MefX2zzZXLv2ktHQS6Yc8BlTToQKTSr7i8a/McgPsA3PxjTZ/7Fv/66+sAsHJk6L2gwMJDOG6lxh7i420Z4tOoPPT29F3lN60cGfqGsI1UjVoe7ufdiF8q9qJ6eH+Z7etyyAsKUFXJcC0VoWywqpLt73kajawX6+gbkfDD0Xp9uCnomFeQeu6RMj9GLbMNiFbVqvjmU8qgACA6WsnyPA+rjYPZ4Y6jRQyGRTWG2hu5OkuNJQLkT7GwwzEbQeTHs8J5iRinCX9vW2vZQgDZKtaea6g/4b7/eEnokN7h4xQKheerpUwFfuu8JgLhKVBA7woNqHTBDaRIePY8+8kGsHBGSlUuMvXj4DTaa3P3B30ul4E8OkTFDQ8K6FwVGSsA9AMRCGZQRtoP9NnPBAXsY0DKC9blr4fL9upB3y+eOD9c1QjiY0dyQfyuYjq8p+P6rss73vQxIMJGAyepcSuIBBOz779LudYVxAkIiobfeRzZIHPVB0Ald6IvkBVUauMP4DvQtTgM8tGYD/JpMMGprBPBgRQN2SA1ihxAJUgBoQJd1+MXGpRgSvkU6PtOnJNpIKJCgoTfBNGH4Up2nVi3bp3P448/3q6DxG233dbwySef+AwbNsz47rvvBsnlcl6r1To+/fTTUwBw77331vTp0ydWr9eb3nrrrXYkR/fu3W0vvPBCxahRo6J5nmfGjBnTeN999zUCQEZGRsndd98dabVaGQB44YUXKuLi4iwA0NDQIIuOjo5VKpX82rVrTwLAhx9+eHrGjBnh0dHRsQ6Hgxk6dGhLYmJi2fmOJSkpyTRp0qR6vV7f19fX1xYXF9eOjLz//vvrv/vuO+/Jkyc3X8o5mjFjRv3NN9/cKzo6OjYuLs4UERFh7mr9+Ph484IFCypHjBgRw7Isr9frTevXry+5lOOx2+1MWlpaREtLi4zneWbGjBln/fz8HJ2tK0HCHwkMz1+UYkeChN+EyTtS40HZy25wuaFznX7M+YVuHNq3mrOByi/2AYgEBdJiMN0AYDvoRjMAlMV6A8AqFwPK6wLrUl/p61emy1Ly8gAGDORq85qEh1euApAtdqxICk2bCLoB90P74MwK4F8A7tcoHf76cJMsv4zU7XcPr+EmDK6zbD3kY992yKvgpkH1ffqHG7Ij/E3dNGpZnwYDUFDpxvrpHNic1YsLOTOAPW6t4waoAthvTKX8MXu9FRR0Ge/WRskHqvw1tQ7z9reaD3rD2QZxMYgEeQfAB5djbJgUmhahZeSLktTdygEsfb7o9TYZ88qRoRGgIC4cwACGV5rcbT2LHYx5+l0//3Rk5chQsdXd2zHDh9zWd9SwMfu3/tB02ubwQsEJX1isrNYWAT/zCNSqf0KhrRFlPW7AgOl6GL7MQkD9SXtiglK+62QkV3b4dNHo6MrI+CEa9nSZ1dYjUqnOPS7j/Xw1TESkCpv3edrraho35hSqSk/U+9hGBxw/EKpt3CKUgwAAsuM+GAcKtANB890I4NHEvDmdZj8FJUMigOy8t0Zf/rzO1E8EqSnE4B1wdl9oBgX83wN4okvJP5EMrwBYuPKjRpFsyJ7+gBfg9NZYAjKHPArgDChAfRrp+dsEwiNZ2FoW0vMNyNT/DdQKdCfoc/4WSPF0G4gAcUUTgNdA3w/zhf2cT3lwPmNK15IH12WXo/yzu2yrI4nRCJLtd31OfyNWFa0LBGXdT4HOT9a0qKnX1XegK1wMG119XCA86uFsT/o/AMdACrkWUEneKZCBqRZAKYAJswZndWrKdinGkBkHkhcBeBodSIxZg7OkTKSETpGbm1vSv3//S8qwX28ICQnpl5OTc0z0NLhaeP755wObmppk77zzTpftOyVIkNA1cnNz/fr379+js9ckRYOEa4INKZtyAIRN3pHaF0Q4ROLcm/DzoeM6MgA9XbYhwgLKGt0IcvQ/BCDreiMYREzdtPBIdtwHw0DZXr3drP5OaDPoiiwAGaDs8CLQzfQgUOnJJgCDWq2ylF9OUNn7kF4tGNe/gXVTc6oUfYPKV2cbNCauibXaMUopA0WTWRUAACAASURBVMuyYH09gEQPI3JOaLmsU0AAV8JX2o0ocxi5Ensz4DTDO/y96XRwiNztZCvvWAAig8RuCRc0vuyIzGnJgWG+5idjQkxDvN3tv+xehDeyl85eBQpwd8Al4z59V/kpUIeS86Gt1V2BI/bHM/tM0/Ore3/+0+7TQyLcQm9jwacGqlo1Mepsm1VergyUOWRMZQ5kX5fAVlgLm6yZLTgTw41K6c/us5qigEqDTAaVt69cYbLK8UORH6POa8QogxyW4qPyMzW6kUdrQ+0A45dr7LV04dwTjcjUtxFCwnjWAXgElJFXAXg4O+6DanTSgUIgF36bQoeC++GgoN31MyaSUZ6goG0ygEhk6j8ESc3/Bsrmbukw/oUAskU/DeQsawvIkP8OQOqFGNB8NMLZOQXC8zeF548Ix7YNwhwF8DmI9IgFcAAUNI4E/YZxIPVKHICbQCVYdhBR4olzSQVG2L+oNOj4Wmf/XyrhIP62dsw4McKYvr8aJMOqonXuoHKhOaBzrQSN2w4gY1XRuqeuR7Ih40DyCBBR91/Q78sPoM//c6DOH7FwGobeAlJg1IBUC0bQnG4FlfnM78p7QSAXLvjZEwiJKBC55NoB5eBlHKIECRKuIMaOHduztLRU9eOPPxb+3mORIOF6hkQ0SLjWCIEz2+jA+SXJrv+Luge+w+stwv8VoPruf4EC2O9B5RXX3Q11RyTmzTmVHffBrWgftLVB6GxwCCTXbdpdvma+6+tJoWmPAtgAIm3QbGJYluFhdzCsg2eM3/3qxTUY5Mpu3pZjFbWs+o7hTdFqJV2D2DATONaWXWRtiAdYZaG9EUqwbLTCy15ia7ZYweXWwvzW282H9sEpFb9s6DSO6fG9DI/o1JyMB4aB1CrvQwhwXY6pXQtI4f9kUNAsYCRAgStw8lR/kFkpAFX6sZZQH4BzO2W0svKAInuY0laqYHA6BGeHlB+0KQ40dFcM9rZBfqyI/aVZBlvFcfas3O7R2OjgAgKU7I/5WjSebbVFaesUh/dzvFwOfmBPo49VVVM/ODaUcVhOJzk4JMtYZzlCi7zAX20PGaVUKBmPbtVoORMEzqaIB2XpD2XHffBMYt6cKxaYJoWmuT89yXv+zQMb/s6yUKHzQFoGp6Q8EURSfQ0iHmpAmXkKuNLzOwu+nCUUpFpoBJUO7BUIMVIykBoiF+S3ADivZX9hn4XCYy2onOMOAHtAkvY+wviaQHNMlKdzoCDbKByDSEiIKAeZSYpzomPXCXEZg3O/d1zRVdtOoHOzSQbAg8jUb7sKZMMkUAlOx/I0OcjbpnFV0brXr0OyYT2oPOIN0Ly5BUQ4TQD5j9hBShgVaF4cAJGTrt+ZMwHEC++/WCLhHGWDsHwSyFtkEKg0YzmoRCwCRLRJkCDhPKioqDh8tfexffv24qu9DwkSJEhEg4Rrj2wQETAFgINh2s1B15t5B6jeWgfKNP0KkloHg27Uy0FtMzUAsl1IhZ+u6uj/gBCy3Z3eGCeFpsWDDDTfA6kbOiIEJBfmAdTMu7XarZuP3dPOgffQctopw+pMDIP6YB+rfvPBbozZoaidNro2gOPBN5oUXGxY66B9RZ5qgHMAsh29Fd5hyZrQnt+iVFloa7wddJ2Ac7PVl4yWVtnKnBPuAaKiQavCysT5/3EzWdhhz6wJ93oidI/YLjMZJLNflxx2R4mbzHGP0aHuAzAK4TgtwqNJGI+Yjc8Fyfo9taw1oa9nRT81a934S30Pn966qjwPhXmL2SH3j3I/u8hf1eLN2O2jrKW5YAC1T6AcGjXLVFXbcOywEVHaZpmC5QCADwiQs/oBGmhCwv2S+ifjy12aIWu3/7qpb2B9rinuA3cLWzOJ51UvsVD30HUrt4cnHmDLshPQVBrGgjoz3AUKqn9TPb9IwMxIqcp9YaplXohX6xweEE0eOwbSYgBtB3Uq8QMFr3JQOUMeOiG2OqBN5XCe/4H2ZMRGQWWRiEy9WBt/FnQ9fYTHOaDyklEdxqwCqZyaQEqGbqCAEnASB0ZQyUYYiFjrqDYwg4gBMfMsvrcrIkE0fex4/rpSQPCg0q7puPIeDWk4vweODOQPErGqaN38aVFTr6eOCb7Cowx0bnNA5sAeoBKJ24R1kkFKty2zBmcZXMiCDGE5cOF5LeJuUDlQY8aB5LUAxgJ4FGR0PAvO66AEKW3emzU4SzKClCBBggQJfxlIRIOEa4oNKZsMk3ekPgJyiO8NMnLTwxnUiDfnG0BZKoACANes+HVbDnEV8B9QVo3bXb7mg05ezwawACQ7zys3eTV6NeHfZ2uxN0BnD61pVgxIjW8I2nHYkztcpuUPl2l9j1a440Slmu8VbJbll2k1wV5mLv3GGmb1j/5xBfUNHhbeIS91tBwFZb+ngIL3jtnqS0b6qqxqUP29E5n6p1QK7un4SGNNTrGHmGEP0KhlIQP6es0+dKROZTRzWnIDYayg77zDAH4E8DOcpRwi+bFReFwNAEmhaeO8NOb3kno336+yWM2Ld/26EcC9QteIZFDpwd9qztrrTpXY+nTvrpD1DDDiYLEnG6hugoLl2LNn7Tj0qxE5FcUor1Xj24P1ihiVZby1wjy0N2CWcW5L1dD5WjgH01QeJCvLHoLmM4EABavfgOrJV17OOXNVdwBIdFM5Fg+MMOT1CzdNZJh27Sw7ggcpCOQgP4+loABdD2BFJyU65yJ+ZnsCzD/BDTbDMLSUeCFTL7YbVYNq6F1LKBaDPvtKkIlsjrDfj0BtSn8B8CKILEhy2Q5AxKQOTvPFOhDZ1V9YHgWSsbeCvm84EElhBX3P6ECEBOeyHaB9K0zxufjYGaHQVZlFPSiwvaxrej6sKloXAZqPnUEkPjwB3APAY1XRunuuI2VDLkj1UgcinHrAOQe+B82vmaBSiqUAbsk4kDwY9BuUBOD/LoMEuBN0Pj1BZIYMpFwIQXuyhwUpb54DdZORIEGCBAkS/hKQiAYJ1xwbUjZVT96RagS50L8N4FVQoFsgrBIBIGNDyqaOGbeNkHCpmA0iG2Z39qIQZH8h/Ino1QMUpPYLM95tssoWffmzb0CrVWYBoBb8HNhfTsjtAFu4OK2MHRxpiOGBoNe/0qDQ3sgDKFQNs61QRDkMvJHZt/Xd9V0ZCTqDYWfN/8VipcXGKnNOuh2HM1gd0S/GQzPrvgj236s45f5D9aIpnxiM9gcZhX57ESaU2ek31nx660DHvRzn7PAo+BBsXDkyNAvADpsNRUWF1r9bzHzv3DKPPgfqw3oN9ilFqLaBtdvBn620McEoRenRJkQpZFApOVVkT48HZIXVmx0t3rsY2EefxdnDYY6w4Y2lYSwHjpNRzfmWjv4MlwjXDhDZ8245s0kfbprBsp2SDK5ZeAYUgNtBvxN6kMFeCC6XLHKYp/OGqqfB97SAqXyI4WsbhW0u7OD1sB6k4jgKKodaASIIioTjGAoiPexwmsTycAb9PIhIKAb5T2wX/h8GZ7nWTuG5FpT1Fuvm0wBUwRmoihC3ywj7EVtXAs7z5nr+xDF1RjgUIz3/uU6W/1YsglOhIsIGOk8NIMNPLZw+BdMAdEY+/hkxBRTIp6N9GcxROP1gogA8A2AeSNnhAada5S4I5OIl4P8AfAg6r5vgVDTEAfgn2pMNHOj6SJAgQYIECX8ZSESDhN8Lx0F13nkbUjZ1DHQlXCHsLl+TA2oPel6UvPtihFrt8ZzZ3LyoxyMvnnJ5ryEpNK3s8Gk3BwBeo3Qo9eEmHKnSwB7FAwBrO8b4rMzy/5JleG7VzoDecAZhcYoox13aMbZJoCCuLbM9Vz880MLJp6tY+8r38/dUQwiGa2WyxQ/tSDWjfSlM10jPr9YCL/9L6GgvZPB/OlrUErp+S0XL4ePNqS7KdwecnQDycREB8+7yNQZk6pcCzF6AOWf9NuNDwpMA8Jl+eGCQpnmWn6qlG8hkjgWgVLAc292tHuUmb97mHiCLiWmYUNVUP0bnb2wyVgW908PUY6kVpmmAaikPawageeY3kgwQjnExBJJlwuCGU6DyI9GzQAyGeQitYUGBaSWoDKEvqGyB2s2m519+RlamXgn5kLHgBiUBhXGwrn8PZNyXCwDI1AeCygk+B2WA7wQpm4aA1AZ/AylaZHCa64nXUzwWsUziFKiNpAeAf4BULK0g8sQo/N0Bpw9DbwBbhffGCI8icdAKmjsVIAKCEc6f3eV1ubBtEQwA8FABbCTAnQQDiw2knFh+eSfwgigXxmkRxqMQ9ucmPHctFWEBPL2qaN2q60HVMGtw1ikAD2YcSJ4KUqIAdH3eEZ7PB7XCPQwiuDVoXxITdhn7PIL2CpKnhMefMg4kF4HmrkZYtl8YowQJEiRIkPCXgUQ0SPi9sAVkJnfZUvrrCs4ga+XVbHnXGdRqj+d8PaPT61AInCvtzQZl7gaPH9AQf9fw2tHLavyYX/UqBmDAfKzUBJ/se9e7q0MWVNqbPUDZw48BFPNGZh+IZGh3jTkw86rNHo/7DmrpMXlHaoxbSHhLakvzDz9o3cW2dAtx+R0VEgE8ZjDamU3bK39Ce0NRDuTL4AmgO4DEpNC07AuqGjo3OsTkHaltSgxXYkQgT14Wyiu2CPtLADAGQAQDx+Zjp1WJ3+xTV8J8Rj95TJlSo5AdUs3bZ0CmfhUoc3856o5zIJBFZpCqwQxSC7kGWDwoQBVNWgH6XYhAe6NDC4BAZOojQAShU4FysYqUWz+qxg/vZMBUHQU33wI0xU1H/eEpAH8QOcuyoQ15F6Yzk6HQpcJmaBY4gzGgTHEDaF7sFfYl73AcDlBQvROkeAgAkQwsiAAYBCIXNoI6DpTBSQ4kCdsaBVIz+LhslxPOm+gBIRIMrQAKeKhiwPZUgCuWM7A44CRuKKPORgKyCQA228Adex/UDaEzr5QrgaUgM8NRwvE44Ax05Tj39z4IwC2ritaZQUSUGX/+Fph3gNQFYueb10GlIuEAvEHmkCIBA9B1NQPYlXEgObCrbhOXglmDs7bhXHWJBAl/CpSVlckffvjh8NzcXK2Hh4fDz8/P9t57752Oi4uz/NZtX2rrynfffdc3JyfH7eOPPy6bP39+N3d3d8fLL798PfnLSJBwXUMiGiRcU0zekToRVJtcByBN8lpow3RQkA1cCYM4CgifA7AI6fldZtLM5uZFdSiE2dx8jrS3rbSCaurnG3mmjzbIrrEc8WqEid92d1WMV5TGa2ISgiOSD//jZZwrxW4L0CfvSA1UGmzzhoZVjg1oapYdvyd4CgA/Iyvj1np63wjyI3gbHYgJIWBPhLN1YlfIBhm0vYRzSRMFnNnOSFC28x+uY3Q1TEy/8Wx/dB08u5YlbOvY7UIsrxDWbZNlT+l9146o7qHB9Y5jdS2G6rdabNY8Lx8uq7NtXuBYLwozUqpydRrHVy2tslyQesANFHBFg0iGYHQu8XcN5NUgub0SlJF/GeS7sdFlzIuRqTeji3PGGHN/hMP8b8gHWhCYOAWcfRMaj6rRVDQNjGISADlszcPQRg4pWMDGgRQWHiDDvgQ4M/QsqPuMVRjvFwCWgcidJ0GKDPFYeBBJcDsoADcJx/CRsM07Qf4JNji9GyA8quBUTliE8xANNlIN+QQG9k0Ad0w8TLHcxAiu5Ajwgx1c1SoAa68EedQRQktL0YejCNTe1/WYz9cZww6S+U8FGWzqAHwGyvz/KTFrcNa2jAPJc0AlNxyIXLgRZBBaCGCwsKoRwHeg614FMm80QVAlSZDwZ8EXP1f4/Hd7aUhdi1Xpq1Na/z62e8WdN4TUX+72OI7DxIkTe6WlpdVt3rz5JAD8/PPPmjNnziguhmjgOA48z0Mm66zhzl8LNpsNCsX5vHklSPhrQCIaJFxrrAEFOj6ggGDg7zucPwxWdnj8rRDrlYELGJAJ5RKdrpMd94E7gMTBM93UKp0xlWXx33JfxSH3lNasDSmbDNlxHwSCwa8ysBcct1tN61MmT+U/TqUE8EHHmxirWqbjqXEpyzDQgufvkBtsvexa+WOTd6TqAOwIPNb4dgLVT08BnO0gzwchgz8B1HUAcAZZYqAlBtRKUCcFdVJomrtLG8y5AKb4edg2AUiD0mcxcpaFAFiJ+JkdsyjZABYav1bmJk1LGwfKmr4KMtc8b733lFFxR8cOihitk5d3V8vL8pCe71o2dE5nhqTQtDa1y+7yNZecyREIk0mgenUzKPNtAwVXHqCAvasuCSJ4UFlBTIfl4pjVuBBJUrOfxtJ8cjFiHnwWLScDASxD1R4v2JrEOzLG+WjzEcY2Es4SIBmorAOgEpgQkEKhFwAL0vOJ3MnUbwO1NrwVpKxJAKkXMkClW2YAWYIqIwuAAdRJIEs4xiBhX9tB5MLNoO8uNxCxYQR3UkMkw6njwvtMIMVEdwB5DFq34P6VV41MFUiGuXB+PvrBqWa40O+7A9Q9xAoi5gDK/v9piQYBa0HdSp4G+XIARDxFg+ZqHcgPaAyoLXIqaO7fD4lokPAnwhc/V/i8ubG4u9XOsQBQ22JVvrmxuDsAXC7ZsHnzZp1cLuefeOKJGnHZDTfc0Co+X7hwYeBXX33lY7VamVtvvbXxn//855mCggLl+PHjowcOHGg4fPiw25YtW4ry8/PVL7/8cjer1cp0797dsnbt2hJPT08OAF566aWgrKwsD5VKxX/22Wcn9Xq95cyZM/Lp06d3r6ioUALA0qVLy8aNG2c83zgTEhJ6L1my5PTIkSNNlZWV8vj4+D4VFRWH3333Xd+NGzd6mUwmtrS0VD1nzpwqq9XKfv75575KpZLbtm1bUWBgYLuOQ1OmTOmh0+kcubm5bjU1NYpXXnmlfPr06Q1dHe+ECROiioqKjgDA888/H2gwGGRLly49k5CQ0Fuv15v279/vPmXKlPq0tLSGadOm9aivr5f7+vraP/7445KoqCjr+fZZWlqqmDJlSqTBYJA5HA7mvffeK73pppukhJyEPy0kokHCtYbG5fnu320UfzSk51cjU78SwHRk6q9E+cSiDo+XjMk7Ut3HJMY+1edAt2n7N9/yzIh71j2r4fns92/a2vajl5g3pxpdKDDELL/bZEvuUHnN6LpwN6auu45hrQ4LbDBDCRUYgOfBMABjd1MM4oU8LAM+pi7cbUzpAG9Z4ImW1WqD/WLLbCJcnjOgbCYHylaHgYJHI0j58CwAc1JoWjYoYJvq46X8OiUpSInWBgZe0WMATBa29XrmtORAncYxvaVVtnIDdcHYljQtbSKonaYVVH7wRFJomgXAj1qZZejNwXnwUrZmrTg5EgAS1z9+utxHHcSx/Ek3AHchU7+lLdPdeZnG5atdqKxBDVKKDAeZHa4C1alvAzAR7evV+Q7PxUexHEAJMmvcC5EMEcdM+zLjfOVQzrEsht2QBcAN7t03oKnAH7YmxpmAb3vCoL2xn2jmyYCy7xyIYOgBMqsU/RkgjKsaRFKJpUmzQATDlnOUBUQ2vA8iJHJB5Rfi+VoDKoG5BaSAGSbsy5+B5RS4YxoALyE9/4tLURJdISSCSIb1wrg9hOUXk05cDTquI6CuCToQSfKnxqzBWQYAGzMOJN8LJ9EAENnQE4A/SMWyA/S5FedXwLUcpwQJvxX/3V4aIpIMIqx2jv3v9tKQyyUa8vLyNP379zd19tqGDRs8Tpw4oc7LyzvG8zzGjBnT69tvv3WPjIy0lpWVqVasWHEqJSWlpLKyUv7qq68G79q1q9DDw4N79tlng1555ZXAJUuWVAKAp6envbCw8Oj777/vO2/evLCdO3eemDVrVtj8+fOrx48fbygqKlKOHz8+6uTJk0cu5xgKCws1ubm5R1tbW9nevXvrFy5cWHHs2LGjDz74YFhGRobv888/f7bje6qrqxU5OTnHDx06pJ40aVKv6dOnN3R1vF3t32q1Mvn5+ccAIDk5ude9995bN2/evLp//etfvrNnzw77/vvvi8+3z48++sgnJSWl6Y033qiy2+1oaWm5UAJAgoQ/NCSiQcI1gVDPnuyyiANlViU4MQuUhZsuBEbuoGxq+iUTDxTk/KZWanaze3JJROOs7sd9PcrDWiNGpOdfcg94b1Y1KUimfbu6rm5n4eQeHt1OetrtaGDL9AGsAjLPbse9uQY/ExuTG4TDCRWwqRwIKfFCRY9G2FV22LXy6PxbwmoLTY5DayZvvVhW/0GQhL4bgP0AZod10/R85+UB2JJVtW/5mlNUEkGZaebOCSH1sb09v/zsf2Vhs/4WWT+4n/dUGezuaPDfBZXPm3BpL6nTOKYP6GF49lCJO+AM+tUAfDVKh6ZfuAmHy7QxrVbZRwA+tDhkU481B6GPR9Wc+J7N6qSY5qWe2ga1DA1yIZy/BcBGZOof7CIw/S1ql7tBhMphkOu+L6jzwLPC/1PRPrB3ldgzHR4BUoHMB/CPToP1zpQMNJfnAUwUGMUgeEV/jpYSwG5MQNCIYbA0MDCfBeQ+HJRuQGuNDXyr6MHQsfODiDMgmf9/QSaRs+D0UzgX9Pl5udPXOh//RkHlsAPOUpAvkKn/UdhXHUgh8SbEThxEpPwH5CkBXJtWhtmga5krjMtVbdKVSoUDYJsWNdWwqmjdfNBnoRxOUu16wFMgNYuby7JqAC8AyJo1OMuQcSA5CqR6kW7mJfzpUNdiVV7K8t+K7777zmPXrl0esbGxsQBgMpnY48ePqyMjI63BwcHWlJQUIwD88MMPbsXFxeqEhIQYALDZbMzgwYPbfi+mTZtWDwAzZ86sf+6558IAYM+ePR5FRUVtiSiDwSBramq6rM9lYmJii7e3N+ft7c25u7s7pk6d2ggA/fr1M+Xl5Wk7e8/EiRMbZTIZBg8ebK6rq1Nc6Hi72v8999zTRvL8+uuvbt9++20xAMyePbv+pZdeCu1qn8OGDTPOmjWrh81mY++4446GxMTE1nP3IEHCnwcS0SDhWiEZFBS44snJO1If+8v4NGTq+4Lq24MA3I/0/J9cXnMHZYsdIImviJtAGe3f7ttwiajYPxUtmsoGZf+GLd5yS4bLOC+6FWWswmfMILW/385620SfQ56y5O295bmDyvnsoFJZ3JkQvk9BMFp9zHxIiQ/DmcCd7dXMDv8uCj+POcmx4CFrlJeU9q372uxn3Xex4xY6bUS2W5izrC+AJfeM6f7mLSFhk3wHs0ONNmtVRZXBMzrSazHLsuyoYX42uZyVMwzDAEob/AYmAghB/My2c//NAZ89B0+6n6lsUO75p6DWCPS0eozoU6++N6lG7eXO4+cCbXNhlZv3twd9h1Y3KVDQHMzUcP4Jn/yjMEmrRkiH4apA5n3P4TyBqVAucbnX/z5QJvcGACWgmnUTqJZ/BZwt+EQyobNWjSLsIOJmEGgOXNhDgubLmwDSAJ6FTMVD6T0bfPFdaDqxH779G6H284D5rBz2OjscjXbwDgWAI1D5lcHWPAKclQcFiy4tLBktwO9Aev4p6gqCQ8Ier5zRYmfESXvCQvQjOSIc6ziQ18F2XKNWhoJx47ZVRevGgYxYXZUMXd2kswBihedt6qdpUVOvm84IswZnnco4kDwUwP9A3welAO4TukWIWAUy0PwbnOUjEiT8KeCrU1prOyEVfHXKLgPhrtCvX7/W//3vf96dvcbzPB599NHKxx9/vNZ1eUFBgVKr1XKu6yUlJTVv2rSp0+8TlnV+NTEMw4vvOXjw4DGtVst39p6OkMvlvMNBFRAmk6mdB41SqWzbBsuyUKvVvPjcbrd35lfTto44lq6Ot7i4WMFxTgGd2Wxu912r0+naq+vOg872efPNNxt27dpVsH79es8HHnggYu7cudVz586tu5jtSZDwR4RENEi4lhBd4CE8TgO1ALsipnd/aFBWdwOcJMIPyNSnw6kSSATJ2beCaobFANCEK+fbcEmwmXyy6kw+czcqkZ331miRVLgos8LJO1LdbSXsLTalrtkU2FqjTXD49zwWxKhNSgzcG8bI+7NMv8MhqPBvtoeUeCmKg2q4vJiK1tYzzGGZssJu267sM84Y4a0wybrvVByfWuhX9V1X+zsvRGIkZkYc5G5+TUWO2VU7uFiFB+ClV/aM6eVsMMAZoKjY40BAEsspvVgeQHbmFyW5y29PGwfKGPcHNI8XVmqiQVnkpQBeGR7TfOLv485qVAqa28P7mLyGRZswaUh9t3990+2lE1Wa+AeSqx9TK+HqCiXeiBwDkHv4tF/msfmzVpiajYvSl39CN2c5y5ykTvzMyyXjDoJKAI4L+/wEwHugLG5oJ+t3JBlcn/Mg88AtEFtSAhcin24R/qwAo4G9hUVrlRb+QzbDK0YP4B0Yz4wVxtgK3rEVREpqYKldJozVud31EyNgN74IS9P34MyupRsb8Tsgf+uMEd6ms8sbtAEz9LQoAGQueK2RDVLWdHoTDSrrEVt58qASiUcAQCAXroX64ppDIBWiunjdACJyp1+zQUmQcIXw97HdK1w9GgBAKWe5v4/tXnG520xNTW1ZuHAhs2TJEr/HHnusFgD27dunaWhokN18883NL774YreHHnqo3tPTkzt16pTCNagXMXr0aOOCBQvC8/PzVXq93tLc3MyWlJS0mUl+/PHHPq+++mrVihUrvAcOHGgEgKSkpObXXnst4JVXXqkGgOzsbE1X2fywsDDL/v373W688UbTp59+2ikx8ltxvuMNDQ2119fXy6uqqmSenp7c1q1bPVNSUpo728bAgQONy5cv954zZ059RkaGT3x8fJe/5YWFhcrIyEjrggULai0WC3Pw4EEtSEEnQcKfEhLRIOFaIQsUnC6BM5hZhb9Oe8vpoMDOgbasLDKRqY8G8AacEmgvAP1BWWgfAOuudbtLEQK50DG4P8es8DxIZHX8EmsPa2BrMVpHb47irVo775A7WM4O9DsQyqkgZ7tXeMtkYOFbqWNM/1M7hp0ORaKsW4SMZ7xaZBa7FkpWv7/b1sLEqsudJ0SMlHz9NnqlPeK2ogAAIABJREFUvWwPtqWGTVLCrYeFg+EsC00wGDk1Fzj7E28q/4bTGCv4Hb1nsbsBZAhlFq8A+ApkqLgcdA0XgDKkC38u8AjsHWQcd9OgJp2Dh/14uXprd39LnI/OEfnKPadv2LDPZ8DI2CaVgwPHsgDHwSyTYQnIkDED6fnVRx99aNWgYT3vO7j3hBxEwAHATJC55DMA/nmZx/8GyBhvD5zkSDTI2PB8sIHUNa43kByI/FKAujZsF84F0DX5FAPAHWA2QhfZAJvxBnhGfQjPqC9B8zwbtsbVAF4E8L3w/qHCe7POIS6mbDwF5/n53eFtOrs8uOlkNIDlDmqR0Q1AN6YLhcrVgFAC0akkGHQdV4PKiQTCDNl/8jaWEiT85SH6MFzJrhMsy2Ljxo3FDz/8cNg777wTpFKp+NDQUMt77713ul+/fpYjR46ohwwZEgMAWq2W+/TTT0/J5fJ2ZEO3bt3sGRkZJXfffXek1WplAOCFF16oEImGhoYGWXR0dKxSqeTXrl17EgA+/PDD0zNmzAiPjo6OdTgczNChQ1sSExPLzjfOp556qvquu+6KzMzM9B87dmzj5R5vV5g8eXJzZ8cbEhJiX7BgQeWQIUP6BAYG2nr16tV5yR6A//73v2X3339/j3feeSdINIPsap9bt27Vvfvuu0FyuZzXarWOTz/99LpRmUn4a4IR5ToSJFxtTN6R+hQocGIgGOf9hcomRJO4g2hfE8wBqAUFAYtAJoVzQUGmDsDcDl0JLmYfTiO6Syx1uFIQFQ2Nb2sGa1qUnhM0PR7or/JTcAwPlmMgZ1hw4MGCPAA5nkODw2L0YJRaBStrl5VlGOZgYt6cwefbV5dwPX79P9DUZLnbaObu8bAeqdGYCycj4AbIPCMaAeyv2OZ4sXStYwyAlYLJJVxaVrYFaELLzzYkhaa5h/mZk9+Zfgr/WBmB07XqZ8f1r3v/+akVowEsevebYN+IQPMyN5Utd2QfQ5hcjnAQWfEI0vO3AcDKWwf/TeUf+nZ9ZdUza4/0LAOQu+2zEQc0alkIwzBFiJ/pWk5zOedhFaiMIg8k7w9G5+URommmN5weCbyw3FWWn430/OEu5zgZTrNG124ObR0zfi/C7GrCVdHg11L+VoChfKiFVdVoOMvQa2QG2YZVRevEdp+u4AD8BOCuaVFTr7vzL0HC9Ybc3NyS/v371154TQkSJEj4YyA3N9evf//+PTp7TVI0SLiWWAlqZ3krgNl/GZKBEAVyxi8S/qLgdNQPAJEL0wBsApmyvQhq4XcpNeftW1pSAOja+u6alagI1/aLpBfSGk2wL//eVMZ5siq7t0zFNthbWyIVntpGztqkY5U+LAdexrIyX5lG2+CwlOl4RsbyCOQZ5oicijlnXdLOc5Z1DG63CcvHeXqqZnp6YiHy92QD+Ajmqlx4RkwHMDVknGyMw8ivLN/E9c+O+yA7MW+OQSAVxPPW6fkT1tkIAKffSnMHYN6W65v9/DdbVwPAI+k4BbGNqzMoB1xVIS3VX5kZ1mLqlpSmKa5NbjU7Gp9cnKtZ+GjflhWfncr+ZkdahIfObYi+T88YHy+PjKc+evhSg8bvAdwBIkta0Z5ksMM5F1mQksaVWOhoyggAgcjUT4STUFADeB9EIBpAsvxtwvm/5v4i1wr68ct/AtA7BMDh7bMebFV5vm1Qey3oNzbj98hCfQjgIZDhoQOkZPkRwBsSySBBggQJEiRIuNaQiAYJ1wwbUjY528392fDblQFiyUEuSNUAUEs6d5d1dKA+9lYAL+LSuzx0bGnp2vru9ypRyQbwWB+V7609FLqBVXbT4UiFV6SMZRN8WQ0DoAIk758KoM5Hrn4sMW/Obw3SztcOsq3sI+m5OMTHeXm9+VzcUiVJ9fcAmGI3AryMu1N1d2uZ45eMCBnDfghgFeJnGoTOKYkAss9HkrkSE0mhaW0tFe+cEHIytrfnoqqzty2477XF5/gJTN9Vbvjy+Wdibujjk1pZY2V/OdSg+zW/GZNn/AwQATW1xWDkjh4/KRsY19uKSw/etwGwgDpOuErsORDRoBYe5TiXVGgBlUwoXV7rDjKTnI9MfRNonnmCjPc+xV+nJKoN/cZmHAGZt/4umBY1dTaA2b/X/iVIkCBBggQJElwhEQ0SJJwPruQCZaHfBPAELsd4jrK+2XAlKzL1DwP4NyjwczXc2wvgc5dxxINM/AJArS473/+5LS1F34fOyRGStc8CGQVuuRqlFULg/UV23AdbACSHKXRTAAwGZckrACwGsCUxb84HXWzmosCv3RUBcK9DFapFYNUWRmn/vN0KZKi4jd+5NuLTV1MyDp4qGSiTMR4g5cg3AD6VuyHXfHtL1KAbvNNZMCzI0yAcOcsOzNEMiPmu9dSU/3Mb3HB679uN/jJtgZqRL0X8zLZssUupRTaI8Hjax0tpTR0bLO8e5q6tqzf3Q86yQa7vEdF8cPvxT3MHWPOPt4olCK6qAy3Pg5PLZVluWs2lmYPSPJ4Fp8GoaEQpKhjE/TnQXtkgQtfJVmUg5cPDwqMb6DyuP+/8lCDhIsGv3XVOyRJz98i/kgJOggQJEiRI+NNDIhokSDg/bgEFmj8C2AkiBNRdvqMzOAkLNSjwF03zmgCcAQW5owAMANAAqp+PAiBm9v8DoLfw/GNk6kMvihTorD1fe4iZfyWoZGPipRzWpSAxb44hO+4DM6h85CsAfgB8ATQm5s25UgHEcwAzFRYNUO/Xwvuf9WFylj2E+JmnhA4OyQDCwLo9G+YeGuTez+E4kn/mYFTvoB80atmPiJ9ZHR4P7Nv92DOM0utWkJeBHMA/AMhHK0Ibb5AFKTSsIs5ln+ORs2w8AGN1ZdPdrz3ea15tM+Ovc5N/tCenbsWJ4rpbX33EOzE0WM2iVQs/byYInbQrTQpNc9fKws1R7vmPtZq7vw2wKpyrLCisrW+67zLKJhJB/gxm0LXuaPQoQtHJPl3BwdnVgBfWHQhSg3wGkupfufaSEv4yIJIQqwEkgEisX0DzcR3IhLXLLjcSJEiQIEGChD8eJKJBwl8Xmfq+IDf/LwDsAKkBFADyQSTACFDQfyeAcSBpeKTw3q5N7tqrIURX/sVo37EhC8A8l3UWgwL+Q2gvPZ8NUjR0A7XOS8SVueleKewTIN+Mq41sAE/DeWyJuLIS+0WQW3pCYY2HReWOJs8xvFfjcexfbkGrmx0ak5ZheQYak5Lx5eCjZuTuQYHxPI/obbuqbC/fnvb67vI1hqlJS6qRs2wCgK9BKhIVALBgfDWsot0OeR5xVgcyVHJYfPzcbxse6KFiGIDnMWfMyKD9xtMHyrSWvCQ0h4E3RYL3rMtjbdpN+CJrEUJOg1FZ3xPUDYkmh+rZ3KaIhRAUBy5MAMcABwDctft/NxqRs2wcLqblpXMO1oNMRoNB3/k8Om+D2BnJIKoqOhpHisNTABgBhYcDXjF/w60fSVlnCRcFQbWQDPp+vQft59cQAKdBpSh+IF8RCRIkSJAgQcKfCFLXCQl/LRBBMB9ALIhI8AS182sCScAByvxyICm5Z4ctiMtNIEl5BYAtoM4RK0BKhI7EwXG4OvF3Pq4Le0BcjQ4SmfqzAPwB1CA9P+CKbPP3RM4yd55jD6LRMwoGHeAnmHfX+QG+tWC0JgBAx++9rT9WVS361/Fpu8vXbHPZViCANQBGApDxPN8WnHM8x7EMy/I8w/OAVcZCxvO8HAAcZh6NhZyjxNLytZd7zWu9gm2LePfeA2Dw3wyOfRYNftMB7iV41/GMb/2LiJ/5eoeSi0YAMnGEvEKZx9qs43aXr6kWSAZqJRk/s2uyKVM/DjQHWZBaRkRXhEJnEH0cZHAaRPJwEhY2yNw4ePZ8CRPXXLfGjxKuLPi1u8aBDCzD0Tnx5UqIcSCyTQMiXKczd4+USnQkXHeQuk5IkCDhz4auuk50JZOVIOH6QKY+EJn6pcjU7wOpFR4HMAFOEkEBypqJtekakEljR5IBwusK4TUWQBioFeXTAApB6ohVoM4Sm4TXXgFg7pIcSM83ID1/229e59JR1OHxz434mQaG5dLg1VQBv1oO6lYe6lbAtxZQO5OiDMO0/cFhxYiYJuuMlKrcDtuqBpAGun7zGIb5mWEYBwCHA7By4B1gwINIp0aGoZio5QSPkvV2mbpRcdvH39gykqafnc3cMDuIGXvHDGb85GoAK8Fyb8HdsASkKsHu8jWG3eVrtgmeFoxGLUPCAG+o1LImxmblQXXqgIuh5UWcjWzQHAwW/u/oveCKC/0WuBpBcgAKAPYs3Lo3Qx34LdxC3oZMfWneERL+kuDX7org1+76FKRk6Ib2JAMHp4eI63IWpHLQgwjhTwRFhAQJEq4wysrK5BMmTIgMCwvT9+3bt8+oUaN65eXlqTZv3qy78cYbe13KthISEnrv2rVLCwCjRo3qVVv7/+xdeVxU5fr/njMzDIOALAoIKCA7DIyKoZLgvtxyyQUlNcUyS7PlarZo0rXlZjdtQe1mVuByi9yyfl5vpZmSkQsuKCgK7oKggOwDzHJ+fzzndQ7DAGruzffz4QPM2d7znve88z7f5/s8T4mstWP+LLp27Rpyu69xL+Cll17y3Lx5s6UcSrcU48eP9zlw4MCNhw3fBG5mjN0okpOTXc+ePXtNntrc/SUnJ7tOnjy506267p0a//carKETVjzYIAXDVgDdWtjL3JtrybvW0jHS320BPAbyAG8EGaH3Vhb+VLUfqBTmhwBSQW186W426Zai+9OZXObKmbCr/RKAMwAOopIBpjAAQEx+yGmLZG2q9jsn9ivTwDwkhciGtwAgI3L5KpUnRrXryQ/1iJMVyxy54QD2gcMZ0DOvEwS4w8NQcsq1TH90f1m7vUfLugLY3tt7wnsQcxjsvvh1Maj/m0BUNhjVIY780xP8UFBUN3/hh8fywMaPmNDyuvqBEo4eBlWN0INIMaBl9UJL2zmQl7kekKcCxkjoq+WoLwtEXfGnFkOIrLCiKd4AkICmBEMKKB/OdJC6h4dJOSMludjftyqEzAor7lvkfr/a5XDqx17a0is2Ktf2DV0SXyoIGTm57GbPZzQaMWLEiIAJEyaUbtmy5TQA/PHHH6rCwkJFa8e2hl27duX/2XMAgE6ng0LRfHMOHTqUeyuuc6/j448/LrwV52mtP7/99ttzt+I69wrWrl3brkuXLlpfX18dcOvur7V+vFXj/8+gtTbeDlgVDVY8mEhVD0aq+gqAi2ieZGDqdGZ8Gi3sY+lzttgVLGxji+LhINLhP6Ckjv3F0Ic7i1S1vdgXfkhVJyFV/XcAuwEkAtgHYBkAPwBfIVW9GanqJ+5KO289dgB4DpR08XEAbaGv/RRV54ww1P0E4D1QTo4XYee+AzZOc9A6GRSjLcT8cxsN3T+fc7H8ckndHFA1h0WgcJzuPM8Ny8gq+az7SEcXToE8AMJDXZz9VLayxaCqJTGtXQPAmezcyrq9h67+a1Cc+yqJ0oHCOTJXZiNzpaHht4/2/PelgUmpU/q7t9IPzwMYDRqHbDyz8KDmIEj2Fb8nuHpAYQCvkkNhNwAwjkZ9aQwgrMO9RKRZca/jHQA7QeFqOtAYWwlSg80CkQ0GcV8ORJSVgcZkIWj+mg3rmLPiL47c71e77Fu60EdbetkGEKAtvWyzb+lCn9zvV7u0frRlbNmyxUEulwuvvPLKFfZZr169tEOHDq0GgJqaGtnQoUM7+/n5hY8YMcLPaKSvke+//94hNDQ0LCgoKCw+Pt5Xq9U2cdh4eXlFXLp0SX7ixAmbzp07hyckJPgEBASEP/zww4HV1dUcAOTk5ChjY2MDw8PDQ6OiooIPHTpkCwBjxozxnTBhQqfIyMiQGTNmeBcWFspjYmICAwICwsePH+/j6ekZcenSJTkA2NnZdWXXnD9/vkdQUFBYcHBw2MyZM71auvfZs2d7jh492jcqKirY09MzYtWqVU7PPvusd1BQUFhsbGxgfX0919K9enl5Rfz973/3DAsLCw0KCgpjbS8uLpYNHDjQPygoKEyj0YTs3btXBQC//vqrXZcuXUJCQ0PDunbtGpKVlaUEyJM+cOBA/5iYmEAvL6+If/7zn+3/8Y9/uIeGhoZpNJqQ4uJiGeuTlJQU55auXVlZycfHx/tGRESEhoaGhq1du9aJXaN///4BPXv2DIqJiQk2GAyYNGlSJz8/v/CYmJjAPn36BLBzS1UpGzZscAwLCwsNDg4O69WrV5B5H544ccImKioqOCwsLDQsLCx027Ztbdi4io6ODrY0djZs2ODo5+cXHhYWFrphwwYnS88mOTnZdcCAAf7R0dHBPj4+6jlz5jClJj799FOXiIiI0JCQkLAJEyb46PV6sHHw1FNPdQwICAjv1atXUGFhoTwlJcU5OzvbbvLkyZ1DQkLCqqurOen9ffLJJ66+vr7qiIiI0IyMjGtr4cLCQvmQIUP81Wp1qFqtDv3555/bsDHz2GOP+XXr1i1k9OjRflVVVfwjjzzS2d/fP3zQoEH+kZGRIezcbPwDwLJly1zZuHzsscf8LI3F+Ph43+jo6GBvb++Id95551poc0v3y/ZJSUlxHjNmjC8bJ9J3p7nx2Nw1Kysr+b59+wYEBweHBQYGhq9cudLZ0jNqDlZFgxUPFkjB8DzIQ9+mhT3NvbaMbDD3oDFCoQYUn64HxdAzya/5lyn7XwlaDDtKzrkKqep9kORqSInzvhabPzX94u1IpMdyRVwVBKGP0QiO56EQZf524j7+4g/bvxh30VM4YGy8H6cSFtfvVbgKtdxzAM6J7cq4ZnC3BvL8rxN/CGn9foNeOxJyVaqAhbsgJvPkEuKWo8fzls9D1SpY7oQMAK9k1l/usqX8zPBvpuXvkbSH9dfPV9bNz6rLKW+oqdX/35C+7knjhncc+POuolXb0i/vWfpOF1tkrrRvIZFjBoBZ2jpDxtQP3rW0z1QA4QCg0Jf06OpXrfmjTtkAsyoW1yCtPJKq7gVSsfQBEWBPgMiwQgA+4hE6EAlhCxrDMlzLS6LgYe/1OxRtdqOu5BvoKsfgNpZGteLBBJcQd0ZISx8JSgTZFkR0rgDNsQtAYVyvSA6pA6kgZgAIAbCcS4j74o422gor7kEcTv3Yy9BQ38hhaGio5w+nfux1s6qGI0eOqDQaTW1z248fP646fPjwaV9fX11UVFTItm3b7GNjY2ueeeYZv59//vlEZGRk/ahRo3w/+OCD9klJSZebO8/58+dt165dezomJubcI4880nn16tXOM2fOLJs2bZrP559/fi4iIqJ+x44dbWbMmNFpz549JwHg0qVLNgcPHsyVy+WYPHlypz59+lS99957RRs2bHBct25dO/NrrFu3znHr1q1OBw4cyHVwcDAyA70lnDt3TpmRkXHy4MGDtv379w9ZtWrVqc8+++zioEGD/NetW9d2zJgxFS3da7t27fTHjh07vmjRovaLFi1y//bbb8+98sornhqNpnb79u2nfvjhB4cpU6b45ebmHtNoNHX79+/PVSgU2Lx5s8Mrr7zi/dNPP50CgJMnT6qysrKOabVaPjg4WL1gwYKC48ePH3vqqac6rlixwtVS31q69rx58zr069evcv369WdLSkpk3bt3Dx0xYkQlAOTk5NgdOXIkx93d3ZCSkuJ84cIFm/z8/JyCggK5Wq1WJyYmlkrPX1hYKJ81a5bvzp07c0NCQhos9aenp6f+t99+O2lnZyccPXpU+fjjj3fOzs4+3tLYmTVrlu+2bdtOhIeH1w8bNqxzc8/myJEjbY4ePZpjb29v7Nq1a9jIkSMr7O3tjRs2bHDJzMzMVSqVwqRJkzp99tlnrrNmzSrVarV89+7da7788ssLL7/8cofXXnvNc/Xq1ef//e9/uy1evPhCXFxco3F+7tw5xaJFizwPHDhw3MXFxRATExOsVqtrAeCZZ57pOHv27OIhQ4ZU5+Xl2QwZMiTw9OnTOQCQl5dnu3fv3lx7e3shKSnJ3cnJyXDq1Kmc/fv32/bq1Svc/D4yMzNtFy9e3OGPP/7I7dChg765cZmfn2+bkZFxory8XBYaGqqeO3fulZycHGVz99tcvwGN350pU6Z0tDQem7vmpk2bHD08PHQ7d+7MB4DS0tIbCv+wEg1W3P+gUICPQMoFTxA5YE4AmGfPF8w+h4XtjHDgQIZXsfi/HWhRnAMKO4gELZhlkvPwAIIAVAP4DUBf0EJ5CoDnkKreASDG0ZG3raw0UsnLVDVLInnrkj1SHfpjAIYIAnhA4I1GGGUyjt2rlCypAbAQd8tTmKq2L5HJ+gd295zTq3117ObK9lzRAdsjgq1dhk19tWuQfVHN290jEhdkHs0REzVS1Q8Kb2gddVe2AiiHvjoDtpgFKu0JNGekE64l9Yz5yuYwgB2vPJa/A8AemPWTNKHj7otfLxoDAJkrnwIQM21C54xpEzqz6iN1aIbIEYmLlkieFJAxZmdUulcdOmO/uEprvL7cCInZxUhVPyO2sQCAB4Dt4vX6gCqPaKByz4KzegDqKuS4ergGgn4bOFktBEM86oo/xdh9jLzJua7rWvHAQSxHuRhEUClByRkzr/d4LiGuGoClZI4/i0kipaESW0B5dd4B4AUxr8lfHWKOihgAGWJ/WvEXg7b0is2NfH4rEBERUePv768DgPDw8NpTp07ZODo6Gry9vesjIyPrASAxMbF0+fLlbqBwQYvw8vKqj4mJ0QJA165da8+ePausqKjgDx06ZB8fH88cH2hoaLi2lhs9evRVuZzMln379tlv3rw5HwDGjh1b6ejoaDC7BLZt2+Y4adKkEgcHByMAuLu7N9nHHAMHDqxQKpVCdHS01mAwcGPHjq0U71V75swZm6ysLNuW7nXChAlXASA6Orr2hx9+cBbb6rBx48Z8ABgxYkTV9OnT5WVlZXxZWZls/PjxfmfPnrXlOE7Q6XTX7jUmJqbK2dnZ6OzsbLS3tzfEx8eXi/1fe+TIETtYgKVr79y50/Gnn35ySk5O9gCA+vp6Lj8/3wYAYmNjK1mf/Pbbb/ajR4++KpPJ0KlTJ33Pnj2rzM+/c+fONtHR0VUhISENzfVnQ0MD99RTT/kcO3ZMxfM8zp07p2TbLI0dBwcHg7e3d31EREQ9AEycOLH0iy++aG/p/nr37l3p4eFhAIBHH3306s6dO+3lcrmQnZ1tp9FoQgGgrq6Od3Nz0wMAz/OYNm1aGQA8+eSTpaNHj24x90N6enqbnj17Vnl6euoBYPTo0WUnT560BYDff//dMS8vT8X2ra6ullVUVPAAMHTo0HJ7e3sBADIyMuxffPHFywDw0EMP1QUFBTUh7X766SfH4cOHX+3QoYO+uX4EgMGDB5erVCpBpVLpXVxcdBcvXpT/+OOPDs3db0swe3csjsfmrtmtWzft/PnzO86YMcNr5MiRFUzddL2wEg1WPAh4A8AItJxbgcnEmSS8FKYEkFJSgf2vB70f0nh+DwBHQFnSmadXAyIZDOL12TFSZUM0KMGkDKSy+BfIa9dtxEiHtT98X/VuZaUxA+Th+xfIm3erMqr3AfAIAHuOg1xUMkgl8/ViX3QAJfyLBiWzvOMokcn6A1g2yae0TVStltM62mANbHmhrjamnXPN5Sud7ENdyqqThbT0kXBsl2zUy0d/t+/wnI8fmzB698Wvf2v1AlLvflo6M1ZaM1oyQLk2poISQ74gVqZoQgbEBFU83T+i/J87jjrNAxFfjXMqZK68kUSOltH96WJkruwD4N8yG9WMRz/eft3GHQBTH6SqXwOFUxwXcyusQ6p6K4AY2PtkwUXdAwDg+fAOdH+6Wgyn+RH62ntarr4kyVSZZc5bVpXFzUI0Yp8GMBPAtyDVwY+gsawBhQyNlhzyb1CyxubOdSMGcYZ4zfHi71jQ/FnBJcStuZn7uRch6eNZAH4F9e1WLiGuWkLkuAJ4jkuIyxH3HwUKBQsGhb0lgOYUa66KvyBUru0bKGyi6ec3e86IiAjt5s2bm5VGK5XKayWbZDIZ9Hr99eS0agIbGxvpeQStVssbDAY4ODjomXfVHPb29i2F+90SsPuTyWSQy+UCzxPnyfP8dd2rra2tAAByuVxobf9XX33Vq0+fPlXbtm07deLECZv+/fsHs23S/uF5/tp5W2qHpWsLgoANGzbkazSaeum+u3fvbmNnZ3fL+/Pdd991d3Nz023cuPGM0WiESqWKYtv+7NhhCbel/wuCwMXHx5cuX7684EaPvxEIgoCDBw8et7Oza1KqsU2bNrdlXFrqr5buV3p/5qFL1/vuWLpmZGRk/cGDB49t3Lix7YIFC7y2b99euXjx4kvXex9WosGKBwEfApgIMuoZDDCRBgaQt74MtGAWQCQDB1O5yvMAvAGcBZV7lIEqTwAUE/w9gL8B+B/Is6YC0AMkNW8AKR44NCYoeLFN7cT/5eJPMKgMJhQKbsaYsY7eAE6BFtYOADohVf0fUH6JD284yR4Zhf3F/6IAOAHQchzHrg9J+2SgevXnQLLkGNylJGvz3DzdvBsaXC9yctsz1YrSrcedXG3UeujyZFyDP1frOLLBeGW3g/ZyeXV/N7gMgAC5o86jHXD8u7Wvz+8zaYzvcFynwoFLiCtGy0oGQvenq4e8ua0k1Lmuk4vSkFmr5/MWdadNkXN3SsMqkNyj8uXYwApbF3v9LKSqc2CuTLmRRI4ttykTzRh1N4CmRIuUiDEnuhpvu6tYkqSOBZWSXQd6n38FUAkKU3oOVJmgeEmSOgX0js4DVSqwkg8tQEhLZ2FnIaAcM8yAYcqfMQA+AamztKA5pAxAEYDZohKBEVGjAAwEsFQ8ZwyANUJa+ofi+VcA+ArAKnPyQTS0nwERnlmgZwhQvpH7DmK/sjAt6dzUHzQH2QDoDFK7bReIBB0DE5Hzi5CWngvgICjciUnEFwP4DtRHVvwF0SXxpYJ9Sxf6SMMnZDZKY5fEl1o1uppoiZAsAAAgAElEQVTD8OHDqxYsWMAtXry43csvv1wCAHv37lVdvXq1Wbm0RqOpKygosMnOzlaq1er61atXu8bGxjbxiLcGFxcXo7e3d8NXX33l/OSTT141Go3Yu3evqlevXlrzfR966KHqNWvWuLz77rtFmzZtcqysrGzSviFDhlS+++67ntOnTy9joRPu7u6Gf/7zn+0BYN68eVfMj2kNN3OvPXr0qEpJSXH94IMPLm3ZssXB2dlZ7+LiYqysrJR5e3s3AMCKFSuahH7cCvTr169yyZIl7qmpqed5nsfvv/+uevjhh5v0Z+/evavXrFnjOmvWrNLCwkL53r17HR5//PFG4Td9+/atmT17tk9ubq4NC50w98ZXVFTIvL29G2QyGZYtW+ZqMLQsIunSpUtdQUGBTU5OjjI8PLw+LS2t2fwiu3fvdiwuLpa1adPGuHXrVqcvvvjibJs2bYyjR48OmDdvXrGXl5e+uLhYVlFRIQsKCmowGo1ISUlxnj59+tXU1FTX6OjoKgCwt7c3VFRUNBkvcXFxNa+++mrHoqIimbOzs/G7775zDg8P14r9U/nee++5vf3228UAkJGRoWKKHCl69epVnZaW5jx8+PCqAwcO2J48eVJlvs+QIUMqx44dGzB//vwiDw8Pg6V+bA5Dhw6tbO5+XV1ddQcPHrTVaDR133//vbO9vb3FczY3Hpu75tmzZxVubm76mTNnljk7Oxu+/PLLGxqrVqLhL4hvctYuaZDJX7Ix6D9+PHzSnLvdnlsAL1CiO1+QwX9c/GHVH/SgRfFWAG4g438ATDkc5KDF22WQTN4PFLceKG4/D1r0dQaRENIvFRtQWUt3UGiFA0zZ/VkdeA5k0BtgIiIYCdIGwFix7T3FfZ4DhV2w3BBv3WB/xICUEQCwQWxXPii231m8th4moqEHyFBbBkoQeVe81iVyeewVmdwOADb5ujjXj22oc+gIW12+TKjcbNPR7rt6ofi0c48X2399+MPgcf84dfrqvJX/2+8OwJnnuR+MRsGD5zngegiEG8ClWsUnl2oVtgB6A8jfOnfnIZBRMBxkEMwHgI/PDmyoq93e0KbG+Zjgf+Zdjhfm43qM81STF/6O5Tog8uqW9tPtgkSlkAVgMMh73gZUUpYDhXzoQOQfe6e9QAQDDyLb9ACOL0lSfwkyzoB7Vflwh8eD6Cn/O4DXYMrLYQk2oLlqA4DfQWFq34Lmqjkg8mstKLTsGdA81w+mfDZzALwMekY8aD66AAvqLZF8sEx63YNoRbUxFWZhWuL+0Wjc13IAQ0DfA4UwkcHu4k8USH3GwvR4EKFzBDf+HWHFAwCWh+FWVp3geR4//PDDqZkzZ3b85JNPPJRKpeDt7V2/dOnSC+fOnbMYkmFnZyd89tlnZ+Pj4/0NBgM0Gk3tyy+/fMNGPAB88803p59++mmf999/v4Ner+dGjRpVZoloWLRoUeHYsWM7BwYGukZFRVW3a9dO5+Tk1MiwGjt2bOXBgwftunTpEqpQKISBAwdWLFu2rCA3N1f18MMP39TcejP3+v777xdOnDjRNygoKEylUhlTU1PPAMCrr75aNG3aNL/333/fc9CgQeU3057WsGjRosLp06d3CgkJCTMajVzHjh3rf/311ybVD6ZMmXJ1+/btDgEBAeEdOnRoCA8PrzXvT09PT31ycvLZUaNGBRiNRri6uuoyMjIalUZ/6aWXLo8ZM8Y/LS3NtX///hUqlapFT7qdnZ2wdOnSc8OGDQtQqVTGHj16VFdXV1v8DoqMjKwZMWKEf1FRkc3YsWNLWY6FN954o2DAgAFBRqMRCoVCSE5OPh8UFNSgUqmM+/bta/PBBx94urq66jZt2nQaACZPnlzy/PPP+8ydO9eYmZl5nJ3fx8dH9+qrrxb27Nkz1MHBwcDyMwDA559/fmHatGmdgoKCwgwGA9ejR4+qmJiY8+ZtnDt37pVx48b5+vv7h/v7+9cFBATUOTs7N+rH7t27182ZM+dSbGxsCM/zglqtrt24cePZlvqJISoqqq65+124cGHByJEjA1xcXPQajaa2pqbGYsGH5sZjczhw4IDq9ddf9+Z5HnK5XPj0009vqEoHJwhNVCBWPOBYlZtmAM/zMBqNU0IS7v+arqaFeR5IFZACMkiWghZjKlCIgBZkxG8FLaqfBy2Kj4n7snjC+QCWgIiF0wCWgySsTCVxCVS1wB/AkzARGfXibyeQGkL6kleIv9NAEvxS0GLRE+QVdIaJ+NOKf+tAnr+l1+7retQNjRUNe0EL3XixPZHib514nXQQOeINYDYSs+/aon70L8P9jAZkcjxcAMCohwENEHgl5DVb5ajdaKNTRhhgP7mhrG6f4oua9UqW8FNwdlIYXnwq8JeBse5Trjtnw3Uicu7OAtBzkqIKNA4AUtPk8pXl/cenH+rS185xpG/s/vUdumQvuy5DMVU9GJS7YQESs+8J1cC9ApFkWACqEHIGQBc0VuWcBHACZOQqQUaYdDtgIvsEUH6MU+JPGMjw3YF7iXRIVQ8WYPsu5CM2Qt532e2MvxcN3tfEHxYOBjQuA8uUWldAIValIOVXN5jUXCxcrEb831w5ZQl6ABO5hLh1zWy/LyD24SyIpCOXEPez+DkrI/xvkLrjmqJBVH/8GxSGZ+7wYf1eA/rugrhPOWh8S5MVs/1HcQlx9zwhY0XryMrKOqvRaErudjvudWi1Wk4ulwsKhQLbt29vM2vWLJ/mQi7M0a9fv4D//e9/p1iogRWEiooKvm3btsaioiLZQw89FPr777/ndurUqdX4/zuB5ORk18zMzDarV69uYtw3Bzs7u661tbWHbme7zKHX69HQ0MDZ2dkJOTk5ysGDBwedOnUq+0Efa1lZWe00Go2vpW1WRcNfDKvy1kvLqFyXVOeeR2NZN3lpU9XfgQz/nqAFcQRIWh2CxOx1SFUvAnmCSMbLDHgy0uvE4+YC+ABEDjwDWkjrQN7S1wCcF4wwGgUoOAG/8nKcABAAYCQa54swgFQC/cTr2YEMZGYAsTAOliviMkx5IMaBDKLeje6vtf4Qk02CFqtMHl8FIkjY4nUziHgBGDGRqra/WxUENg34vzOjfxkeZ1Np3ByMus555W1411xXoejhq4KNkRcCgpxkMAj86bUVbvX5+kCYQmW4mnKDYdfysrMnv9ncR2W7cOstruCRAPKqOsK0wHeAyTP7B4BFRken9yf1ObtXblvfoK9TptxAP/753A0PCKQ5FkCJBjeB3ikeJjUOAw9SArUFsEb8exCI5GMEqtQoY0ld1SBvuhGU2wUgcvFeiXXPgHzERsjixoDk8rezTTEgdQ7LXZMO4BAoye1SUI6XxSCP+TBQ/xpA4/83kEKMxRVzMOWiYQSDJZLBCJpHL4Pm2nsKInHwCEhBoADN1yEgtUYaI36E/6SEg7NfAvD/B67NVIBfD87WSUhLHweaK16HqLLjEuImml0mAxSSZ/45YOqzWhDRXQci0n4G5avwAM3rCpDKhAfwBehZWGHFXwL5+fk248aN82de3RUrVpy93mMtefStAAYNGhRYWVkp0+l03Ny5cy/dKyTD/YSqqio+NjY2WKfTcYIg4KOPPjr3oJMMrcGqaPiLYVXe+v/Y1lc9HnD5MJffPuJyna2T/5TA+LvvxbsdIE9xMmgBvB7kfVtxnaoAU1UDIhleB3nyPAXBRCIIAgyGBpmRlwtVMrnRAFp8M+NFBlOOiP8DSb/bgxKsSbOql4AWjStBC8itIClzMWix+ShowekE4AUkZueIbWxeYp2qHgFTYsk6UEjITyDDoQdooTterEQg9cixqgjM6L2zkn4Alz/pOqJOj5TM8x2chP09+F86X6wVtMCjJYF2yno5/s/pJA5ll5YJUOy1AT/EV+4IGThukF1HlHLnqzxsDEvdDZGLYo48d0vbHDl35zhQWUhLBK0RwCc/B72x00NRTkarVZ1ww1iSdE3dsQRk4Ha0sJs0easRpMwpAj0XJxABZOkZmZeuBejd3ALKh+IMGvfvApg+563su+Zpl0rxxY9uS3UB8ToJILLhMkjhcQVEPNqCSIRZoP5kCeI4kAosFzTXXQKRr82RCgbQnKIC9X+JeK5NoNCLHfdK1QRRhfANgO5oGkKiAz2PqVxC3Bnh6/U/gms/BMYreTBsFcAH/wfymOdB+UCkKpqPuYS42Rau5Q4KQ4kR96sFETgM9SC1zh8AEgH8F5QfQwZSuq2GqdrSk1ZFw4MBq6LBCiusuN9gVTQ84FiVt97iorQZAqEs4PJhLvLib+AgtDvaMe6uJP67Q8iAqR77jkbGsiUjvfFnFMNOn+WClABbYUpABwDgOMg4mVDC8cYzoAWfTNw/HI1LRw4FJZLMBYVOOIAW4Jkgj9nrZobpREmb0kHx5n0AfI1U9bsgb/4w8bNSpKrzQAZTJWhR3xZkcD0M4ACIAHlebJ8MRJrUiKVBvwCpJnaAjAvm3QWYpP8OjhG3trod+/Lsf4z0uTz+Z/cjwumdClvbgfWXLx1rZxeU1QEezkoI4BsAaP0VbUsH23Zsf9lQi8v6WqGjwtfeQa+cCg7pt7rNRz7ouy5y7s4c0ILfx2wzD2D6jPzndG+4/ze3ulaW1+dWXvwBQkbk8mskXsyR58xJP6buiAOF80jBvO7svWJecxUojOk0TPlQpMew0CYFmlamkYFybehA3uFxoO/FL0Dv+l2BNEeBKLNfAkAppKV/CXr3V5glFrwuSCoXDAOFP3wICgvqAeqrchAh+RFoDlHBMoHgBfLWy0CedPZsLO37G8h4DwQZ8DEgI/oMaK5pttzrnYBEwRACoBdMySfNoQCNyxVCWvpoCNVzAFV78O1cIASngLNbA+BNNO4DPUghYn499n39uXg9BZr2nRIU6uYO4FNQn9uBxuXb4hgZbOm89wpxY4UVVlhhxV8bVqLhPsOqvPVskb4dFP8ZAlqkeIGkmEqQF6p4Vd76bQDeBy1O3gBVS6jId++i4yAI512CP8KDLNcmAqE5L08MpEY0KRj+CYoBfx2palZusgsoqeS6XTmOpTZyw+bu/jWP8jwCeA48x8EoVxjbgzypPGjBypQMUqPGDtT/bwM4ClrQZgCYBlqAN34OjfNOxIGUEADJvr8xO787iCjoC1JEtAGwE2QkzBDbw7xlLP53JMiLawcaPwKIDCkGkSEFILLkv7iTWc1T1fbHtd6PvFc/7uIjp9cn/yrTK2rO2bjYdNJrd3Y+ObXUo4Y/aFtUj9/kKgCjTukq9EUKV2hs2qFeMHJ76i4JSpl8bW7D1YJXvCf8D5RwzRHAK7svfv3Rn20eV13ZTVZR7qh366CHQmE+f6q8ZXUzHTpGyfOOlJ3q7T1hD4CM3Re/ti76G2MqgPl1qkrnJUlqD9BcthuUh6EdaE7LBSmQpJVkpCQCKycLmJQNZ0HPmsW2s/dQBzJm7WGqpCAFB1PeElYdRrskST0N5OXfcZdzN+SB5PLtQHMUD2C4kJbe7yYMyhgQadEOZARfBSUe1IJyxXQHVaCxhSmxpiWwXAECGie3lcIAyhHjI7b7Kmi+3QV6vrtAJOrd/g6KAalnWHUgS2AkigCaU/ZC5p8LyrPwCuQxwaDxWwXqGyMo0SUADBXS0tl5WJjJEBDJUgHqdxeYQtqk1wNofh8JGtdfA5jXzHNv/J1mhRVWWGGFFXcZVqLh/gPLYD0T5PGTGrP9YJJrtgcZn67itkni73l1Ng7VRzvGpUwJjL+lSfPuObScvT0DVGVhClLVnUCxr2MAbAQtskcB+Bi02NYBeLFnUOUrANoKAnQiySCVYSvRPJi31A0kyzaAFpJhACKaScDYH5Qf4iKIaGDXaU6ezLy8diBjaxgaG2J24t9Sb29byd8ciCx5EmSQ9QTliSgCsAe3a+EqDVEhFUl/d/nVZZ0cK9su5V5dA2AQRgADdi7f/3SXk9jqcVnvuc/zf88+Yzd47wlb7c49FQ0nG8rbhilcOMgEFMtquMO1ZToASSDihOFfIC/tn4Lr5RND1Q1ZTpmK/ly1W2fzzfy+Bk+7lVn63NO/5J2BddFvEWfUu7erqp3GFAQcDgapCVjZ2MdhyqbfG0A16L3hQB5w6TvWAPKIh8JEFPQFGcznQGO3DWguVIBKYHYBEbJAY0OOE/cxwmRouoGMzyIAL+AOP0MhLT0cRAh8KP5mJb9Ym7uB5ogblctngKo/DAORiadBXnMVqM/+B1JJOV7HuaSKLZj9XQHKcfMDKPRsMoDDoHmlFsAuUZFxp/vVktc/A5SD4i1QsktL4EHttgX1TVsIQhiE2ghwNofAKcaCQk7+C5qv54AUcJ+AFGyPgogdNucWgcb+hwCmA5gN6vfm4AvKFfRiC+SSNdeLFVZYYYUV9xSsRMM9CLNQiPYwZa5+BLTAroDl2GWpYckwAJRfQA/gHZFcuC/K2t0CmDw8pFBIAOU+8Be314MWjWNgkv52BvX1JVAcMQcycOxs5NSv9TrIBAFGjmsikZWb/c+SoSnFcxeAjKAwmEIYGoPIkVFiOwMkbZVmgzcH8+gy40CweG7TeZrLAs+L9yADlawDiHT4c4oGUxUMW8mnTHkTCFpwv4xUtQrACgVv0Ck4YyVISeLqIqvES5p877ZKA58ov8pffNi1i6/rFVV0l+76knLBribHoNPbGBUuwTzsyyEgGxNBBI30Xi/+qXsQ8X6vX350VtYOX3K5SvZ7HeQwedl5ANBySv6n+sAAros7HC6kWRf9FlAUcHQGyFA+DUqwtw80PupBBj8bg8woY2EPAM19FSADrlL8zTzBPIhcCAGRew3isSrQPFgmOR8bF0wZxN4vA0zvcRWA73EnFT0mJIP6pDcsKwsabuakopG6BsAaMSSD5cM4BXrnB6Jl0rSl+YNt50Dz53YQwekv/j1ZPLcAIFRIS1+FO5ifQSQZZoPyHVwV0tIncwlxOVxCXLWQlh4IyyQDU20YQSU9O4PUJfRMOFt/GEtOQeZeDRqPbUDfryEA9oPCUGxBKoiDoDLJcpCTYAboO2EZKMQtFi33bTcAW4S09Oe4hLgc841mJUGtsMIKK6yw4q7DSjTcRUgIhSwAGtCiYx7ouYSDjKMakMHVDWSgMpmqJTDpqpRwYAm8XpwSGF8tJTEe2CSQJjAPTxYoxwILI2BgC2ppfOzDoH50Q2MPXRnHwcloBK9UNHpvjDDFgPMwKUosvVunQdLk70He2COgvAjMGH8E5PkaAVIX3AgY2cDa0JxBwOS/Bpik5dL97Mz2dwTQA6lqShBJFS1aUoo0Bu07G8CzgiC0NRoFgec5nuM4Rmqw6zNj8UU7rq6qk3B+S7/6q4P3yh8qGd8+XW6v1Klq6yFvMHDcTxn1igF9A/+orFD9eDyvKE5hkK3OrCterslr71BeJfAcjJUCeA+QVLsEZHROvY4+bBUPtbv4XZHOqeKMwbstgD5KvWFs53Kt4yUXHqEO53BY2xlao9JGsHd8o/Lx6e/cims+gHjH7Pc6UFUYpj5g7520cgFTGyhBhl4U6B2qFT+T5m0AGhvL7LgOaDreBcmxTNnA4AnyRv+CO2/AHQMpNMxJBtYnhaCwhD8D6fzIztscySAlYsz7j30mnf+CQVVtSkGe+jMgg5ttHwpSN2wX0tJfuZl8EzcCkWRYAOBZ0BznByBZSEv/BKR0mSXZnZElAojQyQLNId1A5NdJEJHAAzwPvl0YTJUpQkDKQhWIWKkC5W1YJZ6nE+i+IZ4nBTSX9gd9PwSI27QwKXIaQMRNkHj+ZBBxZoUVtwUymSwqMDBQq9frOZlMJiQkJJQmJSUVy2QypKen23311VeuqampF1o/081jzJgxvsOGDauYOnXq1dt5nbuJmynZGB0dHbx48eILcXFxtbezbVZYcatgJRruAkRjPwGUnM8TtJhwgskzagB5vgeBZOtVINIhHI2rFZh75Sx5sm1BHnr1qrz1H4JkrE8AWLMqb/2HDzjZ0AYUCjAKzRM0Uo8mM8LrYPK8McOkLQCe55skkwMax32zRXcDTAbSGZCM9kuQ9z7FQuWLGJC03x1NE9Y1B/M8ENL7MzfWpNALUDaA9+dgPGXDoV46bqTnqwctkoHGYQA3EgscA0ps2RaAHcdx0jYbJdeFACXAd+Z5nG77rOf2MeeVKvQ8s+e9x90urQAwtcYoK9XW88lni3mvqQurXIGqTwB8BOhjTmmremmNl7+uMhSFutpwjhUNdot1sLkCepY7/myehMi5O0VyZVkGiByaDWBScGmNcvTJIpzsWYVH3XZg2eVh+KMmFCClxkcAvCLn7kwBEYkZRz7o+yC/b9eFOW9lnwHFs2NJkvo/IAPuHEyx7QLo/ZN+P7ExbAMy+h4FzW2/gsiGYTC9h5beczbmzJMWSok3Nkew8os1IGMw72bv9U/gDJreBzP2y0HfF4NB6oSbAvOAi8qG6ymNqLPQJpbssJtkWwXoWapBXv4dIMXEdLNj7QHEAziO26+ySwSpxFiITB3I8E8GqQMtVQE6DiLAuoHGGCNSzgDwB8eJ55I5guYxVgrOETSmOoNy5VxTbQhp6RNA4SShAH7kEuKKhbT0DFA4ZAGAz0ChLBWg769iAC+CSIj1IKLi2K3sGCvub2xevd0l5eNNXqVXym1c2zs1TH1pdMFjkweWtX5k81Aqlcbc3NxjAFBQUCCPj4/vXFlZKfvoo48K4+Liaq1GrhXNQafTQaFQtL6jFX8ZtCTTs+L2oT9I8hsJSkDVHo296jwosVkVaKEGkCe8CAB0DQ24ePYUr2u4pp6VeqgtwQ5EMDwP4GnQInUiyAh8kDEblEWe1RpnYIZELajfGHTibwWoLznJcYwEMicBGKlQCzJMUkHJGmeB+jgeQHckZs9GYnYOErMXNVNeMw+0+G2u9rw5mEHWXPIyNiYYWSI9rhZ8ZxVkj9qB7ywtw8aO04ESmfVGYvb7IENBGgbQeixwqnocUtXlIOJkLoAVgoBarnHvMYLnPIDd4DsbIRsG8J0hl8HG3qYhM9yjYiMAzYorQ1OeKJhXfLTI+eWDZ+xPA+Cd2+iiZg0tnO2ulH3aQ+m+vK8y8A9HO9tSvzA/Xyd7zJZBHym2M6a39wT33t4Tknp7TxjX23uCfctdaxGMXOkPYFYbXjslps0xmVKoFaA3IK/UC8suD8NhbaO8DQWg8fcMTFL4vwSWJKntlySpBy9JUtubfe6+JEmdtCRJPW1JkvodkDHFwooENE1CaD6+edD77AgiI/qCSIqWcphIjUj2v/S80vec7VcKmoN7Ahjf0r3eJjxq9r9U3eEImsMf/7MXET39tqD8ApbAlCRsjjCC5rt68fcBUGgESzjLwRQixYGe1UoQYbkcZCTvA32XfQUq++slpKW/I5Z7vF2YhsZqlQwQ8cuS+AKmUqntQPPfFdA9a8XtO0CGP+uHalCZybUghYkMpsS6bF7dL4Zm2IuEzhXQPFIIciKAS4ir5hLifhbDIf4G+s6YBnIuXAURSy6gebkUVMXjGoS0dHchLf2129x/VtyD2Lx6u0vywjU+pZfLbSAApZfLbZIXrvHZvHq7S+tHXx+8vLz0X3zxxdmUlBQ3o9GILVu2OPTr1y+gteOWLFnSTq1WhwYHB4cNGTLEv6qqigdIqZCYmNixa9euId7e3hEpKSnOAGA0GjF58uROvr6+6piYmKCSkhKLjtDo6Ojgp556qqNarQ7t3Llz+K5du+wGDx7s7+Pjo37hhRc82X6ffvqpS0RERGhISEjYhAkTfPR6PfR6PcaMGeMbGBgYHhQUFLZw4UI3AMjJyVHGxsYGhoeHh0ZFRQUfOnTItqW2AsD8+fM9goKCwoKDg8Nmzpzp1dp5JkyY0EmtVof6+vqqv/nmm2v5sYqKihSxsbGBPj4+6mefffZapaWJEyd2UqvVoQEBAeF///vfr92XFCtWrHAJCgoKCwwMDJ8xYwbLPwQ7O7uuzz//vFdwcHCYRqMJuXDhQpO+nD17tmd8fLxvdHR0sLe3d8Q777xzjWy21HfsvGyflJQU5zFjxvhK7y8yMjJkxowZ3sXFxbKBAwf6BwUFhWk0mpC9e/eqWrpmZWUl37dv34Dg4OCwwMDA8JUrVzrDigcGVkXD3YEtGi96gKbJyfxBC5nuIKJAB4rxbF9ccEGWuXuHENW7n7GjX4AtWjZMmSEMUObqepCM8yyAiavy1udNCYw/8+dv6Z6EebJMBubF1IOMFak3E2hqmBjQeAHJttWJ27aBZMIVMC+jaQmp6nCQ5wogKe85UB4OTzQOaZCGwnBoLFGWtkkK6SKX3YdBcjzFvhtPA9giwHiaqRlqxWOXgwiT4WAJ6Oh+TMoF8/8t43OQV3MpgK4ApvA8dwH0TCpAHlQelHU+AUANjBfeA7bMhPE0z3PgPZwNGg/n2tkAIh352o1nG9xHzUXSAsf6z18HsHhcTIluaNerE+VXfDyCjTWdqwo9ev/NNbA+uK+XsFrO+e7KvPL3tqqG7NBgt3lHT1Xm1lQZnxT7IbO394QJuy9+fSPjnpErtmhoSIgWMtpM63RAvrKkJzY3aHFSb49aUjIANDZOi/f3jfj3AwmRSGD5N5QgSfoKkILDkurlGZD3VguSl18BjYEQUOUJNcgTfBVEwLakTjCCxuoAyf/mhIG5yoe9L9Ugg92cfKgTr30cFKLxG4BvW+mG24EtIBLFXHkhDf0IYzuLhAEjslrMe2BWXjEG9DwszZMCKK9FEWiuDAfNOToQCS6A5Py5oOfIQloOgZ6nEvSu14nnKwCwEMCrICPbE5QTwkk8fzVun7LhC/HcFwHkgBRHX8KU18YIUjh0FdviBVP/nocgdIRQ7QfO5ltwyifFe/sdFL6yFESOSb+/jaD+/VD8TKoC2woiD5oQtWYqkyJQcmKWq+kPUK6LrWaHsQTRwF8n/5IVAFI+3uTVUK9rNEc21Ov4lI83ef1ZVYMUYWFhDQaDAbKV5/8AACAASURBVAUFBddtM0ycOPHqnDlzSgDghRde8ExOTm43f/78ywBQXFysyMzMzD18+LDtqFGjAqZOnXp1zZo1Tvn5+cr8/PzsixcvKiIiIsITExNLLZ3bxsbGmJ2dffztt992i4+PD9i/f/9xNzc3va+vb8S8efOKCwsLFRs2bHDJzMzMVSqVwqRJkzp99tlnrhqNRnvp0iVFXl5eDgCUlJTIAGDatGk+n3/++bmIiIj6HTt2tJkxY0anPXv2nGyurevWrXPcunWr04EDB3IdHByMxcXFrZ7nwoULyqysrOPHjh1TDhw4MHjkyJFHAeDYsWN2WVlZx1QqlTEgIED98ssvFwcEBOg+/PDDAnd3d4Ner0dMTEzw3r17VT169GCkJ86ePav4xz/+4XXgwIHj7du318fGxgatWbPG6YknnijXarV8r169qpcuXVrw7LPPei9durT9v/71r0vm/Zifn2+bkZFxory8XBYaGqqeO3fulZycHKWlvps1a5bFZ8Fw6dIlm4MHD+bK5XJMmTKlo0ajqd2+ffupH374wWHKlCl+TCFj6ZqbNm1y9PDw0O3cuTMfAEpLS5tzmlpxH8JKNNw96GEiG1icP5P+CiA5ZwYo03QMaMH3OACtu5e3Mqp3P8HDq6MczcfiS41iG5jyPhwXr/s4aCGrhyhjfgDxBkg1EgZT1nqpQsERdP9XQAtnX5CChLGpG8XtsaDF9W+gBXRXUB8eBBmRagDFSMy+3jjuJaAkb+zvT0GZyn8AGWy9QZLbSPEaA8XPjaCScAIorthTbLsDyGiTGmCsTB9gMqLYeNNzqJfDeJx55ThQ0rLOoHj0DJAh8GcSGU4HkQ0/gRb5fWEylpg6hAfQlSk8uFT16zAevwLgVQFKW/BBtTDmRXOoWz/Q8dDpX6silrzl9R/bHwfY7PriF49pndrV5ynlBkX/QfkjHWyOBZ7PCs27crFNx8pfeM5dpsRDATV2Nnbtu49yUPDwUXXZk13Dkjf2APCP3t4TnoNocElDK8QwiWtG25EP+lYf+aBvdeTcnRkAHlEUnC0/cqYs4lP0wkGbrmiIvGo0tPMwz8DvBzJQU8TrvQKWj+M+g0gmxADIkJZ5XJKkdgeRCkNA741S/GkAJbh7F4DTkiT1CJju3QY0ZteJ/7uDvMS9QXH7zJiWJg41J/ikoT5VoHmNzYPSECgepqSQtpJjmEeaKZh0YrvPg96pYrFdBSDj+YklSerDuLNlLj3Q1Pg374cfJdtiQJVVALFKRjMVFgBTRZv/gJ7fEgCrLbSBJcINBRnGUrL1OIiIDAElOswAzVc7AbwE+n5RgBQPO0AKrzdA+WmyQXNaO1DfG0AkU0orfXJdED37U8XztRPv7w1QIsYMECmwCaRWYaSAXmw7D+r7PaB5P5fu0SiAU6khXC0G174MFAryFqhspz1M41Y67koBPC+kpX8JGn/vwvQsGn1XiM9qlNgv/xL3fxsiaSSkpc8S23/cAomUYvbbir8ISq+UWyrX2+zndxIHDhxQJSUleVVVVclqampkffr0qWDbRowYUS6TyRAVFVVXWlqqAIBdu3Y5jBs3rkwul8PX11fXq1evqubOPWrUqHIA0Gg02oCAAK2Pj48OADp27Fh/+vRpm507d9pnZ2fbaTSaUACoq6vj3dzc9OPHjy+/cOGCcsqUKR2HDx9eMWrUqMqKigr+0KFD9vHx8SzxNhoaGq7NvZbaum3bNsdJkyaVODg4GAHA3d3d0Np5xowZUyaTyRAREVHfsWPH+sOHD9sCQO/evStdXV0NABAQEFB36tQpZUBAgG7VqlUuqamp7fR6PXflyhVFVlaWrZRo2L17d5uePXtWeXp66gFg/PjxZbt27bJ/4oknyhUKhZCQkFABAFFRUTXbt2+3WEVo8ODB5SqVSlCpVHoXFxfdxYsX5T/++KODpb5r7XmPHj36qlxOS859+/Y5bNy4MV/sv6rp06fLy8rK+Oau2a1bN+38+fM7zpgxw2vkyJEVQ4cO/cuHmD5IsBINdwfMa8YyrLPEeFJwIK/gZdCCJxPkIb6qsFHad/QL4EELZ7bwNl+UWvJQcSAD6GXQwvEXmBKyPXhIzD4DirFlZRQXglQdrqC+04EWvkGgZFu+MJERv4IImEdAJMAGkPe0C8izpAY9mxiwRWuqugAsBwPt21zCxDkwycPngNQrBpCnbTjICGBx/XkA/iG2g8qqUV16L5jKmDLDCjAtdK9Y2CbFKdACeRVMnjl2vdmgBfbNIzF7HVLVkSDvJZOh+4rtYaQJAJQiVT0YpoSo/gBswXc2Qv43N+jbHIFxz+H2iqoFn/suPwIgMrEf5ieu2vGzeNzjKmWFBwChU7es/5UAgQahcFAYV1w9LkDrWNkgyLzsOBTulzscBF/ZAKMRgNEoV+wzOrl8Irta0ov3CxGemLDJLte9rX29XJYJqlXPPISvRM7dWac8sj/LtrbmGX17j2eMPO9UHvgQ9zvvBcgUQAd7S0Qfy82hEc+14H7LzyAhGGwh3gNEA0nclgxKXMqBFAqloHH1O8hb7A1KCtgAUv38F5RL4QTo3dOJx38CktRrYCIB2EKZjROpGoEDecqP41pSPmhB73QRaNyz0Kc6NFYmMRUT299ebM9WkHLsCRDRuB9kkO8AhUBNwJ0tc6lD4zlcSqQcAnm2P5RszwKN21yYCMJrXnQxD0CMZJsKdF+HQWEaza0FHMTfzjApPg6DyAeWsJb1Yy2Ao6AqPuNA4+Z30VBOAakIYkHPkRnFZ0HkzgoxXwFLihvCPmuhj5rDq6AwwYdAc+ZA0JzfT2zLqyCSQaps2wzgfRDBsARETJeI7XsI4HUQ6goA+x2gubUnKD+GeX4HaS6R4eK9dBTvZ4EkV4N5Imh3UC6lNqBS1FoAL0lIhWtkQiMCqW4mODp+WatKOiseOLi2d2oovdyUVHBt73RTVWmaw7Fjx2xkMhm8vLz0WVmmIjy9e/cOLCkpUWg0mppvv/32nPSY6dOn+23YsCG/V69e2uTkZNddu3axuQS2trbX1iSCYGl50jLY8TzPQ6lUXjsBz/PQ6/WcIAhcfHx86fLlywvMj83Ozj723XffOX722Wftv/32W5cVK1acd3Bw0DOPe3PXaq2tBoMBLZ2HM48bFf+3sbG5dlKZTCbodDouNzfXZtmyZe6iWsEwZswY37q6uusOdZfL5QLP8+xv6PV6i3m/pH0nk8la7TvpPWi12kbntLe3by6Mt9VrRkZG1h88ePDYxo0b2y5YsMBr+/btlYsXL26iwLDi/oQ1R8PdwQ6QJ4nJT/VoGkvPvOzM89MTtKBrB1NpRPMqEy1Buhhi2dWX3c9hE8La19yFtW+9Jqx9rfXY1MTsYiRmP4vE7A4AxgLIB3mjnEHkQZy4pwNokZ8qLtyY7PdJkJc+C7TAzgQt8GxBBsmboAX4OyBP9iOgRX4MUtX2SFUPFiswQMzVEAuKx/UCGfkvgMbEfNCikSkkAkGKjBUgNUNP0MKVA5EZBlA8MWOcefHvVFAcMFv8MlJKDzJINorHBoDUBs+L93IAQBIorvqRVvuVwfweTTCACK2BIIMyT+xflmW5DuTpmwoynPoD0MJ4mof+/wwwHj0t9s9G0HMiKXHmSnsETraFzHY+KOniEoXMUNtVo+sWqc6T99bUOLdrq1QEeGt4m7aPoqeHP//qyICyoPaOrwyy9/tS0ckj3rZz+QRjgJvzQ04Xw+Y6/+A7QfFHOxVfPxTkjT8PetZuAJKh1z+jOHNyou2hPQ62OYfsIJdzUCgEldxo7OVeA5XcKP2SNYK8o0txH9S2F/MpjFiSpB4n/mbPkBmqbiBi1HZJkvqXJUnqVJAhPgomj7AAMtL8QGNnnHi8C+gdmwAaYzWgKgR/h0kJcQSkHtDDFOYjDR9i12DhQDzIsPUHeccFmKql2KIx8VoOenf+DSLyWOlFHmQUV4M82DpQcsUqUNK9EJBxClCOkVdwZ5/hUpjeaaBxCEUI6L57CGnpI4S0dD/QXDYSQLnEOJWOPfYsY2BSGMwV9+uNlr9DpCFbSoi5gkDEAhv3weLf8SAC4w+QsWwrhgHUgJIavixedxGXEPcGlxD3BZcQ95aEUOgPCit7E6RCgJjfYJaQln5ASEvv3lwjhbR0P1E90ANEnAwHqdBOgoiV/kJa+ggQuStNpFwOmpdqQPORIJ6jFjQe9oPjbMGrfMCrFor36AsifKVELgtpY++DATS+iiCqGSTNZc9jqrjtKTQt0zqO7cwlxBVzCXGLxH669iwFzq2/wD+0VJBPmi0SEFb8hTD1pdEFNkpFIwPPRqkwTn1pdBMj8WZRWFgof/rpp32mTp16mRmvDLt3787Lzc09Zk4yAEBtbS3fqVMnXX19PZeWltZqzog+ffpUbdiwwUWv1+PcuXOKPXv2OLR2THMYOnRo5ZYtW5xZqEdxcbHs5MmTNpcuXZIbDAYkJiaWv/feewVHjx61c3FxMXp7ezd89dVX13JF/PHHH6qWzj9kyJDKtWvXtmN5J4qLi2WtnWfTpk3OBoMBOTk5ygsXLig1Gk1dc+e/evWqTKVSGV1cXAwXLlyQ79y5s635PrGxsTV79+51uHTpklyv12P9+vUuffv+eWdGc30HAK6urrqDBw/aGgwGfP/9983mUejRo0dVSkqKKwBs2bLFwdnZWe/i4tIsEXH27FmFg4ODcebMmWWzZ88uOnz4sHn1MyvuY1gVDXcPhSCvnCNM8fFsMmGLlBSQ53yoZBvQOBb0RulgLWix+D/cp1JuE+ymgg+cD2MecGOxqTtARk8GyHD9CKRsGATy7mUCqBeN5h0gw2k6qK//C/KkxsMkN34DjZM4ssX8uzAREQsAbESqepmkROQskPdvviTswtxjyjKSM8nvbFDohj+A10HG2k6xDd5iG2phIjGkUmsDyIjaCGAxaEw9JrmWNNzCHmTQXC8sVaJYClposyobs5GqThL3LQB5Dr3Ev58DeTZ5AOBQD8F4nBeAp/WAg4Ke0SDQAv81VJ07AgefOQh9ZgG6P70GqepxAD6Sy7lq8HI556KWc/B24nRRPK+6jPahv2CAc0ef0+dc3uxw0dWuOCK37YXYiuq2FQ4X/qY85hFiuAo3Qx5yC13xR00o84LqxT5wqQ+JmMpXV5wz2Nq52nh1QHSEIw6XG7kurlrMiijBynMGftdpbwgGGxab/cSRD/oy4+meqm0vhjw8AyIbnwKpELrBZKD/uCRJPRNEtGwDGWbBIIOUJW80z4VgBD2b9jB5wSGej6m2eqJxDhJWqeBTEBnB5jO2jwFE+PiBwhk8xHNWggzZABB54AIy0jxBxqI9TMZxLojkYOqHGpDx+Zh4T1kgozIUNHaVoLKEwaD3aMuct7J/uN6+vVUQvfvlIGJZChYC9Q/QPRtAOS5Gg8JRMiTnuCbRFxUNC2CS7v8gfu4Ok9qoOQhmf7OQk1KQsc1KMJ4Aqbw2i9fVgEjZZACvcAlxP7DrtgIV6Nmx8LIY0PzeBkQYPdTMcW+AqktsBs0pHcRzVIOImUdBJAgr4cnGrSMo+WJHUL/OAs2zcSBFHCuvxxKRSsHmVVaq1zyxaAOITOgBwE1IS68EkRl7QfNfT/H/7mjsDLgA4G0hLT0JNIa3mhFIywD8CzYLIiAIVeD4iSCi756aa6y4vWB5GG511Yn6+no+JCQkjJW3HD9+fOmbb755Q+qi1157rTA6OjrUxcVF361bt+rq6uoW4+6feOKJ8l9++cUxICBA7enpWd+1a9ebNpqjoqLq3njjjYIBAwYEGY1GKBQKITk5+bydnZ3xqaee8jUajRwAvPXWWxcB4Jtvvjn99NNP+7z//vsd9Ho9N2rUqLJevXppmzv/2LFjKw8ePGjXpUuXUIVCIQwcOLBi2bJlBS2dx8vLq0Gj0YRWV1fLPv7443N2dnbNrt179eqlVavVtf7+/uoOHTo0REVFNekLHx8f3ZtvvlnQp0+fIEEQuIEDB5ZPmjSp/Gb7rLW+CwoKali4cGHByJEjA1xcXPQajaa2pqbGorP6/fffL5w4caJvUFBQmEqlMqamprbo0Dxw4IDq9ddf9+Z5HnK5XPj000+bEFdW3L/gbka2ZMWfw6q89SNA8bH2MGW6ZlJ6luDsoPh/R5Cs0g5m5QBFWAqbsARmHNSDjOoXpgTG39ba5bcbpGSwmwrUpnCTFv35e6Hwin+CFpgGAC8gMftnkRS4FrMv/pbKkJ8DeTwdQAvOYpCh6gUyvt8FqRJMpALJ/t8FGf2NZa/UDooxtlShIlU9AhResUc879swlWt7BlRZxAZkgDGD7RjIKJgLSlA4G+RhZPJn8y+MDACjm6mQ0RSppjh+kUhhxuwZWEqSSfs/AsroPwIWSE82MxkBg4xiuONgUgDthoNfJmqLD8BQmwMifTRglUE4+SWBb2MDPtBZcOsAoa6IN5YerS8p4/akXIhQFDxc1Gtq9ZViP73haK1B/vCRMm+bX+t6yH+r6wKtkdkhpsoesksXFbYHfhcAcF1G9cTMPhyWHXXF4VKVsYffWRS4ZegLTsTU15Z0rgMw68gHfdfhHoSoVlgOSsApLefIPLNMPbAfRBq4ggwxRgKwH/N5yCA5hzkJAfF8BSBijBnIOtB8ZAOa38wVDDLxGEcQURAtuc5OkLR9GEyeYGkSPoByjuwBebYZ4VECMiZHg4zrCwAeBhnIU0GG3+sgQ3A3gKVz3rrOd+AWQ0hLPw+a/83BnherdPMt6D2awyXEXXe5S9EDPhuUkNYNTZ8pM6JZThV23UugcVEDena/gUieMNDzywGNsXrQ83kYwGwuIa7Vd0Js0xSQguwZLiEuU/wsEfR8nuES4jKbOdYPRDawcMA3QCqp4eI9SsMcBNCcKYOJJDCC8iPsE9vASNjWlJ96NCb/gcYqEKbGqRR/akEKtm6gcD6mUJSebyKIGJsPGrP7QeRyBkgB9CpMY8MIUhe1mATUivsDWVlZZzUaTcndbocVtw5jxozxHTZsWMXUqVOvtr63FVbcf8jKymqn0Wh8LW2zKhruMFblrfcDeUyYNcMWIpD87wCKZeVAuQK2gRY97WBKYmYDU3WB6wWLfdbftyRDqnoLyFvfwAHjkZh967JsJ2YXI1X9IkykQob4+TUPoKQdWSAPoS8oXnoqyMP6HmgRPg/khWcVFlJAxr676NVfA6ZUMCkc+oNUBk+AFudA80oNFYBeoBJxJiM+Vf0hyLifAzLQfgcZawNAZNZQEBGyBuS1/RuI8GJgBsxnADRIVVvKMdEU0koUdC8pAIYKgiAYjUI1x3FH+FR1KSh55imQoRcitsm8ykY1a5NotdaL98tIhjIAMag6owH1+1FQWMVZkNfbFoJexhkqkoFjuZerOjleOnH18Qi3ui4ezkKPsfp9P1Rdtc3vJq8LEHi4nchvV3W5LBaH9G681lVpMip0OshKimBwdT9iaOfmXqeJ9gQ47mCDB5Yd1RsPl6p4rZ7n0s94ZXm1F96zdbq0q7akswb3cJgEiAyKRVOSAWisfnkIpqR20hKHzXmlzCujmG9zQmOjleWlYTlRGJEhnc8EkAGrB82BCtC7VQF63jLxeD3IaCwXr1MKIjQKYApBswURurUgI00LIhIeBuVYyYMpLOqKuG0QSC1wt7zEH4Hef2lFHEj+rgG9G7Wgd6ICN4YY0FzjgOar87AxUA0iX2JAIVBK0Lv7GIik6QtTqIcGpFJhY6IcphC0FiEaysvFH+lny8Sflo49I6Slz4NI0nIJcU8BgJCWfg5EVEhJhko0DsUBaIw/ASIlKkBkVghoDErBjrcBjcl60BzUHqS+MT+vFjQGd4CqXhwAzRFZoHkuFDQvM7mwAUR2pcFU4nq4eL4wNHZMsHu/baqbFpKKWmGFFVZYYUWLsBINdx5vgIy+GtAihXmN8mFapBhAnjgfkPwzArTAZvHLlaCFhqU4puaqULCFvBaNs5XfP0hVJ4MMY5ZQ8AuQEX8z52rsgTfBBxRW0Zr37XmYYn1Z2dBCmIyCx0CLwkiQLPZd8XMVaIE6EsBYybVZ1ngX0MJ/F5rPIs5irOlvafuJtPACkR67QMqZN0CGVDuQ0iIalKchDuR97AmTQakHyZMrYLk84fWgv3gd0dAQ7DmOYzJoI0hOrAQZL+dAHlIbkER4Kmh8O3AAx9GYV4r3Uwd67i4gA7AMpIbIApEMH4Ky3SeCclR8iCf2VbsDcE9VpwF45PJlw6OHdlSrPTzkb+uiXWdDEMJ8XIadeygiMliVV5L9z3JdKBQKBQDISop528N76+ojunvovX1PGDr6eQLQaQHFH8XKS6BnfdCoVz7331FfMmngvS5dzgIRUT7i/4xsMICeuVb8/xiomkM9aO4RQM9ISkpJExRKoUPj0rEyUK4RFsdfAnqG5gltAZMMnSVrVYjXZflC9oLeK0fQONLDRIRkgQgGlgiyMyhsYj9IQn8VRLCxEo0nQJL2HND72AHkDX8ZVKZxG4C8JUnqwTCruHGHsBJEyg0CMBON53UB5Nn2Bhmu/8/emYdJVV1d/1fVc9PMkzIpgoDaQgyKioqK8xyDENQkgkPMoCYBkvg6EF+MCVEwfprBKdqSaBqMMRqHSBI0reFVQzRoQ9A2iCgqgmJDAz3W/f5YZ3NOXaqZREC9+3n6qa6qO5x77rmnzl577bWtWszW2FzUH98n29m2e9qEQISfuXaYYOxcJ6pY5s5b4/Y9DN2DU/BAeAZVntlRaXpZpR5dG8eSzQyxdAlLA6lFAG8+0Md9vxzN27nAmwjNVeXufQman9qSDWaY5SEAdLQ73r+cw14HXOXSV65CvxmmN/Q5NF+3Q+CX9eULaO4+Hs3jHVB1n202xwT5GWJM3IWezwPQb+0ANCekERvuF60cJrHEEmvFHnjggSU7uw2JJbazLAEadpDdU3O/Raz/Dy1meqEFbxe0eJ+DIiLtEBX1XbxI11q80FSj2641a43mmUKL8j+z6ztDG5uo+N/CX18Tyqu171sDDlqz4cBNQCcqylcBX2Fc9TyUTzzS/X2RivK7kSL9dxHlei1iN1iE3ZyhInQfLTJYihZpc9ECbvfg3Bm0kJtBRflJrr1zkQjjUHfMW1pNW8jFsMi2sNzZwe7/t1Fk0RgzA9E4Wo1PqSly1/cPNB7r2fbo/NsoEpgfE5AyxkSj+ytHi+ta4LfIIR2B70dzOMNI+jykzA5S4PcpJhXlUxEIMXcjAAZmPTqi12PA8MWLm+fuVVw+O7+4YXzZ7qk/Lfrv8iuW/7F65BFt36hZeOApgwa0/yD97269aSgf+nZz9x7NKN1kCaKpTwAmvnTDUQu2sW92uDldhvGIUbIHHrSxyGuEnH9QPvuD6Blph8CgRxGo0xZPMw8FCkH3dTEaW1a+13Q/wuoRRjW3Y4QDpAWffrEQzYMDUEWAbyMmTF5wXPsNK0JjJ4WAkQb3XYO7FhDAciSKIH+AnLv73OfVKP3AjnkWcrLrEC19WwC3j2SmpRBVVoEi7aFOTwoxN65nGyPNDiy4Ed2nK8i+F/Z/CvhvEDGfHe6P14CwUrHzkRM8BgE9c4Afba9I+BZE1+OlHs9E7AobiyF7BgSIrMWDNu8hx3owvprGhtO71+XoN7wQP49Voeu2OTQUk2vAl1fthIDdEMg+GAGkJq72LzQ398GXJK53x94fWJMaO+KkHNe+rTYVAW2g8WSltm0sGHviwqiyqoaE2ZBYYoklltgWWgI07DgL65xPRouwZSiSkEIUzd8jVsNlbvuz0D2qRz/2aTyLIRdzoTU2A2gxlIcWOUP45IEN4/EOzhJgeOBcZgsrbtm1zUVOU3f3dzcV5dfjBcjSKDr6ORRNPALvpJ+NovD3ocX+OWhBWObed0cOUwlyvK5ATnQndP/sHg1G93k2ur9nIEdogkvj2FrwRKZ+mer6Zg4SgByH1zEY7rY0sUOjsX9IdirG1o8RtbkY0dHHplKp3fAAWSHq978jp/Ast5dpAUwLjmSOqbUtdLKWBCBMdmrJZto9vuqt0DmyBfTKFde9+MqBhd3GDN1tfee3Wu6tLu/WtN+d6ROWzMn73Fh03+a+dMNRd7rtd/qzM31yeV88rf5x5CBZxHk+GkeDUMWFtUh7ZAxyqNJovIZzRQjklCOn3gCInigqbLW403igwixCANX9iOJu1VteQqDTcHSfU3igNEyXaMYzGaxdnRBIV4Lo+t3wAIXltYdifL3dcfZxx52NnrejUZqRpYxESJuhHaLJl6J5o9n1W1fX9vuBP6GxalH7nWFzkBjtvfjf7Cbghtb0CjZnocOOQLswEv8+Sh+pQn23WQtBB8SS2GKtiK00E4Z8MaqsuiJe/tK9D+eEC/DO+lrUb6EuTQtKkViFWAnvIKAzL9jGrAmlMqxx2xUjcCCNBCg7u/bVks02NF2L3dBYz1UlKd999zrq9zPxKWTvofmvGIEN10SVVZUfk7MfgndmLWj838m2s9wSSyyxxBL7DFoCNOw4m4soqu1R5OxIJHD2RxSt2gdFVJrwVEiLKlkNc6Mh23cWTYfWQQajJVsk5xl27Rzy1mwmimYOQZTita7SwCBEYx2FlT7cElOKwTmI4VGK+vZ25Aw3oT6z0mRW+tKEO/+IGAWPuc87o3taBXzHteV9RG29hnHVoqpXbHAOO6B7PQc5hSAg5Sx0f8+govwxxKq4Ho2brcvBzdZ8ONS1cQiKZj+MtCVM3R133hLkgA13GhTSG9hSkEPnnICAmc74+SUPLc5/gkqAXolo6U1ka5W0R4DEanx0HbLzx1fgyt5tB9tAsy5O5d/WdUjZwN0OOfyo/Zr6T+tY+vPlN/W5c+sAnh1kDmT4C3LCQdT6NBpzhQhwOA717edRRPVINHYPxdOwN2X7IDBhLV67wNK30ggk7Rdsn0IO+XfQfV2F7qFpKViaQ1g9ohEBEfujsdKEaNu7uW17SlBLGQAAIABJREFU4Oe77mRXpLD57m3Xvr4oAlyE1wbojdI/6hHboiNeR2AwEuLrgubQr+Mp9nu4/ZvQeB4DvDF9cvmz7IQUCudQzooqqy7FA6EFwDeiyqpvb6PDORwvRhtqEMwDTnNVL6wixXPb3PjtZK4t4xHw8x66JzVRZdXPaUWo1/WLlcPMoDE7AI2HtxCw8Cqa53oiRz4EGeK/qc1oTNfiy01bagju2O/iq/1YNY570Bj9CUrTic9fVgXpRNeuYxEwvci1dRg+HQR3fAOoN2J5bIOmwuVontiLbFFYs+Xot+kNBDh8EtcPiSWWWGKJ7QRLgIYdZOftPbrunpr75wC/xqtZH4Yv6deIftD7o0WCLarDxU4+2Tm0YRTSgId08HmzO04+Wpy1AA+ct/foXc552gI7CC0IC5EzswbRqfNQmbAHEBgxfCsEDJ+movwltGjsjl84/hxFuUC5yZNRH/4ReHCjY1eUP4oifzOQYwNip9wDjKSifH/EEnidivLLEHhQjgnWVZTPRMDDuyiaejzKSd9YQE0pJBMQUPBIVnvCahWi4/4cLZ7LkENmZeH6kF1ODTxb5pvIuZzjjjWRLYlOVpR/BQE1dfjqG+Gx8xHIYCkZNe585+CdyFp0D4sR+Gbtszz8/wDnOuBme9gGmvXwl761PKqsuhgYDp3mMnaXBBgMPJqAnAIDC0rR2LXSkyfjI6RG5zYzByK89zanGHBpzoZ9txsC5IwBlIfGiLFUDICzNLAIOV8d0DjbDzlYnVEaw/7uPN2Rs9QBAVwmDhqCqiYSauljpcH3BsKehKewW7sX41OZit3f2wjMauM+7xFsPwhV40mjsbkHcjq7o3S313HR3OmTy+fjnrMdXJHiq8j5NDD0AAKHc0vNOe2HoHtk6SKgfj0tYAkMQZH1F7b2HNvTnIbAk+ienIHG2R/Q82tMwRRifOC2+WtUWbUbArzsfr+GxtksBL7diMbe04gtYyCVjbk4m6HF/f+y+39vBF5ciVhrXclOP4lQis4KBJAsxYMBVpXHSlf+JqqsMjbGi3gm0kg0NsPf/b+S7ezHSwtnvQ+Ah2Vo7vhRauyIDXOoE9E8HImkHuS2OwPPWOqBgI8jUmNHJEyGxBJLLLHEttgSoGEHmdNouByV+woXMD3xVST6kJ0/moePDBJ8F5ajCy1ehixc8ETALfgo/CfNDsGzN7oCP0ROxyoUyb0Y9eEpbB21cwFyyrvgy99NjoEJX9rMMR5DaQfFyLnviqK9/8WrutdRUX4HEgI9AgElL6JI1in4dAbcdQ5Gi87LyF5UjkeLxTwEVAykovx5fFTseyia/QfUP/9Ckbbd8LoSg9HYqEXOWEhXH4Aosscjx/BYtowG/St3/YVoUd3DnbMDWpjfSpiSofKeh7nrG4Qcub5IXf0J158lKIq20H13OeOqt5suQpxmHaN/73RzwILlvR+M2AHnovFl80CuOdzEDpvce4vwZxDIE69YYyKzBlDafFGAxukCVG6wAP8M7o8c+zUIxNwd3bduCGzqhsCzP+NLVFp6w4HIOXzWXVuVe52AUqAMHAA5W83oGXoLjenweiz9xbY36veA4PrstYu7/ij4PNymn+uzejT2Dg768ETEIrESsv/j+nEKO8icQzgaOdj16LncluiytX86erYGu8/fiqUizEVz6c6OYFfixUsPQmkIjzrWhTEF7dkAsfYuQL+by/DVakqRBs4sBHj1dfvUIIc+HFthRL8FgchHuW2NXVCBxskE9PsU6pGk0HPwJJrTno056ePRemAlmutmo36ehRhxQ9x209C4tN/1acC1MeaCVUCy+7ThvjlA4zb0mxC5a+wcVVbd5badkxo7os7d9wkAUWXV8Uhw1arF7I4At60GtRL7ZFpeXt7Qvffee729/+IXv/jBj3/843db2/6RRx5pW1RUlDnuuOPWbs15qqqqSu+6667OFRUVb36U9iaWWGK7riVAw46z4ag2d0nsc4sIhrW0Lec4gxb2uWrUx8vI5SpTZ8J7j6CI/2OfSDaDKPkHkX1dXdz7jkh/YC/ksO8BTKKivA9ygOa0ym7QcfsHxwQo2Gq6vHeey5Bi/SmIon4IWvSNQQu1Keg+PY2c+cH4sqURWhSvR45kAWEJM6/XMBMPTLRFi94Mojz/E42bEWhcdUCL4wK88/gWiqIdw8Yii7j357n9VwK3bKFWxDcQ2PAIWhgvRM7omyjfvxPQhopyWxgXI0enyJ2rHKXGzETVAJ5GYEd/BMb9ip3v8Oxos8jkg8hx6oicqBJ8bnjczCFZg8ZPEYre2jxied8pPHvB5pbFbvsO7jUf3ZvD2LjCje3fiNfPeA2xeEahe7ceRd3bo/GWhwComfiI7cnI+TsG3etCdywbr/YMZPDMjAjPdmiN8WXVe0LwpDA4lm0TRoqNebMuOG8Bes6+iNctWOT+X0QOCwCi7Z5mkRo74uGosmolAlK+nho74mfbcBhr/0vufQtyxK8PN9qFgLcDY+9TOLZXIJg5BzGmQKke/RCguwTNPZYWkY9YWn9z216N5s0CBCgboGHWjAD6nyKA5stofN+IWGwTENPkQ8QyC9tYhJhxM4HiqLKqLEhluBtfaWeuXYtLBXkBP9f9EzEnzK4NjrGBuRCCGDGRzkvwpTHNDkS/p0XArVFl1dTgeHMRmPJPVIFlHZ558Vmbfz8RNuNvszrd9NDtPVd8uLKwa4cujd8542vLvnrMmA8+yjGLiooyixYtWril28+ZM6dtWVlZy9YADU1NTYwYMWLdiBEj1m1bK7efNTc3k5+fuEOJJfZxWPJk7TibixY430YOaAgK2GK4Dv34W9RwPXIq8oJtCf4P38dBB/vuEeCCTyTA4C1OH4XsSOQg1GcGRhyHZw1cQuulKocjFkkI4LzyEdtajxT6n0H51HciR6sfop8W4Z0s8BHF9xDgYCro5yAg4Cy8eKiJXd7i2h4qxA9DC+p6tDiuxY+tOrzj1DPYr0uO9mfw0eEuCAipQ6XZ7qei/Oc5wYZx1RKA86BEHwSw9EML1s7IsRmMQK8zXT9dhhzbdcghLnefleDnp7YtpPf87WFXD6fm/rmf8LG8SQudVATIPIoAqAXIYTiVbBFFgv8tUp9GY2Eluv8NeCBzJRqPIfvJIrE9kMOUca9FCNAI0yjsNULP19N4R2YiihDPQqlDy9z25yDGSiFittgCdgpK1Sl0x2tA46DWvfZDQMYCNBZ3RwwmU+fPZQbApvDCj9Z+66d4dQWbVxrR89PBvRpdvi0SurwN3ZPT0Tyx2EpfumPZfYtT2be3XYjmlQs3t2ErZgysuX9/+fV/dm5X0rtn5/Z/7VhW8uDmdtzR5iLyoaXQ+MnSjTDAIdhvPNJCOAIPktk977e+vnFGXl76kPfXrLtx907tQAwDq0xhINUqNA67I42TRWhuPBeBGvXus7aIHREH/gqQ7sdt6Nmox40HxyDYiA0TB3eiyqqn0Dyaj1ImiCqrTndfP8fmGSd3I1bdvsFnVpWlDLHgQOP5OgSU7+XOWZMaO2JqrnYmtmvYjL/N6vS/903bo6GpMQ3w3ocrC//3vml7AHxUsCGX9ezZc/8xY8a8/8QTT7Rvbm5OzZw5c3FpaWlmxowZXdPpdDRr1qzON91009LBgwfXjx8/fo9ly5YVAtx4441Ljz/++LUTJkzosXjx4qKlS5cW9ezZs+Hiiy9eOX369O5PPvnka7W1tekLLrigz0svvVQKcMUVV7w9bty4Dzd3/gMOOKB+9erV6QsuuKDPokWLSpqbm1NXXnnl21/+8pc/vPnmmzvPmzevzYwZM5YCHH300f0nTpy4/NRTT11TWlp6wLnnnruiqqqq3c0337y0vr4+dfnll/duaWlhyJAh62bMmPFGSUlJ1No5H3300bKJEyf2AUilUsydO3dRx44dN6d9lFhinzlLgIYdZM45mgXMuqfmfqtbPRg5ZKYNYOXmzOLsB9g4XSJuoXbD28Dln2jHrKJ8PyTaZqXqCtD1mZPVRO5xbBH9wbQONMxFlSP+J/gsu3TollZ+UDv/iBZpKdfWQuQYdkBOV3cEEgxBUarr3D4ZVBLQ8s8X40Uc8xCQchrZYpe/QCyC0Gnqh8+Xb0AOWwMeUDBwqpd7Hxc7y7h9OgbvT3Rt/SsCOpSvHe8XvT8ZX+mg2F3zYrTgvxQt1p9zn/8cgRiWP13k/j+VjVkWmTc79V/CZ0PxfCRwA8o3X4SiiW3ZeG4IwcTW8so7uu/aBtuWIoe6iGwxzmb3eQ1Kb/gviu4WuHO/i9gp++CZMPchB8Zy1w0oHY0EW09x19AZ/1ydgijjT+EFK8EzDvKRA7QKPbdvoUjxEHy6TygUCl7LxnQprH8McDDmwjp3vkxs31BU19gZjSgaPhA5iAYIDkXCkWVonGfwVHobnx9rykFq7IinXbu2df8NzuxVk24djp7Ze595675d8XdiPNlj+k00llqtnOTAia+gMfaF2NeZTCZqamxuOejVpcvTtz763J6//u5ZTei+v40XDt0TPSs1iOlwAgLFLsELOh/mtm+NXZRBYPP/se3jYXf8vPxrsqtXXZYaO2J2VFlV5tIdNhJ/dOklp6KUi/0Q0PwMYsS0Q3Pu19Hz1pWo6Qwaf/tnCs++gVTxzADUmJOUtNz17KaHbu9pIINZQ1Nj+qaHbu/5UYCGhoaG9KBBgzaAUxMnTnznoosuWgXQpUuX5oULF/5n6tSpXadOndp95syZb3z1q19dUVZW1jJlypTlAKeddlrfCRMmLD/hhBPqampqCk844YS9Fy9evACgpqam+LnnnltUVlYWPfLII23tHJdffvnu7dq1a3n11VcXAqxYsSK+DqC1819xxRW7H3300avvv//+JStXrsw78MAD9zn99NNXb+oa169fnz744IPX3nHHHW+tW7cutddee+0/e/bsVwYPHtxw5pln7nnDDTd0nTx58nutnXP69Om73XzzzW8cf/zxa2tra9OlpaUJyJBYYjksARp2gp239+jXcQuge2ruvxW4CC3SX0JOwWB85C0OLMTzjs1CXQYrUbYSOYnbSzxvZ9h0lH/biBe6tIjTW2gxeTaexg3e4WoECqgoL2slCl/n9A2MTh4hBzi0zUcn5WT/Fp+GAZ4VYE5Rb8RmOQYtlK1k49n4FBlcu/dDJSCPQM7WbSiffW4g/PgIvuSmOR2vocXxbOSwhmUhLXIbRsFtgRKOHSv9Zvsc4F53Qw5VGL2NC5BNc9e2BxJHuwKxd6Yhh7QtXqPiJQRMWDsKyB7TYcT+H7Wl3W9EdOfPAn23BEVNH8Wn0ZiFEfrQQpV7SxGwhVoTclTaItZCMxpPjyCQbRlylgqQI1LjtjOthDRySGrRnFKLnNN90X1/C42zpxGwNdS14ToEWPRCTJVq93l7JOC4PriWRnfczsh5W+7+P8W138aCgWQpt3/GXY/pTtg4tpKZ1g9p9IxDNsCWwQO6TXgB3XXIGbN0im/gWRcNrr09yE7puRoxHqwKwifBdhUdhtbsT2gc2f0qRGDn/Fb38PoHJkwbWjqKMoXpdJo+3TrUf/fMw/+K5u0m5NQ/hYDR29A8bGLN+UC9S125HDGy8sl+NkNbga9mtAQ9RyOjyqqNHHYHjExAY/uqUKjRtQOcaKT7//vuda7TadhkaWen7XEb6sc/oGevM37OKEQA+dvQfDCppqGphgmXRcW/3JsA1CCbaRECyw+487+Ono16EmBih9iKD1cW5v78/Zyfb6ltKnXinHPOWQUwbNiwdQ8//HBOdtk//vGPdjU1NRsCZXV1dXm1tbVpgBNPPPHDsrKyjX7Dqqqq2lVWVi629127dm2Jb9Pa+Z966ql2TzzxRIebb755N4CGhobUa6+9tsk+yMvLY9y4casA5s+fX9yrV6+GwYMHNwCMGzfu/V/84hfdENM05zkPOeSQukmTJvUeM2bMB2efffaqfv36JUBDYonlsARo2Pn2Q7TQPRZFvBeiBbwtjuOAQmsWotqNSCjsKXbdBeTmTQ78A2gxNxstkPbDO6bd8aJbHfDRyXy0sMsgh20tFeU3uqPG2QlzENAzGDm/f6ei/PTguy1ZiI9EzJR4pDTPvYaL0XL3avc1H6/4nxd89hu0iHuUcdXL2XgBORctuM9BC7snkFPWxrXnWpTXfwTZ1HcbJ+8jECSFxNV2Z+PxZuAE+Kh6eH45VhJ2rEGCaX0RuPAYWmCfTrZAmjl7x7OxGJ+10cxyx/865NhfLR/y6WYymM1BjgPIuTU6d5gyZOCMpTCEJW/DOT0Kvu+Ed6KLUJ52CxLaOwsBW39E4/MrqMJHX5Qe8SYCDE5AWiD7orKATyHBxPYoOnqs2/5l9KxWIeCiHWJw3Y2YOT0Q8GbR6UVoPJ7q2rgGMSf2cddRh2d92TVlkCPXhIASy2MPx1PaXaOBVja2wnSJELi05/RDfOWUdXgWlelEvI6elwiVBXwYMXbmAz9GWgBpoGb65PJvTJyy3aqkbHdzLIZd+bkK9QUyyJG1CkQbtds5wCsRi2A+/lkCaIiiqCidTuetb2ia1660+MnuHdr2R7oLtWgcDgC+iwCkI9C9XI0cfiufeTcam2eRG2hI4x35NYj9sCcaY5c5AcuT0W/OEpSmcBQOzEDzNtBqikWYInI8W1baeS4CIjqg38Bm/JxvgsolkP886UPfiArOOwZVyZjv2jjfASKWavESHlg+A80b6931riUGTCT28VjXDl0a38sBNnTt0Lkx1/bbw4qLiyOA/Pz8qLm5Oef6NIoiXnjhhf+UlpZuBCi0adPmIznkuc4fRRG///3vXxsyZEhDuO2zzz5bmsn40zU0NGxYJxcWFma2VJch1zl//OMfv/uFL3yh9qGHHmp/xBFHDHr00UdrDjjggI0rhSWW2GfcctH9EtuBdt7eo5cD30JOxW8RHdOcT7OtmZhb0GLsVKD+E502ocXY1WiR9yJyoh9Gi6LVKNp/DVoUfoCcgvXI8fgAAS5d0MLtErwD7is8jKuuY1z1LMZVX8W46lloAXu9+xvuvp+dkxFRUV7mnOx9kNMzD+W4PoWc9zmIbluPd7RDh9Hua5j+kUEL3gdRDnbuKiFqzxXunGUoGvY+WjD3Qg7eeNeWdXgnzcAEi/RZ6cAmcpu1dz8kanYRFeVjUARuvusvAzXOQ9HtRuSAnRGcw87bxv3lWqBbdNoWsZ9DaTO35dj2U2lOPNAcGgOl4vN0mArwEuqzluC7cLswtSY/+LwQOVHHIxDiRCSqWoCcpMEoNWISArT+gp6pHoj9UI4XeHwZaRgUINB0T+B+5NzMRakgZWjMfB+N2dXuPL9Ec9Uf8SkMnfDlKo02HoIqH7jPDBzoH1xrCFhZaV+rOmB9GSHnyraxPrG/jmSL8Ta6vq5z//d2/fcGAtKORkDfnQiksTST49D8tEuaUe6dc76r2t34eTKF2FIRrTMaxiJW2pFoTrJ5JgP8samlpTaKolR+XqqgpKjgC6iaShH67f0/5IiPRalpfd25KoEbLULvnP9vAXex6d/mRpSOcSSKjP7etdvYX99DqXRHoZSy3yE9m01a7L7NRyDD3ZtiELjKErMRoDgXzf829nuhMd2FVMEx5O8/nlTR94CbEYhxGaqe8WfEgPoflNZ3JWIxdUC/u2uRGOz3+SQHOD5B9p0zvrasqKAwawwWFRRmvnPG15a1ts/HYW3btm1Zs2bNhlSHww8/fPVPfvKTbvZ+7ty5udKAs+zII49c/bOf/WzDPq2lTuSyo48+evX06dO7G6jwj3/8owSgX79+jQsWLChtaWnhtddeK3jppZfa5Np/yJAh9cuWLSusrq4uApgxY0bnI444Ys2mzrlgwYKiYcOGrb/uuuveHTx48Nrq6uriTW2fWGKfVUsYDbuAOTDg4Xtq7p+DFibfRnT4cJG0OVDIfmxsUf0ycN49NffXuFSNT6INRqwFK/V4Jb7UH1hFiYryqcDzqKTeFSi6GTpcTWjR+QfkMHVoNZ1CC6TvB/97q8gS6gOBF6NdG4tdOx9F7IZSFAW+CjklYQQ6VLuH7Mh+GijaUMli89bO7bsPvmb880gbYQUqwXY4GyvxW8WBCE8dDiO+cUu7a/yB29Z0H/6N0ltGuX64AC8waAuFMGUjbvGIxzPAOY7FARIC/KzZcOScd8vxnUXzjdkwAPVzIxsLxJoZgFOAovXrUJrNEMSg2gMBBEe57X+PosbPo/v6ZbwY3RiU1tAJARD/RYDQTxFA8joCAZ7DPyt7ITG8BjR2DkAOzzEoAjoBRUbrUXqFlVE72LW1F9kMhC7uem0shwvS+HNl76PY553IHnv2XTw1xfq1GA8YtnWv5qC3IACmm+vjzyMndF/gGROM3N4VKD6KOSf1cgQOTqR1HZudak5jYMNb99qTGKMhoPJfjdf9yEPOb4nbt3deOr10Vd36psL8/LtQRH+c+25PBLyBxoJVW0kjwPdlBD5Yu+qiyqo42GFzeyMC0wagZysPpRPtgYDBevRbdiX6nc8Af0mNHdG66OK8O4LfnoFh2hoojeMFx5Q4EwF3SxA4sjwogzkXPVPHuj4K2T9r0dzQLrgOm2OK8ewiu86vuTZ0QXP2X9Fzn6RM7EAzHQZVnXi/sGuHztul6kRco2HkyJG1v/zlL1sFL0aNGvXhWWed1e/xxx/vcNNNNy29/fbb37zwwgv7DBgwYN+WlpbUwQcfvGb48OFLN3XOn/zkJ++MHz++z957771fOp2OrrjiirfPO++8Dze1j9nUqVPf/trXvtZn0KBB+2YymVTv3r0bnnzyydeOO+64ul/84hcN/fv3369///71++67b84KF6WlpdGtt966ZPTo0f1MDHLSpEkrNnXO66+/vtvcuXPbpVKpaODAgevPOuus2i1pa2KJfdYsFUWb0xZMbEfaPTX3H49yKZeiBYOVv9wS9kkDcnT/g0TkOgEV5+09+oJN7rUrmpx6vxAW22BT2w5HlP2r0MLOFOOb0CLxc/i+jJBDg/v8PcSCeBE5NuuAWxhXvTw49nzEELBcWNB9WogWqO3ceZ9y3x2FwIZ4fn0zPi0m1CEwawBOZ1z15kGGivLLUcQ0QvfcBPPeRCDD/cgZ+h6eJRNWKDAwoAVf0i9Xu0LgoBEBGg8hx3IiGnNfx4uixamcucZvI4og9kF9/z5SPr8xABk+k+YqT0wAvoPSEuIAQoTu2Tzk4Fp1iLjORRjpWoPGXie375MoAv9P93o4mnOaECvlN4hVMxZFX/+GWAuTEcukCEWbwZe/e2zilOq6oP1fQeAeyJH7PMq57+yu6zS330D8c7EOOT3t3H5WPjOkzxuA0Ix3KuPXnGt7Yw4ZwL4WPaOhvkVB8B7EvDAGTgbNA+UoVWgZYjMY+6EeD+D9yl1rH5ROcQWeir4BKN1Z4IOj3P8agSM3oDSXjcQEHV1+PIqY75TnMqqsWo9P21qN5pzKsK1OtPBuFF23e9+E1yzJR+P6p2i+Phz9vtSi34CvI6A0g9hopfjUMlA6xknISZ+JSvdOIYqGbGhoKmWsoocQQ+dGdxxr+yrU5+cgttAE/Fh8BxjQqpM+747jceBC9NrADkhsdY67vkfQs3qy6wMTFf5/6HkagtJAJrn++zUeJINsMC6eYrQpW4eA+4NdWyagZ2o4OcZSYltm8+fPXzJkyJCVO7sdiSWWWGJbavPnz+8yZMiQPXN9lzAadj2zXMr5aEF+pPu8Aaeqnt/SmO62+k3ea9eb5rxC8GrqdYi6OAc5bceyBVTMXdSGo5zwXxCmD1SU28J3Joq6DkVOzEGIytyEF7hKBceC7MXUQcG5erq/A/EOxuFUlH8JnxrwIBvnwl6J2BOj0WIyQuBQA35xWY9nNIRU7sh9Z5RCA0C2LBIhAGQNWqCWIYfGFoad0QJwFKK/vomiyiGAEE/jeBY5fN3QYrEtHjAIo72FSBjyMHRfpiMmR4G7NhP0C6sgxNOA/ohSSjYsSjdZ0eMzZs5Zvw05uAORA3QQ2eKOBShiOh+vCWDlGYvxAJc5zW+ge1qCGAOHIkd31vTJ5RciwOppxFg4Bo2ZB9GzBb56SiHQduKU6ikALlp/jtumfvrkctDYON99dgECCp5FoEMvJIS7GM1X7fCaK41oTKxDYyx0fNYG12XPSlixogWNv5ZgG3P87HcuDkoUk/08WBlQe0Zx12xAW9r1eRMCeAYF50+5vrUxvz+ae3+AnodLUf8bOFLCpkvvftxm5ZbtGh4G/h1VVl2A0qS+iIAgA2cL2XllDi1lB+RENyBhRRBzZgheJ8TmGtN3CanSvZGzbUKnx6IUiDnoORuDxtkkBMBNQ+N/TwQ6/Ar1195ojHvx3yjTQirvbQTgXY+AJQNxF7ltn0BjpzsCEcN5MQ/Nha0BzHOBq6O3e9QgllcJStuxdJJx6PfAfndSiIlk4G8a/V69TbbWDmT/DuQC6loDHEpRylUBEjVuhwCQK/n0VwdKLLHEEktsCywBGnYxc2kUswHuqbl/PEoZAEWdvgGM6LZ6aa8D3niyaH6fEe+81Wngs8BPUDRjETDHHeM37u+Taga41ABTqCg/HC2auqIF+9mIzmkibaAFV65FkTkCzSjSb7XZw23NKVmNF8y6GKUGXIcWtC+QXflhGUpNMLDAFmpWZs8o12ZxCrc5JubsPIkWaluS33oeEtgzpyq8jruQk3MwitYVxc5rZlFkgGF42qwJaYZtCxeeoH66EjmjJ6AxexwenEihRfVSdF8sUn4u46rnBW1IFqO57WA0xg0QClNRDMRZhEQZDYwswjvK9Yip0xXd524IfGyLgKnXgJXTJ5f3RSyg4Sg6PwWBDD9CY2wSnslSg1gor0+fXH46XizVUo1A81R35IQ8jZyf0xDbaH/0/C1CrIBm5PRFaGyUueMPRSkY/0SASEeUapGHnMP8HH0RggOQDXC1kF3m0vZLkQ2IlQVYN9StAAAgAElEQVT7mECr7WPzTBt3PJsvzBFbjdeoaECAyvXIaW907y26/a47lzn5O9xctHlWVFk1FTGe0mjMPYBAyyKyAcZtTr8LqfvbGOWejsaVpc6cisZqLUrvOQZf0jcUng3zwjNEURoyPYmaHiNdvBaNweGuROQE/DX+3bE3Rrn22zPyK7IZDTOA9kSZDKnUEvQbsR6BNr3xGh//h4C2G9F4WIpKEVu6WhXSoWl93j/wojqXGlEZXJfNC0PwqT3r8SWDuwZ90eTatMeG/mg9RY5gmzD9KL6PaabYZyej38taoE9UWVWWsBoSSyyxxD7blgANu7A5bYUv2ft7au7/NjC8qHl9zcq2Pb/UZc2yu485+Cqjs87LeZBPoilafzJyTu5EC6S4leMXPxYl3JQOQB1aoD2PHON4dNMciRa08HsOOUSKzoSVH8Sq+F9EDS+NHceU/98nu4RYGDkNo0YGgDwBXLDJtIFsNsfVaBFrzpUp7N8MTHbaFd2QoxhqRNi2kK0BYmUwLTpsJQb3JHtx2YjSRb6NnMRixPh4Djkq/4OnhqdcW6v4jLAWpk8u3w/dg4XI0X6sNXq8SzEYTix/331+GBpbz6B+3i3YtQjdswHIabU0GBt7GbTg/xxiByxHjJaD8XN+F0Qjfws5vK8iB2VvRP0uxadI4I59CmIYXeneP40AiW4oGnwFStcZ787fG43rejQW8hBYV4Wq7fwXgQr/ddsaS6MrGrN5yOmNUOTUNBmayWYGpYP/47ogueYE+9yYIbZ9PIpbgIASO5e9hmKq1jftEHjTFwEy/8AzPda6PngXgaNvorlkpwLBUWXVEXiQwawf2X3YgsChrc4/dmkXlyOHvRfwTlRZdUZq7Iit/a26HY3dUeh5WIbmq5dRilpXPDCUy4FuARZDpi/RmjxaqmpJn34NAg9qYIMWRBWay56PKqvqccCIKzdp6YfzHHDSHsgnlYJU3vsIdLgMn24RArX7oLFzGtJQeBzpPtQiMdQbY2kgYbrekUjE+Eh0X/rFrutx1x/j8KlHBjSW4e+lAdLh79DmbA2aE8LfGPB9nCJ7DZlG48n0LUywObHEEksssc+oJUDDJ8g2sB32Hg2e6fDpMoEM0xD1OoyWmDXgnYO1yBEyR8bSJeKLzXy0MGxBi756vFNmQIBFQEuQTsDDri0fsnGkaTwqiRlvWyiiZaUEQ1V7s3C/t9w13b4F2gTj8ekaXYPjmsjjlcAvAod+PNkO0XIEfsQ1FFKIUrsaXyawGTE76tBiMw9FyP+JHNi9UN+EQMzDVJQfhmj+61CO+i2fZs2F6ZPLuxNoFKDo60jkAH0B9VFrrA3TP7hr+uTyyYG+wSWoLGt7fAQ/rjmwFN2j1/D3yNJ0IhRFXYoAuUHkHqttkGBhGp+qcA6qFrIEMQlaELi0BDFoHkYgyigEgua7/9u4tv0Slf17zl3DCLdNE3pWj3HtaUbjsRZR402nYT/882ipEPVu31DvJO4whc9B+B68KGBp8L1Fx8Ntbd4IQbkS145GNtYusWNZezu4tu6Dr66yBrgViQB2RfNaVwTgPcpHYApsB/sd2eNqKWrzCuTQFrht7mXbqghcD3w1eN8D+HtUWXXk1oANTnjxAqQtYIKHta5tq9HzdiJKf4tH5EHPQndIrSDzejXR+9egtJFxwBFRZdVraGxMQgCugaet0f+HI3ZCCRpXF6I5MawclCY7ZacWjZPX3H6NSKfhxhxR/+F4/Z/TyNYoMbMqQQtdW1biNYFORoCXCZmGqRHWvtZAOIJtVgP/QoB/HNgzUGcJ+s2wcxgwWQx8KaqssrLRjyXshsQSSyyxz54lQENiu5pdQrYTH+ZpN+EjKfZnC/wmPAjRWlmkPBQxrQVucp8NR9Gy1Si6dTswx4EMI9GC6WQqrNIg9ch5CC2M8OBed2Nja8brH6Td+yuQw7UlC3kT3/uTa9ue+P5Zj8QscSU348czlfz82GeW6tHVHWs5is6+gyLVBpo0o+jUF9y2g9AC9+rYuW501yjH+1PIYnBgwEjkAHwFCa2tRH0yF6/1cQ8wN2Au1CDBtJHImTsIOQcXA89On1xej1JYxqC+PgY5rY14UMwAoTtRJBOU/z8QpUaAnIzXUepDh1YuI0Ll6myfauQkHI0ETSPEypmHcr0Hojzz65HC/DWIidAGX1b1CQQwXObOO971ySLk7A1CDtn+6Hk9xp2nm3sNgYQMYl0YIGKf1aPneD2e9WDfhakQxD63ygOhEGt8jgFfpSBMobBXYw3ZvmHalmlGrHXt7YwYHy+je3wGut/GpGgHnDJ9cvnfEfV9Z1SlsLGRQfPe4+j+GpPlWOAaF9HfFjstx2elwJ+iyqoZiCVwM7md7Sxz388GiCqrDJx6LzV2xMNRZdWHqH/DuS1Cz8ljiLmwP6k05B8wR0VPOAndw73x4Golej6fI5jXXOrENMS6aY9YNk+i+znB7Xuu2z9kudncuQ++qovN1cVojLSJKqsuIVtscy5KYbmIbN2JBjT+6pD472HoubwU6QTdj+aSDvjnKZ7yth6xQvZx5zdtJ/tNsvLY61ybD8IDbS3BtdWhlKk/IbHTcWSnVzQhkPFUt/1f3D1PAIfEEksssc+QJUBDYjvXKrKctnKkQxFGbwxk+ADRUsPvwrQFE97KVerOHA8TYLwNuNalF2xQVc+KvMtZvwFFdQ3QKMEv3kInxc6xuQhRGp+iAKLbPrjFzrjaN9W1rQUBHm+jSOGdaIE6EjkL30cRswPJFrqLtzEPIIooQBFBUtpiABtHeNfhBeR+gxaX49Hidl7QxilbdD27uOVKbZg+ufxA5ATs7jZLIZr8M6g/euKpyyXIadkbH1UvY+N7UIgYSu3QAt0o0KXIgX8bORQr0b3eHfguYg+cCVw9cUr1b1z7uiPH4wtu/zRSu49XblgKXDlxSvWG8qHTJ5dPRiKAzegeX40qRQzEAyhHISftyyjv/CeuXeWI3dIZRTCLkDN2LIqOd3HXtADvvBiwEO8Te15NWyRkKDW4a1nm+sK+a8anCMX7N4zehs5QqNNgz3geAttMFNWqXxio0OTO3ddt/yF6Ftu587dBTlmz2+8cPJuqEjmYX0CR6HL0/Jzp+nr2iK8P3jDmqm596WNzyBw93/o/jXL35yCQyLQUNpvaEddfcOkSFyOg6x2MFRNFfl5Opboi9kAapdC8jeavLbU5aH4rjiqrxqAx2jn4PoMc6hbgIQeUvB6093LXrjjzrS+a8y89YtKtc4HhTLp17tPTvv4zBGSENhKJMdqcC/45X4UHtmz+DIGtJlRC9ho8Sw0cS9H148/Rc/E1/O9WLzzD51B3vBPc+dMIeDBgIhcDwlL06tE89Q5+HqtH84Ux0UaRnV4Y/33riNLnVrtjjnbtfReB7E+iefJ6t+3JaC6YGFVW1ZBUpUgsscQS+0xYAjQktrNtJKIWd2HjVAmLRqbd962ZOSbrUARxMNmLpGVIm+EN5Bz5SLs57xvbXEQZvhg5EEVkq9SH57ZFZJySGi5kbbFpubszgW9vY8R/Lj6S9jKisN/tgJNwu9XIUf0t6ptLyBZIi+fcEvsu/KwJLT7vZ1z1LAd2TEWO1a/IruLxabGxiKGRN31y+ToEDF1EdjpKBoEMUxEzxgRJS9FCPAS+2uIji81ka2z0xeuD9ETO+vOIAXAWYhlch+5hPqrcMRsTKPV2KXKkwnFoz0ItcqL7uXPcPH1y+UIE4F2FmAcrkbPwAGJgnIhApGok9lmNQIovImbLEuRoNqHn5GLktOCurxg/5ha5Pgqf87Ciw3rXbxFy1qzyiZUoDIG6sIyh6SfAxuPYnrk42BCCj+E+hXix2JJgO9uvGN0rO1Y+6j87Ty1ifByLmC716F7PR/1qzv1s1Jenons7330+nE3T9rfZwlKViEURMkg+vxXH2Q8BA71Qn7QF7okqq15B81JvPLCJf81kiDJNkK4jldfJfZdGDJ4tBhqcI16PnNi2yLGN3/ciNOfVxz4fjgDBtmxs65G2SM3lY46aUL3knS8P3bvXLHLPbTWpsSOMYTEH9emF+PKbfRGjaAkC6CzFrxB4IDV2xAVuX2OpzXQlOgHmuGt8ye0zCQF09yEmQp+gHfbblMJXnwktZNwUIsbUFe64Fa7N/0bP877o92QISpPr5fZ7F1Ux6o3YSIWu//ZFwsM/QAysU/HPS6E7RkPQhiKk4ZDn+ijRb9hFrbS09IB169a9aO9vvvnmzvPmzWszY8aMpddff33X0tLSzCWXXPL+lh5v2LBhA6dNm/bmiBEj1sWPvbV27733tl+wYEHJj3/843e39RiJJZbYjrMEaEhs55nYDMehhX18gWQW4Z2YUP0asvUQ8vDRTvA171cBlzHOR263yOS034gWYUaRPwofqf4QleibiRZkpuZttPnOKGLUDx9xXYocvJnAt7Y5rUD7PQxARfnpaOG80H22ISqJFv1l+HryT6HFYJi3mwEWRREDUqmc80GYb9wP+CMV5UPd8S9Hjss3tuk6dlFzlRimooW7OSSlbFySLoNymP+IAID/QQKLRyAnLhfDBrKd22YEYPRHoMEtKDIaT305A0Us70UR0OUTp3iBUse+OBk4nuz7C57J0g4JET6ExufhSGTONBrOQVVPznXXdY37LELaCbMR8GBoVgpRqDN4xf8mJCy5EIEO5sy2wVPH7Zld59oUVm5Iob7uEvRXXMx0HdksiBDoi88jufrfWA8mKhnuF+qs5KEUl0Z8SUS7bgM/ioPjGhuiC4qgf95df4Hrs88BjyBmw/3IOd0PzSVrp08u//eA0tRzr65LxdORPrJFlVXHI8euBDmLDwXXmkH6G8aGui6qrBrodr0FX4Z2PhpfVlkktPNRX4TA6hvIOXWgZSpNtCIi1b0Ncl67obSbh7ahQoFVO7kaX/oytGrEtpmTY797kYNdiMYRrg9K0PhuPunAgYccP3Tv7vnp9HfxY8jSCl5FjB4gi4Gw0H1k53wW32fHor48Fp8Ch0uXmOruz/Xu48tchYnBqO9Gomd+LzZes6Vz/B/+TtrcX4dYBg+hCi4NiA3xqDvPgSjVpzPSDToSPWcfAt90KSqmH/NN/PNX6j7rH7RtGXoGhiEdjYPRs2AgXr47ZwI0bAd74q3HO816/Xc9VzWuKuxY2LFxTN+zl53Q66QtK5O9Dfb9739/xcd1bLOmpiYKCgpyfnfuuefWsg3itNvbNtXGxBJLzFsCNCS2M+1kcosqmtnnBbH38f8tf313PBV0GVpI/3SrQQYzc+grysfgnccleBbD1Yyr9lHHig1U+xHIyX8QLcRGoBzoxwjZBx+H6bhWHeM2tMgsQ5Gk/4echjFkOwR7pdMbBAZ7ke0M2nZGHe8EjGdc9VTg5+7vU2HOWT8TOdh93cdhZYJQSNCi5Pug3PZS9/9taIF+cnDolQiwsLSbBrwgZxMwe+KU6guD7V937TkdOXX3IwdhFHJmrkbaD5Ym0QWBEL3xudZmTcix3Qct9gchUGECAjasIsB+CDRahMb2IETPrkVOWy/kSJaSrYVQ6z57F1Gk/4sAjF60DrLYX2ns+93xVU9CsKQl2M6cm82lKuWyEKwIc8lDpkMLXqE/D/VtMz5H3URni1DqUshyanDb74MX/7MIr4n3HYXAz33ddinkzF0ErNunLLr0zhtf3m5MBuccTsPPsxnkRA517W/vru0RfEWDYxGbx5g5eyFnuRH1SRuyLUN2OUzc/30IU1miqIHm+ddQeHRbyGuDnPVmFBF/EwNPt8AcKPFwVFn1K7tUd54liAl0uWlLBOkchQiUrXDXUYIfB2sQ6FUGHJVOp1anybOx/m93jjuBe3IBItYee+/6HWBtauyIsNR0ayKYYZnY+ch5H4P69TyyyyRvzgzYbnb7vY00Un6G5qWhCGhph1gcS1Fq4m8QUFaDgPXX0Th+zgEhxWi+WIEA8xWov/rg15ItCODZHaV9mAisicoWus+WbOoC3D271L29JdCvSCywJ956vNNdNXfs0ZRpSgOsalxVeFfNHXsAfFxgw4QJE3qUlZW1TJkyZfmwYcMGDh06tO6ZZ55pt2bNmrxbb711yYknnlhXV1eXGjt2bN+FCxeW9OvXr76+vj5rrr7gggt6//3vf2/XtWvXpgceeGBxjx49mocNGzawvLx83fPPP182atSoDwYOHFg/derU3ZuamtIdO3Zsnjlz5uLevXs3h+yKUaNG7dm2bduW+fPnt1mxYkXBtdde+9b48eNXhed65ZVXCk866aS9hw0bVjdv3ryy7t27Nz7xxBOvlZWVRQsWLCj6+te/3ueDDz7ILy4uztx5551vHHDAAfWjRo3a89RTT621YxkL45FHHmn7wx/+sEf79u1bFi9eXLxkyZLqa665pvu9997bBeArX/nKismTJ7+3qXP+6Ec/6nb33Xd3zcvLiwYMGFD/yCOPLP447lNiie0qlgANie1MG8Tmx2DIXjCLC1w1oQVRCVpALkZOUyc2FxlsTaMh+/vvowVSLVq8VaLFZ/axzcmvKJ+PIki5jrm9q4WEDIZs85oOffGiZy8iJyI0W8SuQ4vLpihiwLoG3i3KZ2V+PuWoX19Gi8s/bedr2CHWiuZCWEpuPGJptMdT+i3FAbzzCYq+l6C+K3PvD0PRu+vQ4nqwO5bR/Fvwzu07yCltBAZvQhSwBOU/fw9FSO0+G6D1fXwqgpUjNCaFOVH9kENh5zZrj6LKa1CUczwCMb6PQIs3UZT1byi14kR8JQh77emO1cEdx9gv/YM2WRrOCvRMhoKMuON8gFIxWhBNuy+aHzJ4rYRmNk6vsmPkYjOEn4dshnhKU5i+URC8t3OFaRm1+OelDZ7d0ILATXPW27rv3kKgS5vgnAZQveKu6ffoGf4XcMz0yeUd2ERZ1K20kcjhC+fPaxALoAcCIAoR8HQFcsL/5No3FDnoxlQwxzwEeVrrd/AOb5pUKo9UXiFFJ6VQetB1KMJfiMb3tlrYpxF6Fn8dE7C8GLg6kk5EKpOJLsvLS1sVErvfleieLUfz4PeCa3w1NXbEl9g6sxSY68JSma1tHAIVzqkfhVLeCoGzUd93I/u5xv3/qrv2YsSsexsxqyLENOmJQIt90TNV5/opja888zICymoQGH6bK/nZHYk9fg6JhF6J5pI7Xdua3fEG4J+F3VHfdkC/J13JfuYL8AKcrdmP0Xxkz+1Vm9n+M2mzXv9dTwMZzJoyTelZr/+u50cBGhoaGtKDBg3a197X1tbmHXfccTlZBM3NzamXX375PzNnzmw/ZcqUHieeeOKr06ZN61ZSUpJZvHjxgueee67ksMMO23Cs9evXpw888MC1v/71r9+cNGnS7pdffnmPGTNmLAVobGxMVVdX/wdgxYoVeWPHjl2UTqe58cYbu0yZMmW3O+644634+ZcvX14wb968Rf/+97+LzzzzzP5xoAFg6dKlxb/97W8XDx8+/I2TTz55rxkzZnT85je/+cGFF164x+233/7G/vvv3zBnzpw23/jGN/o8++yzr26qbxYuXFj64osvLhg0aFDj008/XXrfffd1/te//vWfKIoYOnToPsccc8yaLl26tLR2zptvvnm3N9544+WSkpJo5cqVrQmXJ5bYp8YSoCGxnWm3oYXOJWRHN+OWayG7Di2M2qLFyxrgFwgAmLMVjIFLUQ5sGfHFTEX5gSjf2tIxShHN/eebPH7rug/b30IGQ+u2N6om0Bk5D0aXjvdrP3esjlEERQX0QFHq1Wih+CGKcp+GcoY/ERaUoGyDhPi+h48+mkPwIAJgLFprffMmqu6xAo2zXu5zo/xbJZQG5KBZDnRn5Eh+gCL8VnJypPu/IxqrfZGjVUcgCujOMQc9G/a/tbcYOYur0aI/3332AVrcN+Aj7SkEfrzvzvdP4BQk8HgQYjA8iZgIR7vr2gvlUHdGTIU93T4rECBzrGtLqHtgffY59CyaYx1nVzSTzSAw8ctCFOnv4Nr7Lh40sWc8F8gQpjOEFgIcdq5cYEN8O0uZCMGH8DotlcbSNZrxGhh74p8tA0B2Q/e2GH+f9kDPj+nOnOWu+wQktLmcTZdF3VoL+6nZnfcUVIq1wJ3zHTQfH0V22dNc87JFqSHb4Q1TYnDH/gu6rjyUSrEMjaEr8aBZFduQKuKc4FBzphGN572jyqpj8Gkrn3ftTGUymUxzJmrIy0u/jcZ3I3q2ViJhyrXoOQxLVV6+tW1z17MlpTJb2zfePzXA19HzPwDNS51R1Y47AiHO8QQVLKLKqnvQfR2NxuIsNP5GkK1TMhCBE6sRyNQtqqx6Fs2bh6N+uh4BU+MQePE8utfHueNkUH9/y7U3D81vBXiGhYENIeCZy05k4xSwxGK2qnFVvFT1Jj/fUisqKsosWrTIUoE2aDTk2nb06NGrAIYPH772e9/7XiHAM888U3bZZZe9B3DwwQevHzBggM0JpNNpLrzwwg8Azj///Pe/+MUv9rfvzj777A3gyOuvv174hS98odeKFSsKGhsb0717927Idf7TTz/9w7y8PIYOHVr//vvv58xl6NmzZ8Pw4cPXAxxwwAHrlixZUlRbW5t+8cUXy0aPHm3gMI2NjZtlyQ0ePHjtoEGDGgGeeuqpspNPPvnDdu3aZQBOOeWUVU8++WTb0aNHf5jrnAADBw5cf+aZZ/Y9/fTTPzz33HM/3Nz5Ekvsk24J0JDYzjM55D+govxvqBTgWrRYD1HeMGKZQWW9SlGeaAe0EBqOlP9v3IaUhC5oIXQ4FeX/cP+/iXQUfoOnuEdsCciwa9pc5DieiBZ4fdg4Mgbe6cikUqyrr2dlSTEl6N40oUjXAwQ5xruyudSDCuSsdkCL5XVA++mTN5QAXYYc3BeQNkE8wrAX6qMeaHw2uPd5KFq9AIEvT6J+PQS/mO6MND0WI9G0k/DpE4UoungLAgBedueb79puTIs5AfvieFyUFDFTznH7mPaHHbsWOXV7us8Wo7SdI5FjZqKLTcih7IpAhPYoetoVRUXz3DWdgICRbnitAtM3wB1nHb6EqvUPZDv0VvLVHPQWVJWjh7s/xvwwFsh6t324aI7rLMTHsDn5eWQLtYbfG7BgDo/1W3gsq/wRr4gR6jNYO2wRXUh2WonRxd9HDlfYFw3u2BHKWzcWyps4nYzpk8trJk6p3tbSkmZzUJ782a5tc9B4uM79fz6KQE/CgwffRWBDHIyBbGAhnirxPvBrfJrF5xCgYeOkF3KMXwAuDCL8Ww2ouNSE6107IzRW/olAqgPwQp4GEq3LZKKGhUvf+1P/Hl3eQhUjliDmTh7SX+mNALeL8OPshm0p72nlOF0769kKICUs5enM/v9BvMpHbL+NAG4HQExAoKdKDuv5nopAUdMWKkLPuM0j5yMQ3u5xIQIj/oQf+8PILgVrwEAHdA/ioOAqPMtwSVRZNRnHnMjRDSZa+mc0RyaWwzoWdmzMBSp0LOzYuKPaUFxcHAHk5+fT0tKytelspFJ+l7Zt29pzxyWXXNLn29/+9rvnnntu7SOPPNJ2ypQpubRYNpwfIIqiXJtQWFi44Yu8vLxo/fr16ZaWFtq2bdscAipm+fn5UUuLMvZaWlpoamra0MjS0tJMfPstPSfAk08+WfP444+3feihh9pPmzZt91deeWVBovWQ2KfZEqAhsZ1v0jmQtoJo/r9EkfeliFa8P1pwbFwKsqLcKN/bqnswCC2IjsAvjA5Cke9wUX0/8J1PHMig/pyGnN7fokh4Cp+LHrc0+u1fUlZKZ+SILUU5vaXAozlTTHYxc2Uof0922oNRwL+JHOjLUXTuaOQUdSPbeY1H5E0bwECarm7fFhTlryM7wm56AvuhPPRuwfdr0cL/AbS43x9FUl+YPrl8LmIyjAKudO8tveNq99oeRZ/7ojSFCAEf7ZDjHirx741ys19352hCjkQZcpKXozQPE318Bz0Xi5AzYRoJKeSMrEeshe74SLbR0MELJIZOvrEXbBv77TnY/W+aBmF6SiguGOo2xHUWQgtTMlpb9IYOaDg+4o5zKdnAhm0Tlt0ED9rYeZvR/TU2Rr9gf7sOU+9fhmfHpBA49B00tg6dPrn8WuDBbU2jcM7ol6PKqol4Vs+1wPecEzoROYtD8ECDsQTibJDwHsb7fQ0aN9XIyTyKjeeXfDRuR6I5adS2XJNztq9Gc7SBOu+hCjE/RH1ougB5SNdkWl5e+sHB//OlOrd/HQJO73P7TUC/AYcgYHUYAhA/UsQxB2jwkWxbjucc+SnBR3VRZdWv0W/sevz9bovmiD5kl2EOtXrCz3OZ9Xm8Ckwemv+a3es0NBaHRJVV84kBDq6ih6VlJdaKjel79rJQowGgIF2QGdP37GU7s12HH3543b333tvp9NNPX/PPf/6z+NVXX93AjMpkMtx9990dv/a1r62qqKjoPGzYsDW5jrFmzZq8Pn36NAFUVFR0zrXNR7FOnTplevXq1XjXXXd1PP/881dlMhmee+65kkMPPXT9Hnvs0fivf/2r9MILL1x13333dWhubs75W3L00UfXnX/++Xtee+2170ZRxGOPPdaxoqKiVc2FlpYW/vvf/xaedtppa44//vi63r17d6qtrc3r0qVLS2v7JJbYJ90SoCGxXcvGVb+OIr9buv1HTVP4LXJ24mJb9sPdDHyDcdVbU+d9V7KrkHNpkdgK5Fz/GznJoTNnFqFIdzNidXwbOatr2bhc3C5ljglgjkwuIKUY7zwfi64zQo6dRaM3Rdc1J9jym83RSaFIngkHWqTPxAa7BsewspbDkB7CIERjfxQ57xMQnf5dPEviNODKiVOqZzumxlTk9LcgJ7AOpfl8mWz6v7V1IHJ4Q9G2PORclOMZAp3cZ8ZSqEOOsEXuS5BTZzoW8xGV25zUkOkQZxKE9yMUSky545kTa46NgQGr8UyJ8Ljx8FUIIETBq50jTvMn9lnY1njahCQNDVgAACAASURBVAEE9ajfwpxzA59s33x0b9aRTcEHf1+M/ZBB46UgOFdX93l/lAtfxFaUf8xlLt/+WRQdLgk+XxBVVj2O6PFmucZ/Gj9mQrAnQtoSpgVwInr+wmPUuffFeIBzmxyHqLLqQKSn0AM/Z0cIXLgdPYMPIer+Erdd3Im1KhFhadgnEFDyEor6t8FH1T82CxgK8xHYY6+hFstc1+ZW2QzbYFaVaCK696vROGyH+mI/xHZIsfE6MS7EGmcCmeWaCwpQaoY9g6cgdskVbhyuA67aFhbJZ9FMh2FHVp3YEps0adJ7Y8eO7bvXXnvt179///p99913rX1XUlKSef7559vccMMNPTp37tz0hz/8IadjfuWVV7599tln92vfvn3z4Ycfvmbp0qVFubb7KPa73/1u8UUXXbTHT3/6092bm5tTZ5555geHHnro+ksvvXTFqaee2n/gwIH7jhw5srakpCQni+Hwww9fd84557z/+c9/fh+QGORhhx22/pVXXsmZutLc3Jw655xz+q5ZsyYviqLUhRde+F4CMiT2abdUa1SjxBL7TFjFhrKAPyC7lnwGLTKv/CRE8Fu1bEbDtxCl3ha2FyMHwRzOeMUCEM16NxR5vR6o3FVYHTEhx4ORs3osWjh3wFdJADk48xGwsM79fymilIe0cbO44Jo5gfZ5rqoHofNlTnNejm1bkPjigSgd5yXgq8jBsDzUZQiAiFD++HpES7d2TsY7wi3AM+66TsCLNdrkbu1d7/ZPIbCgm/suTAUwa3bXsApfDWKe29aqJUSunfsE52sNpLH+CIEZSy8wZ8SEN9chUMvAmYbgujNkAwiby+E25oH1wea2D48fAgnhueJjAwRARK7tXclmACxBKTgh+GP7FKPrbsADHG3Jdu7+MnFK9fGbafdmzTmqI93bOeasutz+36NnKa67AeEaQTTnBuSYLkSlIpejVJv7gH5EkeubTAZYQypvKhonA1F6xUnAd1NjR2yxzotr+8mockIPstNiwhSPJte259Gc3pOYYx447B8gXZ+BaNy9g0o5zo4qq/oikPZHW+P0xsGAzYEDUWWVVZZ5Fs1ND+C1WsDrO8xHzIsjgQmpsSNmtXY/t8aiyqqvIJDzHcRqyiCA8wN8Gdu4hQAmKO2qI17oMZwvs06X47Pwcxto92+D+OanxubPn79kyJAhK3d2OxJLLLHEttTmz5/fZciQIXvm+i5hNCT22TY5zbNcCsaDyGEFuJ1x1d/YeQ3bTiaGiKcoC3g4F0XOb0ML3GIkPvYPlCIRKuT/BTlKuatyfMwWMBS6IQelGaU8rHCv56EI3LnIOW2DwJEO7j14On5v9/5dBCrdg5zvR9DiPqTdhxY6MYVkO5qtAQ6h455rcb0XiiyPQuBAf7edMUzaItp2V8QyKAV+FBzLnFYrOfi5YN/QUbYUhvWuTRZx7EZ2ucZc1/yh+64R5a6X4/v2ZXeMgYg2b+U7cf1kJWetr+y3xhx/+976xypKmBlFO0M2CGTX1VpaRJieYdunYu8N3Kh324fjPXRcQ4ChtaiT7WfRdQNTTDAxHznGBjiZPoWVV7SI8SIE6hyK7zdQSsnj0yeXl33UKhTxEozB58ujyqqzEPA2DDmz+e4aMmQLLmYQaNAeaS4cgsZeBzSmvUXNQOMbpNqeD0xKjR1hYrs/24bmn4y0K8J0FqviEmoEFKBn5hTXvhKyxV9B88n1bv99g88b8EyCqxDzC+CCrWznNGBKVFnVBfXVZOD5qLLqz+h+vhwccwl6vo9F8/FM9Mx1QGCkgQw/RiynFDDIgQyXINZFhKqWbEuKxoMIKPoAiUHWINDghNh2Ns/koXk1LDHbgE9f2d0dqxOtiw7HgYq4oOuoqLLqwtTYEZ9UFmFiiSWWWGLOEqAhscTAUjCG7+xm7AC7CtHrTwK+zLjq2Y7VUY8WtH9GTtOv3P8/3JEAgwMWrNZ7d+QwmEigRcm644XsuiJF9WIUTX4CrzJvFQJsQdsZ0aUHISr+UPf5SjZe9IZmi+U4yBDf3pzUeMpGGMW3tIY98YKIe+FTLOqR42vibBb1ho3na0sHyHf7NATnDp1VAx7i+4fOfvx6LZ3EnOLZyGE+w51zfzzYYdFMqyARF2K0CKe1J5+NnY2QgRECIvH0izBFwb7fIPKZ43pMFM3AFHPsI/dZcdBWa298LNh540yGuKaD7dOEHLgzkRP3f2hu6YwHv9q486dRNHkfNA5/j8bGMHyk+LtIdHS75frHzaUWXOUE+iz14XU0DvsGm6Zd+9YCa4gyBxN9sJxU531JpdSHmfXLid4qI71XCamyfdC9iaemba0Nxgs8mln0POz7UCdjD3Qv4ucuRikC8YoadcDwqLJqLmIP9HSvW2TO+T8FPTdjEGj0GgLkRiOgwFKcLJ3tYbzmRRsEQIx313UFAvLOQ7oXi9zxHsNXCPktSl3pHlVW/Qixo/6OS7/YHMshpvlwjCuteQu6v41ILHkl0ngoQMyrSvT7cRiav9qj52N3vBikzVv2/De7c9iY35TlAb+IKqsqt0OKSGKJJZZYYjvREqAhscQ+W/Yj9Nz/FYveZZfItNeNIp/b22KgQnu0MO9LtpJ5Lv2IoUgX4FW0OC9FdOk27nhhukLoQK4AnkZOxoDgeO1aaaJFTN9B4EYYcTdnPCxvmKu90Ho+cz5yZkK9gKLY94Vkl6ELHfa0a5O1s4Ds6KClVRjIEOX4PmxX2P40Ai4akKM40r0WoohlOzwI8QE+6l3o/prxoEnoCOYScgxBGDt3mFKRy8yBMWDHQAZjDHzoPivCOznheeMaEaHgXWi50i3i4EeYVmNtPsGduw/eATP2yELEYMnDl4NMIcf+IcQSOBj13XPImSzeHqyGLbDbkKO7J3pG1pBKvY6AORBYstJd22CiKCLqsA8pUkqxyKwhXdyRqF8eqXQeqdSbaAxtk7aLc97PQ2leeT4tA0vjCNOYbGw1IkfZgLSi2PEGoTkjfO4z7hqn4stZWhWGVlM8YhoLFyPn+2nEargFrwEDuvcdEUCwBo1ba0szSm1rRPe9HqVN9ELPwlKkk9MXiVZ2cce9GIFZu6NnsBZpvZQD9zstCthybYe5iAECAkhuQoBtWWrsiKlRZdUC167pCOxtg1hZ/0KMBqsMYHoT9hw879pfigeAM2ju6Ew2qJd21z+cjxFcSyyxxBJL7OO3BGhILLHPkimV4ryd2YTpk8v7kkr/iNJ2HVm3+mi8g5zL0YtHjs1BLEVOnOV+z0NR4d3dduvxjrgBBB0Q3ThkaLSWOmDO4zvIaQkZFeE2xiBoLf/YrBGBOwPwVSLsmGGkPhQPNCeqkexKByFjwNphtho5UGvJTqWI60rYayNyejqS3c8tKJ/eRB8Ho+hwE3IghgdtMhCj2b2uQffnVdeGfdwxQ7ZAqMsQpkJk3P5r8BoScdAprApignMha8KqG9S5doT9bABFvOpKCHrE+7i1dA1jkISpKtZ3S1wbwn1rULR7L/z9yEMinSkUtb4b6W00Icd3AHIqLwKYPrm8Hpj7cQEOLo3iOgQ4GCvoZRQlfwKJUvZym6dJpWO9km5PKhV+9hbwv2xFeceYfQ1pGGwuCv4B8P+QgwvSjPgqcr5DRsZwJKx6m/t/X5RK1Rvdl/uCtl69qXYH6QtfRXOLgTFtUBpCH9cuE5oF3fNuaGxaiVvwVXEMZC3AVyNZj8ZAnju2OeopNMZsnBlI8SXERhuLnPsnkOjj1WzGcQ/Ta9z1rXZtvNu9L8aVRg10Iv7hdp8T06WYj9JwBrs2HoCfM2zumum2KUfz8h1IHLLK7Z9YYoklltgn2BKgIbHEEvtYLRBtrEHU4NOIMvuzbnUouNeacFgjPmodKt7XowjwQe6zo5Bzag6nlViMsxAK8WJy4eI/pGQboJDGR+jMAbUovgn4rUJOSiMq7WcMAisX+D5yVh5A0cZrkRPaJzifVR0wen5YFcE0BUxPIWQEQHaKQQE+imv5/yGQkctZswoJLWQ79Cl8JN4c8zz3+eEIyKjFpwMYo6AsOO8BZKdPEPSPHasRn75g11yAL20X39/aFwIA9n+o85CHnKQCBDYYGLAeDz6ExwiPa8doRkBTHQKz7LrWuuN0DNpoZmOzB3Jgu7r3ZSgqXYDYAnHGSR6ezXI0Hkj5LwIfFiIthNOQnsrHGemtREKl5yPAYzh6zo5xbX8Ljd/MBlZBFGWA9LqW+pa5KxdES9cvT5/R4/DGzkXt73alCrfVLmGjcRtl3FCxsf0aGsf/Rs/394A/ogj7XAQqmOjlIQi4eMwdLHSIB9m25GAABOKLxfjUgPMRcGCaIqbj0hWfnhOCqPbahuw5L6w6EhcvNbAvTA0JzcA6A87S6P6tQiDIAMRA2Cqwx4EGj7lrNe2iKxHQMDyqrDLBS2Nf1AOz3WfG8DAhzbGufW+gsfMqAmeqXNuuQmy7L7n27o70gT52Zl1iiSWWWGIfnyVAw6fAfnDtlA3K1j+9evJGkS73/Uj3dk6ubRJL7OOw6ZPLuyPhtSORU9UH7ziaQ2dOYhhhtkV4MYp0dcEL8rVHzuLnkNMzAF85w6Lc6/HlJ+1YRu+P6xdYG6xNuZgJKUShfh9RlwuQI/sj9/6vwIMTp1TXOWDlTCTwds3EKdWvu74oQxoHXyEbAAlzyMM52RyOMDfd+shSC5rQAt8cnTDKb8cIHRjbJn5tccsgSr+dyyKuha4NRfgKFpb+EbIS7LjWr3ZOu24DTiwdIyyDZ7nzIbgQgi9hioV9l4co/RZ1TSEAKBRxzMMDUCFDxd6vRn3ZxW2fjxcQtTGa767bHMh42kcKRdN740GfNu67Pii6HiEnsGOwX1gpJIXGWZegP/ZFY+oFPuZIr3MUJyFn/Cfu3EUIbAClIPXGp7hkSKUagOhv773wl1tee+CI5kxTuwWrF/9h+pBLK7elLGNQ9eF5BG7I6U6lMpBaixgWbRG7wsq6HoWehwgxnV4E7nYsjTI0D40BfpIaO2KWO5WBIPYep1Ng1R5mu8/KUDnIi9B96oAfb2EKRih6amyq+HMXZ/GE39l4jc9PraWS2Wfxz3dzf2kErjzaStWLeHnNDfcoh+Dk91GfFOP6x+lZFCOwoNjtA9K2GINAOdMl+T0SAv0GXgPDzneBO+fdiAH1WdBLSiyxxBL71FsCNHw6zJSuJxEsmAIbjn7gOwNP/ODaKZcjSmVOYCKxxD6qTZ9c/hUkKPkCWuiGDrw5d/XIYbNoedwskm5AQxNapJe47TugRf7LiJ5rZuKIdi47VuiYxp3DMMXCzKpYNKPqHF9FTt/NwH9QNO6xiVOqs9TRHa39N+4v6/Ppk8utRN1esbY148UmzYE19fYIOTXt0aJ+LV5ArgBf6SATvFp0//+zd+bxVVbX+v+ek5FAAoQhICigoCjBVEFUWlHROktr0ZZWbyk/bb1tqVelLb1VcmvQVr1Ge9Vr9VrF1GqjaG2V2motalpptTggoLFBwQEQZQqEzMn5/fHsxd7nzUlAZQjtWZ9PPknOecf97r3fvZ71rGfFkTMf6l6EFk0ZsL8tolqAIvG4c1tKyrv4KH/IhsD9bw7fUuSklpDsVLe5z6P52XZNBqxYDrdpRmTinTEDWgyoKYxchzlzoUBgyIqx/tWKF+DMJ3WfSSAGR3/3fZiuEYJkCeSE50T2t+uz6+qDHwd/ROkTo1DEdwtyoBNIFPLnSNhwJWKJlLCbc9eNQp+orDoBRfptjLS5/zfi2TCNwK3Acx80bXq+qb35eGD04k1v3OFAiy8ix/LZRGXV5U54ckdmVR9eoGNFkjw0fp4AbkMaAQWIXfAd4EcIaLgCeC1RWbUUzUWHoj60kq5tEYra5yYqq3q5tjgDVXawNKtcvH5CV/03n47jIwpmGtgWAhNRFk9onQmRhmZgy2toDM7r5FgT8RU4ClCKxaPBd1NRCdNqNP98Cs1rllYyEbWziZ82or59Jn6cbEasj7OBSe7/Ia6MaC8H7CyKTZtU50Chb7vjftx0m7R9QsvLyzuivr7+5T11vrKysoGXXXbZ+vz8/M76/D5ne7oN05a27mppoGEft9lzy4qAb6HoxdTZc8seD8EDx2bogxavw5Hy9SS0YF42e27ZF66bU7qjhVfa0rZT5qL2X0fAVxyJo0FHAb1GfKk6yy+29AHwkd0M5NC24jUHQE5fAjl04ee2X7jYD89r0b8wQh7uZ9fyd0Qh/08UPb0cOfgz0YL80Vllyz6Os3c2GqsWsbZr3IIHDnqgudm+34qcbcvrjoplWhnCaBlIu6dcvM5D1EkBrzUQ/dwYIYPxjrSlDaxGjrFpDwxE8wzo2eagZzfO7dOOIv398c/c8rXtOWxCzkx4b73wIMgGdy0hI8EcMwM2DHRowjMbrI1rkWMc9oN40C6Q7DSuxWt+tOBF+8y5TKUdEiPZ+Yzh29fOHYJccQRAWDpPH+TAbkZCkCXuJ4HAhruB1eWlxXcBVxtbZjfadajkbagL0BOJVl6PotxPAY/Epk2qu4hJXMT1UbB7NOof5yKn99qdOO/V7vcYklk81naHonlhMypPeyounSc2bdK9icqqKW67T6HSt6fgQcvazk4aRPhBDnQjAnWOwzNQwnklrILSmfMfMrWi845tGzIcwv7fHvkdPXYqnRbbZn/g+di0SbPp3BahFJOT0RwU/e4K93syApN6o/SGjWh+3ILEJx/As21uxAO9eXgdCtP8aMCDCBOJsEcilTDStiP74LVC1r40hJaGbLJ6NDP4yNUMPGzj3r6sj2J33HFH0de//vWNHwVoaGlpISsrWuBpz9nePn/a0ravWBpo2Adt9tyyEciRG4GE5UzZ/Qzgrtlzy6oQmPABWuB9Bi2Qja47yB3qU8CvZ88tOy7NbNj9VlEzfzyKrH1z+qjzFlfUzI/m/JpjsWj6qPP2qefhAIbJqE/9kOSIXCqHzOqwg+ahBny0OkytiKGFektwvEa0YN2CV/ZvIrlCQyoqf9QRsHOZ87AO0cWfwOdw5wNfBn6NFu4FyNn7uNE2iyw+hcbwWKRSvw3l5vd29xo6FQXuGvuQWs8ijo+wh+0eOruhCJs5+uYsh9UqwrYyJ96cqa2oHb6AHC8TTDwInyaw0d2LpS3YSmwtvkxpFh4oasNrJpgjF+ao5+LTCiylwe7X/oZkUcaEa8O3UeTbUhJMq8HOEU0psQoVRnsvwjNCWtBzsO/Cdgp1MOw41pbW9i34lIpo6sbh+FQMe8Z1wbGaUJ/8g2u376NUgSHlpcXnOqZMEaK4z5tVtuvK0boI87koql3grrsK+L5jJkzficPcgWcCdBZZj553JXBhorLq5/iUjRrUBmMRK2gLirQ/jgcPFga/56GI/FvuM0t5eB5Spw3gHd9rSBaDNC0YSA1QpmIVEPk8Fchn17Wj/W08pEpzqseDgjaPvo3WBpkhYyC6o2Oc3EuibRxtry2jbcEm7vnWXcDVMQkHP+na6VN4JtGh7jqOROPd7Ea0BjnN/W/jIbzmdtR/Lk9UVt2B2rdL0c20dWEfvFbIu38bRqJN/aSlIZt3/zYMYFeADQsWLMi/6qqr9isoKGh944038qZMmbJx7NixDbfddltRU1NT7JFHHnlzzJgxTVOnTh2em5vbvmzZsrwNGzZk3X777asqKir6vfjiiz2POOKIbQ8//PAqgPPPP/+AJUuW9GxsbIyfffbZm2666aY1V1999cAPPvgg6/jjjz+4b9++rc8///w/QjbAvHnz+i5YsKD3ww8/vGrq1KnDc3Jy2pctW5Y3YcKEuhtvvHHNhRdeeEB1dXWP1tbW2BVXXLHmggsu2By9h7Kysv0KCwtb3njjjR5jx46t/81vfrMyHo/z5z//Oe/yyy/fv76+Pt63b9/W++67b9WwYcNaJkyYcMgNN9zw7qRJk+rXrl2bOX78+ENXr1699Oabb+73m9/8pm99fX28ra0t9tRTT9WcdtppI2trazNaW1tjpaWlHc6ftrT9q1saaNiHzLEXZiAq+udSbNITRY2+gHcC2pBjZmJjJlZlL//DgQWz55bNSDMbdp85UOF+FAWuqKiZn+H+tooGWcDTyDF6sKJm/qtu10ZgYXcFHgKhxyKUnpOJ7iks02gWpgW0uM/MGX0TRS+Nih5SiON4ECGB11qwCHEOcuI+RKwdo6ibhewGo8yDInGNaHH8KIqQPhAyFcpLi+ehCN6x+DHz/9x3131Up85tf63b/yzUdrlosf1nVJ++h7suE68Mq2KEjrhZBqkjnonIPhsRs2FQcC+WJrENLyAJycBOtvu/B3K4zGG3lJdmJHA5yN2LqeBvQs+iN96BTrj7/Atq1wEIwDAn3vqDnfs9oAIp0ecgPY7w/toQoGrMh574UqcG0IR6HyEQY46TgU5hSoR9byVGrTxjKq2LqKMZ6lGEf29G46QOnz4RJ1mjA9Se+yMArQ052KMRG2AmiiwXuZ+J5aXFi5CTd65rhyvZhebEHAc4McUZON2Dj7D/uk9wTdYn16Jn8Hs0T2SiPvd4kObRi2SRwltRlH0jGtujESBxNBrvExGgsAmBfE1obM/BOeaO2j8Fz9YJLRXbIGoGDnS1TbgOMy0QY9CE5wL1h3r0nNtRtZ3/QsyTQcAvUBWINxEL6xkijIEU9gitj5xA23NfjNF0I3Bkgp6ZVFY9jPrmaODzqITl6cH9VKGxNhmV5pyGmFep2gp3T4+jtUsBAii//QnFQv+1be1LQ7aDDGaJtjhrXxqyq1gN1dXVPZYtW7Z84MCBrcOGDRubk5OzfunSpa/PnTt3YHl5+cC77777XYDa2trMl19+ufr+++/vM23atJELFy6sHjduXMPhhx9+6KJFi3pMnDix4cYbb1xdVFTU1traysSJEw95/vnne1x55ZUf/OxnPyt69tln/zF48ODWHV3P2rVrs1966aXqzMxMZs6cOeTEE0/cMn/+/FXr16/PGD9+/KFTpkzZUlBQkMSMeP3113u88sorbw0fPrxl3Lhxo//4xz/2OuGEE7ZdcsklB/zud79bsd9++7Xeeeedfb/73e8OmT9//qquzr98+fK8V199dXlRUVFbS0sLv/vd71YUFha2r127NvPoo48e/ZWvfGVzPN7ZUE9b2v71LA007CPmUiCuR4vzVMrT4BfVYcRuFXqh28xntGazOIoOPTp7btnJ180p3WXRsH91c+CC5ZpORourRrQgNGc4P9jls+777yHHrwkttL9DJN9/b1mKyKlFAFfhSySaQ2mOqLENQBE3EzeMIWX+fsgRCCPFUYtGxWqRA2uA2lCSKxVEI3/hZ1sQ3fr7qJ2jUc3QjiZZTwF33v8ATnSaC0/avh+l7KDb9sny0uIxKC2gCjnU0QhnVAgxHvncfqL3bc/Bouf9I59tRM/MwBuL3hsg+Ufk7JyIf1cciJ5nHDloRSiNYigeAPoAOSiWkmDpFqbH0A9R2ZvRc2zHp1SsxVfkiLt2eRulDTTiQagWlGt/oDueMSda0TjbjNIerGqJfZdNMmPGWC1xvEif9eHVCLjahgdKwFcdacCnZ5iF/Td0QuNIVBF3nMLIPu3uWvPwaSwJ9/eRCFxYjcCxv+Aj+BaFP56OfX6XmgMMdibtYVfalei5bxcPBC5D4oOzIsKFl6M0ie/hdQZy3faHoTbtj+9fi4DHgFL0TDOBXyHn/CIECE1GOhD9SB5fCaRdYoKL9hMtm9qGn8ftOYcaDdFUmmaSU4BAzzqOZy72RODc74DLHevE+kHIXLjQtcs6umAMxKZNquOeb/0QqEnE9n+O+IhSMk7ZiKpzGOi5FL2fwrnnVSRsa312YPCd/bbt20g030/bkvfIGHcUsXiDu97JrmrFTguFpi2wloZUpZk7//xj2NixY7cNGzasBeCAAw5oOv3002sBSkpKGp599tnt65czzzxzczwe58gjj6zv169fy4QJExoADj744IY333wzZ+LEiQ0VFRWF99xzT//W1tbYhx9+mLVkyZLco48+uuGjXM8XvvCFTZmZeh0988wzBU888USfm2++eRBAU1NTbMWKFdlHHnlkY7jP2LFjtx100EEtAGPGjKl/8803swsLC1tramp6TJ48+WCA9vZ2BgwY0MIO7LjjjttSVFTU5vaJXXrppUP/9re/9YrH43zwwQfZ7733XuYBBxywQ8AkbWn7V7E00LDv2DmIqZAqlxr8IjiBFvpWd3tAZLs8UtthqBxV2Se+0rQZyDATRXmWo8VhFnLOBkc2D50/ez7moOcCX62omf9IN2E1zECL/6PKS4v7IZHCYcjhDC2GnL31aAFqFHhzzIy5MJBkqrlFkaMCcKFQYQbJjpqJRIaaD2HkuQnpLByAHIOfI3AE4Afunt5x2327vLT4beRgHIScF1tM2cLZFiOfQk7INndNl/PxAKHL0SJ+HYref53kBT0kO5GhQ0zwvaVFRHO9wffB/u53M3o+Jn64EQEolqaSjVKuepKcQhGW0LN+fCChQ+Gj94XIGS/At2EcKHZ/JxCwYMCTpYnYdsZ0uMDtvxE9YysNWO/uIxuf8tCO+s9a5BxuQHOggTehfkIryVHpBvfbrtXuNQQD7T7teNbXUrW5WZgq0YaP+IYlQ41JEQWV7Dq+ifqZLaDPRNF4S736T5RKZ+UZ/ynMUijcv8uD36dFNp2IQIYekAQ8fAMvYtoD9ZVLEpVV852D/grqqwaSxlGbXhmcN6wysv3SSC5RC8k6C+EYDavfGMOwBxpn9lk9XlizZ3DMdjzDx/qRpdIcDJQEIEMNqgQxFLgyNm3Syp3RO0hUVvUi97ajUYrJqYjBMByxQUBssW+QvHbYgpgJ/fAitmE7RC2DRNtxtL/RBpm/JPOIFgRU9EYgWmdC1mnryrJ6NKcEFbJ6NO+qU+Tk5GwPbMXjcXJzcxP2d1tb2/YxYZ9nZGSQnZ2dtE9ra2usuro6+9Zbby168cUXVYtRQwAAIABJREFUXx8wYEDb1KlThzc2NqYM/cdifqg1NDQkAai9evXa3tcSiQQPPfTQipKSkqadvYeMjAxaW1tjiUQiNnLkyIZXXnmlOrp9ZmZmoq1NhLf6+vqk8+fl5W0//x133FG4YcOGzKVLl76ek5OTGDJkyNiGhoY0nSFtaQssPSC6uc2eWzZi9tyyPwF3Ibpkqmf2AVpk2OKmH945KIhs21nUqx0YMHtuWalL0Ujbx7CKmvm9Kmrmn4IWq1NRxP5cJALYh44gQ5SGbf9noIVdG9LbqK6omX9LRc38vf1s5iEhuDNRFHUqAhmG4R0262P90WLYHKsoxbyB5EWq6QKY4xWlo4f58GEKRDZ+EdwCrEAL9xfd9b7gPuvtPn/OsQkmImdiMEpHOh74K9Ih+CXSUBhKRycyrFCR5+6/H3BTeWnx+LCxykuLe5WXFp/iUkw6s6uBe5Ba/uPI8QkdCwNPmoLPoukRkOzwhp8nUN16E2g0jYMR7v8PkBNbj1IVHkXzSb37voFksCGqmm+AtT3f3qg/5ONV9zfg2QVWLhME/Jhj1Y4cLaOPZ6L23ezuvSi4jkzEIsHdm4Eg1l42TkxnAXfe14O2sZQlc/gzURQ7hqLIVhkCd36z0KkKAd6teCDAtrNnZRUzQsFTu1YDyqJ9PLRc5IjmuOuqRukVdwE/RdT2Q+gcSP5nt0WIyTAT6TOEwEMPxBba4H6GInARpNfwO+RkP4z6/LN4McqFCLyppyOTMB75CVPDoCNrwcyAVetzYRQ1H8/y2YTGoZV/tX5T7z6fi2e0XINS876LNGV+lLqZvAUVH85Azn450tyIIeD1ZBTcODa4D/CiqofgmWpdrSWVKhfL6UXWublkHLoNAaub0Rw0AC8YmbaPYoOPXE0sI3muiGW0M/jI1Xvpijq1TZs2ZfTo0aO9sLCw7d1338185plnrCQzPXv2bKutrd3eh/r169fy0ksv5ba1tfHb3/62b2fHPPHEE7eUl5cXtberCZ577rkenW0btcMPP7xx48aNmU899VRPEBti8eLFuQD7779/0wsvvNAT4L777uv0/LW1tRn9+/dvycnJSTz22GP5a9as2WVMkrSl7Z/F0oyGbmguTWIiosouQNGFrqwvcuq2IYdqAL4knFlI+0yVL9qG6KIgh2BPU2S7lUXSHrC/Q1ZBim0mo3J0X0LO7c1oUdkTrz8QzV+NPocoEGRR1SGousghFTXz/236qPP2VorLNgRe2Qu1q36VoCNYEFovkp3j0EIHLiraFz2Ondcigz1RzfbDkFNxHGIfbAXuBBY6xz8XRckHBcfKB06KnKMFr0XwodvGNE+24Ett9nPH/iPwXVcRYEelZ3HbXQhQXlr8IXApooqH4ocmKhi2Tdg+IfMh6hBtQNoKg/BR/zhyGHLRfPGuO/5wBBJYm5uIYchaSESOE4Ihr+FE6PDPP+7OEe0f9r8BFzG3nWl45CDH/3C3XQ56xiaqGHPXvwzPjLHz9aGj4ONfERBwWHD8FvRcM92xBrrtTYfCQI+t7vto227Bi1gaRT68N6uGEQXYjJVhoBpu267eyQkUBbZc+M8gUKcdRZftHOd3cYx90hxDYTJ67l9B9/zvsWmTlgNEdRpQKtQc9zMGpUBloz6/EnjKOdm5SFyyCrEEhiFwYWyisupDp9NwLWr3i4ET8HONMSEgGfwLGSo2NsADdeF29cArCOhsR+/8fghksIBBA2JxrEOASAtwHvCCu75FCCT5ET4F6KmdaM9rUQnRP6P3zEbXNttQtZhcdx9H4MVeCf7eEYsBfGngQcTiD0DuCwjc+Rt6b9agNu8WqYH7nJkOwz5QdeLYY49tKC4urj/ooIOKBw8e3Dxu3Ljta6np06evP+200w4uKipqfv755/9x1VVXrf7c5z43srCwsLWkpKR+27ZtKfvXtddeu+Yb3/jGAaNHjz6svb09tv/++zc9/fTTK3bmenJzcxOVlZVvXnLJJQds3bo1o62tLfbNb35z3fjx4xt/8IMfrPvSl7504D333DPgs5/9bKfijhdddNHG008/feTBBx982OGHH14/YsSIxs62TVva/lUtlkh0lu6ftr1ls+eWTUGORj4+7zFqRgHe6v4vQC/1NXgRsVD1eUeiVCYGtxY49bo5pctTbPMvY46VYCJa2N/TR533ZLDNFCSAuAotbM9DbdwLORK/RvnthfjcXXPSwrSA0DpTFgcf5a4G/gdFohe67zoAIbvDykuLv4ii/WHJObufVNcd9rdUjqZZZ5Tz8Psdia7VIbG4T6Hnthkt3L+PFtPWXj3R+DoBOdgTUlz7h8hB34iibX3cvhcgp8VyqC1/+SbE8jAHexNatPdCjvfNs8qWXd7JtW83B4DMRBHXg0gu92n3H3VcQU5IP7oe8xY9Nxq4lV2MampY9Q87j53bNA2WIwDHqtr0wTvIb+GZV4XB/l3pB9j34XYGgtQiIOEI5FQ3IDD1QDRfrUMU+YsRODDBnTMUt7Rjtrh7NiE9kAPZhMpLmsaHpfWYQ2XtAx37Yfh3C35cGDvD+ondW+h42nXVk5w+EoJF4TNcgto0C4FG9jwNYCsEbppVtqyrcob7jt1T3CsRK/sTWX3Hq2VitcTi2XgGzNOoDO32VAEHHvwE9Zl3URpS/0QiEU8kEu2JBKsyMuL7IdbOp5GYYh8EYDyE2vVY1LaXhEKFzjm/C0X5n3P7LUfR+TBFMUzPgdRgajuan34GvIEX0r0CaXJMQONrM2Ja5QPXxKZNKnPilNejyh+PBtf2DQRG/y9wp4lZIoAGgqpGCAC1edxAQmMLNeHTGka5fa1fpkrbCu/JxoClIVWgvvlNBCp81cAhd93b37NpUUjZkiVLVpWUlKzf29eRtrSlLW07a0uWLOlfUlIyPNV3aUZD97RcROfujAYbUjFD0bUstGCy3OPw+XaVQwxyPlYhqvHZ+HzYfdJcisEMYN7HjP5Hy25t/ztgMhQhB2U4igDVocXcYWiBdgxa/IdU+1TCcWGkuCuHzBZ4xYgu3YqEItexY2XxXWXjSAZJwjry0WsPHeLQSQ4j3V2ZOWWhYF8qa0VpEv3RYvY+/KL67Vlly6aHG5eXFs9BUWGQ01mHFvJ2D28DZ80qW7bcOf7nIBrxj5wA5qNErLy0+AJ3zP9AwEQhyToSpzqQ5lm6Fo6ciNJR3kNAg4FLLcFnUee9HUXWi/AOqjmg2YgG3Q85s1vwopDmAIXOs7V39Nk14x33Ovf3wSRHNrfidR56BccL0zk6Y680kDzfmS5CX3fPy5EDlo1PmWhH4++nyEGsR30hCy9u2Y7Ail6or67Hl5LtjQCMPLxjZJFoA85CfQU7Z6iFEd5jnTumtVsuqfusfdbo7ifMyd/m9mtFjvRB7uctRDPPx8/rW5FDfYzbZxXwTnlp8RRg4UcRJu1ulqis6kXGlfcS7zueWDzuWr8PSnM4AQ/uXo7a89hEZdU69CxGI7CxHaWukUjQ3t6egBiD0LMdjd6xZ7tTtpJovxRat0CiklhOAZpLtptz3C8BXgYeQHP8IpRucANeb2E56lf/QEDBBHx/sjHwGgIVDCgegYQV+6HywEe7e/g8YlocD2Q74GAhAk9JVFb1ik2bVOcYHTe6H2vDIsSqOw2Bpk8jZtOHJPfNKOss120XpvGZ+GpvkkGVdtQ3i1DFi8HumrMQkPFHBOCYqOlPETBjli5vmba0pS1t/8SWBhq6ibl0icnBR1mdbUvywiDMMbbv2jrskRyVDC2MZixGi6h5O33h3cwcwHAxWtScAZxWUTP/S9NHnbcuTHWYPuq8Ovf/OWgh9geC6gGOGRA67eHfk1FE6UW0aFwG/Aktxi7Cp0cY6JOKvZAqOt8V6yRKA89BDtLXUA7xNeyZxVpnqsypItepUirC6PDOWGfgQjtq9xXAb9DzuRjRxl9FINNUtJh/MqiW8RpiDNhxe6Dn8yZiQ9QCt1jZSues3csOqL1uu5+XlxZXIsfjp/j5NYFqz/8COUoH2HWlONQi991JSJvDAJYsBFwY5T/azpZyYM6MaRa04cU4raqBWab7LHSCwndC6GjUIUd+DXJ8sxD9uRA5be2I2XEkqXPV20nu22Gf+AcCQMbT0fmJo2c0Cs8OsFQW63O98SUBs4JzJ5Az3+KOv8a1U5X7/Ci8KJ/t145YXQODazB2Qztytgbh0zTCeww1Heyz9a6NB9NxbGfjQTSzHOQINiNwZIT73n6H2/ZE89X77vqHoXlgKyo32AEQ24fsHDIHTyH5fuvROLwFjRMTNZ2KGC4jIseII5ZKWyxGZkZGHHz/HxNskwCy9XgyCkk0n02MwYgRkVSWM1J5YyVAorKqwm37aZTW8RcEPvd2265CzLaVaH7JQ2Udw8j+jcG9/A31z7NRutUDKC3ii4j9MAQ597Pcvo0IdDkevfNOce10CgKlMxCANUH3yYkkz8Nhn2xD/a83AigN7NxKMsMqtAfQmFqCgO9jUOWOHwbtdDAaZ0npYzsjVrmz5kCYaaidLgvbN21pS1va0rZ3LA00dB8zQaZmFKWpxedoQ7ITEX4WRwvofsgZaESL2pDCaxG5VIuEGHrRZwJlnaVMzJ5bNgZXUqw7pVVEwQPUdrPwDI+JQEVFzfzpaFE+F7imomY+aOE1E6WdnIqAlgFIwT3l4sedbwJyAjaiRdZT7jh/xOfRN6JnmYsWWHG8rkEMLeYsMmR5tebMpLIwcmo5wFvdPd0J3A4sraiZP5OPz+LYGbsFOVpfo2N/6gwkSaXTEAUfumJytKOFskV+rT9XIypzDQIZ+rhtvoIW/XcDq8tLi+9D6QvjSBakbEfOfxZiK6zs4hp2yhzg8L/lpcWD0EI7jIbnoPKVLwInlZcWr0aORTXw+KyyZXVBycua4Jr3Q47VXe4YZyHdCWNMmLNt9xQCina/q5FDalohxlxowKdP/APlq2eS3E4gx/s9NLdYLvffUVvj7mkpYvP0QXPRZnz5P7uWKLPKgIBiNCYK8E689ZH+ro364MtThiwJAyBMO4Ngfyu9V+/u72B8isua4DiWLtLu7jE8fwh6DEOgxdv4kqxmlgJh5hxY3nfH7ExMkOBcGSg6vB7NXQYc27YhIyjL/eTj03WyXHuE5RH3RTuLWMzudxMqO1kFPB6UQawDZicqq25HjIL9EOAQphsq6SIWC+dO8OwD2ya2/eNY7lD32aGJyqpSJND6TeBqVwUjyRzTYSFiINQjsL4IOf5NaG6Klp5MdYxbUaUHA4yvwIs9GmvlZDRnPIxApVwEet+P0kSGuHv5bqQN+iNWw0DXbnmkBnytok8WvoytpUFB8juoxR1rBHovHI3SB+chQOQ6NFdtRO/Ge4FHUt1/KnOMjBnAPAfwdLadrQEOQFWAsoD/Q++AtKUtbWlL2160NNDQfWw0chZq0eL7dygyahYurKOUZKsj3xdfMivc1v5OFWFuQ85FAcpN7QxEuAlfR/uUj3pzu8oqauaPQIrgW9EC2yLAKytq5r+HnBGL0LagtILJaCF2EaJpFiHQpNX91LvjHY+iUTVOo2EJcuSX4PPyD0eLn54oV9eiPXVoMWeOUBtq0/BZhc+uMPgbksXzzFnszGE3Z8OEJmMogjkUObKwm8Q8XaT/ovLS4geRk55NMl18R+kQofNmfzfiAZo4WuA2oqhdHwT6nOTO8wBa0I9GkcJrkCNyPHKaW1E/7YHAu3PwecZRexW4dDdRzP+OwJF8d00mFJiJgKpxKIL/GeRgH1heWnyr23ciuu/j3P4LgenGsnCsia+jvtwbr/8BHQEC0DPaz/0Yg8nSCVpQP21CwMZmklMrwD8n0zAY6D4bi5yImLu3/4d/p1gZytAxDlOFDOhYisaYOUFRx9ucwSLXntZ3NuCdHzueAXqhA9WO2j8fjdO3kTMaQ8yFPu7e++MBjK7SzEw0cjj+mRpAEQr9mYhkTwRIvO/OYeM8FmyTheaPZvy80J9kpzg8bmhr3LkHuHtJuOOMLi8t7rUPp0+sQm3zOqqiMIROHHWnzTAdzfOzSQYaOpuPrE2tnG5CNfVireidsQ059WeiKH0hMCJRWfVTYGGK65iIGAjz0Ngaj9PpCAQbJyYqq7oEG0jBoktUVi1B6WDVaF5pRXPgwyQaD4dYAWRMJJbZiubAXNQ3DbDD7XMIyQGI8DfB/x8gYMzStCyVyioBfYj658tozfF5NI7fQuN+LAL77V2YCRzXFVgQWgAcHIMqiUAn7zO37Q+QPoWVkAYYkfjVwj/R/OtfxRLPVPK1fXYcpC1taUvbPm1poKH7mNU/r0aORRSND3OqwTvSoQhfKupwqihzdNFvDsQls+eWVV43pzTVS/lB5CDt7VrXV6OFp93bmchRsrKRm9xvu9+BqI0+4/Z9Ay1I8lGe9gbkxA5BC6oRqALA19y2fdE9z3B/N6MF1Db0rA5BAoEFCIxodeddiRz/FncuU6y3dg8dw+izDa8/tHC7mDtGM4qw9nHn+G9gXgqmxy61WWXLngQGuZSE2SiybYr9XVkIMNi9r0RteTpy3J5CucWLZpUtq3M6CZZWZDnNpiy/Dfh3BExYCUHQszyM1O3YggCqGbvREVuIwJ+rUBR8Ez7KbGMyDzmYixEjpheqVnIFAhprkSL9N2eVLVvn2mGiO87lqM+1I6dsJB21LMI0hU14HQoTfATv2JoDbWU4w3ljKxozGSQ7ula5oxWNIwPIzOExoC20KCDV0/2d5/Z92x0zGy9u2YK0LVagsVmAHK5hCGixsR+2rwnyWb/IRbn9Q5DTX+j+ttKoBgaagxaCLOH14o7Z191nm7s+S2cyACXDbWfOz0B8NY2wHKj9XeD2a8RXvIha+DwNWNmIgDRjobyLwLizEQV/XxXYuxH12XloXr0eKE1UVm1GDKYvofscG+xzPR7Y29Haxp51I5o/93OfZ+Lfh+0IdBiEns2h7hyX0LFdDfA2NoI5yDZfTXb7fp8uUlo6ieKXoPfc3xyo8kO3zaGQNYv2uhjxAgsCtCDmzmA0l1qfMRFUM2PRhYycBAIa7V1l7JwoeLkS6T2MR4DbS/jnlECpGeEYqnP3sLN9cSJiHpYjIHleAD5EgZqJKIBgVbbsfosgYzCZR42j5Zl3PsK505a2tKUtbbvQ0kBDN7Hr5pSuA8rs/9lzy17EO7XgncytaMGwBi3GxiONgXwUmbdyWxYpg46L/VA5+kPkFAxFUYqJpH4pV6Kczb0t2rSVZDX83pHvw/8tXaEJLVpPB85F+fMPosh3Dr6NM1EbHIUcg8HIofk0ehZ1iBnxKmI2nI8crD+hCNNQfHR/DKKaD0H5/yPpWG0ijPZCR0AodCwIvgvBpizkmLQgR+p15HzbYm23CkS6KPvl5aXFt6CoU3/k+GSjKHQbPmoI6m9NqC+Nddv1QU7Db/CiiyuDc9TRcXH+JEB5afErqE/8GvgqXkQwmo5hkfwFwMXGDthdZtoOjqUQR33DzK7tKDQWx6G+dhHqZ9cgEMxytkehxb0902uQmvsMlAu9EZ8/bboEcaRhMQL1SQMUWvGOSCYa+2+h/t6A+msYqY/hmVYGCpiGwH74OeYYPMgZRlLNIc5x+7STrLUwBA9eZLl2smOaI9Tujr/QXetBri0KgvutR86GjSdLT8rHp3IY6DiAZIeyFQ9OmIMURnpTjUEribkJjb9QfNIAQBMZxR0znHetP4ZzQBPJGhqJ4HcI8Ng1ZCJmz0Y8y6QPcsQ3ISBun7RQCyFRWQW6158gx7YWtfehyLkFOfDfR311Air3OYTUz87MAK0taD4pQmMxnIc/QP2lFjG4niPFOzAFG6FLfYwuHOcZCLAYlaisuhoPqISCiUe77XpDPJd4QdhXs1B6kImb2n1EwTMDF0Oz/mPztfXxkCWUcG3QGz2LFcAXY9MmrUtUVi1FAMRQBDaYvQ3kmnBlV+3ibLtApG3vqlNcAzyWqKxaiQD+rQjgNiZReL/6Heu9lL2/Zklb2tKWtn9ZSwMN3df+jpyiA9HL/20UlX8Nr/zfDzkgs5EA0nH4aE4qPQZzcIy+24qckU3IUfsrnbyUHcuhO0QF8kl2YqLRls7ozkV4wbgT0CIKtAALHaPeaJG0HkWyjsA7UKsQy2E5yrsdgPJCh7vjVqPIjanzm8J5Ll5kayByigwQCfOuIRl46CwdIQpIWOQq211XM2LIzAEW7W52A4ADBr5k/5eXFo9AAmZ/Rg5UMWqju5DjbOkoE1Beb+OssmUP8tHrqS9E93kD3qmDZJZPEwKXrtsLVPLbUXTTnNeN6Dqz8Dn5lhMeQ8BCM7qvaM52qNC+kGQBtnUI9NoPn6NfQnI+dRYaCweh+eV9JIA5HLGE1qA+axF100/IQ/3V+qk59OBZVdE0rVDM1oC8DJId+RbUd01nwAQl+yC2ggETeWjc/MidO+aufRMeqDABVktlysdXmrDorI01mxuNJp/nvvvAHa8eMQMOI5mlYO9Le3aWu27P1a6tHZ+nv85dh4HEzUgkNkwhskiy3YO1oYHFqeY0YzVkunNb6kc+SrkbiN4V+7IgpDnkueiZFKN27Iv65lNIqwB82drrETD8K9Sn9yeZLRaCt02oDw5H8/hm93k96hOZCFCOo2fznJWT/Bi2EDEhbCxPROyz5kRl1dcC4cJ5CIA9E4GuQ9z1rQJWJyqrprpr6Qf0RNoTNga3offWMHx/N7O+a4yjbJItfJc2u2NuRX0YPFD2B6S9YIDY1Y5l0QsJZ1rFnJi7HmMLhYyUTtNHoFOByEXoWV+EQFJLtTPGU8dUw1isnljhfydybwOVBIXUaS9pi1heXt4R9fX1L+/t69gdtmDBgvzy8vKip59+esXNN9/cb/HixT1/8YtfvLO3ryttaftntTTQ0H3tS0ig8Hm0iPwlesk/hl7m2WhB2R9F2S9GEaAz8GrmoQMNnqr+Flpk7Y9y2wF+ed2c0r2dFrEzdiVaFE7AAyZdWSiMZ5Txo9z/K/GMhjBSWev+fxsPCPRG7fVj5OQciRa7W9GC/ig8jXez22Z/vOr/ChRpynD7FJDMTIhStrsyS8EIafLg83P/HYEem93ne4TdEJoDHqZ38rWxFR4tLy02h/ljRZ1casU41NahbUWA0EWzypbtTfHS55CeQh/UX1rdtf0D34dBz70/cnZnAm854OXJ8tLiXuWlxaegVJLtz6+8tHgJ3rG6HYFLx6MSm8fgnWwTJTTLQCDPzUiX5UgUBT8ezyLIdH/3IjnqboCI0bQ3I8DNoupG4Q8dOiu1+Sw+pWV/fMlGS//6EDF/DEwMS0wasIprP3MSH0HASYm7tm2uDa0MpAmsvoWA1FMQmGKAQ3+3D+44IOfIAIQw1cmcMdNFMRbEy4hZYOO4FfW9Q/HjuRlpQhTi5xiLzBaQXLHC2i4Ljek46js2F9mcl+Hu9X4ENhvVvQSfLrKv2zkIcDPB0hgCb64EHgmdxkRl1UwEuLehUpEvoLatRWDeOuTwT8UDR+YIx1H7/hIx1D6N0nQyUVteg0+D+MhmznOisqrIXecDqB+MR1H6v6J3y0HouQ3Az+1fdn9/CYEtVikmCwEQVnXkCDwLqQG9T6zErY3FGH4uCJkexnZqQYClHbMPYp+9hhz9sM0vhO1g0OWoSs7D7p5wx3kYgc3fRWPkTHbiPeRSSC5G4Od+aG57CriU5BQp+x1Wb7H7zEPaUhWIfZggddrLPm1/Xby48E9/rhqyta4uO79Xr+aTjpu0+tjx4zfu7etKW9rSljZIAw3d2eahyO/paAE/6ro5pbPdd8tdOcxX0Yv7U27bGrS4iFLywS+mEiQLRtl3T+3Om9mFVo+isSNQZCMEEszCe41SZ20hlkDRGlObt8jIL1FbFiMnq4c75za00GxCztlKtIAZiRa0o9y2OYiKO8j9X4scJMvztahjM8nRSgMZLDplFqWEkuJ+bbue+EjdmQj4eBD1pW5Zq9yqLHzCwwzFt1ELWlzO7SZCeAsRnXgKivS1osX7PBS9/wrJugiHoL55Znlp8ePuHjoARU4b414EDrTgy/VtRv26Ec8GiKbmWJ86B80tDyER1Cw8Nf1wvNORgY+2hzTqTDy7wSLqBNuZo74RMSdOQ0yBA9y+xW5bO17oXJmzsAmNmx74UpRNSLvCwLkj8KkHL7o2sfQJS2UYiRys/iSnNGSj57IGOZchi8j603tobG1C4zzm2iXP/YwMzmUaC8fgAQkDNUa788QR6HAgHnBsRHNMlts2Dy/yGWpqhClBMfT8zkRzzhI8c6iJfVAQMkVKwefxWiT/QAyNGzsRFpyHwJ2TUbuNQY7uz3BVIxCY9SBy2s8jWeMoA5gYmzbpa4nKqlo0ZvOA78SmTfqoTKvO7GJU1ehQlCLxSwS6DUNjaTgeUAOv8bMWzXNxPGMOt/1w1Hcy0HvRQLAQqAuZf6lSSWybLMSisHKsW4DvpmJyODDgSqQH0sdd6+9QBYr70DvuPvTeeQf1z7/RyXvIPftz0Jx4NAJ+bM76DhpPYVUXSy2NrmO3uvYYhNryUjS+/ruzc++r9tfFiwsX/PHJYa2trXGArXV12Qv++OQwgF0BNixYsCD/qquu2q+goKD1jTfeyJsyZcrGsWPHNtx2221FTU1NsUceeeTNMWPGNE2dOnV4bm5u+7Jly/I2bNiQdfvtt6+qqKjo9+KLL/Y84ogjtj388MOrIJkpMW/evL4LFizo/fDDD6+aOnXq8Pz8/LYlS5b0/PDDD7Pmzp373owZMzYBzJkzp+iRRx4pbG5ujp155pmbb7rppjXR63zooYcKSktLh7S1tcUKCwtb//rXv/5jy5Yt8QsvvPCA6urqHq2trbErrrhizQUXXLA5uq/Z3Xff3fcnP/nJfvF4PJGfn9+2ePHiNz5p+6UtbWlLAw3d1q6bU7pu9tyy/0CAwLnA8Nlzy6Yg5+RCt9ktqPTWRPQCPcB9nk3HygXzzCMIAAAgAElEQVRhNPIQFHF7DEXjavkE0Zo9YY7+Pw0tzg6g46IpNLtXyzENF1nh7zCvOo7a4YtoQRMKa2ahdmpCDkSR+/w41PbD0CK4xB17EF4orhkvHmd06HqSU0DALwjDCPeO2BqhtQe/1yNnqwYtpl+aPuq8f6ooTsSs1v17wI27W3/ho5hjXLyOcsat+kUd6mdjkCPTiMa59bVa5DybXkqYMmE2A/U/0zz4AhrTNyIWxfl4QCqB+q6VrzRHZKjbdiiKiJtYYQnqm31JnbZjx4w68wbihdHFBuS0nICABHNgCLaFZPDMxCSzEZhq7Awbx32QvoqxLmyflxGtexRylra6/WrdPjZvGBiXjcbnELwTZ+Pe5sosNL4NpDSHrG9wzX2Cz003YjB6rkvRfJuLwEkr93mgu8etKH2tEAERUScwnJ/C32EbjnXXbSyM9W677yIH9U72AQsi4+cD33MlIwe5r+Mo2p8FHJ2orOpAgXc6Ad9Gwou5qD3PBgpj0yZdGGz6aKKy6nn0PEYj59ba9WfuOsA7qCnfjZ3pLLjPJ7t/o9dZjcb/ZxEo1AONwdV4RsNIVBrXyky247WXwjSQEEg34CwErsP/rc8YGJaDnHHbNzrO46hfXt/Z/aM56FvBvrWoysbiRGXVuEjb2Punq/fQRAQSh0CLHbt/ZNsGBPB/Bg/k2f31dve0CaWenYTa72uxaZNu6uL8+5z96c9VQwxkMGttbY3/6c9VQ3YVq6G6urrHsmXLlg8cOLB12LBhY3NyctYvXbr09blz5w4sLy8fePfdd78LUFtbm/nyyy9X33///X2mTZs2cuHChdXjxo1rOPzwww9dtGhRj4kTJzZ0dZ5169ZlLV68uPqVV17JPeecc0bOmDFj069//euCFStW5L766quvJxIJTj755JG///3ve51++unbx9SaNWsyZ86cOfyZZ56pHj16dPO6desyAH74wx8OPvHEE7fMnz9/1fr16zPGjx9/6JQpU7Z0dv5rr7128JNPPvmPESNGtKxfvz5VMCdtaUvbx7A00NCN7bo5pXWz55ZdjhYGJ7qfPyLgIQHUXTen9NrZc8sWoZSJL+MXv7n4uuphxBS3jbEgfgpc20mliW5hDmS4HIl99XQfdyZoZc65OQnmsEdZDkT+b8dHTIy2bZaDL0lYixfK+j1ynsJIaEj1tkikAT9xvEhilGkRXv8mvNBlJh0XgWYhmBS2xyh8fvtfgUkVNfOXTB91XrdxwHeluTSN8/f2dXRhtyAWzCTkEL+Gj7pnkBylAy2SewIHBBHp6AJ9Hlo8H4Yck4EoWn4GiravRQ50i9t3ifvsejQ3WGrQOjSfbEZO0LEkpz4Q+TtkNIQWC77LjOyXQIBBFqJBG0BhejFheoVpOxg1/NPB8erxmgktKNpqc1scpYDcgi9vaI73e8gx6YXa35ww0Hg0XRtLsRiK2t+AhdWuLXsjdsYAklOcQt2HZpTO8CZygjYgFkcxciJtvsnDlxdtQc9utLvO99AYNocwg+RKCtFI9Psoqm06GqHWw7fKS4t/tY+wGiaitKF+yPlvRBF6c5IzUFrYt1Elgv/Ci0EujE2bVOec2kdhu8PfIYIeVHa4xYET4xE1/27EUpuIAO05sWmTduQYp0pJm4jGWQZQ5UQdTZem0Z3rYgQQLXXXUo8q1HweOdUmgmrjyuaIEGSqpWNll5DNaO+XWPC/pZQdgoDoo/CVU0LhxwXAxTsoSTkPlZbs7e7ruzhQohOdhR3ZIsSsOpvkdxokg5et7nxD3flMVPg9BCYWuO36oTQOm69yEpVVRTtbZnNfsK11dVGtjS4//zg2duzYbcOGDWsBOOCAA5pOP/30WoCSkpKGZ599drsu0plnnrk5Ho9z5JFH1vfr169lwoQJDQAHH3xww5tvvpmzI6BhypQpmzMyMhg3blzjhg0bsgD+8Ic/FFRVVRUcdthhhwHU19fHq6urc0Og4Zlnnuk5YcKEraNHj24GKCoqanOfFzzxxBN9br755kEATU1NsRUrVnTaLuPHj687//zzh0+dOnXT+eefv6mz7dKWtrR9NEsDDd3cAmbD/6Io6HuIApgH9Jo9t6wILbZuRS/YDfhST9GKDJD88s5Di7YX6N6iYZNRnruZRRtb6Jh/bk5SA3LY++DF7RJ0dKLssw346KotuN5B0cHD3HdvowXjeuQkjEGRScuhfgc5JGEli754hXlzRuxabDFlqRv2uxc+rWJHrAZzkGLIkRyAV/lPILq6lVFMWYs8lVXUzP8TikI/M33UeSft7H5p62iuNOVsVLmlAK8fAB2rY4Ce+wg05ikvLa7ERQcDh3EbckhOwedkfwefjtCI7+tbEADxuvvcWDbP4COhhQh83IDmjTp8FDVkQ9k1k+K6bVuC720cTMQDAGbtyCmPIYdwAErZyELO/UiS31GmORFD/bvAXeeH7jybkKPxnru3A9DcOAbvgJmui1VDacWDlwl86ko7auN8NKZD0NC+t5SHsLpPNnKADEw8ETmERvNuwos/FrrjHu/u18DO4XiNBmM/RcEbmzeMcWHf2TxjbI7edF5JqNuYAwWK8O+uf0P6G/fi9RaKEOAbR++DU/GgU4fc+y6c3RkISAC4NjZt0mIEItu1pGIQpbLOtluEQPGp6J19PHoP3YXeZY+5+7oIPetfoDn6yMhxogBzVHB1C96pTrVPKjZSFnqf5eJFoxOoxO7/oXnnjp1xxh1IcxV6r3wAvLOzQoshG8R9ZOWKLZ0zmvqZEfm7F9Kvut99dhrqG88CZwXb9w32OxA9+51+D3Z3y+/VqzkVqJDfq1dzqu0/juXk5Gxfg8TjcXJzcxP2d1tb2/Z3gH2ekZFBdnZ20j6tra0xgFjMvzIaGhqS3h+2P0Aikdj++9JLL137ve99b/1Hve5EIsFDDz20oqSkpCn8fM2aNVmptr///vvfWbhwYc9HH32097hx4w578cUXXxs0aFBbqm3Tlra07bx1FilNWzcyxzb4EUqT+AxayH4OmAXchsTftiIKpkUrwecZhxZ1sAuA6Q6w6K7WG08tB++od6bIbgv+/dDC3ZwMi1CCd5Zs/7+iXHVzejYiB6PR/SRQ/nfCHTMPMUiM+l7vrufveDq3pULYgs5e/iH91aLaCRRdBs/ECKNXUYtGmWNokZ6BFmvNyNHqifrNAxU1809x7JCdsRPccU/Yye3T1rV9By10eyMH2koqRoEkc5zMSS9FIMFctBg3m4aYCO8gB9so9HGSx30WcnaX4qOXceTUT8fTxkFjzGjL4QKdyHWGqUh2za10HIvGKgKfbx2m+KxFfdVArWPReMpCoES072cjh/wFJDBn+g8DkeNRiMbhEHyUv8BdWytiGdj5zUk3oK6W5HEbx+snhPNMBmrz37vfuWjuDedVc+gS7ru7EZDbhgAhuxeC/QbgxSytzTLxuisE25sTFgIc/0BzTwMSCHwHpZFcSjfPS3dO50wEoJutc/+/iFgMD6NnHM55wxHY9hfgpERl1ReDtIeubB4Sd5yX6kvHjHhyR05zuF2isqpXorLqFCvh6DQNvo9YQiNQ3xyOntM3kfaSiS1+hmSQIRwn4WdG+25C/XvIDvaJmonRWlWaPNRXWlGKw72xaZPKPmLE/06UnvcddqKfWTuheecuxIiYjJ7Hj9EclQp8Bd3fBvy7/0SUKnaWO8ZAkgVY20lulxrgrZ3sI/uEnXTcpNWZmZlJzz0zM7P9pOMmrd5b19SV9evXr+Wll17KbWtr47e//W30HdPBTj/99C333ntv/9ra2jjAypUrs1avXp0UID3hhBO2vfDCC/nV1dXZAJY6ceKJJ24pLy8vam9X8zz33HNR5mCSLV++PGfy5MnbfvrTn67p27dv61tvvbXLWCFpS9u/su3TjIbqh5aPAcqBWaPPHbM3leV3mznRx2nAZahqQSbJ9ObPo0XXOnz1gx1Z6MDG3TGGuBSMeuCW6+aUdid64RFooRYuPuKR36GFWgfhNpmRbex47yNH7k60+DoWn+5wBD4N4lQUSV2BFo+mWF6PFvYtKCJrUU87j53fXlzb3HHCqDYksxEgmS4aXXxFc2+Nhh7HK9sbTbwVOaH/DdxXUTP/xp0oc/kMjtGwg+3StnP2Jp6i/DISTbsYr6tiFmWxDEGAYjmwyIlAzkACgAV458u0P3JQ7vk7aCFujvK77pygSOvnEV2/N778ogFWUUDAdE7AA3ZxvCZDWP0lEdnG9sty19QHH4UdiCL775HMOtpGcqTWrqcFr6fwPAIHGlBfN4ZDzLUDaIzauVe584yItLGBevnBtda76y/AAxKW0tSKxtepiE3R7O7LWAdhJDaGUkZAwMgRJFe2MLO2slK4Jihp49qYCiHQau0SpqZMdvstQikAi7p7yoRz+uYghkIIJPTBlx4+EDHvBuHv2dhfBcBX3Wfvo/7UJXvDOdIfPaq9+E4fhR//9agmw0w0rq4Izr8NVXb5MmIR9MWLhJr2RPiegGQQOmq2jaXf2XZh6o8xZ8Ltw/dPHHgCaYj0cZ/9CjnsH9nCVJWdtIkIVNji7uH/ofG1DYlv5pKsLRNtCyvlvAkBa5MR0NCC+s/BwbYbEUA1BOnZPIHm0h32kX3FTIdhX6k6cdVVV63+3Oc+N7KwsLC1pKSkftu2bV0GO7/whS9sWb58ee5RRx01GiAvL6/9vvvuWzlkyBADhdlvv/1ab7755lXnnHPOyPb2dvr169eyaNGimmuvvXbNN77xjQNGjx59WHt7e2z//fdvevrpp1d0dq7LLrts6KpVq3ISiUTsM5/5zJZjjjmmy1SPtKUtbTtnMaMo7YtW/dDy1xCCbWj/g0Dp6HPHdCcn+ROZE4C8H0/v7co2ogXtaLTw7YePpoeW6gVuDkIr8F/XzSntNvTCipr5DyOxOxN4zOli82iaQmjmAJlwlP3fitIihqKFTi1yJkJxuw+QY5SBqK9HoYWNOUTRqCN0jPIa5fklJLgXlswzB8XOGc2PjwrBbcHT3u3Y9vxWu//rEBX8t6icWrnb9tvTR53XnVNl/umsvLT4ZZSakEDP5WdI1DWV4GKYZw165jfMKls2u7y0+Ad4Z+Y0vAJ9O/A/CBhaiOj0t6B87FrkGF8xq2zZkw6s+DECkrKRE340WoD3Rf1nfzoyLuL40ozvIpryf5DcP61qil37NnwZxzq33yB3zkI0ll9H4J4BHqm0S+oRO6CH264OX53hHQTY2L5PIEfkEESJD4GPOF5nJWQLpKKZh2bOnN1rg7sXc2zCOSecX9chh64Vr/8SnX8bUZQ6VNkPr8PAHpsbWt39rUHP+SkUGT4JUfUv28slXVNaQJdfgua/Re7/++go9md6AreivvsEvgqT6WcYm8X66OMoHaGEiEDjLrHFd56CaTKM//p2R9VF6K9BTu2txnBAz+RrqO+OIvU7Ajo+c/ssCjqEwFIi8rf1DXu31eMBb2O+NKJxczlKS/kBYjXNik2btEdKWzt9jBvR3JVA7MGT3LXa/XbGaACvo1IE/Bdiex2H5q4oC6odsRQfBsoQO2wzu6Nv7EJbsmTJqpKSko+cKpC2tKUtbXvLlixZ0r+kpGR4qu/29dQJU3GPo8XcN4Cl1Q8t/3b1Q8v/aehxyKlsomtqZAJFdMajxedyklXdQ0v1ErfP/gI8MHtu2SmOTbHXrKJmfq+KmvmnIBCgLfjZEUU0NKMyQ3LUNvw/FzklPfFRMhNkbEIL4g1ocfYYXgzuJwiACCmate7YRg2P5pqCwDFzSux62oJtTexqW7BvdGEali1ch6fTZqBF+c/w0dFj3HZ34qO9aduz9gu8M9ATgVobEbXanAWzKNAUA4aWlxb3QiDAr1HfuwlFE1vcdvWzypY96qLYQ9x5HkPR2ytIpja/jdKxZiGH1RzYP6E+vDG4jpDpsNAdZyTqg/eifv5TpF0SphnYWPglGju5KLL7PopSj0SAxsnuO0stsnOGFSUykHORg9efeB3152eQ42+6Cp9CLLADg/s1kKANAXRWPjIEA6OOYDhfrHSf23ySjWdMgB/PlkZiWgyW1tLDXZvpyoSWg1JbDCS1ud4cy0z3mV1PNpqDxiBg+RKkU9Hf/Zzt+kp3MxNPvBhF+s9A7+1Uwms2R7+K+u61CJi7CLgBV8IWzb/WX4oQyBBNM9pV1pUmw1zEKOzpgIcz0HqkCM9i2VE6HCSn9KXSbrHjhKAzeNZdIvhJleLUANQ6R/sF1Ccbu7ieXW0lSBS3Jxq/R+PTPQ1ksP5vZSrDd3gcjbt84ALUxovwgYHQ4ug5gMbI4zuTEpO2tKUtbWnbdbZPp06gsnY/JvllOgAtYm6sfmj5rUhIaiYOyR597ph97SWzEOWoTkCLLBMQSwUS9QN+jhbsj6MF2VF4enBXlkBOycOozc4mmQa6N2wyovv/DuW2HoIW2VtJptmmSimA1Hmuttiz6Gx2iu2NBdKEQJ6/AG+gKMxpaJGzBQEGdWjR9Fvk0PdDjkY7Aizq6VgpwyKb4XlrkZMQilu2pGwVH7my4w1w/1t0/Gx3zY8jh8sWr6+4n4WdHDdtu8/eRM59HXLmb0FAaQ3wG5RyYxZGOFtQnvo65OR8BZ9rfRzqS4+5v8O639udohRVKy4G/hM5qT9HDv+DyJm+y11XA3AP6s/t7toPQ33z02gMTEMR9JnIsXsCMWcKEHjRF/X9C9x5c1DfHQk8jZgNB7ljZaHUhgF4EUTLqbVofhNe5DSB16SwMp+gcWAU+wL8mG9BgEijO2dUSDacK6z9Q0DSql204aOvCXdMy7XPQM+zD35cHoQXeg0ZH/Ybd5wB7vgrENvjSHy0PoaABGOA2PyfjRypU1D/mIrScqYi1lR3o4cvQpH/MxDQ9h+Iwj8ksl07ctp/ifpqJT4K/SDwoHPmbyZ5HhyNGCbXAEvcNrsueq10iQ5t6hgMoHfV31CKjDFc7P+e+Hnb2DFR4CEc9yGjIcp42EbyOyS6jznm1jag98sViIVj8//zaIw/v/ON8IltESrd+TW0NglZecZYzEbzz2OIzWKgugG1i1B7ZqE5YAsdGYzWZnFUcrO7jYW0pS1tafuXsH0WaHCMhRieNhulqGcjIaw4ykFcCfy8+qHlt+5LYIMTgnx09tyyhSgCkYsWpV9HCvNhJYN1wF3XzSldOXtu2SlokZuJIgdWWi23i9MdhhyeXFRDu2aX39BHtx7ImTE18kZ8NY2w5FsqCxdgLSjimocvA7cBrw4fbg8+qlKLoiFv49uwDjnsR+EX/qeQrCNhKvUWebLrCEGRNnwJuz7u+zp8OkZWsF94jVFqfVjSFLR4PxdFejejfnM4jj6/E/oMu8yc+OREYNGePG83tIVIBC7Mm18JUF5a/Hk03ixvvwWfs9+OGADfRovvbYjVcBpyYo5Djn8eQUpRJyUxcZFuq35wHAKk/hP1nSuAqllly55025bgq938A0WXD0cldk9GgMlqpAdwEoouW9+1KgqtCFjIxeuZ5KI+YboSG5FQXiHq/7l4p8KcKmM1gGc2ZeFLIJq4Zl6wbwgYZCH2hKUXWblLswa3TRR4NEe/LxrPDUE7x9D8ETp0dg0GLob6FnkksxnW4EX59nfbjiC58oXNHwYyhNRyc2YvReP9CpLTErqbDQBmI0A27n6voeMc3gbcjubdg1H6yzrnzPdGbXQvElvsjfqnAcD/g4Boq1KxOFFZ9cPdVdLQpUhMRmlIRWhM5aA5OOHuDzRGE6i/Z+LT5ux+zSkOI/f2vogGFaJAtVkIvMfQ+/tBFKS4LDZtUjSdpgSJMu4xUMqBMuvQms0AN3vPxdD4W4EYIn9B7CQDO2sQS6kKMVwmojY/jeT2MFZENvAH0sB62tKWtrTtNdsngQYHMlyORNHaUGTuOPSCGoxHyUPnbATwPbQAvndPXu+uMAMcgo/+14EJv8RH2A4GvoRewosQLXocSqc4CP9CT2UxFMlcixbVxyNmQ9kuvZGdsIqa+SZ49xiKuA5Dglq1+MgidJ76E+Z42mItC7/4s8W+VdqoRYv9enyetP22muaHomhQP1TdYwKKpOyHFraF+HJ4rXTUidjijtmGH3cZ+GdnJemiysihWJgJRZqTYfcZAi4WdW1EtdBPddd6FKqsUVRRM38KsHBXOf4BmGBOTg3qh/NQP7oB1VjfI3nA3dE6c/zddyvLS4u/jMYy+L5jFRiG4p/3ehTFy0eL7q3IOR8AXFBeWvwBfmFtz+Ro9/9C99mpKPe9GbgD9dvJKBK83UGdVbasrry0+HcoL/4I9Azvc8ecgSLnl6MKOJZyZOPuSNRfw/4cx4scmiOVjSLTF6HxUYMi0xvcPdr+pqdgqQ7x4Hjgc9NbESPgQPf/u679DKCzVIQ+7v6b3DFy3fWahksIHIAHJWrxjp79EPweiXccW13b5gXbxoLth7htmiPtFEa6w7H9nruOrYjt0IhAmtHAl2aVLbu2u6VMBLoMNahSxyHB13E0f0bZXVvRnDXU7WfVKK5H82wvoDk2bdK17hyPIHbNrWh+7A18FrEivuSOsXs0hxKbJkPebZDVl1jcgN581Nda0FhrQoymAfhUvWhK3Vb3eQ7qD9ZPoXMh4A5Xg9qvCQHhX41Nm7Syk22h81SQ3WZBGdM6vMgq+PH2GpqTJqC5ZQAaT39FzJYJwF0OsFiEGJhRzaZ6BL7th0p1/isD3GlLW9rStldtnwIaqh9aPh69hF5HSLzl9g1FC/A56MV0PXqR9Sd5IdgXuKL6oeVP/jMIRl43p/RJYKDTUjgDLTjnue/qgAdnzy07EL2c38HninZlFuFvBrbOnlv2A2DeHq5CMQNF5w5FUa9MtBC3hVwLWkxY+kSUWhqL/B3DL97C/mAL+l7IuTBl/KH4qhPvoz61BQEV2ciZ7oMciAZ3bU0IxDo4OIc5CuZ4JEgGBGwxaU5kzxT3EwqChRGgaATHzICMJQhgOgEtxt4AzkeVCOpJUXf+o5gDFya7f3ujFKYqdP+voigj+Hzyn1bUzD8OCYGNYhcxHAJQat70Ued16zHtnMCJOFZD+D9yGmuQw3EkyYryh+IX5WOQs7sUtfWvUd88CF9Z5HuInTQVPfuvuePNcz9XEDArykuLp7j97qNj9M8E9kAU6xLkPN+KIqHm6PZDY2UEGidZ+Kjt+yidIeyzxkrKQPT4wajvfgr1GRvbxk4wCynRZonIj81hrWiMPoLAkMxg/4S7/iWoakEPPMAQaqVEI82D8fOjfRcyLRYiUCbH3UdvfKpXqnnK0rTs8yx3bJsrNuJTo4aiftLLHT+OHKoh+FKNpoMwh+6ROmFVBjbhNZXMWvDPJEwTeAWlFr0OzItNm7TOOajGYBhBx9KU77jz/IhEwrVlex9iGVen2HaXWKKyqoj2pm9AjyLiMZvXewebZOGdYNP1MbN73Yie6fuIoQjJrBqC/ULAwQCx0DahdliHSmZe61ggHyJm2y0hs8M54Hu6j0xG+hoFaN77LZqfBiFR098i0WIDoF5Bc001PugxCrHBJqP3WdTejU2b9PPddgdpS1va0pa2nbZ9CmhAAndHosVu+JLNQYuvt9z/NyFnZwRwFX4xZ1H7l6ofWv5fQOW+lEbRmRmo0MnXtsgyUbhT8VE0i5iGZovqDKTqnA9cPntu2RnXzSldvIsvvTOza16NFhImAGfK7BaRDK/ZLExPCGnGmZFt7XOjYLe732GELYYiaFn4tIhm9/9m1Oda8BHLkSgSa2ke1o5hNDXqKCXcfQ5F1PO+eAfJvic4hgl+hYvWcGFaj57ZZsRi2IIWb8+671ciRzVlFGtnUh3cNpcjIa6EO1YR6lu3I8fKBCofcNsNAb7l7qsYuKaiZn5jV+dJcd4i5JRWu/v5CnKae7o22OPsm49oUScw/H8Juq8z8dFt0xYwR9WU1beieW0mer7n4MfHfW5fAxnmoSjhp1C9e4t4F5WXFtfigYU8lI71CgFzyoERjwKUlxaH6vo2Rj+PAK0m5IRvA/4PpVIc6P63iPMK95npERgDoidiUuW6z0w/webskKWTSt/EnFTbZy0etOuD+pu1pb036hFoMw4/LqOsoRBkWIno3pmob7/i7jHq7H3o9s93bdLi7ivMnY+CDTbGm9FYykQsFAM6nnO/JyBg83kEID7j9nsLOLq8tNiEOvdolHoHtgSl3Zwa+dzmRrv3MB3kWJRm9EOgJFFZtWgHZRRtHF1DWB44wSBi26tAhHPH47soyj2DeNHpdM4w6Az8Dr/vg573fp1sE5q9fzJQu37KfbYBvRf/B6VT1SD9qinoXWTpQnXsLmbHx7O/xKZNuilRWXUnHnAFpWddiMCXC2LTJi13QFMOKmV5UqKyaonbdi0aaxsRWPEBYmKmLW1pS1vauoHtU+UtA0aDVQgILYEWgz3Qi3sTPrL3bTpSNLchOuOfgOv+GRgOOzLHfLAo9KdQeastqK1GkExBjC6IX71uTmnJnrhOSHIq16Bo5KlogRWNjnaWRhF1xqMABJHvos5LM1q0ZiNgYT2KgNYj1WwrWdeAnCNzgrbhnZxaUpesi5qJ3IUK+Km2D4/TiK+LHt0mhqimTyMmg6n/X4F3amcgEGCs+24pohm/hVJu5kwfdd6T0BF8cJVAbkVObz1yJOw4i/EOWAuirj+EnOJXkADcye48s91nP9wZNkJFzfwfIPCrDo3do/AAzHPAud2Z1RBhMIBnId2LFtbfwQMBXT1/qzAzELVFDnI6X0SR4G0EzIng3DPRXGgMnY2I2bIIAUfno/6xmSDXP8UxzkPP7XAkAHkxAiqy3bm/gZgQNtccBFyNwN/lSBzSwDEDEtagMr4jXZscQjJ4EOqSRMeTsXzs8/Xu+O8jgCsTP7etR6Bh2MZNwfdvIsAkBPosxeIN9D4xUMJYF3YPjUgP6Cz0bFoj3xsTIjc4tm1j91jvzlWPT+16Hc0pL6K0kGVovlvg0EsAACAASURBVD4fz8TIAmbOKlu2R9OTEpVVI1D1kqeAR6IOfKKyagoCpYzZUYBvg87mxRbUH+5GwFs56hMpQYIgPWMR8CiJhEW524nFrkIsqsvR/LcFjYFPXObQgRdXIqDPGDmQDC7be6izd47pACXc/iYIDL6/ZOHLGT+DUmhuQePQAOp/B34SmzapLGiTcxALZDTqQ5/bQSrFbjd3XTbv3ZFKOyN8nuHzSVRW/QD1tRh6D9xK8nzaYZ990bpDectYLDbuoosuWnfnnXe+B1BaWlpUV1eXceONN67Z0b6fxCZMmHDIDTfc8O6kSZN2e3WssrKygZdddtn6/Pz8qGg4CxYsyC8vLy96+umnV9x33329ly9f3uPHP/7x+1OnTh1+1lln1c6YMSNVpZyPZW+88Ub2WWedNaqmpmZ5VVVV3t13393vnnvueXdXHT9tadsT1lV5y32K0TD63DGLgXHVDy1/l45AA0hQDLT4XoYWM/OQU/NvKG8zC0WkCtBC+XDgouqHlp85+twxf96tN7CXLdR5mD237Hm0iJmHHIr7SQYaogvAPbZAqaiZPwJRwscAf0cRU2Ne9CYZZDB6dRgZiy5gW0kWVgyjiBa5tAVhyDh4FzFgtuHLZx1DsmNtYm/2uwnPdFiAFoPmVEVFHM3MScoIvg/TLUKznO1UIAP4xekIFM26BzmH1+AjnTPRAvkEt5056l9EFPpy4JiKmvlLnOMejcQvQgyaCxHQcgxywoaSHOXNQhHss1BlhRNRWcbj0MI3EzlLfSpq5tehReSHpFg8ur9XIyDjCBTdDYGjzwC3VdTMn95dRSdNp8E57D9AzkE26ufHo36Tyikxs/7QjqfTW9rP8Shyb7oL2y0AOGwuPBwPQJmCO+j5NqJn/QhyVK4pLy1uxKd7PICnLN/jztXs7iWP1OXyvui+Oxo56m+4a92Ij+iuR33wywhIjDJ5wnETHf+h7oEJJybQ+yADnw9urIaQxRRHTr3NfbaPWQyfBmHgRx4+VcoYVsZC+ar7vwUvdpmPd66tZGcoEGvXYmkiffCVK1pdm9UjsPxk1L6/BObjq+F8ARgdTc9h99stCAz4N9Q+VwaO4hI0Tq3tTfDW5sMP0LOyNLUwvWww6uPXICHFy9z2m4nQ/S0FwJ13ArHtw6cdgVrN6P1Vh8bLRWgOiScqq778cSsSOCf5O+4H2O4kz0Vz41IEFr2IQJaj3d/5CNw/xN1TCVqPbMUzik52PxsRI+kx5FwfgsCz+2LTJs125yxCa5rtKSKuTe5NVFY1IYe8Bz7loFPrzMnfVRZUDtnRNqmeyTw8EDsvxXbdIVVoj9sbm98sXLLxtSENbY3ZPTJym0sKD1t9SJ+DNn6SY2ZnZycef/zxvmvXrn1/8ODBrTveY+esvb2dRCJBRkaUCPbJrKWlhaysriTIOtodd9xR9PWvf31jKqAhtPPPP78WH6zZrTZp0qT6PQGypC1te9L2KaAhsK+gyFERyYJJtgAtQBHwoxGN9xWUz7wFLRBnuW3ucPvkA/OrH1o+8p8hlWJnzGkuXAswe27ZIrSQOws5F+GM3Y4UvC/bg5d3JVp8JdAzNAeiDR9pbMULs+UF+4Yq3QYehE6F7RtV8w6dFVC/GumOYQ7gJHc+U+0H0cHHo0jjSJLF6UagPmdpF1GWRWjmVJgTFCO10xnmi6eyduS0LUUL/QfQQhU8I2Eeyvv/DKLa/hYBOjVoQVuOwIBhFTXzf4gcpGuARQ4EugFFpHqgCFwzctCiJdtsTCaQM9ITOalxxKgx4bIz8I7efXhQIxfprZQi59icWStxGG2bKcBPKmrm/2d3BRucTQSmozSZGJqr6kkNLEEya8f6RCPJc585aOe7z76CopmPkkwtBz2vJ2eVLVsHUF5aPBOxv65Bi/lHUL95Cf/sHy4vLb4VsV6OwzlFs8qWrSsvLb4RzbG57rpMdPJ6dz4r02rXfitK7bBytdnAL9w2ZWj8DwvuLVqtxf7vjM20DY2BYW67Arx2iYEYVoI2M7jubDqmQuD2XYGc3FF4Qb8heNZT3P3ORHTulSgFwOYD01UJrxs0P5iWUDjP2fU24/PxbS7MQM93G3rGv3XnW4kArPPQ8+ws1WBX2mHudwz4fqKyKgs507NQWeIZ7vsa/HxqNpCO4JGBtdmIKXMyPuL/LClSQgLnuA8e8GlH/SgH9bX/RikGhQgks7ScxxKVVf8LXLcrKlO4NI05SPhyOyuoM6c9qFqxfey4Y9wTfG420/1uJGgHd92dpUSEGis7k07T3TQ+tpu7zyv39nV0J3tj85uFf1//yrC2RHscoKGtMfvv618ZBvBJwIaMjIzEV7/61Q9//OMfF91yyy2rw+/WrFmTOWPG/2fv3MOjqq7+/5nJlZBwh8j9GggkJCIKmgooKlAtVgtRBBWwWMTyohW8tFZ+itWiGNoXseJd8KUNgvVSsEqFStQolYIBgtGA4AUxCARIyH1mfn+svTh7TiYBFBDJrOfJk+TMueyzzz579vqu7/quiZ137twZDTB37twvhg0bdui2225rFx8f75s1a1YRQFJSUsry5csLAYYPH96zX79+pZs2bWr8+uuvF953331n5OXlNa6oqPCOHDmy+E9/+lO9TIk1a9bE3XrrrZ3Kysq80dHRgZycnE9eeOGF5q+88krzsrIyr8/n83z44Yef3HPPPYkvv/xyi6qqKs9ll122/09/+tPXBw8e9F5++eXddu3aFe33+z133HHH10VFRVG7d++OGjJkSM/mzZvXrF279tO6rj1v3ryW69ata7xo0aIv7O233HJLu6+++ip6yZIlO95///242267rWNZWZm3efPmNYsXL97RuXPnoPLk+fn5MWPHju1aXl7uHTFixP6nn346saysbIO9j82k+Pe//x33m9/8plNlZaU3NjbW//zzz29PT0+vJGxh+5HZjxJoMMyDDgXL8rsiAnS2qrguSFU88B6E3fBvZJFZidB3uyPlk87HyZUcyslZnJ1SZpgOLwAv3Hn/rKsQEEcXvdcgi5WMO++f9a3Z94SYoehfiTynjxEAqKP5uAr4C7JITEOes5aprMRZyPtdv9UJU+ekAmfxpjW7dczYSvMKUFQhC+fuyBgJIAvWAgRg0LJ7+UiKwCjECQdxpvNxIqx6brfpIlsptxp5DVB3OouPYEBFz+1FIp9nIE7AKpwoNUYXIQ9hazRBIo5LEAdqPkIxHoKT1z8Rhypv09s9ONU1qnGU1vV+7LZqmVU3uONFInuJSD93Q6LEWQhLQnPtf46wL5pZ/ReqHyOBGxCw5JRaJLssF3Gob0PAKNUA2Y/0o+3s2uKEOr95cRwqHd+qt/IJDnNrQNbMVJCcfgVuHkEixVU4zslzyHuwE3HuRiIgQy4yJ/4DGdfrrX0LCBaOrABWW2kWuYgTDDJunkEc82eQObYIiVTfgIyfsxHn+R8IAKOmqRP6NzjjKxJ5DzQvvxJx3KsRBs05CFsEZOxUI4yO1qZ/lIFRYo5VgVcFspTOXoGAFt1whBox5ytHQMae5jwxyLviw2FSudkY9vvRkuD5xxaH9Ji+1rnwc9PvzZESoweRd3Qkopvx/5D3/iAnzx5FnHgt5zsdGVcPICyLNjg6HB5CAzluxsoeZG5KQOZSFYj8fR0OewbClNpP8PyyHQEnqsz/KrjaDKffoxFGwm6Ok36BK9Je7zxUl/aEbg9k5wxDqrIATDtW9sURtC1C2amm8RG2eixv35b2CjKo+QJ+b96+Le2/L6vh9ttv3923b9+Ue++99xt7++TJkzvedtttRcOHDy8tLCyMHj58eNJnn33mLp0aZF988UXMM888s/2iiy7aATB37tydiYmJvpqaGjIyMnqtXbu20cCBA8tDHVtRUeEZN25c98WLF28bMmRI2b59+7zx8fF+gPz8/LiNGzfmJyYm+v7+97832bp1a+zGjRs/DgQCXHzxxT3++c9/xhcVFUWeccYZ1W+//fZWgL1790a0bNnS9/jjjyeuWbPm0+/C2Jg8eXKHkpIS79KlS3dUV1d7pk2b1mnFihVb27VrV/PUU081nzFjRvulS5fusI+ZOnVqx5tvvnn35MmT9z388MOtj3SN9PT0ig8//LAgKiqKV155JeGOO+7o8Oabb2471raGLWw/tP0ogQbLyhC67SiC65VD8ILjTITqrp93QKJIHiRa3x+nfnuDtofumfninffPikGEN+fj5Fo/jBMhPVGWgQh5tkQW6YU4DrUfib6DAA3qgFQjES+duO3ImJtarbRndd4g9JixwYpI86OCfFGIo93OnCPdnOMyxGlujyxoNW+6i+u8dhTWna5hL7aVVWKDJLazYjMkPDj5wcqK2ItEOlU062VkfN9t/s407XzBbM9CQJEk5L16HFEwzzPbxiAUbbtEqEYFbaX1UKb52Nq+SNdnem+qUB+LRM0/NX+fh4yFHQh4EkrEVO0bpIRn/KnKajApCC0Rx1WrjhQRDJjqWHCPSTU7/cceM4PMZ3kIa+IqJNf9OSQifjfCJPgsa2Zq/PRZm0sNK+EDxKHxmH1zkedxN/JMPsCh489y3VKtKKgtIgmQNTP1JzhpMBlm3y04KQUjEBZOJ3PIIRzWQQwyJr80n2s5zzhkDK41+7fDGe9DkKhysumXGtPHmxFQYx8yllRPpREy3hNwwEiPOaYp4pB+jjj5jXG0Gw4h4E4vHCHX4TjPEIJZDAqeqZit3n8lwd9h+rsGcdbzEVCqnfnZgDjH4xAW0D8QMc84JL3IXT3kRNlTCLhxC8GVevogwJGO7w7WMW42UhVOiWAFSsEZ8wpkHqb+W0wAkOf/kbme6m0EEICyApMChJRnzQOGIWk/eo1HOUGVKY6D2YDdCXf+T1QliuOdknGiUzx+LFbuq3BXKKl3+7FYixYt/JmZmXtnz57dplGjRodZWO+9916TwsLCw+V4S0tLIw4cOBAqjfOwtW3btuqiiy46pP8vXLiwxfPPP9+qpqbG8+2330bl5eXF1gU0bNy4MbZNmzbVQ4YMKdN26WeDBg06mJiY6AN44403muTk5DTp06dPH4CysjJvQUFB7EUXXVRy9913d5wyZUr7n//85wdGjBjxvcbL7Nmz25511lmH/va3v31u2hdTWFjYaOjQoT1B0kNat25d7T5uw4YN8StXrtwKMGnSpL333ntvB/c+tu3bty/i6quv7rpjx45Yj8cTqK6uPpJYbNjCdkrajx1o0GhrqEg2BCtZR1ifDUQWfAeRRY2W2YopWJZ/+AusoaRRuO2he2a+gDigANx5v9uvOGGWhzwXXXD2QhaOuuhvgjjy6rArCNDKHK+icOA8d40suiOJVciC1D5GzWYN+JBF81YclsEOZHHdGHF6lHkwAUfboR0i2tURZxxWIwtfdczVGdmDw6rR6+7DSbnQxbptoVIqapDoZxninF+HRD51sZ2FQ4vXdIrVyIK9KRIZ9eI4bdlmnzcRwGSQdS3bWXA7DjaAokCI3aehmBm6bzoCZuxFotE+JGp8Po6z2Y26rSsCihVxarMankPAzyuRsaPCfxpd0T5RR9S9kKuLPYI5RkUFNyDid+2RtIe7kTF7F9Ata2bqfAMK2A7NagOG5OKkWwTl/LuELfOQcaVOfi1TfQpzbC4Sgb8aERHUsfcTBARQzYhKHOCsBeJsliDCkkkIy6UvMh4mI4DzRTg5+f3NuSKRd7wDjihwR5yUDk1v0HEabfr3EAIuqjDsKwgDIxKJnm9EQFFl89gApo5nFZ/0mvtoaz5TJtRWZMxmI2OhkWmrfjdHIPPeuebvaGT++QeiB9ESAThmIqwLP7D2JOkzaKrA/eY+fof08SAcPQuoPVe5/4/AAZdtYLjMbFuFzFm2o22n5kwz1/4cYc9cgsxp55jj7sFySI2Apf2+vHE80iZOhH0HRsKpahkIY6QwkJ2jLL1vcNg5u3HSRpTZeDHyjFOQ4MJGJMXvOuQ9GI7MZ6fyPH9CrVFEbFUoUKFRRGxVqP2P1X77298WnXXWWX3GjBlzWJwyEAiwfv36j+Pi4oLWJJGRkQG/38kKq6ysPPyex8XFHf6goKAgev78+Yn//e9/P27durVv1KhRXSoqKuoFKuoy+7yBQIBbb7111+23315LSHP9+vVbXnrppab33HNP+7feeuvgI488ssv+fNGiRc0efPDBdgBPPvnkjvqueeaZZx7auHFjXFFRUURiYqIvEAh4evToUf7RRx8VfJd7qMvuvPPO9kOGDCn517/+te2TTz6JHjp0aK/jef6whe1k2Y8daHgOyZsfTvDCYTeO82k7PDZFXdFEzc2NRRa6FyNfii8ULMuf21DBBpetxlGnPyFm0iYmI8/NFm9UvYJoJG1CS+mVI89Rqc5qttPlIVjh3M71VsG2IgQ0iKS2w2bnzPfCEWtsixM122/Or/ndfkTQsD3ipFQiDoayITTvOtK6lrIx7IV5axwnUh33Q+Z+bQDNfa8qoOnHcaSUDhuLLODWj0/KPLx4XVi4dDWS/zsUGfv/QaKjexEgry+yyNP7t1kJfhw1ebv9NtvCNnXI/IgTvAUYjUQke+OAMHGm32KQd1Xp1+rA1IXuKyD1vWijJ8EOIYyTLogTqak/ajb4Ypu9zQZ6dLvm9p+DOP+tzM9gxJkbY35XI47+ekSvwS5jmZg1M/U2hKZfgSzmK3CELDNw2DH3mDZcqeeq76bN8UORFIaXkfKYh0z78xCnvavZfS8CwOg4jzaf/4/Zlm/usxESod5rtm9FGA3KEAgg7+QOxOnvjpMaZb/jHQkGqWORMb8Hec/PQ8CGsQgoOBhHUwDrnDq+I5B5yl6Q24CnB3G0dpm2v4V8l+k5MP2kqUp6jQ5IGdk+ph2/tK7xPlJl4aSZcQ6LkDkugHz/9iC0WK2CZxE4jCidiz9EnqfOM3EIS+wc5J3+EAHJwBXpNw75LIBAdk42DtthdYiI99c4uhdeRFizzXe7+7AdJbsgF5nvVSwVnO/bK5HnXRDIzonGKT0bjTz7dshYOICwjvog3znzgTyTXhLy2i7my9cISDndM2ZwvVT/H4ult+iz09ZoAIjweP3pLfrsrO+4o7XExETfyJEji//617+2uuaaa/YCnH/++Qf/+Mc/trn//vuLAHJzcxtlZGSUd+nSpfL1119vBvDuu+/G7dy5MybUOYuLiyMaNWrkb9Gihe/LL7+MfPvtt5sOGTKkpK42pKWlVezevTtqzZo1cUOGDCkrLi4+nDph209/+tOD9957b7tf/epX+5o2berfvn17VHR0dKC6utrTpk2bmptvvnlf8+bNfc8880wrgMaNG/sOHDjgbdu2Lddff/3+66+/fr+ea/ny5Ql1tWfEiBEHhw8ffnD48OFJ//73vz9NS0ur2LdvX+Rbb73V+OKLLz5UWVnp2bRpU8zZZ58dJIp85plnlj7//PPNb7zxxuJnn322RV3nVzt48GBEhw4dqgCeeOKJVkfaP2xhO1XtRw00JI9OKSpYln8V8kV1E/KltAcpmzcC+ClOCTK36ZecnS5xI/KFV4GoVX9AA0bL1Ywuw4nuhwyEBlyDRDpUm0EBgEPIYvNrHNX3TgQLq7nLiikF2nbMbRqzLtpLEAdGzY7EK9Ch5jNt6ImTKqFRuAM40Wcv4uApUKDlL+1UD5sJ4Ba3s+nz2p4Y1zabVu/DATvWI5okUaY9jE/KXGnAnCAhMfNZKfDiwsKl+5EocCfT9t8jIAyIU2WzENS8rr5Tc/eZtrsKAS16mjZeiTzzMxEgQfugBIlQqvieTaMGceAaEfrdjgMeRCjSp6oNRRxCu9wfBDOzdMy6mToQ7CDrPjE474BS0M9E8tT/gVT12I5EvjsimiJ5WTNTh+FUlYhH+m4s4pxPRcCEwqyZqXfhlD+1q5jA0ed1ZyA5/XGI0x5h2ug17U/CeRcSCRZQ/NrcUyqSVnMRjkMfB/wZAaw2ISD0+VZ/tkQo9tUIUKjX0/5TZk014uhoWoYfeRdam7a1wQEKFXTbi7xXXXCeWyXiFOv8gNUW+91ohwB7N+Cwkr5CnlE1Ilg8BBFMVQc8CgHFdf6INW3yAB+qyOfJMlP14GrT9lycZ2TryKh5cfQc7H6IQvRiNCVlM3AWAsQkIP34eCA7Z7RnzODS+iL9BvhQQVJ3W+MRxoWy0yKpXSUlbHWYedYTkaoPOs6OVkDyTeQZp+AEfnQ8g7AW3GbPBwlmn6+QcXELImAdA/wtkJ0z32KtpCAMvveQ9eA3yPf12ea8I47lvk9VUx2G4111wra77777m4ULFx7WFHjyySe/nDRpUqeePXv28fl8noEDB5ZkZGR8cf311xcvXry4ZY8ePVL69et3qHPnziHfq/POO688NTW1rHv37qlt27at6t+/f73BvNjY2MDixYu3TZs2rVNFRYU3NjbWn5OTU0u88Re/+MXB/Pz82HPOOScZhO2wePHi7QUFBTG//e1vO3i9XiIjIwN/+ctfPgcYP378nhEjRvRMTEysqk8MMpTdcMMNxQcPHvSOGDGix6pVqwqzs7O3TZs2rVNJSUmEz+fzTJkypcgNNDz66KNfjhs3ruucOXPaDh069GB8fLyvrvMD3Hnnnd9MmjSp60MPPdTukksu2V/fvmEL26lsnkDAzcj+8Zqd9mA2KSU8FVm0XktoMSq3+RFHZhlwZ/LolGNeuFltOaw8HWZH1G3GCdbIw+VIlE5No2DKAlBnMwYBlmxBNbcdwhHOsyPhGvFXp07p0m7n3U6rqDbH70fGlTqI5QigpY5EpTmXLTRpC/pVERzBDuVcQu1Fug2qfIOAANofa3GirnchztFEc65pBmhIxCnF9uj4pMwi0+/2OzMVcTIbI46T9plbT8JtdhTXvucSBFhpZz4vRhywDohjCPKsP0Pe0xhzTAUOqwPrfHU9Z302kYjD85vxSZnz69j3B7esmalXIRVxGiP3rlF8P8GRdlsPw6bna06/7luD9Jtd/rUKeAyJJIKkFlyKAzC/hug3KDNBS59ejYBBK4DXDQAxE/gtsnjP4TuWT8yamZqI0KEvNveegDO+vkTe5caIs/8W8v4OQ96x9cgcoeNJgas9iMjlU5YY5a+BhxDGUhec9yTK9N0BgqPYOh+U4Yi+QnDanQ9HsDMGp3qNzYbS81SYtnlxxC31mdYg7Ioupj3fmvv2mXa9hKRhRSL9DQLu2Kw8NR0f+u69BfziZKVOAASyc2bisF40na3S/NiROO0jt05LNdJXjZC5IQZxSi/Ced93mO23ecYMPmIqgYlyP4D0pe2ADkOEIz9CGEVzgEmeMYNP69LW9ZkBDyYjDKYPkff/uVDpJIHsnLswui2eMYNnm23u75Ba7AbT7/cDTyNzTUfqNxUatse8jgVNe1QrQ+aJJ4HZBmj6F0550BbIHDsBSTF6EfjbqartkJeXtyM9Pb0W/T9sP24rKSnxNm7c2O/1ennyySebL1mypMWqVavC4o5hOy0sLy+vVXp6epdQn/2oGQ1uM468jagHLUgKluX/HSmjpjRc92JTzYssdscDmQXL8h9FvsyeOBrQwYAMf0MEAsuRheQ0d3vC5piJqr8GsLBw6QBqO7TROMwDu4xiI0I7wvpbPw+lIWCnZ+h2Pc69oLejyvbiWVXvFSSoRhY9KjJXQW0HQcXpFDix22szFnD1g96HCsT5kHEZheiO5CLR1XYIJf0DxFnUBeBEpOwdyIJ+NuK4zUEE5eYidNR4pJxpKPp+XbmU7j5TRy0BJ4ddFfW7mrb3NZ81QYCHGuR5xZttatr3NtCgjp+OBXX4KhBncHcd7fzBzbAG+uPk7Xcg9P25tTA8OECp27mNJFiUU7f9FHEiwBEo1XMOQpz3B3BEGkchee3zXc5qATKPbZw+a/Mxs5sMwDAZeY/TkIo//a02ewguaQkCNPmQ995v2tsYh8lSiWgbvI6wNi7NmpmqudxDEVZCJ5z5Qb/vInFSkPR9U8aQzXKwwR5wtB/6mr91vKrp81AASFkREJwGVW3udae5npZ59CJj39Zk6WX6wQYgi00/NrbOre/AhQiY9CInz7YjekdVOEKOkVb7bOBU51gbbFCQsNxsy8FJn9uP3P9+ZN49M5CdEyodwm15yDi4iuCUnlwEMMs9jfQPvq9NRPoEnO/F8YHsnEs9YwZvd+37nOv3YQFJAzjMQtg5zwayc2Zaz0n1Xs6k9lznNn1XbRabDfi7yxvHmZ87gNRAds5fcarO/BeZCxqZn6cQUPIAJ/cdCVsDt/feey/ulltu6RQIBGjSpInv+eef3/FDtylsYTsZdloBDUey5NEprwHNLLZBIQI8ZFA72q1OUxzinAWAoQXL8icilNyLgXuTR6cEfREXLMtPAZYj0Sp8nsq4A7GftW9U3arBV7Q4BnsUqf2dSPCzSEAcj7eRBfzZyMIcgp0GNRUXs50F+383u8U9Bmzqry50bGFJjd5r6oBNA1VzP/d9SMTUBktsJ13bZqcG2E4n1r7q1OhxvZBqEdsR+ngqsNiqvvAcTn8dXiia64xDonwVWCkXrja60zUIsR3rOFz7+nEo5QlIPrqmxbyAOJ4XWue0nTSoTcW22RNecx7tt/4LC5e+fopWnrgUSfXS6gV6n6EW3m7Wi/szNxDk1m/ogTNGSs2PsnHeRoTVQHRYcpFoZSi2wuuIs3dUOi22WKQ512ScqPdyJFodqsSXDRBmEDzOlEGg9+1DwNzzEKBsqrm3ciS1Qt+XItP27sj7twNxeEL1m7ZBx30FTqWaRgidXyP2tgib+71QgBMc51rTW0DYG12tY7Wt0YhzrIyJC3HeWZ17ShEgzk4Js9txWdbM1NdPJqsBaa/mHYfSZ7G32To1ys7xm+O/RqqF/A55jiqIW4mAUz8nREqjJSB4BdJPyxFA60XCpRrrNNNvnyH93s36qCewPJCdcx/y7oPDVKirDOhQRHg2GnkXFrUfZgAAIABJREFUEwPZObd5xgwuMiyDCkT09WOEwfOZuaabNajzur5TodapoebKKHP+C5CUo18h70kZMteMREC9VjglqMMWtpNiI0aMKP3kk0+2HHnPsIXt9LIGBTSo2cwHw1boglA1L0QW/2+Yvxvh0PK9yBft00j0uBGQWrAsvwdSFi4bQdTPMucD4EDsZ/5PWv11F1DRjwtO+L2dDmYo/fcj1Oe1yMLBg0TrWyF9HIuT0+8GGMBx3m1qcaT1mZbOg9rCimqaq22DApGIQ6tOhlbFUPPjVI9wWxWOZojqFriva/99AKFdK00+AiftAoKBlDIENPsIJwf+KaxF9vikzCJEd8E2FYJsilBmz0EW6WU4QIcNbNjttBkgENoZtvdVppBGOn3IIvYz5J1JRRaaKurXDieK7waGQj3vxjg6FdciavWnlMaKiexPQtqqaTihxkspjgCm/QzcfazpPe60GzcAUY30RTeE5fIOMsfFISDTB4apELK/7KoRR2lDCS6Jux3H0f7K3J8bSITgseZ3bdNxAI42QTVSznE7wnzwIXNEL/P3FwhNu7m5ZnNz3m2Is1FXKoK+o9U47x+u326gssa0oz2OVsYBnPKcrc2130Oc5t9TW3vDBi7BmS/8OABde2t/d9rVXuQ9yuAkjH3jqF6DM57rmgO0rW5tJA8Oe6kG+b79OfJOKBgBMm6bAksJDRxkIGkmCl79zOz7GdA4kJ1jl1YNqSfQQEsmZiCBFC0ta39HJgN/RcbdRwiT5+lAds79VnWIS3EqQrS2jo9CRH43Icw5cIDMQuS7ZifCorNTH91AvaZNRlN7DrRBf9vikXS8gabNmk52HvLd/TICRIQtbGELW9hOsDVIoMFl7khdBqICfxWyaFuGLJo/RRbIg5CFYwWOiNnvEMEhjdTowtTbpLJzLjCnKnL/6pN0P6eLLUQWJIWIUvQFwN+RKIWq5mskTB0Oe5FiR/s0hcFelLhzhN1OgxeH1q1ghi7m7XJ4UcjiJRIHfLBp16pu7rE+V6dBadJuxoCfYIf8S3NsZ4J1I3YgTpSe91JEkXszsvj7qL6IvqXPoGDDLxBnrau5R3dUUu/NvkeNMmtObTShnQ09j1anqEbep/U47BXtzzMITofR40NFSN2mz/EDTrFIponyz0XE/YrNZp1L7BKWfhznyy2i42aQ2BUK7GcCwWO62myfizi/ZyJaKP9FFvyFIdrbFXGG/zB91mY3hdq9r+p/tELE3krM3+OyZqauNdfchQgfViPR6o8Qp9lmNtjgghtcUkdWtymD6ELz/weIc+NFIrQbkRz953Cq01QhIENTZHykIe9UMxzGjI7PCOS51Jhj3SKotinDohvBY/Zrc39akSECcYDaErrSjQ0URVifeZDovp1aZTN8tL8+Q8Q8T/jYN47mXUg5Sbud7nfTY22va80RQPQBvsJhfdhpKR0R/Yn5dYAAuchzno4jDnodAoKsR+aXrUhKWF3CpUcrang6mZaa/RXBzDNwnmlTRAwY4Fbg/EB2zrvI3JGFzNd7CB6vAaQCSq0UC/PvbDN+eiFppY2p/U5pG0KxQfeZYw4h4JuOfw8ynzVDmKfutMV4pLrRsEB2zgFCVyUJW9jCFrawHSdr8EBDCF2HlQXL8rUefAGS+5tOsLr6rTjOJtTOs/8bEsmbGOmPG3Xel/dXhIUgj82Mg7wSYGHh0sk4wpoexDEtR8SfmiLR6zUIFV/z9t2LFi2XqJF6O/obEWJ/27zmOBV3tJ1tD8ELYnBYDhqN0coXtlMQTzDt3/1bheFUdb2NOZ9d3rITstD6EMnzTzU/FQjLZjUEAQq5LuDBXlgvQdgMLRFFbncqh/6v/QfigGl+dRGOw1hfX6qzqDnxlQiYdwkSQVM1eC0NqCVCbbHOuqKmmvetgMfQhYVLV58K6RMGZLgN6dtIHJ0P7d9QOhiVOPnMut1+Fm6HQLcp1dyel6IQ/YV2yHich0TW0xFg9RNMeUDLfo8IqAH80p0OYf7XiGYSQl1XEEzbNgpxtotxUifuQVId5pvzz8HROfkScQTAYTBU4uhL6P1UI+yBNojD8QskbWgLkiLRHWHJDMaZq9V5T0JAiE9xIp7u1AkFGmKozTqA2noaajpWvzXnbY84uGebe99urjuUYPBMAUkbnPRZ++xBxrebBaKpHdqmZCD2JKVNDEVSYmzmmAI1oYBA9zbttwjEceyCzGkVSMrDhQgggznvlch3b/BJHSaCRscn4Mzp0Qiw40cA1BwkKBAKaNAywKcUQHkizTAT5iPv3DUEs/yCdsURTT4PqR7xENKfF+EIu4I85wPAX0IJSrquPRt5Fwch6aidcKrn1GVeBEjwmn3tNvsR0PR8QqdHgsx5TyOBpJsI63SELWxhC9sJswYPNLjN6DekA3MtcGCl9fkXBKPn9mLfi0Tt7jClN+cj0ZQGs3A5EeYCHXIQ2mUl4iidiSwmOlG74oHbMY0gGBTw4NAy3ewDt+liXvfBtEGFqdYji/zGiGNSSjDtfQ8SpdZ3zkPo8m9qETgLt5bm/70I8KDq+PGm7V8jQIOmVcQAPcYnZZYakGEq4vDdv7BwaQUO4JCH0Ei1Msogc60qc10Vbdtj7rUjwboRCuh4zPXdqRR1mTrO7RDndg8OE6IMhx2yBUmTKcGh19Z3flvr4EIkz/h/ODWikxkIIOamBttj1H1/dmUS29y6Hu7ttmI7OGPWi/TnMuR5tkH6OBkoCKGr8AdzvP52R3yHImBBcwQUcqdA6D2NRBzkFxFncCNSbjMDSePQ3PxBOOU4z0XeOQWyDiDgjDreUQiwVY7MA8rISEKqCQxEwIbWSIWWJogD60cA5CFmnyoErIvFefeVyWDP80rT1j7WtApNq1KmkQKdbc3fsQjLQY/9FAE/CoF+OPOBnrfEnMfWXlGGiz0eSpD3357PvMiccbLyz5si76nOofZ8pmKO4AAm9vygqWt6n7HIu/9X5Hk8AdyHRNp1HMcgY87tGNrj8jVgDA4bDIJFVFsA/wscDGTn7MdKk3BF3BuMGYf/DgTkS0UAewXV7flI34etyPfGRiRFJQoZd7aW0Os42g71Xht40TyLYTjvlI6NUN/F2haoLdr6H0TrR79P6jo2mmDB4bCFLWxhC9sJsCM5BA3GCpblxxcsy78coYI+BtxmQIegfZBoeiTiHG1EosZ2hPEKrUyRPDqlNHl0ysowm+G4WgUS/XoS6XtdkL5Hbb0A1UGwqcZK21aLwnFmNEVCj4dgujSu8x8EPjfHNEYWycXI2FCQQWnACTgOoC6yv0EWdz5CL4r0WuU4UfoqBMTYi4hqxSAO2osIVVXvrc3CwqWXIwvzTGSsxiIL8gwDQExGFMe1YsXdpv3KxsBcs6350Sgr1C1A6KXuBR4h9vMizzMaeRZfmN/RiFMcgTiY2neh5qxQrAAPdedz/xCWiyzOY6gdCXcv5tVsIBPXdrfZKRa6UNfj1yL9qnW9f44ssl9EIoNPmM+GIsrwUw3o8C3CdCnLmpl6udn3ASAva2bqMASo0DFpMy/UFAzpiqT5XIowKa5GKNcPmL+vRMZif0RvYRICYOxFhAH3IkwA+/wBhDn2JOLI5wDPmPb+3tzjanO9OxBK/uOIWN165D0pQ8p7Pm3aB/JO29c5aPWlAgwBc8+fEQzmeBHH347UexGQTMfuzxBnN9Wcew8O4ySAgAc+IN+cX/swhuCxoZoI7rGwg5OXfz7AtEO/32zQxP5b+8HNHNPqO1uQZ7bQbB+HzEl7CGaPBdWlt0yZCHnW//VZLDIWVgBrAtk5XY+w/2lvRrDx98j7YKcbuOdbTQGciwAJedQGGaoRhtuxWC6iFfE/yPqrGAdg1HGgGh9a2Um/E7412zYigKamPLqZXmo1CNh4K4b1F7batm3btqiLLrqoe+fOnVM7duyYOnHixI4VFRWe5cuXJ1x44YU9ABYvXtz0d7/73RnH43qjRo3q8txzzzU/8p5HtgEDBvTKycmJO/Ke382WL1+e8K9//avxkfcMW9jCFmY0OJaBiJdp+bRbgJSCZfn/QqK9AxBnqwWyQM0121LtkySPTll3EtvcEG01shjRqHwpMHth4dLrEMqsvSCPQBYg4CzkNZphazhotEt1E+zIoUbfbCYCCEigFM9IJIrYwfzfFFkU62cR5pqaFvAJkpuqJR134ZSFswXTInAAEl1MJSLR0BykokAyIux3BkLPXo5EmJMQB2o64nCPQiK9DyAL7UuBG801YxEnswe1I6TqVOUj74FdmsxeXNpMEttCMRAOESw02Q1ZJO43fXLIuo6e29bgcJ+zrqjXxlMhbQJETDFrZqpqedh9V5e2CDiOpN/6uwqn3/Qzu8Snmi6yvIhuRzOcmvKNEJr0JLPPTiR9YSkiinsDMl73IuPlbSStJhpJ/5iM5L9/Yc6lIFgowEfbnoRT8nE3wlxYhTAbRiGg0nqcNIz7zed5CBh2NcJ6aG/1zQHE4VE9jgxkPk4ywpYXweG0lSJrn8sQRoA6tpOQd8aDk++tdgBHkBGCS+J2NseUI4BhN2o/XzUvTpS9xvpMo/0KVOh81A4nbcQuuWmDHqEsAnl/TpgZx/xPCNU9AqcyhG22zsXhQ6397Hv9FgFjMnHG7QBknvwLMlY+Qua5Wo6hicjn4jC3DtZxTTUvToWMs4B/B7Jz7gFeDufrsxr5zvg98p0Syjohz+JT5LmVIf2sKUhRwFhEVPuoGCJ2qdFAdk575H0MINWI5iIsrD4IQNCTYMaKMmtSqDvlwn5fAsCdnjGDT4uUiY/+s6TFB28vaH+odE904/hWVedecNPOMwdcve/7nNPv93PFFVf0mDRp0u5bbrllW01NDWPHju18yy23tB85cuQB3W/cuHEHkDnyR2E1NTVERn5/t2f16tUJ8fHxvksuueSEzrVhC9vpYA0eaChYlq+CZ3ORyFdThK6ZgUTafkawHkMA2IAsaBMJ/gLbceJb3LDNTqNw2QEkatkCWaSohYq0guMUFeFQspu5PrPpmbb5zDXskm4enJJzNYjgXAezzY84I0rP7oMDcqj4oX0tvbbSSJsR7EimmHN7EfX6HUju/7sII+dbJCp0FRJN1gjnWgQsuB/4l9m2EKGXP2Sup2wGdWoqTdu74+T+a7qITYl2O8dq7qjSLmRR+CUSpe5s7ucc5Fm0Q8Qs9yOOm/a9x2xTAU09X6ioLki/1hUB/aHsJcTB70ltxxVqp2PZ2/W35iR7cdKHzsFh0Og5FKTyIHNaifmtoFlfJCXiJ+aYNsAUBFRojoAJu80xl+JUYJmBjJc4ZFyW4mgdqICobXqfCkQkmB8PoqtwFpKSsw+JbK/ASd1YCZA1M1VT0O7BARqqgY2u/Q7n2BtwYajZd7UBHnSf2632zcFx5G0WCObvdlZfBnDKp1aa/7/Cyf+3qd7u90Lfa+2TSBwQUtlNyhrSeUIBT/td1O1u0zbHYCpOhEiFOV6mjBE1m51kAyw2g8HNNtP+qUTy6fsijukbyHfrZOR9/7VnzOD/OYo2ZSAgw0uIaOT/IYBVqOu7rTMiWviTQHbOjIYMNph7fyyQnbMQYQxdRmgQKcn8gPM89W8vMi/kAt+lmsdzyHi4AHjHM2bw9kB2zqUI4LgECTRMxXkPVAxZAdgjpe9t4zRhMnz0nyUt3n7joc6+miovwKHSb6PffuOhzgDfB2z4xz/+kRATE+O/5ZZb9gJERkayYMGCL7t165Y2dOjQEt1v3rx5LdetW9d40aJFX+Tn58eMHTu2a3l5uXfEiBH7n3766cSysrINfr+fKVOmdFi9enVTj8cTuP3223fdeOONxX6/nwkTJnTKyclp0q5du6qoqKjD33vvvPNO3G233daxrKzM27x585rFixfv6Ny5c/WAAQN69e/fv/Tdd99tUlJSErFgwYIdI0aMKC0tLfWMGTOm65YtWxp17969oqKi4vCYjYuL6zdu3Lhvc3JymsybN++LiooKz1133dXR5/ORnp5etmjRos8bNWoUWLNmTdytt97aqayszBsdHR3Iycn55JJLLkmaN2/eFxkZGeUA/fv37zV//vwvFi1a1Nrr9QZefPHFln/+85+/SEtLq5g4cWLnnTt3RgPMnTv3i2HDhh1asWJF/PTp0zsBeDwecnNzC5o3b34kxmfYwnZaWYMHGrAEz5JHp/zSpE/0MZ9pjq2a5tj+FlnQdsD5stuDqLiH7Yex1cDNyOIni2BWguZNqxNxEHHMbFE+n9muTr2aRmhsU4dP/1ZnV524KMR5dud0Kwih0Wj7GmoROFEZd6TaZ47PQRgGYxFGQ09z/32ApPFJmdsXFi691+z7AkJDnoBQsUGcue2I09QRyVdvjDj6+iVYiVMucidOnrk6Rvp3XY6+28nS/Q6Y9jZHgLpFiEORjlM6dA0C8h1E3rdOCKBTTd06ALb5EQrwD7qYtB09pC+zEMG7coKdch/BDqoNGCj13I6Cq9MahQizRRIcEXdHjb04VQ90ezPgpwgQpiKEsRgWANLXnyPObxvEcYhDxnWZ2ae5OfZc13VDmZ1LrfvF4ugJNEZYEvHAmqyZqXa/TUScj5uABeY8z+HKAXeBDsMQhhqIqv1Ka5/XzD7xCOiQiETnNQ3Jniu0/xNwQObuyNjsiSPqWkzw3GGDFe5oaqjn1BwB4VT8sAJhLiXhMKds3Qi37TV98l+CKyg9ALyUNTN1/nEEG/6AAGbKfqoLyHW/q2oe60erpsQiTu0byNhrgrCc8jg6y0UAVHDK4qoQr/v6oSwCYXiVBbJzZjZksAEOs0TuQt6NZJzxH+odd8CcQAAC/ko8Vdl4DpPjjqmah2fM4KJAdo4KQOfqNkyZzEB2zkzkPbwK+d5ogaMXtMP8XY28S6ofolVjtgOjT5fn+8HbC9oryKDmq6nyfvD2gvbfB2jYtGlTo/T09DJ7W4sWLfxt27atKiwsjAl1zNSpUzvefPPNuydPnrzv4YcfPlxJaNGiRc02bdrU6OOPP87ftWtX5IABA3oPGzas9O233268devWmK1bt27+6quvovr27ZsyYcKEvZWVlZ5p06Z1WrFixdZ27drVPPXUU81nzJjRfunSpTsAampqPJs2bfp4yZIlTWfNmtVuxIgRnz7yyCNtGjVq5P/ss8/y165d2+gnP/mJruEpLy/3Dhw48NBTTz31VVlZmadbt259V65c+UlaWlrllVde2WXOnDmt77jjjm/HjRvXffHixduGDBlStm/fPm98fLx//Pjxe55++ulWGRkZX27cuDGmsrLSe95555Vff/3138bHx/tmzZpVBDBy5Miut912W9Hw4cNLCwsLo4cPH5702Wef5WdlZZ0xb968z4cNG3bowIED3ri4uDDIELYGZ2GgobbgWSwOrdtdVkkFz65GFtdeZDH0R+D1sBbDD2fKdFhYuDQXiejfjdCwwclZV4siOOJ4CIncKqPBHSl3R0jUwVamgqZrqOO7F3FA/DjUcq2esAURgbPPbdM97WifrXdgO/fpiIN+yLQ5E4niLcZxMvoi0eq+yDiOM/0xGke/4EtksbbKnEtFF+1IawQOyOA2d9TW/Zm2+xCOQ/aN+d3DtOsqRCsg2bT/A9POMoSJEY0wjdRZdkcoQ9H0VwEzToG0CXuBfRYSuVXqb11Ak3ub7YzqeLDBnpgQ+2F9bo9lD9KvEQjgY1dx2I3j4GmqzjBkYd4aWcyrU7iT2tT+coJFVUNVaHGn3Lh/exBmzlwkDeh2BDy7G2D6rM2zESDgaCwXGTf6dy2zQQfgKYsFofN+GjLXb0QEHAsQ9tG/ECDMgwDMn5l2uvta71srteg91iDgWjnyvpWY7S2t/vgYeUeykYosNuvJHi8KXt43fdbmx0L0wUtIpH89x0no0ESY1yDPSucnTQGyn3ddwJP9mZYHjkb66GcIkOpH+nRgIDtnG8EAU7bbUTSOcQXyvmUhc0k3RO/haNc5XiRl8hDyzjZ0G4lUo5iDpK4MQXQN6gJtZLvHEwMxvwWuD2Tn/ILvUM2jPmFO89kLwAuGLTEU+f7uarYnIe/w3QjQVoEjeHy0rIofhR0q3RN9LNtPpG3YsCF+5cqVWwEmTZq099577+0A8M477yRcddVV+yIjI+nYsWPNwIEDS9999924NWvWHN7epUuX6vPOO68EYOPGjTGFhYWNhg4d2hMkjaN169aaAktmZmYxQEZGxqHbb789GuDdd9+NnzZt2m6AgQMHlvfs2fMwSBIREcGECROKAfLy8mI7dOhQmZaWVgkwYcKEvY899libESNGlLRp06Z6yJAhZSCgivm8eM6cOW0rKyu/WrBgQauxY8fuCXXv7733XpPCwsLDyFppaWnEgQMHvOeee27pjBkzOl511VX7rrnmmuLu3buHgYawNTgLAw2O4Nm3BcvyUxBhQTdi60cWmX9CFuSbkAVmB+D3yaNTtp+85oatPjPO5YsLC5f+HKHgKtXZjzixPiSqDBLN1+iyikx5cRwqCHb83c4fSMTVdpg2I+NCVdR1f/3RNI1vzWfKqNDr6SKuGsdZ059SRBzvJWTxdBnCTjgEHBiflFmXI1Fh2rIRUeX+CRIdVhp+D8T57OE6TqPkbufVdnhCMQps8yL9W4r0STuc1AGloyci/bjf/AxHhP3mI7T9b809dLWuF8rp0nP+5xQAGSBYpK4Zks7Sy/y4wasqapdltZ03e3xoH9gVUNTs/nE7vj4ErAFxalXoMAJhLagT7Efo5F7keZWa9mlZ2Pbm/2IEZB2LPONvEEHHRjg6AXvMfam+Qmtqa214XX+rAzsAoW8nIXP0UZsLRPhOx2TNTH0dufdMxHn9DKmSsgLpv58i/ZGII4gHDrNJ55RCZE5QUci9CCPpfLNPGQL8eJHxr+fQUrOtCe3c6bhYiqM3EXQ/VtrJcRFFNY7dpQjbpQIHlHF/Zx6JQWCPfzs1sQTRzIhB+jQW6fvzzecpiDZIqLlO37dYxEkuJjTboj7zArcHsnMeOp0c0u9oz7l+X42kOMXglGwup5auiQfzf0fgcc+Ywedwgqp52NoOlm0347SCYGDhtKso0ji+VdWh0m9rgQqN41tVhdr/aC01NbX8lVdeCRJm3Ldvn3fXrl3RSUlJlatWrfo+p6/XAoGAp0ePHuUfffRRQajPY2NjAyDpHD6fr761BwDR0dH+76rLkJCQ4B80aNDBv/71r81ee+21Fhs2bNhSR5tZv379x3FxcUHzzYMPPvjNFVdcceDVV19tOmjQoOQVK1YU9uvX71RL6Qxb2E6oHWkxcFrbnffPin//mw1Tq3zVDyNOwBtIZNWdV3538uiUlOTRKU8nj055LXl0yvbk0Sl3Jo9OGRcGGU5Z+z3wPJLn+UdEg+BJxHFaizhCXsQp+hhZpO5EIjfgjAGNQtZFGbVFJasRx2Qp4sTpcepwRCHOVjUOXV6dPbdDr47KNoQF8SSyAP/l+KTMF5G0gHuQqP80aqcJrLa269+vj0/KfA2HwdEIWTD2xMlztRH3MmubDaZo1NpNj7ZNj8HcZ2OE1tre/F+N0Lx/hzyrWeZecpHnNg7Js87HEQN8C4lY1RcV+Bp4tJ7PT5pNn7W51GgDpCMCa2cgTppdeUKtlODnD8ElDm0Qws14sYEIPZdWUrFZH/b5/cgYiLD+V3E+FW2MwgEXIhFwodL8/SbCiFmHPK8KRN+hGhEk/TUi9NgWefZnIu9eBQ6gUY042H5E60DfySaI8zIFGRfDEB2Ko7bBN6WdPfimtA8H35R29rEcZ5sBHuYjqXLzkVSNaeb3R6b9XyEggg1Iqu1G7jed4NKcrREQQcHEBMSBjkH69hnEKfKZ/SC0w1yCVNW4ra60CB2DxzFtIgN4BEcwNJTV937qPFKD9J0K5FaZbb0QgLjG/DyIpEG9i8wNM6gDNPGMGVzqGTN4JTLfvYSAZ7k4gsD1tcm2KBx9jwZrphLFbJO2MBFhwzUFHvGMGRztGTM4Bhn705AUtz14PGvMzx5EzHPKD9T2Us+YwStPd7Do3Atu2hkRGR00fiMio/3nXnDTzu9z3ssvv7ykoqLCO3/+/JYgIoo333xzx8zMzD2NGzcO+X6feeaZpc8//3xzgGeffVa1qxg8eHDJsmXLWtTU1PD1119H/uc//4kfNGjQoSFDhhze/vnnn0d98MEHCQBpaWkV+/bti3zrrbcaA1RWVnrWrVvnZhcH2fnnn1+6ePHiFgAffvhh7Keffhqy4kR6enrFzp07ozdv3hwDsGjRopaDBg0qSUtLq9i9e3fUmjVr4gCKi4u91dUybdx000177rzzzo7p6emHWrdu7QNISEjwlZSUHAb5zz///IN//OMf2+j/ubm5jQDy8/NjBgwYUP7AAw98k5aWdmjz5s313kfYwnY6WkNnNGR8Wrz9qrZxraO7NOlwLbUBhvdwFphh+xHZ+KTM7cAvzb/rAEy5x1jgQmRx/xESmfsJ4gwtRJyCfjjOuFooMTM3hTkayZk/iKhyn2mu1wxxOBJwStp9hNCxX0Acj9E40WkvsjiuMOf9jZut4BLFrBWpCSGaaf+tKQm9zb3vR/LO3WwFzctVh9ZeYNSlJu9mZoD05QdI1Yu7ETr+VoS50N48q9m688LCpRMQZyPVtOHnyMK/M45zUxc1+z/jkzKPtbTaiTaNtCYi0XGN6Cv45EFSE9y5+3aaAdbfNsCj+9j6C3Y+tZ9g5oOtAYF1jN2XxcjYV3aP7qtVBGIQinsTxNHtavZPQBgTedNnbX4ha2ZqjLnfFta5ExCnMgIBMaIRME3Hn5pqnfzcXH9y1szUDxF2w9GIGz6OpF88zjGCFHUJSRpTDYhHEUBnCwKI2SX+vEif5yBjVq9vP6O+yPvtxemfMmQuuAyZMyIRIKYJoXVIGgH7p8/afMLGeyA7JxGjkWEczlwE/JmIgEidqD0m6wtg6GeRyPug70IMMk/4rH1/FSw7AAAgAElEQVQaI+Pqbs+YwemWqGC9ZtIolMnRDIms+xDgocacsxjp10gEPIuy2nKsLIiGYM/hsKEOl09V8UjzE7aTbKrDcLyrTni9Xl555ZWtv/rVrzrPmTOnrd/vZ+jQoQfmzZu3c9WqVfGhjnn00Ue/HDduXNc5c+a0HTp06MH4+HgfwHXXXbc/Nzc3vnfv3ikejydw3333fdWpU6ea6667bv+qVaua9OjRI7Vdu3aV/fr1KwVhLGRnZ2+bNm1ap5KSkgifz+eZMmVK0dlnn10nE2DGjBm7x4wZ07Vbt24pPXr0qOjTp0/IahBxcXGBBQsW7MjMzOyuYpAzZsz4NjY2NrB48eJt06ZN61RRUeGNjY315+TkfNq0aVP/oEGDyho3buybOHHi4bSJUaNG7R89enT3f/7zn83+/Oc/f/Hkk09+OWnSpE49e/bs4/P5PAMHDizJyMj44uGHH26Tm5vbxOPxBHr16lU+evToH02FjrCF7XiZJxBouN+pK575Z1evx/tah/gz+kR5I90gw9+SR6dc+0O1LWzH3xYWLnXncz6BqJv/FtEJWIHkh0cjQMHPCBYscy+gbce7GBF0U/pzE4I1FsrMecuRaDCIeOiLCAW7tXUudRyXI4vlJ76L87ywcOlhJ8E+3vRDBkLpH4I4/30JzVDQ9pfgRLlDmYIvXyJR2MaI45CD0N9fRvp+PtIH9yA51M+5721h4dKzETpsG0SgciIC2MzHofnblSe0n7cBww1wcUpZ1szURGSsDUfAKKXQt3btqs/eZiG4x15dWgf2OaD2s3SnWtjH6+dRBJdzPYBTSs6LPNNSJEIfgQBl7yLigMsRh/t1JJJZhgi02u2oQZxzLQvsR965X+JE792mX1IqiLkaEY48RB1VFQyT4XFgSs6CjcdUctgISc4z/05zAQ32fvGIcOlZpm0QDN77ceaCCMSh3Y+MX5/ZR4EJ1bCwGSb6ToV658qROec302dtfvFY7u9YzAgC3oOwD36BALP/C1xLcJUNqA2M1XtqhOHUHId678Fx+lXP5QzgNc+YwVcEsnOGYTRPDHPhaNofVPHAAk6WIADQZAKBQRDwgEeencfzITD0dI+Gh+3UtLy8vB3p6ekhtQBOZSspKfE2btzY7/V6efLJJ5svWbKkxapVq7b90O36vrZjx46oCy64oNe2bds2R0TUVT01bGFr2JaXl9cqPT29S6jPGiyjoWBZfmL3pp3+gVBW1TRf9ilg5g/SsLCdMDNR/qB8zoWFS59AIqzPIdUZDiFpCi8hDlZ/nIij0r41H9leUCvI4EccC9uqzTU+Q1gEl+GU/3rHfD7ROp86HYfMvh9QR37pwsKlXZHUgz+EcLAnYoT0sBgDBIsUdkNSJ2zn0javaTsEOzxl5jNlGNSYvzsgfVQBTBmflPmC1dbVSFkygNV16SiMT8pct7BwaT/EMS/AoUpPRQCHCUjuuuoOKGX9xVMRZDA2ERE3/AhxEFsQXFbRHkuh2CI2m6GuvFRbu8EWQHWPK1zn0HGr7AXVi1DgAZyxodRPTe9JR3RtWgHjEfDqEUS0sQAZw3uRcQEi0rcPeUe6mGtPwilJG8pRtYUDQSpj/A8CYoVUszfgwhGZDHWUgDyikKSxoUg6yCGE1TAUp+QfOM5zhPVbS3uuRUCKXQizaac5z6+Rfs1H3qnzzLnscaB99AYnnm23xbSnJ6KVMBfp1wpqz3NwZJDBrkDTEWENnoUjmrvNbI9H3pVdOELN31tU0K5egOTyryFw4HU8jc/Ev7cCT9RaPC0nhkGGsIXt2Oy9996Lu+WWWzoFAgGaNGnie/7553f80G36vjZ//vyWf/jDH9o/+OCDX4ZBhrCF7btZgwEaCpbl21Hc24DfUDu6NwGJoOaGK0g0DDPR9Nlw2BH+H2Qhm4FEWHOQKLQfGTsDCe0M2ZFl+3ONSMYhzqVSoVUb4bc4efB6XBkSjV2AoYrXcwt/RSqg9MFxStTcYl5qumAvRJy2dQijQZ0g9/1FE6zh4MUptVeMQ4GvQSKtAdP2l+2LhgJ66jLzXGa5Nr8GsLBw6RrgTsRBeRWJelZzimgz1GHPIXohF+CMERUhtfu6LifbDRBoNQOtxGGfR8tV2gCDrddgp1r4re1qKvSm9HzdT6/fCAHKKhCg9jXkXSlHmA0VCFjRFgFT9iM0+aUI2HY78BckH38AjiBqFcJ+SUZSKRTgs+9DLYrv4HhCrbKjtUrvHaOQZIk57j1EFFNNAbBInIocXiRinwtMnj5r83bTlkKcUp6DEOHDFAR4UBFNuw9eRPLfnziO2gt12SOm7T5EKyEX0bRxA/Ru3ZBQzCgdZ8rmKEGAV9Wm8Jv/dYxu8owZfKeeoL5KBN/VPGMGFwUWzxtNoNG91Kx4yxPY+jITTnifhi1sp52NGDGi9JNPPgkplvhjtalTp+6dOnXq3h+6HWEL24/ZGgzQgCwo5yAL4vauzyqBS5JHp7xz0lsVtlPGbF0DUybzbiTa+DMEIPgQ0W9oROgqAaFylNXJi0GitnHWuaqQBfsZZj+lCscAxSY6f6QIfX/Xb/t+7OhdrftcWLj0LgRYCRCsGh/KSVBHwhYerMYRc1uLRHUPIo5mnYyF72vmvm47Eec+gXYIWIaUTNTnfaT5N5TDps/ASzBQoQKddkUOt76DmubBh6reYTuLtpipfq4OoV0K9nDNcgRsqjTbFyOgWj8EZHjL7DMHSVtKR3L8dd5djqRhqPDnw4jOSTxCs2+JAAyVwH+Nk13L8ayDpWCbDS58J7DC2FrT9n5IJH43wuLogPRxAvLc30Z0YaIRAG621S5tywNIGcoMnPnCTllR+wz4/fRZm08Wc2cB8rwiEHCvAqciiT0u3GlXPhzWi60LosyOGmQMqeijVvxRfYtCpITvCTfPuGnbgfFw48m4XNjCFrawhS1sDcYaEtAwHMkLducyFwGXJ49OOaY83rCd3mY543lmUxwCUGlJNzvVwKaoax55qMi0Og5NgIut4wLIOGyFLLLXUJuFUJfdiVTUuPNIO9pmdBr2IBHTriHaa5s7Sqm5/h8A/0YE/+aaihZhC20ZSDqWVhuoQsZUXf2uTpy7IomH2qwTP05uv0bNY3CcNj1GQSIVwNNItY5pBT/UabS1A9yAhd8cp2wcBd4ikXHVHUkrWI0wG97FSeOZNn3W5qKsmam5wL1m22pTjvEuJBXoApxqDvsRZ7wSAWo2Un/KQC2WgssOgwt1gRVHaemmPS8i7+sHOIDFUKRPKxBAYqDZvtoFfthCoSNxnm1dFR3aICynkwU0PIn0+w0Io2EOThqMG6TC2q6pOLbpfj4EgIlFxouW2bXFcHsgwEv+8biJsIUtbGELW9jCdvKtQQANBcvyU4BbCV4sbwSuTR6dEl7IhK1OG5+UWbSwcGkO8DQi3Pd3ZJG/A8lJPxdxxHRRrfnYavaY248T1WuOOCFvmt99Ead9O/Do0Yo/jk/K/BPwp+9waxnA/0MclzzEaarP6bXBFHUq30UYEzl8t4hwQzItzXc14kQXIQ6jOv5qbmAhYG2zwQX7f7tKBTigA67PqxHWTEeCtRaKERCtDBmLjV3X8ljn0/8D5hxVONR3W1jSiwiNDkfAiG8RRgdAoRFczDXXu9/8Xok47EkIQ+f/EIc7E2EBYf6uon6goV6WwvcEF9zXuZvQgIUbdAsJwulxWTNTZ+IIyNanwaHpCyfF7KoCRlixLSL0qalTR2OhKqc0RQCHnchcaCvZVyGpOduNAGRuWDMhbGELW9jCFrYfnzUIoAERsbIXwX9PHp2S+QO2J2w/LlMnMRmrAsTCwqX/QAQLKxF1/bY41HK3A+nFyTnXHPm15rjD6vknKt3ANsNmiEX0Ha5F0h00/90tNmg7vp8jgEoJkpM+N0QZzbCFMBOtfx0R94zH0Viw632HcsjsMqpuHRA197OLxCmd6XdtV7FOm6nQzvytjJsagsth2vR4t2aEXkPbHEBEHsvMTwSwAQFWJljHZSKpFS8gWh6FWTNTLzef/Q4BvlS3oBSHFdAIGEc9AqnHEUio147zdZ5A0qh+TnAak21lwMyToMsQ0kzVho3IM69Lg0Gtvr8VPItA5sytiDaDHxF/rACyEQ2PmcCSQHbOf4DVYcAhbGELW9jCFrYfjzUUoGG6/g4zGMJ2rGac6Vol5GzBwoWFSx8DrgSuQBbNvQheUNumC+9BwECTcnDMDotVvvItRGvhLeDlowArMpBI7ANINPuXCNuiCGE4fIvMDT0IdhISkYh8BScJFDnNLANJ33oF6d8IJOofTzAjwEttBoOb6WAzHnRfG4SItPYrw0lxsBkSoVIwqOMzvZYNetjbfYjooYJYUYhGQQBJFxlr/vchegUtEJCrCnlvooFfmWMP6xhkzUy9FAHjdiKshhmY8QdHpcfwozCTSvJL4F9IlYVE1y5+4OFT5B5jXf/b48I9ZmytD3v/AwiroQyZK8FhOmxH5tDBiI7DnUiK1wuB7Jwq4AlTPSJsYQvbCbCIiIj+SUlJ5T6fz9OjR4/yF198cUdCQoL/yEeKXX311Z3vuOOOov79+1fUtc+AAQN6PfLII18OHjy47Pi02rH27dv3Xbdu3cdt27atOfLeYQtb2E6kNQigwYALI37odpzKNvimtHgkrxhgdc6CjafCgvZHY8bpfmFh4dIiJFKri+uvEUcLnAW66jsc+i7XMoyES5GygBkI2NAD+CkCFhwJtLCp5auREnZnIiDJo0hZuZ8ggos6RwQQIOOEiTw2AMtFqoyca35XImPgG8TZqiE4rz1UJNh26ELt46M2I8XOgbcZCe4qAfWlZ4RiUtjaD5EI+yAUG6YXwZUXBph7fwHRcyhASmDGI2DETcB/smamrkYYIK2RfP0LEKDhdSDDaDxkIIDZS1kzU+e7HXE3EHEqAxOmfV8gfaCmjvo7fLcUqeNtWuIXQlcscTMddExGuI5pZo5rau2rVU7Ow7lvnX/ikVS1lgg4VUvkNmxha4iWv/u1Fut3LWpfVrMvOi6yRdVZba/fmdLm8n3f55wxMTH+goKCLQCXX35516ysrNb33nvvUYF7NTU1LFmy5PPvc/0jWXV1NVFRbgmYU99qamqIjPzh3K4f+vpha5h2tDmWYTv9bSgmFxcHcAjbsVsu4qC/hog6PgR8iZT9a4qAAcnAfUjaxeqjPfHCwqXxCwuXXoc4/C8gkeFtiCO5EZMvfqTzjE/KLB2flLnS/C41jIq5wF0IyHA38F/T9tXIwv4NYFIYZPjuNn3W5tLpszavRGjyLyLOdT4yTnyu3SuR9AdNR3BHZryubTqXK5gQsLZHmPPrNQLW527zuz4PBT7oj7Ieql3HYj7birBjtP2rzf/fmjYlAg8CY5DotZ7DgwBdCxFA7XXknWmKUOkXAs8j/VgIvIRTscFtKgypn12JaD/cY0CHU81ykXcQHE2Ge4GrTxFgZDXCggIBHWxB27qsBlDHR8ftOhzdDXusgbwP28x+ytR5GWEmzubohXLDFrbT2vJ3v9bi/a/+0rmsZl80QFnNvuj3v/pL5/zdr7U40rFHa+eff37p1q1bYwD+8pe/tOjbt2/v5OTkPmPHju1cUyNfQXFxcf1uvPHGDr169eqzatWq+AEDBvTKycmJ089++ctfduzRo0fKeeed1/Prr78+7On+7W9/a963b9/eXbp0SX3jjTfiQZzhyZMnd0hNTe3ds2fPPnPmzGkFsHz58oT+/fv3Gjp0aI+kpKRUgIsvvrh7SkpK7x49eqQ88sgjrajHampqGDVqVJekpKSUnj179rnvvvvaAOTn58cMGjQoKSUlpXf//v17bdiwIRZg1KhRXSZMmNCxX79+yR06dOj73HPPNQf4/PPPo84+++xeycnJfZKSklK03U888USLnj179klKSkqZMmXK4cp2dt/cddddbS+++OLu+tnLL7/c5JJLLumOy9q3b9/3N7/5Tbs+ffr07tmzZx9t08GDB72ZmZld+vbt27t37959/u///q8ZwLx581pef/31nfT4Cy+8sMfy5csTQj2bV199NaF37959evbs2SczM7NLeXm5p75rrlixIj45OblPcnJyn969e/cpLi4O+41hOyYLQ1sNzCzmQlOExl0NPINEtG0HJWzfwZTZYH6UfVCIk2qgbINZ3+H0Y4DHCWYZxCKR5Eig6LsCAVaVjXic1IgXzf8nTT+iIZihyf8a+F8kFWUDsBmpYKBOfTRSLlEXTz4cBoLH2qcuU4fdh1MNApw0B3vut1kObjFTqA02gFDeo8x5oqz9bGHKKEToLwKZb7oCqxBQ4BNk7PoRtsNOhN2Qj0S7JyORfQ8S4Vb9iPaIpgTAaGATjuOpFWJsO8zeMcDCTUifzgB6Z81MHXuKOPDAYVbDtUjljT+cxDKWR2VGp2EUIo77LFITMgJJudJxEIXDvtEKGv9FwKKvkfSQIcizPldPbX57zbZYnO+jLxAdmTWeMYNrpbCFLWwN1dbvWtTeF6gKcvx8gSrv+l2L2n9fVgMIc+DNN99sMmzYsIPr16+PXbZsWYt169YVxMTEBK699tpOCxYsaDl16tS95eXl3oEDBx566qmnvgK45557Dp+jvLzce/bZZx965plnvpwxY0bbu+66q92iRYu+AKipqfFs2rTp4yVLljSdNWtWuxEjRnz65z//uVXTpk19mzdv/ri8vNxzzjnnJI8cOfIgwJYtW+I2bNiQn5ycXAWwePHiHYmJib7S0lJPv379+lx77bXFZ5xxhhu0B+D999+P27VrV1RhYWE+wJ49eyIAJk2a1PnJJ5/8vG/fvpWrV69uPGXKlE4ffPDBpwBFRUVR69atK/joo49ir7zyyh4TJ04sfvbZZ1tcdNFFBx566KFvampqKCkp8e7YsSPq3nvvbf/f//7349atW9cMGjSo5wsvvNDsuuuu22/3jd/vp3v37ilff/11ZLt27WqeffbZlhMnTtwTqr2tWrWq2bJly8ezZ89uPXv27MQlS5Z8/rvf/a7thRdeeHDp0qU79uzZE3H22Wf3vvzyyw/W9wzt65eVlXm6devWd+XKlZ+kpaVVXnnllV3mzJnTeubMmbvrumZWVtYZ8+bN+3zYsGGHDhw44I2LizvqFJqwhQ3CQEODMAMuZCBiYw/jOBpKfZ6EUFO/Rmj0HQfflDYTiRgeFioMp1Mcux0vscSFhUtTENaB20HsiYBFB6mdO33M5m5vWOzxxJhxKH+HgFA7EYHDUuRZxiPOWSvkuTbGARXcqQkgjBNNS4DagIQep45bKeL426CAnYqhjAaPda4AwUyHCBzQIlQ5Rg/QGadKiQcBDrqYY7oijuenwBnIHPMVAn62NOdUyv0ZyNxUjFQ6USbCJwjIkI4wFT7LmpnaDViCVK5Qdk8swow4FzjH6ovLEND1lCrLasCFX/7Q7ajLPGMGvwP0MlUoPIiw53NICsy5yPdJFSLs2BYBHp5DAAN9JlMR3Y5tyJiwQS63QGoHRKdjN+GUibCF7bApk+Fotx+tVVZWepOTk/sADBw4sOSWW27ZM3fu3FabN2+OS09P7w1QUVHhbdOmTQ1AREQEEyZMKA51Lq/Xy6RJk/YB3HDDDXt/8Ytf9NDPMjMziwEyMjIO3X777dEAb731VpOCgoK41157rTlASUlJxJYtW2Kjo6MDaWlphxRkAHjooYcSV6xY0Qzgm2++icrPz48944wzQqakJicnV3755Zcx48eP7zhy5MgDV1555cEDBw54N2zYEJ+ZmXmYVVBVVXX4O/byyy/fHxERQf/+/Sv27t0bBXDuuecemjx5cpfq6mrv6NGjizMyMspXrFjR5Nxzzy1p165dDcDVV1+9b82aNfHXXXfdfrtvvF4vV1111d6nnnqqxa9//eu969evj//73/8eEkweO3ZsMcCAAQPKtC/efvvtJm+++WazefPmnWGek2fr1q31Pmv7+nl5ebEdOnSoTEtLqwSYMGHC3scee6wNMreGvOa5555bOmPGjI5XXXXVvmuuuaa4e/fuYaAhbMdkYaChYZjmMfdDFnPu567CY0mIA/AzZIE3HlnMDwCWD74pLR1YhCwOwzoOJ8kWFi7tCrxN7dxtdSKjkKhwncJLYTv1bPqszUXAbFNt4RCSDtAVidSrc94MebbKELArmqjDHApkUNFHNRtUaII47c3MddwAlZ5XK1dgnVNByhgcMEIp7m4tB71mlPm7jfnBnKfa3G8sUlGiBicNw87r9yAshmokpeQcxBndjETGAeYjzmgGwoaIRcquFgPX4TBA7OifB4g9lXUbTmUz7Ib5wHpMCcpAds7ryFwFTqUQcFWMMMeBMHr+BnyMPLdOOM+oGvm+2Wv+DqdMhC1slsVFtqgKBSrERbaoCrX/0Zqt0aAWCAQ8mZmZex977LGd7v2jo6P9R5v77/E4WHlsbGwAIDIyEp/P59HrZGVlfTFq1KigSP3y5csT7Gj68uXLE9asWZOwbt26goSEBP+AAQN6lZeX10nrb926tW/z5s1bXn755SYLFixovWTJkhZPPPHEFwkJCTXue3W3z7QLgJ/+9KelOTk5n7z00ktNb7jhhq5Tp04tatasWUgWBdTumylTpuy97LLLesTGxgZGjhxZXJfWhNU3gZqaGu0bli1btjU9Pb3S3veDDz6I8/udr/vKysrD/XAszybUNR988MFvrrjiigOvvvpq00GDBiWvWLGisF+/fuG1ZtiO2sK5Ng3D8hCmQl312dV0poowP92A4UiEcRySXvEw4hC9OvimtK6Db0qLH3xT2jDDmgjbibHf41DoQZ5TCc7zqkDyl49a7yFsp5StRqqGzAZeRaILryKVQKoJ1kDwEPzs64ouKCjgnuN14aRpCTYo4a4+oX/vsc4VSW3GQ4Rpq1vLQRdfVdZnNjgWiwAWpTgMhkbWtd2MjAMIUPGx2W86TonMB5EqLhFIhDwRAWxuRECJ2BB9oeCcW8chbEdpnjGDSz1jBq9UEMH8/5r5KbL+LnUfh4BDdwK/9owZPBvoi4AJVYgOx2jgVs+YwXd6xgz+fbjSRNjCFmxntb1+Z4QnOug7IMIT7T+r7fW1wIDvayNGjDi4fPny5jt37owEKCoqivj000+PyJzw+/2ovsHzzz/fcsCAASX17X/JJZccePzxx1tXVlZ6ADZu3Bhz8ODBWr7K/v37I5o2bepLSEjwb9iwITYvL69x7bM5tmvXrkifz8eECRP2//GPf9y5adOmuBYtWvg7dOhQ9eyzzzbXtr7//vuN6jvPp59+Gt2hQ4fq6dOn77n++uu/Xb9+fdygQYMOrV27NmHXrl2RNTU1LF26tMUFF1wQErTu0qVLdWJiYnVWVlbbX/3qVyHTJuqyCy+88GBWVlaiggrvvfdeI4Du3btX5efnx/l8PrZu3Rq1cePGkH2Rnp5esXPnzujNmzfHACxatKjloEGD6n0e+fn5MQMGDCh/4IEHvklLSzu0efPm782cDVvDsjCjoWFYOkJpLUaimRoNtVXsIXT5PPcEH4nkXV8IvI84RJcCcwfflDYccYpbEE61OJ72B6AjEsldCexAcp73I/oaJcD+sIbCj9NMFH0lQNbM1NeR55qHpMqMwVHmVzaDKvar827rqriF9QKufT2un0iESRBh7WtXCtCSg/b5bcaCzh9NrDbqtfX7Jcb6zEftyhoJ5neMtd1dEtaPgG0/Q/Qhogn+/upgflf+f/bOPD6K+v7/z9nNTUIg3Ge4EwgEEaUSMSKgpYpWCwhetdSD4EE9G78/BdtEq2i1ioKordQDRRu1UsUbBSxaRTkXgoACyqVcISH37vz+eH8+mc9ONuG+5/V45LHZ2dmZ2Z3PzL7fr8/r/Xqr7bhbKpqwkWvoLzjknO7C4uEIQZENH5jP7ZnzbkGMUhe4yYljHbn5eTXKmEkTJpbsbbkHDwcL7cNwqLtORELfvn3L77nnno2DBw/uFgqFiI6OtidPnryhW7du9aon4uPjQ19++WWDhx9+uHWTJk2q3njjje/qW//WW2/dtm7duthevXp1t23bSklJqZo9e/Za93rDhw8veuaZZ5p16tQpo1OnTuW9e/eut4vXunXroq+55poOoVDIAsjLy/sR4JVXXvnuuuuuS500aVKr6upq65JLLtnRv3//srq28/777ydNnjy5ZVRUlJ2QkBCcMWPG96mpqVX33nvvxrPPPrubbdvWkCFDdl155ZW76trG6NGjt0+ZMiXq1FNP3S9lwIMPPrjp+uuvb5+ent4jFApZ7dq1q/jkk0/WnHvuuSVTpkyp6NKlS0aXLl3Ke/ToEbFlaEJCgj1t2rR1I0eO7BwMBundu3fpHXfc8XN9+3zooYeaL1iwoKFlWXZaWlrZiBEjiupb34MHNywtB/Jw4sEwftQMZDkiTX0ACcLj2XdVi9sMTkOb1O1BEpKVar2JSMA/FOlY8KZHPBx6eGaNJy4emdjzUqTdaDQyK68VBZFIwkhwX7O28Wi2y3R3mQBRUfgJVzlUIyoATSrosglNUtR3L4nk+1DXPcX9Hvexus0s3e/R6ou6tl2M3BdT8MolPBwC5ObntUDUfv3V4znIGB2C0/b1Q+CaSRMmesoMD3ViyZIl63r37r1fM93HMhISEvqUlpYuOtrHcSzht7/9bfs+ffqU3nrrrSfMefZwcmPJkiVNe/fu3SHSa56i4QSFIhluAsYgwff4edOWfpCdkzkJIR6qcaTN2p3ebRxXF8w6bL961MlHqnr8I2LsFgNciMiej5jpmmGAuQRRdOjHE0pp4Zk1ntCYjZQv3YQYIFYj11Ejwjs8mKoF04tBw0zoIxECelkVjmeDW1Gg1Q9JrveVq2PTRIXbq8F8v3m8eyMZ3O8xDSv1skjb0MdZF2wk8XsAuWdNwLt+PBwEFMnwMtKiNYiU4TSn9tj8FfBobn7eWE/Z4MHDyYmMjIzu8fHxoRZ+eS4AACAASURBVKeffvqHo30sHjwcCXhEw4mLLKSN3AykL7uWBUfjGKO5Z0aLCZdJm/BF+N+UWGtox/A+xnoxwJnZOZlz5k1bWmKQAPqYDrqrhdrm+UBfJGHajPSf/x9wJo6B3cvZOZlv4JlZnlQoLAjUjLn0ERnHxXlXnSmeBL5DjA7PRK7P3YiPQyv13JSluUuhzEedrGuTR1NdAOFGsZoscJdchIz9BamthHBvE8JLPHRpht62eazue4mbSND/B42/+mqE3WUjIERKBSLPX49XLuHhIJCbn3ca8G/EE0S3kW2JQ+Kb8AGDgZty8/Oe9MgGDycDPDVDOAKBwMqjfQwePBxJeETDiYsFwN0YyXx2TuYSJCDSAbh2ltf1zg1d26hr1rECh6jwq+eoZWWEG69pafWVwM7snMzpwG3A79XjVmQG6IBmFo3ykH7AjUjiFQR+Rkwsf0n4OL8Kacf2B6QXvIcTEIUFgdMQ/4rvketgODIGXyosCDx6PJENwGuPTOwJ0hXmM/XSxYiaoAIZ77oUKhLcCb1WIbkJATORN40YzftAGUImakLDrXCoIry8w9yHjdwPoql9X9HqKPN43coI874F4V4PkaDX09uxkTKub4BZquuHBw/7DaViGIt0ZmpDbVNWN8GFer4T+D+kk8azh/9IPXjw4MGDh6MHj2g4QaFm6z8AyM7JPA9J5t9B2o2tAtLUqnpGUM8wVlO7DRzGOiB+DOb7TBM3t7t7NbAF+BZxiW+DlHPEI/WrN3KARmzZOZktEEO3Qcgskvai8CHS1UgGJD4kUXo6Oyczdt60pVP2d78ejm0okuEzZFxmImSTTp7HIgqfI1bGc4hgmkTmIgl9NXIPj0fGeimOKkGTCT6EkIhHPn8FTktMs9TCLHmIZNioX48h3CPBJAVswhUGmmww16mrJKu+0g4Nt/fC3gyGzP1WAG8i5rW3A1/glUx4OHCMQQgD3VFF+5UUIWT3GvU3GMectBox9W0AXIpBNBjExWZEqfS05+XgwYMHDx6Od3hEw8mBBcAjSM/y5kQ2gdTKhm1qnSocebZbgp2Ck+S4t+N+HoO0m2uHJD+j1f6LgNcRVUPhAX6uMUjAth2HZNDHYEqzIxnn+YDJ2TmZAM97ZRQnFJ6lNvkFMh5aAq8WFgTWq2W7EGXLzGNZ5aA7Uzwysed5yHUcB3yEJN+DcVo4liGf01Q3xOAk5dGEex3oa6Mch2A0fxdMAkIrmKrVa+a1rssx3O/R5VhBY7k+FrePhKlWCCFJnHkeTZIhktrKJBb086DaRhlCMgDcj1cy4eHgMB3oDpyLEO9bkd/NckRF1QwxhCxHrpdSnFKjKsTk1cQY4C5kzMcAqbn5eX/wyis8ePDgwcPxDI9oOAmgfBEyEbm1WWutg3rtMG8jqofTgY6Et6xzu8X7qS2xhsgtM8GRRaeoZY2RYK0hEqhdj9RN7w+mq8eLcUwoQYI1bVLnA0pwXPtN+IDH1fE+uZ/79nAMorAg0AI595Hk9/r8x+EoekB8PWILCwKrMUxDj1HiYQHwKTACuW66IQn0WcAGJLE/V62rr0PdwrJK/TXEKWvQvgjbEbVRpC4V4KgRbMLVDtU433Ep8t0GjfV3IOUV+h4SSdUQydNBqy6sOtbRhIW53E1C+JF7QTXwHyBPvT7e6zTh4WAwacLErbn5eTfidHWKRe4pCQiZXoaQfbuQckVN/OmY64Xc/Lw1yNhciBAVWxE/Fj9yT8rCU9148ODBg4fjGF57y5ME2TmZ9yG16hrmjOAipPVWB6S//FVIcFSJJBXFiNO9hpZDlyIBVF2ElTmzqUsy9HIITyJeRAwkfwBeRQKsX6jX6zVuzM7JvBYhCvTs50pgGXABjmxVJzmRyJEdwGnzpi39vq59eDg+UFgQuAtpraqTYh3g763Lwc+IpPkHZKx/DNyWPiJjqyIvxgDT00dkHHU58yMTe+rjeRXohSgGOiLKoEdwVEhuxYANzEWIRN1yT3uoVKrHaGq3tqxSj5pg0OSC7XrEWN+9rq+O5yYitbPUx+4ul3CrHUzjx2KETKlWy3YinTt0z/I5HtHg4VAhNz/vPGCyelqIdFkqRsZbM/atlWsFMp5DSKnhe8CjalvTdRmFKrEYYy7zcGLhWGhvmZub2/L1119v4vP5bJ/Px9SpU9cPGjRoT13rP/TQQ80SEhJCN9100/b93deqVatihg0b1nX16tWBgzvq+uG12fTg4fChvvaWHtFwkkD5GTyNtNjahQQ9eqbxD4gD+2QkMC9FZnXXq3VikMTEbRZnJgM24XXh7sDKrXQwkxGb8MRhB+LkfRaSKD4LPFoX2WB0nBgEnAE8j9Rha0WDNqwrBQLAqTgKDst4LQ+Y4pVRHF8wO0oggf0/kPEbjYwpH3Unt1B7bOqb4irEA6QjcCdwf/qIjAcP9fEfDFQphU5y/ojMrvZFZlcHIdeuTmCqcEg3sw2kuzWl28hOG05GE05AmDDXL1X79RNOFmiYRIL5vkjJWKT7xs/IzO8AHBLTUse3FSEZViKKp5bAUmC2Ry54OBzIzc/ThsRxiN9QT+T3zCwt1IReJDWPNmWORsiJHervG6Q0sBCYgnSbuQBRMxUgpYdzvPKKEwv7SzRseW1Zyg/TFrap2l4aE90kobJdzmkbW17aa8eB7v+jjz5qcMcdd7T7/PPPV8XHx9ubN2+OqqiosDp06FC193fvPw4H0VBVVUV0dLhX8JEkGiLt34OHExn1EQ1e6cRJgnnTlm5FSgzMTg1xSGAzByESCtTq7wOPIWUO3yLKApOR8hvPzVIMqC1jhvA2eTrJMEkGG4dkCCFEyOnAV4gM/ErqMW9TxMBr2TmZAUSVkYjMSpvJTUh93j6I4VZzwo3rEpDa7aTsnMwHPbLh6GJf2lEa6+gA/30kyD8DGU+6xVwUtV3hNfT4MxMA/X86QnK9h5TYvFpYELhIvTbnGCmtWIAQDODM1L/2yMSe+ho/E7l+ViPXVAKSBBUjBFwUznfgpzZCSPLeSr0v3vXaGoSIMX9LEggvzYLaBIImKrWHw94ISv0YpY4lCiE0tuEQKcuB/4eYzE73ukp4OILQapkGSEnTS8gYvQT5LdJqukg+IyZJXwK0Vo+d1Hq9EU+HUrWf/yL3vXMQpc7xZmzr4RBhy2vLUr5/6L+pdmXQB1C1rTTm+4f+mwpwoGTDxo0bo1NSUqrj4+NtgFatWmlPHtq0adPrwgsv3DlnzpyGsbGx9iuvvPJdz549K2677bbWiYmJwby8vK39+vVL69u3b8lnn33WsLi42D9t2rR1Q4cOLVm1alXM5Zdf3rGsrMwH8Pjjj28499xz61RJANx9990t//Wvf6VYlsXgwYOLpk6dunHBggXx48aNSy0rK/OlpqZWvPzyy+uaNWsW7NevX1rPnj1Lv/zyy8Thw4fvuOyyy3aOHj26U2lpqW/o0KG7Im1/1apVMb/61a+69uvXr2ThwoWJLVq0qHz//ffXJCYm2oFAIDYnJ6f9jh07ouLi4kJ///vf1/fp06d8+PDhHYYNG1Y0ZsyYneAQGG+//XbSvffe2zo5OTn43Xffxa1bt275n/70pxYzZsxoCnDVVVf9PHHixJ/q2+d9993XfPr06c38fr/drVu38rfffvu7AzmHHjwcS/CIhpMQKokOC06yczKzkJkSjVQcIgKclpY2YhKp/Rt2IoRESC2LNCvpDq7MxMEkKnTQ1Qr4HEdF8SZ7MW9T5Mk/gdOQZDMBR4YdRBKSFup5GyK73/uBG4Av8YK3IwaDMCgDnkH8O+KQxHZ+YUFgDZJEFuGQYmOQWe3bEYLoP0jQ3YRwskuX+NjImIDa6oX6JM2xiHy5ETIzXlPnzzFQP62IhVpjVS9/ZGLPOcDXSPeNWciM6BvINXY5sBHxZ2iHkHD6u9HXZAVCBFQi5+BH4NfIb0c5QgDGqvdr8tEkLXTJRojaZpE24e0x3dBlFm4EEeWKjSgWZqJmd9XnXljPNj14ONS4BClZuhsxdOxrvLYDKYVIxyHoTfIMnN9AGyHJQci7bojSYZdaHqe2MV+9Hgfclpuf1x0xhL110oSJh1V+7uHYwg/TFrbRJIOGXRn0/TBtYZsDJRouvvji3Q888EDrDh069BwwYMDuyy67bMcFF1xQQ6onJydXf/vttyuefPLJJjfffHO7Tz75ZI17G9XV1dayZctWvvrqq8l5eXmthw4d+m3r1q2r58+f/21CQoK9bNmy2Msuu6zT8uXLV9Z1HK+99lrD2bNnN/r6668Lk5KSQlu3bvUD/O53v+v4t7/9bcMFF1xQcsstt7TOzc1t/dxzz/0AUFlZaeltDho0qMu1117780033bT9gQceaFbXfjZs2BD30ksvfZeVlbX+/PPP7/TCCy80vuGGG3Zce+21qc8888z6Xr16VcyZM6fBuHHj2n/xxRff1vfdrVixImHRokWB9PT0yvnz5ye8/PLLTb7++uuVtm3Tt2/f7oMHDy5u2rRpsK59Tp48ueX69euXxcfH29u2bYtE/HvwcNzBIxo86CS9EaJoWIokJclIUNMNSUY+QEyuEpAgvxgZPw0QX4cMtbn6kjaoHWCZ0CaTNhKsadO6Dq5j1TJ51P9LkNZgLRFC4f+Qbha9cGayU4x96DINXaNuXgfJwG9UN4p6vSE8HDgUuXAJIg3uhcyU+xGiwMSvkfNUgkiHbwR6IEH9w4iSYQlwilpnnfpfd1coR2bdexLellGjLq8A92z8WciYeQtJ3I+LrgW35y0veWRiz05IedT9t+ctHwygFA/zkGTlISSR2Y6UJXTDuQ61EigauSajCW9leSWOD0JDapde+HBk5CBkUSzhLXTd16C+ZjU5GESSrcaIn8w2pDSkAeIt80dPveDhSECVSWhS9DGk7ew1yH0rDyG5dMlfCXIdrSGcTHCbMGtYOB1yoo3HZGTMJyPXzl3I73BQ7as/cj09kZufdxmeh8NJg6rtpTH7s3xfkJycHFq+fPmK9957L+njjz9OuvrqqztPnDjxx/Hjx28HuPrqq3cAXHfddTvuueeedpG2MXLkyJ0AWVlZe+68884YEBLgmmuuSV2xYkW8z+dj/fr1sZHeq/Hhhx82vPLKK7clJSWFAFq0aBHcvn27v7i42K+Jj+uuu277yJEjtfKHyy67rIZc+eabbxLffffdtQBjx47dnp+f3zbSftq0aVORlZVVBtCnT5/SdevWxRYVFfkWLVqUOHLkyM56vcrKyrpaM9cgMzNzT3p6eiXAp59+mnj++efvatiwYQjgggsu2PnJJ58kjRw5clekfQKkpaWVXXLJJR0vuuiiXVdccUVEFYYHD8cbPKLBA4jE+jEkeLkZkWl2BYYjgVAbJHAqRpKCZCTQaozMSFbg+CxombqlnicQ7sdgEg1mSzs9+wzh0msb8ZXIz87J3InUq96JzGBuRGrovwYGqn3EA0ORBOgloC0yo7QdKQFZgARlmUii4oYPuAIp2RiXnZP5P1TgpspPPOwH3CUQxvMWwFM450Cfe90FQS/TEn/dNWQgkmw+joyFBWp7FwKfIGNFy5CDSGL7MnAdkkSfppYHkXFpyvY1IgUUPqAfQoqMO0bKJvYV012PZrvMRBxvh3Lgf8BI4FaEnIvGIRdArvuf1Wv/Q66rwch1/y2ilGiA3Cf0dbwcIYdikbKlLjglEyDnqxhHwaSN8Rqq9Zsi95J/qud3IufoFeBGz3vBwxFEFqKgao+U352Cc220RBRDuttKGXI9aKWQWR6k21jWZRJpEhIWcg2g1tXKLO27UoiUV/RADGLPAs7Izc8bq5Yv8HwcTkxEN0morNpWm1SIbpJQeTDbjYqKYtiwYcXDhg0rzszMLHvxxRebaKLB53OGq2VZEU3e4uLibL2dYDBoAdx///0tmjdvXvX6669/HwqFiI+P7xvpvQcDTUpo+Hy+vZrQxcTE1Kzj9/vtsrIyXzAYJCkpqbqwsHCFe/2oqCg7GJSqzGAwSFVVVU28kJCQUFeJ5l73CfDJJ5+sfvfdd5Peeuut5L/+9a+tVq1aFfC8Hjwc79jb7LOHkwcJSGB/FVJjeg0yWxhCEohPkKBG12i3Vv/7kADLRhIV7VwfhRMQRbpTRjLn00GYO/hqgMji70USoG+RmfChSMJ6rjr27eq9HYDfInJVENIkA0lmTkMIh+VIhwE9O6uhpfRxSH37W0hgeU+Ez+ChDhQWBBILCwKXIiZmDwLnFxYEHkXqi59Bkls9lsxOBOYMnw85r7o2PwFRrjyISPXvxFG35CNtHzfgEFd+ZKxcjtQ791Pb2YjM8H+JUzcdQmYN6wsUfMhYvEt1ojgucHve8q235y1/MNKs/+15y0tuz1s+6/a85a+px6235y1/EklQ/oYk9l/gnKP+SNJTinSwGIuUOSUhhGQZ8h0XqnUsoDNOm9ldODO3+lrfgFyL63GSp+8RBQuI0uoW5Jw9oZ7/BLxzopEM9sx5GfbMeR+XzJj79+X/mDvj46fmdjzax+QhDAuQMq3G6nkk7xEQIk63i3X7FunfF7dRqkm2mr5HFvLbBfIbW0R4eVIVcq20QEgGH0K4Tkaul0H79xE9HC9ol3PaRivGH/abZcX4Q+1yTtt4oNtcsmRJ7LJly2rUBosWLYpv27ZtDXHxwgsvpAD84x//aNynT596PRZMFBUV+Vu1alXl9/uZOnVqE52s14Vf/vKXu1966aWmxcXFPoCtW7f6mzRpEmzYsGHwvffeS1TH0KR///4RfwNOPfXUkmeffTYF4Nlnn3UrJetFSkpKqG3btpXPPfdcY4BQKMTnn38eD5Camlr59ddfJwC8/PLLjaqrqyMqHc4555yS2bNnNyouLvbt3r3bN3v27MbnnHNOcaR1QUiLtWvXxlx44YXFU6ZM2VhSUuIvKiryyic8HPfwFA0eQOreb0ZIhmY4ydvXiFzajwT2dyCJY1PCHeb1rKeeHQYnWNJjzJzN0UGU2VJPw534u4O0fkiS+SSSGJ6HqBvikWArR62jk80mSCLzptrflWr7aTilGW7zOX2cN6ntWojDvYd9RxbwV2SsvIKQPjcj48HdPnFvsJDxpGX8jYBhwEdIcH0+olD5HU4CsANJdruqYzD9OqrVsfQkXL3QEAniGxLZGFGjOxAoLAj8CfjncaZu2CeokotHEZJhCeLfcj1C0vRGSJ1fIuUXY5GONqciCdYa5L6gicZkY9N91KMfIRdaIbOwe5D7z1Lk2nwZuV6vAF69PW+59qEoeWRiz7GEl08d87BnzkvcVmWf/1kR6T9V8fTYW8/eas+cV6P2sUZn6zH0CDAozkeoWzxWoo8m9sx5I4zXPRxi5ObndUSI5PsmTZgYscWxUTKxGiHOfkJIx1KcVtC6w82+wvQnMv/KcO4/cTgqBj0GTHVgPEKcmx0B9qj3Xai2d3Vuft4y5F7oqRtOIGgfhkPZdWL37t3+8ePHt9+9e7ff7/fbHTp0qHj++efX69d37tzp79atW4+YmBh75syZ+2xWeMstt/w0fPjwzjNnzmwyaNCgovj4+Hpn/0eMGLH7m2++STjllFO6R0dH20OGDCl68sknN06fPv37cePGpY4fP97Xvn37ildeeWVdpPdPnTp1w+jRozs99thjLesyg6wPr7zyynfXXXdd6qRJk1pVV1dbl1xyyY7+/fuX3XzzzT8PGzasS1paWo/6PseAAQNKL7/88u2nnnpqdxAzyDPPPLNs1apVEctaqqurrcsvv7xjcXGx37Zt69prr/2padOm9bMxHjwcB/DaW3qogcv/QEtE/4MkbCCkww1I0FOGBDrbkCSuvfq/AmdmUysgonECKt0mT0O3/YLwJN+iNgmAev9kYCIyU/MQ8Md505bOUi08H0XIBwtJSP1IMLgDcaRPQxKf05CkJ4bIUnmz7dgeYNS8aUuPuvnf8QJVInE+QjA8rRb/BxlDu3AMRH3I+QkS3kJVowohJ/QPboV6jEVIgZCxLEFtQ5dFbEOMAjsCv8Gpf96EyJ7dJBgRntclbQYZF79JH5Fx0owLVWqhfVF6AwsUKaHP9wUIgfMU4qfRETkvbmIpBExFiIvpSLL0EHLPaaOW7VH7WnCsKxfqIA3M18/bE7T/vrOKZjN/tl9+8Sf+sKSvLwtR4kywRmd/8PTf5rboHm/n9m9o/SIIRdurSGgRQ1O/ZS1BFB5PWKOzt+7L/jzsO3Lz8/6BkJT/nDRh4jXGcvfvYT6ihBuFtL5NRkjuPYjSZyjye2Z2VYJ9I1bNZKUCIcc7urajyY0GhKshzLJD9//afLIAuV7/OGnCRM/o+BjG/ra3PJJo06ZNr4ULF640O1F48ODBQ33tLT2iwUNERCAdJiPJYWMkECpGZi6DyKykrsdORozl/oeUVDRWf+6ACOO5rlfVMzwapreDho1IrP+KKBg6Ak9r/wSjdWcyMgPbHwkES5AZ8DPU50gxtrc3eVo1EmR+ASzwDCIPDIUFgX8DF+F0KoHaAbKZ4OsAXXt9hIAVSEeUFTiz6/9DTNjuQMbcesSbYyWqpStSBpCFKFzK1TpdkfEbi5MgVCPkmCbFdPtHn7GsHGn5+DgnqKLhQGEQEVptoK/FMxF/DQshiNoCt9yet/w19/uOdVIhEuyZ887DIA0ivJ64o8q+ZMMe+6aWcWSu3MPjZzW2BgdtuvotNkT5rE07Ku1Wu4N02R2kIDPR6g8sQ0p+eqjN3I/cgzYi9+MeiBrtNk1AeNh/RFI0KJIhD/g98DFyb+mKKBruQVpLpiAk6ecIgdoeh/jU9yuo/bsWCW4DWtO/qBrHu8bnWn9fFBQ2cn9sjficPOipGo5deESDBw8ejjd4RIOHg4IreU9Dkv/rcSTRlWpZsfo/DvgKMcpKItzbQTvZV+KUJZgkhJlobkZIDPdrIMFcOUIijEMC7iwMIiA7J7Mj8CekHVhrpIPBXKR7RkvqVzKYWIUE+TcBEzxlw/6jsCDQEfH50MF4JHLHDK7B6UbgHhe6ZtlGEq/+iEqiHTLrtwEJ/KuRGcg1CMFRgYy5KMQtPhZJfkPAYkTh8j0OAbFRHe8ete73CMnxGnCjRzAcGI53UsHEomfnZjSJ4m/JUcxqGG1tAOZYo7NLPn5qbsdEP39aUGQvvqaVdV5RlZ3WJJpVcX7rPMAXsgn5LOd+ZlkWtm3b5UG2xfjY5vdZbXCMUlchqrKfkRKW3TideGxEKXQrMtM+3SMd9g25+XktqKNDQ25+3kVI+U4DnPvDb5GyieZICV4mcj/RJPlP6u1N1HNtSqvPsybOTXLVRCRllcZqhHQyiXf9W1Wf6kqvV6UeNyEeOU96ZMOxiWOZaPDgwYOHSKiPaPA8GjyEQZUfhHVZUIn7LGOdRKSeupFapI2DkhFDxiAy42PKO/fg1GxH4bTBg3AfBrPHeHPjf70dvW4MEnTFIh0zHkECwfuzczLLkRnVXogk+3UkGAdJLFsgnSia47Txg/CaWXPmKFX9P4HjqC78cMLdTcJYNhq4DPFl+BiV/CAKlFSclpOROn5A7baS5qNpkrYdSbaykWA/ETlXDRCvgD2I6uAUhCyYj0j6tZ9II+Rc9gBmqO3rQP5TpOziNKTk5jnExDIZGWf91Wf3CKcDgO54cbSPY3/hLlewZ85LbBPLP5tEc1pViLO2VdlXN7vq7BKAFtHc1zWBy9rHcFmin+hEvwWivsKyLHzYtZJMy7J88VE0QXxygjhKmq5IOUkD5N7Z2HjNQspVUpG2pN3tmfPeQa6xOV5ZhcAsgzAS7DFIm1wQg1k3fkaUcI0REnIWQqIn4pRoRSHnYAOicEhECE2tmDJLAf2Et3J1t3V1K/70MhCCo1htJ5lwQtathHB7DoH8XurtXYMowrwSCg8ePHjwcFjhEQ0nKczSCFcpQFjwVRfxkJ2T+TDwMM6sjVYqJCJJoGm0GIsEOpU4Mvi6gqPdSCDlM7atEaluPhYJ4K9Hkts4dfwTXB+5N9J9IArppnE7qhuCOpbtyEyU2xfCUsfeZ960pbdxEsAgEZYgRE07pAThlvQRGQG1Wo0/RmFBYI563g/pDtAAh+AZjpgnahNAH07QC+EKkkhdSEyEcGb0dLu3eBwCYgUyfvoDa5GWljcjJTMfqGNqjhAh6Qjh9KR63wRkpq8dQlA9jkNAzDFacxapZR7hdIJBEQnaoX+OejwfUcdUIUaVtyNj5QMgKyWa2J8r7Y1gNd1axQVNZ86bC5ydlkB7y8JuHG35bXW/skOEfL46Z57dpJq7+0pShHU0opDZ9SLEn2Ywjpmtl0wKavwwcvPzdPnDU+q16VBDRujz/z/EW+Ya5PcqCkfdpM+NLmXQJV3aMLgKUUN1xil50MbHFTjlWOZvW10qB40o5L66GCFAzd/PSOSs+Zpt/KUgJFRybn7eeXjmkB48ePDg4TDCIxpOXtQEXoTPLr6KzMS8qp7XNevzLDLrey+S6JcggVlbJODaiiRyGjZSTqFb5JXjkBA6OKpAgjSTSDDJBR0saeVDyHgeA4wHXkTKHHSS/EecpOGP6nGOIkuuRUyy+iMJal0tkHzIjOIJCdWqcQyi+rgQIV3+BHyHkAQJSEI/GUliNHwIkRCHjI1WyDmsQL7PLcA7SLLW2HifWQ5RV+1yJDmwOTvYAGc8VCCzg7HANGTmrwUSVN+IJA/nofw8EInzwwiR8KTadjkQV+0L/urnlF2LqqOC/zt3wDlhcmql3PAStxMXWcBDQdv276xgd+MYMvw+KxrAtm3Kgvwc7WNRtM/6jT1z3jXAh37Luqeomk2No3kqowF9kA4cN0f5rIbBkL3BB00skP5zFlEoY1zVf16P/XIclZcPIBiyq32yPpZlAfhs2w6FQuDz4VPLTPgQlY6NjO94YKA9c542QD1pFA5u9YIqkTgDeAHpYpKM3OcSgX+o91yKlNadg5yj5xCVwhZE1dQJIRerEb+RrcjvhSbF6VpqMAAAIABJREFUTc+fGKTNZJRaH7VNbXyrVV363EBtNUOkEr4ohPTSr0cyS44EU6W3Xh1fT+R38g6kFMyDBw8ePHg45PCIhpMXC4hcCtAVCUK6IjXp09Xy6eZKSgXxYnZO5mdIUvoR0kJyEBJkxSDBzRdIsAYyU7wMCb5+wqnX1wFVNM5MtTbe07NHmlzQgZW7BrabWj4Oact5NiLXv0ORColIcLcEyMrOyVwwb9rSrdk5mXMRQqKlaz9uxcV8TiCo2Xk9e9cPCThvRr7/CiSRb4V8F9uR89m6sCDwN+Abtc7nCNGwFknuOyKzqi8inhi/RcioZIRA0uZl2nRRKxvqIhXcsIy/IiRQ10H7MiRpeFP9mUamDyFJQUOEBBurPusCw2fhg8KCQOKPrX7q8XPjXZd32Njq+cKCwLj0ERkR290dCUQqT/FwWLEEKCitYlSTWE4FIRg04v20sOE827YtAMuyhgNrujWwNiLlOj8g10UMUO33WQ39MpMd5YcyrJpSna5I8toMJzENl8zb+PQdyDgGn6/2VeEu8/Ih97IQ0iFoLHLt/YQoHI67kpUDgCbR78/NzytHyqtuR1QHpyDfhY2oQKYjpPrvcUxgo4E/qG3FqHXLkN8YTTg3Qc61Tvijce5n+nzuVK81RGIt7WG0EyGFNLmgDYndJQ+RSFhd6qeVEe4uE3VBv9YaUWs1QO716fW8x4MHDx48eDgoeETDSQpFFEQKOsMICFUuEal+VW/ne+Bq/Tw7J3MOEtAmY3SEyM7JvAhJ+N5F1A+dEKIhUp2pGWRtw5ktcrcJ08aTGMsaIMF1MZIo91eeDf2AkcC/EEO1PyKz00+o7TRDEuNYlLTU9VFPqes7OJ5gJK+nAn9GEv6NyPeqiQV3aUMTRDLcDTEDrVTPJ6r3v4gEzj3VumOQQLiRel6p1tMKBjMot5HvWxuDumGOhWpgEZKo7UTGkB4D/VElDupzapJhiTrO4Uj7ubMQ2fQf3Ml7+oiMkl3vlfwnuTjxxoYlDTLUetdw9DAI8R+xCwsC7yLJU03LR498OOToDVyaGE0n9wshmz1AfMimyoZovwU+bD+QZlmWrsVPRUq4dBeTSmAdktB+jaiB/oyUQbRBDAXB8Q2pQR0lFpGST3M9n0FKWJZlae+cIPAlci2cEKjDc0FjAeKlMgohkbcivx27cTre2Mh9xELuDQ2Q+2AScu/X9yftlRGP3PtASAc/jk8DCIkejXOf08vjcM7ReiSxj1Te4CZbI51/k2SPQ+6bfmq3uqwLQeS+fC2iUszDNYHgwQNAbm5uy9dff72Jz+ezfT4fU6dOXT9o0KA9R/o47rrrrpYPPvjgFoBVq1bFDBs2rOvq1asDe3ufxosvvtioR48e5X379i0HuOWWW1oPHDiw+OKLLy4+XMfswYOHcHhEg4cw1ENA7PP7FdmQBbxp+D/MQZL7fkjArY0eTdWCWw4ahcy66OhZB84aMUiwFa22oVsPDkQkqjaSPJyPY0TZHQk0k9XxbgXylOKhCJnZ2k3tmuihB/B1HDNQXR/+hHy2EQgBoBP+rsaqOmg1zwuEmzdqI86RCJnwIHJuZiPJf1eE6AkCPyLj6Vz1WqS65AQkAHafX61q0UlYDEKQRCHnb5c6Rm3I1qmwIHAf4tLfEZFH363W64SQSu0Q0qiWmWNhQSCxEYl/Q2b91gL3cQAwSlGmp4/IONgOAK2QpGIcUpY0EBmblYUFgVHpIzK8Uo5DhyXAOsuyuri7Mfl9Vrxt2z5sfJYPrMh18SGEsKwI2XbJpnIKW8dxhs+yEpAxlwwMQzxi+iEGox2RGfb+GAmjZVk1CaOLPCDCciKUUpjJ5w6E7LjNnjnvv5wYJRTnoxRrufl5s3G6IvVE7lVnIkl9EGXCiSgbLBzCWt9/2qrHZPVetz8GyO9KIXJv052UNiPlFRnqvbba307kmo3DUR3o/WjFi96/VmiZA25vhIGGVltU4NwH64PeZzlQNGnCxBf3YR8ejnE89/qXKQ8/+2mbn7aVxDRvmlh553UDN/5+eL8dB7q9jz76qMH777/faNmyZSvi4+PtzZs3R1VUVOytPethweTJk1tpouFA8O9//7tRdXV1kSYaHnvssU2H7ugODFVVVURHu7u2e/Bw4mJffsw8eNhnqIT9JqT+M0svV4RDOTLLlIQQATuR4M3s+qCl9TqA0iUSu5AgKUR4UFZNuDlXA5yZ87VI4FkOPIqQHf1x1BY1UMf3JRKsdUYCySJjFXcSfFygsCCQWFgQOA/4G9IpZCxC8tT1S6e/3yDhfhlmT/cqte7pyHm5BGkfOQSns0RTRI3SBviN2ob25cDYrn7uPh79up4lDOHUO2sSKRmps45T610B3InMZuYg42s1MsN5N0KI/BFRtUSa3c1CCIliIP8gyib+jIz/P+vvXylJ9hdzgP+HeGXMRsgaXRMeB7x0gNv1EBm9g7bduipol5oLq0NUIGOCoNMtQo9Ft1ltA6BhVYj44qB9WsiuuY9NBd5HDGuHI+VHzZGxOR1RV2kpvulBg2VZNX8mbAhFak4dCoUdkwU1nSxuAKbglEwdNfzh3tyPr7jzsuAf7s39+AA3kY58pr6I78o/kHvcbQgpl4kQLKtxflfAKdnS6pCg8RiD4x+zCek6EULud2UIqRqrnlchv2NZiP/MNoR0iEa+7wZqXf3bFEJKKOJwSHFNNJhtKiE8LnN3oTCh1/sW5/5c5VpHv1/fz3Xr6WW5+XnnKWWIh+MUz73+Zcrdj7yXunVbSYwNbN1WEnP3I++lPvf6lyl7fXMd2LhxY3RKSkp1fHy8DdCqVavqDh06VM2aNStpyJAhnfV6b775ZsNzzz23M0BCQkKfsWPHtu3SpUtGVlZWt08++SShX79+aW3btu01Y8aMZIDJkyc3GTx4cOd+/fqlpaam9rz99ttb6W0NGTKkc0ZGRvcuXbpk/PWvf20KcMMNN7SpqKjwpaen97jooos6AgSDQUaPHp3apUuXjDPPPLNrSUmJBRAIBGLPOuusrhkZGd379u2btmjRorgPP/ywwUcffdTonnvuaZuent4jEAjEDh8+vMP06dMbA8ydOzehT58+6WlpaT169erVfefOnWH50Ntvv53Ur1+/tKFDh3bq2LFjxkUXXdQxFJLLaf78+Qmnn356WkZGRvcBAwZ0Xb9+fTRAv3790ubNm5cAsHnz5qg2bdr00p990KBBXc4444xuWVlZaaFQiLFjx7bt2rVrRrdu3Xo8++yzjfe2zxtuuKFN586dM7p169bj+uuvb4sHD8cJPKLBw6FGFhJIv05t/4clapmeTWuAJPzu8gkdNMXhBGINcRJTHaT5cJQKJkJq292QoC8TkYsOUcf1b6BRdk7mpdk5mYnq7zzEafwZJJGNQgJJjYb78yUcC1Az648DDyDfhS5b2Nt1rzttmAGwPjcVyEweiJy4GZJAbVDvScA5TwlIgvY9kiDHIt+t7j6ig+C6WrJp+Iw/3cJSqxjKEFn6TiSAL0QStyjEj2FU+oiMkvQRGVq9MAYZn70jfO4FSKLyW8Tn4UAxBEcFswz5fvbJcO3Dzz5pMWfu3GmBN5Z+jySlujQkCfE7MROPBsDz6jx7OAgUFgQSv/M3ittYaW31WSTYxvcc5SMWSApBMNpn+RHFwReWZVValhU0NlNzvUT7SEhvYCVZFn4kCf0KKTFrjRiR/j9gJmJSeAdyX/ovcm/U47+upBGouSDDl1kWfn8YKaGTbAu5XhOAM+2Z8y5SXTaOCraVbhi4sbjQt610w8AD3MSLwBvI782liEpNl9Xp760l4olhMjTa40Dfc75DEvUo5LvRxEMzHB8FTWp2RIiKlWodTT5rEsFM7nRL5x0459OHQzJUqefaOFe/HkLGi/6Ns433R+KVfAjpou+HlRFer1T7iVbHeAFSEpaPMRng4fjDw89+2qaisjrs97yistr38LOfHrB59cUXX7x706ZNMR06dOh55ZVXtn/nnXcSAYYNG1a8du3auE2bNkUBPPfcc03GjBmzDaCsrMw3ePDg3WvWrAk0aNAgeM8997SZP3/+t//617/W5Ofn1xzL0qVLG8yaNWtNIBAIzJo1K0Un5TNmzFgXCARWLl68eMXTTz/dYsuWLf6pU6dujI2NDRUWFq6YNWvW9wAbNmyIGz9+/E9r1qwJJCcnB1944YXGANdee23q1KlTNwQCgZUPP/zwj+PGjWt/7rnn7hkyZMiu++6778fCwsIVGRkZFfo4ysvLrSuuuKLzY489tmHVqlUr5s6duyoxMbEWqbdy5cr4KVOm/LBmzZrAhg0bYj/88MPEiooKa/z48e3feuuttYFAYOXVV1+97Y477tjr9x0IBBLeeuuttV999dWqF154odGyZcviV65cGfj444+/nThxYltNVkTa55YtW/yzZ89uvHr16sC333674i9/+cvmve3Pg4djBV7pxDGOetpQHhIMzjn9xioqHo0m9raPp3015RBsUs8eRzre3kjg9zSSfKYhAdzvcUgFt0SvitrJsTkTXl8tq/l/mtr2KUhg1lDt93pkVl534HgQaWM3CgnIdML9XL2f+tjEWKSl52fIZ+i+j+8rRWrLU3HICdT/FYgEWM8CdkGCch3kaiM0HfguR3wRdBBtkkd1nUM9DnSgbba/NGcIfUgg31E9L0GIhhdwko1XlaJjCUIyXIok/QsgouHioShFGAP8HTEH1F06fllYEEisz1ehsCCQGNXU/5ftjYt+l1AW52tUkpiKSMRBSigChCcbPkRJ8hV1+Kh4hpL7jCzb5t6UaEqxCVpWrd/GkN+ydgDNLBnbPXHuDdhSx2AhJMRmn2U1ASr9kmxq9dZfgNEIwTAb6XjyHXI/6omcpzXIvepHRPEQi6PkAuPasKSQwmxfWN89URMODRFl06+RLj1HxRyyaUL7T4GB6vFAcDpyf9ZkiYUQAxCuPjO9YMzvJWg8b4lzv9GeB1HGe2Jw7l87ELLIh3NuNOkQ7dqOJl718WmlmPZWACEu4nAMcjXB7S4njPTbqGESHtrQ0vysMeqvCrkntUV+d5cjig8Pxyl+2lYSsz/L9wXJycmh5cuXr3jvvfeSPv7446Srr76688SJE38cP3789ksvvXT7s88+m3LjjTdu/+abbxLfeOON7wGio6PtESNG7AbIyMgoi42NDcXGxtr9+vUr27hxY82xDBgwYHfLli2DABdccMHOTz/9NDE7O7t00qRJLd55551GAFu2bIkOBAJxLVu2rOUJ0aZNm4qsrKwygD59+pSuW7cutqioyLdo0aLEkSNH1qgtKisr6y31WLp0aVzz5s2rzj777FKAlJSUiMqhXr167encuXOV+lyla9eujUlJSalevXp1/KBBg7oBhEIhmjVr5iaFa+Gss87a3aJFiyDA/Pnzky699NIdUVFRtGvXrvoXv/hFyWeffZaQnJwcirTPQYMGlcTGxoZGjRrVYdiwYbtGjRpVVP/ePHg4duARDcc+6mpDeUhQRcXfgGj1eNBEg9vjwSRK1F++ekm3mGyBlCh0RyS92hRQj02dWJmBYV1GaRqWaz3tNxBCZhCrEHKhESK9/Rppw7haHetsHK8HnUxs37dv4JjCZuSzd0bIkxCOmiCJ2oGr/g4TEJKhgWu5hQSoOmiuQozWPkbKJ7Sz+nzku/0KSWzOwgl+3aUvtrE9HbTXdZ51IG6ioTreUsRs7WK1XjdklrgXcu0sxTnX042k+5BfX+kjMuYXFgT6Am8bn68KGd81REYEL4esZjsanRJTHbU4qTQ+k3BCLYSUoehkRc+0bgTaFBYErkKuozkuQuGw3j9OFDQJli5Jsst2xPg428KygJBt2+b424WQBXuQRNNtFgvUnOxWyPnRZV1dEWl/GUKEDULutQ8hyXJ/REHzM+Lf0AgZrwOQMd0cR42gZ9y1SgHCibn61Eq6DC0eKRM7aknm43+eNHjva9WLTIRkML8Hc/Y/Enlp3se090saDpGjDSNTcEop9Hdahdx79DXoQ+45mohwey2UI99vD8J/w75Hxocfp3OEboOpk50ynN8d9+erL4HSx7RBHacuKdOIQsZulXo8Tx1jnWbPHo5tNG+aWLk1AqnQvGmiW9myX4iKimLYsGHFw4YNK87MzCx78cUXm4wfP377uHHjtl9wwQVd4uLi7AsvvHCn9hqIioqydUscn89HbGysDeD3+wkGg468ylX+ZVkWb7/9dtLcuXOTFi5cWJiUlBTq169fWllZWcT7WExMTE3s4Pf77bKyMl8wGCQpKam6sLBwxcF85kjQn0N/lurqasu2batLly5lixcvLnSvHxUVZQeDInIrLS0N+7AJCQn1lUHVu8/o6GgWL168ctasWQ0LCgoaP/XUU82/+OKLbw/4g3nwcAThlU4c+6irDeWhwkIkgFl4mLavE51B6u8U5PNoyWZvpPb8eaTGdhMiZS3GCeIgPMGM9CNqUfesj9kiMwoJtJvi1Fnn+/Bf6yPqKhw56Rwb4mywbNnm0v34zEcdyvzxBuTz6uQIZParIeHBqyn/3qOWmT3eze/VJAHiEEJiKE45gw9J6Dsi6oFzcLwdygjvQGGSDjoJct+TzB9nP7WhiY8ooIP6/0eEZBiJjLf/qMev1eMYw9vgcF1fg5CZw/8iipIfwfHMUOfnZcTP4WZ9LFEh//812ZX8rj/kr0YSTw39feuZ1hBiItgaUa48pf5uc/k26M+3pLAgcJH6a+H6/9LCgsDEk7kEo6ld1jsWUq3I49BGks84nGQfnPEbAuyQTdAKJ4eikGutGkn8diP1/M2RjhMzkWukETI2b0LKY54FTkPIwOVI0voRkkTqa7GU2p41+/J7rmf+hwH3HM3yiQNBbn5eYm5+3kVIsu6+35skZSSYiXqCa139W5OEELRustPGKXHQhEIczu+T3rdWXcUiJIP+3apUy7sgBIn2g4hGVBK71Xt3I/fJnTjnV//e6Q4YZsmZ+/MlIORJPEIi6JnWCoQUjlL7XoP4yHhdJ45j3HndwI2xMVFh4yA2Jip053UDNx7oNpcsWRK7bNmyGlXQokWL4tu2bVsJ0KFDh6oWLVpUPfLII62uv/76bfu77c8++6zh1q1b/SUlJdbs2bMbnX322SW7du3yJycnB5OSkkKLFi2KW7JkSY3xdFRUlL03I8qUlJRQ27ZtK5977rnGIAqDzz//PB4gMTExuHv37lr3hMzMzPKffvopeu7cuQkAO3fu9FVV7VWUUPPeHTt2RH300UcNACoqKqyFCxfGAbRr167iyy+/bAAwY8aMxnVtIzs7u7igoCClurqaTZs2RX355ZeJZ511Vp1dPYqKinw7duzwjxo1qmjatGk/FBYWRioZ9uDhmISnaDjK2FtpxMF2gdiH/T6HBD9PHep9KOhEJw6ZwbOQ4GaB6/UFiPldFkI8RAqi9A/O3ix7dXAWV8drOiCMA37vJ2ZN24bdmyVENSxcuWP+F/rYgsQX+inrEyRhcRSls/fx8x5VqGTxZkQanYEkOemEz7iZhnOlSFCqkYgzewe1Z0m16kAvr0CC/u2IH0a8WvYFMmPmQ0ijnTjKlbVI4rUTpy99pH2BQ0gQ4TX9WXRgvxuZqV2OlH78HvgdcBewGDnfmxEVwQpglpr9P2TXl1Gq0AIhQd5FPvdSZHwPQq6D/yEz1jXkiT6WwoLAEqQMpALx19Azpn5kXH+KdN/QXVfWI8lFE8QEczEwS42Fscgs+tkIkReDEHm91G6/R8aJBYwqLAgMOwgTzOMZCxCvhFzbts9AjTVjBs6HtOPVs80gpFySXs9vhZX6mPeZEJIAnoYoWoap5anIOJyOjN8LkBKKx4C5CDm1GlE8vI4QU3ciRF4C4WShG3WpG/TyErWdWt1XjnFkIca+qdRNKuzvclMZEofThQKc86mvQXfpkltloElU89zbyLgxSwCrcciJJkjb3g4IEaVJkCDhXXf24BBXupzDJGr1cZQh94Uk5DwnI/dg7WHzIfDopAkTD7YjjoejDN1d4lB2ndi9e7d//Pjx7Xfv3u33+/12hw4dKp5//vn1+vXRo0dvnzJlStSpp55avr/bzszM3HPRRRd13rJlS8yIESO2Z2dnl5aVlZU988wzzTp16pTRqVOn8t69e9ck3FdcccXP3bt379GzZ8/Shx9+uE7y5JVXXvnuuuuuS500aVKr6upq65JLLtnRv3//siuuuGLHuHHjOkybNq1FQUHBWr1+XFycPWPGjLXjx49vX15e7ouLiwvNmzfv2+Tk5L2qDuLi4uyZM2euHT9+fPvi4mJ/MBi0xo0bt/W0004rv+uuu7aOGjWq0z//+c9m55577q66tnHVVVftWrBgQWL37t0zLMuy//znP//Yvn376qVLI89n7dq1yz9s2LAumnTJz8//YW/H6cHDsQLL3cbLw5GFMiHMBybMm7Y0YsnB4fBmMPZ7PxKUHNB+9vU41XqD1NM5da2bnZPZEfFw+AXhBoymIaFZ7+o3npsBX10yU1PCbwaee5BgLxlJfh+34ZKQ1XRr0Ndq3BdTZhzzyZdKch9HZks1UbADMRfrilM+YsrxLSRB11Jwd3mCbsemSyVKkGB8N5JM70EC4SK1PIjMvqYj8mYtH1+HqEiikED4R4R00GqESITG3mZozXXWIR4GZyGkR7X6zHuQxL4Q6RtfgLT3/OPBtoY0/Q/UokFI28LLkMSxNfJZfQhh8AUym3w/jkJmDfBo+oiMrW4/hcKCwEWI6V0SQspoYuE2RDFyPXJeHgRuRZKvIsSs7ie1/WHIuZoH/BKnNCnStWEjJMZj1C7BOGGReeenNd/7kr6+xbZt19T6uqS+eoZZXx9mC1h39wlzbG5CfABASJ7vq0N2SnE1DRpF09myrJVIkpmIjM9qxLD0PoSIeAj4ozU6e5Y9c94iRJVTjZBOurZfq120JH9vpq8VSHvIB4+nVpe5+XktEIVQF9dLe7vf7w3mem7PC3NZFXLv6kjdRIM2nCxGvudk5NrdhpAIMThjR48l7UWkiSy973KkQ0kP4BOEMNyGlIaZJKx5Tdtq3RRkrJhmktXAvZMmTPTKJY5RLFmyZF3v3r33Wy1wpPDb3/62fZ8+fUpvvfXW/TrGyZMnN1m4cGGDF154YcPhOjYPHjwcHSxZsqRp7969O0R6zVM0HCHUk5AvQepzlxjrrAb+pP6/HaOu+xASEDVKgoPczj7VgKt97Eti9zNikvYakrCdgQTRlTgzS+U4gbUbZlAVKfCsq/6/ATLDrHGzBVf47W0L/jtlzjEfiKsk9SZk9rMEIWm+RL7PoYSXPpizrTZiEKbLKcykP4jMlichpMIGJOE9DUctEock9v8AViEdF/qr7axFgvIzkKQ7BifwBscroyWRyZ+6oNfbhQTT1YgcXdeyV+EkBQFE3aGxCvgnoig4WOix/yTSWrOL2n8KQjToGdIfkc89HSFEypHZ658RUq1bYUHgG8RUcwNS8vIBooC4CflOqxAlho3Men8N/B9CbNwGtMORR5+pjk/Pmiar/WmzurqkqDZybp9CWgQeCmPM4wHmPeyGkM1snxWxTMccm7rFYYIi68PGbQgq/Zalv+8GOIqfdKCbZROM8kF5iF3xfnoj5057npwNvIqMlz2IaaMms8Yj19pziD/Hn5GkV495PQOufQU0Ees+57EoXxx75rw5xxHZ0Bs5dvP+HgK24JSHuRVQkchlrdqKRE6bnheacNBdIKIRxYPeji6J0PdN01uoAeJf0xdRwzTCOSf6vIDju6F/3/6L3DdOMfaXguOpkIKMh1/gqB00EbILITNWI2qlnmq7qxDyYRdeuYSHA0RGRkb3+Pj40NNPP+3NqHvw4GGf4BENRw7nIzNIk7JzMvshs76fAfcggUgikiz0QmbWL0X1bnehJijOzslcwAGSDoewJOOAa9zrIE2ykK4VE+ZNWzo4OyfzPkQuPAuZpf0tElS2QoLzeJyZ+kokqWuN4+QdTe2AUncoSCI8AA0hgX4scI2pMDkOMAi4DglmGyFJZnvEnV0HtZU4PgrlOEoF7dYeyYyxC04Q3AVHGlyOfE9RSAvHJxCZ/plIEv894pGQYez/e+BzxDhym9peS2rPJJrHEIl80GSSqdCIV8em6+m1MqMz8BaiDNCKgt+hSicOsiuDHvtXIOU+n6rP9UscqbWW3F+nPmsrHOO5f6ltfI2TJLUH4pSHw1WIEuNGxMNEJyLjkLH/ItK2NQ4nudKzqRB53NdnKKeTYj91mB2eoDDvYef7fZaelQbnO9NjTX/P1YS32a1BCGxsorHCOj2A0VbR58OnzBGa4JRxgRARfwbM5N+8D8UjZN838h67iVpeDtZPCOlQiFzrjZBrwca515kYhpCAxxOptAApf2pHuDFmQ5wk3pzpN2F+fneCHklNZd6XtPeChZBG+r5ldrhw37OCiDquDQ5JYRK8kY7rR0TV8he1fgVCDoTU/5lIuVsJzv1cE4p+JI6IQrqbvKNemw9c5ZVKeDhYBAKBlQf63vHjx2/n+DTV9uDBw0HAIxoOI1QifT4SPDZDgpN7EQn5COSmq4OQbkgSsQoJXrYgdbpnZudk9kBm9xchgeYLwIXAJCRIuTM7J3M1QlrcN2/a0u+N/R+28gs4cMJCdZt4CJn9vgMn0HUTF08gQdV0ZAbnKkQSr1vGPYEE1iDf868Rt//+iCpiFRJQn4XMsHfHCRz1TNg4pJ3lj8Cj86YtPR4DsmQc1YCFMxMG8jlLkfHWTi3z4xiFNSe864OZ6JfjOKNjPMYhge9qZOb9RaATEvAPQr73d5DvfCRCoPkQc8h44zhMRFIzuIkgHfDbhCfDtjrWH9Tn+QLHePIUoGv6iIwPVDmCiQPuymB4KqxW+/kGKc+IlKQ3w/kuv0Ok8qVq/zcjYzVJHffdyJgejZyzXyJjXY/bBupPK0e0DDsBR1LuTmYJ/JJqAAAgAElEQVT0Mo26CJwQMoauKSwIVACzT/QSiqUPD6y5h9kz56UT7gHjNgzchoxfXXef5NpcyA5R6ffV8oexEOLYApIsy3Ins/rxbOScJtsz532A3PPAIR4WIKU3cfJnx6gmClsh6jKk3OJ2dZyPIiUYug2jGzE47SGPC0yaMLEkNz+vqXqqy7XikBKx9hhkjlqnLoWUXsckGdwkp4apCDHL9cxtudfV5FRPwq9FbeiojR9DCBlUhIy75oiCKcHYTnv13hS1LB7Hf8dG7iftcMiq3Wo/CxASeLpHMnjw4MGDh6MBj2g4DFAJ/tUIqdAMCSb0THATHEm1TrbM5Kk7Tu16W8JnU7QMMhsJSHyI8dtGZDa1HTA8OyezEmnRWIwQFP/IzsnMP1xkQ13YC9ExBpdqw+XjoJ/3Bp5UrTDnIEnZUEQG+hOG34Na/wukHKW33m92Tuab6jiWIIF7HKIiuRy4fd60pbM4fmb0akHNyg9FAtUQjkmdlv/q4LQpTo/3aMJn80zZbwhJiqNx+sDr5WZpShCZob9L7W8pMjv/S2SM9gCeQZLfzxFZfnOE4NAGhqY/RF011pFqzvW6ehYT5Lw2Qergz0bOdxLi7q+JqzmES9HNrgznsY/KBqM15X+QZO59ZEw1quMtMTgJyrvI2P0Oh+QYgpRRvImQM80RknI7Mk634CQv4Cg3zKTHMl7Xz03Pjb0lYD6cMZOFEDQ3FxYEtmJ8LwepAjnW8TRyz0wnsm+INuqLRa6pDZZlmVJ6X5S/RmGiry0LuZ7iEXIp0gw6OEnuEEQZ9B5CkO5CSmg0EdYDGXP5WPYdEPw9UcGxVEWlIGaCndXrQ3C6lNSFFcg1cTzhd0j5yDqc3wutONHfYX3KHYh8HdS1vkl0+pEyMg29H30/1I8xxutupUMxQlK1xOlgogkfP6IyWYJcf9uR3/Iq5D7aAfm974JDJLdFxmM5Mj6T1H6bTJowMa+e78CDBw8ePHg4rPCIhsODLGS2XidpPsJ7coMjXdfnIJKhYRQSYOggZwNCXDQz1vMjgWJz9VwnbVfhBEa3AAnZOZlfImRFFfDEEZi5r2+2eDry+QtxAt1BSL17GU7Lv5r3K0LhRfVXCy51xQd1LJ8FkJ2TORupnz1cbUOPCFTSdxuSVIAje9eJjq7ZtpFE3Ow8Ac54NMedluiDY1Jmmm/uwCnBAJHmtkdIhxYI6dUIKQ0YhATUQ3A8AiqQ774NkkDrlqORJMualHMnZxrmbGE0cm00VceZBbyEjLWswoLAAneXCUOVcBHKdI99I53GIgTLuYi3hzZcNJM683qPQUwBE4BF6SMyXlRkRSckqfgLkli0Q8w8f0Kug2bqs7TBaceqk9aOOMRApO9jbyUoZlmACT0WkpDrsA3wUmFB4L84nWHygfsLCwJxSFL+dPqIjBNh1nQPci6uR8qOtEJIQ3f+mIOc8x3IWHD7ILi/a23wF0ntot9rdsqJQ8jDKISYWqBaUd6EKIT+BcyxRp1TAkwBUK8/gpAMA5Br0K8+ky4tcs/G7zyO/Bk0fkSIylNwyidaqdf0OF+H/CYm4PwOagI1Umu4SJ1t9H1yE6IWM1VVpmeDLoWKwekuoScO9H1NE7k+hFRYjRBIzXFaarZX6zXHuTbbIZMW3yBlEwkIkVSJnFd9r9b3yWrEl2YX0qnEgwcPHjx4OGrwiIbDgwVIMNTNWOaWriYgMxsNCJesuxMC08yrJVJGoF3GY3Ha1LnhM94Xg9SIX4NjpBUN5B7IhzNhqBbCVATq5Tr9GxTJEWm2pQyYYbzngPwf9obD1Tb0KCALCUQbE54wussMzORdB7Hm9e82RduDBKubkYQLnPrnFOQ8hZCg+Rwk6fIj47IfEogPQpIljdVIl4Ufkdn/OOQa+QkJyDWxplHXbKPp9u9Oks33lCHqgPlq27dw6JQrhYg8fTWikokFBuKUR7gVGD4kYS8DYhWxMRAhBLch5EwcotRppLb3ApIwZiOJBWq7Zl243rYmLusqiYhEOOiEyZyVNbfpQ5I5C1Fv/BpHDaJb1v4VIUMGFBYE/s7xX2qRhSTza5DzqpNUcLxAYpHyrWaIUkcbAu5tBj2SKSHG+/T1WI2jalmDlIcNQq6rSxHy7ElrdHaJIhf0rP4chPS5FPFJSUCIvEeR87UBUfqYZF7qXr+RYw/3IJ85iKPYMMe3jZCZ65H7olYgWDi/f/o9euxXEX5dmcRDG8LNdPV7zXOoy1M0MVuqlmmCw/T8iELUXfo+5sNRN+pja4rcgxOR6+wMY//ayHKhWtYfuf5NH4n+iDLnngjfnwcPHjx48HBE4BENhw9FrufuWVpw6nt18KBnR8AJRitwzN6ikQAE1zq4lunAJogEl9FIwGXW447Ozsmcpv0cDgJ6dvNNxOSvRr1wAMm8Lo0wyYoTgQw4LFBqhjikXKQdMoa0bNcshYDwmuRIjvru9ZIQEqwN4RJwbYJXioynGPW3EDE3HY0QCRmIS3ocTsDcFimt+A0yjnWArx3bKwkP/vcH5rWgA/4iJJHvggTi9UGXVNSUUKjPPwaYHmG2frba5tU4XVHiCG+36oaNJB9341yPFpIQtEHUFMlIuUkpQt48gZyL7QihU5cMPgY5NzGu5e77g1uy70eIjkaEdxypwvkeFyOqkN3qM6L8LhIRf5XrEIl/OkJOHc/XrCZRWiBJuY2MW3N8WQjRYCFE0C6EaO2DYz4b6d7slutXG//rUqUQcn21RUiNZOBv6nkrZJx9D2DPnKevr4fUsUxXr21DTG1bIW1Vr0VmwTchxAM4svvjkWi4D6dN7tWEj3n9fXZFrmkzgTdLiyzXck0yaAVhOXL9m+vieh5y/a8VK5rId6vGwFGX+Aj3AomkhklC7gPaALSJ8bpWkQH8G1FcdEU62XREvp+Rufl5/5g0YeLB/sZ7OImwZcsW/8CBA9MAtm3bFu3z+eyUlJRqgMWLF6+Mi4ur9RvUokWLzEAgEGjatGnQ/dqRQt++fdOeeOKJDVlZWWX7sv7y5ctjR4wY0bmwsHDFwe57xIgRHSZMmLC5d+/eFXWt8/zzzzfKzMws79Onz95iEQ8eTih4RMMhhprhvwkJ7CKhrjr0bUhw6w5GdZs8nRREkr/qbZizLBXI+W1F+Iylfl9b4P3snMyHcHkd1PO5Ivkt1NS4I/LOA1YfnEAqgyOFLEQVsh3xChiImDHuQep4O+GUK4BDYrkTefdz91gK4rTf0xLdNUhgux5JkqMRufILiPS/Aklquqv3b0ZmgM9BxsgpSHKr6421qWEUkrg1No4XwoP1SLPzenyXIAm8D2kR+KP6jqZTTy26UUJxHk65zqkIKQDwoHt9VTYwGEc1pK/PuuritYIgFYe82YFcOzrxG4IkGIuRWenRiGqplL3PmpcRWdVQ1/k1CU+fOvYy5PxqwicBSaA/Q8bVr4EehQWB6YiCaTZiWjsWUXkc16VIqozgA3vmvBbI9TQEx91foxIZ75uQUoUkJMF/EBnj4JS3RPreNUwFQ5zxmlbEaEXa6cg1EauW90SuuVGIL8r9SHIpvg1SSjEaOWeo14oRJcQGJEGdqrb17b5+N8cKVOJ8dW5+XiLSCjIHUXtogsdCrvsNiBoIwtWC5vXgJt50pxZdrhVJDRRyLXdv3/RMMRHCab8br5ZFIiP0ey0cU8hGruOJQe75pch5vBgZRwkIQeJD7s/3IEpGDycqCl9NYcm0NpRtiyG+aSW9czaSPmrHgW6uZcuWQZ1833bbba0TExODeXl5J0JZ3GFDQUHBur2t88YbbzT2+Xw794doqKqqIjo6eu8rHgAO57Y9eDARqebZw8EhCwn4CglPlNyJR8i13CQZNCqQoHYV0g4zhMwqmsGJ2S0AY7mu9dWmZeuRYLMIx6G/CyJ9/jtiPBcRqkPE40ggnWW+psiBJcjM75IjbTh5kmMBkswPRBLsv+PMrnYi3IsBHPLAHQDrMaTXqcbxA9BBuXao1z4PbZDZu+3qPWcgs6eLkeD2Y7XNueoxDgmM5yNGdy0QgqsrjgonGgnCv8YJqH/Gae9WVxcFc/xraXQUQmq8iSgo/m8fJf1muc90JImbrl8sLAgkFhYEzlOz+WfgmG5qokMnj+4ERj/qEog96vnnwAVIudAc4E+IeeVWpG3mL9Q2k3GSGf1d2MZzH04bRbcPh/t862RJl01oAiMKOZ/upCwW+L06nsXI/W0sora4DdiTPiIjL31ExmvHedmEid5ITfynSMK62ngtFvludH39hUgy/xnOjLnuTGGWArn9NDTcMnzTHLIauS4a4ngMnIeoYLYi3YsGIiaWdyNj6ElkDGWq9W9H2gLPtEZnf6BeuwH4EFH8HNPIzc9rkZufd1dufp5pwsikCRNLJk2Y+CLSfWk7TheGLcjYHonjjWJ6HbnJn3Lku9xhvBZCfi+1WXG1et0sSTPJe63GcsN9/9V+NBpmySSudc1j1SVw5Uhc8AvkmrwZISKr1J82K0U9vy/CMXk4UVD4agpfTUql7OcYsKHs5xi+mpRK4asph2N3TzzxRJNevXp1T09P73HllVe2DwZrCxgirVNVVUVSUtIpN9xwQ5u0tLQep5xySvrGjRujADZs2BA1ePDgzt26deuRlpbWY86cOQ32tp3f/e537bp06ZKRlZXVdcuWLTUKzZdeeimlV69e3Tt06NDzww8/bAAQCARi+/btm9a9e/ceGRkZ3fX2TZSUlFi/+c1vOnTr1q1Hjx49ur/77ruJAI8++mjToUOHdhowYEDX1NTUnjfeeGObSN9L37590xYsWBBf1+d87733Ej/99NPku+66q116enqPVatWxSxbtix2wIABXTMyMrqfdtppaUuXLo0F+PWvf93xiiuuaN+rV6/uN998c9vx48e3vvTSS1NPP/30tLZt2/Z64IEHNJld73ek13nmmWcajxo1KjXStjdv3hw1aNCgLt26devRp0+f9K+++ioOoK597ty505ednd01LS2tR9euXTOmT5/eeL8HkYeTDh7RcOixAJlV+tG1PFKwr5froCKE4y4PEtDq9l26VZ+e2ahAAp9I9ff6T+/HRmSXZep9mxAFRSmSxCUhsue6MAap+11E5BnLMUiQO6aebXg4xFBJ3ThkpvJLZFa8CGdGXc/OaSWCOS5MuBMdPTOok9UqnP7X+kddO+2n44zttUhC3BdJhnYipAfIGGuCSM2rECJOS4NN8iOIzA5XqX03VNvVSbGbPDFnibVkWV9P6cCo9BH/n70zj4+qPNv/d2ayTDYCIewgewgJBBQERQ1uVVyqdUOqaAGXxvVV61YVfhbU6mu1r0UUxYpFqVtQoXWpVVqDxaJo2QJhEzd2CGQhCZnMzO+P+7k5z5xMCFRQ0HN9PvnM5OznOc85c+7rue7rzn93XwPg3Avzq3X53AvzN+demP+gK21iGEI+3IIoHtxBuRIJbjWG/l9v1klDyAY1cltk9rkOIRbPQUp1KjFob1PbS58dbrLRZ/01Bfc1D+EEtbXEBithpFTpTCTf/29Iakw6Qj4McxEwPwTMR55ptwPXIQZ77r6nz+DtCPnSgHP9wziBZTxzToV7m+Bcm4i1TrI1vcAstxpJmzoKpwymqjIeN8c/1zeqcI75q9b5vlGFz/hGFZ7mG1VYuu9N8r2hud+XuYh552s498ifELVDFFFTRXGuSQOx91I9QigssaZrSlELa7nWxJKb4NxnTQ0NutVkNsFnI2p96v0cj3wKIs9MJW8zEL8KJS/stLivvLSJHzgWT+1EuD72mRKu97N4atyA+Nvgk08+Cc6ePbvlZ599tqKsrGx5OBz2TZs2LWtfl6murg6ceOKJVStXrlw+ePDg6ilTpmQDXHXVVV1POeWUylWrVi1ftmzZ8oEDB9Y2t53jjjuues2aNaVDhgzZddddd3XU/UejUZYuXbri/vvv/3rixIkdAY444ojQvHnzVq1YsWL5Cy+8sO6mm25qVFr7t7/9bbvk5OToqlWrls+YMWPdFVdc0b2urs4HsGLFitQ5c+asLS0tLZ01a1brL774Yq8ygHjnOWLEiOoTTzyx4sEHH/y6rKxseZ8+feqvvPLKrk899dRXpaWlKx544IFvrrnmGk2FYvPmzYmLFi1aMXXq1G8A1q5dG5w3b96qBQsWrHjooYc6NTQ07NP1iAd727feemvHo48+unrVqlXLx48fv2Hs2LHddbl4+ywuLs7s0qXL7pUrVy5fvXp16TnnnFPZ3P48ePBSJw4wTDnFOmSk9gtkpMtOXXB7Nbhl6uvNn5YB7IbkmWqesM9a3nZEt82tojhBA2Z6G+SlWKsSrETy5DsgL1hxKzkY6IjudFcpSTWB/Bx42FrOw0GGXWIQcRd/BrmuZYiM2H4htn8Y1aW9qRQeu2+BM6LtflbYfVdfgvMRx/4gkvrQBkmRKDXz+iPB2TKcdIlF5jw097wHohTYiJNmUGCdQwQJvtXfxD4ud7rAazTTJ612XI1I0eP5MdiYjwTaN5j9rUCk6EpwJOKMrGpQY9/fOpriN+dwCjLCHDQGkXORa1iFtJmeZ7zziyJBhz2KGYmzrBtuEtL2FOiEtP02ZCT/ODP/NESB0tV8zzZt8RHyvHoMCXhfLysuLUNGXucergoHTaEAiL5UMh+5TjuR82qPI2u/GCe1JA+nXKFdTUjh9hFRuOX89nT39yDyu9AVUZWEEDXDEGQE/zYO41K9TWC66zMGD42fUA3MuWPSxAXIs6YMSel5CmnTrxGT1VOQe2opQhokIv09gjw7dRRQ1QzqT2OTALb6wL5m9vR49559bd0eObZvjg95RlbgVJeyf9t9OCWylbTQZ0+ma5sr75g0Md20j4cfImq3uT159j79W+Dtt99usWTJkrT+/fvnAdTV1fk7d+5cv6/LBIPByMiRIysBBg0aVDNv3rx0gAULFmTMmTPnc4DExESysrIijz/+eJPbCQQC0XHjxu0AGDdu3PZLLrmkh+7/oosu2gkwbNiwmnvuuSfJrOu74ooruq5YsSI1EAhEv/76a7eZMh999FH6bbfdtglg8ODBdW3btg2VlpYmAxx//PGVWVlZEYAePXrUrl27Nqlbt24h9zYUTZ2njW3btgUWL16cfsEFF+xJsQ6Hw3ueERdccMGOQMB5TIwYMaIiGAxGO3Xq1JCZmdmwYcOGhH25HvFgb/uTTz5Jv/fee9cAnH/++ZVFRUXdKisr/U3tc9CgQbX33ntv52uvvbbTz372s52nnXbarub258GDRzQcHMxHSt+tR4yaehAb+Ntw53MeaT4bzLqbkBcg9WrYhmOkp7ADPj/OCHQSUiFAX4A0134bQlwkINL0FODiwqKCx+OlPpgKEXty1C0fip+b/XVBgqVfFhYV/AMZffpVydQlh8No2eEKNeF8BMmbV38AHdmKIIGRlrqExqPY7uDG/YIcz9DOhuYO29tfj5PzH0QCa62MEkb6Wy8cs9TNSHrDkUiA9EskYHsNIequwCkRpyRbS2IVGu6RP/VC2JfnmyoUtiNqC3D5MbiQhqRk6MiB3j9K7oETNOgIqrZxgzXNby2Thhj+RZDR8w+QoOpchLAER56dhBP8aBBiQ30q4pVRVLhzzvW7ztM0rlQc1/w2CKlykjmO9xBiIQ95DnRGCKTROKOuN5YVl2paVXMEzqGMYcio8ceIQaT2LR+i3nkdSRe6GDnXHsSSN24yWWHL5ptTF9rb0lz/gNnPecjIexTINCaRLRFvkn0t13rI4qHxE2J+f5pZbqL+f8ekifcgxNCjwF04FVS6IMTYy0jqyAbkPhuJ83tp3z9uQlXvu1qcez+e+iAeMRiP4LUVL6qKySL2WaEqRn1eQPw+ZW+zD9J3Pd+jHypSsuslbSLO9AOMaDTKz3/+822PPfbYhv1dJhQKkZCQsOe3KhAIRO3A2u/3R/d1O3tDMBiMuLc/adKkdp07d65/44031tXX1/syMjKO3OtGXEhOTraPm4aGhr0pBfd6nopoNErLli0bmjKiTE9Pj1GeJicn7/nf7/dHQ6GQr6k2cqez1NXVxTwj3NtuCvH2edRRR9V9+umny2fNmpX561//uvPcuXMrHnzwwU17244HD17qxEGACdbnIyMi/4soCRYRO4rlJhi0bCUIUbAJ8U0YgONmr0FLJY1HxOzPAOJCrS7VPkSmrSWwuiAvK5uQAKILkrP7VGFRwUhDJFBYVNCusKjgvsKigocKiwouKywqOKewqCAfKEaCi3pkhCgTyRe9G/gjcDrwj8Kigq2FRQVlhUUFg/e99TzsI+YjAfK5SJ52EAlCe1jL2P3GTtXB9b2pHx6dH6IxIWEHR/qZiBjX9UVGBF9ETCMDOCUA85GgtD/SJ89B1AT3mP2otwOIt8J663+VFDfQOB0kbKa9iNxrfsQgrbl0HlWEtEMCj+ZUOTcgigtt0144EvkknBFtmxjREdIKGpe9DCA59pnW9AHAJYihrJ0GkehaD+RZYAegAZyg0065sF88bY8XXW8njftINaIy2WmOvxvSTj2QwCwTUa50M+v0RgKkLCQo6o2YDk4CnigrLj3nME2t0DSKaxAfknsQEqwWae+VCLGShPh7PEvT6gWFPsvttBtdtqnldVmt9KLrdUKIhRTEKHISogJKA4aYEpg/Ojw0fsK6h8ZPuAJpn+FmchRYjtzr/ZFnxRbkORo28zbg3Me2mkCfO2twDB1V5RLGUYspoVCJkyZhX++QtW31XKrH8WCaS6zpcyXSp14z6y0xy2xCninzEZVGtdlfBCG/fsVhbs7qoRkMKFpPICn2eRFIijCgaP2B3tUZZ5xRNXv27KyNGzcmgFSnWL16ddL+LuPGMcccU/nwww+3AWhoaKC8vNy/t+2Ew2HfjBkzWgE899xzrYcOHbpXxU5FRUWgQ4cOIb/fz5QpU1pHo25uHoYNG1b1/PPPtwb47LPPglu3bk3Mz89vsoLEf4P09PSwqgXatGkTbtOmTWjGjBktzTnx0Ucfpex9C7Foqo0CgQAtWrQIL126NDkcDjN79uyWTW1jyJAh1c8++2wWwBtvvJHRtm3bUIsWLZokItatW5eYmZkZue6668pvuummzYsWLUrdn2P28OOEp2g4eBiGkAxbkXzuI5HRr6agTtEg10Vz4PXFRNUIWt5SHwYhYuu823SmLvMNIvfVUZo65EW1HRK8qNHfxUiu8bTCooK/I3LcO3BUEt8ggcfJyAvaNTiSaX0oz0cCDTWsyQY+LCwqmIvIWbOAp4xKwsO3w0AkSK1EXpw7IC/VeQjJoDL+HTgqmHIkONwbcaDQl2a77KoNDWb9SNCVbP1/BEJGaTWGCoRQOBonYK5BAtgvEEPEkcAfEAKlrTmfZGL7NDijySD3VyucqhhzkBfsG8z8vRIHpnrE45iKKU1J/U2AfDISPGtQkUCsDLrcnHuFOf56a5kMYssZKrR9ExHjQZB0pFcRN33brNFNBPiITa2wt6m55/qMVyWErluFMzKq6TWqmPCZ8+tk9r0eUTf0QNo5ijwvUnEUUna5TpBA9/9wCKYRSGD3bFlx6eOHU0qFnUaBGTGPvlQyBSGCcxGJ/lgkbeF+ZPT9GOQ+tK/b3tIj3KaB8WAvq9fYj1NhKAUhl+9HlCcZyD1Ywo97VHs+8hs8CPldzUPIouORdrwdmIDjLdMRhzT4HCHSwLlXW+AQCOlI2+8yn+rJ4UOewVpyVtUJpYgp7m0IWTAL+H/I/a4ERDccJdFS4PyHxk9YZ8wwlwLTjXrDw48dWl3iAFadaApDhgypvfPOOzecdNJJOZFIhMTExOgTTzzxZe/eveubW6Zbt25NKiyefvrpr8aMGdPtueeeaxMIBHjiiSe+OOmkk2qa2k56enq4pKQk/f777+/Ytm3b0GuvvbZ2b8d9yy23bLnooot6vvDCC9k/+clPKpKSkhoxDXfeeeeWyy67rGtOTk5eQkJC9JlnnlkXr4znt8Ho0aPLr7vuuq6TJ09uP3v27DUvv/zy2quvvrrr/fff3zEUCvkuuuii7ccee+w+leaEvV+Pe++995szzjgjp3Xr1qGCgoKa+vr6uCqMhx9+eMOll17aLScnJy8tLS0yffr0vXq6fPzxx6njx4/v5Pf7SUxMjD755JNf7m87ePjxwReP3fPw7WFUAa8heaEvIGZqTyEvKfFeJOMFehVIvnQHJO80ERktribWmMq9HR0B/Ronn9ge1baloJrjab+81iP52R8jo8LKtG4B3kFygXcCNyMv2peZ5fsiwUcdEtjaQZiORoOMAJYhqSHvIiO47rKZHppAWXFpO0QOfCZy7f6GEA2TkH6RjrzcVuG8HCfjvLy6A599kW7Hg72eviTrPlRFswx5ce+KEB7VCAkRwVH97MAJVH+FSH5vQPqSmlKqSaHWoNe+VI280H9l9jkDePRABrKmvR9ASJ0WiMlcoTlmG6pIqjLHux0h5E41x63khNurZTsySrkQUQN9ipAw1xDf2HEXcs62/4aOgELjYNb9kLfzySPmmELIsyYbJ0AKWPNtefcmhHwYjEOeuD1oFLqfWsRMtgdwc+6F+a9wkLH2jYUjU/w109NqCKZBXUCCuTt8owoPeJBmymGORYitAQjZsA0hHNKJff4l4FQpiEf2u9UQ8TxVbNLCXv4bJMWlP0LqvgRcp0aQP3aYkpjXI79hs5H7cy4yMPAMQpB/jdxXJUgVmKMR4mwTcCtOedpK5Hmg98TXyIBCKfJbqKaqEaTazlDk+VaBDELcbu17vvn8g1nnE4TUn+yRCj8uLF68+IsBAwZs+76P41BEKBQiKytrYFVV1aLv+1g8ePDgYPHixdkDBgzoFm+ep2g4SDCmkM8jo87vIaP57yJy8Dqc8pNu93h9WdeXx95m/tHmcyPOy40inowdJIjT/+3RVJ+1rB1A6HQtM9gPeeE5GgmU2iLEwy6z3gWIJLUaUUcsA0dnPqkAACAASURBVE5AAt8WyOiQHVBp4JJn/kYgruktgV/z4x512x/8EnlR1vv3RIS02YJcc02RycDpG25CQeW6ycT2GTv3f6+5iMT2WSWrdJ06pA8dgZBslyEv418iqTpfIH0khNwja4HfIEH5qcj9oS/yenxpyMt8O7OfOuTeaom8xH+IlBr8N/CupUIAWIAEgIvNp0qKh7m/G5WDbbb5SySVYYM5ntY45QbttvObY/oXovLpioygattriTq3pH4NQq70Md9HIwoLEBVIpTlmVXIoEVCH44dh5/vbBrHqDWPniIfNutp/dCS2C9KHUqzt7UaIkNY43g+ZZnl9pmgQ7adxAKxIQogJP3BWWXHpW2a6Xp8DbhyZlFj3dJvwrtSACDdSkT5Yxj7k++8vDHnxIOwxj1RVQ0sk6G8wx6BlFrVMobuSgU0I6zWrM+vG7NJaxu6D7ZFnwCLkeXqvRzI4eGj8hOo7Jk3co2BSo8Q7Jk2cj5AIuYgxcm9r/jrgFbPchch9rb/Z/0KI1E7IfbYD+f1LxfF+mI6UgrWfOzda23/XOobbzfy5nomjBw8ePHg43OERDQcXryNmd/MN8fAFTo5tJTJqqYoDDRDsl/YMnBf3EBLgt7OW246MqrpdrBV+nGDAvtZac1sNxdzwIbnn25Cg4h0kmK1CAjstdXgl8iLdFiFArkEku0mIDH6r+d4Ch2SwVRRBRL76Ak3kkhYWFbTDkcFP9lIuAKcqgbarDyF9jjH/78SRuNuj0Xbf8uFUloincnCTDW7iwd6Obn8LMoraAKxCgqhWSLCdjCMlfxUhA85FXuy/ROTCRUAO8DYS9PYz263CMbVcj4w6asWD05GKJ39HVB29iSUO/td8f8600XLTTuOR0fWLzPEsR/Lwx5cVl85HRj0vMNM0Z1u9D/bUsSZ2xFmDxBMQUqQGJ/UjgBPgpyD3/+c4aUfahtq/tXJFexxi0SYTA8jzQNMfanBIB/uY7H6iRIAfxxBWy+ZGkWfC12Y7AWREFjNvKXL9Qsh9q2kiIXPMSmqqAsXuR0p2JCBkRSFCMNRZ1+dGDjDRWB8KXr3VH5meRoyioTkPjm8N36jC6uhLJYOA/7Em28Z+dYjypi3irwKNSSslklQJ41Y5uEljhT7ne5j1ekdfKtmKIc080mFPlYp340yzVTZNSYivA6Yi7f8c4gmThpCR6xC1wgKEcLgCuQYfG1WCvc9GfV2rZ+z3CXnw8CNBYmIinprBg4fDCx7RcBBhUgHsF4pPkRftdCRAz6Rx+TsN/JVYSCU2z1tfLPVFVHN0a81fNk5OvW53h9mfbjvJWt6HM6ptB40JSMqGSqoDSGByHkIgFOP4RyQiAdGvzDHmm2VWI4HURzi5+QEkUO6KSFM/RQiEpl6ANfcZoL6wqODf/BdpFufedHr3UKT+nkR/0n2z/+9ve81DOwzwFvBXxFOjAQnmT0cC4i7ItdaR0CSc4M8OVqFx6bR40ndwAlc3IYH1PYIEoDo6XmAt295aNwfpj7/FIUHKzHF3wenPfiQYVym/Ht8GhIh4Crk/Soj1VrCvrT1CuMB8jjPby0XUD4twCIjxOBLmC5D86fk4BE4lQp7k4JAHIPdAFU7pQb3vks25fGXWUS+G7UgAud4s96jZ3zyz/AZECTIHUXvsRgjGkGnHL81nD4QoSMUhHCD2GmbgPOeV6GtAFCVdzDw7/eVIHCKwEnlWaXC80JzjUeZTCY/O1vaVfAjQuC/5rO0NQUZ59foccNO6nj8b/AqxweN3guhLJd0R5Zp9f6Ugz9FkpA22Itf7VBxCWT1VlMzZgVwLt5mjPre1P8VLfUpFyL7FOBVqxuOpxr4VHho/oRQhEm1UY1W7ADCqCXWUn/sdHJoHDx48ePBwyMEjGr5bvAVcheRhqjJhB069bBuJSPCgL+whZNRSA4okRLIZRV5eE5EgKxVnZBOzXitig01wStfZo2Qqp9dgUXPAhxJr+rcdMc5Sk6svEYf8gUiAFEVG63YgOcNvAt2RQGW3Od6eSF56CRIwNoXpOC/a65DgZGZhUcGj+0M2hCL19+yq3zEmLakVyEjTYQsj7b8d6UMnItcgCSEYapEgRadpNQZN1bFHu90qBfeIqU1OuM0gA9ayikwcQmCnmZeFjKgnmGlRJFDVIPwds+6b5vNohETpg5ACSxBS6xSk33UDnrJKJcYNnKzUhz2SfGP6qC//CxBVxZ5UCms5rTKwGilTm43kvffASYGwn52JOAabDeZcTsYhYzq5Dm8N4lNyCkJc3AAcZ9rvRCSN5EhE3l1t2sKP4/HQCyf1Qe9T23fFTSbV43g5aPpFf2uZhchI7ADkuih5mIVDMhxrzmeVWUcVJjZJ2mC27SYZdN/anxKBUea4FnEQ0iYONI7vfMmeVJoPv/lzc8d6D9JXapHnrN4Ts3DKuipR9gyiBAO5HxIRUqKK2NQ3+z5LQ9r6PeS52pvG96wacK40+1ISzcN3AE+d4MGDBw8ePHhEw3cKExi/UlhU8DlSBrIaCVBSiM2nVyTi+Dkk4lSNUELAR2y99oFmnl2aS/Oz/TgBRxSRO7czy2mwoCkVagBnB6MakGplA1se3YBjyleNEAxpZh+5SEWBCPBnJJCZYs6rG+KQfndhUYGOJM9H1A6PAL8qmbqktLCoYDJOmcJ0JDBLKiwqeHBfyYZEf9J9aUmtSPQn3bcvyx/qyL0wf3NZcek1SD86BulDiQjpk46jbNHUCvt6xUuPaMpA0K10aKo6hR1sauCtcvG2OITYZ8jofgQZVW9rjr8WISL+iVPBpBUiUw4h+f2zEB+H+RBDJsRUizDTX0AUC09gUm/MMvbLv5IU75YVl6aXFZeehuPRMB+pqDIa516wFT+2okHbAJy0hM04BqlJSOpBLqJWyEZUDG+aaRsRMqE7co/3MNsrIDb1ZSVCSrYx+w6ZNgVRH4TMtqPWOraaARwTQiUfExCyo4/Z924kfaILzmh6qrW9njjPFd2ubtvt0eCGTk9Ang83mO3cgOTFH8o4GcfAr8kA0pSS/CfybOuPoxZrQIjinyDPNjWOBGnfCoQEOwppQzXgrcAxRdWKICDXfxhCvrmJJcy0lghRNgC41kub8ODBgwcPHjx8l4gnk/ZwkFEydcnCkqlLBiBy9xnIqP82nAAhgryUrkECE3Be9G1lgi3NteXtIAE/OCkRfuSl3pY76//gjHxrOTsNIGqRkbcGHH8FO2DdjQQluxAiYR0ilf8bYiQJorxIB65FJOLpZj9HIbLSS5GUjEnIy/P/mbZ5o7Co4DJEAfIbJGf/n8gLdBFwZ2FRwTmmwsdeMfv//rburT/844ofQNoEsCeYvhi5jjXmz4cEjJ0R0kd9AcDpJ/HSJxR2wGgblLrTJezRVff6AWtZ7ZdqnJiKEAYfIv4EfZE+UIv0vSRkRP8RpG/+FhmNnY74L/QD6ixSQSXhw+y2CQUafrszvfrcsD/iR3Kn9wV7tmXa9npzrGvMsamPgX2+9VabKPyI70gE6ffLED+D6Yhh52PINckF3sq9MH8ikud9PfAns3yN2dZXSNrRFvN/a/PnM8u8ibRdBLmn5iGEQy1yX+q1V/PWsNmmkiFV5hy6IkqFZYiCpDdyrVqY9e3nhP1dz9f9Z5tBNlWTuz2O2mVEE8scjhiGXMsPiL0HdyL96H7k2t+DEBfdzPxFiFJFU1KUyFmEc1+FkX5YhdOuc3HI4Xh9MQXxQhmLBw8ePHjw4MHDdwiPaPgeYUbjHwduQgKsCpwAL4i8hKYgUmod5bVf+DW/OoIEDDo/hCgktiEvp/Y8XdYOGpVACCIBRk8zTyW4WlJQSQolNHaZ72lm/QFIMBhAUiguNevoSO1wJCe5BhlxvhtJyxiNkAuLEBLhfeSF+gjgaeAMs5+hyEu2tsUvkJf1mEDzR4KTkdSCT5Ayo4/jXNfdNA6M3Sky9nV39y2sZd2khPYD3YZNRNTSOAjVYFaD3mQkZaAHjlnlZ2bdUoRQAKlu8m+kjw0A7kIqk6wuKy6905ScnE8cSfiu1LpfftN+C1WpNeD0/SZhiIUgEgTOR8qG3miOvxtCgGi/t9sigcaj99omOci9dDxCNPwEIYY6IqTDT4nttyciREdvs0wEUQ/1Q8gFECWDBvIhhIR5HfgHcr0X4ZAPShpGkFHvAPLMUGPJGkRV9LnZZj5yn+Za+9BnhPsc4ykWIq5PzHbipUXZqThhoMpcg0MZc5E+0Vy+vfbJpxAvkQaEZPgbQp7WmXkvI+koRyHPwjsQJZh9n/qQPpJMrJ9JCk4p1UokFewx5D5SQlixDUk3O+gmmB48eDj8sWnTpkBubm5ebm5uXnZ29oC2bdsW6P91dXVx1Wrt2rUr2LZtW1Om5N8JBg0a1Gf+/PkpzS8pWLZsWXJubm7ewTymfcWMGTNajh8/vl3zS8YiHA5z1113tdf/GxoaGDRoUJ8De3QePHw7eKkT3zPUMLKwqOAcnPJ3u83fUiTQ/gx5gdU8e3uEWQO9IM4LpkptQ8iLptsl361+0BE0t+EfOHJdO0DV+Qk40u0uODnFVcgLsi5nS/PTzDYykJduLd+ZBYxBAq6FOKX5Usx5lJptHmu2sxrJbz4CKCwsKlisFSmMwmEY/4Vp5GGEIELKDEFGwkGC6neRYLELjUeW4ylhsJZxT/fhpAjY3g64lrNz9cPE9i31C4laf8lIv9LlW5v/hyHeCXcBZyOVSx4yy41H+stlGHPQ3AvzHySOR0PGrpSazpvaZmbUpIKoCJqEpV64AAn0Tzbf25o/t2oogtybOlrtxi7EU+Jm5D5MQXwnegKzkUDxA5x7GnPe44jN51dzTTvgt/0Q2pj9pCKKhyBwtdlfAqJOGIJjFlmGKF2+NMv3wfFv8Vn7qsEhZ9RHQ1Nw7OdOvBdOVTtpSgY497s7PcdvfY4BWpUVl95oeW8cUvjwmz9X/2n1q4uB6/+0+tXpv+h90Z7jNOkSe6o6mPKWw5C0hVKcaz4U5xk+GyEf6hDy4nrkWQixz1hVNoCo1CpxyOQUpGrKPxEyozuxfQSg0jeq8OZve/4ePHg4NBFdsyGLZV91oq4+iWBSPf2OWO/r1bH8v91e+/btw2VlZcsBbrnllo7p6enhiRMnHpLP5cMJoVCIxMTEuPMuv/zynf/NNiORCJMnT27/wAMPbAJISEjg008/XfktDvOgY2/t4OGHCU/RcAigsKigOyKlTccJxIKItHwE8HMzzR6F1pd39zXUoA4ksGiDvKCGEQIggVjCwg4q3evrdPWEcAcY7truuq0kmoYGIscjgdBis219iQ4jo3xhnMDrY0Rur7nh25Fg+lzgEqTaxZ8LiwruKywqyMeR25+5l+M43FGHjIA+jwSs/RDVSF+cOu/gXDu3qsUeVdYR7Hj9ySaf3CP6bql2Cg45YSsfanCMIJXUqjL/B5BrWWXmpSJk088QufdAhIg4E0kx+DlCLqjyoRECkcAjLavTQ4GIfy4wp5nRcrvCRCZCXhUibbvaLKNtpZVe6oltPxvp5rgrzbnVA/9BAvZ1CEkyllhficVmf9C4/XB92oSRlqHUkZAOCPmUBmxCvAS+QFQMc5A0DDVxxaybhENu1CPXShUcURxCCOt8bQ8IhfYdrZCjcKdTuFPAqs0+LgD+bJQqhyrGJoR9d/fdnPqAIRcU7hSeMxHvlCsQL463EFUOiIprrPlMRs77eqTMpaoU4vUtJVu14snbiJrrV2b+pUAqRH0QjZpmrkFIHA8ePPwAEV2zIYvPPu9KXb080+vqk/js867RNRuyDsb+Jk+e3Lp///59c3Nz80aPHn1EOBzep2VCoRAZGRkDr7322k59+vTJGzhwYO769esTAL766quEU045pWdOTk5enz598ubOnZvW3HbGjBnTpVevXvnDhg3rvWnTpj2/Ny+88EJW//79+3br1q3f3//+9zSA0tLS5EGDBvXp27dvXn5+fl/dvo3q6mrf+eef3y0nJycvLy+v79tvv50O8Oijj2aPGDGix/HHH9+7a9eu/a677jq3sTMgio5rrrmmU05OTl5BQUHu8uXLkwDOPffc7pdeeukR/fv373vDDTd03rhxY8LJJ5/cKycnJ+/II4/M/eSTT4K6n3HjxnUB+PrrrxNOO+20nv369evbv3//vu+//34awI4dO/x6jDk5OXnPP/98y+uuu65zTU1NIDc3N++8887rpu0D8MYbb2Qce+yxOaeddlrPbt269TvvvPO66fHOnDkzs1u3bv3y8/P7/uIXv+hy6qmn9nSf097O/ZVXXmkxcODA3Ly8vL5nnXVWj8rKSr+2gypb3n///bRhw4blANx4440dzzvvvG5HHXVU7oUXXth9f9s7FArxs5/9rHtOTk5e79698++777628Xuoh0MRHtFwaOAeJB/cNu5Tw7aLkeBrljXdPWoFThAEsaOHESQAjOCkHbjlte4AIl4QobnDav5ow728vV07T1uVGn5ETj4OSaVIRoIjDZRzcUbR/YiK4VIkiGlA8skjyAjhm4iJ2vFIEPcczoh0bhPH9UPAXOR8taa0jjpnEVuyUl3+NXiExoErSCCjsK+bO8iM589gk1OaXmP7hqj6wlbTpCOB7W4kKF+EBFB5CNFUDExDPBKyECVDK+SajgDu3UtQ+ntEEfEYEojtLbVGK0xMR/piuplWYp1fBRKwJyD3opYPjZdqsg1JfdDqLduQVCAlyf6GBJonW+sNMG2wmVjyR9Oc3G0edU2zr+MnyP1wHJKi0gtJpRiK+DPUIeoK9VpYj0P4JOKkaeg2VaGi0/TTJp1so1G9vvoGqsqqqDXP3sZOc1wgZQP31VPj+8D0vltSXxv0TXohcHL0pZL86Esl/0L62XbglOhLJe0QE8/2wK1IdQ0lIkDScz5HfEjGIP36NwgBZFeGsdUjm5H+0QO5X15EzB3v8Y0qfAXnWfA5YIiGSD003O4bVTjv4DSFBw8evncs+6oTkUjs71Ak4mfZV3ED4m+DTz75JDh79uyWn3322YqysrLl4XDYN23atKx9Xaa6ujpw4oknVq1cuXL54MGDq6dMmZINcNVVV3U95ZRTKletWrV82bJlywcOHFjb3HaOO+646jVr1pQOGTJk11133dVR9x+NRlm6dOmK+++//+uJEyd2BDjiiCNC8+bNW7VixYrlL7zwwrqbbrqpi/vcfvvb37ZLTk6Orlq1avmMGTPWXXHFFd01TWTFihWpc+bMWVtaWlo6a9as1l988UXc4fisrKzwqlWrlo8bN27rjTfeuGcfmzdvTly0aNGKqVOnfnPrrbd2PProo6tXrVq1fPz48RvGjh3b3b2doqKiI+64445Ny5YtW1FcXLy2qKioG8Dtt9/eMTs7u2HVqlXLy8rKlo8YMaJqypQp36SmpobLysqWv/7661+4t1VaWpr69NNPf7VmzZplq1evTnn//ffTqqqq/LfcckvXd999d9XSpUtXbNmypUl5QbxzX79+fcLDDz/cYd68eauWL1++ol+/fjUPPPBAs4H/mjVrUj788MOVs2fPXre/7T1v3ry08vLyhFWrVi1fvXp1aVFR0fbm9ufh0IGXOnFo4D7kZbQPTsCm9dFBXtTfQUbwddS4gdjrF8Z5uQenxGASjseClsOsxyl5GG+U2h1cQqz8Xfcbr3oBOJUH7O3YJpW7zDGpKaTuO2DWbTDnsg1JjUi1jjWAVCnQVI/jzLY/MvO6ILLkrcBRhUUFbyBu/Y+a7dwD3FcydclhbQppKiPUIQFMHY7Eug7x9Cgw33cjJI5dncSGH2nvJGt+vGXc323jR7226h0Qwknf8VnLab8JIX2gEiGXXsIJyjsh6RNvIiPBbYB7zTqjcUiTy4ALyopLLzHnuUchYD7fNUqGOlweDu5qFabCxC1IX3obCc7zrONuYc7Hj6gcshHSw41FiHnrmQgZkWyWX4PcHz8DZpplB5YVl85Frk0h8BeEhLgXuXbZZjn3S4Den3oP6fUII4RCOvIciSAj2h+ZbRQi6hC97zXwb0Ps9fUh92gasaoUd6qEPc0mJ3fipGNAbH+ynxN1pn06IQToRwiJdMjel7/ofdHm6KclWrr0OIhMBV8HmevLQYidXTjP7mQk/ecyxEtjqfl+GaI4GYBcj3KcykPxyCstbfxnRN01164gYb7Pib5U0haYDL5EUqsryd729YFuAw8ePBxCUCXDvk7/Fnj77bdbLFmyJK1///55AHV1df7OnTvX7+sywWAwMnLkyEqAQYMG1cybNy8dYMGCBRlz5sz5HCAxMZGsrKzI448/3uR2AoFAdNy4cTsAxo0bt/2SSy7pofu/6KKLdgIMGzas5p577kky6/quuOKKritWrEgNBALRr7/+WtN99+Cjjz5Kv+222zYBDB48uK5t27ah0tLSZIDjjz++MisrKwLQo0eP2rVr1yZ169Yt5N7GmDFjygF++ctflk+cOLGzTr/gggt2BALyKvHJJ5+k33vvvWsAzj///MqioqJuqgZQ/Otf/2qxdu1aJZ2pqKgIVFdX+0pKSlrMnj17DYDf76dNmzbhUKjRYcRg4MCBu/RY+/XrV7N27dqkxMTEaI8ePepycnLqAUaNGlX+/PPPt463frxz37BhQ+KaNWuCRx99dC5AKBTyDRkypNkU5TPPPHNHampqFPa/vfPz8+s+//zz4JgxY7r89Kc/rTjvvPMqm9ufh0MHHtFwCKBk6pJ1hUUFJyLB8GlI8KUjfnVIvnV3YgOLKDIS2BlHjm5XhPDhlMP0u6YnIi+67YklCsApgRlv1NsOGvUY1M3eDog05UHJEN1WMo4fg71dO8BVkz2s/yE2WFGSwYcEhRoo7zbH93fgJOAc65huwFF3nF1YVPAhcOthTjisRvrGamSU2o9T+rAKCRY14LdHk93Q9m5qfjyCIuCaZysgUptYR+Xyu8yfsv4nI6O1AaTU47HICPxOJGA/CSGP9Bjt/P8XkRH8u3H5NSjhEOd8dIR5vJk/CilbCHLPtMYh5NSjRM+3FdLmJ7jOSwmWY83/S5H7YiiiwPgIUUVoGcdLEWJiCGIEuBUhIvoihEG5ORZV9rhhE44bgRUIUZKL45GQgJALG82xlJrtnmjOT+9TJRY2I88ElZYqqaHGkm5PDl3GTrmpMPNbW8ftJjIg1lwzGVEElJv1G8GoV8YiKTO9cZU0/Q7RCWgbiUb/Bx+JjnQsGgFfMeLHMAk5n42IV8dQ4CrgSoRMSEP6u9437Wls3GqTdJ0R35oHmylR+RL4KiFcQObOJb6kUHPGlR48eDicEUyqj0sqBJOaNUHeX0SjUX7+859ve+yxxzbs7zKhUIiEhIQ9ytdAIBANh8N73jf8fn90X7ezNwSDwYh7+5MmTWrXuXPn+jfeeGNdfX29LyMj48h9ON09SE5Oto+bhoaGuO9JPp8vrrI3PT093u93k4hGoyxatGhFMBhsSim8z0hKStqzb7/fH23q2JtCvHOPRqMMHz688o033mj07pyQkBDVdJra2tqY97+0tLR9aod4+2zfvn3YKBwyp0yZ0ra4uLjViy+++OX+nIuH7w9e6sQhAmNk+Eskr/cOnOCgBnn5fgoJivRmTUBeQCFWMWDLqjV41/xye7n2xAYLmg6RTGy/cAeM0Tjf3akUGnjEM8uzc77jpYE0IEGonfLh3p8SEGqYl262q4H1AzjBlL7Ap+CM0rZFymlOKywqqDqhqCA0rGjwimOvPSc/zvEeyrgYGTG9GAmAFK2IVTGAc43d6Sw24vkuuINKXMu4R65tDwj3dQUZ8d+C+AqsNd+zcNJ+WgJ/RUZvFyOS8vbAWWa+kmmKIJJWFKNaaAbuahUjcfqOlo9UpcdynHKTmPn9XNtT8utopB9uRwiDMoT8Go5TYrA/QjDY5xCx1vEjffXPwJM43hXQWFmgx9kaSTdJwvE7qUEUIhlIYL4ZafNjzblmmP2W4dwXyTipEva1U88IdzpNPHRFrqe9jbqmFyeCU9Z0JrCgrLh0ZFlx6X3mU70Q1NfgDbPceXvZ5sHEaCDJB8k+fOb8olH8u/+BkCAfIGkMRcBw36jCUrNeO6AnRLMgkmS8FICoTaC6sQbHn2ZMMyQDvlGF1b5Rha/4Rp10j2/46FcYfNUP1QjXgwcPAP2OWI/fH/tb7vdH6HfE+gO9qzPOOKNq9uzZWRs3bkwAqU6xevXqpP1dxo1jjjmm8uGHH24DUjWhvLzcv7fthMNh34wZM1oBPPfcc62HDh261+dcRUVFoEOHDiG/38+UKVNaR6ON4/dhw4ZV6aj+Z599Fty6dWtifn7+7kYL7gUzZszIApg2bVrWoEGD4h7TkCFDqp999tksEA+Ftm3bhlq0aBFz/Y477rjKhx56aI+Bu1bSGD58eOWjjz7aFsQEcuvWrQE1VWyOgLFx5JFH1n3++efBNWvWJEYiEV555ZX98vM46aSTqhcsWJCuPhSVlZX+pUuXJgN06tSpfv78+WkAr776asumtrG/7b1hw4aESCTCuHHjdtx3333rly5d6vaH83AIwyMaDiGUTF1SXTJ1yZySqUt+DxyJjIxdB8w1RMSTiLR4CTLaW4WMNmpQr4ECNK4OYKdiaA61O6/erhYQJjZIjVrrKnSU000I7Atz2RRbm2i2p2iK5HDPs9EGSROB2Lx39/GdhJAUCQHqc/2Ryv/b6xEfengZeMV8XoEEjesRYkpJhigyQq5SM5sA0HQb+1fKHfDEC3Ld+fn66e4DDdZ0Ld3XCVGhJCABblscYiSCXH8NzIfiKHY6I4HzTHM+UXPcrwLTmxrdLisubWeVwwRE6ZB7Yf671jo3IwHifxCSq85sPw0J/rXyyS4zPdPahaoZynEIrXbmnIYiRMoM4F9m/QsQguU5JEXkj0jqyNo4hz+M2HvL9l7RYwEJ1DcgJU7/B3jPHFMLpE+EEKPIzYiywvZiqbG23xKnjKKOuW6S9wAAIABJREFUqIcQ4sJ+VoDz/LBJSp0eL9VG28qerqRFLY4J5VDgd0iw/jhwS1lx6ZXA/0OIoAKEyLjzuzCOjL5Ukh59qeQ085kOPAvhkF6FCJEGsrdsIr32Y4QIGY6oSNoCtxjPhqUQWQxR86z2ucqiRhFFRCTsesSlIykXFwKdXAaUHjx4+JHD16tjOUf1+HKPgiGYVM9RPb78NlUnmsKQIUNq77zzzg0nnXRSTk5OTt4pp5ySs2HDhoT9XcaNp59++qv33nsvMycnJ69///55ixcvDu5tO+np6eGSkpL0Xr165f/73/9Ov//++5tUWADccsstW55//vnsPn365K1bty45KSmp0bvnnXfeuaW2ttaXk5OTN3r06O7PPPPMuv1VFGzbti0hJycn75lnnmnz2GOPxU1be/jhhzcsWLAgPScnJ+83v/lNp+nTp+9RBfh88pPwzDPPfPXRRx+l5+Tk5PXs2TP/ySefbAPw0EMPbdiyZUti79698/v27Zv37rvvpgOMGjVqW25ubr5t9rg3ZGRkRH73u999deqpp/bp379/38zMzHCLFi0au3o2gS5dujQ88cQTX44cObJnnz598o4++ujc0tLSIMCECRM23HTTTUf069evb7x2Vuxve3/++edJw4YNy83Nzc274ooruk+cOPGAE2keDh588dg9D4cmrLKNi5EX8kxEbn0q8hKuwaWOaqYi6RU5xCobEoj1eFCCQeXimu6wASffWl9yK4gNtMAJtjSlodos787hrscxPMOaB6LgsFUW9rZVvt1cTRz7nDRQipfaEe8AGqKkXPvh1AXTmtnHIYOy4tLTEHO5WUhQ9hhiMLcKCXQycHLmtV2UWNBrFY+saW7U2kYdDkHgNoEsx1GdBMxnHXKdKhDioA+OzD9qpqcgKRGzkQDzC8SXoz0SLFcgffoDhAwYn3thfrwUCcqKS+9EAsD7TTnMeMt0R0i8HISs+wIh+lS6HkKUCVoNJp7iR30yGsxymxGS4W3kns0DvkFSXD5EVAh3m238AQmy25pzrwLmIV4PWl4UGpu86n3xjWmbR828InOcyciI+GCz/TrEB+KnZjk7fUnvw2pzrtmu/bnVFLb5o27LPq69wd5WGPgUIZJ2AS8gZOoIJGjfhagk3Hm1YeCepq7pgUL0pZLTcNJsgOj90DAAEhIhEqHl5s992VV/jG5vPZ0d2UMhcin4zgdqolGSFlXz55xUvkwL8Gv8IYgkup9/igiB3bWEkxrA78epGHI6cn+fB4z3jSqM2889ePDww8DixYu/GDBgwLbv+zgORYRCIbKysgZWVVUtan7p7w7t2rUrKC0tLc3Ozt7ngN3G3Xff3b6+vt738MMPbzzQxxYPFRUV/szMzEgkEuHSSy/t2q9fv9q77757y3exbw8/TCxevDh7wIAB3eLN8zwaDiOUTF1i55zPKSwqOA0xS3wEGcEfi1zTRCS4jOJ4F2hOtzto2W6WT8XJ69+BBD3ZxBrH7SaWaHCnaGAtG8Ipo6f7ckvowta8bBr7QdgIWOvES8mAWJLB3u/e0OCT0eY/+qh9vZllDzXMR4KQC4DPEFPRoxBpfy1C7GgqQA1OBYggcn1s8sEmHezRZv1UsshtDminwkCs70Zr1zQ1p9TqBp3Nce3GCXTTEC+Bj8xxPoSoG9QcsYNZ5u9IRYlO7D1tYrrrcw+MLP9kRAVwIhLchxCvg3rgS6QtS833n8Y5V/0/EVEorEBGoH+PkAtDzfZ0mY8QxcPL1nFPMOep3giZCEn0T5wRfDuQDyMkYEekTVMQguM8xPciaI6/Fkk9SUBInzQkjUK9TPQP5DokmP3aRKJ93W24vVNs1ZPdNk2pm2wCoz8ycr8O8a4Yj5BM5UgbtsMhGiJIdYW/EOeaHgTEptlk7pxFcmVHtnRqDwGobflZdHuSIRk4DjgdIhCo/8fSncGKO9ZFB3YL8renc6KPQGQIYhjppFxABHx+iG4nnFiDXMfuCNHVE7m+7yH39/6kB3nw4MGDh0McDzzwQJtXX3016/XXX4+najwo+N3vftfm1VdfbV1fX+8rKCioufnmm7d+V/v28OODp2g4jGEpHOYjAdOTSJCxAHlZ7YDIp+MFiPU4Xgf2vJ1IwBFAqjUMQMzvMpCUBA1qas33JBwjPB2ta0AIjA7ElryzR0AhNpC1obngTRFhTY2auoPjva1Xbs7x1pKpS55pYj+HPOwKCmbSnxCVSw0y0q1qljBCEmn5S70WfjNdHe/tNnK3o47qu30bbKNRXU8DYu1LSkDp9xAy4r8BUTWkIkHVl0i/ARldT0QC5CCS/rEeeMZ87w48lXth/ub9ajQDowj5A05ViWcRA9EqJI1gGUI0BBEzvrNoPKqv55KI9KmngMsRQuFnrnPYZfa3xJzrXFP14jLgaaRto8h1W2LWGU7jvlyDkwrVgCgkTkTuxSjiK9HGfG9njnk1ci2OID75VoUQDPa9qoSE7fVh9w1c23Jv192XFPa0SqSfXGumPYp4E/TG8ZL4G0K4PA48/j2ZQAoWTksHTo6u73AbtenDILoVAmsgeqxzG0TrIXzDgE8DfwHGnpHFyw92918GkV+Z84lCQ4jU2rnUZGSA/xiz9RKI9kPu0QTTnFFgum9U4RXf/cl68ODhu4anaPDgwcPhBk/R8AOFrXAoLCqYC1xjZl2ASI81dUAl1ipz16oWdgDa0nzPxBkBV/l0dyQVogGHmMjEKUW5GCEkdHQ0gOOkDk4AspvYyhfxRkpxTYs3ouoOhKNIoJSAU4bQjQaERNmCBDF/QkqGHtajhHZlhbLi0pFIGo1CDSKjOG2v6gUlA3w4ZoB2YGgrHjRI3IxDNilsAsnub5j11UgxhKOqUYVNR3OMNQip1dH8ZeIQXh1x0nr+AdyIYwyYDfQpKy79pQaf7tKVzTTffKTahKZ0ZCJmf1FzPKfhECNHE9+MUas+lANTkXup1syvRPp5ImIA+R4iha9G1Ad1prTmtTgknaYpnUlj8kbb1VYpJSFBuOb91yMKHS0F2mDOoad13O77R58JEJtq1FT1GXsbuo5e+6buad2fVtHQ50gLoBgpZXqyOZ9jkXZ8FlG+3Jt7YX4jh+vvBWKwOIfP52aCbyj42kHUlAjdY2GSCIl33tIxkpEU4PrhLRgB4Rzw+yDSgD/qI7NyNjuzVoN/FJLekg9UQTRBDCL3NGEtolTy4MGDBw8ePHg4rOARDT8QGNJhDkBhUcFSxJAsHwkadiNkQEdkBPkoHCmyplloUKMjtElm+Sh7RuH25NEr1ECyp9n+QJx0Dcy8WiRA7UjjPGs7cFPHWVsirSRCc+qFMBKcrQW60bgUnw8Z5b0f+ASp0jDdGGz+kJCLkAY+xJujq5keQQI3vc7qJ6DXT4PlFJwUBXfaiR+R5e9t1LoBIQ3Scdo9Bbk+6hKso/E1Zr0g0l86mU81sfojUn3iOYS0CiJk1i0ImaI+FCOQAHWOWc9durLpxhIiYg7sKZ/4vwhh9TGiKOiA3C+FZl/xgv4QUIJI/L9E7oMPkZSFyUg6QHdE6bALeN8ss9yc08kIUWAjA/GvyMbp/7aiIAHp62pY2d7M32DmtULaOISkQZUjygzbyFGvuxJAek5KrNj3XSTO//Y9aZuExlM4aF/QdI+Aa50Wph0WANeb9lgCvPW9qhf2hkjC6/jrryaSeBwx6qCIIfACPUe3893q89HO56MDSTVhQsEQGRWVpOyOUJuymqj/IuR5l4pc80Lga/C3MturAkb6RhUeGiSLBw8ePHjw4MHDfsAjGn6AKJm6ZJ3xb/glTqDzM0Q+fRxSIi4InI0Ed/rSr+7x65EgtQ4nQLQDCB09VWJCSxTWIIFPgzWvFhmVjOeroMtg9tUCJ4jRvhkilqDQ+bbpjho99qRxIKz/9wZpG+CgGsh9j3gKaf/OCLlzPc7IcQectlOvjnIcD4cvET8BcNJWdHm3Z4N93fyu7WZY+wyZ5Xbh5P+n4phHgqO2aGHWSTXr9ULIhF8jVRv8SIrFWERevw0htuqBi8uKS4PIqPhi4E2gZVlxafp+BKpjESXQh4h54lnmeBchhMdF1rLa/3Yjbf5HhABZbdrwBCR14qncC/M3lxWXnoCQEVeqaWVZcWkd8FukKsRHSJCpbR3AIYy0/9rP6ijS1wPmGMLIPZKNtHVHhGBoh9xX1cS/fkk09k1xp1k1R/TpOtCYiHETEJ2IVTTovEKEFL0eMfjsAXxwyJIMgG9UYXV0zpuXEw49SNSfQCjwCYHICAINa9id3AdfeHDAn5BlfBggEEmkIZpAIPqOL6Pq5WhyHewOVlHPrUSTCowBZAvwT0b6z3vA682VtPTw3yFr8ITuwL2Ydi5fONFrZw8ePHjw4OEAwyMafqAwo/UTYY+Xw7+RQOhixERtKCJRbokECkHE/X0VMtr6ayRgOw/xW0hHgpo6nJFykEDhCMT8LhX4BRI8DUKCn9Y0DTsw0QDV9g7QHHRb5aCVKxJpPLLqNur7Aglgs5GAKncvx3JY4W/P/vWE7XVb/9i9Ra8nWwWzppmSjZuBO2DPCH0NEjAPwJHEq4pAR7uzkLYZhKQr1OOQEva1sCX00LjqANb27SAygPSLSrMfrXiSaC2bgjOSXolcs3ORPvE8MhKuJToXI31xPUKmfAmcjwSrPZC+e4nZ/06aUTVYUGPBlxFzwrbAT4BNSAnNfjj9x4fcK48g5Mb1wEjk/lKlwrHA2LLi0scRT4kcYFZZcWl/c53mIyTGJTilIv9q9tGTxgSADR8OEZSMU7ElgNzPxyH3bBRRNwyz1lWSxL627m2r6sWtQLK3oYhn8tMU2WcTVDZhWIJcO3DSYuBQJgQXTksnO7E/O1utJnn3KKK0pEVVJ7a2hd2B44hGfU5ThiPsTgUiUSK+L6PhwAmEksYSrKlld6vsPc0T2P0B4eBLvlGFh61nzOGArMETpgHjkP73c+R37ZXv9aA8ePDgwYOHHyA8ouFHAFe1igdhj6fDVMS4Ts0e30Wc/DMR2e47iJfBYuABhKTYZbajZRLVbyEbCezTkdHJJYgEWs3kFCrLVjNJv/muyopKRIVRgAQ7qpKwpeOaLuGztlOLBEYaFC9HAsRfm2N+Exl9/kFge93WZ975Yk7vc3pc+ECrYFYpVkBtfAqGIikA2UhbqsIAnGvWA0dCH0Tacbe1TDz/Bc3f91vLhXFULNCYnFCCQascqOLBlutjtrMSCbaTETl9FXCTmf8wMMrMew2YiVN1Iw0JHl5BKhYUASeXFZe2xDJebKo9TfCvge06047ZwD0IURMCXkLIjXbmGHcaM8eXgb4I+aHy/4E41UCuNMeZBtxQVlwKTgWKdGQEex6iDDoTJ0WpKc8D2+MAhBTR1CadloVD/LjVBwFrOzrNTQ7YZRhtg0gbdqUJX5zp7uO2FRXar6LIPT4TabsFZtnvoqLEf41oKOE8qlo8TnW6j6oWSQTCXUls2E3rLa0I1EdoCETZlRklZZefxN0+qltASi1k7hxAXfA4ylsnk1meQsZ2qM0I02JnOek1T/mGjfFG1g8isgZPSMchGUDuw1wzrx1S/UXNdN9CnqtveYoHDz8WDB06NOf222/fdMEFF1TqtIkTJ7ZduXJlcObMmV8dzH136tSp/8KFC1d06NChofmlvz1SU1OPrKmp+c/KlSuTzj777N6rV68u/S7268HDjwke0fAjRcnUJdWFRQWPIuqEnyNSdJDA9CycvO4+SGCl5QaXIKOwnXFM5BIR87mdSFCZaNarMuvY6RdRnDKHqpBob+areuFNYCviuJ+BUy5TDerqkYCvM7EmervMvtYiAameUxnwliFcfhBoHWxz5Yhu5/yxU/oRT9LY0HIY4jcAEvxqUFeNvEC3xDH81PbqglP5QdUiGsyqlB5iU2BsokB9IOqQ/gKxgWYr1zru+fpicRRCEvU2889EAuk/IcTRJYgSYwhi0NjTLD8G6btXmH0lA7cifRJgallx6YP7Kcefbo7jKIRkeNwQC3sMJ833yxD1w7kImfWoWX8RxpSyrLi0PzJanw7chrTtZmAuDnk3DielRI0T3W0GTtqQpjbZ6S1bzflrehPEqgggVs3QXNkhd8pFPOh2mrq2Or0W5/6tRsgQgJOQZ82/TWrJoatkUOxKP5Vd6Rmk7opSmxolowJSaiI+f3Ql2eV9ohFfIrU1CaTUSHngABF2pfvZnXwCPqK02g6pNX5fi2qgvBqpqFHHwmnp0TV9wPQvL3XiwCBr8ITTEDIrnVgC7hvgLTO/ELmX1ex4HHLPfpo1eMLF5Qsnel4ZHg45zP7glazn3praaXvF9qTWma3rx5xZtP7c4SPLm18zPi666KLyF198McsmGmbNmpX14IMPfnNgjvjAoaGhgYQEL4zx4OFQhneH/ohgUihONv/ONWTDXYj0OhGnUkQCIl+/ESEOVL1Qh+Svv4r4P/iRoK8aWIqQCxuR4KcGcY3vi5QMXIUEsymIHB0keJwF3IWM5pYi+eWqPHgBOB5RXHTHqVSRiOR7V+OYHiYj0vNS4CnL6HHit2mzQxWnjzt7HiLHjwetpgBCItyFEEcViOrkEeSFO80so22ZRuNyqHbpSpucgMYlLtXETqcpOWSPXkP8EpobzXJZSD9RwiMZSQXog5SAnAccac4xiJBktvmnpmHocSjBMRaR6O9rKgXGX+F/cFWxcFX6OA24FAlMUhD1wg4k5Wi8tc5m4MGy4tJ8hEBLQFJCBiJk0NnmfLSd3EacdlspuRPAuTe17Ky7IohNEMRLM9Jrp21m+zNEiN2nDbdqQZUt7v3o/0pE1JjjTCfWL2Qmh1MFmED4Xlru6EpqdT92pySQUpPp80fTkLSbuT5/9DjSdv0lGvGdT11KCi12QrAuQkPga3a26kbrbRGfP6rtlBGN+G6nJjUVfDtJrZpETcYo9sHQ1MM+YwaO0S1ImtPrSMWXm5HfxZBrHX3mHQ28kDV4wumessHDoYTZH7ySNfnV/+1a31DvB9hesS1p8qv/2xXgvyUbLrvssh0PPPBAp7q6Ol8wGIyuXLkyacuWLYmnn356dUVFhX/EiBG9KioqAg0NDb4JEyZsGD169M7Kykr/Oeec02Pjxo1JkUjEd/vtt2+46qqrdnzwwQepN9100xE1NTX+pKSkaElJycrnn3++1cKFC9NmzJjxFcBJJ53U61e/+tXms88+u8o+jlNPPbXnxo0bk3bv3u0vKirafOutt24DUSFceumlW0tKSlr84Q9/+Or000/fc08uW7Ys+eqrr+66ffv2hEAgEH311Vc/z8/P3z1+/Ph2r7/+elZ9fb3vrLPO2vn73/9+Q1Pnv3DhwuDYsWO7h0IhXyQSYdasWWv79++/u6nlPXjwsHd4RMOPBIVFBUoQtEcCnJsLiwrqkJf7j4EpSFCmL7+2dFrNGYNm3fOQoOJ5ZMR4A2Ks9QGSbnEPEjg8ioxIH42MUu5GArFhSC78o0ZlcGlhUUF3s959ShIUFhWMRgLE9WZbqUiQkoAExn9DiI+3kRfHyT/AShL7DbuagsGlZcWl5yCpB28C/0ECoqD5S0PadhdOlYhEa3239D7iWkan+YglFyA2yFUn/RZxtt3Z2lYYub7pSGD6H6RayC+QUfGPEA+E5cSmEIDzTHOPvm9DUoD2Czap0ATmIwqFXwA/RczlpiMpEzGBs1E/3IKoMXwIUbTTfOr9tjelgdsDQw0ptcpHlNjyrtqWEKtwUDQQP8XBTUzoPedexr7utm/E3vAeQiychVzTLxGC8PVD2fzRDV9G1VaEXD0m6q9JpCZVLlhqTZrPHz0KIZ5+Ql1KCtuzofU2fKk1RCO+ViSEIVgbS/rUpaSxvQ1E/K3I2nYzNRlXcjgRL4c+diJKqzDyO3Ul8mzZgSim2tK4hKuNwcRWt/Hg4XvHc29N7aQkg6K+od7/3FtTO/23REO7du3CAwYM2FVcXJw5evTonX/605+yfvrTn+7w+/2kpqZG3nzzzTVZWVmRjRs3JgwdOjT3kksu2fnaa6+1aN++feif//znGoDt27cH6urqfJdeemnPmTNnrh0+fHhNeXm5Pz09vbnfhz2YOXPmF+3atQtXV1f7jjzyyLzRo0fvaN++fbi2ttY/dOjQXdOmTWuksLjkkku633rrrZsuv/zynTU1Nb5wOOx77bXXWqxZsya4ZMmSFdFolFNPPbXX22+/nX7GGWfE/b2ZPHlym2uvvXbzNddcU15XV+draPhOsjg8ePjBIp6buIcfJp5ESj8GcYKPSUjQH0RGjutp+kVLA7o886cmczcgQVYbRD6+20xTEuEt5KXudeQF72Pg/4C7XKkM9yDy93t0QsnUJZtLpi550Kx7HUJWXG8+b0UCm2zEyLDEIxmaRRpwNXAM8oL9IhLM6EhCCo4hox1A2s8JDWQ1vSJi/e9H+pEGqrXEVgdRSbK7ggk4Qa8eRztEnfI00m+Wm3mvIGRJCiK513Vt6HEptIrF+LLi0nNMwH9AYEw45yAjoxMQFdCuJhY/GZFnv4EE3C0Qn4wkHPImROORVUU9olDRNk1ESAYlFOrMn0LVAkoyaBUQcAgG228j3u+BkozxoEqHBvMXz1xSr4Uazh6D468yP/fC/IuR+3vYgbwuBxULp6Ujz6GxQDJ1KX62t4HtbfzUpShplgC0IVgLrbdBsBYAnz9a6Uutifj8Ub3O0m7BWkivBF8EfNFdvlGF73ppEwcUo4GFwDHlCyeeYtIgUpHUqM04zxDtv2HX+gnAb7IGT8j/Lg7Wg4d9wfaK7XFNg5uavq8YOXJk+csvv9wK4LXXXsu67LLLygEikYjvpptu6pyTk5N30kkn5WzZsiXpm2++STjqqKNq582b1+Kaa67p9M4776S3bt06vGTJkmDbtm1Dw4cPrwHIysqKJCYm7m23MXjooYfa9enTJ2/QoEF9N23alFhaWhoECAQCjBkzZod7+R07dvg3b96cdPnll+8ESE1NjWZkZETeeeedFiUlJS3y8vLy8vPz89auXRssKysLutdXHHvssbseeeSRDnfffXf71atXJ6WnpzeXXujBg4e9wFM0/HhwDZKqAE5lCFU0nIwEnhsR8mAbQiTE+1Wwy+0NMd+PQdIh6oG6kqlL9owA20aUhUUFdyIEwe/ikAL3uT4x67TDKdP5MU7Kx52I3NwH/Atv9K85zAXeR6ojbENSUbIRMuBrJAjsgfQDW2LsNuGExrJ+t4x+GxI8Z+KMekP8INROydBUDp85jgVIWclsnOu70nyWACOs47LTDdxpAbVIUD8WSeO5nm85MmmC4j1pSLaZpEmnmER86XsYSRf6f8Q+f+2yrtuQc3YH7mlImpC7zfUzFSe9CGJTXiDW/wEat5O7moTb48FWrNhQIiOKBGzuVAs9xhBS/eZL0w7tLL+LptrrUMQwJHCVNJVgLbTeyp7vVrlgnz+6g9SabOSatkSIuD8jRFMFoooo8vmjg6OtdhSQXL+dQMNl392p/DhQvnDiQkRZp4aQo4D7ESXDZpz7xq3EsjEQqa502sE+Xg8e9gWtM1vXb6/Y1ohUaJ3Zuv7bbPeSSy7Zeffdd3f58MMPU+vq6vwnnHBCDcBTTz2VtX379oSlS5euSE5Ojnbq1Kl/bW2tv6CgYPdnn322fNasWZnjx4/v9N5771WOHDlyZ7xtJyQkRCMRZxxg9+7djQjuv/71rxkffPBBxsKFC8syMjIiQ4YM6VNbKyqwpKSkyP74MkSjUW666aaNt91227bml4aioqLyE044Ydfrr7+eefbZZ/eePHnyl+ecc05V82t68OAhHjyi4UeCkqlLFiLVIGwoATAXUSG0Q4LNu5E8+KuQF2M1pavCGRVtY20nAcnzX4jjGt8UfED3wqKCdFvRUDJ1yTrEyM+NsYjHgB8hRq5DUjamI6O5ICkT3ujfXmAMCW8HViBBzg1ICcRxSL/QoDaFxlUG3JULsP63pf76vRWxL+o6gq3BqC4fxpHbNyApMkeY6VmI/8JUJN3neKQvHokYttUjvh+DcCpZ2NDgeCtiKLoNqUZxwr61WLM4E3jCHP8NSJ9UzEeCZjf5NRdRPLRE1EXgGDhmIYRIAGm/cmJLw0ZwqrNou9fjeDWsRu7fTGLJF/VfiOf70JR5oxv2evFSamxCQq+n9iG99kpADEHSpjKRazmMptvrUMV85DnoB/D5o5C+K4RTLcSPpP5MQJ6xtyCKsjMxxrQMvsp+Xt0BTTttejgwMATDeYiqawhOCdk2xFbMUXVQGrH3RwQvdcLDIYQxZxattz0aAJISkiJjzixa/222m5mZGTn22GOrrrzyym7nnXfenhSMioqKQHZ2dig5OTn6l7/8JWPDhg1JAF988UVi27ZtG6699tryVq1ahf/4xz9m33fffZu2bNmS+MEHH6QOHz68ZseOHf709PRIz54966dNm5YaDodZt25d4pIlS9Lc+9+5c2cgMzMznJGREfnPf/4TXLx4caNl3GjVqlWkffv29c8//3zLyy67bGdtba2voaHBd8YZZ1Tee++9Ha+++uryzMzMyLp16xKTkpKinTp1ipsTsXz58qS+ffvuzs/P3/LVV18lLVq0KMUjGjx4+O/hEQ0e9qgOjFnkZuRF+nXEb2ExUioRhEQYgAQFbVyb8Zl5AwqLCuYjL9UFyAh0BRJkTQY6ICNDw7BGLy3lQhkilR9gjmM6Egz3RvrrqcDzRhGxJ83CQ/Nwjbpfh7x0lyFVFZRMSiXWDFChI9L6zHDnM9vBrQb+uh0NwKqRoN9epx4hEFogfc+HECBqbghiANkZCc6HIkF5PRKIN6XF1LKnqQiB0QFR3cwEMo1nxV5LXjaDXIQw8GP6ZLMrGL8Hs28lQrYgKp47kf5+Ao4pp1sVYJN+ICkuqeZPSQnbvLG5fFjbyDFe2oPCrWRpLib2I32lHFFm+JDA7S8I2fOW+bNNNg8HFiOrAAAgAElEQVR9JYOkTAxD+pMScnYguhjpZ6XAlQy+SqsUKIG68Ls72H3H8Z0v2VNF5cNv/vxDJmxvRryEbHNVEP+GLNeyf0dIzCNw+nsIyMoaPKE7Uup5evnCiXuUeSat4hHgV+ULJ3pl8jwcdKgPw4GsOqEYNWpU+eWXX97zxRdf/FynXXnlleVnnHFGr5ycnLyCgoKa7t271wF8+umnKb/+9a87+/1+EhISok888cSXwWAwOnPmzLU33njjEXV1df5gMBgpKSlZ9ZOf/KR6ypQpu3v16pXfq1evury8vBr3vi+44IKKp59+uk2PHj3ye/ToUTdgwICm0hFj8MILL6y76qqruk6aNKljYmJi9NVXX117/vnnV5aWlgaPPvroXIDU1NTIzJkz1zVFNLzwwgtZr7zySuuEhIRomzZtQpMmTdr437WgBw8eAHzRqJd+5GH/UFhUkI/kymsJwnWIuuAxpEzbMOAZZIS1Agk4biyZuuTdwqKC9NTElmf2bX18blIg+NRD4yeo8eOdiJy8CsnLvxC4vWTqkjlmf1OQAPRWo37w8C1g5P3PIIGRvnjXI6N8XyLVPxKQAFENIu3qDrbqwf5uy+wrEWJBCQfd/m7+P3tnHh9Vfe7/9zkz2TcIYATiAm6pwYAatxgpFy0utS6FVptqW6pcsNTaq6XaWq11af3VpbdVK9xoqV2oemPttRUtWoqIwSUqILFRK7iwhzX7MjPn98fz/eacTCbshCzP+/XiNTNnm3POfGfI8/k+z+fxZ+a9wD62XWM7Ylw63rxntnl8Cfnjf0jgfLoLkm0Q0YQIHO8hQsYpSLr0BuBbpp3iHlNTUZ2HzFTnAz8qmFK4OrD854hZ5feMf0P8vplIZs53gF8h36XLkED8K8j36hRz7PgMgmD2gIt/H4LXbD8ze1/tfe9OSEhUOhG/PtjuNH6dE/e6Ab8TRj0iNswHvtLrDR9FTLgAEZLmUDxtI1XlVgQdgaTcVyOfr73ubcBvESH1GKAyLmOh12JEhieBcxEB5fwla+b1yyA5t/jWzXTNEgpmWVk2Ij4qVyO/VTHk/zfb7u+fyP16GbjSig25xbcuQHyK3gI+qx0qlL1h+fLlH40dO3a30vwVRVF6A8uXLx86duzYIxOt04wGZY9ZPHtF9fgZRacR+IM86LlgMhq+R+eMhkqzb8ONd9w+GnHqb8PMsCNB1leRGvQrCJhxLZ69ohqYcGCvasBRiZTIXIgEtKfjGzkGTQJtymKiDAbo3B0hWPtvWy0GxQjM8/g2jl5geaP5twb4CXAdvkfDNUiZwrfxA/AIMlZSAucTDBzsNZ2ABIefmvNaxT6k6pvskBsTrJqK+GB0m2ppyliORUpCfooEJ4cjrWDPRMoKFiLCWrHZLYYICBHkPnl0LqMIzqwn0/ke2O0TYUs3htH5M7L722DMihlBYSEofNhOISnm/V9AMlBGmfV1vV5kEEqAe5H7key9+htozvosGQ2nOa5nhbZCJABNRYLSC5EZ8dpAFkNf4VLgfPP8MOCp0vyyT4HvBgWHvp71YMom3kHEy6CoECwRswwGJiNjPgn5fqTjt2T9HH523V9yi2+9xIgNtjRrJNqhQlEURVFUaFD2DlNu8eQu1iVcj5RDBB9BWgN+BvnjLgtJM164X05W6YIJdjciweB9wBLgEqQ7QzoSiI+ic6q+JZjBEC8k2D/i430T4g0CbRlGC1LmYA0ordBRhggBnzHLc5DZyDyzvYtkK7SaYwwP7Bs8nyYkIKwCjkLM3E4C7jxAge9cJNCuYefj905EUEtGumc0IkFQO5LBcR4SvO/AN9X8EyJEHIeIENuQa7LCgBUTwnR2zQ8KEfa5bVnpIvferm8PLIeuGQwR/DHhImnntmtNtjmHRmAMIhq+AXxC3ylzsiJpAZBBa8p/sX1wiFDUIb3Jjucc5HNZhRjr5iLGgk9RVf5gX8lmMHw58DyGtFo9DvhjaX7ZucBnEcE4HTFmvYHuf9d7MyWIz0t8OVI8VsCzZqxJSCaLB6xE7k96YPvTgXdzi2+9BPl92mr2PzW3+NaFmtWgKIqiDGRUaFB6HFMucTd0eDNMpash1zfU4PGA02HCVzCl8Mmaiup8JPW7DjEFDc6WB7MZrIljEBv42z/CE9XxB8sAks1x0vEzHOz7rEHEphTkD/x6JNiZh8y6NgIfIVkAKXTukhFPFpLKPNVc2wdmWZe60H3lhSX/zIsNj03P25Jbk9qWPH9nQkbBlMLVNRXVVgxwkXuwChES3kWuLcecp7035yO+EA4S1FiDRUvwnofo6tHQnQ+D3dbB/4ysQBPcPliuYsnCFx6azP5ZyOxvOxKkPYvMCvd+iqc1UFU+H/k9+gZpza0M2ZxGanP8eM5BrrMMEVv+jsyCv0Vf8JsArr9wZuHpJ+V+dvm7O2huiULnz/oooAL53qUh2WdJwOdL88vm98GshkpEHBqyi+3iP+dW/Kyo4PcjSC6SkVeLCLZnI9lX5BbfereKDcoeEovFYo7rulrXrChKrycWi8V7unVChQblYHM9YtIVrP/3TLnEgCbQ+q/yQMy+B034zHs9b1YtRWak7SydNXi0BNskWp+FNnyDPPBnzT06lzmEEaEgbJZHEEPEHGSG/lPECHQdndP1m5HA7u/ITPlgJJMhROcZ+CAtwDKkc0WtCe5vQkpGwC/b2SviP59oKDq1LqPpB9mNGbWpbcnb2XXAOR0oN+c+D0nD/wzSTeOfiF/DKnON45BSi5VmmzByP2wgZO97FL8FZfw9Cb623SHiO4eA3xEkvstI0KPBbhvMlEhB7nkM8W1JRUSiqxDxpHenkosXw1SkBOQXQNhxvRjpTcF7Y7GlJV/zPAY1R9gU8Zy7spO9vtI5gyGDU35x6fkjssrnreaNZZ3a0scQATDoQxFBzIDHEGfk2xfYWnV7Q27xrZcCzyHlYDszP7XYMiQQEaEO+V5EAsstWcg9ewn5buUgvzOFSHmKouwuK2tra48fNmzYDhUbFEXpzcRiMae2tjYH+ds0ISo0KAebfGQcBmeSPjhI53LQCAatZtF/Aj9A/mBdUVNR/Uvg6QNY516CeB88jbSPvAVpCTkL+eP8cGR2MxhsBoNU65Gws3KJYMbC0eZ5CD81ORUJArYjM6p5wGKkZOB0xMPjJKQG+lOkfCC/m+uxng/Z5lp2IIFuorKdvaUEuMMcf0EoGpqbU5+RnNaSUgNU7kooKphS+PuaiuoFSHC7Cvm8WxEj1E8QseBk5HN4FhF9DjfLbTCYyAzTBsbNdM5KCBp1QtdshWApTHrc9vGdSNxulrUiZphzEP+JMnbiV9FbmPPYU3nnHRZ+7PCsyHjHoYnO3VWg60y3fT2EmENaW9rw1qTm24A6qsqf7u3lE6X5ZXmZGeHQ5m2trKypS7RJI/K55pjXachnuh1YW5pf9hjSJvfpvpLdsLXq9pdzi289Cvm+tSLlS8HvRyLxwbZofQ3xZNiEiJyvm/VtyG9TO3KvJiBim71vF+UW33rH1qrbbzkAl6T0QyKRyNUbNmx4ZMOGDWPYtRimKIpyMIkBKyORyNXdbaBCg3Kw+RHiE3AiEqxuQAKtgUYJts5bZstuww9mTkZc7Y9FgtoDgS2jWI6kf1ea5w3AE0iHCisUxJsPQudZcRt8NplrCc7+OcjnHe/xYI9hsx+sAdvhyEzi+UhQMBL5w/8ws10bnUsvLPY8jkdEBqBzi8/9QEfpCcDnSv9jI3C7XWk6e3QIEd0cYywy4/kgMlN6uLmOI836HUh2QBHy/Yia1x5yLxJlLoDcl2D3iTZzfDuba2dlbfZDkHb8Uph3ELHmWLPOtiiN7zbxCSIYPQ/cbzxAfoh0MNmVX8XBQ7pMTDxnZPibQ1MjZ0djtIVDDKZzWUrQFDMo7LQDLdHGjE3O9iFHpAzePMRr5VFa007l8cU/cC4f35sD8OsbGiMT4jIZQEShGDLuDsXvIjIPma1vBP6G+Il8FfhsaX7Zd5FxNRWYu2TNvI3xB+0tGNPGu4055IdIB5pLkCyp9YiJY1BM3Wb+vYu01m0DHgd+uLXq9o3mOCWIqPeIWf8B4rGCOc4Pc4tvLdladfvZB/4Klb7OySefvAm46GCfh6Ioyv5A21sqB53xM4o6Zn4Hii+DaYM4FZhbMKVwo5n9vh74JjJLn8iEsbFgSmFmD54m0BEwP4SffbIFqWFP1FJyd2Zggin78a0TbXmF3W4xvvgUQmYUM5Eg2aH7sgk7Q+8iAcSJRmToMXan9CWwzXjk82+ks+eEzRaYD/zePL8YaYfp4reuDAbAwc/BCgwh/G4UtiwiPrPBiztOBBF53kUyUJoQg9AYklEyBBGDlgKPIULDnJ6+z3uNiAzXA9/yPIZivnMmo8F2HIhvf2iFNjtGd3iR0ByaMopwo5PYPMwl5rYRS/qSc/n4XlcqYrpHTEQylUoTbPIBEnTb71U7YuiZB/wOER3+H/71e4go2oaUCty1ZM28/SXk9Si5xbfaNqatiFHrh8g1jURE19PMpglNHgOiQx5SejOYwLjZWnV7ot90RVEURem3qNCgKD1IXGB5A/CzgimFt5t1t9I5kyGegyU05CFtJT+DuNBvR8ofzsTvcLCrP6ITiRDx3gDxhjL1SLDrIALDsXH7NiMzqfVIUNiE/HFvzyViHj8FZhRMKTyodeXdCQ9m+U3ANCRbIC3B7ssKphSeaESfB5HZ5lTk2o7Ev4/2n4Pcn4+RWdsQIgok4fs62EAyZB4/RoKkZUgtfg6+4PAHRFC4G/iHOd9TkO4MNchYvuVg3+Nd4vsw/BX4IZIpY68zeO/Abym6Bgm+bUZJMBPwYyDPiznJNKa7NKfhNeREnFj4q87l43tdd4bS/LJJwAOIgWEWvkhlsaKfFaNakHHWYra17VWDRJESmdH08oyGniAgOPwaKQGLAcu2Vt1+8kE9MUVRFEXpYbR0QlF6CBOw/wpxJf8TUh6w2gSPlUgw353I4AH/3RPnmYCxSEaBg5z/MqRmeRESaG5HAnxLvKAQn2Yfv50T2C44I5+DLzy0BNY1mW1tCn8SEgQmIV4HtuWji2QIvAEcXlNRfSewAthpR4gDSCdPh5qK6kIkUySEXGs7XUWGNiR1+2c1FdWPAg8jPhqXI+UjRwa2tbPvEeR+tCLBXz0S9AxFZmorgVOR+/MO8lkmIcHiMmQGdxtivLkVESoWIY7692KycJASDiuUbMf3F+l9SPbCBcC3kM9hGnL/rAmtHaNW2LKmpa1I69R6/O4DwXKTwwCXljTYNoRYuDUWjdGYJOO1N1IJ/BHxYanDz1CJL4VyEXPSMDIm0/DvS/z3OYQIUXMO8Ln3CUy2w4Lc4lsvRjIbnkTKLRRFURRlQKFCg6L0HBVIFoCDpKJfjcwO2uBzZ2UH+9NbYE+pBL5vni+0QXpNRfV9iPjRiASlpyOBbdAc0AYxNushPkjxdvLcprAnIzPtVohIonPXBBsERfFLJuy2yUgWxhfN8o1AS01FdQsHqJvHTujwdKipqB6FmOnZOvh/IgaVPwucfwxJ344gY+VspPvECOR+xPtb2HIIGwQnAy8jJRdPI6JGGnJPNyIz9SWIJ8cXEbGoEJnxXob4KpQg4tJYxCfkUuJaOAa7l/Q6JIPheuQ6xiLX7yIBth2L8WOyzixLR+7TUUi5DnT2EQH7nU1thsx6nLosx0lvfJOmnF7pSbFkzbyG0vyyOUiGxqmI0egwEhuKDjfPtyFjcJh5HW8ACnJPvmUeZxyo8+9LbK26vRqYdLDPQ1EURVEOFio0KErPUYIf0BQjIsNrSPBp2zd2R8pBmoW3gWSievPHkZIGkKC0APg/JCD5EL+2uQg/UAtmL9ggz6Znx2c4ZCB+EDZV26Z1pwReBz0eNiCBO4H1USRgtL91tqPJHcB9NRXVo/Fn6A8oce1Ef4lkCoBcQxXS6vJD5HpPRurFj0Xu8+vI+DkKGSfxGSJW1NmBXOMYJHtjGPI5gJTlRJCsiHOQWfoTkDKeEchM/WICPgs1FdVBkek1fKPQvsL1wPfoXBoRHGfxIkMScv/rkHu+ERFgkpD7Hm+c6ZPc1u4M2vFaOBS72rnoC73Sa8Z4NExFzHf/CLyCCFzDke9JkGBmUX3c8kQilwt8sTS/7Ht9pROFoiiKoigHDhUaFKXn2IwfXOYiM8wzkQDuTXYWxPTCNlfxAkRNRXUuEuy/gwS3HyAZDo34JQGfIKaSdvbdRQIcKxrY4MVmQAQ7HCRqPeeZ7erMcdvw68vbgeRoW1Nb/fp3Y1nDC9tCyWlv4Ys7pyOmeNRUVD/ILowb9zN3AoOQAO9VTKcGzP00GRdfRYSBTKRUws7Gx9OC3NcwUpKTg2QmHIVkmkwG3jL+CVfVVFR/GZl9rkDu11rzHhmIQDEWI4gkEJl6Z+ZCENNJAvGQ+C5dZ+qDJGoPmoTci9PpvqsH+CJXC00Z66nNC4F3j/Ols1fv+0Xsf0rzy/KQlqOnIdksTwDHIJlDO+gs6LmI8eMWJHsmGz9byG4TQgQI6/8BMqYnkliYpDS/rBgR1F5DRI4dwEIVJhRFURSl/6FCg6L0HCuRP8JB/lBPR9KXxyEO/t11bYgB9/fECe4jlcAPkCyGnyI1/w3IzHopEpS8jqT0Z+Kn8dvZ0aDQYA0dg60xg1hhwm6XiV8qYY/1IeDWr3/32E9ffczNP+1rDD7ylM8jgf1opJPDEUgJwxxgAvBfSE31AaVgSuFqRADojoWIGDAZERni70Mdcv+WIR0oahGhJBVps5eOn+XxFJ2zEAoQv4aRyH07Bwn45iCfT1/KWOiMiAy3ANcgYkHw+9Sd/0l8W9RW/LHUjNyb+FIVezwRytIbh5O147/ZPqRXlUwYcWE64qdxDuK1shYpJ5qBiIC1iDD1LiIsPA98Dmm1ejOSXTMTGVtWmFmFjJ0ZZvkkxAOjbRfnMh/JsilCOuxEgJWl+WWrgZuWrJnXK0UaRVEURVH2HO06oSg9RE1FdSt+wNiMzCRuQQKjicDX6ezoHkMc/X8BPH6wSif2BmN8OR05/zcQ87lkJBj5CxLYTKZrucTOWmTuqn2mLcPYjNzHHCAcbWv26tauqMs45NiHkjMGHw8cj3hktOOXYdjSg3sLphTeuGdXe+AIdPz4AlLm4CGdJq5EhIblSAZCZcGUwgZjzHgp8A1EUGgw+1diMjaQAHy6eZyMtP8sAK4vmFJoW2j2TarKr0RmzFPsf20xj1jI3eWYCo6tHUiG0aGIx8X5SNC9s30jwF0UT7t9v1zHfqI0v+wm4MfI9yIbEQIakUmGfyDfx0eQzIYKpMwkHfl9mot0OMFsNxk4CyknKTTHeAa4wng/2EwS26WiU6ZCaX7ZreZcuvss2hGR7xlgvmY5KIqiKErfRoUGRekhaiqqbUo/SDDzIDKD/BoS0BwTt8sc4Ht9SWDYGcH2jkjgcj/iTXA8fqcI6H7meXew9zg+JT6CZFYU0X0ml4eUHkzvbffcCA5TifOTMB1L7iCutaS51zZ75jWz72Tg5oIphQvMfnch2Q6ZSHvKt5DZ/GsLphRWH/irOgBUlS9AZuMJ/tfmdB1R3XVCCa63pRHWf6DrRh54EIt6vJzkchnF03pVa8fS/LIrkd8R284U5Jo2IX4cZwIvIV4gd5j13QkF9vu7Fil7yCHwnUGEq0eR+78OmL5kzbwFgf0OR7qWZCLjrN0cI4iHZOv855I183pde1BFURRFUXYfLZ1QlB7AdBloRv4YjyJ//N9tZqEnIbP9bfimkP9LPxIZoIsZ4nz8logZSEr1OLOpFQmi7Ny3Iigk2OehwPpgIOkiHg62/aUbWG+DyjXAZ5CgqEe9CIIiTKLP3IgLibqOdHSyiNs+6PcwCREZgiUUlUhavL3/pyJdLVzgPuC8fb6og8MJwReeR8SDdXgMdR3xAnGcTp99d1kytkxip94oMY9YU4TYkvWZS86f/JXeJjJkIsaf8S1TQUpqTkayGsYgvze78krIAE5CfBiC5q6XI6UZoxAB0THb2rFm27o+i2RDWIF1GzImbTcLkO9hDvDj0vyyl5asmder7qmiKIqiKLuPCg2K0jP8CN8w7Q3gjkBAWQl8xzxf2J/Ehe6Ia4nYUFNRPQXpihACvkTnNo3gt76MFw8SPbfE19NnIh0ErBgRMfu1An8FbkKySg6GR4ENxm5hD0SO3Wwt2SEq2LGV4P4/ggTpG5Dshr7KdcBjQLLjsNJxeB44519bk14dnBK96JD0WDJ+SUU05O529kxCQcJ1iK2uS5n3SUPyA/vp/Pcn1rMjnhgyzmPAEqT7RGUikSGQjVCJZMXcDPwS8TmxXXSakVKdFPxMkZbA8awYttzsV2lKLW5Cvo/Be2sfjweeLc0vm6AlFIqiKIrSN1GhQVF6hjvxZwFvCooJO2kfOWAw5ohfNzP7zyAeBOcgQUgS8lvVjC/WtCOCgQ1M1pllmUh2SLwRYBMSBIXNYzOSVdKECBt/NOewuqaiOtNkAfRUBwroJjNhX4jLktiVGNGRYdLHha7RSCbMbxHRAeCVvPRozsc7wvVDU9suwmGw4+E6Dm40SmsolDAYh117gsQchxeKhrbOLDrva73qnhmBYBAyzjPjVofNvyhi8njpToL5oAA21yxbBfyneR4F3kbGzkTE8DUKPFSaX5a5ZM28BnNsO/6C43AuIjRMp2sJBUjGxQX0gDmroiiKoij7HxUaFKUHMEHsZQf7PHo7ppRkO2LE9zPETDIbMaF7CvEVqEOyD76KpPivRFr1fQdJBf8sUiaREjh0OtJ28wgkeHwOqSf/CpI2/lpg273KLtgXustMiPO1sOfWYQCZaFlAKOh0HaZ850fAnWY87vL9+yBzOx6LpzWYLhTH56bGpuaktOW5kB2J4bkurgO0xSAkfgS2Cwwk9mWIN4z0kMykqRRP61Uig2Ei8Cv8a0qEg3iW3FuaX7Yc+a7FmzBWIt+5VHOsZOR79RP8bihjkfuSZrbbjnSUeLs0v6zDhDRezDBlETeW5pf9DRGGPkW+5/YeRxGTUkVRFEVR+iAqNCiK0tvokuqPOOND58yPl+0TE5BvRALu6cDXEHf9IrOJh7TbnAxMQ/wgvo+IP7bt5jPmOIOQevLlu/JO6AGCYsEgxD+hEqmV/ytSXz8MeBq5vruMoLAOODKwPYjI8A0gXFNR/ZRZ1r9KdcSMMehlUYJ85n98ZV3aoNzU6NXHDmpLRrJkXBfeR1qEnoIEzYciAXWibhQOkgHThIgy1/c288cAOci4CAojHonFkwnAhUhG0EOl+WV3xIkCxyP38F3k+xIF/obcqxHI/YoGjpmDfKe+gLQFvRn5riXM2lqyZt7LiG8KpfllLyOtcKNIi9c5e3HtiqIoiqL0ArTrhKIo/YqAOLAcESOOApYVTCk8Oa4bQw7w38gs7AtIxsRxSJvRNqR+/VPgXEy3hp68DuiS0XAL4p/QgPhLZCDB8T8QoeQypJvAafgdE15ChJdS4BIkjf4zyLVvAf4HeLBfiQ1BJKOhBKgseuIYfnLKhusvOrL+ayGXENIFoRIJllcBPwDuinmc7cWIuS5JjkMECbCXAlnvb0v6xZub08+pawvNnf71yb1VZOiulWQ7Xc1Vg4KKg/iVVCHZGq+abe5AfBzWAw8gY85DzBzT6Jo1YX0arFhwBDBryZp5uywPK80vG4WUma0B7lczSEVRFEXpu6jQoChKv6W7tpCBdS8ChUhwtAURGFKQjIACJKB/ASnPeCD+GD2FERweBb6IpKZXIwF0NfAQMmP8KXA6fuAXQQLIxUh6+xBkptnWw281x/oT8B6SHdHOQbzOA44vPKwFrkeC6Z8i5TmPA5M9jwzPg8YIq7KSqQDut5kLRbMWdbQTXXHPhF5balKaX5YHvAmMNIti5jGR54TNdrA45vV6RMQ6HZhl1j0K5AaOE+9jYUWGCCL0jUJKlr6HeK48gTFcVZNHRVEURenfqNCgKMqAwwTu1yMZAtlIwBRDZnQjiNFkClJe1mp2e8asex54uiezAIw55UOISDAXEROuRALAHcCvkdnlZPySuCginLQgM/YjzPIh+K1Am8x2YSSzIwrcWjClMFErzf6HCA+3IJ4CPwDGeR5TNza5y6q3pV1x9iVlnbwsimYt6siQWHHPhF4dKJfml81EMnbiSyStGAC+UGDFhrXI+GhDxLWjgN8gmQynAYcgY+4Y/FKMGH7rXnvMNuR7k4OMqX8DRyPjbTPw3d3JcFAURVEUpe+iHg2KogxESpoisSveqWsLR2KQ7DruCTnJbmrICeOnkVsVth34EDgbccn/PHBeTUX1P5HgfU4PZABUArcjs8IrER+CdCR4a0dEgjRzzlFERLAtQlOAE83rBiSd/WREYFmDBI0gQeNH+IaK/R8xjHwFuBgxGV3gOCw4NCO28NDPlnUREoy40GszGeL4FLmm3Ljl8S1i7XhvR0SGKFJyMwEZUwDXImLM+8jfDZsQ/wUrVCQjYyuMjLcUZEza9ceYxyxk3J5Zml+2ULMaFEVRFKX/srPWXYqiKP2Vyoq1TX+Y+1FD9LGP671HP6rnnR1tdp2LBF72MQMJlLKRjIZ0xA/hl4jB4vU1FdWTTJbEfqPpd5cWtj52zvNNv7u00GRPjERKJ44DFiHBHsBV+HXzwX8ggV/QzDALESvCiAhxlFnXjsw2/6zflk10z0KkY0kLYlzY0ks7Sew2xutgMlCOmKLajJ0gMfysBCtKpSPjfQgiFrwErEB8S4YBZyICQy6dMxrCiCjRZI5txYzgd4nAumuAOabEQ1EURVGUfohmNCiKMuAomFLY8LdZi+4fmRpaPTo99L33GyNjTGTehszEBl36Xfy08GDaeap5PBqZ/b2rpqK6hf3UoSLk1d+X7G0418gf5+FnGqxCDCp/C5yBpLMHAztL/Mx1x6GRzAy73F7TKsT0cGAhosICU/H6flQAACAASURBVEbRgt+loy9zG3AF4smRgYgNqXHbbDDrsuksBtjHZ4BaJPvlJ0j3idORjidBU0nHHD8JMRttMscNGk1a7PcpE2mPmV2aX/YVzWxQFEVRlP6HCg2KogxITBr872/7xesLhqSErj02KwnEAO92xPfgUPxyBBu0xwfvHnAs8H9IIHczUvO/z+n1USfrhjbzCGAyDe42mRMpwK2IyJGI4DnH04QxjHScKGkp9U5za1ay54XGINc+eW/Otxe0At03rODQP3gRKQexJRERpPznGPxxcah57uCLAfb16XQW3ZYibUDHIoJCDOk6kYMvHEQR8eIHwGxkjDUif2c45tGKD/Z9LkAEB9u+VlEURVGUfoKaQSqKohhMsPxLpDTiHeAUJAMAujrsB6kHngP+hogUCw9ksF1TUf0oUjO/N3RcR3rqdoYN/oTabYfT1DIIYDVQtDfnvuovVRclJzfd29Kc9fagSPsHHs4Dwy47ZaCVYfQKSvPLrNnpfyIiQBrirzCmm12C2Qe2NaoVCOzrFjpn9tQimTF2u1akhOcZpOTip4jnh/VnCBpQgv9dqgFO0awGRVEURelfqNCgKIoSINASczASrO1O5pc1YXwSSTX/PlL7v19n+I0QMhE4DEmPH7qLXYLiSNQ8bgYGAVHHiaanpdTT0pZOanITza1ZEc8Lfb5gSuEezezXVFRnHjJ49fWZ6du+s33jkTlZLW6s2Qn/OPey0/a9e4XfkrKyr3sn9CRGbJiIZB2MQtq3/hy//WmQGJL5YEUD8IWHHYgA1YSU6thMHhfJekg229ssmhZEnGs362L4HiLgZ1mEzDEiwDeXrJn3+32/akVRFEVRegtaOqEoihIgUKIwCihGshoy6OxnEI+HBF12Bj8V+DZShnAz+5CSHxAXUoECpPZ+MFJbD11r4Onmtc3M6DDg87wQTS2DTGbDx15dw7B52+pH7I1HQcnWHcMvxYl92uCFoxk5WzZkZW3bTNWKzP0gDpQAd7CfSlIGCiZDoKOFpBEeapGxdCWdBQfrnRBFSiNsOYWLiFLjAtuB/x1IDhwj3rskCfleBEsmLEFBIwxcAqjQoCiKoij9CBUaFEVREnMCkA88jBjhTURmYnNIXEYRBY4A7kKyGq4E/gks38fzKAHuQcSFNvzWg0Hzvu4EkO7KPez2MYDm1qz2hqbcX+cN+ej/5Q35qISqyj3NHqjMytz6VnpK/eWpwxrfTUtpGBOL8bNYjCvcqvLXkJr9E8y2C/f02IjI0B9MGg8aRnh4EniyNL/sEWA+khFjx0EYGV/gZyy04rdPtX8vtONnKcTwBazgmHLxvUA8OpdmOHQerx6S1aAoiqIoSj9C21sqiqJ0jwe8gsy2bgEqkNTwaIJtM4AvIB0ivolkDvwHcNo+tr+sBP6IBHgeYjx5DdJ20NKd8WN3v/F2+yZgo+eF7t2y47BbEbO/OxBxY7cpmFLYEHIjP2xuzb4DnG80t2Y82dqWnuQ4jEfKT+4GfmOu4xWqygt3++DF0xoonraA4mkNVJXnUVV+E1Xl2hZxH1iyZl4VIvxU43dYcRCT0eBYCiPdSP6IeDysNa+3AFuRMWnxAo8fAP8KvI6abZvxRQxLC3DT/rkyRVEURVF6C5rRoCiKkpiFwHeQQH8iEpRvQIKsbPwac+gcOKXgO/P/C8mA+Dni2/AMe0jBlMKGmorq+5E6+XOANwqmFK6uqag+EziRzrPICTHdJWhuzcLz7AQ0UeT/gOeAuwumFDZQVbnX2QNDzzl/IyIoUFNRPTMrfdOLqSn/vspxOAa5J4PNuRYBb1NV/iSwCBgBzKF42kYjIEwF/gp8xRz6AYqn2ZKUqUgpCva9lL1jyZp5G0vzyy5CvD6GA2ebVUHjRgcxkDwC6SAx3Cy3wkQEf+y5gXW5wPNIyUUIyX5w6NwW07JoyZp5q/fXdSmKoiiK0jtQM0hFUZRdEGjduBy4FpiBCAiJxNooknJeixjpPQV8DZkJnl4wpXCvgqqaiuqbgB8BTwDXIRkUc4Fz6RzkdUpNd5wog7PWk5WxhdptR9juEnaG+SXg68aXYr9S/9JTo2JJTb/YmhYtOLS1fXBqiEOcznkXHjLDnQzcD7wJTEfu8wbgcLPdvRRPuxEgIETMDYgPyj7ykwvOHxX1nIql72Wd0NwWCgpolmDpA/jlOi3I5xfszAJ+xk8SXUWwYNlEEzBGhQZFURRF6X9oRoOiKF0YP6MoDwn6aoD5i2evGFBu/zUV1YXAfYjfwplIcJ8H/Aq/lr0JaR0YH5SFkNr0w5EshH8CFyMzxrcBX9/L05qLtAocB5QUTClcUFNR/SpwfoJtI5jZ47SUerIytlDfOISWtnQvM33L+y2tmQ9Goim5wJwDITIAhMNN925Ni1xUnZrqJOMxItKlDN+m6oOUmlxnztl6XTgd66rKPwD+gfhe1CCz68peUppfNgkZTy8A8/88a0PL4IyIc88zIxfOfyv3MCT75BCzuUPnDIfg8xQ6j383wXbxHiJBb4Y/qcigKIqiKP0TFRoURUnEdCRFfROwHVgwfkaR7X4AsLA/ig9FsxZl5ia7E28+LvuOqm1tRROGpZ41ODmUhNyPQxABIQLMA0YjIkR3RowO0lbwESSArgde3NtzK5hSuLGmovo6bKtHYQ5wFvK5uIiwkYb8tqe6TmSz60QP37w932luyfk0O2Pzm4cMWT3Kdbz3KZ52QDo4bH7xubykcPO1WektJcMiOCe2tDCsq8gAfq2+h9+m00OEmuD9HAo8iNT9FyDj8Sr2ogxF6eB3iHD2NWDidXNHfftP333/phGD25YjPh0fIKJOBjAFyVw4Dvlsgm0qd+YBkkhcABGJUoAGtIuIoiiKovRbtHRCUZQujJ9R9GXgF8AbgcWtSKvHPGAd4kS/FQkY5gKP93XxoWjWokkZIedXJ2QnDXtre9vgySPTnUtHZlinfJDgqRr4HFCG+ATE8Fv6xRMx//6MZEUsLJhSuN/vUU1F9ZVIBsYNSLnGPcAfhw36ODkjbdsNOxoOeXJb/YiZBUdWghUq9r3tZPD9M9NTtl8w8pB3z/I890LH8Q5zXc91HL+zhSE+MLVGhInWBQkeIwrcCJRzAK5lIFCaX1aDCAe1SBvW+aYrRfx2wZKhLyFZJ8OALHZtJh0DXjbvsw04CvgQETdGmG0WJnpfRVEURVH6PprRoCgDmECWQg5i+paEzNqfiASAn6fz74Sttz7avM43j2OR7Ie+PstcmRJyvn/y4OSc9S3RU0uHpIwD3gMuQgKsOsTXYBJwIXJvVgJLkRng9LjjhZH7VVkwpfBA3pungY34mQ7XApUeTkZza3ZDKBSZKwJHIRyAWeTkcPPEnKwND7ouQyDmeh6e5xFxnA7DTNt5IIpvohnD92ioRkwiE9Xyx+icfu8iJSiHIdkcNx+Ia+rnXIG0bb3GdKBIiBEB7L19sDS/7LfABcDpwLGIYHAIIkJuAhYDk5FOLZ8CDxjTSStYVKqwoCiKoigDA81oUJQByvgZRaOQTISTkVTmMF1bz+0ubcCXFs9e0deFhoTUVFQ/BVyCP7MeQQKrTYg48zIwzTxPdP/+XDClcHIPnGqPMnd8fiZQMvlHNw7KyMm4C6JDXJfBZnXMcToEBVsS0U5ig8BWfL+G4LpNyL0eEdjWrm9APDQe1IyG3oMRFS4Hvgz815I186oP8ikpiqIoinIQ2JuAQlGU/sGPkBnhTCT421nNtSWGBItbzXNbGvB7pB1kf+V7SMaAzVBIRvwQLgRuBc7DN8aLJdg/u2dOs8cpAe54Yc4jLaFQdGYkmnpxJBpa6XmscRw8xDDzE/zSk/j2hl5gefC+tZrXrcCQuH3sGE0B/qoiQ6+jBLgXKS+67yCfi6IoiqIoBwkVGhRl4HInkva/K2L4s9IgweMjSOr1v5AU6Wf6uj/DzjAtKR9H7oN11F9pOja0mH/tZvNEv6t5PXGeB4FK4Ja62i0LKZ62IKXkypeTTv/mCa7LDYgwsx659g0k9moIdiZwA8+TzfPD6CpOWJIQkUfpXVQiwtwLiGeIoiiKoigDEC2dUJQBivFnuBypcT8cCZRtgOchmQpVSC12inn9KdIFoAEJIscD7wCTFs9ecUDaJPYWaiqqi4HX8P0CFgOXIS76JcBpwC34HgSWHcg9fuxAGEH2SqrKM4FfIvenDqnjT5Qx04QYaQaXx5CMmaGB192J4i8hWSUQ6IhiHtUoUlEURVEU5SChGQ2KMnC5FPhvZMb5LURI+F/8VPf3gdnIbH2d2XYScA0wC9hijpOPmEH2d66hc5u+04GpBVMKGwqmFC4A/gfp1BFkM2LM+DUk8O3XbH7xubxtC/96U31j7jBgESJUDYJOXg3tiFAFIjJ4+FkzmG0HBQ4bX45i1fEdSPvQ54C/IWn6P0fucwlwB3tyz6vKR1FV/ihV5aN2ex9FURRFURQlIdp1QlEGLucAacAy4BvAF4BVyMywg7SlGwXMRLpSjAKwho/jZxR9iHgPPInf7aA/cyciqhQjpSONiJmm5bPAd/EF3CgwAfiYzh0h+i0pSfU3pqfWXRuNhT6PCFiHINkwnyDC1dnI/alGxKlkJAPElqTYzhJhOmcy2Mf2wLqQeY98s88/gTmIePEakl3yAVXlNwFzKZ7WNeNGMi9KgA+QcXySWX4dmhGhKIqiKIqy16jQoCgDl9uQLIY7F89esRqoNuUUPwW+jdRYz1k8e8XG8TOKbgK+DxSOn1HUAjyAzNQfgQTRjJ9RNAmotF4N5lglwFrgr2bbKHDV4tkrft9jV7mfMD4N5+5sE3w/AQ94vGBKoXXcHxDtFzPSdpziOIRdN3IiUAtkICLBCEQMaEbErWMQYWCMWZ6FCBJrzLZRs8xijUdfQD6DsDm27ZISAVaYY9wHPAbcjYzjHwHHUFX+Q6S8BaTc50pE6DjX7Hs8ktlzJ35GxH1UlY9GBCVbIiPig4gUfrmGChKKoiiKoigdqNCgKAMUIy5cFbesYfyMojHAkZig2YgMfwU+A1yMZDGcgfg6tCDu8tchQsJPTNvM1UgQ+VX8FHeQoPBhpEtFf2MOcArSgeI5BpoRXlV5nuviIiJBMzKGLEnIeIkiWQkO8B9I9kEjIhSEgeHm8TdIdkEu4gtyEtJ94lz80gkrMsSQ9qqfN9sfAnwdeB0Rud5Gxu0gRGgIAfWI0WQ9UvLyHlLmAvIZvoS0zpxs/iWb95iM+G0sQESHn5t9vsOz31xOtGUqodS5fP43/dqvRFEURVEUZVeoGaSiKACMn1GUB0wFXkT8CO5EshauBZ5FOlRMRwK27Uj6+wlAOhKItSNlGMWIAGFN/qJ0FjXfWzx7RcGBvyKlx6gqz9xB/aMf8snkAkY76aRBYg8g68fwEiJc5SCiwuuIQHMa8Arw/U6lDlXl1yMZCiFEpPgY2IQIEGvNVh8i4/RkJFtiBJK1cBgyPhuQdpnZiPCxDRFEwkiL161mnwZz7IeRsqE2JLtiECKILEREj+eBE837v8r6xQU0fDKLjJF3cdG8u/fiLiqKoiiKovQbNKNBURQCJROXASyeveIqIzxcjPxOTALOxBcNBpnnmUjQ5pjHcUiAmYYfaMb/zmxB6W+U1MRWnbPSfS+EByc5hYm28RCB4Gmk9OYEs3yhKUV4mu59Ef4NfISMnROAexGD0gfM+i3I+CxCRK6I2W41klnhATVIJ4tc89qaVFrjyQzzmIUIID9BBIqUuHP5onk8C2tuGW29iFj746Tl3UP68CeoKp/UzXUoiqIoiqIMCFRoUBQFpNZ8AmKKVz9+RtFFSEnEkUjQ1ggMxnf/D5vtg7PWLiI2eHSdzbbGflHgt/v/9JWDTKW3veDF47PDk48LjQ55Hjh+f456RIhahmS/PE/xtNWICOAjQXl3XhYLEb+F8YiYdTFSfrMReBUJ+qOI4eQoxHTyf4D5iCiRgogQqeZ4jvlnu124yBgPCmRZ+GUa0LXNpi3bSKFxbTo73ruEQ896j0EFl5p1s3ZyPYqiKIqiKP0aLZ1QFAUjLDyKiAmNSL16JlLvbjsCgB+c7ao1bgwppbDiA4hgMRv4gTWMVPoPcx57Km9ERuvfz81vLEoK4TgOLcAfEa+DY4DlSKeJvZ/pryrPQ4wezwTuQUSGD5AuIOMRYeBNYJh5HIpkScQQkcGOXTt+7dgOjmvb+SKeYPtNzHYiqkXbYjR80k4smoQbdsk8bDOh5POR7Il9v25FURRFUZQ+hgoNiqLY0ok5wJcQ88b1QCG7FhQSETX7bUICrSgSuG0FZtj2mEr/46YHnhl1fG7L3V8ctYPMZG4ymQv7FxEbpmJbVkqZwhzEbNJFfBfWIJkNHmImmY+UQcCuhbJE69uQUo2h3W6z40NY/09wkyCv1MN1t5M+sp1Q8kpEwLuJ4mma4aAoiqIoyoBAhQZFUYAOM8hrkW4R5+Knme8ptUiq+lZgMbABqWv/PXC/ZjP0Q347xrYyreQbK3v28xXhYSHSnjIGrEMycYL/uSUFnu+ueGa7WdhMiChiRmmP0TnzYceHsOFlGFQAqcNg8xtwyBmQdUQM+IimdZex6n9zyRr9Ack5V5JbVEPKoPma5aAoiqIoSn9EPRoURbE0IjX0F9A5MNsZwZndWvwOFB8BDwJ/Muv+AVSqyNBvKQHuAG6h530JxiIZC5ZM8xjF73xCYJkds91lNthyihgylq1gkRRYD/HlFZmHwYj/gPTh8toNy/P2Rpdt744mnPEc4fR63PC/afz0LDJG1pIyaDvq46AoiqIoSj9EhQZFUSwXIG0t40WG7mrWoXON+zLgKMRAcgwwYfHsFeVmOw2m+jeViMhQ2WPvKJkM1yPj7T6k7Gc7YhbZnVAWCjwPigxBfwZrZir/P0bboGm9Q/rwGKFkl+4yIkLJkHWEf7ysI+R7UfuWZDcMOWkoIz+XQ8oQj7p/P0nKkGfpyfulKIqiKIrSg6jQoCiK9Wi4GJnBjZ/p7c4YzwZmIDPFYxETPrssbI5bADwMXLN49oqq/X/2ykFHyiV6WkyaCvwX8v/YUERcKMYvdQjR2fSxu5IJKyzYbUKdtm9aD5uWwiFnuAEhIX5/BzE7xZyP/16pgyGcDmlDIevIJKJt2YTTjqZtRzVnztIMH0VRFEVR+iUqNCjKAGT8jKKrgQcQT4bHkdaBpyKCwe6UTUTN44dIS8AjEJFhM/K7sgI4GkmpvwsJAB8GTtlvF6EMPKrKrRfEcmAV8BpwGvAu8BIydm+k6xjurkQiPoshuL2sTx8Oh5zRTvrwEImPY8ssQviinH/srFEOodQY6cNl3/rVQ9nwylCGnvwY8r1QFEVRFEXpd6jQoCgDDJNl8DDy/X8YGAFMAVYDTUBRgt2sMV4KIjI0Ap8g9fDvII7/DtACZABvAYuQ1PBrzPtcc4AuSRk4WOFqBTJO7wGeBbYgpRvLkYDfZtV0V/IDvpjg0bmkwhIBkggle2QdERTggm0u2/AzGNppbwyx7V2Hwce7JGXI+4eSxauhab08tmyGSBO07Th6b26AoiiKoihKX0CFBkUZeEzEn5kNIynoHwBnIa0twXfYb8cPpHYAefgB2r+B8cAJ+BkOI83jdLPvQlMuoZkMyv6gEngK8WP4P0TYegJ4BBmbE/DHtkfXkgk7ru3yRD4NliQSZyo4dBYyHKJtMZrWh2laD5vfdGjdFmPEhK2EktOItqWxZblL3QeQd6ZHylCHcBok5/x7n++GoiiKoihKL0WFBkUZmASDqiagFQmashCBoA4JplqAbKAZCbxazeO7wBlALjAE6TKRh3SdwDxeA7wCPHOgL0YZIBRPa6Cq/EEkYyYVuBkRvM4yW2TgeybYf+BnIdQiJT623CG+XCJI/P5tiNg2DBEs2szy9TStH8GmpSFyx3lkHx2lpdalaX0uWUfU07Tepe7fkH0MpA/3gCYcagilfn3fb4iiKIqiKErvZHf7iSuK0n9YCHwL+Bi/3d+5SFlEOhJcJSNB1f8iZRKHAIPMsgjwBvAyIkR4iGiZEngPO5usKPuX4mkNFE9bgPgzPIsIDg4ikDXRtVzCjlkHGcfWa2Fn//8FyyOkhELGOkiZRgRYa957O+l5z3LI6Q1kj24gr+RDco6OkjrUBbJIHw7DToWUXDleKDmVQcdVk3XEx/twFxRFURRFUXo1mtGgKAOMxbNXNADl42cUPQP8FClr2AwMN5uEEKFhPZKibpc7SPbCQuBfgJ2RjQL5cW/jIAHZwgNzFUqQolmL8oDp7qb16zJeem6EE4nMWbJm3sYE21xrXj6w4p4JHesbHnnmy6mpGf/T0tL4n5lXX/RkD5763iGmkFOBKxCfkDWIT4g1d7TCQwgRA5LNnjvzbLAkynRwERHOA7Yhfib3IG01jyKUWkPWkTEghab1R1O3yiHt0BhJGS6h5Bhu2GXTUnDDkHVEDDgT8ZvQtq+KoiiKovRLVGhQlAHK4tkrNo6fUXQdEvBsBf4EjMbPaCih66xvDHH7/waS4WCXQecgLgbcb0SNHmH8jKJC4FeIIeD/Wzx7xcZd7NIvMALCE0BpeNO6ViJRa1J4t1lvOzWcDdxgdhtVNGvR9BX3TGjwHl98ZWpa1m9d13VT0zJ+C/R+oUGuZzKwFLmuwYF1dhyGEWEghT3DjvkdiLjQjl821Ij4mZQA3zTvW2m2T0e6VDQw9MR3SB9+vHnvVtKHp3DI6Q7pw2uREqNXAvspiqIoiqL0O1RoUJQBjBECFgCMn1HkkLjdn53htTPEU5HAywZ0iVLQXwceO2Annpj7EKPLzwKbMIF2f8FmLQA1wPwV90ywIs50oBRw20YXRMIb1/0lvGn9XLP9VEQYmoWk/tvPeBJQ4j2+eDnwiOu65jN09zQoP1hUIv4My5EsjW8ipUBj8Mdj0GMhiNfNcrsORKjZAByLjPv5SBbCH4DZwGXAX4EvIPf3RqTMaA2h5DcZVPAS0nYzFYBQMmQd2YJ0aLkMmEvxtB4T4RRFURRFUXoaFRoURbE8BNxLZ4HBATYCQ5Ga9Hw6iwyJ2AqU9WQ2g+EG5NyWA3N7+L0PCIFshOVImUsZUA9cXTRr0UJEWMlAvAnSSc94vunsL3wfEVuuBU5HhIkXga9hPtv0tm1pV25//IbNjV86bXBKbhjHieE4tLZFft8n/lOQIF3KDqrKX0fas9YAhYGt4ksgLDsbu0HxbARigtqClAdlA+0UT1uNL2JVmzKO7UBlnHjQnQlqvxLAFEVRFEVREtEn/qZUFKVHKEeC2B8Ao/CDtKFmeRuS8j0WqYtP9PuxCZi4ePaK1Qf8bONYPHtFNZJG3zeRgLWEzgFrCXAH8DQwDjHgHA2cal5fgaTsv4AYFL6KlFGchAgQDhJ8u0g2yg4g66StS5LPPWXCpNz0oTRtayJGS1127tCfpaelPNgzF7tfWQh8B/FouBi5zv1BKrAEOALxJtkMrOiyVVD0UBRFURRFUQAVGhRFMZgMhEfGzyj6KzJ7fhGQhgSsuUgLyz8gQWxwpjgGVCMGkTcdDJGhnzAR+DnwfZOtUIL4ASxCDDtfAt5GRKCvIgF1PRIEn5PuEhmXySVvN5DULK4ZwRKB0UA4w21ZW5S2OivjqNKkpGR4f9u66Cc1n8S2Dzn86S/n8qBz+fi+mM6fgYzJLUjmgWVPuioFfUa2IuUSC4EfmX9fRrwr5u/rySqKoiiKogwEVGhQFKUTxkTxqvEziiYhgW8IOAx4GEknz0CCuFYk8L3aZBMolrjshIBfwhPAMUBlwGOhE5ubQ6nATWb7V4HzkNn1BsSM8ENE1DkUKMb4aYzLpP1bI5zkh9Z5saV1uPgig4eUlHBi+oe51wx71imvvZif1h4XS/cynOmFg5Ne3OieftebMVZcfiBuxgFnKuLX8GckYyOMXG/QZyQRscCjNY7ciBihxoD3gFrgh4jgo74KiqIoiqIou4kKDYqidEcl8H3EH2CseZ0BDEPMB6ctnr2i6uCdXq/GljzcgqTV22D4BGA88FjRrEV3W7GhaNaivNyUUWeek9+wfMGnWRORkohU4Bykc0EEaTeaj5gSesjsvTXv5O0Gkh5a67GsMWFw7QLOm01HZfy69sLWt5uOqGuKET6u/v2ctz9+i4+STjySrBP6artF68fxBPAUcDTwY8RTAXyvBvsYQXwX0sxjPdI9og0Z4xcg2TuXAK9SPG0B6qugKIqiKIqyRzie5+16K0VRFGX36T6jYTBwHTJTfhUS2JYgpo03I9kjrUgQ3AysRLwYQkiGQhQRiB06mx3G0lzccRmwrJFYc2yXM/kNQENypGnd8IZ/j92YMep3LUlZ3+0uy6JPUVX+KHKv7X9uG4DhiGFmM3A90p71eOB3wJtmO9sV4kdIKcYTwIOaxaAoiqIoirLnqNCgKIrSA5gOEhcARYip4HxEZPgZMoNegATEa83zj5DyiAygDsiicymA9WCIAZyRhTtzhMND6zyW1ic8heDMfjPSDvJQZCZ/wQWH19129xkbj6Fr94S+RVX5KOBOxGthB/B3JLPkSeBx4NuIqPMkG5f+kNrXJVvnGysbzP6JTDkVRVEURVGUPUBLJxRFUQ4QgUyGF7OSoj875ZDmo97enPqXba3hi5ESlKOQ3+EvICJAFDjOLBuNb26YadZ7aS6My8BZ1ojTHPOzGpY1wkPrPJY17vSU2hH/gjREzIgis/hfHJIajQBj8Ms9+ibSfvKrHa+ryicBOcAnFE9r4MUbniDSdAzh9DuNyBAscdEuEoqiKIqiKPsBFRoURVH2B4GZ8KInjskApmcnR8eOyW25cPnm1GljcluO/NaYLc3/vWLoVUvWh7OAychvsPUN8PCNCDHLbQZDCCAtHHO+MqKNc9JTeWAtLK3vMHykOYbNZAiWVBA4Xhvi+wBiKpmOGB5+F/jGv+uS7wdGIuUc/YlKREiQ61rz92MQQeWYLusURVEU3WTwvwAAEsBJREFURVGU/YIKDYqiKPuHEuCODU3huxAR4fITclsiM8dsCT9cPST3zdq05r99nPWHZZtTxwKn4vsuOPhdD6Brp4QO4WDckGY+d8xWXvhgGMsaU8HvLEH8/mkuXiDzwcEXGWJIOQZIF5GrM5OiYyaMaPwC/dGToGuGgi8uSLmEZi8oiqIoiqLsZ9SjQVEUZT8w57Gn8vLS2qe3xZzkXywfenljJHRoejjmnDysuWX5ltRFdW2h0xFvhrPxDR3bMdkKgJvmwrhMvGUNOM2xjkPbDhPhtHCMcUOaY8u2pLnNkQ49IoIYHjYjpRguwBlZMHOEw6/Xe5HKuk7ZEZZWxBRy25XHblt8/djNRSGXm02XBespUcJOWnEqiqIoiqIoSiJ25kyuKIqi7CYPrRxy2sK1mVefk9942XmHN6wEkpsibtLL6zPeqWsL1QKDEJEhCd/EMUSgRGJcBswc4TjjJN/ASg0244HmiMvSjRmOERmiZhsPyUw4Ej/DIWY9G95u6CjPiOcDpOvCE1taQneGXG6mcwmBbdFZso+3RlEURVEURRlgaOmEoijKblJZ9FDHLH/JipldZvnf2JTWev/yoasXrs3IQwSFKHACcCJiwGjFAduqMsnsGgPcZY3w0NoOQ8egONCIlD5YIcF2nNgKDMEvwbDrnYBnQzx23+OBUcCO+Z9kv373tRc9E3+5qH+BoiiKoiiKshdo6YSiKMpuUln00CRMl4KSFTMXmPKCS4HzkID8cuB0YDVSxuAhJQrtQDaBdpT4wkDQ/BF8ISBIcBsrOoQQg8fuMhZ2Bw8puZiK325TSyUURVEURVGUfUJLJxRFUXafSuCW/C+4y6kqn3RkVttE4D7gK+axBBEAjkDEhVYkkyEH36zRmjtakWGdeR4x6yOB7UDEBMw2rUgWhP3tTiZxhwkC72efx6vKEaAF2GEeS4C7gG8bAUVRFEVRFEVR9goVGhRFUXaTkhUzG2acW7j8iSMP/WlLxPn5uYfVTwA+yHBZe0Y2SWkuLhLQu0imQS2+UGAJlqy145dShPCFBIsVJaJmmxREXHDwsyVsOQb4HSc4IwsnrfMvvBUamsx+W4D7kfaWOYh/xPvAZajYoCiKoiiKouwDKjQoiqLsGVOfWpVz2a2v57X//v3BVwJnFGUy4tsjHMZldogGzYgAcDi+cNCMZA/Um3UeIhwcbtbbUooIvsljLfI7nUTnbAW7fRK+oWQH4zLxZo5wMKaSHtJdwoogaeb5P4DXkWyGu4EbgAnAW8AVwD+LZi0q3NubpCiKoiiKogxc1KNBURRlNzAz/BOR2f/zEC+G94H/SHNJOTGT9rcbCDfHaMPPOoinHT+jwWYlBLeL92sIGkeG8LMS4kXiWGCZl+bijMuAZY1g2mS2Bta7wCfAAuDzwA+BCxGvideAx4DbgeHAKuC/gIXq26AoiqIoiqLsLio0KIqi7AZFsxZNAn6VEY6Gzji0qa1yQ8bopogbBdKR8oikNBd3XCaxZQ0dAX6wE0SQWIJ1VnSwP8qJ9rP7QldvhnjxIShiRIA6IKPj2O1tzaHNG9OjucOeJiV1LfBNxGgyDAw1x4sBscykaPV5hzd8+PK69IUbm5NsBsabwHwVIBRFURRFUZR4tHRCURRlFxTNWpQHjAf+b2J+4ydTC7Z9ZuyQZttu0noyuKZkwR2XIa8RPwSLF3i0ZpDB5U7g0R43qATHgFiai3tGtvgwBEj0Wx48fiNSspGMlEpUJL/3zm9Sq17ZEa7dUAp8Dcm2+AO+aGEfwyfktoydPHrHFy87evsvZhy/+Xu5KZHvAb8DblcvB0VRFEVRFCWe0G233Xawz0FRFKXXYgLpe4HpwPG1zaFFG5vDWVW16YPaY04KgTaVW9rh01ZY1ogTEYkg2RwmmF0QXzLhxW3TjpRJJMp4iJ2SiXvtSIeMEF5NE06SA6dkyXtHvK5ZEGkunJ5F2+Z2kts9UoGPgGWx7MHt4Q1rf99WcMIGwkknmfd6GxEYjkoLx9xThjWxpTXMhuYkMpKisSmjd4RKhje7LVHHebM2PQycCFRfM+nI6n25x4qiKIqiKEr/QjMaFEVRdk4JcBJQA7jbWsOTX1yTld0UcdfiiwEu4DTHcJfW4TTH2zb6rSwTlUXY7AZr1mg9HGKI3wJnZIHpaBFe1ggvbve8c3Icd1wGjMuAmSMcd1znvAJ7BrETM4nOGOFkn5JFG2JIORL4tpea9t2msy8c6aWmvWKWe8BXgbHAshOHNse+fcIWxg1pjjVH3LZ5Hwx2/7I629nW4rK6zuonJAM/Lpq1aNSe31ZFURRFURSlv6IeDYqiKDvBlE3cjxhAusBWJCgvB24DUuN2SeSXkGhZIoLeDR7gnJEFM0c4PLTeiy2t89tXGi8IF0RsWNZIrDmGC7Sku7SMzSR7eQMNIYdXTspk3Bv1hJpkfTvwMvAB8CdgMrAOOAbJUDgL+GN6OPblsUOaM5dtSXOaI66UbYRjzrghzc6yLWk0R9zgea4CPrfingmrd+eeKoqiKIqiKP0bFRoURVESYEomLgAuA85HWlHGkLKGT4DGNJeCcRm4prvDrsSE+PUxwE1zO4QCewz7Hg5Amkss8B4gxpPhBO/VkUdxRjZcN8Jpf6LWe/HpLUxA2mA2A38HnsKYOBbNWnQr8APgXaSbhm3HWYNkPmQFjt9u1nmBc7TCCMCfVtwz4as7uX5FURRFURRlgKClE4qiKHGYLIaHgIeBS4A0s8qWNxwOHD8uE2fmCIdxGR3rLPEmjsF9Cbzm1GyPG4/wODXbs8uCpRM0x3CX1ktwHyihCL5XPb7h4w7AW9aA89gm780XtjHSnPs6s89ZQBEw0QgpNUjryyKz/wZgI3AYnUUGEKHBln/UAavNo81q2JroXiqKoiiKoigDDxUaFEVRujIduBy/HWR7gm1iyxpwHlrnsayRKF09GIImj93ipDbhDKkllNbUGlyMnynQDjjjMnBnjnAYl9nxu91k/iWZ924HBgOh5hgfP7eVDQ0xChDxYCWSkZEOfB34OTAR6UDxD/Ne+UhJRWbguoOkmu0rgN8CQ5AsCHu+O3Z2nYqiKIqiKMrAIbzrTRRFUQYcW8yjLVHYDGwCxgBeusuGU7Jw32lkGA44EEVKCdoRg8RgV4mQeQwu6yijeG1zGnd7sGxLWlLcuuC2LGsEI2oERQzM+znmfWxZxUrgs2b9MMTM8iXgSSQLocWsuxl4Acl0aDf72tKI+K4XEeBB4A7gekR4aEIyIp4GHuj+diqKoiiKoigDCRUaFEVRApiyiauRTAEb1A9FsgEAnJMyyZw23MlcuM1zzh7sxMrXe+HWGO6yRkLGRyHYZcJmIMR3mogAYWIuNGd4xLpsZwWHFBB/hqX17EBKGhwk0G8LbPcvxDviTEQAyETKIZ5HyhzOBT5Zcc+EBeY6MxHBYTnwOjAOuAIRIt5EyinSECHjU+BO4HHj7TDHnGMNxu9hz+6yoiiKoiiK0p9RM0hFUZQAxiDxFgKGjAYb0MfSXZpOzCT8ryZSj0uDFAeuHt7RGSLGrkXcGCISpJ6RBTNHOjy01mNpfZftPMTE0YocbYCT5pJkDCQjjhtj7JBmt3pr6vy69tBxwBtAJdKW83kk2yADmArMXXHPhI3dXHcmUk4B8BpwGlIaMQqY091+iqIoiqIoihKPZjQoiqJ0pgYSigU248BtipH+Sp2IEEvr8XLDOAu3e7zX1LWrRDfv4WJKKkxJRGxZY8Jto3QuxQgD0XEZ0vLyNxu9j8JZjU3fLNha8OdVOU1PfjgoFRiPlEI0AY+ZDIQS4FLgLWBBohMyWQnPBBY9k2g7RVEURVEURdkVKjQoiqJ05iWk1OC4uOVB4SBo+ugclwYTBzn/v727i62zrgM4/v0/fdt5Ge0Y7KWb2USQAQ7qFF9m1EXQZIlyQ9ALwgXGF2BwI6IxXBhNTDSLiEJDeDFKkETEYAw6NE6yEGmRxHFaiLDBMLxsBbayrj2nT9u1z9+L55y1TiRRH9hCv5+b0/b0nD7tRS+++T+/H09PRgYn5oMEQNfsXHbGWJo831Niur1t4Xu0AzHNCIPjbxokFv7MKaCz1iD2j8T4VIPnYqO8amVp9hcvNTpuAi5tXvt083sHmq8dID+lMYAkSZL0FvPWCUla4Pwbdn0euIv59Y7/6WRCawAkpQT6qmS1er6OkjwKxFJCcslcypl/G+GB9St4+rRqBiSlhNhXIdQa+ewF8kGMbcyvwJxuvn9r+0NGvsbyELCO+QGTrwM/IT+FsQH4HHBjaw6DJEmSdCJ4okHSotacTbAZeBa44pQ2Np1XodKMBq1hjhnzweGNhju2Pg8LP+6rkl2yqpQ8lKyOp5e74qvT+emHrkA4bqZDG/nshayvQlJr0EWSxb7laVYbLYV0Nhla2sb4xgofqdU5Mpkdu44/A/+oJFM/vLCyNzyZrrtjdLbbUwuSJEk6oQwNkha7zcB2YDlw+sYKs1f3hqQ5nLG1daI1H6G1+jEyf6qAvgpsWx3CXSOR6ZjPXUgzqNXh1hHoWloKX+kN4eGxmH2qO4Q7X4mtVZWw4P9wX4WwrTfQPxJJyunR6zYeCj8ePq1j8NXKyg9WWXZVb4g374/LBscZBv4OnAdMXVjZm3xr1f1ZCLG26st/cQOEJEmSTihDg6RFq3maYQl5PFgDZLvrtPfvPxYBWpFhrvmShZsoWrdOZLUGSf9IjJ0BtvWG0H8gxsFxQpqRDE5AKSHGkRi6ILkzjfHxCUKaHTshMdd8z6Q1e6FWh/JM18zug6WX9h7pOgNY/fgE+z4xyY1PNdgKnEE+9PFh4H1PputuDyHWVnWMPfw2/NkkSZKkN2VokLSYbSYfkjhH89aINGPhmslWaGhb8JrjvzaTZrQPjtNeSmAmi+xJCR89hVirE9IM0oxAiFyzFm5+mZhmYbb1+nLCkQ8tpTGcZmvPXpaGodFSPc2S6vt7ppd8YEX6nicOpc/+6eWlZzUy1n7nhdhJvj3ia8DlwO+Be0dnuwc8ySBJkqSThaFB0mI2BBwGLubfZy5QSkj6qvPBoCnwrwMiO1sfpxnZ4ARHPtlNz/VrQtjxepy7+7X8ZENSmszC8kbCoUrSnPGYAGyq0n5Nb1i+c3JmestZo52/2tf96M6Xqr3Lu46e89pk++jesc4yMArsBB4ErgXuAR4DBpprKSVJkqSThqFB0qK1rJ1vbChz0XEh4Zi+ClzbG+g/EI8OjB87wbDwVorWrQ8p8xsiukIkdLXB1lND29BknBscJz52sJT8IHK0dqiUAR1AAyjtrjP7o/2xum+qY894xylP7Hy5uuGL5xx+8Atnjq2rdMRTO5KDD1z1yNo9wO3AlcCNAMPbt3z/LfqzSJIkSf8XQ4OkRevcMtdcvTok/Qdi63aJ1naJOaBraGomu6t+ONbSZW3Q2do+0QoNE8AM+RDJ1oDICDSGJyn/5mCce3Gaeq1ONxBLJGykMrUHOifheeCnwKcnMzYNjgO0df/yuZ4/ANyzt6d88dr615d2Hu39+Z5lzwDXk59g+Fnz0luPkiRJ0knH0CBp0do3xXW3jcRbnprkUfJg8E1gE7AbuJXu/Wv2rh0ox/RjGaPrM2Ac6Gk+DgIfBv4KnAUsBcaAtrPLxI/3hOS2AzFJMyaAA1esYMnlK0NvJM7c8Qo95KcT7gXeBewAfgtsAC49PN0eLnlo/beHt2/57mP37ao237d1m4QnGSRJknRSCzHGE30NknRSOf+GXZ8Bvpe0T724pOfAxVNjvY9ks0vGgT7y0LCC+a0TDeAIMAV8CfhsOWHbBVVKQ3Xqkxm/A9575hL+uPVUrvz1QZ4dOcoa8jkLtwMX0IwI59+wayVwXfMybhnevuXVt/P3liRJkopgaJCk4zTXXm4mHxZ5ATDQfGozcBH51geAF8hvpbgbuKkZC6rkAeEy8gCxjfxEwhD5KYbLgPuBWx3kKEmSpHciQ4Mk/RcWnDp4N3AucB/HRYPm93wVeAbY0XpuQcBwW4QkSZLesQwNkvQ/MBpIkiRJb8zQIEmSJEmSCpOc6AuQJEmSJEnvHIYGSZIkSZJUGEODJEmSJEkqjKFBkiRJkiQVxtAgSZIkSZIKY2iQJEmSJEmFMTRIkiRJkqTCGBokSZIkSVJhDA2SJEmSJKkwhgZJkiRJklQYQ4MkSZIkSSqMoUGSJEmSJBXG0CBJkiRJkgpjaJAkSZIkSYUxNEiSJEmSpMIYGiRJkiRJUmEMDZIkSZIkqTCGBkmSJEmSVBhDgyRJkiRJKoyhQZIkSZIkFcbQIEmSJEmSCmNokCRJkiRJhTE0SJIkSZKkwhgaJEmSJElSYQwNkiRJkiSpMIYGSZIkSZJUGEODJEmSJEkqjKFBkiRJkiQVxtAgSZIkSZIKY2iQJEmSJEmF+Sc27bGTzMMr0QAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%time\n", "embedding = TSNEEmbedding(\n", " initialization.random(x_reduced.shape[0], n_components=2, random_state=np.random.RandomState(42)),\n", " affinities, negative_gradient_method='fft', learning_rate=1000, n_jobs=8)\n", "\n", "embedding.optimize(n_iter=500, exaggeration=12, momentum=0.5, inplace=True)\n", "embedding.optimize(n_iter=1000, momentum=0.8, inplace=True)\n", "\n", "utils.plot(embedding, y, ax=plt.figure(figsize=(13, 9)).gca(), colors=utils.ZEISEL_COLORS)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "ordinary_embedding_50 = embedding.view(np.ndarray)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Larger perplexity" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 33min 36s, sys: 5.54 s, total: 33min 41s\n", "Wall time: 9min 23s\n" ] } ], "source": [ "%time affinities = PerplexityBasedNN(x_reduced, perplexity=500, method='approx', n_jobs=8)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1h 55min, sys: 49.6 s, total: 1h 55min 49s\n", "Wall time: 14min 31s\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%time\n", "embedding = TSNEEmbedding(\n", " initialization.random(x_reduced.shape[0], n_components=2, random_state=np.random.RandomState(42)),\n", " affinities, negative_gradient_method='fft', learning_rate=1000, n_jobs=8)\n", "\n", "embedding.optimize(n_iter=500, exaggeration=12, momentum=0.5, inplace=True)\n", "embedding.optimize(n_iter=1000, momentum=0.8, inplace=True)\n", "\n", "utils.plot(embedding, y, ax=plt.figure(figsize=(13, 9)).gca(), colors=utils.ZEISEL_COLORS)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "ordinary_embedding_500 = embedding.view(np.ndarray)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sample + transform" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "indices = np.random.RandomState(42).permutation(list(range(x.shape[0])))\n", "reverse = np.argsort(indices)\n", "\n", "x_sample, y_sample = x_reduced[indices[:25000]], y[indices[:25000]]\n", "x_rest, y_rest = x_reduced[indices[25000:]], y[indices[25000:]]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 552 ms, sys: 16 ms, total: 568 ms\n", "Wall time: 94.4 ms\n" ] } ], "source": [ "%time init = initialization.pca(x_sample)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 5min 8s, sys: 1.37 s, total: 5min 9s\n", "Wall time: 1min 21s\n" ] } ], "source": [ "%time affinities = PerplexityBasedNN(x_sample, perplexity=500, method='approx', n_jobs=8)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "sample_embedding = TSNEEmbedding(\n", " init, affinities, negative_gradient_method='fft',\n", " learning_rate=500, n_jobs=8, callbacks=ErrorLogger(),\n", ")" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 1.6248, 50 iterations in 5.4902 sec\n", "Iteration 100, KL divergence 1.1036, 50 iterations in 5.4445 sec\n", "Iteration 150, KL divergence 0.9183, 50 iterations in 5.5410 sec\n", "Iteration 200, KL divergence 0.8235, 50 iterations in 5.8950 sec\n", "Iteration 250, KL divergence 0.7690, 50 iterations in 5.8143 sec\n", "Iteration 300, KL divergence 0.7310, 50 iterations in 5.8807 sec\n", "Iteration 350, KL divergence 0.7063, 50 iterations in 6.0369 sec\n", "Iteration 400, KL divergence 0.6884, 50 iterations in 5.9994 sec\n", "Iteration 450, KL divergence 0.6754, 50 iterations in 6.5505 sec\n", "Iteration 500, KL divergence 0.6655, 50 iterations in 5.9735 sec\n", "CPU times: user 7min 48s, sys: 3.3 s, total: 7min 51s\n", "Wall time: 59.1 s\n" ] } ], "source": [ "%time sample_embedding1 = sample_embedding.optimize(n_iter=500)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(sample_embedding1, y_sample, ax=plt.figure(figsize=(13, 9)).gca(), colors=utils.ZEISEL_COLORS)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 10 s, sys: 48 ms, total: 10.1 s\n", "Wall time: 4.48 s\n" ] } ], "source": [ "%time rest_init = sample_embedding1.prepare_partial(x_rest, perplexity=3)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "x_full = np.vstack((x_sample, x_rest))\n", "y_full = np.hstack((y_sample, y_rest))\n", "init_full = np.vstack((sample_embedding1, rest_init))" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "x_full = x_full[reverse]\n", "y_full = y_full[reverse]\n", "init_full = init_full[reverse]" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3min 1s, sys: 2.62 s, total: 3min 4s\n", "Wall time: 1min 14s\n" ] } ], "source": [ "%time full_affinities = PerplexityBasedNN(x_full, perplexity=50, method='approx', n_jobs=8)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "embedding = TSNEEmbedding(\n", " init_full, full_affinities, negative_gradient_method='fft',\n", " learning_rate=1000, n_jobs=8, callbacks=ErrorLogger(),\n", ")" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 3.5675, 50 iterations in 6.5972 sec\n", "Iteration 100, KL divergence 3.4631, 50 iterations in 6.0860 sec\n", "Iteration 150, KL divergence 3.4004, 50 iterations in 5.5690 sec\n", "Iteration 200, KL divergence 3.3445, 50 iterations in 6.5093 sec\n", "Iteration 250, KL divergence 3.2878, 50 iterations in 6.5866 sec\n", "Iteration 300, KL divergence 3.2325, 50 iterations in 6.3539 sec\n", "Iteration 350, KL divergence 3.1789, 50 iterations in 6.6469 sec\n", "Iteration 400, KL divergence 3.1291, 50 iterations in 7.4059 sec\n", "Iteration 450, KL divergence 3.0840, 50 iterations in 8.0026 sec\n", "Iteration 500, KL divergence 3.0435, 50 iterations in 8.3102 sec\n", "Iteration 550, KL divergence 3.0071, 50 iterations in 8.3426 sec\n", "Iteration 600, KL divergence 2.9745, 50 iterations in 9.0023 sec\n", "Iteration 650, KL divergence 2.9452, 50 iterations in 9.9497 sec\n", "Iteration 700, KL divergence 2.9190, 50 iterations in 11.7780 sec\n", "Iteration 750, KL divergence 2.8953, 50 iterations in 12.2506 sec\n", "Iteration 800, KL divergence 2.8737, 50 iterations in 10.9720 sec\n", "Iteration 850, KL divergence 2.8544, 50 iterations in 12.8205 sec\n", "Iteration 900, KL divergence 2.8375, 50 iterations in 13.2817 sec\n", "Iteration 950, KL divergence 2.8226, 50 iterations in 12.4245 sec\n", "Iteration 1000, KL divergence 2.8091, 50 iterations in 15.0679 sec\n", "CPU times: user 22min 46s, sys: 55.3 s, total: 23min 41s\n", "Wall time: 3min 4s\n" ] } ], "source": [ "%time embedding1 = embedding.optimize(n_iter=1000, exaggeration=1, momentum=0.8)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(embedding1, y_full, ax=plt.figure(figsize=(13, 9)).gca(), colors=utils.ZEISEL_COLORS)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "sample_embedding = embedding1.view(np.ndarray)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multiscale" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.28 s, sys: 156 ms, total: 2.43 s\n", "Wall time: 406 ms\n" ] } ], "source": [ "%time init = initialization.pca(x_reduced)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 38min 4s, sys: 1min 34s, total: 39min 39s\n", "Wall time: 10min 57s\n" ] } ], "source": [ "%time affinities = Multiscale(x_reduced, perplexities=[50, 500], method='approx', n_jobs=8)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "embedding = TSNEEmbedding(\n", " init, affinities, negative_gradient_method='fft',\n", " learning_rate=1000, n_jobs=8, callbacks=ErrorLogger(),\n", ")" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 5.3015, 50 iterations in 29.0996 sec\n", "Iteration 100, KL divergence 4.5650, 50 iterations in 28.6809 sec\n", "Iteration 150, KL divergence 4.3430, 50 iterations in 30.2004 sec\n", "Iteration 200, KL divergence 4.2352, 50 iterations in 29.2549 sec\n", "Iteration 250, KL divergence 4.1620, 50 iterations in 28.6163 sec\n", "Iteration 300, KL divergence 4.1256, 50 iterations in 29.3443 sec\n", "Iteration 350, KL divergence 4.0897, 50 iterations in 29.4359 sec\n", "Iteration 400, KL divergence 4.0635, 50 iterations in 29.1292 sec\n", "Iteration 450, KL divergence 4.0302, 50 iterations in 29.5134 sec\n", "Iteration 500, KL divergence 4.0267, 50 iterations in 29.3356 sec\n", "Iteration 550, KL divergence 4.0153, 50 iterations in 29.4545 sec\n", "Iteration 600, KL divergence 3.9921, 50 iterations in 29.0434 sec\n", "Iteration 650, KL divergence 3.9832, 50 iterations in 30.4126 sec\n", "Iteration 700, KL divergence 3.9718, 50 iterations in 29.9948 sec\n", "Iteration 750, KL divergence 3.9646, 50 iterations in 29.2089 sec\n", "CPU times: user 58min 44s, sys: 14.1 s, total: 58min 58s\n", "Wall time: 7min 23s\n" ] } ], "source": [ "%time embedding1 = embedding.optimize(n_iter=750, exaggeration=12, momentum=0.5)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(embedding1, y, ax=plt.figure(figsize=(13, 9)).gca(), colors=utils.ZEISEL_COLORS)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 3.1924, 50 iterations in 29.1546 sec\n", "Iteration 100, KL divergence 2.9228, 50 iterations in 29.0786 sec\n", "Iteration 150, KL divergence 2.7098, 50 iterations in 30.3512 sec\n", "Iteration 200, KL divergence 2.5516, 50 iterations in 30.2337 sec\n", "Iteration 250, KL divergence 2.4343, 50 iterations in 29.2714 sec\n", "Iteration 300, KL divergence 2.3441, 50 iterations in 30.4938 sec\n", "Iteration 350, KL divergence 2.2738, 50 iterations in 30.8689 sec\n", "Iteration 400, KL divergence 2.2177, 50 iterations in 31.5323 sec\n", "Iteration 450, KL divergence 2.1719, 50 iterations in 31.2829 sec\n", "Iteration 500, KL divergence 2.1342, 50 iterations in 31.7163 sec\n", "Iteration 550, KL divergence 2.1028, 50 iterations in 32.5540 sec\n", "Iteration 600, KL divergence 2.0764, 50 iterations in 33.4860 sec\n", "Iteration 650, KL divergence 2.0541, 50 iterations in 32.8353 sec\n", "Iteration 700, KL divergence 2.0353, 50 iterations in 33.7149 sec\n", "Iteration 750, KL divergence 2.0193, 50 iterations in 33.6959 sec\n", "Iteration 800, KL divergence 2.0055, 50 iterations in 34.1486 sec\n", "Iteration 850, KL divergence 1.9937, 50 iterations in 36.5616 sec\n", "Iteration 900, KL divergence 1.9833, 50 iterations in 35.0898 sec\n", "Iteration 950, KL divergence 1.9741, 50 iterations in 37.8398 sec\n", "Iteration 1000, KL divergence 1.9659, 50 iterations in 36.1480 sec\n", "CPU times: user 1h 25min 48s, sys: 43.7 s, total: 1h 26min 32s\n", "Wall time: 10min 52s\n" ] } ], "source": [ "%time embedding2 = embedding1.optimize(n_iter=1000, exaggeration=1, momentum=0.8)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(embedding2, y, ax=plt.figure(figsize=(13, 9)).gca(), colors=utils.ZEISEL_COLORS)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "multiscale_embedding = embedding2.view(np.ndarray)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Perplexity annealing" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.15 s, sys: 76 ms, total: 2.23 s\n", "Wall time: 371 ms\n" ] } ], "source": [ "%time init = initialization.pca(x_reduced)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 33min 39s, sys: 2min 12s, total: 35min 51s\n", "Wall time: 10min 56s\n" ] } ], "source": [ "%time affinities = PerplexityBasedNN(x_reduced, perplexity=500, method='approx', n_jobs=8)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n", "Wall time: 9.06 µs\n" ] } ], "source": [ "%time affinities.set_perplexity(500)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "embedding = TSNEEmbedding(\n", " init, affinities, negative_gradient_method='fft',\n", " learning_rate=1000, n_jobs=8, callbacks=ErrorLogger(),\n", ")" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 4.3671, 50 iterations in 29.0653 sec\n", "Iteration 100, KL divergence 3.7081, 50 iterations in 28.8980 sec\n", "Iteration 150, KL divergence 3.5288, 50 iterations in 29.0465 sec\n", "Iteration 200, KL divergence 3.4395, 50 iterations in 28.8904 sec\n", "Iteration 250, KL divergence 3.3766, 50 iterations in 28.5725 sec\n", "Iteration 300, KL divergence 3.3453, 50 iterations in 28.8256 sec\n", "Iteration 350, KL divergence 3.3134, 50 iterations in 29.3564 sec\n", "Iteration 400, KL divergence 3.3029, 50 iterations in 29.1447 sec\n", "Iteration 450, KL divergence 3.2975, 50 iterations in 28.5378 sec\n", "Iteration 500, KL divergence 3.2735, 50 iterations in 28.8535 sec\n", "Iteration 550, KL divergence 3.2668, 50 iterations in 28.7411 sec\n", "Iteration 600, KL divergence 3.2550, 50 iterations in 28.3898 sec\n", "Iteration 650, KL divergence 3.2515, 50 iterations in 29.2148 sec\n", "Iteration 700, KL divergence 3.2439, 50 iterations in 29.1691 sec\n", "Iteration 750, KL divergence 3.2209, 50 iterations in 29.0541 sec\n", "CPU times: user 57min 44s, sys: 18 s, total: 58min 2s\n", "Wall time: 7min 16s\n" ] } ], "source": [ "%time embedding1 = embedding.optimize(n_iter=750, exaggeration=12, momentum=0.5)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(embedding1, y, ax=plt.figure(figsize=(13, 9)).gca(), colors=utils.ZEISEL_COLORS)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 2.5423, 50 iterations in 29.1516 sec\n", "Iteration 100, KL divergence 2.3373, 50 iterations in 30.4027 sec\n", "Iteration 150, KL divergence 2.1789, 50 iterations in 29.1872 sec\n", "Iteration 200, KL divergence 2.0512, 50 iterations in 30.1521 sec\n", "Iteration 250, KL divergence 1.9483, 50 iterations in 30.4029 sec\n", "Iteration 300, KL divergence 1.8664, 50 iterations in 29.6052 sec\n", "Iteration 350, KL divergence 1.8009, 50 iterations in 29.3506 sec\n", "Iteration 400, KL divergence 1.7449, 50 iterations in 29.9098 sec\n", "Iteration 450, KL divergence 1.6996, 50 iterations in 30.5409 sec\n", "Iteration 500, KL divergence 1.6616, 50 iterations in 29.8158 sec\n", "Iteration 550, KL divergence 1.6287, 50 iterations in 30.5988 sec\n", "Iteration 600, KL divergence 1.6014, 50 iterations in 30.7133 sec\n", "Iteration 650, KL divergence 1.5774, 50 iterations in 30.0793 sec\n", "Iteration 700, KL divergence 1.5569, 50 iterations in 31.3257 sec\n", "Iteration 750, KL divergence 1.5387, 50 iterations in 32.1624 sec\n", "CPU times: user 1h 23s, sys: 16.8 s, total: 1h 40s\n", "Wall time: 7min 35s\n" ] } ], "source": [ "%time embedding2 = embedding1.optimize(n_iter=750, exaggeration=1, momentum=0.5)" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(embedding2, y, ax=plt.figure(figsize=(13, 9)).gca(), colors=utils.ZEISEL_COLORS)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 29.1 s, sys: 488 ms, total: 29.6 s\n", "Wall time: 4.27 s\n" ] } ], "source": [ "%time affinities.set_perplexity(50)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 50, KL divergence 3.1849, 50 iterations in 7.2072 sec\n", "Iteration 100, KL divergence 3.1635, 50 iterations in 6.8647 sec\n", "Iteration 150, KL divergence 3.1378, 50 iterations in 7.2357 sec\n", "Iteration 200, KL divergence 3.1087, 50 iterations in 8.2757 sec\n", "Iteration 250, KL divergence 3.0774, 50 iterations in 8.1145 sec\n", "Iteration 300, KL divergence 3.0457, 50 iterations in 8.3896 sec\n", "Iteration 350, KL divergence 3.0145, 50 iterations in 8.7548 sec\n", "Iteration 400, KL divergence 2.9847, 50 iterations in 8.9934 sec\n", "Iteration 450, KL divergence 2.9566, 50 iterations in 10.1486 sec\n", "Iteration 500, KL divergence 2.9303, 50 iterations in 11.3661 sec\n", "Iteration 550, KL divergence 2.9060, 50 iterations in 10.6805 sec\n", "Iteration 600, KL divergence 2.8840, 50 iterations in 12.0887 sec\n", "Iteration 650, KL divergence 2.8640, 50 iterations in 12.8427 sec\n", "Iteration 700, KL divergence 2.8459, 50 iterations in 10.9404 sec\n", "Iteration 750, KL divergence 2.8296, 50 iterations in 12.5964 sec\n", "Iteration 800, KL divergence 2.8149, 50 iterations in 13.8190 sec\n", "Iteration 850, KL divergence 2.8016, 50 iterations in 17.6422 sec\n", "Iteration 900, KL divergence 2.7898, 50 iterations in 16.1330 sec\n", "Iteration 950, KL divergence 2.7789, 50 iterations in 15.3107 sec\n", "Iteration 1000, KL divergence 2.7691, 50 iterations in 17.9017 sec\n", "Iteration 1050, KL divergence 2.7600, 50 iterations in 19.1546 sec\n", "Iteration 1100, KL divergence 2.7519, 50 iterations in 21.1566 sec\n", "Iteration 1150, KL divergence 2.7444, 50 iterations in 16.2710 sec\n", "Iteration 1200, KL divergence 2.7375, 50 iterations in 26.4706 sec\n", "Iteration 1250, KL divergence 2.7312, 50 iterations in 21.7731 sec\n", "Iteration 1300, KL divergence 2.7253, 50 iterations in 20.7189 sec\n", "Iteration 1350, KL divergence 2.7198, 50 iterations in 21.5166 sec\n", "Iteration 1400, KL divergence 2.7148, 50 iterations in 28.8205 sec\n", "Iteration 1450, KL divergence 2.7100, 50 iterations in 20.8507 sec\n", "Iteration 1500, KL divergence 2.7055, 50 iterations in 25.6662 sec\n", "CPU times: user 45min 44s, sys: 1min 53s, total: 47min 38s\n", "Wall time: 7min 28s\n" ] } ], "source": [ "%time embedding3 = embedding2.optimize(n_iter=1500, exaggeration=1, momentum=0.8)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "utils.plot(embedding3, y, ax=plt.figure(figsize=(13, 9)).gca(), colors=utils.ZEISEL_COLORS)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "annealing_embedding = embedding3.view(np.ndarray)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } openTSNE-0.6.1/examples/prepare_10x.ipynb000066400000000000000000002637121413546205200201750ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ppolicar/nfs/miniconda/envs/tsne/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", " return f(*args, **kwds)\n", "/home/ppolicar/nfs/miniconda/envs/tsne/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", " return f(*args, **kwds)\n" ] } ], "source": [ "import pickle\n", "import gzip\n", "from os.path import join\n", "\n", "import scanpy.api as sc\n", "\n", "import numpy as np\n", "import scipy.sparse as sp\n", "import pandas as pd\n", "from fbpca import pca\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2min 15s, sys: 10.3 s, total: 2min 25s\n", "Wall time: 2min 25s\n" ] } ], "source": [ "%time adata = sc.read_10x_h5(join(\"data\", \"1M_neurons_filtered_gene_bc_matrices_h5.h5\"))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 9min 58s, sys: 2min 57s, total: 12min 56s\n", "Wall time: 5min 22s\n" ] } ], "source": [ "%time sc.pp.recipe_zheng17(adata, plot=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ppolicar/nfs/miniconda/envs/tsne/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: Use X instead of data, data will be removed in the future.\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] }, { "data": { "text/plain": [ "array([[-0.09066167, -0.14078538, -0.05399211, ..., 1.2903655 ,\n", " 1.2008361 , 0.9360502 ],\n", " [-0.09066167, -0.14078538, -0.05399211, ..., 0.54371107,\n", " 0.8728276 , -0.18804006],\n", " [-0.09066167, -0.14078538, -0.05399211, ..., -0.610376 ,\n", " -2.5165358 , -0.46038082],\n", " ...,\n", " [-0.09066167, -0.14078538, -0.05399211, ..., -0.7364305 ,\n", " 0.8866803 , 0.37244514],\n", " [-0.09066167, -0.14078538, -0.05399211, ..., 0.47101277,\n", " 0.13998368, 0.726267 ],\n", " [-0.09066167, -0.14078538, -0.05399211, ..., 0.6153352 ,\n", " 1.2735354 , 0.9248333 ]], dtype=float32)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata.X" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "x_log = adata.X.copy()\n", "x_log -= x_log.mean(axis=0)\n", "x_log /= x_log.std(axis=0)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 4min 8s, sys: 25.7 s, total: 4min 34s\n", "Wall time: 35.7 s\n" ] } ], "source": [ "%time U, S, V = pca(x_log, k=50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Change the sign of the eigenvector so the figures are reproducible." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "U[:, np.sum(V, axis=1) < 0] *= -1" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.13 s, sys: 248 ms, total: 1.38 s\n", "Wall time: 397 ms\n" ] } ], "source": [ "%time x_reduced = np.dot(U, np.diag(S))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "x_reduced = x_reduced[:, np.argsort(S)[::-1]][:, :50]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1306127, 50)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_reduced.shape" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0\n", "AAACCTGAGATAGGAG-1 14\n", "AAACCTGAGCGGCTTC-1 1\n", "AAACCTGAGGAATCGC-1 7\n", "AAACCTGAGGACACCA-1 4\n", "AAACCTGAGGCCCGTT-1 0\n", "Name: 1, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cluster_ids = pd.read_csv(\"data/louvain.csv.gz\", header=None, index_col=0, squeeze=True)\n", "cluster_ids.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "cell_ids = pd.Series(adata.obs.index)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "assert all(cluster_ids.index.values.astype(str) == cell_ids.values.astype(str))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "data_dict = {\"pca_50\": x_reduced,\n", " \"CellID\": cell_ids.values.astype(str),\n", " \"CellType1\": cluster_ids.values}" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "with gzip.open(\"data/10x_mouse_zheng.pkl.gz\", \"wb\") as f:\n", " pickle.dump(data_dict, f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } openTSNE-0.6.1/examples/prepare_macosko_2015.ipynb000066400000000000000000001176561413546205200216750ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ppolicar/nfs/miniconda/envs/tsne/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", " return f(*args, **kwds)\n", "/home/ppolicar/nfs/miniconda/envs/tsne/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", " return f(*args, **kwds)\n" ] } ], "source": [ "import pickle\n", "import gzip\n", "\n", "import utils\n", "\n", "import loompy\n", "import pandas as pd\n", "import numpy as np\n", "import scipy.sparse as sp\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 10min 4s, sys: 11.2 s, total: 10min 15s\n", "Wall time: 10min 15s\n" ] } ], "source": [ "%%time\n", "data = pd.read_table(\"data/GSE63472_P14Retina_merged_digital_expression.txt.gz\", index_col=0)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 48 ms, sys: 8 ms, total: 56 ms\n", "Wall time: 58.3 ms\n" ] } ], "source": [ "%%time\n", "cluster_ids = pd.read_table(\"data/retina_clusteridentities.txt\", header=None, index_col=0, squeeze=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0\n", "r1_GGCCGCAGTCCG 2\n", "r1_CTTGTGCGGGAA 2\n", "r1_GCGCAACTGCTC 2\n", "r1_GATTGGGAGGCA 2\n", "r1_GTGCCGCCTCTC 25\n", "Name: 1, dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cluster_ids.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Reorder\n", "cluster_ids = cluster_ids[data.columns.values]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(24658, 49300)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Only use cells where metadata is available\n", "ind = data.columns.isin(cluster_ids.index)\n", "data = data.loc[:, ind]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((24658, 49300), (49300,))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape, cluster_ids.shape" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "mask = ~cluster_ids.isna()\n", "data = data.loc[:, mask.values]\n", "cluster_ids = cluster_ids[mask]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "assert not cluster_ids.isna().any(), \"Did not properly remove cells with NaN label\"" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((24658, 44808), (44808,))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape, cluster_ids.shape" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 12.4 s, sys: 0 ns, total: 12.4 s\n", "Wall time: 12.4 s\n" ] } ], "source": [ "%%time\n", "counts = sp.csr_matrix(data.values)\n", "counts" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.34 s, sys: 52 ms, total: 2.39 s\n", "Wall time: 1.12 s\n" ] } ], "source": [ "%%time\n", "cpm_counts = utils.calculate_cpm(counts, axis=0)\n", "log_counts = utils.log_normalize(cpm_counts)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Rods 29400\n", "Bipolar cells 6285\n", "Amacrine cells 4426\n", "Cones 1868\n", "Muller glia 1624\n", "Retinal ganglion cells 432\n", "Horizontal cells 252\n", "Vascular endothelium 252\n", "Fibroblasts 85\n", "Microglia 67\n", "Pericytes 63\n", "Astrocytes 54\n", "Name: 1, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cell_types = cluster_ids.astype(object)\n", "\n", "cell_types.loc[cell_types == 1] = \"Horizontal cells\"\n", "cell_types.loc[cell_types == 2] = \"Retinal ganglion cells\"\n", "cell_types.loc[cell_types.isin(range(3, 24))] = \"Amacrine cells\"\n", "cell_types.loc[cell_types == 24] = \"Rods\"\n", "cell_types.loc[cell_types == 25] = \"Cones\"\n", "cell_types.loc[cell_types.isin(range(26, 34))] = \"Bipolar cells\"\n", "cell_types.loc[cell_types == 34] = \"Muller glia\"\n", "cell_types.loc[cell_types == 35] = \"Astrocytes\"\n", "cell_types.loc[cell_types == 36] = \"Fibroblasts\"\n", "cell_types.loc[cell_types == 37] = \"Vascular endothelium\"\n", "cell_types.loc[cell_types == 38] = \"Pericytes\"\n", "cell_types.loc[cell_types == 39] = \"Microglia\"\n", "\n", "cell_types.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preprocess data set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dropout based feature selection" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chosen offset: 0.19\n", "CPU times: user 3.87 s, sys: 148 ms, total: 4.02 s\n", "Wall time: 1.13 s\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%time gene_mask = utils.select_genes(counts.T, n=3000, threshold=0)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(44808, 3000)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = log_counts.T[:, gene_mask].toarray()\n", "x.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Standardize data" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "x -= x.mean(axis=0)\n", "x /= x.std(axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PCA preprocessing" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2min 2s, sys: 1.76 s, total: 2min 3s\n", "Wall time: 23.1 s\n" ] } ], "source": [ "%%time\n", "U, S, V = np.linalg.svd(x, full_matrices=False)\n", "U[:, np.sum(V, axis=1) < 0] *= -1\n", "x_reduced = np.dot(U, np.diag(S))\n", "x_reduced = x_reduced[:, np.argsort(S)[::-1]][:, :50]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(44808, 50)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_reduced.shape" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(44808,)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cell_types.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Write data" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "data_dict = {\"pca_50\": x_reduced,\n", " \"CellType1\": cell_types.values.astype(str),\n", " \"CellType2\": cluster_ids.values.astype(str)}" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 732 ms, sys: 16 ms, total: 748 ms\n", "Wall time: 755 ms\n" ] } ], "source": [ "%%time\n", "with gzip.open(\"data/macosko_2015.pkl.gz\", \"wb\") as f:\n", " pickle.dump(data_dict, f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } openTSNE-0.6.1/examples/utils.py000066400000000000000000000307021413546205200165050ustar00rootroot00000000000000from os.path import abspath, dirname, join import numpy as np import scipy.sparse as sp FILE_DIR = dirname(abspath(__file__)) DATA_DIR = join(FILE_DIR, "data") MACOSKO_COLORS = { "Amacrine cells": "#A5C93D", "Astrocytes": "#8B006B", "Bipolar cells": "#2000D7", "Cones": "#538CBA", "Fibroblasts": "#8B006B", "Horizontal cells": "#B33B19", "Microglia": "#8B006B", "Muller glia": "#8B006B", "Pericytes": "#8B006B", "Retinal ganglion cells": "#C38A1F", "Rods": "#538CBA", "Vascular endothelium": "#8B006B", } ZEISEL_COLORS = { "Astroependymal cells": "#d7abd4", "Cerebellum neurons": "#2d74bf", "Cholinergic, monoaminergic and peptidergic neurons": "#9e3d1b", "Di- and mesencephalon neurons": "#3b1b59", "Enteric neurons": "#1b5d2f", "Hindbrain neurons": "#51bc4c", "Immature neural": "#ffcb9a", "Immune cells": "#768281", "Neural crest-like glia": "#a0daaa", "Oligodendrocytes": "#8c7d2b", "Peripheral sensory neurons": "#98cc41", "Spinal cord neurons": "#c52d94", "Sympathetic neurons": "#11337d", "Telencephalon interneurons": "#ff9f2b", "Telencephalon projecting neurons": "#fea7c1", "Vascular cells": "#3d672d", } MOUSE_10X_COLORS = { 0: "#FFFF00", 1: "#1CE6FF", 2: "#FF34FF", 3: "#FF4A46", 4: "#008941", 5: "#006FA6", 6: "#A30059", 7: "#FFDBE5", 8: "#7A4900", 9: "#0000A6", 10: "#63FFAC", 11: "#B79762", 12: "#004D43", 13: "#8FB0FF", 14: "#997D87", 15: "#5A0007", 16: "#809693", 17: "#FEFFE6", 18: "#1B4400", 19: "#4FC601", 20: "#3B5DFF", 21: "#4A3B53", 22: "#FF2F80", 23: "#61615A", 24: "#BA0900", 25: "#6B7900", 26: "#00C2A0", 27: "#FFAA92", 28: "#FF90C9", 29: "#B903AA", 30: "#D16100", 31: "#DDEFFF", 32: "#000035", 33: "#7B4F4B", 34: "#A1C299", 35: "#300018", 36: "#0AA6D8", 37: "#013349", 38: "#00846F", } def calculate_cpm(x, axis=1): """Calculate counts-per-million on data where the rows are genes. Parameters ---------- x : array_like axis : int Axis accross which to compute CPM. 0 for genes being in rows and 1 for genes in columns. """ normalization = np.sum(x, axis=axis) # On sparse matrices, the sum will be 2d. We want a 1d array normalization = np.squeeze(np.asarray(normalization)) # Straight up division is not an option since this will form a full dense # matrix if `x` is sparse. Divison can be expressed as the dot product with # a reciprocal diagonal matrix normalization = sp.diags(1 / normalization, offsets=0) if axis == 0: cpm_counts = np.dot(x, normalization) elif axis == 1: cpm_counts = np.dot(normalization, x) return cpm_counts * 1e6 def log_normalize(data): """Perform log transform log(x + 1). Parameters ---------- data : array_like """ if sp.issparse(data): data = data.copy() data.data = np.log2(data.data + 1) return data return np.log2(data.astype(np.float64) + 1) def pca(x, n_components=50): if sp.issparse(x): x = x.toarray() U, S, V = np.linalg.svd(x, full_matrices=False) U[:, np.sum(V, axis=1) < 0] *= -1 x_reduced = np.dot(U, np.diag(S)) x_reduced = x_reduced[:, np.argsort(S)[::-1]][:, :n_components] return x_reduced def select_genes( data, threshold=0, atleast=10, yoffset=0.02, xoffset=5, decay=1, n=None, plot=True, markers=None, genes=None, figsize=(6, 3.5), markeroffsets=None, labelsize=10, alpha=1, ): if sp.issparse(data): zeroRate = 1 - np.squeeze(np.array((data > threshold).mean(axis=0))) A = data.multiply(data > threshold) A.data = np.log2(A.data) meanExpr = np.zeros_like(zeroRate) * np.nan detected = zeroRate < 1 meanExpr[detected] = np.squeeze(np.array(A[:, detected].mean(axis=0))) / ( 1 - zeroRate[detected] ) else: zeroRate = 1 - np.mean(data > threshold, axis=0) meanExpr = np.zeros_like(zeroRate) * np.nan detected = zeroRate < 1 meanExpr[detected] = np.nanmean( np.where(data[:, detected] > threshold, np.log2(data[:, detected]), np.nan), axis=0, ) lowDetection = np.array(np.sum(data > threshold, axis=0)).squeeze() < atleast # lowDetection = (1 - zeroRate) * data.shape[0] < atleast - .00001 zeroRate[lowDetection] = np.nan meanExpr[lowDetection] = np.nan if n is not None: up = 10 low = 0 for t in range(100): nonan = ~np.isnan(zeroRate) selected = np.zeros_like(zeroRate).astype(bool) selected[nonan] = ( zeroRate[nonan] > np.exp(-decay * (meanExpr[nonan] - xoffset)) + yoffset ) if np.sum(selected) == n: break elif np.sum(selected) < n: up = xoffset xoffset = (xoffset + low) / 2 else: low = xoffset xoffset = (xoffset + up) / 2 print("Chosen offset: {:.2f}".format(xoffset)) else: nonan = ~np.isnan(zeroRate) selected = np.zeros_like(zeroRate).astype(bool) selected[nonan] = ( zeroRate[nonan] > np.exp(-decay * (meanExpr[nonan] - xoffset)) + yoffset ) if plot: import matplotlib.pyplot as plt if figsize is not None: plt.figure(figsize=figsize) plt.ylim([0, 1]) if threshold > 0: plt.xlim([np.log2(threshold), np.ceil(np.nanmax(meanExpr))]) else: plt.xlim([0, np.ceil(np.nanmax(meanExpr))]) x = np.arange(plt.xlim()[0], plt.xlim()[1] + 0.1, 0.1) y = np.exp(-decay * (x - xoffset)) + yoffset if decay == 1: plt.text( 0.4, 0.2, "{} genes selected\ny = exp(-x+{:.2f})+{:.2f}".format( np.sum(selected), xoffset, yoffset ), color="k", fontsize=labelsize, transform=plt.gca().transAxes, ) else: plt.text( 0.4, 0.2, "{} genes selected\ny = exp(-{:.1f}*(x-{:.2f}))+{:.2f}".format( np.sum(selected), decay, xoffset, yoffset ), color="k", fontsize=labelsize, transform=plt.gca().transAxes, ) plt.plot(x, y, linewidth=2) xy = np.concatenate( ( np.concatenate((x[:, None], y[:, None]), axis=1), np.array([[plt.xlim()[1], 1]]), ) ) t = plt.matplotlib.patches.Polygon(xy, color="r", alpha=0.2) plt.gca().add_patch(t) plt.scatter(meanExpr, zeroRate, s=3, alpha=alpha, rasterized=True) if threshold == 0: plt.xlabel("Mean log2 nonzero expression") plt.ylabel("Frequency of zero expression") else: plt.xlabel("Mean log2 nonzero expression") plt.ylabel("Frequency of near-zero expression") plt.tight_layout() if markers is not None and genes is not None: if markeroffsets is None: markeroffsets = [(0, 0) for g in markers] for num, g in enumerate(markers): i = np.where(genes == g)[0] plt.scatter(meanExpr[i], zeroRate[i], s=10, color="k") dx, dy = markeroffsets[num] plt.text( meanExpr[i] + dx + 0.1, zeroRate[i] + dy, g, color="k", fontsize=labelsize, ) return selected def plot( x, y, ax=None, title=None, draw_legend=True, draw_centers=False, draw_cluster_labels=False, colors=None, legend_kwargs=None, label_order=None, **kwargs ): import matplotlib if ax is None: _, ax = matplotlib.pyplot.subplots(figsize=(8, 8)) if title is not None: ax.set_title(title) plot_params = {"alpha": kwargs.get("alpha", 0.6), "s": kwargs.get("s", 1)} # Create main plot if label_order is not None: assert all(np.isin(np.unique(y), label_order)) classes = [l for l in label_order if l in np.unique(y)] else: classes = np.unique(y) if colors is None: default_colors = matplotlib.rcParams["axes.prop_cycle"] colors = {k: v["color"] for k, v in zip(classes, default_colors())} point_colors = list(map(colors.get, y)) ax.scatter(x[:, 0], x[:, 1], c=point_colors, rasterized=True, **plot_params) # Plot mediods if draw_centers: centers = [] for yi in classes: mask = yi == y centers.append(np.median(x[mask, :2], axis=0)) centers = np.array(centers) center_colors = list(map(colors.get, classes)) ax.scatter( centers[:, 0], centers[:, 1], c=center_colors, s=48, alpha=1, edgecolor="k" ) # Draw mediod labels if draw_cluster_labels: for idx, label in enumerate(classes): ax.text( centers[idx, 0], centers[idx, 1] + 2.2, label, fontsize=kwargs.get("fontsize", 6), horizontalalignment="center", ) # Hide ticks and axis ax.set_xticks([]), ax.set_yticks([]), ax.axis("off") if draw_legend: legend_handles = [ matplotlib.lines.Line2D( [], [], marker="s", color="w", markerfacecolor=colors[yi], ms=10, alpha=1, linewidth=0, label=yi, markeredgecolor="k", ) for yi in classes ] legend_kwargs_ = dict(loc="center left", bbox_to_anchor=(1, 0.5), frameon=False, ) if legend_kwargs is not None: legend_kwargs_.update(legend_kwargs) ax.legend(handles=legend_handles, **legend_kwargs_) def evaluate_embedding( embedding, labels, projection_embedding=None, projection_labels=None, sample=None ): """Evaluate the embedding using Moran's I index. Parameters ---------- embedding: np.ndarray The data embedding. labels: np.ndarray A 1d numpy array containing the labels of each point. projection_embedding: Optional[np.ndarray] If this is given, the score will relate to how well the projection fits the embedding. projection_labels: Optional[np.ndarray] A 1d numpy array containing the labels of each projection point. sample: Optional[int] If this is specified, the score will be computed on a sample of points. Returns ------- float Moran's I index. """ has_projection = projection_embedding is not None if projection_embedding is None: projection_embedding = embedding if projection_labels is not None: raise ValueError( "If `projection_embedding` is None then `projection_labels make no sense`" ) projection_labels = labels if embedding.shape[0] != labels.shape[0]: raise ValueError("The shape of the embedding and labels don't match") if projection_embedding.shape[0] != projection_labels.shape[0]: raise ValueError("The shape of the reference embedding and labels don't match") if sample is not None: n_samples = embedding.shape[0] sample_indices = np.random.choice( n_samples, size=min(sample, n_samples), replace=False ) embedding = embedding[sample_indices] labels = labels[sample_indices] n_samples = projection_embedding.shape[0] sample_indices = np.random.choice( n_samples, size=min(sample, n_samples), replace=False ) projection_embedding = projection_embedding[sample_indices] projection_labels = projection_labels[sample_indices] weights = projection_labels[:, None] == labels if not has_projection: np.fill_diagonal(weights, 0) mu = np.asarray(embedding.mean(axis=0)).ravel() numerator = np.sum(weights * ((projection_embedding - mu) @ (embedding - mu).T)) denominator = np.sum((projection_embedding - mu) ** 2) return projection_embedding.shape[0] / np.sum(weights) * numerator / denominator openTSNE-0.6.1/notes/000077500000000000000000000000001413546205200143035ustar00rootroot00000000000000openTSNE-0.6.1/notes/.gitignore000066400000000000000000000000421413546205200162670ustar00rootroot00000000000000* !.gitignore !*.pdf !*.tex !*.bibopenTSNE-0.6.1/notes/notes.pdf000066400000000000000000006053611413546205200161410ustar00rootroot00000000000000%PDF-1.5 % 76 0 obj << /Length 2699 /Filter /FlateDecode >> stream xZ[sۺ~ӈ`g3MLۉ@KńbAaEqO_,\,|{-̂Tpw Uʌ^XnR)z>Wo_].%ۺ+vyi-pMR)s6ϗzahK&ԥ2"Y T J%!%X\,5)\r.t ҌeWR-s&w`/L;&Kv=EzTV&˦p#M1_hAWn.+7Si&{elkPz nz a\9^Rq*8vdW`]t7 SPBQ>Ƈ[3%1O;ֈ'q Q,| =cɛbUdPWOR!ʀ)Rp(}U| " V>om #A hb@`E t@x syXQzv C(a3`{EY)%[[{5-A`@i2CP}lkݸ8Zfq<Ų kOgE.'Qtg"}SU$y<%nWlq?Fyc̩bEFQRŨqŵc,p$y ~2gh(Ubs+Ӿ\>Zp'YRcECk%.h.X{W|swD|f!7K >TNwxxhʤ-Y1M.CPS.=qܻ*e7U`@EXM N=h:Jc/LU7i@7@]!̍ KUl_VG6 , &IM YUmfz-ӂhUXr Wēj~n֖=6b|p}-ɲpx5ςCa{/裂O>9\uupeGSc :~wuSv~܁O~a=%ueO4mGmp 9 d9T!_˿.VHOj,;NڥX\1H7Y7] !馏=csގOTx'>nL7&B$y쒹ƯޘrZ @巓N<Ψ@p-Zd};O4 B>}^U6 _^Ûy4D &q`xV tğWϖvi P$qcz;\K@r8`XVE'61u <'&pCU>e0b hwM%f2/`Ǜm/N/oæfhL; .DR)T$ Y/g endstream endobj 101 0 obj << /Length 2936 /Filter /FlateDecode >> stream xrF]_GNr&x7NEU{yIHD4گ #*O =}7=?ꙶ3YS1;I%0ffǔgٯяO4cxa'WpbLj5`F{KpA~qX3b6xlblxPYZYש-b+ x"'!ma^//d-\&xL7g(S>3e8Sl/qt3Y.g|!!2 N$Q}pb1L"،S2 2,o TtYg%tT)bS/zx7M%M$f0aC4_¶&sD9-ͮFڳʛ⏹=@V~eEbsf9LXzmdOu/eY Bޭݩ8fPaǢcȘ4tYL#$m h` ~/]*=S,Iv'%#>|`n:oN}fV)Dvcayx"c&x&do<S2nVE[y2g2nl˜`!(kXbxCmji`m@bKu(e5ȃg]zf\EY@,z;$].o*ymiCux!\r7jZ2)v`Q^ܦ;D-YY_PJtEVB5":$1ZyF,ҤS=۶pHִj7@^ksVl-8m=E)"+`cyE Ho3*&[X]ԍ5ļ9kijmA]JBz >luo7TT\Я+pBЀVpWB =ˬ# Eg5pMvX{l.J[$gŲ7"rUʃ0a憞`&kRO2lm"#ue^q:iUfssCV2E Jcu̻!d|:T&>I`TH"07`zoD);9)  Lڐ^ TߣR}C 2䴑mJR-tlXa ®j|7E6}E ,XJ^e]F6@e#o}[P_xm5j8gjȭ\_Qlƣv~Po&X2pTD KM:I͇ՓQBҫ~I/ hjs͆kp-u;Vc>sj[gԊ&!iV ̪hPzJ.\ps{a&[{W2oF?*o&0致j=m3ֳA^lz-֮JwzzU۾l']Z9nM`޶@膴NmGeHm=-i]q>ޮX?zWlOuؾ؞Լ芧oi?ˮc -b%Rcl~n}׾ueeo5zivUq7TPd@WCIxP6, ',xs>T<5G 1=SWBY[ ,v@ A玊;aRڻJ. ͣO (%De}5G* ;]) X)N.ꐚ^@[h_ut؃}3Р\OA-Wse~>bܣܱa/HUW<:ۭLIYf4N !p! [ЭH>#P2bdEtPHIa6cRuqD0$| EθN}p"w]ZkkLt,q}ڰʅAI J>|u0t­Zpcrq\MQP_X8T%)L݆t9V(*Fouл4u?wבY{tc&AN#RiFY8/Cy0p]'2z˴3z9$?Ha(J!ANNbd;16U1 S2u#2d_dݪp w^sfݍ4%P~*U6OfYM7,&jb4!m] Mw4n:/ZMqYTn"Jgzч?$`4w#p?ܔb!mٖM~ї$9`-ʽmP M>X N'Y1{?Ƈ'u=Ӡj>8L/\646u?B Wyl KN脤*.rBgzV43DnBB xmvM4EV͚FZjw]6vMK2Ca,=GEQ\t XjvDbiKLw@g˦n:^ÈTI=$+Wޏ 9eN,znsOup[3:RB LJF~?'W  endstream endobj 117 0 obj << /Length 3525 /Filter /FlateDecode >> stream xˎ>_IiQrv;0\[z8?*VQ-hvl b,ֻl#(N6wM"6y8ӛz&JDr2s*}tG3vrt荌EW7$E8nF}o:`GS[9P7WA``O4en"2= 3ШwNy灡_ֿZ;نwmGKVpNUB2`|m+RmR 4w¦-#Qn솃?9N4RsMW[m?8mÇGV8?'ALsl{dZFYoed]g{{TdT8X LEv%V)wӳvD[^~IB=a((ni"(#~=ȎjҎMMsU{ݎxny~j;[ꔗ`cBFߞvL5"]O((3;@G^\ *nY@ϣٟ0v%L?x,P(2ee_3MӠ!%, ~Lc7{^}8! f=)ъUUU5jo)BA@oOЬPYTwY' Pt-v 5A}Ba , QZ(<< |C/ 3 j9L. VT ^p'j6v7Ј<ޏ ^%\_ς(OK`F]K]3<R'LB= ( dKZsf~ tGWsY<)?ץ$G |d2Q/ $ypI}# Np$fB ˗.Fk!th*Ki/K>KwHo@qT?wGذ,J]{e6Ȋ+k,vSrw=i8d0]}tnwrM€<]}*x#}ejΞ6T&npعם8`"S:a-3Cf+ S1l䨉3[Jf%6'rtjFL8YO0ihGypirA5-Alͨu&M5B|Půz5wM jݏ$NNbD`)jnZEx$qi<0|vfߞLtEL'ܹC0gҌZvD7I_r9(䄄{+&8e!/[;vljNPS-d?{$g x`Pm1ʷ0JlShOuO\b8K8EoaUJ{R`!:_B=;g|~k=0Ii)X)^Pm |Sum7zERd5=i;`lypA#B/`*<}-麂g|| mOB &67ר}x{Wrj5@(͘HGf/E3 :Ky -S~Q07Vt~R@?Bʺ9g1[.UV.&%HfADy07vG endstream endobj 126 0 obj << /Length 2489 /Filter /FlateDecode >> stream x\KsWqQ6\; bTX H @K)O̾1X,D"\,p;랆RX)Yr6є(J&WuʈL//Fcet-$fߖ#ioHWI>_-Go~(Ԃ&cFft >&Qa~$U:Ydi^<:ld﷫ux1AZ |r.f@ Ԗa1SVyY$XM1 )&cY׶ĀH/r%ܿƴ@exhA 1Nx^@EO/L4NI''\ .2 \DIK[L6[ܒ; SF5e'zhBJR0K#P Z-RR0|~A4gޞuz=CC1[2Ż Fk ;"]~C~cvEЈQ9H,\;SRn򗘥@`'=GώcꋸܢF3c&Dzb[oO"iH9֜׈Aw!VF- ( p~!wDəO[hA85}GC.&XaGZ9)a60qf|"12)'L]!6E$ R qь|)G,+ׇ"Ҋ?>B5DNw\ۂJbу>kO#I@a Қ kn5}Wn [mE((ᙔ}: PWɹ q!RlٸH~6V]%_/1{uX@8`{3yc:R+3V԰Iw!c!L3|yĠC 390g3N8F6W#HbWAjKА)lM,6Pry}ۺ-~1+ڛmLUDg넂izr6?=L.Ơ-)GA_5qJUex^C:#-UJ L%é6;8KT!Hz9!ό8z".t-EnvuSh1H~W&4L=Ctz vL~7(:Y;jw\z';BNw cK<FPr_"Sԛ1n}onux A1*MiyeUR7Ylp|3zS!d5*M̼{7pMt\Ze9حn)Q#kN2 Nm}}-ZVT BV]wn4 t`c븎i52 لCA!SY._*!ɋ{:T=nٲT=8'IOKu͠bx%ye_o60ju!T&ܞӅ$dtBhަ[^ j eGrk,Ҥٛb齽H Dh,E%O;{ec]_*>k>M{tJWH3`&d%boy!=H=#JяA k"Ho6Gh ϓx sAy@/m=L7(= ws{ jSVwҕƅwWbkDZ4aS.{ ھvCSUaGU(m7;FbPgJ'POcv`RխI';ڃq>#. j?7#]R۾V٣mNaU6j`تO$CPwBTJ,ܝ*x隿 \j9]gyu?7Y4?Ba0qF܍PT7@_˺m [-܍ hA\/ΔA2yvvZ<^0SU:jLJ]cԿ!%CSS'tɣ lǭM$e6`Bcr gX vtkgJɃ}B=#]W!͊{_.rQ0*μҊDᡴX?Q01:Z8cea?%P hޚ endstream endobj 137 0 obj << /Length 3102 /Filter /FlateDecode >> stream xZY~_h>M0,E6b' DK2ώ}IQnfÌW_5곗2Y8L,n G1 k(6vnR7-CՕ?]t}ۦ]u󚈯WG]Wl豬/}Gf;J(ݰίWU ?RAdddX%"R$F (1EH+\p8SOȚl%O5^YGS}'`xL#& )L``.C;b%,( 5\oM(M? &_1QHɄl!f)Hf6\ (G\pIlm)38;tMuބ6asx{p )u `R:Nw?ċ t# S;"URhV\c L%Nrېp0bOMH2_|`RIg9 Q/H|3˦tv#֪UGGlͦӑ*M7<<]H Xc!G#q Pŕ>I!urQHIXl1"UPZ%p e$%āleZρb<=et&JB5X l .|e#N5"-EV2L +H֛K8tϘ _SP0g O/>.SeE>PRQG9عHP+af`6<:B+eD)B_ sC}y 0 Pa`. 1wK$qdyJ`h.S_ 0.FX/g`h?)x?2АW)4ϘE҆sCCL~bh8J+!]AadⳂQ |Z"L(%n!?}!1<0j_A BIuaH!|,\cȸuȱPn[x'e+;Ckd1VWkHC{OGn߸ o UAA( 3; ly --l#83-1v+W;e%U `Q ߉@IcK,VMP72i=ܫ/Z,[g mWe&-^Ì\s=7+:b+;b+ZE=14'C!)|=adv? bO^ N,H3dUԱ5GgLo9%ρ9 jC &˾xωF խ JHxy{%8dP[zAOdnw+(ca%KŗIR84Q"5-q(C.6s go{,7~l5ҰzRN_ TI탙 EFQH2cM@uSOvB$$3ـ , J"p8G[ Ju.F % Ir"S!5^8Tg` ;^b30 D|YTyz6fY,IvCU?;^dšy;N4vvf }'Rwzʟۦ=魊m75q^&Vo#)]/#{AM$҅ W*JR6ѩu?pgԗdE5 ,z΀..rժuC_TM8]X5PL66# 0R#)YⓁRg>j45\ݧ&]}U- xlH&u Q[52vz,n;8}>c eU JQ0-`fb0!A@) BL+=LBll:P c_ӾfL <29p}Zn:O8Bo>t]gP6J) DHٻJGWI0u79V`Le}6v1:gGB@ժ59+0 R9WѺGpEN4[]=,ܕ7y;f"59l>̇)έJ7ձq m- gwm:mbnƝ(6;n@j<^XZ-X;C^LϚ)"ծCIl=2z=xNz.tn9v`ϐQi9Vk;aA|C)ӭ䖂q+_w7D1oM1Q:8ԭ(nW[[ǒ '=\{\$"hqu^L/1{(aH|1bM"1߃-[W~S^wMOͼ'%kc 0PwPر͍%t X|9$XY_`KE,"F{FƿY !+Lm`pѸmTݯ{t ~"#U$xop?Jh?@Βŷ05}"vWck!Q{wxi972פx['"&oX{gsE l q mbvŁ^F(9,e3SM&߻dQ-o0G3 endstream endobj 144 0 obj << /Length 3142 /Filter /FlateDecode >> stream x[KsW!T9*W%!ĪJ^ Za$hC#΅ʽL>V7 7@*xWtdXWK GO!avNRlٴU?II ah j ,Ed}^.zut N9](w}]P ]|~"n`hm^7סn2b92h&,] *Ye#6|o驟(m2nȬz;H, :I´D m3u0u35fCOv0f#1 وF#llhDZiAhf & Zv4¿u ^럐9େz{WPbۭٙ׭r+^9`f'ez us0pJHiYVq< Ebi(1Iz7!v٫shsCjoӾh\R:1',ΓW#[/"Ƹ"`\IsgRr>/ it )"jXRqǧ~ m w&8p&tʜS?t ZR_Q;AJ<@|x?֛Y9Ǧ\0rx| MJEXmBǨlEضyS! k*'+8diB몋9Baic )wb n)\my=kVod1XѼ^@ێp.;c@9}L6f[7abm`Fslr|rH\7܍A,Ƅ:t]^N:jRhi~2;y!xJLWD@hET>ӏb}Z|)Q}1 )Q0Sk$FeAlRD1&(. @Pxb|xsg&Sj~҆FKpTMb¸90z`hVPO/x@O,큯#?/EO< ͞DP*!xb@(J\? ڶ0'%WD@a1|0e1ۏbsTuD׆h6xF 6v1I,Oq"4f#Ԋî:bD>uTHG7}wnYՄK}V J|GXJ*p)X"cnJ!*_ZaKKp>%o@u 3Pӫ%@#bgA#%;|I_&q< mo C}:dA<_4n ` ]+Ysm{W;kr<1Pb;u>X,U1lV8Ͼnh0ހh Kq/d >CfX!Nh;OBisG0 q0FO⎱-0i~Rvܯs9?y;ȁ Tk\.*?t3lcccFq,96uڲ'~N|99zQDgLO\]}{S)ٱk v<<ocn.(l~/uSqOnD9_4?+7B2ou >Aс*󮃋 ŷrj_=$3̠bf^ְK"y_XIkhİy"VnGU Ͷ6snv:p Y,}V2[} a̗S esO,?Vy"pi9׈`4IJ0ad}<9\ڂo A2ŋ;Gf2l`tx#yo+euN7H'NɺJCLf f_?͎|C].C6Õ_ͱcH >_v8osPrx] /x//DHdQq& endstream endobj 2 0 obj << /Type /ObjStm /N 100 /First 796 /Length 1841 /Filter /FlateDecode >> stream xY]o}ׯI}M/ڸmFKѩ_ДM9(<;{,cH')K)$2'd?|2'Pbl@HxcD!}x5 -o84 v,D1gX2K,䊭 e!:pe X9& 8ѱ nt40,`x% #lxfMWֲ| 'Zj,LX?her/N(5hDybTE !x;4d*XYKX<A7\0SaoøP(JOe G7ظՅOT8d?E[%mYc4ΛlY6]&=mz-kGUG\uժxe 2t2 辸{@տPu@@n:.%=? jUw&Wf/W%lCF8Zv'!O~)ϫMsC8Q)0N$[u`bdfdleCO?vc6Ӵef? r&IUF9dU58Y^CUWU}Z[j}],zh{o2"QE]+b͏Uﺸ_! 0&I$`Uq7AFrY|BԨcFoPI TB˳Ш{hFi `1P+P(t{^ "1[I&ȷ_=Ra3"?'=atlRTn6|M) &۴4\h7s ɷüY 1O˛}%6DOLJ8UN[XxA+Q* T|~`1D(ʂ~u^eD#lB#.SG endstream endobj 159 0 obj << /Length 3141 /Filter /FlateDecode >> stream xZKFϯ-wXH qHHh3C['$s߾U]Idy,^f|d"{y9 k3gӺ^n_ ׫[]kWV7@6?~3}sVo|}sf[lS_׫__~ W03N.JdnRz`^=-=vwݍf x8gvcXN1KiC׼> MʛFmxDjQ8"֕Kȧfx[A"3Y8R9 SA7-D&f)Uf/l/8!eOXɽHO7?m0VɏtVsQSA 8E8d]!N\@U.$]CZ`@ZGͮV= .y-LJf +҃{ZcǸ [ƦF S@HwBRÞs҅)ۊ>`I;؃;U!vծ(gq؉sr6řs32FoPi@g"GXhB OHX:p&{yB1BMb`9_:JcǕJy{G=P߭rU&ʠz8ߴ4þvߒdoFxPz~=!\i>7 48ѡw](I ݆Yr-A_8DH-C}l`ϦA꧐iM(d9l7BPp}hb$G8CXc0oEF: ]cGG̦Nd=RR_D8o1 &n~:"OchHjqsʠ@ ,bG_z<,OuaXNpƃW-$-+]v9,ˣv|p#x,]vP{WDoIJƳEb TwhIc8_u G4^CI@O,,oE9{*khΥ#d.7e6:`ԟw TnHbz!t !i3Ơ߁ u4鸘cC2^ScuoԓDbjʹ9 <_Lf^IK՗z)BM\ *:OeL[]q)RdIZ$}2D0FrR J4S[HLD/#ZDW\!Vmpq^3)$]hL8- 6PlJSf[mXI?˂"X#sUR5 *+I &\G/҇Q#0M1թM K/,TOW0 z>}ԀhnZ]i:3^w-p.VF3?u5Se!KS׉r#]9b׊/Y47Gz,$Pnz/'!;@~"b%DzTmeT K \1'|29=V1d6_yOcIҿ],XtL(Fȳ:1.D](bZTg9T,3PIs7XfDc6a0ݼn!׫]]mB&:ȶ];$XQ:):LNu{n,emPjQzvRx4g?8GЋ@L!{i_ }`|/߆4kl-^7 DQ]@`v$*6 6'CDǜJ>w~fهJ@;$3rZ?Es,,nJ^I,ԙ8Rp9Mt{վgIH y-@uU?iZI%M\%Ĥ8 Vb.&_WǦxD (r5k\߁A_[&¦*kP:(Im~աG] ř. R; MQ.EW/~G!Nr9Y*/ r;N @bO”;ao'L AS*|ʧR.JO}M3- sƗlT|ZҲV*'Eۓ=W֠XsZs endstream endobj 170 0 obj << /Length 3110 /Filter /FlateDecode >> stream xZn#}W𑂇$=@l#pACliasjiU$[#IZrWj(qԱۉj=1Z)b/VL뙰nE|mov_n<ܽIZ6ԛzz[ڿjlz篤鯊[FwD/1W_`XY ">] =Nfhc+&~zzq=TL۴a)y]s K̄9QQ ƻS<1>3;}Yکp&/O$adƥts,XNh Z)p5rkgp'MnW_z{ŰP:a.%o'bWW?D' f ˠ̋c9OW_zW]73`ډ%;401v9qZ9u[nr0QgK3e,w 2+n`ƈ2`V̰ȷE2b!"N!qk *ƻaY)$aCxv} U]PvуUXxpc!-NaB/KYz@F@@ ۽`6#ujۺm<6B#drQ_=]4 #Dz21ǰ1^T/`{9UG*tvJ]ER w삋"czZXRbX㭝/Z#0\2`Dc)7&K'}"F.n]j O}$#Y?߃s nD%6pVUpjmZ/.Xy$izƤKQ) ABD=Nhh&PPjmK'-QƻBfԜsȸ\lkNXaqaeоRK\^ 926݋(e]yU7ڷf& Pc^] $CqC2|6xQ]S]\W^`gP#xF_cyxSR!4 a–E`Lz ,!,wYwN=P}բcA>__Rҋ< >Z݄.4bLnQV4{$ V*"9z  qr‡˓-q'.,<ݵҽ=eq3 {mo'а` &GA~U4r >U6m(ס$i[)<|?mg?.fm}*K-(D02}_ǭn`dRJHfiPӊj Exz %v ^/Ԗ{0۟Y}Xإ&|ý^Ԍ,(WE^`T3ẺxhlIʼn4r'!B 1ت|T4]( >"3TiسΩL``Jc8?gT r*eu;}m%8,aaV(!bs8^pHE:&m/ H|/{Tg/$`~'E6 Sm:q..<Ί?yΎ }{[H bÐ։ .Pz&+=Hnޑ@>$Dj/c̰#rq*'BIӦPuW5~ݡwٺ^f!^a^\D[wN w8f9}\Ƅ [w !z ]]pl1ptc9dX`0f:o>I mw}&}|`z>nS'YCżekUnE ^GY݃,O(w仒pc٪w C2 %X`˳B?[Q"GG(:kz) ,*TRS6YW7f^sp6}wUL<ä`ֳvFOH endstream endobj 189 0 obj << /Length 2738 /Filter /FlateDecode >> stream x[ms۸_~N8@o2dLoډgiL-7IC;/'Ab} ONO~zx(Բ<ӔHPw냔PYEq2Fq\drM a}V^iB'ppo!K'bJIn1ɴ4(~Be*}T̎J+\@%nմkbI,H,aր{)Du&"-3c3^7:p-uygn;j REwkD> ӳc٦jG wBJ5j6htކ$;wcvn|_w&J)exv-77#zF܃\u]cHHUA[ ؼI~l4Pǟ-yTo9b ?'?l#{K20mo3#m&D;& [n  `9R* h°?p;/HysZ,:+$m'@@cY7ss+ d |!ŧE" a81<.-B< "DNCEgAO>7 DNBo녮T; ;Iŏ8j5=-mc6|No/i4E1}At_WUJt Mt_f|'6:([x(K|ޗz };}d[mٙdti/`2c h8 b^^]t~&>,osMWO6q(ם)oiXDeZ8I~yvTvbώF!w ǁU{ϊ:_rO-w{SŖ^fs~'/yFR;b/܎S#`b.M!Μ~ M$VLA~E m7$'K\~} tu3FWu4tz fїMeQ h`n‹pUebf &d 7-fێ&vnGb#ݏ]2geCX-iŨXwc<]]HO>TE2ǯRe?;6&ơm+|n endstream endobj 205 0 obj << /Length 2462 /Filter /FlateDecode >> stream xڥXKs8ϯ𑪊8 'F̦29lLZntd[H`}oQQĥ(qeGy&b飋ѷHj6Okl=ŻH}e[.yr&h?=OUd:~A#)}6i-kSmpG}KoaTDj;\UsOnWXZD񪛙қwS#%nhQZdźrpb*Gu(Qkڞ;zx6WY;Y^wfaX\mʟ5͊&75MOJ a[3}EyqȰˁ1醆mS\-K5 W7Q:V.` ]uy&%#aЯv^F`jTt?:g=<<ǙdB NuˮӮxi;Z= LۑI`k:Bۄt]@bT Yoe-4 ek]QX[?u6Ev[vws,5hwMڑ"ms>r ~DSFy/cn0(]DM%bnۙgca8 k ;Dh,`kVQ a %2Lz  _\g[f;`^)xckVe&{MuP;JָϮIV>/Bi SԧTeV O|W\$ʚLpִhPe"<~n+_E_ܿa'@1dz뗧+oҹN @tPx؁@!M)(2)@ @VW? &=P Ԧ$ZW*ԤH~!)GR~hkvJ u64qV䀂?n3g?dqƳ0a~E[=x|1c0o w (G&NOT內\cAK`)ۋh'RǵB{ \f{X}ߞa4~;8b S9” Ie5 ?getf61ױILsVlS3^&L1`7=`p2N ()}܎u> PD εi0BRyS{xjekr^ ӏ=T 0¿c{sI[͒uZQ8| ?~$Zܙ͖Z LG2L4mÕTW-IxZ3<0JA̡i" oI`O$ek- Cir9O3KhcrE/–P^?P5pC-35Jj2ԚBYM`A5@k~cCr7vR9s1u=췉~ۇ\(3Hɞ&**,TN[\m{AB)DȝU؉,68 Skf x悵/X@RF hb\H` l,޽V41|B>̕Q=96dLG4U^t$v'cs,$B:qBEb,uo/3E`hz=EujV x뢷-3TWX ?a#?p2p3Kݼ n9t|U )jÇe=><@ <`0sZr6v]wГUlWmibǸgθ'B/RXhHH)2B3`̂M\PೡXau7!pPL,;O>[{ J ЧX1, TCkֽexc붫2Jɠ7eDG?y$hM˫`8HxʌcJ<#t!J!Azhؑ!g̎@vQ0Kh &,>`U2P?e<$k"C~2pKGU(Q;|4U)<.k 9|ؘj, k2] !h>xߐ3_6c3\yy(E'i aO)'7}Xڙ CuDF˥,D;jK,A?՟s'-,+RP$T"e@C<\ken]ؠ,C 4Q<"_U蜫 2XyYzJޘqSigpUu}x/@*.+&Y36 (YZldEXy_'6#">$t#p/q&RUiRA+hDo/~/ endstream endobj 224 0 obj << /Length1 1632 /Length2 10691 /Length3 0 /Length 11759 /Filter /FlateDecode >> stream xڍP-;-.!nCpkpwIpwwww{wWW[;tZ %UHl Sba03123"PPY8YeG98Zmysl@7 l `app23XyE;ā.&F,@!sw03wz%ژEg8@`a (A6oURP;9212mf4W 's 22@h5F _U+x3X[lBmM@Uy';_dtG`1hnak0>I3:9&֎x hFs@29;X992:ZX#iގYD lcurDc swgrlB&alǤnka fB `ff 7cs? ہta~l0}kma zAtN o:!L,F 3 [d3Lo`a~ ϿWzo 3Z3i((}$Jwv Vf ';my2`_};mo P= 4Υ~S.@2s0}?ϐ?*ݑ~?oƛrަ@6 K5`k8fAh(i2Qp26K.4k [⏧?2z{>4 6<]Rlǔ0vo6& ?U `b;ޚQ.fI߈ 7byߐd vvd `0)-7ߐjV?n7&?o6l-)X 1 x tޘ?v>h aalhYRߕagT`bG; / 4ikE:і$o=j7)JE ~m홮ƩcJ*8ϯRqddZ&t:ڏOhbDlM|e*NH$B-*~09-2Y͘` mdۍƍ4hTwv-|@;`͠'dk܉-"^G$_ #:1 DszIВ)1 H%\:Qo*1`|@(6i/AsxM>S?njyw= ?vÎ<%fRWy \_hևvK gzU*}_ɞA 0rJ19Տ)|>GE~SptpSN/]rtϕ5beDf~<|oJEy()#E^*lQˣM;"/C 19Z"W묐 iIFn:q+N5b$WU">NoܳDQ*t{KUlȪ]2bi%_Y5)q a7tӓ$ DLzUmRJB2<]j=}͕NNk<[f# GL?t?s~ԡ.FyQiCbWvC΃ r<3?5i6::ϰW|텩*c 6w4 ']3U_|?c.}{4hG"YDusDAc?;Q>8 GeGJ@YUބ褽Nׁ׋#AƟx2b4A<*EԒ.>xẂtR7[s25gE Zxzeۮn c[vYDg뜊|o`F,^MߍXt{|"uȖ2XZH^w?Cn-v.A`aUF59w MT4=vD}[rzzb{I%Z62agA1$_Ğt܆Gm;,Ѯ-4.,vh 2_FbQK~EJWv=ޟ- $?0d=bBA w!9Yr!±(ozoi7>`)Z^m8;^r ѐ1/YGK~G 0TQ棲K;Gk(ܭT-7 xᚑ^BWujU_ );Jv^V?Uꮉ-C`C@tJst QUzyiu Ķ` 69MyNNG|x/xʰMW51af-fIked BX?C,SOXo2kڪr ɿ>ꌇĎ Ei: ekD#U!(Y,L)]*Яɂ15 1 5@#:v*h bLV aoxͮ?;16!YO|Bkl,(H(&50dpX^F{b韊M<N^ lH>A>=KnbEYԽ2.em5X%~YDw!8콍[N|"w0zO0D7^XٞA>ۆ/ 0ߑ ZOQ &~v?Jw'*Xf}(OĦ0&FL  .\Ȏ_k]|l$ [/DV M (z0R9E@" s=Xqy"ad?rZB 5uA)Zw+nI+$\Hp&6ЛIߦ^འ+1ā8}aG )}wwI\c =uGcBH-$C_y ˹ױQov/JbS{̼0fźUw)DE O%:wPtCU{GH%O*Μ}vK\5OeAb`i6s_vaFA>|~ҟa8Q\N8xiQ ̛ t ("(3 ^ 8=;SDt[E@( I3fʒ+#9gGzD{#'k icEj<)DG=E7v7!].eKW D>#4 $c&;9N) ?J F;bJ^ݕV,s6hE"a*s##QYS~/@.C;5XY#emңvQXK}zZ\7CSh wЁ`ϥ p6Ʌ80n[~ؕ}mk ѩM{3l)J&;ŀN NqȐIvHXޮ )L?Jr,IvxZOӼ>Vo1ǶWmɧp yPI4~[{qxUg7KY})yTAEK~͗vPMڜ{xW{-F]f|5̶o PY % S2) 㥺p,[)!N?S3&G|W-1kj| K>ZxGgE2PP+ C3K 7u G1Թ? 65MOՀ%]R*N:#'?0iq{HߢO<.}Є&!k췛Nh%!o*t%jj4Or&K/r=ř^\3Rx٭/62AI#my!@6n,DHE}Yw,\E;r|k#` G"!J88!ZI  bE##CfmEgWL~KZ9eBӮ )UY8U-NJ$S g*3?3!oLi]J:$d5|Guxl~A;D8@DGj'O'XriLNkՐ]ٖ)nJ-Ty`A6(UtYQfb F9! s A~C7GVMb"mwQP<#o^]LTl ^+[+"*GlL}n9fLg݅\$~ ,xgMxKenZZdP2\Qg6 e7yTZ`IvwzIKziVW'gǫ/wCU϶M3pL %8f; Vr鱣;Xdr8:Hs=HYx0|&Mhﺪ6{EݕUǴ<^2lC-H"cMkpŪ$ kj,qȢFL[-1M!65mG4b= Mn4p<ǯ|<5ڽL#UC|)jV-St;>@[v1% ͝v]E!^CmJ<EuS:LzQ͍FM|1D;Jwզ1#++eUD>6ĥ:$>h75Z))H8|=!VYZO߈ʜgU 겘2H jyIye:IH,Ң̢ lgls_\+ūjJ}L6TR]j/Ke f6>)ڬ!^0"I*wyH&ԫKiɗ #jŲb-_I{sCzĄ[^"FuE2 >}-Rɱ3+||m#-Gѯ2=l][臧z{9v=^*m@ӀQԡ2,mtF8!$gwYV!kYSTQWl6;(N[$'`EacHY d(k2}Ǚ٦c8 %}<>֦ΞWdk4 ݆3,q}t/^oӋgoΝطW4DE=8Qnioj3N-#H ݮl9}Lu>@Fe[K|Fm[AaS@pve+hTC;}GÎrf^z4#2C)VNL>* ">`l KhϪFQ@OST3yK$loQY3h͌ڊZ5ND`lO7^]]rY%+㳢 :YTz4582QD4,c).uUb<`~ؖv1$-XAȖ,su[1:K@v)@hDFΆg[پW ɜ~BfOUF3́uxln1V$S/uʰƋ.H~+6(|KW;$j;c"0-OLHkf>y VZlz.I 2ÈO)7% OX0l #gw/q.4|E|f#AqVUS]WT,1[~}KK~KCl {HMx>:5Cq/71@dld(}_O49%y~*n8u+r 1ձ$p>znvя4#C}jL!]V\h>JbdX(n3y6gxwgzO8+f~^ڳ=\ca^D0mtP fCiDAg@l.@QWzbX,Lc&:gw.i2M#dK#"j|ZwC.n`J2#nO1l&`KBLOx?* p`PԅsnTPڊ3}tH17HUzvŪXD&{-E T%uq"< ku"媣PD bھ5$f?_8G1<J!skb X9 s4'~H6<_7G.vh0Y$Ia¶>ǯxbTw̲- (S{`umg;9IkLdt,8Fע`a2#'WSwMpeM_VU2(ŧrk5rqz3yh+COĭA5ͨ(4劜e'>Dү7To*x*mi1Djt8>7&-#@X€H3Drߥ4Ιj¬_5>G [!5I\I!k*b_3vS974Fze9Tɖ=SVy+ȫE=XƧz4"sMhJ*RHRP %m9Z@=3(>񧱠Ch}*`|Qw\nš^)_ro@ lXUGK6[ g3E1Lqޤf⠙JC]{npȯXO¾Bd\yo \b%bMk/]eeUB2y"aD _0鵓 xiuET3StEҜM>"γxq\1U$JXf5cD?-˟4ӆJe+?Y6҆[{`/m} k9. Ȅ`Հruee|X]A:1f`: `>߰׺ "#of"omR},)ox)d*q4’([b&a\+|Zi|#DT5l-aE- #5/+x4y5H_$PGv- %lZElO~(bߔw)>YeCMeH uBUOW'w~1#2ɂϼbD¨M2 1 UyMDH}t4+Tn޷'aIL[{nb_jw3 ͱyQHstUj5@t٭yBHe?PC$<ݹUj=v7Bg^=^eNAh=*ym߸Em^f P'D]zȚ E~={sWwVEjӈ+-~JZcPcc;]AO U:b΍bk^ z[x&h (-MD)ZK|4zwTfZrZq>/.5֘6؁>:nZ;_ݕW"1`Ƃ_K,W<'!HQC1Y,7y+U%R: D-0pcThlD6>J9)5YD-aHF rW jY1S`&Ce21y78QIC1㴑nst?+O4|i-Sz_HA(got GJCj_M Uyu3,gC86rr2un8L;|,(U sMyu >8x#VҶE$V;1 ɌiF6v:J. q,e -4h7mZ?'◅ 2cļղM;P6_똼b'¾ p뱥3A+.>'YTI #O4?RNhܟ*'MuqqID6~t,|oHMZ0Pt0lCOX\yu眮œ\bHMlrE*֥tppiam !ɜQ'Ao*@u<沏ZSJU^K,!:f;1AZP:&DeBuT(Ez_uP{P܈FhAWԇjj15P~z*3!5^Vd(܆\Bn̅Cl.V:ElW? 0N̰Jˊh@e=tpl|čТ?KLX 枢kZ=+$+ab1RUj:K<݇7~8>Y!l?auY+>eR(M2Q듉)粎8֐Gκ]u+yqOtl-5KM~ EA{sLLu C=*,"G)'0s =;ƾ2<~D7ҋ- /+jcP{r]sB/NC: dv߉꿶!tm1pU컝vAi0WCw'fH3tSV}JJHZeGyQIT`CAd HKq4cW}+X"i5/EoyL|4!NJpf5`EBh"H”ž >^@Q0V`4rO& Ҳ\{ws#^nS xss^>)各MwJ2 ]ϔ: 6)3Zk0&:2inYOE X*a: |4gRZS D^UoG@ArS9 7dd۾L z:e<4 vT+Xկ*Gb:DQ;X8`%g:Ů,ͧS}=QX=s x].Z<'{[Dښ'k' 1Ȭl|LhQV~+)acMM/C-3O5 byLw!, K1wIrC zU5\٦b0wx4#ɠNNFhI&ɠZ7D/5mKXɡMQE8L-I+& "qk #],~pj3oC{džnAN>gY ki!9;H c J*Ʈ 1(#uڤMrj@O7>a'k_"cKt,DwQg6kazJFϞ `Ex}w+jU*WWXu ʋL+@Zo5f4$.Oj"*%Kn(]nthP1BKI:(NփD˓Zɱ;u1lVy"C+'S@p\ҌtNL\O]Ru9dZĊ[ajE,BTA-#]'6ccu1> stream xڍPk׀ #EKpwww( @^ܝZhBq"]kqw;J9$u?k )H.Pss"h]lA#hXH8.2I˫= j p r 8ch$Jy{F li?f >bv 'PX^3mf`rqqdcswwg9;Y 30.Vu3 de2XhV`)4-\܁N l8BAN 9E/c0}8V_ffv@'b ۂ*Ҋ.. /C? ]: -v͜.ά`ۿzd+1KA%@g;^ݓbY!a;$m*B#xx G̊_{vwX[^n +%$9` CD,ů g?_3|0s{1ӿ[R\`ppp x>Gw| zN)3@ao,e ;qߔuoEҮeځm=m:.[d kۚ_ u =F4d v1׸Kעق! U{g_W ^p~ɿUߔR3{󿶌trz"'uAO1bxm`a%?M ؤK|6?`C6?`Ck>?`SCٕKTk?Aq4kkz`^3/,Xy"{W_Kd_~=+O׻ū |-Z?T?Z?B/lھ~-|^rV^8^Kszi5OIesqk/^AWokxk^,kuCw@ ¬ٻ`ob,ۣhuX\WŒ16DɟšZ>>RnAr V- 'Ǐ6M4hط2?~ nT* ?MhEOfP»"2bzO_]Oa匽bB9*[㌽Z.t$&# "?]R'UdAj4 ܆t䮡X*["3(Cߜ3S۠ܳi'ʭ+|0.@rVk}󊪘r q9R KI% o;ew0$]]‹yN<.oQ; ؘ #aq(A*O+w&Gu*^lKc&`kV;^z?, ܉9jnϛ&b3 o"85~db +A*D5Y^ߨ MN#W'|+?5rWB:<ʥX:<~!ϊ;W9M*I4xH]8؉\9s FUpu׵Uw4gM: ueVBzIܶF.L4i1s[c RPՆ2XO ϒ5̑WRؾkՍjeWYV3 y]QF> pH[,%^Z#ȸw}7e 񃼶afZfGX&p< Rˈ3"y3x~MgBO1y=bʴC3t9}(O4ˇG-Ӟ xyiT7D% Ij >8&>`0P.T*}rKn@df~MrkJ/QlfE':4;`t;]5Alf:AiO%A? KG.Lvd 8>WtV堖S9]/0K.\dLCef;\?m~ [۶AW;Q;OөGrfOJ[&P4z]|sY]2lS]Lj)2د ӆy8)du ĂH8 ]'7*6?}*j1O2"p`Gj[CSe:E'T~@僢xGȴ uR.Oۍ5bV*w,q/'E?N]Z*Wb=[U ;ʴO ,]&YtV_*;KMnmK@ Xn{Ub✮W15{VݕD+l[wJ&ZBɦ/i*A :0RU.oYےMIG .NKRE {(8l9e7(/Z _L\NduB-ʿ;g2C%''|%mC;k @ 0o;uC7zR9 %Q8Yl.B鲂""8QrǦ/U1&c!S ;pOǡ4;yB--7ܓQ:,l_&ᥧFl8ˮqMoUQu߈'@9!B8dM/#WEyvJ SȰ7-[*(/sLȺY%CIHv q ~Mo0s}H\vɖoYRH,D6Ay)&S@SC@vT-u&Rр',js W?X.–FI/)BHgNl¦09KPxQݨͰ~*DG\Ş鰐Lf&(},rPAS$WZ2E+ 5y 8bWWPD_ X+OiWԁ^ڜr]QԿ6yFk 7ӭ}=BYdIbxpAte±3Ujw)/W6J-|،Z >9Vf+A.N6P.n^yg•$;q9}t_79'7p⡋5m)jHХUUҋu.BI}%q=֌ )2ŊEp^J\tr%Vi`;w1jEpJk]]g'%'}GLZb~ 0paunzRw)."߇oR `?_v"]o:QTrBpߊ\[R땩(f>iE%襬!Ibm7?;t| ä︂*;\\ */mO[׆ %b\;C[..{+X@2RY7x%K7]nZOxߕ/7TIKpM_4@I{n_ўx_3E';6"15"lAW Rr(v%2UZo*>Uwg*d+`D ~(,/yK7'zm-:Н*MfIc^ixy'IjQBN{lD[*.e;% 2t9inJ-+J?q=B}!8POWzށ1=+(GCtzoF4樾-~񦛿thzC_GTͿfowalw+6 B;ls'|q`BnXvrەtpuZƱB"E hw(wI5+8F Xkb̡QBIsHFN]/K`Cs̚=|JI-qSv 7۰qDUÚ 'c]arem|3uN6pIh ^DT13/s-9Kow/kI"_6U*mI|3$>Ne0jf/>v侭+q6tŝǡVޕ9nٻi3_`(B *Sn ! ikN$ t\C&b|qL;iT2[bڽ.+uog7\W~y]nWU|u0qTM9:b|HKj$7~^}~mZgh6KVΝifB jirM$+4p L8`{*IW&($db;V l_ -TO5Lo>JI+U-oG|A"^R 0)wf1BlQ;@_;AR/`)ktj9pNYX|+./c}ce/DQk0 i/[f+jW6|IЇa1iGU󝿛df1&,=EtsQ0̱tx[JdN}%Hc)6|rQ7()"5 ^w~d3\)"ؠj7UJxRkhtt*7Z˜.ԵzQOy" {;~H[]m"@{&h0~.ԋ@UFmYWhi#mLuf?N7. h~vR2=:PkO^@.L5Fk[]&c匿 #I+Eb^o4d +k36!]xd:LV~@0 Sobk 7%uT,Eb>y/t В}^wS n6}ĩRk.'x8nH\QJ3LF7c7VSiɭ)j6szX!b#MW־ 74tsz׶qYOܼu+ǑI2:8} !5%' 4:m@-nHBHp 9|A/]|A;M6JJ8dz 7Fbve4'UM,,!8':=vOuGP&F9|Zta27wV~\CS(au| ?+v%u FЫS("tč?\xӆ1\* #ͥIQ5` H`U !qh# pt G=œ*+꠰DǛV`oCWcP o3oI1pp;*5Gi :wRI7 d %FCO Xm̳2ݰOK9).}Ui@6A"&ss &>)̀"1CUU0jrWr^c+ *zxR],q\R IfvQߐ~dvOxS M{lǘ#-!I36#Y~Ho[f}:o/6 ξ)޺:ŏ*Ls{(`YNpsIk`^U!Pz&z |S$ GCc7Us?Cm{Xt L00˪݈b~3V>- a3A%Jց7BHƫƍ8#ӻ) cY)88j 2dZ]by1]ߞѤ@)MwP&j# ^"xە;R_ kذTLR.1e;Oo샥͡#nĚ-Iw7ۊ8YfYI :ҥgm$/4R@>7ɇ! oYDj܎/Jw mBC|(p C|ϞiZ5!IbD~Z$URLTu-V<[Xn|zj##GxJ V&6qsma]=7Ft#Ӊc+A!fѝc&5Y\<zaW.GWQ0^J&uqr6$lcbhIa+%3\쯳(q 0Dsm ˆʌa]C^T%dpYQ~@ RM<"ȕq= -<0/&d7FA3s _.~q(FKXUd)Z&gf}x~;9T5vA7ߗ]b~/&G-~و\^{&4lffTE~M ?|D }ڎq\,pKQ6.?Pf*VMc.9)I@ܥ7^;XL\](]B0zK;>z3X6pqќFщkNJۦtUꗞ0Uha[{A0>jY͕Ѡ^w*fLJz\wX} JS& UJX,+4RQm)ۂh*4ɘBX 5),ɩ_ |%jĐ}/~ PdﴪOQrBC3y>s_fPs&%-@N< Ɖ弙h1̷W]5;\g6ljƙ287 xɢr~>8OE1˜g.zl.%j* z`p }ba`3:J% n,rVMQޝ!FK'Pt=i|\6Bڭnk*YrikV]ryQMk ̏d30dLE`PS{՗{@L$ Of3nZ?~jʇVfBV$PTƒc:"+P3OՑ[AM XT&w.W82UESna *Qb=Bé2aIkbrom1c'93[ke3=G/Y%ghk-ey#8ز(b9KhL)LkP29򧣧򽻾3; ]@q|hm͖ςy7`A 2ōj| $e9|(dPƥ#4K`3.=H[~3-iSpXϵzgU1Gb} e|3q[ )~dg4އ_݃8JH闽`2'3 "-fsl2\//Z`(/ ~$'2z$abf#!7.~`=6bl< X.hg>>;*,0vUx4*:8"Egջr<6y`܈g;hMe)MU?\Y H8?2]r/'Kt\L7orq|k Z7fkM#:3UUoDx#W-=N()9^@YE/L,22c=oՇa"Ȏ0B 8Oo?m8~| %cQSj{+0A 4_`c?U:9KtFnvшD\|q\ʖ7guoq¦ 2C:=UKr;J0ܚ4%~93N5PuIC!矉}B`h/iS˒&."=Rs #!tmD>iPq4>|b, F9$_[DDdZ :A-뙽m'^Քœk^cNU\kRXer闞yb_Q5ʈ~˦y_:4QeƯGh C4Oo^THgRcNQUp)rob./:_W0^ (՗zB,k9֌g]ޚ-u449"$NuKV[xv_OdwY| &.ҷԱsPD`r×Eh]jv~g7ElLwoGXOnld71WZ;쵱\$ ICoۃe-ZF҆[` R/Gޗe bTzu}Sŋ<詶"Hڡw ǴZvpW{NϕCe?;V7M yßcegAU>zBR> hɉVk9 #L`CϻQ|4;$yƆ #.p'_!_`FSj|8AbC*K>u@h`b$Ե6ୟ Ɍ:UZ-rbR0H 1jxj+wv=H4,eȻd]QwW_UꛆTm8|᭑c'esܾwNrrύuȝ]я?{._D{~^,GX,tjƒ9wzulf"KiO( '0(l;CR1ӭ>sB QȢ/'5V2Gkgͼ/_Bm~S5t)R :LU%gp|Xq~h4/2Y \)ԒV1MBsy oEPUBne_е?ZfLc>6V-S)QKpLy.~i[,B){fm޻ ɶ{_rHB ƫoryYt-ԅ u6OsS7S"洿[\+ nxYB|  ?HC>8)wdZu+^LrF;+uOT! VJNd1B /j4tD .LM-z~[3,xVוfJԐGyNR^^khد{/ 'rszSza=rHj<a׳C)1o0ĔP=ctu}MFѰ(-uyL DA{Dh>n͚@;Am|ڛ9"wTb2պ^{XekunN-z×cl@bggђ܁y:ڌlI 7{4 i%F͒+X= "=~J;|֚_b`7`c']kEdTo4<>4et5#l5Xƥ|Pѐ\7 RF nrB?'i 4E3˫˘pmB")Ϲ,j֦oQș@:3o_~rK+d8*McQ9.ʔڸ} ]{lcUf;7'vov?G^Zz<\Dބ=X@]xu0Ul͋MB]e T9fh[Zp֧#TYA;n~f)9;̧AS mpw 4^^;/P&$e3KQ.S9!_drm H>*2"3Tut=>g6RwLc2q!tol 2TbO9&1> Rz׮vn ZEpMa?؏*bf_tUW3q}ݶqHZm(+?KR0!POiwV6QږLJNw{W|H)3f!ܿjʀ#9f>Nlg^ΐsw*mE>@QRrj^[nڂNׂ|ю$?H(~ͷӹ*l|sc%;bp7TV_mחE8Θ'&{NOǻid-L"*'?Bk[{Mڔ`Bq>-l 7{L4<>*tTEyI㲓sdDKBiatm%n!&$~v{ [t]t'Ƨ|J.Tk2Ko3hz6 Y gx2>NygZ4p:Gڿ u2(JdM"vO(ƺvG, +%w:p7-C"b^JhHQql8$4YQdLE-T쫁ć3t7T H_RKksL < QWYդ0Wt6= :xA^$|V }݋o ?tz7D!5o4?{4yǠ-%ap< HGLh=iL2)'B\p/(䛯5*FvY I!󵍿+b5̮jiHPdԓ1T o`drțc)+4S&QMehI!{D,!.a/IQG4=hYX6UG7uf :PND]E(ϝ?!7WË$c'TOͣ>*͑=1lB`<GoN:H_2Dot~GZheg xQ?ө9#S>XVסH}M~\H#\\6>ډ}BIPp%'ydX:?KX-WEm˯Zof6GGҐSa| '>$嘔Sgݔki;`P #==|[r= ܬ&lla抁nu=nFP{/1S5Hȸ~ٟ˳>'.0+Y6$ pĈGRP\ÅQu>@n6)\6DτOTgnrYj^,i") y1B{!|'N7G,t`&kEe3;hMC~;g4A':;'57ZҠ ѵ P[ؠ ?HT%ܹ)qj4GmL˕ỏTV*R5fQieHK@lvļ *X/_HK"!(9YʮZ>&*D8yȌa%LQ"QыAz̽z/wq i[P1x,F+ഁ:O`)Wv?Ezf+h IQNmx #PX֍^w:4i+V酠j\\p!:he4nHWDUu, 5qi⤑Cs_֩,;D MT>Jn$/E|l<O7 R($Ҋf]47B+v_-2`_?IP!#ߕؠӳ8 \Eʤ7\}pd5-VqtM/J}10"`HV{Ĥm{9ϒʕlq:4պ3M:9Tl(K,{ sҞ?͊Ghz1A=t%r˖A\9bee !@bux@|yUQڷ71 e;]0Mv6l!j ?\~~ld""$ѯx[@"{ө7W- tlht*F9gcNlQ8edaZuLnxD]: [G%hebFx5jN1A_lD@&vAگFG?Y`A mp=#Ω8/:K:dJjxX9{IFF'Cpg*8y2K\-I຤ۻ:4MGXBX *=% KJfH;2MX,ee g[v)Z̸ _FK8ڷ뾆]| ܫ= *ֻr97 -|Tw .d5LHO嘼&p OF:PCEjvLb*]a CG endstream endobj 228 0 obj << /Length1 1853 /Length2 8384 /Length3 0 /Length 9491 /Filter /FlateDecode >> stream xڍTT6L7H "0t) 0 Ct%% %% HKw  }kkͽg?qγ0iqlJ0( +P4xyyyp!?v&C0!w6Qa!!u@ 4sߵ؃^8 /0v[ k}wpN~@/;.:bc59y]y[\|B@Xg mB~#Baѿۨj[OX;)ݜW_?UV g\!.{Iq7aA<]UEAFB< >`;m/e79k.(Xy q'_n~L4>A!JH;/!x0p^ y8|w # ?~a|`6cbO"O&Pvw@_H?@?!Ŀv" m#%?o VJGF<<]]1A<\@_YЍ'.l߉ MlÝ;RysmJ~d4bwz~'Lg ]_Ȧ%\XWd=3-U*f;}~j\2[? CQ[1{h|~_}VuWAyH$SM=Gp$%^N-VytS2RRPgxtER$]ۭم~3E\ O2b]#N.@ƛ6u,R;J^N !j$?W;@prr!G(Yi3x\r1fOB G`"ܒ^VhGHJZ<Φl(ϝCR* {zy?{_6́y:CHeyzcpJ;GK$SyOT^p3#}K4%oZ賃SyU NFݐo"!>2v~팖H:/$SK^06[~']0e@3@8'rUTTrUô]E-!lK֞D&shf5vm y+2 L!$:&T& #4RlDJGgrX@VKs:B̴KzD;zeIV\ clXJDy/cf#%7Oӈ6!U0?[Djxq*^a/V`nWUovXⓍό jO]}I)0Q{%qՐOefI~\GeT{ F@? _ިF%-O7"%z O[SN-*s82Vb9:!*#-pJ{`8ގ&za]yoSz׈F7N ~_jKm [sջo%}gJ!fHA\tU_v [ z^'uoXϾKml3'C5?s!J,+UY-R4-&:_ۿ0{E5췸_ '29]/D/FqkZ/0giEZbTA_may3 lHIF\ tGRۘ`կej]1Mˮ42t4Ǭ{_Mice_.D>&*b||k<~w}nhJ*?bc+7ʠE.YT^r%vӛ|.2j6ºbFժWѲfg90H;eGzޅ.$.\ШD0F<ßb|L}ؒ?ZˠB9l U”<)X\R{Ne"QHAPvS+96aV6ȫ25i}:?I\`?g~8jUMxtMCKc^bH]{t_eٛRr f(ݦSXxy[_;Scd,P/HD|)DžxLH(n*|lWɞ|B;SJ:b5N,ls=3|G8oՂKa;KV-*C1o=WBa6k() (=7:ݐ*»0$na+v9LIWah<"5!R &(s8j>U_LDy#fҘHgဠ0,fG /R1T-DTjB q&V|g^ء>iɦMGjGejKHPjߒRh*th)׃eǺPx |3 fA=,+12OfתboL:j5Xs]a GǿeG"ڝ1L.OMN 8j׸ALsRkl]OiIa 23BT X> oabӈO4B bQ5,γe+Y, %Ϩe$KGņ5&A֟p.R[c)RFD3Mc,J;ji>H!NvgG|lcԴ14<\Z^D=2q6[񷗧>AH鰇I"6rmuWz&t jLAV6syppPF*nr 8y]\Wg1mF?]5J$ho_sJ.sɔ+9}|7S jo[+ݯF+0P]L~m&ޫ~&ia"L̞8 ӹ,| W\8 it>`on|0%g%f?rWB-+kֈ7Ay.ʌtr.Ktt Rl<'rTMYpmInt߼ E2B14yR Β߻7e (<ãñ'/7P`h yg尅< e- m'AÃ1pk!Fu$5^'Ћ >-.6J&"#DߗzXH /$RHmd߿+E֋g/P/(YJ'RˮD 29m-jd{R׋rW}oӆs~>ڲSAU[ri=)zOMVuiTzVsgXj !ι-KKDN vMm wdGc+(X'>r*.|Ipup$9TnJL.}SXoO|1j3ƶk·FƗ.FU(eBV$Hv v馍-d| mc@>oMH4_Yhd]vp ?6r}1V)a ~ y%?g$+4{~뷢w"z,㸶Y̠%P_2-b1IiĴ~Jpj!ZY`2~6ʘB> ֆKPL^,7ؗ{`]'IRz)yWh#٣ bˀ8UuNnpۍtyʙq7x;7AYLŦ\$  ⟣L=?`wjI԰{3Bغ|38c|?Bܯj<ß6$iދ-/֙l}El2辇␚mGhv~n[B|~ z~fy 6):-QY˚qsN^Q~gvEw[jb2ILM.07YkIa@!>h˄,y75C~LJWH᝱:SIYQ;jN%몚("hacбNJ`TQxm#9 sрFeN:sw[O"FqJCRtJ:=1ዱUbWapj K56]9/<0՝a %Z5%7AXV?%]XwoLl6s԰,!}=_-mo(tC8=Oz72-x'f@{YDAiE'e@jcRs?jXP97YT>wS14# : ځ9x0mN:&H sH #O2^@uE[GdTɕ`ޔ+v/Q+*xqnm#ޑqHޭQ+on pӱCE~LiaJκ#OF79Y;2ezڀ7QvK#b閺z߯ y]ȵvj,z_8%tgG[ŸmIje~a&,g&J-Jt'˩"1Q#>R2 _ZtNQ)]YzYܲ @L&ԻwiöqZ6.m!`dA6]$^'ճD#xV0 nLFyC)Zb~[A`5N+x ˮ +[C~Ijύ-qƂ֘LT.yT x*}8XE>lF;φA. E&_Σ{}a(_%wɗ/cꨬC[[%8ѦPwģ{;/7e ®gs KOCOA<*ώcD@A-bΌǹQ(Pr&:Ʉ-' :b.ﵭy fAH ͗v `QgVgscAD^:%[x >tɿWgAfz}uE2:t&&Xz,bM{AĔeX`4q3m ҋ:%g{֪ )ߒ',ί.#=¬~*^OBڬH9;$,С~9s.$Xxy '2 Sz7ĩ6Ÿ?«ѯ}%\~}/%nCAmq'OLbb%XQƖB(c^ ?Z$3 Eqi'1u)J9')vz32ohf! ÏaȌ3]vԭ(K @;MUAx6{hB7) gh [1bRv?I~5Uܷe/X䊾)Vt5|92"JzT=M\hK60RyJaF~-c;tT&qn;ŝRKbJX6W|$᛽Y*$DBs1W L»,z%W 9K5{>JLDj?gum>R i3Uu_*UiSS(S. a>Pʗp><0Ii Vq&Q`fN14 +6Cc5W]lOYv|K6N ^ϵ]$` =i8j((@Wh/6nGW%+r}ߡ3ܦrZ@HQ#g..\qƮ':y4Y V0*:hw,㊀e؟o@[Y.M\vƼRK+Il/Bj:K3kDa%grTaI* !t/>xRQ+͚- /Ԩ0.&=XsшD5b&[%˲e>V/XRי(M"qiwZ#3͢JL 73בUE~j8F[a|EP#Fi,[[C14iME̱=NN|P00y4]-4 a,D,bkM_d ]ɛi'W~<^(76 (.9\^]X2q!kEb/ێuK(UF5(pynnBtMϪ߆'u_T^h (a7ǦYU>qR6e-0"Dσp6ۄza{\V'_K>O!%}RlKq [5Ne\HZES-{_ܤ9lƇ܎P'0Au{yj'4J{j7ʠٻYU^6h1.*oڀ߈0B&lUn/Ѓn/ 7BZEp`>8L.pgfcJf[C aLbcyiEYvAMr05K4dxr<\"SYRcNxGpʢ۽&RAJ'irV_gbNΛ=UK;sy$NNm񠾰uyN4 wUn`d-ꓽz3)޲y, 3 i_{1MN_TĒo?_|Kv\5㻎#sVo1}+/О{u;'=v%z2z\2X1}iXaOE4gc'IO}Iefp&w\ПDILoeo|δ8:jdx(\uM`1O:TP*Bf$lQQMnjY*9s(*ޗ}3r*_/R 45t~u#7Oni {wֹZfUaQT)J 畖W+Oz5qHa!!?U{1Rc|&yM hgz3>%Vf8<*%eΨ 89+6\$b9|!3;h"þJJ 9mm1=VV҇vXs619CڕEg^2DV7 Jk1EĴ(t,8 jy+&4 K߈=ts0+'ikWɳM243ar>>鱉º l\IF oBEeWz_+*:6 B!Ixgq n S1+2TB:4&!Ϻ&[]zDwiO)lk%ޣZ%NB{ UcҰa!/L7B~H6d~}suhL#h2UEoo5U#*~D endstream endobj 230 0 obj << /Length1 1972 /Length2 14014 /Length3 0 /Length 15249 /Filter /FlateDecode >> stream xڍP 6h 0Npw] sr{kwugu?@EIAb8202dXl̬(TTj`G+(T {0Ć_ 1{&t|%*@lNV6 '/ /33?D=/@ 6(0d!6 *1=<hi,<<\ X@dz1 1' -/ #ځbo&Hv4@ %֠KcDrBL] l qx q1^OlA6"| #G"͟@cc- lc0[J򌎮@?@+k<H(]=сlGLymd>q=nL_ ?lcbG&NL6`;'ߜW?63#\͙8@k ^[k /)  8;<_0;@f`At_ǏOfr3h+I]W+ z}?Yg3`GL !"qvJ\Ǭ"T,#)lcusc}2]\_M#Y~cڮ&g텛,g6 8h{ʷ-eCܖfz'7dti>* f+#L~: eX+g{p~]P{iN-{ar:ɋBj>Ea:Gk9 &s{Մ⸷B{nazՅ7*NUeZ+CÈ 71-A3QLeĬ!cTe^9L=ñ? /M``BHol|D5~"Ġ˫7rƇ{(( tDMG})l(SLj5ʬ:lCR,/2U4 aA&%`5ŋ)VgJtr՞z a.>;sx1"aSr~c `>1ШkD\e* c> x:t[:ߒ֎sujݑdL#O3!wWV1rD;o˰DQ$f/F`K\]yK34 Q.A)R~N(fktCW tH-KtPh ?*Wr[eE"攩vqVMl%S\u%՛Sw ~ :%F?ٵ2uGF-%f,k|kȴt4 ƪ'U]mBդl@/A"p+ 61O}R@~{ߪbuR,L<0|2G_) ;|``M9]6Pj\j,+  'Iy5ɰzn6Z25eHI-Aٺx+վXI2t0 XxlӴTmU`ەT4(w1EdH+_'VQ7b#쒫̅JAț01WOPl;E=UY%4́7-(4{3ď"(ϧ=3%ׇGÛ.('P6$j,Y`ya|L,zgr7@m=F^%F( bNd͊[yܱ5T .Rk):/j8<_%a 4#ѫmр$HzO=LS$,a,j OSnrSIgK &AlK]jsm{5ϳlO϶6o44KҮRkX3f@r:[Ce@Mf\0$Ts!)t)ԯXH<n:*N?j]4 ^*w-*`~ BBlS+b? x)Ty/MxWyj_)̩1'Nf}C?Ԗxj<"3њ'L˶́D˂ˇV9 U5|{]:-]XH)U@3⺰Q]Q#ggaZ+"O"Wp&|;)qmIMbRhYU}|9N.ACÝ ^&TwP:1/Ttz  {KG")ma-}]zk|ْ@zAșÁvKEW.^I .u ~[ƟSIq4d\buJS#L򃨉hQٖ@X^G)Q')e En- q]efELjI$so0e-?)툔@R#Zʴf02t쭰wR/HJ7:'ے8j+mo?ROޫ,W;~_г5dT vKF|~>KIo[n:m؛9>SQn:+6PmY6x!7`lH$fQ_a`zgYj 7~#ƿ֘G,UmcE`̋PC e?[F5JIP7* 2y"Ȓ!z@$}Gԟuߨ3HL mvrT6v>~,)j[<˄:4 ;1$enKv+I&YDIA_IS)i·>aWڥ\n_8wF4]Y9u/pYrcV A0&GދNo\Rfw yDx(S5r^ۣ_ F|4Mo%7ǐׄYv \U%vobG*Kc+B饯8Y< e2.5_y\]5cwe)`l tXolv˘K ⺼޶?Jc]%x[|ԯ\2g֬g?0~mZf5i&=ۣ{Fc@TC5sYcH8b'**j,;M!xs=4RMEwj*1eirVjdpTS0K62' ïIH4|U}Í2w8Ĺ "ufR?UÍXg[WfŢ?foFT4L}ԡDBn7!'ZSv`_%>rJg7Xk?Gu3GWRu֘N% ѥmn ]q(X"K7񯋝֚qFDOZL ^0 phg`P%gsn5τӕaq|ꊚpqE?Gp*=np]l9ˑdp(]`bi˵k2?H*pɐ (5U|n2sY9@ m1NS&i ~LERKBw*B[TXWd(zd\WS;-%mV_/o"#VmVDU;'a:Ό$7@0vߠ7m2RJA-L#)4 J&5)ԦxL"f+!U.4J6`=T!VPOFnCOy7s%)+6<"ްs'~. X QX#qH PT(~<;j}dec 1nRKKWŭNWA0!ؗ{ ܆Sۏ2jݔ!Qcni!'҉ cL}|+U CJp$2qbL *ZD vk |ҽQ =o)>+v[ԊOmtDpę#FWѵ̧t rA K阃g1 tĜ_aӁI=oz\J^HxAʒY:ҳQ{&+V%1[s8G 9'GaR'H# %ߊlMmm}jO$9p{N GYmP6 "dvCNW:z_Aԟ'I0'8?m7k#6J:G͝;1d]CRq. H;Ԟ6bׁz _IiV'.]0/}|1Op]]̋+D c ZPt=~C׶,CA2',k7 "9axt(nϵ{3k:, M%O^nÌyY9fMCzhaͿpo7VJ;,:4P0JzhHqŻ NLCTdbw+f5_kgEp ,dL1XtEխ |3Πq`WԉmZt(8 >DU#%OmQghBX*@Rݠ)C3nԔhz)litTl--rV1UWg-<hY!z$kxPNQS>4(]O&~"0!m0Zc'> (Rݮ0 ԶaG! IqE-'!  wq4-_n?k ncaK8t9`kP,w b.P> ecUhjسDw7{ z/C~87RV}[ubΝ)?pxxs3st?:UG۞Ysu. ̨h özoRlTts81gwW Q^gwR`RO2:{@\Zf[ֵqԏY#dޣ^b,kgzLҩ&|?mȺD󚟓EJX ? I9`0<1ZN{&%4ȥm+q[}N(mTru,ʹ]eoC0}-" G5)EOu(y- XݹœˎS͔k*MSiA9~k]#lr-Aq:/<ۦ[~/nݙȁm‘1"nk`Y8'">[X=ցsr(ΞT{b\\}<#]J^#Q !tnϸῨCuKnZq5GN :*$gr|rڛ,}/M,7f-1{Jό1㔆xl\3'(b/Kf:m 2\mRm Emb]td5pz -BS.#7w~TfL+SenԵhcuIni ~bt {otX}[3ш`^<> 7g GEw~kGK|%UzpW*co .dE'pɁ:o@df#%*XƀTj.%DM;vmf. Z XbnTC'ʼnwˉ-#s-ڢF)aVìt56ot|\$C-"6%D*p)<~/nywm6TW<72 K0^ĶG،V9i䩮Ł-h)Ou6ۍc_(sDޤ5簡+|{VGpsVT{qwpz.N і.:1RŖέǚJ7ȢVd6=R`s=r*ۀFKh? liI lΉ䠭!e*)r 2uNSťq]}b%xth,yND^'滎KN~Z 8c[SI^ogZDQX-dBp m1$׀ӤUㆀHSb|-96X+E)O݁%YYֳ 6 $Wqb 硳VoGZf٦YGqES/6g+ ͆qZK-C'JԜ o &D`ReE>=hH>64l sOn[D™,щ-woBշ:x#G=iY7$-3k~Aq+9=j9~BV'T/Tu)(_梴 6 B10l6\ `ۑT)MA!Ρș8*~g!9DvGS".967tm;5:pZ`d&4X!~d{В/`kQ2MRj.WGZ_1bƋHN]ᘫ=iK2s.6ƞc>BӍ;),ً1Oę2Y58־} #Jsl]'*Nfo s^*yϒ%7Ysca xU`.d>pj[1PC𬬮H3s+p%wtHyߦl%Բf)Kt|1z#,&F#]2!ZF9gvQSgAht%&薖$,Q)>W$?F)Z F߆tV]=%$,= y+7t%RD^+#L=!#jqSU.gj&whK-޲ʣ,oY0ܭ+\pH2 88bdWB Zɱ:{YrFO- 1qy#H&3zKk)zh:E֒Eݮȳ:Plom\FyYtVI攺uZs roeh[ٕoa7oMxc*XT hˋK䇱: ~vLPO羀fϣR"BviiL [^K5# \_{ 5%kIh'>mDl'9[c<){k|)43PR`Ηl |3CFuaIOȀIb? |U}^xlW6s+'%.&_hvɏT2?{(!(&R?Ѱ`'†9H@!±fK{RV*87rN/ϓ^!29k\ Ix4 ^O1m,m`37ߔ=H pb`8]x #TW)>%ctF0 MP a sֶ8CmX0a;jfʆd&1n29C?IMI ¯D,T0xLe&WÏUQPk]du;b6էO='&"bVpC5=>Zss&&S 8 *?XU\|%T|_ D/d?7rܰL$ֱaMOo"s UҢ&QcE{G>~]aG+K$wsmTgn>&AI[mKlZɝKi*;ꑸmD"%(vjؽR*riTM`dnki7"To˵fw%!sG$α;n"M^~]$i2;AkoHM7=Q\V2SJ5.ldKNW˙m挠.p~hwG]yؐ('F"Jļ m3^2:q3T fq}s\DȔGgw;-)A &I w(NI\BV,COCG尗`+ܠrEKsh TAM:uFxqT FwAV^VfHO(^z_Q;|t>zKEyǀo_ tydIPWw1{s;V 0@8\9tg|:Bhx^ c ٺLb o"ވ!wjo {Y\m6u*tDi~㷍H2E&M`:F]k Cs'KIBV8|bߏ.QQ};Hhӡ&ͥy-\hF/QRMK{Ӈ <}ӫ'0cm}7GɹX hl%|]I Vf--`7:E'wTwW]zS¸&g4fSw(A(%@H4,3Ow@5c~܄;ZC9 C|:$Qrqs%dÄV? =i&dW— m=F`bW5.i`}ܰXqM'+1l4u{S_9c?[ckӠP'< {;& $^uĥ|<{0Y7jKZ ^b0Ruqưmvi"< ]y,yZUFCH%kѓIrGIL`)vۙ6CE `].l<ږso3 ͥ=׆"nx|ۍEMF4i)?߇pj*4d>BA,}ňqejVϪYARI4!w+lgǮx-Bjb/,p=T*J5HZEE/k΢͵srEifF Ҡ"#nd7Z:tH\1c#T->iRrFh͙ 嚗ӮuD[GR-%C EjxYDxy}X1);E[ DK>dA:OeޓDRq&="Ѿwp~(I;S}4jq [%R߀@`s|+"{]SloUJſ?P‡}vNӇ#W8+`)B`-E~n;BUO]WE{S{ XAQdfɐdq?6*7c>nsU豐=Xԩ;^]qNRII` MaGi:J+CkP[IpIaX ND3x$VY+iK1~7IaJ+AbRJ'|Ȼ}܀R[mMԱwv)Axe}_=}( sÌ7Y ^RAz3f/&mzPu3ʼ3Ϭ. JӘ;vx&[RCO]I(evJN]h:۩y lCfp,R RfV %b_8q,J\:4yd#ڲV(=g懻IVBu-ZFHP%_\CC֐!\d}D%@0uh p|* !EcG/ؼ %n+3eɟe 5lWdP!e0%my]3"R}I$y cs濝qMZuF( :`ʶ1&4i% ]֫:Af!{8C(t4?cma5[*X'D{7Ԩ@؟5~Z1&7jE=jWd]Y9Y7, |3O慗X/XY4ċ\|'L]kPyɎcnܗķg:U0vZMII?Rxq8XⷎYNZu=@<2wEf|ۛQɇ9{> stream xڍt4ڮ BnAѣDb$ 3QfQF-Ѣ"5Zу|r޵]oeϳy +0U!Mi $!xy XG_k"(A@8L A(#PL(&-'&#A 1r@5;(+WAaqk dee~$ BX;nG!XWbDE=<X[BQ_PGW4.E:Bq;ʺ@(n0Hg+׈NYWE;9!PXWԐ w^n@5l(ͯ!n΢(BSoE`R[2Ā f'37 E pW;Ÿ!|}@L G°@k-Ouc.p~zrO)߇>'(&.)-|Y R5Q6hOcW WAX ) U9:vnonX h Pj#Ztso&2Gfa1I Fz"X IB<@"-,|8}p+\PWذ}`h/݉KI z%cNpofEEPh,.h~]3,PGg;/" ZM\D3Sh|}Mp eVaq^qx{e1}I}>ryI s7m9c~O9f>EՈg!W{IܶW1_Mt VHN% lF;+z&~5&(OK}z;#׾bIT_=)vN֠UeЇq1UϹIL3LTmv')R]ARr_jWX=iot`xxi#Lv%~#S`"ƹ)ڼ0qXgv9C;>9eMtzHvY5Ep40 mR"k̿}Zx|5mÁPQhD2ŕ3zrM3.Ev@P鳁[#-s<ߘv  KwHU*2RQZ0] hݍtԺ 31-Gugb922O(Cݷ&?+s71OH^Z1?|6(@JGJfVbX!6+O{T!kxA 8lsBlI[?1 T*PE(G>lXmok.?B; E7Gp@t"w:ebs4o|$Qk/D=mFu]sm}K%_ʦUf㜌,,&! K[gѓC0J}d\LC ӊz28GgxDu x]){G[/ÚDJ/&Gc)QΐHp&rM̱Nb]l l>\6˻uG(#7hWHb${8䊢,O;fcw W^пU |࡝yÓ:|2E4mQ(#xh7?(/Z߈^txox`t3K}?N1Ɏ7J }Em\Ѭ?7?F{7 c~"•R(jk{f8:&[A,E Vu"ڗUm7c~Ft A`!6=AU4;k"7-ejE~Q%s:=~^/mMױ멡?5(ޯf8JQKnoIy,GO0eRO"x7 #p=pNx xDލ3Z)o] jK٩3JYna6Ypr0`˚~T]iml!h梳pUA3x Y 7aݝ(%WWy`Ðc~"N*LU[k>ܙ6֍sL4x+؝0i%>«޴F&zi;iŊloh]qUY Q,g4^ۼUs2苪>sT;,?^ɸnEQTū{F)8}]{/}^ x03PNswIk1ekܕ`ő1E2rNdCǛ=/ Sfۼb[~\Vl {b0'?٢L$MpSqI^9rk7 3:)oR/U \O=Ak$}gͿ%+dMQ1JEO\ Ktȹ9 3C2Iʾ4=*0eǓBVwq/ۅ9ÍUw39,cmuʽ;O8H I| gԡ|MRÄ`/k Z۳nUSZ L[vF@_BNy.y-aćɄnFwoiEG{w#|x:w\P;#n1ڢ A+XkΌNQU3Ϗ30 Y} Yy KU3(/4EIpuxF+~P@U 3'|g%wukS+Ycڔx"p,;/M4R4jTjJsO|݀t7DEc4;:X2K&~_ԪKֳ!z4mYŭ7̨>.sv<amZy͞>Y" ,DP1<]?>ŏd7~#1Ϋ:+ZNPL  Ҵ>Ҥ!E Ca-h6NjHnʼn EUɖO-u:׮HeF6$pxLD~UhvYɷ$U %,r\o׆ bnn++/\aS3Nrl3:q#}| Q[7FurIN8Yytҟ-t7EJ'I,RYjs}9^kUh[XŸq{XëRl_Y"rT|&&3r3P@*<tzHGG]KYCtuy,͛(Ҵˁ0yji>֟ϝ tYL- @U!_%V Q|]LJf5z&ީnǃjܴu ռ͆z>P>#~/Ȝ g..vJ?yz{{ᝌ d EQ(&+~&"$VۄOx8|821y1%;ъBEp/eXc&"r6W>|F{hX'k;yyClG*O7eAnL)6:i-|Gɱ2pO2O QcOiY)>bTZQ!irxДEb@a yˠ"Е[́$ej[s >Ӡz<_%H#4vaU0yؖq#I3/ޤtzܲpQDA^3V^^5sz`yhWir%/M?IlgTf`s2wgR̂{GATq{<]-eenUOeF쐛~87$sm\i,p7f]~2R㗖Lsd3 ȥkAjW?mRr͞pp WM]a Pqy'Y#k4cR^hJџRHnUOCW̍b B$' OY];=3s !j'  j":sz iR>|;HˣWLA1$ҵ>O^ ]KUQc%x3(9k|@AjdzrRͷiU' /oM7-ק|Խ-fq+5JTBZi=HPj;tvV_4?=ԑi{=\%NCRb!KlVdAW"ײj_0fOP3ƌr+I|2* V6yb!d: k;)K Fk gwIrm gu&0 2a8rBGt$v|C *>!>W<ٝ@Ʃ뽥}2m~5c|ЌH1\#RAx™RVVnJqgb?I{f𞎠ˬ&Ufү2 mgE('cxQiGNZOv!22*RK?& endstream endobj 234 0 obj << /Length1 1546 /Length2 7966 /Length3 0 /Length 9013 /Filter /FlateDecode >> stream xڍ46,JѣE3{/!:Q f{A.JHh{AD>9{}k֚<{a咶[B0$/7P , @>l&H(o36>aG+Ɂqp@  > P_pWQ P l&Y~-Vl^!?i'P# N;Z]=_%XHgQnj+ Gt ;{` dLvp7@ 0} t V+xy]߅aVVp'g0f B jHO$' "`w=lys0@AZVH7{DeOYf- wrٻB݋篛u=`>{!ݜy`.neCMl!H(,/ VvgT(/ehD\Pc:IZ2̧x0?)WR kh+Q#lx :&g[wJkE*~QKqh`+&H=x'c8P^lJNV:{",z9laj5CN] wrYJጼihkeIա (Nx+kai+#.Y^"v+ Y"ogn_af{LZơU|.J7e-Bi88صOZTXv1T$_#+hfR2ϱJE/jD'=jF!$?ZW 3,a"б>~(pq'rLE-ڊo*,g',R0k~A«ә̶6'cJӂ틙=ghC;}9e=ӍO y>gMZ;2Lwvr$(pJn?p,^A>n+rfyeEge3*Sb-nOr?[sk(8$_Q6 ZA+),5ʐGçWc[D^YJ:1}=Z{SM8ѱړ?UHB}YH[x;L%n&}}zW&Xumk6Zv.MnV@!<ѽ~;^AojnTC+Cޞ:m.,ݤsFZ;5/!Dw9^R0v^@:anlQ%9L,EI@(+mMdb8?R1nWx{lI(WF=Pl7&zQPğЯS[h\F>M7<~ `93P;F=Fxj 'JQtiK.9^ Ľ#PucB]$)hIɀ30}C>6/*WvQF:;W:dY䞁9iu&e  Zy +_\TH^Jaѣ5G8Hi @VZŗH/Os"9Dp8*9-O yDyObZN+DPBw /dl/,jͩc$bJxE"ck`-q6rv`BITGL | ko㽎 n_Tz#2$e I>6$#',bDOd\"4!-hQE^k˞?*֑z#-W^+X7TޣyeNa< 9dn!_$)EV[gznO0R!o~;2k0o=*XÒnv(t?p#~6yy194X^wӎJB1fޙ uǘe8<0)Ƥ!Zu蔃i8Uq0p&%H?PID?C)8ia4Xxf?M@$mbo(g bniѵAx> ]]ܤohs“kȓHkt;mY+؅AGsb72L$gñ8!"֪E%1"̆;]y!Hg$yYTݫ>V~Xj;}j*S{1Z(7=ֆOlhG7LL( *eZ*ZUKAY)97T Kjm=9#gC(?w&|xkvUkC))&O펟 [V8BBØ+>yfE|ި 7~s&5Y|3F^;Fx(Mt!n¢_:g|<fO`Xwo)X~TuWtGE;-* \p`}a-YP z fﰾ7QʍϦk8E;pǧ_J%iᔧ@+2#7S0\fnbqP&V-V3mĒ6pDtXnp~"Y.˝6ޏp+Y|_u:&bz$3 4 {BoeBNx ^vߠ&zEUdwNz;ϪbDdݘ5{Y.mNKZr J~V. M"fdINZ.<򞮨mOkJ{SwW^ .qsUՒA5UT0^C\uγxede'n,G*lڢq,RNm[fYji(T UjgIRꗾ+ &xK~bhv渔{Fgj2`ad[>I)5TQԗYT徹~^LCЍ6\%Xŷ c8L2 qObOɺH2|b(yz/aBv3PFvDSz^4!jW˲Wq/zkw -"}0-' [[ a].#t! OE*Ս b |IW]7sp#y].V>?;Oݭ>Ex}hd922%l0uT|.]9ڄoV|x->(UʄP6Vz+1Ka Q{/-qfcn]lJY̝#Ot]~:[LP լN px,1DowI&oƼvEs+}R,R5ƶڑ O x%`8}LZ\5)~wS3!GX 896v$aLrҁL1\[@ h^LMO&Ajncvm%5 bJɽVOۢ[{`Ʉl }d;F 23: ocCsEέ5MD^ sjgd &/6@9!2Ma6}j)^ `jSkTO؝r\u$iegŊ3!LD<]\އ 768?hټ~B32A@p T)Siv'Ɣ׏zj+U?i11A*/ZjFP2sQOܞo,MRb/=xݼyR#ی@~T`$ғ~@oV[=[-mf2,H#4wC|%iˤHEƉ,5PaūHlTn|ABSԤ_GD2}L5g1>Z:T,4pDFd a.[[Y#3(GnfVuѥ&PǛ^HR;bV 9MR4KIٵacx"_xSx1m8*JaOV*M|9*.M'HYd\q!NwOsD#seO:Jd܀<. nea+#&/R(0u]7 ݍ>Mq%?z4BK+RxFΎdadxt, $l`kU{ƙY2kݘnr=N3r._I-?5L>bQaͰCCXs;Xf(RڒYi!uLjc.+vy%u GaGv)yc/Mwb[N/k&KT*[2,M} eD<2)9XʧGs (-8h` o=Pl_3' aʫR젾/ͫO/?HXvPggCC+CuJP~ub@Xv_sfCDqHAի #xiBQ<$59Y?S< 07K; Kr*xSAw9&$Svu [ #q&Վu%E9t5r(<=鎱!I2O5>TR'~=or=; AR^K4Z^.A wשAΘQdV'?Gb֒϶w\ d ܲpzWZ_Uo>mfU[T>JWVHJܤUV?, -}>+؄$boauu aYJGR#ô[d'z|n$E3q,'9u`(.ī@eǬ$8vƃθq\ڣ_*X1.WSѐ|_$c?Vnba#JGF St2sMpt ~/Zq"g.En2FV$4`U.E +5EU8,m*&ZLi,ڌ jA_u;{ndH;dYA<;Z^C8^ j5#Tmc,hƸ9pPpYͲ\~Z2jh5SL] k#v Lό2?DMK4դn~_hNXi!aذֵhUIWhl^y|}jȵM!zY44wtvER9΄1NOQhoCE%qL &hc b녜LYsd]>;}LY۹d9E4 9c6T6' #ZUdg ,IGl> {̴wʶ{V'}K5NGk`D"T\%饽'S!ʔwH5hoP_0b{"wXMjޏg}GVoub)n#OJ!NZHAh1Y ZP_܂Z$j7><T\O[_ᬵq;@9,AT\s-ty z>5m?M?ՍSNuzl#|vO&#dJ=DK.xEsG~T131%MOO,]-/6?E^y\W|vy͙xƱ{/qE!@f]&YDӋj:W 1$߲>͓qRM{bߞùubzM5L_tX44־pYeNn&n0U Fl4hG&tIDgN'8IZaR] -`sG׬*|Ķ'7D=o{ !zs>(s33z;-ȩmVvṵqNuAiJp _o\C!.+^L6ӥV)f9s+o:A9,̣lv*q#=C;-_铽]L壿2 f@TݺBS6}C#mQ_\}8i齖y^{['2 u~[n*-nY~RmIqqw}?o|WhqvDr?>?A͓6TO _cjWU 0{#~g^ڕ$ð6.mW«(4| s2 hJ{_rkWiV od8wqtevK},vݾG-D sw) I0HQvv e-qaY|iͻR># <=y 9@Tb㧧5V)QǁJ"雂=gE>랩bPBUFJΧA}dƹz|?Kg-~Kn|Y ]k|'L~zǺӛͰ=2>XUf/~gW@a07]D/!M$EѤ/ŌJ O)%  endstream endobj 236 0 obj << /Length1 2628 /Length2 22644 /Length3 0 /Length 24129 /Filter /FlateDecode >> stream xڌPm Na ݃;!5H kpLd:jyȈUL쌀vtL9e&F## =##35b2u-_"@CgLd'gg v0ع8̌\1sZv@'2;{G 3sgP|PS8hu dt06:m@ *v@gqAklFohDohOE pp6(@62G [8#W3uv3t@k c脋  P(m1ǀߧ;aCcc;{C[ [35 .KL 05mhhd:ojhamh2sCs2vwvw"o7*ٚm~pOgll L-lML'abϠfk HGft122rp9o)~AxLAI},L^N@ o"&&3hfa;H 4haˠ%MO ۹XtlCn -M㯳Rv؂Ʈ/}ہ3二lƠϣ#&!]Gmhcaoк8@TM,\lVBf-;DiG{ˬ-lvN5GZ-c+ߐbv&W`hj2@htmAG|v;`-  ` bE? 1$  `@\d ?Eq@\8A\ ?EqQ@\T Pt?]/ 3? z:W~Wrm([8ؘZ1(5hd#am =K &AD 4]Nz h'YC|a[9os;ǿ رhHW|FPAP26lKaQ9=gmg X-`݌v *_A?b 41,ߚ3n_-`UOXW@Nt38; o kTg7|5/b?3"zASG'O?5vq5_O:w 4FX3 js۝&HZtpy|HUx+8ae[Fp븥65^Y?Vyj aasGP<>d x4Y ;\{~ 屐]*vҟtjQE3d9F$0tpԨgnnQx#A9f)`0ZԍCMy:6E%|$5U\2[@ 6yszrmS_b'Nv" om' ]ߑDM?͹ Hpi*~cj 0e~T:}.$~vFN2w:A2t8DP k"3a0W/G\|FFmN>13l__|*.;7fPPߌ/.qY 2B(szsb(xj 3 %Dq[l H X$v+G"t<,+ھ)Dҍ4 sݚlݖ 8Sp ީI!UB(n{㩪+MS{ Ug7R)Q2ECPEт>j?A&?{J0_2!?w4zSQ7s|xtj˺B iKN3\?ˆ?q" /X-Aϲ|$itҭ]C .']S;{fO]"Gv /-gǢl.!x~ A F'+ z3qifJ$6,pmL/e4E : IwkE'K8` |W>ŋd(N|̍D-!Jw՛D2"━o#o&!c7\oۡy}BڈZvzš\CCuuG1&X6Nb"/+,{I<->2.L,Oij"`iS>؄sƄd~{ :wRuU{Ƒerrߴb\ױtFHXp 2UjЯ`PbI|2 @ $X|!CǬ8\.HJjtXLӍ}<…Ñ'g&3K0 Iu+g\sKaNk.DE.z(^ϓI4q{a͹\dˏ'[cvly]%۹DIJ&?+}wkOzr<HO&+F066J/R]rma-$ {t/đemT.K莧$<)v(^ڥvPR e]gm0- ɇINl4fC*+lVS ҎasLo2;Řs ՅTexK1JE2y*F)?.hBۊđEqz)/šbQk\@ ~CGN@Yŏ@ؑ;xc+ˮqgE'tÐ &(Ńc{9IUHVQaQKHz+P 1>T_VT-phs׊ΕQ11 :ģ K;3:d u&4PUn\i_p??6ǻ`e@c wT/h΂+h:剣6_m}Ptg(*Ff/kt ޿B@]g!}] AG{Nu/֔f=KF?+WJgfGu7_ Y|e\[Ec8OVN&_myVsfy}A(uD(- t팔"x%Z0۴fWh䕁=VW"$5$O\{z 0@\F+FOx,^Ρ~O!ɂo,:-[LB.HB?uXTh%g@\ls|ѱUTbBߝ2{|7wT- Olb+WZ$6/( ЦΘ V8]\hζIy.h?{O!صUgC38 j089H^F7{4,Le}N.wA$M](w 'Űmazw.5xW 6i%:)S =͓}4o/H %QγY`Zt Wy=w0'q{-ҩA"YD X0|c C)~BNk %3i);Eyh'Pp B%na雡&"A@O̬n%֞{3hn' \Foy$ оkQ2xF)$*%SXb>$5ϓ3Q!SoሩQN;q,Hֺ@;r,#D-h*>^#L\F]LvϾmVx/LѭSLOm>aTsE1lۨBz*m"l&"e B~dBEk71b"n9)MVM̤wA6FCnMO0g6!i9mcW^KCEW7Ԋk;xxLWbƇdzuC{EwV?l #Xze 9Zh$,lW{$e:0LIl\m/W N gf%Ju*8%Im<*,p"\G-{o5#e?( ZwgkerײUA-G}ix߀2?xc`1g X\otgNIFrhT~E;YE-_ eIOlW{;dm"0?/5faDVO]{VbcCpr?OSu4f>4\2DP }jq_B 8"KtDk_ p]{y6p`7UšadRņvtKO@~u1 `x4I+x\ nFkx فqXU S:☔ t+1YR-_WC6D$!T4Tfd _%.}F,?bf_`ӈehm' G$=^ɹVɕKGZiQ^85}\6\6`p%4h~QC[s3+!F*oAfY#R eU C [/˪l#};5 Nl= nNeJ}!z$ &F?w~tIm]Sׅn0BIOd~[͍,|ؕ6qt,Aw+_MX5+߼R УRl-Z{<asc"ỉU 6|jpaӗHoXd*|z5=u^Ta$cCgZjeNJX<'x.`g-[LqԎùP1PK_NeOT-71K&x!gcүT[#[?tf\TdpPUeJ 5IwFu #<(-?Ju7xvzn֦MR=q# "o^cn%>m;CԓEj0rNDcbYyn='xH9{AX>߫G"v}>;hMݵ uhK]vUdwH}S"{:)'s~A}1YARj׈S@h'p8F4kAYv$ыEjSWfGf;_ԋgOi>z6OBwwq.*=ڪgH1bkPuXh"%/.Aч:d]vDjv;G}ܗO3 $^ p,jKF\v3L!o׋S} |VFUya10ýabj#a E0AR#f[i2bq}/;[QgZ:k^+r3wχ;SgҴ,RIXi|fԹӒij0]h5{gLڵV>Gׯ)JA:+kj=PK4d5m+*l\!=uѥY$}suf;J4`)kIQn0TQ+< x BȱyLD },잺֗^"2dXqepW5 Tv#+J%(1W&3/LJ*r`UjhՊ o}#ELƫԲiippsǥ!/B{ 0G#";8.q5/[Qv+>ESLU@RNQĝ]{)f |'J}&2畱6=fdf1ՄGiF"_D1/éP?1wLDR"FАkTS vXGGC!srZྈ j/StQ 2_0*}tӍGL-mWf6 @fCtΪS4q'Hȉ?2ƒ0t]RQЧ 7Tx }܂> Qy2ɲb^);v]0*zW^߷Ę.5yufmڒRy{p|·|/&}E= cN "rUl} |#:R=OV#b|nպ55ru|PaN=c&ҥfd8WbD:{] E 8A[!"W8Y`. 'b4;іiQD3L rS D|$/,r|G>=mZ+QS`^օ>*Kߑ g?F{i'M@ Z؏=]-v]+80>S)% &% T;լz Cv[qx^뫻f}פ_,̵]]9Zվ#"*p?8vz䇶XRe ^kkq0};J=Z#ы ,?,`!" ynqIPHE;'q #y9+_8tl e_x[N!]Wz%ka5,+hJP7",%2aU+עګPqkyd>m)<~ey47η:,臰kñ?77@8F3/C+s\h c$9"N RWY2q]4| `J AT6ɳ7_Q> +B˓#|3\B~Vl+n ˜ X\Q 6w;BN ;KG"AXrWhG$$BmXkbuZ=sbdě`-d ;N)QTJGEyLAؼ zcS:ay: R_iyd&ӊЫĿ0cl⫩d8,9Ѹʏ$f$$GLK0DSO.TYXىU3(FjE)t5!%*.M be(̶;#x[]@AHRp.0r^1<chnEGċۧ%4^]hMN7C$L.0Z=B:z}͚dI\+sڛ`/%)3ݧDw/КMD en9y<֝q4LWyZWz~(('8;ifEL>it3*/)1qZƌ9/|r^5l 7rn9n#y>+*iߖLAy=dk(Z WAK)],%N۞ԃf/Աv8brs | @&"+l)o]ZB[$LEý qaO9[3,wKY dߪWT!:R7LI#z-/](|7LskvMbvyQ&sVć`E7u4 CMzl+6jaoo  y}p}V'GC: :蔦f% ?ok._L`~6Cofý_l%O,XdRyUV:xH4QtW@/ Zr$Ń+^xmu{#s ; ={˰~RƼ,3z[KqȞ&kH"͕[_yZٙN}dgFoRhSd^,<`Yp%2D}v_xZ 9뎢ʃ*zL3*[e&3t͋`*f09S$:wx0VYZ wF/?=_-R-WJs,ea`vIU Rc8ۧY%xE o >TC|])2Z57#+k*WnؠC߼mnF!VfE`54Q{;*nђ(l!0BLODTw0d(ix%ywץ$wV_H=r4~0Q-+L5㪞5t7OUwڐ:)6s) X~>O2>{A {jYj5* e[2w&"$䌲ٰGD0z}oAp+g.!dO5Cہ\fBA0J5U-,٩\ƍ)Ss\ 4-= BמCmܴ2|o<1-SI3t&i2?Bp ^!2n^걎,@NbQ[U B(a\_9$ϚNnfhZJ%q>:ukIj[tK!_ E@T5/) sN(we_򶏇1Q,oQa>lk-&Iw܁@i7<Þ8{Dra0qb9IMZ_D[BӐnțF7M霯")K-޾e_F&܌pFs/*!ˇA U Q'0a0. 7k.%1{V5 6+Ǔq~i"aIO\ 3~P%jMg.^^\pJ֗:>3?;pٰE:$Q䜁݀Oɕ-MZU6ç+g^[J~ڮMZ+gYia̱$u+qR1x⫯{z m Yb6f];J箐P 6nK'e~^L#b.V+TХ*>@1̢(s@K*׽\!Z3:24|rr:DH癒1-m|W;kҽScym:x43m'4R3KVN y_b4SQR^fM,kCkH'2YW9%8ڑPfI50*$Yxӗ#xN<\Pm;v%~"R'~Dڲʤ>5s셩TNWf6&njw]ھ6w)DQ^0&!!gAU2IL2^ƴ;XvgM؆l/ 4ϋ('.M*SE}C7j΄$4Pe#)n\fT3Osyz&{5/9QѻKz"4J 1J]i.B]\.H3ʨճBLx_SɽT:6 ɻ]56 u:*=@>j^sh\xRbyqnr< dGc;-}B]ZK.3# ͽĜGxs%/G$m{TKcg]br){'4kd-b^(mrfAM6ĩdD>**dMJz~~STcL3 + TGBOdu=!V yj8~UY`-ZudT`}(ZYaI.,?QddNauSn) rka,:m%Jf6Bp(F c̰a/?Ŭn;kFLo#_"t`h red+&1Ж33vjH,)U?|U=H>'b D%Ӫ|2uuO]EX8tk=\{i߬BA.m@ƒk\:{]ҏ6(ʩw$?KK-P_cD1s48 ;WS,硆<>hDcWU+! B/tZΧZi妗yY`y~Rj3TOIe|Ԅr`̠D֮YgDġɁbo v6E黠AW* iU Js[߉IC3LE1.N;~/@ֵm v. ;FV>ɨҙ x))02 ݺ`Bb[RE.}/ d '؛IJ0:$:EZv''ݲC2ٻ)gELX #}Z;KLAl u5Ӄ~0U3)܉/"GEje1!lIv4!KӾ=|.?HRrn8]-WjC$S ˉ$| {ciMx|onZ~2@ZKDwDѲśB#"MfI !-C!A:Zȇ51;EL X&'jٺ^qQ0|k31ʏDb/nwZh?:e$JmuZ?_{xSt֎[ol:tg!]^wGĈ Y bNιƳ`mrc"_SzMlgP(kk(<Ic~A׃ABEI1b 1B% RMj\>`ܰ:2N4$- 𑧪^B=+B&}swzJNfjN,-M&WʃC{>eFRðPMEk+RlKѲQ 5|Okaظfɋkh*M 驪,/Ϋ?rѣ+D/",5/kHӀ,TKinuib#R >:j : >tv{+V:FHU56f۷T| [Iv"G@W A !ًfj-WF%P*M\Xڹ@r YŚ'.`F`} aseW=Gٲ9RuKe=G-:VuݖEig#݉⬚E$tb ѲDCU a< [ߊWa`S]~iYe ƪY@24xe>29 *XhJtc;"ȝƘ2hRdEUM{Z@5Ѽ1OOϣwL?y"qj]P@[;6ui*K)JMpmjfd~|=hE3 qr=Š8މSX& !T5խd+Qw$凟B).aCA3#$_ȇXt x="C~y}-ߨ@9XӎE/_n'PT#QL9=@6GE򑷖Uy[+@].+Zc RQ̄v﫿#^[qSu^ |EL1~r’`&Z`}֬F)DcO_^9%ynÆYct$ECM<6q+[͕O3oU;;-H4SLӯr3E q^,W?% ŸnUX>¡Mu. ArBK·QA*L-*7e`ߤߡ{+yLzx,?oȒq/:3|vX^cY !R}|"|-687_zwn39~9?{B(:Ř<=WN {R\,^fFffgD=0"a#߹QPJ>G,W!~q;C9VӁddGgpG;R_v)&ǥ_p;]wN=.;وh Zq ۧMm]ih]$-='JB0jp)y$X+Kpר7cF)G 'ofթI|z/7X;;03l[{)ɍÒvS-`)5U*71$3)3ϝ^ g}l,ПJ $A,Y<&4?eK?_P:R+UH!WR/I@00c>@̚8_Sg&W+8kM H֝ * y-s1vфMܡ M[C aR!dZ zGAڨ61b  4 _]+ f\RPuc΢UÇ3; SԵݦ.}pƷ$&܌[ce}WY~]npIl ڮ:(? .t1%l %}tIgܰ2(b.qJ-r EE/ʄ) xg_CQ>^\~[x)ؤǾ "#y)N՚DVg(,xfhhyd83ry}^^0[~Rwx zަw J{haBJMY'^dJ鄝I9\ Ls]+%iznOsgjRL%3}vx_r,Q9(/ HEՇd˭ًs>RlnBu;fN_gP} %?gi'MQǘL@G87(M>Rxt|b.#RgÓ'(TVivu^NO"8[R븖T$Tk}˘ /(@iU(̩M ПIlȸcjawsߪguйLYx0KE߮QFWSX9ĜzM91opvq%zZ̓Uf_s6l'[c=ׂ{ |\ 0uPvJ7#\}YTs0y"IJNmi1h X10t$D cc+ݳ'E\(Д(R_fS\xwxZAl1{6W8E(,v}gynm O?*XvYJ61:P2sm%Honcck`ѵOaTuR .؎n?j㪙jݎSa>@K,YCb/Ku\BfPCY=3ELU."^9S# 7t{uK4G]Y2,/Y) vCNy^#C'*ĬP&IOBpgrfUuC˸-w{:NJ+&\f(Gm꩑jgy PgJ32JrE"m1nݣ8Pn%6E7*H0> <d?6 iPͨNIJsU|fӐƖ#pd" i).U)oE5n#:ts6!iG4pju 1hh@Y?o{`RSvbhcA;հ]t$~;7xBfs$[_r錻-ńB=!-"A > t y/5 Rivk}Q1F`}(i:i<n550|;3,4.yb3 99Ӈz8AloL2"]M&@fhaX&P8kwBBH ڎ,b: 4 N)v|6ţr-WGkUjO (摑G aD4Ӆrp~QMg/b/PK5Jm18۶~ {&< 0!'k7ulIfUX[f6joŁ=.'f3ƑURw`aU4*O5AYވ^@%niǜ Dg&";p7zSbp4MoVOk99 P(!W|1En _iʨ֣ĺAQ++ ˫0W瞓 lE@Uzr{+)’Pr0#b3!h\Md1lY%~~? 󤜉Teh[@vuKϙe|`!K("Lz Jo90Dy<$@9$m1xWk){ذ3v"ObqypE6Ocğg6/Sq~f/F Oɔû`a `r/nؙ79FB^\XbzK}֋9{;eH;qT38A`YZ!k5wS'F}S.ۈ98#lRMm ]e)?f/x2E0҈<2ewD~ڝ%lv  O7Hֳڪ{Vԍ^KϕZ!WJTݢN ׭; %%6sR[g0),wb^y9]9ޭO ,?*5J҂%Ӻݭ'Rj D eR_F@aa_iBF.9^UML`c@3fʯl>:[`Nչaf7 O-4toQOc%jP"kEKZkF_VDݑbUASMcqܷt&rR(2N)Hnz暥j_@"a_//[R\ᤢ>Ň}i6H녳! Yu^C(ŏ} -7aJQcU7O;U:sp%_w6y g{kae $ rNxgrُ8JvB jot;/dkstꉝ\8R`-Q[mz!Cw:@_;[xY8sr:n]8Fb@iĤ*o;K;6O L3cdYЌ4"y,u`):E]@>~u_rݫAtX^y.ȎY O3|QJ7'Wѽ,Bd#(~y*Wfz;証A3:dh)U 1'|Vꗒ=^|c(\{eO/\U5O9 bOTceoӭy@:.9TɊ  *TYT#SM'uyhf$OR8v|VLӶGQ4@lU9+#tq&|s7Bqޔt- E#1O$I? |]Yz2k 04c GOt ;׫ےk|A x y} ղqS? {K0O%Jwj<&h! +_A+5]5 ґ t=q^eH`1#- ^C$zָ*uTA"Bf?+~>dw\r-X E}X6y4ðĴS;0hj\ppL'4_bMEY4=`;@hyIRYM)/Qad+FMPk!ѤFd%ީvI i ,^lJS?2KtM'J`:h;6薉\9(z29{ CVv^c7:`e[5!4zdL?L2* -Vn+4#k{b,,!M[4dM>q!!_Rn u?_R#k|.@7ĬJRxs.tg->?IDbb6S`[IkT{yK5{}쩻8Uj.ؤ > 0nԃ$)(4,ǜ-N&uF(T5jD$BewBg65mTD^QEY1j:infS2iJWJg2WD4T6 F^}-F6,`}))@Dxt78Y->W4]LHtLFbŲtMa"Gi\QOH$Ҥ}L Wtv!Akc{g dN]B\t] 9)|N1)}zp&ҐQhd[ /-׭|)`Asur xG1y _Wbl:ž f zQ P]eRsWIἸ-ԏU@*(2u.${izM|TíA7EO0BdD=ؾ <@ 6z&B\[6)R#D3 #S.G0$EZ\v4;n&b6"t ৳2E>?0S!Dںtd/XJ +EſÞϑ)_N?,Bp'?B&@䠳A$9cU4l15.d*]63܆pLFd^ջE>C'yXK :JT>OX( Zu=5e7@^M짌Ihab2 /DN"`LK'f!~ /?,냗Uj0YtMGC!b; #L?s%W:ݷ၌ 9%C DEZO \xM" h*y5Ŋ 2(L4(laFeJ6^M*u &qa-U M0,vD)2oQ'u ׹rm.74sw \x WzNZ/Zy) 7+ãq_7FL[ƒ'i$"QItqо\l $<7n|; Qu惡gy3lиXtk$N-2 \"OVdj>xnETlT% KAo4.kC,̌IJ. ЊE,>ꑷOWʯj3*!LaI ^Ggt>h駿xBӮwXLNH@d6Zw\۪IQ?CG ZGlxW7ප *4.ްЎD0f^K-I=C m`|u9[ tɔ3 i6{bPRV䅐f q1ec p!;D0 rZv>4=Ej/I5Y dZ5jڢˌR,&;qS8uhcʸ3a "nҌ%gQ/: n%1*ѧ\J*pSN9T"ܯ^Q7@ V4ďXq g0Um*buδ(sk@gZG=2OwpƋGJ9E5҃e@rF@͐(Y}jNؗszYVPφ@wzD̍N?ᏍOT2_Eޔ>$?h峵uS]0vu5A $*Oc만_J0[{HN$gߏZlAo)*#Vbv .72,U4ٝ'XIc"TBÚ@ edz*S;ZdlsI-"!eH7CUdžB=c"]gBMV!|Y =H8-#NB}VHfְ^+lw>4K=ۏLy=){d0‚nثŢD?&$}9N:%2-vJ5_& 1*)q^ f!0nnRT< HfT<0+j85aEЛS"/-'kNt;z7uEI˯[v1xMav\LηMcb›JQ̙|1S|%,U"uf邃|zs tK҆N#>vܽ9:oBէbU:sŋ"]Qff͸ u~EI}gߦ_E2xUH3&c <箃9"{1c2& endstream endobj 238 0 obj << /Length1 1498 /Length2 7306 /Length3 0 /Length 8316 /Filter /FlateDecode >> stream xڍTk6L#! =tJt7C 0P) t#""!*) ʇzZ߷klz| `GUwO(P6@> ˌfAxBaR (! Mi^Aa%CtGHAP0@x)}PG'2ps%%y =! n+ڃ\P_)8eH?͓( "Ojr `c7tw@jyFxvq@!k! +;do|0GUG rtyGߕ mi‘P_- Js*0U2v_?'s0ï&^pc ք@qIA; Jo v 2vwnB |O7DxA7H##w[3=|jOV^`w+hQ>a @RB . ;WC5`? 9 .sߊ[V@QKYC+M[o7o rEլVS_)jCP/j Asst6B=U(Xw#?v_C A=n ?ɲw9X ۯPK۩0to841.F4f46])gLxڠ>حLW&Bݔu.AMϒU!ܢG0lkݸ+nk"zpҸF@"OieFu)Q ?9#NLk^L8b]t4T$!qMM2S Jo#OSNnϪ71Ũ9ջy=1hd{6O?9"bw GZ!۱60eoC MKP :ɸ9Opz> 2ZSpÓY.a)Zpof?^6Mϐeָ<~fCX7djORO"dF>{Лq f`ѵ\)QU߿yHhyyq\;Q^j)aΉ$5)n<vդUn cIV4 j: ٞ-{HؗI!JN;uƀ,W7 Iy q"6r}&3`X{%$xZq/rHn89ȷBYV[3I,Q=hOX MlTT_@ے!\+9rV/ߝOq cw\Bcue?lj5)syVe9UM`xZ3*xv|orPtF )ƍ{uyّV=^A ڡZkYB#?eрx UdJ\fҝՂtG6.ygD:/SO,+Y[Ng6.]-%-w ZOvöZXmydm|ڭe2N}U.9njS̈́~g əؾ A tEbycOŝ5D<ʠ~FxL^\@j_3^7π &i'x&*{,0Q8ڜ02TFMӖ™Dn#.R&aN%f&gn۸=꽀 尸pLX6<6Lǐ%3{ 6EA֠w0dz&]!"am^oLH%N}ԄJwKqڗ]&:~yx4n4ny-}NA))ݟ-v>$lSQ'4Wi+>a2WSH rRk= ;@P wVmaKa'>ASVhv6#(,ZPp6INzuJ)OZkҩ GOo\[9k+ID ,8Uj4=! E?+GT>u@S@Z13Gx22Ňl+<^Hpug6 ]xSf#0Q cJ;Qƒlo1GzI8<ж "sP/q}.k%K+F5X9#\{3$S:A 0#.XRC>]%>j4MmPب恛< E iNFXxjުR^DQ[U){_&-Q@e^!AǑû]9T&sh8K7Dk[22MCSE}$;pL:7랟.0xrөȡ$,CpϐF߳3gk- U?Ey,dt<6cϰ ?ޣÜd]8 6j㳋_#Ѿ w||Uiz޽p3HBr7?/6R>U6b,S#`|$!ڎᆼ 5X2CIt(f Ӭ q |3TfIB_af w6Q׺pjm00[3Վ5>1FB6}s d6w7~BjK#Foo;mOrѕfO\x9ɶfE$+9;Fʒ෨iLadFm|,;ZI`u:CcaT@d% eJ#yF右ї}iӤh~X3xlӷS.Ulf&-mT[0'3jELT)55xHbJJ 83~J Qrs;29R0}QVt (+Y0.wTs*liޝzWgwMt4\/D!f%sVS ﬉?d??&BL= =o~ v k,K-?"i}^zq _cs3oLg$_yH L 9>~.(?0 gF'6j`e#B"*Pg`nק+|xf8Bl{@AZ.xi =&?c|t>'femj%9PVs9Ȏ dr3r0#+OvryNjo~Y8 kxO6JtrWu0D'h[:}/|ղzd Qyv]UڮKk(~ jGǁzquϵ}:Ru;)a?#&'99[go2gBLz,wyx3-TdV,SԒGXѰĵ~9WsOMhIcD\tu &ۅ&Z  Ԁ "~},x$mQQ8|f;e]ݟv43:AoICCIBT`O I$}dh#Ty$WZ]32V/.r׬/[5Ӣ J)S>3'>„Re1gzS2\3霼Ċ2PkЧg\-\-|_Mw5Tlz@llc8K߿D=TfKg{-Yf=ο&ōE۪>>#>^˞1@o> 3`u~BUguk/h(DzW6c~ PȴHRs1| ' O;UL{RN(}dY12i^Xo$뼆5!u. ⣦an-8}EPg6MbUNggE驞pL5"cqty>Dk_tZu;Rgx~XcsO,LwHj+n5V供mԟjYif'>nuhGǦ)cbyv[(QN,G2hgo[uJP3+8W&Dk,=%],W;I}ґGV~;'ƚCkvt^rjܙvBw)mop,dIۄ,v8KSkGv'|}ԥ %U//zt:>De}†^>ۍ\|ދB:OyId캃!kS8zJ_ endstream endobj 240 0 obj << /Length1 1469 /Length2 7181 /Length3 0 /Length 8177 /Filter /FlateDecode >> stream xڍvT]6݂ 3tww0 0 5tw)J HI-) !HJ|Z߷g_\af㖱E@pOn^(@NCW@|x0Og_nX/ aQ?$fQAg/.AөrRǙk{"~L3ZKJקRFkΣ`cnk@W1tw/}3R;I5kg )\&/U(,FT_壽;bM{1#N¯Ԃ}q(ɼ(%?Nl Hk[=2$AM)I :)+>#(G{PfO/|BBCDLP!x@m+/hƒE?+Wv!W_VB+*,TF{/ŏxx3*Shqh ?B%M8yۻ*nWnh;+;_ ˟:M(ь |\OiYJ/f-RcȋX|C!Ԡ6@0aaπ=FE#6̚ﵘ|#֡tr$qOdN]99Bv{8 fNaDeܦ#.y$Y-A/EtI ֬fⓦdN >>/.62^ _d09ω5v3+Bd+&\D쐹K$ȥ3xioW{S_"nW]3 VbiCkҷ.Bw=#}Re!V`/>+8itjS5rQ@X"dvQfnQV_wssVCJݎ5W'ߐOt6{n(5j_ euT6!Sb=MT V\"-lV|:);IY|l6EMZc]~kx1Ț(`#4sU + [n癟+d6(`4x,6w4 P`/`N3+p[D9I2yѤ/ RG>TW2oC99.eB:8}llJzs|d3,=yMw!?twwGU&/P TL3ktP\J)`SKݮO#).D!uNs4i\q ti lh)Yth&Q# C&>~ qTBh(5G\j\ -y~?j`Oc"x_+ۉIHs1±L/3[iHQ3|an+ۓDy[&=18brn4v`W?7 >ѕxI̬eo+xdf-CK'%;߲+O' s{헅婍;)p8b;?|0D4bw^/'6 jx,ǑX- wK!،LvS=XE`Iלl܊ ORQz}6# ]H˳مvaCb<а9U\$+t棧Zoư.lj7Qƹ8!-&R+%sedT^*Тqnjl4):C'oeˉ1u`yb% 6D-9h:΄ GbL䟙In\?s }?yŴ30vo>7c|0Z mzB7)x?,tj}M<쬍4b0PIkK-ՋTÈN*M\{^ldy$4]fϸ7J q 7R>Bh̦]@ņc`J{-X1wNuҦH`[ZwV66/gLZg%U|S[_m%9JchL|+ŝylcC 2*`|:E]S)~%Wx]Ɇ*mqZS}'zE.(&Ջ鹸>Uϙ?z; ~!zPLZ x[рu#S'3 ?<3_-1JNUH<&2YYC%-Gۊwi6͵Wa]oDӴ5ߔVV;xBs!5593_[#ڼ͂; TN8 w81iR. ACcIيz a1o XA?C619?rγ%'4oj=s@5iJ zGI86yԆ n?WuWb-1o4EA %* 7"oGc|kon" Nu+5;Pj6[j)H0Z޽ б˒1,9 P6z[Eq>խwB;8[zmoh\縝{M<gJrzȝ RtAxڃtfo/<k:GC3̀rsm̅dhd8(i(21pq1Oũ_G9&,G\N㻾JY^yśhqd%kC}="GҷN̵rh)\Fz6Zlj8 XD=҃^'\kq&)z~ &4'eqd&J(}3҂<\aV-l@L1/zsAוFTlvGH56l(I&J~ܣ~ɐ\$zGe%#:LLxS!Ǜ jfu-g8T&u4م+H{,msT 5o8hߵv dKߛ>lyi zF';t_ ӧ+K^NdrW ǺaOPQ&][9>~[fsY;YpH´pΓᧂr xj vk(&|Aw՛I4pNr>NZ3lw+kyf6o'sSᭊQ*7_$wTut{Ѵrv/(&jsߜX-0GgY<0g=1_lT{@TH"bHpUA)Ù+/v*/3JePMy:7ome.TH~ؓc>d-h "\^{9E?AMU4YGLĆeUnK15虈&*;>cqMm$z=-ٔJ$PZ&&eK욱qXV- },nmLF< RxEJa^TcC܂VϾ9 hL%ae[wkҲIU#o-& ;]2a:)ncSgX7f9ʔpNd| ɻPnȅYM^kcR!R_M'ZB8;^}f<68X="6yO;uLfrXzT6/G*T4!X$"{z;,=/i!o6c/h^w:W5nhhU J9t/Lu* ʩtcy&ډg" b %*4Sp-*WfV_ya]I“x+יpj+R xG/G-D]>^zMs[G(^ K|%pɠ$/(Cu&zjљ%:?ؿkOfByb]:'&ĵt.n_Rh QVL#<ZZ*w#Vgױ16 cӦ+bܲ.Ur&Ez\ SP*1(FD|x 4?-dϾ2G^}rKZaa@S=ѦE jmaPEDM 'ڀ6^z;rlIشʐs"A *ԉG(Z8lsDT75C>$֩TA$b`X\T  U_^ U|bͪ@׊od:ͼњ -iφ*K+eJĆSp 4sܗcAV1-W[ӄM( ix>I CyjвRsRoga~3{ lBpn6&RI qPmQTVI13n4<3ԋ7m4w=UT0n n"G'=Yjjƌ 2eGۀKW`3-ȠNVGXy1^+Jh R*i(2e4fr&)_̽-'U,CIۛmPf׻xbN*,F>o%Z͊cpAz}bzۨj{a|>7],g~q9\dofr>`h}o#v,M։`Lu,qZ&6&ѽQwٟcS$Rɣ8ztrmB'[b~sn O?OgsDmEsħ$kCmžT RayE) ϭEݛ /V{T޺F{KHUåU!ΐO;_ئf"^12 B?xQ`!5J-H-]5xb knazoW TKWEwgzr*yI@Fވ S) Q%=PVɋY~ޅ9 ɧ`n9,NI륊.AS^UY O;5,{]}XPGLRx&>[#{R[zIc[y1vC9W>j> {PX\dZ$]MXQMi590Bivc]aeYV>:Ӡ)Dw,i쁣eYx xqIEg^# :Z)[Cgx*qxTHrv%B" 32iX# ҦI%{' ulBdžF/" UUVpܝ(&>r9S?/lZ#;xiCvb.J.E|_eLk>?T"yZLgSӢ\ǓJf_#z׳~'wfKy9_xEGm?A̞\]$jbmi2DӀM>>k=:%R_-GgK5Pi%Oޔd`پĹ+X=sPZcq *ٳ؀)xlsE/ ȋl?OlՑ endstream endobj 242 0 obj << /Length1 1374 /Length2 6053 /Length3 0 /Length 6996 /Filter /FlateDecode >> stream xڍWTlPa] R&--]tAH7H  Hܩ=޳sw~9(;"8J@X$P3@ )h CAHIP' ozU$(r%d%e@ $/CR98ԓ a.(l$ow; =0LGn9CFHG)@:+|`(1:~ C7&H K!7/߂dx!XF EC!slZ[>UL9dqOMN2rZkJX.Znd?9jV#Cv^>.ogR: ,pȝb`#B ܹ{cM9y}FvivhՊHbg#c~*7g/#3ۇ> s 7p32j-Mv/eM&_^Fk}lq7H]ITN9ޥ60,@!Z$[u;yCrRl`FUф@t[Ǖy'q,c.,#:`pt=-QdzQ?\ٜiI|XIKRNmf+U@nM-F3XEjvd>ԙm#7ETkgBWy L?'ܮW_ NZ\yE6LxrJL},ksrL7̌ѕnuN8֏֚En y͠;5ׅqd9AFk{KabW e/̢aW`L%c>9ȼrd5ʥ 5,ghLHg8QU̧Qת' ?N_;q=P %Z }%HZ]xV3NUp}ZAOAYQG99+PxѦ8˜ہjPۈohNAy5kZx K~hzDvPi.o4S[7s*Z>!;B N0ppL%nˍL/؂;=TO0QC tըjca xb'K ƉΓꃠTHxuF!> 1u8QSer4g15|^豦VJ%*p Oޖp*>p7%w /Pl)e k?| Jc.,sZU%FL/zYGR菻?ِ_{a,ʞeIuNݤ폸$@x4IVc>)z+{ m Ɋ `cZ5Ae̍> %[tҤsR 9%͙nCfЕF& qٍJnFbmGb).|uҮ{W$ļxt{k7j#<;㏉6_l=Հco9:z!1tr'[ d5 U 9?h;X&~T06ʼ>wɷ›h&SòLiv#||.9Zo.CYT\9QkKky { t3mi#]}^Nwk=XтG]l&=fqe`>qTmBnp<%ښ1Y4eE"&OqjK?T~w~ .d>]5&BTxӽ~}* й|~ooIbyv/sUduľ݊{4.ct-6U*J,F@CyUMf%ǹ:¦~ea#Peha{ߪ8s+B^^/Yl/6oc8}j *l)J_ 5O4l\-=qPMͬJ{~zyE7\{m:焈iƌb.$& eX["R 'MzIt1.5]۴wh"nK44"]tl$ִ_k:ߙK$Hų_6;řz꺜q{In.Hi=lFN$o[I#{[m{c7!W a1~$%Z?\HLnap@^T)/2d9޿{Ϲo]p9I4( )/%uܹx\355I"UJ8CΘm: ^% c i]{l"*ÒiZ㩚ÅOutHH|waXgUM&R/L|gO6TI(!B`#Ӥ׬l:3PST;Yyܲ*+uv4⍥Y˜6/?%鿪x w"oheؚk=_1eJ&Z$!J~hC4bh{%4 lug"~,T<>lyK)JyhU%Ėn/jw5=h[v׫rxGu ޘmtViT5Pq&ydQٛFJuGh=eJ~y:Km(REQEY]ܿsZp }Dqpn)l(6F^r4r}nӪe~^߳[i WH-~T}hUtnjwk:ЂaeY7i߻(&E^ `)(lY۾+KpOc 5&ذ|ЖS11]yDpǿD%˟e5+cZ&NoP̈́V\I\-t *umTZ4LK~ o<_e`z .V2n54%' k:m _6B'owVIÏP{e*өY^Mq$LYҒ<ފ93Մ%dVaU -Cɲ~tG7kSc>;Ik, uN4(ΚK+8]"&@F`ˋm}NT{Km=m«d+8qFJQ+5;ԾBEgˢE+蒊xh>yUST@9ݑCX=ֵw2M'?ї'Moix&Na->Z(Υ?ϓ@ v8A-w E&m5P)gY_um4\#[V>Kˑ3BE;,?Df3UҪFnЌ[ջG U Z^s6znl݇ĩ/i [ihʢ̣{_׏KlQF(u׹>p^v뭍[ՠn oA\'u\W\w7U`b`M9%M0fe: ZyjrSToM]sz[4?{+d4e\V3tij?<鏊> :k&MwQ$Cw ~ TQH1З`"WkߛBfU'~&ld/G/8gKAŗیPK~moEٌ rӭ C%, M%^goa*(eb^wUOѷ^_s' rOx֞ɮ /M=%5.mϬI~bjv0?&,2`bLpT+^t?#,~k.t1o#T>+qё^+q،yy{}ے™gҟP HgҴq>_)" cU3oK BD|6$ 4|aU[Ʒjeڜ3uBܔΜ\9N+ӥvd@3ϪX0֙BF$76;jW#kJ~zgBebMd6gáPXcE#}*|xF2#=_EFɶgU6<<+hΕi g#aR$pD/!4s x%U.fV1f u@{;:>hQu5K;cߪ~?̀lG .:U"j I [6"sXTsr0kGSK]R@Z?$Ux@QVй j-g)̑JA/J]nk)|^ɲ,H~eQ]l, Y'BzqMBKuFB}!uUx9$MEilh*f;8MZBG5ӗRLjdnBfwu3{ J9SZV?Ƙ\͒nh_hj / GM!^d'igL1*kDFBINT U ℄܋Շw W.'y(R.MulW`~B}JĄb |zK›_<<>$lWaCii{qm;Gb>˻>T v S~ə]⒑^cߒ酆|՝Sg-;ҥNfiN|hu@PXw险9QTw:oOJGaQ.AHQ; ~Wt{v r[j|$WK.tKL4Ci>7}2N s]Qi(ڭ`ATr(䕍J[hq2`b_,"irp` K`#0=7w=:4gxퟡݑr (:xdJpL95xUM*TH.^ݰefseeSZv"`aF&^O-WV0ͺ)\[1@MyB7g? [jwlmv+ջ:&b>Z4fecئn W󅻗=O{A +%[tUWwrX3Ӳ.J9ru;қ17YUQ4i4tmۓG.)si tRt{YBPhqOYYw}ɽx<#Hyl{j}N҇Gډىgn^ĥwUIr&s/F{[c(ZІzش/+%~0 "X7ޙ L>{̠_kɛgrۻ}(lhjf: ZkFM(aȝ}g endstream endobj 244 0 obj << /Length1 1606 /Length2 8079 /Length3 0 /Length 9136 /Filter /FlateDecode >> stream xڍT6LtI3 C tt00CHw7R"H# - )-)9{}k֚ywtm`V  Ȫk? 03_(> ^d G`rp: PqPD'/C@lP 7fY+_G5;(** r[[Bp{FkK@f +3{8Yӓɍj An W w K'Пxp``-@5ppڀ\:jMg/c ;޿-aNΖPo0` j

QKWCa6_H`j鍃1Bkh`/Gla8)* %x-l[-#/Dk swAO "#@?j>x-7 `C\#h ws9pO:j vKx@^ kk-WԞ#/3ع}\[o1S٫r^-^Ht~!v.5O3)EߝyF3η1ҵ=4ܺR~.~*.O I<j{f6^lV >+Ջ1 ~7\`;M >":$~3@ȉ+h?zgB %-98vYҷˮCëN)@6φΉ %/*rtrR;$ ܣ*^'"Vn\IMH yxmFfjVzPnz]C?ث닲J@!RrS[ed[Jkq%l!6D-^۔L꓾X+eV7%ـtj ?vTTr kM?Tbd׵O,5%"BI`ōSi9ZR'cɭJ9COYN1HbR$[٬*TK! 1*uDtHDPi45!ݢs jbW*ضbGv >ֿ{fLDF#V4,{><-JlElFĴwJ:A^ h6CBy?:_BP̭*1$"0R5+5eD[QZ)>(uscQ/_ bk5rKgvg1U7Y$vŽr \rmIREJdh_FcB<^L Ci}i1XC7vyog|:%gF)xu`ܖ?!oX"I׾vWSS\빅[:&w 2ҹ(^atg{-k܇8v3@=Nc̽B[阨0o1.Un]M< Zݙk5J#%nNɆٶf0s.|n~޲e.u61\ֵbhs=Yn<4_+F%?$)vnG+At1Mku:VlaԆC*=}5^1ѭ>| +9>@gl>>>L*:3P&5.:vܛDm\ݐK),m[s9\wM?#= :޹jc!Wp,#'ٙr+nb1 n}ͭ͟2'~N܂\9lC.^Ƕ~uvk0`vNz@r_?:mrSݝsEh !#C#3 P@Q2x0k2gUƗ}zK1`v#mUh^}dy6 {әE!lBӬ񭮳G\Yziw7&}LޙSƧ}N[ٺWR"eHG%PG9+ٍ:u@pe/h 3Tdi;=n6?e zoۃ\ e{ 6f;t6Xדs/m:ٜeYP#+u#Q;'V}Ԙ{!Mfܲ6=mz|‘͏l2Peu?3+lރQ?b# ӂ2ͨT;wDAS\+} :d,_{ .cC Yq2HF I v+Tߊ B{Ϩ-wVГ=#_5'x,`/hz 0J!2~Vںct Ƥo~y9 }!GmȪo]^drjH 5)(%h94Ǿ^ȏ"Tŀ.qf帤S׻tPtve`)~ϯRLlD^^ #YYs"?/n&M!3䖓ev`8x<,~TSiU-ur})XY[(f}02S|${S].߈{T~|zD7ӊv\P-]m>ơ芅\=\d$_-tVhB-.V5iJKԭƁK4=i}^/@dOUly:d/MAPYlR9Y@F)[`ޑm9:U$pW%{e~',&Fm#O3Gu5X(Gua[B  gŒ hB&BmD'yMꏶ/P1gWJ3o}HFgWqyQ"kK.a襵ϸ8c {2kx [dIp]yOqjQV=>?@q񴔇@_&?ZkD uAmMCgOZ*kº*`농"QٵPZ7 :݃sR&a3~fƯb#c$ۄ)cGKAG#):ePCF~_`A-:ulʧ_9*wsI~hlm6OY@ y[bOa\坕QwG", HA##W,LǾ%V7]e!1,~aMW_L.ut`G<yԱ,RȄ:Y1砢hTEpkޣ>(Yrr[#-RTR6 2L,6" Q@فee,R+kuKKx2daV]w2`oTzӱ{oR5oέɍY2Pͼ +N`m]'.qHQ]LB6v<y9x7۴G-l2|˃Q\i'OFpL`:9ѥs_1´Y*ѬҐ&{C'/ml8gd΍&ǣ2ݦxtMmы-g o05vK/Nu+&d:qzIL.|w0nGjĎѠJ,o6gr\CWr|89p}0Gk\}?*4qTLހ1X苖Og mprk1ߙ݋n'# 1s/TIUJ~Ȟ4;YA7$& ՗-Z_4^cO\`k6qϿ`Hp?^ؽAԦk(+Md@X'^' 6l${*

{I{:[D N٥/i~-`KwкsYK0 VJ: xHL+ ԑGG簟톜e Q ]=Pn>ڎAצ>\y.QNGfq失޷, &8uE$M?eoP?a9 Lrbdsx"Qŗw:.@ȡ@6S/T@7MBww>q N7Gߝt|G_g3z骈=r_  )(aGZS@ע/k -jϼl>.%z2¹3'?<-i]eIJwGa^&˔plr5ᖺD/-{KŇ3iaŃEәVOtnw#K}w{.1 15N k%mQӰ ͧZ |"|^+(ah z:U!XXb鍮Jǜv]4ؗﲾ #ZƞoBkis'{WR>/QʲjZݛ83}_&򇹑 m he9)Fث&-y"4ZոZ]-f% 6Aً|UbZ(Tl>#̴ [/K5?W~JP;:5a\MwX`AYEkU>.S|r'ۄ 1a^WWSlhj}9/ V;2({rGSnMX0 \_" ([0yo&z>9Y򰧍 0|z`. .ԡ6n?V0㤹 -~'~M#;5*pJ\Gyv̮_u0(uPw8M7cIe'NuѪ=]ZWTiNhk)w˪(:d ^M 2~O{wKDr{+H23wVS=OF2ޒGŬWK=, f/*>%F؉機@0Z<[=us5ix|'#N!c^gjN^cm;է!(v7tGM<>ǫzvN~r"4 ƽeL=$1Uz9KjCcgw)N2v?R/ c9J>Ŧ=6>EE(nˑqŠ4OGɟ'y2oQYWw쓔O }"ez37E x{.TGoc5hPr4XE.[4t;7La:sl\KL p,kڨf=rq:f582sәb= (9]ǷBy2x0O8C c}=%~j_Pٲnm+2^PQyfMʱE.}wC1k̓@8c:skZعs7ojUR`zSRnXWĞ Re;{n҈hcӇ;#E'M9d:j{!Sd`.JyZ xjH&ޝX)$B{Wg" v; E[3] iCh"t ˠ bBOmt&%+ ~i'͐EEjs=ь3&%SP&f.lSHV {g-X[\Y#BҸ2-՚UŢM*5ڳY v鐟@^CAӦ [ظΐʑfo_&ӑ}LI"T"ހ~vq e}^:ugoLJud˟@բMɉRJGiF7N3> ZngZj(,2yT gNUR ktw1{η[}o/2ԫ՟ j^4TWV*"k/q_,KG7U*X{XSqa{;Cr/2wgjrޏPm> stream xڍX}64Ht8Nƀp!%ݍ - ! %-HI|yv_+޽0lA*0(G_e`&ga1#!q|cA%BA0%$P w w DlA`(`_2jp'E??W(1ןQ*(<>< Q~(@LL86?y:+]TN,߱a(!~~ M]X+[Gox@1 -jkjku@v`7ժ#mP u@1G@_/P{tH_ 7o0 CaP^C-uA[BпUavMPD`xfD>y&3 C\08It~A%~Q A {B࿦7ʒek=$| @na1+ % nc:>5?%U@78oN&y0TS}hy<JWdYKGMLg Ot&f}i9z>yDS_S8q`qK\#=HУ'~jid2{\Ghkub\b(>%aLEv0cd^A=!Hw3T7F=S舦Z]@QFohU}njÉ ُ_#XDNR♨\X0-*hOrkrV ٭&2+۳tDgexGHՒ]ƬRcv:~P#Ť``7t-jMJJ4qCS?LL~y ap7Yz a!UftZq~U?Ad]PO!=dPignNkuiRcNeQdGX5G\nT~^.n{8Sq!GCMtm<3S$Dd*E?nN_?GRvW*~'keD1R8ܼ`6t(6pc`MV$>g͓nEzOFƪBά囁\=j`T%A<J#-+agi Y#@b'g]%ʯDb0,._mM=[}d~q|dI*I[he }jőhhCxRm~a"Ӏ)_*n+ _^(CTAta[W7|⬌̒{''bb\T* L0ѝ2ͨ+ZԗrB`GEa.v!EF`HMYɖ 7-2H"pc]A"`Ϩz6.zVM=eyM_&؛q.ͼRGڋYk}~#׀${n iPNs)9&{Y3gm&7*sR{qfƬXH寮\BѢG!.!w{ezmU^9dIq p1%+7c1v?US"c\2uݵm2~˵lȗY˾j<}9J"aOkr{ g/Sɇ\x8T PM՛Rv@y{R<Է5RÀ0 kse˱Jx%nDûַHHWݨua'bTn$gK,ؾ y!\MC 43{O?wXk, w/xS|߷H03&ek C so2HxcoF(.!ðJ`i0}S2O6^C+j$EC1 [dуh=o~A~tnÌaG2ioF2݃m+%pE$QΧ/}Rb_OE< ;D=bo!2%0o$qs9rru!U|,c )m$wO}0A*R?{- DHCM>zq8Qn55pG~2@dU|[ğPB"I,b+ƚXDHTwpBz֞|iŽgc,M7D1Əֵƫ<5=Y{ /,H1'g̷yE;c~w?$.4֥2 LzkHypg9/6ߦv_xLoB`K\6KZW2\R@߇F!D&LƊj[9Δ3 XγmqvJ;yБڍE}zgyupJsff&l쌸RNX+*G κ&hk̗s%{!emI2Zwu,Xg+Rmf^O8g!Ii 骶}1PNNNIƢOԒǸ2# Ij⟓cJa* 8/QIԈ_f듆T(?5Tńnw~'rW:y^"wDH W*I^WVj0BgVϖ xmn %E+\VHD>0DžE5uG|V-_M!̯rXfwD-@gYZ0z3@B*Oߢr<W;*97R~? βK΁dOg3hD8comEgV&flNX4|i})xvIwx'.{=[ҾiK P%x/R 81mՕ:੫lJ tFDN.E_*tdM. :чMkBSd<2*( sZD|$" !xHK=ufbG.,Vwa-oyҚN}w;InX";wfaf7<`d'}9b*0>']bգ*I Q9ѣbOtʒ.9 5A1_/Y`nN?Hg~4؏G\hC+;vdm=u ˨bn G#\${HZ7e:r,Z Kۄ9w쐰'4|kGZ*C"\&2~e$}'B۶|^_Ư'g[OiF9ǚ#o9/$=d QK|]9{Ax>R ][w,j.:Nͅ{iр^c*׵gCҥ<7rl 咑:"˺QKBLVn$إ\oyU3ûAkQx:w-qMLiY.UTLոVqSi`Z'ÄIqB gޗb*-KYXLI2#aRO1 ?X1b[УTӌhb+C3nvz n^ 혒_rm x~a8Z5W$V`~n ai_l1c-r`E,/^7>YC,xǝrðer}ۇYw,g1A3"iv{OKcytF\&lz}m5ZB '𑗂X3K,| .sm} ZW36oC2B?c({;^YT'Sqd։kՙ$/i$| BCaDeZ x97 #Ͳ҆ 4_\ w^ˏ>k{"rXlx}#[Iʵ=o" ô kfE{W!ԙE-C/xgzBQs~Ipnpb;qǮVR~ܣz S:0H@=@f;#UieeoPOU[n(=D$+m]Hc%etfv|sH\| Lj;iP ` ~iLN&`ozovK5TT_2ɯ@vdZ4SfJ~\# q YyV_~D; O4LXW<\ʩQ$؟ ZJ#;r/Sw'(QRI+ CSv̳4D)r(J>QtJkΟϐJ ޏʮ.֏` >PכXǬzcOcApEP8Nc ʪ S4&:*8.l_VL:jkշ%={}YkT>նAYδ~W.4 a} 1R MSYJ\JF=_E3"6 F8Fׅ ;Y#"־/F{elּ+M rg:e6#De/t0k(f9DP8Y%>t(OMOF;ZŸȡӶM>`٧0z/s{ |/KGQz'ѧ?{JqadِnGU ɋlb2GSb\C>gzǏv:It@+Zoe.{?=@klc O\B6z-_^P_pc&79wh2À`ŦsR ''͚j]7v(**f}Ro+,1|g ZjMJhw o1wԥ1 M\,sby4'@1nRX7OP+Ce* L1ၴD%jck&\x);GUKB[CxKw"5W-4-dž,xJ'oŋMsŔ-J]f|++UŞ k#k l@L_6\S*BwcJԐ`aP$%\f"OWщ&FpP6;#+Sϛw +>%8*k$pKg9.=llJ9IQIB5?l81 IG4=Πb/@i?g6wҭM}t5ܥWA륎<| sgȻ]=_Vżkf4B' U28B-TɅUrY従Gqˋ<0]nײ Mg endstream endobj 248 0 obj << /Length1 1414 /Length2 6062 /Length3 0 /Length 7035 /Filter /FlateDecode >> stream xڍuT-HB轃t5$@( $^D:RDȡwK) R&G=xods͵Z{2mFA` ";wL80EQ2A@qxL ( PX(,!#,)E` ꁀh v qm%~]$ AqW0  C"p>(+ù yyyX'BF,㉀ uE 4qDbh{$ 3 E@ߝC Pan8,t5Я2SVGUѮ ՟G:^(Hp7!S79 p@q4Dx~7qC ݀!H{z"8" Hhp@ 0^{@ϿlQ.>_!cs m3&wLE ŁR@IIi`?@O -ձ|ֺƫȭ`K[So U%n螇0"]|&E@꿩?CAFPF9,(,{Ho9\(ng `B}Q04D%P $o?a!J Ph>1h~]0X(dB`+7 .B!~.P?y`0x3 ;F3hl*ef/I>$YvG㸧9^sWy/5n02rtҟxۺ='d>M@{IeG0d G39 K,Qo?6MB yb>y!!ZRxт]tR1)|'d{giM@dxh_!TO| rzEwu:+%#ioj$2=ѮpLM`|=OH7㠌9Wm?޿WX2=-~=9Fi3:|:[GU "/Wд(foXW"I`wcQ57sTzLS # +}WifQrn,0N!Ǘ~l+ (N`a.jNLae=kϿ)os~P<55p=MG8U-_~e:=&NA|G:?V~v@H6+G0u|wE<K%˸ŏ"oI:}:i[xUHs|~\|X~ p|G{GOHYP,IcHԖaػ4T/?`9ZgZzgxU<N9YK$TNⱃz5q&ˡ$$rKNp]̙!ʧ+|8B9\1z~Q&ť>T26Rgf{7MljCQf<7͠yɅݢ65^lG'Y1CTLb2S%#%@d*tьC3!rgYکbyh?v;p| 4SIxYJ9> J~D榩Vs`h%po$Hdj*Aˌ-Hu*( rrE?K#R>rZg(\s#GzG( ,Ӥ c1`n/lLc MhGj9Ր(~'`,1Dƭ[]66ulQljS'dy4e3OKV^땒 PAv^8*9#@ :'FVb9{tEۨL>X6sY?f˅&~~yM38K?= "j>'3mPk*&K#>Y@qd ɟu!D2CL@. r]ƬuV!##Y7۵ng*j@JY:GQﳃ-O9hRgnMl^k͈[gja3?C{Wvcc=%>'ZZOVt).xGv\82 k);i/2]8|wہ$RlZ j_N pvd {?=´XM`9P^zA(i601ѝ}D:O&"1r\S|+1O͡u!h4W0X`o8X)k-148}: YX^L7: ayÝu"yX^X q?MUG-<UW1: {TRdV8㹺{ η^%Mǹ\ZX n)6..Rű0;$^J_:[D!])= 8#[ɓ`vF?R†Hӕ;BomU+ N_%Pw)ƒEnNjeۼ1Zmۗԩa/s-_{YEiԝ'i/gEru/shHuIں}ٳ9g#ˇhv huϨK-wIs̳2c˯V`m3j&* i|*(9r)&<9-=+K!Km<Y/ԕ#=]k[>vt4Q׍鲈68vSv(Z, C$*2,1RY G üpϛzvS׫`)>LGIP4gqyއqpfC@c}2&r+XK"6b &`3~4FB7p>J{6IyNJKXW"*-B곸h7|cD7}PM::+=@]ywmԵtOR9 Y_NJ_c6:7?9`gQ:ڢa& q,lxh lXMq>4%ߟ&1Hk}9e-n%> N<~3,R~NUX€{:=Ί<)avLmk:sb˄wDA=$j\.psQcRPXVjM8!Oh;w(?/29-d X݌LvYdAǽHNWLX0+iz/Q?_O.qx8L\jvw[:tYq0$P%sCYFY/V٢ GL[0ʱR.{:'u)ң9!#)ZxnvWŞNU[%|5i 8ѳRTw~iA+cv敿ﲘYky}P] sh eɑ4W }FJ[j2tHX%ru31y*\/4LcP}gk &rg*Pa䊁_󙟏r< m֊V4>Ge"a Tww>@$1gT=ї:gm3E v6ͯ{ĖW1ciJ0AZН{OD8>߮s$\!phuYk?g/х0g:eU{Wӟ5od><ʮyD[mFqsO.%.HH>,>,D)KfEƗџgyiL&7*m}e>wɎR&]lDBN3hY0p"O/<-Un[Nnwv&0Y($>Vкb)ih`!?EvtVopޡ(bJMsW/H*ty=mx pmAԭ{I8µ@ʵto,hkxR3j169t>-/ISQ7}ٔY*^O_8r"|`]盓F}#P@qxCNE30hat2:z\^jeqU5Z]BB67*R!uyRbfuzSNqrV@|!qVxVp8wD38 8ƚن(x3S9Ys2S q}cĐ:PRzXb@_-b@AwGkQUJ2{N$_}3Ȥ54=J"z@U +:=j77,74zG`ȶ+#ųK%Jk wq囼 39SwUуI=M.񤛭 ,M=DvQkV.1%X}Τr.u,FВ:+8I!zK2ku1`p y>mڬ=Q)LstXb\o.WiR1bQ0d0BWg4< Lt=tO bxi4p99=Ed}"p3y|~ӝ~;Wv_NZ苒.L5})$:̲7qj6y&$TЌTi>M5{JpBnT~e:h2 N_2yP[?y$lZaTwqTURc?,qP2Im*`W0kpi>43!h[HFn4F'$QmlN:"=E˘{Brd{5OaVyeLk\ oL.g):\n6]A;(Y9Y2nS2~򲡪gQYV( BhU72}} V<@F%EPx'|o ݩm%/X'h썙17ŐiuݵnO77ޡ?ldslc88m5N.q_L;N\*ѻge?OQ r:2pz<ŖXja;cx8`&Ko؆گT?NdO*&x(G + tW@1HzU}-:b۞Td$Vڇ:塱!ӑS B[դ#IBl0~Ni RE3گuY􁴙SdQ}%"*fy[R7냴~uc]4QM(a^uk\n7G=R0Gɒ3 Z1 (ପp~%yqeF8FIqq>#_U^| %YOeq[t>݇yQ\}k\7 yIQM]Ake&JW;Idr;|h߱*r >oM!t>q!#a'bh/}\}SY*ߠyΩdIk[V&A9U9&+o|z|Tܔ:P{L>|FЍGȔPV᥾҇#&|& >o\wwl/rR0 K&T^ )ƾ#g/^UAR|k7dވ$I9 FŞ?Ԇ endstream endobj 250 0 obj << /Length1 2014 /Length2 15776 /Length3 0 /Length 17016 /Filter /FlateDecode >> stream xڌP\ #܂C[nA; \>ιszcs1wJEL팁vYyb j2,ff6FffVxJJ55?vxJ Ζb@#w{-@`eef23'Α n 2(0dlNbv s s1p1:Ll F@Mv& pvebrssc4qbs4e-*@'+WE#YP3sv3r {)~:@UFh`0= #;B ۿLLll=@35IRݙ`dkW{=oFIe{dGʼYTh?q#}L\+[;7[ 3_m3ۂ\2y7ܬnbj,{񲷳߿ཛྷ\gG?YX g1d h/~ wgwI]avb& 1M e_;##+3u@\[3;Ͽ?]/-k)ڽ+#t]ff?,w?UU~l@xW(ؽ ku q6z[wEdagdf$ r*M,7k-P =ޗ-.Ϳ]s%lMLZ6VN]#V3=ޣ0e0A&?$_ `L2;IzTk~/f0)A5U<"Fлzi_3~,M?ﭚ xerG_n;$}wm}fCmwYN={<tNY99/tcx,5]Lnvp3t|od}gλϻ9݁&+v&|AA"nN# ^jI(fˋXߏ~Zdl;5{x{]Iޅu_KǐYeFxBƑ?Us}9 u/FDɨ\nn6DJN +"ɣߩc; Tc${corӀ9ʽ^ ܧX/4NB|2{WW{Q2*C[)b[d[p"ݍwċ[W <8K{ʤ0$$g~2~GWsJʜK-`KhTRZb1t1veEX.YlL9רH#P_>hzsNKrRƟکZNjLY5" B{òj] U?48_m {Q%Lkfz:̀^Zv Bނ!Bq=k~ғ3/4Sނk|rM˓B8{}˪vՖ׹R$݀(.k?E禑|XQ:8e*+g,u/4Auͫm!KWC> P ch~57\׆$E4*&: т+>fSԉ҇J8 Wu>#.Y/_TA.m[$!'4h}y'glT" .&UR/E~?j; d_EpJs[!g|qLwZ  ``n: ^}2z doDheeXS˄ QHҤ pA5i'oY\[󘍕$ JaFmIe!UˑŖY.JD ٌYzyv9A*Tx3y1pbV5ޫ/R%UL3zu< KLK5*K/"'!, y `yZ .)J3w<XR0[}0uCeK'#y(/*Z{t*$\NA#G0`R[Qgs_imLUEbu򉘰ܖ,i M/x[*i:! PO`,ͩ.IhO/gIaE Zk*2O rUy0 uI?eTCZg]t"sKʹjӚ]$! 9-hnuwDGҤO͂cIaQgi;o¡zU%nn/5-':<$Qds<'$|D3: *+٨]ٕy]侹uۊv-:ہe1 yiru~\LMUa☎uc ؎ A\" v؄x(r<¡#"^3Q2`^}!U5^,䄉'9JopP(F4,4&A<@@ gTT{m2M!Tz2IcnvU`&Uy:̭}m]K A%MݪXuQgU!?EyrˇOs9֚\8z_&+?wψ(e-h 5GZR/lϿS6t N0b"`wk} ޔ({='hh.SiA P+!)."h'$I`0ǑSSL3-@8~-=`{bIiBmEpf}ZՋm퍆XRTGJêm{?W3o/śAq)#k;&^HD{e[MA.(%LzgH%i-w#5 A?7ҳBnOu9̈́nɲ6z1FR3+t8|118x(Iopȶ][B%G)O z0V\0 :C jfEzs1hoEǠ6XJf9!_jb^ JX.<(;Y'[nd<[ޮu:.IIQkŠNKOwA\!j%&ssTgqiZmh cqxi+3Pb0U0:6nU}n ᎀO4((벑k[ 搏zfhn6ۏo/,QMHc!]&M{*2w UqO`B)*hAzyyޤ9Eڥ̫4>ޭ{=Vxt*l,0e $ԭOq:GrT%n4-bh|Hؤ/)ؘO+=a M9 :!<ʒSWjr}:MD₌0˧m"D3V?Wc g6!97n3~sF?ZB1{"[Qt.n@C;"Dy r2 sxoZ+_hU9.n"]:qvcO1I9F ٚΏ7T7? MEA c㕧+RV&wg^pE*!^Qq0]D,)Qh8rfQ[vwMMG2{g\fhruNm9٬NPů=G~a?yEl՘WޢG 6rԢ^KUzmaO{a!7M<.n~K.R$߱=OElKHg0Q;jǐ>Lr@<+@FSW6g/r=~8a~l+b&uIE`5k0bQU2߬?j8}%HsEzdG>T1Dz `j`kqMvT@S^AKHJNA:}vgW~Gv:v`R>l}Sq5ijoBPڡ0"'M`@(Op:RB63CgoKJ6U_JmXq #?U U3 ;$&C+^uz8L{ٗ %D'3 T3[ޠ8GSt%ͻ䦗p֓vXS'Fr۳r~ujnVK':g<ĆK u$ŧ:AyZJHS(a3K8}LG>6FM>_GJJK+ v`Yxn ΂ڧ{rZn%pHV0 ~v j1O3HEl?C|펋[%c:@[r[G  ʔ fCܼ$[|qWz2?'uتU(W-ãrLQPl'c1h'Ay_Ia2NS|c.MZ@L٢! ݼT)d ޮ5ZDƐ™J+arm!QEjmB+4lh 5U^/dP$[D+7z AaB6u~y(m 3u-&ԉVx$y3"Ke`@3; Z^iw|xyF66Cѥ2lkP3Bc! &8k*9PLxHk?٪1S!x :ɧל$â7c0컍rBHw2g{PsȮ8NKGIayo,okO)_d뽲dC~C;lAzwlI%+Y E!> 0B4Vݏ:"!ZUyλn)+"G.Fc i)O_ En'zhQc4Yu{~dM{OV̛؉^ lѧL=)\0 Lh" ~ZM /I8tG"ݠRr 9rqQY_JQ|S gkH>"O^nfflC0 gIK9ŷG!v)J*HhCOKX@vP!4ҝK wˏ-J^iUms|qZ Aj5=ƩoX$?fA#O bL-^5@ h Ne8V̄h@}`4*;nVy2]?3cS> /a.q1>}l.7\kCWGʣL:Aa0Y#c/^oR9-ܑ͜Ą7r,z51FdfV:Džpb61~#n1a`n)w ?_oэ‡߀Hu~&DG@UɗU閛ZlI@gv"yÚZ-~^=f;)z<{^s0kuF)ϟ=DgJjh$A/ePͩGn=kE8`2܄TGK*ak)>_lIo:r4.r|AǮ8l2=Df̞P^ 6*p ,Ix>?J%4R/w=&ˑ̕KvܬF:GG&+y'?&Ba>$ɻĔmxDGh|9З9ͽ XL!n[u,3 gp^<h[WLARΟ`>L\ /c#wRX% 2Az+rhwI)&ˀ; PaϨ Oܮ{^"1vJMOildϟϽ!)H4U~8Un;h8-e&nVUy{臗q3 kֵfN6VPd*D :5^ԨA/5̥7[WZM W̋Uu\ 8LM܂nNZV=DDZ;1ygb9: ̋&Yb KxvE_m<@*qX&s6!Ytw*C ٳ {^J1de [il5#HW-'ϔ5vs25j{vh#nlJ(!J}ttT\1;׉e9Ji 7Qwz^z7{>0uGQv+?*C]9`טAu_WTcDUH Mb"8 83yauLT7?Me|R&'te.:EE&0z@X ITn-]UЏnFHR*Fskvɀ⹝b ̗=Z1iźGO(l /2RE ?3O meܷB 3AQ6y^ڃEd $4#՝X¦y4jJM+ĤR~][m(zC/|s|ݔa=)eGn܄leJC[̭&L[>Z_yE!i~dBn | cY/H|»jƺOჲ_ "~/yo`\~Mpn vZݩxWͶ6fky;闭 i;C$؄N[] vvP)Ջ~td#\Q1VbTv$Wr fų$;q XpM/j}lqAb/O,~jmQvfKH)q'9yȹ`0rw0o[pqnk3ے~R7tLSRu -kl$z LyLZ SW:VKy 9>iˤ2:`~'oԮB*Qe`H‹м%\>uaw::oX_1_9_=&_K/`ƺib{gP`xB\sh^(qRfЌNRLBωH^50-4aϡ E7b4|jZv͕%Y'75Te,@Omxx屓=:Gk$AU%|"xD=4Pm塲$  n\S"mFT MǘS]|^eF&.]]P2?gͺV LI8֗2sLA~uvu?ܾ>Bqˍء}l?A44U/lX&N 5O\ Gy 줯A4jiu4wt."16 'Qe[qblĸ]u+¯KTsx#1~G ur;TQwyM[mw^.RrOUFO6ƽ0iyvr^!y.dx؀^ e6ֆ/B2AJఘ>,Q~ځ#&M'!D3PjdM Qfì(/7O|9Z~ұDh߾SjF  %*8-D\KF{l`/ӓSn9&hFcu>؍xcL IH(=1:FikaMN]}Re?OVhD3 ;hq 0G|h%N*Z(#w3Ry5}J`8!=?ǧzi,r/U&7218a8W!{U?".W♉xvs&7-8 \17ROB[r[Ը"sLBͨq6C< Bx̝w74hǷIyLpW 3!W4,L:36MuKaAI'teZ}p>^'e I q]l ⾥I0cz[ g\Xۿ{ls\jd>ޚg)l}z <;8 7UJJ΃ᖜΣyE}>[iD#X:#U=%"$Ѕ)1M@ךlKUDGFWY)J 1;L$7 bۆ|Th)]y(_0GRhE pDxkÕn69^ROzф?z321YJ =]f(܉L k_.~cN8UNz(C̳u֓Yn%<>0wSj k}|C-!.1q%k1qa~JdagT6oY{`E>FL@ zyL˚kGiNn]O>!SR-~#NqDъG HDd]=G V{U?acS|ݻ*F3=A0ho1SS>B`*mDb)1xޭCb#aM!a?NVc"G]I <~oovWyA;tB72uhRq-Xp%3HHВwI^qRY.ܢstijTsHIwFmY,NB3JzbPs`$+Rp1/";$;N~^s-M.=Kq^Y9C3eb<4'$0|Zڢb?)Q7Ёi3|2eEAqFbܷ-5X:6ܯ6eù^ۓ(ar™ka[lT"Oѓ_=Xu,&Rf/d"Csy3\ \P14>l\?^Jt[M10= ߗ!vyL0!L HXWH|'+Kh򕢌k|Q0 [*J3H:B.[]65zoy%,{ L<;؇k%tc [).A\ۑ_4:6BQ;v : R LG>$58w-x-yMnη}O~6rv95w4jE`+G$lGȊ"+ȣCTɔc`L=QWEtcإm߮]#ؖnpTRdBN/+ q!Ӷ4Vi'Qz.ؘo[uMH0:t~Pp# "^%(~Ql[6/Φ\!)W@P,/y&)/i1ftjƅwmGz>q<*DdBJ/"}XIC3 ~M&ԈnÇ,=¤UAۭ fQ:~h<7>@lc\fzkfftG^1->8H'EB'S ~XYA`4ƺߒ-4mUdqjJk yR@XPe'19 US ʾ:DU;աw [ZLFGfa_+?BaIk$le/"gI"GRTH7p}; v&U {KG1@d%4|%pHlO@縉NɴPcgG':ZLnZX(٧R{>O`΃ v tX$M<`)d2iB!Sљ0/(Vٰ9X~|2p8〆ޗK:;-Gw_g TLSX-PhҦR>^;fN h>Yݹc7 1:Jpi$-@@mb.ľɣTsdW\`=.IKэ# )mS舰 VR::RSUqmQ+?|MVn4Vekl ֿޕy bŐp^Ԛ tǕ$dI6'#M5pڱzkK m'alrwgym/w2ȶaw>]V^_Ӛp:Y)Y Cxnyf|pW~2%> o;"Z2hY*C9)/70i"Ӻ6:Sv8N-̜8TjXAz*B賟I."u+} D_%C,3IX+W=ϱЁ_ZW2b<7*FHi3K?ŵUe:'5yus xOM) Ѯ,=ae7ڏ`;&DPE8>C5Mkt&3( +:S89EA#܅*<3WC$ib%q*߼KL!RQımхC\N%zL(2rAsj!r= )[q\\lgݦp4a*0BFP,8=Up'@2^' U,jpd4e&oF":v|6B;Hr /8~?JT ޚ㗊UpAc`vmW8梇Iߛ׶ahIBJCܳ9khEThP:a5ШAޥ2D}F &d9%JG'HiY9ܲcTv@6KUl3Pk_DB2}Y!CnFa9BMiRv27`3r<ՄpL|Fp5kr A9A/e+Vx R)_n ~OY{M G_ Oyxnb稰ˆ7kبh!VNӐתݱ"4 QHbdMH+<=֯5 fV,; Gw Jly}LZ!/{ 1֦U>CZPjGR$;iEXF7vY>#gN&a!ۏ DUAJ%N&!P - n|-n=O2Qʷ |FGn~v>w}d s:>*3,bl^lm<+L%ʋBHݍ-E^C6%xU!ZLGM'V}jUq4JGtdhd!쐾!1^U/j%LJu|:f:22"k7|]zH>z$"Ҥ~,1W鵊$l6N V@M}~ 5۝y? sn:~s0~gКi]P̓hJeY^6 ])74%i;Hv1~4SJA>ZF蜕Aһ^D~YXH+ۃ긔d!Q]YTUsfba In2x0ۛa}j[J-:=pƽS+(j۸~f|+86{_A6؁S-lM~OH-JDT6̃L&9t5)r78ˀ!a(#K83Ϯo6O?w~S/M9*W$tHL, q >m "In G(2.x%9DFWDv>"g;g)@bzl[eiιZA~5Nu摞,Zoh ={2cj |H<*fa(Nq Mؙ&PDѥAXn~39ŰtFkmRs+f 1N? 'ܬyeε=r5 ODX!hjwTsKᯭWqƧ*#*5D$vJ+ endstream endobj 156 0 obj << /Type /ObjStm /N 100 /First 885 /Length 4886 /Filter /FlateDecode >> stream xڭ[[s9~ǝڢ[KVð Z<8Ixq0~s$jN$UDVtslՕ5UēQ*u%dz+hXUVx|Z5T6Jg+kXI'5 ,WX4FAP lW\lQ ;ƺ" KA;`@l^$BD+7J2H& ! aȶ#g ,੠;86Hm$  ~u<>ICjO5T;&Wu#1a=8Ct~j3vʳB0DI}(>qEfzHd㸏?gӓvQ}?V͛Z͏/gy]Ŝu;^N9zў'ڒV`up@7Pc B K%^ZBE<-_\-c?b-j (z#0lYaU?`Skkr~w)/ٔ ?cSd 8і6Vx&_tvxyZ}׫$f.fjPGv;$㪳vΆuKKxzըZ[*aKCڐ8]h:ٌ&a4eZfU|0#$6/gj*{J~@m?bp};\O0p5rFz*U{z"LNC?Zl _'NSZGX =(JxG';ƻ$*;yRP8{jan(TFY{r7Z_ rWH^܎Άb6:yt :m6ZKݏJ- S-6ZOR!FۯF|oZ(:ۯRjo26C:$5U iV=,σO=ݥM|ֻMipZ[BQ9Obe:O( N5+(LxB6FS8+e$LmR4bSz+Ш Qu}0?%V޴f 8B[5dA`FWk4umQ ݰ2 =Xz6zj҇ѠBk΃GFvt!'I5G"֍iVےgt:{&l PPH4tBdqRx4a0HdN:i3f"M*t!Ć >T.BbH=A:VDR`vQ2Qż3ӃtVd'<md0FZ ʊteOo&>eֲBԦ@ݖFơ2fpϔ:иLQ9(XTc0Mr!V-9nq߁*g} S vGz KxhJDc$[(6_[D)XC6Y &ޞ,`eK[Q,όl.J A$D& D.Bڹ+R2Ws_y6pg`q#N/GNqGVSBo =BAƐrq)N^- 3vfRVcWS/Sٔs2䖃.-ǵ񤘴rjGm.,R0`s[,[*A?)X9v+V&@G,--f7>^QhuT`g`L'bfj%ՌE (~V e?MvMgjw,e0IAj94GjXD6 T"\J *rUdδ6+HsjmP|<9r2ˆႝbE+ $5|IpT)og()Sl{D2> jH_*Qij"Z:Qwo( bN7&`(Oz&r֊+0␂t?TZ:Q˒b7",aV=St˧sA>G(&Asʵ|JAۤfގӜ|tbj?홓QsVjS1z|Nc$e\:ͥT\d!g5JL- >j$Tg_9(/{JD:r ǡ)E LFO~8 |Ch#DJve vuԮQIŎUdd7E3ghGQH<1>ZU,؆%;:R̳."[xCrYSTeNЩܢ5Ǻ<,bz|FBPD2^IUiY3+L'SK&'d@z0kBNTeZ=U x濪@jy,ׂn YF/ǣ5P+D| )&r{mɍ ݵM|rktSʗ Ϸjiԯ9hP׸$-vBIr޲3Z"[[`A s=a:ZXoOS7ZvMhQ̔VΥѥ<M#C?[Ѧr2Œ]B(szhTb93#62Э&N4n/g<[Om~j_޷S|8),%--dt) ӯ5T;t*]K33o(JS'r_ MRǺb<Ǭt+nF_ok&Ww|[m!Uh@_p V)We@7o07>sٹ(+S yMnMvG \"q_$Fp]Js8B[d@[T(_Tӗ{ltҗ}^|yG/Kټ2‿`]@Q_#5WgAhx/􃆖w$|G''gE{~!=hIq>whgͰi/ӫY3jE3it6e;MOE8mK+(e&߼zdW;d$;/x?SA < 3{9{vT 8!P Hzj^j7p|yOhd !F}u ǧ/_py>j.G_2bx!W[C}@<Κٴr~?8bI@ ~qa䤱l:Bo/X!HM JO3M91w%+z}W.}\{C\!3r2HMǴa׹KN :ښ6g$zz;bdtĶ{/3&p4/HX 2|z7ݛk]+X=Bv竍ŵ\\ʝ8lo^]bu#6W.o[Vwm_3:UZv vv endstream endobj 277 0 obj << /Producer (pdfTeX-1.40.21) /Author()/Title()/Subject()/Creator(LaTeX with hyperref)/Keywords() /CreationDate (D:20200419203419+02'00') /ModDate (D:20200419203419+02'00') /Trapped /False /PTEX.Fullbanner (This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2) >> endobj 252 0 obj << /Type /ObjStm /N 49 /First 405 /Length 1679 /Filter /FlateDecode >> stream xڝXYo6~_lx9 uĹM6C)t@7%nO˧Mi̶eՔ{E)8\z5Gk G!.U)iGU})W w;mA:ͦZRҮU^akY7:A9޶MuS5#_Cyi1'#x4\{-}ZYn) W]q]].rCU,5Zք k?bU]k^^MAwX^/Y)UhT IbT&QQD5i8[f4*dⒼui\Q!uq!>-Y2,iSGlT1%Dj&%,`!L a2ʨ4NF=!DbR9l3aSM)rئ(2ʦ+KQM qqŔEti)rإKK.aΠ"|&rB1UL OOOiMyMFy('QE"D􇈉"ę(@dEppM‰, ێLjG@e$D@V d(I ~uD/(n`}w>8a{ÚS82A@LYGV:%SRQ>LbMuCѾ䇯oզ,ڵOtW;DWtǏ0ks iӑ>={\Ewz + <400141E594062C18C75B34B37EF7236B>] /Length 662 /Filter /FlateDecode >> stream x%IsQ{BBb)1$1A#<TYڰPvS,-k+;KbVN}9vJ)-rTPk([Q28l ԃ6 M\e֊Z AFTXm@`zT XSXr-@U/}3 Odh3/M{bru266fyfGvW`{>)q`<w}nN^wPDy3bϺb[#QԸ8NS48.\ux%O'H_}Mp ` a0fXo~lLI0;\̹H_2&fʘml8EWUi/$}-2(*%HNU.++J1E1s)\BW D䚑@[ǯ<      gZ*qU^E"sY,R[g9_WRlT^<>kj8HDhģ?Vco38i5j5ӅTKO[D~j6VsQQ 1"+ (pn݂w ->cpny=E]\ endstream endobj startxref 198491 %%EOF openTSNE-0.6.1/notes/notes.tex000066400000000000000000000635431413546205200161700ustar00rootroot00000000000000\documentclass[11pt]{article} \usepackage{parskip} \usepackage{geometry} \usepackage[utf8]{inputenc} \usepackage{amsmath, amssymb} \usepackage{subcaption} \usepackage{hyperref} \geometry{ a4paper, bottom=2.5cm, right =2.5cm, left =2.5cm, top =2.5cm, } \begin{document} \title{t-SNE Notes} \author{Pavlin Poličar} \date{} \maketitle \section{t-SNE} t-SNE was presented in \cite{maaten2008visualizing} and aims to preserve local structure of high dimensional spaces $X$ with some low dimensional embedding $Y$. First for each point $i$, we find its nearest nearest neighbours and compute the probability of this point $p_j$ based on the PDF of a Gaussian centred on the point $i$: \begin{equation}\label{eq:sne_pij} p_{j \mid i} = \frac{\exp{\left (- || \mathbf{x}_i - \mathbf{x}_j ||^2 / 2\sigma_i^2 \right )}}{\sum_{k \neq i}\exp{\left (- || \mathbf{x}_i - \mathbf{x}_k ||^2 / 2\sigma_i^2 \right )}} \end{equation} where $\sigma_i$ is the bandwidth of the Gaussian density. These bandwidths are controlled by the ``perplexity'' parameter. Perplexity can be thought of as a continuous analogue to the number $k$-nearest neighbours: \begin{equation} Perp(P_i) = 2^{H(P_i)} \end{equation} where $H(P_i)$ is the Shannon entropy of the distribution $P_i$. t-SNE actually doesn't use Equation~\ref{eq:sne_pij} directly, but symmetrizes this conditional probability, so the actual $p_{ij}$s used by t-SNE are \begin{equation} p_{ij} = \frac{p_{j\mid i} + p_{i \mid j}}{2} \end{equation} In their experiments, van der Maaten et al. found that this doesn't affect embedding quality and simplifies the gradient expression. Similarly, we represent the embedding $Y$ as a probability distribution. In the original SNE paper \cite{hinton2003stochastic}, a Gaussian was used, however this often led to the crowding problem, where all the points were clumped into a single ball in a single point in space. t-SNE, as the name would suggest, uses a Student-t distribution, therefore the probability density of $Y$ is \begin{equation} q_{ij} = \frac{\left ( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 \right )^{-1}}{\sum_{k \neq l}\left ( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 \right )^{-1}} \end{equation} We now have two probability distributions over the local point affinities. Now we'd like some way to match these two distributions, so the local structure of $X$ is reflected in $Y$. A natural way of doing this is to use Kullback-Leibler divergence (from here on referred to as the KL divergence), which is defined as \begin{equation} KL(P \mid \mid Q) = \sum_{ij} p_{ij} \log \frac{p_{ij}}{q_{ij}} \end{equation} Our goal is to minimize this error $C$, so we can take the derivative and obtain \begin{equation} \frac{\partial C}{\partial \mathbf{y}_i} = 4 \sum_{j \neq i} \left ( p_{ij} - q_{ij} \right ) \left ( \mathbf{y}_i - \mathbf{y}_j \right ) \left ( 1 + || \mathbf{y}_i - \mathbf{y}_j || ^2 \right )^{-1} \end{equation} This is t-SNE in essence. In practice various tricks are used to speed up convergence e.g. using a momentum term helps a lot. The embedding $Y$ is typically initialized using an isotropic Gaussian with small variance (e.g. 0.01). Often times, PCA is used for initialization. This can sometimes be problematic if the PCA embedding provides very scattered embeddings (sometimes most points are clumped to one side with very long stretched out tails). In these cases, using a random initialization produces better embeddings. \section{Performance improvements} It quickly became apparent that t-SNE, while nice, was infeasible to run for larger data sets, because of its quadratic time complexity $\mathcal{O}(n^2)$ (due to the normalization term in $q_{ij}$). For convenience, we will write the gradient in a different form seen in many papers, that makes the attractive and repulsive forces clearer. \begin{align} \frac{\partial C}{\partial \mathbf{y}_i} &= 4 \sum_{j \neq i} \left ( p_{ij} - q_{ij} \right ) \left ( \mathbf{y}_i - \mathbf{y}_j \right ) \left ( 1 + || \mathbf{y}_i - \mathbf{y}_j || ^2 \right )^{-1} \intertext{Notice that the right most term is just the unnormalized $q_{ij}$} &= 4 \sum_{j \neq i} \left ( p_{ij} - q_{ij} \right ) \left ( \mathbf{y}_i - \mathbf{y}_j \right ) \left ( 1 + || \mathbf{y}_i - \mathbf{y}_j || ^2 \right )^{-1} \frac{Z}{Z} \intertext{Where $Z$ is the normalization term of $Q$: $Z = \sum_{k \neq l}\left ( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 \right )^{-1}$} &= 4 \sum_{j \neq i} \left ( p_{ij} - q_{ij} \right ) q_{ij} Z \left ( \mathbf{y}_i - \mathbf{y}_j \right ) \\ &= 4 \left (\sum_{j \neq i} p_{ij} q_{ij} Z \left ( \mathbf{y}_i - \mathbf{y}_j \right ) -\sum_{j \neq i} q_{ij}^2 Z \left ( \mathbf{y}_i - \mathbf{y}_j \right ) \right ) \\ \intertext{which can in turn be throught of as attractive and repulsive forces} &= 4 \left ( F_{\text{attr}} + F_{\text{rep}} \right ) \end{align} \subsection{Landmark points} In fact, van der Maaten and Hinton provide a solution to this in their original paper: instead of visualizing all the points, embed only a sample of carefully chosen landmark points. The points must be carefully chosen because a random subset may not properly describe the manifold. First, we construct the k-neighbourhood graph on all the points. Next, they approximate the $P$ of the landmark points using random walks across the neighbourhood graph. Then, we proceed with t-SNE on the landmark points. \subsection{Approximating P} An observation made in \cite{van2014accelerating} was that since we use a Gaussian kernel for $P$, points further than 3 standard deviations from the mean have almost zero probabilities, and as such, do not affect the KL divergence term. Therefore, no harm would come if we simply ignored these terms. In practice, this means that we only compute the $p_{ij}$ terms for $\left \lfloor 3u \right \rfloor$ neighbours, where $u$ is the perplexity. In \cite{van2014accelerating}, exact nearest neighbours are used. These can be efficiently computed in $\mathcal{O}(n \log n)$ using tree structures, thus reducing the complexity from $\mathcal{O}(n^2)$ needed for pairwise distances. The preferred exact nearest neighbour method are vantage point trees (also referred to as VP trees). \cite{yianilos1993data} presented VP trees and compared their performance to another popular tree based nearest neighbour search method -- KD trees. VP trees were shown to require far fewer queries when dealing with high dimensions, as t-SNE often does.\cite{van2014accelerating} also provide a comparison with dual-trees, where VP trees, again, perform favourably. More recently, it was shown in \cite{linderman2017efficient} that approximate nearest neighbours perform just as well. Approximate nearest neighbour algorithms are often orders of magnitude faster than exact nearest neighbour search, allowing us to scale this step to much larger data than before. \subsection{Barnes-Hut} Having drastically improved the complexity of $F_\text{attr}$, we are still left with quadratic $\mathcal{O}(n^2)$ complexity for $F_\text{rep}$, required by the normalization term $Z$. \cite{van2014accelerating} notice that computing $F_\text{rep}$ can be posed as an N-body simulation problem. This problem has been addressed physics simulation community and can be efficiently solved in $\mathcal{O}(n \log n)$ time using Barnes-Hut trees. The main idea behind this approximation is that clusters of points far away for the current point $i$ will have similar contribution, therefore we can summarize entire regions of space (denoted cells in the following) by computing the center of mass of the region $\mathbf{y}_{\text{cell}}$, computing the interaction between $i$ and $\mathbf{y}_{\text{cell}}$ and adding this interaction up $N_{\text{cell}}$ times, where $N_{\text{cell}}$ is the number of points in the given region, given they are far enough from our query point $i$. The space is split into square regions and represented by a space splitting tree (a quad-tree in 2D and an oct-tree in 3D) which can be built in linear time. The ``far enough'' is determined by a parameter $\theta$, which controls how accurate our estimations are. If the following relation holds, then the cell is summarized \begin{equation} \frac{r_\text{cell}}{|| \mathbf{y}_i - \mathbf{y}_{\text{cell}} || ^2} < \theta \end{equation} where $r_\text{cell}$ represents the length of the diagonal of the cell. Larger values of $\theta$ produce more accurate estimates. Setting $\theta$ to 0 computes all the pairwise interactions as the condition can never be met. Scikit-learn recommends values between 0.2 and 0.8, as anything above and below that quickly result in long computation time and large error, respectively. It is worth noting that this approach scales fairly well for 1, 2 and 3 dimensions, but further than that, the complexity becomes prohibitively expensive. This is not really an issue, since we humans can only perceive 3 dimensions, and most visualizations are 2D. \subsection{FFT Accelerated Interpolation} We can write an equivalent expression for the repulsive forces \begin{align} F_\text{rep} &= \sum_{j \neq i} q_{ij}^2 Z \left ( \mathbf{y}_i - \mathbf{y}_j \right ) \intertext{Plugging in the expressions for $q_{ij}$ and $Z$} &= \sum_{j \neq i} \frac{\left ( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 \right )^{-2}}{\sum_{k \neq l}\left ( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 \right )^{-2}} \frac{\left ( \mathbf{y}_i - \mathbf{y}_j \right )}{\sum_{k \neq l}\left ( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 \right )} \\ \intertext{Putting the top and bottom terms together} &= \left ( \sum_{j \neq i} \frac{\mathbf{y}_i - \mathbf{y}_j}{\left ( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 \right )^{2}} \right ) \bigg/ \left( \sum_{k \neq l} \frac{1 + || \mathbf{y}_k - \mathbf{y}_l ||^2}{\left ( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 \right )^{2}} \right) \\ &= \left ( \sum_{j \neq i} \frac{\mathbf{y}_i - \mathbf{y}_j}{\left ( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 \right )^{2}} \right ) \bigg/ \left( \sum_{k \neq l} \frac{1}{\left ( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 \right )} \right) \end{align} We can also write an expression for each term of $\mathbf{y}_i$ individually: \begin{equation} F_{\text{rep}, i}(m) = \left ( \sum_{j \neq i} \frac{\mathbf{y}_i(m) - \mathbf{y}_j(m)}{\left ( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 \right )^{2}} \right ) \bigg/ \left( \sum_{k \neq l} \frac{1}{\left ( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 \right )} \right) \end{equation} where $\mathbf{y}_i(m)$ denotes the $m^{\text{th}}$ component of $\mathbf{y}$ i.e. $m \in \{1, 2\}$ in the 2D case. \cite{linderman2017efficient} make the acute observation that the repulsive forces $F_\text{rep}$ can be written as $s + 2$ sums of the form \begin{equation} \phi(\mathbf{y}_i) = \sum_j K (\mathbf{y}_i, \mathbf{y}_j) q_{ij} \end{equation} where $K(y, z)$ is either the Cauchy kernel or the squared Cauchy kernel and $s$ is the dimensionality of $Y$ \begin{equation} K_1(y, z) = \frac{1}{\left( 1 + || \mathbf{y} - \mathbf{z} ||^2 \right)}, \quad\text{or}\quad K_2(y, z) = \frac{1}{ \left( 1 + || \mathbf{y} - \mathbf{z} ||^2 \right) ^2} \end{equation} To make the sums concrete, consider the 2D case: \begin{align} \phi_{1, i} &= \sum_{j \neq i} \frac{1}{\left( 1 + || \mathbf{y}_j - \mathbf{y}_i ||^2 \right)} \notag \\ \phi_{2, i} &= \sum_{j \neq i} \frac{\mathbf{y}_j(1)}{\left( 1 + || \mathbf{y}_j - \mathbf{y}_i ||^2 \right)^2} \notag \\ \phi_{3, i} &= \sum_{j \neq i} \frac{\mathbf{y}_j(2)}{\left( 1 + || \mathbf{y}_j - \mathbf{y}_i ||^2 \right)^2} \notag \\ \phi_{4, i} &= \sum_{j \neq i} \frac{1}{\left( 1 + || \mathbf{y}_j - \mathbf{y}_i ||^2 \right)^2} \notag \end{align} the the repulsive forces can be expressed in terms of these 4 sums as follows: \begin{align} F_{\text{rep}, i}(1) &= \left ( \sum_{j \neq i} \frac{\mathbf{y}_i(1) - \mathbf{y}_j(1)}{\left ( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 \right )^{2}} \right ) \bigg/ \left( \sum_{k \neq l} \frac{1}{\left ( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 \right )} \right) \notag \\ &= (\phi_{2, i} - \mathbf{y}_{i}(1)\phi_{4, i}) / Z, \\ F_{\text{rep}, i}(2) &= \left ( \sum_{j \neq i} \frac{\mathbf{y}_i(2) - \mathbf{y}_j(2)}{\left ( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 \right )^{2}} \right ) \bigg/ \left( \sum_{k \neq l} \frac{1}{\left ( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 \right )} \right) \notag \\ &= (\phi_{3, i} - \mathbf{y}_{i}(2)\phi_{4, i}) / Z, \end{align} where \begin{align} Z &= \sum_j \phi_{1, j} \end{align} The key idea in this approach is that since we have smooth kernels $K_1$ and $K_2$, we can approximate them using polynomial interpolation. Of course, the choice of interpolants is entirely up to us, but we we evaluate our kernel functions at these points and interpolate our true data using these. To make things computationally efficient, we can set the interpolants to be equispaced points on the space spanned by the data. This is very convenient, because the kernels in question are all translation invariant and when we evaluate them at the interpolants, then the kernel matrix $K$ will be Toeplitz. This means that it is enough to evaluate the Kernel for the left-most point in space in 1D. In 2d, our $K$ is actually a 3D tensor, but is again, Toeplitz. Linear algebra tells us we can embed any Toeplitz matrix into a circulant matrix. This is desirable, because now we can perform matrix-vector multiplication in the frequency domain in linear time, with the slowest part being the FFT and IFFT transforms in $\mathcal{O}(n \log n)$ time. Finally, having evaluated the repulsive forces at the interpolants, we just need to interpolate the forces on our true data. This can be done in linear time $\mathcal{O}(n)$. Doing this, we have successfully made the overall complexity independent of $N$, and have shifted the brunt of the work onto the number of chosen interpolation points, so the time complexity will rely heavily on that. In practice, we split the input space into equally sized intervals, and then have 3 interpolation points in each interval. While we could increase the number of interpolation points, it is preferable to increase the number of intervals (due to the Runge phenomenon in interpolation). Increasing the number of interpolation points also increases the accuracy of the approximation, but comes at a computation cost. Like the Barnes-Hut variant, this method becomes very inefficient for higher dimensions, as the number of interpolation points needed scales exponentially with $d$. In practice, this isn't an issue because most often, we want to inspect 2D embeddings. \section{Implementation details} \subsection{Perplexity} The following section explains how perplexity is formulated so the code can run efficiently. Perplexity is defined as \begin{align} \text{Perplexity}(P_i) &= 2^{H(P_i)} \intertext{where $H$ is the Shannon entropy of a discrete distribution} H(P_i) = -\sum_i p_{j \mid i} \log_2 (p_{j \mid i}) \intertext{In code, the following is more practical to avoid computing $2^{x}$ whereas perplexity stays fixed:} \log(\text{Perplexity}(P_i)) &= -\sum_i p_{j \mid i} \log (p_{j \mid i}) \end{align} Remember that $P_i$ is just a Gaussian distribution centered on point $i$, given by \begin{align} p_i(d_i) &= \frac{1}{\sqrt{2 \pi} \sigma} \exp \left ( -\frac{d_{ij}^2}{2 \sigma^2} \right ) \intertext{however, since we'll be performing row-normalization by hand, something proportional is sufficient} &\sim \exp \left ( -\frac{d_{ij}^2}{2 \sigma^2} \right ) \end{align} In most implementations this Gaussian is parameterized with $\beta = 1 / 2\sigma^2$ and therefore we compute $\exp \left ( -d_{ij}^2 \beta \right )$ in practice. In our case, we actually compute $ \frac{1}{\sigma} \exp \left ( -d_{ij}^2 \beta \right )$ because we allow a multiscale approach, which mixes several Gaussians together. We also reparameterize our distribution to use the more interpretable precision $\tau = 1 / \sigma^2$ instead of $\beta$. Therefore our probability density is given by \begin{equation} p_i(d_i) \sim \sqrt{\tau} \exp \left ( -\frac{d_{ij}^2 \tau}{2} \right ) \end{equation} We now plug in our parametrization into the entropy and arrive at a convenient form which can be coded efficiently. \begin{align} H_i &= -\sum_j \frac{\sqrt{\tau} \exp \left ( -d_{ij}^2 \tau / 2 \right ) }{\sum_k \sqrt{\tau} \exp \left ( -d_{ik}^2 \tau / 2 \right )} \log \left ( \frac{\sqrt{\tau} \exp \left ( -d_{ij}^2 \tau / 2 \right ) }{\sum_k \sqrt{\tau} \exp \left ( -d_{ik}^2 \tau / 2 \right )} \right ) \\ \intertext{The first term is just $p_{j\mid i}$ and we can split up the log into two parts} &= -\sum_j p_{j\mid i} \left [ \log \left ( \sqrt{\tau} \exp \left ( -d_{ij}^2 \tau / 2 \right ) \right ) - \log \left ( \sum_k \sqrt{\tau} \exp \left ( -d_{ik}^2 \tau / 2 \right ) \right ) \right ] \intertext{Notice now that the first term in the square brackets almost has the form $\log (\exp (x))$. For clarity, we will also denote the normalization sum as $Z$.} &= -\sum_j \left [ p_{j\mid i} \left ( \frac{1}{2} \log \tau - d_{ij}^2 \tau / 2 \right ) \right ] + \sum_j p_{j\mid i} \log Z \\ &= -\frac{1}{2} \log \tau \sum_j p_{j\mid i} + \frac{\tau}{2} \sum_j p_{j\mid i} d_{ij}^2 + \sum_j p_{j\mid i} \log Z \intertext{We move the first term to the end to make the sign unmissable. Since $p_i$ is a proper probability distribution, its elements sum up to 1, leaving us with} &= \frac{\tau}{2} \sum_j p_{j\mid i} d_{ij}^2 + \log Z-\frac{1}{2} \log \tau \end{align} This can be computed in two passes over the data. The first pass computes the unnormalized probabilities $\tilde{p}_{j\mid i}$ and accumulate the normalization constant $Z$. In the second pass, the first term can be computed. In other implementation e.g. scikit-learn, the expression is computed without $\sqrt{\tau}$. It's easy to see that the result will be similar (and indeed, this is used in their code), but without the $-1/2 \log \tau$ term and parameterized with $\beta = \tau / 2$. \subsection{Fast KL Divergence} During computation of negative gradients, we do not know the value of the normalization term $Z$ during intermediate steps. Therefore, in order to compute the KL divergence of the embedding, we would need at least two passes over the data points, first to compute the unnormalized $q_{ij}$s, and secondly to normalize them and compute the KL divergence. By rewriting the KL divergence in terms of unnormalized $q_{ij}$s, we can compute the entire error with a single pass over the data points by accumulating the $\sum_{ij} p_{ij}$ and $\sum_{ij}q_{ij}$ in the first pass. \begin{align} KL(P \mid \mid Q) &= \sum_{ij} p_{ij} \log \frac{p_{ij}}{q_{ij}} \\ &= \sum_{ij} p_{ij} \log \left ( p_{ij} \frac{Z}{\hat{q}_{ij}} \right ) \intertext{where $\hat{q}_{ij}$ denotes the unnormalized values $q_{ij}$} &= \sum_{ij} p_{ij} \log \frac{p_{ij}}{\hat{q}_{ij}} + \sum_{ij} p_{ij} \log Z \end{align} Therefore the first term requires a single pass over all $i, j$s and the second term can be computed in constant time if we accumulate the sums of $P$ and $Q$. This is already included in most software packages e.g. scikit-learn. \subsection{KL Divergence with exaggeration} The implemented optimization methods don't have a notion of exaggeration, they simply take an affinity matrix $P$ containing the probabilities of points $j$ appearing close to $i$. Exaggeration is used to scale $P$ by some constant factor $\alpha$ (this means that entries in the affinity matrix $P$ are not proper probabilities) to help separate clusters in the beginning of the optimization. These methods also compute the KL divergence during optimization (for efficiency), and, as such, the error is incorrect because we don't account for the scaling $\alpha$. This section derives a simple correction for the KL divergence error term so we can get the true error of the embedding even when $P$ is exaggerated. \begin{align} KL(P \mid \mid Q) &= \sum_{ij} p_{ij} \log \frac{p_{ij}}{q_{ij}} \intertext{We need to introduce the scaling i.e. exaggeration factor $\alpha$ to every $p_{ij}$ term, so we multiply some terms by $1 = \alpha/\alpha$.} &= \sum_{ij} \frac{\alpha}{\alpha}p_{ij} \log \frac{\alpha p_{ij}}{\alpha q_{ij}} \\ \intertext{Exaggeration means that the $p_{ij}$ terms get multiplied by $\alpha$, so we need to find an expression for the KL divergence that includes only $\alpha p_{ij}$ and $q_{ij}$ and some other factor that will correct for $\alpha$.} &= \frac{1}{\alpha} \sum_{ij} \alpha p_{ij} \left ( \log \frac{\alpha p_{ij}}{q_{ij}} - \log \alpha \right ) \\ &= \frac{1}{\alpha} \left ( \sum_{ij} \alpha p_{ij} \log \frac{\alpha p_{ij}}{q_{ij}} \right ) - \frac{1}{\alpha} \left ( \sum_{ij} \alpha p_{ij} \log \alpha \right ) \intertext{We notice in the first term is exactly the KL divergence where $p_{ij}$s are scaled by $\alpha$. We also notice in the second term that $\sum_{ij} P_{ij} = 1$ and that $\alpha$ cancels out, leaving us with} &= \frac{1}{\alpha} \left ( \sum_{ij} \alpha p_{ij} \log \frac{\alpha p_{ij}}{q_{ij}} \right ) - \log \alpha \end{align} The first term is computed by the gradient method (since it only knows about the scaled $P$), the second term can easily be computed post-optimization, allowing us to get the correct KL divergence. \subsection{Variable Degrees of Freedom} Kobak \textit{et al.}~\cite{kobak2019heavy} suggest that using variable degrees of freedom can be used to improve embeddings. Standard t-SNE uses the t-distribution with a single degree of freedom. This is defined as \begin{equation} q_{ij} \propto \left ( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 / \alpha \right )^{-\alpha} = \frac{1}{\left( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 / \alpha \right)^\alpha }. \end{equation} In standard t-SNE $\alpha=1$ so this simplifies to the standard formulation \begin{equation} q_{ij} \propto \left ( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 \right )^{-1} = \frac{1}{1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 } \end{equation} where we have omitted the normalization constant. The gradient of the t-SNE loss function then becomes \begin{align} \frac{\partial C}{\partial \mathbf{y}_i} &= 4 \sum_{j \neq i} \left ( p_{ij} - q_{ij} \right ) q_{ij}^{1/\alpha} \left ( \mathbf{y}_i - \mathbf{y}_j \right ) \end{align} where $q_{ij}$ is, again, the unnormalized kernel between points $i$ and $j$. Decomposing this into the attractive and repulsive forces gives us \begin{align} \mathbf{F}_{\text{attr}} &= 4 \sum_j p_{ij} q_{ij}^{1/\alpha} (\mathbf{y}_i - \mathbf{y}_j), \\ \mathbf{F}_{\text{rep}} &= - 4 \sum_j q_{ij}^{\frac{\alpha+1}{\alpha}} / Z (\mathbf{y}_i - \mathbf{y}_j). \end{align} See the original publication for more details. \subsubsection{Implementation} Adapting the implementation for computing the attractive forces and the Barnes-Hut repulsive forces is straightforward. Adapting the interpolation based computation of repulsive forces is a bit more involved. The direct implementation of the approach described in the paper leads to a solution requiring two different kernels. We describe the 1D case, but the extension to the 2D case is straightforward. \begin{align} \mathbf{F}_\text{rep} &= \sum_{j \neq i} q_{ij}^{\frac{\alpha+1}{\alpha}} Z \left ( \mathbf{y}_i - \mathbf{y}_j \right ) \\ &= \sum_{j \neq i} \left( \frac{\left ( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 / \alpha \right )^{-\alpha}}{\sum_{k \neq l} \left ( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 / \alpha \right )^{-\alpha}} \right) ^{\frac{\alpha+1}{\alpha}} \frac{\mathbf{y}_i - \mathbf{y}_j}{\sum_{k \neq l}\left ( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 / \alpha \right )^{\alpha}} \\ &= \sum_{j \neq i} \frac{\mathbf{y}_i - \mathbf{y}_j}{\left ( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 / \alpha \right )^{\alpha \left( \frac{\alpha+1}{\alpha} \right)}} \bigg/ \left( \sum_{k \neq l} \frac{\left( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 / \alpha \right)^\alpha}{\left( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 / \alpha \right)^{\alpha \left(\frac{\alpha+1}{\alpha} \right) }} \right) \\ &= \sum_{j \neq i} \frac{\mathbf{y}_i - \mathbf{y}_j}{\left ( 1 + || \mathbf{y}_i - \mathbf{y}_j ||^2 / \alpha \right )^{\alpha + 1}} \bigg/ \left( \sum_{k \neq l} \frac{1}{\left( 1 + || \mathbf{y}_k - \mathbf{y}_l ||^2 / \alpha \right)} \right) \end{align} Evaluating this sum using the interpolation scheme would require two separate kernels with three terms \begin{align} \phi_{1,j} &= \sum_{j \neq i} \frac{1}{\left( 1 + || \mathbf{y}_j - \mathbf{y}_i ||^2 / \alpha \right)^{\alpha+1}}, \\ \phi_{2,j} &= \sum_{j \neq i} \frac{\mathbf{y}_j}{\left( 1 + || \mathbf{y}_j - \mathbf{y}_i ||^2 / \alpha \right)^{\alpha+1}}, \\ \phi_{3,j} &= \sum_{j \neq i} \frac{1}{\left( 1 + || \mathbf{y}_j - \mathbf{y}_i ||^2 / \alpha \right)}. \end{align} Then, we can calculate the necessary quantities \begin{align} N_i &= \mathbf{y}_i \phi_{1,j} - \phi_{2,j} \\ Z &= \sum_j \phi_{3,j} \end{align} where $N_i$ is the unnormalized numerator of the repulsive forces. % \section{Transform} \subsection{Direct optimization} \subsection{General framework of cost functions} \cite{bunte2012general} \subsection{MDS interpolation} MDS Interpolation~\cite{bae2010dimension}. A similar approach might be able to be applied to t-SNE. In essence, they run MDS on a sample of points. Then for each new point, we compute the k-nearest neighbours and optimize the stress function w.r.t. only those points. In their paper, they derive equations that can be used for efficient optimization via majorization. \subsection{Kernel t-SNE} \cite{gisbrecht2012out} claim to outperform direct mapping t-SNE using a direct kernel mapping. This paper is not very useful. The graph is misleading and the table at the end is informative, but run only on small datasets. Their subsequent paper is much better and throughout. In \cite{gisbrecht2015parametric}, kernel t-SNE is described in more detail and parameters are chosen in a more principled manner. Describes how to integrate class labels into embedding using Fischer information. The issue of kernel t-SNE is that we have to compute the inverse of the interaction matrix K. We can use P as the interaction matrix, and P is sparse, but the inverse of that is very dense, and for any reasonably sized data set, this is unfeasable. \bibliography{references} \bibliographystyle{apalike} \end{document} openTSNE-0.6.1/notes/references.bib000066400000000000000000000057161413546205200171130ustar00rootroot00000000000000@inproceedings{hinton2003stochastic, title={Stochastic neighbor embedding}, author={Hinton, Geoffrey E and Roweis, Sam T}, booktitle={Advances in neural information processing systems}, pages={857--864}, year={2003} } @article{maaten2008visualizing, title={Visualizing data using t-SNE}, author={Maaten, Laurens van der and Hinton, Geoffrey}, journal={Journal of machine learning research}, volume={9}, number={Nov}, pages={2579--2605}, year={2008} } @inproceedings{maaten2009learning, title={Learning a parametric embedding by preserving local structure}, author={Maaten, Laurens}, booktitle={Artificial Intelligence and Statistics}, pages={384--391}, year={2009} } @inproceedings{yianilos1993data, title={Data structures and algorithms for nearest neighbor search in general metric spaces}, author={Yianilos, Peter N}, booktitle={SODA}, volume={93}, number={194}, pages={311--321}, year={1993} } @article{linderman2017efficient, title={Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding}, author={Linderman, George C and Rachh, Manas and Hoskins, Jeremy G and Steinerberger, Stefan and Kluger, Yuval}, journal={arXiv preprint arXiv:1712.09005}, year={2017} } @article{van2014accelerating, title={Accelerating t-SNE using tree-based algorithms.}, author={Van Der Maaten, Laurens}, journal={Journal of machine learning research}, volume={15}, number={1}, pages={3221--3245}, year={2014} } @inproceedings{bae2010dimension, title={Dimension reduction and visualization of large high-dimensional data via interpolation}, author={Bae, Seung-Hee and Choi, Jong Youl and Qiu, Judy and Fox, Geoffrey C}, booktitle={Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing}, pages={203--214}, year={2010}, organization={ACM} } @article{bunte2012general, title={A general framework for dimensionality-reducing data visualization mapping}, author={Bunte, Kerstin and Biehl, Michael and Hammer, Barbara}, journal={Neural Computation}, volume={24}, number={3}, pages={771--804}, year={2012}, publisher={MIT Press} } @inproceedings{gisbrecht2012out, title={Out-of-sample kernel extensions for nonparametric dimensionality reduction.}, author={Gisbrecht, Andrej and Lueks, Wouter and Mokbel, Bassam and Hammer, Barbara}, booktitle={ESANN}, volume={2012}, pages={531--536}, year={2012} } @article{gisbrecht2015parametric, title={Parametric nonlinear dimensionality reduction using kernel t-SNE}, author={Gisbrecht, Andrej and Schulz, Alexander and Hammer, Barbara}, journal={Neurocomputing}, volume={147}, pages={71--82}, year={2015}, publisher={Elsevier} } @article{kobak2019heavy, title={Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations}, author={Kobak, Dmitry and Linderman, George and Steinerberger, Stefan and Kluger, Yuval and Berens, Philipp}, journal={arXiv preprint arXiv:1902.05804}, year={2019} }openTSNE-0.6.1/openTSNE/000077500000000000000000000000001413546205200146065ustar00rootroot00000000000000openTSNE-0.6.1/openTSNE/__init__.py000066400000000000000000000001641413546205200167200ustar00rootroot00000000000000from .tsne import TSNE, TSNEEmbedding, PartialTSNEEmbedding, OptimizationInterrupt from .version import __version__ openTSNE-0.6.1/openTSNE/_matrix_mul/000077500000000000000000000000001413546205200171265ustar00rootroot00000000000000openTSNE-0.6.1/openTSNE/_matrix_mul/__init__.py000066400000000000000000000000001413546205200212250ustar00rootroot00000000000000openTSNE-0.6.1/openTSNE/_matrix_mul/matrix_mul.pxd000066400000000000000000000006521413546205200220270ustar00rootroot00000000000000# cython: boundscheck=False # cython: wraparound=False # cython: cdivision=True # cython: initializedcheck=False # cython: warn.undeclared=True # cython: language_level=3 cdef void matrix_multiply_fft_1d( double[::1] kernel_tilde, double[:, ::1] w_coefficients, double[:, ::1] out, ) cdef void matrix_multiply_fft_2d( double[:, ::1] kernel_tilde, double[:, ::1] w_coefficients, double[:, ::1] out, ) openTSNE-0.6.1/openTSNE/_matrix_mul/matrix_mul_fftw3.cpp000066400000000000000000034605211413546205200231370ustar00rootroot00000000000000/* Generated by Cython 0.29.15 */ /* BEGIN: Cython Metadata { "distutils": { "depends": [], "language": "c++", "libraries": [ "fftw3" ], "name": "openTSNE._matrix_mul.matrix_mul", "sources": [ "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx" ] }, "module_name": "openTSNE._matrix_mul.matrix_mul" } END: Cython Metadata */ #define PY_SSIZE_T_CLEAN #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) #error Cython requires Python 2.6+ or Python 3.3+. #else #define CYTHON_ABI "0_29_15" #define CYTHON_HEX_VERSION 0x001D0FF0 #define CYTHON_FUTURE_DIVISION 1 #include #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #define __PYX_COMMA , #ifndef HAVE_LONG_LONG #if PY_VERSION_HEX >= 0x02070000 #define HAVE_LONG_LONG #endif #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #ifndef Py_HUGE_VAL #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_PYSTON 0 #define CYTHON_COMPILING_IN_CPYTHON 0 #undef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 0 #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #if PY_VERSION_HEX < 0x03050000 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #elif !defined(CYTHON_USE_ASYNC_SLOTS) #define CYTHON_USE_ASYNC_SLOTS 1 #endif #undef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 0 #undef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 0 #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #undef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 1 #undef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 0 #undef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 0 #undef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 0 #undef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 0 #undef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT 0 #undef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE 0 #undef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS 0 #undef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK 0 #elif defined(PYSTON_VERSION) #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_PYSTON 1 #define CYTHON_COMPILING_IN_CPYTHON 0 #ifndef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 1 #endif #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #undef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 0 #ifndef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 1 #endif #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #ifndef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 0 #endif #ifndef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 1 #endif #ifndef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 1 #endif #undef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 0 #undef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 0 #undef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT 0 #undef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE 0 #undef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS 0 #undef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_PYSTON 0 #define CYTHON_COMPILING_IN_CPYTHON 1 #ifndef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 1 #endif #if PY_VERSION_HEX < 0x02070000 #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) #define CYTHON_USE_PYTYPE_LOOKUP 1 #endif #if PY_MAJOR_VERSION < 3 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #elif !defined(CYTHON_USE_ASYNC_SLOTS) #define CYTHON_USE_ASYNC_SLOTS 1 #endif #if PY_VERSION_HEX < 0x02070000 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #elif !defined(CYTHON_USE_PYLONG_INTERNALS) #define CYTHON_USE_PYLONG_INTERNALS 1 #endif #ifndef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 1 #endif #ifndef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 1 #endif #if PY_VERSION_HEX < 0x030300F0 #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #elif !defined(CYTHON_USE_UNICODE_WRITER) #define CYTHON_USE_UNICODE_WRITER 1 #endif #ifndef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 0 #endif #ifndef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 1 #endif #ifndef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 1 #endif #ifndef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 1 #endif #ifndef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 1 #endif #ifndef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000) #endif #ifndef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) #endif #ifndef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1) #endif #ifndef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3) #endif #endif #if !defined(CYTHON_FAST_PYCCALL) #define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) #endif #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #undef SHIFT #undef BASE #undef MASK #ifdef SIZEOF_VOID_P enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; #endif #endif #ifndef __has_attribute #define __has_attribute(x) 0 #endif #ifndef __has_cpp_attribute #define __has_cpp_attribute(x) 0 #endif #ifndef CYTHON_RESTRICT #if defined(__GNUC__) #define CYTHON_RESTRICT __restrict__ #elif defined(_MSC_VER) && _MSC_VER >= 1400 #define CYTHON_RESTRICT __restrict #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_RESTRICT restrict #else #define CYTHON_RESTRICT #endif #endif #ifndef CYTHON_UNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif # elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif #endif #ifndef CYTHON_MAYBE_UNUSED_VAR # if defined(__cplusplus) template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } # else # define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) # endif #endif #ifndef CYTHON_NCP_UNUSED # if CYTHON_COMPILING_IN_CPYTHON # define CYTHON_NCP_UNUSED # else # define CYTHON_NCP_UNUSED CYTHON_UNUSED # endif #endif #define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) #ifdef _MSC_VER #ifndef _MSC_STDINT_H_ #if _MSC_VER < 1300 typedef unsigned char uint8_t; typedef unsigned int uint32_t; #else typedef unsigned __int8 uint8_t; typedef unsigned __int32 uint32_t; #endif #endif #else #include #endif #ifndef CYTHON_FALLTHROUGH #if defined(__cplusplus) && __cplusplus >= 201103L #if __has_cpp_attribute(fallthrough) #define CYTHON_FALLTHROUGH [[fallthrough]] #elif __has_cpp_attribute(clang::fallthrough) #define CYTHON_FALLTHROUGH [[clang::fallthrough]] #elif __has_cpp_attribute(gnu::fallthrough) #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] #endif #endif #ifndef CYTHON_FALLTHROUGH #if __has_attribute(fallthrough) #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) #else #define CYTHON_FALLTHROUGH #endif #endif #if defined(__clang__ ) && defined(__apple_build_version__) #if __apple_build_version__ < 7000000 #undef CYTHON_FALLTHROUGH #define CYTHON_FALLTHROUGH #endif #endif #endif #ifndef __cplusplus #error "Cython files generated with the C++ option must be compiled with a C++ compiler." #endif #ifndef CYTHON_INLINE #if defined(__clang__) #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) #else #define CYTHON_INLINE inline #endif #endif template void __Pyx_call_destructor(T& x) { x.~T(); } template class __Pyx_FakeReference { public: __Pyx_FakeReference() : ptr(NULL) { } __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } T *operator->() { return ptr; } T *operator&() { return ptr; } operator T&() { return *ptr; } template bool operator ==(U other) { return *ptr == other; } template bool operator !=(U other) { return *ptr != other; } private: T *ptr; }; #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) #define Py_OptimizeFlag 0 #endif #define __PYX_BUILD_PY_SSIZE_T "n" #define CYTHON_FORMAT_SSIZE_T "z" #if PY_MAJOR_VERSION < 3 #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #define __Pyx_DefaultClassType PyClass_Type #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" #if PY_VERSION_HEX >= 0x030800A4 && PY_VERSION_HEX < 0x030800B2 #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a, 0, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #else #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #endif #define __Pyx_DefaultClassType PyType_Type #endif #ifndef Py_TPFLAGS_CHECKTYPES #define Py_TPFLAGS_CHECKTYPES 0 #endif #ifndef Py_TPFLAGS_HAVE_INDEX #define Py_TPFLAGS_HAVE_INDEX 0 #endif #ifndef Py_TPFLAGS_HAVE_NEWBUFFER #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif #ifndef Py_TPFLAGS_HAVE_FINALIZE #define Py_TPFLAGS_HAVE_FINALIZE 0 #endif #ifndef METH_STACKLESS #define METH_STACKLESS 0 #endif #if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) #ifndef METH_FASTCALL #define METH_FASTCALL 0x80 #endif typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, Py_ssize_t nargs, PyObject *kwnames); #else #define __Pyx_PyCFunctionFast _PyCFunctionFast #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords #endif #if CYTHON_FAST_PYCCALL #define __Pyx_PyFastCFunction_Check(func)\ ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))))) #else #define __Pyx_PyFastCFunction_Check(func) 0 #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) #define PyObject_Malloc(s) PyMem_Malloc(s) #define PyObject_Free(p) PyMem_Free(p) #define PyObject_Realloc(p) PyMem_Realloc(p) #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1 #define PyMem_RawMalloc(n) PyMem_Malloc(n) #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n) #define PyMem_RawFree(p) PyMem_Free(p) #endif #if CYTHON_COMPILING_IN_PYSTON #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) #else #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) #endif #if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 #define __Pyx_PyThreadState_Current PyThreadState_GET() #elif PY_VERSION_HEX >= 0x03060000 #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() #elif PY_VERSION_HEX >= 0x03000000 #define __Pyx_PyThreadState_Current PyThreadState_GET() #else #define __Pyx_PyThreadState_Current _PyThreadState_Current #endif #if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) #include "pythread.h" #define Py_tss_NEEDS_INIT 0 typedef int Py_tss_t; static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { *key = PyThread_create_key(); return 0; } static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); *key = Py_tss_NEEDS_INIT; return key; } static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { PyObject_Free(key); } static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { return *key != Py_tss_NEEDS_INIT; } static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { PyThread_delete_key(*key); *key = Py_tss_NEEDS_INIT; } static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { return PyThread_set_key_value(*key, value); } static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { return PyThread_get_key_value(*key); } #endif #if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) #define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) #else #define __Pyx_PyDict_NewPresized(n) PyDict_New() #endif #if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) #else #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS #define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) #else #define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name) #endif #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) #define CYTHON_PEP393_ENABLED 1 #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ 0 : _PyUnicode_Ready((PyObject *)(op))) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) #else #define CYTHON_PEP393_ENABLED 0 #define PyUnicode_1BYTE_KIND 1 #define PyUnicode_2BYTE_KIND 2 #define PyUnicode_4BYTE_KIND 4 #define __Pyx_PyUnicode_READY(op) (0) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) #endif #if CYTHON_COMPILING_IN_PYPY #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) #else #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) #endif #define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) #define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) #else #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) #endif #if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) #define PyObject_ASCII(o) PyObject_Repr(o) #endif #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type #define PyStringObject PyUnicodeObject #define PyString_Type PyUnicode_Type #define PyString_Check PyUnicode_Check #define PyString_CheckExact PyUnicode_CheckExact #define PyObject_Unicode PyObject_Str #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) #else #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) #endif #ifndef PySet_CheckExact #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) #endif #if CYTHON_ASSUME_SAFE_MACROS #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) #else #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) #endif #if PY_MAJOR_VERSION >= 3 #define PyIntObject PyLongObject #define PyInt_Type PyLong_Type #define PyInt_Check(op) PyLong_Check(op) #define PyInt_CheckExact(op) PyLong_CheckExact(op) #define PyInt_FromString PyLong_FromString #define PyInt_FromUnicode PyLong_FromUnicode #define PyInt_FromLong PyLong_FromLong #define PyInt_FromSize_t PyLong_FromSize_t #define PyInt_FromSsize_t PyLong_FromSsize_t #define PyInt_AsLong PyLong_AsLong #define PyInt_AS_LONG PyLong_AS_LONG #define PyInt_AsSsize_t PyLong_AsSsize_t #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask #define PyNumber_Int PyNumber_Long #endif #if PY_MAJOR_VERSION >= 3 #define PyBoolObject PyLongObject #endif #if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY #ifndef PyUnicode_InternFromString #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) #endif #endif #if PY_VERSION_HEX < 0x030200A4 typedef long Py_hash_t; #define __Pyx_PyInt_FromHash_t PyInt_FromLong #define __Pyx_PyInt_AsHash_t PyInt_AsLong #else #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), func)) #else #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) #endif #if CYTHON_USE_ASYNC_SLOTS #if PY_VERSION_HEX >= 0x030500B1 #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) #else #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) #endif #else #define __Pyx_PyType_AsAsync(obj) NULL #endif #ifndef __Pyx_PyAsyncMethodsStruct typedef struct { unaryfunc am_await; unaryfunc am_aiter; unaryfunc am_anext; } __Pyx_PyAsyncMethodsStruct; #endif #if defined(WIN32) || defined(MS_WINDOWS) #define _USE_MATH_DEFINES #endif #include #ifdef NAN #define __PYX_NAN() ((float) NAN) #else static CYTHON_INLINE float __PYX_NAN() { float value; memset(&value, 0xFF, sizeof(value)); return value; } #endif #if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) #define __Pyx_truncl trunc #else #define __Pyx_truncl truncl #endif #define __PYX_ERR(f_index, lineno, Ln_error) \ { \ __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ } #ifndef __PYX_EXTERN_C #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else #define __PYX_EXTERN_C extern #endif #endif #define __PYX_HAVE__openTSNE___matrix_mul__matrix_mul #define __PYX_HAVE_API__openTSNE___matrix_mul__matrix_mul /* Early includes */ #include #include #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" #include "fftw3.h" #include "pythread.h" #include #include "pystate.h" #ifdef _OPENMP #include #endif /* _OPENMP */ #if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) #define CYTHON_WITHOUT_ASSERTIONS #endif typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; #define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) #define __PYX_DEFAULT_STRING_ENCODING "" #define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString #define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #define __Pyx_uchar_cast(c) ((unsigned char)c) #define __Pyx_long_cast(x) ((long)x) #define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ (sizeof(type) < sizeof(Py_ssize_t)) ||\ (sizeof(type) > sizeof(Py_ssize_t) &&\ likely(v < (type)PY_SSIZE_T_MAX ||\ v == (type)PY_SSIZE_T_MAX) &&\ (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ v == (type)PY_SSIZE_T_MIN))) ||\ (sizeof(type) == sizeof(Py_ssize_t) &&\ (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ v == (type)PY_SSIZE_T_MAX))) ) static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { return (size_t) i < (size_t) limit; } #if defined (__cplusplus) && __cplusplus >= 201103L #include #define __Pyx_sst_abs(value) std::abs(value) #elif SIZEOF_INT >= SIZEOF_SIZE_T #define __Pyx_sst_abs(value) abs(value) #elif SIZEOF_LONG >= SIZEOF_SIZE_T #define __Pyx_sst_abs(value) labs(value) #elif defined (_MSC_VER) #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define __Pyx_sst_abs(value) llabs(value) #elif defined (__GNUC__) #define __Pyx_sst_abs(value) __builtin_llabs(value) #else #define __Pyx_sst_abs(value) ((value<0) ? -value : value) #endif static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); #define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) #define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) #define __Pyx_PyBytes_FromString PyBytes_FromString #define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); #if PY_MAJOR_VERSION < 3 #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #else #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize #endif #define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) #define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) #define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) #define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) #define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) #define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { const Py_UNICODE *u_end = u; while (*u_end++) ; return (size_t)(u_end - u - 1); } #define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) #define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode #define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode #define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) #define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); #define __Pyx_PySequence_Tuple(obj)\ (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); #if CYTHON_ASSUME_SAFE_MACROS #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) #endif #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) #else #define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) #endif #define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII static int __Pyx_sys_getdefaultencoding_not_ascii; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; PyObject* ascii_chars_u = NULL; PyObject* ascii_chars_b = NULL; const char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; if (strcmp(default_encoding_c, "ascii") == 0) { __Pyx_sys_getdefaultencoding_not_ascii = 0; } else { char ascii_chars[128]; int c; for (c = 0; c < 128; c++) { ascii_chars[c] = c; } __Pyx_sys_getdefaultencoding_not_ascii = 1; ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); if (!ascii_chars_u) goto bad; ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { PyErr_Format( PyExc_ValueError, "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", default_encoding_c); goto bad; } Py_DECREF(ascii_chars_u); Py_DECREF(ascii_chars_b); } Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); Py_XDECREF(ascii_chars_u); Py_XDECREF(ascii_chars_b); return -1; } #endif #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) #else #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT static char* __PYX_DEFAULT_STRING_ENCODING; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); return -1; } #endif #endif /* Test for GCC > 2.95 */ #if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #else /* !__GNUC__ or GCC < 2.95 */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ */ static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } static PyObject *__pyx_m = NULL; static PyObject *__pyx_d; static PyObject *__pyx_b; static PyObject *__pyx_cython_runtime = NULL; static PyObject *__pyx_empty_tuple; static PyObject *__pyx_empty_bytes; static PyObject *__pyx_empty_unicode; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; /* Header.proto */ #if !defined(CYTHON_CCOMPLEX) #if defined(__cplusplus) #define CYTHON_CCOMPLEX 1 #elif defined(_Complex_I) #define CYTHON_CCOMPLEX 1 #else #define CYTHON_CCOMPLEX 0 #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus #include #else #include #endif #endif #if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) #undef _Complex_I #define _Complex_I 1.0fj #endif static const char *__pyx_f[] = { "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx", "__init__.pxd", "stringsource", "type.pxd", }; /* MemviewSliceStruct.proto */ struct __pyx_memoryview_obj; typedef struct { struct __pyx_memoryview_obj *memview; char *data; Py_ssize_t shape[8]; Py_ssize_t strides[8]; Py_ssize_t suboffsets[8]; } __Pyx_memviewslice; #define __Pyx_MemoryView_Len(m) (m.shape[0]) /* Atomics.proto */ #include #ifndef CYTHON_ATOMICS #define CYTHON_ATOMICS 1 #endif #define __pyx_atomic_int_type int #if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\ (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\ !defined(__i386__) #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1) #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1) #ifdef __PYX_DEBUG_ATOMICS #warning "Using GNU atomics" #endif #elif CYTHON_ATOMICS && defined(_MSC_VER) && 0 #include #undef __pyx_atomic_int_type #define __pyx_atomic_int_type LONG #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value) #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value) #ifdef __PYX_DEBUG_ATOMICS #pragma message ("Using MSVC atomics") #endif #elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0 #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value) #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value) #ifdef __PYX_DEBUG_ATOMICS #warning "Using Intel atomics" #endif #else #undef CYTHON_ATOMICS #define CYTHON_ATOMICS 0 #ifdef __PYX_DEBUG_ATOMICS #warning "Not using atomics" #endif #endif typedef volatile __pyx_atomic_int_type __pyx_atomic_int; #if CYTHON_ATOMICS #define __pyx_add_acquisition_count(memview)\ __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) #define __pyx_sub_acquisition_count(memview)\ __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) #else #define __pyx_add_acquisition_count(memview)\ __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) #define __pyx_sub_acquisition_count(memview)\ __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) #endif /* ForceInitThreads.proto */ #ifndef __PYX_FORCE_INIT_THREADS #define __PYX_FORCE_INIT_THREADS 0 #endif /* NoFastGil.proto */ #define __Pyx_PyGILState_Ensure PyGILState_Ensure #define __Pyx_PyGILState_Release PyGILState_Release #define __Pyx_FastGIL_Remember() #define __Pyx_FastGIL_Forget() #define __Pyx_FastGilFuncInit() /* BufferFormatStructs.proto */ #define IS_UNSIGNED(type) (((type) -1) > 0) struct __Pyx_StructField_; #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) typedef struct { const char* name; struct __Pyx_StructField_* fields; size_t size; size_t arraysize[8]; int ndim; char typegroup; char is_unsigned; int flags; } __Pyx_TypeInfo; typedef struct __Pyx_StructField_ { __Pyx_TypeInfo* type; const char* name; size_t offset; } __Pyx_StructField; typedef struct { __Pyx_StructField* field; size_t parent_offset; } __Pyx_BufFmt_StackElem; typedef struct { __Pyx_StructField root; __Pyx_BufFmt_StackElem* head; size_t fmt_offset; size_t new_count, enc_count; size_t struct_alignment; int is_complex; char enc_type; char new_packmode; char enc_packmode; char is_valid_array; } __Pyx_BufFmt_Context; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":776 * # in Cython to enable them only on the right systems. * * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t */ typedef npy_int8 __pyx_t_5numpy_int8_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":777 * * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t */ typedef npy_int16 __pyx_t_5numpy_int16_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":778 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< * ctypedef npy_int64 int64_t * #ctypedef npy_int96 int96_t */ typedef npy_int32 __pyx_t_5numpy_int32_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":779 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< * #ctypedef npy_int96 int96_t * #ctypedef npy_int128 int128_t */ typedef npy_int64 __pyx_t_5numpy_int64_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":783 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":784 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":785 * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< * ctypedef npy_uint64 uint64_t * #ctypedef npy_uint96 uint96_t */ typedef npy_uint32 __pyx_t_5numpy_uint32_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":786 * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< * #ctypedef npy_uint96 uint96_t * #ctypedef npy_uint128 uint128_t */ typedef npy_uint64 __pyx_t_5numpy_uint64_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":790 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< * ctypedef npy_float64 float64_t * #ctypedef npy_float80 float80_t */ typedef npy_float32 __pyx_t_5numpy_float32_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":791 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< * #ctypedef npy_float80 float80_t * #ctypedef npy_float128 float128_t */ typedef npy_float64 __pyx_t_5numpy_float64_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":800 * # The int types are mapped a bit surprising -- * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t # <<<<<<<<<<<<<< * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t */ typedef npy_long __pyx_t_5numpy_int_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":801 * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< * ctypedef npy_longlong longlong_t * */ typedef npy_longlong __pyx_t_5numpy_long_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":802 * ctypedef npy_long int_t * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< * * ctypedef npy_ulong uint_t */ typedef npy_longlong __pyx_t_5numpy_longlong_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":804 * ctypedef npy_longlong longlong_t * * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t */ typedef npy_ulong __pyx_t_5numpy_uint_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":805 * * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulonglong_t * */ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":806 * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< * * ctypedef npy_intp intp_t */ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":808 * ctypedef npy_ulonglong ulonglong_t * * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< * ctypedef npy_uintp uintp_t * */ typedef npy_intp __pyx_t_5numpy_intp_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":809 * * ctypedef npy_intp intp_t * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< * * ctypedef npy_double float_t */ typedef npy_uintp __pyx_t_5numpy_uintp_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":811 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t */ typedef npy_double __pyx_t_5numpy_float_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":812 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< * ctypedef npy_longdouble longdouble_t * */ typedef npy_double __pyx_t_5numpy_double_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":813 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cfloat cfloat_t */ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; /* Declarations.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< double > __pyx_t_double_complex; #else typedef double _Complex __pyx_t_double_complex; #endif #else typedef struct { double real, imag; } __pyx_t_double_complex; #endif static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); /* Declarations.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< float > __pyx_t_float_complex; #else typedef float _Complex __pyx_t_float_complex; #endif #else typedef struct { float real, imag; } __pyx_t_float_complex; #endif static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); /*--- Type declarations ---*/ struct __pyx_array_obj; struct __pyx_MemviewEnum_obj; struct __pyx_memoryview_obj; struct __pyx_memoryviewslice_obj; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":815 * ctypedef npy_longdouble longdouble_t * * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t */ typedef npy_cfloat __pyx_t_5numpy_cfloat_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":816 * * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< * ctypedef npy_clongdouble clongdouble_t * */ typedef npy_cdouble __pyx_t_5numpy_cdouble_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":817 * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cdouble complex_t */ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":819 * ctypedef npy_clongdouble clongdouble_t * * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew1(a): */ typedef npy_cdouble __pyx_t_5numpy_complex_t; /* "View.MemoryView":105 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< * * cdef: */ struct __pyx_array_obj { PyObject_HEAD struct __pyx_vtabstruct_array *__pyx_vtab; char *data; Py_ssize_t len; char *format; int ndim; Py_ssize_t *_shape; Py_ssize_t *_strides; Py_ssize_t itemsize; PyObject *mode; PyObject *_format; void (*callback_free_data)(void *); int free_data; int dtype_is_object; }; /* "View.MemoryView":279 * * @cname('__pyx_MemviewEnum') * cdef class Enum(object): # <<<<<<<<<<<<<< * cdef object name * def __init__(self, name): */ struct __pyx_MemviewEnum_obj { PyObject_HEAD PyObject *name; }; /* "View.MemoryView":330 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< * * cdef object obj */ struct __pyx_memoryview_obj { PyObject_HEAD struct __pyx_vtabstruct_memoryview *__pyx_vtab; PyObject *obj; PyObject *_size; PyObject *_array_interface; PyThread_type_lock lock; __pyx_atomic_int acquisition_count[2]; __pyx_atomic_int *acquisition_count_aligned_p; Py_buffer view; int flags; int dtype_is_object; __Pyx_TypeInfo *typeinfo; }; /* "View.MemoryView":965 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< * "Internal class for passing memoryview slices to Python" * */ struct __pyx_memoryviewslice_obj { struct __pyx_memoryview_obj __pyx_base; __Pyx_memviewslice from_slice; PyObject *from_object; PyObject *(*to_object_func)(char *); int (*to_dtype_func)(char *, PyObject *); }; /* "View.MemoryView":105 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< * * cdef: */ struct __pyx_vtabstruct_array { PyObject *(*get_memview)(struct __pyx_array_obj *); }; static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; /* "View.MemoryView":330 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< * * cdef object obj */ struct __pyx_vtabstruct_memoryview { char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *); PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *); PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *); PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *); PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *); }; static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; /* "View.MemoryView":965 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< * "Internal class for passing memoryview slices to Python" * */ struct __pyx_vtabstruct__memoryviewslice { struct __pyx_vtabstruct_memoryview __pyx_base; }; static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice; /* --- Runtime support code (head) --- */ /* Refnanny.proto */ #ifndef CYTHON_REFNANNY #define CYTHON_REFNANNY 0 #endif #if CYTHON_REFNANNY typedef struct { void (*INCREF)(void*, PyObject*, int); void (*DECREF)(void*, PyObject*, int); void (*GOTREF)(void*, PyObject*, int); void (*GIVEREF)(void*, PyObject*, int); void* (*SetupContext)(const char*, int, const char*); void (*FinishContext)(void**); } __Pyx_RefNannyAPIStruct; static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; #ifdef WITH_THREAD #define __Pyx_RefNannySetupContext(name, acquire_gil)\ if (acquire_gil) {\ PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ PyGILState_Release(__pyx_gilstate_save);\ } else {\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ } #else #define __Pyx_RefNannySetupContext(name, acquire_gil)\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) #endif #define __Pyx_RefNannyFinishContext()\ __Pyx_RefNanny->FinishContext(&__pyx_refnanny) #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) #else #define __Pyx_RefNannyDeclarations #define __Pyx_RefNannySetupContext(name, acquire_gil) #define __Pyx_RefNannyFinishContext() #define __Pyx_INCREF(r) Py_INCREF(r) #define __Pyx_DECREF(r) Py_DECREF(r) #define __Pyx_GOTREF(r) #define __Pyx_GIVEREF(r) #define __Pyx_XINCREF(r) Py_XINCREF(r) #define __Pyx_XDECREF(r) Py_XDECREF(r) #define __Pyx_XGOTREF(r) #define __Pyx_XGIVEREF(r) #endif #define __Pyx_XDECREF_SET(r, v) do {\ PyObject *tmp = (PyObject *) r;\ r = v; __Pyx_XDECREF(tmp);\ } while (0) #define __Pyx_DECREF_SET(r, v) do {\ PyObject *tmp = (PyObject *) r;\ r = v; __Pyx_DECREF(tmp);\ } while (0) #define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) #define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) /* PyObjectGetAttrStr.proto */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) #endif /* GetBuiltinName.proto */ static PyObject *__Pyx_GetBuiltinName(PyObject *name); /* PyDictVersioning.proto */ #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS #define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) #define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) #define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ (version_var) = __PYX_GET_DICT_VERSION(dict);\ (cache_var) = (value); #define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ static PY_UINT64_T __pyx_dict_version = 0;\ static PyObject *__pyx_dict_cached_value = NULL;\ if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ (VAR) = __pyx_dict_cached_value;\ } else {\ (VAR) = __pyx_dict_cached_value = (LOOKUP);\ __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ }\ } static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); #else #define __PYX_GET_DICT_VERSION(dict) (0) #define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) #define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); #endif /* GetModuleGlobalName.proto */ #if CYTHON_USE_DICT_VERSIONS #define __Pyx_GetModuleGlobalName(var, name) {\ static PY_UINT64_T __pyx_dict_version = 0;\ static PyObject *__pyx_dict_cached_value = NULL;\ (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ } #define __Pyx_GetModuleGlobalNameUncached(var, name) {\ PY_UINT64_T __pyx_dict_version;\ PyObject *__pyx_dict_cached_value;\ (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ } static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); #else #define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) #define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); #endif /* PyObjectCall.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); #else #define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) #endif /* MemviewSliceInit.proto */ #define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d #define __Pyx_MEMVIEW_DIRECT 1 #define __Pyx_MEMVIEW_PTR 2 #define __Pyx_MEMVIEW_FULL 4 #define __Pyx_MEMVIEW_CONTIG 8 #define __Pyx_MEMVIEW_STRIDED 16 #define __Pyx_MEMVIEW_FOLLOW 32 #define __Pyx_IS_C_CONTIG 1 #define __Pyx_IS_F_CONTIG 2 static int __Pyx_init_memviewslice( struct __pyx_memoryview_obj *memview, int ndim, __Pyx_memviewslice *memviewslice, int memview_is_new_reference); static CYTHON_INLINE int __pyx_add_acquisition_count_locked( __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); #define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p) #define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview)) #define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) #define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__) static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int); /* PyThreadStateGet.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; #define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; #define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type #else #define __Pyx_PyThreadState_declare #define __Pyx_PyThreadState_assign #define __Pyx_PyErr_Occurred() PyErr_Occurred() #endif /* PyErrFetchRestore.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) #define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) #define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) #define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) #define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #if CYTHON_COMPILING_IN_CPYTHON #define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) #else #define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) #endif #else #define __Pyx_PyErr_Clear() PyErr_Clear() #define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) #define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) #define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) #define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) #endif /* WriteUnraisableException.proto */ static void __Pyx_WriteUnraisable(const char *name, int clineno, int lineno, const char *filename, int full_traceback, int nogil); /* PyCFunctionFastCall.proto */ #if CYTHON_FAST_PYCCALL static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); #else #define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) #endif /* PyFunctionFastCall.proto */ #if CYTHON_FAST_PYCALL #define __Pyx_PyFunction_FastCall(func, args, nargs)\ __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) #if 1 || PY_VERSION_HEX < 0x030600B1 static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); #else #define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) #endif #define __Pyx_BUILD_ASSERT_EXPR(cond)\ (sizeof(char [1 - 2*!(cond)]) - 1) #ifndef Py_MEMBER_SIZE #define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) #endif static size_t __pyx_pyframe_localsplus_offset = 0; #include "frameobject.h" #define __Pxy_PyFrame_Initialize_Offsets()\ ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) #define __Pyx_PyFrame_GetLocalsplus(frame)\ (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) #endif /* PyObjectCall2Args.proto */ static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2); /* PyObjectCallMethO.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); #endif /* PyObjectCallOneArg.proto */ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); /* RaiseException.proto */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /* DictGetItem.proto */ #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key); #define __Pyx_PyObject_Dict_GetItem(obj, name)\ (likely(PyDict_CheckExact(obj)) ?\ __Pyx_PyDict_GetItem(obj, name) : PyObject_GetItem(obj, name)) #else #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) #define __Pyx_PyObject_Dict_GetItem(obj, name) PyObject_GetItem(obj, name) #endif /* RaiseTooManyValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); /* RaiseNeedMoreValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); /* RaiseNoneIterError.proto */ static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); /* ExtTypeTest.proto */ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /* GetTopmostException.proto */ #if CYTHON_USE_EXC_INFO_STACK static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); #endif /* SaveResetException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); #else #define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) #define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) #endif /* PyErrExceptionMatches.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); #else #define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) #endif /* GetException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #else static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); #endif /* RaiseArgTupleInvalid.proto */ static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /* RaiseDoubleKeywords.proto */ static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /* ParseKeywords.proto */ static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ const char* function_name); /* ArgTypeTest.proto */ #define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\ __Pyx__ArgTypeTest(obj, type, name, exact)) static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); /* IncludeStringH.proto */ #include /* BytesEquals.proto */ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); /* UnicodeEquals.proto */ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); /* StrEquals.proto */ #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals #else #define __Pyx_PyString_Equals __Pyx_PyBytes_Equals #endif /* UnaryNegOverflows.proto */ #define UNARY_NEG_WOULD_OVERFLOW(x)\ (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ /* GetAttr.proto */ static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); /* GetItemInt.proto */ #define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ __Pyx_GetItemInt_Generic(o, to_py_func(i)))) #define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); #define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, int wraparound, int boundscheck); /* ObjectGetItem.proto */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key); #else #define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) #endif /* decode_c_string_utf16.proto */ static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) { int byteorder = 0; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) { int byteorder = -1; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) { int byteorder = 1; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } /* decode_c_string.proto */ static CYTHON_INLINE PyObject* __Pyx_decode_c_string( const char* cstring, Py_ssize_t start, Py_ssize_t stop, const char* encoding, const char* errors, PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); /* GetAttr3.proto */ static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); /* SwapException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #else static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); #endif /* Import.proto */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /* FastTypeChecks.proto */ #if CYTHON_COMPILING_IN_CPYTHON #define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); #else #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) #define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) #define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) #endif #define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ /* ListCompAppend.proto */ #if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len)) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); Py_SIZE(list) = len+1; return 0; } return PyList_Append(list, x); } #else #define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) #endif /* PyIntBinop.proto */ #if !CYTHON_COMPILING_IN_PYPY static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check); #else #define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\ (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) #endif /* ListExtend.proto */ static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) { #if CYTHON_COMPILING_IN_CPYTHON PyObject* none = _PyList_Extend((PyListObject*)L, v); if (unlikely(!none)) return -1; Py_DECREF(none); return 0; #else return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v); #endif } /* ListAppend.proto */ #if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); Py_SIZE(list) = len+1; return 0; } return PyList_Append(list, x); } #else #define __Pyx_PyList_Append(L,x) PyList_Append(L,x) #endif /* None.proto */ static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); /* ImportFrom.proto */ static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); /* HasAttr.proto */ static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); /* PyObject_GenericGetAttrNoDict.proto */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr #endif /* PyObject_GenericGetAttr.proto */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr #endif /* SetVTable.proto */ static int __Pyx_SetVtable(PyObject *dict, void *vtable); /* SetupReduce.proto */ static int __Pyx_setup_reduce(PyObject* type_obj); /* TypeImport.proto */ #ifndef __PYX_HAVE_RT_ImportType_proto #define __PYX_HAVE_RT_ImportType_proto enum __Pyx_ImportType_CheckSize { __Pyx_ImportType_CheckSize_Error = 0, __Pyx_ImportType_CheckSize_Warn = 1, __Pyx_ImportType_CheckSize_Ignore = 2 }; static PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size); #endif /* CLineInTraceback.proto */ #ifdef CYTHON_CLINE_IN_TRACEBACK #define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) #else static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); #endif /* CodeObjectCache.proto */ typedef struct { PyCodeObject* code_object; int code_line; } __Pyx_CodeObjectCacheEntry; struct __Pyx_CodeObjectCache { int count; int max_count; __Pyx_CodeObjectCacheEntry* entries; }; static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); static PyCodeObject *__pyx_find_code_object(int code_line); static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); /* AddTraceback.proto */ static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename); #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); static void __Pyx_ReleaseBuffer(Py_buffer *view); #else #define __Pyx_GetBuffer PyObject_GetBuffer #define __Pyx_ReleaseBuffer PyBuffer_Release #endif /* BufferStructDeclare.proto */ typedef struct { Py_ssize_t shape, strides, suboffsets; } __Pyx_Buf_DimInfo; typedef struct { size_t refcount; Py_buffer pybuffer; } __Pyx_Buffer; typedef struct { __Pyx_Buffer *rcbuffer; char *data; __Pyx_Buf_DimInfo diminfo[8]; } __Pyx_LocalBuf_ND; /* MemviewSliceIsContig.proto */ static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); /* OverlappingSlices.proto */ static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, __Pyx_memviewslice *slice2, int ndim, size_t itemsize); /* Capsule.proto */ static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); /* RealImag.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus #define __Pyx_CREAL(z) ((z).real()) #define __Pyx_CIMAG(z) ((z).imag()) #else #define __Pyx_CREAL(z) (__real__(z)) #define __Pyx_CIMAG(z) (__imag__(z)) #endif #else #define __Pyx_CREAL(z) ((z).real) #define __Pyx_CIMAG(z) ((z).imag) #endif #if defined(__cplusplus) && CYTHON_CCOMPLEX\ && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) #define __Pyx_SET_CREAL(z,x) ((z).real(x)) #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) #else #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) #endif /* Arithmetic.proto */ #if CYTHON_CCOMPLEX #define __Pyx_c_eq_double(a, b) ((a)==(b)) #define __Pyx_c_sum_double(a, b) ((a)+(b)) #define __Pyx_c_diff_double(a, b) ((a)-(b)) #define __Pyx_c_prod_double(a, b) ((a)*(b)) #define __Pyx_c_quot_double(a, b) ((a)/(b)) #define __Pyx_c_neg_double(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero_double(z) ((z)==(double)0) #define __Pyx_c_conj_double(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs_double(z) (::std::abs(z)) #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero_double(z) ((z)==0) #define __Pyx_c_conj_double(z) (conj(z)) #if 1 #define __Pyx_c_abs_double(z) (cabs(z)) #define __Pyx_c_pow_double(a, b) (cpow(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); #if 1 static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); #endif #endif /* None.proto */ static CYTHON_INLINE Py_ssize_t __Pyx_pow_Py_ssize_t(Py_ssize_t, Py_ssize_t); /* Arithmetic.proto */ #if CYTHON_CCOMPLEX #define __Pyx_c_eq_float(a, b) ((a)==(b)) #define __Pyx_c_sum_float(a, b) ((a)+(b)) #define __Pyx_c_diff_float(a, b) ((a)-(b)) #define __Pyx_c_prod_float(a, b) ((a)*(b)) #define __Pyx_c_quot_float(a, b) ((a)/(b)) #define __Pyx_c_neg_float(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero_float(z) ((z)==(float)0) #define __Pyx_c_conj_float(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs_float(z) (::std::abs(z)) #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero_float(z) ((z)==0) #define __Pyx_c_conj_float(z) (conjf(z)) #if 1 #define __Pyx_c_abs_float(z) (cabsf(z)) #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); #if 1 static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); #endif #endif /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); /* MemviewSliceCopyTemplate.proto */ static __Pyx_memviewslice __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, const char *mode, int ndim, size_t sizeof_dtype, int contig_flag, int dtype_is_object); /* CIntFromPy.proto */ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); /* CIntFromPy.proto */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); /* CIntFromPy.proto */ static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); /* IsLittleEndian.proto */ static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); /* BufferFormatCheck.proto */ static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type); /* TypeInfoCompare.proto */ static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); /* MemviewSliceValidateAndInit.proto */ static int __Pyx_ValidateAndInit_memviewslice( int *axes_specs, int c_or_f_flag, int buf_flags, int ndim, __Pyx_TypeInfo *dtype, __Pyx_BufFmt_StackElem stack[], __Pyx_memviewslice *memviewslice, PyObject *original_obj); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(PyObject *, int writable_flag); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_double(PyObject *, int writable_flag); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(PyObject *, int writable_flag); /* CheckBinaryVersion.proto */ static int __Pyx_check_binary_version(void); /* FunctionExport.proto */ static int __Pyx_ExportFunction(const char *name, void (*f)(void), const char *sig); /* InitStrings.proto */ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ /* Module declarations from 'openTSNE._matrix_mul' */ /* Module declarations from 'openTSNE' */ /* Module declarations from 'cpython.buffer' */ /* Module declarations from 'libc.string' */ /* Module declarations from 'libc.stdio' */ /* Module declarations from '__builtin__' */ /* Module declarations from 'cpython.type' */ static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; /* Module declarations from 'cpython' */ /* Module declarations from 'cpython.object' */ /* Module declarations from 'cpython.ref' */ /* Module declarations from 'cpython.mem' */ /* Module declarations from 'numpy' */ /* Module declarations from 'numpy' */ static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ /* Module declarations from 'openTSNE._matrix_mul.matrix_mul' */ static PyTypeObject *__pyx_array_type = 0; static PyTypeObject *__pyx_MemviewEnum_type = 0; static PyTypeObject *__pyx_memoryview_type = 0; static PyTypeObject *__pyx_memoryviewslice_type = 0; static PyObject *generic = 0; static PyObject *strided = 0; static PyObject *indirect = 0; static PyObject *contiguous = 0; static PyObject *indirect_contiguous = 0; static int __pyx_memoryview_thread_locks_used; static PyThread_type_lock __pyx_memoryview_thread_locks[8]; static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ static void *__pyx_align_pointer(void *, size_t); /*proto*/ static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ static PyObject *_unellipsify(PyObject *, int); /*proto*/ static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ static __Pyx_TypeInfo __Pyx_TypeInfo___pyx_t_double_complex = { "double complex", NULL, sizeof(__pyx_t_double_complex), { 0 }, 0, 'C', 0, 0 }; static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; #define __Pyx_MODULE_NAME "openTSNE._matrix_mul.matrix_mul" extern int __pyx_module_is_main_openTSNE___matrix_mul__matrix_mul; int __pyx_module_is_main_openTSNE___matrix_mul__matrix_mul = 0; /* Implementation of 'openTSNE._matrix_mul.matrix_mul' */ static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_RuntimeError; static PyObject *__pyx_builtin_ImportError; static PyObject *__pyx_builtin_MemoryError; static PyObject *__pyx_builtin_enumerate; static PyObject *__pyx_builtin_TypeError; static PyObject *__pyx_builtin_Ellipsis; static PyObject *__pyx_builtin_id; static PyObject *__pyx_builtin_IndexError; static const char __pyx_k_O[] = "O"; static const char __pyx_k_c[] = "c"; static const char __pyx_k_id[] = "id"; static const char __pyx_k_np[] = "np"; static const char __pyx_k_new[] = "__new__"; static const char __pyx_k_obj[] = "obj"; static const char __pyx_k_base[] = "base"; static const char __pyx_k_dict[] = "__dict__"; static const char __pyx_k_main[] = "__main__"; static const char __pyx_k_mode[] = "mode"; static const char __pyx_k_name[] = "name"; static const char __pyx_k_ndim[] = "ndim"; static const char __pyx_k_pack[] = "pack"; static const char __pyx_k_real[] = "real"; static const char __pyx_k_size[] = "size"; static const char __pyx_k_step[] = "step"; static const char __pyx_k_stop[] = "stop"; static const char __pyx_k_test[] = "__test__"; static const char __pyx_k_ASCII[] = "ASCII"; static const char __pyx_k_class[] = "__class__"; static const char __pyx_k_dtype[] = "dtype"; static const char __pyx_k_empty[] = "empty"; static const char __pyx_k_error[] = "error"; static const char __pyx_k_flags[] = "flags"; static const char __pyx_k_numpy[] = "numpy"; static const char __pyx_k_range[] = "range"; static const char __pyx_k_shape[] = "shape"; static const char __pyx_k_start[] = "start"; static const char __pyx_k_zeros[] = "zeros"; static const char __pyx_k_encode[] = "encode"; static const char __pyx_k_format[] = "format"; static const char __pyx_k_import[] = "__import__"; static const char __pyx_k_name_2[] = "__name__"; static const char __pyx_k_pickle[] = "pickle"; static const char __pyx_k_reduce[] = "__reduce__"; static const char __pyx_k_struct[] = "struct"; static const char __pyx_k_unpack[] = "unpack"; static const char __pyx_k_update[] = "update"; static const char __pyx_k_fortran[] = "fortran"; static const char __pyx_k_memview[] = "memview"; static const char __pyx_k_Ellipsis[] = "Ellipsis"; static const char __pyx_k_getstate[] = "__getstate__"; static const char __pyx_k_itemsize[] = "itemsize"; static const char __pyx_k_pyx_type[] = "__pyx_type"; static const char __pyx_k_setstate[] = "__setstate__"; static const char __pyx_k_TypeError[] = "TypeError"; static const char __pyx_k_enumerate[] = "enumerate"; static const char __pyx_k_pyx_state[] = "__pyx_state"; static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; static const char __pyx_k_IndexError[] = "IndexError"; static const char __pyx_k_ValueError[] = "ValueError"; static const char __pyx_k_pyx_result[] = "__pyx_result"; static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; static const char __pyx_k_ImportError[] = "ImportError"; static const char __pyx_k_MemoryError[] = "MemoryError"; static const char __pyx_k_PickleError[] = "PickleError"; static const char __pyx_k_RuntimeError[] = "RuntimeError"; static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; static const char __pyx_k_stringsource[] = "stringsource"; static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; static const char __pyx_k_strided_and_direct[] = ""; static const char __pyx_k_strided_and_indirect[] = ""; static const char __pyx_k_contiguous_and_direct[] = ""; static const char __pyx_k_MemoryView_of_r_object[] = ""; static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; static const char __pyx_k_contiguous_and_indirect[] = ""; static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'"; static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; static const char __pyx_k_strided_and_direct_or_indirect[] = ""; static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))"; static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; static const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous"; static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short."; static PyObject *__pyx_n_s_ASCII; static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; static PyObject *__pyx_kp_s_Cannot_index_with_type_s; static PyObject *__pyx_n_s_Ellipsis; static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; static PyObject *__pyx_kp_u_Format_string_allocated_too_shor; static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2; static PyObject *__pyx_n_s_ImportError; static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0; static PyObject *__pyx_n_s_IndexError; static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; static PyObject *__pyx_n_s_MemoryError; static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; static PyObject *__pyx_kp_s_MemoryView_of_r_object; static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor; static PyObject *__pyx_n_b_O; static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; static PyObject *__pyx_n_s_PickleError; static PyObject *__pyx_n_s_RuntimeError; static PyObject *__pyx_n_s_TypeError; static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; static PyObject *__pyx_n_s_ValueError; static PyObject *__pyx_n_s_View_MemoryView; static PyObject *__pyx_n_s_allocate_buffer; static PyObject *__pyx_n_s_base; static PyObject *__pyx_n_s_c; static PyObject *__pyx_n_u_c; static PyObject *__pyx_n_s_class; static PyObject *__pyx_n_s_cline_in_traceback; static PyObject *__pyx_kp_s_contiguous_and_direct; static PyObject *__pyx_kp_s_contiguous_and_indirect; static PyObject *__pyx_n_s_dict; static PyObject *__pyx_n_s_dtype; static PyObject *__pyx_n_s_dtype_is_object; static PyObject *__pyx_n_s_empty; static PyObject *__pyx_n_s_encode; static PyObject *__pyx_n_s_enumerate; static PyObject *__pyx_n_s_error; static PyObject *__pyx_n_s_flags; static PyObject *__pyx_n_s_format; static PyObject *__pyx_n_s_fortran; static PyObject *__pyx_n_u_fortran; static PyObject *__pyx_n_s_getstate; static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi; static PyObject *__pyx_n_s_id; static PyObject *__pyx_n_s_import; static PyObject *__pyx_n_s_itemsize; static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; static PyObject *__pyx_n_s_main; static PyObject *__pyx_n_s_memview; static PyObject *__pyx_n_s_mode; static PyObject *__pyx_n_s_name; static PyObject *__pyx_n_s_name_2; static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous; static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou; static PyObject *__pyx_n_s_ndim; static PyObject *__pyx_n_s_new; static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; static PyObject *__pyx_n_s_np; static PyObject *__pyx_n_s_numpy; static PyObject *__pyx_kp_u_numpy_core_multiarray_failed_to; static PyObject *__pyx_kp_u_numpy_core_umath_failed_to_impor; static PyObject *__pyx_n_s_obj; static PyObject *__pyx_n_s_pack; static PyObject *__pyx_n_s_pickle; static PyObject *__pyx_n_s_pyx_PickleError; static PyObject *__pyx_n_s_pyx_checksum; static PyObject *__pyx_n_s_pyx_getbuffer; static PyObject *__pyx_n_s_pyx_result; static PyObject *__pyx_n_s_pyx_state; static PyObject *__pyx_n_s_pyx_type; static PyObject *__pyx_n_s_pyx_unpickle_Enum; static PyObject *__pyx_n_s_pyx_vtable; static PyObject *__pyx_n_s_range; static PyObject *__pyx_n_s_real; static PyObject *__pyx_n_s_reduce; static PyObject *__pyx_n_s_reduce_cython; static PyObject *__pyx_n_s_reduce_ex; static PyObject *__pyx_n_s_setstate; static PyObject *__pyx_n_s_setstate_cython; static PyObject *__pyx_n_s_shape; static PyObject *__pyx_n_s_size; static PyObject *__pyx_n_s_start; static PyObject *__pyx_n_s_step; static PyObject *__pyx_n_s_stop; static PyObject *__pyx_kp_s_strided_and_direct; static PyObject *__pyx_kp_s_strided_and_direct_or_indirect; static PyObject *__pyx_kp_s_strided_and_indirect; static PyObject *__pyx_kp_s_stringsource; static PyObject *__pyx_n_s_struct; static PyObject *__pyx_n_s_test; static PyObject *__pyx_kp_s_unable_to_allocate_array_data; static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd; static PyObject *__pyx_n_s_unpack; static PyObject *__pyx_n_s_update; static PyObject *__pyx_n_s_zeros; static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */ static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_int_0; static PyObject *__pyx_int_1; static PyObject *__pyx_int_184977713; static PyObject *__pyx_int_neg_1; static PyObject *__pyx_tuple_; static PyObject *__pyx_tuple__2; static PyObject *__pyx_tuple__3; static PyObject *__pyx_tuple__4; static PyObject *__pyx_tuple__5; static PyObject *__pyx_tuple__6; static PyObject *__pyx_tuple__7; static PyObject *__pyx_tuple__8; static PyObject *__pyx_tuple__9; static PyObject *__pyx_slice__22; static PyObject *__pyx_tuple__10; static PyObject *__pyx_tuple__11; static PyObject *__pyx_tuple__12; static PyObject *__pyx_tuple__13; static PyObject *__pyx_tuple__14; static PyObject *__pyx_tuple__15; static PyObject *__pyx_tuple__16; static PyObject *__pyx_tuple__17; static PyObject *__pyx_tuple__18; static PyObject *__pyx_tuple__19; static PyObject *__pyx_tuple__20; static PyObject *__pyx_tuple__21; static PyObject *__pyx_tuple__23; static PyObject *__pyx_tuple__24; static PyObject *__pyx_tuple__25; static PyObject *__pyx_tuple__26; static PyObject *__pyx_tuple__27; static PyObject *__pyx_tuple__28; static PyObject *__pyx_tuple__29; static PyObject *__pyx_tuple__30; static PyObject *__pyx_tuple__31; static PyObject *__pyx_codeobj__32; /* Late includes */ /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":33 * * * cdef void matrix_multiply_fft_1d( # <<<<<<<<<<<<<< * double[::1] kernel_tilde, * double[:, ::1] w_coefficients, */ static void __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_1d(__Pyx_memviewslice __pyx_v_kernel_tilde, __Pyx_memviewslice __pyx_v_w_coefficients, __Pyx_memviewslice __pyx_v_out) { Py_ssize_t __pyx_v_n_interpolation_points_1d; Py_ssize_t __pyx_v_n_terms; Py_ssize_t __pyx_v_n_fft_coeffs; __Pyx_memviewslice __pyx_v_fft_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_fft_w_coeffs = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_fft_in_buffer = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_fft_out_buffer = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_d; Py_ssize_t __pyx_v_i; fftw_plan __pyx_v_plan_dft; fftw_plan __pyx_v_plan_idft; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_t_6 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_7; Py_ssize_t __pyx_t_8; Py_ssize_t __pyx_t_9; Py_ssize_t __pyx_t_10; Py_ssize_t __pyx_t_11; Py_ssize_t __pyx_t_12; Py_ssize_t __pyx_t_13; Py_ssize_t __pyx_t_14; Py_ssize_t __pyx_t_15; Py_ssize_t __pyx_t_16; Py_ssize_t __pyx_t_17; Py_ssize_t __pyx_t_18; Py_ssize_t __pyx_t_19; Py_ssize_t __pyx_t_20; Py_ssize_t __pyx_t_21; Py_ssize_t __pyx_t_22; Py_ssize_t __pyx_t_23; Py_ssize_t __pyx_t_24; Py_ssize_t __pyx_t_25; double __pyx_t_26; Py_ssize_t __pyx_t_27; Py_ssize_t __pyx_t_28; __Pyx_RefNannySetupContext("matrix_multiply_fft_1d", 0); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":57 * """ * cdef: * Py_ssize_t n_interpolation_points_1d = w_coefficients.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t n_terms = w_coefficients.shape[1] * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] */ __pyx_v_n_interpolation_points_1d = (__pyx_v_w_coefficients.shape[0]); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":58 * cdef: * Py_ssize_t n_interpolation_points_1d = w_coefficients.shape[0] * Py_ssize_t n_terms = w_coefficients.shape[1] # <<<<<<<<<<<<<< * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] * */ __pyx_v_n_terms = (__pyx_v_w_coefficients.shape[1]); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":59 * Py_ssize_t n_interpolation_points_1d = w_coefficients.shape[0] * Py_ssize_t n_terms = w_coefficients.shape[1] * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] # <<<<<<<<<<<<<< * * complex[::1] fft_kernel_tilde = np.empty(n_fft_coeffs, dtype=complex) */ __pyx_v_n_fft_coeffs = (__pyx_v_kernel_tilde.shape[0]); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":61 * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] * * complex[::1] fft_kernel_tilde = np.empty(n_fft_coeffs, dtype=complex) # <<<<<<<<<<<<<< * complex[::1] fft_w_coeffs = np.empty(n_fft_coeffs, dtype=complex) * # Note that we can't use the same buffer for the input and output since */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 61, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_empty); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 61, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 61, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 61, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 61, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_dtype, ((PyObject *)(&PyComplex_Type))) < 0) __PYX_ERR(0, 61, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 61, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 61, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_fft_kernel_tilde = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":62 * * complex[::1] fft_kernel_tilde = np.empty(n_fft_coeffs, dtype=complex) * complex[::1] fft_w_coeffs = np.empty(n_fft_coeffs, dtype=complex) # <<<<<<<<<<<<<< * # Note that we can't use the same buffer for the input and output since * # we only write to the first half of the vector - we'd need to */ __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_np); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_4, __pyx_n_s_empty); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (PyDict_SetItem(__pyx_t_4, __pyx_n_s_dtype, ((PyObject *)(&PyComplex_Type))) < 0) __PYX_ERR(0, 62, __pyx_L1_error) __pyx_t_2 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_3, __pyx_t_4); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(__pyx_t_2, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v_fft_w_coeffs = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":68 * # changed during the IDFT, so it's faster to use two buffers, at the * # cost of some memory * double[::1] fft_in_buffer = np.zeros(n_fft_coeffs, dtype=float) # <<<<<<<<<<<<<< * double[::1] fft_out_buffer = np.zeros(n_fft_coeffs, dtype=float) * */ __Pyx_GetModuleGlobalName(__pyx_t_2, __pyx_n_s_np); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 68, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_zeros); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 68, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 68, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 68, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 68, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (PyDict_SetItem(__pyx_t_2, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 68, __pyx_L1_error) __pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_4, __pyx_t_3, __pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 68, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_6 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_1, PyBUF_WRITABLE); if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 68, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_v_fft_in_buffer = __pyx_t_6; __pyx_t_6.memview = NULL; __pyx_t_6.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":69 * # cost of some memory * double[::1] fft_in_buffer = np.zeros(n_fft_coeffs, dtype=float) * double[::1] fft_out_buffer = np.zeros(n_fft_coeffs, dtype=float) # <<<<<<<<<<<<<< * * Py_ssize_t d, i */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 69, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_zeros); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 69, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 69, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 69, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 69, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 69, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 69, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_6 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 69, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_fft_out_buffer = __pyx_t_6; __pyx_t_6.memview = NULL; __pyx_t_6.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":77 * plan_dft = fftw_plan_dft_r2c_1d( * n_fft_coeffs, * &kernel_tilde[0], (&fft_kernel_tilde[0]), # <<<<<<<<<<<<<< * FFTW_ESTIMATE, * ) */ __pyx_t_7 = 0; __pyx_t_8 = 0; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":75 * # Compute the FFT of the kernel vector * cdef fftw_plan plan_dft, plan_idft * plan_dft = fftw_plan_dft_r2c_1d( # <<<<<<<<<<<<<< * n_fft_coeffs, * &kernel_tilde[0], (&fft_kernel_tilde[0]), */ __pyx_v_plan_dft = fftw_plan_dft_r2c_1d(__pyx_v_n_fft_coeffs, (&(*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_kernel_tilde.data) + __pyx_t_7)) )))), ((fftw_complex *)(&(*((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_kernel_tilde.data) + __pyx_t_8)) ))))), FFTW_ESTIMATE); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":80 * FFTW_ESTIMATE, * ) * fftw_execute(plan_dft) # <<<<<<<<<<<<<< * fftw_destroy_plan(plan_dft) * */ fftw_execute(__pyx_v_plan_dft); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":81 * ) * fftw_execute(plan_dft) * fftw_destroy_plan(plan_dft) # <<<<<<<<<<<<<< * * plan_dft = fftw_plan_dft_r2c_1d( */ fftw_destroy_plan(__pyx_v_plan_dft); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":85 * plan_dft = fftw_plan_dft_r2c_1d( * n_fft_coeffs, * &fft_in_buffer[0], (&fft_w_coeffs[0]), # <<<<<<<<<<<<<< * FFTW_ESTIMATE | FFTW_DESTROY_INPUT, * ) */ __pyx_t_9 = 0; __pyx_t_10 = 0; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":83 * fftw_destroy_plan(plan_dft) * * plan_dft = fftw_plan_dft_r2c_1d( # <<<<<<<<<<<<<< * n_fft_coeffs, * &fft_in_buffer[0], (&fft_w_coeffs[0]), */ __pyx_v_plan_dft = fftw_plan_dft_r2c_1d(__pyx_v_n_fft_coeffs, (&(*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_fft_in_buffer.data) + __pyx_t_9)) )))), ((fftw_complex *)(&(*((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_w_coeffs.data) + __pyx_t_10)) ))))), (FFTW_ESTIMATE | FFTW_DESTROY_INPUT)); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":90 * plan_idft = fftw_plan_dft_c2r_1d( * n_fft_coeffs, * (&fft_w_coeffs[0]), &fft_out_buffer[0], # <<<<<<<<<<<<<< * FFTW_ESTIMATE | FFTW_DESTROY_INPUT, * ) */ __pyx_t_11 = 0; __pyx_t_12 = 0; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":88 * FFTW_ESTIMATE | FFTW_DESTROY_INPUT, * ) * plan_idft = fftw_plan_dft_c2r_1d( # <<<<<<<<<<<<<< * n_fft_coeffs, * (&fft_w_coeffs[0]), &fft_out_buffer[0], */ __pyx_v_plan_idft = fftw_plan_dft_c2r_1d(__pyx_v_n_fft_coeffs, ((fftw_complex *)(&(*((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_w_coeffs.data) + __pyx_t_11)) ))))), (&(*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_fft_out_buffer.data) + __pyx_t_12)) )))), (FFTW_ESTIMATE | FFTW_DESTROY_INPUT)); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":94 * ) * * for d in range(n_terms): # <<<<<<<<<<<<<< * for i in range(n_interpolation_points_1d): * fft_in_buffer[i] = w_coefficients[i, d] */ __pyx_t_13 = __pyx_v_n_terms; __pyx_t_14 = __pyx_t_13; for (__pyx_t_15 = 0; __pyx_t_15 < __pyx_t_14; __pyx_t_15+=1) { __pyx_v_d = __pyx_t_15; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":95 * * for d in range(n_terms): * for i in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * fft_in_buffer[i] = w_coefficients[i, d] * */ __pyx_t_16 = __pyx_v_n_interpolation_points_1d; __pyx_t_17 = __pyx_t_16; for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) { __pyx_v_i = __pyx_t_18; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":96 * for d in range(n_terms): * for i in range(n_interpolation_points_1d): * fft_in_buffer[i] = w_coefficients[i, d] # <<<<<<<<<<<<<< * * fftw_execute(plan_dft) */ __pyx_t_19 = __pyx_v_i; __pyx_t_20 = __pyx_v_d; __pyx_t_21 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_fft_in_buffer.data) + __pyx_t_21)) )) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_w_coefficients.data + __pyx_t_19 * __pyx_v_w_coefficients.strides[0]) )) + __pyx_t_20)) ))); } /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":98 * fft_in_buffer[i] = w_coefficients[i, d] * * fftw_execute(plan_dft) # <<<<<<<<<<<<<< * * # Take the Hadamard product of two complex vectors */ fftw_execute(__pyx_v_plan_dft); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":101 * * # Take the Hadamard product of two complex vectors * for i in range(n_fft_coeffs): # <<<<<<<<<<<<<< * fft_w_coeffs[i] = fft_w_coeffs[i] * fft_kernel_tilde[i] * */ __pyx_t_16 = __pyx_v_n_fft_coeffs; __pyx_t_17 = __pyx_t_16; for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) { __pyx_v_i = __pyx_t_18; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":102 * # Take the Hadamard product of two complex vectors * for i in range(n_fft_coeffs): * fft_w_coeffs[i] = fft_w_coeffs[i] * fft_kernel_tilde[i] # <<<<<<<<<<<<<< * * fftw_execute(plan_idft) */ __pyx_t_22 = __pyx_v_i; __pyx_t_23 = __pyx_v_i; __pyx_t_24 = __pyx_v_i; *((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_w_coeffs.data) + __pyx_t_24)) )) = __Pyx_c_prod_double((*((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_w_coeffs.data) + __pyx_t_22)) ))), (*((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_kernel_tilde.data) + __pyx_t_23)) )))); } /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":104 * fft_w_coeffs[i] = fft_w_coeffs[i] * fft_kernel_tilde[i] * * fftw_execute(plan_idft) # <<<<<<<<<<<<<< * * for i in range(n_interpolation_points_1d): */ fftw_execute(__pyx_v_plan_idft); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":106 * fftw_execute(plan_idft) * * for i in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * # FFTW doesn't perform IDFT normalization, so we have to do it * # ourselves. This is done by multiplying the result with the number */ __pyx_t_16 = __pyx_v_n_interpolation_points_1d; __pyx_t_17 = __pyx_t_16; for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) { __pyx_v_i = __pyx_t_18; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":110 * # ourselves. This is done by multiplying the result with the number * # of points in the input * out[i, d] = fft_out_buffer[n_interpolation_points_1d + i].real / n_fft_coeffs # <<<<<<<<<<<<<< * * fftw_destroy_plan(plan_dft) */ __pyx_t_25 = (__pyx_v_n_interpolation_points_1d + __pyx_v_i); __pyx_t_4 = PyFloat_FromDouble((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_fft_out_buffer.data) + __pyx_t_25)) )))); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 110, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_4, __pyx_n_s_real); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 110, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 110, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = __Pyx_PyNumber_Divide(__pyx_t_1, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 110, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_26 = __pyx_PyFloat_AsDouble(__pyx_t_3); if (unlikely((__pyx_t_26 == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 110, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_27 = __pyx_v_i; __pyx_t_28 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_out.data + __pyx_t_27 * __pyx_v_out.strides[0]) )) + __pyx_t_28)) )) = __pyx_t_26; } } /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":112 * out[i, d] = fft_out_buffer[n_interpolation_points_1d + i].real / n_fft_coeffs * * fftw_destroy_plan(plan_dft) # <<<<<<<<<<<<<< * fftw_destroy_plan(plan_idft) * */ fftw_destroy_plan(__pyx_v_plan_dft); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":113 * * fftw_destroy_plan(plan_dft) * fftw_destroy_plan(plan_idft) # <<<<<<<<<<<<<< * * */ fftw_destroy_plan(__pyx_v_plan_idft); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":33 * * * cdef void matrix_multiply_fft_1d( # <<<<<<<<<<<<<< * double[::1] kernel_tilde, * double[:, ::1] w_coefficients, */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_6, 1); __Pyx_WriteUnraisable("openTSNE._matrix_mul.matrix_mul.matrix_multiply_fft_1d", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_fft_kernel_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_fft_w_coeffs, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_fft_in_buffer, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_fft_out_buffer, 1); __Pyx_RefNannyFinishContext(); } /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":116 * * * cdef void matrix_multiply_fft_2d( # <<<<<<<<<<<<<< * double[:, ::1] kernel_tilde, * double[:, ::1] w_coefficients, */ static void __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_2d(__Pyx_memviewslice __pyx_v_kernel_tilde, __Pyx_memviewslice __pyx_v_w_coefficients, __Pyx_memviewslice __pyx_v_out) { CYTHON_UNUSED Py_ssize_t __pyx_v_total_interpolation_points; Py_ssize_t __pyx_v_n_terms; Py_ssize_t __pyx_v_n_fft_coeffs; Py_ssize_t __pyx_v_n_interpolation_points_1d; fftw_plan __pyx_v_plan_dft; fftw_plan __pyx_v_plan_idft; __Pyx_memviewslice __pyx_v_fft_w_coefficients = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_fft_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_fft_in_buffer = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_fft_out_buffer = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_d; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; Py_ssize_t __pyx_v_idx; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } }; PyObject *__pyx_t_6 = NULL; __Pyx_memviewslice __pyx_t_7 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_8; Py_ssize_t __pyx_t_9; Py_ssize_t __pyx_t_10; Py_ssize_t __pyx_t_11; Py_ssize_t __pyx_t_12; Py_ssize_t __pyx_t_13; Py_ssize_t __pyx_t_14; Py_ssize_t __pyx_t_15; Py_ssize_t __pyx_t_16; Py_ssize_t __pyx_t_17; Py_ssize_t __pyx_t_18; Py_ssize_t __pyx_t_19; Py_ssize_t __pyx_t_20; Py_ssize_t __pyx_t_21; Py_ssize_t __pyx_t_22; Py_ssize_t __pyx_t_23; Py_ssize_t __pyx_t_24; Py_ssize_t __pyx_t_25; Py_ssize_t __pyx_t_26; Py_ssize_t __pyx_t_27; Py_ssize_t __pyx_t_28; Py_ssize_t __pyx_t_29; Py_ssize_t __pyx_t_30; Py_ssize_t __pyx_t_31; Py_ssize_t __pyx_t_32; Py_ssize_t __pyx_t_33; Py_ssize_t __pyx_t_34; Py_ssize_t __pyx_t_35; Py_ssize_t __pyx_t_36; __Pyx_RefNannySetupContext("matrix_multiply_fft_2d", 0); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":140 * """ * cdef: * Py_ssize_t total_interpolation_points = w_coefficients.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t n_terms = w_coefficients.shape[1] * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] */ __pyx_v_total_interpolation_points = (__pyx_v_w_coefficients.shape[0]); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":141 * cdef: * Py_ssize_t total_interpolation_points = w_coefficients.shape[0] * Py_ssize_t n_terms = w_coefficients.shape[1] # <<<<<<<<<<<<<< * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] * Py_ssize_t n_interpolation_points_1d = n_fft_coeffs / 2 */ __pyx_v_n_terms = (__pyx_v_w_coefficients.shape[1]); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":142 * Py_ssize_t total_interpolation_points = w_coefficients.shape[0] * Py_ssize_t n_terms = w_coefficients.shape[1] * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t n_interpolation_points_1d = n_fft_coeffs / 2 * */ __pyx_v_n_fft_coeffs = (__pyx_v_kernel_tilde.shape[0]); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":143 * Py_ssize_t n_terms = w_coefficients.shape[1] * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] * Py_ssize_t n_interpolation_points_1d = n_fft_coeffs / 2 # <<<<<<<<<<<<<< * * fftw_plan plan_dft, plan_idft */ __pyx_v_n_interpolation_points_1d = (__pyx_v_n_fft_coeffs / 2); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":146 * * fftw_plan plan_dft, plan_idft * complex[::1] fft_w_coefficients = np.empty(n_fft_coeffs * (n_fft_coeffs / 2 + 1), dtype=complex) # <<<<<<<<<<<<<< * complex[::1] fft_kernel_tilde = np.empty(n_fft_coeffs * (n_fft_coeffs / 2 + 1), dtype=complex) * # Note that we can't use the same buffer for the input and output since */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 146, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_empty); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 146, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t((__pyx_v_n_fft_coeffs * ((__pyx_v_n_fft_coeffs / 2) + 1))); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 146, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 146, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 146, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_dtype, ((PyObject *)(&PyComplex_Type))) < 0) __PYX_ERR(0, 146, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 146, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 146, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_fft_w_coefficients = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":147 * fftw_plan plan_dft, plan_idft * complex[::1] fft_w_coefficients = np.empty(n_fft_coeffs * (n_fft_coeffs / 2 + 1), dtype=complex) * complex[::1] fft_kernel_tilde = np.empty(n_fft_coeffs * (n_fft_coeffs / 2 + 1), dtype=complex) # <<<<<<<<<<<<<< * # Note that we can't use the same buffer for the input and output since * # we only write to the top quadrant of the in matrix - we'd need to */ __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_np); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 147, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_4, __pyx_n_s_empty); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 147, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = PyInt_FromSsize_t((__pyx_v_n_fft_coeffs * ((__pyx_v_n_fft_coeffs / 2) + 1))); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 147, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 147, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 147, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (PyDict_SetItem(__pyx_t_4, __pyx_n_s_dtype, ((PyObject *)(&PyComplex_Type))) < 0) __PYX_ERR(0, 147, __pyx_L1_error) __pyx_t_2 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_3, __pyx_t_4); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 147, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(__pyx_t_2, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 147, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v_fft_kernel_tilde = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":153 * # changed during the IDFT, so it's faster to use two buffers, at the * # cost of some memory * double[:, ::1] fft_in_buffer = np.zeros((n_fft_coeffs, n_fft_coeffs)) # <<<<<<<<<<<<<< * double[:, ::1] fft_out_buffer = np.zeros((n_fft_coeffs, n_fft_coeffs)) * */ __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_np); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_4, __pyx_n_s_zeros); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_6 = PyTuple_New(2); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_6, 1, __pyx_t_1); __pyx_t_4 = 0; __pyx_t_1 = 0; __pyx_t_1 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_3))) { __pyx_t_1 = PyMethod_GET_SELF(__pyx_t_3); if (likely(__pyx_t_1)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_3); __Pyx_INCREF(__pyx_t_1); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_3, function); } } __pyx_t_2 = (__pyx_t_1) ? __Pyx_PyObject_Call2Args(__pyx_t_3, __pyx_t_1, __pyx_t_6) : __Pyx_PyObject_CallOneArg(__pyx_t_3, __pyx_t_6); __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_7 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_2, PyBUF_WRITABLE); if (unlikely(!__pyx_t_7.memview)) __PYX_ERR(0, 153, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v_fft_in_buffer = __pyx_t_7; __pyx_t_7.memview = NULL; __pyx_t_7.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":154 * # cost of some memory * double[:, ::1] fft_in_buffer = np.zeros((n_fft_coeffs, n_fft_coeffs)) * double[:, ::1] fft_out_buffer = np.zeros((n_fft_coeffs, n_fft_coeffs)) # <<<<<<<<<<<<<< * * Py_ssize_t d, i, j, idx */ __Pyx_GetModuleGlobalName(__pyx_t_3, __pyx_n_s_np); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 154, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_zeros); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 154, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 154, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 154, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 154, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_1); __pyx_t_3 = 0; __pyx_t_1 = 0; __pyx_t_1 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_6))) { __pyx_t_1 = PyMethod_GET_SELF(__pyx_t_6); if (likely(__pyx_t_1)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_6); __Pyx_INCREF(__pyx_t_1); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_6, function); } } __pyx_t_2 = (__pyx_t_1) ? __Pyx_PyObject_Call2Args(__pyx_t_6, __pyx_t_1, __pyx_t_4) : __Pyx_PyObject_CallOneArg(__pyx_t_6, __pyx_t_4); __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 154, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_7 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_2, PyBUF_WRITABLE); if (unlikely(!__pyx_t_7.memview)) __PYX_ERR(0, 154, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v_fft_out_buffer = __pyx_t_7; __pyx_t_7.memview = NULL; __pyx_t_7.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":160 * plan_dft = fftw_plan_dft_r2c_2d( * n_fft_coeffs, n_fft_coeffs, * &kernel_tilde[0, 0], (&fft_kernel_tilde[0]), # <<<<<<<<<<<<<< * FFTW_ESTIMATE, * ) */ __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":158 * Py_ssize_t d, i, j, idx * * plan_dft = fftw_plan_dft_r2c_2d( # <<<<<<<<<<<<<< * n_fft_coeffs, n_fft_coeffs, * &kernel_tilde[0, 0], (&fft_kernel_tilde[0]), */ __pyx_v_plan_dft = fftw_plan_dft_r2c_2d(__pyx_v_n_fft_coeffs, __pyx_v_n_fft_coeffs, (&(*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_kernel_tilde.data + __pyx_t_8 * __pyx_v_kernel_tilde.strides[0]) )) + __pyx_t_9)) )))), ((fftw_complex *)(&(*((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_kernel_tilde.data) + __pyx_t_10)) ))))), FFTW_ESTIMATE); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":163 * FFTW_ESTIMATE, * ) * fftw_execute(plan_dft) # <<<<<<<<<<<<<< * fftw_destroy_plan(plan_dft) * */ fftw_execute(__pyx_v_plan_dft); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":164 * ) * fftw_execute(plan_dft) * fftw_destroy_plan(plan_dft) # <<<<<<<<<<<<<< * * plan_dft = fftw_plan_dft_r2c_2d( */ fftw_destroy_plan(__pyx_v_plan_dft); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":168 * plan_dft = fftw_plan_dft_r2c_2d( * n_fft_coeffs, n_fft_coeffs, * &fft_in_buffer[0, 0], (&fft_w_coefficients[0]), # <<<<<<<<<<<<<< * FFTW_ESTIMATE | FFTW_DESTROY_INPUT, * ) */ __pyx_t_11 = 0; __pyx_t_12 = 0; __pyx_t_13 = 0; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":166 * fftw_destroy_plan(plan_dft) * * plan_dft = fftw_plan_dft_r2c_2d( # <<<<<<<<<<<<<< * n_fft_coeffs, n_fft_coeffs, * &fft_in_buffer[0, 0], (&fft_w_coefficients[0]), */ __pyx_v_plan_dft = fftw_plan_dft_r2c_2d(__pyx_v_n_fft_coeffs, __pyx_v_n_fft_coeffs, (&(*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_fft_in_buffer.data + __pyx_t_11 * __pyx_v_fft_in_buffer.strides[0]) )) + __pyx_t_12)) )))), ((fftw_complex *)(&(*((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_w_coefficients.data) + __pyx_t_13)) ))))), (FFTW_ESTIMATE | FFTW_DESTROY_INPUT)); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":173 * plan_idft = fftw_plan_dft_c2r_2d( * n_fft_coeffs, n_fft_coeffs, * (&fft_w_coefficients[0]), &fft_out_buffer[0, 0], # <<<<<<<<<<<<<< * FFTW_ESTIMATE | FFTW_DESTROY_INPUT, * ) */ __pyx_t_14 = 0; __pyx_t_15 = 0; __pyx_t_16 = 0; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":171 * FFTW_ESTIMATE | FFTW_DESTROY_INPUT, * ) * plan_idft = fftw_plan_dft_c2r_2d( # <<<<<<<<<<<<<< * n_fft_coeffs, n_fft_coeffs, * (&fft_w_coefficients[0]), &fft_out_buffer[0, 0], */ __pyx_v_plan_idft = fftw_plan_dft_c2r_2d(__pyx_v_n_fft_coeffs, __pyx_v_n_fft_coeffs, ((fftw_complex *)(&(*((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_w_coefficients.data) + __pyx_t_14)) ))))), (&(*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_fft_out_buffer.data + __pyx_t_15 * __pyx_v_fft_out_buffer.strides[0]) )) + __pyx_t_16)) )))), (FFTW_ESTIMATE | FFTW_DESTROY_INPUT)); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":177 * ) * * for d in range(n_terms): # <<<<<<<<<<<<<< * for i in range(n_interpolation_points_1d): * for j in range(n_interpolation_points_1d): */ __pyx_t_17 = __pyx_v_n_terms; __pyx_t_18 = __pyx_t_17; for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) { __pyx_v_d = __pyx_t_19; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":178 * * for d in range(n_terms): * for i in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * for j in range(n_interpolation_points_1d): * fft_in_buffer[i, j] = w_coefficients[i * n_interpolation_points_1d + j, d] */ __pyx_t_20 = __pyx_v_n_interpolation_points_1d; __pyx_t_21 = __pyx_t_20; for (__pyx_t_22 = 0; __pyx_t_22 < __pyx_t_21; __pyx_t_22+=1) { __pyx_v_i = __pyx_t_22; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":179 * for d in range(n_terms): * for i in range(n_interpolation_points_1d): * for j in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * fft_in_buffer[i, j] = w_coefficients[i * n_interpolation_points_1d + j, d] * */ __pyx_t_23 = __pyx_v_n_interpolation_points_1d; __pyx_t_24 = __pyx_t_23; for (__pyx_t_25 = 0; __pyx_t_25 < __pyx_t_24; __pyx_t_25+=1) { __pyx_v_j = __pyx_t_25; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":180 * for i in range(n_interpolation_points_1d): * for j in range(n_interpolation_points_1d): * fft_in_buffer[i, j] = w_coefficients[i * n_interpolation_points_1d + j, d] # <<<<<<<<<<<<<< * * fftw_execute(plan_dft) */ __pyx_t_26 = ((__pyx_v_i * __pyx_v_n_interpolation_points_1d) + __pyx_v_j); __pyx_t_27 = __pyx_v_d; __pyx_t_28 = __pyx_v_i; __pyx_t_29 = __pyx_v_j; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_fft_in_buffer.data + __pyx_t_28 * __pyx_v_fft_in_buffer.strides[0]) )) + __pyx_t_29)) )) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_w_coefficients.data + __pyx_t_26 * __pyx_v_w_coefficients.strides[0]) )) + __pyx_t_27)) ))); } } /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":182 * fft_in_buffer[i, j] = w_coefficients[i * n_interpolation_points_1d + j, d] * * fftw_execute(plan_dft) # <<<<<<<<<<<<<< * * # Take the Hadamard product of two complex vectors */ fftw_execute(__pyx_v_plan_dft); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":185 * * # Take the Hadamard product of two complex vectors * for i in range(n_fft_coeffs * (n_fft_coeffs / 2 + 1)): # <<<<<<<<<<<<<< * fft_w_coefficients[i] = fft_w_coefficients[i] * fft_kernel_tilde[i] * */ __pyx_t_20 = (__pyx_v_n_fft_coeffs * ((__pyx_v_n_fft_coeffs / 2) + 1)); __pyx_t_21 = __pyx_t_20; for (__pyx_t_22 = 0; __pyx_t_22 < __pyx_t_21; __pyx_t_22+=1) { __pyx_v_i = __pyx_t_22; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":186 * # Take the Hadamard product of two complex vectors * for i in range(n_fft_coeffs * (n_fft_coeffs / 2 + 1)): * fft_w_coefficients[i] = fft_w_coefficients[i] * fft_kernel_tilde[i] # <<<<<<<<<<<<<< * * # Invert the computed values at the interpolated nodes */ __pyx_t_30 = __pyx_v_i; __pyx_t_31 = __pyx_v_i; __pyx_t_32 = __pyx_v_i; *((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_w_coefficients.data) + __pyx_t_32)) )) = __Pyx_c_prod_double((*((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_w_coefficients.data) + __pyx_t_30)) ))), (*((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_kernel_tilde.data) + __pyx_t_31)) )))); } /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":189 * * # Invert the computed values at the interpolated nodes * fftw_execute(plan_idft) # <<<<<<<<<<<<<< * # FFTW doesn't perform IDFT normalization, so we have to do it * # ourselves. This is done by multiplying the result with the number of */ fftw_execute(__pyx_v_plan_idft); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":193 * # ourselves. This is done by multiplying the result with the number of * # points in the input * for i in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * for j in range(n_interpolation_points_1d): * idx = i * n_interpolation_points_1d + j */ __pyx_t_20 = __pyx_v_n_interpolation_points_1d; __pyx_t_21 = __pyx_t_20; for (__pyx_t_22 = 0; __pyx_t_22 < __pyx_t_21; __pyx_t_22+=1) { __pyx_v_i = __pyx_t_22; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":194 * # points in the input * for i in range(n_interpolation_points_1d): * for j in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * idx = i * n_interpolation_points_1d + j * out[idx, d] = fft_out_buffer[n_interpolation_points_1d + i, */ __pyx_t_23 = __pyx_v_n_interpolation_points_1d; __pyx_t_24 = __pyx_t_23; for (__pyx_t_25 = 0; __pyx_t_25 < __pyx_t_24; __pyx_t_25+=1) { __pyx_v_j = __pyx_t_25; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":195 * for i in range(n_interpolation_points_1d): * for j in range(n_interpolation_points_1d): * idx = i * n_interpolation_points_1d + j # <<<<<<<<<<<<<< * out[idx, d] = fft_out_buffer[n_interpolation_points_1d + i, * n_interpolation_points_1d + j] / n_fft_coeffs ** 2 */ __pyx_v_idx = ((__pyx_v_i * __pyx_v_n_interpolation_points_1d) + __pyx_v_j); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":196 * for j in range(n_interpolation_points_1d): * idx = i * n_interpolation_points_1d + j * out[idx, d] = fft_out_buffer[n_interpolation_points_1d + i, # <<<<<<<<<<<<<< * n_interpolation_points_1d + j] / n_fft_coeffs ** 2 * */ __pyx_t_33 = (__pyx_v_n_interpolation_points_1d + __pyx_v_i); __pyx_t_34 = (__pyx_v_n_interpolation_points_1d + __pyx_v_j); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":197 * idx = i * n_interpolation_points_1d + j * out[idx, d] = fft_out_buffer[n_interpolation_points_1d + i, * n_interpolation_points_1d + j] / n_fft_coeffs ** 2 # <<<<<<<<<<<<<< * * fftw_destroy_plan(plan_dft) */ __pyx_t_35 = __pyx_v_idx; __pyx_t_36 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_out.data + __pyx_t_35 * __pyx_v_out.strides[0]) )) + __pyx_t_36)) )) = ((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_fft_out_buffer.data + __pyx_t_33 * __pyx_v_fft_out_buffer.strides[0]) )) + __pyx_t_34)) ))) / ((double)__Pyx_pow_Py_ssize_t(__pyx_v_n_fft_coeffs, 2))); } } } /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":199 * n_interpolation_points_1d + j] / n_fft_coeffs ** 2 * * fftw_destroy_plan(plan_dft) # <<<<<<<<<<<<<< * fftw_destroy_plan(plan_idft) */ fftw_destroy_plan(__pyx_v_plan_dft); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":200 * * fftw_destroy_plan(plan_dft) * fftw_destroy_plan(plan_idft) # <<<<<<<<<<<<<< */ fftw_destroy_plan(__pyx_v_plan_idft); /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":116 * * * cdef void matrix_multiply_fft_2d( # <<<<<<<<<<<<<< * double[:, ::1] kernel_tilde, * double[:, ::1] w_coefficients, */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1); __Pyx_XDECREF(__pyx_t_6); __PYX_XDEC_MEMVIEW(&__pyx_t_7, 1); __Pyx_WriteUnraisable("openTSNE._matrix_mul.matrix_mul.matrix_multiply_fft_2d", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_fft_w_coefficients, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_fft_kernel_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_fft_in_buffer, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_fft_out_buffer, 1); __Pyx_RefNannyFinishContext(); } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":258 * # experimental exception made for __getbuffer__ and __releasebuffer__ * # -- the details of this may change. * def __getbuffer__(ndarray self, Py_buffer* info, int flags): # <<<<<<<<<<<<<< * # This implementation of getbuffer is geared towards Cython * # requirements, and does not yet fulfill the PEP. */ /* Python wrapper */ static CYTHON_UNUSED int __pyx_pw_5numpy_7ndarray_1__getbuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static CYTHON_UNUSED int __pyx_pw_5numpy_7ndarray_1__getbuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0); __pyx_r = __pyx_pf_5numpy_7ndarray___getbuffer__(((PyArrayObject *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_v_i; int __pyx_v_ndim; int __pyx_v_endian_detector; int __pyx_v_little_endian; int __pyx_v_t; char *__pyx_v_f; PyArray_Descr *__pyx_v_descr = 0; int __pyx_v_offset; int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; int __pyx_t_5; int __pyx_t_6; PyArray_Descr *__pyx_t_7; PyObject *__pyx_t_8 = NULL; char *__pyx_t_9; if (__pyx_v_info == NULL) { PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete"); return -1; } __Pyx_RefNannySetupContext("__getbuffer__", 0); __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None); __Pyx_GIVEREF(__pyx_v_info->obj); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":265 * * cdef int i, ndim * cdef int endian_detector = 1 # <<<<<<<<<<<<<< * cdef bint little_endian = ((&endian_detector)[0] != 0) * */ __pyx_v_endian_detector = 1; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":266 * cdef int i, ndim * cdef int endian_detector = 1 * cdef bint little_endian = ((&endian_detector)[0] != 0) # <<<<<<<<<<<<<< * * ndim = PyArray_NDIM(self) */ __pyx_v_little_endian = ((((char *)(&__pyx_v_endian_detector))[0]) != 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":268 * cdef bint little_endian = ((&endian_detector)[0] != 0) * * ndim = PyArray_NDIM(self) # <<<<<<<<<<<<<< * * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) */ __pyx_v_ndim = PyArray_NDIM(__pyx_v_self); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":270 * ndim = PyArray_NDIM(self) * * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) # <<<<<<<<<<<<<< * and not PyArray_CHKFLAGS(self, NPY_ARRAY_C_CONTIGUOUS)): * raise ValueError(u"ndarray is not C contiguous") */ __pyx_t_2 = (((__pyx_v_flags & PyBUF_C_CONTIGUOUS) == PyBUF_C_CONTIGUOUS) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":271 * * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_ARRAY_C_CONTIGUOUS)): # <<<<<<<<<<<<<< * raise ValueError(u"ndarray is not C contiguous") * */ __pyx_t_2 = ((!(PyArray_CHKFLAGS(__pyx_v_self, NPY_ARRAY_C_CONTIGUOUS) != 0)) != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":270 * ndim = PyArray_NDIM(self) * * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) # <<<<<<<<<<<<<< * and not PyArray_CHKFLAGS(self, NPY_ARRAY_C_CONTIGUOUS)): * raise ValueError(u"ndarray is not C contiguous") */ if (unlikely(__pyx_t_1)) { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":272 * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_ARRAY_C_CONTIGUOUS)): * raise ValueError(u"ndarray is not C contiguous") # <<<<<<<<<<<<<< * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 272, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 272, __pyx_L1_error) /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":270 * ndim = PyArray_NDIM(self) * * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) # <<<<<<<<<<<<<< * and not PyArray_CHKFLAGS(self, NPY_ARRAY_C_CONTIGUOUS)): * raise ValueError(u"ndarray is not C contiguous") */ } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":274 * raise ValueError(u"ndarray is not C contiguous") * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) # <<<<<<<<<<<<<< * and not PyArray_CHKFLAGS(self, NPY_ARRAY_F_CONTIGUOUS)): * raise ValueError(u"ndarray is not Fortran contiguous") */ __pyx_t_2 = (((__pyx_v_flags & PyBUF_F_CONTIGUOUS) == PyBUF_F_CONTIGUOUS) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L7_bool_binop_done; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":275 * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_ARRAY_F_CONTIGUOUS)): # <<<<<<<<<<<<<< * raise ValueError(u"ndarray is not Fortran contiguous") * */ __pyx_t_2 = ((!(PyArray_CHKFLAGS(__pyx_v_self, NPY_ARRAY_F_CONTIGUOUS) != 0)) != 0); __pyx_t_1 = __pyx_t_2; __pyx_L7_bool_binop_done:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":274 * raise ValueError(u"ndarray is not C contiguous") * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) # <<<<<<<<<<<<<< * and not PyArray_CHKFLAGS(self, NPY_ARRAY_F_CONTIGUOUS)): * raise ValueError(u"ndarray is not Fortran contiguous") */ if (unlikely(__pyx_t_1)) { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":276 * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_ARRAY_F_CONTIGUOUS)): * raise ValueError(u"ndarray is not Fortran contiguous") # <<<<<<<<<<<<<< * * info.buf = PyArray_DATA(self) */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__2, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 276, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 276, __pyx_L1_error) /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":274 * raise ValueError(u"ndarray is not C contiguous") * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) # <<<<<<<<<<<<<< * and not PyArray_CHKFLAGS(self, NPY_ARRAY_F_CONTIGUOUS)): * raise ValueError(u"ndarray is not Fortran contiguous") */ } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":278 * raise ValueError(u"ndarray is not Fortran contiguous") * * info.buf = PyArray_DATA(self) # <<<<<<<<<<<<<< * info.ndim = ndim * if sizeof(npy_intp) != sizeof(Py_ssize_t): */ __pyx_v_info->buf = PyArray_DATA(__pyx_v_self); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":279 * * info.buf = PyArray_DATA(self) * info.ndim = ndim # <<<<<<<<<<<<<< * if sizeof(npy_intp) != sizeof(Py_ssize_t): * # Allocate new buffer for strides and shape info. */ __pyx_v_info->ndim = __pyx_v_ndim; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":280 * info.buf = PyArray_DATA(self) * info.ndim = ndim * if sizeof(npy_intp) != sizeof(Py_ssize_t): # <<<<<<<<<<<<<< * # Allocate new buffer for strides and shape info. * # This is allocated as one block, strides first. */ __pyx_t_1 = (((sizeof(npy_intp)) != (sizeof(Py_ssize_t))) != 0); if (__pyx_t_1) { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":283 * # Allocate new buffer for strides and shape info. * # This is allocated as one block, strides first. * info.strides = PyObject_Malloc(sizeof(Py_ssize_t) * 2 * ndim) # <<<<<<<<<<<<<< * info.shape = info.strides + ndim * for i in range(ndim): */ __pyx_v_info->strides = ((Py_ssize_t *)PyObject_Malloc((((sizeof(Py_ssize_t)) * 2) * ((size_t)__pyx_v_ndim)))); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":284 * # This is allocated as one block, strides first. * info.strides = PyObject_Malloc(sizeof(Py_ssize_t) * 2 * ndim) * info.shape = info.strides + ndim # <<<<<<<<<<<<<< * for i in range(ndim): * info.strides[i] = PyArray_STRIDES(self)[i] */ __pyx_v_info->shape = (__pyx_v_info->strides + __pyx_v_ndim); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":285 * info.strides = PyObject_Malloc(sizeof(Py_ssize_t) * 2 * ndim) * info.shape = info.strides + ndim * for i in range(ndim): # <<<<<<<<<<<<<< * info.strides[i] = PyArray_STRIDES(self)[i] * info.shape[i] = PyArray_DIMS(self)[i] */ __pyx_t_4 = __pyx_v_ndim; __pyx_t_5 = __pyx_t_4; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":286 * info.shape = info.strides + ndim * for i in range(ndim): * info.strides[i] = PyArray_STRIDES(self)[i] # <<<<<<<<<<<<<< * info.shape[i] = PyArray_DIMS(self)[i] * else: */ (__pyx_v_info->strides[__pyx_v_i]) = (PyArray_STRIDES(__pyx_v_self)[__pyx_v_i]); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":287 * for i in range(ndim): * info.strides[i] = PyArray_STRIDES(self)[i] * info.shape[i] = PyArray_DIMS(self)[i] # <<<<<<<<<<<<<< * else: * info.strides = PyArray_STRIDES(self) */ (__pyx_v_info->shape[__pyx_v_i]) = (PyArray_DIMS(__pyx_v_self)[__pyx_v_i]); } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":280 * info.buf = PyArray_DATA(self) * info.ndim = ndim * if sizeof(npy_intp) != sizeof(Py_ssize_t): # <<<<<<<<<<<<<< * # Allocate new buffer for strides and shape info. * # This is allocated as one block, strides first. */ goto __pyx_L9; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":289 * info.shape[i] = PyArray_DIMS(self)[i] * else: * info.strides = PyArray_STRIDES(self) # <<<<<<<<<<<<<< * info.shape = PyArray_DIMS(self) * info.suboffsets = NULL */ /*else*/ { __pyx_v_info->strides = ((Py_ssize_t *)PyArray_STRIDES(__pyx_v_self)); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":290 * else: * info.strides = PyArray_STRIDES(self) * info.shape = PyArray_DIMS(self) # <<<<<<<<<<<<<< * info.suboffsets = NULL * info.itemsize = PyArray_ITEMSIZE(self) */ __pyx_v_info->shape = ((Py_ssize_t *)PyArray_DIMS(__pyx_v_self)); } __pyx_L9:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":291 * info.strides = PyArray_STRIDES(self) * info.shape = PyArray_DIMS(self) * info.suboffsets = NULL # <<<<<<<<<<<<<< * info.itemsize = PyArray_ITEMSIZE(self) * info.readonly = not PyArray_ISWRITEABLE(self) */ __pyx_v_info->suboffsets = NULL; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":292 * info.shape = PyArray_DIMS(self) * info.suboffsets = NULL * info.itemsize = PyArray_ITEMSIZE(self) # <<<<<<<<<<<<<< * info.readonly = not PyArray_ISWRITEABLE(self) * */ __pyx_v_info->itemsize = PyArray_ITEMSIZE(__pyx_v_self); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":293 * info.suboffsets = NULL * info.itemsize = PyArray_ITEMSIZE(self) * info.readonly = not PyArray_ISWRITEABLE(self) # <<<<<<<<<<<<<< * * cdef int t */ __pyx_v_info->readonly = (!(PyArray_ISWRITEABLE(__pyx_v_self) != 0)); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":296 * * cdef int t * cdef char* f = NULL # <<<<<<<<<<<<<< * cdef dtype descr = PyArray_DESCR(self) * cdef int offset */ __pyx_v_f = NULL; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":297 * cdef int t * cdef char* f = NULL * cdef dtype descr = PyArray_DESCR(self) # <<<<<<<<<<<<<< * cdef int offset * */ __pyx_t_7 = PyArray_DESCR(__pyx_v_self); __pyx_t_3 = ((PyObject *)__pyx_t_7); __Pyx_INCREF(__pyx_t_3); __pyx_v_descr = ((PyArray_Descr *)__pyx_t_3); __pyx_t_3 = 0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":300 * cdef int offset * * info.obj = self # <<<<<<<<<<<<<< * * if not PyDataType_HASFIELDS(descr): */ __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = ((PyObject *)__pyx_v_self); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":302 * info.obj = self * * if not PyDataType_HASFIELDS(descr): # <<<<<<<<<<<<<< * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or */ __pyx_t_1 = ((!(PyDataType_HASFIELDS(__pyx_v_descr) != 0)) != 0); if (__pyx_t_1) { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":303 * * if not PyDataType_HASFIELDS(descr): * t = descr.type_num # <<<<<<<<<<<<<< * if ((descr.byteorder == c'>' and little_endian) or * (descr.byteorder == c'<' and not little_endian)): */ __pyx_t_4 = __pyx_v_descr->type_num; __pyx_v_t = __pyx_t_4; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":304 * if not PyDataType_HASFIELDS(descr): * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or # <<<<<<<<<<<<<< * (descr.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") */ __pyx_t_2 = ((__pyx_v_descr->byteorder == '>') != 0); if (!__pyx_t_2) { goto __pyx_L15_next_or; } else { } __pyx_t_2 = (__pyx_v_little_endian != 0); if (!__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L14_bool_binop_done; } __pyx_L15_next_or:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":305 * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or * (descr.byteorder == c'<' and not little_endian)): # <<<<<<<<<<<<<< * raise ValueError(u"Non-native byte order not supported") * if t == NPY_BYTE: f = "b" */ __pyx_t_2 = ((__pyx_v_descr->byteorder == '<') != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L14_bool_binop_done; } __pyx_t_2 = ((!(__pyx_v_little_endian != 0)) != 0); __pyx_t_1 = __pyx_t_2; __pyx_L14_bool_binop_done:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":304 * if not PyDataType_HASFIELDS(descr): * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or # <<<<<<<<<<<<<< * (descr.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") */ if (unlikely(__pyx_t_1)) { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":306 * if ((descr.byteorder == c'>' and little_endian) or * (descr.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") # <<<<<<<<<<<<<< * if t == NPY_BYTE: f = "b" * elif t == NPY_UBYTE: f = "B" */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 306, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 306, __pyx_L1_error) /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":304 * if not PyDataType_HASFIELDS(descr): * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or # <<<<<<<<<<<<<< * (descr.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") */ } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":307 * (descr.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") * if t == NPY_BYTE: f = "b" # <<<<<<<<<<<<<< * elif t == NPY_UBYTE: f = "B" * elif t == NPY_SHORT: f = "h" */ switch (__pyx_v_t) { case NPY_BYTE: __pyx_v_f = ((char *)"b"); break; case NPY_UBYTE: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":308 * raise ValueError(u"Non-native byte order not supported") * if t == NPY_BYTE: f = "b" * elif t == NPY_UBYTE: f = "B" # <<<<<<<<<<<<<< * elif t == NPY_SHORT: f = "h" * elif t == NPY_USHORT: f = "H" */ __pyx_v_f = ((char *)"B"); break; case NPY_SHORT: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":309 * if t == NPY_BYTE: f = "b" * elif t == NPY_UBYTE: f = "B" * elif t == NPY_SHORT: f = "h" # <<<<<<<<<<<<<< * elif t == NPY_USHORT: f = "H" * elif t == NPY_INT: f = "i" */ __pyx_v_f = ((char *)"h"); break; case NPY_USHORT: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":310 * elif t == NPY_UBYTE: f = "B" * elif t == NPY_SHORT: f = "h" * elif t == NPY_USHORT: f = "H" # <<<<<<<<<<<<<< * elif t == NPY_INT: f = "i" * elif t == NPY_UINT: f = "I" */ __pyx_v_f = ((char *)"H"); break; case NPY_INT: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":311 * elif t == NPY_SHORT: f = "h" * elif t == NPY_USHORT: f = "H" * elif t == NPY_INT: f = "i" # <<<<<<<<<<<<<< * elif t == NPY_UINT: f = "I" * elif t == NPY_LONG: f = "l" */ __pyx_v_f = ((char *)"i"); break; case NPY_UINT: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":312 * elif t == NPY_USHORT: f = "H" * elif t == NPY_INT: f = "i" * elif t == NPY_UINT: f = "I" # <<<<<<<<<<<<<< * elif t == NPY_LONG: f = "l" * elif t == NPY_ULONG: f = "L" */ __pyx_v_f = ((char *)"I"); break; case NPY_LONG: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":313 * elif t == NPY_INT: f = "i" * elif t == NPY_UINT: f = "I" * elif t == NPY_LONG: f = "l" # <<<<<<<<<<<<<< * elif t == NPY_ULONG: f = "L" * elif t == NPY_LONGLONG: f = "q" */ __pyx_v_f = ((char *)"l"); break; case NPY_ULONG: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":314 * elif t == NPY_UINT: f = "I" * elif t == NPY_LONG: f = "l" * elif t == NPY_ULONG: f = "L" # <<<<<<<<<<<<<< * elif t == NPY_LONGLONG: f = "q" * elif t == NPY_ULONGLONG: f = "Q" */ __pyx_v_f = ((char *)"L"); break; case NPY_LONGLONG: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":315 * elif t == NPY_LONG: f = "l" * elif t == NPY_ULONG: f = "L" * elif t == NPY_LONGLONG: f = "q" # <<<<<<<<<<<<<< * elif t == NPY_ULONGLONG: f = "Q" * elif t == NPY_FLOAT: f = "f" */ __pyx_v_f = ((char *)"q"); break; case NPY_ULONGLONG: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":316 * elif t == NPY_ULONG: f = "L" * elif t == NPY_LONGLONG: f = "q" * elif t == NPY_ULONGLONG: f = "Q" # <<<<<<<<<<<<<< * elif t == NPY_FLOAT: f = "f" * elif t == NPY_DOUBLE: f = "d" */ __pyx_v_f = ((char *)"Q"); break; case NPY_FLOAT: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":317 * elif t == NPY_LONGLONG: f = "q" * elif t == NPY_ULONGLONG: f = "Q" * elif t == NPY_FLOAT: f = "f" # <<<<<<<<<<<<<< * elif t == NPY_DOUBLE: f = "d" * elif t == NPY_LONGDOUBLE: f = "g" */ __pyx_v_f = ((char *)"f"); break; case NPY_DOUBLE: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":318 * elif t == NPY_ULONGLONG: f = "Q" * elif t == NPY_FLOAT: f = "f" * elif t == NPY_DOUBLE: f = "d" # <<<<<<<<<<<<<< * elif t == NPY_LONGDOUBLE: f = "g" * elif t == NPY_CFLOAT: f = "Zf" */ __pyx_v_f = ((char *)"d"); break; case NPY_LONGDOUBLE: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":319 * elif t == NPY_FLOAT: f = "f" * elif t == NPY_DOUBLE: f = "d" * elif t == NPY_LONGDOUBLE: f = "g" # <<<<<<<<<<<<<< * elif t == NPY_CFLOAT: f = "Zf" * elif t == NPY_CDOUBLE: f = "Zd" */ __pyx_v_f = ((char *)"g"); break; case NPY_CFLOAT: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":320 * elif t == NPY_DOUBLE: f = "d" * elif t == NPY_LONGDOUBLE: f = "g" * elif t == NPY_CFLOAT: f = "Zf" # <<<<<<<<<<<<<< * elif t == NPY_CDOUBLE: f = "Zd" * elif t == NPY_CLONGDOUBLE: f = "Zg" */ __pyx_v_f = ((char *)"Zf"); break; case NPY_CDOUBLE: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":321 * elif t == NPY_LONGDOUBLE: f = "g" * elif t == NPY_CFLOAT: f = "Zf" * elif t == NPY_CDOUBLE: f = "Zd" # <<<<<<<<<<<<<< * elif t == NPY_CLONGDOUBLE: f = "Zg" * elif t == NPY_OBJECT: f = "O" */ __pyx_v_f = ((char *)"Zd"); break; case NPY_CLONGDOUBLE: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":322 * elif t == NPY_CFLOAT: f = "Zf" * elif t == NPY_CDOUBLE: f = "Zd" * elif t == NPY_CLONGDOUBLE: f = "Zg" # <<<<<<<<<<<<<< * elif t == NPY_OBJECT: f = "O" * else: */ __pyx_v_f = ((char *)"Zg"); break; case NPY_OBJECT: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":323 * elif t == NPY_CDOUBLE: f = "Zd" * elif t == NPY_CLONGDOUBLE: f = "Zg" * elif t == NPY_OBJECT: f = "O" # <<<<<<<<<<<<<< * else: * raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t) */ __pyx_v_f = ((char *)"O"); break; default: /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":325 * elif t == NPY_OBJECT: f = "O" * else: * raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t) # <<<<<<<<<<<<<< * info.format = f * return */ __pyx_t_3 = __Pyx_PyInt_From_int(__pyx_v_t); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 325, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_8 = PyUnicode_Format(__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_t_3); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 325, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_8); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 325, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 325, __pyx_L1_error) break; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":326 * else: * raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t) * info.format = f # <<<<<<<<<<<<<< * return * else: */ __pyx_v_info->format = __pyx_v_f; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":327 * raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t) * info.format = f * return # <<<<<<<<<<<<<< * else: * info.format = PyObject_Malloc(_buffer_format_string_len) */ __pyx_r = 0; goto __pyx_L0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":302 * info.obj = self * * if not PyDataType_HASFIELDS(descr): # <<<<<<<<<<<<<< * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or */ } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":329 * return * else: * info.format = PyObject_Malloc(_buffer_format_string_len) # <<<<<<<<<<<<<< * info.format[0] = c'^' # Native data types, manual alignment * offset = 0 */ /*else*/ { __pyx_v_info->format = ((char *)PyObject_Malloc(0xFF)); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":330 * else: * info.format = PyObject_Malloc(_buffer_format_string_len) * info.format[0] = c'^' # Native data types, manual alignment # <<<<<<<<<<<<<< * offset = 0 * f = _util_dtypestring(descr, info.format + 1, */ (__pyx_v_info->format[0]) = '^'; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":331 * info.format = PyObject_Malloc(_buffer_format_string_len) * info.format[0] = c'^' # Native data types, manual alignment * offset = 0 # <<<<<<<<<<<<<< * f = _util_dtypestring(descr, info.format + 1, * info.format + _buffer_format_string_len, */ __pyx_v_offset = 0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":332 * info.format[0] = c'^' # Native data types, manual alignment * offset = 0 * f = _util_dtypestring(descr, info.format + 1, # <<<<<<<<<<<<<< * info.format + _buffer_format_string_len, * &offset) */ __pyx_t_9 = __pyx_f_5numpy__util_dtypestring(__pyx_v_descr, (__pyx_v_info->format + 1), (__pyx_v_info->format + 0xFF), (&__pyx_v_offset)); if (unlikely(__pyx_t_9 == ((char *)NULL))) __PYX_ERR(1, 332, __pyx_L1_error) __pyx_v_f = __pyx_t_9; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":335 * info.format + _buffer_format_string_len, * &offset) * f[0] = c'\0' # Terminate format string # <<<<<<<<<<<<<< * * def __releasebuffer__(ndarray self, Py_buffer* info): */ (__pyx_v_f[0]) = '\x00'; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":258 * # experimental exception made for __getbuffer__ and __releasebuffer__ * # -- the details of this may change. * def __getbuffer__(ndarray self, Py_buffer* info, int flags): # <<<<<<<<<<<<<< * # This implementation of getbuffer is geared towards Cython * # requirements, and does not yet fulfill the PEP. */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("numpy.ndarray.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; if (__pyx_v_info->obj != NULL) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } goto __pyx_L2; __pyx_L0:; if (__pyx_v_info->obj == Py_None) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } __pyx_L2:; __Pyx_XDECREF((PyObject *)__pyx_v_descr); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":337 * f[0] = c'\0' # Terminate format string * * def __releasebuffer__(ndarray self, Py_buffer* info): # <<<<<<<<<<<<<< * if PyArray_HASFIELDS(self): * PyObject_Free(info.format) */ /* Python wrapper */ static CYTHON_UNUSED void __pyx_pw_5numpy_7ndarray_3__releasebuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info); /*proto*/ static CYTHON_UNUSED void __pyx_pw_5numpy_7ndarray_3__releasebuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__releasebuffer__ (wrapper)", 0); __pyx_pf_5numpy_7ndarray_2__releasebuffer__(((PyArrayObject *)__pyx_v_self), ((Py_buffer *)__pyx_v_info)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info) { __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("__releasebuffer__", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":338 * * def __releasebuffer__(ndarray self, Py_buffer* info): * if PyArray_HASFIELDS(self): # <<<<<<<<<<<<<< * PyObject_Free(info.format) * if sizeof(npy_intp) != sizeof(Py_ssize_t): */ __pyx_t_1 = (PyArray_HASFIELDS(__pyx_v_self) != 0); if (__pyx_t_1) { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":339 * def __releasebuffer__(ndarray self, Py_buffer* info): * if PyArray_HASFIELDS(self): * PyObject_Free(info.format) # <<<<<<<<<<<<<< * if sizeof(npy_intp) != sizeof(Py_ssize_t): * PyObject_Free(info.strides) */ PyObject_Free(__pyx_v_info->format); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":338 * * def __releasebuffer__(ndarray self, Py_buffer* info): * if PyArray_HASFIELDS(self): # <<<<<<<<<<<<<< * PyObject_Free(info.format) * if sizeof(npy_intp) != sizeof(Py_ssize_t): */ } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":340 * if PyArray_HASFIELDS(self): * PyObject_Free(info.format) * if sizeof(npy_intp) != sizeof(Py_ssize_t): # <<<<<<<<<<<<<< * PyObject_Free(info.strides) * # info.shape was stored after info.strides in the same block */ __pyx_t_1 = (((sizeof(npy_intp)) != (sizeof(Py_ssize_t))) != 0); if (__pyx_t_1) { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":341 * PyObject_Free(info.format) * if sizeof(npy_intp) != sizeof(Py_ssize_t): * PyObject_Free(info.strides) # <<<<<<<<<<<<<< * # info.shape was stored after info.strides in the same block * */ PyObject_Free(__pyx_v_info->strides); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":340 * if PyArray_HASFIELDS(self): * PyObject_Free(info.format) * if sizeof(npy_intp) != sizeof(Py_ssize_t): # <<<<<<<<<<<<<< * PyObject_Free(info.strides) * # info.shape was stored after info.strides in the same block */ } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":337 * f[0] = c'\0' # Terminate format string * * def __releasebuffer__(ndarray self, Py_buffer* info): # <<<<<<<<<<<<<< * if PyArray_HASFIELDS(self): * PyObject_Free(info.format) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":821 * ctypedef npy_cdouble complex_t * * cdef inline object PyArray_MultiIterNew1(a): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(1, a) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *__pyx_v_a) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("PyArray_MultiIterNew1", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":822 * * cdef inline object PyArray_MultiIterNew1(a): * return PyArray_MultiIterNew(1, a) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew2(a, b): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(1, ((void *)__pyx_v_a)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 822, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":821 * ctypedef npy_cdouble complex_t * * cdef inline object PyArray_MultiIterNew1(a): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(1, a) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew1", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":824 * return PyArray_MultiIterNew(1, a) * * cdef inline object PyArray_MultiIterNew2(a, b): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(2, a, b) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *__pyx_v_a, PyObject *__pyx_v_b) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("PyArray_MultiIterNew2", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":825 * * cdef inline object PyArray_MultiIterNew2(a, b): * return PyArray_MultiIterNew(2, a, b) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew3(a, b, c): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(2, ((void *)__pyx_v_a), ((void *)__pyx_v_b)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 825, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":824 * return PyArray_MultiIterNew(1, a) * * cdef inline object PyArray_MultiIterNew2(a, b): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(2, a, b) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew2", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":827 * return PyArray_MultiIterNew(2, a, b) * * cdef inline object PyArray_MultiIterNew3(a, b, c): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(3, a, b, c) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("PyArray_MultiIterNew3", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":828 * * cdef inline object PyArray_MultiIterNew3(a, b, c): * return PyArray_MultiIterNew(3, a, b, c) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(3, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 828, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":827 * return PyArray_MultiIterNew(2, a, b) * * cdef inline object PyArray_MultiIterNew3(a, b, c): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(3, a, b, c) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew3", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":830 * return PyArray_MultiIterNew(3, a, b, c) * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(4, a, b, c, d) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c, PyObject *__pyx_v_d) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("PyArray_MultiIterNew4", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":831 * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): * return PyArray_MultiIterNew(4, a, b, c, d) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(4, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 831, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":830 * return PyArray_MultiIterNew(3, a, b, c) * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(4, a, b, c, d) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew4", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":833 * return PyArray_MultiIterNew(4, a, b, c, d) * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(5, a, b, c, d, e) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c, PyObject *__pyx_v_d, PyObject *__pyx_v_e) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("PyArray_MultiIterNew5", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":834 * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): * return PyArray_MultiIterNew(5, a, b, c, d, e) # <<<<<<<<<<<<<< * * cdef inline tuple PyDataType_SHAPE(dtype d): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(5, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d), ((void *)__pyx_v_e)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 834, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":833 * return PyArray_MultiIterNew(4, a, b, c, d) * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(5, a, b, c, d, e) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew5", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":836 * return PyArray_MultiIterNew(5, a, b, c, d, e) * * cdef inline tuple PyDataType_SHAPE(dtype d): # <<<<<<<<<<<<<< * if PyDataType_HASSUBARRAY(d): * return d.subarray.shape */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyDataType_SHAPE(PyArray_Descr *__pyx_v_d) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("PyDataType_SHAPE", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":837 * * cdef inline tuple PyDataType_SHAPE(dtype d): * if PyDataType_HASSUBARRAY(d): # <<<<<<<<<<<<<< * return d.subarray.shape * else: */ __pyx_t_1 = (PyDataType_HASSUBARRAY(__pyx_v_d) != 0); if (__pyx_t_1) { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":838 * cdef inline tuple PyDataType_SHAPE(dtype d): * if PyDataType_HASSUBARRAY(d): * return d.subarray.shape # <<<<<<<<<<<<<< * else: * return () */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject*)__pyx_v_d->subarray->shape)); __pyx_r = ((PyObject*)__pyx_v_d->subarray->shape); goto __pyx_L0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":837 * * cdef inline tuple PyDataType_SHAPE(dtype d): * if PyDataType_HASSUBARRAY(d): # <<<<<<<<<<<<<< * return d.subarray.shape * else: */ } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":840 * return d.subarray.shape * else: * return () # <<<<<<<<<<<<<< * * cdef inline char* _util_dtypestring(dtype descr, char* f, char* end, int* offset) except NULL: */ /*else*/ { __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_empty_tuple); __pyx_r = __pyx_empty_tuple; goto __pyx_L0; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":836 * return PyArray_MultiIterNew(5, a, b, c, d, e) * * cdef inline tuple PyDataType_SHAPE(dtype d): # <<<<<<<<<<<<<< * if PyDataType_HASSUBARRAY(d): * return d.subarray.shape */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":842 * return () * * cdef inline char* _util_dtypestring(dtype descr, char* f, char* end, int* offset) except NULL: # <<<<<<<<<<<<<< * # Recursive utility function used in __getbuffer__ to get format * # string. The new location in the format string is returned. */ static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *__pyx_v_descr, char *__pyx_v_f, char *__pyx_v_end, int *__pyx_v_offset) { PyArray_Descr *__pyx_v_child = 0; int __pyx_v_endian_detector; int __pyx_v_little_endian; PyObject *__pyx_v_fields = 0; PyObject *__pyx_v_childname = NULL; PyObject *__pyx_v_new_offset = NULL; PyObject *__pyx_v_t = NULL; char *__pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; Py_ssize_t __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; int __pyx_t_5; int __pyx_t_6; int __pyx_t_7; long __pyx_t_8; char *__pyx_t_9; __Pyx_RefNannySetupContext("_util_dtypestring", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":847 * * cdef dtype child * cdef int endian_detector = 1 # <<<<<<<<<<<<<< * cdef bint little_endian = ((&endian_detector)[0] != 0) * cdef tuple fields */ __pyx_v_endian_detector = 1; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":848 * cdef dtype child * cdef int endian_detector = 1 * cdef bint little_endian = ((&endian_detector)[0] != 0) # <<<<<<<<<<<<<< * cdef tuple fields * */ __pyx_v_little_endian = ((((char *)(&__pyx_v_endian_detector))[0]) != 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":851 * cdef tuple fields * * for childname in descr.names: # <<<<<<<<<<<<<< * fields = descr.fields[childname] * child, new_offset = fields */ if (unlikely(__pyx_v_descr->names == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); __PYX_ERR(1, 851, __pyx_L1_error) } __pyx_t_1 = __pyx_v_descr->names; __Pyx_INCREF(__pyx_t_1); __pyx_t_2 = 0; for (;;) { if (__pyx_t_2 >= PyTuple_GET_SIZE(__pyx_t_1)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_3 = PyTuple_GET_ITEM(__pyx_t_1, __pyx_t_2); __Pyx_INCREF(__pyx_t_3); __pyx_t_2++; if (unlikely(0 < 0)) __PYX_ERR(1, 851, __pyx_L1_error) #else __pyx_t_3 = PySequence_ITEM(__pyx_t_1, __pyx_t_2); __pyx_t_2++; if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 851, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); #endif __Pyx_XDECREF_SET(__pyx_v_childname, __pyx_t_3); __pyx_t_3 = 0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":852 * * for childname in descr.names: * fields = descr.fields[childname] # <<<<<<<<<<<<<< * child, new_offset = fields * */ if (unlikely(__pyx_v_descr->fields == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(1, 852, __pyx_L1_error) } __pyx_t_3 = __Pyx_PyDict_GetItem(__pyx_v_descr->fields, __pyx_v_childname); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 852, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (!(likely(PyTuple_CheckExact(__pyx_t_3))||((__pyx_t_3) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_t_3)->tp_name), 0))) __PYX_ERR(1, 852, __pyx_L1_error) __Pyx_XDECREF_SET(__pyx_v_fields, ((PyObject*)__pyx_t_3)); __pyx_t_3 = 0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":853 * for childname in descr.names: * fields = descr.fields[childname] * child, new_offset = fields # <<<<<<<<<<<<<< * * if (end - f) - (new_offset - offset[0]) < 15: */ if (likely(__pyx_v_fields != Py_None)) { PyObject* sequence = __pyx_v_fields; Py_ssize_t size = __Pyx_PySequence_SIZE(sequence); if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); __PYX_ERR(1, 853, __pyx_L1_error) } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_3 = PyTuple_GET_ITEM(sequence, 0); __pyx_t_4 = PyTuple_GET_ITEM(sequence, 1); __Pyx_INCREF(__pyx_t_3); __Pyx_INCREF(__pyx_t_4); #else __pyx_t_3 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 853, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 853, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); #endif } else { __Pyx_RaiseNoneNotIterableError(); __PYX_ERR(1, 853, __pyx_L1_error) } if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_ptype_5numpy_dtype))))) __PYX_ERR(1, 853, __pyx_L1_error) __Pyx_XDECREF_SET(__pyx_v_child, ((PyArray_Descr *)__pyx_t_3)); __pyx_t_3 = 0; __Pyx_XDECREF_SET(__pyx_v_new_offset, __pyx_t_4); __pyx_t_4 = 0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":855 * child, new_offset = fields * * if (end - f) - (new_offset - offset[0]) < 15: # <<<<<<<<<<<<<< * raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd") * */ __pyx_t_4 = __Pyx_PyInt_From_int((__pyx_v_offset[0])); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 855, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyNumber_Subtract(__pyx_v_new_offset, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 855, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_5 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_5 == (int)-1) && PyErr_Occurred())) __PYX_ERR(1, 855, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_6 = ((((__pyx_v_end - __pyx_v_f) - ((int)__pyx_t_5)) < 15) != 0); if (unlikely(__pyx_t_6)) { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":856 * * if (end - f) - (new_offset - offset[0]) < 15: * raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd") # <<<<<<<<<<<<<< * * if ((child.byteorder == c'>' and little_endian) or */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_tuple__4, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 856, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 856, __pyx_L1_error) /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":855 * child, new_offset = fields * * if (end - f) - (new_offset - offset[0]) < 15: # <<<<<<<<<<<<<< * raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd") * */ } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":858 * raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd") * * if ((child.byteorder == c'>' and little_endian) or # <<<<<<<<<<<<<< * (child.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") */ __pyx_t_7 = ((__pyx_v_child->byteorder == '>') != 0); if (!__pyx_t_7) { goto __pyx_L8_next_or; } else { } __pyx_t_7 = (__pyx_v_little_endian != 0); if (!__pyx_t_7) { } else { __pyx_t_6 = __pyx_t_7; goto __pyx_L7_bool_binop_done; } __pyx_L8_next_or:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":859 * * if ((child.byteorder == c'>' and little_endian) or * (child.byteorder == c'<' and not little_endian)): # <<<<<<<<<<<<<< * raise ValueError(u"Non-native byte order not supported") * # One could encode it in the format string and have Cython */ __pyx_t_7 = ((__pyx_v_child->byteorder == '<') != 0); if (__pyx_t_7) { } else { __pyx_t_6 = __pyx_t_7; goto __pyx_L7_bool_binop_done; } __pyx_t_7 = ((!(__pyx_v_little_endian != 0)) != 0); __pyx_t_6 = __pyx_t_7; __pyx_L7_bool_binop_done:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":858 * raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd") * * if ((child.byteorder == c'>' and little_endian) or # <<<<<<<<<<<<<< * (child.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") */ if (unlikely(__pyx_t_6)) { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":860 * if ((child.byteorder == c'>' and little_endian) or * (child.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") # <<<<<<<<<<<<<< * # One could encode it in the format string and have Cython * # complain instead, BUT: < and > in format strings also imply */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 860, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 860, __pyx_L1_error) /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":858 * raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd") * * if ((child.byteorder == c'>' and little_endian) or # <<<<<<<<<<<<<< * (child.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") */ } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":870 * * # Output padding bytes * while offset[0] < new_offset: # <<<<<<<<<<<<<< * f[0] = 120 # "x"; pad byte * f += 1 */ while (1) { __pyx_t_3 = __Pyx_PyInt_From_int((__pyx_v_offset[0])); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 870, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyObject_RichCompare(__pyx_t_3, __pyx_v_new_offset, Py_LT); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 870, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 870, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (!__pyx_t_6) break; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":871 * # Output padding bytes * while offset[0] < new_offset: * f[0] = 120 # "x"; pad byte # <<<<<<<<<<<<<< * f += 1 * offset[0] += 1 */ (__pyx_v_f[0]) = 0x78; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":872 * while offset[0] < new_offset: * f[0] = 120 # "x"; pad byte * f += 1 # <<<<<<<<<<<<<< * offset[0] += 1 * */ __pyx_v_f = (__pyx_v_f + 1); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":873 * f[0] = 120 # "x"; pad byte * f += 1 * offset[0] += 1 # <<<<<<<<<<<<<< * * offset[0] += child.itemsize */ __pyx_t_8 = 0; (__pyx_v_offset[__pyx_t_8]) = ((__pyx_v_offset[__pyx_t_8]) + 1); } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":875 * offset[0] += 1 * * offset[0] += child.itemsize # <<<<<<<<<<<<<< * * if not PyDataType_HASFIELDS(child): */ __pyx_t_8 = 0; (__pyx_v_offset[__pyx_t_8]) = ((__pyx_v_offset[__pyx_t_8]) + __pyx_v_child->elsize); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":877 * offset[0] += child.itemsize * * if not PyDataType_HASFIELDS(child): # <<<<<<<<<<<<<< * t = child.type_num * if end - f < 5: */ __pyx_t_6 = ((!(PyDataType_HASFIELDS(__pyx_v_child) != 0)) != 0); if (__pyx_t_6) { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":878 * * if not PyDataType_HASFIELDS(child): * t = child.type_num # <<<<<<<<<<<<<< * if end - f < 5: * raise RuntimeError(u"Format string allocated too short.") */ __pyx_t_4 = __Pyx_PyInt_From_int(__pyx_v_child->type_num); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 878, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_XDECREF_SET(__pyx_v_t, __pyx_t_4); __pyx_t_4 = 0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":879 * if not PyDataType_HASFIELDS(child): * t = child.type_num * if end - f < 5: # <<<<<<<<<<<<<< * raise RuntimeError(u"Format string allocated too short.") * */ __pyx_t_6 = (((__pyx_v_end - __pyx_v_f) < 5) != 0); if (unlikely(__pyx_t_6)) { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":880 * t = child.type_num * if end - f < 5: * raise RuntimeError(u"Format string allocated too short.") # <<<<<<<<<<<<<< * * # Until ticket #99 is fixed, use integers to avoid warnings */ __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_tuple__5, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 880, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_Raise(__pyx_t_4, 0, 0, 0); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __PYX_ERR(1, 880, __pyx_L1_error) /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":879 * if not PyDataType_HASFIELDS(child): * t = child.type_num * if end - f < 5: # <<<<<<<<<<<<<< * raise RuntimeError(u"Format string allocated too short.") * */ } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":883 * * # Until ticket #99 is fixed, use integers to avoid warnings * if t == NPY_BYTE: f[0] = 98 #"b" # <<<<<<<<<<<<<< * elif t == NPY_UBYTE: f[0] = 66 #"B" * elif t == NPY_SHORT: f[0] = 104 #"h" */ __pyx_t_4 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_BYTE); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 883, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 883, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 883, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 98; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":884 * # Until ticket #99 is fixed, use integers to avoid warnings * if t == NPY_BYTE: f[0] = 98 #"b" * elif t == NPY_UBYTE: f[0] = 66 #"B" # <<<<<<<<<<<<<< * elif t == NPY_SHORT: f[0] = 104 #"h" * elif t == NPY_USHORT: f[0] = 72 #"H" */ __pyx_t_3 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_UBYTE); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 884, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 884, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 884, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 66; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":885 * if t == NPY_BYTE: f[0] = 98 #"b" * elif t == NPY_UBYTE: f[0] = 66 #"B" * elif t == NPY_SHORT: f[0] = 104 #"h" # <<<<<<<<<<<<<< * elif t == NPY_USHORT: f[0] = 72 #"H" * elif t == NPY_INT: f[0] = 105 #"i" */ __pyx_t_4 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_SHORT); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 885, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 885, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 885, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 0x68; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":886 * elif t == NPY_UBYTE: f[0] = 66 #"B" * elif t == NPY_SHORT: f[0] = 104 #"h" * elif t == NPY_USHORT: f[0] = 72 #"H" # <<<<<<<<<<<<<< * elif t == NPY_INT: f[0] = 105 #"i" * elif t == NPY_UINT: f[0] = 73 #"I" */ __pyx_t_3 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_USHORT); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 886, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 886, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 886, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 72; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":887 * elif t == NPY_SHORT: f[0] = 104 #"h" * elif t == NPY_USHORT: f[0] = 72 #"H" * elif t == NPY_INT: f[0] = 105 #"i" # <<<<<<<<<<<<<< * elif t == NPY_UINT: f[0] = 73 #"I" * elif t == NPY_LONG: f[0] = 108 #"l" */ __pyx_t_4 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_INT); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 887, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 887, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 887, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 0x69; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":888 * elif t == NPY_USHORT: f[0] = 72 #"H" * elif t == NPY_INT: f[0] = 105 #"i" * elif t == NPY_UINT: f[0] = 73 #"I" # <<<<<<<<<<<<<< * elif t == NPY_LONG: f[0] = 108 #"l" * elif t == NPY_ULONG: f[0] = 76 #"L" */ __pyx_t_3 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_UINT); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 888, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 888, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 888, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 73; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":889 * elif t == NPY_INT: f[0] = 105 #"i" * elif t == NPY_UINT: f[0] = 73 #"I" * elif t == NPY_LONG: f[0] = 108 #"l" # <<<<<<<<<<<<<< * elif t == NPY_ULONG: f[0] = 76 #"L" * elif t == NPY_LONGLONG: f[0] = 113 #"q" */ __pyx_t_4 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_LONG); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 889, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 889, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 889, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 0x6C; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":890 * elif t == NPY_UINT: f[0] = 73 #"I" * elif t == NPY_LONG: f[0] = 108 #"l" * elif t == NPY_ULONG: f[0] = 76 #"L" # <<<<<<<<<<<<<< * elif t == NPY_LONGLONG: f[0] = 113 #"q" * elif t == NPY_ULONGLONG: f[0] = 81 #"Q" */ __pyx_t_3 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_ULONG); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 890, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 890, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 890, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 76; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":891 * elif t == NPY_LONG: f[0] = 108 #"l" * elif t == NPY_ULONG: f[0] = 76 #"L" * elif t == NPY_LONGLONG: f[0] = 113 #"q" # <<<<<<<<<<<<<< * elif t == NPY_ULONGLONG: f[0] = 81 #"Q" * elif t == NPY_FLOAT: f[0] = 102 #"f" */ __pyx_t_4 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_LONGLONG); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 891, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 891, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 891, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 0x71; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":892 * elif t == NPY_ULONG: f[0] = 76 #"L" * elif t == NPY_LONGLONG: f[0] = 113 #"q" * elif t == NPY_ULONGLONG: f[0] = 81 #"Q" # <<<<<<<<<<<<<< * elif t == NPY_FLOAT: f[0] = 102 #"f" * elif t == NPY_DOUBLE: f[0] = 100 #"d" */ __pyx_t_3 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_ULONGLONG); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 892, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 892, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 892, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 81; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":893 * elif t == NPY_LONGLONG: f[0] = 113 #"q" * elif t == NPY_ULONGLONG: f[0] = 81 #"Q" * elif t == NPY_FLOAT: f[0] = 102 #"f" # <<<<<<<<<<<<<< * elif t == NPY_DOUBLE: f[0] = 100 #"d" * elif t == NPY_LONGDOUBLE: f[0] = 103 #"g" */ __pyx_t_4 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_FLOAT); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 893, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 893, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 893, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 0x66; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":894 * elif t == NPY_ULONGLONG: f[0] = 81 #"Q" * elif t == NPY_FLOAT: f[0] = 102 #"f" * elif t == NPY_DOUBLE: f[0] = 100 #"d" # <<<<<<<<<<<<<< * elif t == NPY_LONGDOUBLE: f[0] = 103 #"g" * elif t == NPY_CFLOAT: f[0] = 90; f[1] = 102; f += 1 # Zf */ __pyx_t_3 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_DOUBLE); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 894, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 894, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 894, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 0x64; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":895 * elif t == NPY_FLOAT: f[0] = 102 #"f" * elif t == NPY_DOUBLE: f[0] = 100 #"d" * elif t == NPY_LONGDOUBLE: f[0] = 103 #"g" # <<<<<<<<<<<<<< * elif t == NPY_CFLOAT: f[0] = 90; f[1] = 102; f += 1 # Zf * elif t == NPY_CDOUBLE: f[0] = 90; f[1] = 100; f += 1 # Zd */ __pyx_t_4 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_LONGDOUBLE); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 895, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 895, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 895, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 0x67; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":896 * elif t == NPY_DOUBLE: f[0] = 100 #"d" * elif t == NPY_LONGDOUBLE: f[0] = 103 #"g" * elif t == NPY_CFLOAT: f[0] = 90; f[1] = 102; f += 1 # Zf # <<<<<<<<<<<<<< * elif t == NPY_CDOUBLE: f[0] = 90; f[1] = 100; f += 1 # Zd * elif t == NPY_CLONGDOUBLE: f[0] = 90; f[1] = 103; f += 1 # Zg */ __pyx_t_3 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_CFLOAT); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 896, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 896, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 896, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 90; (__pyx_v_f[1]) = 0x66; __pyx_v_f = (__pyx_v_f + 1); goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":897 * elif t == NPY_LONGDOUBLE: f[0] = 103 #"g" * elif t == NPY_CFLOAT: f[0] = 90; f[1] = 102; f += 1 # Zf * elif t == NPY_CDOUBLE: f[0] = 90; f[1] = 100; f += 1 # Zd # <<<<<<<<<<<<<< * elif t == NPY_CLONGDOUBLE: f[0] = 90; f[1] = 103; f += 1 # Zg * elif t == NPY_OBJECT: f[0] = 79 #"O" */ __pyx_t_4 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_CDOUBLE); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 897, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 897, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 897, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 90; (__pyx_v_f[1]) = 0x64; __pyx_v_f = (__pyx_v_f + 1); goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":898 * elif t == NPY_CFLOAT: f[0] = 90; f[1] = 102; f += 1 # Zf * elif t == NPY_CDOUBLE: f[0] = 90; f[1] = 100; f += 1 # Zd * elif t == NPY_CLONGDOUBLE: f[0] = 90; f[1] = 103; f += 1 # Zg # <<<<<<<<<<<<<< * elif t == NPY_OBJECT: f[0] = 79 #"O" * else: */ __pyx_t_3 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_CLONGDOUBLE); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 898, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 898, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 898, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (__pyx_t_6) { (__pyx_v_f[0]) = 90; (__pyx_v_f[1]) = 0x67; __pyx_v_f = (__pyx_v_f + 1); goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":899 * elif t == NPY_CDOUBLE: f[0] = 90; f[1] = 100; f += 1 # Zd * elif t == NPY_CLONGDOUBLE: f[0] = 90; f[1] = 103; f += 1 # Zg * elif t == NPY_OBJECT: f[0] = 79 #"O" # <<<<<<<<<<<<<< * else: * raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t) */ __pyx_t_4 = __Pyx_PyInt_From_enum__NPY_TYPES(NPY_OBJECT); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 899, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 899, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(1, 899, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (likely(__pyx_t_6)) { (__pyx_v_f[0]) = 79; goto __pyx_L15; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":901 * elif t == NPY_OBJECT: f[0] = 79 #"O" * else: * raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t) # <<<<<<<<<<<<<< * f += 1 * else: */ /*else*/ { __pyx_t_3 = __Pyx_PyUnicode_FormatSafe(__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_v_t); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 901, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 901, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_4, 0, 0, 0); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __PYX_ERR(1, 901, __pyx_L1_error) } __pyx_L15:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":902 * else: * raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t) * f += 1 # <<<<<<<<<<<<<< * else: * # Cython ignores struct boundary information ("T{...}"), */ __pyx_v_f = (__pyx_v_f + 1); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":877 * offset[0] += child.itemsize * * if not PyDataType_HASFIELDS(child): # <<<<<<<<<<<<<< * t = child.type_num * if end - f < 5: */ goto __pyx_L13; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":906 * # Cython ignores struct boundary information ("T{...}"), * # so don't output it * f = _util_dtypestring(child, f, end, offset) # <<<<<<<<<<<<<< * return f * */ /*else*/ { __pyx_t_9 = __pyx_f_5numpy__util_dtypestring(__pyx_v_child, __pyx_v_f, __pyx_v_end, __pyx_v_offset); if (unlikely(__pyx_t_9 == ((char *)NULL))) __PYX_ERR(1, 906, __pyx_L1_error) __pyx_v_f = __pyx_t_9; } __pyx_L13:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":851 * cdef tuple fields * * for childname in descr.names: # <<<<<<<<<<<<<< * fields = descr.fields[childname] * child, new_offset = fields */ } __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":907 * # so don't output it * f = _util_dtypestring(child, f, end, offset) * return f # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_f; goto __pyx_L0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":842 * return () * * cdef inline char* _util_dtypestring(dtype descr, char* f, char* end, int* offset) except NULL: # <<<<<<<<<<<<<< * # Recursive utility function used in __getbuffer__ to get format * # string. The new location in the format string is returned. */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("numpy._util_dtypestring", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_child); __Pyx_XDECREF(__pyx_v_fields); __Pyx_XDECREF(__pyx_v_childname); __Pyx_XDECREF(__pyx_v_new_offset); __Pyx_XDECREF(__pyx_v_t); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1022 * int _import_umath() except -1 * * cdef inline void set_array_base(ndarray arr, object base): # <<<<<<<<<<<<<< * Py_INCREF(base) # important to do this before stealing the reference below! * PyArray_SetBaseObject(arr, base) */ static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *__pyx_v_arr, PyObject *__pyx_v_base) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("set_array_base", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1023 * * cdef inline void set_array_base(ndarray arr, object base): * Py_INCREF(base) # important to do this before stealing the reference below! # <<<<<<<<<<<<<< * PyArray_SetBaseObject(arr, base) * */ Py_INCREF(__pyx_v_base); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1024 * cdef inline void set_array_base(ndarray arr, object base): * Py_INCREF(base) # important to do this before stealing the reference below! * PyArray_SetBaseObject(arr, base) # <<<<<<<<<<<<<< * * cdef inline object get_array_base(ndarray arr): */ (void)(PyArray_SetBaseObject(__pyx_v_arr, __pyx_v_base)); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1022 * int _import_umath() except -1 * * cdef inline void set_array_base(ndarray arr, object base): # <<<<<<<<<<<<<< * Py_INCREF(base) # important to do this before stealing the reference below! * PyArray_SetBaseObject(arr, base) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1026 * PyArray_SetBaseObject(arr, base) * * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< * base = PyArray_BASE(arr) * if base is NULL: */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__pyx_v_arr) { PyObject *__pyx_v_base; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("get_array_base", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1027 * * cdef inline object get_array_base(ndarray arr): * base = PyArray_BASE(arr) # <<<<<<<<<<<<<< * if base is NULL: * return None */ __pyx_v_base = PyArray_BASE(__pyx_v_arr); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1028 * cdef inline object get_array_base(ndarray arr): * base = PyArray_BASE(arr) * if base is NULL: # <<<<<<<<<<<<<< * return None * return base */ __pyx_t_1 = ((__pyx_v_base == NULL) != 0); if (__pyx_t_1) { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1029 * base = PyArray_BASE(arr) * if base is NULL: * return None # <<<<<<<<<<<<<< * return base * */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1028 * cdef inline object get_array_base(ndarray arr): * base = PyArray_BASE(arr) * if base is NULL: # <<<<<<<<<<<<<< * return None * return base */ } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1030 * if base is NULL: * return None * return base # <<<<<<<<<<<<<< * * # Versions of the import_* functions which are more suitable for */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_base)); __pyx_r = ((PyObject *)__pyx_v_base); goto __pyx_L0; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1026 * PyArray_SetBaseObject(arr, base) * * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< * base = PyArray_BASE(arr) * if base is NULL: */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1034 * # Versions of the import_* functions which are more suitable for * # Cython code. * cdef inline int import_array() except -1: # <<<<<<<<<<<<<< * try: * _import_array() */ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; __Pyx_RefNannySetupContext("import_array", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1035 * # Cython code. * cdef inline int import_array() except -1: * try: # <<<<<<<<<<<<<< * _import_array() * except Exception: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_1, &__pyx_t_2, &__pyx_t_3); __Pyx_XGOTREF(__pyx_t_1); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); /*try:*/ { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1036 * cdef inline int import_array() except -1: * try: * _import_array() # <<<<<<<<<<<<<< * except Exception: * raise ImportError("numpy.core.multiarray failed to import") */ __pyx_t_4 = _import_array(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 1036, __pyx_L3_error) /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1035 * # Cython code. * cdef inline int import_array() except -1: * try: # <<<<<<<<<<<<<< * _import_array() * except Exception: */ } __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L8_try_end; __pyx_L3_error:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1037 * try: * _import_array() * except Exception: # <<<<<<<<<<<<<< * raise ImportError("numpy.core.multiarray failed to import") * */ __pyx_t_4 = __Pyx_PyErr_ExceptionMatches(((PyObject *)(&((PyTypeObject*)PyExc_Exception)[0]))); if (__pyx_t_4) { __Pyx_AddTraceback("numpy.import_array", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(1, 1037, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_6); __Pyx_GOTREF(__pyx_t_7); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1038 * _import_array() * except Exception: * raise ImportError("numpy.core.multiarray failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_umath() except -1: */ __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ImportError, __pyx_tuple__6, NULL); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 1038, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_Raise(__pyx_t_8, 0, 0, 0); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __PYX_ERR(1, 1038, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1035 * # Cython code. * cdef inline int import_array() except -1: * try: # <<<<<<<<<<<<<< * _import_array() * except Exception: */ __Pyx_XGIVEREF(__pyx_t_1); __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_ExceptionReset(__pyx_t_1, __pyx_t_2, __pyx_t_3); goto __pyx_L1_error; __pyx_L8_try_end:; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1034 * # Versions of the import_* functions which are more suitable for * # Cython code. * cdef inline int import_array() except -1: # <<<<<<<<<<<<<< * try: * _import_array() */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("numpy.import_array", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1040 * raise ImportError("numpy.core.multiarray failed to import") * * cdef inline int import_umath() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; __Pyx_RefNannySetupContext("import_umath", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1041 * * cdef inline int import_umath() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_1, &__pyx_t_2, &__pyx_t_3); __Pyx_XGOTREF(__pyx_t_1); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); /*try:*/ { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1042 * cdef inline int import_umath() except -1: * try: * _import_umath() # <<<<<<<<<<<<<< * except Exception: * raise ImportError("numpy.core.umath failed to import") */ __pyx_t_4 = _import_umath(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 1042, __pyx_L3_error) /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1041 * * cdef inline int import_umath() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ } __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L8_try_end; __pyx_L3_error:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1043 * try: * _import_umath() * except Exception: # <<<<<<<<<<<<<< * raise ImportError("numpy.core.umath failed to import") * */ __pyx_t_4 = __Pyx_PyErr_ExceptionMatches(((PyObject *)(&((PyTypeObject*)PyExc_Exception)[0]))); if (__pyx_t_4) { __Pyx_AddTraceback("numpy.import_umath", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(1, 1043, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_6); __Pyx_GOTREF(__pyx_t_7); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1044 * _import_umath() * except Exception: * raise ImportError("numpy.core.umath failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_ufunc() except -1: */ __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ImportError, __pyx_tuple__7, NULL); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 1044, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_Raise(__pyx_t_8, 0, 0, 0); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __PYX_ERR(1, 1044, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1041 * * cdef inline int import_umath() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ __Pyx_XGIVEREF(__pyx_t_1); __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_ExceptionReset(__pyx_t_1, __pyx_t_2, __pyx_t_3); goto __pyx_L1_error; __pyx_L8_try_end:; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1040 * raise ImportError("numpy.core.multiarray failed to import") * * cdef inline int import_umath() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("numpy.import_umath", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1046 * raise ImportError("numpy.core.umath failed to import") * * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; __Pyx_RefNannySetupContext("import_ufunc", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1047 * * cdef inline int import_ufunc() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_1, &__pyx_t_2, &__pyx_t_3); __Pyx_XGOTREF(__pyx_t_1); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); /*try:*/ { /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1048 * cdef inline int import_ufunc() except -1: * try: * _import_umath() # <<<<<<<<<<<<<< * except Exception: * raise ImportError("numpy.core.umath failed to import") */ __pyx_t_4 = _import_umath(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 1048, __pyx_L3_error) /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1047 * * cdef inline int import_ufunc() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ } __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L8_try_end; __pyx_L3_error:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1049 * try: * _import_umath() * except Exception: # <<<<<<<<<<<<<< * raise ImportError("numpy.core.umath failed to import") */ __pyx_t_4 = __Pyx_PyErr_ExceptionMatches(((PyObject *)(&((PyTypeObject*)PyExc_Exception)[0]))); if (__pyx_t_4) { __Pyx_AddTraceback("numpy.import_ufunc", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(1, 1049, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_6); __Pyx_GOTREF(__pyx_t_7); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1050 * _import_umath() * except Exception: * raise ImportError("numpy.core.umath failed to import") # <<<<<<<<<<<<<< */ __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ImportError, __pyx_tuple__7, NULL); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 1050, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_Raise(__pyx_t_8, 0, 0, 0); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __PYX_ERR(1, 1050, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1047 * * cdef inline int import_ufunc() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ __Pyx_XGIVEREF(__pyx_t_1); __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_ExceptionReset(__pyx_t_1, __pyx_t_2, __pyx_t_3); goto __pyx_L1_error; __pyx_L8_try_end:; } /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1046 * raise ImportError("numpy.core.umath failed to import") * * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("numpy.import_ufunc", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* Python wrapper */ static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_shape = 0; Py_ssize_t __pyx_v_itemsize; PyObject *__pyx_v_format = 0; PyObject *__pyx_v_mode = 0; int __pyx_v_allocate_buffer; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_shape,&__pyx_n_s_itemsize,&__pyx_n_s_format,&__pyx_n_s_mode,&__pyx_n_s_allocate_buffer,0}; PyObject* values[5] = {0,0,0,0,0}; values[3] = ((PyObject *)__pyx_n_s_c); if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_shape)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_itemsize)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 1); __PYX_ERR(2, 122, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_format)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 2); __PYX_ERR(2, 122, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 3: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_mode); if (value) { values[3] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 4: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_allocate_buffer); if (value) { values[4] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) __PYX_ERR(2, 122, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_shape = ((PyObject*)values[0]); __pyx_v_itemsize = __Pyx_PyIndex_AsSsize_t(values[1]); if (unlikely((__pyx_v_itemsize == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 122, __pyx_L3_error) __pyx_v_format = values[2]; __pyx_v_mode = values[3]; if (values[4]) { __pyx_v_allocate_buffer = __Pyx_PyObject_IsTrue(values[4]); if (unlikely((__pyx_v_allocate_buffer == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 123, __pyx_L3_error) } else { /* "View.MemoryView":123 * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, * mode="c", bint allocate_buffer=True): # <<<<<<<<<<<<<< * * cdef int idx */ __pyx_v_allocate_buffer = ((int)1); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 122, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_shape), (&PyTuple_Type), 1, "shape", 1))) __PYX_ERR(2, 122, __pyx_L1_error) if (unlikely(((PyObject *)__pyx_v_format) == Py_None)) { PyErr_Format(PyExc_TypeError, "Argument '%.200s' must not be None", "format"); __PYX_ERR(2, 122, __pyx_L1_error) } __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(((struct __pyx_array_obj *)__pyx_v_self), __pyx_v_shape, __pyx_v_itemsize, __pyx_v_format, __pyx_v_mode, __pyx_v_allocate_buffer); /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer) { int __pyx_v_idx; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_dim; PyObject **__pyx_v_p; char __pyx_v_order; int __pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; char *__pyx_t_7; int __pyx_t_8; Py_ssize_t __pyx_t_9; PyObject *__pyx_t_10 = NULL; Py_ssize_t __pyx_t_11; __Pyx_RefNannySetupContext("__cinit__", 0); __Pyx_INCREF(__pyx_v_format); /* "View.MemoryView":129 * cdef PyObject **p * * self.ndim = len(shape) # <<<<<<<<<<<<<< * self.itemsize = itemsize * */ if (unlikely(__pyx_v_shape == Py_None)) { PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); __PYX_ERR(2, 129, __pyx_L1_error) } __pyx_t_1 = PyTuple_GET_SIZE(__pyx_v_shape); if (unlikely(__pyx_t_1 == ((Py_ssize_t)-1))) __PYX_ERR(2, 129, __pyx_L1_error) __pyx_v_self->ndim = ((int)__pyx_t_1); /* "View.MemoryView":130 * * self.ndim = len(shape) * self.itemsize = itemsize # <<<<<<<<<<<<<< * * if not self.ndim: */ __pyx_v_self->itemsize = __pyx_v_itemsize; /* "View.MemoryView":132 * self.itemsize = itemsize * * if not self.ndim: # <<<<<<<<<<<<<< * raise ValueError("Empty shape tuple for cython.array") * */ __pyx_t_2 = ((!(__pyx_v_self->ndim != 0)) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":133 * * if not self.ndim: * raise ValueError("Empty shape tuple for cython.array") # <<<<<<<<<<<<<< * * if itemsize <= 0: */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__8, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 133, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 133, __pyx_L1_error) /* "View.MemoryView":132 * self.itemsize = itemsize * * if not self.ndim: # <<<<<<<<<<<<<< * raise ValueError("Empty shape tuple for cython.array") * */ } /* "View.MemoryView":135 * raise ValueError("Empty shape tuple for cython.array") * * if itemsize <= 0: # <<<<<<<<<<<<<< * raise ValueError("itemsize <= 0 for cython.array") * */ __pyx_t_2 = ((__pyx_v_itemsize <= 0) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":136 * * if itemsize <= 0: * raise ValueError("itemsize <= 0 for cython.array") # <<<<<<<<<<<<<< * * if not isinstance(format, bytes): */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__9, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 136, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 136, __pyx_L1_error) /* "View.MemoryView":135 * raise ValueError("Empty shape tuple for cython.array") * * if itemsize <= 0: # <<<<<<<<<<<<<< * raise ValueError("itemsize <= 0 for cython.array") * */ } /* "View.MemoryView":138 * raise ValueError("itemsize <= 0 for cython.array") * * if not isinstance(format, bytes): # <<<<<<<<<<<<<< * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string */ __pyx_t_2 = PyBytes_Check(__pyx_v_format); __pyx_t_4 = ((!(__pyx_t_2 != 0)) != 0); if (__pyx_t_4) { /* "View.MemoryView":139 * * if not isinstance(format, bytes): * format = format.encode('ASCII') # <<<<<<<<<<<<<< * self._format = format # keep a reference to the byte string * self.format = self._format */ __pyx_t_5 = __Pyx_PyObject_GetAttrStr(__pyx_v_format, __pyx_n_s_encode); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 139, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_5))) { __pyx_t_6 = PyMethod_GET_SELF(__pyx_t_5); if (likely(__pyx_t_6)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_5); __Pyx_INCREF(__pyx_t_6); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_5, function); } } __pyx_t_3 = (__pyx_t_6) ? __Pyx_PyObject_Call2Args(__pyx_t_5, __pyx_t_6, __pyx_n_s_ASCII) : __Pyx_PyObject_CallOneArg(__pyx_t_5, __pyx_n_s_ASCII); __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 139, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF_SET(__pyx_v_format, __pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":138 * raise ValueError("itemsize <= 0 for cython.array") * * if not isinstance(format, bytes): # <<<<<<<<<<<<<< * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string */ } /* "View.MemoryView":140 * if not isinstance(format, bytes): * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string # <<<<<<<<<<<<<< * self.format = self._format * */ if (!(likely(PyBytes_CheckExact(__pyx_v_format))||((__pyx_v_format) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_v_format)->tp_name), 0))) __PYX_ERR(2, 140, __pyx_L1_error) __pyx_t_3 = __pyx_v_format; __Pyx_INCREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_3); __Pyx_GOTREF(__pyx_v_self->_format); __Pyx_DECREF(__pyx_v_self->_format); __pyx_v_self->_format = ((PyObject*)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":141 * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string * self.format = self._format # <<<<<<<<<<<<<< * * */ if (unlikely(__pyx_v_self->_format == Py_None)) { PyErr_SetString(PyExc_TypeError, "expected bytes, NoneType found"); __PYX_ERR(2, 141, __pyx_L1_error) } __pyx_t_7 = __Pyx_PyBytes_AsWritableString(__pyx_v_self->_format); if (unlikely((!__pyx_t_7) && PyErr_Occurred())) __PYX_ERR(2, 141, __pyx_L1_error) __pyx_v_self->format = __pyx_t_7; /* "View.MemoryView":144 * * * self._shape = PyObject_Malloc(sizeof(Py_ssize_t)*self.ndim*2) # <<<<<<<<<<<<<< * self._strides = self._shape + self.ndim * */ __pyx_v_self->_shape = ((Py_ssize_t *)PyObject_Malloc((((sizeof(Py_ssize_t)) * __pyx_v_self->ndim) * 2))); /* "View.MemoryView":145 * * self._shape = PyObject_Malloc(sizeof(Py_ssize_t)*self.ndim*2) * self._strides = self._shape + self.ndim # <<<<<<<<<<<<<< * * if not self._shape: */ __pyx_v_self->_strides = (__pyx_v_self->_shape + __pyx_v_self->ndim); /* "View.MemoryView":147 * self._strides = self._shape + self.ndim * * if not self._shape: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate shape and strides.") * */ __pyx_t_4 = ((!(__pyx_v_self->_shape != 0)) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":148 * * if not self._shape: * raise MemoryError("unable to allocate shape and strides.") # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__10, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 148, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 148, __pyx_L1_error) /* "View.MemoryView":147 * self._strides = self._shape + self.ndim * * if not self._shape: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate shape and strides.") * */ } /* "View.MemoryView":151 * * * for idx, dim in enumerate(shape): # <<<<<<<<<<<<<< * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) */ __pyx_t_8 = 0; __pyx_t_3 = __pyx_v_shape; __Pyx_INCREF(__pyx_t_3); __pyx_t_1 = 0; for (;;) { if (__pyx_t_1 >= PyTuple_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_1); __Pyx_INCREF(__pyx_t_5); __pyx_t_1++; if (unlikely(0 < 0)) __PYX_ERR(2, 151, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_3, __pyx_t_1); __pyx_t_1++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 151, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_5); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 151, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_dim = __pyx_t_9; __pyx_v_idx = __pyx_t_8; __pyx_t_8 = (__pyx_t_8 + 1); /* "View.MemoryView":152 * * for idx, dim in enumerate(shape): * if dim <= 0: # <<<<<<<<<<<<<< * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim */ __pyx_t_4 = ((__pyx_v_dim <= 0) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":153 * for idx, dim in enumerate(shape): * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) # <<<<<<<<<<<<<< * self._shape[idx] = dim * */ __pyx_t_5 = __Pyx_PyInt_From_int(__pyx_v_idx); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_10 = PyTuple_New(2); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_10, 1, __pyx_t_6); __pyx_t_5 = 0; __pyx_t_6 = 0; __pyx_t_6 = __Pyx_PyString_Format(__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_t_10); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_6); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(2, 153, __pyx_L1_error) /* "View.MemoryView":152 * * for idx, dim in enumerate(shape): * if dim <= 0: # <<<<<<<<<<<<<< * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim */ } /* "View.MemoryView":154 * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim # <<<<<<<<<<<<<< * * cdef char order */ (__pyx_v_self->_shape[__pyx_v_idx]) = __pyx_v_dim; /* "View.MemoryView":151 * * * for idx, dim in enumerate(shape): # <<<<<<<<<<<<<< * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) */ } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":157 * * cdef char order * if mode == 'fortran': # <<<<<<<<<<<<<< * order = b'F' * self.mode = u'fortran' */ __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_fortran, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) __PYX_ERR(2, 157, __pyx_L1_error) if (__pyx_t_4) { /* "View.MemoryView":158 * cdef char order * if mode == 'fortran': * order = b'F' # <<<<<<<<<<<<<< * self.mode = u'fortran' * elif mode == 'c': */ __pyx_v_order = 'F'; /* "View.MemoryView":159 * if mode == 'fortran': * order = b'F' * self.mode = u'fortran' # <<<<<<<<<<<<<< * elif mode == 'c': * order = b'C' */ __Pyx_INCREF(__pyx_n_u_fortran); __Pyx_GIVEREF(__pyx_n_u_fortran); __Pyx_GOTREF(__pyx_v_self->mode); __Pyx_DECREF(__pyx_v_self->mode); __pyx_v_self->mode = __pyx_n_u_fortran; /* "View.MemoryView":157 * * cdef char order * if mode == 'fortran': # <<<<<<<<<<<<<< * order = b'F' * self.mode = u'fortran' */ goto __pyx_L10; } /* "View.MemoryView":160 * order = b'F' * self.mode = u'fortran' * elif mode == 'c': # <<<<<<<<<<<<<< * order = b'C' * self.mode = u'c' */ __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_c, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) __PYX_ERR(2, 160, __pyx_L1_error) if (likely(__pyx_t_4)) { /* "View.MemoryView":161 * self.mode = u'fortran' * elif mode == 'c': * order = b'C' # <<<<<<<<<<<<<< * self.mode = u'c' * else: */ __pyx_v_order = 'C'; /* "View.MemoryView":162 * elif mode == 'c': * order = b'C' * self.mode = u'c' # <<<<<<<<<<<<<< * else: * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) */ __Pyx_INCREF(__pyx_n_u_c); __Pyx_GIVEREF(__pyx_n_u_c); __Pyx_GOTREF(__pyx_v_self->mode); __Pyx_DECREF(__pyx_v_self->mode); __pyx_v_self->mode = __pyx_n_u_c; /* "View.MemoryView":160 * order = b'F' * self.mode = u'fortran' * elif mode == 'c': # <<<<<<<<<<<<<< * order = b'C' * self.mode = u'c' */ goto __pyx_L10; } /* "View.MemoryView":164 * self.mode = u'c' * else: * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) # <<<<<<<<<<<<<< * * self.len = fill_contig_strides_array(self._shape, self._strides, */ /*else*/ { __pyx_t_3 = __Pyx_PyString_FormatSafe(__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_v_mode); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 164, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_10 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_3); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 164, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(2, 164, __pyx_L1_error) } __pyx_L10:; /* "View.MemoryView":166 * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) * * self.len = fill_contig_strides_array(self._shape, self._strides, # <<<<<<<<<<<<<< * itemsize, self.ndim, order) * */ __pyx_v_self->len = __pyx_fill_contig_strides_array(__pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_itemsize, __pyx_v_self->ndim, __pyx_v_order); /* "View.MemoryView":169 * itemsize, self.ndim, order) * * self.free_data = allocate_buffer # <<<<<<<<<<<<<< * self.dtype_is_object = format == b'O' * if allocate_buffer: */ __pyx_v_self->free_data = __pyx_v_allocate_buffer; /* "View.MemoryView":170 * * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' # <<<<<<<<<<<<<< * if allocate_buffer: * */ __pyx_t_10 = PyObject_RichCompare(__pyx_v_format, __pyx_n_b_O, Py_EQ); __Pyx_XGOTREF(__pyx_t_10); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 170, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_IsTrue(__pyx_t_10); if (unlikely((__pyx_t_4 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 170, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_self->dtype_is_object = __pyx_t_4; /* "View.MemoryView":171 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< * * */ __pyx_t_4 = (__pyx_v_allocate_buffer != 0); if (__pyx_t_4) { /* "View.MemoryView":174 * * * self.data = malloc(self.len) # <<<<<<<<<<<<<< * if not self.data: * raise MemoryError("unable to allocate array data.") */ __pyx_v_self->data = ((char *)malloc(__pyx_v_self->len)); /* "View.MemoryView":175 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate array data.") * */ __pyx_t_4 = ((!(__pyx_v_self->data != 0)) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":176 * self.data = malloc(self.len) * if not self.data: * raise MemoryError("unable to allocate array data.") # <<<<<<<<<<<<<< * * if self.dtype_is_object: */ __pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__11, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 176, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(2, 176, __pyx_L1_error) /* "View.MemoryView":175 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate array data.") * */ } /* "View.MemoryView":178 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< * p = self.data * for i in range(self.len / itemsize): */ __pyx_t_4 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_4) { /* "View.MemoryView":179 * * if self.dtype_is_object: * p = self.data # <<<<<<<<<<<<<< * for i in range(self.len / itemsize): * p[i] = Py_None */ __pyx_v_p = ((PyObject **)__pyx_v_self->data); /* "View.MemoryView":180 * if self.dtype_is_object: * p = self.data * for i in range(self.len / itemsize): # <<<<<<<<<<<<<< * p[i] = Py_None * Py_INCREF(Py_None) */ if (unlikely(__pyx_v_itemsize == 0)) { PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); __PYX_ERR(2, 180, __pyx_L1_error) } else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1) && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_self->len))) { PyErr_SetString(PyExc_OverflowError, "value too large to perform division"); __PYX_ERR(2, 180, __pyx_L1_error) } __pyx_t_1 = (__pyx_v_self->len / __pyx_v_itemsize); __pyx_t_9 = __pyx_t_1; for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_9; __pyx_t_11+=1) { __pyx_v_i = __pyx_t_11; /* "View.MemoryView":181 * p = self.data * for i in range(self.len / itemsize): * p[i] = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ (__pyx_v_p[__pyx_v_i]) = Py_None; /* "View.MemoryView":182 * for i in range(self.len / itemsize): * p[i] = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * @cname('getbuffer') */ Py_INCREF(Py_None); } /* "View.MemoryView":178 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< * p = self.data * for i in range(self.len / itemsize): */ } /* "View.MemoryView":171 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_10); __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_XDECREF(__pyx_v_format); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":185 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * cdef int bufmode = -1 * if self.mode == u"c": */ /* Python wrapper */ static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(((struct __pyx_array_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_v_bufmode; int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; char *__pyx_t_4; Py_ssize_t __pyx_t_5; int __pyx_t_6; Py_ssize_t *__pyx_t_7; if (__pyx_v_info == NULL) { PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete"); return -1; } __Pyx_RefNannySetupContext("__getbuffer__", 0); __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None); __Pyx_GIVEREF(__pyx_v_info->obj); /* "View.MemoryView":186 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 # <<<<<<<<<<<<<< * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS */ __pyx_v_bufmode = -1; /* "View.MemoryView":187 * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 * if self.mode == u"c": # <<<<<<<<<<<<<< * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": */ __pyx_t_1 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_c, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 187, __pyx_L1_error) __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":188 * cdef int bufmode = -1 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS # <<<<<<<<<<<<<< * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS */ __pyx_v_bufmode = (PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS); /* "View.MemoryView":187 * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 * if self.mode == u"c": # <<<<<<<<<<<<<< * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": */ goto __pyx_L3; } /* "View.MemoryView":189 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": # <<<<<<<<<<<<<< * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): */ __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_fortran, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(2, 189, __pyx_L1_error) __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":190 * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS # <<<<<<<<<<<<<< * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") */ __pyx_v_bufmode = (PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS); /* "View.MemoryView":189 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": # <<<<<<<<<<<<<< * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): */ } __pyx_L3:; /* "View.MemoryView":191 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data */ __pyx_t_1 = ((!((__pyx_v_flags & __pyx_v_bufmode) != 0)) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":192 * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") # <<<<<<<<<<<<<< * info.buf = self.data * info.len = self.len */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__12, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 192, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 192, __pyx_L1_error) /* "View.MemoryView":191 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data */ } /* "View.MemoryView":193 * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data # <<<<<<<<<<<<<< * info.len = self.len * info.ndim = self.ndim */ __pyx_t_4 = __pyx_v_self->data; __pyx_v_info->buf = __pyx_t_4; /* "View.MemoryView":194 * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data * info.len = self.len # <<<<<<<<<<<<<< * info.ndim = self.ndim * info.shape = self._shape */ __pyx_t_5 = __pyx_v_self->len; __pyx_v_info->len = __pyx_t_5; /* "View.MemoryView":195 * info.buf = self.data * info.len = self.len * info.ndim = self.ndim # <<<<<<<<<<<<<< * info.shape = self._shape * info.strides = self._strides */ __pyx_t_6 = __pyx_v_self->ndim; __pyx_v_info->ndim = __pyx_t_6; /* "View.MemoryView":196 * info.len = self.len * info.ndim = self.ndim * info.shape = self._shape # <<<<<<<<<<<<<< * info.strides = self._strides * info.suboffsets = NULL */ __pyx_t_7 = __pyx_v_self->_shape; __pyx_v_info->shape = __pyx_t_7; /* "View.MemoryView":197 * info.ndim = self.ndim * info.shape = self._shape * info.strides = self._strides # <<<<<<<<<<<<<< * info.suboffsets = NULL * info.itemsize = self.itemsize */ __pyx_t_7 = __pyx_v_self->_strides; __pyx_v_info->strides = __pyx_t_7; /* "View.MemoryView":198 * info.shape = self._shape * info.strides = self._strides * info.suboffsets = NULL # <<<<<<<<<<<<<< * info.itemsize = self.itemsize * info.readonly = 0 */ __pyx_v_info->suboffsets = NULL; /* "View.MemoryView":199 * info.strides = self._strides * info.suboffsets = NULL * info.itemsize = self.itemsize # <<<<<<<<<<<<<< * info.readonly = 0 * */ __pyx_t_5 = __pyx_v_self->itemsize; __pyx_v_info->itemsize = __pyx_t_5; /* "View.MemoryView":200 * info.suboffsets = NULL * info.itemsize = self.itemsize * info.readonly = 0 # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ __pyx_v_info->readonly = 0; /* "View.MemoryView":202 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.format * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":203 * * if flags & PyBUF_FORMAT: * info.format = self.format # <<<<<<<<<<<<<< * else: * info.format = NULL */ __pyx_t_4 = __pyx_v_self->format; __pyx_v_info->format = __pyx_t_4; /* "View.MemoryView":202 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.format * else: */ goto __pyx_L5; } /* "View.MemoryView":205 * info.format = self.format * else: * info.format = NULL # <<<<<<<<<<<<<< * * info.obj = self */ /*else*/ { __pyx_v_info->format = NULL; } __pyx_L5:; /* "View.MemoryView":207 * info.format = NULL * * info.obj = self # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = ((PyObject *)__pyx_v_self); /* "View.MemoryView":185 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * cdef int bufmode = -1 * if self.mode == u"c": */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.array.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; if (__pyx_v_info->obj != NULL) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } goto __pyx_L2; __pyx_L0:; if (__pyx_v_info->obj == Py_None) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } __pyx_L2:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":211 * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") * * def __dealloc__(array self): # <<<<<<<<<<<<<< * if self.callback_free_data != NULL: * self.callback_free_data(self.data) */ /* Python wrapper */ static void __pyx_array___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_array___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self) { __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":212 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< * self.callback_free_data(self.data) * elif self.free_data: */ __pyx_t_1 = ((__pyx_v_self->callback_free_data != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":213 * def __dealloc__(array self): * if self.callback_free_data != NULL: * self.callback_free_data(self.data) # <<<<<<<<<<<<<< * elif self.free_data: * if self.dtype_is_object: */ __pyx_v_self->callback_free_data(__pyx_v_self->data); /* "View.MemoryView":212 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< * self.callback_free_data(self.data) * elif self.free_data: */ goto __pyx_L3; } /* "View.MemoryView":214 * if self.callback_free_data != NULL: * self.callback_free_data(self.data) * elif self.free_data: # <<<<<<<<<<<<<< * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, */ __pyx_t_1 = (__pyx_v_self->free_data != 0); if (__pyx_t_1) { /* "View.MemoryView":215 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) */ __pyx_t_1 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_1) { /* "View.MemoryView":216 * elif self.free_data: * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, # <<<<<<<<<<<<<< * self._strides, self.ndim, False) * free(self.data) */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_self->data, __pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_self->ndim, 0); /* "View.MemoryView":215 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) */ } /* "View.MemoryView":218 * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) * free(self.data) # <<<<<<<<<<<<<< * PyObject_Free(self._shape) * */ free(__pyx_v_self->data); /* "View.MemoryView":214 * if self.callback_free_data != NULL: * self.callback_free_data(self.data) * elif self.free_data: # <<<<<<<<<<<<<< * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, */ } __pyx_L3:; /* "View.MemoryView":219 * self._strides, self.ndim, False) * free(self.data) * PyObject_Free(self._shape) # <<<<<<<<<<<<<< * * @property */ PyObject_Free(__pyx_v_self->_shape); /* "View.MemoryView":211 * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") * * def __dealloc__(array self): # <<<<<<<<<<<<<< * if self.callback_free_data != NULL: * self.callback_free_data(self.data) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":222 * * @property * def memview(self): # <<<<<<<<<<<<<< * return self.get_memview() * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_5array_7memview___get__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":223 * @property * def memview(self): * return self.get_memview() # <<<<<<<<<<<<<< * * @cname('get_memview') */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = ((struct __pyx_vtabstruct_array *)__pyx_v_self->__pyx_vtab)->get_memview(__pyx_v_self); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 223, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":222 * * @property * def memview(self): # <<<<<<<<<<<<<< * return self.get_memview() * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.memview.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":226 * * @cname('get_memview') * cdef get_memview(self): # <<<<<<<<<<<<<< * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) */ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self) { int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("get_memview", 0); /* "View.MemoryView":227 * @cname('get_memview') * cdef get_memview(self): * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE # <<<<<<<<<<<<<< * return memoryview(self, flags, self.dtype_is_object) * */ __pyx_v_flags = ((PyBUF_ANY_CONTIGUOUS | PyBUF_FORMAT) | PyBUF_WRITABLE); /* "View.MemoryView":228 * cdef get_memview(self): * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) # <<<<<<<<<<<<<< * * def __len__(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":226 * * @cname('get_memview') * cdef get_memview(self): # <<<<<<<<<<<<<< * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.array.get_memview", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":230 * return memoryview(self, flags, self.dtype_is_object) * * def __len__(self): # <<<<<<<<<<<<<< * return self._shape[0] * */ /* Python wrapper */ static Py_ssize_t __pyx_array___len__(PyObject *__pyx_v_self); /*proto*/ static Py_ssize_t __pyx_array___len__(PyObject *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__", 0); /* "View.MemoryView":231 * * def __len__(self): * return self._shape[0] # <<<<<<<<<<<<<< * * def __getattr__(self, attr): */ __pyx_r = (__pyx_v_self->_shape[0]); goto __pyx_L0; /* "View.MemoryView":230 * return memoryview(self, flags, self.dtype_is_object) * * def __len__(self): # <<<<<<<<<<<<<< * return self._shape[0] * */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":233 * return self._shape[0] * * def __getattr__(self, attr): # <<<<<<<<<<<<<< * return getattr(self.memview, attr) * */ /* Python wrapper */ static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr); /*proto*/ static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getattr__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_attr)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("__getattr__", 0); /* "View.MemoryView":234 * * def __getattr__(self, attr): * return getattr(self.memview, attr) # <<<<<<<<<<<<<< * * def __getitem__(self, item): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 234, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_GetAttr(__pyx_t_1, __pyx_v_attr); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 234, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":233 * return self._shape[0] * * def __getattr__(self, attr): # <<<<<<<<<<<<<< * return getattr(self.memview, attr) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.array.__getattr__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":236 * return getattr(self.memview, attr) * * def __getitem__(self, item): # <<<<<<<<<<<<<< * return self.memview[item] * */ /* Python wrapper */ static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item); /*proto*/ static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("__getitem__", 0); /* "View.MemoryView":237 * * def __getitem__(self, item): * return self.memview[item] # <<<<<<<<<<<<<< * * def __setitem__(self, item, value): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 237, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetItem(__pyx_t_1, __pyx_v_item); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 237, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":236 * return getattr(self.memview, attr) * * def __getitem__(self, item): # <<<<<<<<<<<<<< * return self.memview[item] * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.array.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":239 * return self.memview[item] * * def __setitem__(self, item, value): # <<<<<<<<<<<<<< * self.memview[item] = value * */ /* Python wrapper */ static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /*proto*/ static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item), ((PyObject *)__pyx_v_value)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__setitem__", 0); /* "View.MemoryView":240 * * def __setitem__(self, item, value): * self.memview[item] = value # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 240, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (unlikely(PyObject_SetItem(__pyx_t_1, __pyx_v_item, __pyx_v_value) < 0)) __PYX_ERR(2, 240, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":239 * return self.memview[item] * * def __setitem__(self, item, value): # <<<<<<<<<<<<<< * self.memview[item] = value * */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_array_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_array_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_array___reduce_cython__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__13, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_array_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_array_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_array_2__setstate_cython__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__14, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":244 * * @cname("__pyx_array_new") * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format, # <<<<<<<<<<<<<< * char *mode, char *buf): * cdef array result */ static struct __pyx_array_obj *__pyx_array_new(PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, char *__pyx_v_format, char *__pyx_v_mode, char *__pyx_v_buf) { struct __pyx_array_obj *__pyx_v_result = 0; struct __pyx_array_obj *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; __Pyx_RefNannySetupContext("array_cwrapper", 0); /* "View.MemoryView":248 * cdef array result * * if buf == NULL: # <<<<<<<<<<<<<< * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: */ __pyx_t_1 = ((__pyx_v_buf == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":249 * * if buf == NULL: * result = array(shape, itemsize, format, mode.decode('ASCII')) # <<<<<<<<<<<<<< * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), */ __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PyTuple_New(4); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_INCREF(__pyx_v_shape); __Pyx_GIVEREF(__pyx_v_shape); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_v_shape); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_5, 2, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 3, __pyx_t_4); __pyx_t_2 = 0; __pyx_t_3 = 0; __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_Call(((PyObject *)__pyx_array_type), __pyx_t_5, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":248 * cdef array result * * if buf == NULL: # <<<<<<<<<<<<<< * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: */ goto __pyx_L3; } /* "View.MemoryView":251 * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), # <<<<<<<<<<<<<< * allocate_buffer=False) * result.data = buf */ /*else*/ { __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_3 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_2 = PyTuple_New(4); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_v_shape); __Pyx_GIVEREF(__pyx_v_shape); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_shape); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_4); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_2, 2, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_2, 3, __pyx_t_3); __pyx_t_4 = 0; __pyx_t_5 = 0; __pyx_t_3 = 0; /* "View.MemoryView":252 * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), * allocate_buffer=False) # <<<<<<<<<<<<<< * result.data = buf * */ __pyx_t_3 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 252, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (PyDict_SetItem(__pyx_t_3, __pyx_n_s_allocate_buffer, Py_False) < 0) __PYX_ERR(2, 252, __pyx_L1_error) /* "View.MemoryView":251 * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), # <<<<<<<<<<<<<< * allocate_buffer=False) * result.data = buf */ __pyx_t_5 = __Pyx_PyObject_Call(((PyObject *)__pyx_array_type), __pyx_t_2, __pyx_t_3); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_5); __pyx_t_5 = 0; /* "View.MemoryView":253 * result = array(shape, itemsize, format, mode.decode('ASCII'), * allocate_buffer=False) * result.data = buf # <<<<<<<<<<<<<< * * return result */ __pyx_v_result->data = __pyx_v_buf; } __pyx_L3:; /* "View.MemoryView":255 * result.data = buf * * return result # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(((PyObject *)__pyx_r)); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = __pyx_v_result; goto __pyx_L0; /* "View.MemoryView":244 * * @cname("__pyx_array_new") * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format, # <<<<<<<<<<<<<< * char *mode, char *buf): * cdef array result */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.array_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF((PyObject *)__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":281 * cdef class Enum(object): * cdef object name * def __init__(self, name): # <<<<<<<<<<<<<< * self.name = name * def __repr__(self): */ /* Python wrapper */ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_name = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__init__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_name,0}; PyObject* values[1] = {0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_name)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__init__") < 0)) __PYX_ERR(2, 281, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 1) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); } __pyx_v_name = values[0]; } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__init__", 1, 1, 1, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 281, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.Enum.__init__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self), __pyx_v_name); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__init__", 0); /* "View.MemoryView":282 * cdef object name * def __init__(self, name): * self.name = name # <<<<<<<<<<<<<< * def __repr__(self): * return self.name */ __Pyx_INCREF(__pyx_v_name); __Pyx_GIVEREF(__pyx_v_name); __Pyx_GOTREF(__pyx_v_self->name); __Pyx_DECREF(__pyx_v_self->name); __pyx_v_self->name = __pyx_v_name; /* "View.MemoryView":281 * cdef class Enum(object): * cdef object name * def __init__(self, name): # <<<<<<<<<<<<<< * self.name = name * def __repr__(self): */ /* function exit code */ __pyx_r = 0; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":283 * def __init__(self, name): * self.name = name * def __repr__(self): # <<<<<<<<<<<<<< * return self.name * */ /* Python wrapper */ static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0); __pyx_r = __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__", 0); /* "View.MemoryView":284 * self.name = name * def __repr__(self): * return self.name # <<<<<<<<<<<<<< * * cdef generic = Enum("") */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->name); __pyx_r = __pyx_v_self->name; goto __pyx_L0; /* "View.MemoryView":283 * def __init__(self, name): * self.name = name * def __repr__(self): # <<<<<<<<<<<<<< * return self.name * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * cdef tuple state * cdef object _dict */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_MemviewEnum_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_MemviewEnum_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_MemviewEnum___reduce_cython__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self) { PyObject *__pyx_v_state = 0; PyObject *__pyx_v__dict = 0; int __pyx_v_use_setstate; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_t_3; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":5 * cdef object _dict * cdef bint use_setstate * state = (self.name,) # <<<<<<<<<<<<<< * _dict = getattr(self, '__dict__', None) * if _dict is not None: */ __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_v_self->name); __Pyx_GIVEREF(__pyx_v_self->name); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v_self->name); __pyx_v_state = ((PyObject*)__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":6 * cdef bint use_setstate * state = (self.name,) * _dict = getattr(self, '__dict__', None) # <<<<<<<<<<<<<< * if _dict is not None: * state += (_dict,) */ __pyx_t_1 = __Pyx_GetAttr3(((PyObject *)__pyx_v_self), __pyx_n_s_dict, Py_None); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v__dict = __pyx_t_1; __pyx_t_1 = 0; /* "(tree fragment)":7 * state = (self.name,) * _dict = getattr(self, '__dict__', None) * if _dict is not None: # <<<<<<<<<<<<<< * state += (_dict,) * use_setstate = True */ __pyx_t_2 = (__pyx_v__dict != Py_None); __pyx_t_3 = (__pyx_t_2 != 0); if (__pyx_t_3) { /* "(tree fragment)":8 * _dict = getattr(self, '__dict__', None) * if _dict is not None: * state += (_dict,) # <<<<<<<<<<<<<< * use_setstate = True * else: */ __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_v__dict); __Pyx_GIVEREF(__pyx_v__dict); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v__dict); __pyx_t_4 = PyNumber_InPlaceAdd(__pyx_v_state, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF_SET(__pyx_v_state, ((PyObject*)__pyx_t_4)); __pyx_t_4 = 0; /* "(tree fragment)":9 * if _dict is not None: * state += (_dict,) * use_setstate = True # <<<<<<<<<<<<<< * else: * use_setstate = self.name is not None */ __pyx_v_use_setstate = 1; /* "(tree fragment)":7 * state = (self.name,) * _dict = getattr(self, '__dict__', None) * if _dict is not None: # <<<<<<<<<<<<<< * state += (_dict,) * use_setstate = True */ goto __pyx_L3; } /* "(tree fragment)":11 * use_setstate = True * else: * use_setstate = self.name is not None # <<<<<<<<<<<<<< * if use_setstate: * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state */ /*else*/ { __pyx_t_3 = (__pyx_v_self->name != Py_None); __pyx_v_use_setstate = __pyx_t_3; } __pyx_L3:; /* "(tree fragment)":12 * else: * use_setstate = self.name is not None * if use_setstate: # <<<<<<<<<<<<<< * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: */ __pyx_t_3 = (__pyx_v_use_setstate != 0); if (__pyx_t_3) { /* "(tree fragment)":13 * use_setstate = self.name is not None * if use_setstate: * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state # <<<<<<<<<<<<<< * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) */ __Pyx_XDECREF(__pyx_r); __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_GIVEREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_INCREF(__pyx_int_184977713); __Pyx_GIVEREF(__pyx_int_184977713); PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_int_184977713); __Pyx_INCREF(Py_None); __Pyx_GIVEREF(Py_None); PyTuple_SET_ITEM(__pyx_t_1, 2, Py_None); __pyx_t_5 = PyTuple_New(3); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_1); __Pyx_INCREF(__pyx_v_state); __Pyx_GIVEREF(__pyx_v_state); PyTuple_SET_ITEM(__pyx_t_5, 2, __pyx_v_state); __pyx_t_4 = 0; __pyx_t_1 = 0; __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "(tree fragment)":12 * else: * use_setstate = self.name is not None * if use_setstate: # <<<<<<<<<<<<<< * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: */ } /* "(tree fragment)":15 * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /*else*/ { __Pyx_XDECREF(__pyx_r); __Pyx_GetModuleGlobalName(__pyx_t_5, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_GIVEREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_INCREF(__pyx_int_184977713); __Pyx_GIVEREF(__pyx_int_184977713); PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_int_184977713); __Pyx_INCREF(__pyx_v_state); __Pyx_GIVEREF(__pyx_v_state); PyTuple_SET_ITEM(__pyx_t_1, 2, __pyx_v_state); __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_1); __pyx_t_5 = 0; __pyx_t_1 = 0; __pyx_r = __pyx_t_4; __pyx_t_4 = 0; goto __pyx_L0; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * cdef tuple state * cdef object _dict */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.Enum.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_state); __Pyx_XDECREF(__pyx_v__dict); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":16 * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_MemviewEnum_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_MemviewEnum_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_MemviewEnum_2__setstate_cython__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":17 * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): * __pyx_unpickle_Enum__set_state(self, __pyx_state) # <<<<<<<<<<<<<< */ if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(2, 17, __pyx_L1_error) __pyx_t_1 = __pyx_unpickle_Enum__set_state(__pyx_v_self, ((PyObject*)__pyx_v___pyx_state)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 17, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":16 * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.Enum.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":298 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory */ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) { Py_intptr_t __pyx_v_aligned_p; size_t __pyx_v_offset; void *__pyx_r; int __pyx_t_1; /* "View.MemoryView":300 * cdef void *align_pointer(void *memory, size_t alignment) nogil: * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory # <<<<<<<<<<<<<< * cdef size_t offset * */ __pyx_v_aligned_p = ((Py_intptr_t)__pyx_v_memory); /* "View.MemoryView":304 * * with cython.cdivision(True): * offset = aligned_p % alignment # <<<<<<<<<<<<<< * * if offset > 0: */ __pyx_v_offset = (__pyx_v_aligned_p % __pyx_v_alignment); /* "View.MemoryView":306 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< * aligned_p += alignment - offset * */ __pyx_t_1 = ((__pyx_v_offset > 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":307 * * if offset > 0: * aligned_p += alignment - offset # <<<<<<<<<<<<<< * * return aligned_p */ __pyx_v_aligned_p = (__pyx_v_aligned_p + (__pyx_v_alignment - __pyx_v_offset)); /* "View.MemoryView":306 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< * aligned_p += alignment - offset * */ } /* "View.MemoryView":309 * aligned_p += alignment - offset * * return aligned_p # <<<<<<<<<<<<<< * * */ __pyx_r = ((void *)__pyx_v_aligned_p); goto __pyx_L0; /* "View.MemoryView":298 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":345 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< * self.obj = obj * self.flags = flags */ /* Python wrapper */ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_obj = 0; int __pyx_v_flags; int __pyx_v_dtype_is_object; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_obj,&__pyx_n_s_flags,&__pyx_n_s_dtype_is_object,0}; PyObject* values[3] = {0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_obj)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_flags)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, 1); __PYX_ERR(2, 345, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dtype_is_object); if (value) { values[2] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) __PYX_ERR(2, 345, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_obj = values[0]; __pyx_v_flags = __Pyx_PyInt_As_int(values[1]); if (unlikely((__pyx_v_flags == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 345, __pyx_L3_error) if (values[2]) { __pyx_v_dtype_is_object = __Pyx_PyObject_IsTrue(values[2]); if (unlikely((__pyx_v_dtype_is_object == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 345, __pyx_L3_error) } else { __pyx_v_dtype_is_object = ((int)0); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 345, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_obj, __pyx_v_flags, __pyx_v_dtype_is_object); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; __Pyx_RefNannySetupContext("__cinit__", 0); /* "View.MemoryView":346 * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj # <<<<<<<<<<<<<< * self.flags = flags * if type(self) is memoryview or obj is not None: */ __Pyx_INCREF(__pyx_v_obj); __Pyx_GIVEREF(__pyx_v_obj); __Pyx_GOTREF(__pyx_v_self->obj); __Pyx_DECREF(__pyx_v_self->obj); __pyx_v_self->obj = __pyx_v_obj; /* "View.MemoryView":347 * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj * self.flags = flags # <<<<<<<<<<<<<< * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) */ __pyx_v_self->flags = __pyx_v_flags; /* "View.MemoryView":348 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: */ __pyx_t_2 = (((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self))) == ((PyObject *)__pyx_memoryview_type)); __pyx_t_3 = (__pyx_t_2 != 0); if (!__pyx_t_3) { } else { __pyx_t_1 = __pyx_t_3; goto __pyx_L4_bool_binop_done; } __pyx_t_3 = (__pyx_v_obj != Py_None); __pyx_t_2 = (__pyx_t_3 != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (__pyx_t_1) { /* "View.MemoryView":349 * self.flags = flags * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) # <<<<<<<<<<<<<< * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None */ __pyx_t_4 = __Pyx_GetBuffer(__pyx_v_obj, (&__pyx_v_self->view), __pyx_v_flags); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 349, __pyx_L1_error) /* "View.MemoryView":350 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) */ __pyx_t_1 = ((((PyObject *)__pyx_v_self->view.obj) == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":351 * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_self->view))->obj = Py_None; /* "View.MemoryView":352 * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * global __pyx_memoryview_thread_locks_used */ Py_INCREF(Py_None); /* "View.MemoryView":350 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) */ } /* "View.MemoryView":348 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: */ } /* "View.MemoryView":355 * * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 */ __pyx_t_1 = ((__pyx_memoryview_thread_locks_used < 8) != 0); if (__pyx_t_1) { /* "View.MemoryView":356 * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: */ __pyx_v_self->lock = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); /* "View.MemoryView":357 * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 # <<<<<<<<<<<<<< * if self.lock is NULL: * self.lock = PyThread_allocate_lock() */ __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used + 1); /* "View.MemoryView":355 * * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 */ } /* "View.MemoryView":358 * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< * self.lock = PyThread_allocate_lock() * if self.lock is NULL: */ __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":359 * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() # <<<<<<<<<<<<<< * if self.lock is NULL: * raise MemoryError */ __pyx_v_self->lock = PyThread_allocate_lock(); /* "View.MemoryView":360 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() * if self.lock is NULL: # <<<<<<<<<<<<<< * raise MemoryError * */ __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":361 * self.lock = PyThread_allocate_lock() * if self.lock is NULL: * raise MemoryError # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ PyErr_NoMemory(); __PYX_ERR(2, 361, __pyx_L1_error) /* "View.MemoryView":360 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() * if self.lock is NULL: # <<<<<<<<<<<<<< * raise MemoryError * */ } /* "View.MemoryView":358 * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< * self.lock = PyThread_allocate_lock() * if self.lock is NULL: */ } /* "View.MemoryView":363 * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":364 * * if flags & PyBUF_FORMAT: * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') # <<<<<<<<<<<<<< * else: * self.dtype_is_object = dtype_is_object */ __pyx_t_2 = (((__pyx_v_self->view.format[0]) == 'O') != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L11_bool_binop_done; } __pyx_t_2 = (((__pyx_v_self->view.format[1]) == '\x00') != 0); __pyx_t_1 = __pyx_t_2; __pyx_L11_bool_binop_done:; __pyx_v_self->dtype_is_object = __pyx_t_1; /* "View.MemoryView":363 * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: */ goto __pyx_L10; } /* "View.MemoryView":366 * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: * self.dtype_is_object = dtype_is_object # <<<<<<<<<<<<<< * * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( */ /*else*/ { __pyx_v_self->dtype_is_object = __pyx_v_dtype_is_object; } __pyx_L10:; /* "View.MemoryView":368 * self.dtype_is_object = dtype_is_object * * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( # <<<<<<<<<<<<<< * &self.acquisition_count[0], sizeof(__pyx_atomic_int)) * self.typeinfo = NULL */ __pyx_v_self->acquisition_count_aligned_p = ((__pyx_atomic_int *)__pyx_align_pointer(((void *)(&(__pyx_v_self->acquisition_count[0]))), (sizeof(__pyx_atomic_int)))); /* "View.MemoryView":370 * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( * &self.acquisition_count[0], sizeof(__pyx_atomic_int)) * self.typeinfo = NULL # <<<<<<<<<<<<<< * * def __dealloc__(memoryview self): */ __pyx_v_self->typeinfo = NULL; /* "View.MemoryView":345 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< * self.obj = obj * self.flags = flags */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":372 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) */ /* Python wrapper */ static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self) { int __pyx_v_i; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_t_5; PyThread_type_lock __pyx_t_6; PyThread_type_lock __pyx_t_7; __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":373 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: */ __pyx_t_1 = (__pyx_v_self->obj != Py_None); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":374 * def __dealloc__(memoryview self): * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) # <<<<<<<<<<<<<< * elif (<__pyx_buffer *> &self.view).obj == Py_None: * */ __Pyx_ReleaseBuffer((&__pyx_v_self->view)); /* "View.MemoryView":373 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: */ goto __pyx_L3; } /* "View.MemoryView":375 * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: # <<<<<<<<<<<<<< * * (<__pyx_buffer *> &self.view).obj = NULL */ __pyx_t_2 = ((((Py_buffer *)(&__pyx_v_self->view))->obj == Py_None) != 0); if (__pyx_t_2) { /* "View.MemoryView":377 * elif (<__pyx_buffer *> &self.view).obj == Py_None: * * (<__pyx_buffer *> &self.view).obj = NULL # <<<<<<<<<<<<<< * Py_DECREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_self->view))->obj = NULL; /* "View.MemoryView":378 * * (<__pyx_buffer *> &self.view).obj = NULL * Py_DECREF(Py_None) # <<<<<<<<<<<<<< * * cdef int i */ Py_DECREF(Py_None); /* "View.MemoryView":375 * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: # <<<<<<<<<<<<<< * * (<__pyx_buffer *> &self.view).obj = NULL */ } __pyx_L3:; /* "View.MemoryView":382 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: */ __pyx_t_2 = ((__pyx_v_self->lock != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":383 * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): # <<<<<<<<<<<<<< * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 */ __pyx_t_3 = __pyx_memoryview_thread_locks_used; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "View.MemoryView":384 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: */ __pyx_t_2 = (((__pyx_memoryview_thread_locks[__pyx_v_i]) == __pyx_v_self->lock) != 0); if (__pyx_t_2) { /* "View.MemoryView":385 * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 # <<<<<<<<<<<<<< * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( */ __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used - 1); /* "View.MemoryView":386 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) */ __pyx_t_2 = ((__pyx_v_i != __pyx_memoryview_thread_locks_used) != 0); if (__pyx_t_2) { /* "View.MemoryView":388 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) # <<<<<<<<<<<<<< * break * else: */ __pyx_t_6 = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); __pyx_t_7 = (__pyx_memoryview_thread_locks[__pyx_v_i]); /* "View.MemoryView":387 * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) * break */ (__pyx_memoryview_thread_locks[__pyx_v_i]) = __pyx_t_6; (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]) = __pyx_t_7; /* "View.MemoryView":386 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) */ } /* "View.MemoryView":389 * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) * break # <<<<<<<<<<<<<< * else: * PyThread_free_lock(self.lock) */ goto __pyx_L6_break; /* "View.MemoryView":384 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: */ } } /*else*/ { /* "View.MemoryView":391 * break * else: * PyThread_free_lock(self.lock) # <<<<<<<<<<<<<< * * cdef char *get_item_pointer(memoryview self, object index) except NULL: */ PyThread_free_lock(__pyx_v_self->lock); } __pyx_L6_break:; /* "View.MemoryView":382 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: */ } /* "View.MemoryView":372 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":393 * PyThread_free_lock(self.lock) * * cdef char *get_item_pointer(memoryview self, object index) except NULL: # <<<<<<<<<<<<<< * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf */ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) { Py_ssize_t __pyx_v_dim; char *__pyx_v_itemp; PyObject *__pyx_v_idx = NULL; char *__pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; PyObject *__pyx_t_2 = NULL; Py_ssize_t __pyx_t_3; PyObject *(*__pyx_t_4)(PyObject *); PyObject *__pyx_t_5 = NULL; Py_ssize_t __pyx_t_6; char *__pyx_t_7; __Pyx_RefNannySetupContext("get_item_pointer", 0); /* "View.MemoryView":395 * cdef char *get_item_pointer(memoryview self, object index) except NULL: * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf # <<<<<<<<<<<<<< * * for dim, idx in enumerate(index): */ __pyx_v_itemp = ((char *)__pyx_v_self->view.buf); /* "View.MemoryView":397 * cdef char *itemp = self.view.buf * * for dim, idx in enumerate(index): # <<<<<<<<<<<<<< * itemp = pybuffer_index(&self.view, itemp, idx, dim) * */ __pyx_t_1 = 0; if (likely(PyList_CheckExact(__pyx_v_index)) || PyTuple_CheckExact(__pyx_v_index)) { __pyx_t_2 = __pyx_v_index; __Pyx_INCREF(__pyx_t_2); __pyx_t_3 = 0; __pyx_t_4 = NULL; } else { __pyx_t_3 = -1; __pyx_t_2 = PyObject_GetIter(__pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = Py_TYPE(__pyx_t_2)->tp_iternext; if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 397, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_4)) { if (likely(PyList_CheckExact(__pyx_t_2))) { if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_2)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyList_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(2, 397, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif } else { if (__pyx_t_3 >= PyTuple_GET_SIZE(__pyx_t_2)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(2, 397, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif } } else { __pyx_t_5 = __pyx_t_4(__pyx_t_2); if (unlikely(!__pyx_t_5)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(2, 397, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_5); } __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_5); __pyx_t_5 = 0; __pyx_v_dim = __pyx_t_1; __pyx_t_1 = (__pyx_t_1 + 1); /* "View.MemoryView":398 * * for dim, idx in enumerate(index): * itemp = pybuffer_index(&self.view, itemp, idx, dim) # <<<<<<<<<<<<<< * * return itemp */ __pyx_t_6 = __Pyx_PyIndex_AsSsize_t(__pyx_v_idx); if (unlikely((__pyx_t_6 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 398, __pyx_L1_error) __pyx_t_7 = __pyx_pybuffer_index((&__pyx_v_self->view), __pyx_v_itemp, __pyx_t_6, __pyx_v_dim); if (unlikely(__pyx_t_7 == ((char *)NULL))) __PYX_ERR(2, 398, __pyx_L1_error) __pyx_v_itemp = __pyx_t_7; /* "View.MemoryView":397 * cdef char *itemp = self.view.buf * * for dim, idx in enumerate(index): # <<<<<<<<<<<<<< * itemp = pybuffer_index(&self.view, itemp, idx, dim) * */ } __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":400 * itemp = pybuffer_index(&self.view, itemp, idx, dim) * * return itemp # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_itemp; goto __pyx_L0; /* "View.MemoryView":393 * PyThread_free_lock(self.lock) * * cdef char *get_item_pointer(memoryview self, object index) except NULL: # <<<<<<<<<<<<<< * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.get_item_pointer", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_idx); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":403 * * * def __getitem__(memoryview self, object index): # <<<<<<<<<<<<<< * if index is Ellipsis: * return self */ /* Python wrapper */ static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index); /*proto*/ static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) { PyObject *__pyx_v_have_slices = NULL; PyObject *__pyx_v_indices = NULL; char *__pyx_v_itemp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; char *__pyx_t_6; __Pyx_RefNannySetupContext("__getitem__", 0); /* "View.MemoryView":404 * * def __getitem__(memoryview self, object index): * if index is Ellipsis: # <<<<<<<<<<<<<< * return self * */ __pyx_t_1 = (__pyx_v_index == __pyx_builtin_Ellipsis); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":405 * def __getitem__(memoryview self, object index): * if index is Ellipsis: * return self # <<<<<<<<<<<<<< * * have_slices, indices = _unellipsify(index, self.view.ndim) */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_self)); __pyx_r = ((PyObject *)__pyx_v_self); goto __pyx_L0; /* "View.MemoryView":404 * * def __getitem__(memoryview self, object index): * if index is Ellipsis: # <<<<<<<<<<<<<< * return self * */ } /* "View.MemoryView":407 * return self * * have_slices, indices = _unellipsify(index, self.view.ndim) # <<<<<<<<<<<<<< * * cdef char *itemp */ __pyx_t_3 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (likely(__pyx_t_3 != Py_None)) { PyObject* sequence = __pyx_t_3; Py_ssize_t size = __Pyx_PySequence_SIZE(sequence); if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); __PYX_ERR(2, 407, __pyx_L1_error) } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_4 = PyTuple_GET_ITEM(sequence, 0); __pyx_t_5 = PyTuple_GET_ITEM(sequence, 1); __Pyx_INCREF(__pyx_t_4); __Pyx_INCREF(__pyx_t_5); #else __pyx_t_4 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } else { __Pyx_RaiseNoneNotIterableError(); __PYX_ERR(2, 407, __pyx_L1_error) } __pyx_v_have_slices = __pyx_t_4; __pyx_t_4 = 0; __pyx_v_indices = __pyx_t_5; __pyx_t_5 = 0; /* "View.MemoryView":410 * * cdef char *itemp * if have_slices: # <<<<<<<<<<<<<< * return memview_slice(self, indices) * else: */ __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(2, 410, __pyx_L1_error) if (__pyx_t_2) { /* "View.MemoryView":411 * cdef char *itemp * if have_slices: * return memview_slice(self, indices) # <<<<<<<<<<<<<< * else: * itemp = self.get_item_pointer(indices) */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = ((PyObject *)__pyx_memview_slice(__pyx_v_self, __pyx_v_indices)); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 411, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":410 * * cdef char *itemp * if have_slices: # <<<<<<<<<<<<<< * return memview_slice(self, indices) * else: */ } /* "View.MemoryView":413 * return memview_slice(self, indices) * else: * itemp = self.get_item_pointer(indices) # <<<<<<<<<<<<<< * return self.convert_item_to_object(itemp) * */ /*else*/ { __pyx_t_6 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_indices); if (unlikely(__pyx_t_6 == ((char *)NULL))) __PYX_ERR(2, 413, __pyx_L1_error) __pyx_v_itemp = __pyx_t_6; /* "View.MemoryView":414 * else: * itemp = self.get_item_pointer(indices) * return self.convert_item_to_object(itemp) # <<<<<<<<<<<<<< * * def __setitem__(memoryview self, object index, object value): */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->convert_item_to_object(__pyx_v_self, __pyx_v_itemp); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 414, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; } /* "View.MemoryView":403 * * * def __getitem__(memoryview self, object index): # <<<<<<<<<<<<<< * if index is Ellipsis: * return self */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_have_slices); __Pyx_XDECREF(__pyx_v_indices); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":416 * return self.convert_item_to_object(itemp) * * def __setitem__(memoryview self, object index, object value): # <<<<<<<<<<<<<< * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") */ /* Python wrapper */ static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /*proto*/ static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index), ((PyObject *)__pyx_v_value)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { PyObject *__pyx_v_have_slices = NULL; PyObject *__pyx_v_obj = NULL; int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_RefNannySetupContext("__setitem__", 0); __Pyx_INCREF(__pyx_v_index); /* "View.MemoryView":417 * * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: # <<<<<<<<<<<<<< * raise TypeError("Cannot assign to read-only memoryview") * */ __pyx_t_1 = (__pyx_v_self->view.readonly != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":418 * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") # <<<<<<<<<<<<<< * * have_slices, index = _unellipsify(index, self.view.ndim) */ __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__15, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 418, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(2, 418, __pyx_L1_error) /* "View.MemoryView":417 * * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: # <<<<<<<<<<<<<< * raise TypeError("Cannot assign to read-only memoryview") * */ } /* "View.MemoryView":420 * raise TypeError("Cannot assign to read-only memoryview") * * have_slices, index = _unellipsify(index, self.view.ndim) # <<<<<<<<<<<<<< * * if have_slices: */ __pyx_t_2 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (likely(__pyx_t_2 != Py_None)) { PyObject* sequence = __pyx_t_2; Py_ssize_t size = __Pyx_PySequence_SIZE(sequence); if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); __PYX_ERR(2, 420, __pyx_L1_error) } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_3 = PyTuple_GET_ITEM(sequence, 0); __pyx_t_4 = PyTuple_GET_ITEM(sequence, 1); __Pyx_INCREF(__pyx_t_3); __Pyx_INCREF(__pyx_t_4); #else __pyx_t_3 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); #endif __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } else { __Pyx_RaiseNoneNotIterableError(); __PYX_ERR(2, 420, __pyx_L1_error) } __pyx_v_have_slices = __pyx_t_3; __pyx_t_3 = 0; __Pyx_DECREF_SET(__pyx_v_index, __pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":422 * have_slices, index = _unellipsify(index, self.view.ndim) * * if have_slices: # <<<<<<<<<<<<<< * obj = self.is_slice(value) * if obj: */ __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 422, __pyx_L1_error) if (__pyx_t_1) { /* "View.MemoryView":423 * * if have_slices: * obj = self.is_slice(value) # <<<<<<<<<<<<<< * if obj: * self.setitem_slice_assignment(self[index], obj) */ __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->is_slice(__pyx_v_self, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 423, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_v_obj = __pyx_t_2; __pyx_t_2 = 0; /* "View.MemoryView":424 * if have_slices: * obj = self.is_slice(value) * if obj: # <<<<<<<<<<<<<< * self.setitem_slice_assignment(self[index], obj) * else: */ __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_v_obj); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 424, __pyx_L1_error) if (__pyx_t_1) { /* "View.MemoryView":425 * obj = self.is_slice(value) * if obj: * self.setitem_slice_assignment(self[index], obj) # <<<<<<<<<<<<<< * else: * self.setitem_slice_assign_scalar(self[index], value) */ __pyx_t_2 = __Pyx_PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 425, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assignment(__pyx_v_self, __pyx_t_2, __pyx_v_obj); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 425, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":424 * if have_slices: * obj = self.is_slice(value) * if obj: # <<<<<<<<<<<<<< * self.setitem_slice_assignment(self[index], obj) * else: */ goto __pyx_L5; } /* "View.MemoryView":427 * self.setitem_slice_assignment(self[index], obj) * else: * self.setitem_slice_assign_scalar(self[index], value) # <<<<<<<<<<<<<< * else: * self.setitem_indexed(index, value) */ /*else*/ { __pyx_t_4 = __Pyx_PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 427, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (!(likely(((__pyx_t_4) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_4, __pyx_memoryview_type))))) __PYX_ERR(2, 427, __pyx_L1_error) __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assign_scalar(__pyx_v_self, ((struct __pyx_memoryview_obj *)__pyx_t_4), __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 427, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_L5:; /* "View.MemoryView":422 * have_slices, index = _unellipsify(index, self.view.ndim) * * if have_slices: # <<<<<<<<<<<<<< * obj = self.is_slice(value) * if obj: */ goto __pyx_L4; } /* "View.MemoryView":429 * self.setitem_slice_assign_scalar(self[index], value) * else: * self.setitem_indexed(index, value) # <<<<<<<<<<<<<< * * cdef is_slice(self, obj): */ /*else*/ { __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_indexed(__pyx_v_self, __pyx_v_index, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 429, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_L4:; /* "View.MemoryView":416 * return self.convert_item_to_object(itemp) * * def __setitem__(memoryview self, object index, object value): # <<<<<<<<<<<<<< * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView.memoryview.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_XDECREF(__pyx_v_have_slices); __Pyx_XDECREF(__pyx_v_obj); __Pyx_XDECREF(__pyx_v_index); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":431 * self.setitem_indexed(index, value) * * cdef is_slice(self, obj): # <<<<<<<<<<<<<< * if not isinstance(obj, memoryview): * try: */ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_t_9; __Pyx_RefNannySetupContext("is_slice", 0); __Pyx_INCREF(__pyx_v_obj); /* "View.MemoryView":432 * * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): # <<<<<<<<<<<<<< * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, */ __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_obj, __pyx_memoryview_type); __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_3, &__pyx_t_4, &__pyx_t_5); __Pyx_XGOTREF(__pyx_t_3); __Pyx_XGOTREF(__pyx_t_4); __Pyx_XGOTREF(__pyx_t_5); /*try:*/ { /* "View.MemoryView":434 * if not isinstance(obj, memoryview): * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, # <<<<<<<<<<<<<< * self.dtype_is_object) * except TypeError: */ __pyx_t_6 = __Pyx_PyInt_From_int(((__pyx_v_self->flags & (~PyBUF_WRITABLE)) | PyBUF_ANY_CONTIGUOUS)); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_6); /* "View.MemoryView":435 * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) # <<<<<<<<<<<<<< * except TypeError: * return None */ __pyx_t_7 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 435, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_7); /* "View.MemoryView":434 * if not isinstance(obj, memoryview): * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, # <<<<<<<<<<<<<< * self.dtype_is_object) * except TypeError: */ __pyx_t_8 = PyTuple_New(3); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_INCREF(__pyx_v_obj); __Pyx_GIVEREF(__pyx_v_obj); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_v_obj); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_8, 1, __pyx_t_6); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_8, 2, __pyx_t_7); __pyx_t_6 = 0; __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_8, NULL); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF_SET(__pyx_v_obj, __pyx_t_7); __pyx_t_7 = 0; /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ } __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; goto __pyx_L9_try_end; __pyx_L4_error:; __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0; /* "View.MemoryView":436 * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) * except TypeError: # <<<<<<<<<<<<<< * return None * */ __pyx_t_9 = __Pyx_PyErr_ExceptionMatches(__pyx_builtin_TypeError); if (__pyx_t_9) { __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_6) < 0) __PYX_ERR(2, 436, __pyx_L6_except_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_GOTREF(__pyx_t_8); __Pyx_GOTREF(__pyx_t_6); /* "View.MemoryView":437 * self.dtype_is_object) * except TypeError: * return None # <<<<<<<<<<<<<< * * return obj */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; goto __pyx_L7_except_return; } goto __pyx_L6_except_error; __pyx_L6_except_error:; /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_XGIVEREF(__pyx_t_5); __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5); goto __pyx_L1_error; __pyx_L7_except_return:; __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_XGIVEREF(__pyx_t_5); __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5); goto __pyx_L0; __pyx_L9_try_end:; } /* "View.MemoryView":432 * * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): # <<<<<<<<<<<<<< * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, */ } /* "View.MemoryView":439 * return None * * return obj # <<<<<<<<<<<<<< * * cdef setitem_slice_assignment(self, dst, src): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_obj); __pyx_r = __pyx_v_obj; goto __pyx_L0; /* "View.MemoryView":431 * self.setitem_indexed(index, value) * * cdef is_slice(self, obj): # <<<<<<<<<<<<<< * if not isinstance(obj, memoryview): * try: */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_obj); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":441 * return obj * * cdef setitem_slice_assignment(self, dst, src): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice dst_slice * cdef __Pyx_memviewslice src_slice */ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src) { __Pyx_memviewslice __pyx_v_dst_slice; __Pyx_memviewslice __pyx_v_src_slice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; __Pyx_memviewslice *__pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; int __pyx_t_5; int __pyx_t_6; __Pyx_RefNannySetupContext("setitem_slice_assignment", 0); /* "View.MemoryView":445 * cdef __Pyx_memviewslice src_slice * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], # <<<<<<<<<<<<<< * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) */ if (!(likely(((__pyx_v_src) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_src, __pyx_memoryview_type))))) __PYX_ERR(2, 445, __pyx_L1_error) __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_src), (&__pyx_v_src_slice)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 445, __pyx_L1_error) /* "View.MemoryView":446 * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], * get_slice_from_memview(dst, &dst_slice)[0], # <<<<<<<<<<<<<< * src.ndim, dst.ndim, self.dtype_is_object) * */ if (!(likely(((__pyx_v_dst) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_dst, __pyx_memoryview_type))))) __PYX_ERR(2, 446, __pyx_L1_error) __pyx_t_2 = __pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_dst), (&__pyx_v_dst_slice)); if (unlikely(__pyx_t_2 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 446, __pyx_L1_error) /* "View.MemoryView":447 * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) # <<<<<<<<<<<<<< * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): */ __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_v_src, __pyx_n_s_ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_4 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_v_dst, __pyx_n_s_ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_5 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_5 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":445 * cdef __Pyx_memviewslice src_slice * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], # <<<<<<<<<<<<<< * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) */ __pyx_t_6 = __pyx_memoryview_copy_contents((__pyx_t_1[0]), (__pyx_t_2[0]), __pyx_t_4, __pyx_t_5, __pyx_v_self->dtype_is_object); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 445, __pyx_L1_error) /* "View.MemoryView":441 * return obj * * cdef setitem_slice_assignment(self, dst, src): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice dst_slice * cdef __Pyx_memviewslice src_slice */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assignment", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":449 * src.ndim, dst.ndim, self.dtype_is_object) * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): # <<<<<<<<<<<<<< * cdef int array[128] * cdef void *tmp = NULL */ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value) { int __pyx_v_array[0x80]; void *__pyx_v_tmp; void *__pyx_v_item; __Pyx_memviewslice *__pyx_v_dst_slice; __Pyx_memviewslice __pyx_v_tmp_slice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; int __pyx_t_5; char const *__pyx_t_6; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; PyObject *__pyx_t_9 = NULL; PyObject *__pyx_t_10 = NULL; PyObject *__pyx_t_11 = NULL; PyObject *__pyx_t_12 = NULL; __Pyx_RefNannySetupContext("setitem_slice_assign_scalar", 0); /* "View.MemoryView":451 * cdef setitem_slice_assign_scalar(self, memoryview dst, value): * cdef int array[128] * cdef void *tmp = NULL # <<<<<<<<<<<<<< * cdef void *item * */ __pyx_v_tmp = NULL; /* "View.MemoryView":456 * cdef __Pyx_memviewslice *dst_slice * cdef __Pyx_memviewslice tmp_slice * dst_slice = get_slice_from_memview(dst, &tmp_slice) # <<<<<<<<<<<<<< * * if self.view.itemsize > sizeof(array): */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_dst, (&__pyx_v_tmp_slice)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 456, __pyx_L1_error) __pyx_v_dst_slice = __pyx_t_1; /* "View.MemoryView":458 * dst_slice = get_slice_from_memview(dst, &tmp_slice) * * if self.view.itemsize > sizeof(array): # <<<<<<<<<<<<<< * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: */ __pyx_t_2 = ((((size_t)__pyx_v_self->view.itemsize) > (sizeof(__pyx_v_array))) != 0); if (__pyx_t_2) { /* "View.MemoryView":459 * * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) # <<<<<<<<<<<<<< * if tmp == NULL: * raise MemoryError */ __pyx_v_tmp = PyMem_Malloc(__pyx_v_self->view.itemsize); /* "View.MemoryView":460 * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: # <<<<<<<<<<<<<< * raise MemoryError * item = tmp */ __pyx_t_2 = ((__pyx_v_tmp == NULL) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":461 * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: * raise MemoryError # <<<<<<<<<<<<<< * item = tmp * else: */ PyErr_NoMemory(); __PYX_ERR(2, 461, __pyx_L1_error) /* "View.MemoryView":460 * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: # <<<<<<<<<<<<<< * raise MemoryError * item = tmp */ } /* "View.MemoryView":462 * if tmp == NULL: * raise MemoryError * item = tmp # <<<<<<<<<<<<<< * else: * item = array */ __pyx_v_item = __pyx_v_tmp; /* "View.MemoryView":458 * dst_slice = get_slice_from_memview(dst, &tmp_slice) * * if self.view.itemsize > sizeof(array): # <<<<<<<<<<<<<< * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: */ goto __pyx_L3; } /* "View.MemoryView":464 * item = tmp * else: * item = array # <<<<<<<<<<<<<< * * try: */ /*else*/ { __pyx_v_item = ((void *)__pyx_v_array); } __pyx_L3:; /* "View.MemoryView":466 * item = array * * try: # <<<<<<<<<<<<<< * if self.dtype_is_object: * ( item)[0] = value */ /*try:*/ { /* "View.MemoryView":467 * * try: * if self.dtype_is_object: # <<<<<<<<<<<<<< * ( item)[0] = value * else: */ __pyx_t_2 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_2) { /* "View.MemoryView":468 * try: * if self.dtype_is_object: * ( item)[0] = value # <<<<<<<<<<<<<< * else: * self.assign_item_from_object( item, value) */ (((PyObject **)__pyx_v_item)[0]) = ((PyObject *)__pyx_v_value); /* "View.MemoryView":467 * * try: * if self.dtype_is_object: # <<<<<<<<<<<<<< * ( item)[0] = value * else: */ goto __pyx_L8; } /* "View.MemoryView":470 * ( item)[0] = value * else: * self.assign_item_from_object( item, value) # <<<<<<<<<<<<<< * * */ /*else*/ { __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, ((char *)__pyx_v_item), __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 470, __pyx_L6_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } __pyx_L8:; /* "View.MemoryView":474 * * * if self.view.suboffsets != NULL: # <<<<<<<<<<<<<< * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, */ __pyx_t_2 = ((__pyx_v_self->view.suboffsets != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":475 * * if self.view.suboffsets != NULL: * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) # <<<<<<<<<<<<<< * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, * item, self.dtype_is_object) */ __pyx_t_3 = assert_direct_dimensions(__pyx_v_self->view.suboffsets, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 475, __pyx_L6_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":474 * * * if self.view.suboffsets != NULL: # <<<<<<<<<<<<<< * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, */ } /* "View.MemoryView":476 * if self.view.suboffsets != NULL: * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, # <<<<<<<<<<<<<< * item, self.dtype_is_object) * finally: */ __pyx_memoryview_slice_assign_scalar(__pyx_v_dst_slice, __pyx_v_dst->view.ndim, __pyx_v_self->view.itemsize, __pyx_v_item, __pyx_v_self->dtype_is_object); } /* "View.MemoryView":479 * item, self.dtype_is_object) * finally: * PyMem_Free(tmp) # <<<<<<<<<<<<<< * * cdef setitem_indexed(self, index, value): */ /*finally:*/ { /*normal exit:*/{ PyMem_Free(__pyx_v_tmp); goto __pyx_L7; } __pyx_L6_error:; /*exception exit:*/{ __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0; __pyx_t_12 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; if (PY_MAJOR_VERSION >= 3) __Pyx_ExceptionSwap(&__pyx_t_10, &__pyx_t_11, &__pyx_t_12); if ((PY_MAJOR_VERSION < 3) || unlikely(__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_9) < 0)) __Pyx_ErrFetch(&__pyx_t_7, &__pyx_t_8, &__pyx_t_9); __Pyx_XGOTREF(__pyx_t_7); __Pyx_XGOTREF(__pyx_t_8); __Pyx_XGOTREF(__pyx_t_9); __Pyx_XGOTREF(__pyx_t_10); __Pyx_XGOTREF(__pyx_t_11); __Pyx_XGOTREF(__pyx_t_12); __pyx_t_4 = __pyx_lineno; __pyx_t_5 = __pyx_clineno; __pyx_t_6 = __pyx_filename; { PyMem_Free(__pyx_v_tmp); } if (PY_MAJOR_VERSION >= 3) { __Pyx_XGIVEREF(__pyx_t_10); __Pyx_XGIVEREF(__pyx_t_11); __Pyx_XGIVEREF(__pyx_t_12); __Pyx_ExceptionReset(__pyx_t_10, __pyx_t_11, __pyx_t_12); } __Pyx_XGIVEREF(__pyx_t_7); __Pyx_XGIVEREF(__pyx_t_8); __Pyx_XGIVEREF(__pyx_t_9); __Pyx_ErrRestore(__pyx_t_7, __pyx_t_8, __pyx_t_9); __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0; __pyx_t_12 = 0; __pyx_lineno = __pyx_t_4; __pyx_clineno = __pyx_t_5; __pyx_filename = __pyx_t_6; goto __pyx_L1_error; } __pyx_L7:; } /* "View.MemoryView":449 * src.ndim, dst.ndim, self.dtype_is_object) * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): # <<<<<<<<<<<<<< * cdef int array[128] * cdef void *tmp = NULL */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assign_scalar", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":481 * PyMem_Free(tmp) * * cdef setitem_indexed(self, index, value): # <<<<<<<<<<<<<< * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) */ static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { char *__pyx_v_itemp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations char *__pyx_t_1; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("setitem_indexed", 0); /* "View.MemoryView":482 * * cdef setitem_indexed(self, index, value): * cdef char *itemp = self.get_item_pointer(index) # <<<<<<<<<<<<<< * self.assign_item_from_object(itemp, value) * */ __pyx_t_1 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_index); if (unlikely(__pyx_t_1 == ((char *)NULL))) __PYX_ERR(2, 482, __pyx_L1_error) __pyx_v_itemp = __pyx_t_1; /* "View.MemoryView":483 * cdef setitem_indexed(self, index, value): * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) # <<<<<<<<<<<<<< * * cdef convert_item_to_object(self, char *itemp): */ __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 483, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":481 * PyMem_Free(tmp) * * cdef setitem_indexed(self, index, value): # <<<<<<<<<<<<<< * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_indexed", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":485 * self.assign_item_from_object(itemp, value) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp) { PyObject *__pyx_v_struct = NULL; PyObject *__pyx_v_bytesitem = 0; PyObject *__pyx_v_result = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; int __pyx_t_8; PyObject *__pyx_t_9 = NULL; size_t __pyx_t_10; int __pyx_t_11; __Pyx_RefNannySetupContext("convert_item_to_object", 0); /* "View.MemoryView":488 * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" * import struct # <<<<<<<<<<<<<< * cdef bytes bytesitem * */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 488, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_struct = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":491 * cdef bytes bytesitem * * bytesitem = itemp[:self.view.itemsize] # <<<<<<<<<<<<<< * try: * result = struct.unpack(self.view.format, bytesitem) */ __pyx_t_1 = __Pyx_PyBytes_FromStringAndSize(__pyx_v_itemp + 0, __pyx_v_self->view.itemsize - 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 491, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_bytesitem = ((PyObject*)__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_2, &__pyx_t_3, &__pyx_t_4); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); __Pyx_XGOTREF(__pyx_t_4); /*try:*/ { /* "View.MemoryView":493 * bytesitem = itemp[:self.view.itemsize] * try: * result = struct.unpack(self.view.format, bytesitem) # <<<<<<<<<<<<<< * except struct.error: * raise ValueError("Unable to convert item to object") */ __pyx_t_5 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_unpack); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = NULL; __pyx_t_8 = 0; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_5))) { __pyx_t_7 = PyMethod_GET_SELF(__pyx_t_5); if (likely(__pyx_t_7)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_5); __Pyx_INCREF(__pyx_t_7); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_5, function); __pyx_t_8 = 1; } } #if CYTHON_FAST_PYCALL if (PyFunction_Check(__pyx_t_5)) { PyObject *__pyx_temp[3] = {__pyx_t_7, __pyx_t_6, __pyx_v_bytesitem}; __pyx_t_1 = __Pyx_PyFunction_FastCall(__pyx_t_5, __pyx_temp+1-__pyx_t_8, 2+__pyx_t_8); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } else #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(__pyx_t_5)) { PyObject *__pyx_temp[3] = {__pyx_t_7, __pyx_t_6, __pyx_v_bytesitem}; __pyx_t_1 = __Pyx_PyCFunction_FastCall(__pyx_t_5, __pyx_temp+1-__pyx_t_8, 2+__pyx_t_8); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } else #endif { __pyx_t_9 = PyTuple_New(2+__pyx_t_8); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_9); if (__pyx_t_7) { __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __pyx_t_7 = NULL; } __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_9, 0+__pyx_t_8, __pyx_t_6); __Pyx_INCREF(__pyx_v_bytesitem); __Pyx_GIVEREF(__pyx_v_bytesitem); PyTuple_SET_ITEM(__pyx_t_9, 1+__pyx_t_8, __pyx_v_bytesitem); __pyx_t_6 = 0; __pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_5, __pyx_t_9, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_result = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ } /* "View.MemoryView":497 * raise ValueError("Unable to convert item to object") * else: * if len(self.view.format) == 1: # <<<<<<<<<<<<<< * return result[0] * return result */ /*else:*/ { __pyx_t_10 = strlen(__pyx_v_self->view.format); __pyx_t_11 = ((__pyx_t_10 == 1) != 0); if (__pyx_t_11) { /* "View.MemoryView":498 * else: * if len(self.view.format) == 1: * return result[0] # <<<<<<<<<<<<<< * return result * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_GetItemInt(__pyx_v_result, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 498, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L6_except_return; /* "View.MemoryView":497 * raise ValueError("Unable to convert item to object") * else: * if len(self.view.format) == 1: # <<<<<<<<<<<<<< * return result[0] * return result */ } /* "View.MemoryView":499 * if len(self.view.format) == 1: * return result[0] * return result # <<<<<<<<<<<<<< * * cdef assign_item_from_object(self, char *itemp, object value): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_result); __pyx_r = __pyx_v_result; goto __pyx_L6_except_return; } __pyx_L3_error:; __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_XDECREF(__pyx_t_9); __pyx_t_9 = 0; /* "View.MemoryView":494 * try: * result = struct.unpack(self.view.format, bytesitem) * except struct.error: # <<<<<<<<<<<<<< * raise ValueError("Unable to convert item to object") * else: */ __Pyx_ErrFetch(&__pyx_t_1, &__pyx_t_5, &__pyx_t_9); __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_error); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 494, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_8 = __Pyx_PyErr_GivenExceptionMatches(__pyx_t_1, __pyx_t_6); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_ErrRestore(__pyx_t_1, __pyx_t_5, __pyx_t_9); __pyx_t_1 = 0; __pyx_t_5 = 0; __pyx_t_9 = 0; if (__pyx_t_8) { __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_9, &__pyx_t_5, &__pyx_t_1) < 0) __PYX_ERR(2, 494, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_1); /* "View.MemoryView":495 * result = struct.unpack(self.view.format, bytesitem) * except struct.error: * raise ValueError("Unable to convert item to object") # <<<<<<<<<<<<<< * else: * if len(self.view.format) == 1: */ __pyx_t_6 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__16, NULL); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 495, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_Raise(__pyx_t_6, 0, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __PYX_ERR(2, 495, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4); goto __pyx_L1_error; __pyx_L6_except_return:; __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4); goto __pyx_L0; } /* "View.MemoryView":485 * self.assign_item_from_object(itemp, value) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_9); __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_struct); __Pyx_XDECREF(__pyx_v_bytesitem); __Pyx_XDECREF(__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":501 * return result * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) { PyObject *__pyx_v_struct = NULL; char __pyx_v_c; PyObject *__pyx_v_bytesvalue = 0; Py_ssize_t __pyx_v_i; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_t_3; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; int __pyx_t_7; PyObject *__pyx_t_8 = NULL; Py_ssize_t __pyx_t_9; PyObject *__pyx_t_10 = NULL; char *__pyx_t_11; char *__pyx_t_12; char *__pyx_t_13; char *__pyx_t_14; __Pyx_RefNannySetupContext("assign_item_from_object", 0); /* "View.MemoryView":504 * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" * import struct # <<<<<<<<<<<<<< * cdef char c * cdef bytes bytesvalue */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 504, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_struct = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":509 * cdef Py_ssize_t i * * if isinstance(value, tuple): # <<<<<<<<<<<<<< * bytesvalue = struct.pack(self.view.format, *value) * else: */ __pyx_t_2 = PyTuple_Check(__pyx_v_value); __pyx_t_3 = (__pyx_t_2 != 0); if (__pyx_t_3) { /* "View.MemoryView":510 * * if isinstance(value, tuple): * bytesvalue = struct.pack(self.view.format, *value) # <<<<<<<<<<<<<< * else: * bytesvalue = struct.pack(self.view.format, value) */ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_4 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PySequence_Tuple(__pyx_v_value); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_6 = PyNumber_Add(__pyx_t_5, __pyx_t_4); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_6, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (!(likely(PyBytes_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_4)->tp_name), 0))) __PYX_ERR(2, 510, __pyx_L1_error) __pyx_v_bytesvalue = ((PyObject*)__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":509 * cdef Py_ssize_t i * * if isinstance(value, tuple): # <<<<<<<<<<<<<< * bytesvalue = struct.pack(self.view.format, *value) * else: */ goto __pyx_L3; } /* "View.MemoryView":512 * bytesvalue = struct.pack(self.view.format, *value) * else: * bytesvalue = struct.pack(self.view.format, value) # <<<<<<<<<<<<<< * * for i, c in enumerate(bytesvalue): */ /*else*/ { __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_1 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_5 = NULL; __pyx_t_7 = 0; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_6))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_6); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_6); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_6, function); __pyx_t_7 = 1; } } #if CYTHON_FAST_PYCALL if (PyFunction_Check(__pyx_t_6)) { PyObject *__pyx_temp[3] = {__pyx_t_5, __pyx_t_1, __pyx_v_value}; __pyx_t_4 = __Pyx_PyFunction_FastCall(__pyx_t_6, __pyx_temp+1-__pyx_t_7, 2+__pyx_t_7); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; } else #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(__pyx_t_6)) { PyObject *__pyx_temp[3] = {__pyx_t_5, __pyx_t_1, __pyx_v_value}; __pyx_t_4 = __Pyx_PyCFunction_FastCall(__pyx_t_6, __pyx_temp+1-__pyx_t_7, 2+__pyx_t_7); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; } else #endif { __pyx_t_8 = PyTuple_New(2+__pyx_t_7); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); if (__pyx_t_5) { __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_5); __pyx_t_5 = NULL; } __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_8, 0+__pyx_t_7, __pyx_t_1); __Pyx_INCREF(__pyx_v_value); __Pyx_GIVEREF(__pyx_v_value); PyTuple_SET_ITEM(__pyx_t_8, 1+__pyx_t_7, __pyx_v_value); __pyx_t_1 = 0; __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_t_8, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; } __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (!(likely(PyBytes_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_4)->tp_name), 0))) __PYX_ERR(2, 512, __pyx_L1_error) __pyx_v_bytesvalue = ((PyObject*)__pyx_t_4); __pyx_t_4 = 0; } __pyx_L3:; /* "View.MemoryView":514 * bytesvalue = struct.pack(self.view.format, value) * * for i, c in enumerate(bytesvalue): # <<<<<<<<<<<<<< * itemp[i] = c * */ __pyx_t_9 = 0; if (unlikely(__pyx_v_bytesvalue == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' is not iterable"); __PYX_ERR(2, 514, __pyx_L1_error) } __Pyx_INCREF(__pyx_v_bytesvalue); __pyx_t_10 = __pyx_v_bytesvalue; __pyx_t_12 = PyBytes_AS_STRING(__pyx_t_10); __pyx_t_13 = (__pyx_t_12 + PyBytes_GET_SIZE(__pyx_t_10)); for (__pyx_t_14 = __pyx_t_12; __pyx_t_14 < __pyx_t_13; __pyx_t_14++) { __pyx_t_11 = __pyx_t_14; __pyx_v_c = (__pyx_t_11[0]); /* "View.MemoryView":515 * * for i, c in enumerate(bytesvalue): * itemp[i] = c # <<<<<<<<<<<<<< * * @cname('getbuffer') */ __pyx_v_i = __pyx_t_9; /* "View.MemoryView":514 * bytesvalue = struct.pack(self.view.format, value) * * for i, c in enumerate(bytesvalue): # <<<<<<<<<<<<<< * itemp[i] = c * */ __pyx_t_9 = (__pyx_t_9 + 1); /* "View.MemoryView":515 * * for i, c in enumerate(bytesvalue): * itemp[i] = c # <<<<<<<<<<<<<< * * @cname('getbuffer') */ (__pyx_v_itemp[__pyx_v_i]) = __pyx_v_c; } __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; /* "View.MemoryView":501 * return result * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_8); __Pyx_XDECREF(__pyx_t_10); __Pyx_AddTraceback("View.MemoryView.memoryview.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_struct); __Pyx_XDECREF(__pyx_v_bytesvalue); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":518 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") */ /* Python wrapper */ static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; Py_ssize_t *__pyx_t_4; char *__pyx_t_5; void *__pyx_t_6; int __pyx_t_7; Py_ssize_t __pyx_t_8; if (__pyx_v_info == NULL) { PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete"); return -1; } __Pyx_RefNannySetupContext("__getbuffer__", 0); __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None); __Pyx_GIVEREF(__pyx_v_info->obj); /* "View.MemoryView":519 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: # <<<<<<<<<<<<<< * raise ValueError("Cannot create writable memory view from read-only memoryview") * */ __pyx_t_2 = ((__pyx_v_flags & PyBUF_WRITABLE) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } __pyx_t_2 = (__pyx_v_self->view.readonly != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (unlikely(__pyx_t_1)) { /* "View.MemoryView":520 * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") # <<<<<<<<<<<<<< * * if flags & PyBUF_ND: */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__17, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 520, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 520, __pyx_L1_error) /* "View.MemoryView":519 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: # <<<<<<<<<<<<<< * raise ValueError("Cannot create writable memory view from read-only memoryview") * */ } /* "View.MemoryView":522 * raise ValueError("Cannot create writable memory view from read-only memoryview") * * if flags & PyBUF_ND: # <<<<<<<<<<<<<< * info.shape = self.view.shape * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_ND) != 0); if (__pyx_t_1) { /* "View.MemoryView":523 * * if flags & PyBUF_ND: * info.shape = self.view.shape # <<<<<<<<<<<<<< * else: * info.shape = NULL */ __pyx_t_4 = __pyx_v_self->view.shape; __pyx_v_info->shape = __pyx_t_4; /* "View.MemoryView":522 * raise ValueError("Cannot create writable memory view from read-only memoryview") * * if flags & PyBUF_ND: # <<<<<<<<<<<<<< * info.shape = self.view.shape * else: */ goto __pyx_L6; } /* "View.MemoryView":525 * info.shape = self.view.shape * else: * info.shape = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_STRIDES: */ /*else*/ { __pyx_v_info->shape = NULL; } __pyx_L6:; /* "View.MemoryView":527 * info.shape = NULL * * if flags & PyBUF_STRIDES: # <<<<<<<<<<<<<< * info.strides = self.view.strides * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_STRIDES) != 0); if (__pyx_t_1) { /* "View.MemoryView":528 * * if flags & PyBUF_STRIDES: * info.strides = self.view.strides # <<<<<<<<<<<<<< * else: * info.strides = NULL */ __pyx_t_4 = __pyx_v_self->view.strides; __pyx_v_info->strides = __pyx_t_4; /* "View.MemoryView":527 * info.shape = NULL * * if flags & PyBUF_STRIDES: # <<<<<<<<<<<<<< * info.strides = self.view.strides * else: */ goto __pyx_L7; } /* "View.MemoryView":530 * info.strides = self.view.strides * else: * info.strides = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_INDIRECT: */ /*else*/ { __pyx_v_info->strides = NULL; } __pyx_L7:; /* "View.MemoryView":532 * info.strides = NULL * * if flags & PyBUF_INDIRECT: # <<<<<<<<<<<<<< * info.suboffsets = self.view.suboffsets * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_INDIRECT) != 0); if (__pyx_t_1) { /* "View.MemoryView":533 * * if flags & PyBUF_INDIRECT: * info.suboffsets = self.view.suboffsets # <<<<<<<<<<<<<< * else: * info.suboffsets = NULL */ __pyx_t_4 = __pyx_v_self->view.suboffsets; __pyx_v_info->suboffsets = __pyx_t_4; /* "View.MemoryView":532 * info.strides = NULL * * if flags & PyBUF_INDIRECT: # <<<<<<<<<<<<<< * info.suboffsets = self.view.suboffsets * else: */ goto __pyx_L8; } /* "View.MemoryView":535 * info.suboffsets = self.view.suboffsets * else: * info.suboffsets = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ /*else*/ { __pyx_v_info->suboffsets = NULL; } __pyx_L8:; /* "View.MemoryView":537 * info.suboffsets = NULL * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.view.format * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":538 * * if flags & PyBUF_FORMAT: * info.format = self.view.format # <<<<<<<<<<<<<< * else: * info.format = NULL */ __pyx_t_5 = __pyx_v_self->view.format; __pyx_v_info->format = __pyx_t_5; /* "View.MemoryView":537 * info.suboffsets = NULL * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.view.format * else: */ goto __pyx_L9; } /* "View.MemoryView":540 * info.format = self.view.format * else: * info.format = NULL # <<<<<<<<<<<<<< * * info.buf = self.view.buf */ /*else*/ { __pyx_v_info->format = NULL; } __pyx_L9:; /* "View.MemoryView":542 * info.format = NULL * * info.buf = self.view.buf # <<<<<<<<<<<<<< * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize */ __pyx_t_6 = __pyx_v_self->view.buf; __pyx_v_info->buf = __pyx_t_6; /* "View.MemoryView":543 * * info.buf = self.view.buf * info.ndim = self.view.ndim # <<<<<<<<<<<<<< * info.itemsize = self.view.itemsize * info.len = self.view.len */ __pyx_t_7 = __pyx_v_self->view.ndim; __pyx_v_info->ndim = __pyx_t_7; /* "View.MemoryView":544 * info.buf = self.view.buf * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize # <<<<<<<<<<<<<< * info.len = self.view.len * info.readonly = self.view.readonly */ __pyx_t_8 = __pyx_v_self->view.itemsize; __pyx_v_info->itemsize = __pyx_t_8; /* "View.MemoryView":545 * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize * info.len = self.view.len # <<<<<<<<<<<<<< * info.readonly = self.view.readonly * info.obj = self */ __pyx_t_8 = __pyx_v_self->view.len; __pyx_v_info->len = __pyx_t_8; /* "View.MemoryView":546 * info.itemsize = self.view.itemsize * info.len = self.view.len * info.readonly = self.view.readonly # <<<<<<<<<<<<<< * info.obj = self * */ __pyx_t_1 = __pyx_v_self->view.readonly; __pyx_v_info->readonly = __pyx_t_1; /* "View.MemoryView":547 * info.len = self.view.len * info.readonly = self.view.readonly * info.obj = self # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = ((PyObject *)__pyx_v_self); /* "View.MemoryView":518 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; if (__pyx_v_info->obj != NULL) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } goto __pyx_L2; __pyx_L0:; if (__pyx_v_info->obj == Py_None) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } __pyx_L2:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":553 * * @property * def T(self): # <<<<<<<<<<<<<< * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self) { struct __pyx_memoryviewslice_obj *__pyx_v_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":554 * @property * def T(self): * cdef _memoryviewslice result = memoryview_copy(self) # <<<<<<<<<<<<<< * transpose_memslice(&result.from_slice) * return result */ __pyx_t_1 = __pyx_memoryview_copy_object(__pyx_v_self); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 554, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (!(likely(((__pyx_t_1) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_1, __pyx_memoryviewslice_type))))) __PYX_ERR(2, 554, __pyx_L1_error) __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":555 * def T(self): * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) # <<<<<<<<<<<<<< * return result * */ __pyx_t_2 = __pyx_memslice_transpose((&__pyx_v_result->from_slice)); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(2, 555, __pyx_L1_error) /* "View.MemoryView":556 * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) * return result # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":553 * * @property * def T(self): # <<<<<<<<<<<<<< * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.T.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":559 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.obj * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":560 * @property * def base(self): * return self.obj # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->obj); __pyx_r = __pyx_v_self->obj; goto __pyx_L0; /* "View.MemoryView":559 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.obj * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":563 * * @property * def shape(self): # <<<<<<<<<<<<<< * return tuple([length for length in self.view.shape[:self.view.ndim]]) * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_length; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; PyObject *__pyx_t_5 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":564 * @property * def shape(self): * return tuple([length for length in self.view.shape[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyList_New(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = (__pyx_v_self->view.shape + __pyx_v_self->view.ndim); for (__pyx_t_4 = __pyx_v_self->view.shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { __pyx_t_2 = __pyx_t_4; __pyx_v_length = (__pyx_t_2[0]); __pyx_t_5 = PyInt_FromSsize_t(__pyx_v_length); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); if (unlikely(__Pyx_ListComp_Append(__pyx_t_1, (PyObject*)__pyx_t_5))) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; } __pyx_t_5 = PyList_AsTuple(((PyObject*)__pyx_t_1)); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "View.MemoryView":563 * * @property * def shape(self): # <<<<<<<<<<<<<< * return tuple([length for length in self.view.shape[:self.view.ndim]]) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.shape.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":567 * * @property * def strides(self): # <<<<<<<<<<<<<< * if self.view.strides == NULL: * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_stride; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; PyObject *__pyx_t_6 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":568 * @property * def strides(self): * if self.view.strides == NULL: # <<<<<<<<<<<<<< * * raise ValueError("Buffer view does not expose strides") */ __pyx_t_1 = ((__pyx_v_self->view.strides == NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":570 * if self.view.strides == NULL: * * raise ValueError("Buffer view does not expose strides") # <<<<<<<<<<<<<< * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) */ __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__18, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 570, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(2, 570, __pyx_L1_error) /* "View.MemoryView":568 * @property * def strides(self): * if self.view.strides == NULL: # <<<<<<<<<<<<<< * * raise ValueError("Buffer view does not expose strides") */ } /* "View.MemoryView":572 * raise ValueError("Buffer view does not expose strides") * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = (__pyx_v_self->view.strides + __pyx_v_self->view.ndim); for (__pyx_t_5 = __pyx_v_self->view.strides; __pyx_t_5 < __pyx_t_4; __pyx_t_5++) { __pyx_t_3 = __pyx_t_5; __pyx_v_stride = (__pyx_t_3[0]); __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_stride); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); if (unlikely(__Pyx_ListComp_Append(__pyx_t_2, (PyObject*)__pyx_t_6))) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } __pyx_t_6 = PyList_AsTuple(((PyObject*)__pyx_t_2)); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_6; __pyx_t_6 = 0; goto __pyx_L0; /* "View.MemoryView":567 * * @property * def strides(self): # <<<<<<<<<<<<<< * if self.view.strides == NULL: * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_6); __Pyx_AddTraceback("View.MemoryView.memoryview.strides.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":575 * * @property * def suboffsets(self): # <<<<<<<<<<<<<< * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; Py_ssize_t *__pyx_t_6; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":576 * @property * def suboffsets(self): * if self.view.suboffsets == NULL: # <<<<<<<<<<<<<< * return (-1,) * self.view.ndim * */ __pyx_t_1 = ((__pyx_v_self->view.suboffsets == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":577 * def suboffsets(self): * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim # <<<<<<<<<<<<<< * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyInt_From_int(__pyx_v_self->view.ndim); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_Multiply(__pyx_tuple__19, __pyx_t_2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":576 * @property * def suboffsets(self): * if self.view.suboffsets == NULL: # <<<<<<<<<<<<<< * return (-1,) * self.view.ndim * */ } /* "View.MemoryView":579 * return (-1,) * self.view.ndim * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_5 = (__pyx_v_self->view.suboffsets + __pyx_v_self->view.ndim); for (__pyx_t_6 = __pyx_v_self->view.suboffsets; __pyx_t_6 < __pyx_t_5; __pyx_t_6++) { __pyx_t_4 = __pyx_t_6; __pyx_v_suboffset = (__pyx_t_4[0]); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_suboffset); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (unlikely(__Pyx_ListComp_Append(__pyx_t_3, (PyObject*)__pyx_t_2))) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_t_2 = PyList_AsTuple(((PyObject*)__pyx_t_3)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":575 * * @property * def suboffsets(self): # <<<<<<<<<<<<<< * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.suboffsets.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":582 * * @property * def ndim(self): # <<<<<<<<<<<<<< * return self.view.ndim * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":583 * @property * def ndim(self): * return self.view.ndim # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_self->view.ndim); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 583, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":582 * * @property * def ndim(self): # <<<<<<<<<<<<<< * return self.view.ndim * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.ndim.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":586 * * @property * def itemsize(self): # <<<<<<<<<<<<<< * return self.view.itemsize * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":587 * @property * def itemsize(self): * return self.view.itemsize # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 587, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":586 * * @property * def itemsize(self): # <<<<<<<<<<<<<< * return self.view.itemsize * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.itemsize.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":590 * * @property * def nbytes(self): # <<<<<<<<<<<<<< * return self.size * self.view.itemsize * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":591 * @property * def nbytes(self): * return self.size * self.view.itemsize # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_size); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_Multiply(__pyx_t_1, __pyx_t_2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":590 * * @property * def nbytes(self): # <<<<<<<<<<<<<< * return self.size * self.view.itemsize * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.nbytes.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":594 * * @property * def size(self): # <<<<<<<<<<<<<< * if self._size is None: * result = 1 */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_v_result = NULL; PyObject *__pyx_v_length = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; PyObject *__pyx_t_6 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":595 * @property * def size(self): * if self._size is None: # <<<<<<<<<<<<<< * result = 1 * */ __pyx_t_1 = (__pyx_v_self->_size == Py_None); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":596 * def size(self): * if self._size is None: * result = 1 # <<<<<<<<<<<<<< * * for length in self.view.shape[:self.view.ndim]: */ __Pyx_INCREF(__pyx_int_1); __pyx_v_result = __pyx_int_1; /* "View.MemoryView":598 * result = 1 * * for length in self.view.shape[:self.view.ndim]: # <<<<<<<<<<<<<< * result *= length * */ __pyx_t_4 = (__pyx_v_self->view.shape + __pyx_v_self->view.ndim); for (__pyx_t_5 = __pyx_v_self->view.shape; __pyx_t_5 < __pyx_t_4; __pyx_t_5++) { __pyx_t_3 = __pyx_t_5; __pyx_t_6 = PyInt_FromSsize_t((__pyx_t_3[0])); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 598, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_6); __pyx_t_6 = 0; /* "View.MemoryView":599 * * for length in self.view.shape[:self.view.ndim]: * result *= length # <<<<<<<<<<<<<< * * self._size = result */ __pyx_t_6 = PyNumber_InPlaceMultiply(__pyx_v_result, __pyx_v_length); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 599, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF_SET(__pyx_v_result, __pyx_t_6); __pyx_t_6 = 0; } /* "View.MemoryView":601 * result *= length * * self._size = result # <<<<<<<<<<<<<< * * return self._size */ __Pyx_INCREF(__pyx_v_result); __Pyx_GIVEREF(__pyx_v_result); __Pyx_GOTREF(__pyx_v_self->_size); __Pyx_DECREF(__pyx_v_self->_size); __pyx_v_self->_size = __pyx_v_result; /* "View.MemoryView":595 * @property * def size(self): * if self._size is None: # <<<<<<<<<<<<<< * result = 1 * */ } /* "View.MemoryView":603 * self._size = result * * return self._size # <<<<<<<<<<<<<< * * def __len__(self): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->_size); __pyx_r = __pyx_v_self->_size; goto __pyx_L0; /* "View.MemoryView":594 * * @property * def size(self): # <<<<<<<<<<<<<< * if self._size is None: * result = 1 */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_6); __Pyx_AddTraceback("View.MemoryView.memoryview.size.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_result); __Pyx_XDECREF(__pyx_v_length); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":605 * return self._size * * def __len__(self): # <<<<<<<<<<<<<< * if self.view.ndim >= 1: * return self.view.shape[0] */ /* Python wrapper */ static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self); /*proto*/ static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("__len__", 0); /* "View.MemoryView":606 * * def __len__(self): * if self.view.ndim >= 1: # <<<<<<<<<<<<<< * return self.view.shape[0] * */ __pyx_t_1 = ((__pyx_v_self->view.ndim >= 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":607 * def __len__(self): * if self.view.ndim >= 1: * return self.view.shape[0] # <<<<<<<<<<<<<< * * return 0 */ __pyx_r = (__pyx_v_self->view.shape[0]); goto __pyx_L0; /* "View.MemoryView":606 * * def __len__(self): * if self.view.ndim >= 1: # <<<<<<<<<<<<<< * return self.view.shape[0] * */ } /* "View.MemoryView":609 * return self.view.shape[0] * * return 0 # <<<<<<<<<<<<<< * * def __repr__(self): */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":605 * return self._size * * def __len__(self): # <<<<<<<<<<<<<< * if self.view.ndim >= 1: * return self.view.shape[0] */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":611 * return 0 * * def __repr__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__, * id(self)) */ /* Python wrapper */ static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("__repr__", 0); /* "View.MemoryView":612 * * def __repr__(self): * return "" % (self.base.__class__.__name__, # <<<<<<<<<<<<<< * id(self)) * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":613 * def __repr__(self): * return "" % (self.base.__class__.__name__, * id(self)) # <<<<<<<<<<<<<< * * def __str__(self): */ __pyx_t_2 = __Pyx_PyObject_CallOneArg(__pyx_builtin_id, ((PyObject *)__pyx_v_self)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 613, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); /* "View.MemoryView":612 * * def __repr__(self): * return "" % (self.base.__class__.__name__, # <<<<<<<<<<<<<< * id(self)) * */ __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_t_3); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":611 * return 0 * * def __repr__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__, * id(self)) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.__repr__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":615 * id(self)) * * def __str__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__,) * */ /* Python wrapper */ static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__str__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("__str__", 0); /* "View.MemoryView":616 * * def __str__(self): * return "" % (self.base.__class__.__name__,) # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_object, __pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":615 * id(self)) * * def __str__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__,) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.__str__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":619 * * * def is_c_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* Python wrapper */ static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("is_c_contig (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice *__pyx_v_mslice; __Pyx_memviewslice __pyx_v_tmp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("is_c_contig", 0); /* "View.MemoryView":622 * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) # <<<<<<<<<<<<<< * return slice_is_contig(mslice[0], 'C', self.view.ndim) * */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 622, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":623 * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) * return slice_is_contig(mslice[0], 'C', self.view.ndim) # <<<<<<<<<<<<<< * * def is_f_contig(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig((__pyx_v_mslice[0]), 'C', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 623, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":619 * * * def is_c_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.is_c_contig", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":625 * return slice_is_contig(mslice[0], 'C', self.view.ndim) * * def is_f_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* Python wrapper */ static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("is_f_contig (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice *__pyx_v_mslice; __Pyx_memviewslice __pyx_v_tmp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("is_f_contig", 0); /* "View.MemoryView":628 * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) # <<<<<<<<<<<<<< * return slice_is_contig(mslice[0], 'F', self.view.ndim) * */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 628, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":629 * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) * return slice_is_contig(mslice[0], 'F', self.view.ndim) # <<<<<<<<<<<<<< * * def copy(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig((__pyx_v_mslice[0]), 'F', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 629, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":625 * return slice_is_contig(mslice[0], 'C', self.view.ndim) * * def is_f_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.is_f_contig", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":631 * return slice_is_contig(mslice[0], 'F', self.view.ndim) * * def copy(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS */ /* Python wrapper */ static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("copy (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice __pyx_v_mslice; int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice __pyx_t_1; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("copy", 0); /* "View.MemoryView":633 * def copy(self): * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS # <<<<<<<<<<<<<< * * slice_copy(self, &mslice) */ __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_F_CONTIGUOUS)); /* "View.MemoryView":635 * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS * * slice_copy(self, &mslice) # <<<<<<<<<<<<<< * mslice = slice_copy_contig(&mslice, "c", self.view.ndim, * self.view.itemsize, */ __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_mslice)); /* "View.MemoryView":636 * * slice_copy(self, &mslice) * mslice = slice_copy_contig(&mslice, "c", self.view.ndim, # <<<<<<<<<<<<<< * self.view.itemsize, * flags|PyBUF_C_CONTIGUOUS, */ __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_mslice), ((char *)"c"), __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_C_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) __PYX_ERR(2, 636, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":641 * self.dtype_is_object) * * return memoryview_copy_from_slice(self, &mslice) # <<<<<<<<<<<<<< * * def copy_fortran(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_mslice)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 641, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":631 * return slice_is_contig(mslice[0], 'F', self.view.ndim) * * def copy(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.copy", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":643 * return memoryview_copy_from_slice(self, &mslice) * * def copy_fortran(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS */ /* Python wrapper */ static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("copy_fortran (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice __pyx_v_src; __Pyx_memviewslice __pyx_v_dst; int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice __pyx_t_1; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("copy_fortran", 0); /* "View.MemoryView":645 * def copy_fortran(self): * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS # <<<<<<<<<<<<<< * * slice_copy(self, &src) */ __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_C_CONTIGUOUS)); /* "View.MemoryView":647 * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS * * slice_copy(self, &src) # <<<<<<<<<<<<<< * dst = slice_copy_contig(&src, "fortran", self.view.ndim, * self.view.itemsize, */ __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_src)); /* "View.MemoryView":648 * * slice_copy(self, &src) * dst = slice_copy_contig(&src, "fortran", self.view.ndim, # <<<<<<<<<<<<<< * self.view.itemsize, * flags|PyBUF_F_CONTIGUOUS, */ __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_src), ((char *)"fortran"), __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_F_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) __PYX_ERR(2, 648, __pyx_L1_error) __pyx_v_dst = __pyx_t_1; /* "View.MemoryView":653 * self.dtype_is_object) * * return memoryview_copy_from_slice(self, &dst) # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_dst)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 653, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":643 * return memoryview_copy_from_slice(self, &mslice) * * def copy_fortran(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.copy_fortran", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryview_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_memoryview_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryview___reduce_cython__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__20, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryview_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_memoryview_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryview_2__setstate_cython__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__21, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":657 * * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): # <<<<<<<<<<<<<< * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo */ static PyObject *__pyx_memoryview_new(PyObject *__pyx_v_o, int __pyx_v_flags, int __pyx_v_dtype_is_object, __Pyx_TypeInfo *__pyx_v_typeinfo) { struct __pyx_memoryview_obj *__pyx_v_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("memoryview_cwrapper", 0); /* "View.MemoryView":658 * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): * cdef memoryview result = memoryview(o, flags, dtype_is_object) # <<<<<<<<<<<<<< * result.typeinfo = typeinfo * return result */ __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_o); __Pyx_GIVEREF(__pyx_v_o); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_o); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_memoryview_obj *)__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":659 * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo # <<<<<<<<<<<<<< * return result * */ __pyx_v_result->typeinfo = __pyx_v_typeinfo; /* "View.MemoryView":660 * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo * return result # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_check') */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":657 * * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): # <<<<<<<<<<<<<< * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":663 * * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): # <<<<<<<<<<<<<< * return isinstance(o, memoryview) * */ static CYTHON_INLINE int __pyx_memoryview_check(PyObject *__pyx_v_o) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("memoryview_check", 0); /* "View.MemoryView":664 * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): * return isinstance(o, memoryview) # <<<<<<<<<<<<<< * * cdef tuple _unellipsify(object index, int ndim): */ __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_o, __pyx_memoryview_type); __pyx_r = __pyx_t_1; goto __pyx_L0; /* "View.MemoryView":663 * * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): # <<<<<<<<<<<<<< * return isinstance(o, memoryview) * */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":666 * return isinstance(o, memoryview) * * cdef tuple _unellipsify(object index, int ndim): # <<<<<<<<<<<<<< * """ * Replace all ellipses with full slices and fill incomplete indices with */ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) { PyObject *__pyx_v_tup = NULL; PyObject *__pyx_v_result = NULL; int __pyx_v_have_slices; int __pyx_v_seen_ellipsis; CYTHON_UNUSED PyObject *__pyx_v_idx = NULL; PyObject *__pyx_v_item = NULL; Py_ssize_t __pyx_v_nslices; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; Py_ssize_t __pyx_t_5; PyObject *(*__pyx_t_6)(PyObject *); PyObject *__pyx_t_7 = NULL; Py_ssize_t __pyx_t_8; int __pyx_t_9; int __pyx_t_10; PyObject *__pyx_t_11 = NULL; __Pyx_RefNannySetupContext("_unellipsify", 0); /* "View.MemoryView":671 * full slices. * """ * if not isinstance(index, tuple): # <<<<<<<<<<<<<< * tup = (index,) * else: */ __pyx_t_1 = PyTuple_Check(__pyx_v_index); __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":672 * """ * if not isinstance(index, tuple): * tup = (index,) # <<<<<<<<<<<<<< * else: * tup = index */ __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 672, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_index); __Pyx_GIVEREF(__pyx_v_index); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_index); __pyx_v_tup = __pyx_t_3; __pyx_t_3 = 0; /* "View.MemoryView":671 * full slices. * """ * if not isinstance(index, tuple): # <<<<<<<<<<<<<< * tup = (index,) * else: */ goto __pyx_L3; } /* "View.MemoryView":674 * tup = (index,) * else: * tup = index # <<<<<<<<<<<<<< * * result = [] */ /*else*/ { __Pyx_INCREF(__pyx_v_index); __pyx_v_tup = __pyx_v_index; } __pyx_L3:; /* "View.MemoryView":676 * tup = index * * result = [] # <<<<<<<<<<<<<< * have_slices = False * seen_ellipsis = False */ __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 676, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_v_result = ((PyObject*)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":677 * * result = [] * have_slices = False # <<<<<<<<<<<<<< * seen_ellipsis = False * for idx, item in enumerate(tup): */ __pyx_v_have_slices = 0; /* "View.MemoryView":678 * result = [] * have_slices = False * seen_ellipsis = False # <<<<<<<<<<<<<< * for idx, item in enumerate(tup): * if item is Ellipsis: */ __pyx_v_seen_ellipsis = 0; /* "View.MemoryView":679 * have_slices = False * seen_ellipsis = False * for idx, item in enumerate(tup): # <<<<<<<<<<<<<< * if item is Ellipsis: * if not seen_ellipsis: */ __Pyx_INCREF(__pyx_int_0); __pyx_t_3 = __pyx_int_0; if (likely(PyList_CheckExact(__pyx_v_tup)) || PyTuple_CheckExact(__pyx_v_tup)) { __pyx_t_4 = __pyx_v_tup; __Pyx_INCREF(__pyx_t_4); __pyx_t_5 = 0; __pyx_t_6 = NULL; } else { __pyx_t_5 = -1; __pyx_t_4 = PyObject_GetIter(__pyx_v_tup); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_6 = Py_TYPE(__pyx_t_4)->tp_iternext; if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 679, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_6)) { if (likely(PyList_CheckExact(__pyx_t_4))) { if (__pyx_t_5 >= PyList_GET_SIZE(__pyx_t_4)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_7 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) __PYX_ERR(2, 679, __pyx_L1_error) #else __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); #endif } else { if (__pyx_t_5 >= PyTuple_GET_SIZE(__pyx_t_4)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_7 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) __PYX_ERR(2, 679, __pyx_L1_error) #else __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); #endif } } else { __pyx_t_7 = __pyx_t_6(__pyx_t_4); if (unlikely(!__pyx_t_7)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(2, 679, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_7); } __Pyx_XDECREF_SET(__pyx_v_item, __pyx_t_7); __pyx_t_7 = 0; __Pyx_INCREF(__pyx_t_3); __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_3); __pyx_t_7 = __Pyx_PyInt_AddObjC(__pyx_t_3, __pyx_int_1, 1, 0, 0); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = __pyx_t_7; __pyx_t_7 = 0; /* "View.MemoryView":680 * seen_ellipsis = False * for idx, item in enumerate(tup): * if item is Ellipsis: # <<<<<<<<<<<<<< * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) */ __pyx_t_2 = (__pyx_v_item == __pyx_builtin_Ellipsis); __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":681 * for idx, item in enumerate(tup): * if item is Ellipsis: * if not seen_ellipsis: # <<<<<<<<<<<<<< * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True */ __pyx_t_1 = ((!(__pyx_v_seen_ellipsis != 0)) != 0); if (__pyx_t_1) { /* "View.MemoryView":682 * if item is Ellipsis: * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) # <<<<<<<<<<<<<< * seen_ellipsis = True * else: */ __pyx_t_8 = PyObject_Length(__pyx_v_tup); if (unlikely(__pyx_t_8 == ((Py_ssize_t)-1))) __PYX_ERR(2, 682, __pyx_L1_error) __pyx_t_7 = PyList_New(1 * ((((__pyx_v_ndim - __pyx_t_8) + 1)<0) ? 0:((__pyx_v_ndim - __pyx_t_8) + 1))); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 682, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); { Py_ssize_t __pyx_temp; for (__pyx_temp=0; __pyx_temp < ((__pyx_v_ndim - __pyx_t_8) + 1); __pyx_temp++) { __Pyx_INCREF(__pyx_slice__22); __Pyx_GIVEREF(__pyx_slice__22); PyList_SET_ITEM(__pyx_t_7, __pyx_temp, __pyx_slice__22); } } __pyx_t_9 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_7); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 682, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; /* "View.MemoryView":683 * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True # <<<<<<<<<<<<<< * else: * result.append(slice(None)) */ __pyx_v_seen_ellipsis = 1; /* "View.MemoryView":681 * for idx, item in enumerate(tup): * if item is Ellipsis: * if not seen_ellipsis: # <<<<<<<<<<<<<< * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True */ goto __pyx_L7; } /* "View.MemoryView":685 * seen_ellipsis = True * else: * result.append(slice(None)) # <<<<<<<<<<<<<< * have_slices = True * else: */ /*else*/ { __pyx_t_9 = __Pyx_PyList_Append(__pyx_v_result, __pyx_slice__22); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 685, __pyx_L1_error) } __pyx_L7:; /* "View.MemoryView":686 * else: * result.append(slice(None)) * have_slices = True # <<<<<<<<<<<<<< * else: * if not isinstance(item, slice) and not PyIndex_Check(item): */ __pyx_v_have_slices = 1; /* "View.MemoryView":680 * seen_ellipsis = False * for idx, item in enumerate(tup): * if item is Ellipsis: # <<<<<<<<<<<<<< * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) */ goto __pyx_L6; } /* "View.MemoryView":688 * have_slices = True * else: * if not isinstance(item, slice) and not PyIndex_Check(item): # <<<<<<<<<<<<<< * raise TypeError("Cannot index with type '%s'" % type(item)) * */ /*else*/ { __pyx_t_2 = PySlice_Check(__pyx_v_item); __pyx_t_10 = ((!(__pyx_t_2 != 0)) != 0); if (__pyx_t_10) { } else { __pyx_t_1 = __pyx_t_10; goto __pyx_L9_bool_binop_done; } __pyx_t_10 = ((!(PyIndex_Check(__pyx_v_item) != 0)) != 0); __pyx_t_1 = __pyx_t_10; __pyx_L9_bool_binop_done:; if (unlikely(__pyx_t_1)) { /* "View.MemoryView":689 * else: * if not isinstance(item, slice) and not PyIndex_Check(item): * raise TypeError("Cannot index with type '%s'" % type(item)) # <<<<<<<<<<<<<< * * have_slices = have_slices or isinstance(item, slice) */ __pyx_t_7 = __Pyx_PyString_FormatSafe(__pyx_kp_s_Cannot_index_with_type_s, ((PyObject *)Py_TYPE(__pyx_v_item))); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 689, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_11 = __Pyx_PyObject_CallOneArg(__pyx_builtin_TypeError, __pyx_t_7); if (unlikely(!__pyx_t_11)) __PYX_ERR(2, 689, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_Raise(__pyx_t_11, 0, 0, 0); __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __PYX_ERR(2, 689, __pyx_L1_error) /* "View.MemoryView":688 * have_slices = True * else: * if not isinstance(item, slice) and not PyIndex_Check(item): # <<<<<<<<<<<<<< * raise TypeError("Cannot index with type '%s'" % type(item)) * */ } /* "View.MemoryView":691 * raise TypeError("Cannot index with type '%s'" % type(item)) * * have_slices = have_slices or isinstance(item, slice) # <<<<<<<<<<<<<< * result.append(item) * */ __pyx_t_10 = (__pyx_v_have_slices != 0); if (!__pyx_t_10) { } else { __pyx_t_1 = __pyx_t_10; goto __pyx_L11_bool_binop_done; } __pyx_t_10 = PySlice_Check(__pyx_v_item); __pyx_t_2 = (__pyx_t_10 != 0); __pyx_t_1 = __pyx_t_2; __pyx_L11_bool_binop_done:; __pyx_v_have_slices = __pyx_t_1; /* "View.MemoryView":692 * * have_slices = have_slices or isinstance(item, slice) * result.append(item) # <<<<<<<<<<<<<< * * nslices = ndim - len(result) */ __pyx_t_9 = __Pyx_PyList_Append(__pyx_v_result, __pyx_v_item); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 692, __pyx_L1_error) } __pyx_L6:; /* "View.MemoryView":679 * have_slices = False * seen_ellipsis = False * for idx, item in enumerate(tup): # <<<<<<<<<<<<<< * if item is Ellipsis: * if not seen_ellipsis: */ } __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":694 * result.append(item) * * nslices = ndim - len(result) # <<<<<<<<<<<<<< * if nslices: * result.extend([slice(None)] * nslices) */ __pyx_t_5 = PyList_GET_SIZE(__pyx_v_result); if (unlikely(__pyx_t_5 == ((Py_ssize_t)-1))) __PYX_ERR(2, 694, __pyx_L1_error) __pyx_v_nslices = (__pyx_v_ndim - __pyx_t_5); /* "View.MemoryView":695 * * nslices = ndim - len(result) * if nslices: # <<<<<<<<<<<<<< * result.extend([slice(None)] * nslices) * */ __pyx_t_1 = (__pyx_v_nslices != 0); if (__pyx_t_1) { /* "View.MemoryView":696 * nslices = ndim - len(result) * if nslices: * result.extend([slice(None)] * nslices) # <<<<<<<<<<<<<< * * return have_slices or nslices, tuple(result) */ __pyx_t_3 = PyList_New(1 * ((__pyx_v_nslices<0) ? 0:__pyx_v_nslices)); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 696, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); { Py_ssize_t __pyx_temp; for (__pyx_temp=0; __pyx_temp < __pyx_v_nslices; __pyx_temp++) { __Pyx_INCREF(__pyx_slice__22); __Pyx_GIVEREF(__pyx_slice__22); PyList_SET_ITEM(__pyx_t_3, __pyx_temp, __pyx_slice__22); } } __pyx_t_9 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_3); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 696, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":695 * * nslices = ndim - len(result) * if nslices: # <<<<<<<<<<<<<< * result.extend([slice(None)] * nslices) * */ } /* "View.MemoryView":698 * result.extend([slice(None)] * nslices) * * return have_slices or nslices, tuple(result) # <<<<<<<<<<<<<< * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): */ __Pyx_XDECREF(__pyx_r); if (!__pyx_v_have_slices) { } else { __pyx_t_4 = __Pyx_PyBool_FromLong(__pyx_v_have_slices); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = __pyx_t_4; __pyx_t_4 = 0; goto __pyx_L14_bool_binop_done; } __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_nslices); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = __pyx_t_4; __pyx_t_4 = 0; __pyx_L14_bool_binop_done:; __pyx_t_4 = PyList_AsTuple(__pyx_v_result); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_11 = PyTuple_New(2); if (unlikely(!__pyx_t_11)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_11, 0, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_11, 1, __pyx_t_4); __pyx_t_3 = 0; __pyx_t_4 = 0; __pyx_r = ((PyObject*)__pyx_t_11); __pyx_t_11 = 0; goto __pyx_L0; /* "View.MemoryView":666 * return isinstance(o, memoryview) * * cdef tuple _unellipsify(object index, int ndim): # <<<<<<<<<<<<<< * """ * Replace all ellipses with full slices and fill incomplete indices with */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_11); __Pyx_AddTraceback("View.MemoryView._unellipsify", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_tup); __Pyx_XDECREF(__pyx_v_result); __Pyx_XDECREF(__pyx_v_idx); __Pyx_XDECREF(__pyx_v_item); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":700 * return have_slices or nslices, tuple(result) * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): # <<<<<<<<<<<<<< * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: */ static PyObject *assert_direct_dimensions(Py_ssize_t *__pyx_v_suboffsets, int __pyx_v_ndim) { Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations Py_ssize_t *__pyx_t_1; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; __Pyx_RefNannySetupContext("assert_direct_dimensions", 0); /* "View.MemoryView":701 * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: # <<<<<<<<<<<<<< * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") */ __pyx_t_2 = (__pyx_v_suboffsets + __pyx_v_ndim); for (__pyx_t_3 = __pyx_v_suboffsets; __pyx_t_3 < __pyx_t_2; __pyx_t_3++) { __pyx_t_1 = __pyx_t_3; __pyx_v_suboffset = (__pyx_t_1[0]); /* "View.MemoryView":702 * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * raise ValueError("Indirect dimensions not supported") * */ __pyx_t_4 = ((__pyx_v_suboffset >= 0) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":703 * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__23, NULL); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 703, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_Raise(__pyx_t_5, 0, 0, 0); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __PYX_ERR(2, 703, __pyx_L1_error) /* "View.MemoryView":702 * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * raise ValueError("Indirect dimensions not supported") * */ } } /* "View.MemoryView":700 * return have_slices or nslices, tuple(result) * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): # <<<<<<<<<<<<<< * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.assert_direct_dimensions", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":710 * * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< * cdef int new_ndim = 0, suboffset_dim = -1, dim * cdef bint negative_step */ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *__pyx_v_memview, PyObject *__pyx_v_indices) { int __pyx_v_new_ndim; int __pyx_v_suboffset_dim; int __pyx_v_dim; __Pyx_memviewslice __pyx_v_src; __Pyx_memviewslice __pyx_v_dst; __Pyx_memviewslice *__pyx_v_p_src; struct __pyx_memoryviewslice_obj *__pyx_v_memviewsliceobj = 0; __Pyx_memviewslice *__pyx_v_p_dst; int *__pyx_v_p_suboffset_dim; Py_ssize_t __pyx_v_start; Py_ssize_t __pyx_v_stop; Py_ssize_t __pyx_v_step; int __pyx_v_have_start; int __pyx_v_have_stop; int __pyx_v_have_step; PyObject *__pyx_v_index = NULL; struct __pyx_memoryview_obj *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; struct __pyx_memoryview_obj *__pyx_t_4; char *__pyx_t_5; int __pyx_t_6; Py_ssize_t __pyx_t_7; PyObject *(*__pyx_t_8)(PyObject *); PyObject *__pyx_t_9 = NULL; Py_ssize_t __pyx_t_10; int __pyx_t_11; Py_ssize_t __pyx_t_12; __Pyx_RefNannySetupContext("memview_slice", 0); /* "View.MemoryView":711 * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): * cdef int new_ndim = 0, suboffset_dim = -1, dim # <<<<<<<<<<<<<< * cdef bint negative_step * cdef __Pyx_memviewslice src, dst */ __pyx_v_new_ndim = 0; __pyx_v_suboffset_dim = -1; /* "View.MemoryView":718 * * * memset(&dst, 0, sizeof(dst)) # <<<<<<<<<<<<<< * * cdef _memoryviewslice memviewsliceobj */ (void)(memset((&__pyx_v_dst), 0, (sizeof(__pyx_v_dst)))); /* "View.MemoryView":722 * cdef _memoryviewslice memviewsliceobj * * assert memview.view.ndim > 0 # <<<<<<<<<<<<<< * * if isinstance(memview, _memoryviewslice): */ #ifndef CYTHON_WITHOUT_ASSERTIONS if (unlikely(!Py_OptimizeFlag)) { if (unlikely(!((__pyx_v_memview->view.ndim > 0) != 0))) { PyErr_SetNone(PyExc_AssertionError); __PYX_ERR(2, 722, __pyx_L1_error) } } #endif /* "View.MemoryView":724 * assert memview.view.ndim > 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":725 * * if isinstance(memview, _memoryviewslice): * memviewsliceobj = memview # <<<<<<<<<<<<<< * p_src = &memviewsliceobj.from_slice * else: */ if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 725, __pyx_L1_error) __pyx_t_3 = ((PyObject *)__pyx_v_memview); __Pyx_INCREF(__pyx_t_3); __pyx_v_memviewsliceobj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":726 * if isinstance(memview, _memoryviewslice): * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice # <<<<<<<<<<<<<< * else: * slice_copy(memview, &src) */ __pyx_v_p_src = (&__pyx_v_memviewsliceobj->from_slice); /* "View.MemoryView":724 * assert memview.view.ndim > 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice */ goto __pyx_L3; } /* "View.MemoryView":728 * p_src = &memviewsliceobj.from_slice * else: * slice_copy(memview, &src) # <<<<<<<<<<<<<< * p_src = &src * */ /*else*/ { __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_src)); /* "View.MemoryView":729 * else: * slice_copy(memview, &src) * p_src = &src # <<<<<<<<<<<<<< * * */ __pyx_v_p_src = (&__pyx_v_src); } __pyx_L3:; /* "View.MemoryView":735 * * * dst.memview = p_src.memview # <<<<<<<<<<<<<< * dst.data = p_src.data * */ __pyx_t_4 = __pyx_v_p_src->memview; __pyx_v_dst.memview = __pyx_t_4; /* "View.MemoryView":736 * * dst.memview = p_src.memview * dst.data = p_src.data # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __pyx_v_p_src->data; __pyx_v_dst.data = __pyx_t_5; /* "View.MemoryView":741 * * * cdef __Pyx_memviewslice *p_dst = &dst # <<<<<<<<<<<<<< * cdef int *p_suboffset_dim = &suboffset_dim * cdef Py_ssize_t start, stop, step */ __pyx_v_p_dst = (&__pyx_v_dst); /* "View.MemoryView":742 * * cdef __Pyx_memviewslice *p_dst = &dst * cdef int *p_suboffset_dim = &suboffset_dim # <<<<<<<<<<<<<< * cdef Py_ssize_t start, stop, step * cdef bint have_start, have_stop, have_step */ __pyx_v_p_suboffset_dim = (&__pyx_v_suboffset_dim); /* "View.MemoryView":746 * cdef bint have_start, have_stop, have_step * * for dim, index in enumerate(indices): # <<<<<<<<<<<<<< * if PyIndex_Check(index): * slice_memviewslice( */ __pyx_t_6 = 0; if (likely(PyList_CheckExact(__pyx_v_indices)) || PyTuple_CheckExact(__pyx_v_indices)) { __pyx_t_3 = __pyx_v_indices; __Pyx_INCREF(__pyx_t_3); __pyx_t_7 = 0; __pyx_t_8 = NULL; } else { __pyx_t_7 = -1; __pyx_t_3 = PyObject_GetIter(__pyx_v_indices); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_8 = Py_TYPE(__pyx_t_3)->tp_iternext; if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 746, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_8)) { if (likely(PyList_CheckExact(__pyx_t_3))) { if (__pyx_t_7 >= PyList_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_9 = PyList_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(2, 746, __pyx_L1_error) #else __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); #endif } else { if (__pyx_t_7 >= PyTuple_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_9 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(2, 746, __pyx_L1_error) #else __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); #endif } } else { __pyx_t_9 = __pyx_t_8(__pyx_t_3); if (unlikely(!__pyx_t_9)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(2, 746, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_9); } __Pyx_XDECREF_SET(__pyx_v_index, __pyx_t_9); __pyx_t_9 = 0; __pyx_v_dim = __pyx_t_6; __pyx_t_6 = (__pyx_t_6 + 1); /* "View.MemoryView":747 * * for dim, index in enumerate(indices): * if PyIndex_Check(index): # <<<<<<<<<<<<<< * slice_memviewslice( * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], */ __pyx_t_2 = (PyIndex_Check(__pyx_v_index) != 0); if (__pyx_t_2) { /* "View.MemoryView":751 * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, * index, 0, 0, # start, stop, step # <<<<<<<<<<<<<< * 0, 0, 0, # have_{start,stop,step} * False) */ __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_v_index); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 751, __pyx_L1_error) /* "View.MemoryView":748 * for dim, index in enumerate(indices): * if PyIndex_Check(index): * slice_memviewslice( # <<<<<<<<<<<<<< * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, */ __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_t_10, 0, 0, 0, 0, 0, 0); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 748, __pyx_L1_error) /* "View.MemoryView":747 * * for dim, index in enumerate(indices): * if PyIndex_Check(index): # <<<<<<<<<<<<<< * slice_memviewslice( * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], */ goto __pyx_L6; } /* "View.MemoryView":754 * 0, 0, 0, # have_{start,stop,step} * False) * elif index is None: # <<<<<<<<<<<<<< * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 */ __pyx_t_2 = (__pyx_v_index == Py_None); __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":755 * False) * elif index is None: * p_dst.shape[new_ndim] = 1 # <<<<<<<<<<<<<< * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 */ (__pyx_v_p_dst->shape[__pyx_v_new_ndim]) = 1; /* "View.MemoryView":756 * elif index is None: * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 # <<<<<<<<<<<<<< * p_dst.suboffsets[new_ndim] = -1 * new_ndim += 1 */ (__pyx_v_p_dst->strides[__pyx_v_new_ndim]) = 0; /* "View.MemoryView":757 * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 # <<<<<<<<<<<<<< * new_ndim += 1 * else: */ (__pyx_v_p_dst->suboffsets[__pyx_v_new_ndim]) = -1L; /* "View.MemoryView":758 * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 * new_ndim += 1 # <<<<<<<<<<<<<< * else: * start = index.start or 0 */ __pyx_v_new_ndim = (__pyx_v_new_ndim + 1); /* "View.MemoryView":754 * 0, 0, 0, # have_{start,stop,step} * False) * elif index is None: # <<<<<<<<<<<<<< * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 */ goto __pyx_L6; } /* "View.MemoryView":760 * new_ndim += 1 * else: * start = index.start or 0 # <<<<<<<<<<<<<< * stop = index.stop or 0 * step = index.step or 0 */ /*else*/ { __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 760, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 760, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 760, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L7_bool_binop_done; } __pyx_t_10 = 0; __pyx_L7_bool_binop_done:; __pyx_v_start = __pyx_t_10; /* "View.MemoryView":761 * else: * start = index.start or 0 * stop = index.stop or 0 # <<<<<<<<<<<<<< * step = index.step or 0 * */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 761, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 761, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 761, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L9_bool_binop_done; } __pyx_t_10 = 0; __pyx_L9_bool_binop_done:; __pyx_v_stop = __pyx_t_10; /* "View.MemoryView":762 * start = index.start or 0 * stop = index.stop or 0 * step = index.step or 0 # <<<<<<<<<<<<<< * * have_start = index.start is not None */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 762, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 762, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 762, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L11_bool_binop_done; } __pyx_t_10 = 0; __pyx_L11_bool_binop_done:; __pyx_v_step = __pyx_t_10; /* "View.MemoryView":764 * step = index.step or 0 * * have_start = index.start is not None # <<<<<<<<<<<<<< * have_stop = index.stop is not None * have_step = index.step is not None */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 764, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_start = __pyx_t_1; /* "View.MemoryView":765 * * have_start = index.start is not None * have_stop = index.stop is not None # <<<<<<<<<<<<<< * have_step = index.step is not None * */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 765, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_stop = __pyx_t_1; /* "View.MemoryView":766 * have_start = index.start is not None * have_stop = index.stop is not None * have_step = index.step is not None # <<<<<<<<<<<<<< * * slice_memviewslice( */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 766, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_step = __pyx_t_1; /* "View.MemoryView":768 * have_step = index.step is not None * * slice_memviewslice( # <<<<<<<<<<<<<< * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, */ __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 768, __pyx_L1_error) /* "View.MemoryView":774 * have_start, have_stop, have_step, * True) * new_ndim += 1 # <<<<<<<<<<<<<< * * if isinstance(memview, _memoryviewslice): */ __pyx_v_new_ndim = (__pyx_v_new_ndim + 1); } __pyx_L6:; /* "View.MemoryView":746 * cdef bint have_start, have_stop, have_step * * for dim, index in enumerate(indices): # <<<<<<<<<<<<<< * if PyIndex_Check(index): * slice_memviewslice( */ } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":776 * new_ndim += 1 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":777 * * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, */ __Pyx_XDECREF(((PyObject *)__pyx_r)); /* "View.MemoryView":778 * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, # <<<<<<<<<<<<<< * memviewsliceobj.to_dtype_func, * memview.dtype_is_object) */ if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 778, __pyx_L1_error) } /* "View.MemoryView":779 * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, # <<<<<<<<<<<<<< * memview.dtype_is_object) * else: */ if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 779, __pyx_L1_error) } /* "View.MemoryView":777 * * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, */ __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 777, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 777, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":776 * new_ndim += 1 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, */ } /* "View.MemoryView":782 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< * memview.dtype_is_object) * */ /*else*/ { __Pyx_XDECREF(((PyObject *)__pyx_r)); /* "View.MemoryView":783 * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 782, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); /* "View.MemoryView":782 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< * memview.dtype_is_object) * */ if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 782, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; } /* "View.MemoryView":710 * * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< * cdef int new_ndim = 0, suboffset_dim = -1, dim * cdef bint negative_step */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_9); __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj); __Pyx_XDECREF(__pyx_v_index); __Pyx_XGIVEREF((PyObject *)__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":807 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, */ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) { Py_ssize_t __pyx_v_new_shape; int __pyx_v_negative_step; int __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; /* "View.MemoryView":827 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< * * if start < 0: */ __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_1) { /* "View.MemoryView":829 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< * start += shape * if not 0 <= start < shape: */ __pyx_t_1 = ((__pyx_v_start < 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":830 * * if start < 0: * start += shape # <<<<<<<<<<<<<< * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); /* "View.MemoryView":829 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< * start += shape * if not 0 <= start < shape: */ } /* "View.MemoryView":831 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) * else: */ __pyx_t_1 = (0 <= __pyx_v_start); if (__pyx_t_1) { __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); } __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":832 * start += shape * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< * else: * */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 832, __pyx_L1_error) /* "View.MemoryView":831 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) * else: */ } /* "View.MemoryView":827 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< * * if start < 0: */ goto __pyx_L3; } /* "View.MemoryView":835 * else: * * negative_step = have_step != 0 and step < 0 # <<<<<<<<<<<<<< * * if have_step and step == 0: */ /*else*/ { __pyx_t_1 = ((__pyx_v_have_step != 0) != 0); if (__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L6_bool_binop_done; } __pyx_t_1 = ((__pyx_v_step < 0) != 0); __pyx_t_2 = __pyx_t_1; __pyx_L6_bool_binop_done:; __pyx_v_negative_step = __pyx_t_2; /* "View.MemoryView":837 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) * */ __pyx_t_1 = (__pyx_v_have_step != 0); if (__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L9_bool_binop_done; } __pyx_t_1 = ((__pyx_v_step == 0) != 0); __pyx_t_2 = __pyx_t_1; __pyx_L9_bool_binop_done:; if (__pyx_t_2) { /* "View.MemoryView":838 * * if have_step and step == 0: * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 838, __pyx_L1_error) /* "View.MemoryView":837 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) * */ } /* "View.MemoryView":841 * * * if have_start: # <<<<<<<<<<<<<< * if start < 0: * start += shape */ __pyx_t_2 = (__pyx_v_have_start != 0); if (__pyx_t_2) { /* "View.MemoryView":842 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< * start += shape * if start < 0: */ __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":843 * if have_start: * if start < 0: * start += shape # <<<<<<<<<<<<<< * if start < 0: * start = 0 */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); /* "View.MemoryView":844 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< * start = 0 * elif start >= shape: */ __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":845 * start += shape * if start < 0: * start = 0 # <<<<<<<<<<<<<< * elif start >= shape: * if negative_step: */ __pyx_v_start = 0; /* "View.MemoryView":844 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< * start = 0 * elif start >= shape: */ } /* "View.MemoryView":842 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< * start += shape * if start < 0: */ goto __pyx_L12; } /* "View.MemoryView":846 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< * if negative_step: * start = shape - 1 */ __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); if (__pyx_t_2) { /* "View.MemoryView":847 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":848 * elif start >= shape: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< * else: * start = shape */ __pyx_v_start = (__pyx_v_shape - 1); /* "View.MemoryView":847 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ goto __pyx_L14; } /* "View.MemoryView":850 * start = shape - 1 * else: * start = shape # <<<<<<<<<<<<<< * else: * if negative_step: */ /*else*/ { __pyx_v_start = __pyx_v_shape; } __pyx_L14:; /* "View.MemoryView":846 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< * if negative_step: * start = shape - 1 */ } __pyx_L12:; /* "View.MemoryView":841 * * * if have_start: # <<<<<<<<<<<<<< * if start < 0: * start += shape */ goto __pyx_L11; } /* "View.MemoryView":852 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ /*else*/ { __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":853 * else: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< * else: * start = 0 */ __pyx_v_start = (__pyx_v_shape - 1); /* "View.MemoryView":852 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ goto __pyx_L15; } /* "View.MemoryView":855 * start = shape - 1 * else: * start = 0 # <<<<<<<<<<<<<< * * if have_stop: */ /*else*/ { __pyx_v_start = 0; } __pyx_L15:; } __pyx_L11:; /* "View.MemoryView":857 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< * if stop < 0: * stop += shape */ __pyx_t_2 = (__pyx_v_have_stop != 0); if (__pyx_t_2) { /* "View.MemoryView":858 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< * stop += shape * if stop < 0: */ __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":859 * if have_stop: * if stop < 0: * stop += shape # <<<<<<<<<<<<<< * if stop < 0: * stop = 0 */ __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); /* "View.MemoryView":860 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< * stop = 0 * elif stop > shape: */ __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":861 * stop += shape * if stop < 0: * stop = 0 # <<<<<<<<<<<<<< * elif stop > shape: * stop = shape */ __pyx_v_stop = 0; /* "View.MemoryView":860 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< * stop = 0 * elif stop > shape: */ } /* "View.MemoryView":858 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< * stop += shape * if stop < 0: */ goto __pyx_L17; } /* "View.MemoryView":862 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< * stop = shape * else: */ __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0); if (__pyx_t_2) { /* "View.MemoryView":863 * stop = 0 * elif stop > shape: * stop = shape # <<<<<<<<<<<<<< * else: * if negative_step: */ __pyx_v_stop = __pyx_v_shape; /* "View.MemoryView":862 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< * stop = shape * else: */ } __pyx_L17:; /* "View.MemoryView":857 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< * if stop < 0: * stop += shape */ goto __pyx_L16; } /* "View.MemoryView":865 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< * stop = -1 * else: */ /*else*/ { __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":866 * else: * if negative_step: * stop = -1 # <<<<<<<<<<<<<< * else: * stop = shape */ __pyx_v_stop = -1L; /* "View.MemoryView":865 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< * stop = -1 * else: */ goto __pyx_L19; } /* "View.MemoryView":868 * stop = -1 * else: * stop = shape # <<<<<<<<<<<<<< * * if not have_step: */ /*else*/ { __pyx_v_stop = __pyx_v_shape; } __pyx_L19:; } __pyx_L16:; /* "View.MemoryView":870 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< * step = 1 * */ __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":871 * * if not have_step: * step = 1 # <<<<<<<<<<<<<< * * */ __pyx_v_step = 1; /* "View.MemoryView":870 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< * step = 1 * */ } /* "View.MemoryView":875 * * with cython.cdivision(True): * new_shape = (stop - start) // step # <<<<<<<<<<<<<< * * if (stop - start) - step * new_shape: */ __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); /* "View.MemoryView":877 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< * new_shape += 1 * */ __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); if (__pyx_t_2) { /* "View.MemoryView":878 * * if (stop - start) - step * new_shape: * new_shape += 1 # <<<<<<<<<<<<<< * * if new_shape < 0: */ __pyx_v_new_shape = (__pyx_v_new_shape + 1); /* "View.MemoryView":877 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< * new_shape += 1 * */ } /* "View.MemoryView":880 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< * new_shape = 0 * */ __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":881 * * if new_shape < 0: * new_shape = 0 # <<<<<<<<<<<<<< * * */ __pyx_v_new_shape = 0; /* "View.MemoryView":880 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< * new_shape = 0 * */ } /* "View.MemoryView":884 * * * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset */ (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); /* "View.MemoryView":885 * * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape # <<<<<<<<<<<<<< * dst.suboffsets[new_ndim] = suboffset * */ (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape; /* "View.MemoryView":886 * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset # <<<<<<<<<<<<<< * * */ (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset; } __pyx_L3:; /* "View.MemoryView":889 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< * dst.data += start * stride * else: */ __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":890 * * if suboffset_dim[0] < 0: * dst.data += start * stride # <<<<<<<<<<<<<< * else: * dst.suboffsets[suboffset_dim[0]] += start * stride */ __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride)); /* "View.MemoryView":889 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< * dst.data += start * stride * else: */ goto __pyx_L23; } /* "View.MemoryView":892 * dst.data += start * stride * else: * dst.suboffsets[suboffset_dim[0]] += start * stride # <<<<<<<<<<<<<< * * if suboffset >= 0: */ /*else*/ { __pyx_t_3 = (__pyx_v_suboffset_dim[0]); (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride)); } __pyx_L23:; /* "View.MemoryView":894 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< * if not is_slice: * if new_ndim == 0: */ __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":895 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset */ __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":896 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< * dst.data = ( dst.data)[0] + suboffset * else: */ __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":897 * if not is_slice: * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset # <<<<<<<<<<<<<< * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " */ __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset); /* "View.MemoryView":896 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< * dst.data = ( dst.data)[0] + suboffset * else: */ goto __pyx_L26; } /* "View.MemoryView":899 * dst.data = ( dst.data)[0] + suboffset * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " # <<<<<<<<<<<<<< * "must be indexed and not sliced", dim) * else: */ /*else*/ { /* "View.MemoryView":900 * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " * "must be indexed and not sliced", dim) # <<<<<<<<<<<<<< * else: * suboffset_dim[0] = new_ndim */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"All dimensions preceding dimension %d must be indexed and not sliced"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 899, __pyx_L1_error) } __pyx_L26:; /* "View.MemoryView":895 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset */ goto __pyx_L25; } /* "View.MemoryView":902 * "must be indexed and not sliced", dim) * else: * suboffset_dim[0] = new_ndim # <<<<<<<<<<<<<< * * return 0 */ /*else*/ { (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim; } __pyx_L25:; /* "View.MemoryView":894 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< * if not is_slice: * if new_ndim == 0: */ } /* "View.MemoryView":904 * suboffset_dim[0] = new_ndim * * return 0 # <<<<<<<<<<<<<< * * */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":807 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.slice_memviewslice", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = -1; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":910 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 */ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, Py_ssize_t __pyx_v_index, Py_ssize_t __pyx_v_dim) { Py_ssize_t __pyx_v_shape; Py_ssize_t __pyx_v_stride; Py_ssize_t __pyx_v_suboffset; Py_ssize_t __pyx_v_itemsize; char *__pyx_v_resultp; char *__pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_RefNannySetupContext("pybuffer_index", 0); /* "View.MemoryView":912 * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 # <<<<<<<<<<<<<< * cdef Py_ssize_t itemsize = view.itemsize * cdef char *resultp */ __pyx_v_suboffset = -1L; /* "View.MemoryView":913 * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 * cdef Py_ssize_t itemsize = view.itemsize # <<<<<<<<<<<<<< * cdef char *resultp * */ __pyx_t_1 = __pyx_v_view->itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":916 * cdef char *resultp * * if view.ndim == 0: # <<<<<<<<<<<<<< * shape = view.len / itemsize * stride = itemsize */ __pyx_t_2 = ((__pyx_v_view->ndim == 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":917 * * if view.ndim == 0: * shape = view.len / itemsize # <<<<<<<<<<<<<< * stride = itemsize * else: */ if (unlikely(__pyx_v_itemsize == 0)) { PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); __PYX_ERR(2, 917, __pyx_L1_error) } else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1) && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_view->len))) { PyErr_SetString(PyExc_OverflowError, "value too large to perform division"); __PYX_ERR(2, 917, __pyx_L1_error) } __pyx_v_shape = (__pyx_v_view->len / __pyx_v_itemsize); /* "View.MemoryView":918 * if view.ndim == 0: * shape = view.len / itemsize * stride = itemsize # <<<<<<<<<<<<<< * else: * shape = view.shape[dim] */ __pyx_v_stride = __pyx_v_itemsize; /* "View.MemoryView":916 * cdef char *resultp * * if view.ndim == 0: # <<<<<<<<<<<<<< * shape = view.len / itemsize * stride = itemsize */ goto __pyx_L3; } /* "View.MemoryView":920 * stride = itemsize * else: * shape = view.shape[dim] # <<<<<<<<<<<<<< * stride = view.strides[dim] * if view.suboffsets != NULL: */ /*else*/ { __pyx_v_shape = (__pyx_v_view->shape[__pyx_v_dim]); /* "View.MemoryView":921 * else: * shape = view.shape[dim] * stride = view.strides[dim] # <<<<<<<<<<<<<< * if view.suboffsets != NULL: * suboffset = view.suboffsets[dim] */ __pyx_v_stride = (__pyx_v_view->strides[__pyx_v_dim]); /* "View.MemoryView":922 * shape = view.shape[dim] * stride = view.strides[dim] * if view.suboffsets != NULL: # <<<<<<<<<<<<<< * suboffset = view.suboffsets[dim] * */ __pyx_t_2 = ((__pyx_v_view->suboffsets != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":923 * stride = view.strides[dim] * if view.suboffsets != NULL: * suboffset = view.suboffsets[dim] # <<<<<<<<<<<<<< * * if index < 0: */ __pyx_v_suboffset = (__pyx_v_view->suboffsets[__pyx_v_dim]); /* "View.MemoryView":922 * shape = view.shape[dim] * stride = view.strides[dim] * if view.suboffsets != NULL: # <<<<<<<<<<<<<< * suboffset = view.suboffsets[dim] * */ } } __pyx_L3:; /* "View.MemoryView":925 * suboffset = view.suboffsets[dim] * * if index < 0: # <<<<<<<<<<<<<< * index += view.shape[dim] * if index < 0: */ __pyx_t_2 = ((__pyx_v_index < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":926 * * if index < 0: * index += view.shape[dim] # <<<<<<<<<<<<<< * if index < 0: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) */ __pyx_v_index = (__pyx_v_index + (__pyx_v_view->shape[__pyx_v_dim])); /* "View.MemoryView":927 * if index < 0: * index += view.shape[dim] * if index < 0: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ __pyx_t_2 = ((__pyx_v_index < 0) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":928 * index += view.shape[dim] * if index < 0: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * if index >= shape: */ __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 928, __pyx_L1_error) /* "View.MemoryView":927 * if index < 0: * index += view.shape[dim] * if index < 0: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ } /* "View.MemoryView":925 * suboffset = view.suboffsets[dim] * * if index < 0: # <<<<<<<<<<<<<< * index += view.shape[dim] * if index < 0: */ } /* "View.MemoryView":930 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ __pyx_t_2 = ((__pyx_v_index >= __pyx_v_shape) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":931 * * if index >= shape: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * resultp = bufp + index * stride */ __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 931, __pyx_L1_error) /* "View.MemoryView":930 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ } /* "View.MemoryView":933 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * resultp = bufp + index * stride # <<<<<<<<<<<<<< * if suboffset >= 0: * resultp = ( resultp)[0] + suboffset */ __pyx_v_resultp = (__pyx_v_bufp + (__pyx_v_index * __pyx_v_stride)); /* "View.MemoryView":934 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< * resultp = ( resultp)[0] + suboffset * */ __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":935 * resultp = bufp + index * stride * if suboffset >= 0: * resultp = ( resultp)[0] + suboffset # <<<<<<<<<<<<<< * * return resultp */ __pyx_v_resultp = ((((char **)__pyx_v_resultp)[0]) + __pyx_v_suboffset); /* "View.MemoryView":934 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< * resultp = ( resultp)[0] + suboffset * */ } /* "View.MemoryView":937 * resultp = ( resultp)[0] + suboffset * * return resultp # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_resultp; goto __pyx_L0; /* "View.MemoryView":910 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView.pybuffer_index", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":943 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< * cdef int ndim = memslice.memview.view.ndim * */ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { int __pyx_v_ndim; Py_ssize_t *__pyx_v_shape; Py_ssize_t *__pyx_v_strides; int __pyx_v_i; int __pyx_v_j; int __pyx_r; int __pyx_t_1; Py_ssize_t *__pyx_t_2; long __pyx_t_3; long __pyx_t_4; Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; int __pyx_t_7; int __pyx_t_8; int __pyx_t_9; /* "View.MemoryView":944 * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: * cdef int ndim = memslice.memview.view.ndim # <<<<<<<<<<<<<< * * cdef Py_ssize_t *shape = memslice.shape */ __pyx_t_1 = __pyx_v_memslice->memview->view.ndim; __pyx_v_ndim = __pyx_t_1; /* "View.MemoryView":946 * cdef int ndim = memslice.memview.view.ndim * * cdef Py_ssize_t *shape = memslice.shape # <<<<<<<<<<<<<< * cdef Py_ssize_t *strides = memslice.strides * */ __pyx_t_2 = __pyx_v_memslice->shape; __pyx_v_shape = __pyx_t_2; /* "View.MemoryView":947 * * cdef Py_ssize_t *shape = memslice.shape * cdef Py_ssize_t *strides = memslice.strides # <<<<<<<<<<<<<< * * */ __pyx_t_2 = __pyx_v_memslice->strides; __pyx_v_strides = __pyx_t_2; /* "View.MemoryView":951 * * cdef int i, j * for i in range(ndim / 2): # <<<<<<<<<<<<<< * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] */ __pyx_t_3 = (__pyx_v_ndim / 2); __pyx_t_4 = __pyx_t_3; for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_4; __pyx_t_1+=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":952 * cdef int i, j * for i in range(ndim / 2): * j = ndim - 1 - i # <<<<<<<<<<<<<< * strides[i], strides[j] = strides[j], strides[i] * shape[i], shape[j] = shape[j], shape[i] */ __pyx_v_j = ((__pyx_v_ndim - 1) - __pyx_v_i); /* "View.MemoryView":953 * for i in range(ndim / 2): * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] # <<<<<<<<<<<<<< * shape[i], shape[j] = shape[j], shape[i] * */ __pyx_t_5 = (__pyx_v_strides[__pyx_v_j]); __pyx_t_6 = (__pyx_v_strides[__pyx_v_i]); (__pyx_v_strides[__pyx_v_i]) = __pyx_t_5; (__pyx_v_strides[__pyx_v_j]) = __pyx_t_6; /* "View.MemoryView":954 * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] * shape[i], shape[j] = shape[j], shape[i] # <<<<<<<<<<<<<< * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: */ __pyx_t_6 = (__pyx_v_shape[__pyx_v_j]); __pyx_t_5 = (__pyx_v_shape[__pyx_v_i]); (__pyx_v_shape[__pyx_v_i]) = __pyx_t_6; (__pyx_v_shape[__pyx_v_j]) = __pyx_t_5; /* "View.MemoryView":956 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * */ __pyx_t_8 = (((__pyx_v_memslice->suboffsets[__pyx_v_i]) >= 0) != 0); if (!__pyx_t_8) { } else { __pyx_t_7 = __pyx_t_8; goto __pyx_L6_bool_binop_done; } __pyx_t_8 = (((__pyx_v_memslice->suboffsets[__pyx_v_j]) >= 0) != 0); __pyx_t_7 = __pyx_t_8; __pyx_L6_bool_binop_done:; if (__pyx_t_7) { /* "View.MemoryView":957 * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") # <<<<<<<<<<<<<< * * return 1 */ __pyx_t_9 = __pyx_memoryview_err(__pyx_builtin_ValueError, ((char *)"Cannot transpose memoryview with indirect dimensions")); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 957, __pyx_L1_error) /* "View.MemoryView":956 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * */ } } /* "View.MemoryView":959 * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * * return 1 # <<<<<<<<<<<<<< * * */ __pyx_r = 1; goto __pyx_L0; /* "View.MemoryView":943 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< * cdef int ndim = memslice.memview.view.ndim * */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.transpose_memslice", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":976 * cdef int (*to_dtype_func)(char *, object) except 0 * * def __dealloc__(self): # <<<<<<<<<<<<<< * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * */ /* Python wrapper */ static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":977 * * def __dealloc__(self): * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) # <<<<<<<<<<<<<< * * cdef convert_item_to_object(self, char *itemp): */ __PYX_XDEC_MEMVIEW((&__pyx_v_self->from_slice), 1); /* "View.MemoryView":976 * cdef int (*to_dtype_func)(char *, object) except 0 * * def __dealloc__(self): # <<<<<<<<<<<<<< * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":979 * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * if self.to_object_func != NULL: * return self.to_object_func(itemp) */ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("convert_item_to_object", 0); /* "View.MemoryView":980 * * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: # <<<<<<<<<<<<<< * return self.to_object_func(itemp) * else: */ __pyx_t_1 = ((__pyx_v_self->to_object_func != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":981 * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: * return self.to_object_func(itemp) # <<<<<<<<<<<<<< * else: * return memoryview.convert_item_to_object(self, itemp) */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_v_self->to_object_func(__pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 981, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":980 * * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: # <<<<<<<<<<<<<< * return self.to_object_func(itemp) * else: */ } /* "View.MemoryView":983 * return self.to_object_func(itemp) * else: * return memoryview.convert_item_to_object(self, itemp) # <<<<<<<<<<<<<< * * cdef assign_item_from_object(self, char *itemp, object value): */ /*else*/ { __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_convert_item_to_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 983, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; } /* "View.MemoryView":979 * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * if self.to_object_func != NULL: * return self.to_object_func(itemp) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":985 * return memoryview.convert_item_to_object(self, itemp) * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) */ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("assign_item_from_object", 0); /* "View.MemoryView":986 * * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: # <<<<<<<<<<<<<< * self.to_dtype_func(itemp, value) * else: */ __pyx_t_1 = ((__pyx_v_self->to_dtype_func != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":987 * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) # <<<<<<<<<<<<<< * else: * memoryview.assign_item_from_object(self, itemp, value) */ __pyx_t_2 = __pyx_v_self->to_dtype_func(__pyx_v_itemp, __pyx_v_value); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(2, 987, __pyx_L1_error) /* "View.MemoryView":986 * * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: # <<<<<<<<<<<<<< * self.to_dtype_func(itemp, value) * else: */ goto __pyx_L3; } /* "View.MemoryView":989 * self.to_dtype_func(itemp, value) * else: * memoryview.assign_item_from_object(self, itemp, value) # <<<<<<<<<<<<<< * * @property */ /*else*/ { __pyx_t_3 = __pyx_memoryview_assign_item_from_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 989, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } __pyx_L3:; /* "View.MemoryView":985 * return memoryview.convert_item_to_object(self, itemp) * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":992 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.from_object * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":993 * @property * def base(self): * return self.from_object # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->from_object); __pyx_r = __pyx_v_self->from_object; goto __pyx_L0; /* "View.MemoryView":992 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.from_object * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryviewslice_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_memoryviewslice_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryviewslice___reduce_cython__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__24, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryviewslice_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_memoryviewslice_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryviewslice_2__setstate_cython__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__25, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":999 * * @cname('__pyx_memoryview_fromslice') * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice, # <<<<<<<<<<<<<< * int ndim, * object (*to_object_func)(char *), */ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewslice, int __pyx_v_ndim, PyObject *(*__pyx_v_to_object_func)(char *), int (*__pyx_v_to_dtype_func)(char *, PyObject *), int __pyx_v_dtype_is_object) { struct __pyx_memoryviewslice_obj *__pyx_v_result = 0; Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_v_length = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; __Pyx_TypeInfo *__pyx_t_4; Py_buffer __pyx_t_5; Py_ssize_t *__pyx_t_6; Py_ssize_t *__pyx_t_7; Py_ssize_t *__pyx_t_8; Py_ssize_t __pyx_t_9; __Pyx_RefNannySetupContext("memoryview_fromslice", 0); /* "View.MemoryView":1007 * cdef _memoryviewslice result * * if memviewslice.memview == Py_None: # <<<<<<<<<<<<<< * return None * */ __pyx_t_1 = ((((PyObject *)__pyx_v_memviewslice.memview) == Py_None) != 0); if (__pyx_t_1) { /* "View.MemoryView":1008 * * if memviewslice.memview == Py_None: * return None # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; /* "View.MemoryView":1007 * cdef _memoryviewslice result * * if memviewslice.memview == Py_None: # <<<<<<<<<<<<<< * return None * */ } /* "View.MemoryView":1013 * * * result = _memoryviewslice(None, 0, dtype_is_object) # <<<<<<<<<<<<<< * * result.from_slice = memviewslice */ __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(Py_None); __Pyx_GIVEREF(Py_None); PyTuple_SET_ITEM(__pyx_t_3, 0, Py_None); __Pyx_INCREF(__pyx_int_0); __Pyx_GIVEREF(__pyx_int_0); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_int_0); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryviewslice_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":1015 * result = _memoryviewslice(None, 0, dtype_is_object) * * result.from_slice = memviewslice # <<<<<<<<<<<<<< * __PYX_INC_MEMVIEW(&memviewslice, 1) * */ __pyx_v_result->from_slice = __pyx_v_memviewslice; /* "View.MemoryView":1016 * * result.from_slice = memviewslice * __PYX_INC_MEMVIEW(&memviewslice, 1) # <<<<<<<<<<<<<< * * result.from_object = ( memviewslice.memview).base */ __PYX_INC_MEMVIEW((&__pyx_v_memviewslice), 1); /* "View.MemoryView":1018 * __PYX_INC_MEMVIEW(&memviewslice, 1) * * result.from_object = ( memviewslice.memview).base # <<<<<<<<<<<<<< * result.typeinfo = memviewslice.memview.typeinfo * */ __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_memviewslice.memview), __pyx_n_s_base); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1018, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); __Pyx_GOTREF(__pyx_v_result->from_object); __Pyx_DECREF(__pyx_v_result->from_object); __pyx_v_result->from_object = __pyx_t_2; __pyx_t_2 = 0; /* "View.MemoryView":1019 * * result.from_object = ( memviewslice.memview).base * result.typeinfo = memviewslice.memview.typeinfo # <<<<<<<<<<<<<< * * result.view = memviewslice.memview.view */ __pyx_t_4 = __pyx_v_memviewslice.memview->typeinfo; __pyx_v_result->__pyx_base.typeinfo = __pyx_t_4; /* "View.MemoryView":1021 * result.typeinfo = memviewslice.memview.typeinfo * * result.view = memviewslice.memview.view # <<<<<<<<<<<<<< * result.view.buf = memviewslice.data * result.view.ndim = ndim */ __pyx_t_5 = __pyx_v_memviewslice.memview->view; __pyx_v_result->__pyx_base.view = __pyx_t_5; /* "View.MemoryView":1022 * * result.view = memviewslice.memview.view * result.view.buf = memviewslice.data # <<<<<<<<<<<<<< * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None */ __pyx_v_result->__pyx_base.view.buf = ((void *)__pyx_v_memviewslice.data); /* "View.MemoryView":1023 * result.view = memviewslice.memview.view * result.view.buf = memviewslice.data * result.view.ndim = ndim # <<<<<<<<<<<<<< * (<__pyx_buffer *> &result.view).obj = Py_None * Py_INCREF(Py_None) */ __pyx_v_result->__pyx_base.view.ndim = __pyx_v_ndim; /* "View.MemoryView":1024 * result.view.buf = memviewslice.data * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_result->__pyx_base.view))->obj = Py_None; /* "View.MemoryView":1025 * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * if (memviewslice.memview).flags & PyBUF_WRITABLE: */ Py_INCREF(Py_None); /* "View.MemoryView":1027 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< * result.flags = PyBUF_RECORDS * else: */ __pyx_t_1 = ((((struct __pyx_memoryview_obj *)__pyx_v_memviewslice.memview)->flags & PyBUF_WRITABLE) != 0); if (__pyx_t_1) { /* "View.MemoryView":1028 * * if (memviewslice.memview).flags & PyBUF_WRITABLE: * result.flags = PyBUF_RECORDS # <<<<<<<<<<<<<< * else: * result.flags = PyBUF_RECORDS_RO */ __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS; /* "View.MemoryView":1027 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< * result.flags = PyBUF_RECORDS * else: */ goto __pyx_L4; } /* "View.MemoryView":1030 * result.flags = PyBUF_RECORDS * else: * result.flags = PyBUF_RECORDS_RO # <<<<<<<<<<<<<< * * result.view.shape = result.from_slice.shape */ /*else*/ { __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS_RO; } __pyx_L4:; /* "View.MemoryView":1032 * result.flags = PyBUF_RECORDS_RO * * result.view.shape = result.from_slice.shape # <<<<<<<<<<<<<< * result.view.strides = result.from_slice.strides * */ __pyx_v_result->__pyx_base.view.shape = ((Py_ssize_t *)__pyx_v_result->from_slice.shape); /* "View.MemoryView":1033 * * result.view.shape = result.from_slice.shape * result.view.strides = result.from_slice.strides # <<<<<<<<<<<<<< * * */ __pyx_v_result->__pyx_base.view.strides = ((Py_ssize_t *)__pyx_v_result->from_slice.strides); /* "View.MemoryView":1036 * * * result.view.suboffsets = NULL # <<<<<<<<<<<<<< * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: */ __pyx_v_result->__pyx_base.view.suboffsets = NULL; /* "View.MemoryView":1037 * * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: # <<<<<<<<<<<<<< * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets */ __pyx_t_7 = (__pyx_v_result->from_slice.suboffsets + __pyx_v_ndim); for (__pyx_t_8 = __pyx_v_result->from_slice.suboffsets; __pyx_t_8 < __pyx_t_7; __pyx_t_8++) { __pyx_t_6 = __pyx_t_8; __pyx_v_suboffset = (__pyx_t_6[0]); /* "View.MemoryView":1038 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * result.view.suboffsets = result.from_slice.suboffsets * break */ __pyx_t_1 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":1039 * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets # <<<<<<<<<<<<<< * break * */ __pyx_v_result->__pyx_base.view.suboffsets = ((Py_ssize_t *)__pyx_v_result->from_slice.suboffsets); /* "View.MemoryView":1040 * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets * break # <<<<<<<<<<<<<< * * result.view.len = result.view.itemsize */ goto __pyx_L6_break; /* "View.MemoryView":1038 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * result.view.suboffsets = result.from_slice.suboffsets * break */ } } __pyx_L6_break:; /* "View.MemoryView":1042 * break * * result.view.len = result.view.itemsize # <<<<<<<<<<<<<< * for length in result.view.shape[:ndim]: * result.view.len *= length */ __pyx_t_9 = __pyx_v_result->__pyx_base.view.itemsize; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; /* "View.MemoryView":1043 * * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: # <<<<<<<<<<<<<< * result.view.len *= length * */ __pyx_t_7 = (__pyx_v_result->__pyx_base.view.shape + __pyx_v_ndim); for (__pyx_t_8 = __pyx_v_result->__pyx_base.view.shape; __pyx_t_8 < __pyx_t_7; __pyx_t_8++) { __pyx_t_6 = __pyx_t_8; __pyx_t_2 = PyInt_FromSsize_t((__pyx_t_6[0])); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1043, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":1044 * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: * result.view.len *= length # <<<<<<<<<<<<<< * * result.to_object_func = to_object_func */ __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_result->__pyx_base.view.len); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_InPlaceMultiply(__pyx_t_2, __pyx_v_length); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_3); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 1044, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; } /* "View.MemoryView":1046 * result.view.len *= length * * result.to_object_func = to_object_func # <<<<<<<<<<<<<< * result.to_dtype_func = to_dtype_func * */ __pyx_v_result->to_object_func = __pyx_v_to_object_func; /* "View.MemoryView":1047 * * result.to_object_func = to_object_func * result.to_dtype_func = to_dtype_func # <<<<<<<<<<<<<< * * return result */ __pyx_v_result->to_dtype_func = __pyx_v_to_dtype_func; /* "View.MemoryView":1049 * result.to_dtype_func = to_dtype_func * * return result # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_get_slice_from_memoryview') */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":999 * * @cname('__pyx_memoryview_fromslice') * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice, # <<<<<<<<<<<<<< * int ndim, * object (*to_object_func)(char *), */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview_fromslice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XDECREF(__pyx_v_length); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1052 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj */ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_mslice) { struct __pyx_memoryviewslice_obj *__pyx_v_obj = 0; __Pyx_memviewslice *__pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("get_slice_from_memview", 0); /* "View.MemoryView":1055 * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * obj = memview * return &obj.from_slice */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":1056 * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): * obj = memview # <<<<<<<<<<<<<< * return &obj.from_slice * else: */ if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 1056, __pyx_L1_error) __pyx_t_3 = ((PyObject *)__pyx_v_memview); __Pyx_INCREF(__pyx_t_3); __pyx_v_obj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":1057 * if isinstance(memview, _memoryviewslice): * obj = memview * return &obj.from_slice # <<<<<<<<<<<<<< * else: * slice_copy(memview, mslice) */ __pyx_r = (&__pyx_v_obj->from_slice); goto __pyx_L0; /* "View.MemoryView":1055 * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * obj = memview * return &obj.from_slice */ } /* "View.MemoryView":1059 * return &obj.from_slice * else: * slice_copy(memview, mslice) # <<<<<<<<<<<<<< * return mslice * */ /*else*/ { __pyx_memoryview_slice_copy(__pyx_v_memview, __pyx_v_mslice); /* "View.MemoryView":1060 * else: * slice_copy(memview, mslice) * return mslice # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_slice_copy') */ __pyx_r = __pyx_v_mslice; goto __pyx_L0; } /* "View.MemoryView":1052 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.get_slice_from_memview", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_obj); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1063 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< * cdef int dim * cdef (Py_ssize_t*) shape, strides, suboffsets */ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_dst) { int __pyx_v_dim; Py_ssize_t *__pyx_v_shape; Py_ssize_t *__pyx_v_strides; Py_ssize_t *__pyx_v_suboffsets; __Pyx_RefNannyDeclarations Py_ssize_t *__pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; Py_ssize_t __pyx_t_5; __Pyx_RefNannySetupContext("slice_copy", 0); /* "View.MemoryView":1067 * cdef (Py_ssize_t*) shape, strides, suboffsets * * shape = memview.view.shape # <<<<<<<<<<<<<< * strides = memview.view.strides * suboffsets = memview.view.suboffsets */ __pyx_t_1 = __pyx_v_memview->view.shape; __pyx_v_shape = __pyx_t_1; /* "View.MemoryView":1068 * * shape = memview.view.shape * strides = memview.view.strides # <<<<<<<<<<<<<< * suboffsets = memview.view.suboffsets * */ __pyx_t_1 = __pyx_v_memview->view.strides; __pyx_v_strides = __pyx_t_1; /* "View.MemoryView":1069 * shape = memview.view.shape * strides = memview.view.strides * suboffsets = memview.view.suboffsets # <<<<<<<<<<<<<< * * dst.memview = <__pyx_memoryview *> memview */ __pyx_t_1 = __pyx_v_memview->view.suboffsets; __pyx_v_suboffsets = __pyx_t_1; /* "View.MemoryView":1071 * suboffsets = memview.view.suboffsets * * dst.memview = <__pyx_memoryview *> memview # <<<<<<<<<<<<<< * dst.data = memview.view.buf * */ __pyx_v_dst->memview = ((struct __pyx_memoryview_obj *)__pyx_v_memview); /* "View.MemoryView":1072 * * dst.memview = <__pyx_memoryview *> memview * dst.data = memview.view.buf # <<<<<<<<<<<<<< * * for dim in range(memview.view.ndim): */ __pyx_v_dst->data = ((char *)__pyx_v_memview->view.buf); /* "View.MemoryView":1074 * dst.data = memview.view.buf * * for dim in range(memview.view.ndim): # <<<<<<<<<<<<<< * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] */ __pyx_t_2 = __pyx_v_memview->view.ndim; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_dim = __pyx_t_4; /* "View.MemoryView":1075 * * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] # <<<<<<<<<<<<<< * dst.strides[dim] = strides[dim] * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 */ (__pyx_v_dst->shape[__pyx_v_dim]) = (__pyx_v_shape[__pyx_v_dim]); /* "View.MemoryView":1076 * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] # <<<<<<<<<<<<<< * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 * */ (__pyx_v_dst->strides[__pyx_v_dim]) = (__pyx_v_strides[__pyx_v_dim]); /* "View.MemoryView":1077 * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_object') */ if ((__pyx_v_suboffsets != 0)) { __pyx_t_5 = (__pyx_v_suboffsets[__pyx_v_dim]); } else { __pyx_t_5 = -1L; } (__pyx_v_dst->suboffsets[__pyx_v_dim]) = __pyx_t_5; } /* "View.MemoryView":1063 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< * cdef int dim * cdef (Py_ssize_t*) shape, strides, suboffsets */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":1080 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice */ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx_v_memview) { __Pyx_memviewslice __pyx_v_memviewslice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("memoryview_copy", 0); /* "View.MemoryView":1083 * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) # <<<<<<<<<<<<<< * return memoryview_copy_from_slice(memview, &memviewslice) * */ __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_memviewslice)); /* "View.MemoryView":1084 * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) * return memoryview_copy_from_slice(memview, &memviewslice) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_object_from_slice') */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __pyx_memoryview_copy_object_from_slice(__pyx_v_memview, (&__pyx_v_memviewslice)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1084, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":1080 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview_copy", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1087 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< * """ * Create a new memoryview object from a given memoryview object and slice. */ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_memviewslice) { PyObject *(*__pyx_v_to_object_func)(char *); int (*__pyx_v_to_dtype_func)(char *, PyObject *); PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *(*__pyx_t_3)(char *); int (*__pyx_t_4)(char *, PyObject *); PyObject *__pyx_t_5 = NULL; __Pyx_RefNannySetupContext("memoryview_copy_from_slice", 0); /* "View.MemoryView":1094 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":1095 * * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func # <<<<<<<<<<<<<< * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func * else: */ __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func; __pyx_v_to_object_func = __pyx_t_3; /* "View.MemoryView":1096 * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func # <<<<<<<<<<<<<< * else: * to_object_func = NULL */ __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func; __pyx_v_to_dtype_func = __pyx_t_4; /* "View.MemoryView":1094 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func */ goto __pyx_L3; } /* "View.MemoryView":1098 * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func * else: * to_object_func = NULL # <<<<<<<<<<<<<< * to_dtype_func = NULL * */ /*else*/ { __pyx_v_to_object_func = NULL; /* "View.MemoryView":1099 * else: * to_object_func = NULL * to_dtype_func = NULL # <<<<<<<<<<<<<< * * return memoryview_fromslice(memviewslice[0], memview.view.ndim, */ __pyx_v_to_dtype_func = NULL; } __pyx_L3:; /* "View.MemoryView":1101 * to_dtype_func = NULL * * return memoryview_fromslice(memviewslice[0], memview.view.ndim, # <<<<<<<<<<<<<< * to_object_func, to_dtype_func, * memview.dtype_is_object) */ __Pyx_XDECREF(__pyx_r); /* "View.MemoryView":1103 * return memoryview_fromslice(memviewslice[0], memview.view.ndim, * to_object_func, to_dtype_func, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 1101, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "View.MemoryView":1087 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< * """ * Create a new memoryview object from a given memoryview object and slice. */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1109 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< * if arg < 0: * return -arg */ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { Py_ssize_t __pyx_r; int __pyx_t_1; /* "View.MemoryView":1110 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< * return -arg * else: */ __pyx_t_1 = ((__pyx_v_arg < 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":1111 * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: * return -arg # <<<<<<<<<<<<<< * else: * return arg */ __pyx_r = (-__pyx_v_arg); goto __pyx_L0; /* "View.MemoryView":1110 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< * return -arg * else: */ } /* "View.MemoryView":1113 * return -arg * else: * return arg # <<<<<<<<<<<<<< * * @cname('__pyx_get_best_slice_order') */ /*else*/ { __pyx_r = __pyx_v_arg; goto __pyx_L0; } /* "View.MemoryView":1109 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< * if arg < 0: * return -arg */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1116 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< * """ * Figure out the best memory access order for a given slice. */ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) { int __pyx_v_i; Py_ssize_t __pyx_v_c_stride; Py_ssize_t __pyx_v_f_stride; char __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; /* "View.MemoryView":1121 * """ * cdef int i * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< * cdef Py_ssize_t f_stride = 0 * */ __pyx_v_c_stride = 0; /* "View.MemoryView":1122 * cdef int i * cdef Py_ssize_t c_stride = 0 * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< * * for i in range(ndim - 1, -1, -1): */ __pyx_v_f_stride = 0; /* "View.MemoryView":1124 * cdef Py_ssize_t f_stride = 0 * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] */ for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":1125 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * c_stride = mslice.strides[i] * break */ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1126 * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< * break * */ __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1127 * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< * * for i in range(ndim): */ goto __pyx_L4_break; /* "View.MemoryView":1125 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * c_stride = mslice.strides[i] * break */ } } __pyx_L4_break:; /* "View.MemoryView":1129 * break * * for i in range(ndim): # <<<<<<<<<<<<<< * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] */ __pyx_t_1 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_1; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1130 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * f_stride = mslice.strides[i] * break */ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1131 * for i in range(ndim): * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< * break * */ __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1132 * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): */ goto __pyx_L7_break; /* "View.MemoryView":1130 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * f_stride = mslice.strides[i] * break */ } } __pyx_L7_break:; /* "View.MemoryView":1134 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< * return 'C' * else: */ __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1135 * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): * return 'C' # <<<<<<<<<<<<<< * else: * return 'F' */ __pyx_r = 'C'; goto __pyx_L0; /* "View.MemoryView":1134 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< * return 'C' * else: */ } /* "View.MemoryView":1137 * return 'C' * else: * return 'F' # <<<<<<<<<<<<<< * * @cython.cdivision(True) */ /*else*/ { __pyx_r = 'F'; goto __pyx_L0; } /* "View.MemoryView":1116 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< * """ * Figure out the best memory access order for a given slice. */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1140 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< * char *dst_data, Py_ssize_t *dst_strides, * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, */ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; Py_ssize_t __pyx_v_dst_extent; Py_ssize_t __pyx_v_src_stride; Py_ssize_t __pyx_v_dst_stride; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; /* "View.MemoryView":1147 * * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] */ __pyx_v_src_extent = (__pyx_v_src_shape[0]); /* "View.MemoryView":1148 * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] */ __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); /* "View.MemoryView":1149 * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t dst_stride = dst_strides[0] * */ __pyx_v_src_stride = (__pyx_v_src_strides[0]); /* "View.MemoryView":1150 * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< * * if ndim == 1: */ __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); /* "View.MemoryView":1152 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): */ __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L5_bool_binop_done; } __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L5_bool_binop_done; } /* "View.MemoryView":1154 * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< * memcpy(dst_data, src_data, itemsize * dst_extent) * else: */ __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); if (__pyx_t_2) { __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); } __pyx_t_3 = (__pyx_t_2 != 0); __pyx_t_1 = __pyx_t_3; __pyx_L5_bool_binop_done:; /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ if (__pyx_t_1) { /* "View.MemoryView":1155 * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< * else: * for i in range(dst_extent): */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ goto __pyx_L4; } /* "View.MemoryView":1157 * memcpy(dst_data, src_data, itemsize * dst_extent) * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< * memcpy(dst_data, src_data, itemsize) * src_data += src_stride */ /*else*/ { __pyx_t_4 = __pyx_v_dst_extent; __pyx_t_5 = __pyx_t_4; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1158 * else: * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< * src_data += src_stride * dst_data += dst_stride */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); /* "View.MemoryView":1159 * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< * dst_data += dst_stride * else: */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); /* "View.MemoryView":1160 * memcpy(dst_data, src_data, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< * else: * for i in range(dst_extent): */ __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); } } __pyx_L4:; /* "View.MemoryView":1152 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): */ goto __pyx_L3; } /* "View.MemoryView":1162 * dst_data += dst_stride * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< * _copy_strided_to_strided(src_data, src_strides + 1, * dst_data, dst_strides + 1, */ /*else*/ { __pyx_t_4 = __pyx_v_dst_extent; __pyx_t_5 = __pyx_t_4; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1163 * else: * for i in range(dst_extent): * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< * dst_data, dst_strides + 1, * src_shape + 1, dst_shape + 1, */ _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); /* "View.MemoryView":1167 * src_shape + 1, dst_shape + 1, * ndim - 1, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< * dst_data += dst_stride * */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); /* "View.MemoryView":1168 * ndim - 1, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, */ __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); } } __pyx_L3:; /* "View.MemoryView":1140 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< * char *dst_data, Py_ssize_t *dst_strides, * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, */ /* function exit code */ } /* "View.MemoryView":1170 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: */ static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { /* "View.MemoryView":1173 * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< * src.shape, dst.shape, ndim, itemsize) * */ _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); /* "View.MemoryView":1170 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: */ /* function exit code */ } /* "View.MemoryView":1177 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize */ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { Py_ssize_t __pyx_v_shape; Py_ssize_t __pyx_v_size; Py_ssize_t __pyx_r; Py_ssize_t __pyx_t_1; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; /* "View.MemoryView":1179 * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< * * for shape in src.shape[:ndim]: */ __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_size = __pyx_t_1; /* "View.MemoryView":1181 * cdef Py_ssize_t shape, size = src.memview.view.itemsize * * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< * size *= shape * */ __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim); for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { __pyx_t_2 = __pyx_t_4; __pyx_v_shape = (__pyx_t_2[0]); /* "View.MemoryView":1182 * * for shape in src.shape[:ndim]: * size *= shape # <<<<<<<<<<<<<< * * return size */ __pyx_v_size = (__pyx_v_size * __pyx_v_shape); } /* "View.MemoryView":1184 * size *= shape * * return size # <<<<<<<<<<<<<< * * @cname('__pyx_fill_contig_strides_array') */ __pyx_r = __pyx_v_size; goto __pyx_L0; /* "View.MemoryView":1177 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1187 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, * int ndim, char order) nogil: */ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { int __pyx_v_idx; Py_ssize_t __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; /* "View.MemoryView":1196 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< * for idx in range(ndim): * strides[idx] = stride */ __pyx_t_1 = ((__pyx_v_order == 'F') != 0); if (__pyx_t_1) { /* "View.MemoryView":1197 * * if order == 'F': * for idx in range(ndim): # <<<<<<<<<<<<<< * strides[idx] = stride * stride *= shape[idx] */ __pyx_t_2 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_idx = __pyx_t_4; /* "View.MemoryView":1198 * if order == 'F': * for idx in range(ndim): * strides[idx] = stride # <<<<<<<<<<<<<< * stride *= shape[idx] * else: */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; /* "View.MemoryView":1199 * for idx in range(ndim): * strides[idx] = stride * stride *= shape[idx] # <<<<<<<<<<<<<< * else: * for idx in range(ndim - 1, -1, -1): */ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } /* "View.MemoryView":1196 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< * for idx in range(ndim): * strides[idx] = stride */ goto __pyx_L3; } /* "View.MemoryView":1201 * stride *= shape[idx] * else: * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * strides[idx] = stride * stride *= shape[idx] */ /*else*/ { for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { __pyx_v_idx = __pyx_t_2; /* "View.MemoryView":1202 * else: * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride # <<<<<<<<<<<<<< * stride *= shape[idx] * */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; /* "View.MemoryView":1203 * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride * stride *= shape[idx] # <<<<<<<<<<<<<< * * return stride */ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } } __pyx_L3:; /* "View.MemoryView":1205 * stride *= shape[idx] * * return stride # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_data_to_temp') */ __pyx_r = __pyx_v_stride; goto __pyx_L0; /* "View.MemoryView":1187 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, * int ndim, char order) nogil: */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1208 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *tmpslice, * char order, */ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) { int __pyx_v_i; void *__pyx_v_result; size_t __pyx_v_itemsize; size_t __pyx_v_size; void *__pyx_r; Py_ssize_t __pyx_t_1; int __pyx_t_2; int __pyx_t_3; struct __pyx_memoryview_obj *__pyx_t_4; int __pyx_t_5; int __pyx_t_6; /* "View.MemoryView":1219 * cdef void *result * * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< * cdef size_t size = slice_get_size(src, ndim) * */ __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":1220 * * cdef size_t itemsize = src.memview.view.itemsize * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< * * result = malloc(size) */ __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); /* "View.MemoryView":1222 * cdef size_t size = slice_get_size(src, ndim) * * result = malloc(size) # <<<<<<<<<<<<<< * if not result: * _err(MemoryError, NULL) */ __pyx_v_result = malloc(__pyx_v_size); /* "View.MemoryView":1223 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< * _err(MemoryError, NULL) * */ __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1224 * result = malloc(size) * if not result: * _err(MemoryError, NULL) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 1224, __pyx_L1_error) /* "View.MemoryView":1223 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< * _err(MemoryError, NULL) * */ } /* "View.MemoryView":1227 * * * tmpslice.data = result # <<<<<<<<<<<<<< * tmpslice.memview = src.memview * for i in range(ndim): */ __pyx_v_tmpslice->data = ((char *)__pyx_v_result); /* "View.MemoryView":1228 * * tmpslice.data = result * tmpslice.memview = src.memview # <<<<<<<<<<<<<< * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] */ __pyx_t_4 = __pyx_v_src->memview; __pyx_v_tmpslice->memview = __pyx_t_4; /* "View.MemoryView":1229 * tmpslice.data = result * tmpslice.memview = src.memview * for i in range(ndim): # <<<<<<<<<<<<<< * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 */ __pyx_t_3 = __pyx_v_ndim; __pyx_t_5 = __pyx_t_3; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1230 * tmpslice.memview = src.memview * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] # <<<<<<<<<<<<<< * tmpslice.suboffsets[i] = -1 * */ (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]); /* "View.MemoryView":1231 * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, */ (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L; } /* "View.MemoryView":1233 * tmpslice.suboffsets[i] = -1 * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, # <<<<<<<<<<<<<< * ndim, order) * */ (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order)); /* "View.MemoryView":1237 * * * for i in range(ndim): # <<<<<<<<<<<<<< * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 */ __pyx_t_3 = __pyx_v_ndim; __pyx_t_5 = __pyx_t_3; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1238 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< * tmpslice.strides[i] = 0 * */ __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1239 * for i in range(ndim): * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 # <<<<<<<<<<<<<< * * if slice_is_contig(src[0], order, ndim): */ (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0; /* "View.MemoryView":1238 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< * tmpslice.strides[i] = 0 * */ } } /* "View.MemoryView":1241 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< * memcpy(result, src.data, size) * else: */ __pyx_t_2 = (__pyx_memviewslice_is_contig((__pyx_v_src[0]), __pyx_v_order, __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1242 * * if slice_is_contig(src[0], order, ndim): * memcpy(result, src.data, size) # <<<<<<<<<<<<<< * else: * copy_strided_to_strided(src, tmpslice, ndim, itemsize) */ (void)(memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size)); /* "View.MemoryView":1241 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< * memcpy(result, src.data, size) * else: */ goto __pyx_L9; } /* "View.MemoryView":1244 * memcpy(result, src.data, size) * else: * copy_strided_to_strided(src, tmpslice, ndim, itemsize) # <<<<<<<<<<<<<< * * return result */ /*else*/ { copy_strided_to_strided(__pyx_v_src, __pyx_v_tmpslice, __pyx_v_ndim, __pyx_v_itemsize); } __pyx_L9:; /* "View.MemoryView":1246 * copy_strided_to_strided(src, tmpslice, ndim, itemsize) * * return result # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_result; goto __pyx_L0; /* "View.MemoryView":1208 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *tmpslice, * char order, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.copy_data_to_temp", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = NULL; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1251 * * @cname('__pyx_memoryview_err_extents') * cdef int _err_extents(int i, Py_ssize_t extent1, # <<<<<<<<<<<<<< * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % */ static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent1, Py_ssize_t __pyx_v_extent2) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err_extents", 0); /* "View.MemoryView":1254 * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % * (i, extent1, extent2)) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_err_dim') */ __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_i); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_extent1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_extent2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_4, 2, __pyx_t_3); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_3 = 0; /* "View.MemoryView":1253 * cdef int _err_extents(int i, Py_ssize_t extent1, * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % # <<<<<<<<<<<<<< * (i, extent1, extent2)) * */ __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1253, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1253, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_4, 0, 0, 0); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __PYX_ERR(2, 1253, __pyx_L1_error) /* "View.MemoryView":1251 * * @cname('__pyx_memoryview_err_extents') * cdef int _err_extents(int i, Py_ssize_t extent1, # <<<<<<<<<<<<<< * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView._err_extents", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1257 * * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii') % dim) * */ static int __pyx_memoryview_err_dim(PyObject *__pyx_v_error, char *__pyx_v_msg, int __pyx_v_dim) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err_dim", 0); __Pyx_INCREF(__pyx_v_error); /* "View.MemoryView":1258 * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: * raise error(msg.decode('ascii') % dim) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_err') */ __pyx_t_2 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyInt_From_int(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyUnicode_Format(__pyx_t_2, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_INCREF(__pyx_v_error); __pyx_t_3 = __pyx_v_error; __pyx_t_2 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_3))) { __pyx_t_2 = PyMethod_GET_SELF(__pyx_t_3); if (likely(__pyx_t_2)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_3); __Pyx_INCREF(__pyx_t_2); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_3, function); } } __pyx_t_1 = (__pyx_t_2) ? __Pyx_PyObject_Call2Args(__pyx_t_3, __pyx_t_2, __pyx_t_4) : __Pyx_PyObject_CallOneArg(__pyx_t_3, __pyx_t_4); __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 1258, __pyx_L1_error) /* "View.MemoryView":1257 * * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii') % dim) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView._err_dim", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_XDECREF(__pyx_v_error); __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1261 * * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: # <<<<<<<<<<<<<< * if msg != NULL: * raise error(msg.decode('ascii')) */ static int __pyx_memoryview_err(PyObject *__pyx_v_error, char *__pyx_v_msg) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err", 0); __Pyx_INCREF(__pyx_v_error); /* "View.MemoryView":1262 * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii')) * else: */ __pyx_t_1 = ((__pyx_v_msg != NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":1263 * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: * raise error(msg.decode('ascii')) # <<<<<<<<<<<<<< * else: * raise error */ __pyx_t_3 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1263, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_error); __pyx_t_4 = __pyx_v_error; __pyx_t_5 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_4))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_4); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_4); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_4, function); } } __pyx_t_2 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_4, __pyx_t_5, __pyx_t_3) : __Pyx_PyObject_CallOneArg(__pyx_t_4, __pyx_t_3); __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1263, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(2, 1263, __pyx_L1_error) /* "View.MemoryView":1262 * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii')) * else: */ } /* "View.MemoryView":1265 * raise error(msg.decode('ascii')) * else: * raise error # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_contents') */ /*else*/ { __Pyx_Raise(__pyx_v_error, 0, 0, 0); __PYX_ERR(2, 1265, __pyx_L1_error) } /* "View.MemoryView":1261 * * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: # <<<<<<<<<<<<<< * if msg != NULL: * raise error(msg.decode('ascii')) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView._err", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_XDECREF(__pyx_v_error); __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1268 * * @cname('__pyx_memoryview_copy_contents') * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< * __Pyx_memviewslice dst, * int src_ndim, int dst_ndim, */ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_memviewslice __pyx_v_dst, int __pyx_v_src_ndim, int __pyx_v_dst_ndim, int __pyx_v_dtype_is_object) { void *__pyx_v_tmpdata; size_t __pyx_v_itemsize; int __pyx_v_i; char __pyx_v_order; int __pyx_v_broadcasting; int __pyx_v_direct_copy; __Pyx_memviewslice __pyx_v_tmp; int __pyx_v_ndim; int __pyx_r; Py_ssize_t __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_t_5; int __pyx_t_6; void *__pyx_t_7; int __pyx_t_8; /* "View.MemoryView":1276 * Check for overlapping memory and verify the shapes. * """ * cdef void *tmpdata = NULL # <<<<<<<<<<<<<< * cdef size_t itemsize = src.memview.view.itemsize * cdef int i */ __pyx_v_tmpdata = NULL; /* "View.MemoryView":1277 * """ * cdef void *tmpdata = NULL * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< * cdef int i * cdef char order = get_best_order(&src, src_ndim) */ __pyx_t_1 = __pyx_v_src.memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":1279 * cdef size_t itemsize = src.memview.view.itemsize * cdef int i * cdef char order = get_best_order(&src, src_ndim) # <<<<<<<<<<<<<< * cdef bint broadcasting = False * cdef bint direct_copy = False */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_src), __pyx_v_src_ndim); /* "View.MemoryView":1280 * cdef int i * cdef char order = get_best_order(&src, src_ndim) * cdef bint broadcasting = False # <<<<<<<<<<<<<< * cdef bint direct_copy = False * cdef __Pyx_memviewslice tmp */ __pyx_v_broadcasting = 0; /* "View.MemoryView":1281 * cdef char order = get_best_order(&src, src_ndim) * cdef bint broadcasting = False * cdef bint direct_copy = False # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice tmp * */ __pyx_v_direct_copy = 0; /* "View.MemoryView":1284 * cdef __Pyx_memviewslice tmp * * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: */ __pyx_t_2 = ((__pyx_v_src_ndim < __pyx_v_dst_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1285 * * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) # <<<<<<<<<<<<<< * elif dst_ndim < src_ndim: * broadcast_leading(&dst, dst_ndim, src_ndim) */ __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim); /* "View.MemoryView":1284 * cdef __Pyx_memviewslice tmp * * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: */ goto __pyx_L3; } /* "View.MemoryView":1286 * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&dst, dst_ndim, src_ndim) * */ __pyx_t_2 = ((__pyx_v_dst_ndim < __pyx_v_src_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1287 * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: * broadcast_leading(&dst, dst_ndim, src_ndim) # <<<<<<<<<<<<<< * * cdef int ndim = max(src_ndim, dst_ndim) */ __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim); /* "View.MemoryView":1286 * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&dst, dst_ndim, src_ndim) * */ } __pyx_L3:; /* "View.MemoryView":1289 * broadcast_leading(&dst, dst_ndim, src_ndim) * * cdef int ndim = max(src_ndim, dst_ndim) # <<<<<<<<<<<<<< * * for i in range(ndim): */ __pyx_t_3 = __pyx_v_dst_ndim; __pyx_t_4 = __pyx_v_src_ndim; if (((__pyx_t_3 > __pyx_t_4) != 0)) { __pyx_t_5 = __pyx_t_3; } else { __pyx_t_5 = __pyx_t_4; } __pyx_v_ndim = __pyx_t_5; /* "View.MemoryView":1291 * cdef int ndim = max(src_ndim, dst_ndim) * * for i in range(ndim): # <<<<<<<<<<<<<< * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: */ __pyx_t_5 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_5; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1292 * * for i in range(ndim): * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< * if src.shape[i] == 1: * broadcasting = True */ __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0); if (__pyx_t_2) { /* "View.MemoryView":1293 * for i in range(ndim): * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: # <<<<<<<<<<<<<< * broadcasting = True * src.strides[i] = 0 */ __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1294 * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: * broadcasting = True # <<<<<<<<<<<<<< * src.strides[i] = 0 * else: */ __pyx_v_broadcasting = 1; /* "View.MemoryView":1295 * if src.shape[i] == 1: * broadcasting = True * src.strides[i] = 0 # <<<<<<<<<<<<<< * else: * _err_extents(i, dst.shape[i], src.shape[i]) */ (__pyx_v_src.strides[__pyx_v_i]) = 0; /* "View.MemoryView":1293 * for i in range(ndim): * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: # <<<<<<<<<<<<<< * broadcasting = True * src.strides[i] = 0 */ goto __pyx_L7; } /* "View.MemoryView":1297 * src.strides[i] = 0 * else: * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<< * * if src.suboffsets[i] >= 0: */ /*else*/ { __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1297, __pyx_L1_error) } __pyx_L7:; /* "View.MemoryView":1292 * * for i in range(ndim): * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< * if src.shape[i] == 1: * broadcasting = True */ } /* "View.MemoryView":1299 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Dimension %d is not direct", i) * */ __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":1300 * * if src.suboffsets[i] >= 0: * _err_dim(ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< * * if slices_overlap(&src, &dst, ndim, itemsize): */ __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1300, __pyx_L1_error) /* "View.MemoryView":1299 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Dimension %d is not direct", i) * */ } } /* "View.MemoryView":1302 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< * * if not slice_is_contig(src, order, ndim): */ __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0); if (__pyx_t_2) { /* "View.MemoryView":1304 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< * order = get_best_order(&dst, ndim) * */ __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1305 * * if not slice_is_contig(src, order, ndim): * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); /* "View.MemoryView":1304 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< * order = get_best_order(&dst, ndim) * */ } /* "View.MemoryView":1307 * order = get_best_order(&dst, ndim) * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< * src = tmp * */ __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(2, 1307, __pyx_L1_error) __pyx_v_tmpdata = __pyx_t_7; /* "View.MemoryView":1308 * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) * src = tmp # <<<<<<<<<<<<<< * * if not broadcasting: */ __pyx_v_src = __pyx_v_tmp; /* "View.MemoryView":1302 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< * * if not slice_is_contig(src, order, ndim): */ } /* "View.MemoryView":1310 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< * * */ __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1313 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): */ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1314 * * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< * elif slice_is_contig(src, 'F', ndim): * direct_copy = slice_is_contig(dst, 'F', ndim) */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); /* "View.MemoryView":1313 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): */ goto __pyx_L12; } /* "View.MemoryView":1315 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'F', ndim) * */ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1316 * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< * * if direct_copy: */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); /* "View.MemoryView":1315 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'F', ndim) * */ } __pyx_L12:; /* "View.MemoryView":1318 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ __pyx_t_2 = (__pyx_v_direct_copy != 0); if (__pyx_t_2) { /* "View.MemoryView":1320 * if direct_copy: * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1321 * * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) */ (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); /* "View.MemoryView":1322 * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * free(tmpdata) * return 0 */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1323 * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) # <<<<<<<<<<<<<< * return 0 * */ free(__pyx_v_tmpdata); /* "View.MemoryView":1324 * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) * return 0 # <<<<<<<<<<<<<< * * if order == 'F' == get_best_order(&dst, ndim): */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":1318 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ } /* "View.MemoryView":1310 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":1326 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< * * */ __pyx_t_2 = (__pyx_v_order == 'F'); if (__pyx_t_2) { __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); } __pyx_t_8 = (__pyx_t_2 != 0); if (__pyx_t_8) { /* "View.MemoryView":1329 * * * transpose_memslice(&src) # <<<<<<<<<<<<<< * transpose_memslice(&dst) * */ __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1329, __pyx_L1_error) /* "View.MemoryView":1330 * * transpose_memslice(&src) * transpose_memslice(&dst) # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1330, __pyx_L1_error) /* "View.MemoryView":1326 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":1332 * transpose_memslice(&dst) * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1333 * * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< * refcount_copying(&dst, dtype_is_object, ndim, True) * */ copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); /* "View.MemoryView":1334 * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * * free(tmpdata) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1336 * refcount_copying(&dst, dtype_is_object, ndim, True) * * free(tmpdata) # <<<<<<<<<<<<<< * return 0 * */ free(__pyx_v_tmpdata); /* "View.MemoryView":1337 * * free(tmpdata) * return 0 # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_broadcast_leading') */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":1268 * * @cname('__pyx_memoryview_copy_contents') * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< * __Pyx_memviewslice dst, * int src_ndim, int dst_ndim, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = -1; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1340 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< * int ndim, * int ndim_other) nogil: */ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) { int __pyx_v_i; int __pyx_v_offset; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; /* "View.MemoryView":1344 * int ndim_other) nogil: * cdef int i * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< * * for i in range(ndim - 1, -1, -1): */ __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); /* "View.MemoryView":1346 * cdef int offset = ndim_other - ndim * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] */ for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":1347 * * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] */ (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); /* "View.MemoryView":1348 * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * */ (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1349 * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< * * for i in range(offset): */ (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]); } /* "View.MemoryView":1351 * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * * for i in range(offset): # <<<<<<<<<<<<<< * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] */ __pyx_t_1 = __pyx_v_offset; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "View.MemoryView":1352 * * for i in range(offset): * mslice.shape[i] = 1 # <<<<<<<<<<<<<< * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 */ (__pyx_v_mslice->shape[__pyx_v_i]) = 1; /* "View.MemoryView":1353 * for i in range(offset): * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] # <<<<<<<<<<<<<< * mslice.suboffsets[i] = -1 * */ (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]); /* "View.MemoryView":1354 * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< * * */ (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L; } /* "View.MemoryView":1340 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< * int ndim, * int ndim_other) nogil: */ /* function exit code */ } /* "View.MemoryView":1362 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< * int ndim, bint inc) nogil: * */ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) { int __pyx_t_1; /* "View.MemoryView":1366 * * * if dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice_with_gil(dst.data, dst.shape, * dst.strides, ndim, inc) */ __pyx_t_1 = (__pyx_v_dtype_is_object != 0); if (__pyx_t_1) { /* "View.MemoryView":1367 * * if dtype_is_object: * refcount_objects_in_slice_with_gil(dst.data, dst.shape, # <<<<<<<<<<<<<< * dst.strides, ndim, inc) * */ __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc); /* "View.MemoryView":1366 * * * if dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice_with_gil(dst.data, dst.shape, * dst.strides, ndim, inc) */ } /* "View.MemoryView":1362 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< * int ndim, bint inc) nogil: * */ /* function exit code */ } /* "View.MemoryView":1371 * * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * bint inc) with gil: */ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { __Pyx_RefNannyDeclarations #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("refcount_objects_in_slice_with_gil", 0); /* "View.MemoryView":1374 * Py_ssize_t *strides, int ndim, * bint inc) with gil: * refcount_objects_in_slice(data, shape, strides, ndim, inc) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_refcount_objects_in_slice') */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc); /* "View.MemoryView":1371 * * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * bint inc) with gil: */ /* function exit code */ __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } /* "View.MemoryView":1377 * * @cname('__pyx_memoryview_refcount_objects_in_slice') * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, bint inc): * cdef Py_ssize_t i */ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; int __pyx_t_4; __Pyx_RefNannySetupContext("refcount_objects_in_slice", 0); /* "View.MemoryView":1381 * cdef Py_ssize_t i * * for i in range(shape[0]): # <<<<<<<<<<<<<< * if ndim == 1: * if inc: */ __pyx_t_1 = (__pyx_v_shape[0]); __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "View.MemoryView":1382 * * for i in range(shape[0]): * if ndim == 1: # <<<<<<<<<<<<<< * if inc: * Py_INCREF(( data)[0]) */ __pyx_t_4 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_4) { /* "View.MemoryView":1383 * for i in range(shape[0]): * if ndim == 1: * if inc: # <<<<<<<<<<<<<< * Py_INCREF(( data)[0]) * else: */ __pyx_t_4 = (__pyx_v_inc != 0); if (__pyx_t_4) { /* "View.MemoryView":1384 * if ndim == 1: * if inc: * Py_INCREF(( data)[0]) # <<<<<<<<<<<<<< * else: * Py_DECREF(( data)[0]) */ Py_INCREF((((PyObject **)__pyx_v_data)[0])); /* "View.MemoryView":1383 * for i in range(shape[0]): * if ndim == 1: * if inc: # <<<<<<<<<<<<<< * Py_INCREF(( data)[0]) * else: */ goto __pyx_L6; } /* "View.MemoryView":1386 * Py_INCREF(( data)[0]) * else: * Py_DECREF(( data)[0]) # <<<<<<<<<<<<<< * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, */ /*else*/ { Py_DECREF((((PyObject **)__pyx_v_data)[0])); } __pyx_L6:; /* "View.MemoryView":1382 * * for i in range(shape[0]): * if ndim == 1: # <<<<<<<<<<<<<< * if inc: * Py_INCREF(( data)[0]) */ goto __pyx_L5; } /* "View.MemoryView":1388 * Py_DECREF(( data)[0]) * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, # <<<<<<<<<<<<<< * ndim - 1, inc) * */ /*else*/ { /* "View.MemoryView":1389 * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, * ndim - 1, inc) # <<<<<<<<<<<<<< * * data += strides[0] */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_inc); } __pyx_L5:; /* "View.MemoryView":1391 * ndim - 1, inc) * * data += strides[0] # <<<<<<<<<<<<<< * * */ __pyx_v_data = (__pyx_v_data + (__pyx_v_strides[0])); } /* "View.MemoryView":1377 * * @cname('__pyx_memoryview_refcount_objects_in_slice') * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, bint inc): * cdef Py_ssize_t i */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":1397 * * @cname('__pyx_memoryview_slice_assign_scalar') * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, # <<<<<<<<<<<<<< * size_t itemsize, void *item, * bint dtype_is_object) nogil: */ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item, int __pyx_v_dtype_is_object) { /* "View.MemoryView":1400 * size_t itemsize, void *item, * bint dtype_is_object) nogil: * refcount_copying(dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, * itemsize, item) */ __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1401 * bint dtype_is_object) nogil: * refcount_copying(dst, dtype_is_object, ndim, False) * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, # <<<<<<<<<<<<<< * itemsize, item) * refcount_copying(dst, dtype_is_object, ndim, True) */ __pyx_memoryview__slice_assign_scalar(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_itemsize, __pyx_v_item); /* "View.MemoryView":1403 * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, * itemsize, item) * refcount_copying(dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * * */ __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1397 * * @cname('__pyx_memoryview_slice_assign_scalar') * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, # <<<<<<<<<<<<<< * size_t itemsize, void *item, * bint dtype_is_object) nogil: */ /* function exit code */ } /* "View.MemoryView":1407 * * @cname('__pyx_memoryview__slice_assign_scalar') * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * size_t itemsize, void *item) nogil: */ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_stride; Py_ssize_t __pyx_v_extent; int __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; /* "View.MemoryView":1411 * size_t itemsize, void *item) nogil: * cdef Py_ssize_t i * cdef Py_ssize_t stride = strides[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t extent = shape[0] * */ __pyx_v_stride = (__pyx_v_strides[0]); /* "View.MemoryView":1412 * cdef Py_ssize_t i * cdef Py_ssize_t stride = strides[0] * cdef Py_ssize_t extent = shape[0] # <<<<<<<<<<<<<< * * if ndim == 1: */ __pyx_v_extent = (__pyx_v_shape[0]); /* "View.MemoryView":1414 * cdef Py_ssize_t extent = shape[0] * * if ndim == 1: # <<<<<<<<<<<<<< * for i in range(extent): * memcpy(data, item, itemsize) */ __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":1415 * * if ndim == 1: * for i in range(extent): # <<<<<<<<<<<<<< * memcpy(data, item, itemsize) * data += stride */ __pyx_t_2 = __pyx_v_extent; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1416 * if ndim == 1: * for i in range(extent): * memcpy(data, item, itemsize) # <<<<<<<<<<<<<< * data += stride * else: */ (void)(memcpy(__pyx_v_data, __pyx_v_item, __pyx_v_itemsize)); /* "View.MemoryView":1417 * for i in range(extent): * memcpy(data, item, itemsize) * data += stride # <<<<<<<<<<<<<< * else: * for i in range(extent): */ __pyx_v_data = (__pyx_v_data + __pyx_v_stride); } /* "View.MemoryView":1414 * cdef Py_ssize_t extent = shape[0] * * if ndim == 1: # <<<<<<<<<<<<<< * for i in range(extent): * memcpy(data, item, itemsize) */ goto __pyx_L3; } /* "View.MemoryView":1419 * data += stride * else: * for i in range(extent): # <<<<<<<<<<<<<< * _slice_assign_scalar(data, shape + 1, strides + 1, * ndim - 1, itemsize, item) */ /*else*/ { __pyx_t_2 = __pyx_v_extent; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1420 * else: * for i in range(extent): * _slice_assign_scalar(data, shape + 1, strides + 1, # <<<<<<<<<<<<<< * ndim - 1, itemsize, item) * data += stride */ __pyx_memoryview__slice_assign_scalar(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize, __pyx_v_item); /* "View.MemoryView":1422 * _slice_assign_scalar(data, shape + 1, strides + 1, * ndim - 1, itemsize, item) * data += stride # <<<<<<<<<<<<<< * * */ __pyx_v_data = (__pyx_v_data + __pyx_v_stride); } } __pyx_L3:; /* "View.MemoryView":1407 * * @cname('__pyx_memoryview__slice_assign_scalar') * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * size_t itemsize, void *item) nogil: */ /* function exit code */ } /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyMethodDef __pyx_mdef_15View_dot_MemoryView_1__pyx_unpickle_Enum = {"__pyx_unpickle_Enum", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum, METH_VARARGS|METH_KEYWORDS, 0}; static PyObject *__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v___pyx_type = 0; long __pyx_v___pyx_checksum; PyObject *__pyx_v___pyx_state = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__pyx_unpickle_Enum (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_pyx_type,&__pyx_n_s_pyx_checksum,&__pyx_n_s_pyx_state,0}; PyObject* values[3] = {0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_type)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_checksum)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, 1); __PYX_ERR(2, 1, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_state)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, 2); __PYX_ERR(2, 1, __pyx_L3_error) } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__pyx_unpickle_Enum") < 0)) __PYX_ERR(2, 1, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 3) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[2] = PyTuple_GET_ITEM(__pyx_args, 2); } __pyx_v___pyx_type = values[0]; __pyx_v___pyx_checksum = __Pyx_PyInt_As_long(values[1]); if (unlikely((__pyx_v___pyx_checksum == (long)-1) && PyErr_Occurred())) __PYX_ERR(2, 1, __pyx_L3_error) __pyx_v___pyx_state = values[2]; } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 1, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(__pyx_self, __pyx_v___pyx_type, __pyx_v___pyx_checksum, __pyx_v___pyx_state); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_v___pyx_PickleError = 0; PyObject *__pyx_v___pyx_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_t_6; __Pyx_RefNannySetupContext("__pyx_unpickle_Enum", 0); /* "(tree fragment)":4 * cdef object __pyx_PickleError * cdef object __pyx_result * if __pyx_checksum != 0xb068931: # <<<<<<<<<<<<<< * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) */ __pyx_t_1 = ((__pyx_v___pyx_checksum != 0xb068931) != 0); if (__pyx_t_1) { /* "(tree fragment)":5 * cdef object __pyx_result * if __pyx_checksum != 0xb068931: * from pickle import PickleError as __pyx_PickleError # <<<<<<<<<<<<<< * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) */ __pyx_t_2 = PyList_New(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_n_s_PickleError); __Pyx_GIVEREF(__pyx_n_s_PickleError); PyList_SET_ITEM(__pyx_t_2, 0, __pyx_n_s_PickleError); __pyx_t_3 = __Pyx_Import(__pyx_n_s_pickle, __pyx_t_2, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_ImportFrom(__pyx_t_3, __pyx_n_s_PickleError); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_t_2); __pyx_v___pyx_PickleError = __pyx_t_2; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "(tree fragment)":6 * if __pyx_checksum != 0xb068931: * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) # <<<<<<<<<<<<<< * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: */ __pyx_t_2 = __Pyx_PyInt_From_long(__pyx_v___pyx_checksum); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_t_2); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_INCREF(__pyx_v___pyx_PickleError); __pyx_t_2 = __pyx_v___pyx_PickleError; __pyx_t_5 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_2))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_3 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_5, __pyx_t_4) : __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 6, __pyx_L1_error) /* "(tree fragment)":4 * cdef object __pyx_PickleError * cdef object __pyx_result * if __pyx_checksum != 0xb068931: # <<<<<<<<<<<<<< * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) */ } /* "(tree fragment)":7 * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) # <<<<<<<<<<<<<< * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) */ __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_MemviewEnum_type), __pyx_n_s_new); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 7, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_2))) { __pyx_t_4 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_4)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_4); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_3 = (__pyx_t_4) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_4, __pyx_v___pyx_type) : __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_v___pyx_type); __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 7, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v___pyx_result = __pyx_t_3; __pyx_t_3 = 0; /* "(tree fragment)":8 * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result */ __pyx_t_1 = (__pyx_v___pyx_state != Py_None); __pyx_t_6 = (__pyx_t_1 != 0); if (__pyx_t_6) { /* "(tree fragment)":9 * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) # <<<<<<<<<<<<<< * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): */ if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(2, 9, __pyx_L1_error) __pyx_t_3 = __pyx_unpickle_Enum__set_state(((struct __pyx_MemviewEnum_obj *)__pyx_v___pyx_result), ((PyObject*)__pyx_v___pyx_state)); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 9, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "(tree fragment)":8 * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result */ } /* "(tree fragment)":10 * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result # <<<<<<<<<<<<<< * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v___pyx_result); __pyx_r = __pyx_v___pyx_result; goto __pyx_L0; /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v___pyx_PickleError); __Pyx_XDECREF(__pyx_v___pyx_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *__pyx_v___pyx_result, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; Py_ssize_t __pyx_t_3; int __pyx_t_4; int __pyx_t_5; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; __Pyx_RefNannySetupContext("__pyx_unpickle_Enum__set_state", 0); /* "(tree fragment)":12 * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] # <<<<<<<<<<<<<< * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): * __pyx_result.__dict__.update(__pyx_state[1]) */ if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(2, 12, __pyx_L1_error) } __pyx_t_1 = __Pyx_GetItemInt_Tuple(__pyx_v___pyx_state, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 12, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __Pyx_GOTREF(__pyx_v___pyx_result->name); __Pyx_DECREF(__pyx_v___pyx_result->name); __pyx_v___pyx_result->name = __pyx_t_1; __pyx_t_1 = 0; /* "(tree fragment)":13 * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): # <<<<<<<<<<<<<< * __pyx_result.__dict__.update(__pyx_state[1]) */ if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); __PYX_ERR(2, 13, __pyx_L1_error) } __pyx_t_3 = PyTuple_GET_SIZE(__pyx_v___pyx_state); if (unlikely(__pyx_t_3 == ((Py_ssize_t)-1))) __PYX_ERR(2, 13, __pyx_L1_error) __pyx_t_4 = ((__pyx_t_3 > 1) != 0); if (__pyx_t_4) { } else { __pyx_t_2 = __pyx_t_4; goto __pyx_L4_bool_binop_done; } __pyx_t_4 = __Pyx_HasAttr(((PyObject *)__pyx_v___pyx_result), __pyx_n_s_dict); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 13, __pyx_L1_error) __pyx_t_5 = (__pyx_t_4 != 0); __pyx_t_2 = __pyx_t_5; __pyx_L4_bool_binop_done:; if (__pyx_t_2) { /* "(tree fragment)":14 * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): * __pyx_result.__dict__.update(__pyx_state[1]) # <<<<<<<<<<<<<< */ __pyx_t_6 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v___pyx_result), __pyx_n_s_dict); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_6, __pyx_n_s_update); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(2, 14, __pyx_L1_error) } __pyx_t_6 = __Pyx_GetItemInt_Tuple(__pyx_v___pyx_state, 1, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_8 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_7))) { __pyx_t_8 = PyMethod_GET_SELF(__pyx_t_7); if (likely(__pyx_t_8)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_7); __Pyx_INCREF(__pyx_t_8); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_7, function); } } __pyx_t_1 = (__pyx_t_8) ? __Pyx_PyObject_Call2Args(__pyx_t_7, __pyx_t_8, __pyx_t_6) : __Pyx_PyObject_CallOneArg(__pyx_t_7, __pyx_t_6); __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":13 * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): # <<<<<<<<<<<<<< * __pyx_result.__dict__.update(__pyx_state[1]) */ } /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum__set_state", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } static struct __pyx_vtabstruct_array __pyx_vtable_array; static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_array_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_array_obj *)o); p->__pyx_vtab = __pyx_vtabptr_array; p->mode = ((PyObject*)Py_None); Py_INCREF(Py_None); p->_format = ((PyObject*)Py_None); Py_INCREF(Py_None); if (unlikely(__pyx_array___cinit__(o, a, k) < 0)) goto bad; return o; bad: Py_DECREF(o); o = 0; return NULL; } static void __pyx_tp_dealloc_array(PyObject *o) { struct __pyx_array_obj *p = (struct __pyx_array_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_array___dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->mode); Py_CLEAR(p->_format); (*Py_TYPE(o)->tp_free)(o); } static PyObject *__pyx_sq_item_array(PyObject *o, Py_ssize_t i) { PyObject *r; PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); Py_DECREF(x); return r; } static int __pyx_mp_ass_subscript_array(PyObject *o, PyObject *i, PyObject *v) { if (v) { return __pyx_array___setitem__(o, i, v); } else { PyErr_Format(PyExc_NotImplementedError, "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); return -1; } } static PyObject *__pyx_tp_getattro_array(PyObject *o, PyObject *n) { PyObject *v = __Pyx_PyObject_GenericGetAttr(o, n); if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Clear(); v = __pyx_array___getattr__(o, n); } return v; } static PyObject *__pyx_getprop___pyx_array_memview(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(o); } static PyMethodDef __pyx_methods_array[] = { {"__getattr__", (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, 0}, {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_array_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_array_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_array[] = { {(char *)"memview", __pyx_getprop___pyx_array_memview, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PySequenceMethods __pyx_tp_as_sequence_array = { __pyx_array___len__, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ __pyx_sq_item_array, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_array = { __pyx_array___len__, /*mp_length*/ __pyx_array___getitem__, /*mp_subscript*/ __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_array = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif __pyx_array_getbuffer, /*bf_getbuffer*/ 0, /*bf_releasebuffer*/ }; static PyTypeObject __pyx_type___pyx_array = { PyVarObject_HEAD_INIT(0, 0) "openTSNE._matrix_mul.matrix_mul.array", /*tp_name*/ sizeof(struct __pyx_array_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_array, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ &__pyx_tp_as_sequence_array, /*tp_as_sequence*/ &__pyx_tp_as_mapping_array, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ __pyx_tp_getattro_array, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_array, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ 0, /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_array, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_array, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_array, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { struct __pyx_MemviewEnum_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_MemviewEnum_obj *)o); p->name = Py_None; Py_INCREF(Py_None); return o; } static void __pyx_tp_dealloc_Enum(PyObject *o) { struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); Py_CLEAR(p->name); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { int e; struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; if (p->name) { e = (*v)(p->name, a); if (e) return e; } return 0; } static int __pyx_tp_clear_Enum(PyObject *o) { PyObject* tmp; struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; tmp = ((PyObject*)p->name); p->name = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); return 0; } static PyMethodDef __pyx_methods_Enum[] = { {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type___pyx_MemviewEnum = { PyVarObject_HEAD_INIT(0, 0) "openTSNE._matrix_mul.matrix_mul.Enum", /*tp_name*/ sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_Enum, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif __pyx_MemviewEnum___repr__, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_Enum, /*tp_traverse*/ __pyx_tp_clear_Enum, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_Enum, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ __pyx_MemviewEnum___init__, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_Enum, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_memoryview_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_memoryview_obj *)o); p->__pyx_vtab = __pyx_vtabptr_memoryview; p->obj = Py_None; Py_INCREF(Py_None); p->_size = Py_None; Py_INCREF(Py_None); p->_array_interface = Py_None; Py_INCREF(Py_None); p->view.obj = NULL; if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; return o; bad: Py_DECREF(o); o = 0; return NULL; } static void __pyx_tp_dealloc_memoryview(PyObject *o) { struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_memoryview___dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->obj); Py_CLEAR(p->_size); Py_CLEAR(p->_array_interface); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { int e; struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; if (p->obj) { e = (*v)(p->obj, a); if (e) return e; } if (p->_size) { e = (*v)(p->_size, a); if (e) return e; } if (p->_array_interface) { e = (*v)(p->_array_interface, a); if (e) return e; } if (p->view.obj) { e = (*v)(p->view.obj, a); if (e) return e; } return 0; } static int __pyx_tp_clear_memoryview(PyObject *o) { PyObject* tmp; struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; tmp = ((PyObject*)p->obj); p->obj = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->_size); p->_size = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->_array_interface); p->_array_interface = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); Py_CLEAR(p->view.obj); return 0; } static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { PyObject *r; PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); Py_DECREF(x); return r; } static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { if (v) { return __pyx_memoryview___setitem__(o, i, v); } else { PyErr_Format(PyExc_NotImplementedError, "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); return -1; } } static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); } static PyMethodDef __pyx_methods_memoryview[] = { {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0}, {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0}, {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0}, {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0}, {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_memoryview[] = { {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PySequenceMethods __pyx_tp_as_sequence_memoryview = { __pyx_memoryview___len__, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ __pyx_sq_item_memoryview, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_memoryview = { __pyx_memoryview___len__, /*mp_length*/ __pyx_memoryview___getitem__, /*mp_subscript*/ __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_memoryview = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif __pyx_memoryview_getbuffer, /*bf_getbuffer*/ 0, /*bf_releasebuffer*/ }; static PyTypeObject __pyx_type___pyx_memoryview = { PyVarObject_HEAD_INIT(0, 0) "openTSNE._matrix_mul.matrix_mul.memoryview", /*tp_name*/ sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif __pyx_memoryview___repr__, /*tp_repr*/ 0, /*tp_as_number*/ &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ __pyx_memoryview___str__, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_memoryview, /*tp_traverse*/ __pyx_tp_clear_memoryview, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_memoryview, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_memoryview, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_memoryview, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_memoryviewslice_obj *p; PyObject *o = __pyx_tp_new_memoryview(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_memoryviewslice_obj *)o); p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; p->from_object = Py_None; Py_INCREF(Py_None); p->from_slice.memview = NULL; return o; } static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_memoryviewslice___dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->from_object); PyObject_GC_Track(o); __pyx_tp_dealloc_memoryview(o); } static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { int e; struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; if (p->from_object) { e = (*v)(p->from_object, a); if (e) return e; } return 0; } static int __pyx_tp_clear__memoryviewslice(PyObject *o) { PyObject* tmp; struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; __pyx_tp_clear_memoryview(o); tmp = ((PyObject*)p->from_object); p->from_object = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); __PYX_XDEC_MEMVIEW(&p->from_slice, 1); return 0; } static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o); } static PyMethodDef __pyx_methods__memoryviewslice[] = { {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = { {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PyTypeObject __pyx_type___pyx_memoryviewslice = { PyVarObject_HEAD_INIT(0, 0) "openTSNE._matrix_mul.matrix_mul._memoryviewslice", /*tp_name*/ sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif #if CYTHON_COMPILING_IN_PYPY __pyx_memoryview___repr__, /*tp_repr*/ #else 0, /*tp_repr*/ #endif 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ #if CYTHON_COMPILING_IN_PYPY __pyx_memoryview___str__, /*tp_str*/ #else 0, /*tp_str*/ #endif 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ "Internal class for passing memoryview slices to Python", /*tp_doc*/ __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ __pyx_tp_clear__memoryviewslice, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods__memoryviewslice, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets__memoryviewslice, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new__memoryviewslice, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static PyMethodDef __pyx_methods[] = { {0, 0, 0, 0} }; #if PY_MAJOR_VERSION >= 3 #if CYTHON_PEP489_MULTI_PHASE_INIT static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/ static int __pyx_pymod_exec_matrix_mul(PyObject* module); /*proto*/ static PyModuleDef_Slot __pyx_moduledef_slots[] = { {Py_mod_create, (void*)__pyx_pymod_create}, {Py_mod_exec, (void*)__pyx_pymod_exec_matrix_mul}, {0, NULL} }; #endif static struct PyModuleDef __pyx_moduledef = { PyModuleDef_HEAD_INIT, "matrix_mul", 0, /* m_doc */ #if CYTHON_PEP489_MULTI_PHASE_INIT 0, /* m_size */ #else -1, /* m_size */ #endif __pyx_methods /* m_methods */, #if CYTHON_PEP489_MULTI_PHASE_INIT __pyx_moduledef_slots, /* m_slots */ #else NULL, /* m_reload */ #endif NULL, /* m_traverse */ NULL, /* m_clear */ NULL /* m_free */ }; #endif #ifndef CYTHON_SMALL_CODE #if defined(__clang__) #define CYTHON_SMALL_CODE #elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3)) #define CYTHON_SMALL_CODE __attribute__((cold)) #else #define CYTHON_SMALL_CODE #endif #endif static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_n_s_ASCII, __pyx_k_ASCII, sizeof(__pyx_k_ASCII), 0, 0, 1, 1}, {&__pyx_kp_s_Buffer_view_does_not_expose_stri, __pyx_k_Buffer_view_does_not_expose_stri, sizeof(__pyx_k_Buffer_view_does_not_expose_stri), 0, 0, 1, 0}, {&__pyx_kp_s_Can_only_create_a_buffer_that_is, __pyx_k_Can_only_create_a_buffer_that_is, sizeof(__pyx_k_Can_only_create_a_buffer_that_is), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_assign_to_read_only_memor, __pyx_k_Cannot_assign_to_read_only_memor, sizeof(__pyx_k_Cannot_assign_to_read_only_memor), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_create_writable_memory_vi, __pyx_k_Cannot_create_writable_memory_vi, sizeof(__pyx_k_Cannot_create_writable_memory_vi), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_index_with_type_s, __pyx_k_Cannot_index_with_type_s, sizeof(__pyx_k_Cannot_index_with_type_s), 0, 0, 1, 0}, {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1}, {&__pyx_kp_s_Empty_shape_tuple_for_cython_arr, __pyx_k_Empty_shape_tuple_for_cython_arr, sizeof(__pyx_k_Empty_shape_tuple_for_cython_arr), 0, 0, 1, 0}, {&__pyx_kp_u_Format_string_allocated_too_shor, __pyx_k_Format_string_allocated_too_shor, sizeof(__pyx_k_Format_string_allocated_too_shor), 0, 1, 0, 0}, {&__pyx_kp_u_Format_string_allocated_too_shor_2, __pyx_k_Format_string_allocated_too_shor_2, sizeof(__pyx_k_Format_string_allocated_too_shor_2), 0, 1, 0, 0}, {&__pyx_n_s_ImportError, __pyx_k_ImportError, sizeof(__pyx_k_ImportError), 0, 0, 1, 1}, {&__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_k_Incompatible_checksums_s_vs_0xb0, sizeof(__pyx_k_Incompatible_checksums_s_vs_0xb0), 0, 0, 1, 0}, {&__pyx_n_s_IndexError, __pyx_k_IndexError, sizeof(__pyx_k_IndexError), 0, 0, 1, 1}, {&__pyx_kp_s_Indirect_dimensions_not_supporte, __pyx_k_Indirect_dimensions_not_supporte, sizeof(__pyx_k_Indirect_dimensions_not_supporte), 0, 0, 1, 0}, {&__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_k_Invalid_mode_expected_c_or_fortr, sizeof(__pyx_k_Invalid_mode_expected_c_or_fortr), 0, 0, 1, 0}, {&__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_k_Invalid_shape_in_axis_d_d, sizeof(__pyx_k_Invalid_shape_in_axis_d_d), 0, 0, 1, 0}, {&__pyx_n_s_MemoryError, __pyx_k_MemoryError, sizeof(__pyx_k_MemoryError), 0, 0, 1, 1}, {&__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_k_MemoryView_of_r_at_0x_x, sizeof(__pyx_k_MemoryView_of_r_at_0x_x), 0, 0, 1, 0}, {&__pyx_kp_s_MemoryView_of_r_object, __pyx_k_MemoryView_of_r_object, sizeof(__pyx_k_MemoryView_of_r_object), 0, 0, 1, 0}, {&__pyx_kp_u_Non_native_byte_order_not_suppor, __pyx_k_Non_native_byte_order_not_suppor, sizeof(__pyx_k_Non_native_byte_order_not_suppor), 0, 1, 0, 0}, {&__pyx_n_b_O, __pyx_k_O, sizeof(__pyx_k_O), 0, 0, 0, 1}, {&__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_k_Out_of_bounds_on_buffer_access_a, sizeof(__pyx_k_Out_of_bounds_on_buffer_access_a), 0, 0, 1, 0}, {&__pyx_n_s_PickleError, __pyx_k_PickleError, sizeof(__pyx_k_PickleError), 0, 0, 1, 1}, {&__pyx_n_s_RuntimeError, __pyx_k_RuntimeError, sizeof(__pyx_k_RuntimeError), 0, 0, 1, 1}, {&__pyx_n_s_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 0, 0, 1, 1}, {&__pyx_kp_s_Unable_to_convert_item_to_object, __pyx_k_Unable_to_convert_item_to_object, sizeof(__pyx_k_Unable_to_convert_item_to_object), 0, 0, 1, 0}, {&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1}, {&__pyx_n_s_View_MemoryView, __pyx_k_View_MemoryView, sizeof(__pyx_k_View_MemoryView), 0, 0, 1, 1}, {&__pyx_n_s_allocate_buffer, __pyx_k_allocate_buffer, sizeof(__pyx_k_allocate_buffer), 0, 0, 1, 1}, {&__pyx_n_s_base, __pyx_k_base, sizeof(__pyx_k_base), 0, 0, 1, 1}, {&__pyx_n_s_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 0, 1, 1}, {&__pyx_n_u_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 1, 0, 1}, {&__pyx_n_s_class, __pyx_k_class, sizeof(__pyx_k_class), 0, 0, 1, 1}, {&__pyx_n_s_cline_in_traceback, __pyx_k_cline_in_traceback, sizeof(__pyx_k_cline_in_traceback), 0, 0, 1, 1}, {&__pyx_kp_s_contiguous_and_direct, __pyx_k_contiguous_and_direct, sizeof(__pyx_k_contiguous_and_direct), 0, 0, 1, 0}, {&__pyx_kp_s_contiguous_and_indirect, __pyx_k_contiguous_and_indirect, sizeof(__pyx_k_contiguous_and_indirect), 0, 0, 1, 0}, {&__pyx_n_s_dict, __pyx_k_dict, sizeof(__pyx_k_dict), 0, 0, 1, 1}, {&__pyx_n_s_dtype, __pyx_k_dtype, sizeof(__pyx_k_dtype), 0, 0, 1, 1}, {&__pyx_n_s_dtype_is_object, __pyx_k_dtype_is_object, sizeof(__pyx_k_dtype_is_object), 0, 0, 1, 1}, {&__pyx_n_s_empty, __pyx_k_empty, sizeof(__pyx_k_empty), 0, 0, 1, 1}, {&__pyx_n_s_encode, __pyx_k_encode, sizeof(__pyx_k_encode), 0, 0, 1, 1}, {&__pyx_n_s_enumerate, __pyx_k_enumerate, sizeof(__pyx_k_enumerate), 0, 0, 1, 1}, {&__pyx_n_s_error, __pyx_k_error, sizeof(__pyx_k_error), 0, 0, 1, 1}, {&__pyx_n_s_flags, __pyx_k_flags, sizeof(__pyx_k_flags), 0, 0, 1, 1}, {&__pyx_n_s_format, __pyx_k_format, sizeof(__pyx_k_format), 0, 0, 1, 1}, {&__pyx_n_s_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 0, 1, 1}, {&__pyx_n_u_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 1, 0, 1}, {&__pyx_n_s_getstate, __pyx_k_getstate, sizeof(__pyx_k_getstate), 0, 0, 1, 1}, {&__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_k_got_differing_extents_in_dimensi, sizeof(__pyx_k_got_differing_extents_in_dimensi), 0, 0, 1, 0}, {&__pyx_n_s_id, __pyx_k_id, sizeof(__pyx_k_id), 0, 0, 1, 1}, {&__pyx_n_s_import, __pyx_k_import, sizeof(__pyx_k_import), 0, 0, 1, 1}, {&__pyx_n_s_itemsize, __pyx_k_itemsize, sizeof(__pyx_k_itemsize), 0, 0, 1, 1}, {&__pyx_kp_s_itemsize_0_for_cython_array, __pyx_k_itemsize_0_for_cython_array, sizeof(__pyx_k_itemsize_0_for_cython_array), 0, 0, 1, 0}, {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1}, {&__pyx_n_s_memview, __pyx_k_memview, sizeof(__pyx_k_memview), 0, 0, 1, 1}, {&__pyx_n_s_mode, __pyx_k_mode, sizeof(__pyx_k_mode), 0, 0, 1, 1}, {&__pyx_n_s_name, __pyx_k_name, sizeof(__pyx_k_name), 0, 0, 1, 1}, {&__pyx_n_s_name_2, __pyx_k_name_2, sizeof(__pyx_k_name_2), 0, 0, 1, 1}, {&__pyx_kp_u_ndarray_is_not_C_contiguous, __pyx_k_ndarray_is_not_C_contiguous, sizeof(__pyx_k_ndarray_is_not_C_contiguous), 0, 1, 0, 0}, {&__pyx_kp_u_ndarray_is_not_Fortran_contiguou, __pyx_k_ndarray_is_not_Fortran_contiguou, sizeof(__pyx_k_ndarray_is_not_Fortran_contiguou), 0, 1, 0, 0}, {&__pyx_n_s_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 0, 0, 1, 1}, {&__pyx_n_s_new, __pyx_k_new, sizeof(__pyx_k_new), 0, 0, 1, 1}, {&__pyx_kp_s_no_default___reduce___due_to_non, __pyx_k_no_default___reduce___due_to_non, sizeof(__pyx_k_no_default___reduce___due_to_non), 0, 0, 1, 0}, {&__pyx_n_s_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 0, 1, 1}, {&__pyx_n_s_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 0, 0, 1, 1}, {&__pyx_kp_u_numpy_core_multiarray_failed_to, __pyx_k_numpy_core_multiarray_failed_to, sizeof(__pyx_k_numpy_core_multiarray_failed_to), 0, 1, 0, 0}, {&__pyx_kp_u_numpy_core_umath_failed_to_impor, __pyx_k_numpy_core_umath_failed_to_impor, sizeof(__pyx_k_numpy_core_umath_failed_to_impor), 0, 1, 0, 0}, {&__pyx_n_s_obj, __pyx_k_obj, sizeof(__pyx_k_obj), 0, 0, 1, 1}, {&__pyx_n_s_pack, __pyx_k_pack, sizeof(__pyx_k_pack), 0, 0, 1, 1}, {&__pyx_n_s_pickle, __pyx_k_pickle, sizeof(__pyx_k_pickle), 0, 0, 1, 1}, {&__pyx_n_s_pyx_PickleError, __pyx_k_pyx_PickleError, sizeof(__pyx_k_pyx_PickleError), 0, 0, 1, 1}, {&__pyx_n_s_pyx_checksum, __pyx_k_pyx_checksum, sizeof(__pyx_k_pyx_checksum), 0, 0, 1, 1}, {&__pyx_n_s_pyx_getbuffer, __pyx_k_pyx_getbuffer, sizeof(__pyx_k_pyx_getbuffer), 0, 0, 1, 1}, {&__pyx_n_s_pyx_result, __pyx_k_pyx_result, sizeof(__pyx_k_pyx_result), 0, 0, 1, 1}, {&__pyx_n_s_pyx_state, __pyx_k_pyx_state, sizeof(__pyx_k_pyx_state), 0, 0, 1, 1}, {&__pyx_n_s_pyx_type, __pyx_k_pyx_type, sizeof(__pyx_k_pyx_type), 0, 0, 1, 1}, {&__pyx_n_s_pyx_unpickle_Enum, __pyx_k_pyx_unpickle_Enum, sizeof(__pyx_k_pyx_unpickle_Enum), 0, 0, 1, 1}, {&__pyx_n_s_pyx_vtable, __pyx_k_pyx_vtable, sizeof(__pyx_k_pyx_vtable), 0, 0, 1, 1}, {&__pyx_n_s_range, __pyx_k_range, sizeof(__pyx_k_range), 0, 0, 1, 1}, {&__pyx_n_s_real, __pyx_k_real, sizeof(__pyx_k_real), 0, 0, 1, 1}, {&__pyx_n_s_reduce, __pyx_k_reduce, sizeof(__pyx_k_reduce), 0, 0, 1, 1}, {&__pyx_n_s_reduce_cython, __pyx_k_reduce_cython, sizeof(__pyx_k_reduce_cython), 0, 0, 1, 1}, {&__pyx_n_s_reduce_ex, __pyx_k_reduce_ex, sizeof(__pyx_k_reduce_ex), 0, 0, 1, 1}, {&__pyx_n_s_setstate, __pyx_k_setstate, sizeof(__pyx_k_setstate), 0, 0, 1, 1}, {&__pyx_n_s_setstate_cython, __pyx_k_setstate_cython, sizeof(__pyx_k_setstate_cython), 0, 0, 1, 1}, {&__pyx_n_s_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 0, 0, 1, 1}, {&__pyx_n_s_size, __pyx_k_size, sizeof(__pyx_k_size), 0, 0, 1, 1}, {&__pyx_n_s_start, __pyx_k_start, sizeof(__pyx_k_start), 0, 0, 1, 1}, {&__pyx_n_s_step, __pyx_k_step, sizeof(__pyx_k_step), 0, 0, 1, 1}, {&__pyx_n_s_stop, __pyx_k_stop, sizeof(__pyx_k_stop), 0, 0, 1, 1}, {&__pyx_kp_s_strided_and_direct, __pyx_k_strided_and_direct, sizeof(__pyx_k_strided_and_direct), 0, 0, 1, 0}, {&__pyx_kp_s_strided_and_direct_or_indirect, __pyx_k_strided_and_direct_or_indirect, sizeof(__pyx_k_strided_and_direct_or_indirect), 0, 0, 1, 0}, {&__pyx_kp_s_strided_and_indirect, __pyx_k_strided_and_indirect, sizeof(__pyx_k_strided_and_indirect), 0, 0, 1, 0}, {&__pyx_kp_s_stringsource, __pyx_k_stringsource, sizeof(__pyx_k_stringsource), 0, 0, 1, 0}, {&__pyx_n_s_struct, __pyx_k_struct, sizeof(__pyx_k_struct), 0, 0, 1, 1}, {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1}, {&__pyx_kp_s_unable_to_allocate_array_data, __pyx_k_unable_to_allocate_array_data, sizeof(__pyx_k_unable_to_allocate_array_data), 0, 0, 1, 0}, {&__pyx_kp_s_unable_to_allocate_shape_and_str, __pyx_k_unable_to_allocate_shape_and_str, sizeof(__pyx_k_unable_to_allocate_shape_and_str), 0, 0, 1, 0}, {&__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_k_unknown_dtype_code_in_numpy_pxd, sizeof(__pyx_k_unknown_dtype_code_in_numpy_pxd), 0, 1, 0, 0}, {&__pyx_n_s_unpack, __pyx_k_unpack, sizeof(__pyx_k_unpack), 0, 0, 1, 1}, {&__pyx_n_s_update, __pyx_k_update, sizeof(__pyx_k_update), 0, 0, 1, 1}, {&__pyx_n_s_zeros, __pyx_k_zeros, sizeof(__pyx_k_zeros), 0, 0, 1, 1}, {0, 0, 0, 0, 0, 0, 0} }; static CYTHON_SMALL_CODE int __Pyx_InitCachedBuiltins(void) { __pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) __PYX_ERR(0, 94, __pyx_L1_error) __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) __PYX_ERR(1, 272, __pyx_L1_error) __pyx_builtin_RuntimeError = __Pyx_GetBuiltinName(__pyx_n_s_RuntimeError); if (!__pyx_builtin_RuntimeError) __PYX_ERR(1, 856, __pyx_L1_error) __pyx_builtin_ImportError = __Pyx_GetBuiltinName(__pyx_n_s_ImportError); if (!__pyx_builtin_ImportError) __PYX_ERR(1, 1038, __pyx_L1_error) __pyx_builtin_MemoryError = __Pyx_GetBuiltinName(__pyx_n_s_MemoryError); if (!__pyx_builtin_MemoryError) __PYX_ERR(2, 148, __pyx_L1_error) __pyx_builtin_enumerate = __Pyx_GetBuiltinName(__pyx_n_s_enumerate); if (!__pyx_builtin_enumerate) __PYX_ERR(2, 151, __pyx_L1_error) __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s_TypeError); if (!__pyx_builtin_TypeError) __PYX_ERR(2, 2, __pyx_L1_error) __pyx_builtin_Ellipsis = __Pyx_GetBuiltinName(__pyx_n_s_Ellipsis); if (!__pyx_builtin_Ellipsis) __PYX_ERR(2, 404, __pyx_L1_error) __pyx_builtin_id = __Pyx_GetBuiltinName(__pyx_n_s_id); if (!__pyx_builtin_id) __PYX_ERR(2, 613, __pyx_L1_error) __pyx_builtin_IndexError = __Pyx_GetBuiltinName(__pyx_n_s_IndexError); if (!__pyx_builtin_IndexError) __PYX_ERR(2, 832, __pyx_L1_error) return 0; __pyx_L1_error:; return -1; } static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":272 * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_ARRAY_C_CONTIGUOUS)): * raise ValueError(u"ndarray is not C contiguous") # <<<<<<<<<<<<<< * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) */ __pyx_tuple_ = PyTuple_Pack(1, __pyx_kp_u_ndarray_is_not_C_contiguous); if (unlikely(!__pyx_tuple_)) __PYX_ERR(1, 272, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple_); __Pyx_GIVEREF(__pyx_tuple_); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":276 * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_ARRAY_F_CONTIGUOUS)): * raise ValueError(u"ndarray is not Fortran contiguous") # <<<<<<<<<<<<<< * * info.buf = PyArray_DATA(self) */ __pyx_tuple__2 = PyTuple_Pack(1, __pyx_kp_u_ndarray_is_not_Fortran_contiguou); if (unlikely(!__pyx_tuple__2)) __PYX_ERR(1, 276, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__2); __Pyx_GIVEREF(__pyx_tuple__2); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":306 * if ((descr.byteorder == c'>' and little_endian) or * (descr.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") # <<<<<<<<<<<<<< * if t == NPY_BYTE: f = "b" * elif t == NPY_UBYTE: f = "B" */ __pyx_tuple__3 = PyTuple_Pack(1, __pyx_kp_u_Non_native_byte_order_not_suppor); if (unlikely(!__pyx_tuple__3)) __PYX_ERR(1, 306, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__3); __Pyx_GIVEREF(__pyx_tuple__3); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":856 * * if (end - f) - (new_offset - offset[0]) < 15: * raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd") # <<<<<<<<<<<<<< * * if ((child.byteorder == c'>' and little_endian) or */ __pyx_tuple__4 = PyTuple_Pack(1, __pyx_kp_u_Format_string_allocated_too_shor); if (unlikely(!__pyx_tuple__4)) __PYX_ERR(1, 856, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__4); __Pyx_GIVEREF(__pyx_tuple__4); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":880 * t = child.type_num * if end - f < 5: * raise RuntimeError(u"Format string allocated too short.") # <<<<<<<<<<<<<< * * # Until ticket #99 is fixed, use integers to avoid warnings */ __pyx_tuple__5 = PyTuple_Pack(1, __pyx_kp_u_Format_string_allocated_too_shor_2); if (unlikely(!__pyx_tuple__5)) __PYX_ERR(1, 880, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__5); __Pyx_GIVEREF(__pyx_tuple__5); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1038 * _import_array() * except Exception: * raise ImportError("numpy.core.multiarray failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_umath() except -1: */ __pyx_tuple__6 = PyTuple_Pack(1, __pyx_kp_u_numpy_core_multiarray_failed_to); if (unlikely(!__pyx_tuple__6)) __PYX_ERR(1, 1038, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__6); __Pyx_GIVEREF(__pyx_tuple__6); /* "../../miniconda3/envs/tsne/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd":1044 * _import_umath() * except Exception: * raise ImportError("numpy.core.umath failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_ufunc() except -1: */ __pyx_tuple__7 = PyTuple_Pack(1, __pyx_kp_u_numpy_core_umath_failed_to_impor); if (unlikely(!__pyx_tuple__7)) __PYX_ERR(1, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__7); __Pyx_GIVEREF(__pyx_tuple__7); /* "View.MemoryView":133 * * if not self.ndim: * raise ValueError("Empty shape tuple for cython.array") # <<<<<<<<<<<<<< * * if itemsize <= 0: */ __pyx_tuple__8 = PyTuple_Pack(1, __pyx_kp_s_Empty_shape_tuple_for_cython_arr); if (unlikely(!__pyx_tuple__8)) __PYX_ERR(2, 133, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__8); __Pyx_GIVEREF(__pyx_tuple__8); /* "View.MemoryView":136 * * if itemsize <= 0: * raise ValueError("itemsize <= 0 for cython.array") # <<<<<<<<<<<<<< * * if not isinstance(format, bytes): */ __pyx_tuple__9 = PyTuple_Pack(1, __pyx_kp_s_itemsize_0_for_cython_array); if (unlikely(!__pyx_tuple__9)) __PYX_ERR(2, 136, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__9); __Pyx_GIVEREF(__pyx_tuple__9); /* "View.MemoryView":148 * * if not self._shape: * raise MemoryError("unable to allocate shape and strides.") # <<<<<<<<<<<<<< * * */ __pyx_tuple__10 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_shape_and_str); if (unlikely(!__pyx_tuple__10)) __PYX_ERR(2, 148, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__10); __Pyx_GIVEREF(__pyx_tuple__10); /* "View.MemoryView":176 * self.data = malloc(self.len) * if not self.data: * raise MemoryError("unable to allocate array data.") # <<<<<<<<<<<<<< * * if self.dtype_is_object: */ __pyx_tuple__11 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_array_data); if (unlikely(!__pyx_tuple__11)) __PYX_ERR(2, 176, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__11); __Pyx_GIVEREF(__pyx_tuple__11); /* "View.MemoryView":192 * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") # <<<<<<<<<<<<<< * info.buf = self.data * info.len = self.len */ __pyx_tuple__12 = PyTuple_Pack(1, __pyx_kp_s_Can_only_create_a_buffer_that_is); if (unlikely(!__pyx_tuple__12)) __PYX_ERR(2, 192, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__12); __Pyx_GIVEREF(__pyx_tuple__12); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__13 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__13)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__13); __Pyx_GIVEREF(__pyx_tuple__13); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__14 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__14)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__14); __Pyx_GIVEREF(__pyx_tuple__14); /* "View.MemoryView":418 * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") # <<<<<<<<<<<<<< * * have_slices, index = _unellipsify(index, self.view.ndim) */ __pyx_tuple__15 = PyTuple_Pack(1, __pyx_kp_s_Cannot_assign_to_read_only_memor); if (unlikely(!__pyx_tuple__15)) __PYX_ERR(2, 418, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__15); __Pyx_GIVEREF(__pyx_tuple__15); /* "View.MemoryView":495 * result = struct.unpack(self.view.format, bytesitem) * except struct.error: * raise ValueError("Unable to convert item to object") # <<<<<<<<<<<<<< * else: * if len(self.view.format) == 1: */ __pyx_tuple__16 = PyTuple_Pack(1, __pyx_kp_s_Unable_to_convert_item_to_object); if (unlikely(!__pyx_tuple__16)) __PYX_ERR(2, 495, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__16); __Pyx_GIVEREF(__pyx_tuple__16); /* "View.MemoryView":520 * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") # <<<<<<<<<<<<<< * * if flags & PyBUF_ND: */ __pyx_tuple__17 = PyTuple_Pack(1, __pyx_kp_s_Cannot_create_writable_memory_vi); if (unlikely(!__pyx_tuple__17)) __PYX_ERR(2, 520, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__17); __Pyx_GIVEREF(__pyx_tuple__17); /* "View.MemoryView":570 * if self.view.strides == NULL: * * raise ValueError("Buffer view does not expose strides") # <<<<<<<<<<<<<< * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) */ __pyx_tuple__18 = PyTuple_Pack(1, __pyx_kp_s_Buffer_view_does_not_expose_stri); if (unlikely(!__pyx_tuple__18)) __PYX_ERR(2, 570, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__18); __Pyx_GIVEREF(__pyx_tuple__18); /* "View.MemoryView":577 * def suboffsets(self): * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim # <<<<<<<<<<<<<< * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) */ __pyx_tuple__19 = PyTuple_New(1); if (unlikely(!__pyx_tuple__19)) __PYX_ERR(2, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__19); __Pyx_INCREF(__pyx_int_neg_1); __Pyx_GIVEREF(__pyx_int_neg_1); PyTuple_SET_ITEM(__pyx_tuple__19, 0, __pyx_int_neg_1); __Pyx_GIVEREF(__pyx_tuple__19); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__20 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__20)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__20); __Pyx_GIVEREF(__pyx_tuple__20); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__21 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__21)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__21); __Pyx_GIVEREF(__pyx_tuple__21); /* "View.MemoryView":682 * if item is Ellipsis: * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) # <<<<<<<<<<<<<< * seen_ellipsis = True * else: */ __pyx_slice__22 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__22)) __PYX_ERR(2, 682, __pyx_L1_error) __Pyx_GOTREF(__pyx_slice__22); __Pyx_GIVEREF(__pyx_slice__22); /* "View.MemoryView":703 * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") # <<<<<<<<<<<<<< * * */ __pyx_tuple__23 = PyTuple_Pack(1, __pyx_kp_s_Indirect_dimensions_not_supporte); if (unlikely(!__pyx_tuple__23)) __PYX_ERR(2, 703, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__23); __Pyx_GIVEREF(__pyx_tuple__23); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__24 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__24)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__24); __Pyx_GIVEREF(__pyx_tuple__24); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__25 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__25)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__25); __Pyx_GIVEREF(__pyx_tuple__25); /* "View.MemoryView":286 * return self.name * * cdef generic = Enum("") # <<<<<<<<<<<<<< * cdef strided = Enum("") # default * cdef indirect = Enum("") */ __pyx_tuple__26 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct_or_indirect); if (unlikely(!__pyx_tuple__26)) __PYX_ERR(2, 286, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__26); __Pyx_GIVEREF(__pyx_tuple__26); /* "View.MemoryView":287 * * cdef generic = Enum("") * cdef strided = Enum("") # default # <<<<<<<<<<<<<< * cdef indirect = Enum("") * */ __pyx_tuple__27 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct); if (unlikely(!__pyx_tuple__27)) __PYX_ERR(2, 287, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__27); __Pyx_GIVEREF(__pyx_tuple__27); /* "View.MemoryView":288 * cdef generic = Enum("") * cdef strided = Enum("") # default * cdef indirect = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_tuple__28 = PyTuple_Pack(1, __pyx_kp_s_strided_and_indirect); if (unlikely(!__pyx_tuple__28)) __PYX_ERR(2, 288, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__28); __Pyx_GIVEREF(__pyx_tuple__28); /* "View.MemoryView":291 * * * cdef contiguous = Enum("") # <<<<<<<<<<<<<< * cdef indirect_contiguous = Enum("") * */ __pyx_tuple__29 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_direct); if (unlikely(!__pyx_tuple__29)) __PYX_ERR(2, 291, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__29); __Pyx_GIVEREF(__pyx_tuple__29); /* "View.MemoryView":292 * * cdef contiguous = Enum("") * cdef indirect_contiguous = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_tuple__30 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_indirect); if (unlikely(!__pyx_tuple__30)) __PYX_ERR(2, 292, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__30); __Pyx_GIVEREF(__pyx_tuple__30); /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ __pyx_tuple__31 = PyTuple_Pack(5, __pyx_n_s_pyx_type, __pyx_n_s_pyx_checksum, __pyx_n_s_pyx_state, __pyx_n_s_pyx_PickleError, __pyx_n_s_pyx_result); if (unlikely(!__pyx_tuple__31)) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__31); __Pyx_GIVEREF(__pyx_tuple__31); __pyx_codeobj__32 = (PyObject*)__Pyx_PyCode_New(3, 0, 5, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__31, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_stringsource, __pyx_n_s_pyx_unpickle_Enum, 1, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__32)) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static CYTHON_SMALL_CODE int __Pyx_InitGlobals(void) { if (__Pyx_InitStrings(__pyx_string_tab) < 0) __PYX_ERR(0, 1, __pyx_L1_error); __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_1 = PyInt_FromLong(1); if (unlikely(!__pyx_int_1)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_184977713 = PyInt_FromLong(184977713L); if (unlikely(!__pyx_int_184977713)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_neg_1 = PyInt_FromLong(-1); if (unlikely(!__pyx_int_neg_1)) __PYX_ERR(0, 1, __pyx_L1_error) return 0; __pyx_L1_error:; return -1; } static CYTHON_SMALL_CODE int __Pyx_modinit_global_init_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_variable_export_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_function_export_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_type_init_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_type_import_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_variable_import_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_function_import_code(void); /*proto*/ static int __Pyx_modinit_global_init_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_global_init_code", 0); /*--- Global init code ---*/ generic = Py_None; Py_INCREF(Py_None); strided = Py_None; Py_INCREF(Py_None); indirect = Py_None; Py_INCREF(Py_None); contiguous = Py_None; Py_INCREF(Py_None); indirect_contiguous = Py_None; Py_INCREF(Py_None); __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_variable_export_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_variable_export_code", 0); /*--- Variable export code ---*/ __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_function_export_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_function_export_code", 0); /*--- Function export code ---*/ if (__Pyx_ExportFunction("matrix_multiply_fft_1d", (void (*)(void))__pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_1d, "void (__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ExportFunction("matrix_multiply_fft_2d", (void (*)(void))__pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_2d, "void (__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_type_init_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_type_init_code", 0); /*--- Type init code ---*/ __pyx_vtabptr_array = &__pyx_vtable_array; __pyx_vtable_array.get_memview = (PyObject *(*)(struct __pyx_array_obj *))__pyx_array_get_memview; if (PyType_Ready(&__pyx_type___pyx_array) < 0) __PYX_ERR(2, 105, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_array.tp_print = 0; #endif if (__Pyx_SetVtable(__pyx_type___pyx_array.tp_dict, __pyx_vtabptr_array) < 0) __PYX_ERR(2, 105, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_array) < 0) __PYX_ERR(2, 105, __pyx_L1_error) __pyx_array_type = &__pyx_type___pyx_array; if (PyType_Ready(&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(2, 279, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_MemviewEnum.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_MemviewEnum.tp_dictoffset && __pyx_type___pyx_MemviewEnum.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_MemviewEnum.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(2, 279, __pyx_L1_error) __pyx_MemviewEnum_type = &__pyx_type___pyx_MemviewEnum; __pyx_vtabptr_memoryview = &__pyx_vtable_memoryview; __pyx_vtable_memoryview.get_item_pointer = (char *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_get_item_pointer; __pyx_vtable_memoryview.is_slice = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_is_slice; __pyx_vtable_memoryview.setitem_slice_assignment = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_slice_assignment; __pyx_vtable_memoryview.setitem_slice_assign_scalar = (PyObject *(*)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_setitem_slice_assign_scalar; __pyx_vtable_memoryview.setitem_indexed = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_indexed; __pyx_vtable_memoryview.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryview_convert_item_to_object; __pyx_vtable_memoryview.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryview_assign_item_from_object; if (PyType_Ready(&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(2, 330, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_memoryview.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_memoryview.tp_dictoffset && __pyx_type___pyx_memoryview.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_memoryview.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_SetVtable(__pyx_type___pyx_memoryview.tp_dict, __pyx_vtabptr_memoryview) < 0) __PYX_ERR(2, 330, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(2, 330, __pyx_L1_error) __pyx_memoryview_type = &__pyx_type___pyx_memoryview; __pyx_vtabptr__memoryviewslice = &__pyx_vtable__memoryviewslice; __pyx_vtable__memoryviewslice.__pyx_base = *__pyx_vtabptr_memoryview; __pyx_vtable__memoryviewslice.__pyx_base.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryviewslice_convert_item_to_object; __pyx_vtable__memoryviewslice.__pyx_base.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryviewslice_assign_item_from_object; __pyx_type___pyx_memoryviewslice.tp_base = __pyx_memoryview_type; if (PyType_Ready(&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(2, 965, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_memoryviewslice.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_memoryviewslice.tp_dictoffset && __pyx_type___pyx_memoryviewslice.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_memoryviewslice.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_SetVtable(__pyx_type___pyx_memoryviewslice.tp_dict, __pyx_vtabptr__memoryviewslice) < 0) __PYX_ERR(2, 965, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(2, 965, __pyx_L1_error) __pyx_memoryviewslice_type = &__pyx_type___pyx_memoryviewslice; __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_type_import_code(void) { __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__Pyx_modinit_type_import_code", 0); /*--- Type import code ---*/ __pyx_t_1 = PyImport_ImportModule(__Pyx_BUILTIN_MODULE_NAME); if (unlikely(!__pyx_t_1)) __PYX_ERR(3, 9, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_ptype_7cpython_4type_type = __Pyx_ImportType(__pyx_t_1, __Pyx_BUILTIN_MODULE_NAME, "type", #if defined(PYPY_VERSION_NUM) && PYPY_VERSION_NUM < 0x050B0000 sizeof(PyTypeObject), #else sizeof(PyHeapTypeObject), #endif __Pyx_ImportType_CheckSize_Warn); if (!__pyx_ptype_7cpython_4type_type) __PYX_ERR(3, 9, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyImport_ImportModule("numpy"); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 206, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_ptype_5numpy_dtype = __Pyx_ImportType(__pyx_t_1, "numpy", "dtype", sizeof(PyArray_Descr), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_dtype) __PYX_ERR(1, 206, __pyx_L1_error) __pyx_ptype_5numpy_flatiter = __Pyx_ImportType(__pyx_t_1, "numpy", "flatiter", sizeof(PyArrayIterObject), __Pyx_ImportType_CheckSize_Warn); if (!__pyx_ptype_5numpy_flatiter) __PYX_ERR(1, 229, __pyx_L1_error) __pyx_ptype_5numpy_broadcast = __Pyx_ImportType(__pyx_t_1, "numpy", "broadcast", sizeof(PyArrayMultiIterObject), __Pyx_ImportType_CheckSize_Warn); if (!__pyx_ptype_5numpy_broadcast) __PYX_ERR(1, 233, __pyx_L1_error) __pyx_ptype_5numpy_ndarray = __Pyx_ImportType(__pyx_t_1, "numpy", "ndarray", sizeof(PyArrayObject), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_ndarray) __PYX_ERR(1, 242, __pyx_L1_error) __pyx_ptype_5numpy_ufunc = __Pyx_ImportType(__pyx_t_1, "numpy", "ufunc", sizeof(PyUFuncObject), __Pyx_ImportType_CheckSize_Warn); if (!__pyx_ptype_5numpy_ufunc) __PYX_ERR(1, 918, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_variable_import_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_variable_import_code", 0); /*--- Variable import code ---*/ __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_function_import_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_function_import_code", 0); /*--- Function import code ---*/ __Pyx_RefNannyFinishContext(); return 0; } #if PY_MAJOR_VERSION < 3 #ifdef CYTHON_NO_PYINIT_EXPORT #define __Pyx_PyMODINIT_FUNC void #else #define __Pyx_PyMODINIT_FUNC PyMODINIT_FUNC #endif #else #ifdef CYTHON_NO_PYINIT_EXPORT #define __Pyx_PyMODINIT_FUNC PyObject * #else #define __Pyx_PyMODINIT_FUNC PyMODINIT_FUNC #endif #endif #if PY_MAJOR_VERSION < 3 __Pyx_PyMODINIT_FUNC initmatrix_mul(void) CYTHON_SMALL_CODE; /*proto*/ __Pyx_PyMODINIT_FUNC initmatrix_mul(void) #else __Pyx_PyMODINIT_FUNC PyInit_matrix_mul(void) CYTHON_SMALL_CODE; /*proto*/ __Pyx_PyMODINIT_FUNC PyInit_matrix_mul(void) #if CYTHON_PEP489_MULTI_PHASE_INIT { return PyModuleDef_Init(&__pyx_moduledef); } static CYTHON_SMALL_CODE int __Pyx_check_single_interpreter(void) { #if PY_VERSION_HEX >= 0x030700A1 static PY_INT64_T main_interpreter_id = -1; PY_INT64_T current_id = PyInterpreterState_GetID(PyThreadState_Get()->interp); if (main_interpreter_id == -1) { main_interpreter_id = current_id; return (unlikely(current_id == -1)) ? -1 : 0; } else if (unlikely(main_interpreter_id != current_id)) #else static PyInterpreterState *main_interpreter = NULL; PyInterpreterState *current_interpreter = PyThreadState_Get()->interp; if (!main_interpreter) { main_interpreter = current_interpreter; } else if (unlikely(main_interpreter != current_interpreter)) #endif { PyErr_SetString( PyExc_ImportError, "Interpreter change detected - this module can only be loaded into one interpreter per process."); return -1; } return 0; } static CYTHON_SMALL_CODE int __Pyx_copy_spec_to_module(PyObject *spec, PyObject *moddict, const char* from_name, const char* to_name, int allow_none) { PyObject *value = PyObject_GetAttrString(spec, from_name); int result = 0; if (likely(value)) { if (allow_none || value != Py_None) { result = PyDict_SetItemString(moddict, to_name, value); } Py_DECREF(value); } else if (PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Clear(); } else { result = -1; } return result; } static CYTHON_SMALL_CODE PyObject* __pyx_pymod_create(PyObject *spec, CYTHON_UNUSED PyModuleDef *def) { PyObject *module = NULL, *moddict, *modname; if (__Pyx_check_single_interpreter()) return NULL; if (__pyx_m) return __Pyx_NewRef(__pyx_m); modname = PyObject_GetAttrString(spec, "name"); if (unlikely(!modname)) goto bad; module = PyModule_NewObject(modname); Py_DECREF(modname); if (unlikely(!module)) goto bad; moddict = PyModule_GetDict(module); if (unlikely(!moddict)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "loader", "__loader__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "origin", "__file__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "parent", "__package__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "submodule_search_locations", "__path__", 0) < 0)) goto bad; return module; bad: Py_XDECREF(module); return NULL; } static CYTHON_SMALL_CODE int __pyx_pymod_exec_matrix_mul(PyObject *__pyx_pyinit_module) #endif #endif { PyObject *__pyx_t_1 = NULL; static PyThread_type_lock __pyx_t_2[8]; __Pyx_RefNannyDeclarations #if CYTHON_PEP489_MULTI_PHASE_INIT if (__pyx_m) { if (__pyx_m == __pyx_pyinit_module) return 0; PyErr_SetString(PyExc_RuntimeError, "Module 'matrix_mul' has already been imported. Re-initialisation is not supported."); return -1; } #elif PY_MAJOR_VERSION >= 3 if (__pyx_m) return __Pyx_NewRef(__pyx_m); #endif #if CYTHON_REFNANNY __Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny"); if (!__Pyx_RefNanny) { PyErr_Clear(); __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny"); if (!__Pyx_RefNanny) Py_FatalError("failed to import 'refnanny' module"); } #endif __Pyx_RefNannySetupContext("__Pyx_PyMODINIT_FUNC PyInit_matrix_mul(void)", 0); if (__Pyx_check_binary_version() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #ifdef __Pxy_PyFrame_Initialize_Offsets __Pxy_PyFrame_Initialize_Offsets(); #endif __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_empty_unicode = PyUnicode_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_unicode)) __PYX_ERR(0, 1, __pyx_L1_error) #ifdef __Pyx_CyFunction_USED if (__pyx_CyFunction_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_FusedFunction_USED if (__pyx_FusedFunction_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_Coroutine_USED if (__pyx_Coroutine_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_Generator_USED if (__pyx_Generator_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_AsyncGen_USED if (__pyx_AsyncGen_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_StopAsyncIteration_USED if (__pyx_StopAsyncIteration_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif /*--- Library function declarations ---*/ /*--- Threads initialization code ---*/ #if defined(__PYX_FORCE_INIT_THREADS) && __PYX_FORCE_INIT_THREADS #ifdef WITH_THREAD /* Python build with threading support? */ PyEval_InitThreads(); #endif #endif /*--- Module creation code ---*/ #if CYTHON_PEP489_MULTI_PHASE_INIT __pyx_m = __pyx_pyinit_module; Py_INCREF(__pyx_m); #else #if PY_MAJOR_VERSION < 3 __pyx_m = Py_InitModule4("matrix_mul", __pyx_methods, 0, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m); #else __pyx_m = PyModule_Create(&__pyx_moduledef); #endif if (unlikely(!__pyx_m)) __PYX_ERR(0, 1, __pyx_L1_error) #endif __pyx_d = PyModule_GetDict(__pyx_m); if (unlikely(!__pyx_d)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_d); __pyx_b = PyImport_AddModule(__Pyx_BUILTIN_MODULE_NAME); if (unlikely(!__pyx_b)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_b); __pyx_cython_runtime = PyImport_AddModule((char *) "cython_runtime"); if (unlikely(!__pyx_cython_runtime)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_cython_runtime); if (PyObject_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) __PYX_ERR(0, 1, __pyx_L1_error); /*--- Initialize various global constants etc. ---*/ if (__Pyx_InitGlobals() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #if PY_MAJOR_VERSION < 3 && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) if (__Pyx_init_sys_getdefaultencoding_params() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif if (__pyx_module_is_main_openTSNE___matrix_mul__matrix_mul) { if (PyObject_SetAttr(__pyx_m, __pyx_n_s_name_2, __pyx_n_s_main) < 0) __PYX_ERR(0, 1, __pyx_L1_error) } #if PY_MAJOR_VERSION >= 3 { PyObject *modules = PyImport_GetModuleDict(); if (unlikely(!modules)) __PYX_ERR(0, 1, __pyx_L1_error) if (!PyDict_GetItemString(modules, "openTSNE._matrix_mul.matrix_mul")) { if (unlikely(PyDict_SetItemString(modules, "openTSNE._matrix_mul.matrix_mul", __pyx_m) < 0)) __PYX_ERR(0, 1, __pyx_L1_error) } } #endif /*--- Builtin init code ---*/ if (__Pyx_InitCachedBuiltins() < 0) goto __pyx_L1_error; /*--- Constants init code ---*/ if (__Pyx_InitCachedConstants() < 0) goto __pyx_L1_error; /*--- Global type/function init code ---*/ (void)__Pyx_modinit_global_init_code(); (void)__Pyx_modinit_variable_export_code(); if (unlikely(__Pyx_modinit_function_export_code() != 0)) goto __pyx_L1_error; if (unlikely(__Pyx_modinit_type_init_code() != 0)) goto __pyx_L1_error; if (unlikely(__Pyx_modinit_type_import_code() != 0)) goto __pyx_L1_error; (void)__Pyx_modinit_variable_import_code(); (void)__Pyx_modinit_function_import_code(); /*--- Execution code ---*/ #if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED) if (__Pyx_patch_abc() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":9 * cimport openTSNE._matrix_mul.matrix_mul * cimport numpy as np * import numpy as np # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_numpy, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 9, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) __PYX_ERR(0, 9, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "openTSNE/_matrix_mul/matrix_mul_fftw3.pyx":1 * # cython: boundscheck=False # <<<<<<<<<<<<<< * # cython: wraparound=False * # cython: cdivision=True */ __pyx_t_1 = __Pyx_PyDict_NewPresized(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":209 * info.obj = self * * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * def __dealloc__(array self): */ __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_array_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 209, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem((PyObject *)__pyx_array_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(2, 209, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; PyType_Modified(__pyx_array_type); /* "View.MemoryView":286 * return self.name * * cdef generic = Enum("") # <<<<<<<<<<<<<< * cdef strided = Enum("") # default * cdef indirect = Enum("") */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__26, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 286, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(generic); __Pyx_DECREF_SET(generic, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":287 * * cdef generic = Enum("") * cdef strided = Enum("") # default # <<<<<<<<<<<<<< * cdef indirect = Enum("") * */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__27, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 287, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(strided); __Pyx_DECREF_SET(strided, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":288 * cdef generic = Enum("") * cdef strided = Enum("") # default * cdef indirect = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__28, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 288, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(indirect); __Pyx_DECREF_SET(indirect, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":291 * * * cdef contiguous = Enum("") # <<<<<<<<<<<<<< * cdef indirect_contiguous = Enum("") * */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__29, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 291, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(contiguous); __Pyx_DECREF_SET(contiguous, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":292 * * cdef contiguous = Enum("") * cdef indirect_contiguous = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__30, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 292, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(indirect_contiguous); __Pyx_DECREF_SET(indirect_contiguous, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":316 * * DEF THREAD_LOCKS_PREALLOCATED = 8 * cdef int __pyx_memoryview_thread_locks_used = 0 # <<<<<<<<<<<<<< * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [ * PyThread_allocate_lock(), */ __pyx_memoryview_thread_locks_used = 0; /* "View.MemoryView":317 * DEF THREAD_LOCKS_PREALLOCATED = 8 * cdef int __pyx_memoryview_thread_locks_used = 0 * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [ # <<<<<<<<<<<<<< * PyThread_allocate_lock(), * PyThread_allocate_lock(), */ __pyx_t_2[0] = PyThread_allocate_lock(); __pyx_t_2[1] = PyThread_allocate_lock(); __pyx_t_2[2] = PyThread_allocate_lock(); __pyx_t_2[3] = PyThread_allocate_lock(); __pyx_t_2[4] = PyThread_allocate_lock(); __pyx_t_2[5] = PyThread_allocate_lock(); __pyx_t_2[6] = PyThread_allocate_lock(); __pyx_t_2[7] = PyThread_allocate_lock(); memcpy(&(__pyx_memoryview_thread_locks[0]), __pyx_t_2, sizeof(__pyx_memoryview_thread_locks[0]) * (8)); /* "View.MemoryView":549 * info.obj = self * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 549, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem((PyObject *)__pyx_memoryview_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(2, 549, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; PyType_Modified(__pyx_memoryview_type); /* "View.MemoryView":995 * return self.from_object * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 995, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem((PyObject *)__pyx_memoryviewslice_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(2, 995, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; PyType_Modified(__pyx_memoryviewslice_type); /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_15View_dot_MemoryView_1__pyx_unpickle_Enum, NULL, __pyx_n_s_View_MemoryView); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_pyx_unpickle_Enum, __pyx_t_1) < 0) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ /*--- Wrapped vars code ---*/ goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); if (__pyx_m) { if (__pyx_d) { __Pyx_AddTraceback("init openTSNE._matrix_mul.matrix_mul", __pyx_clineno, __pyx_lineno, __pyx_filename); } Py_CLEAR(__pyx_m); } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_ImportError, "init openTSNE._matrix_mul.matrix_mul"); } __pyx_L0:; __Pyx_RefNannyFinishContext(); #if CYTHON_PEP489_MULTI_PHASE_INIT return (__pyx_m != NULL) ? 0 : -1; #elif PY_MAJOR_VERSION >= 3 return __pyx_m; #else return; #endif } /* --- Runtime support code --- */ /* Refnanny */ #if CYTHON_REFNANNY static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { PyObject *m = NULL, *p = NULL; void *r = NULL; m = PyImport_ImportModule(modname); if (!m) goto end; p = PyObject_GetAttrString(m, "RefNannyAPI"); if (!p) goto end; r = PyLong_AsVoidPtr(p); end: Py_XDECREF(p); Py_XDECREF(m); return (__Pyx_RefNannyAPIStruct *)r; } #endif /* PyObjectGetAttrStr */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { PyTypeObject* tp = Py_TYPE(obj); if (likely(tp->tp_getattro)) return tp->tp_getattro(obj, attr_name); #if PY_MAJOR_VERSION < 3 if (likely(tp->tp_getattr)) return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); #endif return PyObject_GetAttr(obj, attr_name); } #endif /* GetBuiltinName */ static PyObject *__Pyx_GetBuiltinName(PyObject *name) { PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); if (unlikely(!result)) { PyErr_Format(PyExc_NameError, #if PY_MAJOR_VERSION >= 3 "name '%U' is not defined", name); #else "name '%.200s' is not defined", PyString_AS_STRING(name)); #endif } return result; } /* PyDictVersioning */ #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { PyObject *dict = Py_TYPE(obj)->tp_dict; return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; } static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { PyObject **dictptr = NULL; Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; if (offset) { #if CYTHON_COMPILING_IN_CPYTHON dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); #else dictptr = _PyObject_GetDictPtr(obj); #endif } return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; } static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { PyObject *dict = Py_TYPE(obj)->tp_dict; if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) return 0; return obj_dict_version == __Pyx_get_object_dict_version(obj); } #endif /* GetModuleGlobalName */ #if CYTHON_USE_DICT_VERSIONS static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) #else static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) #endif { PyObject *result; #if !CYTHON_AVOID_BORROWED_REFS #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } else if (unlikely(PyErr_Occurred())) { return NULL; } #else result = PyDict_GetItem(__pyx_d, name); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } #endif #else result = PyObject_GetItem(__pyx_d, name); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } PyErr_Clear(); #endif return __Pyx_GetBuiltinName(name); } /* PyObjectCall */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; ternaryfunc call = func->ob_type->tp_call; if (unlikely(!call)) return PyObject_Call(func, arg, kw); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = (*call)(func, arg, kw); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif /* MemviewSliceInit */ static int __Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, int ndim, __Pyx_memviewslice *memviewslice, int memview_is_new_reference) { __Pyx_RefNannyDeclarations int i, retval=-1; Py_buffer *buf = &memview->view; __Pyx_RefNannySetupContext("init_memviewslice", 0); if (memviewslice->memview || memviewslice->data) { PyErr_SetString(PyExc_ValueError, "memviewslice is already initialized!"); goto fail; } if (buf->strides) { for (i = 0; i < ndim; i++) { memviewslice->strides[i] = buf->strides[i]; } } else { Py_ssize_t stride = buf->itemsize; for (i = ndim - 1; i >= 0; i--) { memviewslice->strides[i] = stride; stride *= buf->shape[i]; } } for (i = 0; i < ndim; i++) { memviewslice->shape[i] = buf->shape[i]; if (buf->suboffsets) { memviewslice->suboffsets[i] = buf->suboffsets[i]; } else { memviewslice->suboffsets[i] = -1; } } memviewslice->memview = memview; memviewslice->data = (char *)buf->buf; if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { Py_INCREF(memview); } retval = 0; goto no_fail; fail: memviewslice->memview = 0; memviewslice->data = 0; retval = -1; no_fail: __Pyx_RefNannyFinishContext(); return retval; } #ifndef Py_NO_RETURN #define Py_NO_RETURN #endif static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { va_list vargs; char msg[200]; #ifdef HAVE_STDARG_PROTOTYPES va_start(vargs, fmt); #else va_start(vargs); #endif vsnprintf(msg, 200, fmt, vargs); va_end(vargs); Py_FatalError(msg); } static CYTHON_INLINE int __pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, PyThread_type_lock lock) { int result; PyThread_acquire_lock(lock, 1); result = (*acquisition_count)++; PyThread_release_lock(lock); return result; } static CYTHON_INLINE int __pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, PyThread_type_lock lock) { int result; PyThread_acquire_lock(lock, 1); result = (*acquisition_count)--; PyThread_release_lock(lock); return result; } static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) { int first_time; struct __pyx_memoryview_obj *memview = memslice->memview; if (!memview || (PyObject *) memview == Py_None) return; if (__pyx_get_slice_count(memview) < 0) __pyx_fatalerror("Acquisition count is %d (line %d)", __pyx_get_slice_count(memview), lineno); first_time = __pyx_add_acquisition_count(memview) == 0; if (first_time) { if (have_gil) { Py_INCREF((PyObject *) memview); } else { PyGILState_STATE _gilstate = PyGILState_Ensure(); Py_INCREF((PyObject *) memview); PyGILState_Release(_gilstate); } } } static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) { int last_time; struct __pyx_memoryview_obj *memview = memslice->memview; if (!memview ) { return; } else if ((PyObject *) memview == Py_None) { memslice->memview = NULL; return; } if (__pyx_get_slice_count(memview) <= 0) __pyx_fatalerror("Acquisition count is %d (line %d)", __pyx_get_slice_count(memview), lineno); last_time = __pyx_sub_acquisition_count(memview) == 1; memslice->data = NULL; if (last_time) { if (have_gil) { Py_CLEAR(memslice->memview); } else { PyGILState_STATE _gilstate = PyGILState_Ensure(); Py_CLEAR(memslice->memview); PyGILState_Release(_gilstate); } } else { memslice->memview = NULL; } } /* PyErrFetchRestore */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; } #endif /* WriteUnraisableException */ static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno, CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename, int full_traceback, CYTHON_UNUSED int nogil) { PyObject *old_exc, *old_val, *old_tb; PyObject *ctx; __Pyx_PyThreadState_declare #ifdef WITH_THREAD PyGILState_STATE state; if (nogil) state = PyGILState_Ensure(); #ifdef _MSC_VER else state = (PyGILState_STATE)-1; #endif #endif __Pyx_PyThreadState_assign __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); if (full_traceback) { Py_XINCREF(old_exc); Py_XINCREF(old_val); Py_XINCREF(old_tb); __Pyx_ErrRestore(old_exc, old_val, old_tb); PyErr_PrintEx(1); } #if PY_MAJOR_VERSION < 3 ctx = PyString_FromString(name); #else ctx = PyUnicode_FromString(name); #endif __Pyx_ErrRestore(old_exc, old_val, old_tb); if (!ctx) { PyErr_WriteUnraisable(Py_None); } else { PyErr_WriteUnraisable(ctx); Py_DECREF(ctx); } #ifdef WITH_THREAD if (nogil) PyGILState_Release(state); #endif } /* PyCFunctionFastCall */ #if CYTHON_FAST_PYCCALL static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { PyCFunctionObject *func = (PyCFunctionObject*)func_obj; PyCFunction meth = PyCFunction_GET_FUNCTION(func); PyObject *self = PyCFunction_GET_SELF(func); int flags = PyCFunction_GET_FLAGS(func); assert(PyCFunction_Check(func)); assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); assert(nargs >= 0); assert(nargs == 0 || args != NULL); /* _PyCFunction_FastCallDict() must not be called with an exception set, because it may clear it (directly or indirectly) and so the caller loses its exception */ assert(!PyErr_Occurred()); if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); } else { return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); } } #endif /* PyFunctionFastCall */ #if CYTHON_FAST_PYCALL static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, PyObject *globals) { PyFrameObject *f; PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject **fastlocals; Py_ssize_t i; PyObject *result; assert(globals != NULL); /* XXX Perhaps we should create a specialized PyFrame_New() that doesn't take locals, but does take builtins without sanity checking them. */ assert(tstate != NULL); f = PyFrame_New(tstate, co, globals, NULL); if (f == NULL) { return NULL; } fastlocals = __Pyx_PyFrame_GetLocalsplus(f); for (i = 0; i < na; i++) { Py_INCREF(*args); fastlocals[i] = *args++; } result = PyEval_EvalFrameEx(f,0); ++tstate->recursion_depth; Py_DECREF(f); --tstate->recursion_depth; return result; } #if 1 || PY_VERSION_HEX < 0x030600B1 static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); PyObject *globals = PyFunction_GET_GLOBALS(func); PyObject *argdefs = PyFunction_GET_DEFAULTS(func); PyObject *closure; #if PY_MAJOR_VERSION >= 3 PyObject *kwdefs; #endif PyObject *kwtuple, **k; PyObject **d; Py_ssize_t nd; Py_ssize_t nk; PyObject *result; assert(kwargs == NULL || PyDict_Check(kwargs)); nk = kwargs ? PyDict_Size(kwargs) : 0; if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { return NULL; } if ( #if PY_MAJOR_VERSION >= 3 co->co_kwonlyargcount == 0 && #endif likely(kwargs == NULL || nk == 0) && co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { if (argdefs == NULL && co->co_argcount == nargs) { result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); goto done; } else if (nargs == 0 && argdefs != NULL && co->co_argcount == Py_SIZE(argdefs)) { /* function called with no arguments, but all parameters have a default value: use default values as arguments .*/ args = &PyTuple_GET_ITEM(argdefs, 0); result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); goto done; } } if (kwargs != NULL) { Py_ssize_t pos, i; kwtuple = PyTuple_New(2 * nk); if (kwtuple == NULL) { result = NULL; goto done; } k = &PyTuple_GET_ITEM(kwtuple, 0); pos = i = 0; while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { Py_INCREF(k[i]); Py_INCREF(k[i+1]); i += 2; } nk = i / 2; } else { kwtuple = NULL; k = NULL; } closure = PyFunction_GET_CLOSURE(func); #if PY_MAJOR_VERSION >= 3 kwdefs = PyFunction_GET_KW_DEFAULTS(func); #endif if (argdefs != NULL) { d = &PyTuple_GET_ITEM(argdefs, 0); nd = Py_SIZE(argdefs); } else { d = NULL; nd = 0; } #if PY_MAJOR_VERSION >= 3 result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, args, (int)nargs, k, (int)nk, d, (int)nd, kwdefs, closure); #else result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, args, (int)nargs, k, (int)nk, d, (int)nd, closure); #endif Py_XDECREF(kwtuple); done: Py_LeaveRecursiveCall(); return result; } #endif #endif /* PyObjectCall2Args */ static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) { PyObject *args, *result = NULL; #if CYTHON_FAST_PYCALL if (PyFunction_Check(function)) { PyObject *args[2] = {arg1, arg2}; return __Pyx_PyFunction_FastCall(function, args, 2); } #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(function)) { PyObject *args[2] = {arg1, arg2}; return __Pyx_PyCFunction_FastCall(function, args, 2); } #endif args = PyTuple_New(2); if (unlikely(!args)) goto done; Py_INCREF(arg1); PyTuple_SET_ITEM(args, 0, arg1); Py_INCREF(arg2); PyTuple_SET_ITEM(args, 1, arg2); Py_INCREF(function); result = __Pyx_PyObject_Call(function, args, NULL); Py_DECREF(args); Py_DECREF(function); done: return result; } /* PyObjectCallMethO */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { PyObject *self, *result; PyCFunction cfunc; cfunc = PyCFunction_GET_FUNCTION(func); self = PyCFunction_GET_SELF(func); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = cfunc(self, arg); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif /* PyObjectCallOneArg */ #if CYTHON_COMPILING_IN_CPYTHON static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_New(1); if (unlikely(!args)) return NULL; Py_INCREF(arg); PyTuple_SET_ITEM(args, 0, arg); result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { #if CYTHON_FAST_PYCALL if (PyFunction_Check(func)) { return __Pyx_PyFunction_FastCall(func, &arg, 1); } #endif if (likely(PyCFunction_Check(func))) { if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { return __Pyx_PyObject_CallMethO(func, arg); #if CYTHON_FAST_PYCCALL } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { return __Pyx_PyCFunction_FastCall(func, &arg, 1); #endif } } return __Pyx__PyObject_CallOneArg(func, arg); } #else static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_Pack(1, arg); if (unlikely(!args)) return NULL; result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } #endif /* RaiseException */ #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { __Pyx_PyThreadState_declare Py_XINCREF(type); if (!value || value == Py_None) value = NULL; else Py_INCREF(value); if (!tb || tb == Py_None) tb = NULL; else { Py_INCREF(tb); if (!PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } } if (PyType_Check(type)) { #if CYTHON_COMPILING_IN_PYPY if (!value) { Py_INCREF(Py_None); value = Py_None; } #endif PyErr_NormalizeException(&type, &value, &tb); } else { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } value = type; type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } } __Pyx_PyThreadState_assign __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (PyExceptionClass_Check(type)) { PyObject *instance_class = NULL; if (value && PyExceptionInstance_Check(value)) { instance_class = (PyObject*) Py_TYPE(value); if (instance_class != type) { int is_subclass = PyObject_IsSubclass(instance_class, type); if (!is_subclass) { instance_class = NULL; } else if (unlikely(is_subclass == -1)) { goto bad; } else { type = instance_class; } } } if (!instance_class) { PyObject *args; if (!value) args = PyTuple_New(0); else if (PyTuple_Check(value)) { Py_INCREF(value); args = value; } else args = PyTuple_Pack(1, value); if (!args) goto bad; owned_instance = PyObject_Call(type, args, NULL); Py_DECREF(args); if (!owned_instance) goto bad; value = owned_instance; if (!PyExceptionInstance_Check(value)) { PyErr_Format(PyExc_TypeError, "calling %R should have returned an instance of " "BaseException, not %R", type, Py_TYPE(value)); goto bad; } } } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } if (cause) { PyObject *fixed_cause; if (cause == Py_None) { fixed_cause = NULL; } else if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { #if CYTHON_COMPILING_IN_PYPY PyObject *tmp_type, *tmp_value, *tmp_tb; PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); Py_INCREF(tb); PyErr_Restore(tmp_type, tmp_value, tb); Py_XDECREF(tmp_tb); #else PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } #endif } bad: Py_XDECREF(owned_instance); return; } #endif /* DictGetItem */ #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { PyObject *value; value = PyDict_GetItemWithError(d, key); if (unlikely(!value)) { if (!PyErr_Occurred()) { if (unlikely(PyTuple_Check(key))) { PyObject* args = PyTuple_Pack(1, key); if (likely(args)) { PyErr_SetObject(PyExc_KeyError, args); Py_DECREF(args); } } else { PyErr_SetObject(PyExc_KeyError, key); } } return NULL; } Py_INCREF(value); return value; } #endif /* RaiseTooManyValuesToUnpack */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } /* RaiseNeedMoreValuesToUnpack */ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", index, (index == 1) ? "" : "s"); } /* RaiseNoneIterError */ static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } /* ExtTypeTest */ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (likely(__Pyx_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } /* GetTopmostException */ #if CYTHON_USE_EXC_INFO_STACK static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate) { _PyErr_StackItem *exc_info = tstate->exc_info; while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && exc_info->previous_item != NULL) { exc_info = exc_info->previous_item; } return exc_info; } #endif /* SaveResetException */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); *type = exc_info->exc_type; *value = exc_info->exc_value; *tb = exc_info->exc_traceback; #else *type = tstate->exc_type; *value = tstate->exc_value; *tb = tstate->exc_traceback; #endif Py_XINCREF(*type); Py_XINCREF(*value); Py_XINCREF(*tb); } static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = type; exc_info->exc_value = value; exc_info->exc_traceback = tb; #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = type; tstate->exc_value = value; tstate->exc_traceback = tb; #endif Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } #endif /* PyErrExceptionMatches */ #if CYTHON_FAST_THREAD_STATE static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { Py_ssize_t i, n; n = PyTuple_GET_SIZE(tuple); #if PY_MAJOR_VERSION >= 3 for (i=0; icurexc_type; if (exc_type == err) return 1; if (unlikely(!exc_type)) return 0; if (unlikely(PyTuple_Check(err))) return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); } #endif /* GetException */ #if CYTHON_FAST_THREAD_STATE static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) #else static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) #endif { PyObject *local_type, *local_value, *local_tb; #if CYTHON_FAST_THREAD_STATE PyObject *tmp_type, *tmp_value, *tmp_tb; local_type = tstate->curexc_type; local_value = tstate->curexc_value; local_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(&local_type, &local_value, &local_tb); #endif PyErr_NormalizeException(&local_type, &local_value, &local_tb); #if CYTHON_FAST_THREAD_STATE if (unlikely(tstate->curexc_type)) #else if (unlikely(PyErr_Occurred())) #endif goto bad; #if PY_MAJOR_VERSION >= 3 if (local_tb) { if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) goto bad; } #endif Py_XINCREF(local_tb); Py_XINCREF(local_type); Py_XINCREF(local_value); *type = local_type; *value = local_value; *tb = local_tb; #if CYTHON_FAST_THREAD_STATE #if CYTHON_USE_EXC_INFO_STACK { _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = local_type; exc_info->exc_value = local_value; exc_info->exc_traceback = local_tb; } #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = local_type; tstate->exc_value = local_value; tstate->exc_traceback = local_tb; #endif Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_SetExcInfo(local_type, local_value, local_tb); #endif return 0; bad: *type = 0; *value = 0; *tb = 0; Py_XDECREF(local_type); Py_XDECREF(local_value); Py_XDECREF(local_tb); return -1; } /* RaiseArgTupleInvalid */ static void __Pyx_RaiseArgtupleInvalid( const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found) { Py_ssize_t num_expected; const char *more_or_less; if (num_found < num_min) { num_expected = num_min; more_or_less = "at least"; } else { num_expected = num_max; more_or_less = "at most"; } if (exact) { more_or_less = "exactly"; } PyErr_Format(PyExc_TypeError, "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", func_name, more_or_less, num_expected, (num_expected == 1) ? "" : "s", num_found); } /* RaiseDoubleKeywords */ static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) { PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION >= 3 "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, PyString_AsString(kw_name)); #endif } /* ParseKeywords */ static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name) { PyObject *key = 0, *value = 0; Py_ssize_t pos = 0; PyObject*** name; PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { name = first_kw_arg; while (*name && (**name != key)) name++; if (*name) { values[name-argnames] = value; continue; } name = first_kw_arg; #if PY_MAJOR_VERSION < 3 if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { while (*name) { if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) && _PyString_Eq(**name, key)) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { if ((**argname == key) || ( (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) && _PyString_Eq(**argname, key))) { goto arg_passed_twice; } argname++; } } } else #endif if (likely(PyUnicode_Check(key))) { while (*name) { int cmp = (**name == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : #endif PyUnicode_Compare(**name, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { int cmp = (**argname == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : #endif PyUnicode_Compare(**argname, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) goto arg_passed_twice; argname++; } } } else goto invalid_keyword_type; if (kwds2) { if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; } else { goto invalid_keyword; } } return 0; arg_passed_twice: __Pyx_RaiseDoubleKeywordsError(function_name, key); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%.200s() keywords must be strings", function_name); goto bad; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%.200s() got an unexpected keyword argument '%.200s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif bad: return -1; } /* ArgTypeTest */ static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } else if (exact) { #if PY_MAJOR_VERSION == 2 if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; #endif } else { if (likely(__Pyx_TypeCheck(obj, type))) return 1; } PyErr_Format(PyExc_TypeError, "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", name, type->tp_name, Py_TYPE(obj)->tp_name); return 0; } /* BytesEquals */ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { #if CYTHON_COMPILING_IN_PYPY return PyObject_RichCompareBool(s1, s2, equals); #else if (s1 == s2) { return (equals == Py_EQ); } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { const char *ps1, *ps2; Py_ssize_t length = PyBytes_GET_SIZE(s1); if (length != PyBytes_GET_SIZE(s2)) return (equals == Py_NE); ps1 = PyBytes_AS_STRING(s1); ps2 = PyBytes_AS_STRING(s2); if (ps1[0] != ps2[0]) { return (equals == Py_NE); } else if (length == 1) { return (equals == Py_EQ); } else { int result; #if CYTHON_USE_UNICODE_INTERNALS Py_hash_t hash1, hash2; hash1 = ((PyBytesObject*)s1)->ob_shash; hash2 = ((PyBytesObject*)s2)->ob_shash; if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { return (equals == Py_NE); } #endif result = memcmp(ps1, ps2, (size_t)length); return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { return (equals == Py_NE); } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { return (equals == Py_NE); } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } #endif } /* UnicodeEquals */ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { #if CYTHON_COMPILING_IN_PYPY return PyObject_RichCompareBool(s1, s2, equals); #else #if PY_MAJOR_VERSION < 3 PyObject* owned_ref = NULL; #endif int s1_is_unicode, s2_is_unicode; if (s1 == s2) { goto return_eq; } s1_is_unicode = PyUnicode_CheckExact(s1); s2_is_unicode = PyUnicode_CheckExact(s2); #if PY_MAJOR_VERSION < 3 if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { owned_ref = PyUnicode_FromObject(s2); if (unlikely(!owned_ref)) return -1; s2 = owned_ref; s2_is_unicode = 1; } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { owned_ref = PyUnicode_FromObject(s1); if (unlikely(!owned_ref)) return -1; s1 = owned_ref; s1_is_unicode = 1; } else if (((!s2_is_unicode) & (!s1_is_unicode))) { return __Pyx_PyBytes_Equals(s1, s2, equals); } #endif if (s1_is_unicode & s2_is_unicode) { Py_ssize_t length; int kind; void *data1, *data2; if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) return -1; length = __Pyx_PyUnicode_GET_LENGTH(s1); if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { goto return_ne; } #if CYTHON_USE_UNICODE_INTERNALS { Py_hash_t hash1, hash2; #if CYTHON_PEP393_ENABLED hash1 = ((PyASCIIObject*)s1)->hash; hash2 = ((PyASCIIObject*)s2)->hash; #else hash1 = ((PyUnicodeObject*)s1)->hash; hash2 = ((PyUnicodeObject*)s2)->hash; #endif if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { goto return_ne; } } #endif kind = __Pyx_PyUnicode_KIND(s1); if (kind != __Pyx_PyUnicode_KIND(s2)) { goto return_ne; } data1 = __Pyx_PyUnicode_DATA(s1); data2 = __Pyx_PyUnicode_DATA(s2); if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { goto return_ne; } else if (length == 1) { goto return_eq; } else { int result = memcmp(data1, data2, (size_t)(length * kind)); #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & s2_is_unicode) { goto return_ne; } else if ((s2 == Py_None) & s1_is_unicode) { goto return_ne; } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } return_eq: #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_EQ); return_ne: #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_NE); #endif } /* GetAttr */ static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { #if CYTHON_USE_TYPE_SLOTS #if PY_MAJOR_VERSION >= 3 if (likely(PyUnicode_Check(n))) #else if (likely(PyString_Check(n))) #endif return __Pyx_PyObject_GetAttrStr(o, n); #endif return PyObject_GetAttr(o, n); } /* GetItemInt */ static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { PyObject *r; if (!j) return NULL; r = PyObject_GetItem(o, j); Py_DECREF(j); return r; } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS Py_ssize_t wrapped_i = i; if (wraparound & unlikely(i < 0)) { wrapped_i += PyList_GET_SIZE(o); } if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { PyObject *r = PyList_GET_ITEM(o, wrapped_i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS Py_ssize_t wrapped_i = i; if (wraparound & unlikely(i < 0)) { wrapped_i += PyTuple_GET_SIZE(o); } if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS if (is_list || PyList_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { PyObject *r = PyList_GET_ITEM(o, n); Py_INCREF(r); return r; } } else if (PyTuple_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, n); Py_INCREF(r); return r; } } else { PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; if (likely(m && m->sq_item)) { if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { Py_ssize_t l = m->sq_length(o); if (likely(l >= 0)) { i += l; } else { if (!PyErr_ExceptionMatches(PyExc_OverflowError)) return NULL; PyErr_Clear(); } } return m->sq_item(o, i); } } #else if (is_list || PySequence_Check(o)) { return PySequence_GetItem(o, i); } #endif return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); } /* ObjectGetItem */ #if CYTHON_USE_TYPE_SLOTS static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { PyObject *runerr; Py_ssize_t key_value; PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence; if (unlikely(!(m && m->sq_item))) { PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name); return NULL; } key_value = __Pyx_PyIndex_AsSsize_t(index); if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); } if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { PyErr_Clear(); PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name); } return NULL; } static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping; if (likely(m && m->mp_subscript)) { return m->mp_subscript(obj, key); } return __Pyx_PyObject_GetIndex(obj, key); } #endif /* decode_c_string */ static CYTHON_INLINE PyObject* __Pyx_decode_c_string( const char* cstring, Py_ssize_t start, Py_ssize_t stop, const char* encoding, const char* errors, PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { Py_ssize_t length; if (unlikely((start < 0) | (stop < 0))) { size_t slen = strlen(cstring); if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { PyErr_SetString(PyExc_OverflowError, "c-string too long to convert to Python"); return NULL; } length = (Py_ssize_t) slen; if (start < 0) { start += length; if (start < 0) start = 0; } if (stop < 0) stop += length; } length = stop - start; if (unlikely(length <= 0)) return PyUnicode_FromUnicode(NULL, 0); cstring += start; if (decode_func) { return decode_func(cstring, length, errors); } else { return PyUnicode_Decode(cstring, length, encoding, errors); } } /* GetAttr3 */ static PyObject *__Pyx_GetAttr3Default(PyObject *d) { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) return NULL; __Pyx_PyErr_Clear(); Py_INCREF(d); return d; } static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { PyObject *r = __Pyx_GetAttr(o, n); return (likely(r)) ? r : __Pyx_GetAttr3Default(d); } /* SwapException */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = *type; exc_info->exc_value = *value; exc_info->exc_traceback = *tb; #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = *type; tstate->exc_value = *value; tstate->exc_traceback = *tb; #endif *type = tmp_type; *value = tmp_value; *tb = tmp_tb; } #else static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); PyErr_SetExcInfo(*type, *value, *tb); *type = tmp_type; *value = tmp_value; *tb = tmp_tb; } #endif /* Import */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; #if PY_MAJOR_VERSION < 3 PyObject *py_import; py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); if (!py_import) goto bad; #endif if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if (strchr(__Pyx_MODULE_NAME, '.')) { module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, 1); if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; } #endif if (!module) { #if PY_MAJOR_VERSION < 3 PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, level); #endif } } bad: #if PY_MAJOR_VERSION < 3 Py_XDECREF(py_import); #endif Py_XDECREF(empty_list); Py_XDECREF(empty_dict); return module; } /* FastTypeChecks */ #if CYTHON_COMPILING_IN_CPYTHON static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { while (a) { a = a->tp_base; if (a == b) return 1; } return b == &PyBaseObject_Type; } static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { PyObject *mro; if (a == b) return 1; mro = a->tp_mro; if (likely(mro)) { Py_ssize_t i, n; n = PyTuple_GET_SIZE(mro); for (i = 0; i < n; i++) { if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) return 1; } return 0; } return __Pyx_InBases(a, b); } #if PY_MAJOR_VERSION == 2 static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { PyObject *exception, *value, *tb; int res; __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ErrFetch(&exception, &value, &tb); res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; if (unlikely(res == -1)) { PyErr_WriteUnraisable(err); res = 0; } if (!res) { res = PyObject_IsSubclass(err, exc_type2); if (unlikely(res == -1)) { PyErr_WriteUnraisable(err); res = 0; } } __Pyx_ErrRestore(exception, value, tb); return res; } #else static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; if (!res) { res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); } return res; } #endif static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { Py_ssize_t i, n; assert(PyExceptionClass_Check(exc_type)); n = PyTuple_GET_SIZE(tuple); #if PY_MAJOR_VERSION >= 3 for (i=0; i= 0 || (x^b) >= 0)) return PyInt_FromLong(x); return PyLong_Type.tp_as_number->nb_add(op1, op2); } #endif #if CYTHON_USE_PYLONG_INTERNALS if (likely(PyLong_CheckExact(op1))) { const long b = intval; long a, x; #ifdef HAVE_LONG_LONG const PY_LONG_LONG llb = intval; PY_LONG_LONG lla, llx; #endif const digit* digits = ((PyLongObject*)op1)->ob_digit; const Py_ssize_t size = Py_SIZE(op1); if (likely(__Pyx_sst_abs(size) <= 1)) { a = likely(size) ? digits[0] : 0; if (size == -1) a = -a; } else { switch (size) { case -2: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 2: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case -3: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 3: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case -4: if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 4: if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; default: return PyLong_Type.tp_as_number->nb_add(op1, op2); } } x = a + b; return PyLong_FromLong(x); #ifdef HAVE_LONG_LONG long_long: llx = lla + llb; return PyLong_FromLongLong(llx); #endif } #endif if (PyFloat_CheckExact(op1)) { const long b = intval; double a = PyFloat_AS_DOUBLE(op1); double result; PyFPE_START_PROTECT("add", return NULL) result = ((double)a) + (double)b; PyFPE_END_PROTECT(result) return PyFloat_FromDouble(result); } return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); } #endif /* None */ static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); } /* ImportFrom */ static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Format(PyExc_ImportError, #if PY_MAJOR_VERSION < 3 "cannot import name %.230s", PyString_AS_STRING(name)); #else "cannot import name %S", name); #endif } return value; } /* HasAttr */ static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { PyObject *r; if (unlikely(!__Pyx_PyBaseString_Check(n))) { PyErr_SetString(PyExc_TypeError, "hasattr(): attribute name must be string"); return -1; } r = __Pyx_GetAttr(o, n); if (unlikely(!r)) { PyErr_Clear(); return 0; } else { Py_DECREF(r); return 1; } } /* PyObject_GenericGetAttrNoDict */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { PyErr_Format(PyExc_AttributeError, #if PY_MAJOR_VERSION >= 3 "'%.50s' object has no attribute '%U'", tp->tp_name, attr_name); #else "'%.50s' object has no attribute '%.400s'", tp->tp_name, PyString_AS_STRING(attr_name)); #endif return NULL; } static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { PyObject *descr; PyTypeObject *tp = Py_TYPE(obj); if (unlikely(!PyString_Check(attr_name))) { return PyObject_GenericGetAttr(obj, attr_name); } assert(!tp->tp_dictoffset); descr = _PyType_Lookup(tp, attr_name); if (unlikely(!descr)) { return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); } Py_INCREF(descr); #if PY_MAJOR_VERSION < 3 if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) #endif { descrgetfunc f = Py_TYPE(descr)->tp_descr_get; if (unlikely(f)) { PyObject *res = f(descr, obj, (PyObject *)tp); Py_DECREF(descr); return res; } } return descr; } #endif /* PyObject_GenericGetAttr */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { return PyObject_GenericGetAttr(obj, attr_name); } return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); } #endif /* SetVTable */ static int __Pyx_SetVtable(PyObject *dict, void *vtable) { #if PY_VERSION_HEX >= 0x02070000 PyObject *ob = PyCapsule_New(vtable, 0, 0); #else PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); #endif if (!ob) goto bad; if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) goto bad; Py_DECREF(ob); return 0; bad: Py_XDECREF(ob); return -1; } /* SetupReduce */ static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { int ret; PyObject *name_attr; name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2); if (likely(name_attr)) { ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); } else { ret = -1; } if (unlikely(ret < 0)) { PyErr_Clear(); ret = 0; } Py_XDECREF(name_attr); return ret; } static int __Pyx_setup_reduce(PyObject* type_obj) { int ret = 0; PyObject *object_reduce = NULL; PyObject *object_reduce_ex = NULL; PyObject *reduce = NULL; PyObject *reduce_ex = NULL; PyObject *reduce_cython = NULL; PyObject *setstate = NULL; PyObject *setstate_cython = NULL; #if CYTHON_USE_PYTYPE_LOOKUP if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; #else if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; #endif #if CYTHON_USE_PYTYPE_LOOKUP object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; #else object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; #endif reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; if (reduce_ex == object_reduce_ex) { #if CYTHON_USE_PYTYPE_LOOKUP object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; #else object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; #endif reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { reduce_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_cython); if (unlikely(!reduce_cython)) goto __PYX_BAD; ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); if (!setstate) PyErr_Clear(); if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { setstate_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate_cython); if (unlikely(!setstate_cython)) goto __PYX_BAD; ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; } PyType_Modified((PyTypeObject*)type_obj); } } goto __PYX_GOOD; __PYX_BAD: if (!PyErr_Occurred()) PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); ret = -1; __PYX_GOOD: #if !CYTHON_USE_PYTYPE_LOOKUP Py_XDECREF(object_reduce); Py_XDECREF(object_reduce_ex); #endif Py_XDECREF(reduce); Py_XDECREF(reduce_ex); Py_XDECREF(reduce_cython); Py_XDECREF(setstate); Py_XDECREF(setstate_cython); return ret; } /* TypeImport */ #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size) { PyObject *result = 0; char warning[200]; Py_ssize_t basicsize; #ifdef Py_LIMITED_API PyObject *py_basicsize; #endif result = PyObject_GetAttrString(module, class_name); if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%.200s.%.200s is not a type object", module_name, class_name); goto bad; } #ifndef Py_LIMITED_API basicsize = ((PyTypeObject *)result)->tp_basicsize; #else py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); if (!py_basicsize) goto bad; basicsize = PyLong_AsSsize_t(py_basicsize); Py_DECREF(py_basicsize); py_basicsize = 0; if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) goto bad; #endif if ((size_t)basicsize < size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); goto bad; } if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); goto bad; } else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(result); return NULL; } #endif /* CLineInTraceback */ #ifndef CYTHON_CLINE_IN_TRACEBACK static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) { PyObject *use_cline; PyObject *ptype, *pvalue, *ptraceback; #if CYTHON_COMPILING_IN_CPYTHON PyObject **cython_runtime_dict; #endif if (unlikely(!__pyx_cython_runtime)) { return c_line; } __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); #if CYTHON_COMPILING_IN_CPYTHON cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); if (likely(cython_runtime_dict)) { __PYX_PY_DICT_LOOKUP_IF_MODIFIED( use_cline, *cython_runtime_dict, __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) } else #endif { PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); if (use_cline_obj) { use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; Py_DECREF(use_cline_obj); } else { PyErr_Clear(); use_cline = NULL; } } if (!use_cline) { c_line = 0; PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); } else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { c_line = 0; } __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); return c_line; } #endif /* CodeObjectCache */ static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = start + (end - start) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } /* AddTraceback */ #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, 0, 0, 0, 0, __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; PyThreadState *tstate = __Pyx_PyThreadState_Current; if (c_line) { c_line = __Pyx_CLineForTraceback(tstate, c_line); } py_code = __pyx_find_code_object(c_line ? -c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); } py_frame = PyFrame_New( tstate, /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ __pyx_d, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; __Pyx_PyFrame_SetLineNumber(py_frame, py_line); PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; if (!obj) return; if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } if ((0)) {} else if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); view->obj = NULL; Py_DECREF(obj); } #endif /* MemviewSliceIsContig */ static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) { int i, index, step, start; Py_ssize_t itemsize = mvs.memview->view.itemsize; if (order == 'F') { step = 1; start = 0; } else { step = -1; start = ndim - 1; } for (i = 0; i < ndim; i++) { index = start + step * i; if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) return 0; itemsize *= mvs.shape[index]; } return 1; } /* OverlappingSlices */ static void __pyx_get_array_memory_extents(__Pyx_memviewslice *slice, void **out_start, void **out_end, int ndim, size_t itemsize) { char *start, *end; int i; start = end = slice->data; for (i = 0; i < ndim; i++) { Py_ssize_t stride = slice->strides[i]; Py_ssize_t extent = slice->shape[i]; if (extent == 0) { *out_start = *out_end = start; return; } else { if (stride > 0) end += stride * (extent - 1); else start += stride * (extent - 1); } } *out_start = start; *out_end = end + itemsize; } static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, __Pyx_memviewslice *slice2, int ndim, size_t itemsize) { void *start1, *end1, *start2, *end2; __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); return (start1 < end2) && (start2 < end1); } /* Capsule */ static CYTHON_INLINE PyObject * __pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) { PyObject *cobj; #if PY_VERSION_HEX >= 0x02070000 cobj = PyCapsule_New(p, sig, NULL); #else cobj = PyCObject_FromVoidPtr(p, NULL); #endif return cobj; } /* Declarations */ #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif /* Arithmetic */ #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } #if 1 static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { if (b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); } else if (fabs(b.real) >= fabs(b.imag)) { if (b.real == 0 && b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); } else { double r = b.imag / b.real; double s = (double)(1.0) / (b.real + b.imag * r); return __pyx_t_double_complex_from_parts( (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); } } else { double r = b.real / b.imag; double s = (double)(1.0) / (b.imag + b.real * r); return __pyx_t_double_complex_from_parts( (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); } } #else static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { if (b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); } else { double denom = b.real * b.real + b.imag * b.imag; return __pyx_t_double_complex_from_parts( (a.real * b.real + a.imag * b.imag) / denom, (a.imag * b.real - a.real * b.imag) / denom); } } #endif static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: return __Pyx_c_prod_double(a, a); case 3: z = __Pyx_c_prod_double(a, a); return __Pyx_c_prod_double(z, a); case 4: z = __Pyx_c_prod_double(a, a); return __Pyx_c_prod_double(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } else if (b.imag == 0) { z.real = pow(a.real, b.real); z.imag = 0; return z; } else if (a.real > 0) { r = a.real; theta = 0; } else { r = -a.real; theta = atan2(0.0, -1.0); } } else { r = __Pyx_c_abs_double(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif /* None */ static CYTHON_INLINE Py_ssize_t __Pyx_pow_Py_ssize_t(Py_ssize_t b, Py_ssize_t e) { Py_ssize_t t = b; switch (e) { case 3: t *= b; CYTHON_FALLTHROUGH; case 2: t *= b; CYTHON_FALLTHROUGH; case 1: return t; case 0: return 1; } #if 1 if (unlikely(e<0)) return 0; #endif t = 1; while (likely(e)) { t *= (b * (e&1)) | ((~e)&1); b *= b; e >>= 1; } return t; } /* Declarations */ #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif /* Arithmetic */ #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } #if 1 static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { if (b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); } else if (fabsf(b.real) >= fabsf(b.imag)) { if (b.real == 0 && b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); } else { float r = b.imag / b.real; float s = (float)(1.0) / (b.real + b.imag * r); return __pyx_t_float_complex_from_parts( (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); } } else { float r = b.real / b.imag; float s = (float)(1.0) / (b.imag + b.real * r); return __pyx_t_float_complex_from_parts( (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); } } #else static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { if (b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); } else { float denom = b.real * b.real + b.imag * b.imag; return __pyx_t_float_complex_from_parts( (a.real * b.real + a.imag * b.imag) / denom, (a.imag * b.real - a.real * b.imag) / denom); } } #endif static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: return __Pyx_c_prod_float(a, a); case 3: z = __Pyx_c_prod_float(a, a); return __Pyx_c_prod_float(z, a); case 4: z = __Pyx_c_prod_float(a, a); return __Pyx_c_prod_float(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } else if (b.imag == 0) { z.real = powf(a.real, b.real); z.imag = 0; return z; } else if (a.real > 0) { r = a.real; theta = 0; } else { r = -a.real; theta = atan2f(0.0, -1.0); } } else { r = __Pyx_c_abs_float(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(int) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(int), little, !is_unsigned); } } /* CIntFromPyVerify */ #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) #define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) #define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ {\ func_type value = func_value;\ if (sizeof(target_type) < sizeof(func_type)) {\ if (unlikely(value != (func_type) (target_type) value)) {\ func_type zero = 0;\ if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ return (target_type) -1;\ if (is_unsigned && unlikely(value < zero))\ goto raise_neg_overflow;\ else\ goto raise_overflow;\ }\ }\ return (target_type) value;\ } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { const enum NPY_TYPES neg_one = (enum NPY_TYPES) ((enum NPY_TYPES) 0 - (enum NPY_TYPES) 1), const_zero = (enum NPY_TYPES) 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(enum NPY_TYPES) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(enum NPY_TYPES) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), little, !is_unsigned); } } /* MemviewSliceCopyTemplate */ static __Pyx_memviewslice __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, const char *mode, int ndim, size_t sizeof_dtype, int contig_flag, int dtype_is_object) { __Pyx_RefNannyDeclarations int i; __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; struct __pyx_memoryview_obj *from_memview = from_mvs->memview; Py_buffer *buf = &from_memview->view; PyObject *shape_tuple = NULL; PyObject *temp_int = NULL; struct __pyx_array_obj *array_obj = NULL; struct __pyx_memoryview_obj *memview_obj = NULL; __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); for (i = 0; i < ndim; i++) { if (from_mvs->suboffsets[i] >= 0) { PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " "indirect dimensions (axis %d)", i); goto fail; } } shape_tuple = PyTuple_New(ndim); if (unlikely(!shape_tuple)) { goto fail; } __Pyx_GOTREF(shape_tuple); for(i = 0; i < ndim; i++) { temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); if(unlikely(!temp_int)) { goto fail; } else { PyTuple_SET_ITEM(shape_tuple, i, temp_int); temp_int = NULL; } } array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); if (unlikely(!array_obj)) { goto fail; } __Pyx_GOTREF(array_obj); memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( (PyObject *) array_obj, contig_flag, dtype_is_object, from_mvs->memview->typeinfo); if (unlikely(!memview_obj)) goto fail; if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) goto fail; if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, dtype_is_object) < 0)) goto fail; goto no_fail; fail: __Pyx_XDECREF(new_mvs.memview); new_mvs.memview = NULL; new_mvs.data = NULL; no_fail: __Pyx_XDECREF(shape_tuple); __Pyx_XDECREF(temp_int); __Pyx_XDECREF(array_obj); __Pyx_RefNannyFinishContext(); return new_mvs; } /* CIntFromPy */ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(int) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (int) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (int) 0; case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) case 2: if (8 * sizeof(int) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; case 3: if (8 * sizeof(int) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; case 4: if (8 * sizeof(int) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (int) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(int) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (int) 0; case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) case -2: if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 2: if (8 * sizeof(int) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case -3: if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 3: if (8 * sizeof(int) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case -4: if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 4: if (8 * sizeof(int) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; } #endif if (sizeof(int) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else int val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (int) -1; } } else { int val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (int) -1; val = __Pyx_PyInt_As_int(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to int"); return (int) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } /* CIntFromPy */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(long) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (long) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) case 2: if (8 * sizeof(long) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; case 3: if (8 * sizeof(long) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; case 4: if (8 * sizeof(long) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (long) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) case -2: if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 2: if (8 * sizeof(long) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case -3: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 3: if (8 * sizeof(long) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case -4: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 4: if (8 * sizeof(long) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; } #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (long) -1; } } else { long val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to long"); return (long) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(long) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(long), little, !is_unsigned); } } /* CIntFromPy */ static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { const char neg_one = (char) ((char) 0 - (char) 1), const_zero = (char) 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(char) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (char) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (char) 0; case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0]) case 2: if (8 * sizeof(char) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) { return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; case 3: if (8 * sizeof(char) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) { return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; case 4: if (8 * sizeof(char) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) { return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (char) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(char) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (char) 0; case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0]) case -2: if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 2: if (8 * sizeof(char) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case -3: if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 3: if (8 * sizeof(char) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case -4: if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 4: if (8 * sizeof(char) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; } #endif if (sizeof(char) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else char val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (char) -1; } } else { char val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (char) -1; val = __Pyx_PyInt_As_char(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to char"); return (char) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to char"); return (char) -1; } /* IsLittleEndian */ static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) { union { uint32_t u32; uint8_t u8[4]; } S; S.u32 = 0x01020304; return S.u8[0] == 4; } /* BufferFormatCheck */ static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type) { stack[0].field = &ctx->root; stack[0].parent_offset = 0; ctx->root.type = type; ctx->root.name = "buffer dtype"; ctx->root.offset = 0; ctx->head = stack; ctx->head->field = &ctx->root; ctx->fmt_offset = 0; ctx->head->parent_offset = 0; ctx->new_packmode = '@'; ctx->enc_packmode = '@'; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->is_complex = 0; ctx->is_valid_array = 0; ctx->struct_alignment = 0; while (type->typegroup == 'S') { ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = 0; type = type->fields->type; } } static int __Pyx_BufFmt_ParseNumber(const char** ts) { int count; const char* t = *ts; if (*t < '0' || *t > '9') { return -1; } else { count = *t++ - '0'; while (*t >= '0' && *t <= '9') { count *= 10; count += *t++ - '0'; } } *ts = t; return count; } static int __Pyx_BufFmt_ExpectNumber(const char **ts) { int number = __Pyx_BufFmt_ParseNumber(ts); if (number == -1) PyErr_Format(PyExc_ValueError,\ "Does not understand character buffer dtype format string ('%c')", **ts); return number; } static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { PyErr_Format(PyExc_ValueError, "Unexpected format string character: '%c'", ch); } static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { switch (ch) { case '?': return "'bool'"; case 'c': return "'char'"; case 'b': return "'signed char'"; case 'B': return "'unsigned char'"; case 'h': return "'short'"; case 'H': return "'unsigned short'"; case 'i': return "'int'"; case 'I': return "'unsigned int'"; case 'l': return "'long'"; case 'L': return "'unsigned long'"; case 'q': return "'long long'"; case 'Q': return "'unsigned long long'"; case 'f': return (is_complex ? "'complex float'" : "'float'"); case 'd': return (is_complex ? "'complex double'" : "'double'"); case 'g': return (is_complex ? "'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; case 's': case 'p': return "a string"; case 0: return "end"; default: return "unparseable format string"; } } static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return 2; case 'i': case 'I': case 'l': case 'L': return 4; case 'q': case 'Q': return 8; case 'f': return (is_complex ? 8 : 4); case 'd': return (is_complex ? 16 : 8); case 'g': { PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); return 0; } case 'O': case 'P': return sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(short); case 'i': case 'I': return sizeof(int); case 'l': case 'L': return sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(float) * (is_complex ? 2 : 1); case 'd': return sizeof(double) * (is_complex ? 2 : 1); case 'g': return sizeof(long double) * (is_complex ? 2 : 1); case 'O': case 'P': return sizeof(void*); default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } typedef struct { char c; short x; } __Pyx_st_short; typedef struct { char c; int x; } __Pyx_st_int; typedef struct { char c; long x; } __Pyx_st_long; typedef struct { char c; float x; } __Pyx_st_float; typedef struct { char c; double x; } __Pyx_st_double; typedef struct { char c; long double x; } __Pyx_st_longdouble; typedef struct { char c; void *x; } __Pyx_st_void_p; #ifdef HAVE_LONG_LONG typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_st_float) - sizeof(float); case 'd': return sizeof(__Pyx_st_double) - sizeof(double); case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } /* These are for computing the padding at the end of the struct to align on the first member of the struct. This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': return 'H'; case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } if ((type->typegroup == 'H' || group == 'H') && type->size == size) { } else { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number; int ndim = ctx->head->field->type->ndim; ; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; while (*ts && *ts != ')') { switch (*ts) { case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; default: break; } number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case '\r': case '\n': ++ts; break; case '<': if (!__Pyx_Is_Little_Endian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_Is_Little_Endian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } CYTHON_FALLTHROUGH; case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 'p': if (ctx->enc_type == *ts && got_Z == ctx->is_complex && ctx->enc_packmode == ctx->new_packmode) { ctx->enc_count += ctx->new_count; ctx->new_count = 1; got_Z = 0; ++ts; break; } CYTHON_FALLTHROUGH; case 's': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } /* TypeInfoCompare */ static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) { int i; if (!a || !b) return 0; if (a == b) return 1; if (a->size != b->size || a->typegroup != b->typegroup || a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { if (a->typegroup == 'H' || b->typegroup == 'H') { return a->size == b->size; } else { return 0; } } if (a->ndim) { for (i = 0; i < a->ndim; i++) if (a->arraysize[i] != b->arraysize[i]) return 0; } if (a->typegroup == 'S') { if (a->flags != b->flags) return 0; if (a->fields || b->fields) { if (!(a->fields && b->fields)) return 0; for (i = 0; a->fields[i].type && b->fields[i].type; i++) { __Pyx_StructField *field_a = a->fields + i; __Pyx_StructField *field_b = b->fields + i; if (field_a->offset != field_b->offset || !__pyx_typeinfo_cmp(field_a->type, field_b->type)) return 0; } return !a->fields[i].type && !b->fields[i].type; } } return 1; } /* MemviewSliceValidateAndInit */ static int __pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) { if (buf->shape[dim] <= 1) return 1; if (buf->strides) { if (spec & __Pyx_MEMVIEW_CONTIG) { if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { if (buf->strides[dim] != sizeof(void *)) { PyErr_Format(PyExc_ValueError, "Buffer is not indirectly contiguous " "in dimension %d.", dim); goto fail; } } else if (buf->strides[dim] != buf->itemsize) { PyErr_SetString(PyExc_ValueError, "Buffer and memoryview are not contiguous " "in the same dimension."); goto fail; } } if (spec & __Pyx_MEMVIEW_FOLLOW) { Py_ssize_t stride = buf->strides[dim]; if (stride < 0) stride = -stride; if (stride < buf->itemsize) { PyErr_SetString(PyExc_ValueError, "Buffer and memoryview are not contiguous " "in the same dimension."); goto fail; } } } else { if (spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1) { PyErr_Format(PyExc_ValueError, "C-contiguous buffer is not contiguous in " "dimension %d", dim); goto fail; } else if (spec & (__Pyx_MEMVIEW_PTR)) { PyErr_Format(PyExc_ValueError, "C-contiguous buffer is not indirect in " "dimension %d", dim); goto fail; } else if (buf->suboffsets) { PyErr_SetString(PyExc_ValueError, "Buffer exposes suboffsets but no strides"); goto fail; } } return 1; fail: return 0; } static int __pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) { if (spec & __Pyx_MEMVIEW_DIRECT) { if (buf->suboffsets && buf->suboffsets[dim] >= 0) { PyErr_Format(PyExc_ValueError, "Buffer not compatible with direct access " "in dimension %d.", dim); goto fail; } } if (spec & __Pyx_MEMVIEW_PTR) { if (!buf->suboffsets || (buf->suboffsets[dim] < 0)) { PyErr_Format(PyExc_ValueError, "Buffer is not indirectly accessible " "in dimension %d.", dim); goto fail; } } return 1; fail: return 0; } static int __pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) { int i; if (c_or_f_flag & __Pyx_IS_F_CONTIG) { Py_ssize_t stride = 1; for (i = 0; i < ndim; i++) { if (stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1) { PyErr_SetString(PyExc_ValueError, "Buffer not fortran contiguous."); goto fail; } stride = stride * buf->shape[i]; } } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { Py_ssize_t stride = 1; for (i = ndim - 1; i >- 1; i--) { if (stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1) { PyErr_SetString(PyExc_ValueError, "Buffer not C contiguous."); goto fail; } stride = stride * buf->shape[i]; } } return 1; fail: return 0; } static int __Pyx_ValidateAndInit_memviewslice( int *axes_specs, int c_or_f_flag, int buf_flags, int ndim, __Pyx_TypeInfo *dtype, __Pyx_BufFmt_StackElem stack[], __Pyx_memviewslice *memviewslice, PyObject *original_obj) { struct __pyx_memoryview_obj *memview, *new_memview; __Pyx_RefNannyDeclarations Py_buffer *buf; int i, spec = 0, retval = -1; __Pyx_BufFmt_Context ctx; int from_memoryview = __pyx_memoryview_check(original_obj); __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) original_obj)->typeinfo)) { memview = (struct __pyx_memoryview_obj *) original_obj; new_memview = NULL; } else { memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( original_obj, buf_flags, 0, dtype); new_memview = memview; if (unlikely(!memview)) goto fail; } buf = &memview->view; if (buf->ndim != ndim) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", ndim, buf->ndim); goto fail; } if (new_memview) { __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if ((unsigned) buf->itemsize != dtype->size) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } for (i = 0; i < ndim; i++) { spec = axes_specs[i]; if (!__pyx_check_strides(buf, i, ndim, spec)) goto fail; if (!__pyx_check_suboffsets(buf, i, ndim, spec)) goto fail; } if (buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)) goto fail; if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, new_memview != NULL) == -1)) { goto fail; } retval = 0; goto no_fail; fail: Py_XDECREF(new_memview); retval = -1; no_fail: __Pyx_RefNannyFinishContext(); return retval; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, &__Pyx_TypeInfo___pyx_t_double_complex, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 2, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* CheckBinaryVersion */ static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); return PyErr_WarnEx(NULL, message, 1); } return 0; } /* FunctionExport */ static int __Pyx_ExportFunction(const char *name, void (*f)(void), const char *sig) { PyObject *d = 0; PyObject *cobj = 0; union { void (*fp)(void); void *p; } tmp; d = PyObject_GetAttrString(__pyx_m, (char *)"__pyx_capi__"); if (!d) { PyErr_Clear(); d = PyDict_New(); if (!d) goto bad; Py_INCREF(d); if (PyModule_AddObject(__pyx_m, (char *)"__pyx_capi__", d) < 0) goto bad; } tmp.fp = f; #if PY_VERSION_HEX >= 0x02070000 cobj = PyCapsule_New(tmp.p, sig, 0); #else cobj = PyCObject_FromVoidPtrAndDesc(tmp.p, (void *)sig, 0); #endif if (!cobj) goto bad; if (PyDict_SetItemString(d, name, cobj) < 0) goto bad; Py_DECREF(cobj); Py_DECREF(d); return 0; bad: Py_XDECREF(cobj); Py_XDECREF(d); return -1; } /* InitStrings */ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; if (PyObject_Hash(*t->p) == -1) return -1; ++t; } return 0; } static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); } static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { Py_ssize_t ignore; return __Pyx_PyObject_AsStringAndSize(o, &ignore); } #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT #if !CYTHON_PEP393_ENABLED static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { char* defenc_c; PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); if (!defenc) return NULL; defenc_c = PyBytes_AS_STRING(defenc); #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII { char* end = defenc_c + PyBytes_GET_SIZE(defenc); char* c; for (c = defenc_c; c < end; c++) { if ((unsigned char) (*c) >= 128) { PyUnicode_AsASCIIString(o); return NULL; } } } #endif *length = PyBytes_GET_SIZE(defenc); return defenc_c; } #else static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII if (likely(PyUnicode_IS_ASCII(o))) { *length = PyUnicode_GET_LENGTH(o); return PyUnicode_AsUTF8(o); } else { PyUnicode_AsASCIIString(o); return NULL; } #else return PyUnicode_AsUTF8AndSize(o, length); #endif } #endif #endif static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT if ( #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII __Pyx_sys_getdefaultencoding_not_ascii && #endif PyUnicode_Check(o)) { return __Pyx_PyUnicode_AsStringAndSize(o, length); } else #endif #if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) if (PyByteArray_Check(o)) { *length = PyByteArray_GET_SIZE(o); return PyByteArray_AS_STRING(o); } else #endif { char* result; int r = PyBytes_AsStringAndSize(o, &result, length); if (unlikely(r < 0)) { return NULL; } else { return result; } } } static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { int retval; if (unlikely(!x)) return -1; retval = __Pyx_PyObject_IsTrue(x); Py_DECREF(x); return retval; } static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { #if PY_MAJOR_VERSION >= 3 if (PyLong_Check(result)) { if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, "__int__ returned non-int (type %.200s). " "The ability to return an instance of a strict subclass of int " "is deprecated, and may be removed in a future version of Python.", Py_TYPE(result)->tp_name)) { Py_DECREF(result); return NULL; } return result; } #endif PyErr_Format(PyExc_TypeError, "__%.4s__ returned non-%.4s (type %.200s)", type_name, type_name, Py_TYPE(result)->tp_name); Py_DECREF(result); return NULL; } static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { #if CYTHON_USE_TYPE_SLOTS PyNumberMethods *m; #endif const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x) || PyLong_Check(x))) #else if (likely(PyLong_Check(x))) #endif return __Pyx_NewRef(x); #if CYTHON_USE_TYPE_SLOTS m = Py_TYPE(x)->tp_as_number; #if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = m->nb_int(x); } else if (m && m->nb_long) { name = "long"; res = m->nb_long(x); } #else if (likely(m && m->nb_int)) { name = "int"; res = m->nb_int(x); } #endif #else if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { res = PyNumber_Int(x); } #endif if (likely(res)) { #if PY_MAJOR_VERSION < 3 if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { #else if (unlikely(!PyLong_CheckExact(res))) { #endif return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject *x; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(b))) { if (sizeof(Py_ssize_t) >= sizeof(long)) return PyInt_AS_LONG(b); else return PyInt_AsSsize_t(b); } #endif if (likely(PyLong_CheckExact(b))) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)b)->ob_digit; const Py_ssize_t size = Py_SIZE(b); if (likely(__Pyx_sst_abs(size) <= 1)) { ival = likely(size) ? digits[0] : 0; if (size == -1) ival = -ival; return ival; } else { switch (size) { case 2: if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -2: if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case 3: if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -3: if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case 4: if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -4: if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; } } #endif return PyLong_AsSsize_t(b); } x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { return PyInt_FromSize_t(ival); } #endif /* Py_PYTHON_H */ openTSNE-0.6.1/openTSNE/_matrix_mul/matrix_mul_fftw3.pyx000066400000000000000000000173441413546205200231730ustar00rootroot00000000000000# cython: boundscheck=False # cython: wraparound=False # cython: cdivision=True # cython: initializedcheck=False # cython: warn.undeclared=True # cython: language_level=3 cimport openTSNE._matrix_mul.matrix_mul cimport numpy as np import numpy as np cdef extern from 'fftw3.h': int fftw_init_threads() void fftw_plan_with_nthreads(int) cdef unsigned FFTW_ESTIMATE cdef unsigned FFTW_DESTROY_INPUT ctypedef double fftw_complex[2] ctypedef struct _fftw_plan: pass ctypedef _fftw_plan *fftw_plan void fftw_execute(fftw_plan) void fftw_destroy_plan(fftw_plan) fftw_plan fftw_plan_dft_r2c_1d(int, double*, fftw_complex*, unsigned) fftw_plan fftw_plan_dft_c2r_1d(int, fftw_complex*, double*, unsigned) fftw_plan fftw_plan_dft_r2c_2d(int, int, double*, fftw_complex*, unsigned) fftw_plan fftw_plan_dft_c2r_2d(int, int, fftw_complex*, double*, unsigned) cdef void matrix_multiply_fft_1d( double[::1] kernel_tilde, double[:, ::1] w_coefficients, double[:, ::1] out, ): """Multiply the the kernel vectr K tilde with the w coefficients. Parameters ---------- kernel_tilde : memoryview The generating vector of the 2d Toeplitz matrix i.e. the kernel evaluated all all interpolation points from the left most interpolation point, embedded in a circulant matrix (doubled in size from (n_interp, n_interp) to (2 * n_interp, 2 * n_interp) and symmetrized. See how to embed Toeplitz into circulant matrices. w_coefficients : memoryview The coefficients calculated in Step 1 of the paper, a (n_total_interp, n_terms) matrix. The coefficients are embedded into a larger matrix in this function, so no prior embedding is needed. out : memoryview Output matrix. Must be same size as ``w_coefficients``. """ cdef: Py_ssize_t n_interpolation_points_1d = w_coefficients.shape[0] Py_ssize_t n_terms = w_coefficients.shape[1] Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] complex[::1] fft_kernel_tilde = np.empty(n_fft_coeffs, dtype=complex) complex[::1] fft_w_coeffs = np.empty(n_fft_coeffs, dtype=complex) # Note that we can't use the same buffer for the input and output since # we only write to the first half of the vector - we'd need to # manually zero out the rest of the entries that were inevitably # changed during the IDFT, so it's faster to use two buffers, at the # cost of some memory double[::1] fft_in_buffer = np.zeros(n_fft_coeffs, dtype=float) double[::1] fft_out_buffer = np.zeros(n_fft_coeffs, dtype=float) Py_ssize_t d, i # Compute the FFT of the kernel vector cdef fftw_plan plan_dft, plan_idft plan_dft = fftw_plan_dft_r2c_1d( n_fft_coeffs, &kernel_tilde[0], (&fft_kernel_tilde[0]), FFTW_ESTIMATE, ) fftw_execute(plan_dft) fftw_destroy_plan(plan_dft) plan_dft = fftw_plan_dft_r2c_1d( n_fft_coeffs, &fft_in_buffer[0], (&fft_w_coeffs[0]), FFTW_ESTIMATE | FFTW_DESTROY_INPUT, ) plan_idft = fftw_plan_dft_c2r_1d( n_fft_coeffs, (&fft_w_coeffs[0]), &fft_out_buffer[0], FFTW_ESTIMATE | FFTW_DESTROY_INPUT, ) for d in range(n_terms): for i in range(n_interpolation_points_1d): fft_in_buffer[i] = w_coefficients[i, d] fftw_execute(plan_dft) # Take the Hadamard product of two complex vectors for i in range(n_fft_coeffs): fft_w_coeffs[i] = fft_w_coeffs[i] * fft_kernel_tilde[i] fftw_execute(plan_idft) for i in range(n_interpolation_points_1d): # FFTW doesn't perform IDFT normalization, so we have to do it # ourselves. This is done by multiplying the result with the number # of points in the input out[i, d] = fft_out_buffer[n_interpolation_points_1d + i].real / n_fft_coeffs fftw_destroy_plan(plan_dft) fftw_destroy_plan(plan_idft) cdef void matrix_multiply_fft_2d( double[:, ::1] kernel_tilde, double[:, ::1] w_coefficients, double[:, ::1] out, ): """Multiply the the kernel matrix K tilde with the w coefficients. Parameters ---------- kernel_tilde : memoryview The generating matrix of the 3d Toeplitz tensor i.e. the kernel evaluated all all interpolation points from the top left most interpolation point, embedded in a circulant matrix (doubled in size from (n_interp, n_interp) to (2 * n_interp, 2 * n_interp) and symmetrized. See how to embed Toeplitz into circulant matrices. w_coefficients : memoryview The coefficients calculated in Step 1 of the paper, a (n_total_interp, n_terms) matrix. The coefficients are embedded into a larger matrix in this function, so no prior embedding is needed. out : memoryview Output matrix. Must be same size as ``w_coefficients``. """ cdef: Py_ssize_t total_interpolation_points = w_coefficients.shape[0] Py_ssize_t n_terms = w_coefficients.shape[1] Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] Py_ssize_t n_interpolation_points_1d = n_fft_coeffs / 2 fftw_plan plan_dft, plan_idft complex[::1] fft_w_coefficients = np.empty(n_fft_coeffs * (n_fft_coeffs / 2 + 1), dtype=complex) complex[::1] fft_kernel_tilde = np.empty(n_fft_coeffs * (n_fft_coeffs / 2 + 1), dtype=complex) # Note that we can't use the same buffer for the input and output since # we only write to the top quadrant of the in matrix - we'd need to # manually zero out the rest of the entries that were inevitably # changed during the IDFT, so it's faster to use two buffers, at the # cost of some memory double[:, ::1] fft_in_buffer = np.zeros((n_fft_coeffs, n_fft_coeffs)) double[:, ::1] fft_out_buffer = np.zeros((n_fft_coeffs, n_fft_coeffs)) Py_ssize_t d, i, j, idx plan_dft = fftw_plan_dft_r2c_2d( n_fft_coeffs, n_fft_coeffs, &kernel_tilde[0, 0], (&fft_kernel_tilde[0]), FFTW_ESTIMATE, ) fftw_execute(plan_dft) fftw_destroy_plan(plan_dft) plan_dft = fftw_plan_dft_r2c_2d( n_fft_coeffs, n_fft_coeffs, &fft_in_buffer[0, 0], (&fft_w_coefficients[0]), FFTW_ESTIMATE | FFTW_DESTROY_INPUT, ) plan_idft = fftw_plan_dft_c2r_2d( n_fft_coeffs, n_fft_coeffs, (&fft_w_coefficients[0]), &fft_out_buffer[0, 0], FFTW_ESTIMATE | FFTW_DESTROY_INPUT, ) for d in range(n_terms): for i in range(n_interpolation_points_1d): for j in range(n_interpolation_points_1d): fft_in_buffer[i, j] = w_coefficients[i * n_interpolation_points_1d + j, d] fftw_execute(plan_dft) # Take the Hadamard product of two complex vectors for i in range(n_fft_coeffs * (n_fft_coeffs / 2 + 1)): fft_w_coefficients[i] = fft_w_coefficients[i] * fft_kernel_tilde[i] # Invert the computed values at the interpolated nodes fftw_execute(plan_idft) # FFTW doesn't perform IDFT normalization, so we have to do it # ourselves. This is done by multiplying the result with the number of # points in the input for i in range(n_interpolation_points_1d): for j in range(n_interpolation_points_1d): idx = i * n_interpolation_points_1d + j out[idx, d] = fft_out_buffer[n_interpolation_points_1d + i, n_interpolation_points_1d + j] / n_fft_coeffs ** 2 fftw_destroy_plan(plan_dft) fftw_destroy_plan(plan_idft) openTSNE-0.6.1/openTSNE/_matrix_mul/matrix_mul_numpy.cpp000066400000000000000000032627261413546205200232650ustar00rootroot00000000000000/* Generated by Cython 0.29.23 */ /* BEGIN: Cython Metadata { "distutils": { "depends": [], "language": "c++", "name": "openTSNE._matrix_mul.matrix_mul", "sources": [ "openTSNE/_matrix_mul/matrix_mul_numpy.pyx" ] }, "module_name": "openTSNE._matrix_mul.matrix_mul" } END: Cython Metadata */ #ifndef PY_SSIZE_T_CLEAN #define PY_SSIZE_T_CLEAN #endif /* PY_SSIZE_T_CLEAN */ #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) #error Cython requires Python 2.6+ or Python 3.3+. #else #define CYTHON_ABI "0_29_23" #define CYTHON_HEX_VERSION 0x001D17F0 #define CYTHON_FUTURE_DIVISION 1 #include #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #define __PYX_COMMA , #ifndef HAVE_LONG_LONG #if PY_VERSION_HEX >= 0x02070000 #define HAVE_LONG_LONG #endif #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #ifndef Py_HUGE_VAL #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_PYSTON 0 #define CYTHON_COMPILING_IN_CPYTHON 0 #undef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 0 #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #if PY_VERSION_HEX < 0x03050000 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #elif !defined(CYTHON_USE_ASYNC_SLOTS) #define CYTHON_USE_ASYNC_SLOTS 1 #endif #undef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 0 #undef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 0 #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #undef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 1 #undef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 0 #undef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 0 #undef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 0 #undef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 0 #undef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT 0 #undef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE 0 #undef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS 0 #undef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK 0 #elif defined(PYSTON_VERSION) #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_PYSTON 1 #define CYTHON_COMPILING_IN_CPYTHON 0 #ifndef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 1 #endif #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #undef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 0 #ifndef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 1 #endif #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #ifndef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 0 #endif #ifndef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 1 #endif #ifndef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 1 #endif #undef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 0 #undef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 0 #undef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT 0 #undef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE 0 #undef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS 0 #undef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_PYSTON 0 #define CYTHON_COMPILING_IN_CPYTHON 1 #ifndef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 1 #endif #if PY_VERSION_HEX < 0x02070000 #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) #define CYTHON_USE_PYTYPE_LOOKUP 1 #endif #if PY_MAJOR_VERSION < 3 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #elif !defined(CYTHON_USE_ASYNC_SLOTS) #define CYTHON_USE_ASYNC_SLOTS 1 #endif #if PY_VERSION_HEX < 0x02070000 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #elif !defined(CYTHON_USE_PYLONG_INTERNALS) #define CYTHON_USE_PYLONG_INTERNALS 1 #endif #ifndef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 1 #endif #ifndef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 1 #endif #if PY_VERSION_HEX < 0x030300F0 #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #elif !defined(CYTHON_USE_UNICODE_WRITER) #define CYTHON_USE_UNICODE_WRITER 1 #endif #ifndef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 0 #endif #ifndef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 1 #endif #ifndef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 1 #endif #ifndef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 1 #endif #ifndef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 1 #endif #ifndef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000) #endif #ifndef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) #endif #ifndef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1) #endif #ifndef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3) #endif #endif #if !defined(CYTHON_FAST_PYCCALL) #define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) #endif #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #undef SHIFT #undef BASE #undef MASK #ifdef SIZEOF_VOID_P enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; #endif #endif #ifndef __has_attribute #define __has_attribute(x) 0 #endif #ifndef __has_cpp_attribute #define __has_cpp_attribute(x) 0 #endif #ifndef CYTHON_RESTRICT #if defined(__GNUC__) #define CYTHON_RESTRICT __restrict__ #elif defined(_MSC_VER) && _MSC_VER >= 1400 #define CYTHON_RESTRICT __restrict #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_RESTRICT restrict #else #define CYTHON_RESTRICT #endif #endif #ifndef CYTHON_UNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif # elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif #endif #ifndef CYTHON_MAYBE_UNUSED_VAR # if defined(__cplusplus) template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } # else # define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) # endif #endif #ifndef CYTHON_NCP_UNUSED # if CYTHON_COMPILING_IN_CPYTHON # define CYTHON_NCP_UNUSED # else # define CYTHON_NCP_UNUSED CYTHON_UNUSED # endif #endif #define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) #ifdef _MSC_VER #ifndef _MSC_STDINT_H_ #if _MSC_VER < 1300 typedef unsigned char uint8_t; typedef unsigned int uint32_t; #else typedef unsigned __int8 uint8_t; typedef unsigned __int32 uint32_t; #endif #endif #else #include #endif #ifndef CYTHON_FALLTHROUGH #if defined(__cplusplus) && __cplusplus >= 201103L #if __has_cpp_attribute(fallthrough) #define CYTHON_FALLTHROUGH [[fallthrough]] #elif __has_cpp_attribute(clang::fallthrough) #define CYTHON_FALLTHROUGH [[clang::fallthrough]] #elif __has_cpp_attribute(gnu::fallthrough) #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] #endif #endif #ifndef CYTHON_FALLTHROUGH #if __has_attribute(fallthrough) #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) #else #define CYTHON_FALLTHROUGH #endif #endif #if defined(__clang__ ) && defined(__apple_build_version__) #if __apple_build_version__ < 7000000 #undef CYTHON_FALLTHROUGH #define CYTHON_FALLTHROUGH #endif #endif #endif #ifndef __cplusplus #error "Cython files generated with the C++ option must be compiled with a C++ compiler." #endif #ifndef CYTHON_INLINE #if defined(__clang__) #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) #else #define CYTHON_INLINE inline #endif #endif template void __Pyx_call_destructor(T& x) { x.~T(); } template class __Pyx_FakeReference { public: __Pyx_FakeReference() : ptr(NULL) { } __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } T *operator->() { return ptr; } T *operator&() { return ptr; } operator T&() { return *ptr; } template bool operator ==(U other) { return *ptr == other; } template bool operator !=(U other) { return *ptr != other; } private: T *ptr; }; #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) #define Py_OptimizeFlag 0 #endif #define __PYX_BUILD_PY_SSIZE_T "n" #define CYTHON_FORMAT_SSIZE_T "z" #if PY_MAJOR_VERSION < 3 #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #define __Pyx_DefaultClassType PyClass_Type #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" #if PY_VERSION_HEX >= 0x030800A4 && PY_VERSION_HEX < 0x030800B2 #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a, 0, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #else #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #endif #define __Pyx_DefaultClassType PyType_Type #endif #ifndef Py_TPFLAGS_CHECKTYPES #define Py_TPFLAGS_CHECKTYPES 0 #endif #ifndef Py_TPFLAGS_HAVE_INDEX #define Py_TPFLAGS_HAVE_INDEX 0 #endif #ifndef Py_TPFLAGS_HAVE_NEWBUFFER #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif #ifndef Py_TPFLAGS_HAVE_FINALIZE #define Py_TPFLAGS_HAVE_FINALIZE 0 #endif #ifndef METH_STACKLESS #define METH_STACKLESS 0 #endif #if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) #ifndef METH_FASTCALL #define METH_FASTCALL 0x80 #endif typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, Py_ssize_t nargs, PyObject *kwnames); #else #define __Pyx_PyCFunctionFast _PyCFunctionFast #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords #endif #if CYTHON_FAST_PYCCALL #define __Pyx_PyFastCFunction_Check(func)\ ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))))) #else #define __Pyx_PyFastCFunction_Check(func) 0 #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) #define PyObject_Malloc(s) PyMem_Malloc(s) #define PyObject_Free(p) PyMem_Free(p) #define PyObject_Realloc(p) PyMem_Realloc(p) #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1 #define PyMem_RawMalloc(n) PyMem_Malloc(n) #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n) #define PyMem_RawFree(p) PyMem_Free(p) #endif #if CYTHON_COMPILING_IN_PYSTON #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) #else #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) #endif #if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 #define __Pyx_PyThreadState_Current PyThreadState_GET() #elif PY_VERSION_HEX >= 0x03060000 #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() #elif PY_VERSION_HEX >= 0x03000000 #define __Pyx_PyThreadState_Current PyThreadState_GET() #else #define __Pyx_PyThreadState_Current _PyThreadState_Current #endif #if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) #include "pythread.h" #define Py_tss_NEEDS_INIT 0 typedef int Py_tss_t; static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { *key = PyThread_create_key(); return 0; } static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); *key = Py_tss_NEEDS_INIT; return key; } static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { PyObject_Free(key); } static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { return *key != Py_tss_NEEDS_INIT; } static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { PyThread_delete_key(*key); *key = Py_tss_NEEDS_INIT; } static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { return PyThread_set_key_value(*key, value); } static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { return PyThread_get_key_value(*key); } #endif #if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) #define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) #else #define __Pyx_PyDict_NewPresized(n) PyDict_New() #endif #if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) #else #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS #define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) #else #define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name) #endif #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) #define CYTHON_PEP393_ENABLED 1 #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ 0 : _PyUnicode_Ready((PyObject *)(op))) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE) #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) #else #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) #endif #else #define CYTHON_PEP393_ENABLED 0 #define PyUnicode_1BYTE_KIND 1 #define PyUnicode_2BYTE_KIND 2 #define PyUnicode_4BYTE_KIND 4 #define __Pyx_PyUnicode_READY(op) (0) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) #endif #if CYTHON_COMPILING_IN_PYPY #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) #else #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) #endif #define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) #define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) #else #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) #endif #if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) #define PyObject_ASCII(o) PyObject_Repr(o) #endif #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type #define PyStringObject PyUnicodeObject #define PyString_Type PyUnicode_Type #define PyString_Check PyUnicode_Check #define PyString_CheckExact PyUnicode_CheckExact #ifndef PyObject_Unicode #define PyObject_Unicode PyObject_Str #endif #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) #else #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) #endif #ifndef PySet_CheckExact #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) #endif #if PY_VERSION_HEX >= 0x030900A4 #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) #else #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) #endif #if CYTHON_ASSUME_SAFE_MACROS #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) #else #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) #endif #if PY_MAJOR_VERSION >= 3 #define PyIntObject PyLongObject #define PyInt_Type PyLong_Type #define PyInt_Check(op) PyLong_Check(op) #define PyInt_CheckExact(op) PyLong_CheckExact(op) #define PyInt_FromString PyLong_FromString #define PyInt_FromUnicode PyLong_FromUnicode #define PyInt_FromLong PyLong_FromLong #define PyInt_FromSize_t PyLong_FromSize_t #define PyInt_FromSsize_t PyLong_FromSsize_t #define PyInt_AsLong PyLong_AsLong #define PyInt_AS_LONG PyLong_AS_LONG #define PyInt_AsSsize_t PyLong_AsSsize_t #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask #define PyNumber_Int PyNumber_Long #endif #if PY_MAJOR_VERSION >= 3 #define PyBoolObject PyLongObject #endif #if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY #ifndef PyUnicode_InternFromString #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) #endif #endif #if PY_VERSION_HEX < 0x030200A4 typedef long Py_hash_t; #define __Pyx_PyInt_FromHash_t PyInt_FromLong #define __Pyx_PyInt_AsHash_t PyInt_AsLong #else #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func)) #else #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) #endif #if CYTHON_USE_ASYNC_SLOTS #if PY_VERSION_HEX >= 0x030500B1 #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) #else #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) #endif #else #define __Pyx_PyType_AsAsync(obj) NULL #endif #ifndef __Pyx_PyAsyncMethodsStruct typedef struct { unaryfunc am_await; unaryfunc am_aiter; unaryfunc am_anext; } __Pyx_PyAsyncMethodsStruct; #endif #if defined(WIN32) || defined(MS_WINDOWS) #define _USE_MATH_DEFINES #endif #include #ifdef NAN #define __PYX_NAN() ((float) NAN) #else static CYTHON_INLINE float __PYX_NAN() { float value; memset(&value, 0xFF, sizeof(value)); return value; } #endif #if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) #define __Pyx_truncl trunc #else #define __Pyx_truncl truncl #endif #define __PYX_MARK_ERR_POS(f_index, lineno) \ { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } #define __PYX_ERR(f_index, lineno, Ln_error) \ { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } #ifndef __PYX_EXTERN_C #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else #define __PYX_EXTERN_C extern #endif #endif #define __PYX_HAVE__openTSNE___matrix_mul__matrix_mul #define __PYX_HAVE_API__openTSNE___matrix_mul__matrix_mul /* Early includes */ #include #include #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" /* NumPy API declarations from "numpy/__init__.pxd" */ #include "pythread.h" #include #include "pystate.h" #ifdef _OPENMP #include #endif /* _OPENMP */ #if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) #define CYTHON_WITHOUT_ASSERTIONS #endif typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; #define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) #define __PYX_DEFAULT_STRING_ENCODING "" #define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString #define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #define __Pyx_uchar_cast(c) ((unsigned char)c) #define __Pyx_long_cast(x) ((long)x) #define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ (sizeof(type) < sizeof(Py_ssize_t)) ||\ (sizeof(type) > sizeof(Py_ssize_t) &&\ likely(v < (type)PY_SSIZE_T_MAX ||\ v == (type)PY_SSIZE_T_MAX) &&\ (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ v == (type)PY_SSIZE_T_MIN))) ||\ (sizeof(type) == sizeof(Py_ssize_t) &&\ (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ v == (type)PY_SSIZE_T_MAX))) ) static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { return (size_t) i < (size_t) limit; } #if defined (__cplusplus) && __cplusplus >= 201103L #include #define __Pyx_sst_abs(value) std::abs(value) #elif SIZEOF_INT >= SIZEOF_SIZE_T #define __Pyx_sst_abs(value) abs(value) #elif SIZEOF_LONG >= SIZEOF_SIZE_T #define __Pyx_sst_abs(value) labs(value) #elif defined (_MSC_VER) #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define __Pyx_sst_abs(value) llabs(value) #elif defined (__GNUC__) #define __Pyx_sst_abs(value) __builtin_llabs(value) #else #define __Pyx_sst_abs(value) ((value<0) ? -value : value) #endif static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); #define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) #define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) #define __Pyx_PyBytes_FromString PyBytes_FromString #define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); #if PY_MAJOR_VERSION < 3 #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #else #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize #endif #define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) #define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) #define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) #define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) #define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) #define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { const Py_UNICODE *u_end = u; while (*u_end++) ; return (size_t)(u_end - u - 1); } #define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) #define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode #define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode #define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) #define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); #define __Pyx_PySequence_Tuple(obj)\ (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); #if CYTHON_ASSUME_SAFE_MACROS #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) #endif #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) #else #define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) #endif #define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII static int __Pyx_sys_getdefaultencoding_not_ascii; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; PyObject* ascii_chars_u = NULL; PyObject* ascii_chars_b = NULL; const char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; if (strcmp(default_encoding_c, "ascii") == 0) { __Pyx_sys_getdefaultencoding_not_ascii = 0; } else { char ascii_chars[128]; int c; for (c = 0; c < 128; c++) { ascii_chars[c] = c; } __Pyx_sys_getdefaultencoding_not_ascii = 1; ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); if (!ascii_chars_u) goto bad; ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { PyErr_Format( PyExc_ValueError, "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", default_encoding_c); goto bad; } Py_DECREF(ascii_chars_u); Py_DECREF(ascii_chars_b); } Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); Py_XDECREF(ascii_chars_u); Py_XDECREF(ascii_chars_b); return -1; } #endif #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) #else #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT static char* __PYX_DEFAULT_STRING_ENCODING; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); return -1; } #endif #endif /* Test for GCC > 2.95 */ #if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #else /* !__GNUC__ or GCC < 2.95 */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ */ static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } static PyObject *__pyx_m = NULL; static PyObject *__pyx_d; static PyObject *__pyx_b; static PyObject *__pyx_cython_runtime = NULL; static PyObject *__pyx_empty_tuple; static PyObject *__pyx_empty_bytes; static PyObject *__pyx_empty_unicode; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; /* Header.proto */ #if !defined(CYTHON_CCOMPLEX) #if defined(__cplusplus) #define CYTHON_CCOMPLEX 1 #elif defined(_Complex_I) #define CYTHON_CCOMPLEX 1 #else #define CYTHON_CCOMPLEX 0 #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus #include #else #include #endif #endif #if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) #undef _Complex_I #define _Complex_I 1.0fj #endif static const char *__pyx_f[] = { "openTSNE/_matrix_mul/matrix_mul_numpy.pyx", "__init__.pxd", "stringsource", "type.pxd", }; /* MemviewSliceStruct.proto */ struct __pyx_memoryview_obj; typedef struct { struct __pyx_memoryview_obj *memview; char *data; Py_ssize_t shape[8]; Py_ssize_t strides[8]; Py_ssize_t suboffsets[8]; } __Pyx_memviewslice; #define __Pyx_MemoryView_Len(m) (m.shape[0]) /* Atomics.proto */ #include #ifndef CYTHON_ATOMICS #define CYTHON_ATOMICS 1 #endif #define __pyx_atomic_int_type int #if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\ (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\ !defined(__i386__) #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1) #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1) #ifdef __PYX_DEBUG_ATOMICS #warning "Using GNU atomics" #endif #elif CYTHON_ATOMICS && defined(_MSC_VER) && 0 #include #undef __pyx_atomic_int_type #define __pyx_atomic_int_type LONG #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value) #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value) #ifdef __PYX_DEBUG_ATOMICS #pragma message ("Using MSVC atomics") #endif #elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0 #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value) #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value) #ifdef __PYX_DEBUG_ATOMICS #warning "Using Intel atomics" #endif #else #undef CYTHON_ATOMICS #define CYTHON_ATOMICS 0 #ifdef __PYX_DEBUG_ATOMICS #warning "Not using atomics" #endif #endif typedef volatile __pyx_atomic_int_type __pyx_atomic_int; #if CYTHON_ATOMICS #define __pyx_add_acquisition_count(memview)\ __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) #define __pyx_sub_acquisition_count(memview)\ __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) #else #define __pyx_add_acquisition_count(memview)\ __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) #define __pyx_sub_acquisition_count(memview)\ __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) #endif /* ForceInitThreads.proto */ #ifndef __PYX_FORCE_INIT_THREADS #define __PYX_FORCE_INIT_THREADS 0 #endif /* NoFastGil.proto */ #define __Pyx_PyGILState_Ensure PyGILState_Ensure #define __Pyx_PyGILState_Release PyGILState_Release #define __Pyx_FastGIL_Remember() #define __Pyx_FastGIL_Forget() #define __Pyx_FastGilFuncInit() /* BufferFormatStructs.proto */ #define IS_UNSIGNED(type) (((type) -1) > 0) struct __Pyx_StructField_; #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) typedef struct { const char* name; struct __Pyx_StructField_* fields; size_t size; size_t arraysize[8]; int ndim; char typegroup; char is_unsigned; int flags; } __Pyx_TypeInfo; typedef struct __Pyx_StructField_ { __Pyx_TypeInfo* type; const char* name; size_t offset; } __Pyx_StructField; typedef struct { __Pyx_StructField* field; size_t parent_offset; } __Pyx_BufFmt_StackElem; typedef struct { __Pyx_StructField root; __Pyx_BufFmt_StackElem* head; size_t fmt_offset; size_t new_count, enc_count; size_t struct_alignment; int is_complex; char enc_type; char new_packmode; char enc_packmode; char is_valid_array; } __Pyx_BufFmt_Context; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":689 * # in Cython to enable them only on the right systems. * * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t */ typedef npy_int8 __pyx_t_5numpy_int8_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":690 * * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t */ typedef npy_int16 __pyx_t_5numpy_int16_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":691 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< * ctypedef npy_int64 int64_t * #ctypedef npy_int96 int96_t */ typedef npy_int32 __pyx_t_5numpy_int32_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":692 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< * #ctypedef npy_int96 int96_t * #ctypedef npy_int128 int128_t */ typedef npy_int64 __pyx_t_5numpy_int64_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":696 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":697 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":698 * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< * ctypedef npy_uint64 uint64_t * #ctypedef npy_uint96 uint96_t */ typedef npy_uint32 __pyx_t_5numpy_uint32_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":699 * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< * #ctypedef npy_uint96 uint96_t * #ctypedef npy_uint128 uint128_t */ typedef npy_uint64 __pyx_t_5numpy_uint64_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":703 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< * ctypedef npy_float64 float64_t * #ctypedef npy_float80 float80_t */ typedef npy_float32 __pyx_t_5numpy_float32_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":704 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< * #ctypedef npy_float80 float80_t * #ctypedef npy_float128 float128_t */ typedef npy_float64 __pyx_t_5numpy_float64_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":713 * # The int types are mapped a bit surprising -- * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t # <<<<<<<<<<<<<< * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t */ typedef npy_long __pyx_t_5numpy_int_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":714 * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< * ctypedef npy_longlong longlong_t * */ typedef npy_longlong __pyx_t_5numpy_long_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":715 * ctypedef npy_long int_t * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< * * ctypedef npy_ulong uint_t */ typedef npy_longlong __pyx_t_5numpy_longlong_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":717 * ctypedef npy_longlong longlong_t * * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t */ typedef npy_ulong __pyx_t_5numpy_uint_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":718 * * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulonglong_t * */ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":719 * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< * * ctypedef npy_intp intp_t */ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":721 * ctypedef npy_ulonglong ulonglong_t * * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< * ctypedef npy_uintp uintp_t * */ typedef npy_intp __pyx_t_5numpy_intp_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":722 * * ctypedef npy_intp intp_t * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< * * ctypedef npy_double float_t */ typedef npy_uintp __pyx_t_5numpy_uintp_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":724 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t */ typedef npy_double __pyx_t_5numpy_float_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":725 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< * ctypedef npy_longdouble longdouble_t * */ typedef npy_double __pyx_t_5numpy_double_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":726 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cfloat cfloat_t */ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; /* Declarations.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< double > __pyx_t_double_complex; #else typedef double _Complex __pyx_t_double_complex; #endif #else typedef struct { double real, imag; } __pyx_t_double_complex; #endif static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); /* Declarations.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< float > __pyx_t_float_complex; #else typedef float _Complex __pyx_t_float_complex; #endif #else typedef struct { float real, imag; } __pyx_t_float_complex; #endif static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); /*--- Type declarations ---*/ struct __pyx_array_obj; struct __pyx_MemviewEnum_obj; struct __pyx_memoryview_obj; struct __pyx_memoryviewslice_obj; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":728 * ctypedef npy_longdouble longdouble_t * * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t */ typedef npy_cfloat __pyx_t_5numpy_cfloat_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":729 * * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< * ctypedef npy_clongdouble clongdouble_t * */ typedef npy_cdouble __pyx_t_5numpy_cdouble_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":730 * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cdouble complex_t */ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":732 * ctypedef npy_clongdouble clongdouble_t * * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew1(a): */ typedef npy_cdouble __pyx_t_5numpy_complex_t; /* "View.MemoryView":105 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< * * cdef: */ struct __pyx_array_obj { PyObject_HEAD struct __pyx_vtabstruct_array *__pyx_vtab; char *data; Py_ssize_t len; char *format; int ndim; Py_ssize_t *_shape; Py_ssize_t *_strides; Py_ssize_t itemsize; PyObject *mode; PyObject *_format; void (*callback_free_data)(void *); int free_data; int dtype_is_object; }; /* "View.MemoryView":279 * * @cname('__pyx_MemviewEnum') * cdef class Enum(object): # <<<<<<<<<<<<<< * cdef object name * def __init__(self, name): */ struct __pyx_MemviewEnum_obj { PyObject_HEAD PyObject *name; }; /* "View.MemoryView":330 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< * * cdef object obj */ struct __pyx_memoryview_obj { PyObject_HEAD struct __pyx_vtabstruct_memoryview *__pyx_vtab; PyObject *obj; PyObject *_size; PyObject *_array_interface; PyThread_type_lock lock; __pyx_atomic_int acquisition_count[2]; __pyx_atomic_int *acquisition_count_aligned_p; Py_buffer view; int flags; int dtype_is_object; __Pyx_TypeInfo *typeinfo; }; /* "View.MemoryView":965 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< * "Internal class for passing memoryview slices to Python" * */ struct __pyx_memoryviewslice_obj { struct __pyx_memoryview_obj __pyx_base; __Pyx_memviewslice from_slice; PyObject *from_object; PyObject *(*to_object_func)(char *); int (*to_dtype_func)(char *, PyObject *); }; /* "View.MemoryView":105 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< * * cdef: */ struct __pyx_vtabstruct_array { PyObject *(*get_memview)(struct __pyx_array_obj *); }; static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; /* "View.MemoryView":330 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< * * cdef object obj */ struct __pyx_vtabstruct_memoryview { char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *); PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *); PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *); PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *); PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *); }; static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; /* "View.MemoryView":965 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< * "Internal class for passing memoryview slices to Python" * */ struct __pyx_vtabstruct__memoryviewslice { struct __pyx_vtabstruct_memoryview __pyx_base; }; static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice; /* --- Runtime support code (head) --- */ /* Refnanny.proto */ #ifndef CYTHON_REFNANNY #define CYTHON_REFNANNY 0 #endif #if CYTHON_REFNANNY typedef struct { void (*INCREF)(void*, PyObject*, int); void (*DECREF)(void*, PyObject*, int); void (*GOTREF)(void*, PyObject*, int); void (*GIVEREF)(void*, PyObject*, int); void* (*SetupContext)(const char*, int, const char*); void (*FinishContext)(void**); } __Pyx_RefNannyAPIStruct; static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; #ifdef WITH_THREAD #define __Pyx_RefNannySetupContext(name, acquire_gil)\ if (acquire_gil) {\ PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ PyGILState_Release(__pyx_gilstate_save);\ } else {\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ } #else #define __Pyx_RefNannySetupContext(name, acquire_gil)\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) #endif #define __Pyx_RefNannyFinishContext()\ __Pyx_RefNanny->FinishContext(&__pyx_refnanny) #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) #else #define __Pyx_RefNannyDeclarations #define __Pyx_RefNannySetupContext(name, acquire_gil) #define __Pyx_RefNannyFinishContext() #define __Pyx_INCREF(r) Py_INCREF(r) #define __Pyx_DECREF(r) Py_DECREF(r) #define __Pyx_GOTREF(r) #define __Pyx_GIVEREF(r) #define __Pyx_XINCREF(r) Py_XINCREF(r) #define __Pyx_XDECREF(r) Py_XDECREF(r) #define __Pyx_XGOTREF(r) #define __Pyx_XGIVEREF(r) #endif #define __Pyx_XDECREF_SET(r, v) do {\ PyObject *tmp = (PyObject *) r;\ r = v; __Pyx_XDECREF(tmp);\ } while (0) #define __Pyx_DECREF_SET(r, v) do {\ PyObject *tmp = (PyObject *) r;\ r = v; __Pyx_DECREF(tmp);\ } while (0) #define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) #define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) /* PyObjectGetAttrStr.proto */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) #endif /* GetBuiltinName.proto */ static PyObject *__Pyx_GetBuiltinName(PyObject *name); /* PyDictVersioning.proto */ #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS #define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) #define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) #define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ (version_var) = __PYX_GET_DICT_VERSION(dict);\ (cache_var) = (value); #define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ static PY_UINT64_T __pyx_dict_version = 0;\ static PyObject *__pyx_dict_cached_value = NULL;\ if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ (VAR) = __pyx_dict_cached_value;\ } else {\ (VAR) = __pyx_dict_cached_value = (LOOKUP);\ __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ }\ } static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); #else #define __PYX_GET_DICT_VERSION(dict) (0) #define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) #define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); #endif /* GetModuleGlobalName.proto */ #if CYTHON_USE_DICT_VERSIONS #define __Pyx_GetModuleGlobalName(var, name) {\ static PY_UINT64_T __pyx_dict_version = 0;\ static PyObject *__pyx_dict_cached_value = NULL;\ (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ } #define __Pyx_GetModuleGlobalNameUncached(var, name) {\ PY_UINT64_T __pyx_dict_version;\ PyObject *__pyx_dict_cached_value;\ (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ } static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); #else #define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) #define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); #endif /* PyObjectCall.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); #else #define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) #endif /* PyCFunctionFastCall.proto */ #if CYTHON_FAST_PYCCALL static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); #else #define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) #endif /* PyFunctionFastCall.proto */ #if CYTHON_FAST_PYCALL #define __Pyx_PyFunction_FastCall(func, args, nargs)\ __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) #if 1 || PY_VERSION_HEX < 0x030600B1 static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); #else #define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) #endif #define __Pyx_BUILD_ASSERT_EXPR(cond)\ (sizeof(char [1 - 2*!(cond)]) - 1) #ifndef Py_MEMBER_SIZE #define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) #endif static size_t __pyx_pyframe_localsplus_offset = 0; #include "frameobject.h" #define __Pxy_PyFrame_Initialize_Offsets()\ ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) #define __Pyx_PyFrame_GetLocalsplus(frame)\ (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) #endif /* PyObjectCall2Args.proto */ static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2); /* PyObjectCallMethO.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); #endif /* PyObjectCallOneArg.proto */ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); /* MemviewSliceInit.proto */ #define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d #define __Pyx_MEMVIEW_DIRECT 1 #define __Pyx_MEMVIEW_PTR 2 #define __Pyx_MEMVIEW_FULL 4 #define __Pyx_MEMVIEW_CONTIG 8 #define __Pyx_MEMVIEW_STRIDED 16 #define __Pyx_MEMVIEW_FOLLOW 32 #define __Pyx_IS_C_CONTIG 1 #define __Pyx_IS_F_CONTIG 2 static int __Pyx_init_memviewslice( struct __pyx_memoryview_obj *memview, int ndim, __Pyx_memviewslice *memviewslice, int memview_is_new_reference); static CYTHON_INLINE int __pyx_add_acquisition_count_locked( __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); #define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p) #define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview)) #define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) #define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__) static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int); /* PyThreadStateGet.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; #define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; #define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type #else #define __Pyx_PyThreadState_declare #define __Pyx_PyThreadState_assign #define __Pyx_PyErr_Occurred() PyErr_Occurred() #endif /* PyErrFetchRestore.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) #define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) #define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) #define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) #define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #if CYTHON_COMPILING_IN_CPYTHON #define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) #else #define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) #endif #else #define __Pyx_PyErr_Clear() PyErr_Clear() #define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) #define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) #define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) #define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) #endif /* WriteUnraisableException.proto */ static void __Pyx_WriteUnraisable(const char *name, int clineno, int lineno, const char *filename, int full_traceback, int nogil); /* GetTopmostException.proto */ #if CYTHON_USE_EXC_INFO_STACK static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); #endif /* SaveResetException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); #else #define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) #define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) #endif /* PyErrExceptionMatches.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); #else #define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) #endif /* GetException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #else static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); #endif /* RaiseException.proto */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /* RaiseArgTupleInvalid.proto */ static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /* RaiseDoubleKeywords.proto */ static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /* ParseKeywords.proto */ static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ const char* function_name); /* ArgTypeTest.proto */ #define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\ __Pyx__ArgTypeTest(obj, type, name, exact)) static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); /* IncludeStringH.proto */ #include /* BytesEquals.proto */ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); /* UnicodeEquals.proto */ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); /* StrEquals.proto */ #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals #else #define __Pyx_PyString_Equals __Pyx_PyBytes_Equals #endif /* UnaryNegOverflows.proto */ #define UNARY_NEG_WOULD_OVERFLOW(x)\ (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ /* GetAttr.proto */ static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); /* GetItemInt.proto */ #define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ __Pyx_GetItemInt_Generic(o, to_py_func(i)))) #define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); #define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, int wraparound, int boundscheck); /* ObjectGetItem.proto */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key); #else #define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) #endif /* decode_c_string_utf16.proto */ static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) { int byteorder = 0; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) { int byteorder = -1; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) { int byteorder = 1; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } /* decode_c_string.proto */ static CYTHON_INLINE PyObject* __Pyx_decode_c_string( const char* cstring, Py_ssize_t start, Py_ssize_t stop, const char* encoding, const char* errors, PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); /* GetAttr3.proto */ static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); /* RaiseTooManyValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); /* RaiseNeedMoreValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); /* RaiseNoneIterError.proto */ static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); /* ExtTypeTest.proto */ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /* SwapException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #else static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); #endif /* Import.proto */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /* FastTypeChecks.proto */ #if CYTHON_COMPILING_IN_CPYTHON #define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); #else #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) #define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) #define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) #endif #define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ /* ListCompAppend.proto */ #if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len)) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); __Pyx_SET_SIZE(list, len + 1); return 0; } return PyList_Append(list, x); } #else #define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) #endif /* PyIntBinop.proto */ #if !CYTHON_COMPILING_IN_PYPY static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check); #else #define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\ (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) #endif /* ListExtend.proto */ static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) { #if CYTHON_COMPILING_IN_CPYTHON PyObject* none = _PyList_Extend((PyListObject*)L, v); if (unlikely(!none)) return -1; Py_DECREF(none); return 0; #else return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v); #endif } /* ListAppend.proto */ #if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); __Pyx_SET_SIZE(list, len + 1); return 0; } return PyList_Append(list, x); } #else #define __Pyx_PyList_Append(L,x) PyList_Append(L,x) #endif /* None.proto */ static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); /* ImportFrom.proto */ static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); /* HasAttr.proto */ static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); /* PyObject_GenericGetAttrNoDict.proto */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr #endif /* PyObject_GenericGetAttr.proto */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr #endif /* SetVTable.proto */ static int __Pyx_SetVtable(PyObject *dict, void *vtable); /* PyObjectGetAttrStrNoError.proto */ static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name); /* SetupReduce.proto */ static int __Pyx_setup_reduce(PyObject* type_obj); /* TypeImport.proto */ #ifndef __PYX_HAVE_RT_ImportType_proto #define __PYX_HAVE_RT_ImportType_proto enum __Pyx_ImportType_CheckSize { __Pyx_ImportType_CheckSize_Error = 0, __Pyx_ImportType_CheckSize_Warn = 1, __Pyx_ImportType_CheckSize_Ignore = 2 }; static PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size); #endif /* CLineInTraceback.proto */ #ifdef CYTHON_CLINE_IN_TRACEBACK #define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) #else static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); #endif /* CodeObjectCache.proto */ typedef struct { PyCodeObject* code_object; int code_line; } __Pyx_CodeObjectCacheEntry; struct __Pyx_CodeObjectCache { int count; int max_count; __Pyx_CodeObjectCacheEntry* entries; }; static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); static PyCodeObject *__pyx_find_code_object(int code_line); static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); /* AddTraceback.proto */ static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename); #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); static void __Pyx_ReleaseBuffer(Py_buffer *view); #else #define __Pyx_GetBuffer PyObject_GetBuffer #define __Pyx_ReleaseBuffer PyBuffer_Release #endif /* BufferStructDeclare.proto */ typedef struct { Py_ssize_t shape, strides, suboffsets; } __Pyx_Buf_DimInfo; typedef struct { size_t refcount; Py_buffer pybuffer; } __Pyx_Buffer; typedef struct { __Pyx_Buffer *rcbuffer; char *data; __Pyx_Buf_DimInfo diminfo[8]; } __Pyx_LocalBuf_ND; /* MemviewSliceIsContig.proto */ static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); /* OverlappingSlices.proto */ static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, __Pyx_memviewslice *slice2, int ndim, size_t itemsize); /* Capsule.proto */ static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); /* RealImag.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus #define __Pyx_CREAL(z) ((z).real()) #define __Pyx_CIMAG(z) ((z).imag()) #else #define __Pyx_CREAL(z) (__real__(z)) #define __Pyx_CIMAG(z) (__imag__(z)) #endif #else #define __Pyx_CREAL(z) ((z).real) #define __Pyx_CIMAG(z) ((z).imag) #endif #if defined(__cplusplus) && CYTHON_CCOMPLEX\ && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) #define __Pyx_SET_CREAL(z,x) ((z).real(x)) #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) #else #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) #endif /* Arithmetic.proto */ #if CYTHON_CCOMPLEX #define __Pyx_c_eq_double(a, b) ((a)==(b)) #define __Pyx_c_sum_double(a, b) ((a)+(b)) #define __Pyx_c_diff_double(a, b) ((a)-(b)) #define __Pyx_c_prod_double(a, b) ((a)*(b)) #define __Pyx_c_quot_double(a, b) ((a)/(b)) #define __Pyx_c_neg_double(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero_double(z) ((z)==(double)0) #define __Pyx_c_conj_double(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs_double(z) (::std::abs(z)) #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero_double(z) ((z)==0) #define __Pyx_c_conj_double(z) (conj(z)) #if 1 #define __Pyx_c_abs_double(z) (cabs(z)) #define __Pyx_c_pow_double(a, b) (cpow(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); #if 1 static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); #endif #endif /* MemviewDtypeToObject.proto */ static CYTHON_INLINE PyObject *__pyx_memview_get_double(const char *itemp); static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *obj); /* ToPy.proto */ #define __pyx_PyComplex_FromComplex(z)\ PyComplex_FromDoubles((double)__Pyx_CREAL(z),\ (double)__Pyx_CIMAG(z)) /* FromPy.proto */ static __pyx_t_double_complex __Pyx_PyComplex_As___pyx_t_double_complex(PyObject*); /* MemviewDtypeToObject.proto */ static CYTHON_INLINE PyObject *__pyx_memview_get___pyx_t_double_complex(const char *itemp); static CYTHON_INLINE int __pyx_memview_set___pyx_t_double_complex(const char *itemp, PyObject *obj); /* Arithmetic.proto */ #if CYTHON_CCOMPLEX #define __Pyx_c_eq_float(a, b) ((a)==(b)) #define __Pyx_c_sum_float(a, b) ((a)+(b)) #define __Pyx_c_diff_float(a, b) ((a)-(b)) #define __Pyx_c_prod_float(a, b) ((a)*(b)) #define __Pyx_c_quot_float(a, b) ((a)/(b)) #define __Pyx_c_neg_float(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero_float(z) ((z)==(float)0) #define __Pyx_c_conj_float(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs_float(z) (::std::abs(z)) #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero_float(z) ((z)==0) #define __Pyx_c_conj_float(z) (conjf(z)) #if 1 #define __Pyx_c_abs_float(z) (cabsf(z)) #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); #if 1 static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); #endif #endif /* MemviewSliceCopyTemplate.proto */ static __Pyx_memviewslice __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, const char *mode, int ndim, size_t sizeof_dtype, int contig_flag, int dtype_is_object); /* GCCDiagnostics.proto */ #if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) #define __Pyx_HAS_GCC_DIAGNOSTIC #endif /* CIntFromPy.proto */ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); /* CIntFromPy.proto */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); /* CIntFromPy.proto */ static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); /* IsLittleEndian.proto */ static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); /* BufferFormatCheck.proto */ static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type); /* TypeInfoCompare.proto */ static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); /* MemviewSliceValidateAndInit.proto */ static int __Pyx_ValidateAndInit_memviewslice( int *axes_specs, int c_or_f_flag, int buf_flags, int ndim, __Pyx_TypeInfo *dtype, __Pyx_BufFmt_StackElem stack[], __Pyx_memviewslice *memviewslice, PyObject *original_obj); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(PyObject *, int writable_flag); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds___pyx_t_double_complex(PyObject *, int writable_flag); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(PyObject *, int writable_flag); /* CheckBinaryVersion.proto */ static int __Pyx_check_binary_version(void); /* FunctionExport.proto */ static int __Pyx_ExportFunction(const char *name, void (*f)(void), const char *sig); /* InitStrings.proto */ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ /* Module declarations from 'openTSNE._matrix_mul' */ /* Module declarations from 'openTSNE' */ /* Module declarations from 'cpython.buffer' */ /* Module declarations from 'libc.string' */ /* Module declarations from 'libc.stdio' */ /* Module declarations from '__builtin__' */ /* Module declarations from 'cpython.type' */ static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; /* Module declarations from 'cpython' */ /* Module declarations from 'cpython.object' */ /* Module declarations from 'cpython.ref' */ /* Module declarations from 'cpython.mem' */ /* Module declarations from 'numpy' */ /* Module declarations from 'numpy' */ static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; /* Module declarations from 'openTSNE._matrix_mul.matrix_mul' */ static PyTypeObject *__pyx_array_type = 0; static PyTypeObject *__pyx_MemviewEnum_type = 0; static PyTypeObject *__pyx_memoryview_type = 0; static PyTypeObject *__pyx_memoryviewslice_type = 0; static PyObject *generic = 0; static PyObject *strided = 0; static PyObject *indirect = 0; static PyObject *contiguous = 0; static PyObject *indirect_contiguous = 0; static int __pyx_memoryview_thread_locks_used; static PyThread_type_lock __pyx_memoryview_thread_locks[8]; static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ static void *__pyx_align_pointer(void *, size_t); /*proto*/ static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ static PyObject *_unellipsify(PyObject *, int); /*proto*/ static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ static __Pyx_TypeInfo __Pyx_TypeInfo___pyx_t_double_complex = { "double complex", NULL, sizeof(__pyx_t_double_complex), { 0 }, 0, 'C', 0, 0 }; static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; #define __Pyx_MODULE_NAME "openTSNE._matrix_mul.matrix_mul" extern int __pyx_module_is_main_openTSNE___matrix_mul__matrix_mul; int __pyx_module_is_main_openTSNE___matrix_mul__matrix_mul = 0; /* Implementation of 'openTSNE._matrix_mul.matrix_mul' */ static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_ImportError; static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_MemoryError; static PyObject *__pyx_builtin_enumerate; static PyObject *__pyx_builtin_TypeError; static PyObject *__pyx_builtin_Ellipsis; static PyObject *__pyx_builtin_id; static PyObject *__pyx_builtin_IndexError; static const char __pyx_k_O[] = "O"; static const char __pyx_k_c[] = "c"; static const char __pyx_k_id[] = "id"; static const char __pyx_k_np[] = "np"; static const char __pyx_k_fft[] = "fft"; static const char __pyx_k_new[] = "__new__"; static const char __pyx_k_obj[] = "obj"; static const char __pyx_k_base[] = "base"; static const char __pyx_k_dict[] = "__dict__"; static const char __pyx_k_ifft[] = "ifft"; static const char __pyx_k_main[] = "__main__"; static const char __pyx_k_mode[] = "mode"; static const char __pyx_k_name[] = "name"; static const char __pyx_k_ndim[] = "ndim"; static const char __pyx_k_pack[] = "pack"; static const char __pyx_k_real[] = "real"; static const char __pyx_k_size[] = "size"; static const char __pyx_k_step[] = "step"; static const char __pyx_k_stop[] = "stop"; static const char __pyx_k_test[] = "__test__"; static const char __pyx_k_ASCII[] = "ASCII"; static const char __pyx_k_class[] = "__class__"; static const char __pyx_k_dtype[] = "dtype"; static const char __pyx_k_empty[] = "empty"; static const char __pyx_k_error[] = "error"; static const char __pyx_k_flags[] = "flags"; static const char __pyx_k_numpy[] = "numpy"; static const char __pyx_k_range[] = "range"; static const char __pyx_k_rfft2[] = "rfft2"; static const char __pyx_k_shape[] = "shape"; static const char __pyx_k_start[] = "start"; static const char __pyx_k_zeros[] = "zeros"; static const char __pyx_k_encode[] = "encode"; static const char __pyx_k_format[] = "format"; static const char __pyx_k_import[] = "__import__"; static const char __pyx_k_irfft2[] = "irfft2"; static const char __pyx_k_name_2[] = "__name__"; static const char __pyx_k_pickle[] = "pickle"; static const char __pyx_k_reduce[] = "__reduce__"; static const char __pyx_k_struct[] = "struct"; static const char __pyx_k_unpack[] = "unpack"; static const char __pyx_k_update[] = "update"; static const char __pyx_k_fortran[] = "fortran"; static const char __pyx_k_memview[] = "memview"; static const char __pyx_k_Ellipsis[] = "Ellipsis"; static const char __pyx_k_getstate[] = "__getstate__"; static const char __pyx_k_itemsize[] = "itemsize"; static const char __pyx_k_multiply[] = "multiply"; static const char __pyx_k_pyx_type[] = "__pyx_type"; static const char __pyx_k_setstate[] = "__setstate__"; static const char __pyx_k_TypeError[] = "TypeError"; static const char __pyx_k_enumerate[] = "enumerate"; static const char __pyx_k_pyx_state[] = "__pyx_state"; static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; static const char __pyx_k_IndexError[] = "IndexError"; static const char __pyx_k_ValueError[] = "ValueError"; static const char __pyx_k_pyx_result[] = "__pyx_result"; static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; static const char __pyx_k_ImportError[] = "ImportError"; static const char __pyx_k_MemoryError[] = "MemoryError"; static const char __pyx_k_PickleError[] = "PickleError"; static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; static const char __pyx_k_stringsource[] = "stringsource"; static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; static const char __pyx_k_strided_and_direct[] = ""; static const char __pyx_k_strided_and_indirect[] = ""; static const char __pyx_k_contiguous_and_direct[] = ""; static const char __pyx_k_MemoryView_of_r_object[] = ""; static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; static const char __pyx_k_contiguous_and_indirect[] = ""; static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'"; static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; static const char __pyx_k_strided_and_direct_or_indirect[] = ""; static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))"; static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; static PyObject *__pyx_n_s_ASCII; static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; static PyObject *__pyx_kp_s_Cannot_index_with_type_s; static PyObject *__pyx_n_s_Ellipsis; static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; static PyObject *__pyx_n_s_ImportError; static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0; static PyObject *__pyx_n_s_IndexError; static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; static PyObject *__pyx_n_s_MemoryError; static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; static PyObject *__pyx_kp_s_MemoryView_of_r_object; static PyObject *__pyx_n_b_O; static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; static PyObject *__pyx_n_s_PickleError; static PyObject *__pyx_n_s_TypeError; static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; static PyObject *__pyx_n_s_ValueError; static PyObject *__pyx_n_s_View_MemoryView; static PyObject *__pyx_n_s_allocate_buffer; static PyObject *__pyx_n_s_base; static PyObject *__pyx_n_s_c; static PyObject *__pyx_n_u_c; static PyObject *__pyx_n_s_class; static PyObject *__pyx_n_s_cline_in_traceback; static PyObject *__pyx_kp_s_contiguous_and_direct; static PyObject *__pyx_kp_s_contiguous_and_indirect; static PyObject *__pyx_n_s_dict; static PyObject *__pyx_n_s_dtype; static PyObject *__pyx_n_s_dtype_is_object; static PyObject *__pyx_n_s_empty; static PyObject *__pyx_n_s_encode; static PyObject *__pyx_n_s_enumerate; static PyObject *__pyx_n_s_error; static PyObject *__pyx_n_s_fft; static PyObject *__pyx_n_s_flags; static PyObject *__pyx_n_s_format; static PyObject *__pyx_n_s_fortran; static PyObject *__pyx_n_u_fortran; static PyObject *__pyx_n_s_getstate; static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi; static PyObject *__pyx_n_s_id; static PyObject *__pyx_n_s_ifft; static PyObject *__pyx_n_s_import; static PyObject *__pyx_n_s_irfft2; static PyObject *__pyx_n_s_itemsize; static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; static PyObject *__pyx_n_s_main; static PyObject *__pyx_n_s_memview; static PyObject *__pyx_n_s_mode; static PyObject *__pyx_n_s_multiply; static PyObject *__pyx_n_s_name; static PyObject *__pyx_n_s_name_2; static PyObject *__pyx_n_s_ndim; static PyObject *__pyx_n_s_new; static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; static PyObject *__pyx_n_s_np; static PyObject *__pyx_n_s_numpy; static PyObject *__pyx_kp_u_numpy_core_multiarray_failed_to; static PyObject *__pyx_kp_u_numpy_core_umath_failed_to_impor; static PyObject *__pyx_n_s_obj; static PyObject *__pyx_n_s_pack; static PyObject *__pyx_n_s_pickle; static PyObject *__pyx_n_s_pyx_PickleError; static PyObject *__pyx_n_s_pyx_checksum; static PyObject *__pyx_n_s_pyx_getbuffer; static PyObject *__pyx_n_s_pyx_result; static PyObject *__pyx_n_s_pyx_state; static PyObject *__pyx_n_s_pyx_type; static PyObject *__pyx_n_s_pyx_unpickle_Enum; static PyObject *__pyx_n_s_pyx_vtable; static PyObject *__pyx_n_s_range; static PyObject *__pyx_n_s_real; static PyObject *__pyx_n_s_reduce; static PyObject *__pyx_n_s_reduce_cython; static PyObject *__pyx_n_s_reduce_ex; static PyObject *__pyx_n_s_rfft2; static PyObject *__pyx_n_s_setstate; static PyObject *__pyx_n_s_setstate_cython; static PyObject *__pyx_n_s_shape; static PyObject *__pyx_n_s_size; static PyObject *__pyx_n_s_start; static PyObject *__pyx_n_s_step; static PyObject *__pyx_n_s_stop; static PyObject *__pyx_kp_s_strided_and_direct; static PyObject *__pyx_kp_s_strided_and_direct_or_indirect; static PyObject *__pyx_kp_s_strided_and_indirect; static PyObject *__pyx_kp_s_stringsource; static PyObject *__pyx_n_s_struct; static PyObject *__pyx_n_s_test; static PyObject *__pyx_kp_s_unable_to_allocate_array_data; static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; static PyObject *__pyx_n_s_unpack; static PyObject *__pyx_n_s_update; static PyObject *__pyx_n_s_zeros; static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */ static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_int_0; static PyObject *__pyx_int_1; static PyObject *__pyx_int_184977713; static PyObject *__pyx_int_neg_1; static PyObject *__pyx_tuple_; static PyObject *__pyx_tuple__2; static PyObject *__pyx_tuple__3; static PyObject *__pyx_tuple__4; static PyObject *__pyx_tuple__5; static PyObject *__pyx_tuple__6; static PyObject *__pyx_tuple__7; static PyObject *__pyx_tuple__8; static PyObject *__pyx_tuple__9; static PyObject *__pyx_slice__17; static PyObject *__pyx_tuple__10; static PyObject *__pyx_tuple__11; static PyObject *__pyx_tuple__12; static PyObject *__pyx_tuple__13; static PyObject *__pyx_tuple__14; static PyObject *__pyx_tuple__15; static PyObject *__pyx_tuple__16; static PyObject *__pyx_tuple__18; static PyObject *__pyx_tuple__19; static PyObject *__pyx_tuple__20; static PyObject *__pyx_tuple__21; static PyObject *__pyx_tuple__22; static PyObject *__pyx_tuple__23; static PyObject *__pyx_tuple__24; static PyObject *__pyx_tuple__25; static PyObject *__pyx_tuple__26; static PyObject *__pyx_codeobj__27; /* Late includes */ /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":12 * * * cdef void matrix_multiply_fft_1d( # <<<<<<<<<<<<<< * double[::1] kernel_tilde, * double[:, ::1] w_coefficients, */ static void __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_1d(__Pyx_memviewslice __pyx_v_kernel_tilde, __Pyx_memviewslice __pyx_v_w_coefficients, __Pyx_memviewslice __pyx_v_out) { Py_ssize_t __pyx_v_n_interpolation_points_1d; Py_ssize_t __pyx_v_n_terms; Py_ssize_t __pyx_v_n_fft_coeffs; __Pyx_memviewslice __pyx_v_fft_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_fft_w_coeffs = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_fft_in_buffer = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_fft_out_buffer = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_d; Py_ssize_t __pyx_v_i; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_6; Py_ssize_t __pyx_t_7; Py_ssize_t __pyx_t_8; Py_ssize_t __pyx_t_9; Py_ssize_t __pyx_t_10; Py_ssize_t __pyx_t_11; Py_ssize_t __pyx_t_12; Py_ssize_t __pyx_t_13; Py_ssize_t __pyx_t_14; PyObject *__pyx_t_15 = NULL; int __pyx_t_16; PyObject *__pyx_t_17 = NULL; double __pyx_t_18; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("matrix_multiply_fft_1d", 0); /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":36 * """ * cdef: * Py_ssize_t n_interpolation_points_1d = w_coefficients.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t n_terms = w_coefficients.shape[1] * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] */ __pyx_v_n_interpolation_points_1d = (__pyx_v_w_coefficients.shape[0]); /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":37 * cdef: * Py_ssize_t n_interpolation_points_1d = w_coefficients.shape[0] * Py_ssize_t n_terms = w_coefficients.shape[1] # <<<<<<<<<<<<<< * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] * */ __pyx_v_n_terms = (__pyx_v_w_coefficients.shape[1]); /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":38 * Py_ssize_t n_interpolation_points_1d = w_coefficients.shape[0] * Py_ssize_t n_terms = w_coefficients.shape[1] * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] # <<<<<<<<<<<<<< * * complex[::1] fft_kernel_tilde = np.empty(n_fft_coeffs, dtype=complex) */ __pyx_v_n_fft_coeffs = (__pyx_v_kernel_tilde.shape[0]); /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":40 * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] * * complex[::1] fft_kernel_tilde = np.empty(n_fft_coeffs, dtype=complex) # <<<<<<<<<<<<<< * complex[::1] fft_w_coeffs = np.empty(n_fft_coeffs, dtype=complex) * # Note that we can't use the same buffer for the input and output since */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 40, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_empty); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 40, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 40, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 40, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 40, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_dtype, ((PyObject *)(&PyComplex_Type))) < 0) __PYX_ERR(0, 40, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 40, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 40, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_fft_kernel_tilde = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":41 * * complex[::1] fft_kernel_tilde = np.empty(n_fft_coeffs, dtype=complex) * complex[::1] fft_w_coeffs = np.empty(n_fft_coeffs, dtype=complex) # <<<<<<<<<<<<<< * # Note that we can't use the same buffer for the input and output since * # we only write to the first half of the vector - we'd need to */ __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_np); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_4, __pyx_n_s_empty); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (PyDict_SetItem(__pyx_t_4, __pyx_n_s_dtype, ((PyObject *)(&PyComplex_Type))) < 0) __PYX_ERR(0, 41, __pyx_L1_error) __pyx_t_2 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_3, __pyx_t_4); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(__pyx_t_2, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v_fft_w_coeffs = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":47 * # changed during the IDFT, so it's faster to use two buffers, at the * # cost of some memory * complex[::1] fft_in_buffer = np.zeros(n_fft_coeffs, dtype=complex) # <<<<<<<<<<<<<< * complex[::1] fft_out_buffer = np.zeros(n_fft_coeffs, dtype=complex) * */ __Pyx_GetModuleGlobalName(__pyx_t_2, __pyx_n_s_np); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 47, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_zeros); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 47, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 47, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 47, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 47, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (PyDict_SetItem(__pyx_t_2, __pyx_n_s_dtype, ((PyObject *)(&PyComplex_Type))) < 0) __PYX_ERR(0, 47, __pyx_L1_error) __pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_4, __pyx_t_3, __pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 47, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(__pyx_t_1, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 47, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_v_fft_in_buffer = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":48 * # cost of some memory * complex[::1] fft_in_buffer = np.zeros(n_fft_coeffs, dtype=complex) * complex[::1] fft_out_buffer = np.zeros(n_fft_coeffs, dtype=complex) # <<<<<<<<<<<<<< * * Py_ssize_t d, i */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 48, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_zeros); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 48, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 48, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 48, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 48, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_dtype, ((PyObject *)(&PyComplex_Type))) < 0) __PYX_ERR(0, 48, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 48, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 48, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_fft_out_buffer = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":53 * * # Compute the FFT of the kernel vector * fft_kernel_tilde = np.fft.fft(kernel_tilde) # <<<<<<<<<<<<<< * * for d in range(n_terms): */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 53, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_fft); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 53, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_fft); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 53, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_kernel_tilde, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 53, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_2 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_1))) { __pyx_t_2 = PyMethod_GET_SELF(__pyx_t_1); if (likely(__pyx_t_2)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_1); __Pyx_INCREF(__pyx_t_2); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_1, function); } } __pyx_t_4 = (__pyx_t_2) ? __Pyx_PyObject_Call2Args(__pyx_t_1, __pyx_t_2, __pyx_t_3) : __Pyx_PyObject_CallOneArg(__pyx_t_1, __pyx_t_3); __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 53, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 53, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __PYX_XDEC_MEMVIEW(&__pyx_v_fft_kernel_tilde, 1); __pyx_v_fft_kernel_tilde = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":55 * fft_kernel_tilde = np.fft.fft(kernel_tilde) * * for d in range(n_terms): # <<<<<<<<<<<<<< * for i in range(n_interpolation_points_1d): * fft_in_buffer[i] = w_coefficients[i, d] */ __pyx_t_6 = __pyx_v_n_terms; __pyx_t_7 = __pyx_t_6; for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_7; __pyx_t_8+=1) { __pyx_v_d = __pyx_t_8; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":56 * * for d in range(n_terms): * for i in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * fft_in_buffer[i] = w_coefficients[i, d] * */ __pyx_t_9 = __pyx_v_n_interpolation_points_1d; __pyx_t_10 = __pyx_t_9; for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) { __pyx_v_i = __pyx_t_11; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":57 * for d in range(n_terms): * for i in range(n_interpolation_points_1d): * fft_in_buffer[i] = w_coefficients[i, d] # <<<<<<<<<<<<<< * * fft_w_coeffs = np.fft.fft(fft_in_buffer) */ __pyx_t_12 = __pyx_v_i; __pyx_t_13 = __pyx_v_d; __pyx_t_14 = __pyx_v_i; *((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_in_buffer.data) + __pyx_t_14)) )) = __pyx_t_double_complex_from_parts((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_w_coefficients.data + __pyx_t_12 * __pyx_v_w_coefficients.strides[0]) )) + __pyx_t_13)) ))), 0); } /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":59 * fft_in_buffer[i] = w_coefficients[i, d] * * fft_w_coeffs = np.fft.fft(fft_in_buffer) # <<<<<<<<<<<<<< * * # Take the Hadamard product of two complex vectors */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 59, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_fft); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 59, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_fft); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 59, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_fft_in_buffer, 1, (PyObject *(*)(char *)) __pyx_memview_get___pyx_t_double_complex, (int (*)(char *, PyObject *)) __pyx_memview_set___pyx_t_double_complex, 0);; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 59, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_2 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_1))) { __pyx_t_2 = PyMethod_GET_SELF(__pyx_t_1); if (likely(__pyx_t_2)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_1); __Pyx_INCREF(__pyx_t_2); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_1, function); } } __pyx_t_4 = (__pyx_t_2) ? __Pyx_PyObject_Call2Args(__pyx_t_1, __pyx_t_2, __pyx_t_3) : __Pyx_PyObject_CallOneArg(__pyx_t_1, __pyx_t_3); __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 59, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 59, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __PYX_XDEC_MEMVIEW(&__pyx_v_fft_w_coeffs, 1); __pyx_v_fft_w_coeffs = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":62 * * # Take the Hadamard product of two complex vectors * fft_w_coeffs = np.multiply(fft_w_coeffs, fft_kernel_tilde) # <<<<<<<<<<<<<< * * fft_out_buffer = np.fft.ifft(fft_w_coeffs) */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_multiply); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_fft_w_coeffs, 1, (PyObject *(*)(char *)) __pyx_memview_get___pyx_t_double_complex, (int (*)(char *, PyObject *)) __pyx_memview_set___pyx_t_double_complex, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __pyx_memoryview_fromslice(__pyx_v_fft_kernel_tilde, 1, (PyObject *(*)(char *)) __pyx_memview_get___pyx_t_double_complex, (int (*)(char *, PyObject *)) __pyx_memview_set___pyx_t_double_complex, 0);; if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_15 = NULL; __pyx_t_16 = 0; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_3))) { __pyx_t_15 = PyMethod_GET_SELF(__pyx_t_3); if (likely(__pyx_t_15)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_3); __Pyx_INCREF(__pyx_t_15); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_3, function); __pyx_t_16 = 1; } } #if CYTHON_FAST_PYCALL if (PyFunction_Check(__pyx_t_3)) { PyObject *__pyx_temp[3] = {__pyx_t_15, __pyx_t_1, __pyx_t_2}; __pyx_t_4 = __Pyx_PyFunction_FastCall(__pyx_t_3, __pyx_temp+1-__pyx_t_16, 2+__pyx_t_16); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_15); __pyx_t_15 = 0; __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } else #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(__pyx_t_3)) { PyObject *__pyx_temp[3] = {__pyx_t_15, __pyx_t_1, __pyx_t_2}; __pyx_t_4 = __Pyx_PyCFunction_FastCall(__pyx_t_3, __pyx_temp+1-__pyx_t_16, 2+__pyx_t_16); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_15); __pyx_t_15 = 0; __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } else #endif { __pyx_t_17 = PyTuple_New(2+__pyx_t_16); if (unlikely(!__pyx_t_17)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_17); if (__pyx_t_15) { __Pyx_GIVEREF(__pyx_t_15); PyTuple_SET_ITEM(__pyx_t_17, 0, __pyx_t_15); __pyx_t_15 = NULL; } __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_17, 0+__pyx_t_16, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_17, 1+__pyx_t_16, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_3, __pyx_t_17, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_17); __pyx_t_17 = 0; } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 62, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __PYX_XDEC_MEMVIEW(&__pyx_v_fft_w_coeffs, 1); __pyx_v_fft_w_coeffs = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":64 * fft_w_coeffs = np.multiply(fft_w_coeffs, fft_kernel_tilde) * * fft_out_buffer = np.fft.ifft(fft_w_coeffs) # <<<<<<<<<<<<<< * * for i in range(n_interpolation_points_1d): */ __Pyx_GetModuleGlobalName(__pyx_t_3, __pyx_n_s_np); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 64, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_17 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_fft); if (unlikely(!__pyx_t_17)) __PYX_ERR(0, 64, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_17); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_17, __pyx_n_s_ifft); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 64, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_17); __pyx_t_17 = 0; __pyx_t_17 = __pyx_memoryview_fromslice(__pyx_v_fft_w_coeffs, 1, (PyObject *(*)(char *)) __pyx_memview_get___pyx_t_double_complex, (int (*)(char *, PyObject *)) __pyx_memview_set___pyx_t_double_complex, 0);; if (unlikely(!__pyx_t_17)) __PYX_ERR(0, 64, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_17); __pyx_t_2 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_3))) { __pyx_t_2 = PyMethod_GET_SELF(__pyx_t_3); if (likely(__pyx_t_2)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_3); __Pyx_INCREF(__pyx_t_2); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_3, function); } } __pyx_t_4 = (__pyx_t_2) ? __Pyx_PyObject_Call2Args(__pyx_t_3, __pyx_t_2, __pyx_t_17) : __Pyx_PyObject_CallOneArg(__pyx_t_3, __pyx_t_17); __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_17); __pyx_t_17 = 0; if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 64, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 64, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __PYX_XDEC_MEMVIEW(&__pyx_v_fft_out_buffer, 1); __pyx_v_fft_out_buffer = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":66 * fft_out_buffer = np.fft.ifft(fft_w_coeffs) * * for i in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * out[i, d] = fft_out_buffer[n_interpolation_points_1d + i].real * */ __pyx_t_9 = __pyx_v_n_interpolation_points_1d; __pyx_t_10 = __pyx_t_9; for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) { __pyx_v_i = __pyx_t_11; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":67 * * for i in range(n_interpolation_points_1d): * out[i, d] = fft_out_buffer[n_interpolation_points_1d + i].real # <<<<<<<<<<<<<< * * */ __pyx_t_13 = (__pyx_v_n_interpolation_points_1d + __pyx_v_i); __pyx_t_18 = __Pyx_CREAL((*((__pyx_t_double_complex *) ( /* dim=0 */ ((char *) (((__pyx_t_double_complex *) __pyx_v_fft_out_buffer.data) + __pyx_t_13)) )))); __pyx_t_13 = __pyx_v_i; __pyx_t_12 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_out.data + __pyx_t_13 * __pyx_v_out.strides[0]) )) + __pyx_t_12)) )) = __pyx_t_18; } } /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":12 * * * cdef void matrix_multiply_fft_1d( # <<<<<<<<<<<<<< * double[::1] kernel_tilde, * double[:, ::1] w_coefficients, */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1); __Pyx_XDECREF(__pyx_t_15); __Pyx_XDECREF(__pyx_t_17); __Pyx_WriteUnraisable("openTSNE._matrix_mul.matrix_mul.matrix_multiply_fft_1d", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_fft_kernel_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_fft_w_coeffs, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_fft_in_buffer, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_fft_out_buffer, 1); __Pyx_RefNannyFinishContext(); } /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":70 * * * cdef void matrix_multiply_fft_2d( # <<<<<<<<<<<<<< * double[:, ::1] kernel_tilde, * double[:, ::1] w_coefficients, */ static void __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_2d(__Pyx_memviewslice __pyx_v_kernel_tilde, __Pyx_memviewslice __pyx_v_w_coefficients, __Pyx_memviewslice __pyx_v_out) { CYTHON_UNUSED Py_ssize_t __pyx_v_total_interpolation_points; Py_ssize_t __pyx_v_n_terms; Py_ssize_t __pyx_v_n_fft_coeffs; Py_ssize_t __pyx_v_n_interpolation_points_1d; __Pyx_memviewslice __pyx_v_fft_w_coefficients = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_fft_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_fft_in_buffer = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_fft_out_buffer = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_d; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; Py_ssize_t __pyx_v_idx; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_t_6 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_7; Py_ssize_t __pyx_t_8; Py_ssize_t __pyx_t_9; Py_ssize_t __pyx_t_10; Py_ssize_t __pyx_t_11; Py_ssize_t __pyx_t_12; Py_ssize_t __pyx_t_13; Py_ssize_t __pyx_t_14; Py_ssize_t __pyx_t_15; Py_ssize_t __pyx_t_16; Py_ssize_t __pyx_t_17; Py_ssize_t __pyx_t_18; Py_ssize_t __pyx_t_19; PyObject *__pyx_t_20 = NULL; int __pyx_t_21; PyObject *__pyx_t_22 = NULL; double __pyx_t_23; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("matrix_multiply_fft_2d", 0); /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":94 * """ * cdef: * Py_ssize_t total_interpolation_points = w_coefficients.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t n_terms = w_coefficients.shape[1] * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] */ __pyx_v_total_interpolation_points = (__pyx_v_w_coefficients.shape[0]); /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":95 * cdef: * Py_ssize_t total_interpolation_points = w_coefficients.shape[0] * Py_ssize_t n_terms = w_coefficients.shape[1] # <<<<<<<<<<<<<< * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] * Py_ssize_t n_interpolation_points_1d = n_fft_coeffs / 2 */ __pyx_v_n_terms = (__pyx_v_w_coefficients.shape[1]); /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":96 * Py_ssize_t total_interpolation_points = w_coefficients.shape[0] * Py_ssize_t n_terms = w_coefficients.shape[1] * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t n_interpolation_points_1d = n_fft_coeffs / 2 * */ __pyx_v_n_fft_coeffs = (__pyx_v_kernel_tilde.shape[0]); /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":97 * Py_ssize_t n_terms = w_coefficients.shape[1] * Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] * Py_ssize_t n_interpolation_points_1d = n_fft_coeffs / 2 # <<<<<<<<<<<<<< * * complex[:, :] fft_w_coefficients = np.empty((n_fft_coeffs, (n_fft_coeffs / 2 + 1)), dtype=complex) */ __pyx_v_n_interpolation_points_1d = (__pyx_v_n_fft_coeffs / 2); /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":99 * Py_ssize_t n_interpolation_points_1d = n_fft_coeffs / 2 * * complex[:, :] fft_w_coefficients = np.empty((n_fft_coeffs, (n_fft_coeffs / 2 + 1)), dtype=complex) # <<<<<<<<<<<<<< * complex[:, :] fft_kernel_tilde = np.empty((n_fft_coeffs, (n_fft_coeffs / 2 + 1)), dtype=complex) * # Note that we can't use the same buffer for the input and output since */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 99, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_empty); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 99, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 99, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyInt_FromSsize_t(((__pyx_v_n_fft_coeffs / 2) + 1)); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 99, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 99, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_3); __pyx_t_1 = 0; __pyx_t_3 = 0; __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 99, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 99, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (PyDict_SetItem(__pyx_t_4, __pyx_n_s_dtype, ((PyObject *)(&PyComplex_Type))) < 0) __PYX_ERR(0, 99, __pyx_L1_error) __pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_4); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 99, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dsds___pyx_t_double_complex(__pyx_t_1, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 99, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_v_fft_w_coefficients = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":100 * * complex[:, :] fft_w_coefficients = np.empty((n_fft_coeffs, (n_fft_coeffs / 2 + 1)), dtype=complex) * complex[:, :] fft_kernel_tilde = np.empty((n_fft_coeffs, (n_fft_coeffs / 2 + 1)), dtype=complex) # <<<<<<<<<<<<<< * # Note that we can't use the same buffer for the input and output since * # we only write to the top quadrant of the in matrix - we'd need to */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 100, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_4 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_empty); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 100, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 100, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyInt_FromSsize_t(((__pyx_v_n_fft_coeffs / 2) + 1)); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 100, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 100, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_3); __pyx_t_1 = 0; __pyx_t_3 = 0; __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 100, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 100, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (PyDict_SetItem(__pyx_t_2, __pyx_n_s_dtype, ((PyObject *)(&PyComplex_Type))) < 0) __PYX_ERR(0, 100, __pyx_L1_error) __pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_4, __pyx_t_3, __pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 100, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dsds___pyx_t_double_complex(__pyx_t_1, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 100, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_v_fft_kernel_tilde = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":106 * # changed during the IDFT, so it's faster to use two buffers, at the * # cost of some memory * double[:, ::1] fft_in_buffer = np.zeros((n_fft_coeffs, n_fft_coeffs), dtype=float) # <<<<<<<<<<<<<< * double[:, ::1] fft_out_buffer = np.zeros((n_fft_coeffs, n_fft_coeffs), dtype=float) * */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 106, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_zeros); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 106, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 106, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 106, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 106, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_3); __pyx_t_1 = 0; __pyx_t_3 = 0; __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 106, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 106, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (PyDict_SetItem(__pyx_t_4, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 106, __pyx_L1_error) __pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_4); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 106, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_6 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_1, PyBUF_WRITABLE); if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 106, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_v_fft_in_buffer = __pyx_t_6; __pyx_t_6.memview = NULL; __pyx_t_6.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":107 * # cost of some memory * double[:, ::1] fft_in_buffer = np.zeros((n_fft_coeffs, n_fft_coeffs), dtype=float) * double[:, ::1] fft_out_buffer = np.zeros((n_fft_coeffs, n_fft_coeffs), dtype=float) # <<<<<<<<<<<<<< * * Py_ssize_t d, i, j, idx */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 107, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_4 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_zeros); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 107, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 107, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_n_fft_coeffs); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 107, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 107, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_3); __pyx_t_1 = 0; __pyx_t_3 = 0; __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 107, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 107, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (PyDict_SetItem(__pyx_t_2, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 107, __pyx_L1_error) __pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_4, __pyx_t_3, __pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 107, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_6 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_1, PyBUF_WRITABLE); if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 107, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_v_fft_out_buffer = __pyx_t_6; __pyx_t_6.memview = NULL; __pyx_t_6.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":111 * Py_ssize_t d, i, j, idx * * fft_kernel_tilde = np.fft.rfft2(kernel_tilde) # <<<<<<<<<<<<<< * * for d in range(n_terms): */ __Pyx_GetModuleGlobalName(__pyx_t_2, __pyx_n_s_np); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 111, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_fft); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 111, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_rfft2); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 111, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_kernel_tilde, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 111, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_2))) { __pyx_t_4 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_4)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_4); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_1 = (__pyx_t_4) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_4, __pyx_t_3) : __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 111, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dsds___pyx_t_double_complex(__pyx_t_1, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 111, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_XDEC_MEMVIEW(&__pyx_v_fft_kernel_tilde, 1); __pyx_v_fft_kernel_tilde = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":113 * fft_kernel_tilde = np.fft.rfft2(kernel_tilde) * * for d in range(n_terms): # <<<<<<<<<<<<<< * for i in range(n_interpolation_points_1d): * for j in range(n_interpolation_points_1d): */ __pyx_t_7 = __pyx_v_n_terms; __pyx_t_8 = __pyx_t_7; for (__pyx_t_9 = 0; __pyx_t_9 < __pyx_t_8; __pyx_t_9+=1) { __pyx_v_d = __pyx_t_9; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":114 * * for d in range(n_terms): * for i in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * for j in range(n_interpolation_points_1d): * fft_in_buffer[i, j] = w_coefficients[i * n_interpolation_points_1d + j, d] */ __pyx_t_10 = __pyx_v_n_interpolation_points_1d; __pyx_t_11 = __pyx_t_10; for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) { __pyx_v_i = __pyx_t_12; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":115 * for d in range(n_terms): * for i in range(n_interpolation_points_1d): * for j in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * fft_in_buffer[i, j] = w_coefficients[i * n_interpolation_points_1d + j, d] * */ __pyx_t_13 = __pyx_v_n_interpolation_points_1d; __pyx_t_14 = __pyx_t_13; for (__pyx_t_15 = 0; __pyx_t_15 < __pyx_t_14; __pyx_t_15+=1) { __pyx_v_j = __pyx_t_15; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":116 * for i in range(n_interpolation_points_1d): * for j in range(n_interpolation_points_1d): * fft_in_buffer[i, j] = w_coefficients[i * n_interpolation_points_1d + j, d] # <<<<<<<<<<<<<< * * fft_w_coefficients = np.fft.rfft2(fft_in_buffer) */ __pyx_t_16 = ((__pyx_v_i * __pyx_v_n_interpolation_points_1d) + __pyx_v_j); __pyx_t_17 = __pyx_v_d; __pyx_t_18 = __pyx_v_i; __pyx_t_19 = __pyx_v_j; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_fft_in_buffer.data + __pyx_t_18 * __pyx_v_fft_in_buffer.strides[0]) )) + __pyx_t_19)) )) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_w_coefficients.data + __pyx_t_16 * __pyx_v_w_coefficients.strides[0]) )) + __pyx_t_17)) ))); } } /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":118 * fft_in_buffer[i, j] = w_coefficients[i * n_interpolation_points_1d + j, d] * * fft_w_coefficients = np.fft.rfft2(fft_in_buffer) # <<<<<<<<<<<<<< * * # Take the Hadamard product of two complex vectors */ __Pyx_GetModuleGlobalName(__pyx_t_2, __pyx_n_s_np); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 118, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_fft); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 118, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_rfft2); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 118, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_fft_in_buffer, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 118, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_2))) { __pyx_t_4 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_4)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_4); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_1 = (__pyx_t_4) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_4, __pyx_t_3) : __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 118, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dsds___pyx_t_double_complex(__pyx_t_1, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 118, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_XDEC_MEMVIEW(&__pyx_v_fft_w_coefficients, 1); __pyx_v_fft_w_coefficients = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":121 * * # Take the Hadamard product of two complex vectors * fft_w_coefficients = np.multiply(fft_w_coefficients, fft_kernel_tilde) # <<<<<<<<<<<<<< * * # Invert the computed values at the interpolated nodes */ __Pyx_GetModuleGlobalName(__pyx_t_2, __pyx_n_s_np); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 121, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_multiply); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 121, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __pyx_memoryview_fromslice(__pyx_v_fft_w_coefficients, 2, (PyObject *(*)(char *)) __pyx_memview_get___pyx_t_double_complex, (int (*)(char *, PyObject *)) __pyx_memview_set___pyx_t_double_complex, 0);; if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 121, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = __pyx_memoryview_fromslice(__pyx_v_fft_kernel_tilde, 2, (PyObject *(*)(char *)) __pyx_memview_get___pyx_t_double_complex, (int (*)(char *, PyObject *)) __pyx_memview_set___pyx_t_double_complex, 0);; if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 121, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_20 = NULL; __pyx_t_21 = 0; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_3))) { __pyx_t_20 = PyMethod_GET_SELF(__pyx_t_3); if (likely(__pyx_t_20)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_3); __Pyx_INCREF(__pyx_t_20); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_3, function); __pyx_t_21 = 1; } } #if CYTHON_FAST_PYCALL if (PyFunction_Check(__pyx_t_3)) { PyObject *__pyx_temp[3] = {__pyx_t_20, __pyx_t_2, __pyx_t_4}; __pyx_t_1 = __Pyx_PyFunction_FastCall(__pyx_t_3, __pyx_temp+1-__pyx_t_21, 2+__pyx_t_21); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 121, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_20); __pyx_t_20 = 0; __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; } else #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(__pyx_t_3)) { PyObject *__pyx_temp[3] = {__pyx_t_20, __pyx_t_2, __pyx_t_4}; __pyx_t_1 = __Pyx_PyCFunction_FastCall(__pyx_t_3, __pyx_temp+1-__pyx_t_21, 2+__pyx_t_21); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 121, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_20); __pyx_t_20 = 0; __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; } else #endif { __pyx_t_22 = PyTuple_New(2+__pyx_t_21); if (unlikely(!__pyx_t_22)) __PYX_ERR(0, 121, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_22); if (__pyx_t_20) { __Pyx_GIVEREF(__pyx_t_20); PyTuple_SET_ITEM(__pyx_t_22, 0, __pyx_t_20); __pyx_t_20 = NULL; } __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_22, 0+__pyx_t_21, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_22, 1+__pyx_t_21, __pyx_t_4); __pyx_t_2 = 0; __pyx_t_4 = 0; __pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_3, __pyx_t_22, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 121, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_22); __pyx_t_22 = 0; } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dsds___pyx_t_double_complex(__pyx_t_1, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 121, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_XDEC_MEMVIEW(&__pyx_v_fft_w_coefficients, 1); __pyx_v_fft_w_coefficients = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":124 * * # Invert the computed values at the interpolated nodes * fft_out_buffer = np.fft.irfft2(fft_w_coefficients) # <<<<<<<<<<<<<< * * for i in range(n_interpolation_points_1d): */ __Pyx_GetModuleGlobalName(__pyx_t_3, __pyx_n_s_np); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 124, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_22 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_fft); if (unlikely(!__pyx_t_22)) __PYX_ERR(0, 124, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_22); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_22, __pyx_n_s_irfft2); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 124, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_22); __pyx_t_22 = 0; __pyx_t_22 = __pyx_memoryview_fromslice(__pyx_v_fft_w_coefficients, 2, (PyObject *(*)(char *)) __pyx_memview_get___pyx_t_double_complex, (int (*)(char *, PyObject *)) __pyx_memview_set___pyx_t_double_complex, 0);; if (unlikely(!__pyx_t_22)) __PYX_ERR(0, 124, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_22); __pyx_t_4 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_3))) { __pyx_t_4 = PyMethod_GET_SELF(__pyx_t_3); if (likely(__pyx_t_4)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_3); __Pyx_INCREF(__pyx_t_4); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_3, function); } } __pyx_t_1 = (__pyx_t_4) ? __Pyx_PyObject_Call2Args(__pyx_t_3, __pyx_t_4, __pyx_t_22) : __Pyx_PyObject_CallOneArg(__pyx_t_3, __pyx_t_22); __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_22); __pyx_t_22 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 124, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_6 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_1, PyBUF_WRITABLE); if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 124, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_XDEC_MEMVIEW(&__pyx_v_fft_out_buffer, 1); __pyx_v_fft_out_buffer = __pyx_t_6; __pyx_t_6.memview = NULL; __pyx_t_6.data = NULL; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":126 * fft_out_buffer = np.fft.irfft2(fft_w_coefficients) * * for i in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * for j in range(n_interpolation_points_1d): * idx = i * n_interpolation_points_1d + j */ __pyx_t_10 = __pyx_v_n_interpolation_points_1d; __pyx_t_11 = __pyx_t_10; for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) { __pyx_v_i = __pyx_t_12; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":127 * * for i in range(n_interpolation_points_1d): * for j in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * idx = i * n_interpolation_points_1d + j * out[idx, d] = fft_out_buffer[n_interpolation_points_1d + i, */ __pyx_t_13 = __pyx_v_n_interpolation_points_1d; __pyx_t_14 = __pyx_t_13; for (__pyx_t_15 = 0; __pyx_t_15 < __pyx_t_14; __pyx_t_15+=1) { __pyx_v_j = __pyx_t_15; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":128 * for i in range(n_interpolation_points_1d): * for j in range(n_interpolation_points_1d): * idx = i * n_interpolation_points_1d + j # <<<<<<<<<<<<<< * out[idx, d] = fft_out_buffer[n_interpolation_points_1d + i, * n_interpolation_points_1d + j].real */ __pyx_v_idx = ((__pyx_v_i * __pyx_v_n_interpolation_points_1d) + __pyx_v_j); /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":129 * for j in range(n_interpolation_points_1d): * idx = i * n_interpolation_points_1d + j * out[idx, d] = fft_out_buffer[n_interpolation_points_1d + i, # <<<<<<<<<<<<<< * n_interpolation_points_1d + j].real */ __pyx_t_17 = (__pyx_v_n_interpolation_points_1d + __pyx_v_i); __pyx_t_16 = (__pyx_v_n_interpolation_points_1d + __pyx_v_j); __pyx_t_1 = PyFloat_FromDouble((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_fft_out_buffer.data + __pyx_t_17 * __pyx_v_fft_out_buffer.strides[0]) )) + __pyx_t_16)) )))); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 129, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":130 * idx = i * n_interpolation_points_1d + j * out[idx, d] = fft_out_buffer[n_interpolation_points_1d + i, * n_interpolation_points_1d + j].real # <<<<<<<<<<<<<< */ __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_real); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 130, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_23 = __pyx_PyFloat_AsDouble(__pyx_t_3); if (unlikely((__pyx_t_23 == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 130, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":129 * for j in range(n_interpolation_points_1d): * idx = i * n_interpolation_points_1d + j * out[idx, d] = fft_out_buffer[n_interpolation_points_1d + i, # <<<<<<<<<<<<<< * n_interpolation_points_1d + j].real */ __pyx_t_16 = __pyx_v_idx; __pyx_t_17 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_out.data + __pyx_t_16 * __pyx_v_out.strides[0]) )) + __pyx_t_17)) )) = __pyx_t_23; } } } /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":70 * * * cdef void matrix_multiply_fft_2d( # <<<<<<<<<<<<<< * double[:, ::1] kernel_tilde, * double[:, ::1] w_coefficients, */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_6, 1); __Pyx_XDECREF(__pyx_t_20); __Pyx_XDECREF(__pyx_t_22); __Pyx_WriteUnraisable("openTSNE._matrix_mul.matrix_mul.matrix_multiply_fft_2d", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_fft_w_coefficients, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_fft_kernel_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_fft_in_buffer, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_fft_out_buffer, 1); __Pyx_RefNannyFinishContext(); } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":734 * ctypedef npy_cdouble complex_t * * cdef inline object PyArray_MultiIterNew1(a): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(1, a) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *__pyx_v_a) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew1", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":735 * * cdef inline object PyArray_MultiIterNew1(a): * return PyArray_MultiIterNew(1, a) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew2(a, b): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(1, ((void *)__pyx_v_a)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 735, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":734 * ctypedef npy_cdouble complex_t * * cdef inline object PyArray_MultiIterNew1(a): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(1, a) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew1", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":737 * return PyArray_MultiIterNew(1, a) * * cdef inline object PyArray_MultiIterNew2(a, b): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(2, a, b) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *__pyx_v_a, PyObject *__pyx_v_b) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew2", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":738 * * cdef inline object PyArray_MultiIterNew2(a, b): * return PyArray_MultiIterNew(2, a, b) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew3(a, b, c): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(2, ((void *)__pyx_v_a), ((void *)__pyx_v_b)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 738, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":737 * return PyArray_MultiIterNew(1, a) * * cdef inline object PyArray_MultiIterNew2(a, b): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(2, a, b) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew2", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":740 * return PyArray_MultiIterNew(2, a, b) * * cdef inline object PyArray_MultiIterNew3(a, b, c): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(3, a, b, c) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew3", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":741 * * cdef inline object PyArray_MultiIterNew3(a, b, c): * return PyArray_MultiIterNew(3, a, b, c) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(3, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 741, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":740 * return PyArray_MultiIterNew(2, a, b) * * cdef inline object PyArray_MultiIterNew3(a, b, c): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(3, a, b, c) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew3", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":743 * return PyArray_MultiIterNew(3, a, b, c) * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(4, a, b, c, d) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c, PyObject *__pyx_v_d) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew4", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":744 * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): * return PyArray_MultiIterNew(4, a, b, c, d) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(4, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 744, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":743 * return PyArray_MultiIterNew(3, a, b, c) * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(4, a, b, c, d) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew4", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":746 * return PyArray_MultiIterNew(4, a, b, c, d) * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(5, a, b, c, d, e) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c, PyObject *__pyx_v_d, PyObject *__pyx_v_e) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew5", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":747 * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): * return PyArray_MultiIterNew(5, a, b, c, d, e) # <<<<<<<<<<<<<< * * cdef inline tuple PyDataType_SHAPE(dtype d): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(5, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d), ((void *)__pyx_v_e)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 747, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":746 * return PyArray_MultiIterNew(4, a, b, c, d) * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(5, a, b, c, d, e) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew5", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":749 * return PyArray_MultiIterNew(5, a, b, c, d, e) * * cdef inline tuple PyDataType_SHAPE(dtype d): # <<<<<<<<<<<<<< * if PyDataType_HASSUBARRAY(d): * return d.subarray.shape */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyDataType_SHAPE(PyArray_Descr *__pyx_v_d) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("PyDataType_SHAPE", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":750 * * cdef inline tuple PyDataType_SHAPE(dtype d): * if PyDataType_HASSUBARRAY(d): # <<<<<<<<<<<<<< * return d.subarray.shape * else: */ __pyx_t_1 = (PyDataType_HASSUBARRAY(__pyx_v_d) != 0); if (__pyx_t_1) { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":751 * cdef inline tuple PyDataType_SHAPE(dtype d): * if PyDataType_HASSUBARRAY(d): * return d.subarray.shape # <<<<<<<<<<<<<< * else: * return () */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject*)__pyx_v_d->subarray->shape)); __pyx_r = ((PyObject*)__pyx_v_d->subarray->shape); goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":750 * * cdef inline tuple PyDataType_SHAPE(dtype d): * if PyDataType_HASSUBARRAY(d): # <<<<<<<<<<<<<< * return d.subarray.shape * else: */ } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":753 * return d.subarray.shape * else: * return () # <<<<<<<<<<<<<< * * */ /*else*/ { __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_empty_tuple); __pyx_r = __pyx_empty_tuple; goto __pyx_L0; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":749 * return PyArray_MultiIterNew(5, a, b, c, d, e) * * cdef inline tuple PyDataType_SHAPE(dtype d): # <<<<<<<<<<<<<< * if PyDataType_HASSUBARRAY(d): * return d.subarray.shape */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":868 * int _import_umath() except -1 * * cdef inline void set_array_base(ndarray arr, object base): # <<<<<<<<<<<<<< * Py_INCREF(base) # important to do this before stealing the reference below! * PyArray_SetBaseObject(arr, base) */ static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *__pyx_v_arr, PyObject *__pyx_v_base) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("set_array_base", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":869 * * cdef inline void set_array_base(ndarray arr, object base): * Py_INCREF(base) # important to do this before stealing the reference below! # <<<<<<<<<<<<<< * PyArray_SetBaseObject(arr, base) * */ Py_INCREF(__pyx_v_base); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":870 * cdef inline void set_array_base(ndarray arr, object base): * Py_INCREF(base) # important to do this before stealing the reference below! * PyArray_SetBaseObject(arr, base) # <<<<<<<<<<<<<< * * cdef inline object get_array_base(ndarray arr): */ (void)(PyArray_SetBaseObject(__pyx_v_arr, __pyx_v_base)); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":868 * int _import_umath() except -1 * * cdef inline void set_array_base(ndarray arr, object base): # <<<<<<<<<<<<<< * Py_INCREF(base) # important to do this before stealing the reference below! * PyArray_SetBaseObject(arr, base) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":872 * PyArray_SetBaseObject(arr, base) * * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< * base = PyArray_BASE(arr) * if base is NULL: */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__pyx_v_arr) { PyObject *__pyx_v_base; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("get_array_base", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":873 * * cdef inline object get_array_base(ndarray arr): * base = PyArray_BASE(arr) # <<<<<<<<<<<<<< * if base is NULL: * return None */ __pyx_v_base = PyArray_BASE(__pyx_v_arr); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":874 * cdef inline object get_array_base(ndarray arr): * base = PyArray_BASE(arr) * if base is NULL: # <<<<<<<<<<<<<< * return None * return base */ __pyx_t_1 = ((__pyx_v_base == NULL) != 0); if (__pyx_t_1) { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":875 * base = PyArray_BASE(arr) * if base is NULL: * return None # <<<<<<<<<<<<<< * return base * */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":874 * cdef inline object get_array_base(ndarray arr): * base = PyArray_BASE(arr) * if base is NULL: # <<<<<<<<<<<<<< * return None * return base */ } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":876 * if base is NULL: * return None * return base # <<<<<<<<<<<<<< * * # Versions of the import_* functions which are more suitable for */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_base)); __pyx_r = ((PyObject *)__pyx_v_base); goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":872 * PyArray_SetBaseObject(arr, base) * * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< * base = PyArray_BASE(arr) * if base is NULL: */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":880 * # Versions of the import_* functions which are more suitable for * # Cython code. * cdef inline int import_array() except -1: # <<<<<<<<<<<<<< * try: * __pyx_import_array() */ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("import_array", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":881 * # Cython code. * cdef inline int import_array() except -1: * try: # <<<<<<<<<<<<<< * __pyx_import_array() * except Exception: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_1, &__pyx_t_2, &__pyx_t_3); __Pyx_XGOTREF(__pyx_t_1); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); /*try:*/ { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":882 * cdef inline int import_array() except -1: * try: * __pyx_import_array() # <<<<<<<<<<<<<< * except Exception: * raise ImportError("numpy.core.multiarray failed to import") */ __pyx_t_4 = _import_array(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 882, __pyx_L3_error) /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":881 * # Cython code. * cdef inline int import_array() except -1: * try: # <<<<<<<<<<<<<< * __pyx_import_array() * except Exception: */ } __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L8_try_end; __pyx_L3_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":883 * try: * __pyx_import_array() * except Exception: # <<<<<<<<<<<<<< * raise ImportError("numpy.core.multiarray failed to import") * */ __pyx_t_4 = __Pyx_PyErr_ExceptionMatches(((PyObject *)(&((PyTypeObject*)PyExc_Exception)[0]))); if (__pyx_t_4) { __Pyx_AddTraceback("numpy.import_array", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(1, 883, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_6); __Pyx_GOTREF(__pyx_t_7); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":884 * __pyx_import_array() * except Exception: * raise ImportError("numpy.core.multiarray failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_umath() except -1: */ __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ImportError, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 884, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_Raise(__pyx_t_8, 0, 0, 0); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __PYX_ERR(1, 884, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":881 * # Cython code. * cdef inline int import_array() except -1: * try: # <<<<<<<<<<<<<< * __pyx_import_array() * except Exception: */ __Pyx_XGIVEREF(__pyx_t_1); __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_ExceptionReset(__pyx_t_1, __pyx_t_2, __pyx_t_3); goto __pyx_L1_error; __pyx_L8_try_end:; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":880 * # Versions of the import_* functions which are more suitable for * # Cython code. * cdef inline int import_array() except -1: # <<<<<<<<<<<<<< * try: * __pyx_import_array() */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("numpy.import_array", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":886 * raise ImportError("numpy.core.multiarray failed to import") * * cdef inline int import_umath() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("import_umath", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":887 * * cdef inline int import_umath() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_1, &__pyx_t_2, &__pyx_t_3); __Pyx_XGOTREF(__pyx_t_1); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); /*try:*/ { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":888 * cdef inline int import_umath() except -1: * try: * _import_umath() # <<<<<<<<<<<<<< * except Exception: * raise ImportError("numpy.core.umath failed to import") */ __pyx_t_4 = _import_umath(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 888, __pyx_L3_error) /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":887 * * cdef inline int import_umath() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ } __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L8_try_end; __pyx_L3_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":889 * try: * _import_umath() * except Exception: # <<<<<<<<<<<<<< * raise ImportError("numpy.core.umath failed to import") * */ __pyx_t_4 = __Pyx_PyErr_ExceptionMatches(((PyObject *)(&((PyTypeObject*)PyExc_Exception)[0]))); if (__pyx_t_4) { __Pyx_AddTraceback("numpy.import_umath", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(1, 889, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_6); __Pyx_GOTREF(__pyx_t_7); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":890 * _import_umath() * except Exception: * raise ImportError("numpy.core.umath failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_ufunc() except -1: */ __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ImportError, __pyx_tuple__2, NULL); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 890, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_Raise(__pyx_t_8, 0, 0, 0); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __PYX_ERR(1, 890, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":887 * * cdef inline int import_umath() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ __Pyx_XGIVEREF(__pyx_t_1); __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_ExceptionReset(__pyx_t_1, __pyx_t_2, __pyx_t_3); goto __pyx_L1_error; __pyx_L8_try_end:; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":886 * raise ImportError("numpy.core.multiarray failed to import") * * cdef inline int import_umath() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("numpy.import_umath", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":892 * raise ImportError("numpy.core.umath failed to import") * * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("import_ufunc", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":893 * * cdef inline int import_ufunc() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_1, &__pyx_t_2, &__pyx_t_3); __Pyx_XGOTREF(__pyx_t_1); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); /*try:*/ { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":894 * cdef inline int import_ufunc() except -1: * try: * _import_umath() # <<<<<<<<<<<<<< * except Exception: * raise ImportError("numpy.core.umath failed to import") */ __pyx_t_4 = _import_umath(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 894, __pyx_L3_error) /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":893 * * cdef inline int import_ufunc() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ } __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L8_try_end; __pyx_L3_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":895 * try: * _import_umath() * except Exception: # <<<<<<<<<<<<<< * raise ImportError("numpy.core.umath failed to import") * */ __pyx_t_4 = __Pyx_PyErr_ExceptionMatches(((PyObject *)(&((PyTypeObject*)PyExc_Exception)[0]))); if (__pyx_t_4) { __Pyx_AddTraceback("numpy.import_ufunc", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(1, 895, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_6); __Pyx_GOTREF(__pyx_t_7); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":896 * _import_umath() * except Exception: * raise ImportError("numpy.core.umath failed to import") # <<<<<<<<<<<<<< * * cdef extern from *: */ __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ImportError, __pyx_tuple__2, NULL); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 896, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_Raise(__pyx_t_8, 0, 0, 0); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __PYX_ERR(1, 896, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":893 * * cdef inline int import_ufunc() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ __Pyx_XGIVEREF(__pyx_t_1); __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_ExceptionReset(__pyx_t_1, __pyx_t_2, __pyx_t_3); goto __pyx_L1_error; __pyx_L8_try_end:; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":892 * raise ImportError("numpy.core.umath failed to import") * * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("numpy.import_ufunc", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* Python wrapper */ static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_shape = 0; Py_ssize_t __pyx_v_itemsize; PyObject *__pyx_v_format = 0; PyObject *__pyx_v_mode = 0; int __pyx_v_allocate_buffer; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_shape,&__pyx_n_s_itemsize,&__pyx_n_s_format,&__pyx_n_s_mode,&__pyx_n_s_allocate_buffer,0}; PyObject* values[5] = {0,0,0,0,0}; values[3] = ((PyObject *)__pyx_n_s_c); if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_shape)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_itemsize)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 1); __PYX_ERR(2, 122, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_format)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 2); __PYX_ERR(2, 122, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 3: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_mode); if (value) { values[3] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 4: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_allocate_buffer); if (value) { values[4] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) __PYX_ERR(2, 122, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_shape = ((PyObject*)values[0]); __pyx_v_itemsize = __Pyx_PyIndex_AsSsize_t(values[1]); if (unlikely((__pyx_v_itemsize == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 122, __pyx_L3_error) __pyx_v_format = values[2]; __pyx_v_mode = values[3]; if (values[4]) { __pyx_v_allocate_buffer = __Pyx_PyObject_IsTrue(values[4]); if (unlikely((__pyx_v_allocate_buffer == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 123, __pyx_L3_error) } else { /* "View.MemoryView":123 * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, * mode="c", bint allocate_buffer=True): # <<<<<<<<<<<<<< * * cdef int idx */ __pyx_v_allocate_buffer = ((int)1); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 122, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_shape), (&PyTuple_Type), 1, "shape", 1))) __PYX_ERR(2, 122, __pyx_L1_error) if (unlikely(((PyObject *)__pyx_v_format) == Py_None)) { PyErr_Format(PyExc_TypeError, "Argument '%.200s' must not be None", "format"); __PYX_ERR(2, 122, __pyx_L1_error) } __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(((struct __pyx_array_obj *)__pyx_v_self), __pyx_v_shape, __pyx_v_itemsize, __pyx_v_format, __pyx_v_mode, __pyx_v_allocate_buffer); /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer) { int __pyx_v_idx; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_dim; PyObject **__pyx_v_p; char __pyx_v_order; int __pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; char *__pyx_t_7; int __pyx_t_8; Py_ssize_t __pyx_t_9; PyObject *__pyx_t_10 = NULL; Py_ssize_t __pyx_t_11; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__cinit__", 0); __Pyx_INCREF(__pyx_v_format); /* "View.MemoryView":129 * cdef PyObject **p * * self.ndim = len(shape) # <<<<<<<<<<<<<< * self.itemsize = itemsize * */ if (unlikely(__pyx_v_shape == Py_None)) { PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); __PYX_ERR(2, 129, __pyx_L1_error) } __pyx_t_1 = PyTuple_GET_SIZE(__pyx_v_shape); if (unlikely(__pyx_t_1 == ((Py_ssize_t)-1))) __PYX_ERR(2, 129, __pyx_L1_error) __pyx_v_self->ndim = ((int)__pyx_t_1); /* "View.MemoryView":130 * * self.ndim = len(shape) * self.itemsize = itemsize # <<<<<<<<<<<<<< * * if not self.ndim: */ __pyx_v_self->itemsize = __pyx_v_itemsize; /* "View.MemoryView":132 * self.itemsize = itemsize * * if not self.ndim: # <<<<<<<<<<<<<< * raise ValueError("Empty shape tuple for cython.array") * */ __pyx_t_2 = ((!(__pyx_v_self->ndim != 0)) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":133 * * if not self.ndim: * raise ValueError("Empty shape tuple for cython.array") # <<<<<<<<<<<<<< * * if itemsize <= 0: */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 133, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 133, __pyx_L1_error) /* "View.MemoryView":132 * self.itemsize = itemsize * * if not self.ndim: # <<<<<<<<<<<<<< * raise ValueError("Empty shape tuple for cython.array") * */ } /* "View.MemoryView":135 * raise ValueError("Empty shape tuple for cython.array") * * if itemsize <= 0: # <<<<<<<<<<<<<< * raise ValueError("itemsize <= 0 for cython.array") * */ __pyx_t_2 = ((__pyx_v_itemsize <= 0) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":136 * * if itemsize <= 0: * raise ValueError("itemsize <= 0 for cython.array") # <<<<<<<<<<<<<< * * if not isinstance(format, bytes): */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__4, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 136, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 136, __pyx_L1_error) /* "View.MemoryView":135 * raise ValueError("Empty shape tuple for cython.array") * * if itemsize <= 0: # <<<<<<<<<<<<<< * raise ValueError("itemsize <= 0 for cython.array") * */ } /* "View.MemoryView":138 * raise ValueError("itemsize <= 0 for cython.array") * * if not isinstance(format, bytes): # <<<<<<<<<<<<<< * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string */ __pyx_t_2 = PyBytes_Check(__pyx_v_format); __pyx_t_4 = ((!(__pyx_t_2 != 0)) != 0); if (__pyx_t_4) { /* "View.MemoryView":139 * * if not isinstance(format, bytes): * format = format.encode('ASCII') # <<<<<<<<<<<<<< * self._format = format # keep a reference to the byte string * self.format = self._format */ __pyx_t_5 = __Pyx_PyObject_GetAttrStr(__pyx_v_format, __pyx_n_s_encode); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 139, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_5))) { __pyx_t_6 = PyMethod_GET_SELF(__pyx_t_5); if (likely(__pyx_t_6)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_5); __Pyx_INCREF(__pyx_t_6); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_5, function); } } __pyx_t_3 = (__pyx_t_6) ? __Pyx_PyObject_Call2Args(__pyx_t_5, __pyx_t_6, __pyx_n_s_ASCII) : __Pyx_PyObject_CallOneArg(__pyx_t_5, __pyx_n_s_ASCII); __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 139, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF_SET(__pyx_v_format, __pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":138 * raise ValueError("itemsize <= 0 for cython.array") * * if not isinstance(format, bytes): # <<<<<<<<<<<<<< * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string */ } /* "View.MemoryView":140 * if not isinstance(format, bytes): * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string # <<<<<<<<<<<<<< * self.format = self._format * */ if (!(likely(PyBytes_CheckExact(__pyx_v_format))||((__pyx_v_format) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_v_format)->tp_name), 0))) __PYX_ERR(2, 140, __pyx_L1_error) __pyx_t_3 = __pyx_v_format; __Pyx_INCREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_3); __Pyx_GOTREF(__pyx_v_self->_format); __Pyx_DECREF(__pyx_v_self->_format); __pyx_v_self->_format = ((PyObject*)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":141 * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string * self.format = self._format # <<<<<<<<<<<<<< * * */ if (unlikely(__pyx_v_self->_format == Py_None)) { PyErr_SetString(PyExc_TypeError, "expected bytes, NoneType found"); __PYX_ERR(2, 141, __pyx_L1_error) } __pyx_t_7 = __Pyx_PyBytes_AsWritableString(__pyx_v_self->_format); if (unlikely((!__pyx_t_7) && PyErr_Occurred())) __PYX_ERR(2, 141, __pyx_L1_error) __pyx_v_self->format = __pyx_t_7; /* "View.MemoryView":144 * * * self._shape = PyObject_Malloc(sizeof(Py_ssize_t)*self.ndim*2) # <<<<<<<<<<<<<< * self._strides = self._shape + self.ndim * */ __pyx_v_self->_shape = ((Py_ssize_t *)PyObject_Malloc((((sizeof(Py_ssize_t)) * __pyx_v_self->ndim) * 2))); /* "View.MemoryView":145 * * self._shape = PyObject_Malloc(sizeof(Py_ssize_t)*self.ndim*2) * self._strides = self._shape + self.ndim # <<<<<<<<<<<<<< * * if not self._shape: */ __pyx_v_self->_strides = (__pyx_v_self->_shape + __pyx_v_self->ndim); /* "View.MemoryView":147 * self._strides = self._shape + self.ndim * * if not self._shape: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate shape and strides.") * */ __pyx_t_4 = ((!(__pyx_v_self->_shape != 0)) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":148 * * if not self._shape: * raise MemoryError("unable to allocate shape and strides.") # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__5, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 148, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 148, __pyx_L1_error) /* "View.MemoryView":147 * self._strides = self._shape + self.ndim * * if not self._shape: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate shape and strides.") * */ } /* "View.MemoryView":151 * * * for idx, dim in enumerate(shape): # <<<<<<<<<<<<<< * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) */ __pyx_t_8 = 0; __pyx_t_3 = __pyx_v_shape; __Pyx_INCREF(__pyx_t_3); __pyx_t_1 = 0; for (;;) { if (__pyx_t_1 >= PyTuple_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_1); __Pyx_INCREF(__pyx_t_5); __pyx_t_1++; if (unlikely(0 < 0)) __PYX_ERR(2, 151, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_3, __pyx_t_1); __pyx_t_1++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 151, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_5); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 151, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_dim = __pyx_t_9; __pyx_v_idx = __pyx_t_8; __pyx_t_8 = (__pyx_t_8 + 1); /* "View.MemoryView":152 * * for idx, dim in enumerate(shape): * if dim <= 0: # <<<<<<<<<<<<<< * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim */ __pyx_t_4 = ((__pyx_v_dim <= 0) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":153 * for idx, dim in enumerate(shape): * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) # <<<<<<<<<<<<<< * self._shape[idx] = dim * */ __pyx_t_5 = __Pyx_PyInt_From_int(__pyx_v_idx); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_10 = PyTuple_New(2); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_10, 1, __pyx_t_6); __pyx_t_5 = 0; __pyx_t_6 = 0; __pyx_t_6 = __Pyx_PyString_Format(__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_t_10); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_6); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(2, 153, __pyx_L1_error) /* "View.MemoryView":152 * * for idx, dim in enumerate(shape): * if dim <= 0: # <<<<<<<<<<<<<< * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim */ } /* "View.MemoryView":154 * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim # <<<<<<<<<<<<<< * * cdef char order */ (__pyx_v_self->_shape[__pyx_v_idx]) = __pyx_v_dim; /* "View.MemoryView":151 * * * for idx, dim in enumerate(shape): # <<<<<<<<<<<<<< * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) */ } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":157 * * cdef char order * if mode == 'fortran': # <<<<<<<<<<<<<< * order = b'F' * self.mode = u'fortran' */ __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_fortran, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) __PYX_ERR(2, 157, __pyx_L1_error) if (__pyx_t_4) { /* "View.MemoryView":158 * cdef char order * if mode == 'fortran': * order = b'F' # <<<<<<<<<<<<<< * self.mode = u'fortran' * elif mode == 'c': */ __pyx_v_order = 'F'; /* "View.MemoryView":159 * if mode == 'fortran': * order = b'F' * self.mode = u'fortran' # <<<<<<<<<<<<<< * elif mode == 'c': * order = b'C' */ __Pyx_INCREF(__pyx_n_u_fortran); __Pyx_GIVEREF(__pyx_n_u_fortran); __Pyx_GOTREF(__pyx_v_self->mode); __Pyx_DECREF(__pyx_v_self->mode); __pyx_v_self->mode = __pyx_n_u_fortran; /* "View.MemoryView":157 * * cdef char order * if mode == 'fortran': # <<<<<<<<<<<<<< * order = b'F' * self.mode = u'fortran' */ goto __pyx_L10; } /* "View.MemoryView":160 * order = b'F' * self.mode = u'fortran' * elif mode == 'c': # <<<<<<<<<<<<<< * order = b'C' * self.mode = u'c' */ __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_c, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) __PYX_ERR(2, 160, __pyx_L1_error) if (likely(__pyx_t_4)) { /* "View.MemoryView":161 * self.mode = u'fortran' * elif mode == 'c': * order = b'C' # <<<<<<<<<<<<<< * self.mode = u'c' * else: */ __pyx_v_order = 'C'; /* "View.MemoryView":162 * elif mode == 'c': * order = b'C' * self.mode = u'c' # <<<<<<<<<<<<<< * else: * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) */ __Pyx_INCREF(__pyx_n_u_c); __Pyx_GIVEREF(__pyx_n_u_c); __Pyx_GOTREF(__pyx_v_self->mode); __Pyx_DECREF(__pyx_v_self->mode); __pyx_v_self->mode = __pyx_n_u_c; /* "View.MemoryView":160 * order = b'F' * self.mode = u'fortran' * elif mode == 'c': # <<<<<<<<<<<<<< * order = b'C' * self.mode = u'c' */ goto __pyx_L10; } /* "View.MemoryView":164 * self.mode = u'c' * else: * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) # <<<<<<<<<<<<<< * * self.len = fill_contig_strides_array(self._shape, self._strides, */ /*else*/ { __pyx_t_3 = __Pyx_PyString_FormatSafe(__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_v_mode); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 164, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_10 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_3); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 164, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(2, 164, __pyx_L1_error) } __pyx_L10:; /* "View.MemoryView":166 * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) * * self.len = fill_contig_strides_array(self._shape, self._strides, # <<<<<<<<<<<<<< * itemsize, self.ndim, order) * */ __pyx_v_self->len = __pyx_fill_contig_strides_array(__pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_itemsize, __pyx_v_self->ndim, __pyx_v_order); /* "View.MemoryView":169 * itemsize, self.ndim, order) * * self.free_data = allocate_buffer # <<<<<<<<<<<<<< * self.dtype_is_object = format == b'O' * if allocate_buffer: */ __pyx_v_self->free_data = __pyx_v_allocate_buffer; /* "View.MemoryView":170 * * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' # <<<<<<<<<<<<<< * if allocate_buffer: * */ __pyx_t_10 = PyObject_RichCompare(__pyx_v_format, __pyx_n_b_O, Py_EQ); __Pyx_XGOTREF(__pyx_t_10); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 170, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_IsTrue(__pyx_t_10); if (unlikely((__pyx_t_4 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 170, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_self->dtype_is_object = __pyx_t_4; /* "View.MemoryView":171 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< * * */ __pyx_t_4 = (__pyx_v_allocate_buffer != 0); if (__pyx_t_4) { /* "View.MemoryView":174 * * * self.data = malloc(self.len) # <<<<<<<<<<<<<< * if not self.data: * raise MemoryError("unable to allocate array data.") */ __pyx_v_self->data = ((char *)malloc(__pyx_v_self->len)); /* "View.MemoryView":175 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate array data.") * */ __pyx_t_4 = ((!(__pyx_v_self->data != 0)) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":176 * self.data = malloc(self.len) * if not self.data: * raise MemoryError("unable to allocate array data.") # <<<<<<<<<<<<<< * * if self.dtype_is_object: */ __pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__6, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 176, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(2, 176, __pyx_L1_error) /* "View.MemoryView":175 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate array data.") * */ } /* "View.MemoryView":178 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< * p = self.data * for i in range(self.len / itemsize): */ __pyx_t_4 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_4) { /* "View.MemoryView":179 * * if self.dtype_is_object: * p = self.data # <<<<<<<<<<<<<< * for i in range(self.len / itemsize): * p[i] = Py_None */ __pyx_v_p = ((PyObject **)__pyx_v_self->data); /* "View.MemoryView":180 * if self.dtype_is_object: * p = self.data * for i in range(self.len / itemsize): # <<<<<<<<<<<<<< * p[i] = Py_None * Py_INCREF(Py_None) */ if (unlikely(__pyx_v_itemsize == 0)) { PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); __PYX_ERR(2, 180, __pyx_L1_error) } else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1) && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_self->len))) { PyErr_SetString(PyExc_OverflowError, "value too large to perform division"); __PYX_ERR(2, 180, __pyx_L1_error) } __pyx_t_1 = (__pyx_v_self->len / __pyx_v_itemsize); __pyx_t_9 = __pyx_t_1; for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_9; __pyx_t_11+=1) { __pyx_v_i = __pyx_t_11; /* "View.MemoryView":181 * p = self.data * for i in range(self.len / itemsize): * p[i] = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ (__pyx_v_p[__pyx_v_i]) = Py_None; /* "View.MemoryView":182 * for i in range(self.len / itemsize): * p[i] = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * @cname('getbuffer') */ Py_INCREF(Py_None); } /* "View.MemoryView":178 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< * p = self.data * for i in range(self.len / itemsize): */ } /* "View.MemoryView":171 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_10); __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_XDECREF(__pyx_v_format); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":185 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * cdef int bufmode = -1 * if self.mode == u"c": */ /* Python wrapper */ static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(((struct __pyx_array_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_v_bufmode; int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; char *__pyx_t_4; Py_ssize_t __pyx_t_5; int __pyx_t_6; Py_ssize_t *__pyx_t_7; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; if (__pyx_v_info == NULL) { PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete"); return -1; } __Pyx_RefNannySetupContext("__getbuffer__", 0); __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None); __Pyx_GIVEREF(__pyx_v_info->obj); /* "View.MemoryView":186 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 # <<<<<<<<<<<<<< * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS */ __pyx_v_bufmode = -1; /* "View.MemoryView":187 * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 * if self.mode == u"c": # <<<<<<<<<<<<<< * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": */ __pyx_t_1 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_c, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 187, __pyx_L1_error) __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":188 * cdef int bufmode = -1 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS # <<<<<<<<<<<<<< * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS */ __pyx_v_bufmode = (PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS); /* "View.MemoryView":187 * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 * if self.mode == u"c": # <<<<<<<<<<<<<< * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": */ goto __pyx_L3; } /* "View.MemoryView":189 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": # <<<<<<<<<<<<<< * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): */ __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_fortran, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(2, 189, __pyx_L1_error) __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":190 * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS # <<<<<<<<<<<<<< * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") */ __pyx_v_bufmode = (PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS); /* "View.MemoryView":189 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": # <<<<<<<<<<<<<< * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): */ } __pyx_L3:; /* "View.MemoryView":191 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data */ __pyx_t_1 = ((!((__pyx_v_flags & __pyx_v_bufmode) != 0)) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":192 * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") # <<<<<<<<<<<<<< * info.buf = self.data * info.len = self.len */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__7, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 192, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 192, __pyx_L1_error) /* "View.MemoryView":191 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data */ } /* "View.MemoryView":193 * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data # <<<<<<<<<<<<<< * info.len = self.len * info.ndim = self.ndim */ __pyx_t_4 = __pyx_v_self->data; __pyx_v_info->buf = __pyx_t_4; /* "View.MemoryView":194 * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data * info.len = self.len # <<<<<<<<<<<<<< * info.ndim = self.ndim * info.shape = self._shape */ __pyx_t_5 = __pyx_v_self->len; __pyx_v_info->len = __pyx_t_5; /* "View.MemoryView":195 * info.buf = self.data * info.len = self.len * info.ndim = self.ndim # <<<<<<<<<<<<<< * info.shape = self._shape * info.strides = self._strides */ __pyx_t_6 = __pyx_v_self->ndim; __pyx_v_info->ndim = __pyx_t_6; /* "View.MemoryView":196 * info.len = self.len * info.ndim = self.ndim * info.shape = self._shape # <<<<<<<<<<<<<< * info.strides = self._strides * info.suboffsets = NULL */ __pyx_t_7 = __pyx_v_self->_shape; __pyx_v_info->shape = __pyx_t_7; /* "View.MemoryView":197 * info.ndim = self.ndim * info.shape = self._shape * info.strides = self._strides # <<<<<<<<<<<<<< * info.suboffsets = NULL * info.itemsize = self.itemsize */ __pyx_t_7 = __pyx_v_self->_strides; __pyx_v_info->strides = __pyx_t_7; /* "View.MemoryView":198 * info.shape = self._shape * info.strides = self._strides * info.suboffsets = NULL # <<<<<<<<<<<<<< * info.itemsize = self.itemsize * info.readonly = 0 */ __pyx_v_info->suboffsets = NULL; /* "View.MemoryView":199 * info.strides = self._strides * info.suboffsets = NULL * info.itemsize = self.itemsize # <<<<<<<<<<<<<< * info.readonly = 0 * */ __pyx_t_5 = __pyx_v_self->itemsize; __pyx_v_info->itemsize = __pyx_t_5; /* "View.MemoryView":200 * info.suboffsets = NULL * info.itemsize = self.itemsize * info.readonly = 0 # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ __pyx_v_info->readonly = 0; /* "View.MemoryView":202 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.format * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":203 * * if flags & PyBUF_FORMAT: * info.format = self.format # <<<<<<<<<<<<<< * else: * info.format = NULL */ __pyx_t_4 = __pyx_v_self->format; __pyx_v_info->format = __pyx_t_4; /* "View.MemoryView":202 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.format * else: */ goto __pyx_L5; } /* "View.MemoryView":205 * info.format = self.format * else: * info.format = NULL # <<<<<<<<<<<<<< * * info.obj = self */ /*else*/ { __pyx_v_info->format = NULL; } __pyx_L5:; /* "View.MemoryView":207 * info.format = NULL * * info.obj = self # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = ((PyObject *)__pyx_v_self); /* "View.MemoryView":185 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * cdef int bufmode = -1 * if self.mode == u"c": */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.array.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; if (__pyx_v_info->obj != NULL) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } goto __pyx_L2; __pyx_L0:; if (__pyx_v_info->obj == Py_None) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } __pyx_L2:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":211 * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") * * def __dealloc__(array self): # <<<<<<<<<<<<<< * if self.callback_free_data != NULL: * self.callback_free_data(self.data) */ /* Python wrapper */ static void __pyx_array___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_array___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self) { __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":212 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< * self.callback_free_data(self.data) * elif self.free_data: */ __pyx_t_1 = ((__pyx_v_self->callback_free_data != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":213 * def __dealloc__(array self): * if self.callback_free_data != NULL: * self.callback_free_data(self.data) # <<<<<<<<<<<<<< * elif self.free_data: * if self.dtype_is_object: */ __pyx_v_self->callback_free_data(__pyx_v_self->data); /* "View.MemoryView":212 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< * self.callback_free_data(self.data) * elif self.free_data: */ goto __pyx_L3; } /* "View.MemoryView":214 * if self.callback_free_data != NULL: * self.callback_free_data(self.data) * elif self.free_data: # <<<<<<<<<<<<<< * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, */ __pyx_t_1 = (__pyx_v_self->free_data != 0); if (__pyx_t_1) { /* "View.MemoryView":215 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) */ __pyx_t_1 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_1) { /* "View.MemoryView":216 * elif self.free_data: * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, # <<<<<<<<<<<<<< * self._strides, self.ndim, False) * free(self.data) */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_self->data, __pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_self->ndim, 0); /* "View.MemoryView":215 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) */ } /* "View.MemoryView":218 * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) * free(self.data) # <<<<<<<<<<<<<< * PyObject_Free(self._shape) * */ free(__pyx_v_self->data); /* "View.MemoryView":214 * if self.callback_free_data != NULL: * self.callback_free_data(self.data) * elif self.free_data: # <<<<<<<<<<<<<< * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, */ } __pyx_L3:; /* "View.MemoryView":219 * self._strides, self.ndim, False) * free(self.data) * PyObject_Free(self._shape) # <<<<<<<<<<<<<< * * @property */ PyObject_Free(__pyx_v_self->_shape); /* "View.MemoryView":211 * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") * * def __dealloc__(array self): # <<<<<<<<<<<<<< * if self.callback_free_data != NULL: * self.callback_free_data(self.data) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":222 * * @property * def memview(self): # <<<<<<<<<<<<<< * return self.get_memview() * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_5array_7memview___get__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":223 * @property * def memview(self): * return self.get_memview() # <<<<<<<<<<<<<< * * @cname('get_memview') */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = ((struct __pyx_vtabstruct_array *)__pyx_v_self->__pyx_vtab)->get_memview(__pyx_v_self); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 223, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":222 * * @property * def memview(self): # <<<<<<<<<<<<<< * return self.get_memview() * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.memview.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":226 * * @cname('get_memview') * cdef get_memview(self): # <<<<<<<<<<<<<< * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) */ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self) { int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("get_memview", 0); /* "View.MemoryView":227 * @cname('get_memview') * cdef get_memview(self): * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE # <<<<<<<<<<<<<< * return memoryview(self, flags, self.dtype_is_object) * */ __pyx_v_flags = ((PyBUF_ANY_CONTIGUOUS | PyBUF_FORMAT) | PyBUF_WRITABLE); /* "View.MemoryView":228 * cdef get_memview(self): * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) # <<<<<<<<<<<<<< * * def __len__(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":226 * * @cname('get_memview') * cdef get_memview(self): # <<<<<<<<<<<<<< * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.array.get_memview", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":230 * return memoryview(self, flags, self.dtype_is_object) * * def __len__(self): # <<<<<<<<<<<<<< * return self._shape[0] * */ /* Python wrapper */ static Py_ssize_t __pyx_array___len__(PyObject *__pyx_v_self); /*proto*/ static Py_ssize_t __pyx_array___len__(PyObject *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__", 0); /* "View.MemoryView":231 * * def __len__(self): * return self._shape[0] # <<<<<<<<<<<<<< * * def __getattr__(self, attr): */ __pyx_r = (__pyx_v_self->_shape[0]); goto __pyx_L0; /* "View.MemoryView":230 * return memoryview(self, flags, self.dtype_is_object) * * def __len__(self): # <<<<<<<<<<<<<< * return self._shape[0] * */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":233 * return self._shape[0] * * def __getattr__(self, attr): # <<<<<<<<<<<<<< * return getattr(self.memview, attr) * */ /* Python wrapper */ static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr); /*proto*/ static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getattr__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_attr)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__getattr__", 0); /* "View.MemoryView":234 * * def __getattr__(self, attr): * return getattr(self.memview, attr) # <<<<<<<<<<<<<< * * def __getitem__(self, item): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 234, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_GetAttr(__pyx_t_1, __pyx_v_attr); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 234, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":233 * return self._shape[0] * * def __getattr__(self, attr): # <<<<<<<<<<<<<< * return getattr(self.memview, attr) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.array.__getattr__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":236 * return getattr(self.memview, attr) * * def __getitem__(self, item): # <<<<<<<<<<<<<< * return self.memview[item] * */ /* Python wrapper */ static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item); /*proto*/ static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__getitem__", 0); /* "View.MemoryView":237 * * def __getitem__(self, item): * return self.memview[item] # <<<<<<<<<<<<<< * * def __setitem__(self, item, value): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 237, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetItem(__pyx_t_1, __pyx_v_item); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 237, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":236 * return getattr(self.memview, attr) * * def __getitem__(self, item): # <<<<<<<<<<<<<< * return self.memview[item] * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.array.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":239 * return self.memview[item] * * def __setitem__(self, item, value): # <<<<<<<<<<<<<< * self.memview[item] = value * */ /* Python wrapper */ static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /*proto*/ static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item), ((PyObject *)__pyx_v_value)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setitem__", 0); /* "View.MemoryView":240 * * def __setitem__(self, item, value): * self.memview[item] = value # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 240, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (unlikely(PyObject_SetItem(__pyx_t_1, __pyx_v_item, __pyx_v_value) < 0)) __PYX_ERR(2, 240, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":239 * return self.memview[item] * * def __setitem__(self, item, value): # <<<<<<<<<<<<<< * self.memview[item] = value * */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_array_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_array_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_array___reduce_cython__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__8, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_array_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_array_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_array_2__setstate_cython__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__9, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":244 * * @cname("__pyx_array_new") * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format, # <<<<<<<<<<<<<< * char *mode, char *buf): * cdef array result */ static struct __pyx_array_obj *__pyx_array_new(PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, char *__pyx_v_format, char *__pyx_v_mode, char *__pyx_v_buf) { struct __pyx_array_obj *__pyx_v_result = 0; struct __pyx_array_obj *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("array_cwrapper", 0); /* "View.MemoryView":248 * cdef array result * * if buf == NULL: # <<<<<<<<<<<<<< * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: */ __pyx_t_1 = ((__pyx_v_buf == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":249 * * if buf == NULL: * result = array(shape, itemsize, format, mode.decode('ASCII')) # <<<<<<<<<<<<<< * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), */ __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PyTuple_New(4); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_INCREF(__pyx_v_shape); __Pyx_GIVEREF(__pyx_v_shape); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_v_shape); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_5, 2, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 3, __pyx_t_4); __pyx_t_2 = 0; __pyx_t_3 = 0; __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_Call(((PyObject *)__pyx_array_type), __pyx_t_5, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":248 * cdef array result * * if buf == NULL: # <<<<<<<<<<<<<< * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: */ goto __pyx_L3; } /* "View.MemoryView":251 * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), # <<<<<<<<<<<<<< * allocate_buffer=False) * result.data = buf */ /*else*/ { __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_3 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_2 = PyTuple_New(4); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_v_shape); __Pyx_GIVEREF(__pyx_v_shape); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_shape); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_4); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_2, 2, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_2, 3, __pyx_t_3); __pyx_t_4 = 0; __pyx_t_5 = 0; __pyx_t_3 = 0; /* "View.MemoryView":252 * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), * allocate_buffer=False) # <<<<<<<<<<<<<< * result.data = buf * */ __pyx_t_3 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 252, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (PyDict_SetItem(__pyx_t_3, __pyx_n_s_allocate_buffer, Py_False) < 0) __PYX_ERR(2, 252, __pyx_L1_error) /* "View.MemoryView":251 * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), # <<<<<<<<<<<<<< * allocate_buffer=False) * result.data = buf */ __pyx_t_5 = __Pyx_PyObject_Call(((PyObject *)__pyx_array_type), __pyx_t_2, __pyx_t_3); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_5); __pyx_t_5 = 0; /* "View.MemoryView":253 * result = array(shape, itemsize, format, mode.decode('ASCII'), * allocate_buffer=False) * result.data = buf # <<<<<<<<<<<<<< * * return result */ __pyx_v_result->data = __pyx_v_buf; } __pyx_L3:; /* "View.MemoryView":255 * result.data = buf * * return result # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(((PyObject *)__pyx_r)); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = __pyx_v_result; goto __pyx_L0; /* "View.MemoryView":244 * * @cname("__pyx_array_new") * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format, # <<<<<<<<<<<<<< * char *mode, char *buf): * cdef array result */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.array_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF((PyObject *)__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":281 * cdef class Enum(object): * cdef object name * def __init__(self, name): # <<<<<<<<<<<<<< * self.name = name * def __repr__(self): */ /* Python wrapper */ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_name = 0; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__init__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_name,0}; PyObject* values[1] = {0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_name)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__init__") < 0)) __PYX_ERR(2, 281, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 1) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); } __pyx_v_name = values[0]; } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__init__", 1, 1, 1, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 281, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.Enum.__init__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self), __pyx_v_name); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__init__", 0); /* "View.MemoryView":282 * cdef object name * def __init__(self, name): * self.name = name # <<<<<<<<<<<<<< * def __repr__(self): * return self.name */ __Pyx_INCREF(__pyx_v_name); __Pyx_GIVEREF(__pyx_v_name); __Pyx_GOTREF(__pyx_v_self->name); __Pyx_DECREF(__pyx_v_self->name); __pyx_v_self->name = __pyx_v_name; /* "View.MemoryView":281 * cdef class Enum(object): * cdef object name * def __init__(self, name): # <<<<<<<<<<<<<< * self.name = name * def __repr__(self): */ /* function exit code */ __pyx_r = 0; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":283 * def __init__(self, name): * self.name = name * def __repr__(self): # <<<<<<<<<<<<<< * return self.name * */ /* Python wrapper */ static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0); __pyx_r = __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__", 0); /* "View.MemoryView":284 * self.name = name * def __repr__(self): * return self.name # <<<<<<<<<<<<<< * * cdef generic = Enum("") */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->name); __pyx_r = __pyx_v_self->name; goto __pyx_L0; /* "View.MemoryView":283 * def __init__(self, name): * self.name = name * def __repr__(self): # <<<<<<<<<<<<<< * return self.name * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * cdef tuple state * cdef object _dict */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_MemviewEnum_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_MemviewEnum_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_MemviewEnum___reduce_cython__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self) { PyObject *__pyx_v_state = 0; PyObject *__pyx_v__dict = 0; int __pyx_v_use_setstate; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_t_3; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":5 * cdef object _dict * cdef bint use_setstate * state = (self.name,) # <<<<<<<<<<<<<< * _dict = getattr(self, '__dict__', None) * if _dict is not None: */ __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_v_self->name); __Pyx_GIVEREF(__pyx_v_self->name); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v_self->name); __pyx_v_state = ((PyObject*)__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":6 * cdef bint use_setstate * state = (self.name,) * _dict = getattr(self, '__dict__', None) # <<<<<<<<<<<<<< * if _dict is not None: * state += (_dict,) */ __pyx_t_1 = __Pyx_GetAttr3(((PyObject *)__pyx_v_self), __pyx_n_s_dict, Py_None); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v__dict = __pyx_t_1; __pyx_t_1 = 0; /* "(tree fragment)":7 * state = (self.name,) * _dict = getattr(self, '__dict__', None) * if _dict is not None: # <<<<<<<<<<<<<< * state += (_dict,) * use_setstate = True */ __pyx_t_2 = (__pyx_v__dict != Py_None); __pyx_t_3 = (__pyx_t_2 != 0); if (__pyx_t_3) { /* "(tree fragment)":8 * _dict = getattr(self, '__dict__', None) * if _dict is not None: * state += (_dict,) # <<<<<<<<<<<<<< * use_setstate = True * else: */ __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_v__dict); __Pyx_GIVEREF(__pyx_v__dict); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v__dict); __pyx_t_4 = PyNumber_InPlaceAdd(__pyx_v_state, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF_SET(__pyx_v_state, ((PyObject*)__pyx_t_4)); __pyx_t_4 = 0; /* "(tree fragment)":9 * if _dict is not None: * state += (_dict,) * use_setstate = True # <<<<<<<<<<<<<< * else: * use_setstate = self.name is not None */ __pyx_v_use_setstate = 1; /* "(tree fragment)":7 * state = (self.name,) * _dict = getattr(self, '__dict__', None) * if _dict is not None: # <<<<<<<<<<<<<< * state += (_dict,) * use_setstate = True */ goto __pyx_L3; } /* "(tree fragment)":11 * use_setstate = True * else: * use_setstate = self.name is not None # <<<<<<<<<<<<<< * if use_setstate: * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state */ /*else*/ { __pyx_t_3 = (__pyx_v_self->name != Py_None); __pyx_v_use_setstate = __pyx_t_3; } __pyx_L3:; /* "(tree fragment)":12 * else: * use_setstate = self.name is not None * if use_setstate: # <<<<<<<<<<<<<< * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: */ __pyx_t_3 = (__pyx_v_use_setstate != 0); if (__pyx_t_3) { /* "(tree fragment)":13 * use_setstate = self.name is not None * if use_setstate: * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state # <<<<<<<<<<<<<< * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) */ __Pyx_XDECREF(__pyx_r); __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_GIVEREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_INCREF(__pyx_int_184977713); __Pyx_GIVEREF(__pyx_int_184977713); PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_int_184977713); __Pyx_INCREF(Py_None); __Pyx_GIVEREF(Py_None); PyTuple_SET_ITEM(__pyx_t_1, 2, Py_None); __pyx_t_5 = PyTuple_New(3); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_1); __Pyx_INCREF(__pyx_v_state); __Pyx_GIVEREF(__pyx_v_state); PyTuple_SET_ITEM(__pyx_t_5, 2, __pyx_v_state); __pyx_t_4 = 0; __pyx_t_1 = 0; __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "(tree fragment)":12 * else: * use_setstate = self.name is not None * if use_setstate: # <<<<<<<<<<<<<< * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: */ } /* "(tree fragment)":15 * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /*else*/ { __Pyx_XDECREF(__pyx_r); __Pyx_GetModuleGlobalName(__pyx_t_5, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_GIVEREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_INCREF(__pyx_int_184977713); __Pyx_GIVEREF(__pyx_int_184977713); PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_int_184977713); __Pyx_INCREF(__pyx_v_state); __Pyx_GIVEREF(__pyx_v_state); PyTuple_SET_ITEM(__pyx_t_1, 2, __pyx_v_state); __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_1); __pyx_t_5 = 0; __pyx_t_1 = 0; __pyx_r = __pyx_t_4; __pyx_t_4 = 0; goto __pyx_L0; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * cdef tuple state * cdef object _dict */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.Enum.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_state); __Pyx_XDECREF(__pyx_v__dict); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":16 * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_MemviewEnum_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_MemviewEnum_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_MemviewEnum_2__setstate_cython__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":17 * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): * __pyx_unpickle_Enum__set_state(self, __pyx_state) # <<<<<<<<<<<<<< */ if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(2, 17, __pyx_L1_error) __pyx_t_1 = __pyx_unpickle_Enum__set_state(__pyx_v_self, ((PyObject*)__pyx_v___pyx_state)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 17, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":16 * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.Enum.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":298 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory */ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) { Py_intptr_t __pyx_v_aligned_p; size_t __pyx_v_offset; void *__pyx_r; int __pyx_t_1; /* "View.MemoryView":300 * cdef void *align_pointer(void *memory, size_t alignment) nogil: * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory # <<<<<<<<<<<<<< * cdef size_t offset * */ __pyx_v_aligned_p = ((Py_intptr_t)__pyx_v_memory); /* "View.MemoryView":304 * * with cython.cdivision(True): * offset = aligned_p % alignment # <<<<<<<<<<<<<< * * if offset > 0: */ __pyx_v_offset = (__pyx_v_aligned_p % __pyx_v_alignment); /* "View.MemoryView":306 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< * aligned_p += alignment - offset * */ __pyx_t_1 = ((__pyx_v_offset > 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":307 * * if offset > 0: * aligned_p += alignment - offset # <<<<<<<<<<<<<< * * return aligned_p */ __pyx_v_aligned_p = (__pyx_v_aligned_p + (__pyx_v_alignment - __pyx_v_offset)); /* "View.MemoryView":306 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< * aligned_p += alignment - offset * */ } /* "View.MemoryView":309 * aligned_p += alignment - offset * * return aligned_p # <<<<<<<<<<<<<< * * */ __pyx_r = ((void *)__pyx_v_aligned_p); goto __pyx_L0; /* "View.MemoryView":298 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":345 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< * self.obj = obj * self.flags = flags */ /* Python wrapper */ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_obj = 0; int __pyx_v_flags; int __pyx_v_dtype_is_object; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_obj,&__pyx_n_s_flags,&__pyx_n_s_dtype_is_object,0}; PyObject* values[3] = {0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_obj)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_flags)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, 1); __PYX_ERR(2, 345, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dtype_is_object); if (value) { values[2] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) __PYX_ERR(2, 345, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_obj = values[0]; __pyx_v_flags = __Pyx_PyInt_As_int(values[1]); if (unlikely((__pyx_v_flags == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 345, __pyx_L3_error) if (values[2]) { __pyx_v_dtype_is_object = __Pyx_PyObject_IsTrue(values[2]); if (unlikely((__pyx_v_dtype_is_object == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 345, __pyx_L3_error) } else { __pyx_v_dtype_is_object = ((int)0); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 345, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_obj, __pyx_v_flags, __pyx_v_dtype_is_object); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__cinit__", 0); /* "View.MemoryView":346 * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj # <<<<<<<<<<<<<< * self.flags = flags * if type(self) is memoryview or obj is not None: */ __Pyx_INCREF(__pyx_v_obj); __Pyx_GIVEREF(__pyx_v_obj); __Pyx_GOTREF(__pyx_v_self->obj); __Pyx_DECREF(__pyx_v_self->obj); __pyx_v_self->obj = __pyx_v_obj; /* "View.MemoryView":347 * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj * self.flags = flags # <<<<<<<<<<<<<< * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) */ __pyx_v_self->flags = __pyx_v_flags; /* "View.MemoryView":348 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: */ __pyx_t_2 = (((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self))) == ((PyObject *)__pyx_memoryview_type)); __pyx_t_3 = (__pyx_t_2 != 0); if (!__pyx_t_3) { } else { __pyx_t_1 = __pyx_t_3; goto __pyx_L4_bool_binop_done; } __pyx_t_3 = (__pyx_v_obj != Py_None); __pyx_t_2 = (__pyx_t_3 != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (__pyx_t_1) { /* "View.MemoryView":349 * self.flags = flags * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) # <<<<<<<<<<<<<< * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None */ __pyx_t_4 = __Pyx_GetBuffer(__pyx_v_obj, (&__pyx_v_self->view), __pyx_v_flags); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 349, __pyx_L1_error) /* "View.MemoryView":350 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) */ __pyx_t_1 = ((((PyObject *)__pyx_v_self->view.obj) == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":351 * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_self->view))->obj = Py_None; /* "View.MemoryView":352 * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * global __pyx_memoryview_thread_locks_used */ Py_INCREF(Py_None); /* "View.MemoryView":350 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) */ } /* "View.MemoryView":348 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: */ } /* "View.MemoryView":355 * * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 */ __pyx_t_1 = ((__pyx_memoryview_thread_locks_used < 8) != 0); if (__pyx_t_1) { /* "View.MemoryView":356 * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: */ __pyx_v_self->lock = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); /* "View.MemoryView":357 * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 # <<<<<<<<<<<<<< * if self.lock is NULL: * self.lock = PyThread_allocate_lock() */ __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used + 1); /* "View.MemoryView":355 * * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 */ } /* "View.MemoryView":358 * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< * self.lock = PyThread_allocate_lock() * if self.lock is NULL: */ __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":359 * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() # <<<<<<<<<<<<<< * if self.lock is NULL: * raise MemoryError */ __pyx_v_self->lock = PyThread_allocate_lock(); /* "View.MemoryView":360 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() * if self.lock is NULL: # <<<<<<<<<<<<<< * raise MemoryError * */ __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":361 * self.lock = PyThread_allocate_lock() * if self.lock is NULL: * raise MemoryError # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ PyErr_NoMemory(); __PYX_ERR(2, 361, __pyx_L1_error) /* "View.MemoryView":360 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() * if self.lock is NULL: # <<<<<<<<<<<<<< * raise MemoryError * */ } /* "View.MemoryView":358 * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< * self.lock = PyThread_allocate_lock() * if self.lock is NULL: */ } /* "View.MemoryView":363 * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":364 * * if flags & PyBUF_FORMAT: * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') # <<<<<<<<<<<<<< * else: * self.dtype_is_object = dtype_is_object */ __pyx_t_2 = (((__pyx_v_self->view.format[0]) == 'O') != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L11_bool_binop_done; } __pyx_t_2 = (((__pyx_v_self->view.format[1]) == '\x00') != 0); __pyx_t_1 = __pyx_t_2; __pyx_L11_bool_binop_done:; __pyx_v_self->dtype_is_object = __pyx_t_1; /* "View.MemoryView":363 * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: */ goto __pyx_L10; } /* "View.MemoryView":366 * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: * self.dtype_is_object = dtype_is_object # <<<<<<<<<<<<<< * * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( */ /*else*/ { __pyx_v_self->dtype_is_object = __pyx_v_dtype_is_object; } __pyx_L10:; /* "View.MemoryView":368 * self.dtype_is_object = dtype_is_object * * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( # <<<<<<<<<<<<<< * &self.acquisition_count[0], sizeof(__pyx_atomic_int)) * self.typeinfo = NULL */ __pyx_v_self->acquisition_count_aligned_p = ((__pyx_atomic_int *)__pyx_align_pointer(((void *)(&(__pyx_v_self->acquisition_count[0]))), (sizeof(__pyx_atomic_int)))); /* "View.MemoryView":370 * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( * &self.acquisition_count[0], sizeof(__pyx_atomic_int)) * self.typeinfo = NULL # <<<<<<<<<<<<<< * * def __dealloc__(memoryview self): */ __pyx_v_self->typeinfo = NULL; /* "View.MemoryView":345 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< * self.obj = obj * self.flags = flags */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":372 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) */ /* Python wrapper */ static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self) { int __pyx_v_i; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_t_5; PyThread_type_lock __pyx_t_6; PyThread_type_lock __pyx_t_7; __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":373 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: */ __pyx_t_1 = (__pyx_v_self->obj != Py_None); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":374 * def __dealloc__(memoryview self): * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) # <<<<<<<<<<<<<< * elif (<__pyx_buffer *> &self.view).obj == Py_None: * */ __Pyx_ReleaseBuffer((&__pyx_v_self->view)); /* "View.MemoryView":373 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: */ goto __pyx_L3; } /* "View.MemoryView":375 * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: # <<<<<<<<<<<<<< * * (<__pyx_buffer *> &self.view).obj = NULL */ __pyx_t_2 = ((((Py_buffer *)(&__pyx_v_self->view))->obj == Py_None) != 0); if (__pyx_t_2) { /* "View.MemoryView":377 * elif (<__pyx_buffer *> &self.view).obj == Py_None: * * (<__pyx_buffer *> &self.view).obj = NULL # <<<<<<<<<<<<<< * Py_DECREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_self->view))->obj = NULL; /* "View.MemoryView":378 * * (<__pyx_buffer *> &self.view).obj = NULL * Py_DECREF(Py_None) # <<<<<<<<<<<<<< * * cdef int i */ Py_DECREF(Py_None); /* "View.MemoryView":375 * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: # <<<<<<<<<<<<<< * * (<__pyx_buffer *> &self.view).obj = NULL */ } __pyx_L3:; /* "View.MemoryView":382 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: */ __pyx_t_2 = ((__pyx_v_self->lock != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":383 * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): # <<<<<<<<<<<<<< * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 */ __pyx_t_3 = __pyx_memoryview_thread_locks_used; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "View.MemoryView":384 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: */ __pyx_t_2 = (((__pyx_memoryview_thread_locks[__pyx_v_i]) == __pyx_v_self->lock) != 0); if (__pyx_t_2) { /* "View.MemoryView":385 * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 # <<<<<<<<<<<<<< * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( */ __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used - 1); /* "View.MemoryView":386 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) */ __pyx_t_2 = ((__pyx_v_i != __pyx_memoryview_thread_locks_used) != 0); if (__pyx_t_2) { /* "View.MemoryView":388 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) # <<<<<<<<<<<<<< * break * else: */ __pyx_t_6 = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); __pyx_t_7 = (__pyx_memoryview_thread_locks[__pyx_v_i]); /* "View.MemoryView":387 * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) * break */ (__pyx_memoryview_thread_locks[__pyx_v_i]) = __pyx_t_6; (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]) = __pyx_t_7; /* "View.MemoryView":386 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) */ } /* "View.MemoryView":389 * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) * break # <<<<<<<<<<<<<< * else: * PyThread_free_lock(self.lock) */ goto __pyx_L6_break; /* "View.MemoryView":384 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: */ } } /*else*/ { /* "View.MemoryView":391 * break * else: * PyThread_free_lock(self.lock) # <<<<<<<<<<<<<< * * cdef char *get_item_pointer(memoryview self, object index) except NULL: */ PyThread_free_lock(__pyx_v_self->lock); } __pyx_L6_break:; /* "View.MemoryView":382 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: */ } /* "View.MemoryView":372 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":393 * PyThread_free_lock(self.lock) * * cdef char *get_item_pointer(memoryview self, object index) except NULL: # <<<<<<<<<<<<<< * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf */ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) { Py_ssize_t __pyx_v_dim; char *__pyx_v_itemp; PyObject *__pyx_v_idx = NULL; char *__pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; PyObject *__pyx_t_2 = NULL; Py_ssize_t __pyx_t_3; PyObject *(*__pyx_t_4)(PyObject *); PyObject *__pyx_t_5 = NULL; Py_ssize_t __pyx_t_6; char *__pyx_t_7; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("get_item_pointer", 0); /* "View.MemoryView":395 * cdef char *get_item_pointer(memoryview self, object index) except NULL: * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf # <<<<<<<<<<<<<< * * for dim, idx in enumerate(index): */ __pyx_v_itemp = ((char *)__pyx_v_self->view.buf); /* "View.MemoryView":397 * cdef char *itemp = self.view.buf * * for dim, idx in enumerate(index): # <<<<<<<<<<<<<< * itemp = pybuffer_index(&self.view, itemp, idx, dim) * */ __pyx_t_1 = 0; if (likely(PyList_CheckExact(__pyx_v_index)) || PyTuple_CheckExact(__pyx_v_index)) { __pyx_t_2 = __pyx_v_index; __Pyx_INCREF(__pyx_t_2); __pyx_t_3 = 0; __pyx_t_4 = NULL; } else { __pyx_t_3 = -1; __pyx_t_2 = PyObject_GetIter(__pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = Py_TYPE(__pyx_t_2)->tp_iternext; if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 397, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_4)) { if (likely(PyList_CheckExact(__pyx_t_2))) { if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_2)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyList_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(2, 397, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif } else { if (__pyx_t_3 >= PyTuple_GET_SIZE(__pyx_t_2)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(2, 397, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif } } else { __pyx_t_5 = __pyx_t_4(__pyx_t_2); if (unlikely(!__pyx_t_5)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(2, 397, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_5); } __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_5); __pyx_t_5 = 0; __pyx_v_dim = __pyx_t_1; __pyx_t_1 = (__pyx_t_1 + 1); /* "View.MemoryView":398 * * for dim, idx in enumerate(index): * itemp = pybuffer_index(&self.view, itemp, idx, dim) # <<<<<<<<<<<<<< * * return itemp */ __pyx_t_6 = __Pyx_PyIndex_AsSsize_t(__pyx_v_idx); if (unlikely((__pyx_t_6 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 398, __pyx_L1_error) __pyx_t_7 = __pyx_pybuffer_index((&__pyx_v_self->view), __pyx_v_itemp, __pyx_t_6, __pyx_v_dim); if (unlikely(__pyx_t_7 == ((char *)NULL))) __PYX_ERR(2, 398, __pyx_L1_error) __pyx_v_itemp = __pyx_t_7; /* "View.MemoryView":397 * cdef char *itemp = self.view.buf * * for dim, idx in enumerate(index): # <<<<<<<<<<<<<< * itemp = pybuffer_index(&self.view, itemp, idx, dim) * */ } __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":400 * itemp = pybuffer_index(&self.view, itemp, idx, dim) * * return itemp # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_itemp; goto __pyx_L0; /* "View.MemoryView":393 * PyThread_free_lock(self.lock) * * cdef char *get_item_pointer(memoryview self, object index) except NULL: # <<<<<<<<<<<<<< * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.get_item_pointer", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_idx); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":403 * * * def __getitem__(memoryview self, object index): # <<<<<<<<<<<<<< * if index is Ellipsis: * return self */ /* Python wrapper */ static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index); /*proto*/ static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) { PyObject *__pyx_v_have_slices = NULL; PyObject *__pyx_v_indices = NULL; char *__pyx_v_itemp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; char *__pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__getitem__", 0); /* "View.MemoryView":404 * * def __getitem__(memoryview self, object index): * if index is Ellipsis: # <<<<<<<<<<<<<< * return self * */ __pyx_t_1 = (__pyx_v_index == __pyx_builtin_Ellipsis); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":405 * def __getitem__(memoryview self, object index): * if index is Ellipsis: * return self # <<<<<<<<<<<<<< * * have_slices, indices = _unellipsify(index, self.view.ndim) */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_self)); __pyx_r = ((PyObject *)__pyx_v_self); goto __pyx_L0; /* "View.MemoryView":404 * * def __getitem__(memoryview self, object index): * if index is Ellipsis: # <<<<<<<<<<<<<< * return self * */ } /* "View.MemoryView":407 * return self * * have_slices, indices = _unellipsify(index, self.view.ndim) # <<<<<<<<<<<<<< * * cdef char *itemp */ __pyx_t_3 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (likely(__pyx_t_3 != Py_None)) { PyObject* sequence = __pyx_t_3; Py_ssize_t size = __Pyx_PySequence_SIZE(sequence); if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); __PYX_ERR(2, 407, __pyx_L1_error) } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_4 = PyTuple_GET_ITEM(sequence, 0); __pyx_t_5 = PyTuple_GET_ITEM(sequence, 1); __Pyx_INCREF(__pyx_t_4); __Pyx_INCREF(__pyx_t_5); #else __pyx_t_4 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } else { __Pyx_RaiseNoneNotIterableError(); __PYX_ERR(2, 407, __pyx_L1_error) } __pyx_v_have_slices = __pyx_t_4; __pyx_t_4 = 0; __pyx_v_indices = __pyx_t_5; __pyx_t_5 = 0; /* "View.MemoryView":410 * * cdef char *itemp * if have_slices: # <<<<<<<<<<<<<< * return memview_slice(self, indices) * else: */ __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(2, 410, __pyx_L1_error) if (__pyx_t_2) { /* "View.MemoryView":411 * cdef char *itemp * if have_slices: * return memview_slice(self, indices) # <<<<<<<<<<<<<< * else: * itemp = self.get_item_pointer(indices) */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = ((PyObject *)__pyx_memview_slice(__pyx_v_self, __pyx_v_indices)); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 411, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":410 * * cdef char *itemp * if have_slices: # <<<<<<<<<<<<<< * return memview_slice(self, indices) * else: */ } /* "View.MemoryView":413 * return memview_slice(self, indices) * else: * itemp = self.get_item_pointer(indices) # <<<<<<<<<<<<<< * return self.convert_item_to_object(itemp) * */ /*else*/ { __pyx_t_6 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_indices); if (unlikely(__pyx_t_6 == ((char *)NULL))) __PYX_ERR(2, 413, __pyx_L1_error) __pyx_v_itemp = __pyx_t_6; /* "View.MemoryView":414 * else: * itemp = self.get_item_pointer(indices) * return self.convert_item_to_object(itemp) # <<<<<<<<<<<<<< * * def __setitem__(memoryview self, object index, object value): */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->convert_item_to_object(__pyx_v_self, __pyx_v_itemp); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 414, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; } /* "View.MemoryView":403 * * * def __getitem__(memoryview self, object index): # <<<<<<<<<<<<<< * if index is Ellipsis: * return self */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_have_slices); __Pyx_XDECREF(__pyx_v_indices); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":416 * return self.convert_item_to_object(itemp) * * def __setitem__(memoryview self, object index, object value): # <<<<<<<<<<<<<< * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") */ /* Python wrapper */ static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /*proto*/ static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index), ((PyObject *)__pyx_v_value)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { PyObject *__pyx_v_have_slices = NULL; PyObject *__pyx_v_obj = NULL; int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setitem__", 0); __Pyx_INCREF(__pyx_v_index); /* "View.MemoryView":417 * * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: # <<<<<<<<<<<<<< * raise TypeError("Cannot assign to read-only memoryview") * */ __pyx_t_1 = (__pyx_v_self->view.readonly != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":418 * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") # <<<<<<<<<<<<<< * * have_slices, index = _unellipsify(index, self.view.ndim) */ __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__10, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 418, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(2, 418, __pyx_L1_error) /* "View.MemoryView":417 * * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: # <<<<<<<<<<<<<< * raise TypeError("Cannot assign to read-only memoryview") * */ } /* "View.MemoryView":420 * raise TypeError("Cannot assign to read-only memoryview") * * have_slices, index = _unellipsify(index, self.view.ndim) # <<<<<<<<<<<<<< * * if have_slices: */ __pyx_t_2 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (likely(__pyx_t_2 != Py_None)) { PyObject* sequence = __pyx_t_2; Py_ssize_t size = __Pyx_PySequence_SIZE(sequence); if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); __PYX_ERR(2, 420, __pyx_L1_error) } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_3 = PyTuple_GET_ITEM(sequence, 0); __pyx_t_4 = PyTuple_GET_ITEM(sequence, 1); __Pyx_INCREF(__pyx_t_3); __Pyx_INCREF(__pyx_t_4); #else __pyx_t_3 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); #endif __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } else { __Pyx_RaiseNoneNotIterableError(); __PYX_ERR(2, 420, __pyx_L1_error) } __pyx_v_have_slices = __pyx_t_3; __pyx_t_3 = 0; __Pyx_DECREF_SET(__pyx_v_index, __pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":422 * have_slices, index = _unellipsify(index, self.view.ndim) * * if have_slices: # <<<<<<<<<<<<<< * obj = self.is_slice(value) * if obj: */ __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 422, __pyx_L1_error) if (__pyx_t_1) { /* "View.MemoryView":423 * * if have_slices: * obj = self.is_slice(value) # <<<<<<<<<<<<<< * if obj: * self.setitem_slice_assignment(self[index], obj) */ __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->is_slice(__pyx_v_self, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 423, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_v_obj = __pyx_t_2; __pyx_t_2 = 0; /* "View.MemoryView":424 * if have_slices: * obj = self.is_slice(value) * if obj: # <<<<<<<<<<<<<< * self.setitem_slice_assignment(self[index], obj) * else: */ __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_v_obj); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 424, __pyx_L1_error) if (__pyx_t_1) { /* "View.MemoryView":425 * obj = self.is_slice(value) * if obj: * self.setitem_slice_assignment(self[index], obj) # <<<<<<<<<<<<<< * else: * self.setitem_slice_assign_scalar(self[index], value) */ __pyx_t_2 = __Pyx_PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 425, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assignment(__pyx_v_self, __pyx_t_2, __pyx_v_obj); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 425, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":424 * if have_slices: * obj = self.is_slice(value) * if obj: # <<<<<<<<<<<<<< * self.setitem_slice_assignment(self[index], obj) * else: */ goto __pyx_L5; } /* "View.MemoryView":427 * self.setitem_slice_assignment(self[index], obj) * else: * self.setitem_slice_assign_scalar(self[index], value) # <<<<<<<<<<<<<< * else: * self.setitem_indexed(index, value) */ /*else*/ { __pyx_t_4 = __Pyx_PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 427, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (!(likely(((__pyx_t_4) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_4, __pyx_memoryview_type))))) __PYX_ERR(2, 427, __pyx_L1_error) __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assign_scalar(__pyx_v_self, ((struct __pyx_memoryview_obj *)__pyx_t_4), __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 427, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_L5:; /* "View.MemoryView":422 * have_slices, index = _unellipsify(index, self.view.ndim) * * if have_slices: # <<<<<<<<<<<<<< * obj = self.is_slice(value) * if obj: */ goto __pyx_L4; } /* "View.MemoryView":429 * self.setitem_slice_assign_scalar(self[index], value) * else: * self.setitem_indexed(index, value) # <<<<<<<<<<<<<< * * cdef is_slice(self, obj): */ /*else*/ { __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_indexed(__pyx_v_self, __pyx_v_index, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 429, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_L4:; /* "View.MemoryView":416 * return self.convert_item_to_object(itemp) * * def __setitem__(memoryview self, object index, object value): # <<<<<<<<<<<<<< * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView.memoryview.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_XDECREF(__pyx_v_have_slices); __Pyx_XDECREF(__pyx_v_obj); __Pyx_XDECREF(__pyx_v_index); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":431 * self.setitem_indexed(index, value) * * cdef is_slice(self, obj): # <<<<<<<<<<<<<< * if not isinstance(obj, memoryview): * try: */ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_t_9; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("is_slice", 0); __Pyx_INCREF(__pyx_v_obj); /* "View.MemoryView":432 * * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): # <<<<<<<<<<<<<< * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, */ __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_obj, __pyx_memoryview_type); __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_3, &__pyx_t_4, &__pyx_t_5); __Pyx_XGOTREF(__pyx_t_3); __Pyx_XGOTREF(__pyx_t_4); __Pyx_XGOTREF(__pyx_t_5); /*try:*/ { /* "View.MemoryView":434 * if not isinstance(obj, memoryview): * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, # <<<<<<<<<<<<<< * self.dtype_is_object) * except TypeError: */ __pyx_t_6 = __Pyx_PyInt_From_int(((__pyx_v_self->flags & (~PyBUF_WRITABLE)) | PyBUF_ANY_CONTIGUOUS)); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_6); /* "View.MemoryView":435 * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) # <<<<<<<<<<<<<< * except TypeError: * return None */ __pyx_t_7 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 435, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_7); /* "View.MemoryView":434 * if not isinstance(obj, memoryview): * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, # <<<<<<<<<<<<<< * self.dtype_is_object) * except TypeError: */ __pyx_t_8 = PyTuple_New(3); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_INCREF(__pyx_v_obj); __Pyx_GIVEREF(__pyx_v_obj); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_v_obj); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_8, 1, __pyx_t_6); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_8, 2, __pyx_t_7); __pyx_t_6 = 0; __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_8, NULL); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF_SET(__pyx_v_obj, __pyx_t_7); __pyx_t_7 = 0; /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ } __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; goto __pyx_L9_try_end; __pyx_L4_error:; __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0; /* "View.MemoryView":436 * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) * except TypeError: # <<<<<<<<<<<<<< * return None * */ __pyx_t_9 = __Pyx_PyErr_ExceptionMatches(__pyx_builtin_TypeError); if (__pyx_t_9) { __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_6) < 0) __PYX_ERR(2, 436, __pyx_L6_except_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_GOTREF(__pyx_t_8); __Pyx_GOTREF(__pyx_t_6); /* "View.MemoryView":437 * self.dtype_is_object) * except TypeError: * return None # <<<<<<<<<<<<<< * * return obj */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; goto __pyx_L7_except_return; } goto __pyx_L6_except_error; __pyx_L6_except_error:; /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_XGIVEREF(__pyx_t_5); __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5); goto __pyx_L1_error; __pyx_L7_except_return:; __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_XGIVEREF(__pyx_t_5); __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5); goto __pyx_L0; __pyx_L9_try_end:; } /* "View.MemoryView":432 * * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): # <<<<<<<<<<<<<< * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, */ } /* "View.MemoryView":439 * return None * * return obj # <<<<<<<<<<<<<< * * cdef setitem_slice_assignment(self, dst, src): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_obj); __pyx_r = __pyx_v_obj; goto __pyx_L0; /* "View.MemoryView":431 * self.setitem_indexed(index, value) * * cdef is_slice(self, obj): # <<<<<<<<<<<<<< * if not isinstance(obj, memoryview): * try: */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_obj); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":441 * return obj * * cdef setitem_slice_assignment(self, dst, src): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice dst_slice * cdef __Pyx_memviewslice src_slice */ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src) { __Pyx_memviewslice __pyx_v_dst_slice; __Pyx_memviewslice __pyx_v_src_slice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; __Pyx_memviewslice *__pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; int __pyx_t_5; int __pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("setitem_slice_assignment", 0); /* "View.MemoryView":445 * cdef __Pyx_memviewslice src_slice * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], # <<<<<<<<<<<<<< * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) */ if (!(likely(((__pyx_v_src) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_src, __pyx_memoryview_type))))) __PYX_ERR(2, 445, __pyx_L1_error) __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_src), (&__pyx_v_src_slice)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 445, __pyx_L1_error) /* "View.MemoryView":446 * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], * get_slice_from_memview(dst, &dst_slice)[0], # <<<<<<<<<<<<<< * src.ndim, dst.ndim, self.dtype_is_object) * */ if (!(likely(((__pyx_v_dst) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_dst, __pyx_memoryview_type))))) __PYX_ERR(2, 446, __pyx_L1_error) __pyx_t_2 = __pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_dst), (&__pyx_v_dst_slice)); if (unlikely(__pyx_t_2 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 446, __pyx_L1_error) /* "View.MemoryView":447 * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) # <<<<<<<<<<<<<< * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): */ __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_v_src, __pyx_n_s_ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_4 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_v_dst, __pyx_n_s_ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_5 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_5 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":445 * cdef __Pyx_memviewslice src_slice * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], # <<<<<<<<<<<<<< * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) */ __pyx_t_6 = __pyx_memoryview_copy_contents((__pyx_t_1[0]), (__pyx_t_2[0]), __pyx_t_4, __pyx_t_5, __pyx_v_self->dtype_is_object); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 445, __pyx_L1_error) /* "View.MemoryView":441 * return obj * * cdef setitem_slice_assignment(self, dst, src): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice dst_slice * cdef __Pyx_memviewslice src_slice */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assignment", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":449 * src.ndim, dst.ndim, self.dtype_is_object) * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): # <<<<<<<<<<<<<< * cdef int array[128] * cdef void *tmp = NULL */ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value) { int __pyx_v_array[0x80]; void *__pyx_v_tmp; void *__pyx_v_item; __Pyx_memviewslice *__pyx_v_dst_slice; __Pyx_memviewslice __pyx_v_tmp_slice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; int __pyx_t_5; char const *__pyx_t_6; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; PyObject *__pyx_t_9 = NULL; PyObject *__pyx_t_10 = NULL; PyObject *__pyx_t_11 = NULL; PyObject *__pyx_t_12 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("setitem_slice_assign_scalar", 0); /* "View.MemoryView":451 * cdef setitem_slice_assign_scalar(self, memoryview dst, value): * cdef int array[128] * cdef void *tmp = NULL # <<<<<<<<<<<<<< * cdef void *item * */ __pyx_v_tmp = NULL; /* "View.MemoryView":456 * cdef __Pyx_memviewslice *dst_slice * cdef __Pyx_memviewslice tmp_slice * dst_slice = get_slice_from_memview(dst, &tmp_slice) # <<<<<<<<<<<<<< * * if self.view.itemsize > sizeof(array): */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_dst, (&__pyx_v_tmp_slice)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 456, __pyx_L1_error) __pyx_v_dst_slice = __pyx_t_1; /* "View.MemoryView":458 * dst_slice = get_slice_from_memview(dst, &tmp_slice) * * if self.view.itemsize > sizeof(array): # <<<<<<<<<<<<<< * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: */ __pyx_t_2 = ((((size_t)__pyx_v_self->view.itemsize) > (sizeof(__pyx_v_array))) != 0); if (__pyx_t_2) { /* "View.MemoryView":459 * * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) # <<<<<<<<<<<<<< * if tmp == NULL: * raise MemoryError */ __pyx_v_tmp = PyMem_Malloc(__pyx_v_self->view.itemsize); /* "View.MemoryView":460 * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: # <<<<<<<<<<<<<< * raise MemoryError * item = tmp */ __pyx_t_2 = ((__pyx_v_tmp == NULL) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":461 * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: * raise MemoryError # <<<<<<<<<<<<<< * item = tmp * else: */ PyErr_NoMemory(); __PYX_ERR(2, 461, __pyx_L1_error) /* "View.MemoryView":460 * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: # <<<<<<<<<<<<<< * raise MemoryError * item = tmp */ } /* "View.MemoryView":462 * if tmp == NULL: * raise MemoryError * item = tmp # <<<<<<<<<<<<<< * else: * item = array */ __pyx_v_item = __pyx_v_tmp; /* "View.MemoryView":458 * dst_slice = get_slice_from_memview(dst, &tmp_slice) * * if self.view.itemsize > sizeof(array): # <<<<<<<<<<<<<< * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: */ goto __pyx_L3; } /* "View.MemoryView":464 * item = tmp * else: * item = array # <<<<<<<<<<<<<< * * try: */ /*else*/ { __pyx_v_item = ((void *)__pyx_v_array); } __pyx_L3:; /* "View.MemoryView":466 * item = array * * try: # <<<<<<<<<<<<<< * if self.dtype_is_object: * ( item)[0] = value */ /*try:*/ { /* "View.MemoryView":467 * * try: * if self.dtype_is_object: # <<<<<<<<<<<<<< * ( item)[0] = value * else: */ __pyx_t_2 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_2) { /* "View.MemoryView":468 * try: * if self.dtype_is_object: * ( item)[0] = value # <<<<<<<<<<<<<< * else: * self.assign_item_from_object( item, value) */ (((PyObject **)__pyx_v_item)[0]) = ((PyObject *)__pyx_v_value); /* "View.MemoryView":467 * * try: * if self.dtype_is_object: # <<<<<<<<<<<<<< * ( item)[0] = value * else: */ goto __pyx_L8; } /* "View.MemoryView":470 * ( item)[0] = value * else: * self.assign_item_from_object( item, value) # <<<<<<<<<<<<<< * * */ /*else*/ { __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, ((char *)__pyx_v_item), __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 470, __pyx_L6_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } __pyx_L8:; /* "View.MemoryView":474 * * * if self.view.suboffsets != NULL: # <<<<<<<<<<<<<< * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, */ __pyx_t_2 = ((__pyx_v_self->view.suboffsets != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":475 * * if self.view.suboffsets != NULL: * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) # <<<<<<<<<<<<<< * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, * item, self.dtype_is_object) */ __pyx_t_3 = assert_direct_dimensions(__pyx_v_self->view.suboffsets, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 475, __pyx_L6_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":474 * * * if self.view.suboffsets != NULL: # <<<<<<<<<<<<<< * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, */ } /* "View.MemoryView":476 * if self.view.suboffsets != NULL: * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, # <<<<<<<<<<<<<< * item, self.dtype_is_object) * finally: */ __pyx_memoryview_slice_assign_scalar(__pyx_v_dst_slice, __pyx_v_dst->view.ndim, __pyx_v_self->view.itemsize, __pyx_v_item, __pyx_v_self->dtype_is_object); } /* "View.MemoryView":479 * item, self.dtype_is_object) * finally: * PyMem_Free(tmp) # <<<<<<<<<<<<<< * * cdef setitem_indexed(self, index, value): */ /*finally:*/ { /*normal exit:*/{ PyMem_Free(__pyx_v_tmp); goto __pyx_L7; } __pyx_L6_error:; /*exception exit:*/{ __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0; __pyx_t_12 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; if (PY_MAJOR_VERSION >= 3) __Pyx_ExceptionSwap(&__pyx_t_10, &__pyx_t_11, &__pyx_t_12); if ((PY_MAJOR_VERSION < 3) || unlikely(__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_9) < 0)) __Pyx_ErrFetch(&__pyx_t_7, &__pyx_t_8, &__pyx_t_9); __Pyx_XGOTREF(__pyx_t_7); __Pyx_XGOTREF(__pyx_t_8); __Pyx_XGOTREF(__pyx_t_9); __Pyx_XGOTREF(__pyx_t_10); __Pyx_XGOTREF(__pyx_t_11); __Pyx_XGOTREF(__pyx_t_12); __pyx_t_4 = __pyx_lineno; __pyx_t_5 = __pyx_clineno; __pyx_t_6 = __pyx_filename; { PyMem_Free(__pyx_v_tmp); } if (PY_MAJOR_VERSION >= 3) { __Pyx_XGIVEREF(__pyx_t_10); __Pyx_XGIVEREF(__pyx_t_11); __Pyx_XGIVEREF(__pyx_t_12); __Pyx_ExceptionReset(__pyx_t_10, __pyx_t_11, __pyx_t_12); } __Pyx_XGIVEREF(__pyx_t_7); __Pyx_XGIVEREF(__pyx_t_8); __Pyx_XGIVEREF(__pyx_t_9); __Pyx_ErrRestore(__pyx_t_7, __pyx_t_8, __pyx_t_9); __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0; __pyx_t_12 = 0; __pyx_lineno = __pyx_t_4; __pyx_clineno = __pyx_t_5; __pyx_filename = __pyx_t_6; goto __pyx_L1_error; } __pyx_L7:; } /* "View.MemoryView":449 * src.ndim, dst.ndim, self.dtype_is_object) * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): # <<<<<<<<<<<<<< * cdef int array[128] * cdef void *tmp = NULL */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assign_scalar", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":481 * PyMem_Free(tmp) * * cdef setitem_indexed(self, index, value): # <<<<<<<<<<<<<< * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) */ static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { char *__pyx_v_itemp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations char *__pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("setitem_indexed", 0); /* "View.MemoryView":482 * * cdef setitem_indexed(self, index, value): * cdef char *itemp = self.get_item_pointer(index) # <<<<<<<<<<<<<< * self.assign_item_from_object(itemp, value) * */ __pyx_t_1 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_index); if (unlikely(__pyx_t_1 == ((char *)NULL))) __PYX_ERR(2, 482, __pyx_L1_error) __pyx_v_itemp = __pyx_t_1; /* "View.MemoryView":483 * cdef setitem_indexed(self, index, value): * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) # <<<<<<<<<<<<<< * * cdef convert_item_to_object(self, char *itemp): */ __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 483, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":481 * PyMem_Free(tmp) * * cdef setitem_indexed(self, index, value): # <<<<<<<<<<<<<< * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_indexed", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":485 * self.assign_item_from_object(itemp, value) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp) { PyObject *__pyx_v_struct = NULL; PyObject *__pyx_v_bytesitem = 0; PyObject *__pyx_v_result = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; int __pyx_t_8; PyObject *__pyx_t_9 = NULL; size_t __pyx_t_10; int __pyx_t_11; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("convert_item_to_object", 0); /* "View.MemoryView":488 * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" * import struct # <<<<<<<<<<<<<< * cdef bytes bytesitem * */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 488, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_struct = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":491 * cdef bytes bytesitem * * bytesitem = itemp[:self.view.itemsize] # <<<<<<<<<<<<<< * try: * result = struct.unpack(self.view.format, bytesitem) */ __pyx_t_1 = __Pyx_PyBytes_FromStringAndSize(__pyx_v_itemp + 0, __pyx_v_self->view.itemsize - 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 491, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_bytesitem = ((PyObject*)__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_2, &__pyx_t_3, &__pyx_t_4); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); __Pyx_XGOTREF(__pyx_t_4); /*try:*/ { /* "View.MemoryView":493 * bytesitem = itemp[:self.view.itemsize] * try: * result = struct.unpack(self.view.format, bytesitem) # <<<<<<<<<<<<<< * except struct.error: * raise ValueError("Unable to convert item to object") */ __pyx_t_5 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_unpack); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = NULL; __pyx_t_8 = 0; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_5))) { __pyx_t_7 = PyMethod_GET_SELF(__pyx_t_5); if (likely(__pyx_t_7)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_5); __Pyx_INCREF(__pyx_t_7); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_5, function); __pyx_t_8 = 1; } } #if CYTHON_FAST_PYCALL if (PyFunction_Check(__pyx_t_5)) { PyObject *__pyx_temp[3] = {__pyx_t_7, __pyx_t_6, __pyx_v_bytesitem}; __pyx_t_1 = __Pyx_PyFunction_FastCall(__pyx_t_5, __pyx_temp+1-__pyx_t_8, 2+__pyx_t_8); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } else #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(__pyx_t_5)) { PyObject *__pyx_temp[3] = {__pyx_t_7, __pyx_t_6, __pyx_v_bytesitem}; __pyx_t_1 = __Pyx_PyCFunction_FastCall(__pyx_t_5, __pyx_temp+1-__pyx_t_8, 2+__pyx_t_8); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } else #endif { __pyx_t_9 = PyTuple_New(2+__pyx_t_8); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_9); if (__pyx_t_7) { __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __pyx_t_7 = NULL; } __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_9, 0+__pyx_t_8, __pyx_t_6); __Pyx_INCREF(__pyx_v_bytesitem); __Pyx_GIVEREF(__pyx_v_bytesitem); PyTuple_SET_ITEM(__pyx_t_9, 1+__pyx_t_8, __pyx_v_bytesitem); __pyx_t_6 = 0; __pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_5, __pyx_t_9, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_result = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ } /* "View.MemoryView":497 * raise ValueError("Unable to convert item to object") * else: * if len(self.view.format) == 1: # <<<<<<<<<<<<<< * return result[0] * return result */ /*else:*/ { __pyx_t_10 = strlen(__pyx_v_self->view.format); __pyx_t_11 = ((__pyx_t_10 == 1) != 0); if (__pyx_t_11) { /* "View.MemoryView":498 * else: * if len(self.view.format) == 1: * return result[0] # <<<<<<<<<<<<<< * return result * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_GetItemInt(__pyx_v_result, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 498, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L6_except_return; /* "View.MemoryView":497 * raise ValueError("Unable to convert item to object") * else: * if len(self.view.format) == 1: # <<<<<<<<<<<<<< * return result[0] * return result */ } /* "View.MemoryView":499 * if len(self.view.format) == 1: * return result[0] * return result # <<<<<<<<<<<<<< * * cdef assign_item_from_object(self, char *itemp, object value): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_result); __pyx_r = __pyx_v_result; goto __pyx_L6_except_return; } __pyx_L3_error:; __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_XDECREF(__pyx_t_9); __pyx_t_9 = 0; /* "View.MemoryView":494 * try: * result = struct.unpack(self.view.format, bytesitem) * except struct.error: # <<<<<<<<<<<<<< * raise ValueError("Unable to convert item to object") * else: */ __Pyx_ErrFetch(&__pyx_t_1, &__pyx_t_5, &__pyx_t_9); __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_error); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 494, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_8 = __Pyx_PyErr_GivenExceptionMatches(__pyx_t_1, __pyx_t_6); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_ErrRestore(__pyx_t_1, __pyx_t_5, __pyx_t_9); __pyx_t_1 = 0; __pyx_t_5 = 0; __pyx_t_9 = 0; if (__pyx_t_8) { __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_9, &__pyx_t_5, &__pyx_t_1) < 0) __PYX_ERR(2, 494, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_1); /* "View.MemoryView":495 * result = struct.unpack(self.view.format, bytesitem) * except struct.error: * raise ValueError("Unable to convert item to object") # <<<<<<<<<<<<<< * else: * if len(self.view.format) == 1: */ __pyx_t_6 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__11, NULL); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 495, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_Raise(__pyx_t_6, 0, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __PYX_ERR(2, 495, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4); goto __pyx_L1_error; __pyx_L6_except_return:; __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4); goto __pyx_L0; } /* "View.MemoryView":485 * self.assign_item_from_object(itemp, value) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_9); __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_struct); __Pyx_XDECREF(__pyx_v_bytesitem); __Pyx_XDECREF(__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":501 * return result * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) { PyObject *__pyx_v_struct = NULL; char __pyx_v_c; PyObject *__pyx_v_bytesvalue = 0; Py_ssize_t __pyx_v_i; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_t_3; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; int __pyx_t_7; PyObject *__pyx_t_8 = NULL; Py_ssize_t __pyx_t_9; PyObject *__pyx_t_10 = NULL; char *__pyx_t_11; char *__pyx_t_12; char *__pyx_t_13; char *__pyx_t_14; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("assign_item_from_object", 0); /* "View.MemoryView":504 * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" * import struct # <<<<<<<<<<<<<< * cdef char c * cdef bytes bytesvalue */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 504, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_struct = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":509 * cdef Py_ssize_t i * * if isinstance(value, tuple): # <<<<<<<<<<<<<< * bytesvalue = struct.pack(self.view.format, *value) * else: */ __pyx_t_2 = PyTuple_Check(__pyx_v_value); __pyx_t_3 = (__pyx_t_2 != 0); if (__pyx_t_3) { /* "View.MemoryView":510 * * if isinstance(value, tuple): * bytesvalue = struct.pack(self.view.format, *value) # <<<<<<<<<<<<<< * else: * bytesvalue = struct.pack(self.view.format, value) */ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_4 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PySequence_Tuple(__pyx_v_value); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_6 = PyNumber_Add(__pyx_t_5, __pyx_t_4); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_6, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (!(likely(PyBytes_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_4)->tp_name), 0))) __PYX_ERR(2, 510, __pyx_L1_error) __pyx_v_bytesvalue = ((PyObject*)__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":509 * cdef Py_ssize_t i * * if isinstance(value, tuple): # <<<<<<<<<<<<<< * bytesvalue = struct.pack(self.view.format, *value) * else: */ goto __pyx_L3; } /* "View.MemoryView":512 * bytesvalue = struct.pack(self.view.format, *value) * else: * bytesvalue = struct.pack(self.view.format, value) # <<<<<<<<<<<<<< * * for i, c in enumerate(bytesvalue): */ /*else*/ { __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_1 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_5 = NULL; __pyx_t_7 = 0; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_6))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_6); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_6); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_6, function); __pyx_t_7 = 1; } } #if CYTHON_FAST_PYCALL if (PyFunction_Check(__pyx_t_6)) { PyObject *__pyx_temp[3] = {__pyx_t_5, __pyx_t_1, __pyx_v_value}; __pyx_t_4 = __Pyx_PyFunction_FastCall(__pyx_t_6, __pyx_temp+1-__pyx_t_7, 2+__pyx_t_7); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; } else #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(__pyx_t_6)) { PyObject *__pyx_temp[3] = {__pyx_t_5, __pyx_t_1, __pyx_v_value}; __pyx_t_4 = __Pyx_PyCFunction_FastCall(__pyx_t_6, __pyx_temp+1-__pyx_t_7, 2+__pyx_t_7); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; } else #endif { __pyx_t_8 = PyTuple_New(2+__pyx_t_7); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); if (__pyx_t_5) { __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_5); __pyx_t_5 = NULL; } __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_8, 0+__pyx_t_7, __pyx_t_1); __Pyx_INCREF(__pyx_v_value); __Pyx_GIVEREF(__pyx_v_value); PyTuple_SET_ITEM(__pyx_t_8, 1+__pyx_t_7, __pyx_v_value); __pyx_t_1 = 0; __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_t_8, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; } __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (!(likely(PyBytes_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_4)->tp_name), 0))) __PYX_ERR(2, 512, __pyx_L1_error) __pyx_v_bytesvalue = ((PyObject*)__pyx_t_4); __pyx_t_4 = 0; } __pyx_L3:; /* "View.MemoryView":514 * bytesvalue = struct.pack(self.view.format, value) * * for i, c in enumerate(bytesvalue): # <<<<<<<<<<<<<< * itemp[i] = c * */ __pyx_t_9 = 0; if (unlikely(__pyx_v_bytesvalue == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' is not iterable"); __PYX_ERR(2, 514, __pyx_L1_error) } __Pyx_INCREF(__pyx_v_bytesvalue); __pyx_t_10 = __pyx_v_bytesvalue; __pyx_t_12 = PyBytes_AS_STRING(__pyx_t_10); __pyx_t_13 = (__pyx_t_12 + PyBytes_GET_SIZE(__pyx_t_10)); for (__pyx_t_14 = __pyx_t_12; __pyx_t_14 < __pyx_t_13; __pyx_t_14++) { __pyx_t_11 = __pyx_t_14; __pyx_v_c = (__pyx_t_11[0]); /* "View.MemoryView":515 * * for i, c in enumerate(bytesvalue): * itemp[i] = c # <<<<<<<<<<<<<< * * @cname('getbuffer') */ __pyx_v_i = __pyx_t_9; /* "View.MemoryView":514 * bytesvalue = struct.pack(self.view.format, value) * * for i, c in enumerate(bytesvalue): # <<<<<<<<<<<<<< * itemp[i] = c * */ __pyx_t_9 = (__pyx_t_9 + 1); /* "View.MemoryView":515 * * for i, c in enumerate(bytesvalue): * itemp[i] = c # <<<<<<<<<<<<<< * * @cname('getbuffer') */ (__pyx_v_itemp[__pyx_v_i]) = __pyx_v_c; } __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; /* "View.MemoryView":501 * return result * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_8); __Pyx_XDECREF(__pyx_t_10); __Pyx_AddTraceback("View.MemoryView.memoryview.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_struct); __Pyx_XDECREF(__pyx_v_bytesvalue); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":518 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") */ /* Python wrapper */ static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; Py_ssize_t *__pyx_t_4; char *__pyx_t_5; void *__pyx_t_6; int __pyx_t_7; Py_ssize_t __pyx_t_8; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; if (__pyx_v_info == NULL) { PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete"); return -1; } __Pyx_RefNannySetupContext("__getbuffer__", 0); __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None); __Pyx_GIVEREF(__pyx_v_info->obj); /* "View.MemoryView":519 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: # <<<<<<<<<<<<<< * raise ValueError("Cannot create writable memory view from read-only memoryview") * */ __pyx_t_2 = ((__pyx_v_flags & PyBUF_WRITABLE) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } __pyx_t_2 = (__pyx_v_self->view.readonly != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (unlikely(__pyx_t_1)) { /* "View.MemoryView":520 * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") # <<<<<<<<<<<<<< * * if flags & PyBUF_ND: */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__12, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 520, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 520, __pyx_L1_error) /* "View.MemoryView":519 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: # <<<<<<<<<<<<<< * raise ValueError("Cannot create writable memory view from read-only memoryview") * */ } /* "View.MemoryView":522 * raise ValueError("Cannot create writable memory view from read-only memoryview") * * if flags & PyBUF_ND: # <<<<<<<<<<<<<< * info.shape = self.view.shape * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_ND) != 0); if (__pyx_t_1) { /* "View.MemoryView":523 * * if flags & PyBUF_ND: * info.shape = self.view.shape # <<<<<<<<<<<<<< * else: * info.shape = NULL */ __pyx_t_4 = __pyx_v_self->view.shape; __pyx_v_info->shape = __pyx_t_4; /* "View.MemoryView":522 * raise ValueError("Cannot create writable memory view from read-only memoryview") * * if flags & PyBUF_ND: # <<<<<<<<<<<<<< * info.shape = self.view.shape * else: */ goto __pyx_L6; } /* "View.MemoryView":525 * info.shape = self.view.shape * else: * info.shape = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_STRIDES: */ /*else*/ { __pyx_v_info->shape = NULL; } __pyx_L6:; /* "View.MemoryView":527 * info.shape = NULL * * if flags & PyBUF_STRIDES: # <<<<<<<<<<<<<< * info.strides = self.view.strides * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_STRIDES) != 0); if (__pyx_t_1) { /* "View.MemoryView":528 * * if flags & PyBUF_STRIDES: * info.strides = self.view.strides # <<<<<<<<<<<<<< * else: * info.strides = NULL */ __pyx_t_4 = __pyx_v_self->view.strides; __pyx_v_info->strides = __pyx_t_4; /* "View.MemoryView":527 * info.shape = NULL * * if flags & PyBUF_STRIDES: # <<<<<<<<<<<<<< * info.strides = self.view.strides * else: */ goto __pyx_L7; } /* "View.MemoryView":530 * info.strides = self.view.strides * else: * info.strides = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_INDIRECT: */ /*else*/ { __pyx_v_info->strides = NULL; } __pyx_L7:; /* "View.MemoryView":532 * info.strides = NULL * * if flags & PyBUF_INDIRECT: # <<<<<<<<<<<<<< * info.suboffsets = self.view.suboffsets * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_INDIRECT) != 0); if (__pyx_t_1) { /* "View.MemoryView":533 * * if flags & PyBUF_INDIRECT: * info.suboffsets = self.view.suboffsets # <<<<<<<<<<<<<< * else: * info.suboffsets = NULL */ __pyx_t_4 = __pyx_v_self->view.suboffsets; __pyx_v_info->suboffsets = __pyx_t_4; /* "View.MemoryView":532 * info.strides = NULL * * if flags & PyBUF_INDIRECT: # <<<<<<<<<<<<<< * info.suboffsets = self.view.suboffsets * else: */ goto __pyx_L8; } /* "View.MemoryView":535 * info.suboffsets = self.view.suboffsets * else: * info.suboffsets = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ /*else*/ { __pyx_v_info->suboffsets = NULL; } __pyx_L8:; /* "View.MemoryView":537 * info.suboffsets = NULL * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.view.format * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":538 * * if flags & PyBUF_FORMAT: * info.format = self.view.format # <<<<<<<<<<<<<< * else: * info.format = NULL */ __pyx_t_5 = __pyx_v_self->view.format; __pyx_v_info->format = __pyx_t_5; /* "View.MemoryView":537 * info.suboffsets = NULL * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.view.format * else: */ goto __pyx_L9; } /* "View.MemoryView":540 * info.format = self.view.format * else: * info.format = NULL # <<<<<<<<<<<<<< * * info.buf = self.view.buf */ /*else*/ { __pyx_v_info->format = NULL; } __pyx_L9:; /* "View.MemoryView":542 * info.format = NULL * * info.buf = self.view.buf # <<<<<<<<<<<<<< * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize */ __pyx_t_6 = __pyx_v_self->view.buf; __pyx_v_info->buf = __pyx_t_6; /* "View.MemoryView":543 * * info.buf = self.view.buf * info.ndim = self.view.ndim # <<<<<<<<<<<<<< * info.itemsize = self.view.itemsize * info.len = self.view.len */ __pyx_t_7 = __pyx_v_self->view.ndim; __pyx_v_info->ndim = __pyx_t_7; /* "View.MemoryView":544 * info.buf = self.view.buf * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize # <<<<<<<<<<<<<< * info.len = self.view.len * info.readonly = self.view.readonly */ __pyx_t_8 = __pyx_v_self->view.itemsize; __pyx_v_info->itemsize = __pyx_t_8; /* "View.MemoryView":545 * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize * info.len = self.view.len # <<<<<<<<<<<<<< * info.readonly = self.view.readonly * info.obj = self */ __pyx_t_8 = __pyx_v_self->view.len; __pyx_v_info->len = __pyx_t_8; /* "View.MemoryView":546 * info.itemsize = self.view.itemsize * info.len = self.view.len * info.readonly = self.view.readonly # <<<<<<<<<<<<<< * info.obj = self * */ __pyx_t_1 = __pyx_v_self->view.readonly; __pyx_v_info->readonly = __pyx_t_1; /* "View.MemoryView":547 * info.len = self.view.len * info.readonly = self.view.readonly * info.obj = self # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = ((PyObject *)__pyx_v_self); /* "View.MemoryView":518 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; if (__pyx_v_info->obj != NULL) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } goto __pyx_L2; __pyx_L0:; if (__pyx_v_info->obj == Py_None) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } __pyx_L2:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":553 * * @property * def T(self): # <<<<<<<<<<<<<< * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self) { struct __pyx_memoryviewslice_obj *__pyx_v_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":554 * @property * def T(self): * cdef _memoryviewslice result = memoryview_copy(self) # <<<<<<<<<<<<<< * transpose_memslice(&result.from_slice) * return result */ __pyx_t_1 = __pyx_memoryview_copy_object(__pyx_v_self); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 554, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (!(likely(((__pyx_t_1) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_1, __pyx_memoryviewslice_type))))) __PYX_ERR(2, 554, __pyx_L1_error) __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":555 * def T(self): * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) # <<<<<<<<<<<<<< * return result * */ __pyx_t_2 = __pyx_memslice_transpose((&__pyx_v_result->from_slice)); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(2, 555, __pyx_L1_error) /* "View.MemoryView":556 * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) * return result # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":553 * * @property * def T(self): # <<<<<<<<<<<<<< * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.T.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":559 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.obj * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":560 * @property * def base(self): * return self.obj # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->obj); __pyx_r = __pyx_v_self->obj; goto __pyx_L0; /* "View.MemoryView":559 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.obj * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":563 * * @property * def shape(self): # <<<<<<<<<<<<<< * return tuple([length for length in self.view.shape[:self.view.ndim]]) * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_length; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":564 * @property * def shape(self): * return tuple([length for length in self.view.shape[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyList_New(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = (__pyx_v_self->view.shape + __pyx_v_self->view.ndim); for (__pyx_t_4 = __pyx_v_self->view.shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { __pyx_t_2 = __pyx_t_4; __pyx_v_length = (__pyx_t_2[0]); __pyx_t_5 = PyInt_FromSsize_t(__pyx_v_length); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); if (unlikely(__Pyx_ListComp_Append(__pyx_t_1, (PyObject*)__pyx_t_5))) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; } __pyx_t_5 = PyList_AsTuple(((PyObject*)__pyx_t_1)); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "View.MemoryView":563 * * @property * def shape(self): # <<<<<<<<<<<<<< * return tuple([length for length in self.view.shape[:self.view.ndim]]) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.shape.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":567 * * @property * def strides(self): # <<<<<<<<<<<<<< * if self.view.strides == NULL: * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_stride; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; PyObject *__pyx_t_6 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":568 * @property * def strides(self): * if self.view.strides == NULL: # <<<<<<<<<<<<<< * * raise ValueError("Buffer view does not expose strides") */ __pyx_t_1 = ((__pyx_v_self->view.strides == NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":570 * if self.view.strides == NULL: * * raise ValueError("Buffer view does not expose strides") # <<<<<<<<<<<<<< * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) */ __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__13, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 570, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(2, 570, __pyx_L1_error) /* "View.MemoryView":568 * @property * def strides(self): * if self.view.strides == NULL: # <<<<<<<<<<<<<< * * raise ValueError("Buffer view does not expose strides") */ } /* "View.MemoryView":572 * raise ValueError("Buffer view does not expose strides") * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = (__pyx_v_self->view.strides + __pyx_v_self->view.ndim); for (__pyx_t_5 = __pyx_v_self->view.strides; __pyx_t_5 < __pyx_t_4; __pyx_t_5++) { __pyx_t_3 = __pyx_t_5; __pyx_v_stride = (__pyx_t_3[0]); __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_stride); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); if (unlikely(__Pyx_ListComp_Append(__pyx_t_2, (PyObject*)__pyx_t_6))) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } __pyx_t_6 = PyList_AsTuple(((PyObject*)__pyx_t_2)); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_6; __pyx_t_6 = 0; goto __pyx_L0; /* "View.MemoryView":567 * * @property * def strides(self): # <<<<<<<<<<<<<< * if self.view.strides == NULL: * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_6); __Pyx_AddTraceback("View.MemoryView.memoryview.strides.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":575 * * @property * def suboffsets(self): # <<<<<<<<<<<<<< * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; Py_ssize_t *__pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":576 * @property * def suboffsets(self): * if self.view.suboffsets == NULL: # <<<<<<<<<<<<<< * return (-1,) * self.view.ndim * */ __pyx_t_1 = ((__pyx_v_self->view.suboffsets == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":577 * def suboffsets(self): * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim # <<<<<<<<<<<<<< * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyInt_From_int(__pyx_v_self->view.ndim); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_Multiply(__pyx_tuple__14, __pyx_t_2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":576 * @property * def suboffsets(self): * if self.view.suboffsets == NULL: # <<<<<<<<<<<<<< * return (-1,) * self.view.ndim * */ } /* "View.MemoryView":579 * return (-1,) * self.view.ndim * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_5 = (__pyx_v_self->view.suboffsets + __pyx_v_self->view.ndim); for (__pyx_t_6 = __pyx_v_self->view.suboffsets; __pyx_t_6 < __pyx_t_5; __pyx_t_6++) { __pyx_t_4 = __pyx_t_6; __pyx_v_suboffset = (__pyx_t_4[0]); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_suboffset); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (unlikely(__Pyx_ListComp_Append(__pyx_t_3, (PyObject*)__pyx_t_2))) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_t_2 = PyList_AsTuple(((PyObject*)__pyx_t_3)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":575 * * @property * def suboffsets(self): # <<<<<<<<<<<<<< * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.suboffsets.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":582 * * @property * def ndim(self): # <<<<<<<<<<<<<< * return self.view.ndim * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":583 * @property * def ndim(self): * return self.view.ndim # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_self->view.ndim); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 583, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":582 * * @property * def ndim(self): # <<<<<<<<<<<<<< * return self.view.ndim * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.ndim.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":586 * * @property * def itemsize(self): # <<<<<<<<<<<<<< * return self.view.itemsize * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":587 * @property * def itemsize(self): * return self.view.itemsize # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 587, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":586 * * @property * def itemsize(self): # <<<<<<<<<<<<<< * return self.view.itemsize * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.itemsize.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":590 * * @property * def nbytes(self): # <<<<<<<<<<<<<< * return self.size * self.view.itemsize * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":591 * @property * def nbytes(self): * return self.size * self.view.itemsize # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_size); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_Multiply(__pyx_t_1, __pyx_t_2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":590 * * @property * def nbytes(self): # <<<<<<<<<<<<<< * return self.size * self.view.itemsize * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.nbytes.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":594 * * @property * def size(self): # <<<<<<<<<<<<<< * if self._size is None: * result = 1 */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_v_result = NULL; PyObject *__pyx_v_length = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; PyObject *__pyx_t_6 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":595 * @property * def size(self): * if self._size is None: # <<<<<<<<<<<<<< * result = 1 * */ __pyx_t_1 = (__pyx_v_self->_size == Py_None); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":596 * def size(self): * if self._size is None: * result = 1 # <<<<<<<<<<<<<< * * for length in self.view.shape[:self.view.ndim]: */ __Pyx_INCREF(__pyx_int_1); __pyx_v_result = __pyx_int_1; /* "View.MemoryView":598 * result = 1 * * for length in self.view.shape[:self.view.ndim]: # <<<<<<<<<<<<<< * result *= length * */ __pyx_t_4 = (__pyx_v_self->view.shape + __pyx_v_self->view.ndim); for (__pyx_t_5 = __pyx_v_self->view.shape; __pyx_t_5 < __pyx_t_4; __pyx_t_5++) { __pyx_t_3 = __pyx_t_5; __pyx_t_6 = PyInt_FromSsize_t((__pyx_t_3[0])); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 598, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_6); __pyx_t_6 = 0; /* "View.MemoryView":599 * * for length in self.view.shape[:self.view.ndim]: * result *= length # <<<<<<<<<<<<<< * * self._size = result */ __pyx_t_6 = PyNumber_InPlaceMultiply(__pyx_v_result, __pyx_v_length); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 599, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF_SET(__pyx_v_result, __pyx_t_6); __pyx_t_6 = 0; } /* "View.MemoryView":601 * result *= length * * self._size = result # <<<<<<<<<<<<<< * * return self._size */ __Pyx_INCREF(__pyx_v_result); __Pyx_GIVEREF(__pyx_v_result); __Pyx_GOTREF(__pyx_v_self->_size); __Pyx_DECREF(__pyx_v_self->_size); __pyx_v_self->_size = __pyx_v_result; /* "View.MemoryView":595 * @property * def size(self): * if self._size is None: # <<<<<<<<<<<<<< * result = 1 * */ } /* "View.MemoryView":603 * self._size = result * * return self._size # <<<<<<<<<<<<<< * * def __len__(self): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->_size); __pyx_r = __pyx_v_self->_size; goto __pyx_L0; /* "View.MemoryView":594 * * @property * def size(self): # <<<<<<<<<<<<<< * if self._size is None: * result = 1 */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_6); __Pyx_AddTraceback("View.MemoryView.memoryview.size.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_result); __Pyx_XDECREF(__pyx_v_length); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":605 * return self._size * * def __len__(self): # <<<<<<<<<<<<<< * if self.view.ndim >= 1: * return self.view.shape[0] */ /* Python wrapper */ static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self); /*proto*/ static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("__len__", 0); /* "View.MemoryView":606 * * def __len__(self): * if self.view.ndim >= 1: # <<<<<<<<<<<<<< * return self.view.shape[0] * */ __pyx_t_1 = ((__pyx_v_self->view.ndim >= 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":607 * def __len__(self): * if self.view.ndim >= 1: * return self.view.shape[0] # <<<<<<<<<<<<<< * * return 0 */ __pyx_r = (__pyx_v_self->view.shape[0]); goto __pyx_L0; /* "View.MemoryView":606 * * def __len__(self): * if self.view.ndim >= 1: # <<<<<<<<<<<<<< * return self.view.shape[0] * */ } /* "View.MemoryView":609 * return self.view.shape[0] * * return 0 # <<<<<<<<<<<<<< * * def __repr__(self): */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":605 * return self._size * * def __len__(self): # <<<<<<<<<<<<<< * if self.view.ndim >= 1: * return self.view.shape[0] */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":611 * return 0 * * def __repr__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__, * id(self)) */ /* Python wrapper */ static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__repr__", 0); /* "View.MemoryView":612 * * def __repr__(self): * return "" % (self.base.__class__.__name__, # <<<<<<<<<<<<<< * id(self)) * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":613 * def __repr__(self): * return "" % (self.base.__class__.__name__, * id(self)) # <<<<<<<<<<<<<< * * def __str__(self): */ __pyx_t_2 = __Pyx_PyObject_CallOneArg(__pyx_builtin_id, ((PyObject *)__pyx_v_self)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 613, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); /* "View.MemoryView":612 * * def __repr__(self): * return "" % (self.base.__class__.__name__, # <<<<<<<<<<<<<< * id(self)) * */ __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_t_3); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":611 * return 0 * * def __repr__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__, * id(self)) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.__repr__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":615 * id(self)) * * def __str__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__,) * */ /* Python wrapper */ static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__str__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__str__", 0); /* "View.MemoryView":616 * * def __str__(self): * return "" % (self.base.__class__.__name__,) # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_object, __pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":615 * id(self)) * * def __str__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__,) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.__str__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":619 * * * def is_c_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* Python wrapper */ static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("is_c_contig (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice *__pyx_v_mslice; __Pyx_memviewslice __pyx_v_tmp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("is_c_contig", 0); /* "View.MemoryView":622 * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) # <<<<<<<<<<<<<< * return slice_is_contig(mslice[0], 'C', self.view.ndim) * */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 622, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":623 * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) * return slice_is_contig(mslice[0], 'C', self.view.ndim) # <<<<<<<<<<<<<< * * def is_f_contig(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig((__pyx_v_mslice[0]), 'C', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 623, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":619 * * * def is_c_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.is_c_contig", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":625 * return slice_is_contig(mslice[0], 'C', self.view.ndim) * * def is_f_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* Python wrapper */ static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("is_f_contig (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice *__pyx_v_mslice; __Pyx_memviewslice __pyx_v_tmp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("is_f_contig", 0); /* "View.MemoryView":628 * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) # <<<<<<<<<<<<<< * return slice_is_contig(mslice[0], 'F', self.view.ndim) * */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 628, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":629 * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) * return slice_is_contig(mslice[0], 'F', self.view.ndim) # <<<<<<<<<<<<<< * * def copy(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig((__pyx_v_mslice[0]), 'F', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 629, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":625 * return slice_is_contig(mslice[0], 'C', self.view.ndim) * * def is_f_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.is_f_contig", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":631 * return slice_is_contig(mslice[0], 'F', self.view.ndim) * * def copy(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS */ /* Python wrapper */ static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("copy (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice __pyx_v_mslice; int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice __pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("copy", 0); /* "View.MemoryView":633 * def copy(self): * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS # <<<<<<<<<<<<<< * * slice_copy(self, &mslice) */ __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_F_CONTIGUOUS)); /* "View.MemoryView":635 * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS * * slice_copy(self, &mslice) # <<<<<<<<<<<<<< * mslice = slice_copy_contig(&mslice, "c", self.view.ndim, * self.view.itemsize, */ __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_mslice)); /* "View.MemoryView":636 * * slice_copy(self, &mslice) * mslice = slice_copy_contig(&mslice, "c", self.view.ndim, # <<<<<<<<<<<<<< * self.view.itemsize, * flags|PyBUF_C_CONTIGUOUS, */ __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_mslice), ((char *)"c"), __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_C_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) __PYX_ERR(2, 636, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":641 * self.dtype_is_object) * * return memoryview_copy_from_slice(self, &mslice) # <<<<<<<<<<<<<< * * def copy_fortran(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_mslice)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 641, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":631 * return slice_is_contig(mslice[0], 'F', self.view.ndim) * * def copy(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.copy", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":643 * return memoryview_copy_from_slice(self, &mslice) * * def copy_fortran(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS */ /* Python wrapper */ static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("copy_fortran (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice __pyx_v_src; __Pyx_memviewslice __pyx_v_dst; int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice __pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("copy_fortran", 0); /* "View.MemoryView":645 * def copy_fortran(self): * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS # <<<<<<<<<<<<<< * * slice_copy(self, &src) */ __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_C_CONTIGUOUS)); /* "View.MemoryView":647 * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS * * slice_copy(self, &src) # <<<<<<<<<<<<<< * dst = slice_copy_contig(&src, "fortran", self.view.ndim, * self.view.itemsize, */ __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_src)); /* "View.MemoryView":648 * * slice_copy(self, &src) * dst = slice_copy_contig(&src, "fortran", self.view.ndim, # <<<<<<<<<<<<<< * self.view.itemsize, * flags|PyBUF_F_CONTIGUOUS, */ __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_src), ((char *)"fortran"), __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_F_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) __PYX_ERR(2, 648, __pyx_L1_error) __pyx_v_dst = __pyx_t_1; /* "View.MemoryView":653 * self.dtype_is_object) * * return memoryview_copy_from_slice(self, &dst) # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_dst)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 653, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":643 * return memoryview_copy_from_slice(self, &mslice) * * def copy_fortran(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.copy_fortran", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryview_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_memoryview_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryview___reduce_cython__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__15, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryview_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_memoryview_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryview_2__setstate_cython__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__16, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":657 * * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): # <<<<<<<<<<<<<< * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo */ static PyObject *__pyx_memoryview_new(PyObject *__pyx_v_o, int __pyx_v_flags, int __pyx_v_dtype_is_object, __Pyx_TypeInfo *__pyx_v_typeinfo) { struct __pyx_memoryview_obj *__pyx_v_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_cwrapper", 0); /* "View.MemoryView":658 * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): * cdef memoryview result = memoryview(o, flags, dtype_is_object) # <<<<<<<<<<<<<< * result.typeinfo = typeinfo * return result */ __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_o); __Pyx_GIVEREF(__pyx_v_o); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_o); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_memoryview_obj *)__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":659 * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo # <<<<<<<<<<<<<< * return result * */ __pyx_v_result->typeinfo = __pyx_v_typeinfo; /* "View.MemoryView":660 * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo * return result # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_check') */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":657 * * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): # <<<<<<<<<<<<<< * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":663 * * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): # <<<<<<<<<<<<<< * return isinstance(o, memoryview) * */ static CYTHON_INLINE int __pyx_memoryview_check(PyObject *__pyx_v_o) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("memoryview_check", 0); /* "View.MemoryView":664 * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): * return isinstance(o, memoryview) # <<<<<<<<<<<<<< * * cdef tuple _unellipsify(object index, int ndim): */ __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_o, __pyx_memoryview_type); __pyx_r = __pyx_t_1; goto __pyx_L0; /* "View.MemoryView":663 * * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): # <<<<<<<<<<<<<< * return isinstance(o, memoryview) * */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":666 * return isinstance(o, memoryview) * * cdef tuple _unellipsify(object index, int ndim): # <<<<<<<<<<<<<< * """ * Replace all ellipses with full slices and fill incomplete indices with */ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) { PyObject *__pyx_v_tup = NULL; PyObject *__pyx_v_result = NULL; int __pyx_v_have_slices; int __pyx_v_seen_ellipsis; CYTHON_UNUSED PyObject *__pyx_v_idx = NULL; PyObject *__pyx_v_item = NULL; Py_ssize_t __pyx_v_nslices; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; Py_ssize_t __pyx_t_5; PyObject *(*__pyx_t_6)(PyObject *); PyObject *__pyx_t_7 = NULL; Py_ssize_t __pyx_t_8; int __pyx_t_9; int __pyx_t_10; PyObject *__pyx_t_11 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("_unellipsify", 0); /* "View.MemoryView":671 * full slices. * """ * if not isinstance(index, tuple): # <<<<<<<<<<<<<< * tup = (index,) * else: */ __pyx_t_1 = PyTuple_Check(__pyx_v_index); __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":672 * """ * if not isinstance(index, tuple): * tup = (index,) # <<<<<<<<<<<<<< * else: * tup = index */ __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 672, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_index); __Pyx_GIVEREF(__pyx_v_index); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_index); __pyx_v_tup = __pyx_t_3; __pyx_t_3 = 0; /* "View.MemoryView":671 * full slices. * """ * if not isinstance(index, tuple): # <<<<<<<<<<<<<< * tup = (index,) * else: */ goto __pyx_L3; } /* "View.MemoryView":674 * tup = (index,) * else: * tup = index # <<<<<<<<<<<<<< * * result = [] */ /*else*/ { __Pyx_INCREF(__pyx_v_index); __pyx_v_tup = __pyx_v_index; } __pyx_L3:; /* "View.MemoryView":676 * tup = index * * result = [] # <<<<<<<<<<<<<< * have_slices = False * seen_ellipsis = False */ __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 676, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_v_result = ((PyObject*)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":677 * * result = [] * have_slices = False # <<<<<<<<<<<<<< * seen_ellipsis = False * for idx, item in enumerate(tup): */ __pyx_v_have_slices = 0; /* "View.MemoryView":678 * result = [] * have_slices = False * seen_ellipsis = False # <<<<<<<<<<<<<< * for idx, item in enumerate(tup): * if item is Ellipsis: */ __pyx_v_seen_ellipsis = 0; /* "View.MemoryView":679 * have_slices = False * seen_ellipsis = False * for idx, item in enumerate(tup): # <<<<<<<<<<<<<< * if item is Ellipsis: * if not seen_ellipsis: */ __Pyx_INCREF(__pyx_int_0); __pyx_t_3 = __pyx_int_0; if (likely(PyList_CheckExact(__pyx_v_tup)) || PyTuple_CheckExact(__pyx_v_tup)) { __pyx_t_4 = __pyx_v_tup; __Pyx_INCREF(__pyx_t_4); __pyx_t_5 = 0; __pyx_t_6 = NULL; } else { __pyx_t_5 = -1; __pyx_t_4 = PyObject_GetIter(__pyx_v_tup); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_6 = Py_TYPE(__pyx_t_4)->tp_iternext; if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 679, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_6)) { if (likely(PyList_CheckExact(__pyx_t_4))) { if (__pyx_t_5 >= PyList_GET_SIZE(__pyx_t_4)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_7 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) __PYX_ERR(2, 679, __pyx_L1_error) #else __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); #endif } else { if (__pyx_t_5 >= PyTuple_GET_SIZE(__pyx_t_4)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_7 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) __PYX_ERR(2, 679, __pyx_L1_error) #else __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); #endif } } else { __pyx_t_7 = __pyx_t_6(__pyx_t_4); if (unlikely(!__pyx_t_7)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(2, 679, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_7); } __Pyx_XDECREF_SET(__pyx_v_item, __pyx_t_7); __pyx_t_7 = 0; __Pyx_INCREF(__pyx_t_3); __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_3); __pyx_t_7 = __Pyx_PyInt_AddObjC(__pyx_t_3, __pyx_int_1, 1, 0, 0); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = __pyx_t_7; __pyx_t_7 = 0; /* "View.MemoryView":680 * seen_ellipsis = False * for idx, item in enumerate(tup): * if item is Ellipsis: # <<<<<<<<<<<<<< * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) */ __pyx_t_2 = (__pyx_v_item == __pyx_builtin_Ellipsis); __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":681 * for idx, item in enumerate(tup): * if item is Ellipsis: * if not seen_ellipsis: # <<<<<<<<<<<<<< * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True */ __pyx_t_1 = ((!(__pyx_v_seen_ellipsis != 0)) != 0); if (__pyx_t_1) { /* "View.MemoryView":682 * if item is Ellipsis: * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) # <<<<<<<<<<<<<< * seen_ellipsis = True * else: */ __pyx_t_8 = PyObject_Length(__pyx_v_tup); if (unlikely(__pyx_t_8 == ((Py_ssize_t)-1))) __PYX_ERR(2, 682, __pyx_L1_error) __pyx_t_7 = PyList_New(1 * ((((__pyx_v_ndim - __pyx_t_8) + 1)<0) ? 0:((__pyx_v_ndim - __pyx_t_8) + 1))); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 682, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); { Py_ssize_t __pyx_temp; for (__pyx_temp=0; __pyx_temp < ((__pyx_v_ndim - __pyx_t_8) + 1); __pyx_temp++) { __Pyx_INCREF(__pyx_slice__17); __Pyx_GIVEREF(__pyx_slice__17); PyList_SET_ITEM(__pyx_t_7, __pyx_temp, __pyx_slice__17); } } __pyx_t_9 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_7); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 682, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; /* "View.MemoryView":683 * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True # <<<<<<<<<<<<<< * else: * result.append(slice(None)) */ __pyx_v_seen_ellipsis = 1; /* "View.MemoryView":681 * for idx, item in enumerate(tup): * if item is Ellipsis: * if not seen_ellipsis: # <<<<<<<<<<<<<< * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True */ goto __pyx_L7; } /* "View.MemoryView":685 * seen_ellipsis = True * else: * result.append(slice(None)) # <<<<<<<<<<<<<< * have_slices = True * else: */ /*else*/ { __pyx_t_9 = __Pyx_PyList_Append(__pyx_v_result, __pyx_slice__17); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 685, __pyx_L1_error) } __pyx_L7:; /* "View.MemoryView":686 * else: * result.append(slice(None)) * have_slices = True # <<<<<<<<<<<<<< * else: * if not isinstance(item, slice) and not PyIndex_Check(item): */ __pyx_v_have_slices = 1; /* "View.MemoryView":680 * seen_ellipsis = False * for idx, item in enumerate(tup): * if item is Ellipsis: # <<<<<<<<<<<<<< * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) */ goto __pyx_L6; } /* "View.MemoryView":688 * have_slices = True * else: * if not isinstance(item, slice) and not PyIndex_Check(item): # <<<<<<<<<<<<<< * raise TypeError("Cannot index with type '%s'" % type(item)) * */ /*else*/ { __pyx_t_2 = PySlice_Check(__pyx_v_item); __pyx_t_10 = ((!(__pyx_t_2 != 0)) != 0); if (__pyx_t_10) { } else { __pyx_t_1 = __pyx_t_10; goto __pyx_L9_bool_binop_done; } __pyx_t_10 = ((!(PyIndex_Check(__pyx_v_item) != 0)) != 0); __pyx_t_1 = __pyx_t_10; __pyx_L9_bool_binop_done:; if (unlikely(__pyx_t_1)) { /* "View.MemoryView":689 * else: * if not isinstance(item, slice) and not PyIndex_Check(item): * raise TypeError("Cannot index with type '%s'" % type(item)) # <<<<<<<<<<<<<< * * have_slices = have_slices or isinstance(item, slice) */ __pyx_t_7 = __Pyx_PyString_FormatSafe(__pyx_kp_s_Cannot_index_with_type_s, ((PyObject *)Py_TYPE(__pyx_v_item))); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 689, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_11 = __Pyx_PyObject_CallOneArg(__pyx_builtin_TypeError, __pyx_t_7); if (unlikely(!__pyx_t_11)) __PYX_ERR(2, 689, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_Raise(__pyx_t_11, 0, 0, 0); __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __PYX_ERR(2, 689, __pyx_L1_error) /* "View.MemoryView":688 * have_slices = True * else: * if not isinstance(item, slice) and not PyIndex_Check(item): # <<<<<<<<<<<<<< * raise TypeError("Cannot index with type '%s'" % type(item)) * */ } /* "View.MemoryView":691 * raise TypeError("Cannot index with type '%s'" % type(item)) * * have_slices = have_slices or isinstance(item, slice) # <<<<<<<<<<<<<< * result.append(item) * */ __pyx_t_10 = (__pyx_v_have_slices != 0); if (!__pyx_t_10) { } else { __pyx_t_1 = __pyx_t_10; goto __pyx_L11_bool_binop_done; } __pyx_t_10 = PySlice_Check(__pyx_v_item); __pyx_t_2 = (__pyx_t_10 != 0); __pyx_t_1 = __pyx_t_2; __pyx_L11_bool_binop_done:; __pyx_v_have_slices = __pyx_t_1; /* "View.MemoryView":692 * * have_slices = have_slices or isinstance(item, slice) * result.append(item) # <<<<<<<<<<<<<< * * nslices = ndim - len(result) */ __pyx_t_9 = __Pyx_PyList_Append(__pyx_v_result, __pyx_v_item); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 692, __pyx_L1_error) } __pyx_L6:; /* "View.MemoryView":679 * have_slices = False * seen_ellipsis = False * for idx, item in enumerate(tup): # <<<<<<<<<<<<<< * if item is Ellipsis: * if not seen_ellipsis: */ } __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":694 * result.append(item) * * nslices = ndim - len(result) # <<<<<<<<<<<<<< * if nslices: * result.extend([slice(None)] * nslices) */ __pyx_t_5 = PyList_GET_SIZE(__pyx_v_result); if (unlikely(__pyx_t_5 == ((Py_ssize_t)-1))) __PYX_ERR(2, 694, __pyx_L1_error) __pyx_v_nslices = (__pyx_v_ndim - __pyx_t_5); /* "View.MemoryView":695 * * nslices = ndim - len(result) * if nslices: # <<<<<<<<<<<<<< * result.extend([slice(None)] * nslices) * */ __pyx_t_1 = (__pyx_v_nslices != 0); if (__pyx_t_1) { /* "View.MemoryView":696 * nslices = ndim - len(result) * if nslices: * result.extend([slice(None)] * nslices) # <<<<<<<<<<<<<< * * return have_slices or nslices, tuple(result) */ __pyx_t_3 = PyList_New(1 * ((__pyx_v_nslices<0) ? 0:__pyx_v_nslices)); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 696, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); { Py_ssize_t __pyx_temp; for (__pyx_temp=0; __pyx_temp < __pyx_v_nslices; __pyx_temp++) { __Pyx_INCREF(__pyx_slice__17); __Pyx_GIVEREF(__pyx_slice__17); PyList_SET_ITEM(__pyx_t_3, __pyx_temp, __pyx_slice__17); } } __pyx_t_9 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_3); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 696, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":695 * * nslices = ndim - len(result) * if nslices: # <<<<<<<<<<<<<< * result.extend([slice(None)] * nslices) * */ } /* "View.MemoryView":698 * result.extend([slice(None)] * nslices) * * return have_slices or nslices, tuple(result) # <<<<<<<<<<<<<< * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): */ __Pyx_XDECREF(__pyx_r); if (!__pyx_v_have_slices) { } else { __pyx_t_4 = __Pyx_PyBool_FromLong(__pyx_v_have_slices); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = __pyx_t_4; __pyx_t_4 = 0; goto __pyx_L14_bool_binop_done; } __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_nslices); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = __pyx_t_4; __pyx_t_4 = 0; __pyx_L14_bool_binop_done:; __pyx_t_4 = PyList_AsTuple(__pyx_v_result); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_11 = PyTuple_New(2); if (unlikely(!__pyx_t_11)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_11, 0, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_11, 1, __pyx_t_4); __pyx_t_3 = 0; __pyx_t_4 = 0; __pyx_r = ((PyObject*)__pyx_t_11); __pyx_t_11 = 0; goto __pyx_L0; /* "View.MemoryView":666 * return isinstance(o, memoryview) * * cdef tuple _unellipsify(object index, int ndim): # <<<<<<<<<<<<<< * """ * Replace all ellipses with full slices and fill incomplete indices with */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_11); __Pyx_AddTraceback("View.MemoryView._unellipsify", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_tup); __Pyx_XDECREF(__pyx_v_result); __Pyx_XDECREF(__pyx_v_idx); __Pyx_XDECREF(__pyx_v_item); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":700 * return have_slices or nslices, tuple(result) * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): # <<<<<<<<<<<<<< * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: */ static PyObject *assert_direct_dimensions(Py_ssize_t *__pyx_v_suboffsets, int __pyx_v_ndim) { Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations Py_ssize_t *__pyx_t_1; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("assert_direct_dimensions", 0); /* "View.MemoryView":701 * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: # <<<<<<<<<<<<<< * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") */ __pyx_t_2 = (__pyx_v_suboffsets + __pyx_v_ndim); for (__pyx_t_3 = __pyx_v_suboffsets; __pyx_t_3 < __pyx_t_2; __pyx_t_3++) { __pyx_t_1 = __pyx_t_3; __pyx_v_suboffset = (__pyx_t_1[0]); /* "View.MemoryView":702 * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * raise ValueError("Indirect dimensions not supported") * */ __pyx_t_4 = ((__pyx_v_suboffset >= 0) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":703 * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__18, NULL); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 703, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_Raise(__pyx_t_5, 0, 0, 0); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __PYX_ERR(2, 703, __pyx_L1_error) /* "View.MemoryView":702 * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * raise ValueError("Indirect dimensions not supported") * */ } } /* "View.MemoryView":700 * return have_slices or nslices, tuple(result) * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): # <<<<<<<<<<<<<< * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.assert_direct_dimensions", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":710 * * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< * cdef int new_ndim = 0, suboffset_dim = -1, dim * cdef bint negative_step */ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *__pyx_v_memview, PyObject *__pyx_v_indices) { int __pyx_v_new_ndim; int __pyx_v_suboffset_dim; int __pyx_v_dim; __Pyx_memviewslice __pyx_v_src; __Pyx_memviewslice __pyx_v_dst; __Pyx_memviewslice *__pyx_v_p_src; struct __pyx_memoryviewslice_obj *__pyx_v_memviewsliceobj = 0; __Pyx_memviewslice *__pyx_v_p_dst; int *__pyx_v_p_suboffset_dim; Py_ssize_t __pyx_v_start; Py_ssize_t __pyx_v_stop; Py_ssize_t __pyx_v_step; int __pyx_v_have_start; int __pyx_v_have_stop; int __pyx_v_have_step; PyObject *__pyx_v_index = NULL; struct __pyx_memoryview_obj *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; struct __pyx_memoryview_obj *__pyx_t_4; char *__pyx_t_5; int __pyx_t_6; Py_ssize_t __pyx_t_7; PyObject *(*__pyx_t_8)(PyObject *); PyObject *__pyx_t_9 = NULL; Py_ssize_t __pyx_t_10; int __pyx_t_11; Py_ssize_t __pyx_t_12; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memview_slice", 0); /* "View.MemoryView":711 * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): * cdef int new_ndim = 0, suboffset_dim = -1, dim # <<<<<<<<<<<<<< * cdef bint negative_step * cdef __Pyx_memviewslice src, dst */ __pyx_v_new_ndim = 0; __pyx_v_suboffset_dim = -1; /* "View.MemoryView":718 * * * memset(&dst, 0, sizeof(dst)) # <<<<<<<<<<<<<< * * cdef _memoryviewslice memviewsliceobj */ (void)(memset((&__pyx_v_dst), 0, (sizeof(__pyx_v_dst)))); /* "View.MemoryView":722 * cdef _memoryviewslice memviewsliceobj * * assert memview.view.ndim > 0 # <<<<<<<<<<<<<< * * if isinstance(memview, _memoryviewslice): */ #ifndef CYTHON_WITHOUT_ASSERTIONS if (unlikely(!Py_OptimizeFlag)) { if (unlikely(!((__pyx_v_memview->view.ndim > 0) != 0))) { PyErr_SetNone(PyExc_AssertionError); __PYX_ERR(2, 722, __pyx_L1_error) } } #endif /* "View.MemoryView":724 * assert memview.view.ndim > 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":725 * * if isinstance(memview, _memoryviewslice): * memviewsliceobj = memview # <<<<<<<<<<<<<< * p_src = &memviewsliceobj.from_slice * else: */ if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 725, __pyx_L1_error) __pyx_t_3 = ((PyObject *)__pyx_v_memview); __Pyx_INCREF(__pyx_t_3); __pyx_v_memviewsliceobj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":726 * if isinstance(memview, _memoryviewslice): * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice # <<<<<<<<<<<<<< * else: * slice_copy(memview, &src) */ __pyx_v_p_src = (&__pyx_v_memviewsliceobj->from_slice); /* "View.MemoryView":724 * assert memview.view.ndim > 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice */ goto __pyx_L3; } /* "View.MemoryView":728 * p_src = &memviewsliceobj.from_slice * else: * slice_copy(memview, &src) # <<<<<<<<<<<<<< * p_src = &src * */ /*else*/ { __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_src)); /* "View.MemoryView":729 * else: * slice_copy(memview, &src) * p_src = &src # <<<<<<<<<<<<<< * * */ __pyx_v_p_src = (&__pyx_v_src); } __pyx_L3:; /* "View.MemoryView":735 * * * dst.memview = p_src.memview # <<<<<<<<<<<<<< * dst.data = p_src.data * */ __pyx_t_4 = __pyx_v_p_src->memview; __pyx_v_dst.memview = __pyx_t_4; /* "View.MemoryView":736 * * dst.memview = p_src.memview * dst.data = p_src.data # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __pyx_v_p_src->data; __pyx_v_dst.data = __pyx_t_5; /* "View.MemoryView":741 * * * cdef __Pyx_memviewslice *p_dst = &dst # <<<<<<<<<<<<<< * cdef int *p_suboffset_dim = &suboffset_dim * cdef Py_ssize_t start, stop, step */ __pyx_v_p_dst = (&__pyx_v_dst); /* "View.MemoryView":742 * * cdef __Pyx_memviewslice *p_dst = &dst * cdef int *p_suboffset_dim = &suboffset_dim # <<<<<<<<<<<<<< * cdef Py_ssize_t start, stop, step * cdef bint have_start, have_stop, have_step */ __pyx_v_p_suboffset_dim = (&__pyx_v_suboffset_dim); /* "View.MemoryView":746 * cdef bint have_start, have_stop, have_step * * for dim, index in enumerate(indices): # <<<<<<<<<<<<<< * if PyIndex_Check(index): * slice_memviewslice( */ __pyx_t_6 = 0; if (likely(PyList_CheckExact(__pyx_v_indices)) || PyTuple_CheckExact(__pyx_v_indices)) { __pyx_t_3 = __pyx_v_indices; __Pyx_INCREF(__pyx_t_3); __pyx_t_7 = 0; __pyx_t_8 = NULL; } else { __pyx_t_7 = -1; __pyx_t_3 = PyObject_GetIter(__pyx_v_indices); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_8 = Py_TYPE(__pyx_t_3)->tp_iternext; if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 746, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_8)) { if (likely(PyList_CheckExact(__pyx_t_3))) { if (__pyx_t_7 >= PyList_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_9 = PyList_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(2, 746, __pyx_L1_error) #else __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); #endif } else { if (__pyx_t_7 >= PyTuple_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_9 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(2, 746, __pyx_L1_error) #else __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); #endif } } else { __pyx_t_9 = __pyx_t_8(__pyx_t_3); if (unlikely(!__pyx_t_9)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(2, 746, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_9); } __Pyx_XDECREF_SET(__pyx_v_index, __pyx_t_9); __pyx_t_9 = 0; __pyx_v_dim = __pyx_t_6; __pyx_t_6 = (__pyx_t_6 + 1); /* "View.MemoryView":747 * * for dim, index in enumerate(indices): * if PyIndex_Check(index): # <<<<<<<<<<<<<< * slice_memviewslice( * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], */ __pyx_t_2 = (PyIndex_Check(__pyx_v_index) != 0); if (__pyx_t_2) { /* "View.MemoryView":751 * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, * index, 0, 0, # start, stop, step # <<<<<<<<<<<<<< * 0, 0, 0, # have_{start,stop,step} * False) */ __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_v_index); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 751, __pyx_L1_error) /* "View.MemoryView":748 * for dim, index in enumerate(indices): * if PyIndex_Check(index): * slice_memviewslice( # <<<<<<<<<<<<<< * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, */ __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_t_10, 0, 0, 0, 0, 0, 0); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 748, __pyx_L1_error) /* "View.MemoryView":747 * * for dim, index in enumerate(indices): * if PyIndex_Check(index): # <<<<<<<<<<<<<< * slice_memviewslice( * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], */ goto __pyx_L6; } /* "View.MemoryView":754 * 0, 0, 0, # have_{start,stop,step} * False) * elif index is None: # <<<<<<<<<<<<<< * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 */ __pyx_t_2 = (__pyx_v_index == Py_None); __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":755 * False) * elif index is None: * p_dst.shape[new_ndim] = 1 # <<<<<<<<<<<<<< * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 */ (__pyx_v_p_dst->shape[__pyx_v_new_ndim]) = 1; /* "View.MemoryView":756 * elif index is None: * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 # <<<<<<<<<<<<<< * p_dst.suboffsets[new_ndim] = -1 * new_ndim += 1 */ (__pyx_v_p_dst->strides[__pyx_v_new_ndim]) = 0; /* "View.MemoryView":757 * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 # <<<<<<<<<<<<<< * new_ndim += 1 * else: */ (__pyx_v_p_dst->suboffsets[__pyx_v_new_ndim]) = -1L; /* "View.MemoryView":758 * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 * new_ndim += 1 # <<<<<<<<<<<<<< * else: * start = index.start or 0 */ __pyx_v_new_ndim = (__pyx_v_new_ndim + 1); /* "View.MemoryView":754 * 0, 0, 0, # have_{start,stop,step} * False) * elif index is None: # <<<<<<<<<<<<<< * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 */ goto __pyx_L6; } /* "View.MemoryView":760 * new_ndim += 1 * else: * start = index.start or 0 # <<<<<<<<<<<<<< * stop = index.stop or 0 * step = index.step or 0 */ /*else*/ { __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 760, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 760, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 760, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L7_bool_binop_done; } __pyx_t_10 = 0; __pyx_L7_bool_binop_done:; __pyx_v_start = __pyx_t_10; /* "View.MemoryView":761 * else: * start = index.start or 0 * stop = index.stop or 0 # <<<<<<<<<<<<<< * step = index.step or 0 * */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 761, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 761, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 761, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L9_bool_binop_done; } __pyx_t_10 = 0; __pyx_L9_bool_binop_done:; __pyx_v_stop = __pyx_t_10; /* "View.MemoryView":762 * start = index.start or 0 * stop = index.stop or 0 * step = index.step or 0 # <<<<<<<<<<<<<< * * have_start = index.start is not None */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 762, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 762, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 762, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L11_bool_binop_done; } __pyx_t_10 = 0; __pyx_L11_bool_binop_done:; __pyx_v_step = __pyx_t_10; /* "View.MemoryView":764 * step = index.step or 0 * * have_start = index.start is not None # <<<<<<<<<<<<<< * have_stop = index.stop is not None * have_step = index.step is not None */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 764, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_start = __pyx_t_1; /* "View.MemoryView":765 * * have_start = index.start is not None * have_stop = index.stop is not None # <<<<<<<<<<<<<< * have_step = index.step is not None * */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 765, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_stop = __pyx_t_1; /* "View.MemoryView":766 * have_start = index.start is not None * have_stop = index.stop is not None * have_step = index.step is not None # <<<<<<<<<<<<<< * * slice_memviewslice( */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 766, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_step = __pyx_t_1; /* "View.MemoryView":768 * have_step = index.step is not None * * slice_memviewslice( # <<<<<<<<<<<<<< * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, */ __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 768, __pyx_L1_error) /* "View.MemoryView":774 * have_start, have_stop, have_step, * True) * new_ndim += 1 # <<<<<<<<<<<<<< * * if isinstance(memview, _memoryviewslice): */ __pyx_v_new_ndim = (__pyx_v_new_ndim + 1); } __pyx_L6:; /* "View.MemoryView":746 * cdef bint have_start, have_stop, have_step * * for dim, index in enumerate(indices): # <<<<<<<<<<<<<< * if PyIndex_Check(index): * slice_memviewslice( */ } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":776 * new_ndim += 1 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":777 * * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, */ __Pyx_XDECREF(((PyObject *)__pyx_r)); /* "View.MemoryView":778 * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, # <<<<<<<<<<<<<< * memviewsliceobj.to_dtype_func, * memview.dtype_is_object) */ if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 778, __pyx_L1_error) } /* "View.MemoryView":779 * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, # <<<<<<<<<<<<<< * memview.dtype_is_object) * else: */ if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 779, __pyx_L1_error) } /* "View.MemoryView":777 * * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, */ __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 777, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 777, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":776 * new_ndim += 1 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, */ } /* "View.MemoryView":782 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< * memview.dtype_is_object) * */ /*else*/ { __Pyx_XDECREF(((PyObject *)__pyx_r)); /* "View.MemoryView":783 * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 782, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); /* "View.MemoryView":782 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< * memview.dtype_is_object) * */ if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 782, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; } /* "View.MemoryView":710 * * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< * cdef int new_ndim = 0, suboffset_dim = -1, dim * cdef bint negative_step */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_9); __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj); __Pyx_XDECREF(__pyx_v_index); __Pyx_XGIVEREF((PyObject *)__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":807 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, */ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) { Py_ssize_t __pyx_v_new_shape; int __pyx_v_negative_step; int __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":827 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< * * if start < 0: */ __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_1) { /* "View.MemoryView":829 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< * start += shape * if not 0 <= start < shape: */ __pyx_t_1 = ((__pyx_v_start < 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":830 * * if start < 0: * start += shape # <<<<<<<<<<<<<< * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); /* "View.MemoryView":829 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< * start += shape * if not 0 <= start < shape: */ } /* "View.MemoryView":831 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) * else: */ __pyx_t_1 = (0 <= __pyx_v_start); if (__pyx_t_1) { __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); } __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":832 * start += shape * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< * else: * */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 832, __pyx_L1_error) /* "View.MemoryView":831 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) * else: */ } /* "View.MemoryView":827 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< * * if start < 0: */ goto __pyx_L3; } /* "View.MemoryView":835 * else: * * negative_step = have_step != 0 and step < 0 # <<<<<<<<<<<<<< * * if have_step and step == 0: */ /*else*/ { __pyx_t_1 = ((__pyx_v_have_step != 0) != 0); if (__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L6_bool_binop_done; } __pyx_t_1 = ((__pyx_v_step < 0) != 0); __pyx_t_2 = __pyx_t_1; __pyx_L6_bool_binop_done:; __pyx_v_negative_step = __pyx_t_2; /* "View.MemoryView":837 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) * */ __pyx_t_1 = (__pyx_v_have_step != 0); if (__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L9_bool_binop_done; } __pyx_t_1 = ((__pyx_v_step == 0) != 0); __pyx_t_2 = __pyx_t_1; __pyx_L9_bool_binop_done:; if (__pyx_t_2) { /* "View.MemoryView":838 * * if have_step and step == 0: * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 838, __pyx_L1_error) /* "View.MemoryView":837 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) * */ } /* "View.MemoryView":841 * * * if have_start: # <<<<<<<<<<<<<< * if start < 0: * start += shape */ __pyx_t_2 = (__pyx_v_have_start != 0); if (__pyx_t_2) { /* "View.MemoryView":842 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< * start += shape * if start < 0: */ __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":843 * if have_start: * if start < 0: * start += shape # <<<<<<<<<<<<<< * if start < 0: * start = 0 */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); /* "View.MemoryView":844 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< * start = 0 * elif start >= shape: */ __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":845 * start += shape * if start < 0: * start = 0 # <<<<<<<<<<<<<< * elif start >= shape: * if negative_step: */ __pyx_v_start = 0; /* "View.MemoryView":844 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< * start = 0 * elif start >= shape: */ } /* "View.MemoryView":842 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< * start += shape * if start < 0: */ goto __pyx_L12; } /* "View.MemoryView":846 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< * if negative_step: * start = shape - 1 */ __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); if (__pyx_t_2) { /* "View.MemoryView":847 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":848 * elif start >= shape: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< * else: * start = shape */ __pyx_v_start = (__pyx_v_shape - 1); /* "View.MemoryView":847 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ goto __pyx_L14; } /* "View.MemoryView":850 * start = shape - 1 * else: * start = shape # <<<<<<<<<<<<<< * else: * if negative_step: */ /*else*/ { __pyx_v_start = __pyx_v_shape; } __pyx_L14:; /* "View.MemoryView":846 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< * if negative_step: * start = shape - 1 */ } __pyx_L12:; /* "View.MemoryView":841 * * * if have_start: # <<<<<<<<<<<<<< * if start < 0: * start += shape */ goto __pyx_L11; } /* "View.MemoryView":852 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ /*else*/ { __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":853 * else: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< * else: * start = 0 */ __pyx_v_start = (__pyx_v_shape - 1); /* "View.MemoryView":852 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ goto __pyx_L15; } /* "View.MemoryView":855 * start = shape - 1 * else: * start = 0 # <<<<<<<<<<<<<< * * if have_stop: */ /*else*/ { __pyx_v_start = 0; } __pyx_L15:; } __pyx_L11:; /* "View.MemoryView":857 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< * if stop < 0: * stop += shape */ __pyx_t_2 = (__pyx_v_have_stop != 0); if (__pyx_t_2) { /* "View.MemoryView":858 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< * stop += shape * if stop < 0: */ __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":859 * if have_stop: * if stop < 0: * stop += shape # <<<<<<<<<<<<<< * if stop < 0: * stop = 0 */ __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); /* "View.MemoryView":860 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< * stop = 0 * elif stop > shape: */ __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":861 * stop += shape * if stop < 0: * stop = 0 # <<<<<<<<<<<<<< * elif stop > shape: * stop = shape */ __pyx_v_stop = 0; /* "View.MemoryView":860 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< * stop = 0 * elif stop > shape: */ } /* "View.MemoryView":858 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< * stop += shape * if stop < 0: */ goto __pyx_L17; } /* "View.MemoryView":862 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< * stop = shape * else: */ __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0); if (__pyx_t_2) { /* "View.MemoryView":863 * stop = 0 * elif stop > shape: * stop = shape # <<<<<<<<<<<<<< * else: * if negative_step: */ __pyx_v_stop = __pyx_v_shape; /* "View.MemoryView":862 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< * stop = shape * else: */ } __pyx_L17:; /* "View.MemoryView":857 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< * if stop < 0: * stop += shape */ goto __pyx_L16; } /* "View.MemoryView":865 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< * stop = -1 * else: */ /*else*/ { __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":866 * else: * if negative_step: * stop = -1 # <<<<<<<<<<<<<< * else: * stop = shape */ __pyx_v_stop = -1L; /* "View.MemoryView":865 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< * stop = -1 * else: */ goto __pyx_L19; } /* "View.MemoryView":868 * stop = -1 * else: * stop = shape # <<<<<<<<<<<<<< * * if not have_step: */ /*else*/ { __pyx_v_stop = __pyx_v_shape; } __pyx_L19:; } __pyx_L16:; /* "View.MemoryView":870 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< * step = 1 * */ __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":871 * * if not have_step: * step = 1 # <<<<<<<<<<<<<< * * */ __pyx_v_step = 1; /* "View.MemoryView":870 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< * step = 1 * */ } /* "View.MemoryView":875 * * with cython.cdivision(True): * new_shape = (stop - start) // step # <<<<<<<<<<<<<< * * if (stop - start) - step * new_shape: */ __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); /* "View.MemoryView":877 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< * new_shape += 1 * */ __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); if (__pyx_t_2) { /* "View.MemoryView":878 * * if (stop - start) - step * new_shape: * new_shape += 1 # <<<<<<<<<<<<<< * * if new_shape < 0: */ __pyx_v_new_shape = (__pyx_v_new_shape + 1); /* "View.MemoryView":877 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< * new_shape += 1 * */ } /* "View.MemoryView":880 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< * new_shape = 0 * */ __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":881 * * if new_shape < 0: * new_shape = 0 # <<<<<<<<<<<<<< * * */ __pyx_v_new_shape = 0; /* "View.MemoryView":880 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< * new_shape = 0 * */ } /* "View.MemoryView":884 * * * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset */ (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); /* "View.MemoryView":885 * * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape # <<<<<<<<<<<<<< * dst.suboffsets[new_ndim] = suboffset * */ (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape; /* "View.MemoryView":886 * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset # <<<<<<<<<<<<<< * * */ (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset; } __pyx_L3:; /* "View.MemoryView":889 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< * dst.data += start * stride * else: */ __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":890 * * if suboffset_dim[0] < 0: * dst.data += start * stride # <<<<<<<<<<<<<< * else: * dst.suboffsets[suboffset_dim[0]] += start * stride */ __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride)); /* "View.MemoryView":889 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< * dst.data += start * stride * else: */ goto __pyx_L23; } /* "View.MemoryView":892 * dst.data += start * stride * else: * dst.suboffsets[suboffset_dim[0]] += start * stride # <<<<<<<<<<<<<< * * if suboffset >= 0: */ /*else*/ { __pyx_t_3 = (__pyx_v_suboffset_dim[0]); (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride)); } __pyx_L23:; /* "View.MemoryView":894 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< * if not is_slice: * if new_ndim == 0: */ __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":895 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset */ __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":896 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< * dst.data = ( dst.data)[0] + suboffset * else: */ __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":897 * if not is_slice: * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset # <<<<<<<<<<<<<< * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " */ __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset); /* "View.MemoryView":896 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< * dst.data = ( dst.data)[0] + suboffset * else: */ goto __pyx_L26; } /* "View.MemoryView":899 * dst.data = ( dst.data)[0] + suboffset * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " # <<<<<<<<<<<<<< * "must be indexed and not sliced", dim) * else: */ /*else*/ { /* "View.MemoryView":900 * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " * "must be indexed and not sliced", dim) # <<<<<<<<<<<<<< * else: * suboffset_dim[0] = new_ndim */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"All dimensions preceding dimension %d must be indexed and not sliced"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 899, __pyx_L1_error) } __pyx_L26:; /* "View.MemoryView":895 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset */ goto __pyx_L25; } /* "View.MemoryView":902 * "must be indexed and not sliced", dim) * else: * suboffset_dim[0] = new_ndim # <<<<<<<<<<<<<< * * return 0 */ /*else*/ { (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim; } __pyx_L25:; /* "View.MemoryView":894 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< * if not is_slice: * if new_ndim == 0: */ } /* "View.MemoryView":904 * suboffset_dim[0] = new_ndim * * return 0 # <<<<<<<<<<<<<< * * */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":807 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.slice_memviewslice", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = -1; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":910 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 */ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, Py_ssize_t __pyx_v_index, Py_ssize_t __pyx_v_dim) { Py_ssize_t __pyx_v_shape; Py_ssize_t __pyx_v_stride; Py_ssize_t __pyx_v_suboffset; Py_ssize_t __pyx_v_itemsize; char *__pyx_v_resultp; char *__pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("pybuffer_index", 0); /* "View.MemoryView":912 * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 # <<<<<<<<<<<<<< * cdef Py_ssize_t itemsize = view.itemsize * cdef char *resultp */ __pyx_v_suboffset = -1L; /* "View.MemoryView":913 * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 * cdef Py_ssize_t itemsize = view.itemsize # <<<<<<<<<<<<<< * cdef char *resultp * */ __pyx_t_1 = __pyx_v_view->itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":916 * cdef char *resultp * * if view.ndim == 0: # <<<<<<<<<<<<<< * shape = view.len / itemsize * stride = itemsize */ __pyx_t_2 = ((__pyx_v_view->ndim == 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":917 * * if view.ndim == 0: * shape = view.len / itemsize # <<<<<<<<<<<<<< * stride = itemsize * else: */ if (unlikely(__pyx_v_itemsize == 0)) { PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); __PYX_ERR(2, 917, __pyx_L1_error) } else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1) && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_view->len))) { PyErr_SetString(PyExc_OverflowError, "value too large to perform division"); __PYX_ERR(2, 917, __pyx_L1_error) } __pyx_v_shape = (__pyx_v_view->len / __pyx_v_itemsize); /* "View.MemoryView":918 * if view.ndim == 0: * shape = view.len / itemsize * stride = itemsize # <<<<<<<<<<<<<< * else: * shape = view.shape[dim] */ __pyx_v_stride = __pyx_v_itemsize; /* "View.MemoryView":916 * cdef char *resultp * * if view.ndim == 0: # <<<<<<<<<<<<<< * shape = view.len / itemsize * stride = itemsize */ goto __pyx_L3; } /* "View.MemoryView":920 * stride = itemsize * else: * shape = view.shape[dim] # <<<<<<<<<<<<<< * stride = view.strides[dim] * if view.suboffsets != NULL: */ /*else*/ { __pyx_v_shape = (__pyx_v_view->shape[__pyx_v_dim]); /* "View.MemoryView":921 * else: * shape = view.shape[dim] * stride = view.strides[dim] # <<<<<<<<<<<<<< * if view.suboffsets != NULL: * suboffset = view.suboffsets[dim] */ __pyx_v_stride = (__pyx_v_view->strides[__pyx_v_dim]); /* "View.MemoryView":922 * shape = view.shape[dim] * stride = view.strides[dim] * if view.suboffsets != NULL: # <<<<<<<<<<<<<< * suboffset = view.suboffsets[dim] * */ __pyx_t_2 = ((__pyx_v_view->suboffsets != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":923 * stride = view.strides[dim] * if view.suboffsets != NULL: * suboffset = view.suboffsets[dim] # <<<<<<<<<<<<<< * * if index < 0: */ __pyx_v_suboffset = (__pyx_v_view->suboffsets[__pyx_v_dim]); /* "View.MemoryView":922 * shape = view.shape[dim] * stride = view.strides[dim] * if view.suboffsets != NULL: # <<<<<<<<<<<<<< * suboffset = view.suboffsets[dim] * */ } } __pyx_L3:; /* "View.MemoryView":925 * suboffset = view.suboffsets[dim] * * if index < 0: # <<<<<<<<<<<<<< * index += view.shape[dim] * if index < 0: */ __pyx_t_2 = ((__pyx_v_index < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":926 * * if index < 0: * index += view.shape[dim] # <<<<<<<<<<<<<< * if index < 0: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) */ __pyx_v_index = (__pyx_v_index + (__pyx_v_view->shape[__pyx_v_dim])); /* "View.MemoryView":927 * if index < 0: * index += view.shape[dim] * if index < 0: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ __pyx_t_2 = ((__pyx_v_index < 0) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":928 * index += view.shape[dim] * if index < 0: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * if index >= shape: */ __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 928, __pyx_L1_error) /* "View.MemoryView":927 * if index < 0: * index += view.shape[dim] * if index < 0: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ } /* "View.MemoryView":925 * suboffset = view.suboffsets[dim] * * if index < 0: # <<<<<<<<<<<<<< * index += view.shape[dim] * if index < 0: */ } /* "View.MemoryView":930 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ __pyx_t_2 = ((__pyx_v_index >= __pyx_v_shape) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":931 * * if index >= shape: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * resultp = bufp + index * stride */ __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 931, __pyx_L1_error) /* "View.MemoryView":930 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ } /* "View.MemoryView":933 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * resultp = bufp + index * stride # <<<<<<<<<<<<<< * if suboffset >= 0: * resultp = ( resultp)[0] + suboffset */ __pyx_v_resultp = (__pyx_v_bufp + (__pyx_v_index * __pyx_v_stride)); /* "View.MemoryView":934 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< * resultp = ( resultp)[0] + suboffset * */ __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":935 * resultp = bufp + index * stride * if suboffset >= 0: * resultp = ( resultp)[0] + suboffset # <<<<<<<<<<<<<< * * return resultp */ __pyx_v_resultp = ((((char **)__pyx_v_resultp)[0]) + __pyx_v_suboffset); /* "View.MemoryView":934 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< * resultp = ( resultp)[0] + suboffset * */ } /* "View.MemoryView":937 * resultp = ( resultp)[0] + suboffset * * return resultp # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_resultp; goto __pyx_L0; /* "View.MemoryView":910 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView.pybuffer_index", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":943 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< * cdef int ndim = memslice.memview.view.ndim * */ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { int __pyx_v_ndim; Py_ssize_t *__pyx_v_shape; Py_ssize_t *__pyx_v_strides; int __pyx_v_i; int __pyx_v_j; int __pyx_r; int __pyx_t_1; Py_ssize_t *__pyx_t_2; long __pyx_t_3; long __pyx_t_4; Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; int __pyx_t_7; int __pyx_t_8; int __pyx_t_9; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":944 * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: * cdef int ndim = memslice.memview.view.ndim # <<<<<<<<<<<<<< * * cdef Py_ssize_t *shape = memslice.shape */ __pyx_t_1 = __pyx_v_memslice->memview->view.ndim; __pyx_v_ndim = __pyx_t_1; /* "View.MemoryView":946 * cdef int ndim = memslice.memview.view.ndim * * cdef Py_ssize_t *shape = memslice.shape # <<<<<<<<<<<<<< * cdef Py_ssize_t *strides = memslice.strides * */ __pyx_t_2 = __pyx_v_memslice->shape; __pyx_v_shape = __pyx_t_2; /* "View.MemoryView":947 * * cdef Py_ssize_t *shape = memslice.shape * cdef Py_ssize_t *strides = memslice.strides # <<<<<<<<<<<<<< * * */ __pyx_t_2 = __pyx_v_memslice->strides; __pyx_v_strides = __pyx_t_2; /* "View.MemoryView":951 * * cdef int i, j * for i in range(ndim / 2): # <<<<<<<<<<<<<< * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] */ __pyx_t_3 = (__pyx_v_ndim / 2); __pyx_t_4 = __pyx_t_3; for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_4; __pyx_t_1+=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":952 * cdef int i, j * for i in range(ndim / 2): * j = ndim - 1 - i # <<<<<<<<<<<<<< * strides[i], strides[j] = strides[j], strides[i] * shape[i], shape[j] = shape[j], shape[i] */ __pyx_v_j = ((__pyx_v_ndim - 1) - __pyx_v_i); /* "View.MemoryView":953 * for i in range(ndim / 2): * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] # <<<<<<<<<<<<<< * shape[i], shape[j] = shape[j], shape[i] * */ __pyx_t_5 = (__pyx_v_strides[__pyx_v_j]); __pyx_t_6 = (__pyx_v_strides[__pyx_v_i]); (__pyx_v_strides[__pyx_v_i]) = __pyx_t_5; (__pyx_v_strides[__pyx_v_j]) = __pyx_t_6; /* "View.MemoryView":954 * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] * shape[i], shape[j] = shape[j], shape[i] # <<<<<<<<<<<<<< * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: */ __pyx_t_6 = (__pyx_v_shape[__pyx_v_j]); __pyx_t_5 = (__pyx_v_shape[__pyx_v_i]); (__pyx_v_shape[__pyx_v_i]) = __pyx_t_6; (__pyx_v_shape[__pyx_v_j]) = __pyx_t_5; /* "View.MemoryView":956 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * */ __pyx_t_8 = (((__pyx_v_memslice->suboffsets[__pyx_v_i]) >= 0) != 0); if (!__pyx_t_8) { } else { __pyx_t_7 = __pyx_t_8; goto __pyx_L6_bool_binop_done; } __pyx_t_8 = (((__pyx_v_memslice->suboffsets[__pyx_v_j]) >= 0) != 0); __pyx_t_7 = __pyx_t_8; __pyx_L6_bool_binop_done:; if (__pyx_t_7) { /* "View.MemoryView":957 * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") # <<<<<<<<<<<<<< * * return 1 */ __pyx_t_9 = __pyx_memoryview_err(__pyx_builtin_ValueError, ((char *)"Cannot transpose memoryview with indirect dimensions")); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 957, __pyx_L1_error) /* "View.MemoryView":956 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * */ } } /* "View.MemoryView":959 * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * * return 1 # <<<<<<<<<<<<<< * * */ __pyx_r = 1; goto __pyx_L0; /* "View.MemoryView":943 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< * cdef int ndim = memslice.memview.view.ndim * */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.transpose_memslice", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":976 * cdef int (*to_dtype_func)(char *, object) except 0 * * def __dealloc__(self): # <<<<<<<<<<<<<< * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * */ /* Python wrapper */ static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":977 * * def __dealloc__(self): * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) # <<<<<<<<<<<<<< * * cdef convert_item_to_object(self, char *itemp): */ __PYX_XDEC_MEMVIEW((&__pyx_v_self->from_slice), 1); /* "View.MemoryView":976 * cdef int (*to_dtype_func)(char *, object) except 0 * * def __dealloc__(self): # <<<<<<<<<<<<<< * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":979 * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * if self.to_object_func != NULL: * return self.to_object_func(itemp) */ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("convert_item_to_object", 0); /* "View.MemoryView":980 * * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: # <<<<<<<<<<<<<< * return self.to_object_func(itemp) * else: */ __pyx_t_1 = ((__pyx_v_self->to_object_func != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":981 * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: * return self.to_object_func(itemp) # <<<<<<<<<<<<<< * else: * return memoryview.convert_item_to_object(self, itemp) */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_v_self->to_object_func(__pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 981, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":980 * * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: # <<<<<<<<<<<<<< * return self.to_object_func(itemp) * else: */ } /* "View.MemoryView":983 * return self.to_object_func(itemp) * else: * return memoryview.convert_item_to_object(self, itemp) # <<<<<<<<<<<<<< * * cdef assign_item_from_object(self, char *itemp, object value): */ /*else*/ { __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_convert_item_to_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 983, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; } /* "View.MemoryView":979 * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * if self.to_object_func != NULL: * return self.to_object_func(itemp) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":985 * return memoryview.convert_item_to_object(self, itemp) * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) */ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("assign_item_from_object", 0); /* "View.MemoryView":986 * * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: # <<<<<<<<<<<<<< * self.to_dtype_func(itemp, value) * else: */ __pyx_t_1 = ((__pyx_v_self->to_dtype_func != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":987 * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) # <<<<<<<<<<<<<< * else: * memoryview.assign_item_from_object(self, itemp, value) */ __pyx_t_2 = __pyx_v_self->to_dtype_func(__pyx_v_itemp, __pyx_v_value); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(2, 987, __pyx_L1_error) /* "View.MemoryView":986 * * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: # <<<<<<<<<<<<<< * self.to_dtype_func(itemp, value) * else: */ goto __pyx_L3; } /* "View.MemoryView":989 * self.to_dtype_func(itemp, value) * else: * memoryview.assign_item_from_object(self, itemp, value) # <<<<<<<<<<<<<< * * @property */ /*else*/ { __pyx_t_3 = __pyx_memoryview_assign_item_from_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 989, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } __pyx_L3:; /* "View.MemoryView":985 * return memoryview.convert_item_to_object(self, itemp) * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":992 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.from_object * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":993 * @property * def base(self): * return self.from_object # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->from_object); __pyx_r = __pyx_v_self->from_object; goto __pyx_L0; /* "View.MemoryView":992 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.from_object * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryviewslice_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_memoryviewslice_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryviewslice___reduce_cython__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__19, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryviewslice_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_memoryviewslice_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryviewslice_2__setstate_cython__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__20, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":999 * * @cname('__pyx_memoryview_fromslice') * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice, # <<<<<<<<<<<<<< * int ndim, * object (*to_object_func)(char *), */ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewslice, int __pyx_v_ndim, PyObject *(*__pyx_v_to_object_func)(char *), int (*__pyx_v_to_dtype_func)(char *, PyObject *), int __pyx_v_dtype_is_object) { struct __pyx_memoryviewslice_obj *__pyx_v_result = 0; Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_v_length = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; __Pyx_TypeInfo *__pyx_t_4; Py_buffer __pyx_t_5; Py_ssize_t *__pyx_t_6; Py_ssize_t *__pyx_t_7; Py_ssize_t *__pyx_t_8; Py_ssize_t __pyx_t_9; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_fromslice", 0); /* "View.MemoryView":1007 * cdef _memoryviewslice result * * if memviewslice.memview == Py_None: # <<<<<<<<<<<<<< * return None * */ __pyx_t_1 = ((((PyObject *)__pyx_v_memviewslice.memview) == Py_None) != 0); if (__pyx_t_1) { /* "View.MemoryView":1008 * * if memviewslice.memview == Py_None: * return None # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; /* "View.MemoryView":1007 * cdef _memoryviewslice result * * if memviewslice.memview == Py_None: # <<<<<<<<<<<<<< * return None * */ } /* "View.MemoryView":1013 * * * result = _memoryviewslice(None, 0, dtype_is_object) # <<<<<<<<<<<<<< * * result.from_slice = memviewslice */ __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(Py_None); __Pyx_GIVEREF(Py_None); PyTuple_SET_ITEM(__pyx_t_3, 0, Py_None); __Pyx_INCREF(__pyx_int_0); __Pyx_GIVEREF(__pyx_int_0); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_int_0); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryviewslice_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":1015 * result = _memoryviewslice(None, 0, dtype_is_object) * * result.from_slice = memviewslice # <<<<<<<<<<<<<< * __PYX_INC_MEMVIEW(&memviewslice, 1) * */ __pyx_v_result->from_slice = __pyx_v_memviewslice; /* "View.MemoryView":1016 * * result.from_slice = memviewslice * __PYX_INC_MEMVIEW(&memviewslice, 1) # <<<<<<<<<<<<<< * * result.from_object = ( memviewslice.memview).base */ __PYX_INC_MEMVIEW((&__pyx_v_memviewslice), 1); /* "View.MemoryView":1018 * __PYX_INC_MEMVIEW(&memviewslice, 1) * * result.from_object = ( memviewslice.memview).base # <<<<<<<<<<<<<< * result.typeinfo = memviewslice.memview.typeinfo * */ __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_memviewslice.memview), __pyx_n_s_base); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1018, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); __Pyx_GOTREF(__pyx_v_result->from_object); __Pyx_DECREF(__pyx_v_result->from_object); __pyx_v_result->from_object = __pyx_t_2; __pyx_t_2 = 0; /* "View.MemoryView":1019 * * result.from_object = ( memviewslice.memview).base * result.typeinfo = memviewslice.memview.typeinfo # <<<<<<<<<<<<<< * * result.view = memviewslice.memview.view */ __pyx_t_4 = __pyx_v_memviewslice.memview->typeinfo; __pyx_v_result->__pyx_base.typeinfo = __pyx_t_4; /* "View.MemoryView":1021 * result.typeinfo = memviewslice.memview.typeinfo * * result.view = memviewslice.memview.view # <<<<<<<<<<<<<< * result.view.buf = memviewslice.data * result.view.ndim = ndim */ __pyx_t_5 = __pyx_v_memviewslice.memview->view; __pyx_v_result->__pyx_base.view = __pyx_t_5; /* "View.MemoryView":1022 * * result.view = memviewslice.memview.view * result.view.buf = memviewslice.data # <<<<<<<<<<<<<< * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None */ __pyx_v_result->__pyx_base.view.buf = ((void *)__pyx_v_memviewslice.data); /* "View.MemoryView":1023 * result.view = memviewslice.memview.view * result.view.buf = memviewslice.data * result.view.ndim = ndim # <<<<<<<<<<<<<< * (<__pyx_buffer *> &result.view).obj = Py_None * Py_INCREF(Py_None) */ __pyx_v_result->__pyx_base.view.ndim = __pyx_v_ndim; /* "View.MemoryView":1024 * result.view.buf = memviewslice.data * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_result->__pyx_base.view))->obj = Py_None; /* "View.MemoryView":1025 * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * if (memviewslice.memview).flags & PyBUF_WRITABLE: */ Py_INCREF(Py_None); /* "View.MemoryView":1027 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< * result.flags = PyBUF_RECORDS * else: */ __pyx_t_1 = ((((struct __pyx_memoryview_obj *)__pyx_v_memviewslice.memview)->flags & PyBUF_WRITABLE) != 0); if (__pyx_t_1) { /* "View.MemoryView":1028 * * if (memviewslice.memview).flags & PyBUF_WRITABLE: * result.flags = PyBUF_RECORDS # <<<<<<<<<<<<<< * else: * result.flags = PyBUF_RECORDS_RO */ __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS; /* "View.MemoryView":1027 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< * result.flags = PyBUF_RECORDS * else: */ goto __pyx_L4; } /* "View.MemoryView":1030 * result.flags = PyBUF_RECORDS * else: * result.flags = PyBUF_RECORDS_RO # <<<<<<<<<<<<<< * * result.view.shape = result.from_slice.shape */ /*else*/ { __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS_RO; } __pyx_L4:; /* "View.MemoryView":1032 * result.flags = PyBUF_RECORDS_RO * * result.view.shape = result.from_slice.shape # <<<<<<<<<<<<<< * result.view.strides = result.from_slice.strides * */ __pyx_v_result->__pyx_base.view.shape = ((Py_ssize_t *)__pyx_v_result->from_slice.shape); /* "View.MemoryView":1033 * * result.view.shape = result.from_slice.shape * result.view.strides = result.from_slice.strides # <<<<<<<<<<<<<< * * */ __pyx_v_result->__pyx_base.view.strides = ((Py_ssize_t *)__pyx_v_result->from_slice.strides); /* "View.MemoryView":1036 * * * result.view.suboffsets = NULL # <<<<<<<<<<<<<< * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: */ __pyx_v_result->__pyx_base.view.suboffsets = NULL; /* "View.MemoryView":1037 * * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: # <<<<<<<<<<<<<< * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets */ __pyx_t_7 = (__pyx_v_result->from_slice.suboffsets + __pyx_v_ndim); for (__pyx_t_8 = __pyx_v_result->from_slice.suboffsets; __pyx_t_8 < __pyx_t_7; __pyx_t_8++) { __pyx_t_6 = __pyx_t_8; __pyx_v_suboffset = (__pyx_t_6[0]); /* "View.MemoryView":1038 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * result.view.suboffsets = result.from_slice.suboffsets * break */ __pyx_t_1 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":1039 * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets # <<<<<<<<<<<<<< * break * */ __pyx_v_result->__pyx_base.view.suboffsets = ((Py_ssize_t *)__pyx_v_result->from_slice.suboffsets); /* "View.MemoryView":1040 * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets * break # <<<<<<<<<<<<<< * * result.view.len = result.view.itemsize */ goto __pyx_L6_break; /* "View.MemoryView":1038 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * result.view.suboffsets = result.from_slice.suboffsets * break */ } } __pyx_L6_break:; /* "View.MemoryView":1042 * break * * result.view.len = result.view.itemsize # <<<<<<<<<<<<<< * for length in result.view.shape[:ndim]: * result.view.len *= length */ __pyx_t_9 = __pyx_v_result->__pyx_base.view.itemsize; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; /* "View.MemoryView":1043 * * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: # <<<<<<<<<<<<<< * result.view.len *= length * */ __pyx_t_7 = (__pyx_v_result->__pyx_base.view.shape + __pyx_v_ndim); for (__pyx_t_8 = __pyx_v_result->__pyx_base.view.shape; __pyx_t_8 < __pyx_t_7; __pyx_t_8++) { __pyx_t_6 = __pyx_t_8; __pyx_t_2 = PyInt_FromSsize_t((__pyx_t_6[0])); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1043, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":1044 * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: * result.view.len *= length # <<<<<<<<<<<<<< * * result.to_object_func = to_object_func */ __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_result->__pyx_base.view.len); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_InPlaceMultiply(__pyx_t_2, __pyx_v_length); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_3); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 1044, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; } /* "View.MemoryView":1046 * result.view.len *= length * * result.to_object_func = to_object_func # <<<<<<<<<<<<<< * result.to_dtype_func = to_dtype_func * */ __pyx_v_result->to_object_func = __pyx_v_to_object_func; /* "View.MemoryView":1047 * * result.to_object_func = to_object_func * result.to_dtype_func = to_dtype_func # <<<<<<<<<<<<<< * * return result */ __pyx_v_result->to_dtype_func = __pyx_v_to_dtype_func; /* "View.MemoryView":1049 * result.to_dtype_func = to_dtype_func * * return result # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_get_slice_from_memoryview') */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":999 * * @cname('__pyx_memoryview_fromslice') * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice, # <<<<<<<<<<<<<< * int ndim, * object (*to_object_func)(char *), */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview_fromslice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XDECREF(__pyx_v_length); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1052 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj */ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_mslice) { struct __pyx_memoryviewslice_obj *__pyx_v_obj = 0; __Pyx_memviewslice *__pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("get_slice_from_memview", 0); /* "View.MemoryView":1055 * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * obj = memview * return &obj.from_slice */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":1056 * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): * obj = memview # <<<<<<<<<<<<<< * return &obj.from_slice * else: */ if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 1056, __pyx_L1_error) __pyx_t_3 = ((PyObject *)__pyx_v_memview); __Pyx_INCREF(__pyx_t_3); __pyx_v_obj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":1057 * if isinstance(memview, _memoryviewslice): * obj = memview * return &obj.from_slice # <<<<<<<<<<<<<< * else: * slice_copy(memview, mslice) */ __pyx_r = (&__pyx_v_obj->from_slice); goto __pyx_L0; /* "View.MemoryView":1055 * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * obj = memview * return &obj.from_slice */ } /* "View.MemoryView":1059 * return &obj.from_slice * else: * slice_copy(memview, mslice) # <<<<<<<<<<<<<< * return mslice * */ /*else*/ { __pyx_memoryview_slice_copy(__pyx_v_memview, __pyx_v_mslice); /* "View.MemoryView":1060 * else: * slice_copy(memview, mslice) * return mslice # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_slice_copy') */ __pyx_r = __pyx_v_mslice; goto __pyx_L0; } /* "View.MemoryView":1052 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.get_slice_from_memview", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_obj); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1063 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< * cdef int dim * cdef (Py_ssize_t*) shape, strides, suboffsets */ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_dst) { int __pyx_v_dim; Py_ssize_t *__pyx_v_shape; Py_ssize_t *__pyx_v_strides; Py_ssize_t *__pyx_v_suboffsets; __Pyx_RefNannyDeclarations Py_ssize_t *__pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; Py_ssize_t __pyx_t_5; __Pyx_RefNannySetupContext("slice_copy", 0); /* "View.MemoryView":1067 * cdef (Py_ssize_t*) shape, strides, suboffsets * * shape = memview.view.shape # <<<<<<<<<<<<<< * strides = memview.view.strides * suboffsets = memview.view.suboffsets */ __pyx_t_1 = __pyx_v_memview->view.shape; __pyx_v_shape = __pyx_t_1; /* "View.MemoryView":1068 * * shape = memview.view.shape * strides = memview.view.strides # <<<<<<<<<<<<<< * suboffsets = memview.view.suboffsets * */ __pyx_t_1 = __pyx_v_memview->view.strides; __pyx_v_strides = __pyx_t_1; /* "View.MemoryView":1069 * shape = memview.view.shape * strides = memview.view.strides * suboffsets = memview.view.suboffsets # <<<<<<<<<<<<<< * * dst.memview = <__pyx_memoryview *> memview */ __pyx_t_1 = __pyx_v_memview->view.suboffsets; __pyx_v_suboffsets = __pyx_t_1; /* "View.MemoryView":1071 * suboffsets = memview.view.suboffsets * * dst.memview = <__pyx_memoryview *> memview # <<<<<<<<<<<<<< * dst.data = memview.view.buf * */ __pyx_v_dst->memview = ((struct __pyx_memoryview_obj *)__pyx_v_memview); /* "View.MemoryView":1072 * * dst.memview = <__pyx_memoryview *> memview * dst.data = memview.view.buf # <<<<<<<<<<<<<< * * for dim in range(memview.view.ndim): */ __pyx_v_dst->data = ((char *)__pyx_v_memview->view.buf); /* "View.MemoryView":1074 * dst.data = memview.view.buf * * for dim in range(memview.view.ndim): # <<<<<<<<<<<<<< * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] */ __pyx_t_2 = __pyx_v_memview->view.ndim; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_dim = __pyx_t_4; /* "View.MemoryView":1075 * * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] # <<<<<<<<<<<<<< * dst.strides[dim] = strides[dim] * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 */ (__pyx_v_dst->shape[__pyx_v_dim]) = (__pyx_v_shape[__pyx_v_dim]); /* "View.MemoryView":1076 * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] # <<<<<<<<<<<<<< * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 * */ (__pyx_v_dst->strides[__pyx_v_dim]) = (__pyx_v_strides[__pyx_v_dim]); /* "View.MemoryView":1077 * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_object') */ if ((__pyx_v_suboffsets != 0)) { __pyx_t_5 = (__pyx_v_suboffsets[__pyx_v_dim]); } else { __pyx_t_5 = -1L; } (__pyx_v_dst->suboffsets[__pyx_v_dim]) = __pyx_t_5; } /* "View.MemoryView":1063 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< * cdef int dim * cdef (Py_ssize_t*) shape, strides, suboffsets */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":1080 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice */ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx_v_memview) { __Pyx_memviewslice __pyx_v_memviewslice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_copy", 0); /* "View.MemoryView":1083 * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) # <<<<<<<<<<<<<< * return memoryview_copy_from_slice(memview, &memviewslice) * */ __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_memviewslice)); /* "View.MemoryView":1084 * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) * return memoryview_copy_from_slice(memview, &memviewslice) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_object_from_slice') */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __pyx_memoryview_copy_object_from_slice(__pyx_v_memview, (&__pyx_v_memviewslice)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1084, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":1080 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview_copy", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1087 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< * """ * Create a new memoryview object from a given memoryview object and slice. */ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_memviewslice) { PyObject *(*__pyx_v_to_object_func)(char *); int (*__pyx_v_to_dtype_func)(char *, PyObject *); PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *(*__pyx_t_3)(char *); int (*__pyx_t_4)(char *, PyObject *); PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_copy_from_slice", 0); /* "View.MemoryView":1094 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":1095 * * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func # <<<<<<<<<<<<<< * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func * else: */ __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func; __pyx_v_to_object_func = __pyx_t_3; /* "View.MemoryView":1096 * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func # <<<<<<<<<<<<<< * else: * to_object_func = NULL */ __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func; __pyx_v_to_dtype_func = __pyx_t_4; /* "View.MemoryView":1094 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func */ goto __pyx_L3; } /* "View.MemoryView":1098 * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func * else: * to_object_func = NULL # <<<<<<<<<<<<<< * to_dtype_func = NULL * */ /*else*/ { __pyx_v_to_object_func = NULL; /* "View.MemoryView":1099 * else: * to_object_func = NULL * to_dtype_func = NULL # <<<<<<<<<<<<<< * * return memoryview_fromslice(memviewslice[0], memview.view.ndim, */ __pyx_v_to_dtype_func = NULL; } __pyx_L3:; /* "View.MemoryView":1101 * to_dtype_func = NULL * * return memoryview_fromslice(memviewslice[0], memview.view.ndim, # <<<<<<<<<<<<<< * to_object_func, to_dtype_func, * memview.dtype_is_object) */ __Pyx_XDECREF(__pyx_r); /* "View.MemoryView":1103 * return memoryview_fromslice(memviewslice[0], memview.view.ndim, * to_object_func, to_dtype_func, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 1101, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "View.MemoryView":1087 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< * """ * Create a new memoryview object from a given memoryview object and slice. */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1109 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< * if arg < 0: * return -arg */ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { Py_ssize_t __pyx_r; int __pyx_t_1; /* "View.MemoryView":1110 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< * return -arg * else: */ __pyx_t_1 = ((__pyx_v_arg < 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":1111 * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: * return -arg # <<<<<<<<<<<<<< * else: * return arg */ __pyx_r = (-__pyx_v_arg); goto __pyx_L0; /* "View.MemoryView":1110 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< * return -arg * else: */ } /* "View.MemoryView":1113 * return -arg * else: * return arg # <<<<<<<<<<<<<< * * @cname('__pyx_get_best_slice_order') */ /*else*/ { __pyx_r = __pyx_v_arg; goto __pyx_L0; } /* "View.MemoryView":1109 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< * if arg < 0: * return -arg */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1116 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< * """ * Figure out the best memory access order for a given slice. */ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) { int __pyx_v_i; Py_ssize_t __pyx_v_c_stride; Py_ssize_t __pyx_v_f_stride; char __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; /* "View.MemoryView":1121 * """ * cdef int i * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< * cdef Py_ssize_t f_stride = 0 * */ __pyx_v_c_stride = 0; /* "View.MemoryView":1122 * cdef int i * cdef Py_ssize_t c_stride = 0 * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< * * for i in range(ndim - 1, -1, -1): */ __pyx_v_f_stride = 0; /* "View.MemoryView":1124 * cdef Py_ssize_t f_stride = 0 * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] */ for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":1125 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * c_stride = mslice.strides[i] * break */ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1126 * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< * break * */ __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1127 * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< * * for i in range(ndim): */ goto __pyx_L4_break; /* "View.MemoryView":1125 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * c_stride = mslice.strides[i] * break */ } } __pyx_L4_break:; /* "View.MemoryView":1129 * break * * for i in range(ndim): # <<<<<<<<<<<<<< * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] */ __pyx_t_1 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_1; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1130 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * f_stride = mslice.strides[i] * break */ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1131 * for i in range(ndim): * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< * break * */ __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1132 * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): */ goto __pyx_L7_break; /* "View.MemoryView":1130 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * f_stride = mslice.strides[i] * break */ } } __pyx_L7_break:; /* "View.MemoryView":1134 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< * return 'C' * else: */ __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1135 * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): * return 'C' # <<<<<<<<<<<<<< * else: * return 'F' */ __pyx_r = 'C'; goto __pyx_L0; /* "View.MemoryView":1134 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< * return 'C' * else: */ } /* "View.MemoryView":1137 * return 'C' * else: * return 'F' # <<<<<<<<<<<<<< * * @cython.cdivision(True) */ /*else*/ { __pyx_r = 'F'; goto __pyx_L0; } /* "View.MemoryView":1116 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< * """ * Figure out the best memory access order for a given slice. */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1140 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< * char *dst_data, Py_ssize_t *dst_strides, * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, */ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; Py_ssize_t __pyx_v_dst_extent; Py_ssize_t __pyx_v_src_stride; Py_ssize_t __pyx_v_dst_stride; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; /* "View.MemoryView":1147 * * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] */ __pyx_v_src_extent = (__pyx_v_src_shape[0]); /* "View.MemoryView":1148 * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] */ __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); /* "View.MemoryView":1149 * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t dst_stride = dst_strides[0] * */ __pyx_v_src_stride = (__pyx_v_src_strides[0]); /* "View.MemoryView":1150 * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< * * if ndim == 1: */ __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); /* "View.MemoryView":1152 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): */ __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L5_bool_binop_done; } __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L5_bool_binop_done; } /* "View.MemoryView":1154 * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< * memcpy(dst_data, src_data, itemsize * dst_extent) * else: */ __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); if (__pyx_t_2) { __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); } __pyx_t_3 = (__pyx_t_2 != 0); __pyx_t_1 = __pyx_t_3; __pyx_L5_bool_binop_done:; /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ if (__pyx_t_1) { /* "View.MemoryView":1155 * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< * else: * for i in range(dst_extent): */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ goto __pyx_L4; } /* "View.MemoryView":1157 * memcpy(dst_data, src_data, itemsize * dst_extent) * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< * memcpy(dst_data, src_data, itemsize) * src_data += src_stride */ /*else*/ { __pyx_t_4 = __pyx_v_dst_extent; __pyx_t_5 = __pyx_t_4; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1158 * else: * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< * src_data += src_stride * dst_data += dst_stride */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); /* "View.MemoryView":1159 * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< * dst_data += dst_stride * else: */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); /* "View.MemoryView":1160 * memcpy(dst_data, src_data, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< * else: * for i in range(dst_extent): */ __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); } } __pyx_L4:; /* "View.MemoryView":1152 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): */ goto __pyx_L3; } /* "View.MemoryView":1162 * dst_data += dst_stride * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< * _copy_strided_to_strided(src_data, src_strides + 1, * dst_data, dst_strides + 1, */ /*else*/ { __pyx_t_4 = __pyx_v_dst_extent; __pyx_t_5 = __pyx_t_4; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1163 * else: * for i in range(dst_extent): * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< * dst_data, dst_strides + 1, * src_shape + 1, dst_shape + 1, */ _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); /* "View.MemoryView":1167 * src_shape + 1, dst_shape + 1, * ndim - 1, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< * dst_data += dst_stride * */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); /* "View.MemoryView":1168 * ndim - 1, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, */ __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); } } __pyx_L3:; /* "View.MemoryView":1140 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< * char *dst_data, Py_ssize_t *dst_strides, * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, */ /* function exit code */ } /* "View.MemoryView":1170 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: */ static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { /* "View.MemoryView":1173 * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< * src.shape, dst.shape, ndim, itemsize) * */ _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); /* "View.MemoryView":1170 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: */ /* function exit code */ } /* "View.MemoryView":1177 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize */ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { Py_ssize_t __pyx_v_shape; Py_ssize_t __pyx_v_size; Py_ssize_t __pyx_r; Py_ssize_t __pyx_t_1; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; /* "View.MemoryView":1179 * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< * * for shape in src.shape[:ndim]: */ __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_size = __pyx_t_1; /* "View.MemoryView":1181 * cdef Py_ssize_t shape, size = src.memview.view.itemsize * * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< * size *= shape * */ __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim); for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { __pyx_t_2 = __pyx_t_4; __pyx_v_shape = (__pyx_t_2[0]); /* "View.MemoryView":1182 * * for shape in src.shape[:ndim]: * size *= shape # <<<<<<<<<<<<<< * * return size */ __pyx_v_size = (__pyx_v_size * __pyx_v_shape); } /* "View.MemoryView":1184 * size *= shape * * return size # <<<<<<<<<<<<<< * * @cname('__pyx_fill_contig_strides_array') */ __pyx_r = __pyx_v_size; goto __pyx_L0; /* "View.MemoryView":1177 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1187 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, * int ndim, char order) nogil: */ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { int __pyx_v_idx; Py_ssize_t __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; /* "View.MemoryView":1196 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< * for idx in range(ndim): * strides[idx] = stride */ __pyx_t_1 = ((__pyx_v_order == 'F') != 0); if (__pyx_t_1) { /* "View.MemoryView":1197 * * if order == 'F': * for idx in range(ndim): # <<<<<<<<<<<<<< * strides[idx] = stride * stride *= shape[idx] */ __pyx_t_2 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_idx = __pyx_t_4; /* "View.MemoryView":1198 * if order == 'F': * for idx in range(ndim): * strides[idx] = stride # <<<<<<<<<<<<<< * stride *= shape[idx] * else: */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; /* "View.MemoryView":1199 * for idx in range(ndim): * strides[idx] = stride * stride *= shape[idx] # <<<<<<<<<<<<<< * else: * for idx in range(ndim - 1, -1, -1): */ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } /* "View.MemoryView":1196 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< * for idx in range(ndim): * strides[idx] = stride */ goto __pyx_L3; } /* "View.MemoryView":1201 * stride *= shape[idx] * else: * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * strides[idx] = stride * stride *= shape[idx] */ /*else*/ { for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { __pyx_v_idx = __pyx_t_2; /* "View.MemoryView":1202 * else: * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride # <<<<<<<<<<<<<< * stride *= shape[idx] * */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; /* "View.MemoryView":1203 * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride * stride *= shape[idx] # <<<<<<<<<<<<<< * * return stride */ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } } __pyx_L3:; /* "View.MemoryView":1205 * stride *= shape[idx] * * return stride # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_data_to_temp') */ __pyx_r = __pyx_v_stride; goto __pyx_L0; /* "View.MemoryView":1187 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, * int ndim, char order) nogil: */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1208 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *tmpslice, * char order, */ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) { int __pyx_v_i; void *__pyx_v_result; size_t __pyx_v_itemsize; size_t __pyx_v_size; void *__pyx_r; Py_ssize_t __pyx_t_1; int __pyx_t_2; int __pyx_t_3; struct __pyx_memoryview_obj *__pyx_t_4; int __pyx_t_5; int __pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":1219 * cdef void *result * * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< * cdef size_t size = slice_get_size(src, ndim) * */ __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":1220 * * cdef size_t itemsize = src.memview.view.itemsize * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< * * result = malloc(size) */ __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); /* "View.MemoryView":1222 * cdef size_t size = slice_get_size(src, ndim) * * result = malloc(size) # <<<<<<<<<<<<<< * if not result: * _err(MemoryError, NULL) */ __pyx_v_result = malloc(__pyx_v_size); /* "View.MemoryView":1223 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< * _err(MemoryError, NULL) * */ __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1224 * result = malloc(size) * if not result: * _err(MemoryError, NULL) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 1224, __pyx_L1_error) /* "View.MemoryView":1223 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< * _err(MemoryError, NULL) * */ } /* "View.MemoryView":1227 * * * tmpslice.data = result # <<<<<<<<<<<<<< * tmpslice.memview = src.memview * for i in range(ndim): */ __pyx_v_tmpslice->data = ((char *)__pyx_v_result); /* "View.MemoryView":1228 * * tmpslice.data = result * tmpslice.memview = src.memview # <<<<<<<<<<<<<< * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] */ __pyx_t_4 = __pyx_v_src->memview; __pyx_v_tmpslice->memview = __pyx_t_4; /* "View.MemoryView":1229 * tmpslice.data = result * tmpslice.memview = src.memview * for i in range(ndim): # <<<<<<<<<<<<<< * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 */ __pyx_t_3 = __pyx_v_ndim; __pyx_t_5 = __pyx_t_3; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1230 * tmpslice.memview = src.memview * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] # <<<<<<<<<<<<<< * tmpslice.suboffsets[i] = -1 * */ (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]); /* "View.MemoryView":1231 * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, */ (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L; } /* "View.MemoryView":1233 * tmpslice.suboffsets[i] = -1 * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, # <<<<<<<<<<<<<< * ndim, order) * */ (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order)); /* "View.MemoryView":1237 * * * for i in range(ndim): # <<<<<<<<<<<<<< * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 */ __pyx_t_3 = __pyx_v_ndim; __pyx_t_5 = __pyx_t_3; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1238 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< * tmpslice.strides[i] = 0 * */ __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1239 * for i in range(ndim): * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 # <<<<<<<<<<<<<< * * if slice_is_contig(src[0], order, ndim): */ (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0; /* "View.MemoryView":1238 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< * tmpslice.strides[i] = 0 * */ } } /* "View.MemoryView":1241 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< * memcpy(result, src.data, size) * else: */ __pyx_t_2 = (__pyx_memviewslice_is_contig((__pyx_v_src[0]), __pyx_v_order, __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1242 * * if slice_is_contig(src[0], order, ndim): * memcpy(result, src.data, size) # <<<<<<<<<<<<<< * else: * copy_strided_to_strided(src, tmpslice, ndim, itemsize) */ (void)(memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size)); /* "View.MemoryView":1241 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< * memcpy(result, src.data, size) * else: */ goto __pyx_L9; } /* "View.MemoryView":1244 * memcpy(result, src.data, size) * else: * copy_strided_to_strided(src, tmpslice, ndim, itemsize) # <<<<<<<<<<<<<< * * return result */ /*else*/ { copy_strided_to_strided(__pyx_v_src, __pyx_v_tmpslice, __pyx_v_ndim, __pyx_v_itemsize); } __pyx_L9:; /* "View.MemoryView":1246 * copy_strided_to_strided(src, tmpslice, ndim, itemsize) * * return result # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_result; goto __pyx_L0; /* "View.MemoryView":1208 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *tmpslice, * char order, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.copy_data_to_temp", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = NULL; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1251 * * @cname('__pyx_memoryview_err_extents') * cdef int _err_extents(int i, Py_ssize_t extent1, # <<<<<<<<<<<<<< * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % */ static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent1, Py_ssize_t __pyx_v_extent2) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err_extents", 0); /* "View.MemoryView":1254 * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % * (i, extent1, extent2)) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_err_dim') */ __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_i); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_extent1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_extent2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_4, 2, __pyx_t_3); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_3 = 0; /* "View.MemoryView":1253 * cdef int _err_extents(int i, Py_ssize_t extent1, * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % # <<<<<<<<<<<<<< * (i, extent1, extent2)) * */ __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1253, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1253, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_4, 0, 0, 0); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __PYX_ERR(2, 1253, __pyx_L1_error) /* "View.MemoryView":1251 * * @cname('__pyx_memoryview_err_extents') * cdef int _err_extents(int i, Py_ssize_t extent1, # <<<<<<<<<<<<<< * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView._err_extents", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1257 * * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii') % dim) * */ static int __pyx_memoryview_err_dim(PyObject *__pyx_v_error, char *__pyx_v_msg, int __pyx_v_dim) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err_dim", 0); __Pyx_INCREF(__pyx_v_error); /* "View.MemoryView":1258 * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: * raise error(msg.decode('ascii') % dim) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_err') */ __pyx_t_2 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyInt_From_int(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyUnicode_Format(__pyx_t_2, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_INCREF(__pyx_v_error); __pyx_t_3 = __pyx_v_error; __pyx_t_2 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_3))) { __pyx_t_2 = PyMethod_GET_SELF(__pyx_t_3); if (likely(__pyx_t_2)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_3); __Pyx_INCREF(__pyx_t_2); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_3, function); } } __pyx_t_1 = (__pyx_t_2) ? __Pyx_PyObject_Call2Args(__pyx_t_3, __pyx_t_2, __pyx_t_4) : __Pyx_PyObject_CallOneArg(__pyx_t_3, __pyx_t_4); __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 1258, __pyx_L1_error) /* "View.MemoryView":1257 * * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii') % dim) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView._err_dim", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_XDECREF(__pyx_v_error); __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1261 * * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: # <<<<<<<<<<<<<< * if msg != NULL: * raise error(msg.decode('ascii')) */ static int __pyx_memoryview_err(PyObject *__pyx_v_error, char *__pyx_v_msg) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err", 0); __Pyx_INCREF(__pyx_v_error); /* "View.MemoryView":1262 * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii')) * else: */ __pyx_t_1 = ((__pyx_v_msg != NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":1263 * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: * raise error(msg.decode('ascii')) # <<<<<<<<<<<<<< * else: * raise error */ __pyx_t_3 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1263, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_error); __pyx_t_4 = __pyx_v_error; __pyx_t_5 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_4))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_4); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_4); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_4, function); } } __pyx_t_2 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_4, __pyx_t_5, __pyx_t_3) : __Pyx_PyObject_CallOneArg(__pyx_t_4, __pyx_t_3); __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1263, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(2, 1263, __pyx_L1_error) /* "View.MemoryView":1262 * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii')) * else: */ } /* "View.MemoryView":1265 * raise error(msg.decode('ascii')) * else: * raise error # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_contents') */ /*else*/ { __Pyx_Raise(__pyx_v_error, 0, 0, 0); __PYX_ERR(2, 1265, __pyx_L1_error) } /* "View.MemoryView":1261 * * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: # <<<<<<<<<<<<<< * if msg != NULL: * raise error(msg.decode('ascii')) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView._err", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_XDECREF(__pyx_v_error); __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1268 * * @cname('__pyx_memoryview_copy_contents') * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< * __Pyx_memviewslice dst, * int src_ndim, int dst_ndim, */ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_memviewslice __pyx_v_dst, int __pyx_v_src_ndim, int __pyx_v_dst_ndim, int __pyx_v_dtype_is_object) { void *__pyx_v_tmpdata; size_t __pyx_v_itemsize; int __pyx_v_i; char __pyx_v_order; int __pyx_v_broadcasting; int __pyx_v_direct_copy; __Pyx_memviewslice __pyx_v_tmp; int __pyx_v_ndim; int __pyx_r; Py_ssize_t __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_t_5; int __pyx_t_6; void *__pyx_t_7; int __pyx_t_8; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":1276 * Check for overlapping memory and verify the shapes. * """ * cdef void *tmpdata = NULL # <<<<<<<<<<<<<< * cdef size_t itemsize = src.memview.view.itemsize * cdef int i */ __pyx_v_tmpdata = NULL; /* "View.MemoryView":1277 * """ * cdef void *tmpdata = NULL * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< * cdef int i * cdef char order = get_best_order(&src, src_ndim) */ __pyx_t_1 = __pyx_v_src.memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":1279 * cdef size_t itemsize = src.memview.view.itemsize * cdef int i * cdef char order = get_best_order(&src, src_ndim) # <<<<<<<<<<<<<< * cdef bint broadcasting = False * cdef bint direct_copy = False */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_src), __pyx_v_src_ndim); /* "View.MemoryView":1280 * cdef int i * cdef char order = get_best_order(&src, src_ndim) * cdef bint broadcasting = False # <<<<<<<<<<<<<< * cdef bint direct_copy = False * cdef __Pyx_memviewslice tmp */ __pyx_v_broadcasting = 0; /* "View.MemoryView":1281 * cdef char order = get_best_order(&src, src_ndim) * cdef bint broadcasting = False * cdef bint direct_copy = False # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice tmp * */ __pyx_v_direct_copy = 0; /* "View.MemoryView":1284 * cdef __Pyx_memviewslice tmp * * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: */ __pyx_t_2 = ((__pyx_v_src_ndim < __pyx_v_dst_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1285 * * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) # <<<<<<<<<<<<<< * elif dst_ndim < src_ndim: * broadcast_leading(&dst, dst_ndim, src_ndim) */ __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim); /* "View.MemoryView":1284 * cdef __Pyx_memviewslice tmp * * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: */ goto __pyx_L3; } /* "View.MemoryView":1286 * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&dst, dst_ndim, src_ndim) * */ __pyx_t_2 = ((__pyx_v_dst_ndim < __pyx_v_src_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1287 * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: * broadcast_leading(&dst, dst_ndim, src_ndim) # <<<<<<<<<<<<<< * * cdef int ndim = max(src_ndim, dst_ndim) */ __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim); /* "View.MemoryView":1286 * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&dst, dst_ndim, src_ndim) * */ } __pyx_L3:; /* "View.MemoryView":1289 * broadcast_leading(&dst, dst_ndim, src_ndim) * * cdef int ndim = max(src_ndim, dst_ndim) # <<<<<<<<<<<<<< * * for i in range(ndim): */ __pyx_t_3 = __pyx_v_dst_ndim; __pyx_t_4 = __pyx_v_src_ndim; if (((__pyx_t_3 > __pyx_t_4) != 0)) { __pyx_t_5 = __pyx_t_3; } else { __pyx_t_5 = __pyx_t_4; } __pyx_v_ndim = __pyx_t_5; /* "View.MemoryView":1291 * cdef int ndim = max(src_ndim, dst_ndim) * * for i in range(ndim): # <<<<<<<<<<<<<< * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: */ __pyx_t_5 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_5; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1292 * * for i in range(ndim): * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< * if src.shape[i] == 1: * broadcasting = True */ __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0); if (__pyx_t_2) { /* "View.MemoryView":1293 * for i in range(ndim): * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: # <<<<<<<<<<<<<< * broadcasting = True * src.strides[i] = 0 */ __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1294 * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: * broadcasting = True # <<<<<<<<<<<<<< * src.strides[i] = 0 * else: */ __pyx_v_broadcasting = 1; /* "View.MemoryView":1295 * if src.shape[i] == 1: * broadcasting = True * src.strides[i] = 0 # <<<<<<<<<<<<<< * else: * _err_extents(i, dst.shape[i], src.shape[i]) */ (__pyx_v_src.strides[__pyx_v_i]) = 0; /* "View.MemoryView":1293 * for i in range(ndim): * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: # <<<<<<<<<<<<<< * broadcasting = True * src.strides[i] = 0 */ goto __pyx_L7; } /* "View.MemoryView":1297 * src.strides[i] = 0 * else: * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<< * * if src.suboffsets[i] >= 0: */ /*else*/ { __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1297, __pyx_L1_error) } __pyx_L7:; /* "View.MemoryView":1292 * * for i in range(ndim): * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< * if src.shape[i] == 1: * broadcasting = True */ } /* "View.MemoryView":1299 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Dimension %d is not direct", i) * */ __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":1300 * * if src.suboffsets[i] >= 0: * _err_dim(ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< * * if slices_overlap(&src, &dst, ndim, itemsize): */ __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1300, __pyx_L1_error) /* "View.MemoryView":1299 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Dimension %d is not direct", i) * */ } } /* "View.MemoryView":1302 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< * * if not slice_is_contig(src, order, ndim): */ __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0); if (__pyx_t_2) { /* "View.MemoryView":1304 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< * order = get_best_order(&dst, ndim) * */ __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1305 * * if not slice_is_contig(src, order, ndim): * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); /* "View.MemoryView":1304 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< * order = get_best_order(&dst, ndim) * */ } /* "View.MemoryView":1307 * order = get_best_order(&dst, ndim) * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< * src = tmp * */ __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(2, 1307, __pyx_L1_error) __pyx_v_tmpdata = __pyx_t_7; /* "View.MemoryView":1308 * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) * src = tmp # <<<<<<<<<<<<<< * * if not broadcasting: */ __pyx_v_src = __pyx_v_tmp; /* "View.MemoryView":1302 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< * * if not slice_is_contig(src, order, ndim): */ } /* "View.MemoryView":1310 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< * * */ __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1313 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): */ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1314 * * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< * elif slice_is_contig(src, 'F', ndim): * direct_copy = slice_is_contig(dst, 'F', ndim) */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); /* "View.MemoryView":1313 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): */ goto __pyx_L12; } /* "View.MemoryView":1315 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'F', ndim) * */ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1316 * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< * * if direct_copy: */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); /* "View.MemoryView":1315 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'F', ndim) * */ } __pyx_L12:; /* "View.MemoryView":1318 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ __pyx_t_2 = (__pyx_v_direct_copy != 0); if (__pyx_t_2) { /* "View.MemoryView":1320 * if direct_copy: * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1321 * * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) */ (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); /* "View.MemoryView":1322 * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * free(tmpdata) * return 0 */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1323 * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) # <<<<<<<<<<<<<< * return 0 * */ free(__pyx_v_tmpdata); /* "View.MemoryView":1324 * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) * return 0 # <<<<<<<<<<<<<< * * if order == 'F' == get_best_order(&dst, ndim): */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":1318 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ } /* "View.MemoryView":1310 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":1326 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< * * */ __pyx_t_2 = (__pyx_v_order == 'F'); if (__pyx_t_2) { __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); } __pyx_t_8 = (__pyx_t_2 != 0); if (__pyx_t_8) { /* "View.MemoryView":1329 * * * transpose_memslice(&src) # <<<<<<<<<<<<<< * transpose_memslice(&dst) * */ __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1329, __pyx_L1_error) /* "View.MemoryView":1330 * * transpose_memslice(&src) * transpose_memslice(&dst) # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1330, __pyx_L1_error) /* "View.MemoryView":1326 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":1332 * transpose_memslice(&dst) * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1333 * * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< * refcount_copying(&dst, dtype_is_object, ndim, True) * */ copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); /* "View.MemoryView":1334 * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * * free(tmpdata) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1336 * refcount_copying(&dst, dtype_is_object, ndim, True) * * free(tmpdata) # <<<<<<<<<<<<<< * return 0 * */ free(__pyx_v_tmpdata); /* "View.MemoryView":1337 * * free(tmpdata) * return 0 # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_broadcast_leading') */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":1268 * * @cname('__pyx_memoryview_copy_contents') * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< * __Pyx_memviewslice dst, * int src_ndim, int dst_ndim, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = -1; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1340 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< * int ndim, * int ndim_other) nogil: */ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) { int __pyx_v_i; int __pyx_v_offset; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; /* "View.MemoryView":1344 * int ndim_other) nogil: * cdef int i * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< * * for i in range(ndim - 1, -1, -1): */ __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); /* "View.MemoryView":1346 * cdef int offset = ndim_other - ndim * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] */ for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":1347 * * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] */ (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); /* "View.MemoryView":1348 * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * */ (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1349 * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< * * for i in range(offset): */ (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]); } /* "View.MemoryView":1351 * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * * for i in range(offset): # <<<<<<<<<<<<<< * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] */ __pyx_t_1 = __pyx_v_offset; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "View.MemoryView":1352 * * for i in range(offset): * mslice.shape[i] = 1 # <<<<<<<<<<<<<< * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 */ (__pyx_v_mslice->shape[__pyx_v_i]) = 1; /* "View.MemoryView":1353 * for i in range(offset): * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] # <<<<<<<<<<<<<< * mslice.suboffsets[i] = -1 * */ (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]); /* "View.MemoryView":1354 * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< * * */ (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L; } /* "View.MemoryView":1340 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< * int ndim, * int ndim_other) nogil: */ /* function exit code */ } /* "View.MemoryView":1362 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< * int ndim, bint inc) nogil: * */ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) { int __pyx_t_1; /* "View.MemoryView":1366 * * * if dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice_with_gil(dst.data, dst.shape, * dst.strides, ndim, inc) */ __pyx_t_1 = (__pyx_v_dtype_is_object != 0); if (__pyx_t_1) { /* "View.MemoryView":1367 * * if dtype_is_object: * refcount_objects_in_slice_with_gil(dst.data, dst.shape, # <<<<<<<<<<<<<< * dst.strides, ndim, inc) * */ __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc); /* "View.MemoryView":1366 * * * if dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice_with_gil(dst.data, dst.shape, * dst.strides, ndim, inc) */ } /* "View.MemoryView":1362 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< * int ndim, bint inc) nogil: * */ /* function exit code */ } /* "View.MemoryView":1371 * * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * bint inc) with gil: */ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { __Pyx_RefNannyDeclarations #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("refcount_objects_in_slice_with_gil", 0); /* "View.MemoryView":1374 * Py_ssize_t *strides, int ndim, * bint inc) with gil: * refcount_objects_in_slice(data, shape, strides, ndim, inc) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_refcount_objects_in_slice') */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc); /* "View.MemoryView":1371 * * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * bint inc) with gil: */ /* function exit code */ __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } /* "View.MemoryView":1377 * * @cname('__pyx_memoryview_refcount_objects_in_slice') * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, bint inc): * cdef Py_ssize_t i */ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; int __pyx_t_4; __Pyx_RefNannySetupContext("refcount_objects_in_slice", 0); /* "View.MemoryView":1381 * cdef Py_ssize_t i * * for i in range(shape[0]): # <<<<<<<<<<<<<< * if ndim == 1: * if inc: */ __pyx_t_1 = (__pyx_v_shape[0]); __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "View.MemoryView":1382 * * for i in range(shape[0]): * if ndim == 1: # <<<<<<<<<<<<<< * if inc: * Py_INCREF(( data)[0]) */ __pyx_t_4 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_4) { /* "View.MemoryView":1383 * for i in range(shape[0]): * if ndim == 1: * if inc: # <<<<<<<<<<<<<< * Py_INCREF(( data)[0]) * else: */ __pyx_t_4 = (__pyx_v_inc != 0); if (__pyx_t_4) { /* "View.MemoryView":1384 * if ndim == 1: * if inc: * Py_INCREF(( data)[0]) # <<<<<<<<<<<<<< * else: * Py_DECREF(( data)[0]) */ Py_INCREF((((PyObject **)__pyx_v_data)[0])); /* "View.MemoryView":1383 * for i in range(shape[0]): * if ndim == 1: * if inc: # <<<<<<<<<<<<<< * Py_INCREF(( data)[0]) * else: */ goto __pyx_L6; } /* "View.MemoryView":1386 * Py_INCREF(( data)[0]) * else: * Py_DECREF(( data)[0]) # <<<<<<<<<<<<<< * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, */ /*else*/ { Py_DECREF((((PyObject **)__pyx_v_data)[0])); } __pyx_L6:; /* "View.MemoryView":1382 * * for i in range(shape[0]): * if ndim == 1: # <<<<<<<<<<<<<< * if inc: * Py_INCREF(( data)[0]) */ goto __pyx_L5; } /* "View.MemoryView":1388 * Py_DECREF(( data)[0]) * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, # <<<<<<<<<<<<<< * ndim - 1, inc) * */ /*else*/ { /* "View.MemoryView":1389 * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, * ndim - 1, inc) # <<<<<<<<<<<<<< * * data += strides[0] */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_inc); } __pyx_L5:; /* "View.MemoryView":1391 * ndim - 1, inc) * * data += strides[0] # <<<<<<<<<<<<<< * * */ __pyx_v_data = (__pyx_v_data + (__pyx_v_strides[0])); } /* "View.MemoryView":1377 * * @cname('__pyx_memoryview_refcount_objects_in_slice') * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, bint inc): * cdef Py_ssize_t i */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":1397 * * @cname('__pyx_memoryview_slice_assign_scalar') * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, # <<<<<<<<<<<<<< * size_t itemsize, void *item, * bint dtype_is_object) nogil: */ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item, int __pyx_v_dtype_is_object) { /* "View.MemoryView":1400 * size_t itemsize, void *item, * bint dtype_is_object) nogil: * refcount_copying(dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, * itemsize, item) */ __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1401 * bint dtype_is_object) nogil: * refcount_copying(dst, dtype_is_object, ndim, False) * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, # <<<<<<<<<<<<<< * itemsize, item) * refcount_copying(dst, dtype_is_object, ndim, True) */ __pyx_memoryview__slice_assign_scalar(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_itemsize, __pyx_v_item); /* "View.MemoryView":1403 * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, * itemsize, item) * refcount_copying(dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * * */ __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1397 * * @cname('__pyx_memoryview_slice_assign_scalar') * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, # <<<<<<<<<<<<<< * size_t itemsize, void *item, * bint dtype_is_object) nogil: */ /* function exit code */ } /* "View.MemoryView":1407 * * @cname('__pyx_memoryview__slice_assign_scalar') * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * size_t itemsize, void *item) nogil: */ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_stride; Py_ssize_t __pyx_v_extent; int __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; /* "View.MemoryView":1411 * size_t itemsize, void *item) nogil: * cdef Py_ssize_t i * cdef Py_ssize_t stride = strides[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t extent = shape[0] * */ __pyx_v_stride = (__pyx_v_strides[0]); /* "View.MemoryView":1412 * cdef Py_ssize_t i * cdef Py_ssize_t stride = strides[0] * cdef Py_ssize_t extent = shape[0] # <<<<<<<<<<<<<< * * if ndim == 1: */ __pyx_v_extent = (__pyx_v_shape[0]); /* "View.MemoryView":1414 * cdef Py_ssize_t extent = shape[0] * * if ndim == 1: # <<<<<<<<<<<<<< * for i in range(extent): * memcpy(data, item, itemsize) */ __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":1415 * * if ndim == 1: * for i in range(extent): # <<<<<<<<<<<<<< * memcpy(data, item, itemsize) * data += stride */ __pyx_t_2 = __pyx_v_extent; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1416 * if ndim == 1: * for i in range(extent): * memcpy(data, item, itemsize) # <<<<<<<<<<<<<< * data += stride * else: */ (void)(memcpy(__pyx_v_data, __pyx_v_item, __pyx_v_itemsize)); /* "View.MemoryView":1417 * for i in range(extent): * memcpy(data, item, itemsize) * data += stride # <<<<<<<<<<<<<< * else: * for i in range(extent): */ __pyx_v_data = (__pyx_v_data + __pyx_v_stride); } /* "View.MemoryView":1414 * cdef Py_ssize_t extent = shape[0] * * if ndim == 1: # <<<<<<<<<<<<<< * for i in range(extent): * memcpy(data, item, itemsize) */ goto __pyx_L3; } /* "View.MemoryView":1419 * data += stride * else: * for i in range(extent): # <<<<<<<<<<<<<< * _slice_assign_scalar(data, shape + 1, strides + 1, * ndim - 1, itemsize, item) */ /*else*/ { __pyx_t_2 = __pyx_v_extent; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1420 * else: * for i in range(extent): * _slice_assign_scalar(data, shape + 1, strides + 1, # <<<<<<<<<<<<<< * ndim - 1, itemsize, item) * data += stride */ __pyx_memoryview__slice_assign_scalar(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize, __pyx_v_item); /* "View.MemoryView":1422 * _slice_assign_scalar(data, shape + 1, strides + 1, * ndim - 1, itemsize, item) * data += stride # <<<<<<<<<<<<<< * * */ __pyx_v_data = (__pyx_v_data + __pyx_v_stride); } } __pyx_L3:; /* "View.MemoryView":1407 * * @cname('__pyx_memoryview__slice_assign_scalar') * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * size_t itemsize, void *item) nogil: */ /* function exit code */ } /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyMethodDef __pyx_mdef_15View_dot_MemoryView_1__pyx_unpickle_Enum = {"__pyx_unpickle_Enum", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum, METH_VARARGS|METH_KEYWORDS, 0}; static PyObject *__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v___pyx_type = 0; long __pyx_v___pyx_checksum; PyObject *__pyx_v___pyx_state = 0; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__pyx_unpickle_Enum (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_pyx_type,&__pyx_n_s_pyx_checksum,&__pyx_n_s_pyx_state,0}; PyObject* values[3] = {0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_type)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_checksum)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, 1); __PYX_ERR(2, 1, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_state)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, 2); __PYX_ERR(2, 1, __pyx_L3_error) } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__pyx_unpickle_Enum") < 0)) __PYX_ERR(2, 1, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 3) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[2] = PyTuple_GET_ITEM(__pyx_args, 2); } __pyx_v___pyx_type = values[0]; __pyx_v___pyx_checksum = __Pyx_PyInt_As_long(values[1]); if (unlikely((__pyx_v___pyx_checksum == (long)-1) && PyErr_Occurred())) __PYX_ERR(2, 1, __pyx_L3_error) __pyx_v___pyx_state = values[2]; } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 1, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(__pyx_self, __pyx_v___pyx_type, __pyx_v___pyx_checksum, __pyx_v___pyx_state); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_v___pyx_PickleError = 0; PyObject *__pyx_v___pyx_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__pyx_unpickle_Enum", 0); /* "(tree fragment)":4 * cdef object __pyx_PickleError * cdef object __pyx_result * if __pyx_checksum != 0xb068931: # <<<<<<<<<<<<<< * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) */ __pyx_t_1 = ((__pyx_v___pyx_checksum != 0xb068931) != 0); if (__pyx_t_1) { /* "(tree fragment)":5 * cdef object __pyx_result * if __pyx_checksum != 0xb068931: * from pickle import PickleError as __pyx_PickleError # <<<<<<<<<<<<<< * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) */ __pyx_t_2 = PyList_New(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_n_s_PickleError); __Pyx_GIVEREF(__pyx_n_s_PickleError); PyList_SET_ITEM(__pyx_t_2, 0, __pyx_n_s_PickleError); __pyx_t_3 = __Pyx_Import(__pyx_n_s_pickle, __pyx_t_2, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_ImportFrom(__pyx_t_3, __pyx_n_s_PickleError); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_t_2); __pyx_v___pyx_PickleError = __pyx_t_2; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "(tree fragment)":6 * if __pyx_checksum != 0xb068931: * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) # <<<<<<<<<<<<<< * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: */ __pyx_t_2 = __Pyx_PyInt_From_long(__pyx_v___pyx_checksum); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_t_2); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_INCREF(__pyx_v___pyx_PickleError); __pyx_t_2 = __pyx_v___pyx_PickleError; __pyx_t_5 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_2))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_3 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_5, __pyx_t_4) : __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 6, __pyx_L1_error) /* "(tree fragment)":4 * cdef object __pyx_PickleError * cdef object __pyx_result * if __pyx_checksum != 0xb068931: # <<<<<<<<<<<<<< * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) */ } /* "(tree fragment)":7 * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) # <<<<<<<<<<<<<< * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) */ __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_MemviewEnum_type), __pyx_n_s_new); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 7, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_2))) { __pyx_t_4 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_4)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_4); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_3 = (__pyx_t_4) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_4, __pyx_v___pyx_type) : __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_v___pyx_type); __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 7, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v___pyx_result = __pyx_t_3; __pyx_t_3 = 0; /* "(tree fragment)":8 * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result */ __pyx_t_1 = (__pyx_v___pyx_state != Py_None); __pyx_t_6 = (__pyx_t_1 != 0); if (__pyx_t_6) { /* "(tree fragment)":9 * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) # <<<<<<<<<<<<<< * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): */ if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(2, 9, __pyx_L1_error) __pyx_t_3 = __pyx_unpickle_Enum__set_state(((struct __pyx_MemviewEnum_obj *)__pyx_v___pyx_result), ((PyObject*)__pyx_v___pyx_state)); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 9, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "(tree fragment)":8 * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result */ } /* "(tree fragment)":10 * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result # <<<<<<<<<<<<<< * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v___pyx_result); __pyx_r = __pyx_v___pyx_result; goto __pyx_L0; /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v___pyx_PickleError); __Pyx_XDECREF(__pyx_v___pyx_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *__pyx_v___pyx_result, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; Py_ssize_t __pyx_t_3; int __pyx_t_4; int __pyx_t_5; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__pyx_unpickle_Enum__set_state", 0); /* "(tree fragment)":12 * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] # <<<<<<<<<<<<<< * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): * __pyx_result.__dict__.update(__pyx_state[1]) */ if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(2, 12, __pyx_L1_error) } __pyx_t_1 = __Pyx_GetItemInt_Tuple(__pyx_v___pyx_state, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 12, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __Pyx_GOTREF(__pyx_v___pyx_result->name); __Pyx_DECREF(__pyx_v___pyx_result->name); __pyx_v___pyx_result->name = __pyx_t_1; __pyx_t_1 = 0; /* "(tree fragment)":13 * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): # <<<<<<<<<<<<<< * __pyx_result.__dict__.update(__pyx_state[1]) */ if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); __PYX_ERR(2, 13, __pyx_L1_error) } __pyx_t_3 = PyTuple_GET_SIZE(__pyx_v___pyx_state); if (unlikely(__pyx_t_3 == ((Py_ssize_t)-1))) __PYX_ERR(2, 13, __pyx_L1_error) __pyx_t_4 = ((__pyx_t_3 > 1) != 0); if (__pyx_t_4) { } else { __pyx_t_2 = __pyx_t_4; goto __pyx_L4_bool_binop_done; } __pyx_t_4 = __Pyx_HasAttr(((PyObject *)__pyx_v___pyx_result), __pyx_n_s_dict); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 13, __pyx_L1_error) __pyx_t_5 = (__pyx_t_4 != 0); __pyx_t_2 = __pyx_t_5; __pyx_L4_bool_binop_done:; if (__pyx_t_2) { /* "(tree fragment)":14 * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): * __pyx_result.__dict__.update(__pyx_state[1]) # <<<<<<<<<<<<<< */ __pyx_t_6 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v___pyx_result), __pyx_n_s_dict); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_6, __pyx_n_s_update); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(2, 14, __pyx_L1_error) } __pyx_t_6 = __Pyx_GetItemInt_Tuple(__pyx_v___pyx_state, 1, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_8 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_7))) { __pyx_t_8 = PyMethod_GET_SELF(__pyx_t_7); if (likely(__pyx_t_8)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_7); __Pyx_INCREF(__pyx_t_8); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_7, function); } } __pyx_t_1 = (__pyx_t_8) ? __Pyx_PyObject_Call2Args(__pyx_t_7, __pyx_t_8, __pyx_t_6) : __Pyx_PyObject_CallOneArg(__pyx_t_7, __pyx_t_6); __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":13 * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): # <<<<<<<<<<<<<< * __pyx_result.__dict__.update(__pyx_state[1]) */ } /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum__set_state", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } static struct __pyx_vtabstruct_array __pyx_vtable_array; static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_array_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_array_obj *)o); p->__pyx_vtab = __pyx_vtabptr_array; p->mode = ((PyObject*)Py_None); Py_INCREF(Py_None); p->_format = ((PyObject*)Py_None); Py_INCREF(Py_None); if (unlikely(__pyx_array___cinit__(o, a, k) < 0)) goto bad; return o; bad: Py_DECREF(o); o = 0; return NULL; } static void __pyx_tp_dealloc_array(PyObject *o) { struct __pyx_array_obj *p = (struct __pyx_array_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); __pyx_array___dealloc__(o); __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->mode); Py_CLEAR(p->_format); (*Py_TYPE(o)->tp_free)(o); } static PyObject *__pyx_sq_item_array(PyObject *o, Py_ssize_t i) { PyObject *r; PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); Py_DECREF(x); return r; } static int __pyx_mp_ass_subscript_array(PyObject *o, PyObject *i, PyObject *v) { if (v) { return __pyx_array___setitem__(o, i, v); } else { PyErr_Format(PyExc_NotImplementedError, "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); return -1; } } static PyObject *__pyx_tp_getattro_array(PyObject *o, PyObject *n) { PyObject *v = __Pyx_PyObject_GenericGetAttr(o, n); if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Clear(); v = __pyx_array___getattr__(o, n); } return v; } static PyObject *__pyx_getprop___pyx_array_memview(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(o); } static PyMethodDef __pyx_methods_array[] = { {"__getattr__", (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, 0}, {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_array_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_array_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_array[] = { {(char *)"memview", __pyx_getprop___pyx_array_memview, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PySequenceMethods __pyx_tp_as_sequence_array = { __pyx_array___len__, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ __pyx_sq_item_array, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_array = { __pyx_array___len__, /*mp_length*/ __pyx_array___getitem__, /*mp_subscript*/ __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_array = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif __pyx_array_getbuffer, /*bf_getbuffer*/ 0, /*bf_releasebuffer*/ }; static PyTypeObject __pyx_type___pyx_array = { PyVarObject_HEAD_INIT(0, 0) "openTSNE._matrix_mul.matrix_mul.array", /*tp_name*/ sizeof(struct __pyx_array_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_array, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ &__pyx_tp_as_sequence_array, /*tp_as_sequence*/ &__pyx_tp_as_mapping_array, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ __pyx_tp_getattro_array, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_array, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ 0, /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_array, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_array, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_array, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { struct __pyx_MemviewEnum_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_MemviewEnum_obj *)o); p->name = Py_None; Py_INCREF(Py_None); return o; } static void __pyx_tp_dealloc_Enum(PyObject *o) { struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); Py_CLEAR(p->name); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { int e; struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; if (p->name) { e = (*v)(p->name, a); if (e) return e; } return 0; } static int __pyx_tp_clear_Enum(PyObject *o) { PyObject* tmp; struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; tmp = ((PyObject*)p->name); p->name = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); return 0; } static PyMethodDef __pyx_methods_Enum[] = { {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type___pyx_MemviewEnum = { PyVarObject_HEAD_INIT(0, 0) "openTSNE._matrix_mul.matrix_mul.Enum", /*tp_name*/ sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_Enum, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif __pyx_MemviewEnum___repr__, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_Enum, /*tp_traverse*/ __pyx_tp_clear_Enum, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_Enum, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ __pyx_MemviewEnum___init__, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_Enum, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_memoryview_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_memoryview_obj *)o); p->__pyx_vtab = __pyx_vtabptr_memoryview; p->obj = Py_None; Py_INCREF(Py_None); p->_size = Py_None; Py_INCREF(Py_None); p->_array_interface = Py_None; Py_INCREF(Py_None); p->view.obj = NULL; if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; return o; bad: Py_DECREF(o); o = 0; return NULL; } static void __pyx_tp_dealloc_memoryview(PyObject *o) { struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); __pyx_memoryview___dealloc__(o); __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->obj); Py_CLEAR(p->_size); Py_CLEAR(p->_array_interface); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { int e; struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; if (p->obj) { e = (*v)(p->obj, a); if (e) return e; } if (p->_size) { e = (*v)(p->_size, a); if (e) return e; } if (p->_array_interface) { e = (*v)(p->_array_interface, a); if (e) return e; } if (p->view.obj) { e = (*v)(p->view.obj, a); if (e) return e; } return 0; } static int __pyx_tp_clear_memoryview(PyObject *o) { PyObject* tmp; struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; tmp = ((PyObject*)p->obj); p->obj = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->_size); p->_size = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->_array_interface); p->_array_interface = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); Py_CLEAR(p->view.obj); return 0; } static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { PyObject *r; PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); Py_DECREF(x); return r; } static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { if (v) { return __pyx_memoryview___setitem__(o, i, v); } else { PyErr_Format(PyExc_NotImplementedError, "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); return -1; } } static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); } static PyMethodDef __pyx_methods_memoryview[] = { {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0}, {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0}, {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0}, {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0}, {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_memoryview[] = { {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PySequenceMethods __pyx_tp_as_sequence_memoryview = { __pyx_memoryview___len__, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ __pyx_sq_item_memoryview, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_memoryview = { __pyx_memoryview___len__, /*mp_length*/ __pyx_memoryview___getitem__, /*mp_subscript*/ __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_memoryview = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif __pyx_memoryview_getbuffer, /*bf_getbuffer*/ 0, /*bf_releasebuffer*/ }; static PyTypeObject __pyx_type___pyx_memoryview = { PyVarObject_HEAD_INIT(0, 0) "openTSNE._matrix_mul.matrix_mul.memoryview", /*tp_name*/ sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif __pyx_memoryview___repr__, /*tp_repr*/ 0, /*tp_as_number*/ &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ __pyx_memoryview___str__, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_memoryview, /*tp_traverse*/ __pyx_tp_clear_memoryview, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_memoryview, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_memoryview, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_memoryview, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_memoryviewslice_obj *p; PyObject *o = __pyx_tp_new_memoryview(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_memoryviewslice_obj *)o); p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; p->from_object = Py_None; Py_INCREF(Py_None); p->from_slice.memview = NULL; return o; } static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); __pyx_memoryviewslice___dealloc__(o); __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->from_object); PyObject_GC_Track(o); __pyx_tp_dealloc_memoryview(o); } static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { int e; struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; if (p->from_object) { e = (*v)(p->from_object, a); if (e) return e; } return 0; } static int __pyx_tp_clear__memoryviewslice(PyObject *o) { PyObject* tmp; struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; __pyx_tp_clear_memoryview(o); tmp = ((PyObject*)p->from_object); p->from_object = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); __PYX_XDEC_MEMVIEW(&p->from_slice, 1); return 0; } static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o); } static PyMethodDef __pyx_methods__memoryviewslice[] = { {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = { {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PyTypeObject __pyx_type___pyx_memoryviewslice = { PyVarObject_HEAD_INIT(0, 0) "openTSNE._matrix_mul.matrix_mul._memoryviewslice", /*tp_name*/ sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif #if CYTHON_COMPILING_IN_PYPY __pyx_memoryview___repr__, /*tp_repr*/ #else 0, /*tp_repr*/ #endif 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ #if CYTHON_COMPILING_IN_PYPY __pyx_memoryview___str__, /*tp_str*/ #else 0, /*tp_str*/ #endif 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ "Internal class for passing memoryview slices to Python", /*tp_doc*/ __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ __pyx_tp_clear__memoryviewslice, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods__memoryviewslice, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets__memoryviewslice, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new__memoryviewslice, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static PyMethodDef __pyx_methods[] = { {0, 0, 0, 0} }; #if PY_MAJOR_VERSION >= 3 #if CYTHON_PEP489_MULTI_PHASE_INIT static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/ static int __pyx_pymod_exec_matrix_mul(PyObject* module); /*proto*/ static PyModuleDef_Slot __pyx_moduledef_slots[] = { {Py_mod_create, (void*)__pyx_pymod_create}, {Py_mod_exec, (void*)__pyx_pymod_exec_matrix_mul}, {0, NULL} }; #endif static struct PyModuleDef __pyx_moduledef = { PyModuleDef_HEAD_INIT, "matrix_mul", 0, /* m_doc */ #if CYTHON_PEP489_MULTI_PHASE_INIT 0, /* m_size */ #else -1, /* m_size */ #endif __pyx_methods /* m_methods */, #if CYTHON_PEP489_MULTI_PHASE_INIT __pyx_moduledef_slots, /* m_slots */ #else NULL, /* m_reload */ #endif NULL, /* m_traverse */ NULL, /* m_clear */ NULL /* m_free */ }; #endif #ifndef CYTHON_SMALL_CODE #if defined(__clang__) #define CYTHON_SMALL_CODE #elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3)) #define CYTHON_SMALL_CODE __attribute__((cold)) #else #define CYTHON_SMALL_CODE #endif #endif static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_n_s_ASCII, __pyx_k_ASCII, sizeof(__pyx_k_ASCII), 0, 0, 1, 1}, {&__pyx_kp_s_Buffer_view_does_not_expose_stri, __pyx_k_Buffer_view_does_not_expose_stri, sizeof(__pyx_k_Buffer_view_does_not_expose_stri), 0, 0, 1, 0}, {&__pyx_kp_s_Can_only_create_a_buffer_that_is, __pyx_k_Can_only_create_a_buffer_that_is, sizeof(__pyx_k_Can_only_create_a_buffer_that_is), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_assign_to_read_only_memor, __pyx_k_Cannot_assign_to_read_only_memor, sizeof(__pyx_k_Cannot_assign_to_read_only_memor), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_create_writable_memory_vi, __pyx_k_Cannot_create_writable_memory_vi, sizeof(__pyx_k_Cannot_create_writable_memory_vi), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_index_with_type_s, __pyx_k_Cannot_index_with_type_s, sizeof(__pyx_k_Cannot_index_with_type_s), 0, 0, 1, 0}, {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1}, {&__pyx_kp_s_Empty_shape_tuple_for_cython_arr, __pyx_k_Empty_shape_tuple_for_cython_arr, sizeof(__pyx_k_Empty_shape_tuple_for_cython_arr), 0, 0, 1, 0}, {&__pyx_n_s_ImportError, __pyx_k_ImportError, sizeof(__pyx_k_ImportError), 0, 0, 1, 1}, {&__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_k_Incompatible_checksums_s_vs_0xb0, sizeof(__pyx_k_Incompatible_checksums_s_vs_0xb0), 0, 0, 1, 0}, {&__pyx_n_s_IndexError, __pyx_k_IndexError, sizeof(__pyx_k_IndexError), 0, 0, 1, 1}, {&__pyx_kp_s_Indirect_dimensions_not_supporte, __pyx_k_Indirect_dimensions_not_supporte, sizeof(__pyx_k_Indirect_dimensions_not_supporte), 0, 0, 1, 0}, {&__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_k_Invalid_mode_expected_c_or_fortr, sizeof(__pyx_k_Invalid_mode_expected_c_or_fortr), 0, 0, 1, 0}, {&__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_k_Invalid_shape_in_axis_d_d, sizeof(__pyx_k_Invalid_shape_in_axis_d_d), 0, 0, 1, 0}, {&__pyx_n_s_MemoryError, __pyx_k_MemoryError, sizeof(__pyx_k_MemoryError), 0, 0, 1, 1}, {&__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_k_MemoryView_of_r_at_0x_x, sizeof(__pyx_k_MemoryView_of_r_at_0x_x), 0, 0, 1, 0}, {&__pyx_kp_s_MemoryView_of_r_object, __pyx_k_MemoryView_of_r_object, sizeof(__pyx_k_MemoryView_of_r_object), 0, 0, 1, 0}, {&__pyx_n_b_O, __pyx_k_O, sizeof(__pyx_k_O), 0, 0, 0, 1}, {&__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_k_Out_of_bounds_on_buffer_access_a, sizeof(__pyx_k_Out_of_bounds_on_buffer_access_a), 0, 0, 1, 0}, {&__pyx_n_s_PickleError, __pyx_k_PickleError, sizeof(__pyx_k_PickleError), 0, 0, 1, 1}, {&__pyx_n_s_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 0, 0, 1, 1}, {&__pyx_kp_s_Unable_to_convert_item_to_object, __pyx_k_Unable_to_convert_item_to_object, sizeof(__pyx_k_Unable_to_convert_item_to_object), 0, 0, 1, 0}, {&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1}, {&__pyx_n_s_View_MemoryView, __pyx_k_View_MemoryView, sizeof(__pyx_k_View_MemoryView), 0, 0, 1, 1}, {&__pyx_n_s_allocate_buffer, __pyx_k_allocate_buffer, sizeof(__pyx_k_allocate_buffer), 0, 0, 1, 1}, {&__pyx_n_s_base, __pyx_k_base, sizeof(__pyx_k_base), 0, 0, 1, 1}, {&__pyx_n_s_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 0, 1, 1}, {&__pyx_n_u_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 1, 0, 1}, {&__pyx_n_s_class, __pyx_k_class, sizeof(__pyx_k_class), 0, 0, 1, 1}, {&__pyx_n_s_cline_in_traceback, __pyx_k_cline_in_traceback, sizeof(__pyx_k_cline_in_traceback), 0, 0, 1, 1}, {&__pyx_kp_s_contiguous_and_direct, __pyx_k_contiguous_and_direct, sizeof(__pyx_k_contiguous_and_direct), 0, 0, 1, 0}, {&__pyx_kp_s_contiguous_and_indirect, __pyx_k_contiguous_and_indirect, sizeof(__pyx_k_contiguous_and_indirect), 0, 0, 1, 0}, {&__pyx_n_s_dict, __pyx_k_dict, sizeof(__pyx_k_dict), 0, 0, 1, 1}, {&__pyx_n_s_dtype, __pyx_k_dtype, sizeof(__pyx_k_dtype), 0, 0, 1, 1}, {&__pyx_n_s_dtype_is_object, __pyx_k_dtype_is_object, sizeof(__pyx_k_dtype_is_object), 0, 0, 1, 1}, {&__pyx_n_s_empty, __pyx_k_empty, sizeof(__pyx_k_empty), 0, 0, 1, 1}, {&__pyx_n_s_encode, __pyx_k_encode, sizeof(__pyx_k_encode), 0, 0, 1, 1}, {&__pyx_n_s_enumerate, __pyx_k_enumerate, sizeof(__pyx_k_enumerate), 0, 0, 1, 1}, {&__pyx_n_s_error, __pyx_k_error, sizeof(__pyx_k_error), 0, 0, 1, 1}, {&__pyx_n_s_fft, __pyx_k_fft, sizeof(__pyx_k_fft), 0, 0, 1, 1}, {&__pyx_n_s_flags, __pyx_k_flags, sizeof(__pyx_k_flags), 0, 0, 1, 1}, {&__pyx_n_s_format, __pyx_k_format, sizeof(__pyx_k_format), 0, 0, 1, 1}, {&__pyx_n_s_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 0, 1, 1}, {&__pyx_n_u_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 1, 0, 1}, {&__pyx_n_s_getstate, __pyx_k_getstate, sizeof(__pyx_k_getstate), 0, 0, 1, 1}, {&__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_k_got_differing_extents_in_dimensi, sizeof(__pyx_k_got_differing_extents_in_dimensi), 0, 0, 1, 0}, {&__pyx_n_s_id, __pyx_k_id, sizeof(__pyx_k_id), 0, 0, 1, 1}, {&__pyx_n_s_ifft, __pyx_k_ifft, sizeof(__pyx_k_ifft), 0, 0, 1, 1}, {&__pyx_n_s_import, __pyx_k_import, sizeof(__pyx_k_import), 0, 0, 1, 1}, {&__pyx_n_s_irfft2, __pyx_k_irfft2, sizeof(__pyx_k_irfft2), 0, 0, 1, 1}, {&__pyx_n_s_itemsize, __pyx_k_itemsize, sizeof(__pyx_k_itemsize), 0, 0, 1, 1}, {&__pyx_kp_s_itemsize_0_for_cython_array, __pyx_k_itemsize_0_for_cython_array, sizeof(__pyx_k_itemsize_0_for_cython_array), 0, 0, 1, 0}, {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1}, {&__pyx_n_s_memview, __pyx_k_memview, sizeof(__pyx_k_memview), 0, 0, 1, 1}, {&__pyx_n_s_mode, __pyx_k_mode, sizeof(__pyx_k_mode), 0, 0, 1, 1}, {&__pyx_n_s_multiply, __pyx_k_multiply, sizeof(__pyx_k_multiply), 0, 0, 1, 1}, {&__pyx_n_s_name, __pyx_k_name, sizeof(__pyx_k_name), 0, 0, 1, 1}, {&__pyx_n_s_name_2, __pyx_k_name_2, sizeof(__pyx_k_name_2), 0, 0, 1, 1}, {&__pyx_n_s_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 0, 0, 1, 1}, {&__pyx_n_s_new, __pyx_k_new, sizeof(__pyx_k_new), 0, 0, 1, 1}, {&__pyx_kp_s_no_default___reduce___due_to_non, __pyx_k_no_default___reduce___due_to_non, sizeof(__pyx_k_no_default___reduce___due_to_non), 0, 0, 1, 0}, {&__pyx_n_s_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 0, 1, 1}, {&__pyx_n_s_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 0, 0, 1, 1}, {&__pyx_kp_u_numpy_core_multiarray_failed_to, __pyx_k_numpy_core_multiarray_failed_to, sizeof(__pyx_k_numpy_core_multiarray_failed_to), 0, 1, 0, 0}, {&__pyx_kp_u_numpy_core_umath_failed_to_impor, __pyx_k_numpy_core_umath_failed_to_impor, sizeof(__pyx_k_numpy_core_umath_failed_to_impor), 0, 1, 0, 0}, {&__pyx_n_s_obj, __pyx_k_obj, sizeof(__pyx_k_obj), 0, 0, 1, 1}, {&__pyx_n_s_pack, __pyx_k_pack, sizeof(__pyx_k_pack), 0, 0, 1, 1}, {&__pyx_n_s_pickle, __pyx_k_pickle, sizeof(__pyx_k_pickle), 0, 0, 1, 1}, {&__pyx_n_s_pyx_PickleError, __pyx_k_pyx_PickleError, sizeof(__pyx_k_pyx_PickleError), 0, 0, 1, 1}, {&__pyx_n_s_pyx_checksum, __pyx_k_pyx_checksum, sizeof(__pyx_k_pyx_checksum), 0, 0, 1, 1}, {&__pyx_n_s_pyx_getbuffer, __pyx_k_pyx_getbuffer, sizeof(__pyx_k_pyx_getbuffer), 0, 0, 1, 1}, {&__pyx_n_s_pyx_result, __pyx_k_pyx_result, sizeof(__pyx_k_pyx_result), 0, 0, 1, 1}, {&__pyx_n_s_pyx_state, __pyx_k_pyx_state, sizeof(__pyx_k_pyx_state), 0, 0, 1, 1}, {&__pyx_n_s_pyx_type, __pyx_k_pyx_type, sizeof(__pyx_k_pyx_type), 0, 0, 1, 1}, {&__pyx_n_s_pyx_unpickle_Enum, __pyx_k_pyx_unpickle_Enum, sizeof(__pyx_k_pyx_unpickle_Enum), 0, 0, 1, 1}, {&__pyx_n_s_pyx_vtable, __pyx_k_pyx_vtable, sizeof(__pyx_k_pyx_vtable), 0, 0, 1, 1}, {&__pyx_n_s_range, __pyx_k_range, sizeof(__pyx_k_range), 0, 0, 1, 1}, {&__pyx_n_s_real, __pyx_k_real, sizeof(__pyx_k_real), 0, 0, 1, 1}, {&__pyx_n_s_reduce, __pyx_k_reduce, sizeof(__pyx_k_reduce), 0, 0, 1, 1}, {&__pyx_n_s_reduce_cython, __pyx_k_reduce_cython, sizeof(__pyx_k_reduce_cython), 0, 0, 1, 1}, {&__pyx_n_s_reduce_ex, __pyx_k_reduce_ex, sizeof(__pyx_k_reduce_ex), 0, 0, 1, 1}, {&__pyx_n_s_rfft2, __pyx_k_rfft2, sizeof(__pyx_k_rfft2), 0, 0, 1, 1}, {&__pyx_n_s_setstate, __pyx_k_setstate, sizeof(__pyx_k_setstate), 0, 0, 1, 1}, {&__pyx_n_s_setstate_cython, __pyx_k_setstate_cython, sizeof(__pyx_k_setstate_cython), 0, 0, 1, 1}, {&__pyx_n_s_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 0, 0, 1, 1}, {&__pyx_n_s_size, __pyx_k_size, sizeof(__pyx_k_size), 0, 0, 1, 1}, {&__pyx_n_s_start, __pyx_k_start, sizeof(__pyx_k_start), 0, 0, 1, 1}, {&__pyx_n_s_step, __pyx_k_step, sizeof(__pyx_k_step), 0, 0, 1, 1}, {&__pyx_n_s_stop, __pyx_k_stop, sizeof(__pyx_k_stop), 0, 0, 1, 1}, {&__pyx_kp_s_strided_and_direct, __pyx_k_strided_and_direct, sizeof(__pyx_k_strided_and_direct), 0, 0, 1, 0}, {&__pyx_kp_s_strided_and_direct_or_indirect, __pyx_k_strided_and_direct_or_indirect, sizeof(__pyx_k_strided_and_direct_or_indirect), 0, 0, 1, 0}, {&__pyx_kp_s_strided_and_indirect, __pyx_k_strided_and_indirect, sizeof(__pyx_k_strided_and_indirect), 0, 0, 1, 0}, {&__pyx_kp_s_stringsource, __pyx_k_stringsource, sizeof(__pyx_k_stringsource), 0, 0, 1, 0}, {&__pyx_n_s_struct, __pyx_k_struct, sizeof(__pyx_k_struct), 0, 0, 1, 1}, {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1}, {&__pyx_kp_s_unable_to_allocate_array_data, __pyx_k_unable_to_allocate_array_data, sizeof(__pyx_k_unable_to_allocate_array_data), 0, 0, 1, 0}, {&__pyx_kp_s_unable_to_allocate_shape_and_str, __pyx_k_unable_to_allocate_shape_and_str, sizeof(__pyx_k_unable_to_allocate_shape_and_str), 0, 0, 1, 0}, {&__pyx_n_s_unpack, __pyx_k_unpack, sizeof(__pyx_k_unpack), 0, 0, 1, 1}, {&__pyx_n_s_update, __pyx_k_update, sizeof(__pyx_k_update), 0, 0, 1, 1}, {&__pyx_n_s_zeros, __pyx_k_zeros, sizeof(__pyx_k_zeros), 0, 0, 1, 1}, {0, 0, 0, 0, 0, 0, 0} }; static CYTHON_SMALL_CODE int __Pyx_InitCachedBuiltins(void) { __pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) __PYX_ERR(0, 55, __pyx_L1_error) __pyx_builtin_ImportError = __Pyx_GetBuiltinName(__pyx_n_s_ImportError); if (!__pyx_builtin_ImportError) __PYX_ERR(1, 884, __pyx_L1_error) __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) __PYX_ERR(2, 133, __pyx_L1_error) __pyx_builtin_MemoryError = __Pyx_GetBuiltinName(__pyx_n_s_MemoryError); if (!__pyx_builtin_MemoryError) __PYX_ERR(2, 148, __pyx_L1_error) __pyx_builtin_enumerate = __Pyx_GetBuiltinName(__pyx_n_s_enumerate); if (!__pyx_builtin_enumerate) __PYX_ERR(2, 151, __pyx_L1_error) __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s_TypeError); if (!__pyx_builtin_TypeError) __PYX_ERR(2, 2, __pyx_L1_error) __pyx_builtin_Ellipsis = __Pyx_GetBuiltinName(__pyx_n_s_Ellipsis); if (!__pyx_builtin_Ellipsis) __PYX_ERR(2, 404, __pyx_L1_error) __pyx_builtin_id = __Pyx_GetBuiltinName(__pyx_n_s_id); if (!__pyx_builtin_id) __PYX_ERR(2, 613, __pyx_L1_error) __pyx_builtin_IndexError = __Pyx_GetBuiltinName(__pyx_n_s_IndexError); if (!__pyx_builtin_IndexError) __PYX_ERR(2, 832, __pyx_L1_error) return 0; __pyx_L1_error:; return -1; } static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":884 * __pyx_import_array() * except Exception: * raise ImportError("numpy.core.multiarray failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_umath() except -1: */ __pyx_tuple_ = PyTuple_Pack(1, __pyx_kp_u_numpy_core_multiarray_failed_to); if (unlikely(!__pyx_tuple_)) __PYX_ERR(1, 884, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple_); __Pyx_GIVEREF(__pyx_tuple_); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":890 * _import_umath() * except Exception: * raise ImportError("numpy.core.umath failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_ufunc() except -1: */ __pyx_tuple__2 = PyTuple_Pack(1, __pyx_kp_u_numpy_core_umath_failed_to_impor); if (unlikely(!__pyx_tuple__2)) __PYX_ERR(1, 890, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__2); __Pyx_GIVEREF(__pyx_tuple__2); /* "View.MemoryView":133 * * if not self.ndim: * raise ValueError("Empty shape tuple for cython.array") # <<<<<<<<<<<<<< * * if itemsize <= 0: */ __pyx_tuple__3 = PyTuple_Pack(1, __pyx_kp_s_Empty_shape_tuple_for_cython_arr); if (unlikely(!__pyx_tuple__3)) __PYX_ERR(2, 133, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__3); __Pyx_GIVEREF(__pyx_tuple__3); /* "View.MemoryView":136 * * if itemsize <= 0: * raise ValueError("itemsize <= 0 for cython.array") # <<<<<<<<<<<<<< * * if not isinstance(format, bytes): */ __pyx_tuple__4 = PyTuple_Pack(1, __pyx_kp_s_itemsize_0_for_cython_array); if (unlikely(!__pyx_tuple__4)) __PYX_ERR(2, 136, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__4); __Pyx_GIVEREF(__pyx_tuple__4); /* "View.MemoryView":148 * * if not self._shape: * raise MemoryError("unable to allocate shape and strides.") # <<<<<<<<<<<<<< * * */ __pyx_tuple__5 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_shape_and_str); if (unlikely(!__pyx_tuple__5)) __PYX_ERR(2, 148, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__5); __Pyx_GIVEREF(__pyx_tuple__5); /* "View.MemoryView":176 * self.data = malloc(self.len) * if not self.data: * raise MemoryError("unable to allocate array data.") # <<<<<<<<<<<<<< * * if self.dtype_is_object: */ __pyx_tuple__6 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_array_data); if (unlikely(!__pyx_tuple__6)) __PYX_ERR(2, 176, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__6); __Pyx_GIVEREF(__pyx_tuple__6); /* "View.MemoryView":192 * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") # <<<<<<<<<<<<<< * info.buf = self.data * info.len = self.len */ __pyx_tuple__7 = PyTuple_Pack(1, __pyx_kp_s_Can_only_create_a_buffer_that_is); if (unlikely(!__pyx_tuple__7)) __PYX_ERR(2, 192, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__7); __Pyx_GIVEREF(__pyx_tuple__7); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__8 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__8)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__8); __Pyx_GIVEREF(__pyx_tuple__8); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__9 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__9)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__9); __Pyx_GIVEREF(__pyx_tuple__9); /* "View.MemoryView":418 * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") # <<<<<<<<<<<<<< * * have_slices, index = _unellipsify(index, self.view.ndim) */ __pyx_tuple__10 = PyTuple_Pack(1, __pyx_kp_s_Cannot_assign_to_read_only_memor); if (unlikely(!__pyx_tuple__10)) __PYX_ERR(2, 418, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__10); __Pyx_GIVEREF(__pyx_tuple__10); /* "View.MemoryView":495 * result = struct.unpack(self.view.format, bytesitem) * except struct.error: * raise ValueError("Unable to convert item to object") # <<<<<<<<<<<<<< * else: * if len(self.view.format) == 1: */ __pyx_tuple__11 = PyTuple_Pack(1, __pyx_kp_s_Unable_to_convert_item_to_object); if (unlikely(!__pyx_tuple__11)) __PYX_ERR(2, 495, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__11); __Pyx_GIVEREF(__pyx_tuple__11); /* "View.MemoryView":520 * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") # <<<<<<<<<<<<<< * * if flags & PyBUF_ND: */ __pyx_tuple__12 = PyTuple_Pack(1, __pyx_kp_s_Cannot_create_writable_memory_vi); if (unlikely(!__pyx_tuple__12)) __PYX_ERR(2, 520, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__12); __Pyx_GIVEREF(__pyx_tuple__12); /* "View.MemoryView":570 * if self.view.strides == NULL: * * raise ValueError("Buffer view does not expose strides") # <<<<<<<<<<<<<< * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) */ __pyx_tuple__13 = PyTuple_Pack(1, __pyx_kp_s_Buffer_view_does_not_expose_stri); if (unlikely(!__pyx_tuple__13)) __PYX_ERR(2, 570, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__13); __Pyx_GIVEREF(__pyx_tuple__13); /* "View.MemoryView":577 * def suboffsets(self): * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim # <<<<<<<<<<<<<< * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) */ __pyx_tuple__14 = PyTuple_New(1); if (unlikely(!__pyx_tuple__14)) __PYX_ERR(2, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__14); __Pyx_INCREF(__pyx_int_neg_1); __Pyx_GIVEREF(__pyx_int_neg_1); PyTuple_SET_ITEM(__pyx_tuple__14, 0, __pyx_int_neg_1); __Pyx_GIVEREF(__pyx_tuple__14); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__15 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__15)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__15); __Pyx_GIVEREF(__pyx_tuple__15); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__16 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__16)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__16); __Pyx_GIVEREF(__pyx_tuple__16); /* "View.MemoryView":682 * if item is Ellipsis: * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) # <<<<<<<<<<<<<< * seen_ellipsis = True * else: */ __pyx_slice__17 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__17)) __PYX_ERR(2, 682, __pyx_L1_error) __Pyx_GOTREF(__pyx_slice__17); __Pyx_GIVEREF(__pyx_slice__17); /* "View.MemoryView":703 * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") # <<<<<<<<<<<<<< * * */ __pyx_tuple__18 = PyTuple_Pack(1, __pyx_kp_s_Indirect_dimensions_not_supporte); if (unlikely(!__pyx_tuple__18)) __PYX_ERR(2, 703, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__18); __Pyx_GIVEREF(__pyx_tuple__18); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__19 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__19)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__19); __Pyx_GIVEREF(__pyx_tuple__19); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__20 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__20)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__20); __Pyx_GIVEREF(__pyx_tuple__20); /* "View.MemoryView":286 * return self.name * * cdef generic = Enum("") # <<<<<<<<<<<<<< * cdef strided = Enum("") # default * cdef indirect = Enum("") */ __pyx_tuple__21 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct_or_indirect); if (unlikely(!__pyx_tuple__21)) __PYX_ERR(2, 286, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__21); __Pyx_GIVEREF(__pyx_tuple__21); /* "View.MemoryView":287 * * cdef generic = Enum("") * cdef strided = Enum("") # default # <<<<<<<<<<<<<< * cdef indirect = Enum("") * */ __pyx_tuple__22 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct); if (unlikely(!__pyx_tuple__22)) __PYX_ERR(2, 287, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__22); __Pyx_GIVEREF(__pyx_tuple__22); /* "View.MemoryView":288 * cdef generic = Enum("") * cdef strided = Enum("") # default * cdef indirect = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_tuple__23 = PyTuple_Pack(1, __pyx_kp_s_strided_and_indirect); if (unlikely(!__pyx_tuple__23)) __PYX_ERR(2, 288, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__23); __Pyx_GIVEREF(__pyx_tuple__23); /* "View.MemoryView":291 * * * cdef contiguous = Enum("") # <<<<<<<<<<<<<< * cdef indirect_contiguous = Enum("") * */ __pyx_tuple__24 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_direct); if (unlikely(!__pyx_tuple__24)) __PYX_ERR(2, 291, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__24); __Pyx_GIVEREF(__pyx_tuple__24); /* "View.MemoryView":292 * * cdef contiguous = Enum("") * cdef indirect_contiguous = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_tuple__25 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_indirect); if (unlikely(!__pyx_tuple__25)) __PYX_ERR(2, 292, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__25); __Pyx_GIVEREF(__pyx_tuple__25); /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ __pyx_tuple__26 = PyTuple_Pack(5, __pyx_n_s_pyx_type, __pyx_n_s_pyx_checksum, __pyx_n_s_pyx_state, __pyx_n_s_pyx_PickleError, __pyx_n_s_pyx_result); if (unlikely(!__pyx_tuple__26)) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__26); __Pyx_GIVEREF(__pyx_tuple__26); __pyx_codeobj__27 = (PyObject*)__Pyx_PyCode_New(3, 0, 5, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__26, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_stringsource, __pyx_n_s_pyx_unpickle_Enum, 1, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__27)) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static CYTHON_SMALL_CODE int __Pyx_InitGlobals(void) { if (__Pyx_InitStrings(__pyx_string_tab) < 0) __PYX_ERR(0, 1, __pyx_L1_error); __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_1 = PyInt_FromLong(1); if (unlikely(!__pyx_int_1)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_184977713 = PyInt_FromLong(184977713L); if (unlikely(!__pyx_int_184977713)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_neg_1 = PyInt_FromLong(-1); if (unlikely(!__pyx_int_neg_1)) __PYX_ERR(0, 1, __pyx_L1_error) return 0; __pyx_L1_error:; return -1; } static CYTHON_SMALL_CODE int __Pyx_modinit_global_init_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_variable_export_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_function_export_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_type_init_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_type_import_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_variable_import_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_function_import_code(void); /*proto*/ static int __Pyx_modinit_global_init_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_global_init_code", 0); /*--- Global init code ---*/ generic = Py_None; Py_INCREF(Py_None); strided = Py_None; Py_INCREF(Py_None); indirect = Py_None; Py_INCREF(Py_None); contiguous = Py_None; Py_INCREF(Py_None); indirect_contiguous = Py_None; Py_INCREF(Py_None); __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_variable_export_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_variable_export_code", 0); /*--- Variable export code ---*/ __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_function_export_code(void) { __Pyx_RefNannyDeclarations int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__Pyx_modinit_function_export_code", 0); /*--- Function export code ---*/ if (__Pyx_ExportFunction("matrix_multiply_fft_1d", (void (*)(void))__pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_1d, "void (__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ExportFunction("matrix_multiply_fft_2d", (void (*)(void))__pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_2d, "void (__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_type_init_code(void) { __Pyx_RefNannyDeclarations int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__Pyx_modinit_type_init_code", 0); /*--- Type init code ---*/ __pyx_vtabptr_array = &__pyx_vtable_array; __pyx_vtable_array.get_memview = (PyObject *(*)(struct __pyx_array_obj *))__pyx_array_get_memview; if (PyType_Ready(&__pyx_type___pyx_array) < 0) __PYX_ERR(2, 105, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_array.tp_print = 0; #endif if (__Pyx_SetVtable(__pyx_type___pyx_array.tp_dict, __pyx_vtabptr_array) < 0) __PYX_ERR(2, 105, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_array) < 0) __PYX_ERR(2, 105, __pyx_L1_error) __pyx_array_type = &__pyx_type___pyx_array; if (PyType_Ready(&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(2, 279, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_MemviewEnum.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_MemviewEnum.tp_dictoffset && __pyx_type___pyx_MemviewEnum.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_MemviewEnum.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(2, 279, __pyx_L1_error) __pyx_MemviewEnum_type = &__pyx_type___pyx_MemviewEnum; __pyx_vtabptr_memoryview = &__pyx_vtable_memoryview; __pyx_vtable_memoryview.get_item_pointer = (char *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_get_item_pointer; __pyx_vtable_memoryview.is_slice = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_is_slice; __pyx_vtable_memoryview.setitem_slice_assignment = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_slice_assignment; __pyx_vtable_memoryview.setitem_slice_assign_scalar = (PyObject *(*)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_setitem_slice_assign_scalar; __pyx_vtable_memoryview.setitem_indexed = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_indexed; __pyx_vtable_memoryview.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryview_convert_item_to_object; __pyx_vtable_memoryview.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryview_assign_item_from_object; if (PyType_Ready(&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(2, 330, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_memoryview.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_memoryview.tp_dictoffset && __pyx_type___pyx_memoryview.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_memoryview.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_SetVtable(__pyx_type___pyx_memoryview.tp_dict, __pyx_vtabptr_memoryview) < 0) __PYX_ERR(2, 330, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(2, 330, __pyx_L1_error) __pyx_memoryview_type = &__pyx_type___pyx_memoryview; __pyx_vtabptr__memoryviewslice = &__pyx_vtable__memoryviewslice; __pyx_vtable__memoryviewslice.__pyx_base = *__pyx_vtabptr_memoryview; __pyx_vtable__memoryviewslice.__pyx_base.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryviewslice_convert_item_to_object; __pyx_vtable__memoryviewslice.__pyx_base.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryviewslice_assign_item_from_object; __pyx_type___pyx_memoryviewslice.tp_base = __pyx_memoryview_type; if (PyType_Ready(&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(2, 965, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_memoryviewslice.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_memoryviewslice.tp_dictoffset && __pyx_type___pyx_memoryviewslice.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_memoryviewslice.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_SetVtable(__pyx_type___pyx_memoryviewslice.tp_dict, __pyx_vtabptr__memoryviewslice) < 0) __PYX_ERR(2, 965, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(2, 965, __pyx_L1_error) __pyx_memoryviewslice_type = &__pyx_type___pyx_memoryviewslice; __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_type_import_code(void) { __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__Pyx_modinit_type_import_code", 0); /*--- Type import code ---*/ __pyx_t_1 = PyImport_ImportModule(__Pyx_BUILTIN_MODULE_NAME); if (unlikely(!__pyx_t_1)) __PYX_ERR(3, 9, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_ptype_7cpython_4type_type = __Pyx_ImportType(__pyx_t_1, __Pyx_BUILTIN_MODULE_NAME, "type", #if defined(PYPY_VERSION_NUM) && PYPY_VERSION_NUM < 0x050B0000 sizeof(PyTypeObject), #else sizeof(PyHeapTypeObject), #endif __Pyx_ImportType_CheckSize_Warn); if (!__pyx_ptype_7cpython_4type_type) __PYX_ERR(3, 9, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyImport_ImportModule("numpy"); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 199, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_ptype_5numpy_dtype = __Pyx_ImportType(__pyx_t_1, "numpy", "dtype", sizeof(PyArray_Descr), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_dtype) __PYX_ERR(1, 199, __pyx_L1_error) __pyx_ptype_5numpy_flatiter = __Pyx_ImportType(__pyx_t_1, "numpy", "flatiter", sizeof(PyArrayIterObject), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_flatiter) __PYX_ERR(1, 222, __pyx_L1_error) __pyx_ptype_5numpy_broadcast = __Pyx_ImportType(__pyx_t_1, "numpy", "broadcast", sizeof(PyArrayMultiIterObject), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_broadcast) __PYX_ERR(1, 226, __pyx_L1_error) __pyx_ptype_5numpy_ndarray = __Pyx_ImportType(__pyx_t_1, "numpy", "ndarray", sizeof(PyArrayObject), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_ndarray) __PYX_ERR(1, 238, __pyx_L1_error) __pyx_ptype_5numpy_ufunc = __Pyx_ImportType(__pyx_t_1, "numpy", "ufunc", sizeof(PyUFuncObject), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_ufunc) __PYX_ERR(1, 764, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_variable_import_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_variable_import_code", 0); /*--- Variable import code ---*/ __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_function_import_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_function_import_code", 0); /*--- Function import code ---*/ __Pyx_RefNannyFinishContext(); return 0; } #ifndef CYTHON_NO_PYINIT_EXPORT #define __Pyx_PyMODINIT_FUNC PyMODINIT_FUNC #elif PY_MAJOR_VERSION < 3 #ifdef __cplusplus #define __Pyx_PyMODINIT_FUNC extern "C" void #else #define __Pyx_PyMODINIT_FUNC void #endif #else #ifdef __cplusplus #define __Pyx_PyMODINIT_FUNC extern "C" PyObject * #else #define __Pyx_PyMODINIT_FUNC PyObject * #endif #endif #if PY_MAJOR_VERSION < 3 __Pyx_PyMODINIT_FUNC initmatrix_mul(void) CYTHON_SMALL_CODE; /*proto*/ __Pyx_PyMODINIT_FUNC initmatrix_mul(void) #else __Pyx_PyMODINIT_FUNC PyInit_matrix_mul(void) CYTHON_SMALL_CODE; /*proto*/ __Pyx_PyMODINIT_FUNC PyInit_matrix_mul(void) #if CYTHON_PEP489_MULTI_PHASE_INIT { return PyModuleDef_Init(&__pyx_moduledef); } static CYTHON_SMALL_CODE int __Pyx_check_single_interpreter(void) { #if PY_VERSION_HEX >= 0x030700A1 static PY_INT64_T main_interpreter_id = -1; PY_INT64_T current_id = PyInterpreterState_GetID(PyThreadState_Get()->interp); if (main_interpreter_id == -1) { main_interpreter_id = current_id; return (unlikely(current_id == -1)) ? -1 : 0; } else if (unlikely(main_interpreter_id != current_id)) #else static PyInterpreterState *main_interpreter = NULL; PyInterpreterState *current_interpreter = PyThreadState_Get()->interp; if (!main_interpreter) { main_interpreter = current_interpreter; } else if (unlikely(main_interpreter != current_interpreter)) #endif { PyErr_SetString( PyExc_ImportError, "Interpreter change detected - this module can only be loaded into one interpreter per process."); return -1; } return 0; } static CYTHON_SMALL_CODE int __Pyx_copy_spec_to_module(PyObject *spec, PyObject *moddict, const char* from_name, const char* to_name, int allow_none) { PyObject *value = PyObject_GetAttrString(spec, from_name); int result = 0; if (likely(value)) { if (allow_none || value != Py_None) { result = PyDict_SetItemString(moddict, to_name, value); } Py_DECREF(value); } else if (PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Clear(); } else { result = -1; } return result; } static CYTHON_SMALL_CODE PyObject* __pyx_pymod_create(PyObject *spec, CYTHON_UNUSED PyModuleDef *def) { PyObject *module = NULL, *moddict, *modname; if (__Pyx_check_single_interpreter()) return NULL; if (__pyx_m) return __Pyx_NewRef(__pyx_m); modname = PyObject_GetAttrString(spec, "name"); if (unlikely(!modname)) goto bad; module = PyModule_NewObject(modname); Py_DECREF(modname); if (unlikely(!module)) goto bad; moddict = PyModule_GetDict(module); if (unlikely(!moddict)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "loader", "__loader__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "origin", "__file__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "parent", "__package__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "submodule_search_locations", "__path__", 0) < 0)) goto bad; return module; bad: Py_XDECREF(module); return NULL; } static CYTHON_SMALL_CODE int __pyx_pymod_exec_matrix_mul(PyObject *__pyx_pyinit_module) #endif #endif { PyObject *__pyx_t_1 = NULL; static PyThread_type_lock __pyx_t_2[8]; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannyDeclarations #if CYTHON_PEP489_MULTI_PHASE_INIT if (__pyx_m) { if (__pyx_m == __pyx_pyinit_module) return 0; PyErr_SetString(PyExc_RuntimeError, "Module 'matrix_mul' has already been imported. Re-initialisation is not supported."); return -1; } #elif PY_MAJOR_VERSION >= 3 if (__pyx_m) return __Pyx_NewRef(__pyx_m); #endif #if CYTHON_REFNANNY __Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny"); if (!__Pyx_RefNanny) { PyErr_Clear(); __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny"); if (!__Pyx_RefNanny) Py_FatalError("failed to import 'refnanny' module"); } #endif __Pyx_RefNannySetupContext("__Pyx_PyMODINIT_FUNC PyInit_matrix_mul(void)", 0); if (__Pyx_check_binary_version() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #ifdef __Pxy_PyFrame_Initialize_Offsets __Pxy_PyFrame_Initialize_Offsets(); #endif __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_empty_unicode = PyUnicode_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_unicode)) __PYX_ERR(0, 1, __pyx_L1_error) #ifdef __Pyx_CyFunction_USED if (__pyx_CyFunction_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_FusedFunction_USED if (__pyx_FusedFunction_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_Coroutine_USED if (__pyx_Coroutine_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_Generator_USED if (__pyx_Generator_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_AsyncGen_USED if (__pyx_AsyncGen_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_StopAsyncIteration_USED if (__pyx_StopAsyncIteration_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif /*--- Library function declarations ---*/ /*--- Threads initialization code ---*/ #if defined(__PYX_FORCE_INIT_THREADS) && __PYX_FORCE_INIT_THREADS #ifdef WITH_THREAD /* Python build with threading support? */ PyEval_InitThreads(); #endif #endif /*--- Module creation code ---*/ #if CYTHON_PEP489_MULTI_PHASE_INIT __pyx_m = __pyx_pyinit_module; Py_INCREF(__pyx_m); #else #if PY_MAJOR_VERSION < 3 __pyx_m = Py_InitModule4("matrix_mul", __pyx_methods, 0, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m); #else __pyx_m = PyModule_Create(&__pyx_moduledef); #endif if (unlikely(!__pyx_m)) __PYX_ERR(0, 1, __pyx_L1_error) #endif __pyx_d = PyModule_GetDict(__pyx_m); if (unlikely(!__pyx_d)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_d); __pyx_b = PyImport_AddModule(__Pyx_BUILTIN_MODULE_NAME); if (unlikely(!__pyx_b)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_b); __pyx_cython_runtime = PyImport_AddModule((char *) "cython_runtime"); if (unlikely(!__pyx_cython_runtime)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_cython_runtime); if (PyObject_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) __PYX_ERR(0, 1, __pyx_L1_error); /*--- Initialize various global constants etc. ---*/ if (__Pyx_InitGlobals() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #if PY_MAJOR_VERSION < 3 && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) if (__Pyx_init_sys_getdefaultencoding_params() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif if (__pyx_module_is_main_openTSNE___matrix_mul__matrix_mul) { if (PyObject_SetAttr(__pyx_m, __pyx_n_s_name_2, __pyx_n_s_main) < 0) __PYX_ERR(0, 1, __pyx_L1_error) } #if PY_MAJOR_VERSION >= 3 { PyObject *modules = PyImport_GetModuleDict(); if (unlikely(!modules)) __PYX_ERR(0, 1, __pyx_L1_error) if (!PyDict_GetItemString(modules, "openTSNE._matrix_mul.matrix_mul")) { if (unlikely(PyDict_SetItemString(modules, "openTSNE._matrix_mul.matrix_mul", __pyx_m) < 0)) __PYX_ERR(0, 1, __pyx_L1_error) } } #endif /*--- Builtin init code ---*/ if (__Pyx_InitCachedBuiltins() < 0) __PYX_ERR(0, 1, __pyx_L1_error) /*--- Constants init code ---*/ if (__Pyx_InitCachedConstants() < 0) __PYX_ERR(0, 1, __pyx_L1_error) /*--- Global type/function init code ---*/ (void)__Pyx_modinit_global_init_code(); (void)__Pyx_modinit_variable_export_code(); if (unlikely(__Pyx_modinit_function_export_code() < 0)) __PYX_ERR(0, 1, __pyx_L1_error) if (unlikely(__Pyx_modinit_type_init_code() < 0)) __PYX_ERR(0, 1, __pyx_L1_error) if (unlikely(__Pyx_modinit_type_import_code() < 0)) __PYX_ERR(0, 1, __pyx_L1_error) (void)__Pyx_modinit_variable_import_code(); (void)__Pyx_modinit_function_import_code(); /*--- Execution code ---*/ #if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED) if (__Pyx_patch_abc() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":9 * cimport openTSNE._matrix_mul.matrix_mul * cimport numpy as np * import numpy as np # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_numpy, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 9, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) __PYX_ERR(0, 9, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "openTSNE/_matrix_mul/matrix_mul_numpy.pyx":1 * # cython: boundscheck=False # <<<<<<<<<<<<<< * # cython: wraparound=False * # cython: cdivision=True */ __pyx_t_1 = __Pyx_PyDict_NewPresized(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":209 * info.obj = self * * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * def __dealloc__(array self): */ __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_array_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 209, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem((PyObject *)__pyx_array_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(2, 209, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; PyType_Modified(__pyx_array_type); /* "View.MemoryView":286 * return self.name * * cdef generic = Enum("") # <<<<<<<<<<<<<< * cdef strided = Enum("") # default * cdef indirect = Enum("") */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__21, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 286, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(generic); __Pyx_DECREF_SET(generic, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":287 * * cdef generic = Enum("") * cdef strided = Enum("") # default # <<<<<<<<<<<<<< * cdef indirect = Enum("") * */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__22, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 287, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(strided); __Pyx_DECREF_SET(strided, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":288 * cdef generic = Enum("") * cdef strided = Enum("") # default * cdef indirect = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__23, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 288, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(indirect); __Pyx_DECREF_SET(indirect, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":291 * * * cdef contiguous = Enum("") # <<<<<<<<<<<<<< * cdef indirect_contiguous = Enum("") * */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__24, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 291, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(contiguous); __Pyx_DECREF_SET(contiguous, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":292 * * cdef contiguous = Enum("") * cdef indirect_contiguous = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__25, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 292, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(indirect_contiguous); __Pyx_DECREF_SET(indirect_contiguous, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":316 * * DEF THREAD_LOCKS_PREALLOCATED = 8 * cdef int __pyx_memoryview_thread_locks_used = 0 # <<<<<<<<<<<<<< * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [ * PyThread_allocate_lock(), */ __pyx_memoryview_thread_locks_used = 0; /* "View.MemoryView":317 * DEF THREAD_LOCKS_PREALLOCATED = 8 * cdef int __pyx_memoryview_thread_locks_used = 0 * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [ # <<<<<<<<<<<<<< * PyThread_allocate_lock(), * PyThread_allocate_lock(), */ __pyx_t_2[0] = PyThread_allocate_lock(); __pyx_t_2[1] = PyThread_allocate_lock(); __pyx_t_2[2] = PyThread_allocate_lock(); __pyx_t_2[3] = PyThread_allocate_lock(); __pyx_t_2[4] = PyThread_allocate_lock(); __pyx_t_2[5] = PyThread_allocate_lock(); __pyx_t_2[6] = PyThread_allocate_lock(); __pyx_t_2[7] = PyThread_allocate_lock(); memcpy(&(__pyx_memoryview_thread_locks[0]), __pyx_t_2, sizeof(__pyx_memoryview_thread_locks[0]) * (8)); /* "View.MemoryView":549 * info.obj = self * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 549, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem((PyObject *)__pyx_memoryview_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(2, 549, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; PyType_Modified(__pyx_memoryview_type); /* "View.MemoryView":995 * return self.from_object * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 995, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem((PyObject *)__pyx_memoryviewslice_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(2, 995, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; PyType_Modified(__pyx_memoryviewslice_type); /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_15View_dot_MemoryView_1__pyx_unpickle_Enum, NULL, __pyx_n_s_View_MemoryView); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_pyx_unpickle_Enum, __pyx_t_1) < 0) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ /*--- Wrapped vars code ---*/ goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); if (__pyx_m) { if (__pyx_d) { __Pyx_AddTraceback("init openTSNE._matrix_mul.matrix_mul", __pyx_clineno, __pyx_lineno, __pyx_filename); } Py_CLEAR(__pyx_m); } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_ImportError, "init openTSNE._matrix_mul.matrix_mul"); } __pyx_L0:; __Pyx_RefNannyFinishContext(); #if CYTHON_PEP489_MULTI_PHASE_INIT return (__pyx_m != NULL) ? 0 : -1; #elif PY_MAJOR_VERSION >= 3 return __pyx_m; #else return; #endif } /* --- Runtime support code --- */ /* Refnanny */ #if CYTHON_REFNANNY static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { PyObject *m = NULL, *p = NULL; void *r = NULL; m = PyImport_ImportModule(modname); if (!m) goto end; p = PyObject_GetAttrString(m, "RefNannyAPI"); if (!p) goto end; r = PyLong_AsVoidPtr(p); end: Py_XDECREF(p); Py_XDECREF(m); return (__Pyx_RefNannyAPIStruct *)r; } #endif /* PyObjectGetAttrStr */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { PyTypeObject* tp = Py_TYPE(obj); if (likely(tp->tp_getattro)) return tp->tp_getattro(obj, attr_name); #if PY_MAJOR_VERSION < 3 if (likely(tp->tp_getattr)) return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); #endif return PyObject_GetAttr(obj, attr_name); } #endif /* GetBuiltinName */ static PyObject *__Pyx_GetBuiltinName(PyObject *name) { PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); if (unlikely(!result)) { PyErr_Format(PyExc_NameError, #if PY_MAJOR_VERSION >= 3 "name '%U' is not defined", name); #else "name '%.200s' is not defined", PyString_AS_STRING(name)); #endif } return result; } /* PyDictVersioning */ #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { PyObject *dict = Py_TYPE(obj)->tp_dict; return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; } static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { PyObject **dictptr = NULL; Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; if (offset) { #if CYTHON_COMPILING_IN_CPYTHON dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); #else dictptr = _PyObject_GetDictPtr(obj); #endif } return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; } static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { PyObject *dict = Py_TYPE(obj)->tp_dict; if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) return 0; return obj_dict_version == __Pyx_get_object_dict_version(obj); } #endif /* GetModuleGlobalName */ #if CYTHON_USE_DICT_VERSIONS static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) #else static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) #endif { PyObject *result; #if !CYTHON_AVOID_BORROWED_REFS #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } else if (unlikely(PyErr_Occurred())) { return NULL; } #else result = PyDict_GetItem(__pyx_d, name); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } #endif #else result = PyObject_GetItem(__pyx_d, name); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } PyErr_Clear(); #endif return __Pyx_GetBuiltinName(name); } /* PyObjectCall */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; ternaryfunc call = func->ob_type->tp_call; if (unlikely(!call)) return PyObject_Call(func, arg, kw); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = (*call)(func, arg, kw); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif /* PyCFunctionFastCall */ #if CYTHON_FAST_PYCCALL static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { PyCFunctionObject *func = (PyCFunctionObject*)func_obj; PyCFunction meth = PyCFunction_GET_FUNCTION(func); PyObject *self = PyCFunction_GET_SELF(func); int flags = PyCFunction_GET_FLAGS(func); assert(PyCFunction_Check(func)); assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); assert(nargs >= 0); assert(nargs == 0 || args != NULL); /* _PyCFunction_FastCallDict() must not be called with an exception set, because it may clear it (directly or indirectly) and so the caller loses its exception */ assert(!PyErr_Occurred()); if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); } else { return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); } } #endif /* PyFunctionFastCall */ #if CYTHON_FAST_PYCALL static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, PyObject *globals) { PyFrameObject *f; PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject **fastlocals; Py_ssize_t i; PyObject *result; assert(globals != NULL); /* XXX Perhaps we should create a specialized PyFrame_New() that doesn't take locals, but does take builtins without sanity checking them. */ assert(tstate != NULL); f = PyFrame_New(tstate, co, globals, NULL); if (f == NULL) { return NULL; } fastlocals = __Pyx_PyFrame_GetLocalsplus(f); for (i = 0; i < na; i++) { Py_INCREF(*args); fastlocals[i] = *args++; } result = PyEval_EvalFrameEx(f,0); ++tstate->recursion_depth; Py_DECREF(f); --tstate->recursion_depth; return result; } #if 1 || PY_VERSION_HEX < 0x030600B1 static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); PyObject *globals = PyFunction_GET_GLOBALS(func); PyObject *argdefs = PyFunction_GET_DEFAULTS(func); PyObject *closure; #if PY_MAJOR_VERSION >= 3 PyObject *kwdefs; #endif PyObject *kwtuple, **k; PyObject **d; Py_ssize_t nd; Py_ssize_t nk; PyObject *result; assert(kwargs == NULL || PyDict_Check(kwargs)); nk = kwargs ? PyDict_Size(kwargs) : 0; if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { return NULL; } if ( #if PY_MAJOR_VERSION >= 3 co->co_kwonlyargcount == 0 && #endif likely(kwargs == NULL || nk == 0) && co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { if (argdefs == NULL && co->co_argcount == nargs) { result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); goto done; } else if (nargs == 0 && argdefs != NULL && co->co_argcount == Py_SIZE(argdefs)) { /* function called with no arguments, but all parameters have a default value: use default values as arguments .*/ args = &PyTuple_GET_ITEM(argdefs, 0); result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); goto done; } } if (kwargs != NULL) { Py_ssize_t pos, i; kwtuple = PyTuple_New(2 * nk); if (kwtuple == NULL) { result = NULL; goto done; } k = &PyTuple_GET_ITEM(kwtuple, 0); pos = i = 0; while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { Py_INCREF(k[i]); Py_INCREF(k[i+1]); i += 2; } nk = i / 2; } else { kwtuple = NULL; k = NULL; } closure = PyFunction_GET_CLOSURE(func); #if PY_MAJOR_VERSION >= 3 kwdefs = PyFunction_GET_KW_DEFAULTS(func); #endif if (argdefs != NULL) { d = &PyTuple_GET_ITEM(argdefs, 0); nd = Py_SIZE(argdefs); } else { d = NULL; nd = 0; } #if PY_MAJOR_VERSION >= 3 result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, args, (int)nargs, k, (int)nk, d, (int)nd, kwdefs, closure); #else result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, args, (int)nargs, k, (int)nk, d, (int)nd, closure); #endif Py_XDECREF(kwtuple); done: Py_LeaveRecursiveCall(); return result; } #endif #endif /* PyObjectCall2Args */ static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) { PyObject *args, *result = NULL; #if CYTHON_FAST_PYCALL if (PyFunction_Check(function)) { PyObject *args[2] = {arg1, arg2}; return __Pyx_PyFunction_FastCall(function, args, 2); } #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(function)) { PyObject *args[2] = {arg1, arg2}; return __Pyx_PyCFunction_FastCall(function, args, 2); } #endif args = PyTuple_New(2); if (unlikely(!args)) goto done; Py_INCREF(arg1); PyTuple_SET_ITEM(args, 0, arg1); Py_INCREF(arg2); PyTuple_SET_ITEM(args, 1, arg2); Py_INCREF(function); result = __Pyx_PyObject_Call(function, args, NULL); Py_DECREF(args); Py_DECREF(function); done: return result; } /* PyObjectCallMethO */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { PyObject *self, *result; PyCFunction cfunc; cfunc = PyCFunction_GET_FUNCTION(func); self = PyCFunction_GET_SELF(func); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = cfunc(self, arg); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif /* PyObjectCallOneArg */ #if CYTHON_COMPILING_IN_CPYTHON static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_New(1); if (unlikely(!args)) return NULL; Py_INCREF(arg); PyTuple_SET_ITEM(args, 0, arg); result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { #if CYTHON_FAST_PYCALL if (PyFunction_Check(func)) { return __Pyx_PyFunction_FastCall(func, &arg, 1); } #endif if (likely(PyCFunction_Check(func))) { if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { return __Pyx_PyObject_CallMethO(func, arg); #if CYTHON_FAST_PYCCALL } else if (__Pyx_PyFastCFunction_Check(func)) { return __Pyx_PyCFunction_FastCall(func, &arg, 1); #endif } } return __Pyx__PyObject_CallOneArg(func, arg); } #else static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_Pack(1, arg); if (unlikely(!args)) return NULL; result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } #endif /* MemviewSliceInit */ static int __Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, int ndim, __Pyx_memviewslice *memviewslice, int memview_is_new_reference) { __Pyx_RefNannyDeclarations int i, retval=-1; Py_buffer *buf = &memview->view; __Pyx_RefNannySetupContext("init_memviewslice", 0); if (unlikely(memviewslice->memview || memviewslice->data)) { PyErr_SetString(PyExc_ValueError, "memviewslice is already initialized!"); goto fail; } if (buf->strides) { for (i = 0; i < ndim; i++) { memviewslice->strides[i] = buf->strides[i]; } } else { Py_ssize_t stride = buf->itemsize; for (i = ndim - 1; i >= 0; i--) { memviewslice->strides[i] = stride; stride *= buf->shape[i]; } } for (i = 0; i < ndim; i++) { memviewslice->shape[i] = buf->shape[i]; if (buf->suboffsets) { memviewslice->suboffsets[i] = buf->suboffsets[i]; } else { memviewslice->suboffsets[i] = -1; } } memviewslice->memview = memview; memviewslice->data = (char *)buf->buf; if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { Py_INCREF(memview); } retval = 0; goto no_fail; fail: memviewslice->memview = 0; memviewslice->data = 0; retval = -1; no_fail: __Pyx_RefNannyFinishContext(); return retval; } #ifndef Py_NO_RETURN #define Py_NO_RETURN #endif static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { va_list vargs; char msg[200]; #ifdef HAVE_STDARG_PROTOTYPES va_start(vargs, fmt); #else va_start(vargs); #endif vsnprintf(msg, 200, fmt, vargs); va_end(vargs); Py_FatalError(msg); } static CYTHON_INLINE int __pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, PyThread_type_lock lock) { int result; PyThread_acquire_lock(lock, 1); result = (*acquisition_count)++; PyThread_release_lock(lock); return result; } static CYTHON_INLINE int __pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, PyThread_type_lock lock) { int result; PyThread_acquire_lock(lock, 1); result = (*acquisition_count)--; PyThread_release_lock(lock); return result; } static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) { int first_time; struct __pyx_memoryview_obj *memview = memslice->memview; if (unlikely(!memview || (PyObject *) memview == Py_None)) return; if (unlikely(__pyx_get_slice_count(memview) < 0)) __pyx_fatalerror("Acquisition count is %d (line %d)", __pyx_get_slice_count(memview), lineno); first_time = __pyx_add_acquisition_count(memview) == 0; if (unlikely(first_time)) { if (have_gil) { Py_INCREF((PyObject *) memview); } else { PyGILState_STATE _gilstate = PyGILState_Ensure(); Py_INCREF((PyObject *) memview); PyGILState_Release(_gilstate); } } } static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) { int last_time; struct __pyx_memoryview_obj *memview = memslice->memview; if (unlikely(!memview || (PyObject *) memview == Py_None)) { memslice->memview = NULL; return; } if (unlikely(__pyx_get_slice_count(memview) <= 0)) __pyx_fatalerror("Acquisition count is %d (line %d)", __pyx_get_slice_count(memview), lineno); last_time = __pyx_sub_acquisition_count(memview) == 1; memslice->data = NULL; if (unlikely(last_time)) { if (have_gil) { Py_CLEAR(memslice->memview); } else { PyGILState_STATE _gilstate = PyGILState_Ensure(); Py_CLEAR(memslice->memview); PyGILState_Release(_gilstate); } } else { memslice->memview = NULL; } } /* PyErrFetchRestore */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; } #endif /* WriteUnraisableException */ static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno, CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename, int full_traceback, CYTHON_UNUSED int nogil) { PyObject *old_exc, *old_val, *old_tb; PyObject *ctx; __Pyx_PyThreadState_declare #ifdef WITH_THREAD PyGILState_STATE state; if (nogil) state = PyGILState_Ensure(); #ifdef _MSC_VER else state = (PyGILState_STATE)-1; #endif #endif __Pyx_PyThreadState_assign __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); if (full_traceback) { Py_XINCREF(old_exc); Py_XINCREF(old_val); Py_XINCREF(old_tb); __Pyx_ErrRestore(old_exc, old_val, old_tb); PyErr_PrintEx(1); } #if PY_MAJOR_VERSION < 3 ctx = PyString_FromString(name); #else ctx = PyUnicode_FromString(name); #endif __Pyx_ErrRestore(old_exc, old_val, old_tb); if (!ctx) { PyErr_WriteUnraisable(Py_None); } else { PyErr_WriteUnraisable(ctx); Py_DECREF(ctx); } #ifdef WITH_THREAD if (nogil) PyGILState_Release(state); #endif } /* GetTopmostException */ #if CYTHON_USE_EXC_INFO_STACK static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate) { _PyErr_StackItem *exc_info = tstate->exc_info; while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && exc_info->previous_item != NULL) { exc_info = exc_info->previous_item; } return exc_info; } #endif /* SaveResetException */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); *type = exc_info->exc_type; *value = exc_info->exc_value; *tb = exc_info->exc_traceback; #else *type = tstate->exc_type; *value = tstate->exc_value; *tb = tstate->exc_traceback; #endif Py_XINCREF(*type); Py_XINCREF(*value); Py_XINCREF(*tb); } static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = type; exc_info->exc_value = value; exc_info->exc_traceback = tb; #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = type; tstate->exc_value = value; tstate->exc_traceback = tb; #endif Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } #endif /* PyErrExceptionMatches */ #if CYTHON_FAST_THREAD_STATE static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { Py_ssize_t i, n; n = PyTuple_GET_SIZE(tuple); #if PY_MAJOR_VERSION >= 3 for (i=0; icurexc_type; if (exc_type == err) return 1; if (unlikely(!exc_type)) return 0; if (unlikely(PyTuple_Check(err))) return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); } #endif /* GetException */ #if CYTHON_FAST_THREAD_STATE static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) #else static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) #endif { PyObject *local_type, *local_value, *local_tb; #if CYTHON_FAST_THREAD_STATE PyObject *tmp_type, *tmp_value, *tmp_tb; local_type = tstate->curexc_type; local_value = tstate->curexc_value; local_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(&local_type, &local_value, &local_tb); #endif PyErr_NormalizeException(&local_type, &local_value, &local_tb); #if CYTHON_FAST_THREAD_STATE if (unlikely(tstate->curexc_type)) #else if (unlikely(PyErr_Occurred())) #endif goto bad; #if PY_MAJOR_VERSION >= 3 if (local_tb) { if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) goto bad; } #endif Py_XINCREF(local_tb); Py_XINCREF(local_type); Py_XINCREF(local_value); *type = local_type; *value = local_value; *tb = local_tb; #if CYTHON_FAST_THREAD_STATE #if CYTHON_USE_EXC_INFO_STACK { _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = local_type; exc_info->exc_value = local_value; exc_info->exc_traceback = local_tb; } #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = local_type; tstate->exc_value = local_value; tstate->exc_traceback = local_tb; #endif Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_SetExcInfo(local_type, local_value, local_tb); #endif return 0; bad: *type = 0; *value = 0; *tb = 0; Py_XDECREF(local_type); Py_XDECREF(local_value); Py_XDECREF(local_tb); return -1; } /* RaiseException */ #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { __Pyx_PyThreadState_declare Py_XINCREF(type); if (!value || value == Py_None) value = NULL; else Py_INCREF(value); if (!tb || tb == Py_None) tb = NULL; else { Py_INCREF(tb); if (!PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } } if (PyType_Check(type)) { #if CYTHON_COMPILING_IN_PYPY if (!value) { Py_INCREF(Py_None); value = Py_None; } #endif PyErr_NormalizeException(&type, &value, &tb); } else { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } value = type; type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } } __Pyx_PyThreadState_assign __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (PyExceptionClass_Check(type)) { PyObject *instance_class = NULL; if (value && PyExceptionInstance_Check(value)) { instance_class = (PyObject*) Py_TYPE(value); if (instance_class != type) { int is_subclass = PyObject_IsSubclass(instance_class, type); if (!is_subclass) { instance_class = NULL; } else if (unlikely(is_subclass == -1)) { goto bad; } else { type = instance_class; } } } if (!instance_class) { PyObject *args; if (!value) args = PyTuple_New(0); else if (PyTuple_Check(value)) { Py_INCREF(value); args = value; } else args = PyTuple_Pack(1, value); if (!args) goto bad; owned_instance = PyObject_Call(type, args, NULL); Py_DECREF(args); if (!owned_instance) goto bad; value = owned_instance; if (!PyExceptionInstance_Check(value)) { PyErr_Format(PyExc_TypeError, "calling %R should have returned an instance of " "BaseException, not %R", type, Py_TYPE(value)); goto bad; } } } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } if (cause) { PyObject *fixed_cause; if (cause == Py_None) { fixed_cause = NULL; } else if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { #if CYTHON_COMPILING_IN_PYPY PyObject *tmp_type, *tmp_value, *tmp_tb; PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); Py_INCREF(tb); PyErr_Restore(tmp_type, tmp_value, tb); Py_XDECREF(tmp_tb); #else PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } #endif } bad: Py_XDECREF(owned_instance); return; } #endif /* RaiseArgTupleInvalid */ static void __Pyx_RaiseArgtupleInvalid( const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found) { Py_ssize_t num_expected; const char *more_or_less; if (num_found < num_min) { num_expected = num_min; more_or_less = "at least"; } else { num_expected = num_max; more_or_less = "at most"; } if (exact) { more_or_less = "exactly"; } PyErr_Format(PyExc_TypeError, "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", func_name, more_or_less, num_expected, (num_expected == 1) ? "" : "s", num_found); } /* RaiseDoubleKeywords */ static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) { PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION >= 3 "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, PyString_AsString(kw_name)); #endif } /* ParseKeywords */ static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name) { PyObject *key = 0, *value = 0; Py_ssize_t pos = 0; PyObject*** name; PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { name = first_kw_arg; while (*name && (**name != key)) name++; if (*name) { values[name-argnames] = value; continue; } name = first_kw_arg; #if PY_MAJOR_VERSION < 3 if (likely(PyString_Check(key))) { while (*name) { if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) && _PyString_Eq(**name, key)) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { if ((**argname == key) || ( (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) && _PyString_Eq(**argname, key))) { goto arg_passed_twice; } argname++; } } } else #endif if (likely(PyUnicode_Check(key))) { while (*name) { int cmp = (**name == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : #endif PyUnicode_Compare(**name, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { int cmp = (**argname == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : #endif PyUnicode_Compare(**argname, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) goto arg_passed_twice; argname++; } } } else goto invalid_keyword_type; if (kwds2) { if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; } else { goto invalid_keyword; } } return 0; arg_passed_twice: __Pyx_RaiseDoubleKeywordsError(function_name, key); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%.200s() keywords must be strings", function_name); goto bad; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%.200s() got an unexpected keyword argument '%.200s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif bad: return -1; } /* ArgTypeTest */ static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } else if (exact) { #if PY_MAJOR_VERSION == 2 if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; #endif } else { if (likely(__Pyx_TypeCheck(obj, type))) return 1; } PyErr_Format(PyExc_TypeError, "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", name, type->tp_name, Py_TYPE(obj)->tp_name); return 0; } /* BytesEquals */ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { #if CYTHON_COMPILING_IN_PYPY return PyObject_RichCompareBool(s1, s2, equals); #else if (s1 == s2) { return (equals == Py_EQ); } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { const char *ps1, *ps2; Py_ssize_t length = PyBytes_GET_SIZE(s1); if (length != PyBytes_GET_SIZE(s2)) return (equals == Py_NE); ps1 = PyBytes_AS_STRING(s1); ps2 = PyBytes_AS_STRING(s2); if (ps1[0] != ps2[0]) { return (equals == Py_NE); } else if (length == 1) { return (equals == Py_EQ); } else { int result; #if CYTHON_USE_UNICODE_INTERNALS Py_hash_t hash1, hash2; hash1 = ((PyBytesObject*)s1)->ob_shash; hash2 = ((PyBytesObject*)s2)->ob_shash; if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { return (equals == Py_NE); } #endif result = memcmp(ps1, ps2, (size_t)length); return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { return (equals == Py_NE); } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { return (equals == Py_NE); } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } #endif } /* UnicodeEquals */ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { #if CYTHON_COMPILING_IN_PYPY return PyObject_RichCompareBool(s1, s2, equals); #else #if PY_MAJOR_VERSION < 3 PyObject* owned_ref = NULL; #endif int s1_is_unicode, s2_is_unicode; if (s1 == s2) { goto return_eq; } s1_is_unicode = PyUnicode_CheckExact(s1); s2_is_unicode = PyUnicode_CheckExact(s2); #if PY_MAJOR_VERSION < 3 if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { owned_ref = PyUnicode_FromObject(s2); if (unlikely(!owned_ref)) return -1; s2 = owned_ref; s2_is_unicode = 1; } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { owned_ref = PyUnicode_FromObject(s1); if (unlikely(!owned_ref)) return -1; s1 = owned_ref; s1_is_unicode = 1; } else if (((!s2_is_unicode) & (!s1_is_unicode))) { return __Pyx_PyBytes_Equals(s1, s2, equals); } #endif if (s1_is_unicode & s2_is_unicode) { Py_ssize_t length; int kind; void *data1, *data2; if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) return -1; length = __Pyx_PyUnicode_GET_LENGTH(s1); if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { goto return_ne; } #if CYTHON_USE_UNICODE_INTERNALS { Py_hash_t hash1, hash2; #if CYTHON_PEP393_ENABLED hash1 = ((PyASCIIObject*)s1)->hash; hash2 = ((PyASCIIObject*)s2)->hash; #else hash1 = ((PyUnicodeObject*)s1)->hash; hash2 = ((PyUnicodeObject*)s2)->hash; #endif if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { goto return_ne; } } #endif kind = __Pyx_PyUnicode_KIND(s1); if (kind != __Pyx_PyUnicode_KIND(s2)) { goto return_ne; } data1 = __Pyx_PyUnicode_DATA(s1); data2 = __Pyx_PyUnicode_DATA(s2); if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { goto return_ne; } else if (length == 1) { goto return_eq; } else { int result = memcmp(data1, data2, (size_t)(length * kind)); #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & s2_is_unicode) { goto return_ne; } else if ((s2 == Py_None) & s1_is_unicode) { goto return_ne; } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } return_eq: #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_EQ); return_ne: #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_NE); #endif } /* GetAttr */ static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { #if CYTHON_USE_TYPE_SLOTS #if PY_MAJOR_VERSION >= 3 if (likely(PyUnicode_Check(n))) #else if (likely(PyString_Check(n))) #endif return __Pyx_PyObject_GetAttrStr(o, n); #endif return PyObject_GetAttr(o, n); } /* GetItemInt */ static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { PyObject *r; if (!j) return NULL; r = PyObject_GetItem(o, j); Py_DECREF(j); return r; } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS Py_ssize_t wrapped_i = i; if (wraparound & unlikely(i < 0)) { wrapped_i += PyList_GET_SIZE(o); } if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { PyObject *r = PyList_GET_ITEM(o, wrapped_i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS Py_ssize_t wrapped_i = i; if (wraparound & unlikely(i < 0)) { wrapped_i += PyTuple_GET_SIZE(o); } if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS if (is_list || PyList_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { PyObject *r = PyList_GET_ITEM(o, n); Py_INCREF(r); return r; } } else if (PyTuple_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, n); Py_INCREF(r); return r; } } else { PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; if (likely(m && m->sq_item)) { if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { Py_ssize_t l = m->sq_length(o); if (likely(l >= 0)) { i += l; } else { if (!PyErr_ExceptionMatches(PyExc_OverflowError)) return NULL; PyErr_Clear(); } } return m->sq_item(o, i); } } #else if (is_list || PySequence_Check(o)) { return PySequence_GetItem(o, i); } #endif return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); } /* ObjectGetItem */ #if CYTHON_USE_TYPE_SLOTS static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { PyObject *runerr; Py_ssize_t key_value; PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence; if (unlikely(!(m && m->sq_item))) { PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name); return NULL; } key_value = __Pyx_PyIndex_AsSsize_t(index); if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); } if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { PyErr_Clear(); PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name); } return NULL; } static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping; if (likely(m && m->mp_subscript)) { return m->mp_subscript(obj, key); } return __Pyx_PyObject_GetIndex(obj, key); } #endif /* decode_c_string */ static CYTHON_INLINE PyObject* __Pyx_decode_c_string( const char* cstring, Py_ssize_t start, Py_ssize_t stop, const char* encoding, const char* errors, PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { Py_ssize_t length; if (unlikely((start < 0) | (stop < 0))) { size_t slen = strlen(cstring); if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { PyErr_SetString(PyExc_OverflowError, "c-string too long to convert to Python"); return NULL; } length = (Py_ssize_t) slen; if (start < 0) { start += length; if (start < 0) start = 0; } if (stop < 0) stop += length; } if (unlikely(stop <= start)) return __Pyx_NewRef(__pyx_empty_unicode); length = stop - start; cstring += start; if (decode_func) { return decode_func(cstring, length, errors); } else { return PyUnicode_Decode(cstring, length, encoding, errors); } } /* GetAttr3 */ static PyObject *__Pyx_GetAttr3Default(PyObject *d) { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) return NULL; __Pyx_PyErr_Clear(); Py_INCREF(d); return d; } static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { PyObject *r = __Pyx_GetAttr(o, n); return (likely(r)) ? r : __Pyx_GetAttr3Default(d); } /* RaiseTooManyValuesToUnpack */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } /* RaiseNeedMoreValuesToUnpack */ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", index, (index == 1) ? "" : "s"); } /* RaiseNoneIterError */ static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } /* ExtTypeTest */ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (likely(__Pyx_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } /* SwapException */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = *type; exc_info->exc_value = *value; exc_info->exc_traceback = *tb; #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = *type; tstate->exc_value = *value; tstate->exc_traceback = *tb; #endif *type = tmp_type; *value = tmp_value; *tb = tmp_tb; } #else static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); PyErr_SetExcInfo(*type, *value, *tb); *type = tmp_type; *value = tmp_value; *tb = tmp_tb; } #endif /* Import */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; #if PY_MAJOR_VERSION < 3 PyObject *py_import; py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); if (!py_import) goto bad; #endif if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, 1); if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; } #endif if (!module) { #if PY_MAJOR_VERSION < 3 PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, level); #endif } } bad: #if PY_MAJOR_VERSION < 3 Py_XDECREF(py_import); #endif Py_XDECREF(empty_list); Py_XDECREF(empty_dict); return module; } /* FastTypeChecks */ #if CYTHON_COMPILING_IN_CPYTHON static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { while (a) { a = a->tp_base; if (a == b) return 1; } return b == &PyBaseObject_Type; } static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { PyObject *mro; if (a == b) return 1; mro = a->tp_mro; if (likely(mro)) { Py_ssize_t i, n; n = PyTuple_GET_SIZE(mro); for (i = 0; i < n; i++) { if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) return 1; } return 0; } return __Pyx_InBases(a, b); } #if PY_MAJOR_VERSION == 2 static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { PyObject *exception, *value, *tb; int res; __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ErrFetch(&exception, &value, &tb); res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; if (unlikely(res == -1)) { PyErr_WriteUnraisable(err); res = 0; } if (!res) { res = PyObject_IsSubclass(err, exc_type2); if (unlikely(res == -1)) { PyErr_WriteUnraisable(err); res = 0; } } __Pyx_ErrRestore(exception, value, tb); return res; } #else static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; if (!res) { res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); } return res; } #endif static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { Py_ssize_t i, n; assert(PyExceptionClass_Check(exc_type)); n = PyTuple_GET_SIZE(tuple); #if PY_MAJOR_VERSION >= 3 for (i=0; i= 0 || (x^b) >= 0)) return PyInt_FromLong(x); return PyLong_Type.tp_as_number->nb_add(op1, op2); } #endif #if CYTHON_USE_PYLONG_INTERNALS if (likely(PyLong_CheckExact(op1))) { const long b = intval; long a, x; #ifdef HAVE_LONG_LONG const PY_LONG_LONG llb = intval; PY_LONG_LONG lla, llx; #endif const digit* digits = ((PyLongObject*)op1)->ob_digit; const Py_ssize_t size = Py_SIZE(op1); if (likely(__Pyx_sst_abs(size) <= 1)) { a = likely(size) ? digits[0] : 0; if (size == -1) a = -a; } else { switch (size) { case -2: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 2: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case -3: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 3: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case -4: if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 4: if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; default: return PyLong_Type.tp_as_number->nb_add(op1, op2); } } x = a + b; return PyLong_FromLong(x); #ifdef HAVE_LONG_LONG long_long: llx = lla + llb; return PyLong_FromLongLong(llx); #endif } #endif if (PyFloat_CheckExact(op1)) { const long b = intval; double a = PyFloat_AS_DOUBLE(op1); double result; PyFPE_START_PROTECT("add", return NULL) result = ((double)a) + (double)b; PyFPE_END_PROTECT(result) return PyFloat_FromDouble(result); } return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); } #endif /* None */ static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); } /* ImportFrom */ static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Format(PyExc_ImportError, #if PY_MAJOR_VERSION < 3 "cannot import name %.230s", PyString_AS_STRING(name)); #else "cannot import name %S", name); #endif } return value; } /* HasAttr */ static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { PyObject *r; if (unlikely(!__Pyx_PyBaseString_Check(n))) { PyErr_SetString(PyExc_TypeError, "hasattr(): attribute name must be string"); return -1; } r = __Pyx_GetAttr(o, n); if (unlikely(!r)) { PyErr_Clear(); return 0; } else { Py_DECREF(r); return 1; } } /* PyObject_GenericGetAttrNoDict */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { PyErr_Format(PyExc_AttributeError, #if PY_MAJOR_VERSION >= 3 "'%.50s' object has no attribute '%U'", tp->tp_name, attr_name); #else "'%.50s' object has no attribute '%.400s'", tp->tp_name, PyString_AS_STRING(attr_name)); #endif return NULL; } static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { PyObject *descr; PyTypeObject *tp = Py_TYPE(obj); if (unlikely(!PyString_Check(attr_name))) { return PyObject_GenericGetAttr(obj, attr_name); } assert(!tp->tp_dictoffset); descr = _PyType_Lookup(tp, attr_name); if (unlikely(!descr)) { return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); } Py_INCREF(descr); #if PY_MAJOR_VERSION < 3 if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) #endif { descrgetfunc f = Py_TYPE(descr)->tp_descr_get; if (unlikely(f)) { PyObject *res = f(descr, obj, (PyObject *)tp); Py_DECREF(descr); return res; } } return descr; } #endif /* PyObject_GenericGetAttr */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { return PyObject_GenericGetAttr(obj, attr_name); } return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); } #endif /* SetVTable */ static int __Pyx_SetVtable(PyObject *dict, void *vtable) { #if PY_VERSION_HEX >= 0x02070000 PyObject *ob = PyCapsule_New(vtable, 0, 0); #else PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); #endif if (!ob) goto bad; if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) goto bad; Py_DECREF(ob); return 0; bad: Py_XDECREF(ob); return -1; } /* PyObjectGetAttrStrNoError */ static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) __Pyx_PyErr_Clear(); } static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) { PyObject *result; #if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1 PyTypeObject* tp = Py_TYPE(obj); if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) { return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1); } #endif result = __Pyx_PyObject_GetAttrStr(obj, attr_name); if (unlikely(!result)) { __Pyx_PyObject_GetAttrStr_ClearAttributeError(); } return result; } /* SetupReduce */ static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { int ret; PyObject *name_attr; name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2); if (likely(name_attr)) { ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); } else { ret = -1; } if (unlikely(ret < 0)) { PyErr_Clear(); ret = 0; } Py_XDECREF(name_attr); return ret; } static int __Pyx_setup_reduce(PyObject* type_obj) { int ret = 0; PyObject *object_reduce = NULL; PyObject *object_reduce_ex = NULL; PyObject *reduce = NULL; PyObject *reduce_ex = NULL; PyObject *reduce_cython = NULL; PyObject *setstate = NULL; PyObject *setstate_cython = NULL; #if CYTHON_USE_PYTYPE_LOOKUP if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; #else if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; #endif #if CYTHON_USE_PYTYPE_LOOKUP object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; #else object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; #endif reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; if (reduce_ex == object_reduce_ex) { #if CYTHON_USE_PYTYPE_LOOKUP object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; #else object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; #endif reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); if (likely(reduce_cython)) { ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; } else if (reduce == object_reduce || PyErr_Occurred()) { goto __PYX_BAD; } setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); if (!setstate) PyErr_Clear(); if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); if (likely(setstate_cython)) { ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; } else if (!setstate || PyErr_Occurred()) { goto __PYX_BAD; } } PyType_Modified((PyTypeObject*)type_obj); } } goto __PYX_GOOD; __PYX_BAD: if (!PyErr_Occurred()) PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); ret = -1; __PYX_GOOD: #if !CYTHON_USE_PYTYPE_LOOKUP Py_XDECREF(object_reduce); Py_XDECREF(object_reduce_ex); #endif Py_XDECREF(reduce); Py_XDECREF(reduce_ex); Py_XDECREF(reduce_cython); Py_XDECREF(setstate); Py_XDECREF(setstate_cython); return ret; } /* TypeImport */ #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size) { PyObject *result = 0; char warning[200]; Py_ssize_t basicsize; #ifdef Py_LIMITED_API PyObject *py_basicsize; #endif result = PyObject_GetAttrString(module, class_name); if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%.200s.%.200s is not a type object", module_name, class_name); goto bad; } #ifndef Py_LIMITED_API basicsize = ((PyTypeObject *)result)->tp_basicsize; #else py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); if (!py_basicsize) goto bad; basicsize = PyLong_AsSsize_t(py_basicsize); Py_DECREF(py_basicsize); py_basicsize = 0; if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) goto bad; #endif if ((size_t)basicsize < size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); goto bad; } if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); goto bad; } else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(result); return NULL; } #endif /* CLineInTraceback */ #ifndef CYTHON_CLINE_IN_TRACEBACK static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) { PyObject *use_cline; PyObject *ptype, *pvalue, *ptraceback; #if CYTHON_COMPILING_IN_CPYTHON PyObject **cython_runtime_dict; #endif if (unlikely(!__pyx_cython_runtime)) { return c_line; } __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); #if CYTHON_COMPILING_IN_CPYTHON cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); if (likely(cython_runtime_dict)) { __PYX_PY_DICT_LOOKUP_IF_MODIFIED( use_cline, *cython_runtime_dict, __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) } else #endif { PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); if (use_cline_obj) { use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; Py_DECREF(use_cline_obj); } else { PyErr_Clear(); use_cline = NULL; } } if (!use_cline) { c_line = 0; PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); } else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { c_line = 0; } __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); return c_line; } #endif /* CodeObjectCache */ static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = start + (end - start) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } /* AddTraceback */ #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, 0, 0, 0, 0, __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; PyThreadState *tstate = __Pyx_PyThreadState_Current; if (c_line) { c_line = __Pyx_CLineForTraceback(tstate, c_line); } py_code = __pyx_find_code_object(c_line ? -c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); } py_frame = PyFrame_New( tstate, /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ __pyx_d, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; __Pyx_PyFrame_SetLineNumber(py_frame, py_line); PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; if (!obj) return; if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } if ((0)) {} view->obj = NULL; Py_DECREF(obj); } #endif /* MemviewSliceIsContig */ static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) { int i, index, step, start; Py_ssize_t itemsize = mvs.memview->view.itemsize; if (order == 'F') { step = 1; start = 0; } else { step = -1; start = ndim - 1; } for (i = 0; i < ndim; i++) { index = start + step * i; if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) return 0; itemsize *= mvs.shape[index]; } return 1; } /* OverlappingSlices */ static void __pyx_get_array_memory_extents(__Pyx_memviewslice *slice, void **out_start, void **out_end, int ndim, size_t itemsize) { char *start, *end; int i; start = end = slice->data; for (i = 0; i < ndim; i++) { Py_ssize_t stride = slice->strides[i]; Py_ssize_t extent = slice->shape[i]; if (extent == 0) { *out_start = *out_end = start; return; } else { if (stride > 0) end += stride * (extent - 1); else start += stride * (extent - 1); } } *out_start = start; *out_end = end + itemsize; } static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, __Pyx_memviewslice *slice2, int ndim, size_t itemsize) { void *start1, *end1, *start2, *end2; __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); return (start1 < end2) && (start2 < end1); } /* Capsule */ static CYTHON_INLINE PyObject * __pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) { PyObject *cobj; #if PY_VERSION_HEX >= 0x02070000 cobj = PyCapsule_New(p, sig, NULL); #else cobj = PyCObject_FromVoidPtr(p, NULL); #endif return cobj; } /* Declarations */ #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif /* Arithmetic */ #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } #if 1 static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { if (b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); } else if (fabs(b.real) >= fabs(b.imag)) { if (b.real == 0 && b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); } else { double r = b.imag / b.real; double s = (double)(1.0) / (b.real + b.imag * r); return __pyx_t_double_complex_from_parts( (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); } } else { double r = b.real / b.imag; double s = (double)(1.0) / (b.imag + b.real * r); return __pyx_t_double_complex_from_parts( (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); } } #else static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { if (b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); } else { double denom = b.real * b.real + b.imag * b.imag; return __pyx_t_double_complex_from_parts( (a.real * b.real + a.imag * b.imag) / denom, (a.imag * b.real - a.real * b.imag) / denom); } } #endif static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: return __Pyx_c_prod_double(a, a); case 3: z = __Pyx_c_prod_double(a, a); return __Pyx_c_prod_double(z, a); case 4: z = __Pyx_c_prod_double(a, a); return __Pyx_c_prod_double(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } else if (b.imag == 0) { z.real = pow(a.real, b.real); z.imag = 0; return z; } else if (a.real > 0) { r = a.real; theta = 0; } else { r = -a.real; theta = atan2(0.0, -1.0); } } else { r = __Pyx_c_abs_double(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif /* MemviewDtypeToObject */ static CYTHON_INLINE PyObject *__pyx_memview_get_double(const char *itemp) { return (PyObject *) PyFloat_FromDouble(*(double *) itemp); } static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *obj) { double value = __pyx_PyFloat_AsDouble(obj); if ((value == (double)-1) && PyErr_Occurred()) return 0; *(double *) itemp = value; return 1; } /* FromPy */ static __pyx_t_double_complex __Pyx_PyComplex_As___pyx_t_double_complex(PyObject* o) { Py_complex cval; #if !CYTHON_COMPILING_IN_PYPY if (PyComplex_CheckExact(o)) cval = ((PyComplexObject *)o)->cval; else #endif cval = PyComplex_AsCComplex(o); return __pyx_t_double_complex_from_parts( (double)cval.real, (double)cval.imag); } /* MemviewDtypeToObject */ static CYTHON_INLINE PyObject *__pyx_memview_get___pyx_t_double_complex(const char *itemp) { return (PyObject *) __pyx_PyComplex_FromComplex(*(__pyx_t_double_complex *) itemp); } static CYTHON_INLINE int __pyx_memview_set___pyx_t_double_complex(const char *itemp, PyObject *obj) { __pyx_t_double_complex value = __Pyx_PyComplex_As___pyx_t_double_complex(obj); if (PyErr_Occurred()) return 0; *(__pyx_t_double_complex *) itemp = value; return 1; } /* Declarations */ #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif /* Arithmetic */ #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } #if 1 static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { if (b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); } else if (fabsf(b.real) >= fabsf(b.imag)) { if (b.real == 0 && b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); } else { float r = b.imag / b.real; float s = (float)(1.0) / (b.real + b.imag * r); return __pyx_t_float_complex_from_parts( (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); } } else { float r = b.real / b.imag; float s = (float)(1.0) / (b.imag + b.real * r); return __pyx_t_float_complex_from_parts( (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); } } #else static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { if (b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); } else { float denom = b.real * b.real + b.imag * b.imag; return __pyx_t_float_complex_from_parts( (a.real * b.real + a.imag * b.imag) / denom, (a.imag * b.real - a.real * b.imag) / denom); } } #endif static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: return __Pyx_c_prod_float(a, a); case 3: z = __Pyx_c_prod_float(a, a); return __Pyx_c_prod_float(z, a); case 4: z = __Pyx_c_prod_float(a, a); return __Pyx_c_prod_float(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } else if (b.imag == 0) { z.real = powf(a.real, b.real); z.imag = 0; return z; } else if (a.real > 0) { r = a.real; theta = 0; } else { r = -a.real; theta = atan2f(0.0, -1.0); } } else { r = __Pyx_c_abs_float(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif /* MemviewSliceCopyTemplate */ static __Pyx_memviewslice __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, const char *mode, int ndim, size_t sizeof_dtype, int contig_flag, int dtype_is_object) { __Pyx_RefNannyDeclarations int i; __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; struct __pyx_memoryview_obj *from_memview = from_mvs->memview; Py_buffer *buf = &from_memview->view; PyObject *shape_tuple = NULL; PyObject *temp_int = NULL; struct __pyx_array_obj *array_obj = NULL; struct __pyx_memoryview_obj *memview_obj = NULL; __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); for (i = 0; i < ndim; i++) { if (unlikely(from_mvs->suboffsets[i] >= 0)) { PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " "indirect dimensions (axis %d)", i); goto fail; } } shape_tuple = PyTuple_New(ndim); if (unlikely(!shape_tuple)) { goto fail; } __Pyx_GOTREF(shape_tuple); for(i = 0; i < ndim; i++) { temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); if(unlikely(!temp_int)) { goto fail; } else { PyTuple_SET_ITEM(shape_tuple, i, temp_int); temp_int = NULL; } } array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); if (unlikely(!array_obj)) { goto fail; } __Pyx_GOTREF(array_obj); memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( (PyObject *) array_obj, contig_flag, dtype_is_object, from_mvs->memview->typeinfo); if (unlikely(!memview_obj)) goto fail; if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) goto fail; if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, dtype_is_object) < 0)) goto fail; goto no_fail; fail: __Pyx_XDECREF(new_mvs.memview); new_mvs.memview = NULL; new_mvs.data = NULL; no_fail: __Pyx_XDECREF(shape_tuple); __Pyx_XDECREF(temp_int); __Pyx_XDECREF(array_obj); __Pyx_RefNannyFinishContext(); return new_mvs; } /* CIntFromPyVerify */ #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) #define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) #define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ {\ func_type value = func_value;\ if (sizeof(target_type) < sizeof(func_type)) {\ if (unlikely(value != (func_type) (target_type) value)) {\ func_type zero = 0;\ if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ return (target_type) -1;\ if (is_unsigned && unlikely(value < zero))\ goto raise_neg_overflow;\ else\ goto raise_overflow;\ }\ }\ return (target_type) value;\ } /* CIntFromPy */ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const int neg_one = (int) -1, const_zero = (int) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(int) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (int) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (int) 0; case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) case 2: if (8 * sizeof(int) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; case 3: if (8 * sizeof(int) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; case 4: if (8 * sizeof(int) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (int) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(int) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (int) 0; case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) case -2: if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 2: if (8 * sizeof(int) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case -3: if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 3: if (8 * sizeof(int) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case -4: if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 4: if (8 * sizeof(int) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; } #endif if (sizeof(int) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else int val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (int) -1; } } else { int val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (int) -1; val = __Pyx_PyInt_As_int(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to int"); return (int) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } /* CIntFromPy */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const long neg_one = (long) -1, const_zero = (long) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(long) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (long) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) case 2: if (8 * sizeof(long) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; case 3: if (8 * sizeof(long) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; case 4: if (8 * sizeof(long) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (long) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) case -2: if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 2: if (8 * sizeof(long) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case -3: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 3: if (8 * sizeof(long) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case -4: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 4: if (8 * sizeof(long) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; } #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (long) -1; } } else { long val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to long"); return (long) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const int neg_one = (int) -1, const_zero = (int) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(int) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(int), little, !is_unsigned); } } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const long neg_one = (long) -1, const_zero = (long) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(long) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(long), little, !is_unsigned); } } /* CIntFromPy */ static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const char neg_one = (char) -1, const_zero = (char) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(char) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (char) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (char) 0; case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0]) case 2: if (8 * sizeof(char) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) { return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; case 3: if (8 * sizeof(char) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) { return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; case 4: if (8 * sizeof(char) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) { return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (char) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(char) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (char) 0; case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0]) case -2: if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 2: if (8 * sizeof(char) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case -3: if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 3: if (8 * sizeof(char) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case -4: if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 4: if (8 * sizeof(char) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; } #endif if (sizeof(char) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else char val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (char) -1; } } else { char val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (char) -1; val = __Pyx_PyInt_As_char(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to char"); return (char) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to char"); return (char) -1; } /* IsLittleEndian */ static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) { union { uint32_t u32; uint8_t u8[4]; } S; S.u32 = 0x01020304; return S.u8[0] == 4; } /* BufferFormatCheck */ static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type) { stack[0].field = &ctx->root; stack[0].parent_offset = 0; ctx->root.type = type; ctx->root.name = "buffer dtype"; ctx->root.offset = 0; ctx->head = stack; ctx->head->field = &ctx->root; ctx->fmt_offset = 0; ctx->head->parent_offset = 0; ctx->new_packmode = '@'; ctx->enc_packmode = '@'; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->is_complex = 0; ctx->is_valid_array = 0; ctx->struct_alignment = 0; while (type->typegroup == 'S') { ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = 0; type = type->fields->type; } } static int __Pyx_BufFmt_ParseNumber(const char** ts) { int count; const char* t = *ts; if (*t < '0' || *t > '9') { return -1; } else { count = *t++ - '0'; while (*t >= '0' && *t <= '9') { count *= 10; count += *t++ - '0'; } } *ts = t; return count; } static int __Pyx_BufFmt_ExpectNumber(const char **ts) { int number = __Pyx_BufFmt_ParseNumber(ts); if (number == -1) PyErr_Format(PyExc_ValueError,\ "Does not understand character buffer dtype format string ('%c')", **ts); return number; } static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { PyErr_Format(PyExc_ValueError, "Unexpected format string character: '%c'", ch); } static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { switch (ch) { case '?': return "'bool'"; case 'c': return "'char'"; case 'b': return "'signed char'"; case 'B': return "'unsigned char'"; case 'h': return "'short'"; case 'H': return "'unsigned short'"; case 'i': return "'int'"; case 'I': return "'unsigned int'"; case 'l': return "'long'"; case 'L': return "'unsigned long'"; case 'q': return "'long long'"; case 'Q': return "'unsigned long long'"; case 'f': return (is_complex ? "'complex float'" : "'float'"); case 'd': return (is_complex ? "'complex double'" : "'double'"); case 'g': return (is_complex ? "'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; case 's': case 'p': return "a string"; case 0: return "end"; default: return "unparseable format string"; } } static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return 2; case 'i': case 'I': case 'l': case 'L': return 4; case 'q': case 'Q': return 8; case 'f': return (is_complex ? 8 : 4); case 'd': return (is_complex ? 16 : 8); case 'g': { PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); return 0; } case 'O': case 'P': return sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(short); case 'i': case 'I': return sizeof(int); case 'l': case 'L': return sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(float) * (is_complex ? 2 : 1); case 'd': return sizeof(double) * (is_complex ? 2 : 1); case 'g': return sizeof(long double) * (is_complex ? 2 : 1); case 'O': case 'P': return sizeof(void*); default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } typedef struct { char c; short x; } __Pyx_st_short; typedef struct { char c; int x; } __Pyx_st_int; typedef struct { char c; long x; } __Pyx_st_long; typedef struct { char c; float x; } __Pyx_st_float; typedef struct { char c; double x; } __Pyx_st_double; typedef struct { char c; long double x; } __Pyx_st_longdouble; typedef struct { char c; void *x; } __Pyx_st_void_p; #ifdef HAVE_LONG_LONG typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_st_float) - sizeof(float); case 'd': return sizeof(__Pyx_st_double) - sizeof(double); case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } /* These are for computing the padding at the end of the struct to align on the first member of the struct. This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': return 'H'; case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } if ((type->typegroup == 'H' || group == 'H') && type->size == size) { } else { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number, ndim; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ndim = ctx->head->field->type->ndim; while (*ts && *ts != ')') { switch (*ts) { case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; default: break; } number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case '\r': case '\n': ++ts; break; case '<': if (!__Pyx_Is_Little_Endian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_Is_Little_Endian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } CYTHON_FALLTHROUGH; case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 'p': if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { ctx->enc_count += ctx->new_count; ctx->new_count = 1; got_Z = 0; ++ts; break; } CYTHON_FALLTHROUGH; case 's': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } /* TypeInfoCompare */ static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) { int i; if (!a || !b) return 0; if (a == b) return 1; if (a->size != b->size || a->typegroup != b->typegroup || a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { if (a->typegroup == 'H' || b->typegroup == 'H') { return a->size == b->size; } else { return 0; } } if (a->ndim) { for (i = 0; i < a->ndim; i++) if (a->arraysize[i] != b->arraysize[i]) return 0; } if (a->typegroup == 'S') { if (a->flags != b->flags) return 0; if (a->fields || b->fields) { if (!(a->fields && b->fields)) return 0; for (i = 0; a->fields[i].type && b->fields[i].type; i++) { __Pyx_StructField *field_a = a->fields + i; __Pyx_StructField *field_b = b->fields + i; if (field_a->offset != field_b->offset || !__pyx_typeinfo_cmp(field_a->type, field_b->type)) return 0; } return !a->fields[i].type && !b->fields[i].type; } } return 1; } /* MemviewSliceValidateAndInit */ static int __pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) { if (buf->shape[dim] <= 1) return 1; if (buf->strides) { if (spec & __Pyx_MEMVIEW_CONTIG) { if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { if (unlikely(buf->strides[dim] != sizeof(void *))) { PyErr_Format(PyExc_ValueError, "Buffer is not indirectly contiguous " "in dimension %d.", dim); goto fail; } } else if (unlikely(buf->strides[dim] != buf->itemsize)) { PyErr_SetString(PyExc_ValueError, "Buffer and memoryview are not contiguous " "in the same dimension."); goto fail; } } if (spec & __Pyx_MEMVIEW_FOLLOW) { Py_ssize_t stride = buf->strides[dim]; if (stride < 0) stride = -stride; if (unlikely(stride < buf->itemsize)) { PyErr_SetString(PyExc_ValueError, "Buffer and memoryview are not contiguous " "in the same dimension."); goto fail; } } } else { if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { PyErr_Format(PyExc_ValueError, "C-contiguous buffer is not contiguous in " "dimension %d", dim); goto fail; } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { PyErr_Format(PyExc_ValueError, "C-contiguous buffer is not indirect in " "dimension %d", dim); goto fail; } else if (unlikely(buf->suboffsets)) { PyErr_SetString(PyExc_ValueError, "Buffer exposes suboffsets but no strides"); goto fail; } } return 1; fail: return 0; } static int __pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) { if (spec & __Pyx_MEMVIEW_DIRECT) { if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { PyErr_Format(PyExc_ValueError, "Buffer not compatible with direct access " "in dimension %d.", dim); goto fail; } } if (spec & __Pyx_MEMVIEW_PTR) { if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { PyErr_Format(PyExc_ValueError, "Buffer is not indirectly accessible " "in dimension %d.", dim); goto fail; } } return 1; fail: return 0; } static int __pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) { int i; if (c_or_f_flag & __Pyx_IS_F_CONTIG) { Py_ssize_t stride = 1; for (i = 0; i < ndim; i++) { if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { PyErr_SetString(PyExc_ValueError, "Buffer not fortran contiguous."); goto fail; } stride = stride * buf->shape[i]; } } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { Py_ssize_t stride = 1; for (i = ndim - 1; i >- 1; i--) { if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { PyErr_SetString(PyExc_ValueError, "Buffer not C contiguous."); goto fail; } stride = stride * buf->shape[i]; } } return 1; fail: return 0; } static int __Pyx_ValidateAndInit_memviewslice( int *axes_specs, int c_or_f_flag, int buf_flags, int ndim, __Pyx_TypeInfo *dtype, __Pyx_BufFmt_StackElem stack[], __Pyx_memviewslice *memviewslice, PyObject *original_obj) { struct __pyx_memoryview_obj *memview, *new_memview; __Pyx_RefNannyDeclarations Py_buffer *buf; int i, spec = 0, retval = -1; __Pyx_BufFmt_Context ctx; int from_memoryview = __pyx_memoryview_check(original_obj); __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) original_obj)->typeinfo)) { memview = (struct __pyx_memoryview_obj *) original_obj; new_memview = NULL; } else { memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( original_obj, buf_flags, 0, dtype); new_memview = memview; if (unlikely(!memview)) goto fail; } buf = &memview->view; if (unlikely(buf->ndim != ndim)) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", ndim, buf->ndim); goto fail; } if (new_memview) { __Pyx_BufFmt_Init(&ctx, stack, dtype); if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; } if (unlikely((unsigned) buf->itemsize != dtype->size)) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->len > 0) { for (i = 0; i < ndim; i++) { spec = axes_specs[i]; if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) goto fail; if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) goto fail; } if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) goto fail; } if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, new_memview != NULL) == -1)) { goto fail; } retval = 0; goto no_fail; fail: Py_XDECREF(new_memview); retval = -1; no_fail: __Pyx_RefNannyFinishContext(); return retval; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc___pyx_t_double_complex(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, &__Pyx_TypeInfo___pyx_t_double_complex, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds___pyx_t_double_complex(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, PyBUF_RECORDS_RO | writable_flag, 2, &__Pyx_TypeInfo___pyx_t_double_complex, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 2, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* CheckBinaryVersion */ static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); return PyErr_WarnEx(NULL, message, 1); } return 0; } /* FunctionExport */ static int __Pyx_ExportFunction(const char *name, void (*f)(void), const char *sig) { PyObject *d = 0; PyObject *cobj = 0; union { void (*fp)(void); void *p; } tmp; d = PyObject_GetAttrString(__pyx_m, (char *)"__pyx_capi__"); if (!d) { PyErr_Clear(); d = PyDict_New(); if (!d) goto bad; Py_INCREF(d); if (PyModule_AddObject(__pyx_m, (char *)"__pyx_capi__", d) < 0) goto bad; } tmp.fp = f; #if PY_VERSION_HEX >= 0x02070000 cobj = PyCapsule_New(tmp.p, sig, 0); #else cobj = PyCObject_FromVoidPtrAndDesc(tmp.p, (void *)sig, 0); #endif if (!cobj) goto bad; if (PyDict_SetItemString(d, name, cobj) < 0) goto bad; Py_DECREF(cobj); Py_DECREF(d); return 0; bad: Py_XDECREF(cobj); Py_XDECREF(d); return -1; } /* InitStrings */ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; if (PyObject_Hash(*t->p) == -1) return -1; ++t; } return 0; } static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); } static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { Py_ssize_t ignore; return __Pyx_PyObject_AsStringAndSize(o, &ignore); } #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT #if !CYTHON_PEP393_ENABLED static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { char* defenc_c; PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); if (!defenc) return NULL; defenc_c = PyBytes_AS_STRING(defenc); #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII { char* end = defenc_c + PyBytes_GET_SIZE(defenc); char* c; for (c = defenc_c; c < end; c++) { if ((unsigned char) (*c) >= 128) { PyUnicode_AsASCIIString(o); return NULL; } } } #endif *length = PyBytes_GET_SIZE(defenc); return defenc_c; } #else static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII if (likely(PyUnicode_IS_ASCII(o))) { *length = PyUnicode_GET_LENGTH(o); return PyUnicode_AsUTF8(o); } else { PyUnicode_AsASCIIString(o); return NULL; } #else return PyUnicode_AsUTF8AndSize(o, length); #endif } #endif #endif static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT if ( #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII __Pyx_sys_getdefaultencoding_not_ascii && #endif PyUnicode_Check(o)) { return __Pyx_PyUnicode_AsStringAndSize(o, length); } else #endif #if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) if (PyByteArray_Check(o)) { *length = PyByteArray_GET_SIZE(o); return PyByteArray_AS_STRING(o); } else #endif { char* result; int r = PyBytes_AsStringAndSize(o, &result, length); if (unlikely(r < 0)) { return NULL; } else { return result; } } } static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { int retval; if (unlikely(!x)) return -1; retval = __Pyx_PyObject_IsTrue(x); Py_DECREF(x); return retval; } static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { #if PY_MAJOR_VERSION >= 3 if (PyLong_Check(result)) { if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, "__int__ returned non-int (type %.200s). " "The ability to return an instance of a strict subclass of int " "is deprecated, and may be removed in a future version of Python.", Py_TYPE(result)->tp_name)) { Py_DECREF(result); return NULL; } return result; } #endif PyErr_Format(PyExc_TypeError, "__%.4s__ returned non-%.4s (type %.200s)", type_name, type_name, Py_TYPE(result)->tp_name); Py_DECREF(result); return NULL; } static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { #if CYTHON_USE_TYPE_SLOTS PyNumberMethods *m; #endif const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x) || PyLong_Check(x))) #else if (likely(PyLong_Check(x))) #endif return __Pyx_NewRef(x); #if CYTHON_USE_TYPE_SLOTS m = Py_TYPE(x)->tp_as_number; #if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = m->nb_int(x); } else if (m && m->nb_long) { name = "long"; res = m->nb_long(x); } #else if (likely(m && m->nb_int)) { name = "int"; res = m->nb_int(x); } #endif #else if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { res = PyNumber_Int(x); } #endif if (likely(res)) { #if PY_MAJOR_VERSION < 3 if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { #else if (unlikely(!PyLong_CheckExact(res))) { #endif return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject *x; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(b))) { if (sizeof(Py_ssize_t) >= sizeof(long)) return PyInt_AS_LONG(b); else return PyInt_AsSsize_t(b); } #endif if (likely(PyLong_CheckExact(b))) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)b)->ob_digit; const Py_ssize_t size = Py_SIZE(b); if (likely(__Pyx_sst_abs(size) <= 1)) { ival = likely(size) ? digits[0] : 0; if (size == -1) ival = -ival; return ival; } else { switch (size) { case 2: if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -2: if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case 3: if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -3: if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case 4: if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -4: if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; } } #endif return PyLong_AsSsize_t(b); } x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { return PyInt_FromSize_t(ival); } #endif /* Py_PYTHON_H */ openTSNE-0.6.1/openTSNE/_matrix_mul/matrix_mul_numpy.pyx000066400000000000000000000126571413546205200233140ustar00rootroot00000000000000# cython: boundscheck=False # cython: wraparound=False # cython: cdivision=True # cython: initializedcheck=False # cython: warn.undeclared=True # cython: language_level=3 cimport openTSNE._matrix_mul.matrix_mul cimport numpy as np import numpy as np cdef void matrix_multiply_fft_1d( double[::1] kernel_tilde, double[:, ::1] w_coefficients, double[:, ::1] out, ): """Multiply the the kernel vectr K tilde with the w coefficients. Parameters ---------- kernel_tilde : memoryview The generating vector of the 2d Toeplitz matrix i.e. the kernel evaluated all all interpolation points from the left most interpolation point, embedded in a circulant matrix (doubled in size from (n_interp, n_interp) to (2 * n_interp, 2 * n_interp) and symmetrized. See how to embed Toeplitz into circulant matrices. w_coefficients : memoryview The coefficients calculated in Step 1 of the paper, a (n_total_interp, n_terms) matrix. The coefficients are embedded into a larger matrix in this function, so no prior embedding is needed. out : memoryview Output matrix. Must be same size as ``w_coefficients``. """ cdef: Py_ssize_t n_interpolation_points_1d = w_coefficients.shape[0] Py_ssize_t n_terms = w_coefficients.shape[1] Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] complex[::1] fft_kernel_tilde = np.empty(n_fft_coeffs, dtype=complex) complex[::1] fft_w_coeffs = np.empty(n_fft_coeffs, dtype=complex) # Note that we can't use the same buffer for the input and output since # we only write to the first half of the vector - we'd need to # manually zero out the rest of the entries that were inevitably # changed during the IDFT, so it's faster to use two buffers, at the # cost of some memory complex[::1] fft_in_buffer = np.zeros(n_fft_coeffs, dtype=complex) complex[::1] fft_out_buffer = np.zeros(n_fft_coeffs, dtype=complex) Py_ssize_t d, i # Compute the FFT of the kernel vector fft_kernel_tilde = np.fft.fft(kernel_tilde) for d in range(n_terms): for i in range(n_interpolation_points_1d): fft_in_buffer[i] = w_coefficients[i, d] fft_w_coeffs = np.fft.fft(fft_in_buffer) # Take the Hadamard product of two complex vectors fft_w_coeffs = np.multiply(fft_w_coeffs, fft_kernel_tilde) fft_out_buffer = np.fft.ifft(fft_w_coeffs) for i in range(n_interpolation_points_1d): out[i, d] = fft_out_buffer[n_interpolation_points_1d + i].real cdef void matrix_multiply_fft_2d( double[:, ::1] kernel_tilde, double[:, ::1] w_coefficients, double[:, ::1] out, ): """Multiply the the kernel matrix K tilde with the w coefficients. Parameters ---------- kernel_tilde : memoryview The generating matrix of the 3d Toeplitz tensor i.e. the kernel evaluated all all interpolation points from the top left most interpolation point, embedded in a circulant matrix (doubled in size from (n_interp, n_interp) to (2 * n_interp, 2 * n_interp) and symmetrized. See how to embed Toeplitz into circulant matrices. w_coefficients : memoryview The coefficients calculated in Step 1 of the paper, a (n_total_interp, n_terms) matrix. The coefficients are embedded into a larger matrix in this function, so no prior embedding is needed. out : memoryview Output matrix. Must be same size as ``w_coefficients``. """ cdef: Py_ssize_t total_interpolation_points = w_coefficients.shape[0] Py_ssize_t n_terms = w_coefficients.shape[1] Py_ssize_t n_fft_coeffs = kernel_tilde.shape[0] Py_ssize_t n_interpolation_points_1d = n_fft_coeffs / 2 complex[:, :] fft_w_coefficients = np.empty((n_fft_coeffs, (n_fft_coeffs / 2 + 1)), dtype=complex) complex[:, :] fft_kernel_tilde = np.empty((n_fft_coeffs, (n_fft_coeffs / 2 + 1)), dtype=complex) # Note that we can't use the same buffer for the input and output since # we only write to the top quadrant of the in matrix - we'd need to # manually zero out the rest of the entries that were inevitably # changed during the IDFT, so it's faster to use two buffers, at the # cost of some memory double[:, ::1] fft_in_buffer = np.zeros((n_fft_coeffs, n_fft_coeffs), dtype=float) double[:, ::1] fft_out_buffer = np.zeros((n_fft_coeffs, n_fft_coeffs), dtype=float) Py_ssize_t d, i, j, idx fft_kernel_tilde = np.fft.rfft2(kernel_tilde) for d in range(n_terms): for i in range(n_interpolation_points_1d): for j in range(n_interpolation_points_1d): fft_in_buffer[i, j] = w_coefficients[i * n_interpolation_points_1d + j, d] fft_w_coefficients = np.fft.rfft2(fft_in_buffer) # Take the Hadamard product of two complex vectors fft_w_coefficients = np.multiply(fft_w_coefficients, fft_kernel_tilde) # Invert the computed values at the interpolated nodes fft_out_buffer = np.fft.irfft2(fft_w_coefficients) for i in range(n_interpolation_points_1d): for j in range(n_interpolation_points_1d): idx = i * n_interpolation_points_1d + j out[idx, d] = fft_out_buffer[n_interpolation_points_1d + i, n_interpolation_points_1d + j].real openTSNE-0.6.1/openTSNE/_tsne.cpp000066400000000000000000061565721413546205200164500ustar00rootroot00000000000000/* Generated by Cython 0.29.23 */ /* BEGIN: Cython Metadata { "distutils": { "depends": [], "language": "c++", "name": "openTSNE._tsne", "sources": [ "openTSNE/_tsne.pyx" ] }, "module_name": "openTSNE._tsne" } END: Cython Metadata */ #ifndef PY_SSIZE_T_CLEAN #define PY_SSIZE_T_CLEAN #endif /* PY_SSIZE_T_CLEAN */ #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) #error Cython requires Python 2.6+ or Python 3.3+. #else #define CYTHON_ABI "0_29_23" #define CYTHON_HEX_VERSION 0x001D17F0 #define CYTHON_FUTURE_DIVISION 1 #include #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #define __PYX_COMMA , #ifndef HAVE_LONG_LONG #if PY_VERSION_HEX >= 0x02070000 #define HAVE_LONG_LONG #endif #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #ifndef Py_HUGE_VAL #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_PYSTON 0 #define CYTHON_COMPILING_IN_CPYTHON 0 #undef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 0 #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #if PY_VERSION_HEX < 0x03050000 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #elif !defined(CYTHON_USE_ASYNC_SLOTS) #define CYTHON_USE_ASYNC_SLOTS 1 #endif #undef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 0 #undef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 0 #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #undef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 1 #undef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 0 #undef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 0 #undef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 0 #undef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 0 #undef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT 0 #undef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE 0 #undef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS 0 #undef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK 0 #elif defined(PYSTON_VERSION) #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_PYSTON 1 #define CYTHON_COMPILING_IN_CPYTHON 0 #ifndef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 1 #endif #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #undef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 0 #ifndef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 1 #endif #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #ifndef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 0 #endif #ifndef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 1 #endif #ifndef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 1 #endif #undef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 0 #undef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 0 #undef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT 0 #undef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE 0 #undef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS 0 #undef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_PYSTON 0 #define CYTHON_COMPILING_IN_CPYTHON 1 #ifndef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 1 #endif #if PY_VERSION_HEX < 0x02070000 #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) #define CYTHON_USE_PYTYPE_LOOKUP 1 #endif #if PY_MAJOR_VERSION < 3 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #elif !defined(CYTHON_USE_ASYNC_SLOTS) #define CYTHON_USE_ASYNC_SLOTS 1 #endif #if PY_VERSION_HEX < 0x02070000 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #elif !defined(CYTHON_USE_PYLONG_INTERNALS) #define CYTHON_USE_PYLONG_INTERNALS 1 #endif #ifndef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 1 #endif #ifndef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 1 #endif #if PY_VERSION_HEX < 0x030300F0 #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #elif !defined(CYTHON_USE_UNICODE_WRITER) #define CYTHON_USE_UNICODE_WRITER 1 #endif #ifndef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 0 #endif #ifndef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 1 #endif #ifndef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 1 #endif #ifndef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 1 #endif #ifndef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 1 #endif #ifndef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000) #endif #ifndef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) #endif #ifndef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1) #endif #ifndef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3) #endif #endif #if !defined(CYTHON_FAST_PYCCALL) #define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) #endif #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #undef SHIFT #undef BASE #undef MASK #ifdef SIZEOF_VOID_P enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; #endif #endif #ifndef __has_attribute #define __has_attribute(x) 0 #endif #ifndef __has_cpp_attribute #define __has_cpp_attribute(x) 0 #endif #ifndef CYTHON_RESTRICT #if defined(__GNUC__) #define CYTHON_RESTRICT __restrict__ #elif defined(_MSC_VER) && _MSC_VER >= 1400 #define CYTHON_RESTRICT __restrict #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_RESTRICT restrict #else #define CYTHON_RESTRICT #endif #endif #ifndef CYTHON_UNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif # elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif #endif #ifndef CYTHON_MAYBE_UNUSED_VAR # if defined(__cplusplus) template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } # else # define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) # endif #endif #ifndef CYTHON_NCP_UNUSED # if CYTHON_COMPILING_IN_CPYTHON # define CYTHON_NCP_UNUSED # else # define CYTHON_NCP_UNUSED CYTHON_UNUSED # endif #endif #define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) #ifdef _MSC_VER #ifndef _MSC_STDINT_H_ #if _MSC_VER < 1300 typedef unsigned char uint8_t; typedef unsigned int uint32_t; #else typedef unsigned __int8 uint8_t; typedef unsigned __int32 uint32_t; #endif #endif #else #include #endif #ifndef CYTHON_FALLTHROUGH #if defined(__cplusplus) && __cplusplus >= 201103L #if __has_cpp_attribute(fallthrough) #define CYTHON_FALLTHROUGH [[fallthrough]] #elif __has_cpp_attribute(clang::fallthrough) #define CYTHON_FALLTHROUGH [[clang::fallthrough]] #elif __has_cpp_attribute(gnu::fallthrough) #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] #endif #endif #ifndef CYTHON_FALLTHROUGH #if __has_attribute(fallthrough) #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) #else #define CYTHON_FALLTHROUGH #endif #endif #if defined(__clang__ ) && defined(__apple_build_version__) #if __apple_build_version__ < 7000000 #undef CYTHON_FALLTHROUGH #define CYTHON_FALLTHROUGH #endif #endif #endif #ifndef __cplusplus #error "Cython files generated with the C++ option must be compiled with a C++ compiler." #endif #ifndef CYTHON_INLINE #if defined(__clang__) #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) #else #define CYTHON_INLINE inline #endif #endif template void __Pyx_call_destructor(T& x) { x.~T(); } template class __Pyx_FakeReference { public: __Pyx_FakeReference() : ptr(NULL) { } __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } T *operator->() { return ptr; } T *operator&() { return ptr; } operator T&() { return *ptr; } template bool operator ==(U other) { return *ptr == other; } template bool operator !=(U other) { return *ptr != other; } private: T *ptr; }; #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) #define Py_OptimizeFlag 0 #endif #define __PYX_BUILD_PY_SSIZE_T "n" #define CYTHON_FORMAT_SSIZE_T "z" #if PY_MAJOR_VERSION < 3 #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #define __Pyx_DefaultClassType PyClass_Type #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" #if PY_VERSION_HEX >= 0x030800A4 && PY_VERSION_HEX < 0x030800B2 #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a, 0, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #else #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #endif #define __Pyx_DefaultClassType PyType_Type #endif #ifndef Py_TPFLAGS_CHECKTYPES #define Py_TPFLAGS_CHECKTYPES 0 #endif #ifndef Py_TPFLAGS_HAVE_INDEX #define Py_TPFLAGS_HAVE_INDEX 0 #endif #ifndef Py_TPFLAGS_HAVE_NEWBUFFER #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif #ifndef Py_TPFLAGS_HAVE_FINALIZE #define Py_TPFLAGS_HAVE_FINALIZE 0 #endif #ifndef METH_STACKLESS #define METH_STACKLESS 0 #endif #if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) #ifndef METH_FASTCALL #define METH_FASTCALL 0x80 #endif typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, Py_ssize_t nargs, PyObject *kwnames); #else #define __Pyx_PyCFunctionFast _PyCFunctionFast #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords #endif #if CYTHON_FAST_PYCCALL #define __Pyx_PyFastCFunction_Check(func)\ ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))))) #else #define __Pyx_PyFastCFunction_Check(func) 0 #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) #define PyObject_Malloc(s) PyMem_Malloc(s) #define PyObject_Free(p) PyMem_Free(p) #define PyObject_Realloc(p) PyMem_Realloc(p) #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1 #define PyMem_RawMalloc(n) PyMem_Malloc(n) #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n) #define PyMem_RawFree(p) PyMem_Free(p) #endif #if CYTHON_COMPILING_IN_PYSTON #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) #else #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) #endif #if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 #define __Pyx_PyThreadState_Current PyThreadState_GET() #elif PY_VERSION_HEX >= 0x03060000 #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() #elif PY_VERSION_HEX >= 0x03000000 #define __Pyx_PyThreadState_Current PyThreadState_GET() #else #define __Pyx_PyThreadState_Current _PyThreadState_Current #endif #if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) #include "pythread.h" #define Py_tss_NEEDS_INIT 0 typedef int Py_tss_t; static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { *key = PyThread_create_key(); return 0; } static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); *key = Py_tss_NEEDS_INIT; return key; } static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { PyObject_Free(key); } static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { return *key != Py_tss_NEEDS_INIT; } static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { PyThread_delete_key(*key); *key = Py_tss_NEEDS_INIT; } static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { return PyThread_set_key_value(*key, value); } static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { return PyThread_get_key_value(*key); } #endif #if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) #define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) #else #define __Pyx_PyDict_NewPresized(n) PyDict_New() #endif #if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) #else #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS #define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) #else #define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name) #endif #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) #define CYTHON_PEP393_ENABLED 1 #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ 0 : _PyUnicode_Ready((PyObject *)(op))) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE) #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) #else #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) #endif #else #define CYTHON_PEP393_ENABLED 0 #define PyUnicode_1BYTE_KIND 1 #define PyUnicode_2BYTE_KIND 2 #define PyUnicode_4BYTE_KIND 4 #define __Pyx_PyUnicode_READY(op) (0) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) #endif #if CYTHON_COMPILING_IN_PYPY #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) #else #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) #endif #define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) #define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) #else #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) #endif #if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) #define PyObject_ASCII(o) PyObject_Repr(o) #endif #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type #define PyStringObject PyUnicodeObject #define PyString_Type PyUnicode_Type #define PyString_Check PyUnicode_Check #define PyString_CheckExact PyUnicode_CheckExact #ifndef PyObject_Unicode #define PyObject_Unicode PyObject_Str #endif #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) #else #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) #endif #ifndef PySet_CheckExact #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) #endif #if PY_VERSION_HEX >= 0x030900A4 #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) #else #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) #endif #if CYTHON_ASSUME_SAFE_MACROS #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) #else #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) #endif #if PY_MAJOR_VERSION >= 3 #define PyIntObject PyLongObject #define PyInt_Type PyLong_Type #define PyInt_Check(op) PyLong_Check(op) #define PyInt_CheckExact(op) PyLong_CheckExact(op) #define PyInt_FromString PyLong_FromString #define PyInt_FromUnicode PyLong_FromUnicode #define PyInt_FromLong PyLong_FromLong #define PyInt_FromSize_t PyLong_FromSize_t #define PyInt_FromSsize_t PyLong_FromSsize_t #define PyInt_AsLong PyLong_AsLong #define PyInt_AS_LONG PyLong_AS_LONG #define PyInt_AsSsize_t PyLong_AsSsize_t #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask #define PyNumber_Int PyNumber_Long #endif #if PY_MAJOR_VERSION >= 3 #define PyBoolObject PyLongObject #endif #if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY #ifndef PyUnicode_InternFromString #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) #endif #endif #if PY_VERSION_HEX < 0x030200A4 typedef long Py_hash_t; #define __Pyx_PyInt_FromHash_t PyInt_FromLong #define __Pyx_PyInt_AsHash_t PyInt_AsLong #else #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func)) #else #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) #endif #if CYTHON_USE_ASYNC_SLOTS #if PY_VERSION_HEX >= 0x030500B1 #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) #else #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) #endif #else #define __Pyx_PyType_AsAsync(obj) NULL #endif #ifndef __Pyx_PyAsyncMethodsStruct typedef struct { unaryfunc am_await; unaryfunc am_aiter; unaryfunc am_anext; } __Pyx_PyAsyncMethodsStruct; #endif #if defined(WIN32) || defined(MS_WINDOWS) #define _USE_MATH_DEFINES #endif #include #ifdef NAN #define __PYX_NAN() ((float) NAN) #else static CYTHON_INLINE float __PYX_NAN() { float value; memset(&value, 0xFF, sizeof(value)); return value; } #endif #if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) #define __Pyx_truncl trunc #else #define __Pyx_truncl truncl #endif #define __PYX_MARK_ERR_POS(f_index, lineno) \ { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } #define __PYX_ERR(f_index, lineno, Ln_error) \ { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } #ifndef __PYX_EXTERN_C #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else #define __PYX_EXTERN_C extern #endif #endif #define __PYX_HAVE__openTSNE___tsne #define __PYX_HAVE_API__openTSNE___tsne /* Early includes */ #include #include #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" /* NumPy API declarations from "numpy/__init__.pxd" */ #include #include "math.h" #include "pythread.h" #include "pystate.h" #ifdef _OPENMP #include #endif /* _OPENMP */ #if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) #define CYTHON_WITHOUT_ASSERTIONS #endif typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; #define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) #define __PYX_DEFAULT_STRING_ENCODING "" #define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString #define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #define __Pyx_uchar_cast(c) ((unsigned char)c) #define __Pyx_long_cast(x) ((long)x) #define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ (sizeof(type) < sizeof(Py_ssize_t)) ||\ (sizeof(type) > sizeof(Py_ssize_t) &&\ likely(v < (type)PY_SSIZE_T_MAX ||\ v == (type)PY_SSIZE_T_MAX) &&\ (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ v == (type)PY_SSIZE_T_MIN))) ||\ (sizeof(type) == sizeof(Py_ssize_t) &&\ (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ v == (type)PY_SSIZE_T_MAX))) ) static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { return (size_t) i < (size_t) limit; } #if defined (__cplusplus) && __cplusplus >= 201103L #include #define __Pyx_sst_abs(value) std::abs(value) #elif SIZEOF_INT >= SIZEOF_SIZE_T #define __Pyx_sst_abs(value) abs(value) #elif SIZEOF_LONG >= SIZEOF_SIZE_T #define __Pyx_sst_abs(value) labs(value) #elif defined (_MSC_VER) #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define __Pyx_sst_abs(value) llabs(value) #elif defined (__GNUC__) #define __Pyx_sst_abs(value) __builtin_llabs(value) #else #define __Pyx_sst_abs(value) ((value<0) ? -value : value) #endif static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); #define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) #define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) #define __Pyx_PyBytes_FromString PyBytes_FromString #define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); #if PY_MAJOR_VERSION < 3 #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #else #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize #endif #define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) #define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) #define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) #define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) #define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) #define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { const Py_UNICODE *u_end = u; while (*u_end++) ; return (size_t)(u_end - u - 1); } #define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) #define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode #define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode #define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) #define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); #define __Pyx_PySequence_Tuple(obj)\ (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); #if CYTHON_ASSUME_SAFE_MACROS #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) #endif #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) #else #define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) #endif #define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII static int __Pyx_sys_getdefaultencoding_not_ascii; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; PyObject* ascii_chars_u = NULL; PyObject* ascii_chars_b = NULL; const char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; if (strcmp(default_encoding_c, "ascii") == 0) { __Pyx_sys_getdefaultencoding_not_ascii = 0; } else { char ascii_chars[128]; int c; for (c = 0; c < 128; c++) { ascii_chars[c] = c; } __Pyx_sys_getdefaultencoding_not_ascii = 1; ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); if (!ascii_chars_u) goto bad; ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { PyErr_Format( PyExc_ValueError, "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", default_encoding_c); goto bad; } Py_DECREF(ascii_chars_u); Py_DECREF(ascii_chars_b); } Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); Py_XDECREF(ascii_chars_u); Py_XDECREF(ascii_chars_b); return -1; } #endif #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) #else #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT static char* __PYX_DEFAULT_STRING_ENCODING; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); return -1; } #endif #endif /* Test for GCC > 2.95 */ #if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #else /* !__GNUC__ or GCC < 2.95 */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ */ static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } static PyObject *__pyx_m = NULL; static PyObject *__pyx_d; static PyObject *__pyx_b; static PyObject *__pyx_cython_runtime = NULL; static PyObject *__pyx_empty_tuple; static PyObject *__pyx_empty_bytes; static PyObject *__pyx_empty_unicode; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; /* Header.proto */ #if !defined(CYTHON_CCOMPLEX) #if defined(__cplusplus) #define CYTHON_CCOMPLEX 1 #elif defined(_Complex_I) #define CYTHON_CCOMPLEX 1 #else #define CYTHON_CCOMPLEX 0 #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus #include #else #include #endif #endif #if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) #undef _Complex_I #define _Complex_I 1.0fj #endif static const char *__pyx_f[] = { "openTSNE/_tsne.pyx", "__init__.pxd", "stringsource", "type.pxd", "openTSNE/quad_tree.pxd", }; /* NoFastGil.proto */ #define __Pyx_PyGILState_Ensure PyGILState_Ensure #define __Pyx_PyGILState_Release PyGILState_Release #define __Pyx_FastGIL_Remember() #define __Pyx_FastGIL_Forget() #define __Pyx_FastGilFuncInit() /* MemviewSliceStruct.proto */ struct __pyx_memoryview_obj; typedef struct { struct __pyx_memoryview_obj *memview; char *data; Py_ssize_t shape[8]; Py_ssize_t strides[8]; Py_ssize_t suboffsets[8]; } __Pyx_memviewslice; #define __Pyx_MemoryView_Len(m) (m.shape[0]) /* Atomics.proto */ #include #ifndef CYTHON_ATOMICS #define CYTHON_ATOMICS 1 #endif #define __pyx_atomic_int_type int #if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\ (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\ !defined(__i386__) #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1) #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1) #ifdef __PYX_DEBUG_ATOMICS #warning "Using GNU atomics" #endif #elif CYTHON_ATOMICS && defined(_MSC_VER) && 0 #include #undef __pyx_atomic_int_type #define __pyx_atomic_int_type LONG #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value) #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value) #ifdef __PYX_DEBUG_ATOMICS #pragma message ("Using MSVC atomics") #endif #elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0 #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value) #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value) #ifdef __PYX_DEBUG_ATOMICS #warning "Using Intel atomics" #endif #else #undef CYTHON_ATOMICS #define CYTHON_ATOMICS 0 #ifdef __PYX_DEBUG_ATOMICS #warning "Not using atomics" #endif #endif typedef volatile __pyx_atomic_int_type __pyx_atomic_int; #if CYTHON_ATOMICS #define __pyx_add_acquisition_count(memview)\ __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) #define __pyx_sub_acquisition_count(memview)\ __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) #else #define __pyx_add_acquisition_count(memview)\ __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) #define __pyx_sub_acquisition_count(memview)\ __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) #endif /* ForceInitThreads.proto */ #ifndef __PYX_FORCE_INIT_THREADS #define __PYX_FORCE_INIT_THREADS 0 #endif /* BufferFormatStructs.proto */ #define IS_UNSIGNED(type) (((type) -1) > 0) struct __Pyx_StructField_; #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) typedef struct { const char* name; struct __Pyx_StructField_* fields; size_t size; size_t arraysize[8]; int ndim; char typegroup; char is_unsigned; int flags; } __Pyx_TypeInfo; typedef struct __Pyx_StructField_ { __Pyx_TypeInfo* type; const char* name; size_t offset; } __Pyx_StructField; typedef struct { __Pyx_StructField* field; size_t parent_offset; } __Pyx_BufFmt_StackElem; typedef struct { __Pyx_StructField root; __Pyx_BufFmt_StackElem* head; size_t fmt_offset; size_t new_count, enc_count; size_t struct_alignment; int is_complex; char enc_type; char new_packmode; char enc_packmode; char is_valid_array; } __Pyx_BufFmt_Context; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":689 * # in Cython to enable them only on the right systems. * * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t */ typedef npy_int8 __pyx_t_5numpy_int8_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":690 * * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t */ typedef npy_int16 __pyx_t_5numpy_int16_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":691 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< * ctypedef npy_int64 int64_t * #ctypedef npy_int96 int96_t */ typedef npy_int32 __pyx_t_5numpy_int32_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":692 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< * #ctypedef npy_int96 int96_t * #ctypedef npy_int128 int128_t */ typedef npy_int64 __pyx_t_5numpy_int64_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":696 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":697 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":698 * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< * ctypedef npy_uint64 uint64_t * #ctypedef npy_uint96 uint96_t */ typedef npy_uint32 __pyx_t_5numpy_uint32_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":699 * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< * #ctypedef npy_uint96 uint96_t * #ctypedef npy_uint128 uint128_t */ typedef npy_uint64 __pyx_t_5numpy_uint64_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":703 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< * ctypedef npy_float64 float64_t * #ctypedef npy_float80 float80_t */ typedef npy_float32 __pyx_t_5numpy_float32_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":704 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< * #ctypedef npy_float80 float80_t * #ctypedef npy_float128 float128_t */ typedef npy_float64 __pyx_t_5numpy_float64_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":713 * # The int types are mapped a bit surprising -- * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t # <<<<<<<<<<<<<< * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t */ typedef npy_long __pyx_t_5numpy_int_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":714 * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< * ctypedef npy_longlong longlong_t * */ typedef npy_longlong __pyx_t_5numpy_long_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":715 * ctypedef npy_long int_t * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< * * ctypedef npy_ulong uint_t */ typedef npy_longlong __pyx_t_5numpy_longlong_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":717 * ctypedef npy_longlong longlong_t * * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t */ typedef npy_ulong __pyx_t_5numpy_uint_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":718 * * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulonglong_t * */ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":719 * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< * * ctypedef npy_intp intp_t */ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":721 * ctypedef npy_ulonglong ulonglong_t * * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< * ctypedef npy_uintp uintp_t * */ typedef npy_intp __pyx_t_5numpy_intp_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":722 * * ctypedef npy_intp intp_t * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< * * ctypedef npy_double float_t */ typedef npy_uintp __pyx_t_5numpy_uintp_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":724 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t */ typedef npy_double __pyx_t_5numpy_float_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":725 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< * ctypedef npy_longdouble longdouble_t * */ typedef npy_double __pyx_t_5numpy_double_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":726 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cfloat cfloat_t */ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; /* Declarations.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< float > __pyx_t_float_complex; #else typedef float _Complex __pyx_t_float_complex; #endif #else typedef struct { float real, imag; } __pyx_t_float_complex; #endif static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); /* Declarations.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< double > __pyx_t_double_complex; #else typedef double _Complex __pyx_t_double_complex; #endif #else typedef struct { double real, imag; } __pyx_t_double_complex; #endif static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); /*--- Type declarations ---*/ struct __pyx_obj_8openTSNE_9quad_tree_QuadTree; struct __pyx_array_obj; struct __pyx_MemviewEnum_obj; struct __pyx_memoryview_obj; struct __pyx_memoryviewslice_obj; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":728 * ctypedef npy_longdouble longdouble_t * * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t */ typedef npy_cfloat __pyx_t_5numpy_cfloat_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":729 * * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< * ctypedef npy_clongdouble clongdouble_t * */ typedef npy_cdouble __pyx_t_5numpy_cdouble_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":730 * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cdouble complex_t */ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":732 * ctypedef npy_clongdouble clongdouble_t * * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew1(a): */ typedef npy_cdouble __pyx_t_5numpy_complex_t; struct __pyx_t_8openTSNE_9quad_tree_Node; typedef struct __pyx_t_8openTSNE_9quad_tree_Node __pyx_t_8openTSNE_9quad_tree_Node; struct __pyx_opt_args_8openTSNE_9quad_tree_is_duplicate; /* "quad_tree.pxd":10 * cdef double EPSILON = np.finfo(np.float64).eps * * ctypedef struct Node: # <<<<<<<<<<<<<< * Py_ssize_t n_dims * double *center */ struct __pyx_t_8openTSNE_9quad_tree_Node { Py_ssize_t n_dims; double *center; double length; int is_leaf; __pyx_t_8openTSNE_9quad_tree_Node *children; double *center_of_mass; Py_ssize_t num_points; }; /* "quad_tree.pxd":22 * * * cdef bint is_duplicate(Node * node, double * point, double duplicate_eps=*) nogil # <<<<<<<<<<<<<< * * */ struct __pyx_opt_args_8openTSNE_9quad_tree_is_duplicate { int __pyx_n; double duplicate_eps; }; struct __pyx_opt_args_8openTSNE_5_tsne_compute_gaussian_perplexity; struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_bh; struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d; struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_1d; struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d; struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_2d; struct __pyx_fuse_0__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn; struct __pyx_fuse_1__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn; /* "openTSNE/_tsne.pxd":18 * * * cpdef double[:, ::1] compute_gaussian_perplexity( # <<<<<<<<<<<<<< * double[:, :] distances, * double[:] desired_perplexities, */ struct __pyx_opt_args_8openTSNE_5_tsne_compute_gaussian_perplexity { int __pyx_n; double perplexity_tol; Py_ssize_t max_iter; Py_ssize_t num_threads; }; /* "openTSNE/_tsne.pxd":38 * ) * * cpdef double estimate_negative_gradient_bh( # <<<<<<<<<<<<<< * QuadTree tree, * double[:, ::1] embedding, */ struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_bh { int __pyx_n; double theta; double dof; Py_ssize_t num_threads; int pairwise_normalization; }; /* "openTSNE/_tsne.pxd":48 * ) * * cpdef double estimate_negative_gradient_fft_1d( # <<<<<<<<<<<<<< * double[::1] embedding, * double[::1] gradient, */ struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d { int __pyx_n; Py_ssize_t n_interpolation_points; Py_ssize_t min_num_intervals; double ints_in_interval; double dof; }; /* "openTSNE/_tsne.pxd":57 * ) * * cpdef tuple prepare_negative_gradient_fft_interpolation_grid_1d( # <<<<<<<<<<<<<< * double[::1] reference_embedding, * Py_ssize_t n_interpolation_points=*, */ struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_1d { int __pyx_n; Py_ssize_t n_interpolation_points; Py_ssize_t min_num_intervals; double ints_in_interval; double dof; double padding; }; /* "openTSNE/_tsne.pxd":75 * ) * * cpdef double estimate_negative_gradient_fft_2d( # <<<<<<<<<<<<<< * double[:, ::1] embedding, * double[:, ::1] gradient, */ struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d { int __pyx_n; Py_ssize_t n_interpolation_points; Py_ssize_t min_num_intervals; double ints_in_interval; double dof; }; /* "openTSNE/_tsne.pxd":84 * ) * * cpdef tuple prepare_negative_gradient_fft_interpolation_grid_2d( # <<<<<<<<<<<<<< * double[:, ::1] reference_embedding, * Py_ssize_t n_interpolation_points=*, */ struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_2d { int __pyx_n; Py_ssize_t n_interpolation_points; Py_ssize_t min_num_intervals; double ints_in_interval; double dof; double padding; }; /* "openTSNE/_tsne.pyx":103 * * * cpdef tuple estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * sparse_index_type[:] indices, * sparse_index_type[:] indptr, */ struct __pyx_fuse_0__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn { int __pyx_n; double dof; Py_ssize_t num_threads; int should_eval_error; }; struct __pyx_fuse_1__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn { int __pyx_n; double dof; Py_ssize_t num_threads; int should_eval_error; }; /* "quad_tree.pxd":25 * * * cdef class QuadTree: # <<<<<<<<<<<<<< * cdef Node root * cpdef void add_points(self, double[:, ::1] points) */ struct __pyx_obj_8openTSNE_9quad_tree_QuadTree { PyObject_HEAD struct __pyx_vtabstruct_8openTSNE_9quad_tree_QuadTree *__pyx_vtab; __pyx_t_8openTSNE_9quad_tree_Node root; }; /* "View.MemoryView":105 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< * * cdef: */ struct __pyx_array_obj { PyObject_HEAD struct __pyx_vtabstruct_array *__pyx_vtab; char *data; Py_ssize_t len; char *format; int ndim; Py_ssize_t *_shape; Py_ssize_t *_strides; Py_ssize_t itemsize; PyObject *mode; PyObject *_format; void (*callback_free_data)(void *); int free_data; int dtype_is_object; }; /* "View.MemoryView":279 * * @cname('__pyx_MemviewEnum') * cdef class Enum(object): # <<<<<<<<<<<<<< * cdef object name * def __init__(self, name): */ struct __pyx_MemviewEnum_obj { PyObject_HEAD PyObject *name; }; /* "View.MemoryView":330 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< * * cdef object obj */ struct __pyx_memoryview_obj { PyObject_HEAD struct __pyx_vtabstruct_memoryview *__pyx_vtab; PyObject *obj; PyObject *_size; PyObject *_array_interface; PyThread_type_lock lock; __pyx_atomic_int acquisition_count[2]; __pyx_atomic_int *acquisition_count_aligned_p; Py_buffer view; int flags; int dtype_is_object; __Pyx_TypeInfo *typeinfo; }; /* "View.MemoryView":965 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< * "Internal class for passing memoryview slices to Python" * */ struct __pyx_memoryviewslice_obj { struct __pyx_memoryview_obj __pyx_base; __Pyx_memviewslice from_slice; PyObject *from_object; PyObject *(*to_object_func)(char *); int (*to_dtype_func)(char *, PyObject *); }; /* "quad_tree.pxd":25 * * * cdef class QuadTree: # <<<<<<<<<<<<<< * cdef Node root * cpdef void add_points(self, double[:, ::1] points) */ struct __pyx_vtabstruct_8openTSNE_9quad_tree_QuadTree { void (*add_points)(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *, __Pyx_memviewslice, int __pyx_skip_dispatch); void (*add_point)(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *, __Pyx_memviewslice, int __pyx_skip_dispatch); }; static struct __pyx_vtabstruct_8openTSNE_9quad_tree_QuadTree *__pyx_vtabptr_8openTSNE_9quad_tree_QuadTree; /* "View.MemoryView":105 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< * * cdef: */ struct __pyx_vtabstruct_array { PyObject *(*get_memview)(struct __pyx_array_obj *); }; static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; /* "View.MemoryView":330 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< * * cdef object obj */ struct __pyx_vtabstruct_memoryview { char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *); PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *); PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *); PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *); PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *); }; static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; /* "View.MemoryView":965 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< * "Internal class for passing memoryview slices to Python" * */ struct __pyx_vtabstruct__memoryviewslice { struct __pyx_vtabstruct_memoryview __pyx_base; }; static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice; /* --- Runtime support code (head) --- */ /* Refnanny.proto */ #ifndef CYTHON_REFNANNY #define CYTHON_REFNANNY 0 #endif #if CYTHON_REFNANNY typedef struct { void (*INCREF)(void*, PyObject*, int); void (*DECREF)(void*, PyObject*, int); void (*GOTREF)(void*, PyObject*, int); void (*GIVEREF)(void*, PyObject*, int); void* (*SetupContext)(const char*, int, const char*); void (*FinishContext)(void**); } __Pyx_RefNannyAPIStruct; static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; #ifdef WITH_THREAD #define __Pyx_RefNannySetupContext(name, acquire_gil)\ if (acquire_gil) {\ PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ PyGILState_Release(__pyx_gilstate_save);\ } else {\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ } #else #define __Pyx_RefNannySetupContext(name, acquire_gil)\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) #endif #define __Pyx_RefNannyFinishContext()\ __Pyx_RefNanny->FinishContext(&__pyx_refnanny) #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) #else #define __Pyx_RefNannyDeclarations #define __Pyx_RefNannySetupContext(name, acquire_gil) #define __Pyx_RefNannyFinishContext() #define __Pyx_INCREF(r) Py_INCREF(r) #define __Pyx_DECREF(r) Py_DECREF(r) #define __Pyx_GOTREF(r) #define __Pyx_GIVEREF(r) #define __Pyx_XINCREF(r) Py_XINCREF(r) #define __Pyx_XDECREF(r) Py_XDECREF(r) #define __Pyx_XGOTREF(r) #define __Pyx_XGIVEREF(r) #endif #define __Pyx_XDECREF_SET(r, v) do {\ PyObject *tmp = (PyObject *) r;\ r = v; __Pyx_XDECREF(tmp);\ } while (0) #define __Pyx_DECREF_SET(r, v) do {\ PyObject *tmp = (PyObject *) r;\ r = v; __Pyx_DECREF(tmp);\ } while (0) #define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) #define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) /* PyObjectGetAttrStr.proto */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) #endif /* GetBuiltinName.proto */ static PyObject *__Pyx_GetBuiltinName(PyObject *name); /* PyDictVersioning.proto */ #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS #define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) #define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) #define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ (version_var) = __PYX_GET_DICT_VERSION(dict);\ (cache_var) = (value); #define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ static PY_UINT64_T __pyx_dict_version = 0;\ static PyObject *__pyx_dict_cached_value = NULL;\ if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ (VAR) = __pyx_dict_cached_value;\ } else {\ (VAR) = __pyx_dict_cached_value = (LOOKUP);\ __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ }\ } static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); #else #define __PYX_GET_DICT_VERSION(dict) (0) #define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) #define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); #endif /* GetModuleGlobalName.proto */ #if CYTHON_USE_DICT_VERSIONS #define __Pyx_GetModuleGlobalName(var, name) {\ static PY_UINT64_T __pyx_dict_version = 0;\ static PyObject *__pyx_dict_cached_value = NULL;\ (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ } #define __Pyx_GetModuleGlobalNameUncached(var, name) {\ PY_UINT64_T __pyx_dict_version;\ PyObject *__pyx_dict_cached_value;\ (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ } static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); #else #define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) #define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); #endif /* PyObjectCall.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); #else #define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) #endif /* PyCFunctionFastCall.proto */ #if CYTHON_FAST_PYCCALL static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); #else #define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) #endif /* PyFunctionFastCall.proto */ #if CYTHON_FAST_PYCALL #define __Pyx_PyFunction_FastCall(func, args, nargs)\ __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) #if 1 || PY_VERSION_HEX < 0x030600B1 static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); #else #define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) #endif #define __Pyx_BUILD_ASSERT_EXPR(cond)\ (sizeof(char [1 - 2*!(cond)]) - 1) #ifndef Py_MEMBER_SIZE #define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) #endif static size_t __pyx_pyframe_localsplus_offset = 0; #include "frameobject.h" #define __Pxy_PyFrame_Initialize_Offsets()\ ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) #define __Pyx_PyFrame_GetLocalsplus(frame)\ (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) #endif /* PyObjectCall2Args.proto */ static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2); /* PyObjectCallMethO.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); #endif /* PyObjectCallOneArg.proto */ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); /* MemviewSliceInit.proto */ #define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d #define __Pyx_MEMVIEW_DIRECT 1 #define __Pyx_MEMVIEW_PTR 2 #define __Pyx_MEMVIEW_FULL 4 #define __Pyx_MEMVIEW_CONTIG 8 #define __Pyx_MEMVIEW_STRIDED 16 #define __Pyx_MEMVIEW_FOLLOW 32 #define __Pyx_IS_C_CONTIG 1 #define __Pyx_IS_F_CONTIG 2 static int __Pyx_init_memviewslice( struct __pyx_memoryview_obj *memview, int ndim, __Pyx_memviewslice *memviewslice, int memview_is_new_reference); static CYTHON_INLINE int __pyx_add_acquisition_count_locked( __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); #define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p) #define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview)) #define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) #define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__) static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int); /* RaiseArgTupleInvalid.proto */ static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /* RaiseDoubleKeywords.proto */ static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /* ParseKeywords.proto */ static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ const char* function_name); /* PyDictContains.proto */ static CYTHON_INLINE int __Pyx_PyDict_ContainsTF(PyObject* item, PyObject* dict, int eq) { int result = PyDict_Contains(dict, item); return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); } /* DictGetItem.proto */ #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key); #define __Pyx_PyObject_Dict_GetItem(obj, name)\ (likely(PyDict_CheckExact(obj)) ?\ __Pyx_PyDict_GetItem(obj, name) : PyObject_GetItem(obj, name)) #else #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) #define __Pyx_PyObject_Dict_GetItem(obj, name) PyObject_GetItem(obj, name) #endif /* PyThreadStateGet.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; #define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; #define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type #else #define __Pyx_PyThreadState_declare #define __Pyx_PyThreadState_assign #define __Pyx_PyErr_Occurred() PyErr_Occurred() #endif /* PyErrFetchRestore.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) #define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) #define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) #define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) #define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #if CYTHON_COMPILING_IN_CPYTHON #define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) #else #define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) #endif #else #define __Pyx_PyErr_Clear() PyErr_Clear() #define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) #define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) #define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) #define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) #endif /* RaiseException.proto */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /* UnicodeAsUCS4.proto */ static CYTHON_INLINE Py_UCS4 __Pyx_PyUnicode_AsPy_UCS4(PyObject*); /* object_ord.proto */ #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyObject_Ord(c)\ (likely(PyUnicode_Check(c)) ? (long)__Pyx_PyUnicode_AsPy_UCS4(c) : __Pyx__PyObject_Ord(c)) #else #define __Pyx_PyObject_Ord(c) __Pyx__PyObject_Ord(c) #endif static long __Pyx__PyObject_Ord(PyObject* c); /* SetItemInt.proto */ #define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\ (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\ __Pyx_SetItemInt_Generic(o, to_py_func(i), v))) static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v); static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list, int wraparound, int boundscheck); /* IterFinish.proto */ static CYTHON_INLINE int __Pyx_IterFinish(void); /* PyObjectCallNoArg.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); #else #define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL) #endif /* PyObjectGetMethod.proto */ static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method); /* PyObjectCallMethod0.proto */ static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name); /* RaiseNeedMoreValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); /* RaiseTooManyValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); /* UnpackItemEndCheck.proto */ static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); /* RaiseNoneIterError.proto */ static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); /* UnpackTupleError.proto */ static void __Pyx_UnpackTupleError(PyObject *, Py_ssize_t index); /* UnpackTuple2.proto */ #define __Pyx_unpack_tuple2(tuple, value1, value2, is_tuple, has_known_size, decref_tuple)\ (likely(is_tuple || PyTuple_Check(tuple)) ?\ (likely(has_known_size || PyTuple_GET_SIZE(tuple) == 2) ?\ __Pyx_unpack_tuple2_exact(tuple, value1, value2, decref_tuple) :\ (__Pyx_UnpackTupleError(tuple, 2), -1)) :\ __Pyx_unpack_tuple2_generic(tuple, value1, value2, has_known_size, decref_tuple)) static CYTHON_INLINE int __Pyx_unpack_tuple2_exact( PyObject* tuple, PyObject** value1, PyObject** value2, int decref_tuple); static int __Pyx_unpack_tuple2_generic( PyObject* tuple, PyObject** value1, PyObject** value2, int has_known_size, int decref_tuple); /* dict_iter.proto */ static CYTHON_INLINE PyObject* __Pyx_dict_iterator(PyObject* dict, int is_dict, PyObject* method_name, Py_ssize_t* p_orig_length, int* p_is_dict); static CYTHON_INLINE int __Pyx_dict_iter_next(PyObject* dict_or_iter, Py_ssize_t orig_length, Py_ssize_t* ppos, PyObject** pkey, PyObject** pvalue, PyObject** pitem, int is_dict); /* GetItemInt.proto */ #define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ __Pyx_GetItemInt_Generic(o, to_py_func(i)))) #define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); #define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, int wraparound, int boundscheck); /* ListAppend.proto */ #if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); __Pyx_SET_SIZE(list, len + 1); return 0; } return PyList_Append(list, x); } #else #define __Pyx_PyList_Append(L,x) PyList_Append(L,x) #endif /* WriteUnraisableException.proto */ static void __Pyx_WriteUnraisable(const char *name, int clineno, int lineno, const char *filename, int full_traceback, int nogil); /* ArgTypeTest.proto */ #define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\ __Pyx__ArgTypeTest(obj, type, name, exact)) static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); /* GetTopmostException.proto */ #if CYTHON_USE_EXC_INFO_STACK static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); #endif /* SaveResetException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); #else #define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) #define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) #endif /* PyErrExceptionMatches.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); #else #define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) #endif /* GetException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #else static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); #endif /* IncludeStringH.proto */ #include /* BytesEquals.proto */ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); /* UnicodeEquals.proto */ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); /* StrEquals.proto */ #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals #else #define __Pyx_PyString_Equals __Pyx_PyBytes_Equals #endif /* UnaryNegOverflows.proto */ #define UNARY_NEG_WOULD_OVERFLOW(x)\ (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ /* GetAttr.proto */ static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); /* ObjectGetItem.proto */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key); #else #define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) #endif /* decode_c_string_utf16.proto */ static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) { int byteorder = 0; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) { int byteorder = -1; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) { int byteorder = 1; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } /* decode_c_string.proto */ static CYTHON_INLINE PyObject* __Pyx_decode_c_string( const char* cstring, Py_ssize_t start, Py_ssize_t stop, const char* encoding, const char* errors, PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); /* GetAttr3.proto */ static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); /* ExtTypeTest.proto */ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /* SwapException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #else static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); #endif /* Import.proto */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /* FastTypeChecks.proto */ #if CYTHON_COMPILING_IN_CPYTHON #define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); #else #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) #define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) #define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) #endif #define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ /* ListCompAppend.proto */ #if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len)) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); __Pyx_SET_SIZE(list, len + 1); return 0; } return PyList_Append(list, x); } #else #define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) #endif /* PyIntBinop.proto */ #if !CYTHON_COMPILING_IN_PYPY static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check); #else #define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\ (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) #endif /* ListExtend.proto */ static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) { #if CYTHON_COMPILING_IN_CPYTHON PyObject* none = _PyList_Extend((PyListObject*)L, v); if (unlikely(!none)) return -1; Py_DECREF(none); return 0; #else return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v); #endif } /* None.proto */ static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); /* ImportFrom.proto */ static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); /* HasAttr.proto */ static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); /* PyObject_GenericGetAttrNoDict.proto */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr #endif /* PyObject_GenericGetAttr.proto */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr #endif /* SetVTable.proto */ static int __Pyx_SetVtable(PyObject *dict, void *vtable); /* PyObjectGetAttrStrNoError.proto */ static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name); /* SetupReduce.proto */ static int __Pyx_setup_reduce(PyObject* type_obj); /* TypeImport.proto */ #ifndef __PYX_HAVE_RT_ImportType_proto #define __PYX_HAVE_RT_ImportType_proto enum __Pyx_ImportType_CheckSize { __Pyx_ImportType_CheckSize_Error = 0, __Pyx_ImportType_CheckSize_Warn = 1, __Pyx_ImportType_CheckSize_Ignore = 2 }; static PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size); #endif /* GetVTable.proto */ static void* __Pyx_GetVtable(PyObject *dict); /* FetchCommonType.proto */ static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type); /* CythonFunctionShared.proto */ #define __Pyx_CyFunction_USED 1 #define __Pyx_CYFUNCTION_STATICMETHOD 0x01 #define __Pyx_CYFUNCTION_CLASSMETHOD 0x02 #define __Pyx_CYFUNCTION_CCLASS 0x04 #define __Pyx_CyFunction_GetClosure(f)\ (((__pyx_CyFunctionObject *) (f))->func_closure) #define __Pyx_CyFunction_GetClassObj(f)\ (((__pyx_CyFunctionObject *) (f))->func_classobj) #define __Pyx_CyFunction_Defaults(type, f)\ ((type *)(((__pyx_CyFunctionObject *) (f))->defaults)) #define __Pyx_CyFunction_SetDefaultsGetter(f, g)\ ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g) typedef struct { PyCFunctionObject func; #if PY_VERSION_HEX < 0x030500A0 PyObject *func_weakreflist; #endif PyObject *func_dict; PyObject *func_name; PyObject *func_qualname; PyObject *func_doc; PyObject *func_globals; PyObject *func_code; PyObject *func_closure; PyObject *func_classobj; void *defaults; int defaults_pyobjects; size_t defaults_size; // used by FusedFunction for copying defaults int flags; PyObject *defaults_tuple; PyObject *defaults_kwdict; PyObject *(*defaults_getter)(PyObject *); PyObject *func_annotations; } __pyx_CyFunctionObject; static PyTypeObject *__pyx_CyFunctionType = 0; #define __Pyx_CyFunction_Check(obj) (__Pyx_TypeCheck(obj, __pyx_CyFunctionType)) static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml, int flags, PyObject* qualname, PyObject *self, PyObject *module, PyObject *globals, PyObject* code); static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m, size_t size, int pyobjects); static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m, PyObject *tuple); static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m, PyObject *dict); static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m, PyObject *dict); static int __pyx_CyFunction_init(void); /* FusedFunction.proto */ typedef struct { __pyx_CyFunctionObject func; PyObject *__signatures__; PyObject *type; PyObject *self; } __pyx_FusedFunctionObject; static PyObject *__pyx_FusedFunction_New(PyMethodDef *ml, int flags, PyObject *qualname, PyObject *closure, PyObject *module, PyObject *globals, PyObject *code); static int __pyx_FusedFunction_clear(__pyx_FusedFunctionObject *self); static PyTypeObject *__pyx_FusedFunctionType = NULL; static int __pyx_FusedFunction_init(void); #define __Pyx_FusedFunction_USED /* CLineInTraceback.proto */ #ifdef CYTHON_CLINE_IN_TRACEBACK #define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) #else static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); #endif /* CodeObjectCache.proto */ typedef struct { PyCodeObject* code_object; int code_line; } __Pyx_CodeObjectCacheEntry; struct __Pyx_CodeObjectCache { int count; int max_count; __Pyx_CodeObjectCacheEntry* entries; }; static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); static PyCodeObject *__pyx_find_code_object(int code_line); static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); /* AddTraceback.proto */ static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename); #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); static void __Pyx_ReleaseBuffer(Py_buffer *view); #else #define __Pyx_GetBuffer PyObject_GetBuffer #define __Pyx_ReleaseBuffer PyBuffer_Release #endif /* BufferStructDeclare.proto */ typedef struct { Py_ssize_t shape, strides, suboffsets; } __Pyx_Buf_DimInfo; typedef struct { size_t refcount; Py_buffer pybuffer; } __Pyx_Buffer; typedef struct { __Pyx_Buffer *rcbuffer; char *data; __Pyx_Buf_DimInfo diminfo[8]; } __Pyx_LocalBuf_ND; /* MemviewSliceIsContig.proto */ static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); /* OverlappingSlices.proto */ static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, __Pyx_memviewslice *slice2, int ndim, size_t itemsize); /* Capsule.proto */ static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); /* IsLittleEndian.proto */ static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); /* BufferFormatCheck.proto */ static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type); /* TypeInfoCompare.proto */ static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); /* MemviewSliceValidateAndInit.proto */ static int __Pyx_ValidateAndInit_memviewslice( int *axes_specs, int c_or_f_flag, int buf_flags, int ndim, __Pyx_TypeInfo *dtype, __Pyx_BufFmt_StackElem stack[], __Pyx_memviewslice *memviewslice, PyObject *original_obj); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_int32_t(PyObject *, int writable_flag); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_int64_t(PyObject *, int writable_flag); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_double(PyObject *, int writable_flag); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *, int writable_flag); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(PyObject *, int writable_flag); /* GCCDiagnostics.proto */ #if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) #define __Pyx_HAS_GCC_DIAGNOSTIC #endif /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_double(PyObject *, int writable_flag); /* MemviewDtypeToObject.proto */ static CYTHON_INLINE PyObject *__pyx_memview_get_double(const char *itemp); static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *obj); /* None.proto */ static CYTHON_INLINE long __Pyx_pow_long(long, long); /* None.proto */ static CYTHON_INLINE Py_ssize_t __Pyx_pow_Py_ssize_t(Py_ssize_t, Py_ssize_t); /* RealImag.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus #define __Pyx_CREAL(z) ((z).real()) #define __Pyx_CIMAG(z) ((z).imag()) #else #define __Pyx_CREAL(z) (__real__(z)) #define __Pyx_CIMAG(z) (__imag__(z)) #endif #else #define __Pyx_CREAL(z) ((z).real) #define __Pyx_CIMAG(z) ((z).imag) #endif #if defined(__cplusplus) && CYTHON_CCOMPLEX\ && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) #define __Pyx_SET_CREAL(z,x) ((z).real(x)) #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) #else #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) #endif /* Arithmetic.proto */ #if CYTHON_CCOMPLEX #define __Pyx_c_eq_float(a, b) ((a)==(b)) #define __Pyx_c_sum_float(a, b) ((a)+(b)) #define __Pyx_c_diff_float(a, b) ((a)-(b)) #define __Pyx_c_prod_float(a, b) ((a)*(b)) #define __Pyx_c_quot_float(a, b) ((a)/(b)) #define __Pyx_c_neg_float(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero_float(z) ((z)==(float)0) #define __Pyx_c_conj_float(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs_float(z) (::std::abs(z)) #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero_float(z) ((z)==0) #define __Pyx_c_conj_float(z) (conjf(z)) #if 1 #define __Pyx_c_abs_float(z) (cabsf(z)) #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); #if 1 static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); #endif #endif /* Arithmetic.proto */ #if CYTHON_CCOMPLEX #define __Pyx_c_eq_double(a, b) ((a)==(b)) #define __Pyx_c_sum_double(a, b) ((a)+(b)) #define __Pyx_c_diff_double(a, b) ((a)-(b)) #define __Pyx_c_prod_double(a, b) ((a)*(b)) #define __Pyx_c_quot_double(a, b) ((a)/(b)) #define __Pyx_c_neg_double(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero_double(z) ((z)==(double)0) #define __Pyx_c_conj_double(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs_double(z) (::std::abs(z)) #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero_double(z) ((z)==0) #define __Pyx_c_conj_double(z) (conj(z)) #if 1 #define __Pyx_c_abs_double(z) (cabs(z)) #define __Pyx_c_pow_double(a, b) (cpow(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); #if 1 static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); #endif #endif /* MemviewSliceCopyTemplate.proto */ static __Pyx_memviewslice __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, const char *mode, int ndim, size_t sizeof_dtype, int contig_flag, int dtype_is_object); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int32(npy_int32 value); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int64(npy_int64 value); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); /* CIntFromPy.proto */ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); /* BytesContains.proto */ static CYTHON_INLINE int __Pyx_BytesContains(PyObject* bytes, char character); /* ImportNumPyArray.proto */ static PyObject *__pyx_numpy_ndarray = NULL; static PyObject* __Pyx_ImportNumPyArrayTypeIfAvailable(void); /* CIntFromPy.proto */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); /* CIntFromPy.proto */ static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_double(PyObject *, int writable_flag); /* CheckBinaryVersion.proto */ static int __Pyx_check_binary_version(void); /* FunctionExport.proto */ static int __Pyx_ExportFunction(const char *name, void (*f)(void), const char *sig); /* VoidPtrImport.proto */ static int __Pyx_ImportVoidPtr(PyObject *module, const char *name, void **p, const char *sig); /* FunctionImport.proto */ static int __Pyx_ImportFunction(PyObject *module, const char *funcname, void (**f)(void), const char *sig); /* InitStrings.proto */ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ /* Module declarations from 'cpython.buffer' */ /* Module declarations from 'libc.string' */ /* Module declarations from 'libc.stdio' */ /* Module declarations from '__builtin__' */ /* Module declarations from 'cpython.type' */ static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; /* Module declarations from 'cpython' */ /* Module declarations from 'cpython.object' */ /* Module declarations from 'cpython.ref' */ /* Module declarations from 'cpython.mem' */ /* Module declarations from 'numpy' */ /* Module declarations from 'numpy' */ static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; /* Module declarations from 'openTSNE.quad_tree' */ static PyTypeObject *__pyx_ptype_8openTSNE_9quad_tree_QuadTree = 0; static double *__pyx_vp_8openTSNE_9quad_tree_EPSILON = 0; #define __pyx_v_8openTSNE_9quad_tree_EPSILON (*__pyx_vp_8openTSNE_9quad_tree_EPSILON) static int (*__pyx_f_8openTSNE_9quad_tree_is_duplicate)(__pyx_t_8openTSNE_9quad_tree_Node *, double *, struct __pyx_opt_args_8openTSNE_9quad_tree_is_duplicate *__pyx_optional_args); /*proto*/ /* Module declarations from 'libc.stdlib' */ /* Module declarations from 'openTSNE._matrix_mul.matrix_mul' */ static void (*__pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_1d)(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice); /*proto*/ static void (*__pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_2d)(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice); /*proto*/ /* Module declarations from 'openTSNE._tsne' */ static PyTypeObject *__pyx_array_type = 0; static PyTypeObject *__pyx_MemviewEnum_type = 0; static PyTypeObject *__pyx_memoryview_type = 0; static PyTypeObject *__pyx_memoryviewslice_type = 0; static double __pyx_v_8openTSNE_5_tsne_EPSILON; static PyObject *generic = 0; static PyObject *strided = 0; static PyObject *indirect = 0; static PyObject *contiguous = 0; static PyObject *indirect_contiguous = 0; static int __pyx_memoryview_thread_locks_used; static PyThread_type_lock __pyx_memoryview_thread_locks[8]; static __Pyx_memviewslice __pyx_f_8openTSNE_5_tsne_compute_gaussian_perplexity(__Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_compute_gaussian_perplexity *__pyx_optional_args); /*proto*/ static double __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_bh(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_bh *__pyx_optional_args); /*proto*/ static double __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d(__Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d *__pyx_optional_args); /*proto*/ static PyObject *__pyx_f_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_1d(__Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_1d *__pyx_optional_args); /*proto*/ static double __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d_with_grid(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, Py_ssize_t, double, int __pyx_skip_dispatch); /*proto*/ static double __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d(__Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d *__pyx_optional_args); /*proto*/ static PyObject *__pyx_f_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_2d(__Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_2d *__pyx_optional_args); /*proto*/ static double __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d_with_grid(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, Py_ssize_t, double, int __pyx_skip_dispatch); /*proto*/ static void __pyx_f_8openTSNE_5_tsne__estimate_negative_gradient_single(__pyx_t_8openTSNE_9quad_tree_Node *, double *, double *, double *, double, double); /*proto*/ static CYTHON_INLINE double __pyx_f_8openTSNE_5_tsne_cauchy_1d(double, double, double); /*proto*/ static CYTHON_INLINE double __pyx_f_8openTSNE_5_tsne_cauchy_1d_exp1p(double, double, double); /*proto*/ static CYTHON_INLINE double __pyx_f_8openTSNE_5_tsne_cauchy_2d(double, double, double, double, double); /*proto*/ static CYTHON_INLINE double __pyx_f_8openTSNE_5_tsne_cauchy_2d_exp1p(double, double, double, double, double); /*proto*/ static __Pyx_memviewslice __pyx_f_8openTSNE_5_tsne_interpolate(__Pyx_memviewslice, __Pyx_memviewslice); /*proto*/ static __Pyx_memviewslice __pyx_f_8openTSNE_5_tsne_compute_kernel_tilde_1d(double (*)(double, double, double), Py_ssize_t, double, double, double); /*proto*/ static __Pyx_memviewslice __pyx_f_8openTSNE_5_tsne_compute_kernel_tilde_2d(double (*)(double, double, double, double, double), Py_ssize_t, double, double, double); /*proto*/ static PyObject *__pyx_fuse_0__pyx_f_8openTSNE_5_tsne_estimate_positive_gradient_nn(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_fuse_0__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn *__pyx_optional_args); /*proto*/ static PyObject *__pyx_fuse_1__pyx_f_8openTSNE_5_tsne_estimate_positive_gradient_nn(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_fuse_1__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn *__pyx_optional_args); /*proto*/ static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ static void *__pyx_align_pointer(void *, size_t); /*proto*/ static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ static PyObject *_unellipsify(PyObject *, int); /*proto*/ static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_int32_t = { "int32_t", NULL, sizeof(__pyx_t_5numpy_int32_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_int32_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_int32_t), 0 }; static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_int64_t = { "int64_t", NULL, sizeof(__pyx_t_5numpy_int64_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_int64_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_int64_t), 0 }; static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; #define __Pyx_MODULE_NAME "openTSNE._tsne" extern int __pyx_module_is_main_openTSNE___tsne; int __pyx_module_is_main_openTSNE___tsne = 0; /* Implementation of 'openTSNE._tsne' */ static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_TypeError; static PyObject *__pyx_builtin_MemoryError; static PyObject *__pyx_builtin_ImportError; static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_enumerate; static PyObject *__pyx_builtin_Ellipsis; static PyObject *__pyx_builtin_id; static PyObject *__pyx_builtin_IndexError; static const char __pyx_k_[] = "()"; static const char __pyx_k_C[] = "C"; static const char __pyx_k_O[] = "O"; static const char __pyx_k_c[] = "c"; static const char __pyx_k_s[] = "s"; static const char __pyx_k__2[] = "|"; static const char __pyx_k_id[] = "id"; static const char __pyx_k_np[] = "np"; static const char __pyx_k_dof[] = "dof"; static const char __pyx_k_eps[] = "eps"; static const char __pyx_k_log[] = "log"; static const char __pyx_k_new[] = "__new__"; static const char __pyx_k_obj[] = "obj"; static const char __pyx_k_args[] = "args"; static const char __pyx_k_base[] = "base"; static const char __pyx_k_dict[] = "__dict__"; static const char __pyx_k_kind[] = "kind"; static const char __pyx_k_main[] = "__main__"; static const char __pyx_k_mode[] = "mode"; static const char __pyx_k_name[] = "name"; static const char __pyx_k_ndim[] = "ndim"; static const char __pyx_k_ones[] = "ones"; static const char __pyx_k_pack[] = "pack"; static const char __pyx_k_size[] = "size"; static const char __pyx_k_step[] = "step"; static const char __pyx_k_stop[] = "stop"; static const char __pyx_k_test[] = "__test__"; static const char __pyx_k_tree[] = "tree"; static const char __pyx_k_ASCII[] = "ASCII"; static const char __pyx_k_class[] = "__class__"; static const char __pyx_k_dtype[] = "dtype"; static const char __pyx_k_empty[] = "empty"; static const char __pyx_k_error[] = "error"; static const char __pyx_k_finfo[] = "finfo"; static const char __pyx_k_flags[] = "flags"; static const char __pyx_k_numpy[] = "numpy"; static const char __pyx_k_order[] = "order"; static const char __pyx_k_range[] = "range"; static const char __pyx_k_shape[] = "shape"; static const char __pyx_k_split[] = "split"; static const char __pyx_k_start[] = "start"; static const char __pyx_k_strip[] = "strip"; static const char __pyx_k_theta[] = "theta"; static const char __pyx_k_zeros[] = "zeros"; static const char __pyx_k_P_data[] = "P_data"; static const char __pyx_k_encode[] = "encode"; static const char __pyx_k_format[] = "format"; static const char __pyx_k_import[] = "__import__"; static const char __pyx_k_indptr[] = "indptr"; static const char __pyx_k_kwargs[] = "kwargs"; static const char __pyx_k_name_2[] = "__name__"; static const char __pyx_k_pickle[] = "pickle"; static const char __pyx_k_reduce[] = "__reduce__"; static const char __pyx_k_struct[] = "struct"; static const char __pyx_k_unpack[] = "unpack"; static const char __pyx_k_update[] = "update"; static const char __pyx_k_asarray[] = "asarray"; static const char __pyx_k_float64[] = "float64"; static const char __pyx_k_fortran[] = "fortran"; static const char __pyx_k_indices[] = "indices"; static const char __pyx_k_int32_t[] = "int32_t"; static const char __pyx_k_int64_t[] = "int64_t"; static const char __pyx_k_memview[] = "memview"; static const char __pyx_k_padding[] = "padding"; static const char __pyx_k_Ellipsis[] = "Ellipsis"; static const char __pyx_k_defaults[] = "defaults"; static const char __pyx_k_getstate[] = "__getstate__"; static const char __pyx_k_gradient[] = "gradient"; static const char __pyx_k_itemsize[] = "itemsize"; static const char __pyx_k_max_iter[] = "max_iter"; static const char __pyx_k_pyx_type[] = "__pyx_type"; static const char __pyx_k_setstate[] = "__setstate__"; static const char __pyx_k_TypeError[] = "TypeError"; static const char __pyx_k_distances[] = "distances"; static const char __pyx_k_embedding[] = "embedding"; static const char __pyx_k_enumerate[] = "enumerate"; static const char __pyx_k_pyx_state[] = "__pyx_state"; static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; static const char __pyx_k_IndexError[] = "IndexError"; static const char __pyx_k_ValueError[] = "ValueError"; static const char __pyx_k_pyx_result[] = "__pyx_result"; static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; static const char __pyx_k_signatures[] = "signatures"; static const char __pyx_k_zeros_like[] = "zeros_like"; static const char __pyx_k_ImportError[] = "ImportError"; static const char __pyx_k_MemoryError[] = "MemoryError"; static const char __pyx_k_PickleError[] = "PickleError"; static const char __pyx_k_num_threads[] = "num_threads"; static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; static const char __pyx_k_stringsource[] = "stringsource"; static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; static const char __pyx_k_openTSNE__tsne[] = "openTSNE._tsne"; static const char __pyx_k_perplexity_tol[] = "perplexity_tol"; static const char __pyx_k_y_tilde_values[] = "y_tilde_values"; static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; static const char __pyx_k_box_lower_bounds[] = "box_lower_bounds"; static const char __pyx_k_ints_in_interval[] = "ints_in_interval"; static const char __pyx_k_min_num_intervals[] = "min_num_intervals"; static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; static const char __pyx_k_should_eval_error[] = "should_eval_error"; static const char __pyx_k_box_x_lower_bounds[] = "box_x_lower_bounds"; static const char __pyx_k_box_y_lower_bounds[] = "box_y_lower_bounds"; static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; static const char __pyx_k_openTSNE__tsne_pyx[] = "openTSNE/_tsne.pyx"; static const char __pyx_k_strided_and_direct[] = ""; static const char __pyx_k_reference_embedding[] = "reference_embedding"; static const char __pyx_k_desired_perplexities[] = "desired_perplexities"; static const char __pyx_k_strided_and_indirect[] = ""; static const char __pyx_k_contiguous_and_direct[] = ""; static const char __pyx_k_MemoryView_of_r_object[] = ""; static const char __pyx_k_n_interpolation_points[] = "n_interpolation_points"; static const char __pyx_k_pairwise_normalization[] = "pairwise_normalization"; static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; static const char __pyx_k_contiguous_and_indirect[] = ""; static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'"; static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; static const char __pyx_k_No_matching_signature_found[] = "No matching signature found"; static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; static const char __pyx_k_estimate_positive_gradient_nn[] = "estimate_positive_gradient_nn"; static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; static const char __pyx_k_pyx_fuse_0estimate_positive_gr[] = "__pyx_fuse_0estimate_positive_gradient_nn"; static const char __pyx_k_pyx_fuse_1estimate_positive_gr[] = "__pyx_fuse_1estimate_positive_gradient_nn"; static const char __pyx_k_strided_and_direct_or_indirect[] = ""; static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; static const char __pyx_k_Expected_at_least_d_argument_s_g[] = "Expected at least %d argument%s, got %d"; static const char __pyx_k_Function_call_with_ambiguous_arg[] = "Function call with ambiguous argument types"; static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))"; static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; static PyObject *__pyx_kp_s_; static PyObject *__pyx_n_s_ASCII; static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; static PyObject *__pyx_n_u_C; static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; static PyObject *__pyx_kp_s_Cannot_index_with_type_s; static PyObject *__pyx_n_s_Ellipsis; static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; static PyObject *__pyx_kp_s_Expected_at_least_d_argument_s_g; static PyObject *__pyx_kp_s_Function_call_with_ambiguous_arg; static PyObject *__pyx_n_s_ImportError; static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0; static PyObject *__pyx_n_s_IndexError; static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; static PyObject *__pyx_n_s_MemoryError; static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; static PyObject *__pyx_kp_s_MemoryView_of_r_object; static PyObject *__pyx_kp_s_No_matching_signature_found; static PyObject *__pyx_n_b_O; static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; static PyObject *__pyx_n_s_P_data; static PyObject *__pyx_n_s_PickleError; static PyObject *__pyx_n_s_TypeError; static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; static PyObject *__pyx_n_s_ValueError; static PyObject *__pyx_n_s_View_MemoryView; static PyObject *__pyx_kp_s__2; static PyObject *__pyx_n_s_allocate_buffer; static PyObject *__pyx_n_s_args; static PyObject *__pyx_n_s_asarray; static PyObject *__pyx_n_s_base; static PyObject *__pyx_n_s_box_lower_bounds; static PyObject *__pyx_n_s_box_x_lower_bounds; static PyObject *__pyx_n_s_box_y_lower_bounds; static PyObject *__pyx_n_s_c; static PyObject *__pyx_n_u_c; static PyObject *__pyx_n_s_class; static PyObject *__pyx_n_s_cline_in_traceback; static PyObject *__pyx_kp_s_contiguous_and_direct; static PyObject *__pyx_kp_s_contiguous_and_indirect; static PyObject *__pyx_n_s_defaults; static PyObject *__pyx_n_s_desired_perplexities; static PyObject *__pyx_n_s_dict; static PyObject *__pyx_n_s_distances; static PyObject *__pyx_n_s_dof; static PyObject *__pyx_n_s_dtype; static PyObject *__pyx_n_s_dtype_is_object; static PyObject *__pyx_n_s_embedding; static PyObject *__pyx_n_s_empty; static PyObject *__pyx_n_s_encode; static PyObject *__pyx_n_s_enumerate; static PyObject *__pyx_n_s_eps; static PyObject *__pyx_n_s_error; static PyObject *__pyx_n_s_estimate_positive_gradient_nn; static PyObject *__pyx_n_s_finfo; static PyObject *__pyx_n_s_flags; static PyObject *__pyx_n_s_float64; static PyObject *__pyx_n_s_format; static PyObject *__pyx_n_s_fortran; static PyObject *__pyx_n_u_fortran; static PyObject *__pyx_n_s_getstate; static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi; static PyObject *__pyx_n_s_gradient; static PyObject *__pyx_n_s_id; static PyObject *__pyx_n_s_import; static PyObject *__pyx_n_s_indices; static PyObject *__pyx_n_s_indptr; static PyObject *__pyx_n_s_int32_t; static PyObject *__pyx_n_s_int64_t; static PyObject *__pyx_n_s_ints_in_interval; static PyObject *__pyx_n_s_itemsize; static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; static PyObject *__pyx_n_s_kind; static PyObject *__pyx_n_s_kwargs; static PyObject *__pyx_n_s_log; static PyObject *__pyx_n_s_main; static PyObject *__pyx_n_s_max_iter; static PyObject *__pyx_n_s_memview; static PyObject *__pyx_n_s_min_num_intervals; static PyObject *__pyx_n_s_mode; static PyObject *__pyx_n_s_n_interpolation_points; static PyObject *__pyx_n_s_name; static PyObject *__pyx_n_s_name_2; static PyObject *__pyx_n_s_ndim; static PyObject *__pyx_n_s_new; static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; static PyObject *__pyx_n_s_np; static PyObject *__pyx_n_s_num_threads; static PyObject *__pyx_n_s_numpy; static PyObject *__pyx_kp_u_numpy_core_multiarray_failed_to; static PyObject *__pyx_kp_u_numpy_core_umath_failed_to_impor; static PyObject *__pyx_n_s_obj; static PyObject *__pyx_n_s_ones; static PyObject *__pyx_n_s_openTSNE__tsne; static PyObject *__pyx_kp_s_openTSNE__tsne_pyx; static PyObject *__pyx_n_s_order; static PyObject *__pyx_n_s_pack; static PyObject *__pyx_n_s_padding; static PyObject *__pyx_n_s_pairwise_normalization; static PyObject *__pyx_n_s_perplexity_tol; static PyObject *__pyx_n_s_pickle; static PyObject *__pyx_n_s_pyx_PickleError; static PyObject *__pyx_n_s_pyx_checksum; static PyObject *__pyx_n_s_pyx_fuse_0estimate_positive_gr; static PyObject *__pyx_n_s_pyx_fuse_1estimate_positive_gr; static PyObject *__pyx_n_s_pyx_getbuffer; static PyObject *__pyx_n_s_pyx_result; static PyObject *__pyx_n_s_pyx_state; static PyObject *__pyx_n_s_pyx_type; static PyObject *__pyx_n_s_pyx_unpickle_Enum; static PyObject *__pyx_n_s_pyx_vtable; static PyObject *__pyx_n_s_range; static PyObject *__pyx_n_s_reduce; static PyObject *__pyx_n_s_reduce_cython; static PyObject *__pyx_n_s_reduce_ex; static PyObject *__pyx_n_s_reference_embedding; static PyObject *__pyx_n_s_s; static PyObject *__pyx_n_s_setstate; static PyObject *__pyx_n_s_setstate_cython; static PyObject *__pyx_n_s_shape; static PyObject *__pyx_n_s_should_eval_error; static PyObject *__pyx_n_s_signatures; static PyObject *__pyx_n_s_size; static PyObject *__pyx_n_s_split; static PyObject *__pyx_n_s_start; static PyObject *__pyx_n_s_step; static PyObject *__pyx_n_s_stop; static PyObject *__pyx_kp_s_strided_and_direct; static PyObject *__pyx_kp_s_strided_and_direct_or_indirect; static PyObject *__pyx_kp_s_strided_and_indirect; static PyObject *__pyx_kp_s_stringsource; static PyObject *__pyx_n_s_strip; static PyObject *__pyx_n_s_struct; static PyObject *__pyx_n_s_test; static PyObject *__pyx_n_s_theta; static PyObject *__pyx_n_s_tree; static PyObject *__pyx_kp_s_unable_to_allocate_array_data; static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; static PyObject *__pyx_n_s_unpack; static PyObject *__pyx_n_s_update; static PyObject *__pyx_n_s_y_tilde_values; static PyObject *__pyx_n_s_zeros; static PyObject *__pyx_n_s_zeros_like; static PyObject *__pyx_pf_8openTSNE_5_tsne_compute_gaussian_perplexity(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_distances, __Pyx_memviewslice __pyx_v_desired_perplexities, double __pyx_v_perplexity_tol, Py_ssize_t __pyx_v_max_iter, Py_ssize_t __pyx_v_num_threads); /* proto */ static PyObject *__pyx_pf_8openTSNE_5_tsne_2estimate_positive_gradient_nn(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_signatures, PyObject *__pyx_v_args, PyObject *__pyx_v_kwargs, CYTHON_UNUSED PyObject *__pyx_v_defaults); /* proto */ static PyObject *__pyx_pf_8openTSNE_5_tsne_18__pyx_fuse_0estimate_positive_gradient_nn(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_indices, __Pyx_memviewslice __pyx_v_indptr, __Pyx_memviewslice __pyx_v_P_data, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_reference_embedding, __Pyx_memviewslice __pyx_v_gradient, double __pyx_v_dof, Py_ssize_t __pyx_v_num_threads, int __pyx_v_should_eval_error); /* proto */ static PyObject *__pyx_pf_8openTSNE_5_tsne_20__pyx_fuse_1estimate_positive_gradient_nn(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_indices, __Pyx_memviewslice __pyx_v_indptr, __Pyx_memviewslice __pyx_v_P_data, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_reference_embedding, __Pyx_memviewslice __pyx_v_gradient, double __pyx_v_dof, Py_ssize_t __pyx_v_num_threads, int __pyx_v_should_eval_error); /* proto */ static PyObject *__pyx_pf_8openTSNE_5_tsne_4estimate_negative_gradient_bh(CYTHON_UNUSED PyObject *__pyx_self, struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_tree, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, double __pyx_v_theta, double __pyx_v_dof, Py_ssize_t __pyx_v_num_threads, int __pyx_v_pairwise_normalization); /* proto */ static PyObject *__pyx_pf_8openTSNE_5_tsne_6estimate_negative_gradient_fft_1d(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, Py_ssize_t __pyx_v_n_interpolation_points, Py_ssize_t __pyx_v_min_num_intervals, double __pyx_v_ints_in_interval, double __pyx_v_dof); /* proto */ static PyObject *__pyx_pf_8openTSNE_5_tsne_8prepare_negative_gradient_fft_interpolation_grid_1d(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_reference_embedding, Py_ssize_t __pyx_v_n_interpolation_points, Py_ssize_t __pyx_v_min_num_intervals, double __pyx_v_ints_in_interval, double __pyx_v_dof, double __pyx_v_padding); /* proto */ static PyObject *__pyx_pf_8openTSNE_5_tsne_10estimate_negative_gradient_fft_1d_with_grid(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, __Pyx_memviewslice __pyx_v_y_tilde_values, __Pyx_memviewslice __pyx_v_box_lower_bounds, Py_ssize_t __pyx_v_n_interpolation_points, double __pyx_v_dof); /* proto */ static PyObject *__pyx_pf_8openTSNE_5_tsne_12estimate_negative_gradient_fft_2d(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, Py_ssize_t __pyx_v_n_interpolation_points, Py_ssize_t __pyx_v_min_num_intervals, double __pyx_v_ints_in_interval, double __pyx_v_dof); /* proto */ static PyObject *__pyx_pf_8openTSNE_5_tsne_14prepare_negative_gradient_fft_interpolation_grid_2d(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_reference_embedding, Py_ssize_t __pyx_v_n_interpolation_points, Py_ssize_t __pyx_v_min_num_intervals, double __pyx_v_ints_in_interval, double __pyx_v_dof, double __pyx_v_padding); /* proto */ static PyObject *__pyx_pf_8openTSNE_5_tsne_16estimate_negative_gradient_fft_2d_with_grid(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, __Pyx_memviewslice __pyx_v_y_tilde_values, __Pyx_memviewslice __pyx_v_box_x_lower_bounds, __Pyx_memviewslice __pyx_v_box_y_lower_bounds, Py_ssize_t __pyx_v_n_interpolation_points, double __pyx_v_dof); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */ static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_int_0; static PyObject *__pyx_int_1; static PyObject *__pyx_int_6; static PyObject *__pyx_int_20; static PyObject *__pyx_int_21; static PyObject *__pyx_int_22; static PyObject *__pyx_int_24; static PyObject *__pyx_int_25; static PyObject *__pyx_int_26; static PyObject *__pyx_int_27; static PyObject *__pyx_int_28; static PyObject *__pyx_int_30; static PyObject *__pyx_int_32; static PyObject *__pyx_int_33; static PyObject *__pyx_int_35; static PyObject *__pyx_int_36; static PyObject *__pyx_int_39; static PyObject *__pyx_int_40; static PyObject *__pyx_int_42; static PyObject *__pyx_int_44; static PyObject *__pyx_int_45; static PyObject *__pyx_int_48; static PyObject *__pyx_int_49; static PyObject *__pyx_int_50; static PyObject *__pyx_int_52; static PyObject *__pyx_int_54; static PyObject *__pyx_int_55; static PyObject *__pyx_int_56; static PyObject *__pyx_int_60; static PyObject *__pyx_int_63; static PyObject *__pyx_int_64; static PyObject *__pyx_int_65; static PyObject *__pyx_int_66; static PyObject *__pyx_int_70; static PyObject *__pyx_int_72; static PyObject *__pyx_int_75; static PyObject *__pyx_int_77; static PyObject *__pyx_int_78; static PyObject *__pyx_int_80; static PyObject *__pyx_int_81; static PyObject *__pyx_int_84; static PyObject *__pyx_int_88; static PyObject *__pyx_int_90; static PyObject *__pyx_int_91; static PyObject *__pyx_int_96; static PyObject *__pyx_int_98; static PyObject *__pyx_int_99; static PyObject *__pyx_int_100; static PyObject *__pyx_int_104; static PyObject *__pyx_int_105; static PyObject *__pyx_int_108; static PyObject *__pyx_int_110; static PyObject *__pyx_int_112; static PyObject *__pyx_int_117; static PyObject *__pyx_int_120; static PyObject *__pyx_int_125; static PyObject *__pyx_int_126; static PyObject *__pyx_int_128; static PyObject *__pyx_int_130; static PyObject *__pyx_int_132; static PyObject *__pyx_int_135; static PyObject *__pyx_int_140; static PyObject *__pyx_int_144; static PyObject *__pyx_int_147; static PyObject *__pyx_int_150; static PyObject *__pyx_int_154; static PyObject *__pyx_int_156; static PyObject *__pyx_int_160; static PyObject *__pyx_int_162; static PyObject *__pyx_int_165; static PyObject *__pyx_int_168; static PyObject *__pyx_int_175; static PyObject *__pyx_int_176; static PyObject *__pyx_int_180; static PyObject *__pyx_int_182; static PyObject *__pyx_int_189; static PyObject *__pyx_int_192; static PyObject *__pyx_int_195; static PyObject *__pyx_int_196; static PyObject *__pyx_int_198; static PyObject *__pyx_int_200; static PyObject *__pyx_int_208; static PyObject *__pyx_int_210; static PyObject *__pyx_int_216; static PyObject *__pyx_int_220; static PyObject *__pyx_int_224; static PyObject *__pyx_int_225; static PyObject *__pyx_int_231; static PyObject *__pyx_int_234; static PyObject *__pyx_int_240; static PyObject *__pyx_int_243; static PyObject *__pyx_int_245; static PyObject *__pyx_int_250; static PyObject *__pyx_int_252; static PyObject *__pyx_int_256; static PyObject *__pyx_int_260; static PyObject *__pyx_int_264; static PyObject *__pyx_int_270; static PyObject *__pyx_int_273; static PyObject *__pyx_int_275; static PyObject *__pyx_int_280; static PyObject *__pyx_int_288; static PyObject *__pyx_int_294; static PyObject *__pyx_int_297; static PyObject *__pyx_int_300; static PyObject *__pyx_int_308; static PyObject *__pyx_int_312; static PyObject *__pyx_int_315; static PyObject *__pyx_int_320; static PyObject *__pyx_int_324; static PyObject *__pyx_int_325; static PyObject *__pyx_int_330; static PyObject *__pyx_int_336; static PyObject *__pyx_int_343; static PyObject *__pyx_int_350; static PyObject *__pyx_int_351; static PyObject *__pyx_int_352; static PyObject *__pyx_int_360; static PyObject *__pyx_int_364; static PyObject *__pyx_int_375; static PyObject *__pyx_int_378; static PyObject *__pyx_int_384; static PyObject *__pyx_int_385; static PyObject *__pyx_int_390; static PyObject *__pyx_int_392; static PyObject *__pyx_int_396; static PyObject *__pyx_int_400; static PyObject *__pyx_int_405; static PyObject *__pyx_int_416; static PyObject *__pyx_int_420; static PyObject *__pyx_int_432; static PyObject *__pyx_int_440; static PyObject *__pyx_int_441; static PyObject *__pyx_int_448; static PyObject *__pyx_int_450; static PyObject *__pyx_int_455; static PyObject *__pyx_int_462; static PyObject *__pyx_int_468; static PyObject *__pyx_int_480; static PyObject *__pyx_int_486; static PyObject *__pyx_int_490; static PyObject *__pyx_int_495; static PyObject *__pyx_int_500; static PyObject *__pyx_int_504; static PyObject *__pyx_int_512; static PyObject *__pyx_int_520; static PyObject *__pyx_int_525; static PyObject *__pyx_int_528; static PyObject *__pyx_int_539; static PyObject *__pyx_int_540; static PyObject *__pyx_int_546; static PyObject *__pyx_int_550; static PyObject *__pyx_int_560; static PyObject *__pyx_int_567; static PyObject *__pyx_int_576; static PyObject *__pyx_int_585; static PyObject *__pyx_int_588; static PyObject *__pyx_int_594; static PyObject *__pyx_int_600; static PyObject *__pyx_int_616; static PyObject *__pyx_int_624; static PyObject *__pyx_int_625; static PyObject *__pyx_int_630; static PyObject *__pyx_int_637; static PyObject *__pyx_int_640; static PyObject *__pyx_int_648; static PyObject *__pyx_int_650; static PyObject *__pyx_int_660; static PyObject *__pyx_int_672; static PyObject *__pyx_int_675; static PyObject *__pyx_int_686; static PyObject *__pyx_int_693; static PyObject *__pyx_int_700; static PyObject *__pyx_int_702; static PyObject *__pyx_int_704; static PyObject *__pyx_int_720; static PyObject *__pyx_int_728; static PyObject *__pyx_int_729; static PyObject *__pyx_int_735; static PyObject *__pyx_int_750; static PyObject *__pyx_int_756; static PyObject *__pyx_int_768; static PyObject *__pyx_int_770; static PyObject *__pyx_int_780; static PyObject *__pyx_int_784; static PyObject *__pyx_int_792; static PyObject *__pyx_int_800; static PyObject *__pyx_int_810; static PyObject *__pyx_int_819; static PyObject *__pyx_int_825; static PyObject *__pyx_int_832; static PyObject *__pyx_int_840; static PyObject *__pyx_int_864; static PyObject *__pyx_int_875; static PyObject *__pyx_int_880; static PyObject *__pyx_int_882; static PyObject *__pyx_int_891; static PyObject *__pyx_int_896; static PyObject *__pyx_int_900; static PyObject *__pyx_int_910; static PyObject *__pyx_int_924; static PyObject *__pyx_int_936; static PyObject *__pyx_int_945; static PyObject *__pyx_int_960; static PyObject *__pyx_int_972; static PyObject *__pyx_int_975; static PyObject *__pyx_int_980; static PyObject *__pyx_int_990; static PyObject *__pyx_int_1000; static PyObject *__pyx_int_184977713; static PyObject *__pyx_int_neg_1; static double __pyx_k__5; static Py_ssize_t __pyx_k__6; static int __pyx_k__7; static double __pyx_k__8; static Py_ssize_t __pyx_k__9; static int __pyx_k__10; static PyObject *__pyx_tuple__3; static PyObject *__pyx_tuple__4; static PyObject *__pyx_slice__27; static PyObject *__pyx_tuple__11; static PyObject *__pyx_tuple__12; static PyObject *__pyx_tuple__13; static PyObject *__pyx_tuple__14; static PyObject *__pyx_tuple__15; static PyObject *__pyx_tuple__16; static PyObject *__pyx_tuple__17; static PyObject *__pyx_tuple__18; static PyObject *__pyx_tuple__19; static PyObject *__pyx_tuple__20; static PyObject *__pyx_tuple__21; static PyObject *__pyx_tuple__22; static PyObject *__pyx_tuple__23; static PyObject *__pyx_tuple__24; static PyObject *__pyx_tuple__25; static PyObject *__pyx_tuple__26; static PyObject *__pyx_tuple__28; static PyObject *__pyx_tuple__29; static PyObject *__pyx_tuple__30; static PyObject *__pyx_tuple__31; static PyObject *__pyx_tuple__33; static PyObject *__pyx_tuple__34; static PyObject *__pyx_tuple__35; static PyObject *__pyx_tuple__36; static PyObject *__pyx_tuple__37; static PyObject *__pyx_tuple__38; static PyObject *__pyx_codeobj__32; static PyObject *__pyx_codeobj__39; /* Late includes */ /* "openTSNE/_tsne.pyx":30 * * * cpdef double[:, ::1] compute_gaussian_perplexity( # <<<<<<<<<<<<<< * double[:, :] distances, * double[:] desired_perplexities, */ static PyObject *__pyx_pw_8openTSNE_5_tsne_1compute_gaussian_perplexity(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static __Pyx_memviewslice __pyx_f_8openTSNE_5_tsne_compute_gaussian_perplexity(__Pyx_memviewslice __pyx_v_distances, __Pyx_memviewslice __pyx_v_desired_perplexities, CYTHON_UNUSED int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_compute_gaussian_perplexity *__pyx_optional_args) { double __pyx_v_perplexity_tol = ((double)1e-8); Py_ssize_t __pyx_v_max_iter = ((Py_ssize_t)0xC8); Py_ssize_t __pyx_v_num_threads = ((Py_ssize_t)1); Py_ssize_t __pyx_v_n_samples; Py_ssize_t __pyx_v_n_scales; Py_ssize_t __pyx_v_k_neighbors; __Pyx_memviewslice __pyx_v_P = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_multiscale_P = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_tau = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; Py_ssize_t __pyx_v_h; CYTHON_UNUSED Py_ssize_t __pyx_v_iteration; __Pyx_memviewslice __pyx_v_desired_entropies = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_min_tau; double __pyx_v_max_tau; double __pyx_v_sum_Pi; double __pyx_v_sum_PiDj; double __pyx_v_entropy; double __pyx_v_entropy_diff; double __pyx_v_sqrt_tau; __Pyx_memviewslice __pyx_r = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } }; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; __Pyx_memviewslice __pyx_t_8 = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_t_9 = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_t_10; Py_ssize_t __pyx_t_11; Py_ssize_t __pyx_t_12; Py_ssize_t __pyx_t_13; double __pyx_t_14; double __pyx_t_15; Py_ssize_t __pyx_t_16; Py_ssize_t __pyx_t_17; Py_ssize_t __pyx_t_18; Py_ssize_t __pyx_t_19; Py_ssize_t __pyx_t_20; Py_ssize_t __pyx_t_21; Py_ssize_t __pyx_t_22; Py_ssize_t __pyx_t_23; Py_ssize_t __pyx_t_24; Py_ssize_t __pyx_t_25; Py_ssize_t __pyx_t_26; Py_ssize_t __pyx_t_27; Py_ssize_t __pyx_t_28; Py_ssize_t __pyx_t_29; Py_ssize_t __pyx_t_30; Py_ssize_t __pyx_t_31; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("compute_gaussian_perplexity", 0); if (__pyx_optional_args) { if (__pyx_optional_args->__pyx_n > 0) { __pyx_v_perplexity_tol = __pyx_optional_args->perplexity_tol; if (__pyx_optional_args->__pyx_n > 1) { __pyx_v_max_iter = __pyx_optional_args->max_iter; if (__pyx_optional_args->__pyx_n > 2) { __pyx_v_num_threads = __pyx_optional_args->num_threads; } } } } /* "openTSNE/_tsne.pyx":38 * ): * cdef: * Py_ssize_t n_samples = distances.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t n_scales = desired_perplexities.shape[0] * Py_ssize_t k_neighbors = distances.shape[1] */ __pyx_v_n_samples = (__pyx_v_distances.shape[0]); /* "openTSNE/_tsne.pyx":39 * cdef: * Py_ssize_t n_samples = distances.shape[0] * Py_ssize_t n_scales = desired_perplexities.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t k_neighbors = distances.shape[1] * double[:, ::1] P = np.zeros_like(distances, dtype=float, order="C") */ __pyx_v_n_scales = (__pyx_v_desired_perplexities.shape[0]); /* "openTSNE/_tsne.pyx":40 * Py_ssize_t n_samples = distances.shape[0] * Py_ssize_t n_scales = desired_perplexities.shape[0] * Py_ssize_t k_neighbors = distances.shape[1] # <<<<<<<<<<<<<< * double[:, ::1] P = np.zeros_like(distances, dtype=float, order="C") * double[:, :, ::1] multiscale_P = np.zeros((n_samples, n_scales, k_neighbors)) */ __pyx_v_k_neighbors = (__pyx_v_distances.shape[1]); /* "openTSNE/_tsne.pyx":41 * Py_ssize_t n_scales = desired_perplexities.shape[0] * Py_ssize_t k_neighbors = distances.shape[1] * double[:, ::1] P = np.zeros_like(distances, dtype=float, order="C") # <<<<<<<<<<<<<< * double[:, :, ::1] multiscale_P = np.zeros((n_samples, n_scales, k_neighbors)) * double[:, ::1] tau = np.ones((n_samples, n_scales)) */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_zeros_like); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_distances, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyDict_NewPresized(2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 41, __pyx_L1_error) if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_order, __pyx_n_u_C) < 0) __PYX_ERR(0, 41, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 41, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_P = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_tsne.pyx":42 * Py_ssize_t k_neighbors = distances.shape[1] * double[:, ::1] P = np.zeros_like(distances, dtype=float, order="C") * double[:, :, ::1] multiscale_P = np.zeros((n_samples, n_scales, k_neighbors)) # <<<<<<<<<<<<<< * double[:, ::1] tau = np.ones((n_samples, n_scales)) * */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 42, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_zeros); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 42, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 42, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_n_scales); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 42, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_k_neighbors); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 42, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = PyTuple_New(3); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 42, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_7, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_7, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_7, 2, __pyx_t_6); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_6 = 0; __pyx_t_6 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_3))) { __pyx_t_6 = PyMethod_GET_SELF(__pyx_t_3); if (likely(__pyx_t_6)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_3); __Pyx_INCREF(__pyx_t_6); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_3, function); } } __pyx_t_4 = (__pyx_t_6) ? __Pyx_PyObject_Call2Args(__pyx_t_3, __pyx_t_6, __pyx_t_7) : __Pyx_PyObject_CallOneArg(__pyx_t_3, __pyx_t_7); __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 42, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_8 = __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_8.memview)) __PYX_ERR(0, 42, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_multiscale_P = __pyx_t_8; __pyx_t_8.memview = NULL; __pyx_t_8.data = NULL; /* "openTSNE/_tsne.pyx":43 * double[:, ::1] P = np.zeros_like(distances, dtype=float, order="C") * double[:, :, ::1] multiscale_P = np.zeros((n_samples, n_scales, k_neighbors)) * double[:, ::1] tau = np.ones((n_samples, n_scales)) # <<<<<<<<<<<<<< * * Py_ssize_t i, j, h, iteration */ __Pyx_GetModuleGlobalName(__pyx_t_3, __pyx_n_s_np); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 43, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_ones); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 43, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 43, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_n_scales); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 43, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 43, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_6); __pyx_t_3 = 0; __pyx_t_6 = 0; __pyx_t_6 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_7))) { __pyx_t_6 = PyMethod_GET_SELF(__pyx_t_7); if (likely(__pyx_t_6)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_7); __Pyx_INCREF(__pyx_t_6); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_7, function); } } __pyx_t_4 = (__pyx_t_6) ? __Pyx_PyObject_Call2Args(__pyx_t_7, __pyx_t_6, __pyx_t_2) : __Pyx_PyObject_CallOneArg(__pyx_t_7, __pyx_t_2); __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 43, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 43, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_tau = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_tsne.pyx":46 * * Py_ssize_t i, j, h, iteration * double[:] desired_entropies = np.log(desired_perplexities) # <<<<<<<<<<<<<< * * double min_tau, max_tau, sum_Pi, sum_PiDj, entropy, entropy_diff, sqrt_tau */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 46, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_log); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 46, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __pyx_memoryview_fromslice(__pyx_v_desired_perplexities, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 46, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_6 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_2))) { __pyx_t_6 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_6)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_6); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_4 = (__pyx_t_6) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_6, __pyx_t_7) : __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_t_7); __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 46, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_9 = __Pyx_PyObject_to_MemoryviewSlice_ds_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_9.memview)) __PYX_ERR(0, 46, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_desired_entropies = __pyx_t_9; __pyx_t_9.memview = NULL; __pyx_t_9.data = NULL; /* "openTSNE/_tsne.pyx":50 * double min_tau, max_tau, sum_Pi, sum_PiDj, entropy, entropy_diff, sqrt_tau * * if num_threads < 1: # <<<<<<<<<<<<<< * num_threads = 1 * */ __pyx_t_10 = ((__pyx_v_num_threads < 1) != 0); if (__pyx_t_10) { /* "openTSNE/_tsne.pyx":51 * * if num_threads < 1: * num_threads = 1 # <<<<<<<<<<<<<< * * for i in prange(n_samples, nogil=True, schedule="guided", num_threads=num_threads): */ __pyx_v_num_threads = 1; /* "openTSNE/_tsne.pyx":50 * double min_tau, max_tau, sum_Pi, sum_PiDj, entropy, entropy_diff, sqrt_tau * * if num_threads < 1: # <<<<<<<<<<<<<< * num_threads = 1 * */ } /* "openTSNE/_tsne.pyx":53 * num_threads = 1 * * for i in prange(n_samples, nogil=True, schedule="guided", num_threads=num_threads): # <<<<<<<<<<<<<< * min_tau, max_tau = -INFINITY, INFINITY * */ { #ifdef WITH_THREAD PyThreadState *_save; Py_UNBLOCK_THREADS __Pyx_FastGIL_Remember(); #endif /*try:*/ { __pyx_t_11 = __pyx_v_n_samples; if ((1 == 0)) abort(); { #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))))) #undef likely #undef unlikely #define likely(x) (x) #define unlikely(x) (x) #endif __pyx_t_13 = (__pyx_t_11 - 0 + 1 - 1/abs(1)) / 1; if (__pyx_t_13 > 0) { #ifdef _OPENMP #pragma omp parallel num_threads(__pyx_v_num_threads) private(__pyx_t_10, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_20, __pyx_t_21, __pyx_t_22, __pyx_t_23, __pyx_t_24, __pyx_t_25, __pyx_t_26, __pyx_t_27, __pyx_t_28, __pyx_t_29, __pyx_t_30, __pyx_t_31) #endif /* _OPENMP */ { #ifdef _OPENMP #pragma omp for lastprivate(__pyx_v_entropy) lastprivate(__pyx_v_entropy_diff) lastprivate(__pyx_v_h) firstprivate(__pyx_v_i) lastprivate(__pyx_v_i) lastprivate(__pyx_v_iteration) lastprivate(__pyx_v_j) lastprivate(__pyx_v_max_tau) lastprivate(__pyx_v_min_tau) lastprivate(__pyx_v_sqrt_tau) lastprivate(__pyx_v_sum_Pi) lastprivate(__pyx_v_sum_PiDj) schedule(guided) #endif /* _OPENMP */ for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_13; __pyx_t_12++){ { __pyx_v_i = (Py_ssize_t)(0 + 1 * __pyx_t_12); /* Initialize private variables to invalid values */ __pyx_v_entropy = ((double)__PYX_NAN()); __pyx_v_entropy_diff = ((double)__PYX_NAN()); __pyx_v_h = ((Py_ssize_t)0xbad0bad0); __pyx_v_iteration = ((Py_ssize_t)0xbad0bad0); __pyx_v_j = ((Py_ssize_t)0xbad0bad0); __pyx_v_max_tau = ((double)__PYX_NAN()); __pyx_v_min_tau = ((double)__PYX_NAN()); __pyx_v_sqrt_tau = ((double)__PYX_NAN()); __pyx_v_sum_Pi = ((double)__PYX_NAN()); __pyx_v_sum_PiDj = ((double)__PYX_NAN()); /* "openTSNE/_tsne.pyx":54 * * for i in prange(n_samples, nogil=True, schedule="guided", num_threads=num_threads): * min_tau, max_tau = -INFINITY, INFINITY # <<<<<<<<<<<<<< * * # For every scale find a precision tau that fits the perplexity */ __pyx_t_14 = (-INFINITY); __pyx_t_15 = INFINITY; __pyx_v_min_tau = __pyx_t_14; __pyx_v_max_tau = __pyx_t_15; /* "openTSNE/_tsne.pyx":57 * * # For every scale find a precision tau that fits the perplexity * for h in range(n_scales): # <<<<<<<<<<<<<< * for iteration in range(max_iter): * sum_Pi, sum_PiDj = 0, 0 */ __pyx_t_16 = __pyx_v_n_scales; __pyx_t_17 = __pyx_t_16; for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) { __pyx_v_h = __pyx_t_18; /* "openTSNE/_tsne.pyx":58 * # For every scale find a precision tau that fits the perplexity * for h in range(n_scales): * for iteration in range(max_iter): # <<<<<<<<<<<<<< * sum_Pi, sum_PiDj = 0, 0 * sqrt_tau = sqrt(tau[i, h]) */ __pyx_t_19 = __pyx_v_max_iter; __pyx_t_20 = __pyx_t_19; for (__pyx_t_21 = 0; __pyx_t_21 < __pyx_t_20; __pyx_t_21+=1) { __pyx_v_iteration = __pyx_t_21; /* "openTSNE/_tsne.pyx":59 * for h in range(n_scales): * for iteration in range(max_iter): * sum_Pi, sum_PiDj = 0, 0 # <<<<<<<<<<<<<< * sqrt_tau = sqrt(tau[i, h]) * */ __pyx_t_15 = 0.0; __pyx_t_14 = 0.0; __pyx_v_sum_Pi = __pyx_t_15; __pyx_v_sum_PiDj = __pyx_t_14; /* "openTSNE/_tsne.pyx":60 * for iteration in range(max_iter): * sum_Pi, sum_PiDj = 0, 0 * sqrt_tau = sqrt(tau[i, h]) # <<<<<<<<<<<<<< * * for j in range(k_neighbors): */ __pyx_t_22 = __pyx_v_i; __pyx_t_23 = __pyx_v_h; __pyx_v_sqrt_tau = sqrt((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_tau.data + __pyx_t_22 * __pyx_v_tau.strides[0]) )) + __pyx_t_23)) )))); /* "openTSNE/_tsne.pyx":62 * sqrt_tau = sqrt(tau[i, h]) * * for j in range(k_neighbors): # <<<<<<<<<<<<<< * multiscale_P[i, h, j] = sqrt_tau * exp(-distances[i, j] ** 2 * tau[i, h] * 0.5) * sum_Pi = sum_Pi + multiscale_P[i, h, j] */ __pyx_t_24 = __pyx_v_k_neighbors; __pyx_t_25 = __pyx_t_24; for (__pyx_t_26 = 0; __pyx_t_26 < __pyx_t_25; __pyx_t_26+=1) { __pyx_v_j = __pyx_t_26; /* "openTSNE/_tsne.pyx":63 * * for j in range(k_neighbors): * multiscale_P[i, h, j] = sqrt_tau * exp(-distances[i, j] ** 2 * tau[i, h] * 0.5) # <<<<<<<<<<<<<< * sum_Pi = sum_Pi + multiscale_P[i, h, j] * sum_Pi = sum_Pi + EPSILON */ __pyx_t_23 = __pyx_v_i; __pyx_t_22 = __pyx_v_j; __pyx_t_27 = __pyx_v_i; __pyx_t_28 = __pyx_v_h; __pyx_t_29 = __pyx_v_i; __pyx_t_30 = __pyx_v_h; __pyx_t_31 = __pyx_v_j; *((double *) ( /* dim=2 */ ((char *) (((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_multiscale_P.data + __pyx_t_29 * __pyx_v_multiscale_P.strides[0]) ) + __pyx_t_30 * __pyx_v_multiscale_P.strides[1]) )) + __pyx_t_31)) )) = (__pyx_v_sqrt_tau * exp((((-pow((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_distances.data + __pyx_t_23 * __pyx_v_distances.strides[0]) ) + __pyx_t_22 * __pyx_v_distances.strides[1]) ))), 2.0)) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_tau.data + __pyx_t_27 * __pyx_v_tau.strides[0]) )) + __pyx_t_28)) )))) * 0.5))); /* "openTSNE/_tsne.pyx":64 * for j in range(k_neighbors): * multiscale_P[i, h, j] = sqrt_tau * exp(-distances[i, j] ** 2 * tau[i, h] * 0.5) * sum_Pi = sum_Pi + multiscale_P[i, h, j] # <<<<<<<<<<<<<< * sum_Pi = sum_Pi + EPSILON * */ __pyx_t_28 = __pyx_v_i; __pyx_t_27 = __pyx_v_h; __pyx_t_22 = __pyx_v_j; __pyx_v_sum_Pi = (__pyx_v_sum_Pi + (*((double *) ( /* dim=2 */ ((char *) (((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_multiscale_P.data + __pyx_t_28 * __pyx_v_multiscale_P.strides[0]) ) + __pyx_t_27 * __pyx_v_multiscale_P.strides[1]) )) + __pyx_t_22)) )))); } /* "openTSNE/_tsne.pyx":65 * multiscale_P[i, h, j] = sqrt_tau * exp(-distances[i, j] ** 2 * tau[i, h] * 0.5) * sum_Pi = sum_Pi + multiscale_P[i, h, j] * sum_Pi = sum_Pi + EPSILON # <<<<<<<<<<<<<< * * for j in range(k_neighbors): */ __pyx_v_sum_Pi = (__pyx_v_sum_Pi + __pyx_v_8openTSNE_5_tsne_EPSILON); /* "openTSNE/_tsne.pyx":67 * sum_Pi = sum_Pi + EPSILON * * for j in range(k_neighbors): # <<<<<<<<<<<<<< * sum_PiDj = sum_PiDj + multiscale_P[i, h, j] / sum_Pi * distances[i, j] ** 2 * */ __pyx_t_24 = __pyx_v_k_neighbors; __pyx_t_25 = __pyx_t_24; for (__pyx_t_26 = 0; __pyx_t_26 < __pyx_t_25; __pyx_t_26+=1) { __pyx_v_j = __pyx_t_26; /* "openTSNE/_tsne.pyx":68 * * for j in range(k_neighbors): * sum_PiDj = sum_PiDj + multiscale_P[i, h, j] / sum_Pi * distances[i, j] ** 2 # <<<<<<<<<<<<<< * * entropy = tau[i, h] * 0.5 * sum_PiDj + log(sum_Pi) - log(tau[i, h]) * 0.5 */ __pyx_t_22 = __pyx_v_i; __pyx_t_27 = __pyx_v_h; __pyx_t_28 = __pyx_v_j; __pyx_t_23 = __pyx_v_i; __pyx_t_31 = __pyx_v_j; __pyx_v_sum_PiDj = (__pyx_v_sum_PiDj + (((*((double *) ( /* dim=2 */ ((char *) (((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_multiscale_P.data + __pyx_t_22 * __pyx_v_multiscale_P.strides[0]) ) + __pyx_t_27 * __pyx_v_multiscale_P.strides[1]) )) + __pyx_t_28)) ))) / __pyx_v_sum_Pi) * pow((*((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_distances.data + __pyx_t_23 * __pyx_v_distances.strides[0]) ) + __pyx_t_31 * __pyx_v_distances.strides[1]) ))), 2.0))); } /* "openTSNE/_tsne.pyx":70 * sum_PiDj = sum_PiDj + multiscale_P[i, h, j] / sum_Pi * distances[i, j] ** 2 * * entropy = tau[i, h] * 0.5 * sum_PiDj + log(sum_Pi) - log(tau[i, h]) * 0.5 # <<<<<<<<<<<<<< * entropy_diff = entropy - desired_entropies[h] * */ __pyx_t_31 = __pyx_v_i; __pyx_t_23 = __pyx_v_h; __pyx_t_28 = __pyx_v_i; __pyx_t_27 = __pyx_v_h; __pyx_v_entropy = (((((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_tau.data + __pyx_t_31 * __pyx_v_tau.strides[0]) )) + __pyx_t_23)) ))) * 0.5) * __pyx_v_sum_PiDj) + log(__pyx_v_sum_Pi)) - (log((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_tau.data + __pyx_t_28 * __pyx_v_tau.strides[0]) )) + __pyx_t_27)) )))) * 0.5)); /* "openTSNE/_tsne.pyx":71 * * entropy = tau[i, h] * 0.5 * sum_PiDj + log(sum_Pi) - log(tau[i, h]) * 0.5 * entropy_diff = entropy - desired_entropies[h] # <<<<<<<<<<<<<< * * if fabs(entropy_diff) <= perplexity_tol: */ __pyx_t_27 = __pyx_v_h; __pyx_v_entropy_diff = (__pyx_v_entropy - (*((double *) ( /* dim=0 */ (__pyx_v_desired_entropies.data + __pyx_t_27 * __pyx_v_desired_entropies.strides[0]) )))); /* "openTSNE/_tsne.pyx":73 * entropy_diff = entropy - desired_entropies[h] * * if fabs(entropy_diff) <= perplexity_tol: # <<<<<<<<<<<<<< * break * */ __pyx_t_10 = ((fabs(__pyx_v_entropy_diff) <= __pyx_v_perplexity_tol) != 0); if (__pyx_t_10) { /* "openTSNE/_tsne.pyx":74 * * if fabs(entropy_diff) <= perplexity_tol: * break # <<<<<<<<<<<<<< * * if entropy_diff > 0: */ goto __pyx_L14_break; /* "openTSNE/_tsne.pyx":73 * entropy_diff = entropy - desired_entropies[h] * * if fabs(entropy_diff) <= perplexity_tol: # <<<<<<<<<<<<<< * break * */ } /* "openTSNE/_tsne.pyx":76 * break * * if entropy_diff > 0: # <<<<<<<<<<<<<< * min_tau = tau[i, h] * if isinf(max_tau): */ __pyx_t_10 = ((__pyx_v_entropy_diff > 0.0) != 0); if (__pyx_t_10) { /* "openTSNE/_tsne.pyx":77 * * if entropy_diff > 0: * min_tau = tau[i, h] # <<<<<<<<<<<<<< * if isinf(max_tau): * tau[i, h] *= 2 */ __pyx_t_27 = __pyx_v_i; __pyx_t_28 = __pyx_v_h; __pyx_v_min_tau = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_tau.data + __pyx_t_27 * __pyx_v_tau.strides[0]) )) + __pyx_t_28)) ))); /* "openTSNE/_tsne.pyx":78 * if entropy_diff > 0: * min_tau = tau[i, h] * if isinf(max_tau): # <<<<<<<<<<<<<< * tau[i, h] *= 2 * else: */ __pyx_t_10 = (isinf(__pyx_v_max_tau) != 0); if (__pyx_t_10) { /* "openTSNE/_tsne.pyx":79 * min_tau = tau[i, h] * if isinf(max_tau): * tau[i, h] *= 2 # <<<<<<<<<<<<<< * else: * tau[i, h] = (tau[i, h] + max_tau) * 0.5 */ __pyx_t_28 = __pyx_v_i; __pyx_t_27 = __pyx_v_h; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_tau.data + __pyx_t_28 * __pyx_v_tau.strides[0]) )) + __pyx_t_27)) )) *= 2.0; /* "openTSNE/_tsne.pyx":78 * if entropy_diff > 0: * min_tau = tau[i, h] * if isinf(max_tau): # <<<<<<<<<<<<<< * tau[i, h] *= 2 * else: */ goto __pyx_L21; } /* "openTSNE/_tsne.pyx":81 * tau[i, h] *= 2 * else: * tau[i, h] = (tau[i, h] + max_tau) * 0.5 # <<<<<<<<<<<<<< * else: * max_tau = tau[i, h] */ /*else*/ { __pyx_t_27 = __pyx_v_i; __pyx_t_28 = __pyx_v_h; __pyx_t_23 = __pyx_v_i; __pyx_t_31 = __pyx_v_h; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_tau.data + __pyx_t_23 * __pyx_v_tau.strides[0]) )) + __pyx_t_31)) )) = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_tau.data + __pyx_t_27 * __pyx_v_tau.strides[0]) )) + __pyx_t_28)) ))) + __pyx_v_max_tau) * 0.5); } __pyx_L21:; /* "openTSNE/_tsne.pyx":76 * break * * if entropy_diff > 0: # <<<<<<<<<<<<<< * min_tau = tau[i, h] * if isinf(max_tau): */ goto __pyx_L20; } /* "openTSNE/_tsne.pyx":83 * tau[i, h] = (tau[i, h] + max_tau) * 0.5 * else: * max_tau = tau[i, h] # <<<<<<<<<<<<<< * if isinf(min_tau): * tau[i, h] /= 2 */ /*else*/ { __pyx_t_28 = __pyx_v_i; __pyx_t_27 = __pyx_v_h; __pyx_v_max_tau = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_tau.data + __pyx_t_28 * __pyx_v_tau.strides[0]) )) + __pyx_t_27)) ))); /* "openTSNE/_tsne.pyx":84 * else: * max_tau = tau[i, h] * if isinf(min_tau): # <<<<<<<<<<<<<< * tau[i, h] /= 2 * else: */ __pyx_t_10 = (isinf(__pyx_v_min_tau) != 0); if (__pyx_t_10) { /* "openTSNE/_tsne.pyx":85 * max_tau = tau[i, h] * if isinf(min_tau): * tau[i, h] /= 2 # <<<<<<<<<<<<<< * else: * tau[i, h] = (tau[i, h] + min_tau) * 0.5 */ __pyx_t_27 = __pyx_v_i; __pyx_t_28 = __pyx_v_h; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_tau.data + __pyx_t_27 * __pyx_v_tau.strides[0]) )) + __pyx_t_28)) )) /= 2.0; /* "openTSNE/_tsne.pyx":84 * else: * max_tau = tau[i, h] * if isinf(min_tau): # <<<<<<<<<<<<<< * tau[i, h] /= 2 * else: */ goto __pyx_L22; } /* "openTSNE/_tsne.pyx":87 * tau[i, h] /= 2 * else: * tau[i, h] = (tau[i, h] + min_tau) * 0.5 # <<<<<<<<<<<<<< * * # Get the probability of the mixture of Gaussians with different precisions */ /*else*/ { __pyx_t_28 = __pyx_v_i; __pyx_t_27 = __pyx_v_h; __pyx_t_31 = __pyx_v_i; __pyx_t_23 = __pyx_v_h; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_tau.data + __pyx_t_31 * __pyx_v_tau.strides[0]) )) + __pyx_t_23)) )) = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_tau.data + __pyx_t_28 * __pyx_v_tau.strides[0]) )) + __pyx_t_27)) ))) + __pyx_v_min_tau) * 0.5); } __pyx_L22:; } __pyx_L20:; } __pyx_L14_break:; } /* "openTSNE/_tsne.pyx":90 * * # Get the probability of the mixture of Gaussians with different precisions * sum_Pi = 0 # <<<<<<<<<<<<<< * for j in range(k_neighbors): * for h in range(n_scales): */ __pyx_v_sum_Pi = 0.0; /* "openTSNE/_tsne.pyx":91 * # Get the probability of the mixture of Gaussians with different precisions * sum_Pi = 0 * for j in range(k_neighbors): # <<<<<<<<<<<<<< * for h in range(n_scales): * P[i, j] = P[i, j] + multiscale_P[i, h, j] */ __pyx_t_16 = __pyx_v_k_neighbors; __pyx_t_17 = __pyx_t_16; for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) { __pyx_v_j = __pyx_t_18; /* "openTSNE/_tsne.pyx":92 * sum_Pi = 0 * for j in range(k_neighbors): * for h in range(n_scales): # <<<<<<<<<<<<<< * P[i, j] = P[i, j] + multiscale_P[i, h, j] * sum_Pi = sum_Pi + multiscale_P[i, h, j] */ __pyx_t_19 = __pyx_v_n_scales; __pyx_t_20 = __pyx_t_19; for (__pyx_t_21 = 0; __pyx_t_21 < __pyx_t_20; __pyx_t_21+=1) { __pyx_v_h = __pyx_t_21; /* "openTSNE/_tsne.pyx":93 * for j in range(k_neighbors): * for h in range(n_scales): * P[i, j] = P[i, j] + multiscale_P[i, h, j] # <<<<<<<<<<<<<< * sum_Pi = sum_Pi + multiscale_P[i, h, j] * */ __pyx_t_27 = __pyx_v_i; __pyx_t_28 = __pyx_v_j; __pyx_t_23 = __pyx_v_i; __pyx_t_31 = __pyx_v_h; __pyx_t_22 = __pyx_v_j; __pyx_t_30 = __pyx_v_i; __pyx_t_29 = __pyx_v_j; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_P.data + __pyx_t_30 * __pyx_v_P.strides[0]) )) + __pyx_t_29)) )) = ((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_P.data + __pyx_t_27 * __pyx_v_P.strides[0]) )) + __pyx_t_28)) ))) + (*((double *) ( /* dim=2 */ ((char *) (((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_multiscale_P.data + __pyx_t_23 * __pyx_v_multiscale_P.strides[0]) ) + __pyx_t_31 * __pyx_v_multiscale_P.strides[1]) )) + __pyx_t_22)) )))); /* "openTSNE/_tsne.pyx":94 * for h in range(n_scales): * P[i, j] = P[i, j] + multiscale_P[i, h, j] * sum_Pi = sum_Pi + multiscale_P[i, h, j] # <<<<<<<<<<<<<< * * # Perform row-normalization */ __pyx_t_22 = __pyx_v_i; __pyx_t_31 = __pyx_v_h; __pyx_t_23 = __pyx_v_j; __pyx_v_sum_Pi = (__pyx_v_sum_Pi + (*((double *) ( /* dim=2 */ ((char *) (((double *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_multiscale_P.data + __pyx_t_22 * __pyx_v_multiscale_P.strides[0]) ) + __pyx_t_31 * __pyx_v_multiscale_P.strides[1]) )) + __pyx_t_23)) )))); } } /* "openTSNE/_tsne.pyx":97 * * # Perform row-normalization * for j in range(k_neighbors): # <<<<<<<<<<<<<< * P[i, j] /= sum_Pi * */ __pyx_t_16 = __pyx_v_k_neighbors; __pyx_t_17 = __pyx_t_16; for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) { __pyx_v_j = __pyx_t_18; /* "openTSNE/_tsne.pyx":98 * # Perform row-normalization * for j in range(k_neighbors): * P[i, j] /= sum_Pi # <<<<<<<<<<<<<< * * return P */ __pyx_t_23 = __pyx_v_i; __pyx_t_31 = __pyx_v_j; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_P.data + __pyx_t_23 * __pyx_v_P.strides[0]) )) + __pyx_t_31)) )) /= __pyx_v_sum_Pi; } } } } } } #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))))) #undef likely #undef unlikely #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #endif } /* "openTSNE/_tsne.pyx":53 * num_threads = 1 * * for i in prange(n_samples, nogil=True, schedule="guided", num_threads=num_threads): # <<<<<<<<<<<<<< * min_tau, max_tau = -INFINITY, INFINITY * */ /*finally:*/ { /*normal exit:*/{ #ifdef WITH_THREAD __Pyx_FastGIL_Forget(); Py_BLOCK_THREADS #endif goto __pyx_L6; } __pyx_L6:; } } /* "openTSNE/_tsne.pyx":100 * P[i, j] /= sum_Pi * * return P # <<<<<<<<<<<<<< * * */ __PYX_INC_MEMVIEW(&__pyx_v_P, 0); __pyx_r = __pyx_v_P; goto __pyx_L0; /* "openTSNE/_tsne.pyx":30 * * * cpdef double[:, ::1] compute_gaussian_perplexity( # <<<<<<<<<<<<<< * double[:, :] distances, * double[:] desired_perplexities, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __PYX_XDEC_MEMVIEW(&__pyx_t_8, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_9, 1); __pyx_r.data = NULL; __pyx_r.memview = NULL; __Pyx_AddTraceback("openTSNE._tsne.compute_gaussian_perplexity", __pyx_clineno, __pyx_lineno, __pyx_filename); goto __pyx_L2; __pyx_L0:; if (unlikely(!__pyx_r.memview)) { PyErr_SetString(PyExc_TypeError, "Memoryview return value is not initialized"); } __pyx_L2:; __PYX_XDEC_MEMVIEW(&__pyx_v_P, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_multiscale_P, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_tau, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_desired_entropies, 1); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_5_tsne_1compute_gaussian_perplexity(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_pw_8openTSNE_5_tsne_1compute_gaussian_perplexity(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __Pyx_memviewslice __pyx_v_distances = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_desired_perplexities = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_perplexity_tol; Py_ssize_t __pyx_v_max_iter; Py_ssize_t __pyx_v_num_threads; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("compute_gaussian_perplexity (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_distances,&__pyx_n_s_desired_perplexities,&__pyx_n_s_perplexity_tol,&__pyx_n_s_max_iter,&__pyx_n_s_num_threads,0}; PyObject* values[5] = {0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_distances)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_desired_perplexities)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("compute_gaussian_perplexity", 0, 2, 5, 1); __PYX_ERR(0, 30, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_perplexity_tol); if (value) { values[2] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 3: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_max_iter); if (value) { values[3] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 4: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_num_threads); if (value) { values[4] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "compute_gaussian_perplexity") < 0)) __PYX_ERR(0, 30, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_distances = __Pyx_PyObject_to_MemoryviewSlice_dsds_double(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_distances.memview)) __PYX_ERR(0, 31, __pyx_L3_error) __pyx_v_desired_perplexities = __Pyx_PyObject_to_MemoryviewSlice_ds_double(values[1], PyBUF_WRITABLE); if (unlikely(!__pyx_v_desired_perplexities.memview)) __PYX_ERR(0, 32, __pyx_L3_error) if (values[2]) { __pyx_v_perplexity_tol = __pyx_PyFloat_AsDouble(values[2]); if (unlikely((__pyx_v_perplexity_tol == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 33, __pyx_L3_error) } else { __pyx_v_perplexity_tol = ((double)1e-8); } if (values[3]) { __pyx_v_max_iter = __Pyx_PyIndex_AsSsize_t(values[3]); if (unlikely((__pyx_v_max_iter == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 34, __pyx_L3_error) } else { __pyx_v_max_iter = ((Py_ssize_t)0xC8); } if (values[4]) { __pyx_v_num_threads = __Pyx_PyIndex_AsSsize_t(values[4]); if (unlikely((__pyx_v_num_threads == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 35, __pyx_L3_error) } else { __pyx_v_num_threads = ((Py_ssize_t)1); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("compute_gaussian_perplexity", 0, 2, 5, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 30, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE._tsne.compute_gaussian_perplexity", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_5_tsne_compute_gaussian_perplexity(__pyx_self, __pyx_v_distances, __pyx_v_desired_perplexities, __pyx_v_perplexity_tol, __pyx_v_max_iter, __pyx_v_num_threads); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_5_tsne_compute_gaussian_perplexity(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_distances, __Pyx_memviewslice __pyx_v_desired_perplexities, double __pyx_v_perplexity_tol, Py_ssize_t __pyx_v_max_iter, Py_ssize_t __pyx_v_num_threads) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice __pyx_t_1 = { 0, 0, { 0 }, { 0 }, { 0 } }; struct __pyx_opt_args_8openTSNE_5_tsne_compute_gaussian_perplexity __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("compute_gaussian_perplexity", 0); __Pyx_XDECREF(__pyx_r); __pyx_t_2.__pyx_n = 3; __pyx_t_2.perplexity_tol = __pyx_v_perplexity_tol; __pyx_t_2.max_iter = __pyx_v_max_iter; __pyx_t_2.num_threads = __pyx_v_num_threads; __pyx_t_1 = __pyx_f_8openTSNE_5_tsne_compute_gaussian_perplexity(__pyx_v_distances, __pyx_v_desired_perplexities, 0, &__pyx_t_2); if (unlikely(!__pyx_t_1.memview)) __PYX_ERR(0, 30, __pyx_L1_error) __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_t_1, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 30, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __PYX_XDEC_MEMVIEW(&__pyx_t_1, 1); __pyx_t_1.memview = NULL; __pyx_t_1.data = NULL; __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __PYX_XDEC_MEMVIEW(&__pyx_t_1, 1); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("openTSNE._tsne.compute_gaussian_perplexity", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_distances, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_desired_perplexities, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/_tsne.pyx":103 * * * cpdef tuple estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * sparse_index_type[:] indices, * sparse_index_type[:] indptr, */ /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_5_tsne_3estimate_positive_gradient_nn(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyMethodDef __pyx_mdef_8openTSNE_5_tsne_3estimate_positive_gradient_nn = {"estimate_positive_gradient_nn", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_5_tsne_3estimate_positive_gradient_nn, METH_VARARGS|METH_KEYWORDS, 0}; static PyObject *__pyx_pw_8openTSNE_5_tsne_3estimate_positive_gradient_nn(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_signatures = 0; PyObject *__pyx_v_args = 0; PyObject *__pyx_v_kwargs = 0; CYTHON_UNUSED PyObject *__pyx_v_defaults = 0; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__pyx_fused_cpdef (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_signatures,&__pyx_n_s_args,&__pyx_n_s_kwargs,&__pyx_n_s_defaults,0}; PyObject* values[4] = {0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_signatures)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_args)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_fused_cpdef", 1, 4, 4, 1); __PYX_ERR(0, 103, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_kwargs)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_fused_cpdef", 1, 4, 4, 2); __PYX_ERR(0, 103, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 3: if (likely((values[3] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_defaults)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_fused_cpdef", 1, 4, 4, 3); __PYX_ERR(0, 103, __pyx_L3_error) } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__pyx_fused_cpdef") < 0)) __PYX_ERR(0, 103, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 4) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[2] = PyTuple_GET_ITEM(__pyx_args, 2); values[3] = PyTuple_GET_ITEM(__pyx_args, 3); } __pyx_v_signatures = values[0]; __pyx_v_args = values[1]; __pyx_v_kwargs = values[2]; __pyx_v_defaults = values[3]; } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__pyx_fused_cpdef", 1, 4, 4, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 103, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE._tsne.__pyx_fused_cpdef", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_5_tsne_2estimate_positive_gradient_nn(__pyx_self, __pyx_v_signatures, __pyx_v_args, __pyx_v_kwargs, __pyx_v_defaults); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_5_tsne_2estimate_positive_gradient_nn(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_signatures, PyObject *__pyx_v_args, PyObject *__pyx_v_kwargs, CYTHON_UNUSED PyObject *__pyx_v_defaults) { PyObject *__pyx_v_dest_sig = NULL; Py_ssize_t __pyx_v_i; PyTypeObject *__pyx_v_ndarray = 0; __Pyx_memviewslice __pyx_v_memslice; Py_ssize_t __pyx_v_itemsize; int __pyx_v_dtype_signed; char __pyx_v_kind; int __pyx_v____pyx_int32_t_is_signed; int __pyx_v____pyx_int64_t_is_signed; PyObject *__pyx_v_arg = NULL; PyObject *__pyx_v_dtype = NULL; PyObject *__pyx_v_arg_base = NULL; PyObject *__pyx_v_candidates = NULL; PyObject *__pyx_v_sig = NULL; int __pyx_v_match_found; PyObject *__pyx_v_src_sig = NULL; PyObject *__pyx_v_dst_type = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; Py_ssize_t __pyx_t_5; PyObject *__pyx_t_6 = NULL; long __pyx_t_7; __Pyx_memviewslice __pyx_t_8; Py_ssize_t __pyx_t_9; int __pyx_t_10; int __pyx_t_11; PyObject *__pyx_t_12 = NULL; PyObject *__pyx_t_13 = NULL; PyObject *__pyx_t_14 = NULL; Py_ssize_t __pyx_t_15; Py_ssize_t __pyx_t_16; Py_ssize_t __pyx_t_17; int __pyx_t_18; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("estimate_positive_gradient_nn", 0); __Pyx_INCREF(__pyx_v_kwargs); __pyx_t_1 = PyList_New(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(Py_None); __Pyx_GIVEREF(Py_None); PyList_SET_ITEM(__pyx_t_1, 0, Py_None); __pyx_v_dest_sig = ((PyObject*)__pyx_t_1); __pyx_t_1 = 0; __pyx_t_3 = (__pyx_v_kwargs != Py_None); __pyx_t_4 = (__pyx_t_3 != 0); if (__pyx_t_4) { } else { __pyx_t_2 = __pyx_t_4; goto __pyx_L4_bool_binop_done; } __pyx_t_4 = __Pyx_PyObject_IsTrue(__pyx_v_kwargs); if (unlikely(__pyx_t_4 < 0)) __PYX_ERR(0, 103, __pyx_L1_error) __pyx_t_3 = ((!__pyx_t_4) != 0); __pyx_t_2 = __pyx_t_3; __pyx_L4_bool_binop_done:; if (__pyx_t_2) { __Pyx_INCREF(Py_None); __Pyx_DECREF_SET(__pyx_v_kwargs, Py_None); } __pyx_t_1 = ((PyObject *)__Pyx_ImportNumPyArrayTypeIfAvailable()); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_ndarray = ((PyTypeObject*)__pyx_t_1); __pyx_t_1 = 0; __pyx_v_itemsize = -1L; __pyx_v____pyx_int32_t_is_signed = (!((((__pyx_t_5numpy_int32_t)-1L) > 0) != 0)); __pyx_v____pyx_int64_t_is_signed = (!((((__pyx_t_5numpy_int64_t)-1L) > 0) != 0)); if (unlikely(__pyx_v_args == Py_None)) { PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); __PYX_ERR(0, 103, __pyx_L1_error) } __pyx_t_5 = PyTuple_GET_SIZE(((PyObject*)__pyx_v_args)); if (unlikely(__pyx_t_5 == ((Py_ssize_t)-1))) __PYX_ERR(0, 103, __pyx_L1_error) __pyx_t_2 = ((0 < __pyx_t_5) != 0); if (__pyx_t_2) { if (unlikely(__pyx_v_args == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(0, 103, __pyx_L1_error) } __pyx_t_1 = PyTuple_GET_ITEM(((PyObject*)__pyx_v_args), 0); __Pyx_INCREF(__pyx_t_1); __pyx_v_arg = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L6; } __pyx_t_3 = (__pyx_v_kwargs != Py_None); __pyx_t_4 = (__pyx_t_3 != 0); if (__pyx_t_4) { } else { __pyx_t_2 = __pyx_t_4; goto __pyx_L7_bool_binop_done; } if (unlikely(__pyx_v_kwargs == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); __PYX_ERR(0, 103, __pyx_L1_error) } __pyx_t_4 = (__Pyx_PyDict_ContainsTF(__pyx_n_s_indices, ((PyObject*)__pyx_v_kwargs), Py_EQ)); if (unlikely(__pyx_t_4 < 0)) __PYX_ERR(0, 103, __pyx_L1_error) __pyx_t_3 = (__pyx_t_4 != 0); __pyx_t_2 = __pyx_t_3; __pyx_L7_bool_binop_done:; if (__pyx_t_2) { if (unlikely(__pyx_v_kwargs == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(0, 103, __pyx_L1_error) } __pyx_t_1 = __Pyx_PyDict_GetItem(((PyObject*)__pyx_v_kwargs), __pyx_n_s_indices); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_arg = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L6; } /*else*/ { if (unlikely(__pyx_v_args == Py_None)) { PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); __PYX_ERR(0, 103, __pyx_L1_error) } __pyx_t_5 = PyTuple_GET_SIZE(((PyObject*)__pyx_v_args)); if (unlikely(__pyx_t_5 == ((Py_ssize_t)-1))) __PYX_ERR(0, 103, __pyx_L1_error) __pyx_t_1 = PyInt_FromSsize_t(__pyx_t_5); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_6 = PyTuple_New(3); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_INCREF(__pyx_int_6); __Pyx_GIVEREF(__pyx_int_6); PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_int_6); __Pyx_INCREF(__pyx_n_s_s); __Pyx_GIVEREF(__pyx_n_s_s); PyTuple_SET_ITEM(__pyx_t_6, 1, __pyx_n_s_s); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_6, 2, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyString_Format(__pyx_kp_s_Expected_at_least_d_argument_s_g, __pyx_t_6); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = __Pyx_PyObject_CallOneArg(__pyx_builtin_TypeError, __pyx_t_1); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_Raise(__pyx_t_6, 0, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __PYX_ERR(0, 103, __pyx_L1_error) } __pyx_L6:; while (1) { __pyx_t_2 = (__pyx_v_ndarray != ((PyTypeObject*)Py_None)); __pyx_t_3 = (__pyx_t_2 != 0); if (__pyx_t_3) { __pyx_t_3 = __Pyx_TypeCheck(__pyx_v_arg, __pyx_v_ndarray); __pyx_t_2 = (__pyx_t_3 != 0); if (__pyx_t_2) { __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_arg, __pyx_n_s_dtype); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_v_dtype = __pyx_t_6; __pyx_t_6 = 0; goto __pyx_L12; } __pyx_t_2 = __pyx_memoryview_check(__pyx_v_arg); __pyx_t_3 = (__pyx_t_2 != 0); if (__pyx_t_3) { __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_arg, __pyx_n_s_base); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_v_arg_base = __pyx_t_6; __pyx_t_6 = 0; __pyx_t_3 = __Pyx_TypeCheck(__pyx_v_arg_base, __pyx_v_ndarray); __pyx_t_2 = (__pyx_t_3 != 0); if (__pyx_t_2) { __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_arg_base, __pyx_n_s_dtype); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_v_dtype = __pyx_t_6; __pyx_t_6 = 0; goto __pyx_L13; } /*else*/ { __Pyx_INCREF(Py_None); __pyx_v_dtype = Py_None; } __pyx_L13:; goto __pyx_L12; } /*else*/ { __Pyx_INCREF(Py_None); __pyx_v_dtype = Py_None; } __pyx_L12:; __pyx_v_itemsize = -1L; __pyx_t_2 = (__pyx_v_dtype != Py_None); __pyx_t_3 = (__pyx_t_2 != 0); if (__pyx_t_3) { __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_dtype, __pyx_n_s_itemsize); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_5 = __Pyx_PyIndex_AsSsize_t(__pyx_t_6); if (unlikely((__pyx_t_5 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_v_itemsize = __pyx_t_5; __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_dtype, __pyx_n_s_kind); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = __Pyx_PyObject_Ord(__pyx_t_6); if (unlikely(__pyx_t_7 == ((long)(long)(Py_UCS4)-1))) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_v_kind = __pyx_t_7; __pyx_v_dtype_signed = (__pyx_v_kind == 'i'); switch (__pyx_v_kind) { case 'i': case 'u': __pyx_t_2 = (((sizeof(__pyx_t_5numpy_int32_t)) == __pyx_v_itemsize) != 0); if (__pyx_t_2) { } else { __pyx_t_3 = __pyx_t_2; goto __pyx_L16_bool_binop_done; } __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_arg, __pyx_n_s_ndim); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_5 = __Pyx_PyIndex_AsSsize_t(__pyx_t_6); if (unlikely((__pyx_t_5 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_2 = ((((Py_ssize_t)__pyx_t_5) == 1) != 0); if (__pyx_t_2) { } else { __pyx_t_3 = __pyx_t_2; goto __pyx_L16_bool_binop_done; } __pyx_t_2 = ((!((__pyx_v____pyx_int32_t_is_signed ^ __pyx_v_dtype_signed) != 0)) != 0); __pyx_t_3 = __pyx_t_2; __pyx_L16_bool_binop_done:; if (__pyx_t_3) { if (unlikely(__Pyx_SetItemInt(__pyx_v_dest_sig, 0, __pyx_n_s_int32_t, long, 1, __Pyx_PyInt_From_long, 1, 0, 0) < 0)) __PYX_ERR(0, 103, __pyx_L1_error) goto __pyx_L10_break; } __pyx_t_2 = (((sizeof(__pyx_t_5numpy_int64_t)) == __pyx_v_itemsize) != 0); if (__pyx_t_2) { } else { __pyx_t_3 = __pyx_t_2; goto __pyx_L20_bool_binop_done; } __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_arg, __pyx_n_s_ndim); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_5 = __Pyx_PyIndex_AsSsize_t(__pyx_t_6); if (unlikely((__pyx_t_5 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_2 = ((((Py_ssize_t)__pyx_t_5) == 1) != 0); if (__pyx_t_2) { } else { __pyx_t_3 = __pyx_t_2; goto __pyx_L20_bool_binop_done; } __pyx_t_2 = ((!((__pyx_v____pyx_int64_t_is_signed ^ __pyx_v_dtype_signed) != 0)) != 0); __pyx_t_3 = __pyx_t_2; __pyx_L20_bool_binop_done:; if (__pyx_t_3) { if (unlikely(__Pyx_SetItemInt(__pyx_v_dest_sig, 0, __pyx_n_s_int64_t, long, 1, __Pyx_PyInt_From_long, 1, 0, 0) < 0)) __PYX_ERR(0, 103, __pyx_L1_error) goto __pyx_L10_break; } break; case 'f': break; case 'c': break; case 'O': break; default: break; } } } __pyx_t_2 = ((__pyx_v_itemsize == -1L) != 0); if (!__pyx_t_2) { } else { __pyx_t_3 = __pyx_t_2; goto __pyx_L24_bool_binop_done; } __pyx_t_2 = ((__pyx_v_itemsize == (sizeof(__pyx_t_5numpy_int32_t))) != 0); __pyx_t_3 = __pyx_t_2; __pyx_L24_bool_binop_done:; if (__pyx_t_3) { __pyx_t_8 = __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_int32_t(__pyx_v_arg, 0); __pyx_v_memslice = __pyx_t_8; __pyx_t_3 = (__pyx_v_memslice.memview != 0); if (__pyx_t_3) { __PYX_XDEC_MEMVIEW((&__pyx_v_memslice), 1); if (unlikely(__Pyx_SetItemInt(__pyx_v_dest_sig, 0, __pyx_n_s_int32_t, long, 1, __Pyx_PyInt_From_long, 1, 0, 0) < 0)) __PYX_ERR(0, 103, __pyx_L1_error) goto __pyx_L10_break; } /*else*/ { PyErr_Clear(); } } __pyx_t_2 = ((__pyx_v_itemsize == -1L) != 0); if (!__pyx_t_2) { } else { __pyx_t_3 = __pyx_t_2; goto __pyx_L28_bool_binop_done; } __pyx_t_2 = ((__pyx_v_itemsize == (sizeof(__pyx_t_5numpy_int64_t))) != 0); __pyx_t_3 = __pyx_t_2; __pyx_L28_bool_binop_done:; if (__pyx_t_3) { __pyx_t_8 = __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_int64_t(__pyx_v_arg, 0); __pyx_v_memslice = __pyx_t_8; __pyx_t_3 = (__pyx_v_memslice.memview != 0); if (__pyx_t_3) { __PYX_XDEC_MEMVIEW((&__pyx_v_memslice), 1); if (unlikely(__Pyx_SetItemInt(__pyx_v_dest_sig, 0, __pyx_n_s_int64_t, long, 1, __Pyx_PyInt_From_long, 1, 0, 0) < 0)) __PYX_ERR(0, 103, __pyx_L1_error) goto __pyx_L10_break; } /*else*/ { PyErr_Clear(); } } if (unlikely(__Pyx_SetItemInt(__pyx_v_dest_sig, 0, Py_None, long, 1, __Pyx_PyInt_From_long, 1, 0, 0) < 0)) __PYX_ERR(0, 103, __pyx_L1_error) goto __pyx_L10_break; } __pyx_L10_break:; __pyx_t_6 = PyList_New(0); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_v_candidates = ((PyObject*)__pyx_t_6); __pyx_t_6 = 0; __pyx_t_5 = 0; if (unlikely(__pyx_v_signatures == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); __PYX_ERR(0, 103, __pyx_L1_error) } __pyx_t_1 = __Pyx_dict_iterator(((PyObject*)__pyx_v_signatures), 1, ((PyObject *)NULL), (&__pyx_t_9), (&__pyx_t_10)); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = __pyx_t_1; __pyx_t_1 = 0; while (1) { __pyx_t_11 = __Pyx_dict_iter_next(__pyx_t_6, __pyx_t_9, &__pyx_t_5, &__pyx_t_1, NULL, NULL, __pyx_t_10); if (unlikely(__pyx_t_11 == 0)) break; if (unlikely(__pyx_t_11 == -1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XDECREF_SET(__pyx_v_sig, __pyx_t_1); __pyx_t_1 = 0; __pyx_v_match_found = 0; __pyx_t_13 = __Pyx_PyObject_GetAttrStr(__pyx_v_sig, __pyx_n_s_strip); if (unlikely(!__pyx_t_13)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_13); __pyx_t_14 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_13))) { __pyx_t_14 = PyMethod_GET_SELF(__pyx_t_13); if (likely(__pyx_t_14)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_13); __Pyx_INCREF(__pyx_t_14); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_13, function); } } __pyx_t_12 = (__pyx_t_14) ? __Pyx_PyObject_Call2Args(__pyx_t_13, __pyx_t_14, __pyx_kp_s_) : __Pyx_PyObject_CallOneArg(__pyx_t_13, __pyx_kp_s_); __Pyx_XDECREF(__pyx_t_14); __pyx_t_14 = 0; if (unlikely(!__pyx_t_12)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_12); __Pyx_DECREF(__pyx_t_13); __pyx_t_13 = 0; __pyx_t_13 = __Pyx_PyObject_GetAttrStr(__pyx_t_12, __pyx_n_s_split); if (unlikely(!__pyx_t_13)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_13); __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0; __pyx_t_12 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_13))) { __pyx_t_12 = PyMethod_GET_SELF(__pyx_t_13); if (likely(__pyx_t_12)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_13); __Pyx_INCREF(__pyx_t_12); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_13, function); } } __pyx_t_1 = (__pyx_t_12) ? __Pyx_PyObject_Call2Args(__pyx_t_13, __pyx_t_12, __pyx_kp_s__2) : __Pyx_PyObject_CallOneArg(__pyx_t_13, __pyx_kp_s__2); __Pyx_XDECREF(__pyx_t_12); __pyx_t_12 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_13); __pyx_t_13 = 0; __Pyx_XDECREF_SET(__pyx_v_src_sig, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_15 = PyList_GET_SIZE(__pyx_v_dest_sig); if (unlikely(__pyx_t_15 == ((Py_ssize_t)-1))) __PYX_ERR(0, 103, __pyx_L1_error) __pyx_t_16 = __pyx_t_15; for (__pyx_t_17 = 0; __pyx_t_17 < __pyx_t_16; __pyx_t_17+=1) { __pyx_v_i = __pyx_t_17; __pyx_t_1 = PyList_GET_ITEM(__pyx_v_dest_sig, __pyx_v_i); __Pyx_INCREF(__pyx_t_1); __Pyx_XDECREF_SET(__pyx_v_dst_type, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_3 = (__pyx_v_dst_type != Py_None); __pyx_t_2 = (__pyx_t_3 != 0); if (__pyx_t_2) { __pyx_t_1 = __Pyx_GetItemInt(__pyx_v_src_sig, __pyx_v_i, Py_ssize_t, 1, PyInt_FromSsize_t, 0, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_13 = PyObject_RichCompare(__pyx_t_1, __pyx_v_dst_type, Py_EQ); __Pyx_XGOTREF(__pyx_t_13); if (unlikely(!__pyx_t_13)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_13); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_DECREF(__pyx_t_13); __pyx_t_13 = 0; if (__pyx_t_2) { __pyx_v_match_found = 1; goto __pyx_L36; } /*else*/ { __pyx_v_match_found = 0; goto __pyx_L34_break; } __pyx_L36:; } } __pyx_L34_break:; __pyx_t_2 = (__pyx_v_match_found != 0); if (__pyx_t_2) { __pyx_t_18 = __Pyx_PyList_Append(__pyx_v_candidates, __pyx_v_sig); if (unlikely(__pyx_t_18 == ((int)-1))) __PYX_ERR(0, 103, __pyx_L1_error) } } __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_2 = (PyList_GET_SIZE(__pyx_v_candidates) != 0); __pyx_t_3 = ((!__pyx_t_2) != 0); if (__pyx_t_3) { __pyx_t_6 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_Raise(__pyx_t_6, 0, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __PYX_ERR(0, 103, __pyx_L1_error) } __pyx_t_9 = PyList_GET_SIZE(__pyx_v_candidates); if (unlikely(__pyx_t_9 == ((Py_ssize_t)-1))) __PYX_ERR(0, 103, __pyx_L1_error) __pyx_t_3 = ((__pyx_t_9 > 1) != 0); if (__pyx_t_3) { __pyx_t_6 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__4, NULL); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_Raise(__pyx_t_6, 0, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __PYX_ERR(0, 103, __pyx_L1_error) } /*else*/ { __Pyx_XDECREF(__pyx_r); if (unlikely(__pyx_v_signatures == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(0, 103, __pyx_L1_error) } __pyx_t_6 = __Pyx_PyDict_GetItem(((PyObject*)__pyx_v_signatures), PyList_GET_ITEM(__pyx_v_candidates, 0)); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_r = __pyx_t_6; __pyx_t_6 = 0; goto __pyx_L0; } /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_12); __Pyx_XDECREF(__pyx_t_13); __Pyx_XDECREF(__pyx_t_14); __Pyx_AddTraceback("openTSNE._tsne.__pyx_fused_cpdef", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_dest_sig); __Pyx_XDECREF(__pyx_v_ndarray); __Pyx_XDECREF(__pyx_v_arg); __Pyx_XDECREF(__pyx_v_dtype); __Pyx_XDECREF(__pyx_v_arg_base); __Pyx_XDECREF(__pyx_v_candidates); __Pyx_XDECREF(__pyx_v_sig); __Pyx_XDECREF(__pyx_v_src_sig); __Pyx_XDECREF(__pyx_v_dst_type); __Pyx_XDECREF(__pyx_v_kwargs); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pw_8openTSNE_5_tsne_19__pyx_fuse_0estimate_positive_gradient_nn(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_pw_8openTSNE_5_tsne_3estimate_positive_gradient_nn(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_fuse_0__pyx_f_8openTSNE_5_tsne_estimate_positive_gradient_nn(__Pyx_memviewslice __pyx_v_indices, __Pyx_memviewslice __pyx_v_indptr, __Pyx_memviewslice __pyx_v_P_data, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_reference_embedding, __Pyx_memviewslice __pyx_v_gradient, CYTHON_UNUSED int __pyx_skip_dispatch, struct __pyx_fuse_0__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn *__pyx_optional_args) { double __pyx_v_dof = __pyx_k__5; Py_ssize_t __pyx_v_num_threads = __pyx_k__6; int __pyx_v_should_eval_error = __pyx_k__7; CYTHON_UNUSED Py_ssize_t __pyx_v_n_samples; Py_ssize_t __pyx_v_n_dims; double *__pyx_v_diff; double __pyx_v_d_ij; double __pyx_v_p_ij; double __pyx_v_q_ij; double __pyx_v_kl_divergence; double __pyx_v_sum_P; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; Py_ssize_t __pyx_v_k; Py_ssize_t __pyx_v_d; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; __pyx_t_5numpy_int32_t __pyx_t_6; __pyx_t_5numpy_int32_t __pyx_t_7; Py_ssize_t __pyx_t_8; Py_ssize_t __pyx_t_9; Py_ssize_t __pyx_t_10; Py_ssize_t __pyx_t_11; Py_ssize_t __pyx_t_12; Py_ssize_t __pyx_t_13; Py_ssize_t __pyx_t_14; Py_ssize_t __pyx_t_15; PyObject *__pyx_t_16 = NULL; PyObject *__pyx_t_17 = NULL; PyObject *__pyx_t_18 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__pyx_fuse_0estimate_positive_gradient_nn", 0); if (__pyx_optional_args) { if (__pyx_optional_args->__pyx_n > 0) { __pyx_v_dof = __pyx_optional_args->dof; if (__pyx_optional_args->__pyx_n > 1) { __pyx_v_num_threads = __pyx_optional_args->num_threads; if (__pyx_optional_args->__pyx_n > 2) { __pyx_v_should_eval_error = __pyx_optional_args->should_eval_error; } } } } /* "openTSNE/_tsne.pyx":115 * ): * cdef: * Py_ssize_t n_samples = gradient.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t n_dims = gradient.shape[1] * double * diff */ __pyx_v_n_samples = (__pyx_v_gradient.shape[0]); /* "openTSNE/_tsne.pyx":116 * cdef: * Py_ssize_t n_samples = gradient.shape[0] * Py_ssize_t n_dims = gradient.shape[1] # <<<<<<<<<<<<<< * double * diff * double d_ij, p_ij, q_ij, kl_divergence = 0, sum_P = 0 */ __pyx_v_n_dims = (__pyx_v_gradient.shape[1]); /* "openTSNE/_tsne.pyx":118 * Py_ssize_t n_dims = gradient.shape[1] * double * diff * double d_ij, p_ij, q_ij, kl_divergence = 0, sum_P = 0 # <<<<<<<<<<<<<< * * Py_ssize_t i, j, k, d */ __pyx_v_kl_divergence = 0.0; __pyx_v_sum_P = 0.0; /* "openTSNE/_tsne.pyx":122 * Py_ssize_t i, j, k, d * * if num_threads < 1: # <<<<<<<<<<<<<< * num_threads = 1 * */ __pyx_t_1 = ((__pyx_v_num_threads < 1) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":123 * * if num_threads < 1: * num_threads = 1 # <<<<<<<<<<<<<< * * # Degrees of freedom cannot be negative */ __pyx_v_num_threads = 1; /* "openTSNE/_tsne.pyx":122 * Py_ssize_t i, j, k, d * * if num_threads < 1: # <<<<<<<<<<<<<< * num_threads = 1 * */ } /* "openTSNE/_tsne.pyx":126 * * # Degrees of freedom cannot be negative * if dof <= 0: # <<<<<<<<<<<<<< * dof = 1e-8 * */ __pyx_t_1 = ((__pyx_v_dof <= 0.0) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":127 * # Degrees of freedom cannot be negative * if dof <= 0: * dof = 1e-8 # <<<<<<<<<<<<<< * * with nogil, parallel(num_threads=num_threads): */ __pyx_v_dof = 1e-8; /* "openTSNE/_tsne.pyx":126 * * # Degrees of freedom cannot be negative * if dof <= 0: # <<<<<<<<<<<<<< * dof = 1e-8 * */ } /* "openTSNE/_tsne.pyx":129 * dof = 1e-8 * * with nogil, parallel(num_threads=num_threads): # <<<<<<<<<<<<<< * # Use `malloc` here instead of `PyMem_Malloc` because we're in a * # `nogil` clause and we won't be allocating much memory */ { #ifdef WITH_THREAD PyThreadState *_save; Py_UNBLOCK_THREADS __Pyx_FastGIL_Remember(); #endif /*try:*/ { { const char *__pyx_parallel_filename = NULL; int __pyx_parallel_lineno = 0, __pyx_parallel_clineno = 0; PyObject *__pyx_parallel_exc_type = NULL, *__pyx_parallel_exc_value = NULL, *__pyx_parallel_exc_tb = NULL; int __pyx_parallel_why; __pyx_parallel_why = 0; #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))))) #undef likely #undef unlikely #define likely(x) (x) #define unlikely(x) (x) #endif #ifdef _OPENMP #pragma omp parallel private(__pyx_v_diff) reduction(+:__pyx_v_kl_divergence) reduction(+:__pyx_v_sum_P) private(__pyx_t_1, __pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_num_threads) #endif /* _OPENMP */ { #ifdef _OPENMP #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif Py_BEGIN_ALLOW_THREADS #endif /* _OPENMP */ /* Initialize private variables to invalid values */ __pyx_v_diff = ((double *)1); /* "openTSNE/_tsne.pyx":132 * # Use `malloc` here instead of `PyMem_Malloc` because we're in a * # `nogil` clause and we won't be allocating much memory * diff = malloc(n_dims * sizeof(double)) # <<<<<<<<<<<<<< * if not diff: * with gil: */ __pyx_v_diff = ((double *)malloc((__pyx_v_n_dims * (sizeof(double))))); /* "openTSNE/_tsne.pyx":133 * # `nogil` clause and we won't be allocating much memory * diff = malloc(n_dims * sizeof(double)) * if not diff: # <<<<<<<<<<<<<< * with gil: * raise MemoryError() */ __pyx_t_1 = ((!(__pyx_v_diff != 0)) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":134 * diff = malloc(n_dims * sizeof(double)) * if not diff: * with gil: # <<<<<<<<<<<<<< * raise MemoryError() * */ { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif /*try:*/ { /* "openTSNE/_tsne.pyx":135 * if not diff: * with gil: * raise MemoryError() # <<<<<<<<<<<<<< * * for i in prange(n_samples, schedule="guided"): */ PyErr_NoMemory(); __PYX_ERR(0, 135, __pyx_L16_error) } /* "openTSNE/_tsne.pyx":134 * diff = malloc(n_dims * sizeof(double)) * if not diff: * with gil: # <<<<<<<<<<<<<< * raise MemoryError() * */ /*finally:*/ { __pyx_L16_error: { #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif goto __pyx_L10_error; } } } /* "openTSNE/_tsne.pyx":133 * # `nogil` clause and we won't be allocating much memory * diff = malloc(n_dims * sizeof(double)) * if not diff: # <<<<<<<<<<<<<< * with gil: * raise MemoryError() */ } /* "openTSNE/_tsne.pyx":137 * raise MemoryError() * * for i in prange(n_samples, schedule="guided"): # <<<<<<<<<<<<<< * # Iterate over all the neighbors `j` and sum up their contribution * for k in range(indptr[i], indptr[i + 1]): */ __pyx_t_2 = __pyx_v_n_samples; if ((1 == 0)) abort(); { __pyx_t_4 = (__pyx_t_2 - 0 + 1 - 1/abs(1)) / 1; if (__pyx_t_4 > 0) { #ifdef _OPENMP #pragma omp for lastprivate(__pyx_v_d) lastprivate(__pyx_v_d_ij) firstprivate(__pyx_v_i) lastprivate(__pyx_v_i) lastprivate(__pyx_v_j) lastprivate(__pyx_v_k) lastprivate(__pyx_v_p_ij) lastprivate(__pyx_v_q_ij) schedule(guided) #endif /* _OPENMP */ for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_4; __pyx_t_3++){ { __pyx_v_i = (Py_ssize_t)(0 + 1 * __pyx_t_3); /* Initialize private variables to invalid values */ __pyx_v_d = ((Py_ssize_t)0xbad0bad0); __pyx_v_d_ij = ((double)__PYX_NAN()); __pyx_v_j = ((Py_ssize_t)0xbad0bad0); __pyx_v_k = ((Py_ssize_t)0xbad0bad0); __pyx_v_p_ij = ((double)__PYX_NAN()); __pyx_v_q_ij = ((double)__PYX_NAN()); /* "openTSNE/_tsne.pyx":139 * for i in prange(n_samples, schedule="guided"): * # Iterate over all the neighbors `j` and sum up their contribution * for k in range(indptr[i], indptr[i + 1]): # <<<<<<<<<<<<<< * j = indices[k] * p_ij = P_data[k] */ __pyx_t_5 = (__pyx_v_i + 1); __pyx_t_6 = (*((__pyx_t_5numpy_int32_t *) ( /* dim=0 */ (__pyx_v_indptr.data + __pyx_t_5 * __pyx_v_indptr.strides[0]) ))); __pyx_t_5 = __pyx_v_i; __pyx_t_7 = __pyx_t_6; for (__pyx_t_8 = (*((__pyx_t_5numpy_int32_t *) ( /* dim=0 */ (__pyx_v_indptr.data + __pyx_t_5 * __pyx_v_indptr.strides[0]) ))); __pyx_t_8 < __pyx_t_7; __pyx_t_8+=1) { __pyx_v_k = __pyx_t_8; /* "openTSNE/_tsne.pyx":140 * # Iterate over all the neighbors `j` and sum up their contribution * for k in range(indptr[i], indptr[i + 1]): * j = indices[k] # <<<<<<<<<<<<<< * p_ij = P_data[k] * # Compute the direction of the points attraction and the */ __pyx_t_9 = __pyx_v_k; __pyx_v_j = (*((__pyx_t_5numpy_int32_t *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_9 * __pyx_v_indices.strides[0]) ))); /* "openTSNE/_tsne.pyx":141 * for k in range(indptr[i], indptr[i + 1]): * j = indices[k] * p_ij = P_data[k] # <<<<<<<<<<<<<< * # Compute the direction of the points attraction and the * # squared euclidean distance between the points */ __pyx_t_9 = __pyx_v_k; __pyx_v_p_ij = (*((double *) ( /* dim=0 */ (__pyx_v_P_data.data + __pyx_t_9 * __pyx_v_P_data.strides[0]) ))); /* "openTSNE/_tsne.pyx":144 * # Compute the direction of the points attraction and the * # squared euclidean distance between the points * d_ij = 0 # <<<<<<<<<<<<<< * for d in range(n_dims): * diff[d] = embedding[i, d] - reference_embedding[j, d] */ __pyx_v_d_ij = 0.0; /* "openTSNE/_tsne.pyx":145 * # squared euclidean distance between the points * d_ij = 0 * for d in range(n_dims): # <<<<<<<<<<<<<< * diff[d] = embedding[i, d] - reference_embedding[j, d] * d_ij = d_ij + diff[d] * diff[d] */ __pyx_t_10 = __pyx_v_n_dims; __pyx_t_11 = __pyx_t_10; for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) { __pyx_v_d = __pyx_t_12; /* "openTSNE/_tsne.pyx":146 * d_ij = 0 * for d in range(n_dims): * diff[d] = embedding[i, d] - reference_embedding[j, d] # <<<<<<<<<<<<<< * d_ij = d_ij + diff[d] * diff[d] * */ __pyx_t_9 = __pyx_v_i; __pyx_t_13 = __pyx_v_d; __pyx_t_14 = __pyx_v_j; __pyx_t_15 = __pyx_v_d; (__pyx_v_diff[__pyx_v_d]) = ((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_9 * __pyx_v_embedding.strides[0]) )) + __pyx_t_13)) ))) - (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_14 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_15)) )))); /* "openTSNE/_tsne.pyx":147 * for d in range(n_dims): * diff[d] = embedding[i, d] - reference_embedding[j, d] * d_ij = d_ij + diff[d] * diff[d] # <<<<<<<<<<<<<< * * if dof != 1: */ __pyx_v_d_ij = (__pyx_v_d_ij + ((__pyx_v_diff[__pyx_v_d]) * (__pyx_v_diff[__pyx_v_d]))); } /* "openTSNE/_tsne.pyx":149 * d_ij = d_ij + diff[d] * diff[d] * * if dof != 1: # <<<<<<<<<<<<<< * # No need exp by dof here because the terms cancel out * q_ij = 1 / (1 + d_ij / dof) */ __pyx_t_1 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":151 * if dof != 1: * # No need exp by dof here because the terms cancel out * q_ij = 1 / (1 + d_ij / dof) # <<<<<<<<<<<<<< * else: * q_ij = 1 / (1 + d_ij) */ __pyx_v_q_ij = (1.0 / (1.0 + (__pyx_v_d_ij / __pyx_v_dof))); /* "openTSNE/_tsne.pyx":149 * d_ij = d_ij + diff[d] * diff[d] * * if dof != 1: # <<<<<<<<<<<<<< * # No need exp by dof here because the terms cancel out * q_ij = 1 / (1 + d_ij / dof) */ goto __pyx_L26; } /* "openTSNE/_tsne.pyx":153 * q_ij = 1 / (1 + d_ij / dof) * else: * q_ij = 1 / (1 + d_ij) # <<<<<<<<<<<<<< * * # Compute F_{attr} of point `j` on point `i` */ /*else*/ { __pyx_v_q_ij = (1.0 / (1.0 + __pyx_v_d_ij)); } __pyx_L26:; /* "openTSNE/_tsne.pyx":156 * * # Compute F_{attr} of point `j` on point `i` * for d in range(n_dims): # <<<<<<<<<<<<<< * gradient[i, d] = gradient[i, d] + q_ij * p_ij * diff[d] * */ __pyx_t_10 = __pyx_v_n_dims; __pyx_t_11 = __pyx_t_10; for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) { __pyx_v_d = __pyx_t_12; /* "openTSNE/_tsne.pyx":157 * # Compute F_{attr} of point `j` on point `i` * for d in range(n_dims): * gradient[i, d] = gradient[i, d] + q_ij * p_ij * diff[d] # <<<<<<<<<<<<<< * * # Evaluating the following expressions can slow things down */ __pyx_t_15 = __pyx_v_i; __pyx_t_14 = __pyx_v_d; __pyx_t_13 = __pyx_v_i; __pyx_t_9 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_gradient.data + __pyx_t_13 * __pyx_v_gradient.strides[0]) )) + __pyx_t_9)) )) = ((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_gradient.data + __pyx_t_15 * __pyx_v_gradient.strides[0]) )) + __pyx_t_14)) ))) + ((__pyx_v_q_ij * __pyx_v_p_ij) * (__pyx_v_diff[__pyx_v_d]))); } /* "openTSNE/_tsne.pyx":163 * # is unnormalized, so we need to normalize once the sum of q_ij * # is known * if should_eval_error: # <<<<<<<<<<<<<< * sum_P += p_ij * kl_divergence += p_ij * log((p_ij / (q_ij + EPSILON)) + EPSILON) */ __pyx_t_1 = (__pyx_v_should_eval_error != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":164 * # is known * if should_eval_error: * sum_P += p_ij # <<<<<<<<<<<<<< * kl_divergence += p_ij * log((p_ij / (q_ij + EPSILON)) + EPSILON) * */ __pyx_v_sum_P = (__pyx_v_sum_P + __pyx_v_p_ij); /* "openTSNE/_tsne.pyx":165 * if should_eval_error: * sum_P += p_ij * kl_divergence += p_ij * log((p_ij / (q_ij + EPSILON)) + EPSILON) # <<<<<<<<<<<<<< * * free(diff) */ __pyx_v_kl_divergence = (__pyx_v_kl_divergence + (__pyx_v_p_ij * log(((__pyx_v_p_ij / (__pyx_v_q_ij + __pyx_v_8openTSNE_5_tsne_EPSILON)) + __pyx_v_8openTSNE_5_tsne_EPSILON)))); /* "openTSNE/_tsne.pyx":163 * # is unnormalized, so we need to normalize once the sum of q_ij * # is known * if should_eval_error: # <<<<<<<<<<<<<< * sum_P += p_ij * kl_divergence += p_ij * log((p_ij / (q_ij + EPSILON)) + EPSILON) */ } } } } } } /* "openTSNE/_tsne.pyx":167 * kl_divergence += p_ij * log((p_ij / (q_ij + EPSILON)) + EPSILON) * * free(diff) # <<<<<<<<<<<<<< * * return sum_P, kl_divergence */ free(__pyx_v_diff); goto __pyx_L33; __pyx_L10_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif #ifdef _OPENMP #pragma omp flush(__pyx_parallel_exc_type) #endif /* _OPENMP */ if (!__pyx_parallel_exc_type) { __Pyx_ErrFetchWithState(&__pyx_parallel_exc_type, &__pyx_parallel_exc_value, &__pyx_parallel_exc_tb); __pyx_parallel_filename = __pyx_filename; __pyx_parallel_lineno = __pyx_lineno; __pyx_parallel_clineno = __pyx_clineno; __Pyx_GOTREF(__pyx_parallel_exc_type); } #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_parallel_why = 4; goto __pyx_L33; __pyx_L33:; #ifdef _OPENMP Py_END_ALLOW_THREADS #else { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif #endif /* _OPENMP */ /* Clean up any temporaries */ #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif #ifndef _OPENMP } #endif /* _OPENMP */ } if (__pyx_parallel_exc_type) { /* This may have been overridden by a continue, break or return in another thread. Prefer the error. */ __pyx_parallel_why = 4; } if (__pyx_parallel_why) { switch (__pyx_parallel_why) { case 4: { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_GIVEREF(__pyx_parallel_exc_type); __Pyx_ErrRestoreWithState(__pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb); __pyx_filename = __pyx_parallel_filename; __pyx_lineno = __pyx_parallel_lineno; __pyx_clineno = __pyx_parallel_clineno; #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } goto __pyx_L6_error; } } } #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))))) #undef likely #undef unlikely #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #endif } /* "openTSNE/_tsne.pyx":129 * dof = 1e-8 * * with nogil, parallel(num_threads=num_threads): # <<<<<<<<<<<<<< * # Use `malloc` here instead of `PyMem_Malloc` because we're in a * # `nogil` clause and we won't be allocating much memory */ /*finally:*/ { /*normal exit:*/{ #ifdef WITH_THREAD __Pyx_FastGIL_Forget(); Py_BLOCK_THREADS #endif goto __pyx_L7; } __pyx_L6_error: { #ifdef WITH_THREAD __Pyx_FastGIL_Forget(); Py_BLOCK_THREADS #endif goto __pyx_L1_error; } __pyx_L7:; } } /* "openTSNE/_tsne.pyx":169 * free(diff) * * return sum_P, kl_divergence # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_t_16 = PyFloat_FromDouble(__pyx_v_sum_P); if (unlikely(!__pyx_t_16)) __PYX_ERR(0, 169, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_16); __pyx_t_17 = PyFloat_FromDouble(__pyx_v_kl_divergence); if (unlikely(!__pyx_t_17)) __PYX_ERR(0, 169, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_17); __pyx_t_18 = PyTuple_New(2); if (unlikely(!__pyx_t_18)) __PYX_ERR(0, 169, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_18); __Pyx_GIVEREF(__pyx_t_16); PyTuple_SET_ITEM(__pyx_t_18, 0, __pyx_t_16); __Pyx_GIVEREF(__pyx_t_17); PyTuple_SET_ITEM(__pyx_t_18, 1, __pyx_t_17); __pyx_t_16 = 0; __pyx_t_17 = 0; __pyx_r = ((PyObject*)__pyx_t_18); __pyx_t_18 = 0; goto __pyx_L0; /* "openTSNE/_tsne.pyx":103 * * * cpdef tuple estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * sparse_index_type[:] indices, * sparse_index_type[:] indptr, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_16); __Pyx_XDECREF(__pyx_t_17); __Pyx_XDECREF(__pyx_t_18); __Pyx_AddTraceback("openTSNE._tsne.estimate_positive_gradient_nn", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_5_tsne_19__pyx_fuse_0estimate_positive_gradient_nn(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyMethodDef __pyx_fuse_0__pyx_mdef_8openTSNE_5_tsne_19__pyx_fuse_0estimate_positive_gradient_nn = {"__pyx_fuse_0estimate_positive_gradient_nn", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_5_tsne_19__pyx_fuse_0estimate_positive_gradient_nn, METH_VARARGS|METH_KEYWORDS, 0}; static PyObject *__pyx_pw_8openTSNE_5_tsne_19__pyx_fuse_0estimate_positive_gradient_nn(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __Pyx_memviewslice __pyx_v_indices = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_indptr = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_P_data = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_reference_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_gradient = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_dof; Py_ssize_t __pyx_v_num_threads; int __pyx_v_should_eval_error; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__pyx_fuse_0estimate_positive_gradient_nn (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_indices,&__pyx_n_s_indptr,&__pyx_n_s_P_data,&__pyx_n_s_embedding,&__pyx_n_s_reference_embedding,&__pyx_n_s_gradient,&__pyx_n_s_dof,&__pyx_n_s_num_threads,&__pyx_n_s_should_eval_error,0}; PyObject* values[9] = {0,0,0,0,0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 9: values[8] = PyTuple_GET_ITEM(__pyx_args, 8); CYTHON_FALLTHROUGH; case 8: values[7] = PyTuple_GET_ITEM(__pyx_args, 7); CYTHON_FALLTHROUGH; case 7: values[6] = PyTuple_GET_ITEM(__pyx_args, 6); CYTHON_FALLTHROUGH; case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_indices)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_indptr)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_fuse_0estimate_positive_gradient_nn", 0, 6, 9, 1); __PYX_ERR(0, 103, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_P_data)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_fuse_0estimate_positive_gradient_nn", 0, 6, 9, 2); __PYX_ERR(0, 103, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 3: if (likely((values[3] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_embedding)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_fuse_0estimate_positive_gradient_nn", 0, 6, 9, 3); __PYX_ERR(0, 103, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 4: if (likely((values[4] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_reference_embedding)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_fuse_0estimate_positive_gradient_nn", 0, 6, 9, 4); __PYX_ERR(0, 103, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 5: if (likely((values[5] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_gradient)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_fuse_0estimate_positive_gradient_nn", 0, 6, 9, 5); __PYX_ERR(0, 103, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 6: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dof); if (value) { values[6] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 7: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_num_threads); if (value) { values[7] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 8: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_should_eval_error); if (value) { values[8] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__pyx_fuse_0estimate_positive_gradient_nn") < 0)) __PYX_ERR(0, 103, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 9: values[8] = PyTuple_GET_ITEM(__pyx_args, 8); CYTHON_FALLTHROUGH; case 8: values[7] = PyTuple_GET_ITEM(__pyx_args, 7); CYTHON_FALLTHROUGH; case 7: values[6] = PyTuple_GET_ITEM(__pyx_args, 6); CYTHON_FALLTHROUGH; case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); values[4] = PyTuple_GET_ITEM(__pyx_args, 4); values[3] = PyTuple_GET_ITEM(__pyx_args, 3); values[2] = PyTuple_GET_ITEM(__pyx_args, 2); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_indices = __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_int32_t(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_indices.memview)) __PYX_ERR(0, 104, __pyx_L3_error) __pyx_v_indptr = __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_int32_t(values[1], PyBUF_WRITABLE); if (unlikely(!__pyx_v_indptr.memview)) __PYX_ERR(0, 105, __pyx_L3_error) __pyx_v_P_data = __Pyx_PyObject_to_MemoryviewSlice_ds_double(values[2], PyBUF_WRITABLE); if (unlikely(!__pyx_v_P_data.memview)) __PYX_ERR(0, 106, __pyx_L3_error) __pyx_v_embedding = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[3], PyBUF_WRITABLE); if (unlikely(!__pyx_v_embedding.memview)) __PYX_ERR(0, 107, __pyx_L3_error) __pyx_v_reference_embedding = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[4], PyBUF_WRITABLE); if (unlikely(!__pyx_v_reference_embedding.memview)) __PYX_ERR(0, 108, __pyx_L3_error) __pyx_v_gradient = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[5], PyBUF_WRITABLE); if (unlikely(!__pyx_v_gradient.memview)) __PYX_ERR(0, 109, __pyx_L3_error) if (values[6]) { __pyx_v_dof = __pyx_PyFloat_AsDouble(values[6]); if (unlikely((__pyx_v_dof == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 110, __pyx_L3_error) } else { __pyx_v_dof = __pyx_k__5; } if (values[7]) { __pyx_v_num_threads = __Pyx_PyIndex_AsSsize_t(values[7]); if (unlikely((__pyx_v_num_threads == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 111, __pyx_L3_error) } else { __pyx_v_num_threads = __pyx_k__6; } if (values[8]) { __pyx_v_should_eval_error = __Pyx_PyObject_IsTrue(values[8]); if (unlikely((__pyx_v_should_eval_error == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 112, __pyx_L3_error) } else { __pyx_v_should_eval_error = __pyx_k__7; } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__pyx_fuse_0estimate_positive_gradient_nn", 0, 6, 9, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 103, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE._tsne.__pyx_fuse_0estimate_positive_gradient_nn", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_5_tsne_18__pyx_fuse_0estimate_positive_gradient_nn(__pyx_self, __pyx_v_indices, __pyx_v_indptr, __pyx_v_P_data, __pyx_v_embedding, __pyx_v_reference_embedding, __pyx_v_gradient, __pyx_v_dof, __pyx_v_num_threads, __pyx_v_should_eval_error); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_5_tsne_18__pyx_fuse_0estimate_positive_gradient_nn(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_indices, __Pyx_memviewslice __pyx_v_indptr, __Pyx_memviewslice __pyx_v_P_data, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_reference_embedding, __Pyx_memviewslice __pyx_v_gradient, double __pyx_v_dof, Py_ssize_t __pyx_v_num_threads, int __pyx_v_should_eval_error) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; struct __pyx_fuse_0__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn __pyx_t_2; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__pyx_fuse_0estimate_positive_gradient_nn", 0); __Pyx_XDECREF(__pyx_r); __pyx_t_2.__pyx_n = 3; __pyx_t_2.dof = __pyx_v_dof; __pyx_t_2.num_threads = __pyx_v_num_threads; __pyx_t_2.should_eval_error = __pyx_v_should_eval_error; __pyx_t_1 = __pyx_fuse_0__pyx_f_8openTSNE_5_tsne_estimate_positive_gradient_nn(__pyx_v_indices, __pyx_v_indptr, __pyx_v_P_data, __pyx_v_embedding, __pyx_v_reference_embedding, __pyx_v_gradient, 0, &__pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("openTSNE._tsne.__pyx_fuse_0estimate_positive_gradient_nn", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_indices, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_indptr, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_P_data, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_embedding, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_reference_embedding, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_gradient, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pw_8openTSNE_5_tsne_21__pyx_fuse_1estimate_positive_gradient_nn(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_pw_8openTSNE_5_tsne_3estimate_positive_gradient_nn(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_fuse_1__pyx_f_8openTSNE_5_tsne_estimate_positive_gradient_nn(__Pyx_memviewslice __pyx_v_indices, __Pyx_memviewslice __pyx_v_indptr, __Pyx_memviewslice __pyx_v_P_data, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_reference_embedding, __Pyx_memviewslice __pyx_v_gradient, CYTHON_UNUSED int __pyx_skip_dispatch, struct __pyx_fuse_1__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn *__pyx_optional_args) { double __pyx_v_dof = __pyx_k__8; Py_ssize_t __pyx_v_num_threads = __pyx_k__9; int __pyx_v_should_eval_error = __pyx_k__10; CYTHON_UNUSED Py_ssize_t __pyx_v_n_samples; Py_ssize_t __pyx_v_n_dims; double *__pyx_v_diff; double __pyx_v_d_ij; double __pyx_v_p_ij; double __pyx_v_q_ij; double __pyx_v_kl_divergence; double __pyx_v_sum_P; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; Py_ssize_t __pyx_v_k; Py_ssize_t __pyx_v_d; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; __pyx_t_5numpy_int64_t __pyx_t_6; __pyx_t_5numpy_int64_t __pyx_t_7; Py_ssize_t __pyx_t_8; Py_ssize_t __pyx_t_9; Py_ssize_t __pyx_t_10; Py_ssize_t __pyx_t_11; Py_ssize_t __pyx_t_12; Py_ssize_t __pyx_t_13; Py_ssize_t __pyx_t_14; Py_ssize_t __pyx_t_15; PyObject *__pyx_t_16 = NULL; PyObject *__pyx_t_17 = NULL; PyObject *__pyx_t_18 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__pyx_fuse_1estimate_positive_gradient_nn", 0); if (__pyx_optional_args) { if (__pyx_optional_args->__pyx_n > 0) { __pyx_v_dof = __pyx_optional_args->dof; if (__pyx_optional_args->__pyx_n > 1) { __pyx_v_num_threads = __pyx_optional_args->num_threads; if (__pyx_optional_args->__pyx_n > 2) { __pyx_v_should_eval_error = __pyx_optional_args->should_eval_error; } } } } /* "openTSNE/_tsne.pyx":115 * ): * cdef: * Py_ssize_t n_samples = gradient.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t n_dims = gradient.shape[1] * double * diff */ __pyx_v_n_samples = (__pyx_v_gradient.shape[0]); /* "openTSNE/_tsne.pyx":116 * cdef: * Py_ssize_t n_samples = gradient.shape[0] * Py_ssize_t n_dims = gradient.shape[1] # <<<<<<<<<<<<<< * double * diff * double d_ij, p_ij, q_ij, kl_divergence = 0, sum_P = 0 */ __pyx_v_n_dims = (__pyx_v_gradient.shape[1]); /* "openTSNE/_tsne.pyx":118 * Py_ssize_t n_dims = gradient.shape[1] * double * diff * double d_ij, p_ij, q_ij, kl_divergence = 0, sum_P = 0 # <<<<<<<<<<<<<< * * Py_ssize_t i, j, k, d */ __pyx_v_kl_divergence = 0.0; __pyx_v_sum_P = 0.0; /* "openTSNE/_tsne.pyx":122 * Py_ssize_t i, j, k, d * * if num_threads < 1: # <<<<<<<<<<<<<< * num_threads = 1 * */ __pyx_t_1 = ((__pyx_v_num_threads < 1) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":123 * * if num_threads < 1: * num_threads = 1 # <<<<<<<<<<<<<< * * # Degrees of freedom cannot be negative */ __pyx_v_num_threads = 1; /* "openTSNE/_tsne.pyx":122 * Py_ssize_t i, j, k, d * * if num_threads < 1: # <<<<<<<<<<<<<< * num_threads = 1 * */ } /* "openTSNE/_tsne.pyx":126 * * # Degrees of freedom cannot be negative * if dof <= 0: # <<<<<<<<<<<<<< * dof = 1e-8 * */ __pyx_t_1 = ((__pyx_v_dof <= 0.0) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":127 * # Degrees of freedom cannot be negative * if dof <= 0: * dof = 1e-8 # <<<<<<<<<<<<<< * * with nogil, parallel(num_threads=num_threads): */ __pyx_v_dof = 1e-8; /* "openTSNE/_tsne.pyx":126 * * # Degrees of freedom cannot be negative * if dof <= 0: # <<<<<<<<<<<<<< * dof = 1e-8 * */ } /* "openTSNE/_tsne.pyx":129 * dof = 1e-8 * * with nogil, parallel(num_threads=num_threads): # <<<<<<<<<<<<<< * # Use `malloc` here instead of `PyMem_Malloc` because we're in a * # `nogil` clause and we won't be allocating much memory */ { #ifdef WITH_THREAD PyThreadState *_save; Py_UNBLOCK_THREADS __Pyx_FastGIL_Remember(); #endif /*try:*/ { { const char *__pyx_parallel_filename = NULL; int __pyx_parallel_lineno = 0, __pyx_parallel_clineno = 0; PyObject *__pyx_parallel_exc_type = NULL, *__pyx_parallel_exc_value = NULL, *__pyx_parallel_exc_tb = NULL; int __pyx_parallel_why; __pyx_parallel_why = 0; #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))))) #undef likely #undef unlikely #define likely(x) (x) #define unlikely(x) (x) #endif #ifdef _OPENMP #pragma omp parallel private(__pyx_v_diff) reduction(+:__pyx_v_kl_divergence) reduction(+:__pyx_v_sum_P) private(__pyx_t_1, __pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_num_threads) #endif /* _OPENMP */ { #ifdef _OPENMP #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif Py_BEGIN_ALLOW_THREADS #endif /* _OPENMP */ /* Initialize private variables to invalid values */ __pyx_v_diff = ((double *)1); /* "openTSNE/_tsne.pyx":132 * # Use `malloc` here instead of `PyMem_Malloc` because we're in a * # `nogil` clause and we won't be allocating much memory * diff = malloc(n_dims * sizeof(double)) # <<<<<<<<<<<<<< * if not diff: * with gil: */ __pyx_v_diff = ((double *)malloc((__pyx_v_n_dims * (sizeof(double))))); /* "openTSNE/_tsne.pyx":133 * # `nogil` clause and we won't be allocating much memory * diff = malloc(n_dims * sizeof(double)) * if not diff: # <<<<<<<<<<<<<< * with gil: * raise MemoryError() */ __pyx_t_1 = ((!(__pyx_v_diff != 0)) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":134 * diff = malloc(n_dims * sizeof(double)) * if not diff: * with gil: # <<<<<<<<<<<<<< * raise MemoryError() * */ { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif /*try:*/ { /* "openTSNE/_tsne.pyx":135 * if not diff: * with gil: * raise MemoryError() # <<<<<<<<<<<<<< * * for i in prange(n_samples, schedule="guided"): */ PyErr_NoMemory(); __PYX_ERR(0, 135, __pyx_L16_error) } /* "openTSNE/_tsne.pyx":134 * diff = malloc(n_dims * sizeof(double)) * if not diff: * with gil: # <<<<<<<<<<<<<< * raise MemoryError() * */ /*finally:*/ { __pyx_L16_error: { #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif goto __pyx_L10_error; } } } /* "openTSNE/_tsne.pyx":133 * # `nogil` clause and we won't be allocating much memory * diff = malloc(n_dims * sizeof(double)) * if not diff: # <<<<<<<<<<<<<< * with gil: * raise MemoryError() */ } /* "openTSNE/_tsne.pyx":137 * raise MemoryError() * * for i in prange(n_samples, schedule="guided"): # <<<<<<<<<<<<<< * # Iterate over all the neighbors `j` and sum up their contribution * for k in range(indptr[i], indptr[i + 1]): */ __pyx_t_2 = __pyx_v_n_samples; if ((1 == 0)) abort(); { __pyx_t_4 = (__pyx_t_2 - 0 + 1 - 1/abs(1)) / 1; if (__pyx_t_4 > 0) { #ifdef _OPENMP #pragma omp for lastprivate(__pyx_v_d) lastprivate(__pyx_v_d_ij) firstprivate(__pyx_v_i) lastprivate(__pyx_v_i) lastprivate(__pyx_v_j) lastprivate(__pyx_v_k) lastprivate(__pyx_v_p_ij) lastprivate(__pyx_v_q_ij) schedule(guided) #endif /* _OPENMP */ for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_4; __pyx_t_3++){ { __pyx_v_i = (Py_ssize_t)(0 + 1 * __pyx_t_3); /* Initialize private variables to invalid values */ __pyx_v_d = ((Py_ssize_t)0xbad0bad0); __pyx_v_d_ij = ((double)__PYX_NAN()); __pyx_v_j = ((Py_ssize_t)0xbad0bad0); __pyx_v_k = ((Py_ssize_t)0xbad0bad0); __pyx_v_p_ij = ((double)__PYX_NAN()); __pyx_v_q_ij = ((double)__PYX_NAN()); /* "openTSNE/_tsne.pyx":139 * for i in prange(n_samples, schedule="guided"): * # Iterate over all the neighbors `j` and sum up their contribution * for k in range(indptr[i], indptr[i + 1]): # <<<<<<<<<<<<<< * j = indices[k] * p_ij = P_data[k] */ __pyx_t_5 = (__pyx_v_i + 1); __pyx_t_6 = (*((__pyx_t_5numpy_int64_t *) ( /* dim=0 */ (__pyx_v_indptr.data + __pyx_t_5 * __pyx_v_indptr.strides[0]) ))); __pyx_t_5 = __pyx_v_i; __pyx_t_7 = __pyx_t_6; for (__pyx_t_8 = (*((__pyx_t_5numpy_int64_t *) ( /* dim=0 */ (__pyx_v_indptr.data + __pyx_t_5 * __pyx_v_indptr.strides[0]) ))); __pyx_t_8 < __pyx_t_7; __pyx_t_8+=1) { __pyx_v_k = __pyx_t_8; /* "openTSNE/_tsne.pyx":140 * # Iterate over all the neighbors `j` and sum up their contribution * for k in range(indptr[i], indptr[i + 1]): * j = indices[k] # <<<<<<<<<<<<<< * p_ij = P_data[k] * # Compute the direction of the points attraction and the */ __pyx_t_9 = __pyx_v_k; __pyx_v_j = (*((__pyx_t_5numpy_int64_t *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_9 * __pyx_v_indices.strides[0]) ))); /* "openTSNE/_tsne.pyx":141 * for k in range(indptr[i], indptr[i + 1]): * j = indices[k] * p_ij = P_data[k] # <<<<<<<<<<<<<< * # Compute the direction of the points attraction and the * # squared euclidean distance between the points */ __pyx_t_9 = __pyx_v_k; __pyx_v_p_ij = (*((double *) ( /* dim=0 */ (__pyx_v_P_data.data + __pyx_t_9 * __pyx_v_P_data.strides[0]) ))); /* "openTSNE/_tsne.pyx":144 * # Compute the direction of the points attraction and the * # squared euclidean distance between the points * d_ij = 0 # <<<<<<<<<<<<<< * for d in range(n_dims): * diff[d] = embedding[i, d] - reference_embedding[j, d] */ __pyx_v_d_ij = 0.0; /* "openTSNE/_tsne.pyx":145 * # squared euclidean distance between the points * d_ij = 0 * for d in range(n_dims): # <<<<<<<<<<<<<< * diff[d] = embedding[i, d] - reference_embedding[j, d] * d_ij = d_ij + diff[d] * diff[d] */ __pyx_t_10 = __pyx_v_n_dims; __pyx_t_11 = __pyx_t_10; for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) { __pyx_v_d = __pyx_t_12; /* "openTSNE/_tsne.pyx":146 * d_ij = 0 * for d in range(n_dims): * diff[d] = embedding[i, d] - reference_embedding[j, d] # <<<<<<<<<<<<<< * d_ij = d_ij + diff[d] * diff[d] * */ __pyx_t_9 = __pyx_v_i; __pyx_t_13 = __pyx_v_d; __pyx_t_14 = __pyx_v_j; __pyx_t_15 = __pyx_v_d; (__pyx_v_diff[__pyx_v_d]) = ((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_9 * __pyx_v_embedding.strides[0]) )) + __pyx_t_13)) ))) - (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_14 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_15)) )))); /* "openTSNE/_tsne.pyx":147 * for d in range(n_dims): * diff[d] = embedding[i, d] - reference_embedding[j, d] * d_ij = d_ij + diff[d] * diff[d] # <<<<<<<<<<<<<< * * if dof != 1: */ __pyx_v_d_ij = (__pyx_v_d_ij + ((__pyx_v_diff[__pyx_v_d]) * (__pyx_v_diff[__pyx_v_d]))); } /* "openTSNE/_tsne.pyx":149 * d_ij = d_ij + diff[d] * diff[d] * * if dof != 1: # <<<<<<<<<<<<<< * # No need exp by dof here because the terms cancel out * q_ij = 1 / (1 + d_ij / dof) */ __pyx_t_1 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":151 * if dof != 1: * # No need exp by dof here because the terms cancel out * q_ij = 1 / (1 + d_ij / dof) # <<<<<<<<<<<<<< * else: * q_ij = 1 / (1 + d_ij) */ __pyx_v_q_ij = (1.0 / (1.0 + (__pyx_v_d_ij / __pyx_v_dof))); /* "openTSNE/_tsne.pyx":149 * d_ij = d_ij + diff[d] * diff[d] * * if dof != 1: # <<<<<<<<<<<<<< * # No need exp by dof here because the terms cancel out * q_ij = 1 / (1 + d_ij / dof) */ goto __pyx_L26; } /* "openTSNE/_tsne.pyx":153 * q_ij = 1 / (1 + d_ij / dof) * else: * q_ij = 1 / (1 + d_ij) # <<<<<<<<<<<<<< * * # Compute F_{attr} of point `j` on point `i` */ /*else*/ { __pyx_v_q_ij = (1.0 / (1.0 + __pyx_v_d_ij)); } __pyx_L26:; /* "openTSNE/_tsne.pyx":156 * * # Compute F_{attr} of point `j` on point `i` * for d in range(n_dims): # <<<<<<<<<<<<<< * gradient[i, d] = gradient[i, d] + q_ij * p_ij * diff[d] * */ __pyx_t_10 = __pyx_v_n_dims; __pyx_t_11 = __pyx_t_10; for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) { __pyx_v_d = __pyx_t_12; /* "openTSNE/_tsne.pyx":157 * # Compute F_{attr} of point `j` on point `i` * for d in range(n_dims): * gradient[i, d] = gradient[i, d] + q_ij * p_ij * diff[d] # <<<<<<<<<<<<<< * * # Evaluating the following expressions can slow things down */ __pyx_t_15 = __pyx_v_i; __pyx_t_14 = __pyx_v_d; __pyx_t_13 = __pyx_v_i; __pyx_t_9 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_gradient.data + __pyx_t_13 * __pyx_v_gradient.strides[0]) )) + __pyx_t_9)) )) = ((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_gradient.data + __pyx_t_15 * __pyx_v_gradient.strides[0]) )) + __pyx_t_14)) ))) + ((__pyx_v_q_ij * __pyx_v_p_ij) * (__pyx_v_diff[__pyx_v_d]))); } /* "openTSNE/_tsne.pyx":163 * # is unnormalized, so we need to normalize once the sum of q_ij * # is known * if should_eval_error: # <<<<<<<<<<<<<< * sum_P += p_ij * kl_divergence += p_ij * log((p_ij / (q_ij + EPSILON)) + EPSILON) */ __pyx_t_1 = (__pyx_v_should_eval_error != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":164 * # is known * if should_eval_error: * sum_P += p_ij # <<<<<<<<<<<<<< * kl_divergence += p_ij * log((p_ij / (q_ij + EPSILON)) + EPSILON) * */ __pyx_v_sum_P = (__pyx_v_sum_P + __pyx_v_p_ij); /* "openTSNE/_tsne.pyx":165 * if should_eval_error: * sum_P += p_ij * kl_divergence += p_ij * log((p_ij / (q_ij + EPSILON)) + EPSILON) # <<<<<<<<<<<<<< * * free(diff) */ __pyx_v_kl_divergence = (__pyx_v_kl_divergence + (__pyx_v_p_ij * log(((__pyx_v_p_ij / (__pyx_v_q_ij + __pyx_v_8openTSNE_5_tsne_EPSILON)) + __pyx_v_8openTSNE_5_tsne_EPSILON)))); /* "openTSNE/_tsne.pyx":163 * # is unnormalized, so we need to normalize once the sum of q_ij * # is known * if should_eval_error: # <<<<<<<<<<<<<< * sum_P += p_ij * kl_divergence += p_ij * log((p_ij / (q_ij + EPSILON)) + EPSILON) */ } } } } } } /* "openTSNE/_tsne.pyx":167 * kl_divergence += p_ij * log((p_ij / (q_ij + EPSILON)) + EPSILON) * * free(diff) # <<<<<<<<<<<<<< * * return sum_P, kl_divergence */ free(__pyx_v_diff); goto __pyx_L33; __pyx_L10_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif #ifdef _OPENMP #pragma omp flush(__pyx_parallel_exc_type) #endif /* _OPENMP */ if (!__pyx_parallel_exc_type) { __Pyx_ErrFetchWithState(&__pyx_parallel_exc_type, &__pyx_parallel_exc_value, &__pyx_parallel_exc_tb); __pyx_parallel_filename = __pyx_filename; __pyx_parallel_lineno = __pyx_lineno; __pyx_parallel_clineno = __pyx_clineno; __Pyx_GOTREF(__pyx_parallel_exc_type); } #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_parallel_why = 4; goto __pyx_L33; __pyx_L33:; #ifdef _OPENMP Py_END_ALLOW_THREADS #else { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif #endif /* _OPENMP */ /* Clean up any temporaries */ #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif #ifndef _OPENMP } #endif /* _OPENMP */ } if (__pyx_parallel_exc_type) { /* This may have been overridden by a continue, break or return in another thread. Prefer the error. */ __pyx_parallel_why = 4; } if (__pyx_parallel_why) { switch (__pyx_parallel_why) { case 4: { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_GIVEREF(__pyx_parallel_exc_type); __Pyx_ErrRestoreWithState(__pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb); __pyx_filename = __pyx_parallel_filename; __pyx_lineno = __pyx_parallel_lineno; __pyx_clineno = __pyx_parallel_clineno; #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } goto __pyx_L6_error; } } } #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))))) #undef likely #undef unlikely #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #endif } /* "openTSNE/_tsne.pyx":129 * dof = 1e-8 * * with nogil, parallel(num_threads=num_threads): # <<<<<<<<<<<<<< * # Use `malloc` here instead of `PyMem_Malloc` because we're in a * # `nogil` clause and we won't be allocating much memory */ /*finally:*/ { /*normal exit:*/{ #ifdef WITH_THREAD __Pyx_FastGIL_Forget(); Py_BLOCK_THREADS #endif goto __pyx_L7; } __pyx_L6_error: { #ifdef WITH_THREAD __Pyx_FastGIL_Forget(); Py_BLOCK_THREADS #endif goto __pyx_L1_error; } __pyx_L7:; } } /* "openTSNE/_tsne.pyx":169 * free(diff) * * return sum_P, kl_divergence # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_t_16 = PyFloat_FromDouble(__pyx_v_sum_P); if (unlikely(!__pyx_t_16)) __PYX_ERR(0, 169, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_16); __pyx_t_17 = PyFloat_FromDouble(__pyx_v_kl_divergence); if (unlikely(!__pyx_t_17)) __PYX_ERR(0, 169, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_17); __pyx_t_18 = PyTuple_New(2); if (unlikely(!__pyx_t_18)) __PYX_ERR(0, 169, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_18); __Pyx_GIVEREF(__pyx_t_16); PyTuple_SET_ITEM(__pyx_t_18, 0, __pyx_t_16); __Pyx_GIVEREF(__pyx_t_17); PyTuple_SET_ITEM(__pyx_t_18, 1, __pyx_t_17); __pyx_t_16 = 0; __pyx_t_17 = 0; __pyx_r = ((PyObject*)__pyx_t_18); __pyx_t_18 = 0; goto __pyx_L0; /* "openTSNE/_tsne.pyx":103 * * * cpdef tuple estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * sparse_index_type[:] indices, * sparse_index_type[:] indptr, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_16); __Pyx_XDECREF(__pyx_t_17); __Pyx_XDECREF(__pyx_t_18); __Pyx_AddTraceback("openTSNE._tsne.estimate_positive_gradient_nn", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_5_tsne_21__pyx_fuse_1estimate_positive_gradient_nn(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyMethodDef __pyx_fuse_1__pyx_mdef_8openTSNE_5_tsne_21__pyx_fuse_1estimate_positive_gradient_nn = {"__pyx_fuse_1estimate_positive_gradient_nn", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_5_tsne_21__pyx_fuse_1estimate_positive_gradient_nn, METH_VARARGS|METH_KEYWORDS, 0}; static PyObject *__pyx_pw_8openTSNE_5_tsne_21__pyx_fuse_1estimate_positive_gradient_nn(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __Pyx_memviewslice __pyx_v_indices = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_indptr = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_P_data = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_reference_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_gradient = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_dof; Py_ssize_t __pyx_v_num_threads; int __pyx_v_should_eval_error; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__pyx_fuse_1estimate_positive_gradient_nn (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_indices,&__pyx_n_s_indptr,&__pyx_n_s_P_data,&__pyx_n_s_embedding,&__pyx_n_s_reference_embedding,&__pyx_n_s_gradient,&__pyx_n_s_dof,&__pyx_n_s_num_threads,&__pyx_n_s_should_eval_error,0}; PyObject* values[9] = {0,0,0,0,0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 9: values[8] = PyTuple_GET_ITEM(__pyx_args, 8); CYTHON_FALLTHROUGH; case 8: values[7] = PyTuple_GET_ITEM(__pyx_args, 7); CYTHON_FALLTHROUGH; case 7: values[6] = PyTuple_GET_ITEM(__pyx_args, 6); CYTHON_FALLTHROUGH; case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_indices)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_indptr)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_fuse_1estimate_positive_gradient_nn", 0, 6, 9, 1); __PYX_ERR(0, 103, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_P_data)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_fuse_1estimate_positive_gradient_nn", 0, 6, 9, 2); __PYX_ERR(0, 103, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 3: if (likely((values[3] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_embedding)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_fuse_1estimate_positive_gradient_nn", 0, 6, 9, 3); __PYX_ERR(0, 103, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 4: if (likely((values[4] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_reference_embedding)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_fuse_1estimate_positive_gradient_nn", 0, 6, 9, 4); __PYX_ERR(0, 103, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 5: if (likely((values[5] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_gradient)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_fuse_1estimate_positive_gradient_nn", 0, 6, 9, 5); __PYX_ERR(0, 103, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 6: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dof); if (value) { values[6] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 7: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_num_threads); if (value) { values[7] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 8: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_should_eval_error); if (value) { values[8] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__pyx_fuse_1estimate_positive_gradient_nn") < 0)) __PYX_ERR(0, 103, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 9: values[8] = PyTuple_GET_ITEM(__pyx_args, 8); CYTHON_FALLTHROUGH; case 8: values[7] = PyTuple_GET_ITEM(__pyx_args, 7); CYTHON_FALLTHROUGH; case 7: values[6] = PyTuple_GET_ITEM(__pyx_args, 6); CYTHON_FALLTHROUGH; case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); values[4] = PyTuple_GET_ITEM(__pyx_args, 4); values[3] = PyTuple_GET_ITEM(__pyx_args, 3); values[2] = PyTuple_GET_ITEM(__pyx_args, 2); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_indices = __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_int64_t(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_indices.memview)) __PYX_ERR(0, 104, __pyx_L3_error) __pyx_v_indptr = __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_int64_t(values[1], PyBUF_WRITABLE); if (unlikely(!__pyx_v_indptr.memview)) __PYX_ERR(0, 105, __pyx_L3_error) __pyx_v_P_data = __Pyx_PyObject_to_MemoryviewSlice_ds_double(values[2], PyBUF_WRITABLE); if (unlikely(!__pyx_v_P_data.memview)) __PYX_ERR(0, 106, __pyx_L3_error) __pyx_v_embedding = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[3], PyBUF_WRITABLE); if (unlikely(!__pyx_v_embedding.memview)) __PYX_ERR(0, 107, __pyx_L3_error) __pyx_v_reference_embedding = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[4], PyBUF_WRITABLE); if (unlikely(!__pyx_v_reference_embedding.memview)) __PYX_ERR(0, 108, __pyx_L3_error) __pyx_v_gradient = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[5], PyBUF_WRITABLE); if (unlikely(!__pyx_v_gradient.memview)) __PYX_ERR(0, 109, __pyx_L3_error) if (values[6]) { __pyx_v_dof = __pyx_PyFloat_AsDouble(values[6]); if (unlikely((__pyx_v_dof == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 110, __pyx_L3_error) } else { __pyx_v_dof = __pyx_k__8; } if (values[7]) { __pyx_v_num_threads = __Pyx_PyIndex_AsSsize_t(values[7]); if (unlikely((__pyx_v_num_threads == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 111, __pyx_L3_error) } else { __pyx_v_num_threads = __pyx_k__9; } if (values[8]) { __pyx_v_should_eval_error = __Pyx_PyObject_IsTrue(values[8]); if (unlikely((__pyx_v_should_eval_error == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 112, __pyx_L3_error) } else { __pyx_v_should_eval_error = __pyx_k__10; } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__pyx_fuse_1estimate_positive_gradient_nn", 0, 6, 9, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 103, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE._tsne.__pyx_fuse_1estimate_positive_gradient_nn", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_5_tsne_20__pyx_fuse_1estimate_positive_gradient_nn(__pyx_self, __pyx_v_indices, __pyx_v_indptr, __pyx_v_P_data, __pyx_v_embedding, __pyx_v_reference_embedding, __pyx_v_gradient, __pyx_v_dof, __pyx_v_num_threads, __pyx_v_should_eval_error); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_5_tsne_20__pyx_fuse_1estimate_positive_gradient_nn(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_indices, __Pyx_memviewslice __pyx_v_indptr, __Pyx_memviewslice __pyx_v_P_data, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_reference_embedding, __Pyx_memviewslice __pyx_v_gradient, double __pyx_v_dof, Py_ssize_t __pyx_v_num_threads, int __pyx_v_should_eval_error) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; struct __pyx_fuse_1__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn __pyx_t_2; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__pyx_fuse_1estimate_positive_gradient_nn", 0); __Pyx_XDECREF(__pyx_r); __pyx_t_2.__pyx_n = 3; __pyx_t_2.dof = __pyx_v_dof; __pyx_t_2.num_threads = __pyx_v_num_threads; __pyx_t_2.should_eval_error = __pyx_v_should_eval_error; __pyx_t_1 = __pyx_fuse_1__pyx_f_8openTSNE_5_tsne_estimate_positive_gradient_nn(__pyx_v_indices, __pyx_v_indptr, __pyx_v_P_data, __pyx_v_embedding, __pyx_v_reference_embedding, __pyx_v_gradient, 0, &__pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("openTSNE._tsne.__pyx_fuse_1estimate_positive_gradient_nn", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_indices, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_indptr, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_P_data, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_embedding, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_reference_embedding, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_gradient, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/_tsne.pyx":172 * * * cpdef double estimate_negative_gradient_bh( # <<<<<<<<<<<<<< * QuadTree tree, * double[:, ::1] embedding, */ static PyObject *__pyx_pw_8openTSNE_5_tsne_5estimate_negative_gradient_bh(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static double __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_bh(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_tree, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, CYTHON_UNUSED int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_bh *__pyx_optional_args) { double __pyx_v_theta = ((double)0.5); double __pyx_v_dof = ((double)1.0); Py_ssize_t __pyx_v_num_threads = ((Py_ssize_t)1); /* "openTSNE/_tsne.pyx":179 * double dof=1, * Py_ssize_t num_threads=1, * bint pairwise_normalization=True, # <<<<<<<<<<<<<< * ): * """Estimate the negative tSNE gradient using the Barnes Hut approximation. */ int __pyx_v_pairwise_normalization = ((int)1); Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; Py_ssize_t __pyx_v_num_points; double __pyx_v_sum_Q; __Pyx_memviewslice __pyx_v_sum_Qi = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_t_6; Py_ssize_t __pyx_t_7; Py_ssize_t __pyx_t_8; Py_ssize_t __pyx_t_9; Py_ssize_t __pyx_t_10; Py_ssize_t __pyx_t_11; Py_ssize_t __pyx_t_12; Py_ssize_t __pyx_t_13; Py_ssize_t __pyx_t_14; Py_ssize_t __pyx_t_15; Py_ssize_t __pyx_t_16; Py_ssize_t __pyx_t_17; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("estimate_negative_gradient_bh", 0); if (__pyx_optional_args) { if (__pyx_optional_args->__pyx_n > 0) { __pyx_v_theta = __pyx_optional_args->theta; if (__pyx_optional_args->__pyx_n > 1) { __pyx_v_dof = __pyx_optional_args->dof; if (__pyx_optional_args->__pyx_n > 2) { __pyx_v_num_threads = __pyx_optional_args->num_threads; if (__pyx_optional_args->__pyx_n > 3) { __pyx_v_pairwise_normalization = __pyx_optional_args->pairwise_normalization; } } } } } /* "openTSNE/_tsne.pyx":192 * """ * cdef: * Py_ssize_t i, j, num_points = embedding.shape[0] # <<<<<<<<<<<<<< * double sum_Q = 0 * double[::1] sum_Qi = np.zeros(num_points, dtype=float) */ __pyx_v_num_points = (__pyx_v_embedding.shape[0]); /* "openTSNE/_tsne.pyx":193 * cdef: * Py_ssize_t i, j, num_points = embedding.shape[0] * double sum_Q = 0 # <<<<<<<<<<<<<< * double[::1] sum_Qi = np.zeros(num_points, dtype=float) * */ __pyx_v_sum_Q = 0.0; /* "openTSNE/_tsne.pyx":194 * Py_ssize_t i, j, num_points = embedding.shape[0] * double sum_Q = 0 * double[::1] sum_Qi = np.zeros(num_points, dtype=float) # <<<<<<<<<<<<<< * * if num_threads < 1: */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 194, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_zeros); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 194, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_num_points); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 194, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 194, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 194, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 194, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 194, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 194, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_sum_Qi = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_tsne.pyx":196 * double[::1] sum_Qi = np.zeros(num_points, dtype=float) * * if num_threads < 1: # <<<<<<<<<<<<<< * num_threads = 1 * */ __pyx_t_6 = ((__pyx_v_num_threads < 1) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":197 * * if num_threads < 1: * num_threads = 1 # <<<<<<<<<<<<<< * * # In order to run gradient estimation in parallel, we need to pass each */ __pyx_v_num_threads = 1; /* "openTSNE/_tsne.pyx":196 * double[::1] sum_Qi = np.zeros(num_points, dtype=float) * * if num_threads < 1: # <<<<<<<<<<<<<< * num_threads = 1 * */ } /* "openTSNE/_tsne.pyx":201 * # In order to run gradient estimation in parallel, we need to pass each * # worker it's own memory slot to write sum_Qs * for i in prange(num_points, nogil=True, num_threads=num_threads, schedule="guided"): # <<<<<<<<<<<<<< * _estimate_negative_gradient_single( * &tree.root, &embedding[i, 0], &gradient[i, 0], &sum_Qi[i], theta, dof */ { #ifdef WITH_THREAD PyThreadState *_save; Py_UNBLOCK_THREADS __Pyx_FastGIL_Remember(); #endif /*try:*/ { __pyx_t_7 = __pyx_v_num_points; if ((1 == 0)) abort(); { #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))))) #undef likely #undef unlikely #define likely(x) (x) #define unlikely(x) (x) #endif __pyx_t_9 = (__pyx_t_7 - 0 + 1 - 1/abs(1)) / 1; if (__pyx_t_9 > 0) { #ifdef _OPENMP #pragma omp parallel num_threads(__pyx_v_num_threads) private(__pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14) #endif /* _OPENMP */ { #ifdef _OPENMP #pragma omp for firstprivate(__pyx_v_i) lastprivate(__pyx_v_i) schedule(guided) #endif /* _OPENMP */ for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_9; __pyx_t_8++){ { __pyx_v_i = (Py_ssize_t)(0 + 1 * __pyx_t_8); /* "openTSNE/_tsne.pyx":203 * for i in prange(num_points, nogil=True, num_threads=num_threads, schedule="guided"): * _estimate_negative_gradient_single( * &tree.root, &embedding[i, 0], &gradient[i, 0], &sum_Qi[i], theta, dof # <<<<<<<<<<<<<< * ) * */ __pyx_t_10 = __pyx_v_i; __pyx_t_11 = 0; __pyx_t_12 = __pyx_v_i; __pyx_t_13 = 0; __pyx_t_14 = __pyx_v_i; /* "openTSNE/_tsne.pyx":202 * # worker it's own memory slot to write sum_Qs * for i in prange(num_points, nogil=True, num_threads=num_threads, schedule="guided"): * _estimate_negative_gradient_single( # <<<<<<<<<<<<<< * &tree.root, &embedding[i, 0], &gradient[i, 0], &sum_Qi[i], theta, dof * ) */ __pyx_f_8openTSNE_5_tsne__estimate_negative_gradient_single((&__pyx_v_tree->root), (&(*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_10 * __pyx_v_embedding.strides[0]) )) + __pyx_t_11)) )))), (&(*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_gradient.data + __pyx_t_12 * __pyx_v_gradient.strides[0]) )) + __pyx_t_13)) )))), (&(*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_sum_Qi.data) + __pyx_t_14)) )))), __pyx_v_theta, __pyx_v_dof); } } } } } #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))))) #undef likely #undef unlikely #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #endif } /* "openTSNE/_tsne.pyx":201 * # In order to run gradient estimation in parallel, we need to pass each * # worker it's own memory slot to write sum_Qs * for i in prange(num_points, nogil=True, num_threads=num_threads, schedule="guided"): # <<<<<<<<<<<<<< * _estimate_negative_gradient_single( * &tree.root, &embedding[i, 0], &gradient[i, 0], &sum_Qi[i], theta, dof */ /*finally:*/ { /*normal exit:*/{ #ifdef WITH_THREAD __Pyx_FastGIL_Forget(); Py_BLOCK_THREADS #endif goto __pyx_L6; } __pyx_L6:; } } /* "openTSNE/_tsne.pyx":206 * ) * * for i in range(num_points): # <<<<<<<<<<<<<< * sum_Q += sum_Qi[i] * */ __pyx_t_9 = __pyx_v_num_points; __pyx_t_8 = __pyx_t_9; for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_8; __pyx_t_7+=1) { __pyx_v_i = __pyx_t_7; /* "openTSNE/_tsne.pyx":207 * * for i in range(num_points): * sum_Q += sum_Qi[i] # <<<<<<<<<<<<<< * * # Normalize q_{ij}s */ __pyx_t_14 = __pyx_v_i; __pyx_v_sum_Q = (__pyx_v_sum_Q + (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_sum_Qi.data) + __pyx_t_14)) )))); } /* "openTSNE/_tsne.pyx":210 * * # Normalize q_{ij}s * for i in range(gradient.shape[0]): # <<<<<<<<<<<<<< * for j in range(gradient.shape[1]): * if pairwise_normalization: */ __pyx_t_9 = (__pyx_v_gradient.shape[0]); __pyx_t_8 = __pyx_t_9; for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_8; __pyx_t_7+=1) { __pyx_v_i = __pyx_t_7; /* "openTSNE/_tsne.pyx":211 * # Normalize q_{ij}s * for i in range(gradient.shape[0]): * for j in range(gradient.shape[1]): # <<<<<<<<<<<<<< * if pairwise_normalization: * gradient[i, j] /= sum_Q + EPSILON */ __pyx_t_15 = (__pyx_v_gradient.shape[1]); __pyx_t_16 = __pyx_t_15; for (__pyx_t_17 = 0; __pyx_t_17 < __pyx_t_16; __pyx_t_17+=1) { __pyx_v_j = __pyx_t_17; /* "openTSNE/_tsne.pyx":212 * for i in range(gradient.shape[0]): * for j in range(gradient.shape[1]): * if pairwise_normalization: # <<<<<<<<<<<<<< * gradient[i, j] /= sum_Q + EPSILON * else: */ __pyx_t_6 = (__pyx_v_pairwise_normalization != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":213 * for j in range(gradient.shape[1]): * if pairwise_normalization: * gradient[i, j] /= sum_Q + EPSILON # <<<<<<<<<<<<<< * else: * gradient[i, j] /= sum_Qi[i] + EPSILON */ __pyx_t_14 = __pyx_v_i; __pyx_t_13 = __pyx_v_j; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_gradient.data + __pyx_t_14 * __pyx_v_gradient.strides[0]) )) + __pyx_t_13)) )) /= (__pyx_v_sum_Q + __pyx_v_8openTSNE_5_tsne_EPSILON); /* "openTSNE/_tsne.pyx":212 * for i in range(gradient.shape[0]): * for j in range(gradient.shape[1]): * if pairwise_normalization: # <<<<<<<<<<<<<< * gradient[i, j] /= sum_Q + EPSILON * else: */ goto __pyx_L19; } /* "openTSNE/_tsne.pyx":215 * gradient[i, j] /= sum_Q + EPSILON * else: * gradient[i, j] /= sum_Qi[i] + EPSILON # <<<<<<<<<<<<<< * * return sum_Q */ /*else*/ { __pyx_t_13 = __pyx_v_i; __pyx_t_14 = __pyx_v_i; __pyx_t_12 = __pyx_v_j; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_gradient.data + __pyx_t_14 * __pyx_v_gradient.strides[0]) )) + __pyx_t_12)) )) /= ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_sum_Qi.data) + __pyx_t_13)) ))) + __pyx_v_8openTSNE_5_tsne_EPSILON); } __pyx_L19:; } } /* "openTSNE/_tsne.pyx":217 * gradient[i, j] /= sum_Qi[i] + EPSILON * * return sum_Q # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_sum_Q; goto __pyx_L0; /* "openTSNE/_tsne.pyx":172 * * * cpdef double estimate_negative_gradient_bh( # <<<<<<<<<<<<<< * QuadTree tree, * double[:, ::1] embedding, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1); __Pyx_WriteUnraisable("openTSNE._tsne.estimate_negative_gradient_bh", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_r = 0; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_sum_Qi, 1); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_5_tsne_5estimate_negative_gradient_bh(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static char __pyx_doc_8openTSNE_5_tsne_4estimate_negative_gradient_bh[] = "Estimate the negative tSNE gradient using the Barnes Hut approximation.\n \n Notes\n -----\n Changes the gradient inplace to avoid needless memory allocation. As\n such, this must be run before estimating the positive gradients, since\n the negative gradient must be normalized at the end with the sum of\n q_{ij}s.\n \n "; static PyObject *__pyx_pw_8openTSNE_5_tsne_5estimate_negative_gradient_bh(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_tree = 0; __Pyx_memviewslice __pyx_v_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_gradient = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_theta; double __pyx_v_dof; Py_ssize_t __pyx_v_num_threads; int __pyx_v_pairwise_normalization; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("estimate_negative_gradient_bh (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_tree,&__pyx_n_s_embedding,&__pyx_n_s_gradient,&__pyx_n_s_theta,&__pyx_n_s_dof,&__pyx_n_s_num_threads,&__pyx_n_s_pairwise_normalization,0}; PyObject* values[7] = {0,0,0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 7: values[6] = PyTuple_GET_ITEM(__pyx_args, 6); CYTHON_FALLTHROUGH; case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_tree)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_embedding)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_bh", 0, 3, 7, 1); __PYX_ERR(0, 172, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_gradient)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_bh", 0, 3, 7, 2); __PYX_ERR(0, 172, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 3: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_theta); if (value) { values[3] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 4: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dof); if (value) { values[4] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 5: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_num_threads); if (value) { values[5] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 6: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pairwise_normalization); if (value) { values[6] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "estimate_negative_gradient_bh") < 0)) __PYX_ERR(0, 172, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 7: values[6] = PyTuple_GET_ITEM(__pyx_args, 6); CYTHON_FALLTHROUGH; case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_tree = ((struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *)values[0]); __pyx_v_embedding = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[1], PyBUF_WRITABLE); if (unlikely(!__pyx_v_embedding.memview)) __PYX_ERR(0, 174, __pyx_L3_error) __pyx_v_gradient = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[2], PyBUF_WRITABLE); if (unlikely(!__pyx_v_gradient.memview)) __PYX_ERR(0, 175, __pyx_L3_error) if (values[3]) { __pyx_v_theta = __pyx_PyFloat_AsDouble(values[3]); if (unlikely((__pyx_v_theta == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 176, __pyx_L3_error) } else { __pyx_v_theta = ((double)0.5); } if (values[4]) { __pyx_v_dof = __pyx_PyFloat_AsDouble(values[4]); if (unlikely((__pyx_v_dof == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 177, __pyx_L3_error) } else { __pyx_v_dof = ((double)1.0); } if (values[5]) { __pyx_v_num_threads = __Pyx_PyIndex_AsSsize_t(values[5]); if (unlikely((__pyx_v_num_threads == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 178, __pyx_L3_error) } else { __pyx_v_num_threads = ((Py_ssize_t)1); } if (values[6]) { __pyx_v_pairwise_normalization = __Pyx_PyObject_IsTrue(values[6]); if (unlikely((__pyx_v_pairwise_normalization == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 179, __pyx_L3_error) } else { /* "openTSNE/_tsne.pyx":179 * double dof=1, * Py_ssize_t num_threads=1, * bint pairwise_normalization=True, # <<<<<<<<<<<<<< * ): * """Estimate the negative tSNE gradient using the Barnes Hut approximation. */ __pyx_v_pairwise_normalization = ((int)1); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_bh", 0, 3, 7, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 172, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE._tsne.estimate_negative_gradient_bh", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_tree), __pyx_ptype_8openTSNE_9quad_tree_QuadTree, 1, "tree", 0))) __PYX_ERR(0, 173, __pyx_L1_error) __pyx_r = __pyx_pf_8openTSNE_5_tsne_4estimate_negative_gradient_bh(__pyx_self, __pyx_v_tree, __pyx_v_embedding, __pyx_v_gradient, __pyx_v_theta, __pyx_v_dof, __pyx_v_num_threads, __pyx_v_pairwise_normalization); /* "openTSNE/_tsne.pyx":172 * * * cpdef double estimate_negative_gradient_bh( # <<<<<<<<<<<<<< * QuadTree tree, * double[:, ::1] embedding, */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __pyx_r = NULL; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_5_tsne_4estimate_negative_gradient_bh(CYTHON_UNUSED PyObject *__pyx_self, struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_tree, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, double __pyx_v_theta, double __pyx_v_dof, Py_ssize_t __pyx_v_num_threads, int __pyx_v_pairwise_normalization) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations double __pyx_t_1; struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_bh __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("estimate_negative_gradient_bh", 0); __Pyx_XDECREF(__pyx_r); __pyx_t_2.__pyx_n = 4; __pyx_t_2.theta = __pyx_v_theta; __pyx_t_2.dof = __pyx_v_dof; __pyx_t_2.num_threads = __pyx_v_num_threads; __pyx_t_2.pairwise_normalization = __pyx_v_pairwise_normalization; __pyx_t_1 = __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_bh(__pyx_v_tree, __pyx_v_embedding, __pyx_v_gradient, 0, &__pyx_t_2); __pyx_t_3 = PyFloat_FromDouble(__pyx_t_1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 172, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("openTSNE._tsne.estimate_negative_gradient_bh", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_embedding, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_gradient, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/_tsne.pyx":220 * * * cdef void _estimate_negative_gradient_single( # <<<<<<<<<<<<<< * Node * node, * double * point, */ static void __pyx_f_8openTSNE_5_tsne__estimate_negative_gradient_single(__pyx_t_8openTSNE_9quad_tree_Node *__pyx_v_node, double *__pyx_v_point, double *__pyx_v_gradient, double *__pyx_v_sum_Q, double __pyx_v_theta, double __pyx_v_dof) { double __pyx_v_distance; double __pyx_v_q_ij; double __pyx_v_tmp; Py_ssize_t __pyx_v_d; int __pyx_t_1; int __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; long __pyx_t_6; Py_ssize_t __pyx_t_7; /* "openTSNE/_tsne.pyx":229 * ) nogil: * # Make sure that we spend no time on empty nodes or self-interactions * if node.num_points == 0 or node.is_leaf and is_duplicate(node, point): # <<<<<<<<<<<<<< * return * */ __pyx_t_2 = ((__pyx_v_node->num_points == 0) != 0); if (!__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } __pyx_t_2 = (__pyx_v_node->is_leaf != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } __pyx_t_2 = (__pyx_f_8openTSNE_9quad_tree_is_duplicate(__pyx_v_node, __pyx_v_point, NULL) != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":230 * # Make sure that we spend no time on empty nodes or self-interactions * if node.num_points == 0 or node.is_leaf and is_duplicate(node, point): * return # <<<<<<<<<<<<<< * * cdef: */ goto __pyx_L0; /* "openTSNE/_tsne.pyx":229 * ) nogil: * # Make sure that we spend no time on empty nodes or self-interactions * if node.num_points == 0 or node.is_leaf and is_duplicate(node, point): # <<<<<<<<<<<<<< * return * */ } /* "openTSNE/_tsne.pyx":233 * * cdef: * double distance = EPSILON # <<<<<<<<<<<<<< * double q_ij, tmp * Py_ssize_t d */ __pyx_v_distance = __pyx_v_8openTSNE_5_tsne_EPSILON; /* "openTSNE/_tsne.pyx":239 * # Compute the squared euclidean disstance in the embedding space from the * # new point to the center of mass * for d in range(node.n_dims): # <<<<<<<<<<<<<< * tmp = node.center_of_mass[d] - point[d] * distance += (tmp * tmp) */ __pyx_t_3 = __pyx_v_node->n_dims; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_d = __pyx_t_5; /* "openTSNE/_tsne.pyx":240 * # new point to the center of mass * for d in range(node.n_dims): * tmp = node.center_of_mass[d] - point[d] # <<<<<<<<<<<<<< * distance += (tmp * tmp) * */ __pyx_v_tmp = ((__pyx_v_node->center_of_mass[__pyx_v_d]) - (__pyx_v_point[__pyx_v_d])); /* "openTSNE/_tsne.pyx":241 * for d in range(node.n_dims): * tmp = node.center_of_mass[d] - point[d] * distance += (tmp * tmp) # <<<<<<<<<<<<<< * * # Degrees of freedom cannot be negative */ __pyx_v_distance = (__pyx_v_distance + (__pyx_v_tmp * __pyx_v_tmp)); } /* "openTSNE/_tsne.pyx":244 * * # Degrees of freedom cannot be negative * if dof <= 0: # <<<<<<<<<<<<<< * dof = 1e-8 * */ __pyx_t_1 = ((__pyx_v_dof <= 0.0) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":245 * # Degrees of freedom cannot be negative * if dof <= 0: * dof = 1e-8 # <<<<<<<<<<<<<< * * # Check whether we can use this node as a summary */ __pyx_v_dof = 1e-8; /* "openTSNE/_tsne.pyx":244 * * # Degrees of freedom cannot be negative * if dof <= 0: # <<<<<<<<<<<<<< * dof = 1e-8 * */ } /* "openTSNE/_tsne.pyx":248 * * # Check whether we can use this node as a summary * if node.is_leaf or node.length / sqrt(distance) < theta: # <<<<<<<<<<<<<< * if dof != 1: * q_ij = 1 / (1 + distance / dof) ** dof */ __pyx_t_2 = (__pyx_v_node->is_leaf != 0); if (!__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L11_bool_binop_done; } __pyx_t_2 = (((__pyx_v_node->length / sqrt(__pyx_v_distance)) < __pyx_v_theta) != 0); __pyx_t_1 = __pyx_t_2; __pyx_L11_bool_binop_done:; if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":249 * # Check whether we can use this node as a summary * if node.is_leaf or node.length / sqrt(distance) < theta: * if dof != 1: # <<<<<<<<<<<<<< * q_ij = 1 / (1 + distance / dof) ** dof * else: */ __pyx_t_1 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":250 * if node.is_leaf or node.length / sqrt(distance) < theta: * if dof != 1: * q_ij = 1 / (1 + distance / dof) ** dof # <<<<<<<<<<<<<< * else: * q_ij = 1 / (1 + distance) */ __pyx_v_q_ij = (1.0 / pow((1.0 + (__pyx_v_distance / __pyx_v_dof)), __pyx_v_dof)); /* "openTSNE/_tsne.pyx":249 * # Check whether we can use this node as a summary * if node.is_leaf or node.length / sqrt(distance) < theta: * if dof != 1: # <<<<<<<<<<<<<< * q_ij = 1 / (1 + distance / dof) ** dof * else: */ goto __pyx_L13; } /* "openTSNE/_tsne.pyx":252 * q_ij = 1 / (1 + distance / dof) ** dof * else: * q_ij = 1 / (1 + distance) # <<<<<<<<<<<<<< * * sum_Q[0] += node.num_points * q_ij */ /*else*/ { __pyx_v_q_ij = (1.0 / (1.0 + __pyx_v_distance)); } __pyx_L13:; /* "openTSNE/_tsne.pyx":254 * q_ij = 1 / (1 + distance) * * sum_Q[0] += node.num_points * q_ij # <<<<<<<<<<<<<< * * # These two expressions are the same, but multiplication with itself is */ __pyx_t_6 = 0; (__pyx_v_sum_Q[__pyx_t_6]) = ((__pyx_v_sum_Q[__pyx_t_6]) + (__pyx_v_node->num_points * __pyx_v_q_ij)); /* "openTSNE/_tsne.pyx":258 * # These two expressions are the same, but multiplication with itself is * # faster (dof=1: (1 + 1) / 1 = 2 * if dof != 1: # <<<<<<<<<<<<<< * q_ij = q_ij ** ((dof + 1) / dof) * else: */ __pyx_t_1 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":259 * # faster (dof=1: (1 + 1) / 1 = 2 * if dof != 1: * q_ij = q_ij ** ((dof + 1) / dof) # <<<<<<<<<<<<<< * else: * q_ij = q_ij * q_ij */ __pyx_v_q_ij = pow(__pyx_v_q_ij, ((__pyx_v_dof + 1.0) / __pyx_v_dof)); /* "openTSNE/_tsne.pyx":258 * # These two expressions are the same, but multiplication with itself is * # faster (dof=1: (1 + 1) / 1 = 2 * if dof != 1: # <<<<<<<<<<<<<< * q_ij = q_ij ** ((dof + 1) / dof) * else: */ goto __pyx_L14; } /* "openTSNE/_tsne.pyx":261 * q_ij = q_ij ** ((dof + 1) / dof) * else: * q_ij = q_ij * q_ij # <<<<<<<<<<<<<< * * for d in range(node.n_dims): */ /*else*/ { __pyx_v_q_ij = (__pyx_v_q_ij * __pyx_v_q_ij); } __pyx_L14:; /* "openTSNE/_tsne.pyx":263 * q_ij = q_ij * q_ij * * for d in range(node.n_dims): # <<<<<<<<<<<<<< * gradient[d] -= node.num_points * q_ij * (point[d] - node.center_of_mass[d]) * */ __pyx_t_3 = __pyx_v_node->n_dims; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_d = __pyx_t_5; /* "openTSNE/_tsne.pyx":264 * * for d in range(node.n_dims): * gradient[d] -= node.num_points * q_ij * (point[d] - node.center_of_mass[d]) # <<<<<<<<<<<<<< * * return */ __pyx_t_7 = __pyx_v_d; (__pyx_v_gradient[__pyx_t_7]) = ((__pyx_v_gradient[__pyx_t_7]) - ((__pyx_v_node->num_points * __pyx_v_q_ij) * ((__pyx_v_point[__pyx_v_d]) - (__pyx_v_node->center_of_mass[__pyx_v_d])))); } /* "openTSNE/_tsne.pyx":266 * gradient[d] -= node.num_points * q_ij * (point[d] - node.center_of_mass[d]) * * return # <<<<<<<<<<<<<< * * # Otherwise we have to look for summaries in the children */ goto __pyx_L0; /* "openTSNE/_tsne.pyx":248 * * # Check whether we can use this node as a summary * if node.is_leaf or node.length / sqrt(distance) < theta: # <<<<<<<<<<<<<< * if dof != 1: * q_ij = 1 / (1 + distance / dof) ** dof */ } /* "openTSNE/_tsne.pyx":269 * * # Otherwise we have to look for summaries in the children * for d in range(1 << node.n_dims): # <<<<<<<<<<<<<< * _estimate_negative_gradient_single(&node.children[d], point, gradient, sum_Q, theta, dof) * */ __pyx_t_3 = (1 << __pyx_v_node->n_dims); __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_d = __pyx_t_5; /* "openTSNE/_tsne.pyx":270 * # Otherwise we have to look for summaries in the children * for d in range(1 << node.n_dims): * _estimate_negative_gradient_single(&node.children[d], point, gradient, sum_Q, theta, dof) # <<<<<<<<<<<<<< * * */ __pyx_f_8openTSNE_5_tsne__estimate_negative_gradient_single((&(__pyx_v_node->children[__pyx_v_d])), __pyx_v_point, __pyx_v_gradient, __pyx_v_sum_Q, __pyx_v_theta, __pyx_v_dof); } /* "openTSNE/_tsne.pyx":220 * * * cdef void _estimate_negative_gradient_single( # <<<<<<<<<<<<<< * Node * node, * double * point, */ /* function exit code */ __pyx_L0:; } /* "openTSNE/_tsne.pyx":273 * * * cdef inline double cauchy_1d(double x, double y, double dof) nogil: # <<<<<<<<<<<<<< * if dof != 1: * return (1 + ((x - y) ** 2) / dof) ** -dof */ static CYTHON_INLINE double __pyx_f_8openTSNE_5_tsne_cauchy_1d(double __pyx_v_x, double __pyx_v_y, double __pyx_v_dof) { double __pyx_r; int __pyx_t_1; /* "openTSNE/_tsne.pyx":274 * * cdef inline double cauchy_1d(double x, double y, double dof) nogil: * if dof != 1: # <<<<<<<<<<<<<< * return (1 + ((x - y) ** 2) / dof) ** -dof * else: */ __pyx_t_1 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":275 * cdef inline double cauchy_1d(double x, double y, double dof) nogil: * if dof != 1: * return (1 + ((x - y) ** 2) / dof) ** -dof # <<<<<<<<<<<<<< * else: * return (1 + (x - y) ** 2) ** -1 */ __pyx_r = pow((1.0 + (pow((__pyx_v_x - __pyx_v_y), 2.0) / __pyx_v_dof)), (-__pyx_v_dof)); goto __pyx_L0; /* "openTSNE/_tsne.pyx":274 * * cdef inline double cauchy_1d(double x, double y, double dof) nogil: * if dof != 1: # <<<<<<<<<<<<<< * return (1 + ((x - y) ** 2) / dof) ** -dof * else: */ } /* "openTSNE/_tsne.pyx":277 * return (1 + ((x - y) ** 2) / dof) ** -dof * else: * return (1 + (x - y) ** 2) ** -1 # <<<<<<<<<<<<<< * * */ /*else*/ { __pyx_r = pow((1.0 + pow((__pyx_v_x - __pyx_v_y), 2.0)), -1.0); goto __pyx_L0; } /* "openTSNE/_tsne.pyx":273 * * * cdef inline double cauchy_1d(double x, double y, double dof) nogil: # <<<<<<<<<<<<<< * if dof != 1: * return (1 + ((x - y) ** 2) / dof) ** -dof */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "openTSNE/_tsne.pyx":280 * * * cdef inline double cauchy_1d_exp1p(double x, double y, double dof) nogil: # <<<<<<<<<<<<<< * if dof != 1: * return (1 + ((x - y) ** 2) / dof) ** -(dof + 1) */ static CYTHON_INLINE double __pyx_f_8openTSNE_5_tsne_cauchy_1d_exp1p(double __pyx_v_x, double __pyx_v_y, double __pyx_v_dof) { double __pyx_r; int __pyx_t_1; /* "openTSNE/_tsne.pyx":281 * * cdef inline double cauchy_1d_exp1p(double x, double y, double dof) nogil: * if dof != 1: # <<<<<<<<<<<<<< * return (1 + ((x - y) ** 2) / dof) ** -(dof + 1) * else: */ __pyx_t_1 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":282 * cdef inline double cauchy_1d_exp1p(double x, double y, double dof) nogil: * if dof != 1: * return (1 + ((x - y) ** 2) / dof) ** -(dof + 1) # <<<<<<<<<<<<<< * else: * return (1 + (x - y) ** 2) ** -2 */ __pyx_r = pow((1.0 + (pow((__pyx_v_x - __pyx_v_y), 2.0) / __pyx_v_dof)), (-(__pyx_v_dof + 1.0))); goto __pyx_L0; /* "openTSNE/_tsne.pyx":281 * * cdef inline double cauchy_1d_exp1p(double x, double y, double dof) nogil: * if dof != 1: # <<<<<<<<<<<<<< * return (1 + ((x - y) ** 2) / dof) ** -(dof + 1) * else: */ } /* "openTSNE/_tsne.pyx":284 * return (1 + ((x - y) ** 2) / dof) ** -(dof + 1) * else: * return (1 + (x - y) ** 2) ** -2 # <<<<<<<<<<<<<< * * */ /*else*/ { __pyx_r = pow((1.0 + pow((__pyx_v_x - __pyx_v_y), 2.0)), -2.0); goto __pyx_L0; } /* "openTSNE/_tsne.pyx":280 * * * cdef inline double cauchy_1d_exp1p(double x, double y, double dof) nogil: # <<<<<<<<<<<<<< * if dof != 1: * return (1 + ((x - y) ** 2) / dof) ** -(dof + 1) */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "openTSNE/_tsne.pyx":287 * * * cdef inline double cauchy_2d(double x1, double x2, double y1, double y2, double dof) nogil: # <<<<<<<<<<<<<< * if dof != 1: * return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -dof */ static CYTHON_INLINE double __pyx_f_8openTSNE_5_tsne_cauchy_2d(double __pyx_v_x1, double __pyx_v_x2, double __pyx_v_y1, double __pyx_v_y2, double __pyx_v_dof) { double __pyx_r; int __pyx_t_1; /* "openTSNE/_tsne.pyx":288 * * cdef inline double cauchy_2d(double x1, double x2, double y1, double y2, double dof) nogil: * if dof != 1: # <<<<<<<<<<<<<< * return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -dof * else: */ __pyx_t_1 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":289 * cdef inline double cauchy_2d(double x1, double x2, double y1, double y2, double dof) nogil: * if dof != 1: * return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -dof # <<<<<<<<<<<<<< * else: * return (1 + (x1 - y1) ** 2 + (x2 - y2) ** 2) ** -1 */ __pyx_r = pow((1.0 + ((pow((__pyx_v_x1 - __pyx_v_y1), 2.0) + pow((__pyx_v_x2 - __pyx_v_y2), 2.0)) / __pyx_v_dof)), (-__pyx_v_dof)); goto __pyx_L0; /* "openTSNE/_tsne.pyx":288 * * cdef inline double cauchy_2d(double x1, double x2, double y1, double y2, double dof) nogil: * if dof != 1: # <<<<<<<<<<<<<< * return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -dof * else: */ } /* "openTSNE/_tsne.pyx":291 * return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -dof * else: * return (1 + (x1 - y1) ** 2 + (x2 - y2) ** 2) ** -1 # <<<<<<<<<<<<<< * * */ /*else*/ { __pyx_r = pow(((1.0 + pow((__pyx_v_x1 - __pyx_v_y1), 2.0)) + pow((__pyx_v_x2 - __pyx_v_y2), 2.0)), -1.0); goto __pyx_L0; } /* "openTSNE/_tsne.pyx":287 * * * cdef inline double cauchy_2d(double x1, double x2, double y1, double y2, double dof) nogil: # <<<<<<<<<<<<<< * if dof != 1: * return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -dof */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "openTSNE/_tsne.pyx":294 * * * cdef inline double cauchy_2d_exp1p(double x1, double x2, double y1, double y2, double dof) nogil: # <<<<<<<<<<<<<< * if dof != 1: * return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -(dof + 1) */ static CYTHON_INLINE double __pyx_f_8openTSNE_5_tsne_cauchy_2d_exp1p(double __pyx_v_x1, double __pyx_v_x2, double __pyx_v_y1, double __pyx_v_y2, double __pyx_v_dof) { double __pyx_r; int __pyx_t_1; /* "openTSNE/_tsne.pyx":295 * * cdef inline double cauchy_2d_exp1p(double x1, double x2, double y1, double y2, double dof) nogil: * if dof != 1: # <<<<<<<<<<<<<< * return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -(dof + 1) * else: */ __pyx_t_1 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_1) { /* "openTSNE/_tsne.pyx":296 * cdef inline double cauchy_2d_exp1p(double x1, double x2, double y1, double y2, double dof) nogil: * if dof != 1: * return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -(dof + 1) # <<<<<<<<<<<<<< * else: * return (1 + (x1 - y1) ** 2 + (x2 - y2) ** 2) ** -2 */ __pyx_r = pow((1.0 + ((pow((__pyx_v_x1 - __pyx_v_y1), 2.0) + pow((__pyx_v_x2 - __pyx_v_y2), 2.0)) / __pyx_v_dof)), (-(__pyx_v_dof + 1.0))); goto __pyx_L0; /* "openTSNE/_tsne.pyx":295 * * cdef inline double cauchy_2d_exp1p(double x1, double x2, double y1, double y2, double dof) nogil: * if dof != 1: # <<<<<<<<<<<<<< * return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -(dof + 1) * else: */ } /* "openTSNE/_tsne.pyx":298 * return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -(dof + 1) * else: * return (1 + (x1 - y1) ** 2 + (x2 - y2) ** 2) ** -2 # <<<<<<<<<<<<<< * * */ /*else*/ { __pyx_r = pow(((1.0 + pow((__pyx_v_x1 - __pyx_v_y1), 2.0)) + pow((__pyx_v_x2 - __pyx_v_y2), 2.0)), -2.0); goto __pyx_L0; } /* "openTSNE/_tsne.pyx":294 * * * cdef inline double cauchy_2d_exp1p(double x1, double x2, double y1, double y2, double dof) nogil: # <<<<<<<<<<<<<< * if dof != 1: * return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -(dof + 1) */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "openTSNE/_tsne.pyx":301 * * * cdef double[:, ::1] interpolate(double[::1] y_in_box, double[::1] y_tilde): # <<<<<<<<<<<<<< * """Lagrangian polynomial interpolation.""" * cdef Py_ssize_t N = y_in_box.shape[0] */ static __Pyx_memviewslice __pyx_f_8openTSNE_5_tsne_interpolate(__Pyx_memviewslice __pyx_v_y_in_box, __Pyx_memviewslice __pyx_v_y_tilde) { Py_ssize_t __pyx_v_N; Py_ssize_t __pyx_v_n_interpolation_points; __Pyx_memviewslice __pyx_v_interpolated_values = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_denominator = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; Py_ssize_t __pyx_v_k; __Pyx_memviewslice __pyx_r = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_t_6 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_7; Py_ssize_t __pyx_t_8; Py_ssize_t __pyx_t_9; Py_ssize_t __pyx_t_10; Py_ssize_t __pyx_t_11; Py_ssize_t __pyx_t_12; Py_ssize_t __pyx_t_13; int __pyx_t_14; Py_ssize_t __pyx_t_15; Py_ssize_t __pyx_t_16; Py_ssize_t __pyx_t_17; Py_ssize_t __pyx_t_18; Py_ssize_t __pyx_t_19; Py_ssize_t __pyx_t_20; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("interpolate", 0); /* "openTSNE/_tsne.pyx":303 * cdef double[:, ::1] interpolate(double[::1] y_in_box, double[::1] y_tilde): * """Lagrangian polynomial interpolation.""" * cdef Py_ssize_t N = y_in_box.shape[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t n_interpolation_points = y_tilde.shape[0] * */ __pyx_v_N = (__pyx_v_y_in_box.shape[0]); /* "openTSNE/_tsne.pyx":304 * """Lagrangian polynomial interpolation.""" * cdef Py_ssize_t N = y_in_box.shape[0] * cdef Py_ssize_t n_interpolation_points = y_tilde.shape[0] # <<<<<<<<<<<<<< * * cdef double[:, ::1] interpolated_values = np.empty((N, n_interpolation_points), dtype=float) */ __pyx_v_n_interpolation_points = (__pyx_v_y_tilde.shape[0]); /* "openTSNE/_tsne.pyx":306 * cdef Py_ssize_t n_interpolation_points = y_tilde.shape[0] * * cdef double[:, ::1] interpolated_values = np.empty((N, n_interpolation_points), dtype=float) # <<<<<<<<<<<<<< * cdef double[::1] denominator = np.empty(n_interpolation_points, dtype=float) * cdef Py_ssize_t i, j, k */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 306, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_empty); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 306, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_N); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 306, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_n_interpolation_points); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 306, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 306, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_3); __pyx_t_1 = 0; __pyx_t_3 = 0; __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 306, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 306, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (PyDict_SetItem(__pyx_t_4, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 306, __pyx_L1_error) __pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_4); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 306, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_1, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 306, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_v_interpolated_values = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_tsne.pyx":307 * * cdef double[:, ::1] interpolated_values = np.empty((N, n_interpolation_points), dtype=float) * cdef double[::1] denominator = np.empty(n_interpolation_points, dtype=float) # <<<<<<<<<<<<<< * cdef Py_ssize_t i, j, k * */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 307, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_4 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_empty); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 307, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_interpolation_points); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 307, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 307, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 307, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 307, __pyx_L1_error) __pyx_t_2 = __Pyx_PyObject_Call(__pyx_t_4, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 307, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_6 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_2, PyBUF_WRITABLE); if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 307, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v_denominator = __pyx_t_6; __pyx_t_6.memview = NULL; __pyx_t_6.data = NULL; /* "openTSNE/_tsne.pyx":310 * cdef Py_ssize_t i, j, k * * for i in range(n_interpolation_points): # <<<<<<<<<<<<<< * denominator[i] = 1 * for j in range(n_interpolation_points): */ __pyx_t_7 = __pyx_v_n_interpolation_points; __pyx_t_8 = __pyx_t_7; for (__pyx_t_9 = 0; __pyx_t_9 < __pyx_t_8; __pyx_t_9+=1) { __pyx_v_i = __pyx_t_9; /* "openTSNE/_tsne.pyx":311 * * for i in range(n_interpolation_points): * denominator[i] = 1 # <<<<<<<<<<<<<< * for j in range(n_interpolation_points): * if i != j: */ __pyx_t_10 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_denominator.data) + __pyx_t_10)) )) = 1.0; /* "openTSNE/_tsne.pyx":312 * for i in range(n_interpolation_points): * denominator[i] = 1 * for j in range(n_interpolation_points): # <<<<<<<<<<<<<< * if i != j: * denominator[i] *= y_tilde[i] - y_tilde[j] */ __pyx_t_11 = __pyx_v_n_interpolation_points; __pyx_t_12 = __pyx_t_11; for (__pyx_t_13 = 0; __pyx_t_13 < __pyx_t_12; __pyx_t_13+=1) { __pyx_v_j = __pyx_t_13; /* "openTSNE/_tsne.pyx":313 * denominator[i] = 1 * for j in range(n_interpolation_points): * if i != j: # <<<<<<<<<<<<<< * denominator[i] *= y_tilde[i] - y_tilde[j] * */ __pyx_t_14 = ((__pyx_v_i != __pyx_v_j) != 0); if (__pyx_t_14) { /* "openTSNE/_tsne.pyx":314 * for j in range(n_interpolation_points): * if i != j: * denominator[i] *= y_tilde[i] - y_tilde[j] # <<<<<<<<<<<<<< * * for i in range(N): */ __pyx_t_10 = __pyx_v_i; __pyx_t_15 = __pyx_v_j; __pyx_t_16 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_denominator.data) + __pyx_t_16)) )) *= ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_10)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_15)) )))); /* "openTSNE/_tsne.pyx":313 * denominator[i] = 1 * for j in range(n_interpolation_points): * if i != j: # <<<<<<<<<<<<<< * denominator[i] *= y_tilde[i] - y_tilde[j] * */ } } } /* "openTSNE/_tsne.pyx":316 * denominator[i] *= y_tilde[i] - y_tilde[j] * * for i in range(N): # <<<<<<<<<<<<<< * for j in range(n_interpolation_points): * interpolated_values[i, j] = 1 */ __pyx_t_7 = __pyx_v_N; __pyx_t_8 = __pyx_t_7; for (__pyx_t_9 = 0; __pyx_t_9 < __pyx_t_8; __pyx_t_9+=1) { __pyx_v_i = __pyx_t_9; /* "openTSNE/_tsne.pyx":317 * * for i in range(N): * for j in range(n_interpolation_points): # <<<<<<<<<<<<<< * interpolated_values[i, j] = 1 * for k in range(n_interpolation_points): */ __pyx_t_11 = __pyx_v_n_interpolation_points; __pyx_t_12 = __pyx_t_11; for (__pyx_t_13 = 0; __pyx_t_13 < __pyx_t_12; __pyx_t_13+=1) { __pyx_v_j = __pyx_t_13; /* "openTSNE/_tsne.pyx":318 * for i in range(N): * for j in range(n_interpolation_points): * interpolated_values[i, j] = 1 # <<<<<<<<<<<<<< * for k in range(n_interpolation_points): * if j != k: */ __pyx_t_15 = __pyx_v_i; __pyx_t_10 = __pyx_v_j; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_interpolated_values.data + __pyx_t_15 * __pyx_v_interpolated_values.strides[0]) )) + __pyx_t_10)) )) = 1.0; /* "openTSNE/_tsne.pyx":319 * for j in range(n_interpolation_points): * interpolated_values[i, j] = 1 * for k in range(n_interpolation_points): # <<<<<<<<<<<<<< * if j != k: * interpolated_values[i, j] *= y_in_box[i] - y_tilde[k] */ __pyx_t_17 = __pyx_v_n_interpolation_points; __pyx_t_18 = __pyx_t_17; for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) { __pyx_v_k = __pyx_t_19; /* "openTSNE/_tsne.pyx":320 * interpolated_values[i, j] = 1 * for k in range(n_interpolation_points): * if j != k: # <<<<<<<<<<<<<< * interpolated_values[i, j] *= y_in_box[i] - y_tilde[k] * interpolated_values[i, j] /= denominator[j] */ __pyx_t_14 = ((__pyx_v_j != __pyx_v_k) != 0); if (__pyx_t_14) { /* "openTSNE/_tsne.pyx":321 * for k in range(n_interpolation_points): * if j != k: * interpolated_values[i, j] *= y_in_box[i] - y_tilde[k] # <<<<<<<<<<<<<< * interpolated_values[i, j] /= denominator[j] * */ __pyx_t_10 = __pyx_v_i; __pyx_t_15 = __pyx_v_k; __pyx_t_16 = __pyx_v_i; __pyx_t_20 = __pyx_v_j; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_interpolated_values.data + __pyx_t_16 * __pyx_v_interpolated_values.strides[0]) )) + __pyx_t_20)) )) *= ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_in_box.data) + __pyx_t_10)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_15)) )))); /* "openTSNE/_tsne.pyx":320 * interpolated_values[i, j] = 1 * for k in range(n_interpolation_points): * if j != k: # <<<<<<<<<<<<<< * interpolated_values[i, j] *= y_in_box[i] - y_tilde[k] * interpolated_values[i, j] /= denominator[j] */ } } /* "openTSNE/_tsne.pyx":322 * if j != k: * interpolated_values[i, j] *= y_in_box[i] - y_tilde[k] * interpolated_values[i, j] /= denominator[j] # <<<<<<<<<<<<<< * * return interpolated_values */ __pyx_t_15 = __pyx_v_j; __pyx_t_10 = __pyx_v_i; __pyx_t_20 = __pyx_v_j; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_interpolated_values.data + __pyx_t_10 * __pyx_v_interpolated_values.strides[0]) )) + __pyx_t_20)) )) /= (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_denominator.data) + __pyx_t_15)) ))); } } /* "openTSNE/_tsne.pyx":324 * interpolated_values[i, j] /= denominator[j] * * return interpolated_values # <<<<<<<<<<<<<< * * */ __PYX_INC_MEMVIEW(&__pyx_v_interpolated_values, 0); __pyx_r = __pyx_v_interpolated_values; goto __pyx_L0; /* "openTSNE/_tsne.pyx":301 * * * cdef double[:, ::1] interpolate(double[::1] y_in_box, double[::1] y_tilde): # <<<<<<<<<<<<<< * """Lagrangian polynomial interpolation.""" * cdef Py_ssize_t N = y_in_box.shape[0] */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_6, 1); __pyx_r.data = NULL; __pyx_r.memview = NULL; __Pyx_AddTraceback("openTSNE._tsne.interpolate", __pyx_clineno, __pyx_lineno, __pyx_filename); goto __pyx_L2; __pyx_L0:; if (unlikely(!__pyx_r.memview)) { PyErr_SetString(PyExc_TypeError, "Memoryview return value is not initialized"); } __pyx_L2:; __PYX_XDEC_MEMVIEW(&__pyx_v_interpolated_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_denominator, 1); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/_tsne.pyx":327 * * * cdef double[::1] compute_kernel_tilde_1d( # <<<<<<<<<<<<<< * double (*kernel)(double, double, double), * Py_ssize_t n_interpolation_points_1d, */ static __Pyx_memviewslice __pyx_f_8openTSNE_5_tsne_compute_kernel_tilde_1d(double (*__pyx_v_kernel)(double, double, double), Py_ssize_t __pyx_v_n_interpolation_points_1d, double __pyx_v_coord_min, double __pyx_v_coord_spacing, double __pyx_v_dof) { __Pyx_memviewslice __pyx_v_y_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_embedded_size; __Pyx_memviewslice __pyx_v_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_i; double __pyx_v_tmp; __Pyx_memviewslice __pyx_r = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_6; Py_ssize_t __pyx_t_7; Py_ssize_t __pyx_t_8; Py_ssize_t __pyx_t_9; Py_ssize_t __pyx_t_10; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("compute_kernel_tilde_1d", 0); /* "openTSNE/_tsne.pyx":335 * ): * cdef: * double[::1] y_tilde = np.empty(n_interpolation_points_1d, dtype=float) # <<<<<<<<<<<<<< * * Py_ssize_t embedded_size = 2 * n_interpolation_points_1d */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 335, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_empty); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 335, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_interpolation_points_1d); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 335, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 335, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 335, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 335, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 335, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 335, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_y_tilde = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_tsne.pyx":337 * double[::1] y_tilde = np.empty(n_interpolation_points_1d, dtype=float) * * Py_ssize_t embedded_size = 2 * n_interpolation_points_1d # <<<<<<<<<<<<<< * double[::1] kernel_tilde = np.zeros(embedded_size, dtype=float) * */ __pyx_v_embedded_size = (2 * __pyx_v_n_interpolation_points_1d); /* "openTSNE/_tsne.pyx":338 * * Py_ssize_t embedded_size = 2 * n_interpolation_points_1d * double[::1] kernel_tilde = np.zeros(embedded_size, dtype=float) # <<<<<<<<<<<<<< * * Py_ssize_t i */ __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_np); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 338, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_4, __pyx_n_s_zeros); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 338, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_embedded_size); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 338, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 338, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 338, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (PyDict_SetItem(__pyx_t_4, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 338, __pyx_L1_error) __pyx_t_2 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_3, __pyx_t_4); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 338, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_2, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 338, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v_kernel_tilde = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_tsne.pyx":342 * Py_ssize_t i * * y_tilde[0] = coord_spacing / 2 + coord_min # <<<<<<<<<<<<<< * for i in range(1, n_interpolation_points_1d): * y_tilde[i] = y_tilde[i - 1] + coord_spacing */ __pyx_t_6 = 0; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_6)) )) = ((__pyx_v_coord_spacing / 2.0) + __pyx_v_coord_min); /* "openTSNE/_tsne.pyx":343 * * y_tilde[0] = coord_spacing / 2 + coord_min * for i in range(1, n_interpolation_points_1d): # <<<<<<<<<<<<<< * y_tilde[i] = y_tilde[i - 1] + coord_spacing * */ __pyx_t_7 = __pyx_v_n_interpolation_points_1d; __pyx_t_8 = __pyx_t_7; for (__pyx_t_9 = 1; __pyx_t_9 < __pyx_t_8; __pyx_t_9+=1) { __pyx_v_i = __pyx_t_9; /* "openTSNE/_tsne.pyx":344 * y_tilde[0] = coord_spacing / 2 + coord_min * for i in range(1, n_interpolation_points_1d): * y_tilde[i] = y_tilde[i - 1] + coord_spacing # <<<<<<<<<<<<<< * * # Evaluate the kernel at the interpolation nodes and form the embedded */ __pyx_t_6 = (__pyx_v_i - 1); __pyx_t_10 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_10)) )) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_6)) ))) + __pyx_v_coord_spacing); } /* "openTSNE/_tsne.pyx":349 * # generating kernel vector for a circulant matrix * cdef double tmp * for i in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * tmp = kernel(y_tilde[0], y_tilde[i], dof) * */ __pyx_t_7 = __pyx_v_n_interpolation_points_1d; __pyx_t_8 = __pyx_t_7; for (__pyx_t_9 = 0; __pyx_t_9 < __pyx_t_8; __pyx_t_9+=1) { __pyx_v_i = __pyx_t_9; /* "openTSNE/_tsne.pyx":350 * cdef double tmp * for i in range(n_interpolation_points_1d): * tmp = kernel(y_tilde[0], y_tilde[i], dof) # <<<<<<<<<<<<<< * * kernel_tilde[n_interpolation_points_1d + i] = tmp */ __pyx_t_6 = 0; __pyx_t_10 = __pyx_v_i; __pyx_v_tmp = __pyx_v_kernel((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_6)) ))), (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_10)) ))), __pyx_v_dof); /* "openTSNE/_tsne.pyx":352 * tmp = kernel(y_tilde[0], y_tilde[i], dof) * * kernel_tilde[n_interpolation_points_1d + i] = tmp # <<<<<<<<<<<<<< * kernel_tilde[n_interpolation_points_1d - i] = tmp * */ __pyx_t_10 = (__pyx_v_n_interpolation_points_1d + __pyx_v_i); *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_kernel_tilde.data) + __pyx_t_10)) )) = __pyx_v_tmp; /* "openTSNE/_tsne.pyx":353 * * kernel_tilde[n_interpolation_points_1d + i] = tmp * kernel_tilde[n_interpolation_points_1d - i] = tmp # <<<<<<<<<<<<<< * * return kernel_tilde */ __pyx_t_10 = (__pyx_v_n_interpolation_points_1d - __pyx_v_i); *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_kernel_tilde.data) + __pyx_t_10)) )) = __pyx_v_tmp; } /* "openTSNE/_tsne.pyx":355 * kernel_tilde[n_interpolation_points_1d - i] = tmp * * return kernel_tilde # <<<<<<<<<<<<<< * * */ __PYX_INC_MEMVIEW(&__pyx_v_kernel_tilde, 0); __pyx_r = __pyx_v_kernel_tilde; goto __pyx_L0; /* "openTSNE/_tsne.pyx":327 * * * cdef double[::1] compute_kernel_tilde_1d( # <<<<<<<<<<<<<< * double (*kernel)(double, double, double), * Py_ssize_t n_interpolation_points_1d, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1); __pyx_r.data = NULL; __pyx_r.memview = NULL; __Pyx_AddTraceback("openTSNE._tsne.compute_kernel_tilde_1d", __pyx_clineno, __pyx_lineno, __pyx_filename); goto __pyx_L2; __pyx_L0:; if (unlikely(!__pyx_r.memview)) { PyErr_SetString(PyExc_TypeError, "Memoryview return value is not initialized"); } __pyx_L2:; __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_kernel_tilde, 1); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/_tsne.pyx":358 * * * cpdef double estimate_negative_gradient_fft_1d( # <<<<<<<<<<<<<< * double[::1] embedding, * double[::1] gradient, */ static PyObject *__pyx_pw_8openTSNE_5_tsne_7estimate_negative_gradient_fft_1d(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static double __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d(__Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, CYTHON_UNUSED int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d *__pyx_optional_args) { Py_ssize_t __pyx_v_n_interpolation_points = ((Py_ssize_t)3); Py_ssize_t __pyx_v_min_num_intervals = ((Py_ssize_t)10); double __pyx_v_ints_in_interval = ((double)1.0); double __pyx_v_dof = ((double)1.0); Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; Py_ssize_t __pyx_v_d; Py_ssize_t __pyx_v_box_idx; Py_ssize_t __pyx_v_n_samples; double __pyx_v_y_max; double __pyx_v_y_min; int __pyx_v_n_boxes; PyObject *__pyx_v_recommended_boxes = 0; double __pyx_v_box_width; __Pyx_memviewslice __pyx_v_box_lower_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_box_upper_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; int *__pyx_v_point_box_idx; int __pyx_v_n_interpolation_points_1d; __Pyx_memviewslice __pyx_v_y_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_h; __Pyx_memviewslice __pyx_v_sq_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_v_n_terms; __Pyx_memviewslice __pyx_v_q_j = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_y_in_box = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_interpolated_values = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_w_coefficients = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_y_tilde_values = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_phi = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_sum_Q; double __pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; int __pyx_t_5; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; int __pyx_t_8; PyObject *__pyx_t_9 = NULL; PyObject *__pyx_t_10 = NULL; __Pyx_memviewslice __pyx_t_11 = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_t_12; Py_ssize_t __pyx_t_13; __Pyx_memviewslice __pyx_t_14 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_15; Py_ssize_t __pyx_t_16; Py_ssize_t __pyx_t_17; Py_ssize_t __pyx_t_18; Py_ssize_t __pyx_t_19; Py_ssize_t __pyx_t_20; Py_ssize_t __pyx_t_21; Py_ssize_t __pyx_t_22; PyObject *__pyx_t_23 = NULL; __Pyx_memviewslice __pyx_t_24 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_25; Py_ssize_t __pyx_t_26; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("estimate_negative_gradient_fft_1d", 0); if (__pyx_optional_args) { if (__pyx_optional_args->__pyx_n > 0) { __pyx_v_n_interpolation_points = __pyx_optional_args->n_interpolation_points; if (__pyx_optional_args->__pyx_n > 1) { __pyx_v_min_num_intervals = __pyx_optional_args->min_num_intervals; if (__pyx_optional_args->__pyx_n > 2) { __pyx_v_ints_in_interval = __pyx_optional_args->ints_in_interval; if (__pyx_optional_args->__pyx_n > 3) { __pyx_v_dof = __pyx_optional_args->dof; } } } } } /* "openTSNE/_tsne.pyx":366 * double dof=1, * ): * cdef Py_ssize_t i, j, d, box_idx, n_samples = embedding.shape[0] # <<<<<<<<<<<<<< * cdef double y_max = -INFINITY, y_min = INFINITY * # Determine the min/max values of the embedding */ __pyx_v_n_samples = (__pyx_v_embedding.shape[0]); /* "openTSNE/_tsne.pyx":367 * ): * cdef Py_ssize_t i, j, d, box_idx, n_samples = embedding.shape[0] * cdef double y_max = -INFINITY, y_min = INFINITY # <<<<<<<<<<<<<< * # Determine the min/max values of the embedding * for i in range(n_samples): */ __pyx_v_y_max = (-INFINITY); __pyx_v_y_min = INFINITY; /* "openTSNE/_tsne.pyx":369 * cdef double y_max = -INFINITY, y_min = INFINITY * # Determine the min/max values of the embedding * for i in range(n_samples): # <<<<<<<<<<<<<< * if embedding[i] < y_min: * y_min = embedding[i] */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":370 * # Determine the min/max values of the embedding * for i in range(n_samples): * if embedding[i] < y_min: # <<<<<<<<<<<<<< * y_min = embedding[i] * elif embedding[i] > y_max: */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = (((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_4)) ))) < __pyx_v_y_min) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":371 * for i in range(n_samples): * if embedding[i] < y_min: * y_min = embedding[i] # <<<<<<<<<<<<<< * elif embedding[i] > y_max: * y_max = embedding[i] */ __pyx_t_4 = __pyx_v_i; __pyx_v_y_min = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":370 * # Determine the min/max values of the embedding * for i in range(n_samples): * if embedding[i] < y_min: # <<<<<<<<<<<<<< * y_min = embedding[i] * elif embedding[i] > y_max: */ goto __pyx_L5; } /* "openTSNE/_tsne.pyx":372 * if embedding[i] < y_min: * y_min = embedding[i] * elif embedding[i] > y_max: # <<<<<<<<<<<<<< * y_max = embedding[i] * */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = (((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_4)) ))) > __pyx_v_y_max) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":373 * y_min = embedding[i] * elif embedding[i] > y_max: * y_max = embedding[i] # <<<<<<<<<<<<<< * * cdef int n_boxes = fmax(min_num_intervals, (y_max - y_min) / ints_in_interval) */ __pyx_t_4 = __pyx_v_i; __pyx_v_y_max = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":372 * if embedding[i] < y_min: * y_min = embedding[i] * elif embedding[i] > y_max: # <<<<<<<<<<<<<< * y_max = embedding[i] * */ } __pyx_L5:; } /* "openTSNE/_tsne.pyx":375 * y_max = embedding[i] * * cdef int n_boxes = fmax(min_num_intervals, (y_max - y_min) / ints_in_interval) # <<<<<<<<<<<<<< * # FFTW works faster on numbers that can be written as 2^a 3^b 5^c 7^d * # 11^e 13^f, where e+f is either 0 or 1, and the other exponents are arbitrary */ __pyx_v_n_boxes = ((int)fmax(__pyx_v_min_num_intervals, ((__pyx_v_y_max - __pyx_v_y_min) / __pyx_v_ints_in_interval))); /* "openTSNE/_tsne.pyx":378 * # FFTW works faster on numbers that can be written as 2^a 3^b 5^c 7^d * # 11^e 13^f, where e+f is either 0 or 1, and the other exponents are arbitrary * cdef list recommended_boxes = [ # <<<<<<<<<<<<<< * 20, 21, 22, 24, 25, 26, 27, 28, 30, 32, 33, 35, 36, 39, 40, 42, 44, 45, 48, 49, 50, * 52, 54, 55, 56, 60, 63, 64, 65, 66, 70, 72, 75, 77, 78, 80, 81, 84, 88, 90, 91, 96, */ __pyx_t_6 = PyList_New(206); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 378, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_INCREF(__pyx_int_20); __Pyx_GIVEREF(__pyx_int_20); PyList_SET_ITEM(__pyx_t_6, 0, __pyx_int_20); __Pyx_INCREF(__pyx_int_21); __Pyx_GIVEREF(__pyx_int_21); PyList_SET_ITEM(__pyx_t_6, 1, __pyx_int_21); __Pyx_INCREF(__pyx_int_22); __Pyx_GIVEREF(__pyx_int_22); PyList_SET_ITEM(__pyx_t_6, 2, __pyx_int_22); __Pyx_INCREF(__pyx_int_24); __Pyx_GIVEREF(__pyx_int_24); PyList_SET_ITEM(__pyx_t_6, 3, __pyx_int_24); __Pyx_INCREF(__pyx_int_25); __Pyx_GIVEREF(__pyx_int_25); PyList_SET_ITEM(__pyx_t_6, 4, __pyx_int_25); __Pyx_INCREF(__pyx_int_26); __Pyx_GIVEREF(__pyx_int_26); PyList_SET_ITEM(__pyx_t_6, 5, __pyx_int_26); __Pyx_INCREF(__pyx_int_27); __Pyx_GIVEREF(__pyx_int_27); PyList_SET_ITEM(__pyx_t_6, 6, __pyx_int_27); __Pyx_INCREF(__pyx_int_28); __Pyx_GIVEREF(__pyx_int_28); PyList_SET_ITEM(__pyx_t_6, 7, __pyx_int_28); __Pyx_INCREF(__pyx_int_30); __Pyx_GIVEREF(__pyx_int_30); PyList_SET_ITEM(__pyx_t_6, 8, __pyx_int_30); __Pyx_INCREF(__pyx_int_32); __Pyx_GIVEREF(__pyx_int_32); PyList_SET_ITEM(__pyx_t_6, 9, __pyx_int_32); __Pyx_INCREF(__pyx_int_33); __Pyx_GIVEREF(__pyx_int_33); PyList_SET_ITEM(__pyx_t_6, 10, __pyx_int_33); __Pyx_INCREF(__pyx_int_35); __Pyx_GIVEREF(__pyx_int_35); PyList_SET_ITEM(__pyx_t_6, 11, __pyx_int_35); __Pyx_INCREF(__pyx_int_36); __Pyx_GIVEREF(__pyx_int_36); PyList_SET_ITEM(__pyx_t_6, 12, __pyx_int_36); __Pyx_INCREF(__pyx_int_39); __Pyx_GIVEREF(__pyx_int_39); PyList_SET_ITEM(__pyx_t_6, 13, __pyx_int_39); __Pyx_INCREF(__pyx_int_40); __Pyx_GIVEREF(__pyx_int_40); PyList_SET_ITEM(__pyx_t_6, 14, __pyx_int_40); __Pyx_INCREF(__pyx_int_42); __Pyx_GIVEREF(__pyx_int_42); PyList_SET_ITEM(__pyx_t_6, 15, __pyx_int_42); __Pyx_INCREF(__pyx_int_44); __Pyx_GIVEREF(__pyx_int_44); PyList_SET_ITEM(__pyx_t_6, 16, __pyx_int_44); __Pyx_INCREF(__pyx_int_45); __Pyx_GIVEREF(__pyx_int_45); PyList_SET_ITEM(__pyx_t_6, 17, __pyx_int_45); __Pyx_INCREF(__pyx_int_48); __Pyx_GIVEREF(__pyx_int_48); PyList_SET_ITEM(__pyx_t_6, 18, __pyx_int_48); __Pyx_INCREF(__pyx_int_49); __Pyx_GIVEREF(__pyx_int_49); PyList_SET_ITEM(__pyx_t_6, 19, __pyx_int_49); __Pyx_INCREF(__pyx_int_50); __Pyx_GIVEREF(__pyx_int_50); PyList_SET_ITEM(__pyx_t_6, 20, __pyx_int_50); __Pyx_INCREF(__pyx_int_52); __Pyx_GIVEREF(__pyx_int_52); PyList_SET_ITEM(__pyx_t_6, 21, __pyx_int_52); __Pyx_INCREF(__pyx_int_54); __Pyx_GIVEREF(__pyx_int_54); PyList_SET_ITEM(__pyx_t_6, 22, __pyx_int_54); __Pyx_INCREF(__pyx_int_55); __Pyx_GIVEREF(__pyx_int_55); PyList_SET_ITEM(__pyx_t_6, 23, __pyx_int_55); __Pyx_INCREF(__pyx_int_56); __Pyx_GIVEREF(__pyx_int_56); PyList_SET_ITEM(__pyx_t_6, 24, __pyx_int_56); __Pyx_INCREF(__pyx_int_60); __Pyx_GIVEREF(__pyx_int_60); PyList_SET_ITEM(__pyx_t_6, 25, __pyx_int_60); __Pyx_INCREF(__pyx_int_63); __Pyx_GIVEREF(__pyx_int_63); PyList_SET_ITEM(__pyx_t_6, 26, __pyx_int_63); __Pyx_INCREF(__pyx_int_64); __Pyx_GIVEREF(__pyx_int_64); PyList_SET_ITEM(__pyx_t_6, 27, __pyx_int_64); __Pyx_INCREF(__pyx_int_65); __Pyx_GIVEREF(__pyx_int_65); PyList_SET_ITEM(__pyx_t_6, 28, __pyx_int_65); __Pyx_INCREF(__pyx_int_66); __Pyx_GIVEREF(__pyx_int_66); PyList_SET_ITEM(__pyx_t_6, 29, __pyx_int_66); __Pyx_INCREF(__pyx_int_70); __Pyx_GIVEREF(__pyx_int_70); PyList_SET_ITEM(__pyx_t_6, 30, __pyx_int_70); __Pyx_INCREF(__pyx_int_72); __Pyx_GIVEREF(__pyx_int_72); PyList_SET_ITEM(__pyx_t_6, 31, __pyx_int_72); __Pyx_INCREF(__pyx_int_75); __Pyx_GIVEREF(__pyx_int_75); PyList_SET_ITEM(__pyx_t_6, 32, __pyx_int_75); __Pyx_INCREF(__pyx_int_77); __Pyx_GIVEREF(__pyx_int_77); PyList_SET_ITEM(__pyx_t_6, 33, __pyx_int_77); __Pyx_INCREF(__pyx_int_78); __Pyx_GIVEREF(__pyx_int_78); PyList_SET_ITEM(__pyx_t_6, 34, __pyx_int_78); __Pyx_INCREF(__pyx_int_80); __Pyx_GIVEREF(__pyx_int_80); PyList_SET_ITEM(__pyx_t_6, 35, __pyx_int_80); __Pyx_INCREF(__pyx_int_81); __Pyx_GIVEREF(__pyx_int_81); PyList_SET_ITEM(__pyx_t_6, 36, __pyx_int_81); __Pyx_INCREF(__pyx_int_84); __Pyx_GIVEREF(__pyx_int_84); PyList_SET_ITEM(__pyx_t_6, 37, __pyx_int_84); __Pyx_INCREF(__pyx_int_88); __Pyx_GIVEREF(__pyx_int_88); PyList_SET_ITEM(__pyx_t_6, 38, __pyx_int_88); __Pyx_INCREF(__pyx_int_90); __Pyx_GIVEREF(__pyx_int_90); PyList_SET_ITEM(__pyx_t_6, 39, __pyx_int_90); __Pyx_INCREF(__pyx_int_91); __Pyx_GIVEREF(__pyx_int_91); PyList_SET_ITEM(__pyx_t_6, 40, __pyx_int_91); __Pyx_INCREF(__pyx_int_96); __Pyx_GIVEREF(__pyx_int_96); PyList_SET_ITEM(__pyx_t_6, 41, __pyx_int_96); __Pyx_INCREF(__pyx_int_98); __Pyx_GIVEREF(__pyx_int_98); PyList_SET_ITEM(__pyx_t_6, 42, __pyx_int_98); __Pyx_INCREF(__pyx_int_99); __Pyx_GIVEREF(__pyx_int_99); PyList_SET_ITEM(__pyx_t_6, 43, __pyx_int_99); __Pyx_INCREF(__pyx_int_100); __Pyx_GIVEREF(__pyx_int_100); PyList_SET_ITEM(__pyx_t_6, 44, __pyx_int_100); __Pyx_INCREF(__pyx_int_104); __Pyx_GIVEREF(__pyx_int_104); PyList_SET_ITEM(__pyx_t_6, 45, __pyx_int_104); __Pyx_INCREF(__pyx_int_105); __Pyx_GIVEREF(__pyx_int_105); PyList_SET_ITEM(__pyx_t_6, 46, __pyx_int_105); __Pyx_INCREF(__pyx_int_108); __Pyx_GIVEREF(__pyx_int_108); PyList_SET_ITEM(__pyx_t_6, 47, __pyx_int_108); __Pyx_INCREF(__pyx_int_110); __Pyx_GIVEREF(__pyx_int_110); PyList_SET_ITEM(__pyx_t_6, 48, __pyx_int_110); __Pyx_INCREF(__pyx_int_112); __Pyx_GIVEREF(__pyx_int_112); PyList_SET_ITEM(__pyx_t_6, 49, __pyx_int_112); __Pyx_INCREF(__pyx_int_117); __Pyx_GIVEREF(__pyx_int_117); PyList_SET_ITEM(__pyx_t_6, 50, __pyx_int_117); __Pyx_INCREF(__pyx_int_120); __Pyx_GIVEREF(__pyx_int_120); PyList_SET_ITEM(__pyx_t_6, 51, __pyx_int_120); __Pyx_INCREF(__pyx_int_125); __Pyx_GIVEREF(__pyx_int_125); PyList_SET_ITEM(__pyx_t_6, 52, __pyx_int_125); __Pyx_INCREF(__pyx_int_126); __Pyx_GIVEREF(__pyx_int_126); PyList_SET_ITEM(__pyx_t_6, 53, __pyx_int_126); __Pyx_INCREF(__pyx_int_128); __Pyx_GIVEREF(__pyx_int_128); PyList_SET_ITEM(__pyx_t_6, 54, __pyx_int_128); __Pyx_INCREF(__pyx_int_130); __Pyx_GIVEREF(__pyx_int_130); PyList_SET_ITEM(__pyx_t_6, 55, __pyx_int_130); __Pyx_INCREF(__pyx_int_132); __Pyx_GIVEREF(__pyx_int_132); PyList_SET_ITEM(__pyx_t_6, 56, __pyx_int_132); __Pyx_INCREF(__pyx_int_135); __Pyx_GIVEREF(__pyx_int_135); PyList_SET_ITEM(__pyx_t_6, 57, __pyx_int_135); __Pyx_INCREF(__pyx_int_140); __Pyx_GIVEREF(__pyx_int_140); PyList_SET_ITEM(__pyx_t_6, 58, __pyx_int_140); __Pyx_INCREF(__pyx_int_144); __Pyx_GIVEREF(__pyx_int_144); PyList_SET_ITEM(__pyx_t_6, 59, __pyx_int_144); __Pyx_INCREF(__pyx_int_147); __Pyx_GIVEREF(__pyx_int_147); PyList_SET_ITEM(__pyx_t_6, 60, __pyx_int_147); __Pyx_INCREF(__pyx_int_150); __Pyx_GIVEREF(__pyx_int_150); PyList_SET_ITEM(__pyx_t_6, 61, __pyx_int_150); __Pyx_INCREF(__pyx_int_154); __Pyx_GIVEREF(__pyx_int_154); PyList_SET_ITEM(__pyx_t_6, 62, __pyx_int_154); __Pyx_INCREF(__pyx_int_156); __Pyx_GIVEREF(__pyx_int_156); PyList_SET_ITEM(__pyx_t_6, 63, __pyx_int_156); __Pyx_INCREF(__pyx_int_160); __Pyx_GIVEREF(__pyx_int_160); PyList_SET_ITEM(__pyx_t_6, 64, __pyx_int_160); __Pyx_INCREF(__pyx_int_162); __Pyx_GIVEREF(__pyx_int_162); PyList_SET_ITEM(__pyx_t_6, 65, __pyx_int_162); __Pyx_INCREF(__pyx_int_165); __Pyx_GIVEREF(__pyx_int_165); PyList_SET_ITEM(__pyx_t_6, 66, __pyx_int_165); __Pyx_INCREF(__pyx_int_168); __Pyx_GIVEREF(__pyx_int_168); PyList_SET_ITEM(__pyx_t_6, 67, __pyx_int_168); __Pyx_INCREF(__pyx_int_175); __Pyx_GIVEREF(__pyx_int_175); PyList_SET_ITEM(__pyx_t_6, 68, __pyx_int_175); __Pyx_INCREF(__pyx_int_176); __Pyx_GIVEREF(__pyx_int_176); PyList_SET_ITEM(__pyx_t_6, 69, __pyx_int_176); __Pyx_INCREF(__pyx_int_180); __Pyx_GIVEREF(__pyx_int_180); PyList_SET_ITEM(__pyx_t_6, 70, __pyx_int_180); __Pyx_INCREF(__pyx_int_182); __Pyx_GIVEREF(__pyx_int_182); PyList_SET_ITEM(__pyx_t_6, 71, __pyx_int_182); __Pyx_INCREF(__pyx_int_189); __Pyx_GIVEREF(__pyx_int_189); PyList_SET_ITEM(__pyx_t_6, 72, __pyx_int_189); __Pyx_INCREF(__pyx_int_192); __Pyx_GIVEREF(__pyx_int_192); PyList_SET_ITEM(__pyx_t_6, 73, __pyx_int_192); __Pyx_INCREF(__pyx_int_195); __Pyx_GIVEREF(__pyx_int_195); PyList_SET_ITEM(__pyx_t_6, 74, __pyx_int_195); __Pyx_INCREF(__pyx_int_196); __Pyx_GIVEREF(__pyx_int_196); PyList_SET_ITEM(__pyx_t_6, 75, __pyx_int_196); __Pyx_INCREF(__pyx_int_198); __Pyx_GIVEREF(__pyx_int_198); PyList_SET_ITEM(__pyx_t_6, 76, __pyx_int_198); __Pyx_INCREF(__pyx_int_200); __Pyx_GIVEREF(__pyx_int_200); PyList_SET_ITEM(__pyx_t_6, 77, __pyx_int_200); __Pyx_INCREF(__pyx_int_208); __Pyx_GIVEREF(__pyx_int_208); PyList_SET_ITEM(__pyx_t_6, 78, __pyx_int_208); __Pyx_INCREF(__pyx_int_210); __Pyx_GIVEREF(__pyx_int_210); PyList_SET_ITEM(__pyx_t_6, 79, __pyx_int_210); __Pyx_INCREF(__pyx_int_216); __Pyx_GIVEREF(__pyx_int_216); PyList_SET_ITEM(__pyx_t_6, 80, __pyx_int_216); __Pyx_INCREF(__pyx_int_220); __Pyx_GIVEREF(__pyx_int_220); PyList_SET_ITEM(__pyx_t_6, 81, __pyx_int_220); __Pyx_INCREF(__pyx_int_224); __Pyx_GIVEREF(__pyx_int_224); PyList_SET_ITEM(__pyx_t_6, 82, __pyx_int_224); __Pyx_INCREF(__pyx_int_225); __Pyx_GIVEREF(__pyx_int_225); PyList_SET_ITEM(__pyx_t_6, 83, __pyx_int_225); __Pyx_INCREF(__pyx_int_231); __Pyx_GIVEREF(__pyx_int_231); PyList_SET_ITEM(__pyx_t_6, 84, __pyx_int_231); __Pyx_INCREF(__pyx_int_234); __Pyx_GIVEREF(__pyx_int_234); PyList_SET_ITEM(__pyx_t_6, 85, __pyx_int_234); __Pyx_INCREF(__pyx_int_240); __Pyx_GIVEREF(__pyx_int_240); PyList_SET_ITEM(__pyx_t_6, 86, __pyx_int_240); __Pyx_INCREF(__pyx_int_243); __Pyx_GIVEREF(__pyx_int_243); PyList_SET_ITEM(__pyx_t_6, 87, __pyx_int_243); __Pyx_INCREF(__pyx_int_245); __Pyx_GIVEREF(__pyx_int_245); PyList_SET_ITEM(__pyx_t_6, 88, __pyx_int_245); __Pyx_INCREF(__pyx_int_250); __Pyx_GIVEREF(__pyx_int_250); PyList_SET_ITEM(__pyx_t_6, 89, __pyx_int_250); __Pyx_INCREF(__pyx_int_252); __Pyx_GIVEREF(__pyx_int_252); PyList_SET_ITEM(__pyx_t_6, 90, __pyx_int_252); __Pyx_INCREF(__pyx_int_256); __Pyx_GIVEREF(__pyx_int_256); PyList_SET_ITEM(__pyx_t_6, 91, __pyx_int_256); __Pyx_INCREF(__pyx_int_260); __Pyx_GIVEREF(__pyx_int_260); PyList_SET_ITEM(__pyx_t_6, 92, __pyx_int_260); __Pyx_INCREF(__pyx_int_264); __Pyx_GIVEREF(__pyx_int_264); PyList_SET_ITEM(__pyx_t_6, 93, __pyx_int_264); __Pyx_INCREF(__pyx_int_270); __Pyx_GIVEREF(__pyx_int_270); PyList_SET_ITEM(__pyx_t_6, 94, __pyx_int_270); __Pyx_INCREF(__pyx_int_273); __Pyx_GIVEREF(__pyx_int_273); PyList_SET_ITEM(__pyx_t_6, 95, __pyx_int_273); __Pyx_INCREF(__pyx_int_275); __Pyx_GIVEREF(__pyx_int_275); PyList_SET_ITEM(__pyx_t_6, 96, __pyx_int_275); __Pyx_INCREF(__pyx_int_280); __Pyx_GIVEREF(__pyx_int_280); PyList_SET_ITEM(__pyx_t_6, 97, __pyx_int_280); __Pyx_INCREF(__pyx_int_288); __Pyx_GIVEREF(__pyx_int_288); PyList_SET_ITEM(__pyx_t_6, 98, __pyx_int_288); __Pyx_INCREF(__pyx_int_294); __Pyx_GIVEREF(__pyx_int_294); PyList_SET_ITEM(__pyx_t_6, 99, __pyx_int_294); __Pyx_INCREF(__pyx_int_297); __Pyx_GIVEREF(__pyx_int_297); PyList_SET_ITEM(__pyx_t_6, 100, __pyx_int_297); __Pyx_INCREF(__pyx_int_300); __Pyx_GIVEREF(__pyx_int_300); PyList_SET_ITEM(__pyx_t_6, 101, __pyx_int_300); __Pyx_INCREF(__pyx_int_308); __Pyx_GIVEREF(__pyx_int_308); PyList_SET_ITEM(__pyx_t_6, 102, __pyx_int_308); __Pyx_INCREF(__pyx_int_312); __Pyx_GIVEREF(__pyx_int_312); PyList_SET_ITEM(__pyx_t_6, 103, __pyx_int_312); __Pyx_INCREF(__pyx_int_315); __Pyx_GIVEREF(__pyx_int_315); PyList_SET_ITEM(__pyx_t_6, 104, __pyx_int_315); __Pyx_INCREF(__pyx_int_320); __Pyx_GIVEREF(__pyx_int_320); PyList_SET_ITEM(__pyx_t_6, 105, __pyx_int_320); __Pyx_INCREF(__pyx_int_324); __Pyx_GIVEREF(__pyx_int_324); PyList_SET_ITEM(__pyx_t_6, 106, __pyx_int_324); __Pyx_INCREF(__pyx_int_325); __Pyx_GIVEREF(__pyx_int_325); PyList_SET_ITEM(__pyx_t_6, 107, __pyx_int_325); __Pyx_INCREF(__pyx_int_330); __Pyx_GIVEREF(__pyx_int_330); PyList_SET_ITEM(__pyx_t_6, 108, __pyx_int_330); __Pyx_INCREF(__pyx_int_336); __Pyx_GIVEREF(__pyx_int_336); PyList_SET_ITEM(__pyx_t_6, 109, __pyx_int_336); __Pyx_INCREF(__pyx_int_343); __Pyx_GIVEREF(__pyx_int_343); PyList_SET_ITEM(__pyx_t_6, 110, __pyx_int_343); __Pyx_INCREF(__pyx_int_350); __Pyx_GIVEREF(__pyx_int_350); PyList_SET_ITEM(__pyx_t_6, 111, __pyx_int_350); __Pyx_INCREF(__pyx_int_351); __Pyx_GIVEREF(__pyx_int_351); PyList_SET_ITEM(__pyx_t_6, 112, __pyx_int_351); __Pyx_INCREF(__pyx_int_352); __Pyx_GIVEREF(__pyx_int_352); PyList_SET_ITEM(__pyx_t_6, 113, __pyx_int_352); __Pyx_INCREF(__pyx_int_360); __Pyx_GIVEREF(__pyx_int_360); PyList_SET_ITEM(__pyx_t_6, 114, __pyx_int_360); __Pyx_INCREF(__pyx_int_364); __Pyx_GIVEREF(__pyx_int_364); PyList_SET_ITEM(__pyx_t_6, 115, __pyx_int_364); __Pyx_INCREF(__pyx_int_375); __Pyx_GIVEREF(__pyx_int_375); PyList_SET_ITEM(__pyx_t_6, 116, __pyx_int_375); __Pyx_INCREF(__pyx_int_378); __Pyx_GIVEREF(__pyx_int_378); PyList_SET_ITEM(__pyx_t_6, 117, __pyx_int_378); __Pyx_INCREF(__pyx_int_384); __Pyx_GIVEREF(__pyx_int_384); PyList_SET_ITEM(__pyx_t_6, 118, __pyx_int_384); __Pyx_INCREF(__pyx_int_385); __Pyx_GIVEREF(__pyx_int_385); PyList_SET_ITEM(__pyx_t_6, 119, __pyx_int_385); __Pyx_INCREF(__pyx_int_390); __Pyx_GIVEREF(__pyx_int_390); PyList_SET_ITEM(__pyx_t_6, 120, __pyx_int_390); __Pyx_INCREF(__pyx_int_392); __Pyx_GIVEREF(__pyx_int_392); PyList_SET_ITEM(__pyx_t_6, 121, __pyx_int_392); __Pyx_INCREF(__pyx_int_396); __Pyx_GIVEREF(__pyx_int_396); PyList_SET_ITEM(__pyx_t_6, 122, __pyx_int_396); __Pyx_INCREF(__pyx_int_400); __Pyx_GIVEREF(__pyx_int_400); PyList_SET_ITEM(__pyx_t_6, 123, __pyx_int_400); __Pyx_INCREF(__pyx_int_405); __Pyx_GIVEREF(__pyx_int_405); PyList_SET_ITEM(__pyx_t_6, 124, __pyx_int_405); __Pyx_INCREF(__pyx_int_416); __Pyx_GIVEREF(__pyx_int_416); PyList_SET_ITEM(__pyx_t_6, 125, __pyx_int_416); __Pyx_INCREF(__pyx_int_420); __Pyx_GIVEREF(__pyx_int_420); PyList_SET_ITEM(__pyx_t_6, 126, __pyx_int_420); __Pyx_INCREF(__pyx_int_432); __Pyx_GIVEREF(__pyx_int_432); PyList_SET_ITEM(__pyx_t_6, 127, __pyx_int_432); __Pyx_INCREF(__pyx_int_440); __Pyx_GIVEREF(__pyx_int_440); PyList_SET_ITEM(__pyx_t_6, 128, __pyx_int_440); __Pyx_INCREF(__pyx_int_441); __Pyx_GIVEREF(__pyx_int_441); PyList_SET_ITEM(__pyx_t_6, 129, __pyx_int_441); __Pyx_INCREF(__pyx_int_448); __Pyx_GIVEREF(__pyx_int_448); PyList_SET_ITEM(__pyx_t_6, 130, __pyx_int_448); __Pyx_INCREF(__pyx_int_450); __Pyx_GIVEREF(__pyx_int_450); PyList_SET_ITEM(__pyx_t_6, 131, __pyx_int_450); __Pyx_INCREF(__pyx_int_455); __Pyx_GIVEREF(__pyx_int_455); PyList_SET_ITEM(__pyx_t_6, 132, __pyx_int_455); __Pyx_INCREF(__pyx_int_462); __Pyx_GIVEREF(__pyx_int_462); PyList_SET_ITEM(__pyx_t_6, 133, __pyx_int_462); __Pyx_INCREF(__pyx_int_468); __Pyx_GIVEREF(__pyx_int_468); PyList_SET_ITEM(__pyx_t_6, 134, __pyx_int_468); __Pyx_INCREF(__pyx_int_480); __Pyx_GIVEREF(__pyx_int_480); PyList_SET_ITEM(__pyx_t_6, 135, __pyx_int_480); __Pyx_INCREF(__pyx_int_486); __Pyx_GIVEREF(__pyx_int_486); PyList_SET_ITEM(__pyx_t_6, 136, __pyx_int_486); __Pyx_INCREF(__pyx_int_490); __Pyx_GIVEREF(__pyx_int_490); PyList_SET_ITEM(__pyx_t_6, 137, __pyx_int_490); __Pyx_INCREF(__pyx_int_495); __Pyx_GIVEREF(__pyx_int_495); PyList_SET_ITEM(__pyx_t_6, 138, __pyx_int_495); __Pyx_INCREF(__pyx_int_500); __Pyx_GIVEREF(__pyx_int_500); PyList_SET_ITEM(__pyx_t_6, 139, __pyx_int_500); __Pyx_INCREF(__pyx_int_504); __Pyx_GIVEREF(__pyx_int_504); PyList_SET_ITEM(__pyx_t_6, 140, __pyx_int_504); __Pyx_INCREF(__pyx_int_512); __Pyx_GIVEREF(__pyx_int_512); PyList_SET_ITEM(__pyx_t_6, 141, __pyx_int_512); __Pyx_INCREF(__pyx_int_520); __Pyx_GIVEREF(__pyx_int_520); PyList_SET_ITEM(__pyx_t_6, 142, __pyx_int_520); __Pyx_INCREF(__pyx_int_525); __Pyx_GIVEREF(__pyx_int_525); PyList_SET_ITEM(__pyx_t_6, 143, __pyx_int_525); __Pyx_INCREF(__pyx_int_528); __Pyx_GIVEREF(__pyx_int_528); PyList_SET_ITEM(__pyx_t_6, 144, __pyx_int_528); __Pyx_INCREF(__pyx_int_539); __Pyx_GIVEREF(__pyx_int_539); PyList_SET_ITEM(__pyx_t_6, 145, __pyx_int_539); __Pyx_INCREF(__pyx_int_540); __Pyx_GIVEREF(__pyx_int_540); PyList_SET_ITEM(__pyx_t_6, 146, __pyx_int_540); __Pyx_INCREF(__pyx_int_546); __Pyx_GIVEREF(__pyx_int_546); PyList_SET_ITEM(__pyx_t_6, 147, __pyx_int_546); __Pyx_INCREF(__pyx_int_550); __Pyx_GIVEREF(__pyx_int_550); PyList_SET_ITEM(__pyx_t_6, 148, __pyx_int_550); __Pyx_INCREF(__pyx_int_560); __Pyx_GIVEREF(__pyx_int_560); PyList_SET_ITEM(__pyx_t_6, 149, __pyx_int_560); __Pyx_INCREF(__pyx_int_567); __Pyx_GIVEREF(__pyx_int_567); PyList_SET_ITEM(__pyx_t_6, 150, __pyx_int_567); __Pyx_INCREF(__pyx_int_576); __Pyx_GIVEREF(__pyx_int_576); PyList_SET_ITEM(__pyx_t_6, 151, __pyx_int_576); __Pyx_INCREF(__pyx_int_585); __Pyx_GIVEREF(__pyx_int_585); PyList_SET_ITEM(__pyx_t_6, 152, __pyx_int_585); __Pyx_INCREF(__pyx_int_588); __Pyx_GIVEREF(__pyx_int_588); PyList_SET_ITEM(__pyx_t_6, 153, __pyx_int_588); __Pyx_INCREF(__pyx_int_594); __Pyx_GIVEREF(__pyx_int_594); PyList_SET_ITEM(__pyx_t_6, 154, __pyx_int_594); __Pyx_INCREF(__pyx_int_600); __Pyx_GIVEREF(__pyx_int_600); PyList_SET_ITEM(__pyx_t_6, 155, __pyx_int_600); __Pyx_INCREF(__pyx_int_616); __Pyx_GIVEREF(__pyx_int_616); PyList_SET_ITEM(__pyx_t_6, 156, __pyx_int_616); __Pyx_INCREF(__pyx_int_624); __Pyx_GIVEREF(__pyx_int_624); PyList_SET_ITEM(__pyx_t_6, 157, __pyx_int_624); __Pyx_INCREF(__pyx_int_625); __Pyx_GIVEREF(__pyx_int_625); PyList_SET_ITEM(__pyx_t_6, 158, __pyx_int_625); __Pyx_INCREF(__pyx_int_630); __Pyx_GIVEREF(__pyx_int_630); PyList_SET_ITEM(__pyx_t_6, 159, __pyx_int_630); __Pyx_INCREF(__pyx_int_637); __Pyx_GIVEREF(__pyx_int_637); PyList_SET_ITEM(__pyx_t_6, 160, __pyx_int_637); __Pyx_INCREF(__pyx_int_640); __Pyx_GIVEREF(__pyx_int_640); PyList_SET_ITEM(__pyx_t_6, 161, __pyx_int_640); __Pyx_INCREF(__pyx_int_648); __Pyx_GIVEREF(__pyx_int_648); PyList_SET_ITEM(__pyx_t_6, 162, __pyx_int_648); __Pyx_INCREF(__pyx_int_650); __Pyx_GIVEREF(__pyx_int_650); PyList_SET_ITEM(__pyx_t_6, 163, __pyx_int_650); __Pyx_INCREF(__pyx_int_660); __Pyx_GIVEREF(__pyx_int_660); PyList_SET_ITEM(__pyx_t_6, 164, __pyx_int_660); __Pyx_INCREF(__pyx_int_672); __Pyx_GIVEREF(__pyx_int_672); PyList_SET_ITEM(__pyx_t_6, 165, __pyx_int_672); __Pyx_INCREF(__pyx_int_675); __Pyx_GIVEREF(__pyx_int_675); PyList_SET_ITEM(__pyx_t_6, 166, __pyx_int_675); __Pyx_INCREF(__pyx_int_686); __Pyx_GIVEREF(__pyx_int_686); PyList_SET_ITEM(__pyx_t_6, 167, __pyx_int_686); __Pyx_INCREF(__pyx_int_693); __Pyx_GIVEREF(__pyx_int_693); PyList_SET_ITEM(__pyx_t_6, 168, __pyx_int_693); __Pyx_INCREF(__pyx_int_700); __Pyx_GIVEREF(__pyx_int_700); PyList_SET_ITEM(__pyx_t_6, 169, __pyx_int_700); __Pyx_INCREF(__pyx_int_702); __Pyx_GIVEREF(__pyx_int_702); PyList_SET_ITEM(__pyx_t_6, 170, __pyx_int_702); __Pyx_INCREF(__pyx_int_704); __Pyx_GIVEREF(__pyx_int_704); PyList_SET_ITEM(__pyx_t_6, 171, __pyx_int_704); __Pyx_INCREF(__pyx_int_720); __Pyx_GIVEREF(__pyx_int_720); PyList_SET_ITEM(__pyx_t_6, 172, __pyx_int_720); __Pyx_INCREF(__pyx_int_728); __Pyx_GIVEREF(__pyx_int_728); PyList_SET_ITEM(__pyx_t_6, 173, __pyx_int_728); __Pyx_INCREF(__pyx_int_729); __Pyx_GIVEREF(__pyx_int_729); PyList_SET_ITEM(__pyx_t_6, 174, __pyx_int_729); __Pyx_INCREF(__pyx_int_735); __Pyx_GIVEREF(__pyx_int_735); PyList_SET_ITEM(__pyx_t_6, 175, __pyx_int_735); __Pyx_INCREF(__pyx_int_750); __Pyx_GIVEREF(__pyx_int_750); PyList_SET_ITEM(__pyx_t_6, 176, __pyx_int_750); __Pyx_INCREF(__pyx_int_756); __Pyx_GIVEREF(__pyx_int_756); PyList_SET_ITEM(__pyx_t_6, 177, __pyx_int_756); __Pyx_INCREF(__pyx_int_768); __Pyx_GIVEREF(__pyx_int_768); PyList_SET_ITEM(__pyx_t_6, 178, __pyx_int_768); __Pyx_INCREF(__pyx_int_770); __Pyx_GIVEREF(__pyx_int_770); PyList_SET_ITEM(__pyx_t_6, 179, __pyx_int_770); __Pyx_INCREF(__pyx_int_780); __Pyx_GIVEREF(__pyx_int_780); PyList_SET_ITEM(__pyx_t_6, 180, __pyx_int_780); __Pyx_INCREF(__pyx_int_784); __Pyx_GIVEREF(__pyx_int_784); PyList_SET_ITEM(__pyx_t_6, 181, __pyx_int_784); __Pyx_INCREF(__pyx_int_792); __Pyx_GIVEREF(__pyx_int_792); PyList_SET_ITEM(__pyx_t_6, 182, __pyx_int_792); __Pyx_INCREF(__pyx_int_800); __Pyx_GIVEREF(__pyx_int_800); PyList_SET_ITEM(__pyx_t_6, 183, __pyx_int_800); __Pyx_INCREF(__pyx_int_810); __Pyx_GIVEREF(__pyx_int_810); PyList_SET_ITEM(__pyx_t_6, 184, __pyx_int_810); __Pyx_INCREF(__pyx_int_819); __Pyx_GIVEREF(__pyx_int_819); PyList_SET_ITEM(__pyx_t_6, 185, __pyx_int_819); __Pyx_INCREF(__pyx_int_825); __Pyx_GIVEREF(__pyx_int_825); PyList_SET_ITEM(__pyx_t_6, 186, __pyx_int_825); __Pyx_INCREF(__pyx_int_832); __Pyx_GIVEREF(__pyx_int_832); PyList_SET_ITEM(__pyx_t_6, 187, __pyx_int_832); __Pyx_INCREF(__pyx_int_840); __Pyx_GIVEREF(__pyx_int_840); PyList_SET_ITEM(__pyx_t_6, 188, __pyx_int_840); __Pyx_INCREF(__pyx_int_864); __Pyx_GIVEREF(__pyx_int_864); PyList_SET_ITEM(__pyx_t_6, 189, __pyx_int_864); __Pyx_INCREF(__pyx_int_875); __Pyx_GIVEREF(__pyx_int_875); PyList_SET_ITEM(__pyx_t_6, 190, __pyx_int_875); __Pyx_INCREF(__pyx_int_880); __Pyx_GIVEREF(__pyx_int_880); PyList_SET_ITEM(__pyx_t_6, 191, __pyx_int_880); __Pyx_INCREF(__pyx_int_882); __Pyx_GIVEREF(__pyx_int_882); PyList_SET_ITEM(__pyx_t_6, 192, __pyx_int_882); __Pyx_INCREF(__pyx_int_891); __Pyx_GIVEREF(__pyx_int_891); PyList_SET_ITEM(__pyx_t_6, 193, __pyx_int_891); __Pyx_INCREF(__pyx_int_896); __Pyx_GIVEREF(__pyx_int_896); PyList_SET_ITEM(__pyx_t_6, 194, __pyx_int_896); __Pyx_INCREF(__pyx_int_900); __Pyx_GIVEREF(__pyx_int_900); PyList_SET_ITEM(__pyx_t_6, 195, __pyx_int_900); __Pyx_INCREF(__pyx_int_910); __Pyx_GIVEREF(__pyx_int_910); PyList_SET_ITEM(__pyx_t_6, 196, __pyx_int_910); __Pyx_INCREF(__pyx_int_924); __Pyx_GIVEREF(__pyx_int_924); PyList_SET_ITEM(__pyx_t_6, 197, __pyx_int_924); __Pyx_INCREF(__pyx_int_936); __Pyx_GIVEREF(__pyx_int_936); PyList_SET_ITEM(__pyx_t_6, 198, __pyx_int_936); __Pyx_INCREF(__pyx_int_945); __Pyx_GIVEREF(__pyx_int_945); PyList_SET_ITEM(__pyx_t_6, 199, __pyx_int_945); __Pyx_INCREF(__pyx_int_960); __Pyx_GIVEREF(__pyx_int_960); PyList_SET_ITEM(__pyx_t_6, 200, __pyx_int_960); __Pyx_INCREF(__pyx_int_972); __Pyx_GIVEREF(__pyx_int_972); PyList_SET_ITEM(__pyx_t_6, 201, __pyx_int_972); __Pyx_INCREF(__pyx_int_975); __Pyx_GIVEREF(__pyx_int_975); PyList_SET_ITEM(__pyx_t_6, 202, __pyx_int_975); __Pyx_INCREF(__pyx_int_980); __Pyx_GIVEREF(__pyx_int_980); PyList_SET_ITEM(__pyx_t_6, 203, __pyx_int_980); __Pyx_INCREF(__pyx_int_990); __Pyx_GIVEREF(__pyx_int_990); PyList_SET_ITEM(__pyx_t_6, 204, __pyx_int_990); __Pyx_INCREF(__pyx_int_1000); __Pyx_GIVEREF(__pyx_int_1000); PyList_SET_ITEM(__pyx_t_6, 205, __pyx_int_1000); __pyx_v_recommended_boxes = ((PyObject*)__pyx_t_6); __pyx_t_6 = 0; /* "openTSNE/_tsne.pyx":392 * 900, 910, 924, 936, 945, 960, 972, 975, 980, 990, 1000 * ] * if n_boxes < recommended_boxes[205]: # <<<<<<<<<<<<<< * i = 0 * while n_boxes > recommended_boxes[i]: */ __pyx_t_6 = __Pyx_PyInt_From_int(__pyx_v_n_boxes); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 392, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = PyObject_RichCompare(__pyx_t_6, PyList_GET_ITEM(__pyx_v_recommended_boxes, 0xCD), Py_LT); __Pyx_XGOTREF(__pyx_t_7); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 392, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_5 = __Pyx_PyObject_IsTrue(__pyx_t_7); if (unlikely(__pyx_t_5 < 0)) __PYX_ERR(0, 392, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":393 * ] * if n_boxes < recommended_boxes[205]: * i = 0 # <<<<<<<<<<<<<< * while n_boxes > recommended_boxes[i]: * i += 1 */ __pyx_v_i = 0; /* "openTSNE/_tsne.pyx":394 * if n_boxes < recommended_boxes[205]: * i = 0 * while n_boxes > recommended_boxes[i]: # <<<<<<<<<<<<<< * i += 1 * n_boxes = recommended_boxes[i] */ while (1) { __pyx_t_7 = __Pyx_PyInt_From_int(__pyx_v_n_boxes); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 394, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_6 = PyObject_RichCompare(__pyx_t_7, PyList_GET_ITEM(__pyx_v_recommended_boxes, __pyx_v_i), Py_GT); __Pyx_XGOTREF(__pyx_t_6); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 394, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_5 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_5 < 0)) __PYX_ERR(0, 394, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (!__pyx_t_5) break; /* "openTSNE/_tsne.pyx":395 * i = 0 * while n_boxes > recommended_boxes[i]: * i += 1 # <<<<<<<<<<<<<< * n_boxes = recommended_boxes[i] * else: */ __pyx_v_i = (__pyx_v_i + 1); } /* "openTSNE/_tsne.pyx":396 * while n_boxes > recommended_boxes[i]: * i += 1 * n_boxes = recommended_boxes[i] # <<<<<<<<<<<<<< * else: * n_boxes = 1000 */ __pyx_t_8 = __Pyx_PyInt_As_int(PyList_GET_ITEM(__pyx_v_recommended_boxes, __pyx_v_i)); if (unlikely((__pyx_t_8 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 396, __pyx_L1_error) __pyx_v_n_boxes = __pyx_t_8; /* "openTSNE/_tsne.pyx":392 * 900, 910, 924, 936, 945, 960, 972, 975, 980, 990, 1000 * ] * if n_boxes < recommended_boxes[205]: # <<<<<<<<<<<<<< * i = 0 * while n_boxes > recommended_boxes[i]: */ goto __pyx_L6; } /* "openTSNE/_tsne.pyx":398 * n_boxes = recommended_boxes[i] * else: * n_boxes = 1000 # <<<<<<<<<<<<<< * * cdef double box_width = (y_max - y_min) / n_boxes */ /*else*/ { __pyx_v_n_boxes = 0x3E8; } __pyx_L6:; /* "openTSNE/_tsne.pyx":400 * n_boxes = 1000 * * cdef double box_width = (y_max - y_min) / n_boxes # <<<<<<<<<<<<<< * * # Compute the box bounds */ __pyx_v_box_width = ((__pyx_v_y_max - __pyx_v_y_min) / ((double)__pyx_v_n_boxes)); /* "openTSNE/_tsne.pyx":403 * * # Compute the box bounds * cdef double[::1] box_lower_bounds = np.empty(n_boxes, dtype=float) # <<<<<<<<<<<<<< * cdef double[::1] box_upper_bounds = np.empty(n_boxes, dtype=float) * for box_idx in range(n_boxes): */ __Pyx_GetModuleGlobalName(__pyx_t_6, __pyx_n_s_np); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 403, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_6, __pyx_n_s_empty); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 403, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = __Pyx_PyInt_From_int(__pyx_v_n_boxes); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 403, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 403, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 403, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); if (PyDict_SetItem(__pyx_t_6, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 403, __pyx_L1_error) __pyx_t_10 = __Pyx_PyObject_Call(__pyx_t_7, __pyx_t_9, __pyx_t_6); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 403, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_10, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 403, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_box_lower_bounds = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":404 * # Compute the box bounds * cdef double[::1] box_lower_bounds = np.empty(n_boxes, dtype=float) * cdef double[::1] box_upper_bounds = np.empty(n_boxes, dtype=float) # <<<<<<<<<<<<<< * for box_idx in range(n_boxes): * box_lower_bounds[box_idx] = box_idx * box_width + y_min */ __Pyx_GetModuleGlobalName(__pyx_t_10, __pyx_n_s_np); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 404, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_t_10, __pyx_n_s_empty); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 404, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyInt_From_int(__pyx_v_n_boxes); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 404, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 404, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 404, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); if (PyDict_SetItem(__pyx_t_10, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 404, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_t_9, __pyx_t_10); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 404, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 404, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_box_upper_bounds = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":405 * cdef double[::1] box_lower_bounds = np.empty(n_boxes, dtype=float) * cdef double[::1] box_upper_bounds = np.empty(n_boxes, dtype=float) * for box_idx in range(n_boxes): # <<<<<<<<<<<<<< * box_lower_bounds[box_idx] = box_idx * box_width + y_min * box_upper_bounds[box_idx] = (box_idx + 1) * box_width + y_min */ __pyx_t_8 = __pyx_v_n_boxes; __pyx_t_12 = __pyx_t_8; for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_12; __pyx_t_1+=1) { __pyx_v_box_idx = __pyx_t_1; /* "openTSNE/_tsne.pyx":406 * cdef double[::1] box_upper_bounds = np.empty(n_boxes, dtype=float) * for box_idx in range(n_boxes): * box_lower_bounds[box_idx] = box_idx * box_width + y_min # <<<<<<<<<<<<<< * box_upper_bounds[box_idx] = (box_idx + 1) * box_width + y_min * */ __pyx_t_4 = __pyx_v_box_idx; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_lower_bounds.data) + __pyx_t_4)) )) = ((__pyx_v_box_idx * __pyx_v_box_width) + __pyx_v_y_min); /* "openTSNE/_tsne.pyx":407 * for box_idx in range(n_boxes): * box_lower_bounds[box_idx] = box_idx * box_width + y_min * box_upper_bounds[box_idx] = (box_idx + 1) * box_width + y_min # <<<<<<<<<<<<<< * * # Determine which box each point belongs to */ __pyx_t_4 = __pyx_v_box_idx; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_upper_bounds.data) + __pyx_t_4)) )) = (((__pyx_v_box_idx + 1) * __pyx_v_box_width) + __pyx_v_y_min); } /* "openTSNE/_tsne.pyx":410 * * # Determine which box each point belongs to * cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) # <<<<<<<<<<<<<< * for i in range(n_samples): * box_idx = ((embedding[i] - y_min) / box_width) */ __pyx_v_point_box_idx = ((int *)PyMem_Malloc((__pyx_v_n_samples * (sizeof(int))))); /* "openTSNE/_tsne.pyx":411 * # Determine which box each point belongs to * cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) * for i in range(n_samples): # <<<<<<<<<<<<<< * box_idx = ((embedding[i] - y_min) / box_width) * # The right most point maps directly into `n_boxes`, while it should */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":412 * cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) * for i in range(n_samples): * box_idx = ((embedding[i] - y_min) / box_width) # <<<<<<<<<<<<<< * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box */ __pyx_t_4 = __pyx_v_i; __pyx_v_box_idx = ((int)(((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_4)) ))) - __pyx_v_y_min) / __pyx_v_box_width)); /* "openTSNE/_tsne.pyx":415 * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box * if box_idx >= n_boxes: # <<<<<<<<<<<<<< * box_idx = n_boxes - 1 * */ __pyx_t_5 = ((__pyx_v_box_idx >= __pyx_v_n_boxes) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":416 * # belong to the last box * if box_idx >= n_boxes: * box_idx = n_boxes - 1 # <<<<<<<<<<<<<< * * point_box_idx[i] = box_idx */ __pyx_v_box_idx = (__pyx_v_n_boxes - 1); /* "openTSNE/_tsne.pyx":415 * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box * if box_idx >= n_boxes: # <<<<<<<<<<<<<< * box_idx = n_boxes - 1 * */ } /* "openTSNE/_tsne.pyx":418 * box_idx = n_boxes - 1 * * point_box_idx[i] = box_idx # <<<<<<<<<<<<<< * * cdef int n_interpolation_points_1d = n_interpolation_points * n_boxes */ (__pyx_v_point_box_idx[__pyx_v_i]) = __pyx_v_box_idx; } /* "openTSNE/_tsne.pyx":420 * point_box_idx[i] = box_idx * * cdef int n_interpolation_points_1d = n_interpolation_points * n_boxes # <<<<<<<<<<<<<< * # Prepare the interpolants for a single interval, so we can use their * # relative positions later on */ __pyx_v_n_interpolation_points_1d = (__pyx_v_n_interpolation_points * __pyx_v_n_boxes); /* "openTSNE/_tsne.pyx":423 * # Prepare the interpolants for a single interval, so we can use their * # relative positions later on * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) # <<<<<<<<<<<<<< * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 423, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_empty); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 423, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = PyInt_FromSsize_t(__pyx_v_n_interpolation_points); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 423, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 423, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 423, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); if (PyDict_SetItem(__pyx_t_7, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 423, __pyx_L1_error) __pyx_t_6 = __Pyx_PyObject_Call(__pyx_t_10, __pyx_t_9, __pyx_t_7); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 423, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_6, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 423, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_v_y_tilde = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":424 * # relative positions later on * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) * cdef double h = 1. / n_interpolation_points # <<<<<<<<<<<<<< * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): */ __pyx_v_h = (1. / ((double)__pyx_v_n_interpolation_points)); /* "openTSNE/_tsne.pyx":425 * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 # <<<<<<<<<<<<<< * for i in range(1, n_interpolation_points): * y_tilde[i] = y_tilde[i - 1] + h */ __pyx_t_4 = 0; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_4)) )) = (__pyx_v_h / 2.0); /* "openTSNE/_tsne.pyx":426 * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): # <<<<<<<<<<<<<< * y_tilde[i] = y_tilde[i - 1] + h * */ __pyx_t_1 = __pyx_v_n_interpolation_points; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 1; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":427 * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): * y_tilde[i] = y_tilde[i - 1] + h # <<<<<<<<<<<<<< * * # Evaluate the the squared cauchy kernel at the interpolation nodes */ __pyx_t_4 = (__pyx_v_i - 1); __pyx_t_13 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_13)) )) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_4)) ))) + __pyx_v_h); } /* "openTSNE/_tsne.pyx":430 * * # Evaluate the the squared cauchy kernel at the interpolation nodes * cdef double[::1] sq_kernel_tilde = compute_kernel_tilde_1d( # <<<<<<<<<<<<<< * &cauchy_1d_exp1p, n_interpolation_points_1d, y_min, h * box_width, dof * ) */ __pyx_t_11 = __pyx_f_8openTSNE_5_tsne_compute_kernel_tilde_1d((&__pyx_f_8openTSNE_5_tsne_cauchy_1d_exp1p), __pyx_v_n_interpolation_points_1d, __pyx_v_y_min, (__pyx_v_h * __pyx_v_box_width), __pyx_v_dof); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 430, __pyx_L1_error) __pyx_v_sq_kernel_tilde = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":435 * # The non-square cauchy kernel is only used if dof != 1, so don't do unnecessary work * cdef double[::1] kernel_tilde * if dof != 1: # <<<<<<<<<<<<<< * kernel_tilde = compute_kernel_tilde_1d( * &cauchy_1d, n_interpolation_points_1d, y_min, h * box_width, dof */ __pyx_t_5 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":436 * cdef double[::1] kernel_tilde * if dof != 1: * kernel_tilde = compute_kernel_tilde_1d( # <<<<<<<<<<<<<< * &cauchy_1d, n_interpolation_points_1d, y_min, h * box_width, dof * ) */ __pyx_t_11 = __pyx_f_8openTSNE_5_tsne_compute_kernel_tilde_1d((&__pyx_f_8openTSNE_5_tsne_cauchy_1d), __pyx_v_n_interpolation_points_1d, __pyx_v_y_min, (__pyx_v_h * __pyx_v_box_width), __pyx_v_dof); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 436, __pyx_L1_error) __pyx_v_kernel_tilde = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":435 * # The non-square cauchy kernel is only used if dof != 1, so don't do unnecessary work * cdef double[::1] kernel_tilde * if dof != 1: # <<<<<<<<<<<<<< * kernel_tilde = compute_kernel_tilde_1d( * &cauchy_1d, n_interpolation_points_1d, y_min, h * box_width, dof */ } /* "openTSNE/_tsne.pyx":442 * # STEP 1: Compute the w coefficients * # Set up q_j values * cdef int n_terms = 3 # <<<<<<<<<<<<<< * cdef double[:, ::1] q_j = np.empty((n_samples, n_terms), dtype=float) * if dof != 1: */ __pyx_v_n_terms = 3; /* "openTSNE/_tsne.pyx":443 * # Set up q_j values * cdef int n_terms = 3 * cdef double[:, ::1] q_j = np.empty((n_samples, n_terms), dtype=float) # <<<<<<<<<<<<<< * if dof != 1: * for i in range(n_samples): */ __Pyx_GetModuleGlobalName(__pyx_t_6, __pyx_n_s_np); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 443, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_6, __pyx_n_s_empty); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 443, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 443, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_9 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 443, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_10 = PyTuple_New(2); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 443, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_6); __Pyx_GIVEREF(__pyx_t_9); PyTuple_SET_ITEM(__pyx_t_10, 1, __pyx_t_9); __pyx_t_6 = 0; __pyx_t_9 = 0; __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 443, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 443, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); if (PyDict_SetItem(__pyx_t_10, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 443, __pyx_L1_error) __pyx_t_6 = __Pyx_PyObject_Call(__pyx_t_7, __pyx_t_9, __pyx_t_10); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 443, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_14 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_6, PyBUF_WRITABLE); if (unlikely(!__pyx_t_14.memview)) __PYX_ERR(0, 443, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_v_q_j = __pyx_t_14; __pyx_t_14.memview = NULL; __pyx_t_14.data = NULL; /* "openTSNE/_tsne.pyx":444 * cdef int n_terms = 3 * cdef double[:, ::1] q_j = np.empty((n_samples, n_terms), dtype=float) * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_samples): * q_j[i, 0] = 1 */ __pyx_t_5 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":445 * cdef double[:, ::1] q_j = np.empty((n_samples, n_terms), dtype=float) * if dof != 1: * for i in range(n_samples): # <<<<<<<<<<<<<< * q_j[i, 0] = 1 * q_j[i, 1] = embedding[i] */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":446 * if dof != 1: * for i in range(n_samples): * q_j[i, 0] = 1 # <<<<<<<<<<<<<< * q_j[i, 1] = embedding[i] * q_j[i, 2] = 1 */ __pyx_t_4 = __pyx_v_i; __pyx_t_13 = 0; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_4 * __pyx_v_q_j.strides[0]) )) + __pyx_t_13)) )) = 1.0; /* "openTSNE/_tsne.pyx":447 * for i in range(n_samples): * q_j[i, 0] = 1 * q_j[i, 1] = embedding[i] # <<<<<<<<<<<<<< * q_j[i, 2] = 1 * else: */ __pyx_t_13 = __pyx_v_i; __pyx_t_4 = __pyx_v_i; __pyx_t_15 = 1; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_4 * __pyx_v_q_j.strides[0]) )) + __pyx_t_15)) )) = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_13)) ))); /* "openTSNE/_tsne.pyx":448 * q_j[i, 0] = 1 * q_j[i, 1] = embedding[i] * q_j[i, 2] = 1 # <<<<<<<<<<<<<< * else: * for i in range(n_samples): */ __pyx_t_13 = __pyx_v_i; __pyx_t_15 = 2; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_13 * __pyx_v_q_j.strides[0]) )) + __pyx_t_15)) )) = 1.0; } /* "openTSNE/_tsne.pyx":444 * cdef int n_terms = 3 * cdef double[:, ::1] q_j = np.empty((n_samples, n_terms), dtype=float) * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_samples): * q_j[i, 0] = 1 */ goto __pyx_L17; } /* "openTSNE/_tsne.pyx":450 * q_j[i, 2] = 1 * else: * for i in range(n_samples): # <<<<<<<<<<<<<< * q_j[i, 0] = 1 * q_j[i, 1] = embedding[i] */ /*else*/ { __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":451 * else: * for i in range(n_samples): * q_j[i, 0] = 1 # <<<<<<<<<<<<<< * q_j[i, 1] = embedding[i] * q_j[i, 2] = embedding[i] ** 2 */ __pyx_t_15 = __pyx_v_i; __pyx_t_13 = 0; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_15 * __pyx_v_q_j.strides[0]) )) + __pyx_t_13)) )) = 1.0; /* "openTSNE/_tsne.pyx":452 * for i in range(n_samples): * q_j[i, 0] = 1 * q_j[i, 1] = embedding[i] # <<<<<<<<<<<<<< * q_j[i, 2] = embedding[i] ** 2 * */ __pyx_t_13 = __pyx_v_i; __pyx_t_15 = __pyx_v_i; __pyx_t_4 = 1; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_15 * __pyx_v_q_j.strides[0]) )) + __pyx_t_4)) )) = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_13)) ))); /* "openTSNE/_tsne.pyx":453 * q_j[i, 0] = 1 * q_j[i, 1] = embedding[i] * q_j[i, 2] = embedding[i] ** 2 # <<<<<<<<<<<<<< * * # Compute the relative position of each reference point in its box */ __pyx_t_13 = __pyx_v_i; __pyx_t_4 = __pyx_v_i; __pyx_t_15 = 2; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_4 * __pyx_v_q_j.strides[0]) )) + __pyx_t_15)) )) = pow((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_13)) ))), 2.0); } } __pyx_L17:; /* "openTSNE/_tsne.pyx":456 * * # Compute the relative position of each reference point in its box * cdef double[::1] y_in_box = np.empty(n_samples, dtype=float) # <<<<<<<<<<<<<< * for i in range(n_samples): * box_idx = point_box_idx[i] */ __Pyx_GetModuleGlobalName(__pyx_t_6, __pyx_n_s_np); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 456, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_t_6, __pyx_n_s_empty); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 456, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 456, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 456, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 456, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); if (PyDict_SetItem(__pyx_t_6, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 456, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_10, __pyx_t_9, __pyx_t_6); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 456, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 456, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_y_in_box = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":457 * # Compute the relative position of each reference point in its box * cdef double[::1] y_in_box = np.empty(n_samples, dtype=float) * for i in range(n_samples): # <<<<<<<<<<<<<< * box_idx = point_box_idx[i] * y_in_box[i] = (embedding[i] - box_lower_bounds[box_idx]) / box_width */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":458 * cdef double[::1] y_in_box = np.empty(n_samples, dtype=float) * for i in range(n_samples): * box_idx = point_box_idx[i] # <<<<<<<<<<<<<< * y_in_box[i] = (embedding[i] - box_lower_bounds[box_idx]) / box_width * */ __pyx_v_box_idx = (__pyx_v_point_box_idx[__pyx_v_i]); /* "openTSNE/_tsne.pyx":459 * for i in range(n_samples): * box_idx = point_box_idx[i] * y_in_box[i] = (embedding[i] - box_lower_bounds[box_idx]) / box_width # <<<<<<<<<<<<<< * * # Interpolate kernel using Lagrange polynomials */ __pyx_t_13 = __pyx_v_i; __pyx_t_15 = __pyx_v_box_idx; __pyx_t_4 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_in_box.data) + __pyx_t_4)) )) = (((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_13)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_lower_bounds.data) + __pyx_t_15)) )))) / __pyx_v_box_width); } /* "openTSNE/_tsne.pyx":462 * * # Interpolate kernel using Lagrange polynomials * cdef double[:, ::1] interpolated_values = interpolate(y_in_box, y_tilde) # <<<<<<<<<<<<<< * * # Actually compute w_{ij}s */ __pyx_t_14 = __pyx_f_8openTSNE_5_tsne_interpolate(__pyx_v_y_in_box, __pyx_v_y_tilde); if (unlikely(!__pyx_t_14.memview)) __PYX_ERR(0, 462, __pyx_L1_error) __pyx_v_interpolated_values = __pyx_t_14; __pyx_t_14.memview = NULL; __pyx_t_14.data = NULL; /* "openTSNE/_tsne.pyx":465 * * # Actually compute w_{ij}s * cdef double[:, ::1] w_coefficients = np.zeros((n_interpolation_points_1d, n_terms), dtype=float) # <<<<<<<<<<<<<< * for i in range(n_samples): * box_idx = point_box_idx[i] * n_interpolation_points */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 465, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_zeros); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 465, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyInt_From_int(__pyx_v_n_interpolation_points_1d); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 465, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_9 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 465, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_10 = PyTuple_New(2); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 465, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_7); __Pyx_GIVEREF(__pyx_t_9); PyTuple_SET_ITEM(__pyx_t_10, 1, __pyx_t_9); __pyx_t_7 = 0; __pyx_t_9 = 0; __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 465, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 465, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); if (PyDict_SetItem(__pyx_t_10, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 465, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_t_9, __pyx_t_10); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 465, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_14 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_14.memview)) __PYX_ERR(0, 465, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_w_coefficients = __pyx_t_14; __pyx_t_14.memview = NULL; __pyx_t_14.data = NULL; /* "openTSNE/_tsne.pyx":466 * # Actually compute w_{ij}s * cdef double[:, ::1] w_coefficients = np.zeros((n_interpolation_points_1d, n_terms), dtype=float) * for i in range(n_samples): # <<<<<<<<<<<<<< * box_idx = point_box_idx[i] * n_interpolation_points * for j in range(n_interpolation_points): */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":467 * cdef double[:, ::1] w_coefficients = np.zeros((n_interpolation_points_1d, n_terms), dtype=float) * for i in range(n_samples): * box_idx = point_box_idx[i] * n_interpolation_points # <<<<<<<<<<<<<< * for j in range(n_interpolation_points): * for d in range(n_terms): */ __pyx_v_box_idx = ((__pyx_v_point_box_idx[__pyx_v_i]) * __pyx_v_n_interpolation_points); /* "openTSNE/_tsne.pyx":468 * for i in range(n_samples): * box_idx = point_box_idx[i] * n_interpolation_points * for j in range(n_interpolation_points): # <<<<<<<<<<<<<< * for d in range(n_terms): * w_coefficients[box_idx + j, d] += interpolated_values[i, j] * q_j[i, d] */ __pyx_t_16 = __pyx_v_n_interpolation_points; __pyx_t_17 = __pyx_t_16; for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) { __pyx_v_j = __pyx_t_18; /* "openTSNE/_tsne.pyx":469 * box_idx = point_box_idx[i] * n_interpolation_points * for j in range(n_interpolation_points): * for d in range(n_terms): # <<<<<<<<<<<<<< * w_coefficients[box_idx + j, d] += interpolated_values[i, j] * q_j[i, d] * */ __pyx_t_8 = __pyx_v_n_terms; __pyx_t_12 = __pyx_t_8; for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_12; __pyx_t_19+=1) { __pyx_v_d = __pyx_t_19; /* "openTSNE/_tsne.pyx":470 * for j in range(n_interpolation_points): * for d in range(n_terms): * w_coefficients[box_idx + j, d] += interpolated_values[i, j] * q_j[i, d] # <<<<<<<<<<<<<< * * # STEP 2: Compute the kernel values evaluated at the interpolation nodes */ __pyx_t_15 = __pyx_v_i; __pyx_t_13 = __pyx_v_j; __pyx_t_4 = __pyx_v_i; __pyx_t_20 = __pyx_v_d; __pyx_t_21 = (__pyx_v_box_idx + __pyx_v_j); __pyx_t_22 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_w_coefficients.data + __pyx_t_21 * __pyx_v_w_coefficients.strides[0]) )) + __pyx_t_22)) )) += ((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_interpolated_values.data + __pyx_t_15 * __pyx_v_interpolated_values.strides[0]) )) + __pyx_t_13)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_4 * __pyx_v_q_j.strides[0]) )) + __pyx_t_20)) )))); } } } /* "openTSNE/_tsne.pyx":473 * * # STEP 2: Compute the kernel values evaluated at the interpolation nodes * cdef double[:, ::1] y_tilde_values = np.empty((n_interpolation_points_1d, n_terms)) # <<<<<<<<<<<<<< * if dof != 1: * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients[:, :2], y_tilde_values[:, :2]) */ __Pyx_GetModuleGlobalName(__pyx_t_10, __pyx_n_s_np); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 473, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_t_10, __pyx_n_s_empty); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 473, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyInt_From_int(__pyx_v_n_interpolation_points_1d); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 473, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_6 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 473, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_23 = PyTuple_New(2); if (unlikely(!__pyx_t_23)) __PYX_ERR(0, 473, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_23); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_23, 0, __pyx_t_10); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_23, 1, __pyx_t_6); __pyx_t_10 = 0; __pyx_t_6 = 0; __pyx_t_6 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_9))) { __pyx_t_6 = PyMethod_GET_SELF(__pyx_t_9); if (likely(__pyx_t_6)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_9); __Pyx_INCREF(__pyx_t_6); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_9, function); } } __pyx_t_7 = (__pyx_t_6) ? __Pyx_PyObject_Call2Args(__pyx_t_9, __pyx_t_6, __pyx_t_23) : __Pyx_PyObject_CallOneArg(__pyx_t_9, __pyx_t_23); __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_23); __pyx_t_23 = 0; if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 473, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_14 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_14.memview)) __PYX_ERR(0, 473, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_y_tilde_values = __pyx_t_14; __pyx_t_14.memview = NULL; __pyx_t_14.data = NULL; /* "openTSNE/_tsne.pyx":474 * # STEP 2: Compute the kernel values evaluated at the interpolation nodes * cdef double[:, ::1] y_tilde_values = np.empty((n_interpolation_points_1d, n_terms)) * if dof != 1: # <<<<<<<<<<<<<< * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients[:, :2], y_tilde_values[:, :2]) * matrix_multiply_fft_1d(kernel_tilde, w_coefficients[:, 2:], y_tilde_values[:, 2:]) */ __pyx_t_5 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":475 * cdef double[:, ::1] y_tilde_values = np.empty((n_interpolation_points_1d, n_terms)) * if dof != 1: * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients[:, :2], y_tilde_values[:, :2]) # <<<<<<<<<<<<<< * matrix_multiply_fft_1d(kernel_tilde, w_coefficients[:, 2:], y_tilde_values[:, 2:]) * else: */ __pyx_t_14.data = __pyx_v_w_coefficients.data; __pyx_t_14.memview = __pyx_v_w_coefficients.memview; __PYX_INC_MEMVIEW(&__pyx_t_14, 0); __pyx_t_14.shape[0] = __pyx_v_w_coefficients.shape[0]; __pyx_t_14.strides[0] = __pyx_v_w_coefficients.strides[0]; __pyx_t_14.suboffsets[0] = -1; __pyx_t_8 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_14, __pyx_v_w_coefficients.shape[1], __pyx_v_w_coefficients.strides[1], __pyx_v_w_coefficients.suboffsets[1], 1, 1, &__pyx_t_8, 0, 2, 0, 0, 1, 0, 1) < 0)) { __PYX_ERR(0, 475, __pyx_L1_error) } __pyx_t_24.data = __pyx_v_y_tilde_values.data; __pyx_t_24.memview = __pyx_v_y_tilde_values.memview; __PYX_INC_MEMVIEW(&__pyx_t_24, 0); __pyx_t_24.shape[0] = __pyx_v_y_tilde_values.shape[0]; __pyx_t_24.strides[0] = __pyx_v_y_tilde_values.strides[0]; __pyx_t_24.suboffsets[0] = -1; __pyx_t_8 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_24, __pyx_v_y_tilde_values.shape[1], __pyx_v_y_tilde_values.strides[1], __pyx_v_y_tilde_values.suboffsets[1], 1, 1, &__pyx_t_8, 0, 2, 0, 0, 1, 0, 1) < 0)) { __PYX_ERR(0, 475, __pyx_L1_error) } __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_1d(__pyx_v_sq_kernel_tilde, __pyx_t_14, __pyx_t_24); __PYX_XDEC_MEMVIEW(&__pyx_t_14, 1); __pyx_t_14.memview = NULL; __pyx_t_14.data = NULL; __PYX_XDEC_MEMVIEW(&__pyx_t_24, 1); __pyx_t_24.memview = NULL; __pyx_t_24.data = NULL; /* "openTSNE/_tsne.pyx":476 * if dof != 1: * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients[:, :2], y_tilde_values[:, :2]) * matrix_multiply_fft_1d(kernel_tilde, w_coefficients[:, 2:], y_tilde_values[:, 2:]) # <<<<<<<<<<<<<< * else: * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients, y_tilde_values) */ __pyx_t_24.data = __pyx_v_w_coefficients.data; __pyx_t_24.memview = __pyx_v_w_coefficients.memview; __PYX_INC_MEMVIEW(&__pyx_t_24, 0); __pyx_t_24.shape[0] = __pyx_v_w_coefficients.shape[0]; __pyx_t_24.strides[0] = __pyx_v_w_coefficients.strides[0]; __pyx_t_24.suboffsets[0] = -1; __pyx_t_8 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_24, __pyx_v_w_coefficients.shape[1], __pyx_v_w_coefficients.strides[1], __pyx_v_w_coefficients.suboffsets[1], 1, 1, &__pyx_t_8, 2, 0, 0, 1, 0, 0, 1) < 0)) { __PYX_ERR(0, 476, __pyx_L1_error) } __pyx_t_14.data = __pyx_v_y_tilde_values.data; __pyx_t_14.memview = __pyx_v_y_tilde_values.memview; __PYX_INC_MEMVIEW(&__pyx_t_14, 0); __pyx_t_14.shape[0] = __pyx_v_y_tilde_values.shape[0]; __pyx_t_14.strides[0] = __pyx_v_y_tilde_values.strides[0]; __pyx_t_14.suboffsets[0] = -1; __pyx_t_8 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_14, __pyx_v_y_tilde_values.shape[1], __pyx_v_y_tilde_values.strides[1], __pyx_v_y_tilde_values.suboffsets[1], 1, 1, &__pyx_t_8, 2, 0, 0, 1, 0, 0, 1) < 0)) { __PYX_ERR(0, 476, __pyx_L1_error) } __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_1d(__pyx_v_kernel_tilde, __pyx_t_24, __pyx_t_14); __PYX_XDEC_MEMVIEW(&__pyx_t_24, 1); __pyx_t_24.memview = NULL; __pyx_t_24.data = NULL; __PYX_XDEC_MEMVIEW(&__pyx_t_14, 1); __pyx_t_14.memview = NULL; __pyx_t_14.data = NULL; /* "openTSNE/_tsne.pyx":474 * # STEP 2: Compute the kernel values evaluated at the interpolation nodes * cdef double[:, ::1] y_tilde_values = np.empty((n_interpolation_points_1d, n_terms)) * if dof != 1: # <<<<<<<<<<<<<< * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients[:, :2], y_tilde_values[:, :2]) * matrix_multiply_fft_1d(kernel_tilde, w_coefficients[:, 2:], y_tilde_values[:, 2:]) */ goto __pyx_L30; } /* "openTSNE/_tsne.pyx":478 * matrix_multiply_fft_1d(kernel_tilde, w_coefficients[:, 2:], y_tilde_values[:, 2:]) * else: * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients, y_tilde_values) # <<<<<<<<<<<<<< * * */ /*else*/ { __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_1d(__pyx_v_sq_kernel_tilde, __pyx_v_w_coefficients, __pyx_v_y_tilde_values); } __pyx_L30:; /* "openTSNE/_tsne.pyx":482 * * # STEP 3: Compute the potentials \tilde{\phi(y_i)} * cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) # <<<<<<<<<<<<<< * for i in range(n_samples): * box_idx = point_box_idx[i] * n_interpolation_points */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 482, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_zeros); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 482, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 482, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_23 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_23)) __PYX_ERR(0, 482, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_23); __pyx_t_6 = PyTuple_New(2); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 482, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_7); __Pyx_GIVEREF(__pyx_t_23); PyTuple_SET_ITEM(__pyx_t_6, 1, __pyx_t_23); __pyx_t_7 = 0; __pyx_t_23 = 0; __pyx_t_23 = PyTuple_New(1); if (unlikely(!__pyx_t_23)) __PYX_ERR(0, 482, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_23); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_23, 0, __pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 482, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); if (PyDict_SetItem(__pyx_t_6, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 482, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_9, __pyx_t_23, __pyx_t_6); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 482, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_23); __pyx_t_23 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_14 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_14.memview)) __PYX_ERR(0, 482, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_phi = __pyx_t_14; __pyx_t_14.memview = NULL; __pyx_t_14.data = NULL; /* "openTSNE/_tsne.pyx":483 * # STEP 3: Compute the potentials \tilde{\phi(y_i)} * cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) * for i in range(n_samples): # <<<<<<<<<<<<<< * box_idx = point_box_idx[i] * n_interpolation_points * for j in range(n_interpolation_points): */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":484 * cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) * for i in range(n_samples): * box_idx = point_box_idx[i] * n_interpolation_points # <<<<<<<<<<<<<< * for j in range(n_interpolation_points): * for d in range(n_terms): */ __pyx_v_box_idx = ((__pyx_v_point_box_idx[__pyx_v_i]) * __pyx_v_n_interpolation_points); /* "openTSNE/_tsne.pyx":485 * for i in range(n_samples): * box_idx = point_box_idx[i] * n_interpolation_points * for j in range(n_interpolation_points): # <<<<<<<<<<<<<< * for d in range(n_terms): * phi[i, d] += interpolated_values[i, j] * y_tilde_values[box_idx + j, d] */ __pyx_t_16 = __pyx_v_n_interpolation_points; __pyx_t_17 = __pyx_t_16; for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) { __pyx_v_j = __pyx_t_18; /* "openTSNE/_tsne.pyx":486 * box_idx = point_box_idx[i] * n_interpolation_points * for j in range(n_interpolation_points): * for d in range(n_terms): # <<<<<<<<<<<<<< * phi[i, d] += interpolated_values[i, j] * y_tilde_values[box_idx + j, d] * */ __pyx_t_8 = __pyx_v_n_terms; __pyx_t_12 = __pyx_t_8; for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_12; __pyx_t_19+=1) { __pyx_v_d = __pyx_t_19; /* "openTSNE/_tsne.pyx":487 * for j in range(n_interpolation_points): * for d in range(n_terms): * phi[i, d] += interpolated_values[i, j] * y_tilde_values[box_idx + j, d] # <<<<<<<<<<<<<< * * PyMem_Free(point_box_idx) */ __pyx_t_20 = __pyx_v_i; __pyx_t_4 = __pyx_v_j; __pyx_t_13 = (__pyx_v_box_idx + __pyx_v_j); __pyx_t_15 = __pyx_v_d; __pyx_t_22 = __pyx_v_i; __pyx_t_21 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_22 * __pyx_v_phi.strides[0]) )) + __pyx_t_21)) )) += ((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_interpolated_values.data + __pyx_t_20 * __pyx_v_interpolated_values.strides[0]) )) + __pyx_t_4)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_y_tilde_values.data + __pyx_t_13 * __pyx_v_y_tilde_values.strides[0]) )) + __pyx_t_15)) )))); } } } /* "openTSNE/_tsne.pyx":489 * phi[i, d] += interpolated_values[i, j] * y_tilde_values[box_idx + j, d] * * PyMem_Free(point_box_idx) # <<<<<<<<<<<<<< * * # Compute the normalization term Z or sum of q_{ij}s */ PyMem_Free(__pyx_v_point_box_idx); /* "openTSNE/_tsne.pyx":492 * * # Compute the normalization term Z or sum of q_{ij}s * cdef double sum_Q = 0 # <<<<<<<<<<<<<< * if dof != 1: * for i in range(n_samples): */ __pyx_v_sum_Q = 0.0; /* "openTSNE/_tsne.pyx":493 * # Compute the normalization term Z or sum of q_{ij}s * cdef double sum_Q = 0 * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_samples): * sum_Q += phi[i, 2] */ __pyx_t_5 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":494 * cdef double sum_Q = 0 * if dof != 1: * for i in range(n_samples): # <<<<<<<<<<<<<< * sum_Q += phi[i, 2] * else: */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":495 * if dof != 1: * for i in range(n_samples): * sum_Q += phi[i, 2] # <<<<<<<<<<<<<< * else: * for i in range(n_samples): */ __pyx_t_15 = __pyx_v_i; __pyx_t_13 = 2; __pyx_v_sum_Q = (__pyx_v_sum_Q + (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_15 * __pyx_v_phi.strides[0]) )) + __pyx_t_13)) )))); } /* "openTSNE/_tsne.pyx":493 * # Compute the normalization term Z or sum of q_{ij}s * cdef double sum_Q = 0 * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_samples): * sum_Q += phi[i, 2] */ goto __pyx_L37; } /* "openTSNE/_tsne.pyx":497 * sum_Q += phi[i, 2] * else: * for i in range(n_samples): # <<<<<<<<<<<<<< * sum_Q += (1 + embedding[i] ** 2) * phi[i, 0] - \ * 2 * embedding[i] * phi[i, 1] + \ */ /*else*/ { __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":498 * else: * for i in range(n_samples): * sum_Q += (1 + embedding[i] ** 2) * phi[i, 0] - \ # <<<<<<<<<<<<<< * 2 * embedding[i] * phi[i, 1] + \ * phi[i, 2] */ __pyx_t_13 = __pyx_v_i; __pyx_t_15 = __pyx_v_i; __pyx_t_4 = 0; /* "openTSNE/_tsne.pyx":499 * for i in range(n_samples): * sum_Q += (1 + embedding[i] ** 2) * phi[i, 0] - \ * 2 * embedding[i] * phi[i, 1] + \ # <<<<<<<<<<<<<< * phi[i, 2] * */ __pyx_t_20 = __pyx_v_i; __pyx_t_21 = __pyx_v_i; __pyx_t_22 = 1; /* "openTSNE/_tsne.pyx":500 * sum_Q += (1 + embedding[i] ** 2) * phi[i, 0] - \ * 2 * embedding[i] * phi[i, 1] + \ * phi[i, 2] # <<<<<<<<<<<<<< * * sum_Q -= n_samples */ __pyx_t_25 = __pyx_v_i; __pyx_t_26 = 2; /* "openTSNE/_tsne.pyx":498 * else: * for i in range(n_samples): * sum_Q += (1 + embedding[i] ** 2) * phi[i, 0] - \ # <<<<<<<<<<<<<< * 2 * embedding[i] * phi[i, 1] + \ * phi[i, 2] */ __pyx_v_sum_Q = (__pyx_v_sum_Q + ((((1.0 + pow((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_13)) ))), 2.0)) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_15 * __pyx_v_phi.strides[0]) )) + __pyx_t_4)) )))) - ((2.0 * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_20)) )))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_21 * __pyx_v_phi.strides[0]) )) + __pyx_t_22)) ))))) + (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_25 * __pyx_v_phi.strides[0]) )) + __pyx_t_26)) ))))); } } __pyx_L37:; /* "openTSNE/_tsne.pyx":502 * phi[i, 2] * * sum_Q -= n_samples # <<<<<<<<<<<<<< * * # The phis used here are not affected if dof != 1 */ __pyx_v_sum_Q = (__pyx_v_sum_Q - __pyx_v_n_samples); /* "openTSNE/_tsne.pyx":505 * * # The phis used here are not affected if dof != 1 * for i in range(n_samples): # <<<<<<<<<<<<<< * gradient[i] -= (embedding[i] * phi[i, 0] - phi[i, 1]) / (sum_Q + EPSILON) * */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":506 * # The phis used here are not affected if dof != 1 * for i in range(n_samples): * gradient[i] -= (embedding[i] * phi[i, 0] - phi[i, 1]) / (sum_Q + EPSILON) # <<<<<<<<<<<<<< * * return sum_Q */ __pyx_t_26 = __pyx_v_i; __pyx_t_25 = __pyx_v_i; __pyx_t_22 = 0; __pyx_t_21 = __pyx_v_i; __pyx_t_20 = 1; __pyx_t_4 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_gradient.data) + __pyx_t_4)) )) -= ((((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_26)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_25 * __pyx_v_phi.strides[0]) )) + __pyx_t_22)) )))) - (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_21 * __pyx_v_phi.strides[0]) )) + __pyx_t_20)) )))) / (__pyx_v_sum_Q + __pyx_v_8openTSNE_5_tsne_EPSILON)); } /* "openTSNE/_tsne.pyx":508 * gradient[i] -= (embedding[i] * phi[i, 0] - phi[i, 1]) / (sum_Q + EPSILON) * * return sum_Q # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_sum_Q; goto __pyx_L0; /* "openTSNE/_tsne.pyx":358 * * * cpdef double estimate_negative_gradient_fft_1d( # <<<<<<<<<<<<<< * double[::1] embedding, * double[::1] gradient, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_9); __Pyx_XDECREF(__pyx_t_10); __PYX_XDEC_MEMVIEW(&__pyx_t_11, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_14, 1); __Pyx_XDECREF(__pyx_t_23); __PYX_XDEC_MEMVIEW(&__pyx_t_24, 1); __Pyx_WriteUnraisable("openTSNE._tsne.estimate_negative_gradient_fft_1d", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_recommended_boxes); __PYX_XDEC_MEMVIEW(&__pyx_v_box_lower_bounds, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_box_upper_bounds, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_sq_kernel_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_kernel_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_q_j, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_in_box, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_interpolated_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_w_coefficients, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_phi, 1); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_5_tsne_7estimate_negative_gradient_fft_1d(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_pw_8openTSNE_5_tsne_7estimate_negative_gradient_fft_1d(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __Pyx_memviewslice __pyx_v_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_gradient = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_n_interpolation_points; Py_ssize_t __pyx_v_min_num_intervals; double __pyx_v_ints_in_interval; double __pyx_v_dof; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("estimate_negative_gradient_fft_1d (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_embedding,&__pyx_n_s_gradient,&__pyx_n_s_n_interpolation_points,&__pyx_n_s_min_num_intervals,&__pyx_n_s_ints_in_interval,&__pyx_n_s_dof,0}; PyObject* values[6] = {0,0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_embedding)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_gradient)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_1d", 0, 2, 6, 1); __PYX_ERR(0, 358, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_n_interpolation_points); if (value) { values[2] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 3: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_min_num_intervals); if (value) { values[3] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 4: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_ints_in_interval); if (value) { values[4] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 5: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dof); if (value) { values[5] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "estimate_negative_gradient_fft_1d") < 0)) __PYX_ERR(0, 358, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_embedding = __Pyx_PyObject_to_MemoryviewSlice_dc_double(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_embedding.memview)) __PYX_ERR(0, 359, __pyx_L3_error) __pyx_v_gradient = __Pyx_PyObject_to_MemoryviewSlice_dc_double(values[1], PyBUF_WRITABLE); if (unlikely(!__pyx_v_gradient.memview)) __PYX_ERR(0, 360, __pyx_L3_error) if (values[2]) { __pyx_v_n_interpolation_points = __Pyx_PyIndex_AsSsize_t(values[2]); if (unlikely((__pyx_v_n_interpolation_points == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 361, __pyx_L3_error) } else { __pyx_v_n_interpolation_points = ((Py_ssize_t)3); } if (values[3]) { __pyx_v_min_num_intervals = __Pyx_PyIndex_AsSsize_t(values[3]); if (unlikely((__pyx_v_min_num_intervals == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 362, __pyx_L3_error) } else { __pyx_v_min_num_intervals = ((Py_ssize_t)10); } if (values[4]) { __pyx_v_ints_in_interval = __pyx_PyFloat_AsDouble(values[4]); if (unlikely((__pyx_v_ints_in_interval == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 363, __pyx_L3_error) } else { __pyx_v_ints_in_interval = ((double)1.0); } if (values[5]) { __pyx_v_dof = __pyx_PyFloat_AsDouble(values[5]); if (unlikely((__pyx_v_dof == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 364, __pyx_L3_error) } else { __pyx_v_dof = ((double)1.0); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_1d", 0, 2, 6, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 358, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE._tsne.estimate_negative_gradient_fft_1d", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_5_tsne_6estimate_negative_gradient_fft_1d(__pyx_self, __pyx_v_embedding, __pyx_v_gradient, __pyx_v_n_interpolation_points, __pyx_v_min_num_intervals, __pyx_v_ints_in_interval, __pyx_v_dof); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_5_tsne_6estimate_negative_gradient_fft_1d(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, Py_ssize_t __pyx_v_n_interpolation_points, Py_ssize_t __pyx_v_min_num_intervals, double __pyx_v_ints_in_interval, double __pyx_v_dof) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations double __pyx_t_1; struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("estimate_negative_gradient_fft_1d", 0); __Pyx_XDECREF(__pyx_r); __pyx_t_2.__pyx_n = 4; __pyx_t_2.n_interpolation_points = __pyx_v_n_interpolation_points; __pyx_t_2.min_num_intervals = __pyx_v_min_num_intervals; __pyx_t_2.ints_in_interval = __pyx_v_ints_in_interval; __pyx_t_2.dof = __pyx_v_dof; __pyx_t_1 = __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d(__pyx_v_embedding, __pyx_v_gradient, 0, &__pyx_t_2); __pyx_t_3 = PyFloat_FromDouble(__pyx_t_1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 358, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("openTSNE._tsne.estimate_negative_gradient_fft_1d", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_embedding, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_gradient, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/_tsne.pyx":511 * * * cpdef tuple prepare_negative_gradient_fft_interpolation_grid_1d( # <<<<<<<<<<<<<< * double[::1] reference_embedding, * Py_ssize_t n_interpolation_points=3, */ static PyObject *__pyx_pw_8openTSNE_5_tsne_9prepare_negative_gradient_fft_interpolation_grid_1d(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_f_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_1d(__Pyx_memviewslice __pyx_v_reference_embedding, CYTHON_UNUSED int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_1d *__pyx_optional_args) { Py_ssize_t __pyx_v_n_interpolation_points = ((Py_ssize_t)3); Py_ssize_t __pyx_v_min_num_intervals = ((Py_ssize_t)10); double __pyx_v_ints_in_interval = ((double)1.0); double __pyx_v_dof = ((double)1.0); double __pyx_v_padding = ((double)0.0); Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; Py_ssize_t __pyx_v_d; Py_ssize_t __pyx_v_box_idx; Py_ssize_t __pyx_v_n_reference_samples; double __pyx_v_y_max; double __pyx_v_y_min; CYTHON_UNUSED double __pyx_v_coord_max; CYTHON_UNUSED double __pyx_v_coord_min; int __pyx_v_n_boxes; double __pyx_v_box_width; __Pyx_memviewslice __pyx_v_box_lower_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_box_upper_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; int *__pyx_v_reference_point_box_idx; int __pyx_v_n_interpolation_points_1d; __Pyx_memviewslice __pyx_v_y_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_h; __Pyx_memviewslice __pyx_v_sq_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_v_n_terms; __Pyx_memviewslice __pyx_v_q_j = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_reference_y_in_box = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_reference_interpolated_values = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_w_coefficients = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_y_tilde_values = { 0, 0, { 0 }, { 0 }, { 0 } }; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; int __pyx_t_5; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; PyObject *__pyx_t_9 = NULL; __Pyx_memviewslice __pyx_t_10 = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_t_11; int __pyx_t_12; Py_ssize_t __pyx_t_13; __Pyx_memviewslice __pyx_t_14 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_15; Py_ssize_t __pyx_t_16; Py_ssize_t __pyx_t_17; Py_ssize_t __pyx_t_18; Py_ssize_t __pyx_t_19; Py_ssize_t __pyx_t_20; Py_ssize_t __pyx_t_21; Py_ssize_t __pyx_t_22; PyObject *__pyx_t_23 = NULL; __Pyx_memviewslice __pyx_t_24 = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("prepare_negative_gradient_fft_interpolation_grid_1d", 0); if (__pyx_optional_args) { if (__pyx_optional_args->__pyx_n > 0) { __pyx_v_n_interpolation_points = __pyx_optional_args->n_interpolation_points; if (__pyx_optional_args->__pyx_n > 1) { __pyx_v_min_num_intervals = __pyx_optional_args->min_num_intervals; if (__pyx_optional_args->__pyx_n > 2) { __pyx_v_ints_in_interval = __pyx_optional_args->ints_in_interval; if (__pyx_optional_args->__pyx_n > 3) { __pyx_v_dof = __pyx_optional_args->dof; if (__pyx_optional_args->__pyx_n > 4) { __pyx_v_padding = __pyx_optional_args->padding; } } } } } } /* "openTSNE/_tsne.pyx":521 * cdef: * Py_ssize_t i, j, d, box_idx * Py_ssize_t n_reference_samples = reference_embedding.shape[0] # <<<<<<<<<<<<<< * * double y_max = -INFINITY, y_min = INFINITY */ __pyx_v_n_reference_samples = (__pyx_v_reference_embedding.shape[0]); /* "openTSNE/_tsne.pyx":523 * Py_ssize_t n_reference_samples = reference_embedding.shape[0] * * double y_max = -INFINITY, y_min = INFINITY # <<<<<<<<<<<<<< * # Determine the min/max values of the embedding * # First, check the existing embedding */ __pyx_v_y_max = (-INFINITY); __pyx_v_y_min = INFINITY; /* "openTSNE/_tsne.pyx":526 * # Determine the min/max values of the embedding * # First, check the existing embedding * for i in range(n_reference_samples): # <<<<<<<<<<<<<< * if reference_embedding[i] < y_min: * y_min = reference_embedding[i] */ __pyx_t_1 = __pyx_v_n_reference_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":527 * # First, check the existing embedding * for i in range(n_reference_samples): * if reference_embedding[i] < y_min: # <<<<<<<<<<<<<< * y_min = reference_embedding[i] * elif reference_embedding[i] > y_max: */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = (((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_reference_embedding.data) + __pyx_t_4)) ))) < __pyx_v_y_min) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":528 * for i in range(n_reference_samples): * if reference_embedding[i] < y_min: * y_min = reference_embedding[i] # <<<<<<<<<<<<<< * elif reference_embedding[i] > y_max: * y_max = reference_embedding[i] */ __pyx_t_4 = __pyx_v_i; __pyx_v_y_min = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_reference_embedding.data) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":527 * # First, check the existing embedding * for i in range(n_reference_samples): * if reference_embedding[i] < y_min: # <<<<<<<<<<<<<< * y_min = reference_embedding[i] * elif reference_embedding[i] > y_max: */ goto __pyx_L5; } /* "openTSNE/_tsne.pyx":529 * if reference_embedding[i] < y_min: * y_min = reference_embedding[i] * elif reference_embedding[i] > y_max: # <<<<<<<<<<<<<< * y_max = reference_embedding[i] * */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = (((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_reference_embedding.data) + __pyx_t_4)) ))) > __pyx_v_y_max) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":530 * y_min = reference_embedding[i] * elif reference_embedding[i] > y_max: * y_max = reference_embedding[i] # <<<<<<<<<<<<<< * * # We assume here that the embedding is centered and we want to generate an */ __pyx_t_4 = __pyx_v_i; __pyx_v_y_max = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_reference_embedding.data) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":529 * if reference_embedding[i] < y_min: * y_min = reference_embedding[i] * elif reference_embedding[i] > y_max: # <<<<<<<<<<<<<< * y_max = reference_embedding[i] * */ } __pyx_L5:; } /* "openTSNE/_tsne.pyx":534 * # We assume here that the embedding is centered and we want to generate an * # equal grid in both negative and positive lines * if fabs(y_min) > fabs(y_max): # <<<<<<<<<<<<<< * coord_max = -y_min * elif fabs(y_max) > fabs(y_min): */ __pyx_t_5 = ((fabs(__pyx_v_y_min) > fabs(__pyx_v_y_max)) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":535 * # equal grid in both negative and positive lines * if fabs(y_min) > fabs(y_max): * coord_max = -y_min # <<<<<<<<<<<<<< * elif fabs(y_max) > fabs(y_min): * coord_min = -y_max */ __pyx_v_coord_max = (-__pyx_v_y_min); /* "openTSNE/_tsne.pyx":534 * # We assume here that the embedding is centered and we want to generate an * # equal grid in both negative and positive lines * if fabs(y_min) > fabs(y_max): # <<<<<<<<<<<<<< * coord_max = -y_min * elif fabs(y_max) > fabs(y_min): */ goto __pyx_L6; } /* "openTSNE/_tsne.pyx":536 * if fabs(y_min) > fabs(y_max): * coord_max = -y_min * elif fabs(y_max) > fabs(y_min): # <<<<<<<<<<<<<< * coord_min = -y_max * */ __pyx_t_5 = ((fabs(__pyx_v_y_max) > fabs(__pyx_v_y_min)) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":537 * coord_max = -y_min * elif fabs(y_max) > fabs(y_min): * coord_min = -y_max # <<<<<<<<<<<<<< * * # Apply padding to the min/max coordinates */ __pyx_v_coord_min = (-__pyx_v_y_max); /* "openTSNE/_tsne.pyx":536 * if fabs(y_min) > fabs(y_max): * coord_max = -y_min * elif fabs(y_max) > fabs(y_min): # <<<<<<<<<<<<<< * coord_min = -y_max * */ } __pyx_L6:; /* "openTSNE/_tsne.pyx":540 * * # Apply padding to the min/max coordinates * y_min *= 1 + padding # <<<<<<<<<<<<<< * y_max *= 1 + padding * */ __pyx_v_y_min = (__pyx_v_y_min * (1.0 + __pyx_v_padding)); /* "openTSNE/_tsne.pyx":541 * # Apply padding to the min/max coordinates * y_min *= 1 + padding * y_max *= 1 + padding # <<<<<<<<<<<<<< * * cdef int n_boxes = fmax(min_num_intervals, (y_max - y_min) / ints_in_interval) */ __pyx_v_y_max = (__pyx_v_y_max * (1.0 + __pyx_v_padding)); /* "openTSNE/_tsne.pyx":543 * y_max *= 1 + padding * * cdef int n_boxes = fmax(min_num_intervals, (y_max - y_min) / ints_in_interval) # <<<<<<<<<<<<<< * cdef double box_width = (y_max - y_min) / n_boxes * */ __pyx_v_n_boxes = ((int)fmax(__pyx_v_min_num_intervals, ((__pyx_v_y_max - __pyx_v_y_min) / __pyx_v_ints_in_interval))); /* "openTSNE/_tsne.pyx":544 * * cdef int n_boxes = fmax(min_num_intervals, (y_max - y_min) / ints_in_interval) * cdef double box_width = (y_max - y_min) / n_boxes # <<<<<<<<<<<<<< * * # Compute the box bounds */ __pyx_v_box_width = ((__pyx_v_y_max - __pyx_v_y_min) / ((double)__pyx_v_n_boxes)); /* "openTSNE/_tsne.pyx":547 * * # Compute the box bounds * cdef double[::1] box_lower_bounds = np.empty(n_boxes, dtype=float) # <<<<<<<<<<<<<< * cdef double[::1] box_upper_bounds = np.empty(n_boxes, dtype=float) * for box_idx in range(n_boxes): */ __Pyx_GetModuleGlobalName(__pyx_t_6, __pyx_n_s_np); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 547, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_6, __pyx_n_s_empty); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 547, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = __Pyx_PyInt_From_int(__pyx_v_n_boxes); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 547, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_8 = PyTuple_New(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 547, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 547, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); if (PyDict_SetItem(__pyx_t_6, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 547, __pyx_L1_error) __pyx_t_9 = __Pyx_PyObject_Call(__pyx_t_7, __pyx_t_8, __pyx_t_6); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 547, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_10 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_9, PyBUF_WRITABLE); if (unlikely(!__pyx_t_10.memview)) __PYX_ERR(0, 547, __pyx_L1_error) __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_box_lower_bounds = __pyx_t_10; __pyx_t_10.memview = NULL; __pyx_t_10.data = NULL; /* "openTSNE/_tsne.pyx":548 * # Compute the box bounds * cdef double[::1] box_lower_bounds = np.empty(n_boxes, dtype=float) * cdef double[::1] box_upper_bounds = np.empty(n_boxes, dtype=float) # <<<<<<<<<<<<<< * for box_idx in range(n_boxes): * box_lower_bounds[box_idx] = box_idx * box_width + y_min */ __Pyx_GetModuleGlobalName(__pyx_t_9, __pyx_n_s_np); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 548, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_t_9, __pyx_n_s_empty); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 548, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_9 = __Pyx_PyInt_From_int(__pyx_v_n_boxes); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 548, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_8 = PyTuple_New(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 548, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_GIVEREF(__pyx_t_9); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_9); __pyx_t_9 = 0; __pyx_t_9 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 548, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); if (PyDict_SetItem(__pyx_t_9, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 548, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_t_8, __pyx_t_9); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 548, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_10 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_10.memview)) __PYX_ERR(0, 548, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_box_upper_bounds = __pyx_t_10; __pyx_t_10.memview = NULL; __pyx_t_10.data = NULL; /* "openTSNE/_tsne.pyx":549 * cdef double[::1] box_lower_bounds = np.empty(n_boxes, dtype=float) * cdef double[::1] box_upper_bounds = np.empty(n_boxes, dtype=float) * for box_idx in range(n_boxes): # <<<<<<<<<<<<<< * box_lower_bounds[box_idx] = box_idx * box_width + y_min * box_upper_bounds[box_idx] = (box_idx + 1) * box_width + y_min */ __pyx_t_11 = __pyx_v_n_boxes; __pyx_t_12 = __pyx_t_11; for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_12; __pyx_t_1+=1) { __pyx_v_box_idx = __pyx_t_1; /* "openTSNE/_tsne.pyx":550 * cdef double[::1] box_upper_bounds = np.empty(n_boxes, dtype=float) * for box_idx in range(n_boxes): * box_lower_bounds[box_idx] = box_idx * box_width + y_min # <<<<<<<<<<<<<< * box_upper_bounds[box_idx] = (box_idx + 1) * box_width + y_min * */ __pyx_t_4 = __pyx_v_box_idx; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_lower_bounds.data) + __pyx_t_4)) )) = ((__pyx_v_box_idx * __pyx_v_box_width) + __pyx_v_y_min); /* "openTSNE/_tsne.pyx":551 * for box_idx in range(n_boxes): * box_lower_bounds[box_idx] = box_idx * box_width + y_min * box_upper_bounds[box_idx] = (box_idx + 1) * box_width + y_min # <<<<<<<<<<<<<< * * # Determine which box each reference point belongs to */ __pyx_t_4 = __pyx_v_box_idx; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_upper_bounds.data) + __pyx_t_4)) )) = (((__pyx_v_box_idx + 1) * __pyx_v_box_width) + __pyx_v_y_min); } /* "openTSNE/_tsne.pyx":554 * * # Determine which box each reference point belongs to * cdef int *reference_point_box_idx = PyMem_Malloc(n_reference_samples * sizeof(int)) # <<<<<<<<<<<<<< * for i in range(n_reference_samples): * box_idx = ((reference_embedding[i] - y_min) / box_width) */ __pyx_v_reference_point_box_idx = ((int *)PyMem_Malloc((__pyx_v_n_reference_samples * (sizeof(int))))); /* "openTSNE/_tsne.pyx":555 * # Determine which box each reference point belongs to * cdef int *reference_point_box_idx = PyMem_Malloc(n_reference_samples * sizeof(int)) * for i in range(n_reference_samples): # <<<<<<<<<<<<<< * box_idx = ((reference_embedding[i] - y_min) / box_width) * # The right most point maps directly into `n_boxes`, while it should */ __pyx_t_1 = __pyx_v_n_reference_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":556 * cdef int *reference_point_box_idx = PyMem_Malloc(n_reference_samples * sizeof(int)) * for i in range(n_reference_samples): * box_idx = ((reference_embedding[i] - y_min) / box_width) # <<<<<<<<<<<<<< * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box */ __pyx_t_4 = __pyx_v_i; __pyx_v_box_idx = ((int)(((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_reference_embedding.data) + __pyx_t_4)) ))) - __pyx_v_y_min) / __pyx_v_box_width)); /* "openTSNE/_tsne.pyx":559 * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box * if box_idx >= n_boxes: # <<<<<<<<<<<<<< * box_idx = n_boxes - 1 * */ __pyx_t_5 = ((__pyx_v_box_idx >= __pyx_v_n_boxes) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":560 * # belong to the last box * if box_idx >= n_boxes: * box_idx = n_boxes - 1 # <<<<<<<<<<<<<< * * reference_point_box_idx[i] = box_idx */ __pyx_v_box_idx = (__pyx_v_n_boxes - 1); /* "openTSNE/_tsne.pyx":559 * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box * if box_idx >= n_boxes: # <<<<<<<<<<<<<< * box_idx = n_boxes - 1 * */ } /* "openTSNE/_tsne.pyx":562 * box_idx = n_boxes - 1 * * reference_point_box_idx[i] = box_idx # <<<<<<<<<<<<<< * * cdef int n_interpolation_points_1d = n_interpolation_points * n_boxes */ (__pyx_v_reference_point_box_idx[__pyx_v_i]) = __pyx_v_box_idx; } /* "openTSNE/_tsne.pyx":564 * reference_point_box_idx[i] = box_idx * * cdef int n_interpolation_points_1d = n_interpolation_points * n_boxes # <<<<<<<<<<<<<< * # Prepare the interpolants for a single interval, so we can use their * # relative positions later on */ __pyx_v_n_interpolation_points_1d = (__pyx_v_n_interpolation_points * __pyx_v_n_boxes); /* "openTSNE/_tsne.pyx":567 * # Prepare the interpolants for a single interval, so we can use their * # relative positions later on * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) # <<<<<<<<<<<<<< * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 567, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_empty); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 567, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = PyInt_FromSsize_t(__pyx_v_n_interpolation_points); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 567, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_8 = PyTuple_New(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 567, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 567, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); if (PyDict_SetItem(__pyx_t_7, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 567, __pyx_L1_error) __pyx_t_6 = __Pyx_PyObject_Call(__pyx_t_9, __pyx_t_8, __pyx_t_7); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 567, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_10 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_6, PyBUF_WRITABLE); if (unlikely(!__pyx_t_10.memview)) __PYX_ERR(0, 567, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_v_y_tilde = __pyx_t_10; __pyx_t_10.memview = NULL; __pyx_t_10.data = NULL; /* "openTSNE/_tsne.pyx":568 * # relative positions later on * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) * cdef double h = 1. / n_interpolation_points # <<<<<<<<<<<<<< * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): */ __pyx_v_h = (1. / ((double)__pyx_v_n_interpolation_points)); /* "openTSNE/_tsne.pyx":569 * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 # <<<<<<<<<<<<<< * for i in range(1, n_interpolation_points): * y_tilde[i] = y_tilde[i - 1] + h */ __pyx_t_4 = 0; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_4)) )) = (__pyx_v_h / 2.0); /* "openTSNE/_tsne.pyx":570 * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): # <<<<<<<<<<<<<< * y_tilde[i] = y_tilde[i - 1] + h * */ __pyx_t_1 = __pyx_v_n_interpolation_points; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 1; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":571 * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): * y_tilde[i] = y_tilde[i - 1] + h # <<<<<<<<<<<<<< * * # Evaluate the the squared cauchy kernel at the interpolation nodes */ __pyx_t_4 = (__pyx_v_i - 1); __pyx_t_13 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_13)) )) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_4)) ))) + __pyx_v_h); } /* "openTSNE/_tsne.pyx":574 * * # Evaluate the the squared cauchy kernel at the interpolation nodes * cdef double[::1] sq_kernel_tilde = compute_kernel_tilde_1d( # <<<<<<<<<<<<<< * &cauchy_1d_exp1p, n_interpolation_points_1d, y_min, h * box_width, dof * ) */ __pyx_t_10 = __pyx_f_8openTSNE_5_tsne_compute_kernel_tilde_1d((&__pyx_f_8openTSNE_5_tsne_cauchy_1d_exp1p), __pyx_v_n_interpolation_points_1d, __pyx_v_y_min, (__pyx_v_h * __pyx_v_box_width), __pyx_v_dof); if (unlikely(!__pyx_t_10.memview)) __PYX_ERR(0, 574, __pyx_L1_error) __pyx_v_sq_kernel_tilde = __pyx_t_10; __pyx_t_10.memview = NULL; __pyx_t_10.data = NULL; /* "openTSNE/_tsne.pyx":579 * # The non-square cauchy kernel is only used if dof != 1, so don't do unnecessary work * cdef double[::1] kernel_tilde * if dof != 1: # <<<<<<<<<<<<<< * kernel_tilde = compute_kernel_tilde_1d( * &cauchy_1d, n_interpolation_points_1d, y_min, h * box_width, dof */ __pyx_t_5 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":580 * cdef double[::1] kernel_tilde * if dof != 1: * kernel_tilde = compute_kernel_tilde_1d( # <<<<<<<<<<<<<< * &cauchy_1d, n_interpolation_points_1d, y_min, h * box_width, dof * ) */ __pyx_t_10 = __pyx_f_8openTSNE_5_tsne_compute_kernel_tilde_1d((&__pyx_f_8openTSNE_5_tsne_cauchy_1d), __pyx_v_n_interpolation_points_1d, __pyx_v_y_min, (__pyx_v_h * __pyx_v_box_width), __pyx_v_dof); if (unlikely(!__pyx_t_10.memview)) __PYX_ERR(0, 580, __pyx_L1_error) __pyx_v_kernel_tilde = __pyx_t_10; __pyx_t_10.memview = NULL; __pyx_t_10.data = NULL; /* "openTSNE/_tsne.pyx":579 * # The non-square cauchy kernel is only used if dof != 1, so don't do unnecessary work * cdef double[::1] kernel_tilde * if dof != 1: # <<<<<<<<<<<<<< * kernel_tilde = compute_kernel_tilde_1d( * &cauchy_1d, n_interpolation_points_1d, y_min, h * box_width, dof */ } /* "openTSNE/_tsne.pyx":586 * # STEP 1: Compute the w coefficients * # Set up q_j values * cdef int n_terms = 3 # <<<<<<<<<<<<<< * cdef double[:, ::1] q_j = np.empty((n_reference_samples, n_terms), dtype=float) * if dof != 1: */ __pyx_v_n_terms = 3; /* "openTSNE/_tsne.pyx":587 * # Set up q_j values * cdef int n_terms = 3 * cdef double[:, ::1] q_j = np.empty((n_reference_samples, n_terms), dtype=float) # <<<<<<<<<<<<<< * if dof != 1: * for i in range(n_reference_samples): */ __Pyx_GetModuleGlobalName(__pyx_t_6, __pyx_n_s_np); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 587, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_6, __pyx_n_s_empty); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 587, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_n_reference_samples); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 587, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_8 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 587, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_9 = PyTuple_New(2); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 587, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_6); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_9, 1, __pyx_t_8); __pyx_t_6 = 0; __pyx_t_8 = 0; __pyx_t_8 = PyTuple_New(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 587, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_GIVEREF(__pyx_t_9); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_9); __pyx_t_9 = 0; __pyx_t_9 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 587, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); if (PyDict_SetItem(__pyx_t_9, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 587, __pyx_L1_error) __pyx_t_6 = __Pyx_PyObject_Call(__pyx_t_7, __pyx_t_8, __pyx_t_9); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 587, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_14 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_6, PyBUF_WRITABLE); if (unlikely(!__pyx_t_14.memview)) __PYX_ERR(0, 587, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_v_q_j = __pyx_t_14; __pyx_t_14.memview = NULL; __pyx_t_14.data = NULL; /* "openTSNE/_tsne.pyx":588 * cdef int n_terms = 3 * cdef double[:, ::1] q_j = np.empty((n_reference_samples, n_terms), dtype=float) * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_reference_samples): * q_j[i, 0] = 1 */ __pyx_t_5 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":589 * cdef double[:, ::1] q_j = np.empty((n_reference_samples, n_terms), dtype=float) * if dof != 1: * for i in range(n_reference_samples): # <<<<<<<<<<<<<< * q_j[i, 0] = 1 * q_j[i, 1] = reference_embedding[i] */ __pyx_t_1 = __pyx_v_n_reference_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":590 * if dof != 1: * for i in range(n_reference_samples): * q_j[i, 0] = 1 # <<<<<<<<<<<<<< * q_j[i, 1] = reference_embedding[i] * q_j[i, 2] = 1 */ __pyx_t_4 = __pyx_v_i; __pyx_t_13 = 0; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_4 * __pyx_v_q_j.strides[0]) )) + __pyx_t_13)) )) = 1.0; /* "openTSNE/_tsne.pyx":591 * for i in range(n_reference_samples): * q_j[i, 0] = 1 * q_j[i, 1] = reference_embedding[i] # <<<<<<<<<<<<<< * q_j[i, 2] = 1 * else: */ __pyx_t_13 = __pyx_v_i; __pyx_t_4 = __pyx_v_i; __pyx_t_15 = 1; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_4 * __pyx_v_q_j.strides[0]) )) + __pyx_t_15)) )) = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_reference_embedding.data) + __pyx_t_13)) ))); /* "openTSNE/_tsne.pyx":592 * q_j[i, 0] = 1 * q_j[i, 1] = reference_embedding[i] * q_j[i, 2] = 1 # <<<<<<<<<<<<<< * else: * for i in range(n_reference_samples): */ __pyx_t_13 = __pyx_v_i; __pyx_t_15 = 2; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_13 * __pyx_v_q_j.strides[0]) )) + __pyx_t_15)) )) = 1.0; } /* "openTSNE/_tsne.pyx":588 * cdef int n_terms = 3 * cdef double[:, ::1] q_j = np.empty((n_reference_samples, n_terms), dtype=float) * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_reference_samples): * q_j[i, 0] = 1 */ goto __pyx_L15; } /* "openTSNE/_tsne.pyx":594 * q_j[i, 2] = 1 * else: * for i in range(n_reference_samples): # <<<<<<<<<<<<<< * q_j[i, 0] = 1 * q_j[i, 1] = reference_embedding[i] */ /*else*/ { __pyx_t_1 = __pyx_v_n_reference_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":595 * else: * for i in range(n_reference_samples): * q_j[i, 0] = 1 # <<<<<<<<<<<<<< * q_j[i, 1] = reference_embedding[i] * q_j[i, 2] = reference_embedding[i] ** 2 */ __pyx_t_15 = __pyx_v_i; __pyx_t_13 = 0; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_15 * __pyx_v_q_j.strides[0]) )) + __pyx_t_13)) )) = 1.0; /* "openTSNE/_tsne.pyx":596 * for i in range(n_reference_samples): * q_j[i, 0] = 1 * q_j[i, 1] = reference_embedding[i] # <<<<<<<<<<<<<< * q_j[i, 2] = reference_embedding[i] ** 2 * */ __pyx_t_13 = __pyx_v_i; __pyx_t_15 = __pyx_v_i; __pyx_t_4 = 1; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_15 * __pyx_v_q_j.strides[0]) )) + __pyx_t_4)) )) = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_reference_embedding.data) + __pyx_t_13)) ))); /* "openTSNE/_tsne.pyx":597 * q_j[i, 0] = 1 * q_j[i, 1] = reference_embedding[i] * q_j[i, 2] = reference_embedding[i] ** 2 # <<<<<<<<<<<<<< * * # Compute the relative position of each reference point in its box */ __pyx_t_13 = __pyx_v_i; __pyx_t_4 = __pyx_v_i; __pyx_t_15 = 2; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_4 * __pyx_v_q_j.strides[0]) )) + __pyx_t_15)) )) = pow((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_reference_embedding.data) + __pyx_t_13)) ))), 2.0); } } __pyx_L15:; /* "openTSNE/_tsne.pyx":600 * * # Compute the relative position of each reference point in its box * cdef double[::1] reference_y_in_box = np.empty(n_reference_samples, dtype=float) # <<<<<<<<<<<<<< * for i in range(n_reference_samples): * box_idx = reference_point_box_idx[i] */ __Pyx_GetModuleGlobalName(__pyx_t_6, __pyx_n_s_np); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 600, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_t_6, __pyx_n_s_empty); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 600, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_n_reference_samples); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 600, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_8 = PyTuple_New(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 600, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 600, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); if (PyDict_SetItem(__pyx_t_6, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 600, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_9, __pyx_t_8, __pyx_t_6); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 600, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_10 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_10.memview)) __PYX_ERR(0, 600, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_reference_y_in_box = __pyx_t_10; __pyx_t_10.memview = NULL; __pyx_t_10.data = NULL; /* "openTSNE/_tsne.pyx":601 * # Compute the relative position of each reference point in its box * cdef double[::1] reference_y_in_box = np.empty(n_reference_samples, dtype=float) * for i in range(n_reference_samples): # <<<<<<<<<<<<<< * box_idx = reference_point_box_idx[i] * reference_y_in_box[i] = (reference_embedding[i] - box_lower_bounds[box_idx]) / box_width */ __pyx_t_1 = __pyx_v_n_reference_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":602 * cdef double[::1] reference_y_in_box = np.empty(n_reference_samples, dtype=float) * for i in range(n_reference_samples): * box_idx = reference_point_box_idx[i] # <<<<<<<<<<<<<< * reference_y_in_box[i] = (reference_embedding[i] - box_lower_bounds[box_idx]) / box_width * */ __pyx_v_box_idx = (__pyx_v_reference_point_box_idx[__pyx_v_i]); /* "openTSNE/_tsne.pyx":603 * for i in range(n_reference_samples): * box_idx = reference_point_box_idx[i] * reference_y_in_box[i] = (reference_embedding[i] - box_lower_bounds[box_idx]) / box_width # <<<<<<<<<<<<<< * * # Interpolate kernel using Lagrange polynomials */ __pyx_t_13 = __pyx_v_i; __pyx_t_15 = __pyx_v_box_idx; __pyx_t_4 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_reference_y_in_box.data) + __pyx_t_4)) )) = (((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_reference_embedding.data) + __pyx_t_13)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_lower_bounds.data) + __pyx_t_15)) )))) / __pyx_v_box_width); } /* "openTSNE/_tsne.pyx":606 * * # Interpolate kernel using Lagrange polynomials * cdef double[:, ::1] reference_interpolated_values = interpolate(reference_y_in_box, y_tilde) # <<<<<<<<<<<<<< * * # Actually compute w_{ij}s */ __pyx_t_14 = __pyx_f_8openTSNE_5_tsne_interpolate(__pyx_v_reference_y_in_box, __pyx_v_y_tilde); if (unlikely(!__pyx_t_14.memview)) __PYX_ERR(0, 606, __pyx_L1_error) __pyx_v_reference_interpolated_values = __pyx_t_14; __pyx_t_14.memview = NULL; __pyx_t_14.data = NULL; /* "openTSNE/_tsne.pyx":609 * * # Actually compute w_{ij}s * cdef double[:, ::1] w_coefficients = np.zeros((n_interpolation_points_1d, n_terms), dtype=float) # <<<<<<<<<<<<<< * for i in range(n_reference_samples): * box_idx = reference_point_box_idx[i] * n_interpolation_points */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 609, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_zeros); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 609, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyInt_From_int(__pyx_v_n_interpolation_points_1d); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 609, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_8 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 609, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_9 = PyTuple_New(2); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 609, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_9, 1, __pyx_t_8); __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_8 = PyTuple_New(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 609, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_GIVEREF(__pyx_t_9); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_9); __pyx_t_9 = 0; __pyx_t_9 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 609, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); if (PyDict_SetItem(__pyx_t_9, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 609, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_t_8, __pyx_t_9); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 609, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_14 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_14.memview)) __PYX_ERR(0, 609, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_w_coefficients = __pyx_t_14; __pyx_t_14.memview = NULL; __pyx_t_14.data = NULL; /* "openTSNE/_tsne.pyx":610 * # Actually compute w_{ij}s * cdef double[:, ::1] w_coefficients = np.zeros((n_interpolation_points_1d, n_terms), dtype=float) * for i in range(n_reference_samples): # <<<<<<<<<<<<<< * box_idx = reference_point_box_idx[i] * n_interpolation_points * for j in range(n_interpolation_points): */ __pyx_t_1 = __pyx_v_n_reference_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":611 * cdef double[:, ::1] w_coefficients = np.zeros((n_interpolation_points_1d, n_terms), dtype=float) * for i in range(n_reference_samples): * box_idx = reference_point_box_idx[i] * n_interpolation_points # <<<<<<<<<<<<<< * for j in range(n_interpolation_points): * for d in range(n_terms): */ __pyx_v_box_idx = ((__pyx_v_reference_point_box_idx[__pyx_v_i]) * __pyx_v_n_interpolation_points); /* "openTSNE/_tsne.pyx":612 * for i in range(n_reference_samples): * box_idx = reference_point_box_idx[i] * n_interpolation_points * for j in range(n_interpolation_points): # <<<<<<<<<<<<<< * for d in range(n_terms): * w_coefficients[box_idx + j, d] += reference_interpolated_values[i, j] * q_j[i, d] */ __pyx_t_16 = __pyx_v_n_interpolation_points; __pyx_t_17 = __pyx_t_16; for (__pyx_t_18 = 0; __pyx_t_18 < __pyx_t_17; __pyx_t_18+=1) { __pyx_v_j = __pyx_t_18; /* "openTSNE/_tsne.pyx":613 * box_idx = reference_point_box_idx[i] * n_interpolation_points * for j in range(n_interpolation_points): * for d in range(n_terms): # <<<<<<<<<<<<<< * w_coefficients[box_idx + j, d] += reference_interpolated_values[i, j] * q_j[i, d] * */ __pyx_t_11 = __pyx_v_n_terms; __pyx_t_12 = __pyx_t_11; for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_12; __pyx_t_19+=1) { __pyx_v_d = __pyx_t_19; /* "openTSNE/_tsne.pyx":614 * for j in range(n_interpolation_points): * for d in range(n_terms): * w_coefficients[box_idx + j, d] += reference_interpolated_values[i, j] * q_j[i, d] # <<<<<<<<<<<<<< * * # STEP 2: Compute the kernel values evaluated at the interpolation nodes */ __pyx_t_15 = __pyx_v_i; __pyx_t_13 = __pyx_v_j; __pyx_t_4 = __pyx_v_i; __pyx_t_20 = __pyx_v_d; __pyx_t_21 = (__pyx_v_box_idx + __pyx_v_j); __pyx_t_22 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_w_coefficients.data + __pyx_t_21 * __pyx_v_w_coefficients.strides[0]) )) + __pyx_t_22)) )) += ((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_interpolated_values.data + __pyx_t_15 * __pyx_v_reference_interpolated_values.strides[0]) )) + __pyx_t_13)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_4 * __pyx_v_q_j.strides[0]) )) + __pyx_t_20)) )))); } } } /* "openTSNE/_tsne.pyx":617 * * # STEP 2: Compute the kernel values evaluated at the interpolation nodes * cdef double[:, ::1] y_tilde_values = np.empty((n_interpolation_points_1d, n_terms)) # <<<<<<<<<<<<<< * if dof != 1: * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients[:, :2], y_tilde_values[:, :2]) */ __Pyx_GetModuleGlobalName(__pyx_t_9, __pyx_n_s_np); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 617, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_8 = __Pyx_PyObject_GetAttrStr(__pyx_t_9, __pyx_n_s_empty); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 617, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_9 = __Pyx_PyInt_From_int(__pyx_v_n_interpolation_points_1d); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 617, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_6 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 617, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_23 = PyTuple_New(2); if (unlikely(!__pyx_t_23)) __PYX_ERR(0, 617, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_23); __Pyx_GIVEREF(__pyx_t_9); PyTuple_SET_ITEM(__pyx_t_23, 0, __pyx_t_9); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_23, 1, __pyx_t_6); __pyx_t_9 = 0; __pyx_t_6 = 0; __pyx_t_6 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_8))) { __pyx_t_6 = PyMethod_GET_SELF(__pyx_t_8); if (likely(__pyx_t_6)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_8); __Pyx_INCREF(__pyx_t_6); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_8, function); } } __pyx_t_7 = (__pyx_t_6) ? __Pyx_PyObject_Call2Args(__pyx_t_8, __pyx_t_6, __pyx_t_23) : __Pyx_PyObject_CallOneArg(__pyx_t_8, __pyx_t_23); __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_23); __pyx_t_23 = 0; if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 617, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_14 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_14.memview)) __PYX_ERR(0, 617, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_y_tilde_values = __pyx_t_14; __pyx_t_14.memview = NULL; __pyx_t_14.data = NULL; /* "openTSNE/_tsne.pyx":618 * # STEP 2: Compute the kernel values evaluated at the interpolation nodes * cdef double[:, ::1] y_tilde_values = np.empty((n_interpolation_points_1d, n_terms)) * if dof != 1: # <<<<<<<<<<<<<< * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients[:, :2], y_tilde_values[:, :2]) * matrix_multiply_fft_1d(kernel_tilde, w_coefficients[:, 2:], y_tilde_values[:, 2:]) */ __pyx_t_5 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_5) { /* "openTSNE/_tsne.pyx":619 * cdef double[:, ::1] y_tilde_values = np.empty((n_interpolation_points_1d, n_terms)) * if dof != 1: * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients[:, :2], y_tilde_values[:, :2]) # <<<<<<<<<<<<<< * matrix_multiply_fft_1d(kernel_tilde, w_coefficients[:, 2:], y_tilde_values[:, 2:]) * else: */ __pyx_t_14.data = __pyx_v_w_coefficients.data; __pyx_t_14.memview = __pyx_v_w_coefficients.memview; __PYX_INC_MEMVIEW(&__pyx_t_14, 0); __pyx_t_14.shape[0] = __pyx_v_w_coefficients.shape[0]; __pyx_t_14.strides[0] = __pyx_v_w_coefficients.strides[0]; __pyx_t_14.suboffsets[0] = -1; __pyx_t_11 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_14, __pyx_v_w_coefficients.shape[1], __pyx_v_w_coefficients.strides[1], __pyx_v_w_coefficients.suboffsets[1], 1, 1, &__pyx_t_11, 0, 2, 0, 0, 1, 0, 1) < 0)) { __PYX_ERR(0, 619, __pyx_L1_error) } __pyx_t_24.data = __pyx_v_y_tilde_values.data; __pyx_t_24.memview = __pyx_v_y_tilde_values.memview; __PYX_INC_MEMVIEW(&__pyx_t_24, 0); __pyx_t_24.shape[0] = __pyx_v_y_tilde_values.shape[0]; __pyx_t_24.strides[0] = __pyx_v_y_tilde_values.strides[0]; __pyx_t_24.suboffsets[0] = -1; __pyx_t_11 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_24, __pyx_v_y_tilde_values.shape[1], __pyx_v_y_tilde_values.strides[1], __pyx_v_y_tilde_values.suboffsets[1], 1, 1, &__pyx_t_11, 0, 2, 0, 0, 1, 0, 1) < 0)) { __PYX_ERR(0, 619, __pyx_L1_error) } __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_1d(__pyx_v_sq_kernel_tilde, __pyx_t_14, __pyx_t_24); __PYX_XDEC_MEMVIEW(&__pyx_t_14, 1); __pyx_t_14.memview = NULL; __pyx_t_14.data = NULL; __PYX_XDEC_MEMVIEW(&__pyx_t_24, 1); __pyx_t_24.memview = NULL; __pyx_t_24.data = NULL; /* "openTSNE/_tsne.pyx":620 * if dof != 1: * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients[:, :2], y_tilde_values[:, :2]) * matrix_multiply_fft_1d(kernel_tilde, w_coefficients[:, 2:], y_tilde_values[:, 2:]) # <<<<<<<<<<<<<< * else: * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients, y_tilde_values) */ __pyx_t_24.data = __pyx_v_w_coefficients.data; __pyx_t_24.memview = __pyx_v_w_coefficients.memview; __PYX_INC_MEMVIEW(&__pyx_t_24, 0); __pyx_t_24.shape[0] = __pyx_v_w_coefficients.shape[0]; __pyx_t_24.strides[0] = __pyx_v_w_coefficients.strides[0]; __pyx_t_24.suboffsets[0] = -1; __pyx_t_11 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_24, __pyx_v_w_coefficients.shape[1], __pyx_v_w_coefficients.strides[1], __pyx_v_w_coefficients.suboffsets[1], 1, 1, &__pyx_t_11, 2, 0, 0, 1, 0, 0, 1) < 0)) { __PYX_ERR(0, 620, __pyx_L1_error) } __pyx_t_14.data = __pyx_v_y_tilde_values.data; __pyx_t_14.memview = __pyx_v_y_tilde_values.memview; __PYX_INC_MEMVIEW(&__pyx_t_14, 0); __pyx_t_14.shape[0] = __pyx_v_y_tilde_values.shape[0]; __pyx_t_14.strides[0] = __pyx_v_y_tilde_values.strides[0]; __pyx_t_14.suboffsets[0] = -1; __pyx_t_11 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_14, __pyx_v_y_tilde_values.shape[1], __pyx_v_y_tilde_values.strides[1], __pyx_v_y_tilde_values.suboffsets[1], 1, 1, &__pyx_t_11, 2, 0, 0, 1, 0, 0, 1) < 0)) { __PYX_ERR(0, 620, __pyx_L1_error) } __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_1d(__pyx_v_kernel_tilde, __pyx_t_24, __pyx_t_14); __PYX_XDEC_MEMVIEW(&__pyx_t_24, 1); __pyx_t_24.memview = NULL; __pyx_t_24.data = NULL; __PYX_XDEC_MEMVIEW(&__pyx_t_14, 1); __pyx_t_14.memview = NULL; __pyx_t_14.data = NULL; /* "openTSNE/_tsne.pyx":618 * # STEP 2: Compute the kernel values evaluated at the interpolation nodes * cdef double[:, ::1] y_tilde_values = np.empty((n_interpolation_points_1d, n_terms)) * if dof != 1: # <<<<<<<<<<<<<< * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients[:, :2], y_tilde_values[:, :2]) * matrix_multiply_fft_1d(kernel_tilde, w_coefficients[:, 2:], y_tilde_values[:, 2:]) */ goto __pyx_L28; } /* "openTSNE/_tsne.pyx":622 * matrix_multiply_fft_1d(kernel_tilde, w_coefficients[:, 2:], y_tilde_values[:, 2:]) * else: * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients, y_tilde_values) # <<<<<<<<<<<<<< * * PyMem_Free(reference_point_box_idx) */ /*else*/ { __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_1d(__pyx_v_sq_kernel_tilde, __pyx_v_w_coefficients, __pyx_v_y_tilde_values); } __pyx_L28:; /* "openTSNE/_tsne.pyx":624 * matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients, y_tilde_values) * * PyMem_Free(reference_point_box_idx) # <<<<<<<<<<<<<< * * return np.asarray(y_tilde_values), np.asarray(box_lower_bounds) */ PyMem_Free(__pyx_v_reference_point_box_idx); /* "openTSNE/_tsne.pyx":626 * PyMem_Free(reference_point_box_idx) * * return np.asarray(y_tilde_values), np.asarray(box_lower_bounds) # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __Pyx_GetModuleGlobalName(__pyx_t_8, __pyx_n_s_np); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 626, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_23 = __Pyx_PyObject_GetAttrStr(__pyx_t_8, __pyx_n_s_asarray); if (unlikely(!__pyx_t_23)) __PYX_ERR(0, 626, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_23); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __pyx_memoryview_fromslice(__pyx_v_y_tilde_values, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 626, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_6 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_23))) { __pyx_t_6 = PyMethod_GET_SELF(__pyx_t_23); if (likely(__pyx_t_6)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_23); __Pyx_INCREF(__pyx_t_6); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_23, function); } } __pyx_t_7 = (__pyx_t_6) ? __Pyx_PyObject_Call2Args(__pyx_t_23, __pyx_t_6, __pyx_t_8) : __Pyx_PyObject_CallOneArg(__pyx_t_23, __pyx_t_8); __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 626, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_23); __pyx_t_23 = 0; __Pyx_GetModuleGlobalName(__pyx_t_8, __pyx_n_s_np); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 626, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_t_8, __pyx_n_s_asarray); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 626, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __pyx_memoryview_fromslice(__pyx_v_box_lower_bounds, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 626, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_9 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_6))) { __pyx_t_9 = PyMethod_GET_SELF(__pyx_t_6); if (likely(__pyx_t_9)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_6); __Pyx_INCREF(__pyx_t_9); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_6, function); } } __pyx_t_23 = (__pyx_t_9) ? __Pyx_PyObject_Call2Args(__pyx_t_6, __pyx_t_9, __pyx_t_8) : __Pyx_PyObject_CallOneArg(__pyx_t_6, __pyx_t_8); __Pyx_XDECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; if (unlikely(!__pyx_t_23)) __PYX_ERR(0, 626, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_23); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = PyTuple_New(2); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 626, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_7); __Pyx_GIVEREF(__pyx_t_23); PyTuple_SET_ITEM(__pyx_t_6, 1, __pyx_t_23); __pyx_t_7 = 0; __pyx_t_23 = 0; __pyx_r = ((PyObject*)__pyx_t_6); __pyx_t_6 = 0; goto __pyx_L0; /* "openTSNE/_tsne.pyx":511 * * * cpdef tuple prepare_negative_gradient_fft_interpolation_grid_1d( # <<<<<<<<<<<<<< * double[::1] reference_embedding, * Py_ssize_t n_interpolation_points=3, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_XDECREF(__pyx_t_9); __PYX_XDEC_MEMVIEW(&__pyx_t_10, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_14, 1); __Pyx_XDECREF(__pyx_t_23); __PYX_XDEC_MEMVIEW(&__pyx_t_24, 1); __Pyx_AddTraceback("openTSNE._tsne.prepare_negative_gradient_fft_interpolation_grid_1d", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_box_lower_bounds, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_box_upper_bounds, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_sq_kernel_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_kernel_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_q_j, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_reference_y_in_box, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_reference_interpolated_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_w_coefficients, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde_values, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_5_tsne_9prepare_negative_gradient_fft_interpolation_grid_1d(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_pw_8openTSNE_5_tsne_9prepare_negative_gradient_fft_interpolation_grid_1d(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __Pyx_memviewslice __pyx_v_reference_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_n_interpolation_points; Py_ssize_t __pyx_v_min_num_intervals; double __pyx_v_ints_in_interval; double __pyx_v_dof; double __pyx_v_padding; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("prepare_negative_gradient_fft_interpolation_grid_1d (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_reference_embedding,&__pyx_n_s_n_interpolation_points,&__pyx_n_s_min_num_intervals,&__pyx_n_s_ints_in_interval,&__pyx_n_s_dof,&__pyx_n_s_padding,0}; PyObject* values[6] = {0,0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_reference_embedding)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_n_interpolation_points); if (value) { values[1] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 2: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_min_num_intervals); if (value) { values[2] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 3: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_ints_in_interval); if (value) { values[3] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 4: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dof); if (value) { values[4] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 5: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_padding); if (value) { values[5] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "prepare_negative_gradient_fft_interpolation_grid_1d") < 0)) __PYX_ERR(0, 511, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_reference_embedding = __Pyx_PyObject_to_MemoryviewSlice_dc_double(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_reference_embedding.memview)) __PYX_ERR(0, 512, __pyx_L3_error) if (values[1]) { __pyx_v_n_interpolation_points = __Pyx_PyIndex_AsSsize_t(values[1]); if (unlikely((__pyx_v_n_interpolation_points == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 513, __pyx_L3_error) } else { __pyx_v_n_interpolation_points = ((Py_ssize_t)3); } if (values[2]) { __pyx_v_min_num_intervals = __Pyx_PyIndex_AsSsize_t(values[2]); if (unlikely((__pyx_v_min_num_intervals == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 514, __pyx_L3_error) } else { __pyx_v_min_num_intervals = ((Py_ssize_t)10); } if (values[3]) { __pyx_v_ints_in_interval = __pyx_PyFloat_AsDouble(values[3]); if (unlikely((__pyx_v_ints_in_interval == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 515, __pyx_L3_error) } else { __pyx_v_ints_in_interval = ((double)1.0); } if (values[4]) { __pyx_v_dof = __pyx_PyFloat_AsDouble(values[4]); if (unlikely((__pyx_v_dof == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 516, __pyx_L3_error) } else { __pyx_v_dof = ((double)1.0); } if (values[5]) { __pyx_v_padding = __pyx_PyFloat_AsDouble(values[5]); if (unlikely((__pyx_v_padding == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 517, __pyx_L3_error) } else { __pyx_v_padding = ((double)0.0); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("prepare_negative_gradient_fft_interpolation_grid_1d", 0, 1, 6, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 511, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE._tsne.prepare_negative_gradient_fft_interpolation_grid_1d", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_5_tsne_8prepare_negative_gradient_fft_interpolation_grid_1d(__pyx_self, __pyx_v_reference_embedding, __pyx_v_n_interpolation_points, __pyx_v_min_num_intervals, __pyx_v_ints_in_interval, __pyx_v_dof, __pyx_v_padding); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_5_tsne_8prepare_negative_gradient_fft_interpolation_grid_1d(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_reference_embedding, Py_ssize_t __pyx_v_n_interpolation_points, Py_ssize_t __pyx_v_min_num_intervals, double __pyx_v_ints_in_interval, double __pyx_v_dof, double __pyx_v_padding) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_1d __pyx_t_2; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("prepare_negative_gradient_fft_interpolation_grid_1d", 0); __Pyx_XDECREF(__pyx_r); __pyx_t_2.__pyx_n = 5; __pyx_t_2.n_interpolation_points = __pyx_v_n_interpolation_points; __pyx_t_2.min_num_intervals = __pyx_v_min_num_intervals; __pyx_t_2.ints_in_interval = __pyx_v_ints_in_interval; __pyx_t_2.dof = __pyx_v_dof; __pyx_t_2.padding = __pyx_v_padding; __pyx_t_1 = __pyx_f_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_1d(__pyx_v_reference_embedding, 0, &__pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 511, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("openTSNE._tsne.prepare_negative_gradient_fft_interpolation_grid_1d", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_reference_embedding, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/_tsne.pyx":629 * * * cpdef double estimate_negative_gradient_fft_1d_with_grid( # <<<<<<<<<<<<<< * double[::1] embedding, * double[::1] gradient, */ static PyObject *__pyx_pw_8openTSNE_5_tsne_11estimate_negative_gradient_fft_1d_with_grid(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static double __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d_with_grid(__Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, __Pyx_memviewslice __pyx_v_y_tilde_values, __Pyx_memviewslice __pyx_v_box_lower_bounds, Py_ssize_t __pyx_v_n_interpolation_points, double __pyx_v_dof, CYTHON_UNUSED int __pyx_skip_dispatch) { Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; Py_ssize_t __pyx_v_d; Py_ssize_t __pyx_v_box_idx; Py_ssize_t __pyx_v_n_samples; Py_ssize_t __pyx_v_n_terms; Py_ssize_t __pyx_v_n_boxes; double __pyx_v_y_min; double __pyx_v_box_width; int *__pyx_v_point_box_idx; __Pyx_memviewslice __pyx_v_y_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_h; __Pyx_memviewslice __pyx_v_y_in_box = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_interpolated_values = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_phi = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_sum_Qi = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_sum_Q; double __pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; int __pyx_t_6; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; PyObject *__pyx_t_9 = NULL; PyObject *__pyx_t_10 = NULL; __Pyx_memviewslice __pyx_t_11 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_12; __Pyx_memviewslice __pyx_t_13 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_14; Py_ssize_t __pyx_t_15; Py_ssize_t __pyx_t_16; Py_ssize_t __pyx_t_17; Py_ssize_t __pyx_t_18; Py_ssize_t __pyx_t_19; Py_ssize_t __pyx_t_20; Py_ssize_t __pyx_t_21; Py_ssize_t __pyx_t_22; Py_ssize_t __pyx_t_23; Py_ssize_t __pyx_t_24; Py_ssize_t __pyx_t_25; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("estimate_negative_gradient_fft_1d_with_grid", 0); /* "openTSNE/_tsne.pyx":639 * cdef: * Py_ssize_t i, j, d, box_idx * Py_ssize_t n_samples = embedding.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t n_terms = y_tilde_values.shape[1] * Py_ssize_t n_boxes = box_lower_bounds.shape[0] */ __pyx_v_n_samples = (__pyx_v_embedding.shape[0]); /* "openTSNE/_tsne.pyx":640 * Py_ssize_t i, j, d, box_idx * Py_ssize_t n_samples = embedding.shape[0] * Py_ssize_t n_terms = y_tilde_values.shape[1] # <<<<<<<<<<<<<< * Py_ssize_t n_boxes = box_lower_bounds.shape[0] * double y_min = box_lower_bounds[0] */ __pyx_v_n_terms = (__pyx_v_y_tilde_values.shape[1]); /* "openTSNE/_tsne.pyx":641 * Py_ssize_t n_samples = embedding.shape[0] * Py_ssize_t n_terms = y_tilde_values.shape[1] * Py_ssize_t n_boxes = box_lower_bounds.shape[0] # <<<<<<<<<<<<<< * double y_min = box_lower_bounds[0] * double box_width = box_lower_bounds[1] - box_lower_bounds[0] */ __pyx_v_n_boxes = (__pyx_v_box_lower_bounds.shape[0]); /* "openTSNE/_tsne.pyx":642 * Py_ssize_t n_terms = y_tilde_values.shape[1] * Py_ssize_t n_boxes = box_lower_bounds.shape[0] * double y_min = box_lower_bounds[0] # <<<<<<<<<<<<<< * double box_width = box_lower_bounds[1] - box_lower_bounds[0] * */ __pyx_t_1 = 0; __pyx_v_y_min = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_lower_bounds.data) + __pyx_t_1)) ))); /* "openTSNE/_tsne.pyx":643 * Py_ssize_t n_boxes = box_lower_bounds.shape[0] * double y_min = box_lower_bounds[0] * double box_width = box_lower_bounds[1] - box_lower_bounds[0] # <<<<<<<<<<<<<< * * # Determine which box each point belongs to */ __pyx_t_1 = 1; __pyx_t_2 = 0; __pyx_v_box_width = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_lower_bounds.data) + __pyx_t_1)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_lower_bounds.data) + __pyx_t_2)) )))); /* "openTSNE/_tsne.pyx":646 * * # Determine which box each point belongs to * cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) # <<<<<<<<<<<<<< * for i in range(n_samples): * box_idx = ((embedding[i] - y_min) / box_width) */ __pyx_v_point_box_idx = ((int *)PyMem_Malloc((__pyx_v_n_samples * (sizeof(int))))); /* "openTSNE/_tsne.pyx":647 * # Determine which box each point belongs to * cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) * for i in range(n_samples): # <<<<<<<<<<<<<< * box_idx = ((embedding[i] - y_min) / box_width) * # The right most point maps directly into `n_boxes`, while it should */ __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":648 * cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) * for i in range(n_samples): * box_idx = ((embedding[i] - y_min) / box_width) # <<<<<<<<<<<<<< * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box */ __pyx_t_2 = __pyx_v_i; __pyx_v_box_idx = ((int)(((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_2)) ))) - __pyx_v_y_min) / __pyx_v_box_width)); /* "openTSNE/_tsne.pyx":651 * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box * if box_idx >= n_boxes: # <<<<<<<<<<<<<< * box_idx = n_boxes - 1 * */ __pyx_t_6 = ((__pyx_v_box_idx >= __pyx_v_n_boxes) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":652 * # belong to the last box * if box_idx >= n_boxes: * box_idx = n_boxes - 1 # <<<<<<<<<<<<<< * * point_box_idx[i] = box_idx */ __pyx_v_box_idx = (__pyx_v_n_boxes - 1); /* "openTSNE/_tsne.pyx":651 * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box * if box_idx >= n_boxes: # <<<<<<<<<<<<<< * box_idx = n_boxes - 1 * */ } /* "openTSNE/_tsne.pyx":654 * box_idx = n_boxes - 1 * * point_box_idx[i] = box_idx # <<<<<<<<<<<<<< * * # Prepare the interpolants for a single interval, so we can use their */ (__pyx_v_point_box_idx[__pyx_v_i]) = __pyx_v_box_idx; } /* "openTSNE/_tsne.pyx":658 * # Prepare the interpolants for a single interval, so we can use their * # relative positions later on * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) # <<<<<<<<<<<<<< * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_8 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_empty); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = PyInt_FromSsize_t(__pyx_v_n_interpolation_points); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); if (PyDict_SetItem(__pyx_t_7, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 658, __pyx_L1_error) __pyx_t_10 = __Pyx_PyObject_Call(__pyx_t_8, __pyx_t_9, __pyx_t_7); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_10, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 658, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_y_tilde = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":659 * # relative positions later on * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) * cdef double h = 1. / n_interpolation_points # <<<<<<<<<<<<<< * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): */ __pyx_v_h = (1. / ((double)__pyx_v_n_interpolation_points)); /* "openTSNE/_tsne.pyx":660 * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 # <<<<<<<<<<<<<< * for i in range(1, n_interpolation_points): * y_tilde[i] = y_tilde[i - 1] + h */ __pyx_t_2 = 0; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_2)) )) = (__pyx_v_h / 2.0); /* "openTSNE/_tsne.pyx":661 * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): # <<<<<<<<<<<<<< * y_tilde[i] = y_tilde[i - 1] + h * */ __pyx_t_3 = __pyx_v_n_interpolation_points; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 1; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":662 * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): * y_tilde[i] = y_tilde[i - 1] + h # <<<<<<<<<<<<<< * * # STEP 3: Compute the potentials \tilde{\phi(y_i)} */ __pyx_t_2 = (__pyx_v_i - 1); __pyx_t_1 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_1)) )) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_2)) ))) + __pyx_v_h); } /* "openTSNE/_tsne.pyx":666 * # STEP 3: Compute the potentials \tilde{\phi(y_i)} * # Compute the relative position of each new embedding point in its box * cdef double[::1] y_in_box = np.empty(n_samples, dtype=float) # <<<<<<<<<<<<<< * for i in range(n_samples): * box_idx = point_box_idx[i] */ __Pyx_GetModuleGlobalName(__pyx_t_10, __pyx_n_s_np); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 666, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_10, __pyx_n_s_empty); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 666, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 666, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 666, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 666, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); if (PyDict_SetItem(__pyx_t_10, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 666, __pyx_L1_error) __pyx_t_8 = __Pyx_PyObject_Call(__pyx_t_7, __pyx_t_9, __pyx_t_10); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 666, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_8, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 666, __pyx_L1_error) __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_v_y_in_box = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":667 * # Compute the relative position of each new embedding point in its box * cdef double[::1] y_in_box = np.empty(n_samples, dtype=float) * for i in range(n_samples): # <<<<<<<<<<<<<< * box_idx = point_box_idx[i] * y_in_box[i] = (embedding[i] - box_lower_bounds[box_idx]) / box_width */ __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":668 * cdef double[::1] y_in_box = np.empty(n_samples, dtype=float) * for i in range(n_samples): * box_idx = point_box_idx[i] # <<<<<<<<<<<<<< * y_in_box[i] = (embedding[i] - box_lower_bounds[box_idx]) / box_width * */ __pyx_v_box_idx = (__pyx_v_point_box_idx[__pyx_v_i]); /* "openTSNE/_tsne.pyx":669 * for i in range(n_samples): * box_idx = point_box_idx[i] * y_in_box[i] = (embedding[i] - box_lower_bounds[box_idx]) / box_width # <<<<<<<<<<<<<< * * # Interpolate kernel using Lagrange polynomials */ __pyx_t_2 = __pyx_v_i; __pyx_t_1 = __pyx_v_box_idx; __pyx_t_12 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_in_box.data) + __pyx_t_12)) )) = (((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_2)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_lower_bounds.data) + __pyx_t_1)) )))) / __pyx_v_box_width); } /* "openTSNE/_tsne.pyx":672 * * # Interpolate kernel using Lagrange polynomials * cdef double[:, ::1] interpolated_values = interpolate(y_in_box, y_tilde) # <<<<<<<<<<<<<< * * # Actually compute \tilde{\phi(y_i)} */ __pyx_t_13 = __pyx_f_8openTSNE_5_tsne_interpolate(__pyx_v_y_in_box, __pyx_v_y_tilde); if (unlikely(!__pyx_t_13.memview)) __PYX_ERR(0, 672, __pyx_L1_error) __pyx_v_interpolated_values = __pyx_t_13; __pyx_t_13.memview = NULL; __pyx_t_13.data = NULL; /* "openTSNE/_tsne.pyx":675 * * # Actually compute \tilde{\phi(y_i)} * cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) # <<<<<<<<<<<<<< * for i in range(n_samples): * box_idx = point_box_idx[i] * n_interpolation_points */ __Pyx_GetModuleGlobalName(__pyx_t_8, __pyx_n_s_np); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 675, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_t_8, __pyx_n_s_zeros); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 675, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 675, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_9 = PyInt_FromSsize_t(__pyx_v_n_terms); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 675, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_7 = PyTuple_New(2); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 675, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_7, 0, __pyx_t_8); __Pyx_GIVEREF(__pyx_t_9); PyTuple_SET_ITEM(__pyx_t_7, 1, __pyx_t_9); __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 675, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 675, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); if (PyDict_SetItem(__pyx_t_7, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 675, __pyx_L1_error) __pyx_t_8 = __Pyx_PyObject_Call(__pyx_t_10, __pyx_t_9, __pyx_t_7); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 675, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_13 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_8, PyBUF_WRITABLE); if (unlikely(!__pyx_t_13.memview)) __PYX_ERR(0, 675, __pyx_L1_error) __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_v_phi = __pyx_t_13; __pyx_t_13.memview = NULL; __pyx_t_13.data = NULL; /* "openTSNE/_tsne.pyx":676 * # Actually compute \tilde{\phi(y_i)} * cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) * for i in range(n_samples): # <<<<<<<<<<<<<< * box_idx = point_box_idx[i] * n_interpolation_points * for j in range(n_interpolation_points): */ __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":677 * cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) * for i in range(n_samples): * box_idx = point_box_idx[i] * n_interpolation_points # <<<<<<<<<<<<<< * for j in range(n_interpolation_points): * for d in range(n_terms): */ __pyx_v_box_idx = ((__pyx_v_point_box_idx[__pyx_v_i]) * __pyx_v_n_interpolation_points); /* "openTSNE/_tsne.pyx":678 * for i in range(n_samples): * box_idx = point_box_idx[i] * n_interpolation_points * for j in range(n_interpolation_points): # <<<<<<<<<<<<<< * for d in range(n_terms): * phi[i, d] += interpolated_values[i, j] * y_tilde_values[box_idx + j, d] */ __pyx_t_14 = __pyx_v_n_interpolation_points; __pyx_t_15 = __pyx_t_14; for (__pyx_t_16 = 0; __pyx_t_16 < __pyx_t_15; __pyx_t_16+=1) { __pyx_v_j = __pyx_t_16; /* "openTSNE/_tsne.pyx":679 * box_idx = point_box_idx[i] * n_interpolation_points * for j in range(n_interpolation_points): * for d in range(n_terms): # <<<<<<<<<<<<<< * phi[i, d] += interpolated_values[i, j] * y_tilde_values[box_idx + j, d] * */ __pyx_t_17 = __pyx_v_n_terms; __pyx_t_18 = __pyx_t_17; for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) { __pyx_v_d = __pyx_t_19; /* "openTSNE/_tsne.pyx":680 * for j in range(n_interpolation_points): * for d in range(n_terms): * phi[i, d] += interpolated_values[i, j] * y_tilde_values[box_idx + j, d] # <<<<<<<<<<<<<< * * PyMem_Free(point_box_idx) */ __pyx_t_1 = __pyx_v_i; __pyx_t_2 = __pyx_v_j; __pyx_t_12 = (__pyx_v_box_idx + __pyx_v_j); __pyx_t_20 = __pyx_v_d; __pyx_t_21 = __pyx_v_i; __pyx_t_22 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_21 * __pyx_v_phi.strides[0]) )) + __pyx_t_22)) )) += ((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_interpolated_values.data + __pyx_t_1 * __pyx_v_interpolated_values.strides[0]) )) + __pyx_t_2)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_y_tilde_values.data + __pyx_t_12 * __pyx_v_y_tilde_values.strides[0]) )) + __pyx_t_20)) )))); } } } /* "openTSNE/_tsne.pyx":682 * phi[i, d] += interpolated_values[i, j] * y_tilde_values[box_idx + j, d] * * PyMem_Free(point_box_idx) # <<<<<<<<<<<<<< * * # Compute the normalization term Z or sum of q_{ij}s */ PyMem_Free(__pyx_v_point_box_idx); /* "openTSNE/_tsne.pyx":685 * * # Compute the normalization term Z or sum of q_{ij}s * cdef double[::1] sum_Qi = np.empty(n_samples, dtype=float) # <<<<<<<<<<<<<< * if dof != 1: * for i in range(n_samples): */ __Pyx_GetModuleGlobalName(__pyx_t_8, __pyx_n_s_np); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 685, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_8, __pyx_n_s_empty); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 685, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 685, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 685, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 685, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); if (PyDict_SetItem(__pyx_t_8, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 685, __pyx_L1_error) __pyx_t_10 = __Pyx_PyObject_Call(__pyx_t_7, __pyx_t_9, __pyx_t_8); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 685, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_10, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 685, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_sum_Qi = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":686 * # Compute the normalization term Z or sum of q_{ij}s * cdef double[::1] sum_Qi = np.empty(n_samples, dtype=float) * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_samples): * sum_Qi[i] = phi[i, 2] */ __pyx_t_6 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":687 * cdef double[::1] sum_Qi = np.empty(n_samples, dtype=float) * if dof != 1: * for i in range(n_samples): # <<<<<<<<<<<<<< * sum_Qi[i] = phi[i, 2] * else: */ __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":688 * if dof != 1: * for i in range(n_samples): * sum_Qi[i] = phi[i, 2] # <<<<<<<<<<<<<< * else: * for i in range(n_samples): */ __pyx_t_20 = __pyx_v_i; __pyx_t_12 = 2; __pyx_t_2 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_sum_Qi.data) + __pyx_t_2)) )) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_20 * __pyx_v_phi.strides[0]) )) + __pyx_t_12)) ))); } /* "openTSNE/_tsne.pyx":686 * # Compute the normalization term Z or sum of q_{ij}s * cdef double[::1] sum_Qi = np.empty(n_samples, dtype=float) * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_samples): * sum_Qi[i] = phi[i, 2] */ goto __pyx_L16; } /* "openTSNE/_tsne.pyx":690 * sum_Qi[i] = phi[i, 2] * else: * for i in range(n_samples): # <<<<<<<<<<<<<< * sum_Qi[i] = (1 + embedding[i] ** 2) * phi[i, 0] - \ * 2 * embedding[i] * phi[i, 1] + \ */ /*else*/ { __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":691 * else: * for i in range(n_samples): * sum_Qi[i] = (1 + embedding[i] ** 2) * phi[i, 0] - \ # <<<<<<<<<<<<<< * 2 * embedding[i] * phi[i, 1] + \ * phi[i, 2] */ __pyx_t_12 = __pyx_v_i; __pyx_t_20 = __pyx_v_i; __pyx_t_2 = 0; /* "openTSNE/_tsne.pyx":692 * for i in range(n_samples): * sum_Qi[i] = (1 + embedding[i] ** 2) * phi[i, 0] - \ * 2 * embedding[i] * phi[i, 1] + \ # <<<<<<<<<<<<<< * phi[i, 2] * */ __pyx_t_1 = __pyx_v_i; __pyx_t_22 = __pyx_v_i; __pyx_t_21 = 1; /* "openTSNE/_tsne.pyx":693 * sum_Qi[i] = (1 + embedding[i] ** 2) * phi[i, 0] - \ * 2 * embedding[i] * phi[i, 1] + \ * phi[i, 2] # <<<<<<<<<<<<<< * * cdef double sum_Q = 0 */ __pyx_t_23 = __pyx_v_i; __pyx_t_24 = 2; /* "openTSNE/_tsne.pyx":691 * else: * for i in range(n_samples): * sum_Qi[i] = (1 + embedding[i] ** 2) * phi[i, 0] - \ # <<<<<<<<<<<<<< * 2 * embedding[i] * phi[i, 1] + \ * phi[i, 2] */ __pyx_t_25 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_sum_Qi.data) + __pyx_t_25)) )) = ((((1.0 + pow((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_12)) ))), 2.0)) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_20 * __pyx_v_phi.strides[0]) )) + __pyx_t_2)) )))) - ((2.0 * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_1)) )))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_22 * __pyx_v_phi.strides[0]) )) + __pyx_t_21)) ))))) + (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_23 * __pyx_v_phi.strides[0]) )) + __pyx_t_24)) )))); } } __pyx_L16:; /* "openTSNE/_tsne.pyx":695 * phi[i, 2] * * cdef double sum_Q = 0 # <<<<<<<<<<<<<< * for i in range(n_samples): * sum_Q += sum_Qi[i] */ __pyx_v_sum_Q = 0.0; /* "openTSNE/_tsne.pyx":696 * * cdef double sum_Q = 0 * for i in range(n_samples): # <<<<<<<<<<<<<< * sum_Q += sum_Qi[i] * */ __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":697 * cdef double sum_Q = 0 * for i in range(n_samples): * sum_Q += sum_Qi[i] # <<<<<<<<<<<<<< * * # The phis used here are not affected if dof != 1 */ __pyx_t_24 = __pyx_v_i; __pyx_v_sum_Q = (__pyx_v_sum_Q + (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_sum_Qi.data) + __pyx_t_24)) )))); } /* "openTSNE/_tsne.pyx":700 * * # The phis used here are not affected if dof != 1 * for i in range(n_samples): # <<<<<<<<<<<<<< * gradient[i] -= (embedding[i] * phi[i, 0] - phi[i, 1]) / (sum_Qi[i] + EPSILON) * */ __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":701 * # The phis used here are not affected if dof != 1 * for i in range(n_samples): * gradient[i] -= (embedding[i] * phi[i, 0] - phi[i, 1]) / (sum_Qi[i] + EPSILON) # <<<<<<<<<<<<<< * * return sum_Q */ __pyx_t_24 = __pyx_v_i; __pyx_t_23 = __pyx_v_i; __pyx_t_21 = 0; __pyx_t_22 = __pyx_v_i; __pyx_t_1 = 1; __pyx_t_2 = __pyx_v_i; __pyx_t_20 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_gradient.data) + __pyx_t_20)) )) -= ((((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_embedding.data) + __pyx_t_24)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_23 * __pyx_v_phi.strides[0]) )) + __pyx_t_21)) )))) - (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_22 * __pyx_v_phi.strides[0]) )) + __pyx_t_1)) )))) / ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_sum_Qi.data) + __pyx_t_2)) ))) + __pyx_v_8openTSNE_5_tsne_EPSILON)); } /* "openTSNE/_tsne.pyx":703 * gradient[i] -= (embedding[i] * phi[i, 0] - phi[i, 1]) / (sum_Qi[i] + EPSILON) * * return sum_Q # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_sum_Q; goto __pyx_L0; /* "openTSNE/_tsne.pyx":629 * * * cpdef double estimate_negative_gradient_fft_1d_with_grid( # <<<<<<<<<<<<<< * double[::1] embedding, * double[::1] gradient, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_XDECREF(__pyx_t_9); __Pyx_XDECREF(__pyx_t_10); __PYX_XDEC_MEMVIEW(&__pyx_t_11, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_13, 1); __Pyx_WriteUnraisable("openTSNE._tsne.estimate_negative_gradient_fft_1d_with_grid", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_r = 0; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_in_box, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_interpolated_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_phi, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_sum_Qi, 1); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_5_tsne_11estimate_negative_gradient_fft_1d_with_grid(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_pw_8openTSNE_5_tsne_11estimate_negative_gradient_fft_1d_with_grid(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __Pyx_memviewslice __pyx_v_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_gradient = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_y_tilde_values = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_box_lower_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_n_interpolation_points; double __pyx_v_dof; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("estimate_negative_gradient_fft_1d_with_grid (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_embedding,&__pyx_n_s_gradient,&__pyx_n_s_y_tilde_values,&__pyx_n_s_box_lower_bounds,&__pyx_n_s_n_interpolation_points,&__pyx_n_s_dof,0}; PyObject* values[6] = {0,0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_embedding)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_gradient)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_1d_with_grid", 1, 6, 6, 1); __PYX_ERR(0, 629, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_y_tilde_values)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_1d_with_grid", 1, 6, 6, 2); __PYX_ERR(0, 629, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 3: if (likely((values[3] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_box_lower_bounds)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_1d_with_grid", 1, 6, 6, 3); __PYX_ERR(0, 629, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 4: if (likely((values[4] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_n_interpolation_points)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_1d_with_grid", 1, 6, 6, 4); __PYX_ERR(0, 629, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 5: if (likely((values[5] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dof)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_1d_with_grid", 1, 6, 6, 5); __PYX_ERR(0, 629, __pyx_L3_error) } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "estimate_negative_gradient_fft_1d_with_grid") < 0)) __PYX_ERR(0, 629, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 6) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[2] = PyTuple_GET_ITEM(__pyx_args, 2); values[3] = PyTuple_GET_ITEM(__pyx_args, 3); values[4] = PyTuple_GET_ITEM(__pyx_args, 4); values[5] = PyTuple_GET_ITEM(__pyx_args, 5); } __pyx_v_embedding = __Pyx_PyObject_to_MemoryviewSlice_dc_double(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_embedding.memview)) __PYX_ERR(0, 630, __pyx_L3_error) __pyx_v_gradient = __Pyx_PyObject_to_MemoryviewSlice_dc_double(values[1], PyBUF_WRITABLE); if (unlikely(!__pyx_v_gradient.memview)) __PYX_ERR(0, 631, __pyx_L3_error) __pyx_v_y_tilde_values = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[2], PyBUF_WRITABLE); if (unlikely(!__pyx_v_y_tilde_values.memview)) __PYX_ERR(0, 632, __pyx_L3_error) __pyx_v_box_lower_bounds = __Pyx_PyObject_to_MemoryviewSlice_dc_double(values[3], PyBUF_WRITABLE); if (unlikely(!__pyx_v_box_lower_bounds.memview)) __PYX_ERR(0, 633, __pyx_L3_error) __pyx_v_n_interpolation_points = __Pyx_PyIndex_AsSsize_t(values[4]); if (unlikely((__pyx_v_n_interpolation_points == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 634, __pyx_L3_error) __pyx_v_dof = __pyx_PyFloat_AsDouble(values[5]); if (unlikely((__pyx_v_dof == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 635, __pyx_L3_error) } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_1d_with_grid", 1, 6, 6, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 629, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE._tsne.estimate_negative_gradient_fft_1d_with_grid", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_5_tsne_10estimate_negative_gradient_fft_1d_with_grid(__pyx_self, __pyx_v_embedding, __pyx_v_gradient, __pyx_v_y_tilde_values, __pyx_v_box_lower_bounds, __pyx_v_n_interpolation_points, __pyx_v_dof); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_5_tsne_10estimate_negative_gradient_fft_1d_with_grid(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, __Pyx_memviewslice __pyx_v_y_tilde_values, __Pyx_memviewslice __pyx_v_box_lower_bounds, Py_ssize_t __pyx_v_n_interpolation_points, double __pyx_v_dof) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("estimate_negative_gradient_fft_1d_with_grid", 0); __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyFloat_FromDouble(__pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d_with_grid(__pyx_v_embedding, __pyx_v_gradient, __pyx_v_y_tilde_values, __pyx_v_box_lower_bounds, __pyx_v_n_interpolation_points, __pyx_v_dof, 0)); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 629, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("openTSNE._tsne.estimate_negative_gradient_fft_1d_with_grid", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_embedding, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_gradient, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_box_lower_bounds, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/_tsne.pyx":706 * * * cdef double[:, ::1] compute_kernel_tilde_2d( # <<<<<<<<<<<<<< * double (*kernel)(double, double, double, double, double), * Py_ssize_t n_interpolation_points_1d, */ static __Pyx_memviewslice __pyx_f_8openTSNE_5_tsne_compute_kernel_tilde_2d(double (*__pyx_v_kernel)(double, double, double, double, double), Py_ssize_t __pyx_v_n_interpolation_points_1d, double __pyx_v_coord_min, double __pyx_v_coord_spacing, double __pyx_v_dof) { __Pyx_memviewslice __pyx_v_y_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_x_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_embedded_size; __Pyx_memviewslice __pyx_v_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; double __pyx_v_tmp; __Pyx_memviewslice __pyx_r = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_t_6 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_7; Py_ssize_t __pyx_t_8; Py_ssize_t __pyx_t_9; Py_ssize_t __pyx_t_10; Py_ssize_t __pyx_t_11; Py_ssize_t __pyx_t_12; Py_ssize_t __pyx_t_13; Py_ssize_t __pyx_t_14; Py_ssize_t __pyx_t_15; Py_ssize_t __pyx_t_16; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("compute_kernel_tilde_2d", 0); /* "openTSNE/_tsne.pyx":714 * ): * cdef: * double[::1] y_tilde = np.empty(n_interpolation_points_1d, dtype=float) # <<<<<<<<<<<<<< * double[::1] x_tilde = np.empty(n_interpolation_points_1d, dtype=float) * */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 714, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_empty); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 714, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_n_interpolation_points_1d); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 714, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 714, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 714, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 714, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 714, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 714, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_y_tilde = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_tsne.pyx":715 * cdef: * double[::1] y_tilde = np.empty(n_interpolation_points_1d, dtype=float) * double[::1] x_tilde = np.empty(n_interpolation_points_1d, dtype=float) # <<<<<<<<<<<<<< * * Py_ssize_t embedded_size = 2 * n_interpolation_points_1d */ __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_np); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 715, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_4, __pyx_n_s_empty); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 715, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_n_interpolation_points_1d); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 715, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 715, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 715, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (PyDict_SetItem(__pyx_t_4, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 715, __pyx_L1_error) __pyx_t_2 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_3, __pyx_t_4); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 715, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_2, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 715, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v_x_tilde = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/_tsne.pyx":717 * double[::1] x_tilde = np.empty(n_interpolation_points_1d, dtype=float) * * Py_ssize_t embedded_size = 2 * n_interpolation_points_1d # <<<<<<<<<<<<<< * double[:, ::1] kernel_tilde = np.zeros((embedded_size, embedded_size), dtype=float) * */ __pyx_v_embedded_size = (2 * __pyx_v_n_interpolation_points_1d); /* "openTSNE/_tsne.pyx":718 * * Py_ssize_t embedded_size = 2 * n_interpolation_points_1d * double[:, ::1] kernel_tilde = np.zeros((embedded_size, embedded_size), dtype=float) # <<<<<<<<<<<<<< * * Py_ssize_t i, j */ __Pyx_GetModuleGlobalName(__pyx_t_2, __pyx_n_s_np); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 718, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_zeros); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 718, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_embedded_size); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 718, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_embedded_size); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 718, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_1 = PyTuple_New(2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 718, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_t_3); __pyx_t_2 = 0; __pyx_t_3 = 0; __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 718, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 718, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 718, __pyx_L1_error) __pyx_t_2 = __Pyx_PyObject_Call(__pyx_t_4, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 718, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_6 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_2, PyBUF_WRITABLE); if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 718, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v_kernel_tilde = __pyx_t_6; __pyx_t_6.memview = NULL; __pyx_t_6.data = NULL; /* "openTSNE/_tsne.pyx":722 * Py_ssize_t i, j * * x_tilde[0] = coord_min + coord_spacing / 2 # <<<<<<<<<<<<<< * y_tilde[0] = coord_min + coord_spacing / 2 * for i in range(1, n_interpolation_points_1d): */ __pyx_t_7 = 0; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_x_tilde.data) + __pyx_t_7)) )) = (__pyx_v_coord_min + (__pyx_v_coord_spacing / 2.0)); /* "openTSNE/_tsne.pyx":723 * * x_tilde[0] = coord_min + coord_spacing / 2 * y_tilde[0] = coord_min + coord_spacing / 2 # <<<<<<<<<<<<<< * for i in range(1, n_interpolation_points_1d): * x_tilde[i] = x_tilde[i - 1] + coord_spacing */ __pyx_t_7 = 0; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_7)) )) = (__pyx_v_coord_min + (__pyx_v_coord_spacing / 2.0)); /* "openTSNE/_tsne.pyx":724 * x_tilde[0] = coord_min + coord_spacing / 2 * y_tilde[0] = coord_min + coord_spacing / 2 * for i in range(1, n_interpolation_points_1d): # <<<<<<<<<<<<<< * x_tilde[i] = x_tilde[i - 1] + coord_spacing * y_tilde[i] = y_tilde[i - 1] + coord_spacing */ __pyx_t_8 = __pyx_v_n_interpolation_points_1d; __pyx_t_9 = __pyx_t_8; for (__pyx_t_10 = 1; __pyx_t_10 < __pyx_t_9; __pyx_t_10+=1) { __pyx_v_i = __pyx_t_10; /* "openTSNE/_tsne.pyx":725 * y_tilde[0] = coord_min + coord_spacing / 2 * for i in range(1, n_interpolation_points_1d): * x_tilde[i] = x_tilde[i - 1] + coord_spacing # <<<<<<<<<<<<<< * y_tilde[i] = y_tilde[i - 1] + coord_spacing * */ __pyx_t_7 = (__pyx_v_i - 1); __pyx_t_11 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_x_tilde.data) + __pyx_t_11)) )) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_x_tilde.data) + __pyx_t_7)) ))) + __pyx_v_coord_spacing); /* "openTSNE/_tsne.pyx":726 * for i in range(1, n_interpolation_points_1d): * x_tilde[i] = x_tilde[i - 1] + coord_spacing * y_tilde[i] = y_tilde[i - 1] + coord_spacing # <<<<<<<<<<<<<< * * # Evaluate the kernel at the interpolation nodes and form the embedded */ __pyx_t_7 = (__pyx_v_i - 1); __pyx_t_11 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_11)) )) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_7)) ))) + __pyx_v_coord_spacing); } /* "openTSNE/_tsne.pyx":731 * # generating kernel vector for a circulant matrix * cdef double tmp * for i in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * for j in range(n_interpolation_points_1d): * tmp = kernel(y_tilde[0], x_tilde[0], y_tilde[i], x_tilde[j], dof) */ __pyx_t_8 = __pyx_v_n_interpolation_points_1d; __pyx_t_9 = __pyx_t_8; for (__pyx_t_10 = 0; __pyx_t_10 < __pyx_t_9; __pyx_t_10+=1) { __pyx_v_i = __pyx_t_10; /* "openTSNE/_tsne.pyx":732 * cdef double tmp * for i in range(n_interpolation_points_1d): * for j in range(n_interpolation_points_1d): # <<<<<<<<<<<<<< * tmp = kernel(y_tilde[0], x_tilde[0], y_tilde[i], x_tilde[j], dof) * */ __pyx_t_12 = __pyx_v_n_interpolation_points_1d; __pyx_t_13 = __pyx_t_12; for (__pyx_t_14 = 0; __pyx_t_14 < __pyx_t_13; __pyx_t_14+=1) { __pyx_v_j = __pyx_t_14; /* "openTSNE/_tsne.pyx":733 * for i in range(n_interpolation_points_1d): * for j in range(n_interpolation_points_1d): * tmp = kernel(y_tilde[0], x_tilde[0], y_tilde[i], x_tilde[j], dof) # <<<<<<<<<<<<<< * * kernel_tilde[n_interpolation_points_1d + i, n_interpolation_points_1d + j] = tmp */ __pyx_t_7 = 0; __pyx_t_11 = 0; __pyx_t_15 = __pyx_v_i; __pyx_t_16 = __pyx_v_j; __pyx_v_tmp = __pyx_v_kernel((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_7)) ))), (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_x_tilde.data) + __pyx_t_11)) ))), (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_15)) ))), (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_x_tilde.data) + __pyx_t_16)) ))), __pyx_v_dof); /* "openTSNE/_tsne.pyx":735 * tmp = kernel(y_tilde[0], x_tilde[0], y_tilde[i], x_tilde[j], dof) * * kernel_tilde[n_interpolation_points_1d + i, n_interpolation_points_1d + j] = tmp # <<<<<<<<<<<<<< * kernel_tilde[n_interpolation_points_1d - i, n_interpolation_points_1d + j] = tmp * kernel_tilde[n_interpolation_points_1d + i, n_interpolation_points_1d - j] = tmp */ __pyx_t_16 = (__pyx_v_n_interpolation_points_1d + __pyx_v_i); __pyx_t_15 = (__pyx_v_n_interpolation_points_1d + __pyx_v_j); *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_kernel_tilde.data + __pyx_t_16 * __pyx_v_kernel_tilde.strides[0]) )) + __pyx_t_15)) )) = __pyx_v_tmp; /* "openTSNE/_tsne.pyx":736 * * kernel_tilde[n_interpolation_points_1d + i, n_interpolation_points_1d + j] = tmp * kernel_tilde[n_interpolation_points_1d - i, n_interpolation_points_1d + j] = tmp # <<<<<<<<<<<<<< * kernel_tilde[n_interpolation_points_1d + i, n_interpolation_points_1d - j] = tmp * kernel_tilde[n_interpolation_points_1d - i, n_interpolation_points_1d - j] = tmp */ __pyx_t_15 = (__pyx_v_n_interpolation_points_1d - __pyx_v_i); __pyx_t_16 = (__pyx_v_n_interpolation_points_1d + __pyx_v_j); *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_kernel_tilde.data + __pyx_t_15 * __pyx_v_kernel_tilde.strides[0]) )) + __pyx_t_16)) )) = __pyx_v_tmp; /* "openTSNE/_tsne.pyx":737 * kernel_tilde[n_interpolation_points_1d + i, n_interpolation_points_1d + j] = tmp * kernel_tilde[n_interpolation_points_1d - i, n_interpolation_points_1d + j] = tmp * kernel_tilde[n_interpolation_points_1d + i, n_interpolation_points_1d - j] = tmp # <<<<<<<<<<<<<< * kernel_tilde[n_interpolation_points_1d - i, n_interpolation_points_1d - j] = tmp * */ __pyx_t_16 = (__pyx_v_n_interpolation_points_1d + __pyx_v_i); __pyx_t_15 = (__pyx_v_n_interpolation_points_1d - __pyx_v_j); *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_kernel_tilde.data + __pyx_t_16 * __pyx_v_kernel_tilde.strides[0]) )) + __pyx_t_15)) )) = __pyx_v_tmp; /* "openTSNE/_tsne.pyx":738 * kernel_tilde[n_interpolation_points_1d - i, n_interpolation_points_1d + j] = tmp * kernel_tilde[n_interpolation_points_1d + i, n_interpolation_points_1d - j] = tmp * kernel_tilde[n_interpolation_points_1d - i, n_interpolation_points_1d - j] = tmp # <<<<<<<<<<<<<< * * return kernel_tilde */ __pyx_t_15 = (__pyx_v_n_interpolation_points_1d - __pyx_v_i); __pyx_t_16 = (__pyx_v_n_interpolation_points_1d - __pyx_v_j); *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_kernel_tilde.data + __pyx_t_15 * __pyx_v_kernel_tilde.strides[0]) )) + __pyx_t_16)) )) = __pyx_v_tmp; } } /* "openTSNE/_tsne.pyx":740 * kernel_tilde[n_interpolation_points_1d - i, n_interpolation_points_1d - j] = tmp * * return kernel_tilde # <<<<<<<<<<<<<< * * */ __PYX_INC_MEMVIEW(&__pyx_v_kernel_tilde, 0); __pyx_r = __pyx_v_kernel_tilde; goto __pyx_L0; /* "openTSNE/_tsne.pyx":706 * * * cdef double[:, ::1] compute_kernel_tilde_2d( # <<<<<<<<<<<<<< * double (*kernel)(double, double, double, double, double), * Py_ssize_t n_interpolation_points_1d, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_6, 1); __pyx_r.data = NULL; __pyx_r.memview = NULL; __Pyx_AddTraceback("openTSNE._tsne.compute_kernel_tilde_2d", __pyx_clineno, __pyx_lineno, __pyx_filename); goto __pyx_L2; __pyx_L0:; if (unlikely(!__pyx_r.memview)) { PyErr_SetString(PyExc_TypeError, "Memoryview return value is not initialized"); } __pyx_L2:; __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_x_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_kernel_tilde, 1); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/_tsne.pyx":743 * * * cpdef double estimate_negative_gradient_fft_2d( # <<<<<<<<<<<<<< * double[:, ::1] embedding, * double[:, ::1] gradient, */ static PyObject *__pyx_pw_8openTSNE_5_tsne_13estimate_negative_gradient_fft_2d(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static double __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d(__Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, CYTHON_UNUSED int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d *__pyx_optional_args) { Py_ssize_t __pyx_v_n_interpolation_points = ((Py_ssize_t)3); Py_ssize_t __pyx_v_min_num_intervals = ((Py_ssize_t)10); double __pyx_v_ints_in_interval = ((double)1.0); double __pyx_v_dof = ((double)1.0); Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; Py_ssize_t __pyx_v_d; Py_ssize_t __pyx_v_box_idx; Py_ssize_t __pyx_v_n_samples; CYTHON_UNUSED Py_ssize_t __pyx_v_n_dims; double __pyx_v_coord_max; double __pyx_v_coord_min; int __pyx_v_n_boxes_1d; PyObject *__pyx_v_recommended_boxes = 0; int __pyx_v_n_total_boxes; double __pyx_v_box_width; __Pyx_memviewslice __pyx_v_box_x_lower_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_box_x_upper_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_box_y_lower_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_box_y_upper_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; int *__pyx_v_point_box_idx; int __pyx_v_box_x_idx; int __pyx_v_box_y_idx; __Pyx_memviewslice __pyx_v_y_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_h; __Pyx_memviewslice __pyx_v_sq_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_v_n_terms; __Pyx_memviewslice __pyx_v_q_j = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_x_in_box = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_y_in_box = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_y_min; double __pyx_v_x_min; __Pyx_memviewslice __pyx_v_x_interpolated_values = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_y_interpolated_values = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_v_total_interpolation_points; __Pyx_memviewslice __pyx_v_w_coefficients = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_box_i; Py_ssize_t __pyx_v_box_j; Py_ssize_t __pyx_v_interp_i; Py_ssize_t __pyx_v_interp_j; Py_ssize_t __pyx_v_idx; __Pyx_memviewslice __pyx_v_y_tilde_values = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_phi = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_sum_Q; double __pyx_v_y1; double __pyx_v_y2; double __pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; int __pyx_t_6; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_t_9; PyObject *__pyx_t_10 = NULL; PyObject *__pyx_t_11 = NULL; __Pyx_memviewslice __pyx_t_12 = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_t_13; int __pyx_t_14; int __pyx_t_15; __Pyx_memviewslice __pyx_t_16 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_17; Py_ssize_t __pyx_t_18; Py_ssize_t __pyx_t_19; Py_ssize_t __pyx_t_20; Py_ssize_t __pyx_t_21; Py_ssize_t __pyx_t_22; Py_ssize_t __pyx_t_23; Py_ssize_t __pyx_t_24; Py_ssize_t __pyx_t_25; Py_ssize_t __pyx_t_26; Py_ssize_t __pyx_t_27; Py_ssize_t __pyx_t_28; Py_ssize_t __pyx_t_29; PyObject *__pyx_t_30 = NULL; __Pyx_memviewslice __pyx_t_31 = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("estimate_negative_gradient_fft_2d", 0); if (__pyx_optional_args) { if (__pyx_optional_args->__pyx_n > 0) { __pyx_v_n_interpolation_points = __pyx_optional_args->n_interpolation_points; if (__pyx_optional_args->__pyx_n > 1) { __pyx_v_min_num_intervals = __pyx_optional_args->min_num_intervals; if (__pyx_optional_args->__pyx_n > 2) { __pyx_v_ints_in_interval = __pyx_optional_args->ints_in_interval; if (__pyx_optional_args->__pyx_n > 3) { __pyx_v_dof = __pyx_optional_args->dof; } } } } } /* "openTSNE/_tsne.pyx":753 * cdef: * Py_ssize_t i, j, d, box_idx * Py_ssize_t n_samples = embedding.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t n_dims = embedding.shape[1] * */ __pyx_v_n_samples = (__pyx_v_embedding.shape[0]); /* "openTSNE/_tsne.pyx":754 * Py_ssize_t i, j, d, box_idx * Py_ssize_t n_samples = embedding.shape[0] * Py_ssize_t n_dims = embedding.shape[1] # <<<<<<<<<<<<<< * * double coord_max = -INFINITY, coord_min = INFINITY */ __pyx_v_n_dims = (__pyx_v_embedding.shape[1]); /* "openTSNE/_tsne.pyx":756 * Py_ssize_t n_dims = embedding.shape[1] * * double coord_max = -INFINITY, coord_min = INFINITY # <<<<<<<<<<<<<< * # Determine the min/max values of the embedding * for i in range(n_samples): */ __pyx_v_coord_max = (-INFINITY); __pyx_v_coord_min = INFINITY; /* "openTSNE/_tsne.pyx":758 * double coord_max = -INFINITY, coord_min = INFINITY * # Determine the min/max values of the embedding * for i in range(n_samples): # <<<<<<<<<<<<<< * if embedding[i, 0] < coord_min: * coord_min = embedding[i, 0] */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":759 * # Determine the min/max values of the embedding * for i in range(n_samples): * if embedding[i, 0] < coord_min: # <<<<<<<<<<<<<< * coord_min = embedding[i, 0] * elif embedding[i, 0] > coord_max: */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 0; __pyx_t_6 = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_4 * __pyx_v_embedding.strides[0]) )) + __pyx_t_5)) ))) < __pyx_v_coord_min) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":760 * for i in range(n_samples): * if embedding[i, 0] < coord_min: * coord_min = embedding[i, 0] # <<<<<<<<<<<<<< * elif embedding[i, 0] > coord_max: * coord_max = embedding[i, 0] */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 0; __pyx_v_coord_min = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_5 * __pyx_v_embedding.strides[0]) )) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":759 * # Determine the min/max values of the embedding * for i in range(n_samples): * if embedding[i, 0] < coord_min: # <<<<<<<<<<<<<< * coord_min = embedding[i, 0] * elif embedding[i, 0] > coord_max: */ goto __pyx_L5; } /* "openTSNE/_tsne.pyx":761 * if embedding[i, 0] < coord_min: * coord_min = embedding[i, 0] * elif embedding[i, 0] > coord_max: # <<<<<<<<<<<<<< * coord_max = embedding[i, 0] * if embedding[i, 1] < coord_min: */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 0; __pyx_t_6 = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_4 * __pyx_v_embedding.strides[0]) )) + __pyx_t_5)) ))) > __pyx_v_coord_max) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":762 * coord_min = embedding[i, 0] * elif embedding[i, 0] > coord_max: * coord_max = embedding[i, 0] # <<<<<<<<<<<<<< * if embedding[i, 1] < coord_min: * coord_min = embedding[i, 1] */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 0; __pyx_v_coord_max = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_5 * __pyx_v_embedding.strides[0]) )) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":761 * if embedding[i, 0] < coord_min: * coord_min = embedding[i, 0] * elif embedding[i, 0] > coord_max: # <<<<<<<<<<<<<< * coord_max = embedding[i, 0] * if embedding[i, 1] < coord_min: */ } __pyx_L5:; /* "openTSNE/_tsne.pyx":763 * elif embedding[i, 0] > coord_max: * coord_max = embedding[i, 0] * if embedding[i, 1] < coord_min: # <<<<<<<<<<<<<< * coord_min = embedding[i, 1] * elif embedding[i, 1] > coord_max: */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 1; __pyx_t_6 = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_4 * __pyx_v_embedding.strides[0]) )) + __pyx_t_5)) ))) < __pyx_v_coord_min) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":764 * coord_max = embedding[i, 0] * if embedding[i, 1] < coord_min: * coord_min = embedding[i, 1] # <<<<<<<<<<<<<< * elif embedding[i, 1] > coord_max: * coord_max = embedding[i, 1] */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 1; __pyx_v_coord_min = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_5 * __pyx_v_embedding.strides[0]) )) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":763 * elif embedding[i, 0] > coord_max: * coord_max = embedding[i, 0] * if embedding[i, 1] < coord_min: # <<<<<<<<<<<<<< * coord_min = embedding[i, 1] * elif embedding[i, 1] > coord_max: */ goto __pyx_L6; } /* "openTSNE/_tsne.pyx":765 * if embedding[i, 1] < coord_min: * coord_min = embedding[i, 1] * elif embedding[i, 1] > coord_max: # <<<<<<<<<<<<<< * coord_max = embedding[i, 1] * */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 1; __pyx_t_6 = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_4 * __pyx_v_embedding.strides[0]) )) + __pyx_t_5)) ))) > __pyx_v_coord_max) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":766 * coord_min = embedding[i, 1] * elif embedding[i, 1] > coord_max: * coord_max = embedding[i, 1] # <<<<<<<<<<<<<< * * cdef int n_boxes_1d = fmax(min_num_intervals, (coord_max - coord_min) / ints_in_interval) */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 1; __pyx_v_coord_max = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_5 * __pyx_v_embedding.strides[0]) )) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":765 * if embedding[i, 1] < coord_min: * coord_min = embedding[i, 1] * elif embedding[i, 1] > coord_max: # <<<<<<<<<<<<<< * coord_max = embedding[i, 1] * */ } __pyx_L6:; } /* "openTSNE/_tsne.pyx":768 * coord_max = embedding[i, 1] * * cdef int n_boxes_1d = fmax(min_num_intervals, (coord_max - coord_min) / ints_in_interval) # <<<<<<<<<<<<<< * # FFTW works faster on numbers that can be written as 2^a 3^b 5^c 7^d * # 11^e 13^f, where e+f is either 0 or 1, and the other exponents are arbitrary */ __pyx_v_n_boxes_1d = ((int)fmax(__pyx_v_min_num_intervals, ((__pyx_v_coord_max - __pyx_v_coord_min) / __pyx_v_ints_in_interval))); /* "openTSNE/_tsne.pyx":771 * # FFTW works faster on numbers that can be written as 2^a 3^b 5^c 7^d * # 11^e 13^f, where e+f is either 0 or 1, and the other exponents are arbitrary * cdef list recommended_boxes = [ # <<<<<<<<<<<<<< * 20, 21, 22, 24, 25, 26, 27, 28, 30, 32, 33, 35, 36, 39, 40, 42, 44, 45, 48, 49, 50, * 52, 54, 55, 56, 60, 63, 64, 65, 66, 70, 72, 75, 77, 78, 80, 81, 84, 88, 90, 91, 96, */ __pyx_t_7 = PyList_New(206); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 771, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_INCREF(__pyx_int_20); __Pyx_GIVEREF(__pyx_int_20); PyList_SET_ITEM(__pyx_t_7, 0, __pyx_int_20); __Pyx_INCREF(__pyx_int_21); __Pyx_GIVEREF(__pyx_int_21); PyList_SET_ITEM(__pyx_t_7, 1, __pyx_int_21); __Pyx_INCREF(__pyx_int_22); __Pyx_GIVEREF(__pyx_int_22); PyList_SET_ITEM(__pyx_t_7, 2, __pyx_int_22); __Pyx_INCREF(__pyx_int_24); __Pyx_GIVEREF(__pyx_int_24); PyList_SET_ITEM(__pyx_t_7, 3, __pyx_int_24); __Pyx_INCREF(__pyx_int_25); __Pyx_GIVEREF(__pyx_int_25); PyList_SET_ITEM(__pyx_t_7, 4, __pyx_int_25); __Pyx_INCREF(__pyx_int_26); __Pyx_GIVEREF(__pyx_int_26); PyList_SET_ITEM(__pyx_t_7, 5, __pyx_int_26); __Pyx_INCREF(__pyx_int_27); __Pyx_GIVEREF(__pyx_int_27); PyList_SET_ITEM(__pyx_t_7, 6, __pyx_int_27); __Pyx_INCREF(__pyx_int_28); __Pyx_GIVEREF(__pyx_int_28); PyList_SET_ITEM(__pyx_t_7, 7, __pyx_int_28); __Pyx_INCREF(__pyx_int_30); __Pyx_GIVEREF(__pyx_int_30); PyList_SET_ITEM(__pyx_t_7, 8, __pyx_int_30); __Pyx_INCREF(__pyx_int_32); __Pyx_GIVEREF(__pyx_int_32); PyList_SET_ITEM(__pyx_t_7, 9, __pyx_int_32); __Pyx_INCREF(__pyx_int_33); __Pyx_GIVEREF(__pyx_int_33); PyList_SET_ITEM(__pyx_t_7, 10, __pyx_int_33); __Pyx_INCREF(__pyx_int_35); __Pyx_GIVEREF(__pyx_int_35); PyList_SET_ITEM(__pyx_t_7, 11, __pyx_int_35); __Pyx_INCREF(__pyx_int_36); __Pyx_GIVEREF(__pyx_int_36); PyList_SET_ITEM(__pyx_t_7, 12, __pyx_int_36); __Pyx_INCREF(__pyx_int_39); __Pyx_GIVEREF(__pyx_int_39); PyList_SET_ITEM(__pyx_t_7, 13, __pyx_int_39); __Pyx_INCREF(__pyx_int_40); __Pyx_GIVEREF(__pyx_int_40); PyList_SET_ITEM(__pyx_t_7, 14, __pyx_int_40); __Pyx_INCREF(__pyx_int_42); __Pyx_GIVEREF(__pyx_int_42); PyList_SET_ITEM(__pyx_t_7, 15, __pyx_int_42); __Pyx_INCREF(__pyx_int_44); __Pyx_GIVEREF(__pyx_int_44); PyList_SET_ITEM(__pyx_t_7, 16, __pyx_int_44); __Pyx_INCREF(__pyx_int_45); __Pyx_GIVEREF(__pyx_int_45); PyList_SET_ITEM(__pyx_t_7, 17, __pyx_int_45); __Pyx_INCREF(__pyx_int_48); __Pyx_GIVEREF(__pyx_int_48); PyList_SET_ITEM(__pyx_t_7, 18, __pyx_int_48); __Pyx_INCREF(__pyx_int_49); __Pyx_GIVEREF(__pyx_int_49); PyList_SET_ITEM(__pyx_t_7, 19, __pyx_int_49); __Pyx_INCREF(__pyx_int_50); __Pyx_GIVEREF(__pyx_int_50); PyList_SET_ITEM(__pyx_t_7, 20, __pyx_int_50); __Pyx_INCREF(__pyx_int_52); __Pyx_GIVEREF(__pyx_int_52); PyList_SET_ITEM(__pyx_t_7, 21, __pyx_int_52); __Pyx_INCREF(__pyx_int_54); __Pyx_GIVEREF(__pyx_int_54); PyList_SET_ITEM(__pyx_t_7, 22, __pyx_int_54); __Pyx_INCREF(__pyx_int_55); __Pyx_GIVEREF(__pyx_int_55); PyList_SET_ITEM(__pyx_t_7, 23, __pyx_int_55); __Pyx_INCREF(__pyx_int_56); __Pyx_GIVEREF(__pyx_int_56); PyList_SET_ITEM(__pyx_t_7, 24, __pyx_int_56); __Pyx_INCREF(__pyx_int_60); __Pyx_GIVEREF(__pyx_int_60); PyList_SET_ITEM(__pyx_t_7, 25, __pyx_int_60); __Pyx_INCREF(__pyx_int_63); __Pyx_GIVEREF(__pyx_int_63); PyList_SET_ITEM(__pyx_t_7, 26, __pyx_int_63); __Pyx_INCREF(__pyx_int_64); __Pyx_GIVEREF(__pyx_int_64); PyList_SET_ITEM(__pyx_t_7, 27, __pyx_int_64); __Pyx_INCREF(__pyx_int_65); __Pyx_GIVEREF(__pyx_int_65); PyList_SET_ITEM(__pyx_t_7, 28, __pyx_int_65); __Pyx_INCREF(__pyx_int_66); __Pyx_GIVEREF(__pyx_int_66); PyList_SET_ITEM(__pyx_t_7, 29, __pyx_int_66); __Pyx_INCREF(__pyx_int_70); __Pyx_GIVEREF(__pyx_int_70); PyList_SET_ITEM(__pyx_t_7, 30, __pyx_int_70); __Pyx_INCREF(__pyx_int_72); __Pyx_GIVEREF(__pyx_int_72); PyList_SET_ITEM(__pyx_t_7, 31, __pyx_int_72); __Pyx_INCREF(__pyx_int_75); __Pyx_GIVEREF(__pyx_int_75); PyList_SET_ITEM(__pyx_t_7, 32, __pyx_int_75); __Pyx_INCREF(__pyx_int_77); __Pyx_GIVEREF(__pyx_int_77); PyList_SET_ITEM(__pyx_t_7, 33, __pyx_int_77); __Pyx_INCREF(__pyx_int_78); __Pyx_GIVEREF(__pyx_int_78); PyList_SET_ITEM(__pyx_t_7, 34, __pyx_int_78); __Pyx_INCREF(__pyx_int_80); __Pyx_GIVEREF(__pyx_int_80); PyList_SET_ITEM(__pyx_t_7, 35, __pyx_int_80); __Pyx_INCREF(__pyx_int_81); __Pyx_GIVEREF(__pyx_int_81); PyList_SET_ITEM(__pyx_t_7, 36, __pyx_int_81); __Pyx_INCREF(__pyx_int_84); __Pyx_GIVEREF(__pyx_int_84); PyList_SET_ITEM(__pyx_t_7, 37, __pyx_int_84); __Pyx_INCREF(__pyx_int_88); __Pyx_GIVEREF(__pyx_int_88); PyList_SET_ITEM(__pyx_t_7, 38, __pyx_int_88); __Pyx_INCREF(__pyx_int_90); __Pyx_GIVEREF(__pyx_int_90); PyList_SET_ITEM(__pyx_t_7, 39, __pyx_int_90); __Pyx_INCREF(__pyx_int_91); __Pyx_GIVEREF(__pyx_int_91); PyList_SET_ITEM(__pyx_t_7, 40, __pyx_int_91); __Pyx_INCREF(__pyx_int_96); __Pyx_GIVEREF(__pyx_int_96); PyList_SET_ITEM(__pyx_t_7, 41, __pyx_int_96); __Pyx_INCREF(__pyx_int_98); __Pyx_GIVEREF(__pyx_int_98); PyList_SET_ITEM(__pyx_t_7, 42, __pyx_int_98); __Pyx_INCREF(__pyx_int_99); __Pyx_GIVEREF(__pyx_int_99); PyList_SET_ITEM(__pyx_t_7, 43, __pyx_int_99); __Pyx_INCREF(__pyx_int_100); __Pyx_GIVEREF(__pyx_int_100); PyList_SET_ITEM(__pyx_t_7, 44, __pyx_int_100); __Pyx_INCREF(__pyx_int_104); __Pyx_GIVEREF(__pyx_int_104); PyList_SET_ITEM(__pyx_t_7, 45, __pyx_int_104); __Pyx_INCREF(__pyx_int_105); __Pyx_GIVEREF(__pyx_int_105); PyList_SET_ITEM(__pyx_t_7, 46, __pyx_int_105); __Pyx_INCREF(__pyx_int_108); __Pyx_GIVEREF(__pyx_int_108); PyList_SET_ITEM(__pyx_t_7, 47, __pyx_int_108); __Pyx_INCREF(__pyx_int_110); __Pyx_GIVEREF(__pyx_int_110); PyList_SET_ITEM(__pyx_t_7, 48, __pyx_int_110); __Pyx_INCREF(__pyx_int_112); __Pyx_GIVEREF(__pyx_int_112); PyList_SET_ITEM(__pyx_t_7, 49, __pyx_int_112); __Pyx_INCREF(__pyx_int_117); __Pyx_GIVEREF(__pyx_int_117); PyList_SET_ITEM(__pyx_t_7, 50, __pyx_int_117); __Pyx_INCREF(__pyx_int_120); __Pyx_GIVEREF(__pyx_int_120); PyList_SET_ITEM(__pyx_t_7, 51, __pyx_int_120); __Pyx_INCREF(__pyx_int_125); __Pyx_GIVEREF(__pyx_int_125); PyList_SET_ITEM(__pyx_t_7, 52, __pyx_int_125); __Pyx_INCREF(__pyx_int_126); __Pyx_GIVEREF(__pyx_int_126); PyList_SET_ITEM(__pyx_t_7, 53, __pyx_int_126); __Pyx_INCREF(__pyx_int_128); __Pyx_GIVEREF(__pyx_int_128); PyList_SET_ITEM(__pyx_t_7, 54, __pyx_int_128); __Pyx_INCREF(__pyx_int_130); __Pyx_GIVEREF(__pyx_int_130); PyList_SET_ITEM(__pyx_t_7, 55, __pyx_int_130); __Pyx_INCREF(__pyx_int_132); __Pyx_GIVEREF(__pyx_int_132); PyList_SET_ITEM(__pyx_t_7, 56, __pyx_int_132); __Pyx_INCREF(__pyx_int_135); __Pyx_GIVEREF(__pyx_int_135); PyList_SET_ITEM(__pyx_t_7, 57, __pyx_int_135); __Pyx_INCREF(__pyx_int_140); __Pyx_GIVEREF(__pyx_int_140); PyList_SET_ITEM(__pyx_t_7, 58, __pyx_int_140); __Pyx_INCREF(__pyx_int_144); __Pyx_GIVEREF(__pyx_int_144); PyList_SET_ITEM(__pyx_t_7, 59, __pyx_int_144); __Pyx_INCREF(__pyx_int_147); __Pyx_GIVEREF(__pyx_int_147); PyList_SET_ITEM(__pyx_t_7, 60, __pyx_int_147); __Pyx_INCREF(__pyx_int_150); __Pyx_GIVEREF(__pyx_int_150); PyList_SET_ITEM(__pyx_t_7, 61, __pyx_int_150); __Pyx_INCREF(__pyx_int_154); __Pyx_GIVEREF(__pyx_int_154); PyList_SET_ITEM(__pyx_t_7, 62, __pyx_int_154); __Pyx_INCREF(__pyx_int_156); __Pyx_GIVEREF(__pyx_int_156); PyList_SET_ITEM(__pyx_t_7, 63, __pyx_int_156); __Pyx_INCREF(__pyx_int_160); __Pyx_GIVEREF(__pyx_int_160); PyList_SET_ITEM(__pyx_t_7, 64, __pyx_int_160); __Pyx_INCREF(__pyx_int_162); __Pyx_GIVEREF(__pyx_int_162); PyList_SET_ITEM(__pyx_t_7, 65, __pyx_int_162); __Pyx_INCREF(__pyx_int_165); __Pyx_GIVEREF(__pyx_int_165); PyList_SET_ITEM(__pyx_t_7, 66, __pyx_int_165); __Pyx_INCREF(__pyx_int_168); __Pyx_GIVEREF(__pyx_int_168); PyList_SET_ITEM(__pyx_t_7, 67, __pyx_int_168); __Pyx_INCREF(__pyx_int_175); __Pyx_GIVEREF(__pyx_int_175); PyList_SET_ITEM(__pyx_t_7, 68, __pyx_int_175); __Pyx_INCREF(__pyx_int_176); __Pyx_GIVEREF(__pyx_int_176); PyList_SET_ITEM(__pyx_t_7, 69, __pyx_int_176); __Pyx_INCREF(__pyx_int_180); __Pyx_GIVEREF(__pyx_int_180); PyList_SET_ITEM(__pyx_t_7, 70, __pyx_int_180); __Pyx_INCREF(__pyx_int_182); __Pyx_GIVEREF(__pyx_int_182); PyList_SET_ITEM(__pyx_t_7, 71, __pyx_int_182); __Pyx_INCREF(__pyx_int_189); __Pyx_GIVEREF(__pyx_int_189); PyList_SET_ITEM(__pyx_t_7, 72, __pyx_int_189); __Pyx_INCREF(__pyx_int_192); __Pyx_GIVEREF(__pyx_int_192); PyList_SET_ITEM(__pyx_t_7, 73, __pyx_int_192); __Pyx_INCREF(__pyx_int_195); __Pyx_GIVEREF(__pyx_int_195); PyList_SET_ITEM(__pyx_t_7, 74, __pyx_int_195); __Pyx_INCREF(__pyx_int_196); __Pyx_GIVEREF(__pyx_int_196); PyList_SET_ITEM(__pyx_t_7, 75, __pyx_int_196); __Pyx_INCREF(__pyx_int_198); __Pyx_GIVEREF(__pyx_int_198); PyList_SET_ITEM(__pyx_t_7, 76, __pyx_int_198); __Pyx_INCREF(__pyx_int_200); __Pyx_GIVEREF(__pyx_int_200); PyList_SET_ITEM(__pyx_t_7, 77, __pyx_int_200); __Pyx_INCREF(__pyx_int_208); __Pyx_GIVEREF(__pyx_int_208); PyList_SET_ITEM(__pyx_t_7, 78, __pyx_int_208); __Pyx_INCREF(__pyx_int_210); __Pyx_GIVEREF(__pyx_int_210); PyList_SET_ITEM(__pyx_t_7, 79, __pyx_int_210); __Pyx_INCREF(__pyx_int_216); __Pyx_GIVEREF(__pyx_int_216); PyList_SET_ITEM(__pyx_t_7, 80, __pyx_int_216); __Pyx_INCREF(__pyx_int_220); __Pyx_GIVEREF(__pyx_int_220); PyList_SET_ITEM(__pyx_t_7, 81, __pyx_int_220); __Pyx_INCREF(__pyx_int_224); __Pyx_GIVEREF(__pyx_int_224); PyList_SET_ITEM(__pyx_t_7, 82, __pyx_int_224); __Pyx_INCREF(__pyx_int_225); __Pyx_GIVEREF(__pyx_int_225); PyList_SET_ITEM(__pyx_t_7, 83, __pyx_int_225); __Pyx_INCREF(__pyx_int_231); __Pyx_GIVEREF(__pyx_int_231); PyList_SET_ITEM(__pyx_t_7, 84, __pyx_int_231); __Pyx_INCREF(__pyx_int_234); __Pyx_GIVEREF(__pyx_int_234); PyList_SET_ITEM(__pyx_t_7, 85, __pyx_int_234); __Pyx_INCREF(__pyx_int_240); __Pyx_GIVEREF(__pyx_int_240); PyList_SET_ITEM(__pyx_t_7, 86, __pyx_int_240); __Pyx_INCREF(__pyx_int_243); __Pyx_GIVEREF(__pyx_int_243); PyList_SET_ITEM(__pyx_t_7, 87, __pyx_int_243); __Pyx_INCREF(__pyx_int_245); __Pyx_GIVEREF(__pyx_int_245); PyList_SET_ITEM(__pyx_t_7, 88, __pyx_int_245); __Pyx_INCREF(__pyx_int_250); __Pyx_GIVEREF(__pyx_int_250); PyList_SET_ITEM(__pyx_t_7, 89, __pyx_int_250); __Pyx_INCREF(__pyx_int_252); __Pyx_GIVEREF(__pyx_int_252); PyList_SET_ITEM(__pyx_t_7, 90, __pyx_int_252); __Pyx_INCREF(__pyx_int_256); __Pyx_GIVEREF(__pyx_int_256); PyList_SET_ITEM(__pyx_t_7, 91, __pyx_int_256); __Pyx_INCREF(__pyx_int_260); __Pyx_GIVEREF(__pyx_int_260); PyList_SET_ITEM(__pyx_t_7, 92, __pyx_int_260); __Pyx_INCREF(__pyx_int_264); __Pyx_GIVEREF(__pyx_int_264); PyList_SET_ITEM(__pyx_t_7, 93, __pyx_int_264); __Pyx_INCREF(__pyx_int_270); __Pyx_GIVEREF(__pyx_int_270); PyList_SET_ITEM(__pyx_t_7, 94, __pyx_int_270); __Pyx_INCREF(__pyx_int_273); __Pyx_GIVEREF(__pyx_int_273); PyList_SET_ITEM(__pyx_t_7, 95, __pyx_int_273); __Pyx_INCREF(__pyx_int_275); __Pyx_GIVEREF(__pyx_int_275); PyList_SET_ITEM(__pyx_t_7, 96, __pyx_int_275); __Pyx_INCREF(__pyx_int_280); __Pyx_GIVEREF(__pyx_int_280); PyList_SET_ITEM(__pyx_t_7, 97, __pyx_int_280); __Pyx_INCREF(__pyx_int_288); __Pyx_GIVEREF(__pyx_int_288); PyList_SET_ITEM(__pyx_t_7, 98, __pyx_int_288); __Pyx_INCREF(__pyx_int_294); __Pyx_GIVEREF(__pyx_int_294); PyList_SET_ITEM(__pyx_t_7, 99, __pyx_int_294); __Pyx_INCREF(__pyx_int_297); __Pyx_GIVEREF(__pyx_int_297); PyList_SET_ITEM(__pyx_t_7, 100, __pyx_int_297); __Pyx_INCREF(__pyx_int_300); __Pyx_GIVEREF(__pyx_int_300); PyList_SET_ITEM(__pyx_t_7, 101, __pyx_int_300); __Pyx_INCREF(__pyx_int_308); __Pyx_GIVEREF(__pyx_int_308); PyList_SET_ITEM(__pyx_t_7, 102, __pyx_int_308); __Pyx_INCREF(__pyx_int_312); __Pyx_GIVEREF(__pyx_int_312); PyList_SET_ITEM(__pyx_t_7, 103, __pyx_int_312); __Pyx_INCREF(__pyx_int_315); __Pyx_GIVEREF(__pyx_int_315); PyList_SET_ITEM(__pyx_t_7, 104, __pyx_int_315); __Pyx_INCREF(__pyx_int_320); __Pyx_GIVEREF(__pyx_int_320); PyList_SET_ITEM(__pyx_t_7, 105, __pyx_int_320); __Pyx_INCREF(__pyx_int_324); __Pyx_GIVEREF(__pyx_int_324); PyList_SET_ITEM(__pyx_t_7, 106, __pyx_int_324); __Pyx_INCREF(__pyx_int_325); __Pyx_GIVEREF(__pyx_int_325); PyList_SET_ITEM(__pyx_t_7, 107, __pyx_int_325); __Pyx_INCREF(__pyx_int_330); __Pyx_GIVEREF(__pyx_int_330); PyList_SET_ITEM(__pyx_t_7, 108, __pyx_int_330); __Pyx_INCREF(__pyx_int_336); __Pyx_GIVEREF(__pyx_int_336); PyList_SET_ITEM(__pyx_t_7, 109, __pyx_int_336); __Pyx_INCREF(__pyx_int_343); __Pyx_GIVEREF(__pyx_int_343); PyList_SET_ITEM(__pyx_t_7, 110, __pyx_int_343); __Pyx_INCREF(__pyx_int_350); __Pyx_GIVEREF(__pyx_int_350); PyList_SET_ITEM(__pyx_t_7, 111, __pyx_int_350); __Pyx_INCREF(__pyx_int_351); __Pyx_GIVEREF(__pyx_int_351); PyList_SET_ITEM(__pyx_t_7, 112, __pyx_int_351); __Pyx_INCREF(__pyx_int_352); __Pyx_GIVEREF(__pyx_int_352); PyList_SET_ITEM(__pyx_t_7, 113, __pyx_int_352); __Pyx_INCREF(__pyx_int_360); __Pyx_GIVEREF(__pyx_int_360); PyList_SET_ITEM(__pyx_t_7, 114, __pyx_int_360); __Pyx_INCREF(__pyx_int_364); __Pyx_GIVEREF(__pyx_int_364); PyList_SET_ITEM(__pyx_t_7, 115, __pyx_int_364); __Pyx_INCREF(__pyx_int_375); __Pyx_GIVEREF(__pyx_int_375); PyList_SET_ITEM(__pyx_t_7, 116, __pyx_int_375); __Pyx_INCREF(__pyx_int_378); __Pyx_GIVEREF(__pyx_int_378); PyList_SET_ITEM(__pyx_t_7, 117, __pyx_int_378); __Pyx_INCREF(__pyx_int_384); __Pyx_GIVEREF(__pyx_int_384); PyList_SET_ITEM(__pyx_t_7, 118, __pyx_int_384); __Pyx_INCREF(__pyx_int_385); __Pyx_GIVEREF(__pyx_int_385); PyList_SET_ITEM(__pyx_t_7, 119, __pyx_int_385); __Pyx_INCREF(__pyx_int_390); __Pyx_GIVEREF(__pyx_int_390); PyList_SET_ITEM(__pyx_t_7, 120, __pyx_int_390); __Pyx_INCREF(__pyx_int_392); __Pyx_GIVEREF(__pyx_int_392); PyList_SET_ITEM(__pyx_t_7, 121, __pyx_int_392); __Pyx_INCREF(__pyx_int_396); __Pyx_GIVEREF(__pyx_int_396); PyList_SET_ITEM(__pyx_t_7, 122, __pyx_int_396); __Pyx_INCREF(__pyx_int_400); __Pyx_GIVEREF(__pyx_int_400); PyList_SET_ITEM(__pyx_t_7, 123, __pyx_int_400); __Pyx_INCREF(__pyx_int_405); __Pyx_GIVEREF(__pyx_int_405); PyList_SET_ITEM(__pyx_t_7, 124, __pyx_int_405); __Pyx_INCREF(__pyx_int_416); __Pyx_GIVEREF(__pyx_int_416); PyList_SET_ITEM(__pyx_t_7, 125, __pyx_int_416); __Pyx_INCREF(__pyx_int_420); __Pyx_GIVEREF(__pyx_int_420); PyList_SET_ITEM(__pyx_t_7, 126, __pyx_int_420); __Pyx_INCREF(__pyx_int_432); __Pyx_GIVEREF(__pyx_int_432); PyList_SET_ITEM(__pyx_t_7, 127, __pyx_int_432); __Pyx_INCREF(__pyx_int_440); __Pyx_GIVEREF(__pyx_int_440); PyList_SET_ITEM(__pyx_t_7, 128, __pyx_int_440); __Pyx_INCREF(__pyx_int_441); __Pyx_GIVEREF(__pyx_int_441); PyList_SET_ITEM(__pyx_t_7, 129, __pyx_int_441); __Pyx_INCREF(__pyx_int_448); __Pyx_GIVEREF(__pyx_int_448); PyList_SET_ITEM(__pyx_t_7, 130, __pyx_int_448); __Pyx_INCREF(__pyx_int_450); __Pyx_GIVEREF(__pyx_int_450); PyList_SET_ITEM(__pyx_t_7, 131, __pyx_int_450); __Pyx_INCREF(__pyx_int_455); __Pyx_GIVEREF(__pyx_int_455); PyList_SET_ITEM(__pyx_t_7, 132, __pyx_int_455); __Pyx_INCREF(__pyx_int_462); __Pyx_GIVEREF(__pyx_int_462); PyList_SET_ITEM(__pyx_t_7, 133, __pyx_int_462); __Pyx_INCREF(__pyx_int_468); __Pyx_GIVEREF(__pyx_int_468); PyList_SET_ITEM(__pyx_t_7, 134, __pyx_int_468); __Pyx_INCREF(__pyx_int_480); __Pyx_GIVEREF(__pyx_int_480); PyList_SET_ITEM(__pyx_t_7, 135, __pyx_int_480); __Pyx_INCREF(__pyx_int_486); __Pyx_GIVEREF(__pyx_int_486); PyList_SET_ITEM(__pyx_t_7, 136, __pyx_int_486); __Pyx_INCREF(__pyx_int_490); __Pyx_GIVEREF(__pyx_int_490); PyList_SET_ITEM(__pyx_t_7, 137, __pyx_int_490); __Pyx_INCREF(__pyx_int_495); __Pyx_GIVEREF(__pyx_int_495); PyList_SET_ITEM(__pyx_t_7, 138, __pyx_int_495); __Pyx_INCREF(__pyx_int_500); __Pyx_GIVEREF(__pyx_int_500); PyList_SET_ITEM(__pyx_t_7, 139, __pyx_int_500); __Pyx_INCREF(__pyx_int_504); __Pyx_GIVEREF(__pyx_int_504); PyList_SET_ITEM(__pyx_t_7, 140, __pyx_int_504); __Pyx_INCREF(__pyx_int_512); __Pyx_GIVEREF(__pyx_int_512); PyList_SET_ITEM(__pyx_t_7, 141, __pyx_int_512); __Pyx_INCREF(__pyx_int_520); __Pyx_GIVEREF(__pyx_int_520); PyList_SET_ITEM(__pyx_t_7, 142, __pyx_int_520); __Pyx_INCREF(__pyx_int_525); __Pyx_GIVEREF(__pyx_int_525); PyList_SET_ITEM(__pyx_t_7, 143, __pyx_int_525); __Pyx_INCREF(__pyx_int_528); __Pyx_GIVEREF(__pyx_int_528); PyList_SET_ITEM(__pyx_t_7, 144, __pyx_int_528); __Pyx_INCREF(__pyx_int_539); __Pyx_GIVEREF(__pyx_int_539); PyList_SET_ITEM(__pyx_t_7, 145, __pyx_int_539); __Pyx_INCREF(__pyx_int_540); __Pyx_GIVEREF(__pyx_int_540); PyList_SET_ITEM(__pyx_t_7, 146, __pyx_int_540); __Pyx_INCREF(__pyx_int_546); __Pyx_GIVEREF(__pyx_int_546); PyList_SET_ITEM(__pyx_t_7, 147, __pyx_int_546); __Pyx_INCREF(__pyx_int_550); __Pyx_GIVEREF(__pyx_int_550); PyList_SET_ITEM(__pyx_t_7, 148, __pyx_int_550); __Pyx_INCREF(__pyx_int_560); __Pyx_GIVEREF(__pyx_int_560); PyList_SET_ITEM(__pyx_t_7, 149, __pyx_int_560); __Pyx_INCREF(__pyx_int_567); __Pyx_GIVEREF(__pyx_int_567); PyList_SET_ITEM(__pyx_t_7, 150, __pyx_int_567); __Pyx_INCREF(__pyx_int_576); __Pyx_GIVEREF(__pyx_int_576); PyList_SET_ITEM(__pyx_t_7, 151, __pyx_int_576); __Pyx_INCREF(__pyx_int_585); __Pyx_GIVEREF(__pyx_int_585); PyList_SET_ITEM(__pyx_t_7, 152, __pyx_int_585); __Pyx_INCREF(__pyx_int_588); __Pyx_GIVEREF(__pyx_int_588); PyList_SET_ITEM(__pyx_t_7, 153, __pyx_int_588); __Pyx_INCREF(__pyx_int_594); __Pyx_GIVEREF(__pyx_int_594); PyList_SET_ITEM(__pyx_t_7, 154, __pyx_int_594); __Pyx_INCREF(__pyx_int_600); __Pyx_GIVEREF(__pyx_int_600); PyList_SET_ITEM(__pyx_t_7, 155, __pyx_int_600); __Pyx_INCREF(__pyx_int_616); __Pyx_GIVEREF(__pyx_int_616); PyList_SET_ITEM(__pyx_t_7, 156, __pyx_int_616); __Pyx_INCREF(__pyx_int_624); __Pyx_GIVEREF(__pyx_int_624); PyList_SET_ITEM(__pyx_t_7, 157, __pyx_int_624); __Pyx_INCREF(__pyx_int_625); __Pyx_GIVEREF(__pyx_int_625); PyList_SET_ITEM(__pyx_t_7, 158, __pyx_int_625); __Pyx_INCREF(__pyx_int_630); __Pyx_GIVEREF(__pyx_int_630); PyList_SET_ITEM(__pyx_t_7, 159, __pyx_int_630); __Pyx_INCREF(__pyx_int_637); __Pyx_GIVEREF(__pyx_int_637); PyList_SET_ITEM(__pyx_t_7, 160, __pyx_int_637); __Pyx_INCREF(__pyx_int_640); __Pyx_GIVEREF(__pyx_int_640); PyList_SET_ITEM(__pyx_t_7, 161, __pyx_int_640); __Pyx_INCREF(__pyx_int_648); __Pyx_GIVEREF(__pyx_int_648); PyList_SET_ITEM(__pyx_t_7, 162, __pyx_int_648); __Pyx_INCREF(__pyx_int_650); __Pyx_GIVEREF(__pyx_int_650); PyList_SET_ITEM(__pyx_t_7, 163, __pyx_int_650); __Pyx_INCREF(__pyx_int_660); __Pyx_GIVEREF(__pyx_int_660); PyList_SET_ITEM(__pyx_t_7, 164, __pyx_int_660); __Pyx_INCREF(__pyx_int_672); __Pyx_GIVEREF(__pyx_int_672); PyList_SET_ITEM(__pyx_t_7, 165, __pyx_int_672); __Pyx_INCREF(__pyx_int_675); __Pyx_GIVEREF(__pyx_int_675); PyList_SET_ITEM(__pyx_t_7, 166, __pyx_int_675); __Pyx_INCREF(__pyx_int_686); __Pyx_GIVEREF(__pyx_int_686); PyList_SET_ITEM(__pyx_t_7, 167, __pyx_int_686); __Pyx_INCREF(__pyx_int_693); __Pyx_GIVEREF(__pyx_int_693); PyList_SET_ITEM(__pyx_t_7, 168, __pyx_int_693); __Pyx_INCREF(__pyx_int_700); __Pyx_GIVEREF(__pyx_int_700); PyList_SET_ITEM(__pyx_t_7, 169, __pyx_int_700); __Pyx_INCREF(__pyx_int_702); __Pyx_GIVEREF(__pyx_int_702); PyList_SET_ITEM(__pyx_t_7, 170, __pyx_int_702); __Pyx_INCREF(__pyx_int_704); __Pyx_GIVEREF(__pyx_int_704); PyList_SET_ITEM(__pyx_t_7, 171, __pyx_int_704); __Pyx_INCREF(__pyx_int_720); __Pyx_GIVEREF(__pyx_int_720); PyList_SET_ITEM(__pyx_t_7, 172, __pyx_int_720); __Pyx_INCREF(__pyx_int_728); __Pyx_GIVEREF(__pyx_int_728); PyList_SET_ITEM(__pyx_t_7, 173, __pyx_int_728); __Pyx_INCREF(__pyx_int_729); __Pyx_GIVEREF(__pyx_int_729); PyList_SET_ITEM(__pyx_t_7, 174, __pyx_int_729); __Pyx_INCREF(__pyx_int_735); __Pyx_GIVEREF(__pyx_int_735); PyList_SET_ITEM(__pyx_t_7, 175, __pyx_int_735); __Pyx_INCREF(__pyx_int_750); __Pyx_GIVEREF(__pyx_int_750); PyList_SET_ITEM(__pyx_t_7, 176, __pyx_int_750); __Pyx_INCREF(__pyx_int_756); __Pyx_GIVEREF(__pyx_int_756); PyList_SET_ITEM(__pyx_t_7, 177, __pyx_int_756); __Pyx_INCREF(__pyx_int_768); __Pyx_GIVEREF(__pyx_int_768); PyList_SET_ITEM(__pyx_t_7, 178, __pyx_int_768); __Pyx_INCREF(__pyx_int_770); __Pyx_GIVEREF(__pyx_int_770); PyList_SET_ITEM(__pyx_t_7, 179, __pyx_int_770); __Pyx_INCREF(__pyx_int_780); __Pyx_GIVEREF(__pyx_int_780); PyList_SET_ITEM(__pyx_t_7, 180, __pyx_int_780); __Pyx_INCREF(__pyx_int_784); __Pyx_GIVEREF(__pyx_int_784); PyList_SET_ITEM(__pyx_t_7, 181, __pyx_int_784); __Pyx_INCREF(__pyx_int_792); __Pyx_GIVEREF(__pyx_int_792); PyList_SET_ITEM(__pyx_t_7, 182, __pyx_int_792); __Pyx_INCREF(__pyx_int_800); __Pyx_GIVEREF(__pyx_int_800); PyList_SET_ITEM(__pyx_t_7, 183, __pyx_int_800); __Pyx_INCREF(__pyx_int_810); __Pyx_GIVEREF(__pyx_int_810); PyList_SET_ITEM(__pyx_t_7, 184, __pyx_int_810); __Pyx_INCREF(__pyx_int_819); __Pyx_GIVEREF(__pyx_int_819); PyList_SET_ITEM(__pyx_t_7, 185, __pyx_int_819); __Pyx_INCREF(__pyx_int_825); __Pyx_GIVEREF(__pyx_int_825); PyList_SET_ITEM(__pyx_t_7, 186, __pyx_int_825); __Pyx_INCREF(__pyx_int_832); __Pyx_GIVEREF(__pyx_int_832); PyList_SET_ITEM(__pyx_t_7, 187, __pyx_int_832); __Pyx_INCREF(__pyx_int_840); __Pyx_GIVEREF(__pyx_int_840); PyList_SET_ITEM(__pyx_t_7, 188, __pyx_int_840); __Pyx_INCREF(__pyx_int_864); __Pyx_GIVEREF(__pyx_int_864); PyList_SET_ITEM(__pyx_t_7, 189, __pyx_int_864); __Pyx_INCREF(__pyx_int_875); __Pyx_GIVEREF(__pyx_int_875); PyList_SET_ITEM(__pyx_t_7, 190, __pyx_int_875); __Pyx_INCREF(__pyx_int_880); __Pyx_GIVEREF(__pyx_int_880); PyList_SET_ITEM(__pyx_t_7, 191, __pyx_int_880); __Pyx_INCREF(__pyx_int_882); __Pyx_GIVEREF(__pyx_int_882); PyList_SET_ITEM(__pyx_t_7, 192, __pyx_int_882); __Pyx_INCREF(__pyx_int_891); __Pyx_GIVEREF(__pyx_int_891); PyList_SET_ITEM(__pyx_t_7, 193, __pyx_int_891); __Pyx_INCREF(__pyx_int_896); __Pyx_GIVEREF(__pyx_int_896); PyList_SET_ITEM(__pyx_t_7, 194, __pyx_int_896); __Pyx_INCREF(__pyx_int_900); __Pyx_GIVEREF(__pyx_int_900); PyList_SET_ITEM(__pyx_t_7, 195, __pyx_int_900); __Pyx_INCREF(__pyx_int_910); __Pyx_GIVEREF(__pyx_int_910); PyList_SET_ITEM(__pyx_t_7, 196, __pyx_int_910); __Pyx_INCREF(__pyx_int_924); __Pyx_GIVEREF(__pyx_int_924); PyList_SET_ITEM(__pyx_t_7, 197, __pyx_int_924); __Pyx_INCREF(__pyx_int_936); __Pyx_GIVEREF(__pyx_int_936); PyList_SET_ITEM(__pyx_t_7, 198, __pyx_int_936); __Pyx_INCREF(__pyx_int_945); __Pyx_GIVEREF(__pyx_int_945); PyList_SET_ITEM(__pyx_t_7, 199, __pyx_int_945); __Pyx_INCREF(__pyx_int_960); __Pyx_GIVEREF(__pyx_int_960); PyList_SET_ITEM(__pyx_t_7, 200, __pyx_int_960); __Pyx_INCREF(__pyx_int_972); __Pyx_GIVEREF(__pyx_int_972); PyList_SET_ITEM(__pyx_t_7, 201, __pyx_int_972); __Pyx_INCREF(__pyx_int_975); __Pyx_GIVEREF(__pyx_int_975); PyList_SET_ITEM(__pyx_t_7, 202, __pyx_int_975); __Pyx_INCREF(__pyx_int_980); __Pyx_GIVEREF(__pyx_int_980); PyList_SET_ITEM(__pyx_t_7, 203, __pyx_int_980); __Pyx_INCREF(__pyx_int_990); __Pyx_GIVEREF(__pyx_int_990); PyList_SET_ITEM(__pyx_t_7, 204, __pyx_int_990); __Pyx_INCREF(__pyx_int_1000); __Pyx_GIVEREF(__pyx_int_1000); PyList_SET_ITEM(__pyx_t_7, 205, __pyx_int_1000); __pyx_v_recommended_boxes = ((PyObject*)__pyx_t_7); __pyx_t_7 = 0; /* "openTSNE/_tsne.pyx":785 * 900, 910, 924, 936, 945, 960, 972, 975, 980, 990, 1000 * ] * if n_boxes_1d < recommended_boxes[205]: # <<<<<<<<<<<<<< * i = 0 * while n_boxes_1d > recommended_boxes[i]: */ __pyx_t_7 = __Pyx_PyInt_From_int(__pyx_v_n_boxes_1d); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 785, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_8 = PyObject_RichCompare(__pyx_t_7, PyList_GET_ITEM(__pyx_v_recommended_boxes, 0xCD), Py_LT); __Pyx_XGOTREF(__pyx_t_8); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 785, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_8); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(0, 785, __pyx_L1_error) __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":786 * ] * if n_boxes_1d < recommended_boxes[205]: * i = 0 # <<<<<<<<<<<<<< * while n_boxes_1d > recommended_boxes[i]: * i += 1 */ __pyx_v_i = 0; /* "openTSNE/_tsne.pyx":787 * if n_boxes_1d < recommended_boxes[205]: * i = 0 * while n_boxes_1d > recommended_boxes[i]: # <<<<<<<<<<<<<< * i += 1 * n_boxes_1d = recommended_boxes[i] */ while (1) { __pyx_t_8 = __Pyx_PyInt_From_int(__pyx_v_n_boxes_1d); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 787, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_7 = PyObject_RichCompare(__pyx_t_8, PyList_GET_ITEM(__pyx_v_recommended_boxes, __pyx_v_i), Py_GT); __Pyx_XGOTREF(__pyx_t_7); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 787, __pyx_L1_error) __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_7); if (unlikely(__pyx_t_6 < 0)) __PYX_ERR(0, 787, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; if (!__pyx_t_6) break; /* "openTSNE/_tsne.pyx":788 * i = 0 * while n_boxes_1d > recommended_boxes[i]: * i += 1 # <<<<<<<<<<<<<< * n_boxes_1d = recommended_boxes[i] * else: */ __pyx_v_i = (__pyx_v_i + 1); } /* "openTSNE/_tsne.pyx":789 * while n_boxes_1d > recommended_boxes[i]: * i += 1 * n_boxes_1d = recommended_boxes[i] # <<<<<<<<<<<<<< * else: * n_boxes_1d = 1000 */ __pyx_t_9 = __Pyx_PyInt_As_int(PyList_GET_ITEM(__pyx_v_recommended_boxes, __pyx_v_i)); if (unlikely((__pyx_t_9 == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 789, __pyx_L1_error) __pyx_v_n_boxes_1d = __pyx_t_9; /* "openTSNE/_tsne.pyx":785 * 900, 910, 924, 936, 945, 960, 972, 975, 980, 990, 1000 * ] * if n_boxes_1d < recommended_boxes[205]: # <<<<<<<<<<<<<< * i = 0 * while n_boxes_1d > recommended_boxes[i]: */ goto __pyx_L7; } /* "openTSNE/_tsne.pyx":791 * n_boxes_1d = recommended_boxes[i] * else: * n_boxes_1d = 1000 # <<<<<<<<<<<<<< * * cdef int n_total_boxes = n_boxes_1d ** 2 */ /*else*/ { __pyx_v_n_boxes_1d = 0x3E8; } __pyx_L7:; /* "openTSNE/_tsne.pyx":793 * n_boxes_1d = 1000 * * cdef int n_total_boxes = n_boxes_1d ** 2 # <<<<<<<<<<<<<< * cdef double box_width = (coord_max - coord_min) / n_boxes_1d * */ __pyx_v_n_total_boxes = __Pyx_pow_long(((long)__pyx_v_n_boxes_1d), 2); /* "openTSNE/_tsne.pyx":794 * * cdef int n_total_boxes = n_boxes_1d ** 2 * cdef double box_width = (coord_max - coord_min) / n_boxes_1d # <<<<<<<<<<<<<< * * # Compute the box bounds */ __pyx_v_box_width = ((__pyx_v_coord_max - __pyx_v_coord_min) / ((double)__pyx_v_n_boxes_1d)); /* "openTSNE/_tsne.pyx":798 * # Compute the box bounds * cdef: * double[::1] box_x_lower_bounds = np.empty(n_total_boxes, dtype=float) # <<<<<<<<<<<<<< * double[::1] box_x_upper_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_y_lower_bounds = np.empty(n_total_boxes, dtype=float) */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 798, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_8 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_empty); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 798, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyInt_From_int(__pyx_v_n_total_boxes); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 798, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_10 = PyTuple_New(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 798, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 798, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); if (PyDict_SetItem(__pyx_t_7, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 798, __pyx_L1_error) __pyx_t_11 = __Pyx_PyObject_Call(__pyx_t_8, __pyx_t_10, __pyx_t_7); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 798, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_12 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_11, PyBUF_WRITABLE); if (unlikely(!__pyx_t_12.memview)) __PYX_ERR(0, 798, __pyx_L1_error) __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __pyx_v_box_x_lower_bounds = __pyx_t_12; __pyx_t_12.memview = NULL; __pyx_t_12.data = NULL; /* "openTSNE/_tsne.pyx":799 * cdef: * double[::1] box_x_lower_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_x_upper_bounds = np.empty(n_total_boxes, dtype=float) # <<<<<<<<<<<<<< * double[::1] box_y_lower_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_y_upper_bounds = np.empty(n_total_boxes, dtype=float) */ __Pyx_GetModuleGlobalName(__pyx_t_11, __pyx_n_s_np); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 799, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_11, __pyx_n_s_empty); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 799, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __pyx_t_11 = __Pyx_PyInt_From_int(__pyx_v_n_total_boxes); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 799, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __pyx_t_10 = PyTuple_New(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 799, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_11); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_11); __pyx_t_11 = 0; __pyx_t_11 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 799, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); if (PyDict_SetItem(__pyx_t_11, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 799, __pyx_L1_error) __pyx_t_8 = __Pyx_PyObject_Call(__pyx_t_7, __pyx_t_10, __pyx_t_11); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 799, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __pyx_t_12 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_8, PyBUF_WRITABLE); if (unlikely(!__pyx_t_12.memview)) __PYX_ERR(0, 799, __pyx_L1_error) __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_v_box_x_upper_bounds = __pyx_t_12; __pyx_t_12.memview = NULL; __pyx_t_12.data = NULL; /* "openTSNE/_tsne.pyx":800 * double[::1] box_x_lower_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_x_upper_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_y_lower_bounds = np.empty(n_total_boxes, dtype=float) # <<<<<<<<<<<<<< * double[::1] box_y_upper_bounds = np.empty(n_total_boxes, dtype=float) * */ __Pyx_GetModuleGlobalName(__pyx_t_8, __pyx_n_s_np); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 800, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_11 = __Pyx_PyObject_GetAttrStr(__pyx_t_8, __pyx_n_s_empty); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 800, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __Pyx_PyInt_From_int(__pyx_v_n_total_boxes); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 800, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_10 = PyTuple_New(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 800, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 800, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); if (PyDict_SetItem(__pyx_t_8, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 800, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_11, __pyx_t_10, __pyx_t_8); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 800, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_12 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_12.memview)) __PYX_ERR(0, 800, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_box_y_lower_bounds = __pyx_t_12; __pyx_t_12.memview = NULL; __pyx_t_12.data = NULL; /* "openTSNE/_tsne.pyx":801 * double[::1] box_x_upper_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_y_lower_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_y_upper_bounds = np.empty(n_total_boxes, dtype=float) # <<<<<<<<<<<<<< * * for i in range(n_boxes_1d): */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 801, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_8 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_empty); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 801, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyInt_From_int(__pyx_v_n_total_boxes); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 801, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_10 = PyTuple_New(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 801, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 801, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); if (PyDict_SetItem(__pyx_t_7, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 801, __pyx_L1_error) __pyx_t_11 = __Pyx_PyObject_Call(__pyx_t_8, __pyx_t_10, __pyx_t_7); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 801, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_12 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_11, PyBUF_WRITABLE); if (unlikely(!__pyx_t_12.memview)) __PYX_ERR(0, 801, __pyx_L1_error) __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __pyx_v_box_y_upper_bounds = __pyx_t_12; __pyx_t_12.memview = NULL; __pyx_t_12.data = NULL; /* "openTSNE/_tsne.pyx":803 * double[::1] box_y_upper_bounds = np.empty(n_total_boxes, dtype=float) * * for i in range(n_boxes_1d): # <<<<<<<<<<<<<< * for j in range(n_boxes_1d): * box_x_lower_bounds[i * n_boxes_1d + j] = j * box_width + coord_min */ __pyx_t_9 = __pyx_v_n_boxes_1d; __pyx_t_13 = __pyx_t_9; for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_13; __pyx_t_1+=1) { __pyx_v_i = __pyx_t_1; /* "openTSNE/_tsne.pyx":804 * * for i in range(n_boxes_1d): * for j in range(n_boxes_1d): # <<<<<<<<<<<<<< * box_x_lower_bounds[i * n_boxes_1d + j] = j * box_width + coord_min * box_x_upper_bounds[i * n_boxes_1d + j] = (j + 1) * box_width + coord_min */ __pyx_t_14 = __pyx_v_n_boxes_1d; __pyx_t_15 = __pyx_t_14; for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_15; __pyx_t_2+=1) { __pyx_v_j = __pyx_t_2; /* "openTSNE/_tsne.pyx":805 * for i in range(n_boxes_1d): * for j in range(n_boxes_1d): * box_x_lower_bounds[i * n_boxes_1d + j] = j * box_width + coord_min # <<<<<<<<<<<<<< * box_x_upper_bounds[i * n_boxes_1d + j] = (j + 1) * box_width + coord_min * */ __pyx_t_4 = ((__pyx_v_i * __pyx_v_n_boxes_1d) + __pyx_v_j); *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_x_lower_bounds.data) + __pyx_t_4)) )) = ((__pyx_v_j * __pyx_v_box_width) + __pyx_v_coord_min); /* "openTSNE/_tsne.pyx":806 * for j in range(n_boxes_1d): * box_x_lower_bounds[i * n_boxes_1d + j] = j * box_width + coord_min * box_x_upper_bounds[i * n_boxes_1d + j] = (j + 1) * box_width + coord_min # <<<<<<<<<<<<<< * * box_y_lower_bounds[i * n_boxes_1d + j] = i * box_width + coord_min */ __pyx_t_4 = ((__pyx_v_i * __pyx_v_n_boxes_1d) + __pyx_v_j); *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_x_upper_bounds.data) + __pyx_t_4)) )) = (((__pyx_v_j + 1) * __pyx_v_box_width) + __pyx_v_coord_min); /* "openTSNE/_tsne.pyx":808 * box_x_upper_bounds[i * n_boxes_1d + j] = (j + 1) * box_width + coord_min * * box_y_lower_bounds[i * n_boxes_1d + j] = i * box_width + coord_min # <<<<<<<<<<<<<< * box_y_upper_bounds[i * n_boxes_1d + j] = (i + 1) * box_width + coord_min * */ __pyx_t_4 = ((__pyx_v_i * __pyx_v_n_boxes_1d) + __pyx_v_j); *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_y_lower_bounds.data) + __pyx_t_4)) )) = ((__pyx_v_i * __pyx_v_box_width) + __pyx_v_coord_min); /* "openTSNE/_tsne.pyx":809 * * box_y_lower_bounds[i * n_boxes_1d + j] = i * box_width + coord_min * box_y_upper_bounds[i * n_boxes_1d + j] = (i + 1) * box_width + coord_min # <<<<<<<<<<<<<< * * # Determine which box each reference point belongs to */ __pyx_t_4 = ((__pyx_v_i * __pyx_v_n_boxes_1d) + __pyx_v_j); *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_y_upper_bounds.data) + __pyx_t_4)) )) = (((__pyx_v_i + 1) * __pyx_v_box_width) + __pyx_v_coord_min); } } /* "openTSNE/_tsne.pyx":812 * * # Determine which box each reference point belongs to * cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) # <<<<<<<<<<<<<< * cdef int box_x_idx, box_y_idx * for i in range(n_samples): */ __pyx_v_point_box_idx = ((int *)PyMem_Malloc((__pyx_v_n_samples * (sizeof(int))))); /* "openTSNE/_tsne.pyx":814 * cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) * cdef int box_x_idx, box_y_idx * for i in range(n_samples): # <<<<<<<<<<<<<< * box_x_idx = ((embedding[i, 0] - coord_min) / box_width) * box_y_idx = ((embedding[i, 1] - coord_min) / box_width) */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":815 * cdef int box_x_idx, box_y_idx * for i in range(n_samples): * box_x_idx = ((embedding[i, 0] - coord_min) / box_width) # <<<<<<<<<<<<<< * box_y_idx = ((embedding[i, 1] - coord_min) / box_width) * # The right most point maps directly into `n_boxes`, while it should */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 0; __pyx_v_box_x_idx = ((int)(((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_4 * __pyx_v_embedding.strides[0]) )) + __pyx_t_5)) ))) - __pyx_v_coord_min) / __pyx_v_box_width)); /* "openTSNE/_tsne.pyx":816 * for i in range(n_samples): * box_x_idx = ((embedding[i, 0] - coord_min) / box_width) * box_y_idx = ((embedding[i, 1] - coord_min) / box_width) # <<<<<<<<<<<<<< * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 1; __pyx_v_box_y_idx = ((int)(((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_5 * __pyx_v_embedding.strides[0]) )) + __pyx_t_4)) ))) - __pyx_v_coord_min) / __pyx_v_box_width)); /* "openTSNE/_tsne.pyx":819 * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box * if box_x_idx >= n_boxes_1d: # <<<<<<<<<<<<<< * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: */ __pyx_t_6 = ((__pyx_v_box_x_idx >= __pyx_v_n_boxes_1d) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":820 * # belong to the last box * if box_x_idx >= n_boxes_1d: * box_x_idx = n_boxes_1d - 1 # <<<<<<<<<<<<<< * if box_y_idx >= n_boxes_1d: * box_y_idx = n_boxes_1d - 1 */ __pyx_v_box_x_idx = (__pyx_v_n_boxes_1d - 1); /* "openTSNE/_tsne.pyx":819 * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box * if box_x_idx >= n_boxes_1d: # <<<<<<<<<<<<<< * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: */ } /* "openTSNE/_tsne.pyx":821 * if box_x_idx >= n_boxes_1d: * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: # <<<<<<<<<<<<<< * box_y_idx = n_boxes_1d - 1 * */ __pyx_t_6 = ((__pyx_v_box_y_idx >= __pyx_v_n_boxes_1d) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":822 * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: * box_y_idx = n_boxes_1d - 1 # <<<<<<<<<<<<<< * * point_box_idx[i] = box_y_idx * n_boxes_1d + box_x_idx */ __pyx_v_box_y_idx = (__pyx_v_n_boxes_1d - 1); /* "openTSNE/_tsne.pyx":821 * if box_x_idx >= n_boxes_1d: * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: # <<<<<<<<<<<<<< * box_y_idx = n_boxes_1d - 1 * */ } /* "openTSNE/_tsne.pyx":824 * box_y_idx = n_boxes_1d - 1 * * point_box_idx[i] = box_y_idx * n_boxes_1d + box_x_idx # <<<<<<<<<<<<<< * * # Prepare the interpolants for a single interval, so we can use their */ (__pyx_v_point_box_idx[__pyx_v_i]) = ((__pyx_v_box_y_idx * __pyx_v_n_boxes_1d) + __pyx_v_box_x_idx); } /* "openTSNE/_tsne.pyx":828 * # Prepare the interpolants for a single interval, so we can use their * # relative positions later on * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) # <<<<<<<<<<<<<< * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 */ __Pyx_GetModuleGlobalName(__pyx_t_11, __pyx_n_s_np); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 828, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_11, __pyx_n_s_empty); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 828, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __pyx_t_11 = PyInt_FromSsize_t(__pyx_v_n_interpolation_points); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 828, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __pyx_t_10 = PyTuple_New(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 828, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_11); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_11); __pyx_t_11 = 0; __pyx_t_11 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 828, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); if (PyDict_SetItem(__pyx_t_11, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 828, __pyx_L1_error) __pyx_t_8 = __Pyx_PyObject_Call(__pyx_t_7, __pyx_t_10, __pyx_t_11); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 828, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __pyx_t_12 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_8, PyBUF_WRITABLE); if (unlikely(!__pyx_t_12.memview)) __PYX_ERR(0, 828, __pyx_L1_error) __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_v_y_tilde = __pyx_t_12; __pyx_t_12.memview = NULL; __pyx_t_12.data = NULL; /* "openTSNE/_tsne.pyx":829 * # relative positions later on * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) * cdef double h = 1. / n_interpolation_points # <<<<<<<<<<<<<< * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): */ __pyx_v_h = (1. / ((double)__pyx_v_n_interpolation_points)); /* "openTSNE/_tsne.pyx":830 * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 # <<<<<<<<<<<<<< * for i in range(1, n_interpolation_points): * y_tilde[i] = y_tilde[i - 1] + h */ __pyx_t_4 = 0; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_4)) )) = (__pyx_v_h / 2.0); /* "openTSNE/_tsne.pyx":831 * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): # <<<<<<<<<<<<<< * y_tilde[i] = y_tilde[i - 1] + h * */ __pyx_t_1 = __pyx_v_n_interpolation_points; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 1; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":832 * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): * y_tilde[i] = y_tilde[i - 1] + h # <<<<<<<<<<<<<< * * # Evaluate the the squared cauchy kernel at the interpolation nodes */ __pyx_t_4 = (__pyx_v_i - 1); __pyx_t_5 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_5)) )) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_4)) ))) + __pyx_v_h); } /* "openTSNE/_tsne.pyx":835 * * # Evaluate the the squared cauchy kernel at the interpolation nodes * cdef double[:, ::1] sq_kernel_tilde = compute_kernel_tilde_2d( # <<<<<<<<<<<<<< * &cauchy_2d_exp1p, n_interpolation_points * n_boxes_1d, coord_min, h * box_width, dof, * ) */ __pyx_t_16 = __pyx_f_8openTSNE_5_tsne_compute_kernel_tilde_2d((&__pyx_f_8openTSNE_5_tsne_cauchy_2d_exp1p), (__pyx_v_n_interpolation_points * __pyx_v_n_boxes_1d), __pyx_v_coord_min, (__pyx_v_h * __pyx_v_box_width), __pyx_v_dof); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 835, __pyx_L1_error) __pyx_v_sq_kernel_tilde = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":840 * # The non-square cauchy kernel is only used if dof != 1, so don't do unnecessary work * cdef double[:, ::1] kernel_tilde * if dof != 1: # <<<<<<<<<<<<<< * kernel_tilde = compute_kernel_tilde_2d( * &cauchy_2d, n_interpolation_points * n_boxes_1d, coord_min, h * box_width, dof, */ __pyx_t_6 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":841 * cdef double[:, ::1] kernel_tilde * if dof != 1: * kernel_tilde = compute_kernel_tilde_2d( # <<<<<<<<<<<<<< * &cauchy_2d, n_interpolation_points * n_boxes_1d, coord_min, h * box_width, dof, * ) */ __pyx_t_16 = __pyx_f_8openTSNE_5_tsne_compute_kernel_tilde_2d((&__pyx_f_8openTSNE_5_tsne_cauchy_2d), (__pyx_v_n_interpolation_points * __pyx_v_n_boxes_1d), __pyx_v_coord_min, (__pyx_v_h * __pyx_v_box_width), __pyx_v_dof); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 841, __pyx_L1_error) __pyx_v_kernel_tilde = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":840 * # The non-square cauchy kernel is only used if dof != 1, so don't do unnecessary work * cdef double[:, ::1] kernel_tilde * if dof != 1: # <<<<<<<<<<<<<< * kernel_tilde = compute_kernel_tilde_2d( * &cauchy_2d, n_interpolation_points * n_boxes_1d, coord_min, h * box_width, dof, */ } /* "openTSNE/_tsne.pyx":847 * # STEP 1: Compute the w coefficients * # Set up q_j values * cdef int n_terms = 4 # <<<<<<<<<<<<<< * cdef double[:, ::1] q_j = np.empty((n_samples, n_terms), dtype=float) * if dof != 1: */ __pyx_v_n_terms = 4; /* "openTSNE/_tsne.pyx":848 * # Set up q_j values * cdef int n_terms = 4 * cdef double[:, ::1] q_j = np.empty((n_samples, n_terms), dtype=float) # <<<<<<<<<<<<<< * if dof != 1: * for i in range(n_samples): */ __Pyx_GetModuleGlobalName(__pyx_t_8, __pyx_n_s_np); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 848, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_11 = __Pyx_PyObject_GetAttrStr(__pyx_t_8, __pyx_n_s_empty); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 848, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 848, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_10 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 848, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_7 = PyTuple_New(2); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 848, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_7, 0, __pyx_t_8); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_7, 1, __pyx_t_10); __pyx_t_8 = 0; __pyx_t_10 = 0; __pyx_t_10 = PyTuple_New(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 848, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 848, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); if (PyDict_SetItem(__pyx_t_7, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 848, __pyx_L1_error) __pyx_t_8 = __Pyx_PyObject_Call(__pyx_t_11, __pyx_t_10, __pyx_t_7); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 848, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_16 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_8, PyBUF_WRITABLE); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 848, __pyx_L1_error) __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_v_q_j = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":849 * cdef int n_terms = 4 * cdef double[:, ::1] q_j = np.empty((n_samples, n_terms), dtype=float) * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_samples): * q_j[i, 0] = 1 */ __pyx_t_6 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":850 * cdef double[:, ::1] q_j = np.empty((n_samples, n_terms), dtype=float) * if dof != 1: * for i in range(n_samples): # <<<<<<<<<<<<<< * q_j[i, 0] = 1 * q_j[i, 1] = embedding[i, 0] */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":851 * if dof != 1: * for i in range(n_samples): * q_j[i, 0] = 1 # <<<<<<<<<<<<<< * q_j[i, 1] = embedding[i, 0] * q_j[i, 2] = embedding[i, 1] */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 0; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_4 * __pyx_v_q_j.strides[0]) )) + __pyx_t_5)) )) = 1.0; /* "openTSNE/_tsne.pyx":852 * for i in range(n_samples): * q_j[i, 0] = 1 * q_j[i, 1] = embedding[i, 0] # <<<<<<<<<<<<<< * q_j[i, 2] = embedding[i, 1] * q_j[i, 3] = 1 */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 0; __pyx_t_17 = __pyx_v_i; __pyx_t_18 = 1; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_17 * __pyx_v_q_j.strides[0]) )) + __pyx_t_18)) )) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_5 * __pyx_v_embedding.strides[0]) )) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":853 * q_j[i, 0] = 1 * q_j[i, 1] = embedding[i, 0] * q_j[i, 2] = embedding[i, 1] # <<<<<<<<<<<<<< * q_j[i, 3] = 1 * else: */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 1; __pyx_t_18 = __pyx_v_i; __pyx_t_17 = 2; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_18 * __pyx_v_q_j.strides[0]) )) + __pyx_t_17)) )) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_4 * __pyx_v_embedding.strides[0]) )) + __pyx_t_5)) ))); /* "openTSNE/_tsne.pyx":854 * q_j[i, 1] = embedding[i, 0] * q_j[i, 2] = embedding[i, 1] * q_j[i, 3] = 1 # <<<<<<<<<<<<<< * else: * for i in range(n_samples): */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 3; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_5 * __pyx_v_q_j.strides[0]) )) + __pyx_t_4)) )) = 1.0; } /* "openTSNE/_tsne.pyx":849 * cdef int n_terms = 4 * cdef double[:, ::1] q_j = np.empty((n_samples, n_terms), dtype=float) * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_samples): * q_j[i, 0] = 1 */ goto __pyx_L21; } /* "openTSNE/_tsne.pyx":856 * q_j[i, 3] = 1 * else: * for i in range(n_samples): # <<<<<<<<<<<<<< * q_j[i, 0] = 1 * q_j[i, 1] = embedding[i, 0] */ /*else*/ { __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":857 * else: * for i in range(n_samples): * q_j[i, 0] = 1 # <<<<<<<<<<<<<< * q_j[i, 1] = embedding[i, 0] * q_j[i, 2] = embedding[i, 1] */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 0; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_4 * __pyx_v_q_j.strides[0]) )) + __pyx_t_5)) )) = 1.0; /* "openTSNE/_tsne.pyx":858 * for i in range(n_samples): * q_j[i, 0] = 1 * q_j[i, 1] = embedding[i, 0] # <<<<<<<<<<<<<< * q_j[i, 2] = embedding[i, 1] * q_j[i, 3] = embedding[i, 0] ** 2 + embedding[i, 1] ** 2 */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 0; __pyx_t_17 = __pyx_v_i; __pyx_t_18 = 1; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_17 * __pyx_v_q_j.strides[0]) )) + __pyx_t_18)) )) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_5 * __pyx_v_embedding.strides[0]) )) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":859 * q_j[i, 0] = 1 * q_j[i, 1] = embedding[i, 0] * q_j[i, 2] = embedding[i, 1] # <<<<<<<<<<<<<< * q_j[i, 3] = embedding[i, 0] ** 2 + embedding[i, 1] ** 2 * */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 1; __pyx_t_18 = __pyx_v_i; __pyx_t_17 = 2; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_18 * __pyx_v_q_j.strides[0]) )) + __pyx_t_17)) )) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_4 * __pyx_v_embedding.strides[0]) )) + __pyx_t_5)) ))); /* "openTSNE/_tsne.pyx":860 * q_j[i, 1] = embedding[i, 0] * q_j[i, 2] = embedding[i, 1] * q_j[i, 3] = embedding[i, 0] ** 2 + embedding[i, 1] ** 2 # <<<<<<<<<<<<<< * * # Compute the relative position of each reference point in its box */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 0; __pyx_t_17 = __pyx_v_i; __pyx_t_18 = 1; __pyx_t_19 = __pyx_v_i; __pyx_t_20 = 3; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_19 * __pyx_v_q_j.strides[0]) )) + __pyx_t_20)) )) = (pow((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_5 * __pyx_v_embedding.strides[0]) )) + __pyx_t_4)) ))), 2.0) + pow((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_17 * __pyx_v_embedding.strides[0]) )) + __pyx_t_18)) ))), 2.0)); } } __pyx_L21:; /* "openTSNE/_tsne.pyx":864 * # Compute the relative position of each reference point in its box * cdef: * double[::1] x_in_box = np.empty(n_samples, dtype=float) # <<<<<<<<<<<<<< * double[::1] y_in_box = np.empty(n_samples, dtype=float) * double y_min, x_min */ __Pyx_GetModuleGlobalName(__pyx_t_8, __pyx_n_s_np); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 864, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_8, __pyx_n_s_empty); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 864, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 864, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_10 = PyTuple_New(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 864, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 864, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); if (PyDict_SetItem(__pyx_t_8, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 864, __pyx_L1_error) __pyx_t_11 = __Pyx_PyObject_Call(__pyx_t_7, __pyx_t_10, __pyx_t_8); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 864, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_12 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_11, PyBUF_WRITABLE); if (unlikely(!__pyx_t_12.memview)) __PYX_ERR(0, 864, __pyx_L1_error) __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __pyx_v_x_in_box = __pyx_t_12; __pyx_t_12.memview = NULL; __pyx_t_12.data = NULL; /* "openTSNE/_tsne.pyx":865 * cdef: * double[::1] x_in_box = np.empty(n_samples, dtype=float) * double[::1] y_in_box = np.empty(n_samples, dtype=float) # <<<<<<<<<<<<<< * double y_min, x_min * */ __Pyx_GetModuleGlobalName(__pyx_t_11, __pyx_n_s_np); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 865, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __pyx_t_8 = __Pyx_PyObject_GetAttrStr(__pyx_t_11, __pyx_n_s_empty); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 865, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __pyx_t_11 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 865, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __pyx_t_10 = PyTuple_New(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 865, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_11); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_11); __pyx_t_11 = 0; __pyx_t_11 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 865, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); if (PyDict_SetItem(__pyx_t_11, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 865, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_8, __pyx_t_10, __pyx_t_11); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 865, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __pyx_t_12 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_12.memview)) __PYX_ERR(0, 865, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_y_in_box = __pyx_t_12; __pyx_t_12.memview = NULL; __pyx_t_12.data = NULL; /* "openTSNE/_tsne.pyx":868 * double y_min, x_min * * for i in range(n_samples): # <<<<<<<<<<<<<< * box_idx = point_box_idx[i] * x_min = box_x_lower_bounds[box_idx] */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":869 * * for i in range(n_samples): * box_idx = point_box_idx[i] # <<<<<<<<<<<<<< * x_min = box_x_lower_bounds[box_idx] * y_min = box_y_lower_bounds[box_idx] */ __pyx_v_box_idx = (__pyx_v_point_box_idx[__pyx_v_i]); /* "openTSNE/_tsne.pyx":870 * for i in range(n_samples): * box_idx = point_box_idx[i] * x_min = box_x_lower_bounds[box_idx] # <<<<<<<<<<<<<< * y_min = box_y_lower_bounds[box_idx] * x_in_box[i] = (embedding[i, 0] - x_min) / box_width */ __pyx_t_18 = __pyx_v_box_idx; __pyx_v_x_min = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_x_lower_bounds.data) + __pyx_t_18)) ))); /* "openTSNE/_tsne.pyx":871 * box_idx = point_box_idx[i] * x_min = box_x_lower_bounds[box_idx] * y_min = box_y_lower_bounds[box_idx] # <<<<<<<<<<<<<< * x_in_box[i] = (embedding[i, 0] - x_min) / box_width * y_in_box[i] = (embedding[i, 1] - y_min) / box_width */ __pyx_t_18 = __pyx_v_box_idx; __pyx_v_y_min = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_y_lower_bounds.data) + __pyx_t_18)) ))); /* "openTSNE/_tsne.pyx":872 * x_min = box_x_lower_bounds[box_idx] * y_min = box_y_lower_bounds[box_idx] * x_in_box[i] = (embedding[i, 0] - x_min) / box_width # <<<<<<<<<<<<<< * y_in_box[i] = (embedding[i, 1] - y_min) / box_width * */ __pyx_t_18 = __pyx_v_i; __pyx_t_17 = 0; __pyx_t_4 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_x_in_box.data) + __pyx_t_4)) )) = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_18 * __pyx_v_embedding.strides[0]) )) + __pyx_t_17)) ))) - __pyx_v_x_min) / __pyx_v_box_width); /* "openTSNE/_tsne.pyx":873 * y_min = box_y_lower_bounds[box_idx] * x_in_box[i] = (embedding[i, 0] - x_min) / box_width * y_in_box[i] = (embedding[i, 1] - y_min) / box_width # <<<<<<<<<<<<<< * * # Interpolate kernel using Lagrange polynomials */ __pyx_t_17 = __pyx_v_i; __pyx_t_18 = 1; __pyx_t_4 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_in_box.data) + __pyx_t_4)) )) = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_17 * __pyx_v_embedding.strides[0]) )) + __pyx_t_18)) ))) - __pyx_v_y_min) / __pyx_v_box_width); } /* "openTSNE/_tsne.pyx":876 * * # Interpolate kernel using Lagrange polynomials * cdef double[:, ::1] x_interpolated_values = interpolate(x_in_box, y_tilde) # <<<<<<<<<<<<<< * cdef double[:, ::1] y_interpolated_values = interpolate(y_in_box, y_tilde) * */ __pyx_t_16 = __pyx_f_8openTSNE_5_tsne_interpolate(__pyx_v_x_in_box, __pyx_v_y_tilde); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 876, __pyx_L1_error) __pyx_v_x_interpolated_values = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":877 * # Interpolate kernel using Lagrange polynomials * cdef double[:, ::1] x_interpolated_values = interpolate(x_in_box, y_tilde) * cdef double[:, ::1] y_interpolated_values = interpolate(y_in_box, y_tilde) # <<<<<<<<<<<<<< * * # Actually compute w_{ij}s */ __pyx_t_16 = __pyx_f_8openTSNE_5_tsne_interpolate(__pyx_v_y_in_box, __pyx_v_y_tilde); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 877, __pyx_L1_error) __pyx_v_y_interpolated_values = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":881 * # Actually compute w_{ij}s * cdef: * int total_interpolation_points = n_total_boxes * n_interpolation_points ** 2 # <<<<<<<<<<<<<< * double[:, ::1] w_coefficients = np.zeros((total_interpolation_points, n_terms), dtype=float) * Py_ssize_t box_i, box_j, interp_i, interp_j, idx */ __pyx_v_total_interpolation_points = (__pyx_v_n_total_boxes * __Pyx_pow_Py_ssize_t(__pyx_v_n_interpolation_points, 2)); /* "openTSNE/_tsne.pyx":882 * cdef: * int total_interpolation_points = n_total_boxes * n_interpolation_points ** 2 * double[:, ::1] w_coefficients = np.zeros((total_interpolation_points, n_terms), dtype=float) # <<<<<<<<<<<<<< * Py_ssize_t box_i, box_j, interp_i, interp_j, idx * */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 882, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_11 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_zeros); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 882, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyInt_From_int(__pyx_v_total_interpolation_points); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 882, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_10 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 882, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_8 = PyTuple_New(2); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 882, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_7); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_8, 1, __pyx_t_10); __pyx_t_7 = 0; __pyx_t_10 = 0; __pyx_t_10 = PyTuple_New(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 882, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 882, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); if (PyDict_SetItem(__pyx_t_8, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 882, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_11, __pyx_t_10, __pyx_t_8); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 882, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_16 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 882, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_w_coefficients = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":885 * Py_ssize_t box_i, box_j, interp_i, interp_j, idx * * for i in range(n_samples): # <<<<<<<<<<<<<< * box_idx = point_box_idx[i] * box_i = box_idx % n_boxes_1d */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":886 * * for i in range(n_samples): * box_idx = point_box_idx[i] # <<<<<<<<<<<<<< * box_i = box_idx % n_boxes_1d * box_j = box_idx // n_boxes_1d */ __pyx_v_box_idx = (__pyx_v_point_box_idx[__pyx_v_i]); /* "openTSNE/_tsne.pyx":887 * for i in range(n_samples): * box_idx = point_box_idx[i] * box_i = box_idx % n_boxes_1d # <<<<<<<<<<<<<< * box_j = box_idx // n_boxes_1d * for interp_i in range(n_interpolation_points): */ __pyx_v_box_i = (__pyx_v_box_idx % __pyx_v_n_boxes_1d); /* "openTSNE/_tsne.pyx":888 * box_idx = point_box_idx[i] * box_i = box_idx % n_boxes_1d * box_j = box_idx // n_boxes_1d # <<<<<<<<<<<<<< * for interp_i in range(n_interpolation_points): * for interp_j in range(n_interpolation_points): */ __pyx_v_box_j = (__pyx_v_box_idx / __pyx_v_n_boxes_1d); /* "openTSNE/_tsne.pyx":889 * box_i = box_idx % n_boxes_1d * box_j = box_idx // n_boxes_1d * for interp_i in range(n_interpolation_points): # <<<<<<<<<<<<<< * for interp_j in range(n_interpolation_points): * idx = (box_i * n_interpolation_points + interp_i) * \ */ __pyx_t_21 = __pyx_v_n_interpolation_points; __pyx_t_22 = __pyx_t_21; for (__pyx_t_23 = 0; __pyx_t_23 < __pyx_t_22; __pyx_t_23+=1) { __pyx_v_interp_i = __pyx_t_23; /* "openTSNE/_tsne.pyx":890 * box_j = box_idx // n_boxes_1d * for interp_i in range(n_interpolation_points): * for interp_j in range(n_interpolation_points): # <<<<<<<<<<<<<< * idx = (box_i * n_interpolation_points + interp_i) * \ * (n_boxes_1d * n_interpolation_points) + \ */ __pyx_t_24 = __pyx_v_n_interpolation_points; __pyx_t_25 = __pyx_t_24; for (__pyx_t_26 = 0; __pyx_t_26 < __pyx_t_25; __pyx_t_26+=1) { __pyx_v_interp_j = __pyx_t_26; /* "openTSNE/_tsne.pyx":893 * idx = (box_i * n_interpolation_points + interp_i) * \ * (n_boxes_1d * n_interpolation_points) + \ * (box_j * n_interpolation_points) + \ # <<<<<<<<<<<<<< * interp_j * for d in range(n_terms): */ __pyx_v_idx = (((((__pyx_v_box_i * __pyx_v_n_interpolation_points) + __pyx_v_interp_i) * (__pyx_v_n_boxes_1d * __pyx_v_n_interpolation_points)) + (__pyx_v_box_j * __pyx_v_n_interpolation_points)) + __pyx_v_interp_j); /* "openTSNE/_tsne.pyx":895 * (box_j * n_interpolation_points) + \ * interp_j * for d in range(n_terms): # <<<<<<<<<<<<<< * w_coefficients[idx, d] += \ * x_interpolated_values[i, interp_i] * \ */ __pyx_t_9 = __pyx_v_n_terms; __pyx_t_13 = __pyx_t_9; for (__pyx_t_27 = 0; __pyx_t_27 < __pyx_t_13; __pyx_t_27+=1) { __pyx_v_d = __pyx_t_27; /* "openTSNE/_tsne.pyx":897 * for d in range(n_terms): * w_coefficients[idx, d] += \ * x_interpolated_values[i, interp_i] * \ # <<<<<<<<<<<<<< * y_interpolated_values[i, interp_j] * \ * q_j[i, d] */ __pyx_t_18 = __pyx_v_i; __pyx_t_17 = __pyx_v_interp_i; /* "openTSNE/_tsne.pyx":898 * w_coefficients[idx, d] += \ * x_interpolated_values[i, interp_i] * \ * y_interpolated_values[i, interp_j] * \ # <<<<<<<<<<<<<< * q_j[i, d] * */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = __pyx_v_interp_j; /* "openTSNE/_tsne.pyx":899 * x_interpolated_values[i, interp_i] * \ * y_interpolated_values[i, interp_j] * \ * q_j[i, d] # <<<<<<<<<<<<<< * * # STEP 2: Compute the kernel values evaluated at the interpolation nodes */ __pyx_t_20 = __pyx_v_i; __pyx_t_19 = __pyx_v_d; /* "openTSNE/_tsne.pyx":896 * interp_j * for d in range(n_terms): * w_coefficients[idx, d] += \ # <<<<<<<<<<<<<< * x_interpolated_values[i, interp_i] * \ * y_interpolated_values[i, interp_j] * \ */ __pyx_t_28 = __pyx_v_idx; __pyx_t_29 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_w_coefficients.data + __pyx_t_28 * __pyx_v_w_coefficients.strides[0]) )) + __pyx_t_29)) )) += (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_x_interpolated_values.data + __pyx_t_18 * __pyx_v_x_interpolated_values.strides[0]) )) + __pyx_t_17)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_y_interpolated_values.data + __pyx_t_4 * __pyx_v_y_interpolated_values.strides[0]) )) + __pyx_t_5)) )))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_20 * __pyx_v_q_j.strides[0]) )) + __pyx_t_19)) )))); } } } } /* "openTSNE/_tsne.pyx":902 * * # STEP 2: Compute the kernel values evaluated at the interpolation nodes * cdef double[:, ::1] y_tilde_values = np.empty((total_interpolation_points, n_terms)) # <<<<<<<<<<<<<< * if dof != 1: * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients[:, :3], y_tilde_values[:, :3]) */ __Pyx_GetModuleGlobalName(__pyx_t_8, __pyx_n_s_np); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 902, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_t_8, __pyx_n_s_empty); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 902, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __Pyx_PyInt_From_int(__pyx_v_total_interpolation_points); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 902, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_11 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 902, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __pyx_t_30 = PyTuple_New(2); if (unlikely(!__pyx_t_30)) __PYX_ERR(0, 902, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_30); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_30, 0, __pyx_t_8); __Pyx_GIVEREF(__pyx_t_11); PyTuple_SET_ITEM(__pyx_t_30, 1, __pyx_t_11); __pyx_t_8 = 0; __pyx_t_11 = 0; __pyx_t_11 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_10))) { __pyx_t_11 = PyMethod_GET_SELF(__pyx_t_10); if (likely(__pyx_t_11)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_10); __Pyx_INCREF(__pyx_t_11); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_10, function); } } __pyx_t_7 = (__pyx_t_11) ? __Pyx_PyObject_Call2Args(__pyx_t_10, __pyx_t_11, __pyx_t_30) : __Pyx_PyObject_CallOneArg(__pyx_t_10, __pyx_t_30); __Pyx_XDECREF(__pyx_t_11); __pyx_t_11 = 0; __Pyx_DECREF(__pyx_t_30); __pyx_t_30 = 0; if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 902, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_16 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 902, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_y_tilde_values = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":903 * # STEP 2: Compute the kernel values evaluated at the interpolation nodes * cdef double[:, ::1] y_tilde_values = np.empty((total_interpolation_points, n_terms)) * if dof != 1: # <<<<<<<<<<<<<< * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients[:, :3], y_tilde_values[:, :3]) * matrix_multiply_fft_2d(kernel_tilde, w_coefficients[:, 3:], y_tilde_values[:, 3:]) */ __pyx_t_6 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":904 * cdef double[:, ::1] y_tilde_values = np.empty((total_interpolation_points, n_terms)) * if dof != 1: * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients[:, :3], y_tilde_values[:, :3]) # <<<<<<<<<<<<<< * matrix_multiply_fft_2d(kernel_tilde, w_coefficients[:, 3:], y_tilde_values[:, 3:]) * else: */ __pyx_t_16.data = __pyx_v_w_coefficients.data; __pyx_t_16.memview = __pyx_v_w_coefficients.memview; __PYX_INC_MEMVIEW(&__pyx_t_16, 0); __pyx_t_16.shape[0] = __pyx_v_w_coefficients.shape[0]; __pyx_t_16.strides[0] = __pyx_v_w_coefficients.strides[0]; __pyx_t_16.suboffsets[0] = -1; __pyx_t_9 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_16, __pyx_v_w_coefficients.shape[1], __pyx_v_w_coefficients.strides[1], __pyx_v_w_coefficients.suboffsets[1], 1, 1, &__pyx_t_9, 0, 3, 0, 0, 1, 0, 1) < 0)) { __PYX_ERR(0, 904, __pyx_L1_error) } __pyx_t_31.data = __pyx_v_y_tilde_values.data; __pyx_t_31.memview = __pyx_v_y_tilde_values.memview; __PYX_INC_MEMVIEW(&__pyx_t_31, 0); __pyx_t_31.shape[0] = __pyx_v_y_tilde_values.shape[0]; __pyx_t_31.strides[0] = __pyx_v_y_tilde_values.strides[0]; __pyx_t_31.suboffsets[0] = -1; __pyx_t_9 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_31, __pyx_v_y_tilde_values.shape[1], __pyx_v_y_tilde_values.strides[1], __pyx_v_y_tilde_values.suboffsets[1], 1, 1, &__pyx_t_9, 0, 3, 0, 0, 1, 0, 1) < 0)) { __PYX_ERR(0, 904, __pyx_L1_error) } __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_2d(__pyx_v_sq_kernel_tilde, __pyx_t_16, __pyx_t_31); __PYX_XDEC_MEMVIEW(&__pyx_t_16, 1); __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; __PYX_XDEC_MEMVIEW(&__pyx_t_31, 1); __pyx_t_31.memview = NULL; __pyx_t_31.data = NULL; /* "openTSNE/_tsne.pyx":905 * if dof != 1: * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients[:, :3], y_tilde_values[:, :3]) * matrix_multiply_fft_2d(kernel_tilde, w_coefficients[:, 3:], y_tilde_values[:, 3:]) # <<<<<<<<<<<<<< * else: * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients, y_tilde_values) */ __pyx_t_31.data = __pyx_v_w_coefficients.data; __pyx_t_31.memview = __pyx_v_w_coefficients.memview; __PYX_INC_MEMVIEW(&__pyx_t_31, 0); __pyx_t_31.shape[0] = __pyx_v_w_coefficients.shape[0]; __pyx_t_31.strides[0] = __pyx_v_w_coefficients.strides[0]; __pyx_t_31.suboffsets[0] = -1; __pyx_t_9 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_31, __pyx_v_w_coefficients.shape[1], __pyx_v_w_coefficients.strides[1], __pyx_v_w_coefficients.suboffsets[1], 1, 1, &__pyx_t_9, 3, 0, 0, 1, 0, 0, 1) < 0)) { __PYX_ERR(0, 905, __pyx_L1_error) } __pyx_t_16.data = __pyx_v_y_tilde_values.data; __pyx_t_16.memview = __pyx_v_y_tilde_values.memview; __PYX_INC_MEMVIEW(&__pyx_t_16, 0); __pyx_t_16.shape[0] = __pyx_v_y_tilde_values.shape[0]; __pyx_t_16.strides[0] = __pyx_v_y_tilde_values.strides[0]; __pyx_t_16.suboffsets[0] = -1; __pyx_t_9 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_16, __pyx_v_y_tilde_values.shape[1], __pyx_v_y_tilde_values.strides[1], __pyx_v_y_tilde_values.suboffsets[1], 1, 1, &__pyx_t_9, 3, 0, 0, 1, 0, 0, 1) < 0)) { __PYX_ERR(0, 905, __pyx_L1_error) } __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_2d(__pyx_v_kernel_tilde, __pyx_t_31, __pyx_t_16); __PYX_XDEC_MEMVIEW(&__pyx_t_31, 1); __pyx_t_31.memview = NULL; __pyx_t_31.data = NULL; __PYX_XDEC_MEMVIEW(&__pyx_t_16, 1); __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":903 * # STEP 2: Compute the kernel values evaluated at the interpolation nodes * cdef double[:, ::1] y_tilde_values = np.empty((total_interpolation_points, n_terms)) * if dof != 1: # <<<<<<<<<<<<<< * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients[:, :3], y_tilde_values[:, :3]) * matrix_multiply_fft_2d(kernel_tilde, w_coefficients[:, 3:], y_tilde_values[:, 3:]) */ goto __pyx_L36; } /* "openTSNE/_tsne.pyx":907 * matrix_multiply_fft_2d(kernel_tilde, w_coefficients[:, 3:], y_tilde_values[:, 3:]) * else: * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients, y_tilde_values) # <<<<<<<<<<<<<< * * # STEP 3: Compute the potentials \tilde{\phi(y_i)} */ /*else*/ { __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_2d(__pyx_v_sq_kernel_tilde, __pyx_v_w_coefficients, __pyx_v_y_tilde_values); } __pyx_L36:; /* "openTSNE/_tsne.pyx":910 * * # STEP 3: Compute the potentials \tilde{\phi(y_i)} * cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) # <<<<<<<<<<<<<< * for i in range(n_samples): * box_idx = point_box_idx[i] */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 910, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_zeros); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 910, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 910, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_30 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_30)) __PYX_ERR(0, 910, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_30); __pyx_t_11 = PyTuple_New(2); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 910, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_11, 0, __pyx_t_7); __Pyx_GIVEREF(__pyx_t_30); PyTuple_SET_ITEM(__pyx_t_11, 1, __pyx_t_30); __pyx_t_7 = 0; __pyx_t_30 = 0; __pyx_t_30 = PyTuple_New(1); if (unlikely(!__pyx_t_30)) __PYX_ERR(0, 910, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_30); __Pyx_GIVEREF(__pyx_t_11); PyTuple_SET_ITEM(__pyx_t_30, 0, __pyx_t_11); __pyx_t_11 = 0; __pyx_t_11 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 910, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); if (PyDict_SetItem(__pyx_t_11, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 910, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_10, __pyx_t_30, __pyx_t_11); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 910, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_30); __pyx_t_30 = 0; __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __pyx_t_16 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 910, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_phi = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":911 * # STEP 3: Compute the potentials \tilde{\phi(y_i)} * cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) * for i in range(n_samples): # <<<<<<<<<<<<<< * box_idx = point_box_idx[i] * box_i = box_idx % n_boxes_1d */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":912 * cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) * for i in range(n_samples): * box_idx = point_box_idx[i] # <<<<<<<<<<<<<< * box_i = box_idx % n_boxes_1d * box_j = box_idx // n_boxes_1d */ __pyx_v_box_idx = (__pyx_v_point_box_idx[__pyx_v_i]); /* "openTSNE/_tsne.pyx":913 * for i in range(n_samples): * box_idx = point_box_idx[i] * box_i = box_idx % n_boxes_1d # <<<<<<<<<<<<<< * box_j = box_idx // n_boxes_1d * for interp_i in range(n_interpolation_points): */ __pyx_v_box_i = (__pyx_v_box_idx % __pyx_v_n_boxes_1d); /* "openTSNE/_tsne.pyx":914 * box_idx = point_box_idx[i] * box_i = box_idx % n_boxes_1d * box_j = box_idx // n_boxes_1d # <<<<<<<<<<<<<< * for interp_i in range(n_interpolation_points): * for interp_j in range(n_interpolation_points): */ __pyx_v_box_j = (__pyx_v_box_idx / __pyx_v_n_boxes_1d); /* "openTSNE/_tsne.pyx":915 * box_i = box_idx % n_boxes_1d * box_j = box_idx // n_boxes_1d * for interp_i in range(n_interpolation_points): # <<<<<<<<<<<<<< * for interp_j in range(n_interpolation_points): * idx = (box_i * n_interpolation_points + interp_i) * \ */ __pyx_t_21 = __pyx_v_n_interpolation_points; __pyx_t_22 = __pyx_t_21; for (__pyx_t_23 = 0; __pyx_t_23 < __pyx_t_22; __pyx_t_23+=1) { __pyx_v_interp_i = __pyx_t_23; /* "openTSNE/_tsne.pyx":916 * box_j = box_idx // n_boxes_1d * for interp_i in range(n_interpolation_points): * for interp_j in range(n_interpolation_points): # <<<<<<<<<<<<<< * idx = (box_i * n_interpolation_points + interp_i) * \ * (n_boxes_1d * n_interpolation_points) + \ */ __pyx_t_24 = __pyx_v_n_interpolation_points; __pyx_t_25 = __pyx_t_24; for (__pyx_t_26 = 0; __pyx_t_26 < __pyx_t_25; __pyx_t_26+=1) { __pyx_v_interp_j = __pyx_t_26; /* "openTSNE/_tsne.pyx":919 * idx = (box_i * n_interpolation_points + interp_i) * \ * (n_boxes_1d * n_interpolation_points) + \ * (box_j * n_interpolation_points) + \ # <<<<<<<<<<<<<< * interp_j * for d in range(n_terms): */ __pyx_v_idx = (((((__pyx_v_box_i * __pyx_v_n_interpolation_points) + __pyx_v_interp_i) * (__pyx_v_n_boxes_1d * __pyx_v_n_interpolation_points)) + (__pyx_v_box_j * __pyx_v_n_interpolation_points)) + __pyx_v_interp_j); /* "openTSNE/_tsne.pyx":921 * (box_j * n_interpolation_points) + \ * interp_j * for d in range(n_terms): # <<<<<<<<<<<<<< * phi[i, d] += x_interpolated_values[i, interp_i] * \ * y_interpolated_values[i, interp_j] * \ */ __pyx_t_9 = __pyx_v_n_terms; __pyx_t_13 = __pyx_t_9; for (__pyx_t_27 = 0; __pyx_t_27 < __pyx_t_13; __pyx_t_27+=1) { __pyx_v_d = __pyx_t_27; /* "openTSNE/_tsne.pyx":922 * interp_j * for d in range(n_terms): * phi[i, d] += x_interpolated_values[i, interp_i] * \ # <<<<<<<<<<<<<< * y_interpolated_values[i, interp_j] * \ * y_tilde_values[idx, d] */ __pyx_t_19 = __pyx_v_i; __pyx_t_20 = __pyx_v_interp_i; /* "openTSNE/_tsne.pyx":923 * for d in range(n_terms): * phi[i, d] += x_interpolated_values[i, interp_i] * \ * y_interpolated_values[i, interp_j] * \ # <<<<<<<<<<<<<< * y_tilde_values[idx, d] * */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = __pyx_v_interp_j; /* "openTSNE/_tsne.pyx":924 * phi[i, d] += x_interpolated_values[i, interp_i] * \ * y_interpolated_values[i, interp_j] * \ * y_tilde_values[idx, d] # <<<<<<<<<<<<<< * * PyMem_Free(point_box_idx) */ __pyx_t_17 = __pyx_v_idx; __pyx_t_18 = __pyx_v_d; /* "openTSNE/_tsne.pyx":922 * interp_j * for d in range(n_terms): * phi[i, d] += x_interpolated_values[i, interp_i] * \ # <<<<<<<<<<<<<< * y_interpolated_values[i, interp_j] * \ * y_tilde_values[idx, d] */ __pyx_t_29 = __pyx_v_i; __pyx_t_28 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_29 * __pyx_v_phi.strides[0]) )) + __pyx_t_28)) )) += (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_x_interpolated_values.data + __pyx_t_19 * __pyx_v_x_interpolated_values.strides[0]) )) + __pyx_t_20)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_y_interpolated_values.data + __pyx_t_5 * __pyx_v_y_interpolated_values.strides[0]) )) + __pyx_t_4)) )))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_y_tilde_values.data + __pyx_t_17 * __pyx_v_y_tilde_values.strides[0]) )) + __pyx_t_18)) )))); } } } } /* "openTSNE/_tsne.pyx":926 * y_tilde_values[idx, d] * * PyMem_Free(point_box_idx) # <<<<<<<<<<<<<< * * # Compute the normalization term Z or sum of q_{ij}s */ PyMem_Free(__pyx_v_point_box_idx); /* "openTSNE/_tsne.pyx":929 * * # Compute the normalization term Z or sum of q_{ij}s * cdef double sum_Q = 0, y1, y2 # <<<<<<<<<<<<<< * if dof != 1: * for i in range(n_samples): */ __pyx_v_sum_Q = 0.0; /* "openTSNE/_tsne.pyx":930 * # Compute the normalization term Z or sum of q_{ij}s * cdef double sum_Q = 0, y1, y2 * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_samples): * sum_Q += phi[i, 3] */ __pyx_t_6 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":931 * cdef double sum_Q = 0, y1, y2 * if dof != 1: * for i in range(n_samples): # <<<<<<<<<<<<<< * sum_Q += phi[i, 3] * else: */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":932 * if dof != 1: * for i in range(n_samples): * sum_Q += phi[i, 3] # <<<<<<<<<<<<<< * else: * for i in range(n_samples): */ __pyx_t_18 = __pyx_v_i; __pyx_t_17 = 3; __pyx_v_sum_Q = (__pyx_v_sum_Q + (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_18 * __pyx_v_phi.strides[0]) )) + __pyx_t_17)) )))); } /* "openTSNE/_tsne.pyx":930 * # Compute the normalization term Z or sum of q_{ij}s * cdef double sum_Q = 0, y1, y2 * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_samples): * sum_Q += phi[i, 3] */ goto __pyx_L45; } /* "openTSNE/_tsne.pyx":934 * sum_Q += phi[i, 3] * else: * for i in range(n_samples): # <<<<<<<<<<<<<< * y1 = embedding[i, 0] * y2 = embedding[i, 1] */ /*else*/ { __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":935 * else: * for i in range(n_samples): * y1 = embedding[i, 0] # <<<<<<<<<<<<<< * y2 = embedding[i, 1] * */ __pyx_t_17 = __pyx_v_i; __pyx_t_18 = 0; __pyx_v_y1 = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_17 * __pyx_v_embedding.strides[0]) )) + __pyx_t_18)) ))); /* "openTSNE/_tsne.pyx":936 * for i in range(n_samples): * y1 = embedding[i, 0] * y2 = embedding[i, 1] # <<<<<<<<<<<<<< * * sum_Q += (1 + y1 ** 2 + y2 ** 2) * phi[i, 0] - \ */ __pyx_t_18 = __pyx_v_i; __pyx_t_17 = 1; __pyx_v_y2 = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_18 * __pyx_v_embedding.strides[0]) )) + __pyx_t_17)) ))); /* "openTSNE/_tsne.pyx":938 * y2 = embedding[i, 1] * * sum_Q += (1 + y1 ** 2 + y2 ** 2) * phi[i, 0] - \ # <<<<<<<<<<<<<< * 2 * (y1 * phi[i, 1] + y2 * phi[i, 2]) + \ * phi[i, 3] */ __pyx_t_17 = __pyx_v_i; __pyx_t_18 = 0; /* "openTSNE/_tsne.pyx":939 * * sum_Q += (1 + y1 ** 2 + y2 ** 2) * phi[i, 0] - \ * 2 * (y1 * phi[i, 1] + y2 * phi[i, 2]) + \ # <<<<<<<<<<<<<< * phi[i, 3] * */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 1; __pyx_t_20 = __pyx_v_i; __pyx_t_19 = 2; /* "openTSNE/_tsne.pyx":940 * sum_Q += (1 + y1 ** 2 + y2 ** 2) * phi[i, 0] - \ * 2 * (y1 * phi[i, 1] + y2 * phi[i, 2]) + \ * phi[i, 3] # <<<<<<<<<<<<<< * * sum_Q -= n_samples */ __pyx_t_28 = __pyx_v_i; __pyx_t_29 = 3; /* "openTSNE/_tsne.pyx":938 * y2 = embedding[i, 1] * * sum_Q += (1 + y1 ** 2 + y2 ** 2) * phi[i, 0] - \ # <<<<<<<<<<<<<< * 2 * (y1 * phi[i, 1] + y2 * phi[i, 2]) + \ * phi[i, 3] */ __pyx_v_sum_Q = (__pyx_v_sum_Q + (((((1.0 + pow(__pyx_v_y1, 2.0)) + pow(__pyx_v_y2, 2.0)) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_17 * __pyx_v_phi.strides[0]) )) + __pyx_t_18)) )))) - (2.0 * ((__pyx_v_y1 * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_4 * __pyx_v_phi.strides[0]) )) + __pyx_t_5)) )))) + (__pyx_v_y2 * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_20 * __pyx_v_phi.strides[0]) )) + __pyx_t_19)) ))))))) + (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_28 * __pyx_v_phi.strides[0]) )) + __pyx_t_29)) ))))); } } __pyx_L45:; /* "openTSNE/_tsne.pyx":942 * phi[i, 3] * * sum_Q -= n_samples # <<<<<<<<<<<<<< * * # The phis used here are not affected if dof != 1 */ __pyx_v_sum_Q = (__pyx_v_sum_Q - __pyx_v_n_samples); /* "openTSNE/_tsne.pyx":945 * * # The phis used here are not affected if dof != 1 * for i in range(n_samples): # <<<<<<<<<<<<<< * gradient[i, 0] -= (embedding[i, 0] * phi[i, 0] - phi[i, 1]) / (sum_Q + EPSILON) * gradient[i, 1] -= (embedding[i, 1] * phi[i, 0] - phi[i, 2]) / (sum_Q + EPSILON) */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":946 * # The phis used here are not affected if dof != 1 * for i in range(n_samples): * gradient[i, 0] -= (embedding[i, 0] * phi[i, 0] - phi[i, 1]) / (sum_Q + EPSILON) # <<<<<<<<<<<<<< * gradient[i, 1] -= (embedding[i, 1] * phi[i, 0] - phi[i, 2]) / (sum_Q + EPSILON) * */ __pyx_t_29 = __pyx_v_i; __pyx_t_28 = 0; __pyx_t_19 = __pyx_v_i; __pyx_t_20 = 0; __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 1; __pyx_t_18 = __pyx_v_i; __pyx_t_17 = 0; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_gradient.data + __pyx_t_18 * __pyx_v_gradient.strides[0]) )) + __pyx_t_17)) )) -= ((((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_29 * __pyx_v_embedding.strides[0]) )) + __pyx_t_28)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_19 * __pyx_v_phi.strides[0]) )) + __pyx_t_20)) )))) - (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_5 * __pyx_v_phi.strides[0]) )) + __pyx_t_4)) )))) / (__pyx_v_sum_Q + __pyx_v_8openTSNE_5_tsne_EPSILON)); /* "openTSNE/_tsne.pyx":947 * for i in range(n_samples): * gradient[i, 0] -= (embedding[i, 0] * phi[i, 0] - phi[i, 1]) / (sum_Q + EPSILON) * gradient[i, 1] -= (embedding[i, 1] * phi[i, 0] - phi[i, 2]) / (sum_Q + EPSILON) # <<<<<<<<<<<<<< * * return sum_Q */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 1; __pyx_t_20 = __pyx_v_i; __pyx_t_19 = 0; __pyx_t_28 = __pyx_v_i; __pyx_t_29 = 2; __pyx_t_17 = __pyx_v_i; __pyx_t_18 = 1; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_gradient.data + __pyx_t_17 * __pyx_v_gradient.strides[0]) )) + __pyx_t_18)) )) -= ((((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_4 * __pyx_v_embedding.strides[0]) )) + __pyx_t_5)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_20 * __pyx_v_phi.strides[0]) )) + __pyx_t_19)) )))) - (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_28 * __pyx_v_phi.strides[0]) )) + __pyx_t_29)) )))) / (__pyx_v_sum_Q + __pyx_v_8openTSNE_5_tsne_EPSILON)); } /* "openTSNE/_tsne.pyx":949 * gradient[i, 1] -= (embedding[i, 1] * phi[i, 0] - phi[i, 2]) / (sum_Q + EPSILON) * * return sum_Q # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_sum_Q; goto __pyx_L0; /* "openTSNE/_tsne.pyx":743 * * * cpdef double estimate_negative_gradient_fft_2d( # <<<<<<<<<<<<<< * double[:, ::1] embedding, * double[:, ::1] gradient, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_XDECREF(__pyx_t_10); __Pyx_XDECREF(__pyx_t_11); __PYX_XDEC_MEMVIEW(&__pyx_t_12, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_16, 1); __Pyx_XDECREF(__pyx_t_30); __PYX_XDEC_MEMVIEW(&__pyx_t_31, 1); __Pyx_WriteUnraisable("openTSNE._tsne.estimate_negative_gradient_fft_2d", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_recommended_boxes); __PYX_XDEC_MEMVIEW(&__pyx_v_box_x_lower_bounds, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_box_x_upper_bounds, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_box_y_lower_bounds, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_box_y_upper_bounds, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_sq_kernel_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_kernel_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_q_j, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_x_in_box, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_in_box, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_x_interpolated_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_interpolated_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_w_coefficients, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_phi, 1); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_5_tsne_13estimate_negative_gradient_fft_2d(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_pw_8openTSNE_5_tsne_13estimate_negative_gradient_fft_2d(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __Pyx_memviewslice __pyx_v_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_gradient = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_n_interpolation_points; Py_ssize_t __pyx_v_min_num_intervals; double __pyx_v_ints_in_interval; double __pyx_v_dof; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("estimate_negative_gradient_fft_2d (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_embedding,&__pyx_n_s_gradient,&__pyx_n_s_n_interpolation_points,&__pyx_n_s_min_num_intervals,&__pyx_n_s_ints_in_interval,&__pyx_n_s_dof,0}; PyObject* values[6] = {0,0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_embedding)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_gradient)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_2d", 0, 2, 6, 1); __PYX_ERR(0, 743, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_n_interpolation_points); if (value) { values[2] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 3: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_min_num_intervals); if (value) { values[3] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 4: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_ints_in_interval); if (value) { values[4] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 5: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dof); if (value) { values[5] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "estimate_negative_gradient_fft_2d") < 0)) __PYX_ERR(0, 743, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_embedding = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_embedding.memview)) __PYX_ERR(0, 744, __pyx_L3_error) __pyx_v_gradient = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[1], PyBUF_WRITABLE); if (unlikely(!__pyx_v_gradient.memview)) __PYX_ERR(0, 745, __pyx_L3_error) if (values[2]) { __pyx_v_n_interpolation_points = __Pyx_PyIndex_AsSsize_t(values[2]); if (unlikely((__pyx_v_n_interpolation_points == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 746, __pyx_L3_error) } else { __pyx_v_n_interpolation_points = ((Py_ssize_t)3); } if (values[3]) { __pyx_v_min_num_intervals = __Pyx_PyIndex_AsSsize_t(values[3]); if (unlikely((__pyx_v_min_num_intervals == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 747, __pyx_L3_error) } else { __pyx_v_min_num_intervals = ((Py_ssize_t)10); } if (values[4]) { __pyx_v_ints_in_interval = __pyx_PyFloat_AsDouble(values[4]); if (unlikely((__pyx_v_ints_in_interval == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 748, __pyx_L3_error) } else { __pyx_v_ints_in_interval = ((double)1.0); } if (values[5]) { __pyx_v_dof = __pyx_PyFloat_AsDouble(values[5]); if (unlikely((__pyx_v_dof == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 749, __pyx_L3_error) } else { __pyx_v_dof = ((double)1.0); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_2d", 0, 2, 6, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 743, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE._tsne.estimate_negative_gradient_fft_2d", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_5_tsne_12estimate_negative_gradient_fft_2d(__pyx_self, __pyx_v_embedding, __pyx_v_gradient, __pyx_v_n_interpolation_points, __pyx_v_min_num_intervals, __pyx_v_ints_in_interval, __pyx_v_dof); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_5_tsne_12estimate_negative_gradient_fft_2d(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, Py_ssize_t __pyx_v_n_interpolation_points, Py_ssize_t __pyx_v_min_num_intervals, double __pyx_v_ints_in_interval, double __pyx_v_dof) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations double __pyx_t_1; struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("estimate_negative_gradient_fft_2d", 0); __Pyx_XDECREF(__pyx_r); __pyx_t_2.__pyx_n = 4; __pyx_t_2.n_interpolation_points = __pyx_v_n_interpolation_points; __pyx_t_2.min_num_intervals = __pyx_v_min_num_intervals; __pyx_t_2.ints_in_interval = __pyx_v_ints_in_interval; __pyx_t_2.dof = __pyx_v_dof; __pyx_t_1 = __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d(__pyx_v_embedding, __pyx_v_gradient, 0, &__pyx_t_2); __pyx_t_3 = PyFloat_FromDouble(__pyx_t_1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 743, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("openTSNE._tsne.estimate_negative_gradient_fft_2d", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_embedding, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_gradient, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/_tsne.pyx":952 * * * cpdef tuple prepare_negative_gradient_fft_interpolation_grid_2d( # <<<<<<<<<<<<<< * double[:, ::1] reference_embedding, * Py_ssize_t n_interpolation_points=3, */ static PyObject *__pyx_pw_8openTSNE_5_tsne_15prepare_negative_gradient_fft_interpolation_grid_2d(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_f_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_2d(__Pyx_memviewslice __pyx_v_reference_embedding, CYTHON_UNUSED int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_2d *__pyx_optional_args) { Py_ssize_t __pyx_v_n_interpolation_points = ((Py_ssize_t)3); Py_ssize_t __pyx_v_min_num_intervals = ((Py_ssize_t)10); double __pyx_v_ints_in_interval = ((double)1.0); double __pyx_v_dof = ((double)1.0); double __pyx_v_padding = ((double)0.0); Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; Py_ssize_t __pyx_v_d; Py_ssize_t __pyx_v_box_idx; Py_ssize_t __pyx_v_n_reference_samples; double __pyx_v_coord_max; double __pyx_v_coord_min; int __pyx_v_n_boxes_1d; int __pyx_v_n_total_boxes; double __pyx_v_box_width; __Pyx_memviewslice __pyx_v_box_x_lower_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_box_x_upper_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_box_y_lower_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_box_y_upper_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; int *__pyx_v_reference_point_box_idx; int __pyx_v_box_x_idx; int __pyx_v_box_y_idx; __Pyx_memviewslice __pyx_v_y_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_h; __Pyx_memviewslice __pyx_v_sq_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_kernel_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_v_n_terms; __Pyx_memviewslice __pyx_v_q_j = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_reference_x_in_box = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_reference_y_in_box = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_y_min; double __pyx_v_x_min; __Pyx_memviewslice __pyx_v_reference_x_interpolated_values = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_reference_y_interpolated_values = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_v_total_interpolation_points; __Pyx_memviewslice __pyx_v_w_coefficients = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_box_i; Py_ssize_t __pyx_v_box_j; Py_ssize_t __pyx_v_interp_i; Py_ssize_t __pyx_v_interp_j; Py_ssize_t __pyx_v_idx; __Pyx_memviewslice __pyx_v_y_tilde_values = { 0, 0, { 0 }, { 0 }, { 0 } }; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; int __pyx_t_6; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; PyObject *__pyx_t_9 = NULL; PyObject *__pyx_t_10 = NULL; __Pyx_memviewslice __pyx_t_11 = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_t_12; int __pyx_t_13; int __pyx_t_14; int __pyx_t_15; __Pyx_memviewslice __pyx_t_16 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_17; Py_ssize_t __pyx_t_18; Py_ssize_t __pyx_t_19; Py_ssize_t __pyx_t_20; Py_ssize_t __pyx_t_21; Py_ssize_t __pyx_t_22; Py_ssize_t __pyx_t_23; Py_ssize_t __pyx_t_24; Py_ssize_t __pyx_t_25; Py_ssize_t __pyx_t_26; Py_ssize_t __pyx_t_27; Py_ssize_t __pyx_t_28; Py_ssize_t __pyx_t_29; PyObject *__pyx_t_30 = NULL; __Pyx_memviewslice __pyx_t_31 = { 0, 0, { 0 }, { 0 }, { 0 } }; PyObject *__pyx_t_32 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("prepare_negative_gradient_fft_interpolation_grid_2d", 0); if (__pyx_optional_args) { if (__pyx_optional_args->__pyx_n > 0) { __pyx_v_n_interpolation_points = __pyx_optional_args->n_interpolation_points; if (__pyx_optional_args->__pyx_n > 1) { __pyx_v_min_num_intervals = __pyx_optional_args->min_num_intervals; if (__pyx_optional_args->__pyx_n > 2) { __pyx_v_ints_in_interval = __pyx_optional_args->ints_in_interval; if (__pyx_optional_args->__pyx_n > 3) { __pyx_v_dof = __pyx_optional_args->dof; if (__pyx_optional_args->__pyx_n > 4) { __pyx_v_padding = __pyx_optional_args->padding; } } } } } } /* "openTSNE/_tsne.pyx":962 * cdef: * Py_ssize_t i, j, d, box_idx * Py_ssize_t n_reference_samples = reference_embedding.shape[0] # <<<<<<<<<<<<<< * * double coord_max = -INFINITY, coord_min = INFINITY */ __pyx_v_n_reference_samples = (__pyx_v_reference_embedding.shape[0]); /* "openTSNE/_tsne.pyx":964 * Py_ssize_t n_reference_samples = reference_embedding.shape[0] * * double coord_max = -INFINITY, coord_min = INFINITY # <<<<<<<<<<<<<< * # Determine the min/max values of the embedding * # First, check the existing embedding */ __pyx_v_coord_max = (-INFINITY); __pyx_v_coord_min = INFINITY; /* "openTSNE/_tsne.pyx":967 * # Determine the min/max values of the embedding * # First, check the existing embedding * for i in range(n_reference_samples): # <<<<<<<<<<<<<< * if reference_embedding[i, 0] < coord_min: * coord_min = reference_embedding[i, 0] */ __pyx_t_1 = __pyx_v_n_reference_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":968 * # First, check the existing embedding * for i in range(n_reference_samples): * if reference_embedding[i, 0] < coord_min: # <<<<<<<<<<<<<< * coord_min = reference_embedding[i, 0] * elif reference_embedding[i, 0] > coord_max: */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 0; __pyx_t_6 = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_4 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_5)) ))) < __pyx_v_coord_min) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":969 * for i in range(n_reference_samples): * if reference_embedding[i, 0] < coord_min: * coord_min = reference_embedding[i, 0] # <<<<<<<<<<<<<< * elif reference_embedding[i, 0] > coord_max: * coord_max = reference_embedding[i, 0] */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 0; __pyx_v_coord_min = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_5 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":968 * # First, check the existing embedding * for i in range(n_reference_samples): * if reference_embedding[i, 0] < coord_min: # <<<<<<<<<<<<<< * coord_min = reference_embedding[i, 0] * elif reference_embedding[i, 0] > coord_max: */ goto __pyx_L5; } /* "openTSNE/_tsne.pyx":970 * if reference_embedding[i, 0] < coord_min: * coord_min = reference_embedding[i, 0] * elif reference_embedding[i, 0] > coord_max: # <<<<<<<<<<<<<< * coord_max = reference_embedding[i, 0] * if reference_embedding[i, 1] < coord_min: */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 0; __pyx_t_6 = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_4 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_5)) ))) > __pyx_v_coord_max) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":971 * coord_min = reference_embedding[i, 0] * elif reference_embedding[i, 0] > coord_max: * coord_max = reference_embedding[i, 0] # <<<<<<<<<<<<<< * if reference_embedding[i, 1] < coord_min: * coord_min = reference_embedding[i, 1] */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 0; __pyx_v_coord_max = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_5 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":970 * if reference_embedding[i, 0] < coord_min: * coord_min = reference_embedding[i, 0] * elif reference_embedding[i, 0] > coord_max: # <<<<<<<<<<<<<< * coord_max = reference_embedding[i, 0] * if reference_embedding[i, 1] < coord_min: */ } __pyx_L5:; /* "openTSNE/_tsne.pyx":972 * elif reference_embedding[i, 0] > coord_max: * coord_max = reference_embedding[i, 0] * if reference_embedding[i, 1] < coord_min: # <<<<<<<<<<<<<< * coord_min = reference_embedding[i, 1] * elif reference_embedding[i, 1] > coord_max: */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 1; __pyx_t_6 = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_4 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_5)) ))) < __pyx_v_coord_min) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":973 * coord_max = reference_embedding[i, 0] * if reference_embedding[i, 1] < coord_min: * coord_min = reference_embedding[i, 1] # <<<<<<<<<<<<<< * elif reference_embedding[i, 1] > coord_max: * coord_max = reference_embedding[i, 1] */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 1; __pyx_v_coord_min = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_5 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":972 * elif reference_embedding[i, 0] > coord_max: * coord_max = reference_embedding[i, 0] * if reference_embedding[i, 1] < coord_min: # <<<<<<<<<<<<<< * coord_min = reference_embedding[i, 1] * elif reference_embedding[i, 1] > coord_max: */ goto __pyx_L6; } /* "openTSNE/_tsne.pyx":974 * if reference_embedding[i, 1] < coord_min: * coord_min = reference_embedding[i, 1] * elif reference_embedding[i, 1] > coord_max: # <<<<<<<<<<<<<< * coord_max = reference_embedding[i, 1] * */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 1; __pyx_t_6 = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_4 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_5)) ))) > __pyx_v_coord_max) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":975 * coord_min = reference_embedding[i, 1] * elif reference_embedding[i, 1] > coord_max: * coord_max = reference_embedding[i, 1] # <<<<<<<<<<<<<< * * # We assume here that the embedding is centered and we want to generate an */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 1; __pyx_v_coord_max = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_5 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":974 * if reference_embedding[i, 1] < coord_min: * coord_min = reference_embedding[i, 1] * elif reference_embedding[i, 1] > coord_max: # <<<<<<<<<<<<<< * coord_max = reference_embedding[i, 1] * */ } __pyx_L6:; } /* "openTSNE/_tsne.pyx":979 * # We assume here that the embedding is centered and we want to generate an * # equal grid in all quadrants * if fabs(coord_min) > fabs(coord_max): # <<<<<<<<<<<<<< * coord_max = -coord_min * elif fabs(coord_max) > fabs(coord_min): */ __pyx_t_6 = ((fabs(__pyx_v_coord_min) > fabs(__pyx_v_coord_max)) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":980 * # equal grid in all quadrants * if fabs(coord_min) > fabs(coord_max): * coord_max = -coord_min # <<<<<<<<<<<<<< * elif fabs(coord_max) > fabs(coord_min): * coord_min = -coord_max */ __pyx_v_coord_max = (-__pyx_v_coord_min); /* "openTSNE/_tsne.pyx":979 * # We assume here that the embedding is centered and we want to generate an * # equal grid in all quadrants * if fabs(coord_min) > fabs(coord_max): # <<<<<<<<<<<<<< * coord_max = -coord_min * elif fabs(coord_max) > fabs(coord_min): */ goto __pyx_L7; } /* "openTSNE/_tsne.pyx":981 * if fabs(coord_min) > fabs(coord_max): * coord_max = -coord_min * elif fabs(coord_max) > fabs(coord_min): # <<<<<<<<<<<<<< * coord_min = -coord_max * */ __pyx_t_6 = ((fabs(__pyx_v_coord_max) > fabs(__pyx_v_coord_min)) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":982 * coord_max = -coord_min * elif fabs(coord_max) > fabs(coord_min): * coord_min = -coord_max # <<<<<<<<<<<<<< * * # Apply padding to the min/max coordinates */ __pyx_v_coord_min = (-__pyx_v_coord_max); /* "openTSNE/_tsne.pyx":981 * if fabs(coord_min) > fabs(coord_max): * coord_max = -coord_min * elif fabs(coord_max) > fabs(coord_min): # <<<<<<<<<<<<<< * coord_min = -coord_max * */ } __pyx_L7:; /* "openTSNE/_tsne.pyx":985 * * # Apply padding to the min/max coordinates * coord_min *= 1 + padding # <<<<<<<<<<<<<< * coord_max *= 1 + padding * */ __pyx_v_coord_min = (__pyx_v_coord_min * (1.0 + __pyx_v_padding)); /* "openTSNE/_tsne.pyx":986 * # Apply padding to the min/max coordinates * coord_min *= 1 + padding * coord_max *= 1 + padding # <<<<<<<<<<<<<< * * cdef int n_boxes_1d = fmax(min_num_intervals, (coord_max - coord_min) / ints_in_interval) */ __pyx_v_coord_max = (__pyx_v_coord_max * (1.0 + __pyx_v_padding)); /* "openTSNE/_tsne.pyx":988 * coord_max *= 1 + padding * * cdef int n_boxes_1d = fmax(min_num_intervals, (coord_max - coord_min) / ints_in_interval) # <<<<<<<<<<<<<< * cdef int n_total_boxes = n_boxes_1d ** 2 * cdef double box_width = (coord_max - coord_min) / n_boxes_1d */ __pyx_v_n_boxes_1d = ((int)fmax(__pyx_v_min_num_intervals, ((__pyx_v_coord_max - __pyx_v_coord_min) / __pyx_v_ints_in_interval))); /* "openTSNE/_tsne.pyx":989 * * cdef int n_boxes_1d = fmax(min_num_intervals, (coord_max - coord_min) / ints_in_interval) * cdef int n_total_boxes = n_boxes_1d ** 2 # <<<<<<<<<<<<<< * cdef double box_width = (coord_max - coord_min) / n_boxes_1d * */ __pyx_v_n_total_boxes = __Pyx_pow_long(((long)__pyx_v_n_boxes_1d), 2); /* "openTSNE/_tsne.pyx":990 * cdef int n_boxes_1d = fmax(min_num_intervals, (coord_max - coord_min) / ints_in_interval) * cdef int n_total_boxes = n_boxes_1d ** 2 * cdef double box_width = (coord_max - coord_min) / n_boxes_1d # <<<<<<<<<<<<<< * * # Compute the box bounds */ __pyx_v_box_width = ((__pyx_v_coord_max - __pyx_v_coord_min) / ((double)__pyx_v_n_boxes_1d)); /* "openTSNE/_tsne.pyx":994 * # Compute the box bounds * cdef: * double[::1] box_x_lower_bounds = np.empty(n_total_boxes, dtype=float) # <<<<<<<<<<<<<< * double[::1] box_x_upper_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_y_lower_bounds = np.empty(n_total_boxes, dtype=float) */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 994, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_8 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_empty); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 994, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyInt_From_int(__pyx_v_n_total_boxes); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 994, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 994, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 994, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); if (PyDict_SetItem(__pyx_t_7, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 994, __pyx_L1_error) __pyx_t_10 = __Pyx_PyObject_Call(__pyx_t_8, __pyx_t_9, __pyx_t_7); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 994, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_10, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 994, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_box_x_lower_bounds = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":995 * cdef: * double[::1] box_x_lower_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_x_upper_bounds = np.empty(n_total_boxes, dtype=float) # <<<<<<<<<<<<<< * double[::1] box_y_lower_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_y_upper_bounds = np.empty(n_total_boxes, dtype=float) */ __Pyx_GetModuleGlobalName(__pyx_t_10, __pyx_n_s_np); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 995, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_10, __pyx_n_s_empty); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 995, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyInt_From_int(__pyx_v_n_total_boxes); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 995, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 995, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 995, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); if (PyDict_SetItem(__pyx_t_10, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 995, __pyx_L1_error) __pyx_t_8 = __Pyx_PyObject_Call(__pyx_t_7, __pyx_t_9, __pyx_t_10); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 995, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_8, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 995, __pyx_L1_error) __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_v_box_x_upper_bounds = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":996 * double[::1] box_x_lower_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_x_upper_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_y_lower_bounds = np.empty(n_total_boxes, dtype=float) # <<<<<<<<<<<<<< * double[::1] box_y_upper_bounds = np.empty(n_total_boxes, dtype=float) * */ __Pyx_GetModuleGlobalName(__pyx_t_8, __pyx_n_s_np); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 996, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_t_8, __pyx_n_s_empty); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 996, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __Pyx_PyInt_From_int(__pyx_v_n_total_boxes); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 996, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 996, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 996, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); if (PyDict_SetItem(__pyx_t_8, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 996, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_10, __pyx_t_9, __pyx_t_8); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 996, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 996, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_box_y_lower_bounds = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":997 * double[::1] box_x_upper_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_y_lower_bounds = np.empty(n_total_boxes, dtype=float) * double[::1] box_y_upper_bounds = np.empty(n_total_boxes, dtype=float) # <<<<<<<<<<<<<< * * for i in range(n_boxes_1d): */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 997, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_8 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_empty); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 997, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyInt_From_int(__pyx_v_n_total_boxes); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 997, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 997, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 997, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); if (PyDict_SetItem(__pyx_t_7, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 997, __pyx_L1_error) __pyx_t_10 = __Pyx_PyObject_Call(__pyx_t_8, __pyx_t_9, __pyx_t_7); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 997, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_10, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 997, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_box_y_upper_bounds = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":999 * double[::1] box_y_upper_bounds = np.empty(n_total_boxes, dtype=float) * * for i in range(n_boxes_1d): # <<<<<<<<<<<<<< * for j in range(n_boxes_1d): * box_x_lower_bounds[i * n_boxes_1d + j] = j * box_width + coord_min */ __pyx_t_12 = __pyx_v_n_boxes_1d; __pyx_t_13 = __pyx_t_12; for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_13; __pyx_t_1+=1) { __pyx_v_i = __pyx_t_1; /* "openTSNE/_tsne.pyx":1000 * * for i in range(n_boxes_1d): * for j in range(n_boxes_1d): # <<<<<<<<<<<<<< * box_x_lower_bounds[i * n_boxes_1d + j] = j * box_width + coord_min * box_x_upper_bounds[i * n_boxes_1d + j] = (j + 1) * box_width + coord_min */ __pyx_t_14 = __pyx_v_n_boxes_1d; __pyx_t_15 = __pyx_t_14; for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_15; __pyx_t_2+=1) { __pyx_v_j = __pyx_t_2; /* "openTSNE/_tsne.pyx":1001 * for i in range(n_boxes_1d): * for j in range(n_boxes_1d): * box_x_lower_bounds[i * n_boxes_1d + j] = j * box_width + coord_min # <<<<<<<<<<<<<< * box_x_upper_bounds[i * n_boxes_1d + j] = (j + 1) * box_width + coord_min * */ __pyx_t_4 = ((__pyx_v_i * __pyx_v_n_boxes_1d) + __pyx_v_j); *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_x_lower_bounds.data) + __pyx_t_4)) )) = ((__pyx_v_j * __pyx_v_box_width) + __pyx_v_coord_min); /* "openTSNE/_tsne.pyx":1002 * for j in range(n_boxes_1d): * box_x_lower_bounds[i * n_boxes_1d + j] = j * box_width + coord_min * box_x_upper_bounds[i * n_boxes_1d + j] = (j + 1) * box_width + coord_min # <<<<<<<<<<<<<< * * box_y_lower_bounds[i * n_boxes_1d + j] = i * box_width + coord_min */ __pyx_t_4 = ((__pyx_v_i * __pyx_v_n_boxes_1d) + __pyx_v_j); *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_x_upper_bounds.data) + __pyx_t_4)) )) = (((__pyx_v_j + 1) * __pyx_v_box_width) + __pyx_v_coord_min); /* "openTSNE/_tsne.pyx":1004 * box_x_upper_bounds[i * n_boxes_1d + j] = (j + 1) * box_width + coord_min * * box_y_lower_bounds[i * n_boxes_1d + j] = i * box_width + coord_min # <<<<<<<<<<<<<< * box_y_upper_bounds[i * n_boxes_1d + j] = (i + 1) * box_width + coord_min * */ __pyx_t_4 = ((__pyx_v_i * __pyx_v_n_boxes_1d) + __pyx_v_j); *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_y_lower_bounds.data) + __pyx_t_4)) )) = ((__pyx_v_i * __pyx_v_box_width) + __pyx_v_coord_min); /* "openTSNE/_tsne.pyx":1005 * * box_y_lower_bounds[i * n_boxes_1d + j] = i * box_width + coord_min * box_y_upper_bounds[i * n_boxes_1d + j] = (i + 1) * box_width + coord_min # <<<<<<<<<<<<<< * * # Determine which box each reference point belongs to */ __pyx_t_4 = ((__pyx_v_i * __pyx_v_n_boxes_1d) + __pyx_v_j); *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_y_upper_bounds.data) + __pyx_t_4)) )) = (((__pyx_v_i + 1) * __pyx_v_box_width) + __pyx_v_coord_min); } } /* "openTSNE/_tsne.pyx":1008 * * # Determine which box each reference point belongs to * cdef int *reference_point_box_idx = PyMem_Malloc(n_reference_samples * sizeof(int)) # <<<<<<<<<<<<<< * cdef int box_x_idx, box_y_idx * for i in range(n_reference_samples): */ __pyx_v_reference_point_box_idx = ((int *)PyMem_Malloc((__pyx_v_n_reference_samples * (sizeof(int))))); /* "openTSNE/_tsne.pyx":1010 * cdef int *reference_point_box_idx = PyMem_Malloc(n_reference_samples * sizeof(int)) * cdef int box_x_idx, box_y_idx * for i in range(n_reference_samples): # <<<<<<<<<<<<<< * box_x_idx = ((reference_embedding[i, 0] - coord_min) / box_width) * box_y_idx = ((reference_embedding[i, 1] - coord_min) / box_width) */ __pyx_t_1 = __pyx_v_n_reference_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":1011 * cdef int box_x_idx, box_y_idx * for i in range(n_reference_samples): * box_x_idx = ((reference_embedding[i, 0] - coord_min) / box_width) # <<<<<<<<<<<<<< * box_y_idx = ((reference_embedding[i, 1] - coord_min) / box_width) * # The right most point maps directly into `n_boxes`, while it should */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 0; __pyx_v_box_x_idx = ((int)(((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_4 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_5)) ))) - __pyx_v_coord_min) / __pyx_v_box_width)); /* "openTSNE/_tsne.pyx":1012 * for i in range(n_reference_samples): * box_x_idx = ((reference_embedding[i, 0] - coord_min) / box_width) * box_y_idx = ((reference_embedding[i, 1] - coord_min) / box_width) # <<<<<<<<<<<<<< * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 1; __pyx_v_box_y_idx = ((int)(((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_5 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_4)) ))) - __pyx_v_coord_min) / __pyx_v_box_width)); /* "openTSNE/_tsne.pyx":1015 * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box * if box_x_idx >= n_boxes_1d: # <<<<<<<<<<<<<< * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: */ __pyx_t_6 = ((__pyx_v_box_x_idx >= __pyx_v_n_boxes_1d) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":1016 * # belong to the last box * if box_x_idx >= n_boxes_1d: * box_x_idx = n_boxes_1d - 1 # <<<<<<<<<<<<<< * if box_y_idx >= n_boxes_1d: * box_y_idx = n_boxes_1d - 1 */ __pyx_v_box_x_idx = (__pyx_v_n_boxes_1d - 1); /* "openTSNE/_tsne.pyx":1015 * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box * if box_x_idx >= n_boxes_1d: # <<<<<<<<<<<<<< * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: */ } /* "openTSNE/_tsne.pyx":1017 * if box_x_idx >= n_boxes_1d: * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: # <<<<<<<<<<<<<< * box_y_idx = n_boxes_1d - 1 * */ __pyx_t_6 = ((__pyx_v_box_y_idx >= __pyx_v_n_boxes_1d) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":1018 * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: * box_y_idx = n_boxes_1d - 1 # <<<<<<<<<<<<<< * * reference_point_box_idx[i] = box_y_idx * n_boxes_1d + box_x_idx */ __pyx_v_box_y_idx = (__pyx_v_n_boxes_1d - 1); /* "openTSNE/_tsne.pyx":1017 * if box_x_idx >= n_boxes_1d: * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: # <<<<<<<<<<<<<< * box_y_idx = n_boxes_1d - 1 * */ } /* "openTSNE/_tsne.pyx":1020 * box_y_idx = n_boxes_1d - 1 * * reference_point_box_idx[i] = box_y_idx * n_boxes_1d + box_x_idx # <<<<<<<<<<<<<< * * # Prepare the interpolants for a single interval, so we can use their */ (__pyx_v_reference_point_box_idx[__pyx_v_i]) = ((__pyx_v_box_y_idx * __pyx_v_n_boxes_1d) + __pyx_v_box_x_idx); } /* "openTSNE/_tsne.pyx":1024 * # Prepare the interpolants for a single interval, so we can use their * # relative positions later on * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) # <<<<<<<<<<<<<< * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 */ __Pyx_GetModuleGlobalName(__pyx_t_10, __pyx_n_s_np); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1024, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_10, __pyx_n_s_empty); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1024, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = PyInt_FromSsize_t(__pyx_v_n_interpolation_points); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1024, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1024, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1024, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); if (PyDict_SetItem(__pyx_t_10, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 1024, __pyx_L1_error) __pyx_t_8 = __Pyx_PyObject_Call(__pyx_t_7, __pyx_t_9, __pyx_t_10); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1024, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_8, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 1024, __pyx_L1_error) __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_v_y_tilde = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":1025 * # relative positions later on * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) * cdef double h = 1. / n_interpolation_points # <<<<<<<<<<<<<< * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): */ __pyx_v_h = (1. / ((double)__pyx_v_n_interpolation_points)); /* "openTSNE/_tsne.pyx":1026 * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 # <<<<<<<<<<<<<< * for i in range(1, n_interpolation_points): * y_tilde[i] = y_tilde[i - 1] + h */ __pyx_t_4 = 0; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_4)) )) = (__pyx_v_h / 2.0); /* "openTSNE/_tsne.pyx":1027 * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): # <<<<<<<<<<<<<< * y_tilde[i] = y_tilde[i - 1] + h * */ __pyx_t_1 = __pyx_v_n_interpolation_points; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 1; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":1028 * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): * y_tilde[i] = y_tilde[i - 1] + h # <<<<<<<<<<<<<< * * # Evaluate the the squared cauchy kernel at the interpolation nodes */ __pyx_t_4 = (__pyx_v_i - 1); __pyx_t_5 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_5)) )) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_4)) ))) + __pyx_v_h); } /* "openTSNE/_tsne.pyx":1031 * * # Evaluate the the squared cauchy kernel at the interpolation nodes * cdef double[:, ::1] sq_kernel_tilde = compute_kernel_tilde_2d( # <<<<<<<<<<<<<< * &cauchy_2d_exp1p, n_interpolation_points * n_boxes_1d, coord_min, h * box_width, dof, * ) */ __pyx_t_16 = __pyx_f_8openTSNE_5_tsne_compute_kernel_tilde_2d((&__pyx_f_8openTSNE_5_tsne_cauchy_2d_exp1p), (__pyx_v_n_interpolation_points * __pyx_v_n_boxes_1d), __pyx_v_coord_min, (__pyx_v_h * __pyx_v_box_width), __pyx_v_dof); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 1031, __pyx_L1_error) __pyx_v_sq_kernel_tilde = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":1036 * # The non-square cauchy kernel is only used if dof != 1, so don't do unnecessary work * cdef double[:, ::1] kernel_tilde * if dof != 1: # <<<<<<<<<<<<<< * kernel_tilde = compute_kernel_tilde_2d( * &cauchy_2d, n_interpolation_points * n_boxes_1d, coord_min, h * box_width, dof, */ __pyx_t_6 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":1037 * cdef double[:, ::1] kernel_tilde * if dof != 1: * kernel_tilde = compute_kernel_tilde_2d( # <<<<<<<<<<<<<< * &cauchy_2d, n_interpolation_points * n_boxes_1d, coord_min, h * box_width, dof, * ) */ __pyx_t_16 = __pyx_f_8openTSNE_5_tsne_compute_kernel_tilde_2d((&__pyx_f_8openTSNE_5_tsne_cauchy_2d), (__pyx_v_n_interpolation_points * __pyx_v_n_boxes_1d), __pyx_v_coord_min, (__pyx_v_h * __pyx_v_box_width), __pyx_v_dof); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 1037, __pyx_L1_error) __pyx_v_kernel_tilde = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":1036 * # The non-square cauchy kernel is only used if dof != 1, so don't do unnecessary work * cdef double[:, ::1] kernel_tilde * if dof != 1: # <<<<<<<<<<<<<< * kernel_tilde = compute_kernel_tilde_2d( * &cauchy_2d, n_interpolation_points * n_boxes_1d, coord_min, h * box_width, dof, */ } /* "openTSNE/_tsne.pyx":1043 * # STEP 1: Compute the w coefficients * # Set up q_j values * cdef int n_terms = 4 # <<<<<<<<<<<<<< * cdef double[:, ::1] q_j = np.empty((n_reference_samples, n_terms), dtype=float) * if dof != 1: */ __pyx_v_n_terms = 4; /* "openTSNE/_tsne.pyx":1044 * # Set up q_j values * cdef int n_terms = 4 * cdef double[:, ::1] q_j = np.empty((n_reference_samples, n_terms), dtype=float) # <<<<<<<<<<<<<< * if dof != 1: * for i in range(n_reference_samples): */ __Pyx_GetModuleGlobalName(__pyx_t_8, __pyx_n_s_np); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_t_8, __pyx_n_s_empty); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = PyInt_FromSsize_t(__pyx_v_n_reference_samples); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_9 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_7 = PyTuple_New(2); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_7, 0, __pyx_t_8); __Pyx_GIVEREF(__pyx_t_9); PyTuple_SET_ITEM(__pyx_t_7, 1, __pyx_t_9); __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); if (PyDict_SetItem(__pyx_t_7, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 1044, __pyx_L1_error) __pyx_t_8 = __Pyx_PyObject_Call(__pyx_t_10, __pyx_t_9, __pyx_t_7); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_16 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_8, PyBUF_WRITABLE); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 1044, __pyx_L1_error) __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_v_q_j = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":1045 * cdef int n_terms = 4 * cdef double[:, ::1] q_j = np.empty((n_reference_samples, n_terms), dtype=float) * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_reference_samples): * q_j[i, 0] = 1 */ __pyx_t_6 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":1046 * cdef double[:, ::1] q_j = np.empty((n_reference_samples, n_terms), dtype=float) * if dof != 1: * for i in range(n_reference_samples): # <<<<<<<<<<<<<< * q_j[i, 0] = 1 * q_j[i, 1] = reference_embedding[i, 0] */ __pyx_t_1 = __pyx_v_n_reference_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":1047 * if dof != 1: * for i in range(n_reference_samples): * q_j[i, 0] = 1 # <<<<<<<<<<<<<< * q_j[i, 1] = reference_embedding[i, 0] * q_j[i, 2] = reference_embedding[i, 1] */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 0; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_4 * __pyx_v_q_j.strides[0]) )) + __pyx_t_5)) )) = 1.0; /* "openTSNE/_tsne.pyx":1048 * for i in range(n_reference_samples): * q_j[i, 0] = 1 * q_j[i, 1] = reference_embedding[i, 0] # <<<<<<<<<<<<<< * q_j[i, 2] = reference_embedding[i, 1] * q_j[i, 3] = 1 */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 0; __pyx_t_17 = __pyx_v_i; __pyx_t_18 = 1; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_17 * __pyx_v_q_j.strides[0]) )) + __pyx_t_18)) )) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_5 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":1049 * q_j[i, 0] = 1 * q_j[i, 1] = reference_embedding[i, 0] * q_j[i, 2] = reference_embedding[i, 1] # <<<<<<<<<<<<<< * q_j[i, 3] = 1 * else: */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 1; __pyx_t_18 = __pyx_v_i; __pyx_t_17 = 2; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_18 * __pyx_v_q_j.strides[0]) )) + __pyx_t_17)) )) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_4 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_5)) ))); /* "openTSNE/_tsne.pyx":1050 * q_j[i, 1] = reference_embedding[i, 0] * q_j[i, 2] = reference_embedding[i, 1] * q_j[i, 3] = 1 # <<<<<<<<<<<<<< * else: * for i in range(n_reference_samples): */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 3; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_5 * __pyx_v_q_j.strides[0]) )) + __pyx_t_4)) )) = 1.0; } /* "openTSNE/_tsne.pyx":1045 * cdef int n_terms = 4 * cdef double[:, ::1] q_j = np.empty((n_reference_samples, n_terms), dtype=float) * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_reference_samples): * q_j[i, 0] = 1 */ goto __pyx_L19; } /* "openTSNE/_tsne.pyx":1052 * q_j[i, 3] = 1 * else: * for i in range(n_reference_samples): # <<<<<<<<<<<<<< * q_j[i, 0] = 1 * q_j[i, 1] = reference_embedding[i, 0] */ /*else*/ { __pyx_t_1 = __pyx_v_n_reference_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":1053 * else: * for i in range(n_reference_samples): * q_j[i, 0] = 1 # <<<<<<<<<<<<<< * q_j[i, 1] = reference_embedding[i, 0] * q_j[i, 2] = reference_embedding[i, 1] */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 0; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_4 * __pyx_v_q_j.strides[0]) )) + __pyx_t_5)) )) = 1.0; /* "openTSNE/_tsne.pyx":1054 * for i in range(n_reference_samples): * q_j[i, 0] = 1 * q_j[i, 1] = reference_embedding[i, 0] # <<<<<<<<<<<<<< * q_j[i, 2] = reference_embedding[i, 1] * q_j[i, 3] = reference_embedding[i, 0] ** 2 + reference_embedding[i, 1] ** 2 */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 0; __pyx_t_17 = __pyx_v_i; __pyx_t_18 = 1; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_17 * __pyx_v_q_j.strides[0]) )) + __pyx_t_18)) )) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_5 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_4)) ))); /* "openTSNE/_tsne.pyx":1055 * q_j[i, 0] = 1 * q_j[i, 1] = reference_embedding[i, 0] * q_j[i, 2] = reference_embedding[i, 1] # <<<<<<<<<<<<<< * q_j[i, 3] = reference_embedding[i, 0] ** 2 + reference_embedding[i, 1] ** 2 * */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = 1; __pyx_t_18 = __pyx_v_i; __pyx_t_17 = 2; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_18 * __pyx_v_q_j.strides[0]) )) + __pyx_t_17)) )) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_4 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_5)) ))); /* "openTSNE/_tsne.pyx":1056 * q_j[i, 1] = reference_embedding[i, 0] * q_j[i, 2] = reference_embedding[i, 1] * q_j[i, 3] = reference_embedding[i, 0] ** 2 + reference_embedding[i, 1] ** 2 # <<<<<<<<<<<<<< * * # Compute the relative position of each reference point in its box */ __pyx_t_5 = __pyx_v_i; __pyx_t_4 = 0; __pyx_t_17 = __pyx_v_i; __pyx_t_18 = 1; __pyx_t_19 = __pyx_v_i; __pyx_t_20 = 3; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_19 * __pyx_v_q_j.strides[0]) )) + __pyx_t_20)) )) = (pow((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_5 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_4)) ))), 2.0) + pow((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_17 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_18)) ))), 2.0)); } } __pyx_L19:; /* "openTSNE/_tsne.pyx":1060 * # Compute the relative position of each reference point in its box * cdef: * double[::1] reference_x_in_box = np.empty(n_reference_samples, dtype=float) # <<<<<<<<<<<<<< * double[::1] reference_y_in_box = np.empty(n_reference_samples, dtype=float) * double y_min, x_min */ __Pyx_GetModuleGlobalName(__pyx_t_8, __pyx_n_s_np); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1060, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_8, __pyx_n_s_empty); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1060, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = PyInt_FromSsize_t(__pyx_v_n_reference_samples); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1060, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1060, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1060, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); if (PyDict_SetItem(__pyx_t_8, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 1060, __pyx_L1_error) __pyx_t_10 = __Pyx_PyObject_Call(__pyx_t_7, __pyx_t_9, __pyx_t_8); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1060, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_10, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 1060, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_reference_x_in_box = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":1061 * cdef: * double[::1] reference_x_in_box = np.empty(n_reference_samples, dtype=float) * double[::1] reference_y_in_box = np.empty(n_reference_samples, dtype=float) # <<<<<<<<<<<<<< * double y_min, x_min * */ __Pyx_GetModuleGlobalName(__pyx_t_10, __pyx_n_s_np); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1061, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_8 = __Pyx_PyObject_GetAttrStr(__pyx_t_10, __pyx_n_s_empty); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1061, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = PyInt_FromSsize_t(__pyx_v_n_reference_samples); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1061, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1061, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1061, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); if (PyDict_SetItem(__pyx_t_10, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 1061, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_8, __pyx_t_9, __pyx_t_10); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1061, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 1061, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_reference_y_in_box = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":1064 * double y_min, x_min * * for i in range(n_reference_samples): # <<<<<<<<<<<<<< * box_idx = reference_point_box_idx[i] * x_min = box_x_lower_bounds[box_idx] */ __pyx_t_1 = __pyx_v_n_reference_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":1065 * * for i in range(n_reference_samples): * box_idx = reference_point_box_idx[i] # <<<<<<<<<<<<<< * x_min = box_x_lower_bounds[box_idx] * y_min = box_y_lower_bounds[box_idx] */ __pyx_v_box_idx = (__pyx_v_reference_point_box_idx[__pyx_v_i]); /* "openTSNE/_tsne.pyx":1066 * for i in range(n_reference_samples): * box_idx = reference_point_box_idx[i] * x_min = box_x_lower_bounds[box_idx] # <<<<<<<<<<<<<< * y_min = box_y_lower_bounds[box_idx] * reference_x_in_box[i] = (reference_embedding[i, 0] - x_min) / box_width */ __pyx_t_18 = __pyx_v_box_idx; __pyx_v_x_min = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_x_lower_bounds.data) + __pyx_t_18)) ))); /* "openTSNE/_tsne.pyx":1067 * box_idx = reference_point_box_idx[i] * x_min = box_x_lower_bounds[box_idx] * y_min = box_y_lower_bounds[box_idx] # <<<<<<<<<<<<<< * reference_x_in_box[i] = (reference_embedding[i, 0] - x_min) / box_width * reference_y_in_box[i] = (reference_embedding[i, 1] - y_min) / box_width */ __pyx_t_18 = __pyx_v_box_idx; __pyx_v_y_min = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_y_lower_bounds.data) + __pyx_t_18)) ))); /* "openTSNE/_tsne.pyx":1068 * x_min = box_x_lower_bounds[box_idx] * y_min = box_y_lower_bounds[box_idx] * reference_x_in_box[i] = (reference_embedding[i, 0] - x_min) / box_width # <<<<<<<<<<<<<< * reference_y_in_box[i] = (reference_embedding[i, 1] - y_min) / box_width * */ __pyx_t_18 = __pyx_v_i; __pyx_t_17 = 0; __pyx_t_4 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_reference_x_in_box.data) + __pyx_t_4)) )) = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_18 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_17)) ))) - __pyx_v_x_min) / __pyx_v_box_width); /* "openTSNE/_tsne.pyx":1069 * y_min = box_y_lower_bounds[box_idx] * reference_x_in_box[i] = (reference_embedding[i, 0] - x_min) / box_width * reference_y_in_box[i] = (reference_embedding[i, 1] - y_min) / box_width # <<<<<<<<<<<<<< * * # Interpolate kernel using Lagrange polynomials */ __pyx_t_17 = __pyx_v_i; __pyx_t_18 = 1; __pyx_t_4 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_reference_y_in_box.data) + __pyx_t_4)) )) = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_embedding.data + __pyx_t_17 * __pyx_v_reference_embedding.strides[0]) )) + __pyx_t_18)) ))) - __pyx_v_y_min) / __pyx_v_box_width); } /* "openTSNE/_tsne.pyx":1072 * * # Interpolate kernel using Lagrange polynomials * cdef double[:, ::1] reference_x_interpolated_values = interpolate(reference_x_in_box, y_tilde) # <<<<<<<<<<<<<< * cdef double[:, ::1] reference_y_interpolated_values = interpolate(reference_y_in_box, y_tilde) * */ __pyx_t_16 = __pyx_f_8openTSNE_5_tsne_interpolate(__pyx_v_reference_x_in_box, __pyx_v_y_tilde); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 1072, __pyx_L1_error) __pyx_v_reference_x_interpolated_values = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":1073 * # Interpolate kernel using Lagrange polynomials * cdef double[:, ::1] reference_x_interpolated_values = interpolate(reference_x_in_box, y_tilde) * cdef double[:, ::1] reference_y_interpolated_values = interpolate(reference_y_in_box, y_tilde) # <<<<<<<<<<<<<< * * # Actually compute w_{ij}s */ __pyx_t_16 = __pyx_f_8openTSNE_5_tsne_interpolate(__pyx_v_reference_y_in_box, __pyx_v_y_tilde); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 1073, __pyx_L1_error) __pyx_v_reference_y_interpolated_values = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":1077 * # Actually compute w_{ij}s * cdef: * int total_interpolation_points = n_total_boxes * n_interpolation_points ** 2 # <<<<<<<<<<<<<< * double[:, ::1] w_coefficients = np.zeros((total_interpolation_points, n_terms), dtype=float) * Py_ssize_t box_i, box_j, interp_i, interp_j, idx */ __pyx_v_total_interpolation_points = (__pyx_v_n_total_boxes * __Pyx_pow_Py_ssize_t(__pyx_v_n_interpolation_points, 2)); /* "openTSNE/_tsne.pyx":1078 * cdef: * int total_interpolation_points = n_total_boxes * n_interpolation_points ** 2 * double[:, ::1] w_coefficients = np.zeros((total_interpolation_points, n_terms), dtype=float) # <<<<<<<<<<<<<< * Py_ssize_t box_i, box_j, interp_i, interp_j, idx * */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1078, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_zeros); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1078, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyInt_From_int(__pyx_v_total_interpolation_points); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1078, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_9 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1078, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_8 = PyTuple_New(2); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1078, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_7); __Pyx_GIVEREF(__pyx_t_9); PyTuple_SET_ITEM(__pyx_t_8, 1, __pyx_t_9); __pyx_t_7 = 0; __pyx_t_9 = 0; __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1078, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1078, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); if (PyDict_SetItem(__pyx_t_8, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 1078, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_10, __pyx_t_9, __pyx_t_8); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1078, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_16 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 1078, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_w_coefficients = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":1081 * Py_ssize_t box_i, box_j, interp_i, interp_j, idx * * for i in range(n_reference_samples): # <<<<<<<<<<<<<< * box_idx = reference_point_box_idx[i] * box_i = box_idx % n_boxes_1d */ __pyx_t_1 = __pyx_v_n_reference_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/_tsne.pyx":1082 * * for i in range(n_reference_samples): * box_idx = reference_point_box_idx[i] # <<<<<<<<<<<<<< * box_i = box_idx % n_boxes_1d * box_j = box_idx // n_boxes_1d */ __pyx_v_box_idx = (__pyx_v_reference_point_box_idx[__pyx_v_i]); /* "openTSNE/_tsne.pyx":1083 * for i in range(n_reference_samples): * box_idx = reference_point_box_idx[i] * box_i = box_idx % n_boxes_1d # <<<<<<<<<<<<<< * box_j = box_idx // n_boxes_1d * for interp_i in range(n_interpolation_points): */ __pyx_v_box_i = (__pyx_v_box_idx % __pyx_v_n_boxes_1d); /* "openTSNE/_tsne.pyx":1084 * box_idx = reference_point_box_idx[i] * box_i = box_idx % n_boxes_1d * box_j = box_idx // n_boxes_1d # <<<<<<<<<<<<<< * for interp_i in range(n_interpolation_points): * for interp_j in range(n_interpolation_points): */ __pyx_v_box_j = (__pyx_v_box_idx / __pyx_v_n_boxes_1d); /* "openTSNE/_tsne.pyx":1085 * box_i = box_idx % n_boxes_1d * box_j = box_idx // n_boxes_1d * for interp_i in range(n_interpolation_points): # <<<<<<<<<<<<<< * for interp_j in range(n_interpolation_points): * idx = (box_i * n_interpolation_points + interp_i) * \ */ __pyx_t_21 = __pyx_v_n_interpolation_points; __pyx_t_22 = __pyx_t_21; for (__pyx_t_23 = 0; __pyx_t_23 < __pyx_t_22; __pyx_t_23+=1) { __pyx_v_interp_i = __pyx_t_23; /* "openTSNE/_tsne.pyx":1086 * box_j = box_idx // n_boxes_1d * for interp_i in range(n_interpolation_points): * for interp_j in range(n_interpolation_points): # <<<<<<<<<<<<<< * idx = (box_i * n_interpolation_points + interp_i) * \ * (n_boxes_1d * n_interpolation_points) + \ */ __pyx_t_24 = __pyx_v_n_interpolation_points; __pyx_t_25 = __pyx_t_24; for (__pyx_t_26 = 0; __pyx_t_26 < __pyx_t_25; __pyx_t_26+=1) { __pyx_v_interp_j = __pyx_t_26; /* "openTSNE/_tsne.pyx":1089 * idx = (box_i * n_interpolation_points + interp_i) * \ * (n_boxes_1d * n_interpolation_points) + \ * (box_j * n_interpolation_points) + \ # <<<<<<<<<<<<<< * interp_j * for d in range(n_terms): */ __pyx_v_idx = (((((__pyx_v_box_i * __pyx_v_n_interpolation_points) + __pyx_v_interp_i) * (__pyx_v_n_boxes_1d * __pyx_v_n_interpolation_points)) + (__pyx_v_box_j * __pyx_v_n_interpolation_points)) + __pyx_v_interp_j); /* "openTSNE/_tsne.pyx":1091 * (box_j * n_interpolation_points) + \ * interp_j * for d in range(n_terms): # <<<<<<<<<<<<<< * w_coefficients[idx, d] += \ * reference_x_interpolated_values[i, interp_i] * \ */ __pyx_t_12 = __pyx_v_n_terms; __pyx_t_13 = __pyx_t_12; for (__pyx_t_27 = 0; __pyx_t_27 < __pyx_t_13; __pyx_t_27+=1) { __pyx_v_d = __pyx_t_27; /* "openTSNE/_tsne.pyx":1093 * for d in range(n_terms): * w_coefficients[idx, d] += \ * reference_x_interpolated_values[i, interp_i] * \ # <<<<<<<<<<<<<< * reference_y_interpolated_values[i, interp_j] * \ * q_j[i, d] */ __pyx_t_18 = __pyx_v_i; __pyx_t_17 = __pyx_v_interp_i; /* "openTSNE/_tsne.pyx":1094 * w_coefficients[idx, d] += \ * reference_x_interpolated_values[i, interp_i] * \ * reference_y_interpolated_values[i, interp_j] * \ # <<<<<<<<<<<<<< * q_j[i, d] * */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = __pyx_v_interp_j; /* "openTSNE/_tsne.pyx":1095 * reference_x_interpolated_values[i, interp_i] * \ * reference_y_interpolated_values[i, interp_j] * \ * q_j[i, d] # <<<<<<<<<<<<<< * * # STEP 2: Compute the kernel values evaluated at the interpolation nodes */ __pyx_t_20 = __pyx_v_i; __pyx_t_19 = __pyx_v_d; /* "openTSNE/_tsne.pyx":1092 * interp_j * for d in range(n_terms): * w_coefficients[idx, d] += \ # <<<<<<<<<<<<<< * reference_x_interpolated_values[i, interp_i] * \ * reference_y_interpolated_values[i, interp_j] * \ */ __pyx_t_28 = __pyx_v_idx; __pyx_t_29 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_w_coefficients.data + __pyx_t_28 * __pyx_v_w_coefficients.strides[0]) )) + __pyx_t_29)) )) += (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_x_interpolated_values.data + __pyx_t_18 * __pyx_v_reference_x_interpolated_values.strides[0]) )) + __pyx_t_17)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_reference_y_interpolated_values.data + __pyx_t_4 * __pyx_v_reference_y_interpolated_values.strides[0]) )) + __pyx_t_5)) )))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_q_j.data + __pyx_t_20 * __pyx_v_q_j.strides[0]) )) + __pyx_t_19)) )))); } } } } /* "openTSNE/_tsne.pyx":1098 * * # STEP 2: Compute the kernel values evaluated at the interpolation nodes * cdef double[:, ::1] y_tilde_values = np.empty((total_interpolation_points, n_terms)) # <<<<<<<<<<<<<< * if dof != 1: * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients[:, :3], y_tilde_values[:, :3]) */ __Pyx_GetModuleGlobalName(__pyx_t_8, __pyx_n_s_np); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1098, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_t_8, __pyx_n_s_empty); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1098, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __Pyx_PyInt_From_int(__pyx_v_total_interpolation_points); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1098, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_10 = __Pyx_PyInt_From_int(__pyx_v_n_terms); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1098, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_30 = PyTuple_New(2); if (unlikely(!__pyx_t_30)) __PYX_ERR(0, 1098, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_30); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_30, 0, __pyx_t_8); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_30, 1, __pyx_t_10); __pyx_t_8 = 0; __pyx_t_10 = 0; __pyx_t_10 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_9))) { __pyx_t_10 = PyMethod_GET_SELF(__pyx_t_9); if (likely(__pyx_t_10)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_9); __Pyx_INCREF(__pyx_t_10); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_9, function); } } __pyx_t_7 = (__pyx_t_10) ? __Pyx_PyObject_Call2Args(__pyx_t_9, __pyx_t_10, __pyx_t_30) : __Pyx_PyObject_CallOneArg(__pyx_t_9, __pyx_t_30); __Pyx_XDECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_30); __pyx_t_30 = 0; if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1098, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_16 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_16.memview)) __PYX_ERR(0, 1098, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_y_tilde_values = __pyx_t_16; __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":1099 * # STEP 2: Compute the kernel values evaluated at the interpolation nodes * cdef double[:, ::1] y_tilde_values = np.empty((total_interpolation_points, n_terms)) * if dof != 1: # <<<<<<<<<<<<<< * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients[:, :3], y_tilde_values[:, :3]) * matrix_multiply_fft_2d(kernel_tilde, w_coefficients[:, 3:], y_tilde_values[:, 3:]) */ __pyx_t_6 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":1100 * cdef double[:, ::1] y_tilde_values = np.empty((total_interpolation_points, n_terms)) * if dof != 1: * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients[:, :3], y_tilde_values[:, :3]) # <<<<<<<<<<<<<< * matrix_multiply_fft_2d(kernel_tilde, w_coefficients[:, 3:], y_tilde_values[:, 3:]) * else: */ __pyx_t_16.data = __pyx_v_w_coefficients.data; __pyx_t_16.memview = __pyx_v_w_coefficients.memview; __PYX_INC_MEMVIEW(&__pyx_t_16, 0); __pyx_t_16.shape[0] = __pyx_v_w_coefficients.shape[0]; __pyx_t_16.strides[0] = __pyx_v_w_coefficients.strides[0]; __pyx_t_16.suboffsets[0] = -1; __pyx_t_12 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_16, __pyx_v_w_coefficients.shape[1], __pyx_v_w_coefficients.strides[1], __pyx_v_w_coefficients.suboffsets[1], 1, 1, &__pyx_t_12, 0, 3, 0, 0, 1, 0, 1) < 0)) { __PYX_ERR(0, 1100, __pyx_L1_error) } __pyx_t_31.data = __pyx_v_y_tilde_values.data; __pyx_t_31.memview = __pyx_v_y_tilde_values.memview; __PYX_INC_MEMVIEW(&__pyx_t_31, 0); __pyx_t_31.shape[0] = __pyx_v_y_tilde_values.shape[0]; __pyx_t_31.strides[0] = __pyx_v_y_tilde_values.strides[0]; __pyx_t_31.suboffsets[0] = -1; __pyx_t_12 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_31, __pyx_v_y_tilde_values.shape[1], __pyx_v_y_tilde_values.strides[1], __pyx_v_y_tilde_values.suboffsets[1], 1, 1, &__pyx_t_12, 0, 3, 0, 0, 1, 0, 1) < 0)) { __PYX_ERR(0, 1100, __pyx_L1_error) } __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_2d(__pyx_v_sq_kernel_tilde, __pyx_t_16, __pyx_t_31); __PYX_XDEC_MEMVIEW(&__pyx_t_16, 1); __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; __PYX_XDEC_MEMVIEW(&__pyx_t_31, 1); __pyx_t_31.memview = NULL; __pyx_t_31.data = NULL; /* "openTSNE/_tsne.pyx":1101 * if dof != 1: * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients[:, :3], y_tilde_values[:, :3]) * matrix_multiply_fft_2d(kernel_tilde, w_coefficients[:, 3:], y_tilde_values[:, 3:]) # <<<<<<<<<<<<<< * else: * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients, y_tilde_values) */ __pyx_t_31.data = __pyx_v_w_coefficients.data; __pyx_t_31.memview = __pyx_v_w_coefficients.memview; __PYX_INC_MEMVIEW(&__pyx_t_31, 0); __pyx_t_31.shape[0] = __pyx_v_w_coefficients.shape[0]; __pyx_t_31.strides[0] = __pyx_v_w_coefficients.strides[0]; __pyx_t_31.suboffsets[0] = -1; __pyx_t_12 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_31, __pyx_v_w_coefficients.shape[1], __pyx_v_w_coefficients.strides[1], __pyx_v_w_coefficients.suboffsets[1], 1, 1, &__pyx_t_12, 3, 0, 0, 1, 0, 0, 1) < 0)) { __PYX_ERR(0, 1101, __pyx_L1_error) } __pyx_t_16.data = __pyx_v_y_tilde_values.data; __pyx_t_16.memview = __pyx_v_y_tilde_values.memview; __PYX_INC_MEMVIEW(&__pyx_t_16, 0); __pyx_t_16.shape[0] = __pyx_v_y_tilde_values.shape[0]; __pyx_t_16.strides[0] = __pyx_v_y_tilde_values.strides[0]; __pyx_t_16.suboffsets[0] = -1; __pyx_t_12 = -1; if (unlikely(__pyx_memoryview_slice_memviewslice( &__pyx_t_16, __pyx_v_y_tilde_values.shape[1], __pyx_v_y_tilde_values.strides[1], __pyx_v_y_tilde_values.suboffsets[1], 1, 1, &__pyx_t_12, 3, 0, 0, 1, 0, 0, 1) < 0)) { __PYX_ERR(0, 1101, __pyx_L1_error) } __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_2d(__pyx_v_kernel_tilde, __pyx_t_31, __pyx_t_16); __PYX_XDEC_MEMVIEW(&__pyx_t_31, 1); __pyx_t_31.memview = NULL; __pyx_t_31.data = NULL; __PYX_XDEC_MEMVIEW(&__pyx_t_16, 1); __pyx_t_16.memview = NULL; __pyx_t_16.data = NULL; /* "openTSNE/_tsne.pyx":1099 * # STEP 2: Compute the kernel values evaluated at the interpolation nodes * cdef double[:, ::1] y_tilde_values = np.empty((total_interpolation_points, n_terms)) * if dof != 1: # <<<<<<<<<<<<<< * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients[:, :3], y_tilde_values[:, :3]) * matrix_multiply_fft_2d(kernel_tilde, w_coefficients[:, 3:], y_tilde_values[:, 3:]) */ goto __pyx_L34; } /* "openTSNE/_tsne.pyx":1103 * matrix_multiply_fft_2d(kernel_tilde, w_coefficients[:, 3:], y_tilde_values[:, 3:]) * else: * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients, y_tilde_values) # <<<<<<<<<<<<<< * * return ( */ /*else*/ { __pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_2d(__pyx_v_sq_kernel_tilde, __pyx_v_w_coefficients, __pyx_v_y_tilde_values); } __pyx_L34:; /* "openTSNE/_tsne.pyx":1105 * matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients, y_tilde_values) * * return ( # <<<<<<<<<<<<<< * np.asarray(y_tilde_values), * np.asarray(box_x_lower_bounds), */ __Pyx_XDECREF(__pyx_r); /* "openTSNE/_tsne.pyx":1106 * * return ( * np.asarray(y_tilde_values), # <<<<<<<<<<<<<< * np.asarray(box_x_lower_bounds), * np.asarray(box_y_lower_bounds), */ __Pyx_GetModuleGlobalName(__pyx_t_9, __pyx_n_s_np); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1106, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_30 = __Pyx_PyObject_GetAttrStr(__pyx_t_9, __pyx_n_s_asarray); if (unlikely(!__pyx_t_30)) __PYX_ERR(0, 1106, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_30); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_9 = __pyx_memoryview_fromslice(__pyx_v_y_tilde_values, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1106, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_10 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_30))) { __pyx_t_10 = PyMethod_GET_SELF(__pyx_t_30); if (likely(__pyx_t_10)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_30); __Pyx_INCREF(__pyx_t_10); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_30, function); } } __pyx_t_7 = (__pyx_t_10) ? __Pyx_PyObject_Call2Args(__pyx_t_30, __pyx_t_10, __pyx_t_9) : __Pyx_PyObject_CallOneArg(__pyx_t_30, __pyx_t_9); __Pyx_XDECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1106, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_30); __pyx_t_30 = 0; /* "openTSNE/_tsne.pyx":1107 * return ( * np.asarray(y_tilde_values), * np.asarray(box_x_lower_bounds), # <<<<<<<<<<<<<< * np.asarray(box_y_lower_bounds), * ) */ __Pyx_GetModuleGlobalName(__pyx_t_9, __pyx_n_s_np); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1107, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_t_9, __pyx_n_s_asarray); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1107, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_9 = __pyx_memoryview_fromslice(__pyx_v_box_x_lower_bounds, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1107, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_8 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_10))) { __pyx_t_8 = PyMethod_GET_SELF(__pyx_t_10); if (likely(__pyx_t_8)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_10); __Pyx_INCREF(__pyx_t_8); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_10, function); } } __pyx_t_30 = (__pyx_t_8) ? __Pyx_PyObject_Call2Args(__pyx_t_10, __pyx_t_8, __pyx_t_9) : __Pyx_PyObject_CallOneArg(__pyx_t_10, __pyx_t_9); __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; if (unlikely(!__pyx_t_30)) __PYX_ERR(0, 1107, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_30); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; /* "openTSNE/_tsne.pyx":1108 * np.asarray(y_tilde_values), * np.asarray(box_x_lower_bounds), * np.asarray(box_y_lower_bounds), # <<<<<<<<<<<<<< * ) * */ __Pyx_GetModuleGlobalName(__pyx_t_9, __pyx_n_s_np); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1108, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_8 = __Pyx_PyObject_GetAttrStr(__pyx_t_9, __pyx_n_s_asarray); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1108, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_9 = __pyx_memoryview_fromslice(__pyx_v_box_y_lower_bounds, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1108, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_32 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_8))) { __pyx_t_32 = PyMethod_GET_SELF(__pyx_t_8); if (likely(__pyx_t_32)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_8); __Pyx_INCREF(__pyx_t_32); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_8, function); } } __pyx_t_10 = (__pyx_t_32) ? __Pyx_PyObject_Call2Args(__pyx_t_8, __pyx_t_32, __pyx_t_9) : __Pyx_PyObject_CallOneArg(__pyx_t_8, __pyx_t_9); __Pyx_XDECREF(__pyx_t_32); __pyx_t_32 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1108, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; /* "openTSNE/_tsne.pyx":1106 * * return ( * np.asarray(y_tilde_values), # <<<<<<<<<<<<<< * np.asarray(box_x_lower_bounds), * np.asarray(box_y_lower_bounds), */ __pyx_t_8 = PyTuple_New(3); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1106, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_7); __Pyx_GIVEREF(__pyx_t_30); PyTuple_SET_ITEM(__pyx_t_8, 1, __pyx_t_30); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_8, 2, __pyx_t_10); __pyx_t_7 = 0; __pyx_t_30 = 0; __pyx_t_10 = 0; __pyx_r = ((PyObject*)__pyx_t_8); __pyx_t_8 = 0; goto __pyx_L0; /* "openTSNE/_tsne.pyx":952 * * * cpdef tuple prepare_negative_gradient_fft_interpolation_grid_2d( # <<<<<<<<<<<<<< * double[:, ::1] reference_embedding, * Py_ssize_t n_interpolation_points=3, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_XDECREF(__pyx_t_9); __Pyx_XDECREF(__pyx_t_10); __PYX_XDEC_MEMVIEW(&__pyx_t_11, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_16, 1); __Pyx_XDECREF(__pyx_t_30); __PYX_XDEC_MEMVIEW(&__pyx_t_31, 1); __Pyx_XDECREF(__pyx_t_32); __Pyx_AddTraceback("openTSNE._tsne.prepare_negative_gradient_fft_interpolation_grid_2d", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_box_x_lower_bounds, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_box_x_upper_bounds, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_box_y_lower_bounds, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_box_y_upper_bounds, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_sq_kernel_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_kernel_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_q_j, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_reference_x_in_box, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_reference_y_in_box, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_reference_x_interpolated_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_reference_y_interpolated_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_w_coefficients, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde_values, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_5_tsne_15prepare_negative_gradient_fft_interpolation_grid_2d(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_pw_8openTSNE_5_tsne_15prepare_negative_gradient_fft_interpolation_grid_2d(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __Pyx_memviewslice __pyx_v_reference_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_n_interpolation_points; Py_ssize_t __pyx_v_min_num_intervals; double __pyx_v_ints_in_interval; double __pyx_v_dof; double __pyx_v_padding; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("prepare_negative_gradient_fft_interpolation_grid_2d (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_reference_embedding,&__pyx_n_s_n_interpolation_points,&__pyx_n_s_min_num_intervals,&__pyx_n_s_ints_in_interval,&__pyx_n_s_dof,&__pyx_n_s_padding,0}; PyObject* values[6] = {0,0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_reference_embedding)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_n_interpolation_points); if (value) { values[1] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 2: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_min_num_intervals); if (value) { values[2] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 3: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_ints_in_interval); if (value) { values[3] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 4: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dof); if (value) { values[4] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 5: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_padding); if (value) { values[5] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "prepare_negative_gradient_fft_interpolation_grid_2d") < 0)) __PYX_ERR(0, 952, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_reference_embedding = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_reference_embedding.memview)) __PYX_ERR(0, 953, __pyx_L3_error) if (values[1]) { __pyx_v_n_interpolation_points = __Pyx_PyIndex_AsSsize_t(values[1]); if (unlikely((__pyx_v_n_interpolation_points == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 954, __pyx_L3_error) } else { __pyx_v_n_interpolation_points = ((Py_ssize_t)3); } if (values[2]) { __pyx_v_min_num_intervals = __Pyx_PyIndex_AsSsize_t(values[2]); if (unlikely((__pyx_v_min_num_intervals == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 955, __pyx_L3_error) } else { __pyx_v_min_num_intervals = ((Py_ssize_t)10); } if (values[3]) { __pyx_v_ints_in_interval = __pyx_PyFloat_AsDouble(values[3]); if (unlikely((__pyx_v_ints_in_interval == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 956, __pyx_L3_error) } else { __pyx_v_ints_in_interval = ((double)1.0); } if (values[4]) { __pyx_v_dof = __pyx_PyFloat_AsDouble(values[4]); if (unlikely((__pyx_v_dof == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 957, __pyx_L3_error) } else { __pyx_v_dof = ((double)1.0); } if (values[5]) { __pyx_v_padding = __pyx_PyFloat_AsDouble(values[5]); if (unlikely((__pyx_v_padding == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 958, __pyx_L3_error) } else { __pyx_v_padding = ((double)0.0); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("prepare_negative_gradient_fft_interpolation_grid_2d", 0, 1, 6, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 952, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE._tsne.prepare_negative_gradient_fft_interpolation_grid_2d", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_5_tsne_14prepare_negative_gradient_fft_interpolation_grid_2d(__pyx_self, __pyx_v_reference_embedding, __pyx_v_n_interpolation_points, __pyx_v_min_num_intervals, __pyx_v_ints_in_interval, __pyx_v_dof, __pyx_v_padding); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_5_tsne_14prepare_negative_gradient_fft_interpolation_grid_2d(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_reference_embedding, Py_ssize_t __pyx_v_n_interpolation_points, Py_ssize_t __pyx_v_min_num_intervals, double __pyx_v_ints_in_interval, double __pyx_v_dof, double __pyx_v_padding) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_2d __pyx_t_2; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("prepare_negative_gradient_fft_interpolation_grid_2d", 0); __Pyx_XDECREF(__pyx_r); __pyx_t_2.__pyx_n = 5; __pyx_t_2.n_interpolation_points = __pyx_v_n_interpolation_points; __pyx_t_2.min_num_intervals = __pyx_v_min_num_intervals; __pyx_t_2.ints_in_interval = __pyx_v_ints_in_interval; __pyx_t_2.dof = __pyx_v_dof; __pyx_t_2.padding = __pyx_v_padding; __pyx_t_1 = __pyx_f_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_2d(__pyx_v_reference_embedding, 0, &__pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 952, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("openTSNE._tsne.prepare_negative_gradient_fft_interpolation_grid_2d", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_reference_embedding, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/_tsne.pyx":1112 * * * cpdef double estimate_negative_gradient_fft_2d_with_grid( # <<<<<<<<<<<<<< * double[:, ::1] embedding, * double[:, ::1] gradient, */ static PyObject *__pyx_pw_8openTSNE_5_tsne_17estimate_negative_gradient_fft_2d_with_grid(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static double __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d_with_grid(__Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, __Pyx_memviewslice __pyx_v_y_tilde_values, __Pyx_memviewslice __pyx_v_box_x_lower_bounds, __Pyx_memviewslice __pyx_v_box_y_lower_bounds, Py_ssize_t __pyx_v_n_interpolation_points, double __pyx_v_dof, CYTHON_UNUSED int __pyx_skip_dispatch) { Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_d; Py_ssize_t __pyx_v_box_idx; Py_ssize_t __pyx_v_n_samples; Py_ssize_t __pyx_v_n_terms; Py_ssize_t __pyx_v_n_boxes_1d; double __pyx_v_coord_min; double __pyx_v_box_width; int __pyx_v_box_x_idx; int __pyx_v_box_y_idx; int *__pyx_v_point_box_idx; __Pyx_memviewslice __pyx_v_y_tilde = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_h; __Pyx_memviewslice __pyx_v_x_in_box = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_y_in_box = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_y_min; double __pyx_v_x_min; __Pyx_memviewslice __pyx_v_x_interpolated_values = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_y_interpolated_values = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_box_i; Py_ssize_t __pyx_v_box_j; Py_ssize_t __pyx_v_interp_i; Py_ssize_t __pyx_v_interp_j; Py_ssize_t __pyx_v_idx; __Pyx_memviewslice __pyx_v_phi = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_sum_Qi = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_y1; double __pyx_v_y2; PyObject *__pyx_v_sum_Q = 0; double __pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; int __pyx_t_6; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; PyObject *__pyx_t_9 = NULL; PyObject *__pyx_t_10 = NULL; __Pyx_memviewslice __pyx_t_11 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_12; __Pyx_memviewslice __pyx_t_13 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_14; Py_ssize_t __pyx_t_15; Py_ssize_t __pyx_t_16; Py_ssize_t __pyx_t_17; Py_ssize_t __pyx_t_18; Py_ssize_t __pyx_t_19; Py_ssize_t __pyx_t_20; Py_ssize_t __pyx_t_21; Py_ssize_t __pyx_t_22; Py_ssize_t __pyx_t_23; Py_ssize_t __pyx_t_24; Py_ssize_t __pyx_t_25; Py_ssize_t __pyx_t_26; Py_ssize_t __pyx_t_27; Py_ssize_t __pyx_t_28; double __pyx_t_29; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("estimate_negative_gradient_fft_2d_with_grid", 0); /* "openTSNE/_tsne.pyx":1123 * cdef: * Py_ssize_t i, j, d, box_idx * Py_ssize_t n_samples = embedding.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t n_terms = y_tilde_values.shape[1] * Py_ssize_t n_boxes_1d = int(sqrt(box_x_lower_bounds.shape[0])) */ __pyx_v_n_samples = (__pyx_v_embedding.shape[0]); /* "openTSNE/_tsne.pyx":1124 * Py_ssize_t i, j, d, box_idx * Py_ssize_t n_samples = embedding.shape[0] * Py_ssize_t n_terms = y_tilde_values.shape[1] # <<<<<<<<<<<<<< * Py_ssize_t n_boxes_1d = int(sqrt(box_x_lower_bounds.shape[0])) * double coord_min = box_x_lower_bounds[0] */ __pyx_v_n_terms = (__pyx_v_y_tilde_values.shape[1]); /* "openTSNE/_tsne.pyx":1125 * Py_ssize_t n_samples = embedding.shape[0] * Py_ssize_t n_terms = y_tilde_values.shape[1] * Py_ssize_t n_boxes_1d = int(sqrt(box_x_lower_bounds.shape[0])) # <<<<<<<<<<<<<< * double coord_min = box_x_lower_bounds[0] * double box_width = box_x_lower_bounds[1] - box_x_lower_bounds[0] */ __pyx_v_n_boxes_1d = ((Py_ssize_t)sqrt((__pyx_v_box_x_lower_bounds.shape[0]))); /* "openTSNE/_tsne.pyx":1126 * Py_ssize_t n_terms = y_tilde_values.shape[1] * Py_ssize_t n_boxes_1d = int(sqrt(box_x_lower_bounds.shape[0])) * double coord_min = box_x_lower_bounds[0] # <<<<<<<<<<<<<< * double box_width = box_x_lower_bounds[1] - box_x_lower_bounds[0] * */ __pyx_t_1 = 0; __pyx_v_coord_min = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_x_lower_bounds.data) + __pyx_t_1)) ))); /* "openTSNE/_tsne.pyx":1127 * Py_ssize_t n_boxes_1d = int(sqrt(box_x_lower_bounds.shape[0])) * double coord_min = box_x_lower_bounds[0] * double box_width = box_x_lower_bounds[1] - box_x_lower_bounds[0] # <<<<<<<<<<<<<< * * # Determine which box each point belongs to */ __pyx_t_1 = 1; __pyx_t_2 = 0; __pyx_v_box_width = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_x_lower_bounds.data) + __pyx_t_1)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_x_lower_bounds.data) + __pyx_t_2)) )))); /* "openTSNE/_tsne.pyx":1131 * # Determine which box each point belongs to * cdef int box_x_idx, box_y_idx * cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) # <<<<<<<<<<<<<< * for i in range(n_samples): * box_x_idx = ((embedding[i, 0] - coord_min) / box_width) */ __pyx_v_point_box_idx = ((int *)PyMem_Malloc((__pyx_v_n_samples * (sizeof(int))))); /* "openTSNE/_tsne.pyx":1132 * cdef int box_x_idx, box_y_idx * cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) * for i in range(n_samples): # <<<<<<<<<<<<<< * box_x_idx = ((embedding[i, 0] - coord_min) / box_width) * box_y_idx = ((embedding[i, 1] - coord_min) / box_width) */ __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":1133 * cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) * for i in range(n_samples): * box_x_idx = ((embedding[i, 0] - coord_min) / box_width) # <<<<<<<<<<<<<< * box_y_idx = ((embedding[i, 1] - coord_min) / box_width) * # The right most point maps directly into `n_boxes`, while it should */ __pyx_t_2 = __pyx_v_i; __pyx_t_1 = 0; __pyx_v_box_x_idx = ((int)(((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_2 * __pyx_v_embedding.strides[0]) )) + __pyx_t_1)) ))) - __pyx_v_coord_min) / __pyx_v_box_width)); /* "openTSNE/_tsne.pyx":1134 * for i in range(n_samples): * box_x_idx = ((embedding[i, 0] - coord_min) / box_width) * box_y_idx = ((embedding[i, 1] - coord_min) / box_width) # <<<<<<<<<<<<<< * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box */ __pyx_t_1 = __pyx_v_i; __pyx_t_2 = 1; __pyx_v_box_y_idx = ((int)(((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_1 * __pyx_v_embedding.strides[0]) )) + __pyx_t_2)) ))) - __pyx_v_coord_min) / __pyx_v_box_width)); /* "openTSNE/_tsne.pyx":1137 * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box * if box_x_idx >= n_boxes_1d: # <<<<<<<<<<<<<< * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: */ __pyx_t_6 = ((__pyx_v_box_x_idx >= __pyx_v_n_boxes_1d) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":1138 * # belong to the last box * if box_x_idx >= n_boxes_1d: * box_x_idx = n_boxes_1d - 1 # <<<<<<<<<<<<<< * if box_y_idx >= n_boxes_1d: * box_y_idx = n_boxes_1d - 1 */ __pyx_v_box_x_idx = (__pyx_v_n_boxes_1d - 1); /* "openTSNE/_tsne.pyx":1137 * # The right most point maps directly into `n_boxes`, while it should * # belong to the last box * if box_x_idx >= n_boxes_1d: # <<<<<<<<<<<<<< * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: */ } /* "openTSNE/_tsne.pyx":1139 * if box_x_idx >= n_boxes_1d: * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: # <<<<<<<<<<<<<< * box_y_idx = n_boxes_1d - 1 * */ __pyx_t_6 = ((__pyx_v_box_y_idx >= __pyx_v_n_boxes_1d) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":1140 * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: * box_y_idx = n_boxes_1d - 1 # <<<<<<<<<<<<<< * * point_box_idx[i] = box_y_idx * n_boxes_1d + box_x_idx */ __pyx_v_box_y_idx = (__pyx_v_n_boxes_1d - 1); /* "openTSNE/_tsne.pyx":1139 * if box_x_idx >= n_boxes_1d: * box_x_idx = n_boxes_1d - 1 * if box_y_idx >= n_boxes_1d: # <<<<<<<<<<<<<< * box_y_idx = n_boxes_1d - 1 * */ } /* "openTSNE/_tsne.pyx":1142 * box_y_idx = n_boxes_1d - 1 * * point_box_idx[i] = box_y_idx * n_boxes_1d + box_x_idx # <<<<<<<<<<<<<< * * # Prepare the interpolants for a single interval, so we can use their */ (__pyx_v_point_box_idx[__pyx_v_i]) = ((__pyx_v_box_y_idx * __pyx_v_n_boxes_1d) + __pyx_v_box_x_idx); } /* "openTSNE/_tsne.pyx":1146 * # Prepare the interpolants for a single interval, so we can use their * # relative positions later on * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) # <<<<<<<<<<<<<< * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1146, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_8 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_empty); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1146, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = PyInt_FromSsize_t(__pyx_v_n_interpolation_points); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1146, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1146, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1146, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); if (PyDict_SetItem(__pyx_t_7, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 1146, __pyx_L1_error) __pyx_t_10 = __Pyx_PyObject_Call(__pyx_t_8, __pyx_t_9, __pyx_t_7); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1146, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_10, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 1146, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_y_tilde = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":1147 * # relative positions later on * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) * cdef double h = 1. / n_interpolation_points # <<<<<<<<<<<<<< * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): */ __pyx_v_h = (1. / ((double)__pyx_v_n_interpolation_points)); /* "openTSNE/_tsne.pyx":1148 * cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 # <<<<<<<<<<<<<< * for i in range(1, n_interpolation_points): * y_tilde[i] = y_tilde[i - 1] + h */ __pyx_t_2 = 0; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_2)) )) = (__pyx_v_h / 2.0); /* "openTSNE/_tsne.pyx":1149 * cdef double h = 1. / n_interpolation_points * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): # <<<<<<<<<<<<<< * y_tilde[i] = y_tilde[i - 1] + h * */ __pyx_t_3 = __pyx_v_n_interpolation_points; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 1; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":1150 * y_tilde[0] = h / 2 * for i in range(1, n_interpolation_points): * y_tilde[i] = y_tilde[i - 1] + h # <<<<<<<<<<<<<< * * # STEP 3: Compute the potentials \tilde{\phi(y_i)} */ __pyx_t_2 = (__pyx_v_i - 1); __pyx_t_1 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_1)) )) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_tilde.data) + __pyx_t_2)) ))) + __pyx_v_h); } /* "openTSNE/_tsne.pyx":1155 * # Compute the relative position of each new embedding point in its box * cdef: * double[::1] x_in_box = np.empty(n_samples, dtype=float) # <<<<<<<<<<<<<< * double[::1] y_in_box = np.empty(n_samples, dtype=float) * */ __Pyx_GetModuleGlobalName(__pyx_t_10, __pyx_n_s_np); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1155, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_10, __pyx_n_s_empty); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1155, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1155, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1155, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1155, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); if (PyDict_SetItem(__pyx_t_10, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 1155, __pyx_L1_error) __pyx_t_8 = __Pyx_PyObject_Call(__pyx_t_7, __pyx_t_9, __pyx_t_10); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1155, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_8, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 1155, __pyx_L1_error) __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_v_x_in_box = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":1156 * cdef: * double[::1] x_in_box = np.empty(n_samples, dtype=float) * double[::1] y_in_box = np.empty(n_samples, dtype=float) # <<<<<<<<<<<<<< * * cdef double y_min, x_min */ __Pyx_GetModuleGlobalName(__pyx_t_8, __pyx_n_s_np); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1156, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_t_8, __pyx_n_s_empty); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1156, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1156, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1156, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_8); __pyx_t_8 = 0; __pyx_t_8 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1156, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); if (PyDict_SetItem(__pyx_t_8, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 1156, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_10, __pyx_t_9, __pyx_t_8); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1156, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 1156, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_y_in_box = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":1159 * * cdef double y_min, x_min * for i in range(n_samples): # <<<<<<<<<<<<<< * box_idx = point_box_idx[i] * x_min = box_x_lower_bounds[box_idx] */ __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":1160 * cdef double y_min, x_min * for i in range(n_samples): * box_idx = point_box_idx[i] # <<<<<<<<<<<<<< * x_min = box_x_lower_bounds[box_idx] * y_min = box_y_lower_bounds[box_idx] */ __pyx_v_box_idx = (__pyx_v_point_box_idx[__pyx_v_i]); /* "openTSNE/_tsne.pyx":1161 * for i in range(n_samples): * box_idx = point_box_idx[i] * x_min = box_x_lower_bounds[box_idx] # <<<<<<<<<<<<<< * y_min = box_y_lower_bounds[box_idx] * x_in_box[i] = (embedding[i, 0] - x_min) / box_width */ __pyx_t_2 = __pyx_v_box_idx; __pyx_v_x_min = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_x_lower_bounds.data) + __pyx_t_2)) ))); /* "openTSNE/_tsne.pyx":1162 * box_idx = point_box_idx[i] * x_min = box_x_lower_bounds[box_idx] * y_min = box_y_lower_bounds[box_idx] # <<<<<<<<<<<<<< * x_in_box[i] = (embedding[i, 0] - x_min) / box_width * y_in_box[i] = (embedding[i, 1] - y_min) / box_width */ __pyx_t_2 = __pyx_v_box_idx; __pyx_v_y_min = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_box_y_lower_bounds.data) + __pyx_t_2)) ))); /* "openTSNE/_tsne.pyx":1163 * x_min = box_x_lower_bounds[box_idx] * y_min = box_y_lower_bounds[box_idx] * x_in_box[i] = (embedding[i, 0] - x_min) / box_width # <<<<<<<<<<<<<< * y_in_box[i] = (embedding[i, 1] - y_min) / box_width * */ __pyx_t_2 = __pyx_v_i; __pyx_t_1 = 0; __pyx_t_12 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_x_in_box.data) + __pyx_t_12)) )) = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_2 * __pyx_v_embedding.strides[0]) )) + __pyx_t_1)) ))) - __pyx_v_x_min) / __pyx_v_box_width); /* "openTSNE/_tsne.pyx":1164 * y_min = box_y_lower_bounds[box_idx] * x_in_box[i] = (embedding[i, 0] - x_min) / box_width * y_in_box[i] = (embedding[i, 1] - y_min) / box_width # <<<<<<<<<<<<<< * * # Interpolate kernel using Lagrange polynomials */ __pyx_t_1 = __pyx_v_i; __pyx_t_2 = 1; __pyx_t_12 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_y_in_box.data) + __pyx_t_12)) )) = (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_1 * __pyx_v_embedding.strides[0]) )) + __pyx_t_2)) ))) - __pyx_v_y_min) / __pyx_v_box_width); } /* "openTSNE/_tsne.pyx":1167 * * # Interpolate kernel using Lagrange polynomials * cdef double[:, ::1] x_interpolated_values = interpolate(x_in_box, y_tilde) # <<<<<<<<<<<<<< * cdef double[:, ::1] y_interpolated_values = interpolate(y_in_box, y_tilde) * */ __pyx_t_13 = __pyx_f_8openTSNE_5_tsne_interpolate(__pyx_v_x_in_box, __pyx_v_y_tilde); if (unlikely(!__pyx_t_13.memview)) __PYX_ERR(0, 1167, __pyx_L1_error) __pyx_v_x_interpolated_values = __pyx_t_13; __pyx_t_13.memview = NULL; __pyx_t_13.data = NULL; /* "openTSNE/_tsne.pyx":1168 * # Interpolate kernel using Lagrange polynomials * cdef double[:, ::1] x_interpolated_values = interpolate(x_in_box, y_tilde) * cdef double[:, ::1] y_interpolated_values = interpolate(y_in_box, y_tilde) # <<<<<<<<<<<<<< * * # Actually compute \tilde{\phi(y_i)} */ __pyx_t_13 = __pyx_f_8openTSNE_5_tsne_interpolate(__pyx_v_y_in_box, __pyx_v_y_tilde); if (unlikely(!__pyx_t_13.memview)) __PYX_ERR(0, 1168, __pyx_L1_error) __pyx_v_y_interpolated_values = __pyx_t_13; __pyx_t_13.memview = NULL; __pyx_t_13.data = NULL; /* "openTSNE/_tsne.pyx":1173 * cdef Py_ssize_t box_i, box_j, interp_i, interp_j, idx * * cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) # <<<<<<<<<<<<<< * for i in range(n_samples): * box_idx = point_box_idx[i] */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1173, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_8 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_zeros); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1173, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1173, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_9 = PyInt_FromSsize_t(__pyx_v_n_terms); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1173, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_10 = PyTuple_New(2); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1173, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_7); __Pyx_GIVEREF(__pyx_t_9); PyTuple_SET_ITEM(__pyx_t_10, 1, __pyx_t_9); __pyx_t_7 = 0; __pyx_t_9 = 0; __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1173, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1173, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); if (PyDict_SetItem(__pyx_t_10, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 1173, __pyx_L1_error) __pyx_t_7 = __Pyx_PyObject_Call(__pyx_t_8, __pyx_t_9, __pyx_t_10); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1173, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_13 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_7, PyBUF_WRITABLE); if (unlikely(!__pyx_t_13.memview)) __PYX_ERR(0, 1173, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_v_phi = __pyx_t_13; __pyx_t_13.memview = NULL; __pyx_t_13.data = NULL; /* "openTSNE/_tsne.pyx":1174 * * cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) * for i in range(n_samples): # <<<<<<<<<<<<<< * box_idx = point_box_idx[i] * box_i = box_idx % n_boxes_1d */ __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":1175 * cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) * for i in range(n_samples): * box_idx = point_box_idx[i] # <<<<<<<<<<<<<< * box_i = box_idx % n_boxes_1d * box_j = box_idx // n_boxes_1d */ __pyx_v_box_idx = (__pyx_v_point_box_idx[__pyx_v_i]); /* "openTSNE/_tsne.pyx":1176 * for i in range(n_samples): * box_idx = point_box_idx[i] * box_i = box_idx % n_boxes_1d # <<<<<<<<<<<<<< * box_j = box_idx // n_boxes_1d * for interp_i in range(n_interpolation_points): */ __pyx_v_box_i = (__pyx_v_box_idx % __pyx_v_n_boxes_1d); /* "openTSNE/_tsne.pyx":1177 * box_idx = point_box_idx[i] * box_i = box_idx % n_boxes_1d * box_j = box_idx // n_boxes_1d # <<<<<<<<<<<<<< * for interp_i in range(n_interpolation_points): * for interp_j in range(n_interpolation_points): */ __pyx_v_box_j = (__pyx_v_box_idx / __pyx_v_n_boxes_1d); /* "openTSNE/_tsne.pyx":1178 * box_i = box_idx % n_boxes_1d * box_j = box_idx // n_boxes_1d * for interp_i in range(n_interpolation_points): # <<<<<<<<<<<<<< * for interp_j in range(n_interpolation_points): * idx = (box_i * n_interpolation_points + interp_i) * \ */ __pyx_t_14 = __pyx_v_n_interpolation_points; __pyx_t_15 = __pyx_t_14; for (__pyx_t_16 = 0; __pyx_t_16 < __pyx_t_15; __pyx_t_16+=1) { __pyx_v_interp_i = __pyx_t_16; /* "openTSNE/_tsne.pyx":1179 * box_j = box_idx // n_boxes_1d * for interp_i in range(n_interpolation_points): * for interp_j in range(n_interpolation_points): # <<<<<<<<<<<<<< * idx = (box_i * n_interpolation_points + interp_i) * \ * (n_boxes_1d * n_interpolation_points) + \ */ __pyx_t_17 = __pyx_v_n_interpolation_points; __pyx_t_18 = __pyx_t_17; for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) { __pyx_v_interp_j = __pyx_t_19; /* "openTSNE/_tsne.pyx":1182 * idx = (box_i * n_interpolation_points + interp_i) * \ * (n_boxes_1d * n_interpolation_points) + \ * (box_j * n_interpolation_points) + \ # <<<<<<<<<<<<<< * interp_j * for d in range(n_terms): */ __pyx_v_idx = (((((__pyx_v_box_i * __pyx_v_n_interpolation_points) + __pyx_v_interp_i) * (__pyx_v_n_boxes_1d * __pyx_v_n_interpolation_points)) + (__pyx_v_box_j * __pyx_v_n_interpolation_points)) + __pyx_v_interp_j); /* "openTSNE/_tsne.pyx":1184 * (box_j * n_interpolation_points) + \ * interp_j * for d in range(n_terms): # <<<<<<<<<<<<<< * phi[i, d] += x_interpolated_values[i, interp_i] * \ * y_interpolated_values[i, interp_j] * \ */ __pyx_t_20 = __pyx_v_n_terms; __pyx_t_21 = __pyx_t_20; for (__pyx_t_22 = 0; __pyx_t_22 < __pyx_t_21; __pyx_t_22+=1) { __pyx_v_d = __pyx_t_22; /* "openTSNE/_tsne.pyx":1185 * interp_j * for d in range(n_terms): * phi[i, d] += x_interpolated_values[i, interp_i] * \ # <<<<<<<<<<<<<< * y_interpolated_values[i, interp_j] * \ * y_tilde_values[idx, d] */ __pyx_t_2 = __pyx_v_i; __pyx_t_1 = __pyx_v_interp_i; /* "openTSNE/_tsne.pyx":1186 * for d in range(n_terms): * phi[i, d] += x_interpolated_values[i, interp_i] * \ * y_interpolated_values[i, interp_j] * \ # <<<<<<<<<<<<<< * y_tilde_values[idx, d] * */ __pyx_t_12 = __pyx_v_i; __pyx_t_23 = __pyx_v_interp_j; /* "openTSNE/_tsne.pyx":1187 * phi[i, d] += x_interpolated_values[i, interp_i] * \ * y_interpolated_values[i, interp_j] * \ * y_tilde_values[idx, d] # <<<<<<<<<<<<<< * * PyMem_Free(point_box_idx) */ __pyx_t_24 = __pyx_v_idx; __pyx_t_25 = __pyx_v_d; /* "openTSNE/_tsne.pyx":1185 * interp_j * for d in range(n_terms): * phi[i, d] += x_interpolated_values[i, interp_i] * \ # <<<<<<<<<<<<<< * y_interpolated_values[i, interp_j] * \ * y_tilde_values[idx, d] */ __pyx_t_26 = __pyx_v_i; __pyx_t_27 = __pyx_v_d; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_26 * __pyx_v_phi.strides[0]) )) + __pyx_t_27)) )) += (((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_x_interpolated_values.data + __pyx_t_2 * __pyx_v_x_interpolated_values.strides[0]) )) + __pyx_t_1)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_y_interpolated_values.data + __pyx_t_12 * __pyx_v_y_interpolated_values.strides[0]) )) + __pyx_t_23)) )))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_y_tilde_values.data + __pyx_t_24 * __pyx_v_y_tilde_values.strides[0]) )) + __pyx_t_25)) )))); } } } } /* "openTSNE/_tsne.pyx":1189 * y_tilde_values[idx, d] * * PyMem_Free(point_box_idx) # <<<<<<<<<<<<<< * * # Compute the normalization term Z or sum of q_{ij}s */ PyMem_Free(__pyx_v_point_box_idx); /* "openTSNE/_tsne.pyx":1192 * * # Compute the normalization term Z or sum of q_{ij}s * cdef double[::1] sum_Qi = np.empty(n_samples, dtype=float) # <<<<<<<<<<<<<< * cdef double y1, y2 * if dof != 1: */ __Pyx_GetModuleGlobalName(__pyx_t_7, __pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1192, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_10 = __Pyx_PyObject_GetAttrStr(__pyx_t_7, __pyx_n_s_empty); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 1192, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = PyInt_FromSsize_t(__pyx_v_n_samples); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1192, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 1192, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1192, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); if (PyDict_SetItem(__pyx_t_7, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 1192, __pyx_L1_error) __pyx_t_8 = __Pyx_PyObject_Call(__pyx_t_10, __pyx_t_9, __pyx_t_7); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1192, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __pyx_t_11 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_8, PyBUF_WRITABLE); if (unlikely(!__pyx_t_11.memview)) __PYX_ERR(0, 1192, __pyx_L1_error) __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __pyx_v_sum_Qi = __pyx_t_11; __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; /* "openTSNE/_tsne.pyx":1194 * cdef double[::1] sum_Qi = np.empty(n_samples, dtype=float) * cdef double y1, y2 * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_samples): * sum_Qi[i] = phi[i, 3] */ __pyx_t_6 = ((__pyx_v_dof != 1.0) != 0); if (__pyx_t_6) { /* "openTSNE/_tsne.pyx":1195 * cdef double y1, y2 * if dof != 1: * for i in range(n_samples): # <<<<<<<<<<<<<< * sum_Qi[i] = phi[i, 3] * else: */ __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":1196 * if dof != 1: * for i in range(n_samples): * sum_Qi[i] = phi[i, 3] # <<<<<<<<<<<<<< * else: * for i in range(n_samples): */ __pyx_t_25 = __pyx_v_i; __pyx_t_24 = 3; __pyx_t_23 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_sum_Qi.data) + __pyx_t_23)) )) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_25 * __pyx_v_phi.strides[0]) )) + __pyx_t_24)) ))); } /* "openTSNE/_tsne.pyx":1194 * cdef double[::1] sum_Qi = np.empty(n_samples, dtype=float) * cdef double y1, y2 * if dof != 1: # <<<<<<<<<<<<<< * for i in range(n_samples): * sum_Qi[i] = phi[i, 3] */ goto __pyx_L19; } /* "openTSNE/_tsne.pyx":1198 * sum_Qi[i] = phi[i, 3] * else: * for i in range(n_samples): # <<<<<<<<<<<<<< * y1 = embedding[i, 0] * y2 = embedding[i, 1] */ /*else*/ { __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":1199 * else: * for i in range(n_samples): * y1 = embedding[i, 0] # <<<<<<<<<<<<<< * y2 = embedding[i, 1] * */ __pyx_t_24 = __pyx_v_i; __pyx_t_25 = 0; __pyx_v_y1 = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_24 * __pyx_v_embedding.strides[0]) )) + __pyx_t_25)) ))); /* "openTSNE/_tsne.pyx":1200 * for i in range(n_samples): * y1 = embedding[i, 0] * y2 = embedding[i, 1] # <<<<<<<<<<<<<< * * sum_Qi[i] = (1 + y1 ** 2 + y2 ** 2) * phi[i, 0] - \ */ __pyx_t_25 = __pyx_v_i; __pyx_t_24 = 1; __pyx_v_y2 = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_25 * __pyx_v_embedding.strides[0]) )) + __pyx_t_24)) ))); /* "openTSNE/_tsne.pyx":1202 * y2 = embedding[i, 1] * * sum_Qi[i] = (1 + y1 ** 2 + y2 ** 2) * phi[i, 0] - \ # <<<<<<<<<<<<<< * 2 * (y1 * phi[i, 1] + y2 * phi[i, 2]) + \ * phi[i, 3] */ __pyx_t_24 = __pyx_v_i; __pyx_t_25 = 0; /* "openTSNE/_tsne.pyx":1203 * * sum_Qi[i] = (1 + y1 ** 2 + y2 ** 2) * phi[i, 0] - \ * 2 * (y1 * phi[i, 1] + y2 * phi[i, 2]) + \ # <<<<<<<<<<<<<< * phi[i, 3] * */ __pyx_t_23 = __pyx_v_i; __pyx_t_12 = 1; __pyx_t_1 = __pyx_v_i; __pyx_t_2 = 2; /* "openTSNE/_tsne.pyx":1204 * sum_Qi[i] = (1 + y1 ** 2 + y2 ** 2) * phi[i, 0] - \ * 2 * (y1 * phi[i, 1] + y2 * phi[i, 2]) + \ * phi[i, 3] # <<<<<<<<<<<<<< * * cdef sum_Q = 0 */ __pyx_t_27 = __pyx_v_i; __pyx_t_26 = 3; /* "openTSNE/_tsne.pyx":1202 * y2 = embedding[i, 1] * * sum_Qi[i] = (1 + y1 ** 2 + y2 ** 2) * phi[i, 0] - \ # <<<<<<<<<<<<<< * 2 * (y1 * phi[i, 1] + y2 * phi[i, 2]) + \ * phi[i, 3] */ __pyx_t_28 = __pyx_v_i; *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_sum_Qi.data) + __pyx_t_28)) )) = (((((1.0 + pow(__pyx_v_y1, 2.0)) + pow(__pyx_v_y2, 2.0)) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_24 * __pyx_v_phi.strides[0]) )) + __pyx_t_25)) )))) - (2.0 * ((__pyx_v_y1 * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_23 * __pyx_v_phi.strides[0]) )) + __pyx_t_12)) )))) + (__pyx_v_y2 * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_1 * __pyx_v_phi.strides[0]) )) + __pyx_t_2)) ))))))) + (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_27 * __pyx_v_phi.strides[0]) )) + __pyx_t_26)) )))); } } __pyx_L19:; /* "openTSNE/_tsne.pyx":1206 * phi[i, 3] * * cdef sum_Q = 0 # <<<<<<<<<<<<<< * for i in range(n_samples): * sum_Q += sum_Qi[i] */ __Pyx_INCREF(__pyx_int_0); __pyx_v_sum_Q = __pyx_int_0; /* "openTSNE/_tsne.pyx":1207 * * cdef sum_Q = 0 * for i in range(n_samples): # <<<<<<<<<<<<<< * sum_Q += sum_Qi[i] * */ __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":1208 * cdef sum_Q = 0 * for i in range(n_samples): * sum_Q += sum_Qi[i] # <<<<<<<<<<<<<< * * # The phis used here are not affected if dof != 1 */ __pyx_t_26 = __pyx_v_i; __pyx_t_8 = PyFloat_FromDouble((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_sum_Qi.data) + __pyx_t_26)) )))); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 1208, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); __pyx_t_7 = PyNumber_InPlaceAdd(__pyx_v_sum_Q, __pyx_t_8); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 1208, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF_SET(__pyx_v_sum_Q, __pyx_t_7); __pyx_t_7 = 0; } /* "openTSNE/_tsne.pyx":1211 * * # The phis used here are not affected if dof != 1 * for i in range(n_samples): # <<<<<<<<<<<<<< * gradient[i, 0] -= (embedding[i, 0] * phi[i, 0] - phi[i, 1]) / (sum_Qi[i] + EPSILON) * gradient[i, 1] -= (embedding[i, 1] * phi[i, 0] - phi[i, 2]) / (sum_Qi[i] + EPSILON) */ __pyx_t_3 = __pyx_v_n_samples; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/_tsne.pyx":1212 * # The phis used here are not affected if dof != 1 * for i in range(n_samples): * gradient[i, 0] -= (embedding[i, 0] * phi[i, 0] - phi[i, 1]) / (sum_Qi[i] + EPSILON) # <<<<<<<<<<<<<< * gradient[i, 1] -= (embedding[i, 1] * phi[i, 0] - phi[i, 2]) / (sum_Qi[i] + EPSILON) * */ __pyx_t_26 = __pyx_v_i; __pyx_t_27 = 0; __pyx_t_2 = __pyx_v_i; __pyx_t_1 = 0; __pyx_t_12 = __pyx_v_i; __pyx_t_23 = 1; __pyx_t_25 = __pyx_v_i; __pyx_t_24 = __pyx_v_i; __pyx_t_28 = 0; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_gradient.data + __pyx_t_24 * __pyx_v_gradient.strides[0]) )) + __pyx_t_28)) )) -= ((((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_26 * __pyx_v_embedding.strides[0]) )) + __pyx_t_27)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_2 * __pyx_v_phi.strides[0]) )) + __pyx_t_1)) )))) - (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_12 * __pyx_v_phi.strides[0]) )) + __pyx_t_23)) )))) / ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_sum_Qi.data) + __pyx_t_25)) ))) + __pyx_v_8openTSNE_5_tsne_EPSILON)); /* "openTSNE/_tsne.pyx":1213 * for i in range(n_samples): * gradient[i, 0] -= (embedding[i, 0] * phi[i, 0] - phi[i, 1]) / (sum_Qi[i] + EPSILON) * gradient[i, 1] -= (embedding[i, 1] * phi[i, 0] - phi[i, 2]) / (sum_Qi[i] + EPSILON) # <<<<<<<<<<<<<< * * return sum_Q */ __pyx_t_25 = __pyx_v_i; __pyx_t_23 = 1; __pyx_t_12 = __pyx_v_i; __pyx_t_1 = 0; __pyx_t_2 = __pyx_v_i; __pyx_t_27 = 2; __pyx_t_26 = __pyx_v_i; __pyx_t_28 = __pyx_v_i; __pyx_t_24 = 1; *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_gradient.data + __pyx_t_28 * __pyx_v_gradient.strides[0]) )) + __pyx_t_24)) )) -= ((((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_embedding.data + __pyx_t_25 * __pyx_v_embedding.strides[0]) )) + __pyx_t_23)) ))) * (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_12 * __pyx_v_phi.strides[0]) )) + __pyx_t_1)) )))) - (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_2 * __pyx_v_phi.strides[0]) )) + __pyx_t_27)) )))) / ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_sum_Qi.data) + __pyx_t_26)) ))) + __pyx_v_8openTSNE_5_tsne_EPSILON)); } /* "openTSNE/_tsne.pyx":1215 * gradient[i, 1] -= (embedding[i, 1] * phi[i, 0] - phi[i, 2]) / (sum_Qi[i] + EPSILON) * * return sum_Q # <<<<<<<<<<<<<< */ __pyx_t_29 = __pyx_PyFloat_AsDouble(__pyx_v_sum_Q); if (unlikely((__pyx_t_29 == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 1215, __pyx_L1_error) __pyx_r = __pyx_t_29; goto __pyx_L0; /* "openTSNE/_tsne.pyx":1112 * * * cpdef double estimate_negative_gradient_fft_2d_with_grid( # <<<<<<<<<<<<<< * double[:, ::1] embedding, * double[:, ::1] gradient, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_XDECREF(__pyx_t_9); __Pyx_XDECREF(__pyx_t_10); __PYX_XDEC_MEMVIEW(&__pyx_t_11, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_13, 1); __Pyx_WriteUnraisable("openTSNE._tsne.estimate_negative_gradient_fft_2d_with_grid", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_r = 0; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_x_in_box, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_in_box, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_x_interpolated_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_interpolated_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_phi, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_sum_Qi, 1); __Pyx_XDECREF(__pyx_v_sum_Q); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_5_tsne_17estimate_negative_gradient_fft_2d_with_grid(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_pw_8openTSNE_5_tsne_17estimate_negative_gradient_fft_2d_with_grid(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __Pyx_memviewslice __pyx_v_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_gradient = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_y_tilde_values = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_box_x_lower_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_box_y_lower_bounds = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_v_n_interpolation_points; double __pyx_v_dof; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("estimate_negative_gradient_fft_2d_with_grid (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_embedding,&__pyx_n_s_gradient,&__pyx_n_s_y_tilde_values,&__pyx_n_s_box_x_lower_bounds,&__pyx_n_s_box_y_lower_bounds,&__pyx_n_s_n_interpolation_points,&__pyx_n_s_dof,0}; PyObject* values[7] = {0,0,0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 7: values[6] = PyTuple_GET_ITEM(__pyx_args, 6); CYTHON_FALLTHROUGH; case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_embedding)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_gradient)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_2d_with_grid", 1, 7, 7, 1); __PYX_ERR(0, 1112, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_y_tilde_values)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_2d_with_grid", 1, 7, 7, 2); __PYX_ERR(0, 1112, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 3: if (likely((values[3] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_box_x_lower_bounds)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_2d_with_grid", 1, 7, 7, 3); __PYX_ERR(0, 1112, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 4: if (likely((values[4] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_box_y_lower_bounds)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_2d_with_grid", 1, 7, 7, 4); __PYX_ERR(0, 1112, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 5: if (likely((values[5] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_n_interpolation_points)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_2d_with_grid", 1, 7, 7, 5); __PYX_ERR(0, 1112, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 6: if (likely((values[6] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dof)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_2d_with_grid", 1, 7, 7, 6); __PYX_ERR(0, 1112, __pyx_L3_error) } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "estimate_negative_gradient_fft_2d_with_grid") < 0)) __PYX_ERR(0, 1112, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 7) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[2] = PyTuple_GET_ITEM(__pyx_args, 2); values[3] = PyTuple_GET_ITEM(__pyx_args, 3); values[4] = PyTuple_GET_ITEM(__pyx_args, 4); values[5] = PyTuple_GET_ITEM(__pyx_args, 5); values[6] = PyTuple_GET_ITEM(__pyx_args, 6); } __pyx_v_embedding = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_embedding.memview)) __PYX_ERR(0, 1113, __pyx_L3_error) __pyx_v_gradient = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[1], PyBUF_WRITABLE); if (unlikely(!__pyx_v_gradient.memview)) __PYX_ERR(0, 1114, __pyx_L3_error) __pyx_v_y_tilde_values = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[2], PyBUF_WRITABLE); if (unlikely(!__pyx_v_y_tilde_values.memview)) __PYX_ERR(0, 1115, __pyx_L3_error) __pyx_v_box_x_lower_bounds = __Pyx_PyObject_to_MemoryviewSlice_dc_double(values[3], PyBUF_WRITABLE); if (unlikely(!__pyx_v_box_x_lower_bounds.memview)) __PYX_ERR(0, 1116, __pyx_L3_error) __pyx_v_box_y_lower_bounds = __Pyx_PyObject_to_MemoryviewSlice_dc_double(values[4], PyBUF_WRITABLE); if (unlikely(!__pyx_v_box_y_lower_bounds.memview)) __PYX_ERR(0, 1117, __pyx_L3_error) __pyx_v_n_interpolation_points = __Pyx_PyIndex_AsSsize_t(values[5]); if (unlikely((__pyx_v_n_interpolation_points == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 1118, __pyx_L3_error) __pyx_v_dof = __pyx_PyFloat_AsDouble(values[6]); if (unlikely((__pyx_v_dof == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 1119, __pyx_L3_error) } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("estimate_negative_gradient_fft_2d_with_grid", 1, 7, 7, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 1112, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE._tsne.estimate_negative_gradient_fft_2d_with_grid", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_5_tsne_16estimate_negative_gradient_fft_2d_with_grid(__pyx_self, __pyx_v_embedding, __pyx_v_gradient, __pyx_v_y_tilde_values, __pyx_v_box_x_lower_bounds, __pyx_v_box_y_lower_bounds, __pyx_v_n_interpolation_points, __pyx_v_dof); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_5_tsne_16estimate_negative_gradient_fft_2d_with_grid(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_embedding, __Pyx_memviewslice __pyx_v_gradient, __Pyx_memviewslice __pyx_v_y_tilde_values, __Pyx_memviewslice __pyx_v_box_x_lower_bounds, __Pyx_memviewslice __pyx_v_box_y_lower_bounds, Py_ssize_t __pyx_v_n_interpolation_points, double __pyx_v_dof) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("estimate_negative_gradient_fft_2d_with_grid", 0); __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyFloat_FromDouble(__pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d_with_grid(__pyx_v_embedding, __pyx_v_gradient, __pyx_v_y_tilde_values, __pyx_v_box_x_lower_bounds, __pyx_v_box_y_lower_bounds, __pyx_v_n_interpolation_points, __pyx_v_dof, 0)); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1112, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("openTSNE._tsne.estimate_negative_gradient_fft_2d_with_grid", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_embedding, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_gradient, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_y_tilde_values, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_box_x_lower_bounds, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_box_y_lower_bounds, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":734 * ctypedef npy_cdouble complex_t * * cdef inline object PyArray_MultiIterNew1(a): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(1, a) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *__pyx_v_a) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew1", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":735 * * cdef inline object PyArray_MultiIterNew1(a): * return PyArray_MultiIterNew(1, a) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew2(a, b): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(1, ((void *)__pyx_v_a)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 735, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":734 * ctypedef npy_cdouble complex_t * * cdef inline object PyArray_MultiIterNew1(a): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(1, a) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew1", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":737 * return PyArray_MultiIterNew(1, a) * * cdef inline object PyArray_MultiIterNew2(a, b): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(2, a, b) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *__pyx_v_a, PyObject *__pyx_v_b) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew2", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":738 * * cdef inline object PyArray_MultiIterNew2(a, b): * return PyArray_MultiIterNew(2, a, b) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew3(a, b, c): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(2, ((void *)__pyx_v_a), ((void *)__pyx_v_b)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 738, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":737 * return PyArray_MultiIterNew(1, a) * * cdef inline object PyArray_MultiIterNew2(a, b): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(2, a, b) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew2", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":740 * return PyArray_MultiIterNew(2, a, b) * * cdef inline object PyArray_MultiIterNew3(a, b, c): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(3, a, b, c) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew3", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":741 * * cdef inline object PyArray_MultiIterNew3(a, b, c): * return PyArray_MultiIterNew(3, a, b, c) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(3, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 741, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":740 * return PyArray_MultiIterNew(2, a, b) * * cdef inline object PyArray_MultiIterNew3(a, b, c): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(3, a, b, c) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew3", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":743 * return PyArray_MultiIterNew(3, a, b, c) * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(4, a, b, c, d) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c, PyObject *__pyx_v_d) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew4", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":744 * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): * return PyArray_MultiIterNew(4, a, b, c, d) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(4, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 744, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":743 * return PyArray_MultiIterNew(3, a, b, c) * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(4, a, b, c, d) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew4", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":746 * return PyArray_MultiIterNew(4, a, b, c, d) * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(5, a, b, c, d, e) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c, PyObject *__pyx_v_d, PyObject *__pyx_v_e) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew5", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":747 * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): * return PyArray_MultiIterNew(5, a, b, c, d, e) # <<<<<<<<<<<<<< * * cdef inline tuple PyDataType_SHAPE(dtype d): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(5, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d), ((void *)__pyx_v_e)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 747, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":746 * return PyArray_MultiIterNew(4, a, b, c, d) * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(5, a, b, c, d, e) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew5", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":749 * return PyArray_MultiIterNew(5, a, b, c, d, e) * * cdef inline tuple PyDataType_SHAPE(dtype d): # <<<<<<<<<<<<<< * if PyDataType_HASSUBARRAY(d): * return d.subarray.shape */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyDataType_SHAPE(PyArray_Descr *__pyx_v_d) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("PyDataType_SHAPE", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":750 * * cdef inline tuple PyDataType_SHAPE(dtype d): * if PyDataType_HASSUBARRAY(d): # <<<<<<<<<<<<<< * return d.subarray.shape * else: */ __pyx_t_1 = (PyDataType_HASSUBARRAY(__pyx_v_d) != 0); if (__pyx_t_1) { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":751 * cdef inline tuple PyDataType_SHAPE(dtype d): * if PyDataType_HASSUBARRAY(d): * return d.subarray.shape # <<<<<<<<<<<<<< * else: * return () */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject*)__pyx_v_d->subarray->shape)); __pyx_r = ((PyObject*)__pyx_v_d->subarray->shape); goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":750 * * cdef inline tuple PyDataType_SHAPE(dtype d): * if PyDataType_HASSUBARRAY(d): # <<<<<<<<<<<<<< * return d.subarray.shape * else: */ } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":753 * return d.subarray.shape * else: * return () # <<<<<<<<<<<<<< * * */ /*else*/ { __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_empty_tuple); __pyx_r = __pyx_empty_tuple; goto __pyx_L0; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":749 * return PyArray_MultiIterNew(5, a, b, c, d, e) * * cdef inline tuple PyDataType_SHAPE(dtype d): # <<<<<<<<<<<<<< * if PyDataType_HASSUBARRAY(d): * return d.subarray.shape */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":868 * int _import_umath() except -1 * * cdef inline void set_array_base(ndarray arr, object base): # <<<<<<<<<<<<<< * Py_INCREF(base) # important to do this before stealing the reference below! * PyArray_SetBaseObject(arr, base) */ static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *__pyx_v_arr, PyObject *__pyx_v_base) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("set_array_base", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":869 * * cdef inline void set_array_base(ndarray arr, object base): * Py_INCREF(base) # important to do this before stealing the reference below! # <<<<<<<<<<<<<< * PyArray_SetBaseObject(arr, base) * */ Py_INCREF(__pyx_v_base); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":870 * cdef inline void set_array_base(ndarray arr, object base): * Py_INCREF(base) # important to do this before stealing the reference below! * PyArray_SetBaseObject(arr, base) # <<<<<<<<<<<<<< * * cdef inline object get_array_base(ndarray arr): */ (void)(PyArray_SetBaseObject(__pyx_v_arr, __pyx_v_base)); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":868 * int _import_umath() except -1 * * cdef inline void set_array_base(ndarray arr, object base): # <<<<<<<<<<<<<< * Py_INCREF(base) # important to do this before stealing the reference below! * PyArray_SetBaseObject(arr, base) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":872 * PyArray_SetBaseObject(arr, base) * * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< * base = PyArray_BASE(arr) * if base is NULL: */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__pyx_v_arr) { PyObject *__pyx_v_base; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("get_array_base", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":873 * * cdef inline object get_array_base(ndarray arr): * base = PyArray_BASE(arr) # <<<<<<<<<<<<<< * if base is NULL: * return None */ __pyx_v_base = PyArray_BASE(__pyx_v_arr); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":874 * cdef inline object get_array_base(ndarray arr): * base = PyArray_BASE(arr) * if base is NULL: # <<<<<<<<<<<<<< * return None * return base */ __pyx_t_1 = ((__pyx_v_base == NULL) != 0); if (__pyx_t_1) { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":875 * base = PyArray_BASE(arr) * if base is NULL: * return None # <<<<<<<<<<<<<< * return base * */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":874 * cdef inline object get_array_base(ndarray arr): * base = PyArray_BASE(arr) * if base is NULL: # <<<<<<<<<<<<<< * return None * return base */ } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":876 * if base is NULL: * return None * return base # <<<<<<<<<<<<<< * * # Versions of the import_* functions which are more suitable for */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_base)); __pyx_r = ((PyObject *)__pyx_v_base); goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":872 * PyArray_SetBaseObject(arr, base) * * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< * base = PyArray_BASE(arr) * if base is NULL: */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":880 * # Versions of the import_* functions which are more suitable for * # Cython code. * cdef inline int import_array() except -1: # <<<<<<<<<<<<<< * try: * __pyx_import_array() */ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("import_array", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":881 * # Cython code. * cdef inline int import_array() except -1: * try: # <<<<<<<<<<<<<< * __pyx_import_array() * except Exception: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_1, &__pyx_t_2, &__pyx_t_3); __Pyx_XGOTREF(__pyx_t_1); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); /*try:*/ { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":882 * cdef inline int import_array() except -1: * try: * __pyx_import_array() # <<<<<<<<<<<<<< * except Exception: * raise ImportError("numpy.core.multiarray failed to import") */ __pyx_t_4 = _import_array(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 882, __pyx_L3_error) /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":881 * # Cython code. * cdef inline int import_array() except -1: * try: # <<<<<<<<<<<<<< * __pyx_import_array() * except Exception: */ } __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L8_try_end; __pyx_L3_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":883 * try: * __pyx_import_array() * except Exception: # <<<<<<<<<<<<<< * raise ImportError("numpy.core.multiarray failed to import") * */ __pyx_t_4 = __Pyx_PyErr_ExceptionMatches(((PyObject *)(&((PyTypeObject*)PyExc_Exception)[0]))); if (__pyx_t_4) { __Pyx_AddTraceback("numpy.import_array", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(1, 883, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_6); __Pyx_GOTREF(__pyx_t_7); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":884 * __pyx_import_array() * except Exception: * raise ImportError("numpy.core.multiarray failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_umath() except -1: */ __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ImportError, __pyx_tuple__11, NULL); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 884, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_Raise(__pyx_t_8, 0, 0, 0); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __PYX_ERR(1, 884, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":881 * # Cython code. * cdef inline int import_array() except -1: * try: # <<<<<<<<<<<<<< * __pyx_import_array() * except Exception: */ __Pyx_XGIVEREF(__pyx_t_1); __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_ExceptionReset(__pyx_t_1, __pyx_t_2, __pyx_t_3); goto __pyx_L1_error; __pyx_L8_try_end:; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":880 * # Versions of the import_* functions which are more suitable for * # Cython code. * cdef inline int import_array() except -1: # <<<<<<<<<<<<<< * try: * __pyx_import_array() */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("numpy.import_array", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":886 * raise ImportError("numpy.core.multiarray failed to import") * * cdef inline int import_umath() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("import_umath", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":887 * * cdef inline int import_umath() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_1, &__pyx_t_2, &__pyx_t_3); __Pyx_XGOTREF(__pyx_t_1); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); /*try:*/ { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":888 * cdef inline int import_umath() except -1: * try: * _import_umath() # <<<<<<<<<<<<<< * except Exception: * raise ImportError("numpy.core.umath failed to import") */ __pyx_t_4 = _import_umath(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 888, __pyx_L3_error) /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":887 * * cdef inline int import_umath() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ } __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L8_try_end; __pyx_L3_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":889 * try: * _import_umath() * except Exception: # <<<<<<<<<<<<<< * raise ImportError("numpy.core.umath failed to import") * */ __pyx_t_4 = __Pyx_PyErr_ExceptionMatches(((PyObject *)(&((PyTypeObject*)PyExc_Exception)[0]))); if (__pyx_t_4) { __Pyx_AddTraceback("numpy.import_umath", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(1, 889, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_6); __Pyx_GOTREF(__pyx_t_7); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":890 * _import_umath() * except Exception: * raise ImportError("numpy.core.umath failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_ufunc() except -1: */ __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ImportError, __pyx_tuple__12, NULL); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 890, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_Raise(__pyx_t_8, 0, 0, 0); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __PYX_ERR(1, 890, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":887 * * cdef inline int import_umath() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ __Pyx_XGIVEREF(__pyx_t_1); __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_ExceptionReset(__pyx_t_1, __pyx_t_2, __pyx_t_3); goto __pyx_L1_error; __pyx_L8_try_end:; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":886 * raise ImportError("numpy.core.multiarray failed to import") * * cdef inline int import_umath() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("numpy.import_umath", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":892 * raise ImportError("numpy.core.umath failed to import") * * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("import_ufunc", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":893 * * cdef inline int import_ufunc() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_1, &__pyx_t_2, &__pyx_t_3); __Pyx_XGOTREF(__pyx_t_1); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); /*try:*/ { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":894 * cdef inline int import_ufunc() except -1: * try: * _import_umath() # <<<<<<<<<<<<<< * except Exception: * raise ImportError("numpy.core.umath failed to import") */ __pyx_t_4 = _import_umath(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 894, __pyx_L3_error) /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":893 * * cdef inline int import_ufunc() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ } __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L8_try_end; __pyx_L3_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":895 * try: * _import_umath() * except Exception: # <<<<<<<<<<<<<< * raise ImportError("numpy.core.umath failed to import") * */ __pyx_t_4 = __Pyx_PyErr_ExceptionMatches(((PyObject *)(&((PyTypeObject*)PyExc_Exception)[0]))); if (__pyx_t_4) { __Pyx_AddTraceback("numpy.import_ufunc", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(1, 895, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_6); __Pyx_GOTREF(__pyx_t_7); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":896 * _import_umath() * except Exception: * raise ImportError("numpy.core.umath failed to import") # <<<<<<<<<<<<<< * * cdef extern from *: */ __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ImportError, __pyx_tuple__12, NULL); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 896, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_Raise(__pyx_t_8, 0, 0, 0); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __PYX_ERR(1, 896, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":893 * * cdef inline int import_ufunc() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ __Pyx_XGIVEREF(__pyx_t_1); __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_ExceptionReset(__pyx_t_1, __pyx_t_2, __pyx_t_3); goto __pyx_L1_error; __pyx_L8_try_end:; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":892 * raise ImportError("numpy.core.umath failed to import") * * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("numpy.import_ufunc", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* Python wrapper */ static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_shape = 0; Py_ssize_t __pyx_v_itemsize; PyObject *__pyx_v_format = 0; PyObject *__pyx_v_mode = 0; int __pyx_v_allocate_buffer; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_shape,&__pyx_n_s_itemsize,&__pyx_n_s_format,&__pyx_n_s_mode,&__pyx_n_s_allocate_buffer,0}; PyObject* values[5] = {0,0,0,0,0}; values[3] = ((PyObject *)__pyx_n_s_c); if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_shape)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_itemsize)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 1); __PYX_ERR(2, 122, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_format)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 2); __PYX_ERR(2, 122, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 3: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_mode); if (value) { values[3] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 4: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_allocate_buffer); if (value) { values[4] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) __PYX_ERR(2, 122, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_shape = ((PyObject*)values[0]); __pyx_v_itemsize = __Pyx_PyIndex_AsSsize_t(values[1]); if (unlikely((__pyx_v_itemsize == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 122, __pyx_L3_error) __pyx_v_format = values[2]; __pyx_v_mode = values[3]; if (values[4]) { __pyx_v_allocate_buffer = __Pyx_PyObject_IsTrue(values[4]); if (unlikely((__pyx_v_allocate_buffer == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 123, __pyx_L3_error) } else { /* "View.MemoryView":123 * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, * mode="c", bint allocate_buffer=True): # <<<<<<<<<<<<<< * * cdef int idx */ __pyx_v_allocate_buffer = ((int)1); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 122, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_shape), (&PyTuple_Type), 1, "shape", 1))) __PYX_ERR(2, 122, __pyx_L1_error) if (unlikely(((PyObject *)__pyx_v_format) == Py_None)) { PyErr_Format(PyExc_TypeError, "Argument '%.200s' must not be None", "format"); __PYX_ERR(2, 122, __pyx_L1_error) } __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(((struct __pyx_array_obj *)__pyx_v_self), __pyx_v_shape, __pyx_v_itemsize, __pyx_v_format, __pyx_v_mode, __pyx_v_allocate_buffer); /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer) { int __pyx_v_idx; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_dim; PyObject **__pyx_v_p; char __pyx_v_order; int __pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; char *__pyx_t_7; int __pyx_t_8; Py_ssize_t __pyx_t_9; PyObject *__pyx_t_10 = NULL; Py_ssize_t __pyx_t_11; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__cinit__", 0); __Pyx_INCREF(__pyx_v_format); /* "View.MemoryView":129 * cdef PyObject **p * * self.ndim = len(shape) # <<<<<<<<<<<<<< * self.itemsize = itemsize * */ if (unlikely(__pyx_v_shape == Py_None)) { PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); __PYX_ERR(2, 129, __pyx_L1_error) } __pyx_t_1 = PyTuple_GET_SIZE(__pyx_v_shape); if (unlikely(__pyx_t_1 == ((Py_ssize_t)-1))) __PYX_ERR(2, 129, __pyx_L1_error) __pyx_v_self->ndim = ((int)__pyx_t_1); /* "View.MemoryView":130 * * self.ndim = len(shape) * self.itemsize = itemsize # <<<<<<<<<<<<<< * * if not self.ndim: */ __pyx_v_self->itemsize = __pyx_v_itemsize; /* "View.MemoryView":132 * self.itemsize = itemsize * * if not self.ndim: # <<<<<<<<<<<<<< * raise ValueError("Empty shape tuple for cython.array") * */ __pyx_t_2 = ((!(__pyx_v_self->ndim != 0)) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":133 * * if not self.ndim: * raise ValueError("Empty shape tuple for cython.array") # <<<<<<<<<<<<<< * * if itemsize <= 0: */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__13, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 133, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 133, __pyx_L1_error) /* "View.MemoryView":132 * self.itemsize = itemsize * * if not self.ndim: # <<<<<<<<<<<<<< * raise ValueError("Empty shape tuple for cython.array") * */ } /* "View.MemoryView":135 * raise ValueError("Empty shape tuple for cython.array") * * if itemsize <= 0: # <<<<<<<<<<<<<< * raise ValueError("itemsize <= 0 for cython.array") * */ __pyx_t_2 = ((__pyx_v_itemsize <= 0) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":136 * * if itemsize <= 0: * raise ValueError("itemsize <= 0 for cython.array") # <<<<<<<<<<<<<< * * if not isinstance(format, bytes): */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__14, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 136, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 136, __pyx_L1_error) /* "View.MemoryView":135 * raise ValueError("Empty shape tuple for cython.array") * * if itemsize <= 0: # <<<<<<<<<<<<<< * raise ValueError("itemsize <= 0 for cython.array") * */ } /* "View.MemoryView":138 * raise ValueError("itemsize <= 0 for cython.array") * * if not isinstance(format, bytes): # <<<<<<<<<<<<<< * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string */ __pyx_t_2 = PyBytes_Check(__pyx_v_format); __pyx_t_4 = ((!(__pyx_t_2 != 0)) != 0); if (__pyx_t_4) { /* "View.MemoryView":139 * * if not isinstance(format, bytes): * format = format.encode('ASCII') # <<<<<<<<<<<<<< * self._format = format # keep a reference to the byte string * self.format = self._format */ __pyx_t_5 = __Pyx_PyObject_GetAttrStr(__pyx_v_format, __pyx_n_s_encode); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 139, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_5))) { __pyx_t_6 = PyMethod_GET_SELF(__pyx_t_5); if (likely(__pyx_t_6)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_5); __Pyx_INCREF(__pyx_t_6); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_5, function); } } __pyx_t_3 = (__pyx_t_6) ? __Pyx_PyObject_Call2Args(__pyx_t_5, __pyx_t_6, __pyx_n_s_ASCII) : __Pyx_PyObject_CallOneArg(__pyx_t_5, __pyx_n_s_ASCII); __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 139, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF_SET(__pyx_v_format, __pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":138 * raise ValueError("itemsize <= 0 for cython.array") * * if not isinstance(format, bytes): # <<<<<<<<<<<<<< * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string */ } /* "View.MemoryView":140 * if not isinstance(format, bytes): * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string # <<<<<<<<<<<<<< * self.format = self._format * */ if (!(likely(PyBytes_CheckExact(__pyx_v_format))||((__pyx_v_format) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_v_format)->tp_name), 0))) __PYX_ERR(2, 140, __pyx_L1_error) __pyx_t_3 = __pyx_v_format; __Pyx_INCREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_3); __Pyx_GOTREF(__pyx_v_self->_format); __Pyx_DECREF(__pyx_v_self->_format); __pyx_v_self->_format = ((PyObject*)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":141 * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string * self.format = self._format # <<<<<<<<<<<<<< * * */ if (unlikely(__pyx_v_self->_format == Py_None)) { PyErr_SetString(PyExc_TypeError, "expected bytes, NoneType found"); __PYX_ERR(2, 141, __pyx_L1_error) } __pyx_t_7 = __Pyx_PyBytes_AsWritableString(__pyx_v_self->_format); if (unlikely((!__pyx_t_7) && PyErr_Occurred())) __PYX_ERR(2, 141, __pyx_L1_error) __pyx_v_self->format = __pyx_t_7; /* "View.MemoryView":144 * * * self._shape = PyObject_Malloc(sizeof(Py_ssize_t)*self.ndim*2) # <<<<<<<<<<<<<< * self._strides = self._shape + self.ndim * */ __pyx_v_self->_shape = ((Py_ssize_t *)PyObject_Malloc((((sizeof(Py_ssize_t)) * __pyx_v_self->ndim) * 2))); /* "View.MemoryView":145 * * self._shape = PyObject_Malloc(sizeof(Py_ssize_t)*self.ndim*2) * self._strides = self._shape + self.ndim # <<<<<<<<<<<<<< * * if not self._shape: */ __pyx_v_self->_strides = (__pyx_v_self->_shape + __pyx_v_self->ndim); /* "View.MemoryView":147 * self._strides = self._shape + self.ndim * * if not self._shape: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate shape and strides.") * */ __pyx_t_4 = ((!(__pyx_v_self->_shape != 0)) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":148 * * if not self._shape: * raise MemoryError("unable to allocate shape and strides.") # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__15, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 148, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 148, __pyx_L1_error) /* "View.MemoryView":147 * self._strides = self._shape + self.ndim * * if not self._shape: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate shape and strides.") * */ } /* "View.MemoryView":151 * * * for idx, dim in enumerate(shape): # <<<<<<<<<<<<<< * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) */ __pyx_t_8 = 0; __pyx_t_3 = __pyx_v_shape; __Pyx_INCREF(__pyx_t_3); __pyx_t_1 = 0; for (;;) { if (__pyx_t_1 >= PyTuple_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_1); __Pyx_INCREF(__pyx_t_5); __pyx_t_1++; if (unlikely(0 < 0)) __PYX_ERR(2, 151, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_3, __pyx_t_1); __pyx_t_1++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 151, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_5); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 151, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_dim = __pyx_t_9; __pyx_v_idx = __pyx_t_8; __pyx_t_8 = (__pyx_t_8 + 1); /* "View.MemoryView":152 * * for idx, dim in enumerate(shape): * if dim <= 0: # <<<<<<<<<<<<<< * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim */ __pyx_t_4 = ((__pyx_v_dim <= 0) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":153 * for idx, dim in enumerate(shape): * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) # <<<<<<<<<<<<<< * self._shape[idx] = dim * */ __pyx_t_5 = __Pyx_PyInt_From_int(__pyx_v_idx); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_10 = PyTuple_New(2); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_10, 1, __pyx_t_6); __pyx_t_5 = 0; __pyx_t_6 = 0; __pyx_t_6 = __Pyx_PyString_Format(__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_t_10); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_6); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(2, 153, __pyx_L1_error) /* "View.MemoryView":152 * * for idx, dim in enumerate(shape): * if dim <= 0: # <<<<<<<<<<<<<< * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim */ } /* "View.MemoryView":154 * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim # <<<<<<<<<<<<<< * * cdef char order */ (__pyx_v_self->_shape[__pyx_v_idx]) = __pyx_v_dim; /* "View.MemoryView":151 * * * for idx, dim in enumerate(shape): # <<<<<<<<<<<<<< * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) */ } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":157 * * cdef char order * if mode == 'fortran': # <<<<<<<<<<<<<< * order = b'F' * self.mode = u'fortran' */ __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_fortran, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) __PYX_ERR(2, 157, __pyx_L1_error) if (__pyx_t_4) { /* "View.MemoryView":158 * cdef char order * if mode == 'fortran': * order = b'F' # <<<<<<<<<<<<<< * self.mode = u'fortran' * elif mode == 'c': */ __pyx_v_order = 'F'; /* "View.MemoryView":159 * if mode == 'fortran': * order = b'F' * self.mode = u'fortran' # <<<<<<<<<<<<<< * elif mode == 'c': * order = b'C' */ __Pyx_INCREF(__pyx_n_u_fortran); __Pyx_GIVEREF(__pyx_n_u_fortran); __Pyx_GOTREF(__pyx_v_self->mode); __Pyx_DECREF(__pyx_v_self->mode); __pyx_v_self->mode = __pyx_n_u_fortran; /* "View.MemoryView":157 * * cdef char order * if mode == 'fortran': # <<<<<<<<<<<<<< * order = b'F' * self.mode = u'fortran' */ goto __pyx_L10; } /* "View.MemoryView":160 * order = b'F' * self.mode = u'fortran' * elif mode == 'c': # <<<<<<<<<<<<<< * order = b'C' * self.mode = u'c' */ __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_c, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) __PYX_ERR(2, 160, __pyx_L1_error) if (likely(__pyx_t_4)) { /* "View.MemoryView":161 * self.mode = u'fortran' * elif mode == 'c': * order = b'C' # <<<<<<<<<<<<<< * self.mode = u'c' * else: */ __pyx_v_order = 'C'; /* "View.MemoryView":162 * elif mode == 'c': * order = b'C' * self.mode = u'c' # <<<<<<<<<<<<<< * else: * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) */ __Pyx_INCREF(__pyx_n_u_c); __Pyx_GIVEREF(__pyx_n_u_c); __Pyx_GOTREF(__pyx_v_self->mode); __Pyx_DECREF(__pyx_v_self->mode); __pyx_v_self->mode = __pyx_n_u_c; /* "View.MemoryView":160 * order = b'F' * self.mode = u'fortran' * elif mode == 'c': # <<<<<<<<<<<<<< * order = b'C' * self.mode = u'c' */ goto __pyx_L10; } /* "View.MemoryView":164 * self.mode = u'c' * else: * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) # <<<<<<<<<<<<<< * * self.len = fill_contig_strides_array(self._shape, self._strides, */ /*else*/ { __pyx_t_3 = __Pyx_PyString_FormatSafe(__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_v_mode); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 164, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_10 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_3); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 164, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(2, 164, __pyx_L1_error) } __pyx_L10:; /* "View.MemoryView":166 * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) * * self.len = fill_contig_strides_array(self._shape, self._strides, # <<<<<<<<<<<<<< * itemsize, self.ndim, order) * */ __pyx_v_self->len = __pyx_fill_contig_strides_array(__pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_itemsize, __pyx_v_self->ndim, __pyx_v_order); /* "View.MemoryView":169 * itemsize, self.ndim, order) * * self.free_data = allocate_buffer # <<<<<<<<<<<<<< * self.dtype_is_object = format == b'O' * if allocate_buffer: */ __pyx_v_self->free_data = __pyx_v_allocate_buffer; /* "View.MemoryView":170 * * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' # <<<<<<<<<<<<<< * if allocate_buffer: * */ __pyx_t_10 = PyObject_RichCompare(__pyx_v_format, __pyx_n_b_O, Py_EQ); __Pyx_XGOTREF(__pyx_t_10); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 170, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_IsTrue(__pyx_t_10); if (unlikely((__pyx_t_4 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 170, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_self->dtype_is_object = __pyx_t_4; /* "View.MemoryView":171 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< * * */ __pyx_t_4 = (__pyx_v_allocate_buffer != 0); if (__pyx_t_4) { /* "View.MemoryView":174 * * * self.data = malloc(self.len) # <<<<<<<<<<<<<< * if not self.data: * raise MemoryError("unable to allocate array data.") */ __pyx_v_self->data = ((char *)malloc(__pyx_v_self->len)); /* "View.MemoryView":175 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate array data.") * */ __pyx_t_4 = ((!(__pyx_v_self->data != 0)) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":176 * self.data = malloc(self.len) * if not self.data: * raise MemoryError("unable to allocate array data.") # <<<<<<<<<<<<<< * * if self.dtype_is_object: */ __pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__16, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 176, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(2, 176, __pyx_L1_error) /* "View.MemoryView":175 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate array data.") * */ } /* "View.MemoryView":178 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< * p = self.data * for i in range(self.len / itemsize): */ __pyx_t_4 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_4) { /* "View.MemoryView":179 * * if self.dtype_is_object: * p = self.data # <<<<<<<<<<<<<< * for i in range(self.len / itemsize): * p[i] = Py_None */ __pyx_v_p = ((PyObject **)__pyx_v_self->data); /* "View.MemoryView":180 * if self.dtype_is_object: * p = self.data * for i in range(self.len / itemsize): # <<<<<<<<<<<<<< * p[i] = Py_None * Py_INCREF(Py_None) */ if (unlikely(__pyx_v_itemsize == 0)) { PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); __PYX_ERR(2, 180, __pyx_L1_error) } else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1) && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_self->len))) { PyErr_SetString(PyExc_OverflowError, "value too large to perform division"); __PYX_ERR(2, 180, __pyx_L1_error) } __pyx_t_1 = (__pyx_v_self->len / __pyx_v_itemsize); __pyx_t_9 = __pyx_t_1; for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_9; __pyx_t_11+=1) { __pyx_v_i = __pyx_t_11; /* "View.MemoryView":181 * p = self.data * for i in range(self.len / itemsize): * p[i] = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ (__pyx_v_p[__pyx_v_i]) = Py_None; /* "View.MemoryView":182 * for i in range(self.len / itemsize): * p[i] = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * @cname('getbuffer') */ Py_INCREF(Py_None); } /* "View.MemoryView":178 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< * p = self.data * for i in range(self.len / itemsize): */ } /* "View.MemoryView":171 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_10); __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_XDECREF(__pyx_v_format); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":185 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * cdef int bufmode = -1 * if self.mode == u"c": */ /* Python wrapper */ static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(((struct __pyx_array_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_v_bufmode; int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; char *__pyx_t_4; Py_ssize_t __pyx_t_5; int __pyx_t_6; Py_ssize_t *__pyx_t_7; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; if (__pyx_v_info == NULL) { PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete"); return -1; } __Pyx_RefNannySetupContext("__getbuffer__", 0); __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None); __Pyx_GIVEREF(__pyx_v_info->obj); /* "View.MemoryView":186 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 # <<<<<<<<<<<<<< * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS */ __pyx_v_bufmode = -1; /* "View.MemoryView":187 * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 * if self.mode == u"c": # <<<<<<<<<<<<<< * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": */ __pyx_t_1 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_c, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 187, __pyx_L1_error) __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":188 * cdef int bufmode = -1 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS # <<<<<<<<<<<<<< * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS */ __pyx_v_bufmode = (PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS); /* "View.MemoryView":187 * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 * if self.mode == u"c": # <<<<<<<<<<<<<< * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": */ goto __pyx_L3; } /* "View.MemoryView":189 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": # <<<<<<<<<<<<<< * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): */ __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_fortran, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(2, 189, __pyx_L1_error) __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":190 * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS # <<<<<<<<<<<<<< * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") */ __pyx_v_bufmode = (PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS); /* "View.MemoryView":189 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": # <<<<<<<<<<<<<< * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): */ } __pyx_L3:; /* "View.MemoryView":191 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data */ __pyx_t_1 = ((!((__pyx_v_flags & __pyx_v_bufmode) != 0)) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":192 * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") # <<<<<<<<<<<<<< * info.buf = self.data * info.len = self.len */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__17, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 192, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 192, __pyx_L1_error) /* "View.MemoryView":191 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data */ } /* "View.MemoryView":193 * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data # <<<<<<<<<<<<<< * info.len = self.len * info.ndim = self.ndim */ __pyx_t_4 = __pyx_v_self->data; __pyx_v_info->buf = __pyx_t_4; /* "View.MemoryView":194 * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data * info.len = self.len # <<<<<<<<<<<<<< * info.ndim = self.ndim * info.shape = self._shape */ __pyx_t_5 = __pyx_v_self->len; __pyx_v_info->len = __pyx_t_5; /* "View.MemoryView":195 * info.buf = self.data * info.len = self.len * info.ndim = self.ndim # <<<<<<<<<<<<<< * info.shape = self._shape * info.strides = self._strides */ __pyx_t_6 = __pyx_v_self->ndim; __pyx_v_info->ndim = __pyx_t_6; /* "View.MemoryView":196 * info.len = self.len * info.ndim = self.ndim * info.shape = self._shape # <<<<<<<<<<<<<< * info.strides = self._strides * info.suboffsets = NULL */ __pyx_t_7 = __pyx_v_self->_shape; __pyx_v_info->shape = __pyx_t_7; /* "View.MemoryView":197 * info.ndim = self.ndim * info.shape = self._shape * info.strides = self._strides # <<<<<<<<<<<<<< * info.suboffsets = NULL * info.itemsize = self.itemsize */ __pyx_t_7 = __pyx_v_self->_strides; __pyx_v_info->strides = __pyx_t_7; /* "View.MemoryView":198 * info.shape = self._shape * info.strides = self._strides * info.suboffsets = NULL # <<<<<<<<<<<<<< * info.itemsize = self.itemsize * info.readonly = 0 */ __pyx_v_info->suboffsets = NULL; /* "View.MemoryView":199 * info.strides = self._strides * info.suboffsets = NULL * info.itemsize = self.itemsize # <<<<<<<<<<<<<< * info.readonly = 0 * */ __pyx_t_5 = __pyx_v_self->itemsize; __pyx_v_info->itemsize = __pyx_t_5; /* "View.MemoryView":200 * info.suboffsets = NULL * info.itemsize = self.itemsize * info.readonly = 0 # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ __pyx_v_info->readonly = 0; /* "View.MemoryView":202 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.format * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":203 * * if flags & PyBUF_FORMAT: * info.format = self.format # <<<<<<<<<<<<<< * else: * info.format = NULL */ __pyx_t_4 = __pyx_v_self->format; __pyx_v_info->format = __pyx_t_4; /* "View.MemoryView":202 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.format * else: */ goto __pyx_L5; } /* "View.MemoryView":205 * info.format = self.format * else: * info.format = NULL # <<<<<<<<<<<<<< * * info.obj = self */ /*else*/ { __pyx_v_info->format = NULL; } __pyx_L5:; /* "View.MemoryView":207 * info.format = NULL * * info.obj = self # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = ((PyObject *)__pyx_v_self); /* "View.MemoryView":185 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * cdef int bufmode = -1 * if self.mode == u"c": */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.array.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; if (__pyx_v_info->obj != NULL) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } goto __pyx_L2; __pyx_L0:; if (__pyx_v_info->obj == Py_None) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } __pyx_L2:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":211 * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") * * def __dealloc__(array self): # <<<<<<<<<<<<<< * if self.callback_free_data != NULL: * self.callback_free_data(self.data) */ /* Python wrapper */ static void __pyx_array___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_array___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self) { __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":212 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< * self.callback_free_data(self.data) * elif self.free_data: */ __pyx_t_1 = ((__pyx_v_self->callback_free_data != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":213 * def __dealloc__(array self): * if self.callback_free_data != NULL: * self.callback_free_data(self.data) # <<<<<<<<<<<<<< * elif self.free_data: * if self.dtype_is_object: */ __pyx_v_self->callback_free_data(__pyx_v_self->data); /* "View.MemoryView":212 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< * self.callback_free_data(self.data) * elif self.free_data: */ goto __pyx_L3; } /* "View.MemoryView":214 * if self.callback_free_data != NULL: * self.callback_free_data(self.data) * elif self.free_data: # <<<<<<<<<<<<<< * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, */ __pyx_t_1 = (__pyx_v_self->free_data != 0); if (__pyx_t_1) { /* "View.MemoryView":215 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) */ __pyx_t_1 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_1) { /* "View.MemoryView":216 * elif self.free_data: * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, # <<<<<<<<<<<<<< * self._strides, self.ndim, False) * free(self.data) */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_self->data, __pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_self->ndim, 0); /* "View.MemoryView":215 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) */ } /* "View.MemoryView":218 * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) * free(self.data) # <<<<<<<<<<<<<< * PyObject_Free(self._shape) * */ free(__pyx_v_self->data); /* "View.MemoryView":214 * if self.callback_free_data != NULL: * self.callback_free_data(self.data) * elif self.free_data: # <<<<<<<<<<<<<< * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, */ } __pyx_L3:; /* "View.MemoryView":219 * self._strides, self.ndim, False) * free(self.data) * PyObject_Free(self._shape) # <<<<<<<<<<<<<< * * @property */ PyObject_Free(__pyx_v_self->_shape); /* "View.MemoryView":211 * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") * * def __dealloc__(array self): # <<<<<<<<<<<<<< * if self.callback_free_data != NULL: * self.callback_free_data(self.data) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":222 * * @property * def memview(self): # <<<<<<<<<<<<<< * return self.get_memview() * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_5array_7memview___get__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":223 * @property * def memview(self): * return self.get_memview() # <<<<<<<<<<<<<< * * @cname('get_memview') */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = ((struct __pyx_vtabstruct_array *)__pyx_v_self->__pyx_vtab)->get_memview(__pyx_v_self); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 223, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":222 * * @property * def memview(self): # <<<<<<<<<<<<<< * return self.get_memview() * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.memview.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":226 * * @cname('get_memview') * cdef get_memview(self): # <<<<<<<<<<<<<< * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) */ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self) { int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("get_memview", 0); /* "View.MemoryView":227 * @cname('get_memview') * cdef get_memview(self): * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE # <<<<<<<<<<<<<< * return memoryview(self, flags, self.dtype_is_object) * */ __pyx_v_flags = ((PyBUF_ANY_CONTIGUOUS | PyBUF_FORMAT) | PyBUF_WRITABLE); /* "View.MemoryView":228 * cdef get_memview(self): * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) # <<<<<<<<<<<<<< * * def __len__(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":226 * * @cname('get_memview') * cdef get_memview(self): # <<<<<<<<<<<<<< * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.array.get_memview", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":230 * return memoryview(self, flags, self.dtype_is_object) * * def __len__(self): # <<<<<<<<<<<<<< * return self._shape[0] * */ /* Python wrapper */ static Py_ssize_t __pyx_array___len__(PyObject *__pyx_v_self); /*proto*/ static Py_ssize_t __pyx_array___len__(PyObject *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__", 0); /* "View.MemoryView":231 * * def __len__(self): * return self._shape[0] # <<<<<<<<<<<<<< * * def __getattr__(self, attr): */ __pyx_r = (__pyx_v_self->_shape[0]); goto __pyx_L0; /* "View.MemoryView":230 * return memoryview(self, flags, self.dtype_is_object) * * def __len__(self): # <<<<<<<<<<<<<< * return self._shape[0] * */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":233 * return self._shape[0] * * def __getattr__(self, attr): # <<<<<<<<<<<<<< * return getattr(self.memview, attr) * */ /* Python wrapper */ static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr); /*proto*/ static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getattr__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_attr)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__getattr__", 0); /* "View.MemoryView":234 * * def __getattr__(self, attr): * return getattr(self.memview, attr) # <<<<<<<<<<<<<< * * def __getitem__(self, item): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 234, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_GetAttr(__pyx_t_1, __pyx_v_attr); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 234, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":233 * return self._shape[0] * * def __getattr__(self, attr): # <<<<<<<<<<<<<< * return getattr(self.memview, attr) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.array.__getattr__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":236 * return getattr(self.memview, attr) * * def __getitem__(self, item): # <<<<<<<<<<<<<< * return self.memview[item] * */ /* Python wrapper */ static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item); /*proto*/ static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__getitem__", 0); /* "View.MemoryView":237 * * def __getitem__(self, item): * return self.memview[item] # <<<<<<<<<<<<<< * * def __setitem__(self, item, value): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 237, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetItem(__pyx_t_1, __pyx_v_item); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 237, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":236 * return getattr(self.memview, attr) * * def __getitem__(self, item): # <<<<<<<<<<<<<< * return self.memview[item] * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.array.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":239 * return self.memview[item] * * def __setitem__(self, item, value): # <<<<<<<<<<<<<< * self.memview[item] = value * */ /* Python wrapper */ static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /*proto*/ static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item), ((PyObject *)__pyx_v_value)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setitem__", 0); /* "View.MemoryView":240 * * def __setitem__(self, item, value): * self.memview[item] = value # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 240, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (unlikely(PyObject_SetItem(__pyx_t_1, __pyx_v_item, __pyx_v_value) < 0)) __PYX_ERR(2, 240, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":239 * return self.memview[item] * * def __setitem__(self, item, value): # <<<<<<<<<<<<<< * self.memview[item] = value * */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_array_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_array_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_array___reduce_cython__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__18, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_array_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_array_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_array_2__setstate_cython__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__19, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":244 * * @cname("__pyx_array_new") * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format, # <<<<<<<<<<<<<< * char *mode, char *buf): * cdef array result */ static struct __pyx_array_obj *__pyx_array_new(PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, char *__pyx_v_format, char *__pyx_v_mode, char *__pyx_v_buf) { struct __pyx_array_obj *__pyx_v_result = 0; struct __pyx_array_obj *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("array_cwrapper", 0); /* "View.MemoryView":248 * cdef array result * * if buf == NULL: # <<<<<<<<<<<<<< * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: */ __pyx_t_1 = ((__pyx_v_buf == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":249 * * if buf == NULL: * result = array(shape, itemsize, format, mode.decode('ASCII')) # <<<<<<<<<<<<<< * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), */ __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PyTuple_New(4); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_INCREF(__pyx_v_shape); __Pyx_GIVEREF(__pyx_v_shape); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_v_shape); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_5, 2, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 3, __pyx_t_4); __pyx_t_2 = 0; __pyx_t_3 = 0; __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_Call(((PyObject *)__pyx_array_type), __pyx_t_5, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":248 * cdef array result * * if buf == NULL: # <<<<<<<<<<<<<< * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: */ goto __pyx_L3; } /* "View.MemoryView":251 * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), # <<<<<<<<<<<<<< * allocate_buffer=False) * result.data = buf */ /*else*/ { __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_3 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_2 = PyTuple_New(4); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_v_shape); __Pyx_GIVEREF(__pyx_v_shape); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_shape); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_4); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_2, 2, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_2, 3, __pyx_t_3); __pyx_t_4 = 0; __pyx_t_5 = 0; __pyx_t_3 = 0; /* "View.MemoryView":252 * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), * allocate_buffer=False) # <<<<<<<<<<<<<< * result.data = buf * */ __pyx_t_3 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 252, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (PyDict_SetItem(__pyx_t_3, __pyx_n_s_allocate_buffer, Py_False) < 0) __PYX_ERR(2, 252, __pyx_L1_error) /* "View.MemoryView":251 * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), # <<<<<<<<<<<<<< * allocate_buffer=False) * result.data = buf */ __pyx_t_5 = __Pyx_PyObject_Call(((PyObject *)__pyx_array_type), __pyx_t_2, __pyx_t_3); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_5); __pyx_t_5 = 0; /* "View.MemoryView":253 * result = array(shape, itemsize, format, mode.decode('ASCII'), * allocate_buffer=False) * result.data = buf # <<<<<<<<<<<<<< * * return result */ __pyx_v_result->data = __pyx_v_buf; } __pyx_L3:; /* "View.MemoryView":255 * result.data = buf * * return result # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(((PyObject *)__pyx_r)); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = __pyx_v_result; goto __pyx_L0; /* "View.MemoryView":244 * * @cname("__pyx_array_new") * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format, # <<<<<<<<<<<<<< * char *mode, char *buf): * cdef array result */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.array_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF((PyObject *)__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":281 * cdef class Enum(object): * cdef object name * def __init__(self, name): # <<<<<<<<<<<<<< * self.name = name * def __repr__(self): */ /* Python wrapper */ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_name = 0; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__init__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_name,0}; PyObject* values[1] = {0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_name)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__init__") < 0)) __PYX_ERR(2, 281, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 1) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); } __pyx_v_name = values[0]; } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__init__", 1, 1, 1, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 281, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.Enum.__init__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self), __pyx_v_name); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__init__", 0); /* "View.MemoryView":282 * cdef object name * def __init__(self, name): * self.name = name # <<<<<<<<<<<<<< * def __repr__(self): * return self.name */ __Pyx_INCREF(__pyx_v_name); __Pyx_GIVEREF(__pyx_v_name); __Pyx_GOTREF(__pyx_v_self->name); __Pyx_DECREF(__pyx_v_self->name); __pyx_v_self->name = __pyx_v_name; /* "View.MemoryView":281 * cdef class Enum(object): * cdef object name * def __init__(self, name): # <<<<<<<<<<<<<< * self.name = name * def __repr__(self): */ /* function exit code */ __pyx_r = 0; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":283 * def __init__(self, name): * self.name = name * def __repr__(self): # <<<<<<<<<<<<<< * return self.name * */ /* Python wrapper */ static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0); __pyx_r = __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__", 0); /* "View.MemoryView":284 * self.name = name * def __repr__(self): * return self.name # <<<<<<<<<<<<<< * * cdef generic = Enum("") */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->name); __pyx_r = __pyx_v_self->name; goto __pyx_L0; /* "View.MemoryView":283 * def __init__(self, name): * self.name = name * def __repr__(self): # <<<<<<<<<<<<<< * return self.name * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * cdef tuple state * cdef object _dict */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_MemviewEnum_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_MemviewEnum_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_MemviewEnum___reduce_cython__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self) { PyObject *__pyx_v_state = 0; PyObject *__pyx_v__dict = 0; int __pyx_v_use_setstate; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_t_3; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":5 * cdef object _dict * cdef bint use_setstate * state = (self.name,) # <<<<<<<<<<<<<< * _dict = getattr(self, '__dict__', None) * if _dict is not None: */ __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_v_self->name); __Pyx_GIVEREF(__pyx_v_self->name); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v_self->name); __pyx_v_state = ((PyObject*)__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":6 * cdef bint use_setstate * state = (self.name,) * _dict = getattr(self, '__dict__', None) # <<<<<<<<<<<<<< * if _dict is not None: * state += (_dict,) */ __pyx_t_1 = __Pyx_GetAttr3(((PyObject *)__pyx_v_self), __pyx_n_s_dict, Py_None); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v__dict = __pyx_t_1; __pyx_t_1 = 0; /* "(tree fragment)":7 * state = (self.name,) * _dict = getattr(self, '__dict__', None) * if _dict is not None: # <<<<<<<<<<<<<< * state += (_dict,) * use_setstate = True */ __pyx_t_2 = (__pyx_v__dict != Py_None); __pyx_t_3 = (__pyx_t_2 != 0); if (__pyx_t_3) { /* "(tree fragment)":8 * _dict = getattr(self, '__dict__', None) * if _dict is not None: * state += (_dict,) # <<<<<<<<<<<<<< * use_setstate = True * else: */ __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_v__dict); __Pyx_GIVEREF(__pyx_v__dict); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v__dict); __pyx_t_4 = PyNumber_InPlaceAdd(__pyx_v_state, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF_SET(__pyx_v_state, ((PyObject*)__pyx_t_4)); __pyx_t_4 = 0; /* "(tree fragment)":9 * if _dict is not None: * state += (_dict,) * use_setstate = True # <<<<<<<<<<<<<< * else: * use_setstate = self.name is not None */ __pyx_v_use_setstate = 1; /* "(tree fragment)":7 * state = (self.name,) * _dict = getattr(self, '__dict__', None) * if _dict is not None: # <<<<<<<<<<<<<< * state += (_dict,) * use_setstate = True */ goto __pyx_L3; } /* "(tree fragment)":11 * use_setstate = True * else: * use_setstate = self.name is not None # <<<<<<<<<<<<<< * if use_setstate: * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state */ /*else*/ { __pyx_t_3 = (__pyx_v_self->name != Py_None); __pyx_v_use_setstate = __pyx_t_3; } __pyx_L3:; /* "(tree fragment)":12 * else: * use_setstate = self.name is not None * if use_setstate: # <<<<<<<<<<<<<< * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: */ __pyx_t_3 = (__pyx_v_use_setstate != 0); if (__pyx_t_3) { /* "(tree fragment)":13 * use_setstate = self.name is not None * if use_setstate: * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state # <<<<<<<<<<<<<< * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) */ __Pyx_XDECREF(__pyx_r); __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_GIVEREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_INCREF(__pyx_int_184977713); __Pyx_GIVEREF(__pyx_int_184977713); PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_int_184977713); __Pyx_INCREF(Py_None); __Pyx_GIVEREF(Py_None); PyTuple_SET_ITEM(__pyx_t_1, 2, Py_None); __pyx_t_5 = PyTuple_New(3); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_1); __Pyx_INCREF(__pyx_v_state); __Pyx_GIVEREF(__pyx_v_state); PyTuple_SET_ITEM(__pyx_t_5, 2, __pyx_v_state); __pyx_t_4 = 0; __pyx_t_1 = 0; __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "(tree fragment)":12 * else: * use_setstate = self.name is not None * if use_setstate: # <<<<<<<<<<<<<< * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: */ } /* "(tree fragment)":15 * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /*else*/ { __Pyx_XDECREF(__pyx_r); __Pyx_GetModuleGlobalName(__pyx_t_5, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_GIVEREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_INCREF(__pyx_int_184977713); __Pyx_GIVEREF(__pyx_int_184977713); PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_int_184977713); __Pyx_INCREF(__pyx_v_state); __Pyx_GIVEREF(__pyx_v_state); PyTuple_SET_ITEM(__pyx_t_1, 2, __pyx_v_state); __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_1); __pyx_t_5 = 0; __pyx_t_1 = 0; __pyx_r = __pyx_t_4; __pyx_t_4 = 0; goto __pyx_L0; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * cdef tuple state * cdef object _dict */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.Enum.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_state); __Pyx_XDECREF(__pyx_v__dict); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":16 * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_MemviewEnum_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_MemviewEnum_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_MemviewEnum_2__setstate_cython__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":17 * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): * __pyx_unpickle_Enum__set_state(self, __pyx_state) # <<<<<<<<<<<<<< */ if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(2, 17, __pyx_L1_error) __pyx_t_1 = __pyx_unpickle_Enum__set_state(__pyx_v_self, ((PyObject*)__pyx_v___pyx_state)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 17, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":16 * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.Enum.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":298 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory */ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) { Py_intptr_t __pyx_v_aligned_p; size_t __pyx_v_offset; void *__pyx_r; int __pyx_t_1; /* "View.MemoryView":300 * cdef void *align_pointer(void *memory, size_t alignment) nogil: * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory # <<<<<<<<<<<<<< * cdef size_t offset * */ __pyx_v_aligned_p = ((Py_intptr_t)__pyx_v_memory); /* "View.MemoryView":304 * * with cython.cdivision(True): * offset = aligned_p % alignment # <<<<<<<<<<<<<< * * if offset > 0: */ __pyx_v_offset = (__pyx_v_aligned_p % __pyx_v_alignment); /* "View.MemoryView":306 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< * aligned_p += alignment - offset * */ __pyx_t_1 = ((__pyx_v_offset > 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":307 * * if offset > 0: * aligned_p += alignment - offset # <<<<<<<<<<<<<< * * return aligned_p */ __pyx_v_aligned_p = (__pyx_v_aligned_p + (__pyx_v_alignment - __pyx_v_offset)); /* "View.MemoryView":306 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< * aligned_p += alignment - offset * */ } /* "View.MemoryView":309 * aligned_p += alignment - offset * * return aligned_p # <<<<<<<<<<<<<< * * */ __pyx_r = ((void *)__pyx_v_aligned_p); goto __pyx_L0; /* "View.MemoryView":298 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":345 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< * self.obj = obj * self.flags = flags */ /* Python wrapper */ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_obj = 0; int __pyx_v_flags; int __pyx_v_dtype_is_object; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_obj,&__pyx_n_s_flags,&__pyx_n_s_dtype_is_object,0}; PyObject* values[3] = {0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_obj)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_flags)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, 1); __PYX_ERR(2, 345, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dtype_is_object); if (value) { values[2] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) __PYX_ERR(2, 345, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_obj = values[0]; __pyx_v_flags = __Pyx_PyInt_As_int(values[1]); if (unlikely((__pyx_v_flags == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 345, __pyx_L3_error) if (values[2]) { __pyx_v_dtype_is_object = __Pyx_PyObject_IsTrue(values[2]); if (unlikely((__pyx_v_dtype_is_object == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 345, __pyx_L3_error) } else { __pyx_v_dtype_is_object = ((int)0); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 345, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_obj, __pyx_v_flags, __pyx_v_dtype_is_object); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__cinit__", 0); /* "View.MemoryView":346 * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj # <<<<<<<<<<<<<< * self.flags = flags * if type(self) is memoryview or obj is not None: */ __Pyx_INCREF(__pyx_v_obj); __Pyx_GIVEREF(__pyx_v_obj); __Pyx_GOTREF(__pyx_v_self->obj); __Pyx_DECREF(__pyx_v_self->obj); __pyx_v_self->obj = __pyx_v_obj; /* "View.MemoryView":347 * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj * self.flags = flags # <<<<<<<<<<<<<< * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) */ __pyx_v_self->flags = __pyx_v_flags; /* "View.MemoryView":348 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: */ __pyx_t_2 = (((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self))) == ((PyObject *)__pyx_memoryview_type)); __pyx_t_3 = (__pyx_t_2 != 0); if (!__pyx_t_3) { } else { __pyx_t_1 = __pyx_t_3; goto __pyx_L4_bool_binop_done; } __pyx_t_3 = (__pyx_v_obj != Py_None); __pyx_t_2 = (__pyx_t_3 != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (__pyx_t_1) { /* "View.MemoryView":349 * self.flags = flags * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) # <<<<<<<<<<<<<< * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None */ __pyx_t_4 = __Pyx_GetBuffer(__pyx_v_obj, (&__pyx_v_self->view), __pyx_v_flags); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 349, __pyx_L1_error) /* "View.MemoryView":350 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) */ __pyx_t_1 = ((((PyObject *)__pyx_v_self->view.obj) == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":351 * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_self->view))->obj = Py_None; /* "View.MemoryView":352 * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * global __pyx_memoryview_thread_locks_used */ Py_INCREF(Py_None); /* "View.MemoryView":350 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) */ } /* "View.MemoryView":348 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: */ } /* "View.MemoryView":355 * * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 */ __pyx_t_1 = ((__pyx_memoryview_thread_locks_used < 8) != 0); if (__pyx_t_1) { /* "View.MemoryView":356 * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: */ __pyx_v_self->lock = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); /* "View.MemoryView":357 * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 # <<<<<<<<<<<<<< * if self.lock is NULL: * self.lock = PyThread_allocate_lock() */ __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used + 1); /* "View.MemoryView":355 * * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 */ } /* "View.MemoryView":358 * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< * self.lock = PyThread_allocate_lock() * if self.lock is NULL: */ __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":359 * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() # <<<<<<<<<<<<<< * if self.lock is NULL: * raise MemoryError */ __pyx_v_self->lock = PyThread_allocate_lock(); /* "View.MemoryView":360 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() * if self.lock is NULL: # <<<<<<<<<<<<<< * raise MemoryError * */ __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":361 * self.lock = PyThread_allocate_lock() * if self.lock is NULL: * raise MemoryError # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ PyErr_NoMemory(); __PYX_ERR(2, 361, __pyx_L1_error) /* "View.MemoryView":360 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() * if self.lock is NULL: # <<<<<<<<<<<<<< * raise MemoryError * */ } /* "View.MemoryView":358 * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< * self.lock = PyThread_allocate_lock() * if self.lock is NULL: */ } /* "View.MemoryView":363 * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":364 * * if flags & PyBUF_FORMAT: * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') # <<<<<<<<<<<<<< * else: * self.dtype_is_object = dtype_is_object */ __pyx_t_2 = (((__pyx_v_self->view.format[0]) == 'O') != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L11_bool_binop_done; } __pyx_t_2 = (((__pyx_v_self->view.format[1]) == '\x00') != 0); __pyx_t_1 = __pyx_t_2; __pyx_L11_bool_binop_done:; __pyx_v_self->dtype_is_object = __pyx_t_1; /* "View.MemoryView":363 * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: */ goto __pyx_L10; } /* "View.MemoryView":366 * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: * self.dtype_is_object = dtype_is_object # <<<<<<<<<<<<<< * * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( */ /*else*/ { __pyx_v_self->dtype_is_object = __pyx_v_dtype_is_object; } __pyx_L10:; /* "View.MemoryView":368 * self.dtype_is_object = dtype_is_object * * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( # <<<<<<<<<<<<<< * &self.acquisition_count[0], sizeof(__pyx_atomic_int)) * self.typeinfo = NULL */ __pyx_v_self->acquisition_count_aligned_p = ((__pyx_atomic_int *)__pyx_align_pointer(((void *)(&(__pyx_v_self->acquisition_count[0]))), (sizeof(__pyx_atomic_int)))); /* "View.MemoryView":370 * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( * &self.acquisition_count[0], sizeof(__pyx_atomic_int)) * self.typeinfo = NULL # <<<<<<<<<<<<<< * * def __dealloc__(memoryview self): */ __pyx_v_self->typeinfo = NULL; /* "View.MemoryView":345 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< * self.obj = obj * self.flags = flags */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":372 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) */ /* Python wrapper */ static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self) { int __pyx_v_i; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_t_5; PyThread_type_lock __pyx_t_6; PyThread_type_lock __pyx_t_7; __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":373 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: */ __pyx_t_1 = (__pyx_v_self->obj != Py_None); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":374 * def __dealloc__(memoryview self): * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) # <<<<<<<<<<<<<< * elif (<__pyx_buffer *> &self.view).obj == Py_None: * */ __Pyx_ReleaseBuffer((&__pyx_v_self->view)); /* "View.MemoryView":373 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: */ goto __pyx_L3; } /* "View.MemoryView":375 * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: # <<<<<<<<<<<<<< * * (<__pyx_buffer *> &self.view).obj = NULL */ __pyx_t_2 = ((((Py_buffer *)(&__pyx_v_self->view))->obj == Py_None) != 0); if (__pyx_t_2) { /* "View.MemoryView":377 * elif (<__pyx_buffer *> &self.view).obj == Py_None: * * (<__pyx_buffer *> &self.view).obj = NULL # <<<<<<<<<<<<<< * Py_DECREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_self->view))->obj = NULL; /* "View.MemoryView":378 * * (<__pyx_buffer *> &self.view).obj = NULL * Py_DECREF(Py_None) # <<<<<<<<<<<<<< * * cdef int i */ Py_DECREF(Py_None); /* "View.MemoryView":375 * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: # <<<<<<<<<<<<<< * * (<__pyx_buffer *> &self.view).obj = NULL */ } __pyx_L3:; /* "View.MemoryView":382 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: */ __pyx_t_2 = ((__pyx_v_self->lock != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":383 * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): # <<<<<<<<<<<<<< * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 */ __pyx_t_3 = __pyx_memoryview_thread_locks_used; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "View.MemoryView":384 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: */ __pyx_t_2 = (((__pyx_memoryview_thread_locks[__pyx_v_i]) == __pyx_v_self->lock) != 0); if (__pyx_t_2) { /* "View.MemoryView":385 * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 # <<<<<<<<<<<<<< * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( */ __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used - 1); /* "View.MemoryView":386 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) */ __pyx_t_2 = ((__pyx_v_i != __pyx_memoryview_thread_locks_used) != 0); if (__pyx_t_2) { /* "View.MemoryView":388 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) # <<<<<<<<<<<<<< * break * else: */ __pyx_t_6 = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); __pyx_t_7 = (__pyx_memoryview_thread_locks[__pyx_v_i]); /* "View.MemoryView":387 * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) * break */ (__pyx_memoryview_thread_locks[__pyx_v_i]) = __pyx_t_6; (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]) = __pyx_t_7; /* "View.MemoryView":386 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) */ } /* "View.MemoryView":389 * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) * break # <<<<<<<<<<<<<< * else: * PyThread_free_lock(self.lock) */ goto __pyx_L6_break; /* "View.MemoryView":384 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: */ } } /*else*/ { /* "View.MemoryView":391 * break * else: * PyThread_free_lock(self.lock) # <<<<<<<<<<<<<< * * cdef char *get_item_pointer(memoryview self, object index) except NULL: */ PyThread_free_lock(__pyx_v_self->lock); } __pyx_L6_break:; /* "View.MemoryView":382 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: */ } /* "View.MemoryView":372 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":393 * PyThread_free_lock(self.lock) * * cdef char *get_item_pointer(memoryview self, object index) except NULL: # <<<<<<<<<<<<<< * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf */ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) { Py_ssize_t __pyx_v_dim; char *__pyx_v_itemp; PyObject *__pyx_v_idx = NULL; char *__pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; PyObject *__pyx_t_2 = NULL; Py_ssize_t __pyx_t_3; PyObject *(*__pyx_t_4)(PyObject *); PyObject *__pyx_t_5 = NULL; Py_ssize_t __pyx_t_6; char *__pyx_t_7; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("get_item_pointer", 0); /* "View.MemoryView":395 * cdef char *get_item_pointer(memoryview self, object index) except NULL: * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf # <<<<<<<<<<<<<< * * for dim, idx in enumerate(index): */ __pyx_v_itemp = ((char *)__pyx_v_self->view.buf); /* "View.MemoryView":397 * cdef char *itemp = self.view.buf * * for dim, idx in enumerate(index): # <<<<<<<<<<<<<< * itemp = pybuffer_index(&self.view, itemp, idx, dim) * */ __pyx_t_1 = 0; if (likely(PyList_CheckExact(__pyx_v_index)) || PyTuple_CheckExact(__pyx_v_index)) { __pyx_t_2 = __pyx_v_index; __Pyx_INCREF(__pyx_t_2); __pyx_t_3 = 0; __pyx_t_4 = NULL; } else { __pyx_t_3 = -1; __pyx_t_2 = PyObject_GetIter(__pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = Py_TYPE(__pyx_t_2)->tp_iternext; if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 397, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_4)) { if (likely(PyList_CheckExact(__pyx_t_2))) { if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_2)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyList_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(2, 397, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif } else { if (__pyx_t_3 >= PyTuple_GET_SIZE(__pyx_t_2)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(2, 397, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif } } else { __pyx_t_5 = __pyx_t_4(__pyx_t_2); if (unlikely(!__pyx_t_5)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(2, 397, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_5); } __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_5); __pyx_t_5 = 0; __pyx_v_dim = __pyx_t_1; __pyx_t_1 = (__pyx_t_1 + 1); /* "View.MemoryView":398 * * for dim, idx in enumerate(index): * itemp = pybuffer_index(&self.view, itemp, idx, dim) # <<<<<<<<<<<<<< * * return itemp */ __pyx_t_6 = __Pyx_PyIndex_AsSsize_t(__pyx_v_idx); if (unlikely((__pyx_t_6 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 398, __pyx_L1_error) __pyx_t_7 = __pyx_pybuffer_index((&__pyx_v_self->view), __pyx_v_itemp, __pyx_t_6, __pyx_v_dim); if (unlikely(__pyx_t_7 == ((char *)NULL))) __PYX_ERR(2, 398, __pyx_L1_error) __pyx_v_itemp = __pyx_t_7; /* "View.MemoryView":397 * cdef char *itemp = self.view.buf * * for dim, idx in enumerate(index): # <<<<<<<<<<<<<< * itemp = pybuffer_index(&self.view, itemp, idx, dim) * */ } __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":400 * itemp = pybuffer_index(&self.view, itemp, idx, dim) * * return itemp # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_itemp; goto __pyx_L0; /* "View.MemoryView":393 * PyThread_free_lock(self.lock) * * cdef char *get_item_pointer(memoryview self, object index) except NULL: # <<<<<<<<<<<<<< * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.get_item_pointer", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_idx); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":403 * * * def __getitem__(memoryview self, object index): # <<<<<<<<<<<<<< * if index is Ellipsis: * return self */ /* Python wrapper */ static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index); /*proto*/ static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) { PyObject *__pyx_v_have_slices = NULL; PyObject *__pyx_v_indices = NULL; char *__pyx_v_itemp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; char *__pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__getitem__", 0); /* "View.MemoryView":404 * * def __getitem__(memoryview self, object index): * if index is Ellipsis: # <<<<<<<<<<<<<< * return self * */ __pyx_t_1 = (__pyx_v_index == __pyx_builtin_Ellipsis); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":405 * def __getitem__(memoryview self, object index): * if index is Ellipsis: * return self # <<<<<<<<<<<<<< * * have_slices, indices = _unellipsify(index, self.view.ndim) */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_self)); __pyx_r = ((PyObject *)__pyx_v_self); goto __pyx_L0; /* "View.MemoryView":404 * * def __getitem__(memoryview self, object index): * if index is Ellipsis: # <<<<<<<<<<<<<< * return self * */ } /* "View.MemoryView":407 * return self * * have_slices, indices = _unellipsify(index, self.view.ndim) # <<<<<<<<<<<<<< * * cdef char *itemp */ __pyx_t_3 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (likely(__pyx_t_3 != Py_None)) { PyObject* sequence = __pyx_t_3; Py_ssize_t size = __Pyx_PySequence_SIZE(sequence); if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); __PYX_ERR(2, 407, __pyx_L1_error) } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_4 = PyTuple_GET_ITEM(sequence, 0); __pyx_t_5 = PyTuple_GET_ITEM(sequence, 1); __Pyx_INCREF(__pyx_t_4); __Pyx_INCREF(__pyx_t_5); #else __pyx_t_4 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } else { __Pyx_RaiseNoneNotIterableError(); __PYX_ERR(2, 407, __pyx_L1_error) } __pyx_v_have_slices = __pyx_t_4; __pyx_t_4 = 0; __pyx_v_indices = __pyx_t_5; __pyx_t_5 = 0; /* "View.MemoryView":410 * * cdef char *itemp * if have_slices: # <<<<<<<<<<<<<< * return memview_slice(self, indices) * else: */ __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(2, 410, __pyx_L1_error) if (__pyx_t_2) { /* "View.MemoryView":411 * cdef char *itemp * if have_slices: * return memview_slice(self, indices) # <<<<<<<<<<<<<< * else: * itemp = self.get_item_pointer(indices) */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = ((PyObject *)__pyx_memview_slice(__pyx_v_self, __pyx_v_indices)); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 411, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":410 * * cdef char *itemp * if have_slices: # <<<<<<<<<<<<<< * return memview_slice(self, indices) * else: */ } /* "View.MemoryView":413 * return memview_slice(self, indices) * else: * itemp = self.get_item_pointer(indices) # <<<<<<<<<<<<<< * return self.convert_item_to_object(itemp) * */ /*else*/ { __pyx_t_6 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_indices); if (unlikely(__pyx_t_6 == ((char *)NULL))) __PYX_ERR(2, 413, __pyx_L1_error) __pyx_v_itemp = __pyx_t_6; /* "View.MemoryView":414 * else: * itemp = self.get_item_pointer(indices) * return self.convert_item_to_object(itemp) # <<<<<<<<<<<<<< * * def __setitem__(memoryview self, object index, object value): */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->convert_item_to_object(__pyx_v_self, __pyx_v_itemp); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 414, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; } /* "View.MemoryView":403 * * * def __getitem__(memoryview self, object index): # <<<<<<<<<<<<<< * if index is Ellipsis: * return self */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_have_slices); __Pyx_XDECREF(__pyx_v_indices); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":416 * return self.convert_item_to_object(itemp) * * def __setitem__(memoryview self, object index, object value): # <<<<<<<<<<<<<< * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") */ /* Python wrapper */ static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /*proto*/ static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index), ((PyObject *)__pyx_v_value)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { PyObject *__pyx_v_have_slices = NULL; PyObject *__pyx_v_obj = NULL; int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setitem__", 0); __Pyx_INCREF(__pyx_v_index); /* "View.MemoryView":417 * * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: # <<<<<<<<<<<<<< * raise TypeError("Cannot assign to read-only memoryview") * */ __pyx_t_1 = (__pyx_v_self->view.readonly != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":418 * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") # <<<<<<<<<<<<<< * * have_slices, index = _unellipsify(index, self.view.ndim) */ __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__20, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 418, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(2, 418, __pyx_L1_error) /* "View.MemoryView":417 * * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: # <<<<<<<<<<<<<< * raise TypeError("Cannot assign to read-only memoryview") * */ } /* "View.MemoryView":420 * raise TypeError("Cannot assign to read-only memoryview") * * have_slices, index = _unellipsify(index, self.view.ndim) # <<<<<<<<<<<<<< * * if have_slices: */ __pyx_t_2 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (likely(__pyx_t_2 != Py_None)) { PyObject* sequence = __pyx_t_2; Py_ssize_t size = __Pyx_PySequence_SIZE(sequence); if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); __PYX_ERR(2, 420, __pyx_L1_error) } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_3 = PyTuple_GET_ITEM(sequence, 0); __pyx_t_4 = PyTuple_GET_ITEM(sequence, 1); __Pyx_INCREF(__pyx_t_3); __Pyx_INCREF(__pyx_t_4); #else __pyx_t_3 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); #endif __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } else { __Pyx_RaiseNoneNotIterableError(); __PYX_ERR(2, 420, __pyx_L1_error) } __pyx_v_have_slices = __pyx_t_3; __pyx_t_3 = 0; __Pyx_DECREF_SET(__pyx_v_index, __pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":422 * have_slices, index = _unellipsify(index, self.view.ndim) * * if have_slices: # <<<<<<<<<<<<<< * obj = self.is_slice(value) * if obj: */ __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 422, __pyx_L1_error) if (__pyx_t_1) { /* "View.MemoryView":423 * * if have_slices: * obj = self.is_slice(value) # <<<<<<<<<<<<<< * if obj: * self.setitem_slice_assignment(self[index], obj) */ __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->is_slice(__pyx_v_self, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 423, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_v_obj = __pyx_t_2; __pyx_t_2 = 0; /* "View.MemoryView":424 * if have_slices: * obj = self.is_slice(value) * if obj: # <<<<<<<<<<<<<< * self.setitem_slice_assignment(self[index], obj) * else: */ __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_v_obj); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 424, __pyx_L1_error) if (__pyx_t_1) { /* "View.MemoryView":425 * obj = self.is_slice(value) * if obj: * self.setitem_slice_assignment(self[index], obj) # <<<<<<<<<<<<<< * else: * self.setitem_slice_assign_scalar(self[index], value) */ __pyx_t_2 = __Pyx_PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 425, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assignment(__pyx_v_self, __pyx_t_2, __pyx_v_obj); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 425, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":424 * if have_slices: * obj = self.is_slice(value) * if obj: # <<<<<<<<<<<<<< * self.setitem_slice_assignment(self[index], obj) * else: */ goto __pyx_L5; } /* "View.MemoryView":427 * self.setitem_slice_assignment(self[index], obj) * else: * self.setitem_slice_assign_scalar(self[index], value) # <<<<<<<<<<<<<< * else: * self.setitem_indexed(index, value) */ /*else*/ { __pyx_t_4 = __Pyx_PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 427, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (!(likely(((__pyx_t_4) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_4, __pyx_memoryview_type))))) __PYX_ERR(2, 427, __pyx_L1_error) __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assign_scalar(__pyx_v_self, ((struct __pyx_memoryview_obj *)__pyx_t_4), __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 427, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_L5:; /* "View.MemoryView":422 * have_slices, index = _unellipsify(index, self.view.ndim) * * if have_slices: # <<<<<<<<<<<<<< * obj = self.is_slice(value) * if obj: */ goto __pyx_L4; } /* "View.MemoryView":429 * self.setitem_slice_assign_scalar(self[index], value) * else: * self.setitem_indexed(index, value) # <<<<<<<<<<<<<< * * cdef is_slice(self, obj): */ /*else*/ { __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_indexed(__pyx_v_self, __pyx_v_index, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 429, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_L4:; /* "View.MemoryView":416 * return self.convert_item_to_object(itemp) * * def __setitem__(memoryview self, object index, object value): # <<<<<<<<<<<<<< * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView.memoryview.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_XDECREF(__pyx_v_have_slices); __Pyx_XDECREF(__pyx_v_obj); __Pyx_XDECREF(__pyx_v_index); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":431 * self.setitem_indexed(index, value) * * cdef is_slice(self, obj): # <<<<<<<<<<<<<< * if not isinstance(obj, memoryview): * try: */ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_t_9; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("is_slice", 0); __Pyx_INCREF(__pyx_v_obj); /* "View.MemoryView":432 * * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): # <<<<<<<<<<<<<< * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, */ __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_obj, __pyx_memoryview_type); __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_3, &__pyx_t_4, &__pyx_t_5); __Pyx_XGOTREF(__pyx_t_3); __Pyx_XGOTREF(__pyx_t_4); __Pyx_XGOTREF(__pyx_t_5); /*try:*/ { /* "View.MemoryView":434 * if not isinstance(obj, memoryview): * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, # <<<<<<<<<<<<<< * self.dtype_is_object) * except TypeError: */ __pyx_t_6 = __Pyx_PyInt_From_int(((__pyx_v_self->flags & (~PyBUF_WRITABLE)) | PyBUF_ANY_CONTIGUOUS)); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_6); /* "View.MemoryView":435 * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) # <<<<<<<<<<<<<< * except TypeError: * return None */ __pyx_t_7 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 435, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_7); /* "View.MemoryView":434 * if not isinstance(obj, memoryview): * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, # <<<<<<<<<<<<<< * self.dtype_is_object) * except TypeError: */ __pyx_t_8 = PyTuple_New(3); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_INCREF(__pyx_v_obj); __Pyx_GIVEREF(__pyx_v_obj); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_v_obj); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_8, 1, __pyx_t_6); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_8, 2, __pyx_t_7); __pyx_t_6 = 0; __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_8, NULL); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF_SET(__pyx_v_obj, __pyx_t_7); __pyx_t_7 = 0; /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ } __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; goto __pyx_L9_try_end; __pyx_L4_error:; __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0; /* "View.MemoryView":436 * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) * except TypeError: # <<<<<<<<<<<<<< * return None * */ __pyx_t_9 = __Pyx_PyErr_ExceptionMatches(__pyx_builtin_TypeError); if (__pyx_t_9) { __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_6) < 0) __PYX_ERR(2, 436, __pyx_L6_except_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_GOTREF(__pyx_t_8); __Pyx_GOTREF(__pyx_t_6); /* "View.MemoryView":437 * self.dtype_is_object) * except TypeError: * return None # <<<<<<<<<<<<<< * * return obj */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; goto __pyx_L7_except_return; } goto __pyx_L6_except_error; __pyx_L6_except_error:; /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_XGIVEREF(__pyx_t_5); __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5); goto __pyx_L1_error; __pyx_L7_except_return:; __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_XGIVEREF(__pyx_t_5); __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5); goto __pyx_L0; __pyx_L9_try_end:; } /* "View.MemoryView":432 * * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): # <<<<<<<<<<<<<< * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, */ } /* "View.MemoryView":439 * return None * * return obj # <<<<<<<<<<<<<< * * cdef setitem_slice_assignment(self, dst, src): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_obj); __pyx_r = __pyx_v_obj; goto __pyx_L0; /* "View.MemoryView":431 * self.setitem_indexed(index, value) * * cdef is_slice(self, obj): # <<<<<<<<<<<<<< * if not isinstance(obj, memoryview): * try: */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_obj); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":441 * return obj * * cdef setitem_slice_assignment(self, dst, src): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice dst_slice * cdef __Pyx_memviewslice src_slice */ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src) { __Pyx_memviewslice __pyx_v_dst_slice; __Pyx_memviewslice __pyx_v_src_slice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; __Pyx_memviewslice *__pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; int __pyx_t_5; int __pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("setitem_slice_assignment", 0); /* "View.MemoryView":445 * cdef __Pyx_memviewslice src_slice * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], # <<<<<<<<<<<<<< * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) */ if (!(likely(((__pyx_v_src) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_src, __pyx_memoryview_type))))) __PYX_ERR(2, 445, __pyx_L1_error) __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_src), (&__pyx_v_src_slice)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 445, __pyx_L1_error) /* "View.MemoryView":446 * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], * get_slice_from_memview(dst, &dst_slice)[0], # <<<<<<<<<<<<<< * src.ndim, dst.ndim, self.dtype_is_object) * */ if (!(likely(((__pyx_v_dst) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_dst, __pyx_memoryview_type))))) __PYX_ERR(2, 446, __pyx_L1_error) __pyx_t_2 = __pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_dst), (&__pyx_v_dst_slice)); if (unlikely(__pyx_t_2 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 446, __pyx_L1_error) /* "View.MemoryView":447 * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) # <<<<<<<<<<<<<< * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): */ __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_v_src, __pyx_n_s_ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_4 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_v_dst, __pyx_n_s_ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_5 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_5 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":445 * cdef __Pyx_memviewslice src_slice * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], # <<<<<<<<<<<<<< * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) */ __pyx_t_6 = __pyx_memoryview_copy_contents((__pyx_t_1[0]), (__pyx_t_2[0]), __pyx_t_4, __pyx_t_5, __pyx_v_self->dtype_is_object); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 445, __pyx_L1_error) /* "View.MemoryView":441 * return obj * * cdef setitem_slice_assignment(self, dst, src): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice dst_slice * cdef __Pyx_memviewslice src_slice */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assignment", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":449 * src.ndim, dst.ndim, self.dtype_is_object) * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): # <<<<<<<<<<<<<< * cdef int array[128] * cdef void *tmp = NULL */ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value) { int __pyx_v_array[0x80]; void *__pyx_v_tmp; void *__pyx_v_item; __Pyx_memviewslice *__pyx_v_dst_slice; __Pyx_memviewslice __pyx_v_tmp_slice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; int __pyx_t_5; char const *__pyx_t_6; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; PyObject *__pyx_t_9 = NULL; PyObject *__pyx_t_10 = NULL; PyObject *__pyx_t_11 = NULL; PyObject *__pyx_t_12 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("setitem_slice_assign_scalar", 0); /* "View.MemoryView":451 * cdef setitem_slice_assign_scalar(self, memoryview dst, value): * cdef int array[128] * cdef void *tmp = NULL # <<<<<<<<<<<<<< * cdef void *item * */ __pyx_v_tmp = NULL; /* "View.MemoryView":456 * cdef __Pyx_memviewslice *dst_slice * cdef __Pyx_memviewslice tmp_slice * dst_slice = get_slice_from_memview(dst, &tmp_slice) # <<<<<<<<<<<<<< * * if self.view.itemsize > sizeof(array): */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_dst, (&__pyx_v_tmp_slice)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 456, __pyx_L1_error) __pyx_v_dst_slice = __pyx_t_1; /* "View.MemoryView":458 * dst_slice = get_slice_from_memview(dst, &tmp_slice) * * if self.view.itemsize > sizeof(array): # <<<<<<<<<<<<<< * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: */ __pyx_t_2 = ((((size_t)__pyx_v_self->view.itemsize) > (sizeof(__pyx_v_array))) != 0); if (__pyx_t_2) { /* "View.MemoryView":459 * * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) # <<<<<<<<<<<<<< * if tmp == NULL: * raise MemoryError */ __pyx_v_tmp = PyMem_Malloc(__pyx_v_self->view.itemsize); /* "View.MemoryView":460 * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: # <<<<<<<<<<<<<< * raise MemoryError * item = tmp */ __pyx_t_2 = ((__pyx_v_tmp == NULL) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":461 * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: * raise MemoryError # <<<<<<<<<<<<<< * item = tmp * else: */ PyErr_NoMemory(); __PYX_ERR(2, 461, __pyx_L1_error) /* "View.MemoryView":460 * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: # <<<<<<<<<<<<<< * raise MemoryError * item = tmp */ } /* "View.MemoryView":462 * if tmp == NULL: * raise MemoryError * item = tmp # <<<<<<<<<<<<<< * else: * item = array */ __pyx_v_item = __pyx_v_tmp; /* "View.MemoryView":458 * dst_slice = get_slice_from_memview(dst, &tmp_slice) * * if self.view.itemsize > sizeof(array): # <<<<<<<<<<<<<< * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: */ goto __pyx_L3; } /* "View.MemoryView":464 * item = tmp * else: * item = array # <<<<<<<<<<<<<< * * try: */ /*else*/ { __pyx_v_item = ((void *)__pyx_v_array); } __pyx_L3:; /* "View.MemoryView":466 * item = array * * try: # <<<<<<<<<<<<<< * if self.dtype_is_object: * ( item)[0] = value */ /*try:*/ { /* "View.MemoryView":467 * * try: * if self.dtype_is_object: # <<<<<<<<<<<<<< * ( item)[0] = value * else: */ __pyx_t_2 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_2) { /* "View.MemoryView":468 * try: * if self.dtype_is_object: * ( item)[0] = value # <<<<<<<<<<<<<< * else: * self.assign_item_from_object( item, value) */ (((PyObject **)__pyx_v_item)[0]) = ((PyObject *)__pyx_v_value); /* "View.MemoryView":467 * * try: * if self.dtype_is_object: # <<<<<<<<<<<<<< * ( item)[0] = value * else: */ goto __pyx_L8; } /* "View.MemoryView":470 * ( item)[0] = value * else: * self.assign_item_from_object( item, value) # <<<<<<<<<<<<<< * * */ /*else*/ { __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, ((char *)__pyx_v_item), __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 470, __pyx_L6_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } __pyx_L8:; /* "View.MemoryView":474 * * * if self.view.suboffsets != NULL: # <<<<<<<<<<<<<< * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, */ __pyx_t_2 = ((__pyx_v_self->view.suboffsets != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":475 * * if self.view.suboffsets != NULL: * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) # <<<<<<<<<<<<<< * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, * item, self.dtype_is_object) */ __pyx_t_3 = assert_direct_dimensions(__pyx_v_self->view.suboffsets, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 475, __pyx_L6_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":474 * * * if self.view.suboffsets != NULL: # <<<<<<<<<<<<<< * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, */ } /* "View.MemoryView":476 * if self.view.suboffsets != NULL: * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, # <<<<<<<<<<<<<< * item, self.dtype_is_object) * finally: */ __pyx_memoryview_slice_assign_scalar(__pyx_v_dst_slice, __pyx_v_dst->view.ndim, __pyx_v_self->view.itemsize, __pyx_v_item, __pyx_v_self->dtype_is_object); } /* "View.MemoryView":479 * item, self.dtype_is_object) * finally: * PyMem_Free(tmp) # <<<<<<<<<<<<<< * * cdef setitem_indexed(self, index, value): */ /*finally:*/ { /*normal exit:*/{ PyMem_Free(__pyx_v_tmp); goto __pyx_L7; } __pyx_L6_error:; /*exception exit:*/{ __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0; __pyx_t_12 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; if (PY_MAJOR_VERSION >= 3) __Pyx_ExceptionSwap(&__pyx_t_10, &__pyx_t_11, &__pyx_t_12); if ((PY_MAJOR_VERSION < 3) || unlikely(__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_9) < 0)) __Pyx_ErrFetch(&__pyx_t_7, &__pyx_t_8, &__pyx_t_9); __Pyx_XGOTREF(__pyx_t_7); __Pyx_XGOTREF(__pyx_t_8); __Pyx_XGOTREF(__pyx_t_9); __Pyx_XGOTREF(__pyx_t_10); __Pyx_XGOTREF(__pyx_t_11); __Pyx_XGOTREF(__pyx_t_12); __pyx_t_4 = __pyx_lineno; __pyx_t_5 = __pyx_clineno; __pyx_t_6 = __pyx_filename; { PyMem_Free(__pyx_v_tmp); } if (PY_MAJOR_VERSION >= 3) { __Pyx_XGIVEREF(__pyx_t_10); __Pyx_XGIVEREF(__pyx_t_11); __Pyx_XGIVEREF(__pyx_t_12); __Pyx_ExceptionReset(__pyx_t_10, __pyx_t_11, __pyx_t_12); } __Pyx_XGIVEREF(__pyx_t_7); __Pyx_XGIVEREF(__pyx_t_8); __Pyx_XGIVEREF(__pyx_t_9); __Pyx_ErrRestore(__pyx_t_7, __pyx_t_8, __pyx_t_9); __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0; __pyx_t_12 = 0; __pyx_lineno = __pyx_t_4; __pyx_clineno = __pyx_t_5; __pyx_filename = __pyx_t_6; goto __pyx_L1_error; } __pyx_L7:; } /* "View.MemoryView":449 * src.ndim, dst.ndim, self.dtype_is_object) * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): # <<<<<<<<<<<<<< * cdef int array[128] * cdef void *tmp = NULL */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assign_scalar", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":481 * PyMem_Free(tmp) * * cdef setitem_indexed(self, index, value): # <<<<<<<<<<<<<< * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) */ static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { char *__pyx_v_itemp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations char *__pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("setitem_indexed", 0); /* "View.MemoryView":482 * * cdef setitem_indexed(self, index, value): * cdef char *itemp = self.get_item_pointer(index) # <<<<<<<<<<<<<< * self.assign_item_from_object(itemp, value) * */ __pyx_t_1 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_index); if (unlikely(__pyx_t_1 == ((char *)NULL))) __PYX_ERR(2, 482, __pyx_L1_error) __pyx_v_itemp = __pyx_t_1; /* "View.MemoryView":483 * cdef setitem_indexed(self, index, value): * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) # <<<<<<<<<<<<<< * * cdef convert_item_to_object(self, char *itemp): */ __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 483, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":481 * PyMem_Free(tmp) * * cdef setitem_indexed(self, index, value): # <<<<<<<<<<<<<< * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_indexed", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":485 * self.assign_item_from_object(itemp, value) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp) { PyObject *__pyx_v_struct = NULL; PyObject *__pyx_v_bytesitem = 0; PyObject *__pyx_v_result = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; int __pyx_t_8; PyObject *__pyx_t_9 = NULL; size_t __pyx_t_10; int __pyx_t_11; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("convert_item_to_object", 0); /* "View.MemoryView":488 * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" * import struct # <<<<<<<<<<<<<< * cdef bytes bytesitem * */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 488, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_struct = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":491 * cdef bytes bytesitem * * bytesitem = itemp[:self.view.itemsize] # <<<<<<<<<<<<<< * try: * result = struct.unpack(self.view.format, bytesitem) */ __pyx_t_1 = __Pyx_PyBytes_FromStringAndSize(__pyx_v_itemp + 0, __pyx_v_self->view.itemsize - 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 491, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_bytesitem = ((PyObject*)__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_2, &__pyx_t_3, &__pyx_t_4); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); __Pyx_XGOTREF(__pyx_t_4); /*try:*/ { /* "View.MemoryView":493 * bytesitem = itemp[:self.view.itemsize] * try: * result = struct.unpack(self.view.format, bytesitem) # <<<<<<<<<<<<<< * except struct.error: * raise ValueError("Unable to convert item to object") */ __pyx_t_5 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_unpack); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = NULL; __pyx_t_8 = 0; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_5))) { __pyx_t_7 = PyMethod_GET_SELF(__pyx_t_5); if (likely(__pyx_t_7)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_5); __Pyx_INCREF(__pyx_t_7); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_5, function); __pyx_t_8 = 1; } } #if CYTHON_FAST_PYCALL if (PyFunction_Check(__pyx_t_5)) { PyObject *__pyx_temp[3] = {__pyx_t_7, __pyx_t_6, __pyx_v_bytesitem}; __pyx_t_1 = __Pyx_PyFunction_FastCall(__pyx_t_5, __pyx_temp+1-__pyx_t_8, 2+__pyx_t_8); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } else #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(__pyx_t_5)) { PyObject *__pyx_temp[3] = {__pyx_t_7, __pyx_t_6, __pyx_v_bytesitem}; __pyx_t_1 = __Pyx_PyCFunction_FastCall(__pyx_t_5, __pyx_temp+1-__pyx_t_8, 2+__pyx_t_8); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } else #endif { __pyx_t_9 = PyTuple_New(2+__pyx_t_8); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_9); if (__pyx_t_7) { __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __pyx_t_7 = NULL; } __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_9, 0+__pyx_t_8, __pyx_t_6); __Pyx_INCREF(__pyx_v_bytesitem); __Pyx_GIVEREF(__pyx_v_bytesitem); PyTuple_SET_ITEM(__pyx_t_9, 1+__pyx_t_8, __pyx_v_bytesitem); __pyx_t_6 = 0; __pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_5, __pyx_t_9, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_result = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ } /* "View.MemoryView":497 * raise ValueError("Unable to convert item to object") * else: * if len(self.view.format) == 1: # <<<<<<<<<<<<<< * return result[0] * return result */ /*else:*/ { __pyx_t_10 = strlen(__pyx_v_self->view.format); __pyx_t_11 = ((__pyx_t_10 == 1) != 0); if (__pyx_t_11) { /* "View.MemoryView":498 * else: * if len(self.view.format) == 1: * return result[0] # <<<<<<<<<<<<<< * return result * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_GetItemInt(__pyx_v_result, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 498, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L6_except_return; /* "View.MemoryView":497 * raise ValueError("Unable to convert item to object") * else: * if len(self.view.format) == 1: # <<<<<<<<<<<<<< * return result[0] * return result */ } /* "View.MemoryView":499 * if len(self.view.format) == 1: * return result[0] * return result # <<<<<<<<<<<<<< * * cdef assign_item_from_object(self, char *itemp, object value): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_result); __pyx_r = __pyx_v_result; goto __pyx_L6_except_return; } __pyx_L3_error:; __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_XDECREF(__pyx_t_9); __pyx_t_9 = 0; /* "View.MemoryView":494 * try: * result = struct.unpack(self.view.format, bytesitem) * except struct.error: # <<<<<<<<<<<<<< * raise ValueError("Unable to convert item to object") * else: */ __Pyx_ErrFetch(&__pyx_t_1, &__pyx_t_5, &__pyx_t_9); __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_error); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 494, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_8 = __Pyx_PyErr_GivenExceptionMatches(__pyx_t_1, __pyx_t_6); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_ErrRestore(__pyx_t_1, __pyx_t_5, __pyx_t_9); __pyx_t_1 = 0; __pyx_t_5 = 0; __pyx_t_9 = 0; if (__pyx_t_8) { __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_9, &__pyx_t_5, &__pyx_t_1) < 0) __PYX_ERR(2, 494, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_1); /* "View.MemoryView":495 * result = struct.unpack(self.view.format, bytesitem) * except struct.error: * raise ValueError("Unable to convert item to object") # <<<<<<<<<<<<<< * else: * if len(self.view.format) == 1: */ __pyx_t_6 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__21, NULL); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 495, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_Raise(__pyx_t_6, 0, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __PYX_ERR(2, 495, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4); goto __pyx_L1_error; __pyx_L6_except_return:; __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4); goto __pyx_L0; } /* "View.MemoryView":485 * self.assign_item_from_object(itemp, value) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_9); __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_struct); __Pyx_XDECREF(__pyx_v_bytesitem); __Pyx_XDECREF(__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":501 * return result * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) { PyObject *__pyx_v_struct = NULL; char __pyx_v_c; PyObject *__pyx_v_bytesvalue = 0; Py_ssize_t __pyx_v_i; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_t_3; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; int __pyx_t_7; PyObject *__pyx_t_8 = NULL; Py_ssize_t __pyx_t_9; PyObject *__pyx_t_10 = NULL; char *__pyx_t_11; char *__pyx_t_12; char *__pyx_t_13; char *__pyx_t_14; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("assign_item_from_object", 0); /* "View.MemoryView":504 * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" * import struct # <<<<<<<<<<<<<< * cdef char c * cdef bytes bytesvalue */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 504, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_struct = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":509 * cdef Py_ssize_t i * * if isinstance(value, tuple): # <<<<<<<<<<<<<< * bytesvalue = struct.pack(self.view.format, *value) * else: */ __pyx_t_2 = PyTuple_Check(__pyx_v_value); __pyx_t_3 = (__pyx_t_2 != 0); if (__pyx_t_3) { /* "View.MemoryView":510 * * if isinstance(value, tuple): * bytesvalue = struct.pack(self.view.format, *value) # <<<<<<<<<<<<<< * else: * bytesvalue = struct.pack(self.view.format, value) */ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_4 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PySequence_Tuple(__pyx_v_value); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_6 = PyNumber_Add(__pyx_t_5, __pyx_t_4); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_6, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (!(likely(PyBytes_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_4)->tp_name), 0))) __PYX_ERR(2, 510, __pyx_L1_error) __pyx_v_bytesvalue = ((PyObject*)__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":509 * cdef Py_ssize_t i * * if isinstance(value, tuple): # <<<<<<<<<<<<<< * bytesvalue = struct.pack(self.view.format, *value) * else: */ goto __pyx_L3; } /* "View.MemoryView":512 * bytesvalue = struct.pack(self.view.format, *value) * else: * bytesvalue = struct.pack(self.view.format, value) # <<<<<<<<<<<<<< * * for i, c in enumerate(bytesvalue): */ /*else*/ { __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_1 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_5 = NULL; __pyx_t_7 = 0; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_6))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_6); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_6); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_6, function); __pyx_t_7 = 1; } } #if CYTHON_FAST_PYCALL if (PyFunction_Check(__pyx_t_6)) { PyObject *__pyx_temp[3] = {__pyx_t_5, __pyx_t_1, __pyx_v_value}; __pyx_t_4 = __Pyx_PyFunction_FastCall(__pyx_t_6, __pyx_temp+1-__pyx_t_7, 2+__pyx_t_7); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; } else #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(__pyx_t_6)) { PyObject *__pyx_temp[3] = {__pyx_t_5, __pyx_t_1, __pyx_v_value}; __pyx_t_4 = __Pyx_PyCFunction_FastCall(__pyx_t_6, __pyx_temp+1-__pyx_t_7, 2+__pyx_t_7); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; } else #endif { __pyx_t_8 = PyTuple_New(2+__pyx_t_7); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); if (__pyx_t_5) { __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_5); __pyx_t_5 = NULL; } __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_8, 0+__pyx_t_7, __pyx_t_1); __Pyx_INCREF(__pyx_v_value); __Pyx_GIVEREF(__pyx_v_value); PyTuple_SET_ITEM(__pyx_t_8, 1+__pyx_t_7, __pyx_v_value); __pyx_t_1 = 0; __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_t_8, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; } __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (!(likely(PyBytes_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_4)->tp_name), 0))) __PYX_ERR(2, 512, __pyx_L1_error) __pyx_v_bytesvalue = ((PyObject*)__pyx_t_4); __pyx_t_4 = 0; } __pyx_L3:; /* "View.MemoryView":514 * bytesvalue = struct.pack(self.view.format, value) * * for i, c in enumerate(bytesvalue): # <<<<<<<<<<<<<< * itemp[i] = c * */ __pyx_t_9 = 0; if (unlikely(__pyx_v_bytesvalue == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' is not iterable"); __PYX_ERR(2, 514, __pyx_L1_error) } __Pyx_INCREF(__pyx_v_bytesvalue); __pyx_t_10 = __pyx_v_bytesvalue; __pyx_t_12 = PyBytes_AS_STRING(__pyx_t_10); __pyx_t_13 = (__pyx_t_12 + PyBytes_GET_SIZE(__pyx_t_10)); for (__pyx_t_14 = __pyx_t_12; __pyx_t_14 < __pyx_t_13; __pyx_t_14++) { __pyx_t_11 = __pyx_t_14; __pyx_v_c = (__pyx_t_11[0]); /* "View.MemoryView":515 * * for i, c in enumerate(bytesvalue): * itemp[i] = c # <<<<<<<<<<<<<< * * @cname('getbuffer') */ __pyx_v_i = __pyx_t_9; /* "View.MemoryView":514 * bytesvalue = struct.pack(self.view.format, value) * * for i, c in enumerate(bytesvalue): # <<<<<<<<<<<<<< * itemp[i] = c * */ __pyx_t_9 = (__pyx_t_9 + 1); /* "View.MemoryView":515 * * for i, c in enumerate(bytesvalue): * itemp[i] = c # <<<<<<<<<<<<<< * * @cname('getbuffer') */ (__pyx_v_itemp[__pyx_v_i]) = __pyx_v_c; } __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; /* "View.MemoryView":501 * return result * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_8); __Pyx_XDECREF(__pyx_t_10); __Pyx_AddTraceback("View.MemoryView.memoryview.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_struct); __Pyx_XDECREF(__pyx_v_bytesvalue); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":518 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") */ /* Python wrapper */ static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; Py_ssize_t *__pyx_t_4; char *__pyx_t_5; void *__pyx_t_6; int __pyx_t_7; Py_ssize_t __pyx_t_8; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; if (__pyx_v_info == NULL) { PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete"); return -1; } __Pyx_RefNannySetupContext("__getbuffer__", 0); __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None); __Pyx_GIVEREF(__pyx_v_info->obj); /* "View.MemoryView":519 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: # <<<<<<<<<<<<<< * raise ValueError("Cannot create writable memory view from read-only memoryview") * */ __pyx_t_2 = ((__pyx_v_flags & PyBUF_WRITABLE) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } __pyx_t_2 = (__pyx_v_self->view.readonly != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (unlikely(__pyx_t_1)) { /* "View.MemoryView":520 * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") # <<<<<<<<<<<<<< * * if flags & PyBUF_ND: */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__22, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 520, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 520, __pyx_L1_error) /* "View.MemoryView":519 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: # <<<<<<<<<<<<<< * raise ValueError("Cannot create writable memory view from read-only memoryview") * */ } /* "View.MemoryView":522 * raise ValueError("Cannot create writable memory view from read-only memoryview") * * if flags & PyBUF_ND: # <<<<<<<<<<<<<< * info.shape = self.view.shape * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_ND) != 0); if (__pyx_t_1) { /* "View.MemoryView":523 * * if flags & PyBUF_ND: * info.shape = self.view.shape # <<<<<<<<<<<<<< * else: * info.shape = NULL */ __pyx_t_4 = __pyx_v_self->view.shape; __pyx_v_info->shape = __pyx_t_4; /* "View.MemoryView":522 * raise ValueError("Cannot create writable memory view from read-only memoryview") * * if flags & PyBUF_ND: # <<<<<<<<<<<<<< * info.shape = self.view.shape * else: */ goto __pyx_L6; } /* "View.MemoryView":525 * info.shape = self.view.shape * else: * info.shape = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_STRIDES: */ /*else*/ { __pyx_v_info->shape = NULL; } __pyx_L6:; /* "View.MemoryView":527 * info.shape = NULL * * if flags & PyBUF_STRIDES: # <<<<<<<<<<<<<< * info.strides = self.view.strides * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_STRIDES) != 0); if (__pyx_t_1) { /* "View.MemoryView":528 * * if flags & PyBUF_STRIDES: * info.strides = self.view.strides # <<<<<<<<<<<<<< * else: * info.strides = NULL */ __pyx_t_4 = __pyx_v_self->view.strides; __pyx_v_info->strides = __pyx_t_4; /* "View.MemoryView":527 * info.shape = NULL * * if flags & PyBUF_STRIDES: # <<<<<<<<<<<<<< * info.strides = self.view.strides * else: */ goto __pyx_L7; } /* "View.MemoryView":530 * info.strides = self.view.strides * else: * info.strides = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_INDIRECT: */ /*else*/ { __pyx_v_info->strides = NULL; } __pyx_L7:; /* "View.MemoryView":532 * info.strides = NULL * * if flags & PyBUF_INDIRECT: # <<<<<<<<<<<<<< * info.suboffsets = self.view.suboffsets * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_INDIRECT) != 0); if (__pyx_t_1) { /* "View.MemoryView":533 * * if flags & PyBUF_INDIRECT: * info.suboffsets = self.view.suboffsets # <<<<<<<<<<<<<< * else: * info.suboffsets = NULL */ __pyx_t_4 = __pyx_v_self->view.suboffsets; __pyx_v_info->suboffsets = __pyx_t_4; /* "View.MemoryView":532 * info.strides = NULL * * if flags & PyBUF_INDIRECT: # <<<<<<<<<<<<<< * info.suboffsets = self.view.suboffsets * else: */ goto __pyx_L8; } /* "View.MemoryView":535 * info.suboffsets = self.view.suboffsets * else: * info.suboffsets = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ /*else*/ { __pyx_v_info->suboffsets = NULL; } __pyx_L8:; /* "View.MemoryView":537 * info.suboffsets = NULL * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.view.format * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":538 * * if flags & PyBUF_FORMAT: * info.format = self.view.format # <<<<<<<<<<<<<< * else: * info.format = NULL */ __pyx_t_5 = __pyx_v_self->view.format; __pyx_v_info->format = __pyx_t_5; /* "View.MemoryView":537 * info.suboffsets = NULL * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.view.format * else: */ goto __pyx_L9; } /* "View.MemoryView":540 * info.format = self.view.format * else: * info.format = NULL # <<<<<<<<<<<<<< * * info.buf = self.view.buf */ /*else*/ { __pyx_v_info->format = NULL; } __pyx_L9:; /* "View.MemoryView":542 * info.format = NULL * * info.buf = self.view.buf # <<<<<<<<<<<<<< * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize */ __pyx_t_6 = __pyx_v_self->view.buf; __pyx_v_info->buf = __pyx_t_6; /* "View.MemoryView":543 * * info.buf = self.view.buf * info.ndim = self.view.ndim # <<<<<<<<<<<<<< * info.itemsize = self.view.itemsize * info.len = self.view.len */ __pyx_t_7 = __pyx_v_self->view.ndim; __pyx_v_info->ndim = __pyx_t_7; /* "View.MemoryView":544 * info.buf = self.view.buf * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize # <<<<<<<<<<<<<< * info.len = self.view.len * info.readonly = self.view.readonly */ __pyx_t_8 = __pyx_v_self->view.itemsize; __pyx_v_info->itemsize = __pyx_t_8; /* "View.MemoryView":545 * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize * info.len = self.view.len # <<<<<<<<<<<<<< * info.readonly = self.view.readonly * info.obj = self */ __pyx_t_8 = __pyx_v_self->view.len; __pyx_v_info->len = __pyx_t_8; /* "View.MemoryView":546 * info.itemsize = self.view.itemsize * info.len = self.view.len * info.readonly = self.view.readonly # <<<<<<<<<<<<<< * info.obj = self * */ __pyx_t_1 = __pyx_v_self->view.readonly; __pyx_v_info->readonly = __pyx_t_1; /* "View.MemoryView":547 * info.len = self.view.len * info.readonly = self.view.readonly * info.obj = self # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = ((PyObject *)__pyx_v_self); /* "View.MemoryView":518 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; if (__pyx_v_info->obj != NULL) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } goto __pyx_L2; __pyx_L0:; if (__pyx_v_info->obj == Py_None) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } __pyx_L2:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":553 * * @property * def T(self): # <<<<<<<<<<<<<< * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self) { struct __pyx_memoryviewslice_obj *__pyx_v_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":554 * @property * def T(self): * cdef _memoryviewslice result = memoryview_copy(self) # <<<<<<<<<<<<<< * transpose_memslice(&result.from_slice) * return result */ __pyx_t_1 = __pyx_memoryview_copy_object(__pyx_v_self); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 554, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (!(likely(((__pyx_t_1) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_1, __pyx_memoryviewslice_type))))) __PYX_ERR(2, 554, __pyx_L1_error) __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":555 * def T(self): * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) # <<<<<<<<<<<<<< * return result * */ __pyx_t_2 = __pyx_memslice_transpose((&__pyx_v_result->from_slice)); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(2, 555, __pyx_L1_error) /* "View.MemoryView":556 * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) * return result # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":553 * * @property * def T(self): # <<<<<<<<<<<<<< * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.T.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":559 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.obj * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":560 * @property * def base(self): * return self.obj # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->obj); __pyx_r = __pyx_v_self->obj; goto __pyx_L0; /* "View.MemoryView":559 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.obj * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":563 * * @property * def shape(self): # <<<<<<<<<<<<<< * return tuple([length for length in self.view.shape[:self.view.ndim]]) * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_length; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":564 * @property * def shape(self): * return tuple([length for length in self.view.shape[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyList_New(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = (__pyx_v_self->view.shape + __pyx_v_self->view.ndim); for (__pyx_t_4 = __pyx_v_self->view.shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { __pyx_t_2 = __pyx_t_4; __pyx_v_length = (__pyx_t_2[0]); __pyx_t_5 = PyInt_FromSsize_t(__pyx_v_length); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); if (unlikely(__Pyx_ListComp_Append(__pyx_t_1, (PyObject*)__pyx_t_5))) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; } __pyx_t_5 = PyList_AsTuple(((PyObject*)__pyx_t_1)); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "View.MemoryView":563 * * @property * def shape(self): # <<<<<<<<<<<<<< * return tuple([length for length in self.view.shape[:self.view.ndim]]) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.shape.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":567 * * @property * def strides(self): # <<<<<<<<<<<<<< * if self.view.strides == NULL: * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_stride; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; PyObject *__pyx_t_6 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":568 * @property * def strides(self): * if self.view.strides == NULL: # <<<<<<<<<<<<<< * * raise ValueError("Buffer view does not expose strides") */ __pyx_t_1 = ((__pyx_v_self->view.strides == NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":570 * if self.view.strides == NULL: * * raise ValueError("Buffer view does not expose strides") # <<<<<<<<<<<<<< * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) */ __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__23, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 570, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(2, 570, __pyx_L1_error) /* "View.MemoryView":568 * @property * def strides(self): * if self.view.strides == NULL: # <<<<<<<<<<<<<< * * raise ValueError("Buffer view does not expose strides") */ } /* "View.MemoryView":572 * raise ValueError("Buffer view does not expose strides") * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = (__pyx_v_self->view.strides + __pyx_v_self->view.ndim); for (__pyx_t_5 = __pyx_v_self->view.strides; __pyx_t_5 < __pyx_t_4; __pyx_t_5++) { __pyx_t_3 = __pyx_t_5; __pyx_v_stride = (__pyx_t_3[0]); __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_stride); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); if (unlikely(__Pyx_ListComp_Append(__pyx_t_2, (PyObject*)__pyx_t_6))) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } __pyx_t_6 = PyList_AsTuple(((PyObject*)__pyx_t_2)); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_6; __pyx_t_6 = 0; goto __pyx_L0; /* "View.MemoryView":567 * * @property * def strides(self): # <<<<<<<<<<<<<< * if self.view.strides == NULL: * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_6); __Pyx_AddTraceback("View.MemoryView.memoryview.strides.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":575 * * @property * def suboffsets(self): # <<<<<<<<<<<<<< * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; Py_ssize_t *__pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":576 * @property * def suboffsets(self): * if self.view.suboffsets == NULL: # <<<<<<<<<<<<<< * return (-1,) * self.view.ndim * */ __pyx_t_1 = ((__pyx_v_self->view.suboffsets == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":577 * def suboffsets(self): * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim # <<<<<<<<<<<<<< * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyInt_From_int(__pyx_v_self->view.ndim); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_Multiply(__pyx_tuple__24, __pyx_t_2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":576 * @property * def suboffsets(self): * if self.view.suboffsets == NULL: # <<<<<<<<<<<<<< * return (-1,) * self.view.ndim * */ } /* "View.MemoryView":579 * return (-1,) * self.view.ndim * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_5 = (__pyx_v_self->view.suboffsets + __pyx_v_self->view.ndim); for (__pyx_t_6 = __pyx_v_self->view.suboffsets; __pyx_t_6 < __pyx_t_5; __pyx_t_6++) { __pyx_t_4 = __pyx_t_6; __pyx_v_suboffset = (__pyx_t_4[0]); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_suboffset); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (unlikely(__Pyx_ListComp_Append(__pyx_t_3, (PyObject*)__pyx_t_2))) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_t_2 = PyList_AsTuple(((PyObject*)__pyx_t_3)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":575 * * @property * def suboffsets(self): # <<<<<<<<<<<<<< * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.suboffsets.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":582 * * @property * def ndim(self): # <<<<<<<<<<<<<< * return self.view.ndim * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":583 * @property * def ndim(self): * return self.view.ndim # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_self->view.ndim); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 583, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":582 * * @property * def ndim(self): # <<<<<<<<<<<<<< * return self.view.ndim * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.ndim.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":586 * * @property * def itemsize(self): # <<<<<<<<<<<<<< * return self.view.itemsize * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":587 * @property * def itemsize(self): * return self.view.itemsize # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 587, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":586 * * @property * def itemsize(self): # <<<<<<<<<<<<<< * return self.view.itemsize * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.itemsize.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":590 * * @property * def nbytes(self): # <<<<<<<<<<<<<< * return self.size * self.view.itemsize * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":591 * @property * def nbytes(self): * return self.size * self.view.itemsize # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_size); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_Multiply(__pyx_t_1, __pyx_t_2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":590 * * @property * def nbytes(self): # <<<<<<<<<<<<<< * return self.size * self.view.itemsize * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.nbytes.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":594 * * @property * def size(self): # <<<<<<<<<<<<<< * if self._size is None: * result = 1 */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_v_result = NULL; PyObject *__pyx_v_length = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; PyObject *__pyx_t_6 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":595 * @property * def size(self): * if self._size is None: # <<<<<<<<<<<<<< * result = 1 * */ __pyx_t_1 = (__pyx_v_self->_size == Py_None); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":596 * def size(self): * if self._size is None: * result = 1 # <<<<<<<<<<<<<< * * for length in self.view.shape[:self.view.ndim]: */ __Pyx_INCREF(__pyx_int_1); __pyx_v_result = __pyx_int_1; /* "View.MemoryView":598 * result = 1 * * for length in self.view.shape[:self.view.ndim]: # <<<<<<<<<<<<<< * result *= length * */ __pyx_t_4 = (__pyx_v_self->view.shape + __pyx_v_self->view.ndim); for (__pyx_t_5 = __pyx_v_self->view.shape; __pyx_t_5 < __pyx_t_4; __pyx_t_5++) { __pyx_t_3 = __pyx_t_5; __pyx_t_6 = PyInt_FromSsize_t((__pyx_t_3[0])); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 598, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_6); __pyx_t_6 = 0; /* "View.MemoryView":599 * * for length in self.view.shape[:self.view.ndim]: * result *= length # <<<<<<<<<<<<<< * * self._size = result */ __pyx_t_6 = PyNumber_InPlaceMultiply(__pyx_v_result, __pyx_v_length); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 599, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF_SET(__pyx_v_result, __pyx_t_6); __pyx_t_6 = 0; } /* "View.MemoryView":601 * result *= length * * self._size = result # <<<<<<<<<<<<<< * * return self._size */ __Pyx_INCREF(__pyx_v_result); __Pyx_GIVEREF(__pyx_v_result); __Pyx_GOTREF(__pyx_v_self->_size); __Pyx_DECREF(__pyx_v_self->_size); __pyx_v_self->_size = __pyx_v_result; /* "View.MemoryView":595 * @property * def size(self): * if self._size is None: # <<<<<<<<<<<<<< * result = 1 * */ } /* "View.MemoryView":603 * self._size = result * * return self._size # <<<<<<<<<<<<<< * * def __len__(self): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->_size); __pyx_r = __pyx_v_self->_size; goto __pyx_L0; /* "View.MemoryView":594 * * @property * def size(self): # <<<<<<<<<<<<<< * if self._size is None: * result = 1 */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_6); __Pyx_AddTraceback("View.MemoryView.memoryview.size.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_result); __Pyx_XDECREF(__pyx_v_length); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":605 * return self._size * * def __len__(self): # <<<<<<<<<<<<<< * if self.view.ndim >= 1: * return self.view.shape[0] */ /* Python wrapper */ static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self); /*proto*/ static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("__len__", 0); /* "View.MemoryView":606 * * def __len__(self): * if self.view.ndim >= 1: # <<<<<<<<<<<<<< * return self.view.shape[0] * */ __pyx_t_1 = ((__pyx_v_self->view.ndim >= 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":607 * def __len__(self): * if self.view.ndim >= 1: * return self.view.shape[0] # <<<<<<<<<<<<<< * * return 0 */ __pyx_r = (__pyx_v_self->view.shape[0]); goto __pyx_L0; /* "View.MemoryView":606 * * def __len__(self): * if self.view.ndim >= 1: # <<<<<<<<<<<<<< * return self.view.shape[0] * */ } /* "View.MemoryView":609 * return self.view.shape[0] * * return 0 # <<<<<<<<<<<<<< * * def __repr__(self): */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":605 * return self._size * * def __len__(self): # <<<<<<<<<<<<<< * if self.view.ndim >= 1: * return self.view.shape[0] */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":611 * return 0 * * def __repr__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__, * id(self)) */ /* Python wrapper */ static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__repr__", 0); /* "View.MemoryView":612 * * def __repr__(self): * return "" % (self.base.__class__.__name__, # <<<<<<<<<<<<<< * id(self)) * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":613 * def __repr__(self): * return "" % (self.base.__class__.__name__, * id(self)) # <<<<<<<<<<<<<< * * def __str__(self): */ __pyx_t_2 = __Pyx_PyObject_CallOneArg(__pyx_builtin_id, ((PyObject *)__pyx_v_self)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 613, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); /* "View.MemoryView":612 * * def __repr__(self): * return "" % (self.base.__class__.__name__, # <<<<<<<<<<<<<< * id(self)) * */ __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_t_3); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":611 * return 0 * * def __repr__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__, * id(self)) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.__repr__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":615 * id(self)) * * def __str__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__,) * */ /* Python wrapper */ static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__str__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__str__", 0); /* "View.MemoryView":616 * * def __str__(self): * return "" % (self.base.__class__.__name__,) # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_object, __pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":615 * id(self)) * * def __str__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__,) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.__str__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":619 * * * def is_c_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* Python wrapper */ static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("is_c_contig (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice *__pyx_v_mslice; __Pyx_memviewslice __pyx_v_tmp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("is_c_contig", 0); /* "View.MemoryView":622 * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) # <<<<<<<<<<<<<< * return slice_is_contig(mslice[0], 'C', self.view.ndim) * */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 622, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":623 * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) * return slice_is_contig(mslice[0], 'C', self.view.ndim) # <<<<<<<<<<<<<< * * def is_f_contig(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig((__pyx_v_mslice[0]), 'C', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 623, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":619 * * * def is_c_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.is_c_contig", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":625 * return slice_is_contig(mslice[0], 'C', self.view.ndim) * * def is_f_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* Python wrapper */ static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("is_f_contig (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice *__pyx_v_mslice; __Pyx_memviewslice __pyx_v_tmp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("is_f_contig", 0); /* "View.MemoryView":628 * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) # <<<<<<<<<<<<<< * return slice_is_contig(mslice[0], 'F', self.view.ndim) * */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 628, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":629 * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) * return slice_is_contig(mslice[0], 'F', self.view.ndim) # <<<<<<<<<<<<<< * * def copy(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig((__pyx_v_mslice[0]), 'F', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 629, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":625 * return slice_is_contig(mslice[0], 'C', self.view.ndim) * * def is_f_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.is_f_contig", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":631 * return slice_is_contig(mslice[0], 'F', self.view.ndim) * * def copy(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS */ /* Python wrapper */ static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("copy (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice __pyx_v_mslice; int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice __pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("copy", 0); /* "View.MemoryView":633 * def copy(self): * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS # <<<<<<<<<<<<<< * * slice_copy(self, &mslice) */ __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_F_CONTIGUOUS)); /* "View.MemoryView":635 * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS * * slice_copy(self, &mslice) # <<<<<<<<<<<<<< * mslice = slice_copy_contig(&mslice, "c", self.view.ndim, * self.view.itemsize, */ __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_mslice)); /* "View.MemoryView":636 * * slice_copy(self, &mslice) * mslice = slice_copy_contig(&mslice, "c", self.view.ndim, # <<<<<<<<<<<<<< * self.view.itemsize, * flags|PyBUF_C_CONTIGUOUS, */ __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_mslice), ((char *)"c"), __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_C_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) __PYX_ERR(2, 636, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":641 * self.dtype_is_object) * * return memoryview_copy_from_slice(self, &mslice) # <<<<<<<<<<<<<< * * def copy_fortran(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_mslice)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 641, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":631 * return slice_is_contig(mslice[0], 'F', self.view.ndim) * * def copy(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.copy", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":643 * return memoryview_copy_from_slice(self, &mslice) * * def copy_fortran(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS */ /* Python wrapper */ static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("copy_fortran (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice __pyx_v_src; __Pyx_memviewslice __pyx_v_dst; int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice __pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("copy_fortran", 0); /* "View.MemoryView":645 * def copy_fortran(self): * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS # <<<<<<<<<<<<<< * * slice_copy(self, &src) */ __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_C_CONTIGUOUS)); /* "View.MemoryView":647 * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS * * slice_copy(self, &src) # <<<<<<<<<<<<<< * dst = slice_copy_contig(&src, "fortran", self.view.ndim, * self.view.itemsize, */ __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_src)); /* "View.MemoryView":648 * * slice_copy(self, &src) * dst = slice_copy_contig(&src, "fortran", self.view.ndim, # <<<<<<<<<<<<<< * self.view.itemsize, * flags|PyBUF_F_CONTIGUOUS, */ __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_src), ((char *)"fortran"), __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_F_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) __PYX_ERR(2, 648, __pyx_L1_error) __pyx_v_dst = __pyx_t_1; /* "View.MemoryView":653 * self.dtype_is_object) * * return memoryview_copy_from_slice(self, &dst) # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_dst)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 653, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":643 * return memoryview_copy_from_slice(self, &mslice) * * def copy_fortran(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.copy_fortran", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryview_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_memoryview_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryview___reduce_cython__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__25, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryview_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_memoryview_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryview_2__setstate_cython__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__26, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":657 * * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): # <<<<<<<<<<<<<< * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo */ static PyObject *__pyx_memoryview_new(PyObject *__pyx_v_o, int __pyx_v_flags, int __pyx_v_dtype_is_object, __Pyx_TypeInfo *__pyx_v_typeinfo) { struct __pyx_memoryview_obj *__pyx_v_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_cwrapper", 0); /* "View.MemoryView":658 * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): * cdef memoryview result = memoryview(o, flags, dtype_is_object) # <<<<<<<<<<<<<< * result.typeinfo = typeinfo * return result */ __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_o); __Pyx_GIVEREF(__pyx_v_o); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_o); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_memoryview_obj *)__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":659 * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo # <<<<<<<<<<<<<< * return result * */ __pyx_v_result->typeinfo = __pyx_v_typeinfo; /* "View.MemoryView":660 * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo * return result # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_check') */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":657 * * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): # <<<<<<<<<<<<<< * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":663 * * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): # <<<<<<<<<<<<<< * return isinstance(o, memoryview) * */ static CYTHON_INLINE int __pyx_memoryview_check(PyObject *__pyx_v_o) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("memoryview_check", 0); /* "View.MemoryView":664 * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): * return isinstance(o, memoryview) # <<<<<<<<<<<<<< * * cdef tuple _unellipsify(object index, int ndim): */ __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_o, __pyx_memoryview_type); __pyx_r = __pyx_t_1; goto __pyx_L0; /* "View.MemoryView":663 * * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): # <<<<<<<<<<<<<< * return isinstance(o, memoryview) * */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":666 * return isinstance(o, memoryview) * * cdef tuple _unellipsify(object index, int ndim): # <<<<<<<<<<<<<< * """ * Replace all ellipses with full slices and fill incomplete indices with */ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) { PyObject *__pyx_v_tup = NULL; PyObject *__pyx_v_result = NULL; int __pyx_v_have_slices; int __pyx_v_seen_ellipsis; CYTHON_UNUSED PyObject *__pyx_v_idx = NULL; PyObject *__pyx_v_item = NULL; Py_ssize_t __pyx_v_nslices; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; Py_ssize_t __pyx_t_5; PyObject *(*__pyx_t_6)(PyObject *); PyObject *__pyx_t_7 = NULL; Py_ssize_t __pyx_t_8; int __pyx_t_9; int __pyx_t_10; PyObject *__pyx_t_11 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("_unellipsify", 0); /* "View.MemoryView":671 * full slices. * """ * if not isinstance(index, tuple): # <<<<<<<<<<<<<< * tup = (index,) * else: */ __pyx_t_1 = PyTuple_Check(__pyx_v_index); __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":672 * """ * if not isinstance(index, tuple): * tup = (index,) # <<<<<<<<<<<<<< * else: * tup = index */ __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 672, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_index); __Pyx_GIVEREF(__pyx_v_index); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_index); __pyx_v_tup = __pyx_t_3; __pyx_t_3 = 0; /* "View.MemoryView":671 * full slices. * """ * if not isinstance(index, tuple): # <<<<<<<<<<<<<< * tup = (index,) * else: */ goto __pyx_L3; } /* "View.MemoryView":674 * tup = (index,) * else: * tup = index # <<<<<<<<<<<<<< * * result = [] */ /*else*/ { __Pyx_INCREF(__pyx_v_index); __pyx_v_tup = __pyx_v_index; } __pyx_L3:; /* "View.MemoryView":676 * tup = index * * result = [] # <<<<<<<<<<<<<< * have_slices = False * seen_ellipsis = False */ __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 676, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_v_result = ((PyObject*)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":677 * * result = [] * have_slices = False # <<<<<<<<<<<<<< * seen_ellipsis = False * for idx, item in enumerate(tup): */ __pyx_v_have_slices = 0; /* "View.MemoryView":678 * result = [] * have_slices = False * seen_ellipsis = False # <<<<<<<<<<<<<< * for idx, item in enumerate(tup): * if item is Ellipsis: */ __pyx_v_seen_ellipsis = 0; /* "View.MemoryView":679 * have_slices = False * seen_ellipsis = False * for idx, item in enumerate(tup): # <<<<<<<<<<<<<< * if item is Ellipsis: * if not seen_ellipsis: */ __Pyx_INCREF(__pyx_int_0); __pyx_t_3 = __pyx_int_0; if (likely(PyList_CheckExact(__pyx_v_tup)) || PyTuple_CheckExact(__pyx_v_tup)) { __pyx_t_4 = __pyx_v_tup; __Pyx_INCREF(__pyx_t_4); __pyx_t_5 = 0; __pyx_t_6 = NULL; } else { __pyx_t_5 = -1; __pyx_t_4 = PyObject_GetIter(__pyx_v_tup); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_6 = Py_TYPE(__pyx_t_4)->tp_iternext; if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 679, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_6)) { if (likely(PyList_CheckExact(__pyx_t_4))) { if (__pyx_t_5 >= PyList_GET_SIZE(__pyx_t_4)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_7 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) __PYX_ERR(2, 679, __pyx_L1_error) #else __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); #endif } else { if (__pyx_t_5 >= PyTuple_GET_SIZE(__pyx_t_4)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_7 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) __PYX_ERR(2, 679, __pyx_L1_error) #else __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); #endif } } else { __pyx_t_7 = __pyx_t_6(__pyx_t_4); if (unlikely(!__pyx_t_7)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(2, 679, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_7); } __Pyx_XDECREF_SET(__pyx_v_item, __pyx_t_7); __pyx_t_7 = 0; __Pyx_INCREF(__pyx_t_3); __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_3); __pyx_t_7 = __Pyx_PyInt_AddObjC(__pyx_t_3, __pyx_int_1, 1, 0, 0); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = __pyx_t_7; __pyx_t_7 = 0; /* "View.MemoryView":680 * seen_ellipsis = False * for idx, item in enumerate(tup): * if item is Ellipsis: # <<<<<<<<<<<<<< * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) */ __pyx_t_2 = (__pyx_v_item == __pyx_builtin_Ellipsis); __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":681 * for idx, item in enumerate(tup): * if item is Ellipsis: * if not seen_ellipsis: # <<<<<<<<<<<<<< * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True */ __pyx_t_1 = ((!(__pyx_v_seen_ellipsis != 0)) != 0); if (__pyx_t_1) { /* "View.MemoryView":682 * if item is Ellipsis: * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) # <<<<<<<<<<<<<< * seen_ellipsis = True * else: */ __pyx_t_8 = PyObject_Length(__pyx_v_tup); if (unlikely(__pyx_t_8 == ((Py_ssize_t)-1))) __PYX_ERR(2, 682, __pyx_L1_error) __pyx_t_7 = PyList_New(1 * ((((__pyx_v_ndim - __pyx_t_8) + 1)<0) ? 0:((__pyx_v_ndim - __pyx_t_8) + 1))); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 682, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); { Py_ssize_t __pyx_temp; for (__pyx_temp=0; __pyx_temp < ((__pyx_v_ndim - __pyx_t_8) + 1); __pyx_temp++) { __Pyx_INCREF(__pyx_slice__27); __Pyx_GIVEREF(__pyx_slice__27); PyList_SET_ITEM(__pyx_t_7, __pyx_temp, __pyx_slice__27); } } __pyx_t_9 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_7); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 682, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; /* "View.MemoryView":683 * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True # <<<<<<<<<<<<<< * else: * result.append(slice(None)) */ __pyx_v_seen_ellipsis = 1; /* "View.MemoryView":681 * for idx, item in enumerate(tup): * if item is Ellipsis: * if not seen_ellipsis: # <<<<<<<<<<<<<< * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True */ goto __pyx_L7; } /* "View.MemoryView":685 * seen_ellipsis = True * else: * result.append(slice(None)) # <<<<<<<<<<<<<< * have_slices = True * else: */ /*else*/ { __pyx_t_9 = __Pyx_PyList_Append(__pyx_v_result, __pyx_slice__27); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 685, __pyx_L1_error) } __pyx_L7:; /* "View.MemoryView":686 * else: * result.append(slice(None)) * have_slices = True # <<<<<<<<<<<<<< * else: * if not isinstance(item, slice) and not PyIndex_Check(item): */ __pyx_v_have_slices = 1; /* "View.MemoryView":680 * seen_ellipsis = False * for idx, item in enumerate(tup): * if item is Ellipsis: # <<<<<<<<<<<<<< * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) */ goto __pyx_L6; } /* "View.MemoryView":688 * have_slices = True * else: * if not isinstance(item, slice) and not PyIndex_Check(item): # <<<<<<<<<<<<<< * raise TypeError("Cannot index with type '%s'" % type(item)) * */ /*else*/ { __pyx_t_2 = PySlice_Check(__pyx_v_item); __pyx_t_10 = ((!(__pyx_t_2 != 0)) != 0); if (__pyx_t_10) { } else { __pyx_t_1 = __pyx_t_10; goto __pyx_L9_bool_binop_done; } __pyx_t_10 = ((!(PyIndex_Check(__pyx_v_item) != 0)) != 0); __pyx_t_1 = __pyx_t_10; __pyx_L9_bool_binop_done:; if (unlikely(__pyx_t_1)) { /* "View.MemoryView":689 * else: * if not isinstance(item, slice) and not PyIndex_Check(item): * raise TypeError("Cannot index with type '%s'" % type(item)) # <<<<<<<<<<<<<< * * have_slices = have_slices or isinstance(item, slice) */ __pyx_t_7 = __Pyx_PyString_FormatSafe(__pyx_kp_s_Cannot_index_with_type_s, ((PyObject *)Py_TYPE(__pyx_v_item))); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 689, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_11 = __Pyx_PyObject_CallOneArg(__pyx_builtin_TypeError, __pyx_t_7); if (unlikely(!__pyx_t_11)) __PYX_ERR(2, 689, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_Raise(__pyx_t_11, 0, 0, 0); __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __PYX_ERR(2, 689, __pyx_L1_error) /* "View.MemoryView":688 * have_slices = True * else: * if not isinstance(item, slice) and not PyIndex_Check(item): # <<<<<<<<<<<<<< * raise TypeError("Cannot index with type '%s'" % type(item)) * */ } /* "View.MemoryView":691 * raise TypeError("Cannot index with type '%s'" % type(item)) * * have_slices = have_slices or isinstance(item, slice) # <<<<<<<<<<<<<< * result.append(item) * */ __pyx_t_10 = (__pyx_v_have_slices != 0); if (!__pyx_t_10) { } else { __pyx_t_1 = __pyx_t_10; goto __pyx_L11_bool_binop_done; } __pyx_t_10 = PySlice_Check(__pyx_v_item); __pyx_t_2 = (__pyx_t_10 != 0); __pyx_t_1 = __pyx_t_2; __pyx_L11_bool_binop_done:; __pyx_v_have_slices = __pyx_t_1; /* "View.MemoryView":692 * * have_slices = have_slices or isinstance(item, slice) * result.append(item) # <<<<<<<<<<<<<< * * nslices = ndim - len(result) */ __pyx_t_9 = __Pyx_PyList_Append(__pyx_v_result, __pyx_v_item); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 692, __pyx_L1_error) } __pyx_L6:; /* "View.MemoryView":679 * have_slices = False * seen_ellipsis = False * for idx, item in enumerate(tup): # <<<<<<<<<<<<<< * if item is Ellipsis: * if not seen_ellipsis: */ } __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":694 * result.append(item) * * nslices = ndim - len(result) # <<<<<<<<<<<<<< * if nslices: * result.extend([slice(None)] * nslices) */ __pyx_t_5 = PyList_GET_SIZE(__pyx_v_result); if (unlikely(__pyx_t_5 == ((Py_ssize_t)-1))) __PYX_ERR(2, 694, __pyx_L1_error) __pyx_v_nslices = (__pyx_v_ndim - __pyx_t_5); /* "View.MemoryView":695 * * nslices = ndim - len(result) * if nslices: # <<<<<<<<<<<<<< * result.extend([slice(None)] * nslices) * */ __pyx_t_1 = (__pyx_v_nslices != 0); if (__pyx_t_1) { /* "View.MemoryView":696 * nslices = ndim - len(result) * if nslices: * result.extend([slice(None)] * nslices) # <<<<<<<<<<<<<< * * return have_slices or nslices, tuple(result) */ __pyx_t_3 = PyList_New(1 * ((__pyx_v_nslices<0) ? 0:__pyx_v_nslices)); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 696, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); { Py_ssize_t __pyx_temp; for (__pyx_temp=0; __pyx_temp < __pyx_v_nslices; __pyx_temp++) { __Pyx_INCREF(__pyx_slice__27); __Pyx_GIVEREF(__pyx_slice__27); PyList_SET_ITEM(__pyx_t_3, __pyx_temp, __pyx_slice__27); } } __pyx_t_9 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_3); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 696, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":695 * * nslices = ndim - len(result) * if nslices: # <<<<<<<<<<<<<< * result.extend([slice(None)] * nslices) * */ } /* "View.MemoryView":698 * result.extend([slice(None)] * nslices) * * return have_slices or nslices, tuple(result) # <<<<<<<<<<<<<< * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): */ __Pyx_XDECREF(__pyx_r); if (!__pyx_v_have_slices) { } else { __pyx_t_4 = __Pyx_PyBool_FromLong(__pyx_v_have_slices); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = __pyx_t_4; __pyx_t_4 = 0; goto __pyx_L14_bool_binop_done; } __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_nslices); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = __pyx_t_4; __pyx_t_4 = 0; __pyx_L14_bool_binop_done:; __pyx_t_4 = PyList_AsTuple(__pyx_v_result); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_11 = PyTuple_New(2); if (unlikely(!__pyx_t_11)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_11, 0, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_11, 1, __pyx_t_4); __pyx_t_3 = 0; __pyx_t_4 = 0; __pyx_r = ((PyObject*)__pyx_t_11); __pyx_t_11 = 0; goto __pyx_L0; /* "View.MemoryView":666 * return isinstance(o, memoryview) * * cdef tuple _unellipsify(object index, int ndim): # <<<<<<<<<<<<<< * """ * Replace all ellipses with full slices and fill incomplete indices with */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_11); __Pyx_AddTraceback("View.MemoryView._unellipsify", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_tup); __Pyx_XDECREF(__pyx_v_result); __Pyx_XDECREF(__pyx_v_idx); __Pyx_XDECREF(__pyx_v_item); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":700 * return have_slices or nslices, tuple(result) * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): # <<<<<<<<<<<<<< * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: */ static PyObject *assert_direct_dimensions(Py_ssize_t *__pyx_v_suboffsets, int __pyx_v_ndim) { Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations Py_ssize_t *__pyx_t_1; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("assert_direct_dimensions", 0); /* "View.MemoryView":701 * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: # <<<<<<<<<<<<<< * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") */ __pyx_t_2 = (__pyx_v_suboffsets + __pyx_v_ndim); for (__pyx_t_3 = __pyx_v_suboffsets; __pyx_t_3 < __pyx_t_2; __pyx_t_3++) { __pyx_t_1 = __pyx_t_3; __pyx_v_suboffset = (__pyx_t_1[0]); /* "View.MemoryView":702 * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * raise ValueError("Indirect dimensions not supported") * */ __pyx_t_4 = ((__pyx_v_suboffset >= 0) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":703 * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__28, NULL); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 703, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_Raise(__pyx_t_5, 0, 0, 0); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __PYX_ERR(2, 703, __pyx_L1_error) /* "View.MemoryView":702 * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * raise ValueError("Indirect dimensions not supported") * */ } } /* "View.MemoryView":700 * return have_slices or nslices, tuple(result) * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): # <<<<<<<<<<<<<< * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.assert_direct_dimensions", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":710 * * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< * cdef int new_ndim = 0, suboffset_dim = -1, dim * cdef bint negative_step */ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *__pyx_v_memview, PyObject *__pyx_v_indices) { int __pyx_v_new_ndim; int __pyx_v_suboffset_dim; int __pyx_v_dim; __Pyx_memviewslice __pyx_v_src; __Pyx_memviewslice __pyx_v_dst; __Pyx_memviewslice *__pyx_v_p_src; struct __pyx_memoryviewslice_obj *__pyx_v_memviewsliceobj = 0; __Pyx_memviewslice *__pyx_v_p_dst; int *__pyx_v_p_suboffset_dim; Py_ssize_t __pyx_v_start; Py_ssize_t __pyx_v_stop; Py_ssize_t __pyx_v_step; int __pyx_v_have_start; int __pyx_v_have_stop; int __pyx_v_have_step; PyObject *__pyx_v_index = NULL; struct __pyx_memoryview_obj *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; struct __pyx_memoryview_obj *__pyx_t_4; char *__pyx_t_5; int __pyx_t_6; Py_ssize_t __pyx_t_7; PyObject *(*__pyx_t_8)(PyObject *); PyObject *__pyx_t_9 = NULL; Py_ssize_t __pyx_t_10; int __pyx_t_11; Py_ssize_t __pyx_t_12; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memview_slice", 0); /* "View.MemoryView":711 * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): * cdef int new_ndim = 0, suboffset_dim = -1, dim # <<<<<<<<<<<<<< * cdef bint negative_step * cdef __Pyx_memviewslice src, dst */ __pyx_v_new_ndim = 0; __pyx_v_suboffset_dim = -1; /* "View.MemoryView":718 * * * memset(&dst, 0, sizeof(dst)) # <<<<<<<<<<<<<< * * cdef _memoryviewslice memviewsliceobj */ (void)(memset((&__pyx_v_dst), 0, (sizeof(__pyx_v_dst)))); /* "View.MemoryView":722 * cdef _memoryviewslice memviewsliceobj * * assert memview.view.ndim > 0 # <<<<<<<<<<<<<< * * if isinstance(memview, _memoryviewslice): */ #ifndef CYTHON_WITHOUT_ASSERTIONS if (unlikely(!Py_OptimizeFlag)) { if (unlikely(!((__pyx_v_memview->view.ndim > 0) != 0))) { PyErr_SetNone(PyExc_AssertionError); __PYX_ERR(2, 722, __pyx_L1_error) } } #endif /* "View.MemoryView":724 * assert memview.view.ndim > 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":725 * * if isinstance(memview, _memoryviewslice): * memviewsliceobj = memview # <<<<<<<<<<<<<< * p_src = &memviewsliceobj.from_slice * else: */ if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 725, __pyx_L1_error) __pyx_t_3 = ((PyObject *)__pyx_v_memview); __Pyx_INCREF(__pyx_t_3); __pyx_v_memviewsliceobj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":726 * if isinstance(memview, _memoryviewslice): * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice # <<<<<<<<<<<<<< * else: * slice_copy(memview, &src) */ __pyx_v_p_src = (&__pyx_v_memviewsliceobj->from_slice); /* "View.MemoryView":724 * assert memview.view.ndim > 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice */ goto __pyx_L3; } /* "View.MemoryView":728 * p_src = &memviewsliceobj.from_slice * else: * slice_copy(memview, &src) # <<<<<<<<<<<<<< * p_src = &src * */ /*else*/ { __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_src)); /* "View.MemoryView":729 * else: * slice_copy(memview, &src) * p_src = &src # <<<<<<<<<<<<<< * * */ __pyx_v_p_src = (&__pyx_v_src); } __pyx_L3:; /* "View.MemoryView":735 * * * dst.memview = p_src.memview # <<<<<<<<<<<<<< * dst.data = p_src.data * */ __pyx_t_4 = __pyx_v_p_src->memview; __pyx_v_dst.memview = __pyx_t_4; /* "View.MemoryView":736 * * dst.memview = p_src.memview * dst.data = p_src.data # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __pyx_v_p_src->data; __pyx_v_dst.data = __pyx_t_5; /* "View.MemoryView":741 * * * cdef __Pyx_memviewslice *p_dst = &dst # <<<<<<<<<<<<<< * cdef int *p_suboffset_dim = &suboffset_dim * cdef Py_ssize_t start, stop, step */ __pyx_v_p_dst = (&__pyx_v_dst); /* "View.MemoryView":742 * * cdef __Pyx_memviewslice *p_dst = &dst * cdef int *p_suboffset_dim = &suboffset_dim # <<<<<<<<<<<<<< * cdef Py_ssize_t start, stop, step * cdef bint have_start, have_stop, have_step */ __pyx_v_p_suboffset_dim = (&__pyx_v_suboffset_dim); /* "View.MemoryView":746 * cdef bint have_start, have_stop, have_step * * for dim, index in enumerate(indices): # <<<<<<<<<<<<<< * if PyIndex_Check(index): * slice_memviewslice( */ __pyx_t_6 = 0; if (likely(PyList_CheckExact(__pyx_v_indices)) || PyTuple_CheckExact(__pyx_v_indices)) { __pyx_t_3 = __pyx_v_indices; __Pyx_INCREF(__pyx_t_3); __pyx_t_7 = 0; __pyx_t_8 = NULL; } else { __pyx_t_7 = -1; __pyx_t_3 = PyObject_GetIter(__pyx_v_indices); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_8 = Py_TYPE(__pyx_t_3)->tp_iternext; if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 746, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_8)) { if (likely(PyList_CheckExact(__pyx_t_3))) { if (__pyx_t_7 >= PyList_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_9 = PyList_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(2, 746, __pyx_L1_error) #else __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); #endif } else { if (__pyx_t_7 >= PyTuple_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_9 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(2, 746, __pyx_L1_error) #else __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); #endif } } else { __pyx_t_9 = __pyx_t_8(__pyx_t_3); if (unlikely(!__pyx_t_9)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(2, 746, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_9); } __Pyx_XDECREF_SET(__pyx_v_index, __pyx_t_9); __pyx_t_9 = 0; __pyx_v_dim = __pyx_t_6; __pyx_t_6 = (__pyx_t_6 + 1); /* "View.MemoryView":747 * * for dim, index in enumerate(indices): * if PyIndex_Check(index): # <<<<<<<<<<<<<< * slice_memviewslice( * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], */ __pyx_t_2 = (PyIndex_Check(__pyx_v_index) != 0); if (__pyx_t_2) { /* "View.MemoryView":751 * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, * index, 0, 0, # start, stop, step # <<<<<<<<<<<<<< * 0, 0, 0, # have_{start,stop,step} * False) */ __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_v_index); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 751, __pyx_L1_error) /* "View.MemoryView":748 * for dim, index in enumerate(indices): * if PyIndex_Check(index): * slice_memviewslice( # <<<<<<<<<<<<<< * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, */ __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_t_10, 0, 0, 0, 0, 0, 0); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 748, __pyx_L1_error) /* "View.MemoryView":747 * * for dim, index in enumerate(indices): * if PyIndex_Check(index): # <<<<<<<<<<<<<< * slice_memviewslice( * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], */ goto __pyx_L6; } /* "View.MemoryView":754 * 0, 0, 0, # have_{start,stop,step} * False) * elif index is None: # <<<<<<<<<<<<<< * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 */ __pyx_t_2 = (__pyx_v_index == Py_None); __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":755 * False) * elif index is None: * p_dst.shape[new_ndim] = 1 # <<<<<<<<<<<<<< * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 */ (__pyx_v_p_dst->shape[__pyx_v_new_ndim]) = 1; /* "View.MemoryView":756 * elif index is None: * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 # <<<<<<<<<<<<<< * p_dst.suboffsets[new_ndim] = -1 * new_ndim += 1 */ (__pyx_v_p_dst->strides[__pyx_v_new_ndim]) = 0; /* "View.MemoryView":757 * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 # <<<<<<<<<<<<<< * new_ndim += 1 * else: */ (__pyx_v_p_dst->suboffsets[__pyx_v_new_ndim]) = -1L; /* "View.MemoryView":758 * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 * new_ndim += 1 # <<<<<<<<<<<<<< * else: * start = index.start or 0 */ __pyx_v_new_ndim = (__pyx_v_new_ndim + 1); /* "View.MemoryView":754 * 0, 0, 0, # have_{start,stop,step} * False) * elif index is None: # <<<<<<<<<<<<<< * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 */ goto __pyx_L6; } /* "View.MemoryView":760 * new_ndim += 1 * else: * start = index.start or 0 # <<<<<<<<<<<<<< * stop = index.stop or 0 * step = index.step or 0 */ /*else*/ { __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 760, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 760, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 760, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L7_bool_binop_done; } __pyx_t_10 = 0; __pyx_L7_bool_binop_done:; __pyx_v_start = __pyx_t_10; /* "View.MemoryView":761 * else: * start = index.start or 0 * stop = index.stop or 0 # <<<<<<<<<<<<<< * step = index.step or 0 * */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 761, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 761, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 761, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L9_bool_binop_done; } __pyx_t_10 = 0; __pyx_L9_bool_binop_done:; __pyx_v_stop = __pyx_t_10; /* "View.MemoryView":762 * start = index.start or 0 * stop = index.stop or 0 * step = index.step or 0 # <<<<<<<<<<<<<< * * have_start = index.start is not None */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 762, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 762, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 762, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L11_bool_binop_done; } __pyx_t_10 = 0; __pyx_L11_bool_binop_done:; __pyx_v_step = __pyx_t_10; /* "View.MemoryView":764 * step = index.step or 0 * * have_start = index.start is not None # <<<<<<<<<<<<<< * have_stop = index.stop is not None * have_step = index.step is not None */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 764, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_start = __pyx_t_1; /* "View.MemoryView":765 * * have_start = index.start is not None * have_stop = index.stop is not None # <<<<<<<<<<<<<< * have_step = index.step is not None * */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 765, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_stop = __pyx_t_1; /* "View.MemoryView":766 * have_start = index.start is not None * have_stop = index.stop is not None * have_step = index.step is not None # <<<<<<<<<<<<<< * * slice_memviewslice( */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 766, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_step = __pyx_t_1; /* "View.MemoryView":768 * have_step = index.step is not None * * slice_memviewslice( # <<<<<<<<<<<<<< * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, */ __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 768, __pyx_L1_error) /* "View.MemoryView":774 * have_start, have_stop, have_step, * True) * new_ndim += 1 # <<<<<<<<<<<<<< * * if isinstance(memview, _memoryviewslice): */ __pyx_v_new_ndim = (__pyx_v_new_ndim + 1); } __pyx_L6:; /* "View.MemoryView":746 * cdef bint have_start, have_stop, have_step * * for dim, index in enumerate(indices): # <<<<<<<<<<<<<< * if PyIndex_Check(index): * slice_memviewslice( */ } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":776 * new_ndim += 1 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":777 * * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, */ __Pyx_XDECREF(((PyObject *)__pyx_r)); /* "View.MemoryView":778 * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, # <<<<<<<<<<<<<< * memviewsliceobj.to_dtype_func, * memview.dtype_is_object) */ if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 778, __pyx_L1_error) } /* "View.MemoryView":779 * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, # <<<<<<<<<<<<<< * memview.dtype_is_object) * else: */ if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 779, __pyx_L1_error) } /* "View.MemoryView":777 * * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, */ __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 777, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 777, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":776 * new_ndim += 1 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, */ } /* "View.MemoryView":782 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< * memview.dtype_is_object) * */ /*else*/ { __Pyx_XDECREF(((PyObject *)__pyx_r)); /* "View.MemoryView":783 * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 782, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); /* "View.MemoryView":782 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< * memview.dtype_is_object) * */ if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 782, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; } /* "View.MemoryView":710 * * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< * cdef int new_ndim = 0, suboffset_dim = -1, dim * cdef bint negative_step */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_9); __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj); __Pyx_XDECREF(__pyx_v_index); __Pyx_XGIVEREF((PyObject *)__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":807 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, */ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) { Py_ssize_t __pyx_v_new_shape; int __pyx_v_negative_step; int __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":827 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< * * if start < 0: */ __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_1) { /* "View.MemoryView":829 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< * start += shape * if not 0 <= start < shape: */ __pyx_t_1 = ((__pyx_v_start < 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":830 * * if start < 0: * start += shape # <<<<<<<<<<<<<< * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); /* "View.MemoryView":829 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< * start += shape * if not 0 <= start < shape: */ } /* "View.MemoryView":831 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) * else: */ __pyx_t_1 = (0 <= __pyx_v_start); if (__pyx_t_1) { __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); } __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":832 * start += shape * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< * else: * */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 832, __pyx_L1_error) /* "View.MemoryView":831 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) * else: */ } /* "View.MemoryView":827 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< * * if start < 0: */ goto __pyx_L3; } /* "View.MemoryView":835 * else: * * negative_step = have_step != 0 and step < 0 # <<<<<<<<<<<<<< * * if have_step and step == 0: */ /*else*/ { __pyx_t_1 = ((__pyx_v_have_step != 0) != 0); if (__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L6_bool_binop_done; } __pyx_t_1 = ((__pyx_v_step < 0) != 0); __pyx_t_2 = __pyx_t_1; __pyx_L6_bool_binop_done:; __pyx_v_negative_step = __pyx_t_2; /* "View.MemoryView":837 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) * */ __pyx_t_1 = (__pyx_v_have_step != 0); if (__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L9_bool_binop_done; } __pyx_t_1 = ((__pyx_v_step == 0) != 0); __pyx_t_2 = __pyx_t_1; __pyx_L9_bool_binop_done:; if (__pyx_t_2) { /* "View.MemoryView":838 * * if have_step and step == 0: * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 838, __pyx_L1_error) /* "View.MemoryView":837 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) * */ } /* "View.MemoryView":841 * * * if have_start: # <<<<<<<<<<<<<< * if start < 0: * start += shape */ __pyx_t_2 = (__pyx_v_have_start != 0); if (__pyx_t_2) { /* "View.MemoryView":842 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< * start += shape * if start < 0: */ __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":843 * if have_start: * if start < 0: * start += shape # <<<<<<<<<<<<<< * if start < 0: * start = 0 */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); /* "View.MemoryView":844 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< * start = 0 * elif start >= shape: */ __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":845 * start += shape * if start < 0: * start = 0 # <<<<<<<<<<<<<< * elif start >= shape: * if negative_step: */ __pyx_v_start = 0; /* "View.MemoryView":844 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< * start = 0 * elif start >= shape: */ } /* "View.MemoryView":842 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< * start += shape * if start < 0: */ goto __pyx_L12; } /* "View.MemoryView":846 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< * if negative_step: * start = shape - 1 */ __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); if (__pyx_t_2) { /* "View.MemoryView":847 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":848 * elif start >= shape: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< * else: * start = shape */ __pyx_v_start = (__pyx_v_shape - 1); /* "View.MemoryView":847 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ goto __pyx_L14; } /* "View.MemoryView":850 * start = shape - 1 * else: * start = shape # <<<<<<<<<<<<<< * else: * if negative_step: */ /*else*/ { __pyx_v_start = __pyx_v_shape; } __pyx_L14:; /* "View.MemoryView":846 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< * if negative_step: * start = shape - 1 */ } __pyx_L12:; /* "View.MemoryView":841 * * * if have_start: # <<<<<<<<<<<<<< * if start < 0: * start += shape */ goto __pyx_L11; } /* "View.MemoryView":852 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ /*else*/ { __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":853 * else: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< * else: * start = 0 */ __pyx_v_start = (__pyx_v_shape - 1); /* "View.MemoryView":852 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ goto __pyx_L15; } /* "View.MemoryView":855 * start = shape - 1 * else: * start = 0 # <<<<<<<<<<<<<< * * if have_stop: */ /*else*/ { __pyx_v_start = 0; } __pyx_L15:; } __pyx_L11:; /* "View.MemoryView":857 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< * if stop < 0: * stop += shape */ __pyx_t_2 = (__pyx_v_have_stop != 0); if (__pyx_t_2) { /* "View.MemoryView":858 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< * stop += shape * if stop < 0: */ __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":859 * if have_stop: * if stop < 0: * stop += shape # <<<<<<<<<<<<<< * if stop < 0: * stop = 0 */ __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); /* "View.MemoryView":860 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< * stop = 0 * elif stop > shape: */ __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":861 * stop += shape * if stop < 0: * stop = 0 # <<<<<<<<<<<<<< * elif stop > shape: * stop = shape */ __pyx_v_stop = 0; /* "View.MemoryView":860 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< * stop = 0 * elif stop > shape: */ } /* "View.MemoryView":858 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< * stop += shape * if stop < 0: */ goto __pyx_L17; } /* "View.MemoryView":862 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< * stop = shape * else: */ __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0); if (__pyx_t_2) { /* "View.MemoryView":863 * stop = 0 * elif stop > shape: * stop = shape # <<<<<<<<<<<<<< * else: * if negative_step: */ __pyx_v_stop = __pyx_v_shape; /* "View.MemoryView":862 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< * stop = shape * else: */ } __pyx_L17:; /* "View.MemoryView":857 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< * if stop < 0: * stop += shape */ goto __pyx_L16; } /* "View.MemoryView":865 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< * stop = -1 * else: */ /*else*/ { __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":866 * else: * if negative_step: * stop = -1 # <<<<<<<<<<<<<< * else: * stop = shape */ __pyx_v_stop = -1L; /* "View.MemoryView":865 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< * stop = -1 * else: */ goto __pyx_L19; } /* "View.MemoryView":868 * stop = -1 * else: * stop = shape # <<<<<<<<<<<<<< * * if not have_step: */ /*else*/ { __pyx_v_stop = __pyx_v_shape; } __pyx_L19:; } __pyx_L16:; /* "View.MemoryView":870 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< * step = 1 * */ __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":871 * * if not have_step: * step = 1 # <<<<<<<<<<<<<< * * */ __pyx_v_step = 1; /* "View.MemoryView":870 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< * step = 1 * */ } /* "View.MemoryView":875 * * with cython.cdivision(True): * new_shape = (stop - start) // step # <<<<<<<<<<<<<< * * if (stop - start) - step * new_shape: */ __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); /* "View.MemoryView":877 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< * new_shape += 1 * */ __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); if (__pyx_t_2) { /* "View.MemoryView":878 * * if (stop - start) - step * new_shape: * new_shape += 1 # <<<<<<<<<<<<<< * * if new_shape < 0: */ __pyx_v_new_shape = (__pyx_v_new_shape + 1); /* "View.MemoryView":877 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< * new_shape += 1 * */ } /* "View.MemoryView":880 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< * new_shape = 0 * */ __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":881 * * if new_shape < 0: * new_shape = 0 # <<<<<<<<<<<<<< * * */ __pyx_v_new_shape = 0; /* "View.MemoryView":880 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< * new_shape = 0 * */ } /* "View.MemoryView":884 * * * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset */ (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); /* "View.MemoryView":885 * * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape # <<<<<<<<<<<<<< * dst.suboffsets[new_ndim] = suboffset * */ (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape; /* "View.MemoryView":886 * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset # <<<<<<<<<<<<<< * * */ (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset; } __pyx_L3:; /* "View.MemoryView":889 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< * dst.data += start * stride * else: */ __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":890 * * if suboffset_dim[0] < 0: * dst.data += start * stride # <<<<<<<<<<<<<< * else: * dst.suboffsets[suboffset_dim[0]] += start * stride */ __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride)); /* "View.MemoryView":889 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< * dst.data += start * stride * else: */ goto __pyx_L23; } /* "View.MemoryView":892 * dst.data += start * stride * else: * dst.suboffsets[suboffset_dim[0]] += start * stride # <<<<<<<<<<<<<< * * if suboffset >= 0: */ /*else*/ { __pyx_t_3 = (__pyx_v_suboffset_dim[0]); (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride)); } __pyx_L23:; /* "View.MemoryView":894 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< * if not is_slice: * if new_ndim == 0: */ __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":895 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset */ __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":896 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< * dst.data = ( dst.data)[0] + suboffset * else: */ __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":897 * if not is_slice: * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset # <<<<<<<<<<<<<< * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " */ __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset); /* "View.MemoryView":896 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< * dst.data = ( dst.data)[0] + suboffset * else: */ goto __pyx_L26; } /* "View.MemoryView":899 * dst.data = ( dst.data)[0] + suboffset * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " # <<<<<<<<<<<<<< * "must be indexed and not sliced", dim) * else: */ /*else*/ { /* "View.MemoryView":900 * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " * "must be indexed and not sliced", dim) # <<<<<<<<<<<<<< * else: * suboffset_dim[0] = new_ndim */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"All dimensions preceding dimension %d must be indexed and not sliced"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 899, __pyx_L1_error) } __pyx_L26:; /* "View.MemoryView":895 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset */ goto __pyx_L25; } /* "View.MemoryView":902 * "must be indexed and not sliced", dim) * else: * suboffset_dim[0] = new_ndim # <<<<<<<<<<<<<< * * return 0 */ /*else*/ { (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim; } __pyx_L25:; /* "View.MemoryView":894 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< * if not is_slice: * if new_ndim == 0: */ } /* "View.MemoryView":904 * suboffset_dim[0] = new_ndim * * return 0 # <<<<<<<<<<<<<< * * */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":807 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.slice_memviewslice", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = -1; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":910 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 */ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, Py_ssize_t __pyx_v_index, Py_ssize_t __pyx_v_dim) { Py_ssize_t __pyx_v_shape; Py_ssize_t __pyx_v_stride; Py_ssize_t __pyx_v_suboffset; Py_ssize_t __pyx_v_itemsize; char *__pyx_v_resultp; char *__pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("pybuffer_index", 0); /* "View.MemoryView":912 * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 # <<<<<<<<<<<<<< * cdef Py_ssize_t itemsize = view.itemsize * cdef char *resultp */ __pyx_v_suboffset = -1L; /* "View.MemoryView":913 * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 * cdef Py_ssize_t itemsize = view.itemsize # <<<<<<<<<<<<<< * cdef char *resultp * */ __pyx_t_1 = __pyx_v_view->itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":916 * cdef char *resultp * * if view.ndim == 0: # <<<<<<<<<<<<<< * shape = view.len / itemsize * stride = itemsize */ __pyx_t_2 = ((__pyx_v_view->ndim == 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":917 * * if view.ndim == 0: * shape = view.len / itemsize # <<<<<<<<<<<<<< * stride = itemsize * else: */ if (unlikely(__pyx_v_itemsize == 0)) { PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); __PYX_ERR(2, 917, __pyx_L1_error) } else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1) && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_view->len))) { PyErr_SetString(PyExc_OverflowError, "value too large to perform division"); __PYX_ERR(2, 917, __pyx_L1_error) } __pyx_v_shape = (__pyx_v_view->len / __pyx_v_itemsize); /* "View.MemoryView":918 * if view.ndim == 0: * shape = view.len / itemsize * stride = itemsize # <<<<<<<<<<<<<< * else: * shape = view.shape[dim] */ __pyx_v_stride = __pyx_v_itemsize; /* "View.MemoryView":916 * cdef char *resultp * * if view.ndim == 0: # <<<<<<<<<<<<<< * shape = view.len / itemsize * stride = itemsize */ goto __pyx_L3; } /* "View.MemoryView":920 * stride = itemsize * else: * shape = view.shape[dim] # <<<<<<<<<<<<<< * stride = view.strides[dim] * if view.suboffsets != NULL: */ /*else*/ { __pyx_v_shape = (__pyx_v_view->shape[__pyx_v_dim]); /* "View.MemoryView":921 * else: * shape = view.shape[dim] * stride = view.strides[dim] # <<<<<<<<<<<<<< * if view.suboffsets != NULL: * suboffset = view.suboffsets[dim] */ __pyx_v_stride = (__pyx_v_view->strides[__pyx_v_dim]); /* "View.MemoryView":922 * shape = view.shape[dim] * stride = view.strides[dim] * if view.suboffsets != NULL: # <<<<<<<<<<<<<< * suboffset = view.suboffsets[dim] * */ __pyx_t_2 = ((__pyx_v_view->suboffsets != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":923 * stride = view.strides[dim] * if view.suboffsets != NULL: * suboffset = view.suboffsets[dim] # <<<<<<<<<<<<<< * * if index < 0: */ __pyx_v_suboffset = (__pyx_v_view->suboffsets[__pyx_v_dim]); /* "View.MemoryView":922 * shape = view.shape[dim] * stride = view.strides[dim] * if view.suboffsets != NULL: # <<<<<<<<<<<<<< * suboffset = view.suboffsets[dim] * */ } } __pyx_L3:; /* "View.MemoryView":925 * suboffset = view.suboffsets[dim] * * if index < 0: # <<<<<<<<<<<<<< * index += view.shape[dim] * if index < 0: */ __pyx_t_2 = ((__pyx_v_index < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":926 * * if index < 0: * index += view.shape[dim] # <<<<<<<<<<<<<< * if index < 0: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) */ __pyx_v_index = (__pyx_v_index + (__pyx_v_view->shape[__pyx_v_dim])); /* "View.MemoryView":927 * if index < 0: * index += view.shape[dim] * if index < 0: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ __pyx_t_2 = ((__pyx_v_index < 0) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":928 * index += view.shape[dim] * if index < 0: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * if index >= shape: */ __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 928, __pyx_L1_error) /* "View.MemoryView":927 * if index < 0: * index += view.shape[dim] * if index < 0: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ } /* "View.MemoryView":925 * suboffset = view.suboffsets[dim] * * if index < 0: # <<<<<<<<<<<<<< * index += view.shape[dim] * if index < 0: */ } /* "View.MemoryView":930 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ __pyx_t_2 = ((__pyx_v_index >= __pyx_v_shape) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":931 * * if index >= shape: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * resultp = bufp + index * stride */ __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 931, __pyx_L1_error) /* "View.MemoryView":930 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ } /* "View.MemoryView":933 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * resultp = bufp + index * stride # <<<<<<<<<<<<<< * if suboffset >= 0: * resultp = ( resultp)[0] + suboffset */ __pyx_v_resultp = (__pyx_v_bufp + (__pyx_v_index * __pyx_v_stride)); /* "View.MemoryView":934 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< * resultp = ( resultp)[0] + suboffset * */ __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":935 * resultp = bufp + index * stride * if suboffset >= 0: * resultp = ( resultp)[0] + suboffset # <<<<<<<<<<<<<< * * return resultp */ __pyx_v_resultp = ((((char **)__pyx_v_resultp)[0]) + __pyx_v_suboffset); /* "View.MemoryView":934 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< * resultp = ( resultp)[0] + suboffset * */ } /* "View.MemoryView":937 * resultp = ( resultp)[0] + suboffset * * return resultp # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_resultp; goto __pyx_L0; /* "View.MemoryView":910 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView.pybuffer_index", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":943 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< * cdef int ndim = memslice.memview.view.ndim * */ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { int __pyx_v_ndim; Py_ssize_t *__pyx_v_shape; Py_ssize_t *__pyx_v_strides; int __pyx_v_i; int __pyx_v_j; int __pyx_r; int __pyx_t_1; Py_ssize_t *__pyx_t_2; long __pyx_t_3; long __pyx_t_4; Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; int __pyx_t_7; int __pyx_t_8; int __pyx_t_9; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":944 * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: * cdef int ndim = memslice.memview.view.ndim # <<<<<<<<<<<<<< * * cdef Py_ssize_t *shape = memslice.shape */ __pyx_t_1 = __pyx_v_memslice->memview->view.ndim; __pyx_v_ndim = __pyx_t_1; /* "View.MemoryView":946 * cdef int ndim = memslice.memview.view.ndim * * cdef Py_ssize_t *shape = memslice.shape # <<<<<<<<<<<<<< * cdef Py_ssize_t *strides = memslice.strides * */ __pyx_t_2 = __pyx_v_memslice->shape; __pyx_v_shape = __pyx_t_2; /* "View.MemoryView":947 * * cdef Py_ssize_t *shape = memslice.shape * cdef Py_ssize_t *strides = memslice.strides # <<<<<<<<<<<<<< * * */ __pyx_t_2 = __pyx_v_memslice->strides; __pyx_v_strides = __pyx_t_2; /* "View.MemoryView":951 * * cdef int i, j * for i in range(ndim / 2): # <<<<<<<<<<<<<< * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] */ __pyx_t_3 = (__pyx_v_ndim / 2); __pyx_t_4 = __pyx_t_3; for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_4; __pyx_t_1+=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":952 * cdef int i, j * for i in range(ndim / 2): * j = ndim - 1 - i # <<<<<<<<<<<<<< * strides[i], strides[j] = strides[j], strides[i] * shape[i], shape[j] = shape[j], shape[i] */ __pyx_v_j = ((__pyx_v_ndim - 1) - __pyx_v_i); /* "View.MemoryView":953 * for i in range(ndim / 2): * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] # <<<<<<<<<<<<<< * shape[i], shape[j] = shape[j], shape[i] * */ __pyx_t_5 = (__pyx_v_strides[__pyx_v_j]); __pyx_t_6 = (__pyx_v_strides[__pyx_v_i]); (__pyx_v_strides[__pyx_v_i]) = __pyx_t_5; (__pyx_v_strides[__pyx_v_j]) = __pyx_t_6; /* "View.MemoryView":954 * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] * shape[i], shape[j] = shape[j], shape[i] # <<<<<<<<<<<<<< * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: */ __pyx_t_6 = (__pyx_v_shape[__pyx_v_j]); __pyx_t_5 = (__pyx_v_shape[__pyx_v_i]); (__pyx_v_shape[__pyx_v_i]) = __pyx_t_6; (__pyx_v_shape[__pyx_v_j]) = __pyx_t_5; /* "View.MemoryView":956 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * */ __pyx_t_8 = (((__pyx_v_memslice->suboffsets[__pyx_v_i]) >= 0) != 0); if (!__pyx_t_8) { } else { __pyx_t_7 = __pyx_t_8; goto __pyx_L6_bool_binop_done; } __pyx_t_8 = (((__pyx_v_memslice->suboffsets[__pyx_v_j]) >= 0) != 0); __pyx_t_7 = __pyx_t_8; __pyx_L6_bool_binop_done:; if (__pyx_t_7) { /* "View.MemoryView":957 * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") # <<<<<<<<<<<<<< * * return 1 */ __pyx_t_9 = __pyx_memoryview_err(__pyx_builtin_ValueError, ((char *)"Cannot transpose memoryview with indirect dimensions")); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 957, __pyx_L1_error) /* "View.MemoryView":956 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * */ } } /* "View.MemoryView":959 * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * * return 1 # <<<<<<<<<<<<<< * * */ __pyx_r = 1; goto __pyx_L0; /* "View.MemoryView":943 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< * cdef int ndim = memslice.memview.view.ndim * */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.transpose_memslice", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":976 * cdef int (*to_dtype_func)(char *, object) except 0 * * def __dealloc__(self): # <<<<<<<<<<<<<< * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * */ /* Python wrapper */ static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":977 * * def __dealloc__(self): * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) # <<<<<<<<<<<<<< * * cdef convert_item_to_object(self, char *itemp): */ __PYX_XDEC_MEMVIEW((&__pyx_v_self->from_slice), 1); /* "View.MemoryView":976 * cdef int (*to_dtype_func)(char *, object) except 0 * * def __dealloc__(self): # <<<<<<<<<<<<<< * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":979 * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * if self.to_object_func != NULL: * return self.to_object_func(itemp) */ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("convert_item_to_object", 0); /* "View.MemoryView":980 * * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: # <<<<<<<<<<<<<< * return self.to_object_func(itemp) * else: */ __pyx_t_1 = ((__pyx_v_self->to_object_func != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":981 * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: * return self.to_object_func(itemp) # <<<<<<<<<<<<<< * else: * return memoryview.convert_item_to_object(self, itemp) */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_v_self->to_object_func(__pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 981, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":980 * * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: # <<<<<<<<<<<<<< * return self.to_object_func(itemp) * else: */ } /* "View.MemoryView":983 * return self.to_object_func(itemp) * else: * return memoryview.convert_item_to_object(self, itemp) # <<<<<<<<<<<<<< * * cdef assign_item_from_object(self, char *itemp, object value): */ /*else*/ { __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_convert_item_to_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 983, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; } /* "View.MemoryView":979 * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * if self.to_object_func != NULL: * return self.to_object_func(itemp) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":985 * return memoryview.convert_item_to_object(self, itemp) * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) */ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("assign_item_from_object", 0); /* "View.MemoryView":986 * * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: # <<<<<<<<<<<<<< * self.to_dtype_func(itemp, value) * else: */ __pyx_t_1 = ((__pyx_v_self->to_dtype_func != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":987 * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) # <<<<<<<<<<<<<< * else: * memoryview.assign_item_from_object(self, itemp, value) */ __pyx_t_2 = __pyx_v_self->to_dtype_func(__pyx_v_itemp, __pyx_v_value); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(2, 987, __pyx_L1_error) /* "View.MemoryView":986 * * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: # <<<<<<<<<<<<<< * self.to_dtype_func(itemp, value) * else: */ goto __pyx_L3; } /* "View.MemoryView":989 * self.to_dtype_func(itemp, value) * else: * memoryview.assign_item_from_object(self, itemp, value) # <<<<<<<<<<<<<< * * @property */ /*else*/ { __pyx_t_3 = __pyx_memoryview_assign_item_from_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 989, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } __pyx_L3:; /* "View.MemoryView":985 * return memoryview.convert_item_to_object(self, itemp) * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":992 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.from_object * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":993 * @property * def base(self): * return self.from_object # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->from_object); __pyx_r = __pyx_v_self->from_object; goto __pyx_L0; /* "View.MemoryView":992 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.from_object * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryviewslice_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_memoryviewslice_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryviewslice___reduce_cython__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__29, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryviewslice_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_memoryviewslice_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryviewslice_2__setstate_cython__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__30, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":999 * * @cname('__pyx_memoryview_fromslice') * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice, # <<<<<<<<<<<<<< * int ndim, * object (*to_object_func)(char *), */ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewslice, int __pyx_v_ndim, PyObject *(*__pyx_v_to_object_func)(char *), int (*__pyx_v_to_dtype_func)(char *, PyObject *), int __pyx_v_dtype_is_object) { struct __pyx_memoryviewslice_obj *__pyx_v_result = 0; Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_v_length = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; __Pyx_TypeInfo *__pyx_t_4; Py_buffer __pyx_t_5; Py_ssize_t *__pyx_t_6; Py_ssize_t *__pyx_t_7; Py_ssize_t *__pyx_t_8; Py_ssize_t __pyx_t_9; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_fromslice", 0); /* "View.MemoryView":1007 * cdef _memoryviewslice result * * if memviewslice.memview == Py_None: # <<<<<<<<<<<<<< * return None * */ __pyx_t_1 = ((((PyObject *)__pyx_v_memviewslice.memview) == Py_None) != 0); if (__pyx_t_1) { /* "View.MemoryView":1008 * * if memviewslice.memview == Py_None: * return None # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; /* "View.MemoryView":1007 * cdef _memoryviewslice result * * if memviewslice.memview == Py_None: # <<<<<<<<<<<<<< * return None * */ } /* "View.MemoryView":1013 * * * result = _memoryviewslice(None, 0, dtype_is_object) # <<<<<<<<<<<<<< * * result.from_slice = memviewslice */ __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(Py_None); __Pyx_GIVEREF(Py_None); PyTuple_SET_ITEM(__pyx_t_3, 0, Py_None); __Pyx_INCREF(__pyx_int_0); __Pyx_GIVEREF(__pyx_int_0); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_int_0); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryviewslice_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":1015 * result = _memoryviewslice(None, 0, dtype_is_object) * * result.from_slice = memviewslice # <<<<<<<<<<<<<< * __PYX_INC_MEMVIEW(&memviewslice, 1) * */ __pyx_v_result->from_slice = __pyx_v_memviewslice; /* "View.MemoryView":1016 * * result.from_slice = memviewslice * __PYX_INC_MEMVIEW(&memviewslice, 1) # <<<<<<<<<<<<<< * * result.from_object = ( memviewslice.memview).base */ __PYX_INC_MEMVIEW((&__pyx_v_memviewslice), 1); /* "View.MemoryView":1018 * __PYX_INC_MEMVIEW(&memviewslice, 1) * * result.from_object = ( memviewslice.memview).base # <<<<<<<<<<<<<< * result.typeinfo = memviewslice.memview.typeinfo * */ __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_memviewslice.memview), __pyx_n_s_base); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1018, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); __Pyx_GOTREF(__pyx_v_result->from_object); __Pyx_DECREF(__pyx_v_result->from_object); __pyx_v_result->from_object = __pyx_t_2; __pyx_t_2 = 0; /* "View.MemoryView":1019 * * result.from_object = ( memviewslice.memview).base * result.typeinfo = memviewslice.memview.typeinfo # <<<<<<<<<<<<<< * * result.view = memviewslice.memview.view */ __pyx_t_4 = __pyx_v_memviewslice.memview->typeinfo; __pyx_v_result->__pyx_base.typeinfo = __pyx_t_4; /* "View.MemoryView":1021 * result.typeinfo = memviewslice.memview.typeinfo * * result.view = memviewslice.memview.view # <<<<<<<<<<<<<< * result.view.buf = memviewslice.data * result.view.ndim = ndim */ __pyx_t_5 = __pyx_v_memviewslice.memview->view; __pyx_v_result->__pyx_base.view = __pyx_t_5; /* "View.MemoryView":1022 * * result.view = memviewslice.memview.view * result.view.buf = memviewslice.data # <<<<<<<<<<<<<< * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None */ __pyx_v_result->__pyx_base.view.buf = ((void *)__pyx_v_memviewslice.data); /* "View.MemoryView":1023 * result.view = memviewslice.memview.view * result.view.buf = memviewslice.data * result.view.ndim = ndim # <<<<<<<<<<<<<< * (<__pyx_buffer *> &result.view).obj = Py_None * Py_INCREF(Py_None) */ __pyx_v_result->__pyx_base.view.ndim = __pyx_v_ndim; /* "View.MemoryView":1024 * result.view.buf = memviewslice.data * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_result->__pyx_base.view))->obj = Py_None; /* "View.MemoryView":1025 * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * if (memviewslice.memview).flags & PyBUF_WRITABLE: */ Py_INCREF(Py_None); /* "View.MemoryView":1027 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< * result.flags = PyBUF_RECORDS * else: */ __pyx_t_1 = ((((struct __pyx_memoryview_obj *)__pyx_v_memviewslice.memview)->flags & PyBUF_WRITABLE) != 0); if (__pyx_t_1) { /* "View.MemoryView":1028 * * if (memviewslice.memview).flags & PyBUF_WRITABLE: * result.flags = PyBUF_RECORDS # <<<<<<<<<<<<<< * else: * result.flags = PyBUF_RECORDS_RO */ __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS; /* "View.MemoryView":1027 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< * result.flags = PyBUF_RECORDS * else: */ goto __pyx_L4; } /* "View.MemoryView":1030 * result.flags = PyBUF_RECORDS * else: * result.flags = PyBUF_RECORDS_RO # <<<<<<<<<<<<<< * * result.view.shape = result.from_slice.shape */ /*else*/ { __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS_RO; } __pyx_L4:; /* "View.MemoryView":1032 * result.flags = PyBUF_RECORDS_RO * * result.view.shape = result.from_slice.shape # <<<<<<<<<<<<<< * result.view.strides = result.from_slice.strides * */ __pyx_v_result->__pyx_base.view.shape = ((Py_ssize_t *)__pyx_v_result->from_slice.shape); /* "View.MemoryView":1033 * * result.view.shape = result.from_slice.shape * result.view.strides = result.from_slice.strides # <<<<<<<<<<<<<< * * */ __pyx_v_result->__pyx_base.view.strides = ((Py_ssize_t *)__pyx_v_result->from_slice.strides); /* "View.MemoryView":1036 * * * result.view.suboffsets = NULL # <<<<<<<<<<<<<< * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: */ __pyx_v_result->__pyx_base.view.suboffsets = NULL; /* "View.MemoryView":1037 * * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: # <<<<<<<<<<<<<< * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets */ __pyx_t_7 = (__pyx_v_result->from_slice.suboffsets + __pyx_v_ndim); for (__pyx_t_8 = __pyx_v_result->from_slice.suboffsets; __pyx_t_8 < __pyx_t_7; __pyx_t_8++) { __pyx_t_6 = __pyx_t_8; __pyx_v_suboffset = (__pyx_t_6[0]); /* "View.MemoryView":1038 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * result.view.suboffsets = result.from_slice.suboffsets * break */ __pyx_t_1 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":1039 * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets # <<<<<<<<<<<<<< * break * */ __pyx_v_result->__pyx_base.view.suboffsets = ((Py_ssize_t *)__pyx_v_result->from_slice.suboffsets); /* "View.MemoryView":1040 * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets * break # <<<<<<<<<<<<<< * * result.view.len = result.view.itemsize */ goto __pyx_L6_break; /* "View.MemoryView":1038 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * result.view.suboffsets = result.from_slice.suboffsets * break */ } } __pyx_L6_break:; /* "View.MemoryView":1042 * break * * result.view.len = result.view.itemsize # <<<<<<<<<<<<<< * for length in result.view.shape[:ndim]: * result.view.len *= length */ __pyx_t_9 = __pyx_v_result->__pyx_base.view.itemsize; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; /* "View.MemoryView":1043 * * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: # <<<<<<<<<<<<<< * result.view.len *= length * */ __pyx_t_7 = (__pyx_v_result->__pyx_base.view.shape + __pyx_v_ndim); for (__pyx_t_8 = __pyx_v_result->__pyx_base.view.shape; __pyx_t_8 < __pyx_t_7; __pyx_t_8++) { __pyx_t_6 = __pyx_t_8; __pyx_t_2 = PyInt_FromSsize_t((__pyx_t_6[0])); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1043, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":1044 * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: * result.view.len *= length # <<<<<<<<<<<<<< * * result.to_object_func = to_object_func */ __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_result->__pyx_base.view.len); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_InPlaceMultiply(__pyx_t_2, __pyx_v_length); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_3); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 1044, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; } /* "View.MemoryView":1046 * result.view.len *= length * * result.to_object_func = to_object_func # <<<<<<<<<<<<<< * result.to_dtype_func = to_dtype_func * */ __pyx_v_result->to_object_func = __pyx_v_to_object_func; /* "View.MemoryView":1047 * * result.to_object_func = to_object_func * result.to_dtype_func = to_dtype_func # <<<<<<<<<<<<<< * * return result */ __pyx_v_result->to_dtype_func = __pyx_v_to_dtype_func; /* "View.MemoryView":1049 * result.to_dtype_func = to_dtype_func * * return result # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_get_slice_from_memoryview') */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":999 * * @cname('__pyx_memoryview_fromslice') * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice, # <<<<<<<<<<<<<< * int ndim, * object (*to_object_func)(char *), */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview_fromslice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XDECREF(__pyx_v_length); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1052 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj */ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_mslice) { struct __pyx_memoryviewslice_obj *__pyx_v_obj = 0; __Pyx_memviewslice *__pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("get_slice_from_memview", 0); /* "View.MemoryView":1055 * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * obj = memview * return &obj.from_slice */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":1056 * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): * obj = memview # <<<<<<<<<<<<<< * return &obj.from_slice * else: */ if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 1056, __pyx_L1_error) __pyx_t_3 = ((PyObject *)__pyx_v_memview); __Pyx_INCREF(__pyx_t_3); __pyx_v_obj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":1057 * if isinstance(memview, _memoryviewslice): * obj = memview * return &obj.from_slice # <<<<<<<<<<<<<< * else: * slice_copy(memview, mslice) */ __pyx_r = (&__pyx_v_obj->from_slice); goto __pyx_L0; /* "View.MemoryView":1055 * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * obj = memview * return &obj.from_slice */ } /* "View.MemoryView":1059 * return &obj.from_slice * else: * slice_copy(memview, mslice) # <<<<<<<<<<<<<< * return mslice * */ /*else*/ { __pyx_memoryview_slice_copy(__pyx_v_memview, __pyx_v_mslice); /* "View.MemoryView":1060 * else: * slice_copy(memview, mslice) * return mslice # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_slice_copy') */ __pyx_r = __pyx_v_mslice; goto __pyx_L0; } /* "View.MemoryView":1052 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.get_slice_from_memview", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_obj); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1063 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< * cdef int dim * cdef (Py_ssize_t*) shape, strides, suboffsets */ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_dst) { int __pyx_v_dim; Py_ssize_t *__pyx_v_shape; Py_ssize_t *__pyx_v_strides; Py_ssize_t *__pyx_v_suboffsets; __Pyx_RefNannyDeclarations Py_ssize_t *__pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; Py_ssize_t __pyx_t_5; __Pyx_RefNannySetupContext("slice_copy", 0); /* "View.MemoryView":1067 * cdef (Py_ssize_t*) shape, strides, suboffsets * * shape = memview.view.shape # <<<<<<<<<<<<<< * strides = memview.view.strides * suboffsets = memview.view.suboffsets */ __pyx_t_1 = __pyx_v_memview->view.shape; __pyx_v_shape = __pyx_t_1; /* "View.MemoryView":1068 * * shape = memview.view.shape * strides = memview.view.strides # <<<<<<<<<<<<<< * suboffsets = memview.view.suboffsets * */ __pyx_t_1 = __pyx_v_memview->view.strides; __pyx_v_strides = __pyx_t_1; /* "View.MemoryView":1069 * shape = memview.view.shape * strides = memview.view.strides * suboffsets = memview.view.suboffsets # <<<<<<<<<<<<<< * * dst.memview = <__pyx_memoryview *> memview */ __pyx_t_1 = __pyx_v_memview->view.suboffsets; __pyx_v_suboffsets = __pyx_t_1; /* "View.MemoryView":1071 * suboffsets = memview.view.suboffsets * * dst.memview = <__pyx_memoryview *> memview # <<<<<<<<<<<<<< * dst.data = memview.view.buf * */ __pyx_v_dst->memview = ((struct __pyx_memoryview_obj *)__pyx_v_memview); /* "View.MemoryView":1072 * * dst.memview = <__pyx_memoryview *> memview * dst.data = memview.view.buf # <<<<<<<<<<<<<< * * for dim in range(memview.view.ndim): */ __pyx_v_dst->data = ((char *)__pyx_v_memview->view.buf); /* "View.MemoryView":1074 * dst.data = memview.view.buf * * for dim in range(memview.view.ndim): # <<<<<<<<<<<<<< * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] */ __pyx_t_2 = __pyx_v_memview->view.ndim; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_dim = __pyx_t_4; /* "View.MemoryView":1075 * * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] # <<<<<<<<<<<<<< * dst.strides[dim] = strides[dim] * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 */ (__pyx_v_dst->shape[__pyx_v_dim]) = (__pyx_v_shape[__pyx_v_dim]); /* "View.MemoryView":1076 * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] # <<<<<<<<<<<<<< * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 * */ (__pyx_v_dst->strides[__pyx_v_dim]) = (__pyx_v_strides[__pyx_v_dim]); /* "View.MemoryView":1077 * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_object') */ if ((__pyx_v_suboffsets != 0)) { __pyx_t_5 = (__pyx_v_suboffsets[__pyx_v_dim]); } else { __pyx_t_5 = -1L; } (__pyx_v_dst->suboffsets[__pyx_v_dim]) = __pyx_t_5; } /* "View.MemoryView":1063 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< * cdef int dim * cdef (Py_ssize_t*) shape, strides, suboffsets */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":1080 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice */ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx_v_memview) { __Pyx_memviewslice __pyx_v_memviewslice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_copy", 0); /* "View.MemoryView":1083 * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) # <<<<<<<<<<<<<< * return memoryview_copy_from_slice(memview, &memviewslice) * */ __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_memviewslice)); /* "View.MemoryView":1084 * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) * return memoryview_copy_from_slice(memview, &memviewslice) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_object_from_slice') */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __pyx_memoryview_copy_object_from_slice(__pyx_v_memview, (&__pyx_v_memviewslice)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1084, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":1080 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview_copy", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1087 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< * """ * Create a new memoryview object from a given memoryview object and slice. */ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_memviewslice) { PyObject *(*__pyx_v_to_object_func)(char *); int (*__pyx_v_to_dtype_func)(char *, PyObject *); PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *(*__pyx_t_3)(char *); int (*__pyx_t_4)(char *, PyObject *); PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_copy_from_slice", 0); /* "View.MemoryView":1094 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":1095 * * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func # <<<<<<<<<<<<<< * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func * else: */ __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func; __pyx_v_to_object_func = __pyx_t_3; /* "View.MemoryView":1096 * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func # <<<<<<<<<<<<<< * else: * to_object_func = NULL */ __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func; __pyx_v_to_dtype_func = __pyx_t_4; /* "View.MemoryView":1094 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func */ goto __pyx_L3; } /* "View.MemoryView":1098 * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func * else: * to_object_func = NULL # <<<<<<<<<<<<<< * to_dtype_func = NULL * */ /*else*/ { __pyx_v_to_object_func = NULL; /* "View.MemoryView":1099 * else: * to_object_func = NULL * to_dtype_func = NULL # <<<<<<<<<<<<<< * * return memoryview_fromslice(memviewslice[0], memview.view.ndim, */ __pyx_v_to_dtype_func = NULL; } __pyx_L3:; /* "View.MemoryView":1101 * to_dtype_func = NULL * * return memoryview_fromslice(memviewslice[0], memview.view.ndim, # <<<<<<<<<<<<<< * to_object_func, to_dtype_func, * memview.dtype_is_object) */ __Pyx_XDECREF(__pyx_r); /* "View.MemoryView":1103 * return memoryview_fromslice(memviewslice[0], memview.view.ndim, * to_object_func, to_dtype_func, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 1101, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "View.MemoryView":1087 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< * """ * Create a new memoryview object from a given memoryview object and slice. */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1109 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< * if arg < 0: * return -arg */ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { Py_ssize_t __pyx_r; int __pyx_t_1; /* "View.MemoryView":1110 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< * return -arg * else: */ __pyx_t_1 = ((__pyx_v_arg < 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":1111 * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: * return -arg # <<<<<<<<<<<<<< * else: * return arg */ __pyx_r = (-__pyx_v_arg); goto __pyx_L0; /* "View.MemoryView":1110 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< * return -arg * else: */ } /* "View.MemoryView":1113 * return -arg * else: * return arg # <<<<<<<<<<<<<< * * @cname('__pyx_get_best_slice_order') */ /*else*/ { __pyx_r = __pyx_v_arg; goto __pyx_L0; } /* "View.MemoryView":1109 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< * if arg < 0: * return -arg */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1116 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< * """ * Figure out the best memory access order for a given slice. */ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) { int __pyx_v_i; Py_ssize_t __pyx_v_c_stride; Py_ssize_t __pyx_v_f_stride; char __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; /* "View.MemoryView":1121 * """ * cdef int i * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< * cdef Py_ssize_t f_stride = 0 * */ __pyx_v_c_stride = 0; /* "View.MemoryView":1122 * cdef int i * cdef Py_ssize_t c_stride = 0 * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< * * for i in range(ndim - 1, -1, -1): */ __pyx_v_f_stride = 0; /* "View.MemoryView":1124 * cdef Py_ssize_t f_stride = 0 * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] */ for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":1125 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * c_stride = mslice.strides[i] * break */ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1126 * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< * break * */ __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1127 * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< * * for i in range(ndim): */ goto __pyx_L4_break; /* "View.MemoryView":1125 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * c_stride = mslice.strides[i] * break */ } } __pyx_L4_break:; /* "View.MemoryView":1129 * break * * for i in range(ndim): # <<<<<<<<<<<<<< * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] */ __pyx_t_1 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_1; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1130 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * f_stride = mslice.strides[i] * break */ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1131 * for i in range(ndim): * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< * break * */ __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1132 * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): */ goto __pyx_L7_break; /* "View.MemoryView":1130 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * f_stride = mslice.strides[i] * break */ } } __pyx_L7_break:; /* "View.MemoryView":1134 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< * return 'C' * else: */ __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1135 * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): * return 'C' # <<<<<<<<<<<<<< * else: * return 'F' */ __pyx_r = 'C'; goto __pyx_L0; /* "View.MemoryView":1134 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< * return 'C' * else: */ } /* "View.MemoryView":1137 * return 'C' * else: * return 'F' # <<<<<<<<<<<<<< * * @cython.cdivision(True) */ /*else*/ { __pyx_r = 'F'; goto __pyx_L0; } /* "View.MemoryView":1116 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< * """ * Figure out the best memory access order for a given slice. */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1140 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< * char *dst_data, Py_ssize_t *dst_strides, * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, */ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; Py_ssize_t __pyx_v_dst_extent; Py_ssize_t __pyx_v_src_stride; Py_ssize_t __pyx_v_dst_stride; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; /* "View.MemoryView":1147 * * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] */ __pyx_v_src_extent = (__pyx_v_src_shape[0]); /* "View.MemoryView":1148 * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] */ __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); /* "View.MemoryView":1149 * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t dst_stride = dst_strides[0] * */ __pyx_v_src_stride = (__pyx_v_src_strides[0]); /* "View.MemoryView":1150 * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< * * if ndim == 1: */ __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); /* "View.MemoryView":1152 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): */ __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L5_bool_binop_done; } __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L5_bool_binop_done; } /* "View.MemoryView":1154 * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< * memcpy(dst_data, src_data, itemsize * dst_extent) * else: */ __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); if (__pyx_t_2) { __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); } __pyx_t_3 = (__pyx_t_2 != 0); __pyx_t_1 = __pyx_t_3; __pyx_L5_bool_binop_done:; /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ if (__pyx_t_1) { /* "View.MemoryView":1155 * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< * else: * for i in range(dst_extent): */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ goto __pyx_L4; } /* "View.MemoryView":1157 * memcpy(dst_data, src_data, itemsize * dst_extent) * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< * memcpy(dst_data, src_data, itemsize) * src_data += src_stride */ /*else*/ { __pyx_t_4 = __pyx_v_dst_extent; __pyx_t_5 = __pyx_t_4; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1158 * else: * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< * src_data += src_stride * dst_data += dst_stride */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); /* "View.MemoryView":1159 * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< * dst_data += dst_stride * else: */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); /* "View.MemoryView":1160 * memcpy(dst_data, src_data, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< * else: * for i in range(dst_extent): */ __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); } } __pyx_L4:; /* "View.MemoryView":1152 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): */ goto __pyx_L3; } /* "View.MemoryView":1162 * dst_data += dst_stride * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< * _copy_strided_to_strided(src_data, src_strides + 1, * dst_data, dst_strides + 1, */ /*else*/ { __pyx_t_4 = __pyx_v_dst_extent; __pyx_t_5 = __pyx_t_4; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1163 * else: * for i in range(dst_extent): * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< * dst_data, dst_strides + 1, * src_shape + 1, dst_shape + 1, */ _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); /* "View.MemoryView":1167 * src_shape + 1, dst_shape + 1, * ndim - 1, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< * dst_data += dst_stride * */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); /* "View.MemoryView":1168 * ndim - 1, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, */ __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); } } __pyx_L3:; /* "View.MemoryView":1140 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< * char *dst_data, Py_ssize_t *dst_strides, * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, */ /* function exit code */ } /* "View.MemoryView":1170 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: */ static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { /* "View.MemoryView":1173 * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< * src.shape, dst.shape, ndim, itemsize) * */ _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); /* "View.MemoryView":1170 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: */ /* function exit code */ } /* "View.MemoryView":1177 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize */ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { Py_ssize_t __pyx_v_shape; Py_ssize_t __pyx_v_size; Py_ssize_t __pyx_r; Py_ssize_t __pyx_t_1; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; /* "View.MemoryView":1179 * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< * * for shape in src.shape[:ndim]: */ __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_size = __pyx_t_1; /* "View.MemoryView":1181 * cdef Py_ssize_t shape, size = src.memview.view.itemsize * * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< * size *= shape * */ __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim); for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { __pyx_t_2 = __pyx_t_4; __pyx_v_shape = (__pyx_t_2[0]); /* "View.MemoryView":1182 * * for shape in src.shape[:ndim]: * size *= shape # <<<<<<<<<<<<<< * * return size */ __pyx_v_size = (__pyx_v_size * __pyx_v_shape); } /* "View.MemoryView":1184 * size *= shape * * return size # <<<<<<<<<<<<<< * * @cname('__pyx_fill_contig_strides_array') */ __pyx_r = __pyx_v_size; goto __pyx_L0; /* "View.MemoryView":1177 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1187 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, * int ndim, char order) nogil: */ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { int __pyx_v_idx; Py_ssize_t __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; /* "View.MemoryView":1196 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< * for idx in range(ndim): * strides[idx] = stride */ __pyx_t_1 = ((__pyx_v_order == 'F') != 0); if (__pyx_t_1) { /* "View.MemoryView":1197 * * if order == 'F': * for idx in range(ndim): # <<<<<<<<<<<<<< * strides[idx] = stride * stride *= shape[idx] */ __pyx_t_2 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_idx = __pyx_t_4; /* "View.MemoryView":1198 * if order == 'F': * for idx in range(ndim): * strides[idx] = stride # <<<<<<<<<<<<<< * stride *= shape[idx] * else: */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; /* "View.MemoryView":1199 * for idx in range(ndim): * strides[idx] = stride * stride *= shape[idx] # <<<<<<<<<<<<<< * else: * for idx in range(ndim - 1, -1, -1): */ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } /* "View.MemoryView":1196 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< * for idx in range(ndim): * strides[idx] = stride */ goto __pyx_L3; } /* "View.MemoryView":1201 * stride *= shape[idx] * else: * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * strides[idx] = stride * stride *= shape[idx] */ /*else*/ { for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { __pyx_v_idx = __pyx_t_2; /* "View.MemoryView":1202 * else: * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride # <<<<<<<<<<<<<< * stride *= shape[idx] * */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; /* "View.MemoryView":1203 * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride * stride *= shape[idx] # <<<<<<<<<<<<<< * * return stride */ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } } __pyx_L3:; /* "View.MemoryView":1205 * stride *= shape[idx] * * return stride # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_data_to_temp') */ __pyx_r = __pyx_v_stride; goto __pyx_L0; /* "View.MemoryView":1187 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, * int ndim, char order) nogil: */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1208 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *tmpslice, * char order, */ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) { int __pyx_v_i; void *__pyx_v_result; size_t __pyx_v_itemsize; size_t __pyx_v_size; void *__pyx_r; Py_ssize_t __pyx_t_1; int __pyx_t_2; int __pyx_t_3; struct __pyx_memoryview_obj *__pyx_t_4; int __pyx_t_5; int __pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":1219 * cdef void *result * * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< * cdef size_t size = slice_get_size(src, ndim) * */ __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":1220 * * cdef size_t itemsize = src.memview.view.itemsize * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< * * result = malloc(size) */ __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); /* "View.MemoryView":1222 * cdef size_t size = slice_get_size(src, ndim) * * result = malloc(size) # <<<<<<<<<<<<<< * if not result: * _err(MemoryError, NULL) */ __pyx_v_result = malloc(__pyx_v_size); /* "View.MemoryView":1223 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< * _err(MemoryError, NULL) * */ __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1224 * result = malloc(size) * if not result: * _err(MemoryError, NULL) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 1224, __pyx_L1_error) /* "View.MemoryView":1223 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< * _err(MemoryError, NULL) * */ } /* "View.MemoryView":1227 * * * tmpslice.data = result # <<<<<<<<<<<<<< * tmpslice.memview = src.memview * for i in range(ndim): */ __pyx_v_tmpslice->data = ((char *)__pyx_v_result); /* "View.MemoryView":1228 * * tmpslice.data = result * tmpslice.memview = src.memview # <<<<<<<<<<<<<< * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] */ __pyx_t_4 = __pyx_v_src->memview; __pyx_v_tmpslice->memview = __pyx_t_4; /* "View.MemoryView":1229 * tmpslice.data = result * tmpslice.memview = src.memview * for i in range(ndim): # <<<<<<<<<<<<<< * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 */ __pyx_t_3 = __pyx_v_ndim; __pyx_t_5 = __pyx_t_3; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1230 * tmpslice.memview = src.memview * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] # <<<<<<<<<<<<<< * tmpslice.suboffsets[i] = -1 * */ (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]); /* "View.MemoryView":1231 * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, */ (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L; } /* "View.MemoryView":1233 * tmpslice.suboffsets[i] = -1 * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, # <<<<<<<<<<<<<< * ndim, order) * */ (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order)); /* "View.MemoryView":1237 * * * for i in range(ndim): # <<<<<<<<<<<<<< * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 */ __pyx_t_3 = __pyx_v_ndim; __pyx_t_5 = __pyx_t_3; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1238 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< * tmpslice.strides[i] = 0 * */ __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1239 * for i in range(ndim): * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 # <<<<<<<<<<<<<< * * if slice_is_contig(src[0], order, ndim): */ (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0; /* "View.MemoryView":1238 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< * tmpslice.strides[i] = 0 * */ } } /* "View.MemoryView":1241 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< * memcpy(result, src.data, size) * else: */ __pyx_t_2 = (__pyx_memviewslice_is_contig((__pyx_v_src[0]), __pyx_v_order, __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1242 * * if slice_is_contig(src[0], order, ndim): * memcpy(result, src.data, size) # <<<<<<<<<<<<<< * else: * copy_strided_to_strided(src, tmpslice, ndim, itemsize) */ (void)(memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size)); /* "View.MemoryView":1241 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< * memcpy(result, src.data, size) * else: */ goto __pyx_L9; } /* "View.MemoryView":1244 * memcpy(result, src.data, size) * else: * copy_strided_to_strided(src, tmpslice, ndim, itemsize) # <<<<<<<<<<<<<< * * return result */ /*else*/ { copy_strided_to_strided(__pyx_v_src, __pyx_v_tmpslice, __pyx_v_ndim, __pyx_v_itemsize); } __pyx_L9:; /* "View.MemoryView":1246 * copy_strided_to_strided(src, tmpslice, ndim, itemsize) * * return result # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_result; goto __pyx_L0; /* "View.MemoryView":1208 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *tmpslice, * char order, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.copy_data_to_temp", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = NULL; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1251 * * @cname('__pyx_memoryview_err_extents') * cdef int _err_extents(int i, Py_ssize_t extent1, # <<<<<<<<<<<<<< * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % */ static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent1, Py_ssize_t __pyx_v_extent2) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err_extents", 0); /* "View.MemoryView":1254 * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % * (i, extent1, extent2)) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_err_dim') */ __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_i); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_extent1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_extent2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_4, 2, __pyx_t_3); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_3 = 0; /* "View.MemoryView":1253 * cdef int _err_extents(int i, Py_ssize_t extent1, * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % # <<<<<<<<<<<<<< * (i, extent1, extent2)) * */ __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1253, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1253, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_4, 0, 0, 0); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __PYX_ERR(2, 1253, __pyx_L1_error) /* "View.MemoryView":1251 * * @cname('__pyx_memoryview_err_extents') * cdef int _err_extents(int i, Py_ssize_t extent1, # <<<<<<<<<<<<<< * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView._err_extents", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1257 * * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii') % dim) * */ static int __pyx_memoryview_err_dim(PyObject *__pyx_v_error, char *__pyx_v_msg, int __pyx_v_dim) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err_dim", 0); __Pyx_INCREF(__pyx_v_error); /* "View.MemoryView":1258 * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: * raise error(msg.decode('ascii') % dim) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_err') */ __pyx_t_2 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyInt_From_int(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyUnicode_Format(__pyx_t_2, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_INCREF(__pyx_v_error); __pyx_t_3 = __pyx_v_error; __pyx_t_2 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_3))) { __pyx_t_2 = PyMethod_GET_SELF(__pyx_t_3); if (likely(__pyx_t_2)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_3); __Pyx_INCREF(__pyx_t_2); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_3, function); } } __pyx_t_1 = (__pyx_t_2) ? __Pyx_PyObject_Call2Args(__pyx_t_3, __pyx_t_2, __pyx_t_4) : __Pyx_PyObject_CallOneArg(__pyx_t_3, __pyx_t_4); __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 1258, __pyx_L1_error) /* "View.MemoryView":1257 * * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii') % dim) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView._err_dim", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_XDECREF(__pyx_v_error); __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1261 * * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: # <<<<<<<<<<<<<< * if msg != NULL: * raise error(msg.decode('ascii')) */ static int __pyx_memoryview_err(PyObject *__pyx_v_error, char *__pyx_v_msg) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err", 0); __Pyx_INCREF(__pyx_v_error); /* "View.MemoryView":1262 * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii')) * else: */ __pyx_t_1 = ((__pyx_v_msg != NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":1263 * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: * raise error(msg.decode('ascii')) # <<<<<<<<<<<<<< * else: * raise error */ __pyx_t_3 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1263, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_error); __pyx_t_4 = __pyx_v_error; __pyx_t_5 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_4))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_4); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_4); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_4, function); } } __pyx_t_2 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_4, __pyx_t_5, __pyx_t_3) : __Pyx_PyObject_CallOneArg(__pyx_t_4, __pyx_t_3); __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1263, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(2, 1263, __pyx_L1_error) /* "View.MemoryView":1262 * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii')) * else: */ } /* "View.MemoryView":1265 * raise error(msg.decode('ascii')) * else: * raise error # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_contents') */ /*else*/ { __Pyx_Raise(__pyx_v_error, 0, 0, 0); __PYX_ERR(2, 1265, __pyx_L1_error) } /* "View.MemoryView":1261 * * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: # <<<<<<<<<<<<<< * if msg != NULL: * raise error(msg.decode('ascii')) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView._err", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_XDECREF(__pyx_v_error); __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1268 * * @cname('__pyx_memoryview_copy_contents') * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< * __Pyx_memviewslice dst, * int src_ndim, int dst_ndim, */ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_memviewslice __pyx_v_dst, int __pyx_v_src_ndim, int __pyx_v_dst_ndim, int __pyx_v_dtype_is_object) { void *__pyx_v_tmpdata; size_t __pyx_v_itemsize; int __pyx_v_i; char __pyx_v_order; int __pyx_v_broadcasting; int __pyx_v_direct_copy; __Pyx_memviewslice __pyx_v_tmp; int __pyx_v_ndim; int __pyx_r; Py_ssize_t __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_t_5; int __pyx_t_6; void *__pyx_t_7; int __pyx_t_8; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":1276 * Check for overlapping memory and verify the shapes. * """ * cdef void *tmpdata = NULL # <<<<<<<<<<<<<< * cdef size_t itemsize = src.memview.view.itemsize * cdef int i */ __pyx_v_tmpdata = NULL; /* "View.MemoryView":1277 * """ * cdef void *tmpdata = NULL * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< * cdef int i * cdef char order = get_best_order(&src, src_ndim) */ __pyx_t_1 = __pyx_v_src.memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":1279 * cdef size_t itemsize = src.memview.view.itemsize * cdef int i * cdef char order = get_best_order(&src, src_ndim) # <<<<<<<<<<<<<< * cdef bint broadcasting = False * cdef bint direct_copy = False */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_src), __pyx_v_src_ndim); /* "View.MemoryView":1280 * cdef int i * cdef char order = get_best_order(&src, src_ndim) * cdef bint broadcasting = False # <<<<<<<<<<<<<< * cdef bint direct_copy = False * cdef __Pyx_memviewslice tmp */ __pyx_v_broadcasting = 0; /* "View.MemoryView":1281 * cdef char order = get_best_order(&src, src_ndim) * cdef bint broadcasting = False * cdef bint direct_copy = False # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice tmp * */ __pyx_v_direct_copy = 0; /* "View.MemoryView":1284 * cdef __Pyx_memviewslice tmp * * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: */ __pyx_t_2 = ((__pyx_v_src_ndim < __pyx_v_dst_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1285 * * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) # <<<<<<<<<<<<<< * elif dst_ndim < src_ndim: * broadcast_leading(&dst, dst_ndim, src_ndim) */ __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim); /* "View.MemoryView":1284 * cdef __Pyx_memviewslice tmp * * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: */ goto __pyx_L3; } /* "View.MemoryView":1286 * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&dst, dst_ndim, src_ndim) * */ __pyx_t_2 = ((__pyx_v_dst_ndim < __pyx_v_src_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1287 * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: * broadcast_leading(&dst, dst_ndim, src_ndim) # <<<<<<<<<<<<<< * * cdef int ndim = max(src_ndim, dst_ndim) */ __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim); /* "View.MemoryView":1286 * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&dst, dst_ndim, src_ndim) * */ } __pyx_L3:; /* "View.MemoryView":1289 * broadcast_leading(&dst, dst_ndim, src_ndim) * * cdef int ndim = max(src_ndim, dst_ndim) # <<<<<<<<<<<<<< * * for i in range(ndim): */ __pyx_t_3 = __pyx_v_dst_ndim; __pyx_t_4 = __pyx_v_src_ndim; if (((__pyx_t_3 > __pyx_t_4) != 0)) { __pyx_t_5 = __pyx_t_3; } else { __pyx_t_5 = __pyx_t_4; } __pyx_v_ndim = __pyx_t_5; /* "View.MemoryView":1291 * cdef int ndim = max(src_ndim, dst_ndim) * * for i in range(ndim): # <<<<<<<<<<<<<< * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: */ __pyx_t_5 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_5; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1292 * * for i in range(ndim): * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< * if src.shape[i] == 1: * broadcasting = True */ __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0); if (__pyx_t_2) { /* "View.MemoryView":1293 * for i in range(ndim): * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: # <<<<<<<<<<<<<< * broadcasting = True * src.strides[i] = 0 */ __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1294 * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: * broadcasting = True # <<<<<<<<<<<<<< * src.strides[i] = 0 * else: */ __pyx_v_broadcasting = 1; /* "View.MemoryView":1295 * if src.shape[i] == 1: * broadcasting = True * src.strides[i] = 0 # <<<<<<<<<<<<<< * else: * _err_extents(i, dst.shape[i], src.shape[i]) */ (__pyx_v_src.strides[__pyx_v_i]) = 0; /* "View.MemoryView":1293 * for i in range(ndim): * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: # <<<<<<<<<<<<<< * broadcasting = True * src.strides[i] = 0 */ goto __pyx_L7; } /* "View.MemoryView":1297 * src.strides[i] = 0 * else: * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<< * * if src.suboffsets[i] >= 0: */ /*else*/ { __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1297, __pyx_L1_error) } __pyx_L7:; /* "View.MemoryView":1292 * * for i in range(ndim): * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< * if src.shape[i] == 1: * broadcasting = True */ } /* "View.MemoryView":1299 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Dimension %d is not direct", i) * */ __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":1300 * * if src.suboffsets[i] >= 0: * _err_dim(ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< * * if slices_overlap(&src, &dst, ndim, itemsize): */ __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1300, __pyx_L1_error) /* "View.MemoryView":1299 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Dimension %d is not direct", i) * */ } } /* "View.MemoryView":1302 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< * * if not slice_is_contig(src, order, ndim): */ __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0); if (__pyx_t_2) { /* "View.MemoryView":1304 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< * order = get_best_order(&dst, ndim) * */ __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1305 * * if not slice_is_contig(src, order, ndim): * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); /* "View.MemoryView":1304 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< * order = get_best_order(&dst, ndim) * */ } /* "View.MemoryView":1307 * order = get_best_order(&dst, ndim) * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< * src = tmp * */ __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(2, 1307, __pyx_L1_error) __pyx_v_tmpdata = __pyx_t_7; /* "View.MemoryView":1308 * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) * src = tmp # <<<<<<<<<<<<<< * * if not broadcasting: */ __pyx_v_src = __pyx_v_tmp; /* "View.MemoryView":1302 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< * * if not slice_is_contig(src, order, ndim): */ } /* "View.MemoryView":1310 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< * * */ __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1313 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): */ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1314 * * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< * elif slice_is_contig(src, 'F', ndim): * direct_copy = slice_is_contig(dst, 'F', ndim) */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); /* "View.MemoryView":1313 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): */ goto __pyx_L12; } /* "View.MemoryView":1315 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'F', ndim) * */ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1316 * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< * * if direct_copy: */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); /* "View.MemoryView":1315 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'F', ndim) * */ } __pyx_L12:; /* "View.MemoryView":1318 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ __pyx_t_2 = (__pyx_v_direct_copy != 0); if (__pyx_t_2) { /* "View.MemoryView":1320 * if direct_copy: * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1321 * * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) */ (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); /* "View.MemoryView":1322 * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * free(tmpdata) * return 0 */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1323 * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) # <<<<<<<<<<<<<< * return 0 * */ free(__pyx_v_tmpdata); /* "View.MemoryView":1324 * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) * return 0 # <<<<<<<<<<<<<< * * if order == 'F' == get_best_order(&dst, ndim): */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":1318 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ } /* "View.MemoryView":1310 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":1326 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< * * */ __pyx_t_2 = (__pyx_v_order == 'F'); if (__pyx_t_2) { __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); } __pyx_t_8 = (__pyx_t_2 != 0); if (__pyx_t_8) { /* "View.MemoryView":1329 * * * transpose_memslice(&src) # <<<<<<<<<<<<<< * transpose_memslice(&dst) * */ __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1329, __pyx_L1_error) /* "View.MemoryView":1330 * * transpose_memslice(&src) * transpose_memslice(&dst) # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1330, __pyx_L1_error) /* "View.MemoryView":1326 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":1332 * transpose_memslice(&dst) * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1333 * * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< * refcount_copying(&dst, dtype_is_object, ndim, True) * */ copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); /* "View.MemoryView":1334 * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * * free(tmpdata) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1336 * refcount_copying(&dst, dtype_is_object, ndim, True) * * free(tmpdata) # <<<<<<<<<<<<<< * return 0 * */ free(__pyx_v_tmpdata); /* "View.MemoryView":1337 * * free(tmpdata) * return 0 # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_broadcast_leading') */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":1268 * * @cname('__pyx_memoryview_copy_contents') * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< * __Pyx_memviewslice dst, * int src_ndim, int dst_ndim, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = -1; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1340 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< * int ndim, * int ndim_other) nogil: */ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) { int __pyx_v_i; int __pyx_v_offset; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; /* "View.MemoryView":1344 * int ndim_other) nogil: * cdef int i * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< * * for i in range(ndim - 1, -1, -1): */ __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); /* "View.MemoryView":1346 * cdef int offset = ndim_other - ndim * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] */ for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":1347 * * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] */ (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); /* "View.MemoryView":1348 * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * */ (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1349 * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< * * for i in range(offset): */ (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]); } /* "View.MemoryView":1351 * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * * for i in range(offset): # <<<<<<<<<<<<<< * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] */ __pyx_t_1 = __pyx_v_offset; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "View.MemoryView":1352 * * for i in range(offset): * mslice.shape[i] = 1 # <<<<<<<<<<<<<< * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 */ (__pyx_v_mslice->shape[__pyx_v_i]) = 1; /* "View.MemoryView":1353 * for i in range(offset): * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] # <<<<<<<<<<<<<< * mslice.suboffsets[i] = -1 * */ (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]); /* "View.MemoryView":1354 * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< * * */ (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L; } /* "View.MemoryView":1340 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< * int ndim, * int ndim_other) nogil: */ /* function exit code */ } /* "View.MemoryView":1362 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< * int ndim, bint inc) nogil: * */ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) { int __pyx_t_1; /* "View.MemoryView":1366 * * * if dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice_with_gil(dst.data, dst.shape, * dst.strides, ndim, inc) */ __pyx_t_1 = (__pyx_v_dtype_is_object != 0); if (__pyx_t_1) { /* "View.MemoryView":1367 * * if dtype_is_object: * refcount_objects_in_slice_with_gil(dst.data, dst.shape, # <<<<<<<<<<<<<< * dst.strides, ndim, inc) * */ __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc); /* "View.MemoryView":1366 * * * if dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice_with_gil(dst.data, dst.shape, * dst.strides, ndim, inc) */ } /* "View.MemoryView":1362 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< * int ndim, bint inc) nogil: * */ /* function exit code */ } /* "View.MemoryView":1371 * * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * bint inc) with gil: */ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { __Pyx_RefNannyDeclarations #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("refcount_objects_in_slice_with_gil", 0); /* "View.MemoryView":1374 * Py_ssize_t *strides, int ndim, * bint inc) with gil: * refcount_objects_in_slice(data, shape, strides, ndim, inc) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_refcount_objects_in_slice') */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc); /* "View.MemoryView":1371 * * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * bint inc) with gil: */ /* function exit code */ __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } /* "View.MemoryView":1377 * * @cname('__pyx_memoryview_refcount_objects_in_slice') * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, bint inc): * cdef Py_ssize_t i */ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; int __pyx_t_4; __Pyx_RefNannySetupContext("refcount_objects_in_slice", 0); /* "View.MemoryView":1381 * cdef Py_ssize_t i * * for i in range(shape[0]): # <<<<<<<<<<<<<< * if ndim == 1: * if inc: */ __pyx_t_1 = (__pyx_v_shape[0]); __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "View.MemoryView":1382 * * for i in range(shape[0]): * if ndim == 1: # <<<<<<<<<<<<<< * if inc: * Py_INCREF(( data)[0]) */ __pyx_t_4 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_4) { /* "View.MemoryView":1383 * for i in range(shape[0]): * if ndim == 1: * if inc: # <<<<<<<<<<<<<< * Py_INCREF(( data)[0]) * else: */ __pyx_t_4 = (__pyx_v_inc != 0); if (__pyx_t_4) { /* "View.MemoryView":1384 * if ndim == 1: * if inc: * Py_INCREF(( data)[0]) # <<<<<<<<<<<<<< * else: * Py_DECREF(( data)[0]) */ Py_INCREF((((PyObject **)__pyx_v_data)[0])); /* "View.MemoryView":1383 * for i in range(shape[0]): * if ndim == 1: * if inc: # <<<<<<<<<<<<<< * Py_INCREF(( data)[0]) * else: */ goto __pyx_L6; } /* "View.MemoryView":1386 * Py_INCREF(( data)[0]) * else: * Py_DECREF(( data)[0]) # <<<<<<<<<<<<<< * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, */ /*else*/ { Py_DECREF((((PyObject **)__pyx_v_data)[0])); } __pyx_L6:; /* "View.MemoryView":1382 * * for i in range(shape[0]): * if ndim == 1: # <<<<<<<<<<<<<< * if inc: * Py_INCREF(( data)[0]) */ goto __pyx_L5; } /* "View.MemoryView":1388 * Py_DECREF(( data)[0]) * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, # <<<<<<<<<<<<<< * ndim - 1, inc) * */ /*else*/ { /* "View.MemoryView":1389 * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, * ndim - 1, inc) # <<<<<<<<<<<<<< * * data += strides[0] */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_inc); } __pyx_L5:; /* "View.MemoryView":1391 * ndim - 1, inc) * * data += strides[0] # <<<<<<<<<<<<<< * * */ __pyx_v_data = (__pyx_v_data + (__pyx_v_strides[0])); } /* "View.MemoryView":1377 * * @cname('__pyx_memoryview_refcount_objects_in_slice') * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, bint inc): * cdef Py_ssize_t i */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":1397 * * @cname('__pyx_memoryview_slice_assign_scalar') * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, # <<<<<<<<<<<<<< * size_t itemsize, void *item, * bint dtype_is_object) nogil: */ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item, int __pyx_v_dtype_is_object) { /* "View.MemoryView":1400 * size_t itemsize, void *item, * bint dtype_is_object) nogil: * refcount_copying(dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, * itemsize, item) */ __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1401 * bint dtype_is_object) nogil: * refcount_copying(dst, dtype_is_object, ndim, False) * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, # <<<<<<<<<<<<<< * itemsize, item) * refcount_copying(dst, dtype_is_object, ndim, True) */ __pyx_memoryview__slice_assign_scalar(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_itemsize, __pyx_v_item); /* "View.MemoryView":1403 * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, * itemsize, item) * refcount_copying(dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * * */ __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1397 * * @cname('__pyx_memoryview_slice_assign_scalar') * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, # <<<<<<<<<<<<<< * size_t itemsize, void *item, * bint dtype_is_object) nogil: */ /* function exit code */ } /* "View.MemoryView":1407 * * @cname('__pyx_memoryview__slice_assign_scalar') * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * size_t itemsize, void *item) nogil: */ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_stride; Py_ssize_t __pyx_v_extent; int __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; /* "View.MemoryView":1411 * size_t itemsize, void *item) nogil: * cdef Py_ssize_t i * cdef Py_ssize_t stride = strides[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t extent = shape[0] * */ __pyx_v_stride = (__pyx_v_strides[0]); /* "View.MemoryView":1412 * cdef Py_ssize_t i * cdef Py_ssize_t stride = strides[0] * cdef Py_ssize_t extent = shape[0] # <<<<<<<<<<<<<< * * if ndim == 1: */ __pyx_v_extent = (__pyx_v_shape[0]); /* "View.MemoryView":1414 * cdef Py_ssize_t extent = shape[0] * * if ndim == 1: # <<<<<<<<<<<<<< * for i in range(extent): * memcpy(data, item, itemsize) */ __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":1415 * * if ndim == 1: * for i in range(extent): # <<<<<<<<<<<<<< * memcpy(data, item, itemsize) * data += stride */ __pyx_t_2 = __pyx_v_extent; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1416 * if ndim == 1: * for i in range(extent): * memcpy(data, item, itemsize) # <<<<<<<<<<<<<< * data += stride * else: */ (void)(memcpy(__pyx_v_data, __pyx_v_item, __pyx_v_itemsize)); /* "View.MemoryView":1417 * for i in range(extent): * memcpy(data, item, itemsize) * data += stride # <<<<<<<<<<<<<< * else: * for i in range(extent): */ __pyx_v_data = (__pyx_v_data + __pyx_v_stride); } /* "View.MemoryView":1414 * cdef Py_ssize_t extent = shape[0] * * if ndim == 1: # <<<<<<<<<<<<<< * for i in range(extent): * memcpy(data, item, itemsize) */ goto __pyx_L3; } /* "View.MemoryView":1419 * data += stride * else: * for i in range(extent): # <<<<<<<<<<<<<< * _slice_assign_scalar(data, shape + 1, strides + 1, * ndim - 1, itemsize, item) */ /*else*/ { __pyx_t_2 = __pyx_v_extent; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1420 * else: * for i in range(extent): * _slice_assign_scalar(data, shape + 1, strides + 1, # <<<<<<<<<<<<<< * ndim - 1, itemsize, item) * data += stride */ __pyx_memoryview__slice_assign_scalar(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize, __pyx_v_item); /* "View.MemoryView":1422 * _slice_assign_scalar(data, shape + 1, strides + 1, * ndim - 1, itemsize, item) * data += stride # <<<<<<<<<<<<<< * * */ __pyx_v_data = (__pyx_v_data + __pyx_v_stride); } } __pyx_L3:; /* "View.MemoryView":1407 * * @cname('__pyx_memoryview__slice_assign_scalar') * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * size_t itemsize, void *item) nogil: */ /* function exit code */ } /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyMethodDef __pyx_mdef_15View_dot_MemoryView_1__pyx_unpickle_Enum = {"__pyx_unpickle_Enum", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum, METH_VARARGS|METH_KEYWORDS, 0}; static PyObject *__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v___pyx_type = 0; long __pyx_v___pyx_checksum; PyObject *__pyx_v___pyx_state = 0; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__pyx_unpickle_Enum (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_pyx_type,&__pyx_n_s_pyx_checksum,&__pyx_n_s_pyx_state,0}; PyObject* values[3] = {0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_type)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_checksum)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, 1); __PYX_ERR(2, 1, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_state)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, 2); __PYX_ERR(2, 1, __pyx_L3_error) } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__pyx_unpickle_Enum") < 0)) __PYX_ERR(2, 1, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 3) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[2] = PyTuple_GET_ITEM(__pyx_args, 2); } __pyx_v___pyx_type = values[0]; __pyx_v___pyx_checksum = __Pyx_PyInt_As_long(values[1]); if (unlikely((__pyx_v___pyx_checksum == (long)-1) && PyErr_Occurred())) __PYX_ERR(2, 1, __pyx_L3_error) __pyx_v___pyx_state = values[2]; } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 1, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(__pyx_self, __pyx_v___pyx_type, __pyx_v___pyx_checksum, __pyx_v___pyx_state); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_v___pyx_PickleError = 0; PyObject *__pyx_v___pyx_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__pyx_unpickle_Enum", 0); /* "(tree fragment)":4 * cdef object __pyx_PickleError * cdef object __pyx_result * if __pyx_checksum != 0xb068931: # <<<<<<<<<<<<<< * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) */ __pyx_t_1 = ((__pyx_v___pyx_checksum != 0xb068931) != 0); if (__pyx_t_1) { /* "(tree fragment)":5 * cdef object __pyx_result * if __pyx_checksum != 0xb068931: * from pickle import PickleError as __pyx_PickleError # <<<<<<<<<<<<<< * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) */ __pyx_t_2 = PyList_New(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_n_s_PickleError); __Pyx_GIVEREF(__pyx_n_s_PickleError); PyList_SET_ITEM(__pyx_t_2, 0, __pyx_n_s_PickleError); __pyx_t_3 = __Pyx_Import(__pyx_n_s_pickle, __pyx_t_2, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_ImportFrom(__pyx_t_3, __pyx_n_s_PickleError); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_t_2); __pyx_v___pyx_PickleError = __pyx_t_2; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "(tree fragment)":6 * if __pyx_checksum != 0xb068931: * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) # <<<<<<<<<<<<<< * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: */ __pyx_t_2 = __Pyx_PyInt_From_long(__pyx_v___pyx_checksum); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_t_2); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_INCREF(__pyx_v___pyx_PickleError); __pyx_t_2 = __pyx_v___pyx_PickleError; __pyx_t_5 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_2))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_3 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_5, __pyx_t_4) : __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 6, __pyx_L1_error) /* "(tree fragment)":4 * cdef object __pyx_PickleError * cdef object __pyx_result * if __pyx_checksum != 0xb068931: # <<<<<<<<<<<<<< * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) */ } /* "(tree fragment)":7 * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) # <<<<<<<<<<<<<< * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) */ __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_MemviewEnum_type), __pyx_n_s_new); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 7, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_2))) { __pyx_t_4 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_4)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_4); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_3 = (__pyx_t_4) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_4, __pyx_v___pyx_type) : __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_v___pyx_type); __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 7, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v___pyx_result = __pyx_t_3; __pyx_t_3 = 0; /* "(tree fragment)":8 * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result */ __pyx_t_1 = (__pyx_v___pyx_state != Py_None); __pyx_t_6 = (__pyx_t_1 != 0); if (__pyx_t_6) { /* "(tree fragment)":9 * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) # <<<<<<<<<<<<<< * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): */ if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(2, 9, __pyx_L1_error) __pyx_t_3 = __pyx_unpickle_Enum__set_state(((struct __pyx_MemviewEnum_obj *)__pyx_v___pyx_result), ((PyObject*)__pyx_v___pyx_state)); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 9, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "(tree fragment)":8 * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result */ } /* "(tree fragment)":10 * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result # <<<<<<<<<<<<<< * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v___pyx_result); __pyx_r = __pyx_v___pyx_result; goto __pyx_L0; /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v___pyx_PickleError); __Pyx_XDECREF(__pyx_v___pyx_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *__pyx_v___pyx_result, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; Py_ssize_t __pyx_t_3; int __pyx_t_4; int __pyx_t_5; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__pyx_unpickle_Enum__set_state", 0); /* "(tree fragment)":12 * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] # <<<<<<<<<<<<<< * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): * __pyx_result.__dict__.update(__pyx_state[1]) */ if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(2, 12, __pyx_L1_error) } __pyx_t_1 = __Pyx_GetItemInt_Tuple(__pyx_v___pyx_state, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 12, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __Pyx_GOTREF(__pyx_v___pyx_result->name); __Pyx_DECREF(__pyx_v___pyx_result->name); __pyx_v___pyx_result->name = __pyx_t_1; __pyx_t_1 = 0; /* "(tree fragment)":13 * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): # <<<<<<<<<<<<<< * __pyx_result.__dict__.update(__pyx_state[1]) */ if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); __PYX_ERR(2, 13, __pyx_L1_error) } __pyx_t_3 = PyTuple_GET_SIZE(__pyx_v___pyx_state); if (unlikely(__pyx_t_3 == ((Py_ssize_t)-1))) __PYX_ERR(2, 13, __pyx_L1_error) __pyx_t_4 = ((__pyx_t_3 > 1) != 0); if (__pyx_t_4) { } else { __pyx_t_2 = __pyx_t_4; goto __pyx_L4_bool_binop_done; } __pyx_t_4 = __Pyx_HasAttr(((PyObject *)__pyx_v___pyx_result), __pyx_n_s_dict); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 13, __pyx_L1_error) __pyx_t_5 = (__pyx_t_4 != 0); __pyx_t_2 = __pyx_t_5; __pyx_L4_bool_binop_done:; if (__pyx_t_2) { /* "(tree fragment)":14 * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): * __pyx_result.__dict__.update(__pyx_state[1]) # <<<<<<<<<<<<<< */ __pyx_t_6 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v___pyx_result), __pyx_n_s_dict); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_6, __pyx_n_s_update); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(2, 14, __pyx_L1_error) } __pyx_t_6 = __Pyx_GetItemInt_Tuple(__pyx_v___pyx_state, 1, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_8 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_7))) { __pyx_t_8 = PyMethod_GET_SELF(__pyx_t_7); if (likely(__pyx_t_8)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_7); __Pyx_INCREF(__pyx_t_8); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_7, function); } } __pyx_t_1 = (__pyx_t_8) ? __Pyx_PyObject_Call2Args(__pyx_t_7, __pyx_t_8, __pyx_t_6) : __Pyx_PyObject_CallOneArg(__pyx_t_7, __pyx_t_6); __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":13 * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): # <<<<<<<<<<<<<< * __pyx_result.__dict__.update(__pyx_state[1]) */ } /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum__set_state", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } static struct __pyx_vtabstruct_array __pyx_vtable_array; static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_array_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_array_obj *)o); p->__pyx_vtab = __pyx_vtabptr_array; p->mode = ((PyObject*)Py_None); Py_INCREF(Py_None); p->_format = ((PyObject*)Py_None); Py_INCREF(Py_None); if (unlikely(__pyx_array___cinit__(o, a, k) < 0)) goto bad; return o; bad: Py_DECREF(o); o = 0; return NULL; } static void __pyx_tp_dealloc_array(PyObject *o) { struct __pyx_array_obj *p = (struct __pyx_array_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); __pyx_array___dealloc__(o); __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->mode); Py_CLEAR(p->_format); (*Py_TYPE(o)->tp_free)(o); } static PyObject *__pyx_sq_item_array(PyObject *o, Py_ssize_t i) { PyObject *r; PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); Py_DECREF(x); return r; } static int __pyx_mp_ass_subscript_array(PyObject *o, PyObject *i, PyObject *v) { if (v) { return __pyx_array___setitem__(o, i, v); } else { PyErr_Format(PyExc_NotImplementedError, "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); return -1; } } static PyObject *__pyx_tp_getattro_array(PyObject *o, PyObject *n) { PyObject *v = __Pyx_PyObject_GenericGetAttr(o, n); if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Clear(); v = __pyx_array___getattr__(o, n); } return v; } static PyObject *__pyx_getprop___pyx_array_memview(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(o); } static PyMethodDef __pyx_methods_array[] = { {"__getattr__", (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, 0}, {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_array_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_array_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_array[] = { {(char *)"memview", __pyx_getprop___pyx_array_memview, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PySequenceMethods __pyx_tp_as_sequence_array = { __pyx_array___len__, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ __pyx_sq_item_array, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_array = { __pyx_array___len__, /*mp_length*/ __pyx_array___getitem__, /*mp_subscript*/ __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_array = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif __pyx_array_getbuffer, /*bf_getbuffer*/ 0, /*bf_releasebuffer*/ }; static PyTypeObject __pyx_type___pyx_array = { PyVarObject_HEAD_INIT(0, 0) "openTSNE._tsne.array", /*tp_name*/ sizeof(struct __pyx_array_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_array, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ &__pyx_tp_as_sequence_array, /*tp_as_sequence*/ &__pyx_tp_as_mapping_array, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ __pyx_tp_getattro_array, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_array, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ 0, /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_array, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_array, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_array, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { struct __pyx_MemviewEnum_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_MemviewEnum_obj *)o); p->name = Py_None; Py_INCREF(Py_None); return o; } static void __pyx_tp_dealloc_Enum(PyObject *o) { struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); Py_CLEAR(p->name); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { int e; struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; if (p->name) { e = (*v)(p->name, a); if (e) return e; } return 0; } static int __pyx_tp_clear_Enum(PyObject *o) { PyObject* tmp; struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; tmp = ((PyObject*)p->name); p->name = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); return 0; } static PyMethodDef __pyx_methods_Enum[] = { {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type___pyx_MemviewEnum = { PyVarObject_HEAD_INIT(0, 0) "openTSNE._tsne.Enum", /*tp_name*/ sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_Enum, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif __pyx_MemviewEnum___repr__, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_Enum, /*tp_traverse*/ __pyx_tp_clear_Enum, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_Enum, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ __pyx_MemviewEnum___init__, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_Enum, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_memoryview_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_memoryview_obj *)o); p->__pyx_vtab = __pyx_vtabptr_memoryview; p->obj = Py_None; Py_INCREF(Py_None); p->_size = Py_None; Py_INCREF(Py_None); p->_array_interface = Py_None; Py_INCREF(Py_None); p->view.obj = NULL; if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; return o; bad: Py_DECREF(o); o = 0; return NULL; } static void __pyx_tp_dealloc_memoryview(PyObject *o) { struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); __pyx_memoryview___dealloc__(o); __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->obj); Py_CLEAR(p->_size); Py_CLEAR(p->_array_interface); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { int e; struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; if (p->obj) { e = (*v)(p->obj, a); if (e) return e; } if (p->_size) { e = (*v)(p->_size, a); if (e) return e; } if (p->_array_interface) { e = (*v)(p->_array_interface, a); if (e) return e; } if (p->view.obj) { e = (*v)(p->view.obj, a); if (e) return e; } return 0; } static int __pyx_tp_clear_memoryview(PyObject *o) { PyObject* tmp; struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; tmp = ((PyObject*)p->obj); p->obj = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->_size); p->_size = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->_array_interface); p->_array_interface = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); Py_CLEAR(p->view.obj); return 0; } static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { PyObject *r; PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); Py_DECREF(x); return r; } static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { if (v) { return __pyx_memoryview___setitem__(o, i, v); } else { PyErr_Format(PyExc_NotImplementedError, "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); return -1; } } static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); } static PyMethodDef __pyx_methods_memoryview[] = { {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0}, {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0}, {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0}, {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0}, {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_memoryview[] = { {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PySequenceMethods __pyx_tp_as_sequence_memoryview = { __pyx_memoryview___len__, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ __pyx_sq_item_memoryview, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_memoryview = { __pyx_memoryview___len__, /*mp_length*/ __pyx_memoryview___getitem__, /*mp_subscript*/ __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_memoryview = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif __pyx_memoryview_getbuffer, /*bf_getbuffer*/ 0, /*bf_releasebuffer*/ }; static PyTypeObject __pyx_type___pyx_memoryview = { PyVarObject_HEAD_INIT(0, 0) "openTSNE._tsne.memoryview", /*tp_name*/ sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif __pyx_memoryview___repr__, /*tp_repr*/ 0, /*tp_as_number*/ &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ __pyx_memoryview___str__, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_memoryview, /*tp_traverse*/ __pyx_tp_clear_memoryview, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_memoryview, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_memoryview, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_memoryview, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_memoryviewslice_obj *p; PyObject *o = __pyx_tp_new_memoryview(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_memoryviewslice_obj *)o); p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; p->from_object = Py_None; Py_INCREF(Py_None); p->from_slice.memview = NULL; return o; } static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); __pyx_memoryviewslice___dealloc__(o); __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->from_object); PyObject_GC_Track(o); __pyx_tp_dealloc_memoryview(o); } static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { int e; struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; if (p->from_object) { e = (*v)(p->from_object, a); if (e) return e; } return 0; } static int __pyx_tp_clear__memoryviewslice(PyObject *o) { PyObject* tmp; struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; __pyx_tp_clear_memoryview(o); tmp = ((PyObject*)p->from_object); p->from_object = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); __PYX_XDEC_MEMVIEW(&p->from_slice, 1); return 0; } static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o); } static PyMethodDef __pyx_methods__memoryviewslice[] = { {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = { {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PyTypeObject __pyx_type___pyx_memoryviewslice = { PyVarObject_HEAD_INIT(0, 0) "openTSNE._tsne._memoryviewslice", /*tp_name*/ sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif #if CYTHON_COMPILING_IN_PYPY __pyx_memoryview___repr__, /*tp_repr*/ #else 0, /*tp_repr*/ #endif 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ #if CYTHON_COMPILING_IN_PYPY __pyx_memoryview___str__, /*tp_str*/ #else 0, /*tp_str*/ #endif 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ "Internal class for passing memoryview slices to Python", /*tp_doc*/ __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ __pyx_tp_clear__memoryviewslice, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods__memoryviewslice, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets__memoryviewslice, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new__memoryviewslice, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static PyMethodDef __pyx_methods[] = { {"compute_gaussian_perplexity", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_5_tsne_1compute_gaussian_perplexity, METH_VARARGS|METH_KEYWORDS, 0}, {"estimate_negative_gradient_bh", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_5_tsne_5estimate_negative_gradient_bh, METH_VARARGS|METH_KEYWORDS, __pyx_doc_8openTSNE_5_tsne_4estimate_negative_gradient_bh}, {"estimate_negative_gradient_fft_1d", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_5_tsne_7estimate_negative_gradient_fft_1d, METH_VARARGS|METH_KEYWORDS, 0}, {"prepare_negative_gradient_fft_interpolation_grid_1d", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_5_tsne_9prepare_negative_gradient_fft_interpolation_grid_1d, METH_VARARGS|METH_KEYWORDS, 0}, {"estimate_negative_gradient_fft_1d_with_grid", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_5_tsne_11estimate_negative_gradient_fft_1d_with_grid, METH_VARARGS|METH_KEYWORDS, 0}, {"estimate_negative_gradient_fft_2d", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_5_tsne_13estimate_negative_gradient_fft_2d, METH_VARARGS|METH_KEYWORDS, 0}, {"prepare_negative_gradient_fft_interpolation_grid_2d", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_5_tsne_15prepare_negative_gradient_fft_interpolation_grid_2d, METH_VARARGS|METH_KEYWORDS, 0}, {"estimate_negative_gradient_fft_2d_with_grid", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_5_tsne_17estimate_negative_gradient_fft_2d_with_grid, METH_VARARGS|METH_KEYWORDS, 0}, {0, 0, 0, 0} }; #if PY_MAJOR_VERSION >= 3 #if CYTHON_PEP489_MULTI_PHASE_INIT static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/ static int __pyx_pymod_exec__tsne(PyObject* module); /*proto*/ static PyModuleDef_Slot __pyx_moduledef_slots[] = { {Py_mod_create, (void*)__pyx_pymod_create}, {Py_mod_exec, (void*)__pyx_pymod_exec__tsne}, {0, NULL} }; #endif static struct PyModuleDef __pyx_moduledef = { PyModuleDef_HEAD_INIT, "_tsne", 0, /* m_doc */ #if CYTHON_PEP489_MULTI_PHASE_INIT 0, /* m_size */ #else -1, /* m_size */ #endif __pyx_methods /* m_methods */, #if CYTHON_PEP489_MULTI_PHASE_INIT __pyx_moduledef_slots, /* m_slots */ #else NULL, /* m_reload */ #endif NULL, /* m_traverse */ NULL, /* m_clear */ NULL /* m_free */ }; #endif #ifndef CYTHON_SMALL_CODE #if defined(__clang__) #define CYTHON_SMALL_CODE #elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3)) #define CYTHON_SMALL_CODE __attribute__((cold)) #else #define CYTHON_SMALL_CODE #endif #endif static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_kp_s_, __pyx_k_, sizeof(__pyx_k_), 0, 0, 1, 0}, {&__pyx_n_s_ASCII, __pyx_k_ASCII, sizeof(__pyx_k_ASCII), 0, 0, 1, 1}, {&__pyx_kp_s_Buffer_view_does_not_expose_stri, __pyx_k_Buffer_view_does_not_expose_stri, sizeof(__pyx_k_Buffer_view_does_not_expose_stri), 0, 0, 1, 0}, {&__pyx_n_u_C, __pyx_k_C, sizeof(__pyx_k_C), 0, 1, 0, 1}, {&__pyx_kp_s_Can_only_create_a_buffer_that_is, __pyx_k_Can_only_create_a_buffer_that_is, sizeof(__pyx_k_Can_only_create_a_buffer_that_is), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_assign_to_read_only_memor, __pyx_k_Cannot_assign_to_read_only_memor, sizeof(__pyx_k_Cannot_assign_to_read_only_memor), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_create_writable_memory_vi, __pyx_k_Cannot_create_writable_memory_vi, sizeof(__pyx_k_Cannot_create_writable_memory_vi), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_index_with_type_s, __pyx_k_Cannot_index_with_type_s, sizeof(__pyx_k_Cannot_index_with_type_s), 0, 0, 1, 0}, {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1}, {&__pyx_kp_s_Empty_shape_tuple_for_cython_arr, __pyx_k_Empty_shape_tuple_for_cython_arr, sizeof(__pyx_k_Empty_shape_tuple_for_cython_arr), 0, 0, 1, 0}, {&__pyx_kp_s_Expected_at_least_d_argument_s_g, __pyx_k_Expected_at_least_d_argument_s_g, sizeof(__pyx_k_Expected_at_least_d_argument_s_g), 0, 0, 1, 0}, {&__pyx_kp_s_Function_call_with_ambiguous_arg, __pyx_k_Function_call_with_ambiguous_arg, sizeof(__pyx_k_Function_call_with_ambiguous_arg), 0, 0, 1, 0}, {&__pyx_n_s_ImportError, __pyx_k_ImportError, sizeof(__pyx_k_ImportError), 0, 0, 1, 1}, {&__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_k_Incompatible_checksums_s_vs_0xb0, sizeof(__pyx_k_Incompatible_checksums_s_vs_0xb0), 0, 0, 1, 0}, {&__pyx_n_s_IndexError, __pyx_k_IndexError, sizeof(__pyx_k_IndexError), 0, 0, 1, 1}, {&__pyx_kp_s_Indirect_dimensions_not_supporte, __pyx_k_Indirect_dimensions_not_supporte, sizeof(__pyx_k_Indirect_dimensions_not_supporte), 0, 0, 1, 0}, {&__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_k_Invalid_mode_expected_c_or_fortr, sizeof(__pyx_k_Invalid_mode_expected_c_or_fortr), 0, 0, 1, 0}, {&__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_k_Invalid_shape_in_axis_d_d, sizeof(__pyx_k_Invalid_shape_in_axis_d_d), 0, 0, 1, 0}, {&__pyx_n_s_MemoryError, __pyx_k_MemoryError, sizeof(__pyx_k_MemoryError), 0, 0, 1, 1}, {&__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_k_MemoryView_of_r_at_0x_x, sizeof(__pyx_k_MemoryView_of_r_at_0x_x), 0, 0, 1, 0}, {&__pyx_kp_s_MemoryView_of_r_object, __pyx_k_MemoryView_of_r_object, sizeof(__pyx_k_MemoryView_of_r_object), 0, 0, 1, 0}, {&__pyx_kp_s_No_matching_signature_found, __pyx_k_No_matching_signature_found, sizeof(__pyx_k_No_matching_signature_found), 0, 0, 1, 0}, {&__pyx_n_b_O, __pyx_k_O, sizeof(__pyx_k_O), 0, 0, 0, 1}, {&__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_k_Out_of_bounds_on_buffer_access_a, sizeof(__pyx_k_Out_of_bounds_on_buffer_access_a), 0, 0, 1, 0}, {&__pyx_n_s_P_data, __pyx_k_P_data, sizeof(__pyx_k_P_data), 0, 0, 1, 1}, {&__pyx_n_s_PickleError, __pyx_k_PickleError, sizeof(__pyx_k_PickleError), 0, 0, 1, 1}, {&__pyx_n_s_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 0, 0, 1, 1}, {&__pyx_kp_s_Unable_to_convert_item_to_object, __pyx_k_Unable_to_convert_item_to_object, sizeof(__pyx_k_Unable_to_convert_item_to_object), 0, 0, 1, 0}, {&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1}, {&__pyx_n_s_View_MemoryView, __pyx_k_View_MemoryView, sizeof(__pyx_k_View_MemoryView), 0, 0, 1, 1}, {&__pyx_kp_s__2, __pyx_k__2, sizeof(__pyx_k__2), 0, 0, 1, 0}, {&__pyx_n_s_allocate_buffer, __pyx_k_allocate_buffer, sizeof(__pyx_k_allocate_buffer), 0, 0, 1, 1}, {&__pyx_n_s_args, __pyx_k_args, sizeof(__pyx_k_args), 0, 0, 1, 1}, {&__pyx_n_s_asarray, __pyx_k_asarray, sizeof(__pyx_k_asarray), 0, 0, 1, 1}, {&__pyx_n_s_base, __pyx_k_base, sizeof(__pyx_k_base), 0, 0, 1, 1}, {&__pyx_n_s_box_lower_bounds, __pyx_k_box_lower_bounds, sizeof(__pyx_k_box_lower_bounds), 0, 0, 1, 1}, {&__pyx_n_s_box_x_lower_bounds, __pyx_k_box_x_lower_bounds, sizeof(__pyx_k_box_x_lower_bounds), 0, 0, 1, 1}, {&__pyx_n_s_box_y_lower_bounds, __pyx_k_box_y_lower_bounds, sizeof(__pyx_k_box_y_lower_bounds), 0, 0, 1, 1}, {&__pyx_n_s_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 0, 1, 1}, {&__pyx_n_u_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 1, 0, 1}, {&__pyx_n_s_class, __pyx_k_class, sizeof(__pyx_k_class), 0, 0, 1, 1}, {&__pyx_n_s_cline_in_traceback, __pyx_k_cline_in_traceback, sizeof(__pyx_k_cline_in_traceback), 0, 0, 1, 1}, {&__pyx_kp_s_contiguous_and_direct, __pyx_k_contiguous_and_direct, sizeof(__pyx_k_contiguous_and_direct), 0, 0, 1, 0}, {&__pyx_kp_s_contiguous_and_indirect, __pyx_k_contiguous_and_indirect, sizeof(__pyx_k_contiguous_and_indirect), 0, 0, 1, 0}, {&__pyx_n_s_defaults, __pyx_k_defaults, sizeof(__pyx_k_defaults), 0, 0, 1, 1}, {&__pyx_n_s_desired_perplexities, __pyx_k_desired_perplexities, sizeof(__pyx_k_desired_perplexities), 0, 0, 1, 1}, {&__pyx_n_s_dict, __pyx_k_dict, sizeof(__pyx_k_dict), 0, 0, 1, 1}, {&__pyx_n_s_distances, __pyx_k_distances, sizeof(__pyx_k_distances), 0, 0, 1, 1}, {&__pyx_n_s_dof, __pyx_k_dof, sizeof(__pyx_k_dof), 0, 0, 1, 1}, {&__pyx_n_s_dtype, __pyx_k_dtype, sizeof(__pyx_k_dtype), 0, 0, 1, 1}, {&__pyx_n_s_dtype_is_object, __pyx_k_dtype_is_object, sizeof(__pyx_k_dtype_is_object), 0, 0, 1, 1}, {&__pyx_n_s_embedding, __pyx_k_embedding, sizeof(__pyx_k_embedding), 0, 0, 1, 1}, {&__pyx_n_s_empty, __pyx_k_empty, sizeof(__pyx_k_empty), 0, 0, 1, 1}, {&__pyx_n_s_encode, __pyx_k_encode, sizeof(__pyx_k_encode), 0, 0, 1, 1}, {&__pyx_n_s_enumerate, __pyx_k_enumerate, sizeof(__pyx_k_enumerate), 0, 0, 1, 1}, {&__pyx_n_s_eps, __pyx_k_eps, sizeof(__pyx_k_eps), 0, 0, 1, 1}, {&__pyx_n_s_error, __pyx_k_error, sizeof(__pyx_k_error), 0, 0, 1, 1}, {&__pyx_n_s_estimate_positive_gradient_nn, __pyx_k_estimate_positive_gradient_nn, sizeof(__pyx_k_estimate_positive_gradient_nn), 0, 0, 1, 1}, {&__pyx_n_s_finfo, __pyx_k_finfo, sizeof(__pyx_k_finfo), 0, 0, 1, 1}, {&__pyx_n_s_flags, __pyx_k_flags, sizeof(__pyx_k_flags), 0, 0, 1, 1}, {&__pyx_n_s_float64, __pyx_k_float64, sizeof(__pyx_k_float64), 0, 0, 1, 1}, {&__pyx_n_s_format, __pyx_k_format, sizeof(__pyx_k_format), 0, 0, 1, 1}, {&__pyx_n_s_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 0, 1, 1}, {&__pyx_n_u_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 1, 0, 1}, {&__pyx_n_s_getstate, __pyx_k_getstate, sizeof(__pyx_k_getstate), 0, 0, 1, 1}, {&__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_k_got_differing_extents_in_dimensi, sizeof(__pyx_k_got_differing_extents_in_dimensi), 0, 0, 1, 0}, {&__pyx_n_s_gradient, __pyx_k_gradient, sizeof(__pyx_k_gradient), 0, 0, 1, 1}, {&__pyx_n_s_id, __pyx_k_id, sizeof(__pyx_k_id), 0, 0, 1, 1}, {&__pyx_n_s_import, __pyx_k_import, sizeof(__pyx_k_import), 0, 0, 1, 1}, {&__pyx_n_s_indices, __pyx_k_indices, sizeof(__pyx_k_indices), 0, 0, 1, 1}, {&__pyx_n_s_indptr, __pyx_k_indptr, sizeof(__pyx_k_indptr), 0, 0, 1, 1}, {&__pyx_n_s_int32_t, __pyx_k_int32_t, sizeof(__pyx_k_int32_t), 0, 0, 1, 1}, {&__pyx_n_s_int64_t, __pyx_k_int64_t, sizeof(__pyx_k_int64_t), 0, 0, 1, 1}, {&__pyx_n_s_ints_in_interval, __pyx_k_ints_in_interval, sizeof(__pyx_k_ints_in_interval), 0, 0, 1, 1}, {&__pyx_n_s_itemsize, __pyx_k_itemsize, sizeof(__pyx_k_itemsize), 0, 0, 1, 1}, {&__pyx_kp_s_itemsize_0_for_cython_array, __pyx_k_itemsize_0_for_cython_array, sizeof(__pyx_k_itemsize_0_for_cython_array), 0, 0, 1, 0}, {&__pyx_n_s_kind, __pyx_k_kind, sizeof(__pyx_k_kind), 0, 0, 1, 1}, {&__pyx_n_s_kwargs, __pyx_k_kwargs, sizeof(__pyx_k_kwargs), 0, 0, 1, 1}, {&__pyx_n_s_log, __pyx_k_log, sizeof(__pyx_k_log), 0, 0, 1, 1}, {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1}, {&__pyx_n_s_max_iter, __pyx_k_max_iter, sizeof(__pyx_k_max_iter), 0, 0, 1, 1}, {&__pyx_n_s_memview, __pyx_k_memview, sizeof(__pyx_k_memview), 0, 0, 1, 1}, {&__pyx_n_s_min_num_intervals, __pyx_k_min_num_intervals, sizeof(__pyx_k_min_num_intervals), 0, 0, 1, 1}, {&__pyx_n_s_mode, __pyx_k_mode, sizeof(__pyx_k_mode), 0, 0, 1, 1}, {&__pyx_n_s_n_interpolation_points, __pyx_k_n_interpolation_points, sizeof(__pyx_k_n_interpolation_points), 0, 0, 1, 1}, {&__pyx_n_s_name, __pyx_k_name, sizeof(__pyx_k_name), 0, 0, 1, 1}, {&__pyx_n_s_name_2, __pyx_k_name_2, sizeof(__pyx_k_name_2), 0, 0, 1, 1}, {&__pyx_n_s_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 0, 0, 1, 1}, {&__pyx_n_s_new, __pyx_k_new, sizeof(__pyx_k_new), 0, 0, 1, 1}, {&__pyx_kp_s_no_default___reduce___due_to_non, __pyx_k_no_default___reduce___due_to_non, sizeof(__pyx_k_no_default___reduce___due_to_non), 0, 0, 1, 0}, {&__pyx_n_s_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 0, 1, 1}, {&__pyx_n_s_num_threads, __pyx_k_num_threads, sizeof(__pyx_k_num_threads), 0, 0, 1, 1}, {&__pyx_n_s_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 0, 0, 1, 1}, {&__pyx_kp_u_numpy_core_multiarray_failed_to, __pyx_k_numpy_core_multiarray_failed_to, sizeof(__pyx_k_numpy_core_multiarray_failed_to), 0, 1, 0, 0}, {&__pyx_kp_u_numpy_core_umath_failed_to_impor, __pyx_k_numpy_core_umath_failed_to_impor, sizeof(__pyx_k_numpy_core_umath_failed_to_impor), 0, 1, 0, 0}, {&__pyx_n_s_obj, __pyx_k_obj, sizeof(__pyx_k_obj), 0, 0, 1, 1}, {&__pyx_n_s_ones, __pyx_k_ones, sizeof(__pyx_k_ones), 0, 0, 1, 1}, {&__pyx_n_s_openTSNE__tsne, __pyx_k_openTSNE__tsne, sizeof(__pyx_k_openTSNE__tsne), 0, 0, 1, 1}, {&__pyx_kp_s_openTSNE__tsne_pyx, __pyx_k_openTSNE__tsne_pyx, sizeof(__pyx_k_openTSNE__tsne_pyx), 0, 0, 1, 0}, {&__pyx_n_s_order, __pyx_k_order, sizeof(__pyx_k_order), 0, 0, 1, 1}, {&__pyx_n_s_pack, __pyx_k_pack, sizeof(__pyx_k_pack), 0, 0, 1, 1}, {&__pyx_n_s_padding, __pyx_k_padding, sizeof(__pyx_k_padding), 0, 0, 1, 1}, {&__pyx_n_s_pairwise_normalization, __pyx_k_pairwise_normalization, sizeof(__pyx_k_pairwise_normalization), 0, 0, 1, 1}, {&__pyx_n_s_perplexity_tol, __pyx_k_perplexity_tol, sizeof(__pyx_k_perplexity_tol), 0, 0, 1, 1}, {&__pyx_n_s_pickle, __pyx_k_pickle, sizeof(__pyx_k_pickle), 0, 0, 1, 1}, {&__pyx_n_s_pyx_PickleError, __pyx_k_pyx_PickleError, sizeof(__pyx_k_pyx_PickleError), 0, 0, 1, 1}, {&__pyx_n_s_pyx_checksum, __pyx_k_pyx_checksum, sizeof(__pyx_k_pyx_checksum), 0, 0, 1, 1}, {&__pyx_n_s_pyx_fuse_0estimate_positive_gr, __pyx_k_pyx_fuse_0estimate_positive_gr, sizeof(__pyx_k_pyx_fuse_0estimate_positive_gr), 0, 0, 1, 1}, {&__pyx_n_s_pyx_fuse_1estimate_positive_gr, __pyx_k_pyx_fuse_1estimate_positive_gr, sizeof(__pyx_k_pyx_fuse_1estimate_positive_gr), 0, 0, 1, 1}, {&__pyx_n_s_pyx_getbuffer, __pyx_k_pyx_getbuffer, sizeof(__pyx_k_pyx_getbuffer), 0, 0, 1, 1}, {&__pyx_n_s_pyx_result, __pyx_k_pyx_result, sizeof(__pyx_k_pyx_result), 0, 0, 1, 1}, {&__pyx_n_s_pyx_state, __pyx_k_pyx_state, sizeof(__pyx_k_pyx_state), 0, 0, 1, 1}, {&__pyx_n_s_pyx_type, __pyx_k_pyx_type, sizeof(__pyx_k_pyx_type), 0, 0, 1, 1}, {&__pyx_n_s_pyx_unpickle_Enum, __pyx_k_pyx_unpickle_Enum, sizeof(__pyx_k_pyx_unpickle_Enum), 0, 0, 1, 1}, {&__pyx_n_s_pyx_vtable, __pyx_k_pyx_vtable, sizeof(__pyx_k_pyx_vtable), 0, 0, 1, 1}, {&__pyx_n_s_range, __pyx_k_range, sizeof(__pyx_k_range), 0, 0, 1, 1}, {&__pyx_n_s_reduce, __pyx_k_reduce, sizeof(__pyx_k_reduce), 0, 0, 1, 1}, {&__pyx_n_s_reduce_cython, __pyx_k_reduce_cython, sizeof(__pyx_k_reduce_cython), 0, 0, 1, 1}, {&__pyx_n_s_reduce_ex, __pyx_k_reduce_ex, sizeof(__pyx_k_reduce_ex), 0, 0, 1, 1}, {&__pyx_n_s_reference_embedding, __pyx_k_reference_embedding, sizeof(__pyx_k_reference_embedding), 0, 0, 1, 1}, {&__pyx_n_s_s, __pyx_k_s, sizeof(__pyx_k_s), 0, 0, 1, 1}, {&__pyx_n_s_setstate, __pyx_k_setstate, sizeof(__pyx_k_setstate), 0, 0, 1, 1}, {&__pyx_n_s_setstate_cython, __pyx_k_setstate_cython, sizeof(__pyx_k_setstate_cython), 0, 0, 1, 1}, {&__pyx_n_s_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 0, 0, 1, 1}, {&__pyx_n_s_should_eval_error, __pyx_k_should_eval_error, sizeof(__pyx_k_should_eval_error), 0, 0, 1, 1}, {&__pyx_n_s_signatures, __pyx_k_signatures, sizeof(__pyx_k_signatures), 0, 0, 1, 1}, {&__pyx_n_s_size, __pyx_k_size, sizeof(__pyx_k_size), 0, 0, 1, 1}, {&__pyx_n_s_split, __pyx_k_split, sizeof(__pyx_k_split), 0, 0, 1, 1}, {&__pyx_n_s_start, __pyx_k_start, sizeof(__pyx_k_start), 0, 0, 1, 1}, {&__pyx_n_s_step, __pyx_k_step, sizeof(__pyx_k_step), 0, 0, 1, 1}, {&__pyx_n_s_stop, __pyx_k_stop, sizeof(__pyx_k_stop), 0, 0, 1, 1}, {&__pyx_kp_s_strided_and_direct, __pyx_k_strided_and_direct, sizeof(__pyx_k_strided_and_direct), 0, 0, 1, 0}, {&__pyx_kp_s_strided_and_direct_or_indirect, __pyx_k_strided_and_direct_or_indirect, sizeof(__pyx_k_strided_and_direct_or_indirect), 0, 0, 1, 0}, {&__pyx_kp_s_strided_and_indirect, __pyx_k_strided_and_indirect, sizeof(__pyx_k_strided_and_indirect), 0, 0, 1, 0}, {&__pyx_kp_s_stringsource, __pyx_k_stringsource, sizeof(__pyx_k_stringsource), 0, 0, 1, 0}, {&__pyx_n_s_strip, __pyx_k_strip, sizeof(__pyx_k_strip), 0, 0, 1, 1}, {&__pyx_n_s_struct, __pyx_k_struct, sizeof(__pyx_k_struct), 0, 0, 1, 1}, {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1}, {&__pyx_n_s_theta, __pyx_k_theta, sizeof(__pyx_k_theta), 0, 0, 1, 1}, {&__pyx_n_s_tree, __pyx_k_tree, sizeof(__pyx_k_tree), 0, 0, 1, 1}, {&__pyx_kp_s_unable_to_allocate_array_data, __pyx_k_unable_to_allocate_array_data, sizeof(__pyx_k_unable_to_allocate_array_data), 0, 0, 1, 0}, {&__pyx_kp_s_unable_to_allocate_shape_and_str, __pyx_k_unable_to_allocate_shape_and_str, sizeof(__pyx_k_unable_to_allocate_shape_and_str), 0, 0, 1, 0}, {&__pyx_n_s_unpack, __pyx_k_unpack, sizeof(__pyx_k_unpack), 0, 0, 1, 1}, {&__pyx_n_s_update, __pyx_k_update, sizeof(__pyx_k_update), 0, 0, 1, 1}, {&__pyx_n_s_y_tilde_values, __pyx_k_y_tilde_values, sizeof(__pyx_k_y_tilde_values), 0, 0, 1, 1}, {&__pyx_n_s_zeros, __pyx_k_zeros, sizeof(__pyx_k_zeros), 0, 0, 1, 1}, {&__pyx_n_s_zeros_like, __pyx_k_zeros_like, sizeof(__pyx_k_zeros_like), 0, 0, 1, 1}, {0, 0, 0, 0, 0, 0, 0} }; static CYTHON_SMALL_CODE int __Pyx_InitCachedBuiltins(void) { __pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) __PYX_ERR(0, 57, __pyx_L1_error) __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s_TypeError); if (!__pyx_builtin_TypeError) __PYX_ERR(0, 103, __pyx_L1_error) __pyx_builtin_MemoryError = __Pyx_GetBuiltinName(__pyx_n_s_MemoryError); if (!__pyx_builtin_MemoryError) __PYX_ERR(0, 135, __pyx_L1_error) __pyx_builtin_ImportError = __Pyx_GetBuiltinName(__pyx_n_s_ImportError); if (!__pyx_builtin_ImportError) __PYX_ERR(1, 884, __pyx_L1_error) __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) __PYX_ERR(2, 133, __pyx_L1_error) __pyx_builtin_enumerate = __Pyx_GetBuiltinName(__pyx_n_s_enumerate); if (!__pyx_builtin_enumerate) __PYX_ERR(2, 151, __pyx_L1_error) __pyx_builtin_Ellipsis = __Pyx_GetBuiltinName(__pyx_n_s_Ellipsis); if (!__pyx_builtin_Ellipsis) __PYX_ERR(2, 404, __pyx_L1_error) __pyx_builtin_id = __Pyx_GetBuiltinName(__pyx_n_s_id); if (!__pyx_builtin_id) __PYX_ERR(2, 613, __pyx_L1_error) __pyx_builtin_IndexError = __Pyx_GetBuiltinName(__pyx_n_s_IndexError); if (!__pyx_builtin_IndexError) __PYX_ERR(2, 832, __pyx_L1_error) return 0; __pyx_L1_error:; return -1; } static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0); /* "openTSNE/_tsne.pyx":103 * * * cpdef tuple estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * sparse_index_type[:] indices, * sparse_index_type[:] indptr, */ __pyx_tuple__3 = PyTuple_Pack(1, __pyx_kp_s_No_matching_signature_found); if (unlikely(!__pyx_tuple__3)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__3); __Pyx_GIVEREF(__pyx_tuple__3); __pyx_tuple__4 = PyTuple_Pack(1, __pyx_kp_s_Function_call_with_ambiguous_arg); if (unlikely(!__pyx_tuple__4)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__4); __Pyx_GIVEREF(__pyx_tuple__4); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":884 * __pyx_import_array() * except Exception: * raise ImportError("numpy.core.multiarray failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_umath() except -1: */ __pyx_tuple__11 = PyTuple_Pack(1, __pyx_kp_u_numpy_core_multiarray_failed_to); if (unlikely(!__pyx_tuple__11)) __PYX_ERR(1, 884, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__11); __Pyx_GIVEREF(__pyx_tuple__11); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":890 * _import_umath() * except Exception: * raise ImportError("numpy.core.umath failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_ufunc() except -1: */ __pyx_tuple__12 = PyTuple_Pack(1, __pyx_kp_u_numpy_core_umath_failed_to_impor); if (unlikely(!__pyx_tuple__12)) __PYX_ERR(1, 890, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__12); __Pyx_GIVEREF(__pyx_tuple__12); /* "View.MemoryView":133 * * if not self.ndim: * raise ValueError("Empty shape tuple for cython.array") # <<<<<<<<<<<<<< * * if itemsize <= 0: */ __pyx_tuple__13 = PyTuple_Pack(1, __pyx_kp_s_Empty_shape_tuple_for_cython_arr); if (unlikely(!__pyx_tuple__13)) __PYX_ERR(2, 133, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__13); __Pyx_GIVEREF(__pyx_tuple__13); /* "View.MemoryView":136 * * if itemsize <= 0: * raise ValueError("itemsize <= 0 for cython.array") # <<<<<<<<<<<<<< * * if not isinstance(format, bytes): */ __pyx_tuple__14 = PyTuple_Pack(1, __pyx_kp_s_itemsize_0_for_cython_array); if (unlikely(!__pyx_tuple__14)) __PYX_ERR(2, 136, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__14); __Pyx_GIVEREF(__pyx_tuple__14); /* "View.MemoryView":148 * * if not self._shape: * raise MemoryError("unable to allocate shape and strides.") # <<<<<<<<<<<<<< * * */ __pyx_tuple__15 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_shape_and_str); if (unlikely(!__pyx_tuple__15)) __PYX_ERR(2, 148, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__15); __Pyx_GIVEREF(__pyx_tuple__15); /* "View.MemoryView":176 * self.data = malloc(self.len) * if not self.data: * raise MemoryError("unable to allocate array data.") # <<<<<<<<<<<<<< * * if self.dtype_is_object: */ __pyx_tuple__16 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_array_data); if (unlikely(!__pyx_tuple__16)) __PYX_ERR(2, 176, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__16); __Pyx_GIVEREF(__pyx_tuple__16); /* "View.MemoryView":192 * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") # <<<<<<<<<<<<<< * info.buf = self.data * info.len = self.len */ __pyx_tuple__17 = PyTuple_Pack(1, __pyx_kp_s_Can_only_create_a_buffer_that_is); if (unlikely(!__pyx_tuple__17)) __PYX_ERR(2, 192, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__17); __Pyx_GIVEREF(__pyx_tuple__17); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__18 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__18)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__18); __Pyx_GIVEREF(__pyx_tuple__18); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__19 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__19)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__19); __Pyx_GIVEREF(__pyx_tuple__19); /* "View.MemoryView":418 * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") # <<<<<<<<<<<<<< * * have_slices, index = _unellipsify(index, self.view.ndim) */ __pyx_tuple__20 = PyTuple_Pack(1, __pyx_kp_s_Cannot_assign_to_read_only_memor); if (unlikely(!__pyx_tuple__20)) __PYX_ERR(2, 418, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__20); __Pyx_GIVEREF(__pyx_tuple__20); /* "View.MemoryView":495 * result = struct.unpack(self.view.format, bytesitem) * except struct.error: * raise ValueError("Unable to convert item to object") # <<<<<<<<<<<<<< * else: * if len(self.view.format) == 1: */ __pyx_tuple__21 = PyTuple_Pack(1, __pyx_kp_s_Unable_to_convert_item_to_object); if (unlikely(!__pyx_tuple__21)) __PYX_ERR(2, 495, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__21); __Pyx_GIVEREF(__pyx_tuple__21); /* "View.MemoryView":520 * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") # <<<<<<<<<<<<<< * * if flags & PyBUF_ND: */ __pyx_tuple__22 = PyTuple_Pack(1, __pyx_kp_s_Cannot_create_writable_memory_vi); if (unlikely(!__pyx_tuple__22)) __PYX_ERR(2, 520, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__22); __Pyx_GIVEREF(__pyx_tuple__22); /* "View.MemoryView":570 * if self.view.strides == NULL: * * raise ValueError("Buffer view does not expose strides") # <<<<<<<<<<<<<< * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) */ __pyx_tuple__23 = PyTuple_Pack(1, __pyx_kp_s_Buffer_view_does_not_expose_stri); if (unlikely(!__pyx_tuple__23)) __PYX_ERR(2, 570, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__23); __Pyx_GIVEREF(__pyx_tuple__23); /* "View.MemoryView":577 * def suboffsets(self): * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim # <<<<<<<<<<<<<< * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) */ __pyx_tuple__24 = PyTuple_New(1); if (unlikely(!__pyx_tuple__24)) __PYX_ERR(2, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__24); __Pyx_INCREF(__pyx_int_neg_1); __Pyx_GIVEREF(__pyx_int_neg_1); PyTuple_SET_ITEM(__pyx_tuple__24, 0, __pyx_int_neg_1); __Pyx_GIVEREF(__pyx_tuple__24); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__25 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__25)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__25); __Pyx_GIVEREF(__pyx_tuple__25); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__26 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__26)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__26); __Pyx_GIVEREF(__pyx_tuple__26); /* "View.MemoryView":682 * if item is Ellipsis: * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) # <<<<<<<<<<<<<< * seen_ellipsis = True * else: */ __pyx_slice__27 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__27)) __PYX_ERR(2, 682, __pyx_L1_error) __Pyx_GOTREF(__pyx_slice__27); __Pyx_GIVEREF(__pyx_slice__27); /* "View.MemoryView":703 * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") # <<<<<<<<<<<<<< * * */ __pyx_tuple__28 = PyTuple_Pack(1, __pyx_kp_s_Indirect_dimensions_not_supporte); if (unlikely(!__pyx_tuple__28)) __PYX_ERR(2, 703, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__28); __Pyx_GIVEREF(__pyx_tuple__28); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__29 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__29)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__29); __Pyx_GIVEREF(__pyx_tuple__29); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__30 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__30)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__30); __Pyx_GIVEREF(__pyx_tuple__30); /* "openTSNE/_tsne.pyx":103 * * * cpdef tuple estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * sparse_index_type[:] indices, * sparse_index_type[:] indptr, */ __pyx_tuple__31 = PyTuple_Pack(9, __pyx_n_s_indices, __pyx_n_s_indptr, __pyx_n_s_P_data, __pyx_n_s_embedding, __pyx_n_s_reference_embedding, __pyx_n_s_gradient, __pyx_n_s_dof, __pyx_n_s_num_threads, __pyx_n_s_should_eval_error); if (unlikely(!__pyx_tuple__31)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__31); __Pyx_GIVEREF(__pyx_tuple__31); __pyx_codeobj__32 = (PyObject*)__Pyx_PyCode_New(9, 0, 9, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__31, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_openTSNE__tsne_pyx, __pyx_n_s_pyx_fuse_0estimate_positive_gr, 103, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__32)) __PYX_ERR(0, 103, __pyx_L1_error) /* "View.MemoryView":286 * return self.name * * cdef generic = Enum("") # <<<<<<<<<<<<<< * cdef strided = Enum("") # default * cdef indirect = Enum("") */ __pyx_tuple__33 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct_or_indirect); if (unlikely(!__pyx_tuple__33)) __PYX_ERR(2, 286, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__33); __Pyx_GIVEREF(__pyx_tuple__33); /* "View.MemoryView":287 * * cdef generic = Enum("") * cdef strided = Enum("") # default # <<<<<<<<<<<<<< * cdef indirect = Enum("") * */ __pyx_tuple__34 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct); if (unlikely(!__pyx_tuple__34)) __PYX_ERR(2, 287, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__34); __Pyx_GIVEREF(__pyx_tuple__34); /* "View.MemoryView":288 * cdef generic = Enum("") * cdef strided = Enum("") # default * cdef indirect = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_tuple__35 = PyTuple_Pack(1, __pyx_kp_s_strided_and_indirect); if (unlikely(!__pyx_tuple__35)) __PYX_ERR(2, 288, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__35); __Pyx_GIVEREF(__pyx_tuple__35); /* "View.MemoryView":291 * * * cdef contiguous = Enum("") # <<<<<<<<<<<<<< * cdef indirect_contiguous = Enum("") * */ __pyx_tuple__36 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_direct); if (unlikely(!__pyx_tuple__36)) __PYX_ERR(2, 291, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__36); __Pyx_GIVEREF(__pyx_tuple__36); /* "View.MemoryView":292 * * cdef contiguous = Enum("") * cdef indirect_contiguous = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_tuple__37 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_indirect); if (unlikely(!__pyx_tuple__37)) __PYX_ERR(2, 292, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__37); __Pyx_GIVEREF(__pyx_tuple__37); /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ __pyx_tuple__38 = PyTuple_Pack(5, __pyx_n_s_pyx_type, __pyx_n_s_pyx_checksum, __pyx_n_s_pyx_state, __pyx_n_s_pyx_PickleError, __pyx_n_s_pyx_result); if (unlikely(!__pyx_tuple__38)) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__38); __Pyx_GIVEREF(__pyx_tuple__38); __pyx_codeobj__39 = (PyObject*)__Pyx_PyCode_New(3, 0, 5, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__38, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_stringsource, __pyx_n_s_pyx_unpickle_Enum, 1, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__39)) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static CYTHON_SMALL_CODE int __Pyx_InitGlobals(void) { /* InitThreads.init */ #ifdef WITH_THREAD PyEval_InitThreads(); #endif if (unlikely(PyErr_Occurred())) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_InitStrings(__pyx_string_tab) < 0) __PYX_ERR(0, 1, __pyx_L1_error); __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_1 = PyInt_FromLong(1); if (unlikely(!__pyx_int_1)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_6 = PyInt_FromLong(6); if (unlikely(!__pyx_int_6)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_20 = PyInt_FromLong(20); if (unlikely(!__pyx_int_20)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_21 = PyInt_FromLong(21); if (unlikely(!__pyx_int_21)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_22 = PyInt_FromLong(22); if (unlikely(!__pyx_int_22)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_24 = PyInt_FromLong(24); if (unlikely(!__pyx_int_24)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_25 = PyInt_FromLong(25); if (unlikely(!__pyx_int_25)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_26 = PyInt_FromLong(26); if (unlikely(!__pyx_int_26)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_27 = PyInt_FromLong(27); if (unlikely(!__pyx_int_27)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_28 = PyInt_FromLong(28); if (unlikely(!__pyx_int_28)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_30 = PyInt_FromLong(30); if (unlikely(!__pyx_int_30)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_32 = PyInt_FromLong(32); if (unlikely(!__pyx_int_32)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_33 = PyInt_FromLong(33); if (unlikely(!__pyx_int_33)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_35 = PyInt_FromLong(35); if (unlikely(!__pyx_int_35)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_36 = PyInt_FromLong(36); if (unlikely(!__pyx_int_36)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_39 = PyInt_FromLong(39); if (unlikely(!__pyx_int_39)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_40 = PyInt_FromLong(40); if (unlikely(!__pyx_int_40)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_42 = PyInt_FromLong(42); if (unlikely(!__pyx_int_42)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_44 = PyInt_FromLong(44); if (unlikely(!__pyx_int_44)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_45 = PyInt_FromLong(45); if (unlikely(!__pyx_int_45)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_48 = PyInt_FromLong(48); if (unlikely(!__pyx_int_48)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_49 = PyInt_FromLong(49); if (unlikely(!__pyx_int_49)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_50 = PyInt_FromLong(50); if (unlikely(!__pyx_int_50)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_52 = PyInt_FromLong(52); if (unlikely(!__pyx_int_52)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_54 = PyInt_FromLong(54); if (unlikely(!__pyx_int_54)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_55 = PyInt_FromLong(55); if (unlikely(!__pyx_int_55)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_56 = PyInt_FromLong(56); if (unlikely(!__pyx_int_56)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_60 = PyInt_FromLong(60); if (unlikely(!__pyx_int_60)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_63 = PyInt_FromLong(63); if (unlikely(!__pyx_int_63)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_64 = PyInt_FromLong(64); if (unlikely(!__pyx_int_64)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_65 = PyInt_FromLong(65); if (unlikely(!__pyx_int_65)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_66 = PyInt_FromLong(66); if (unlikely(!__pyx_int_66)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_70 = PyInt_FromLong(70); if (unlikely(!__pyx_int_70)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_72 = PyInt_FromLong(72); if (unlikely(!__pyx_int_72)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_75 = PyInt_FromLong(75); if (unlikely(!__pyx_int_75)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_77 = PyInt_FromLong(77); if (unlikely(!__pyx_int_77)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_78 = PyInt_FromLong(78); if (unlikely(!__pyx_int_78)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_80 = PyInt_FromLong(80); if (unlikely(!__pyx_int_80)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_81 = PyInt_FromLong(81); if (unlikely(!__pyx_int_81)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_84 = PyInt_FromLong(84); if (unlikely(!__pyx_int_84)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_88 = PyInt_FromLong(88); if (unlikely(!__pyx_int_88)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_90 = PyInt_FromLong(90); if (unlikely(!__pyx_int_90)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_91 = PyInt_FromLong(91); if (unlikely(!__pyx_int_91)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_96 = PyInt_FromLong(96); if (unlikely(!__pyx_int_96)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_98 = PyInt_FromLong(98); if (unlikely(!__pyx_int_98)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_99 = PyInt_FromLong(99); if (unlikely(!__pyx_int_99)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_100 = PyInt_FromLong(100); if (unlikely(!__pyx_int_100)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_104 = PyInt_FromLong(104); if (unlikely(!__pyx_int_104)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_105 = PyInt_FromLong(105); if (unlikely(!__pyx_int_105)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_108 = PyInt_FromLong(108); if (unlikely(!__pyx_int_108)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_110 = PyInt_FromLong(110); if (unlikely(!__pyx_int_110)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_112 = PyInt_FromLong(112); if (unlikely(!__pyx_int_112)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_117 = PyInt_FromLong(117); if (unlikely(!__pyx_int_117)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_120 = PyInt_FromLong(120); if (unlikely(!__pyx_int_120)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_125 = PyInt_FromLong(125); if (unlikely(!__pyx_int_125)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_126 = PyInt_FromLong(126); if (unlikely(!__pyx_int_126)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_128 = PyInt_FromLong(128); if (unlikely(!__pyx_int_128)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_130 = PyInt_FromLong(130); if (unlikely(!__pyx_int_130)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_132 = PyInt_FromLong(132); if (unlikely(!__pyx_int_132)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_135 = PyInt_FromLong(135); if (unlikely(!__pyx_int_135)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_140 = PyInt_FromLong(140); if (unlikely(!__pyx_int_140)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_144 = PyInt_FromLong(144); if (unlikely(!__pyx_int_144)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_147 = PyInt_FromLong(147); if (unlikely(!__pyx_int_147)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_150 = PyInt_FromLong(150); if (unlikely(!__pyx_int_150)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_154 = PyInt_FromLong(154); if (unlikely(!__pyx_int_154)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_156 = PyInt_FromLong(156); if (unlikely(!__pyx_int_156)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_160 = PyInt_FromLong(160); if (unlikely(!__pyx_int_160)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_162 = PyInt_FromLong(162); if (unlikely(!__pyx_int_162)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_165 = PyInt_FromLong(165); if (unlikely(!__pyx_int_165)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_168 = PyInt_FromLong(168); if (unlikely(!__pyx_int_168)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_175 = PyInt_FromLong(175); if (unlikely(!__pyx_int_175)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_176 = PyInt_FromLong(176); if (unlikely(!__pyx_int_176)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_180 = PyInt_FromLong(180); if (unlikely(!__pyx_int_180)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_182 = PyInt_FromLong(182); if (unlikely(!__pyx_int_182)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_189 = PyInt_FromLong(189); if (unlikely(!__pyx_int_189)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_192 = PyInt_FromLong(192); if (unlikely(!__pyx_int_192)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_195 = PyInt_FromLong(195); if (unlikely(!__pyx_int_195)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_196 = PyInt_FromLong(196); if (unlikely(!__pyx_int_196)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_198 = PyInt_FromLong(198); if (unlikely(!__pyx_int_198)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_200 = PyInt_FromLong(200); if (unlikely(!__pyx_int_200)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_208 = PyInt_FromLong(208); if (unlikely(!__pyx_int_208)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_210 = PyInt_FromLong(210); if (unlikely(!__pyx_int_210)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_216 = PyInt_FromLong(216); if (unlikely(!__pyx_int_216)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_220 = PyInt_FromLong(220); if (unlikely(!__pyx_int_220)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_224 = PyInt_FromLong(224); if (unlikely(!__pyx_int_224)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_225 = PyInt_FromLong(225); if (unlikely(!__pyx_int_225)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_231 = PyInt_FromLong(231); if (unlikely(!__pyx_int_231)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_234 = PyInt_FromLong(234); if (unlikely(!__pyx_int_234)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_240 = PyInt_FromLong(240); if (unlikely(!__pyx_int_240)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_243 = PyInt_FromLong(243); if (unlikely(!__pyx_int_243)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_245 = PyInt_FromLong(245); if (unlikely(!__pyx_int_245)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_250 = PyInt_FromLong(250); if (unlikely(!__pyx_int_250)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_252 = PyInt_FromLong(252); if (unlikely(!__pyx_int_252)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_256 = PyInt_FromLong(256); if (unlikely(!__pyx_int_256)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_260 = PyInt_FromLong(260); if (unlikely(!__pyx_int_260)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_264 = PyInt_FromLong(264); if (unlikely(!__pyx_int_264)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_270 = PyInt_FromLong(270); if (unlikely(!__pyx_int_270)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_273 = PyInt_FromLong(273); if (unlikely(!__pyx_int_273)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_275 = PyInt_FromLong(275); if (unlikely(!__pyx_int_275)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_280 = PyInt_FromLong(280); if (unlikely(!__pyx_int_280)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_288 = PyInt_FromLong(288); if (unlikely(!__pyx_int_288)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_294 = PyInt_FromLong(294); if (unlikely(!__pyx_int_294)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_297 = PyInt_FromLong(297); if (unlikely(!__pyx_int_297)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_300 = PyInt_FromLong(300); if (unlikely(!__pyx_int_300)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_308 = PyInt_FromLong(308); if (unlikely(!__pyx_int_308)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_312 = PyInt_FromLong(312); if (unlikely(!__pyx_int_312)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_315 = PyInt_FromLong(315); if (unlikely(!__pyx_int_315)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_320 = PyInt_FromLong(320); if (unlikely(!__pyx_int_320)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_324 = PyInt_FromLong(324); if (unlikely(!__pyx_int_324)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_325 = PyInt_FromLong(325); if (unlikely(!__pyx_int_325)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_330 = PyInt_FromLong(330); if (unlikely(!__pyx_int_330)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_336 = PyInt_FromLong(336); if (unlikely(!__pyx_int_336)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_343 = PyInt_FromLong(343); if (unlikely(!__pyx_int_343)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_350 = PyInt_FromLong(350); if (unlikely(!__pyx_int_350)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_351 = PyInt_FromLong(351); if (unlikely(!__pyx_int_351)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_352 = PyInt_FromLong(352); if (unlikely(!__pyx_int_352)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_360 = PyInt_FromLong(360); if (unlikely(!__pyx_int_360)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_364 = PyInt_FromLong(364); if (unlikely(!__pyx_int_364)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_375 = PyInt_FromLong(375); if (unlikely(!__pyx_int_375)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_378 = PyInt_FromLong(378); if (unlikely(!__pyx_int_378)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_384 = PyInt_FromLong(384); if (unlikely(!__pyx_int_384)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_385 = PyInt_FromLong(385); if (unlikely(!__pyx_int_385)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_390 = PyInt_FromLong(390); if (unlikely(!__pyx_int_390)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_392 = PyInt_FromLong(392); if (unlikely(!__pyx_int_392)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_396 = PyInt_FromLong(396); if (unlikely(!__pyx_int_396)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_400 = PyInt_FromLong(400); if (unlikely(!__pyx_int_400)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_405 = PyInt_FromLong(405); if (unlikely(!__pyx_int_405)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_416 = PyInt_FromLong(416); if (unlikely(!__pyx_int_416)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_420 = PyInt_FromLong(420); if (unlikely(!__pyx_int_420)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_432 = PyInt_FromLong(432); if (unlikely(!__pyx_int_432)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_440 = PyInt_FromLong(440); if (unlikely(!__pyx_int_440)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_441 = PyInt_FromLong(441); if (unlikely(!__pyx_int_441)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_448 = PyInt_FromLong(448); if (unlikely(!__pyx_int_448)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_450 = PyInt_FromLong(450); if (unlikely(!__pyx_int_450)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_455 = PyInt_FromLong(455); if (unlikely(!__pyx_int_455)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_462 = PyInt_FromLong(462); if (unlikely(!__pyx_int_462)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_468 = PyInt_FromLong(468); if (unlikely(!__pyx_int_468)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_480 = PyInt_FromLong(480); if (unlikely(!__pyx_int_480)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_486 = PyInt_FromLong(486); if (unlikely(!__pyx_int_486)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_490 = PyInt_FromLong(490); if (unlikely(!__pyx_int_490)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_495 = PyInt_FromLong(495); if (unlikely(!__pyx_int_495)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_500 = PyInt_FromLong(500); if (unlikely(!__pyx_int_500)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_504 = PyInt_FromLong(504); if (unlikely(!__pyx_int_504)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_512 = PyInt_FromLong(512); if (unlikely(!__pyx_int_512)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_520 = PyInt_FromLong(520); if (unlikely(!__pyx_int_520)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_525 = PyInt_FromLong(525); if (unlikely(!__pyx_int_525)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_528 = PyInt_FromLong(528); if (unlikely(!__pyx_int_528)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_539 = PyInt_FromLong(539); if (unlikely(!__pyx_int_539)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_540 = PyInt_FromLong(540); if (unlikely(!__pyx_int_540)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_546 = PyInt_FromLong(546); if (unlikely(!__pyx_int_546)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_550 = PyInt_FromLong(550); if (unlikely(!__pyx_int_550)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_560 = PyInt_FromLong(560); if (unlikely(!__pyx_int_560)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_567 = PyInt_FromLong(567); if (unlikely(!__pyx_int_567)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_576 = PyInt_FromLong(576); if (unlikely(!__pyx_int_576)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_585 = PyInt_FromLong(585); if (unlikely(!__pyx_int_585)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_588 = PyInt_FromLong(588); if (unlikely(!__pyx_int_588)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_594 = PyInt_FromLong(594); if (unlikely(!__pyx_int_594)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_600 = PyInt_FromLong(600); if (unlikely(!__pyx_int_600)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_616 = PyInt_FromLong(616); if (unlikely(!__pyx_int_616)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_624 = PyInt_FromLong(624); if (unlikely(!__pyx_int_624)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_625 = PyInt_FromLong(625); if (unlikely(!__pyx_int_625)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_630 = PyInt_FromLong(630); if (unlikely(!__pyx_int_630)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_637 = PyInt_FromLong(637); if (unlikely(!__pyx_int_637)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_640 = PyInt_FromLong(640); if (unlikely(!__pyx_int_640)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_648 = PyInt_FromLong(648); if (unlikely(!__pyx_int_648)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_650 = PyInt_FromLong(650); if (unlikely(!__pyx_int_650)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_660 = PyInt_FromLong(660); if (unlikely(!__pyx_int_660)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_672 = PyInt_FromLong(672); if (unlikely(!__pyx_int_672)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_675 = PyInt_FromLong(675); if (unlikely(!__pyx_int_675)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_686 = PyInt_FromLong(686); if (unlikely(!__pyx_int_686)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_693 = PyInt_FromLong(693); if (unlikely(!__pyx_int_693)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_700 = PyInt_FromLong(700); if (unlikely(!__pyx_int_700)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_702 = PyInt_FromLong(702); if (unlikely(!__pyx_int_702)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_704 = PyInt_FromLong(704); if (unlikely(!__pyx_int_704)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_720 = PyInt_FromLong(720); if (unlikely(!__pyx_int_720)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_728 = PyInt_FromLong(728); if (unlikely(!__pyx_int_728)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_729 = PyInt_FromLong(729); if (unlikely(!__pyx_int_729)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_735 = PyInt_FromLong(735); if (unlikely(!__pyx_int_735)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_750 = PyInt_FromLong(750); if (unlikely(!__pyx_int_750)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_756 = PyInt_FromLong(756); if (unlikely(!__pyx_int_756)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_768 = PyInt_FromLong(768); if (unlikely(!__pyx_int_768)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_770 = PyInt_FromLong(770); if (unlikely(!__pyx_int_770)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_780 = PyInt_FromLong(780); if (unlikely(!__pyx_int_780)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_784 = PyInt_FromLong(784); if (unlikely(!__pyx_int_784)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_792 = PyInt_FromLong(792); if (unlikely(!__pyx_int_792)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_800 = PyInt_FromLong(800); if (unlikely(!__pyx_int_800)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_810 = PyInt_FromLong(810); if (unlikely(!__pyx_int_810)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_819 = PyInt_FromLong(819); if (unlikely(!__pyx_int_819)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_825 = PyInt_FromLong(825); if (unlikely(!__pyx_int_825)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_832 = PyInt_FromLong(832); if (unlikely(!__pyx_int_832)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_840 = PyInt_FromLong(840); if (unlikely(!__pyx_int_840)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_864 = PyInt_FromLong(864); if (unlikely(!__pyx_int_864)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_875 = PyInt_FromLong(875); if (unlikely(!__pyx_int_875)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_880 = PyInt_FromLong(880); if (unlikely(!__pyx_int_880)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_882 = PyInt_FromLong(882); if (unlikely(!__pyx_int_882)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_891 = PyInt_FromLong(891); if (unlikely(!__pyx_int_891)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_896 = PyInt_FromLong(896); if (unlikely(!__pyx_int_896)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_900 = PyInt_FromLong(900); if (unlikely(!__pyx_int_900)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_910 = PyInt_FromLong(910); if (unlikely(!__pyx_int_910)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_924 = PyInt_FromLong(924); if (unlikely(!__pyx_int_924)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_936 = PyInt_FromLong(936); if (unlikely(!__pyx_int_936)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_945 = PyInt_FromLong(945); if (unlikely(!__pyx_int_945)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_960 = PyInt_FromLong(960); if (unlikely(!__pyx_int_960)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_972 = PyInt_FromLong(972); if (unlikely(!__pyx_int_972)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_975 = PyInt_FromLong(975); if (unlikely(!__pyx_int_975)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_980 = PyInt_FromLong(980); if (unlikely(!__pyx_int_980)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_990 = PyInt_FromLong(990); if (unlikely(!__pyx_int_990)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_1000 = PyInt_FromLong(1000); if (unlikely(!__pyx_int_1000)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_184977713 = PyInt_FromLong(184977713L); if (unlikely(!__pyx_int_184977713)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_neg_1 = PyInt_FromLong(-1); if (unlikely(!__pyx_int_neg_1)) __PYX_ERR(0, 1, __pyx_L1_error) return 0; __pyx_L1_error:; return -1; } static CYTHON_SMALL_CODE int __Pyx_modinit_global_init_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_variable_export_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_function_export_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_type_init_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_type_import_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_variable_import_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_function_import_code(void); /*proto*/ static int __Pyx_modinit_global_init_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_global_init_code", 0); /*--- Global init code ---*/ generic = Py_None; Py_INCREF(Py_None); strided = Py_None; Py_INCREF(Py_None); indirect = Py_None; Py_INCREF(Py_None); contiguous = Py_None; Py_INCREF(Py_None); indirect_contiguous = Py_None; Py_INCREF(Py_None); __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_variable_export_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_variable_export_code", 0); /*--- Variable export code ---*/ __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_function_export_code(void) { __Pyx_RefNannyDeclarations int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__Pyx_modinit_function_export_code", 0); /*--- Function export code ---*/ if (__Pyx_ExportFunction("compute_gaussian_perplexity", (void (*)(void))__pyx_f_8openTSNE_5_tsne_compute_gaussian_perplexity, "__Pyx_memviewslice (__Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_compute_gaussian_perplexity *__pyx_optional_args)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ExportFunction("estimate_negative_gradient_bh", (void (*)(void))__pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_bh, "double (struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_bh *__pyx_optional_args)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ExportFunction("estimate_negative_gradient_fft_1d", (void (*)(void))__pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d, "double (__Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d *__pyx_optional_args)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ExportFunction("prepare_negative_gradient_fft_interpolation_grid_1d", (void (*)(void))__pyx_f_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_1d, "PyObject *(__Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_1d *__pyx_optional_args)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ExportFunction("estimate_negative_gradient_fft_1d_with_grid", (void (*)(void))__pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d_with_grid, "double (__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, Py_ssize_t, double, int __pyx_skip_dispatch)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ExportFunction("estimate_negative_gradient_fft_2d", (void (*)(void))__pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d, "double (__Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d *__pyx_optional_args)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ExportFunction("prepare_negative_gradient_fft_interpolation_grid_2d", (void (*)(void))__pyx_f_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_2d, "PyObject *(__Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_2d *__pyx_optional_args)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ExportFunction("estimate_negative_gradient_fft_2d_with_grid", (void (*)(void))__pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d_with_grid, "double (__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, Py_ssize_t, double, int __pyx_skip_dispatch)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ExportFunction("__pyx_fuse_0estimate_positive_gradient_nn", (void (*)(void))__pyx_fuse_0__pyx_f_8openTSNE_5_tsne_estimate_positive_gradient_nn, "PyObject *(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_fuse_0__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn *__pyx_optional_args)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ExportFunction("__pyx_fuse_1estimate_positive_gradient_nn", (void (*)(void))__pyx_fuse_1__pyx_f_8openTSNE_5_tsne_estimate_positive_gradient_nn, "PyObject *(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_fuse_1__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn *__pyx_optional_args)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_type_init_code(void) { __Pyx_RefNannyDeclarations int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__Pyx_modinit_type_init_code", 0); /*--- Type init code ---*/ __pyx_vtabptr_array = &__pyx_vtable_array; __pyx_vtable_array.get_memview = (PyObject *(*)(struct __pyx_array_obj *))__pyx_array_get_memview; if (PyType_Ready(&__pyx_type___pyx_array) < 0) __PYX_ERR(2, 105, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_array.tp_print = 0; #endif if (__Pyx_SetVtable(__pyx_type___pyx_array.tp_dict, __pyx_vtabptr_array) < 0) __PYX_ERR(2, 105, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_array) < 0) __PYX_ERR(2, 105, __pyx_L1_error) __pyx_array_type = &__pyx_type___pyx_array; if (PyType_Ready(&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(2, 279, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_MemviewEnum.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_MemviewEnum.tp_dictoffset && __pyx_type___pyx_MemviewEnum.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_MemviewEnum.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(2, 279, __pyx_L1_error) __pyx_MemviewEnum_type = &__pyx_type___pyx_MemviewEnum; __pyx_vtabptr_memoryview = &__pyx_vtable_memoryview; __pyx_vtable_memoryview.get_item_pointer = (char *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_get_item_pointer; __pyx_vtable_memoryview.is_slice = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_is_slice; __pyx_vtable_memoryview.setitem_slice_assignment = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_slice_assignment; __pyx_vtable_memoryview.setitem_slice_assign_scalar = (PyObject *(*)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_setitem_slice_assign_scalar; __pyx_vtable_memoryview.setitem_indexed = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_indexed; __pyx_vtable_memoryview.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryview_convert_item_to_object; __pyx_vtable_memoryview.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryview_assign_item_from_object; if (PyType_Ready(&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(2, 330, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_memoryview.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_memoryview.tp_dictoffset && __pyx_type___pyx_memoryview.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_memoryview.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_SetVtable(__pyx_type___pyx_memoryview.tp_dict, __pyx_vtabptr_memoryview) < 0) __PYX_ERR(2, 330, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(2, 330, __pyx_L1_error) __pyx_memoryview_type = &__pyx_type___pyx_memoryview; __pyx_vtabptr__memoryviewslice = &__pyx_vtable__memoryviewslice; __pyx_vtable__memoryviewslice.__pyx_base = *__pyx_vtabptr_memoryview; __pyx_vtable__memoryviewslice.__pyx_base.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryviewslice_convert_item_to_object; __pyx_vtable__memoryviewslice.__pyx_base.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryviewslice_assign_item_from_object; __pyx_type___pyx_memoryviewslice.tp_base = __pyx_memoryview_type; if (PyType_Ready(&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(2, 965, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_memoryviewslice.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_memoryviewslice.tp_dictoffset && __pyx_type___pyx_memoryviewslice.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_memoryviewslice.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_SetVtable(__pyx_type___pyx_memoryviewslice.tp_dict, __pyx_vtabptr__memoryviewslice) < 0) __PYX_ERR(2, 965, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(2, 965, __pyx_L1_error) __pyx_memoryviewslice_type = &__pyx_type___pyx_memoryviewslice; __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_type_import_code(void) { __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__Pyx_modinit_type_import_code", 0); /*--- Type import code ---*/ __pyx_t_1 = PyImport_ImportModule(__Pyx_BUILTIN_MODULE_NAME); if (unlikely(!__pyx_t_1)) __PYX_ERR(3, 9, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_ptype_7cpython_4type_type = __Pyx_ImportType(__pyx_t_1, __Pyx_BUILTIN_MODULE_NAME, "type", #if defined(PYPY_VERSION_NUM) && PYPY_VERSION_NUM < 0x050B0000 sizeof(PyTypeObject), #else sizeof(PyHeapTypeObject), #endif __Pyx_ImportType_CheckSize_Warn); if (!__pyx_ptype_7cpython_4type_type) __PYX_ERR(3, 9, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyImport_ImportModule("numpy"); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 199, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_ptype_5numpy_dtype = __Pyx_ImportType(__pyx_t_1, "numpy", "dtype", sizeof(PyArray_Descr), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_dtype) __PYX_ERR(1, 199, __pyx_L1_error) __pyx_ptype_5numpy_flatiter = __Pyx_ImportType(__pyx_t_1, "numpy", "flatiter", sizeof(PyArrayIterObject), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_flatiter) __PYX_ERR(1, 222, __pyx_L1_error) __pyx_ptype_5numpy_broadcast = __Pyx_ImportType(__pyx_t_1, "numpy", "broadcast", sizeof(PyArrayMultiIterObject), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_broadcast) __PYX_ERR(1, 226, __pyx_L1_error) __pyx_ptype_5numpy_ndarray = __Pyx_ImportType(__pyx_t_1, "numpy", "ndarray", sizeof(PyArrayObject), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_ndarray) __PYX_ERR(1, 238, __pyx_L1_error) __pyx_ptype_5numpy_ufunc = __Pyx_ImportType(__pyx_t_1, "numpy", "ufunc", sizeof(PyUFuncObject), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_ufunc) __PYX_ERR(1, 764, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyImport_ImportModule("openTSNE.quad_tree"); if (unlikely(!__pyx_t_1)) __PYX_ERR(4, 25, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_ptype_8openTSNE_9quad_tree_QuadTree = __Pyx_ImportType(__pyx_t_1, "openTSNE.quad_tree", "QuadTree", sizeof(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree), __Pyx_ImportType_CheckSize_Warn); if (!__pyx_ptype_8openTSNE_9quad_tree_QuadTree) __PYX_ERR(4, 25, __pyx_L1_error) __pyx_vtabptr_8openTSNE_9quad_tree_QuadTree = (struct __pyx_vtabstruct_8openTSNE_9quad_tree_QuadTree*)__Pyx_GetVtable(__pyx_ptype_8openTSNE_9quad_tree_QuadTree->tp_dict); if (unlikely(!__pyx_vtabptr_8openTSNE_9quad_tree_QuadTree)) __PYX_ERR(4, 25, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_variable_import_code(void) { __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__Pyx_modinit_variable_import_code", 0); /*--- Variable import code ---*/ __pyx_t_1 = PyImport_ImportModule("openTSNE.quad_tree"); if (!__pyx_t_1) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (__Pyx_ImportVoidPtr(__pyx_t_1, "EPSILON", (void **)&__pyx_vp_8openTSNE_9quad_tree_EPSILON, "double") < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_function_import_code(void) { __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__Pyx_modinit_function_import_code", 0); /*--- Function import code ---*/ __pyx_t_1 = PyImport_ImportModule("openTSNE.quad_tree"); if (!__pyx_t_1) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (__Pyx_ImportFunction(__pyx_t_1, "is_duplicate", (void (**)(void))&__pyx_f_8openTSNE_9quad_tree_is_duplicate, "int (__pyx_t_8openTSNE_9quad_tree_Node *, double *, struct __pyx_opt_args_8openTSNE_9quad_tree_is_duplicate *__pyx_optional_args)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyImport_ImportModule("openTSNE._matrix_mul.matrix_mul"); if (!__pyx_t_1) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (__Pyx_ImportFunction(__pyx_t_1, "matrix_multiply_fft_1d", (void (**)(void))&__pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_1d, "void (__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ImportFunction(__pyx_t_1, "matrix_multiply_fft_2d", (void (**)(void))&__pyx_f_8openTSNE_11_matrix_mul_10matrix_mul_matrix_multiply_fft_2d, "void (__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_RefNannyFinishContext(); return -1; } #ifndef CYTHON_NO_PYINIT_EXPORT #define __Pyx_PyMODINIT_FUNC PyMODINIT_FUNC #elif PY_MAJOR_VERSION < 3 #ifdef __cplusplus #define __Pyx_PyMODINIT_FUNC extern "C" void #else #define __Pyx_PyMODINIT_FUNC void #endif #else #ifdef __cplusplus #define __Pyx_PyMODINIT_FUNC extern "C" PyObject * #else #define __Pyx_PyMODINIT_FUNC PyObject * #endif #endif #if PY_MAJOR_VERSION < 3 __Pyx_PyMODINIT_FUNC init_tsne(void) CYTHON_SMALL_CODE; /*proto*/ __Pyx_PyMODINIT_FUNC init_tsne(void) #else __Pyx_PyMODINIT_FUNC PyInit__tsne(void) CYTHON_SMALL_CODE; /*proto*/ __Pyx_PyMODINIT_FUNC PyInit__tsne(void) #if CYTHON_PEP489_MULTI_PHASE_INIT { return PyModuleDef_Init(&__pyx_moduledef); } static CYTHON_SMALL_CODE int __Pyx_check_single_interpreter(void) { #if PY_VERSION_HEX >= 0x030700A1 static PY_INT64_T main_interpreter_id = -1; PY_INT64_T current_id = PyInterpreterState_GetID(PyThreadState_Get()->interp); if (main_interpreter_id == -1) { main_interpreter_id = current_id; return (unlikely(current_id == -1)) ? -1 : 0; } else if (unlikely(main_interpreter_id != current_id)) #else static PyInterpreterState *main_interpreter = NULL; PyInterpreterState *current_interpreter = PyThreadState_Get()->interp; if (!main_interpreter) { main_interpreter = current_interpreter; } else if (unlikely(main_interpreter != current_interpreter)) #endif { PyErr_SetString( PyExc_ImportError, "Interpreter change detected - this module can only be loaded into one interpreter per process."); return -1; } return 0; } static CYTHON_SMALL_CODE int __Pyx_copy_spec_to_module(PyObject *spec, PyObject *moddict, const char* from_name, const char* to_name, int allow_none) { PyObject *value = PyObject_GetAttrString(spec, from_name); int result = 0; if (likely(value)) { if (allow_none || value != Py_None) { result = PyDict_SetItemString(moddict, to_name, value); } Py_DECREF(value); } else if (PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Clear(); } else { result = -1; } return result; } static CYTHON_SMALL_CODE PyObject* __pyx_pymod_create(PyObject *spec, CYTHON_UNUSED PyModuleDef *def) { PyObject *module = NULL, *moddict, *modname; if (__Pyx_check_single_interpreter()) return NULL; if (__pyx_m) return __Pyx_NewRef(__pyx_m); modname = PyObject_GetAttrString(spec, "name"); if (unlikely(!modname)) goto bad; module = PyModule_NewObject(modname); Py_DECREF(modname); if (unlikely(!module)) goto bad; moddict = PyModule_GetDict(module); if (unlikely(!moddict)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "loader", "__loader__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "origin", "__file__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "parent", "__package__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "submodule_search_locations", "__path__", 0) < 0)) goto bad; return module; bad: Py_XDECREF(module); return NULL; } static CYTHON_SMALL_CODE int __pyx_pymod_exec__tsne(PyObject *__pyx_pyinit_module) #endif #endif { PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; double __pyx_t_4; PyObject *__pyx_t_5 = NULL; static PyThread_type_lock __pyx_t_6[8]; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannyDeclarations #if CYTHON_PEP489_MULTI_PHASE_INIT if (__pyx_m) { if (__pyx_m == __pyx_pyinit_module) return 0; PyErr_SetString(PyExc_RuntimeError, "Module '_tsne' has already been imported. Re-initialisation is not supported."); return -1; } #elif PY_MAJOR_VERSION >= 3 if (__pyx_m) return __Pyx_NewRef(__pyx_m); #endif #if CYTHON_REFNANNY __Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny"); if (!__Pyx_RefNanny) { PyErr_Clear(); __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny"); if (!__Pyx_RefNanny) Py_FatalError("failed to import 'refnanny' module"); } #endif __Pyx_RefNannySetupContext("__Pyx_PyMODINIT_FUNC PyInit__tsne(void)", 0); if (__Pyx_check_binary_version() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #ifdef __Pxy_PyFrame_Initialize_Offsets __Pxy_PyFrame_Initialize_Offsets(); #endif __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_empty_unicode = PyUnicode_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_unicode)) __PYX_ERR(0, 1, __pyx_L1_error) #ifdef __Pyx_CyFunction_USED if (__pyx_CyFunction_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_FusedFunction_USED if (__pyx_FusedFunction_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_Coroutine_USED if (__pyx_Coroutine_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_Generator_USED if (__pyx_Generator_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_AsyncGen_USED if (__pyx_AsyncGen_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_StopAsyncIteration_USED if (__pyx_StopAsyncIteration_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif /*--- Library function declarations ---*/ /*--- Threads initialization code ---*/ #if defined(__PYX_FORCE_INIT_THREADS) && __PYX_FORCE_INIT_THREADS #ifdef WITH_THREAD /* Python build with threading support? */ PyEval_InitThreads(); #endif #endif /*--- Module creation code ---*/ #if CYTHON_PEP489_MULTI_PHASE_INIT __pyx_m = __pyx_pyinit_module; Py_INCREF(__pyx_m); #else #if PY_MAJOR_VERSION < 3 __pyx_m = Py_InitModule4("_tsne", __pyx_methods, 0, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m); #else __pyx_m = PyModule_Create(&__pyx_moduledef); #endif if (unlikely(!__pyx_m)) __PYX_ERR(0, 1, __pyx_L1_error) #endif __pyx_d = PyModule_GetDict(__pyx_m); if (unlikely(!__pyx_d)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_d); __pyx_b = PyImport_AddModule(__Pyx_BUILTIN_MODULE_NAME); if (unlikely(!__pyx_b)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_b); __pyx_cython_runtime = PyImport_AddModule((char *) "cython_runtime"); if (unlikely(!__pyx_cython_runtime)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_cython_runtime); if (PyObject_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) __PYX_ERR(0, 1, __pyx_L1_error); /*--- Initialize various global constants etc. ---*/ if (__Pyx_InitGlobals() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #if PY_MAJOR_VERSION < 3 && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) if (__Pyx_init_sys_getdefaultencoding_params() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif if (__pyx_module_is_main_openTSNE___tsne) { if (PyObject_SetAttr(__pyx_m, __pyx_n_s_name_2, __pyx_n_s_main) < 0) __PYX_ERR(0, 1, __pyx_L1_error) } #if PY_MAJOR_VERSION >= 3 { PyObject *modules = PyImport_GetModuleDict(); if (unlikely(!modules)) __PYX_ERR(0, 1, __pyx_L1_error) if (!PyDict_GetItemString(modules, "openTSNE._tsne")) { if (unlikely(PyDict_SetItemString(modules, "openTSNE._tsne", __pyx_m) < 0)) __PYX_ERR(0, 1, __pyx_L1_error) } } #endif /*--- Builtin init code ---*/ if (__Pyx_InitCachedBuiltins() < 0) __PYX_ERR(0, 1, __pyx_L1_error) /*--- Constants init code ---*/ if (__Pyx_InitCachedConstants() < 0) __PYX_ERR(0, 1, __pyx_L1_error) /*--- Global type/function init code ---*/ (void)__Pyx_modinit_global_init_code(); (void)__Pyx_modinit_variable_export_code(); if (unlikely(__Pyx_modinit_function_export_code() < 0)) __PYX_ERR(0, 1, __pyx_L1_error) if (unlikely(__Pyx_modinit_type_init_code() < 0)) __PYX_ERR(0, 1, __pyx_L1_error) if (unlikely(__Pyx_modinit_type_import_code() < 0)) __PYX_ERR(0, 1, __pyx_L1_error) if (unlikely(__Pyx_modinit_variable_import_code() < 0)) __PYX_ERR(0, 1, __pyx_L1_error) if (unlikely(__Pyx_modinit_function_import_code() < 0)) __PYX_ERR(0, 1, __pyx_L1_error) /*--- Execution code ---*/ #if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED) if (__Pyx_patch_abc() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif /* "openTSNE/_tsne.pyx":8 * # cython: language_level=3 * cimport numpy as np * import numpy as np # <<<<<<<<<<<<<< * from cython.parallel import prange, parallel * from cpython.mem cimport PyMem_Malloc, PyMem_Free */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_numpy, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) __PYX_ERR(0, 8, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "openTSNE/_tsne.pyx":17 * * * cdef double EPSILON = np.finfo(np.float64).eps # <<<<<<<<<<<<<< * * */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 17, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_finfo); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 17, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 17, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_float64); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 17, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_t_3); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 17, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_eps); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 17, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_4 = __pyx_PyFloat_AsDouble(__pyx_t_3); if (unlikely((__pyx_t_4 == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 17, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_8openTSNE_5_tsne_EPSILON = __pyx_t_4; /* "openTSNE/_tsne.pyx":110 * double[:, ::1] reference_embedding, * double[:, ::1] gradient, * double dof=1, # <<<<<<<<<<<<<< * Py_ssize_t num_threads=1, * bint should_eval_error=False, */ __pyx_t_3 = __Pyx_PyInt_From_long(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 110, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); /* "openTSNE/_tsne.pyx":111 * double[:, ::1] gradient, * double dof=1, * Py_ssize_t num_threads=1, # <<<<<<<<<<<<<< * bint should_eval_error=False, * ): */ __pyx_t_1 = __Pyx_PyInt_From_long(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 111, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); /* "openTSNE/_tsne.pyx":112 * double dof=1, * Py_ssize_t num_threads=1, * bint should_eval_error=False, # <<<<<<<<<<<<<< * ): * cdef: */ __pyx_t_2 = __Pyx_PyBool_FromLong(0); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 112, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); /* "openTSNE/_tsne.pyx":103 * * * cpdef tuple estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * sparse_index_type[:] indices, * sparse_index_type[:] indptr, */ __pyx_t_5 = PyTuple_New(3); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_5, 2, __pyx_t_2); __pyx_t_3 = 0; __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_k__5 = 1; __pyx_k__6 = 1; __pyx_k__7 = 0; __pyx_k__5 = 1; __pyx_k__6 = 1; __pyx_k__7 = 0; __pyx_k__8 = 1; __pyx_k__9 = 1; __pyx_k__10 = 0; __pyx_k__8 = 1; __pyx_k__9 = 1; __pyx_k__10 = 0; __pyx_t_2 = __Pyx_PyDict_NewPresized(2); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_1 = __pyx_FusedFunction_New(&__pyx_fuse_0__pyx_mdef_8openTSNE_5_tsne_19__pyx_fuse_0estimate_positive_gradient_nn, 0, __pyx_n_s_pyx_fuse_0estimate_positive_gr, NULL, __pyx_n_s_openTSNE__tsne, __pyx_d, ((PyObject *)__pyx_codeobj__32)); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_CyFunction_SetDefaultsTuple(__pyx_t_1, __pyx_t_5); if (PyDict_SetItem(__pyx_t_2, __pyx_n_s_int32_t, __pyx_t_1) < 0) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __pyx_FusedFunction_New(&__pyx_fuse_1__pyx_mdef_8openTSNE_5_tsne_21__pyx_fuse_1estimate_positive_gradient_nn, 0, __pyx_n_s_pyx_fuse_1estimate_positive_gr, NULL, __pyx_n_s_openTSNE__tsne, __pyx_d, ((PyObject *)__pyx_codeobj__32)); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_CyFunction_SetDefaultsTuple(__pyx_t_1, __pyx_t_5); if (PyDict_SetItem(__pyx_t_2, __pyx_n_s_int64_t, __pyx_t_1) < 0) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __pyx_FusedFunction_New(&__pyx_mdef_8openTSNE_5_tsne_3estimate_positive_gradient_nn, 0, __pyx_n_s_estimate_positive_gradient_nn, NULL, __pyx_n_s_openTSNE__tsne, __pyx_d, ((PyObject *)__pyx_codeobj__32)); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_CyFunction_SetDefaultsTuple(__pyx_t_1, __pyx_t_5); ((__pyx_FusedFunctionObject *) __pyx_t_1)->__signatures__ = __pyx_t_2; __Pyx_GIVEREF(__pyx_t_2); __pyx_t_2 = 0; if (PyDict_SetItem(__pyx_d, __pyx_n_s_estimate_positive_gradient_nn, __pyx_t_1) < 0) __PYX_ERR(0, 103, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; /* "openTSNE/_tsne.pyx":1 * # cython: boundscheck=False # <<<<<<<<<<<<<< * # cython: wraparound=False * # cython: cdivision=True */ __pyx_t_5 = __Pyx_PyDict_NewPresized(0); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_5) < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; /* "View.MemoryView":209 * info.obj = self * * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * def __dealloc__(array self): */ __pyx_t_5 = __pyx_capsule_create(((void *)(&__pyx_array_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 209, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); if (PyDict_SetItem((PyObject *)__pyx_array_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_5) < 0) __PYX_ERR(2, 209, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; PyType_Modified(__pyx_array_type); /* "View.MemoryView":286 * return self.name * * cdef generic = Enum("") # <<<<<<<<<<<<<< * cdef strided = Enum("") # default * cdef indirect = Enum("") */ __pyx_t_5 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__33, NULL); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 286, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_XGOTREF(generic); __Pyx_DECREF_SET(generic, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_5); __pyx_t_5 = 0; /* "View.MemoryView":287 * * cdef generic = Enum("") * cdef strided = Enum("") # default # <<<<<<<<<<<<<< * cdef indirect = Enum("") * */ __pyx_t_5 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__34, NULL); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 287, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_XGOTREF(strided); __Pyx_DECREF_SET(strided, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_5); __pyx_t_5 = 0; /* "View.MemoryView":288 * cdef generic = Enum("") * cdef strided = Enum("") # default * cdef indirect = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__35, NULL); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 288, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_XGOTREF(indirect); __Pyx_DECREF_SET(indirect, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_5); __pyx_t_5 = 0; /* "View.MemoryView":291 * * * cdef contiguous = Enum("") # <<<<<<<<<<<<<< * cdef indirect_contiguous = Enum("") * */ __pyx_t_5 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__36, NULL); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 291, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_XGOTREF(contiguous); __Pyx_DECREF_SET(contiguous, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_5); __pyx_t_5 = 0; /* "View.MemoryView":292 * * cdef contiguous = Enum("") * cdef indirect_contiguous = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__37, NULL); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 292, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_XGOTREF(indirect_contiguous); __Pyx_DECREF_SET(indirect_contiguous, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_5); __pyx_t_5 = 0; /* "View.MemoryView":316 * * DEF THREAD_LOCKS_PREALLOCATED = 8 * cdef int __pyx_memoryview_thread_locks_used = 0 # <<<<<<<<<<<<<< * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [ * PyThread_allocate_lock(), */ __pyx_memoryview_thread_locks_used = 0; /* "View.MemoryView":317 * DEF THREAD_LOCKS_PREALLOCATED = 8 * cdef int __pyx_memoryview_thread_locks_used = 0 * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [ # <<<<<<<<<<<<<< * PyThread_allocate_lock(), * PyThread_allocate_lock(), */ __pyx_t_6[0] = PyThread_allocate_lock(); __pyx_t_6[1] = PyThread_allocate_lock(); __pyx_t_6[2] = PyThread_allocate_lock(); __pyx_t_6[3] = PyThread_allocate_lock(); __pyx_t_6[4] = PyThread_allocate_lock(); __pyx_t_6[5] = PyThread_allocate_lock(); __pyx_t_6[6] = PyThread_allocate_lock(); __pyx_t_6[7] = PyThread_allocate_lock(); memcpy(&(__pyx_memoryview_thread_locks[0]), __pyx_t_6, sizeof(__pyx_memoryview_thread_locks[0]) * (8)); /* "View.MemoryView":549 * info.obj = self * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 549, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); if (PyDict_SetItem((PyObject *)__pyx_memoryview_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_5) < 0) __PYX_ERR(2, 549, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; PyType_Modified(__pyx_memoryview_type); /* "View.MemoryView":995 * return self.from_object * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 995, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); if (PyDict_SetItem((PyObject *)__pyx_memoryviewslice_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_5) < 0) __PYX_ERR(2, 995, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; PyType_Modified(__pyx_memoryviewslice_type); /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ __pyx_t_5 = PyCFunction_NewEx(&__pyx_mdef_15View_dot_MemoryView_1__pyx_unpickle_Enum, NULL, __pyx_n_s_View_MemoryView); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); if (PyDict_SetItem(__pyx_d, __pyx_n_s_pyx_unpickle_Enum, __pyx_t_5) < 0) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ /*--- Wrapped vars code ---*/ goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_5); if (__pyx_m) { if (__pyx_d) { __Pyx_AddTraceback("init openTSNE._tsne", __pyx_clineno, __pyx_lineno, __pyx_filename); } Py_CLEAR(__pyx_m); } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_ImportError, "init openTSNE._tsne"); } __pyx_L0:; __Pyx_RefNannyFinishContext(); #if CYTHON_PEP489_MULTI_PHASE_INIT return (__pyx_m != NULL) ? 0 : -1; #elif PY_MAJOR_VERSION >= 3 return __pyx_m; #else return; #endif } /* --- Runtime support code --- */ /* Refnanny */ #if CYTHON_REFNANNY static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { PyObject *m = NULL, *p = NULL; void *r = NULL; m = PyImport_ImportModule(modname); if (!m) goto end; p = PyObject_GetAttrString(m, "RefNannyAPI"); if (!p) goto end; r = PyLong_AsVoidPtr(p); end: Py_XDECREF(p); Py_XDECREF(m); return (__Pyx_RefNannyAPIStruct *)r; } #endif /* PyObjectGetAttrStr */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { PyTypeObject* tp = Py_TYPE(obj); if (likely(tp->tp_getattro)) return tp->tp_getattro(obj, attr_name); #if PY_MAJOR_VERSION < 3 if (likely(tp->tp_getattr)) return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); #endif return PyObject_GetAttr(obj, attr_name); } #endif /* GetBuiltinName */ static PyObject *__Pyx_GetBuiltinName(PyObject *name) { PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); if (unlikely(!result)) { PyErr_Format(PyExc_NameError, #if PY_MAJOR_VERSION >= 3 "name '%U' is not defined", name); #else "name '%.200s' is not defined", PyString_AS_STRING(name)); #endif } return result; } /* PyDictVersioning */ #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { PyObject *dict = Py_TYPE(obj)->tp_dict; return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; } static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { PyObject **dictptr = NULL; Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; if (offset) { #if CYTHON_COMPILING_IN_CPYTHON dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); #else dictptr = _PyObject_GetDictPtr(obj); #endif } return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; } static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { PyObject *dict = Py_TYPE(obj)->tp_dict; if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) return 0; return obj_dict_version == __Pyx_get_object_dict_version(obj); } #endif /* GetModuleGlobalName */ #if CYTHON_USE_DICT_VERSIONS static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) #else static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) #endif { PyObject *result; #if !CYTHON_AVOID_BORROWED_REFS #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } else if (unlikely(PyErr_Occurred())) { return NULL; } #else result = PyDict_GetItem(__pyx_d, name); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } #endif #else result = PyObject_GetItem(__pyx_d, name); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } PyErr_Clear(); #endif return __Pyx_GetBuiltinName(name); } /* PyObjectCall */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; ternaryfunc call = func->ob_type->tp_call; if (unlikely(!call)) return PyObject_Call(func, arg, kw); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = (*call)(func, arg, kw); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif /* PyCFunctionFastCall */ #if CYTHON_FAST_PYCCALL static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { PyCFunctionObject *func = (PyCFunctionObject*)func_obj; PyCFunction meth = PyCFunction_GET_FUNCTION(func); PyObject *self = PyCFunction_GET_SELF(func); int flags = PyCFunction_GET_FLAGS(func); assert(PyCFunction_Check(func)); assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); assert(nargs >= 0); assert(nargs == 0 || args != NULL); /* _PyCFunction_FastCallDict() must not be called with an exception set, because it may clear it (directly or indirectly) and so the caller loses its exception */ assert(!PyErr_Occurred()); if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); } else { return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); } } #endif /* PyFunctionFastCall */ #if CYTHON_FAST_PYCALL static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, PyObject *globals) { PyFrameObject *f; PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject **fastlocals; Py_ssize_t i; PyObject *result; assert(globals != NULL); /* XXX Perhaps we should create a specialized PyFrame_New() that doesn't take locals, but does take builtins without sanity checking them. */ assert(tstate != NULL); f = PyFrame_New(tstate, co, globals, NULL); if (f == NULL) { return NULL; } fastlocals = __Pyx_PyFrame_GetLocalsplus(f); for (i = 0; i < na; i++) { Py_INCREF(*args); fastlocals[i] = *args++; } result = PyEval_EvalFrameEx(f,0); ++tstate->recursion_depth; Py_DECREF(f); --tstate->recursion_depth; return result; } #if 1 || PY_VERSION_HEX < 0x030600B1 static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); PyObject *globals = PyFunction_GET_GLOBALS(func); PyObject *argdefs = PyFunction_GET_DEFAULTS(func); PyObject *closure; #if PY_MAJOR_VERSION >= 3 PyObject *kwdefs; #endif PyObject *kwtuple, **k; PyObject **d; Py_ssize_t nd; Py_ssize_t nk; PyObject *result; assert(kwargs == NULL || PyDict_Check(kwargs)); nk = kwargs ? PyDict_Size(kwargs) : 0; if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { return NULL; } if ( #if PY_MAJOR_VERSION >= 3 co->co_kwonlyargcount == 0 && #endif likely(kwargs == NULL || nk == 0) && co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { if (argdefs == NULL && co->co_argcount == nargs) { result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); goto done; } else if (nargs == 0 && argdefs != NULL && co->co_argcount == Py_SIZE(argdefs)) { /* function called with no arguments, but all parameters have a default value: use default values as arguments .*/ args = &PyTuple_GET_ITEM(argdefs, 0); result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); goto done; } } if (kwargs != NULL) { Py_ssize_t pos, i; kwtuple = PyTuple_New(2 * nk); if (kwtuple == NULL) { result = NULL; goto done; } k = &PyTuple_GET_ITEM(kwtuple, 0); pos = i = 0; while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { Py_INCREF(k[i]); Py_INCREF(k[i+1]); i += 2; } nk = i / 2; } else { kwtuple = NULL; k = NULL; } closure = PyFunction_GET_CLOSURE(func); #if PY_MAJOR_VERSION >= 3 kwdefs = PyFunction_GET_KW_DEFAULTS(func); #endif if (argdefs != NULL) { d = &PyTuple_GET_ITEM(argdefs, 0); nd = Py_SIZE(argdefs); } else { d = NULL; nd = 0; } #if PY_MAJOR_VERSION >= 3 result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, args, (int)nargs, k, (int)nk, d, (int)nd, kwdefs, closure); #else result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, args, (int)nargs, k, (int)nk, d, (int)nd, closure); #endif Py_XDECREF(kwtuple); done: Py_LeaveRecursiveCall(); return result; } #endif #endif /* PyObjectCall2Args */ static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) { PyObject *args, *result = NULL; #if CYTHON_FAST_PYCALL if (PyFunction_Check(function)) { PyObject *args[2] = {arg1, arg2}; return __Pyx_PyFunction_FastCall(function, args, 2); } #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(function)) { PyObject *args[2] = {arg1, arg2}; return __Pyx_PyCFunction_FastCall(function, args, 2); } #endif args = PyTuple_New(2); if (unlikely(!args)) goto done; Py_INCREF(arg1); PyTuple_SET_ITEM(args, 0, arg1); Py_INCREF(arg2); PyTuple_SET_ITEM(args, 1, arg2); Py_INCREF(function); result = __Pyx_PyObject_Call(function, args, NULL); Py_DECREF(args); Py_DECREF(function); done: return result; } /* PyObjectCallMethO */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { PyObject *self, *result; PyCFunction cfunc; cfunc = PyCFunction_GET_FUNCTION(func); self = PyCFunction_GET_SELF(func); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = cfunc(self, arg); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif /* PyObjectCallOneArg */ #if CYTHON_COMPILING_IN_CPYTHON static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_New(1); if (unlikely(!args)) return NULL; Py_INCREF(arg); PyTuple_SET_ITEM(args, 0, arg); result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { #if CYTHON_FAST_PYCALL if (PyFunction_Check(func)) { return __Pyx_PyFunction_FastCall(func, &arg, 1); } #endif if (likely(PyCFunction_Check(func))) { if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { return __Pyx_PyObject_CallMethO(func, arg); #if CYTHON_FAST_PYCCALL } else if (__Pyx_PyFastCFunction_Check(func)) { return __Pyx_PyCFunction_FastCall(func, &arg, 1); #endif } } return __Pyx__PyObject_CallOneArg(func, arg); } #else static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_Pack(1, arg); if (unlikely(!args)) return NULL; result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } #endif /* MemviewSliceInit */ static int __Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, int ndim, __Pyx_memviewslice *memviewslice, int memview_is_new_reference) { __Pyx_RefNannyDeclarations int i, retval=-1; Py_buffer *buf = &memview->view; __Pyx_RefNannySetupContext("init_memviewslice", 0); if (unlikely(memviewslice->memview || memviewslice->data)) { PyErr_SetString(PyExc_ValueError, "memviewslice is already initialized!"); goto fail; } if (buf->strides) { for (i = 0; i < ndim; i++) { memviewslice->strides[i] = buf->strides[i]; } } else { Py_ssize_t stride = buf->itemsize; for (i = ndim - 1; i >= 0; i--) { memviewslice->strides[i] = stride; stride *= buf->shape[i]; } } for (i = 0; i < ndim; i++) { memviewslice->shape[i] = buf->shape[i]; if (buf->suboffsets) { memviewslice->suboffsets[i] = buf->suboffsets[i]; } else { memviewslice->suboffsets[i] = -1; } } memviewslice->memview = memview; memviewslice->data = (char *)buf->buf; if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { Py_INCREF(memview); } retval = 0; goto no_fail; fail: memviewslice->memview = 0; memviewslice->data = 0; retval = -1; no_fail: __Pyx_RefNannyFinishContext(); return retval; } #ifndef Py_NO_RETURN #define Py_NO_RETURN #endif static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { va_list vargs; char msg[200]; #ifdef HAVE_STDARG_PROTOTYPES va_start(vargs, fmt); #else va_start(vargs); #endif vsnprintf(msg, 200, fmt, vargs); va_end(vargs); Py_FatalError(msg); } static CYTHON_INLINE int __pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, PyThread_type_lock lock) { int result; PyThread_acquire_lock(lock, 1); result = (*acquisition_count)++; PyThread_release_lock(lock); return result; } static CYTHON_INLINE int __pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, PyThread_type_lock lock) { int result; PyThread_acquire_lock(lock, 1); result = (*acquisition_count)--; PyThread_release_lock(lock); return result; } static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) { int first_time; struct __pyx_memoryview_obj *memview = memslice->memview; if (unlikely(!memview || (PyObject *) memview == Py_None)) return; if (unlikely(__pyx_get_slice_count(memview) < 0)) __pyx_fatalerror("Acquisition count is %d (line %d)", __pyx_get_slice_count(memview), lineno); first_time = __pyx_add_acquisition_count(memview) == 0; if (unlikely(first_time)) { if (have_gil) { Py_INCREF((PyObject *) memview); } else { PyGILState_STATE _gilstate = PyGILState_Ensure(); Py_INCREF((PyObject *) memview); PyGILState_Release(_gilstate); } } } static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) { int last_time; struct __pyx_memoryview_obj *memview = memslice->memview; if (unlikely(!memview || (PyObject *) memview == Py_None)) { memslice->memview = NULL; return; } if (unlikely(__pyx_get_slice_count(memview) <= 0)) __pyx_fatalerror("Acquisition count is %d (line %d)", __pyx_get_slice_count(memview), lineno); last_time = __pyx_sub_acquisition_count(memview) == 1; memslice->data = NULL; if (unlikely(last_time)) { if (have_gil) { Py_CLEAR(memslice->memview); } else { PyGILState_STATE _gilstate = PyGILState_Ensure(); Py_CLEAR(memslice->memview); PyGILState_Release(_gilstate); } } else { memslice->memview = NULL; } } /* RaiseArgTupleInvalid */ static void __Pyx_RaiseArgtupleInvalid( const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found) { Py_ssize_t num_expected; const char *more_or_less; if (num_found < num_min) { num_expected = num_min; more_or_less = "at least"; } else { num_expected = num_max; more_or_less = "at most"; } if (exact) { more_or_less = "exactly"; } PyErr_Format(PyExc_TypeError, "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", func_name, more_or_less, num_expected, (num_expected == 1) ? "" : "s", num_found); } /* RaiseDoubleKeywords */ static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) { PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION >= 3 "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, PyString_AsString(kw_name)); #endif } /* ParseKeywords */ static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name) { PyObject *key = 0, *value = 0; Py_ssize_t pos = 0; PyObject*** name; PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { name = first_kw_arg; while (*name && (**name != key)) name++; if (*name) { values[name-argnames] = value; continue; } name = first_kw_arg; #if PY_MAJOR_VERSION < 3 if (likely(PyString_Check(key))) { while (*name) { if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) && _PyString_Eq(**name, key)) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { if ((**argname == key) || ( (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) && _PyString_Eq(**argname, key))) { goto arg_passed_twice; } argname++; } } } else #endif if (likely(PyUnicode_Check(key))) { while (*name) { int cmp = (**name == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : #endif PyUnicode_Compare(**name, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { int cmp = (**argname == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : #endif PyUnicode_Compare(**argname, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) goto arg_passed_twice; argname++; } } } else goto invalid_keyword_type; if (kwds2) { if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; } else { goto invalid_keyword; } } return 0; arg_passed_twice: __Pyx_RaiseDoubleKeywordsError(function_name, key); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%.200s() keywords must be strings", function_name); goto bad; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%.200s() got an unexpected keyword argument '%.200s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif bad: return -1; } /* DictGetItem */ #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { PyObject *value; value = PyDict_GetItemWithError(d, key); if (unlikely(!value)) { if (!PyErr_Occurred()) { if (unlikely(PyTuple_Check(key))) { PyObject* args = PyTuple_Pack(1, key); if (likely(args)) { PyErr_SetObject(PyExc_KeyError, args); Py_DECREF(args); } } else { PyErr_SetObject(PyExc_KeyError, key); } } return NULL; } Py_INCREF(value); return value; } #endif /* PyErrFetchRestore */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; } #endif /* RaiseException */ #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { __Pyx_PyThreadState_declare Py_XINCREF(type); if (!value || value == Py_None) value = NULL; else Py_INCREF(value); if (!tb || tb == Py_None) tb = NULL; else { Py_INCREF(tb); if (!PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } } if (PyType_Check(type)) { #if CYTHON_COMPILING_IN_PYPY if (!value) { Py_INCREF(Py_None); value = Py_None; } #endif PyErr_NormalizeException(&type, &value, &tb); } else { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } value = type; type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } } __Pyx_PyThreadState_assign __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (PyExceptionClass_Check(type)) { PyObject *instance_class = NULL; if (value && PyExceptionInstance_Check(value)) { instance_class = (PyObject*) Py_TYPE(value); if (instance_class != type) { int is_subclass = PyObject_IsSubclass(instance_class, type); if (!is_subclass) { instance_class = NULL; } else if (unlikely(is_subclass == -1)) { goto bad; } else { type = instance_class; } } } if (!instance_class) { PyObject *args; if (!value) args = PyTuple_New(0); else if (PyTuple_Check(value)) { Py_INCREF(value); args = value; } else args = PyTuple_Pack(1, value); if (!args) goto bad; owned_instance = PyObject_Call(type, args, NULL); Py_DECREF(args); if (!owned_instance) goto bad; value = owned_instance; if (!PyExceptionInstance_Check(value)) { PyErr_Format(PyExc_TypeError, "calling %R should have returned an instance of " "BaseException, not %R", type, Py_TYPE(value)); goto bad; } } } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } if (cause) { PyObject *fixed_cause; if (cause == Py_None) { fixed_cause = NULL; } else if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { #if CYTHON_COMPILING_IN_PYPY PyObject *tmp_type, *tmp_value, *tmp_tb; PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); Py_INCREF(tb); PyErr_Restore(tmp_type, tmp_value, tb); Py_XDECREF(tmp_tb); #else PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } #endif } bad: Py_XDECREF(owned_instance); return; } #endif /* UnicodeAsUCS4 */ static CYTHON_INLINE Py_UCS4 __Pyx_PyUnicode_AsPy_UCS4(PyObject* x) { Py_ssize_t length; #if CYTHON_PEP393_ENABLED length = PyUnicode_GET_LENGTH(x); if (likely(length == 1)) { return PyUnicode_READ_CHAR(x, 0); } #else length = PyUnicode_GET_SIZE(x); if (likely(length == 1)) { return PyUnicode_AS_UNICODE(x)[0]; } #if Py_UNICODE_SIZE == 2 else if (PyUnicode_GET_SIZE(x) == 2) { Py_UCS4 high_val = PyUnicode_AS_UNICODE(x)[0]; if (high_val >= 0xD800 && high_val <= 0xDBFF) { Py_UCS4 low_val = PyUnicode_AS_UNICODE(x)[1]; if (low_val >= 0xDC00 && low_val <= 0xDFFF) { return 0x10000 + (((high_val & ((1<<10)-1)) << 10) | (low_val & ((1<<10)-1))); } } } #endif #endif PyErr_Format(PyExc_ValueError, "only single character unicode strings can be converted to Py_UCS4, " "got length %" CYTHON_FORMAT_SSIZE_T "d", length); return (Py_UCS4)-1; } /* object_ord */ static long __Pyx__PyObject_Ord(PyObject* c) { Py_ssize_t size; if (PyBytes_Check(c)) { size = PyBytes_GET_SIZE(c); if (likely(size == 1)) { return (unsigned char) PyBytes_AS_STRING(c)[0]; } #if PY_MAJOR_VERSION < 3 } else if (PyUnicode_Check(c)) { return (long)__Pyx_PyUnicode_AsPy_UCS4(c); #endif #if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) } else if (PyByteArray_Check(c)) { size = PyByteArray_GET_SIZE(c); if (likely(size == 1)) { return (unsigned char) PyByteArray_AS_STRING(c)[0]; } #endif } else { PyErr_Format(PyExc_TypeError, "ord() expected string of length 1, but %.200s found", c->ob_type->tp_name); return (long)(Py_UCS4)-1; } PyErr_Format(PyExc_TypeError, "ord() expected a character, but string of length %zd found", size); return (long)(Py_UCS4)-1; } /* SetItemInt */ static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) { int r; if (!j) return -1; r = PyObject_SetItem(o, j, v); Py_DECREF(j); return r; } static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS if (is_list || PyList_CheckExact(o)) { Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o)); if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o)))) { PyObject* old = PyList_GET_ITEM(o, n); Py_INCREF(v); PyList_SET_ITEM(o, n, v); Py_DECREF(old); return 1; } } else { PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; if (likely(m && m->sq_ass_item)) { if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { Py_ssize_t l = m->sq_length(o); if (likely(l >= 0)) { i += l; } else { if (!PyErr_ExceptionMatches(PyExc_OverflowError)) return -1; PyErr_Clear(); } } return m->sq_ass_item(o, i, v); } } #else #if CYTHON_COMPILING_IN_PYPY if (is_list || (PySequence_Check(o) && !PyDict_Check(o))) #else if (is_list || PySequence_Check(o)) #endif { return PySequence_SetItem(o, i, v); } #endif return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v); } /* IterFinish */ static CYTHON_INLINE int __Pyx_IterFinish(void) { #if CYTHON_FAST_THREAD_STATE PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject* exc_type = tstate->curexc_type; if (unlikely(exc_type)) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) { PyObject *exc_value, *exc_tb; exc_value = tstate->curexc_value; exc_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; Py_DECREF(exc_type); Py_XDECREF(exc_value); Py_XDECREF(exc_tb); return 0; } else { return -1; } } return 0; #else if (unlikely(PyErr_Occurred())) { if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) { PyErr_Clear(); return 0; } else { return -1; } } return 0; #endif } /* PyObjectCallNoArg */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { #if CYTHON_FAST_PYCALL if (PyFunction_Check(func)) { return __Pyx_PyFunction_FastCall(func, NULL, 0); } #endif #ifdef __Pyx_CyFunction_USED if (likely(PyCFunction_Check(func) || __Pyx_CyFunction_Check(func))) #else if (likely(PyCFunction_Check(func))) #endif { if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { return __Pyx_PyObject_CallMethO(func, NULL); } } return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); } #endif /* PyObjectGetMethod */ static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) { PyObject *attr; #if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP PyTypeObject *tp = Py_TYPE(obj); PyObject *descr; descrgetfunc f = NULL; PyObject **dictptr, *dict; int meth_found = 0; assert (*method == NULL); if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) { attr = __Pyx_PyObject_GetAttrStr(obj, name); goto try_unpack; } if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) { return 0; } descr = _PyType_Lookup(tp, name); if (likely(descr != NULL)) { Py_INCREF(descr); #if PY_MAJOR_VERSION >= 3 #ifdef __Pyx_CyFunction_USED if (likely(PyFunction_Check(descr) || (Py_TYPE(descr) == &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr))) #else if (likely(PyFunction_Check(descr) || (Py_TYPE(descr) == &PyMethodDescr_Type))) #endif #else #ifdef __Pyx_CyFunction_USED if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr))) #else if (likely(PyFunction_Check(descr))) #endif #endif { meth_found = 1; } else { f = Py_TYPE(descr)->tp_descr_get; if (f != NULL && PyDescr_IsData(descr)) { attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); Py_DECREF(descr); goto try_unpack; } } } dictptr = _PyObject_GetDictPtr(obj); if (dictptr != NULL && (dict = *dictptr) != NULL) { Py_INCREF(dict); attr = __Pyx_PyDict_GetItemStr(dict, name); if (attr != NULL) { Py_INCREF(attr); Py_DECREF(dict); Py_XDECREF(descr); goto try_unpack; } Py_DECREF(dict); } if (meth_found) { *method = descr; return 1; } if (f != NULL) { attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); Py_DECREF(descr); goto try_unpack; } if (descr != NULL) { *method = descr; return 0; } PyErr_Format(PyExc_AttributeError, #if PY_MAJOR_VERSION >= 3 "'%.50s' object has no attribute '%U'", tp->tp_name, name); #else "'%.50s' object has no attribute '%.400s'", tp->tp_name, PyString_AS_STRING(name)); #endif return 0; #else attr = __Pyx_PyObject_GetAttrStr(obj, name); goto try_unpack; #endif try_unpack: #if CYTHON_UNPACK_METHODS if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) { PyObject *function = PyMethod_GET_FUNCTION(attr); Py_INCREF(function); Py_DECREF(attr); *method = function; return 1; } #endif *method = attr; return 0; } /* PyObjectCallMethod0 */ static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) { PyObject *method = NULL, *result = NULL; int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method); if (likely(is_method)) { result = __Pyx_PyObject_CallOneArg(method, obj); Py_DECREF(method); return result; } if (unlikely(!method)) goto bad; result = __Pyx_PyObject_CallNoArg(method); Py_DECREF(method); bad: return result; } /* RaiseNeedMoreValuesToUnpack */ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", index, (index == 1) ? "" : "s"); } /* RaiseTooManyValuesToUnpack */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } /* UnpackItemEndCheck */ static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) { if (unlikely(retval)) { Py_DECREF(retval); __Pyx_RaiseTooManyValuesError(expected); return -1; } else { return __Pyx_IterFinish(); } return 0; } /* RaiseNoneIterError */ static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } /* UnpackTupleError */ static void __Pyx_UnpackTupleError(PyObject *t, Py_ssize_t index) { if (t == Py_None) { __Pyx_RaiseNoneNotIterableError(); } else if (PyTuple_GET_SIZE(t) < index) { __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(t)); } else { __Pyx_RaiseTooManyValuesError(index); } } /* UnpackTuple2 */ static CYTHON_INLINE int __Pyx_unpack_tuple2_exact( PyObject* tuple, PyObject** pvalue1, PyObject** pvalue2, int decref_tuple) { PyObject *value1 = NULL, *value2 = NULL; #if CYTHON_COMPILING_IN_PYPY value1 = PySequence_ITEM(tuple, 0); if (unlikely(!value1)) goto bad; value2 = PySequence_ITEM(tuple, 1); if (unlikely(!value2)) goto bad; #else value1 = PyTuple_GET_ITEM(tuple, 0); Py_INCREF(value1); value2 = PyTuple_GET_ITEM(tuple, 1); Py_INCREF(value2); #endif if (decref_tuple) { Py_DECREF(tuple); } *pvalue1 = value1; *pvalue2 = value2; return 0; #if CYTHON_COMPILING_IN_PYPY bad: Py_XDECREF(value1); Py_XDECREF(value2); if (decref_tuple) { Py_XDECREF(tuple); } return -1; #endif } static int __Pyx_unpack_tuple2_generic(PyObject* tuple, PyObject** pvalue1, PyObject** pvalue2, int has_known_size, int decref_tuple) { Py_ssize_t index; PyObject *value1 = NULL, *value2 = NULL, *iter = NULL; iternextfunc iternext; iter = PyObject_GetIter(tuple); if (unlikely(!iter)) goto bad; if (decref_tuple) { Py_DECREF(tuple); tuple = NULL; } iternext = Py_TYPE(iter)->tp_iternext; value1 = iternext(iter); if (unlikely(!value1)) { index = 0; goto unpacking_failed; } value2 = iternext(iter); if (unlikely(!value2)) { index = 1; goto unpacking_failed; } if (!has_known_size && unlikely(__Pyx_IternextUnpackEndCheck(iternext(iter), 2))) goto bad; Py_DECREF(iter); *pvalue1 = value1; *pvalue2 = value2; return 0; unpacking_failed: if (!has_known_size && __Pyx_IterFinish() == 0) __Pyx_RaiseNeedMoreValuesError(index); bad: Py_XDECREF(iter); Py_XDECREF(value1); Py_XDECREF(value2); if (decref_tuple) { Py_XDECREF(tuple); } return -1; } /* dict_iter */ static CYTHON_INLINE PyObject* __Pyx_dict_iterator(PyObject* iterable, int is_dict, PyObject* method_name, Py_ssize_t* p_orig_length, int* p_source_is_dict) { is_dict = is_dict || likely(PyDict_CheckExact(iterable)); *p_source_is_dict = is_dict; if (is_dict) { #if !CYTHON_COMPILING_IN_PYPY *p_orig_length = PyDict_Size(iterable); Py_INCREF(iterable); return iterable; #elif PY_MAJOR_VERSION >= 3 static PyObject *py_items = NULL, *py_keys = NULL, *py_values = NULL; PyObject **pp = NULL; if (method_name) { const char *name = PyUnicode_AsUTF8(method_name); if (strcmp(name, "iteritems") == 0) pp = &py_items; else if (strcmp(name, "iterkeys") == 0) pp = &py_keys; else if (strcmp(name, "itervalues") == 0) pp = &py_values; if (pp) { if (!*pp) { *pp = PyUnicode_FromString(name + 4); if (!*pp) return NULL; } method_name = *pp; } } #endif } *p_orig_length = 0; if (method_name) { PyObject* iter; iterable = __Pyx_PyObject_CallMethod0(iterable, method_name); if (!iterable) return NULL; #if !CYTHON_COMPILING_IN_PYPY if (PyTuple_CheckExact(iterable) || PyList_CheckExact(iterable)) return iterable; #endif iter = PyObject_GetIter(iterable); Py_DECREF(iterable); return iter; } return PyObject_GetIter(iterable); } static CYTHON_INLINE int __Pyx_dict_iter_next( PyObject* iter_obj, CYTHON_NCP_UNUSED Py_ssize_t orig_length, CYTHON_NCP_UNUSED Py_ssize_t* ppos, PyObject** pkey, PyObject** pvalue, PyObject** pitem, int source_is_dict) { PyObject* next_item; #if !CYTHON_COMPILING_IN_PYPY if (source_is_dict) { PyObject *key, *value; if (unlikely(orig_length != PyDict_Size(iter_obj))) { PyErr_SetString(PyExc_RuntimeError, "dictionary changed size during iteration"); return -1; } if (unlikely(!PyDict_Next(iter_obj, ppos, &key, &value))) { return 0; } if (pitem) { PyObject* tuple = PyTuple_New(2); if (unlikely(!tuple)) { return -1; } Py_INCREF(key); Py_INCREF(value); PyTuple_SET_ITEM(tuple, 0, key); PyTuple_SET_ITEM(tuple, 1, value); *pitem = tuple; } else { if (pkey) { Py_INCREF(key); *pkey = key; } if (pvalue) { Py_INCREF(value); *pvalue = value; } } return 1; } else if (PyTuple_CheckExact(iter_obj)) { Py_ssize_t pos = *ppos; if (unlikely(pos >= PyTuple_GET_SIZE(iter_obj))) return 0; *ppos = pos + 1; next_item = PyTuple_GET_ITEM(iter_obj, pos); Py_INCREF(next_item); } else if (PyList_CheckExact(iter_obj)) { Py_ssize_t pos = *ppos; if (unlikely(pos >= PyList_GET_SIZE(iter_obj))) return 0; *ppos = pos + 1; next_item = PyList_GET_ITEM(iter_obj, pos); Py_INCREF(next_item); } else #endif { next_item = PyIter_Next(iter_obj); if (unlikely(!next_item)) { return __Pyx_IterFinish(); } } if (pitem) { *pitem = next_item; } else if (pkey && pvalue) { if (__Pyx_unpack_tuple2(next_item, pkey, pvalue, source_is_dict, source_is_dict, 1)) return -1; } else if (pkey) { *pkey = next_item; } else { *pvalue = next_item; } return 1; } /* GetItemInt */ static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { PyObject *r; if (!j) return NULL; r = PyObject_GetItem(o, j); Py_DECREF(j); return r; } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS Py_ssize_t wrapped_i = i; if (wraparound & unlikely(i < 0)) { wrapped_i += PyList_GET_SIZE(o); } if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { PyObject *r = PyList_GET_ITEM(o, wrapped_i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS Py_ssize_t wrapped_i = i; if (wraparound & unlikely(i < 0)) { wrapped_i += PyTuple_GET_SIZE(o); } if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS if (is_list || PyList_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { PyObject *r = PyList_GET_ITEM(o, n); Py_INCREF(r); return r; } } else if (PyTuple_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, n); Py_INCREF(r); return r; } } else { PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; if (likely(m && m->sq_item)) { if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { Py_ssize_t l = m->sq_length(o); if (likely(l >= 0)) { i += l; } else { if (!PyErr_ExceptionMatches(PyExc_OverflowError)) return NULL; PyErr_Clear(); } } return m->sq_item(o, i); } } #else if (is_list || PySequence_Check(o)) { return PySequence_GetItem(o, i); } #endif return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); } /* WriteUnraisableException */ static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno, CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename, int full_traceback, CYTHON_UNUSED int nogil) { PyObject *old_exc, *old_val, *old_tb; PyObject *ctx; __Pyx_PyThreadState_declare #ifdef WITH_THREAD PyGILState_STATE state; if (nogil) state = PyGILState_Ensure(); #ifdef _MSC_VER else state = (PyGILState_STATE)-1; #endif #endif __Pyx_PyThreadState_assign __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); if (full_traceback) { Py_XINCREF(old_exc); Py_XINCREF(old_val); Py_XINCREF(old_tb); __Pyx_ErrRestore(old_exc, old_val, old_tb); PyErr_PrintEx(1); } #if PY_MAJOR_VERSION < 3 ctx = PyString_FromString(name); #else ctx = PyUnicode_FromString(name); #endif __Pyx_ErrRestore(old_exc, old_val, old_tb); if (!ctx) { PyErr_WriteUnraisable(Py_None); } else { PyErr_WriteUnraisable(ctx); Py_DECREF(ctx); } #ifdef WITH_THREAD if (nogil) PyGILState_Release(state); #endif } /* ArgTypeTest */ static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } else if (exact) { #if PY_MAJOR_VERSION == 2 if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; #endif } else { if (likely(__Pyx_TypeCheck(obj, type))) return 1; } PyErr_Format(PyExc_TypeError, "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", name, type->tp_name, Py_TYPE(obj)->tp_name); return 0; } /* GetTopmostException */ #if CYTHON_USE_EXC_INFO_STACK static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate) { _PyErr_StackItem *exc_info = tstate->exc_info; while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && exc_info->previous_item != NULL) { exc_info = exc_info->previous_item; } return exc_info; } #endif /* SaveResetException */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); *type = exc_info->exc_type; *value = exc_info->exc_value; *tb = exc_info->exc_traceback; #else *type = tstate->exc_type; *value = tstate->exc_value; *tb = tstate->exc_traceback; #endif Py_XINCREF(*type); Py_XINCREF(*value); Py_XINCREF(*tb); } static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = type; exc_info->exc_value = value; exc_info->exc_traceback = tb; #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = type; tstate->exc_value = value; tstate->exc_traceback = tb; #endif Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } #endif /* PyErrExceptionMatches */ #if CYTHON_FAST_THREAD_STATE static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { Py_ssize_t i, n; n = PyTuple_GET_SIZE(tuple); #if PY_MAJOR_VERSION >= 3 for (i=0; icurexc_type; if (exc_type == err) return 1; if (unlikely(!exc_type)) return 0; if (unlikely(PyTuple_Check(err))) return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); } #endif /* GetException */ #if CYTHON_FAST_THREAD_STATE static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) #else static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) #endif { PyObject *local_type, *local_value, *local_tb; #if CYTHON_FAST_THREAD_STATE PyObject *tmp_type, *tmp_value, *tmp_tb; local_type = tstate->curexc_type; local_value = tstate->curexc_value; local_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(&local_type, &local_value, &local_tb); #endif PyErr_NormalizeException(&local_type, &local_value, &local_tb); #if CYTHON_FAST_THREAD_STATE if (unlikely(tstate->curexc_type)) #else if (unlikely(PyErr_Occurred())) #endif goto bad; #if PY_MAJOR_VERSION >= 3 if (local_tb) { if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) goto bad; } #endif Py_XINCREF(local_tb); Py_XINCREF(local_type); Py_XINCREF(local_value); *type = local_type; *value = local_value; *tb = local_tb; #if CYTHON_FAST_THREAD_STATE #if CYTHON_USE_EXC_INFO_STACK { _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = local_type; exc_info->exc_value = local_value; exc_info->exc_traceback = local_tb; } #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = local_type; tstate->exc_value = local_value; tstate->exc_traceback = local_tb; #endif Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_SetExcInfo(local_type, local_value, local_tb); #endif return 0; bad: *type = 0; *value = 0; *tb = 0; Py_XDECREF(local_type); Py_XDECREF(local_value); Py_XDECREF(local_tb); return -1; } /* BytesEquals */ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { #if CYTHON_COMPILING_IN_PYPY return PyObject_RichCompareBool(s1, s2, equals); #else if (s1 == s2) { return (equals == Py_EQ); } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { const char *ps1, *ps2; Py_ssize_t length = PyBytes_GET_SIZE(s1); if (length != PyBytes_GET_SIZE(s2)) return (equals == Py_NE); ps1 = PyBytes_AS_STRING(s1); ps2 = PyBytes_AS_STRING(s2); if (ps1[0] != ps2[0]) { return (equals == Py_NE); } else if (length == 1) { return (equals == Py_EQ); } else { int result; #if CYTHON_USE_UNICODE_INTERNALS Py_hash_t hash1, hash2; hash1 = ((PyBytesObject*)s1)->ob_shash; hash2 = ((PyBytesObject*)s2)->ob_shash; if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { return (equals == Py_NE); } #endif result = memcmp(ps1, ps2, (size_t)length); return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { return (equals == Py_NE); } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { return (equals == Py_NE); } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } #endif } /* UnicodeEquals */ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { #if CYTHON_COMPILING_IN_PYPY return PyObject_RichCompareBool(s1, s2, equals); #else #if PY_MAJOR_VERSION < 3 PyObject* owned_ref = NULL; #endif int s1_is_unicode, s2_is_unicode; if (s1 == s2) { goto return_eq; } s1_is_unicode = PyUnicode_CheckExact(s1); s2_is_unicode = PyUnicode_CheckExact(s2); #if PY_MAJOR_VERSION < 3 if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { owned_ref = PyUnicode_FromObject(s2); if (unlikely(!owned_ref)) return -1; s2 = owned_ref; s2_is_unicode = 1; } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { owned_ref = PyUnicode_FromObject(s1); if (unlikely(!owned_ref)) return -1; s1 = owned_ref; s1_is_unicode = 1; } else if (((!s2_is_unicode) & (!s1_is_unicode))) { return __Pyx_PyBytes_Equals(s1, s2, equals); } #endif if (s1_is_unicode & s2_is_unicode) { Py_ssize_t length; int kind; void *data1, *data2; if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) return -1; length = __Pyx_PyUnicode_GET_LENGTH(s1); if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { goto return_ne; } #if CYTHON_USE_UNICODE_INTERNALS { Py_hash_t hash1, hash2; #if CYTHON_PEP393_ENABLED hash1 = ((PyASCIIObject*)s1)->hash; hash2 = ((PyASCIIObject*)s2)->hash; #else hash1 = ((PyUnicodeObject*)s1)->hash; hash2 = ((PyUnicodeObject*)s2)->hash; #endif if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { goto return_ne; } } #endif kind = __Pyx_PyUnicode_KIND(s1); if (kind != __Pyx_PyUnicode_KIND(s2)) { goto return_ne; } data1 = __Pyx_PyUnicode_DATA(s1); data2 = __Pyx_PyUnicode_DATA(s2); if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { goto return_ne; } else if (length == 1) { goto return_eq; } else { int result = memcmp(data1, data2, (size_t)(length * kind)); #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & s2_is_unicode) { goto return_ne; } else if ((s2 == Py_None) & s1_is_unicode) { goto return_ne; } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } return_eq: #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_EQ); return_ne: #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_NE); #endif } /* GetAttr */ static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { #if CYTHON_USE_TYPE_SLOTS #if PY_MAJOR_VERSION >= 3 if (likely(PyUnicode_Check(n))) #else if (likely(PyString_Check(n))) #endif return __Pyx_PyObject_GetAttrStr(o, n); #endif return PyObject_GetAttr(o, n); } /* ObjectGetItem */ #if CYTHON_USE_TYPE_SLOTS static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { PyObject *runerr; Py_ssize_t key_value; PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence; if (unlikely(!(m && m->sq_item))) { PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name); return NULL; } key_value = __Pyx_PyIndex_AsSsize_t(index); if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); } if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { PyErr_Clear(); PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name); } return NULL; } static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping; if (likely(m && m->mp_subscript)) { return m->mp_subscript(obj, key); } return __Pyx_PyObject_GetIndex(obj, key); } #endif /* decode_c_string */ static CYTHON_INLINE PyObject* __Pyx_decode_c_string( const char* cstring, Py_ssize_t start, Py_ssize_t stop, const char* encoding, const char* errors, PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { Py_ssize_t length; if (unlikely((start < 0) | (stop < 0))) { size_t slen = strlen(cstring); if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { PyErr_SetString(PyExc_OverflowError, "c-string too long to convert to Python"); return NULL; } length = (Py_ssize_t) slen; if (start < 0) { start += length; if (start < 0) start = 0; } if (stop < 0) stop += length; } if (unlikely(stop <= start)) return __Pyx_NewRef(__pyx_empty_unicode); length = stop - start; cstring += start; if (decode_func) { return decode_func(cstring, length, errors); } else { return PyUnicode_Decode(cstring, length, encoding, errors); } } /* GetAttr3 */ static PyObject *__Pyx_GetAttr3Default(PyObject *d) { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) return NULL; __Pyx_PyErr_Clear(); Py_INCREF(d); return d; } static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { PyObject *r = __Pyx_GetAttr(o, n); return (likely(r)) ? r : __Pyx_GetAttr3Default(d); } /* ExtTypeTest */ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (likely(__Pyx_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } /* SwapException */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = *type; exc_info->exc_value = *value; exc_info->exc_traceback = *tb; #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = *type; tstate->exc_value = *value; tstate->exc_traceback = *tb; #endif *type = tmp_type; *value = tmp_value; *tb = tmp_tb; } #else static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); PyErr_SetExcInfo(*type, *value, *tb); *type = tmp_type; *value = tmp_value; *tb = tmp_tb; } #endif /* Import */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; #if PY_MAJOR_VERSION < 3 PyObject *py_import; py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); if (!py_import) goto bad; #endif if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, 1); if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; } #endif if (!module) { #if PY_MAJOR_VERSION < 3 PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, level); #endif } } bad: #if PY_MAJOR_VERSION < 3 Py_XDECREF(py_import); #endif Py_XDECREF(empty_list); Py_XDECREF(empty_dict); return module; } /* FastTypeChecks */ #if CYTHON_COMPILING_IN_CPYTHON static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { while (a) { a = a->tp_base; if (a == b) return 1; } return b == &PyBaseObject_Type; } static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { PyObject *mro; if (a == b) return 1; mro = a->tp_mro; if (likely(mro)) { Py_ssize_t i, n; n = PyTuple_GET_SIZE(mro); for (i = 0; i < n; i++) { if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) return 1; } return 0; } return __Pyx_InBases(a, b); } #if PY_MAJOR_VERSION == 2 static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { PyObject *exception, *value, *tb; int res; __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ErrFetch(&exception, &value, &tb); res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; if (unlikely(res == -1)) { PyErr_WriteUnraisable(err); res = 0; } if (!res) { res = PyObject_IsSubclass(err, exc_type2); if (unlikely(res == -1)) { PyErr_WriteUnraisable(err); res = 0; } } __Pyx_ErrRestore(exception, value, tb); return res; } #else static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; if (!res) { res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); } return res; } #endif static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { Py_ssize_t i, n; assert(PyExceptionClass_Check(exc_type)); n = PyTuple_GET_SIZE(tuple); #if PY_MAJOR_VERSION >= 3 for (i=0; i= 0 || (x^b) >= 0)) return PyInt_FromLong(x); return PyLong_Type.tp_as_number->nb_add(op1, op2); } #endif #if CYTHON_USE_PYLONG_INTERNALS if (likely(PyLong_CheckExact(op1))) { const long b = intval; long a, x; #ifdef HAVE_LONG_LONG const PY_LONG_LONG llb = intval; PY_LONG_LONG lla, llx; #endif const digit* digits = ((PyLongObject*)op1)->ob_digit; const Py_ssize_t size = Py_SIZE(op1); if (likely(__Pyx_sst_abs(size) <= 1)) { a = likely(size) ? digits[0] : 0; if (size == -1) a = -a; } else { switch (size) { case -2: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 2: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case -3: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 3: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case -4: if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 4: if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; default: return PyLong_Type.tp_as_number->nb_add(op1, op2); } } x = a + b; return PyLong_FromLong(x); #ifdef HAVE_LONG_LONG long_long: llx = lla + llb; return PyLong_FromLongLong(llx); #endif } #endif if (PyFloat_CheckExact(op1)) { const long b = intval; double a = PyFloat_AS_DOUBLE(op1); double result; PyFPE_START_PROTECT("add", return NULL) result = ((double)a) + (double)b; PyFPE_END_PROTECT(result) return PyFloat_FromDouble(result); } return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); } #endif /* None */ static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); } /* ImportFrom */ static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Format(PyExc_ImportError, #if PY_MAJOR_VERSION < 3 "cannot import name %.230s", PyString_AS_STRING(name)); #else "cannot import name %S", name); #endif } return value; } /* HasAttr */ static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { PyObject *r; if (unlikely(!__Pyx_PyBaseString_Check(n))) { PyErr_SetString(PyExc_TypeError, "hasattr(): attribute name must be string"); return -1; } r = __Pyx_GetAttr(o, n); if (unlikely(!r)) { PyErr_Clear(); return 0; } else { Py_DECREF(r); return 1; } } /* PyObject_GenericGetAttrNoDict */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { PyErr_Format(PyExc_AttributeError, #if PY_MAJOR_VERSION >= 3 "'%.50s' object has no attribute '%U'", tp->tp_name, attr_name); #else "'%.50s' object has no attribute '%.400s'", tp->tp_name, PyString_AS_STRING(attr_name)); #endif return NULL; } static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { PyObject *descr; PyTypeObject *tp = Py_TYPE(obj); if (unlikely(!PyString_Check(attr_name))) { return PyObject_GenericGetAttr(obj, attr_name); } assert(!tp->tp_dictoffset); descr = _PyType_Lookup(tp, attr_name); if (unlikely(!descr)) { return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); } Py_INCREF(descr); #if PY_MAJOR_VERSION < 3 if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) #endif { descrgetfunc f = Py_TYPE(descr)->tp_descr_get; if (unlikely(f)) { PyObject *res = f(descr, obj, (PyObject *)tp); Py_DECREF(descr); return res; } } return descr; } #endif /* PyObject_GenericGetAttr */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { return PyObject_GenericGetAttr(obj, attr_name); } return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); } #endif /* SetVTable */ static int __Pyx_SetVtable(PyObject *dict, void *vtable) { #if PY_VERSION_HEX >= 0x02070000 PyObject *ob = PyCapsule_New(vtable, 0, 0); #else PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); #endif if (!ob) goto bad; if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) goto bad; Py_DECREF(ob); return 0; bad: Py_XDECREF(ob); return -1; } /* PyObjectGetAttrStrNoError */ static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) __Pyx_PyErr_Clear(); } static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) { PyObject *result; #if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1 PyTypeObject* tp = Py_TYPE(obj); if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) { return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1); } #endif result = __Pyx_PyObject_GetAttrStr(obj, attr_name); if (unlikely(!result)) { __Pyx_PyObject_GetAttrStr_ClearAttributeError(); } return result; } /* SetupReduce */ static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { int ret; PyObject *name_attr; name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2); if (likely(name_attr)) { ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); } else { ret = -1; } if (unlikely(ret < 0)) { PyErr_Clear(); ret = 0; } Py_XDECREF(name_attr); return ret; } static int __Pyx_setup_reduce(PyObject* type_obj) { int ret = 0; PyObject *object_reduce = NULL; PyObject *object_reduce_ex = NULL; PyObject *reduce = NULL; PyObject *reduce_ex = NULL; PyObject *reduce_cython = NULL; PyObject *setstate = NULL; PyObject *setstate_cython = NULL; #if CYTHON_USE_PYTYPE_LOOKUP if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; #else if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; #endif #if CYTHON_USE_PYTYPE_LOOKUP object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; #else object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; #endif reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; if (reduce_ex == object_reduce_ex) { #if CYTHON_USE_PYTYPE_LOOKUP object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; #else object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; #endif reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); if (likely(reduce_cython)) { ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; } else if (reduce == object_reduce || PyErr_Occurred()) { goto __PYX_BAD; } setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); if (!setstate) PyErr_Clear(); if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); if (likely(setstate_cython)) { ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; } else if (!setstate || PyErr_Occurred()) { goto __PYX_BAD; } } PyType_Modified((PyTypeObject*)type_obj); } } goto __PYX_GOOD; __PYX_BAD: if (!PyErr_Occurred()) PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); ret = -1; __PYX_GOOD: #if !CYTHON_USE_PYTYPE_LOOKUP Py_XDECREF(object_reduce); Py_XDECREF(object_reduce_ex); #endif Py_XDECREF(reduce); Py_XDECREF(reduce_ex); Py_XDECREF(reduce_cython); Py_XDECREF(setstate); Py_XDECREF(setstate_cython); return ret; } /* TypeImport */ #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size) { PyObject *result = 0; char warning[200]; Py_ssize_t basicsize; #ifdef Py_LIMITED_API PyObject *py_basicsize; #endif result = PyObject_GetAttrString(module, class_name); if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%.200s.%.200s is not a type object", module_name, class_name); goto bad; } #ifndef Py_LIMITED_API basicsize = ((PyTypeObject *)result)->tp_basicsize; #else py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); if (!py_basicsize) goto bad; basicsize = PyLong_AsSsize_t(py_basicsize); Py_DECREF(py_basicsize); py_basicsize = 0; if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) goto bad; #endif if ((size_t)basicsize < size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); goto bad; } if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); goto bad; } else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(result); return NULL; } #endif /* GetVTable */ static void* __Pyx_GetVtable(PyObject *dict) { void* ptr; PyObject *ob = PyObject_GetItem(dict, __pyx_n_s_pyx_vtable); if (!ob) goto bad; #if PY_VERSION_HEX >= 0x02070000 ptr = PyCapsule_GetPointer(ob, 0); #else ptr = PyCObject_AsVoidPtr(ob); #endif if (!ptr && !PyErr_Occurred()) PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type"); Py_DECREF(ob); return ptr; bad: Py_XDECREF(ob); return NULL; } /* FetchCommonType */ static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { PyObject* fake_module; PyTypeObject* cached_type = NULL; fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI); if (!fake_module) return NULL; Py_INCREF(fake_module); cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name); if (cached_type) { if (!PyType_Check((PyObject*)cached_type)) { PyErr_Format(PyExc_TypeError, "Shared Cython type %.200s is not a type object", type->tp_name); goto bad; } if (cached_type->tp_basicsize != type->tp_basicsize) { PyErr_Format(PyExc_TypeError, "Shared Cython type %.200s has the wrong size, try recompiling", type->tp_name); goto bad; } } else { if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; PyErr_Clear(); if (PyType_Ready(type) < 0) goto bad; if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0) goto bad; Py_INCREF(type); cached_type = type; } done: Py_DECREF(fake_module); return cached_type; bad: Py_XDECREF(cached_type); cached_type = NULL; goto done; } /* CythonFunctionShared */ #include static PyObject * __Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure) { if (unlikely(op->func_doc == NULL)) { if (op->func.m_ml->ml_doc) { #if PY_MAJOR_VERSION >= 3 op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc); #else op->func_doc = PyString_FromString(op->func.m_ml->ml_doc); #endif if (unlikely(op->func_doc == NULL)) return NULL; } else { Py_INCREF(Py_None); return Py_None; } } Py_INCREF(op->func_doc); return op->func_doc; } static int __Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context) { PyObject *tmp = op->func_doc; if (value == NULL) { value = Py_None; } Py_INCREF(value); op->func_doc = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) { if (unlikely(op->func_name == NULL)) { #if PY_MAJOR_VERSION >= 3 op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name); #else op->func_name = PyString_InternFromString(op->func.m_ml->ml_name); #endif if (unlikely(op->func_name == NULL)) return NULL; } Py_INCREF(op->func_name); return op->func_name; } static int __Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context) { PyObject *tmp; #if PY_MAJOR_VERSION >= 3 if (unlikely(value == NULL || !PyUnicode_Check(value))) #else if (unlikely(value == NULL || !PyString_Check(value))) #endif { PyErr_SetString(PyExc_TypeError, "__name__ must be set to a string object"); return -1; } tmp = op->func_name; Py_INCREF(value); op->func_name = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) { Py_INCREF(op->func_qualname); return op->func_qualname; } static int __Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context) { PyObject *tmp; #if PY_MAJOR_VERSION >= 3 if (unlikely(value == NULL || !PyUnicode_Check(value))) #else if (unlikely(value == NULL || !PyString_Check(value))) #endif { PyErr_SetString(PyExc_TypeError, "__qualname__ must be set to a string object"); return -1; } tmp = op->func_qualname; Py_INCREF(value); op->func_qualname = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure) { PyObject *self; self = m->func_closure; if (self == NULL) self = Py_None; Py_INCREF(self); return self; } static PyObject * __Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) { if (unlikely(op->func_dict == NULL)) { op->func_dict = PyDict_New(); if (unlikely(op->func_dict == NULL)) return NULL; } Py_INCREF(op->func_dict); return op->func_dict; } static int __Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context) { PyObject *tmp; if (unlikely(value == NULL)) { PyErr_SetString(PyExc_TypeError, "function's dictionary may not be deleted"); return -1; } if (unlikely(!PyDict_Check(value))) { PyErr_SetString(PyExc_TypeError, "setting function's dictionary to a non-dict"); return -1; } tmp = op->func_dict; Py_INCREF(value); op->func_dict = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) { Py_INCREF(op->func_globals); return op->func_globals; } static PyObject * __Pyx_CyFunction_get_closure(CYTHON_UNUSED __pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) { Py_INCREF(Py_None); return Py_None; } static PyObject * __Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) { PyObject* result = (op->func_code) ? op->func_code : Py_None; Py_INCREF(result); return result; } static int __Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) { int result = 0; PyObject *res = op->defaults_getter((PyObject *) op); if (unlikely(!res)) return -1; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS op->defaults_tuple = PyTuple_GET_ITEM(res, 0); Py_INCREF(op->defaults_tuple); op->defaults_kwdict = PyTuple_GET_ITEM(res, 1); Py_INCREF(op->defaults_kwdict); #else op->defaults_tuple = PySequence_ITEM(res, 0); if (unlikely(!op->defaults_tuple)) result = -1; else { op->defaults_kwdict = PySequence_ITEM(res, 1); if (unlikely(!op->defaults_kwdict)) result = -1; } #endif Py_DECREF(res); return result; } static int __Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, CYTHON_UNUSED void *context) { PyObject* tmp; if (!value) { value = Py_None; } else if (value != Py_None && !PyTuple_Check(value)) { PyErr_SetString(PyExc_TypeError, "__defaults__ must be set to a tuple object"); return -1; } Py_INCREF(value); tmp = op->defaults_tuple; op->defaults_tuple = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) { PyObject* result = op->defaults_tuple; if (unlikely(!result)) { if (op->defaults_getter) { if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; result = op->defaults_tuple; } else { result = Py_None; } } Py_INCREF(result); return result; } static int __Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, CYTHON_UNUSED void *context) { PyObject* tmp; if (!value) { value = Py_None; } else if (value != Py_None && !PyDict_Check(value)) { PyErr_SetString(PyExc_TypeError, "__kwdefaults__ must be set to a dict object"); return -1; } Py_INCREF(value); tmp = op->defaults_kwdict; op->defaults_kwdict = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) { PyObject* result = op->defaults_kwdict; if (unlikely(!result)) { if (op->defaults_getter) { if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; result = op->defaults_kwdict; } else { result = Py_None; } } Py_INCREF(result); return result; } static int __Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, CYTHON_UNUSED void *context) { PyObject* tmp; if (!value || value == Py_None) { value = NULL; } else if (!PyDict_Check(value)) { PyErr_SetString(PyExc_TypeError, "__annotations__ must be set to a dict object"); return -1; } Py_XINCREF(value); tmp = op->func_annotations; op->func_annotations = value; Py_XDECREF(tmp); return 0; } static PyObject * __Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) { PyObject* result = op->func_annotations; if (unlikely(!result)) { result = PyDict_New(); if (unlikely(!result)) return NULL; op->func_annotations = result; } Py_INCREF(result); return result; } static PyGetSetDef __pyx_CyFunction_getsets[] = { {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, {(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0}, {(char *) "__self__", (getter)__Pyx_CyFunction_get_self, 0, 0, 0}, {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0}, {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0}, {0, 0, 0, 0, 0} }; static PyMemberDef __pyx_CyFunction_members[] = { {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), PY_WRITE_RESTRICTED, 0}, {0, 0, 0, 0, 0} }; static PyObject * __Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args) { #if PY_MAJOR_VERSION >= 3 return PyUnicode_FromString(m->func.m_ml->ml_name); #else return PyString_FromString(m->func.m_ml->ml_name); #endif } static PyMethodDef __pyx_CyFunction_methods[] = { {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, {0, 0, 0, 0} }; #if PY_VERSION_HEX < 0x030500A0 #define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) #else #define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist) #endif static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname, PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { if (unlikely(op == NULL)) return NULL; op->flags = flags; __Pyx_CyFunction_weakreflist(op) = NULL; op->func.m_ml = ml; op->func.m_self = (PyObject *) op; Py_XINCREF(closure); op->func_closure = closure; Py_XINCREF(module); op->func.m_module = module; op->func_dict = NULL; op->func_name = NULL; Py_INCREF(qualname); op->func_qualname = qualname; op->func_doc = NULL; op->func_classobj = NULL; op->func_globals = globals; Py_INCREF(op->func_globals); Py_XINCREF(code); op->func_code = code; op->defaults_pyobjects = 0; op->defaults_size = 0; op->defaults = NULL; op->defaults_tuple = NULL; op->defaults_kwdict = NULL; op->defaults_getter = NULL; op->func_annotations = NULL; return (PyObject *) op; } static int __Pyx_CyFunction_clear(__pyx_CyFunctionObject *m) { Py_CLEAR(m->func_closure); Py_CLEAR(m->func.m_module); Py_CLEAR(m->func_dict); Py_CLEAR(m->func_name); Py_CLEAR(m->func_qualname); Py_CLEAR(m->func_doc); Py_CLEAR(m->func_globals); Py_CLEAR(m->func_code); Py_CLEAR(m->func_classobj); Py_CLEAR(m->defaults_tuple); Py_CLEAR(m->defaults_kwdict); Py_CLEAR(m->func_annotations); if (m->defaults) { PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); int i; for (i = 0; i < m->defaults_pyobjects; i++) Py_XDECREF(pydefaults[i]); PyObject_Free(m->defaults); m->defaults = NULL; } return 0; } static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m) { if (__Pyx_CyFunction_weakreflist(m) != NULL) PyObject_ClearWeakRefs((PyObject *) m); __Pyx_CyFunction_clear(m); PyObject_GC_Del(m); } static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) { PyObject_GC_UnTrack(m); __Pyx__CyFunction_dealloc(m); } static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) { Py_VISIT(m->func_closure); Py_VISIT(m->func.m_module); Py_VISIT(m->func_dict); Py_VISIT(m->func_name); Py_VISIT(m->func_qualname); Py_VISIT(m->func_doc); Py_VISIT(m->func_globals); Py_VISIT(m->func_code); Py_VISIT(m->func_classobj); Py_VISIT(m->defaults_tuple); Py_VISIT(m->defaults_kwdict); if (m->defaults) { PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); int i; for (i = 0; i < m->defaults_pyobjects; i++) Py_VISIT(pydefaults[i]); } return 0; } static PyObject *__Pyx_CyFunction_descr_get(PyObject *func, PyObject *obj, PyObject *type) { #if PY_MAJOR_VERSION < 3 __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; if (m->flags & __Pyx_CYFUNCTION_STATICMETHOD) { Py_INCREF(func); return func; } if (m->flags & __Pyx_CYFUNCTION_CLASSMETHOD) { if (type == NULL) type = (PyObject *)(Py_TYPE(obj)); return __Pyx_PyMethod_New(func, type, (PyObject *)(Py_TYPE(type))); } if (obj == Py_None) obj = NULL; #endif return __Pyx_PyMethod_New(func, obj, type); } static PyObject* __Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) { #if PY_MAJOR_VERSION >= 3 return PyUnicode_FromFormat("", op->func_qualname, (void *)op); #else return PyString_FromFormat("", PyString_AsString(op->func_qualname), (void *)op); #endif } static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) { PyCFunctionObject* f = (PyCFunctionObject*)func; PyCFunction meth = f->m_ml->ml_meth; Py_ssize_t size; switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) { case METH_VARARGS: if (likely(kw == NULL || PyDict_Size(kw) == 0)) return (*meth)(self, arg); break; case METH_VARARGS | METH_KEYWORDS: return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw); case METH_NOARGS: if (likely(kw == NULL || PyDict_Size(kw) == 0)) { size = PyTuple_GET_SIZE(arg); if (likely(size == 0)) return (*meth)(self, NULL); PyErr_Format(PyExc_TypeError, "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", f->m_ml->ml_name, size); return NULL; } break; case METH_O: if (likely(kw == NULL || PyDict_Size(kw) == 0)) { size = PyTuple_GET_SIZE(arg); if (likely(size == 1)) { PyObject *result, *arg0; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS arg0 = PyTuple_GET_ITEM(arg, 0); #else arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL; #endif result = (*meth)(self, arg0); #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) Py_DECREF(arg0); #endif return result; } PyErr_Format(PyExc_TypeError, "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", f->m_ml->ml_name, size); return NULL; } break; default: PyErr_SetString(PyExc_SystemError, "Bad call flags in " "__Pyx_CyFunction_Call. METH_OLDARGS is no " "longer supported!"); return NULL; } PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments", f->m_ml->ml_name); return NULL; } static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw); } static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) { PyObject *result; __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { Py_ssize_t argc; PyObject *new_args; PyObject *self; argc = PyTuple_GET_SIZE(args); new_args = PyTuple_GetSlice(args, 1, argc); if (unlikely(!new_args)) return NULL; self = PyTuple_GetItem(args, 0); if (unlikely(!self)) { Py_DECREF(new_args); return NULL; } result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw); Py_DECREF(new_args); } else { result = __Pyx_CyFunction_Call(func, args, kw); } return result; } static PyTypeObject __pyx_CyFunctionType_type = { PyVarObject_HEAD_INIT(0, 0) "cython_function_or_method", sizeof(__pyx_CyFunctionObject), 0, (destructor) __Pyx_CyFunction_dealloc, 0, 0, 0, #if PY_MAJOR_VERSION < 3 0, #else 0, #endif (reprfunc) __Pyx_CyFunction_repr, 0, 0, 0, 0, __Pyx_CyFunction_CallAsMethod, 0, 0, 0, 0, Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC, 0, (traverseproc) __Pyx_CyFunction_traverse, (inquiry) __Pyx_CyFunction_clear, 0, #if PY_VERSION_HEX < 0x030500A0 offsetof(__pyx_CyFunctionObject, func_weakreflist), #else offsetof(PyCFunctionObject, m_weakreflist), #endif 0, 0, __pyx_CyFunction_methods, __pyx_CyFunction_members, __pyx_CyFunction_getsets, 0, 0, __Pyx_CyFunction_descr_get, 0, offsetof(__pyx_CyFunctionObject, func_dict), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, #if PY_VERSION_HEX >= 0x030400a1 0, #endif #if PY_VERSION_HEX >= 0x030800b1 0, #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, #endif }; static int __pyx_CyFunction_init(void) { __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type); if (unlikely(__pyx_CyFunctionType == NULL)) { return -1; } return 0; } static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) { __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; m->defaults = PyObject_Malloc(size); if (unlikely(!m->defaults)) return PyErr_NoMemory(); memset(m->defaults, 0, size); m->defaults_pyobjects = pyobjects; m->defaults_size = size; return m->defaults; } static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; m->defaults_tuple = tuple; Py_INCREF(tuple); } static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; m->defaults_kwdict = dict; Py_INCREF(dict); } static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; m->func_annotations = dict; Py_INCREF(dict); } /* FusedFunction */ static PyObject * __pyx_FusedFunction_New(PyMethodDef *ml, int flags, PyObject *qualname, PyObject *closure, PyObject *module, PyObject *globals, PyObject *code) { PyObject *op = __Pyx_CyFunction_Init( PyObject_GC_New(__pyx_CyFunctionObject, __pyx_FusedFunctionType), ml, flags, qualname, closure, module, globals, code ); if (likely(op)) { __pyx_FusedFunctionObject *fusedfunc = (__pyx_FusedFunctionObject *) op; fusedfunc->__signatures__ = NULL; fusedfunc->type = NULL; fusedfunc->self = NULL; PyObject_GC_Track(op); } return op; } static void __pyx_FusedFunction_dealloc(__pyx_FusedFunctionObject *self) { PyObject_GC_UnTrack(self); Py_CLEAR(self->self); Py_CLEAR(self->type); Py_CLEAR(self->__signatures__); __Pyx__CyFunction_dealloc((__pyx_CyFunctionObject *) self); } static int __pyx_FusedFunction_traverse(__pyx_FusedFunctionObject *self, visitproc visit, void *arg) { Py_VISIT(self->self); Py_VISIT(self->type); Py_VISIT(self->__signatures__); return __Pyx_CyFunction_traverse((__pyx_CyFunctionObject *) self, visit, arg); } static int __pyx_FusedFunction_clear(__pyx_FusedFunctionObject *self) { Py_CLEAR(self->self); Py_CLEAR(self->type); Py_CLEAR(self->__signatures__); return __Pyx_CyFunction_clear((__pyx_CyFunctionObject *) self); } static PyObject * __pyx_FusedFunction_descr_get(PyObject *self, PyObject *obj, PyObject *type) { __pyx_FusedFunctionObject *func, *meth; func = (__pyx_FusedFunctionObject *) self; if (func->self || func->func.flags & __Pyx_CYFUNCTION_STATICMETHOD) { Py_INCREF(self); return self; } if (obj == Py_None) obj = NULL; meth = (__pyx_FusedFunctionObject *) __pyx_FusedFunction_New( ((PyCFunctionObject *) func)->m_ml, ((__pyx_CyFunctionObject *) func)->flags, ((__pyx_CyFunctionObject *) func)->func_qualname, ((__pyx_CyFunctionObject *) func)->func_closure, ((PyCFunctionObject *) func)->m_module, ((__pyx_CyFunctionObject *) func)->func_globals, ((__pyx_CyFunctionObject *) func)->func_code); if (!meth) return NULL; if (func->func.defaults) { PyObject **pydefaults; int i; if (!__Pyx_CyFunction_InitDefaults((PyObject*)meth, func->func.defaults_size, func->func.defaults_pyobjects)) { Py_XDECREF((PyObject*)meth); return NULL; } memcpy(meth->func.defaults, func->func.defaults, func->func.defaults_size); pydefaults = __Pyx_CyFunction_Defaults(PyObject *, meth); for (i = 0; i < meth->func.defaults_pyobjects; i++) Py_XINCREF(pydefaults[i]); } Py_XINCREF(func->func.func_classobj); meth->func.func_classobj = func->func.func_classobj; Py_XINCREF(func->__signatures__); meth->__signatures__ = func->__signatures__; Py_XINCREF(type); meth->type = type; Py_XINCREF(func->func.defaults_tuple); meth->func.defaults_tuple = func->func.defaults_tuple; if (func->func.flags & __Pyx_CYFUNCTION_CLASSMETHOD) obj = type; Py_XINCREF(obj); meth->self = obj; return (PyObject *) meth; } static PyObject * _obj_to_str(PyObject *obj) { if (PyType_Check(obj)) return PyObject_GetAttr(obj, __pyx_n_s_name_2); else return PyObject_Str(obj); } static PyObject * __pyx_FusedFunction_getitem(__pyx_FusedFunctionObject *self, PyObject *idx) { PyObject *signature = NULL; PyObject *unbound_result_func; PyObject *result_func = NULL; if (self->__signatures__ == NULL) { PyErr_SetString(PyExc_TypeError, "Function is not fused"); return NULL; } if (PyTuple_Check(idx)) { PyObject *list = PyList_New(0); Py_ssize_t n = PyTuple_GET_SIZE(idx); PyObject *sep = NULL; int i; if (unlikely(!list)) return NULL; for (i = 0; i < n; i++) { int ret; PyObject *string; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS PyObject *item = PyTuple_GET_ITEM(idx, i); #else PyObject *item = PySequence_ITEM(idx, i); if (unlikely(!item)) goto __pyx_err; #endif string = _obj_to_str(item); #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) Py_DECREF(item); #endif if (unlikely(!string)) goto __pyx_err; ret = PyList_Append(list, string); Py_DECREF(string); if (unlikely(ret < 0)) goto __pyx_err; } sep = PyUnicode_FromString("|"); if (likely(sep)) signature = PyUnicode_Join(sep, list); __pyx_err: ; Py_DECREF(list); Py_XDECREF(sep); } else { signature = _obj_to_str(idx); } if (!signature) return NULL; unbound_result_func = PyObject_GetItem(self->__signatures__, signature); if (unbound_result_func) { if (self->self || self->type) { __pyx_FusedFunctionObject *unbound = (__pyx_FusedFunctionObject *) unbound_result_func; Py_CLEAR(unbound->func.func_classobj); Py_XINCREF(self->func.func_classobj); unbound->func.func_classobj = self->func.func_classobj; result_func = __pyx_FusedFunction_descr_get(unbound_result_func, self->self, self->type); } else { result_func = unbound_result_func; Py_INCREF(result_func); } } Py_DECREF(signature); Py_XDECREF(unbound_result_func); return result_func; } static PyObject * __pyx_FusedFunction_callfunction(PyObject *func, PyObject *args, PyObject *kw) { __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; int static_specialized = (cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD && !((__pyx_FusedFunctionObject *) func)->__signatures__); if (cyfunc->flags & __Pyx_CYFUNCTION_CCLASS && !static_specialized) { return __Pyx_CyFunction_CallAsMethod(func, args, kw); } else { return __Pyx_CyFunction_Call(func, args, kw); } } static PyObject * __pyx_FusedFunction_call(PyObject *func, PyObject *args, PyObject *kw) { __pyx_FusedFunctionObject *binding_func = (__pyx_FusedFunctionObject *) func; Py_ssize_t argc = PyTuple_GET_SIZE(args); PyObject *new_args = NULL; __pyx_FusedFunctionObject *new_func = NULL; PyObject *result = NULL; PyObject *self = NULL; int is_staticmethod = binding_func->func.flags & __Pyx_CYFUNCTION_STATICMETHOD; int is_classmethod = binding_func->func.flags & __Pyx_CYFUNCTION_CLASSMETHOD; if (binding_func->self) { Py_ssize_t i; new_args = PyTuple_New(argc + 1); if (!new_args) return NULL; self = binding_func->self; #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) Py_INCREF(self); #endif Py_INCREF(self); PyTuple_SET_ITEM(new_args, 0, self); for (i = 0; i < argc; i++) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS PyObject *item = PyTuple_GET_ITEM(args, i); Py_INCREF(item); #else PyObject *item = PySequence_ITEM(args, i); if (unlikely(!item)) goto bad; #endif PyTuple_SET_ITEM(new_args, i + 1, item); } args = new_args; } else if (binding_func->type) { if (argc < 1) { PyErr_SetString(PyExc_TypeError, "Need at least one argument, 0 given."); return NULL; } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS self = PyTuple_GET_ITEM(args, 0); #else self = PySequence_ITEM(args, 0); if (unlikely(!self)) return NULL; #endif } if (self && !is_classmethod && !is_staticmethod) { int is_instance = PyObject_IsInstance(self, binding_func->type); if (unlikely(!is_instance)) { PyErr_Format(PyExc_TypeError, "First argument should be of type %.200s, got %.200s.", ((PyTypeObject *) binding_func->type)->tp_name, self->ob_type->tp_name); goto bad; } else if (unlikely(is_instance == -1)) { goto bad; } } #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) Py_XDECREF(self); self = NULL; #endif if (binding_func->__signatures__) { PyObject *tup; if (is_staticmethod && binding_func->func.flags & __Pyx_CYFUNCTION_CCLASS) { tup = PyTuple_Pack(3, args, kw == NULL ? Py_None : kw, binding_func->func.defaults_tuple); if (unlikely(!tup)) goto bad; new_func = (__pyx_FusedFunctionObject *) __Pyx_CyFunction_CallMethod( func, binding_func->__signatures__, tup, NULL); } else { tup = PyTuple_Pack(4, binding_func->__signatures__, args, kw == NULL ? Py_None : kw, binding_func->func.defaults_tuple); if (unlikely(!tup)) goto bad; new_func = (__pyx_FusedFunctionObject *) __pyx_FusedFunction_callfunction(func, tup, NULL); } Py_DECREF(tup); if (unlikely(!new_func)) goto bad; Py_XINCREF(binding_func->func.func_classobj); Py_CLEAR(new_func->func.func_classobj); new_func->func.func_classobj = binding_func->func.func_classobj; func = (PyObject *) new_func; } result = __pyx_FusedFunction_callfunction(func, args, kw); bad: #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) Py_XDECREF(self); #endif Py_XDECREF(new_args); Py_XDECREF((PyObject *) new_func); return result; } static PyMemberDef __pyx_FusedFunction_members[] = { {(char *) "__signatures__", T_OBJECT, offsetof(__pyx_FusedFunctionObject, __signatures__), READONLY, 0}, {0, 0, 0, 0, 0}, }; static PyMappingMethods __pyx_FusedFunction_mapping_methods = { 0, (binaryfunc) __pyx_FusedFunction_getitem, 0, }; static PyTypeObject __pyx_FusedFunctionType_type = { PyVarObject_HEAD_INIT(0, 0) "fused_cython_function", sizeof(__pyx_FusedFunctionObject), 0, (destructor) __pyx_FusedFunction_dealloc, 0, 0, 0, #if PY_MAJOR_VERSION < 3 0, #else 0, #endif 0, 0, 0, &__pyx_FusedFunction_mapping_methods, 0, (ternaryfunc) __pyx_FusedFunction_call, 0, 0, 0, 0, Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE, 0, (traverseproc) __pyx_FusedFunction_traverse, (inquiry) __pyx_FusedFunction_clear, 0, 0, 0, 0, 0, __pyx_FusedFunction_members, __pyx_CyFunction_getsets, &__pyx_CyFunctionType_type, 0, __pyx_FusedFunction_descr_get, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, #if PY_VERSION_HEX >= 0x030400a1 0, #endif #if PY_VERSION_HEX >= 0x030800b1 0, #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, #endif }; static int __pyx_FusedFunction_init(void) { __pyx_FusedFunctionType_type.tp_base = __pyx_CyFunctionType; __pyx_FusedFunctionType = __Pyx_FetchCommonType(&__pyx_FusedFunctionType_type); if (__pyx_FusedFunctionType == NULL) { return -1; } return 0; } /* CLineInTraceback */ #ifndef CYTHON_CLINE_IN_TRACEBACK static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) { PyObject *use_cline; PyObject *ptype, *pvalue, *ptraceback; #if CYTHON_COMPILING_IN_CPYTHON PyObject **cython_runtime_dict; #endif if (unlikely(!__pyx_cython_runtime)) { return c_line; } __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); #if CYTHON_COMPILING_IN_CPYTHON cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); if (likely(cython_runtime_dict)) { __PYX_PY_DICT_LOOKUP_IF_MODIFIED( use_cline, *cython_runtime_dict, __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) } else #endif { PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); if (use_cline_obj) { use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; Py_DECREF(use_cline_obj); } else { PyErr_Clear(); use_cline = NULL; } } if (!use_cline) { c_line = 0; PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); } else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { c_line = 0; } __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); return c_line; } #endif /* CodeObjectCache */ static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = start + (end - start) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } /* AddTraceback */ #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, 0, 0, 0, 0, __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; PyThreadState *tstate = __Pyx_PyThreadState_Current; if (c_line) { c_line = __Pyx_CLineForTraceback(tstate, c_line); } py_code = __pyx_find_code_object(c_line ? -c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); } py_frame = PyFrame_New( tstate, /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ __pyx_d, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; __Pyx_PyFrame_SetLineNumber(py_frame, py_line); PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; if (!obj) return; if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } if ((0)) {} view->obj = NULL; Py_DECREF(obj); } #endif /* MemviewSliceIsContig */ static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) { int i, index, step, start; Py_ssize_t itemsize = mvs.memview->view.itemsize; if (order == 'F') { step = 1; start = 0; } else { step = -1; start = ndim - 1; } for (i = 0; i < ndim; i++) { index = start + step * i; if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) return 0; itemsize *= mvs.shape[index]; } return 1; } /* OverlappingSlices */ static void __pyx_get_array_memory_extents(__Pyx_memviewslice *slice, void **out_start, void **out_end, int ndim, size_t itemsize) { char *start, *end; int i; start = end = slice->data; for (i = 0; i < ndim; i++) { Py_ssize_t stride = slice->strides[i]; Py_ssize_t extent = slice->shape[i]; if (extent == 0) { *out_start = *out_end = start; return; } else { if (stride > 0) end += stride * (extent - 1); else start += stride * (extent - 1); } } *out_start = start; *out_end = end + itemsize; } static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, __Pyx_memviewslice *slice2, int ndim, size_t itemsize) { void *start1, *end1, *start2, *end2; __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); return (start1 < end2) && (start2 < end1); } /* Capsule */ static CYTHON_INLINE PyObject * __pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) { PyObject *cobj; #if PY_VERSION_HEX >= 0x02070000 cobj = PyCapsule_New(p, sig, NULL); #else cobj = PyCObject_FromVoidPtr(p, NULL); #endif return cobj; } /* IsLittleEndian */ static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) { union { uint32_t u32; uint8_t u8[4]; } S; S.u32 = 0x01020304; return S.u8[0] == 4; } /* BufferFormatCheck */ static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type) { stack[0].field = &ctx->root; stack[0].parent_offset = 0; ctx->root.type = type; ctx->root.name = "buffer dtype"; ctx->root.offset = 0; ctx->head = stack; ctx->head->field = &ctx->root; ctx->fmt_offset = 0; ctx->head->parent_offset = 0; ctx->new_packmode = '@'; ctx->enc_packmode = '@'; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->is_complex = 0; ctx->is_valid_array = 0; ctx->struct_alignment = 0; while (type->typegroup == 'S') { ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = 0; type = type->fields->type; } } static int __Pyx_BufFmt_ParseNumber(const char** ts) { int count; const char* t = *ts; if (*t < '0' || *t > '9') { return -1; } else { count = *t++ - '0'; while (*t >= '0' && *t <= '9') { count *= 10; count += *t++ - '0'; } } *ts = t; return count; } static int __Pyx_BufFmt_ExpectNumber(const char **ts) { int number = __Pyx_BufFmt_ParseNumber(ts); if (number == -1) PyErr_Format(PyExc_ValueError,\ "Does not understand character buffer dtype format string ('%c')", **ts); return number; } static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { PyErr_Format(PyExc_ValueError, "Unexpected format string character: '%c'", ch); } static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { switch (ch) { case '?': return "'bool'"; case 'c': return "'char'"; case 'b': return "'signed char'"; case 'B': return "'unsigned char'"; case 'h': return "'short'"; case 'H': return "'unsigned short'"; case 'i': return "'int'"; case 'I': return "'unsigned int'"; case 'l': return "'long'"; case 'L': return "'unsigned long'"; case 'q': return "'long long'"; case 'Q': return "'unsigned long long'"; case 'f': return (is_complex ? "'complex float'" : "'float'"); case 'd': return (is_complex ? "'complex double'" : "'double'"); case 'g': return (is_complex ? "'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; case 's': case 'p': return "a string"; case 0: return "end"; default: return "unparseable format string"; } } static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return 2; case 'i': case 'I': case 'l': case 'L': return 4; case 'q': case 'Q': return 8; case 'f': return (is_complex ? 8 : 4); case 'd': return (is_complex ? 16 : 8); case 'g': { PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); return 0; } case 'O': case 'P': return sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(short); case 'i': case 'I': return sizeof(int); case 'l': case 'L': return sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(float) * (is_complex ? 2 : 1); case 'd': return sizeof(double) * (is_complex ? 2 : 1); case 'g': return sizeof(long double) * (is_complex ? 2 : 1); case 'O': case 'P': return sizeof(void*); default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } typedef struct { char c; short x; } __Pyx_st_short; typedef struct { char c; int x; } __Pyx_st_int; typedef struct { char c; long x; } __Pyx_st_long; typedef struct { char c; float x; } __Pyx_st_float; typedef struct { char c; double x; } __Pyx_st_double; typedef struct { char c; long double x; } __Pyx_st_longdouble; typedef struct { char c; void *x; } __Pyx_st_void_p; #ifdef HAVE_LONG_LONG typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_st_float) - sizeof(float); case 'd': return sizeof(__Pyx_st_double) - sizeof(double); case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } /* These are for computing the padding at the end of the struct to align on the first member of the struct. This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': return 'H'; case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } if ((type->typegroup == 'H' || group == 'H') && type->size == size) { } else { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number, ndim; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ndim = ctx->head->field->type->ndim; while (*ts && *ts != ')') { switch (*ts) { case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; default: break; } number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case '\r': case '\n': ++ts; break; case '<': if (!__Pyx_Is_Little_Endian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_Is_Little_Endian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } CYTHON_FALLTHROUGH; case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 'p': if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { ctx->enc_count += ctx->new_count; ctx->new_count = 1; got_Z = 0; ++ts; break; } CYTHON_FALLTHROUGH; case 's': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } /* TypeInfoCompare */ static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) { int i; if (!a || !b) return 0; if (a == b) return 1; if (a->size != b->size || a->typegroup != b->typegroup || a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { if (a->typegroup == 'H' || b->typegroup == 'H') { return a->size == b->size; } else { return 0; } } if (a->ndim) { for (i = 0; i < a->ndim; i++) if (a->arraysize[i] != b->arraysize[i]) return 0; } if (a->typegroup == 'S') { if (a->flags != b->flags) return 0; if (a->fields || b->fields) { if (!(a->fields && b->fields)) return 0; for (i = 0; a->fields[i].type && b->fields[i].type; i++) { __Pyx_StructField *field_a = a->fields + i; __Pyx_StructField *field_b = b->fields + i; if (field_a->offset != field_b->offset || !__pyx_typeinfo_cmp(field_a->type, field_b->type)) return 0; } return !a->fields[i].type && !b->fields[i].type; } } return 1; } /* MemviewSliceValidateAndInit */ static int __pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) { if (buf->shape[dim] <= 1) return 1; if (buf->strides) { if (spec & __Pyx_MEMVIEW_CONTIG) { if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { if (unlikely(buf->strides[dim] != sizeof(void *))) { PyErr_Format(PyExc_ValueError, "Buffer is not indirectly contiguous " "in dimension %d.", dim); goto fail; } } else if (unlikely(buf->strides[dim] != buf->itemsize)) { PyErr_SetString(PyExc_ValueError, "Buffer and memoryview are not contiguous " "in the same dimension."); goto fail; } } if (spec & __Pyx_MEMVIEW_FOLLOW) { Py_ssize_t stride = buf->strides[dim]; if (stride < 0) stride = -stride; if (unlikely(stride < buf->itemsize)) { PyErr_SetString(PyExc_ValueError, "Buffer and memoryview are not contiguous " "in the same dimension."); goto fail; } } } else { if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { PyErr_Format(PyExc_ValueError, "C-contiguous buffer is not contiguous in " "dimension %d", dim); goto fail; } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { PyErr_Format(PyExc_ValueError, "C-contiguous buffer is not indirect in " "dimension %d", dim); goto fail; } else if (unlikely(buf->suboffsets)) { PyErr_SetString(PyExc_ValueError, "Buffer exposes suboffsets but no strides"); goto fail; } } return 1; fail: return 0; } static int __pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) { if (spec & __Pyx_MEMVIEW_DIRECT) { if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { PyErr_Format(PyExc_ValueError, "Buffer not compatible with direct access " "in dimension %d.", dim); goto fail; } } if (spec & __Pyx_MEMVIEW_PTR) { if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { PyErr_Format(PyExc_ValueError, "Buffer is not indirectly accessible " "in dimension %d.", dim); goto fail; } } return 1; fail: return 0; } static int __pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) { int i; if (c_or_f_flag & __Pyx_IS_F_CONTIG) { Py_ssize_t stride = 1; for (i = 0; i < ndim; i++) { if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { PyErr_SetString(PyExc_ValueError, "Buffer not fortran contiguous."); goto fail; } stride = stride * buf->shape[i]; } } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { Py_ssize_t stride = 1; for (i = ndim - 1; i >- 1; i--) { if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { PyErr_SetString(PyExc_ValueError, "Buffer not C contiguous."); goto fail; } stride = stride * buf->shape[i]; } } return 1; fail: return 0; } static int __Pyx_ValidateAndInit_memviewslice( int *axes_specs, int c_or_f_flag, int buf_flags, int ndim, __Pyx_TypeInfo *dtype, __Pyx_BufFmt_StackElem stack[], __Pyx_memviewslice *memviewslice, PyObject *original_obj) { struct __pyx_memoryview_obj *memview, *new_memview; __Pyx_RefNannyDeclarations Py_buffer *buf; int i, spec = 0, retval = -1; __Pyx_BufFmt_Context ctx; int from_memoryview = __pyx_memoryview_check(original_obj); __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) original_obj)->typeinfo)) { memview = (struct __pyx_memoryview_obj *) original_obj; new_memview = NULL; } else { memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( original_obj, buf_flags, 0, dtype); new_memview = memview; if (unlikely(!memview)) goto fail; } buf = &memview->view; if (unlikely(buf->ndim != ndim)) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", ndim, buf->ndim); goto fail; } if (new_memview) { __Pyx_BufFmt_Init(&ctx, stack, dtype); if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; } if (unlikely((unsigned) buf->itemsize != dtype->size)) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->len > 0) { for (i = 0; i < ndim; i++) { spec = axes_specs[i]; if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) goto fail; if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) goto fail; } if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) goto fail; } if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, new_memview != NULL) == -1)) { goto fail; } retval = 0; goto no_fail; fail: Py_XDECREF(new_memview); retval = -1; no_fail: __Pyx_RefNannyFinishContext(); return retval; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_int32_t(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, PyBUF_RECORDS_RO | writable_flag, 1, &__Pyx_TypeInfo_nn___pyx_t_5numpy_int32_t, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_nn___pyx_t_5numpy_int64_t(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, PyBUF_RECORDS_RO | writable_flag, 1, &__Pyx_TypeInfo_nn___pyx_t_5numpy_int64_t, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, PyBUF_RECORDS_RO | writable_flag, 2, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, PyBUF_RECORDS_RO | writable_flag, 1, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 2, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* MemviewDtypeToObject */ static CYTHON_INLINE PyObject *__pyx_memview_get_double(const char *itemp) { return (PyObject *) PyFloat_FromDouble(*(double *) itemp); } static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *obj) { double value = __pyx_PyFloat_AsDouble(obj); if ((value == (double)-1) && PyErr_Occurred()) return 0; *(double *) itemp = value; return 1; } /* CIntFromPyVerify */ #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) #define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) #define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ {\ func_type value = func_value;\ if (sizeof(target_type) < sizeof(func_type)) {\ if (unlikely(value != (func_type) (target_type) value)) {\ func_type zero = 0;\ if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ return (target_type) -1;\ if (is_unsigned && unlikely(value < zero))\ goto raise_neg_overflow;\ else\ goto raise_overflow;\ }\ }\ return (target_type) value;\ } /* None */ static CYTHON_INLINE long __Pyx_pow_long(long b, long e) { long t = b; switch (e) { case 3: t *= b; CYTHON_FALLTHROUGH; case 2: t *= b; CYTHON_FALLTHROUGH; case 1: return t; case 0: return 1; } #if 1 if (unlikely(e<0)) return 0; #endif t = 1; while (likely(e)) { t *= (b * (e&1)) | ((~e)&1); b *= b; e >>= 1; } return t; } /* None */ static CYTHON_INLINE Py_ssize_t __Pyx_pow_Py_ssize_t(Py_ssize_t b, Py_ssize_t e) { Py_ssize_t t = b; switch (e) { case 3: t *= b; CYTHON_FALLTHROUGH; case 2: t *= b; CYTHON_FALLTHROUGH; case 1: return t; case 0: return 1; } #if 1 if (unlikely(e<0)) return 0; #endif t = 1; while (likely(e)) { t *= (b * (e&1)) | ((~e)&1); b *= b; e >>= 1; } return t; } /* Declarations */ #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif /* Arithmetic */ #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } #if 1 static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { if (b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); } else if (fabsf(b.real) >= fabsf(b.imag)) { if (b.real == 0 && b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); } else { float r = b.imag / b.real; float s = (float)(1.0) / (b.real + b.imag * r); return __pyx_t_float_complex_from_parts( (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); } } else { float r = b.real / b.imag; float s = (float)(1.0) / (b.imag + b.real * r); return __pyx_t_float_complex_from_parts( (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); } } #else static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { if (b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); } else { float denom = b.real * b.real + b.imag * b.imag; return __pyx_t_float_complex_from_parts( (a.real * b.real + a.imag * b.imag) / denom, (a.imag * b.real - a.real * b.imag) / denom); } } #endif static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: return __Pyx_c_prod_float(a, a); case 3: z = __Pyx_c_prod_float(a, a); return __Pyx_c_prod_float(z, a); case 4: z = __Pyx_c_prod_float(a, a); return __Pyx_c_prod_float(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } else if (b.imag == 0) { z.real = powf(a.real, b.real); z.imag = 0; return z; } else if (a.real > 0) { r = a.real; theta = 0; } else { r = -a.real; theta = atan2f(0.0, -1.0); } } else { r = __Pyx_c_abs_float(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif /* Declarations */ #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif /* Arithmetic */ #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } #if 1 static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { if (b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); } else if (fabs(b.real) >= fabs(b.imag)) { if (b.real == 0 && b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); } else { double r = b.imag / b.real; double s = (double)(1.0) / (b.real + b.imag * r); return __pyx_t_double_complex_from_parts( (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); } } else { double r = b.real / b.imag; double s = (double)(1.0) / (b.imag + b.real * r); return __pyx_t_double_complex_from_parts( (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); } } #else static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { if (b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); } else { double denom = b.real * b.real + b.imag * b.imag; return __pyx_t_double_complex_from_parts( (a.real * b.real + a.imag * b.imag) / denom, (a.imag * b.real - a.real * b.imag) / denom); } } #endif static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: return __Pyx_c_prod_double(a, a); case 3: z = __Pyx_c_prod_double(a, a); return __Pyx_c_prod_double(z, a); case 4: z = __Pyx_c_prod_double(a, a); return __Pyx_c_prod_double(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } else if (b.imag == 0) { z.real = pow(a.real, b.real); z.imag = 0; return z; } else if (a.real > 0) { r = a.real; theta = 0; } else { r = -a.real; theta = atan2(0.0, -1.0); } } else { r = __Pyx_c_abs_double(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif /* MemviewSliceCopyTemplate */ static __Pyx_memviewslice __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, const char *mode, int ndim, size_t sizeof_dtype, int contig_flag, int dtype_is_object) { __Pyx_RefNannyDeclarations int i; __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; struct __pyx_memoryview_obj *from_memview = from_mvs->memview; Py_buffer *buf = &from_memview->view; PyObject *shape_tuple = NULL; PyObject *temp_int = NULL; struct __pyx_array_obj *array_obj = NULL; struct __pyx_memoryview_obj *memview_obj = NULL; __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); for (i = 0; i < ndim; i++) { if (unlikely(from_mvs->suboffsets[i] >= 0)) { PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " "indirect dimensions (axis %d)", i); goto fail; } } shape_tuple = PyTuple_New(ndim); if (unlikely(!shape_tuple)) { goto fail; } __Pyx_GOTREF(shape_tuple); for(i = 0; i < ndim; i++) { temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); if(unlikely(!temp_int)) { goto fail; } else { PyTuple_SET_ITEM(shape_tuple, i, temp_int); temp_int = NULL; } } array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); if (unlikely(!array_obj)) { goto fail; } __Pyx_GOTREF(array_obj); memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( (PyObject *) array_obj, contig_flag, dtype_is_object, from_mvs->memview->typeinfo); if (unlikely(!memview_obj)) goto fail; if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) goto fail; if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, dtype_is_object) < 0)) goto fail; goto no_fail; fail: __Pyx_XDECREF(new_mvs.memview); new_mvs.memview = NULL; new_mvs.data = NULL; no_fail: __Pyx_XDECREF(shape_tuple); __Pyx_XDECREF(temp_int); __Pyx_XDECREF(array_obj); __Pyx_RefNannyFinishContext(); return new_mvs; } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const long neg_one = (long) -1, const_zero = (long) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(long) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(long), little, !is_unsigned); } } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int32(npy_int32 value) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const npy_int32 neg_one = (npy_int32) -1, const_zero = (npy_int32) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(npy_int32) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(npy_int32) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(npy_int32) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(npy_int32) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(npy_int32) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(npy_int32), little, !is_unsigned); } } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int64(npy_int64 value) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const npy_int64 neg_one = (npy_int64) -1, const_zero = (npy_int64) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(npy_int64) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(npy_int64) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(npy_int64) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(npy_int64) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(npy_int64) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(npy_int64), little, !is_unsigned); } } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const int neg_one = (int) -1, const_zero = (int) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(int) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(int), little, !is_unsigned); } } /* CIntFromPy */ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const int neg_one = (int) -1, const_zero = (int) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(int) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (int) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (int) 0; case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) case 2: if (8 * sizeof(int) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; case 3: if (8 * sizeof(int) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; case 4: if (8 * sizeof(int) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (int) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(int) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (int) 0; case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) case -2: if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 2: if (8 * sizeof(int) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case -3: if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 3: if (8 * sizeof(int) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case -4: if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 4: if (8 * sizeof(int) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; } #endif if (sizeof(int) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else int val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (int) -1; } } else { int val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (int) -1; val = __Pyx_PyInt_As_int(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to int"); return (int) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } /* BytesContains */ static CYTHON_INLINE int __Pyx_BytesContains(PyObject* bytes, char character) { const Py_ssize_t length = PyBytes_GET_SIZE(bytes); char* char_start = PyBytes_AS_STRING(bytes); return memchr(char_start, (unsigned char)character, (size_t)length) != NULL; } /* ImportNumPyArray */ static PyObject* __Pyx__ImportNumPyArray(void) { PyObject *numpy_module, *ndarray_object = NULL; numpy_module = __Pyx_Import(__pyx_n_s_numpy, NULL, 0); if (likely(numpy_module)) { ndarray_object = PyObject_GetAttrString(numpy_module, "ndarray"); Py_DECREF(numpy_module); } if (unlikely(!ndarray_object)) { PyErr_Clear(); } if (unlikely(!ndarray_object || !PyObject_TypeCheck(ndarray_object, &PyType_Type))) { Py_XDECREF(ndarray_object); Py_INCREF(Py_None); ndarray_object = Py_None; } return ndarray_object; } static CYTHON_INLINE PyObject* __Pyx_ImportNumPyArrayTypeIfAvailable(void) { if (unlikely(!__pyx_numpy_ndarray)) { __pyx_numpy_ndarray = __Pyx__ImportNumPyArray(); } Py_INCREF(__pyx_numpy_ndarray); return __pyx_numpy_ndarray; } /* CIntFromPy */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const long neg_one = (long) -1, const_zero = (long) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(long) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (long) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) case 2: if (8 * sizeof(long) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; case 3: if (8 * sizeof(long) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; case 4: if (8 * sizeof(long) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (long) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) case -2: if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 2: if (8 * sizeof(long) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case -3: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 3: if (8 * sizeof(long) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case -4: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 4: if (8 * sizeof(long) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; } #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (long) -1; } } else { long val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to long"); return (long) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } /* CIntFromPy */ static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const char neg_one = (char) -1, const_zero = (char) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(char) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (char) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (char) 0; case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0]) case 2: if (8 * sizeof(char) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) { return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; case 3: if (8 * sizeof(char) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) { return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; case 4: if (8 * sizeof(char) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) { return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (char) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(char) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (char) 0; case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0]) case -2: if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 2: if (8 * sizeof(char) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case -3: if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 3: if (8 * sizeof(char) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case -4: if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 4: if (8 * sizeof(char) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; } #endif if (sizeof(char) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else char val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (char) -1; } } else { char val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (char) -1; val = __Pyx_PyInt_As_char(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to char"); return (char) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to char"); return (char) -1; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* CheckBinaryVersion */ static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); return PyErr_WarnEx(NULL, message, 1); } return 0; } /* FunctionExport */ static int __Pyx_ExportFunction(const char *name, void (*f)(void), const char *sig) { PyObject *d = 0; PyObject *cobj = 0; union { void (*fp)(void); void *p; } tmp; d = PyObject_GetAttrString(__pyx_m, (char *)"__pyx_capi__"); if (!d) { PyErr_Clear(); d = PyDict_New(); if (!d) goto bad; Py_INCREF(d); if (PyModule_AddObject(__pyx_m, (char *)"__pyx_capi__", d) < 0) goto bad; } tmp.fp = f; #if PY_VERSION_HEX >= 0x02070000 cobj = PyCapsule_New(tmp.p, sig, 0); #else cobj = PyCObject_FromVoidPtrAndDesc(tmp.p, (void *)sig, 0); #endif if (!cobj) goto bad; if (PyDict_SetItemString(d, name, cobj) < 0) goto bad; Py_DECREF(cobj); Py_DECREF(d); return 0; bad: Py_XDECREF(cobj); Py_XDECREF(d); return -1; } /* VoidPtrImport */ #ifndef __PYX_HAVE_RT_ImportVoidPtr #define __PYX_HAVE_RT_ImportVoidPtr static int __Pyx_ImportVoidPtr(PyObject *module, const char *name, void **p, const char *sig) { PyObject *d = 0; PyObject *cobj = 0; d = PyObject_GetAttrString(module, (char *)"__pyx_capi__"); if (!d) goto bad; cobj = PyDict_GetItemString(d, name); if (!cobj) { PyErr_Format(PyExc_ImportError, "%.200s does not export expected C variable %.200s", PyModule_GetName(module), name); goto bad; } #if PY_VERSION_HEX >= 0x02070000 if (!PyCapsule_IsValid(cobj, sig)) { PyErr_Format(PyExc_TypeError, "C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)", PyModule_GetName(module), name, sig, PyCapsule_GetName(cobj)); goto bad; } *p = PyCapsule_GetPointer(cobj, sig); #else {const char *desc, *s1, *s2; desc = (const char *)PyCObject_GetDesc(cobj); if (!desc) goto bad; s1 = desc; s2 = sig; while (*s1 != '\0' && *s1 == *s2) { s1++; s2++; } if (*s1 != *s2) { PyErr_Format(PyExc_TypeError, "C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)", PyModule_GetName(module), name, sig, desc); goto bad; } *p = PyCObject_AsVoidPtr(cobj);} #endif if (!(*p)) goto bad; Py_DECREF(d); return 0; bad: Py_XDECREF(d); return -1; } #endif /* FunctionImport */ #ifndef __PYX_HAVE_RT_ImportFunction #define __PYX_HAVE_RT_ImportFunction static int __Pyx_ImportFunction(PyObject *module, const char *funcname, void (**f)(void), const char *sig) { PyObject *d = 0; PyObject *cobj = 0; union { void (*fp)(void); void *p; } tmp; d = PyObject_GetAttrString(module, (char *)"__pyx_capi__"); if (!d) goto bad; cobj = PyDict_GetItemString(d, funcname); if (!cobj) { PyErr_Format(PyExc_ImportError, "%.200s does not export expected C function %.200s", PyModule_GetName(module), funcname); goto bad; } #if PY_VERSION_HEX >= 0x02070000 if (!PyCapsule_IsValid(cobj, sig)) { PyErr_Format(PyExc_TypeError, "C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)", PyModule_GetName(module), funcname, sig, PyCapsule_GetName(cobj)); goto bad; } tmp.p = PyCapsule_GetPointer(cobj, sig); #else {const char *desc, *s1, *s2; desc = (const char *)PyCObject_GetDesc(cobj); if (!desc) goto bad; s1 = desc; s2 = sig; while (*s1 != '\0' && *s1 == *s2) { s1++; s2++; } if (*s1 != *s2) { PyErr_Format(PyExc_TypeError, "C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)", PyModule_GetName(module), funcname, sig, desc); goto bad; } tmp.p = PyCObject_AsVoidPtr(cobj);} #endif *f = tmp.fp; if (!(*f)) goto bad; Py_DECREF(d); return 0; bad: Py_XDECREF(d); return -1; } #endif /* InitStrings */ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; if (PyObject_Hash(*t->p) == -1) return -1; ++t; } return 0; } static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); } static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { Py_ssize_t ignore; return __Pyx_PyObject_AsStringAndSize(o, &ignore); } #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT #if !CYTHON_PEP393_ENABLED static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { char* defenc_c; PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); if (!defenc) return NULL; defenc_c = PyBytes_AS_STRING(defenc); #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII { char* end = defenc_c + PyBytes_GET_SIZE(defenc); char* c; for (c = defenc_c; c < end; c++) { if ((unsigned char) (*c) >= 128) { PyUnicode_AsASCIIString(o); return NULL; } } } #endif *length = PyBytes_GET_SIZE(defenc); return defenc_c; } #else static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII if (likely(PyUnicode_IS_ASCII(o))) { *length = PyUnicode_GET_LENGTH(o); return PyUnicode_AsUTF8(o); } else { PyUnicode_AsASCIIString(o); return NULL; } #else return PyUnicode_AsUTF8AndSize(o, length); #endif } #endif #endif static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT if ( #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII __Pyx_sys_getdefaultencoding_not_ascii && #endif PyUnicode_Check(o)) { return __Pyx_PyUnicode_AsStringAndSize(o, length); } else #endif #if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) if (PyByteArray_Check(o)) { *length = PyByteArray_GET_SIZE(o); return PyByteArray_AS_STRING(o); } else #endif { char* result; int r = PyBytes_AsStringAndSize(o, &result, length); if (unlikely(r < 0)) { return NULL; } else { return result; } } } static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { int retval; if (unlikely(!x)) return -1; retval = __Pyx_PyObject_IsTrue(x); Py_DECREF(x); return retval; } static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { #if PY_MAJOR_VERSION >= 3 if (PyLong_Check(result)) { if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, "__int__ returned non-int (type %.200s). " "The ability to return an instance of a strict subclass of int " "is deprecated, and may be removed in a future version of Python.", Py_TYPE(result)->tp_name)) { Py_DECREF(result); return NULL; } return result; } #endif PyErr_Format(PyExc_TypeError, "__%.4s__ returned non-%.4s (type %.200s)", type_name, type_name, Py_TYPE(result)->tp_name); Py_DECREF(result); return NULL; } static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { #if CYTHON_USE_TYPE_SLOTS PyNumberMethods *m; #endif const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x) || PyLong_Check(x))) #else if (likely(PyLong_Check(x))) #endif return __Pyx_NewRef(x); #if CYTHON_USE_TYPE_SLOTS m = Py_TYPE(x)->tp_as_number; #if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = m->nb_int(x); } else if (m && m->nb_long) { name = "long"; res = m->nb_long(x); } #else if (likely(m && m->nb_int)) { name = "int"; res = m->nb_int(x); } #endif #else if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { res = PyNumber_Int(x); } #endif if (likely(res)) { #if PY_MAJOR_VERSION < 3 if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { #else if (unlikely(!PyLong_CheckExact(res))) { #endif return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject *x; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(b))) { if (sizeof(Py_ssize_t) >= sizeof(long)) return PyInt_AS_LONG(b); else return PyInt_AsSsize_t(b); } #endif if (likely(PyLong_CheckExact(b))) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)b)->ob_digit; const Py_ssize_t size = Py_SIZE(b); if (likely(__Pyx_sst_abs(size) <= 1)) { ival = likely(size) ? digits[0] : 0; if (size == -1) ival = -ival; return ival; } else { switch (size) { case 2: if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -2: if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case 3: if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -3: if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case 4: if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -4: if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; } } #endif return PyLong_AsSsize_t(b); } x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { return PyInt_FromSize_t(ival); } #endif /* Py_PYTHON_H */ openTSNE-0.6.1/openTSNE/_tsne.pxd000066400000000000000000000047711413546205200164440ustar00rootroot00000000000000# cython: profile=True # cython: boundscheck=False # cython: wraparound=False # cython: cdivision=True # cython: initializedcheck=False # cython: warn.undeclared=True # cython: language_level=3 cimport numpy as np from .quad_tree cimport QuadTree ctypedef fused sparse_index_type: np.int32_t np.int64_t cpdef double[:, ::1] compute_gaussian_perplexity( double[:, :] distances, double[:] desired_perplexities, double perplexity_tol=*, Py_ssize_t max_iter=*, Py_ssize_t num_threads=*, ) cpdef tuple estimate_positive_gradient_nn( sparse_index_type[:] indices, sparse_index_type[:] indptr, double[:] P_data, double[:, ::1] embedding, double[:, ::1] reference_embedding, double[:, ::1] gradient, double dof=*, Py_ssize_t num_threads=*, bint should_eval_error=*, ) cpdef double estimate_negative_gradient_bh( QuadTree tree, double[:, ::1] embedding, double[:, ::1] gradient, double theta=*, double dof=*, Py_ssize_t num_threads=*, bint pairwise_normalization=*, ) cpdef double estimate_negative_gradient_fft_1d( double[::1] embedding, double[::1] gradient, Py_ssize_t n_interpolation_points=*, Py_ssize_t min_num_intervals=*, double ints_in_interval=*, double dof=*, ) cpdef tuple prepare_negative_gradient_fft_interpolation_grid_1d( double[::1] reference_embedding, Py_ssize_t n_interpolation_points=*, Py_ssize_t min_num_intervals=*, double ints_in_interval=*, double dof=*, double padding=*, ) cpdef double estimate_negative_gradient_fft_1d_with_grid( double[::1] embedding, double[::1] gradient, double[:, ::1] y_tilde_values, double[::1] box_lower_bounds, Py_ssize_t n_interpolation_points, double dof, ) cpdef double estimate_negative_gradient_fft_2d( double[:, ::1] embedding, double[:, ::1] gradient, Py_ssize_t n_interpolation_points=*, Py_ssize_t min_num_intervals=*, double ints_in_interval=*, double dof=*, ) cpdef tuple prepare_negative_gradient_fft_interpolation_grid_2d( double[:, ::1] reference_embedding, Py_ssize_t n_interpolation_points=*, Py_ssize_t min_num_intervals=*, double ints_in_interval=*, double dof=*, double padding=*, ) cpdef double estimate_negative_gradient_fft_2d_with_grid( double[:, ::1] embedding, double[:, ::1] gradient, double[:, ::1] y_tilde_values, double[::1] box_x_lower_bounds, double[::1] box_y_lower_bounds, Py_ssize_t n_interpolation_points, double dof, ) openTSNE-0.6.1/openTSNE/_tsne.pyx000066400000000000000000001364321413546205200164710ustar00rootroot00000000000000# cython: boundscheck=False # cython: wraparound=False # cython: cdivision=True # cython: initializedcheck=False # cython: warn.undeclared=True # cython: language_level=3 cimport numpy as np import numpy as np from cython.parallel import prange, parallel from cpython.mem cimport PyMem_Malloc, PyMem_Free from libc.stdlib cimport malloc, free from .quad_tree cimport QuadTree, Node, is_duplicate from ._matrix_mul.matrix_mul cimport matrix_multiply_fft_1d, matrix_multiply_fft_2d cdef double EPSILON = np.finfo(np.float64).eps cdef extern from "math.h": double sqrt(double x) nogil double log(double x) nogil double exp(double x) nogil double fabs(double x) nogil double fmax(double x, double y) nogil double isinf(long double) nogil double INFINITY cpdef double[:, ::1] compute_gaussian_perplexity( double[:, :] distances, double[:] desired_perplexities, double perplexity_tol=1e-8, Py_ssize_t max_iter=200, Py_ssize_t num_threads=1, ): cdef: Py_ssize_t n_samples = distances.shape[0] Py_ssize_t n_scales = desired_perplexities.shape[0] Py_ssize_t k_neighbors = distances.shape[1] double[:, ::1] P = np.zeros_like(distances, dtype=float, order="C") double[:, :, ::1] multiscale_P = np.zeros((n_samples, n_scales, k_neighbors)) double[:, ::1] tau = np.ones((n_samples, n_scales)) Py_ssize_t i, j, h, iteration double[:] desired_entropies = np.log(desired_perplexities) double min_tau, max_tau, sum_Pi, sum_PiDj, entropy, entropy_diff, sqrt_tau if num_threads < 1: num_threads = 1 for i in prange(n_samples, nogil=True, schedule="guided", num_threads=num_threads): min_tau, max_tau = -INFINITY, INFINITY # For every scale find a precision tau that fits the perplexity for h in range(n_scales): for iteration in range(max_iter): sum_Pi, sum_PiDj = 0, 0 sqrt_tau = sqrt(tau[i, h]) for j in range(k_neighbors): multiscale_P[i, h, j] = sqrt_tau * exp(-distances[i, j] ** 2 * tau[i, h] * 0.5) sum_Pi = sum_Pi + multiscale_P[i, h, j] sum_Pi = sum_Pi + EPSILON for j in range(k_neighbors): sum_PiDj = sum_PiDj + multiscale_P[i, h, j] / sum_Pi * distances[i, j] ** 2 entropy = tau[i, h] * 0.5 * sum_PiDj + log(sum_Pi) - log(tau[i, h]) * 0.5 entropy_diff = entropy - desired_entropies[h] if fabs(entropy_diff) <= perplexity_tol: break if entropy_diff > 0: min_tau = tau[i, h] if isinf(max_tau): tau[i, h] *= 2 else: tau[i, h] = (tau[i, h] + max_tau) * 0.5 else: max_tau = tau[i, h] if isinf(min_tau): tau[i, h] /= 2 else: tau[i, h] = (tau[i, h] + min_tau) * 0.5 # Get the probability of the mixture of Gaussians with different precisions sum_Pi = 0 for j in range(k_neighbors): for h in range(n_scales): P[i, j] = P[i, j] + multiscale_P[i, h, j] sum_Pi = sum_Pi + multiscale_P[i, h, j] # Perform row-normalization for j in range(k_neighbors): P[i, j] /= sum_Pi return P cpdef tuple estimate_positive_gradient_nn( sparse_index_type[:] indices, sparse_index_type[:] indptr, double[:] P_data, double[:, ::1] embedding, double[:, ::1] reference_embedding, double[:, ::1] gradient, double dof=1, Py_ssize_t num_threads=1, bint should_eval_error=False, ): cdef: Py_ssize_t n_samples = gradient.shape[0] Py_ssize_t n_dims = gradient.shape[1] double * diff double d_ij, p_ij, q_ij, kl_divergence = 0, sum_P = 0 Py_ssize_t i, j, k, d if num_threads < 1: num_threads = 1 # Degrees of freedom cannot be negative if dof <= 0: dof = 1e-8 with nogil, parallel(num_threads=num_threads): # Use `malloc` here instead of `PyMem_Malloc` because we're in a # `nogil` clause and we won't be allocating much memory diff = malloc(n_dims * sizeof(double)) if not diff: with gil: raise MemoryError() for i in prange(n_samples, schedule="guided"): # Iterate over all the neighbors `j` and sum up their contribution for k in range(indptr[i], indptr[i + 1]): j = indices[k] p_ij = P_data[k] # Compute the direction of the points attraction and the # squared euclidean distance between the points d_ij = 0 for d in range(n_dims): diff[d] = embedding[i, d] - reference_embedding[j, d] d_ij = d_ij + diff[d] * diff[d] if dof != 1: # No need exp by dof here because the terms cancel out q_ij = 1 / (1 + d_ij / dof) else: q_ij = 1 / (1 + d_ij) # Compute F_{attr} of point `j` on point `i` for d in range(n_dims): gradient[i, d] = gradient[i, d] + q_ij * p_ij * diff[d] # Evaluating the following expressions can slow things down # considerably if evaluated every iteration. Note that the q_ij # is unnormalized, so we need to normalize once the sum of q_ij # is known if should_eval_error: sum_P += p_ij kl_divergence += p_ij * log((p_ij / (q_ij + EPSILON)) + EPSILON) free(diff) return sum_P, kl_divergence cpdef double estimate_negative_gradient_bh( QuadTree tree, double[:, ::1] embedding, double[:, ::1] gradient, double theta=0.5, double dof=1, Py_ssize_t num_threads=1, bint pairwise_normalization=True, ): """Estimate the negative tSNE gradient using the Barnes Hut approximation. Notes ----- Changes the gradient inplace to avoid needless memory allocation. As such, this must be run before estimating the positive gradients, since the negative gradient must be normalized at the end with the sum of q_{ij}s. """ cdef: Py_ssize_t i, j, num_points = embedding.shape[0] double sum_Q = 0 double[::1] sum_Qi = np.zeros(num_points, dtype=float) if num_threads < 1: num_threads = 1 # In order to run gradient estimation in parallel, we need to pass each # worker it's own memory slot to write sum_Qs for i in prange(num_points, nogil=True, num_threads=num_threads, schedule="guided"): _estimate_negative_gradient_single( &tree.root, &embedding[i, 0], &gradient[i, 0], &sum_Qi[i], theta, dof ) for i in range(num_points): sum_Q += sum_Qi[i] # Normalize q_{ij}s for i in range(gradient.shape[0]): for j in range(gradient.shape[1]): if pairwise_normalization: gradient[i, j] /= sum_Q + EPSILON else: gradient[i, j] /= sum_Qi[i] + EPSILON return sum_Q cdef void _estimate_negative_gradient_single( Node * node, double * point, double * gradient, double * sum_Q, double theta, double dof, ) nogil: # Make sure that we spend no time on empty nodes or self-interactions if node.num_points == 0 or node.is_leaf and is_duplicate(node, point): return cdef: double distance = EPSILON double q_ij, tmp Py_ssize_t d # Compute the squared euclidean disstance in the embedding space from the # new point to the center of mass for d in range(node.n_dims): tmp = node.center_of_mass[d] - point[d] distance += (tmp * tmp) # Degrees of freedom cannot be negative if dof <= 0: dof = 1e-8 # Check whether we can use this node as a summary if node.is_leaf or node.length / sqrt(distance) < theta: if dof != 1: q_ij = 1 / (1 + distance / dof) ** dof else: q_ij = 1 / (1 + distance) sum_Q[0] += node.num_points * q_ij # These two expressions are the same, but multiplication with itself is # faster (dof=1: (1 + 1) / 1 = 2 if dof != 1: q_ij = q_ij ** ((dof + 1) / dof) else: q_ij = q_ij * q_ij for d in range(node.n_dims): gradient[d] -= node.num_points * q_ij * (point[d] - node.center_of_mass[d]) return # Otherwise we have to look for summaries in the children for d in range(1 << node.n_dims): _estimate_negative_gradient_single(&node.children[d], point, gradient, sum_Q, theta, dof) cdef inline double cauchy_1d(double x, double y, double dof) nogil: if dof != 1: return (1 + ((x - y) ** 2) / dof) ** -dof else: return (1 + (x - y) ** 2) ** -1 cdef inline double cauchy_1d_exp1p(double x, double y, double dof) nogil: if dof != 1: return (1 + ((x - y) ** 2) / dof) ** -(dof + 1) else: return (1 + (x - y) ** 2) ** -2 cdef inline double cauchy_2d(double x1, double x2, double y1, double y2, double dof) nogil: if dof != 1: return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -dof else: return (1 + (x1 - y1) ** 2 + (x2 - y2) ** 2) ** -1 cdef inline double cauchy_2d_exp1p(double x1, double x2, double y1, double y2, double dof) nogil: if dof != 1: return (1 + ((x1 - y1) ** 2 + (x2 - y2) ** 2) / dof) ** -(dof + 1) else: return (1 + (x1 - y1) ** 2 + (x2 - y2) ** 2) ** -2 cdef double[:, ::1] interpolate(double[::1] y_in_box, double[::1] y_tilde): """Lagrangian polynomial interpolation.""" cdef Py_ssize_t N = y_in_box.shape[0] cdef Py_ssize_t n_interpolation_points = y_tilde.shape[0] cdef double[:, ::1] interpolated_values = np.empty((N, n_interpolation_points), dtype=float) cdef double[::1] denominator = np.empty(n_interpolation_points, dtype=float) cdef Py_ssize_t i, j, k for i in range(n_interpolation_points): denominator[i] = 1 for j in range(n_interpolation_points): if i != j: denominator[i] *= y_tilde[i] - y_tilde[j] for i in range(N): for j in range(n_interpolation_points): interpolated_values[i, j] = 1 for k in range(n_interpolation_points): if j != k: interpolated_values[i, j] *= y_in_box[i] - y_tilde[k] interpolated_values[i, j] /= denominator[j] return interpolated_values cdef double[::1] compute_kernel_tilde_1d( double (*kernel)(double, double, double), Py_ssize_t n_interpolation_points_1d, double coord_min, double coord_spacing, double dof, ): cdef: double[::1] y_tilde = np.empty(n_interpolation_points_1d, dtype=float) Py_ssize_t embedded_size = 2 * n_interpolation_points_1d double[::1] kernel_tilde = np.zeros(embedded_size, dtype=float) Py_ssize_t i y_tilde[0] = coord_spacing / 2 + coord_min for i in range(1, n_interpolation_points_1d): y_tilde[i] = y_tilde[i - 1] + coord_spacing # Evaluate the kernel at the interpolation nodes and form the embedded # generating kernel vector for a circulant matrix cdef double tmp for i in range(n_interpolation_points_1d): tmp = kernel(y_tilde[0], y_tilde[i], dof) kernel_tilde[n_interpolation_points_1d + i] = tmp kernel_tilde[n_interpolation_points_1d - i] = tmp return kernel_tilde cpdef double estimate_negative_gradient_fft_1d( double[::1] embedding, double[::1] gradient, Py_ssize_t n_interpolation_points=3, Py_ssize_t min_num_intervals=10, double ints_in_interval=1, double dof=1, ): cdef Py_ssize_t i, j, d, box_idx, n_samples = embedding.shape[0] cdef double y_max = -INFINITY, y_min = INFINITY # Determine the min/max values of the embedding for i in range(n_samples): if embedding[i] < y_min: y_min = embedding[i] elif embedding[i] > y_max: y_max = embedding[i] cdef int n_boxes = fmax(min_num_intervals, (y_max - y_min) / ints_in_interval) # FFTW works faster on numbers that can be written as 2^a 3^b 5^c 7^d # 11^e 13^f, where e+f is either 0 or 1, and the other exponents are arbitrary cdef list recommended_boxes = [ 20, 21, 22, 24, 25, 26, 27, 28, 30, 32, 33, 35, 36, 39, 40, 42, 44, 45, 48, 49, 50, 52, 54, 55, 56, 60, 63, 64, 65, 66, 70, 72, 75, 77, 78, 80, 81, 84, 88, 90, 91, 96, 98, 99, 100, 104, 105, 108, 110, 112, 117, 120, 125, 126, 128, 130, 132, 135, 140, 144, 147, 150, 154, 156, 160, 162, 165, 168, 175, 176, 180, 182, 189, 192, 195, 196, 198, 200, 208, 210, 216, 220, 224, 225, 231, 234, 240, 243, 245, 250, 252, 256, 260, 264, 270, 273, 275, 280, 288, 294, 297, 300, 308, 312, 315, 320, 324, 325, 330, 336, 343, 350, 351, 352, 360, 364, 375, 378, 384, 385, 390, 392, 396, 400, 405, 416, 420, 432, 440, 441, 448, 450, 455, 462, 468, 480, 486, 490, 495, 500, 504, 512, 520, 525, 528, 539, 540, 546, 550, 560, 567, 576, 585, 588, 594, 600, 616, 624, 625, 630, 637, 640, 648, 650, 660, 672, 675, 686, 693, 700, 702, 704, 720, 728, 729, 735, 750, 756, 768, 770, 780, 784, 792, 800, 810, 819, 825, 832, 840, 864, 875, 880, 882, 891, 896, 900, 910, 924, 936, 945, 960, 972, 975, 980, 990, 1000 ] if n_boxes < recommended_boxes[205]: i = 0 while n_boxes > recommended_boxes[i]: i += 1 n_boxes = recommended_boxes[i] else: n_boxes = 1000 cdef double box_width = (y_max - y_min) / n_boxes # Compute the box bounds cdef double[::1] box_lower_bounds = np.empty(n_boxes, dtype=float) cdef double[::1] box_upper_bounds = np.empty(n_boxes, dtype=float) for box_idx in range(n_boxes): box_lower_bounds[box_idx] = box_idx * box_width + y_min box_upper_bounds[box_idx] = (box_idx + 1) * box_width + y_min # Determine which box each point belongs to cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) for i in range(n_samples): box_idx = ((embedding[i] - y_min) / box_width) # The right most point maps directly into `n_boxes`, while it should # belong to the last box if box_idx >= n_boxes: box_idx = n_boxes - 1 point_box_idx[i] = box_idx cdef int n_interpolation_points_1d = n_interpolation_points * n_boxes # Prepare the interpolants for a single interval, so we can use their # relative positions later on cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) cdef double h = 1. / n_interpolation_points y_tilde[0] = h / 2 for i in range(1, n_interpolation_points): y_tilde[i] = y_tilde[i - 1] + h # Evaluate the the squared cauchy kernel at the interpolation nodes cdef double[::1] sq_kernel_tilde = compute_kernel_tilde_1d( &cauchy_1d_exp1p, n_interpolation_points_1d, y_min, h * box_width, dof ) # The non-square cauchy kernel is only used if dof != 1, so don't do unnecessary work cdef double[::1] kernel_tilde if dof != 1: kernel_tilde = compute_kernel_tilde_1d( &cauchy_1d, n_interpolation_points_1d, y_min, h * box_width, dof ) # STEP 1: Compute the w coefficients # Set up q_j values cdef int n_terms = 3 cdef double[:, ::1] q_j = np.empty((n_samples, n_terms), dtype=float) if dof != 1: for i in range(n_samples): q_j[i, 0] = 1 q_j[i, 1] = embedding[i] q_j[i, 2] = 1 else: for i in range(n_samples): q_j[i, 0] = 1 q_j[i, 1] = embedding[i] q_j[i, 2] = embedding[i] ** 2 # Compute the relative position of each reference point in its box cdef double[::1] y_in_box = np.empty(n_samples, dtype=float) for i in range(n_samples): box_idx = point_box_idx[i] y_in_box[i] = (embedding[i] - box_lower_bounds[box_idx]) / box_width # Interpolate kernel using Lagrange polynomials cdef double[:, ::1] interpolated_values = interpolate(y_in_box, y_tilde) # Actually compute w_{ij}s cdef double[:, ::1] w_coefficients = np.zeros((n_interpolation_points_1d, n_terms), dtype=float) for i in range(n_samples): box_idx = point_box_idx[i] * n_interpolation_points for j in range(n_interpolation_points): for d in range(n_terms): w_coefficients[box_idx + j, d] += interpolated_values[i, j] * q_j[i, d] # STEP 2: Compute the kernel values evaluated at the interpolation nodes cdef double[:, ::1] y_tilde_values = np.empty((n_interpolation_points_1d, n_terms)) if dof != 1: matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients[:, :2], y_tilde_values[:, :2]) matrix_multiply_fft_1d(kernel_tilde, w_coefficients[:, 2:], y_tilde_values[:, 2:]) else: matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients, y_tilde_values) # STEP 3: Compute the potentials \tilde{\phi(y_i)} cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) for i in range(n_samples): box_idx = point_box_idx[i] * n_interpolation_points for j in range(n_interpolation_points): for d in range(n_terms): phi[i, d] += interpolated_values[i, j] * y_tilde_values[box_idx + j, d] PyMem_Free(point_box_idx) # Compute the normalization term Z or sum of q_{ij}s cdef double sum_Q = 0 if dof != 1: for i in range(n_samples): sum_Q += phi[i, 2] else: for i in range(n_samples): sum_Q += (1 + embedding[i] ** 2) * phi[i, 0] - \ 2 * embedding[i] * phi[i, 1] + \ phi[i, 2] sum_Q -= n_samples # The phis used here are not affected if dof != 1 for i in range(n_samples): gradient[i] -= (embedding[i] * phi[i, 0] - phi[i, 1]) / (sum_Q + EPSILON) return sum_Q cpdef tuple prepare_negative_gradient_fft_interpolation_grid_1d( double[::1] reference_embedding, Py_ssize_t n_interpolation_points=3, Py_ssize_t min_num_intervals=10, double ints_in_interval=1, double dof=1, double padding=0, ): cdef: Py_ssize_t i, j, d, box_idx Py_ssize_t n_reference_samples = reference_embedding.shape[0] double y_max = -INFINITY, y_min = INFINITY # Determine the min/max values of the embedding # First, check the existing embedding for i in range(n_reference_samples): if reference_embedding[i] < y_min: y_min = reference_embedding[i] elif reference_embedding[i] > y_max: y_max = reference_embedding[i] # We assume here that the embedding is centered and we want to generate an # equal grid in both negative and positive lines if fabs(y_min) > fabs(y_max): coord_max = -y_min elif fabs(y_max) > fabs(y_min): coord_min = -y_max # Apply padding to the min/max coordinates y_min *= 1 + padding y_max *= 1 + padding cdef int n_boxes = fmax(min_num_intervals, (y_max - y_min) / ints_in_interval) cdef double box_width = (y_max - y_min) / n_boxes # Compute the box bounds cdef double[::1] box_lower_bounds = np.empty(n_boxes, dtype=float) cdef double[::1] box_upper_bounds = np.empty(n_boxes, dtype=float) for box_idx in range(n_boxes): box_lower_bounds[box_idx] = box_idx * box_width + y_min box_upper_bounds[box_idx] = (box_idx + 1) * box_width + y_min # Determine which box each reference point belongs to cdef int *reference_point_box_idx = PyMem_Malloc(n_reference_samples * sizeof(int)) for i in range(n_reference_samples): box_idx = ((reference_embedding[i] - y_min) / box_width) # The right most point maps directly into `n_boxes`, while it should # belong to the last box if box_idx >= n_boxes: box_idx = n_boxes - 1 reference_point_box_idx[i] = box_idx cdef int n_interpolation_points_1d = n_interpolation_points * n_boxes # Prepare the interpolants for a single interval, so we can use their # relative positions later on cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) cdef double h = 1. / n_interpolation_points y_tilde[0] = h / 2 for i in range(1, n_interpolation_points): y_tilde[i] = y_tilde[i - 1] + h # Evaluate the the squared cauchy kernel at the interpolation nodes cdef double[::1] sq_kernel_tilde = compute_kernel_tilde_1d( &cauchy_1d_exp1p, n_interpolation_points_1d, y_min, h * box_width, dof ) # The non-square cauchy kernel is only used if dof != 1, so don't do unnecessary work cdef double[::1] kernel_tilde if dof != 1: kernel_tilde = compute_kernel_tilde_1d( &cauchy_1d, n_interpolation_points_1d, y_min, h * box_width, dof ) # STEP 1: Compute the w coefficients # Set up q_j values cdef int n_terms = 3 cdef double[:, ::1] q_j = np.empty((n_reference_samples, n_terms), dtype=float) if dof != 1: for i in range(n_reference_samples): q_j[i, 0] = 1 q_j[i, 1] = reference_embedding[i] q_j[i, 2] = 1 else: for i in range(n_reference_samples): q_j[i, 0] = 1 q_j[i, 1] = reference_embedding[i] q_j[i, 2] = reference_embedding[i] ** 2 # Compute the relative position of each reference point in its box cdef double[::1] reference_y_in_box = np.empty(n_reference_samples, dtype=float) for i in range(n_reference_samples): box_idx = reference_point_box_idx[i] reference_y_in_box[i] = (reference_embedding[i] - box_lower_bounds[box_idx]) / box_width # Interpolate kernel using Lagrange polynomials cdef double[:, ::1] reference_interpolated_values = interpolate(reference_y_in_box, y_tilde) # Actually compute w_{ij}s cdef double[:, ::1] w_coefficients = np.zeros((n_interpolation_points_1d, n_terms), dtype=float) for i in range(n_reference_samples): box_idx = reference_point_box_idx[i] * n_interpolation_points for j in range(n_interpolation_points): for d in range(n_terms): w_coefficients[box_idx + j, d] += reference_interpolated_values[i, j] * q_j[i, d] # STEP 2: Compute the kernel values evaluated at the interpolation nodes cdef double[:, ::1] y_tilde_values = np.empty((n_interpolation_points_1d, n_terms)) if dof != 1: matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients[:, :2], y_tilde_values[:, :2]) matrix_multiply_fft_1d(kernel_tilde, w_coefficients[:, 2:], y_tilde_values[:, 2:]) else: matrix_multiply_fft_1d(sq_kernel_tilde, w_coefficients, y_tilde_values) PyMem_Free(reference_point_box_idx) return np.asarray(y_tilde_values), np.asarray(box_lower_bounds) cpdef double estimate_negative_gradient_fft_1d_with_grid( double[::1] embedding, double[::1] gradient, double[:, ::1] y_tilde_values, double[::1] box_lower_bounds, Py_ssize_t n_interpolation_points, double dof, ): cdef: Py_ssize_t i, j, d, box_idx Py_ssize_t n_samples = embedding.shape[0] Py_ssize_t n_terms = y_tilde_values.shape[1] Py_ssize_t n_boxes = box_lower_bounds.shape[0] double y_min = box_lower_bounds[0] double box_width = box_lower_bounds[1] - box_lower_bounds[0] # Determine which box each point belongs to cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) for i in range(n_samples): box_idx = ((embedding[i] - y_min) / box_width) # The right most point maps directly into `n_boxes`, while it should # belong to the last box if box_idx >= n_boxes: box_idx = n_boxes - 1 point_box_idx[i] = box_idx # Prepare the interpolants for a single interval, so we can use their # relative positions later on cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) cdef double h = 1. / n_interpolation_points y_tilde[0] = h / 2 for i in range(1, n_interpolation_points): y_tilde[i] = y_tilde[i - 1] + h # STEP 3: Compute the potentials \tilde{\phi(y_i)} # Compute the relative position of each new embedding point in its box cdef double[::1] y_in_box = np.empty(n_samples, dtype=float) for i in range(n_samples): box_idx = point_box_idx[i] y_in_box[i] = (embedding[i] - box_lower_bounds[box_idx]) / box_width # Interpolate kernel using Lagrange polynomials cdef double[:, ::1] interpolated_values = interpolate(y_in_box, y_tilde) # Actually compute \tilde{\phi(y_i)} cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) for i in range(n_samples): box_idx = point_box_idx[i] * n_interpolation_points for j in range(n_interpolation_points): for d in range(n_terms): phi[i, d] += interpolated_values[i, j] * y_tilde_values[box_idx + j, d] PyMem_Free(point_box_idx) # Compute the normalization term Z or sum of q_{ij}s cdef double[::1] sum_Qi = np.empty(n_samples, dtype=float) if dof != 1: for i in range(n_samples): sum_Qi[i] = phi[i, 2] else: for i in range(n_samples): sum_Qi[i] = (1 + embedding[i] ** 2) * phi[i, 0] - \ 2 * embedding[i] * phi[i, 1] + \ phi[i, 2] cdef double sum_Q = 0 for i in range(n_samples): sum_Q += sum_Qi[i] # The phis used here are not affected if dof != 1 for i in range(n_samples): gradient[i] -= (embedding[i] * phi[i, 0] - phi[i, 1]) / (sum_Qi[i] + EPSILON) return sum_Q cdef double[:, ::1] compute_kernel_tilde_2d( double (*kernel)(double, double, double, double, double), Py_ssize_t n_interpolation_points_1d, double coord_min, double coord_spacing, double dof, ): cdef: double[::1] y_tilde = np.empty(n_interpolation_points_1d, dtype=float) double[::1] x_tilde = np.empty(n_interpolation_points_1d, dtype=float) Py_ssize_t embedded_size = 2 * n_interpolation_points_1d double[:, ::1] kernel_tilde = np.zeros((embedded_size, embedded_size), dtype=float) Py_ssize_t i, j x_tilde[0] = coord_min + coord_spacing / 2 y_tilde[0] = coord_min + coord_spacing / 2 for i in range(1, n_interpolation_points_1d): x_tilde[i] = x_tilde[i - 1] + coord_spacing y_tilde[i] = y_tilde[i - 1] + coord_spacing # Evaluate the kernel at the interpolation nodes and form the embedded # generating kernel vector for a circulant matrix cdef double tmp for i in range(n_interpolation_points_1d): for j in range(n_interpolation_points_1d): tmp = kernel(y_tilde[0], x_tilde[0], y_tilde[i], x_tilde[j], dof) kernel_tilde[n_interpolation_points_1d + i, n_interpolation_points_1d + j] = tmp kernel_tilde[n_interpolation_points_1d - i, n_interpolation_points_1d + j] = tmp kernel_tilde[n_interpolation_points_1d + i, n_interpolation_points_1d - j] = tmp kernel_tilde[n_interpolation_points_1d - i, n_interpolation_points_1d - j] = tmp return kernel_tilde cpdef double estimate_negative_gradient_fft_2d( double[:, ::1] embedding, double[:, ::1] gradient, Py_ssize_t n_interpolation_points=3, Py_ssize_t min_num_intervals=10, double ints_in_interval=1, double dof=1, ): cdef: Py_ssize_t i, j, d, box_idx Py_ssize_t n_samples = embedding.shape[0] Py_ssize_t n_dims = embedding.shape[1] double coord_max = -INFINITY, coord_min = INFINITY # Determine the min/max values of the embedding for i in range(n_samples): if embedding[i, 0] < coord_min: coord_min = embedding[i, 0] elif embedding[i, 0] > coord_max: coord_max = embedding[i, 0] if embedding[i, 1] < coord_min: coord_min = embedding[i, 1] elif embedding[i, 1] > coord_max: coord_max = embedding[i, 1] cdef int n_boxes_1d = fmax(min_num_intervals, (coord_max - coord_min) / ints_in_interval) # FFTW works faster on numbers that can be written as 2^a 3^b 5^c 7^d # 11^e 13^f, where e+f is either 0 or 1, and the other exponents are arbitrary cdef list recommended_boxes = [ 20, 21, 22, 24, 25, 26, 27, 28, 30, 32, 33, 35, 36, 39, 40, 42, 44, 45, 48, 49, 50, 52, 54, 55, 56, 60, 63, 64, 65, 66, 70, 72, 75, 77, 78, 80, 81, 84, 88, 90, 91, 96, 98, 99, 100, 104, 105, 108, 110, 112, 117, 120, 125, 126, 128, 130, 132, 135, 140, 144, 147, 150, 154, 156, 160, 162, 165, 168, 175, 176, 180, 182, 189, 192, 195, 196, 198, 200, 208, 210, 216, 220, 224, 225, 231, 234, 240, 243, 245, 250, 252, 256, 260, 264, 270, 273, 275, 280, 288, 294, 297, 300, 308, 312, 315, 320, 324, 325, 330, 336, 343, 350, 351, 352, 360, 364, 375, 378, 384, 385, 390, 392, 396, 400, 405, 416, 420, 432, 440, 441, 448, 450, 455, 462, 468, 480, 486, 490, 495, 500, 504, 512, 520, 525, 528, 539, 540, 546, 550, 560, 567, 576, 585, 588, 594, 600, 616, 624, 625, 630, 637, 640, 648, 650, 660, 672, 675, 686, 693, 700, 702, 704, 720, 728, 729, 735, 750, 756, 768, 770, 780, 784, 792, 800, 810, 819, 825, 832, 840, 864, 875, 880, 882, 891, 896, 900, 910, 924, 936, 945, 960, 972, 975, 980, 990, 1000 ] if n_boxes_1d < recommended_boxes[205]: i = 0 while n_boxes_1d > recommended_boxes[i]: i += 1 n_boxes_1d = recommended_boxes[i] else: n_boxes_1d = 1000 cdef int n_total_boxes = n_boxes_1d ** 2 cdef double box_width = (coord_max - coord_min) / n_boxes_1d # Compute the box bounds cdef: double[::1] box_x_lower_bounds = np.empty(n_total_boxes, dtype=float) double[::1] box_x_upper_bounds = np.empty(n_total_boxes, dtype=float) double[::1] box_y_lower_bounds = np.empty(n_total_boxes, dtype=float) double[::1] box_y_upper_bounds = np.empty(n_total_boxes, dtype=float) for i in range(n_boxes_1d): for j in range(n_boxes_1d): box_x_lower_bounds[i * n_boxes_1d + j] = j * box_width + coord_min box_x_upper_bounds[i * n_boxes_1d + j] = (j + 1) * box_width + coord_min box_y_lower_bounds[i * n_boxes_1d + j] = i * box_width + coord_min box_y_upper_bounds[i * n_boxes_1d + j] = (i + 1) * box_width + coord_min # Determine which box each reference point belongs to cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) cdef int box_x_idx, box_y_idx for i in range(n_samples): box_x_idx = ((embedding[i, 0] - coord_min) / box_width) box_y_idx = ((embedding[i, 1] - coord_min) / box_width) # The right most point maps directly into `n_boxes`, while it should # belong to the last box if box_x_idx >= n_boxes_1d: box_x_idx = n_boxes_1d - 1 if box_y_idx >= n_boxes_1d: box_y_idx = n_boxes_1d - 1 point_box_idx[i] = box_y_idx * n_boxes_1d + box_x_idx # Prepare the interpolants for a single interval, so we can use their # relative positions later on cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) cdef double h = 1. / n_interpolation_points y_tilde[0] = h / 2 for i in range(1, n_interpolation_points): y_tilde[i] = y_tilde[i - 1] + h # Evaluate the the squared cauchy kernel at the interpolation nodes cdef double[:, ::1] sq_kernel_tilde = compute_kernel_tilde_2d( &cauchy_2d_exp1p, n_interpolation_points * n_boxes_1d, coord_min, h * box_width, dof, ) # The non-square cauchy kernel is only used if dof != 1, so don't do unnecessary work cdef double[:, ::1] kernel_tilde if dof != 1: kernel_tilde = compute_kernel_tilde_2d( &cauchy_2d, n_interpolation_points * n_boxes_1d, coord_min, h * box_width, dof, ) # STEP 1: Compute the w coefficients # Set up q_j values cdef int n_terms = 4 cdef double[:, ::1] q_j = np.empty((n_samples, n_terms), dtype=float) if dof != 1: for i in range(n_samples): q_j[i, 0] = 1 q_j[i, 1] = embedding[i, 0] q_j[i, 2] = embedding[i, 1] q_j[i, 3] = 1 else: for i in range(n_samples): q_j[i, 0] = 1 q_j[i, 1] = embedding[i, 0] q_j[i, 2] = embedding[i, 1] q_j[i, 3] = embedding[i, 0] ** 2 + embedding[i, 1] ** 2 # Compute the relative position of each reference point in its box cdef: double[::1] x_in_box = np.empty(n_samples, dtype=float) double[::1] y_in_box = np.empty(n_samples, dtype=float) double y_min, x_min for i in range(n_samples): box_idx = point_box_idx[i] x_min = box_x_lower_bounds[box_idx] y_min = box_y_lower_bounds[box_idx] x_in_box[i] = (embedding[i, 0] - x_min) / box_width y_in_box[i] = (embedding[i, 1] - y_min) / box_width # Interpolate kernel using Lagrange polynomials cdef double[:, ::1] x_interpolated_values = interpolate(x_in_box, y_tilde) cdef double[:, ::1] y_interpolated_values = interpolate(y_in_box, y_tilde) # Actually compute w_{ij}s cdef: int total_interpolation_points = n_total_boxes * n_interpolation_points ** 2 double[:, ::1] w_coefficients = np.zeros((total_interpolation_points, n_terms), dtype=float) Py_ssize_t box_i, box_j, interp_i, interp_j, idx for i in range(n_samples): box_idx = point_box_idx[i] box_i = box_idx % n_boxes_1d box_j = box_idx // n_boxes_1d for interp_i in range(n_interpolation_points): for interp_j in range(n_interpolation_points): idx = (box_i * n_interpolation_points + interp_i) * \ (n_boxes_1d * n_interpolation_points) + \ (box_j * n_interpolation_points) + \ interp_j for d in range(n_terms): w_coefficients[idx, d] += \ x_interpolated_values[i, interp_i] * \ y_interpolated_values[i, interp_j] * \ q_j[i, d] # STEP 2: Compute the kernel values evaluated at the interpolation nodes cdef double[:, ::1] y_tilde_values = np.empty((total_interpolation_points, n_terms)) if dof != 1: matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients[:, :3], y_tilde_values[:, :3]) matrix_multiply_fft_2d(kernel_tilde, w_coefficients[:, 3:], y_tilde_values[:, 3:]) else: matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients, y_tilde_values) # STEP 3: Compute the potentials \tilde{\phi(y_i)} cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) for i in range(n_samples): box_idx = point_box_idx[i] box_i = box_idx % n_boxes_1d box_j = box_idx // n_boxes_1d for interp_i in range(n_interpolation_points): for interp_j in range(n_interpolation_points): idx = (box_i * n_interpolation_points + interp_i) * \ (n_boxes_1d * n_interpolation_points) + \ (box_j * n_interpolation_points) + \ interp_j for d in range(n_terms): phi[i, d] += x_interpolated_values[i, interp_i] * \ y_interpolated_values[i, interp_j] * \ y_tilde_values[idx, d] PyMem_Free(point_box_idx) # Compute the normalization term Z or sum of q_{ij}s cdef double sum_Q = 0, y1, y2 if dof != 1: for i in range(n_samples): sum_Q += phi[i, 3] else: for i in range(n_samples): y1 = embedding[i, 0] y2 = embedding[i, 1] sum_Q += (1 + y1 ** 2 + y2 ** 2) * phi[i, 0] - \ 2 * (y1 * phi[i, 1] + y2 * phi[i, 2]) + \ phi[i, 3] sum_Q -= n_samples # The phis used here are not affected if dof != 1 for i in range(n_samples): gradient[i, 0] -= (embedding[i, 0] * phi[i, 0] - phi[i, 1]) / (sum_Q + EPSILON) gradient[i, 1] -= (embedding[i, 1] * phi[i, 0] - phi[i, 2]) / (sum_Q + EPSILON) return sum_Q cpdef tuple prepare_negative_gradient_fft_interpolation_grid_2d( double[:, ::1] reference_embedding, Py_ssize_t n_interpolation_points=3, Py_ssize_t min_num_intervals=10, double ints_in_interval=1, double dof=1, double padding=0, ): cdef: Py_ssize_t i, j, d, box_idx Py_ssize_t n_reference_samples = reference_embedding.shape[0] double coord_max = -INFINITY, coord_min = INFINITY # Determine the min/max values of the embedding # First, check the existing embedding for i in range(n_reference_samples): if reference_embedding[i, 0] < coord_min: coord_min = reference_embedding[i, 0] elif reference_embedding[i, 0] > coord_max: coord_max = reference_embedding[i, 0] if reference_embedding[i, 1] < coord_min: coord_min = reference_embedding[i, 1] elif reference_embedding[i, 1] > coord_max: coord_max = reference_embedding[i, 1] # We assume here that the embedding is centered and we want to generate an # equal grid in all quadrants if fabs(coord_min) > fabs(coord_max): coord_max = -coord_min elif fabs(coord_max) > fabs(coord_min): coord_min = -coord_max # Apply padding to the min/max coordinates coord_min *= 1 + padding coord_max *= 1 + padding cdef int n_boxes_1d = fmax(min_num_intervals, (coord_max - coord_min) / ints_in_interval) cdef int n_total_boxes = n_boxes_1d ** 2 cdef double box_width = (coord_max - coord_min) / n_boxes_1d # Compute the box bounds cdef: double[::1] box_x_lower_bounds = np.empty(n_total_boxes, dtype=float) double[::1] box_x_upper_bounds = np.empty(n_total_boxes, dtype=float) double[::1] box_y_lower_bounds = np.empty(n_total_boxes, dtype=float) double[::1] box_y_upper_bounds = np.empty(n_total_boxes, dtype=float) for i in range(n_boxes_1d): for j in range(n_boxes_1d): box_x_lower_bounds[i * n_boxes_1d + j] = j * box_width + coord_min box_x_upper_bounds[i * n_boxes_1d + j] = (j + 1) * box_width + coord_min box_y_lower_bounds[i * n_boxes_1d + j] = i * box_width + coord_min box_y_upper_bounds[i * n_boxes_1d + j] = (i + 1) * box_width + coord_min # Determine which box each reference point belongs to cdef int *reference_point_box_idx = PyMem_Malloc(n_reference_samples * sizeof(int)) cdef int box_x_idx, box_y_idx for i in range(n_reference_samples): box_x_idx = ((reference_embedding[i, 0] - coord_min) / box_width) box_y_idx = ((reference_embedding[i, 1] - coord_min) / box_width) # The right most point maps directly into `n_boxes`, while it should # belong to the last box if box_x_idx >= n_boxes_1d: box_x_idx = n_boxes_1d - 1 if box_y_idx >= n_boxes_1d: box_y_idx = n_boxes_1d - 1 reference_point_box_idx[i] = box_y_idx * n_boxes_1d + box_x_idx # Prepare the interpolants for a single interval, so we can use their # relative positions later on cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) cdef double h = 1. / n_interpolation_points y_tilde[0] = h / 2 for i in range(1, n_interpolation_points): y_tilde[i] = y_tilde[i - 1] + h # Evaluate the the squared cauchy kernel at the interpolation nodes cdef double[:, ::1] sq_kernel_tilde = compute_kernel_tilde_2d( &cauchy_2d_exp1p, n_interpolation_points * n_boxes_1d, coord_min, h * box_width, dof, ) # The non-square cauchy kernel is only used if dof != 1, so don't do unnecessary work cdef double[:, ::1] kernel_tilde if dof != 1: kernel_tilde = compute_kernel_tilde_2d( &cauchy_2d, n_interpolation_points * n_boxes_1d, coord_min, h * box_width, dof, ) # STEP 1: Compute the w coefficients # Set up q_j values cdef int n_terms = 4 cdef double[:, ::1] q_j = np.empty((n_reference_samples, n_terms), dtype=float) if dof != 1: for i in range(n_reference_samples): q_j[i, 0] = 1 q_j[i, 1] = reference_embedding[i, 0] q_j[i, 2] = reference_embedding[i, 1] q_j[i, 3] = 1 else: for i in range(n_reference_samples): q_j[i, 0] = 1 q_j[i, 1] = reference_embedding[i, 0] q_j[i, 2] = reference_embedding[i, 1] q_j[i, 3] = reference_embedding[i, 0] ** 2 + reference_embedding[i, 1] ** 2 # Compute the relative position of each reference point in its box cdef: double[::1] reference_x_in_box = np.empty(n_reference_samples, dtype=float) double[::1] reference_y_in_box = np.empty(n_reference_samples, dtype=float) double y_min, x_min for i in range(n_reference_samples): box_idx = reference_point_box_idx[i] x_min = box_x_lower_bounds[box_idx] y_min = box_y_lower_bounds[box_idx] reference_x_in_box[i] = (reference_embedding[i, 0] - x_min) / box_width reference_y_in_box[i] = (reference_embedding[i, 1] - y_min) / box_width # Interpolate kernel using Lagrange polynomials cdef double[:, ::1] reference_x_interpolated_values = interpolate(reference_x_in_box, y_tilde) cdef double[:, ::1] reference_y_interpolated_values = interpolate(reference_y_in_box, y_tilde) # Actually compute w_{ij}s cdef: int total_interpolation_points = n_total_boxes * n_interpolation_points ** 2 double[:, ::1] w_coefficients = np.zeros((total_interpolation_points, n_terms), dtype=float) Py_ssize_t box_i, box_j, interp_i, interp_j, idx for i in range(n_reference_samples): box_idx = reference_point_box_idx[i] box_i = box_idx % n_boxes_1d box_j = box_idx // n_boxes_1d for interp_i in range(n_interpolation_points): for interp_j in range(n_interpolation_points): idx = (box_i * n_interpolation_points + interp_i) * \ (n_boxes_1d * n_interpolation_points) + \ (box_j * n_interpolation_points) + \ interp_j for d in range(n_terms): w_coefficients[idx, d] += \ reference_x_interpolated_values[i, interp_i] * \ reference_y_interpolated_values[i, interp_j] * \ q_j[i, d] # STEP 2: Compute the kernel values evaluated at the interpolation nodes cdef double[:, ::1] y_tilde_values = np.empty((total_interpolation_points, n_terms)) if dof != 1: matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients[:, :3], y_tilde_values[:, :3]) matrix_multiply_fft_2d(kernel_tilde, w_coefficients[:, 3:], y_tilde_values[:, 3:]) else: matrix_multiply_fft_2d(sq_kernel_tilde, w_coefficients, y_tilde_values) return ( np.asarray(y_tilde_values), np.asarray(box_x_lower_bounds), np.asarray(box_y_lower_bounds), ) cpdef double estimate_negative_gradient_fft_2d_with_grid( double[:, ::1] embedding, double[:, ::1] gradient, double[:, ::1] y_tilde_values, double[::1] box_x_lower_bounds, double[::1] box_y_lower_bounds, Py_ssize_t n_interpolation_points, double dof, ): cdef: Py_ssize_t i, j, d, box_idx Py_ssize_t n_samples = embedding.shape[0] Py_ssize_t n_terms = y_tilde_values.shape[1] Py_ssize_t n_boxes_1d = int(sqrt(box_x_lower_bounds.shape[0])) double coord_min = box_x_lower_bounds[0] double box_width = box_x_lower_bounds[1] - box_x_lower_bounds[0] # Determine which box each point belongs to cdef int box_x_idx, box_y_idx cdef int *point_box_idx = PyMem_Malloc(n_samples * sizeof(int)) for i in range(n_samples): box_x_idx = ((embedding[i, 0] - coord_min) / box_width) box_y_idx = ((embedding[i, 1] - coord_min) / box_width) # The right most point maps directly into `n_boxes`, while it should # belong to the last box if box_x_idx >= n_boxes_1d: box_x_idx = n_boxes_1d - 1 if box_y_idx >= n_boxes_1d: box_y_idx = n_boxes_1d - 1 point_box_idx[i] = box_y_idx * n_boxes_1d + box_x_idx # Prepare the interpolants for a single interval, so we can use their # relative positions later on cdef double[::1] y_tilde = np.empty(n_interpolation_points, dtype=float) cdef double h = 1. / n_interpolation_points y_tilde[0] = h / 2 for i in range(1, n_interpolation_points): y_tilde[i] = y_tilde[i - 1] + h # STEP 3: Compute the potentials \tilde{\phi(y_i)} # Compute the relative position of each new embedding point in its box cdef: double[::1] x_in_box = np.empty(n_samples, dtype=float) double[::1] y_in_box = np.empty(n_samples, dtype=float) cdef double y_min, x_min for i in range(n_samples): box_idx = point_box_idx[i] x_min = box_x_lower_bounds[box_idx] y_min = box_y_lower_bounds[box_idx] x_in_box[i] = (embedding[i, 0] - x_min) / box_width y_in_box[i] = (embedding[i, 1] - y_min) / box_width # Interpolate kernel using Lagrange polynomials cdef double[:, ::1] x_interpolated_values = interpolate(x_in_box, y_tilde) cdef double[:, ::1] y_interpolated_values = interpolate(y_in_box, y_tilde) # Actually compute \tilde{\phi(y_i)} cdef Py_ssize_t box_i, box_j, interp_i, interp_j, idx cdef double[:, ::1] phi = np.zeros((n_samples, n_terms), dtype=float) for i in range(n_samples): box_idx = point_box_idx[i] box_i = box_idx % n_boxes_1d box_j = box_idx // n_boxes_1d for interp_i in range(n_interpolation_points): for interp_j in range(n_interpolation_points): idx = (box_i * n_interpolation_points + interp_i) * \ (n_boxes_1d * n_interpolation_points) + \ (box_j * n_interpolation_points) + \ interp_j for d in range(n_terms): phi[i, d] += x_interpolated_values[i, interp_i] * \ y_interpolated_values[i, interp_j] * \ y_tilde_values[idx, d] PyMem_Free(point_box_idx) # Compute the normalization term Z or sum of q_{ij}s cdef double[::1] sum_Qi = np.empty(n_samples, dtype=float) cdef double y1, y2 if dof != 1: for i in range(n_samples): sum_Qi[i] = phi[i, 3] else: for i in range(n_samples): y1 = embedding[i, 0] y2 = embedding[i, 1] sum_Qi[i] = (1 + y1 ** 2 + y2 ** 2) * phi[i, 0] - \ 2 * (y1 * phi[i, 1] + y2 * phi[i, 2]) + \ phi[i, 3] cdef sum_Q = 0 for i in range(n_samples): sum_Q += sum_Qi[i] # The phis used here are not affected if dof != 1 for i in range(n_samples): gradient[i, 0] -= (embedding[i, 0] * phi[i, 0] - phi[i, 1]) / (sum_Qi[i] + EPSILON) gradient[i, 1] -= (embedding[i, 1] * phi[i, 0] - phi[i, 2]) / (sum_Qi[i] + EPSILON) return sum_Q openTSNE-0.6.1/openTSNE/affinity.py000066400000000000000000001271131413546205200167760ustar00rootroot00000000000000import logging import operator from functools import reduce import numpy as np import scipy.sparse as sp from openTSNE import _tsne from openTSNE import nearest_neighbors from openTSNE import utils from openTSNE.utils import is_package_installed log = logging.getLogger(__name__) class Affinities: """Compute the affinities between samples. t-SNE takes as input an affinity matrix :math:`P`, and does not really care about anything else from the data. This means we can use t-SNE for any data where we are able to express interactions between samples with an affinity matrix. Attributes ---------- P: array_like The :math:`N \\times N` affinity matrix expressing interactions between :math:`N` initial data samples. verbose: bool """ def __init__(self, verbose=False): self.P = None self.verbose = verbose self.knn_index: nearest_neighbors.KNNIndex = None def to_new(self, data, return_distances=False): """Compute the affinities of new samples to the initial samples. This is necessary for embedding new data points into an existing embedding. Parameters ---------- data: np.ndarray The data points to be added to the existing embedding. return_distances: bool If needed, the function can return the indices of the nearest neighbors and their corresponding distances. Returns ------- P: array_like An :math:`N \\times M` affinity matrix expressing interactions between :math:`N` new data points the initial :math:`M` data samples. indices: np.ndarray Returned if ``return_distances=True``. The indices of the :math:`k` nearest neighbors in the existing embedding for every new data point. distances: np.ndarray Returned if ``return_distances=True``. The distances to the :math:`k` nearest neighbors in the existing embedding for every new data point. """ @property def n_samples(self): if self.knn_index is None: raise RuntimeError("`knn_index` is not set!") return self.knn_index.n_samples class PerplexityBasedNN(Affinities): """Compute affinities using nearest neighbors. Please see the :ref:`parameter-guide` for more information. Parameters ---------- data: np.ndarray The data matrix. perplexity: float Perplexity can be thought of as the continuous :math:`k` number of nearest neighbors, for which t-SNE will attempt to preserve distances. method: str Specifies the nearest neighbor method to use. Can be ``exact``, ``annoy``, ``pynndescent``, ``hnsw``, ``approx``, or ``auto`` (default). ``approx`` uses Annoy if the input data matrix is not a sparse object and if Annoy supports the given metric. Otherwise it uses Pynndescent. ``auto`` uses exact nearest neighbors for N<1000 and the same heuristic as ``approx`` for N>=1000. metric: Union[str, Callable] The metric to be used to compute affinities between points in the original space. metric_params: dict Additional keyword arguments for the metric function. symmetrize: bool Symmetrize affinity matrix. Standard t-SNE symmetrizes the interactions but when embedding new data, symmetrization is not performed. n_jobs: int The number of threads to use while running t-SNE. This follows the scikit-learn convention, ``-1`` meaning all processors, ``-2`` meaning all but one, etc. random_state: Union[int, RandomState] If the value is an int, random_state is the seed used by the random number generator. If the value is a RandomState instance, then it will be used as the random number generator. If the value is None, the random number generator is the RandomState instance used by `np.random`. verbose: bool k_neighbors: int or ``auto`` The number of neighbors to use in the kNN graph. If ``auto`` (default), it is set to three times the perplexity. knn_index: Optional[nearest_neighbors.KNNIndex] Optionally, a precomptued ``openTSNE.nearest_neighbors.KNNIndex`` object can be specified. This option will ignore any KNN-related parameters. When ``knn_index`` is specified, ``data`` must be set to None. """ def __init__( self, data=None, perplexity=30, method="auto", metric="euclidean", metric_params=None, symmetrize=True, n_jobs=1, random_state=None, verbose=False, k_neighbors="auto", knn_index=None, ): # This can't work if neither data nor the knn index are specified if data is None and knn_index is None: raise ValueError( "At least one of the parameters `data` or `knn_index` must be specified!" ) # This can't work if both data and the knn index are specified if data is not None and knn_index is not None: raise ValueError( "Both `data` or `knn_index` were specified! Please pass only one." ) # Find the nearest neighbors if knn_index is None: n_samples = data.shape[0] if k_neighbors == "auto": _k_neighbors = min(n_samples - 1, int(3 * perplexity)) else: _k_neighbors = k_neighbors self.perplexity = self.check_perplexity(perplexity, _k_neighbors) if _k_neighbors > int(3 * self.perplexity): log.warning( "The k_neighbors value is over 3 times larger than the perplexity value. " "This may result in an unnecessary slowdown." ) self.knn_index = get_knn_index( data, method, _k_neighbors, metric, metric_params, n_jobs, random_state, verbose ) else: self.knn_index = knn_index self.perplexity = self.check_perplexity(perplexity, self.knn_index.k) log.info("KNN index provided. Ignoring KNN-related parameters.") self.__neighbors, self.__distances = self.knn_index.build() with utils.Timer("Calculating affinity matrix...", verbose): self.P = joint_probabilities_nn( self.__neighbors, self.__distances, [self.perplexity], symmetrize=symmetrize, n_jobs=n_jobs, ) self.symmetrize = symmetrize self.n_jobs = n_jobs self.verbose = verbose def set_perplexity(self, new_perplexity): """Change the perplexity of the affinity matrix. Note that we only allow setting the perplexity to a value not larger than the number of neighbors used for the original perplexity. This restriction exists because setting a higher perplexity value requires recomputing all the nearest neighbors, which can take a long time. To avoid potential confusion as to why execution time is slow, this is not allowed. If you would like to increase the perplexity above that value, simply create a new instance. Parameters ---------- new_perplexity: float The new perplexity. """ # If the value hasn't changed, there's nothing to do if new_perplexity == self.perplexity: return # Verify that the perplexity isn't negative new_perplexity = self.check_perplexity(new_perplexity, np.inf) # Verify that the perplexity isn't too large for the kNN graph if new_perplexity > self.__neighbors.shape[1]: raise RuntimeError( "The desired perplexity `%.2f` is larger than the kNN graph " "allows. This would need to recompute the nearest neighbors, " "which is not efficient. Please create a new `%s` instance " "with the increased perplexity." % (new_perplexity, self.__class__.__name__) ) # Warn if the perplexity is larger than the heuristic if 3 * new_perplexity > self.__neighbors.shape[1]: log.warning( "The new perplexity is quite close to the computed number of " "nearest neighbors. The results may be unexpected. Consider " "creating a new `%s` instance with the increased perplexity." % (self.__class__.__name__) ) # Recompute the affinity matrix self.perplexity = new_perplexity k_neighbors = int(3 * new_perplexity) with utils.Timer( "Perplexity changed. Recomputing affinity matrix...", self.verbose ): self.P = joint_probabilities_nn( self.__neighbors[:, :k_neighbors], self.__distances[:, :k_neighbors], [self.perplexity], symmetrize=self.symmetrize, n_jobs=self.n_jobs, ) def to_new( self, data, perplexity=None, return_distances=False, k_neighbors="auto" ): """Compute the affinities of new samples to the initial samples. This is necessary for embedding new data points into an existing embedding. Please see the :ref:`parameter-guide` for more information. Parameters ---------- data: np.ndarray The data points to be added to the existing embedding. perplexity: float Perplexity can be thought of as the continuous :math:`k` number of nearest neighbors, for which t-SNE will attempt to preserve distances. return_distances: bool If needed, the function can return the indices of the nearest neighbors and their corresponding distances. k_neighbors: int or ``auto`` The number of neighbors to query kNN graph for. If ``auto`` (default), it is set to three times the perplexity. Returns ------- P: array_like An :math:`N \\times M` affinity matrix expressing interactions between :math:`N` new data points the initial :math:`M` data samples. indices: np.ndarray Returned if ``return_distances=True``. The indices of the :math:`k` nearest neighbors in the existing embedding for every new data point. distances: np.ndarray Returned if ``return_distances=True``. The distances to the :math:`k` nearest neighbors in the existing embedding for every new data point. """ perplexity = perplexity if perplexity is not None else self.perplexity if k_neighbors == "auto": _k_neighbors = min(self.n_samples, int(3 * perplexity)) else: _k_neighbors = k_neighbors perplexity = self.check_perplexity(perplexity, _k_neighbors) neighbors, distances = self.knn_index.query(data, _k_neighbors) with utils.Timer("Calculating affinity matrix...", self.verbose): P = joint_probabilities_nn( neighbors, distances, [perplexity], symmetrize=False, normalization="point-wise", n_reference_samples=self.n_samples, n_jobs=self.n_jobs, ) if return_distances: return P, neighbors, distances return P def check_perplexity(self, perplexity, k_neighbors): if perplexity <= 0: raise ValueError("Perplexity must be >=0. %.2f given" % perplexity) if perplexity > k_neighbors: old_perplexity, perplexity = perplexity, k_neighbors / 3 log.warning( "Perplexity value %d is too high. Using perplexity %.2f instead" % (old_perplexity, perplexity) ) return perplexity def get_knn_index( data, method, k, metric, metric_params=None, n_jobs=1, random_state=None, verbose=False ): # If we're dealing with a precomputed distance matrix, our job is very easy # so we can skip all the remaining checks if metric == "precomputed": return nearest_neighbors.PrecomputedDistanceMatrix(data, k=k) preferred_approx_method = nearest_neighbors.Annoy if is_package_installed("pynndescent") and (sp.issparse(data) or metric not in [ "cosine", "euclidean", "manhattan", "hamming", "dot", "l1", "l2", "taxicab", ]): preferred_approx_method = nearest_neighbors.NNDescent if data.shape[0] < 1000: preferred_method = nearest_neighbors.Sklearn else: preferred_method = preferred_approx_method methods = { "exact": nearest_neighbors.Sklearn, "auto": preferred_method, "approx": preferred_approx_method, "annoy": nearest_neighbors.Annoy, "pynndescent": nearest_neighbors.NNDescent, "hnsw": nearest_neighbors.HNSW } if isinstance(method, nearest_neighbors.KNNIndex): knn_index = method elif method not in methods: raise ValueError( "Unrecognized nearest neighbor algorithm `%s`. Please choose one " "of the supported methods or provide a valid `KNNIndex` instance." % method ) else: knn_index = methods[method]( data=data, k=k, metric=metric, metric_params=metric_params, n_jobs=n_jobs, random_state=random_state, verbose=verbose, ) return knn_index def joint_probabilities_nn( neighbors, distances, perplexities, symmetrize=True, normalization="pair-wise", n_reference_samples=None, n_jobs=1, ): """Compute the conditional probability matrix P_{j|i}. This method computes an approximation to P using the nearest neighbors. Parameters ---------- neighbors: np.ndarray A `n_samples * k_neighbors` matrix containing the indices to each points" nearest neighbors in descending order. distances: np.ndarray A `n_samples * k_neighbors` matrix containing the distances to the neighbors at indices defined in the neighbors parameter. perplexities: double The desired perplexity of the probability distribution. symmetrize: bool Whether to symmetrize the probability matrix or not. Symmetrizing is used for typical t-SNE, but does not make sense when embedding new data into an existing embedding. normalization: str The normalization scheme to use for the affinities. Standard t-SNE considers interactions between all the data points, therefore the entire affinity matrix is regarded as a probability distribution, and must sum to 1. When embedding new points, we only consider interactions to existing points, and treat each point separately. In this case, we row-normalize the affinity matrix, meaning each point gets its own probability distribution. n_reference_samples: int The number of samples in the existing (reference) embedding. Needed to properly construct the sparse P matrix. n_jobs: int Number of threads. Returns ------- csr_matrix A `n_samples * n_reference_samples` matrix containing the probabilities that a new sample would appear as a neighbor of a reference point. """ assert normalization in ( "pair-wise", "point-wise", ), f"Unrecognized normalization scheme `{normalization}`." n_samples, k_neighbors = distances.shape if n_reference_samples is None: n_reference_samples = n_samples # Compute asymmetric pairwise input similarities conditional_P = _tsne.compute_gaussian_perplexity( np.array(distances, dtype=float), np.array(perplexities, dtype=float), num_threads=n_jobs, ) conditional_P = np.asarray(conditional_P) P = sp.csr_matrix( ( conditional_P.ravel(), neighbors.ravel(), range(0, n_samples * k_neighbors + 1, k_neighbors), ), shape=(n_samples, n_reference_samples), ) # Symmetrize the probability matrix if symmetrize: P = (P + P.T) / 2 if normalization == "pair-wise": P /= np.sum(P) elif normalization == "point-wise": P = sp.diags(np.asarray(1 / P.sum(axis=1)).ravel()) @ P return P class FixedSigmaNN(Affinities): """Compute affinities using using nearest neighbors and a fixed bandwidth for the Gaussians in the ambient space. Using a fixed Gaussian bandwidth can enable us to find smaller clusters of data points than we might be able to using the automatically determined bandwidths using perplexity. Note however that this requires mostly trial and error. Parameters ---------- data: np.ndarray The data matrix. sigma: float The bandwidth to use for the Gaussian kernels in the ambient space. k: int The number of nearest neighbors to consider for each kernel. method: str Specifies the nearest neighbor method to use. Can be ``exact``, ``annoy``, ``pynndescent``, ``hnsw``, ``approx``, or ``auto`` (default). ``approx`` uses Annoy if the input data matrix is not a sparse object and if Annoy supports the given metric. Otherwise it uses Pynndescent. ``auto`` uses exact nearest neighbors for N<1000 and the same heuristic as ``approx`` for N>=1000. metric: Union[str, Callable] The metric to be used to compute affinities between points in the original space. metric_params: dict Additional keyword arguments for the metric function. symmetrize: bool Symmetrize affinity matrix. Standard t-SNE symmetrizes the interactions but when embedding new data, symmetrization is not performed. n_jobs: int The number of threads to use while running t-SNE. This follows the scikit-learn convention, ``-1`` meaning all processors, ``-2`` meaning all but one, etc. random_state: Union[int, RandomState] If the value is an int, random_state is the seed used by the random number generator. If the value is a RandomState instance, then it will be used as the random number generator. If the value is None, the random number generator is the RandomState instance used by `np.random`. verbose: bool knn_index: Optional[nearest_neighbors.KNNIndex] Optionally, a precomptued ``openTSNE.nearest_neighbors.KNNIndex`` object can be specified. This option will ignore any KNN-related parameters. When ``knn_index`` is specified, ``data`` must be set to None. """ def __init__( self, data=None, sigma=None, k=30, method="auto", metric="euclidean", metric_params=None, symmetrize=True, n_jobs=1, random_state=None, verbose=False, knn_index=None, ): # Sigma must be specified, but has default set to none, so the parameter # order makes more sense if sigma is None: raise ValueError("`sigma` must be specified!") # This can't work if neither data nor the knn index are specified if data is None and knn_index is None: raise ValueError( "At least one of the parameters `data` or `knn_index` must be specified!" ) # This can't work if both data and the knn index are specified if data is not None and knn_index is not None: raise ValueError( "Both `data` or `knn_index` were specified! Please pass only one." ) # Find the nearest neighbors if knn_index is None: if k >= data.shape[0]: raise ValueError( "`k` (%d) cannot be larger than N-1 (%d)." % (k, data.shape[0]) ) self.knn_index = get_knn_index( data, method, k, metric, metric_params, n_jobs, random_state, verbose ) else: self.knn_index = knn_index log.info("KNN index provided. Ignoring KNN-related parameters.") neighbors, distances = self.knn_index.build() with utils.Timer("Calculating affinity matrix...", verbose): # Compute asymmetric pairwise input similarities conditional_P = np.exp(-(distances ** 2) / (2 * sigma ** 2)) conditional_P /= np.sum(conditional_P, axis=1)[:, np.newaxis] n_samples = self.knn_index.n_samples P = sp.csr_matrix( ( conditional_P.ravel(), neighbors.ravel(), range(0, n_samples * k + 1, k), ), shape=(n_samples, n_samples), ) # Symmetrize the probability matrix if symmetrize: P = (P + P.T) / 2 # Convert weights to probabilities P /= np.sum(P) self.sigma = sigma self.P = P self.n_jobs = n_jobs self.verbose = verbose def to_new(self, data, k=None, sigma=None, return_distances=False): """Compute the affinities of new samples to the initial samples. This is necessary for embedding new data points into an existing embedding. Parameters ---------- data: np.ndarray The data points to be added to the existing embedding. k: int The number of nearest neighbors to consider for each kernel. sigma: float The bandwidth to use for the Gaussian kernels in the ambient space. return_distances: bool If needed, the function can return the indices of the nearest neighbors and their corresponding distances. Returns ------- P: array_like An :math:`N \\times M` affinity matrix expressing interactions between :math:`N` new data points the initial :math:`M` data samples. indices: np.ndarray Returned if ``return_distances=True``. The indices of the :math:`k` nearest neighbors in the existing embedding for every new data point. distances: np.ndarray Returned if ``return_distances=True``. The distances to the :math:`k` nearest neighbors in the existing embedding for every new data point. """ n_samples = data.shape[0] n_reference_samples = self.n_samples if k is None: k = self.knn_index.k elif k >= n_reference_samples: raise ValueError( "`k` (%d) cannot be larger than the number of reference " "samples (%d)." % (k, self.n_samples) ) if sigma is None: sigma = self.sigma # Find nearest neighbors and the distances to the new points neighbors, distances = self.knn_index.query(data, k) with utils.Timer("Calculating affinity matrix...", self.verbose): # Compute asymmetric pairwise input similarities conditional_P = np.exp(-(distances ** 2) / (2 * sigma ** 2)) # Convert weights to probabilities conditional_P /= np.sum(conditional_P, axis=1)[:, np.newaxis] P = sp.csr_matrix( ( conditional_P.ravel(), neighbors.ravel(), range(0, n_samples * k + 1, k), ), shape=(n_samples, n_reference_samples), ) if return_distances: return P, neighbors, distances return P class MultiscaleMixture(Affinities): """Calculate affinities using a Gaussian mixture kernel. Instead of using a single perplexity to compute the affinities between data points, we can use a multiscale Gaussian kernel instead. This allows us to incorporate long range interactions. Please see the :ref:`parameter-guide` for more information. Parameters ---------- data: np.ndarray The data matrix. perplexities: List[float] A list of perplexity values, which will be used in the multiscale Gaussian kernel. Perplexity can be thought of as the continuous :math:`k` number of nearest neighbors, for which t-SNE will attempt to preserve distances. method: str Specifies the nearest neighbor method to use. Can be ``exact``, ``annoy``, ``pynndescent``, ``hnsw``, ``approx``, or ``auto`` (default). ``approx`` uses Annoy if the input data matrix is not a sparse object and if Annoy supports the given metric. Otherwise it uses Pynndescent. ``auto`` uses exact nearest neighbors for N<1000 and the same heuristic as ``approx`` for N>=1000. metric: Union[str, Callable] The metric to be used to compute affinities between points in the original space. metric_params: dict Additional keyword arguments for the metric function. symmetrize: bool Symmetrize affinity matrix. Standard t-SNE symmetrizes the interactions but when embedding new data, symmetrization is not performed. n_jobs: int The number of threads to use while running t-SNE. This follows the scikit-learn convention, ``-1`` meaning all processors, ``-2`` meaning all but one, etc. random_state: Union[int, RandomState] If the value is an int, random_state is the seed used by the random number generator. If the value is a RandomState instance, then it will be used as the random number generator. If the value is None, the random number generator is the RandomState instance used by `np.random`. verbose: bool knn_index: Optional[nearest_neighbors.KNNIndex] Optionally, a precomptued ``openTSNE.nearest_neighbors.KNNIndex`` object can be specified. This option will ignore any KNN-related parameters. When ``knn_index`` is specified, ``data`` must be set to None. """ def __init__( self, data=None, perplexities=None, method="auto", metric="euclidean", metric_params=None, symmetrize=True, n_jobs=1, random_state=None, verbose=False, knn_index=None, ): # Perplexities must be specified, but has default set to none, so the # parameter order makes more sense if perplexities is None: raise ValueError("`perplexities` must be specified!") # This can't work if neither data nor the knn index are specified if data is None and knn_index is None: raise ValueError( "At least one of the parameters `data` or `knn_index` must be specified!" ) # This can't work if both data and the knn index are specified if data is not None and knn_index is not None: raise ValueError( "Both `data` or `knn_index` were specified! Please pass only one." ) # Find the nearest neighbors if knn_index is None: # We will compute the nearest neighbors to the max value of perplexity, # smaller values can just use indexing to truncate unneeded neighbors n_samples = data.shape[0] perplexities = self.check_perplexities(perplexities, n_samples) max_perplexity = np.max(perplexities) k_neighbors = min(n_samples - 1, int(3 * max_perplexity)) self.knn_index = get_knn_index( data, method, k_neighbors, metric, metric_params, n_jobs, random_state, verbose ) else: self.knn_index = knn_index log.info("KNN index provided. Ignoring KNN-related parameters.") self.__neighbors, self.__distances = self.knn_index.build() with utils.Timer("Calculating affinity matrix...", verbose): self.P = self._calculate_P( self.__neighbors, self.__distances, perplexities, symmetrize=symmetrize, n_jobs=n_jobs, ) self.perplexities = perplexities self.symmetrize = symmetrize self.n_jobs = n_jobs self.verbose = verbose @staticmethod def _calculate_P( neighbors, distances, perplexities, symmetrize=True, normalization="pair-wise", n_reference_samples=None, n_jobs=1, ): return joint_probabilities_nn( neighbors, distances, perplexities, symmetrize=symmetrize, normalization=normalization, n_reference_samples=n_reference_samples, n_jobs=n_jobs, ) def set_perplexities(self, new_perplexities): """Change the perplexities of the affinity matrix. Note that we only allow lowering the perplexities or restoring them to their original maximum value. This restriction exists because setting a higher perplexity value requires recomputing all the nearest neighbors, which can take a long time. To avoid potential confusion as to why execution time is slow, this is not allowed. If you would like to increase the perplexity above the initial value, simply create a new instance. Parameters ---------- new_perplexities: List[float] The new list of perplexities. """ if np.array_equal(self.perplexities, new_perplexities): return new_perplexities = self.check_perplexities(new_perplexities, self.n_samples) max_perplexity = np.max(new_perplexities) k_neighbors = min(self.n_samples - 1, int(3 * max_perplexity)) if k_neighbors > self.__neighbors.shape[1]: raise RuntimeError( "The largest perplexity `%.2f` is larger than the initial one " "used. This would need to recompute the nearest neighbors, " "which is not efficient. Please create a new `%s` instance " "with the increased perplexity." % (max_perplexity, self.__class__.__name__) ) self.perplexities = new_perplexities with utils.Timer( "Perplexity changed. Recomputing affinity matrix...", self.verbose ): self.P = self._calculate_P( self.__neighbors[:, :k_neighbors], self.__distances[:, :k_neighbors], self.perplexities, symmetrize=self.symmetrize, n_jobs=self.n_jobs, ) def to_new(self, data, perplexities=None, return_distances=False): """Compute the affinities of new samples to the initial samples. This is necessary for embedding new data points into an existing embedding. Please see the :ref:`parameter-guide` for more information. Parameters ---------- data: np.ndarray The data points to be added to the existing embedding. perplexities: List[float] A list of perplexity values, which will be used in the multiscale Gaussian kernel. Perplexity can be thought of as the continuous :math:`k` number of nearest neighbors, for which t-SNE will attempt to preserve distances. return_distances: bool If needed, the function can return the indices of the nearest neighbors and their corresponding distances. Returns ------- P: array_like An :math:`N \\times M` affinity matrix expressing interactions between :math:`N` new data points the initial :math:`M` data samples. indices: np.ndarray Returned if ``return_distances=True``. The indices of the :math:`k` nearest neighbors in the existing embedding for every new data point. distances: np.ndarray Returned if ``return_distances=True``. The distances to the :math:`k` nearest neighbors in the existing embedding for every new data point. """ perplexities = perplexities if perplexities is not None else self.perplexities perplexities = self.check_perplexities(perplexities, self.n_samples) max_perplexity = np.max(perplexities) k_neighbors = min(self.n_samples - 1, int(3 * max_perplexity)) neighbors, distances = self.knn_index.query(data, k_neighbors) with utils.Timer("Calculating affinity matrix...", self.verbose): P = self._calculate_P( neighbors, distances, perplexities, symmetrize=False, normalization="point-wise", n_reference_samples=self.n_samples, n_jobs=self.n_jobs, ) if return_distances: return P, neighbors, distances return P def check_perplexities(self, perplexities, n_samples): """Check and correct/truncate perplexities. If a perplexity is too large, it is corrected to the largest allowed value. It is then inserted into the list of perplexities only if that value doesn't already exist in the list. """ usable_perplexities = [] for perplexity in sorted(perplexities): if perplexity <= 0: raise ValueError("Perplexity must be >=0. %.2f given" % perplexity) if 3 * perplexity > n_samples - 1: new_perplexity = (n_samples - 1) / 3 if new_perplexity in usable_perplexities: log.warning( "Perplexity value %d is too high. Dropping " "because the max perplexity is already in the " "list." % perplexity ) else: usable_perplexities.append(new_perplexity) log.warning( "Perplexity value %d is too high. Using " "perplexity %.2f instead" % (perplexity, new_perplexity) ) else: usable_perplexities.append(perplexity) return usable_perplexities class Multiscale(MultiscaleMixture): """Calculate affinities using averaged Gaussian perplexities. In contrast to :class:`MultiscaleMixture`, which uses a Gaussian mixture kernel, here, we first compute single scale Gaussian kernels, convert them to probability distributions, then average them out between scales. Please see the :ref:`parameter-guide` for more information. Parameters ---------- data: np.ndarray The data matrix. perplexities: List[float] A list of perplexity values, which will be used in the multiscale Gaussian kernel. Perplexity can be thought of as the continuous :math:`k` number of nearest neighbors, for which t-SNE will attempt to preserve distances. method: str Specifies the nearest neighbor method to use. Can be ``exact``, ``annoy``, ``pynndescent``, ``hnsw``, ``approx``, or ``auto`` (default). ``approx`` uses Annoy if the input data matrix is not a sparse object and if Annoy supports the given metric. Otherwise it uses Pynndescent. ``auto`` uses exact nearest neighbors for N<1000 and the same heuristic as ``approx`` for N>=1000. metric: Union[str, Callable] The metric to be used to compute affinities between points in the original space. metric_params: dict Additional keyword arguments for the metric function. symmetrize: bool Symmetrize affinity matrix. Standard t-SNE symmetrizes the interactions but when embedding new data, symmetrization is not performed. n_jobs: int The number of threads to use while running t-SNE. This follows the scikit-learn convention, ``-1`` meaning all processors, ``-2`` meaning all but one, etc. random_state: Union[int, RandomState] If the value is an int, random_state is the seed used by the random number generator. If the value is a RandomState instance, then it will be used as the random number generator. If the value is None, the random number generator is the RandomState instance used by `np.random`. verbose: bool knn_index: Optional[nearest_neighbors.KNNIndex] Optionally, a precomptued ``openTSNE.nearest_neighbors.KNNIndex`` object can be specified. This option will ignore any KNN-related parameters. When ``knn_index`` is specified, ``data`` must be set to None. """ @staticmethod def _calculate_P( neighbors, distances, perplexities, symmetrize=True, normalization="pair-wise", n_reference_samples=None, n_jobs=1, ): # Compute normalized probabilities for each perplexity partial_Ps = [ joint_probabilities_nn( neighbors, distances, [perplexity], symmetrize=symmetrize, normalization=normalization, n_reference_samples=n_reference_samples, n_jobs=n_jobs, ) for perplexity in perplexities ] # Sum them together, then normalize P = reduce(operator.add, partial_Ps, 0) # Take care to properly normalize the affinity matrix if normalization == "pair-wise": P /= np.sum(P) elif normalization == "point-wise": P = sp.diags(np.asarray(1 / P.sum(axis=1)).ravel()) @ P return P class Uniform(Affinities): """Compute affinities using using nearest neighbors and uniform kernel in the ambient space. Parameters ---------- data: np.ndarray The data matrix. k_neighbors: int method: str Specifies the nearest neighbor method to use. Can be ``exact``, ``annoy``, ``pynndescent``, ``hnsw``, ``approx``, or ``auto`` (default). ``approx`` uses Annoy if the input data matrix is not a sparse object and if Annoy supports the given metric. Otherwise it uses Pynndescent. ``auto`` uses exact nearest neighbors for N<1000 and the same heuristic as ``approx`` for N>=1000. metric: Union[str, Callable] The metric to be used to compute affinities between points in the original space. metric_params: dict Additional keyword arguments for the metric function. symmetrize: bool Symmetrize affinity matrix. Standard t-SNE symmetrizes the interactions but when embedding new data, symmetrization is not performed. n_jobs: int The number of threads to use while running t-SNE. This follows the scikit-learn convention, ``-1`` meaning all processors, ``-2`` meaning all but one, etc. random_state: Union[int, RandomState] If the value is an int, random_state is the seed used by the random number generator. If the value is a RandomState instance, then it will be used as the random number generator. If the value is None, the random number generator is the RandomState instance used by `np.random`. verbose: bool knn_index: Optional[nearest_neighbors.KNNIndex] Optionally, a precomptued ``openTSNE.nearest_neighbors.KNNIndex`` object can be specified. This option will ignore any KNN-related parameters. When ``knn_index`` is specified, ``data`` must be set to None. """ def __init__( self, data=None, k_neighbors=30, method="auto", metric="euclidean", metric_params=None, symmetrize=True, n_jobs=1, random_state=None, verbose=False, knn_index=None, ): # This can't work if neither data nor the knn index are specified if data is None and knn_index is None: raise ValueError( "At least one of the parameters `data` or `knn_index` must be specified!" ) # This can't work if both data and the knn index are specified if data is not None and knn_index is not None: raise ValueError( "Both `data` or `knn_index` were specified! Please pass only one." ) if knn_index is None: if k_neighbors >= data.shape[0]: raise ValueError( "`k_neighbors` (%d) cannot be larger than N-1 (%d)." % (k_neighbors, data.shape[0]) ) self.knn_index = get_knn_index( data, method, k_neighbors, metric, metric_params, n_jobs, random_state, verbose ) else: self.knn_index = knn_index log.info("KNN index provided. Ignoring KNN-related parameters.") neighbors, distances = self.knn_index.build() k_neighbors = self.knn_index.k n_samples = self.knn_index.n_samples P = sp.csr_matrix( ( np.ones_like(distances).ravel(), neighbors.ravel(), range(0, n_samples * k_neighbors + 1, k_neighbors), ), shape=(n_samples, n_samples), ) # Symmetrize the probability matrix if symmetrize: P = (P + P.T) / 2 # Convert weights to probabilities P /= np.sum(P) self.P = P self.verbose = verbose self.n_jobs = n_jobs def to_new(self, data, k_neighbors=None, return_distances=False): """Compute the affinities of new samples to the initial samples. This is necessary for embedding new data points into an existing embedding. Parameters ---------- data: np.ndarray The data points to be added to the existing embedding. k_neighbors: int The number of nearest neighbors to consider. return_distances: bool If needed, the function can return the indices of the nearest neighbors and their corresponding distances. Returns ------- P: array_like An :math:`N \\times M` affinity matrix expressing interactions between :math:`N` new data points the initial :math:`M` data samples. indices: np.ndarray Returned if ``return_distances=True``. The indices of the :math:`k` nearest neighbors in the existing embedding for every new data point. distances: np.ndarray Returned if ``return_distances=True``. The distances to the :math:`k` nearest neighbors in the existing embedding for every new data point. """ n_samples = data.shape[0] n_reference_samples = self.n_samples if k_neighbors is None: k_neighbors = self.knn_index.k elif k_neighbors >= n_reference_samples: raise ValueError( "`k` (%d) cannot be larger than the number of reference " "samples (%d)." % (k_neighbors, self.n_samples) ) # Find nearest neighbors and the distances to the new points neighbors, distances = self.knn_index.query(data, k_neighbors) values = np.ones_like(distances) values /= np.sum(values, axis=1)[:, np.newaxis] P = sp.csr_matrix( ( values.ravel(), neighbors.ravel(), range(0, n_samples * k_neighbors + 1, k_neighbors), ), shape=(n_samples, n_reference_samples), ) if return_distances: return P, neighbors, distances return P openTSNE-0.6.1/openTSNE/callbacks.py000066400000000000000000000075721413546205200171120ustar00rootroot00000000000000import logging from functools import partial import numpy as np from scipy.sparse import csr_matrix from openTSNE import kl_divergence from openTSNE.tsne import TSNEEmbedding log = logging.getLogger(__name__) class Callback: def optimization_about_to_start(self): """This is called at the beginning of the optimization procedure.""" def __call__(self, iteration, error, embedding): """This is the main method called from the optimization. Parameters ---------- iteration: int The current iteration number. error: float The current KL divergence of the given embedding. embedding: TSNEEmbedding The current t-SNE embedding. Returns ------- stop_optimization: bool If this value is set to ``True``, the optimization will be interrupted. """ class VerifyExaggerationError(Callback): """Used to verify that the exaggeration correction implemented in `gradient_descent` is correct.""" def __init__(self, embedding: TSNEEmbedding) -> None: self.embedding = embedding # Keep a copy of the unexaggerated affinity matrix self.P = self.embedding.affinities.P.copy() def __call__( self, iteration: int, corrected_error: float, embedding: TSNEEmbedding ): params = self.embedding.gradient_descent_params method = params["negative_gradient_method"] if np.sum(embedding.affinities.P) <= 1: log.warning("Are you sure you are testing an exaggerated P matrix?") if method == "fft": f = partial( kl_divergence.kl_divergence_approx_fft, n_interpolation_points=params["n_interpolation_points"], min_num_intervals=params["min_num_intervals"], ints_in_interval=params["ints_in_interval"], dof=params["dof"], ) elif method == "bh": f = partial( kl_divergence.kl_divergence_approx_bh, theta=params["theta"], dof=params["dof"], ) P = self.P true_error = f(P.indices, P.indptr, P.data, embedding) if abs(true_error - corrected_error) > 1e-8: raise RuntimeError("Correction term is wrong.") else: log.info( "Corrected: %.4f - True %.4f [eps %.4f]" % (corrected_error, true_error, abs(true_error - corrected_error)) ) class ErrorApproximations(Callback): """Check how good the error approximations are. Of course, we use an approximation for P so this itself is an approximation.""" def __init__(self, P: csr_matrix): self.P = P.copy() self.exact_errors = [] self.bh_errors = [] self.fft_errors = [] def __call__(self, iteration: int, error: float, embedding: TSNEEmbedding): exact_error = kl_divergence.kl_divergence_exact(self.P.toarray(), embedding) bh_error = kl_divergence.kl_divergence_approx_bh( self.P.indices, self.P.indptr, self.P.data, embedding ) fft_error = kl_divergence.kl_divergence_approx_fft( self.P.indices, self.P.indptr, self.P.data, embedding ) self.exact_errors.append(exact_error) self.bh_errors.append(bh_error) self.fft_errors.append(fft_error) def report(self): exact_errors = np.array(self.exact_errors) bh_errors = np.array(self.bh_errors) fft_errors = np.array(self.fft_errors) bh_diff = bh_errors - exact_errors print( "Barnes-Hut: mean difference %.4f (±%.4f)" % (np.mean(bh_diff), np.std(bh_diff)) ) fft_diff = fft_errors - exact_errors print( "Interpolation: mean difference %.4f (±%.4f)" % (np.mean(fft_diff), np.std(fft_diff)) ) openTSNE-0.6.1/openTSNE/dependencies/000077500000000000000000000000001413546205200172345ustar00rootroot00000000000000openTSNE-0.6.1/openTSNE/dependencies/__init__.py000066400000000000000000000000001413546205200213330ustar00rootroot00000000000000openTSNE-0.6.1/openTSNE/dependencies/annoy/000077500000000000000000000000001413546205200203605ustar00rootroot00000000000000openTSNE-0.6.1/openTSNE/dependencies/annoy/__init__.py000066400000000000000000000000521413546205200224660ustar00rootroot00000000000000from .annoylib import Annoy as AnnoyIndex openTSNE-0.6.1/openTSNE/dependencies/annoy/annoylib.h000066400000000000000000001277751413546205200223670ustar00rootroot00000000000000// Copyright (c) 2013 Spotify AB // // Licensed under the Apache License, Version 2.0 (the "License"); you may not // use this file except in compliance with the License. You may obtain a copy of // the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, WITHOUT // WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the // License for the specific language governing permissions and limitations under // the License. #ifndef ANNOYLIB_H #define ANNOYLIB_H #include #include #ifndef _MSC_VER #include #endif #include #include #include #include #include #if defined(_MSC_VER) && _MSC_VER == 1500 typedef unsigned char uint8_t; typedef signed __int32 int32_t; typedef unsigned __int64 uint64_t; typedef signed __int64 int64_t; #else #include #endif #if defined(_MSC_VER) || defined(__MINGW32__) // a bit hacky, but override some definitions to support 64 bit #define off_t int64_t #define lseek_getsize(fd) _lseeki64(fd, 0, SEEK_END) #ifndef NOMINMAX #define NOMINMAX #endif #include "mman.h" #include #else #include #define lseek_getsize(fd) lseek(fd, 0, SEEK_END) #endif #include #include #include #include #include #include #include #ifdef ANNOYLIB_MULTITHREADED_BUILD #include #include #include #endif #ifdef _MSC_VER // Needed for Visual Studio to disable runtime checks for mempcy #pragma runtime_checks("s", off) #endif // This allows others to supply their own logger / error printer without // requiring Annoy to import their headers. See RcppAnnoy for a use case. #ifndef __ERROR_PRINTER_OVERRIDE__ #define showUpdate(...) { fprintf(stderr, __VA_ARGS__ ); } #else #define showUpdate(...) { __ERROR_PRINTER_OVERRIDE__( __VA_ARGS__ ); } #endif // Portable alloc definition, cf Writing R Extensions, Section 1.6.4 #ifdef __GNUC__ // Includes GCC, clang and Intel compilers # undef alloca # define alloca(x) __builtin_alloca((x)) #elif defined(__sun) || defined(_AIX) // this is necessary (and sufficient) for Solaris 10 and AIX 6: # include #endif inline void set_error_from_errno(char **error, const char* msg) { showUpdate("%s: %s (%d)\n", msg, strerror(errno), errno); if (error) { *error = (char *)malloc(256); // TODO: win doesn't support snprintf sprintf(*error, "%s: %s (%d)", msg, strerror(errno), errno); } } inline void set_error_from_string(char **error, const char* msg) { showUpdate("%s\n", msg); if (error) { *error = (char *)malloc(strlen(msg) + 1); strcpy(*error, msg); } } // We let the v array in the Node struct take whatever space is needed, so this is a mostly insignificant number. // Compilers need *some* size defined for the v array, and some memory checking tools will flag for buffer overruns if this is set too low. #define V_ARRAY_SIZE 65536 #ifndef _MSC_VER #define popcount __builtin_popcountll #else // See #293, #358 #define popcount cole_popcount #endif #if !defined(NO_MANUAL_VECTORIZATION) && defined(__GNUC__) && (__GNUC__ >6) && defined(__AVX512F__) // See #402 #define USE_AVX512 #elif !defined(NO_MANUAL_VECTORIZATION) && defined(__AVX__) && defined (__SSE__) && defined(__SSE2__) && defined(__SSE3__) #define USE_AVX #else #endif #if defined(USE_AVX) || defined(USE_AVX512) #if defined(_MSC_VER) #include #elif defined(__GNUC__) #include #endif #endif #if !defined(__MINGW32__) #define FTRUNCATE_SIZE(x) static_cast(x) #else #define FTRUNCATE_SIZE(x) (x) #endif using std::vector; using std::pair; using std::numeric_limits; using std::make_pair; inline bool remap_memory_and_truncate(void** _ptr, int _fd, size_t old_size, size_t new_size) { #ifdef __linux__ *_ptr = mremap(*_ptr, old_size, new_size, MREMAP_MAYMOVE); bool ok = ftruncate(_fd, new_size) != -1; #else munmap(*_ptr, old_size); bool ok = ftruncate(_fd, FTRUNCATE_SIZE(new_size)) != -1; #ifdef MAP_POPULATE *_ptr = mmap(*_ptr, new_size, PROT_READ | PROT_WRITE, MAP_SHARED | MAP_POPULATE, _fd, 0); #else *_ptr = mmap(*_ptr, new_size, PROT_READ | PROT_WRITE, MAP_SHARED, _fd, 0); #endif #endif return ok; } namespace { template inline Node* get_node_ptr(const void* _nodes, const size_t _s, const S i) { return (Node*)((uint8_t *)_nodes + (_s * i)); } template inline T dot(const T* x, const T* y, int f) { T s = 0; for (int z = 0; z < f; z++) { s += (*x) * (*y); x++; y++; } return s; } template inline T manhattan_distance(const T* x, const T* y, int f) { T d = 0.0; for (int i = 0; i < f; i++) d += fabs(x[i] - y[i]); return d; } template inline T euclidean_distance(const T* x, const T* y, int f) { // Don't use dot-product: avoid catastrophic cancellation in #314. T d = 0.0; for (int i = 0; i < f; ++i) { const T tmp=*x - *y; d += tmp * tmp; ++x; ++y; } return d; } #ifdef USE_AVX // Horizontal single sum of 256bit vector. inline float hsum256_ps_avx(__m256 v) { const __m128 x128 = _mm_add_ps(_mm256_extractf128_ps(v, 1), _mm256_castps256_ps128(v)); const __m128 x64 = _mm_add_ps(x128, _mm_movehl_ps(x128, x128)); const __m128 x32 = _mm_add_ss(x64, _mm_shuffle_ps(x64, x64, 0x55)); return _mm_cvtss_f32(x32); } template<> inline float dot(const float* x, const float *y, int f) { float result = 0; if (f > 7) { __m256 d = _mm256_setzero_ps(); for (; f > 7; f -= 8) { d = _mm256_add_ps(d, _mm256_mul_ps(_mm256_loadu_ps(x), _mm256_loadu_ps(y))); x += 8; y += 8; } // Sum all floats in dot register. result += hsum256_ps_avx(d); } // Don't forget the remaining values. for (; f > 0; f--) { result += *x * *y; x++; y++; } return result; } template<> inline float manhattan_distance(const float* x, const float* y, int f) { float result = 0; int i = f; if (f > 7) { __m256 manhattan = _mm256_setzero_ps(); __m256 minus_zero = _mm256_set1_ps(-0.0f); for (; i > 7; i -= 8) { const __m256 x_minus_y = _mm256_sub_ps(_mm256_loadu_ps(x), _mm256_loadu_ps(y)); const __m256 distance = _mm256_andnot_ps(minus_zero, x_minus_y); // Absolute value of x_minus_y (forces sign bit to zero) manhattan = _mm256_add_ps(manhattan, distance); x += 8; y += 8; } // Sum all floats in manhattan register. result = hsum256_ps_avx(manhattan); } // Don't forget the remaining values. for (; i > 0; i--) { result += fabsf(*x - *y); x++; y++; } return result; } template<> inline float euclidean_distance(const float* x, const float* y, int f) { float result=0; if (f > 7) { __m256 d = _mm256_setzero_ps(); for (; f > 7; f -= 8) { const __m256 diff = _mm256_sub_ps(_mm256_loadu_ps(x), _mm256_loadu_ps(y)); d = _mm256_add_ps(d, _mm256_mul_ps(diff, diff)); // no support for fmadd in AVX... x += 8; y += 8; } // Sum all floats in dot register. result = hsum256_ps_avx(d); } // Don't forget the remaining values. for (; f > 0; f--) { float tmp = *x - *y; result += tmp * tmp; x++; y++; } return result; } #endif #ifdef USE_AVX512 template<> inline float dot(const float* x, const float *y, int f) { float result = 0; if (f > 15) { __m512 d = _mm512_setzero_ps(); for (; f > 15; f -= 16) { //AVX512F includes FMA d = _mm512_fmadd_ps(_mm512_loadu_ps(x), _mm512_loadu_ps(y), d); x += 16; y += 16; } // Sum all floats in dot register. result += _mm512_reduce_add_ps(d); } // Don't forget the remaining values. for (; f > 0; f--) { result += *x * *y; x++; y++; } return result; } template<> inline float manhattan_distance(const float* x, const float* y, int f) { float result = 0; int i = f; if (f > 15) { __m512 manhattan = _mm512_setzero_ps(); for (; i > 15; i -= 16) { const __m512 x_minus_y = _mm512_sub_ps(_mm512_loadu_ps(x), _mm512_loadu_ps(y)); manhattan = _mm512_add_ps(manhattan, _mm512_abs_ps(x_minus_y)); x += 16; y += 16; } // Sum all floats in manhattan register. result = _mm512_reduce_add_ps(manhattan); } // Don't forget the remaining values. for (; i > 0; i--) { result += fabsf(*x - *y); x++; y++; } return result; } template<> inline float euclidean_distance(const float* x, const float* y, int f) { float result=0; if (f > 15) { __m512 d = _mm512_setzero_ps(); for (; f > 15; f -= 16) { const __m512 diff = _mm512_sub_ps(_mm512_loadu_ps(x), _mm512_loadu_ps(y)); d = _mm512_fmadd_ps(diff, diff, d); x += 16; y += 16; } // Sum all floats in dot register. result = _mm512_reduce_add_ps(d); } // Don't forget the remaining values. for (; f > 0; f--) { float tmp = *x - *y; result += tmp * tmp; x++; y++; } return result; } #endif template inline T get_norm(T* v, int f) { return sqrt(dot(v, v, f)); } template inline void two_means(const vector& nodes, int f, Random& random, bool cosine, Node* p, Node* q) { /* This algorithm is a huge heuristic. Empirically it works really well, but I can't motivate it well. The basic idea is to keep two centroids and assign points to either one of them. We weight each centroid by the number of points assigned to it, so to balance it. */ static int iteration_steps = 200; size_t count = nodes.size(); size_t i = random.index(count); size_t j = random.index(count-1); j += (j >= i); // ensure that i != j Distance::template copy_node(p, nodes[i], f); Distance::template copy_node(q, nodes[j], f); if (cosine) { Distance::template normalize(p, f); Distance::template normalize(q, f); } Distance::init_node(p, f); Distance::init_node(q, f); int ic = 1, jc = 1; for (int l = 0; l < iteration_steps; l++) { size_t k = random.index(count); T di = ic * Distance::distance(p, nodes[k], f), dj = jc * Distance::distance(q, nodes[k], f); T norm = cosine ? get_norm(nodes[k]->v, f) : 1; if (!(norm > T(0))) { continue; } if (di < dj) { for (int z = 0; z < f; z++) p->v[z] = (p->v[z] * ic + nodes[k]->v[z] / norm) / (ic + 1); Distance::init_node(p, f); ic++; } else if (dj < di) { for (int z = 0; z < f; z++) q->v[z] = (q->v[z] * jc + nodes[k]->v[z] / norm) / (jc + 1); Distance::init_node(q, f); jc++; } } } } // namespace struct Base { template static inline void preprocess(void* nodes, size_t _s, const S node_count, const int f) { // Override this in specific metric structs below if you need to do any pre-processing // on the entire set of nodes passed into this index. } template static inline void zero_value(Node* dest) { // Initialize any fields that require sane defaults within this node. } template static inline void copy_node(Node* dest, const Node* source, const int f) { memcpy(dest->v, source->v, f * sizeof(T)); } template static inline void normalize(Node* node, int f) { T norm = get_norm(node->v, f); if (norm > 0) { for (int z = 0; z < f; z++) node->v[z] /= norm; } } }; struct Angular : Base { template struct Node { /* * We store a binary tree where each node has two things * - A vector associated with it * - Two children * All nodes occupy the same amount of memory * All nodes with n_descendants == 1 are leaf nodes. * A memory optimization is that for nodes with 2 <= n_descendants <= K, * we skip the vector. Instead we store a list of all descendants. K is * determined by the number of items that fits in the space of the vector. * For nodes with n_descendants == 1 the vector is a data point. * For nodes with n_descendants > K the vector is the normal of the split plane. * Note that we can't really do sizeof(node) because we cheat and allocate * more memory to be able to fit the vector outside */ S n_descendants; union { S children[2]; // Will possibly store more than 2 T norm; }; T v[V_ARRAY_SIZE]; }; template static inline T distance(const Node* x, const Node* y, int f) { // want to calculate (a/|a| - b/|b|)^2 // = a^2 / a^2 + b^2 / b^2 - 2ab/|a||b| // = 2 - 2cos T pp = x->norm ? x->norm : dot(x->v, x->v, f); // For backwards compatibility reasons, we need to fall back and compute the norm here T qq = y->norm ? y->norm : dot(y->v, y->v, f); T pq = dot(x->v, y->v, f); T ppqq = pp * qq; if (ppqq > 0) return 2.0 - 2.0 * pq / sqrt(ppqq); else return 2.0; // cos is 0 } template static inline T margin(const Node* n, const T* y, int f) { return dot(n->v, y, f); } template static inline bool side(const Node* n, const T* y, int f, Random& random) { T dot = margin(n, y, f); if (dot != 0) return (dot > 0); else return (bool)random.flip(); } template static inline void create_split(const vector*>& nodes, int f, size_t s, Random& random, Node* n) { Node* p = (Node*)alloca(s); Node* q = (Node*)alloca(s); two_means >(nodes, f, random, true, p, q); for (int z = 0; z < f; z++) n->v[z] = p->v[z] - q->v[z]; Base::normalize >(n, f); } template static inline T normalized_distance(T distance) { // Used when requesting distances from Python layer // Turns out sometimes the squared distance is -0.0 // so we have to make sure it's a positive number. return sqrt(std::max(distance, T(0))); } template static inline T pq_distance(T distance, T margin, int child_nr) { if (child_nr == 0) margin = -margin; return std::min(distance, margin); } template static inline T pq_initial_value() { return numeric_limits::infinity(); } template static inline void init_node(Node* n, int f) { n->norm = dot(n->v, n->v, f); } static const char* name() { return "angular"; } }; struct DotProduct : Angular { template struct Node { /* * This is an extension of the Angular node with an extra attribute for the scaled norm. */ S n_descendants; S children[2]; // Will possibly store more than 2 T dot_factor; T v[V_ARRAY_SIZE]; }; static const char* name() { return "dot"; } template static inline T distance(const Node* x, const Node* y, int f) { return -dot(x->v, y->v, f); } template static inline void zero_value(Node* dest) { dest->dot_factor = 0; } template static inline void init_node(Node* n, int f) { } template static inline void copy_node(Node* dest, const Node* source, const int f) { memcpy(dest->v, source->v, f * sizeof(T)); dest->dot_factor = source->dot_factor; } template static inline void create_split(const vector*>& nodes, int f, size_t s, Random& random, Node* n) { Node* p = (Node*)alloca(s); Node* q = (Node*)alloca(s); DotProduct::zero_value(p); DotProduct::zero_value(q); two_means >(nodes, f, random, true, p, q); for (int z = 0; z < f; z++) n->v[z] = p->v[z] - q->v[z]; n->dot_factor = p->dot_factor - q->dot_factor; DotProduct::normalize >(n, f); } template static inline void normalize(Node* node, int f) { T norm = sqrt(dot(node->v, node->v, f) + pow(node->dot_factor, 2)); if (norm > 0) { for (int z = 0; z < f; z++) node->v[z] /= norm; node->dot_factor /= norm; } } template static inline T margin(const Node* n, const T* y, int f) { return dot(n->v, y, f) + (n->dot_factor * n->dot_factor); } template static inline bool side(const Node* n, const T* y, int f, Random& random) { T dot = margin(n, y, f); if (dot != 0) return (dot > 0); else return (bool)random.flip(); } template static inline T normalized_distance(T distance) { return -distance; } template static inline void preprocess(void* nodes, size_t _s, const S node_count, const int f) { // This uses a method from Microsoft Research for transforming inner product spaces to cosine/angular-compatible spaces. // (Bachrach et al., 2014, see https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/XboxInnerProduct.pdf) // Step one: compute the norm of each vector and store that in its extra dimension (f-1) for (S i = 0; i < node_count; i++) { Node* node = get_node_ptr(nodes, _s, i); T d = dot(node->v, node->v, f); T norm = d < 0 ? 0 : sqrt(d); node->dot_factor = norm; } // Step two: find the maximum norm T max_norm = 0; for (S i = 0; i < node_count; i++) { Node* node = get_node_ptr(nodes, _s, i); if (node->dot_factor > max_norm) { max_norm = node->dot_factor; } } // Step three: set each vector's extra dimension to sqrt(max_norm^2 - norm^2) for (S i = 0; i < node_count; i++) { Node* node = get_node_ptr(nodes, _s, i); T node_norm = node->dot_factor; T squared_norm_diff = pow(max_norm, static_cast(2.0)) - pow(node_norm, static_cast(2.0)); T dot_factor = squared_norm_diff < 0 ? 0 : sqrt(squared_norm_diff); node->dot_factor = dot_factor; } } }; struct Hamming : Base { template struct Node { S n_descendants; S children[2]; T v[V_ARRAY_SIZE]; }; static const size_t max_iterations = 20; template static inline T pq_distance(T distance, T margin, int child_nr) { return distance - (margin != (unsigned int) child_nr); } template static inline T pq_initial_value() { return numeric_limits::max(); } template static inline int cole_popcount(T v) { // Note: Only used with MSVC 9, which lacks intrinsics and fails to // calculate std::bitset::count for v > 32bit. Uses the generalized // approach by Eric Cole. // See https://graphics.stanford.edu/~seander/bithacks.html#CountBitsSet64 v = v - ((v >> 1) & (T)~(T)0/3); v = (v & (T)~(T)0/15*3) + ((v >> 2) & (T)~(T)0/15*3); v = (v + (v >> 4)) & (T)~(T)0/255*15; return (T)(v * ((T)~(T)0/255)) >> (sizeof(T) - 1) * 8; } template static inline T distance(const Node* x, const Node* y, int f) { size_t dist = 0; for (int i = 0; i < f; i++) { dist += popcount(x->v[i] ^ y->v[i]); } return dist; } template static inline bool margin(const Node* n, const T* y, int f) { static const size_t n_bits = sizeof(T) * 8; T chunk = n->v[0] / n_bits; return (y[chunk] & (static_cast(1) << (n_bits - 1 - (n->v[0] % n_bits)))) != 0; } template static inline bool side(const Node* n, const T* y, int f, Random& random) { return margin(n, y, f); } template static inline void create_split(const vector*>& nodes, int f, size_t s, Random& random, Node* n) { size_t cur_size = 0; size_t i = 0; int dim = f * 8 * sizeof(T); for (; i < max_iterations; i++) { // choose random position to split at n->v[0] = random.index(dim); cur_size = 0; for (typename vector*>::const_iterator it = nodes.begin(); it != nodes.end(); ++it) { if (margin(n, (*it)->v, f)) { cur_size++; } } if (cur_size > 0 && cur_size < nodes.size()) { break; } } // brute-force search for splitting coordinate if (i == max_iterations) { int j = 0; for (; j < dim; j++) { n->v[0] = j; cur_size = 0; for (typename vector*>::const_iterator it = nodes.begin(); it != nodes.end(); ++it) { if (margin(n, (*it)->v, f)) { cur_size++; } } if (cur_size > 0 && cur_size < nodes.size()) { break; } } } } template static inline T normalized_distance(T distance) { return distance; } template static inline void init_node(Node* n, int f) { } static const char* name() { return "hamming"; } }; struct Minkowski : Base { template struct Node { S n_descendants; T a; // need an extra constant term to determine the offset of the plane S children[2]; T v[V_ARRAY_SIZE]; }; template static inline T margin(const Node* n, const T* y, int f) { return n->a + dot(n->v, y, f); } template static inline bool side(const Node* n, const T* y, int f, Random& random) { T dot = margin(n, y, f); if (dot != 0) return (dot > 0); else return (bool)random.flip(); } template static inline T pq_distance(T distance, T margin, int child_nr) { if (child_nr == 0) margin = -margin; return std::min(distance, margin); } template static inline T pq_initial_value() { return numeric_limits::infinity(); } }; struct Euclidean : Minkowski { template static inline T distance(const Node* x, const Node* y, int f) { return euclidean_distance(x->v, y->v, f); } template static inline void create_split(const vector*>& nodes, int f, size_t s, Random& random, Node* n) { Node* p = (Node*)alloca(s); Node* q = (Node*)alloca(s); two_means >(nodes, f, random, false, p, q); for (int z = 0; z < f; z++) n->v[z] = p->v[z] - q->v[z]; Base::normalize >(n, f); n->a = 0.0; for (int z = 0; z < f; z++) n->a += -n->v[z] * (p->v[z] + q->v[z]) / 2; } template static inline T normalized_distance(T distance) { return sqrt(std::max(distance, T(0))); } template static inline void init_node(Node* n, int f) { } static const char* name() { return "euclidean"; } }; struct Manhattan : Minkowski { template static inline T distance(const Node* x, const Node* y, int f) { return manhattan_distance(x->v, y->v, f); } template static inline void create_split(const vector*>& nodes, int f, size_t s, Random& random, Node* n) { Node* p = (Node*)alloca(s); Node* q = (Node*)alloca(s); two_means >(nodes, f, random, false, p, q); for (int z = 0; z < f; z++) n->v[z] = p->v[z] - q->v[z]; Base::normalize >(n, f); n->a = 0.0; for (int z = 0; z < f; z++) n->a += -n->v[z] * (p->v[z] + q->v[z]) / 2; } template static inline T normalized_distance(T distance) { return std::max(distance, T(0)); } template static inline void init_node(Node* n, int f) { } static const char* name() { return "manhattan"; } }; template class AnnoyIndexInterface { public: // Note that the methods with an **error argument will allocate memory and write the pointer to that string if error is non-NULL virtual ~AnnoyIndexInterface() {}; virtual bool add_item(S item, const T* w, char** error=NULL) = 0; virtual bool build(int q, int n_threads=-1, char** error=NULL) = 0; virtual bool unbuild(char** error=NULL) = 0; virtual bool save(const char* filename, bool prefault=false, char** error=NULL) = 0; virtual void unload() = 0; virtual bool load(const char* filename, bool prefault=false, char** error=NULL) = 0; virtual T get_distance(S i, S j) const = 0; virtual void get_nns_by_item(S item, size_t n, int search_k, vector* result, vector* distances) const = 0; virtual void get_nns_by_vector(const T* w, size_t n, int search_k, vector* result, vector* distances) const = 0; virtual S get_n_items() const = 0; virtual S get_n_trees() const = 0; virtual void verbose(bool v) = 0; virtual void get_item(S item, T* v) const = 0; virtual void set_seed(int q) = 0; virtual bool on_disk_build(const char* filename, char** error=NULL) = 0; }; template class AnnoyIndex : public AnnoyIndexInterface { /* * We use random projection to build a forest of binary trees of all items. * Basically just split the hyperspace into two sides by a hyperplane, * then recursively split each of those subtrees etc. * We create a tree like this q times. The default q is determined automatically * in such a way that we at most use 2x as much memory as the vectors take. */ public: typedef Distance D; typedef typename D::template Node Node; protected: const int _f; size_t _s; S _n_items; void* _nodes; // Could either be mmapped, or point to a memory buffer that we reallocate S _n_nodes; S _nodes_size; vector _roots; S _K; bool _is_seeded; int _seed; bool _loaded; bool _verbose; int _fd; bool _on_disk; bool _built; public: AnnoyIndex(int f) : _f(f) { _s = offsetof(Node, v) + _f * sizeof(T); // Size of each node _verbose = false; _built = false; _K = (S) (((size_t) (_s - offsetof(Node, children))) / sizeof(S)); // Max number of descendants to fit into node reinitialize(); // Reset everything } ~AnnoyIndex() { unload(); } int get_f() const { return _f; } bool add_item(S item, const T* w, char** error=NULL) { return add_item_impl(item, w, error); } template bool add_item_impl(S item, const W& w, char** error=NULL) { if (_loaded) { set_error_from_string(error, "You can't add an item to a loaded index"); return false; } _allocate_size(item + 1); Node* n = _get(item); D::zero_value(n); n->children[0] = 0; n->children[1] = 0; n->n_descendants = 1; for (int z = 0; z < _f; z++) n->v[z] = w[z]; D::init_node(n, _f); if (item >= _n_items) _n_items = item + 1; return true; } bool on_disk_build(const char* file, char** error=NULL) { _on_disk = true; _fd = open(file, O_RDWR | O_CREAT | O_TRUNC, (int) 0600); if (_fd == -1) { set_error_from_errno(error, "Unable to open"); _fd = 0; return false; } _nodes_size = 1; if (ftruncate(_fd, FTRUNCATE_SIZE(_s) * FTRUNCATE_SIZE(_nodes_size)) == -1) { set_error_from_errno(error, "Unable to truncate"); return false; } #ifdef MAP_POPULATE _nodes = (Node*) mmap(0, _s * _nodes_size, PROT_READ | PROT_WRITE, MAP_SHARED | MAP_POPULATE, _fd, 0); #else _nodes = (Node*) mmap(0, _s * _nodes_size, PROT_READ | PROT_WRITE, MAP_SHARED, _fd, 0); #endif return true; } bool build(int q, int n_threads=-1, char** error=NULL) { if (_loaded) { set_error_from_string(error, "You can't build a loaded index"); return false; } if (_built) { set_error_from_string(error, "You can't build a built index"); return false; } D::template preprocess(_nodes, _s, _n_items, _f); _n_nodes = _n_items; ThreadedBuildPolicy::template build(this, q, n_threads); // Also, copy the roots into the last segment of the array // This way we can load them faster without reading the whole file _allocate_size(_n_nodes + (S)_roots.size()); for (size_t i = 0; i < _roots.size(); i++) memcpy(_get(_n_nodes + (S)i), _get(_roots[i]), _s); _n_nodes += _roots.size(); if (_verbose) showUpdate("has %d nodes\n", _n_nodes); if (_on_disk) { if (!remap_memory_and_truncate(&_nodes, _fd, static_cast(_s) * static_cast(_nodes_size), static_cast(_s) * static_cast(_n_nodes))) { // TODO: this probably creates an index in a corrupt state... not sure what to do set_error_from_errno(error, "Unable to truncate"); return false; } _nodes_size = _n_nodes; } _built = true; return true; } bool unbuild(char** error=NULL) { if (_loaded) { set_error_from_string(error, "You can't unbuild a loaded index"); return false; } _roots.clear(); _n_nodes = _n_items; _built = false; return true; } bool save(const char* filename, bool prefault=false, char** error=NULL) { if (!_built) { set_error_from_string(error, "You can't save an index that hasn't been built"); return false; } if (_on_disk) { return true; } else { // Delete file if it already exists (See issue #335) unlink(filename); FILE *f = fopen(filename, "wb"); if (f == NULL) { set_error_from_errno(error, "Unable to open"); return false; } if (fwrite(_nodes, _s, _n_nodes, f) != (size_t) _n_nodes) { set_error_from_errno(error, "Unable to write"); return false; } if (fclose(f) == EOF) { set_error_from_errno(error, "Unable to close"); return false; } unload(); return load(filename, prefault, error); } } void reinitialize() { _fd = 0; _nodes = NULL; _loaded = false; _n_items = 0; _n_nodes = 0; _nodes_size = 0; _on_disk = false; _is_seeded = false; _roots.clear(); } void unload() { if (_on_disk && _fd) { close(_fd); munmap(_nodes, _s * _nodes_size); } else { if (_fd) { // we have mmapped data close(_fd); munmap(_nodes, _n_nodes * _s); } else if (_nodes) { // We have heap allocated data free(_nodes); } } reinitialize(); if (_verbose) showUpdate("unloaded\n"); } bool load(const char* filename, bool prefault=false, char** error=NULL) { _fd = open(filename, O_RDONLY, (int)0400); if (_fd == -1) { set_error_from_errno(error, "Unable to open"); _fd = 0; return false; } off_t size = lseek_getsize(_fd); if (size == -1) { set_error_from_errno(error, "Unable to get size"); return false; } else if (size == 0) { set_error_from_errno(error, "Size of file is zero"); return false; } else if (size % _s) { // Something is fishy with this index! set_error_from_errno(error, "Index size is not a multiple of vector size. Ensure you are opening using the same metric you used to create the index."); return false; } int flags = MAP_SHARED; if (prefault) { #ifdef MAP_POPULATE flags |= MAP_POPULATE; #else showUpdate("prefault is set to true, but MAP_POPULATE is not defined on this platform"); #endif } _nodes = (Node*)mmap(0, size, PROT_READ, flags, _fd, 0); _n_nodes = (S)(size / _s); // Find the roots by scanning the end of the file and taking the nodes with most descendants _roots.clear(); S m = -1; for (S i = _n_nodes - 1; i >= 0; i--) { S k = _get(i)->n_descendants; if (m == -1 || k == m) { _roots.push_back(i); m = k; } else { break; } } // hacky fix: since the last root precedes the copy of all roots, delete it if (_roots.size() > 1 && _get(_roots.front())->children[0] == _get(_roots.back())->children[0]) _roots.pop_back(); _loaded = true; _built = true; _n_items = m; if (_verbose) showUpdate("found %lu roots with degree %d\n", _roots.size(), m); return true; } T get_distance(S i, S j) const { return D::normalized_distance(D::distance(_get(i), _get(j), _f)); } void get_nns_by_item(S item, size_t n, int search_k, vector* result, vector* distances) const { // TODO: handle OOB const Node* m = _get(item); _get_all_nns(m->v, n, search_k, result, distances); } void get_nns_by_vector(const T* w, size_t n, int search_k, vector* result, vector* distances) const { _get_all_nns(w, n, search_k, result, distances); } S get_n_items() const { return _n_items; } S get_n_trees() const { return (S)_roots.size(); } void verbose(bool v) { _verbose = v; } void get_item(S item, T* v) const { // TODO: handle OOB Node* m = _get(item); memcpy(v, m->v, (_f) * sizeof(T)); } void set_seed(int seed) { _is_seeded = true; _seed = seed; } void thread_build(int q, int thread_idx, ThreadedBuildPolicy& threaded_build_policy) { Random _random; // Each thread needs its own seed, otherwise each thread would be building the same tree(s) int seed = _is_seeded ? _seed + thread_idx : thread_idx; _random.set_seed(seed); vector thread_roots; while (1) { if (q == -1) { threaded_build_policy.lock_n_nodes(); if (_n_nodes >= 2 * _n_items) { threaded_build_policy.unlock_n_nodes(); break; } threaded_build_policy.unlock_n_nodes(); } else { if (thread_roots.size() >= (size_t)q) { break; } } if (_verbose) showUpdate("pass %zd...\n", thread_roots.size()); vector indices; threaded_build_policy.lock_shared_nodes(); for (S i = 0; i < _n_items; i++) { if (_get(i)->n_descendants >= 1) { // Issue #223 indices.push_back(i); } } threaded_build_policy.unlock_shared_nodes(); thread_roots.push_back(_make_tree(indices, true, _random, threaded_build_policy)); } threaded_build_policy.lock_roots(); _roots.insert(_roots.end(), thread_roots.begin(), thread_roots.end()); threaded_build_policy.unlock_roots(); } protected: void _reallocate_nodes(S n) { const double reallocation_factor = 1.3; S new_nodes_size = std::max(n, (S) ((_nodes_size + 1) * reallocation_factor)); void *old = _nodes; if (_on_disk) { if (!remap_memory_and_truncate(&_nodes, _fd, static_cast(_s) * static_cast(_nodes_size), static_cast(_s) * static_cast(new_nodes_size)) && _verbose) showUpdate("File truncation error\n"); } else { _nodes = realloc(_nodes, _s * new_nodes_size); memset((char *) _nodes + (_nodes_size * _s) / sizeof(char), 0, (new_nodes_size - _nodes_size) * _s); } _nodes_size = new_nodes_size; if (_verbose) showUpdate("Reallocating to %d nodes: old_address=%p, new_address=%p\n", new_nodes_size, old, _nodes); } void _allocate_size(S n, ThreadedBuildPolicy& threaded_build_policy) { if (n > _nodes_size) { threaded_build_policy.lock_nodes(); _reallocate_nodes(n); threaded_build_policy.unlock_nodes(); } } void _allocate_size(S n) { if (n > _nodes_size) { _reallocate_nodes(n); } } Node* _get(const S i) const { return get_node_ptr(_nodes, _s, i); } double _split_imbalance(const vector& left_indices, const vector& right_indices) { double ls = (float)left_indices.size(); double rs = (float)right_indices.size(); float f = ls / (ls + rs + 1e-9); // Avoid 0/0 return std::max(f, 1-f); } S _make_tree(const vector& indices, bool is_root, Random& _random, ThreadedBuildPolicy& threaded_build_policy) { // The basic rule is that if we have <= _K items, then it's a leaf node, otherwise it's a split node. // There's some regrettable complications caused by the problem that root nodes have to be "special": // 1. We identify root nodes by the arguable logic that _n_items == n->n_descendants, regardless of how many descendants they actually have // 2. Root nodes with only 1 child need to be a "dummy" parent // 3. Due to the _n_items "hack", we need to be careful with the cases where _n_items <= _K or _n_items > _K if (indices.size() == 1 && !is_root) return indices[0]; if (indices.size() <= (size_t)_K && (!is_root || (size_t)_n_items <= (size_t)_K || indices.size() == 1)) { threaded_build_policy.lock_n_nodes(); _allocate_size(_n_nodes + 1, threaded_build_policy); S item = _n_nodes++; threaded_build_policy.unlock_n_nodes(); threaded_build_policy.lock_shared_nodes(); Node* m = _get(item); m->n_descendants = is_root ? _n_items : (S)indices.size(); // Using std::copy instead of a loop seems to resolve issues #3 and #13, // probably because gcc 4.8 goes overboard with optimizations. // Using memcpy instead of std::copy for MSVC compatibility. #235 // Only copy when necessary to avoid crash in MSVC 9. #293 if (!indices.empty()) memcpy(m->children, &indices[0], indices.size() * sizeof(S)); threaded_build_policy.unlock_shared_nodes(); return item; } threaded_build_policy.lock_shared_nodes(); vector children; for (size_t i = 0; i < indices.size(); i++) { S j = indices[i]; Node* n = _get(j); if (n) children.push_back(n); } vector children_indices[2]; Node* m = (Node*)alloca(_s); for (int attempt = 0; attempt < 3; attempt++) { children_indices[0].clear(); children_indices[1].clear(); D::create_split(children, _f, _s, _random, m); for (size_t i = 0; i < indices.size(); i++) { S j = indices[i]; Node* n = _get(j); if (n) { bool side = D::side(m, n->v, _f, _random); children_indices[side].push_back(j); } else { showUpdate("No node for index %d?\n", j); } } if (_split_imbalance(children_indices[0], children_indices[1]) < 0.95) break; } threaded_build_policy.unlock_shared_nodes(); // If we didn't find a hyperplane, just randomize sides as a last option while (_split_imbalance(children_indices[0], children_indices[1]) > 0.99) { if (_verbose) showUpdate("\tNo hyperplane found (left has %ld children, right has %ld children)\n", children_indices[0].size(), children_indices[1].size()); children_indices[0].clear(); children_indices[1].clear(); // Set the vector to 0.0 for (int z = 0; z < _f; z++) m->v[z] = 0; for (size_t i = 0; i < indices.size(); i++) { S j = indices[i]; // Just randomize... children_indices[_random.flip()].push_back(j); } } int flip = (children_indices[0].size() > children_indices[1].size()); m->n_descendants = is_root ? _n_items : (S)indices.size(); for (int side = 0; side < 2; side++) { // run _make_tree for the smallest child first (for cache locality) m->children[side^flip] = _make_tree(children_indices[side^flip], false, _random, threaded_build_policy); } threaded_build_policy.lock_n_nodes(); _allocate_size(_n_nodes + 1, threaded_build_policy); S item = _n_nodes++; threaded_build_policy.unlock_n_nodes(); threaded_build_policy.lock_shared_nodes(); memcpy(_get(item), m, _s); threaded_build_policy.unlock_shared_nodes(); return item; } void _get_all_nns(const T* v, size_t n, int search_k, vector* result, vector* distances) const { Node* v_node = (Node *)alloca(_s); D::template zero_value(v_node); memcpy(v_node->v, v, sizeof(T) * _f); D::init_node(v_node, _f); std::priority_queue > q; if (search_k == -1) { search_k = n * _roots.size(); } for (size_t i = 0; i < _roots.size(); i++) { q.push(make_pair(Distance::template pq_initial_value(), _roots[i])); } std::vector nns; while (nns.size() < (size_t)search_k && !q.empty()) { const pair& top = q.top(); T d = top.first; S i = top.second; Node* nd = _get(i); q.pop(); if (nd->n_descendants == 1 && i < _n_items) { nns.push_back(i); } else if (nd->n_descendants <= _K) { const S* dst = nd->children; nns.insert(nns.end(), dst, &dst[nd->n_descendants]); } else { T margin = D::margin(nd, v, _f); q.push(make_pair(D::pq_distance(d, margin, 1), static_cast(nd->children[1]))); q.push(make_pair(D::pq_distance(d, margin, 0), static_cast(nd->children[0]))); } } // Get distances for all items // To avoid calculating distance multiple times for any items, sort by id std::sort(nns.begin(), nns.end()); vector > nns_dist; S last = -1; for (size_t i = 0; i < nns.size(); i++) { S j = nns[i]; if (j == last) continue; last = j; if (_get(j)->n_descendants == 1) // This is only to guard a really obscure case, #284 nns_dist.push_back(make_pair(D::distance(v_node, _get(j), _f), j)); } size_t m = nns_dist.size(); size_t p = n < m ? n : m; // Return this many items std::partial_sort(nns_dist.begin(), nns_dist.begin() + p, nns_dist.end()); for (size_t i = 0; i < p; i++) { if (distances) distances->push_back(D::normalized_distance(nns_dist[i].first)); result->push_back(nns_dist[i].second); } } }; class AnnoyIndexSingleThreadedBuildPolicy { public: template static void build(AnnoyIndex* annoy, int q, int n_threads) { AnnoyIndexSingleThreadedBuildPolicy threaded_build_policy; annoy->thread_build(q, 0, threaded_build_policy); } void lock_n_nodes() {} void unlock_n_nodes() {} void lock_nodes() {} void unlock_nodes() {} void lock_shared_nodes() {} void unlock_shared_nodes() {} void lock_roots() {} void unlock_roots() {} }; #ifdef ANNOYLIB_MULTITHREADED_BUILD class AnnoyIndexMultiThreadedBuildPolicy { private: std::shared_timed_mutex nodes_mutex; std::mutex n_nodes_mutex; std::mutex roots_mutex; public: template static void build(AnnoyIndex* annoy, int q, int n_threads) { AnnoyIndexMultiThreadedBuildPolicy threaded_build_policy; if (n_threads == -1) { // If the hardware_concurrency() value is not well defined or not computable, it returns 0. // We guard against this by using at least 1 thread. n_threads = std::max(1, (int)std::thread::hardware_concurrency()); } vector threads(n_threads); for (int thread_idx = 0; thread_idx < n_threads; thread_idx++) { int trees_per_thread = q == -1 ? -1 : (int)floor((q + thread_idx) / n_threads); threads[thread_idx] = std::thread( &AnnoyIndex::thread_build, annoy, trees_per_thread, thread_idx, std::ref(threaded_build_policy) ); } for (auto& thread : threads) { thread.join(); } } void lock_n_nodes() { n_nodes_mutex.lock(); } void unlock_n_nodes() { n_nodes_mutex.unlock(); } void lock_nodes() { nodes_mutex.lock(); } void unlock_nodes() { nodes_mutex.unlock(); } void lock_shared_nodes() { nodes_mutex.lock_shared(); } void unlock_shared_nodes() { nodes_mutex.unlock_shared(); } void lock_roots() { roots_mutex.lock(); } void unlock_roots() { roots_mutex.unlock(); } }; #endif #endif // vim: tabstop=2 shiftwidth=2 openTSNE-0.6.1/openTSNE/dependencies/annoy/annoymodule.cc000066400000000000000000000502451413546205200232270ustar00rootroot00000000000000// Copyright (c) 2013 Spotify AB // // Licensed under the Apache License, Version 2.0 (the "License"); you may not // use this file except in compliance with the License. You may obtain a copy of // the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, WITHOUT // WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the // License for the specific language governing permissions and limitations under // the License. #include "annoylib.h" #include "kissrandom.h" #include "Python.h" #include "structmember.h" #include #if defined(_MSC_VER) && _MSC_VER == 1500 typedef signed __int32 int32_t; #else #include #endif #if defined(USE_AVX512) #define AVX_INFO "Using 512-bit AVX instructions" #elif defined(USE_AVX128) #define AVX_INFO "Using 128-bit AVX instructions" #else #define AVX_INFO "Not using AVX instructions" #endif #if defined(_MSC_VER) #define COMPILER_INFO "Compiled using MSC" #elif defined(__GNUC__) #define COMPILER_INFO "Compiled on GCC" #else #define COMPILER_INFO "Compiled on unknown platform" #endif #define ANNOY_DOC (COMPILER_INFO ". " AVX_INFO ".") #if PY_MAJOR_VERSION >= 3 #define IS_PY3K #endif #ifndef Py_TYPE #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type) #endif #ifdef IS_PY3K #define PyInt_FromLong PyLong_FromLong #endif #ifdef ANNOYLIB_MULTITHREADED_BUILD typedef AnnoyIndexMultiThreadedBuildPolicy AnnoyIndexThreadedBuildPolicy; #else typedef AnnoyIndexSingleThreadedBuildPolicy AnnoyIndexThreadedBuildPolicy; #endif template class AnnoyIndexInterface; class HammingWrapper : public AnnoyIndexInterface { // Wrapper class for Hamming distance, using composition. // This translates binary (float) vectors into packed uint64_t vectors. // This is questionable from a performance point of view. Should reconsider this solution. private: int32_t _f_external, _f_internal; AnnoyIndex _index; void _pack(const float* src, uint64_t* dst) const { for (int32_t i = 0; i < _f_internal; i++) { dst[i] = 0; for (int32_t j = 0; j < 64 && i*64+j < _f_external; j++) { dst[i] |= (uint64_t)(src[i * 64 + j] > 0.5) << j; } } }; void _unpack(const uint64_t* src, float* dst) const { for (int32_t i = 0; i < _f_external; i++) { dst[i] = (src[i / 64] >> (i % 64)) & 1; } }; public: HammingWrapper(int f) : _f_external(f), _f_internal((f + 63) / 64), _index((f + 63) / 64) {}; bool add_item(int32_t item, const float* w, char**error) { vector w_internal(_f_internal, 0); _pack(w, &w_internal[0]); return _index.add_item(item, &w_internal[0], error); }; bool build(int q, int n_threads, char** error) { return _index.build(q, n_threads, error); }; bool unbuild(char** error) { return _index.unbuild(error); }; bool save(const char* filename, bool prefault, char** error) { return _index.save(filename, prefault, error); }; void unload() { _index.unload(); }; bool load(const char* filename, bool prefault, char** error) { return _index.load(filename, prefault, error); }; float get_distance(int32_t i, int32_t j) const { return _index.get_distance(i, j); }; void get_nns_by_item(int32_t item, size_t n, int search_k, vector* result, vector* distances) const { if (distances) { vector distances_internal; _index.get_nns_by_item(item, n, search_k, result, &distances_internal); distances->insert(distances->begin(), distances_internal.begin(), distances_internal.end()); } else { _index.get_nns_by_item(item, n, search_k, result, NULL); } }; void get_nns_by_vector(const float* w, size_t n, int search_k, vector* result, vector* distances) const { vector w_internal(_f_internal, 0); _pack(w, &w_internal[0]); if (distances) { vector distances_internal; _index.get_nns_by_vector(&w_internal[0], n, search_k, result, &distances_internal); distances->insert(distances->begin(), distances_internal.begin(), distances_internal.end()); } else { _index.get_nns_by_vector(&w_internal[0], n, search_k, result, NULL); } }; int32_t get_n_items() const { return _index.get_n_items(); }; int32_t get_n_trees() const { return _index.get_n_trees(); }; void verbose(bool v) { _index.verbose(v); }; void get_item(int32_t item, float* v) const { vector v_internal(_f_internal, 0); _index.get_item(item, &v_internal[0]); _unpack(&v_internal[0], v); }; void set_seed(int q) { _index.set_seed(q); }; bool on_disk_build(const char* filename, char** error) { return _index.on_disk_build(filename, error); }; }; // annoy python object typedef struct { PyObject_HEAD int f; AnnoyIndexInterface* ptr; } py_annoy; static PyObject * py_an_new(PyTypeObject *type, PyObject *args, PyObject *kwargs) { py_annoy *self = (py_annoy *)type->tp_alloc(type, 0); if (self == NULL) { return NULL; } const char *metric = NULL; static char const * kwlist[] = {"f", "metric", NULL}; if (!PyArg_ParseTupleAndKeywords(args, kwargs, "i|s", (char**)kwlist, &self->f, &metric)) return NULL; if (!metric) { // This keeps coming up, see #368 etc PyErr_WarnEx(PyExc_FutureWarning, "The default argument for metric will be removed " "in future version of Annoy. Please pass metric='angular' explicitly.", 1); self->ptr = new AnnoyIndex(self->f); } else if (!strcmp(metric, "angular")) { self->ptr = new AnnoyIndex(self->f); } else if (!strcmp(metric, "euclidean")) { self->ptr = new AnnoyIndex(self->f); } else if (!strcmp(metric, "manhattan")) { self->ptr = new AnnoyIndex(self->f); } else if (!strcmp(metric, "hamming")) { self->ptr = new HammingWrapper(self->f); } else if (!strcmp(metric, "dot")) { self->ptr = new AnnoyIndex(self->f); } else { PyErr_SetString(PyExc_ValueError, "No such metric"); return NULL; } return (PyObject *)self; } static int py_an_init(py_annoy *self, PyObject *args, PyObject *kwargs) { // Seems to be needed for Python 3 const char *metric = NULL; int f; static char const * kwlist[] = {"f", "metric", NULL}; if (!PyArg_ParseTupleAndKeywords(args, kwargs, "i|s", (char**)kwlist, &f, &metric)) return (int) NULL; return 0; } static void py_an_dealloc(py_annoy* self) { delete self->ptr; Py_TYPE(self)->tp_free((PyObject*)self); } static PyMemberDef py_annoy_members[] = { {(char*)"f", T_INT, offsetof(py_annoy, f), 0, (char*)""}, {NULL} /* Sentinel */ }; static PyObject * py_an_load(py_annoy *self, PyObject *args, PyObject *kwargs) { char *filename, *error; bool prefault = false; if (!self->ptr) return NULL; static char const * kwlist[] = {"fn", "prefault", NULL}; if (!PyArg_ParseTupleAndKeywords(args, kwargs, "s|b", (char**)kwlist, &filename, &prefault)) return NULL; if (!self->ptr->load(filename, prefault, &error)) { PyErr_SetString(PyExc_IOError, error); free(error); return NULL; } Py_RETURN_TRUE; } static PyObject * py_an_save(py_annoy *self, PyObject *args, PyObject *kwargs) { char *filename, *error; bool prefault = false; if (!self->ptr) return NULL; static char const * kwlist[] = {"fn", "prefault", NULL}; if (!PyArg_ParseTupleAndKeywords(args, kwargs, "s|b", (char**)kwlist, &filename, &prefault)) return NULL; if (!self->ptr->save(filename, prefault, &error)) { PyErr_SetString(PyExc_IOError, error); free(error); return NULL; } Py_RETURN_TRUE; } PyObject* get_nns_to_python(const vector& result, const vector& distances, int include_distances) { PyObject* l = PyList_New(result.size()); for (size_t i = 0; i < result.size(); i++) PyList_SetItem(l, i, PyInt_FromLong(result[i])); if (!include_distances) return l; PyObject* d = PyList_New(distances.size()); for (size_t i = 0; i < distances.size(); i++) PyList_SetItem(d, i, PyFloat_FromDouble(distances[i])); PyObject* t = PyTuple_New(2); PyTuple_SetItem(t, 0, l); PyTuple_SetItem(t, 1, d); return t; } bool check_constraints(py_annoy *self, int32_t item, bool building) { if (item < 0) { PyErr_SetString(PyExc_IndexError, "Item index can not be negative"); return false; } else if (!building && item >= self->ptr->get_n_items()) { PyErr_SetString(PyExc_IndexError, "Item index larger than the largest item index"); return false; } else { return true; } } static PyObject* py_an_get_nns_by_item(py_annoy *self, PyObject *args, PyObject *kwargs) { int32_t item, n, search_k=-1, include_distances=0; if (!self->ptr) return NULL; static char const * kwlist[] = {"i", "n", "search_k", "include_distances", NULL}; if (!PyArg_ParseTupleAndKeywords(args, kwargs, "ii|ii", (char**)kwlist, &item, &n, &search_k, &include_distances)) return NULL; if (!check_constraints(self, item, false)) { return NULL; } vector result; vector distances; Py_BEGIN_ALLOW_THREADS; self->ptr->get_nns_by_item(item, n, search_k, &result, include_distances ? &distances : NULL); Py_END_ALLOW_THREADS; return get_nns_to_python(result, distances, include_distances); } bool convert_list_to_vector(PyObject* v, int f, vector* w) { if (PyObject_Size(v) == -1) { char buf[256]; snprintf(buf, 256, "Expected an iterable, got an object of type \"%s\"", v->ob_type->tp_name); PyErr_SetString(PyExc_ValueError, buf); return false; } if (PyObject_Size(v) != f) { char buf[128]; snprintf(buf, 128, "Vector has wrong length (expected %d, got %ld)", f, PyObject_Size(v)); PyErr_SetString(PyExc_IndexError, buf); return false; } for (int z = 0; z < f; z++) { PyObject *key = PyInt_FromLong(z); PyObject *pf = PyObject_GetItem(v, key); (*w)[z] = PyFloat_AsDouble(pf); Py_DECREF(key); Py_DECREF(pf); } return true; } static PyObject* py_an_get_nns_by_vector(py_annoy *self, PyObject *args, PyObject *kwargs) { PyObject* v; int32_t n, search_k=-1, include_distances=0; if (!self->ptr) return NULL; static char const * kwlist[] = {"vector", "n", "search_k", "include_distances", NULL}; if (!PyArg_ParseTupleAndKeywords(args, kwargs, "Oi|ii", (char**)kwlist, &v, &n, &search_k, &include_distances)) return NULL; vector w(self->f); if (!convert_list_to_vector(v, self->f, &w)) { return NULL; } vector result; vector distances; Py_BEGIN_ALLOW_THREADS; self->ptr->get_nns_by_vector(&w[0], n, search_k, &result, include_distances ? &distances : NULL); Py_END_ALLOW_THREADS; return get_nns_to_python(result, distances, include_distances); } static PyObject* py_an_get_item_vector(py_annoy *self, PyObject *args) { int32_t item; if (!self->ptr) return NULL; if (!PyArg_ParseTuple(args, "i", &item)) return NULL; if (!check_constraints(self, item, false)) { return NULL; } vector v(self->f); self->ptr->get_item(item, &v[0]); PyObject* l = PyList_New(self->f); for (int z = 0; z < self->f; z++) { PyList_SetItem(l, z, PyFloat_FromDouble(v[z])); } return l; } static PyObject* py_an_add_item(py_annoy *self, PyObject *args, PyObject* kwargs) { PyObject* v; int32_t item; if (!self->ptr) return NULL; static char const * kwlist[] = {"i", "vector", NULL}; if (!PyArg_ParseTupleAndKeywords(args, kwargs, "iO", (char**)kwlist, &item, &v)) return NULL; if (!check_constraints(self, item, true)) { return NULL; } vector w(self->f); if (!convert_list_to_vector(v, self->f, &w)) { return NULL; } char* error; if (!self->ptr->add_item(item, &w[0], &error)) { PyErr_SetString(PyExc_Exception, error); free(error); return NULL; } Py_RETURN_NONE; } static PyObject * py_an_on_disk_build(py_annoy *self, PyObject *args, PyObject *kwargs) { char *filename, *error; if (!self->ptr) return NULL; static char const * kwlist[] = {"fn", NULL}; if (!PyArg_ParseTupleAndKeywords(args, kwargs, "s", (char**)kwlist, &filename)) return NULL; if (!self->ptr->on_disk_build(filename, &error)) { PyErr_SetString(PyExc_IOError, error); free(error); return NULL; } Py_RETURN_TRUE; } static PyObject * py_an_build(py_annoy *self, PyObject *args, PyObject *kwargs) { int q; int n_jobs = -1; if (!self->ptr) return NULL; static char const * kwlist[] = {"n_trees", "n_jobs", NULL}; if (!PyArg_ParseTupleAndKeywords(args, kwargs, "i|i", (char**)kwlist, &q, &n_jobs)) return NULL; bool res; char* error; Py_BEGIN_ALLOW_THREADS; res = self->ptr->build(q, n_jobs, &error); Py_END_ALLOW_THREADS; if (!res) { PyErr_SetString(PyExc_Exception, error); free(error); return NULL; } Py_RETURN_TRUE; } static PyObject * py_an_unbuild(py_annoy *self) { if (!self->ptr) return NULL; char* error; if (!self->ptr->unbuild(&error)) { PyErr_SetString(PyExc_Exception, error); free(error); return NULL; } Py_RETURN_TRUE; } static PyObject * py_an_unload(py_annoy *self) { if (!self->ptr) return NULL; self->ptr->unload(); Py_RETURN_TRUE; } static PyObject * py_an_get_distance(py_annoy *self, PyObject *args) { int32_t i, j; if (!self->ptr) return NULL; if (!PyArg_ParseTuple(args, "ii", &i, &j)) return NULL; if (!check_constraints(self, i, false) || !check_constraints(self, j, false)) { return NULL; } double d = self->ptr->get_distance(i,j); return PyFloat_FromDouble(d); } static PyObject * py_an_get_n_items(py_annoy *self) { if (!self->ptr) return NULL; int32_t n = self->ptr->get_n_items(); return PyInt_FromLong(n); } static PyObject * py_an_get_n_trees(py_annoy *self) { if (!self->ptr) return NULL; int32_t n = self->ptr->get_n_trees(); return PyInt_FromLong(n); } static PyObject * py_an_verbose(py_annoy *self, PyObject *args) { int verbose; if (!self->ptr) return NULL; if (!PyArg_ParseTuple(args, "i", &verbose)) return NULL; self->ptr->verbose((bool)verbose); Py_RETURN_TRUE; } static PyObject * py_an_set_seed(py_annoy *self, PyObject *args) { int q; if (!self->ptr) return NULL; if (!PyArg_ParseTuple(args, "i", &q)) return NULL; self->ptr->set_seed(q); Py_RETURN_NONE; } static PyMethodDef AnnoyMethods[] = { {"load", (PyCFunction)py_an_load, METH_VARARGS | METH_KEYWORDS, "Loads (mmaps) an index from disk."}, {"save", (PyCFunction)py_an_save, METH_VARARGS | METH_KEYWORDS, "Saves the index to disk."}, {"get_nns_by_item",(PyCFunction)py_an_get_nns_by_item, METH_VARARGS | METH_KEYWORDS, "Returns the `n` closest items to item `i`.\n\n:param search_k: the query will inspect up to `search_k` nodes.\n`search_k` gives you a run-time tradeoff between better accuracy and speed.\n`search_k` defaults to `n_trees * n` if not provided.\n\n:param include_distances: If `True`, this function will return a\n2 element tuple of lists. The first list contains the `n` closest items.\nThe second list contains the corresponding distances."}, {"get_nns_by_vector",(PyCFunction)py_an_get_nns_by_vector, METH_VARARGS | METH_KEYWORDS, "Returns the `n` closest items to vector `vector`.\n\n:param search_k: the query will inspect up to `search_k` nodes.\n`search_k` gives you a run-time tradeoff between better accuracy and speed.\n`search_k` defaults to `n_trees * n` if not provided.\n\n:param include_distances: If `True`, this function will return a\n2 element tuple of lists. The first list contains the `n` closest items.\nThe second list contains the corresponding distances."}, {"get_item_vector",(PyCFunction)py_an_get_item_vector, METH_VARARGS, "Returns the vector for item `i` that was previously added."}, {"add_item",(PyCFunction)py_an_add_item, METH_VARARGS | METH_KEYWORDS, "Adds item `i` (any nonnegative integer) with vector `v`.\n\nNote that it will allocate memory for `max(i)+1` items."}, {"on_disk_build",(PyCFunction)py_an_on_disk_build, METH_VARARGS | METH_KEYWORDS, "Build will be performed with storage on disk instead of RAM."}, {"build",(PyCFunction)py_an_build, METH_VARARGS | METH_KEYWORDS, "Builds a forest of `n_trees` trees.\n\nMore trees give higher precision when querying. After calling `build`,\nno more items can be added. `n_jobs` specifies the number of threads used to build the trees. `n_jobs=-1` uses all available CPU cores."}, {"unbuild",(PyCFunction)py_an_unbuild, METH_NOARGS, "Unbuilds the tree in order to allows adding new items.\n\nbuild() has to be called again afterwards in order to\nrun queries."}, {"unload",(PyCFunction)py_an_unload, METH_NOARGS, "Unloads an index from disk."}, {"get_distance",(PyCFunction)py_an_get_distance, METH_VARARGS, "Returns the distance between items `i` and `j`."}, {"get_n_items",(PyCFunction)py_an_get_n_items, METH_NOARGS, "Returns the number of items in the index."}, {"get_n_trees",(PyCFunction)py_an_get_n_trees, METH_NOARGS, "Returns the number of trees in the index."}, {"verbose",(PyCFunction)py_an_verbose, METH_VARARGS, ""}, {"set_seed",(PyCFunction)py_an_set_seed, METH_VARARGS, "Sets the seed of Annoy's random number generator."}, {NULL, NULL, 0, NULL} /* Sentinel */ }; static PyTypeObject PyAnnoyType = { PyVarObject_HEAD_INIT(NULL, 0) "annoy.Annoy", /*tp_name*/ sizeof(py_annoy), /*tp_basicsize*/ 0, /*tp_itemsize*/ (destructor)py_an_dealloc, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ 0, /*tp_compare*/ 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash */ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /*tp_flags*/ ANNOY_DOC, /* tp_doc */ 0, /* tp_traverse */ 0, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ 0, /* tp_iter */ 0, /* tp_iternext */ AnnoyMethods, /* tp_methods */ py_annoy_members, /* tp_members */ 0, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ 0, /* tp_dictoffset */ (initproc)py_an_init, /* tp_init */ 0, /* tp_alloc */ py_an_new, /* tp_new */ }; static PyMethodDef module_methods[] = { {NULL} /* Sentinel */ }; #if PY_MAJOR_VERSION >= 3 static struct PyModuleDef moduledef = { PyModuleDef_HEAD_INIT, "annoylib", /* m_name */ ANNOY_DOC, /* m_doc */ -1, /* m_size */ module_methods, /* m_methods */ NULL, /* m_reload */ NULL, /* m_traverse */ NULL, /* m_clear */ NULL, /* m_free */ }; #endif PyObject *create_module(void) { PyObject *m; if (PyType_Ready(&PyAnnoyType) < 0) return NULL; #if PY_MAJOR_VERSION >= 3 m = PyModule_Create(&moduledef); #else m = Py_InitModule("annoylib", module_methods); #endif if (m == NULL) return NULL; Py_INCREF(&PyAnnoyType); PyModule_AddObject(m, "Annoy", (PyObject *)&PyAnnoyType); return m; } #if PY_MAJOR_VERSION >= 3 PyMODINIT_FUNC PyInit_annoylib(void) { return create_module(); // it should return moudule object in py3 } #else PyMODINIT_FUNC initannoylib(void) { create_module(); } #endif // vim: tabstop=2 shiftwidth=2 openTSNE-0.6.1/openTSNE/dependencies/annoy/kissrandom.h000066400000000000000000000044751413546205200227150ustar00rootroot00000000000000#ifndef KISSRANDOM_H #define KISSRANDOM_H #if defined(_MSC_VER) && _MSC_VER == 1500 typedef unsigned __int32 uint32_t; typedef unsigned __int64 uint64_t; #else #include #endif // KISS = "keep it simple, stupid", but high quality random number generator // http://www0.cs.ucl.ac.uk/staff/d.jones/GoodPracticeRNG.pdf -> "Use a good RNG and build it into your code" // http://mathforum.org/kb/message.jspa?messageID=6627731 // https://de.wikipedia.org/wiki/KISS_(Zufallszahlengenerator) // 32 bit KISS struct Kiss32Random { uint32_t x; uint32_t y; uint32_t z; uint32_t c; // seed must be != 0 Kiss32Random(uint32_t seed = 123456789) { x = seed; y = 362436000; z = 521288629; c = 7654321; } uint32_t kiss() { // Linear congruence generator x = 69069 * x + 12345; // Xor shift y ^= y << 13; y ^= y >> 17; y ^= y << 5; // Multiply-with-carry uint64_t t = 698769069ULL * z + c; c = t >> 32; z = (uint32_t) t; return x + y + z; } inline int flip() { // Draw random 0 or 1 return kiss() & 1; } inline size_t index(size_t n) { // Draw random integer between 0 and n-1 where n is at most the number of data points you have return kiss() % n; } inline void set_seed(uint32_t seed) { x = seed; } }; // 64 bit KISS. Use this if you have more than about 2^24 data points ("big data" ;) ) struct Kiss64Random { uint64_t x; uint64_t y; uint64_t z; uint64_t c; // seed must be != 0 Kiss64Random(uint64_t seed = 1234567890987654321ULL) { x = seed; y = 362436362436362436ULL; z = 1066149217761810ULL; c = 123456123456123456ULL; } uint64_t kiss() { // Linear congruence generator z = 6906969069LL*z+1234567; // Xor shift y ^= (y<<13); y ^= (y>>17); y ^= (y<<43); // Multiply-with-carry (uint128_t t = (2^58 + 1) * x + c; c = t >> 64; x = (uint64_t) t) uint64_t t = (x<<58)+c; c = (x>>6); x += t; c += (x #include #include #include #define PROT_NONE 0 #define PROT_READ 1 #define PROT_WRITE 2 #define PROT_EXEC 4 #define MAP_FILE 0 #define MAP_SHARED 1 #define MAP_PRIVATE 2 #define MAP_TYPE 0xf #define MAP_FIXED 0x10 #define MAP_ANONYMOUS 0x20 #define MAP_ANON MAP_ANONYMOUS #define MAP_FAILED ((void *)-1) /* Flags for msync. */ #define MS_ASYNC 1 #define MS_SYNC 2 #define MS_INVALIDATE 4 #ifndef FILE_MAP_EXECUTE #define FILE_MAP_EXECUTE 0x0020 #endif static int __map_mman_error(const DWORD err, const int deferr) { if (err == 0) return 0; //TODO: implement return err; } static DWORD __map_mmap_prot_page(const int prot) { DWORD protect = 0; if (prot == PROT_NONE) return protect; if ((prot & PROT_EXEC) != 0) { protect = ((prot & PROT_WRITE) != 0) ? PAGE_EXECUTE_READWRITE : PAGE_EXECUTE_READ; } else { protect = ((prot & PROT_WRITE) != 0) ? PAGE_READWRITE : PAGE_READONLY; } return protect; } static DWORD __map_mmap_prot_file(const int prot) { DWORD desiredAccess = 0; if (prot == PROT_NONE) return desiredAccess; if ((prot & PROT_READ) != 0) desiredAccess |= FILE_MAP_READ; if ((prot & PROT_WRITE) != 0) desiredAccess |= FILE_MAP_WRITE; if ((prot & PROT_EXEC) != 0) desiredAccess |= FILE_MAP_EXECUTE; return desiredAccess; } inline void* mmap(void *addr, size_t len, int prot, int flags, int fildes, off_t off) { HANDLE fm, h; void * map = MAP_FAILED; #ifdef _MSC_VER #pragma warning(push) #pragma warning(disable: 4293) #endif const DWORD dwFileOffsetLow = (sizeof(off_t) <= sizeof(DWORD)) ? (DWORD)off : (DWORD)(off & 0xFFFFFFFFL); const DWORD dwFileOffsetHigh = (sizeof(off_t) <= sizeof(DWORD)) ? (DWORD)0 : (DWORD)((off >> 32) & 0xFFFFFFFFL); const DWORD protect = __map_mmap_prot_page(prot); const DWORD desiredAccess = __map_mmap_prot_file(prot); const off_t maxSize = off + (off_t)len; const DWORD dwMaxSizeLow = (sizeof(off_t) <= sizeof(DWORD)) ? (DWORD)maxSize : (DWORD)(maxSize & 0xFFFFFFFFL); const DWORD dwMaxSizeHigh = (sizeof(off_t) <= sizeof(DWORD)) ? (DWORD)0 : (DWORD)((maxSize >> 32) & 0xFFFFFFFFL); #ifdef _MSC_VER #pragma warning(pop) #endif errno = 0; if (len == 0 /* Unsupported flag combinations */ || (flags & MAP_FIXED) != 0 /* Usupported protection combinations */ || prot == PROT_EXEC) { errno = EINVAL; return MAP_FAILED; } h = ((flags & MAP_ANONYMOUS) == 0) ? (HANDLE)_get_osfhandle(fildes) : INVALID_HANDLE_VALUE; if ((flags & MAP_ANONYMOUS) == 0 && h == INVALID_HANDLE_VALUE) { errno = EBADF; return MAP_FAILED; } fm = CreateFileMapping(h, NULL, protect, dwMaxSizeHigh, dwMaxSizeLow, NULL); if (fm == NULL) { errno = __map_mman_error(GetLastError(), EPERM); return MAP_FAILED; } map = MapViewOfFile(fm, desiredAccess, dwFileOffsetHigh, dwFileOffsetLow, len); CloseHandle(fm); if (map == NULL) { errno = __map_mman_error(GetLastError(), EPERM); return MAP_FAILED; } return map; } inline int munmap(void *addr, size_t len) { if (UnmapViewOfFile(addr)) return 0; errno = __map_mman_error(GetLastError(), EPERM); return -1; } inline int mprotect(void *addr, size_t len, int prot) { DWORD newProtect = __map_mmap_prot_page(prot); DWORD oldProtect = 0; if (VirtualProtect(addr, len, newProtect, &oldProtect)) return 0; errno = __map_mman_error(GetLastError(), EPERM); return -1; } inline int msync(void *addr, size_t len, int flags) { if (FlushViewOfFile(addr, len)) return 0; errno = __map_mman_error(GetLastError(), EPERM); return -1; } inline int mlock(const void *addr, size_t len) { if (VirtualLock((LPVOID)addr, len)) return 0; errno = __map_mman_error(GetLastError(), EPERM); return -1; } inline int munlock(const void *addr, size_t len) { if (VirtualUnlock((LPVOID)addr, len)) return 0; errno = __map_mman_error(GetLastError(), EPERM); return -1; } #if !defined(__MINGW32__) inline int ftruncate(const int fd, const int64_t size) { if (fd < 0) { errno = EBADF; return -1; } HANDLE h = reinterpret_cast(_get_osfhandle(fd)); LARGE_INTEGER li_start, li_size; li_start.QuadPart = static_cast(0); li_size.QuadPart = size; if (SetFilePointerEx(h, li_start, NULL, FILE_CURRENT) == ~0 || SetFilePointerEx(h, li_size, NULL, FILE_BEGIN) == ~0 || !SetEndOfFile(h)) { unsigned long error = GetLastError(); fprintf(stderr, "I/O error while truncating: %lu\n", error); switch (error) { case ERROR_INVALID_HANDLE: errno = EBADF; break; default: errno = EIO; break; } return -1; } return 0; } #endif #endif openTSNE-0.6.1/openTSNE/initialization.py000066400000000000000000000133661413546205200202200ustar00rootroot00000000000000import numpy as np import scipy.sparse as sp from sklearn.decomposition import PCA from sklearn.utils import check_random_state from openTSNE import utils def rescale(x, inplace=False): """Rescale an embedding so optimization will not have convergence issues. Parameters ---------- x: np.ndarray inplace: bool Returns ------- np.ndarray A scaled-down version of ``x``. """ if not inplace: x = np.array(x, copy=True) x /= np.std(x[:, 0]) * 10000 return x def random(n_samples, n_components=2, random_state=None, verbose=False): """Initialize an embedding using samples from an isotropic Gaussian. Parameters ---------- n_samples: Union[int, np.ndarray] The number of samples. Also accepts a data matrix. n_components: int The dimension of the embedding space. random_state: Union[int, RandomState] If the value is an int, random_state is the seed used by the random number generator. If the value is a RandomState instance, then it will be used as the random number generator. If the value is None, the random number generator is the RandomState instance used by `np.random`. verbose: bool Returns ------- initialization: np.ndarray """ random_state = check_random_state(random_state) if isinstance(n_samples, np.ndarray): n_samples = n_samples.shape[0] embedding = random_state.normal(0, 1e-4, (n_samples, n_components)) return np.ascontiguousarray(embedding) def pca(X, n_components=2, svd_solver="auto", random_state=None, verbose=False): """Initialize an embedding using the top principal components. Parameters ---------- X: np.ndarray The data matrix. n_components: int The dimension of the embedding space. svd_solver: str See sklearn.decomposition.PCA documentation. random_state: Union[int, RandomState] If the value is an int, random_state is the seed used by the random number generator. If the value is a RandomState instance, then it will be used as the random number generator. If the value is None, the random number generator is the RandomState instance used by `np.random`. verbose: bool Returns ------- initialization: np.ndarray """ timer = utils.Timer("Calculating PCA-based initialization...", verbose) timer.__enter__() pca_ = PCA( n_components=n_components, svd_solver=svd_solver, random_state=random_state ) embedding = pca_.fit_transform(X) rescale(embedding, inplace=True) timer.__exit__() return np.ascontiguousarray(embedding) def spectral(A, n_components=2, tol=1e-4, max_iter=None, random_state=None, verbose=False): """Initialize an embedding using the spectral embedding of the KNN graph. Specifically, we initialize data points by computing the diffusion map on the random walk transition matrix of the weighted graph given by the affiniy matrix. Parameters ---------- A: Union[sp.csr_matrix, sp.csc_matrix, ...] The graph adjacency matrix. n_components: int The dimension of the embedding space. tol: float See scipy.sparse.linalg.eigsh documentation. max_iter: float See scipy.sparse.linalg.eigsh documentation. random_state: Any Unused, but kept for consistency between initialization schemes. verbose: bool Returns ------- initialization: np.ndarray """ if A.ndim != 2: raise ValueError("The graph adjacency matrix must be a 2-dimensional matrix.") if A.shape[0] != A.shape[1]: raise ValueError("The graph adjacency matrix must be a square matrix.") timer = utils.Timer("Calculating spectral initialization...", verbose) timer.__enter__() D = sp.diags(np.ravel(np.sum(A, axis=1))) # Find leading eigenvectors k = n_components + 1 v0 = np.ones(A.shape[0]) / np.sqrt(A.shape[0]) eigvals, eigvecs = sp.linalg.eigsh( A, M=D, k=k, tol=tol, maxiter=max_iter, which="LM", v0=v0 ) # Sort the eigenvalues in decreasing order order = np.argsort(eigvals)[::-1] eigvecs = eigvecs[:, order] # In diffusion maps, we multiply the eigenvectors by their eigenvalues eigvecs *= eigvals # Drop the leading eigenvector embedding = eigvecs[:, 1:] rescale(embedding, inplace=True) timer.__exit__() return embedding def weighted_mean(X, embedding, neighbors, distances, verbose=False): """Initialize points onto an existing embedding by placing them in the weighted mean position of their nearest neighbors on the reference embedding. Parameters ---------- X: np.ndarray embedding: TSNEEmbedding neighbors: np.ndarray distances: np.ndarray verbose: bool Returns ------- np.ndarray """ n_samples = X.shape[0] n_components = embedding.shape[1] with utils.Timer("Calculating weighted-mean initialization...", verbose): partial_embedding = np.zeros((n_samples, n_components), order="C") for i in range(n_samples): partial_embedding[i] = np.average( embedding[neighbors[i]], axis=0, weights=distances[i] ) return partial_embedding def median(embedding, neighbors, verbose=False): """Initialize points onto an existing embedding by placing them in the median position of their nearest neighbors on the reference embedding. Parameters ---------- embedding: TSNEEmbedding neighbors: np.ndarray verbose: bool Returns ------- np.ndarray """ with utils.Timer("Calculating meadian initialization...", verbose): embedding = np.median(embedding[neighbors], axis=1) return np.ascontiguousarray(embedding) openTSNE-0.6.1/openTSNE/kl_divergence.cpp000066400000000000000000034064431413546205200201310ustar00rootroot00000000000000/* Generated by Cython 0.29.23 */ /* BEGIN: Cython Metadata { "distutils": { "depends": [], "language": "c++", "name": "openTSNE.kl_divergence", "sources": [ "openTSNE/kl_divergence.pyx" ] }, "module_name": "openTSNE.kl_divergence" } END: Cython Metadata */ #ifndef PY_SSIZE_T_CLEAN #define PY_SSIZE_T_CLEAN #endif /* PY_SSIZE_T_CLEAN */ #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) #error Cython requires Python 2.6+ or Python 3.3+. #else #define CYTHON_ABI "0_29_23" #define CYTHON_HEX_VERSION 0x001D17F0 #define CYTHON_FUTURE_DIVISION 1 #include #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #define __PYX_COMMA , #ifndef HAVE_LONG_LONG #if PY_VERSION_HEX >= 0x02070000 #define HAVE_LONG_LONG #endif #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #ifndef Py_HUGE_VAL #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_PYSTON 0 #define CYTHON_COMPILING_IN_CPYTHON 0 #undef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 0 #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #if PY_VERSION_HEX < 0x03050000 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #elif !defined(CYTHON_USE_ASYNC_SLOTS) #define CYTHON_USE_ASYNC_SLOTS 1 #endif #undef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 0 #undef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 0 #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #undef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 1 #undef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 0 #undef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 0 #undef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 0 #undef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 0 #undef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT 0 #undef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE 0 #undef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS 0 #undef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK 0 #elif defined(PYSTON_VERSION) #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_PYSTON 1 #define CYTHON_COMPILING_IN_CPYTHON 0 #ifndef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 1 #endif #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #undef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 0 #ifndef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 1 #endif #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #ifndef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 0 #endif #ifndef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 1 #endif #ifndef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 1 #endif #undef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 0 #undef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 0 #undef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT 0 #undef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE 0 #undef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS 0 #undef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_PYSTON 0 #define CYTHON_COMPILING_IN_CPYTHON 1 #ifndef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 1 #endif #if PY_VERSION_HEX < 0x02070000 #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) #define CYTHON_USE_PYTYPE_LOOKUP 1 #endif #if PY_MAJOR_VERSION < 3 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #elif !defined(CYTHON_USE_ASYNC_SLOTS) #define CYTHON_USE_ASYNC_SLOTS 1 #endif #if PY_VERSION_HEX < 0x02070000 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #elif !defined(CYTHON_USE_PYLONG_INTERNALS) #define CYTHON_USE_PYLONG_INTERNALS 1 #endif #ifndef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 1 #endif #ifndef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 1 #endif #if PY_VERSION_HEX < 0x030300F0 #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #elif !defined(CYTHON_USE_UNICODE_WRITER) #define CYTHON_USE_UNICODE_WRITER 1 #endif #ifndef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 0 #endif #ifndef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 1 #endif #ifndef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 1 #endif #ifndef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 1 #endif #ifndef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 1 #endif #ifndef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000) #endif #ifndef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) #endif #ifndef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1) #endif #ifndef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3) #endif #endif #if !defined(CYTHON_FAST_PYCCALL) #define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) #endif #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #undef SHIFT #undef BASE #undef MASK #ifdef SIZEOF_VOID_P enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; #endif #endif #ifndef __has_attribute #define __has_attribute(x) 0 #endif #ifndef __has_cpp_attribute #define __has_cpp_attribute(x) 0 #endif #ifndef CYTHON_RESTRICT #if defined(__GNUC__) #define CYTHON_RESTRICT __restrict__ #elif defined(_MSC_VER) && _MSC_VER >= 1400 #define CYTHON_RESTRICT __restrict #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_RESTRICT restrict #else #define CYTHON_RESTRICT #endif #endif #ifndef CYTHON_UNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif # elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif #endif #ifndef CYTHON_MAYBE_UNUSED_VAR # if defined(__cplusplus) template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } # else # define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) # endif #endif #ifndef CYTHON_NCP_UNUSED # if CYTHON_COMPILING_IN_CPYTHON # define CYTHON_NCP_UNUSED # else # define CYTHON_NCP_UNUSED CYTHON_UNUSED # endif #endif #define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) #ifdef _MSC_VER #ifndef _MSC_STDINT_H_ #if _MSC_VER < 1300 typedef unsigned char uint8_t; typedef unsigned int uint32_t; #else typedef unsigned __int8 uint8_t; typedef unsigned __int32 uint32_t; #endif #endif #else #include #endif #ifndef CYTHON_FALLTHROUGH #if defined(__cplusplus) && __cplusplus >= 201103L #if __has_cpp_attribute(fallthrough) #define CYTHON_FALLTHROUGH [[fallthrough]] #elif __has_cpp_attribute(clang::fallthrough) #define CYTHON_FALLTHROUGH [[clang::fallthrough]] #elif __has_cpp_attribute(gnu::fallthrough) #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] #endif #endif #ifndef CYTHON_FALLTHROUGH #if __has_attribute(fallthrough) #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) #else #define CYTHON_FALLTHROUGH #endif #endif #if defined(__clang__ ) && defined(__apple_build_version__) #if __apple_build_version__ < 7000000 #undef CYTHON_FALLTHROUGH #define CYTHON_FALLTHROUGH #endif #endif #endif #ifndef __cplusplus #error "Cython files generated with the C++ option must be compiled with a C++ compiler." #endif #ifndef CYTHON_INLINE #if defined(__clang__) #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) #else #define CYTHON_INLINE inline #endif #endif template void __Pyx_call_destructor(T& x) { x.~T(); } template class __Pyx_FakeReference { public: __Pyx_FakeReference() : ptr(NULL) { } __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } T *operator->() { return ptr; } T *operator&() { return ptr; } operator T&() { return *ptr; } template bool operator ==(U other) { return *ptr == other; } template bool operator !=(U other) { return *ptr != other; } private: T *ptr; }; #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) #define Py_OptimizeFlag 0 #endif #define __PYX_BUILD_PY_SSIZE_T "n" #define CYTHON_FORMAT_SSIZE_T "z" #if PY_MAJOR_VERSION < 3 #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #define __Pyx_DefaultClassType PyClass_Type #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" #if PY_VERSION_HEX >= 0x030800A4 && PY_VERSION_HEX < 0x030800B2 #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a, 0, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #else #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #endif #define __Pyx_DefaultClassType PyType_Type #endif #ifndef Py_TPFLAGS_CHECKTYPES #define Py_TPFLAGS_CHECKTYPES 0 #endif #ifndef Py_TPFLAGS_HAVE_INDEX #define Py_TPFLAGS_HAVE_INDEX 0 #endif #ifndef Py_TPFLAGS_HAVE_NEWBUFFER #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif #ifndef Py_TPFLAGS_HAVE_FINALIZE #define Py_TPFLAGS_HAVE_FINALIZE 0 #endif #ifndef METH_STACKLESS #define METH_STACKLESS 0 #endif #if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) #ifndef METH_FASTCALL #define METH_FASTCALL 0x80 #endif typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, Py_ssize_t nargs, PyObject *kwnames); #else #define __Pyx_PyCFunctionFast _PyCFunctionFast #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords #endif #if CYTHON_FAST_PYCCALL #define __Pyx_PyFastCFunction_Check(func)\ ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))))) #else #define __Pyx_PyFastCFunction_Check(func) 0 #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) #define PyObject_Malloc(s) PyMem_Malloc(s) #define PyObject_Free(p) PyMem_Free(p) #define PyObject_Realloc(p) PyMem_Realloc(p) #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1 #define PyMem_RawMalloc(n) PyMem_Malloc(n) #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n) #define PyMem_RawFree(p) PyMem_Free(p) #endif #if CYTHON_COMPILING_IN_PYSTON #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) #else #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) #endif #if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 #define __Pyx_PyThreadState_Current PyThreadState_GET() #elif PY_VERSION_HEX >= 0x03060000 #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() #elif PY_VERSION_HEX >= 0x03000000 #define __Pyx_PyThreadState_Current PyThreadState_GET() #else #define __Pyx_PyThreadState_Current _PyThreadState_Current #endif #if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) #include "pythread.h" #define Py_tss_NEEDS_INIT 0 typedef int Py_tss_t; static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { *key = PyThread_create_key(); return 0; } static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); *key = Py_tss_NEEDS_INIT; return key; } static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { PyObject_Free(key); } static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { return *key != Py_tss_NEEDS_INIT; } static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { PyThread_delete_key(*key); *key = Py_tss_NEEDS_INIT; } static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { return PyThread_set_key_value(*key, value); } static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { return PyThread_get_key_value(*key); } #endif #if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) #define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) #else #define __Pyx_PyDict_NewPresized(n) PyDict_New() #endif #if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) #else #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS #define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) #else #define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name) #endif #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) #define CYTHON_PEP393_ENABLED 1 #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ 0 : _PyUnicode_Ready((PyObject *)(op))) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE) #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) #else #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) #endif #else #define CYTHON_PEP393_ENABLED 0 #define PyUnicode_1BYTE_KIND 1 #define PyUnicode_2BYTE_KIND 2 #define PyUnicode_4BYTE_KIND 4 #define __Pyx_PyUnicode_READY(op) (0) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) #endif #if CYTHON_COMPILING_IN_PYPY #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) #else #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) #endif #define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) #define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) #else #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) #endif #if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) #define PyObject_ASCII(o) PyObject_Repr(o) #endif #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type #define PyStringObject PyUnicodeObject #define PyString_Type PyUnicode_Type #define PyString_Check PyUnicode_Check #define PyString_CheckExact PyUnicode_CheckExact #ifndef PyObject_Unicode #define PyObject_Unicode PyObject_Str #endif #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) #else #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) #endif #ifndef PySet_CheckExact #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) #endif #if PY_VERSION_HEX >= 0x030900A4 #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) #else #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) #endif #if CYTHON_ASSUME_SAFE_MACROS #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) #else #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) #endif #if PY_MAJOR_VERSION >= 3 #define PyIntObject PyLongObject #define PyInt_Type PyLong_Type #define PyInt_Check(op) PyLong_Check(op) #define PyInt_CheckExact(op) PyLong_CheckExact(op) #define PyInt_FromString PyLong_FromString #define PyInt_FromUnicode PyLong_FromUnicode #define PyInt_FromLong PyLong_FromLong #define PyInt_FromSize_t PyLong_FromSize_t #define PyInt_FromSsize_t PyLong_FromSsize_t #define PyInt_AsLong PyLong_AsLong #define PyInt_AS_LONG PyLong_AS_LONG #define PyInt_AsSsize_t PyLong_AsSsize_t #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask #define PyNumber_Int PyNumber_Long #endif #if PY_MAJOR_VERSION >= 3 #define PyBoolObject PyLongObject #endif #if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY #ifndef PyUnicode_InternFromString #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) #endif #endif #if PY_VERSION_HEX < 0x030200A4 typedef long Py_hash_t; #define __Pyx_PyInt_FromHash_t PyInt_FromLong #define __Pyx_PyInt_AsHash_t PyInt_AsLong #else #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func)) #else #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) #endif #if CYTHON_USE_ASYNC_SLOTS #if PY_VERSION_HEX >= 0x030500B1 #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) #else #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) #endif #else #define __Pyx_PyType_AsAsync(obj) NULL #endif #ifndef __Pyx_PyAsyncMethodsStruct typedef struct { unaryfunc am_await; unaryfunc am_aiter; unaryfunc am_anext; } __Pyx_PyAsyncMethodsStruct; #endif #if defined(WIN32) || defined(MS_WINDOWS) #define _USE_MATH_DEFINES #endif #include #ifdef NAN #define __PYX_NAN() ((float) NAN) #else static CYTHON_INLINE float __PYX_NAN() { float value; memset(&value, 0xFF, sizeof(value)); return value; } #endif #if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) #define __Pyx_truncl trunc #else #define __Pyx_truncl truncl #endif #define __PYX_MARK_ERR_POS(f_index, lineno) \ { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } #define __PYX_ERR(f_index, lineno, Ln_error) \ { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } #ifndef __PYX_EXTERN_C #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else #define __PYX_EXTERN_C extern #endif #endif #define __PYX_HAVE__openTSNE__kl_divergence #define __PYX_HAVE_API__openTSNE__kl_divergence /* Early includes */ #include #include #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" /* NumPy API declarations from "numpy/__init__.pxd" */ #include "math.h" #include "pythread.h" #include #include "pystate.h" #ifdef _OPENMP #include #endif /* _OPENMP */ #if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) #define CYTHON_WITHOUT_ASSERTIONS #endif typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; #define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) #define __PYX_DEFAULT_STRING_ENCODING "" #define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString #define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #define __Pyx_uchar_cast(c) ((unsigned char)c) #define __Pyx_long_cast(x) ((long)x) #define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ (sizeof(type) < sizeof(Py_ssize_t)) ||\ (sizeof(type) > sizeof(Py_ssize_t) &&\ likely(v < (type)PY_SSIZE_T_MAX ||\ v == (type)PY_SSIZE_T_MAX) &&\ (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ v == (type)PY_SSIZE_T_MIN))) ||\ (sizeof(type) == sizeof(Py_ssize_t) &&\ (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ v == (type)PY_SSIZE_T_MAX))) ) static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { return (size_t) i < (size_t) limit; } #if defined (__cplusplus) && __cplusplus >= 201103L #include #define __Pyx_sst_abs(value) std::abs(value) #elif SIZEOF_INT >= SIZEOF_SIZE_T #define __Pyx_sst_abs(value) abs(value) #elif SIZEOF_LONG >= SIZEOF_SIZE_T #define __Pyx_sst_abs(value) labs(value) #elif defined (_MSC_VER) #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define __Pyx_sst_abs(value) llabs(value) #elif defined (__GNUC__) #define __Pyx_sst_abs(value) __builtin_llabs(value) #else #define __Pyx_sst_abs(value) ((value<0) ? -value : value) #endif static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); #define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) #define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) #define __Pyx_PyBytes_FromString PyBytes_FromString #define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); #if PY_MAJOR_VERSION < 3 #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #else #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize #endif #define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) #define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) #define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) #define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) #define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) #define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { const Py_UNICODE *u_end = u; while (*u_end++) ; return (size_t)(u_end - u - 1); } #define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) #define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode #define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode #define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) #define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); #define __Pyx_PySequence_Tuple(obj)\ (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); #if CYTHON_ASSUME_SAFE_MACROS #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) #endif #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) #else #define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) #endif #define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII static int __Pyx_sys_getdefaultencoding_not_ascii; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; PyObject* ascii_chars_u = NULL; PyObject* ascii_chars_b = NULL; const char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; if (strcmp(default_encoding_c, "ascii") == 0) { __Pyx_sys_getdefaultencoding_not_ascii = 0; } else { char ascii_chars[128]; int c; for (c = 0; c < 128; c++) { ascii_chars[c] = c; } __Pyx_sys_getdefaultencoding_not_ascii = 1; ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); if (!ascii_chars_u) goto bad; ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { PyErr_Format( PyExc_ValueError, "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", default_encoding_c); goto bad; } Py_DECREF(ascii_chars_u); Py_DECREF(ascii_chars_b); } Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); Py_XDECREF(ascii_chars_u); Py_XDECREF(ascii_chars_b); return -1; } #endif #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) #else #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT static char* __PYX_DEFAULT_STRING_ENCODING; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); return -1; } #endif #endif /* Test for GCC > 2.95 */ #if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #else /* !__GNUC__ or GCC < 2.95 */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ */ static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } static PyObject *__pyx_m = NULL; static PyObject *__pyx_d; static PyObject *__pyx_b; static PyObject *__pyx_cython_runtime = NULL; static PyObject *__pyx_empty_tuple; static PyObject *__pyx_empty_bytes; static PyObject *__pyx_empty_unicode; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; /* Header.proto */ #if !defined(CYTHON_CCOMPLEX) #if defined(__cplusplus) #define CYTHON_CCOMPLEX 1 #elif defined(_Complex_I) #define CYTHON_CCOMPLEX 1 #else #define CYTHON_CCOMPLEX 0 #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus #include #else #include #endif #endif #if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) #undef _Complex_I #define _Complex_I 1.0fj #endif static const char *__pyx_f[] = { "openTSNE/kl_divergence.pyx", "__init__.pxd", "stringsource", "type.pxd", "openTSNE/quad_tree.pxd", }; /* MemviewSliceStruct.proto */ struct __pyx_memoryview_obj; typedef struct { struct __pyx_memoryview_obj *memview; char *data; Py_ssize_t shape[8]; Py_ssize_t strides[8]; Py_ssize_t suboffsets[8]; } __Pyx_memviewslice; #define __Pyx_MemoryView_Len(m) (m.shape[0]) /* Atomics.proto */ #include #ifndef CYTHON_ATOMICS #define CYTHON_ATOMICS 1 #endif #define __pyx_atomic_int_type int #if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\ (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\ !defined(__i386__) #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1) #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1) #ifdef __PYX_DEBUG_ATOMICS #warning "Using GNU atomics" #endif #elif CYTHON_ATOMICS && defined(_MSC_VER) && 0 #include #undef __pyx_atomic_int_type #define __pyx_atomic_int_type LONG #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value) #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value) #ifdef __PYX_DEBUG_ATOMICS #pragma message ("Using MSVC atomics") #endif #elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0 #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value) #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value) #ifdef __PYX_DEBUG_ATOMICS #warning "Using Intel atomics" #endif #else #undef CYTHON_ATOMICS #define CYTHON_ATOMICS 0 #ifdef __PYX_DEBUG_ATOMICS #warning "Not using atomics" #endif #endif typedef volatile __pyx_atomic_int_type __pyx_atomic_int; #if CYTHON_ATOMICS #define __pyx_add_acquisition_count(memview)\ __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) #define __pyx_sub_acquisition_count(memview)\ __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) #else #define __pyx_add_acquisition_count(memview)\ __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) #define __pyx_sub_acquisition_count(memview)\ __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) #endif /* ForceInitThreads.proto */ #ifndef __PYX_FORCE_INIT_THREADS #define __PYX_FORCE_INIT_THREADS 0 #endif /* NoFastGil.proto */ #define __Pyx_PyGILState_Ensure PyGILState_Ensure #define __Pyx_PyGILState_Release PyGILState_Release #define __Pyx_FastGIL_Remember() #define __Pyx_FastGIL_Forget() #define __Pyx_FastGilFuncInit() /* BufferFormatStructs.proto */ #define IS_UNSIGNED(type) (((type) -1) > 0) struct __Pyx_StructField_; #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) typedef struct { const char* name; struct __Pyx_StructField_* fields; size_t size; size_t arraysize[8]; int ndim; char typegroup; char is_unsigned; int flags; } __Pyx_TypeInfo; typedef struct __Pyx_StructField_ { __Pyx_TypeInfo* type; const char* name; size_t offset; } __Pyx_StructField; typedef struct { __Pyx_StructField* field; size_t parent_offset; } __Pyx_BufFmt_StackElem; typedef struct { __Pyx_StructField root; __Pyx_BufFmt_StackElem* head; size_t fmt_offset; size_t new_count, enc_count; size_t struct_alignment; int is_complex; char enc_type; char new_packmode; char enc_packmode; char is_valid_array; } __Pyx_BufFmt_Context; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":689 * # in Cython to enable them only on the right systems. * * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t */ typedef npy_int8 __pyx_t_5numpy_int8_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":690 * * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t */ typedef npy_int16 __pyx_t_5numpy_int16_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":691 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< * ctypedef npy_int64 int64_t * #ctypedef npy_int96 int96_t */ typedef npy_int32 __pyx_t_5numpy_int32_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":692 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< * #ctypedef npy_int96 int96_t * #ctypedef npy_int128 int128_t */ typedef npy_int64 __pyx_t_5numpy_int64_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":696 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":697 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":698 * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< * ctypedef npy_uint64 uint64_t * #ctypedef npy_uint96 uint96_t */ typedef npy_uint32 __pyx_t_5numpy_uint32_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":699 * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< * #ctypedef npy_uint96 uint96_t * #ctypedef npy_uint128 uint128_t */ typedef npy_uint64 __pyx_t_5numpy_uint64_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":703 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< * ctypedef npy_float64 float64_t * #ctypedef npy_float80 float80_t */ typedef npy_float32 __pyx_t_5numpy_float32_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":704 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< * #ctypedef npy_float80 float80_t * #ctypedef npy_float128 float128_t */ typedef npy_float64 __pyx_t_5numpy_float64_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":713 * # The int types are mapped a bit surprising -- * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t # <<<<<<<<<<<<<< * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t */ typedef npy_long __pyx_t_5numpy_int_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":714 * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< * ctypedef npy_longlong longlong_t * */ typedef npy_longlong __pyx_t_5numpy_long_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":715 * ctypedef npy_long int_t * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< * * ctypedef npy_ulong uint_t */ typedef npy_longlong __pyx_t_5numpy_longlong_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":717 * ctypedef npy_longlong longlong_t * * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t */ typedef npy_ulong __pyx_t_5numpy_uint_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":718 * * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulonglong_t * */ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":719 * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< * * ctypedef npy_intp intp_t */ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":721 * ctypedef npy_ulonglong ulonglong_t * * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< * ctypedef npy_uintp uintp_t * */ typedef npy_intp __pyx_t_5numpy_intp_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":722 * * ctypedef npy_intp intp_t * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< * * ctypedef npy_double float_t */ typedef npy_uintp __pyx_t_5numpy_uintp_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":724 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t */ typedef npy_double __pyx_t_5numpy_float_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":725 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< * ctypedef npy_longdouble longdouble_t * */ typedef npy_double __pyx_t_5numpy_double_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":726 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cfloat cfloat_t */ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; /* Declarations.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< float > __pyx_t_float_complex; #else typedef float _Complex __pyx_t_float_complex; #endif #else typedef struct { float real, imag; } __pyx_t_float_complex; #endif static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); /* Declarations.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< double > __pyx_t_double_complex; #else typedef double _Complex __pyx_t_double_complex; #endif #else typedef struct { double real, imag; } __pyx_t_double_complex; #endif static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); /*--- Type declarations ---*/ struct __pyx_obj_8openTSNE_9quad_tree_QuadTree; struct __pyx_array_obj; struct __pyx_MemviewEnum_obj; struct __pyx_memoryview_obj; struct __pyx_memoryviewslice_obj; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":728 * ctypedef npy_longdouble longdouble_t * * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t */ typedef npy_cfloat __pyx_t_5numpy_cfloat_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":729 * * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< * ctypedef npy_clongdouble clongdouble_t * */ typedef npy_cdouble __pyx_t_5numpy_cdouble_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":730 * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cdouble complex_t */ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":732 * ctypedef npy_clongdouble clongdouble_t * * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew1(a): */ typedef npy_cdouble __pyx_t_5numpy_complex_t; struct __pyx_t_8openTSNE_9quad_tree_Node; typedef struct __pyx_t_8openTSNE_9quad_tree_Node __pyx_t_8openTSNE_9quad_tree_Node; struct __pyx_opt_args_8openTSNE_9quad_tree_is_duplicate; /* "quad_tree.pxd":10 * cdef double EPSILON = np.finfo(np.float64).eps * * ctypedef struct Node: # <<<<<<<<<<<<<< * Py_ssize_t n_dims * double *center */ struct __pyx_t_8openTSNE_9quad_tree_Node { Py_ssize_t n_dims; double *center; double length; int is_leaf; __pyx_t_8openTSNE_9quad_tree_Node *children; double *center_of_mass; Py_ssize_t num_points; }; /* "quad_tree.pxd":22 * * * cdef bint is_duplicate(Node * node, double * point, double duplicate_eps=*) nogil # <<<<<<<<<<<<<< * * */ struct __pyx_opt_args_8openTSNE_9quad_tree_is_duplicate { int __pyx_n; double duplicate_eps; }; struct __pyx_opt_args_8openTSNE_5_tsne_compute_gaussian_perplexity; struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_bh; struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d; struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_1d; struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d; struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_2d; struct __pyx_fuse_0__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn; struct __pyx_fuse_1__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn; /* "_tsne.pxd":18 * * * cpdef double[:, ::1] compute_gaussian_perplexity( # <<<<<<<<<<<<<< * double[:, :] distances, * double[:] desired_perplexities, */ struct __pyx_opt_args_8openTSNE_5_tsne_compute_gaussian_perplexity { int __pyx_n; double perplexity_tol; Py_ssize_t max_iter; Py_ssize_t num_threads; }; /* "_tsne.pxd":38 * ) * * cpdef double estimate_negative_gradient_bh( # <<<<<<<<<<<<<< * QuadTree tree, * double[:, ::1] embedding, */ struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_bh { int __pyx_n; double theta; double dof; Py_ssize_t num_threads; int pairwise_normalization; }; /* "_tsne.pxd":48 * ) * * cpdef double estimate_negative_gradient_fft_1d( # <<<<<<<<<<<<<< * double[::1] embedding, * double[::1] gradient, */ struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d { int __pyx_n; Py_ssize_t n_interpolation_points; Py_ssize_t min_num_intervals; double ints_in_interval; double dof; }; /* "_tsne.pxd":57 * ) * * cpdef tuple prepare_negative_gradient_fft_interpolation_grid_1d( # <<<<<<<<<<<<<< * double[::1] reference_embedding, * Py_ssize_t n_interpolation_points=*, */ struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_1d { int __pyx_n; Py_ssize_t n_interpolation_points; Py_ssize_t min_num_intervals; double ints_in_interval; double dof; double padding; }; /* "_tsne.pxd":75 * ) * * cpdef double estimate_negative_gradient_fft_2d( # <<<<<<<<<<<<<< * double[:, ::1] embedding, * double[:, ::1] gradient, */ struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d { int __pyx_n; Py_ssize_t n_interpolation_points; Py_ssize_t min_num_intervals; double ints_in_interval; double dof; }; /* "_tsne.pxd":84 * ) * * cpdef tuple prepare_negative_gradient_fft_interpolation_grid_2d( # <<<<<<<<<<<<<< * double[:, ::1] reference_embedding, * Py_ssize_t n_interpolation_points=*, */ struct __pyx_opt_args_8openTSNE_5_tsne_prepare_negative_gradient_fft_interpolation_grid_2d { int __pyx_n; Py_ssize_t n_interpolation_points; Py_ssize_t min_num_intervals; double ints_in_interval; double dof; double padding; }; /* "_tsne.pxd":26 * ) * * cpdef tuple estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * sparse_index_type[:] indices, * sparse_index_type[:] indptr, */ struct __pyx_fuse_0__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn { int __pyx_n; double dof; Py_ssize_t num_threads; int should_eval_error; }; struct __pyx_fuse_1__pyx_opt_args_8openTSNE_5_tsne_estimate_positive_gradient_nn { int __pyx_n; double dof; Py_ssize_t num_threads; int should_eval_error; }; struct __pyx_opt_args_8openTSNE_13kl_divergence_kl_divergence_approx_bh; struct __pyx_opt_args_8openTSNE_13kl_divergence_kl_divergence_approx_fft; /* "openTSNE/kl_divergence.pyx":61 * * * cpdef double kl_divergence_approx_bh( # <<<<<<<<<<<<<< * int[:] indices, * int[:] indptr, */ struct __pyx_opt_args_8openTSNE_13kl_divergence_kl_divergence_approx_bh { int __pyx_n; double theta; double dof; }; /* "openTSNE/kl_divergence.pyx":100 * * * cpdef double kl_divergence_approx_fft( # <<<<<<<<<<<<<< * int[:] indices, * int[:] indptr, */ struct __pyx_opt_args_8openTSNE_13kl_divergence_kl_divergence_approx_fft { int __pyx_n; double dof; Py_ssize_t n_interpolation_points; Py_ssize_t min_num_intervals; double ints_in_interval; }; /* "quad_tree.pxd":25 * * * cdef class QuadTree: # <<<<<<<<<<<<<< * cdef Node root * cpdef void add_points(self, double[:, ::1] points) */ struct __pyx_obj_8openTSNE_9quad_tree_QuadTree { PyObject_HEAD struct __pyx_vtabstruct_8openTSNE_9quad_tree_QuadTree *__pyx_vtab; __pyx_t_8openTSNE_9quad_tree_Node root; }; /* "View.MemoryView":105 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< * * cdef: */ struct __pyx_array_obj { PyObject_HEAD struct __pyx_vtabstruct_array *__pyx_vtab; char *data; Py_ssize_t len; char *format; int ndim; Py_ssize_t *_shape; Py_ssize_t *_strides; Py_ssize_t itemsize; PyObject *mode; PyObject *_format; void (*callback_free_data)(void *); int free_data; int dtype_is_object; }; /* "View.MemoryView":279 * * @cname('__pyx_MemviewEnum') * cdef class Enum(object): # <<<<<<<<<<<<<< * cdef object name * def __init__(self, name): */ struct __pyx_MemviewEnum_obj { PyObject_HEAD PyObject *name; }; /* "View.MemoryView":330 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< * * cdef object obj */ struct __pyx_memoryview_obj { PyObject_HEAD struct __pyx_vtabstruct_memoryview *__pyx_vtab; PyObject *obj; PyObject *_size; PyObject *_array_interface; PyThread_type_lock lock; __pyx_atomic_int acquisition_count[2]; __pyx_atomic_int *acquisition_count_aligned_p; Py_buffer view; int flags; int dtype_is_object; __Pyx_TypeInfo *typeinfo; }; /* "View.MemoryView":965 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< * "Internal class for passing memoryview slices to Python" * */ struct __pyx_memoryviewslice_obj { struct __pyx_memoryview_obj __pyx_base; __Pyx_memviewslice from_slice; PyObject *from_object; PyObject *(*to_object_func)(char *); int (*to_dtype_func)(char *, PyObject *); }; /* "quad_tree.pxd":25 * * * cdef class QuadTree: # <<<<<<<<<<<<<< * cdef Node root * cpdef void add_points(self, double[:, ::1] points) */ struct __pyx_vtabstruct_8openTSNE_9quad_tree_QuadTree { void (*add_points)(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *, __Pyx_memviewslice, int __pyx_skip_dispatch); void (*add_point)(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *, __Pyx_memviewslice, int __pyx_skip_dispatch); }; static struct __pyx_vtabstruct_8openTSNE_9quad_tree_QuadTree *__pyx_vtabptr_8openTSNE_9quad_tree_QuadTree; /* "View.MemoryView":105 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< * * cdef: */ struct __pyx_vtabstruct_array { PyObject *(*get_memview)(struct __pyx_array_obj *); }; static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; /* "View.MemoryView":330 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< * * cdef object obj */ struct __pyx_vtabstruct_memoryview { char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *); PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *); PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *); PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *); PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *); }; static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; /* "View.MemoryView":965 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< * "Internal class for passing memoryview slices to Python" * */ struct __pyx_vtabstruct__memoryviewslice { struct __pyx_vtabstruct_memoryview __pyx_base; }; static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice; /* --- Runtime support code (head) --- */ /* Refnanny.proto */ #ifndef CYTHON_REFNANNY #define CYTHON_REFNANNY 0 #endif #if CYTHON_REFNANNY typedef struct { void (*INCREF)(void*, PyObject*, int); void (*DECREF)(void*, PyObject*, int); void (*GOTREF)(void*, PyObject*, int); void (*GIVEREF)(void*, PyObject*, int); void* (*SetupContext)(const char*, int, const char*); void (*FinishContext)(void**); } __Pyx_RefNannyAPIStruct; static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; #ifdef WITH_THREAD #define __Pyx_RefNannySetupContext(name, acquire_gil)\ if (acquire_gil) {\ PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ PyGILState_Release(__pyx_gilstate_save);\ } else {\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ } #else #define __Pyx_RefNannySetupContext(name, acquire_gil)\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) #endif #define __Pyx_RefNannyFinishContext()\ __Pyx_RefNanny->FinishContext(&__pyx_refnanny) #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) #else #define __Pyx_RefNannyDeclarations #define __Pyx_RefNannySetupContext(name, acquire_gil) #define __Pyx_RefNannyFinishContext() #define __Pyx_INCREF(r) Py_INCREF(r) #define __Pyx_DECREF(r) Py_DECREF(r) #define __Pyx_GOTREF(r) #define __Pyx_GIVEREF(r) #define __Pyx_XINCREF(r) Py_XINCREF(r) #define __Pyx_XDECREF(r) Py_XDECREF(r) #define __Pyx_XGOTREF(r) #define __Pyx_XGIVEREF(r) #endif #define __Pyx_XDECREF_SET(r, v) do {\ PyObject *tmp = (PyObject *) r;\ r = v; __Pyx_XDECREF(tmp);\ } while (0) #define __Pyx_DECREF_SET(r, v) do {\ PyObject *tmp = (PyObject *) r;\ r = v; __Pyx_DECREF(tmp);\ } while (0) #define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) #define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) /* PyObjectGetAttrStr.proto */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) #endif /* GetBuiltinName.proto */ static PyObject *__Pyx_GetBuiltinName(PyObject *name); /* PyIntBinop.proto */ #if !CYTHON_COMPILING_IN_PYPY static PyObject* __Pyx_PyInt_AddCObj(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check); #else #define __Pyx_PyInt_AddCObj(op1, op2, intval, inplace, zerodivision_check)\ (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) #endif /* MemviewSliceInit.proto */ #define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d #define __Pyx_MEMVIEW_DIRECT 1 #define __Pyx_MEMVIEW_PTR 2 #define __Pyx_MEMVIEW_FULL 4 #define __Pyx_MEMVIEW_CONTIG 8 #define __Pyx_MEMVIEW_STRIDED 16 #define __Pyx_MEMVIEW_FOLLOW 32 #define __Pyx_IS_C_CONTIG 1 #define __Pyx_IS_F_CONTIG 2 static int __Pyx_init_memviewslice( struct __pyx_memoryview_obj *memview, int ndim, __Pyx_memviewslice *memviewslice, int memview_is_new_reference); static CYTHON_INLINE int __pyx_add_acquisition_count_locked( __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); #define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p) #define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview)) #define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) #define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__) static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int); /* PyThreadStateGet.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; #define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; #define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type #else #define __Pyx_PyThreadState_declare #define __Pyx_PyThreadState_assign #define __Pyx_PyErr_Occurred() PyErr_Occurred() #endif /* PyErrFetchRestore.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) #define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) #define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) #define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) #define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #if CYTHON_COMPILING_IN_CPYTHON #define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) #else #define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) #endif #else #define __Pyx_PyErr_Clear() PyErr_Clear() #define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) #define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) #define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) #define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) #endif /* WriteUnraisableException.proto */ static void __Pyx_WriteUnraisable(const char *name, int clineno, int lineno, const char *filename, int full_traceback, int nogil); /* RaiseArgTupleInvalid.proto */ static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /* RaiseDoubleKeywords.proto */ static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /* ParseKeywords.proto */ static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ const char* function_name); /* PyCFunctionFastCall.proto */ #if CYTHON_FAST_PYCCALL static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); #else #define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) #endif /* PyFunctionFastCall.proto */ #if CYTHON_FAST_PYCALL #define __Pyx_PyFunction_FastCall(func, args, nargs)\ __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) #if 1 || PY_VERSION_HEX < 0x030600B1 static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); #else #define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) #endif #define __Pyx_BUILD_ASSERT_EXPR(cond)\ (sizeof(char [1 - 2*!(cond)]) - 1) #ifndef Py_MEMBER_SIZE #define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) #endif static size_t __pyx_pyframe_localsplus_offset = 0; #include "frameobject.h" #define __Pxy_PyFrame_Initialize_Offsets()\ ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) #define __Pyx_PyFrame_GetLocalsplus(frame)\ (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) #endif /* PyObjectCall.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); #else #define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) #endif /* PyObjectCallMethO.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); #endif /* PyObjectCallOneArg.proto */ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); /* PyDictVersioning.proto */ #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS #define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) #define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) #define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ (version_var) = __PYX_GET_DICT_VERSION(dict);\ (cache_var) = (value); #define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ static PY_UINT64_T __pyx_dict_version = 0;\ static PyObject *__pyx_dict_cached_value = NULL;\ if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ (VAR) = __pyx_dict_cached_value;\ } else {\ (VAR) = __pyx_dict_cached_value = (LOOKUP);\ __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ }\ } static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); #else #define __PYX_GET_DICT_VERSION(dict) (0) #define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) #define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); #endif /* GetModuleGlobalName.proto */ #if CYTHON_USE_DICT_VERSIONS #define __Pyx_GetModuleGlobalName(var, name) {\ static PY_UINT64_T __pyx_dict_version = 0;\ static PyObject *__pyx_dict_cached_value = NULL;\ (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ } #define __Pyx_GetModuleGlobalNameUncached(var, name) {\ PY_UINT64_T __pyx_dict_version;\ PyObject *__pyx_dict_cached_value;\ (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ } static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); #else #define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) #define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); #endif /* RaiseTooManyValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); /* RaiseNeedMoreValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); /* IterFinish.proto */ static CYTHON_INLINE int __Pyx_IterFinish(void); /* UnpackItemEndCheck.proto */ static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); /* PyObjectCallNoArg.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); #else #define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL) #endif /* GetTopmostException.proto */ #if CYTHON_USE_EXC_INFO_STACK static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); #endif /* SaveResetException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); #else #define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) #define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) #endif /* PyErrExceptionMatches.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); #else #define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) #endif /* GetException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #else static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); #endif /* RaiseException.proto */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /* ArgTypeTest.proto */ #define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\ __Pyx__ArgTypeTest(obj, type, name, exact)) static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); /* PyObjectCall2Args.proto */ static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2); /* IncludeStringH.proto */ #include /* BytesEquals.proto */ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); /* UnicodeEquals.proto */ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); /* StrEquals.proto */ #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals #else #define __Pyx_PyString_Equals __Pyx_PyBytes_Equals #endif /* UnaryNegOverflows.proto */ #define UNARY_NEG_WOULD_OVERFLOW(x)\ (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ /* GetAttr.proto */ static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); /* GetItemInt.proto */ #define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ __Pyx_GetItemInt_Generic(o, to_py_func(i)))) #define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); #define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, int wraparound, int boundscheck); /* ObjectGetItem.proto */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key); #else #define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) #endif /* decode_c_string_utf16.proto */ static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) { int byteorder = 0; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) { int byteorder = -1; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) { int byteorder = 1; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } /* decode_c_string.proto */ static CYTHON_INLINE PyObject* __Pyx_decode_c_string( const char* cstring, Py_ssize_t start, Py_ssize_t stop, const char* encoding, const char* errors, PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); /* GetAttr3.proto */ static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); /* RaiseNoneIterError.proto */ static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); /* ExtTypeTest.proto */ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /* SwapException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #else static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); #endif /* Import.proto */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /* FastTypeChecks.proto */ #if CYTHON_COMPILING_IN_CPYTHON #define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); #else #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) #define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) #define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) #endif #define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ /* ListCompAppend.proto */ #if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len)) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); __Pyx_SET_SIZE(list, len + 1); return 0; } return PyList_Append(list, x); } #else #define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) #endif /* PyIntBinop.proto */ #if !CYTHON_COMPILING_IN_PYPY static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check); #else #define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\ (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) #endif /* ListExtend.proto */ static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) { #if CYTHON_COMPILING_IN_CPYTHON PyObject* none = _PyList_Extend((PyListObject*)L, v); if (unlikely(!none)) return -1; Py_DECREF(none); return 0; #else return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v); #endif } /* ListAppend.proto */ #if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); __Pyx_SET_SIZE(list, len + 1); return 0; } return PyList_Append(list, x); } #else #define __Pyx_PyList_Append(L,x) PyList_Append(L,x) #endif /* None.proto */ static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); /* ImportFrom.proto */ static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); /* HasAttr.proto */ static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); /* PyObject_GenericGetAttrNoDict.proto */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr #endif /* PyObject_GenericGetAttr.proto */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr #endif /* SetVTable.proto */ static int __Pyx_SetVtable(PyObject *dict, void *vtable); /* PyObjectGetAttrStrNoError.proto */ static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name); /* SetupReduce.proto */ static int __Pyx_setup_reduce(PyObject* type_obj); /* TypeImport.proto */ #ifndef __PYX_HAVE_RT_ImportType_proto #define __PYX_HAVE_RT_ImportType_proto enum __Pyx_ImportType_CheckSize { __Pyx_ImportType_CheckSize_Error = 0, __Pyx_ImportType_CheckSize_Warn = 1, __Pyx_ImportType_CheckSize_Ignore = 2 }; static PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size); #endif /* GetVTable.proto */ static void* __Pyx_GetVtable(PyObject *dict); /* CLineInTraceback.proto */ #ifdef CYTHON_CLINE_IN_TRACEBACK #define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) #else static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); #endif /* CodeObjectCache.proto */ typedef struct { PyCodeObject* code_object; int code_line; } __Pyx_CodeObjectCacheEntry; struct __Pyx_CodeObjectCache { int count; int max_count; __Pyx_CodeObjectCacheEntry* entries; }; static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); static PyCodeObject *__pyx_find_code_object(int code_line); static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); /* AddTraceback.proto */ static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename); #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); static void __Pyx_ReleaseBuffer(Py_buffer *view); #else #define __Pyx_GetBuffer PyObject_GetBuffer #define __Pyx_ReleaseBuffer PyBuffer_Release #endif /* BufferStructDeclare.proto */ typedef struct { Py_ssize_t shape, strides, suboffsets; } __Pyx_Buf_DimInfo; typedef struct { size_t refcount; Py_buffer pybuffer; } __Pyx_Buffer; typedef struct { __Pyx_Buffer *rcbuffer; char *data; __Pyx_Buf_DimInfo diminfo[8]; } __Pyx_LocalBuf_ND; /* MemviewSliceIsContig.proto */ static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); /* OverlappingSlices.proto */ static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, __Pyx_memviewslice *slice2, int ndim, size_t itemsize); /* Capsule.proto */ static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); /* IsLittleEndian.proto */ static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); /* BufferFormatCheck.proto */ static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type); /* TypeInfoCompare.proto */ static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); /* MemviewSliceValidateAndInit.proto */ static int __Pyx_ValidateAndInit_memviewslice( int *axes_specs, int c_or_f_flag, int buf_flags, int ndim, __Pyx_TypeInfo *dtype, __Pyx_BufFmt_StackElem stack[], __Pyx_memviewslice *memviewslice, PyObject *original_obj); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(PyObject *, int writable_flag); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_int(PyObject *, int writable_flag); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *, int writable_flag); /* MemviewDtypeToObject.proto */ static CYTHON_INLINE PyObject *__pyx_memview_get_double(const char *itemp); static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *obj); /* GCCDiagnostics.proto */ #if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) #define __Pyx_HAS_GCC_DIAGNOSTIC #endif /* MemviewDtypeToObject.proto */ static CYTHON_INLINE PyObject *__pyx_memview_get_int(const char *itemp); static CYTHON_INLINE int __pyx_memview_set_int(const char *itemp, PyObject *obj); /* RealImag.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus #define __Pyx_CREAL(z) ((z).real()) #define __Pyx_CIMAG(z) ((z).imag()) #else #define __Pyx_CREAL(z) (__real__(z)) #define __Pyx_CIMAG(z) (__imag__(z)) #endif #else #define __Pyx_CREAL(z) ((z).real) #define __Pyx_CIMAG(z) ((z).imag) #endif #if defined(__cplusplus) && CYTHON_CCOMPLEX\ && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) #define __Pyx_SET_CREAL(z,x) ((z).real(x)) #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) #else #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) #endif /* Arithmetic.proto */ #if CYTHON_CCOMPLEX #define __Pyx_c_eq_float(a, b) ((a)==(b)) #define __Pyx_c_sum_float(a, b) ((a)+(b)) #define __Pyx_c_diff_float(a, b) ((a)-(b)) #define __Pyx_c_prod_float(a, b) ((a)*(b)) #define __Pyx_c_quot_float(a, b) ((a)/(b)) #define __Pyx_c_neg_float(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero_float(z) ((z)==(float)0) #define __Pyx_c_conj_float(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs_float(z) (::std::abs(z)) #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero_float(z) ((z)==0) #define __Pyx_c_conj_float(z) (conjf(z)) #if 1 #define __Pyx_c_abs_float(z) (cabsf(z)) #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); #if 1 static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); #endif #endif /* Arithmetic.proto */ #if CYTHON_CCOMPLEX #define __Pyx_c_eq_double(a, b) ((a)==(b)) #define __Pyx_c_sum_double(a, b) ((a)+(b)) #define __Pyx_c_diff_double(a, b) ((a)-(b)) #define __Pyx_c_prod_double(a, b) ((a)*(b)) #define __Pyx_c_quot_double(a, b) ((a)/(b)) #define __Pyx_c_neg_double(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero_double(z) ((z)==(double)0) #define __Pyx_c_conj_double(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs_double(z) (::std::abs(z)) #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero_double(z) ((z)==0) #define __Pyx_c_conj_double(z) (conj(z)) #if 1 #define __Pyx_c_abs_double(z) (cabs(z)) #define __Pyx_c_pow_double(a, b) (cpow(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); #if 1 static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); #endif #endif /* MemviewSliceCopyTemplate.proto */ static __Pyx_memviewslice __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, const char *mode, int ndim, size_t sizeof_dtype, int contig_flag, int dtype_is_object); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); /* CIntFromPy.proto */ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); /* CIntFromPy.proto */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); /* CIntFromPy.proto */ static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_double(PyObject *, int writable_flag); /* CheckBinaryVersion.proto */ static int __Pyx_check_binary_version(void); /* VoidPtrImport.proto */ static int __Pyx_ImportVoidPtr(PyObject *module, const char *name, void **p, const char *sig); /* FunctionImport.proto */ static int __Pyx_ImportFunction(PyObject *module, const char *funcname, void (**f)(void), const char *sig); /* InitStrings.proto */ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ /* Module declarations from 'cpython.buffer' */ /* Module declarations from 'libc.string' */ /* Module declarations from 'libc.stdio' */ /* Module declarations from '__builtin__' */ /* Module declarations from 'cpython.type' */ static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; /* Module declarations from 'cpython' */ /* Module declarations from 'cpython.object' */ /* Module declarations from 'cpython.ref' */ /* Module declarations from 'cpython.mem' */ /* Module declarations from 'numpy' */ /* Module declarations from 'numpy' */ static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; /* Module declarations from 'openTSNE.quad_tree' */ static PyTypeObject *__pyx_ptype_8openTSNE_9quad_tree_QuadTree = 0; static double *__pyx_vp_8openTSNE_9quad_tree_EPSILON = 0; #define __pyx_v_8openTSNE_9quad_tree_EPSILON (*__pyx_vp_8openTSNE_9quad_tree_EPSILON) /* Module declarations from 'openTSNE._tsne' */ static double (*__pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_bh)(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_bh *__pyx_optional_args); /*proto*/ static double (*__pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d)(__Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d *__pyx_optional_args); /*proto*/ static double (*__pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d)(__Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d *__pyx_optional_args); /*proto*/ /* Module declarations from 'openTSNE.kl_divergence' */ static PyTypeObject *__pyx_array_type = 0; static PyTypeObject *__pyx_MemviewEnum_type = 0; static PyTypeObject *__pyx_memoryview_type = 0; static PyTypeObject *__pyx_memoryviewslice_type = 0; static double __pyx_v_8openTSNE_13kl_divergence_EPSILON; static PyObject *generic = 0; static PyObject *strided = 0; static PyObject *indirect = 0; static PyObject *contiguous = 0; static PyObject *indirect_contiguous = 0; static int __pyx_memoryview_thread_locks_used; static PyThread_type_lock __pyx_memoryview_thread_locks[8]; static PyObject *__pyx_f_8openTSNE_13kl_divergence_sqeuclidean(__Pyx_memviewslice, __Pyx_memviewslice); /*proto*/ static double __pyx_f_8openTSNE_13kl_divergence_kl_divergence_exact(__Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/ static double __pyx_f_8openTSNE_13kl_divergence_kl_divergence_approx_bh(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_13kl_divergence_kl_divergence_approx_bh *__pyx_optional_args); /*proto*/ static double __pyx_f_8openTSNE_13kl_divergence_kl_divergence_approx_fft(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_13kl_divergence_kl_divergence_approx_fft *__pyx_optional_args); /*proto*/ static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ static void *__pyx_align_pointer(void *, size_t); /*proto*/ static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ static PyObject *_unellipsify(PyObject *, int); /*proto*/ static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, IS_UNSIGNED(int) ? 'U' : 'I', IS_UNSIGNED(int), 0 }; #define __Pyx_MODULE_NAME "openTSNE.kl_divergence" extern int __pyx_module_is_main_openTSNE__kl_divergence; int __pyx_module_is_main_openTSNE__kl_divergence = 0; /* Implementation of 'openTSNE.kl_divergence' */ static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_ImportError; static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_MemoryError; static PyObject *__pyx_builtin_enumerate; static PyObject *__pyx_builtin_TypeError; static PyObject *__pyx_builtin_Ellipsis; static PyObject *__pyx_builtin_id; static PyObject *__pyx_builtin_IndexError; static const char __pyx_k_O[] = "O"; static const char __pyx_k_P[] = "P"; static const char __pyx_k_c[] = "c"; static const char __pyx_k_id[] = "id"; static const char __pyx_k_np[] = "np"; static const char __pyx_k_dof[] = "dof"; static const char __pyx_k_eps[] = "eps"; static const char __pyx_k_new[] = "__new__"; static const char __pyx_k_obj[] = "obj"; static const char __pyx_k_base[] = "base"; static const char __pyx_k_dict[] = "__dict__"; static const char __pyx_k_main[] = "__main__"; static const char __pyx_k_mode[] = "mode"; static const char __pyx_k_name[] = "name"; static const char __pyx_k_ndim[] = "ndim"; static const char __pyx_k_pack[] = "pack"; static const char __pyx_k_size[] = "size"; static const char __pyx_k_step[] = "step"; static const char __pyx_k_stop[] = "stop"; static const char __pyx_k_test[] = "__test__"; static const char __pyx_k_tsne[] = "_tsne"; static const char __pyx_k_ASCII[] = "ASCII"; static const char __pyx_k_class[] = "__class__"; static const char __pyx_k_dtype[] = "dtype"; static const char __pyx_k_error[] = "error"; static const char __pyx_k_finfo[] = "finfo"; static const char __pyx_k_flags[] = "flags"; static const char __pyx_k_numpy[] = "numpy"; static const char __pyx_k_range[] = "range"; static const char __pyx_k_ravel[] = "ravel"; static const char __pyx_k_shape[] = "shape"; static const char __pyx_k_start[] = "start"; static const char __pyx_k_theta[] = "theta"; static const char __pyx_k_P_data[] = "P_data"; static const char __pyx_k_encode[] = "encode"; static const char __pyx_k_format[] = "format"; static const char __pyx_k_import[] = "__import__"; static const char __pyx_k_indptr[] = "indptr"; static const char __pyx_k_name_2[] = "__name__"; static const char __pyx_k_pickle[] = "pickle"; static const char __pyx_k_reduce[] = "__reduce__"; static const char __pyx_k_struct[] = "struct"; static const char __pyx_k_unpack[] = "unpack"; static const char __pyx_k_update[] = "update"; static const char __pyx_k_float64[] = "float64"; static const char __pyx_k_fortran[] = "fortran"; static const char __pyx_k_indices[] = "indices"; static const char __pyx_k_memview[] = "memview"; static const char __pyx_k_Ellipsis[] = "Ellipsis"; static const char __pyx_k_getstate[] = "__getstate__"; static const char __pyx_k_itemsize[] = "itemsize"; static const char __pyx_k_pyx_type[] = "__pyx_type"; static const char __pyx_k_setstate[] = "__setstate__"; static const char __pyx_k_TypeError[] = "TypeError"; static const char __pyx_k_embedding[] = "embedding"; static const char __pyx_k_enumerate[] = "enumerate"; static const char __pyx_k_pyx_state[] = "__pyx_state"; static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; static const char __pyx_k_IndexError[] = "IndexError"; static const char __pyx_k_ValueError[] = "ValueError"; static const char __pyx_k_empty_like[] = "empty_like"; static const char __pyx_k_pyx_result[] = "__pyx_result"; static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; static const char __pyx_k_ImportError[] = "ImportError"; static const char __pyx_k_MemoryError[] = "MemoryError"; static const char __pyx_k_PickleError[] = "PickleError"; static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; static const char __pyx_k_stringsource[] = "stringsource"; static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; static const char __pyx_k_ints_in_interval[] = "ints_in_interval"; static const char __pyx_k_min_num_intervals[] = "min_num_intervals"; static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; static const char __pyx_k_should_eval_error[] = "should_eval_error"; static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; static const char __pyx_k_strided_and_direct[] = ""; static const char __pyx_k_strided_and_indirect[] = ""; static const char __pyx_k_contiguous_and_direct[] = ""; static const char __pyx_k_MemoryView_of_r_object[] = ""; static const char __pyx_k_n_interpolation_points[] = "n_interpolation_points"; static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; static const char __pyx_k_contiguous_and_indirect[] = ""; static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'"; static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; static const char __pyx_k_estimate_positive_gradient_nn[] = "estimate_positive_gradient_nn"; static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; static const char __pyx_k_strided_and_direct_or_indirect[] = ""; static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))"; static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; static PyObject *__pyx_n_s_ASCII; static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; static PyObject *__pyx_kp_s_Cannot_index_with_type_s; static PyObject *__pyx_n_s_Ellipsis; static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; static PyObject *__pyx_n_s_ImportError; static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0; static PyObject *__pyx_n_s_IndexError; static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; static PyObject *__pyx_n_s_MemoryError; static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; static PyObject *__pyx_kp_s_MemoryView_of_r_object; static PyObject *__pyx_n_b_O; static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; static PyObject *__pyx_n_s_P; static PyObject *__pyx_n_s_P_data; static PyObject *__pyx_n_s_PickleError; static PyObject *__pyx_n_s_TypeError; static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; static PyObject *__pyx_n_s_ValueError; static PyObject *__pyx_n_s_View_MemoryView; static PyObject *__pyx_n_s_allocate_buffer; static PyObject *__pyx_n_s_base; static PyObject *__pyx_n_s_c; static PyObject *__pyx_n_u_c; static PyObject *__pyx_n_s_class; static PyObject *__pyx_n_s_cline_in_traceback; static PyObject *__pyx_kp_s_contiguous_and_direct; static PyObject *__pyx_kp_s_contiguous_and_indirect; static PyObject *__pyx_n_s_dict; static PyObject *__pyx_n_s_dof; static PyObject *__pyx_n_s_dtype; static PyObject *__pyx_n_s_dtype_is_object; static PyObject *__pyx_n_s_embedding; static PyObject *__pyx_n_s_empty_like; static PyObject *__pyx_n_s_encode; static PyObject *__pyx_n_s_enumerate; static PyObject *__pyx_n_s_eps; static PyObject *__pyx_n_s_error; static PyObject *__pyx_n_s_estimate_positive_gradient_nn; static PyObject *__pyx_n_s_finfo; static PyObject *__pyx_n_s_flags; static PyObject *__pyx_n_s_float64; static PyObject *__pyx_n_s_format; static PyObject *__pyx_n_s_fortran; static PyObject *__pyx_n_u_fortran; static PyObject *__pyx_n_s_getstate; static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi; static PyObject *__pyx_n_s_id; static PyObject *__pyx_n_s_import; static PyObject *__pyx_n_s_indices; static PyObject *__pyx_n_s_indptr; static PyObject *__pyx_n_s_ints_in_interval; static PyObject *__pyx_n_s_itemsize; static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; static PyObject *__pyx_n_s_main; static PyObject *__pyx_n_s_memview; static PyObject *__pyx_n_s_min_num_intervals; static PyObject *__pyx_n_s_mode; static PyObject *__pyx_n_s_n_interpolation_points; static PyObject *__pyx_n_s_name; static PyObject *__pyx_n_s_name_2; static PyObject *__pyx_n_s_ndim; static PyObject *__pyx_n_s_new; static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; static PyObject *__pyx_n_s_np; static PyObject *__pyx_n_s_numpy; static PyObject *__pyx_kp_u_numpy_core_multiarray_failed_to; static PyObject *__pyx_kp_u_numpy_core_umath_failed_to_impor; static PyObject *__pyx_n_s_obj; static PyObject *__pyx_n_s_pack; static PyObject *__pyx_n_s_pickle; static PyObject *__pyx_n_s_pyx_PickleError; static PyObject *__pyx_n_s_pyx_checksum; static PyObject *__pyx_n_s_pyx_getbuffer; static PyObject *__pyx_n_s_pyx_result; static PyObject *__pyx_n_s_pyx_state; static PyObject *__pyx_n_s_pyx_type; static PyObject *__pyx_n_s_pyx_unpickle_Enum; static PyObject *__pyx_n_s_pyx_vtable; static PyObject *__pyx_n_s_range; static PyObject *__pyx_n_s_ravel; static PyObject *__pyx_n_s_reduce; static PyObject *__pyx_n_s_reduce_cython; static PyObject *__pyx_n_s_reduce_ex; static PyObject *__pyx_n_s_setstate; static PyObject *__pyx_n_s_setstate_cython; static PyObject *__pyx_n_s_shape; static PyObject *__pyx_n_s_should_eval_error; static PyObject *__pyx_n_s_size; static PyObject *__pyx_n_s_start; static PyObject *__pyx_n_s_step; static PyObject *__pyx_n_s_stop; static PyObject *__pyx_kp_s_strided_and_direct; static PyObject *__pyx_kp_s_strided_and_direct_or_indirect; static PyObject *__pyx_kp_s_strided_and_indirect; static PyObject *__pyx_kp_s_stringsource; static PyObject *__pyx_n_s_struct; static PyObject *__pyx_n_s_test; static PyObject *__pyx_n_s_theta; static PyObject *__pyx_n_s_tsne; static PyObject *__pyx_kp_s_unable_to_allocate_array_data; static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; static PyObject *__pyx_n_s_unpack; static PyObject *__pyx_n_s_update; static PyObject *__pyx_pf_8openTSNE_13kl_divergence_kl_divergence_exact(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_P, __Pyx_memviewslice __pyx_v_embedding); /* proto */ static PyObject *__pyx_pf_8openTSNE_13kl_divergence_2kl_divergence_approx_bh(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_indices, __Pyx_memviewslice __pyx_v_indptr, __Pyx_memviewslice __pyx_v_P_data, __Pyx_memviewslice __pyx_v_embedding, double __pyx_v_theta, double __pyx_v_dof); /* proto */ static PyObject *__pyx_pf_8openTSNE_13kl_divergence_4kl_divergence_approx_fft(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_indices, __Pyx_memviewslice __pyx_v_indptr, __Pyx_memviewslice __pyx_v_P_data, __Pyx_memviewslice __pyx_v_embedding, double __pyx_v_dof, Py_ssize_t __pyx_v_n_interpolation_points, Py_ssize_t __pyx_v_min_num_intervals, double __pyx_v_ints_in_interval); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */ static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_int_0; static PyObject *__pyx_int_1; static PyObject *__pyx_int_184977713; static PyObject *__pyx_int_neg_1; static PyObject *__pyx_tuple_; static PyObject *__pyx_tuple__2; static PyObject *__pyx_tuple__3; static PyObject *__pyx_tuple__4; static PyObject *__pyx_tuple__5; static PyObject *__pyx_tuple__6; static PyObject *__pyx_tuple__7; static PyObject *__pyx_tuple__8; static PyObject *__pyx_tuple__9; static PyObject *__pyx_slice__17; static PyObject *__pyx_tuple__10; static PyObject *__pyx_tuple__11; static PyObject *__pyx_tuple__12; static PyObject *__pyx_tuple__13; static PyObject *__pyx_tuple__14; static PyObject *__pyx_tuple__15; static PyObject *__pyx_tuple__16; static PyObject *__pyx_tuple__18; static PyObject *__pyx_tuple__19; static PyObject *__pyx_tuple__20; static PyObject *__pyx_tuple__21; static PyObject *__pyx_tuple__22; static PyObject *__pyx_tuple__23; static PyObject *__pyx_tuple__24; static PyObject *__pyx_tuple__25; static PyObject *__pyx_tuple__26; static PyObject *__pyx_codeobj__27; /* Late includes */ /* "openTSNE/kl_divergence.pyx":25 * * * cdef sqeuclidean(double[:] x, double[:] y): # <<<<<<<<<<<<<< * cdef: * Py_ssize_t n_dims = x.shape[0] */ static PyObject *__pyx_f_8openTSNE_13kl_divergence_sqeuclidean(__Pyx_memviewslice __pyx_v_x, __Pyx_memviewslice __pyx_v_y) { Py_ssize_t __pyx_v_n_dims; double __pyx_v_result; Py_ssize_t __pyx_v_i; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; PyObject *__pyx_t_6 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("sqeuclidean", 0); /* "openTSNE/kl_divergence.pyx":27 * cdef sqeuclidean(double[:] x, double[:] y): * cdef: * Py_ssize_t n_dims = x.shape[0] # <<<<<<<<<<<<<< * double result = 0 * Py_ssize_t i */ __pyx_v_n_dims = (__pyx_v_x.shape[0]); /* "openTSNE/kl_divergence.pyx":28 * cdef: * Py_ssize_t n_dims = x.shape[0] * double result = 0 # <<<<<<<<<<<<<< * Py_ssize_t i * */ __pyx_v_result = 0.0; /* "openTSNE/kl_divergence.pyx":31 * Py_ssize_t i * * for i in range(n_dims): # <<<<<<<<<<<<<< * result += (x[i] - y[i]) ** 2 * */ __pyx_t_1 = __pyx_v_n_dims; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/kl_divergence.pyx":32 * * for i in range(n_dims): * result += (x[i] - y[i]) ** 2 # <<<<<<<<<<<<<< * * return result */ __pyx_t_4 = __pyx_v_i; __pyx_t_5 = __pyx_v_i; __pyx_v_result = (__pyx_v_result + pow(((*((double *) ( /* dim=0 */ (__pyx_v_x.data + __pyx_t_4 * __pyx_v_x.strides[0]) ))) - (*((double *) ( /* dim=0 */ (__pyx_v_y.data + __pyx_t_5 * __pyx_v_y.strides[0]) )))), 2.0)); } /* "openTSNE/kl_divergence.pyx":34 * result += (x[i] - y[i]) ** 2 * * return result # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_t_6 = PyFloat_FromDouble(__pyx_v_result); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 34, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_r = __pyx_t_6; __pyx_t_6 = 0; goto __pyx_L0; /* "openTSNE/kl_divergence.pyx":25 * * * cdef sqeuclidean(double[:] x, double[:] y): # <<<<<<<<<<<<<< * cdef: * Py_ssize_t n_dims = x.shape[0] */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_6); __Pyx_AddTraceback("openTSNE.kl_divergence.sqeuclidean", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/kl_divergence.pyx":37 * * * cpdef double kl_divergence_exact(double[:, ::1] P, double[:, ::1] embedding): # <<<<<<<<<<<<<< * """Compute the exact KL divergence.""" * cdef: */ static PyObject *__pyx_pw_8openTSNE_13kl_divergence_1kl_divergence_exact(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static double __pyx_f_8openTSNE_13kl_divergence_kl_divergence_exact(__Pyx_memviewslice __pyx_v_P, __Pyx_memviewslice __pyx_v_embedding, CYTHON_UNUSED int __pyx_skip_dispatch) { Py_ssize_t __pyx_v_n_samples; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_j; double __pyx_v_sum_P; double __pyx_v_sum_Q; double __pyx_v_p_ij; double __pyx_v_q_ij; double __pyx_v_kl_divergence; double __pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; int __pyx_t_7; Py_ssize_t __pyx_t_8; Py_ssize_t __pyx_t_9; __Pyx_memviewslice __pyx_t_10 = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_t_11 = { 0, 0, { 0 }, { 0 }, { 0 } }; PyObject *__pyx_t_12 = NULL; PyObject *__pyx_t_13 = NULL; double __pyx_t_14; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("kl_divergence_exact", 0); /* "openTSNE/kl_divergence.pyx":40 * """Compute the exact KL divergence.""" * cdef: * Py_ssize_t n_samples = embedding.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t i, j * */ __pyx_v_n_samples = (__pyx_v_embedding.shape[0]); /* "openTSNE/kl_divergence.pyx":43 * Py_ssize_t i, j * * double sum_P = 0, sum_Q = 0, p_ij, q_ij # <<<<<<<<<<<<<< * double kl_divergence = 0 * */ __pyx_v_sum_P = 0.0; __pyx_v_sum_Q = 0.0; /* "openTSNE/kl_divergence.pyx":44 * * double sum_P = 0, sum_Q = 0, p_ij, q_ij * double kl_divergence = 0 # <<<<<<<<<<<<<< * * for i in range(n_samples): */ __pyx_v_kl_divergence = 0.0; /* "openTSNE/kl_divergence.pyx":46 * double kl_divergence = 0 * * for i in range(n_samples): # <<<<<<<<<<<<<< * for j in range(n_samples): * if i != j: */ __pyx_t_1 = __pyx_v_n_samples; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "openTSNE/kl_divergence.pyx":47 * * for i in range(n_samples): * for j in range(n_samples): # <<<<<<<<<<<<<< * if i != j: * p_ij = P[i, j] */ __pyx_t_4 = __pyx_v_n_samples; __pyx_t_5 = __pyx_t_4; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_j = __pyx_t_6; /* "openTSNE/kl_divergence.pyx":48 * for i in range(n_samples): * for j in range(n_samples): * if i != j: # <<<<<<<<<<<<<< * p_ij = P[i, j] * q_ij = 1 / (1 + sqeuclidean(embedding[i], embedding[j])) */ __pyx_t_7 = ((__pyx_v_i != __pyx_v_j) != 0); if (__pyx_t_7) { /* "openTSNE/kl_divergence.pyx":49 * for j in range(n_samples): * if i != j: * p_ij = P[i, j] # <<<<<<<<<<<<<< * q_ij = 1 / (1 + sqeuclidean(embedding[i], embedding[j])) * sum_Q += q_ij */ __pyx_t_8 = __pyx_v_i; __pyx_t_9 = __pyx_v_j; __pyx_v_p_ij = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_P.data + __pyx_t_8 * __pyx_v_P.strides[0]) )) + __pyx_t_9)) ))); /* "openTSNE/kl_divergence.pyx":50 * if i != j: * p_ij = P[i, j] * q_ij = 1 / (1 + sqeuclidean(embedding[i], embedding[j])) # <<<<<<<<<<<<<< * sum_Q += q_ij * sum_P += p_ij */ __pyx_t_10.data = __pyx_v_embedding.data; __pyx_t_10.memview = __pyx_v_embedding.memview; __PYX_INC_MEMVIEW(&__pyx_t_10, 0); { Py_ssize_t __pyx_tmp_idx = __pyx_v_i; Py_ssize_t __pyx_tmp_stride = __pyx_v_embedding.strides[0]; __pyx_t_10.data += __pyx_tmp_idx * __pyx_tmp_stride; } __pyx_t_10.shape[0] = __pyx_v_embedding.shape[1]; __pyx_t_10.strides[0] = __pyx_v_embedding.strides[1]; __pyx_t_10.suboffsets[0] = -1; __pyx_t_11.data = __pyx_v_embedding.data; __pyx_t_11.memview = __pyx_v_embedding.memview; __PYX_INC_MEMVIEW(&__pyx_t_11, 0); { Py_ssize_t __pyx_tmp_idx = __pyx_v_j; Py_ssize_t __pyx_tmp_stride = __pyx_v_embedding.strides[0]; __pyx_t_11.data += __pyx_tmp_idx * __pyx_tmp_stride; } __pyx_t_11.shape[0] = __pyx_v_embedding.shape[1]; __pyx_t_11.strides[0] = __pyx_v_embedding.strides[1]; __pyx_t_11.suboffsets[0] = -1; __pyx_t_12 = __pyx_f_8openTSNE_13kl_divergence_sqeuclidean(__pyx_t_10, __pyx_t_11); if (unlikely(!__pyx_t_12)) __PYX_ERR(0, 50, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_12); __PYX_XDEC_MEMVIEW(&__pyx_t_10, 1); __pyx_t_10.memview = NULL; __pyx_t_10.data = NULL; __PYX_XDEC_MEMVIEW(&__pyx_t_11, 1); __pyx_t_11.memview = NULL; __pyx_t_11.data = NULL; __pyx_t_13 = __Pyx_PyInt_AddCObj(__pyx_int_1, __pyx_t_12, 1, 0, 0); if (unlikely(!__pyx_t_13)) __PYX_ERR(0, 50, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_13); __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0; __pyx_t_12 = __Pyx_PyNumber_Divide(__pyx_int_1, __pyx_t_13); if (unlikely(!__pyx_t_12)) __PYX_ERR(0, 50, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_12); __Pyx_DECREF(__pyx_t_13); __pyx_t_13 = 0; __pyx_t_14 = __pyx_PyFloat_AsDouble(__pyx_t_12); if (unlikely((__pyx_t_14 == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 50, __pyx_L1_error) __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0; __pyx_v_q_ij = __pyx_t_14; /* "openTSNE/kl_divergence.pyx":51 * p_ij = P[i, j] * q_ij = 1 / (1 + sqeuclidean(embedding[i], embedding[j])) * sum_Q += q_ij # <<<<<<<<<<<<<< * sum_P += p_ij * if p_ij > 0: */ __pyx_v_sum_Q = (__pyx_v_sum_Q + __pyx_v_q_ij); /* "openTSNE/kl_divergence.pyx":52 * q_ij = 1 / (1 + sqeuclidean(embedding[i], embedding[j])) * sum_Q += q_ij * sum_P += p_ij # <<<<<<<<<<<<<< * if p_ij > 0: * kl_divergence += p_ij * log(p_ij / (q_ij + EPSILON)) */ __pyx_v_sum_P = (__pyx_v_sum_P + __pyx_v_p_ij); /* "openTSNE/kl_divergence.pyx":53 * sum_Q += q_ij * sum_P += p_ij * if p_ij > 0: # <<<<<<<<<<<<<< * kl_divergence += p_ij * log(p_ij / (q_ij + EPSILON)) * */ __pyx_t_7 = ((__pyx_v_p_ij > 0.0) != 0); if (__pyx_t_7) { /* "openTSNE/kl_divergence.pyx":54 * sum_P += p_ij * if p_ij > 0: * kl_divergence += p_ij * log(p_ij / (q_ij + EPSILON)) # <<<<<<<<<<<<<< * * kl_divergence += sum_P * log(sum_Q + EPSILON) */ __pyx_v_kl_divergence = (__pyx_v_kl_divergence + (__pyx_v_p_ij * log((__pyx_v_p_ij / (__pyx_v_q_ij + __pyx_v_8openTSNE_13kl_divergence_EPSILON))))); /* "openTSNE/kl_divergence.pyx":53 * sum_Q += q_ij * sum_P += p_ij * if p_ij > 0: # <<<<<<<<<<<<<< * kl_divergence += p_ij * log(p_ij / (q_ij + EPSILON)) * */ } /* "openTSNE/kl_divergence.pyx":48 * for i in range(n_samples): * for j in range(n_samples): * if i != j: # <<<<<<<<<<<<<< * p_ij = P[i, j] * q_ij = 1 / (1 + sqeuclidean(embedding[i], embedding[j])) */ } } } /* "openTSNE/kl_divergence.pyx":56 * kl_divergence += p_ij * log(p_ij / (q_ij + EPSILON)) * * kl_divergence += sum_P * log(sum_Q + EPSILON) # <<<<<<<<<<<<<< * * return kl_divergence */ __pyx_v_kl_divergence = (__pyx_v_kl_divergence + (__pyx_v_sum_P * log((__pyx_v_sum_Q + __pyx_v_8openTSNE_13kl_divergence_EPSILON)))); /* "openTSNE/kl_divergence.pyx":58 * kl_divergence += sum_P * log(sum_Q + EPSILON) * * return kl_divergence # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_kl_divergence; goto __pyx_L0; /* "openTSNE/kl_divergence.pyx":37 * * * cpdef double kl_divergence_exact(double[:, ::1] P, double[:, ::1] embedding): # <<<<<<<<<<<<<< * """Compute the exact KL divergence.""" * cdef: */ /* function exit code */ __pyx_L1_error:; __PYX_XDEC_MEMVIEW(&__pyx_t_10, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_11, 1); __Pyx_XDECREF(__pyx_t_12); __Pyx_XDECREF(__pyx_t_13); __Pyx_WriteUnraisable("openTSNE.kl_divergence.kl_divergence_exact", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_r = 0; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_13kl_divergence_1kl_divergence_exact(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static char __pyx_doc_8openTSNE_13kl_divergence_kl_divergence_exact[] = "Compute the exact KL divergence."; static PyObject *__pyx_pw_8openTSNE_13kl_divergence_1kl_divergence_exact(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __Pyx_memviewslice __pyx_v_P = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("kl_divergence_exact (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_P,&__pyx_n_s_embedding,0}; PyObject* values[2] = {0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_P)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_embedding)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("kl_divergence_exact", 1, 2, 2, 1); __PYX_ERR(0, 37, __pyx_L3_error) } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "kl_divergence_exact") < 0)) __PYX_ERR(0, 37, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 2) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); } __pyx_v_P = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_P.memview)) __PYX_ERR(0, 37, __pyx_L3_error) __pyx_v_embedding = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[1], PyBUF_WRITABLE); if (unlikely(!__pyx_v_embedding.memview)) __PYX_ERR(0, 37, __pyx_L3_error) } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("kl_divergence_exact", 1, 2, 2, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 37, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE.kl_divergence.kl_divergence_exact", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_13kl_divergence_kl_divergence_exact(__pyx_self, __pyx_v_P, __pyx_v_embedding); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_13kl_divergence_kl_divergence_exact(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_P, __Pyx_memviewslice __pyx_v_embedding) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("kl_divergence_exact", 0); __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyFloat_FromDouble(__pyx_f_8openTSNE_13kl_divergence_kl_divergence_exact(__pyx_v_P, __pyx_v_embedding, 0)); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 37, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("openTSNE.kl_divergence.kl_divergence_exact", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_P, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_embedding, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/kl_divergence.pyx":61 * * * cpdef double kl_divergence_approx_bh( # <<<<<<<<<<<<<< * int[:] indices, * int[:] indptr, */ static PyObject *__pyx_pw_8openTSNE_13kl_divergence_3kl_divergence_approx_bh(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static double __pyx_f_8openTSNE_13kl_divergence_kl_divergence_approx_bh(__Pyx_memviewslice __pyx_v_indices, __Pyx_memviewslice __pyx_v_indptr, __Pyx_memviewslice __pyx_v_P_data, __Pyx_memviewslice __pyx_v_embedding, CYTHON_UNUSED int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_13kl_divergence_kl_divergence_approx_bh *__pyx_optional_args) { double __pyx_v_theta = ((double)0.5); double __pyx_v_dof = ((double)1.0); CYTHON_UNUSED Py_ssize_t __pyx_v_n_samples; struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_tree = 0; __Pyx_memviewslice __pyx_v_gradient = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_sum_P; double __pyx_v_sum_Q; double __pyx_v_kl_divergence; double __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_t_6; struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_bh __pyx_t_7; PyObject *__pyx_t_8 = NULL; PyObject *__pyx_t_9 = NULL; PyObject *__pyx_t_10 = NULL; PyObject *__pyx_t_11 = NULL; PyObject *(*__pyx_t_12)(PyObject *); double __pyx_t_13; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("kl_divergence_approx_bh", 0); if (__pyx_optional_args) { if (__pyx_optional_args->__pyx_n > 0) { __pyx_v_theta = __pyx_optional_args->theta; if (__pyx_optional_args->__pyx_n > 1) { __pyx_v_dof = __pyx_optional_args->dof; } } } /* "openTSNE/kl_divergence.pyx":71 * """Compute the KL divergence using the Barnes-Hut approximation.""" * cdef: * Py_ssize_t n_samples = embedding.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t i, j * */ __pyx_v_n_samples = (__pyx_v_embedding.shape[0]); /* "openTSNE/kl_divergence.pyx":74 * Py_ssize_t i, j * * QuadTree tree = QuadTree(embedding) # <<<<<<<<<<<<<< * # We don"t actually care about the gradient, so don"t waste time * # initializing memory */ __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_embedding, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 74, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_CallOneArg(((PyObject *)__pyx_ptype_8openTSNE_9quad_tree_QuadTree), __pyx_t_1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 74, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_v_tree = ((struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *)__pyx_t_2); __pyx_t_2 = 0; /* "openTSNE/kl_divergence.pyx":77 * # We don"t actually care about the gradient, so don"t waste time * # initializing memory * double[:, ::1] gradient = np.empty_like(embedding, dtype=float) # <<<<<<<<<<<<<< * * double sum_P = 0, sum_Q = 0 */ __Pyx_GetModuleGlobalName(__pyx_t_2, __pyx_n_s_np); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 77, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_empty_like); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 77, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __pyx_memoryview_fromslice(__pyx_v_embedding, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 77, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 77, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 77, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (PyDict_SetItem(__pyx_t_2, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 77, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_3, __pyx_t_2); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 77, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 77, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_gradient = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/kl_divergence.pyx":79 * double[:, ::1] gradient = np.empty_like(embedding, dtype=float) * * double sum_P = 0, sum_Q = 0 # <<<<<<<<<<<<<< * double kl_divergence = 0 * */ __pyx_v_sum_P = 0.0; __pyx_v_sum_Q = 0.0; /* "openTSNE/kl_divergence.pyx":80 * * double sum_P = 0, sum_Q = 0 * double kl_divergence = 0 # <<<<<<<<<<<<<< * * sum_Q = estimate_negative_gradient_bh(tree, embedding, gradient, theta, dof) */ __pyx_v_kl_divergence = 0.0; /* "openTSNE/kl_divergence.pyx":82 * double kl_divergence = 0 * * sum_Q = estimate_negative_gradient_bh(tree, embedding, gradient, theta, dof) # <<<<<<<<<<<<<< * sum_P, kl_divergence = estimate_positive_gradient_nn( * indices, */ __pyx_t_7.__pyx_n = 2; __pyx_t_7.theta = __pyx_v_theta; __pyx_t_7.dof = __pyx_v_dof; __pyx_t_6 = __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_bh(__pyx_v_tree, __pyx_v_embedding, __pyx_v_gradient, 0, &__pyx_t_7); __pyx_v_sum_Q = __pyx_t_6; /* "openTSNE/kl_divergence.pyx":83 * * sum_Q = estimate_negative_gradient_bh(tree, embedding, gradient, theta, dof) * sum_P, kl_divergence = estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * indices, * indptr, */ __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_estimate_positive_gradient_nn); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 83, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); /* "openTSNE/kl_divergence.pyx":84 * sum_Q = estimate_negative_gradient_bh(tree, embedding, gradient, theta, dof) * sum_P, kl_divergence = estimate_positive_gradient_nn( * indices, # <<<<<<<<<<<<<< * indptr, * P_data, */ __pyx_t_2 = __pyx_memoryview_fromslice(__pyx_v_indices, 1, (PyObject *(*)(char *)) __pyx_memview_get_int, (int (*)(char *, PyObject *)) __pyx_memview_set_int, 0);; if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 84, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); /* "openTSNE/kl_divergence.pyx":85 * sum_P, kl_divergence = estimate_positive_gradient_nn( * indices, * indptr, # <<<<<<<<<<<<<< * P_data, * embedding, */ __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_indptr, 1, (PyObject *(*)(char *)) __pyx_memview_get_int, (int (*)(char *, PyObject *)) __pyx_memview_set_int, 0);; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 85, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); /* "openTSNE/kl_divergence.pyx":86 * indices, * indptr, * P_data, # <<<<<<<<<<<<<< * embedding, * embedding, */ __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_P_data, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 86, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); /* "openTSNE/kl_divergence.pyx":87 * indptr, * P_data, * embedding, # <<<<<<<<<<<<<< * embedding, * gradient, */ __pyx_t_8 = __pyx_memoryview_fromslice(__pyx_v_embedding, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 87, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); /* "openTSNE/kl_divergence.pyx":88 * P_data, * embedding, * embedding, # <<<<<<<<<<<<<< * gradient, * dof=dof, */ __pyx_t_9 = __pyx_memoryview_fromslice(__pyx_v_embedding, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 88, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); /* "openTSNE/kl_divergence.pyx":89 * embedding, * embedding, * gradient, # <<<<<<<<<<<<<< * dof=dof, * should_eval_error=True, */ __pyx_t_10 = __pyx_memoryview_fromslice(__pyx_v_gradient, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 89, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); /* "openTSNE/kl_divergence.pyx":83 * * sum_Q = estimate_negative_gradient_bh(tree, embedding, gradient, theta, dof) * sum_P, kl_divergence = estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * indices, * indptr, */ __pyx_t_11 = PyTuple_New(6); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 83, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_11, 0, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_11, 1, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_11, 2, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_8); PyTuple_SET_ITEM(__pyx_t_11, 3, __pyx_t_8); __Pyx_GIVEREF(__pyx_t_9); PyTuple_SET_ITEM(__pyx_t_11, 4, __pyx_t_9); __Pyx_GIVEREF(__pyx_t_10); PyTuple_SET_ITEM(__pyx_t_11, 5, __pyx_t_10); __pyx_t_2 = 0; __pyx_t_3 = 0; __pyx_t_1 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; /* "openTSNE/kl_divergence.pyx":90 * embedding, * gradient, * dof=dof, # <<<<<<<<<<<<<< * should_eval_error=True, * ) */ __pyx_t_10 = __Pyx_PyDict_NewPresized(2); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 90, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_9 = PyFloat_FromDouble(__pyx_v_dof); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 90, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); if (PyDict_SetItem(__pyx_t_10, __pyx_n_s_dof, __pyx_t_9) < 0) __PYX_ERR(0, 90, __pyx_L1_error) __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; /* "openTSNE/kl_divergence.pyx":91 * gradient, * dof=dof, * should_eval_error=True, # <<<<<<<<<<<<<< * ) * */ if (PyDict_SetItem(__pyx_t_10, __pyx_n_s_should_eval_error, Py_True) < 0) __PYX_ERR(0, 90, __pyx_L1_error) /* "openTSNE/kl_divergence.pyx":83 * * sum_Q = estimate_negative_gradient_bh(tree, embedding, gradient, theta, dof) * sum_P, kl_divergence = estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * indices, * indptr, */ __pyx_t_9 = __Pyx_PyObject_Call(__pyx_t_4, __pyx_t_11, __pyx_t_10); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 83, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; if ((likely(PyTuple_CheckExact(__pyx_t_9))) || (PyList_CheckExact(__pyx_t_9))) { PyObject* sequence = __pyx_t_9; Py_ssize_t size = __Pyx_PySequence_SIZE(sequence); if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); __PYX_ERR(0, 83, __pyx_L1_error) } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS if (likely(PyTuple_CheckExact(sequence))) { __pyx_t_10 = PyTuple_GET_ITEM(sequence, 0); __pyx_t_11 = PyTuple_GET_ITEM(sequence, 1); } else { __pyx_t_10 = PyList_GET_ITEM(sequence, 0); __pyx_t_11 = PyList_GET_ITEM(sequence, 1); } __Pyx_INCREF(__pyx_t_10); __Pyx_INCREF(__pyx_t_11); #else __pyx_t_10 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 83, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __pyx_t_11 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 83, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); #endif __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { Py_ssize_t index = -1; __pyx_t_4 = PyObject_GetIter(__pyx_t_9); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 83, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_t_12 = Py_TYPE(__pyx_t_4)->tp_iternext; index = 0; __pyx_t_10 = __pyx_t_12(__pyx_t_4); if (unlikely(!__pyx_t_10)) goto __pyx_L3_unpacking_failed; __Pyx_GOTREF(__pyx_t_10); index = 1; __pyx_t_11 = __pyx_t_12(__pyx_t_4); if (unlikely(!__pyx_t_11)) goto __pyx_L3_unpacking_failed; __Pyx_GOTREF(__pyx_t_11); if (__Pyx_IternextUnpackEndCheck(__pyx_t_12(__pyx_t_4), 2) < 0) __PYX_ERR(0, 83, __pyx_L1_error) __pyx_t_12 = NULL; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; goto __pyx_L4_unpacking_done; __pyx_L3_unpacking_failed:; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_12 = NULL; if (__Pyx_IterFinish() == 0) __Pyx_RaiseNeedMoreValuesError(index); __PYX_ERR(0, 83, __pyx_L1_error) __pyx_L4_unpacking_done:; } __pyx_t_6 = __pyx_PyFloat_AsDouble(__pyx_t_10); if (unlikely((__pyx_t_6 == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 83, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_13 = __pyx_PyFloat_AsDouble(__pyx_t_11); if (unlikely((__pyx_t_13 == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 83, __pyx_L1_error) __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __pyx_v_sum_P = __pyx_t_6; __pyx_v_kl_divergence = __pyx_t_13; /* "openTSNE/kl_divergence.pyx":94 * ) * * kl_divergence += sum_P * log(sum_Q + EPSILON) # <<<<<<<<<<<<<< * * return kl_divergence */ __pyx_v_kl_divergence = (__pyx_v_kl_divergence + (__pyx_v_sum_P * log((__pyx_v_sum_Q + __pyx_v_8openTSNE_13kl_divergence_EPSILON)))); /* "openTSNE/kl_divergence.pyx":96 * kl_divergence += sum_P * log(sum_Q + EPSILON) * * return kl_divergence # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_kl_divergence; goto __pyx_L0; /* "openTSNE/kl_divergence.pyx":61 * * * cpdef double kl_divergence_approx_bh( # <<<<<<<<<<<<<< * int[:] indices, * int[:] indptr, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1); __Pyx_XDECREF(__pyx_t_8); __Pyx_XDECREF(__pyx_t_9); __Pyx_XDECREF(__pyx_t_10); __Pyx_XDECREF(__pyx_t_11); __Pyx_WriteUnraisable("openTSNE.kl_divergence.kl_divergence_approx_bh", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_tree); __PYX_XDEC_MEMVIEW(&__pyx_v_gradient, 1); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_13kl_divergence_3kl_divergence_approx_bh(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static char __pyx_doc_8openTSNE_13kl_divergence_2kl_divergence_approx_bh[] = "Compute the KL divergence using the Barnes-Hut approximation."; static PyObject *__pyx_pw_8openTSNE_13kl_divergence_3kl_divergence_approx_bh(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __Pyx_memviewslice __pyx_v_indices = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_indptr = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_P_data = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_theta; double __pyx_v_dof; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("kl_divergence_approx_bh (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_indices,&__pyx_n_s_indptr,&__pyx_n_s_P_data,&__pyx_n_s_embedding,&__pyx_n_s_theta,&__pyx_n_s_dof,0}; PyObject* values[6] = {0,0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_indices)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_indptr)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("kl_divergence_approx_bh", 0, 4, 6, 1); __PYX_ERR(0, 61, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_P_data)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("kl_divergence_approx_bh", 0, 4, 6, 2); __PYX_ERR(0, 61, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 3: if (likely((values[3] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_embedding)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("kl_divergence_approx_bh", 0, 4, 6, 3); __PYX_ERR(0, 61, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 4: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_theta); if (value) { values[4] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 5: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dof); if (value) { values[5] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "kl_divergence_approx_bh") < 0)) __PYX_ERR(0, 61, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); values[2] = PyTuple_GET_ITEM(__pyx_args, 2); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_indices = __Pyx_PyObject_to_MemoryviewSlice_ds_int(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_indices.memview)) __PYX_ERR(0, 62, __pyx_L3_error) __pyx_v_indptr = __Pyx_PyObject_to_MemoryviewSlice_ds_int(values[1], PyBUF_WRITABLE); if (unlikely(!__pyx_v_indptr.memview)) __PYX_ERR(0, 63, __pyx_L3_error) __pyx_v_P_data = __Pyx_PyObject_to_MemoryviewSlice_ds_double(values[2], PyBUF_WRITABLE); if (unlikely(!__pyx_v_P_data.memview)) __PYX_ERR(0, 64, __pyx_L3_error) __pyx_v_embedding = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[3], PyBUF_WRITABLE); if (unlikely(!__pyx_v_embedding.memview)) __PYX_ERR(0, 65, __pyx_L3_error) if (values[4]) { __pyx_v_theta = __pyx_PyFloat_AsDouble(values[4]); if (unlikely((__pyx_v_theta == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 66, __pyx_L3_error) } else { __pyx_v_theta = ((double)0.5); } if (values[5]) { __pyx_v_dof = __pyx_PyFloat_AsDouble(values[5]); if (unlikely((__pyx_v_dof == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 67, __pyx_L3_error) } else { __pyx_v_dof = ((double)1.0); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("kl_divergence_approx_bh", 0, 4, 6, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 61, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE.kl_divergence.kl_divergence_approx_bh", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_13kl_divergence_2kl_divergence_approx_bh(__pyx_self, __pyx_v_indices, __pyx_v_indptr, __pyx_v_P_data, __pyx_v_embedding, __pyx_v_theta, __pyx_v_dof); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_13kl_divergence_2kl_divergence_approx_bh(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_indices, __Pyx_memviewslice __pyx_v_indptr, __Pyx_memviewslice __pyx_v_P_data, __Pyx_memviewslice __pyx_v_embedding, double __pyx_v_theta, double __pyx_v_dof) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations double __pyx_t_1; struct __pyx_opt_args_8openTSNE_13kl_divergence_kl_divergence_approx_bh __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("kl_divergence_approx_bh", 0); __Pyx_XDECREF(__pyx_r); __pyx_t_2.__pyx_n = 2; __pyx_t_2.theta = __pyx_v_theta; __pyx_t_2.dof = __pyx_v_dof; __pyx_t_1 = __pyx_f_8openTSNE_13kl_divergence_kl_divergence_approx_bh(__pyx_v_indices, __pyx_v_indptr, __pyx_v_P_data, __pyx_v_embedding, 0, &__pyx_t_2); __pyx_t_3 = PyFloat_FromDouble(__pyx_t_1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 61, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("openTSNE.kl_divergence.kl_divergence_approx_bh", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_indices, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_indptr, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_P_data, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_embedding, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/kl_divergence.pyx":100 * * * cpdef double kl_divergence_approx_fft( # <<<<<<<<<<<<<< * int[:] indices, * int[:] indptr, */ static PyObject *__pyx_pw_8openTSNE_13kl_divergence_5kl_divergence_approx_fft(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static double __pyx_f_8openTSNE_13kl_divergence_kl_divergence_approx_fft(__Pyx_memviewslice __pyx_v_indices, __Pyx_memviewslice __pyx_v_indptr, __Pyx_memviewslice __pyx_v_P_data, __Pyx_memviewslice __pyx_v_embedding, CYTHON_UNUSED int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_13kl_divergence_kl_divergence_approx_fft *__pyx_optional_args) { double __pyx_v_dof = ((double)1.0); Py_ssize_t __pyx_v_n_interpolation_points = ((Py_ssize_t)3); Py_ssize_t __pyx_v_min_num_intervals = ((Py_ssize_t)10); double __pyx_v_ints_in_interval = ((double)1.0); CYTHON_UNUSED Py_ssize_t __pyx_v_n_samples; Py_ssize_t __pyx_v_n_dims; __Pyx_memviewslice __pyx_v_gradient = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_sum_P; double __pyx_v_sum_Q; double __pyx_v_kl_divergence; double __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_t_6 = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_t_7 = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_t_8; struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d __pyx_t_9; struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d __pyx_t_10; PyObject *__pyx_t_11 = NULL; PyObject *__pyx_t_12 = NULL; PyObject *__pyx_t_13 = NULL; PyObject *__pyx_t_14 = NULL; PyObject *(*__pyx_t_15)(PyObject *); double __pyx_t_16; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("kl_divergence_approx_fft", 0); if (__pyx_optional_args) { if (__pyx_optional_args->__pyx_n > 0) { __pyx_v_dof = __pyx_optional_args->dof; if (__pyx_optional_args->__pyx_n > 1) { __pyx_v_n_interpolation_points = __pyx_optional_args->n_interpolation_points; if (__pyx_optional_args->__pyx_n > 2) { __pyx_v_min_num_intervals = __pyx_optional_args->min_num_intervals; if (__pyx_optional_args->__pyx_n > 3) { __pyx_v_ints_in_interval = __pyx_optional_args->ints_in_interval; } } } } } /* "openTSNE/kl_divergence.pyx":112 * """Compute the KL divergence using the interpolation based approximation.""" * cdef: * Py_ssize_t n_samples = embedding.shape[0] # <<<<<<<<<<<<<< * Py_ssize_t n_dims = embedding.shape[1] * Py_ssize_t i, j */ __pyx_v_n_samples = (__pyx_v_embedding.shape[0]); /* "openTSNE/kl_divergence.pyx":113 * cdef: * Py_ssize_t n_samples = embedding.shape[0] * Py_ssize_t n_dims = embedding.shape[1] # <<<<<<<<<<<<<< * Py_ssize_t i, j * */ __pyx_v_n_dims = (__pyx_v_embedding.shape[1]); /* "openTSNE/kl_divergence.pyx":118 * # We don"t actually care about the gradient, so don"t waste time * # initializing memory * double[:, ::1] gradient = np.empty_like(embedding, dtype=float) # <<<<<<<<<<<<<< * * double sum_P = 0, sum_Q = 0 */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 118, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_empty_like); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 118, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_embedding, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 118, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 118, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 118, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_dtype, ((PyObject *)(&PyFloat_Type))) < 0) __PYX_ERR(0, 118, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 118, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 118, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_gradient = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/kl_divergence.pyx":120 * double[:, ::1] gradient = np.empty_like(embedding, dtype=float) * * double sum_P = 0, sum_Q = 0 # <<<<<<<<<<<<<< * double kl_divergence = 0 * */ __pyx_v_sum_P = 0.0; __pyx_v_sum_Q = 0.0; /* "openTSNE/kl_divergence.pyx":121 * * double sum_P = 0, sum_Q = 0 * double kl_divergence = 0 # <<<<<<<<<<<<<< * * */ __pyx_v_kl_divergence = 0.0; /* "openTSNE/kl_divergence.pyx":124 * * * if n_dims == 1: # <<<<<<<<<<<<<< * sum_Q = estimate_negative_gradient_fft_1d( * embedding.ravel(), */ switch (__pyx_v_n_dims) { case 1: /* "openTSNE/kl_divergence.pyx":126 * if n_dims == 1: * sum_Q = estimate_negative_gradient_fft_1d( * embedding.ravel(), # <<<<<<<<<<<<<< * gradient.ravel(), * n_interpolation_points, */ __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_embedding, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 126, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_ravel); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 126, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_3))) { __pyx_t_1 = PyMethod_GET_SELF(__pyx_t_3); if (likely(__pyx_t_1)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_3); __Pyx_INCREF(__pyx_t_1); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_3, function); } } __pyx_t_4 = (__pyx_t_1) ? __Pyx_PyObject_CallOneArg(__pyx_t_3, __pyx_t_1) : __Pyx_PyObject_CallNoArg(__pyx_t_3); __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 126, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_6 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 126, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; /* "openTSNE/kl_divergence.pyx":127 * sum_Q = estimate_negative_gradient_fft_1d( * embedding.ravel(), * gradient.ravel(), # <<<<<<<<<<<<<< * n_interpolation_points, * min_num_intervals, */ __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_gradient, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 127, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_ravel); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 127, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_1))) { __pyx_t_3 = PyMethod_GET_SELF(__pyx_t_1); if (likely(__pyx_t_3)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_1); __Pyx_INCREF(__pyx_t_3); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_1, function); } } __pyx_t_4 = (__pyx_t_3) ? __Pyx_PyObject_CallOneArg(__pyx_t_1, __pyx_t_3) : __Pyx_PyObject_CallNoArg(__pyx_t_1); __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 127, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_7 = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_7.memview)) __PYX_ERR(0, 127, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; /* "openTSNE/kl_divergence.pyx":125 * * if n_dims == 1: * sum_Q = estimate_negative_gradient_fft_1d( # <<<<<<<<<<<<<< * embedding.ravel(), * gradient.ravel(), */ __pyx_t_9.__pyx_n = 4; __pyx_t_9.n_interpolation_points = __pyx_v_n_interpolation_points; __pyx_t_9.min_num_intervals = __pyx_v_min_num_intervals; __pyx_t_9.ints_in_interval = __pyx_v_ints_in_interval; __pyx_t_9.dof = __pyx_v_dof; __pyx_t_8 = __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d(__pyx_t_6, __pyx_t_7, 0, &__pyx_t_9); __PYX_XDEC_MEMVIEW(&__pyx_t_6, 1); __pyx_t_6.memview = NULL; __pyx_t_6.data = NULL; __PYX_XDEC_MEMVIEW(&__pyx_t_7, 1); __pyx_t_7.memview = NULL; __pyx_t_7.data = NULL; __pyx_v_sum_Q = __pyx_t_8; /* "openTSNE/kl_divergence.pyx":124 * * * if n_dims == 1: # <<<<<<<<<<<<<< * sum_Q = estimate_negative_gradient_fft_1d( * embedding.ravel(), */ break; case 2: /* "openTSNE/kl_divergence.pyx":134 * ) * elif n_dims == 2: * sum_Q = estimate_negative_gradient_fft_2d( # <<<<<<<<<<<<<< * embedding, * gradient, */ __pyx_t_10.__pyx_n = 4; __pyx_t_10.n_interpolation_points = __pyx_v_n_interpolation_points; __pyx_t_10.min_num_intervals = __pyx_v_min_num_intervals; __pyx_t_10.ints_in_interval = __pyx_v_ints_in_interval; __pyx_t_10.dof = __pyx_v_dof; __pyx_t_8 = __pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d(__pyx_v_embedding, __pyx_v_gradient, 0, &__pyx_t_10); __pyx_v_sum_Q = __pyx_t_8; /* "openTSNE/kl_divergence.pyx":133 * dof, * ) * elif n_dims == 2: # <<<<<<<<<<<<<< * sum_Q = estimate_negative_gradient_fft_2d( * embedding, */ break; default: /* "openTSNE/kl_divergence.pyx":143 * ) * else: * return -1 # <<<<<<<<<<<<<< * * sum_P, kl_divergence = estimate_positive_gradient_nn( */ __pyx_r = -1.0; goto __pyx_L0; break; } /* "openTSNE/kl_divergence.pyx":145 * return -1 * * sum_P, kl_divergence = estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * indices, * indptr, */ __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_estimate_positive_gradient_nn); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 145, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); /* "openTSNE/kl_divergence.pyx":146 * * sum_P, kl_divergence = estimate_positive_gradient_nn( * indices, # <<<<<<<<<<<<<< * indptr, * P_data, */ __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_indices, 1, (PyObject *(*)(char *)) __pyx_memview_get_int, (int (*)(char *, PyObject *)) __pyx_memview_set_int, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 146, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); /* "openTSNE/kl_divergence.pyx":147 * sum_P, kl_divergence = estimate_positive_gradient_nn( * indices, * indptr, # <<<<<<<<<<<<<< * P_data, * embedding, */ __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_indptr, 1, (PyObject *(*)(char *)) __pyx_memview_get_int, (int (*)(char *, PyObject *)) __pyx_memview_set_int, 0);; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 147, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); /* "openTSNE/kl_divergence.pyx":148 * indices, * indptr, * P_data, # <<<<<<<<<<<<<< * embedding, * embedding, */ __pyx_t_2 = __pyx_memoryview_fromslice(__pyx_v_P_data, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 148, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); /* "openTSNE/kl_divergence.pyx":149 * indptr, * P_data, * embedding, # <<<<<<<<<<<<<< * embedding, * gradient, */ __pyx_t_11 = __pyx_memoryview_fromslice(__pyx_v_embedding, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 149, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); /* "openTSNE/kl_divergence.pyx":150 * P_data, * embedding, * embedding, # <<<<<<<<<<<<<< * gradient, * dof=dof, */ __pyx_t_12 = __pyx_memoryview_fromslice(__pyx_v_embedding, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_12)) __PYX_ERR(0, 150, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_12); /* "openTSNE/kl_divergence.pyx":151 * embedding, * embedding, * gradient, # <<<<<<<<<<<<<< * dof=dof, * should_eval_error=True, */ __pyx_t_13 = __pyx_memoryview_fromslice(__pyx_v_gradient, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_13)) __PYX_ERR(0, 151, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_13); /* "openTSNE/kl_divergence.pyx":145 * return -1 * * sum_P, kl_divergence = estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * indices, * indptr, */ __pyx_t_14 = PyTuple_New(6); if (unlikely(!__pyx_t_14)) __PYX_ERR(0, 145, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_14); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_14, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_14, 1, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_14, 2, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_11); PyTuple_SET_ITEM(__pyx_t_14, 3, __pyx_t_11); __Pyx_GIVEREF(__pyx_t_12); PyTuple_SET_ITEM(__pyx_t_14, 4, __pyx_t_12); __Pyx_GIVEREF(__pyx_t_13); PyTuple_SET_ITEM(__pyx_t_14, 5, __pyx_t_13); __pyx_t_1 = 0; __pyx_t_3 = 0; __pyx_t_2 = 0; __pyx_t_11 = 0; __pyx_t_12 = 0; __pyx_t_13 = 0; /* "openTSNE/kl_divergence.pyx":152 * embedding, * gradient, * dof=dof, # <<<<<<<<<<<<<< * should_eval_error=True, * ) */ __pyx_t_13 = __Pyx_PyDict_NewPresized(2); if (unlikely(!__pyx_t_13)) __PYX_ERR(0, 152, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_13); __pyx_t_12 = PyFloat_FromDouble(__pyx_v_dof); if (unlikely(!__pyx_t_12)) __PYX_ERR(0, 152, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_12); if (PyDict_SetItem(__pyx_t_13, __pyx_n_s_dof, __pyx_t_12) < 0) __PYX_ERR(0, 152, __pyx_L1_error) __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0; /* "openTSNE/kl_divergence.pyx":153 * gradient, * dof=dof, * should_eval_error=True, # <<<<<<<<<<<<<< * ) * */ if (PyDict_SetItem(__pyx_t_13, __pyx_n_s_should_eval_error, Py_True) < 0) __PYX_ERR(0, 152, __pyx_L1_error) /* "openTSNE/kl_divergence.pyx":145 * return -1 * * sum_P, kl_divergence = estimate_positive_gradient_nn( # <<<<<<<<<<<<<< * indices, * indptr, */ __pyx_t_12 = __Pyx_PyObject_Call(__pyx_t_4, __pyx_t_14, __pyx_t_13); if (unlikely(!__pyx_t_12)) __PYX_ERR(0, 145, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_12); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_14); __pyx_t_14 = 0; __Pyx_DECREF(__pyx_t_13); __pyx_t_13 = 0; if ((likely(PyTuple_CheckExact(__pyx_t_12))) || (PyList_CheckExact(__pyx_t_12))) { PyObject* sequence = __pyx_t_12; Py_ssize_t size = __Pyx_PySequence_SIZE(sequence); if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); __PYX_ERR(0, 145, __pyx_L1_error) } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS if (likely(PyTuple_CheckExact(sequence))) { __pyx_t_13 = PyTuple_GET_ITEM(sequence, 0); __pyx_t_14 = PyTuple_GET_ITEM(sequence, 1); } else { __pyx_t_13 = PyList_GET_ITEM(sequence, 0); __pyx_t_14 = PyList_GET_ITEM(sequence, 1); } __Pyx_INCREF(__pyx_t_13); __Pyx_INCREF(__pyx_t_14); #else __pyx_t_13 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_13)) __PYX_ERR(0, 145, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_13); __pyx_t_14 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_14)) __PYX_ERR(0, 145, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_14); #endif __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0; } else { Py_ssize_t index = -1; __pyx_t_4 = PyObject_GetIter(__pyx_t_12); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 145, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0; __pyx_t_15 = Py_TYPE(__pyx_t_4)->tp_iternext; index = 0; __pyx_t_13 = __pyx_t_15(__pyx_t_4); if (unlikely(!__pyx_t_13)) goto __pyx_L3_unpacking_failed; __Pyx_GOTREF(__pyx_t_13); index = 1; __pyx_t_14 = __pyx_t_15(__pyx_t_4); if (unlikely(!__pyx_t_14)) goto __pyx_L3_unpacking_failed; __Pyx_GOTREF(__pyx_t_14); if (__Pyx_IternextUnpackEndCheck(__pyx_t_15(__pyx_t_4), 2) < 0) __PYX_ERR(0, 145, __pyx_L1_error) __pyx_t_15 = NULL; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; goto __pyx_L4_unpacking_done; __pyx_L3_unpacking_failed:; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_15 = NULL; if (__Pyx_IterFinish() == 0) __Pyx_RaiseNeedMoreValuesError(index); __PYX_ERR(0, 145, __pyx_L1_error) __pyx_L4_unpacking_done:; } __pyx_t_8 = __pyx_PyFloat_AsDouble(__pyx_t_13); if (unlikely((__pyx_t_8 == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 145, __pyx_L1_error) __Pyx_DECREF(__pyx_t_13); __pyx_t_13 = 0; __pyx_t_16 = __pyx_PyFloat_AsDouble(__pyx_t_14); if (unlikely((__pyx_t_16 == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 145, __pyx_L1_error) __Pyx_DECREF(__pyx_t_14); __pyx_t_14 = 0; __pyx_v_sum_P = __pyx_t_8; __pyx_v_kl_divergence = __pyx_t_16; /* "openTSNE/kl_divergence.pyx":156 * ) * * kl_divergence += sum_P * log(sum_Q + EPSILON) # <<<<<<<<<<<<<< * * return kl_divergence */ __pyx_v_kl_divergence = (__pyx_v_kl_divergence + (__pyx_v_sum_P * log((__pyx_v_sum_Q + __pyx_v_8openTSNE_13kl_divergence_EPSILON)))); /* "openTSNE/kl_divergence.pyx":158 * kl_divergence += sum_P * log(sum_Q + EPSILON) * * return kl_divergence # <<<<<<<<<<<<<< */ __pyx_r = __pyx_v_kl_divergence; goto __pyx_L0; /* "openTSNE/kl_divergence.pyx":100 * * * cpdef double kl_divergence_approx_fft( # <<<<<<<<<<<<<< * int[:] indices, * int[:] indptr, */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_6, 1); __PYX_XDEC_MEMVIEW(&__pyx_t_7, 1); __Pyx_XDECREF(__pyx_t_11); __Pyx_XDECREF(__pyx_t_12); __Pyx_XDECREF(__pyx_t_13); __Pyx_XDECREF(__pyx_t_14); __Pyx_WriteUnraisable("openTSNE.kl_divergence.kl_divergence_approx_fft", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_r = 0; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_gradient, 1); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_13kl_divergence_5kl_divergence_approx_fft(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static char __pyx_doc_8openTSNE_13kl_divergence_4kl_divergence_approx_fft[] = "Compute the KL divergence using the interpolation based approximation."; static PyObject *__pyx_pw_8openTSNE_13kl_divergence_5kl_divergence_approx_fft(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __Pyx_memviewslice __pyx_v_indices = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_indptr = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_P_data = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_embedding = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_dof; Py_ssize_t __pyx_v_n_interpolation_points; Py_ssize_t __pyx_v_min_num_intervals; double __pyx_v_ints_in_interval; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("kl_divergence_approx_fft (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_indices,&__pyx_n_s_indptr,&__pyx_n_s_P_data,&__pyx_n_s_embedding,&__pyx_n_s_dof,&__pyx_n_s_n_interpolation_points,&__pyx_n_s_min_num_intervals,&__pyx_n_s_ints_in_interval,0}; PyObject* values[8] = {0,0,0,0,0,0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 8: values[7] = PyTuple_GET_ITEM(__pyx_args, 7); CYTHON_FALLTHROUGH; case 7: values[6] = PyTuple_GET_ITEM(__pyx_args, 6); CYTHON_FALLTHROUGH; case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_indices)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_indptr)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("kl_divergence_approx_fft", 0, 4, 8, 1); __PYX_ERR(0, 100, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_P_data)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("kl_divergence_approx_fft", 0, 4, 8, 2); __PYX_ERR(0, 100, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 3: if (likely((values[3] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_embedding)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("kl_divergence_approx_fft", 0, 4, 8, 3); __PYX_ERR(0, 100, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 4: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dof); if (value) { values[4] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 5: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_n_interpolation_points); if (value) { values[5] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 6: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_min_num_intervals); if (value) { values[6] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 7: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_ints_in_interval); if (value) { values[7] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "kl_divergence_approx_fft") < 0)) __PYX_ERR(0, 100, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 8: values[7] = PyTuple_GET_ITEM(__pyx_args, 7); CYTHON_FALLTHROUGH; case 7: values[6] = PyTuple_GET_ITEM(__pyx_args, 6); CYTHON_FALLTHROUGH; case 6: values[5] = PyTuple_GET_ITEM(__pyx_args, 5); CYTHON_FALLTHROUGH; case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); values[2] = PyTuple_GET_ITEM(__pyx_args, 2); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_indices = __Pyx_PyObject_to_MemoryviewSlice_ds_int(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_indices.memview)) __PYX_ERR(0, 101, __pyx_L3_error) __pyx_v_indptr = __Pyx_PyObject_to_MemoryviewSlice_ds_int(values[1], PyBUF_WRITABLE); if (unlikely(!__pyx_v_indptr.memview)) __PYX_ERR(0, 102, __pyx_L3_error) __pyx_v_P_data = __Pyx_PyObject_to_MemoryviewSlice_ds_double(values[2], PyBUF_WRITABLE); if (unlikely(!__pyx_v_P_data.memview)) __PYX_ERR(0, 103, __pyx_L3_error) __pyx_v_embedding = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[3], PyBUF_WRITABLE); if (unlikely(!__pyx_v_embedding.memview)) __PYX_ERR(0, 104, __pyx_L3_error) if (values[4]) { __pyx_v_dof = __pyx_PyFloat_AsDouble(values[4]); if (unlikely((__pyx_v_dof == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 105, __pyx_L3_error) } else { __pyx_v_dof = ((double)1.0); } if (values[5]) { __pyx_v_n_interpolation_points = __Pyx_PyIndex_AsSsize_t(values[5]); if (unlikely((__pyx_v_n_interpolation_points == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 106, __pyx_L3_error) } else { __pyx_v_n_interpolation_points = ((Py_ssize_t)3); } if (values[6]) { __pyx_v_min_num_intervals = __Pyx_PyIndex_AsSsize_t(values[6]); if (unlikely((__pyx_v_min_num_intervals == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(0, 107, __pyx_L3_error) } else { __pyx_v_min_num_intervals = ((Py_ssize_t)10); } if (values[7]) { __pyx_v_ints_in_interval = __pyx_PyFloat_AsDouble(values[7]); if (unlikely((__pyx_v_ints_in_interval == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 108, __pyx_L3_error) } else { __pyx_v_ints_in_interval = ((double)1.0); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("kl_divergence_approx_fft", 0, 4, 8, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 100, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE.kl_divergence.kl_divergence_approx_fft", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_13kl_divergence_4kl_divergence_approx_fft(__pyx_self, __pyx_v_indices, __pyx_v_indptr, __pyx_v_P_data, __pyx_v_embedding, __pyx_v_dof, __pyx_v_n_interpolation_points, __pyx_v_min_num_intervals, __pyx_v_ints_in_interval); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_13kl_divergence_4kl_divergence_approx_fft(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_indices, __Pyx_memviewslice __pyx_v_indptr, __Pyx_memviewslice __pyx_v_P_data, __Pyx_memviewslice __pyx_v_embedding, double __pyx_v_dof, Py_ssize_t __pyx_v_n_interpolation_points, Py_ssize_t __pyx_v_min_num_intervals, double __pyx_v_ints_in_interval) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations double __pyx_t_1; struct __pyx_opt_args_8openTSNE_13kl_divergence_kl_divergence_approx_fft __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("kl_divergence_approx_fft", 0); __Pyx_XDECREF(__pyx_r); __pyx_t_2.__pyx_n = 4; __pyx_t_2.dof = __pyx_v_dof; __pyx_t_2.n_interpolation_points = __pyx_v_n_interpolation_points; __pyx_t_2.min_num_intervals = __pyx_v_min_num_intervals; __pyx_t_2.ints_in_interval = __pyx_v_ints_in_interval; __pyx_t_1 = __pyx_f_8openTSNE_13kl_divergence_kl_divergence_approx_fft(__pyx_v_indices, __pyx_v_indptr, __pyx_v_P_data, __pyx_v_embedding, 0, &__pyx_t_2); __pyx_t_3 = PyFloat_FromDouble(__pyx_t_1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 100, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("openTSNE.kl_divergence.kl_divergence_approx_fft", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_indices, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_indptr, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_P_data, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_embedding, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":734 * ctypedef npy_cdouble complex_t * * cdef inline object PyArray_MultiIterNew1(a): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(1, a) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *__pyx_v_a) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew1", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":735 * * cdef inline object PyArray_MultiIterNew1(a): * return PyArray_MultiIterNew(1, a) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew2(a, b): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(1, ((void *)__pyx_v_a)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 735, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":734 * ctypedef npy_cdouble complex_t * * cdef inline object PyArray_MultiIterNew1(a): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(1, a) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew1", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":737 * return PyArray_MultiIterNew(1, a) * * cdef inline object PyArray_MultiIterNew2(a, b): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(2, a, b) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *__pyx_v_a, PyObject *__pyx_v_b) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew2", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":738 * * cdef inline object PyArray_MultiIterNew2(a, b): * return PyArray_MultiIterNew(2, a, b) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew3(a, b, c): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(2, ((void *)__pyx_v_a), ((void *)__pyx_v_b)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 738, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":737 * return PyArray_MultiIterNew(1, a) * * cdef inline object PyArray_MultiIterNew2(a, b): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(2, a, b) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew2", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":740 * return PyArray_MultiIterNew(2, a, b) * * cdef inline object PyArray_MultiIterNew3(a, b, c): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(3, a, b, c) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew3", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":741 * * cdef inline object PyArray_MultiIterNew3(a, b, c): * return PyArray_MultiIterNew(3, a, b, c) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(3, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 741, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":740 * return PyArray_MultiIterNew(2, a, b) * * cdef inline object PyArray_MultiIterNew3(a, b, c): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(3, a, b, c) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew3", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":743 * return PyArray_MultiIterNew(3, a, b, c) * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(4, a, b, c, d) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c, PyObject *__pyx_v_d) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew4", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":744 * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): * return PyArray_MultiIterNew(4, a, b, c, d) # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(4, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 744, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":743 * return PyArray_MultiIterNew(3, a, b, c) * * cdef inline object PyArray_MultiIterNew4(a, b, c, d): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(4, a, b, c, d) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew4", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":746 * return PyArray_MultiIterNew(4, a, b, c, d) * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(5, a, b, c, d, e) * */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c, PyObject *__pyx_v_d, PyObject *__pyx_v_e) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("PyArray_MultiIterNew5", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":747 * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): * return PyArray_MultiIterNew(5, a, b, c, d, e) # <<<<<<<<<<<<<< * * cdef inline tuple PyDataType_SHAPE(dtype d): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyArray_MultiIterNew(5, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d), ((void *)__pyx_v_e)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 747, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":746 * return PyArray_MultiIterNew(4, a, b, c, d) * * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e): # <<<<<<<<<<<<<< * return PyArray_MultiIterNew(5, a, b, c, d, e) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("numpy.PyArray_MultiIterNew5", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":749 * return PyArray_MultiIterNew(5, a, b, c, d, e) * * cdef inline tuple PyDataType_SHAPE(dtype d): # <<<<<<<<<<<<<< * if PyDataType_HASSUBARRAY(d): * return d.subarray.shape */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyDataType_SHAPE(PyArray_Descr *__pyx_v_d) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("PyDataType_SHAPE", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":750 * * cdef inline tuple PyDataType_SHAPE(dtype d): * if PyDataType_HASSUBARRAY(d): # <<<<<<<<<<<<<< * return d.subarray.shape * else: */ __pyx_t_1 = (PyDataType_HASSUBARRAY(__pyx_v_d) != 0); if (__pyx_t_1) { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":751 * cdef inline tuple PyDataType_SHAPE(dtype d): * if PyDataType_HASSUBARRAY(d): * return d.subarray.shape # <<<<<<<<<<<<<< * else: * return () */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject*)__pyx_v_d->subarray->shape)); __pyx_r = ((PyObject*)__pyx_v_d->subarray->shape); goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":750 * * cdef inline tuple PyDataType_SHAPE(dtype d): * if PyDataType_HASSUBARRAY(d): # <<<<<<<<<<<<<< * return d.subarray.shape * else: */ } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":753 * return d.subarray.shape * else: * return () # <<<<<<<<<<<<<< * * */ /*else*/ { __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_empty_tuple); __pyx_r = __pyx_empty_tuple; goto __pyx_L0; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":749 * return PyArray_MultiIterNew(5, a, b, c, d, e) * * cdef inline tuple PyDataType_SHAPE(dtype d): # <<<<<<<<<<<<<< * if PyDataType_HASSUBARRAY(d): * return d.subarray.shape */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":868 * int _import_umath() except -1 * * cdef inline void set_array_base(ndarray arr, object base): # <<<<<<<<<<<<<< * Py_INCREF(base) # important to do this before stealing the reference below! * PyArray_SetBaseObject(arr, base) */ static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *__pyx_v_arr, PyObject *__pyx_v_base) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("set_array_base", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":869 * * cdef inline void set_array_base(ndarray arr, object base): * Py_INCREF(base) # important to do this before stealing the reference below! # <<<<<<<<<<<<<< * PyArray_SetBaseObject(arr, base) * */ Py_INCREF(__pyx_v_base); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":870 * cdef inline void set_array_base(ndarray arr, object base): * Py_INCREF(base) # important to do this before stealing the reference below! * PyArray_SetBaseObject(arr, base) # <<<<<<<<<<<<<< * * cdef inline object get_array_base(ndarray arr): */ (void)(PyArray_SetBaseObject(__pyx_v_arr, __pyx_v_base)); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":868 * int _import_umath() except -1 * * cdef inline void set_array_base(ndarray arr, object base): # <<<<<<<<<<<<<< * Py_INCREF(base) # important to do this before stealing the reference below! * PyArray_SetBaseObject(arr, base) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":872 * PyArray_SetBaseObject(arr, base) * * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< * base = PyArray_BASE(arr) * if base is NULL: */ static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__pyx_v_arr) { PyObject *__pyx_v_base; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("get_array_base", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":873 * * cdef inline object get_array_base(ndarray arr): * base = PyArray_BASE(arr) # <<<<<<<<<<<<<< * if base is NULL: * return None */ __pyx_v_base = PyArray_BASE(__pyx_v_arr); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":874 * cdef inline object get_array_base(ndarray arr): * base = PyArray_BASE(arr) * if base is NULL: # <<<<<<<<<<<<<< * return None * return base */ __pyx_t_1 = ((__pyx_v_base == NULL) != 0); if (__pyx_t_1) { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":875 * base = PyArray_BASE(arr) * if base is NULL: * return None # <<<<<<<<<<<<<< * return base * */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":874 * cdef inline object get_array_base(ndarray arr): * base = PyArray_BASE(arr) * if base is NULL: # <<<<<<<<<<<<<< * return None * return base */ } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":876 * if base is NULL: * return None * return base # <<<<<<<<<<<<<< * * # Versions of the import_* functions which are more suitable for */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_base)); __pyx_r = ((PyObject *)__pyx_v_base); goto __pyx_L0; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":872 * PyArray_SetBaseObject(arr, base) * * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< * base = PyArray_BASE(arr) * if base is NULL: */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":880 * # Versions of the import_* functions which are more suitable for * # Cython code. * cdef inline int import_array() except -1: # <<<<<<<<<<<<<< * try: * __pyx_import_array() */ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("import_array", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":881 * # Cython code. * cdef inline int import_array() except -1: * try: # <<<<<<<<<<<<<< * __pyx_import_array() * except Exception: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_1, &__pyx_t_2, &__pyx_t_3); __Pyx_XGOTREF(__pyx_t_1); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); /*try:*/ { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":882 * cdef inline int import_array() except -1: * try: * __pyx_import_array() # <<<<<<<<<<<<<< * except Exception: * raise ImportError("numpy.core.multiarray failed to import") */ __pyx_t_4 = _import_array(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 882, __pyx_L3_error) /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":881 * # Cython code. * cdef inline int import_array() except -1: * try: # <<<<<<<<<<<<<< * __pyx_import_array() * except Exception: */ } __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L8_try_end; __pyx_L3_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":883 * try: * __pyx_import_array() * except Exception: # <<<<<<<<<<<<<< * raise ImportError("numpy.core.multiarray failed to import") * */ __pyx_t_4 = __Pyx_PyErr_ExceptionMatches(((PyObject *)(&((PyTypeObject*)PyExc_Exception)[0]))); if (__pyx_t_4) { __Pyx_AddTraceback("numpy.import_array", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(1, 883, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_6); __Pyx_GOTREF(__pyx_t_7); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":884 * __pyx_import_array() * except Exception: * raise ImportError("numpy.core.multiarray failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_umath() except -1: */ __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ImportError, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 884, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_Raise(__pyx_t_8, 0, 0, 0); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __PYX_ERR(1, 884, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":881 * # Cython code. * cdef inline int import_array() except -1: * try: # <<<<<<<<<<<<<< * __pyx_import_array() * except Exception: */ __Pyx_XGIVEREF(__pyx_t_1); __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_ExceptionReset(__pyx_t_1, __pyx_t_2, __pyx_t_3); goto __pyx_L1_error; __pyx_L8_try_end:; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":880 * # Versions of the import_* functions which are more suitable for * # Cython code. * cdef inline int import_array() except -1: # <<<<<<<<<<<<<< * try: * __pyx_import_array() */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("numpy.import_array", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":886 * raise ImportError("numpy.core.multiarray failed to import") * * cdef inline int import_umath() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ static CYTHON_INLINE int __pyx_f_5numpy_import_umath(void) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("import_umath", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":887 * * cdef inline int import_umath() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_1, &__pyx_t_2, &__pyx_t_3); __Pyx_XGOTREF(__pyx_t_1); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); /*try:*/ { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":888 * cdef inline int import_umath() except -1: * try: * _import_umath() # <<<<<<<<<<<<<< * except Exception: * raise ImportError("numpy.core.umath failed to import") */ __pyx_t_4 = _import_umath(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 888, __pyx_L3_error) /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":887 * * cdef inline int import_umath() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ } __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L8_try_end; __pyx_L3_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":889 * try: * _import_umath() * except Exception: # <<<<<<<<<<<<<< * raise ImportError("numpy.core.umath failed to import") * */ __pyx_t_4 = __Pyx_PyErr_ExceptionMatches(((PyObject *)(&((PyTypeObject*)PyExc_Exception)[0]))); if (__pyx_t_4) { __Pyx_AddTraceback("numpy.import_umath", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(1, 889, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_6); __Pyx_GOTREF(__pyx_t_7); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":890 * _import_umath() * except Exception: * raise ImportError("numpy.core.umath failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_ufunc() except -1: */ __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ImportError, __pyx_tuple__2, NULL); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 890, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_Raise(__pyx_t_8, 0, 0, 0); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __PYX_ERR(1, 890, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":887 * * cdef inline int import_umath() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ __Pyx_XGIVEREF(__pyx_t_1); __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_ExceptionReset(__pyx_t_1, __pyx_t_2, __pyx_t_3); goto __pyx_L1_error; __pyx_L8_try_end:; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":886 * raise ImportError("numpy.core.multiarray failed to import") * * cdef inline int import_umath() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("numpy.import_umath", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":892 * raise ImportError("numpy.core.umath failed to import") * * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ static CYTHON_INLINE int __pyx_f_5numpy_import_ufunc(void) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("import_ufunc", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":893 * * cdef inline int import_ufunc() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_1, &__pyx_t_2, &__pyx_t_3); __Pyx_XGOTREF(__pyx_t_1); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); /*try:*/ { /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":894 * cdef inline int import_ufunc() except -1: * try: * _import_umath() # <<<<<<<<<<<<<< * except Exception: * raise ImportError("numpy.core.umath failed to import") */ __pyx_t_4 = _import_umath(); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 894, __pyx_L3_error) /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":893 * * cdef inline int import_ufunc() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ } __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L8_try_end; __pyx_L3_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":895 * try: * _import_umath() * except Exception: # <<<<<<<<<<<<<< * raise ImportError("numpy.core.umath failed to import") * */ __pyx_t_4 = __Pyx_PyErr_ExceptionMatches(((PyObject *)(&((PyTypeObject*)PyExc_Exception)[0]))); if (__pyx_t_4) { __Pyx_AddTraceback("numpy.import_ufunc", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) __PYX_ERR(1, 895, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_6); __Pyx_GOTREF(__pyx_t_7); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":896 * _import_umath() * except Exception: * raise ImportError("numpy.core.umath failed to import") # <<<<<<<<<<<<<< * * cdef extern from *: */ __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ImportError, __pyx_tuple__2, NULL); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 896, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_Raise(__pyx_t_8, 0, 0, 0); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __PYX_ERR(1, 896, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":893 * * cdef inline int import_ufunc() except -1: * try: # <<<<<<<<<<<<<< * _import_umath() * except Exception: */ __Pyx_XGIVEREF(__pyx_t_1); __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_ExceptionReset(__pyx_t_1, __pyx_t_2, __pyx_t_3); goto __pyx_L1_error; __pyx_L8_try_end:; } /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":892 * raise ImportError("numpy.core.umath failed to import") * * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< * try: * _import_umath() */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("numpy.import_ufunc", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* Python wrapper */ static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_shape = 0; Py_ssize_t __pyx_v_itemsize; PyObject *__pyx_v_format = 0; PyObject *__pyx_v_mode = 0; int __pyx_v_allocate_buffer; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_shape,&__pyx_n_s_itemsize,&__pyx_n_s_format,&__pyx_n_s_mode,&__pyx_n_s_allocate_buffer,0}; PyObject* values[5] = {0,0,0,0,0}; values[3] = ((PyObject *)__pyx_n_s_c); if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_shape)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_itemsize)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 1); __PYX_ERR(2, 122, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_format)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 2); __PYX_ERR(2, 122, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 3: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_mode); if (value) { values[3] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 4: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_allocate_buffer); if (value) { values[4] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) __PYX_ERR(2, 122, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_shape = ((PyObject*)values[0]); __pyx_v_itemsize = __Pyx_PyIndex_AsSsize_t(values[1]); if (unlikely((__pyx_v_itemsize == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 122, __pyx_L3_error) __pyx_v_format = values[2]; __pyx_v_mode = values[3]; if (values[4]) { __pyx_v_allocate_buffer = __Pyx_PyObject_IsTrue(values[4]); if (unlikely((__pyx_v_allocate_buffer == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 123, __pyx_L3_error) } else { /* "View.MemoryView":123 * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, * mode="c", bint allocate_buffer=True): # <<<<<<<<<<<<<< * * cdef int idx */ __pyx_v_allocate_buffer = ((int)1); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 122, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_shape), (&PyTuple_Type), 1, "shape", 1))) __PYX_ERR(2, 122, __pyx_L1_error) if (unlikely(((PyObject *)__pyx_v_format) == Py_None)) { PyErr_Format(PyExc_TypeError, "Argument '%.200s' must not be None", "format"); __PYX_ERR(2, 122, __pyx_L1_error) } __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(((struct __pyx_array_obj *)__pyx_v_self), __pyx_v_shape, __pyx_v_itemsize, __pyx_v_format, __pyx_v_mode, __pyx_v_allocate_buffer); /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer) { int __pyx_v_idx; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_dim; PyObject **__pyx_v_p; char __pyx_v_order; int __pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; char *__pyx_t_7; int __pyx_t_8; Py_ssize_t __pyx_t_9; PyObject *__pyx_t_10 = NULL; Py_ssize_t __pyx_t_11; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__cinit__", 0); __Pyx_INCREF(__pyx_v_format); /* "View.MemoryView":129 * cdef PyObject **p * * self.ndim = len(shape) # <<<<<<<<<<<<<< * self.itemsize = itemsize * */ if (unlikely(__pyx_v_shape == Py_None)) { PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); __PYX_ERR(2, 129, __pyx_L1_error) } __pyx_t_1 = PyTuple_GET_SIZE(__pyx_v_shape); if (unlikely(__pyx_t_1 == ((Py_ssize_t)-1))) __PYX_ERR(2, 129, __pyx_L1_error) __pyx_v_self->ndim = ((int)__pyx_t_1); /* "View.MemoryView":130 * * self.ndim = len(shape) * self.itemsize = itemsize # <<<<<<<<<<<<<< * * if not self.ndim: */ __pyx_v_self->itemsize = __pyx_v_itemsize; /* "View.MemoryView":132 * self.itemsize = itemsize * * if not self.ndim: # <<<<<<<<<<<<<< * raise ValueError("Empty shape tuple for cython.array") * */ __pyx_t_2 = ((!(__pyx_v_self->ndim != 0)) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":133 * * if not self.ndim: * raise ValueError("Empty shape tuple for cython.array") # <<<<<<<<<<<<<< * * if itemsize <= 0: */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 133, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 133, __pyx_L1_error) /* "View.MemoryView":132 * self.itemsize = itemsize * * if not self.ndim: # <<<<<<<<<<<<<< * raise ValueError("Empty shape tuple for cython.array") * */ } /* "View.MemoryView":135 * raise ValueError("Empty shape tuple for cython.array") * * if itemsize <= 0: # <<<<<<<<<<<<<< * raise ValueError("itemsize <= 0 for cython.array") * */ __pyx_t_2 = ((__pyx_v_itemsize <= 0) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":136 * * if itemsize <= 0: * raise ValueError("itemsize <= 0 for cython.array") # <<<<<<<<<<<<<< * * if not isinstance(format, bytes): */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__4, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 136, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 136, __pyx_L1_error) /* "View.MemoryView":135 * raise ValueError("Empty shape tuple for cython.array") * * if itemsize <= 0: # <<<<<<<<<<<<<< * raise ValueError("itemsize <= 0 for cython.array") * */ } /* "View.MemoryView":138 * raise ValueError("itemsize <= 0 for cython.array") * * if not isinstance(format, bytes): # <<<<<<<<<<<<<< * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string */ __pyx_t_2 = PyBytes_Check(__pyx_v_format); __pyx_t_4 = ((!(__pyx_t_2 != 0)) != 0); if (__pyx_t_4) { /* "View.MemoryView":139 * * if not isinstance(format, bytes): * format = format.encode('ASCII') # <<<<<<<<<<<<<< * self._format = format # keep a reference to the byte string * self.format = self._format */ __pyx_t_5 = __Pyx_PyObject_GetAttrStr(__pyx_v_format, __pyx_n_s_encode); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 139, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_5))) { __pyx_t_6 = PyMethod_GET_SELF(__pyx_t_5); if (likely(__pyx_t_6)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_5); __Pyx_INCREF(__pyx_t_6); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_5, function); } } __pyx_t_3 = (__pyx_t_6) ? __Pyx_PyObject_Call2Args(__pyx_t_5, __pyx_t_6, __pyx_n_s_ASCII) : __Pyx_PyObject_CallOneArg(__pyx_t_5, __pyx_n_s_ASCII); __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 139, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF_SET(__pyx_v_format, __pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":138 * raise ValueError("itemsize <= 0 for cython.array") * * if not isinstance(format, bytes): # <<<<<<<<<<<<<< * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string */ } /* "View.MemoryView":140 * if not isinstance(format, bytes): * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string # <<<<<<<<<<<<<< * self.format = self._format * */ if (!(likely(PyBytes_CheckExact(__pyx_v_format))||((__pyx_v_format) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_v_format)->tp_name), 0))) __PYX_ERR(2, 140, __pyx_L1_error) __pyx_t_3 = __pyx_v_format; __Pyx_INCREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_3); __Pyx_GOTREF(__pyx_v_self->_format); __Pyx_DECREF(__pyx_v_self->_format); __pyx_v_self->_format = ((PyObject*)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":141 * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string * self.format = self._format # <<<<<<<<<<<<<< * * */ if (unlikely(__pyx_v_self->_format == Py_None)) { PyErr_SetString(PyExc_TypeError, "expected bytes, NoneType found"); __PYX_ERR(2, 141, __pyx_L1_error) } __pyx_t_7 = __Pyx_PyBytes_AsWritableString(__pyx_v_self->_format); if (unlikely((!__pyx_t_7) && PyErr_Occurred())) __PYX_ERR(2, 141, __pyx_L1_error) __pyx_v_self->format = __pyx_t_7; /* "View.MemoryView":144 * * * self._shape = PyObject_Malloc(sizeof(Py_ssize_t)*self.ndim*2) # <<<<<<<<<<<<<< * self._strides = self._shape + self.ndim * */ __pyx_v_self->_shape = ((Py_ssize_t *)PyObject_Malloc((((sizeof(Py_ssize_t)) * __pyx_v_self->ndim) * 2))); /* "View.MemoryView":145 * * self._shape = PyObject_Malloc(sizeof(Py_ssize_t)*self.ndim*2) * self._strides = self._shape + self.ndim # <<<<<<<<<<<<<< * * if not self._shape: */ __pyx_v_self->_strides = (__pyx_v_self->_shape + __pyx_v_self->ndim); /* "View.MemoryView":147 * self._strides = self._shape + self.ndim * * if not self._shape: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate shape and strides.") * */ __pyx_t_4 = ((!(__pyx_v_self->_shape != 0)) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":148 * * if not self._shape: * raise MemoryError("unable to allocate shape and strides.") # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__5, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 148, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 148, __pyx_L1_error) /* "View.MemoryView":147 * self._strides = self._shape + self.ndim * * if not self._shape: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate shape and strides.") * */ } /* "View.MemoryView":151 * * * for idx, dim in enumerate(shape): # <<<<<<<<<<<<<< * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) */ __pyx_t_8 = 0; __pyx_t_3 = __pyx_v_shape; __Pyx_INCREF(__pyx_t_3); __pyx_t_1 = 0; for (;;) { if (__pyx_t_1 >= PyTuple_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_1); __Pyx_INCREF(__pyx_t_5); __pyx_t_1++; if (unlikely(0 < 0)) __PYX_ERR(2, 151, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_3, __pyx_t_1); __pyx_t_1++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 151, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_5); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 151, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_dim = __pyx_t_9; __pyx_v_idx = __pyx_t_8; __pyx_t_8 = (__pyx_t_8 + 1); /* "View.MemoryView":152 * * for idx, dim in enumerate(shape): * if dim <= 0: # <<<<<<<<<<<<<< * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim */ __pyx_t_4 = ((__pyx_v_dim <= 0) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":153 * for idx, dim in enumerate(shape): * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) # <<<<<<<<<<<<<< * self._shape[idx] = dim * */ __pyx_t_5 = __Pyx_PyInt_From_int(__pyx_v_idx); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_10 = PyTuple_New(2); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_10, 1, __pyx_t_6); __pyx_t_5 = 0; __pyx_t_6 = 0; __pyx_t_6 = __Pyx_PyString_Format(__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_t_10); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_6); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(2, 153, __pyx_L1_error) /* "View.MemoryView":152 * * for idx, dim in enumerate(shape): * if dim <= 0: # <<<<<<<<<<<<<< * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim */ } /* "View.MemoryView":154 * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim # <<<<<<<<<<<<<< * * cdef char order */ (__pyx_v_self->_shape[__pyx_v_idx]) = __pyx_v_dim; /* "View.MemoryView":151 * * * for idx, dim in enumerate(shape): # <<<<<<<<<<<<<< * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) */ } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":157 * * cdef char order * if mode == 'fortran': # <<<<<<<<<<<<<< * order = b'F' * self.mode = u'fortran' */ __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_fortran, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) __PYX_ERR(2, 157, __pyx_L1_error) if (__pyx_t_4) { /* "View.MemoryView":158 * cdef char order * if mode == 'fortran': * order = b'F' # <<<<<<<<<<<<<< * self.mode = u'fortran' * elif mode == 'c': */ __pyx_v_order = 'F'; /* "View.MemoryView":159 * if mode == 'fortran': * order = b'F' * self.mode = u'fortran' # <<<<<<<<<<<<<< * elif mode == 'c': * order = b'C' */ __Pyx_INCREF(__pyx_n_u_fortran); __Pyx_GIVEREF(__pyx_n_u_fortran); __Pyx_GOTREF(__pyx_v_self->mode); __Pyx_DECREF(__pyx_v_self->mode); __pyx_v_self->mode = __pyx_n_u_fortran; /* "View.MemoryView":157 * * cdef char order * if mode == 'fortran': # <<<<<<<<<<<<<< * order = b'F' * self.mode = u'fortran' */ goto __pyx_L10; } /* "View.MemoryView":160 * order = b'F' * self.mode = u'fortran' * elif mode == 'c': # <<<<<<<<<<<<<< * order = b'C' * self.mode = u'c' */ __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_c, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) __PYX_ERR(2, 160, __pyx_L1_error) if (likely(__pyx_t_4)) { /* "View.MemoryView":161 * self.mode = u'fortran' * elif mode == 'c': * order = b'C' # <<<<<<<<<<<<<< * self.mode = u'c' * else: */ __pyx_v_order = 'C'; /* "View.MemoryView":162 * elif mode == 'c': * order = b'C' * self.mode = u'c' # <<<<<<<<<<<<<< * else: * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) */ __Pyx_INCREF(__pyx_n_u_c); __Pyx_GIVEREF(__pyx_n_u_c); __Pyx_GOTREF(__pyx_v_self->mode); __Pyx_DECREF(__pyx_v_self->mode); __pyx_v_self->mode = __pyx_n_u_c; /* "View.MemoryView":160 * order = b'F' * self.mode = u'fortran' * elif mode == 'c': # <<<<<<<<<<<<<< * order = b'C' * self.mode = u'c' */ goto __pyx_L10; } /* "View.MemoryView":164 * self.mode = u'c' * else: * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) # <<<<<<<<<<<<<< * * self.len = fill_contig_strides_array(self._shape, self._strides, */ /*else*/ { __pyx_t_3 = __Pyx_PyString_FormatSafe(__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_v_mode); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 164, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_10 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_3); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 164, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(2, 164, __pyx_L1_error) } __pyx_L10:; /* "View.MemoryView":166 * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) * * self.len = fill_contig_strides_array(self._shape, self._strides, # <<<<<<<<<<<<<< * itemsize, self.ndim, order) * */ __pyx_v_self->len = __pyx_fill_contig_strides_array(__pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_itemsize, __pyx_v_self->ndim, __pyx_v_order); /* "View.MemoryView":169 * itemsize, self.ndim, order) * * self.free_data = allocate_buffer # <<<<<<<<<<<<<< * self.dtype_is_object = format == b'O' * if allocate_buffer: */ __pyx_v_self->free_data = __pyx_v_allocate_buffer; /* "View.MemoryView":170 * * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' # <<<<<<<<<<<<<< * if allocate_buffer: * */ __pyx_t_10 = PyObject_RichCompare(__pyx_v_format, __pyx_n_b_O, Py_EQ); __Pyx_XGOTREF(__pyx_t_10); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 170, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_IsTrue(__pyx_t_10); if (unlikely((__pyx_t_4 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 170, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_self->dtype_is_object = __pyx_t_4; /* "View.MemoryView":171 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< * * */ __pyx_t_4 = (__pyx_v_allocate_buffer != 0); if (__pyx_t_4) { /* "View.MemoryView":174 * * * self.data = malloc(self.len) # <<<<<<<<<<<<<< * if not self.data: * raise MemoryError("unable to allocate array data.") */ __pyx_v_self->data = ((char *)malloc(__pyx_v_self->len)); /* "View.MemoryView":175 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate array data.") * */ __pyx_t_4 = ((!(__pyx_v_self->data != 0)) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":176 * self.data = malloc(self.len) * if not self.data: * raise MemoryError("unable to allocate array data.") # <<<<<<<<<<<<<< * * if self.dtype_is_object: */ __pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__6, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 176, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(2, 176, __pyx_L1_error) /* "View.MemoryView":175 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate array data.") * */ } /* "View.MemoryView":178 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< * p = self.data * for i in range(self.len / itemsize): */ __pyx_t_4 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_4) { /* "View.MemoryView":179 * * if self.dtype_is_object: * p = self.data # <<<<<<<<<<<<<< * for i in range(self.len / itemsize): * p[i] = Py_None */ __pyx_v_p = ((PyObject **)__pyx_v_self->data); /* "View.MemoryView":180 * if self.dtype_is_object: * p = self.data * for i in range(self.len / itemsize): # <<<<<<<<<<<<<< * p[i] = Py_None * Py_INCREF(Py_None) */ if (unlikely(__pyx_v_itemsize == 0)) { PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); __PYX_ERR(2, 180, __pyx_L1_error) } else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1) && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_self->len))) { PyErr_SetString(PyExc_OverflowError, "value too large to perform division"); __PYX_ERR(2, 180, __pyx_L1_error) } __pyx_t_1 = (__pyx_v_self->len / __pyx_v_itemsize); __pyx_t_9 = __pyx_t_1; for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_9; __pyx_t_11+=1) { __pyx_v_i = __pyx_t_11; /* "View.MemoryView":181 * p = self.data * for i in range(self.len / itemsize): * p[i] = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ (__pyx_v_p[__pyx_v_i]) = Py_None; /* "View.MemoryView":182 * for i in range(self.len / itemsize): * p[i] = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * @cname('getbuffer') */ Py_INCREF(Py_None); } /* "View.MemoryView":178 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< * p = self.data * for i in range(self.len / itemsize): */ } /* "View.MemoryView":171 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_10); __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_XDECREF(__pyx_v_format); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":185 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * cdef int bufmode = -1 * if self.mode == u"c": */ /* Python wrapper */ static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(((struct __pyx_array_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_v_bufmode; int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; char *__pyx_t_4; Py_ssize_t __pyx_t_5; int __pyx_t_6; Py_ssize_t *__pyx_t_7; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; if (__pyx_v_info == NULL) { PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete"); return -1; } __Pyx_RefNannySetupContext("__getbuffer__", 0); __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None); __Pyx_GIVEREF(__pyx_v_info->obj); /* "View.MemoryView":186 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 # <<<<<<<<<<<<<< * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS */ __pyx_v_bufmode = -1; /* "View.MemoryView":187 * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 * if self.mode == u"c": # <<<<<<<<<<<<<< * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": */ __pyx_t_1 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_c, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 187, __pyx_L1_error) __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":188 * cdef int bufmode = -1 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS # <<<<<<<<<<<<<< * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS */ __pyx_v_bufmode = (PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS); /* "View.MemoryView":187 * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 * if self.mode == u"c": # <<<<<<<<<<<<<< * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": */ goto __pyx_L3; } /* "View.MemoryView":189 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": # <<<<<<<<<<<<<< * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): */ __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_fortran, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(2, 189, __pyx_L1_error) __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":190 * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS # <<<<<<<<<<<<<< * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") */ __pyx_v_bufmode = (PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS); /* "View.MemoryView":189 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": # <<<<<<<<<<<<<< * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): */ } __pyx_L3:; /* "View.MemoryView":191 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data */ __pyx_t_1 = ((!((__pyx_v_flags & __pyx_v_bufmode) != 0)) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":192 * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") # <<<<<<<<<<<<<< * info.buf = self.data * info.len = self.len */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__7, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 192, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 192, __pyx_L1_error) /* "View.MemoryView":191 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data */ } /* "View.MemoryView":193 * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data # <<<<<<<<<<<<<< * info.len = self.len * info.ndim = self.ndim */ __pyx_t_4 = __pyx_v_self->data; __pyx_v_info->buf = __pyx_t_4; /* "View.MemoryView":194 * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data * info.len = self.len # <<<<<<<<<<<<<< * info.ndim = self.ndim * info.shape = self._shape */ __pyx_t_5 = __pyx_v_self->len; __pyx_v_info->len = __pyx_t_5; /* "View.MemoryView":195 * info.buf = self.data * info.len = self.len * info.ndim = self.ndim # <<<<<<<<<<<<<< * info.shape = self._shape * info.strides = self._strides */ __pyx_t_6 = __pyx_v_self->ndim; __pyx_v_info->ndim = __pyx_t_6; /* "View.MemoryView":196 * info.len = self.len * info.ndim = self.ndim * info.shape = self._shape # <<<<<<<<<<<<<< * info.strides = self._strides * info.suboffsets = NULL */ __pyx_t_7 = __pyx_v_self->_shape; __pyx_v_info->shape = __pyx_t_7; /* "View.MemoryView":197 * info.ndim = self.ndim * info.shape = self._shape * info.strides = self._strides # <<<<<<<<<<<<<< * info.suboffsets = NULL * info.itemsize = self.itemsize */ __pyx_t_7 = __pyx_v_self->_strides; __pyx_v_info->strides = __pyx_t_7; /* "View.MemoryView":198 * info.shape = self._shape * info.strides = self._strides * info.suboffsets = NULL # <<<<<<<<<<<<<< * info.itemsize = self.itemsize * info.readonly = 0 */ __pyx_v_info->suboffsets = NULL; /* "View.MemoryView":199 * info.strides = self._strides * info.suboffsets = NULL * info.itemsize = self.itemsize # <<<<<<<<<<<<<< * info.readonly = 0 * */ __pyx_t_5 = __pyx_v_self->itemsize; __pyx_v_info->itemsize = __pyx_t_5; /* "View.MemoryView":200 * info.suboffsets = NULL * info.itemsize = self.itemsize * info.readonly = 0 # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ __pyx_v_info->readonly = 0; /* "View.MemoryView":202 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.format * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":203 * * if flags & PyBUF_FORMAT: * info.format = self.format # <<<<<<<<<<<<<< * else: * info.format = NULL */ __pyx_t_4 = __pyx_v_self->format; __pyx_v_info->format = __pyx_t_4; /* "View.MemoryView":202 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.format * else: */ goto __pyx_L5; } /* "View.MemoryView":205 * info.format = self.format * else: * info.format = NULL # <<<<<<<<<<<<<< * * info.obj = self */ /*else*/ { __pyx_v_info->format = NULL; } __pyx_L5:; /* "View.MemoryView":207 * info.format = NULL * * info.obj = self # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = ((PyObject *)__pyx_v_self); /* "View.MemoryView":185 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * cdef int bufmode = -1 * if self.mode == u"c": */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.array.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; if (__pyx_v_info->obj != NULL) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } goto __pyx_L2; __pyx_L0:; if (__pyx_v_info->obj == Py_None) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } __pyx_L2:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":211 * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") * * def __dealloc__(array self): # <<<<<<<<<<<<<< * if self.callback_free_data != NULL: * self.callback_free_data(self.data) */ /* Python wrapper */ static void __pyx_array___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_array___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self) { __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":212 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< * self.callback_free_data(self.data) * elif self.free_data: */ __pyx_t_1 = ((__pyx_v_self->callback_free_data != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":213 * def __dealloc__(array self): * if self.callback_free_data != NULL: * self.callback_free_data(self.data) # <<<<<<<<<<<<<< * elif self.free_data: * if self.dtype_is_object: */ __pyx_v_self->callback_free_data(__pyx_v_self->data); /* "View.MemoryView":212 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< * self.callback_free_data(self.data) * elif self.free_data: */ goto __pyx_L3; } /* "View.MemoryView":214 * if self.callback_free_data != NULL: * self.callback_free_data(self.data) * elif self.free_data: # <<<<<<<<<<<<<< * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, */ __pyx_t_1 = (__pyx_v_self->free_data != 0); if (__pyx_t_1) { /* "View.MemoryView":215 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) */ __pyx_t_1 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_1) { /* "View.MemoryView":216 * elif self.free_data: * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, # <<<<<<<<<<<<<< * self._strides, self.ndim, False) * free(self.data) */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_self->data, __pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_self->ndim, 0); /* "View.MemoryView":215 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) */ } /* "View.MemoryView":218 * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) * free(self.data) # <<<<<<<<<<<<<< * PyObject_Free(self._shape) * */ free(__pyx_v_self->data); /* "View.MemoryView":214 * if self.callback_free_data != NULL: * self.callback_free_data(self.data) * elif self.free_data: # <<<<<<<<<<<<<< * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, */ } __pyx_L3:; /* "View.MemoryView":219 * self._strides, self.ndim, False) * free(self.data) * PyObject_Free(self._shape) # <<<<<<<<<<<<<< * * @property */ PyObject_Free(__pyx_v_self->_shape); /* "View.MemoryView":211 * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") * * def __dealloc__(array self): # <<<<<<<<<<<<<< * if self.callback_free_data != NULL: * self.callback_free_data(self.data) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":222 * * @property * def memview(self): # <<<<<<<<<<<<<< * return self.get_memview() * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_5array_7memview___get__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":223 * @property * def memview(self): * return self.get_memview() # <<<<<<<<<<<<<< * * @cname('get_memview') */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = ((struct __pyx_vtabstruct_array *)__pyx_v_self->__pyx_vtab)->get_memview(__pyx_v_self); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 223, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":222 * * @property * def memview(self): # <<<<<<<<<<<<<< * return self.get_memview() * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.memview.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":226 * * @cname('get_memview') * cdef get_memview(self): # <<<<<<<<<<<<<< * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) */ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self) { int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("get_memview", 0); /* "View.MemoryView":227 * @cname('get_memview') * cdef get_memview(self): * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE # <<<<<<<<<<<<<< * return memoryview(self, flags, self.dtype_is_object) * */ __pyx_v_flags = ((PyBUF_ANY_CONTIGUOUS | PyBUF_FORMAT) | PyBUF_WRITABLE); /* "View.MemoryView":228 * cdef get_memview(self): * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) # <<<<<<<<<<<<<< * * def __len__(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":226 * * @cname('get_memview') * cdef get_memview(self): # <<<<<<<<<<<<<< * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.array.get_memview", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":230 * return memoryview(self, flags, self.dtype_is_object) * * def __len__(self): # <<<<<<<<<<<<<< * return self._shape[0] * */ /* Python wrapper */ static Py_ssize_t __pyx_array___len__(PyObject *__pyx_v_self); /*proto*/ static Py_ssize_t __pyx_array___len__(PyObject *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__", 0); /* "View.MemoryView":231 * * def __len__(self): * return self._shape[0] # <<<<<<<<<<<<<< * * def __getattr__(self, attr): */ __pyx_r = (__pyx_v_self->_shape[0]); goto __pyx_L0; /* "View.MemoryView":230 * return memoryview(self, flags, self.dtype_is_object) * * def __len__(self): # <<<<<<<<<<<<<< * return self._shape[0] * */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":233 * return self._shape[0] * * def __getattr__(self, attr): # <<<<<<<<<<<<<< * return getattr(self.memview, attr) * */ /* Python wrapper */ static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr); /*proto*/ static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getattr__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_attr)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__getattr__", 0); /* "View.MemoryView":234 * * def __getattr__(self, attr): * return getattr(self.memview, attr) # <<<<<<<<<<<<<< * * def __getitem__(self, item): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 234, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_GetAttr(__pyx_t_1, __pyx_v_attr); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 234, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":233 * return self._shape[0] * * def __getattr__(self, attr): # <<<<<<<<<<<<<< * return getattr(self.memview, attr) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.array.__getattr__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":236 * return getattr(self.memview, attr) * * def __getitem__(self, item): # <<<<<<<<<<<<<< * return self.memview[item] * */ /* Python wrapper */ static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item); /*proto*/ static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__getitem__", 0); /* "View.MemoryView":237 * * def __getitem__(self, item): * return self.memview[item] # <<<<<<<<<<<<<< * * def __setitem__(self, item, value): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 237, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetItem(__pyx_t_1, __pyx_v_item); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 237, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":236 * return getattr(self.memview, attr) * * def __getitem__(self, item): # <<<<<<<<<<<<<< * return self.memview[item] * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.array.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":239 * return self.memview[item] * * def __setitem__(self, item, value): # <<<<<<<<<<<<<< * self.memview[item] = value * */ /* Python wrapper */ static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /*proto*/ static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item), ((PyObject *)__pyx_v_value)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setitem__", 0); /* "View.MemoryView":240 * * def __setitem__(self, item, value): * self.memview[item] = value # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 240, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (unlikely(PyObject_SetItem(__pyx_t_1, __pyx_v_item, __pyx_v_value) < 0)) __PYX_ERR(2, 240, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":239 * return self.memview[item] * * def __setitem__(self, item, value): # <<<<<<<<<<<<<< * self.memview[item] = value * */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_array_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_array_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_array___reduce_cython__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__8, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_array_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_array_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_array_2__setstate_cython__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__9, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":244 * * @cname("__pyx_array_new") * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format, # <<<<<<<<<<<<<< * char *mode, char *buf): * cdef array result */ static struct __pyx_array_obj *__pyx_array_new(PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, char *__pyx_v_format, char *__pyx_v_mode, char *__pyx_v_buf) { struct __pyx_array_obj *__pyx_v_result = 0; struct __pyx_array_obj *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("array_cwrapper", 0); /* "View.MemoryView":248 * cdef array result * * if buf == NULL: # <<<<<<<<<<<<<< * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: */ __pyx_t_1 = ((__pyx_v_buf == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":249 * * if buf == NULL: * result = array(shape, itemsize, format, mode.decode('ASCII')) # <<<<<<<<<<<<<< * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), */ __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PyTuple_New(4); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_INCREF(__pyx_v_shape); __Pyx_GIVEREF(__pyx_v_shape); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_v_shape); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_5, 2, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 3, __pyx_t_4); __pyx_t_2 = 0; __pyx_t_3 = 0; __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_Call(((PyObject *)__pyx_array_type), __pyx_t_5, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":248 * cdef array result * * if buf == NULL: # <<<<<<<<<<<<<< * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: */ goto __pyx_L3; } /* "View.MemoryView":251 * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), # <<<<<<<<<<<<<< * allocate_buffer=False) * result.data = buf */ /*else*/ { __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_3 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_2 = PyTuple_New(4); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_v_shape); __Pyx_GIVEREF(__pyx_v_shape); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_shape); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_4); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_2, 2, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_2, 3, __pyx_t_3); __pyx_t_4 = 0; __pyx_t_5 = 0; __pyx_t_3 = 0; /* "View.MemoryView":252 * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), * allocate_buffer=False) # <<<<<<<<<<<<<< * result.data = buf * */ __pyx_t_3 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 252, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (PyDict_SetItem(__pyx_t_3, __pyx_n_s_allocate_buffer, Py_False) < 0) __PYX_ERR(2, 252, __pyx_L1_error) /* "View.MemoryView":251 * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), # <<<<<<<<<<<<<< * allocate_buffer=False) * result.data = buf */ __pyx_t_5 = __Pyx_PyObject_Call(((PyObject *)__pyx_array_type), __pyx_t_2, __pyx_t_3); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_5); __pyx_t_5 = 0; /* "View.MemoryView":253 * result = array(shape, itemsize, format, mode.decode('ASCII'), * allocate_buffer=False) * result.data = buf # <<<<<<<<<<<<<< * * return result */ __pyx_v_result->data = __pyx_v_buf; } __pyx_L3:; /* "View.MemoryView":255 * result.data = buf * * return result # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(((PyObject *)__pyx_r)); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = __pyx_v_result; goto __pyx_L0; /* "View.MemoryView":244 * * @cname("__pyx_array_new") * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format, # <<<<<<<<<<<<<< * char *mode, char *buf): * cdef array result */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.array_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF((PyObject *)__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":281 * cdef class Enum(object): * cdef object name * def __init__(self, name): # <<<<<<<<<<<<<< * self.name = name * def __repr__(self): */ /* Python wrapper */ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_name = 0; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__init__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_name,0}; PyObject* values[1] = {0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_name)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__init__") < 0)) __PYX_ERR(2, 281, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 1) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); } __pyx_v_name = values[0]; } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__init__", 1, 1, 1, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 281, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.Enum.__init__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self), __pyx_v_name); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__init__", 0); /* "View.MemoryView":282 * cdef object name * def __init__(self, name): * self.name = name # <<<<<<<<<<<<<< * def __repr__(self): * return self.name */ __Pyx_INCREF(__pyx_v_name); __Pyx_GIVEREF(__pyx_v_name); __Pyx_GOTREF(__pyx_v_self->name); __Pyx_DECREF(__pyx_v_self->name); __pyx_v_self->name = __pyx_v_name; /* "View.MemoryView":281 * cdef class Enum(object): * cdef object name * def __init__(self, name): # <<<<<<<<<<<<<< * self.name = name * def __repr__(self): */ /* function exit code */ __pyx_r = 0; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":283 * def __init__(self, name): * self.name = name * def __repr__(self): # <<<<<<<<<<<<<< * return self.name * */ /* Python wrapper */ static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0); __pyx_r = __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__", 0); /* "View.MemoryView":284 * self.name = name * def __repr__(self): * return self.name # <<<<<<<<<<<<<< * * cdef generic = Enum("") */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->name); __pyx_r = __pyx_v_self->name; goto __pyx_L0; /* "View.MemoryView":283 * def __init__(self, name): * self.name = name * def __repr__(self): # <<<<<<<<<<<<<< * return self.name * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * cdef tuple state * cdef object _dict */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_MemviewEnum_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_MemviewEnum_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_MemviewEnum___reduce_cython__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self) { PyObject *__pyx_v_state = 0; PyObject *__pyx_v__dict = 0; int __pyx_v_use_setstate; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_t_3; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":5 * cdef object _dict * cdef bint use_setstate * state = (self.name,) # <<<<<<<<<<<<<< * _dict = getattr(self, '__dict__', None) * if _dict is not None: */ __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_v_self->name); __Pyx_GIVEREF(__pyx_v_self->name); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v_self->name); __pyx_v_state = ((PyObject*)__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":6 * cdef bint use_setstate * state = (self.name,) * _dict = getattr(self, '__dict__', None) # <<<<<<<<<<<<<< * if _dict is not None: * state += (_dict,) */ __pyx_t_1 = __Pyx_GetAttr3(((PyObject *)__pyx_v_self), __pyx_n_s_dict, Py_None); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v__dict = __pyx_t_1; __pyx_t_1 = 0; /* "(tree fragment)":7 * state = (self.name,) * _dict = getattr(self, '__dict__', None) * if _dict is not None: # <<<<<<<<<<<<<< * state += (_dict,) * use_setstate = True */ __pyx_t_2 = (__pyx_v__dict != Py_None); __pyx_t_3 = (__pyx_t_2 != 0); if (__pyx_t_3) { /* "(tree fragment)":8 * _dict = getattr(self, '__dict__', None) * if _dict is not None: * state += (_dict,) # <<<<<<<<<<<<<< * use_setstate = True * else: */ __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_v__dict); __Pyx_GIVEREF(__pyx_v__dict); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v__dict); __pyx_t_4 = PyNumber_InPlaceAdd(__pyx_v_state, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF_SET(__pyx_v_state, ((PyObject*)__pyx_t_4)); __pyx_t_4 = 0; /* "(tree fragment)":9 * if _dict is not None: * state += (_dict,) * use_setstate = True # <<<<<<<<<<<<<< * else: * use_setstate = self.name is not None */ __pyx_v_use_setstate = 1; /* "(tree fragment)":7 * state = (self.name,) * _dict = getattr(self, '__dict__', None) * if _dict is not None: # <<<<<<<<<<<<<< * state += (_dict,) * use_setstate = True */ goto __pyx_L3; } /* "(tree fragment)":11 * use_setstate = True * else: * use_setstate = self.name is not None # <<<<<<<<<<<<<< * if use_setstate: * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state */ /*else*/ { __pyx_t_3 = (__pyx_v_self->name != Py_None); __pyx_v_use_setstate = __pyx_t_3; } __pyx_L3:; /* "(tree fragment)":12 * else: * use_setstate = self.name is not None * if use_setstate: # <<<<<<<<<<<<<< * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: */ __pyx_t_3 = (__pyx_v_use_setstate != 0); if (__pyx_t_3) { /* "(tree fragment)":13 * use_setstate = self.name is not None * if use_setstate: * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state # <<<<<<<<<<<<<< * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) */ __Pyx_XDECREF(__pyx_r); __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_GIVEREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_INCREF(__pyx_int_184977713); __Pyx_GIVEREF(__pyx_int_184977713); PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_int_184977713); __Pyx_INCREF(Py_None); __Pyx_GIVEREF(Py_None); PyTuple_SET_ITEM(__pyx_t_1, 2, Py_None); __pyx_t_5 = PyTuple_New(3); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_1); __Pyx_INCREF(__pyx_v_state); __Pyx_GIVEREF(__pyx_v_state); PyTuple_SET_ITEM(__pyx_t_5, 2, __pyx_v_state); __pyx_t_4 = 0; __pyx_t_1 = 0; __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "(tree fragment)":12 * else: * use_setstate = self.name is not None * if use_setstate: # <<<<<<<<<<<<<< * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: */ } /* "(tree fragment)":15 * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /*else*/ { __Pyx_XDECREF(__pyx_r); __Pyx_GetModuleGlobalName(__pyx_t_5, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_GIVEREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_INCREF(__pyx_int_184977713); __Pyx_GIVEREF(__pyx_int_184977713); PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_int_184977713); __Pyx_INCREF(__pyx_v_state); __Pyx_GIVEREF(__pyx_v_state); PyTuple_SET_ITEM(__pyx_t_1, 2, __pyx_v_state); __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_1); __pyx_t_5 = 0; __pyx_t_1 = 0; __pyx_r = __pyx_t_4; __pyx_t_4 = 0; goto __pyx_L0; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * cdef tuple state * cdef object _dict */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.Enum.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_state); __Pyx_XDECREF(__pyx_v__dict); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":16 * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_MemviewEnum_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_MemviewEnum_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_MemviewEnum_2__setstate_cython__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":17 * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): * __pyx_unpickle_Enum__set_state(self, __pyx_state) # <<<<<<<<<<<<<< */ if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(2, 17, __pyx_L1_error) __pyx_t_1 = __pyx_unpickle_Enum__set_state(__pyx_v_self, ((PyObject*)__pyx_v___pyx_state)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 17, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":16 * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.Enum.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":298 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory */ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) { Py_intptr_t __pyx_v_aligned_p; size_t __pyx_v_offset; void *__pyx_r; int __pyx_t_1; /* "View.MemoryView":300 * cdef void *align_pointer(void *memory, size_t alignment) nogil: * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory # <<<<<<<<<<<<<< * cdef size_t offset * */ __pyx_v_aligned_p = ((Py_intptr_t)__pyx_v_memory); /* "View.MemoryView":304 * * with cython.cdivision(True): * offset = aligned_p % alignment # <<<<<<<<<<<<<< * * if offset > 0: */ __pyx_v_offset = (__pyx_v_aligned_p % __pyx_v_alignment); /* "View.MemoryView":306 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< * aligned_p += alignment - offset * */ __pyx_t_1 = ((__pyx_v_offset > 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":307 * * if offset > 0: * aligned_p += alignment - offset # <<<<<<<<<<<<<< * * return aligned_p */ __pyx_v_aligned_p = (__pyx_v_aligned_p + (__pyx_v_alignment - __pyx_v_offset)); /* "View.MemoryView":306 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< * aligned_p += alignment - offset * */ } /* "View.MemoryView":309 * aligned_p += alignment - offset * * return aligned_p # <<<<<<<<<<<<<< * * */ __pyx_r = ((void *)__pyx_v_aligned_p); goto __pyx_L0; /* "View.MemoryView":298 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":345 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< * self.obj = obj * self.flags = flags */ /* Python wrapper */ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_obj = 0; int __pyx_v_flags; int __pyx_v_dtype_is_object; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_obj,&__pyx_n_s_flags,&__pyx_n_s_dtype_is_object,0}; PyObject* values[3] = {0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_obj)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_flags)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, 1); __PYX_ERR(2, 345, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dtype_is_object); if (value) { values[2] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) __PYX_ERR(2, 345, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_obj = values[0]; __pyx_v_flags = __Pyx_PyInt_As_int(values[1]); if (unlikely((__pyx_v_flags == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 345, __pyx_L3_error) if (values[2]) { __pyx_v_dtype_is_object = __Pyx_PyObject_IsTrue(values[2]); if (unlikely((__pyx_v_dtype_is_object == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 345, __pyx_L3_error) } else { __pyx_v_dtype_is_object = ((int)0); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 345, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_obj, __pyx_v_flags, __pyx_v_dtype_is_object); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__cinit__", 0); /* "View.MemoryView":346 * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj # <<<<<<<<<<<<<< * self.flags = flags * if type(self) is memoryview or obj is not None: */ __Pyx_INCREF(__pyx_v_obj); __Pyx_GIVEREF(__pyx_v_obj); __Pyx_GOTREF(__pyx_v_self->obj); __Pyx_DECREF(__pyx_v_self->obj); __pyx_v_self->obj = __pyx_v_obj; /* "View.MemoryView":347 * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj * self.flags = flags # <<<<<<<<<<<<<< * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) */ __pyx_v_self->flags = __pyx_v_flags; /* "View.MemoryView":348 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: */ __pyx_t_2 = (((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self))) == ((PyObject *)__pyx_memoryview_type)); __pyx_t_3 = (__pyx_t_2 != 0); if (!__pyx_t_3) { } else { __pyx_t_1 = __pyx_t_3; goto __pyx_L4_bool_binop_done; } __pyx_t_3 = (__pyx_v_obj != Py_None); __pyx_t_2 = (__pyx_t_3 != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (__pyx_t_1) { /* "View.MemoryView":349 * self.flags = flags * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) # <<<<<<<<<<<<<< * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None */ __pyx_t_4 = __Pyx_GetBuffer(__pyx_v_obj, (&__pyx_v_self->view), __pyx_v_flags); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 349, __pyx_L1_error) /* "View.MemoryView":350 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) */ __pyx_t_1 = ((((PyObject *)__pyx_v_self->view.obj) == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":351 * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_self->view))->obj = Py_None; /* "View.MemoryView":352 * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * global __pyx_memoryview_thread_locks_used */ Py_INCREF(Py_None); /* "View.MemoryView":350 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) */ } /* "View.MemoryView":348 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: */ } /* "View.MemoryView":355 * * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 */ __pyx_t_1 = ((__pyx_memoryview_thread_locks_used < 8) != 0); if (__pyx_t_1) { /* "View.MemoryView":356 * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: */ __pyx_v_self->lock = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); /* "View.MemoryView":357 * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 # <<<<<<<<<<<<<< * if self.lock is NULL: * self.lock = PyThread_allocate_lock() */ __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used + 1); /* "View.MemoryView":355 * * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 */ } /* "View.MemoryView":358 * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< * self.lock = PyThread_allocate_lock() * if self.lock is NULL: */ __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":359 * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() # <<<<<<<<<<<<<< * if self.lock is NULL: * raise MemoryError */ __pyx_v_self->lock = PyThread_allocate_lock(); /* "View.MemoryView":360 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() * if self.lock is NULL: # <<<<<<<<<<<<<< * raise MemoryError * */ __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":361 * self.lock = PyThread_allocate_lock() * if self.lock is NULL: * raise MemoryError # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ PyErr_NoMemory(); __PYX_ERR(2, 361, __pyx_L1_error) /* "View.MemoryView":360 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() * if self.lock is NULL: # <<<<<<<<<<<<<< * raise MemoryError * */ } /* "View.MemoryView":358 * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< * self.lock = PyThread_allocate_lock() * if self.lock is NULL: */ } /* "View.MemoryView":363 * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":364 * * if flags & PyBUF_FORMAT: * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') # <<<<<<<<<<<<<< * else: * self.dtype_is_object = dtype_is_object */ __pyx_t_2 = (((__pyx_v_self->view.format[0]) == 'O') != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L11_bool_binop_done; } __pyx_t_2 = (((__pyx_v_self->view.format[1]) == '\x00') != 0); __pyx_t_1 = __pyx_t_2; __pyx_L11_bool_binop_done:; __pyx_v_self->dtype_is_object = __pyx_t_1; /* "View.MemoryView":363 * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: */ goto __pyx_L10; } /* "View.MemoryView":366 * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: * self.dtype_is_object = dtype_is_object # <<<<<<<<<<<<<< * * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( */ /*else*/ { __pyx_v_self->dtype_is_object = __pyx_v_dtype_is_object; } __pyx_L10:; /* "View.MemoryView":368 * self.dtype_is_object = dtype_is_object * * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( # <<<<<<<<<<<<<< * &self.acquisition_count[0], sizeof(__pyx_atomic_int)) * self.typeinfo = NULL */ __pyx_v_self->acquisition_count_aligned_p = ((__pyx_atomic_int *)__pyx_align_pointer(((void *)(&(__pyx_v_self->acquisition_count[0]))), (sizeof(__pyx_atomic_int)))); /* "View.MemoryView":370 * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( * &self.acquisition_count[0], sizeof(__pyx_atomic_int)) * self.typeinfo = NULL # <<<<<<<<<<<<<< * * def __dealloc__(memoryview self): */ __pyx_v_self->typeinfo = NULL; /* "View.MemoryView":345 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< * self.obj = obj * self.flags = flags */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":372 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) */ /* Python wrapper */ static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self) { int __pyx_v_i; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_t_5; PyThread_type_lock __pyx_t_6; PyThread_type_lock __pyx_t_7; __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":373 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: */ __pyx_t_1 = (__pyx_v_self->obj != Py_None); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":374 * def __dealloc__(memoryview self): * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) # <<<<<<<<<<<<<< * elif (<__pyx_buffer *> &self.view).obj == Py_None: * */ __Pyx_ReleaseBuffer((&__pyx_v_self->view)); /* "View.MemoryView":373 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: */ goto __pyx_L3; } /* "View.MemoryView":375 * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: # <<<<<<<<<<<<<< * * (<__pyx_buffer *> &self.view).obj = NULL */ __pyx_t_2 = ((((Py_buffer *)(&__pyx_v_self->view))->obj == Py_None) != 0); if (__pyx_t_2) { /* "View.MemoryView":377 * elif (<__pyx_buffer *> &self.view).obj == Py_None: * * (<__pyx_buffer *> &self.view).obj = NULL # <<<<<<<<<<<<<< * Py_DECREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_self->view))->obj = NULL; /* "View.MemoryView":378 * * (<__pyx_buffer *> &self.view).obj = NULL * Py_DECREF(Py_None) # <<<<<<<<<<<<<< * * cdef int i */ Py_DECREF(Py_None); /* "View.MemoryView":375 * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: # <<<<<<<<<<<<<< * * (<__pyx_buffer *> &self.view).obj = NULL */ } __pyx_L3:; /* "View.MemoryView":382 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: */ __pyx_t_2 = ((__pyx_v_self->lock != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":383 * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): # <<<<<<<<<<<<<< * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 */ __pyx_t_3 = __pyx_memoryview_thread_locks_used; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "View.MemoryView":384 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: */ __pyx_t_2 = (((__pyx_memoryview_thread_locks[__pyx_v_i]) == __pyx_v_self->lock) != 0); if (__pyx_t_2) { /* "View.MemoryView":385 * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 # <<<<<<<<<<<<<< * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( */ __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used - 1); /* "View.MemoryView":386 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) */ __pyx_t_2 = ((__pyx_v_i != __pyx_memoryview_thread_locks_used) != 0); if (__pyx_t_2) { /* "View.MemoryView":388 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) # <<<<<<<<<<<<<< * break * else: */ __pyx_t_6 = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); __pyx_t_7 = (__pyx_memoryview_thread_locks[__pyx_v_i]); /* "View.MemoryView":387 * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) * break */ (__pyx_memoryview_thread_locks[__pyx_v_i]) = __pyx_t_6; (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]) = __pyx_t_7; /* "View.MemoryView":386 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) */ } /* "View.MemoryView":389 * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) * break # <<<<<<<<<<<<<< * else: * PyThread_free_lock(self.lock) */ goto __pyx_L6_break; /* "View.MemoryView":384 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: */ } } /*else*/ { /* "View.MemoryView":391 * break * else: * PyThread_free_lock(self.lock) # <<<<<<<<<<<<<< * * cdef char *get_item_pointer(memoryview self, object index) except NULL: */ PyThread_free_lock(__pyx_v_self->lock); } __pyx_L6_break:; /* "View.MemoryView":382 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: */ } /* "View.MemoryView":372 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":393 * PyThread_free_lock(self.lock) * * cdef char *get_item_pointer(memoryview self, object index) except NULL: # <<<<<<<<<<<<<< * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf */ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) { Py_ssize_t __pyx_v_dim; char *__pyx_v_itemp; PyObject *__pyx_v_idx = NULL; char *__pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; PyObject *__pyx_t_2 = NULL; Py_ssize_t __pyx_t_3; PyObject *(*__pyx_t_4)(PyObject *); PyObject *__pyx_t_5 = NULL; Py_ssize_t __pyx_t_6; char *__pyx_t_7; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("get_item_pointer", 0); /* "View.MemoryView":395 * cdef char *get_item_pointer(memoryview self, object index) except NULL: * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf # <<<<<<<<<<<<<< * * for dim, idx in enumerate(index): */ __pyx_v_itemp = ((char *)__pyx_v_self->view.buf); /* "View.MemoryView":397 * cdef char *itemp = self.view.buf * * for dim, idx in enumerate(index): # <<<<<<<<<<<<<< * itemp = pybuffer_index(&self.view, itemp, idx, dim) * */ __pyx_t_1 = 0; if (likely(PyList_CheckExact(__pyx_v_index)) || PyTuple_CheckExact(__pyx_v_index)) { __pyx_t_2 = __pyx_v_index; __Pyx_INCREF(__pyx_t_2); __pyx_t_3 = 0; __pyx_t_4 = NULL; } else { __pyx_t_3 = -1; __pyx_t_2 = PyObject_GetIter(__pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = Py_TYPE(__pyx_t_2)->tp_iternext; if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 397, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_4)) { if (likely(PyList_CheckExact(__pyx_t_2))) { if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_2)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyList_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(2, 397, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif } else { if (__pyx_t_3 >= PyTuple_GET_SIZE(__pyx_t_2)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(2, 397, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif } } else { __pyx_t_5 = __pyx_t_4(__pyx_t_2); if (unlikely(!__pyx_t_5)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(2, 397, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_5); } __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_5); __pyx_t_5 = 0; __pyx_v_dim = __pyx_t_1; __pyx_t_1 = (__pyx_t_1 + 1); /* "View.MemoryView":398 * * for dim, idx in enumerate(index): * itemp = pybuffer_index(&self.view, itemp, idx, dim) # <<<<<<<<<<<<<< * * return itemp */ __pyx_t_6 = __Pyx_PyIndex_AsSsize_t(__pyx_v_idx); if (unlikely((__pyx_t_6 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 398, __pyx_L1_error) __pyx_t_7 = __pyx_pybuffer_index((&__pyx_v_self->view), __pyx_v_itemp, __pyx_t_6, __pyx_v_dim); if (unlikely(__pyx_t_7 == ((char *)NULL))) __PYX_ERR(2, 398, __pyx_L1_error) __pyx_v_itemp = __pyx_t_7; /* "View.MemoryView":397 * cdef char *itemp = self.view.buf * * for dim, idx in enumerate(index): # <<<<<<<<<<<<<< * itemp = pybuffer_index(&self.view, itemp, idx, dim) * */ } __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":400 * itemp = pybuffer_index(&self.view, itemp, idx, dim) * * return itemp # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_itemp; goto __pyx_L0; /* "View.MemoryView":393 * PyThread_free_lock(self.lock) * * cdef char *get_item_pointer(memoryview self, object index) except NULL: # <<<<<<<<<<<<<< * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.get_item_pointer", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_idx); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":403 * * * def __getitem__(memoryview self, object index): # <<<<<<<<<<<<<< * if index is Ellipsis: * return self */ /* Python wrapper */ static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index); /*proto*/ static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) { PyObject *__pyx_v_have_slices = NULL; PyObject *__pyx_v_indices = NULL; char *__pyx_v_itemp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; char *__pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__getitem__", 0); /* "View.MemoryView":404 * * def __getitem__(memoryview self, object index): * if index is Ellipsis: # <<<<<<<<<<<<<< * return self * */ __pyx_t_1 = (__pyx_v_index == __pyx_builtin_Ellipsis); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":405 * def __getitem__(memoryview self, object index): * if index is Ellipsis: * return self # <<<<<<<<<<<<<< * * have_slices, indices = _unellipsify(index, self.view.ndim) */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_self)); __pyx_r = ((PyObject *)__pyx_v_self); goto __pyx_L0; /* "View.MemoryView":404 * * def __getitem__(memoryview self, object index): * if index is Ellipsis: # <<<<<<<<<<<<<< * return self * */ } /* "View.MemoryView":407 * return self * * have_slices, indices = _unellipsify(index, self.view.ndim) # <<<<<<<<<<<<<< * * cdef char *itemp */ __pyx_t_3 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (likely(__pyx_t_3 != Py_None)) { PyObject* sequence = __pyx_t_3; Py_ssize_t size = __Pyx_PySequence_SIZE(sequence); if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); __PYX_ERR(2, 407, __pyx_L1_error) } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_4 = PyTuple_GET_ITEM(sequence, 0); __pyx_t_5 = PyTuple_GET_ITEM(sequence, 1); __Pyx_INCREF(__pyx_t_4); __Pyx_INCREF(__pyx_t_5); #else __pyx_t_4 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } else { __Pyx_RaiseNoneNotIterableError(); __PYX_ERR(2, 407, __pyx_L1_error) } __pyx_v_have_slices = __pyx_t_4; __pyx_t_4 = 0; __pyx_v_indices = __pyx_t_5; __pyx_t_5 = 0; /* "View.MemoryView":410 * * cdef char *itemp * if have_slices: # <<<<<<<<<<<<<< * return memview_slice(self, indices) * else: */ __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(2, 410, __pyx_L1_error) if (__pyx_t_2) { /* "View.MemoryView":411 * cdef char *itemp * if have_slices: * return memview_slice(self, indices) # <<<<<<<<<<<<<< * else: * itemp = self.get_item_pointer(indices) */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = ((PyObject *)__pyx_memview_slice(__pyx_v_self, __pyx_v_indices)); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 411, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":410 * * cdef char *itemp * if have_slices: # <<<<<<<<<<<<<< * return memview_slice(self, indices) * else: */ } /* "View.MemoryView":413 * return memview_slice(self, indices) * else: * itemp = self.get_item_pointer(indices) # <<<<<<<<<<<<<< * return self.convert_item_to_object(itemp) * */ /*else*/ { __pyx_t_6 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_indices); if (unlikely(__pyx_t_6 == ((char *)NULL))) __PYX_ERR(2, 413, __pyx_L1_error) __pyx_v_itemp = __pyx_t_6; /* "View.MemoryView":414 * else: * itemp = self.get_item_pointer(indices) * return self.convert_item_to_object(itemp) # <<<<<<<<<<<<<< * * def __setitem__(memoryview self, object index, object value): */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->convert_item_to_object(__pyx_v_self, __pyx_v_itemp); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 414, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; } /* "View.MemoryView":403 * * * def __getitem__(memoryview self, object index): # <<<<<<<<<<<<<< * if index is Ellipsis: * return self */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_have_slices); __Pyx_XDECREF(__pyx_v_indices); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":416 * return self.convert_item_to_object(itemp) * * def __setitem__(memoryview self, object index, object value): # <<<<<<<<<<<<<< * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") */ /* Python wrapper */ static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /*proto*/ static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index), ((PyObject *)__pyx_v_value)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { PyObject *__pyx_v_have_slices = NULL; PyObject *__pyx_v_obj = NULL; int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setitem__", 0); __Pyx_INCREF(__pyx_v_index); /* "View.MemoryView":417 * * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: # <<<<<<<<<<<<<< * raise TypeError("Cannot assign to read-only memoryview") * */ __pyx_t_1 = (__pyx_v_self->view.readonly != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":418 * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") # <<<<<<<<<<<<<< * * have_slices, index = _unellipsify(index, self.view.ndim) */ __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__10, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 418, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(2, 418, __pyx_L1_error) /* "View.MemoryView":417 * * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: # <<<<<<<<<<<<<< * raise TypeError("Cannot assign to read-only memoryview") * */ } /* "View.MemoryView":420 * raise TypeError("Cannot assign to read-only memoryview") * * have_slices, index = _unellipsify(index, self.view.ndim) # <<<<<<<<<<<<<< * * if have_slices: */ __pyx_t_2 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (likely(__pyx_t_2 != Py_None)) { PyObject* sequence = __pyx_t_2; Py_ssize_t size = __Pyx_PySequence_SIZE(sequence); if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); __PYX_ERR(2, 420, __pyx_L1_error) } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_3 = PyTuple_GET_ITEM(sequence, 0); __pyx_t_4 = PyTuple_GET_ITEM(sequence, 1); __Pyx_INCREF(__pyx_t_3); __Pyx_INCREF(__pyx_t_4); #else __pyx_t_3 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); #endif __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } else { __Pyx_RaiseNoneNotIterableError(); __PYX_ERR(2, 420, __pyx_L1_error) } __pyx_v_have_slices = __pyx_t_3; __pyx_t_3 = 0; __Pyx_DECREF_SET(__pyx_v_index, __pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":422 * have_slices, index = _unellipsify(index, self.view.ndim) * * if have_slices: # <<<<<<<<<<<<<< * obj = self.is_slice(value) * if obj: */ __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 422, __pyx_L1_error) if (__pyx_t_1) { /* "View.MemoryView":423 * * if have_slices: * obj = self.is_slice(value) # <<<<<<<<<<<<<< * if obj: * self.setitem_slice_assignment(self[index], obj) */ __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->is_slice(__pyx_v_self, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 423, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_v_obj = __pyx_t_2; __pyx_t_2 = 0; /* "View.MemoryView":424 * if have_slices: * obj = self.is_slice(value) * if obj: # <<<<<<<<<<<<<< * self.setitem_slice_assignment(self[index], obj) * else: */ __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_v_obj); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 424, __pyx_L1_error) if (__pyx_t_1) { /* "View.MemoryView":425 * obj = self.is_slice(value) * if obj: * self.setitem_slice_assignment(self[index], obj) # <<<<<<<<<<<<<< * else: * self.setitem_slice_assign_scalar(self[index], value) */ __pyx_t_2 = __Pyx_PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 425, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assignment(__pyx_v_self, __pyx_t_2, __pyx_v_obj); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 425, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":424 * if have_slices: * obj = self.is_slice(value) * if obj: # <<<<<<<<<<<<<< * self.setitem_slice_assignment(self[index], obj) * else: */ goto __pyx_L5; } /* "View.MemoryView":427 * self.setitem_slice_assignment(self[index], obj) * else: * self.setitem_slice_assign_scalar(self[index], value) # <<<<<<<<<<<<<< * else: * self.setitem_indexed(index, value) */ /*else*/ { __pyx_t_4 = __Pyx_PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 427, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (!(likely(((__pyx_t_4) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_4, __pyx_memoryview_type))))) __PYX_ERR(2, 427, __pyx_L1_error) __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assign_scalar(__pyx_v_self, ((struct __pyx_memoryview_obj *)__pyx_t_4), __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 427, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_L5:; /* "View.MemoryView":422 * have_slices, index = _unellipsify(index, self.view.ndim) * * if have_slices: # <<<<<<<<<<<<<< * obj = self.is_slice(value) * if obj: */ goto __pyx_L4; } /* "View.MemoryView":429 * self.setitem_slice_assign_scalar(self[index], value) * else: * self.setitem_indexed(index, value) # <<<<<<<<<<<<<< * * cdef is_slice(self, obj): */ /*else*/ { __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_indexed(__pyx_v_self, __pyx_v_index, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 429, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_L4:; /* "View.MemoryView":416 * return self.convert_item_to_object(itemp) * * def __setitem__(memoryview self, object index, object value): # <<<<<<<<<<<<<< * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView.memoryview.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_XDECREF(__pyx_v_have_slices); __Pyx_XDECREF(__pyx_v_obj); __Pyx_XDECREF(__pyx_v_index); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":431 * self.setitem_indexed(index, value) * * cdef is_slice(self, obj): # <<<<<<<<<<<<<< * if not isinstance(obj, memoryview): * try: */ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_t_9; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("is_slice", 0); __Pyx_INCREF(__pyx_v_obj); /* "View.MemoryView":432 * * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): # <<<<<<<<<<<<<< * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, */ __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_obj, __pyx_memoryview_type); __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_3, &__pyx_t_4, &__pyx_t_5); __Pyx_XGOTREF(__pyx_t_3); __Pyx_XGOTREF(__pyx_t_4); __Pyx_XGOTREF(__pyx_t_5); /*try:*/ { /* "View.MemoryView":434 * if not isinstance(obj, memoryview): * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, # <<<<<<<<<<<<<< * self.dtype_is_object) * except TypeError: */ __pyx_t_6 = __Pyx_PyInt_From_int(((__pyx_v_self->flags & (~PyBUF_WRITABLE)) | PyBUF_ANY_CONTIGUOUS)); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_6); /* "View.MemoryView":435 * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) # <<<<<<<<<<<<<< * except TypeError: * return None */ __pyx_t_7 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 435, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_7); /* "View.MemoryView":434 * if not isinstance(obj, memoryview): * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, # <<<<<<<<<<<<<< * self.dtype_is_object) * except TypeError: */ __pyx_t_8 = PyTuple_New(3); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_INCREF(__pyx_v_obj); __Pyx_GIVEREF(__pyx_v_obj); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_v_obj); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_8, 1, __pyx_t_6); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_8, 2, __pyx_t_7); __pyx_t_6 = 0; __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_8, NULL); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF_SET(__pyx_v_obj, __pyx_t_7); __pyx_t_7 = 0; /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ } __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; goto __pyx_L9_try_end; __pyx_L4_error:; __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0; /* "View.MemoryView":436 * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) * except TypeError: # <<<<<<<<<<<<<< * return None * */ __pyx_t_9 = __Pyx_PyErr_ExceptionMatches(__pyx_builtin_TypeError); if (__pyx_t_9) { __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_6) < 0) __PYX_ERR(2, 436, __pyx_L6_except_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_GOTREF(__pyx_t_8); __Pyx_GOTREF(__pyx_t_6); /* "View.MemoryView":437 * self.dtype_is_object) * except TypeError: * return None # <<<<<<<<<<<<<< * * return obj */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; goto __pyx_L7_except_return; } goto __pyx_L6_except_error; __pyx_L6_except_error:; /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_XGIVEREF(__pyx_t_5); __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5); goto __pyx_L1_error; __pyx_L7_except_return:; __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_XGIVEREF(__pyx_t_5); __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5); goto __pyx_L0; __pyx_L9_try_end:; } /* "View.MemoryView":432 * * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): # <<<<<<<<<<<<<< * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, */ } /* "View.MemoryView":439 * return None * * return obj # <<<<<<<<<<<<<< * * cdef setitem_slice_assignment(self, dst, src): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_obj); __pyx_r = __pyx_v_obj; goto __pyx_L0; /* "View.MemoryView":431 * self.setitem_indexed(index, value) * * cdef is_slice(self, obj): # <<<<<<<<<<<<<< * if not isinstance(obj, memoryview): * try: */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_obj); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":441 * return obj * * cdef setitem_slice_assignment(self, dst, src): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice dst_slice * cdef __Pyx_memviewslice src_slice */ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src) { __Pyx_memviewslice __pyx_v_dst_slice; __Pyx_memviewslice __pyx_v_src_slice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; __Pyx_memviewslice *__pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; int __pyx_t_5; int __pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("setitem_slice_assignment", 0); /* "View.MemoryView":445 * cdef __Pyx_memviewslice src_slice * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], # <<<<<<<<<<<<<< * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) */ if (!(likely(((__pyx_v_src) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_src, __pyx_memoryview_type))))) __PYX_ERR(2, 445, __pyx_L1_error) __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_src), (&__pyx_v_src_slice)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 445, __pyx_L1_error) /* "View.MemoryView":446 * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], * get_slice_from_memview(dst, &dst_slice)[0], # <<<<<<<<<<<<<< * src.ndim, dst.ndim, self.dtype_is_object) * */ if (!(likely(((__pyx_v_dst) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_dst, __pyx_memoryview_type))))) __PYX_ERR(2, 446, __pyx_L1_error) __pyx_t_2 = __pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_dst), (&__pyx_v_dst_slice)); if (unlikely(__pyx_t_2 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 446, __pyx_L1_error) /* "View.MemoryView":447 * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) # <<<<<<<<<<<<<< * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): */ __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_v_src, __pyx_n_s_ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_4 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_v_dst, __pyx_n_s_ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_5 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_5 == (int)-1) && PyErr_Occurred())) __PYX_ERR(2, 447, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":445 * cdef __Pyx_memviewslice src_slice * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], # <<<<<<<<<<<<<< * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) */ __pyx_t_6 = __pyx_memoryview_copy_contents((__pyx_t_1[0]), (__pyx_t_2[0]), __pyx_t_4, __pyx_t_5, __pyx_v_self->dtype_is_object); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 445, __pyx_L1_error) /* "View.MemoryView":441 * return obj * * cdef setitem_slice_assignment(self, dst, src): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice dst_slice * cdef __Pyx_memviewslice src_slice */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assignment", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":449 * src.ndim, dst.ndim, self.dtype_is_object) * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): # <<<<<<<<<<<<<< * cdef int array[128] * cdef void *tmp = NULL */ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value) { int __pyx_v_array[0x80]; void *__pyx_v_tmp; void *__pyx_v_item; __Pyx_memviewslice *__pyx_v_dst_slice; __Pyx_memviewslice __pyx_v_tmp_slice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; int __pyx_t_5; char const *__pyx_t_6; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; PyObject *__pyx_t_9 = NULL; PyObject *__pyx_t_10 = NULL; PyObject *__pyx_t_11 = NULL; PyObject *__pyx_t_12 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("setitem_slice_assign_scalar", 0); /* "View.MemoryView":451 * cdef setitem_slice_assign_scalar(self, memoryview dst, value): * cdef int array[128] * cdef void *tmp = NULL # <<<<<<<<<<<<<< * cdef void *item * */ __pyx_v_tmp = NULL; /* "View.MemoryView":456 * cdef __Pyx_memviewslice *dst_slice * cdef __Pyx_memviewslice tmp_slice * dst_slice = get_slice_from_memview(dst, &tmp_slice) # <<<<<<<<<<<<<< * * if self.view.itemsize > sizeof(array): */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_dst, (&__pyx_v_tmp_slice)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 456, __pyx_L1_error) __pyx_v_dst_slice = __pyx_t_1; /* "View.MemoryView":458 * dst_slice = get_slice_from_memview(dst, &tmp_slice) * * if self.view.itemsize > sizeof(array): # <<<<<<<<<<<<<< * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: */ __pyx_t_2 = ((((size_t)__pyx_v_self->view.itemsize) > (sizeof(__pyx_v_array))) != 0); if (__pyx_t_2) { /* "View.MemoryView":459 * * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) # <<<<<<<<<<<<<< * if tmp == NULL: * raise MemoryError */ __pyx_v_tmp = PyMem_Malloc(__pyx_v_self->view.itemsize); /* "View.MemoryView":460 * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: # <<<<<<<<<<<<<< * raise MemoryError * item = tmp */ __pyx_t_2 = ((__pyx_v_tmp == NULL) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":461 * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: * raise MemoryError # <<<<<<<<<<<<<< * item = tmp * else: */ PyErr_NoMemory(); __PYX_ERR(2, 461, __pyx_L1_error) /* "View.MemoryView":460 * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: # <<<<<<<<<<<<<< * raise MemoryError * item = tmp */ } /* "View.MemoryView":462 * if tmp == NULL: * raise MemoryError * item = tmp # <<<<<<<<<<<<<< * else: * item = array */ __pyx_v_item = __pyx_v_tmp; /* "View.MemoryView":458 * dst_slice = get_slice_from_memview(dst, &tmp_slice) * * if self.view.itemsize > sizeof(array): # <<<<<<<<<<<<<< * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: */ goto __pyx_L3; } /* "View.MemoryView":464 * item = tmp * else: * item = array # <<<<<<<<<<<<<< * * try: */ /*else*/ { __pyx_v_item = ((void *)__pyx_v_array); } __pyx_L3:; /* "View.MemoryView":466 * item = array * * try: # <<<<<<<<<<<<<< * if self.dtype_is_object: * ( item)[0] = value */ /*try:*/ { /* "View.MemoryView":467 * * try: * if self.dtype_is_object: # <<<<<<<<<<<<<< * ( item)[0] = value * else: */ __pyx_t_2 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_2) { /* "View.MemoryView":468 * try: * if self.dtype_is_object: * ( item)[0] = value # <<<<<<<<<<<<<< * else: * self.assign_item_from_object( item, value) */ (((PyObject **)__pyx_v_item)[0]) = ((PyObject *)__pyx_v_value); /* "View.MemoryView":467 * * try: * if self.dtype_is_object: # <<<<<<<<<<<<<< * ( item)[0] = value * else: */ goto __pyx_L8; } /* "View.MemoryView":470 * ( item)[0] = value * else: * self.assign_item_from_object( item, value) # <<<<<<<<<<<<<< * * */ /*else*/ { __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, ((char *)__pyx_v_item), __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 470, __pyx_L6_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } __pyx_L8:; /* "View.MemoryView":474 * * * if self.view.suboffsets != NULL: # <<<<<<<<<<<<<< * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, */ __pyx_t_2 = ((__pyx_v_self->view.suboffsets != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":475 * * if self.view.suboffsets != NULL: * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) # <<<<<<<<<<<<<< * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, * item, self.dtype_is_object) */ __pyx_t_3 = assert_direct_dimensions(__pyx_v_self->view.suboffsets, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 475, __pyx_L6_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":474 * * * if self.view.suboffsets != NULL: # <<<<<<<<<<<<<< * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, */ } /* "View.MemoryView":476 * if self.view.suboffsets != NULL: * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, # <<<<<<<<<<<<<< * item, self.dtype_is_object) * finally: */ __pyx_memoryview_slice_assign_scalar(__pyx_v_dst_slice, __pyx_v_dst->view.ndim, __pyx_v_self->view.itemsize, __pyx_v_item, __pyx_v_self->dtype_is_object); } /* "View.MemoryView":479 * item, self.dtype_is_object) * finally: * PyMem_Free(tmp) # <<<<<<<<<<<<<< * * cdef setitem_indexed(self, index, value): */ /*finally:*/ { /*normal exit:*/{ PyMem_Free(__pyx_v_tmp); goto __pyx_L7; } __pyx_L6_error:; /*exception exit:*/{ __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0; __pyx_t_12 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; if (PY_MAJOR_VERSION >= 3) __Pyx_ExceptionSwap(&__pyx_t_10, &__pyx_t_11, &__pyx_t_12); if ((PY_MAJOR_VERSION < 3) || unlikely(__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_9) < 0)) __Pyx_ErrFetch(&__pyx_t_7, &__pyx_t_8, &__pyx_t_9); __Pyx_XGOTREF(__pyx_t_7); __Pyx_XGOTREF(__pyx_t_8); __Pyx_XGOTREF(__pyx_t_9); __Pyx_XGOTREF(__pyx_t_10); __Pyx_XGOTREF(__pyx_t_11); __Pyx_XGOTREF(__pyx_t_12); __pyx_t_4 = __pyx_lineno; __pyx_t_5 = __pyx_clineno; __pyx_t_6 = __pyx_filename; { PyMem_Free(__pyx_v_tmp); } if (PY_MAJOR_VERSION >= 3) { __Pyx_XGIVEREF(__pyx_t_10); __Pyx_XGIVEREF(__pyx_t_11); __Pyx_XGIVEREF(__pyx_t_12); __Pyx_ExceptionReset(__pyx_t_10, __pyx_t_11, __pyx_t_12); } __Pyx_XGIVEREF(__pyx_t_7); __Pyx_XGIVEREF(__pyx_t_8); __Pyx_XGIVEREF(__pyx_t_9); __Pyx_ErrRestore(__pyx_t_7, __pyx_t_8, __pyx_t_9); __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0; __pyx_t_12 = 0; __pyx_lineno = __pyx_t_4; __pyx_clineno = __pyx_t_5; __pyx_filename = __pyx_t_6; goto __pyx_L1_error; } __pyx_L7:; } /* "View.MemoryView":449 * src.ndim, dst.ndim, self.dtype_is_object) * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): # <<<<<<<<<<<<<< * cdef int array[128] * cdef void *tmp = NULL */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assign_scalar", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":481 * PyMem_Free(tmp) * * cdef setitem_indexed(self, index, value): # <<<<<<<<<<<<<< * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) */ static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { char *__pyx_v_itemp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations char *__pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("setitem_indexed", 0); /* "View.MemoryView":482 * * cdef setitem_indexed(self, index, value): * cdef char *itemp = self.get_item_pointer(index) # <<<<<<<<<<<<<< * self.assign_item_from_object(itemp, value) * */ __pyx_t_1 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_index); if (unlikely(__pyx_t_1 == ((char *)NULL))) __PYX_ERR(2, 482, __pyx_L1_error) __pyx_v_itemp = __pyx_t_1; /* "View.MemoryView":483 * cdef setitem_indexed(self, index, value): * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) # <<<<<<<<<<<<<< * * cdef convert_item_to_object(self, char *itemp): */ __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 483, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":481 * PyMem_Free(tmp) * * cdef setitem_indexed(self, index, value): # <<<<<<<<<<<<<< * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_indexed", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":485 * self.assign_item_from_object(itemp, value) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp) { PyObject *__pyx_v_struct = NULL; PyObject *__pyx_v_bytesitem = 0; PyObject *__pyx_v_result = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; int __pyx_t_8; PyObject *__pyx_t_9 = NULL; size_t __pyx_t_10; int __pyx_t_11; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("convert_item_to_object", 0); /* "View.MemoryView":488 * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" * import struct # <<<<<<<<<<<<<< * cdef bytes bytesitem * */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 488, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_struct = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":491 * cdef bytes bytesitem * * bytesitem = itemp[:self.view.itemsize] # <<<<<<<<<<<<<< * try: * result = struct.unpack(self.view.format, bytesitem) */ __pyx_t_1 = __Pyx_PyBytes_FromStringAndSize(__pyx_v_itemp + 0, __pyx_v_self->view.itemsize - 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 491, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_bytesitem = ((PyObject*)__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_2, &__pyx_t_3, &__pyx_t_4); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); __Pyx_XGOTREF(__pyx_t_4); /*try:*/ { /* "View.MemoryView":493 * bytesitem = itemp[:self.view.itemsize] * try: * result = struct.unpack(self.view.format, bytesitem) # <<<<<<<<<<<<<< * except struct.error: * raise ValueError("Unable to convert item to object") */ __pyx_t_5 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_unpack); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = NULL; __pyx_t_8 = 0; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_5))) { __pyx_t_7 = PyMethod_GET_SELF(__pyx_t_5); if (likely(__pyx_t_7)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_5); __Pyx_INCREF(__pyx_t_7); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_5, function); __pyx_t_8 = 1; } } #if CYTHON_FAST_PYCALL if (PyFunction_Check(__pyx_t_5)) { PyObject *__pyx_temp[3] = {__pyx_t_7, __pyx_t_6, __pyx_v_bytesitem}; __pyx_t_1 = __Pyx_PyFunction_FastCall(__pyx_t_5, __pyx_temp+1-__pyx_t_8, 2+__pyx_t_8); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } else #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(__pyx_t_5)) { PyObject *__pyx_temp[3] = {__pyx_t_7, __pyx_t_6, __pyx_v_bytesitem}; __pyx_t_1 = __Pyx_PyCFunction_FastCall(__pyx_t_5, __pyx_temp+1-__pyx_t_8, 2+__pyx_t_8); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } else #endif { __pyx_t_9 = PyTuple_New(2+__pyx_t_8); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_9); if (__pyx_t_7) { __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __pyx_t_7 = NULL; } __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_9, 0+__pyx_t_8, __pyx_t_6); __Pyx_INCREF(__pyx_v_bytesitem); __Pyx_GIVEREF(__pyx_v_bytesitem); PyTuple_SET_ITEM(__pyx_t_9, 1+__pyx_t_8, __pyx_v_bytesitem); __pyx_t_6 = 0; __pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_5, __pyx_t_9, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_result = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ } /* "View.MemoryView":497 * raise ValueError("Unable to convert item to object") * else: * if len(self.view.format) == 1: # <<<<<<<<<<<<<< * return result[0] * return result */ /*else:*/ { __pyx_t_10 = strlen(__pyx_v_self->view.format); __pyx_t_11 = ((__pyx_t_10 == 1) != 0); if (__pyx_t_11) { /* "View.MemoryView":498 * else: * if len(self.view.format) == 1: * return result[0] # <<<<<<<<<<<<<< * return result * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_GetItemInt(__pyx_v_result, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 498, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L6_except_return; /* "View.MemoryView":497 * raise ValueError("Unable to convert item to object") * else: * if len(self.view.format) == 1: # <<<<<<<<<<<<<< * return result[0] * return result */ } /* "View.MemoryView":499 * if len(self.view.format) == 1: * return result[0] * return result # <<<<<<<<<<<<<< * * cdef assign_item_from_object(self, char *itemp, object value): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_result); __pyx_r = __pyx_v_result; goto __pyx_L6_except_return; } __pyx_L3_error:; __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_XDECREF(__pyx_t_9); __pyx_t_9 = 0; /* "View.MemoryView":494 * try: * result = struct.unpack(self.view.format, bytesitem) * except struct.error: # <<<<<<<<<<<<<< * raise ValueError("Unable to convert item to object") * else: */ __Pyx_ErrFetch(&__pyx_t_1, &__pyx_t_5, &__pyx_t_9); __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_error); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 494, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_8 = __Pyx_PyErr_GivenExceptionMatches(__pyx_t_1, __pyx_t_6); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_ErrRestore(__pyx_t_1, __pyx_t_5, __pyx_t_9); __pyx_t_1 = 0; __pyx_t_5 = 0; __pyx_t_9 = 0; if (__pyx_t_8) { __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_9, &__pyx_t_5, &__pyx_t_1) < 0) __PYX_ERR(2, 494, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_1); /* "View.MemoryView":495 * result = struct.unpack(self.view.format, bytesitem) * except struct.error: * raise ValueError("Unable to convert item to object") # <<<<<<<<<<<<<< * else: * if len(self.view.format) == 1: */ __pyx_t_6 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__11, NULL); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 495, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_Raise(__pyx_t_6, 0, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __PYX_ERR(2, 495, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4); goto __pyx_L1_error; __pyx_L6_except_return:; __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4); goto __pyx_L0; } /* "View.MemoryView":485 * self.assign_item_from_object(itemp, value) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_9); __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_struct); __Pyx_XDECREF(__pyx_v_bytesitem); __Pyx_XDECREF(__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":501 * return result * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) { PyObject *__pyx_v_struct = NULL; char __pyx_v_c; PyObject *__pyx_v_bytesvalue = 0; Py_ssize_t __pyx_v_i; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_t_3; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; int __pyx_t_7; PyObject *__pyx_t_8 = NULL; Py_ssize_t __pyx_t_9; PyObject *__pyx_t_10 = NULL; char *__pyx_t_11; char *__pyx_t_12; char *__pyx_t_13; char *__pyx_t_14; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("assign_item_from_object", 0); /* "View.MemoryView":504 * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" * import struct # <<<<<<<<<<<<<< * cdef char c * cdef bytes bytesvalue */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 504, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_struct = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":509 * cdef Py_ssize_t i * * if isinstance(value, tuple): # <<<<<<<<<<<<<< * bytesvalue = struct.pack(self.view.format, *value) * else: */ __pyx_t_2 = PyTuple_Check(__pyx_v_value); __pyx_t_3 = (__pyx_t_2 != 0); if (__pyx_t_3) { /* "View.MemoryView":510 * * if isinstance(value, tuple): * bytesvalue = struct.pack(self.view.format, *value) # <<<<<<<<<<<<<< * else: * bytesvalue = struct.pack(self.view.format, value) */ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_4 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PySequence_Tuple(__pyx_v_value); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_6 = PyNumber_Add(__pyx_t_5, __pyx_t_4); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_6, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (!(likely(PyBytes_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_4)->tp_name), 0))) __PYX_ERR(2, 510, __pyx_L1_error) __pyx_v_bytesvalue = ((PyObject*)__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":509 * cdef Py_ssize_t i * * if isinstance(value, tuple): # <<<<<<<<<<<<<< * bytesvalue = struct.pack(self.view.format, *value) * else: */ goto __pyx_L3; } /* "View.MemoryView":512 * bytesvalue = struct.pack(self.view.format, *value) * else: * bytesvalue = struct.pack(self.view.format, value) # <<<<<<<<<<<<<< * * for i, c in enumerate(bytesvalue): */ /*else*/ { __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_1 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_5 = NULL; __pyx_t_7 = 0; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_6))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_6); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_6); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_6, function); __pyx_t_7 = 1; } } #if CYTHON_FAST_PYCALL if (PyFunction_Check(__pyx_t_6)) { PyObject *__pyx_temp[3] = {__pyx_t_5, __pyx_t_1, __pyx_v_value}; __pyx_t_4 = __Pyx_PyFunction_FastCall(__pyx_t_6, __pyx_temp+1-__pyx_t_7, 2+__pyx_t_7); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; } else #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(__pyx_t_6)) { PyObject *__pyx_temp[3] = {__pyx_t_5, __pyx_t_1, __pyx_v_value}; __pyx_t_4 = __Pyx_PyCFunction_FastCall(__pyx_t_6, __pyx_temp+1-__pyx_t_7, 2+__pyx_t_7); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; } else #endif { __pyx_t_8 = PyTuple_New(2+__pyx_t_7); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); if (__pyx_t_5) { __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_5); __pyx_t_5 = NULL; } __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_8, 0+__pyx_t_7, __pyx_t_1); __Pyx_INCREF(__pyx_v_value); __Pyx_GIVEREF(__pyx_v_value); PyTuple_SET_ITEM(__pyx_t_8, 1+__pyx_t_7, __pyx_v_value); __pyx_t_1 = 0; __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_t_8, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; } __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (!(likely(PyBytes_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_4)->tp_name), 0))) __PYX_ERR(2, 512, __pyx_L1_error) __pyx_v_bytesvalue = ((PyObject*)__pyx_t_4); __pyx_t_4 = 0; } __pyx_L3:; /* "View.MemoryView":514 * bytesvalue = struct.pack(self.view.format, value) * * for i, c in enumerate(bytesvalue): # <<<<<<<<<<<<<< * itemp[i] = c * */ __pyx_t_9 = 0; if (unlikely(__pyx_v_bytesvalue == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' is not iterable"); __PYX_ERR(2, 514, __pyx_L1_error) } __Pyx_INCREF(__pyx_v_bytesvalue); __pyx_t_10 = __pyx_v_bytesvalue; __pyx_t_12 = PyBytes_AS_STRING(__pyx_t_10); __pyx_t_13 = (__pyx_t_12 + PyBytes_GET_SIZE(__pyx_t_10)); for (__pyx_t_14 = __pyx_t_12; __pyx_t_14 < __pyx_t_13; __pyx_t_14++) { __pyx_t_11 = __pyx_t_14; __pyx_v_c = (__pyx_t_11[0]); /* "View.MemoryView":515 * * for i, c in enumerate(bytesvalue): * itemp[i] = c # <<<<<<<<<<<<<< * * @cname('getbuffer') */ __pyx_v_i = __pyx_t_9; /* "View.MemoryView":514 * bytesvalue = struct.pack(self.view.format, value) * * for i, c in enumerate(bytesvalue): # <<<<<<<<<<<<<< * itemp[i] = c * */ __pyx_t_9 = (__pyx_t_9 + 1); /* "View.MemoryView":515 * * for i, c in enumerate(bytesvalue): * itemp[i] = c # <<<<<<<<<<<<<< * * @cname('getbuffer') */ (__pyx_v_itemp[__pyx_v_i]) = __pyx_v_c; } __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; /* "View.MemoryView":501 * return result * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_8); __Pyx_XDECREF(__pyx_t_10); __Pyx_AddTraceback("View.MemoryView.memoryview.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_struct); __Pyx_XDECREF(__pyx_v_bytesvalue); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":518 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") */ /* Python wrapper */ static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; Py_ssize_t *__pyx_t_4; char *__pyx_t_5; void *__pyx_t_6; int __pyx_t_7; Py_ssize_t __pyx_t_8; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; if (__pyx_v_info == NULL) { PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete"); return -1; } __Pyx_RefNannySetupContext("__getbuffer__", 0); __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None); __Pyx_GIVEREF(__pyx_v_info->obj); /* "View.MemoryView":519 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: # <<<<<<<<<<<<<< * raise ValueError("Cannot create writable memory view from read-only memoryview") * */ __pyx_t_2 = ((__pyx_v_flags & PyBUF_WRITABLE) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } __pyx_t_2 = (__pyx_v_self->view.readonly != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (unlikely(__pyx_t_1)) { /* "View.MemoryView":520 * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") # <<<<<<<<<<<<<< * * if flags & PyBUF_ND: */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__12, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 520, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 520, __pyx_L1_error) /* "View.MemoryView":519 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: # <<<<<<<<<<<<<< * raise ValueError("Cannot create writable memory view from read-only memoryview") * */ } /* "View.MemoryView":522 * raise ValueError("Cannot create writable memory view from read-only memoryview") * * if flags & PyBUF_ND: # <<<<<<<<<<<<<< * info.shape = self.view.shape * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_ND) != 0); if (__pyx_t_1) { /* "View.MemoryView":523 * * if flags & PyBUF_ND: * info.shape = self.view.shape # <<<<<<<<<<<<<< * else: * info.shape = NULL */ __pyx_t_4 = __pyx_v_self->view.shape; __pyx_v_info->shape = __pyx_t_4; /* "View.MemoryView":522 * raise ValueError("Cannot create writable memory view from read-only memoryview") * * if flags & PyBUF_ND: # <<<<<<<<<<<<<< * info.shape = self.view.shape * else: */ goto __pyx_L6; } /* "View.MemoryView":525 * info.shape = self.view.shape * else: * info.shape = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_STRIDES: */ /*else*/ { __pyx_v_info->shape = NULL; } __pyx_L6:; /* "View.MemoryView":527 * info.shape = NULL * * if flags & PyBUF_STRIDES: # <<<<<<<<<<<<<< * info.strides = self.view.strides * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_STRIDES) != 0); if (__pyx_t_1) { /* "View.MemoryView":528 * * if flags & PyBUF_STRIDES: * info.strides = self.view.strides # <<<<<<<<<<<<<< * else: * info.strides = NULL */ __pyx_t_4 = __pyx_v_self->view.strides; __pyx_v_info->strides = __pyx_t_4; /* "View.MemoryView":527 * info.shape = NULL * * if flags & PyBUF_STRIDES: # <<<<<<<<<<<<<< * info.strides = self.view.strides * else: */ goto __pyx_L7; } /* "View.MemoryView":530 * info.strides = self.view.strides * else: * info.strides = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_INDIRECT: */ /*else*/ { __pyx_v_info->strides = NULL; } __pyx_L7:; /* "View.MemoryView":532 * info.strides = NULL * * if flags & PyBUF_INDIRECT: # <<<<<<<<<<<<<< * info.suboffsets = self.view.suboffsets * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_INDIRECT) != 0); if (__pyx_t_1) { /* "View.MemoryView":533 * * if flags & PyBUF_INDIRECT: * info.suboffsets = self.view.suboffsets # <<<<<<<<<<<<<< * else: * info.suboffsets = NULL */ __pyx_t_4 = __pyx_v_self->view.suboffsets; __pyx_v_info->suboffsets = __pyx_t_4; /* "View.MemoryView":532 * info.strides = NULL * * if flags & PyBUF_INDIRECT: # <<<<<<<<<<<<<< * info.suboffsets = self.view.suboffsets * else: */ goto __pyx_L8; } /* "View.MemoryView":535 * info.suboffsets = self.view.suboffsets * else: * info.suboffsets = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ /*else*/ { __pyx_v_info->suboffsets = NULL; } __pyx_L8:; /* "View.MemoryView":537 * info.suboffsets = NULL * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.view.format * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":538 * * if flags & PyBUF_FORMAT: * info.format = self.view.format # <<<<<<<<<<<<<< * else: * info.format = NULL */ __pyx_t_5 = __pyx_v_self->view.format; __pyx_v_info->format = __pyx_t_5; /* "View.MemoryView":537 * info.suboffsets = NULL * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.view.format * else: */ goto __pyx_L9; } /* "View.MemoryView":540 * info.format = self.view.format * else: * info.format = NULL # <<<<<<<<<<<<<< * * info.buf = self.view.buf */ /*else*/ { __pyx_v_info->format = NULL; } __pyx_L9:; /* "View.MemoryView":542 * info.format = NULL * * info.buf = self.view.buf # <<<<<<<<<<<<<< * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize */ __pyx_t_6 = __pyx_v_self->view.buf; __pyx_v_info->buf = __pyx_t_6; /* "View.MemoryView":543 * * info.buf = self.view.buf * info.ndim = self.view.ndim # <<<<<<<<<<<<<< * info.itemsize = self.view.itemsize * info.len = self.view.len */ __pyx_t_7 = __pyx_v_self->view.ndim; __pyx_v_info->ndim = __pyx_t_7; /* "View.MemoryView":544 * info.buf = self.view.buf * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize # <<<<<<<<<<<<<< * info.len = self.view.len * info.readonly = self.view.readonly */ __pyx_t_8 = __pyx_v_self->view.itemsize; __pyx_v_info->itemsize = __pyx_t_8; /* "View.MemoryView":545 * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize * info.len = self.view.len # <<<<<<<<<<<<<< * info.readonly = self.view.readonly * info.obj = self */ __pyx_t_8 = __pyx_v_self->view.len; __pyx_v_info->len = __pyx_t_8; /* "View.MemoryView":546 * info.itemsize = self.view.itemsize * info.len = self.view.len * info.readonly = self.view.readonly # <<<<<<<<<<<<<< * info.obj = self * */ __pyx_t_1 = __pyx_v_self->view.readonly; __pyx_v_info->readonly = __pyx_t_1; /* "View.MemoryView":547 * info.len = self.view.len * info.readonly = self.view.readonly * info.obj = self # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = ((PyObject *)__pyx_v_self); /* "View.MemoryView":518 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; if (__pyx_v_info->obj != NULL) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } goto __pyx_L2; __pyx_L0:; if (__pyx_v_info->obj == Py_None) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } __pyx_L2:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":553 * * @property * def T(self): # <<<<<<<<<<<<<< * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self) { struct __pyx_memoryviewslice_obj *__pyx_v_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":554 * @property * def T(self): * cdef _memoryviewslice result = memoryview_copy(self) # <<<<<<<<<<<<<< * transpose_memslice(&result.from_slice) * return result */ __pyx_t_1 = __pyx_memoryview_copy_object(__pyx_v_self); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 554, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (!(likely(((__pyx_t_1) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_1, __pyx_memoryviewslice_type))))) __PYX_ERR(2, 554, __pyx_L1_error) __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":555 * def T(self): * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) # <<<<<<<<<<<<<< * return result * */ __pyx_t_2 = __pyx_memslice_transpose((&__pyx_v_result->from_slice)); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(2, 555, __pyx_L1_error) /* "View.MemoryView":556 * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) * return result # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":553 * * @property * def T(self): # <<<<<<<<<<<<<< * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.T.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":559 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.obj * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":560 * @property * def base(self): * return self.obj # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->obj); __pyx_r = __pyx_v_self->obj; goto __pyx_L0; /* "View.MemoryView":559 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.obj * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":563 * * @property * def shape(self): # <<<<<<<<<<<<<< * return tuple([length for length in self.view.shape[:self.view.ndim]]) * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_length; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":564 * @property * def shape(self): * return tuple([length for length in self.view.shape[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyList_New(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = (__pyx_v_self->view.shape + __pyx_v_self->view.ndim); for (__pyx_t_4 = __pyx_v_self->view.shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { __pyx_t_2 = __pyx_t_4; __pyx_v_length = (__pyx_t_2[0]); __pyx_t_5 = PyInt_FromSsize_t(__pyx_v_length); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); if (unlikely(__Pyx_ListComp_Append(__pyx_t_1, (PyObject*)__pyx_t_5))) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; } __pyx_t_5 = PyList_AsTuple(((PyObject*)__pyx_t_1)); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "View.MemoryView":563 * * @property * def shape(self): # <<<<<<<<<<<<<< * return tuple([length for length in self.view.shape[:self.view.ndim]]) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.shape.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":567 * * @property * def strides(self): # <<<<<<<<<<<<<< * if self.view.strides == NULL: * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_stride; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; PyObject *__pyx_t_6 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":568 * @property * def strides(self): * if self.view.strides == NULL: # <<<<<<<<<<<<<< * * raise ValueError("Buffer view does not expose strides") */ __pyx_t_1 = ((__pyx_v_self->view.strides == NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":570 * if self.view.strides == NULL: * * raise ValueError("Buffer view does not expose strides") # <<<<<<<<<<<<<< * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) */ __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__13, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 570, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(2, 570, __pyx_L1_error) /* "View.MemoryView":568 * @property * def strides(self): * if self.view.strides == NULL: # <<<<<<<<<<<<<< * * raise ValueError("Buffer view does not expose strides") */ } /* "View.MemoryView":572 * raise ValueError("Buffer view does not expose strides") * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = (__pyx_v_self->view.strides + __pyx_v_self->view.ndim); for (__pyx_t_5 = __pyx_v_self->view.strides; __pyx_t_5 < __pyx_t_4; __pyx_t_5++) { __pyx_t_3 = __pyx_t_5; __pyx_v_stride = (__pyx_t_3[0]); __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_stride); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); if (unlikely(__Pyx_ListComp_Append(__pyx_t_2, (PyObject*)__pyx_t_6))) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } __pyx_t_6 = PyList_AsTuple(((PyObject*)__pyx_t_2)); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_6; __pyx_t_6 = 0; goto __pyx_L0; /* "View.MemoryView":567 * * @property * def strides(self): # <<<<<<<<<<<<<< * if self.view.strides == NULL: * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_6); __Pyx_AddTraceback("View.MemoryView.memoryview.strides.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":575 * * @property * def suboffsets(self): # <<<<<<<<<<<<<< * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; Py_ssize_t *__pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":576 * @property * def suboffsets(self): * if self.view.suboffsets == NULL: # <<<<<<<<<<<<<< * return (-1,) * self.view.ndim * */ __pyx_t_1 = ((__pyx_v_self->view.suboffsets == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":577 * def suboffsets(self): * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim # <<<<<<<<<<<<<< * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyInt_From_int(__pyx_v_self->view.ndim); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_Multiply(__pyx_tuple__14, __pyx_t_2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":576 * @property * def suboffsets(self): * if self.view.suboffsets == NULL: # <<<<<<<<<<<<<< * return (-1,) * self.view.ndim * */ } /* "View.MemoryView":579 * return (-1,) * self.view.ndim * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_5 = (__pyx_v_self->view.suboffsets + __pyx_v_self->view.ndim); for (__pyx_t_6 = __pyx_v_self->view.suboffsets; __pyx_t_6 < __pyx_t_5; __pyx_t_6++) { __pyx_t_4 = __pyx_t_6; __pyx_v_suboffset = (__pyx_t_4[0]); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_suboffset); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (unlikely(__Pyx_ListComp_Append(__pyx_t_3, (PyObject*)__pyx_t_2))) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_t_2 = PyList_AsTuple(((PyObject*)__pyx_t_3)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":575 * * @property * def suboffsets(self): # <<<<<<<<<<<<<< * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.suboffsets.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":582 * * @property * def ndim(self): # <<<<<<<<<<<<<< * return self.view.ndim * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":583 * @property * def ndim(self): * return self.view.ndim # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_self->view.ndim); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 583, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":582 * * @property * def ndim(self): # <<<<<<<<<<<<<< * return self.view.ndim * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.ndim.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":586 * * @property * def itemsize(self): # <<<<<<<<<<<<<< * return self.view.itemsize * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":587 * @property * def itemsize(self): * return self.view.itemsize # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 587, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":586 * * @property * def itemsize(self): # <<<<<<<<<<<<<< * return self.view.itemsize * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.itemsize.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":590 * * @property * def nbytes(self): # <<<<<<<<<<<<<< * return self.size * self.view.itemsize * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":591 * @property * def nbytes(self): * return self.size * self.view.itemsize # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_size); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_Multiply(__pyx_t_1, __pyx_t_2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":590 * * @property * def nbytes(self): # <<<<<<<<<<<<<< * return self.size * self.view.itemsize * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.nbytes.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":594 * * @property * def size(self): # <<<<<<<<<<<<<< * if self._size is None: * result = 1 */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_v_result = NULL; PyObject *__pyx_v_length = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; PyObject *__pyx_t_6 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":595 * @property * def size(self): * if self._size is None: # <<<<<<<<<<<<<< * result = 1 * */ __pyx_t_1 = (__pyx_v_self->_size == Py_None); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":596 * def size(self): * if self._size is None: * result = 1 # <<<<<<<<<<<<<< * * for length in self.view.shape[:self.view.ndim]: */ __Pyx_INCREF(__pyx_int_1); __pyx_v_result = __pyx_int_1; /* "View.MemoryView":598 * result = 1 * * for length in self.view.shape[:self.view.ndim]: # <<<<<<<<<<<<<< * result *= length * */ __pyx_t_4 = (__pyx_v_self->view.shape + __pyx_v_self->view.ndim); for (__pyx_t_5 = __pyx_v_self->view.shape; __pyx_t_5 < __pyx_t_4; __pyx_t_5++) { __pyx_t_3 = __pyx_t_5; __pyx_t_6 = PyInt_FromSsize_t((__pyx_t_3[0])); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 598, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_6); __pyx_t_6 = 0; /* "View.MemoryView":599 * * for length in self.view.shape[:self.view.ndim]: * result *= length # <<<<<<<<<<<<<< * * self._size = result */ __pyx_t_6 = PyNumber_InPlaceMultiply(__pyx_v_result, __pyx_v_length); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 599, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF_SET(__pyx_v_result, __pyx_t_6); __pyx_t_6 = 0; } /* "View.MemoryView":601 * result *= length * * self._size = result # <<<<<<<<<<<<<< * * return self._size */ __Pyx_INCREF(__pyx_v_result); __Pyx_GIVEREF(__pyx_v_result); __Pyx_GOTREF(__pyx_v_self->_size); __Pyx_DECREF(__pyx_v_self->_size); __pyx_v_self->_size = __pyx_v_result; /* "View.MemoryView":595 * @property * def size(self): * if self._size is None: # <<<<<<<<<<<<<< * result = 1 * */ } /* "View.MemoryView":603 * self._size = result * * return self._size # <<<<<<<<<<<<<< * * def __len__(self): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->_size); __pyx_r = __pyx_v_self->_size; goto __pyx_L0; /* "View.MemoryView":594 * * @property * def size(self): # <<<<<<<<<<<<<< * if self._size is None: * result = 1 */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_6); __Pyx_AddTraceback("View.MemoryView.memoryview.size.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_result); __Pyx_XDECREF(__pyx_v_length); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":605 * return self._size * * def __len__(self): # <<<<<<<<<<<<<< * if self.view.ndim >= 1: * return self.view.shape[0] */ /* Python wrapper */ static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self); /*proto*/ static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("__len__", 0); /* "View.MemoryView":606 * * def __len__(self): * if self.view.ndim >= 1: # <<<<<<<<<<<<<< * return self.view.shape[0] * */ __pyx_t_1 = ((__pyx_v_self->view.ndim >= 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":607 * def __len__(self): * if self.view.ndim >= 1: * return self.view.shape[0] # <<<<<<<<<<<<<< * * return 0 */ __pyx_r = (__pyx_v_self->view.shape[0]); goto __pyx_L0; /* "View.MemoryView":606 * * def __len__(self): * if self.view.ndim >= 1: # <<<<<<<<<<<<<< * return self.view.shape[0] * */ } /* "View.MemoryView":609 * return self.view.shape[0] * * return 0 # <<<<<<<<<<<<<< * * def __repr__(self): */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":605 * return self._size * * def __len__(self): # <<<<<<<<<<<<<< * if self.view.ndim >= 1: * return self.view.shape[0] */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":611 * return 0 * * def __repr__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__, * id(self)) */ /* Python wrapper */ static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__repr__", 0); /* "View.MemoryView":612 * * def __repr__(self): * return "" % (self.base.__class__.__name__, # <<<<<<<<<<<<<< * id(self)) * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":613 * def __repr__(self): * return "" % (self.base.__class__.__name__, * id(self)) # <<<<<<<<<<<<<< * * def __str__(self): */ __pyx_t_2 = __Pyx_PyObject_CallOneArg(__pyx_builtin_id, ((PyObject *)__pyx_v_self)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 613, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); /* "View.MemoryView":612 * * def __repr__(self): * return "" % (self.base.__class__.__name__, # <<<<<<<<<<<<<< * id(self)) * */ __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_t_3); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":611 * return 0 * * def __repr__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__, * id(self)) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.__repr__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":615 * id(self)) * * def __str__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__,) * */ /* Python wrapper */ static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__str__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__str__", 0); /* "View.MemoryView":616 * * def __str__(self): * return "" % (self.base.__class__.__name__,) # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_object, __pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":615 * id(self)) * * def __str__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__,) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.__str__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":619 * * * def is_c_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* Python wrapper */ static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("is_c_contig (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice *__pyx_v_mslice; __Pyx_memviewslice __pyx_v_tmp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("is_c_contig", 0); /* "View.MemoryView":622 * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) # <<<<<<<<<<<<<< * return slice_is_contig(mslice[0], 'C', self.view.ndim) * */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 622, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":623 * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) * return slice_is_contig(mslice[0], 'C', self.view.ndim) # <<<<<<<<<<<<<< * * def is_f_contig(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig((__pyx_v_mslice[0]), 'C', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 623, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":619 * * * def is_c_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.is_c_contig", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":625 * return slice_is_contig(mslice[0], 'C', self.view.ndim) * * def is_f_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* Python wrapper */ static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("is_f_contig (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice *__pyx_v_mslice; __Pyx_memviewslice __pyx_v_tmp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("is_f_contig", 0); /* "View.MemoryView":628 * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) # <<<<<<<<<<<<<< * return slice_is_contig(mslice[0], 'F', self.view.ndim) * */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(2, 628, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":629 * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) * return slice_is_contig(mslice[0], 'F', self.view.ndim) # <<<<<<<<<<<<<< * * def copy(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig((__pyx_v_mslice[0]), 'F', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 629, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":625 * return slice_is_contig(mslice[0], 'C', self.view.ndim) * * def is_f_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.is_f_contig", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":631 * return slice_is_contig(mslice[0], 'F', self.view.ndim) * * def copy(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS */ /* Python wrapper */ static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("copy (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice __pyx_v_mslice; int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice __pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("copy", 0); /* "View.MemoryView":633 * def copy(self): * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS # <<<<<<<<<<<<<< * * slice_copy(self, &mslice) */ __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_F_CONTIGUOUS)); /* "View.MemoryView":635 * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS * * slice_copy(self, &mslice) # <<<<<<<<<<<<<< * mslice = slice_copy_contig(&mslice, "c", self.view.ndim, * self.view.itemsize, */ __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_mslice)); /* "View.MemoryView":636 * * slice_copy(self, &mslice) * mslice = slice_copy_contig(&mslice, "c", self.view.ndim, # <<<<<<<<<<<<<< * self.view.itemsize, * flags|PyBUF_C_CONTIGUOUS, */ __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_mslice), ((char *)"c"), __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_C_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) __PYX_ERR(2, 636, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":641 * self.dtype_is_object) * * return memoryview_copy_from_slice(self, &mslice) # <<<<<<<<<<<<<< * * def copy_fortran(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_mslice)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 641, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":631 * return slice_is_contig(mslice[0], 'F', self.view.ndim) * * def copy(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.copy", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":643 * return memoryview_copy_from_slice(self, &mslice) * * def copy_fortran(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS */ /* Python wrapper */ static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("copy_fortran (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice __pyx_v_src; __Pyx_memviewslice __pyx_v_dst; int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice __pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("copy_fortran", 0); /* "View.MemoryView":645 * def copy_fortran(self): * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS # <<<<<<<<<<<<<< * * slice_copy(self, &src) */ __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_C_CONTIGUOUS)); /* "View.MemoryView":647 * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS * * slice_copy(self, &src) # <<<<<<<<<<<<<< * dst = slice_copy_contig(&src, "fortran", self.view.ndim, * self.view.itemsize, */ __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_src)); /* "View.MemoryView":648 * * slice_copy(self, &src) * dst = slice_copy_contig(&src, "fortran", self.view.ndim, # <<<<<<<<<<<<<< * self.view.itemsize, * flags|PyBUF_F_CONTIGUOUS, */ __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_src), ((char *)"fortran"), __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_F_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) __PYX_ERR(2, 648, __pyx_L1_error) __pyx_v_dst = __pyx_t_1; /* "View.MemoryView":653 * self.dtype_is_object) * * return memoryview_copy_from_slice(self, &dst) # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_dst)); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 653, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":643 * return memoryview_copy_from_slice(self, &mslice) * * def copy_fortran(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.copy_fortran", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryview_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_memoryview_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryview___reduce_cython__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__15, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryview_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_memoryview_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryview_2__setstate_cython__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__16, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":657 * * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): # <<<<<<<<<<<<<< * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo */ static PyObject *__pyx_memoryview_new(PyObject *__pyx_v_o, int __pyx_v_flags, int __pyx_v_dtype_is_object, __Pyx_TypeInfo *__pyx_v_typeinfo) { struct __pyx_memoryview_obj *__pyx_v_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_cwrapper", 0); /* "View.MemoryView":658 * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): * cdef memoryview result = memoryview(o, flags, dtype_is_object) # <<<<<<<<<<<<<< * result.typeinfo = typeinfo * return result */ __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_o); __Pyx_GIVEREF(__pyx_v_o); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_o); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_memoryview_obj *)__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":659 * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo # <<<<<<<<<<<<<< * return result * */ __pyx_v_result->typeinfo = __pyx_v_typeinfo; /* "View.MemoryView":660 * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo * return result # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_check') */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":657 * * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): # <<<<<<<<<<<<<< * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":663 * * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): # <<<<<<<<<<<<<< * return isinstance(o, memoryview) * */ static CYTHON_INLINE int __pyx_memoryview_check(PyObject *__pyx_v_o) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("memoryview_check", 0); /* "View.MemoryView":664 * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): * return isinstance(o, memoryview) # <<<<<<<<<<<<<< * * cdef tuple _unellipsify(object index, int ndim): */ __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_o, __pyx_memoryview_type); __pyx_r = __pyx_t_1; goto __pyx_L0; /* "View.MemoryView":663 * * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): # <<<<<<<<<<<<<< * return isinstance(o, memoryview) * */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":666 * return isinstance(o, memoryview) * * cdef tuple _unellipsify(object index, int ndim): # <<<<<<<<<<<<<< * """ * Replace all ellipses with full slices and fill incomplete indices with */ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) { PyObject *__pyx_v_tup = NULL; PyObject *__pyx_v_result = NULL; int __pyx_v_have_slices; int __pyx_v_seen_ellipsis; CYTHON_UNUSED PyObject *__pyx_v_idx = NULL; PyObject *__pyx_v_item = NULL; Py_ssize_t __pyx_v_nslices; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; Py_ssize_t __pyx_t_5; PyObject *(*__pyx_t_6)(PyObject *); PyObject *__pyx_t_7 = NULL; Py_ssize_t __pyx_t_8; int __pyx_t_9; int __pyx_t_10; PyObject *__pyx_t_11 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("_unellipsify", 0); /* "View.MemoryView":671 * full slices. * """ * if not isinstance(index, tuple): # <<<<<<<<<<<<<< * tup = (index,) * else: */ __pyx_t_1 = PyTuple_Check(__pyx_v_index); __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":672 * """ * if not isinstance(index, tuple): * tup = (index,) # <<<<<<<<<<<<<< * else: * tup = index */ __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 672, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_index); __Pyx_GIVEREF(__pyx_v_index); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_index); __pyx_v_tup = __pyx_t_3; __pyx_t_3 = 0; /* "View.MemoryView":671 * full slices. * """ * if not isinstance(index, tuple): # <<<<<<<<<<<<<< * tup = (index,) * else: */ goto __pyx_L3; } /* "View.MemoryView":674 * tup = (index,) * else: * tup = index # <<<<<<<<<<<<<< * * result = [] */ /*else*/ { __Pyx_INCREF(__pyx_v_index); __pyx_v_tup = __pyx_v_index; } __pyx_L3:; /* "View.MemoryView":676 * tup = index * * result = [] # <<<<<<<<<<<<<< * have_slices = False * seen_ellipsis = False */ __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 676, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_v_result = ((PyObject*)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":677 * * result = [] * have_slices = False # <<<<<<<<<<<<<< * seen_ellipsis = False * for idx, item in enumerate(tup): */ __pyx_v_have_slices = 0; /* "View.MemoryView":678 * result = [] * have_slices = False * seen_ellipsis = False # <<<<<<<<<<<<<< * for idx, item in enumerate(tup): * if item is Ellipsis: */ __pyx_v_seen_ellipsis = 0; /* "View.MemoryView":679 * have_slices = False * seen_ellipsis = False * for idx, item in enumerate(tup): # <<<<<<<<<<<<<< * if item is Ellipsis: * if not seen_ellipsis: */ __Pyx_INCREF(__pyx_int_0); __pyx_t_3 = __pyx_int_0; if (likely(PyList_CheckExact(__pyx_v_tup)) || PyTuple_CheckExact(__pyx_v_tup)) { __pyx_t_4 = __pyx_v_tup; __Pyx_INCREF(__pyx_t_4); __pyx_t_5 = 0; __pyx_t_6 = NULL; } else { __pyx_t_5 = -1; __pyx_t_4 = PyObject_GetIter(__pyx_v_tup); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_6 = Py_TYPE(__pyx_t_4)->tp_iternext; if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 679, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_6)) { if (likely(PyList_CheckExact(__pyx_t_4))) { if (__pyx_t_5 >= PyList_GET_SIZE(__pyx_t_4)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_7 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) __PYX_ERR(2, 679, __pyx_L1_error) #else __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); #endif } else { if (__pyx_t_5 >= PyTuple_GET_SIZE(__pyx_t_4)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_7 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) __PYX_ERR(2, 679, __pyx_L1_error) #else __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); #endif } } else { __pyx_t_7 = __pyx_t_6(__pyx_t_4); if (unlikely(!__pyx_t_7)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(2, 679, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_7); } __Pyx_XDECREF_SET(__pyx_v_item, __pyx_t_7); __pyx_t_7 = 0; __Pyx_INCREF(__pyx_t_3); __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_3); __pyx_t_7 = __Pyx_PyInt_AddObjC(__pyx_t_3, __pyx_int_1, 1, 0, 0); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = __pyx_t_7; __pyx_t_7 = 0; /* "View.MemoryView":680 * seen_ellipsis = False * for idx, item in enumerate(tup): * if item is Ellipsis: # <<<<<<<<<<<<<< * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) */ __pyx_t_2 = (__pyx_v_item == __pyx_builtin_Ellipsis); __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":681 * for idx, item in enumerate(tup): * if item is Ellipsis: * if not seen_ellipsis: # <<<<<<<<<<<<<< * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True */ __pyx_t_1 = ((!(__pyx_v_seen_ellipsis != 0)) != 0); if (__pyx_t_1) { /* "View.MemoryView":682 * if item is Ellipsis: * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) # <<<<<<<<<<<<<< * seen_ellipsis = True * else: */ __pyx_t_8 = PyObject_Length(__pyx_v_tup); if (unlikely(__pyx_t_8 == ((Py_ssize_t)-1))) __PYX_ERR(2, 682, __pyx_L1_error) __pyx_t_7 = PyList_New(1 * ((((__pyx_v_ndim - __pyx_t_8) + 1)<0) ? 0:((__pyx_v_ndim - __pyx_t_8) + 1))); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 682, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); { Py_ssize_t __pyx_temp; for (__pyx_temp=0; __pyx_temp < ((__pyx_v_ndim - __pyx_t_8) + 1); __pyx_temp++) { __Pyx_INCREF(__pyx_slice__17); __Pyx_GIVEREF(__pyx_slice__17); PyList_SET_ITEM(__pyx_t_7, __pyx_temp, __pyx_slice__17); } } __pyx_t_9 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_7); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 682, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; /* "View.MemoryView":683 * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True # <<<<<<<<<<<<<< * else: * result.append(slice(None)) */ __pyx_v_seen_ellipsis = 1; /* "View.MemoryView":681 * for idx, item in enumerate(tup): * if item is Ellipsis: * if not seen_ellipsis: # <<<<<<<<<<<<<< * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True */ goto __pyx_L7; } /* "View.MemoryView":685 * seen_ellipsis = True * else: * result.append(slice(None)) # <<<<<<<<<<<<<< * have_slices = True * else: */ /*else*/ { __pyx_t_9 = __Pyx_PyList_Append(__pyx_v_result, __pyx_slice__17); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 685, __pyx_L1_error) } __pyx_L7:; /* "View.MemoryView":686 * else: * result.append(slice(None)) * have_slices = True # <<<<<<<<<<<<<< * else: * if not isinstance(item, slice) and not PyIndex_Check(item): */ __pyx_v_have_slices = 1; /* "View.MemoryView":680 * seen_ellipsis = False * for idx, item in enumerate(tup): * if item is Ellipsis: # <<<<<<<<<<<<<< * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) */ goto __pyx_L6; } /* "View.MemoryView":688 * have_slices = True * else: * if not isinstance(item, slice) and not PyIndex_Check(item): # <<<<<<<<<<<<<< * raise TypeError("Cannot index with type '%s'" % type(item)) * */ /*else*/ { __pyx_t_2 = PySlice_Check(__pyx_v_item); __pyx_t_10 = ((!(__pyx_t_2 != 0)) != 0); if (__pyx_t_10) { } else { __pyx_t_1 = __pyx_t_10; goto __pyx_L9_bool_binop_done; } __pyx_t_10 = ((!(PyIndex_Check(__pyx_v_item) != 0)) != 0); __pyx_t_1 = __pyx_t_10; __pyx_L9_bool_binop_done:; if (unlikely(__pyx_t_1)) { /* "View.MemoryView":689 * else: * if not isinstance(item, slice) and not PyIndex_Check(item): * raise TypeError("Cannot index with type '%s'" % type(item)) # <<<<<<<<<<<<<< * * have_slices = have_slices or isinstance(item, slice) */ __pyx_t_7 = __Pyx_PyString_FormatSafe(__pyx_kp_s_Cannot_index_with_type_s, ((PyObject *)Py_TYPE(__pyx_v_item))); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 689, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_11 = __Pyx_PyObject_CallOneArg(__pyx_builtin_TypeError, __pyx_t_7); if (unlikely(!__pyx_t_11)) __PYX_ERR(2, 689, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_Raise(__pyx_t_11, 0, 0, 0); __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __PYX_ERR(2, 689, __pyx_L1_error) /* "View.MemoryView":688 * have_slices = True * else: * if not isinstance(item, slice) and not PyIndex_Check(item): # <<<<<<<<<<<<<< * raise TypeError("Cannot index with type '%s'" % type(item)) * */ } /* "View.MemoryView":691 * raise TypeError("Cannot index with type '%s'" % type(item)) * * have_slices = have_slices or isinstance(item, slice) # <<<<<<<<<<<<<< * result.append(item) * */ __pyx_t_10 = (__pyx_v_have_slices != 0); if (!__pyx_t_10) { } else { __pyx_t_1 = __pyx_t_10; goto __pyx_L11_bool_binop_done; } __pyx_t_10 = PySlice_Check(__pyx_v_item); __pyx_t_2 = (__pyx_t_10 != 0); __pyx_t_1 = __pyx_t_2; __pyx_L11_bool_binop_done:; __pyx_v_have_slices = __pyx_t_1; /* "View.MemoryView":692 * * have_slices = have_slices or isinstance(item, slice) * result.append(item) # <<<<<<<<<<<<<< * * nslices = ndim - len(result) */ __pyx_t_9 = __Pyx_PyList_Append(__pyx_v_result, __pyx_v_item); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 692, __pyx_L1_error) } __pyx_L6:; /* "View.MemoryView":679 * have_slices = False * seen_ellipsis = False * for idx, item in enumerate(tup): # <<<<<<<<<<<<<< * if item is Ellipsis: * if not seen_ellipsis: */ } __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":694 * result.append(item) * * nslices = ndim - len(result) # <<<<<<<<<<<<<< * if nslices: * result.extend([slice(None)] * nslices) */ __pyx_t_5 = PyList_GET_SIZE(__pyx_v_result); if (unlikely(__pyx_t_5 == ((Py_ssize_t)-1))) __PYX_ERR(2, 694, __pyx_L1_error) __pyx_v_nslices = (__pyx_v_ndim - __pyx_t_5); /* "View.MemoryView":695 * * nslices = ndim - len(result) * if nslices: # <<<<<<<<<<<<<< * result.extend([slice(None)] * nslices) * */ __pyx_t_1 = (__pyx_v_nslices != 0); if (__pyx_t_1) { /* "View.MemoryView":696 * nslices = ndim - len(result) * if nslices: * result.extend([slice(None)] * nslices) # <<<<<<<<<<<<<< * * return have_slices or nslices, tuple(result) */ __pyx_t_3 = PyList_New(1 * ((__pyx_v_nslices<0) ? 0:__pyx_v_nslices)); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 696, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); { Py_ssize_t __pyx_temp; for (__pyx_temp=0; __pyx_temp < __pyx_v_nslices; __pyx_temp++) { __Pyx_INCREF(__pyx_slice__17); __Pyx_GIVEREF(__pyx_slice__17); PyList_SET_ITEM(__pyx_t_3, __pyx_temp, __pyx_slice__17); } } __pyx_t_9 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_3); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 696, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":695 * * nslices = ndim - len(result) * if nslices: # <<<<<<<<<<<<<< * result.extend([slice(None)] * nslices) * */ } /* "View.MemoryView":698 * result.extend([slice(None)] * nslices) * * return have_slices or nslices, tuple(result) # <<<<<<<<<<<<<< * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): */ __Pyx_XDECREF(__pyx_r); if (!__pyx_v_have_slices) { } else { __pyx_t_4 = __Pyx_PyBool_FromLong(__pyx_v_have_slices); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = __pyx_t_4; __pyx_t_4 = 0; goto __pyx_L14_bool_binop_done; } __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_nslices); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = __pyx_t_4; __pyx_t_4 = 0; __pyx_L14_bool_binop_done:; __pyx_t_4 = PyList_AsTuple(__pyx_v_result); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_11 = PyTuple_New(2); if (unlikely(!__pyx_t_11)) __PYX_ERR(2, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_11, 0, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_11, 1, __pyx_t_4); __pyx_t_3 = 0; __pyx_t_4 = 0; __pyx_r = ((PyObject*)__pyx_t_11); __pyx_t_11 = 0; goto __pyx_L0; /* "View.MemoryView":666 * return isinstance(o, memoryview) * * cdef tuple _unellipsify(object index, int ndim): # <<<<<<<<<<<<<< * """ * Replace all ellipses with full slices and fill incomplete indices with */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_11); __Pyx_AddTraceback("View.MemoryView._unellipsify", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_tup); __Pyx_XDECREF(__pyx_v_result); __Pyx_XDECREF(__pyx_v_idx); __Pyx_XDECREF(__pyx_v_item); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":700 * return have_slices or nslices, tuple(result) * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): # <<<<<<<<<<<<<< * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: */ static PyObject *assert_direct_dimensions(Py_ssize_t *__pyx_v_suboffsets, int __pyx_v_ndim) { Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations Py_ssize_t *__pyx_t_1; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("assert_direct_dimensions", 0); /* "View.MemoryView":701 * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: # <<<<<<<<<<<<<< * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") */ __pyx_t_2 = (__pyx_v_suboffsets + __pyx_v_ndim); for (__pyx_t_3 = __pyx_v_suboffsets; __pyx_t_3 < __pyx_t_2; __pyx_t_3++) { __pyx_t_1 = __pyx_t_3; __pyx_v_suboffset = (__pyx_t_1[0]); /* "View.MemoryView":702 * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * raise ValueError("Indirect dimensions not supported") * */ __pyx_t_4 = ((__pyx_v_suboffset >= 0) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":703 * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__18, NULL); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 703, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_Raise(__pyx_t_5, 0, 0, 0); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __PYX_ERR(2, 703, __pyx_L1_error) /* "View.MemoryView":702 * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * raise ValueError("Indirect dimensions not supported") * */ } } /* "View.MemoryView":700 * return have_slices or nslices, tuple(result) * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): # <<<<<<<<<<<<<< * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.assert_direct_dimensions", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":710 * * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< * cdef int new_ndim = 0, suboffset_dim = -1, dim * cdef bint negative_step */ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *__pyx_v_memview, PyObject *__pyx_v_indices) { int __pyx_v_new_ndim; int __pyx_v_suboffset_dim; int __pyx_v_dim; __Pyx_memviewslice __pyx_v_src; __Pyx_memviewslice __pyx_v_dst; __Pyx_memviewslice *__pyx_v_p_src; struct __pyx_memoryviewslice_obj *__pyx_v_memviewsliceobj = 0; __Pyx_memviewslice *__pyx_v_p_dst; int *__pyx_v_p_suboffset_dim; Py_ssize_t __pyx_v_start; Py_ssize_t __pyx_v_stop; Py_ssize_t __pyx_v_step; int __pyx_v_have_start; int __pyx_v_have_stop; int __pyx_v_have_step; PyObject *__pyx_v_index = NULL; struct __pyx_memoryview_obj *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; struct __pyx_memoryview_obj *__pyx_t_4; char *__pyx_t_5; int __pyx_t_6; Py_ssize_t __pyx_t_7; PyObject *(*__pyx_t_8)(PyObject *); PyObject *__pyx_t_9 = NULL; Py_ssize_t __pyx_t_10; int __pyx_t_11; Py_ssize_t __pyx_t_12; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memview_slice", 0); /* "View.MemoryView":711 * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): * cdef int new_ndim = 0, suboffset_dim = -1, dim # <<<<<<<<<<<<<< * cdef bint negative_step * cdef __Pyx_memviewslice src, dst */ __pyx_v_new_ndim = 0; __pyx_v_suboffset_dim = -1; /* "View.MemoryView":718 * * * memset(&dst, 0, sizeof(dst)) # <<<<<<<<<<<<<< * * cdef _memoryviewslice memviewsliceobj */ (void)(memset((&__pyx_v_dst), 0, (sizeof(__pyx_v_dst)))); /* "View.MemoryView":722 * cdef _memoryviewslice memviewsliceobj * * assert memview.view.ndim > 0 # <<<<<<<<<<<<<< * * if isinstance(memview, _memoryviewslice): */ #ifndef CYTHON_WITHOUT_ASSERTIONS if (unlikely(!Py_OptimizeFlag)) { if (unlikely(!((__pyx_v_memview->view.ndim > 0) != 0))) { PyErr_SetNone(PyExc_AssertionError); __PYX_ERR(2, 722, __pyx_L1_error) } } #endif /* "View.MemoryView":724 * assert memview.view.ndim > 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":725 * * if isinstance(memview, _memoryviewslice): * memviewsliceobj = memview # <<<<<<<<<<<<<< * p_src = &memviewsliceobj.from_slice * else: */ if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 725, __pyx_L1_error) __pyx_t_3 = ((PyObject *)__pyx_v_memview); __Pyx_INCREF(__pyx_t_3); __pyx_v_memviewsliceobj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":726 * if isinstance(memview, _memoryviewslice): * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice # <<<<<<<<<<<<<< * else: * slice_copy(memview, &src) */ __pyx_v_p_src = (&__pyx_v_memviewsliceobj->from_slice); /* "View.MemoryView":724 * assert memview.view.ndim > 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice */ goto __pyx_L3; } /* "View.MemoryView":728 * p_src = &memviewsliceobj.from_slice * else: * slice_copy(memview, &src) # <<<<<<<<<<<<<< * p_src = &src * */ /*else*/ { __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_src)); /* "View.MemoryView":729 * else: * slice_copy(memview, &src) * p_src = &src # <<<<<<<<<<<<<< * * */ __pyx_v_p_src = (&__pyx_v_src); } __pyx_L3:; /* "View.MemoryView":735 * * * dst.memview = p_src.memview # <<<<<<<<<<<<<< * dst.data = p_src.data * */ __pyx_t_4 = __pyx_v_p_src->memview; __pyx_v_dst.memview = __pyx_t_4; /* "View.MemoryView":736 * * dst.memview = p_src.memview * dst.data = p_src.data # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __pyx_v_p_src->data; __pyx_v_dst.data = __pyx_t_5; /* "View.MemoryView":741 * * * cdef __Pyx_memviewslice *p_dst = &dst # <<<<<<<<<<<<<< * cdef int *p_suboffset_dim = &suboffset_dim * cdef Py_ssize_t start, stop, step */ __pyx_v_p_dst = (&__pyx_v_dst); /* "View.MemoryView":742 * * cdef __Pyx_memviewslice *p_dst = &dst * cdef int *p_suboffset_dim = &suboffset_dim # <<<<<<<<<<<<<< * cdef Py_ssize_t start, stop, step * cdef bint have_start, have_stop, have_step */ __pyx_v_p_suboffset_dim = (&__pyx_v_suboffset_dim); /* "View.MemoryView":746 * cdef bint have_start, have_stop, have_step * * for dim, index in enumerate(indices): # <<<<<<<<<<<<<< * if PyIndex_Check(index): * slice_memviewslice( */ __pyx_t_6 = 0; if (likely(PyList_CheckExact(__pyx_v_indices)) || PyTuple_CheckExact(__pyx_v_indices)) { __pyx_t_3 = __pyx_v_indices; __Pyx_INCREF(__pyx_t_3); __pyx_t_7 = 0; __pyx_t_8 = NULL; } else { __pyx_t_7 = -1; __pyx_t_3 = PyObject_GetIter(__pyx_v_indices); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_8 = Py_TYPE(__pyx_t_3)->tp_iternext; if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 746, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_8)) { if (likely(PyList_CheckExact(__pyx_t_3))) { if (__pyx_t_7 >= PyList_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_9 = PyList_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(2, 746, __pyx_L1_error) #else __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); #endif } else { if (__pyx_t_7 >= PyTuple_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_9 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(2, 746, __pyx_L1_error) #else __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); #endif } } else { __pyx_t_9 = __pyx_t_8(__pyx_t_3); if (unlikely(!__pyx_t_9)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(2, 746, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_9); } __Pyx_XDECREF_SET(__pyx_v_index, __pyx_t_9); __pyx_t_9 = 0; __pyx_v_dim = __pyx_t_6; __pyx_t_6 = (__pyx_t_6 + 1); /* "View.MemoryView":747 * * for dim, index in enumerate(indices): * if PyIndex_Check(index): # <<<<<<<<<<<<<< * slice_memviewslice( * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], */ __pyx_t_2 = (PyIndex_Check(__pyx_v_index) != 0); if (__pyx_t_2) { /* "View.MemoryView":751 * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, * index, 0, 0, # start, stop, step # <<<<<<<<<<<<<< * 0, 0, 0, # have_{start,stop,step} * False) */ __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_v_index); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 751, __pyx_L1_error) /* "View.MemoryView":748 * for dim, index in enumerate(indices): * if PyIndex_Check(index): * slice_memviewslice( # <<<<<<<<<<<<<< * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, */ __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_t_10, 0, 0, 0, 0, 0, 0); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 748, __pyx_L1_error) /* "View.MemoryView":747 * * for dim, index in enumerate(indices): * if PyIndex_Check(index): # <<<<<<<<<<<<<< * slice_memviewslice( * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], */ goto __pyx_L6; } /* "View.MemoryView":754 * 0, 0, 0, # have_{start,stop,step} * False) * elif index is None: # <<<<<<<<<<<<<< * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 */ __pyx_t_2 = (__pyx_v_index == Py_None); __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":755 * False) * elif index is None: * p_dst.shape[new_ndim] = 1 # <<<<<<<<<<<<<< * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 */ (__pyx_v_p_dst->shape[__pyx_v_new_ndim]) = 1; /* "View.MemoryView":756 * elif index is None: * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 # <<<<<<<<<<<<<< * p_dst.suboffsets[new_ndim] = -1 * new_ndim += 1 */ (__pyx_v_p_dst->strides[__pyx_v_new_ndim]) = 0; /* "View.MemoryView":757 * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 # <<<<<<<<<<<<<< * new_ndim += 1 * else: */ (__pyx_v_p_dst->suboffsets[__pyx_v_new_ndim]) = -1L; /* "View.MemoryView":758 * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 * new_ndim += 1 # <<<<<<<<<<<<<< * else: * start = index.start or 0 */ __pyx_v_new_ndim = (__pyx_v_new_ndim + 1); /* "View.MemoryView":754 * 0, 0, 0, # have_{start,stop,step} * False) * elif index is None: # <<<<<<<<<<<<<< * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 */ goto __pyx_L6; } /* "View.MemoryView":760 * new_ndim += 1 * else: * start = index.start or 0 # <<<<<<<<<<<<<< * stop = index.stop or 0 * step = index.step or 0 */ /*else*/ { __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 760, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 760, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 760, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L7_bool_binop_done; } __pyx_t_10 = 0; __pyx_L7_bool_binop_done:; __pyx_v_start = __pyx_t_10; /* "View.MemoryView":761 * else: * start = index.start or 0 * stop = index.stop or 0 # <<<<<<<<<<<<<< * step = index.step or 0 * */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 761, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 761, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 761, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L9_bool_binop_done; } __pyx_t_10 = 0; __pyx_L9_bool_binop_done:; __pyx_v_stop = __pyx_t_10; /* "View.MemoryView":762 * start = index.start or 0 * stop = index.stop or 0 * step = index.step or 0 # <<<<<<<<<<<<<< * * have_start = index.start is not None */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 762, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 762, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 762, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L11_bool_binop_done; } __pyx_t_10 = 0; __pyx_L11_bool_binop_done:; __pyx_v_step = __pyx_t_10; /* "View.MemoryView":764 * step = index.step or 0 * * have_start = index.start is not None # <<<<<<<<<<<<<< * have_stop = index.stop is not None * have_step = index.step is not None */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 764, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_start = __pyx_t_1; /* "View.MemoryView":765 * * have_start = index.start is not None * have_stop = index.stop is not None # <<<<<<<<<<<<<< * have_step = index.step is not None * */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 765, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_stop = __pyx_t_1; /* "View.MemoryView":766 * have_start = index.start is not None * have_stop = index.stop is not None * have_step = index.step is not None # <<<<<<<<<<<<<< * * slice_memviewslice( */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 766, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_step = __pyx_t_1; /* "View.MemoryView":768 * have_step = index.step is not None * * slice_memviewslice( # <<<<<<<<<<<<<< * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, */ __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 768, __pyx_L1_error) /* "View.MemoryView":774 * have_start, have_stop, have_step, * True) * new_ndim += 1 # <<<<<<<<<<<<<< * * if isinstance(memview, _memoryviewslice): */ __pyx_v_new_ndim = (__pyx_v_new_ndim + 1); } __pyx_L6:; /* "View.MemoryView":746 * cdef bint have_start, have_stop, have_step * * for dim, index in enumerate(indices): # <<<<<<<<<<<<<< * if PyIndex_Check(index): * slice_memviewslice( */ } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":776 * new_ndim += 1 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":777 * * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, */ __Pyx_XDECREF(((PyObject *)__pyx_r)); /* "View.MemoryView":778 * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, # <<<<<<<<<<<<<< * memviewsliceobj.to_dtype_func, * memview.dtype_is_object) */ if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 778, __pyx_L1_error) } /* "View.MemoryView":779 * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, # <<<<<<<<<<<<<< * memview.dtype_is_object) * else: */ if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 779, __pyx_L1_error) } /* "View.MemoryView":777 * * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, */ __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 777, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 777, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":776 * new_ndim += 1 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, */ } /* "View.MemoryView":782 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< * memview.dtype_is_object) * */ /*else*/ { __Pyx_XDECREF(((PyObject *)__pyx_r)); /* "View.MemoryView":783 * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 782, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); /* "View.MemoryView":782 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< * memview.dtype_is_object) * */ if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 782, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; } /* "View.MemoryView":710 * * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< * cdef int new_ndim = 0, suboffset_dim = -1, dim * cdef bint negative_step */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_9); __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj); __Pyx_XDECREF(__pyx_v_index); __Pyx_XGIVEREF((PyObject *)__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":807 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, */ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) { Py_ssize_t __pyx_v_new_shape; int __pyx_v_negative_step; int __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":827 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< * * if start < 0: */ __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_1) { /* "View.MemoryView":829 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< * start += shape * if not 0 <= start < shape: */ __pyx_t_1 = ((__pyx_v_start < 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":830 * * if start < 0: * start += shape # <<<<<<<<<<<<<< * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); /* "View.MemoryView":829 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< * start += shape * if not 0 <= start < shape: */ } /* "View.MemoryView":831 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) * else: */ __pyx_t_1 = (0 <= __pyx_v_start); if (__pyx_t_1) { __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); } __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":832 * start += shape * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< * else: * */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 832, __pyx_L1_error) /* "View.MemoryView":831 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) * else: */ } /* "View.MemoryView":827 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< * * if start < 0: */ goto __pyx_L3; } /* "View.MemoryView":835 * else: * * negative_step = have_step != 0 and step < 0 # <<<<<<<<<<<<<< * * if have_step and step == 0: */ /*else*/ { __pyx_t_1 = ((__pyx_v_have_step != 0) != 0); if (__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L6_bool_binop_done; } __pyx_t_1 = ((__pyx_v_step < 0) != 0); __pyx_t_2 = __pyx_t_1; __pyx_L6_bool_binop_done:; __pyx_v_negative_step = __pyx_t_2; /* "View.MemoryView":837 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) * */ __pyx_t_1 = (__pyx_v_have_step != 0); if (__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L9_bool_binop_done; } __pyx_t_1 = ((__pyx_v_step == 0) != 0); __pyx_t_2 = __pyx_t_1; __pyx_L9_bool_binop_done:; if (__pyx_t_2) { /* "View.MemoryView":838 * * if have_step and step == 0: * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 838, __pyx_L1_error) /* "View.MemoryView":837 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) * */ } /* "View.MemoryView":841 * * * if have_start: # <<<<<<<<<<<<<< * if start < 0: * start += shape */ __pyx_t_2 = (__pyx_v_have_start != 0); if (__pyx_t_2) { /* "View.MemoryView":842 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< * start += shape * if start < 0: */ __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":843 * if have_start: * if start < 0: * start += shape # <<<<<<<<<<<<<< * if start < 0: * start = 0 */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); /* "View.MemoryView":844 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< * start = 0 * elif start >= shape: */ __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":845 * start += shape * if start < 0: * start = 0 # <<<<<<<<<<<<<< * elif start >= shape: * if negative_step: */ __pyx_v_start = 0; /* "View.MemoryView":844 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< * start = 0 * elif start >= shape: */ } /* "View.MemoryView":842 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< * start += shape * if start < 0: */ goto __pyx_L12; } /* "View.MemoryView":846 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< * if negative_step: * start = shape - 1 */ __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); if (__pyx_t_2) { /* "View.MemoryView":847 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":848 * elif start >= shape: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< * else: * start = shape */ __pyx_v_start = (__pyx_v_shape - 1); /* "View.MemoryView":847 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ goto __pyx_L14; } /* "View.MemoryView":850 * start = shape - 1 * else: * start = shape # <<<<<<<<<<<<<< * else: * if negative_step: */ /*else*/ { __pyx_v_start = __pyx_v_shape; } __pyx_L14:; /* "View.MemoryView":846 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< * if negative_step: * start = shape - 1 */ } __pyx_L12:; /* "View.MemoryView":841 * * * if have_start: # <<<<<<<<<<<<<< * if start < 0: * start += shape */ goto __pyx_L11; } /* "View.MemoryView":852 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ /*else*/ { __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":853 * else: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< * else: * start = 0 */ __pyx_v_start = (__pyx_v_shape - 1); /* "View.MemoryView":852 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ goto __pyx_L15; } /* "View.MemoryView":855 * start = shape - 1 * else: * start = 0 # <<<<<<<<<<<<<< * * if have_stop: */ /*else*/ { __pyx_v_start = 0; } __pyx_L15:; } __pyx_L11:; /* "View.MemoryView":857 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< * if stop < 0: * stop += shape */ __pyx_t_2 = (__pyx_v_have_stop != 0); if (__pyx_t_2) { /* "View.MemoryView":858 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< * stop += shape * if stop < 0: */ __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":859 * if have_stop: * if stop < 0: * stop += shape # <<<<<<<<<<<<<< * if stop < 0: * stop = 0 */ __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); /* "View.MemoryView":860 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< * stop = 0 * elif stop > shape: */ __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":861 * stop += shape * if stop < 0: * stop = 0 # <<<<<<<<<<<<<< * elif stop > shape: * stop = shape */ __pyx_v_stop = 0; /* "View.MemoryView":860 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< * stop = 0 * elif stop > shape: */ } /* "View.MemoryView":858 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< * stop += shape * if stop < 0: */ goto __pyx_L17; } /* "View.MemoryView":862 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< * stop = shape * else: */ __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0); if (__pyx_t_2) { /* "View.MemoryView":863 * stop = 0 * elif stop > shape: * stop = shape # <<<<<<<<<<<<<< * else: * if negative_step: */ __pyx_v_stop = __pyx_v_shape; /* "View.MemoryView":862 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< * stop = shape * else: */ } __pyx_L17:; /* "View.MemoryView":857 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< * if stop < 0: * stop += shape */ goto __pyx_L16; } /* "View.MemoryView":865 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< * stop = -1 * else: */ /*else*/ { __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":866 * else: * if negative_step: * stop = -1 # <<<<<<<<<<<<<< * else: * stop = shape */ __pyx_v_stop = -1L; /* "View.MemoryView":865 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< * stop = -1 * else: */ goto __pyx_L19; } /* "View.MemoryView":868 * stop = -1 * else: * stop = shape # <<<<<<<<<<<<<< * * if not have_step: */ /*else*/ { __pyx_v_stop = __pyx_v_shape; } __pyx_L19:; } __pyx_L16:; /* "View.MemoryView":870 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< * step = 1 * */ __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":871 * * if not have_step: * step = 1 # <<<<<<<<<<<<<< * * */ __pyx_v_step = 1; /* "View.MemoryView":870 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< * step = 1 * */ } /* "View.MemoryView":875 * * with cython.cdivision(True): * new_shape = (stop - start) // step # <<<<<<<<<<<<<< * * if (stop - start) - step * new_shape: */ __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); /* "View.MemoryView":877 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< * new_shape += 1 * */ __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); if (__pyx_t_2) { /* "View.MemoryView":878 * * if (stop - start) - step * new_shape: * new_shape += 1 # <<<<<<<<<<<<<< * * if new_shape < 0: */ __pyx_v_new_shape = (__pyx_v_new_shape + 1); /* "View.MemoryView":877 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< * new_shape += 1 * */ } /* "View.MemoryView":880 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< * new_shape = 0 * */ __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":881 * * if new_shape < 0: * new_shape = 0 # <<<<<<<<<<<<<< * * */ __pyx_v_new_shape = 0; /* "View.MemoryView":880 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< * new_shape = 0 * */ } /* "View.MemoryView":884 * * * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset */ (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); /* "View.MemoryView":885 * * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape # <<<<<<<<<<<<<< * dst.suboffsets[new_ndim] = suboffset * */ (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape; /* "View.MemoryView":886 * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset # <<<<<<<<<<<<<< * * */ (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset; } __pyx_L3:; /* "View.MemoryView":889 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< * dst.data += start * stride * else: */ __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":890 * * if suboffset_dim[0] < 0: * dst.data += start * stride # <<<<<<<<<<<<<< * else: * dst.suboffsets[suboffset_dim[0]] += start * stride */ __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride)); /* "View.MemoryView":889 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< * dst.data += start * stride * else: */ goto __pyx_L23; } /* "View.MemoryView":892 * dst.data += start * stride * else: * dst.suboffsets[suboffset_dim[0]] += start * stride # <<<<<<<<<<<<<< * * if suboffset >= 0: */ /*else*/ { __pyx_t_3 = (__pyx_v_suboffset_dim[0]); (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride)); } __pyx_L23:; /* "View.MemoryView":894 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< * if not is_slice: * if new_ndim == 0: */ __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":895 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset */ __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":896 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< * dst.data = ( dst.data)[0] + suboffset * else: */ __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":897 * if not is_slice: * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset # <<<<<<<<<<<<<< * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " */ __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset); /* "View.MemoryView":896 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< * dst.data = ( dst.data)[0] + suboffset * else: */ goto __pyx_L26; } /* "View.MemoryView":899 * dst.data = ( dst.data)[0] + suboffset * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " # <<<<<<<<<<<<<< * "must be indexed and not sliced", dim) * else: */ /*else*/ { /* "View.MemoryView":900 * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " * "must be indexed and not sliced", dim) # <<<<<<<<<<<<<< * else: * suboffset_dim[0] = new_ndim */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"All dimensions preceding dimension %d must be indexed and not sliced"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 899, __pyx_L1_error) } __pyx_L26:; /* "View.MemoryView":895 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset */ goto __pyx_L25; } /* "View.MemoryView":902 * "must be indexed and not sliced", dim) * else: * suboffset_dim[0] = new_ndim # <<<<<<<<<<<<<< * * return 0 */ /*else*/ { (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim; } __pyx_L25:; /* "View.MemoryView":894 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< * if not is_slice: * if new_ndim == 0: */ } /* "View.MemoryView":904 * suboffset_dim[0] = new_ndim * * return 0 # <<<<<<<<<<<<<< * * */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":807 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.slice_memviewslice", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = -1; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":910 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 */ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, Py_ssize_t __pyx_v_index, Py_ssize_t __pyx_v_dim) { Py_ssize_t __pyx_v_shape; Py_ssize_t __pyx_v_stride; Py_ssize_t __pyx_v_suboffset; Py_ssize_t __pyx_v_itemsize; char *__pyx_v_resultp; char *__pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("pybuffer_index", 0); /* "View.MemoryView":912 * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 # <<<<<<<<<<<<<< * cdef Py_ssize_t itemsize = view.itemsize * cdef char *resultp */ __pyx_v_suboffset = -1L; /* "View.MemoryView":913 * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 * cdef Py_ssize_t itemsize = view.itemsize # <<<<<<<<<<<<<< * cdef char *resultp * */ __pyx_t_1 = __pyx_v_view->itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":916 * cdef char *resultp * * if view.ndim == 0: # <<<<<<<<<<<<<< * shape = view.len / itemsize * stride = itemsize */ __pyx_t_2 = ((__pyx_v_view->ndim == 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":917 * * if view.ndim == 0: * shape = view.len / itemsize # <<<<<<<<<<<<<< * stride = itemsize * else: */ if (unlikely(__pyx_v_itemsize == 0)) { PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); __PYX_ERR(2, 917, __pyx_L1_error) } else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1) && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_view->len))) { PyErr_SetString(PyExc_OverflowError, "value too large to perform division"); __PYX_ERR(2, 917, __pyx_L1_error) } __pyx_v_shape = (__pyx_v_view->len / __pyx_v_itemsize); /* "View.MemoryView":918 * if view.ndim == 0: * shape = view.len / itemsize * stride = itemsize # <<<<<<<<<<<<<< * else: * shape = view.shape[dim] */ __pyx_v_stride = __pyx_v_itemsize; /* "View.MemoryView":916 * cdef char *resultp * * if view.ndim == 0: # <<<<<<<<<<<<<< * shape = view.len / itemsize * stride = itemsize */ goto __pyx_L3; } /* "View.MemoryView":920 * stride = itemsize * else: * shape = view.shape[dim] # <<<<<<<<<<<<<< * stride = view.strides[dim] * if view.suboffsets != NULL: */ /*else*/ { __pyx_v_shape = (__pyx_v_view->shape[__pyx_v_dim]); /* "View.MemoryView":921 * else: * shape = view.shape[dim] * stride = view.strides[dim] # <<<<<<<<<<<<<< * if view.suboffsets != NULL: * suboffset = view.suboffsets[dim] */ __pyx_v_stride = (__pyx_v_view->strides[__pyx_v_dim]); /* "View.MemoryView":922 * shape = view.shape[dim] * stride = view.strides[dim] * if view.suboffsets != NULL: # <<<<<<<<<<<<<< * suboffset = view.suboffsets[dim] * */ __pyx_t_2 = ((__pyx_v_view->suboffsets != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":923 * stride = view.strides[dim] * if view.suboffsets != NULL: * suboffset = view.suboffsets[dim] # <<<<<<<<<<<<<< * * if index < 0: */ __pyx_v_suboffset = (__pyx_v_view->suboffsets[__pyx_v_dim]); /* "View.MemoryView":922 * shape = view.shape[dim] * stride = view.strides[dim] * if view.suboffsets != NULL: # <<<<<<<<<<<<<< * suboffset = view.suboffsets[dim] * */ } } __pyx_L3:; /* "View.MemoryView":925 * suboffset = view.suboffsets[dim] * * if index < 0: # <<<<<<<<<<<<<< * index += view.shape[dim] * if index < 0: */ __pyx_t_2 = ((__pyx_v_index < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":926 * * if index < 0: * index += view.shape[dim] # <<<<<<<<<<<<<< * if index < 0: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) */ __pyx_v_index = (__pyx_v_index + (__pyx_v_view->shape[__pyx_v_dim])); /* "View.MemoryView":927 * if index < 0: * index += view.shape[dim] * if index < 0: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ __pyx_t_2 = ((__pyx_v_index < 0) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":928 * index += view.shape[dim] * if index < 0: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * if index >= shape: */ __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 928, __pyx_L1_error) /* "View.MemoryView":927 * if index < 0: * index += view.shape[dim] * if index < 0: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ } /* "View.MemoryView":925 * suboffset = view.suboffsets[dim] * * if index < 0: # <<<<<<<<<<<<<< * index += view.shape[dim] * if index < 0: */ } /* "View.MemoryView":930 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ __pyx_t_2 = ((__pyx_v_index >= __pyx_v_shape) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":931 * * if index >= shape: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * resultp = bufp + index * stride */ __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 931, __pyx_L1_error) /* "View.MemoryView":930 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ } /* "View.MemoryView":933 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * resultp = bufp + index * stride # <<<<<<<<<<<<<< * if suboffset >= 0: * resultp = ( resultp)[0] + suboffset */ __pyx_v_resultp = (__pyx_v_bufp + (__pyx_v_index * __pyx_v_stride)); /* "View.MemoryView":934 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< * resultp = ( resultp)[0] + suboffset * */ __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":935 * resultp = bufp + index * stride * if suboffset >= 0: * resultp = ( resultp)[0] + suboffset # <<<<<<<<<<<<<< * * return resultp */ __pyx_v_resultp = ((((char **)__pyx_v_resultp)[0]) + __pyx_v_suboffset); /* "View.MemoryView":934 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< * resultp = ( resultp)[0] + suboffset * */ } /* "View.MemoryView":937 * resultp = ( resultp)[0] + suboffset * * return resultp # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_resultp; goto __pyx_L0; /* "View.MemoryView":910 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView.pybuffer_index", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":943 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< * cdef int ndim = memslice.memview.view.ndim * */ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { int __pyx_v_ndim; Py_ssize_t *__pyx_v_shape; Py_ssize_t *__pyx_v_strides; int __pyx_v_i; int __pyx_v_j; int __pyx_r; int __pyx_t_1; Py_ssize_t *__pyx_t_2; long __pyx_t_3; long __pyx_t_4; Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; int __pyx_t_7; int __pyx_t_8; int __pyx_t_9; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":944 * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: * cdef int ndim = memslice.memview.view.ndim # <<<<<<<<<<<<<< * * cdef Py_ssize_t *shape = memslice.shape */ __pyx_t_1 = __pyx_v_memslice->memview->view.ndim; __pyx_v_ndim = __pyx_t_1; /* "View.MemoryView":946 * cdef int ndim = memslice.memview.view.ndim * * cdef Py_ssize_t *shape = memslice.shape # <<<<<<<<<<<<<< * cdef Py_ssize_t *strides = memslice.strides * */ __pyx_t_2 = __pyx_v_memslice->shape; __pyx_v_shape = __pyx_t_2; /* "View.MemoryView":947 * * cdef Py_ssize_t *shape = memslice.shape * cdef Py_ssize_t *strides = memslice.strides # <<<<<<<<<<<<<< * * */ __pyx_t_2 = __pyx_v_memslice->strides; __pyx_v_strides = __pyx_t_2; /* "View.MemoryView":951 * * cdef int i, j * for i in range(ndim / 2): # <<<<<<<<<<<<<< * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] */ __pyx_t_3 = (__pyx_v_ndim / 2); __pyx_t_4 = __pyx_t_3; for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_4; __pyx_t_1+=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":952 * cdef int i, j * for i in range(ndim / 2): * j = ndim - 1 - i # <<<<<<<<<<<<<< * strides[i], strides[j] = strides[j], strides[i] * shape[i], shape[j] = shape[j], shape[i] */ __pyx_v_j = ((__pyx_v_ndim - 1) - __pyx_v_i); /* "View.MemoryView":953 * for i in range(ndim / 2): * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] # <<<<<<<<<<<<<< * shape[i], shape[j] = shape[j], shape[i] * */ __pyx_t_5 = (__pyx_v_strides[__pyx_v_j]); __pyx_t_6 = (__pyx_v_strides[__pyx_v_i]); (__pyx_v_strides[__pyx_v_i]) = __pyx_t_5; (__pyx_v_strides[__pyx_v_j]) = __pyx_t_6; /* "View.MemoryView":954 * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] * shape[i], shape[j] = shape[j], shape[i] # <<<<<<<<<<<<<< * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: */ __pyx_t_6 = (__pyx_v_shape[__pyx_v_j]); __pyx_t_5 = (__pyx_v_shape[__pyx_v_i]); (__pyx_v_shape[__pyx_v_i]) = __pyx_t_6; (__pyx_v_shape[__pyx_v_j]) = __pyx_t_5; /* "View.MemoryView":956 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * */ __pyx_t_8 = (((__pyx_v_memslice->suboffsets[__pyx_v_i]) >= 0) != 0); if (!__pyx_t_8) { } else { __pyx_t_7 = __pyx_t_8; goto __pyx_L6_bool_binop_done; } __pyx_t_8 = (((__pyx_v_memslice->suboffsets[__pyx_v_j]) >= 0) != 0); __pyx_t_7 = __pyx_t_8; __pyx_L6_bool_binop_done:; if (__pyx_t_7) { /* "View.MemoryView":957 * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") # <<<<<<<<<<<<<< * * return 1 */ __pyx_t_9 = __pyx_memoryview_err(__pyx_builtin_ValueError, ((char *)"Cannot transpose memoryview with indirect dimensions")); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 957, __pyx_L1_error) /* "View.MemoryView":956 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * */ } } /* "View.MemoryView":959 * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * * return 1 # <<<<<<<<<<<<<< * * */ __pyx_r = 1; goto __pyx_L0; /* "View.MemoryView":943 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< * cdef int ndim = memslice.memview.view.ndim * */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.transpose_memslice", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":976 * cdef int (*to_dtype_func)(char *, object) except 0 * * def __dealloc__(self): # <<<<<<<<<<<<<< * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * */ /* Python wrapper */ static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":977 * * def __dealloc__(self): * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) # <<<<<<<<<<<<<< * * cdef convert_item_to_object(self, char *itemp): */ __PYX_XDEC_MEMVIEW((&__pyx_v_self->from_slice), 1); /* "View.MemoryView":976 * cdef int (*to_dtype_func)(char *, object) except 0 * * def __dealloc__(self): # <<<<<<<<<<<<<< * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":979 * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * if self.to_object_func != NULL: * return self.to_object_func(itemp) */ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("convert_item_to_object", 0); /* "View.MemoryView":980 * * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: # <<<<<<<<<<<<<< * return self.to_object_func(itemp) * else: */ __pyx_t_1 = ((__pyx_v_self->to_object_func != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":981 * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: * return self.to_object_func(itemp) # <<<<<<<<<<<<<< * else: * return memoryview.convert_item_to_object(self, itemp) */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_v_self->to_object_func(__pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 981, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":980 * * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: # <<<<<<<<<<<<<< * return self.to_object_func(itemp) * else: */ } /* "View.MemoryView":983 * return self.to_object_func(itemp) * else: * return memoryview.convert_item_to_object(self, itemp) # <<<<<<<<<<<<<< * * cdef assign_item_from_object(self, char *itemp, object value): */ /*else*/ { __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_convert_item_to_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 983, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; } /* "View.MemoryView":979 * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * if self.to_object_func != NULL: * return self.to_object_func(itemp) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":985 * return memoryview.convert_item_to_object(self, itemp) * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) */ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("assign_item_from_object", 0); /* "View.MemoryView":986 * * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: # <<<<<<<<<<<<<< * self.to_dtype_func(itemp, value) * else: */ __pyx_t_1 = ((__pyx_v_self->to_dtype_func != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":987 * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) # <<<<<<<<<<<<<< * else: * memoryview.assign_item_from_object(self, itemp, value) */ __pyx_t_2 = __pyx_v_self->to_dtype_func(__pyx_v_itemp, __pyx_v_value); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(2, 987, __pyx_L1_error) /* "View.MemoryView":986 * * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: # <<<<<<<<<<<<<< * self.to_dtype_func(itemp, value) * else: */ goto __pyx_L3; } /* "View.MemoryView":989 * self.to_dtype_func(itemp, value) * else: * memoryview.assign_item_from_object(self, itemp, value) # <<<<<<<<<<<<<< * * @property */ /*else*/ { __pyx_t_3 = __pyx_memoryview_assign_item_from_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 989, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } __pyx_L3:; /* "View.MemoryView":985 * return memoryview.convert_item_to_object(self, itemp) * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":992 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.from_object * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":993 * @property * def base(self): * return self.from_object # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->from_object); __pyx_r = __pyx_v_self->from_object; goto __pyx_L0; /* "View.MemoryView":992 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.from_object * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryviewslice_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_memoryviewslice_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryviewslice___reduce_cython__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__19, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryviewslice_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_memoryviewslice_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryviewslice_2__setstate_cython__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__20, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":999 * * @cname('__pyx_memoryview_fromslice') * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice, # <<<<<<<<<<<<<< * int ndim, * object (*to_object_func)(char *), */ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewslice, int __pyx_v_ndim, PyObject *(*__pyx_v_to_object_func)(char *), int (*__pyx_v_to_dtype_func)(char *, PyObject *), int __pyx_v_dtype_is_object) { struct __pyx_memoryviewslice_obj *__pyx_v_result = 0; Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_v_length = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; __Pyx_TypeInfo *__pyx_t_4; Py_buffer __pyx_t_5; Py_ssize_t *__pyx_t_6; Py_ssize_t *__pyx_t_7; Py_ssize_t *__pyx_t_8; Py_ssize_t __pyx_t_9; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_fromslice", 0); /* "View.MemoryView":1007 * cdef _memoryviewslice result * * if memviewslice.memview == Py_None: # <<<<<<<<<<<<<< * return None * */ __pyx_t_1 = ((((PyObject *)__pyx_v_memviewslice.memview) == Py_None) != 0); if (__pyx_t_1) { /* "View.MemoryView":1008 * * if memviewslice.memview == Py_None: * return None # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; /* "View.MemoryView":1007 * cdef _memoryviewslice result * * if memviewslice.memview == Py_None: # <<<<<<<<<<<<<< * return None * */ } /* "View.MemoryView":1013 * * * result = _memoryviewslice(None, 0, dtype_is_object) # <<<<<<<<<<<<<< * * result.from_slice = memviewslice */ __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(Py_None); __Pyx_GIVEREF(Py_None); PyTuple_SET_ITEM(__pyx_t_3, 0, Py_None); __Pyx_INCREF(__pyx_int_0); __Pyx_GIVEREF(__pyx_int_0); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_int_0); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryviewslice_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":1015 * result = _memoryviewslice(None, 0, dtype_is_object) * * result.from_slice = memviewslice # <<<<<<<<<<<<<< * __PYX_INC_MEMVIEW(&memviewslice, 1) * */ __pyx_v_result->from_slice = __pyx_v_memviewslice; /* "View.MemoryView":1016 * * result.from_slice = memviewslice * __PYX_INC_MEMVIEW(&memviewslice, 1) # <<<<<<<<<<<<<< * * result.from_object = ( memviewslice.memview).base */ __PYX_INC_MEMVIEW((&__pyx_v_memviewslice), 1); /* "View.MemoryView":1018 * __PYX_INC_MEMVIEW(&memviewslice, 1) * * result.from_object = ( memviewslice.memview).base # <<<<<<<<<<<<<< * result.typeinfo = memviewslice.memview.typeinfo * */ __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_memviewslice.memview), __pyx_n_s_base); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1018, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); __Pyx_GOTREF(__pyx_v_result->from_object); __Pyx_DECREF(__pyx_v_result->from_object); __pyx_v_result->from_object = __pyx_t_2; __pyx_t_2 = 0; /* "View.MemoryView":1019 * * result.from_object = ( memviewslice.memview).base * result.typeinfo = memviewslice.memview.typeinfo # <<<<<<<<<<<<<< * * result.view = memviewslice.memview.view */ __pyx_t_4 = __pyx_v_memviewslice.memview->typeinfo; __pyx_v_result->__pyx_base.typeinfo = __pyx_t_4; /* "View.MemoryView":1021 * result.typeinfo = memviewslice.memview.typeinfo * * result.view = memviewslice.memview.view # <<<<<<<<<<<<<< * result.view.buf = memviewslice.data * result.view.ndim = ndim */ __pyx_t_5 = __pyx_v_memviewslice.memview->view; __pyx_v_result->__pyx_base.view = __pyx_t_5; /* "View.MemoryView":1022 * * result.view = memviewslice.memview.view * result.view.buf = memviewslice.data # <<<<<<<<<<<<<< * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None */ __pyx_v_result->__pyx_base.view.buf = ((void *)__pyx_v_memviewslice.data); /* "View.MemoryView":1023 * result.view = memviewslice.memview.view * result.view.buf = memviewslice.data * result.view.ndim = ndim # <<<<<<<<<<<<<< * (<__pyx_buffer *> &result.view).obj = Py_None * Py_INCREF(Py_None) */ __pyx_v_result->__pyx_base.view.ndim = __pyx_v_ndim; /* "View.MemoryView":1024 * result.view.buf = memviewslice.data * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_result->__pyx_base.view))->obj = Py_None; /* "View.MemoryView":1025 * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * if (memviewslice.memview).flags & PyBUF_WRITABLE: */ Py_INCREF(Py_None); /* "View.MemoryView":1027 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< * result.flags = PyBUF_RECORDS * else: */ __pyx_t_1 = ((((struct __pyx_memoryview_obj *)__pyx_v_memviewslice.memview)->flags & PyBUF_WRITABLE) != 0); if (__pyx_t_1) { /* "View.MemoryView":1028 * * if (memviewslice.memview).flags & PyBUF_WRITABLE: * result.flags = PyBUF_RECORDS # <<<<<<<<<<<<<< * else: * result.flags = PyBUF_RECORDS_RO */ __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS; /* "View.MemoryView":1027 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< * result.flags = PyBUF_RECORDS * else: */ goto __pyx_L4; } /* "View.MemoryView":1030 * result.flags = PyBUF_RECORDS * else: * result.flags = PyBUF_RECORDS_RO # <<<<<<<<<<<<<< * * result.view.shape = result.from_slice.shape */ /*else*/ { __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS_RO; } __pyx_L4:; /* "View.MemoryView":1032 * result.flags = PyBUF_RECORDS_RO * * result.view.shape = result.from_slice.shape # <<<<<<<<<<<<<< * result.view.strides = result.from_slice.strides * */ __pyx_v_result->__pyx_base.view.shape = ((Py_ssize_t *)__pyx_v_result->from_slice.shape); /* "View.MemoryView":1033 * * result.view.shape = result.from_slice.shape * result.view.strides = result.from_slice.strides # <<<<<<<<<<<<<< * * */ __pyx_v_result->__pyx_base.view.strides = ((Py_ssize_t *)__pyx_v_result->from_slice.strides); /* "View.MemoryView":1036 * * * result.view.suboffsets = NULL # <<<<<<<<<<<<<< * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: */ __pyx_v_result->__pyx_base.view.suboffsets = NULL; /* "View.MemoryView":1037 * * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: # <<<<<<<<<<<<<< * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets */ __pyx_t_7 = (__pyx_v_result->from_slice.suboffsets + __pyx_v_ndim); for (__pyx_t_8 = __pyx_v_result->from_slice.suboffsets; __pyx_t_8 < __pyx_t_7; __pyx_t_8++) { __pyx_t_6 = __pyx_t_8; __pyx_v_suboffset = (__pyx_t_6[0]); /* "View.MemoryView":1038 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * result.view.suboffsets = result.from_slice.suboffsets * break */ __pyx_t_1 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":1039 * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets # <<<<<<<<<<<<<< * break * */ __pyx_v_result->__pyx_base.view.suboffsets = ((Py_ssize_t *)__pyx_v_result->from_slice.suboffsets); /* "View.MemoryView":1040 * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets * break # <<<<<<<<<<<<<< * * result.view.len = result.view.itemsize */ goto __pyx_L6_break; /* "View.MemoryView":1038 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * result.view.suboffsets = result.from_slice.suboffsets * break */ } } __pyx_L6_break:; /* "View.MemoryView":1042 * break * * result.view.len = result.view.itemsize # <<<<<<<<<<<<<< * for length in result.view.shape[:ndim]: * result.view.len *= length */ __pyx_t_9 = __pyx_v_result->__pyx_base.view.itemsize; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; /* "View.MemoryView":1043 * * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: # <<<<<<<<<<<<<< * result.view.len *= length * */ __pyx_t_7 = (__pyx_v_result->__pyx_base.view.shape + __pyx_v_ndim); for (__pyx_t_8 = __pyx_v_result->__pyx_base.view.shape; __pyx_t_8 < __pyx_t_7; __pyx_t_8++) { __pyx_t_6 = __pyx_t_8; __pyx_t_2 = PyInt_FromSsize_t((__pyx_t_6[0])); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1043, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":1044 * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: * result.view.len *= length # <<<<<<<<<<<<<< * * result.to_object_func = to_object_func */ __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_result->__pyx_base.view.len); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_InPlaceMultiply(__pyx_t_2, __pyx_v_length); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_3); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 1044, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; } /* "View.MemoryView":1046 * result.view.len *= length * * result.to_object_func = to_object_func # <<<<<<<<<<<<<< * result.to_dtype_func = to_dtype_func * */ __pyx_v_result->to_object_func = __pyx_v_to_object_func; /* "View.MemoryView":1047 * * result.to_object_func = to_object_func * result.to_dtype_func = to_dtype_func # <<<<<<<<<<<<<< * * return result */ __pyx_v_result->to_dtype_func = __pyx_v_to_dtype_func; /* "View.MemoryView":1049 * result.to_dtype_func = to_dtype_func * * return result # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_get_slice_from_memoryview') */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":999 * * @cname('__pyx_memoryview_fromslice') * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice, # <<<<<<<<<<<<<< * int ndim, * object (*to_object_func)(char *), */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview_fromslice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XDECREF(__pyx_v_length); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1052 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj */ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_mslice) { struct __pyx_memoryviewslice_obj *__pyx_v_obj = 0; __Pyx_memviewslice *__pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("get_slice_from_memview", 0); /* "View.MemoryView":1055 * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * obj = memview * return &obj.from_slice */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":1056 * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): * obj = memview # <<<<<<<<<<<<<< * return &obj.from_slice * else: */ if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 1056, __pyx_L1_error) __pyx_t_3 = ((PyObject *)__pyx_v_memview); __Pyx_INCREF(__pyx_t_3); __pyx_v_obj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":1057 * if isinstance(memview, _memoryviewslice): * obj = memview * return &obj.from_slice # <<<<<<<<<<<<<< * else: * slice_copy(memview, mslice) */ __pyx_r = (&__pyx_v_obj->from_slice); goto __pyx_L0; /* "View.MemoryView":1055 * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * obj = memview * return &obj.from_slice */ } /* "View.MemoryView":1059 * return &obj.from_slice * else: * slice_copy(memview, mslice) # <<<<<<<<<<<<<< * return mslice * */ /*else*/ { __pyx_memoryview_slice_copy(__pyx_v_memview, __pyx_v_mslice); /* "View.MemoryView":1060 * else: * slice_copy(memview, mslice) * return mslice # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_slice_copy') */ __pyx_r = __pyx_v_mslice; goto __pyx_L0; } /* "View.MemoryView":1052 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.get_slice_from_memview", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_obj); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1063 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< * cdef int dim * cdef (Py_ssize_t*) shape, strides, suboffsets */ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_dst) { int __pyx_v_dim; Py_ssize_t *__pyx_v_shape; Py_ssize_t *__pyx_v_strides; Py_ssize_t *__pyx_v_suboffsets; __Pyx_RefNannyDeclarations Py_ssize_t *__pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; Py_ssize_t __pyx_t_5; __Pyx_RefNannySetupContext("slice_copy", 0); /* "View.MemoryView":1067 * cdef (Py_ssize_t*) shape, strides, suboffsets * * shape = memview.view.shape # <<<<<<<<<<<<<< * strides = memview.view.strides * suboffsets = memview.view.suboffsets */ __pyx_t_1 = __pyx_v_memview->view.shape; __pyx_v_shape = __pyx_t_1; /* "View.MemoryView":1068 * * shape = memview.view.shape * strides = memview.view.strides # <<<<<<<<<<<<<< * suboffsets = memview.view.suboffsets * */ __pyx_t_1 = __pyx_v_memview->view.strides; __pyx_v_strides = __pyx_t_1; /* "View.MemoryView":1069 * shape = memview.view.shape * strides = memview.view.strides * suboffsets = memview.view.suboffsets # <<<<<<<<<<<<<< * * dst.memview = <__pyx_memoryview *> memview */ __pyx_t_1 = __pyx_v_memview->view.suboffsets; __pyx_v_suboffsets = __pyx_t_1; /* "View.MemoryView":1071 * suboffsets = memview.view.suboffsets * * dst.memview = <__pyx_memoryview *> memview # <<<<<<<<<<<<<< * dst.data = memview.view.buf * */ __pyx_v_dst->memview = ((struct __pyx_memoryview_obj *)__pyx_v_memview); /* "View.MemoryView":1072 * * dst.memview = <__pyx_memoryview *> memview * dst.data = memview.view.buf # <<<<<<<<<<<<<< * * for dim in range(memview.view.ndim): */ __pyx_v_dst->data = ((char *)__pyx_v_memview->view.buf); /* "View.MemoryView":1074 * dst.data = memview.view.buf * * for dim in range(memview.view.ndim): # <<<<<<<<<<<<<< * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] */ __pyx_t_2 = __pyx_v_memview->view.ndim; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_dim = __pyx_t_4; /* "View.MemoryView":1075 * * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] # <<<<<<<<<<<<<< * dst.strides[dim] = strides[dim] * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 */ (__pyx_v_dst->shape[__pyx_v_dim]) = (__pyx_v_shape[__pyx_v_dim]); /* "View.MemoryView":1076 * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] # <<<<<<<<<<<<<< * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 * */ (__pyx_v_dst->strides[__pyx_v_dim]) = (__pyx_v_strides[__pyx_v_dim]); /* "View.MemoryView":1077 * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_object') */ if ((__pyx_v_suboffsets != 0)) { __pyx_t_5 = (__pyx_v_suboffsets[__pyx_v_dim]); } else { __pyx_t_5 = -1L; } (__pyx_v_dst->suboffsets[__pyx_v_dim]) = __pyx_t_5; } /* "View.MemoryView":1063 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< * cdef int dim * cdef (Py_ssize_t*) shape, strides, suboffsets */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":1080 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice */ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx_v_memview) { __Pyx_memviewslice __pyx_v_memviewslice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_copy", 0); /* "View.MemoryView":1083 * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) # <<<<<<<<<<<<<< * return memoryview_copy_from_slice(memview, &memviewslice) * */ __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_memviewslice)); /* "View.MemoryView":1084 * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) * return memoryview_copy_from_slice(memview, &memviewslice) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_object_from_slice') */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __pyx_memoryview_copy_object_from_slice(__pyx_v_memview, (&__pyx_v_memviewslice)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1084, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":1080 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview_copy", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1087 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< * """ * Create a new memoryview object from a given memoryview object and slice. */ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_memviewslice) { PyObject *(*__pyx_v_to_object_func)(char *); int (*__pyx_v_to_dtype_func)(char *, PyObject *); PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *(*__pyx_t_3)(char *); int (*__pyx_t_4)(char *, PyObject *); PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_copy_from_slice", 0); /* "View.MemoryView":1094 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":1095 * * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func # <<<<<<<<<<<<<< * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func * else: */ __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func; __pyx_v_to_object_func = __pyx_t_3; /* "View.MemoryView":1096 * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func # <<<<<<<<<<<<<< * else: * to_object_func = NULL */ __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func; __pyx_v_to_dtype_func = __pyx_t_4; /* "View.MemoryView":1094 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func */ goto __pyx_L3; } /* "View.MemoryView":1098 * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func * else: * to_object_func = NULL # <<<<<<<<<<<<<< * to_dtype_func = NULL * */ /*else*/ { __pyx_v_to_object_func = NULL; /* "View.MemoryView":1099 * else: * to_object_func = NULL * to_dtype_func = NULL # <<<<<<<<<<<<<< * * return memoryview_fromslice(memviewslice[0], memview.view.ndim, */ __pyx_v_to_dtype_func = NULL; } __pyx_L3:; /* "View.MemoryView":1101 * to_dtype_func = NULL * * return memoryview_fromslice(memviewslice[0], memview.view.ndim, # <<<<<<<<<<<<<< * to_object_func, to_dtype_func, * memview.dtype_is_object) */ __Pyx_XDECREF(__pyx_r); /* "View.MemoryView":1103 * return memoryview_fromslice(memviewslice[0], memview.view.ndim, * to_object_func, to_dtype_func, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 1101, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "View.MemoryView":1087 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< * """ * Create a new memoryview object from a given memoryview object and slice. */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1109 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< * if arg < 0: * return -arg */ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { Py_ssize_t __pyx_r; int __pyx_t_1; /* "View.MemoryView":1110 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< * return -arg * else: */ __pyx_t_1 = ((__pyx_v_arg < 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":1111 * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: * return -arg # <<<<<<<<<<<<<< * else: * return arg */ __pyx_r = (-__pyx_v_arg); goto __pyx_L0; /* "View.MemoryView":1110 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< * return -arg * else: */ } /* "View.MemoryView":1113 * return -arg * else: * return arg # <<<<<<<<<<<<<< * * @cname('__pyx_get_best_slice_order') */ /*else*/ { __pyx_r = __pyx_v_arg; goto __pyx_L0; } /* "View.MemoryView":1109 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< * if arg < 0: * return -arg */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1116 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< * """ * Figure out the best memory access order for a given slice. */ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) { int __pyx_v_i; Py_ssize_t __pyx_v_c_stride; Py_ssize_t __pyx_v_f_stride; char __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; /* "View.MemoryView":1121 * """ * cdef int i * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< * cdef Py_ssize_t f_stride = 0 * */ __pyx_v_c_stride = 0; /* "View.MemoryView":1122 * cdef int i * cdef Py_ssize_t c_stride = 0 * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< * * for i in range(ndim - 1, -1, -1): */ __pyx_v_f_stride = 0; /* "View.MemoryView":1124 * cdef Py_ssize_t f_stride = 0 * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] */ for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":1125 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * c_stride = mslice.strides[i] * break */ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1126 * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< * break * */ __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1127 * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< * * for i in range(ndim): */ goto __pyx_L4_break; /* "View.MemoryView":1125 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * c_stride = mslice.strides[i] * break */ } } __pyx_L4_break:; /* "View.MemoryView":1129 * break * * for i in range(ndim): # <<<<<<<<<<<<<< * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] */ __pyx_t_1 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_1; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1130 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * f_stride = mslice.strides[i] * break */ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1131 * for i in range(ndim): * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< * break * */ __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1132 * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): */ goto __pyx_L7_break; /* "View.MemoryView":1130 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * f_stride = mslice.strides[i] * break */ } } __pyx_L7_break:; /* "View.MemoryView":1134 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< * return 'C' * else: */ __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1135 * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): * return 'C' # <<<<<<<<<<<<<< * else: * return 'F' */ __pyx_r = 'C'; goto __pyx_L0; /* "View.MemoryView":1134 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< * return 'C' * else: */ } /* "View.MemoryView":1137 * return 'C' * else: * return 'F' # <<<<<<<<<<<<<< * * @cython.cdivision(True) */ /*else*/ { __pyx_r = 'F'; goto __pyx_L0; } /* "View.MemoryView":1116 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< * """ * Figure out the best memory access order for a given slice. */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1140 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< * char *dst_data, Py_ssize_t *dst_strides, * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, */ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; Py_ssize_t __pyx_v_dst_extent; Py_ssize_t __pyx_v_src_stride; Py_ssize_t __pyx_v_dst_stride; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; /* "View.MemoryView":1147 * * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] */ __pyx_v_src_extent = (__pyx_v_src_shape[0]); /* "View.MemoryView":1148 * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] */ __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); /* "View.MemoryView":1149 * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t dst_stride = dst_strides[0] * */ __pyx_v_src_stride = (__pyx_v_src_strides[0]); /* "View.MemoryView":1150 * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< * * if ndim == 1: */ __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); /* "View.MemoryView":1152 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): */ __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L5_bool_binop_done; } __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L5_bool_binop_done; } /* "View.MemoryView":1154 * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< * memcpy(dst_data, src_data, itemsize * dst_extent) * else: */ __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); if (__pyx_t_2) { __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); } __pyx_t_3 = (__pyx_t_2 != 0); __pyx_t_1 = __pyx_t_3; __pyx_L5_bool_binop_done:; /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ if (__pyx_t_1) { /* "View.MemoryView":1155 * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< * else: * for i in range(dst_extent): */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ goto __pyx_L4; } /* "View.MemoryView":1157 * memcpy(dst_data, src_data, itemsize * dst_extent) * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< * memcpy(dst_data, src_data, itemsize) * src_data += src_stride */ /*else*/ { __pyx_t_4 = __pyx_v_dst_extent; __pyx_t_5 = __pyx_t_4; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1158 * else: * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< * src_data += src_stride * dst_data += dst_stride */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); /* "View.MemoryView":1159 * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< * dst_data += dst_stride * else: */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); /* "View.MemoryView":1160 * memcpy(dst_data, src_data, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< * else: * for i in range(dst_extent): */ __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); } } __pyx_L4:; /* "View.MemoryView":1152 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): */ goto __pyx_L3; } /* "View.MemoryView":1162 * dst_data += dst_stride * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< * _copy_strided_to_strided(src_data, src_strides + 1, * dst_data, dst_strides + 1, */ /*else*/ { __pyx_t_4 = __pyx_v_dst_extent; __pyx_t_5 = __pyx_t_4; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1163 * else: * for i in range(dst_extent): * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< * dst_data, dst_strides + 1, * src_shape + 1, dst_shape + 1, */ _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); /* "View.MemoryView":1167 * src_shape + 1, dst_shape + 1, * ndim - 1, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< * dst_data += dst_stride * */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); /* "View.MemoryView":1168 * ndim - 1, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, */ __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); } } __pyx_L3:; /* "View.MemoryView":1140 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< * char *dst_data, Py_ssize_t *dst_strides, * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, */ /* function exit code */ } /* "View.MemoryView":1170 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: */ static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { /* "View.MemoryView":1173 * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< * src.shape, dst.shape, ndim, itemsize) * */ _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); /* "View.MemoryView":1170 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: */ /* function exit code */ } /* "View.MemoryView":1177 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize */ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { Py_ssize_t __pyx_v_shape; Py_ssize_t __pyx_v_size; Py_ssize_t __pyx_r; Py_ssize_t __pyx_t_1; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; /* "View.MemoryView":1179 * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< * * for shape in src.shape[:ndim]: */ __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_size = __pyx_t_1; /* "View.MemoryView":1181 * cdef Py_ssize_t shape, size = src.memview.view.itemsize * * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< * size *= shape * */ __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim); for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { __pyx_t_2 = __pyx_t_4; __pyx_v_shape = (__pyx_t_2[0]); /* "View.MemoryView":1182 * * for shape in src.shape[:ndim]: * size *= shape # <<<<<<<<<<<<<< * * return size */ __pyx_v_size = (__pyx_v_size * __pyx_v_shape); } /* "View.MemoryView":1184 * size *= shape * * return size # <<<<<<<<<<<<<< * * @cname('__pyx_fill_contig_strides_array') */ __pyx_r = __pyx_v_size; goto __pyx_L0; /* "View.MemoryView":1177 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1187 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, * int ndim, char order) nogil: */ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { int __pyx_v_idx; Py_ssize_t __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; /* "View.MemoryView":1196 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< * for idx in range(ndim): * strides[idx] = stride */ __pyx_t_1 = ((__pyx_v_order == 'F') != 0); if (__pyx_t_1) { /* "View.MemoryView":1197 * * if order == 'F': * for idx in range(ndim): # <<<<<<<<<<<<<< * strides[idx] = stride * stride *= shape[idx] */ __pyx_t_2 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_idx = __pyx_t_4; /* "View.MemoryView":1198 * if order == 'F': * for idx in range(ndim): * strides[idx] = stride # <<<<<<<<<<<<<< * stride *= shape[idx] * else: */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; /* "View.MemoryView":1199 * for idx in range(ndim): * strides[idx] = stride * stride *= shape[idx] # <<<<<<<<<<<<<< * else: * for idx in range(ndim - 1, -1, -1): */ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } /* "View.MemoryView":1196 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< * for idx in range(ndim): * strides[idx] = stride */ goto __pyx_L3; } /* "View.MemoryView":1201 * stride *= shape[idx] * else: * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * strides[idx] = stride * stride *= shape[idx] */ /*else*/ { for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { __pyx_v_idx = __pyx_t_2; /* "View.MemoryView":1202 * else: * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride # <<<<<<<<<<<<<< * stride *= shape[idx] * */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; /* "View.MemoryView":1203 * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride * stride *= shape[idx] # <<<<<<<<<<<<<< * * return stride */ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } } __pyx_L3:; /* "View.MemoryView":1205 * stride *= shape[idx] * * return stride # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_data_to_temp') */ __pyx_r = __pyx_v_stride; goto __pyx_L0; /* "View.MemoryView":1187 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, * int ndim, char order) nogil: */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1208 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *tmpslice, * char order, */ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) { int __pyx_v_i; void *__pyx_v_result; size_t __pyx_v_itemsize; size_t __pyx_v_size; void *__pyx_r; Py_ssize_t __pyx_t_1; int __pyx_t_2; int __pyx_t_3; struct __pyx_memoryview_obj *__pyx_t_4; int __pyx_t_5; int __pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":1219 * cdef void *result * * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< * cdef size_t size = slice_get_size(src, ndim) * */ __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":1220 * * cdef size_t itemsize = src.memview.view.itemsize * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< * * result = malloc(size) */ __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); /* "View.MemoryView":1222 * cdef size_t size = slice_get_size(src, ndim) * * result = malloc(size) # <<<<<<<<<<<<<< * if not result: * _err(MemoryError, NULL) */ __pyx_v_result = malloc(__pyx_v_size); /* "View.MemoryView":1223 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< * _err(MemoryError, NULL) * */ __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1224 * result = malloc(size) * if not result: * _err(MemoryError, NULL) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 1224, __pyx_L1_error) /* "View.MemoryView":1223 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< * _err(MemoryError, NULL) * */ } /* "View.MemoryView":1227 * * * tmpslice.data = result # <<<<<<<<<<<<<< * tmpslice.memview = src.memview * for i in range(ndim): */ __pyx_v_tmpslice->data = ((char *)__pyx_v_result); /* "View.MemoryView":1228 * * tmpslice.data = result * tmpslice.memview = src.memview # <<<<<<<<<<<<<< * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] */ __pyx_t_4 = __pyx_v_src->memview; __pyx_v_tmpslice->memview = __pyx_t_4; /* "View.MemoryView":1229 * tmpslice.data = result * tmpslice.memview = src.memview * for i in range(ndim): # <<<<<<<<<<<<<< * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 */ __pyx_t_3 = __pyx_v_ndim; __pyx_t_5 = __pyx_t_3; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1230 * tmpslice.memview = src.memview * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] # <<<<<<<<<<<<<< * tmpslice.suboffsets[i] = -1 * */ (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]); /* "View.MemoryView":1231 * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, */ (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L; } /* "View.MemoryView":1233 * tmpslice.suboffsets[i] = -1 * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, # <<<<<<<<<<<<<< * ndim, order) * */ (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order)); /* "View.MemoryView":1237 * * * for i in range(ndim): # <<<<<<<<<<<<<< * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 */ __pyx_t_3 = __pyx_v_ndim; __pyx_t_5 = __pyx_t_3; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1238 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< * tmpslice.strides[i] = 0 * */ __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1239 * for i in range(ndim): * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 # <<<<<<<<<<<<<< * * if slice_is_contig(src[0], order, ndim): */ (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0; /* "View.MemoryView":1238 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< * tmpslice.strides[i] = 0 * */ } } /* "View.MemoryView":1241 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< * memcpy(result, src.data, size) * else: */ __pyx_t_2 = (__pyx_memviewslice_is_contig((__pyx_v_src[0]), __pyx_v_order, __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1242 * * if slice_is_contig(src[0], order, ndim): * memcpy(result, src.data, size) # <<<<<<<<<<<<<< * else: * copy_strided_to_strided(src, tmpslice, ndim, itemsize) */ (void)(memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size)); /* "View.MemoryView":1241 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< * memcpy(result, src.data, size) * else: */ goto __pyx_L9; } /* "View.MemoryView":1244 * memcpy(result, src.data, size) * else: * copy_strided_to_strided(src, tmpslice, ndim, itemsize) # <<<<<<<<<<<<<< * * return result */ /*else*/ { copy_strided_to_strided(__pyx_v_src, __pyx_v_tmpslice, __pyx_v_ndim, __pyx_v_itemsize); } __pyx_L9:; /* "View.MemoryView":1246 * copy_strided_to_strided(src, tmpslice, ndim, itemsize) * * return result # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_result; goto __pyx_L0; /* "View.MemoryView":1208 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *tmpslice, * char order, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.copy_data_to_temp", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = NULL; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1251 * * @cname('__pyx_memoryview_err_extents') * cdef int _err_extents(int i, Py_ssize_t extent1, # <<<<<<<<<<<<<< * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % */ static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent1, Py_ssize_t __pyx_v_extent2) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err_extents", 0); /* "View.MemoryView":1254 * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % * (i, extent1, extent2)) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_err_dim') */ __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_i); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_extent1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_extent2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_4, 2, __pyx_t_3); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_3 = 0; /* "View.MemoryView":1253 * cdef int _err_extents(int i, Py_ssize_t extent1, * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % # <<<<<<<<<<<<<< * (i, extent1, extent2)) * */ __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1253, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1253, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_4, 0, 0, 0); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __PYX_ERR(2, 1253, __pyx_L1_error) /* "View.MemoryView":1251 * * @cname('__pyx_memoryview_err_extents') * cdef int _err_extents(int i, Py_ssize_t extent1, # <<<<<<<<<<<<<< * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView._err_extents", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1257 * * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii') % dim) * */ static int __pyx_memoryview_err_dim(PyObject *__pyx_v_error, char *__pyx_v_msg, int __pyx_v_dim) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err_dim", 0); __Pyx_INCREF(__pyx_v_error); /* "View.MemoryView":1258 * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: * raise error(msg.decode('ascii') % dim) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_err') */ __pyx_t_2 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyInt_From_int(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyUnicode_Format(__pyx_t_2, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_INCREF(__pyx_v_error); __pyx_t_3 = __pyx_v_error; __pyx_t_2 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_3))) { __pyx_t_2 = PyMethod_GET_SELF(__pyx_t_3); if (likely(__pyx_t_2)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_3); __Pyx_INCREF(__pyx_t_2); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_3, function); } } __pyx_t_1 = (__pyx_t_2) ? __Pyx_PyObject_Call2Args(__pyx_t_3, __pyx_t_2, __pyx_t_4) : __Pyx_PyObject_CallOneArg(__pyx_t_3, __pyx_t_4); __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(2, 1258, __pyx_L1_error) /* "View.MemoryView":1257 * * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii') % dim) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView._err_dim", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_XDECREF(__pyx_v_error); __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1261 * * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: # <<<<<<<<<<<<<< * if msg != NULL: * raise error(msg.decode('ascii')) */ static int __pyx_memoryview_err(PyObject *__pyx_v_error, char *__pyx_v_msg) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err", 0); __Pyx_INCREF(__pyx_v_error); /* "View.MemoryView":1262 * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii')) * else: */ __pyx_t_1 = ((__pyx_v_msg != NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":1263 * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: * raise error(msg.decode('ascii')) # <<<<<<<<<<<<<< * else: * raise error */ __pyx_t_3 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1263, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_error); __pyx_t_4 = __pyx_v_error; __pyx_t_5 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_4))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_4); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_4); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_4, function); } } __pyx_t_2 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_4, __pyx_t_5, __pyx_t_3) : __Pyx_PyObject_CallOneArg(__pyx_t_4, __pyx_t_3); __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1263, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(2, 1263, __pyx_L1_error) /* "View.MemoryView":1262 * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii')) * else: */ } /* "View.MemoryView":1265 * raise error(msg.decode('ascii')) * else: * raise error # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_contents') */ /*else*/ { __Pyx_Raise(__pyx_v_error, 0, 0, 0); __PYX_ERR(2, 1265, __pyx_L1_error) } /* "View.MemoryView":1261 * * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: # <<<<<<<<<<<<<< * if msg != NULL: * raise error(msg.decode('ascii')) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView._err", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_XDECREF(__pyx_v_error); __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1268 * * @cname('__pyx_memoryview_copy_contents') * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< * __Pyx_memviewslice dst, * int src_ndim, int dst_ndim, */ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_memviewslice __pyx_v_dst, int __pyx_v_src_ndim, int __pyx_v_dst_ndim, int __pyx_v_dtype_is_object) { void *__pyx_v_tmpdata; size_t __pyx_v_itemsize; int __pyx_v_i; char __pyx_v_order; int __pyx_v_broadcasting; int __pyx_v_direct_copy; __Pyx_memviewslice __pyx_v_tmp; int __pyx_v_ndim; int __pyx_r; Py_ssize_t __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_t_5; int __pyx_t_6; void *__pyx_t_7; int __pyx_t_8; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; /* "View.MemoryView":1276 * Check for overlapping memory and verify the shapes. * """ * cdef void *tmpdata = NULL # <<<<<<<<<<<<<< * cdef size_t itemsize = src.memview.view.itemsize * cdef int i */ __pyx_v_tmpdata = NULL; /* "View.MemoryView":1277 * """ * cdef void *tmpdata = NULL * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< * cdef int i * cdef char order = get_best_order(&src, src_ndim) */ __pyx_t_1 = __pyx_v_src.memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":1279 * cdef size_t itemsize = src.memview.view.itemsize * cdef int i * cdef char order = get_best_order(&src, src_ndim) # <<<<<<<<<<<<<< * cdef bint broadcasting = False * cdef bint direct_copy = False */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_src), __pyx_v_src_ndim); /* "View.MemoryView":1280 * cdef int i * cdef char order = get_best_order(&src, src_ndim) * cdef bint broadcasting = False # <<<<<<<<<<<<<< * cdef bint direct_copy = False * cdef __Pyx_memviewslice tmp */ __pyx_v_broadcasting = 0; /* "View.MemoryView":1281 * cdef char order = get_best_order(&src, src_ndim) * cdef bint broadcasting = False * cdef bint direct_copy = False # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice tmp * */ __pyx_v_direct_copy = 0; /* "View.MemoryView":1284 * cdef __Pyx_memviewslice tmp * * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: */ __pyx_t_2 = ((__pyx_v_src_ndim < __pyx_v_dst_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1285 * * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) # <<<<<<<<<<<<<< * elif dst_ndim < src_ndim: * broadcast_leading(&dst, dst_ndim, src_ndim) */ __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim); /* "View.MemoryView":1284 * cdef __Pyx_memviewslice tmp * * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: */ goto __pyx_L3; } /* "View.MemoryView":1286 * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&dst, dst_ndim, src_ndim) * */ __pyx_t_2 = ((__pyx_v_dst_ndim < __pyx_v_src_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1287 * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: * broadcast_leading(&dst, dst_ndim, src_ndim) # <<<<<<<<<<<<<< * * cdef int ndim = max(src_ndim, dst_ndim) */ __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim); /* "View.MemoryView":1286 * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&dst, dst_ndim, src_ndim) * */ } __pyx_L3:; /* "View.MemoryView":1289 * broadcast_leading(&dst, dst_ndim, src_ndim) * * cdef int ndim = max(src_ndim, dst_ndim) # <<<<<<<<<<<<<< * * for i in range(ndim): */ __pyx_t_3 = __pyx_v_dst_ndim; __pyx_t_4 = __pyx_v_src_ndim; if (((__pyx_t_3 > __pyx_t_4) != 0)) { __pyx_t_5 = __pyx_t_3; } else { __pyx_t_5 = __pyx_t_4; } __pyx_v_ndim = __pyx_t_5; /* "View.MemoryView":1291 * cdef int ndim = max(src_ndim, dst_ndim) * * for i in range(ndim): # <<<<<<<<<<<<<< * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: */ __pyx_t_5 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_5; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1292 * * for i in range(ndim): * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< * if src.shape[i] == 1: * broadcasting = True */ __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0); if (__pyx_t_2) { /* "View.MemoryView":1293 * for i in range(ndim): * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: # <<<<<<<<<<<<<< * broadcasting = True * src.strides[i] = 0 */ __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1294 * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: * broadcasting = True # <<<<<<<<<<<<<< * src.strides[i] = 0 * else: */ __pyx_v_broadcasting = 1; /* "View.MemoryView":1295 * if src.shape[i] == 1: * broadcasting = True * src.strides[i] = 0 # <<<<<<<<<<<<<< * else: * _err_extents(i, dst.shape[i], src.shape[i]) */ (__pyx_v_src.strides[__pyx_v_i]) = 0; /* "View.MemoryView":1293 * for i in range(ndim): * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: # <<<<<<<<<<<<<< * broadcasting = True * src.strides[i] = 0 */ goto __pyx_L7; } /* "View.MemoryView":1297 * src.strides[i] = 0 * else: * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<< * * if src.suboffsets[i] >= 0: */ /*else*/ { __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1297, __pyx_L1_error) } __pyx_L7:; /* "View.MemoryView":1292 * * for i in range(ndim): * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< * if src.shape[i] == 1: * broadcasting = True */ } /* "View.MemoryView":1299 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Dimension %d is not direct", i) * */ __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":1300 * * if src.suboffsets[i] >= 0: * _err_dim(ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< * * if slices_overlap(&src, &dst, ndim, itemsize): */ __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1300, __pyx_L1_error) /* "View.MemoryView":1299 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Dimension %d is not direct", i) * */ } } /* "View.MemoryView":1302 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< * * if not slice_is_contig(src, order, ndim): */ __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0); if (__pyx_t_2) { /* "View.MemoryView":1304 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< * order = get_best_order(&dst, ndim) * */ __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1305 * * if not slice_is_contig(src, order, ndim): * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); /* "View.MemoryView":1304 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< * order = get_best_order(&dst, ndim) * */ } /* "View.MemoryView":1307 * order = get_best_order(&dst, ndim) * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< * src = tmp * */ __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(2, 1307, __pyx_L1_error) __pyx_v_tmpdata = __pyx_t_7; /* "View.MemoryView":1308 * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) * src = tmp # <<<<<<<<<<<<<< * * if not broadcasting: */ __pyx_v_src = __pyx_v_tmp; /* "View.MemoryView":1302 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< * * if not slice_is_contig(src, order, ndim): */ } /* "View.MemoryView":1310 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< * * */ __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1313 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): */ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1314 * * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< * elif slice_is_contig(src, 'F', ndim): * direct_copy = slice_is_contig(dst, 'F', ndim) */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); /* "View.MemoryView":1313 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): */ goto __pyx_L12; } /* "View.MemoryView":1315 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'F', ndim) * */ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1316 * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< * * if direct_copy: */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); /* "View.MemoryView":1315 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'F', ndim) * */ } __pyx_L12:; /* "View.MemoryView":1318 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ __pyx_t_2 = (__pyx_v_direct_copy != 0); if (__pyx_t_2) { /* "View.MemoryView":1320 * if direct_copy: * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1321 * * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) */ (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); /* "View.MemoryView":1322 * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * free(tmpdata) * return 0 */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1323 * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) # <<<<<<<<<<<<<< * return 0 * */ free(__pyx_v_tmpdata); /* "View.MemoryView":1324 * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) * return 0 # <<<<<<<<<<<<<< * * if order == 'F' == get_best_order(&dst, ndim): */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":1318 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ } /* "View.MemoryView":1310 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":1326 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< * * */ __pyx_t_2 = (__pyx_v_order == 'F'); if (__pyx_t_2) { __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); } __pyx_t_8 = (__pyx_t_2 != 0); if (__pyx_t_8) { /* "View.MemoryView":1329 * * * transpose_memslice(&src) # <<<<<<<<<<<<<< * transpose_memslice(&dst) * */ __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1329, __pyx_L1_error) /* "View.MemoryView":1330 * * transpose_memslice(&src) * transpose_memslice(&dst) # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1330, __pyx_L1_error) /* "View.MemoryView":1326 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":1332 * transpose_memslice(&dst) * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1333 * * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< * refcount_copying(&dst, dtype_is_object, ndim, True) * */ copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); /* "View.MemoryView":1334 * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * * free(tmpdata) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1336 * refcount_copying(&dst, dtype_is_object, ndim, True) * * free(tmpdata) # <<<<<<<<<<<<<< * return 0 * */ free(__pyx_v_tmpdata); /* "View.MemoryView":1337 * * free(tmpdata) * return 0 # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_broadcast_leading') */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":1268 * * @cname('__pyx_memoryview_copy_contents') * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< * __Pyx_memviewslice dst, * int src_ndim, int dst_ndim, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = -1; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1340 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< * int ndim, * int ndim_other) nogil: */ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) { int __pyx_v_i; int __pyx_v_offset; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; /* "View.MemoryView":1344 * int ndim_other) nogil: * cdef int i * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< * * for i in range(ndim - 1, -1, -1): */ __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); /* "View.MemoryView":1346 * cdef int offset = ndim_other - ndim * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] */ for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":1347 * * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] */ (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); /* "View.MemoryView":1348 * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * */ (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1349 * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< * * for i in range(offset): */ (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]); } /* "View.MemoryView":1351 * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * * for i in range(offset): # <<<<<<<<<<<<<< * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] */ __pyx_t_1 = __pyx_v_offset; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "View.MemoryView":1352 * * for i in range(offset): * mslice.shape[i] = 1 # <<<<<<<<<<<<<< * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 */ (__pyx_v_mslice->shape[__pyx_v_i]) = 1; /* "View.MemoryView":1353 * for i in range(offset): * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] # <<<<<<<<<<<<<< * mslice.suboffsets[i] = -1 * */ (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]); /* "View.MemoryView":1354 * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< * * */ (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L; } /* "View.MemoryView":1340 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< * int ndim, * int ndim_other) nogil: */ /* function exit code */ } /* "View.MemoryView":1362 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< * int ndim, bint inc) nogil: * */ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) { int __pyx_t_1; /* "View.MemoryView":1366 * * * if dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice_with_gil(dst.data, dst.shape, * dst.strides, ndim, inc) */ __pyx_t_1 = (__pyx_v_dtype_is_object != 0); if (__pyx_t_1) { /* "View.MemoryView":1367 * * if dtype_is_object: * refcount_objects_in_slice_with_gil(dst.data, dst.shape, # <<<<<<<<<<<<<< * dst.strides, ndim, inc) * */ __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc); /* "View.MemoryView":1366 * * * if dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice_with_gil(dst.data, dst.shape, * dst.strides, ndim, inc) */ } /* "View.MemoryView":1362 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< * int ndim, bint inc) nogil: * */ /* function exit code */ } /* "View.MemoryView":1371 * * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * bint inc) with gil: */ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { __Pyx_RefNannyDeclarations #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("refcount_objects_in_slice_with_gil", 0); /* "View.MemoryView":1374 * Py_ssize_t *strides, int ndim, * bint inc) with gil: * refcount_objects_in_slice(data, shape, strides, ndim, inc) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_refcount_objects_in_slice') */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc); /* "View.MemoryView":1371 * * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * bint inc) with gil: */ /* function exit code */ __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } /* "View.MemoryView":1377 * * @cname('__pyx_memoryview_refcount_objects_in_slice') * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, bint inc): * cdef Py_ssize_t i */ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; int __pyx_t_4; __Pyx_RefNannySetupContext("refcount_objects_in_slice", 0); /* "View.MemoryView":1381 * cdef Py_ssize_t i * * for i in range(shape[0]): # <<<<<<<<<<<<<< * if ndim == 1: * if inc: */ __pyx_t_1 = (__pyx_v_shape[0]); __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "View.MemoryView":1382 * * for i in range(shape[0]): * if ndim == 1: # <<<<<<<<<<<<<< * if inc: * Py_INCREF(( data)[0]) */ __pyx_t_4 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_4) { /* "View.MemoryView":1383 * for i in range(shape[0]): * if ndim == 1: * if inc: # <<<<<<<<<<<<<< * Py_INCREF(( data)[0]) * else: */ __pyx_t_4 = (__pyx_v_inc != 0); if (__pyx_t_4) { /* "View.MemoryView":1384 * if ndim == 1: * if inc: * Py_INCREF(( data)[0]) # <<<<<<<<<<<<<< * else: * Py_DECREF(( data)[0]) */ Py_INCREF((((PyObject **)__pyx_v_data)[0])); /* "View.MemoryView":1383 * for i in range(shape[0]): * if ndim == 1: * if inc: # <<<<<<<<<<<<<< * Py_INCREF(( data)[0]) * else: */ goto __pyx_L6; } /* "View.MemoryView":1386 * Py_INCREF(( data)[0]) * else: * Py_DECREF(( data)[0]) # <<<<<<<<<<<<<< * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, */ /*else*/ { Py_DECREF((((PyObject **)__pyx_v_data)[0])); } __pyx_L6:; /* "View.MemoryView":1382 * * for i in range(shape[0]): * if ndim == 1: # <<<<<<<<<<<<<< * if inc: * Py_INCREF(( data)[0]) */ goto __pyx_L5; } /* "View.MemoryView":1388 * Py_DECREF(( data)[0]) * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, # <<<<<<<<<<<<<< * ndim - 1, inc) * */ /*else*/ { /* "View.MemoryView":1389 * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, * ndim - 1, inc) # <<<<<<<<<<<<<< * * data += strides[0] */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_inc); } __pyx_L5:; /* "View.MemoryView":1391 * ndim - 1, inc) * * data += strides[0] # <<<<<<<<<<<<<< * * */ __pyx_v_data = (__pyx_v_data + (__pyx_v_strides[0])); } /* "View.MemoryView":1377 * * @cname('__pyx_memoryview_refcount_objects_in_slice') * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, bint inc): * cdef Py_ssize_t i */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":1397 * * @cname('__pyx_memoryview_slice_assign_scalar') * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, # <<<<<<<<<<<<<< * size_t itemsize, void *item, * bint dtype_is_object) nogil: */ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item, int __pyx_v_dtype_is_object) { /* "View.MemoryView":1400 * size_t itemsize, void *item, * bint dtype_is_object) nogil: * refcount_copying(dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, * itemsize, item) */ __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1401 * bint dtype_is_object) nogil: * refcount_copying(dst, dtype_is_object, ndim, False) * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, # <<<<<<<<<<<<<< * itemsize, item) * refcount_copying(dst, dtype_is_object, ndim, True) */ __pyx_memoryview__slice_assign_scalar(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_itemsize, __pyx_v_item); /* "View.MemoryView":1403 * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, * itemsize, item) * refcount_copying(dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * * */ __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1397 * * @cname('__pyx_memoryview_slice_assign_scalar') * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, # <<<<<<<<<<<<<< * size_t itemsize, void *item, * bint dtype_is_object) nogil: */ /* function exit code */ } /* "View.MemoryView":1407 * * @cname('__pyx_memoryview__slice_assign_scalar') * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * size_t itemsize, void *item) nogil: */ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_stride; Py_ssize_t __pyx_v_extent; int __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; /* "View.MemoryView":1411 * size_t itemsize, void *item) nogil: * cdef Py_ssize_t i * cdef Py_ssize_t stride = strides[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t extent = shape[0] * */ __pyx_v_stride = (__pyx_v_strides[0]); /* "View.MemoryView":1412 * cdef Py_ssize_t i * cdef Py_ssize_t stride = strides[0] * cdef Py_ssize_t extent = shape[0] # <<<<<<<<<<<<<< * * if ndim == 1: */ __pyx_v_extent = (__pyx_v_shape[0]); /* "View.MemoryView":1414 * cdef Py_ssize_t extent = shape[0] * * if ndim == 1: # <<<<<<<<<<<<<< * for i in range(extent): * memcpy(data, item, itemsize) */ __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":1415 * * if ndim == 1: * for i in range(extent): # <<<<<<<<<<<<<< * memcpy(data, item, itemsize) * data += stride */ __pyx_t_2 = __pyx_v_extent; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1416 * if ndim == 1: * for i in range(extent): * memcpy(data, item, itemsize) # <<<<<<<<<<<<<< * data += stride * else: */ (void)(memcpy(__pyx_v_data, __pyx_v_item, __pyx_v_itemsize)); /* "View.MemoryView":1417 * for i in range(extent): * memcpy(data, item, itemsize) * data += stride # <<<<<<<<<<<<<< * else: * for i in range(extent): */ __pyx_v_data = (__pyx_v_data + __pyx_v_stride); } /* "View.MemoryView":1414 * cdef Py_ssize_t extent = shape[0] * * if ndim == 1: # <<<<<<<<<<<<<< * for i in range(extent): * memcpy(data, item, itemsize) */ goto __pyx_L3; } /* "View.MemoryView":1419 * data += stride * else: * for i in range(extent): # <<<<<<<<<<<<<< * _slice_assign_scalar(data, shape + 1, strides + 1, * ndim - 1, itemsize, item) */ /*else*/ { __pyx_t_2 = __pyx_v_extent; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1420 * else: * for i in range(extent): * _slice_assign_scalar(data, shape + 1, strides + 1, # <<<<<<<<<<<<<< * ndim - 1, itemsize, item) * data += stride */ __pyx_memoryview__slice_assign_scalar(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize, __pyx_v_item); /* "View.MemoryView":1422 * _slice_assign_scalar(data, shape + 1, strides + 1, * ndim - 1, itemsize, item) * data += stride # <<<<<<<<<<<<<< * * */ __pyx_v_data = (__pyx_v_data + __pyx_v_stride); } } __pyx_L3:; /* "View.MemoryView":1407 * * @cname('__pyx_memoryview__slice_assign_scalar') * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * size_t itemsize, void *item) nogil: */ /* function exit code */ } /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyMethodDef __pyx_mdef_15View_dot_MemoryView_1__pyx_unpickle_Enum = {"__pyx_unpickle_Enum", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum, METH_VARARGS|METH_KEYWORDS, 0}; static PyObject *__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v___pyx_type = 0; long __pyx_v___pyx_checksum; PyObject *__pyx_v___pyx_state = 0; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__pyx_unpickle_Enum (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_pyx_type,&__pyx_n_s_pyx_checksum,&__pyx_n_s_pyx_state,0}; PyObject* values[3] = {0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_type)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_checksum)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, 1); __PYX_ERR(2, 1, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_state)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, 2); __PYX_ERR(2, 1, __pyx_L3_error) } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__pyx_unpickle_Enum") < 0)) __PYX_ERR(2, 1, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 3) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[2] = PyTuple_GET_ITEM(__pyx_args, 2); } __pyx_v___pyx_type = values[0]; __pyx_v___pyx_checksum = __Pyx_PyInt_As_long(values[1]); if (unlikely((__pyx_v___pyx_checksum == (long)-1) && PyErr_Occurred())) __PYX_ERR(2, 1, __pyx_L3_error) __pyx_v___pyx_state = values[2]; } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 1, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(__pyx_self, __pyx_v___pyx_type, __pyx_v___pyx_checksum, __pyx_v___pyx_state); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_v___pyx_PickleError = 0; PyObject *__pyx_v___pyx_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_t_6; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__pyx_unpickle_Enum", 0); /* "(tree fragment)":4 * cdef object __pyx_PickleError * cdef object __pyx_result * if __pyx_checksum != 0xb068931: # <<<<<<<<<<<<<< * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) */ __pyx_t_1 = ((__pyx_v___pyx_checksum != 0xb068931) != 0); if (__pyx_t_1) { /* "(tree fragment)":5 * cdef object __pyx_result * if __pyx_checksum != 0xb068931: * from pickle import PickleError as __pyx_PickleError # <<<<<<<<<<<<<< * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) */ __pyx_t_2 = PyList_New(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_n_s_PickleError); __Pyx_GIVEREF(__pyx_n_s_PickleError); PyList_SET_ITEM(__pyx_t_2, 0, __pyx_n_s_PickleError); __pyx_t_3 = __Pyx_Import(__pyx_n_s_pickle, __pyx_t_2, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_ImportFrom(__pyx_t_3, __pyx_n_s_PickleError); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_t_2); __pyx_v___pyx_PickleError = __pyx_t_2; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "(tree fragment)":6 * if __pyx_checksum != 0xb068931: * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) # <<<<<<<<<<<<<< * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: */ __pyx_t_2 = __Pyx_PyInt_From_long(__pyx_v___pyx_checksum); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_t_2); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_INCREF(__pyx_v___pyx_PickleError); __pyx_t_2 = __pyx_v___pyx_PickleError; __pyx_t_5 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_2))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_3 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_5, __pyx_t_4) : __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(2, 6, __pyx_L1_error) /* "(tree fragment)":4 * cdef object __pyx_PickleError * cdef object __pyx_result * if __pyx_checksum != 0xb068931: # <<<<<<<<<<<<<< * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) */ } /* "(tree fragment)":7 * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) # <<<<<<<<<<<<<< * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) */ __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_MemviewEnum_type), __pyx_n_s_new); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 7, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_2))) { __pyx_t_4 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_4)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_4); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_3 = (__pyx_t_4) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_4, __pyx_v___pyx_type) : __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_v___pyx_type); __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 7, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v___pyx_result = __pyx_t_3; __pyx_t_3 = 0; /* "(tree fragment)":8 * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result */ __pyx_t_1 = (__pyx_v___pyx_state != Py_None); __pyx_t_6 = (__pyx_t_1 != 0); if (__pyx_t_6) { /* "(tree fragment)":9 * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) # <<<<<<<<<<<<<< * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): */ if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(2, 9, __pyx_L1_error) __pyx_t_3 = __pyx_unpickle_Enum__set_state(((struct __pyx_MemviewEnum_obj *)__pyx_v___pyx_result), ((PyObject*)__pyx_v___pyx_state)); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 9, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "(tree fragment)":8 * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result */ } /* "(tree fragment)":10 * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result # <<<<<<<<<<<<<< * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v___pyx_result); __pyx_r = __pyx_v___pyx_result; goto __pyx_L0; /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v___pyx_PickleError); __Pyx_XDECREF(__pyx_v___pyx_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *__pyx_v___pyx_result, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; Py_ssize_t __pyx_t_3; int __pyx_t_4; int __pyx_t_5; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__pyx_unpickle_Enum__set_state", 0); /* "(tree fragment)":12 * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] # <<<<<<<<<<<<<< * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): * __pyx_result.__dict__.update(__pyx_state[1]) */ if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(2, 12, __pyx_L1_error) } __pyx_t_1 = __Pyx_GetItemInt_Tuple(__pyx_v___pyx_state, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 12, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __Pyx_GOTREF(__pyx_v___pyx_result->name); __Pyx_DECREF(__pyx_v___pyx_result->name); __pyx_v___pyx_result->name = __pyx_t_1; __pyx_t_1 = 0; /* "(tree fragment)":13 * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): # <<<<<<<<<<<<<< * __pyx_result.__dict__.update(__pyx_state[1]) */ if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); __PYX_ERR(2, 13, __pyx_L1_error) } __pyx_t_3 = PyTuple_GET_SIZE(__pyx_v___pyx_state); if (unlikely(__pyx_t_3 == ((Py_ssize_t)-1))) __PYX_ERR(2, 13, __pyx_L1_error) __pyx_t_4 = ((__pyx_t_3 > 1) != 0); if (__pyx_t_4) { } else { __pyx_t_2 = __pyx_t_4; goto __pyx_L4_bool_binop_done; } __pyx_t_4 = __Pyx_HasAttr(((PyObject *)__pyx_v___pyx_result), __pyx_n_s_dict); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 13, __pyx_L1_error) __pyx_t_5 = (__pyx_t_4 != 0); __pyx_t_2 = __pyx_t_5; __pyx_L4_bool_binop_done:; if (__pyx_t_2) { /* "(tree fragment)":14 * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): * __pyx_result.__dict__.update(__pyx_state[1]) # <<<<<<<<<<<<<< */ __pyx_t_6 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v___pyx_result), __pyx_n_s_dict); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_6, __pyx_n_s_update); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(2, 14, __pyx_L1_error) } __pyx_t_6 = __Pyx_GetItemInt_Tuple(__pyx_v___pyx_state, 1, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_6)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_8 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_7))) { __pyx_t_8 = PyMethod_GET_SELF(__pyx_t_7); if (likely(__pyx_t_8)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_7); __Pyx_INCREF(__pyx_t_8); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_7, function); } } __pyx_t_1 = (__pyx_t_8) ? __Pyx_PyObject_Call2Args(__pyx_t_7, __pyx_t_8, __pyx_t_6) : __Pyx_PyObject_CallOneArg(__pyx_t_7, __pyx_t_6); __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":13 * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): # <<<<<<<<<<<<<< * __pyx_result.__dict__.update(__pyx_state[1]) */ } /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum__set_state", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } static struct __pyx_vtabstruct_array __pyx_vtable_array; static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_array_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_array_obj *)o); p->__pyx_vtab = __pyx_vtabptr_array; p->mode = ((PyObject*)Py_None); Py_INCREF(Py_None); p->_format = ((PyObject*)Py_None); Py_INCREF(Py_None); if (unlikely(__pyx_array___cinit__(o, a, k) < 0)) goto bad; return o; bad: Py_DECREF(o); o = 0; return NULL; } static void __pyx_tp_dealloc_array(PyObject *o) { struct __pyx_array_obj *p = (struct __pyx_array_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); __pyx_array___dealloc__(o); __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->mode); Py_CLEAR(p->_format); (*Py_TYPE(o)->tp_free)(o); } static PyObject *__pyx_sq_item_array(PyObject *o, Py_ssize_t i) { PyObject *r; PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); Py_DECREF(x); return r; } static int __pyx_mp_ass_subscript_array(PyObject *o, PyObject *i, PyObject *v) { if (v) { return __pyx_array___setitem__(o, i, v); } else { PyErr_Format(PyExc_NotImplementedError, "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); return -1; } } static PyObject *__pyx_tp_getattro_array(PyObject *o, PyObject *n) { PyObject *v = __Pyx_PyObject_GenericGetAttr(o, n); if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Clear(); v = __pyx_array___getattr__(o, n); } return v; } static PyObject *__pyx_getprop___pyx_array_memview(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(o); } static PyMethodDef __pyx_methods_array[] = { {"__getattr__", (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, 0}, {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_array_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_array_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_array[] = { {(char *)"memview", __pyx_getprop___pyx_array_memview, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PySequenceMethods __pyx_tp_as_sequence_array = { __pyx_array___len__, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ __pyx_sq_item_array, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_array = { __pyx_array___len__, /*mp_length*/ __pyx_array___getitem__, /*mp_subscript*/ __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_array = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif __pyx_array_getbuffer, /*bf_getbuffer*/ 0, /*bf_releasebuffer*/ }; static PyTypeObject __pyx_type___pyx_array = { PyVarObject_HEAD_INIT(0, 0) "openTSNE.kl_divergence.array", /*tp_name*/ sizeof(struct __pyx_array_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_array, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ &__pyx_tp_as_sequence_array, /*tp_as_sequence*/ &__pyx_tp_as_mapping_array, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ __pyx_tp_getattro_array, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_array, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ 0, /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_array, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_array, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_array, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { struct __pyx_MemviewEnum_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_MemviewEnum_obj *)o); p->name = Py_None; Py_INCREF(Py_None); return o; } static void __pyx_tp_dealloc_Enum(PyObject *o) { struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); Py_CLEAR(p->name); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { int e; struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; if (p->name) { e = (*v)(p->name, a); if (e) return e; } return 0; } static int __pyx_tp_clear_Enum(PyObject *o) { PyObject* tmp; struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; tmp = ((PyObject*)p->name); p->name = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); return 0; } static PyMethodDef __pyx_methods_Enum[] = { {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type___pyx_MemviewEnum = { PyVarObject_HEAD_INIT(0, 0) "openTSNE.kl_divergence.Enum", /*tp_name*/ sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_Enum, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif __pyx_MemviewEnum___repr__, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_Enum, /*tp_traverse*/ __pyx_tp_clear_Enum, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_Enum, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ __pyx_MemviewEnum___init__, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_Enum, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_memoryview_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_memoryview_obj *)o); p->__pyx_vtab = __pyx_vtabptr_memoryview; p->obj = Py_None; Py_INCREF(Py_None); p->_size = Py_None; Py_INCREF(Py_None); p->_array_interface = Py_None; Py_INCREF(Py_None); p->view.obj = NULL; if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; return o; bad: Py_DECREF(o); o = 0; return NULL; } static void __pyx_tp_dealloc_memoryview(PyObject *o) { struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); __pyx_memoryview___dealloc__(o); __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->obj); Py_CLEAR(p->_size); Py_CLEAR(p->_array_interface); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { int e; struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; if (p->obj) { e = (*v)(p->obj, a); if (e) return e; } if (p->_size) { e = (*v)(p->_size, a); if (e) return e; } if (p->_array_interface) { e = (*v)(p->_array_interface, a); if (e) return e; } if (p->view.obj) { e = (*v)(p->view.obj, a); if (e) return e; } return 0; } static int __pyx_tp_clear_memoryview(PyObject *o) { PyObject* tmp; struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; tmp = ((PyObject*)p->obj); p->obj = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->_size); p->_size = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->_array_interface); p->_array_interface = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); Py_CLEAR(p->view.obj); return 0; } static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { PyObject *r; PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); Py_DECREF(x); return r; } static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { if (v) { return __pyx_memoryview___setitem__(o, i, v); } else { PyErr_Format(PyExc_NotImplementedError, "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); return -1; } } static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); } static PyMethodDef __pyx_methods_memoryview[] = { {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0}, {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0}, {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0}, {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0}, {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_memoryview[] = { {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PySequenceMethods __pyx_tp_as_sequence_memoryview = { __pyx_memoryview___len__, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ __pyx_sq_item_memoryview, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_memoryview = { __pyx_memoryview___len__, /*mp_length*/ __pyx_memoryview___getitem__, /*mp_subscript*/ __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_memoryview = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif __pyx_memoryview_getbuffer, /*bf_getbuffer*/ 0, /*bf_releasebuffer*/ }; static PyTypeObject __pyx_type___pyx_memoryview = { PyVarObject_HEAD_INIT(0, 0) "openTSNE.kl_divergence.memoryview", /*tp_name*/ sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif __pyx_memoryview___repr__, /*tp_repr*/ 0, /*tp_as_number*/ &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ __pyx_memoryview___str__, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_memoryview, /*tp_traverse*/ __pyx_tp_clear_memoryview, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_memoryview, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_memoryview, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_memoryview, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_memoryviewslice_obj *p; PyObject *o = __pyx_tp_new_memoryview(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_memoryviewslice_obj *)o); p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; p->from_object = Py_None; Py_INCREF(Py_None); p->from_slice.memview = NULL; return o; } static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); __pyx_memoryviewslice___dealloc__(o); __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->from_object); PyObject_GC_Track(o); __pyx_tp_dealloc_memoryview(o); } static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { int e; struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; if (p->from_object) { e = (*v)(p->from_object, a); if (e) return e; } return 0; } static int __pyx_tp_clear__memoryviewslice(PyObject *o) { PyObject* tmp; struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; __pyx_tp_clear_memoryview(o); tmp = ((PyObject*)p->from_object); p->from_object = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); __PYX_XDEC_MEMVIEW(&p->from_slice, 1); return 0; } static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o); } static PyMethodDef __pyx_methods__memoryviewslice[] = { {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = { {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PyTypeObject __pyx_type___pyx_memoryviewslice = { PyVarObject_HEAD_INIT(0, 0) "openTSNE.kl_divergence._memoryviewslice", /*tp_name*/ sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif #if CYTHON_COMPILING_IN_PYPY __pyx_memoryview___repr__, /*tp_repr*/ #else 0, /*tp_repr*/ #endif 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ #if CYTHON_COMPILING_IN_PYPY __pyx_memoryview___str__, /*tp_str*/ #else 0, /*tp_str*/ #endif 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ "Internal class for passing memoryview slices to Python", /*tp_doc*/ __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ __pyx_tp_clear__memoryviewslice, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods__memoryviewslice, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets__memoryviewslice, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new__memoryviewslice, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static PyMethodDef __pyx_methods[] = { {"kl_divergence_exact", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_13kl_divergence_1kl_divergence_exact, METH_VARARGS|METH_KEYWORDS, __pyx_doc_8openTSNE_13kl_divergence_kl_divergence_exact}, {"kl_divergence_approx_bh", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_13kl_divergence_3kl_divergence_approx_bh, METH_VARARGS|METH_KEYWORDS, __pyx_doc_8openTSNE_13kl_divergence_2kl_divergence_approx_bh}, {"kl_divergence_approx_fft", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8openTSNE_13kl_divergence_5kl_divergence_approx_fft, METH_VARARGS|METH_KEYWORDS, __pyx_doc_8openTSNE_13kl_divergence_4kl_divergence_approx_fft}, {0, 0, 0, 0} }; #if PY_MAJOR_VERSION >= 3 #if CYTHON_PEP489_MULTI_PHASE_INIT static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/ static int __pyx_pymod_exec_kl_divergence(PyObject* module); /*proto*/ static PyModuleDef_Slot __pyx_moduledef_slots[] = { {Py_mod_create, (void*)__pyx_pymod_create}, {Py_mod_exec, (void*)__pyx_pymod_exec_kl_divergence}, {0, NULL} }; #endif static struct PyModuleDef __pyx_moduledef = { PyModuleDef_HEAD_INIT, "kl_divergence", 0, /* m_doc */ #if CYTHON_PEP489_MULTI_PHASE_INIT 0, /* m_size */ #else -1, /* m_size */ #endif __pyx_methods /* m_methods */, #if CYTHON_PEP489_MULTI_PHASE_INIT __pyx_moduledef_slots, /* m_slots */ #else NULL, /* m_reload */ #endif NULL, /* m_traverse */ NULL, /* m_clear */ NULL /* m_free */ }; #endif #ifndef CYTHON_SMALL_CODE #if defined(__clang__) #define CYTHON_SMALL_CODE #elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3)) #define CYTHON_SMALL_CODE __attribute__((cold)) #else #define CYTHON_SMALL_CODE #endif #endif static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_n_s_ASCII, __pyx_k_ASCII, sizeof(__pyx_k_ASCII), 0, 0, 1, 1}, {&__pyx_kp_s_Buffer_view_does_not_expose_stri, __pyx_k_Buffer_view_does_not_expose_stri, sizeof(__pyx_k_Buffer_view_does_not_expose_stri), 0, 0, 1, 0}, {&__pyx_kp_s_Can_only_create_a_buffer_that_is, __pyx_k_Can_only_create_a_buffer_that_is, sizeof(__pyx_k_Can_only_create_a_buffer_that_is), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_assign_to_read_only_memor, __pyx_k_Cannot_assign_to_read_only_memor, sizeof(__pyx_k_Cannot_assign_to_read_only_memor), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_create_writable_memory_vi, __pyx_k_Cannot_create_writable_memory_vi, sizeof(__pyx_k_Cannot_create_writable_memory_vi), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_index_with_type_s, __pyx_k_Cannot_index_with_type_s, sizeof(__pyx_k_Cannot_index_with_type_s), 0, 0, 1, 0}, {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1}, {&__pyx_kp_s_Empty_shape_tuple_for_cython_arr, __pyx_k_Empty_shape_tuple_for_cython_arr, sizeof(__pyx_k_Empty_shape_tuple_for_cython_arr), 0, 0, 1, 0}, {&__pyx_n_s_ImportError, __pyx_k_ImportError, sizeof(__pyx_k_ImportError), 0, 0, 1, 1}, {&__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_k_Incompatible_checksums_s_vs_0xb0, sizeof(__pyx_k_Incompatible_checksums_s_vs_0xb0), 0, 0, 1, 0}, {&__pyx_n_s_IndexError, __pyx_k_IndexError, sizeof(__pyx_k_IndexError), 0, 0, 1, 1}, {&__pyx_kp_s_Indirect_dimensions_not_supporte, __pyx_k_Indirect_dimensions_not_supporte, sizeof(__pyx_k_Indirect_dimensions_not_supporte), 0, 0, 1, 0}, {&__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_k_Invalid_mode_expected_c_or_fortr, sizeof(__pyx_k_Invalid_mode_expected_c_or_fortr), 0, 0, 1, 0}, {&__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_k_Invalid_shape_in_axis_d_d, sizeof(__pyx_k_Invalid_shape_in_axis_d_d), 0, 0, 1, 0}, {&__pyx_n_s_MemoryError, __pyx_k_MemoryError, sizeof(__pyx_k_MemoryError), 0, 0, 1, 1}, {&__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_k_MemoryView_of_r_at_0x_x, sizeof(__pyx_k_MemoryView_of_r_at_0x_x), 0, 0, 1, 0}, {&__pyx_kp_s_MemoryView_of_r_object, __pyx_k_MemoryView_of_r_object, sizeof(__pyx_k_MemoryView_of_r_object), 0, 0, 1, 0}, {&__pyx_n_b_O, __pyx_k_O, sizeof(__pyx_k_O), 0, 0, 0, 1}, {&__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_k_Out_of_bounds_on_buffer_access_a, sizeof(__pyx_k_Out_of_bounds_on_buffer_access_a), 0, 0, 1, 0}, {&__pyx_n_s_P, __pyx_k_P, sizeof(__pyx_k_P), 0, 0, 1, 1}, {&__pyx_n_s_P_data, __pyx_k_P_data, sizeof(__pyx_k_P_data), 0, 0, 1, 1}, {&__pyx_n_s_PickleError, __pyx_k_PickleError, sizeof(__pyx_k_PickleError), 0, 0, 1, 1}, {&__pyx_n_s_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 0, 0, 1, 1}, {&__pyx_kp_s_Unable_to_convert_item_to_object, __pyx_k_Unable_to_convert_item_to_object, sizeof(__pyx_k_Unable_to_convert_item_to_object), 0, 0, 1, 0}, {&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1}, {&__pyx_n_s_View_MemoryView, __pyx_k_View_MemoryView, sizeof(__pyx_k_View_MemoryView), 0, 0, 1, 1}, {&__pyx_n_s_allocate_buffer, __pyx_k_allocate_buffer, sizeof(__pyx_k_allocate_buffer), 0, 0, 1, 1}, {&__pyx_n_s_base, __pyx_k_base, sizeof(__pyx_k_base), 0, 0, 1, 1}, {&__pyx_n_s_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 0, 1, 1}, {&__pyx_n_u_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 1, 0, 1}, {&__pyx_n_s_class, __pyx_k_class, sizeof(__pyx_k_class), 0, 0, 1, 1}, {&__pyx_n_s_cline_in_traceback, __pyx_k_cline_in_traceback, sizeof(__pyx_k_cline_in_traceback), 0, 0, 1, 1}, {&__pyx_kp_s_contiguous_and_direct, __pyx_k_contiguous_and_direct, sizeof(__pyx_k_contiguous_and_direct), 0, 0, 1, 0}, {&__pyx_kp_s_contiguous_and_indirect, __pyx_k_contiguous_and_indirect, sizeof(__pyx_k_contiguous_and_indirect), 0, 0, 1, 0}, {&__pyx_n_s_dict, __pyx_k_dict, sizeof(__pyx_k_dict), 0, 0, 1, 1}, {&__pyx_n_s_dof, __pyx_k_dof, sizeof(__pyx_k_dof), 0, 0, 1, 1}, {&__pyx_n_s_dtype, __pyx_k_dtype, sizeof(__pyx_k_dtype), 0, 0, 1, 1}, {&__pyx_n_s_dtype_is_object, __pyx_k_dtype_is_object, sizeof(__pyx_k_dtype_is_object), 0, 0, 1, 1}, {&__pyx_n_s_embedding, __pyx_k_embedding, sizeof(__pyx_k_embedding), 0, 0, 1, 1}, {&__pyx_n_s_empty_like, __pyx_k_empty_like, sizeof(__pyx_k_empty_like), 0, 0, 1, 1}, {&__pyx_n_s_encode, __pyx_k_encode, sizeof(__pyx_k_encode), 0, 0, 1, 1}, {&__pyx_n_s_enumerate, __pyx_k_enumerate, sizeof(__pyx_k_enumerate), 0, 0, 1, 1}, {&__pyx_n_s_eps, __pyx_k_eps, sizeof(__pyx_k_eps), 0, 0, 1, 1}, {&__pyx_n_s_error, __pyx_k_error, sizeof(__pyx_k_error), 0, 0, 1, 1}, {&__pyx_n_s_estimate_positive_gradient_nn, __pyx_k_estimate_positive_gradient_nn, sizeof(__pyx_k_estimate_positive_gradient_nn), 0, 0, 1, 1}, {&__pyx_n_s_finfo, __pyx_k_finfo, sizeof(__pyx_k_finfo), 0, 0, 1, 1}, {&__pyx_n_s_flags, __pyx_k_flags, sizeof(__pyx_k_flags), 0, 0, 1, 1}, {&__pyx_n_s_float64, __pyx_k_float64, sizeof(__pyx_k_float64), 0, 0, 1, 1}, {&__pyx_n_s_format, __pyx_k_format, sizeof(__pyx_k_format), 0, 0, 1, 1}, {&__pyx_n_s_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 0, 1, 1}, {&__pyx_n_u_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 1, 0, 1}, {&__pyx_n_s_getstate, __pyx_k_getstate, sizeof(__pyx_k_getstate), 0, 0, 1, 1}, {&__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_k_got_differing_extents_in_dimensi, sizeof(__pyx_k_got_differing_extents_in_dimensi), 0, 0, 1, 0}, {&__pyx_n_s_id, __pyx_k_id, sizeof(__pyx_k_id), 0, 0, 1, 1}, {&__pyx_n_s_import, __pyx_k_import, sizeof(__pyx_k_import), 0, 0, 1, 1}, {&__pyx_n_s_indices, __pyx_k_indices, sizeof(__pyx_k_indices), 0, 0, 1, 1}, {&__pyx_n_s_indptr, __pyx_k_indptr, sizeof(__pyx_k_indptr), 0, 0, 1, 1}, {&__pyx_n_s_ints_in_interval, __pyx_k_ints_in_interval, sizeof(__pyx_k_ints_in_interval), 0, 0, 1, 1}, {&__pyx_n_s_itemsize, __pyx_k_itemsize, sizeof(__pyx_k_itemsize), 0, 0, 1, 1}, {&__pyx_kp_s_itemsize_0_for_cython_array, __pyx_k_itemsize_0_for_cython_array, sizeof(__pyx_k_itemsize_0_for_cython_array), 0, 0, 1, 0}, {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1}, {&__pyx_n_s_memview, __pyx_k_memview, sizeof(__pyx_k_memview), 0, 0, 1, 1}, {&__pyx_n_s_min_num_intervals, __pyx_k_min_num_intervals, sizeof(__pyx_k_min_num_intervals), 0, 0, 1, 1}, {&__pyx_n_s_mode, __pyx_k_mode, sizeof(__pyx_k_mode), 0, 0, 1, 1}, {&__pyx_n_s_n_interpolation_points, __pyx_k_n_interpolation_points, sizeof(__pyx_k_n_interpolation_points), 0, 0, 1, 1}, {&__pyx_n_s_name, __pyx_k_name, sizeof(__pyx_k_name), 0, 0, 1, 1}, {&__pyx_n_s_name_2, __pyx_k_name_2, sizeof(__pyx_k_name_2), 0, 0, 1, 1}, {&__pyx_n_s_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 0, 0, 1, 1}, {&__pyx_n_s_new, __pyx_k_new, sizeof(__pyx_k_new), 0, 0, 1, 1}, {&__pyx_kp_s_no_default___reduce___due_to_non, __pyx_k_no_default___reduce___due_to_non, sizeof(__pyx_k_no_default___reduce___due_to_non), 0, 0, 1, 0}, {&__pyx_n_s_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 0, 1, 1}, {&__pyx_n_s_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 0, 0, 1, 1}, {&__pyx_kp_u_numpy_core_multiarray_failed_to, __pyx_k_numpy_core_multiarray_failed_to, sizeof(__pyx_k_numpy_core_multiarray_failed_to), 0, 1, 0, 0}, {&__pyx_kp_u_numpy_core_umath_failed_to_impor, __pyx_k_numpy_core_umath_failed_to_impor, sizeof(__pyx_k_numpy_core_umath_failed_to_impor), 0, 1, 0, 0}, {&__pyx_n_s_obj, __pyx_k_obj, sizeof(__pyx_k_obj), 0, 0, 1, 1}, {&__pyx_n_s_pack, __pyx_k_pack, sizeof(__pyx_k_pack), 0, 0, 1, 1}, {&__pyx_n_s_pickle, __pyx_k_pickle, sizeof(__pyx_k_pickle), 0, 0, 1, 1}, {&__pyx_n_s_pyx_PickleError, __pyx_k_pyx_PickleError, sizeof(__pyx_k_pyx_PickleError), 0, 0, 1, 1}, {&__pyx_n_s_pyx_checksum, __pyx_k_pyx_checksum, sizeof(__pyx_k_pyx_checksum), 0, 0, 1, 1}, {&__pyx_n_s_pyx_getbuffer, __pyx_k_pyx_getbuffer, sizeof(__pyx_k_pyx_getbuffer), 0, 0, 1, 1}, {&__pyx_n_s_pyx_result, __pyx_k_pyx_result, sizeof(__pyx_k_pyx_result), 0, 0, 1, 1}, {&__pyx_n_s_pyx_state, __pyx_k_pyx_state, sizeof(__pyx_k_pyx_state), 0, 0, 1, 1}, {&__pyx_n_s_pyx_type, __pyx_k_pyx_type, sizeof(__pyx_k_pyx_type), 0, 0, 1, 1}, {&__pyx_n_s_pyx_unpickle_Enum, __pyx_k_pyx_unpickle_Enum, sizeof(__pyx_k_pyx_unpickle_Enum), 0, 0, 1, 1}, {&__pyx_n_s_pyx_vtable, __pyx_k_pyx_vtable, sizeof(__pyx_k_pyx_vtable), 0, 0, 1, 1}, {&__pyx_n_s_range, __pyx_k_range, sizeof(__pyx_k_range), 0, 0, 1, 1}, {&__pyx_n_s_ravel, __pyx_k_ravel, sizeof(__pyx_k_ravel), 0, 0, 1, 1}, {&__pyx_n_s_reduce, __pyx_k_reduce, sizeof(__pyx_k_reduce), 0, 0, 1, 1}, {&__pyx_n_s_reduce_cython, __pyx_k_reduce_cython, sizeof(__pyx_k_reduce_cython), 0, 0, 1, 1}, {&__pyx_n_s_reduce_ex, __pyx_k_reduce_ex, sizeof(__pyx_k_reduce_ex), 0, 0, 1, 1}, {&__pyx_n_s_setstate, __pyx_k_setstate, sizeof(__pyx_k_setstate), 0, 0, 1, 1}, {&__pyx_n_s_setstate_cython, __pyx_k_setstate_cython, sizeof(__pyx_k_setstate_cython), 0, 0, 1, 1}, {&__pyx_n_s_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 0, 0, 1, 1}, {&__pyx_n_s_should_eval_error, __pyx_k_should_eval_error, sizeof(__pyx_k_should_eval_error), 0, 0, 1, 1}, {&__pyx_n_s_size, __pyx_k_size, sizeof(__pyx_k_size), 0, 0, 1, 1}, {&__pyx_n_s_start, __pyx_k_start, sizeof(__pyx_k_start), 0, 0, 1, 1}, {&__pyx_n_s_step, __pyx_k_step, sizeof(__pyx_k_step), 0, 0, 1, 1}, {&__pyx_n_s_stop, __pyx_k_stop, sizeof(__pyx_k_stop), 0, 0, 1, 1}, {&__pyx_kp_s_strided_and_direct, __pyx_k_strided_and_direct, sizeof(__pyx_k_strided_and_direct), 0, 0, 1, 0}, {&__pyx_kp_s_strided_and_direct_or_indirect, __pyx_k_strided_and_direct_or_indirect, sizeof(__pyx_k_strided_and_direct_or_indirect), 0, 0, 1, 0}, {&__pyx_kp_s_strided_and_indirect, __pyx_k_strided_and_indirect, sizeof(__pyx_k_strided_and_indirect), 0, 0, 1, 0}, {&__pyx_kp_s_stringsource, __pyx_k_stringsource, sizeof(__pyx_k_stringsource), 0, 0, 1, 0}, {&__pyx_n_s_struct, __pyx_k_struct, sizeof(__pyx_k_struct), 0, 0, 1, 1}, {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1}, {&__pyx_n_s_theta, __pyx_k_theta, sizeof(__pyx_k_theta), 0, 0, 1, 1}, {&__pyx_n_s_tsne, __pyx_k_tsne, sizeof(__pyx_k_tsne), 0, 0, 1, 1}, {&__pyx_kp_s_unable_to_allocate_array_data, __pyx_k_unable_to_allocate_array_data, sizeof(__pyx_k_unable_to_allocate_array_data), 0, 0, 1, 0}, {&__pyx_kp_s_unable_to_allocate_shape_and_str, __pyx_k_unable_to_allocate_shape_and_str, sizeof(__pyx_k_unable_to_allocate_shape_and_str), 0, 0, 1, 0}, {&__pyx_n_s_unpack, __pyx_k_unpack, sizeof(__pyx_k_unpack), 0, 0, 1, 1}, {&__pyx_n_s_update, __pyx_k_update, sizeof(__pyx_k_update), 0, 0, 1, 1}, {0, 0, 0, 0, 0, 0, 0} }; static CYTHON_SMALL_CODE int __Pyx_InitCachedBuiltins(void) { __pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) __PYX_ERR(0, 31, __pyx_L1_error) __pyx_builtin_ImportError = __Pyx_GetBuiltinName(__pyx_n_s_ImportError); if (!__pyx_builtin_ImportError) __PYX_ERR(1, 884, __pyx_L1_error) __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) __PYX_ERR(2, 133, __pyx_L1_error) __pyx_builtin_MemoryError = __Pyx_GetBuiltinName(__pyx_n_s_MemoryError); if (!__pyx_builtin_MemoryError) __PYX_ERR(2, 148, __pyx_L1_error) __pyx_builtin_enumerate = __Pyx_GetBuiltinName(__pyx_n_s_enumerate); if (!__pyx_builtin_enumerate) __PYX_ERR(2, 151, __pyx_L1_error) __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s_TypeError); if (!__pyx_builtin_TypeError) __PYX_ERR(2, 2, __pyx_L1_error) __pyx_builtin_Ellipsis = __Pyx_GetBuiltinName(__pyx_n_s_Ellipsis); if (!__pyx_builtin_Ellipsis) __PYX_ERR(2, 404, __pyx_L1_error) __pyx_builtin_id = __Pyx_GetBuiltinName(__pyx_n_s_id); if (!__pyx_builtin_id) __PYX_ERR(2, 613, __pyx_L1_error) __pyx_builtin_IndexError = __Pyx_GetBuiltinName(__pyx_n_s_IndexError); if (!__pyx_builtin_IndexError) __PYX_ERR(2, 832, __pyx_L1_error) return 0; __pyx_L1_error:; return -1; } static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":884 * __pyx_import_array() * except Exception: * raise ImportError("numpy.core.multiarray failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_umath() except -1: */ __pyx_tuple_ = PyTuple_Pack(1, __pyx_kp_u_numpy_core_multiarray_failed_to); if (unlikely(!__pyx_tuple_)) __PYX_ERR(1, 884, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple_); __Pyx_GIVEREF(__pyx_tuple_); /* "../../miniconda3/envs/ml/lib/python3.8/site-packages/numpy/__init__.pxd":890 * _import_umath() * except Exception: * raise ImportError("numpy.core.umath failed to import") # <<<<<<<<<<<<<< * * cdef inline int import_ufunc() except -1: */ __pyx_tuple__2 = PyTuple_Pack(1, __pyx_kp_u_numpy_core_umath_failed_to_impor); if (unlikely(!__pyx_tuple__2)) __PYX_ERR(1, 890, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__2); __Pyx_GIVEREF(__pyx_tuple__2); /* "View.MemoryView":133 * * if not self.ndim: * raise ValueError("Empty shape tuple for cython.array") # <<<<<<<<<<<<<< * * if itemsize <= 0: */ __pyx_tuple__3 = PyTuple_Pack(1, __pyx_kp_s_Empty_shape_tuple_for_cython_arr); if (unlikely(!__pyx_tuple__3)) __PYX_ERR(2, 133, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__3); __Pyx_GIVEREF(__pyx_tuple__3); /* "View.MemoryView":136 * * if itemsize <= 0: * raise ValueError("itemsize <= 0 for cython.array") # <<<<<<<<<<<<<< * * if not isinstance(format, bytes): */ __pyx_tuple__4 = PyTuple_Pack(1, __pyx_kp_s_itemsize_0_for_cython_array); if (unlikely(!__pyx_tuple__4)) __PYX_ERR(2, 136, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__4); __Pyx_GIVEREF(__pyx_tuple__4); /* "View.MemoryView":148 * * if not self._shape: * raise MemoryError("unable to allocate shape and strides.") # <<<<<<<<<<<<<< * * */ __pyx_tuple__5 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_shape_and_str); if (unlikely(!__pyx_tuple__5)) __PYX_ERR(2, 148, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__5); __Pyx_GIVEREF(__pyx_tuple__5); /* "View.MemoryView":176 * self.data = malloc(self.len) * if not self.data: * raise MemoryError("unable to allocate array data.") # <<<<<<<<<<<<<< * * if self.dtype_is_object: */ __pyx_tuple__6 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_array_data); if (unlikely(!__pyx_tuple__6)) __PYX_ERR(2, 176, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__6); __Pyx_GIVEREF(__pyx_tuple__6); /* "View.MemoryView":192 * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") # <<<<<<<<<<<<<< * info.buf = self.data * info.len = self.len */ __pyx_tuple__7 = PyTuple_Pack(1, __pyx_kp_s_Can_only_create_a_buffer_that_is); if (unlikely(!__pyx_tuple__7)) __PYX_ERR(2, 192, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__7); __Pyx_GIVEREF(__pyx_tuple__7); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__8 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__8)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__8); __Pyx_GIVEREF(__pyx_tuple__8); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__9 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__9)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__9); __Pyx_GIVEREF(__pyx_tuple__9); /* "View.MemoryView":418 * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") # <<<<<<<<<<<<<< * * have_slices, index = _unellipsify(index, self.view.ndim) */ __pyx_tuple__10 = PyTuple_Pack(1, __pyx_kp_s_Cannot_assign_to_read_only_memor); if (unlikely(!__pyx_tuple__10)) __PYX_ERR(2, 418, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__10); __Pyx_GIVEREF(__pyx_tuple__10); /* "View.MemoryView":495 * result = struct.unpack(self.view.format, bytesitem) * except struct.error: * raise ValueError("Unable to convert item to object") # <<<<<<<<<<<<<< * else: * if len(self.view.format) == 1: */ __pyx_tuple__11 = PyTuple_Pack(1, __pyx_kp_s_Unable_to_convert_item_to_object); if (unlikely(!__pyx_tuple__11)) __PYX_ERR(2, 495, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__11); __Pyx_GIVEREF(__pyx_tuple__11); /* "View.MemoryView":520 * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") # <<<<<<<<<<<<<< * * if flags & PyBUF_ND: */ __pyx_tuple__12 = PyTuple_Pack(1, __pyx_kp_s_Cannot_create_writable_memory_vi); if (unlikely(!__pyx_tuple__12)) __PYX_ERR(2, 520, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__12); __Pyx_GIVEREF(__pyx_tuple__12); /* "View.MemoryView":570 * if self.view.strides == NULL: * * raise ValueError("Buffer view does not expose strides") # <<<<<<<<<<<<<< * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) */ __pyx_tuple__13 = PyTuple_Pack(1, __pyx_kp_s_Buffer_view_does_not_expose_stri); if (unlikely(!__pyx_tuple__13)) __PYX_ERR(2, 570, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__13); __Pyx_GIVEREF(__pyx_tuple__13); /* "View.MemoryView":577 * def suboffsets(self): * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim # <<<<<<<<<<<<<< * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) */ __pyx_tuple__14 = PyTuple_New(1); if (unlikely(!__pyx_tuple__14)) __PYX_ERR(2, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__14); __Pyx_INCREF(__pyx_int_neg_1); __Pyx_GIVEREF(__pyx_int_neg_1); PyTuple_SET_ITEM(__pyx_tuple__14, 0, __pyx_int_neg_1); __Pyx_GIVEREF(__pyx_tuple__14); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__15 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__15)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__15); __Pyx_GIVEREF(__pyx_tuple__15); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__16 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__16)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__16); __Pyx_GIVEREF(__pyx_tuple__16); /* "View.MemoryView":682 * if item is Ellipsis: * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) # <<<<<<<<<<<<<< * seen_ellipsis = True * else: */ __pyx_slice__17 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__17)) __PYX_ERR(2, 682, __pyx_L1_error) __Pyx_GOTREF(__pyx_slice__17); __Pyx_GIVEREF(__pyx_slice__17); /* "View.MemoryView":703 * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") # <<<<<<<<<<<<<< * * */ __pyx_tuple__18 = PyTuple_Pack(1, __pyx_kp_s_Indirect_dimensions_not_supporte); if (unlikely(!__pyx_tuple__18)) __PYX_ERR(2, 703, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__18); __Pyx_GIVEREF(__pyx_tuple__18); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__19 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__19)) __PYX_ERR(2, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__19); __Pyx_GIVEREF(__pyx_tuple__19); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__20 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__20)) __PYX_ERR(2, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__20); __Pyx_GIVEREF(__pyx_tuple__20); /* "View.MemoryView":286 * return self.name * * cdef generic = Enum("") # <<<<<<<<<<<<<< * cdef strided = Enum("") # default * cdef indirect = Enum("") */ __pyx_tuple__21 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct_or_indirect); if (unlikely(!__pyx_tuple__21)) __PYX_ERR(2, 286, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__21); __Pyx_GIVEREF(__pyx_tuple__21); /* "View.MemoryView":287 * * cdef generic = Enum("") * cdef strided = Enum("") # default # <<<<<<<<<<<<<< * cdef indirect = Enum("") * */ __pyx_tuple__22 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct); if (unlikely(!__pyx_tuple__22)) __PYX_ERR(2, 287, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__22); __Pyx_GIVEREF(__pyx_tuple__22); /* "View.MemoryView":288 * cdef generic = Enum("") * cdef strided = Enum("") # default * cdef indirect = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_tuple__23 = PyTuple_Pack(1, __pyx_kp_s_strided_and_indirect); if (unlikely(!__pyx_tuple__23)) __PYX_ERR(2, 288, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__23); __Pyx_GIVEREF(__pyx_tuple__23); /* "View.MemoryView":291 * * * cdef contiguous = Enum("") # <<<<<<<<<<<<<< * cdef indirect_contiguous = Enum("") * */ __pyx_tuple__24 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_direct); if (unlikely(!__pyx_tuple__24)) __PYX_ERR(2, 291, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__24); __Pyx_GIVEREF(__pyx_tuple__24); /* "View.MemoryView":292 * * cdef contiguous = Enum("") * cdef indirect_contiguous = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_tuple__25 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_indirect); if (unlikely(!__pyx_tuple__25)) __PYX_ERR(2, 292, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__25); __Pyx_GIVEREF(__pyx_tuple__25); /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ __pyx_tuple__26 = PyTuple_Pack(5, __pyx_n_s_pyx_type, __pyx_n_s_pyx_checksum, __pyx_n_s_pyx_state, __pyx_n_s_pyx_PickleError, __pyx_n_s_pyx_result); if (unlikely(!__pyx_tuple__26)) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__26); __Pyx_GIVEREF(__pyx_tuple__26); __pyx_codeobj__27 = (PyObject*)__Pyx_PyCode_New(3, 0, 5, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__26, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_stringsource, __pyx_n_s_pyx_unpickle_Enum, 1, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__27)) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static CYTHON_SMALL_CODE int __Pyx_InitGlobals(void) { if (__Pyx_InitStrings(__pyx_string_tab) < 0) __PYX_ERR(0, 1, __pyx_L1_error); __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_1 = PyInt_FromLong(1); if (unlikely(!__pyx_int_1)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_184977713 = PyInt_FromLong(184977713L); if (unlikely(!__pyx_int_184977713)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_neg_1 = PyInt_FromLong(-1); if (unlikely(!__pyx_int_neg_1)) __PYX_ERR(0, 1, __pyx_L1_error) return 0; __pyx_L1_error:; return -1; } static CYTHON_SMALL_CODE int __Pyx_modinit_global_init_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_variable_export_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_function_export_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_type_init_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_type_import_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_variable_import_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_function_import_code(void); /*proto*/ static int __Pyx_modinit_global_init_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_global_init_code", 0); /*--- Global init code ---*/ generic = Py_None; Py_INCREF(Py_None); strided = Py_None; Py_INCREF(Py_None); indirect = Py_None; Py_INCREF(Py_None); contiguous = Py_None; Py_INCREF(Py_None); indirect_contiguous = Py_None; Py_INCREF(Py_None); __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_variable_export_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_variable_export_code", 0); /*--- Variable export code ---*/ __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_function_export_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_function_export_code", 0); /*--- Function export code ---*/ __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_type_init_code(void) { __Pyx_RefNannyDeclarations int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__Pyx_modinit_type_init_code", 0); /*--- Type init code ---*/ __pyx_vtabptr_array = &__pyx_vtable_array; __pyx_vtable_array.get_memview = (PyObject *(*)(struct __pyx_array_obj *))__pyx_array_get_memview; if (PyType_Ready(&__pyx_type___pyx_array) < 0) __PYX_ERR(2, 105, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_array.tp_print = 0; #endif if (__Pyx_SetVtable(__pyx_type___pyx_array.tp_dict, __pyx_vtabptr_array) < 0) __PYX_ERR(2, 105, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_array) < 0) __PYX_ERR(2, 105, __pyx_L1_error) __pyx_array_type = &__pyx_type___pyx_array; if (PyType_Ready(&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(2, 279, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_MemviewEnum.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_MemviewEnum.tp_dictoffset && __pyx_type___pyx_MemviewEnum.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_MemviewEnum.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(2, 279, __pyx_L1_error) __pyx_MemviewEnum_type = &__pyx_type___pyx_MemviewEnum; __pyx_vtabptr_memoryview = &__pyx_vtable_memoryview; __pyx_vtable_memoryview.get_item_pointer = (char *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_get_item_pointer; __pyx_vtable_memoryview.is_slice = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_is_slice; __pyx_vtable_memoryview.setitem_slice_assignment = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_slice_assignment; __pyx_vtable_memoryview.setitem_slice_assign_scalar = (PyObject *(*)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_setitem_slice_assign_scalar; __pyx_vtable_memoryview.setitem_indexed = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_indexed; __pyx_vtable_memoryview.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryview_convert_item_to_object; __pyx_vtable_memoryview.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryview_assign_item_from_object; if (PyType_Ready(&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(2, 330, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_memoryview.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_memoryview.tp_dictoffset && __pyx_type___pyx_memoryview.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_memoryview.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_SetVtable(__pyx_type___pyx_memoryview.tp_dict, __pyx_vtabptr_memoryview) < 0) __PYX_ERR(2, 330, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(2, 330, __pyx_L1_error) __pyx_memoryview_type = &__pyx_type___pyx_memoryview; __pyx_vtabptr__memoryviewslice = &__pyx_vtable__memoryviewslice; __pyx_vtable__memoryviewslice.__pyx_base = *__pyx_vtabptr_memoryview; __pyx_vtable__memoryviewslice.__pyx_base.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryviewslice_convert_item_to_object; __pyx_vtable__memoryviewslice.__pyx_base.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryviewslice_assign_item_from_object; __pyx_type___pyx_memoryviewslice.tp_base = __pyx_memoryview_type; if (PyType_Ready(&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(2, 965, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_memoryviewslice.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_memoryviewslice.tp_dictoffset && __pyx_type___pyx_memoryviewslice.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_memoryviewslice.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_SetVtable(__pyx_type___pyx_memoryviewslice.tp_dict, __pyx_vtabptr__memoryviewslice) < 0) __PYX_ERR(2, 965, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(2, 965, __pyx_L1_error) __pyx_memoryviewslice_type = &__pyx_type___pyx_memoryviewslice; __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_type_import_code(void) { __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__Pyx_modinit_type_import_code", 0); /*--- Type import code ---*/ __pyx_t_1 = PyImport_ImportModule(__Pyx_BUILTIN_MODULE_NAME); if (unlikely(!__pyx_t_1)) __PYX_ERR(3, 9, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_ptype_7cpython_4type_type = __Pyx_ImportType(__pyx_t_1, __Pyx_BUILTIN_MODULE_NAME, "type", #if defined(PYPY_VERSION_NUM) && PYPY_VERSION_NUM < 0x050B0000 sizeof(PyTypeObject), #else sizeof(PyHeapTypeObject), #endif __Pyx_ImportType_CheckSize_Warn); if (!__pyx_ptype_7cpython_4type_type) __PYX_ERR(3, 9, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyImport_ImportModule("numpy"); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 199, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_ptype_5numpy_dtype = __Pyx_ImportType(__pyx_t_1, "numpy", "dtype", sizeof(PyArray_Descr), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_dtype) __PYX_ERR(1, 199, __pyx_L1_error) __pyx_ptype_5numpy_flatiter = __Pyx_ImportType(__pyx_t_1, "numpy", "flatiter", sizeof(PyArrayIterObject), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_flatiter) __PYX_ERR(1, 222, __pyx_L1_error) __pyx_ptype_5numpy_broadcast = __Pyx_ImportType(__pyx_t_1, "numpy", "broadcast", sizeof(PyArrayMultiIterObject), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_broadcast) __PYX_ERR(1, 226, __pyx_L1_error) __pyx_ptype_5numpy_ndarray = __Pyx_ImportType(__pyx_t_1, "numpy", "ndarray", sizeof(PyArrayObject), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_ndarray) __PYX_ERR(1, 238, __pyx_L1_error) __pyx_ptype_5numpy_ufunc = __Pyx_ImportType(__pyx_t_1, "numpy", "ufunc", sizeof(PyUFuncObject), __Pyx_ImportType_CheckSize_Ignore); if (!__pyx_ptype_5numpy_ufunc) __PYX_ERR(1, 764, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyImport_ImportModule("openTSNE.quad_tree"); if (unlikely(!__pyx_t_1)) __PYX_ERR(4, 25, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_ptype_8openTSNE_9quad_tree_QuadTree = __Pyx_ImportType(__pyx_t_1, "openTSNE.quad_tree", "QuadTree", sizeof(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree), __Pyx_ImportType_CheckSize_Warn); if (!__pyx_ptype_8openTSNE_9quad_tree_QuadTree) __PYX_ERR(4, 25, __pyx_L1_error) __pyx_vtabptr_8openTSNE_9quad_tree_QuadTree = (struct __pyx_vtabstruct_8openTSNE_9quad_tree_QuadTree*)__Pyx_GetVtable(__pyx_ptype_8openTSNE_9quad_tree_QuadTree->tp_dict); if (unlikely(!__pyx_vtabptr_8openTSNE_9quad_tree_QuadTree)) __PYX_ERR(4, 25, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_variable_import_code(void) { __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__Pyx_modinit_variable_import_code", 0); /*--- Variable import code ---*/ __pyx_t_1 = PyImport_ImportModule("openTSNE.quad_tree"); if (!__pyx_t_1) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (__Pyx_ImportVoidPtr(__pyx_t_1, "EPSILON", (void **)&__pyx_vp_8openTSNE_9quad_tree_EPSILON, "double") < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_function_import_code(void) { __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__Pyx_modinit_function_import_code", 0); /*--- Function import code ---*/ __pyx_t_1 = PyImport_ImportModule("openTSNE._tsne"); if (!__pyx_t_1) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (__Pyx_ImportFunction(__pyx_t_1, "estimate_negative_gradient_bh", (void (**)(void))&__pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_bh, "double (struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_bh *__pyx_optional_args)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ImportFunction(__pyx_t_1, "estimate_negative_gradient_fft_1d", (void (**)(void))&__pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d, "double (__Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_1d *__pyx_optional_args)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) if (__Pyx_ImportFunction(__pyx_t_1, "estimate_negative_gradient_fft_2d", (void (**)(void))&__pyx_f_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d, "double (__Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_8openTSNE_5_tsne_estimate_negative_gradient_fft_2d *__pyx_optional_args)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_RefNannyFinishContext(); return -1; } #ifndef CYTHON_NO_PYINIT_EXPORT #define __Pyx_PyMODINIT_FUNC PyMODINIT_FUNC #elif PY_MAJOR_VERSION < 3 #ifdef __cplusplus #define __Pyx_PyMODINIT_FUNC extern "C" void #else #define __Pyx_PyMODINIT_FUNC void #endif #else #ifdef __cplusplus #define __Pyx_PyMODINIT_FUNC extern "C" PyObject * #else #define __Pyx_PyMODINIT_FUNC PyObject * #endif #endif #if PY_MAJOR_VERSION < 3 __Pyx_PyMODINIT_FUNC initkl_divergence(void) CYTHON_SMALL_CODE; /*proto*/ __Pyx_PyMODINIT_FUNC initkl_divergence(void) #else __Pyx_PyMODINIT_FUNC PyInit_kl_divergence(void) CYTHON_SMALL_CODE; /*proto*/ __Pyx_PyMODINIT_FUNC PyInit_kl_divergence(void) #if CYTHON_PEP489_MULTI_PHASE_INIT { return PyModuleDef_Init(&__pyx_moduledef); } static CYTHON_SMALL_CODE int __Pyx_check_single_interpreter(void) { #if PY_VERSION_HEX >= 0x030700A1 static PY_INT64_T main_interpreter_id = -1; PY_INT64_T current_id = PyInterpreterState_GetID(PyThreadState_Get()->interp); if (main_interpreter_id == -1) { main_interpreter_id = current_id; return (unlikely(current_id == -1)) ? -1 : 0; } else if (unlikely(main_interpreter_id != current_id)) #else static PyInterpreterState *main_interpreter = NULL; PyInterpreterState *current_interpreter = PyThreadState_Get()->interp; if (!main_interpreter) { main_interpreter = current_interpreter; } else if (unlikely(main_interpreter != current_interpreter)) #endif { PyErr_SetString( PyExc_ImportError, "Interpreter change detected - this module can only be loaded into one interpreter per process."); return -1; } return 0; } static CYTHON_SMALL_CODE int __Pyx_copy_spec_to_module(PyObject *spec, PyObject *moddict, const char* from_name, const char* to_name, int allow_none) { PyObject *value = PyObject_GetAttrString(spec, from_name); int result = 0; if (likely(value)) { if (allow_none || value != Py_None) { result = PyDict_SetItemString(moddict, to_name, value); } Py_DECREF(value); } else if (PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Clear(); } else { result = -1; } return result; } static CYTHON_SMALL_CODE PyObject* __pyx_pymod_create(PyObject *spec, CYTHON_UNUSED PyModuleDef *def) { PyObject *module = NULL, *moddict, *modname; if (__Pyx_check_single_interpreter()) return NULL; if (__pyx_m) return __Pyx_NewRef(__pyx_m); modname = PyObject_GetAttrString(spec, "name"); if (unlikely(!modname)) goto bad; module = PyModule_NewObject(modname); Py_DECREF(modname); if (unlikely(!module)) goto bad; moddict = PyModule_GetDict(module); if (unlikely(!moddict)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "loader", "__loader__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "origin", "__file__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "parent", "__package__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "submodule_search_locations", "__path__", 0) < 0)) goto bad; return module; bad: Py_XDECREF(module); return NULL; } static CYTHON_SMALL_CODE int __pyx_pymod_exec_kl_divergence(PyObject *__pyx_pyinit_module) #endif #endif { PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; double __pyx_t_4; static PyThread_type_lock __pyx_t_5[8]; int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; __Pyx_RefNannyDeclarations #if CYTHON_PEP489_MULTI_PHASE_INIT if (__pyx_m) { if (__pyx_m == __pyx_pyinit_module) return 0; PyErr_SetString(PyExc_RuntimeError, "Module 'kl_divergence' has already been imported. Re-initialisation is not supported."); return -1; } #elif PY_MAJOR_VERSION >= 3 if (__pyx_m) return __Pyx_NewRef(__pyx_m); #endif #if CYTHON_REFNANNY __Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny"); if (!__Pyx_RefNanny) { PyErr_Clear(); __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny"); if (!__Pyx_RefNanny) Py_FatalError("failed to import 'refnanny' module"); } #endif __Pyx_RefNannySetupContext("__Pyx_PyMODINIT_FUNC PyInit_kl_divergence(void)", 0); if (__Pyx_check_binary_version() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #ifdef __Pxy_PyFrame_Initialize_Offsets __Pxy_PyFrame_Initialize_Offsets(); #endif __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_empty_unicode = PyUnicode_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_unicode)) __PYX_ERR(0, 1, __pyx_L1_error) #ifdef __Pyx_CyFunction_USED if (__pyx_CyFunction_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_FusedFunction_USED if (__pyx_FusedFunction_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_Coroutine_USED if (__pyx_Coroutine_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_Generator_USED if (__pyx_Generator_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_AsyncGen_USED if (__pyx_AsyncGen_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_StopAsyncIteration_USED if (__pyx_StopAsyncIteration_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif /*--- Library function declarations ---*/ /*--- Threads initialization code ---*/ #if defined(__PYX_FORCE_INIT_THREADS) && __PYX_FORCE_INIT_THREADS #ifdef WITH_THREAD /* Python build with threading support? */ PyEval_InitThreads(); #endif #endif /*--- Module creation code ---*/ #if CYTHON_PEP489_MULTI_PHASE_INIT __pyx_m = __pyx_pyinit_module; Py_INCREF(__pyx_m); #else #if PY_MAJOR_VERSION < 3 __pyx_m = Py_InitModule4("kl_divergence", __pyx_methods, 0, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m); #else __pyx_m = PyModule_Create(&__pyx_moduledef); #endif if (unlikely(!__pyx_m)) __PYX_ERR(0, 1, __pyx_L1_error) #endif __pyx_d = PyModule_GetDict(__pyx_m); if (unlikely(!__pyx_d)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_d); __pyx_b = PyImport_AddModule(__Pyx_BUILTIN_MODULE_NAME); if (unlikely(!__pyx_b)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_b); __pyx_cython_runtime = PyImport_AddModule((char *) "cython_runtime"); if (unlikely(!__pyx_cython_runtime)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_cython_runtime); if (PyObject_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) __PYX_ERR(0, 1, __pyx_L1_error); /*--- Initialize various global constants etc. ---*/ if (__Pyx_InitGlobals() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #if PY_MAJOR_VERSION < 3 && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) if (__Pyx_init_sys_getdefaultencoding_params() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif if (__pyx_module_is_main_openTSNE__kl_divergence) { if (PyObject_SetAttr(__pyx_m, __pyx_n_s_name_2, __pyx_n_s_main) < 0) __PYX_ERR(0, 1, __pyx_L1_error) } #if PY_MAJOR_VERSION >= 3 { PyObject *modules = PyImport_GetModuleDict(); if (unlikely(!modules)) __PYX_ERR(0, 1, __pyx_L1_error) if (!PyDict_GetItemString(modules, "openTSNE.kl_divergence")) { if (unlikely(PyDict_SetItemString(modules, "openTSNE.kl_divergence", __pyx_m) < 0)) __PYX_ERR(0, 1, __pyx_L1_error) } } #endif /*--- Builtin init code ---*/ if (__Pyx_InitCachedBuiltins() < 0) __PYX_ERR(0, 1, __pyx_L1_error) /*--- Constants init code ---*/ if (__Pyx_InitCachedConstants() < 0) __PYX_ERR(0, 1, __pyx_L1_error) /*--- Global type/function init code ---*/ (void)__Pyx_modinit_global_init_code(); (void)__Pyx_modinit_variable_export_code(); (void)__Pyx_modinit_function_export_code(); if (unlikely(__Pyx_modinit_type_init_code() < 0)) __PYX_ERR(0, 1, __pyx_L1_error) if (unlikely(__Pyx_modinit_type_import_code() < 0)) __PYX_ERR(0, 1, __pyx_L1_error) if (unlikely(__Pyx_modinit_variable_import_code() < 0)) __PYX_ERR(0, 1, __pyx_L1_error) if (unlikely(__Pyx_modinit_function_import_code() < 0)) __PYX_ERR(0, 1, __pyx_L1_error) /*--- Execution code ---*/ #if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED) if (__Pyx_patch_abc() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif /* "openTSNE/kl_divergence.pyx":8 * # cython: language_level=3 * cimport numpy as np * import numpy as np # <<<<<<<<<<<<<< * from .quad_tree cimport QuadTree * from ._tsne cimport ( */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_numpy, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) __PYX_ERR(0, 8, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "openTSNE/kl_divergence.pyx":16 * ) * # This returns a tuple, and can"t be called from C * from ._tsne import estimate_positive_gradient_nn # <<<<<<<<<<<<<< * * */ __pyx_t_1 = PyList_New(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 16, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_n_s_estimate_positive_gradient_nn); __Pyx_GIVEREF(__pyx_n_s_estimate_positive_gradient_nn); PyList_SET_ITEM(__pyx_t_1, 0, __pyx_n_s_estimate_positive_gradient_nn); __pyx_t_2 = __Pyx_Import(__pyx_n_s_tsne, __pyx_t_1, 1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 16, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_ImportFrom(__pyx_t_2, __pyx_n_s_estimate_positive_gradient_nn); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 16, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_estimate_positive_gradient_nn, __pyx_t_1) < 0) __PYX_ERR(0, 16, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "openTSNE/kl_divergence.pyx":19 * * * cdef double EPSILON = np.finfo(np.float64).eps # <<<<<<<<<<<<<< * * cdef extern from "math.h": */ __Pyx_GetModuleGlobalName(__pyx_t_2, __pyx_n_s_np); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 19, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_finfo); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 19, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_GetModuleGlobalName(__pyx_t_2, __pyx_n_s_np); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 19, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_float64); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 19, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_CallOneArg(__pyx_t_1, __pyx_t_3); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 19, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_eps); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 19, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_4 = __pyx_PyFloat_AsDouble(__pyx_t_3); if (unlikely((__pyx_t_4 == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 19, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_8openTSNE_13kl_divergence_EPSILON = __pyx_t_4; /* "openTSNE/kl_divergence.pyx":1 * # cython: boundscheck=False # <<<<<<<<<<<<<< * # cython: wraparound=False * # cython: cdivision=True */ __pyx_t_3 = __Pyx_PyDict_NewPresized(0); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_3) < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":209 * info.obj = self * * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * def __dealloc__(array self): */ __pyx_t_3 = __pyx_capsule_create(((void *)(&__pyx_array_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 209, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (PyDict_SetItem((PyObject *)__pyx_array_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_3) < 0) __PYX_ERR(2, 209, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; PyType_Modified(__pyx_array_type); /* "View.MemoryView":286 * return self.name * * cdef generic = Enum("") # <<<<<<<<<<<<<< * cdef strided = Enum("") # default * cdef indirect = Enum("") */ __pyx_t_3 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__21, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 286, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_XGOTREF(generic); __Pyx_DECREF_SET(generic, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":287 * * cdef generic = Enum("") * cdef strided = Enum("") # default # <<<<<<<<<<<<<< * cdef indirect = Enum("") * */ __pyx_t_3 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__22, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 287, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_XGOTREF(strided); __Pyx_DECREF_SET(strided, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":288 * cdef generic = Enum("") * cdef strided = Enum("") # default * cdef indirect = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__23, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 288, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_XGOTREF(indirect); __Pyx_DECREF_SET(indirect, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":291 * * * cdef contiguous = Enum("") # <<<<<<<<<<<<<< * cdef indirect_contiguous = Enum("") * */ __pyx_t_3 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__24, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 291, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_XGOTREF(contiguous); __Pyx_DECREF_SET(contiguous, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":292 * * cdef contiguous = Enum("") * cdef indirect_contiguous = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__25, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 292, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_XGOTREF(indirect_contiguous); __Pyx_DECREF_SET(indirect_contiguous, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":316 * * DEF THREAD_LOCKS_PREALLOCATED = 8 * cdef int __pyx_memoryview_thread_locks_used = 0 # <<<<<<<<<<<<<< * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [ * PyThread_allocate_lock(), */ __pyx_memoryview_thread_locks_used = 0; /* "View.MemoryView":317 * DEF THREAD_LOCKS_PREALLOCATED = 8 * cdef int __pyx_memoryview_thread_locks_used = 0 * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [ # <<<<<<<<<<<<<< * PyThread_allocate_lock(), * PyThread_allocate_lock(), */ __pyx_t_5[0] = PyThread_allocate_lock(); __pyx_t_5[1] = PyThread_allocate_lock(); __pyx_t_5[2] = PyThread_allocate_lock(); __pyx_t_5[3] = PyThread_allocate_lock(); __pyx_t_5[4] = PyThread_allocate_lock(); __pyx_t_5[5] = PyThread_allocate_lock(); __pyx_t_5[6] = PyThread_allocate_lock(); __pyx_t_5[7] = PyThread_allocate_lock(); memcpy(&(__pyx_memoryview_thread_locks[0]), __pyx_t_5, sizeof(__pyx_memoryview_thread_locks[0]) * (8)); /* "View.MemoryView":549 * info.obj = self * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 549, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (PyDict_SetItem((PyObject *)__pyx_memoryview_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_3) < 0) __PYX_ERR(2, 549, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; PyType_Modified(__pyx_memoryview_type); /* "View.MemoryView":995 * return self.from_object * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 995, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (PyDict_SetItem((PyObject *)__pyx_memoryviewslice_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_3) < 0) __PYX_ERR(2, 995, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; PyType_Modified(__pyx_memoryviewslice_type); /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ __pyx_t_3 = PyCFunction_NewEx(&__pyx_mdef_15View_dot_MemoryView_1__pyx_unpickle_Enum, NULL, __pyx_n_s_View_MemoryView); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (PyDict_SetItem(__pyx_d, __pyx_n_s_pyx_unpickle_Enum, __pyx_t_3) < 0) __PYX_ERR(2, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ /*--- Wrapped vars code ---*/ goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); if (__pyx_m) { if (__pyx_d) { __Pyx_AddTraceback("init openTSNE.kl_divergence", __pyx_clineno, __pyx_lineno, __pyx_filename); } Py_CLEAR(__pyx_m); } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_ImportError, "init openTSNE.kl_divergence"); } __pyx_L0:; __Pyx_RefNannyFinishContext(); #if CYTHON_PEP489_MULTI_PHASE_INIT return (__pyx_m != NULL) ? 0 : -1; #elif PY_MAJOR_VERSION >= 3 return __pyx_m; #else return; #endif } /* --- Runtime support code --- */ /* Refnanny */ #if CYTHON_REFNANNY static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { PyObject *m = NULL, *p = NULL; void *r = NULL; m = PyImport_ImportModule(modname); if (!m) goto end; p = PyObject_GetAttrString(m, "RefNannyAPI"); if (!p) goto end; r = PyLong_AsVoidPtr(p); end: Py_XDECREF(p); Py_XDECREF(m); return (__Pyx_RefNannyAPIStruct *)r; } #endif /* PyObjectGetAttrStr */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { PyTypeObject* tp = Py_TYPE(obj); if (likely(tp->tp_getattro)) return tp->tp_getattro(obj, attr_name); #if PY_MAJOR_VERSION < 3 if (likely(tp->tp_getattr)) return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); #endif return PyObject_GetAttr(obj, attr_name); } #endif /* GetBuiltinName */ static PyObject *__Pyx_GetBuiltinName(PyObject *name) { PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); if (unlikely(!result)) { PyErr_Format(PyExc_NameError, #if PY_MAJOR_VERSION >= 3 "name '%U' is not defined", name); #else "name '%.200s' is not defined", PyString_AS_STRING(name)); #endif } return result; } /* PyIntBinop */ #if !CYTHON_COMPILING_IN_PYPY static PyObject* __Pyx_PyInt_AddCObj(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) { (void)inplace; (void)zerodivision_check; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(op2))) { const long a = intval; long x; long b = PyInt_AS_LONG(op2); x = (long)((unsigned long)a + b); if (likely((x^a) >= 0 || (x^b) >= 0)) return PyInt_FromLong(x); return PyLong_Type.tp_as_number->nb_add(op1, op2); } #endif #if CYTHON_USE_PYLONG_INTERNALS if (likely(PyLong_CheckExact(op2))) { const long a = intval; long b, x; #ifdef HAVE_LONG_LONG const PY_LONG_LONG lla = intval; PY_LONG_LONG llb, llx; #endif const digit* digits = ((PyLongObject*)op2)->ob_digit; const Py_ssize_t size = Py_SIZE(op2); if (likely(__Pyx_sst_abs(size) <= 1)) { b = likely(size) ? digits[0] : 0; if (size == -1) b = -b; } else { switch (size) { case -2: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { b = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { llb = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 2: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { b = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { llb = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case -3: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { b = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { llb = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 3: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { b = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { llb = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case -4: if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { b = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { llb = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 4: if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { b = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { llb = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; default: return PyLong_Type.tp_as_number->nb_add(op1, op2); } } x = a + b; return PyLong_FromLong(x); #ifdef HAVE_LONG_LONG long_long: llx = lla + llb; return PyLong_FromLongLong(llx); #endif } #endif if (PyFloat_CheckExact(op2)) { const long a = intval; double b = PyFloat_AS_DOUBLE(op2); double result; PyFPE_START_PROTECT("add", return NULL) result = ((double)a) + (double)b; PyFPE_END_PROTECT(result) return PyFloat_FromDouble(result); } return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); } #endif /* MemviewSliceInit */ static int __Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, int ndim, __Pyx_memviewslice *memviewslice, int memview_is_new_reference) { __Pyx_RefNannyDeclarations int i, retval=-1; Py_buffer *buf = &memview->view; __Pyx_RefNannySetupContext("init_memviewslice", 0); if (unlikely(memviewslice->memview || memviewslice->data)) { PyErr_SetString(PyExc_ValueError, "memviewslice is already initialized!"); goto fail; } if (buf->strides) { for (i = 0; i < ndim; i++) { memviewslice->strides[i] = buf->strides[i]; } } else { Py_ssize_t stride = buf->itemsize; for (i = ndim - 1; i >= 0; i--) { memviewslice->strides[i] = stride; stride *= buf->shape[i]; } } for (i = 0; i < ndim; i++) { memviewslice->shape[i] = buf->shape[i]; if (buf->suboffsets) { memviewslice->suboffsets[i] = buf->suboffsets[i]; } else { memviewslice->suboffsets[i] = -1; } } memviewslice->memview = memview; memviewslice->data = (char *)buf->buf; if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { Py_INCREF(memview); } retval = 0; goto no_fail; fail: memviewslice->memview = 0; memviewslice->data = 0; retval = -1; no_fail: __Pyx_RefNannyFinishContext(); return retval; } #ifndef Py_NO_RETURN #define Py_NO_RETURN #endif static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { va_list vargs; char msg[200]; #ifdef HAVE_STDARG_PROTOTYPES va_start(vargs, fmt); #else va_start(vargs); #endif vsnprintf(msg, 200, fmt, vargs); va_end(vargs); Py_FatalError(msg); } static CYTHON_INLINE int __pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, PyThread_type_lock lock) { int result; PyThread_acquire_lock(lock, 1); result = (*acquisition_count)++; PyThread_release_lock(lock); return result; } static CYTHON_INLINE int __pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, PyThread_type_lock lock) { int result; PyThread_acquire_lock(lock, 1); result = (*acquisition_count)--; PyThread_release_lock(lock); return result; } static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) { int first_time; struct __pyx_memoryview_obj *memview = memslice->memview; if (unlikely(!memview || (PyObject *) memview == Py_None)) return; if (unlikely(__pyx_get_slice_count(memview) < 0)) __pyx_fatalerror("Acquisition count is %d (line %d)", __pyx_get_slice_count(memview), lineno); first_time = __pyx_add_acquisition_count(memview) == 0; if (unlikely(first_time)) { if (have_gil) { Py_INCREF((PyObject *) memview); } else { PyGILState_STATE _gilstate = PyGILState_Ensure(); Py_INCREF((PyObject *) memview); PyGILState_Release(_gilstate); } } } static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) { int last_time; struct __pyx_memoryview_obj *memview = memslice->memview; if (unlikely(!memview || (PyObject *) memview == Py_None)) { memslice->memview = NULL; return; } if (unlikely(__pyx_get_slice_count(memview) <= 0)) __pyx_fatalerror("Acquisition count is %d (line %d)", __pyx_get_slice_count(memview), lineno); last_time = __pyx_sub_acquisition_count(memview) == 1; memslice->data = NULL; if (unlikely(last_time)) { if (have_gil) { Py_CLEAR(memslice->memview); } else { PyGILState_STATE _gilstate = PyGILState_Ensure(); Py_CLEAR(memslice->memview); PyGILState_Release(_gilstate); } } else { memslice->memview = NULL; } } /* PyErrFetchRestore */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; } #endif /* WriteUnraisableException */ static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno, CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename, int full_traceback, CYTHON_UNUSED int nogil) { PyObject *old_exc, *old_val, *old_tb; PyObject *ctx; __Pyx_PyThreadState_declare #ifdef WITH_THREAD PyGILState_STATE state; if (nogil) state = PyGILState_Ensure(); #ifdef _MSC_VER else state = (PyGILState_STATE)-1; #endif #endif __Pyx_PyThreadState_assign __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); if (full_traceback) { Py_XINCREF(old_exc); Py_XINCREF(old_val); Py_XINCREF(old_tb); __Pyx_ErrRestore(old_exc, old_val, old_tb); PyErr_PrintEx(1); } #if PY_MAJOR_VERSION < 3 ctx = PyString_FromString(name); #else ctx = PyUnicode_FromString(name); #endif __Pyx_ErrRestore(old_exc, old_val, old_tb); if (!ctx) { PyErr_WriteUnraisable(Py_None); } else { PyErr_WriteUnraisable(ctx); Py_DECREF(ctx); } #ifdef WITH_THREAD if (nogil) PyGILState_Release(state); #endif } /* RaiseArgTupleInvalid */ static void __Pyx_RaiseArgtupleInvalid( const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found) { Py_ssize_t num_expected; const char *more_or_less; if (num_found < num_min) { num_expected = num_min; more_or_less = "at least"; } else { num_expected = num_max; more_or_less = "at most"; } if (exact) { more_or_less = "exactly"; } PyErr_Format(PyExc_TypeError, "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", func_name, more_or_less, num_expected, (num_expected == 1) ? "" : "s", num_found); } /* RaiseDoubleKeywords */ static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) { PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION >= 3 "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, PyString_AsString(kw_name)); #endif } /* ParseKeywords */ static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name) { PyObject *key = 0, *value = 0; Py_ssize_t pos = 0; PyObject*** name; PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { name = first_kw_arg; while (*name && (**name != key)) name++; if (*name) { values[name-argnames] = value; continue; } name = first_kw_arg; #if PY_MAJOR_VERSION < 3 if (likely(PyString_Check(key))) { while (*name) { if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) && _PyString_Eq(**name, key)) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { if ((**argname == key) || ( (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) && _PyString_Eq(**argname, key))) { goto arg_passed_twice; } argname++; } } } else #endif if (likely(PyUnicode_Check(key))) { while (*name) { int cmp = (**name == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : #endif PyUnicode_Compare(**name, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { int cmp = (**argname == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : #endif PyUnicode_Compare(**argname, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) goto arg_passed_twice; argname++; } } } else goto invalid_keyword_type; if (kwds2) { if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; } else { goto invalid_keyword; } } return 0; arg_passed_twice: __Pyx_RaiseDoubleKeywordsError(function_name, key); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%.200s() keywords must be strings", function_name); goto bad; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%.200s() got an unexpected keyword argument '%.200s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif bad: return -1; } /* PyCFunctionFastCall */ #if CYTHON_FAST_PYCCALL static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { PyCFunctionObject *func = (PyCFunctionObject*)func_obj; PyCFunction meth = PyCFunction_GET_FUNCTION(func); PyObject *self = PyCFunction_GET_SELF(func); int flags = PyCFunction_GET_FLAGS(func); assert(PyCFunction_Check(func)); assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); assert(nargs >= 0); assert(nargs == 0 || args != NULL); /* _PyCFunction_FastCallDict() must not be called with an exception set, because it may clear it (directly or indirectly) and so the caller loses its exception */ assert(!PyErr_Occurred()); if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); } else { return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); } } #endif /* PyFunctionFastCall */ #if CYTHON_FAST_PYCALL static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, PyObject *globals) { PyFrameObject *f; PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject **fastlocals; Py_ssize_t i; PyObject *result; assert(globals != NULL); /* XXX Perhaps we should create a specialized PyFrame_New() that doesn't take locals, but does take builtins without sanity checking them. */ assert(tstate != NULL); f = PyFrame_New(tstate, co, globals, NULL); if (f == NULL) { return NULL; } fastlocals = __Pyx_PyFrame_GetLocalsplus(f); for (i = 0; i < na; i++) { Py_INCREF(*args); fastlocals[i] = *args++; } result = PyEval_EvalFrameEx(f,0); ++tstate->recursion_depth; Py_DECREF(f); --tstate->recursion_depth; return result; } #if 1 || PY_VERSION_HEX < 0x030600B1 static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); PyObject *globals = PyFunction_GET_GLOBALS(func); PyObject *argdefs = PyFunction_GET_DEFAULTS(func); PyObject *closure; #if PY_MAJOR_VERSION >= 3 PyObject *kwdefs; #endif PyObject *kwtuple, **k; PyObject **d; Py_ssize_t nd; Py_ssize_t nk; PyObject *result; assert(kwargs == NULL || PyDict_Check(kwargs)); nk = kwargs ? PyDict_Size(kwargs) : 0; if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { return NULL; } if ( #if PY_MAJOR_VERSION >= 3 co->co_kwonlyargcount == 0 && #endif likely(kwargs == NULL || nk == 0) && co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { if (argdefs == NULL && co->co_argcount == nargs) { result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); goto done; } else if (nargs == 0 && argdefs != NULL && co->co_argcount == Py_SIZE(argdefs)) { /* function called with no arguments, but all parameters have a default value: use default values as arguments .*/ args = &PyTuple_GET_ITEM(argdefs, 0); result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); goto done; } } if (kwargs != NULL) { Py_ssize_t pos, i; kwtuple = PyTuple_New(2 * nk); if (kwtuple == NULL) { result = NULL; goto done; } k = &PyTuple_GET_ITEM(kwtuple, 0); pos = i = 0; while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { Py_INCREF(k[i]); Py_INCREF(k[i+1]); i += 2; } nk = i / 2; } else { kwtuple = NULL; k = NULL; } closure = PyFunction_GET_CLOSURE(func); #if PY_MAJOR_VERSION >= 3 kwdefs = PyFunction_GET_KW_DEFAULTS(func); #endif if (argdefs != NULL) { d = &PyTuple_GET_ITEM(argdefs, 0); nd = Py_SIZE(argdefs); } else { d = NULL; nd = 0; } #if PY_MAJOR_VERSION >= 3 result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, args, (int)nargs, k, (int)nk, d, (int)nd, kwdefs, closure); #else result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, args, (int)nargs, k, (int)nk, d, (int)nd, closure); #endif Py_XDECREF(kwtuple); done: Py_LeaveRecursiveCall(); return result; } #endif #endif /* PyObjectCall */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; ternaryfunc call = func->ob_type->tp_call; if (unlikely(!call)) return PyObject_Call(func, arg, kw); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = (*call)(func, arg, kw); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif /* PyObjectCallMethO */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { PyObject *self, *result; PyCFunction cfunc; cfunc = PyCFunction_GET_FUNCTION(func); self = PyCFunction_GET_SELF(func); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = cfunc(self, arg); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif /* PyObjectCallOneArg */ #if CYTHON_COMPILING_IN_CPYTHON static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_New(1); if (unlikely(!args)) return NULL; Py_INCREF(arg); PyTuple_SET_ITEM(args, 0, arg); result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { #if CYTHON_FAST_PYCALL if (PyFunction_Check(func)) { return __Pyx_PyFunction_FastCall(func, &arg, 1); } #endif if (likely(PyCFunction_Check(func))) { if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { return __Pyx_PyObject_CallMethO(func, arg); #if CYTHON_FAST_PYCCALL } else if (__Pyx_PyFastCFunction_Check(func)) { return __Pyx_PyCFunction_FastCall(func, &arg, 1); #endif } } return __Pyx__PyObject_CallOneArg(func, arg); } #else static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_Pack(1, arg); if (unlikely(!args)) return NULL; result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } #endif /* PyDictVersioning */ #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { PyObject *dict = Py_TYPE(obj)->tp_dict; return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; } static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { PyObject **dictptr = NULL; Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; if (offset) { #if CYTHON_COMPILING_IN_CPYTHON dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); #else dictptr = _PyObject_GetDictPtr(obj); #endif } return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; } static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { PyObject *dict = Py_TYPE(obj)->tp_dict; if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) return 0; return obj_dict_version == __Pyx_get_object_dict_version(obj); } #endif /* GetModuleGlobalName */ #if CYTHON_USE_DICT_VERSIONS static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) #else static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) #endif { PyObject *result; #if !CYTHON_AVOID_BORROWED_REFS #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } else if (unlikely(PyErr_Occurred())) { return NULL; } #else result = PyDict_GetItem(__pyx_d, name); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } #endif #else result = PyObject_GetItem(__pyx_d, name); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } PyErr_Clear(); #endif return __Pyx_GetBuiltinName(name); } /* RaiseTooManyValuesToUnpack */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } /* RaiseNeedMoreValuesToUnpack */ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", index, (index == 1) ? "" : "s"); } /* IterFinish */ static CYTHON_INLINE int __Pyx_IterFinish(void) { #if CYTHON_FAST_THREAD_STATE PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject* exc_type = tstate->curexc_type; if (unlikely(exc_type)) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) { PyObject *exc_value, *exc_tb; exc_value = tstate->curexc_value; exc_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; Py_DECREF(exc_type); Py_XDECREF(exc_value); Py_XDECREF(exc_tb); return 0; } else { return -1; } } return 0; #else if (unlikely(PyErr_Occurred())) { if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) { PyErr_Clear(); return 0; } else { return -1; } } return 0; #endif } /* UnpackItemEndCheck */ static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) { if (unlikely(retval)) { Py_DECREF(retval); __Pyx_RaiseTooManyValuesError(expected); return -1; } else { return __Pyx_IterFinish(); } return 0; } /* PyObjectCallNoArg */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { #if CYTHON_FAST_PYCALL if (PyFunction_Check(func)) { return __Pyx_PyFunction_FastCall(func, NULL, 0); } #endif #ifdef __Pyx_CyFunction_USED if (likely(PyCFunction_Check(func) || __Pyx_CyFunction_Check(func))) #else if (likely(PyCFunction_Check(func))) #endif { if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { return __Pyx_PyObject_CallMethO(func, NULL); } } return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); } #endif /* GetTopmostException */ #if CYTHON_USE_EXC_INFO_STACK static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate) { _PyErr_StackItem *exc_info = tstate->exc_info; while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && exc_info->previous_item != NULL) { exc_info = exc_info->previous_item; } return exc_info; } #endif /* SaveResetException */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); *type = exc_info->exc_type; *value = exc_info->exc_value; *tb = exc_info->exc_traceback; #else *type = tstate->exc_type; *value = tstate->exc_value; *tb = tstate->exc_traceback; #endif Py_XINCREF(*type); Py_XINCREF(*value); Py_XINCREF(*tb); } static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = type; exc_info->exc_value = value; exc_info->exc_traceback = tb; #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = type; tstate->exc_value = value; tstate->exc_traceback = tb; #endif Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } #endif /* PyErrExceptionMatches */ #if CYTHON_FAST_THREAD_STATE static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { Py_ssize_t i, n; n = PyTuple_GET_SIZE(tuple); #if PY_MAJOR_VERSION >= 3 for (i=0; icurexc_type; if (exc_type == err) return 1; if (unlikely(!exc_type)) return 0; if (unlikely(PyTuple_Check(err))) return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); } #endif /* GetException */ #if CYTHON_FAST_THREAD_STATE static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) #else static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) #endif { PyObject *local_type, *local_value, *local_tb; #if CYTHON_FAST_THREAD_STATE PyObject *tmp_type, *tmp_value, *tmp_tb; local_type = tstate->curexc_type; local_value = tstate->curexc_value; local_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(&local_type, &local_value, &local_tb); #endif PyErr_NormalizeException(&local_type, &local_value, &local_tb); #if CYTHON_FAST_THREAD_STATE if (unlikely(tstate->curexc_type)) #else if (unlikely(PyErr_Occurred())) #endif goto bad; #if PY_MAJOR_VERSION >= 3 if (local_tb) { if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) goto bad; } #endif Py_XINCREF(local_tb); Py_XINCREF(local_type); Py_XINCREF(local_value); *type = local_type; *value = local_value; *tb = local_tb; #if CYTHON_FAST_THREAD_STATE #if CYTHON_USE_EXC_INFO_STACK { _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = local_type; exc_info->exc_value = local_value; exc_info->exc_traceback = local_tb; } #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = local_type; tstate->exc_value = local_value; tstate->exc_traceback = local_tb; #endif Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_SetExcInfo(local_type, local_value, local_tb); #endif return 0; bad: *type = 0; *value = 0; *tb = 0; Py_XDECREF(local_type); Py_XDECREF(local_value); Py_XDECREF(local_tb); return -1; } /* RaiseException */ #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { __Pyx_PyThreadState_declare Py_XINCREF(type); if (!value || value == Py_None) value = NULL; else Py_INCREF(value); if (!tb || tb == Py_None) tb = NULL; else { Py_INCREF(tb); if (!PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } } if (PyType_Check(type)) { #if CYTHON_COMPILING_IN_PYPY if (!value) { Py_INCREF(Py_None); value = Py_None; } #endif PyErr_NormalizeException(&type, &value, &tb); } else { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } value = type; type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } } __Pyx_PyThreadState_assign __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (PyExceptionClass_Check(type)) { PyObject *instance_class = NULL; if (value && PyExceptionInstance_Check(value)) { instance_class = (PyObject*) Py_TYPE(value); if (instance_class != type) { int is_subclass = PyObject_IsSubclass(instance_class, type); if (!is_subclass) { instance_class = NULL; } else if (unlikely(is_subclass == -1)) { goto bad; } else { type = instance_class; } } } if (!instance_class) { PyObject *args; if (!value) args = PyTuple_New(0); else if (PyTuple_Check(value)) { Py_INCREF(value); args = value; } else args = PyTuple_Pack(1, value); if (!args) goto bad; owned_instance = PyObject_Call(type, args, NULL); Py_DECREF(args); if (!owned_instance) goto bad; value = owned_instance; if (!PyExceptionInstance_Check(value)) { PyErr_Format(PyExc_TypeError, "calling %R should have returned an instance of " "BaseException, not %R", type, Py_TYPE(value)); goto bad; } } } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } if (cause) { PyObject *fixed_cause; if (cause == Py_None) { fixed_cause = NULL; } else if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { #if CYTHON_COMPILING_IN_PYPY PyObject *tmp_type, *tmp_value, *tmp_tb; PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); Py_INCREF(tb); PyErr_Restore(tmp_type, tmp_value, tb); Py_XDECREF(tmp_tb); #else PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } #endif } bad: Py_XDECREF(owned_instance); return; } #endif /* ArgTypeTest */ static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } else if (exact) { #if PY_MAJOR_VERSION == 2 if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; #endif } else { if (likely(__Pyx_TypeCheck(obj, type))) return 1; } PyErr_Format(PyExc_TypeError, "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", name, type->tp_name, Py_TYPE(obj)->tp_name); return 0; } /* PyObjectCall2Args */ static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) { PyObject *args, *result = NULL; #if CYTHON_FAST_PYCALL if (PyFunction_Check(function)) { PyObject *args[2] = {arg1, arg2}; return __Pyx_PyFunction_FastCall(function, args, 2); } #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(function)) { PyObject *args[2] = {arg1, arg2}; return __Pyx_PyCFunction_FastCall(function, args, 2); } #endif args = PyTuple_New(2); if (unlikely(!args)) goto done; Py_INCREF(arg1); PyTuple_SET_ITEM(args, 0, arg1); Py_INCREF(arg2); PyTuple_SET_ITEM(args, 1, arg2); Py_INCREF(function); result = __Pyx_PyObject_Call(function, args, NULL); Py_DECREF(args); Py_DECREF(function); done: return result; } /* BytesEquals */ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { #if CYTHON_COMPILING_IN_PYPY return PyObject_RichCompareBool(s1, s2, equals); #else if (s1 == s2) { return (equals == Py_EQ); } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { const char *ps1, *ps2; Py_ssize_t length = PyBytes_GET_SIZE(s1); if (length != PyBytes_GET_SIZE(s2)) return (equals == Py_NE); ps1 = PyBytes_AS_STRING(s1); ps2 = PyBytes_AS_STRING(s2); if (ps1[0] != ps2[0]) { return (equals == Py_NE); } else if (length == 1) { return (equals == Py_EQ); } else { int result; #if CYTHON_USE_UNICODE_INTERNALS Py_hash_t hash1, hash2; hash1 = ((PyBytesObject*)s1)->ob_shash; hash2 = ((PyBytesObject*)s2)->ob_shash; if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { return (equals == Py_NE); } #endif result = memcmp(ps1, ps2, (size_t)length); return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { return (equals == Py_NE); } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { return (equals == Py_NE); } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } #endif } /* UnicodeEquals */ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { #if CYTHON_COMPILING_IN_PYPY return PyObject_RichCompareBool(s1, s2, equals); #else #if PY_MAJOR_VERSION < 3 PyObject* owned_ref = NULL; #endif int s1_is_unicode, s2_is_unicode; if (s1 == s2) { goto return_eq; } s1_is_unicode = PyUnicode_CheckExact(s1); s2_is_unicode = PyUnicode_CheckExact(s2); #if PY_MAJOR_VERSION < 3 if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { owned_ref = PyUnicode_FromObject(s2); if (unlikely(!owned_ref)) return -1; s2 = owned_ref; s2_is_unicode = 1; } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { owned_ref = PyUnicode_FromObject(s1); if (unlikely(!owned_ref)) return -1; s1 = owned_ref; s1_is_unicode = 1; } else if (((!s2_is_unicode) & (!s1_is_unicode))) { return __Pyx_PyBytes_Equals(s1, s2, equals); } #endif if (s1_is_unicode & s2_is_unicode) { Py_ssize_t length; int kind; void *data1, *data2; if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) return -1; length = __Pyx_PyUnicode_GET_LENGTH(s1); if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { goto return_ne; } #if CYTHON_USE_UNICODE_INTERNALS { Py_hash_t hash1, hash2; #if CYTHON_PEP393_ENABLED hash1 = ((PyASCIIObject*)s1)->hash; hash2 = ((PyASCIIObject*)s2)->hash; #else hash1 = ((PyUnicodeObject*)s1)->hash; hash2 = ((PyUnicodeObject*)s2)->hash; #endif if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { goto return_ne; } } #endif kind = __Pyx_PyUnicode_KIND(s1); if (kind != __Pyx_PyUnicode_KIND(s2)) { goto return_ne; } data1 = __Pyx_PyUnicode_DATA(s1); data2 = __Pyx_PyUnicode_DATA(s2); if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { goto return_ne; } else if (length == 1) { goto return_eq; } else { int result = memcmp(data1, data2, (size_t)(length * kind)); #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & s2_is_unicode) { goto return_ne; } else if ((s2 == Py_None) & s1_is_unicode) { goto return_ne; } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } return_eq: #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_EQ); return_ne: #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_NE); #endif } /* GetAttr */ static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { #if CYTHON_USE_TYPE_SLOTS #if PY_MAJOR_VERSION >= 3 if (likely(PyUnicode_Check(n))) #else if (likely(PyString_Check(n))) #endif return __Pyx_PyObject_GetAttrStr(o, n); #endif return PyObject_GetAttr(o, n); } /* GetItemInt */ static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { PyObject *r; if (!j) return NULL; r = PyObject_GetItem(o, j); Py_DECREF(j); return r; } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS Py_ssize_t wrapped_i = i; if (wraparound & unlikely(i < 0)) { wrapped_i += PyList_GET_SIZE(o); } if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { PyObject *r = PyList_GET_ITEM(o, wrapped_i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS Py_ssize_t wrapped_i = i; if (wraparound & unlikely(i < 0)) { wrapped_i += PyTuple_GET_SIZE(o); } if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS if (is_list || PyList_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { PyObject *r = PyList_GET_ITEM(o, n); Py_INCREF(r); return r; } } else if (PyTuple_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, n); Py_INCREF(r); return r; } } else { PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; if (likely(m && m->sq_item)) { if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { Py_ssize_t l = m->sq_length(o); if (likely(l >= 0)) { i += l; } else { if (!PyErr_ExceptionMatches(PyExc_OverflowError)) return NULL; PyErr_Clear(); } } return m->sq_item(o, i); } } #else if (is_list || PySequence_Check(o)) { return PySequence_GetItem(o, i); } #endif return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); } /* ObjectGetItem */ #if CYTHON_USE_TYPE_SLOTS static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { PyObject *runerr; Py_ssize_t key_value; PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence; if (unlikely(!(m && m->sq_item))) { PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name); return NULL; } key_value = __Pyx_PyIndex_AsSsize_t(index); if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); } if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { PyErr_Clear(); PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name); } return NULL; } static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping; if (likely(m && m->mp_subscript)) { return m->mp_subscript(obj, key); } return __Pyx_PyObject_GetIndex(obj, key); } #endif /* decode_c_string */ static CYTHON_INLINE PyObject* __Pyx_decode_c_string( const char* cstring, Py_ssize_t start, Py_ssize_t stop, const char* encoding, const char* errors, PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { Py_ssize_t length; if (unlikely((start < 0) | (stop < 0))) { size_t slen = strlen(cstring); if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { PyErr_SetString(PyExc_OverflowError, "c-string too long to convert to Python"); return NULL; } length = (Py_ssize_t) slen; if (start < 0) { start += length; if (start < 0) start = 0; } if (stop < 0) stop += length; } if (unlikely(stop <= start)) return __Pyx_NewRef(__pyx_empty_unicode); length = stop - start; cstring += start; if (decode_func) { return decode_func(cstring, length, errors); } else { return PyUnicode_Decode(cstring, length, encoding, errors); } } /* GetAttr3 */ static PyObject *__Pyx_GetAttr3Default(PyObject *d) { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) return NULL; __Pyx_PyErr_Clear(); Py_INCREF(d); return d; } static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { PyObject *r = __Pyx_GetAttr(o, n); return (likely(r)) ? r : __Pyx_GetAttr3Default(d); } /* RaiseNoneIterError */ static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } /* ExtTypeTest */ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (likely(__Pyx_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } /* SwapException */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = *type; exc_info->exc_value = *value; exc_info->exc_traceback = *tb; #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = *type; tstate->exc_value = *value; tstate->exc_traceback = *tb; #endif *type = tmp_type; *value = tmp_value; *tb = tmp_tb; } #else static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); PyErr_SetExcInfo(*type, *value, *tb); *type = tmp_type; *value = tmp_value; *tb = tmp_tb; } #endif /* Import */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; #if PY_MAJOR_VERSION < 3 PyObject *py_import; py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); if (!py_import) goto bad; #endif if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, 1); if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; } #endif if (!module) { #if PY_MAJOR_VERSION < 3 PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, level); #endif } } bad: #if PY_MAJOR_VERSION < 3 Py_XDECREF(py_import); #endif Py_XDECREF(empty_list); Py_XDECREF(empty_dict); return module; } /* FastTypeChecks */ #if CYTHON_COMPILING_IN_CPYTHON static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { while (a) { a = a->tp_base; if (a == b) return 1; } return b == &PyBaseObject_Type; } static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { PyObject *mro; if (a == b) return 1; mro = a->tp_mro; if (likely(mro)) { Py_ssize_t i, n; n = PyTuple_GET_SIZE(mro); for (i = 0; i < n; i++) { if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) return 1; } return 0; } return __Pyx_InBases(a, b); } #if PY_MAJOR_VERSION == 2 static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { PyObject *exception, *value, *tb; int res; __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ErrFetch(&exception, &value, &tb); res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; if (unlikely(res == -1)) { PyErr_WriteUnraisable(err); res = 0; } if (!res) { res = PyObject_IsSubclass(err, exc_type2); if (unlikely(res == -1)) { PyErr_WriteUnraisable(err); res = 0; } } __Pyx_ErrRestore(exception, value, tb); return res; } #else static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; if (!res) { res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); } return res; } #endif static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { Py_ssize_t i, n; assert(PyExceptionClass_Check(exc_type)); n = PyTuple_GET_SIZE(tuple); #if PY_MAJOR_VERSION >= 3 for (i=0; i= 0 || (x^b) >= 0)) return PyInt_FromLong(x); return PyLong_Type.tp_as_number->nb_add(op1, op2); } #endif #if CYTHON_USE_PYLONG_INTERNALS if (likely(PyLong_CheckExact(op1))) { const long b = intval; long a, x; #ifdef HAVE_LONG_LONG const PY_LONG_LONG llb = intval; PY_LONG_LONG lla, llx; #endif const digit* digits = ((PyLongObject*)op1)->ob_digit; const Py_ssize_t size = Py_SIZE(op1); if (likely(__Pyx_sst_abs(size) <= 1)) { a = likely(size) ? digits[0] : 0; if (size == -1) a = -a; } else { switch (size) { case -2: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 2: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case -3: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 3: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case -4: if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 4: if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; default: return PyLong_Type.tp_as_number->nb_add(op1, op2); } } x = a + b; return PyLong_FromLong(x); #ifdef HAVE_LONG_LONG long_long: llx = lla + llb; return PyLong_FromLongLong(llx); #endif } #endif if (PyFloat_CheckExact(op1)) { const long b = intval; double a = PyFloat_AS_DOUBLE(op1); double result; PyFPE_START_PROTECT("add", return NULL) result = ((double)a) + (double)b; PyFPE_END_PROTECT(result) return PyFloat_FromDouble(result); } return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); } #endif /* None */ static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); } /* ImportFrom */ static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Format(PyExc_ImportError, #if PY_MAJOR_VERSION < 3 "cannot import name %.230s", PyString_AS_STRING(name)); #else "cannot import name %S", name); #endif } return value; } /* HasAttr */ static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { PyObject *r; if (unlikely(!__Pyx_PyBaseString_Check(n))) { PyErr_SetString(PyExc_TypeError, "hasattr(): attribute name must be string"); return -1; } r = __Pyx_GetAttr(o, n); if (unlikely(!r)) { PyErr_Clear(); return 0; } else { Py_DECREF(r); return 1; } } /* PyObject_GenericGetAttrNoDict */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { PyErr_Format(PyExc_AttributeError, #if PY_MAJOR_VERSION >= 3 "'%.50s' object has no attribute '%U'", tp->tp_name, attr_name); #else "'%.50s' object has no attribute '%.400s'", tp->tp_name, PyString_AS_STRING(attr_name)); #endif return NULL; } static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { PyObject *descr; PyTypeObject *tp = Py_TYPE(obj); if (unlikely(!PyString_Check(attr_name))) { return PyObject_GenericGetAttr(obj, attr_name); } assert(!tp->tp_dictoffset); descr = _PyType_Lookup(tp, attr_name); if (unlikely(!descr)) { return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); } Py_INCREF(descr); #if PY_MAJOR_VERSION < 3 if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) #endif { descrgetfunc f = Py_TYPE(descr)->tp_descr_get; if (unlikely(f)) { PyObject *res = f(descr, obj, (PyObject *)tp); Py_DECREF(descr); return res; } } return descr; } #endif /* PyObject_GenericGetAttr */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { return PyObject_GenericGetAttr(obj, attr_name); } return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); } #endif /* SetVTable */ static int __Pyx_SetVtable(PyObject *dict, void *vtable) { #if PY_VERSION_HEX >= 0x02070000 PyObject *ob = PyCapsule_New(vtable, 0, 0); #else PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); #endif if (!ob) goto bad; if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) goto bad; Py_DECREF(ob); return 0; bad: Py_XDECREF(ob); return -1; } /* PyObjectGetAttrStrNoError */ static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) __Pyx_PyErr_Clear(); } static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) { PyObject *result; #if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1 PyTypeObject* tp = Py_TYPE(obj); if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) { return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1); } #endif result = __Pyx_PyObject_GetAttrStr(obj, attr_name); if (unlikely(!result)) { __Pyx_PyObject_GetAttrStr_ClearAttributeError(); } return result; } /* SetupReduce */ static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { int ret; PyObject *name_attr; name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2); if (likely(name_attr)) { ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); } else { ret = -1; } if (unlikely(ret < 0)) { PyErr_Clear(); ret = 0; } Py_XDECREF(name_attr); return ret; } static int __Pyx_setup_reduce(PyObject* type_obj) { int ret = 0; PyObject *object_reduce = NULL; PyObject *object_reduce_ex = NULL; PyObject *reduce = NULL; PyObject *reduce_ex = NULL; PyObject *reduce_cython = NULL; PyObject *setstate = NULL; PyObject *setstate_cython = NULL; #if CYTHON_USE_PYTYPE_LOOKUP if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; #else if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; #endif #if CYTHON_USE_PYTYPE_LOOKUP object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; #else object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; #endif reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; if (reduce_ex == object_reduce_ex) { #if CYTHON_USE_PYTYPE_LOOKUP object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; #else object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; #endif reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); if (likely(reduce_cython)) { ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; } else if (reduce == object_reduce || PyErr_Occurred()) { goto __PYX_BAD; } setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); if (!setstate) PyErr_Clear(); if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); if (likely(setstate_cython)) { ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; } else if (!setstate || PyErr_Occurred()) { goto __PYX_BAD; } } PyType_Modified((PyTypeObject*)type_obj); } } goto __PYX_GOOD; __PYX_BAD: if (!PyErr_Occurred()) PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); ret = -1; __PYX_GOOD: #if !CYTHON_USE_PYTYPE_LOOKUP Py_XDECREF(object_reduce); Py_XDECREF(object_reduce_ex); #endif Py_XDECREF(reduce); Py_XDECREF(reduce_ex); Py_XDECREF(reduce_cython); Py_XDECREF(setstate); Py_XDECREF(setstate_cython); return ret; } /* TypeImport */ #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size) { PyObject *result = 0; char warning[200]; Py_ssize_t basicsize; #ifdef Py_LIMITED_API PyObject *py_basicsize; #endif result = PyObject_GetAttrString(module, class_name); if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%.200s.%.200s is not a type object", module_name, class_name); goto bad; } #ifndef Py_LIMITED_API basicsize = ((PyTypeObject *)result)->tp_basicsize; #else py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); if (!py_basicsize) goto bad; basicsize = PyLong_AsSsize_t(py_basicsize); Py_DECREF(py_basicsize); py_basicsize = 0; if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) goto bad; #endif if ((size_t)basicsize < size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); goto bad; } if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); goto bad; } else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(result); return NULL; } #endif /* GetVTable */ static void* __Pyx_GetVtable(PyObject *dict) { void* ptr; PyObject *ob = PyObject_GetItem(dict, __pyx_n_s_pyx_vtable); if (!ob) goto bad; #if PY_VERSION_HEX >= 0x02070000 ptr = PyCapsule_GetPointer(ob, 0); #else ptr = PyCObject_AsVoidPtr(ob); #endif if (!ptr && !PyErr_Occurred()) PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type"); Py_DECREF(ob); return ptr; bad: Py_XDECREF(ob); return NULL; } /* CLineInTraceback */ #ifndef CYTHON_CLINE_IN_TRACEBACK static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) { PyObject *use_cline; PyObject *ptype, *pvalue, *ptraceback; #if CYTHON_COMPILING_IN_CPYTHON PyObject **cython_runtime_dict; #endif if (unlikely(!__pyx_cython_runtime)) { return c_line; } __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); #if CYTHON_COMPILING_IN_CPYTHON cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); if (likely(cython_runtime_dict)) { __PYX_PY_DICT_LOOKUP_IF_MODIFIED( use_cline, *cython_runtime_dict, __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) } else #endif { PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); if (use_cline_obj) { use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; Py_DECREF(use_cline_obj); } else { PyErr_Clear(); use_cline = NULL; } } if (!use_cline) { c_line = 0; PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); } else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { c_line = 0; } __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); return c_line; } #endif /* CodeObjectCache */ static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = start + (end - start) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } /* AddTraceback */ #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, 0, 0, 0, 0, __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; PyThreadState *tstate = __Pyx_PyThreadState_Current; if (c_line) { c_line = __Pyx_CLineForTraceback(tstate, c_line); } py_code = __pyx_find_code_object(c_line ? -c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); } py_frame = PyFrame_New( tstate, /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ __pyx_d, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; __Pyx_PyFrame_SetLineNumber(py_frame, py_line); PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; if (!obj) return; if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } if ((0)) {} view->obj = NULL; Py_DECREF(obj); } #endif /* MemviewSliceIsContig */ static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) { int i, index, step, start; Py_ssize_t itemsize = mvs.memview->view.itemsize; if (order == 'F') { step = 1; start = 0; } else { step = -1; start = ndim - 1; } for (i = 0; i < ndim; i++) { index = start + step * i; if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) return 0; itemsize *= mvs.shape[index]; } return 1; } /* OverlappingSlices */ static void __pyx_get_array_memory_extents(__Pyx_memviewslice *slice, void **out_start, void **out_end, int ndim, size_t itemsize) { char *start, *end; int i; start = end = slice->data; for (i = 0; i < ndim; i++) { Py_ssize_t stride = slice->strides[i]; Py_ssize_t extent = slice->shape[i]; if (extent == 0) { *out_start = *out_end = start; return; } else { if (stride > 0) end += stride * (extent - 1); else start += stride * (extent - 1); } } *out_start = start; *out_end = end + itemsize; } static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, __Pyx_memviewslice *slice2, int ndim, size_t itemsize) { void *start1, *end1, *start2, *end2; __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); return (start1 < end2) && (start2 < end1); } /* Capsule */ static CYTHON_INLINE PyObject * __pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) { PyObject *cobj; #if PY_VERSION_HEX >= 0x02070000 cobj = PyCapsule_New(p, sig, NULL); #else cobj = PyCObject_FromVoidPtr(p, NULL); #endif return cobj; } /* IsLittleEndian */ static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) { union { uint32_t u32; uint8_t u8[4]; } S; S.u32 = 0x01020304; return S.u8[0] == 4; } /* BufferFormatCheck */ static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type) { stack[0].field = &ctx->root; stack[0].parent_offset = 0; ctx->root.type = type; ctx->root.name = "buffer dtype"; ctx->root.offset = 0; ctx->head = stack; ctx->head->field = &ctx->root; ctx->fmt_offset = 0; ctx->head->parent_offset = 0; ctx->new_packmode = '@'; ctx->enc_packmode = '@'; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->is_complex = 0; ctx->is_valid_array = 0; ctx->struct_alignment = 0; while (type->typegroup == 'S') { ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = 0; type = type->fields->type; } } static int __Pyx_BufFmt_ParseNumber(const char** ts) { int count; const char* t = *ts; if (*t < '0' || *t > '9') { return -1; } else { count = *t++ - '0'; while (*t >= '0' && *t <= '9') { count *= 10; count += *t++ - '0'; } } *ts = t; return count; } static int __Pyx_BufFmt_ExpectNumber(const char **ts) { int number = __Pyx_BufFmt_ParseNumber(ts); if (number == -1) PyErr_Format(PyExc_ValueError,\ "Does not understand character buffer dtype format string ('%c')", **ts); return number; } static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { PyErr_Format(PyExc_ValueError, "Unexpected format string character: '%c'", ch); } static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { switch (ch) { case '?': return "'bool'"; case 'c': return "'char'"; case 'b': return "'signed char'"; case 'B': return "'unsigned char'"; case 'h': return "'short'"; case 'H': return "'unsigned short'"; case 'i': return "'int'"; case 'I': return "'unsigned int'"; case 'l': return "'long'"; case 'L': return "'unsigned long'"; case 'q': return "'long long'"; case 'Q': return "'unsigned long long'"; case 'f': return (is_complex ? "'complex float'" : "'float'"); case 'd': return (is_complex ? "'complex double'" : "'double'"); case 'g': return (is_complex ? "'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; case 's': case 'p': return "a string"; case 0: return "end"; default: return "unparseable format string"; } } static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return 2; case 'i': case 'I': case 'l': case 'L': return 4; case 'q': case 'Q': return 8; case 'f': return (is_complex ? 8 : 4); case 'd': return (is_complex ? 16 : 8); case 'g': { PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); return 0; } case 'O': case 'P': return sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(short); case 'i': case 'I': return sizeof(int); case 'l': case 'L': return sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(float) * (is_complex ? 2 : 1); case 'd': return sizeof(double) * (is_complex ? 2 : 1); case 'g': return sizeof(long double) * (is_complex ? 2 : 1); case 'O': case 'P': return sizeof(void*); default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } typedef struct { char c; short x; } __Pyx_st_short; typedef struct { char c; int x; } __Pyx_st_int; typedef struct { char c; long x; } __Pyx_st_long; typedef struct { char c; float x; } __Pyx_st_float; typedef struct { char c; double x; } __Pyx_st_double; typedef struct { char c; long double x; } __Pyx_st_longdouble; typedef struct { char c; void *x; } __Pyx_st_void_p; #ifdef HAVE_LONG_LONG typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_st_float) - sizeof(float); case 'd': return sizeof(__Pyx_st_double) - sizeof(double); case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } /* These are for computing the padding at the end of the struct to align on the first member of the struct. This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': return 'H'; case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } if ((type->typegroup == 'H' || group == 'H') && type->size == size) { } else { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number, ndim; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ndim = ctx->head->field->type->ndim; while (*ts && *ts != ')') { switch (*ts) { case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; default: break; } number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case '\r': case '\n': ++ts; break; case '<': if (!__Pyx_Is_Little_Endian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_Is_Little_Endian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } CYTHON_FALLTHROUGH; case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 'p': if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { ctx->enc_count += ctx->new_count; ctx->new_count = 1; got_Z = 0; ++ts; break; } CYTHON_FALLTHROUGH; case 's': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } /* TypeInfoCompare */ static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) { int i; if (!a || !b) return 0; if (a == b) return 1; if (a->size != b->size || a->typegroup != b->typegroup || a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { if (a->typegroup == 'H' || b->typegroup == 'H') { return a->size == b->size; } else { return 0; } } if (a->ndim) { for (i = 0; i < a->ndim; i++) if (a->arraysize[i] != b->arraysize[i]) return 0; } if (a->typegroup == 'S') { if (a->flags != b->flags) return 0; if (a->fields || b->fields) { if (!(a->fields && b->fields)) return 0; for (i = 0; a->fields[i].type && b->fields[i].type; i++) { __Pyx_StructField *field_a = a->fields + i; __Pyx_StructField *field_b = b->fields + i; if (field_a->offset != field_b->offset || !__pyx_typeinfo_cmp(field_a->type, field_b->type)) return 0; } return !a->fields[i].type && !b->fields[i].type; } } return 1; } /* MemviewSliceValidateAndInit */ static int __pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) { if (buf->shape[dim] <= 1) return 1; if (buf->strides) { if (spec & __Pyx_MEMVIEW_CONTIG) { if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { if (unlikely(buf->strides[dim] != sizeof(void *))) { PyErr_Format(PyExc_ValueError, "Buffer is not indirectly contiguous " "in dimension %d.", dim); goto fail; } } else if (unlikely(buf->strides[dim] != buf->itemsize)) { PyErr_SetString(PyExc_ValueError, "Buffer and memoryview are not contiguous " "in the same dimension."); goto fail; } } if (spec & __Pyx_MEMVIEW_FOLLOW) { Py_ssize_t stride = buf->strides[dim]; if (stride < 0) stride = -stride; if (unlikely(stride < buf->itemsize)) { PyErr_SetString(PyExc_ValueError, "Buffer and memoryview are not contiguous " "in the same dimension."); goto fail; } } } else { if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { PyErr_Format(PyExc_ValueError, "C-contiguous buffer is not contiguous in " "dimension %d", dim); goto fail; } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { PyErr_Format(PyExc_ValueError, "C-contiguous buffer is not indirect in " "dimension %d", dim); goto fail; } else if (unlikely(buf->suboffsets)) { PyErr_SetString(PyExc_ValueError, "Buffer exposes suboffsets but no strides"); goto fail; } } return 1; fail: return 0; } static int __pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) { if (spec & __Pyx_MEMVIEW_DIRECT) { if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { PyErr_Format(PyExc_ValueError, "Buffer not compatible with direct access " "in dimension %d.", dim); goto fail; } } if (spec & __Pyx_MEMVIEW_PTR) { if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { PyErr_Format(PyExc_ValueError, "Buffer is not indirectly accessible " "in dimension %d.", dim); goto fail; } } return 1; fail: return 0; } static int __pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) { int i; if (c_or_f_flag & __Pyx_IS_F_CONTIG) { Py_ssize_t stride = 1; for (i = 0; i < ndim; i++) { if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { PyErr_SetString(PyExc_ValueError, "Buffer not fortran contiguous."); goto fail; } stride = stride * buf->shape[i]; } } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { Py_ssize_t stride = 1; for (i = ndim - 1; i >- 1; i--) { if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { PyErr_SetString(PyExc_ValueError, "Buffer not C contiguous."); goto fail; } stride = stride * buf->shape[i]; } } return 1; fail: return 0; } static int __Pyx_ValidateAndInit_memviewslice( int *axes_specs, int c_or_f_flag, int buf_flags, int ndim, __Pyx_TypeInfo *dtype, __Pyx_BufFmt_StackElem stack[], __Pyx_memviewslice *memviewslice, PyObject *original_obj) { struct __pyx_memoryview_obj *memview, *new_memview; __Pyx_RefNannyDeclarations Py_buffer *buf; int i, spec = 0, retval = -1; __Pyx_BufFmt_Context ctx; int from_memoryview = __pyx_memoryview_check(original_obj); __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) original_obj)->typeinfo)) { memview = (struct __pyx_memoryview_obj *) original_obj; new_memview = NULL; } else { memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( original_obj, buf_flags, 0, dtype); new_memview = memview; if (unlikely(!memview)) goto fail; } buf = &memview->view; if (unlikely(buf->ndim != ndim)) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", ndim, buf->ndim); goto fail; } if (new_memview) { __Pyx_BufFmt_Init(&ctx, stack, dtype); if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; } if (unlikely((unsigned) buf->itemsize != dtype->size)) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->len > 0) { for (i = 0; i < ndim; i++) { spec = axes_specs[i]; if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) goto fail; if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) goto fail; } if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) goto fail; } if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, new_memview != NULL) == -1)) { goto fail; } retval = 0; goto no_fail; fail: Py_XDECREF(new_memview); retval = -1; no_fail: __Pyx_RefNannyFinishContext(); return retval; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 2, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_int(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, PyBUF_RECORDS_RO | writable_flag, 1, &__Pyx_TypeInfo_int, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, PyBUF_RECORDS_RO | writable_flag, 1, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* MemviewDtypeToObject */ static CYTHON_INLINE PyObject *__pyx_memview_get_double(const char *itemp) { return (PyObject *) PyFloat_FromDouble(*(double *) itemp); } static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *obj) { double value = __pyx_PyFloat_AsDouble(obj); if ((value == (double)-1) && PyErr_Occurred()) return 0; *(double *) itemp = value; return 1; } /* CIntFromPyVerify */ #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) #define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) #define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ {\ func_type value = func_value;\ if (sizeof(target_type) < sizeof(func_type)) {\ if (unlikely(value != (func_type) (target_type) value)) {\ func_type zero = 0;\ if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ return (target_type) -1;\ if (is_unsigned && unlikely(value < zero))\ goto raise_neg_overflow;\ else\ goto raise_overflow;\ }\ }\ return (target_type) value;\ } /* MemviewDtypeToObject */ static CYTHON_INLINE PyObject *__pyx_memview_get_int(const char *itemp) { return (PyObject *) __Pyx_PyInt_From_int(*(int *) itemp); } static CYTHON_INLINE int __pyx_memview_set_int(const char *itemp, PyObject *obj) { int value = __Pyx_PyInt_As_int(obj); if ((value == (int)-1) && PyErr_Occurred()) return 0; *(int *) itemp = value; return 1; } /* Declarations */ #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif /* Arithmetic */ #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } #if 1 static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { if (b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); } else if (fabsf(b.real) >= fabsf(b.imag)) { if (b.real == 0 && b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); } else { float r = b.imag / b.real; float s = (float)(1.0) / (b.real + b.imag * r); return __pyx_t_float_complex_from_parts( (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); } } else { float r = b.real / b.imag; float s = (float)(1.0) / (b.imag + b.real * r); return __pyx_t_float_complex_from_parts( (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); } } #else static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { if (b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); } else { float denom = b.real * b.real + b.imag * b.imag; return __pyx_t_float_complex_from_parts( (a.real * b.real + a.imag * b.imag) / denom, (a.imag * b.real - a.real * b.imag) / denom); } } #endif static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: return __Pyx_c_prod_float(a, a); case 3: z = __Pyx_c_prod_float(a, a); return __Pyx_c_prod_float(z, a); case 4: z = __Pyx_c_prod_float(a, a); return __Pyx_c_prod_float(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } else if (b.imag == 0) { z.real = powf(a.real, b.real); z.imag = 0; return z; } else if (a.real > 0) { r = a.real; theta = 0; } else { r = -a.real; theta = atan2f(0.0, -1.0); } } else { r = __Pyx_c_abs_float(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif /* Declarations */ #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif /* Arithmetic */ #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } #if 1 static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { if (b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); } else if (fabs(b.real) >= fabs(b.imag)) { if (b.real == 0 && b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); } else { double r = b.imag / b.real; double s = (double)(1.0) / (b.real + b.imag * r); return __pyx_t_double_complex_from_parts( (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); } } else { double r = b.real / b.imag; double s = (double)(1.0) / (b.imag + b.real * r); return __pyx_t_double_complex_from_parts( (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); } } #else static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { if (b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); } else { double denom = b.real * b.real + b.imag * b.imag; return __pyx_t_double_complex_from_parts( (a.real * b.real + a.imag * b.imag) / denom, (a.imag * b.real - a.real * b.imag) / denom); } } #endif static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: return __Pyx_c_prod_double(a, a); case 3: z = __Pyx_c_prod_double(a, a); return __Pyx_c_prod_double(z, a); case 4: z = __Pyx_c_prod_double(a, a); return __Pyx_c_prod_double(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } else if (b.imag == 0) { z.real = pow(a.real, b.real); z.imag = 0; return z; } else if (a.real > 0) { r = a.real; theta = 0; } else { r = -a.real; theta = atan2(0.0, -1.0); } } else { r = __Pyx_c_abs_double(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif /* MemviewSliceCopyTemplate */ static __Pyx_memviewslice __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, const char *mode, int ndim, size_t sizeof_dtype, int contig_flag, int dtype_is_object) { __Pyx_RefNannyDeclarations int i; __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; struct __pyx_memoryview_obj *from_memview = from_mvs->memview; Py_buffer *buf = &from_memview->view; PyObject *shape_tuple = NULL; PyObject *temp_int = NULL; struct __pyx_array_obj *array_obj = NULL; struct __pyx_memoryview_obj *memview_obj = NULL; __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); for (i = 0; i < ndim; i++) { if (unlikely(from_mvs->suboffsets[i] >= 0)) { PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " "indirect dimensions (axis %d)", i); goto fail; } } shape_tuple = PyTuple_New(ndim); if (unlikely(!shape_tuple)) { goto fail; } __Pyx_GOTREF(shape_tuple); for(i = 0; i < ndim; i++) { temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); if(unlikely(!temp_int)) { goto fail; } else { PyTuple_SET_ITEM(shape_tuple, i, temp_int); temp_int = NULL; } } array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); if (unlikely(!array_obj)) { goto fail; } __Pyx_GOTREF(array_obj); memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( (PyObject *) array_obj, contig_flag, dtype_is_object, from_mvs->memview->typeinfo); if (unlikely(!memview_obj)) goto fail; if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) goto fail; if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, dtype_is_object) < 0)) goto fail; goto no_fail; fail: __Pyx_XDECREF(new_mvs.memview); new_mvs.memview = NULL; new_mvs.data = NULL; no_fail: __Pyx_XDECREF(shape_tuple); __Pyx_XDECREF(temp_int); __Pyx_XDECREF(array_obj); __Pyx_RefNannyFinishContext(); return new_mvs; } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const int neg_one = (int) -1, const_zero = (int) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(int) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(int), little, !is_unsigned); } } /* CIntFromPy */ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const int neg_one = (int) -1, const_zero = (int) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(int) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (int) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (int) 0; case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) case 2: if (8 * sizeof(int) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; case 3: if (8 * sizeof(int) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; case 4: if (8 * sizeof(int) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (int) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(int) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (int) 0; case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) case -2: if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 2: if (8 * sizeof(int) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case -3: if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 3: if (8 * sizeof(int) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case -4: if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 4: if (8 * sizeof(int) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; } #endif if (sizeof(int) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else int val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (int) -1; } } else { int val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (int) -1; val = __Pyx_PyInt_As_int(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to int"); return (int) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } /* CIntFromPy */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const long neg_one = (long) -1, const_zero = (long) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(long) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (long) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) case 2: if (8 * sizeof(long) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; case 3: if (8 * sizeof(long) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; case 4: if (8 * sizeof(long) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (long) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) case -2: if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 2: if (8 * sizeof(long) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case -3: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 3: if (8 * sizeof(long) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case -4: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 4: if (8 * sizeof(long) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; } #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (long) -1; } } else { long val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to long"); return (long) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const long neg_one = (long) -1, const_zero = (long) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(long) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(long), little, !is_unsigned); } } /* CIntFromPy */ static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const char neg_one = (char) -1, const_zero = (char) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(char) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (char) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (char) 0; case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0]) case 2: if (8 * sizeof(char) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) { return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; case 3: if (8 * sizeof(char) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) { return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; case 4: if (8 * sizeof(char) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) { return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (char) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(char) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (char) 0; case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0]) case -2: if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 2: if (8 * sizeof(char) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case -3: if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 3: if (8 * sizeof(char) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case -4: if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 4: if (8 * sizeof(char) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; } #endif if (sizeof(char) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else char val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (char) -1; } } else { char val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (char) -1; val = __Pyx_PyInt_As_char(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to char"); return (char) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to char"); return (char) -1; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* CheckBinaryVersion */ static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); return PyErr_WarnEx(NULL, message, 1); } return 0; } /* VoidPtrImport */ #ifndef __PYX_HAVE_RT_ImportVoidPtr #define __PYX_HAVE_RT_ImportVoidPtr static int __Pyx_ImportVoidPtr(PyObject *module, const char *name, void **p, const char *sig) { PyObject *d = 0; PyObject *cobj = 0; d = PyObject_GetAttrString(module, (char *)"__pyx_capi__"); if (!d) goto bad; cobj = PyDict_GetItemString(d, name); if (!cobj) { PyErr_Format(PyExc_ImportError, "%.200s does not export expected C variable %.200s", PyModule_GetName(module), name); goto bad; } #if PY_VERSION_HEX >= 0x02070000 if (!PyCapsule_IsValid(cobj, sig)) { PyErr_Format(PyExc_TypeError, "C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)", PyModule_GetName(module), name, sig, PyCapsule_GetName(cobj)); goto bad; } *p = PyCapsule_GetPointer(cobj, sig); #else {const char *desc, *s1, *s2; desc = (const char *)PyCObject_GetDesc(cobj); if (!desc) goto bad; s1 = desc; s2 = sig; while (*s1 != '\0' && *s1 == *s2) { s1++; s2++; } if (*s1 != *s2) { PyErr_Format(PyExc_TypeError, "C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)", PyModule_GetName(module), name, sig, desc); goto bad; } *p = PyCObject_AsVoidPtr(cobj);} #endif if (!(*p)) goto bad; Py_DECREF(d); return 0; bad: Py_XDECREF(d); return -1; } #endif /* FunctionImport */ #ifndef __PYX_HAVE_RT_ImportFunction #define __PYX_HAVE_RT_ImportFunction static int __Pyx_ImportFunction(PyObject *module, const char *funcname, void (**f)(void), const char *sig) { PyObject *d = 0; PyObject *cobj = 0; union { void (*fp)(void); void *p; } tmp; d = PyObject_GetAttrString(module, (char *)"__pyx_capi__"); if (!d) goto bad; cobj = PyDict_GetItemString(d, funcname); if (!cobj) { PyErr_Format(PyExc_ImportError, "%.200s does not export expected C function %.200s", PyModule_GetName(module), funcname); goto bad; } #if PY_VERSION_HEX >= 0x02070000 if (!PyCapsule_IsValid(cobj, sig)) { PyErr_Format(PyExc_TypeError, "C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)", PyModule_GetName(module), funcname, sig, PyCapsule_GetName(cobj)); goto bad; } tmp.p = PyCapsule_GetPointer(cobj, sig); #else {const char *desc, *s1, *s2; desc = (const char *)PyCObject_GetDesc(cobj); if (!desc) goto bad; s1 = desc; s2 = sig; while (*s1 != '\0' && *s1 == *s2) { s1++; s2++; } if (*s1 != *s2) { PyErr_Format(PyExc_TypeError, "C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)", PyModule_GetName(module), funcname, sig, desc); goto bad; } tmp.p = PyCObject_AsVoidPtr(cobj);} #endif *f = tmp.fp; if (!(*f)) goto bad; Py_DECREF(d); return 0; bad: Py_XDECREF(d); return -1; } #endif /* InitStrings */ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; if (PyObject_Hash(*t->p) == -1) return -1; ++t; } return 0; } static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); } static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { Py_ssize_t ignore; return __Pyx_PyObject_AsStringAndSize(o, &ignore); } #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT #if !CYTHON_PEP393_ENABLED static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { char* defenc_c; PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); if (!defenc) return NULL; defenc_c = PyBytes_AS_STRING(defenc); #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII { char* end = defenc_c + PyBytes_GET_SIZE(defenc); char* c; for (c = defenc_c; c < end; c++) { if ((unsigned char) (*c) >= 128) { PyUnicode_AsASCIIString(o); return NULL; } } } #endif *length = PyBytes_GET_SIZE(defenc); return defenc_c; } #else static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII if (likely(PyUnicode_IS_ASCII(o))) { *length = PyUnicode_GET_LENGTH(o); return PyUnicode_AsUTF8(o); } else { PyUnicode_AsASCIIString(o); return NULL; } #else return PyUnicode_AsUTF8AndSize(o, length); #endif } #endif #endif static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT if ( #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII __Pyx_sys_getdefaultencoding_not_ascii && #endif PyUnicode_Check(o)) { return __Pyx_PyUnicode_AsStringAndSize(o, length); } else #endif #if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) if (PyByteArray_Check(o)) { *length = PyByteArray_GET_SIZE(o); return PyByteArray_AS_STRING(o); } else #endif { char* result; int r = PyBytes_AsStringAndSize(o, &result, length); if (unlikely(r < 0)) { return NULL; } else { return result; } } } static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { int retval; if (unlikely(!x)) return -1; retval = __Pyx_PyObject_IsTrue(x); Py_DECREF(x); return retval; } static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { #if PY_MAJOR_VERSION >= 3 if (PyLong_Check(result)) { if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, "__int__ returned non-int (type %.200s). " "The ability to return an instance of a strict subclass of int " "is deprecated, and may be removed in a future version of Python.", Py_TYPE(result)->tp_name)) { Py_DECREF(result); return NULL; } return result; } #endif PyErr_Format(PyExc_TypeError, "__%.4s__ returned non-%.4s (type %.200s)", type_name, type_name, Py_TYPE(result)->tp_name); Py_DECREF(result); return NULL; } static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { #if CYTHON_USE_TYPE_SLOTS PyNumberMethods *m; #endif const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x) || PyLong_Check(x))) #else if (likely(PyLong_Check(x))) #endif return __Pyx_NewRef(x); #if CYTHON_USE_TYPE_SLOTS m = Py_TYPE(x)->tp_as_number; #if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = m->nb_int(x); } else if (m && m->nb_long) { name = "long"; res = m->nb_long(x); } #else if (likely(m && m->nb_int)) { name = "int"; res = m->nb_int(x); } #endif #else if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { res = PyNumber_Int(x); } #endif if (likely(res)) { #if PY_MAJOR_VERSION < 3 if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { #else if (unlikely(!PyLong_CheckExact(res))) { #endif return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject *x; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(b))) { if (sizeof(Py_ssize_t) >= sizeof(long)) return PyInt_AS_LONG(b); else return PyInt_AsSsize_t(b); } #endif if (likely(PyLong_CheckExact(b))) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)b)->ob_digit; const Py_ssize_t size = Py_SIZE(b); if (likely(__Pyx_sst_abs(size) <= 1)) { ival = likely(size) ? digits[0] : 0; if (size == -1) ival = -ival; return ival; } else { switch (size) { case 2: if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -2: if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case 3: if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -3: if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case 4: if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -4: if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; } } #endif return PyLong_AsSsize_t(b); } x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { return PyInt_FromSize_t(ival); } #endif /* Py_PYTHON_H */ openTSNE-0.6.1/openTSNE/kl_divergence.pyx000066400000000000000000000077571413546205200201710ustar00rootroot00000000000000# cython: boundscheck=False # cython: wraparound=False # cython: cdivision=True # cython: initializedcheck=False # cython: warn.undeclared=True # cython: language_level=3 cimport numpy as np import numpy as np from .quad_tree cimport QuadTree from ._tsne cimport ( estimate_negative_gradient_bh, estimate_negative_gradient_fft_1d, estimate_negative_gradient_fft_2d, ) # This returns a tuple, and can"t be called from C from ._tsne import estimate_positive_gradient_nn cdef double EPSILON = np.finfo(np.float64).eps cdef extern from "math.h": double log(double x) nogil cdef sqeuclidean(double[:] x, double[:] y): cdef: Py_ssize_t n_dims = x.shape[0] double result = 0 Py_ssize_t i for i in range(n_dims): result += (x[i] - y[i]) ** 2 return result cpdef double kl_divergence_exact(double[:, ::1] P, double[:, ::1] embedding): """Compute the exact KL divergence.""" cdef: Py_ssize_t n_samples = embedding.shape[0] Py_ssize_t i, j double sum_P = 0, sum_Q = 0, p_ij, q_ij double kl_divergence = 0 for i in range(n_samples): for j in range(n_samples): if i != j: p_ij = P[i, j] q_ij = 1 / (1 + sqeuclidean(embedding[i], embedding[j])) sum_Q += q_ij sum_P += p_ij if p_ij > 0: kl_divergence += p_ij * log(p_ij / (q_ij + EPSILON)) kl_divergence += sum_P * log(sum_Q + EPSILON) return kl_divergence cpdef double kl_divergence_approx_bh( int[:] indices, int[:] indptr, double[:] P_data, double[:, ::1] embedding, double theta=0.5, double dof=1, ): """Compute the KL divergence using the Barnes-Hut approximation.""" cdef: Py_ssize_t n_samples = embedding.shape[0] Py_ssize_t i, j QuadTree tree = QuadTree(embedding) # We don"t actually care about the gradient, so don"t waste time # initializing memory double[:, ::1] gradient = np.empty_like(embedding, dtype=float) double sum_P = 0, sum_Q = 0 double kl_divergence = 0 sum_Q = estimate_negative_gradient_bh(tree, embedding, gradient, theta, dof) sum_P, kl_divergence = estimate_positive_gradient_nn( indices, indptr, P_data, embedding, embedding, gradient, dof=dof, should_eval_error=True, ) kl_divergence += sum_P * log(sum_Q + EPSILON) return kl_divergence cpdef double kl_divergence_approx_fft( int[:] indices, int[:] indptr, double[:] P_data, double[:, ::1] embedding, double dof=1, Py_ssize_t n_interpolation_points=3, Py_ssize_t min_num_intervals=10, double ints_in_interval=1, ): """Compute the KL divergence using the interpolation based approximation.""" cdef: Py_ssize_t n_samples = embedding.shape[0] Py_ssize_t n_dims = embedding.shape[1] Py_ssize_t i, j # We don"t actually care about the gradient, so don"t waste time # initializing memory double[:, ::1] gradient = np.empty_like(embedding, dtype=float) double sum_P = 0, sum_Q = 0 double kl_divergence = 0 if n_dims == 1: sum_Q = estimate_negative_gradient_fft_1d( embedding.ravel(), gradient.ravel(), n_interpolation_points, min_num_intervals, ints_in_interval, dof, ) elif n_dims == 2: sum_Q = estimate_negative_gradient_fft_2d( embedding, gradient, n_interpolation_points, min_num_intervals, ints_in_interval, dof, ) else: return -1 sum_P, kl_divergence = estimate_positive_gradient_nn( indices, indptr, P_data, embedding, embedding, gradient, dof=dof, should_eval_error=True, ) kl_divergence += sum_P * log(sum_Q + EPSILON) return kl_divergence openTSNE-0.6.1/openTSNE/metrics.py000066400000000000000000000006211413546205200166250ustar00rootroot00000000000000import numpy as np from openTSNE.tsne import TSNEEmbedding def pBIC(embedding: TSNEEmbedding) -> float: if not hasattr(embedding.affinities, "perplexity"): raise TypeError("The embedding affinity matrix has no attribute `perplexity`") n_samples = embedding.shape[0] return 2 * embedding.kl_divergence + np.log(n_samples) * \ embedding.affinities.perplexity / n_samples openTSNE-0.6.1/openTSNE/nearest_neighbors.py000066400000000000000000000553561413546205200206770ustar00rootroot00000000000000import logging import os import warnings import numpy as np from scipy.spatial.distance import cdist from sklearn import neighbors from sklearn.utils import check_random_state from openTSNE import utils log = logging.getLogger(__name__) class KNNIndex: VALID_METRICS = [] def __init__( self, data, k, metric="euclidean", metric_params=None, n_jobs=1, random_state=None, verbose=False, ): self.data = data self.n_samples = data.shape[0] self.k = k self.metric = self.check_metric(metric) self.metric_params = metric_params self.n_jobs = n_jobs self.random_state = random_state self.verbose = verbose self.index = None def build(self): """Build the nearest neighbor index on the training data. Builds an index on the training data and computes the nearest neighbors on the training data. Returns ------- indices: np.ndarray distances: np.ndarray """ def query(self, query, k): """Query the index with new points. Finds k nearest neighbors from the training data to each row of the query data. Parameters ---------- query: array_like k: int Returns ------- indices: np.ndarray distances: np.ndarray """ def check_metric(self, metric): """Check that the metric is supported by the KNNIndex instance.""" if callable(metric): pass elif metric not in self.VALID_METRICS: raise ValueError( f"`{self.__class__.__name__}` does not support the `{metric}` " f"metric. Please choose one of the supported metrics: " f"{', '.join(self.VALID_METRICS)}." ) return metric class Sklearn(KNNIndex): VALID_METRICS = [ "braycurtis", "canberra", "chebyshev", "cityblock", "dice", "euclidean", "hamming", "haversine", "infinity", "jaccard", "kulsinski", "l1", "l2", "mahalanobis", "manhattan", "matching", "minkowski", "p", "pyfunc", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "wminkowski", ] + ["cosine"] # our own workaround implementation def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.__data = None def build(self): data, k = self.data, self.k timer = utils.Timer( f"Finding {k} nearest neighbors using exact search using " f"{self.metric} distance...", verbose=self.verbose, ) timer.__enter__() if self.metric == "cosine": # The nearest neighbor ranking for cosine distance is the same as # for euclidean distance on normalized data effective_metric = "euclidean" effective_data = data.copy() effective_data = ( effective_data / np.linalg.norm(effective_data, axis=1)[:, None] ) # In order to properly compute cosine distances when querying the # index, we need to store the original data self.__data = data else: effective_metric = self.metric effective_data = data self.index = neighbors.NearestNeighbors( algorithm="auto", metric=effective_metric, metric_params=self.metric_params, n_jobs=self.n_jobs, ) self.index.fit(effective_data) # Return the nearest neighbors in the training set distances, indices = self.index.kneighbors(n_neighbors=k) # If using cosine distance, the computed distances will be wrong and # need to be recomputed if self.metric == "cosine": distances = np.vstack( [ cdist(np.atleast_2d(x), data[idx], metric="cosine") for x, idx in zip(data, indices) ] ) timer.__exit__() return indices, distances def query(self, query, k): timer = utils.Timer( f"Finding {k} nearest neighbors in existing embedding using exact search...", self.verbose, ) timer.__enter__() # The nearest neighbor ranking for cosine distance is the same as for # euclidean distance on normalized data if self.metric == "cosine": effective_data = query.copy() effective_data = ( effective_data / np.linalg.norm(effective_data, axis=1)[:, None] ) else: effective_data = query distances, indices = self.index.kneighbors(effective_data, n_neighbors=k) # If using cosine distance, the computed distances will be wrong and # need to be recomputed if self.metric == "cosine": if self.__data is None: raise RuntimeError( "The original data was unavailable when querying cosine " "distance. Did you change the distance metric after " "building the index? Please rebuild the index using cosine " "similarity." ) distances = np.vstack( [ cdist(np.atleast_2d(x), self.__data[idx], metric="cosine") for x, idx in zip(query, indices) ] ) timer.__exit__() return indices, distances class Annoy(KNNIndex): """Annoy KNN Index. Notes ----- Pickling: Annoy doesn't support pickling. As a workaround, we override the pickling process and save the annoy index file separately. Upon unpickling, this file will attempt to be reloaded. However, since we can't access the actual pickle file location from __getstate__, the annoy index is saved into the current working directory. And, it will also be loaded from cwd. This means that if we pickle an object into a specific directory, our files could end up in different places. And when sharing a pickle, they may need to be put into different directories. Alternatively, the use can set the ``.pickle_fname`` attribute to specify a file name and location for the save annoy index e.g. ``./pickle/my-index.ann``. This should make it at least somewhat easier to specify the pickle names. This is extremely messy, but it is better than not supporting pickling at all. If anyone has a better solution, I would welcome any and all help. """ VALID_METRICS = [ "cosine", "euclidean", "manhattan", "hamming", "dot", "l1", "l2", "taxicab", ] def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def build(self): data, k = self.data, self.k timer = utils.Timer( f"Finding {k} nearest neighbors using Annoy approximate search using " f"{self.metric} distance...", verbose=self.verbose, ) timer.__enter__() from openTSNE.dependencies.annoy import AnnoyIndex N = data.shape[0] annoy_metric = self.metric annoy_aliases = { "cosine": "angular", "l1": "manhattan", "l2": "euclidean", "taxicab": "manhattan", } if annoy_metric in annoy_aliases: annoy_metric = annoy_aliases[annoy_metric] self.index = AnnoyIndex(data.shape[1], annoy_metric) random_state = check_random_state(self.random_state) self.index.set_seed(random_state.randint(np.iinfo(np.int32).max)) for i in range(N): self.index.add_item(i, data[i]) # Number of trees. FIt-SNE uses 50 by default. self.index.build(50, n_jobs=self.n_jobs) # Return the nearest neighbors in the training set distances = np.zeros((N, k)) indices = np.zeros((N, k)).astype(int) def getnns(i): # Annoy returns the query point itself as the first element indices_i, distances_i = self.index.get_nns_by_item( i, k + 1, include_distances=True ) indices[i] = indices_i[1:] distances[i] = distances_i[1:] if self.n_jobs == 1: for i in range(N): getnns(i) else: from joblib import Parallel, delayed Parallel(n_jobs=self.n_jobs, require="sharedmem")( delayed(getnns)(i) for i in range(N) ) timer.__exit__() return indices, distances def query(self, query, k): timer = utils.Timer( f"Finding {k} nearest neighbors in existing embedding using Annoy " f"approximate search...", self.verbose, ) timer.__enter__() N = query.shape[0] distances = np.zeros((N, k)) indices = np.zeros((N, k)).astype(int) def getnns(i): indices[i], distances[i] = self.index.get_nns_by_vector( query[i], k, include_distances=True ) if self.n_jobs == 1: for i in range(N): getnns(i) else: from joblib import Parallel, delayed Parallel(n_jobs=self.n_jobs, require="sharedmem")( delayed(getnns)(i) for i in range(N) ) timer.__exit__() return indices, distances def __getstate__(self): import tempfile import base64 from os import path d = dict(self.__dict__) # If the index is not None, we want to save the encoded index if self.index is not None: with tempfile.TemporaryDirectory() as dirname: self.index.save(path.join(dirname, "tmp.ann")) with open(path.join(dirname, "tmp.ann"), "rb") as f: b64_index = base64.b64encode(f.read()) d["b64_index"] = b64_index del d["index"] return d def __setstate__(self, state): import tempfile import base64 from os import path from openTSNE.dependencies.annoy import AnnoyIndex # If a base64 index is given, we have to load the index if "b64_index" in state: assert "index" not in state b64_index = state["b64_index"] del state["b64_index"] annoy_metric = state["metric"] annoy_aliases = { "cosine": "angular", "l1": "manhattan", "l2": "euclidean", "taxicab": "manhattan", } if annoy_metric in annoy_aliases: annoy_metric = annoy_aliases[annoy_metric] self.index = AnnoyIndex(state["data"].shape[1], annoy_metric) with tempfile.TemporaryDirectory() as dirname: with open(path.join(dirname, "tmp.ann"), "wb") as f: f.write(base64.b64decode(b64_index)) self.index.load(path.join(dirname, "tmp.ann")) self.__dict__.update(state) class NNDescent(KNNIndex): VALID_METRICS = [ "euclidean", "l2", "sqeuclidean", "manhattan", "taxicab", "l1", "chebyshev", "linfinity", "linfty", "linf", "minkowski", "seuclidean", "standardised_euclidean", "wminkowski", "weighted_minkowski", "mahalanobis", "canberra", "cosine", "dot", "correlation", "haversine", "braycurtis", "spearmanr", "tsss", "true_angular", "hellinger", "kantorovich", "wasserstein", "sinkhorn", "jensen-shannon", "jensen_shannon", "symmetric-kl", "symmetric_kl", "symmetric_kullback_liebler", "hamming", "jaccard", "dice", "matching", "kulsinski", "rogerstanimoto", "russellrao", "sokalsneath", "sokalmichener", "yule", ] def __init__(self, *args, **kwargs): try: import pynndescent # pylint: disable=unused-import,unused-variable except ImportError: raise ImportError( "Please install pynndescent: `conda install -c conda-forge " "pynndescent` or `pip install pynndescent`." ) super().__init__(*args, **kwargs) def check_metric(self, metric): import pynndescent if not np.array_equal( list(pynndescent.distances.named_distances), self.VALID_METRICS ): warnings.warn( "`pynndescent` has recently changed which distance metrics are supported, " "and `openTSNE.nearest_neighbors` has not been updated. Please notify the " "developers of this change." ) if callable(metric): from numba.core.registry import CPUDispatcher if not isinstance(metric, CPUDispatcher): warnings.warn( f"`pynndescent` requires callable metrics to be " f"compiled with `numba`, but `{metric.__name__}` is not compiled. " f"`openTSNE.nearest_neighbors.NNDescent` " f"will attempt to compile the function. " f"If this results in an error, then the function may not be " f"compatible with `numba.njit` and should be rewritten. " f"Otherwise, set `neighbors`='exact' to use `scikit-learn` " f"for calculating nearest neighbors." ) from numba import njit metric = njit(fastmath=True)(metric) return super().check_metric(metric) def build(self): data, k = self.data, self.k timer = utils.Timer( f"Finding {k} nearest neighbors using NN descent approximate search using " f"{self.metric} distance...", verbose=self.verbose, ) timer.__enter__() # These values were taken from UMAP, which we assume to be sensible defaults n_trees = 5 + int(round((data.shape[0]) ** 0.5 / 20)) n_iters = max(5, int(round(np.log2(data.shape[0])))) # Numba takes a while to load up, so there's little point in loading it # unless we're actually going to use it import pynndescent # Will use query() only for k>15 if k <= 15: n_neighbors_build = k + 1 else: n_neighbors_build = 15 self.index = pynndescent.NNDescent( data, n_neighbors=n_neighbors_build, metric=self.metric, metric_kwds=self.metric_params, random_state=self.random_state, n_trees=n_trees, n_iters=n_iters, max_candidates=60, n_jobs=self.n_jobs, verbose=self.verbose > 1, ) # -1 in indices means that pynndescent failed indices, distances = self.index.neighbor_graph mask = np.sum(indices == -1, axis=1) > 0 if k > 15: indices, distances = self.index.query(data, k=k + 1) # As a workaround, we let the failed points group together if np.sum(mask) > 0: if self.verbose: opt = np.get_printoptions() np.set_printoptions(threshold=np.inf) warnings.warn( f"`pynndescent` failed to find neighbors for some of the points. " f"As a workaround, openTSNE considers all such points similar to " f"each other, so they will likely form a cluster in the embedding." f"The indices of the failed points are:\n{np.where(mask)[0]}" ) np.set_printoptions(**opt) else: warnings.warn( f"`pynndescent` failed to find neighbors for some of the points. " f"As a workaround, openTSNE considers all such points similar to " f"each other, so they will likely form a cluster in the embedding. " f"Run with verbose=True, to see indices of the failed points." ) distances[mask] = 1 rs = check_random_state(self.random_state) fake_indices = rs.choice( np.sum(mask), size=np.sum(mask) * indices.shape[1], replace=True ) fake_indices = np.where(mask)[0][fake_indices] indices[mask] = np.reshape(fake_indices, (np.sum(mask), indices.shape[1])) timer.__exit__() return indices[:, 1:], distances[:, 1:] def query(self, query, k): timer = utils.Timer( f"Finding {k} nearest neighbors in existing embedding using NN Descent " f"approxmimate search...", self.verbose, ) timer.__enter__() indices, distances = self.index.query(query, k=k) timer.__exit__() return indices, distances class HNSW(KNNIndex): VALID_METRICS = [ "cosine", "euclidean", "dot", "l2", "ip", ] def __init__(self, *args, **kwargs): try: from hnswlib import Index # pylint: disable=unused-import,unused-variable except ImportError: raise ImportError( "Please install hnswlib: `conda install -c conda-forge " "hnswlib` or `pip install hnswlib`." ) super().__init__(*args, **kwargs) def build(self): data, k = self.data, self.k timer = utils.Timer( f"Finding {k} nearest neighbors using HNSWlib approximate search using " f"{self.metric} distance...", verbose=self.verbose, ) timer.__enter__() from hnswlib import Index hnsw_space = { "cosine": "cosine", "dot": "ip", "euclidean": "l2", "ip": "ip", "l2": "l2", }[self.metric] random_state = check_random_state(self.random_state) random_seed = random_state.randint(np.iinfo(np.int32).max) self.index = Index(space=hnsw_space, dim=data.shape[1]) # Initialize HNSW Index self.index.init_index( max_elements=data.shape[0], ef_construction=200, M=16, random_seed=random_seed, ) # Build index tree from data self.index.add_items(data, num_threads=self.n_jobs) # Set ef parameter for (ideal) precision/recall self.index.set_ef(min(2 * k, self.index.get_current_count())) # Query for kNN indices, distances = self.index.knn_query(data, k=k + 1, num_threads=self.n_jobs) # Stop timer timer.__exit__() # return indices and distances, skip first entry, which is always the point itself return indices[:, 1:], distances[:, 1:] def query(self, query, k): timer = utils.Timer( f"Finding {k} nearest neighbors in existing embedding using HNSWlib " f"approximate search...", self.verbose, ) timer.__enter__() # Set ef parameter for (ideal) precision/recall self.index.set_ef(min(2 * k, self.index.get_current_count())) # Query for kNN indices, distances = self.index.knn_query(query, k=k, num_threads=self.n_jobs) # Stop timer timer.__exit__() # return indices and distances return indices, distances def __getstate__(self): import tempfile import base64 from os import path d = dict(self.__dict__) # If the index is not None, we want to save the encoded index if self.index is not None: with tempfile.TemporaryDirectory() as dirname: self.index.save_index(path.join(dirname, "tmp.bin")) with open(path.join(dirname, "tmp.bin"), "rb") as f: b64_index = base64.b64encode(f.read()) d["b64_index"] = b64_index del d["index"] return d def __setstate__(self, state): import tempfile import base64 from os import path from hnswlib import Index # If a base64 index is given, we have to load the index if "b64_index" in state: assert "index" not in state b64_index = state["b64_index"] del state["b64_index"] hnsw_metric = state["metric"] hnsw_aliases = { "cosine": "cosine", "dot": "ip", "euclidean": "l2", "ip": "ip", "l2": "l2", } if hnsw_metric in hnsw_aliases: hnsw_metric = hnsw_aliases[hnsw_metric] self.index = Index(space=hnsw_metric, dim=state["data"].data.shape[1]) with tempfile.TemporaryDirectory() as dirname: with open(path.join(dirname, "tmp.bin"), "wb") as f: f.write(base64.b64decode(b64_index)) self.index.load_index(path.join(dirname, "tmp.bin")) self.__dict__.update(state) class PrecomputedDistanceMatrix(KNNIndex): """Use a precomputed distance matrix to construct the KNNG. Parameters ---------- distance_matrix: np.ndarray A square, symmetric, and contain only poistive values. """ def __init__(self, distance_matrix, k): nn = neighbors.NearestNeighbors(metric="precomputed") nn.fit(distance_matrix) self.distances, self.indices = nn.kneighbors(n_neighbors=k) self.n_samples = distance_matrix.shape[0] self.k = k def build(self): return self.indices, self.distances def query(self, *args, **kwargs): raise RuntimeError("Precomputed distance matrices cannot be queried") class PrecomputedNeighbors(KNNIndex): """Use a precomputed distance matrix to construct the KNNG. Parameters ---------- neighbors: np.ndarray A N x K matrix containing the indices of point i's k nearest neighbors. distances: np.ndarray A N x K matrix containing the distances to from data point i to its k nearest neighbors. """ def __init__(self, neighbors, distances): self.distances, self.indices = distances, neighbors self.n_samples = neighbors.shape[0] self.k = neighbors.shape[1] def build(self): return self.indices, self.distances def query(self, *args, **kwargs): raise RuntimeError("Precomputed distance matrices cannot be queried") openTSNE-0.6.1/openTSNE/quad_tree.cpp000066400000000000000000031161411413546205200172720ustar00rootroot00000000000000/* Generated by Cython 0.29.15 */ /* BEGIN: Cython Metadata { "distutils": { "depends": [], "language": "c++", "name": "openTSNE.quad_tree", "sources": [ "openTSNE/quad_tree.pyx" ] }, "module_name": "openTSNE.quad_tree" } END: Cython Metadata */ #define PY_SSIZE_T_CLEAN #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) #error Cython requires Python 2.6+ or Python 3.3+. #else #define CYTHON_ABI "0_29_15" #define CYTHON_HEX_VERSION 0x001D0FF0 #define CYTHON_FUTURE_DIVISION 1 #include #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #define __PYX_COMMA , #ifndef HAVE_LONG_LONG #if PY_VERSION_HEX >= 0x02070000 #define HAVE_LONG_LONG #endif #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #ifndef Py_HUGE_VAL #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_PYSTON 0 #define CYTHON_COMPILING_IN_CPYTHON 0 #undef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 0 #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #if PY_VERSION_HEX < 0x03050000 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #elif !defined(CYTHON_USE_ASYNC_SLOTS) #define CYTHON_USE_ASYNC_SLOTS 1 #endif #undef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 0 #undef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 0 #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #undef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 1 #undef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 0 #undef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 0 #undef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 0 #undef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 0 #undef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT 0 #undef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE 0 #undef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS 0 #undef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK 0 #elif defined(PYSTON_VERSION) #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_PYSTON 1 #define CYTHON_COMPILING_IN_CPYTHON 0 #ifndef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 1 #endif #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #undef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 0 #ifndef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 1 #endif #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #ifndef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 0 #endif #ifndef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 1 #endif #ifndef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 1 #endif #undef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 0 #undef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 0 #undef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT 0 #undef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE 0 #undef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS 0 #undef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_PYSTON 0 #define CYTHON_COMPILING_IN_CPYTHON 1 #ifndef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 1 #endif #if PY_VERSION_HEX < 0x02070000 #undef CYTHON_USE_PYTYPE_LOOKUP #define CYTHON_USE_PYTYPE_LOOKUP 0 #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) #define CYTHON_USE_PYTYPE_LOOKUP 1 #endif #if PY_MAJOR_VERSION < 3 #undef CYTHON_USE_ASYNC_SLOTS #define CYTHON_USE_ASYNC_SLOTS 0 #elif !defined(CYTHON_USE_ASYNC_SLOTS) #define CYTHON_USE_ASYNC_SLOTS 1 #endif #if PY_VERSION_HEX < 0x02070000 #undef CYTHON_USE_PYLONG_INTERNALS #define CYTHON_USE_PYLONG_INTERNALS 0 #elif !defined(CYTHON_USE_PYLONG_INTERNALS) #define CYTHON_USE_PYLONG_INTERNALS 1 #endif #ifndef CYTHON_USE_PYLIST_INTERNALS #define CYTHON_USE_PYLIST_INTERNALS 1 #endif #ifndef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 1 #endif #if PY_VERSION_HEX < 0x030300F0 #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #elif !defined(CYTHON_USE_UNICODE_WRITER) #define CYTHON_USE_UNICODE_WRITER 1 #endif #ifndef CYTHON_AVOID_BORROWED_REFS #define CYTHON_AVOID_BORROWED_REFS 0 #endif #ifndef CYTHON_ASSUME_SAFE_MACROS #define CYTHON_ASSUME_SAFE_MACROS 1 #endif #ifndef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 1 #endif #ifndef CYTHON_FAST_THREAD_STATE #define CYTHON_FAST_THREAD_STATE 1 #endif #ifndef CYTHON_FAST_PYCALL #define CYTHON_FAST_PYCALL 1 #endif #ifndef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000) #endif #ifndef CYTHON_USE_TP_FINALIZE #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) #endif #ifndef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1) #endif #ifndef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3) #endif #endif #if !defined(CYTHON_FAST_PYCCALL) #define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) #endif #if CYTHON_USE_PYLONG_INTERNALS #include "longintrepr.h" #undef SHIFT #undef BASE #undef MASK #ifdef SIZEOF_VOID_P enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; #endif #endif #ifndef __has_attribute #define __has_attribute(x) 0 #endif #ifndef __has_cpp_attribute #define __has_cpp_attribute(x) 0 #endif #ifndef CYTHON_RESTRICT #if defined(__GNUC__) #define CYTHON_RESTRICT __restrict__ #elif defined(_MSC_VER) && _MSC_VER >= 1400 #define CYTHON_RESTRICT __restrict #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_RESTRICT restrict #else #define CYTHON_RESTRICT #endif #endif #ifndef CYTHON_UNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif # elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif #endif #ifndef CYTHON_MAYBE_UNUSED_VAR # if defined(__cplusplus) template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } # else # define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) # endif #endif #ifndef CYTHON_NCP_UNUSED # if CYTHON_COMPILING_IN_CPYTHON # define CYTHON_NCP_UNUSED # else # define CYTHON_NCP_UNUSED CYTHON_UNUSED # endif #endif #define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) #ifdef _MSC_VER #ifndef _MSC_STDINT_H_ #if _MSC_VER < 1300 typedef unsigned char uint8_t; typedef unsigned int uint32_t; #else typedef unsigned __int8 uint8_t; typedef unsigned __int32 uint32_t; #endif #endif #else #include #endif #ifndef CYTHON_FALLTHROUGH #if defined(__cplusplus) && __cplusplus >= 201103L #if __has_cpp_attribute(fallthrough) #define CYTHON_FALLTHROUGH [[fallthrough]] #elif __has_cpp_attribute(clang::fallthrough) #define CYTHON_FALLTHROUGH [[clang::fallthrough]] #elif __has_cpp_attribute(gnu::fallthrough) #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] #endif #endif #ifndef CYTHON_FALLTHROUGH #if __has_attribute(fallthrough) #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) #else #define CYTHON_FALLTHROUGH #endif #endif #if defined(__clang__ ) && defined(__apple_build_version__) #if __apple_build_version__ < 7000000 #undef CYTHON_FALLTHROUGH #define CYTHON_FALLTHROUGH #endif #endif #endif #ifndef __cplusplus #error "Cython files generated with the C++ option must be compiled with a C++ compiler." #endif #ifndef CYTHON_INLINE #if defined(__clang__) #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) #else #define CYTHON_INLINE inline #endif #endif template void __Pyx_call_destructor(T& x) { x.~T(); } template class __Pyx_FakeReference { public: __Pyx_FakeReference() : ptr(NULL) { } __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } T *operator->() { return ptr; } T *operator&() { return ptr; } operator T&() { return *ptr; } template bool operator ==(U other) { return *ptr == other; } template bool operator !=(U other) { return *ptr != other; } private: T *ptr; }; #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) #define Py_OptimizeFlag 0 #endif #define __PYX_BUILD_PY_SSIZE_T "n" #define CYTHON_FORMAT_SSIZE_T "z" #if PY_MAJOR_VERSION < 3 #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #define __Pyx_DefaultClassType PyClass_Type #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" #if PY_VERSION_HEX >= 0x030800A4 && PY_VERSION_HEX < 0x030800B2 #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a, 0, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #else #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #endif #define __Pyx_DefaultClassType PyType_Type #endif #ifndef Py_TPFLAGS_CHECKTYPES #define Py_TPFLAGS_CHECKTYPES 0 #endif #ifndef Py_TPFLAGS_HAVE_INDEX #define Py_TPFLAGS_HAVE_INDEX 0 #endif #ifndef Py_TPFLAGS_HAVE_NEWBUFFER #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif #ifndef Py_TPFLAGS_HAVE_FINALIZE #define Py_TPFLAGS_HAVE_FINALIZE 0 #endif #ifndef METH_STACKLESS #define METH_STACKLESS 0 #endif #if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) #ifndef METH_FASTCALL #define METH_FASTCALL 0x80 #endif typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, Py_ssize_t nargs, PyObject *kwnames); #else #define __Pyx_PyCFunctionFast _PyCFunctionFast #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords #endif #if CYTHON_FAST_PYCCALL #define __Pyx_PyFastCFunction_Check(func)\ ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))))) #else #define __Pyx_PyFastCFunction_Check(func) 0 #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) #define PyObject_Malloc(s) PyMem_Malloc(s) #define PyObject_Free(p) PyMem_Free(p) #define PyObject_Realloc(p) PyMem_Realloc(p) #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1 #define PyMem_RawMalloc(n) PyMem_Malloc(n) #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n) #define PyMem_RawFree(p) PyMem_Free(p) #endif #if CYTHON_COMPILING_IN_PYSTON #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) #else #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) #endif #if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 #define __Pyx_PyThreadState_Current PyThreadState_GET() #elif PY_VERSION_HEX >= 0x03060000 #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() #elif PY_VERSION_HEX >= 0x03000000 #define __Pyx_PyThreadState_Current PyThreadState_GET() #else #define __Pyx_PyThreadState_Current _PyThreadState_Current #endif #if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) #include "pythread.h" #define Py_tss_NEEDS_INIT 0 typedef int Py_tss_t; static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { *key = PyThread_create_key(); return 0; } static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); *key = Py_tss_NEEDS_INIT; return key; } static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { PyObject_Free(key); } static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { return *key != Py_tss_NEEDS_INIT; } static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { PyThread_delete_key(*key); *key = Py_tss_NEEDS_INIT; } static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { return PyThread_set_key_value(*key, value); } static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { return PyThread_get_key_value(*key); } #endif #if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) #define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) #else #define __Pyx_PyDict_NewPresized(n) PyDict_New() #endif #if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) #else #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) #endif #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS #define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) #else #define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name) #endif #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) #define CYTHON_PEP393_ENABLED 1 #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ 0 : _PyUnicode_Ready((PyObject *)(op))) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) #else #define CYTHON_PEP393_ENABLED 0 #define PyUnicode_1BYTE_KIND 1 #define PyUnicode_2BYTE_KIND 2 #define PyUnicode_4BYTE_KIND 4 #define __Pyx_PyUnicode_READY(op) (0) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) #endif #if CYTHON_COMPILING_IN_PYPY #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) #else #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) #endif #if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) #endif #define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) #define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) #else #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) #endif #if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) #define PyObject_ASCII(o) PyObject_Repr(o) #endif #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type #define PyStringObject PyUnicodeObject #define PyString_Type PyUnicode_Type #define PyString_Check PyUnicode_Check #define PyString_CheckExact PyUnicode_CheckExact #define PyObject_Unicode PyObject_Str #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) #else #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) #endif #ifndef PySet_CheckExact #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) #endif #if CYTHON_ASSUME_SAFE_MACROS #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) #else #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) #endif #if PY_MAJOR_VERSION >= 3 #define PyIntObject PyLongObject #define PyInt_Type PyLong_Type #define PyInt_Check(op) PyLong_Check(op) #define PyInt_CheckExact(op) PyLong_CheckExact(op) #define PyInt_FromString PyLong_FromString #define PyInt_FromUnicode PyLong_FromUnicode #define PyInt_FromLong PyLong_FromLong #define PyInt_FromSize_t PyLong_FromSize_t #define PyInt_FromSsize_t PyLong_FromSsize_t #define PyInt_AsLong PyLong_AsLong #define PyInt_AS_LONG PyLong_AS_LONG #define PyInt_AsSsize_t PyLong_AsSsize_t #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask #define PyNumber_Int PyNumber_Long #endif #if PY_MAJOR_VERSION >= 3 #define PyBoolObject PyLongObject #endif #if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY #ifndef PyUnicode_InternFromString #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) #endif #endif #if PY_VERSION_HEX < 0x030200A4 typedef long Py_hash_t; #define __Pyx_PyInt_FromHash_t PyInt_FromLong #define __Pyx_PyInt_AsHash_t PyInt_AsLong #else #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), func)) #else #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) #endif #if CYTHON_USE_ASYNC_SLOTS #if PY_VERSION_HEX >= 0x030500B1 #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) #else #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) #endif #else #define __Pyx_PyType_AsAsync(obj) NULL #endif #ifndef __Pyx_PyAsyncMethodsStruct typedef struct { unaryfunc am_await; unaryfunc am_aiter; unaryfunc am_anext; } __Pyx_PyAsyncMethodsStruct; #endif #if defined(WIN32) || defined(MS_WINDOWS) #define _USE_MATH_DEFINES #endif #include #ifdef NAN #define __PYX_NAN() ((float) NAN) #else static CYTHON_INLINE float __PYX_NAN() { float value; memset(&value, 0xFF, sizeof(value)); return value; } #endif #if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) #define __Pyx_truncl trunc #else #define __Pyx_truncl truncl #endif #define __PYX_ERR(f_index, lineno, Ln_error) \ { \ __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ } #ifndef __PYX_EXTERN_C #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else #define __PYX_EXTERN_C extern #endif #endif #define __PYX_HAVE__openTSNE__quad_tree #define __PYX_HAVE_API__openTSNE__quad_tree /* Early includes */ #include "math.h" #include "pythread.h" #include #include #include #include "pystate.h" #ifdef _OPENMP #include #endif /* _OPENMP */ #if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) #define CYTHON_WITHOUT_ASSERTIONS #endif typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; #define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) #define __PYX_DEFAULT_STRING_ENCODING "" #define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString #define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #define __Pyx_uchar_cast(c) ((unsigned char)c) #define __Pyx_long_cast(x) ((long)x) #define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ (sizeof(type) < sizeof(Py_ssize_t)) ||\ (sizeof(type) > sizeof(Py_ssize_t) &&\ likely(v < (type)PY_SSIZE_T_MAX ||\ v == (type)PY_SSIZE_T_MAX) &&\ (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ v == (type)PY_SSIZE_T_MIN))) ||\ (sizeof(type) == sizeof(Py_ssize_t) &&\ (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ v == (type)PY_SSIZE_T_MAX))) ) static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { return (size_t) i < (size_t) limit; } #if defined (__cplusplus) && __cplusplus >= 201103L #include #define __Pyx_sst_abs(value) std::abs(value) #elif SIZEOF_INT >= SIZEOF_SIZE_T #define __Pyx_sst_abs(value) abs(value) #elif SIZEOF_LONG >= SIZEOF_SIZE_T #define __Pyx_sst_abs(value) labs(value) #elif defined (_MSC_VER) #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define __Pyx_sst_abs(value) llabs(value) #elif defined (__GNUC__) #define __Pyx_sst_abs(value) __builtin_llabs(value) #else #define __Pyx_sst_abs(value) ((value<0) ? -value : value) #endif static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); #define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) #define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) #define __Pyx_PyBytes_FromString PyBytes_FromString #define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); #if PY_MAJOR_VERSION < 3 #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #else #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize #endif #define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) #define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) #define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) #define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) #define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) #define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { const Py_UNICODE *u_end = u; while (*u_end++) ; return (size_t)(u_end - u - 1); } #define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) #define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode #define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode #define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) #define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); #define __Pyx_PySequence_Tuple(obj)\ (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); #if CYTHON_ASSUME_SAFE_MACROS #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) #endif #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) #else #define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) #endif #define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII static int __Pyx_sys_getdefaultencoding_not_ascii; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; PyObject* ascii_chars_u = NULL; PyObject* ascii_chars_b = NULL; const char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; if (strcmp(default_encoding_c, "ascii") == 0) { __Pyx_sys_getdefaultencoding_not_ascii = 0; } else { char ascii_chars[128]; int c; for (c = 0; c < 128; c++) { ascii_chars[c] = c; } __Pyx_sys_getdefaultencoding_not_ascii = 1; ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); if (!ascii_chars_u) goto bad; ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { PyErr_Format( PyExc_ValueError, "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", default_encoding_c); goto bad; } Py_DECREF(ascii_chars_u); Py_DECREF(ascii_chars_b); } Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); Py_XDECREF(ascii_chars_u); Py_XDECREF(ascii_chars_b); return -1; } #endif #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) #else #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT static char* __PYX_DEFAULT_STRING_ENCODING; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); return -1; } #endif #endif /* Test for GCC > 2.95 */ #if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #else /* !__GNUC__ or GCC < 2.95 */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ */ static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } static PyObject *__pyx_m = NULL; static PyObject *__pyx_d; static PyObject *__pyx_b; static PyObject *__pyx_cython_runtime = NULL; static PyObject *__pyx_empty_tuple; static PyObject *__pyx_empty_bytes; static PyObject *__pyx_empty_unicode; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; static const char *__pyx_f[] = { "openTSNE/quad_tree.pyx", "stringsource", }; /* MemviewSliceStruct.proto */ struct __pyx_memoryview_obj; typedef struct { struct __pyx_memoryview_obj *memview; char *data; Py_ssize_t shape[8]; Py_ssize_t strides[8]; Py_ssize_t suboffsets[8]; } __Pyx_memviewslice; #define __Pyx_MemoryView_Len(m) (m.shape[0]) /* Atomics.proto */ #include #ifndef CYTHON_ATOMICS #define CYTHON_ATOMICS 1 #endif #define __pyx_atomic_int_type int #if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\ (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\ !defined(__i386__) #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1) #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1) #ifdef __PYX_DEBUG_ATOMICS #warning "Using GNU atomics" #endif #elif CYTHON_ATOMICS && defined(_MSC_VER) && 0 #include #undef __pyx_atomic_int_type #define __pyx_atomic_int_type LONG #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value) #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value) #ifdef __PYX_DEBUG_ATOMICS #pragma message ("Using MSVC atomics") #endif #elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0 #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value) #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value) #ifdef __PYX_DEBUG_ATOMICS #warning "Using Intel atomics" #endif #else #undef CYTHON_ATOMICS #define CYTHON_ATOMICS 0 #ifdef __PYX_DEBUG_ATOMICS #warning "Not using atomics" #endif #endif typedef volatile __pyx_atomic_int_type __pyx_atomic_int; #if CYTHON_ATOMICS #define __pyx_add_acquisition_count(memview)\ __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) #define __pyx_sub_acquisition_count(memview)\ __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) #else #define __pyx_add_acquisition_count(memview)\ __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) #define __pyx_sub_acquisition_count(memview)\ __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) #endif /* ForceInitThreads.proto */ #ifndef __PYX_FORCE_INIT_THREADS #define __PYX_FORCE_INIT_THREADS 0 #endif /* NoFastGil.proto */ #define __Pyx_PyGILState_Ensure PyGILState_Ensure #define __Pyx_PyGILState_Release PyGILState_Release #define __Pyx_FastGIL_Remember() #define __Pyx_FastGIL_Forget() #define __Pyx_FastGilFuncInit() /* BufferFormatStructs.proto */ #define IS_UNSIGNED(type) (((type) -1) > 0) struct __Pyx_StructField_; #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) typedef struct { const char* name; struct __Pyx_StructField_* fields; size_t size; size_t arraysize[8]; int ndim; char typegroup; char is_unsigned; int flags; } __Pyx_TypeInfo; typedef struct __Pyx_StructField_ { __Pyx_TypeInfo* type; const char* name; size_t offset; } __Pyx_StructField; typedef struct { __Pyx_StructField* field; size_t parent_offset; } __Pyx_BufFmt_StackElem; typedef struct { __Pyx_StructField root; __Pyx_BufFmt_StackElem* head; size_t fmt_offset; size_t new_count, enc_count; size_t struct_alignment; int is_complex; char enc_type; char new_packmode; char enc_packmode; char is_valid_array; } __Pyx_BufFmt_Context; /*--- Type declarations ---*/ struct __pyx_obj_8openTSNE_9quad_tree_QuadTree; struct __pyx_array_obj; struct __pyx_MemviewEnum_obj; struct __pyx_memoryview_obj; struct __pyx_memoryviewslice_obj; struct __pyx_t_8openTSNE_9quad_tree_Node; typedef struct __pyx_t_8openTSNE_9quad_tree_Node __pyx_t_8openTSNE_9quad_tree_Node; struct __pyx_opt_args_8openTSNE_9quad_tree_is_duplicate; /* "openTSNE/quad_tree.pxd":10 * cdef double EPSILON = np.finfo(np.float64).eps * * ctypedef struct Node: # <<<<<<<<<<<<<< * Py_ssize_t n_dims * double *center */ struct __pyx_t_8openTSNE_9quad_tree_Node { Py_ssize_t n_dims; double *center; double length; int is_leaf; __pyx_t_8openTSNE_9quad_tree_Node *children; double *center_of_mass; Py_ssize_t num_points; }; /* "openTSNE/quad_tree.pxd":22 * * * cdef bint is_duplicate(Node * node, double * point, double duplicate_eps=*) nogil # <<<<<<<<<<<<<< * * */ struct __pyx_opt_args_8openTSNE_9quad_tree_is_duplicate { int __pyx_n; double duplicate_eps; }; /* "openTSNE/quad_tree.pxd":25 * * * cdef class QuadTree: # <<<<<<<<<<<<<< * cdef Node root * cpdef void add_points(self, double[:, ::1] points) */ struct __pyx_obj_8openTSNE_9quad_tree_QuadTree { PyObject_HEAD struct __pyx_vtabstruct_8openTSNE_9quad_tree_QuadTree *__pyx_vtab; __pyx_t_8openTSNE_9quad_tree_Node root; }; /* "View.MemoryView":105 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< * * cdef: */ struct __pyx_array_obj { PyObject_HEAD struct __pyx_vtabstruct_array *__pyx_vtab; char *data; Py_ssize_t len; char *format; int ndim; Py_ssize_t *_shape; Py_ssize_t *_strides; Py_ssize_t itemsize; PyObject *mode; PyObject *_format; void (*callback_free_data)(void *); int free_data; int dtype_is_object; }; /* "View.MemoryView":279 * * @cname('__pyx_MemviewEnum') * cdef class Enum(object): # <<<<<<<<<<<<<< * cdef object name * def __init__(self, name): */ struct __pyx_MemviewEnum_obj { PyObject_HEAD PyObject *name; }; /* "View.MemoryView":330 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< * * cdef object obj */ struct __pyx_memoryview_obj { PyObject_HEAD struct __pyx_vtabstruct_memoryview *__pyx_vtab; PyObject *obj; PyObject *_size; PyObject *_array_interface; PyThread_type_lock lock; __pyx_atomic_int acquisition_count[2]; __pyx_atomic_int *acquisition_count_aligned_p; Py_buffer view; int flags; int dtype_is_object; __Pyx_TypeInfo *typeinfo; }; /* "View.MemoryView":965 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< * "Internal class for passing memoryview slices to Python" * */ struct __pyx_memoryviewslice_obj { struct __pyx_memoryview_obj __pyx_base; __Pyx_memviewslice from_slice; PyObject *from_object; PyObject *(*to_object_func)(char *); int (*to_dtype_func)(char *, PyObject *); }; /* "openTSNE/quad_tree.pyx":147 * * * cdef class QuadTree: # <<<<<<<<<<<<<< * def __init__(self, double[:, ::1] data): * cdef: */ struct __pyx_vtabstruct_8openTSNE_9quad_tree_QuadTree { void (*add_points)(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *, __Pyx_memviewslice, int __pyx_skip_dispatch); void (*add_point)(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *, __Pyx_memviewslice, int __pyx_skip_dispatch); }; static struct __pyx_vtabstruct_8openTSNE_9quad_tree_QuadTree *__pyx_vtabptr_8openTSNE_9quad_tree_QuadTree; /* "View.MemoryView":105 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< * * cdef: */ struct __pyx_vtabstruct_array { PyObject *(*get_memview)(struct __pyx_array_obj *); }; static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; /* "View.MemoryView":330 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< * * cdef object obj */ struct __pyx_vtabstruct_memoryview { char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *); PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *); PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *); PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *); PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *); }; static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; /* "View.MemoryView":965 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< * "Internal class for passing memoryview slices to Python" * */ struct __pyx_vtabstruct__memoryviewslice { struct __pyx_vtabstruct_memoryview __pyx_base; }; static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice; /* --- Runtime support code (head) --- */ /* Refnanny.proto */ #ifndef CYTHON_REFNANNY #define CYTHON_REFNANNY 0 #endif #if CYTHON_REFNANNY typedef struct { void (*INCREF)(void*, PyObject*, int); void (*DECREF)(void*, PyObject*, int); void (*GOTREF)(void*, PyObject*, int); void (*GIVEREF)(void*, PyObject*, int); void* (*SetupContext)(const char*, int, const char*); void (*FinishContext)(void**); } __Pyx_RefNannyAPIStruct; static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; #ifdef WITH_THREAD #define __Pyx_RefNannySetupContext(name, acquire_gil)\ if (acquire_gil) {\ PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ PyGILState_Release(__pyx_gilstate_save);\ } else {\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ } #else #define __Pyx_RefNannySetupContext(name, acquire_gil)\ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) #endif #define __Pyx_RefNannyFinishContext()\ __Pyx_RefNanny->FinishContext(&__pyx_refnanny) #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) #else #define __Pyx_RefNannyDeclarations #define __Pyx_RefNannySetupContext(name, acquire_gil) #define __Pyx_RefNannyFinishContext() #define __Pyx_INCREF(r) Py_INCREF(r) #define __Pyx_DECREF(r) Py_DECREF(r) #define __Pyx_GOTREF(r) #define __Pyx_GIVEREF(r) #define __Pyx_XINCREF(r) Py_XINCREF(r) #define __Pyx_XDECREF(r) Py_XDECREF(r) #define __Pyx_XGOTREF(r) #define __Pyx_XGIVEREF(r) #endif #define __Pyx_XDECREF_SET(r, v) do {\ PyObject *tmp = (PyObject *) r;\ r = v; __Pyx_XDECREF(tmp);\ } while (0) #define __Pyx_DECREF_SET(r, v) do {\ PyObject *tmp = (PyObject *) r;\ r = v; __Pyx_DECREF(tmp);\ } while (0) #define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) #define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) /* PyObjectGetAttrStr.proto */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) #endif /* GetBuiltinName.proto */ static PyObject *__Pyx_GetBuiltinName(PyObject *name); /* PyThreadStateGet.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; #define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; #define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type #else #define __Pyx_PyThreadState_declare #define __Pyx_PyThreadState_assign #define __Pyx_PyErr_Occurred() PyErr_Occurred() #endif /* PyErrFetchRestore.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) #define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) #define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) #define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) #define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #if CYTHON_COMPILING_IN_CPYTHON #define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) #else #define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) #endif #else #define __Pyx_PyErr_Clear() PyErr_Clear() #define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) #define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) #define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) #define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) #endif /* WriteUnraisableException.proto */ static void __Pyx_WriteUnraisable(const char *name, int clineno, int lineno, const char *filename, int full_traceback, int nogil); /* RaiseDoubleKeywords.proto */ static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /* ParseKeywords.proto */ static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ const char* function_name); /* RaiseArgTupleInvalid.proto */ static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /* PyDictVersioning.proto */ #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS #define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) #define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) #define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ (version_var) = __PYX_GET_DICT_VERSION(dict);\ (cache_var) = (value); #define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ static PY_UINT64_T __pyx_dict_version = 0;\ static PyObject *__pyx_dict_cached_value = NULL;\ if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ (VAR) = __pyx_dict_cached_value;\ } else {\ (VAR) = __pyx_dict_cached_value = (LOOKUP);\ __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ }\ } static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); #else #define __PYX_GET_DICT_VERSION(dict) (0) #define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) #define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); #endif /* GetModuleGlobalName.proto */ #if CYTHON_USE_DICT_VERSIONS #define __Pyx_GetModuleGlobalName(var, name) {\ static PY_UINT64_T __pyx_dict_version = 0;\ static PyObject *__pyx_dict_cached_value = NULL;\ (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ } #define __Pyx_GetModuleGlobalNameUncached(var, name) {\ PY_UINT64_T __pyx_dict_version;\ PyObject *__pyx_dict_cached_value;\ (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ } static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); #else #define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) #define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); #endif /* PyObjectCall.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); #else #define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) #endif /* PyCFunctionFastCall.proto */ #if CYTHON_FAST_PYCCALL static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); #else #define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) #endif /* PyFunctionFastCall.proto */ #if CYTHON_FAST_PYCALL #define __Pyx_PyFunction_FastCall(func, args, nargs)\ __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) #if 1 || PY_VERSION_HEX < 0x030600B1 static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); #else #define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) #endif #define __Pyx_BUILD_ASSERT_EXPR(cond)\ (sizeof(char [1 - 2*!(cond)]) - 1) #ifndef Py_MEMBER_SIZE #define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) #endif static size_t __pyx_pyframe_localsplus_offset = 0; #include "frameobject.h" #define __Pxy_PyFrame_Initialize_Offsets()\ ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) #define __Pyx_PyFrame_GetLocalsplus(frame)\ (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) #endif /* PyObjectCall2Args.proto */ static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2); /* PyObjectCallMethO.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); #endif /* PyObjectCallOneArg.proto */ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); /* MemviewSliceInit.proto */ #define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d #define __Pyx_MEMVIEW_DIRECT 1 #define __Pyx_MEMVIEW_PTR 2 #define __Pyx_MEMVIEW_FULL 4 #define __Pyx_MEMVIEW_CONTIG 8 #define __Pyx_MEMVIEW_STRIDED 16 #define __Pyx_MEMVIEW_FOLLOW 32 #define __Pyx_IS_C_CONTIG 1 #define __Pyx_IS_F_CONTIG 2 static int __Pyx_init_memviewslice( struct __pyx_memoryview_obj *memview, int ndim, __Pyx_memviewslice *memviewslice, int memview_is_new_reference); static CYTHON_INLINE int __pyx_add_acquisition_count_locked( __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); #define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p) #define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview)) #define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) #define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__) static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int); /* None.proto */ static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); /* RaiseException.proto */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /* ArgTypeTest.proto */ #define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\ __Pyx__ArgTypeTest(obj, type, name, exact)) static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); /* IncludeStringH.proto */ #include /* BytesEquals.proto */ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); /* UnicodeEquals.proto */ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); /* StrEquals.proto */ #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals #else #define __Pyx_PyString_Equals __Pyx_PyBytes_Equals #endif /* UnaryNegOverflows.proto */ #define UNARY_NEG_WOULD_OVERFLOW(x)\ (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ /* GetAttr.proto */ static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); /* GetItemInt.proto */ #define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ __Pyx_GetItemInt_Generic(o, to_py_func(i)))) #define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); #define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, int wraparound, int boundscheck); /* ObjectGetItem.proto */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key); #else #define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) #endif /* decode_c_string_utf16.proto */ static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) { int byteorder = 0; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) { int byteorder = -1; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) { int byteorder = 1; return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); } /* decode_c_string.proto */ static CYTHON_INLINE PyObject* __Pyx_decode_c_string( const char* cstring, Py_ssize_t start, Py_ssize_t stop, const char* encoding, const char* errors, PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); /* PyErrExceptionMatches.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); #else #define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) #endif /* GetAttr3.proto */ static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); /* RaiseTooManyValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); /* RaiseNeedMoreValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); /* RaiseNoneIterError.proto */ static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); /* ExtTypeTest.proto */ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /* GetTopmostException.proto */ #if CYTHON_USE_EXC_INFO_STACK static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); #endif /* SaveResetException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); #else #define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) #define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) #endif /* GetException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #else static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); #endif /* SwapException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #else static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); #endif /* Import.proto */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /* FastTypeChecks.proto */ #if CYTHON_COMPILING_IN_CPYTHON #define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); #else #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) #define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) #define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) #endif #define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ /* ListCompAppend.proto */ #if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len)) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); Py_SIZE(list) = len+1; return 0; } return PyList_Append(list, x); } #else #define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) #endif /* PyIntBinop.proto */ #if !CYTHON_COMPILING_IN_PYPY static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check); #else #define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\ (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) #endif /* ListExtend.proto */ static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) { #if CYTHON_COMPILING_IN_CPYTHON PyObject* none = _PyList_Extend((PyListObject*)L, v); if (unlikely(!none)) return -1; Py_DECREF(none); return 0; #else return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v); #endif } /* ListAppend.proto */ #if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { PyListObject* L = (PyListObject*) list; Py_ssize_t len = Py_SIZE(list); if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); Py_SIZE(list) = len+1; return 0; } return PyList_Append(list, x); } #else #define __Pyx_PyList_Append(L,x) PyList_Append(L,x) #endif /* ImportFrom.proto */ static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); /* HasAttr.proto */ static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); /* PyObject_GenericGetAttrNoDict.proto */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr #endif /* PyObject_GenericGetAttr.proto */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); #else #define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr #endif /* SetVTable.proto */ static int __Pyx_SetVtable(PyObject *dict, void *vtable); /* SetupReduce.proto */ static int __Pyx_setup_reduce(PyObject* type_obj); /* CLineInTraceback.proto */ #ifdef CYTHON_CLINE_IN_TRACEBACK #define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) #else static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); #endif /* CodeObjectCache.proto */ typedef struct { PyCodeObject* code_object; int code_line; } __Pyx_CodeObjectCacheEntry; struct __Pyx_CodeObjectCache { int count; int max_count; __Pyx_CodeObjectCacheEntry* entries; }; static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); static PyCodeObject *__pyx_find_code_object(int code_line); static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); /* AddTraceback.proto */ static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename); #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); static void __Pyx_ReleaseBuffer(Py_buffer *view); #else #define __Pyx_GetBuffer PyObject_GetBuffer #define __Pyx_ReleaseBuffer PyBuffer_Release #endif /* BufferStructDeclare.proto */ typedef struct { Py_ssize_t shape, strides, suboffsets; } __Pyx_Buf_DimInfo; typedef struct { size_t refcount; Py_buffer pybuffer; } __Pyx_Buffer; typedef struct { __Pyx_Buffer *rcbuffer; char *data; __Pyx_Buf_DimInfo diminfo[8]; } __Pyx_LocalBuf_ND; /* MemviewSliceIsContig.proto */ static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); /* OverlappingSlices.proto */ static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, __Pyx_memviewslice *slice2, int ndim, size_t itemsize); /* Capsule.proto */ static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); /* IsLittleEndian.proto */ static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); /* BufferFormatCheck.proto */ static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type); /* TypeInfoCompare.proto */ static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); /* MemviewSliceValidateAndInit.proto */ static int __Pyx_ValidateAndInit_memviewslice( int *axes_specs, int c_or_f_flag, int buf_flags, int ndim, __Pyx_TypeInfo *dtype, __Pyx_BufFmt_StackElem stack[], __Pyx_memviewslice *memviewslice, PyObject *original_obj); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(PyObject *, int writable_flag); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_double(PyObject *, int writable_flag); /* MemviewDtypeToObject.proto */ static CYTHON_INLINE PyObject *__pyx_memview_get_double(const char *itemp); static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *obj); /* MemviewSliceCopyTemplate.proto */ static __Pyx_memviewslice __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, const char *mode, int ndim, size_t sizeof_dtype, int contig_flag, int dtype_is_object); /* CIntFromPy.proto */ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); /* CIntFromPy.proto */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); /* CIntFromPy.proto */ static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); /* ObjectToMemviewSlice.proto */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *, int writable_flag); /* CheckBinaryVersion.proto */ static int __Pyx_check_binary_version(void); /* PyObjectSetAttrStr.proto */ #if CYTHON_USE_TYPE_SLOTS #define __Pyx_PyObject_DelAttrStr(o,n) __Pyx_PyObject_SetAttrStr(o, n, NULL) static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value); #else #define __Pyx_PyObject_DelAttrStr(o,n) PyObject_DelAttr(o,n) #define __Pyx_PyObject_SetAttrStr(o,n,v) PyObject_SetAttr(o,n,v) #endif /* VoidPtrExport.proto */ static int __Pyx_ExportVoidPtr(PyObject *name, void *p, const char *sig); /* FunctionExport.proto */ static int __Pyx_ExportFunction(const char *name, void (*f)(void), const char *sig); /* InitStrings.proto */ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); static void __pyx_f_8openTSNE_9quad_tree_8QuadTree_add_points(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self, __Pyx_memviewslice __pyx_v_points, int __pyx_skip_dispatch); /* proto*/ static void __pyx_f_8openTSNE_9quad_tree_8QuadTree_add_point(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self, __Pyx_memviewslice __pyx_v_point, int __pyx_skip_dispatch); /* proto*/ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ /* Module declarations from 'cpython.mem' */ /* Module declarations from 'openTSNE.quad_tree' */ static PyTypeObject *__pyx_ptype_8openTSNE_9quad_tree_QuadTree = 0; static PyTypeObject *__pyx_array_type = 0; static PyTypeObject *__pyx_MemviewEnum_type = 0; static PyTypeObject *__pyx_memoryview_type = 0; static PyTypeObject *__pyx_memoryviewslice_type = 0; static double __pyx_v_8openTSNE_9quad_tree_EPSILON; static PyObject *generic = 0; static PyObject *strided = 0; static PyObject *indirect = 0; static PyObject *contiguous = 0; static PyObject *indirect_contiguous = 0; static int __pyx_memoryview_thread_locks_used; static PyThread_type_lock __pyx_memoryview_thread_locks[8]; static CYTHON_INLINE int __pyx_f_8openTSNE_9quad_tree_is_duplicate(__pyx_t_8openTSNE_9quad_tree_Node *, double *, struct __pyx_opt_args_8openTSNE_9quad_tree_is_duplicate *__pyx_optional_args); /*proto*/ static void __pyx_f_8openTSNE_9quad_tree_init_node(__pyx_t_8openTSNE_9quad_tree_Node *, Py_ssize_t, double *, double); /*proto*/ static Py_ssize_t __pyx_f_8openTSNE_9quad_tree_get_child_idx_for(__pyx_t_8openTSNE_9quad_tree_Node *, double *); /*proto*/ static CYTHON_INLINE void __pyx_f_8openTSNE_9quad_tree_update_center_of_mass(__pyx_t_8openTSNE_9quad_tree_Node *, double *); /*proto*/ static void __pyx_f_8openTSNE_9quad_tree_add_point_to(__pyx_t_8openTSNE_9quad_tree_Node *, double *); /*proto*/ static void __pyx_f_8openTSNE_9quad_tree_split_node(__pyx_t_8openTSNE_9quad_tree_Node *); /*proto*/ static void __pyx_f_8openTSNE_9quad_tree_delete_node(__pyx_t_8openTSNE_9quad_tree_Node *); /*proto*/ static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ static void *__pyx_align_pointer(void *, size_t); /*proto*/ static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ static PyObject *_unellipsify(PyObject *, int); /*proto*/ static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; #define __Pyx_MODULE_NAME "openTSNE.quad_tree" extern int __pyx_module_is_main_openTSNE__quad_tree; int __pyx_module_is_main_openTSNE__quad_tree = 0; /* Implementation of 'openTSNE.quad_tree' */ static PyObject *__pyx_builtin_MemoryError; static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_TypeError; static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_enumerate; static PyObject *__pyx_builtin_Ellipsis; static PyObject *__pyx_builtin_id; static PyObject *__pyx_builtin_IndexError; static const char __pyx_k_O[] = "O"; static const char __pyx_k_c[] = "c"; static const char __pyx_k_id[] = "id"; static const char __pyx_k_np[] = "np"; static const char __pyx_k_max[] = "max"; static const char __pyx_k_min[] = "min"; static const char __pyx_k_new[] = "__new__"; static const char __pyx_k_obj[] = "obj"; static const char __pyx_k_axis[] = "axis"; static const char __pyx_k_base[] = "base"; static const char __pyx_k_data[] = "data"; static const char __pyx_k_dict[] = "__dict__"; static const char __pyx_k_main[] = "__main__"; static const char __pyx_k_mode[] = "mode"; static const char __pyx_k_name[] = "name"; static const char __pyx_k_ndim[] = "ndim"; static const char __pyx_k_pack[] = "pack"; static const char __pyx_k_size[] = "size"; static const char __pyx_k_step[] = "step"; static const char __pyx_k_stop[] = "stop"; static const char __pyx_k_test[] = "__test__"; static const char __pyx_k_ASCII[] = "ASCII"; static const char __pyx_k_class[] = "__class__"; static const char __pyx_k_error[] = "error"; static const char __pyx_k_flags[] = "flags"; static const char __pyx_k_numpy[] = "numpy"; static const char __pyx_k_range[] = "range"; static const char __pyx_k_shape[] = "shape"; static const char __pyx_k_start[] = "start"; static const char __pyx_k_zeros[] = "zeros"; static const char __pyx_k_encode[] = "encode"; static const char __pyx_k_format[] = "format"; static const char __pyx_k_import[] = "__import__"; static const char __pyx_k_name_2[] = "__name__"; static const char __pyx_k_pickle[] = "pickle"; static const char __pyx_k_reduce[] = "__reduce__"; static const char __pyx_k_struct[] = "struct"; static const char __pyx_k_unpack[] = "unpack"; static const char __pyx_k_update[] = "update"; static const char __pyx_k_EPSILON[] = "EPSILON"; static const char __pyx_k_fortran[] = "fortran"; static const char __pyx_k_memview[] = "memview"; static const char __pyx_k_Ellipsis[] = "Ellipsis"; static const char __pyx_k_QuadTree[] = "QuadTree"; static const char __pyx_k_getstate[] = "__getstate__"; static const char __pyx_k_itemsize[] = "itemsize"; static const char __pyx_k_pyx_capi[] = "__pyx_capi__"; static const char __pyx_k_pyx_type[] = "__pyx_type"; static const char __pyx_k_setstate[] = "__setstate__"; static const char __pyx_k_TypeError[] = "TypeError"; static const char __pyx_k_add_point[] = "add_point"; static const char __pyx_k_enumerate[] = "enumerate"; static const char __pyx_k_pyx_state[] = "__pyx_state"; static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; static const char __pyx_k_IndexError[] = "IndexError"; static const char __pyx_k_ValueError[] = "ValueError"; static const char __pyx_k_add_points[] = "add_points"; static const char __pyx_k_pyx_result[] = "__pyx_result"; static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; static const char __pyx_k_MemoryError[] = "MemoryError"; static const char __pyx_k_PickleError[] = "PickleError"; static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; static const char __pyx_k_stringsource[] = "stringsource"; static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; static const char __pyx_k_strided_and_direct[] = ""; static const char __pyx_k_strided_and_indirect[] = ""; static const char __pyx_k_contiguous_and_direct[] = ""; static const char __pyx_k_MemoryView_of_r_object[] = ""; static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; static const char __pyx_k_contiguous_and_indirect[] = ""; static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'"; static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; static const char __pyx_k_strided_and_direct_or_indirect[] = ""; static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; static const char __pyx_k_Implements_a_quad_oct_tree_space[] = "Implements a quad/oct-tree space partitioning algorithm primarily used in\nefficiently estimating the t-SNE negative gradient. Lowers the time complexity\nfrom the naive O(n^2) to O(n * log(n)).\n\nNotes\n-----\nI list here several implementation details. Many of these improve efficiency.\n\n - Allocating memory is slow, especially if it has to be done millions of\n times, therefore avoid allocation whereever possible and use buffers.\n Allocation should be done through the use of `PyMem_Malloc` as this is the\n fastest method of allocation. Use this over `libc.stdlib.malloc` because,\n despite requiring the GIL to allocate, it gets tracked in the Python\n virtual environment (which is desirable) and includes some minor\n optimizations. Also, since we need the GIL to allocate, this can warn us of\n any needless memory allocations.\n\n - Structs do not support memoryviews, therefore pointers must be used.\n\n - Prefer pointers over memoryviews where speed is essential. Memoryview\n indexing and slicing is slow compared to raw memory access. We can easily\n convert a memory view to a pointer like so: `&mv[0]` however care must be\n taken to ensure the memoryview is a C contigous array. This can be ensured\n by the type declaration `double[:, ::1]` for 2d arrays.\n\nReferences\n----------\n.. [1] Van Der Maaten, Laurens. \"Accelerating t-SNE using tree-based\n algorithms.\" Journal of machine learning research 15.1 (2014): 3221-3245.\n\n"; static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))"; static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; static const char __pyx_k_self_root_cannot_be_converted_to[] = "self.root cannot be converted to a Python object for pickling"; static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; static PyObject *__pyx_n_s_ASCII; static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; static PyObject *__pyx_kp_s_Cannot_index_with_type_s; static PyObject *__pyx_n_s_EPSILON; static PyObject *__pyx_n_s_Ellipsis; static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0; static PyObject *__pyx_n_s_IndexError; static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; static PyObject *__pyx_n_s_MemoryError; static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; static PyObject *__pyx_kp_s_MemoryView_of_r_object; static PyObject *__pyx_n_b_O; static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; static PyObject *__pyx_n_s_PickleError; static PyObject *__pyx_n_s_QuadTree; static PyObject *__pyx_n_s_TypeError; static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; static PyObject *__pyx_n_s_ValueError; static PyObject *__pyx_n_s_View_MemoryView; static PyObject *__pyx_n_s_add_point; static PyObject *__pyx_n_s_add_points; static PyObject *__pyx_n_s_allocate_buffer; static PyObject *__pyx_n_s_axis; static PyObject *__pyx_n_s_base; static PyObject *__pyx_n_s_c; static PyObject *__pyx_n_u_c; static PyObject *__pyx_n_s_class; static PyObject *__pyx_n_s_cline_in_traceback; static PyObject *__pyx_kp_s_contiguous_and_direct; static PyObject *__pyx_kp_s_contiguous_and_indirect; static PyObject *__pyx_n_s_data; static PyObject *__pyx_n_s_dict; static PyObject *__pyx_n_s_dtype_is_object; static PyObject *__pyx_n_s_encode; static PyObject *__pyx_n_s_enumerate; static PyObject *__pyx_n_s_error; static PyObject *__pyx_n_s_flags; static PyObject *__pyx_n_s_format; static PyObject *__pyx_n_s_fortran; static PyObject *__pyx_n_u_fortran; static PyObject *__pyx_n_s_getstate; static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi; static PyObject *__pyx_n_s_id; static PyObject *__pyx_n_s_import; static PyObject *__pyx_n_s_itemsize; static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; static PyObject *__pyx_n_s_main; static PyObject *__pyx_n_s_max; static PyObject *__pyx_n_s_memview; static PyObject *__pyx_n_s_min; static PyObject *__pyx_n_s_mode; static PyObject *__pyx_n_s_name; static PyObject *__pyx_n_s_name_2; static PyObject *__pyx_n_s_ndim; static PyObject *__pyx_n_s_new; static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; static PyObject *__pyx_n_s_np; static PyObject *__pyx_n_s_numpy; static PyObject *__pyx_n_s_obj; static PyObject *__pyx_n_s_pack; static PyObject *__pyx_n_s_pickle; static PyObject *__pyx_n_s_pyx_PickleError; static PyObject *__pyx_n_s_pyx_capi; static PyObject *__pyx_n_s_pyx_checksum; static PyObject *__pyx_n_s_pyx_getbuffer; static PyObject *__pyx_n_s_pyx_result; static PyObject *__pyx_n_s_pyx_state; static PyObject *__pyx_n_s_pyx_type; static PyObject *__pyx_n_s_pyx_unpickle_Enum; static PyObject *__pyx_n_s_pyx_vtable; static PyObject *__pyx_n_s_range; static PyObject *__pyx_n_s_reduce; static PyObject *__pyx_n_s_reduce_cython; static PyObject *__pyx_n_s_reduce_ex; static PyObject *__pyx_kp_s_self_root_cannot_be_converted_to; static PyObject *__pyx_n_s_setstate; static PyObject *__pyx_n_s_setstate_cython; static PyObject *__pyx_n_s_shape; static PyObject *__pyx_n_s_size; static PyObject *__pyx_n_s_start; static PyObject *__pyx_n_s_step; static PyObject *__pyx_n_s_stop; static PyObject *__pyx_kp_s_strided_and_direct; static PyObject *__pyx_kp_s_strided_and_direct_or_indirect; static PyObject *__pyx_kp_s_strided_and_indirect; static PyObject *__pyx_kp_s_stringsource; static PyObject *__pyx_n_s_struct; static PyObject *__pyx_n_s_test; static PyObject *__pyx_kp_s_unable_to_allocate_array_data; static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; static PyObject *__pyx_n_s_unpack; static PyObject *__pyx_n_s_update; static PyObject *__pyx_n_s_zeros; static int __pyx_pf_8openTSNE_9quad_tree_8QuadTree___init__(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self, __Pyx_memviewslice __pyx_v_data); /* proto */ static PyObject *__pyx_pf_8openTSNE_9quad_tree_8QuadTree_2add_points(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self, __Pyx_memviewslice __pyx_v_points); /* proto */ static PyObject *__pyx_pf_8openTSNE_9quad_tree_8QuadTree_4add_point(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self, __Pyx_memviewslice __pyx_v_point); /* proto */ static void __pyx_pf_8openTSNE_9quad_tree_8QuadTree_6__dealloc__(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_8openTSNE_9quad_tree_8QuadTree_8__reduce_cython__(CYTHON_UNUSED struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_8openTSNE_9quad_tree_8QuadTree_10__setstate_cython__(CYTHON_UNUSED struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */ static PyObject *__pyx_tp_new_8openTSNE_9quad_tree_QuadTree(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ static PyObject *__pyx_int_0; static PyObject *__pyx_int_1; static PyObject *__pyx_int_184977713; static PyObject *__pyx_int_neg_1; static PyObject *__pyx_tuple_; static PyObject *__pyx_tuple__2; static PyObject *__pyx_tuple__3; static PyObject *__pyx_tuple__4; static PyObject *__pyx_tuple__5; static PyObject *__pyx_tuple__6; static PyObject *__pyx_tuple__7; static PyObject *__pyx_tuple__8; static PyObject *__pyx_tuple__9; static PyObject *__pyx_slice__17; static PyObject *__pyx_tuple__10; static PyObject *__pyx_tuple__11; static PyObject *__pyx_tuple__12; static PyObject *__pyx_tuple__13; static PyObject *__pyx_tuple__14; static PyObject *__pyx_tuple__15; static PyObject *__pyx_tuple__16; static PyObject *__pyx_tuple__18; static PyObject *__pyx_tuple__19; static PyObject *__pyx_tuple__20; static PyObject *__pyx_tuple__21; static PyObject *__pyx_tuple__22; static PyObject *__pyx_tuple__23; static PyObject *__pyx_tuple__24; static PyObject *__pyx_tuple__25; static PyObject *__pyx_tuple__26; static PyObject *__pyx_codeobj__27; /* Late includes */ /* "openTSNE/quad_tree.pyx":44 * * * cdef void init_node(Node * node, Py_ssize_t n_dim, double * center, double length): # <<<<<<<<<<<<<< * node.n_dims = n_dim * node.center = PyMem_Malloc(node.n_dims * sizeof(double)) */ static void __pyx_f_8openTSNE_9quad_tree_init_node(__pyx_t_8openTSNE_9quad_tree_Node *__pyx_v_node, Py_ssize_t __pyx_v_n_dim, double *__pyx_v_center, double __pyx_v_length) { Py_ssize_t __pyx_v_i; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; __Pyx_RefNannySetupContext("init_node", 0); /* "openTSNE/quad_tree.pyx":45 * * cdef void init_node(Node * node, Py_ssize_t n_dim, double * center, double length): * node.n_dims = n_dim # <<<<<<<<<<<<<< * node.center = PyMem_Malloc(node.n_dims * sizeof(double)) * node.center_of_mass = PyMem_Malloc(node.n_dims * sizeof(double)) */ __pyx_v_node->n_dims = __pyx_v_n_dim; /* "openTSNE/quad_tree.pyx":46 * cdef void init_node(Node * node, Py_ssize_t n_dim, double * center, double length): * node.n_dims = n_dim * node.center = PyMem_Malloc(node.n_dims * sizeof(double)) # <<<<<<<<<<<<<< * node.center_of_mass = PyMem_Malloc(node.n_dims * sizeof(double)) * if not node.center or not node.center_of_mass: */ __pyx_v_node->center = ((double *)PyMem_Malloc((__pyx_v_node->n_dims * (sizeof(double))))); /* "openTSNE/quad_tree.pyx":47 * node.n_dims = n_dim * node.center = PyMem_Malloc(node.n_dims * sizeof(double)) * node.center_of_mass = PyMem_Malloc(node.n_dims * sizeof(double)) # <<<<<<<<<<<<<< * if not node.center or not node.center_of_mass: * raise MemoryError() */ __pyx_v_node->center_of_mass = ((double *)PyMem_Malloc((__pyx_v_node->n_dims * (sizeof(double))))); /* "openTSNE/quad_tree.pyx":48 * node.center = PyMem_Malloc(node.n_dims * sizeof(double)) * node.center_of_mass = PyMem_Malloc(node.n_dims * sizeof(double)) * if not node.center or not node.center_of_mass: # <<<<<<<<<<<<<< * raise MemoryError() * */ __pyx_t_2 = ((!(__pyx_v_node->center != 0)) != 0); if (!__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } __pyx_t_2 = ((!(__pyx_v_node->center_of_mass != 0)) != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (unlikely(__pyx_t_1)) { /* "openTSNE/quad_tree.pyx":49 * node.center_of_mass = PyMem_Malloc(node.n_dims * sizeof(double)) * if not node.center or not node.center_of_mass: * raise MemoryError() # <<<<<<<<<<<<<< * * cdef Py_ssize_t i */ PyErr_NoMemory(); __PYX_ERR(0, 49, __pyx_L1_error) /* "openTSNE/quad_tree.pyx":48 * node.center = PyMem_Malloc(node.n_dims * sizeof(double)) * node.center_of_mass = PyMem_Malloc(node.n_dims * sizeof(double)) * if not node.center or not node.center_of_mass: # <<<<<<<<<<<<<< * raise MemoryError() * */ } /* "openTSNE/quad_tree.pyx":52 * * cdef Py_ssize_t i * for i in range(node.n_dims): # <<<<<<<<<<<<<< * node.center[i] = center[i] * node.center_of_mass[i] = 0 */ __pyx_t_3 = __pyx_v_node->n_dims; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "openTSNE/quad_tree.pyx":53 * cdef Py_ssize_t i * for i in range(node.n_dims): * node.center[i] = center[i] # <<<<<<<<<<<<<< * node.center_of_mass[i] = 0 * */ (__pyx_v_node->center[__pyx_v_i]) = (__pyx_v_center[__pyx_v_i]); /* "openTSNE/quad_tree.pyx":54 * for i in range(node.n_dims): * node.center[i] = center[i] * node.center_of_mass[i] = 0 # <<<<<<<<<<<<<< * * node.length = length */ (__pyx_v_node->center_of_mass[__pyx_v_i]) = 0.0; } /* "openTSNE/quad_tree.pyx":56 * node.center_of_mass[i] = 0 * * node.length = length # <<<<<<<<<<<<<< * * node.is_leaf = True */ __pyx_v_node->length = __pyx_v_length; /* "openTSNE/quad_tree.pyx":58 * node.length = length * * node.is_leaf = True # <<<<<<<<<<<<<< * node.num_points = 0 * */ __pyx_v_node->is_leaf = 1; /* "openTSNE/quad_tree.pyx":59 * * node.is_leaf = True * node.num_points = 0 # <<<<<<<<<<<<<< * * */ __pyx_v_node->num_points = 0; /* "openTSNE/quad_tree.pyx":44 * * * cdef void init_node(Node * node, Py_ssize_t n_dim, double * center, double length): # <<<<<<<<<<<<<< * node.n_dims = n_dim * node.center = PyMem_Malloc(node.n_dims * sizeof(double)) */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __Pyx_WriteUnraisable("openTSNE.quad_tree.init_node", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_L0:; __Pyx_RefNannyFinishContext(); } /* "openTSNE/quad_tree.pyx":62 * * * cdef Py_ssize_t get_child_idx_for(Node * node, double * point) nogil: # <<<<<<<<<<<<<< * cdef Py_ssize_t idx = 0, d * */ static Py_ssize_t __pyx_f_8openTSNE_9quad_tree_get_child_idx_for(__pyx_t_8openTSNE_9quad_tree_Node *__pyx_v_node, double *__pyx_v_point) { Py_ssize_t __pyx_v_idx; Py_ssize_t __pyx_v_d; Py_ssize_t __pyx_r; Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; /* "openTSNE/quad_tree.pyx":63 * * cdef Py_ssize_t get_child_idx_for(Node * node, double * point) nogil: * cdef Py_ssize_t idx = 0, d # <<<<<<<<<<<<<< * * for d in range(node.n_dims): */ __pyx_v_idx = 0; /* "openTSNE/quad_tree.pyx":65 * cdef Py_ssize_t idx = 0, d * * for d in range(node.n_dims): # <<<<<<<<<<<<<< * idx |= (point[d] > node.center[d]) << d * */ __pyx_t_1 = __pyx_v_node->n_dims; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_d = __pyx_t_3; /* "openTSNE/quad_tree.pyx":66 * * for d in range(node.n_dims): * idx |= (point[d] > node.center[d]) << d # <<<<<<<<<<<<<< * * return idx */ __pyx_v_idx = (__pyx_v_idx | (((__pyx_v_point[__pyx_v_d]) > (__pyx_v_node->center[__pyx_v_d])) << __pyx_v_d)); } /* "openTSNE/quad_tree.pyx":68 * idx |= (point[d] > node.center[d]) << d * * return idx # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_idx; goto __pyx_L0; /* "openTSNE/quad_tree.pyx":62 * * * cdef Py_ssize_t get_child_idx_for(Node * node, double * point) nogil: # <<<<<<<<<<<<<< * cdef Py_ssize_t idx = 0, d * */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "openTSNE/quad_tree.pyx":71 * * * cdef inline void update_center_of_mass(Node * node, double * point) nogil: # <<<<<<<<<<<<<< * cdef Py_ssize_t d * for d in range(node.n_dims): */ static CYTHON_INLINE void __pyx_f_8openTSNE_9quad_tree_update_center_of_mass(__pyx_t_8openTSNE_9quad_tree_Node *__pyx_v_node, double *__pyx_v_point) { Py_ssize_t __pyx_v_d; Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; /* "openTSNE/quad_tree.pyx":73 * cdef inline void update_center_of_mass(Node * node, double * point) nogil: * cdef Py_ssize_t d * for d in range(node.n_dims): # <<<<<<<<<<<<<< * node.center_of_mass[d] = (node.center_of_mass[d] * node.num_points + point[d]) \ * / (node.num_points + 1) */ __pyx_t_1 = __pyx_v_node->n_dims; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_d = __pyx_t_3; /* "openTSNE/quad_tree.pyx":74 * cdef Py_ssize_t d * for d in range(node.n_dims): * node.center_of_mass[d] = (node.center_of_mass[d] * node.num_points + point[d]) \ # <<<<<<<<<<<<<< * / (node.num_points + 1) * node.num_points += 1 */ (__pyx_v_node->center_of_mass[__pyx_v_d]) = ((((__pyx_v_node->center_of_mass[__pyx_v_d]) * __pyx_v_node->num_points) + (__pyx_v_point[__pyx_v_d])) / ((double)(__pyx_v_node->num_points + 1))); } /* "openTSNE/quad_tree.pyx":76 * node.center_of_mass[d] = (node.center_of_mass[d] * node.num_points + point[d]) \ * / (node.num_points + 1) * node.num_points += 1 # <<<<<<<<<<<<<< * * */ __pyx_v_node->num_points = (__pyx_v_node->num_points + 1); /* "openTSNE/quad_tree.pyx":71 * * * cdef inline void update_center_of_mass(Node * node, double * point) nogil: # <<<<<<<<<<<<<< * cdef Py_ssize_t d * for d in range(node.n_dims): */ /* function exit code */ } /* "openTSNE/quad_tree.pyx":79 * * * cdef void add_point_to(Node * node, double * point): # <<<<<<<<<<<<<< * # If the node is a leaf node and empty, we"re done * if node.is_leaf and node.num_points == 0 or is_duplicate(node, point): */ static void __pyx_f_8openTSNE_9quad_tree_add_point_to(__pyx_t_8openTSNE_9quad_tree_Node *__pyx_v_node, double *__pyx_v_point) { Py_ssize_t __pyx_v_child_index; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; __Pyx_RefNannySetupContext("add_point_to", 0); /* "openTSNE/quad_tree.pyx":81 * cdef void add_point_to(Node * node, double * point): * # If the node is a leaf node and empty, we"re done * if node.is_leaf and node.num_points == 0 or is_duplicate(node, point): # <<<<<<<<<<<<<< * update_center_of_mass(node, point) * return */ __pyx_t_2 = (__pyx_v_node->is_leaf != 0); if (!__pyx_t_2) { goto __pyx_L5_next_or; } else { } __pyx_t_2 = ((__pyx_v_node->num_points == 0) != 0); if (!__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } __pyx_L5_next_or:; __pyx_t_2 = (__pyx_f_8openTSNE_9quad_tree_is_duplicate(__pyx_v_node, __pyx_v_point, NULL) != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (__pyx_t_1) { /* "openTSNE/quad_tree.pyx":82 * # If the node is a leaf node and empty, we"re done * if node.is_leaf and node.num_points == 0 or is_duplicate(node, point): * update_center_of_mass(node, point) # <<<<<<<<<<<<<< * return * */ __pyx_f_8openTSNE_9quad_tree_update_center_of_mass(__pyx_v_node, __pyx_v_point); /* "openTSNE/quad_tree.pyx":83 * if node.is_leaf and node.num_points == 0 or is_duplicate(node, point): * update_center_of_mass(node, point) * return # <<<<<<<<<<<<<< * * # Otherwise, we have to split the node and sink the previous, existing */ goto __pyx_L0; /* "openTSNE/quad_tree.pyx":81 * cdef void add_point_to(Node * node, double * point): * # If the node is a leaf node and empty, we"re done * if node.is_leaf and node.num_points == 0 or is_duplicate(node, point): # <<<<<<<<<<<<<< * update_center_of_mass(node, point) * return */ } /* "openTSNE/quad_tree.pyx":89 * cdef Py_ssize_t child_index * * if node.is_leaf: # <<<<<<<<<<<<<< * split_node(node) * child_index = get_child_idx_for(node, node.center_of_mass) */ __pyx_t_1 = (__pyx_v_node->is_leaf != 0); if (__pyx_t_1) { /* "openTSNE/quad_tree.pyx":90 * * if node.is_leaf: * split_node(node) # <<<<<<<<<<<<<< * child_index = get_child_idx_for(node, node.center_of_mass) * update_center_of_mass(&node.children[child_index], node.center_of_mass) */ __pyx_f_8openTSNE_9quad_tree_split_node(__pyx_v_node); /* "openTSNE/quad_tree.pyx":91 * if node.is_leaf: * split_node(node) * child_index = get_child_idx_for(node, node.center_of_mass) # <<<<<<<<<<<<<< * update_center_of_mass(&node.children[child_index], node.center_of_mass) * */ __pyx_v_child_index = __pyx_f_8openTSNE_9quad_tree_get_child_idx_for(__pyx_v_node, __pyx_v_node->center_of_mass); /* "openTSNE/quad_tree.pyx":92 * split_node(node) * child_index = get_child_idx_for(node, node.center_of_mass) * update_center_of_mass(&node.children[child_index], node.center_of_mass) # <<<<<<<<<<<<<< * * update_center_of_mass(node, point) */ __pyx_f_8openTSNE_9quad_tree_update_center_of_mass((&(__pyx_v_node->children[__pyx_v_child_index])), __pyx_v_node->center_of_mass); /* "openTSNE/quad_tree.pyx":89 * cdef Py_ssize_t child_index * * if node.is_leaf: # <<<<<<<<<<<<<< * split_node(node) * child_index = get_child_idx_for(node, node.center_of_mass) */ } /* "openTSNE/quad_tree.pyx":94 * update_center_of_mass(&node.children[child_index], node.center_of_mass) * * update_center_of_mass(node, point) # <<<<<<<<<<<<<< * * # Finally, once the node is properly split, insert the new point into the */ __pyx_f_8openTSNE_9quad_tree_update_center_of_mass(__pyx_v_node, __pyx_v_point); /* "openTSNE/quad_tree.pyx":98 * # Finally, once the node is properly split, insert the new point into the * # corresponding child * child_index = get_child_idx_for(node, point) # <<<<<<<<<<<<<< * add_point_to(&node.children[child_index], point) * */ __pyx_v_child_index = __pyx_f_8openTSNE_9quad_tree_get_child_idx_for(__pyx_v_node, __pyx_v_point); /* "openTSNE/quad_tree.pyx":99 * # corresponding child * child_index = get_child_idx_for(node, point) * add_point_to(&node.children[child_index], point) # <<<<<<<<<<<<<< * * */ __pyx_f_8openTSNE_9quad_tree_add_point_to((&(__pyx_v_node->children[__pyx_v_child_index])), __pyx_v_point); /* "openTSNE/quad_tree.pyx":79 * * * cdef void add_point_to(Node * node, double * point): # <<<<<<<<<<<<<< * # If the node is a leaf node and empty, we"re done * if node.is_leaf and node.num_points == 0 or is_duplicate(node, point): */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); } /* "openTSNE/quad_tree.pyx":102 * * * cdef void split_node(Node * node): # <<<<<<<<<<<<<< * cdef double new_length = node.length / 2 * cdef Py_ssize_t num_children = 1 << node.n_dims */ static void __pyx_f_8openTSNE_9quad_tree_split_node(__pyx_t_8openTSNE_9quad_tree_Node *__pyx_v_node) { double __pyx_v_new_length; Py_ssize_t __pyx_v_num_children; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_d; double *__pyx_v_new_center; __Pyx_RefNannyDeclarations int __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; Py_ssize_t __pyx_t_7; __Pyx_RefNannySetupContext("split_node", 0); /* "openTSNE/quad_tree.pyx":103 * * cdef void split_node(Node * node): * cdef double new_length = node.length / 2 # <<<<<<<<<<<<<< * cdef Py_ssize_t num_children = 1 << node.n_dims * */ __pyx_v_new_length = (__pyx_v_node->length / 2.0); /* "openTSNE/quad_tree.pyx":104 * cdef void split_node(Node * node): * cdef double new_length = node.length / 2 * cdef Py_ssize_t num_children = 1 << node.n_dims # <<<<<<<<<<<<<< * * node.is_leaf = False */ __pyx_v_num_children = (1 << __pyx_v_node->n_dims); /* "openTSNE/quad_tree.pyx":106 * cdef Py_ssize_t num_children = 1 << node.n_dims * * node.is_leaf = False # <<<<<<<<<<<<<< * node.children = PyMem_Malloc(num_children * sizeof(Node)) * if not node.children: */ __pyx_v_node->is_leaf = 0; /* "openTSNE/quad_tree.pyx":107 * * node.is_leaf = False * node.children = PyMem_Malloc(num_children * sizeof(Node)) # <<<<<<<<<<<<<< * if not node.children: * raise MemoryError() */ __pyx_v_node->children = ((__pyx_t_8openTSNE_9quad_tree_Node *)PyMem_Malloc((__pyx_v_num_children * (sizeof(__pyx_t_8openTSNE_9quad_tree_Node))))); /* "openTSNE/quad_tree.pyx":108 * node.is_leaf = False * node.children = PyMem_Malloc(num_children * sizeof(Node)) * if not node.children: # <<<<<<<<<<<<<< * raise MemoryError() * */ __pyx_t_1 = ((!(__pyx_v_node->children != 0)) != 0); if (unlikely(__pyx_t_1)) { /* "openTSNE/quad_tree.pyx":109 * node.children = PyMem_Malloc(num_children * sizeof(Node)) * if not node.children: * raise MemoryError() # <<<<<<<<<<<<<< * * cdef Py_ssize_t i, d */ PyErr_NoMemory(); __PYX_ERR(0, 109, __pyx_L1_error) /* "openTSNE/quad_tree.pyx":108 * node.is_leaf = False * node.children = PyMem_Malloc(num_children * sizeof(Node)) * if not node.children: # <<<<<<<<<<<<<< * raise MemoryError() * */ } /* "openTSNE/quad_tree.pyx":112 * * cdef Py_ssize_t i, d * cdef double * new_center = PyMem_Malloc(node.n_dims * sizeof(double)) # <<<<<<<<<<<<<< * if not new_center: * raise MemoryError() */ __pyx_v_new_center = ((double *)PyMem_Malloc((__pyx_v_node->n_dims * (sizeof(double))))); /* "openTSNE/quad_tree.pyx":113 * cdef Py_ssize_t i, d * cdef double * new_center = PyMem_Malloc(node.n_dims * sizeof(double)) * if not new_center: # <<<<<<<<<<<<<< * raise MemoryError() * */ __pyx_t_1 = ((!(__pyx_v_new_center != 0)) != 0); if (unlikely(__pyx_t_1)) { /* "openTSNE/quad_tree.pyx":114 * cdef double * new_center = PyMem_Malloc(node.n_dims * sizeof(double)) * if not new_center: * raise MemoryError() # <<<<<<<<<<<<<< * * for i in range(num_children): */ PyErr_NoMemory(); __PYX_ERR(0, 114, __pyx_L1_error) /* "openTSNE/quad_tree.pyx":113 * cdef Py_ssize_t i, d * cdef double * new_center = PyMem_Malloc(node.n_dims * sizeof(double)) * if not new_center: # <<<<<<<<<<<<<< * raise MemoryError() * */ } /* "openTSNE/quad_tree.pyx":116 * raise MemoryError() * * for i in range(num_children): # <<<<<<<<<<<<<< * for d in range(node.n_dims): * if i & (1 << d): */ __pyx_t_2 = __pyx_v_num_children; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "openTSNE/quad_tree.pyx":117 * * for i in range(num_children): * for d in range(node.n_dims): # <<<<<<<<<<<<<< * if i & (1 << d): * new_center[d] = node.center[d] + new_length / 2 */ __pyx_t_5 = __pyx_v_node->n_dims; __pyx_t_6 = __pyx_t_5; for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) { __pyx_v_d = __pyx_t_7; /* "openTSNE/quad_tree.pyx":118 * for i in range(num_children): * for d in range(node.n_dims): * if i & (1 << d): # <<<<<<<<<<<<<< * new_center[d] = node.center[d] + new_length / 2 * else: */ __pyx_t_1 = ((__pyx_v_i & (1 << __pyx_v_d)) != 0); if (__pyx_t_1) { /* "openTSNE/quad_tree.pyx":119 * for d in range(node.n_dims): * if i & (1 << d): * new_center[d] = node.center[d] + new_length / 2 # <<<<<<<<<<<<<< * else: * new_center[d] = node.center[d] - new_length / 2 */ (__pyx_v_new_center[__pyx_v_d]) = ((__pyx_v_node->center[__pyx_v_d]) + (__pyx_v_new_length / 2.0)); /* "openTSNE/quad_tree.pyx":118 * for i in range(num_children): * for d in range(node.n_dims): * if i & (1 << d): # <<<<<<<<<<<<<< * new_center[d] = node.center[d] + new_length / 2 * else: */ goto __pyx_L9; } /* "openTSNE/quad_tree.pyx":121 * new_center[d] = node.center[d] + new_length / 2 * else: * new_center[d] = node.center[d] - new_length / 2 # <<<<<<<<<<<<<< * init_node(&node.children[i], node.n_dims, new_center, new_length) * */ /*else*/ { (__pyx_v_new_center[__pyx_v_d]) = ((__pyx_v_node->center[__pyx_v_d]) - (__pyx_v_new_length / 2.0)); } __pyx_L9:; } /* "openTSNE/quad_tree.pyx":122 * else: * new_center[d] = node.center[d] - new_length / 2 * init_node(&node.children[i], node.n_dims, new_center, new_length) # <<<<<<<<<<<<<< * * PyMem_Free(new_center) */ __pyx_f_8openTSNE_9quad_tree_init_node((&(__pyx_v_node->children[__pyx_v_i])), __pyx_v_node->n_dims, __pyx_v_new_center, __pyx_v_new_length); } /* "openTSNE/quad_tree.pyx":124 * init_node(&node.children[i], node.n_dims, new_center, new_length) * * PyMem_Free(new_center) # <<<<<<<<<<<<<< * * */ PyMem_Free(__pyx_v_new_center); /* "openTSNE/quad_tree.pyx":102 * * * cdef void split_node(Node * node): # <<<<<<<<<<<<<< * cdef double new_length = node.length / 2 * cdef Py_ssize_t num_children = 1 << node.n_dims */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __Pyx_WriteUnraisable("openTSNE.quad_tree.split_node", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_L0:; __Pyx_RefNannyFinishContext(); } /* "openTSNE/quad_tree.pyx":127 * * * cdef inline bint is_duplicate(Node * node, double * point, double duplicate_eps=1e-6) nogil: # <<<<<<<<<<<<<< * cdef Py_ssize_t d * for d in range(node.n_dims): */ static CYTHON_INLINE int __pyx_f_8openTSNE_9quad_tree_is_duplicate(__pyx_t_8openTSNE_9quad_tree_Node *__pyx_v_node, double *__pyx_v_point, struct __pyx_opt_args_8openTSNE_9quad_tree_is_duplicate *__pyx_optional_args) { double __pyx_v_duplicate_eps = ((double)1e-6); Py_ssize_t __pyx_v_d; int __pyx_r; Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; int __pyx_t_4; if (__pyx_optional_args) { if (__pyx_optional_args->__pyx_n > 0) { __pyx_v_duplicate_eps = __pyx_optional_args->duplicate_eps; } } /* "openTSNE/quad_tree.pyx":129 * cdef inline bint is_duplicate(Node * node, double * point, double duplicate_eps=1e-6) nogil: * cdef Py_ssize_t d * for d in range(node.n_dims): # <<<<<<<<<<<<<< * if fabs(node.center_of_mass[d] - point[d]) >= duplicate_eps: * return False */ __pyx_t_1 = __pyx_v_node->n_dims; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_d = __pyx_t_3; /* "openTSNE/quad_tree.pyx":130 * cdef Py_ssize_t d * for d in range(node.n_dims): * if fabs(node.center_of_mass[d] - point[d]) >= duplicate_eps: # <<<<<<<<<<<<<< * return False * return True */ __pyx_t_4 = ((fabs(((__pyx_v_node->center_of_mass[__pyx_v_d]) - (__pyx_v_point[__pyx_v_d]))) >= __pyx_v_duplicate_eps) != 0); if (__pyx_t_4) { /* "openTSNE/quad_tree.pyx":131 * for d in range(node.n_dims): * if fabs(node.center_of_mass[d] - point[d]) >= duplicate_eps: * return False # <<<<<<<<<<<<<< * return True * */ __pyx_r = 0; goto __pyx_L0; /* "openTSNE/quad_tree.pyx":130 * cdef Py_ssize_t d * for d in range(node.n_dims): * if fabs(node.center_of_mass[d] - point[d]) >= duplicate_eps: # <<<<<<<<<<<<<< * return False * return True */ } } /* "openTSNE/quad_tree.pyx":132 * if fabs(node.center_of_mass[d] - point[d]) >= duplicate_eps: * return False * return True # <<<<<<<<<<<<<< * * */ __pyx_r = 1; goto __pyx_L0; /* "openTSNE/quad_tree.pyx":127 * * * cdef inline bint is_duplicate(Node * node, double * point, double duplicate_eps=1e-6) nogil: # <<<<<<<<<<<<<< * cdef Py_ssize_t d * for d in range(node.n_dims): */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "openTSNE/quad_tree.pyx":135 * * * cdef void delete_node(Node * node): # <<<<<<<<<<<<<< * PyMem_Free(node.center) * PyMem_Free(node.center_of_mass) */ static void __pyx_f_8openTSNE_9quad_tree_delete_node(__pyx_t_8openTSNE_9quad_tree_Node *__pyx_v_node) { Py_ssize_t __pyx_v_i; __Pyx_RefNannyDeclarations int __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; __Pyx_RefNannySetupContext("delete_node", 0); /* "openTSNE/quad_tree.pyx":136 * * cdef void delete_node(Node * node): * PyMem_Free(node.center) # <<<<<<<<<<<<<< * PyMem_Free(node.center_of_mass) * if node.is_leaf: */ PyMem_Free(__pyx_v_node->center); /* "openTSNE/quad_tree.pyx":137 * cdef void delete_node(Node * node): * PyMem_Free(node.center) * PyMem_Free(node.center_of_mass) # <<<<<<<<<<<<<< * if node.is_leaf: * return */ PyMem_Free(__pyx_v_node->center_of_mass); /* "openTSNE/quad_tree.pyx":138 * PyMem_Free(node.center) * PyMem_Free(node.center_of_mass) * if node.is_leaf: # <<<<<<<<<<<<<< * return * */ __pyx_t_1 = (__pyx_v_node->is_leaf != 0); if (__pyx_t_1) { /* "openTSNE/quad_tree.pyx":139 * PyMem_Free(node.center_of_mass) * if node.is_leaf: * return # <<<<<<<<<<<<<< * * cdef Py_ssize_t i */ goto __pyx_L0; /* "openTSNE/quad_tree.pyx":138 * PyMem_Free(node.center) * PyMem_Free(node.center_of_mass) * if node.is_leaf: # <<<<<<<<<<<<<< * return * */ } /* "openTSNE/quad_tree.pyx":142 * * cdef Py_ssize_t i * for i in range(1 << node.n_dims): # <<<<<<<<<<<<<< * delete_node(&node.children[i]) * PyMem_Free(node.children) */ __pyx_t_2 = (1 << __pyx_v_node->n_dims); __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "openTSNE/quad_tree.pyx":143 * cdef Py_ssize_t i * for i in range(1 << node.n_dims): * delete_node(&node.children[i]) # <<<<<<<<<<<<<< * PyMem_Free(node.children) * */ __pyx_f_8openTSNE_9quad_tree_delete_node((&(__pyx_v_node->children[__pyx_v_i]))); } /* "openTSNE/quad_tree.pyx":144 * for i in range(1 << node.n_dims): * delete_node(&node.children[i]) * PyMem_Free(node.children) # <<<<<<<<<<<<<< * * */ PyMem_Free(__pyx_v_node->children); /* "openTSNE/quad_tree.pyx":135 * * * cdef void delete_node(Node * node): # <<<<<<<<<<<<<< * PyMem_Free(node.center) * PyMem_Free(node.center_of_mass) */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); } /* "openTSNE/quad_tree.pyx":148 * * cdef class QuadTree: * def __init__(self, double[:, ::1] data): # <<<<<<<<<<<<<< * cdef: * Py_ssize_t n_dim = data.shape[1] */ /* Python wrapper */ static int __pyx_pw_8openTSNE_9quad_tree_8QuadTree_1__init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_pw_8openTSNE_9quad_tree_8QuadTree_1__init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { __Pyx_memviewslice __pyx_v_data = { 0, 0, { 0 }, { 0 }, { 0 } }; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__init__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_data,0}; PyObject* values[1] = {0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_data)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__init__") < 0)) __PYX_ERR(0, 148, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 1) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); } __pyx_v_data = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(values[0], PyBUF_WRITABLE); if (unlikely(!__pyx_v_data.memview)) __PYX_ERR(0, 148, __pyx_L3_error) } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__init__", 1, 1, 1, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(0, 148, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("openTSNE.quad_tree.QuadTree.__init__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_9quad_tree_8QuadTree___init__(((struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *)__pyx_v_self), __pyx_v_data); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_pf_8openTSNE_9quad_tree_8QuadTree___init__(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self, __Pyx_memviewslice __pyx_v_data) { Py_ssize_t __pyx_v_n_dim; __Pyx_memviewslice __pyx_v_x_min = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_x_max = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_memviewslice __pyx_v_center = { 0, 0, { 0 }, { 0 }, { 0 } }; double __pyx_v_length; Py_ssize_t __pyx_v_d; int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } }; Py_ssize_t __pyx_t_6; Py_ssize_t __pyx_t_7; Py_ssize_t __pyx_t_8; Py_ssize_t __pyx_t_9; Py_ssize_t __pyx_t_10; Py_ssize_t __pyx_t_11; Py_ssize_t __pyx_t_12; Py_ssize_t __pyx_t_13; int __pyx_t_14; Py_ssize_t __pyx_t_15; Py_ssize_t __pyx_t_16; __pyx_t_8openTSNE_9quad_tree_Node __pyx_t_17; Py_ssize_t __pyx_t_18; __Pyx_RefNannySetupContext("__init__", 0); /* "openTSNE/quad_tree.pyx":150 * def __init__(self, double[:, ::1] data): * cdef: * Py_ssize_t n_dim = data.shape[1] # <<<<<<<<<<<<<< * double[:] x_min = np.min(data, axis=0) * double[:] x_max = np.max(data, axis=0) */ __pyx_v_n_dim = (__pyx_v_data.shape[1]); /* "openTSNE/quad_tree.pyx":151 * cdef: * Py_ssize_t n_dim = data.shape[1] * double[:] x_min = np.min(data, axis=0) # <<<<<<<<<<<<<< * double[:] x_max = np.max(data, axis=0) * */ __Pyx_GetModuleGlobalName(__pyx_t_1, __pyx_n_s_np); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 151, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_min); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 151, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_data, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 151, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 151, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 151, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_t_1, __pyx_n_s_axis, __pyx_int_0) < 0) __PYX_ERR(0, 151, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_2, __pyx_t_3, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 151, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_ds_double(__pyx_t_4, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 151, __pyx_L1_error) __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_x_min = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/quad_tree.pyx":152 * Py_ssize_t n_dim = data.shape[1] * double[:] x_min = np.min(data, axis=0) * double[:] x_max = np.max(data, axis=0) # <<<<<<<<<<<<<< * * double[:] center = np.zeros(n_dim) */ __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_np); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 152, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_4, __pyx_n_s_max); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 152, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __pyx_memoryview_fromslice(__pyx_v_data, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 152, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 152, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 152, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (PyDict_SetItem(__pyx_t_4, __pyx_n_s_axis, __pyx_int_0) < 0) __PYX_ERR(0, 152, __pyx_L1_error) __pyx_t_2 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_3, __pyx_t_4); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 152, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_ds_double(__pyx_t_2, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 152, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v_x_max = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/quad_tree.pyx":154 * double[:] x_max = np.max(data, axis=0) * * double[:] center = np.zeros(n_dim) # <<<<<<<<<<<<<< * double length = 0 * Py_ssize_t d */ __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_np); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 154, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_4, __pyx_n_s_zeros); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 154, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_n_dim); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 154, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_3))) { __pyx_t_1 = PyMethod_GET_SELF(__pyx_t_3); if (likely(__pyx_t_1)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_3); __Pyx_INCREF(__pyx_t_1); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_3, function); } } __pyx_t_2 = (__pyx_t_1) ? __Pyx_PyObject_Call2Args(__pyx_t_3, __pyx_t_1, __pyx_t_4) : __Pyx_PyObject_CallOneArg(__pyx_t_3, __pyx_t_4); __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 154, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_5 = __Pyx_PyObject_to_MemoryviewSlice_ds_double(__pyx_t_2, PyBUF_WRITABLE); if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 154, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v_center = __pyx_t_5; __pyx_t_5.memview = NULL; __pyx_t_5.data = NULL; /* "openTSNE/quad_tree.pyx":155 * * double[:] center = np.zeros(n_dim) * double length = 0 # <<<<<<<<<<<<<< * Py_ssize_t d * */ __pyx_v_length = 0.0; /* "openTSNE/quad_tree.pyx":158 * Py_ssize_t d * * for d in range(n_dim): # <<<<<<<<<<<<<< * center[d] = (x_max[d] + x_min[d]) / 2 * if x_max[d] - x_min[d] > length: */ __pyx_t_6 = __pyx_v_n_dim; __pyx_t_7 = __pyx_t_6; for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_7; __pyx_t_8+=1) { __pyx_v_d = __pyx_t_8; /* "openTSNE/quad_tree.pyx":159 * * for d in range(n_dim): * center[d] = (x_max[d] + x_min[d]) / 2 # <<<<<<<<<<<<<< * if x_max[d] - x_min[d] > length: * length = x_max[d] - x_min[d] */ __pyx_t_9 = __pyx_v_d; __pyx_t_10 = __pyx_v_d; __pyx_t_11 = __pyx_v_d; *((double *) ( /* dim=0 */ (__pyx_v_center.data + __pyx_t_11 * __pyx_v_center.strides[0]) )) = (((*((double *) ( /* dim=0 */ (__pyx_v_x_max.data + __pyx_t_9 * __pyx_v_x_max.strides[0]) ))) + (*((double *) ( /* dim=0 */ (__pyx_v_x_min.data + __pyx_t_10 * __pyx_v_x_min.strides[0]) )))) / 2.0); /* "openTSNE/quad_tree.pyx":160 * for d in range(n_dim): * center[d] = (x_max[d] + x_min[d]) / 2 * if x_max[d] - x_min[d] > length: # <<<<<<<<<<<<<< * length = x_max[d] - x_min[d] * */ __pyx_t_12 = __pyx_v_d; __pyx_t_13 = __pyx_v_d; __pyx_t_14 = ((((*((double *) ( /* dim=0 */ (__pyx_v_x_max.data + __pyx_t_12 * __pyx_v_x_max.strides[0]) ))) - (*((double *) ( /* dim=0 */ (__pyx_v_x_min.data + __pyx_t_13 * __pyx_v_x_min.strides[0]) )))) > __pyx_v_length) != 0); if (__pyx_t_14) { /* "openTSNE/quad_tree.pyx":161 * center[d] = (x_max[d] + x_min[d]) / 2 * if x_max[d] - x_min[d] > length: * length = x_max[d] - x_min[d] # <<<<<<<<<<<<<< * * self.root = Node() */ __pyx_t_15 = __pyx_v_d; __pyx_t_16 = __pyx_v_d; __pyx_v_length = ((*((double *) ( /* dim=0 */ (__pyx_v_x_max.data + __pyx_t_15 * __pyx_v_x_max.strides[0]) ))) - (*((double *) ( /* dim=0 */ (__pyx_v_x_min.data + __pyx_t_16 * __pyx_v_x_min.strides[0]) )))); /* "openTSNE/quad_tree.pyx":160 * for d in range(n_dim): * center[d] = (x_max[d] + x_min[d]) / 2 * if x_max[d] - x_min[d] > length: # <<<<<<<<<<<<<< * length = x_max[d] - x_min[d] * */ } } /* "openTSNE/quad_tree.pyx":163 * length = x_max[d] - x_min[d] * * self.root = Node() # <<<<<<<<<<<<<< * init_node(&self.root, n_dim, ¢er[0], length) * self.add_points(data) */ __pyx_v_self->root = __pyx_t_17; /* "openTSNE/quad_tree.pyx":164 * * self.root = Node() * init_node(&self.root, n_dim, ¢er[0], length) # <<<<<<<<<<<<<< * self.add_points(data) * */ __pyx_t_18 = 0; __pyx_f_8openTSNE_9quad_tree_init_node((&__pyx_v_self->root), __pyx_v_n_dim, (&(*((double *) ( /* dim=0 */ (__pyx_v_center.data + __pyx_t_18 * __pyx_v_center.strides[0]) )))), __pyx_v_length); /* "openTSNE/quad_tree.pyx":165 * self.root = Node() * init_node(&self.root, n_dim, ¢er[0], length) * self.add_points(data) # <<<<<<<<<<<<<< * * cpdef void add_points(self, double[:, ::1] points): */ ((struct __pyx_vtabstruct_8openTSNE_9quad_tree_QuadTree *)__pyx_v_self->__pyx_vtab)->add_points(__pyx_v_self, __pyx_v_data, 0); /* "openTSNE/quad_tree.pyx":148 * * cdef class QuadTree: * def __init__(self, double[:, ::1] data): # <<<<<<<<<<<<<< * cdef: * Py_ssize_t n_dim = data.shape[1] */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1); __Pyx_AddTraceback("openTSNE.quad_tree.QuadTree.__init__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_x_min, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_x_max, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_center, 1); __PYX_XDEC_MEMVIEW(&__pyx_v_data, 1); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/quad_tree.pyx":167 * self.add_points(data) * * cpdef void add_points(self, double[:, ::1] points): # <<<<<<<<<<<<<< * cdef Py_ssize_t i * for i in range(points.shape[0]): */ static PyObject *__pyx_pw_8openTSNE_9quad_tree_8QuadTree_3add_points(PyObject *__pyx_v_self, PyObject *__pyx_arg_points); /*proto*/ static void __pyx_f_8openTSNE_9quad_tree_8QuadTree_add_points(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self, __Pyx_memviewslice __pyx_v_points, int __pyx_skip_dispatch) { Py_ssize_t __pyx_v_i; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; Py_ssize_t __pyx_t_6; Py_ssize_t __pyx_t_7; Py_ssize_t __pyx_t_8; Py_ssize_t __pyx_t_9; Py_ssize_t __pyx_t_10; __Pyx_RefNannySetupContext("add_points", 0); /* Check if called by wrapper */ if (unlikely(__pyx_skip_dispatch)) ; /* Check if overridden in Python */ else if (unlikely((Py_TYPE(((PyObject *)__pyx_v_self))->tp_dictoffset != 0) || (Py_TYPE(((PyObject *)__pyx_v_self))->tp_flags & (Py_TPFLAGS_IS_ABSTRACT | Py_TPFLAGS_HEAPTYPE)))) { #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_PYTYPE_LOOKUP && CYTHON_USE_TYPE_SLOTS static PY_UINT64_T __pyx_tp_dict_version = __PYX_DICT_VERSION_INIT, __pyx_obj_dict_version = __PYX_DICT_VERSION_INIT; if (unlikely(!__Pyx_object_dict_version_matches(((PyObject *)__pyx_v_self), __pyx_tp_dict_version, __pyx_obj_dict_version))) { PY_UINT64_T __pyx_type_dict_guard = __Pyx_get_tp_dict_version(((PyObject *)__pyx_v_self)); #endif __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_add_points); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 167, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (!PyCFunction_Check(__pyx_t_1) || (PyCFunction_GET_FUNCTION(__pyx_t_1) != (PyCFunction)(void*)__pyx_pw_8openTSNE_9quad_tree_8QuadTree_3add_points)) { if (unlikely(!__pyx_v_points.memview)) { __Pyx_RaiseUnboundLocalError("points"); __PYX_ERR(0, 167, __pyx_L1_error) } __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_points, 2, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 167, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_t_1); __pyx_t_4 = __pyx_t_1; __pyx_t_5 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_4))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_4); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_4); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_4, function); } } __pyx_t_2 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_4, __pyx_t_5, __pyx_t_3) : __Pyx_PyObject_CallOneArg(__pyx_t_4, __pyx_t_3); __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 167, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; goto __pyx_L0; } #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_PYTYPE_LOOKUP && CYTHON_USE_TYPE_SLOTS __pyx_tp_dict_version = __Pyx_get_tp_dict_version(((PyObject *)__pyx_v_self)); __pyx_obj_dict_version = __Pyx_get_object_dict_version(((PyObject *)__pyx_v_self)); if (unlikely(__pyx_type_dict_guard != __pyx_tp_dict_version)) { __pyx_tp_dict_version = __pyx_obj_dict_version = __PYX_DICT_VERSION_INIT; } #endif __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_PYTYPE_LOOKUP && CYTHON_USE_TYPE_SLOTS } #endif } /* "openTSNE/quad_tree.pyx":169 * cpdef void add_points(self, double[:, ::1] points): * cdef Py_ssize_t i * for i in range(points.shape[0]): # <<<<<<<<<<<<<< * add_point_to(&self.root, &points[i, 0]) * */ __pyx_t_6 = (__pyx_v_points.shape[0]); __pyx_t_7 = __pyx_t_6; for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_7; __pyx_t_8+=1) { __pyx_v_i = __pyx_t_8; /* "openTSNE/quad_tree.pyx":170 * cdef Py_ssize_t i * for i in range(points.shape[0]): * add_point_to(&self.root, &points[i, 0]) # <<<<<<<<<<<<<< * * cpdef void add_point(self, double[::1] point): */ __pyx_t_9 = __pyx_v_i; __pyx_t_10 = 0; __pyx_f_8openTSNE_9quad_tree_add_point_to((&__pyx_v_self->root), (&(*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_points.data + __pyx_t_9 * __pyx_v_points.strides[0]) )) + __pyx_t_10)) ))))); } /* "openTSNE/quad_tree.pyx":167 * self.add_points(data) * * cpdef void add_points(self, double[:, ::1] points): # <<<<<<<<<<<<<< * cdef Py_ssize_t i * for i in range(points.shape[0]): */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_WriteUnraisable("openTSNE.quad_tree.QuadTree.add_points", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_L0:; __Pyx_RefNannyFinishContext(); } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_9quad_tree_8QuadTree_3add_points(PyObject *__pyx_v_self, PyObject *__pyx_arg_points); /*proto*/ static PyObject *__pyx_pw_8openTSNE_9quad_tree_8QuadTree_3add_points(PyObject *__pyx_v_self, PyObject *__pyx_arg_points) { __Pyx_memviewslice __pyx_v_points = { 0, 0, { 0 }, { 0 }, { 0 } }; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("add_points (wrapper)", 0); assert(__pyx_arg_points); { __pyx_v_points = __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(__pyx_arg_points, PyBUF_WRITABLE); if (unlikely(!__pyx_v_points.memview)) __PYX_ERR(0, 167, __pyx_L3_error) } goto __pyx_L4_argument_unpacking_done; __pyx_L3_error:; __Pyx_AddTraceback("openTSNE.quad_tree.QuadTree.add_points", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_9quad_tree_8QuadTree_2add_points(((struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *)__pyx_v_self), __pyx_v_points); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_9quad_tree_8QuadTree_2add_points(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self, __Pyx_memviewslice __pyx_v_points) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("add_points", 0); __Pyx_XDECREF(__pyx_r); if (unlikely(!__pyx_v_points.memview)) { __Pyx_RaiseUnboundLocalError("points"); __PYX_ERR(0, 167, __pyx_L1_error) } __pyx_t_1 = __Pyx_void_to_None(__pyx_f_8openTSNE_9quad_tree_8QuadTree_add_points(__pyx_v_self, __pyx_v_points, 1)); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 167, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("openTSNE.quad_tree.QuadTree.add_points", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_points, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/quad_tree.pyx":172 * add_point_to(&self.root, &points[i, 0]) * * cpdef void add_point(self, double[::1] point): # <<<<<<<<<<<<<< * add_point_to(&self.root, &point[0]) * */ static PyObject *__pyx_pw_8openTSNE_9quad_tree_8QuadTree_5add_point(PyObject *__pyx_v_self, PyObject *__pyx_arg_point); /*proto*/ static void __pyx_f_8openTSNE_9quad_tree_8QuadTree_add_point(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self, __Pyx_memviewslice __pyx_v_point, int __pyx_skip_dispatch) { __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; Py_ssize_t __pyx_t_6; __Pyx_RefNannySetupContext("add_point", 0); /* Check if called by wrapper */ if (unlikely(__pyx_skip_dispatch)) ; /* Check if overridden in Python */ else if (unlikely((Py_TYPE(((PyObject *)__pyx_v_self))->tp_dictoffset != 0) || (Py_TYPE(((PyObject *)__pyx_v_self))->tp_flags & (Py_TPFLAGS_IS_ABSTRACT | Py_TPFLAGS_HEAPTYPE)))) { #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_PYTYPE_LOOKUP && CYTHON_USE_TYPE_SLOTS static PY_UINT64_T __pyx_tp_dict_version = __PYX_DICT_VERSION_INIT, __pyx_obj_dict_version = __PYX_DICT_VERSION_INIT; if (unlikely(!__Pyx_object_dict_version_matches(((PyObject *)__pyx_v_self), __pyx_tp_dict_version, __pyx_obj_dict_version))) { PY_UINT64_T __pyx_type_dict_guard = __Pyx_get_tp_dict_version(((PyObject *)__pyx_v_self)); #endif __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_add_point); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 172, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (!PyCFunction_Check(__pyx_t_1) || (PyCFunction_GET_FUNCTION(__pyx_t_1) != (PyCFunction)(void*)__pyx_pw_8openTSNE_9quad_tree_8QuadTree_5add_point)) { if (unlikely(!__pyx_v_point.memview)) { __Pyx_RaiseUnboundLocalError("point"); __PYX_ERR(0, 172, __pyx_L1_error) } __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_point, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 172, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_t_1); __pyx_t_4 = __pyx_t_1; __pyx_t_5 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_4))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_4); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_4); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_4, function); } } __pyx_t_2 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_4, __pyx_t_5, __pyx_t_3) : __Pyx_PyObject_CallOneArg(__pyx_t_4, __pyx_t_3); __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 172, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; goto __pyx_L0; } #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_PYTYPE_LOOKUP && CYTHON_USE_TYPE_SLOTS __pyx_tp_dict_version = __Pyx_get_tp_dict_version(((PyObject *)__pyx_v_self)); __pyx_obj_dict_version = __Pyx_get_object_dict_version(((PyObject *)__pyx_v_self)); if (unlikely(__pyx_type_dict_guard != __pyx_tp_dict_version)) { __pyx_tp_dict_version = __pyx_obj_dict_version = __PYX_DICT_VERSION_INIT; } #endif __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_PYTYPE_LOOKUP && CYTHON_USE_TYPE_SLOTS } #endif } /* "openTSNE/quad_tree.pyx":173 * * cpdef void add_point(self, double[::1] point): * add_point_to(&self.root, &point[0]) # <<<<<<<<<<<<<< * * def __dealloc__(self): */ __pyx_t_6 = 0; __pyx_f_8openTSNE_9quad_tree_add_point_to((&__pyx_v_self->root), (&(*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_point.data) + __pyx_t_6)) ))))); /* "openTSNE/quad_tree.pyx":172 * add_point_to(&self.root, &points[i, 0]) * * cpdef void add_point(self, double[::1] point): # <<<<<<<<<<<<<< * add_point_to(&self.root, &point[0]) * */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_WriteUnraisable("openTSNE.quad_tree.QuadTree.add_point", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); __pyx_L0:; __Pyx_RefNannyFinishContext(); } /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_9quad_tree_8QuadTree_5add_point(PyObject *__pyx_v_self, PyObject *__pyx_arg_point); /*proto*/ static PyObject *__pyx_pw_8openTSNE_9quad_tree_8QuadTree_5add_point(PyObject *__pyx_v_self, PyObject *__pyx_arg_point) { __Pyx_memviewslice __pyx_v_point = { 0, 0, { 0 }, { 0 }, { 0 } }; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("add_point (wrapper)", 0); assert(__pyx_arg_point); { __pyx_v_point = __Pyx_PyObject_to_MemoryviewSlice_dc_double(__pyx_arg_point, PyBUF_WRITABLE); if (unlikely(!__pyx_v_point.memview)) __PYX_ERR(0, 172, __pyx_L3_error) } goto __pyx_L4_argument_unpacking_done; __pyx_L3_error:; __Pyx_AddTraceback("openTSNE.quad_tree.QuadTree.add_point", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_8openTSNE_9quad_tree_8QuadTree_4add_point(((struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *)__pyx_v_self), __pyx_v_point); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_9quad_tree_8QuadTree_4add_point(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self, __Pyx_memviewslice __pyx_v_point) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("add_point", 0); __Pyx_XDECREF(__pyx_r); if (unlikely(!__pyx_v_point.memview)) { __Pyx_RaiseUnboundLocalError("point"); __PYX_ERR(0, 172, __pyx_L1_error) } __pyx_t_1 = __Pyx_void_to_None(__pyx_f_8openTSNE_9quad_tree_8QuadTree_add_point(__pyx_v_self, __pyx_v_point, 1)); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 172, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("openTSNE.quad_tree.QuadTree.add_point", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __PYX_XDEC_MEMVIEW(&__pyx_v_point, 1); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "openTSNE/quad_tree.pyx":175 * add_point_to(&self.root, &point[0]) * * def __dealloc__(self): # <<<<<<<<<<<<<< * delete_node(&self.root) */ /* Python wrapper */ static void __pyx_pw_8openTSNE_9quad_tree_8QuadTree_7__dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_pw_8openTSNE_9quad_tree_8QuadTree_7__dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_pf_8openTSNE_9quad_tree_8QuadTree_6__dealloc__(((struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_pf_8openTSNE_9quad_tree_8QuadTree_6__dealloc__(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__", 0); /* "openTSNE/quad_tree.pyx":176 * * def __dealloc__(self): * delete_node(&self.root) # <<<<<<<<<<<<<< */ __pyx_f_8openTSNE_9quad_tree_delete_node((&__pyx_v_self->root)); /* "openTSNE/quad_tree.pyx":175 * add_point_to(&self.root, &point[0]) * * def __dealloc__(self): # <<<<<<<<<<<<<< * delete_node(&self.root) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("self.root cannot be converted to a Python object for pickling") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_9quad_tree_8QuadTree_9__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw_8openTSNE_9quad_tree_8QuadTree_9__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf_8openTSNE_9quad_tree_8QuadTree_8__reduce_cython__(((struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_9quad_tree_8QuadTree_8__reduce_cython__(CYTHON_UNUSED struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("self.root cannot be converted to a Python object for pickling") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("self.root cannot be converted to a Python object for pickling") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(1, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("self.root cannot be converted to a Python object for pickling") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("openTSNE.quad_tree.QuadTree.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("self.root cannot be converted to a Python object for pickling") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("self.root cannot be converted to a Python object for pickling") */ /* Python wrapper */ static PyObject *__pyx_pw_8openTSNE_9quad_tree_8QuadTree_11__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw_8openTSNE_9quad_tree_8QuadTree_11__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf_8openTSNE_9quad_tree_8QuadTree_10__setstate_cython__(((struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_8openTSNE_9quad_tree_8QuadTree_10__setstate_cython__(CYTHON_UNUSED struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("self.root cannot be converted to a Python object for pickling") * def __setstate_cython__(self, __pyx_state): * raise TypeError("self.root cannot be converted to a Python object for pickling") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__2, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(1, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("self.root cannot be converted to a Python object for pickling") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("self.root cannot be converted to a Python object for pickling") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("openTSNE.quad_tree.QuadTree.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* Python wrapper */ static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_shape = 0; Py_ssize_t __pyx_v_itemsize; PyObject *__pyx_v_format = 0; PyObject *__pyx_v_mode = 0; int __pyx_v_allocate_buffer; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_shape,&__pyx_n_s_itemsize,&__pyx_n_s_format,&__pyx_n_s_mode,&__pyx_n_s_allocate_buffer,0}; PyObject* values[5] = {0,0,0,0,0}; values[3] = ((PyObject *)__pyx_n_s_c); if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_shape)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_itemsize)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 1); __PYX_ERR(1, 122, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_format)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 2); __PYX_ERR(1, 122, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 3: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_mode); if (value) { values[3] = value; kw_args--; } } CYTHON_FALLTHROUGH; case 4: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_allocate_buffer); if (value) { values[4] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) __PYX_ERR(1, 122, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4); CYTHON_FALLTHROUGH; case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); CYTHON_FALLTHROUGH; case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_shape = ((PyObject*)values[0]); __pyx_v_itemsize = __Pyx_PyIndex_AsSsize_t(values[1]); if (unlikely((__pyx_v_itemsize == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(1, 122, __pyx_L3_error) __pyx_v_format = values[2]; __pyx_v_mode = values[3]; if (values[4]) { __pyx_v_allocate_buffer = __Pyx_PyObject_IsTrue(values[4]); if (unlikely((__pyx_v_allocate_buffer == (int)-1) && PyErr_Occurred())) __PYX_ERR(1, 123, __pyx_L3_error) } else { /* "View.MemoryView":123 * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, * mode="c", bint allocate_buffer=True): # <<<<<<<<<<<<<< * * cdef int idx */ __pyx_v_allocate_buffer = ((int)1); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(1, 122, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_shape), (&PyTuple_Type), 1, "shape", 1))) __PYX_ERR(1, 122, __pyx_L1_error) if (unlikely(((PyObject *)__pyx_v_format) == Py_None)) { PyErr_Format(PyExc_TypeError, "Argument '%.200s' must not be None", "format"); __PYX_ERR(1, 122, __pyx_L1_error) } __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(((struct __pyx_array_obj *)__pyx_v_self), __pyx_v_shape, __pyx_v_itemsize, __pyx_v_format, __pyx_v_mode, __pyx_v_allocate_buffer); /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* function exit code */ goto __pyx_L0; __pyx_L1_error:; __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer) { int __pyx_v_idx; Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_dim; PyObject **__pyx_v_p; char __pyx_v_order; int __pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; char *__pyx_t_7; int __pyx_t_8; Py_ssize_t __pyx_t_9; PyObject *__pyx_t_10 = NULL; Py_ssize_t __pyx_t_11; __Pyx_RefNannySetupContext("__cinit__", 0); __Pyx_INCREF(__pyx_v_format); /* "View.MemoryView":129 * cdef PyObject **p * * self.ndim = len(shape) # <<<<<<<<<<<<<< * self.itemsize = itemsize * */ if (unlikely(__pyx_v_shape == Py_None)) { PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); __PYX_ERR(1, 129, __pyx_L1_error) } __pyx_t_1 = PyTuple_GET_SIZE(__pyx_v_shape); if (unlikely(__pyx_t_1 == ((Py_ssize_t)-1))) __PYX_ERR(1, 129, __pyx_L1_error) __pyx_v_self->ndim = ((int)__pyx_t_1); /* "View.MemoryView":130 * * self.ndim = len(shape) * self.itemsize = itemsize # <<<<<<<<<<<<<< * * if not self.ndim: */ __pyx_v_self->itemsize = __pyx_v_itemsize; /* "View.MemoryView":132 * self.itemsize = itemsize * * if not self.ndim: # <<<<<<<<<<<<<< * raise ValueError("Empty shape tuple for cython.array") * */ __pyx_t_2 = ((!(__pyx_v_self->ndim != 0)) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":133 * * if not self.ndim: * raise ValueError("Empty shape tuple for cython.array") # <<<<<<<<<<<<<< * * if itemsize <= 0: */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 133, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 133, __pyx_L1_error) /* "View.MemoryView":132 * self.itemsize = itemsize * * if not self.ndim: # <<<<<<<<<<<<<< * raise ValueError("Empty shape tuple for cython.array") * */ } /* "View.MemoryView":135 * raise ValueError("Empty shape tuple for cython.array") * * if itemsize <= 0: # <<<<<<<<<<<<<< * raise ValueError("itemsize <= 0 for cython.array") * */ __pyx_t_2 = ((__pyx_v_itemsize <= 0) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":136 * * if itemsize <= 0: * raise ValueError("itemsize <= 0 for cython.array") # <<<<<<<<<<<<<< * * if not isinstance(format, bytes): */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__4, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 136, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 136, __pyx_L1_error) /* "View.MemoryView":135 * raise ValueError("Empty shape tuple for cython.array") * * if itemsize <= 0: # <<<<<<<<<<<<<< * raise ValueError("itemsize <= 0 for cython.array") * */ } /* "View.MemoryView":138 * raise ValueError("itemsize <= 0 for cython.array") * * if not isinstance(format, bytes): # <<<<<<<<<<<<<< * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string */ __pyx_t_2 = PyBytes_Check(__pyx_v_format); __pyx_t_4 = ((!(__pyx_t_2 != 0)) != 0); if (__pyx_t_4) { /* "View.MemoryView":139 * * if not isinstance(format, bytes): * format = format.encode('ASCII') # <<<<<<<<<<<<<< * self._format = format # keep a reference to the byte string * self.format = self._format */ __pyx_t_5 = __Pyx_PyObject_GetAttrStr(__pyx_v_format, __pyx_n_s_encode); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 139, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_5))) { __pyx_t_6 = PyMethod_GET_SELF(__pyx_t_5); if (likely(__pyx_t_6)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_5); __Pyx_INCREF(__pyx_t_6); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_5, function); } } __pyx_t_3 = (__pyx_t_6) ? __Pyx_PyObject_Call2Args(__pyx_t_5, __pyx_t_6, __pyx_n_s_ASCII) : __Pyx_PyObject_CallOneArg(__pyx_t_5, __pyx_n_s_ASCII); __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 139, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF_SET(__pyx_v_format, __pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":138 * raise ValueError("itemsize <= 0 for cython.array") * * if not isinstance(format, bytes): # <<<<<<<<<<<<<< * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string */ } /* "View.MemoryView":140 * if not isinstance(format, bytes): * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string # <<<<<<<<<<<<<< * self.format = self._format * */ if (!(likely(PyBytes_CheckExact(__pyx_v_format))||((__pyx_v_format) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_v_format)->tp_name), 0))) __PYX_ERR(1, 140, __pyx_L1_error) __pyx_t_3 = __pyx_v_format; __Pyx_INCREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_3); __Pyx_GOTREF(__pyx_v_self->_format); __Pyx_DECREF(__pyx_v_self->_format); __pyx_v_self->_format = ((PyObject*)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":141 * format = format.encode('ASCII') * self._format = format # keep a reference to the byte string * self.format = self._format # <<<<<<<<<<<<<< * * */ if (unlikely(__pyx_v_self->_format == Py_None)) { PyErr_SetString(PyExc_TypeError, "expected bytes, NoneType found"); __PYX_ERR(1, 141, __pyx_L1_error) } __pyx_t_7 = __Pyx_PyBytes_AsWritableString(__pyx_v_self->_format); if (unlikely((!__pyx_t_7) && PyErr_Occurred())) __PYX_ERR(1, 141, __pyx_L1_error) __pyx_v_self->format = __pyx_t_7; /* "View.MemoryView":144 * * * self._shape = PyObject_Malloc(sizeof(Py_ssize_t)*self.ndim*2) # <<<<<<<<<<<<<< * self._strides = self._shape + self.ndim * */ __pyx_v_self->_shape = ((Py_ssize_t *)PyObject_Malloc((((sizeof(Py_ssize_t)) * __pyx_v_self->ndim) * 2))); /* "View.MemoryView":145 * * self._shape = PyObject_Malloc(sizeof(Py_ssize_t)*self.ndim*2) * self._strides = self._shape + self.ndim # <<<<<<<<<<<<<< * * if not self._shape: */ __pyx_v_self->_strides = (__pyx_v_self->_shape + __pyx_v_self->ndim); /* "View.MemoryView":147 * self._strides = self._shape + self.ndim * * if not self._shape: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate shape and strides.") * */ __pyx_t_4 = ((!(__pyx_v_self->_shape != 0)) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":148 * * if not self._shape: * raise MemoryError("unable to allocate shape and strides.") # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__5, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 148, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 148, __pyx_L1_error) /* "View.MemoryView":147 * self._strides = self._shape + self.ndim * * if not self._shape: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate shape and strides.") * */ } /* "View.MemoryView":151 * * * for idx, dim in enumerate(shape): # <<<<<<<<<<<<<< * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) */ __pyx_t_8 = 0; __pyx_t_3 = __pyx_v_shape; __Pyx_INCREF(__pyx_t_3); __pyx_t_1 = 0; for (;;) { if (__pyx_t_1 >= PyTuple_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_1); __Pyx_INCREF(__pyx_t_5); __pyx_t_1++; if (unlikely(0 < 0)) __PYX_ERR(1, 151, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_3, __pyx_t_1); __pyx_t_1++; if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 151, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_5); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(1, 151, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_dim = __pyx_t_9; __pyx_v_idx = __pyx_t_8; __pyx_t_8 = (__pyx_t_8 + 1); /* "View.MemoryView":152 * * for idx, dim in enumerate(shape): * if dim <= 0: # <<<<<<<<<<<<<< * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim */ __pyx_t_4 = ((__pyx_v_dim <= 0) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":153 * for idx, dim in enumerate(shape): * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) # <<<<<<<<<<<<<< * self._shape[idx] = dim * */ __pyx_t_5 = __Pyx_PyInt_From_int(__pyx_v_idx); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_10 = PyTuple_New(2); if (unlikely(!__pyx_t_10)) __PYX_ERR(1, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_10, 1, __pyx_t_6); __pyx_t_5 = 0; __pyx_t_6 = 0; __pyx_t_6 = __Pyx_PyString_Format(__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_t_10); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_t_10 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_6); if (unlikely(!__pyx_t_10)) __PYX_ERR(1, 153, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(1, 153, __pyx_L1_error) /* "View.MemoryView":152 * * for idx, dim in enumerate(shape): * if dim <= 0: # <<<<<<<<<<<<<< * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim */ } /* "View.MemoryView":154 * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) * self._shape[idx] = dim # <<<<<<<<<<<<<< * * cdef char order */ (__pyx_v_self->_shape[__pyx_v_idx]) = __pyx_v_dim; /* "View.MemoryView":151 * * * for idx, dim in enumerate(shape): # <<<<<<<<<<<<<< * if dim <= 0: * raise ValueError("Invalid shape in axis %d: %d." % (idx, dim)) */ } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":157 * * cdef char order * if mode == 'fortran': # <<<<<<<<<<<<<< * order = b'F' * self.mode = u'fortran' */ __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_fortran, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) __PYX_ERR(1, 157, __pyx_L1_error) if (__pyx_t_4) { /* "View.MemoryView":158 * cdef char order * if mode == 'fortran': * order = b'F' # <<<<<<<<<<<<<< * self.mode = u'fortran' * elif mode == 'c': */ __pyx_v_order = 'F'; /* "View.MemoryView":159 * if mode == 'fortran': * order = b'F' * self.mode = u'fortran' # <<<<<<<<<<<<<< * elif mode == 'c': * order = b'C' */ __Pyx_INCREF(__pyx_n_u_fortran); __Pyx_GIVEREF(__pyx_n_u_fortran); __Pyx_GOTREF(__pyx_v_self->mode); __Pyx_DECREF(__pyx_v_self->mode); __pyx_v_self->mode = __pyx_n_u_fortran; /* "View.MemoryView":157 * * cdef char order * if mode == 'fortran': # <<<<<<<<<<<<<< * order = b'F' * self.mode = u'fortran' */ goto __pyx_L10; } /* "View.MemoryView":160 * order = b'F' * self.mode = u'fortran' * elif mode == 'c': # <<<<<<<<<<<<<< * order = b'C' * self.mode = u'c' */ __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_c, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) __PYX_ERR(1, 160, __pyx_L1_error) if (likely(__pyx_t_4)) { /* "View.MemoryView":161 * self.mode = u'fortran' * elif mode == 'c': * order = b'C' # <<<<<<<<<<<<<< * self.mode = u'c' * else: */ __pyx_v_order = 'C'; /* "View.MemoryView":162 * elif mode == 'c': * order = b'C' * self.mode = u'c' # <<<<<<<<<<<<<< * else: * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) */ __Pyx_INCREF(__pyx_n_u_c); __Pyx_GIVEREF(__pyx_n_u_c); __Pyx_GOTREF(__pyx_v_self->mode); __Pyx_DECREF(__pyx_v_self->mode); __pyx_v_self->mode = __pyx_n_u_c; /* "View.MemoryView":160 * order = b'F' * self.mode = u'fortran' * elif mode == 'c': # <<<<<<<<<<<<<< * order = b'C' * self.mode = u'c' */ goto __pyx_L10; } /* "View.MemoryView":164 * self.mode = u'c' * else: * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) # <<<<<<<<<<<<<< * * self.len = fill_contig_strides_array(self._shape, self._strides, */ /*else*/ { __pyx_t_3 = __Pyx_PyString_FormatSafe(__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_v_mode); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 164, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_10 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_3); if (unlikely(!__pyx_t_10)) __PYX_ERR(1, 164, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(1, 164, __pyx_L1_error) } __pyx_L10:; /* "View.MemoryView":166 * raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode) * * self.len = fill_contig_strides_array(self._shape, self._strides, # <<<<<<<<<<<<<< * itemsize, self.ndim, order) * */ __pyx_v_self->len = __pyx_fill_contig_strides_array(__pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_itemsize, __pyx_v_self->ndim, __pyx_v_order); /* "View.MemoryView":169 * itemsize, self.ndim, order) * * self.free_data = allocate_buffer # <<<<<<<<<<<<<< * self.dtype_is_object = format == b'O' * if allocate_buffer: */ __pyx_v_self->free_data = __pyx_v_allocate_buffer; /* "View.MemoryView":170 * * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' # <<<<<<<<<<<<<< * if allocate_buffer: * */ __pyx_t_10 = PyObject_RichCompare(__pyx_v_format, __pyx_n_b_O, Py_EQ); __Pyx_XGOTREF(__pyx_t_10); if (unlikely(!__pyx_t_10)) __PYX_ERR(1, 170, __pyx_L1_error) __pyx_t_4 = __Pyx_PyObject_IsTrue(__pyx_t_10); if (unlikely((__pyx_t_4 == (int)-1) && PyErr_Occurred())) __PYX_ERR(1, 170, __pyx_L1_error) __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __pyx_v_self->dtype_is_object = __pyx_t_4; /* "View.MemoryView":171 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< * * */ __pyx_t_4 = (__pyx_v_allocate_buffer != 0); if (__pyx_t_4) { /* "View.MemoryView":174 * * * self.data = malloc(self.len) # <<<<<<<<<<<<<< * if not self.data: * raise MemoryError("unable to allocate array data.") */ __pyx_v_self->data = ((char *)malloc(__pyx_v_self->len)); /* "View.MemoryView":175 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate array data.") * */ __pyx_t_4 = ((!(__pyx_v_self->data != 0)) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":176 * self.data = malloc(self.len) * if not self.data: * raise MemoryError("unable to allocate array data.") # <<<<<<<<<<<<<< * * if self.dtype_is_object: */ __pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__6, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(1, 176, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; __PYX_ERR(1, 176, __pyx_L1_error) /* "View.MemoryView":175 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< * raise MemoryError("unable to allocate array data.") * */ } /* "View.MemoryView":178 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< * p = self.data * for i in range(self.len / itemsize): */ __pyx_t_4 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_4) { /* "View.MemoryView":179 * * if self.dtype_is_object: * p = self.data # <<<<<<<<<<<<<< * for i in range(self.len / itemsize): * p[i] = Py_None */ __pyx_v_p = ((PyObject **)__pyx_v_self->data); /* "View.MemoryView":180 * if self.dtype_is_object: * p = self.data * for i in range(self.len / itemsize): # <<<<<<<<<<<<<< * p[i] = Py_None * Py_INCREF(Py_None) */ if (unlikely(__pyx_v_itemsize == 0)) { PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); __PYX_ERR(1, 180, __pyx_L1_error) } else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1) && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_self->len))) { PyErr_SetString(PyExc_OverflowError, "value too large to perform division"); __PYX_ERR(1, 180, __pyx_L1_error) } __pyx_t_1 = (__pyx_v_self->len / __pyx_v_itemsize); __pyx_t_9 = __pyx_t_1; for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_9; __pyx_t_11+=1) { __pyx_v_i = __pyx_t_11; /* "View.MemoryView":181 * p = self.data * for i in range(self.len / itemsize): * p[i] = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ (__pyx_v_p[__pyx_v_i]) = Py_None; /* "View.MemoryView":182 * for i in range(self.len / itemsize): * p[i] = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * @cname('getbuffer') */ Py_INCREF(Py_None); } /* "View.MemoryView":178 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< * p = self.data * for i in range(self.len / itemsize): */ } /* "View.MemoryView":171 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< * mode="c", bint allocate_buffer=True): * */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_10); __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_XDECREF(__pyx_v_format); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":185 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * cdef int bufmode = -1 * if self.mode == u"c": */ /* Python wrapper */ static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(((struct __pyx_array_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_v_bufmode; int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; char *__pyx_t_4; Py_ssize_t __pyx_t_5; int __pyx_t_6; Py_ssize_t *__pyx_t_7; if (__pyx_v_info == NULL) { PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete"); return -1; } __Pyx_RefNannySetupContext("__getbuffer__", 0); __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None); __Pyx_GIVEREF(__pyx_v_info->obj); /* "View.MemoryView":186 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 # <<<<<<<<<<<<<< * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS */ __pyx_v_bufmode = -1; /* "View.MemoryView":187 * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 * if self.mode == u"c": # <<<<<<<<<<<<<< * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": */ __pyx_t_1 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_c, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(1, 187, __pyx_L1_error) __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":188 * cdef int bufmode = -1 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS # <<<<<<<<<<<<<< * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS */ __pyx_v_bufmode = (PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS); /* "View.MemoryView":187 * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 * if self.mode == u"c": # <<<<<<<<<<<<<< * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": */ goto __pyx_L3; } /* "View.MemoryView":189 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": # <<<<<<<<<<<<<< * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): */ __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_fortran, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(1, 189, __pyx_L1_error) __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":190 * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS # <<<<<<<<<<<<<< * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") */ __pyx_v_bufmode = (PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS); /* "View.MemoryView":189 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": # <<<<<<<<<<<<<< * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): */ } __pyx_L3:; /* "View.MemoryView":191 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data */ __pyx_t_1 = ((!((__pyx_v_flags & __pyx_v_bufmode) != 0)) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":192 * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") # <<<<<<<<<<<<<< * info.buf = self.data * info.len = self.len */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__7, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 192, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 192, __pyx_L1_error) /* "View.MemoryView":191 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data */ } /* "View.MemoryView":193 * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data # <<<<<<<<<<<<<< * info.len = self.len * info.ndim = self.ndim */ __pyx_t_4 = __pyx_v_self->data; __pyx_v_info->buf = __pyx_t_4; /* "View.MemoryView":194 * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data * info.len = self.len # <<<<<<<<<<<<<< * info.ndim = self.ndim * info.shape = self._shape */ __pyx_t_5 = __pyx_v_self->len; __pyx_v_info->len = __pyx_t_5; /* "View.MemoryView":195 * info.buf = self.data * info.len = self.len * info.ndim = self.ndim # <<<<<<<<<<<<<< * info.shape = self._shape * info.strides = self._strides */ __pyx_t_6 = __pyx_v_self->ndim; __pyx_v_info->ndim = __pyx_t_6; /* "View.MemoryView":196 * info.len = self.len * info.ndim = self.ndim * info.shape = self._shape # <<<<<<<<<<<<<< * info.strides = self._strides * info.suboffsets = NULL */ __pyx_t_7 = __pyx_v_self->_shape; __pyx_v_info->shape = __pyx_t_7; /* "View.MemoryView":197 * info.ndim = self.ndim * info.shape = self._shape * info.strides = self._strides # <<<<<<<<<<<<<< * info.suboffsets = NULL * info.itemsize = self.itemsize */ __pyx_t_7 = __pyx_v_self->_strides; __pyx_v_info->strides = __pyx_t_7; /* "View.MemoryView":198 * info.shape = self._shape * info.strides = self._strides * info.suboffsets = NULL # <<<<<<<<<<<<<< * info.itemsize = self.itemsize * info.readonly = 0 */ __pyx_v_info->suboffsets = NULL; /* "View.MemoryView":199 * info.strides = self._strides * info.suboffsets = NULL * info.itemsize = self.itemsize # <<<<<<<<<<<<<< * info.readonly = 0 * */ __pyx_t_5 = __pyx_v_self->itemsize; __pyx_v_info->itemsize = __pyx_t_5; /* "View.MemoryView":200 * info.suboffsets = NULL * info.itemsize = self.itemsize * info.readonly = 0 # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ __pyx_v_info->readonly = 0; /* "View.MemoryView":202 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.format * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":203 * * if flags & PyBUF_FORMAT: * info.format = self.format # <<<<<<<<<<<<<< * else: * info.format = NULL */ __pyx_t_4 = __pyx_v_self->format; __pyx_v_info->format = __pyx_t_4; /* "View.MemoryView":202 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.format * else: */ goto __pyx_L5; } /* "View.MemoryView":205 * info.format = self.format * else: * info.format = NULL # <<<<<<<<<<<<<< * * info.obj = self */ /*else*/ { __pyx_v_info->format = NULL; } __pyx_L5:; /* "View.MemoryView":207 * info.format = NULL * * info.obj = self # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = ((PyObject *)__pyx_v_self); /* "View.MemoryView":185 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * cdef int bufmode = -1 * if self.mode == u"c": */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.array.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; if (__pyx_v_info->obj != NULL) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } goto __pyx_L2; __pyx_L0:; if (__pyx_v_info->obj == Py_None) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } __pyx_L2:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":211 * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") * * def __dealloc__(array self): # <<<<<<<<<<<<<< * if self.callback_free_data != NULL: * self.callback_free_data(self.data) */ /* Python wrapper */ static void __pyx_array___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_array___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self) { __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":212 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< * self.callback_free_data(self.data) * elif self.free_data: */ __pyx_t_1 = ((__pyx_v_self->callback_free_data != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":213 * def __dealloc__(array self): * if self.callback_free_data != NULL: * self.callback_free_data(self.data) # <<<<<<<<<<<<<< * elif self.free_data: * if self.dtype_is_object: */ __pyx_v_self->callback_free_data(__pyx_v_self->data); /* "View.MemoryView":212 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< * self.callback_free_data(self.data) * elif self.free_data: */ goto __pyx_L3; } /* "View.MemoryView":214 * if self.callback_free_data != NULL: * self.callback_free_data(self.data) * elif self.free_data: # <<<<<<<<<<<<<< * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, */ __pyx_t_1 = (__pyx_v_self->free_data != 0); if (__pyx_t_1) { /* "View.MemoryView":215 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) */ __pyx_t_1 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_1) { /* "View.MemoryView":216 * elif self.free_data: * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, # <<<<<<<<<<<<<< * self._strides, self.ndim, False) * free(self.data) */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_self->data, __pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_self->ndim, 0); /* "View.MemoryView":215 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) */ } /* "View.MemoryView":218 * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) * free(self.data) # <<<<<<<<<<<<<< * PyObject_Free(self._shape) * */ free(__pyx_v_self->data); /* "View.MemoryView":214 * if self.callback_free_data != NULL: * self.callback_free_data(self.data) * elif self.free_data: # <<<<<<<<<<<<<< * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, */ } __pyx_L3:; /* "View.MemoryView":219 * self._strides, self.ndim, False) * free(self.data) * PyObject_Free(self._shape) # <<<<<<<<<<<<<< * * @property */ PyObject_Free(__pyx_v_self->_shape); /* "View.MemoryView":211 * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") * * def __dealloc__(array self): # <<<<<<<<<<<<<< * if self.callback_free_data != NULL: * self.callback_free_data(self.data) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":222 * * @property * def memview(self): # <<<<<<<<<<<<<< * return self.get_memview() * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_5array_7memview___get__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":223 * @property * def memview(self): * return self.get_memview() # <<<<<<<<<<<<<< * * @cname('get_memview') */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = ((struct __pyx_vtabstruct_array *)__pyx_v_self->__pyx_vtab)->get_memview(__pyx_v_self); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 223, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":222 * * @property * def memview(self): # <<<<<<<<<<<<<< * return self.get_memview() * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.memview.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":226 * * @cname('get_memview') * cdef get_memview(self): # <<<<<<<<<<<<<< * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) */ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self) { int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("get_memview", 0); /* "View.MemoryView":227 * @cname('get_memview') * cdef get_memview(self): * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE # <<<<<<<<<<<<<< * return memoryview(self, flags, self.dtype_is_object) * */ __pyx_v_flags = ((PyBUF_ANY_CONTIGUOUS | PyBUF_FORMAT) | PyBUF_WRITABLE); /* "View.MemoryView":228 * cdef get_memview(self): * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) # <<<<<<<<<<<<<< * * def __len__(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":226 * * @cname('get_memview') * cdef get_memview(self): # <<<<<<<<<<<<<< * flags = PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE * return memoryview(self, flags, self.dtype_is_object) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.array.get_memview", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":230 * return memoryview(self, flags, self.dtype_is_object) * * def __len__(self): # <<<<<<<<<<<<<< * return self._shape[0] * */ /* Python wrapper */ static Py_ssize_t __pyx_array___len__(PyObject *__pyx_v_self); /*proto*/ static Py_ssize_t __pyx_array___len__(PyObject *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__", 0); /* "View.MemoryView":231 * * def __len__(self): * return self._shape[0] # <<<<<<<<<<<<<< * * def __getattr__(self, attr): */ __pyx_r = (__pyx_v_self->_shape[0]); goto __pyx_L0; /* "View.MemoryView":230 * return memoryview(self, flags, self.dtype_is_object) * * def __len__(self): # <<<<<<<<<<<<<< * return self._shape[0] * */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":233 * return self._shape[0] * * def __getattr__(self, attr): # <<<<<<<<<<<<<< * return getattr(self.memview, attr) * */ /* Python wrapper */ static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr); /*proto*/ static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getattr__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_attr)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("__getattr__", 0); /* "View.MemoryView":234 * * def __getattr__(self, attr): * return getattr(self.memview, attr) # <<<<<<<<<<<<<< * * def __getitem__(self, item): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 234, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_GetAttr(__pyx_t_1, __pyx_v_attr); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 234, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":233 * return self._shape[0] * * def __getattr__(self, attr): # <<<<<<<<<<<<<< * return getattr(self.memview, attr) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.array.__getattr__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":236 * return getattr(self.memview, attr) * * def __getitem__(self, item): # <<<<<<<<<<<<<< * return self.memview[item] * */ /* Python wrapper */ static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item); /*proto*/ static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("__getitem__", 0); /* "View.MemoryView":237 * * def __getitem__(self, item): * return self.memview[item] # <<<<<<<<<<<<<< * * def __setitem__(self, item, value): */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 237, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetItem(__pyx_t_1, __pyx_v_item); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 237, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":236 * return getattr(self.memview, attr) * * def __getitem__(self, item): # <<<<<<<<<<<<<< * return self.memview[item] * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.array.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":239 * return self.memview[item] * * def __setitem__(self, item, value): # <<<<<<<<<<<<<< * self.memview[item] = value * */ /* Python wrapper */ static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /*proto*/ static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0); __pyx_r = __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item), ((PyObject *)__pyx_v_value)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__setitem__", 0); /* "View.MemoryView":240 * * def __setitem__(self, item, value): * self.memview[item] = value # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 240, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (unlikely(PyObject_SetItem(__pyx_t_1, __pyx_v_item, __pyx_v_value) < 0)) __PYX_ERR(1, 240, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":239 * return self.memview[item] * * def __setitem__(self, item, value): # <<<<<<<<<<<<<< * self.memview[item] = value * */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_array_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_array_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_array___reduce_cython__(((struct __pyx_array_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__8, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(1, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_array_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_array_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_array_2__setstate_cython__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__9, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(1, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.array.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":244 * * @cname("__pyx_array_new") * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format, # <<<<<<<<<<<<<< * char *mode, char *buf): * cdef array result */ static struct __pyx_array_obj *__pyx_array_new(PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, char *__pyx_v_format, char *__pyx_v_mode, char *__pyx_v_buf) { struct __pyx_array_obj *__pyx_v_result = 0; struct __pyx_array_obj *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; __Pyx_RefNannySetupContext("array_cwrapper", 0); /* "View.MemoryView":248 * cdef array result * * if buf == NULL: # <<<<<<<<<<<<<< * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: */ __pyx_t_1 = ((__pyx_v_buf == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":249 * * if buf == NULL: * result = array(shape, itemsize, format, mode.decode('ASCII')) # <<<<<<<<<<<<<< * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), */ __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PyTuple_New(4); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_INCREF(__pyx_v_shape); __Pyx_GIVEREF(__pyx_v_shape); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_v_shape); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_5, 2, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 3, __pyx_t_4); __pyx_t_2 = 0; __pyx_t_3 = 0; __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_Call(((PyObject *)__pyx_array_type), __pyx_t_5, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 249, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":248 * cdef array result * * if buf == NULL: # <<<<<<<<<<<<<< * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: */ goto __pyx_L3; } /* "View.MemoryView":251 * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), # <<<<<<<<<<<<<< * allocate_buffer=False) * result.data = buf */ /*else*/ { __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_3 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_2 = PyTuple_New(4); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_v_shape); __Pyx_GIVEREF(__pyx_v_shape); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_shape); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_4); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_2, 2, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_2, 3, __pyx_t_3); __pyx_t_4 = 0; __pyx_t_5 = 0; __pyx_t_3 = 0; /* "View.MemoryView":252 * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), * allocate_buffer=False) # <<<<<<<<<<<<<< * result.data = buf * */ __pyx_t_3 = __Pyx_PyDict_NewPresized(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 252, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (PyDict_SetItem(__pyx_t_3, __pyx_n_s_allocate_buffer, Py_False) < 0) __PYX_ERR(1, 252, __pyx_L1_error) /* "View.MemoryView":251 * result = array(shape, itemsize, format, mode.decode('ASCII')) * else: * result = array(shape, itemsize, format, mode.decode('ASCII'), # <<<<<<<<<<<<<< * allocate_buffer=False) * result.data = buf */ __pyx_t_5 = __Pyx_PyObject_Call(((PyObject *)__pyx_array_type), __pyx_t_2, __pyx_t_3); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_5); __pyx_t_5 = 0; /* "View.MemoryView":253 * result = array(shape, itemsize, format, mode.decode('ASCII'), * allocate_buffer=False) * result.data = buf # <<<<<<<<<<<<<< * * return result */ __pyx_v_result->data = __pyx_v_buf; } __pyx_L3:; /* "View.MemoryView":255 * result.data = buf * * return result # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(((PyObject *)__pyx_r)); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = __pyx_v_result; goto __pyx_L0; /* "View.MemoryView":244 * * @cname("__pyx_array_new") * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format, # <<<<<<<<<<<<<< * char *mode, char *buf): * cdef array result */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.array_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF((PyObject *)__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":281 * cdef class Enum(object): * cdef object name * def __init__(self, name): # <<<<<<<<<<<<<< * self.name = name * def __repr__(self): */ /* Python wrapper */ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_name = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__init__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_name,0}; PyObject* values[1] = {0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_name)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__init__") < 0)) __PYX_ERR(1, 281, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 1) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); } __pyx_v_name = values[0]; } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__init__", 1, 1, 1, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(1, 281, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.Enum.__init__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self), __pyx_v_name); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__init__", 0); /* "View.MemoryView":282 * cdef object name * def __init__(self, name): * self.name = name # <<<<<<<<<<<<<< * def __repr__(self): * return self.name */ __Pyx_INCREF(__pyx_v_name); __Pyx_GIVEREF(__pyx_v_name); __Pyx_GOTREF(__pyx_v_self->name); __Pyx_DECREF(__pyx_v_self->name); __pyx_v_self->name = __pyx_v_name; /* "View.MemoryView":281 * cdef class Enum(object): * cdef object name * def __init__(self, name): # <<<<<<<<<<<<<< * self.name = name * def __repr__(self): */ /* function exit code */ __pyx_r = 0; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":283 * def __init__(self, name): * self.name = name * def __repr__(self): # <<<<<<<<<<<<<< * return self.name * */ /* Python wrapper */ static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0); __pyx_r = __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__", 0); /* "View.MemoryView":284 * self.name = name * def __repr__(self): * return self.name # <<<<<<<<<<<<<< * * cdef generic = Enum("") */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->name); __pyx_r = __pyx_v_self->name; goto __pyx_L0; /* "View.MemoryView":283 * def __init__(self, name): * self.name = name * def __repr__(self): # <<<<<<<<<<<<<< * return self.name * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * cdef tuple state * cdef object _dict */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_MemviewEnum_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_MemviewEnum_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_MemviewEnum___reduce_cython__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self) { PyObject *__pyx_v_state = 0; PyObject *__pyx_v__dict = 0; int __pyx_v_use_setstate; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_t_3; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":5 * cdef object _dict * cdef bint use_setstate * state = (self.name,) # <<<<<<<<<<<<<< * _dict = getattr(self, '__dict__', None) * if _dict is not None: */ __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_v_self->name); __Pyx_GIVEREF(__pyx_v_self->name); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v_self->name); __pyx_v_state = ((PyObject*)__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":6 * cdef bint use_setstate * state = (self.name,) * _dict = getattr(self, '__dict__', None) # <<<<<<<<<<<<<< * if _dict is not None: * state += (_dict,) */ __pyx_t_1 = __Pyx_GetAttr3(((PyObject *)__pyx_v_self), __pyx_n_s_dict, Py_None); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v__dict = __pyx_t_1; __pyx_t_1 = 0; /* "(tree fragment)":7 * state = (self.name,) * _dict = getattr(self, '__dict__', None) * if _dict is not None: # <<<<<<<<<<<<<< * state += (_dict,) * use_setstate = True */ __pyx_t_2 = (__pyx_v__dict != Py_None); __pyx_t_3 = (__pyx_t_2 != 0); if (__pyx_t_3) { /* "(tree fragment)":8 * _dict = getattr(self, '__dict__', None) * if _dict is not None: * state += (_dict,) # <<<<<<<<<<<<<< * use_setstate = True * else: */ __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_v__dict); __Pyx_GIVEREF(__pyx_v__dict); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v__dict); __pyx_t_4 = PyNumber_InPlaceAdd(__pyx_v_state, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF_SET(__pyx_v_state, ((PyObject*)__pyx_t_4)); __pyx_t_4 = 0; /* "(tree fragment)":9 * if _dict is not None: * state += (_dict,) * use_setstate = True # <<<<<<<<<<<<<< * else: * use_setstate = self.name is not None */ __pyx_v_use_setstate = 1; /* "(tree fragment)":7 * state = (self.name,) * _dict = getattr(self, '__dict__', None) * if _dict is not None: # <<<<<<<<<<<<<< * state += (_dict,) * use_setstate = True */ goto __pyx_L3; } /* "(tree fragment)":11 * use_setstate = True * else: * use_setstate = self.name is not None # <<<<<<<<<<<<<< * if use_setstate: * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state */ /*else*/ { __pyx_t_3 = (__pyx_v_self->name != Py_None); __pyx_v_use_setstate = __pyx_t_3; } __pyx_L3:; /* "(tree fragment)":12 * else: * use_setstate = self.name is not None * if use_setstate: # <<<<<<<<<<<<<< * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: */ __pyx_t_3 = (__pyx_v_use_setstate != 0); if (__pyx_t_3) { /* "(tree fragment)":13 * use_setstate = self.name is not None * if use_setstate: * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state # <<<<<<<<<<<<<< * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) */ __Pyx_XDECREF(__pyx_r); __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_GIVEREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_INCREF(__pyx_int_184977713); __Pyx_GIVEREF(__pyx_int_184977713); PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_int_184977713); __Pyx_INCREF(Py_None); __Pyx_GIVEREF(Py_None); PyTuple_SET_ITEM(__pyx_t_1, 2, Py_None); __pyx_t_5 = PyTuple_New(3); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_1); __Pyx_INCREF(__pyx_v_state); __Pyx_GIVEREF(__pyx_v_state); PyTuple_SET_ITEM(__pyx_t_5, 2, __pyx_v_state); __pyx_t_4 = 0; __pyx_t_1 = 0; __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "(tree fragment)":12 * else: * use_setstate = self.name is not None * if use_setstate: # <<<<<<<<<<<<<< * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: */ } /* "(tree fragment)":15 * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /*else*/ { __Pyx_XDECREF(__pyx_r); __Pyx_GetModuleGlobalName(__pyx_t_5, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_GIVEREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_INCREF(__pyx_int_184977713); __Pyx_GIVEREF(__pyx_int_184977713); PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_int_184977713); __Pyx_INCREF(__pyx_v_state); __Pyx_GIVEREF(__pyx_v_state); PyTuple_SET_ITEM(__pyx_t_1, 2, __pyx_v_state); __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_1); __pyx_t_5 = 0; __pyx_t_1 = 0; __pyx_r = __pyx_t_4; __pyx_t_4 = 0; goto __pyx_L0; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * cdef tuple state * cdef object _dict */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.Enum.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_state); __Pyx_XDECREF(__pyx_v__dict); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":16 * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_MemviewEnum_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_MemviewEnum_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_MemviewEnum_2__setstate_cython__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":17 * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): * __pyx_unpickle_Enum__set_state(self, __pyx_state) # <<<<<<<<<<<<<< */ if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(1, 17, __pyx_L1_error) __pyx_t_1 = __pyx_unpickle_Enum__set_state(__pyx_v_self, ((PyObject*)__pyx_v___pyx_state)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 17, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":16 * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state(self, __pyx_state) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.Enum.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":298 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory */ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) { Py_intptr_t __pyx_v_aligned_p; size_t __pyx_v_offset; void *__pyx_r; int __pyx_t_1; /* "View.MemoryView":300 * cdef void *align_pointer(void *memory, size_t alignment) nogil: * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory # <<<<<<<<<<<<<< * cdef size_t offset * */ __pyx_v_aligned_p = ((Py_intptr_t)__pyx_v_memory); /* "View.MemoryView":304 * * with cython.cdivision(True): * offset = aligned_p % alignment # <<<<<<<<<<<<<< * * if offset > 0: */ __pyx_v_offset = (__pyx_v_aligned_p % __pyx_v_alignment); /* "View.MemoryView":306 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< * aligned_p += alignment - offset * */ __pyx_t_1 = ((__pyx_v_offset > 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":307 * * if offset > 0: * aligned_p += alignment - offset # <<<<<<<<<<<<<< * * return aligned_p */ __pyx_v_aligned_p = (__pyx_v_aligned_p + (__pyx_v_alignment - __pyx_v_offset)); /* "View.MemoryView":306 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< * aligned_p += alignment - offset * */ } /* "View.MemoryView":309 * aligned_p += alignment - offset * * return aligned_p # <<<<<<<<<<<<<< * * */ __pyx_r = ((void *)__pyx_v_aligned_p); goto __pyx_L0; /* "View.MemoryView":298 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":345 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< * self.obj = obj * self.flags = flags */ /* Python wrapper */ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_obj = 0; int __pyx_v_flags; int __pyx_v_dtype_is_object; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_obj,&__pyx_n_s_flags,&__pyx_n_s_dtype_is_object,0}; PyObject* values[3] = {0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_obj)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_flags)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, 1); __PYX_ERR(1, 345, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (kw_args > 0) { PyObject* value = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_dtype_is_object); if (value) { values[2] = value; kw_args--; } } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) __PYX_ERR(1, 345, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[0] = PyTuple_GET_ITEM(__pyx_args, 0); break; default: goto __pyx_L5_argtuple_error; } } __pyx_v_obj = values[0]; __pyx_v_flags = __Pyx_PyInt_As_int(values[1]); if (unlikely((__pyx_v_flags == (int)-1) && PyErr_Occurred())) __PYX_ERR(1, 345, __pyx_L3_error) if (values[2]) { __pyx_v_dtype_is_object = __Pyx_PyObject_IsTrue(values[2]); if (unlikely((__pyx_v_dtype_is_object == (int)-1) && PyErr_Occurred())) __PYX_ERR(1, 345, __pyx_L3_error) } else { __pyx_v_dtype_is_object = ((int)0); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(1, 345, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return -1; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_obj, __pyx_v_flags, __pyx_v_dtype_is_object); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; __Pyx_RefNannySetupContext("__cinit__", 0); /* "View.MemoryView":346 * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj # <<<<<<<<<<<<<< * self.flags = flags * if type(self) is memoryview or obj is not None: */ __Pyx_INCREF(__pyx_v_obj); __Pyx_GIVEREF(__pyx_v_obj); __Pyx_GOTREF(__pyx_v_self->obj); __Pyx_DECREF(__pyx_v_self->obj); __pyx_v_self->obj = __pyx_v_obj; /* "View.MemoryView":347 * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj * self.flags = flags # <<<<<<<<<<<<<< * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) */ __pyx_v_self->flags = __pyx_v_flags; /* "View.MemoryView":348 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: */ __pyx_t_2 = (((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self))) == ((PyObject *)__pyx_memoryview_type)); __pyx_t_3 = (__pyx_t_2 != 0); if (!__pyx_t_3) { } else { __pyx_t_1 = __pyx_t_3; goto __pyx_L4_bool_binop_done; } __pyx_t_3 = (__pyx_v_obj != Py_None); __pyx_t_2 = (__pyx_t_3 != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (__pyx_t_1) { /* "View.MemoryView":349 * self.flags = flags * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) # <<<<<<<<<<<<<< * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None */ __pyx_t_4 = __Pyx_GetBuffer(__pyx_v_obj, (&__pyx_v_self->view), __pyx_v_flags); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 349, __pyx_L1_error) /* "View.MemoryView":350 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) */ __pyx_t_1 = ((((PyObject *)__pyx_v_self->view.obj) == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":351 * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_self->view))->obj = Py_None; /* "View.MemoryView":352 * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * global __pyx_memoryview_thread_locks_used */ Py_INCREF(Py_None); /* "View.MemoryView":350 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) */ } /* "View.MemoryView":348 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: */ } /* "View.MemoryView":355 * * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 */ __pyx_t_1 = ((__pyx_memoryview_thread_locks_used < 8) != 0); if (__pyx_t_1) { /* "View.MemoryView":356 * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: */ __pyx_v_self->lock = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); /* "View.MemoryView":357 * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 # <<<<<<<<<<<<<< * if self.lock is NULL: * self.lock = PyThread_allocate_lock() */ __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used + 1); /* "View.MemoryView":355 * * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 */ } /* "View.MemoryView":358 * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< * self.lock = PyThread_allocate_lock() * if self.lock is NULL: */ __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":359 * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() # <<<<<<<<<<<<<< * if self.lock is NULL: * raise MemoryError */ __pyx_v_self->lock = PyThread_allocate_lock(); /* "View.MemoryView":360 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() * if self.lock is NULL: # <<<<<<<<<<<<<< * raise MemoryError * */ __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":361 * self.lock = PyThread_allocate_lock() * if self.lock is NULL: * raise MemoryError # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ PyErr_NoMemory(); __PYX_ERR(1, 361, __pyx_L1_error) /* "View.MemoryView":360 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() * if self.lock is NULL: # <<<<<<<<<<<<<< * raise MemoryError * */ } /* "View.MemoryView":358 * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< * self.lock = PyThread_allocate_lock() * if self.lock is NULL: */ } /* "View.MemoryView":363 * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":364 * * if flags & PyBUF_FORMAT: * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') # <<<<<<<<<<<<<< * else: * self.dtype_is_object = dtype_is_object */ __pyx_t_2 = (((__pyx_v_self->view.format[0]) == 'O') != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L11_bool_binop_done; } __pyx_t_2 = (((__pyx_v_self->view.format[1]) == '\x00') != 0); __pyx_t_1 = __pyx_t_2; __pyx_L11_bool_binop_done:; __pyx_v_self->dtype_is_object = __pyx_t_1; /* "View.MemoryView":363 * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: */ goto __pyx_L10; } /* "View.MemoryView":366 * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: * self.dtype_is_object = dtype_is_object # <<<<<<<<<<<<<< * * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( */ /*else*/ { __pyx_v_self->dtype_is_object = __pyx_v_dtype_is_object; } __pyx_L10:; /* "View.MemoryView":368 * self.dtype_is_object = dtype_is_object * * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( # <<<<<<<<<<<<<< * &self.acquisition_count[0], sizeof(__pyx_atomic_int)) * self.typeinfo = NULL */ __pyx_v_self->acquisition_count_aligned_p = ((__pyx_atomic_int *)__pyx_align_pointer(((void *)(&(__pyx_v_self->acquisition_count[0]))), (sizeof(__pyx_atomic_int)))); /* "View.MemoryView":370 * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( * &self.acquisition_count[0], sizeof(__pyx_atomic_int)) * self.typeinfo = NULL # <<<<<<<<<<<<<< * * def __dealloc__(memoryview self): */ __pyx_v_self->typeinfo = NULL; /* "View.MemoryView":345 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< * self.obj = obj * self.flags = flags */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":372 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) */ /* Python wrapper */ static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self) { int __pyx_v_i; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_t_5; PyThread_type_lock __pyx_t_6; PyThread_type_lock __pyx_t_7; __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":373 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: */ __pyx_t_1 = (__pyx_v_self->obj != Py_None); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":374 * def __dealloc__(memoryview self): * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) # <<<<<<<<<<<<<< * elif (<__pyx_buffer *> &self.view).obj == Py_None: * */ __Pyx_ReleaseBuffer((&__pyx_v_self->view)); /* "View.MemoryView":373 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: */ goto __pyx_L3; } /* "View.MemoryView":375 * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: # <<<<<<<<<<<<<< * * (<__pyx_buffer *> &self.view).obj = NULL */ __pyx_t_2 = ((((Py_buffer *)(&__pyx_v_self->view))->obj == Py_None) != 0); if (__pyx_t_2) { /* "View.MemoryView":377 * elif (<__pyx_buffer *> &self.view).obj == Py_None: * * (<__pyx_buffer *> &self.view).obj = NULL # <<<<<<<<<<<<<< * Py_DECREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_self->view))->obj = NULL; /* "View.MemoryView":378 * * (<__pyx_buffer *> &self.view).obj = NULL * Py_DECREF(Py_None) # <<<<<<<<<<<<<< * * cdef int i */ Py_DECREF(Py_None); /* "View.MemoryView":375 * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: # <<<<<<<<<<<<<< * * (<__pyx_buffer *> &self.view).obj = NULL */ } __pyx_L3:; /* "View.MemoryView":382 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: */ __pyx_t_2 = ((__pyx_v_self->lock != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":383 * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): # <<<<<<<<<<<<<< * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 */ __pyx_t_3 = __pyx_memoryview_thread_locks_used; __pyx_t_4 = __pyx_t_3; for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; /* "View.MemoryView":384 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: */ __pyx_t_2 = (((__pyx_memoryview_thread_locks[__pyx_v_i]) == __pyx_v_self->lock) != 0); if (__pyx_t_2) { /* "View.MemoryView":385 * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 # <<<<<<<<<<<<<< * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( */ __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used - 1); /* "View.MemoryView":386 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) */ __pyx_t_2 = ((__pyx_v_i != __pyx_memoryview_thread_locks_used) != 0); if (__pyx_t_2) { /* "View.MemoryView":388 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) # <<<<<<<<<<<<<< * break * else: */ __pyx_t_6 = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); __pyx_t_7 = (__pyx_memoryview_thread_locks[__pyx_v_i]); /* "View.MemoryView":387 * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) * break */ (__pyx_memoryview_thread_locks[__pyx_v_i]) = __pyx_t_6; (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]) = __pyx_t_7; /* "View.MemoryView":386 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) */ } /* "View.MemoryView":389 * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) * break # <<<<<<<<<<<<<< * else: * PyThread_free_lock(self.lock) */ goto __pyx_L6_break; /* "View.MemoryView":384 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: */ } } /*else*/ { /* "View.MemoryView":391 * break * else: * PyThread_free_lock(self.lock) # <<<<<<<<<<<<<< * * cdef char *get_item_pointer(memoryview self, object index) except NULL: */ PyThread_free_lock(__pyx_v_self->lock); } __pyx_L6_break:; /* "View.MemoryView":382 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: */ } /* "View.MemoryView":372 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":393 * PyThread_free_lock(self.lock) * * cdef char *get_item_pointer(memoryview self, object index) except NULL: # <<<<<<<<<<<<<< * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf */ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) { Py_ssize_t __pyx_v_dim; char *__pyx_v_itemp; PyObject *__pyx_v_idx = NULL; char *__pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; PyObject *__pyx_t_2 = NULL; Py_ssize_t __pyx_t_3; PyObject *(*__pyx_t_4)(PyObject *); PyObject *__pyx_t_5 = NULL; Py_ssize_t __pyx_t_6; char *__pyx_t_7; __Pyx_RefNannySetupContext("get_item_pointer", 0); /* "View.MemoryView":395 * cdef char *get_item_pointer(memoryview self, object index) except NULL: * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf # <<<<<<<<<<<<<< * * for dim, idx in enumerate(index): */ __pyx_v_itemp = ((char *)__pyx_v_self->view.buf); /* "View.MemoryView":397 * cdef char *itemp = self.view.buf * * for dim, idx in enumerate(index): # <<<<<<<<<<<<<< * itemp = pybuffer_index(&self.view, itemp, idx, dim) * */ __pyx_t_1 = 0; if (likely(PyList_CheckExact(__pyx_v_index)) || PyTuple_CheckExact(__pyx_v_index)) { __pyx_t_2 = __pyx_v_index; __Pyx_INCREF(__pyx_t_2); __pyx_t_3 = 0; __pyx_t_4 = NULL; } else { __pyx_t_3 = -1; __pyx_t_2 = PyObject_GetIter(__pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = Py_TYPE(__pyx_t_2)->tp_iternext; if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 397, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_4)) { if (likely(PyList_CheckExact(__pyx_t_2))) { if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_2)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyList_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(1, 397, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif } else { if (__pyx_t_3 >= PyTuple_GET_SIZE(__pyx_t_2)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(1, 397, __pyx_L1_error) #else __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 397, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif } } else { __pyx_t_5 = __pyx_t_4(__pyx_t_2); if (unlikely(!__pyx_t_5)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(1, 397, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_5); } __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_5); __pyx_t_5 = 0; __pyx_v_dim = __pyx_t_1; __pyx_t_1 = (__pyx_t_1 + 1); /* "View.MemoryView":398 * * for dim, idx in enumerate(index): * itemp = pybuffer_index(&self.view, itemp, idx, dim) # <<<<<<<<<<<<<< * * return itemp */ __pyx_t_6 = __Pyx_PyIndex_AsSsize_t(__pyx_v_idx); if (unlikely((__pyx_t_6 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(1, 398, __pyx_L1_error) __pyx_t_7 = __pyx_pybuffer_index((&__pyx_v_self->view), __pyx_v_itemp, __pyx_t_6, __pyx_v_dim); if (unlikely(__pyx_t_7 == ((char *)NULL))) __PYX_ERR(1, 398, __pyx_L1_error) __pyx_v_itemp = __pyx_t_7; /* "View.MemoryView":397 * cdef char *itemp = self.view.buf * * for dim, idx in enumerate(index): # <<<<<<<<<<<<<< * itemp = pybuffer_index(&self.view, itemp, idx, dim) * */ } __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":400 * itemp = pybuffer_index(&self.view, itemp, idx, dim) * * return itemp # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_itemp; goto __pyx_L0; /* "View.MemoryView":393 * PyThread_free_lock(self.lock) * * cdef char *get_item_pointer(memoryview self, object index) except NULL: # <<<<<<<<<<<<<< * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.get_item_pointer", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_idx); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":403 * * * def __getitem__(memoryview self, object index): # <<<<<<<<<<<<<< * if index is Ellipsis: * return self */ /* Python wrapper */ static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index); /*proto*/ static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) { PyObject *__pyx_v_have_slices = NULL; PyObject *__pyx_v_indices = NULL; char *__pyx_v_itemp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; char *__pyx_t_6; __Pyx_RefNannySetupContext("__getitem__", 0); /* "View.MemoryView":404 * * def __getitem__(memoryview self, object index): * if index is Ellipsis: # <<<<<<<<<<<<<< * return self * */ __pyx_t_1 = (__pyx_v_index == __pyx_builtin_Ellipsis); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":405 * def __getitem__(memoryview self, object index): * if index is Ellipsis: * return self # <<<<<<<<<<<<<< * * have_slices, indices = _unellipsify(index, self.view.ndim) */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_self)); __pyx_r = ((PyObject *)__pyx_v_self); goto __pyx_L0; /* "View.MemoryView":404 * * def __getitem__(memoryview self, object index): * if index is Ellipsis: # <<<<<<<<<<<<<< * return self * */ } /* "View.MemoryView":407 * return self * * have_slices, indices = _unellipsify(index, self.view.ndim) # <<<<<<<<<<<<<< * * cdef char *itemp */ __pyx_t_3 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (likely(__pyx_t_3 != Py_None)) { PyObject* sequence = __pyx_t_3; Py_ssize_t size = __Pyx_PySequence_SIZE(sequence); if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); __PYX_ERR(1, 407, __pyx_L1_error) } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_4 = PyTuple_GET_ITEM(sequence, 0); __pyx_t_5 = PyTuple_GET_ITEM(sequence, 1); __Pyx_INCREF(__pyx_t_4); __Pyx_INCREF(__pyx_t_5); #else __pyx_t_4 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 407, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } else { __Pyx_RaiseNoneNotIterableError(); __PYX_ERR(1, 407, __pyx_L1_error) } __pyx_v_have_slices = __pyx_t_4; __pyx_t_4 = 0; __pyx_v_indices = __pyx_t_5; __pyx_t_5 = 0; /* "View.MemoryView":410 * * cdef char *itemp * if have_slices: # <<<<<<<<<<<<<< * return memview_slice(self, indices) * else: */ __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(1, 410, __pyx_L1_error) if (__pyx_t_2) { /* "View.MemoryView":411 * cdef char *itemp * if have_slices: * return memview_slice(self, indices) # <<<<<<<<<<<<<< * else: * itemp = self.get_item_pointer(indices) */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = ((PyObject *)__pyx_memview_slice(__pyx_v_self, __pyx_v_indices)); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 411, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":410 * * cdef char *itemp * if have_slices: # <<<<<<<<<<<<<< * return memview_slice(self, indices) * else: */ } /* "View.MemoryView":413 * return memview_slice(self, indices) * else: * itemp = self.get_item_pointer(indices) # <<<<<<<<<<<<<< * return self.convert_item_to_object(itemp) * */ /*else*/ { __pyx_t_6 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_indices); if (unlikely(__pyx_t_6 == ((char *)NULL))) __PYX_ERR(1, 413, __pyx_L1_error) __pyx_v_itemp = __pyx_t_6; /* "View.MemoryView":414 * else: * itemp = self.get_item_pointer(indices) * return self.convert_item_to_object(itemp) # <<<<<<<<<<<<<< * * def __setitem__(memoryview self, object index, object value): */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->convert_item_to_object(__pyx_v_self, __pyx_v_itemp); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 414, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; } /* "View.MemoryView":403 * * * def __getitem__(memoryview self, object index): # <<<<<<<<<<<<<< * if index is Ellipsis: * return self */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_have_slices); __Pyx_XDECREF(__pyx_v_indices); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":416 * return self.convert_item_to_object(itemp) * * def __setitem__(memoryview self, object index, object value): # <<<<<<<<<<<<<< * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") */ /* Python wrapper */ static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /*proto*/ static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index), ((PyObject *)__pyx_v_value)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { PyObject *__pyx_v_have_slices = NULL; PyObject *__pyx_v_obj = NULL; int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_RefNannySetupContext("__setitem__", 0); __Pyx_INCREF(__pyx_v_index); /* "View.MemoryView":417 * * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: # <<<<<<<<<<<<<< * raise TypeError("Cannot assign to read-only memoryview") * */ __pyx_t_1 = (__pyx_v_self->view.readonly != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":418 * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") # <<<<<<<<<<<<<< * * have_slices, index = _unellipsify(index, self.view.ndim) */ __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__10, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 418, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(1, 418, __pyx_L1_error) /* "View.MemoryView":417 * * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: # <<<<<<<<<<<<<< * raise TypeError("Cannot assign to read-only memoryview") * */ } /* "View.MemoryView":420 * raise TypeError("Cannot assign to read-only memoryview") * * have_slices, index = _unellipsify(index, self.view.ndim) # <<<<<<<<<<<<<< * * if have_slices: */ __pyx_t_2 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (likely(__pyx_t_2 != Py_None)) { PyObject* sequence = __pyx_t_2; Py_ssize_t size = __Pyx_PySequence_SIZE(sequence); if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); __PYX_ERR(1, 420, __pyx_L1_error) } #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_3 = PyTuple_GET_ITEM(sequence, 0); __pyx_t_4 = PyTuple_GET_ITEM(sequence, 1); __Pyx_INCREF(__pyx_t_3); __Pyx_INCREF(__pyx_t_4); #else __pyx_t_3 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 420, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); #endif __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } else { __Pyx_RaiseNoneNotIterableError(); __PYX_ERR(1, 420, __pyx_L1_error) } __pyx_v_have_slices = __pyx_t_3; __pyx_t_3 = 0; __Pyx_DECREF_SET(__pyx_v_index, __pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":422 * have_slices, index = _unellipsify(index, self.view.ndim) * * if have_slices: # <<<<<<<<<<<<<< * obj = self.is_slice(value) * if obj: */ __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(1, 422, __pyx_L1_error) if (__pyx_t_1) { /* "View.MemoryView":423 * * if have_slices: * obj = self.is_slice(value) # <<<<<<<<<<<<<< * if obj: * self.setitem_slice_assignment(self[index], obj) */ __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->is_slice(__pyx_v_self, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 423, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_v_obj = __pyx_t_2; __pyx_t_2 = 0; /* "View.MemoryView":424 * if have_slices: * obj = self.is_slice(value) * if obj: # <<<<<<<<<<<<<< * self.setitem_slice_assignment(self[index], obj) * else: */ __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_v_obj); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(1, 424, __pyx_L1_error) if (__pyx_t_1) { /* "View.MemoryView":425 * obj = self.is_slice(value) * if obj: * self.setitem_slice_assignment(self[index], obj) # <<<<<<<<<<<<<< * else: * self.setitem_slice_assign_scalar(self[index], value) */ __pyx_t_2 = __Pyx_PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 425, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assignment(__pyx_v_self, __pyx_t_2, __pyx_v_obj); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 425, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":424 * if have_slices: * obj = self.is_slice(value) * if obj: # <<<<<<<<<<<<<< * self.setitem_slice_assignment(self[index], obj) * else: */ goto __pyx_L5; } /* "View.MemoryView":427 * self.setitem_slice_assignment(self[index], obj) * else: * self.setitem_slice_assign_scalar(self[index], value) # <<<<<<<<<<<<<< * else: * self.setitem_indexed(index, value) */ /*else*/ { __pyx_t_4 = __Pyx_PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 427, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); if (!(likely(((__pyx_t_4) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_4, __pyx_memoryview_type))))) __PYX_ERR(1, 427, __pyx_L1_error) __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assign_scalar(__pyx_v_self, ((struct __pyx_memoryview_obj *)__pyx_t_4), __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 427, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_L5:; /* "View.MemoryView":422 * have_slices, index = _unellipsify(index, self.view.ndim) * * if have_slices: # <<<<<<<<<<<<<< * obj = self.is_slice(value) * if obj: */ goto __pyx_L4; } /* "View.MemoryView":429 * self.setitem_slice_assign_scalar(self[index], value) * else: * self.setitem_indexed(index, value) # <<<<<<<<<<<<<< * * cdef is_slice(self, obj): */ /*else*/ { __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_indexed(__pyx_v_self, __pyx_v_index, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 429, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_L4:; /* "View.MemoryView":416 * return self.convert_item_to_object(itemp) * * def __setitem__(memoryview self, object index, object value): # <<<<<<<<<<<<<< * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView.memoryview.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __pyx_L0:; __Pyx_XDECREF(__pyx_v_have_slices); __Pyx_XDECREF(__pyx_v_obj); __Pyx_XDECREF(__pyx_v_index); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":431 * self.setitem_indexed(index, value) * * cdef is_slice(self, obj): # <<<<<<<<<<<<<< * if not isinstance(obj, memoryview): * try: */ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; int __pyx_t_9; __Pyx_RefNannySetupContext("is_slice", 0); __Pyx_INCREF(__pyx_v_obj); /* "View.MemoryView":432 * * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): # <<<<<<<<<<<<<< * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, */ __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_obj, __pyx_memoryview_type); __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_3, &__pyx_t_4, &__pyx_t_5); __Pyx_XGOTREF(__pyx_t_3); __Pyx_XGOTREF(__pyx_t_4); __Pyx_XGOTREF(__pyx_t_5); /*try:*/ { /* "View.MemoryView":434 * if not isinstance(obj, memoryview): * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, # <<<<<<<<<<<<<< * self.dtype_is_object) * except TypeError: */ __pyx_t_6 = __Pyx_PyInt_From_int(((__pyx_v_self->flags & (~PyBUF_WRITABLE)) | PyBUF_ANY_CONTIGUOUS)); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_6); /* "View.MemoryView":435 * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) # <<<<<<<<<<<<<< * except TypeError: * return None */ __pyx_t_7 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_7)) __PYX_ERR(1, 435, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_7); /* "View.MemoryView":434 * if not isinstance(obj, memoryview): * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, # <<<<<<<<<<<<<< * self.dtype_is_object) * except TypeError: */ __pyx_t_8 = PyTuple_New(3); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_INCREF(__pyx_v_obj); __Pyx_GIVEREF(__pyx_v_obj); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_v_obj); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_8, 1, __pyx_t_6); __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_8, 2, __pyx_t_7); __pyx_t_6 = 0; __pyx_t_7 = 0; __pyx_t_7 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_8, NULL); if (unlikely(!__pyx_t_7)) __PYX_ERR(1, 434, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF_SET(__pyx_v_obj, __pyx_t_7); __pyx_t_7 = 0; /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ } __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; goto __pyx_L9_try_end; __pyx_L4_error:; __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0; /* "View.MemoryView":436 * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) * except TypeError: # <<<<<<<<<<<<<< * return None * */ __pyx_t_9 = __Pyx_PyErr_ExceptionMatches(__pyx_builtin_TypeError); if (__pyx_t_9) { __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_6) < 0) __PYX_ERR(1, 436, __pyx_L6_except_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_GOTREF(__pyx_t_8); __Pyx_GOTREF(__pyx_t_6); /* "View.MemoryView":437 * self.dtype_is_object) * except TypeError: * return None # <<<<<<<<<<<<<< * * return obj */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; goto __pyx_L7_except_return; } goto __pyx_L6_except_error; __pyx_L6_except_error:; /* "View.MemoryView":433 * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): * try: # <<<<<<<<<<<<<< * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) */ __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_XGIVEREF(__pyx_t_5); __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5); goto __pyx_L1_error; __pyx_L7_except_return:; __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_XGIVEREF(__pyx_t_5); __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5); goto __pyx_L0; __pyx_L9_try_end:; } /* "View.MemoryView":432 * * cdef is_slice(self, obj): * if not isinstance(obj, memoryview): # <<<<<<<<<<<<<< * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, */ } /* "View.MemoryView":439 * return None * * return obj # <<<<<<<<<<<<<< * * cdef setitem_slice_assignment(self, dst, src): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_obj); __pyx_r = __pyx_v_obj; goto __pyx_L0; /* "View.MemoryView":431 * self.setitem_indexed(index, value) * * cdef is_slice(self, obj): # <<<<<<<<<<<<<< * if not isinstance(obj, memoryview): * try: */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_obj); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":441 * return obj * * cdef setitem_slice_assignment(self, dst, src): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice dst_slice * cdef __Pyx_memviewslice src_slice */ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src) { __Pyx_memviewslice __pyx_v_dst_slice; __Pyx_memviewslice __pyx_v_src_slice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; __Pyx_memviewslice *__pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; int __pyx_t_5; int __pyx_t_6; __Pyx_RefNannySetupContext("setitem_slice_assignment", 0); /* "View.MemoryView":445 * cdef __Pyx_memviewslice src_slice * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], # <<<<<<<<<<<<<< * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) */ if (!(likely(((__pyx_v_src) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_src, __pyx_memoryview_type))))) __PYX_ERR(1, 445, __pyx_L1_error) __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_src), (&__pyx_v_src_slice)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(1, 445, __pyx_L1_error) /* "View.MemoryView":446 * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], * get_slice_from_memview(dst, &dst_slice)[0], # <<<<<<<<<<<<<< * src.ndim, dst.ndim, self.dtype_is_object) * */ if (!(likely(((__pyx_v_dst) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_dst, __pyx_memoryview_type))))) __PYX_ERR(1, 446, __pyx_L1_error) __pyx_t_2 = __pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_dst), (&__pyx_v_dst_slice)); if (unlikely(__pyx_t_2 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(1, 446, __pyx_L1_error) /* "View.MemoryView":447 * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) # <<<<<<<<<<<<<< * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): */ __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_v_src, __pyx_n_s_ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 447, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_4 == (int)-1) && PyErr_Occurred())) __PYX_ERR(1, 447, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_v_dst, __pyx_n_s_ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 447, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_5 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_5 == (int)-1) && PyErr_Occurred())) __PYX_ERR(1, 447, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":445 * cdef __Pyx_memviewslice src_slice * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], # <<<<<<<<<<<<<< * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) */ __pyx_t_6 = __pyx_memoryview_copy_contents((__pyx_t_1[0]), (__pyx_t_2[0]), __pyx_t_4, __pyx_t_5, __pyx_v_self->dtype_is_object); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 445, __pyx_L1_error) /* "View.MemoryView":441 * return obj * * cdef setitem_slice_assignment(self, dst, src): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice dst_slice * cdef __Pyx_memviewslice src_slice */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assignment", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":449 * src.ndim, dst.ndim, self.dtype_is_object) * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): # <<<<<<<<<<<<<< * cdef int array[128] * cdef void *tmp = NULL */ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value) { int __pyx_v_array[0x80]; void *__pyx_v_tmp; void *__pyx_v_item; __Pyx_memviewslice *__pyx_v_dst_slice; __Pyx_memviewslice __pyx_v_tmp_slice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; int __pyx_t_4; int __pyx_t_5; char const *__pyx_t_6; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; PyObject *__pyx_t_9 = NULL; PyObject *__pyx_t_10 = NULL; PyObject *__pyx_t_11 = NULL; PyObject *__pyx_t_12 = NULL; __Pyx_RefNannySetupContext("setitem_slice_assign_scalar", 0); /* "View.MemoryView":451 * cdef setitem_slice_assign_scalar(self, memoryview dst, value): * cdef int array[128] * cdef void *tmp = NULL # <<<<<<<<<<<<<< * cdef void *item * */ __pyx_v_tmp = NULL; /* "View.MemoryView":456 * cdef __Pyx_memviewslice *dst_slice * cdef __Pyx_memviewslice tmp_slice * dst_slice = get_slice_from_memview(dst, &tmp_slice) # <<<<<<<<<<<<<< * * if self.view.itemsize > sizeof(array): */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_dst, (&__pyx_v_tmp_slice)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(1, 456, __pyx_L1_error) __pyx_v_dst_slice = __pyx_t_1; /* "View.MemoryView":458 * dst_slice = get_slice_from_memview(dst, &tmp_slice) * * if self.view.itemsize > sizeof(array): # <<<<<<<<<<<<<< * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: */ __pyx_t_2 = ((((size_t)__pyx_v_self->view.itemsize) > (sizeof(__pyx_v_array))) != 0); if (__pyx_t_2) { /* "View.MemoryView":459 * * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) # <<<<<<<<<<<<<< * if tmp == NULL: * raise MemoryError */ __pyx_v_tmp = PyMem_Malloc(__pyx_v_self->view.itemsize); /* "View.MemoryView":460 * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: # <<<<<<<<<<<<<< * raise MemoryError * item = tmp */ __pyx_t_2 = ((__pyx_v_tmp == NULL) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":461 * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: * raise MemoryError # <<<<<<<<<<<<<< * item = tmp * else: */ PyErr_NoMemory(); __PYX_ERR(1, 461, __pyx_L1_error) /* "View.MemoryView":460 * if self.view.itemsize > sizeof(array): * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: # <<<<<<<<<<<<<< * raise MemoryError * item = tmp */ } /* "View.MemoryView":462 * if tmp == NULL: * raise MemoryError * item = tmp # <<<<<<<<<<<<<< * else: * item = array */ __pyx_v_item = __pyx_v_tmp; /* "View.MemoryView":458 * dst_slice = get_slice_from_memview(dst, &tmp_slice) * * if self.view.itemsize > sizeof(array): # <<<<<<<<<<<<<< * tmp = PyMem_Malloc(self.view.itemsize) * if tmp == NULL: */ goto __pyx_L3; } /* "View.MemoryView":464 * item = tmp * else: * item = array # <<<<<<<<<<<<<< * * try: */ /*else*/ { __pyx_v_item = ((void *)__pyx_v_array); } __pyx_L3:; /* "View.MemoryView":466 * item = array * * try: # <<<<<<<<<<<<<< * if self.dtype_is_object: * ( item)[0] = value */ /*try:*/ { /* "View.MemoryView":467 * * try: * if self.dtype_is_object: # <<<<<<<<<<<<<< * ( item)[0] = value * else: */ __pyx_t_2 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_2) { /* "View.MemoryView":468 * try: * if self.dtype_is_object: * ( item)[0] = value # <<<<<<<<<<<<<< * else: * self.assign_item_from_object( item, value) */ (((PyObject **)__pyx_v_item)[0]) = ((PyObject *)__pyx_v_value); /* "View.MemoryView":467 * * try: * if self.dtype_is_object: # <<<<<<<<<<<<<< * ( item)[0] = value * else: */ goto __pyx_L8; } /* "View.MemoryView":470 * ( item)[0] = value * else: * self.assign_item_from_object( item, value) # <<<<<<<<<<<<<< * * */ /*else*/ { __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, ((char *)__pyx_v_item), __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 470, __pyx_L6_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } __pyx_L8:; /* "View.MemoryView":474 * * * if self.view.suboffsets != NULL: # <<<<<<<<<<<<<< * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, */ __pyx_t_2 = ((__pyx_v_self->view.suboffsets != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":475 * * if self.view.suboffsets != NULL: * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) # <<<<<<<<<<<<<< * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, * item, self.dtype_is_object) */ __pyx_t_3 = assert_direct_dimensions(__pyx_v_self->view.suboffsets, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 475, __pyx_L6_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":474 * * * if self.view.suboffsets != NULL: # <<<<<<<<<<<<<< * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, */ } /* "View.MemoryView":476 * if self.view.suboffsets != NULL: * assert_direct_dimensions(self.view.suboffsets, self.view.ndim) * slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize, # <<<<<<<<<<<<<< * item, self.dtype_is_object) * finally: */ __pyx_memoryview_slice_assign_scalar(__pyx_v_dst_slice, __pyx_v_dst->view.ndim, __pyx_v_self->view.itemsize, __pyx_v_item, __pyx_v_self->dtype_is_object); } /* "View.MemoryView":479 * item, self.dtype_is_object) * finally: * PyMem_Free(tmp) # <<<<<<<<<<<<<< * * cdef setitem_indexed(self, index, value): */ /*finally:*/ { /*normal exit:*/{ PyMem_Free(__pyx_v_tmp); goto __pyx_L7; } __pyx_L6_error:; /*exception exit:*/{ __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0; __pyx_t_12 = 0; __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0; if (PY_MAJOR_VERSION >= 3) __Pyx_ExceptionSwap(&__pyx_t_10, &__pyx_t_11, &__pyx_t_12); if ((PY_MAJOR_VERSION < 3) || unlikely(__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_9) < 0)) __Pyx_ErrFetch(&__pyx_t_7, &__pyx_t_8, &__pyx_t_9); __Pyx_XGOTREF(__pyx_t_7); __Pyx_XGOTREF(__pyx_t_8); __Pyx_XGOTREF(__pyx_t_9); __Pyx_XGOTREF(__pyx_t_10); __Pyx_XGOTREF(__pyx_t_11); __Pyx_XGOTREF(__pyx_t_12); __pyx_t_4 = __pyx_lineno; __pyx_t_5 = __pyx_clineno; __pyx_t_6 = __pyx_filename; { PyMem_Free(__pyx_v_tmp); } if (PY_MAJOR_VERSION >= 3) { __Pyx_XGIVEREF(__pyx_t_10); __Pyx_XGIVEREF(__pyx_t_11); __Pyx_XGIVEREF(__pyx_t_12); __Pyx_ExceptionReset(__pyx_t_10, __pyx_t_11, __pyx_t_12); } __Pyx_XGIVEREF(__pyx_t_7); __Pyx_XGIVEREF(__pyx_t_8); __Pyx_XGIVEREF(__pyx_t_9); __Pyx_ErrRestore(__pyx_t_7, __pyx_t_8, __pyx_t_9); __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0; __pyx_t_12 = 0; __pyx_lineno = __pyx_t_4; __pyx_clineno = __pyx_t_5; __pyx_filename = __pyx_t_6; goto __pyx_L1_error; } __pyx_L7:; } /* "View.MemoryView":449 * src.ndim, dst.ndim, self.dtype_is_object) * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): # <<<<<<<<<<<<<< * cdef int array[128] * cdef void *tmp = NULL */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assign_scalar", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":481 * PyMem_Free(tmp) * * cdef setitem_indexed(self, index, value): # <<<<<<<<<<<<<< * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) */ static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) { char *__pyx_v_itemp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations char *__pyx_t_1; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("setitem_indexed", 0); /* "View.MemoryView":482 * * cdef setitem_indexed(self, index, value): * cdef char *itemp = self.get_item_pointer(index) # <<<<<<<<<<<<<< * self.assign_item_from_object(itemp, value) * */ __pyx_t_1 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_index); if (unlikely(__pyx_t_1 == ((char *)NULL))) __PYX_ERR(1, 482, __pyx_L1_error) __pyx_v_itemp = __pyx_t_1; /* "View.MemoryView":483 * cdef setitem_indexed(self, index, value): * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) # <<<<<<<<<<<<<< * * cdef convert_item_to_object(self, char *itemp): */ __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 483, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":481 * PyMem_Free(tmp) * * cdef setitem_indexed(self, index, value): # <<<<<<<<<<<<<< * cdef char *itemp = self.get_item_pointer(index) * self.assign_item_from_object(itemp, value) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_indexed", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":485 * self.assign_item_from_object(itemp, value) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp) { PyObject *__pyx_v_struct = NULL; PyObject *__pyx_v_bytesitem = 0; PyObject *__pyx_v_result = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; int __pyx_t_8; PyObject *__pyx_t_9 = NULL; size_t __pyx_t_10; int __pyx_t_11; __Pyx_RefNannySetupContext("convert_item_to_object", 0); /* "View.MemoryView":488 * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" * import struct # <<<<<<<<<<<<<< * cdef bytes bytesitem * */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 488, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_struct = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":491 * cdef bytes bytesitem * * bytesitem = itemp[:self.view.itemsize] # <<<<<<<<<<<<<< * try: * result = struct.unpack(self.view.format, bytesitem) */ __pyx_t_1 = __Pyx_PyBytes_FromStringAndSize(__pyx_v_itemp + 0, __pyx_v_self->view.itemsize - 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 491, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_bytesitem = ((PyObject*)__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ExceptionSave(&__pyx_t_2, &__pyx_t_3, &__pyx_t_4); __Pyx_XGOTREF(__pyx_t_2); __Pyx_XGOTREF(__pyx_t_3); __Pyx_XGOTREF(__pyx_t_4); /*try:*/ { /* "View.MemoryView":493 * bytesitem = itemp[:self.view.itemsize] * try: * result = struct.unpack(self.view.format, bytesitem) # <<<<<<<<<<<<<< * except struct.error: * raise ValueError("Unable to convert item to object") */ __pyx_t_5 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_unpack); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_5); __pyx_t_6 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = NULL; __pyx_t_8 = 0; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_5))) { __pyx_t_7 = PyMethod_GET_SELF(__pyx_t_5); if (likely(__pyx_t_7)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_5); __Pyx_INCREF(__pyx_t_7); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_5, function); __pyx_t_8 = 1; } } #if CYTHON_FAST_PYCALL if (PyFunction_Check(__pyx_t_5)) { PyObject *__pyx_temp[3] = {__pyx_t_7, __pyx_t_6, __pyx_v_bytesitem}; __pyx_t_1 = __Pyx_PyFunction_FastCall(__pyx_t_5, __pyx_temp+1-__pyx_t_8, 2+__pyx_t_8); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 493, __pyx_L3_error) __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } else #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(__pyx_t_5)) { PyObject *__pyx_temp[3] = {__pyx_t_7, __pyx_t_6, __pyx_v_bytesitem}; __pyx_t_1 = __Pyx_PyCFunction_FastCall(__pyx_t_5, __pyx_temp+1-__pyx_t_8, 2+__pyx_t_8); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 493, __pyx_L3_error) __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } else #endif { __pyx_t_9 = PyTuple_New(2+__pyx_t_8); if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_9); if (__pyx_t_7) { __Pyx_GIVEREF(__pyx_t_7); PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_7); __pyx_t_7 = NULL; } __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_9, 0+__pyx_t_8, __pyx_t_6); __Pyx_INCREF(__pyx_v_bytesitem); __Pyx_GIVEREF(__pyx_v_bytesitem); PyTuple_SET_ITEM(__pyx_t_9, 1+__pyx_t_8, __pyx_v_bytesitem); __pyx_t_6 = 0; __pyx_t_1 = __Pyx_PyObject_Call(__pyx_t_5, __pyx_t_9, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 493, __pyx_L3_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_result = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ } /* "View.MemoryView":497 * raise ValueError("Unable to convert item to object") * else: * if len(self.view.format) == 1: # <<<<<<<<<<<<<< * return result[0] * return result */ /*else:*/ { __pyx_t_10 = strlen(__pyx_v_self->view.format); __pyx_t_11 = ((__pyx_t_10 == 1) != 0); if (__pyx_t_11) { /* "View.MemoryView":498 * else: * if len(self.view.format) == 1: * return result[0] # <<<<<<<<<<<<<< * return result * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_GetItemInt(__pyx_v_result, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 498, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L6_except_return; /* "View.MemoryView":497 * raise ValueError("Unable to convert item to object") * else: * if len(self.view.format) == 1: # <<<<<<<<<<<<<< * return result[0] * return result */ } /* "View.MemoryView":499 * if len(self.view.format) == 1: * return result[0] * return result # <<<<<<<<<<<<<< * * cdef assign_item_from_object(self, char *itemp, object value): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_result); __pyx_r = __pyx_v_result; goto __pyx_L6_except_return; } __pyx_L3_error:; __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_XDECREF(__pyx_t_9); __pyx_t_9 = 0; /* "View.MemoryView":494 * try: * result = struct.unpack(self.view.format, bytesitem) * except struct.error: # <<<<<<<<<<<<<< * raise ValueError("Unable to convert item to object") * else: */ __Pyx_ErrFetch(&__pyx_t_1, &__pyx_t_5, &__pyx_t_9); __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_error); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 494, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_8 = __Pyx_PyErr_GivenExceptionMatches(__pyx_t_1, __pyx_t_6); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __Pyx_ErrRestore(__pyx_t_1, __pyx_t_5, __pyx_t_9); __pyx_t_1 = 0; __pyx_t_5 = 0; __pyx_t_9 = 0; if (__pyx_t_8) { __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); if (__Pyx_GetException(&__pyx_t_9, &__pyx_t_5, &__pyx_t_1) < 0) __PYX_ERR(1, 494, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_9); __Pyx_GOTREF(__pyx_t_5); __Pyx_GOTREF(__pyx_t_1); /* "View.MemoryView":495 * result = struct.unpack(self.view.format, bytesitem) * except struct.error: * raise ValueError("Unable to convert item to object") # <<<<<<<<<<<<<< * else: * if len(self.view.format) == 1: */ __pyx_t_6 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__11, NULL); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 495, __pyx_L5_except_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_Raise(__pyx_t_6, 0, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __PYX_ERR(1, 495, __pyx_L5_except_error) } goto __pyx_L5_except_error; __pyx_L5_except_error:; /* "View.MemoryView":492 * * bytesitem = itemp[:self.view.itemsize] * try: # <<<<<<<<<<<<<< * result = struct.unpack(self.view.format, bytesitem) * except struct.error: */ __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4); goto __pyx_L1_error; __pyx_L6_except_return:; __Pyx_XGIVEREF(__pyx_t_2); __Pyx_XGIVEREF(__pyx_t_3); __Pyx_XGIVEREF(__pyx_t_4); __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4); goto __pyx_L0; } /* "View.MemoryView":485 * self.assign_item_from_object(itemp, value) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_9); __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_struct); __Pyx_XDECREF(__pyx_v_bytesitem); __Pyx_XDECREF(__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":501 * return result * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) { PyObject *__pyx_v_struct = NULL; char __pyx_v_c; PyObject *__pyx_v_bytesvalue = 0; Py_ssize_t __pyx_v_i; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; int __pyx_t_3; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; int __pyx_t_7; PyObject *__pyx_t_8 = NULL; Py_ssize_t __pyx_t_9; PyObject *__pyx_t_10 = NULL; char *__pyx_t_11; char *__pyx_t_12; char *__pyx_t_13; char *__pyx_t_14; __Pyx_RefNannySetupContext("assign_item_from_object", 0); /* "View.MemoryView":504 * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" * import struct # <<<<<<<<<<<<<< * cdef char c * cdef bytes bytesvalue */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 504, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_struct = __pyx_t_1; __pyx_t_1 = 0; /* "View.MemoryView":509 * cdef Py_ssize_t i * * if isinstance(value, tuple): # <<<<<<<<<<<<<< * bytesvalue = struct.pack(self.view.format, *value) * else: */ __pyx_t_2 = PyTuple_Check(__pyx_v_value); __pyx_t_3 = (__pyx_t_2 != 0); if (__pyx_t_3) { /* "View.MemoryView":510 * * if isinstance(value, tuple): * bytesvalue = struct.pack(self.view.format, *value) # <<<<<<<<<<<<<< * else: * bytesvalue = struct.pack(self.view.format, value) */ __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_4 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PySequence_Tuple(__pyx_v_value); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_6 = PyNumber_Add(__pyx_t_5, __pyx_t_4); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_6, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 510, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (!(likely(PyBytes_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_4)->tp_name), 0))) __PYX_ERR(1, 510, __pyx_L1_error) __pyx_v_bytesvalue = ((PyObject*)__pyx_t_4); __pyx_t_4 = 0; /* "View.MemoryView":509 * cdef Py_ssize_t i * * if isinstance(value, tuple): # <<<<<<<<<<<<<< * bytesvalue = struct.pack(self.view.format, *value) * else: */ goto __pyx_L3; } /* "View.MemoryView":512 * bytesvalue = struct.pack(self.view.format, *value) * else: * bytesvalue = struct.pack(self.view.format, value) # <<<<<<<<<<<<<< * * for i, c in enumerate(bytesvalue): */ /*else*/ { __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_1 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_5 = NULL; __pyx_t_7 = 0; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_6))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_6); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_6); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_6, function); __pyx_t_7 = 1; } } #if CYTHON_FAST_PYCALL if (PyFunction_Check(__pyx_t_6)) { PyObject *__pyx_temp[3] = {__pyx_t_5, __pyx_t_1, __pyx_v_value}; __pyx_t_4 = __Pyx_PyFunction_FastCall(__pyx_t_6, __pyx_temp+1-__pyx_t_7, 2+__pyx_t_7); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 512, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; } else #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(__pyx_t_6)) { PyObject *__pyx_temp[3] = {__pyx_t_5, __pyx_t_1, __pyx_v_value}; __pyx_t_4 = __Pyx_PyCFunction_FastCall(__pyx_t_6, __pyx_temp+1-__pyx_t_7, 2+__pyx_t_7); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 512, __pyx_L1_error) __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; } else #endif { __pyx_t_8 = PyTuple_New(2+__pyx_t_7); if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_8); if (__pyx_t_5) { __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_t_5); __pyx_t_5 = NULL; } __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_8, 0+__pyx_t_7, __pyx_t_1); __Pyx_INCREF(__pyx_v_value); __Pyx_GIVEREF(__pyx_v_value); PyTuple_SET_ITEM(__pyx_t_8, 1+__pyx_t_7, __pyx_v_value); __pyx_t_1 = 0; __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_t_8, NULL); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 512, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0; } __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (!(likely(PyBytes_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_4)->tp_name), 0))) __PYX_ERR(1, 512, __pyx_L1_error) __pyx_v_bytesvalue = ((PyObject*)__pyx_t_4); __pyx_t_4 = 0; } __pyx_L3:; /* "View.MemoryView":514 * bytesvalue = struct.pack(self.view.format, value) * * for i, c in enumerate(bytesvalue): # <<<<<<<<<<<<<< * itemp[i] = c * */ __pyx_t_9 = 0; if (unlikely(__pyx_v_bytesvalue == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' is not iterable"); __PYX_ERR(1, 514, __pyx_L1_error) } __Pyx_INCREF(__pyx_v_bytesvalue); __pyx_t_10 = __pyx_v_bytesvalue; __pyx_t_12 = PyBytes_AS_STRING(__pyx_t_10); __pyx_t_13 = (__pyx_t_12 + PyBytes_GET_SIZE(__pyx_t_10)); for (__pyx_t_14 = __pyx_t_12; __pyx_t_14 < __pyx_t_13; __pyx_t_14++) { __pyx_t_11 = __pyx_t_14; __pyx_v_c = (__pyx_t_11[0]); /* "View.MemoryView":515 * * for i, c in enumerate(bytesvalue): * itemp[i] = c # <<<<<<<<<<<<<< * * @cname('getbuffer') */ __pyx_v_i = __pyx_t_9; /* "View.MemoryView":514 * bytesvalue = struct.pack(self.view.format, value) * * for i, c in enumerate(bytesvalue): # <<<<<<<<<<<<<< * itemp[i] = c * */ __pyx_t_9 = (__pyx_t_9 + 1); /* "View.MemoryView":515 * * for i, c in enumerate(bytesvalue): * itemp[i] = c # <<<<<<<<<<<<<< * * @cname('getbuffer') */ (__pyx_v_itemp[__pyx_v_i]) = __pyx_v_c; } __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; /* "View.MemoryView":501 * return result * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * """Only used if instantiated manually by the user, or if Cython doesn't * know how to convert the type""" */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_8); __Pyx_XDECREF(__pyx_t_10); __Pyx_AddTraceback("View.MemoryView.memoryview.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_struct); __Pyx_XDECREF(__pyx_v_bytesvalue); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":518 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") */ /* Python wrapper */ static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; Py_ssize_t *__pyx_t_4; char *__pyx_t_5; void *__pyx_t_6; int __pyx_t_7; Py_ssize_t __pyx_t_8; if (__pyx_v_info == NULL) { PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete"); return -1; } __Pyx_RefNannySetupContext("__getbuffer__", 0); __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None); __Pyx_GIVEREF(__pyx_v_info->obj); /* "View.MemoryView":519 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: # <<<<<<<<<<<<<< * raise ValueError("Cannot create writable memory view from read-only memoryview") * */ __pyx_t_2 = ((__pyx_v_flags & PyBUF_WRITABLE) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L4_bool_binop_done; } __pyx_t_2 = (__pyx_v_self->view.readonly != 0); __pyx_t_1 = __pyx_t_2; __pyx_L4_bool_binop_done:; if (unlikely(__pyx_t_1)) { /* "View.MemoryView":520 * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") # <<<<<<<<<<<<<< * * if flags & PyBUF_ND: */ __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__12, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 520, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 520, __pyx_L1_error) /* "View.MemoryView":519 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: # <<<<<<<<<<<<<< * raise ValueError("Cannot create writable memory view from read-only memoryview") * */ } /* "View.MemoryView":522 * raise ValueError("Cannot create writable memory view from read-only memoryview") * * if flags & PyBUF_ND: # <<<<<<<<<<<<<< * info.shape = self.view.shape * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_ND) != 0); if (__pyx_t_1) { /* "View.MemoryView":523 * * if flags & PyBUF_ND: * info.shape = self.view.shape # <<<<<<<<<<<<<< * else: * info.shape = NULL */ __pyx_t_4 = __pyx_v_self->view.shape; __pyx_v_info->shape = __pyx_t_4; /* "View.MemoryView":522 * raise ValueError("Cannot create writable memory view from read-only memoryview") * * if flags & PyBUF_ND: # <<<<<<<<<<<<<< * info.shape = self.view.shape * else: */ goto __pyx_L6; } /* "View.MemoryView":525 * info.shape = self.view.shape * else: * info.shape = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_STRIDES: */ /*else*/ { __pyx_v_info->shape = NULL; } __pyx_L6:; /* "View.MemoryView":527 * info.shape = NULL * * if flags & PyBUF_STRIDES: # <<<<<<<<<<<<<< * info.strides = self.view.strides * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_STRIDES) != 0); if (__pyx_t_1) { /* "View.MemoryView":528 * * if flags & PyBUF_STRIDES: * info.strides = self.view.strides # <<<<<<<<<<<<<< * else: * info.strides = NULL */ __pyx_t_4 = __pyx_v_self->view.strides; __pyx_v_info->strides = __pyx_t_4; /* "View.MemoryView":527 * info.shape = NULL * * if flags & PyBUF_STRIDES: # <<<<<<<<<<<<<< * info.strides = self.view.strides * else: */ goto __pyx_L7; } /* "View.MemoryView":530 * info.strides = self.view.strides * else: * info.strides = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_INDIRECT: */ /*else*/ { __pyx_v_info->strides = NULL; } __pyx_L7:; /* "View.MemoryView":532 * info.strides = NULL * * if flags & PyBUF_INDIRECT: # <<<<<<<<<<<<<< * info.suboffsets = self.view.suboffsets * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_INDIRECT) != 0); if (__pyx_t_1) { /* "View.MemoryView":533 * * if flags & PyBUF_INDIRECT: * info.suboffsets = self.view.suboffsets # <<<<<<<<<<<<<< * else: * info.suboffsets = NULL */ __pyx_t_4 = __pyx_v_self->view.suboffsets; __pyx_v_info->suboffsets = __pyx_t_4; /* "View.MemoryView":532 * info.strides = NULL * * if flags & PyBUF_INDIRECT: # <<<<<<<<<<<<<< * info.suboffsets = self.view.suboffsets * else: */ goto __pyx_L8; } /* "View.MemoryView":535 * info.suboffsets = self.view.suboffsets * else: * info.suboffsets = NULL # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ /*else*/ { __pyx_v_info->suboffsets = NULL; } __pyx_L8:; /* "View.MemoryView":537 * info.suboffsets = NULL * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.view.format * else: */ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { /* "View.MemoryView":538 * * if flags & PyBUF_FORMAT: * info.format = self.view.format # <<<<<<<<<<<<<< * else: * info.format = NULL */ __pyx_t_5 = __pyx_v_self->view.format; __pyx_v_info->format = __pyx_t_5; /* "View.MemoryView":537 * info.suboffsets = NULL * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * info.format = self.view.format * else: */ goto __pyx_L9; } /* "View.MemoryView":540 * info.format = self.view.format * else: * info.format = NULL # <<<<<<<<<<<<<< * * info.buf = self.view.buf */ /*else*/ { __pyx_v_info->format = NULL; } __pyx_L9:; /* "View.MemoryView":542 * info.format = NULL * * info.buf = self.view.buf # <<<<<<<<<<<<<< * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize */ __pyx_t_6 = __pyx_v_self->view.buf; __pyx_v_info->buf = __pyx_t_6; /* "View.MemoryView":543 * * info.buf = self.view.buf * info.ndim = self.view.ndim # <<<<<<<<<<<<<< * info.itemsize = self.view.itemsize * info.len = self.view.len */ __pyx_t_7 = __pyx_v_self->view.ndim; __pyx_v_info->ndim = __pyx_t_7; /* "View.MemoryView":544 * info.buf = self.view.buf * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize # <<<<<<<<<<<<<< * info.len = self.view.len * info.readonly = self.view.readonly */ __pyx_t_8 = __pyx_v_self->view.itemsize; __pyx_v_info->itemsize = __pyx_t_8; /* "View.MemoryView":545 * info.ndim = self.view.ndim * info.itemsize = self.view.itemsize * info.len = self.view.len # <<<<<<<<<<<<<< * info.readonly = self.view.readonly * info.obj = self */ __pyx_t_8 = __pyx_v_self->view.len; __pyx_v_info->len = __pyx_t_8; /* "View.MemoryView":546 * info.itemsize = self.view.itemsize * info.len = self.view.len * info.readonly = self.view.readonly # <<<<<<<<<<<<<< * info.obj = self * */ __pyx_t_1 = __pyx_v_self->view.readonly; __pyx_v_info->readonly = __pyx_t_1; /* "View.MemoryView":547 * info.len = self.view.len * info.readonly = self.view.readonly * info.obj = self # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = ((PyObject *)__pyx_v_self); /* "View.MemoryView":518 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") */ /* function exit code */ __pyx_r = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; if (__pyx_v_info->obj != NULL) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } goto __pyx_L2; __pyx_L0:; if (__pyx_v_info->obj == Py_None) { __Pyx_GOTREF(__pyx_v_info->obj); __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = 0; } __pyx_L2:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":553 * * @property * def T(self): # <<<<<<<<<<<<<< * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self) { struct __pyx_memoryviewslice_obj *__pyx_v_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":554 * @property * def T(self): * cdef _memoryviewslice result = memoryview_copy(self) # <<<<<<<<<<<<<< * transpose_memslice(&result.from_slice) * return result */ __pyx_t_1 = __pyx_memoryview_copy_object(__pyx_v_self); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 554, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (!(likely(((__pyx_t_1) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_1, __pyx_memoryviewslice_type))))) __PYX_ERR(1, 554, __pyx_L1_error) __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":555 * def T(self): * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) # <<<<<<<<<<<<<< * return result * */ __pyx_t_2 = __pyx_memslice_transpose((&__pyx_v_result->from_slice)); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(1, 555, __pyx_L1_error) /* "View.MemoryView":556 * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) * return result # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":553 * * @property * def T(self): # <<<<<<<<<<<<<< * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.T.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":559 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.obj * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":560 * @property * def base(self): * return self.obj # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->obj); __pyx_r = __pyx_v_self->obj; goto __pyx_L0; /* "View.MemoryView":559 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.obj * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":563 * * @property * def shape(self): # <<<<<<<<<<<<<< * return tuple([length for length in self.view.shape[:self.view.ndim]]) * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_length; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; PyObject *__pyx_t_5 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":564 * @property * def shape(self): * return tuple([length for length in self.view.shape[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyList_New(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_3 = (__pyx_v_self->view.shape + __pyx_v_self->view.ndim); for (__pyx_t_4 = __pyx_v_self->view.shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { __pyx_t_2 = __pyx_t_4; __pyx_v_length = (__pyx_t_2[0]); __pyx_t_5 = PyInt_FromSsize_t(__pyx_v_length); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); if (unlikely(__Pyx_ListComp_Append(__pyx_t_1, (PyObject*)__pyx_t_5))) __PYX_ERR(1, 564, __pyx_L1_error) __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; } __pyx_t_5 = PyList_AsTuple(((PyObject*)__pyx_t_1)); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 564, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "View.MemoryView":563 * * @property * def shape(self): # <<<<<<<<<<<<<< * return tuple([length for length in self.view.shape[:self.view.ndim]]) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview.shape.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":567 * * @property * def strides(self): # <<<<<<<<<<<<<< * if self.view.strides == NULL: * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_stride; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; PyObject *__pyx_t_6 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":568 * @property * def strides(self): * if self.view.strides == NULL: # <<<<<<<<<<<<<< * * raise ValueError("Buffer view does not expose strides") */ __pyx_t_1 = ((__pyx_v_self->view.strides == NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":570 * if self.view.strides == NULL: * * raise ValueError("Buffer view does not expose strides") # <<<<<<<<<<<<<< * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) */ __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__13, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 570, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(1, 570, __pyx_L1_error) /* "View.MemoryView":568 * @property * def strides(self): * if self.view.strides == NULL: # <<<<<<<<<<<<<< * * raise ValueError("Buffer view does not expose strides") */ } /* "View.MemoryView":572 * raise ValueError("Buffer view does not expose strides") * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = (__pyx_v_self->view.strides + __pyx_v_self->view.ndim); for (__pyx_t_5 = __pyx_v_self->view.strides; __pyx_t_5 < __pyx_t_4; __pyx_t_5++) { __pyx_t_3 = __pyx_t_5; __pyx_v_stride = (__pyx_t_3[0]); __pyx_t_6 = PyInt_FromSsize_t(__pyx_v_stride); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); if (unlikely(__Pyx_ListComp_Append(__pyx_t_2, (PyObject*)__pyx_t_6))) __PYX_ERR(1, 572, __pyx_L1_error) __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; } __pyx_t_6 = PyList_AsTuple(((PyObject*)__pyx_t_2)); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 572, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_6; __pyx_t_6 = 0; goto __pyx_L0; /* "View.MemoryView":567 * * @property * def strides(self): # <<<<<<<<<<<<<< * if self.view.strides == NULL: * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_6); __Pyx_AddTraceback("View.MemoryView.memoryview.strides.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":575 * * @property * def suboffsets(self): # <<<<<<<<<<<<<< * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; Py_ssize_t *__pyx_t_6; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":576 * @property * def suboffsets(self): * if self.view.suboffsets == NULL: # <<<<<<<<<<<<<< * return (-1,) * self.view.ndim * */ __pyx_t_1 = ((__pyx_v_self->view.suboffsets == NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":577 * def suboffsets(self): * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim # <<<<<<<<<<<<<< * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyInt_From_int(__pyx_v_self->view.ndim); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_Multiply(__pyx_tuple__14, __pyx_t_2); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":576 * @property * def suboffsets(self): * if self.view.suboffsets == NULL: # <<<<<<<<<<<<<< * return (-1,) * self.view.ndim * */ } /* "View.MemoryView":579 * return (-1,) * self.view.ndim * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_5 = (__pyx_v_self->view.suboffsets + __pyx_v_self->view.ndim); for (__pyx_t_6 = __pyx_v_self->view.suboffsets; __pyx_t_6 < __pyx_t_5; __pyx_t_6++) { __pyx_t_4 = __pyx_t_6; __pyx_v_suboffset = (__pyx_t_4[0]); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_suboffset); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (unlikely(__Pyx_ListComp_Append(__pyx_t_3, (PyObject*)__pyx_t_2))) __PYX_ERR(1, 579, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; } __pyx_t_2 = PyList_AsTuple(((PyObject*)__pyx_t_3)); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 579, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":575 * * @property * def suboffsets(self): # <<<<<<<<<<<<<< * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.suboffsets.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":582 * * @property * def ndim(self): # <<<<<<<<<<<<<< * return self.view.ndim * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":583 * @property * def ndim(self): * return self.view.ndim # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_self->view.ndim); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 583, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":582 * * @property * def ndim(self): # <<<<<<<<<<<<<< * return self.view.ndim * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.ndim.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":586 * * @property * def itemsize(self): # <<<<<<<<<<<<<< * return self.view.itemsize * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":587 * @property * def itemsize(self): * return self.view.itemsize # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 587, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":586 * * @property * def itemsize(self): # <<<<<<<<<<<<<< * return self.view.itemsize * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.itemsize.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":590 * * @property * def nbytes(self): # <<<<<<<<<<<<<< * return self.size * self.view.itemsize * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":591 * @property * def nbytes(self): * return self.size * self.view.itemsize # <<<<<<<<<<<<<< * * @property */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_size); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_Multiply(__pyx_t_1, __pyx_t_2); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 591, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":590 * * @property * def nbytes(self): # <<<<<<<<<<<<<< * return self.size * self.view.itemsize * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.nbytes.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":594 * * @property * def size(self): # <<<<<<<<<<<<<< * if self._size is None: * result = 1 */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_v_result = NULL; PyObject *__pyx_v_length = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; Py_ssize_t *__pyx_t_5; PyObject *__pyx_t_6 = NULL; __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":595 * @property * def size(self): * if self._size is None: # <<<<<<<<<<<<<< * result = 1 * */ __pyx_t_1 = (__pyx_v_self->_size == Py_None); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":596 * def size(self): * if self._size is None: * result = 1 # <<<<<<<<<<<<<< * * for length in self.view.shape[:self.view.ndim]: */ __Pyx_INCREF(__pyx_int_1); __pyx_v_result = __pyx_int_1; /* "View.MemoryView":598 * result = 1 * * for length in self.view.shape[:self.view.ndim]: # <<<<<<<<<<<<<< * result *= length * */ __pyx_t_4 = (__pyx_v_self->view.shape + __pyx_v_self->view.ndim); for (__pyx_t_5 = __pyx_v_self->view.shape; __pyx_t_5 < __pyx_t_4; __pyx_t_5++) { __pyx_t_3 = __pyx_t_5; __pyx_t_6 = PyInt_FromSsize_t((__pyx_t_3[0])); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 598, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_6); __pyx_t_6 = 0; /* "View.MemoryView":599 * * for length in self.view.shape[:self.view.ndim]: * result *= length # <<<<<<<<<<<<<< * * self._size = result */ __pyx_t_6 = PyNumber_InPlaceMultiply(__pyx_v_result, __pyx_v_length); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 599, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF_SET(__pyx_v_result, __pyx_t_6); __pyx_t_6 = 0; } /* "View.MemoryView":601 * result *= length * * self._size = result # <<<<<<<<<<<<<< * * return self._size */ __Pyx_INCREF(__pyx_v_result); __Pyx_GIVEREF(__pyx_v_result); __Pyx_GOTREF(__pyx_v_self->_size); __Pyx_DECREF(__pyx_v_self->_size); __pyx_v_self->_size = __pyx_v_result; /* "View.MemoryView":595 * @property * def size(self): * if self._size is None: # <<<<<<<<<<<<<< * result = 1 * */ } /* "View.MemoryView":603 * self._size = result * * return self._size # <<<<<<<<<<<<<< * * def __len__(self): */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->_size); __pyx_r = __pyx_v_self->_size; goto __pyx_L0; /* "View.MemoryView":594 * * @property * def size(self): # <<<<<<<<<<<<<< * if self._size is None: * result = 1 */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_6); __Pyx_AddTraceback("View.MemoryView.memoryview.size.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v_result); __Pyx_XDECREF(__pyx_v_length); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":605 * return self._size * * def __len__(self): # <<<<<<<<<<<<<< * if self.view.ndim >= 1: * return self.view.shape[0] */ /* Python wrapper */ static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self); /*proto*/ static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__len__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self) { Py_ssize_t __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("__len__", 0); /* "View.MemoryView":606 * * def __len__(self): * if self.view.ndim >= 1: # <<<<<<<<<<<<<< * return self.view.shape[0] * */ __pyx_t_1 = ((__pyx_v_self->view.ndim >= 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":607 * def __len__(self): * if self.view.ndim >= 1: * return self.view.shape[0] # <<<<<<<<<<<<<< * * return 0 */ __pyx_r = (__pyx_v_self->view.shape[0]); goto __pyx_L0; /* "View.MemoryView":606 * * def __len__(self): * if self.view.ndim >= 1: # <<<<<<<<<<<<<< * return self.view.shape[0] * */ } /* "View.MemoryView":609 * return self.view.shape[0] * * return 0 # <<<<<<<<<<<<<< * * def __repr__(self): */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":605 * return self._size * * def __len__(self): # <<<<<<<<<<<<<< * if self.view.ndim >= 1: * return self.view.shape[0] */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":611 * return 0 * * def __repr__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__, * id(self)) */ /* Python wrapper */ static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("__repr__", 0); /* "View.MemoryView":612 * * def __repr__(self): * return "" % (self.base.__class__.__name__, # <<<<<<<<<<<<<< * id(self)) * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":613 * def __repr__(self): * return "" % (self.base.__class__.__name__, * id(self)) # <<<<<<<<<<<<<< * * def __str__(self): */ __pyx_t_2 = __Pyx_PyObject_CallOneArg(__pyx_builtin_id, ((PyObject *)__pyx_v_self)); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 613, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); /* "View.MemoryView":612 * * def __repr__(self): * return "" % (self.base.__class__.__name__, # <<<<<<<<<<<<<< * id(self)) * */ __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_t_3); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 612, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":611 * return 0 * * def __repr__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__, * id(self)) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview.__repr__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":615 * id(self)) * * def __str__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__,) * */ /* Python wrapper */ static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__str__ (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("__str__", 0); /* "View.MemoryView":616 * * def __str__(self): * return "" % (self.base.__class__.__name__,) # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_object, __pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 616, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":615 * id(self)) * * def __str__(self): # <<<<<<<<<<<<<< * return "" % (self.base.__class__.__name__,) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.__str__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":619 * * * def is_c_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* Python wrapper */ static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("is_c_contig (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice *__pyx_v_mslice; __Pyx_memviewslice __pyx_v_tmp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("is_c_contig", 0); /* "View.MemoryView":622 * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) # <<<<<<<<<<<<<< * return slice_is_contig(mslice[0], 'C', self.view.ndim) * */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(1, 622, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":623 * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) * return slice_is_contig(mslice[0], 'C', self.view.ndim) # <<<<<<<<<<<<<< * * def is_f_contig(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig((__pyx_v_mslice[0]), 'C', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 623, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":619 * * * def is_c_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.is_c_contig", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":625 * return slice_is_contig(mslice[0], 'C', self.view.ndim) * * def is_f_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* Python wrapper */ static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("is_f_contig (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice *__pyx_v_mslice; __Pyx_memviewslice __pyx_v_tmp; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice *__pyx_t_1; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("is_f_contig", 0); /* "View.MemoryView":628 * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) # <<<<<<<<<<<<<< * return slice_is_contig(mslice[0], 'F', self.view.ndim) * */ __pyx_t_1 = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp)); if (unlikely(__pyx_t_1 == ((__Pyx_memviewslice *)NULL))) __PYX_ERR(1, 628, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":629 * cdef __Pyx_memviewslice tmp * mslice = get_slice_from_memview(self, &tmp) * return slice_is_contig(mslice[0], 'F', self.view.ndim) # <<<<<<<<<<<<<< * * def copy(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig((__pyx_v_mslice[0]), 'F', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 629, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":625 * return slice_is_contig(mslice[0], 'C', self.view.ndim) * * def is_f_contig(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice *mslice * cdef __Pyx_memviewslice tmp */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.is_f_contig", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":631 * return slice_is_contig(mslice[0], 'F', self.view.ndim) * * def copy(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS */ /* Python wrapper */ static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("copy (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice __pyx_v_mslice; int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice __pyx_t_1; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("copy", 0); /* "View.MemoryView":633 * def copy(self): * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS # <<<<<<<<<<<<<< * * slice_copy(self, &mslice) */ __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_F_CONTIGUOUS)); /* "View.MemoryView":635 * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS * * slice_copy(self, &mslice) # <<<<<<<<<<<<<< * mslice = slice_copy_contig(&mslice, "c", self.view.ndim, * self.view.itemsize, */ __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_mslice)); /* "View.MemoryView":636 * * slice_copy(self, &mslice) * mslice = slice_copy_contig(&mslice, "c", self.view.ndim, # <<<<<<<<<<<<<< * self.view.itemsize, * flags|PyBUF_C_CONTIGUOUS, */ __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_mslice), ((char *)"c"), __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_C_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) __PYX_ERR(1, 636, __pyx_L1_error) __pyx_v_mslice = __pyx_t_1; /* "View.MemoryView":641 * self.dtype_is_object) * * return memoryview_copy_from_slice(self, &mslice) # <<<<<<<<<<<<<< * * def copy_fortran(self): */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_mslice)); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 641, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":631 * return slice_is_contig(mslice[0], 'F', self.view.ndim) * * def copy(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice mslice * cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.copy", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":643 * return memoryview_copy_from_slice(self, &mslice) * * def copy_fortran(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS */ /* Python wrapper */ static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("copy_fortran (wrapper)", 0); __pyx_r = __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self) { __Pyx_memviewslice __pyx_v_src; __Pyx_memviewslice __pyx_v_dst; int __pyx_v_flags; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_memviewslice __pyx_t_1; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("copy_fortran", 0); /* "View.MemoryView":645 * def copy_fortran(self): * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS # <<<<<<<<<<<<<< * * slice_copy(self, &src) */ __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_C_CONTIGUOUS)); /* "View.MemoryView":647 * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS * * slice_copy(self, &src) # <<<<<<<<<<<<<< * dst = slice_copy_contig(&src, "fortran", self.view.ndim, * self.view.itemsize, */ __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_src)); /* "View.MemoryView":648 * * slice_copy(self, &src) * dst = slice_copy_contig(&src, "fortran", self.view.ndim, # <<<<<<<<<<<<<< * self.view.itemsize, * flags|PyBUF_F_CONTIGUOUS, */ __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_src), ((char *)"fortran"), __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_F_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) __PYX_ERR(1, 648, __pyx_L1_error) __pyx_v_dst = __pyx_t_1; /* "View.MemoryView":653 * self.dtype_is_object) * * return memoryview_copy_from_slice(self, &dst) # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_dst)); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 653, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":643 * return memoryview_copy_from_slice(self, &mslice) * * def copy_fortran(self): # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice src, dst * cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView.memoryview.copy_fortran", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryview_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_memoryview_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryview___reduce_cython__(((struct __pyx_memoryview_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__15, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(1, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryview_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_memoryview_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryview_2__setstate_cython__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__16, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(1, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":657 * * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): # <<<<<<<<<<<<<< * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo */ static PyObject *__pyx_memoryview_new(PyObject *__pyx_v_o, int __pyx_v_flags, int __pyx_v_dtype_is_object, __Pyx_TypeInfo *__pyx_v_typeinfo) { struct __pyx_memoryview_obj *__pyx_v_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("memoryview_cwrapper", 0); /* "View.MemoryView":658 * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): * cdef memoryview result = memoryview(o, flags, dtype_is_object) # <<<<<<<<<<<<<< * result.typeinfo = typeinfo * return result */ __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_o); __Pyx_GIVEREF(__pyx_v_o); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_o); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 658, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_memoryview_obj *)__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":659 * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo # <<<<<<<<<<<<<< * return result * */ __pyx_v_result->typeinfo = __pyx_v_typeinfo; /* "View.MemoryView":660 * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo * return result # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_check') */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":657 * * @cname('__pyx_memoryview_new') * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo): # <<<<<<<<<<<<<< * cdef memoryview result = memoryview(o, flags, dtype_is_object) * result.typeinfo = typeinfo */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":663 * * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): # <<<<<<<<<<<<<< * return isinstance(o, memoryview) * */ static CYTHON_INLINE int __pyx_memoryview_check(PyObject *__pyx_v_o) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; __Pyx_RefNannySetupContext("memoryview_check", 0); /* "View.MemoryView":664 * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): * return isinstance(o, memoryview) # <<<<<<<<<<<<<< * * cdef tuple _unellipsify(object index, int ndim): */ __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_o, __pyx_memoryview_type); __pyx_r = __pyx_t_1; goto __pyx_L0; /* "View.MemoryView":663 * * @cname('__pyx_memoryview_check') * cdef inline bint memoryview_check(object o): # <<<<<<<<<<<<<< * return isinstance(o, memoryview) * */ /* function exit code */ __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":666 * return isinstance(o, memoryview) * * cdef tuple _unellipsify(object index, int ndim): # <<<<<<<<<<<<<< * """ * Replace all ellipses with full slices and fill incomplete indices with */ static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) { PyObject *__pyx_v_tup = NULL; PyObject *__pyx_v_result = NULL; int __pyx_v_have_slices; int __pyx_v_seen_ellipsis; CYTHON_UNUSED PyObject *__pyx_v_idx = NULL; PyObject *__pyx_v_item = NULL; Py_ssize_t __pyx_v_nslices; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; Py_ssize_t __pyx_t_5; PyObject *(*__pyx_t_6)(PyObject *); PyObject *__pyx_t_7 = NULL; Py_ssize_t __pyx_t_8; int __pyx_t_9; int __pyx_t_10; PyObject *__pyx_t_11 = NULL; __Pyx_RefNannySetupContext("_unellipsify", 0); /* "View.MemoryView":671 * full slices. * """ * if not isinstance(index, tuple): # <<<<<<<<<<<<<< * tup = (index,) * else: */ __pyx_t_1 = PyTuple_Check(__pyx_v_index); __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":672 * """ * if not isinstance(index, tuple): * tup = (index,) # <<<<<<<<<<<<<< * else: * tup = index */ __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 672, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_index); __Pyx_GIVEREF(__pyx_v_index); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_index); __pyx_v_tup = __pyx_t_3; __pyx_t_3 = 0; /* "View.MemoryView":671 * full slices. * """ * if not isinstance(index, tuple): # <<<<<<<<<<<<<< * tup = (index,) * else: */ goto __pyx_L3; } /* "View.MemoryView":674 * tup = (index,) * else: * tup = index # <<<<<<<<<<<<<< * * result = [] */ /*else*/ { __Pyx_INCREF(__pyx_v_index); __pyx_v_tup = __pyx_v_index; } __pyx_L3:; /* "View.MemoryView":676 * tup = index * * result = [] # <<<<<<<<<<<<<< * have_slices = False * seen_ellipsis = False */ __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 676, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_v_result = ((PyObject*)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":677 * * result = [] * have_slices = False # <<<<<<<<<<<<<< * seen_ellipsis = False * for idx, item in enumerate(tup): */ __pyx_v_have_slices = 0; /* "View.MemoryView":678 * result = [] * have_slices = False * seen_ellipsis = False # <<<<<<<<<<<<<< * for idx, item in enumerate(tup): * if item is Ellipsis: */ __pyx_v_seen_ellipsis = 0; /* "View.MemoryView":679 * have_slices = False * seen_ellipsis = False * for idx, item in enumerate(tup): # <<<<<<<<<<<<<< * if item is Ellipsis: * if not seen_ellipsis: */ __Pyx_INCREF(__pyx_int_0); __pyx_t_3 = __pyx_int_0; if (likely(PyList_CheckExact(__pyx_v_tup)) || PyTuple_CheckExact(__pyx_v_tup)) { __pyx_t_4 = __pyx_v_tup; __Pyx_INCREF(__pyx_t_4); __pyx_t_5 = 0; __pyx_t_6 = NULL; } else { __pyx_t_5 = -1; __pyx_t_4 = PyObject_GetIter(__pyx_v_tup); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_6 = Py_TYPE(__pyx_t_4)->tp_iternext; if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 679, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_6)) { if (likely(PyList_CheckExact(__pyx_t_4))) { if (__pyx_t_5 >= PyList_GET_SIZE(__pyx_t_4)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_7 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) __PYX_ERR(1, 679, __pyx_L1_error) #else __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) __PYX_ERR(1, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); #endif } else { if (__pyx_t_5 >= PyTuple_GET_SIZE(__pyx_t_4)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_7 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) __PYX_ERR(1, 679, __pyx_L1_error) #else __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) __PYX_ERR(1, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); #endif } } else { __pyx_t_7 = __pyx_t_6(__pyx_t_4); if (unlikely(!__pyx_t_7)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(1, 679, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_7); } __Pyx_XDECREF_SET(__pyx_v_item, __pyx_t_7); __pyx_t_7 = 0; __Pyx_INCREF(__pyx_t_3); __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_3); __pyx_t_7 = __Pyx_PyInt_AddObjC(__pyx_t_3, __pyx_int_1, 1, 0, 0); if (unlikely(!__pyx_t_7)) __PYX_ERR(1, 679, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = __pyx_t_7; __pyx_t_7 = 0; /* "View.MemoryView":680 * seen_ellipsis = False * for idx, item in enumerate(tup): * if item is Ellipsis: # <<<<<<<<<<<<<< * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) */ __pyx_t_2 = (__pyx_v_item == __pyx_builtin_Ellipsis); __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":681 * for idx, item in enumerate(tup): * if item is Ellipsis: * if not seen_ellipsis: # <<<<<<<<<<<<<< * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True */ __pyx_t_1 = ((!(__pyx_v_seen_ellipsis != 0)) != 0); if (__pyx_t_1) { /* "View.MemoryView":682 * if item is Ellipsis: * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) # <<<<<<<<<<<<<< * seen_ellipsis = True * else: */ __pyx_t_8 = PyObject_Length(__pyx_v_tup); if (unlikely(__pyx_t_8 == ((Py_ssize_t)-1))) __PYX_ERR(1, 682, __pyx_L1_error) __pyx_t_7 = PyList_New(1 * ((((__pyx_v_ndim - __pyx_t_8) + 1)<0) ? 0:((__pyx_v_ndim - __pyx_t_8) + 1))); if (unlikely(!__pyx_t_7)) __PYX_ERR(1, 682, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); { Py_ssize_t __pyx_temp; for (__pyx_temp=0; __pyx_temp < ((__pyx_v_ndim - __pyx_t_8) + 1); __pyx_temp++) { __Pyx_INCREF(__pyx_slice__17); __Pyx_GIVEREF(__pyx_slice__17); PyList_SET_ITEM(__pyx_t_7, __pyx_temp, __pyx_slice__17); } } __pyx_t_9 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_7); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(1, 682, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; /* "View.MemoryView":683 * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True # <<<<<<<<<<<<<< * else: * result.append(slice(None)) */ __pyx_v_seen_ellipsis = 1; /* "View.MemoryView":681 * for idx, item in enumerate(tup): * if item is Ellipsis: * if not seen_ellipsis: # <<<<<<<<<<<<<< * result.extend([slice(None)] * (ndim - len(tup) + 1)) * seen_ellipsis = True */ goto __pyx_L7; } /* "View.MemoryView":685 * seen_ellipsis = True * else: * result.append(slice(None)) # <<<<<<<<<<<<<< * have_slices = True * else: */ /*else*/ { __pyx_t_9 = __Pyx_PyList_Append(__pyx_v_result, __pyx_slice__17); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(1, 685, __pyx_L1_error) } __pyx_L7:; /* "View.MemoryView":686 * else: * result.append(slice(None)) * have_slices = True # <<<<<<<<<<<<<< * else: * if not isinstance(item, slice) and not PyIndex_Check(item): */ __pyx_v_have_slices = 1; /* "View.MemoryView":680 * seen_ellipsis = False * for idx, item in enumerate(tup): * if item is Ellipsis: # <<<<<<<<<<<<<< * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) */ goto __pyx_L6; } /* "View.MemoryView":688 * have_slices = True * else: * if not isinstance(item, slice) and not PyIndex_Check(item): # <<<<<<<<<<<<<< * raise TypeError("Cannot index with type '%s'" % type(item)) * */ /*else*/ { __pyx_t_2 = PySlice_Check(__pyx_v_item); __pyx_t_10 = ((!(__pyx_t_2 != 0)) != 0); if (__pyx_t_10) { } else { __pyx_t_1 = __pyx_t_10; goto __pyx_L9_bool_binop_done; } __pyx_t_10 = ((!(PyIndex_Check(__pyx_v_item) != 0)) != 0); __pyx_t_1 = __pyx_t_10; __pyx_L9_bool_binop_done:; if (unlikely(__pyx_t_1)) { /* "View.MemoryView":689 * else: * if not isinstance(item, slice) and not PyIndex_Check(item): * raise TypeError("Cannot index with type '%s'" % type(item)) # <<<<<<<<<<<<<< * * have_slices = have_slices or isinstance(item, slice) */ __pyx_t_7 = __Pyx_PyString_FormatSafe(__pyx_kp_s_Cannot_index_with_type_s, ((PyObject *)Py_TYPE(__pyx_v_item))); if (unlikely(!__pyx_t_7)) __PYX_ERR(1, 689, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_11 = __Pyx_PyObject_CallOneArg(__pyx_builtin_TypeError, __pyx_t_7); if (unlikely(!__pyx_t_11)) __PYX_ERR(1, 689, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_Raise(__pyx_t_11, 0, 0, 0); __Pyx_DECREF(__pyx_t_11); __pyx_t_11 = 0; __PYX_ERR(1, 689, __pyx_L1_error) /* "View.MemoryView":688 * have_slices = True * else: * if not isinstance(item, slice) and not PyIndex_Check(item): # <<<<<<<<<<<<<< * raise TypeError("Cannot index with type '%s'" % type(item)) * */ } /* "View.MemoryView":691 * raise TypeError("Cannot index with type '%s'" % type(item)) * * have_slices = have_slices or isinstance(item, slice) # <<<<<<<<<<<<<< * result.append(item) * */ __pyx_t_10 = (__pyx_v_have_slices != 0); if (!__pyx_t_10) { } else { __pyx_t_1 = __pyx_t_10; goto __pyx_L11_bool_binop_done; } __pyx_t_10 = PySlice_Check(__pyx_v_item); __pyx_t_2 = (__pyx_t_10 != 0); __pyx_t_1 = __pyx_t_2; __pyx_L11_bool_binop_done:; __pyx_v_have_slices = __pyx_t_1; /* "View.MemoryView":692 * * have_slices = have_slices or isinstance(item, slice) * result.append(item) # <<<<<<<<<<<<<< * * nslices = ndim - len(result) */ __pyx_t_9 = __Pyx_PyList_Append(__pyx_v_result, __pyx_v_item); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(1, 692, __pyx_L1_error) } __pyx_L6:; /* "View.MemoryView":679 * have_slices = False * seen_ellipsis = False * for idx, item in enumerate(tup): # <<<<<<<<<<<<<< * if item is Ellipsis: * if not seen_ellipsis: */ } __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":694 * result.append(item) * * nslices = ndim - len(result) # <<<<<<<<<<<<<< * if nslices: * result.extend([slice(None)] * nslices) */ __pyx_t_5 = PyList_GET_SIZE(__pyx_v_result); if (unlikely(__pyx_t_5 == ((Py_ssize_t)-1))) __PYX_ERR(1, 694, __pyx_L1_error) __pyx_v_nslices = (__pyx_v_ndim - __pyx_t_5); /* "View.MemoryView":695 * * nslices = ndim - len(result) * if nslices: # <<<<<<<<<<<<<< * result.extend([slice(None)] * nslices) * */ __pyx_t_1 = (__pyx_v_nslices != 0); if (__pyx_t_1) { /* "View.MemoryView":696 * nslices = ndim - len(result) * if nslices: * result.extend([slice(None)] * nslices) # <<<<<<<<<<<<<< * * return have_slices or nslices, tuple(result) */ __pyx_t_3 = PyList_New(1 * ((__pyx_v_nslices<0) ? 0:__pyx_v_nslices)); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 696, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); { Py_ssize_t __pyx_temp; for (__pyx_temp=0; __pyx_temp < __pyx_v_nslices; __pyx_temp++) { __Pyx_INCREF(__pyx_slice__17); __Pyx_GIVEREF(__pyx_slice__17); PyList_SET_ITEM(__pyx_t_3, __pyx_temp, __pyx_slice__17); } } __pyx_t_9 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_3); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(1, 696, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":695 * * nslices = ndim - len(result) * if nslices: # <<<<<<<<<<<<<< * result.extend([slice(None)] * nslices) * */ } /* "View.MemoryView":698 * result.extend([slice(None)] * nslices) * * return have_slices or nslices, tuple(result) # <<<<<<<<<<<<<< * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): */ __Pyx_XDECREF(__pyx_r); if (!__pyx_v_have_slices) { } else { __pyx_t_4 = __Pyx_PyBool_FromLong(__pyx_v_have_slices); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = __pyx_t_4; __pyx_t_4 = 0; goto __pyx_L14_bool_binop_done; } __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_nslices); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_3 = __pyx_t_4; __pyx_t_4 = 0; __pyx_L14_bool_binop_done:; __pyx_t_4 = PyList_AsTuple(__pyx_v_result); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __pyx_t_11 = PyTuple_New(2); if (unlikely(!__pyx_t_11)) __PYX_ERR(1, 698, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_11); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_11, 0, __pyx_t_3); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_11, 1, __pyx_t_4); __pyx_t_3 = 0; __pyx_t_4 = 0; __pyx_r = ((PyObject*)__pyx_t_11); __pyx_t_11 = 0; goto __pyx_L0; /* "View.MemoryView":666 * return isinstance(o, memoryview) * * cdef tuple _unellipsify(object index, int ndim): # <<<<<<<<<<<<<< * """ * Replace all ellipses with full slices and fill incomplete indices with */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_11); __Pyx_AddTraceback("View.MemoryView._unellipsify", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF(__pyx_v_tup); __Pyx_XDECREF(__pyx_v_result); __Pyx_XDECREF(__pyx_v_idx); __Pyx_XDECREF(__pyx_v_item); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":700 * return have_slices or nslices, tuple(result) * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): # <<<<<<<<<<<<<< * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: */ static PyObject *assert_direct_dimensions(Py_ssize_t *__pyx_v_suboffsets, int __pyx_v_ndim) { Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations Py_ssize_t *__pyx_t_1; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; int __pyx_t_4; PyObject *__pyx_t_5 = NULL; __Pyx_RefNannySetupContext("assert_direct_dimensions", 0); /* "View.MemoryView":701 * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: # <<<<<<<<<<<<<< * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") */ __pyx_t_2 = (__pyx_v_suboffsets + __pyx_v_ndim); for (__pyx_t_3 = __pyx_v_suboffsets; __pyx_t_3 < __pyx_t_2; __pyx_t_3++) { __pyx_t_1 = __pyx_t_3; __pyx_v_suboffset = (__pyx_t_1[0]); /* "View.MemoryView":702 * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * raise ValueError("Indirect dimensions not supported") * */ __pyx_t_4 = ((__pyx_v_suboffset >= 0) != 0); if (unlikely(__pyx_t_4)) { /* "View.MemoryView":703 * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__18, NULL); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 703, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_Raise(__pyx_t_5, 0, 0, 0); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __PYX_ERR(1, 703, __pyx_L1_error) /* "View.MemoryView":702 * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * raise ValueError("Indirect dimensions not supported") * */ } } /* "View.MemoryView":700 * return have_slices or nslices, tuple(result) * * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim): # <<<<<<<<<<<<<< * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.assert_direct_dimensions", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":710 * * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< * cdef int new_ndim = 0, suboffset_dim = -1, dim * cdef bint negative_step */ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *__pyx_v_memview, PyObject *__pyx_v_indices) { int __pyx_v_new_ndim; int __pyx_v_suboffset_dim; int __pyx_v_dim; __Pyx_memviewslice __pyx_v_src; __Pyx_memviewslice __pyx_v_dst; __Pyx_memviewslice *__pyx_v_p_src; struct __pyx_memoryviewslice_obj *__pyx_v_memviewsliceobj = 0; __Pyx_memviewslice *__pyx_v_p_dst; int *__pyx_v_p_suboffset_dim; Py_ssize_t __pyx_v_start; Py_ssize_t __pyx_v_stop; Py_ssize_t __pyx_v_step; int __pyx_v_have_start; int __pyx_v_have_stop; int __pyx_v_have_step; PyObject *__pyx_v_index = NULL; struct __pyx_memoryview_obj *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; struct __pyx_memoryview_obj *__pyx_t_4; char *__pyx_t_5; int __pyx_t_6; Py_ssize_t __pyx_t_7; PyObject *(*__pyx_t_8)(PyObject *); PyObject *__pyx_t_9 = NULL; Py_ssize_t __pyx_t_10; int __pyx_t_11; Py_ssize_t __pyx_t_12; __Pyx_RefNannySetupContext("memview_slice", 0); /* "View.MemoryView":711 * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): * cdef int new_ndim = 0, suboffset_dim = -1, dim # <<<<<<<<<<<<<< * cdef bint negative_step * cdef __Pyx_memviewslice src, dst */ __pyx_v_new_ndim = 0; __pyx_v_suboffset_dim = -1; /* "View.MemoryView":718 * * * memset(&dst, 0, sizeof(dst)) # <<<<<<<<<<<<<< * * cdef _memoryviewslice memviewsliceobj */ (void)(memset((&__pyx_v_dst), 0, (sizeof(__pyx_v_dst)))); /* "View.MemoryView":722 * cdef _memoryviewslice memviewsliceobj * * assert memview.view.ndim > 0 # <<<<<<<<<<<<<< * * if isinstance(memview, _memoryviewslice): */ #ifndef CYTHON_WITHOUT_ASSERTIONS if (unlikely(!Py_OptimizeFlag)) { if (unlikely(!((__pyx_v_memview->view.ndim > 0) != 0))) { PyErr_SetNone(PyExc_AssertionError); __PYX_ERR(1, 722, __pyx_L1_error) } } #endif /* "View.MemoryView":724 * assert memview.view.ndim > 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":725 * * if isinstance(memview, _memoryviewslice): * memviewsliceobj = memview # <<<<<<<<<<<<<< * p_src = &memviewsliceobj.from_slice * else: */ if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(1, 725, __pyx_L1_error) __pyx_t_3 = ((PyObject *)__pyx_v_memview); __Pyx_INCREF(__pyx_t_3); __pyx_v_memviewsliceobj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":726 * if isinstance(memview, _memoryviewslice): * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice # <<<<<<<<<<<<<< * else: * slice_copy(memview, &src) */ __pyx_v_p_src = (&__pyx_v_memviewsliceobj->from_slice); /* "View.MemoryView":724 * assert memview.view.ndim > 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * memviewsliceobj = memview * p_src = &memviewsliceobj.from_slice */ goto __pyx_L3; } /* "View.MemoryView":728 * p_src = &memviewsliceobj.from_slice * else: * slice_copy(memview, &src) # <<<<<<<<<<<<<< * p_src = &src * */ /*else*/ { __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_src)); /* "View.MemoryView":729 * else: * slice_copy(memview, &src) * p_src = &src # <<<<<<<<<<<<<< * * */ __pyx_v_p_src = (&__pyx_v_src); } __pyx_L3:; /* "View.MemoryView":735 * * * dst.memview = p_src.memview # <<<<<<<<<<<<<< * dst.data = p_src.data * */ __pyx_t_4 = __pyx_v_p_src->memview; __pyx_v_dst.memview = __pyx_t_4; /* "View.MemoryView":736 * * dst.memview = p_src.memview * dst.data = p_src.data # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __pyx_v_p_src->data; __pyx_v_dst.data = __pyx_t_5; /* "View.MemoryView":741 * * * cdef __Pyx_memviewslice *p_dst = &dst # <<<<<<<<<<<<<< * cdef int *p_suboffset_dim = &suboffset_dim * cdef Py_ssize_t start, stop, step */ __pyx_v_p_dst = (&__pyx_v_dst); /* "View.MemoryView":742 * * cdef __Pyx_memviewslice *p_dst = &dst * cdef int *p_suboffset_dim = &suboffset_dim # <<<<<<<<<<<<<< * cdef Py_ssize_t start, stop, step * cdef bint have_start, have_stop, have_step */ __pyx_v_p_suboffset_dim = (&__pyx_v_suboffset_dim); /* "View.MemoryView":746 * cdef bint have_start, have_stop, have_step * * for dim, index in enumerate(indices): # <<<<<<<<<<<<<< * if PyIndex_Check(index): * slice_memviewslice( */ __pyx_t_6 = 0; if (likely(PyList_CheckExact(__pyx_v_indices)) || PyTuple_CheckExact(__pyx_v_indices)) { __pyx_t_3 = __pyx_v_indices; __Pyx_INCREF(__pyx_t_3); __pyx_t_7 = 0; __pyx_t_8 = NULL; } else { __pyx_t_7 = -1; __pyx_t_3 = PyObject_GetIter(__pyx_v_indices); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_8 = Py_TYPE(__pyx_t_3)->tp_iternext; if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 746, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_8)) { if (likely(PyList_CheckExact(__pyx_t_3))) { if (__pyx_t_7 >= PyList_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_9 = PyList_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(1, 746, __pyx_L1_error) #else __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); #endif } else { if (__pyx_t_7 >= PyTuple_GET_SIZE(__pyx_t_3)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS __pyx_t_9 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(1, 746, __pyx_L1_error) #else __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 746, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); #endif } } else { __pyx_t_9 = __pyx_t_8(__pyx_t_3); if (unlikely(!__pyx_t_9)) { PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); else __PYX_ERR(1, 746, __pyx_L1_error) } break; } __Pyx_GOTREF(__pyx_t_9); } __Pyx_XDECREF_SET(__pyx_v_index, __pyx_t_9); __pyx_t_9 = 0; __pyx_v_dim = __pyx_t_6; __pyx_t_6 = (__pyx_t_6 + 1); /* "View.MemoryView":747 * * for dim, index in enumerate(indices): * if PyIndex_Check(index): # <<<<<<<<<<<<<< * slice_memviewslice( * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], */ __pyx_t_2 = (PyIndex_Check(__pyx_v_index) != 0); if (__pyx_t_2) { /* "View.MemoryView":751 * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, * index, 0, 0, # start, stop, step # <<<<<<<<<<<<<< * 0, 0, 0, # have_{start,stop,step} * False) */ __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_v_index); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(1, 751, __pyx_L1_error) /* "View.MemoryView":748 * for dim, index in enumerate(indices): * if PyIndex_Check(index): * slice_memviewslice( # <<<<<<<<<<<<<< * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, */ __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_t_10, 0, 0, 0, 0, 0, 0); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(1, 748, __pyx_L1_error) /* "View.MemoryView":747 * * for dim, index in enumerate(indices): * if PyIndex_Check(index): # <<<<<<<<<<<<<< * slice_memviewslice( * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], */ goto __pyx_L6; } /* "View.MemoryView":754 * 0, 0, 0, # have_{start,stop,step} * False) * elif index is None: # <<<<<<<<<<<<<< * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 */ __pyx_t_2 = (__pyx_v_index == Py_None); __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { /* "View.MemoryView":755 * False) * elif index is None: * p_dst.shape[new_ndim] = 1 # <<<<<<<<<<<<<< * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 */ (__pyx_v_p_dst->shape[__pyx_v_new_ndim]) = 1; /* "View.MemoryView":756 * elif index is None: * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 # <<<<<<<<<<<<<< * p_dst.suboffsets[new_ndim] = -1 * new_ndim += 1 */ (__pyx_v_p_dst->strides[__pyx_v_new_ndim]) = 0; /* "View.MemoryView":757 * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 # <<<<<<<<<<<<<< * new_ndim += 1 * else: */ (__pyx_v_p_dst->suboffsets[__pyx_v_new_ndim]) = -1L; /* "View.MemoryView":758 * p_dst.strides[new_ndim] = 0 * p_dst.suboffsets[new_ndim] = -1 * new_ndim += 1 # <<<<<<<<<<<<<< * else: * start = index.start or 0 */ __pyx_v_new_ndim = (__pyx_v_new_ndim + 1); /* "View.MemoryView":754 * 0, 0, 0, # have_{start,stop,step} * False) * elif index is None: # <<<<<<<<<<<<<< * p_dst.shape[new_ndim] = 1 * p_dst.strides[new_ndim] = 0 */ goto __pyx_L6; } /* "View.MemoryView":760 * new_ndim += 1 * else: * start = index.start or 0 # <<<<<<<<<<<<<< * stop = index.stop or 0 * step = index.step or 0 */ /*else*/ { __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 760, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(1, 760, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(1, 760, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L7_bool_binop_done; } __pyx_t_10 = 0; __pyx_L7_bool_binop_done:; __pyx_v_start = __pyx_t_10; /* "View.MemoryView":761 * else: * start = index.start or 0 * stop = index.stop or 0 # <<<<<<<<<<<<<< * step = index.step or 0 * */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 761, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(1, 761, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(1, 761, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L9_bool_binop_done; } __pyx_t_10 = 0; __pyx_L9_bool_binop_done:; __pyx_v_stop = __pyx_t_10; /* "View.MemoryView":762 * start = index.start or 0 * stop = index.stop or 0 * step = index.step or 0 # <<<<<<<<<<<<<< * * have_start = index.start is not None */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 762, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(1, 762, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(1, 762, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L11_bool_binop_done; } __pyx_t_10 = 0; __pyx_L11_bool_binop_done:; __pyx_v_step = __pyx_t_10; /* "View.MemoryView":764 * step = index.step or 0 * * have_start = index.start is not None # <<<<<<<<<<<<<< * have_stop = index.stop is not None * have_step = index.step is not None */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 764, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_start = __pyx_t_1; /* "View.MemoryView":765 * * have_start = index.start is not None * have_stop = index.stop is not None # <<<<<<<<<<<<<< * have_step = index.step is not None * */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 765, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_stop = __pyx_t_1; /* "View.MemoryView":766 * have_start = index.start is not None * have_stop = index.stop is not None * have_step = index.step is not None # <<<<<<<<<<<<<< * * slice_memviewslice( */ __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 766, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_step = __pyx_t_1; /* "View.MemoryView":768 * have_step = index.step is not None * * slice_memviewslice( # <<<<<<<<<<<<<< * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, */ __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(1, 768, __pyx_L1_error) /* "View.MemoryView":774 * have_start, have_stop, have_step, * True) * new_ndim += 1 # <<<<<<<<<<<<<< * * if isinstance(memview, _memoryviewslice): */ __pyx_v_new_ndim = (__pyx_v_new_ndim + 1); } __pyx_L6:; /* "View.MemoryView":746 * cdef bint have_start, have_stop, have_step * * for dim, index in enumerate(indices): # <<<<<<<<<<<<<< * if PyIndex_Check(index): * slice_memviewslice( */ } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":776 * new_ndim += 1 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":777 * * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, */ __Pyx_XDECREF(((PyObject *)__pyx_r)); /* "View.MemoryView":778 * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, # <<<<<<<<<<<<<< * memviewsliceobj.to_dtype_func, * memview.dtype_is_object) */ if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(1, 778, __pyx_L1_error) } /* "View.MemoryView":779 * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, # <<<<<<<<<<<<<< * memview.dtype_is_object) * else: */ if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(1, 779, __pyx_L1_error) } /* "View.MemoryView":777 * * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, */ __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 777, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(1, 777, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; /* "View.MemoryView":776 * new_ndim += 1 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, */ } /* "View.MemoryView":782 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< * memview.dtype_is_object) * */ /*else*/ { __Pyx_XDECREF(((PyObject *)__pyx_r)); /* "View.MemoryView":783 * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 782, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); /* "View.MemoryView":782 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< * memview.dtype_is_object) * */ if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(1, 782, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; } /* "View.MemoryView":710 * * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< * cdef int new_ndim = 0, suboffset_dim = -1, dim * cdef bint negative_step */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_9); __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj); __Pyx_XDECREF(__pyx_v_index); __Pyx_XGIVEREF((PyObject *)__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":807 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, */ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) { Py_ssize_t __pyx_v_new_shape; int __pyx_v_negative_step; int __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; /* "View.MemoryView":827 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< * * if start < 0: */ __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_1) { /* "View.MemoryView":829 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< * start += shape * if not 0 <= start < shape: */ __pyx_t_1 = ((__pyx_v_start < 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":830 * * if start < 0: * start += shape # <<<<<<<<<<<<<< * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); /* "View.MemoryView":829 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< * start += shape * if not 0 <= start < shape: */ } /* "View.MemoryView":831 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) * else: */ __pyx_t_1 = (0 <= __pyx_v_start); if (__pyx_t_1) { __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); } __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":832 * start += shape * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< * else: * */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 832, __pyx_L1_error) /* "View.MemoryView":831 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) * else: */ } /* "View.MemoryView":827 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< * * if start < 0: */ goto __pyx_L3; } /* "View.MemoryView":835 * else: * * negative_step = have_step != 0 and step < 0 # <<<<<<<<<<<<<< * * if have_step and step == 0: */ /*else*/ { __pyx_t_1 = ((__pyx_v_have_step != 0) != 0); if (__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L6_bool_binop_done; } __pyx_t_1 = ((__pyx_v_step < 0) != 0); __pyx_t_2 = __pyx_t_1; __pyx_L6_bool_binop_done:; __pyx_v_negative_step = __pyx_t_2; /* "View.MemoryView":837 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) * */ __pyx_t_1 = (__pyx_v_have_step != 0); if (__pyx_t_1) { } else { __pyx_t_2 = __pyx_t_1; goto __pyx_L9_bool_binop_done; } __pyx_t_1 = ((__pyx_v_step == 0) != 0); __pyx_t_2 = __pyx_t_1; __pyx_L9_bool_binop_done:; if (__pyx_t_2) { /* "View.MemoryView":838 * * if have_step and step == 0: * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 838, __pyx_L1_error) /* "View.MemoryView":837 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) * */ } /* "View.MemoryView":841 * * * if have_start: # <<<<<<<<<<<<<< * if start < 0: * start += shape */ __pyx_t_2 = (__pyx_v_have_start != 0); if (__pyx_t_2) { /* "View.MemoryView":842 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< * start += shape * if start < 0: */ __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":843 * if have_start: * if start < 0: * start += shape # <<<<<<<<<<<<<< * if start < 0: * start = 0 */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); /* "View.MemoryView":844 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< * start = 0 * elif start >= shape: */ __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":845 * start += shape * if start < 0: * start = 0 # <<<<<<<<<<<<<< * elif start >= shape: * if negative_step: */ __pyx_v_start = 0; /* "View.MemoryView":844 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< * start = 0 * elif start >= shape: */ } /* "View.MemoryView":842 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< * start += shape * if start < 0: */ goto __pyx_L12; } /* "View.MemoryView":846 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< * if negative_step: * start = shape - 1 */ __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); if (__pyx_t_2) { /* "View.MemoryView":847 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":848 * elif start >= shape: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< * else: * start = shape */ __pyx_v_start = (__pyx_v_shape - 1); /* "View.MemoryView":847 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ goto __pyx_L14; } /* "View.MemoryView":850 * start = shape - 1 * else: * start = shape # <<<<<<<<<<<<<< * else: * if negative_step: */ /*else*/ { __pyx_v_start = __pyx_v_shape; } __pyx_L14:; /* "View.MemoryView":846 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< * if negative_step: * start = shape - 1 */ } __pyx_L12:; /* "View.MemoryView":841 * * * if have_start: # <<<<<<<<<<<<<< * if start < 0: * start += shape */ goto __pyx_L11; } /* "View.MemoryView":852 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ /*else*/ { __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":853 * else: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< * else: * start = 0 */ __pyx_v_start = (__pyx_v_shape - 1); /* "View.MemoryView":852 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< * start = shape - 1 * else: */ goto __pyx_L15; } /* "View.MemoryView":855 * start = shape - 1 * else: * start = 0 # <<<<<<<<<<<<<< * * if have_stop: */ /*else*/ { __pyx_v_start = 0; } __pyx_L15:; } __pyx_L11:; /* "View.MemoryView":857 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< * if stop < 0: * stop += shape */ __pyx_t_2 = (__pyx_v_have_stop != 0); if (__pyx_t_2) { /* "View.MemoryView":858 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< * stop += shape * if stop < 0: */ __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":859 * if have_stop: * if stop < 0: * stop += shape # <<<<<<<<<<<<<< * if stop < 0: * stop = 0 */ __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); /* "View.MemoryView":860 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< * stop = 0 * elif stop > shape: */ __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":861 * stop += shape * if stop < 0: * stop = 0 # <<<<<<<<<<<<<< * elif stop > shape: * stop = shape */ __pyx_v_stop = 0; /* "View.MemoryView":860 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< * stop = 0 * elif stop > shape: */ } /* "View.MemoryView":858 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< * stop += shape * if stop < 0: */ goto __pyx_L17; } /* "View.MemoryView":862 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< * stop = shape * else: */ __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0); if (__pyx_t_2) { /* "View.MemoryView":863 * stop = 0 * elif stop > shape: * stop = shape # <<<<<<<<<<<<<< * else: * if negative_step: */ __pyx_v_stop = __pyx_v_shape; /* "View.MemoryView":862 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< * stop = shape * else: */ } __pyx_L17:; /* "View.MemoryView":857 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< * if stop < 0: * stop += shape */ goto __pyx_L16; } /* "View.MemoryView":865 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< * stop = -1 * else: */ /*else*/ { __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { /* "View.MemoryView":866 * else: * if negative_step: * stop = -1 # <<<<<<<<<<<<<< * else: * stop = shape */ __pyx_v_stop = -1L; /* "View.MemoryView":865 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< * stop = -1 * else: */ goto __pyx_L19; } /* "View.MemoryView":868 * stop = -1 * else: * stop = shape # <<<<<<<<<<<<<< * * if not have_step: */ /*else*/ { __pyx_v_stop = __pyx_v_shape; } __pyx_L19:; } __pyx_L16:; /* "View.MemoryView":870 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< * step = 1 * */ __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":871 * * if not have_step: * step = 1 # <<<<<<<<<<<<<< * * */ __pyx_v_step = 1; /* "View.MemoryView":870 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< * step = 1 * */ } /* "View.MemoryView":875 * * with cython.cdivision(True): * new_shape = (stop - start) // step # <<<<<<<<<<<<<< * * if (stop - start) - step * new_shape: */ __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); /* "View.MemoryView":877 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< * new_shape += 1 * */ __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); if (__pyx_t_2) { /* "View.MemoryView":878 * * if (stop - start) - step * new_shape: * new_shape += 1 # <<<<<<<<<<<<<< * * if new_shape < 0: */ __pyx_v_new_shape = (__pyx_v_new_shape + 1); /* "View.MemoryView":877 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< * new_shape += 1 * */ } /* "View.MemoryView":880 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< * new_shape = 0 * */ __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":881 * * if new_shape < 0: * new_shape = 0 # <<<<<<<<<<<<<< * * */ __pyx_v_new_shape = 0; /* "View.MemoryView":880 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< * new_shape = 0 * */ } /* "View.MemoryView":884 * * * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset */ (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); /* "View.MemoryView":885 * * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape # <<<<<<<<<<<<<< * dst.suboffsets[new_ndim] = suboffset * */ (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape; /* "View.MemoryView":886 * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset # <<<<<<<<<<<<<< * * */ (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset; } __pyx_L3:; /* "View.MemoryView":889 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< * dst.data += start * stride * else: */ __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":890 * * if suboffset_dim[0] < 0: * dst.data += start * stride # <<<<<<<<<<<<<< * else: * dst.suboffsets[suboffset_dim[0]] += start * stride */ __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride)); /* "View.MemoryView":889 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< * dst.data += start * stride * else: */ goto __pyx_L23; } /* "View.MemoryView":892 * dst.data += start * stride * else: * dst.suboffsets[suboffset_dim[0]] += start * stride # <<<<<<<<<<<<<< * * if suboffset >= 0: */ /*else*/ { __pyx_t_3 = (__pyx_v_suboffset_dim[0]); (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride)); } __pyx_L23:; /* "View.MemoryView":894 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< * if not is_slice: * if new_ndim == 0: */ __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":895 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset */ __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":896 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< * dst.data = ( dst.data)[0] + suboffset * else: */ __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":897 * if not is_slice: * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset # <<<<<<<<<<<<<< * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " */ __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset); /* "View.MemoryView":896 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< * dst.data = ( dst.data)[0] + suboffset * else: */ goto __pyx_L26; } /* "View.MemoryView":899 * dst.data = ( dst.data)[0] + suboffset * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " # <<<<<<<<<<<<<< * "must be indexed and not sliced", dim) * else: */ /*else*/ { /* "View.MemoryView":900 * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " * "must be indexed and not sliced", dim) # <<<<<<<<<<<<<< * else: * suboffset_dim[0] = new_ndim */ __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"All dimensions preceding dimension %d must be indexed and not sliced"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 899, __pyx_L1_error) } __pyx_L26:; /* "View.MemoryView":895 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset */ goto __pyx_L25; } /* "View.MemoryView":902 * "must be indexed and not sliced", dim) * else: * suboffset_dim[0] = new_ndim # <<<<<<<<<<<<<< * * return 0 */ /*else*/ { (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim; } __pyx_L25:; /* "View.MemoryView":894 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< * if not is_slice: * if new_ndim == 0: */ } /* "View.MemoryView":904 * suboffset_dim[0] = new_ndim * * return 0 # <<<<<<<<<<<<<< * * */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":807 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.slice_memviewslice", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = -1; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":910 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 */ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, Py_ssize_t __pyx_v_index, Py_ssize_t __pyx_v_dim) { Py_ssize_t __pyx_v_shape; Py_ssize_t __pyx_v_stride; Py_ssize_t __pyx_v_suboffset; Py_ssize_t __pyx_v_itemsize; char *__pyx_v_resultp; char *__pyx_r; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; __Pyx_RefNannySetupContext("pybuffer_index", 0); /* "View.MemoryView":912 * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 # <<<<<<<<<<<<<< * cdef Py_ssize_t itemsize = view.itemsize * cdef char *resultp */ __pyx_v_suboffset = -1L; /* "View.MemoryView":913 * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 * cdef Py_ssize_t itemsize = view.itemsize # <<<<<<<<<<<<<< * cdef char *resultp * */ __pyx_t_1 = __pyx_v_view->itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":916 * cdef char *resultp * * if view.ndim == 0: # <<<<<<<<<<<<<< * shape = view.len / itemsize * stride = itemsize */ __pyx_t_2 = ((__pyx_v_view->ndim == 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":917 * * if view.ndim == 0: * shape = view.len / itemsize # <<<<<<<<<<<<<< * stride = itemsize * else: */ if (unlikely(__pyx_v_itemsize == 0)) { PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); __PYX_ERR(1, 917, __pyx_L1_error) } else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1) && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_view->len))) { PyErr_SetString(PyExc_OverflowError, "value too large to perform division"); __PYX_ERR(1, 917, __pyx_L1_error) } __pyx_v_shape = (__pyx_v_view->len / __pyx_v_itemsize); /* "View.MemoryView":918 * if view.ndim == 0: * shape = view.len / itemsize * stride = itemsize # <<<<<<<<<<<<<< * else: * shape = view.shape[dim] */ __pyx_v_stride = __pyx_v_itemsize; /* "View.MemoryView":916 * cdef char *resultp * * if view.ndim == 0: # <<<<<<<<<<<<<< * shape = view.len / itemsize * stride = itemsize */ goto __pyx_L3; } /* "View.MemoryView":920 * stride = itemsize * else: * shape = view.shape[dim] # <<<<<<<<<<<<<< * stride = view.strides[dim] * if view.suboffsets != NULL: */ /*else*/ { __pyx_v_shape = (__pyx_v_view->shape[__pyx_v_dim]); /* "View.MemoryView":921 * else: * shape = view.shape[dim] * stride = view.strides[dim] # <<<<<<<<<<<<<< * if view.suboffsets != NULL: * suboffset = view.suboffsets[dim] */ __pyx_v_stride = (__pyx_v_view->strides[__pyx_v_dim]); /* "View.MemoryView":922 * shape = view.shape[dim] * stride = view.strides[dim] * if view.suboffsets != NULL: # <<<<<<<<<<<<<< * suboffset = view.suboffsets[dim] * */ __pyx_t_2 = ((__pyx_v_view->suboffsets != NULL) != 0); if (__pyx_t_2) { /* "View.MemoryView":923 * stride = view.strides[dim] * if view.suboffsets != NULL: * suboffset = view.suboffsets[dim] # <<<<<<<<<<<<<< * * if index < 0: */ __pyx_v_suboffset = (__pyx_v_view->suboffsets[__pyx_v_dim]); /* "View.MemoryView":922 * shape = view.shape[dim] * stride = view.strides[dim] * if view.suboffsets != NULL: # <<<<<<<<<<<<<< * suboffset = view.suboffsets[dim] * */ } } __pyx_L3:; /* "View.MemoryView":925 * suboffset = view.suboffsets[dim] * * if index < 0: # <<<<<<<<<<<<<< * index += view.shape[dim] * if index < 0: */ __pyx_t_2 = ((__pyx_v_index < 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":926 * * if index < 0: * index += view.shape[dim] # <<<<<<<<<<<<<< * if index < 0: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) */ __pyx_v_index = (__pyx_v_index + (__pyx_v_view->shape[__pyx_v_dim])); /* "View.MemoryView":927 * if index < 0: * index += view.shape[dim] * if index < 0: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ __pyx_t_2 = ((__pyx_v_index < 0) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":928 * index += view.shape[dim] * if index < 0: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * if index >= shape: */ __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 928, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 928, __pyx_L1_error) /* "View.MemoryView":927 * if index < 0: * index += view.shape[dim] * if index < 0: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ } /* "View.MemoryView":925 * suboffset = view.suboffsets[dim] * * if index < 0: # <<<<<<<<<<<<<< * index += view.shape[dim] * if index < 0: */ } /* "View.MemoryView":930 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ __pyx_t_2 = ((__pyx_v_index >= __pyx_v_shape) != 0); if (unlikely(__pyx_t_2)) { /* "View.MemoryView":931 * * if index >= shape: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * resultp = bufp + index * stride */ __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 931, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 931, __pyx_L1_error) /* "View.MemoryView":930 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * */ } /* "View.MemoryView":933 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * resultp = bufp + index * stride # <<<<<<<<<<<<<< * if suboffset >= 0: * resultp = ( resultp)[0] + suboffset */ __pyx_v_resultp = (__pyx_v_bufp + (__pyx_v_index * __pyx_v_stride)); /* "View.MemoryView":934 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< * resultp = ( resultp)[0] + suboffset * */ __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":935 * resultp = bufp + index * stride * if suboffset >= 0: * resultp = ( resultp)[0] + suboffset # <<<<<<<<<<<<<< * * return resultp */ __pyx_v_resultp = ((((char **)__pyx_v_resultp)[0]) + __pyx_v_suboffset); /* "View.MemoryView":934 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< * resultp = ( resultp)[0] + suboffset * */ } /* "View.MemoryView":937 * resultp = ( resultp)[0] + suboffset * * return resultp # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_resultp; goto __pyx_L0; /* "View.MemoryView":910 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView.pybuffer_index", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":943 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< * cdef int ndim = memslice.memview.view.ndim * */ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { int __pyx_v_ndim; Py_ssize_t *__pyx_v_shape; Py_ssize_t *__pyx_v_strides; int __pyx_v_i; int __pyx_v_j; int __pyx_r; int __pyx_t_1; Py_ssize_t *__pyx_t_2; long __pyx_t_3; long __pyx_t_4; Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; int __pyx_t_7; int __pyx_t_8; int __pyx_t_9; /* "View.MemoryView":944 * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: * cdef int ndim = memslice.memview.view.ndim # <<<<<<<<<<<<<< * * cdef Py_ssize_t *shape = memslice.shape */ __pyx_t_1 = __pyx_v_memslice->memview->view.ndim; __pyx_v_ndim = __pyx_t_1; /* "View.MemoryView":946 * cdef int ndim = memslice.memview.view.ndim * * cdef Py_ssize_t *shape = memslice.shape # <<<<<<<<<<<<<< * cdef Py_ssize_t *strides = memslice.strides * */ __pyx_t_2 = __pyx_v_memslice->shape; __pyx_v_shape = __pyx_t_2; /* "View.MemoryView":947 * * cdef Py_ssize_t *shape = memslice.shape * cdef Py_ssize_t *strides = memslice.strides # <<<<<<<<<<<<<< * * */ __pyx_t_2 = __pyx_v_memslice->strides; __pyx_v_strides = __pyx_t_2; /* "View.MemoryView":951 * * cdef int i, j * for i in range(ndim / 2): # <<<<<<<<<<<<<< * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] */ __pyx_t_3 = (__pyx_v_ndim / 2); __pyx_t_4 = __pyx_t_3; for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_4; __pyx_t_1+=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":952 * cdef int i, j * for i in range(ndim / 2): * j = ndim - 1 - i # <<<<<<<<<<<<<< * strides[i], strides[j] = strides[j], strides[i] * shape[i], shape[j] = shape[j], shape[i] */ __pyx_v_j = ((__pyx_v_ndim - 1) - __pyx_v_i); /* "View.MemoryView":953 * for i in range(ndim / 2): * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] # <<<<<<<<<<<<<< * shape[i], shape[j] = shape[j], shape[i] * */ __pyx_t_5 = (__pyx_v_strides[__pyx_v_j]); __pyx_t_6 = (__pyx_v_strides[__pyx_v_i]); (__pyx_v_strides[__pyx_v_i]) = __pyx_t_5; (__pyx_v_strides[__pyx_v_j]) = __pyx_t_6; /* "View.MemoryView":954 * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] * shape[i], shape[j] = shape[j], shape[i] # <<<<<<<<<<<<<< * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: */ __pyx_t_6 = (__pyx_v_shape[__pyx_v_j]); __pyx_t_5 = (__pyx_v_shape[__pyx_v_i]); (__pyx_v_shape[__pyx_v_i]) = __pyx_t_6; (__pyx_v_shape[__pyx_v_j]) = __pyx_t_5; /* "View.MemoryView":956 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * */ __pyx_t_8 = (((__pyx_v_memslice->suboffsets[__pyx_v_i]) >= 0) != 0); if (!__pyx_t_8) { } else { __pyx_t_7 = __pyx_t_8; goto __pyx_L6_bool_binop_done; } __pyx_t_8 = (((__pyx_v_memslice->suboffsets[__pyx_v_j]) >= 0) != 0); __pyx_t_7 = __pyx_t_8; __pyx_L6_bool_binop_done:; if (__pyx_t_7) { /* "View.MemoryView":957 * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") # <<<<<<<<<<<<<< * * return 1 */ __pyx_t_9 = __pyx_memoryview_err(__pyx_builtin_ValueError, ((char *)"Cannot transpose memoryview with indirect dimensions")); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(1, 957, __pyx_L1_error) /* "View.MemoryView":956 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * */ } } /* "View.MemoryView":959 * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * * return 1 # <<<<<<<<<<<<<< * * */ __pyx_r = 1; goto __pyx_L0; /* "View.MemoryView":943 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< * cdef int ndim = memslice.memview.view.ndim * */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.transpose_memslice", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":976 * cdef int (*to_dtype_func)(char *, object) except 0 * * def __dealloc__(self): # <<<<<<<<<<<<<< * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * */ /* Python wrapper */ static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0); __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); } static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__dealloc__", 0); /* "View.MemoryView":977 * * def __dealloc__(self): * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) # <<<<<<<<<<<<<< * * cdef convert_item_to_object(self, char *itemp): */ __PYX_XDEC_MEMVIEW((&__pyx_v_self->from_slice), 1); /* "View.MemoryView":976 * cdef int (*to_dtype_func)(char *, object) except 0 * * def __dealloc__(self): # <<<<<<<<<<<<<< * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":979 * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * if self.to_object_func != NULL: * return self.to_object_func(itemp) */ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; __Pyx_RefNannySetupContext("convert_item_to_object", 0); /* "View.MemoryView":980 * * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: # <<<<<<<<<<<<<< * return self.to_object_func(itemp) * else: */ __pyx_t_1 = ((__pyx_v_self->to_object_func != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":981 * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: * return self.to_object_func(itemp) # <<<<<<<<<<<<<< * else: * return memoryview.convert_item_to_object(self, itemp) */ __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_v_self->to_object_func(__pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 981, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; /* "View.MemoryView":980 * * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: # <<<<<<<<<<<<<< * return self.to_object_func(itemp) * else: */ } /* "View.MemoryView":983 * return self.to_object_func(itemp) * else: * return memoryview.convert_item_to_object(self, itemp) # <<<<<<<<<<<<<< * * cdef assign_item_from_object(self, char *itemp, object value): */ /*else*/ { __Pyx_XDECREF(__pyx_r); __pyx_t_2 = __pyx_memoryview_convert_item_to_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 983, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; } /* "View.MemoryView":979 * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< * if self.to_object_func != NULL: * return self.to_object_func(itemp) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":985 * return memoryview.convert_item_to_object(self, itemp) * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) */ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("assign_item_from_object", 0); /* "View.MemoryView":986 * * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: # <<<<<<<<<<<<<< * self.to_dtype_func(itemp, value) * else: */ __pyx_t_1 = ((__pyx_v_self->to_dtype_func != NULL) != 0); if (__pyx_t_1) { /* "View.MemoryView":987 * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) # <<<<<<<<<<<<<< * else: * memoryview.assign_item_from_object(self, itemp, value) */ __pyx_t_2 = __pyx_v_self->to_dtype_func(__pyx_v_itemp, __pyx_v_value); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(1, 987, __pyx_L1_error) /* "View.MemoryView":986 * * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: # <<<<<<<<<<<<<< * self.to_dtype_func(itemp, value) * else: */ goto __pyx_L3; } /* "View.MemoryView":989 * self.to_dtype_func(itemp, value) * else: * memoryview.assign_item_from_object(self, itemp, value) # <<<<<<<<<<<<<< * * @property */ /*else*/ { __pyx_t_3 = __pyx_memoryview_assign_item_from_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 989, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } __pyx_L3:; /* "View.MemoryView":985 * return memoryview.convert_item_to_object(self, itemp) * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":992 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.from_object * */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(PyObject *__pyx_v_self); /*proto*/ static PyObject *__pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(PyObject *__pyx_v_self) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__ (wrapper)", 0); __pyx_r = __pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__", 0); /* "View.MemoryView":993 * @property * def base(self): * return self.from_object # <<<<<<<<<<<<<< * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v_self->from_object); __pyx_r = __pyx_v_self->from_object; goto __pyx_L0; /* "View.MemoryView":992 * * @property * def base(self): # <<<<<<<<<<<<<< * return self.from_object * */ /* function exit code */ __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryviewslice_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ static PyObject *__pyx_pw___pyx_memoryviewslice_1__reduce_cython__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__reduce_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryviewslice___reduce_cython__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__19, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(1, 2, __pyx_L1_error) /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.__reduce_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* Python wrapper */ static PyObject *__pyx_pw___pyx_memoryviewslice_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state); /*proto*/ static PyObject *__pyx_pw___pyx_memoryviewslice_3__setstate_cython__(PyObject *__pyx_v_self, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__setstate_cython__ (wrapper)", 0); __pyx_r = __pyx_pf___pyx_memoryviewslice_2__setstate_cython__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self), ((PyObject *)__pyx_v___pyx_state)); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_tuple__20, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(1, 4, __pyx_L1_error) /* "(tree fragment)":3 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView._memoryviewslice.__setstate_cython__", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":999 * * @cname('__pyx_memoryview_fromslice') * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice, # <<<<<<<<<<<<<< * int ndim, * object (*to_object_func)(char *), */ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewslice, int __pyx_v_ndim, PyObject *(*__pyx_v_to_object_func)(char *), int (*__pyx_v_to_dtype_func)(char *, PyObject *), int __pyx_v_dtype_is_object) { struct __pyx_memoryviewslice_obj *__pyx_v_result = 0; Py_ssize_t __pyx_v_suboffset; PyObject *__pyx_v_length = NULL; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; __Pyx_TypeInfo *__pyx_t_4; Py_buffer __pyx_t_5; Py_ssize_t *__pyx_t_6; Py_ssize_t *__pyx_t_7; Py_ssize_t *__pyx_t_8; Py_ssize_t __pyx_t_9; __Pyx_RefNannySetupContext("memoryview_fromslice", 0); /* "View.MemoryView":1007 * cdef _memoryviewslice result * * if memviewslice.memview == Py_None: # <<<<<<<<<<<<<< * return None * */ __pyx_t_1 = ((((PyObject *)__pyx_v_memviewslice.memview) == Py_None) != 0); if (__pyx_t_1) { /* "View.MemoryView":1008 * * if memviewslice.memview == Py_None: * return None # <<<<<<<<<<<<<< * * */ __Pyx_XDECREF(__pyx_r); __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; /* "View.MemoryView":1007 * cdef _memoryviewslice result * * if memviewslice.memview == Py_None: # <<<<<<<<<<<<<< * return None * */ } /* "View.MemoryView":1013 * * * result = _memoryviewslice(None, 0, dtype_is_object) # <<<<<<<<<<<<<< * * result.from_slice = memviewslice */ __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(Py_None); __Pyx_GIVEREF(Py_None); PyTuple_SET_ITEM(__pyx_t_3, 0, Py_None); __Pyx_INCREF(__pyx_int_0); __Pyx_GIVEREF(__pyx_int_0); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_int_0); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryviewslice_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1013, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":1015 * result = _memoryviewslice(None, 0, dtype_is_object) * * result.from_slice = memviewslice # <<<<<<<<<<<<<< * __PYX_INC_MEMVIEW(&memviewslice, 1) * */ __pyx_v_result->from_slice = __pyx_v_memviewslice; /* "View.MemoryView":1016 * * result.from_slice = memviewslice * __PYX_INC_MEMVIEW(&memviewslice, 1) # <<<<<<<<<<<<<< * * result.from_object = ( memviewslice.memview).base */ __PYX_INC_MEMVIEW((&__pyx_v_memviewslice), 1); /* "View.MemoryView":1018 * __PYX_INC_MEMVIEW(&memviewslice, 1) * * result.from_object = ( memviewslice.memview).base # <<<<<<<<<<<<<< * result.typeinfo = memviewslice.memview.typeinfo * */ __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_memviewslice.memview), __pyx_n_s_base); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1018, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); __Pyx_GOTREF(__pyx_v_result->from_object); __Pyx_DECREF(__pyx_v_result->from_object); __pyx_v_result->from_object = __pyx_t_2; __pyx_t_2 = 0; /* "View.MemoryView":1019 * * result.from_object = ( memviewslice.memview).base * result.typeinfo = memviewslice.memview.typeinfo # <<<<<<<<<<<<<< * * result.view = memviewslice.memview.view */ __pyx_t_4 = __pyx_v_memviewslice.memview->typeinfo; __pyx_v_result->__pyx_base.typeinfo = __pyx_t_4; /* "View.MemoryView":1021 * result.typeinfo = memviewslice.memview.typeinfo * * result.view = memviewslice.memview.view # <<<<<<<<<<<<<< * result.view.buf = memviewslice.data * result.view.ndim = ndim */ __pyx_t_5 = __pyx_v_memviewslice.memview->view; __pyx_v_result->__pyx_base.view = __pyx_t_5; /* "View.MemoryView":1022 * * result.view = memviewslice.memview.view * result.view.buf = memviewslice.data # <<<<<<<<<<<<<< * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None */ __pyx_v_result->__pyx_base.view.buf = ((void *)__pyx_v_memviewslice.data); /* "View.MemoryView":1023 * result.view = memviewslice.memview.view * result.view.buf = memviewslice.data * result.view.ndim = ndim # <<<<<<<<<<<<<< * (<__pyx_buffer *> &result.view).obj = Py_None * Py_INCREF(Py_None) */ __pyx_v_result->__pyx_base.view.ndim = __pyx_v_ndim; /* "View.MemoryView":1024 * result.view.buf = memviewslice.data * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None # <<<<<<<<<<<<<< * Py_INCREF(Py_None) * */ ((Py_buffer *)(&__pyx_v_result->__pyx_base.view))->obj = Py_None; /* "View.MemoryView":1025 * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * * if (memviewslice.memview).flags & PyBUF_WRITABLE: */ Py_INCREF(Py_None); /* "View.MemoryView":1027 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< * result.flags = PyBUF_RECORDS * else: */ __pyx_t_1 = ((((struct __pyx_memoryview_obj *)__pyx_v_memviewslice.memview)->flags & PyBUF_WRITABLE) != 0); if (__pyx_t_1) { /* "View.MemoryView":1028 * * if (memviewslice.memview).flags & PyBUF_WRITABLE: * result.flags = PyBUF_RECORDS # <<<<<<<<<<<<<< * else: * result.flags = PyBUF_RECORDS_RO */ __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS; /* "View.MemoryView":1027 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< * result.flags = PyBUF_RECORDS * else: */ goto __pyx_L4; } /* "View.MemoryView":1030 * result.flags = PyBUF_RECORDS * else: * result.flags = PyBUF_RECORDS_RO # <<<<<<<<<<<<<< * * result.view.shape = result.from_slice.shape */ /*else*/ { __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS_RO; } __pyx_L4:; /* "View.MemoryView":1032 * result.flags = PyBUF_RECORDS_RO * * result.view.shape = result.from_slice.shape # <<<<<<<<<<<<<< * result.view.strides = result.from_slice.strides * */ __pyx_v_result->__pyx_base.view.shape = ((Py_ssize_t *)__pyx_v_result->from_slice.shape); /* "View.MemoryView":1033 * * result.view.shape = result.from_slice.shape * result.view.strides = result.from_slice.strides # <<<<<<<<<<<<<< * * */ __pyx_v_result->__pyx_base.view.strides = ((Py_ssize_t *)__pyx_v_result->from_slice.strides); /* "View.MemoryView":1036 * * * result.view.suboffsets = NULL # <<<<<<<<<<<<<< * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: */ __pyx_v_result->__pyx_base.view.suboffsets = NULL; /* "View.MemoryView":1037 * * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: # <<<<<<<<<<<<<< * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets */ __pyx_t_7 = (__pyx_v_result->from_slice.suboffsets + __pyx_v_ndim); for (__pyx_t_8 = __pyx_v_result->from_slice.suboffsets; __pyx_t_8 < __pyx_t_7; __pyx_t_8++) { __pyx_t_6 = __pyx_t_8; __pyx_v_suboffset = (__pyx_t_6[0]); /* "View.MemoryView":1038 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * result.view.suboffsets = result.from_slice.suboffsets * break */ __pyx_t_1 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":1039 * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets # <<<<<<<<<<<<<< * break * */ __pyx_v_result->__pyx_base.view.suboffsets = ((Py_ssize_t *)__pyx_v_result->from_slice.suboffsets); /* "View.MemoryView":1040 * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets * break # <<<<<<<<<<<<<< * * result.view.len = result.view.itemsize */ goto __pyx_L6_break; /* "View.MemoryView":1038 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< * result.view.suboffsets = result.from_slice.suboffsets * break */ } } __pyx_L6_break:; /* "View.MemoryView":1042 * break * * result.view.len = result.view.itemsize # <<<<<<<<<<<<<< * for length in result.view.shape[:ndim]: * result.view.len *= length */ __pyx_t_9 = __pyx_v_result->__pyx_base.view.itemsize; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; /* "View.MemoryView":1043 * * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: # <<<<<<<<<<<<<< * result.view.len *= length * */ __pyx_t_7 = (__pyx_v_result->__pyx_base.view.shape + __pyx_v_ndim); for (__pyx_t_8 = __pyx_v_result->__pyx_base.view.shape; __pyx_t_8 < __pyx_t_7; __pyx_t_8++) { __pyx_t_6 = __pyx_t_8; __pyx_t_2 = PyInt_FromSsize_t((__pyx_t_6[0])); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1043, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_2); __pyx_t_2 = 0; /* "View.MemoryView":1044 * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: * result.view.len *= length # <<<<<<<<<<<<<< * * result.to_object_func = to_object_func */ __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_result->__pyx_base.view.len); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyNumber_InPlaceMultiply(__pyx_t_2, __pyx_v_length); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 1044, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_3); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(1, 1044, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; } /* "View.MemoryView":1046 * result.view.len *= length * * result.to_object_func = to_object_func # <<<<<<<<<<<<<< * result.to_dtype_func = to_dtype_func * */ __pyx_v_result->to_object_func = __pyx_v_to_object_func; /* "View.MemoryView":1047 * * result.to_object_func = to_object_func * result.to_dtype_func = to_dtype_func # <<<<<<<<<<<<<< * * return result */ __pyx_v_result->to_dtype_func = __pyx_v_to_dtype_func; /* "View.MemoryView":1049 * result.to_dtype_func = to_dtype_func * * return result # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_get_slice_from_memoryview') */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(((PyObject *)__pyx_v_result)); __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; /* "View.MemoryView":999 * * @cname('__pyx_memoryview_fromslice') * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice, # <<<<<<<<<<<<<< * int ndim, * object (*to_object_func)(char *), */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.memoryview_fromslice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_result); __Pyx_XDECREF(__pyx_v_length); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1052 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj */ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_mslice) { struct __pyx_memoryviewslice_obj *__pyx_v_obj = 0; __Pyx_memviewslice *__pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("get_slice_from_memview", 0); /* "View.MemoryView":1055 * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * obj = memview * return &obj.from_slice */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":1056 * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): * obj = memview # <<<<<<<<<<<<<< * return &obj.from_slice * else: */ if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(1, 1056, __pyx_L1_error) __pyx_t_3 = ((PyObject *)__pyx_v_memview); __Pyx_INCREF(__pyx_t_3); __pyx_v_obj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3); __pyx_t_3 = 0; /* "View.MemoryView":1057 * if isinstance(memview, _memoryviewslice): * obj = memview * return &obj.from_slice # <<<<<<<<<<<<<< * else: * slice_copy(memview, mslice) */ __pyx_r = (&__pyx_v_obj->from_slice); goto __pyx_L0; /* "View.MemoryView":1055 * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * obj = memview * return &obj.from_slice */ } /* "View.MemoryView":1059 * return &obj.from_slice * else: * slice_copy(memview, mslice) # <<<<<<<<<<<<<< * return mslice * */ /*else*/ { __pyx_memoryview_slice_copy(__pyx_v_memview, __pyx_v_mslice); /* "View.MemoryView":1060 * else: * slice_copy(memview, mslice) * return mslice # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_slice_copy') */ __pyx_r = __pyx_v_mslice; goto __pyx_L0; } /* "View.MemoryView":1052 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_3); __Pyx_AddTraceback("View.MemoryView.get_slice_from_memview", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF((PyObject *)__pyx_v_obj); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1063 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< * cdef int dim * cdef (Py_ssize_t*) shape, strides, suboffsets */ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_dst) { int __pyx_v_dim; Py_ssize_t *__pyx_v_shape; Py_ssize_t *__pyx_v_strides; Py_ssize_t *__pyx_v_suboffsets; __Pyx_RefNannyDeclarations Py_ssize_t *__pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; Py_ssize_t __pyx_t_5; __Pyx_RefNannySetupContext("slice_copy", 0); /* "View.MemoryView":1067 * cdef (Py_ssize_t*) shape, strides, suboffsets * * shape = memview.view.shape # <<<<<<<<<<<<<< * strides = memview.view.strides * suboffsets = memview.view.suboffsets */ __pyx_t_1 = __pyx_v_memview->view.shape; __pyx_v_shape = __pyx_t_1; /* "View.MemoryView":1068 * * shape = memview.view.shape * strides = memview.view.strides # <<<<<<<<<<<<<< * suboffsets = memview.view.suboffsets * */ __pyx_t_1 = __pyx_v_memview->view.strides; __pyx_v_strides = __pyx_t_1; /* "View.MemoryView":1069 * shape = memview.view.shape * strides = memview.view.strides * suboffsets = memview.view.suboffsets # <<<<<<<<<<<<<< * * dst.memview = <__pyx_memoryview *> memview */ __pyx_t_1 = __pyx_v_memview->view.suboffsets; __pyx_v_suboffsets = __pyx_t_1; /* "View.MemoryView":1071 * suboffsets = memview.view.suboffsets * * dst.memview = <__pyx_memoryview *> memview # <<<<<<<<<<<<<< * dst.data = memview.view.buf * */ __pyx_v_dst->memview = ((struct __pyx_memoryview_obj *)__pyx_v_memview); /* "View.MemoryView":1072 * * dst.memview = <__pyx_memoryview *> memview * dst.data = memview.view.buf # <<<<<<<<<<<<<< * * for dim in range(memview.view.ndim): */ __pyx_v_dst->data = ((char *)__pyx_v_memview->view.buf); /* "View.MemoryView":1074 * dst.data = memview.view.buf * * for dim in range(memview.view.ndim): # <<<<<<<<<<<<<< * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] */ __pyx_t_2 = __pyx_v_memview->view.ndim; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_dim = __pyx_t_4; /* "View.MemoryView":1075 * * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] # <<<<<<<<<<<<<< * dst.strides[dim] = strides[dim] * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 */ (__pyx_v_dst->shape[__pyx_v_dim]) = (__pyx_v_shape[__pyx_v_dim]); /* "View.MemoryView":1076 * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] # <<<<<<<<<<<<<< * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 * */ (__pyx_v_dst->strides[__pyx_v_dim]) = (__pyx_v_strides[__pyx_v_dim]); /* "View.MemoryView":1077 * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_object') */ if ((__pyx_v_suboffsets != 0)) { __pyx_t_5 = (__pyx_v_suboffsets[__pyx_v_dim]); } else { __pyx_t_5 = -1L; } (__pyx_v_dst->suboffsets[__pyx_v_dim]) = __pyx_t_5; } /* "View.MemoryView":1063 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< * cdef int dim * cdef (Py_ssize_t*) shape, strides, suboffsets */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":1080 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice */ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx_v_memview) { __Pyx_memviewslice __pyx_v_memviewslice; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("memoryview_copy", 0); /* "View.MemoryView":1083 * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) # <<<<<<<<<<<<<< * return memoryview_copy_from_slice(memview, &memviewslice) * */ __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_memviewslice)); /* "View.MemoryView":1084 * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) * return memoryview_copy_from_slice(memview, &memviewslice) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_object_from_slice') */ __Pyx_XDECREF(__pyx_r); __pyx_t_1 = __pyx_memoryview_copy_object_from_slice(__pyx_v_memview, (&__pyx_v_memviewslice)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 1084, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; /* "View.MemoryView":1080 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_AddTraceback("View.MemoryView.memoryview_copy", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1087 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< * """ * Create a new memoryview object from a given memoryview object and slice. */ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_memviewslice) { PyObject *(*__pyx_v_to_object_func)(char *); int (*__pyx_v_to_dtype_func)(char *, PyObject *); PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; int __pyx_t_2; PyObject *(*__pyx_t_3)(char *); int (*__pyx_t_4)(char *, PyObject *); PyObject *__pyx_t_5 = NULL; __Pyx_RefNannySetupContext("memoryview_copy_from_slice", 0); /* "View.MemoryView":1094 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func */ __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { /* "View.MemoryView":1095 * * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func # <<<<<<<<<<<<<< * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func * else: */ __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func; __pyx_v_to_object_func = __pyx_t_3; /* "View.MemoryView":1096 * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func # <<<<<<<<<<<<<< * else: * to_object_func = NULL */ __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func; __pyx_v_to_dtype_func = __pyx_t_4; /* "View.MemoryView":1094 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func */ goto __pyx_L3; } /* "View.MemoryView":1098 * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func * else: * to_object_func = NULL # <<<<<<<<<<<<<< * to_dtype_func = NULL * */ /*else*/ { __pyx_v_to_object_func = NULL; /* "View.MemoryView":1099 * else: * to_object_func = NULL * to_dtype_func = NULL # <<<<<<<<<<<<<< * * return memoryview_fromslice(memviewslice[0], memview.view.ndim, */ __pyx_v_to_dtype_func = NULL; } __pyx_L3:; /* "View.MemoryView":1101 * to_dtype_func = NULL * * return memoryview_fromslice(memviewslice[0], memview.view.ndim, # <<<<<<<<<<<<<< * to_object_func, to_dtype_func, * memview.dtype_is_object) */ __Pyx_XDECREF(__pyx_r); /* "View.MemoryView":1103 * return memoryview_fromslice(memviewslice[0], memview.view.ndim, * to_object_func, to_dtype_func, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 1101, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; /* "View.MemoryView":1087 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< * """ * Create a new memoryview object from a given memoryview object and slice. */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "View.MemoryView":1109 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< * if arg < 0: * return -arg */ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { Py_ssize_t __pyx_r; int __pyx_t_1; /* "View.MemoryView":1110 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< * return -arg * else: */ __pyx_t_1 = ((__pyx_v_arg < 0) != 0); if (__pyx_t_1) { /* "View.MemoryView":1111 * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: * return -arg # <<<<<<<<<<<<<< * else: * return arg */ __pyx_r = (-__pyx_v_arg); goto __pyx_L0; /* "View.MemoryView":1110 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< * return -arg * else: */ } /* "View.MemoryView":1113 * return -arg * else: * return arg # <<<<<<<<<<<<<< * * @cname('__pyx_get_best_slice_order') */ /*else*/ { __pyx_r = __pyx_v_arg; goto __pyx_L0; } /* "View.MemoryView":1109 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< * if arg < 0: * return -arg */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1116 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< * """ * Figure out the best memory access order for a given slice. */ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) { int __pyx_v_i; Py_ssize_t __pyx_v_c_stride; Py_ssize_t __pyx_v_f_stride; char __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; /* "View.MemoryView":1121 * """ * cdef int i * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< * cdef Py_ssize_t f_stride = 0 * */ __pyx_v_c_stride = 0; /* "View.MemoryView":1122 * cdef int i * cdef Py_ssize_t c_stride = 0 * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< * * for i in range(ndim - 1, -1, -1): */ __pyx_v_f_stride = 0; /* "View.MemoryView":1124 * cdef Py_ssize_t f_stride = 0 * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] */ for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":1125 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * c_stride = mslice.strides[i] * break */ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1126 * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< * break * */ __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1127 * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< * * for i in range(ndim): */ goto __pyx_L4_break; /* "View.MemoryView":1125 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * c_stride = mslice.strides[i] * break */ } } __pyx_L4_break:; /* "View.MemoryView":1129 * break * * for i in range(ndim): # <<<<<<<<<<<<<< * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] */ __pyx_t_1 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_1; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1130 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * f_stride = mslice.strides[i] * break */ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1131 * for i in range(ndim): * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< * break * */ __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1132 * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): */ goto __pyx_L7_break; /* "View.MemoryView":1130 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< * f_stride = mslice.strides[i] * break */ } } __pyx_L7_break:; /* "View.MemoryView":1134 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< * return 'C' * else: */ __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1135 * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): * return 'C' # <<<<<<<<<<<<<< * else: * return 'F' */ __pyx_r = 'C'; goto __pyx_L0; /* "View.MemoryView":1134 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< * return 'C' * else: */ } /* "View.MemoryView":1137 * return 'C' * else: * return 'F' # <<<<<<<<<<<<<< * * @cython.cdivision(True) */ /*else*/ { __pyx_r = 'F'; goto __pyx_L0; } /* "View.MemoryView":1116 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< * """ * Figure out the best memory access order for a given slice. */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1140 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< * char *dst_data, Py_ssize_t *dst_strides, * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, */ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; Py_ssize_t __pyx_v_dst_extent; Py_ssize_t __pyx_v_src_stride; Py_ssize_t __pyx_v_dst_stride; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; Py_ssize_t __pyx_t_4; Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; /* "View.MemoryView":1147 * * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] */ __pyx_v_src_extent = (__pyx_v_src_shape[0]); /* "View.MemoryView":1148 * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] */ __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); /* "View.MemoryView":1149 * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t dst_stride = dst_strides[0] * */ __pyx_v_src_stride = (__pyx_v_src_strides[0]); /* "View.MemoryView":1150 * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< * * if ndim == 1: */ __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); /* "View.MemoryView":1152 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): */ __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L5_bool_binop_done; } __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; goto __pyx_L5_bool_binop_done; } /* "View.MemoryView":1154 * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< * memcpy(dst_data, src_data, itemsize * dst_extent) * else: */ __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); if (__pyx_t_2) { __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); } __pyx_t_3 = (__pyx_t_2 != 0); __pyx_t_1 = __pyx_t_3; __pyx_L5_bool_binop_done:; /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ if (__pyx_t_1) { /* "View.MemoryView":1155 * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< * else: * for i in range(dst_extent): */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); /* "View.MemoryView":1153 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) */ goto __pyx_L4; } /* "View.MemoryView":1157 * memcpy(dst_data, src_data, itemsize * dst_extent) * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< * memcpy(dst_data, src_data, itemsize) * src_data += src_stride */ /*else*/ { __pyx_t_4 = __pyx_v_dst_extent; __pyx_t_5 = __pyx_t_4; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1158 * else: * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< * src_data += src_stride * dst_data += dst_stride */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); /* "View.MemoryView":1159 * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< * dst_data += dst_stride * else: */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); /* "View.MemoryView":1160 * memcpy(dst_data, src_data, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< * else: * for i in range(dst_extent): */ __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); } } __pyx_L4:; /* "View.MemoryView":1152 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): */ goto __pyx_L3; } /* "View.MemoryView":1162 * dst_data += dst_stride * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< * _copy_strided_to_strided(src_data, src_strides + 1, * dst_data, dst_strides + 1, */ /*else*/ { __pyx_t_4 = __pyx_v_dst_extent; __pyx_t_5 = __pyx_t_4; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1163 * else: * for i in range(dst_extent): * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< * dst_data, dst_strides + 1, * src_shape + 1, dst_shape + 1, */ _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); /* "View.MemoryView":1167 * src_shape + 1, dst_shape + 1, * ndim - 1, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< * dst_data += dst_stride * */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); /* "View.MemoryView":1168 * ndim - 1, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, */ __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); } } __pyx_L3:; /* "View.MemoryView":1140 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< * char *dst_data, Py_ssize_t *dst_strides, * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, */ /* function exit code */ } /* "View.MemoryView":1170 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: */ static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { /* "View.MemoryView":1173 * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< * src.shape, dst.shape, ndim, itemsize) * */ _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); /* "View.MemoryView":1170 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: */ /* function exit code */ } /* "View.MemoryView":1177 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize */ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { Py_ssize_t __pyx_v_shape; Py_ssize_t __pyx_v_size; Py_ssize_t __pyx_r; Py_ssize_t __pyx_t_1; Py_ssize_t *__pyx_t_2; Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; /* "View.MemoryView":1179 * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< * * for shape in src.shape[:ndim]: */ __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_size = __pyx_t_1; /* "View.MemoryView":1181 * cdef Py_ssize_t shape, size = src.memview.view.itemsize * * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< * size *= shape * */ __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim); for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { __pyx_t_2 = __pyx_t_4; __pyx_v_shape = (__pyx_t_2[0]); /* "View.MemoryView":1182 * * for shape in src.shape[:ndim]: * size *= shape # <<<<<<<<<<<<<< * * return size */ __pyx_v_size = (__pyx_v_size * __pyx_v_shape); } /* "View.MemoryView":1184 * size *= shape * * return size # <<<<<<<<<<<<<< * * @cname('__pyx_fill_contig_strides_array') */ __pyx_r = __pyx_v_size; goto __pyx_L0; /* "View.MemoryView":1177 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1187 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, * int ndim, char order) nogil: */ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { int __pyx_v_idx; Py_ssize_t __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; /* "View.MemoryView":1196 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< * for idx in range(ndim): * strides[idx] = stride */ __pyx_t_1 = ((__pyx_v_order == 'F') != 0); if (__pyx_t_1) { /* "View.MemoryView":1197 * * if order == 'F': * for idx in range(ndim): # <<<<<<<<<<<<<< * strides[idx] = stride * stride *= shape[idx] */ __pyx_t_2 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_idx = __pyx_t_4; /* "View.MemoryView":1198 * if order == 'F': * for idx in range(ndim): * strides[idx] = stride # <<<<<<<<<<<<<< * stride *= shape[idx] * else: */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; /* "View.MemoryView":1199 * for idx in range(ndim): * strides[idx] = stride * stride *= shape[idx] # <<<<<<<<<<<<<< * else: * for idx in range(ndim - 1, -1, -1): */ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } /* "View.MemoryView":1196 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< * for idx in range(ndim): * strides[idx] = stride */ goto __pyx_L3; } /* "View.MemoryView":1201 * stride *= shape[idx] * else: * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * strides[idx] = stride * stride *= shape[idx] */ /*else*/ { for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { __pyx_v_idx = __pyx_t_2; /* "View.MemoryView":1202 * else: * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride # <<<<<<<<<<<<<< * stride *= shape[idx] * */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; /* "View.MemoryView":1203 * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride * stride *= shape[idx] # <<<<<<<<<<<<<< * * return stride */ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } } __pyx_L3:; /* "View.MemoryView":1205 * stride *= shape[idx] * * return stride # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_data_to_temp') */ __pyx_r = __pyx_v_stride; goto __pyx_L0; /* "View.MemoryView":1187 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, * int ndim, char order) nogil: */ /* function exit code */ __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1208 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *tmpslice, * char order, */ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) { int __pyx_v_i; void *__pyx_v_result; size_t __pyx_v_itemsize; size_t __pyx_v_size; void *__pyx_r; Py_ssize_t __pyx_t_1; int __pyx_t_2; int __pyx_t_3; struct __pyx_memoryview_obj *__pyx_t_4; int __pyx_t_5; int __pyx_t_6; /* "View.MemoryView":1219 * cdef void *result * * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< * cdef size_t size = slice_get_size(src, ndim) * */ __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":1220 * * cdef size_t itemsize = src.memview.view.itemsize * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< * * result = malloc(size) */ __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); /* "View.MemoryView":1222 * cdef size_t size = slice_get_size(src, ndim) * * result = malloc(size) # <<<<<<<<<<<<<< * if not result: * _err(MemoryError, NULL) */ __pyx_v_result = malloc(__pyx_v_size); /* "View.MemoryView":1223 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< * _err(MemoryError, NULL) * */ __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1224 * result = malloc(size) * if not result: * _err(MemoryError, NULL) # <<<<<<<<<<<<<< * * */ __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 1224, __pyx_L1_error) /* "View.MemoryView":1223 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< * _err(MemoryError, NULL) * */ } /* "View.MemoryView":1227 * * * tmpslice.data = result # <<<<<<<<<<<<<< * tmpslice.memview = src.memview * for i in range(ndim): */ __pyx_v_tmpslice->data = ((char *)__pyx_v_result); /* "View.MemoryView":1228 * * tmpslice.data = result * tmpslice.memview = src.memview # <<<<<<<<<<<<<< * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] */ __pyx_t_4 = __pyx_v_src->memview; __pyx_v_tmpslice->memview = __pyx_t_4; /* "View.MemoryView":1229 * tmpslice.data = result * tmpslice.memview = src.memview * for i in range(ndim): # <<<<<<<<<<<<<< * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 */ __pyx_t_3 = __pyx_v_ndim; __pyx_t_5 = __pyx_t_3; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1230 * tmpslice.memview = src.memview * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] # <<<<<<<<<<<<<< * tmpslice.suboffsets[i] = -1 * */ (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]); /* "View.MemoryView":1231 * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, */ (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L; } /* "View.MemoryView":1233 * tmpslice.suboffsets[i] = -1 * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, # <<<<<<<<<<<<<< * ndim, order) * */ (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order)); /* "View.MemoryView":1237 * * * for i in range(ndim): # <<<<<<<<<<<<<< * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 */ __pyx_t_3 = __pyx_v_ndim; __pyx_t_5 = __pyx_t_3; for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; /* "View.MemoryView":1238 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< * tmpslice.strides[i] = 0 * */ __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1239 * for i in range(ndim): * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 # <<<<<<<<<<<<<< * * if slice_is_contig(src[0], order, ndim): */ (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0; /* "View.MemoryView":1238 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< * tmpslice.strides[i] = 0 * */ } } /* "View.MemoryView":1241 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< * memcpy(result, src.data, size) * else: */ __pyx_t_2 = (__pyx_memviewslice_is_contig((__pyx_v_src[0]), __pyx_v_order, __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1242 * * if slice_is_contig(src[0], order, ndim): * memcpy(result, src.data, size) # <<<<<<<<<<<<<< * else: * copy_strided_to_strided(src, tmpslice, ndim, itemsize) */ (void)(memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size)); /* "View.MemoryView":1241 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< * memcpy(result, src.data, size) * else: */ goto __pyx_L9; } /* "View.MemoryView":1244 * memcpy(result, src.data, size) * else: * copy_strided_to_strided(src, tmpslice, ndim, itemsize) # <<<<<<<<<<<<<< * * return result */ /*else*/ { copy_strided_to_strided(__pyx_v_src, __pyx_v_tmpslice, __pyx_v_ndim, __pyx_v_itemsize); } __pyx_L9:; /* "View.MemoryView":1246 * copy_strided_to_strided(src, tmpslice, ndim, itemsize) * * return result # <<<<<<<<<<<<<< * * */ __pyx_r = __pyx_v_result; goto __pyx_L0; /* "View.MemoryView":1208 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< * __Pyx_memviewslice *tmpslice, * char order, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.copy_data_to_temp", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = NULL; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1251 * * @cname('__pyx_memoryview_err_extents') * cdef int _err_extents(int i, Py_ssize_t extent1, # <<<<<<<<<<<<<< * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % */ static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent1, Py_ssize_t __pyx_v_extent2) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err_extents", 0); /* "View.MemoryView":1254 * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % * (i, extent1, extent2)) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_err_dim') */ __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_i); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_extent1); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_extent2); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 1254, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_2); __Pyx_GIVEREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_4, 2, __pyx_t_3); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_t_3 = 0; /* "View.MemoryView":1253 * cdef int _err_extents(int i, Py_ssize_t extent1, * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % # <<<<<<<<<<<<<< * (i, extent1, extent2)) * */ __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 1253, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_t_4 = __Pyx_PyObject_CallOneArg(__pyx_builtin_ValueError, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 1253, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_4, 0, 0, 0); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __PYX_ERR(1, 1253, __pyx_L1_error) /* "View.MemoryView":1251 * * @cname('__pyx_memoryview_err_extents') * cdef int _err_extents(int i, Py_ssize_t extent1, # <<<<<<<<<<<<<< * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView._err_extents", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1257 * * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii') % dim) * */ static int __pyx_memoryview_err_dim(PyObject *__pyx_v_error, char *__pyx_v_msg, int __pyx_v_dim) { int __pyx_r; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err_dim", 0); __Pyx_INCREF(__pyx_v_error); /* "View.MemoryView":1258 * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: * raise error(msg.decode('ascii') % dim) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_err') */ __pyx_t_2 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = __Pyx_PyInt_From_int(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyUnicode_Format(__pyx_t_2, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_INCREF(__pyx_v_error); __pyx_t_3 = __pyx_v_error; __pyx_t_2 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_3))) { __pyx_t_2 = PyMethod_GET_SELF(__pyx_t_3); if (likely(__pyx_t_2)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_3); __Pyx_INCREF(__pyx_t_2); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_3, function); } } __pyx_t_1 = (__pyx_t_2) ? __Pyx_PyObject_Call2Args(__pyx_t_3, __pyx_t_2, __pyx_t_4) : __Pyx_PyObject_CallOneArg(__pyx_t_3, __pyx_t_4); __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 1258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_1, 0, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __PYX_ERR(1, 1258, __pyx_L1_error) /* "View.MemoryView":1257 * * @cname('__pyx_memoryview_err_dim') * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii') % dim) * */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_AddTraceback("View.MemoryView._err_dim", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_XDECREF(__pyx_v_error); __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1261 * * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: # <<<<<<<<<<<<<< * if msg != NULL: * raise error(msg.decode('ascii')) */ static int __pyx_memoryview_err(PyObject *__pyx_v_error, char *__pyx_v_msg) { int __pyx_r; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err", 0); __Pyx_INCREF(__pyx_v_error); /* "View.MemoryView":1262 * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii')) * else: */ __pyx_t_1 = ((__pyx_v_msg != NULL) != 0); if (unlikely(__pyx_t_1)) { /* "View.MemoryView":1263 * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: * raise error(msg.decode('ascii')) # <<<<<<<<<<<<<< * else: * raise error */ __pyx_t_3 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 1263, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(__pyx_v_error); __pyx_t_4 = __pyx_v_error; __pyx_t_5 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_4))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_4); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_4); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_4, function); } } __pyx_t_2 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_4, __pyx_t_5, __pyx_t_3) : __Pyx_PyObject_CallOneArg(__pyx_t_4, __pyx_t_3); __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1263, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __PYX_ERR(1, 1263, __pyx_L1_error) /* "View.MemoryView":1262 * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: * if msg != NULL: # <<<<<<<<<<<<<< * raise error(msg.decode('ascii')) * else: */ } /* "View.MemoryView":1265 * raise error(msg.decode('ascii')) * else: * raise error # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_copy_contents') */ /*else*/ { __Pyx_Raise(__pyx_v_error, 0, 0, 0); __PYX_ERR(1, 1265, __pyx_L1_error) } /* "View.MemoryView":1261 * * @cname('__pyx_memoryview_err') * cdef int _err(object error, char *msg) except -1 with gil: # <<<<<<<<<<<<<< * if msg != NULL: * raise error(msg.decode('ascii')) */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView._err", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = -1; __Pyx_XDECREF(__pyx_v_error); __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif return __pyx_r; } /* "View.MemoryView":1268 * * @cname('__pyx_memoryview_copy_contents') * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< * __Pyx_memviewslice dst, * int src_ndim, int dst_ndim, */ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_memviewslice __pyx_v_dst, int __pyx_v_src_ndim, int __pyx_v_dst_ndim, int __pyx_v_dtype_is_object) { void *__pyx_v_tmpdata; size_t __pyx_v_itemsize; int __pyx_v_i; char __pyx_v_order; int __pyx_v_broadcasting; int __pyx_v_direct_copy; __Pyx_memviewslice __pyx_v_tmp; int __pyx_v_ndim; int __pyx_r; Py_ssize_t __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_t_5; int __pyx_t_6; void *__pyx_t_7; int __pyx_t_8; /* "View.MemoryView":1276 * Check for overlapping memory and verify the shapes. * """ * cdef void *tmpdata = NULL # <<<<<<<<<<<<<< * cdef size_t itemsize = src.memview.view.itemsize * cdef int i */ __pyx_v_tmpdata = NULL; /* "View.MemoryView":1277 * """ * cdef void *tmpdata = NULL * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< * cdef int i * cdef char order = get_best_order(&src, src_ndim) */ __pyx_t_1 = __pyx_v_src.memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; /* "View.MemoryView":1279 * cdef size_t itemsize = src.memview.view.itemsize * cdef int i * cdef char order = get_best_order(&src, src_ndim) # <<<<<<<<<<<<<< * cdef bint broadcasting = False * cdef bint direct_copy = False */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_src), __pyx_v_src_ndim); /* "View.MemoryView":1280 * cdef int i * cdef char order = get_best_order(&src, src_ndim) * cdef bint broadcasting = False # <<<<<<<<<<<<<< * cdef bint direct_copy = False * cdef __Pyx_memviewslice tmp */ __pyx_v_broadcasting = 0; /* "View.MemoryView":1281 * cdef char order = get_best_order(&src, src_ndim) * cdef bint broadcasting = False * cdef bint direct_copy = False # <<<<<<<<<<<<<< * cdef __Pyx_memviewslice tmp * */ __pyx_v_direct_copy = 0; /* "View.MemoryView":1284 * cdef __Pyx_memviewslice tmp * * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: */ __pyx_t_2 = ((__pyx_v_src_ndim < __pyx_v_dst_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1285 * * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) # <<<<<<<<<<<<<< * elif dst_ndim < src_ndim: * broadcast_leading(&dst, dst_ndim, src_ndim) */ __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim); /* "View.MemoryView":1284 * cdef __Pyx_memviewslice tmp * * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: */ goto __pyx_L3; } /* "View.MemoryView":1286 * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&dst, dst_ndim, src_ndim) * */ __pyx_t_2 = ((__pyx_v_dst_ndim < __pyx_v_src_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1287 * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: * broadcast_leading(&dst, dst_ndim, src_ndim) # <<<<<<<<<<<<<< * * cdef int ndim = max(src_ndim, dst_ndim) */ __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim); /* "View.MemoryView":1286 * if src_ndim < dst_ndim: * broadcast_leading(&src, src_ndim, dst_ndim) * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< * broadcast_leading(&dst, dst_ndim, src_ndim) * */ } __pyx_L3:; /* "View.MemoryView":1289 * broadcast_leading(&dst, dst_ndim, src_ndim) * * cdef int ndim = max(src_ndim, dst_ndim) # <<<<<<<<<<<<<< * * for i in range(ndim): */ __pyx_t_3 = __pyx_v_dst_ndim; __pyx_t_4 = __pyx_v_src_ndim; if (((__pyx_t_3 > __pyx_t_4) != 0)) { __pyx_t_5 = __pyx_t_3; } else { __pyx_t_5 = __pyx_t_4; } __pyx_v_ndim = __pyx_t_5; /* "View.MemoryView":1291 * cdef int ndim = max(src_ndim, dst_ndim) * * for i in range(ndim): # <<<<<<<<<<<<<< * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: */ __pyx_t_5 = __pyx_v_ndim; __pyx_t_3 = __pyx_t_5; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1292 * * for i in range(ndim): * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< * if src.shape[i] == 1: * broadcasting = True */ __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0); if (__pyx_t_2) { /* "View.MemoryView":1293 * for i in range(ndim): * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: # <<<<<<<<<<<<<< * broadcasting = True * src.strides[i] = 0 */ __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { /* "View.MemoryView":1294 * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: * broadcasting = True # <<<<<<<<<<<<<< * src.strides[i] = 0 * else: */ __pyx_v_broadcasting = 1; /* "View.MemoryView":1295 * if src.shape[i] == 1: * broadcasting = True * src.strides[i] = 0 # <<<<<<<<<<<<<< * else: * _err_extents(i, dst.shape[i], src.shape[i]) */ (__pyx_v_src.strides[__pyx_v_i]) = 0; /* "View.MemoryView":1293 * for i in range(ndim): * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: # <<<<<<<<<<<<<< * broadcasting = True * src.strides[i] = 0 */ goto __pyx_L7; } /* "View.MemoryView":1297 * src.strides[i] = 0 * else: * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<< * * if src.suboffsets[i] >= 0: */ /*else*/ { __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1297, __pyx_L1_error) } __pyx_L7:; /* "View.MemoryView":1292 * * for i in range(ndim): * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< * if src.shape[i] == 1: * broadcasting = True */ } /* "View.MemoryView":1299 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Dimension %d is not direct", i) * */ __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0); if (__pyx_t_2) { /* "View.MemoryView":1300 * * if src.suboffsets[i] >= 0: * _err_dim(ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< * * if slices_overlap(&src, &dst, ndim, itemsize): */ __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1300, __pyx_L1_error) /* "View.MemoryView":1299 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< * _err_dim(ValueError, "Dimension %d is not direct", i) * */ } } /* "View.MemoryView":1302 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< * * if not slice_is_contig(src, order, ndim): */ __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0); if (__pyx_t_2) { /* "View.MemoryView":1304 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< * order = get_best_order(&dst, ndim) * */ __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1305 * * if not slice_is_contig(src, order, ndim): * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); /* "View.MemoryView":1304 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< * order = get_best_order(&dst, ndim) * */ } /* "View.MemoryView":1307 * order = get_best_order(&dst, ndim) * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< * src = tmp * */ __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(1, 1307, __pyx_L1_error) __pyx_v_tmpdata = __pyx_t_7; /* "View.MemoryView":1308 * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) * src = tmp # <<<<<<<<<<<<<< * * if not broadcasting: */ __pyx_v_src = __pyx_v_tmp; /* "View.MemoryView":1302 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< * * if not slice_is_contig(src, order, ndim): */ } /* "View.MemoryView":1310 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< * * */ __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0); if (__pyx_t_2) { /* "View.MemoryView":1313 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): */ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1314 * * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< * elif slice_is_contig(src, 'F', ndim): * direct_copy = slice_is_contig(dst, 'F', ndim) */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); /* "View.MemoryView":1313 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): */ goto __pyx_L12; } /* "View.MemoryView":1315 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'F', ndim) * */ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0); if (__pyx_t_2) { /* "View.MemoryView":1316 * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< * * if direct_copy: */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); /* "View.MemoryView":1315 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< * direct_copy = slice_is_contig(dst, 'F', ndim) * */ } __pyx_L12:; /* "View.MemoryView":1318 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ __pyx_t_2 = (__pyx_v_direct_copy != 0); if (__pyx_t_2) { /* "View.MemoryView":1320 * if direct_copy: * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1321 * * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) */ (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); /* "View.MemoryView":1322 * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * free(tmpdata) * return 0 */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1323 * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) # <<<<<<<<<<<<<< * return 0 * */ free(__pyx_v_tmpdata); /* "View.MemoryView":1324 * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) * return 0 # <<<<<<<<<<<<<< * * if order == 'F' == get_best_order(&dst, ndim): */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":1318 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ } /* "View.MemoryView":1310 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":1326 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< * * */ __pyx_t_2 = (__pyx_v_order == 'F'); if (__pyx_t_2) { __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); } __pyx_t_8 = (__pyx_t_2 != 0); if (__pyx_t_8) { /* "View.MemoryView":1329 * * * transpose_memslice(&src) # <<<<<<<<<<<<<< * transpose_memslice(&dst) * */ __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1329, __pyx_L1_error) /* "View.MemoryView":1330 * * transpose_memslice(&src) * transpose_memslice(&dst) # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1330, __pyx_L1_error) /* "View.MemoryView":1326 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< * * */ } /* "View.MemoryView":1332 * transpose_memslice(&dst) * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1333 * * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< * refcount_copying(&dst, dtype_is_object, ndim, True) * */ copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); /* "View.MemoryView":1334 * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * * free(tmpdata) */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1336 * refcount_copying(&dst, dtype_is_object, ndim, True) * * free(tmpdata) # <<<<<<<<<<<<<< * return 0 * */ free(__pyx_v_tmpdata); /* "View.MemoryView":1337 * * free(tmpdata) * return 0 # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_broadcast_leading') */ __pyx_r = 0; goto __pyx_L0; /* "View.MemoryView":1268 * * @cname('__pyx_memoryview_copy_contents') * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< * __Pyx_memviewslice dst, * int src_ndim, int dst_ndim, */ /* function exit code */ __pyx_L1_error:; { #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } __pyx_r = -1; __pyx_L0:; return __pyx_r; } /* "View.MemoryView":1340 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< * int ndim, * int ndim_other) nogil: */ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) { int __pyx_v_i; int __pyx_v_offset; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; /* "View.MemoryView":1344 * int ndim_other) nogil: * cdef int i * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< * * for i in range(ndim - 1, -1, -1): */ __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); /* "View.MemoryView":1346 * cdef int offset = ndim_other - ndim * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] */ for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; /* "View.MemoryView":1347 * * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] */ (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); /* "View.MemoryView":1348 * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * */ (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); /* "View.MemoryView":1349 * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< * * for i in range(offset): */ (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]); } /* "View.MemoryView":1351 * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * * for i in range(offset): # <<<<<<<<<<<<<< * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] */ __pyx_t_1 = __pyx_v_offset; __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "View.MemoryView":1352 * * for i in range(offset): * mslice.shape[i] = 1 # <<<<<<<<<<<<<< * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 */ (__pyx_v_mslice->shape[__pyx_v_i]) = 1; /* "View.MemoryView":1353 * for i in range(offset): * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] # <<<<<<<<<<<<<< * mslice.suboffsets[i] = -1 * */ (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]); /* "View.MemoryView":1354 * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< * * */ (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L; } /* "View.MemoryView":1340 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< * int ndim, * int ndim_other) nogil: */ /* function exit code */ } /* "View.MemoryView":1362 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< * int ndim, bint inc) nogil: * */ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) { int __pyx_t_1; /* "View.MemoryView":1366 * * * if dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice_with_gil(dst.data, dst.shape, * dst.strides, ndim, inc) */ __pyx_t_1 = (__pyx_v_dtype_is_object != 0); if (__pyx_t_1) { /* "View.MemoryView":1367 * * if dtype_is_object: * refcount_objects_in_slice_with_gil(dst.data, dst.shape, # <<<<<<<<<<<<<< * dst.strides, ndim, inc) * */ __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc); /* "View.MemoryView":1366 * * * if dtype_is_object: # <<<<<<<<<<<<<< * refcount_objects_in_slice_with_gil(dst.data, dst.shape, * dst.strides, ndim, inc) */ } /* "View.MemoryView":1362 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< * int ndim, bint inc) nogil: * */ /* function exit code */ } /* "View.MemoryView":1371 * * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * bint inc) with gil: */ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { __Pyx_RefNannyDeclarations #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("refcount_objects_in_slice_with_gil", 0); /* "View.MemoryView":1374 * Py_ssize_t *strides, int ndim, * bint inc) with gil: * refcount_objects_in_slice(data, shape, strides, ndim, inc) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_refcount_objects_in_slice') */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc); /* "View.MemoryView":1371 * * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * bint inc) with gil: */ /* function exit code */ __Pyx_RefNannyFinishContext(); #ifdef WITH_THREAD __Pyx_PyGILState_Release(__pyx_gilstate_save); #endif } /* "View.MemoryView":1377 * * @cname('__pyx_memoryview_refcount_objects_in_slice') * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, bint inc): * cdef Py_ssize_t i */ static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; int __pyx_t_4; __Pyx_RefNannySetupContext("refcount_objects_in_slice", 0); /* "View.MemoryView":1381 * cdef Py_ssize_t i * * for i in range(shape[0]): # <<<<<<<<<<<<<< * if ndim == 1: * if inc: */ __pyx_t_1 = (__pyx_v_shape[0]); __pyx_t_2 = __pyx_t_1; for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; /* "View.MemoryView":1382 * * for i in range(shape[0]): * if ndim == 1: # <<<<<<<<<<<<<< * if inc: * Py_INCREF(( data)[0]) */ __pyx_t_4 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_4) { /* "View.MemoryView":1383 * for i in range(shape[0]): * if ndim == 1: * if inc: # <<<<<<<<<<<<<< * Py_INCREF(( data)[0]) * else: */ __pyx_t_4 = (__pyx_v_inc != 0); if (__pyx_t_4) { /* "View.MemoryView":1384 * if ndim == 1: * if inc: * Py_INCREF(( data)[0]) # <<<<<<<<<<<<<< * else: * Py_DECREF(( data)[0]) */ Py_INCREF((((PyObject **)__pyx_v_data)[0])); /* "View.MemoryView":1383 * for i in range(shape[0]): * if ndim == 1: * if inc: # <<<<<<<<<<<<<< * Py_INCREF(( data)[0]) * else: */ goto __pyx_L6; } /* "View.MemoryView":1386 * Py_INCREF(( data)[0]) * else: * Py_DECREF(( data)[0]) # <<<<<<<<<<<<<< * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, */ /*else*/ { Py_DECREF((((PyObject **)__pyx_v_data)[0])); } __pyx_L6:; /* "View.MemoryView":1382 * * for i in range(shape[0]): * if ndim == 1: # <<<<<<<<<<<<<< * if inc: * Py_INCREF(( data)[0]) */ goto __pyx_L5; } /* "View.MemoryView":1388 * Py_DECREF(( data)[0]) * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, # <<<<<<<<<<<<<< * ndim - 1, inc) * */ /*else*/ { /* "View.MemoryView":1389 * else: * refcount_objects_in_slice(data, shape + 1, strides + 1, * ndim - 1, inc) # <<<<<<<<<<<<<< * * data += strides[0] */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_inc); } __pyx_L5:; /* "View.MemoryView":1391 * ndim - 1, inc) * * data += strides[0] # <<<<<<<<<<<<<< * * */ __pyx_v_data = (__pyx_v_data + (__pyx_v_strides[0])); } /* "View.MemoryView":1377 * * @cname('__pyx_memoryview_refcount_objects_in_slice') * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, bint inc): * cdef Py_ssize_t i */ /* function exit code */ __Pyx_RefNannyFinishContext(); } /* "View.MemoryView":1397 * * @cname('__pyx_memoryview_slice_assign_scalar') * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, # <<<<<<<<<<<<<< * size_t itemsize, void *item, * bint dtype_is_object) nogil: */ static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item, int __pyx_v_dtype_is_object) { /* "View.MemoryView":1400 * size_t itemsize, void *item, * bint dtype_is_object) nogil: * refcount_copying(dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, * itemsize, item) */ __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 0); /* "View.MemoryView":1401 * bint dtype_is_object) nogil: * refcount_copying(dst, dtype_is_object, ndim, False) * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, # <<<<<<<<<<<<<< * itemsize, item) * refcount_copying(dst, dtype_is_object, ndim, True) */ __pyx_memoryview__slice_assign_scalar(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_itemsize, __pyx_v_item); /* "View.MemoryView":1403 * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, * itemsize, item) * refcount_copying(dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< * * */ __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 1); /* "View.MemoryView":1397 * * @cname('__pyx_memoryview_slice_assign_scalar') * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, # <<<<<<<<<<<<<< * size_t itemsize, void *item, * bint dtype_is_object) nogil: */ /* function exit code */ } /* "View.MemoryView":1407 * * @cname('__pyx_memoryview__slice_assign_scalar') * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * size_t itemsize, void *item) nogil: */ static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item) { CYTHON_UNUSED Py_ssize_t __pyx_v_i; Py_ssize_t __pyx_v_stride; Py_ssize_t __pyx_v_extent; int __pyx_t_1; Py_ssize_t __pyx_t_2; Py_ssize_t __pyx_t_3; Py_ssize_t __pyx_t_4; /* "View.MemoryView":1411 * size_t itemsize, void *item) nogil: * cdef Py_ssize_t i * cdef Py_ssize_t stride = strides[0] # <<<<<<<<<<<<<< * cdef Py_ssize_t extent = shape[0] * */ __pyx_v_stride = (__pyx_v_strides[0]); /* "View.MemoryView":1412 * cdef Py_ssize_t i * cdef Py_ssize_t stride = strides[0] * cdef Py_ssize_t extent = shape[0] # <<<<<<<<<<<<<< * * if ndim == 1: */ __pyx_v_extent = (__pyx_v_shape[0]); /* "View.MemoryView":1414 * cdef Py_ssize_t extent = shape[0] * * if ndim == 1: # <<<<<<<<<<<<<< * for i in range(extent): * memcpy(data, item, itemsize) */ __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_1) { /* "View.MemoryView":1415 * * if ndim == 1: * for i in range(extent): # <<<<<<<<<<<<<< * memcpy(data, item, itemsize) * data += stride */ __pyx_t_2 = __pyx_v_extent; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1416 * if ndim == 1: * for i in range(extent): * memcpy(data, item, itemsize) # <<<<<<<<<<<<<< * data += stride * else: */ (void)(memcpy(__pyx_v_data, __pyx_v_item, __pyx_v_itemsize)); /* "View.MemoryView":1417 * for i in range(extent): * memcpy(data, item, itemsize) * data += stride # <<<<<<<<<<<<<< * else: * for i in range(extent): */ __pyx_v_data = (__pyx_v_data + __pyx_v_stride); } /* "View.MemoryView":1414 * cdef Py_ssize_t extent = shape[0] * * if ndim == 1: # <<<<<<<<<<<<<< * for i in range(extent): * memcpy(data, item, itemsize) */ goto __pyx_L3; } /* "View.MemoryView":1419 * data += stride * else: * for i in range(extent): # <<<<<<<<<<<<<< * _slice_assign_scalar(data, shape + 1, strides + 1, * ndim - 1, itemsize, item) */ /*else*/ { __pyx_t_2 = __pyx_v_extent; __pyx_t_3 = __pyx_t_2; for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; /* "View.MemoryView":1420 * else: * for i in range(extent): * _slice_assign_scalar(data, shape + 1, strides + 1, # <<<<<<<<<<<<<< * ndim - 1, itemsize, item) * data += stride */ __pyx_memoryview__slice_assign_scalar(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize, __pyx_v_item); /* "View.MemoryView":1422 * _slice_assign_scalar(data, shape + 1, strides + 1, * ndim - 1, itemsize, item) * data += stride # <<<<<<<<<<<<<< * * */ __pyx_v_data = (__pyx_v_data + __pyx_v_stride); } } __pyx_L3:; /* "View.MemoryView":1407 * * @cname('__pyx_memoryview__slice_assign_scalar') * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< * Py_ssize_t *strides, int ndim, * size_t itemsize, void *item) nogil: */ /* function exit code */ } /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ /* Python wrapper */ static PyObject *__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyMethodDef __pyx_mdef_15View_dot_MemoryView_1__pyx_unpickle_Enum = {"__pyx_unpickle_Enum", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum, METH_VARARGS|METH_KEYWORDS, 0}; static PyObject *__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v___pyx_type = 0; long __pyx_v___pyx_checksum; PyObject *__pyx_v___pyx_state = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__pyx_unpickle_Enum (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_pyx_type,&__pyx_n_s_pyx_checksum,&__pyx_n_s_pyx_state,0}; PyObject* values[3] = {0,0,0}; if (unlikely(__pyx_kwds)) { Py_ssize_t kw_args; const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); switch (pos_args) { case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); CYTHON_FALLTHROUGH; case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); CYTHON_FALLTHROUGH; case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); CYTHON_FALLTHROUGH; case 0: break; default: goto __pyx_L5_argtuple_error; } kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: if (likely((values[0] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_type)) != 0)) kw_args--; else goto __pyx_L5_argtuple_error; CYTHON_FALLTHROUGH; case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_checksum)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, 1); __PYX_ERR(1, 1, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: if (likely((values[2] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_pyx_state)) != 0)) kw_args--; else { __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, 2); __PYX_ERR(1, 1, __pyx_L3_error) } } if (unlikely(kw_args > 0)) { if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__pyx_unpickle_Enum") < 0)) __PYX_ERR(1, 1, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 3) { goto __pyx_L5_argtuple_error; } else { values[0] = PyTuple_GET_ITEM(__pyx_args, 0); values[1] = PyTuple_GET_ITEM(__pyx_args, 1); values[2] = PyTuple_GET_ITEM(__pyx_args, 2); } __pyx_v___pyx_type = values[0]; __pyx_v___pyx_checksum = __Pyx_PyInt_As_long(values[1]); if (unlikely((__pyx_v___pyx_checksum == (long)-1) && PyErr_Occurred())) __PYX_ERR(1, 1, __pyx_L3_error) __pyx_v___pyx_state = values[2]; } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("__pyx_unpickle_Enum", 1, 3, 3, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(1, 1, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(__pyx_self, __pyx_v___pyx_type, __pyx_v___pyx_checksum, __pyx_v___pyx_state); /* function exit code */ __Pyx_RefNannyFinishContext(); return __pyx_r; } static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_v___pyx_PickleError = 0; PyObject *__pyx_v___pyx_result = 0; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; int __pyx_t_6; __Pyx_RefNannySetupContext("__pyx_unpickle_Enum", 0); /* "(tree fragment)":4 * cdef object __pyx_PickleError * cdef object __pyx_result * if __pyx_checksum != 0xb068931: # <<<<<<<<<<<<<< * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) */ __pyx_t_1 = ((__pyx_v___pyx_checksum != 0xb068931) != 0); if (__pyx_t_1) { /* "(tree fragment)":5 * cdef object __pyx_result * if __pyx_checksum != 0xb068931: * from pickle import PickleError as __pyx_PickleError # <<<<<<<<<<<<<< * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) */ __pyx_t_2 = PyList_New(1); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_n_s_PickleError); __Pyx_GIVEREF(__pyx_n_s_PickleError); PyList_SET_ITEM(__pyx_t_2, 0, __pyx_n_s_PickleError); __pyx_t_3 = __Pyx_Import(__pyx_n_s_pickle, __pyx_t_2, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = __Pyx_ImportFrom(__pyx_t_3, __pyx_n_s_PickleError); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 5, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_t_2); __pyx_v___pyx_PickleError = __pyx_t_2; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "(tree fragment)":6 * if __pyx_checksum != 0xb068931: * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) # <<<<<<<<<<<<<< * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: */ __pyx_t_2 = __Pyx_PyInt_From_long(__pyx_v___pyx_checksum); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_t_2); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_INCREF(__pyx_v___pyx_PickleError); __pyx_t_2 = __pyx_v___pyx_PickleError; __pyx_t_5 = NULL; if (CYTHON_UNPACK_METHODS && unlikely(PyMethod_Check(__pyx_t_2))) { __pyx_t_5 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_5)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_5); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_3 = (__pyx_t_5) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_5, __pyx_t_4) : __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 6, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __PYX_ERR(1, 6, __pyx_L1_error) /* "(tree fragment)":4 * cdef object __pyx_PickleError * cdef object __pyx_result * if __pyx_checksum != 0xb068931: # <<<<<<<<<<<<<< * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) */ } /* "(tree fragment)":7 * from pickle import PickleError as __pyx_PickleError * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) # <<<<<<<<<<<<<< * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) */ __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_MemviewEnum_type), __pyx_n_s_new); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 7, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_4 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_2))) { __pyx_t_4 = PyMethod_GET_SELF(__pyx_t_2); if (likely(__pyx_t_4)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_2); __Pyx_INCREF(__pyx_t_4); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_2, function); } } __pyx_t_3 = (__pyx_t_4) ? __Pyx_PyObject_Call2Args(__pyx_t_2, __pyx_t_4, __pyx_v___pyx_type) : __Pyx_PyObject_CallOneArg(__pyx_t_2, __pyx_v___pyx_type); __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0; if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 7, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_v___pyx_result = __pyx_t_3; __pyx_t_3 = 0; /* "(tree fragment)":8 * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result */ __pyx_t_1 = (__pyx_v___pyx_state != Py_None); __pyx_t_6 = (__pyx_t_1 != 0); if (__pyx_t_6) { /* "(tree fragment)":9 * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) # <<<<<<<<<<<<<< * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): */ if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(1, 9, __pyx_L1_error) __pyx_t_3 = __pyx_unpickle_Enum__set_state(((struct __pyx_MemviewEnum_obj *)__pyx_v___pyx_result), ((PyObject*)__pyx_v___pyx_state)); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 9, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "(tree fragment)":8 * raise __pyx_PickleError("Incompatible checksums (%s vs 0xb068931 = (name))" % __pyx_checksum) * __pyx_result = Enum.__new__(__pyx_type) * if __pyx_state is not None: # <<<<<<<<<<<<<< * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result */ } /* "(tree fragment)":10 * if __pyx_state is not None: * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result # <<<<<<<<<<<<<< * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] */ __Pyx_XDECREF(__pyx_r); __Pyx_INCREF(__pyx_v___pyx_result); __pyx_r = __pyx_v___pyx_result; goto __pyx_L0; /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ /* function exit code */ __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; __Pyx_XDECREF(__pyx_v___pyx_PickleError); __Pyx_XDECREF(__pyx_v___pyx_result); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *__pyx_v___pyx_result, PyObject *__pyx_v___pyx_state) { PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; Py_ssize_t __pyx_t_3; int __pyx_t_4; int __pyx_t_5; PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; __Pyx_RefNannySetupContext("__pyx_unpickle_Enum__set_state", 0); /* "(tree fragment)":12 * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] # <<<<<<<<<<<<<< * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): * __pyx_result.__dict__.update(__pyx_state[1]) */ if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(1, 12, __pyx_L1_error) } __pyx_t_1 = __Pyx_GetItemInt_Tuple(__pyx_v___pyx_state, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 12, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __Pyx_GOTREF(__pyx_v___pyx_result->name); __Pyx_DECREF(__pyx_v___pyx_result->name); __pyx_v___pyx_result->name = __pyx_t_1; __pyx_t_1 = 0; /* "(tree fragment)":13 * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): # <<<<<<<<<<<<<< * __pyx_result.__dict__.update(__pyx_state[1]) */ if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); __PYX_ERR(1, 13, __pyx_L1_error) } __pyx_t_3 = PyTuple_GET_SIZE(__pyx_v___pyx_state); if (unlikely(__pyx_t_3 == ((Py_ssize_t)-1))) __PYX_ERR(1, 13, __pyx_L1_error) __pyx_t_4 = ((__pyx_t_3 > 1) != 0); if (__pyx_t_4) { } else { __pyx_t_2 = __pyx_t_4; goto __pyx_L4_bool_binop_done; } __pyx_t_4 = __Pyx_HasAttr(((PyObject *)__pyx_v___pyx_result), __pyx_n_s_dict); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 13, __pyx_L1_error) __pyx_t_5 = (__pyx_t_4 != 0); __pyx_t_2 = __pyx_t_5; __pyx_L4_bool_binop_done:; if (__pyx_t_2) { /* "(tree fragment)":14 * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): * __pyx_result.__dict__.update(__pyx_state[1]) # <<<<<<<<<<<<<< */ __pyx_t_6 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v___pyx_result), __pyx_n_s_dict); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_7 = __Pyx_PyObject_GetAttrStr(__pyx_t_6, __pyx_n_s_update); if (unlikely(!__pyx_t_7)) __PYX_ERR(1, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(__pyx_v___pyx_state == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable"); __PYX_ERR(1, 14, __pyx_L1_error) } __pyx_t_6 = __Pyx_GetItemInt_Tuple(__pyx_v___pyx_state, 1, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); __pyx_t_8 = NULL; if (CYTHON_UNPACK_METHODS && likely(PyMethod_Check(__pyx_t_7))) { __pyx_t_8 = PyMethod_GET_SELF(__pyx_t_7); if (likely(__pyx_t_8)) { PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_7); __Pyx_INCREF(__pyx_t_8); __Pyx_INCREF(function); __Pyx_DECREF_SET(__pyx_t_7, function); } } __pyx_t_1 = (__pyx_t_8) ? __Pyx_PyObject_Call2Args(__pyx_t_7, __pyx_t_8, __pyx_t_6) : __Pyx_PyObject_CallOneArg(__pyx_t_7, __pyx_t_6); __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 14, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":13 * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): # <<<<<<<<<<<<<< * __pyx_result.__dict__.update(__pyx_state[1]) */ } /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ /* function exit code */ __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_6); __Pyx_XDECREF(__pyx_t_7); __Pyx_XDECREF(__pyx_t_8); __Pyx_AddTraceback("View.MemoryView.__pyx_unpickle_Enum__set_state", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = 0; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } static struct __pyx_vtabstruct_8openTSNE_9quad_tree_QuadTree __pyx_vtable_8openTSNE_9quad_tree_QuadTree; static PyObject *__pyx_tp_new_8openTSNE_9quad_tree_QuadTree(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *)o); p->__pyx_vtab = __pyx_vtabptr_8openTSNE_9quad_tree_QuadTree; return o; } static void __pyx_tp_dealloc_8openTSNE_9quad_tree_QuadTree(PyObject *o) { #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_pw_8openTSNE_9quad_tree_8QuadTree_7__dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } (*Py_TYPE(o)->tp_free)(o); } static PyMethodDef __pyx_methods_8openTSNE_9quad_tree_QuadTree[] = { {"add_points", (PyCFunction)__pyx_pw_8openTSNE_9quad_tree_8QuadTree_3add_points, METH_O, 0}, {"add_point", (PyCFunction)__pyx_pw_8openTSNE_9quad_tree_8QuadTree_5add_point, METH_O, 0}, {"__reduce_cython__", (PyCFunction)__pyx_pw_8openTSNE_9quad_tree_8QuadTree_9__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw_8openTSNE_9quad_tree_8QuadTree_11__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type_8openTSNE_9quad_tree_QuadTree = { PyVarObject_HEAD_INIT(0, 0) "openTSNE.quad_tree.QuadTree", /*tp_name*/ sizeof(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_8openTSNE_9quad_tree_QuadTree, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ 0, /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_8openTSNE_9quad_tree_QuadTree, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ __pyx_pw_8openTSNE_9quad_tree_8QuadTree_1__init__, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_8openTSNE_9quad_tree_QuadTree, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static struct __pyx_vtabstruct_array __pyx_vtable_array; static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_array_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_array_obj *)o); p->__pyx_vtab = __pyx_vtabptr_array; p->mode = ((PyObject*)Py_None); Py_INCREF(Py_None); p->_format = ((PyObject*)Py_None); Py_INCREF(Py_None); if (unlikely(__pyx_array___cinit__(o, a, k) < 0)) goto bad; return o; bad: Py_DECREF(o); o = 0; return NULL; } static void __pyx_tp_dealloc_array(PyObject *o) { struct __pyx_array_obj *p = (struct __pyx_array_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_array___dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->mode); Py_CLEAR(p->_format); (*Py_TYPE(o)->tp_free)(o); } static PyObject *__pyx_sq_item_array(PyObject *o, Py_ssize_t i) { PyObject *r; PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); Py_DECREF(x); return r; } static int __pyx_mp_ass_subscript_array(PyObject *o, PyObject *i, PyObject *v) { if (v) { return __pyx_array___setitem__(o, i, v); } else { PyErr_Format(PyExc_NotImplementedError, "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); return -1; } } static PyObject *__pyx_tp_getattro_array(PyObject *o, PyObject *n) { PyObject *v = __Pyx_PyObject_GenericGetAttr(o, n); if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Clear(); v = __pyx_array___getattr__(o, n); } return v; } static PyObject *__pyx_getprop___pyx_array_memview(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(o); } static PyMethodDef __pyx_methods_array[] = { {"__getattr__", (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, 0}, {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_array_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_array_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_array[] = { {(char *)"memview", __pyx_getprop___pyx_array_memview, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PySequenceMethods __pyx_tp_as_sequence_array = { __pyx_array___len__, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ __pyx_sq_item_array, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_array = { __pyx_array___len__, /*mp_length*/ __pyx_array___getitem__, /*mp_subscript*/ __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_array = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif __pyx_array_getbuffer, /*bf_getbuffer*/ 0, /*bf_releasebuffer*/ }; static PyTypeObject __pyx_type___pyx_array = { PyVarObject_HEAD_INIT(0, 0) "openTSNE.quad_tree.array", /*tp_name*/ sizeof(struct __pyx_array_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_array, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ &__pyx_tp_as_sequence_array, /*tp_as_sequence*/ &__pyx_tp_as_mapping_array, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ __pyx_tp_getattro_array, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_array, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ 0, /*tp_doc*/ 0, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_array, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_array, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_array, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { struct __pyx_MemviewEnum_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_MemviewEnum_obj *)o); p->name = Py_None; Py_INCREF(Py_None); return o; } static void __pyx_tp_dealloc_Enum(PyObject *o) { struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); Py_CLEAR(p->name); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { int e; struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; if (p->name) { e = (*v)(p->name, a); if (e) return e; } return 0; } static int __pyx_tp_clear_Enum(PyObject *o) { PyObject* tmp; struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; tmp = ((PyObject*)p->name); p->name = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); return 0; } static PyMethodDef __pyx_methods_Enum[] = { {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_type___pyx_MemviewEnum = { PyVarObject_HEAD_INIT(0, 0) "openTSNE.quad_tree.Enum", /*tp_name*/ sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_Enum, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif __pyx_MemviewEnum___repr__, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_Enum, /*tp_traverse*/ __pyx_tp_clear_Enum, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_Enum, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ __pyx_MemviewEnum___init__, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_Enum, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_memoryview_obj *p; PyObject *o; if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { o = (*t->tp_alloc)(t, 0); } else { o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); } if (unlikely(!o)) return 0; p = ((struct __pyx_memoryview_obj *)o); p->__pyx_vtab = __pyx_vtabptr_memoryview; p->obj = Py_None; Py_INCREF(Py_None); p->_size = Py_None; Py_INCREF(Py_None); p->_array_interface = Py_None; Py_INCREF(Py_None); p->view.obj = NULL; if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; return o; bad: Py_DECREF(o); o = 0; return NULL; } static void __pyx_tp_dealloc_memoryview(PyObject *o) { struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_memoryview___dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->obj); Py_CLEAR(p->_size); Py_CLEAR(p->_array_interface); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { int e; struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; if (p->obj) { e = (*v)(p->obj, a); if (e) return e; } if (p->_size) { e = (*v)(p->_size, a); if (e) return e; } if (p->_array_interface) { e = (*v)(p->_array_interface, a); if (e) return e; } if (p->view.obj) { e = (*v)(p->view.obj, a); if (e) return e; } return 0; } static int __pyx_tp_clear_memoryview(PyObject *o) { PyObject* tmp; struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; tmp = ((PyObject*)p->obj); p->obj = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->_size); p->_size = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->_array_interface); p->_array_interface = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); Py_CLEAR(p->view.obj); return 0; } static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { PyObject *r; PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); Py_DECREF(x); return r; } static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { if (v) { return __pyx_memoryview___setitem__(o, i, v); } else { PyErr_Format(PyExc_NotImplementedError, "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); return -1; } } static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); } static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); } static PyMethodDef __pyx_methods_memoryview[] = { {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0}, {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0}, {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0}, {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0}, {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_memoryview[] = { {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PySequenceMethods __pyx_tp_as_sequence_memoryview = { __pyx_memoryview___len__, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ __pyx_sq_item_memoryview, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_memoryview = { __pyx_memoryview___len__, /*mp_length*/ __pyx_memoryview___getitem__, /*mp_subscript*/ __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_memoryview = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif __pyx_memoryview_getbuffer, /*bf_getbuffer*/ 0, /*bf_releasebuffer*/ }; static PyTypeObject __pyx_type___pyx_memoryview = { PyVarObject_HEAD_INIT(0, 0) "openTSNE.quad_tree.memoryview", /*tp_name*/ sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif __pyx_memoryview___repr__, /*tp_repr*/ 0, /*tp_as_number*/ &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ __pyx_memoryview___str__, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_memoryview, /*tp_traverse*/ __pyx_tp_clear_memoryview, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_memoryview, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_memoryview, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_memoryview, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_memoryviewslice_obj *p; PyObject *o = __pyx_tp_new_memoryview(t, a, k); if (unlikely(!o)) return 0; p = ((struct __pyx_memoryviewslice_obj *)o); p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; p->from_object = Py_None; Py_INCREF(Py_None); p->from_slice.memview = NULL; return o; } static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; #if CYTHON_USE_TP_FINALIZE if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { if (PyObject_CallFinalizerFromDealloc(o)) return; } #endif PyObject_GC_UnTrack(o); { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); ++Py_REFCNT(o); __pyx_memoryviewslice___dealloc__(o); --Py_REFCNT(o); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->from_object); PyObject_GC_Track(o); __pyx_tp_dealloc_memoryview(o); } static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { int e; struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; if (p->from_object) { e = (*v)(p->from_object, a); if (e) return e; } return 0; } static int __pyx_tp_clear__memoryviewslice(PyObject *o) { PyObject* tmp; struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; __pyx_tp_clear_memoryview(o); tmp = ((PyObject*)p->from_object); p->from_object = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); __PYX_XDEC_MEMVIEW(&p->from_slice, 1); return 0; } static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) { return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o); } static PyMethodDef __pyx_methods__memoryviewslice[] = { {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0}, {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = { {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0}, {0, 0, 0, 0, 0} }; static PyTypeObject __pyx_type___pyx_memoryviewslice = { PyVarObject_HEAD_INIT(0, 0) "openTSNE.quad_tree._memoryviewslice", /*tp_name*/ sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ #if PY_VERSION_HEX < 0x030800b4 0, /*tp_print*/ #endif #if PY_VERSION_HEX >= 0x030800b4 0, /*tp_vectorcall_offset*/ #endif 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #endif #if PY_MAJOR_VERSION >= 3 0, /*tp_as_async*/ #endif #if CYTHON_COMPILING_IN_PYPY __pyx_memoryview___repr__, /*tp_repr*/ #else 0, /*tp_repr*/ #endif 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ #if CYTHON_COMPILING_IN_PYPY __pyx_memoryview___str__, /*tp_str*/ #else 0, /*tp_str*/ #endif 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ "Internal class for passing memoryview slices to Python", /*tp_doc*/ __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ __pyx_tp_clear__memoryviewslice, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods__memoryviewslice, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets__memoryviewslice, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new__memoryviewslice, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ 0, /*tp_version_tag*/ #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif #if PY_VERSION_HEX >= 0x030800b1 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif }; static PyMethodDef __pyx_methods[] = { {0, 0, 0, 0} }; #if PY_MAJOR_VERSION >= 3 #if CYTHON_PEP489_MULTI_PHASE_INIT static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/ static int __pyx_pymod_exec_quad_tree(PyObject* module); /*proto*/ static PyModuleDef_Slot __pyx_moduledef_slots[] = { {Py_mod_create, (void*)__pyx_pymod_create}, {Py_mod_exec, (void*)__pyx_pymod_exec_quad_tree}, {0, NULL} }; #endif static struct PyModuleDef __pyx_moduledef = { PyModuleDef_HEAD_INIT, "quad_tree", __pyx_k_Implements_a_quad_oct_tree_space, /* m_doc */ #if CYTHON_PEP489_MULTI_PHASE_INIT 0, /* m_size */ #else -1, /* m_size */ #endif __pyx_methods /* m_methods */, #if CYTHON_PEP489_MULTI_PHASE_INIT __pyx_moduledef_slots, /* m_slots */ #else NULL, /* m_reload */ #endif NULL, /* m_traverse */ NULL, /* m_clear */ NULL /* m_free */ }; #endif #ifndef CYTHON_SMALL_CODE #if defined(__clang__) #define CYTHON_SMALL_CODE #elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3)) #define CYTHON_SMALL_CODE __attribute__((cold)) #else #define CYTHON_SMALL_CODE #endif #endif static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_n_s_ASCII, __pyx_k_ASCII, sizeof(__pyx_k_ASCII), 0, 0, 1, 1}, {&__pyx_kp_s_Buffer_view_does_not_expose_stri, __pyx_k_Buffer_view_does_not_expose_stri, sizeof(__pyx_k_Buffer_view_does_not_expose_stri), 0, 0, 1, 0}, {&__pyx_kp_s_Can_only_create_a_buffer_that_is, __pyx_k_Can_only_create_a_buffer_that_is, sizeof(__pyx_k_Can_only_create_a_buffer_that_is), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_assign_to_read_only_memor, __pyx_k_Cannot_assign_to_read_only_memor, sizeof(__pyx_k_Cannot_assign_to_read_only_memor), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_create_writable_memory_vi, __pyx_k_Cannot_create_writable_memory_vi, sizeof(__pyx_k_Cannot_create_writable_memory_vi), 0, 0, 1, 0}, {&__pyx_kp_s_Cannot_index_with_type_s, __pyx_k_Cannot_index_with_type_s, sizeof(__pyx_k_Cannot_index_with_type_s), 0, 0, 1, 0}, {&__pyx_n_s_EPSILON, __pyx_k_EPSILON, sizeof(__pyx_k_EPSILON), 0, 0, 1, 1}, {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1}, {&__pyx_kp_s_Empty_shape_tuple_for_cython_arr, __pyx_k_Empty_shape_tuple_for_cython_arr, sizeof(__pyx_k_Empty_shape_tuple_for_cython_arr), 0, 0, 1, 0}, {&__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_k_Incompatible_checksums_s_vs_0xb0, sizeof(__pyx_k_Incompatible_checksums_s_vs_0xb0), 0, 0, 1, 0}, {&__pyx_n_s_IndexError, __pyx_k_IndexError, sizeof(__pyx_k_IndexError), 0, 0, 1, 1}, {&__pyx_kp_s_Indirect_dimensions_not_supporte, __pyx_k_Indirect_dimensions_not_supporte, sizeof(__pyx_k_Indirect_dimensions_not_supporte), 0, 0, 1, 0}, {&__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_k_Invalid_mode_expected_c_or_fortr, sizeof(__pyx_k_Invalid_mode_expected_c_or_fortr), 0, 0, 1, 0}, {&__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_k_Invalid_shape_in_axis_d_d, sizeof(__pyx_k_Invalid_shape_in_axis_d_d), 0, 0, 1, 0}, {&__pyx_n_s_MemoryError, __pyx_k_MemoryError, sizeof(__pyx_k_MemoryError), 0, 0, 1, 1}, {&__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_k_MemoryView_of_r_at_0x_x, sizeof(__pyx_k_MemoryView_of_r_at_0x_x), 0, 0, 1, 0}, {&__pyx_kp_s_MemoryView_of_r_object, __pyx_k_MemoryView_of_r_object, sizeof(__pyx_k_MemoryView_of_r_object), 0, 0, 1, 0}, {&__pyx_n_b_O, __pyx_k_O, sizeof(__pyx_k_O), 0, 0, 0, 1}, {&__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_k_Out_of_bounds_on_buffer_access_a, sizeof(__pyx_k_Out_of_bounds_on_buffer_access_a), 0, 0, 1, 0}, {&__pyx_n_s_PickleError, __pyx_k_PickleError, sizeof(__pyx_k_PickleError), 0, 0, 1, 1}, {&__pyx_n_s_QuadTree, __pyx_k_QuadTree, sizeof(__pyx_k_QuadTree), 0, 0, 1, 1}, {&__pyx_n_s_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 0, 0, 1, 1}, {&__pyx_kp_s_Unable_to_convert_item_to_object, __pyx_k_Unable_to_convert_item_to_object, sizeof(__pyx_k_Unable_to_convert_item_to_object), 0, 0, 1, 0}, {&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1}, {&__pyx_n_s_View_MemoryView, __pyx_k_View_MemoryView, sizeof(__pyx_k_View_MemoryView), 0, 0, 1, 1}, {&__pyx_n_s_add_point, __pyx_k_add_point, sizeof(__pyx_k_add_point), 0, 0, 1, 1}, {&__pyx_n_s_add_points, __pyx_k_add_points, sizeof(__pyx_k_add_points), 0, 0, 1, 1}, {&__pyx_n_s_allocate_buffer, __pyx_k_allocate_buffer, sizeof(__pyx_k_allocate_buffer), 0, 0, 1, 1}, {&__pyx_n_s_axis, __pyx_k_axis, sizeof(__pyx_k_axis), 0, 0, 1, 1}, {&__pyx_n_s_base, __pyx_k_base, sizeof(__pyx_k_base), 0, 0, 1, 1}, {&__pyx_n_s_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 0, 1, 1}, {&__pyx_n_u_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 1, 0, 1}, {&__pyx_n_s_class, __pyx_k_class, sizeof(__pyx_k_class), 0, 0, 1, 1}, {&__pyx_n_s_cline_in_traceback, __pyx_k_cline_in_traceback, sizeof(__pyx_k_cline_in_traceback), 0, 0, 1, 1}, {&__pyx_kp_s_contiguous_and_direct, __pyx_k_contiguous_and_direct, sizeof(__pyx_k_contiguous_and_direct), 0, 0, 1, 0}, {&__pyx_kp_s_contiguous_and_indirect, __pyx_k_contiguous_and_indirect, sizeof(__pyx_k_contiguous_and_indirect), 0, 0, 1, 0}, {&__pyx_n_s_data, __pyx_k_data, sizeof(__pyx_k_data), 0, 0, 1, 1}, {&__pyx_n_s_dict, __pyx_k_dict, sizeof(__pyx_k_dict), 0, 0, 1, 1}, {&__pyx_n_s_dtype_is_object, __pyx_k_dtype_is_object, sizeof(__pyx_k_dtype_is_object), 0, 0, 1, 1}, {&__pyx_n_s_encode, __pyx_k_encode, sizeof(__pyx_k_encode), 0, 0, 1, 1}, {&__pyx_n_s_enumerate, __pyx_k_enumerate, sizeof(__pyx_k_enumerate), 0, 0, 1, 1}, {&__pyx_n_s_error, __pyx_k_error, sizeof(__pyx_k_error), 0, 0, 1, 1}, {&__pyx_n_s_flags, __pyx_k_flags, sizeof(__pyx_k_flags), 0, 0, 1, 1}, {&__pyx_n_s_format, __pyx_k_format, sizeof(__pyx_k_format), 0, 0, 1, 1}, {&__pyx_n_s_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 0, 1, 1}, {&__pyx_n_u_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 1, 0, 1}, {&__pyx_n_s_getstate, __pyx_k_getstate, sizeof(__pyx_k_getstate), 0, 0, 1, 1}, {&__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_k_got_differing_extents_in_dimensi, sizeof(__pyx_k_got_differing_extents_in_dimensi), 0, 0, 1, 0}, {&__pyx_n_s_id, __pyx_k_id, sizeof(__pyx_k_id), 0, 0, 1, 1}, {&__pyx_n_s_import, __pyx_k_import, sizeof(__pyx_k_import), 0, 0, 1, 1}, {&__pyx_n_s_itemsize, __pyx_k_itemsize, sizeof(__pyx_k_itemsize), 0, 0, 1, 1}, {&__pyx_kp_s_itemsize_0_for_cython_array, __pyx_k_itemsize_0_for_cython_array, sizeof(__pyx_k_itemsize_0_for_cython_array), 0, 0, 1, 0}, {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1}, {&__pyx_n_s_max, __pyx_k_max, sizeof(__pyx_k_max), 0, 0, 1, 1}, {&__pyx_n_s_memview, __pyx_k_memview, sizeof(__pyx_k_memview), 0, 0, 1, 1}, {&__pyx_n_s_min, __pyx_k_min, sizeof(__pyx_k_min), 0, 0, 1, 1}, {&__pyx_n_s_mode, __pyx_k_mode, sizeof(__pyx_k_mode), 0, 0, 1, 1}, {&__pyx_n_s_name, __pyx_k_name, sizeof(__pyx_k_name), 0, 0, 1, 1}, {&__pyx_n_s_name_2, __pyx_k_name_2, sizeof(__pyx_k_name_2), 0, 0, 1, 1}, {&__pyx_n_s_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 0, 0, 1, 1}, {&__pyx_n_s_new, __pyx_k_new, sizeof(__pyx_k_new), 0, 0, 1, 1}, {&__pyx_kp_s_no_default___reduce___due_to_non, __pyx_k_no_default___reduce___due_to_non, sizeof(__pyx_k_no_default___reduce___due_to_non), 0, 0, 1, 0}, {&__pyx_n_s_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 0, 1, 1}, {&__pyx_n_s_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 0, 0, 1, 1}, {&__pyx_n_s_obj, __pyx_k_obj, sizeof(__pyx_k_obj), 0, 0, 1, 1}, {&__pyx_n_s_pack, __pyx_k_pack, sizeof(__pyx_k_pack), 0, 0, 1, 1}, {&__pyx_n_s_pickle, __pyx_k_pickle, sizeof(__pyx_k_pickle), 0, 0, 1, 1}, {&__pyx_n_s_pyx_PickleError, __pyx_k_pyx_PickleError, sizeof(__pyx_k_pyx_PickleError), 0, 0, 1, 1}, {&__pyx_n_s_pyx_capi, __pyx_k_pyx_capi, sizeof(__pyx_k_pyx_capi), 0, 0, 1, 1}, {&__pyx_n_s_pyx_checksum, __pyx_k_pyx_checksum, sizeof(__pyx_k_pyx_checksum), 0, 0, 1, 1}, {&__pyx_n_s_pyx_getbuffer, __pyx_k_pyx_getbuffer, sizeof(__pyx_k_pyx_getbuffer), 0, 0, 1, 1}, {&__pyx_n_s_pyx_result, __pyx_k_pyx_result, sizeof(__pyx_k_pyx_result), 0, 0, 1, 1}, {&__pyx_n_s_pyx_state, __pyx_k_pyx_state, sizeof(__pyx_k_pyx_state), 0, 0, 1, 1}, {&__pyx_n_s_pyx_type, __pyx_k_pyx_type, sizeof(__pyx_k_pyx_type), 0, 0, 1, 1}, {&__pyx_n_s_pyx_unpickle_Enum, __pyx_k_pyx_unpickle_Enum, sizeof(__pyx_k_pyx_unpickle_Enum), 0, 0, 1, 1}, {&__pyx_n_s_pyx_vtable, __pyx_k_pyx_vtable, sizeof(__pyx_k_pyx_vtable), 0, 0, 1, 1}, {&__pyx_n_s_range, __pyx_k_range, sizeof(__pyx_k_range), 0, 0, 1, 1}, {&__pyx_n_s_reduce, __pyx_k_reduce, sizeof(__pyx_k_reduce), 0, 0, 1, 1}, {&__pyx_n_s_reduce_cython, __pyx_k_reduce_cython, sizeof(__pyx_k_reduce_cython), 0, 0, 1, 1}, {&__pyx_n_s_reduce_ex, __pyx_k_reduce_ex, sizeof(__pyx_k_reduce_ex), 0, 0, 1, 1}, {&__pyx_kp_s_self_root_cannot_be_converted_to, __pyx_k_self_root_cannot_be_converted_to, sizeof(__pyx_k_self_root_cannot_be_converted_to), 0, 0, 1, 0}, {&__pyx_n_s_setstate, __pyx_k_setstate, sizeof(__pyx_k_setstate), 0, 0, 1, 1}, {&__pyx_n_s_setstate_cython, __pyx_k_setstate_cython, sizeof(__pyx_k_setstate_cython), 0, 0, 1, 1}, {&__pyx_n_s_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 0, 0, 1, 1}, {&__pyx_n_s_size, __pyx_k_size, sizeof(__pyx_k_size), 0, 0, 1, 1}, {&__pyx_n_s_start, __pyx_k_start, sizeof(__pyx_k_start), 0, 0, 1, 1}, {&__pyx_n_s_step, __pyx_k_step, sizeof(__pyx_k_step), 0, 0, 1, 1}, {&__pyx_n_s_stop, __pyx_k_stop, sizeof(__pyx_k_stop), 0, 0, 1, 1}, {&__pyx_kp_s_strided_and_direct, __pyx_k_strided_and_direct, sizeof(__pyx_k_strided_and_direct), 0, 0, 1, 0}, {&__pyx_kp_s_strided_and_direct_or_indirect, __pyx_k_strided_and_direct_or_indirect, sizeof(__pyx_k_strided_and_direct_or_indirect), 0, 0, 1, 0}, {&__pyx_kp_s_strided_and_indirect, __pyx_k_strided_and_indirect, sizeof(__pyx_k_strided_and_indirect), 0, 0, 1, 0}, {&__pyx_kp_s_stringsource, __pyx_k_stringsource, sizeof(__pyx_k_stringsource), 0, 0, 1, 0}, {&__pyx_n_s_struct, __pyx_k_struct, sizeof(__pyx_k_struct), 0, 0, 1, 1}, {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1}, {&__pyx_kp_s_unable_to_allocate_array_data, __pyx_k_unable_to_allocate_array_data, sizeof(__pyx_k_unable_to_allocate_array_data), 0, 0, 1, 0}, {&__pyx_kp_s_unable_to_allocate_shape_and_str, __pyx_k_unable_to_allocate_shape_and_str, sizeof(__pyx_k_unable_to_allocate_shape_and_str), 0, 0, 1, 0}, {&__pyx_n_s_unpack, __pyx_k_unpack, sizeof(__pyx_k_unpack), 0, 0, 1, 1}, {&__pyx_n_s_update, __pyx_k_update, sizeof(__pyx_k_update), 0, 0, 1, 1}, {&__pyx_n_s_zeros, __pyx_k_zeros, sizeof(__pyx_k_zeros), 0, 0, 1, 1}, {0, 0, 0, 0, 0, 0, 0} }; static CYTHON_SMALL_CODE int __Pyx_InitCachedBuiltins(void) { __pyx_builtin_MemoryError = __Pyx_GetBuiltinName(__pyx_n_s_MemoryError); if (!__pyx_builtin_MemoryError) __PYX_ERR(0, 49, __pyx_L1_error) __pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) __PYX_ERR(0, 52, __pyx_L1_error) __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s_TypeError); if (!__pyx_builtin_TypeError) __PYX_ERR(1, 2, __pyx_L1_error) __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) __PYX_ERR(1, 133, __pyx_L1_error) __pyx_builtin_enumerate = __Pyx_GetBuiltinName(__pyx_n_s_enumerate); if (!__pyx_builtin_enumerate) __PYX_ERR(1, 151, __pyx_L1_error) __pyx_builtin_Ellipsis = __Pyx_GetBuiltinName(__pyx_n_s_Ellipsis); if (!__pyx_builtin_Ellipsis) __PYX_ERR(1, 404, __pyx_L1_error) __pyx_builtin_id = __Pyx_GetBuiltinName(__pyx_n_s_id); if (!__pyx_builtin_id) __PYX_ERR(1, 613, __pyx_L1_error) __pyx_builtin_IndexError = __Pyx_GetBuiltinName(__pyx_n_s_IndexError); if (!__pyx_builtin_IndexError) __PYX_ERR(1, 832, __pyx_L1_error) return 0; __pyx_L1_error:; return -1; } static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("self.root cannot be converted to a Python object for pickling") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("self.root cannot be converted to a Python object for pickling") */ __pyx_tuple_ = PyTuple_Pack(1, __pyx_kp_s_self_root_cannot_be_converted_to); if (unlikely(!__pyx_tuple_)) __PYX_ERR(1, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple_); __Pyx_GIVEREF(__pyx_tuple_); /* "(tree fragment)":4 * raise TypeError("self.root cannot be converted to a Python object for pickling") * def __setstate_cython__(self, __pyx_state): * raise TypeError("self.root cannot be converted to a Python object for pickling") # <<<<<<<<<<<<<< */ __pyx_tuple__2 = PyTuple_Pack(1, __pyx_kp_s_self_root_cannot_be_converted_to); if (unlikely(!__pyx_tuple__2)) __PYX_ERR(1, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__2); __Pyx_GIVEREF(__pyx_tuple__2); /* "View.MemoryView":133 * * if not self.ndim: * raise ValueError("Empty shape tuple for cython.array") # <<<<<<<<<<<<<< * * if itemsize <= 0: */ __pyx_tuple__3 = PyTuple_Pack(1, __pyx_kp_s_Empty_shape_tuple_for_cython_arr); if (unlikely(!__pyx_tuple__3)) __PYX_ERR(1, 133, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__3); __Pyx_GIVEREF(__pyx_tuple__3); /* "View.MemoryView":136 * * if itemsize <= 0: * raise ValueError("itemsize <= 0 for cython.array") # <<<<<<<<<<<<<< * * if not isinstance(format, bytes): */ __pyx_tuple__4 = PyTuple_Pack(1, __pyx_kp_s_itemsize_0_for_cython_array); if (unlikely(!__pyx_tuple__4)) __PYX_ERR(1, 136, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__4); __Pyx_GIVEREF(__pyx_tuple__4); /* "View.MemoryView":148 * * if not self._shape: * raise MemoryError("unable to allocate shape and strides.") # <<<<<<<<<<<<<< * * */ __pyx_tuple__5 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_shape_and_str); if (unlikely(!__pyx_tuple__5)) __PYX_ERR(1, 148, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__5); __Pyx_GIVEREF(__pyx_tuple__5); /* "View.MemoryView":176 * self.data = malloc(self.len) * if not self.data: * raise MemoryError("unable to allocate array data.") # <<<<<<<<<<<<<< * * if self.dtype_is_object: */ __pyx_tuple__6 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_array_data); if (unlikely(!__pyx_tuple__6)) __PYX_ERR(1, 176, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__6); __Pyx_GIVEREF(__pyx_tuple__6); /* "View.MemoryView":192 * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") # <<<<<<<<<<<<<< * info.buf = self.data * info.len = self.len */ __pyx_tuple__7 = PyTuple_Pack(1, __pyx_kp_s_Can_only_create_a_buffer_that_is); if (unlikely(!__pyx_tuple__7)) __PYX_ERR(1, 192, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__7); __Pyx_GIVEREF(__pyx_tuple__7); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__8 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__8)) __PYX_ERR(1, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__8); __Pyx_GIVEREF(__pyx_tuple__8); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__9 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__9)) __PYX_ERR(1, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__9); __Pyx_GIVEREF(__pyx_tuple__9); /* "View.MemoryView":418 * def __setitem__(memoryview self, object index, object value): * if self.view.readonly: * raise TypeError("Cannot assign to read-only memoryview") # <<<<<<<<<<<<<< * * have_slices, index = _unellipsify(index, self.view.ndim) */ __pyx_tuple__10 = PyTuple_Pack(1, __pyx_kp_s_Cannot_assign_to_read_only_memor); if (unlikely(!__pyx_tuple__10)) __PYX_ERR(1, 418, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__10); __Pyx_GIVEREF(__pyx_tuple__10); /* "View.MemoryView":495 * result = struct.unpack(self.view.format, bytesitem) * except struct.error: * raise ValueError("Unable to convert item to object") # <<<<<<<<<<<<<< * else: * if len(self.view.format) == 1: */ __pyx_tuple__11 = PyTuple_Pack(1, __pyx_kp_s_Unable_to_convert_item_to_object); if (unlikely(!__pyx_tuple__11)) __PYX_ERR(1, 495, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__11); __Pyx_GIVEREF(__pyx_tuple__11); /* "View.MemoryView":520 * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") # <<<<<<<<<<<<<< * * if flags & PyBUF_ND: */ __pyx_tuple__12 = PyTuple_Pack(1, __pyx_kp_s_Cannot_create_writable_memory_vi); if (unlikely(!__pyx_tuple__12)) __PYX_ERR(1, 520, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__12); __Pyx_GIVEREF(__pyx_tuple__12); /* "View.MemoryView":570 * if self.view.strides == NULL: * * raise ValueError("Buffer view does not expose strides") # <<<<<<<<<<<<<< * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) */ __pyx_tuple__13 = PyTuple_Pack(1, __pyx_kp_s_Buffer_view_does_not_expose_stri); if (unlikely(!__pyx_tuple__13)) __PYX_ERR(1, 570, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__13); __Pyx_GIVEREF(__pyx_tuple__13); /* "View.MemoryView":577 * def suboffsets(self): * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim # <<<<<<<<<<<<<< * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) */ __pyx_tuple__14 = PyTuple_New(1); if (unlikely(!__pyx_tuple__14)) __PYX_ERR(1, 577, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__14); __Pyx_INCREF(__pyx_int_neg_1); __Pyx_GIVEREF(__pyx_int_neg_1); PyTuple_SET_ITEM(__pyx_tuple__14, 0, __pyx_int_neg_1); __Pyx_GIVEREF(__pyx_tuple__14); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__15 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__15)) __PYX_ERR(1, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__15); __Pyx_GIVEREF(__pyx_tuple__15); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__16 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__16)) __PYX_ERR(1, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__16); __Pyx_GIVEREF(__pyx_tuple__16); /* "View.MemoryView":682 * if item is Ellipsis: * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) # <<<<<<<<<<<<<< * seen_ellipsis = True * else: */ __pyx_slice__17 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__17)) __PYX_ERR(1, 682, __pyx_L1_error) __Pyx_GOTREF(__pyx_slice__17); __Pyx_GIVEREF(__pyx_slice__17); /* "View.MemoryView":703 * for suboffset in suboffsets[:ndim]: * if suboffset >= 0: * raise ValueError("Indirect dimensions not supported") # <<<<<<<<<<<<<< * * */ __pyx_tuple__18 = PyTuple_Pack(1, __pyx_kp_s_Indirect_dimensions_not_supporte); if (unlikely(!__pyx_tuple__18)) __PYX_ERR(1, 703, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__18); __Pyx_GIVEREF(__pyx_tuple__18); /* "(tree fragment)":2 * def __reduce_cython__(self): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") */ __pyx_tuple__19 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__19)) __PYX_ERR(1, 2, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__19); __Pyx_GIVEREF(__pyx_tuple__19); /* "(tree fragment)":4 * raise TypeError("no default __reduce__ due to non-trivial __cinit__") * def __setstate_cython__(self, __pyx_state): * raise TypeError("no default __reduce__ due to non-trivial __cinit__") # <<<<<<<<<<<<<< */ __pyx_tuple__20 = PyTuple_Pack(1, __pyx_kp_s_no_default___reduce___due_to_non); if (unlikely(!__pyx_tuple__20)) __PYX_ERR(1, 4, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__20); __Pyx_GIVEREF(__pyx_tuple__20); /* "View.MemoryView":286 * return self.name * * cdef generic = Enum("") # <<<<<<<<<<<<<< * cdef strided = Enum("") # default * cdef indirect = Enum("") */ __pyx_tuple__21 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct_or_indirect); if (unlikely(!__pyx_tuple__21)) __PYX_ERR(1, 286, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__21); __Pyx_GIVEREF(__pyx_tuple__21); /* "View.MemoryView":287 * * cdef generic = Enum("") * cdef strided = Enum("") # default # <<<<<<<<<<<<<< * cdef indirect = Enum("") * */ __pyx_tuple__22 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct); if (unlikely(!__pyx_tuple__22)) __PYX_ERR(1, 287, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__22); __Pyx_GIVEREF(__pyx_tuple__22); /* "View.MemoryView":288 * cdef generic = Enum("") * cdef strided = Enum("") # default * cdef indirect = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_tuple__23 = PyTuple_Pack(1, __pyx_kp_s_strided_and_indirect); if (unlikely(!__pyx_tuple__23)) __PYX_ERR(1, 288, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__23); __Pyx_GIVEREF(__pyx_tuple__23); /* "View.MemoryView":291 * * * cdef contiguous = Enum("") # <<<<<<<<<<<<<< * cdef indirect_contiguous = Enum("") * */ __pyx_tuple__24 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_direct); if (unlikely(!__pyx_tuple__24)) __PYX_ERR(1, 291, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__24); __Pyx_GIVEREF(__pyx_tuple__24); /* "View.MemoryView":292 * * cdef contiguous = Enum("") * cdef indirect_contiguous = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_tuple__25 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_indirect); if (unlikely(!__pyx_tuple__25)) __PYX_ERR(1, 292, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__25); __Pyx_GIVEREF(__pyx_tuple__25); /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ __pyx_tuple__26 = PyTuple_Pack(5, __pyx_n_s_pyx_type, __pyx_n_s_pyx_checksum, __pyx_n_s_pyx_state, __pyx_n_s_pyx_PickleError, __pyx_n_s_pyx_result); if (unlikely(!__pyx_tuple__26)) __PYX_ERR(1, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__26); __Pyx_GIVEREF(__pyx_tuple__26); __pyx_codeobj__27 = (PyObject*)__Pyx_PyCode_New(3, 0, 5, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__26, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_stringsource, __pyx_n_s_pyx_unpickle_Enum, 1, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__27)) __PYX_ERR(1, 1, __pyx_L1_error) __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static CYTHON_SMALL_CODE int __Pyx_InitGlobals(void) { if (__Pyx_InitStrings(__pyx_string_tab) < 0) __PYX_ERR(0, 1, __pyx_L1_error); __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_1 = PyInt_FromLong(1); if (unlikely(!__pyx_int_1)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_184977713 = PyInt_FromLong(184977713L); if (unlikely(!__pyx_int_184977713)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_int_neg_1 = PyInt_FromLong(-1); if (unlikely(!__pyx_int_neg_1)) __PYX_ERR(0, 1, __pyx_L1_error) return 0; __pyx_L1_error:; return -1; } static CYTHON_SMALL_CODE int __Pyx_modinit_global_init_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_variable_export_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_function_export_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_type_init_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_type_import_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_variable_import_code(void); /*proto*/ static CYTHON_SMALL_CODE int __Pyx_modinit_function_import_code(void); /*proto*/ static int __Pyx_modinit_global_init_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_global_init_code", 0); /*--- Global init code ---*/ generic = Py_None; Py_INCREF(Py_None); strided = Py_None; Py_INCREF(Py_None); indirect = Py_None; Py_INCREF(Py_None); contiguous = Py_None; Py_INCREF(Py_None); indirect_contiguous = Py_None; Py_INCREF(Py_None); __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_variable_export_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_variable_export_code", 0); /*--- Variable export code ---*/ if (__Pyx_ExportVoidPtr(__pyx_n_s_EPSILON, (void *)&__pyx_v_8openTSNE_9quad_tree_EPSILON, "double") < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_function_export_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_function_export_code", 0); /*--- Function export code ---*/ if (__Pyx_ExportFunction("is_duplicate", (void (*)(void))__pyx_f_8openTSNE_9quad_tree_is_duplicate, "int (__pyx_t_8openTSNE_9quad_tree_Node *, double *, struct __pyx_opt_args_8openTSNE_9quad_tree_is_duplicate *__pyx_optional_args)") < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_type_init_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_type_init_code", 0); /*--- Type init code ---*/ __pyx_vtabptr_8openTSNE_9quad_tree_QuadTree = &__pyx_vtable_8openTSNE_9quad_tree_QuadTree; __pyx_vtable_8openTSNE_9quad_tree_QuadTree.add_points = (void (*)(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *, __Pyx_memviewslice, int __pyx_skip_dispatch))__pyx_f_8openTSNE_9quad_tree_8QuadTree_add_points; __pyx_vtable_8openTSNE_9quad_tree_QuadTree.add_point = (void (*)(struct __pyx_obj_8openTSNE_9quad_tree_QuadTree *, __Pyx_memviewslice, int __pyx_skip_dispatch))__pyx_f_8openTSNE_9quad_tree_8QuadTree_add_point; if (PyType_Ready(&__pyx_type_8openTSNE_9quad_tree_QuadTree) < 0) __PYX_ERR(0, 147, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type_8openTSNE_9quad_tree_QuadTree.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type_8openTSNE_9quad_tree_QuadTree.tp_dictoffset && __pyx_type_8openTSNE_9quad_tree_QuadTree.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type_8openTSNE_9quad_tree_QuadTree.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_SetVtable(__pyx_type_8openTSNE_9quad_tree_QuadTree.tp_dict, __pyx_vtabptr_8openTSNE_9quad_tree_QuadTree) < 0) __PYX_ERR(0, 147, __pyx_L1_error) if (PyObject_SetAttr(__pyx_m, __pyx_n_s_QuadTree, (PyObject *)&__pyx_type_8openTSNE_9quad_tree_QuadTree) < 0) __PYX_ERR(0, 147, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type_8openTSNE_9quad_tree_QuadTree) < 0) __PYX_ERR(0, 147, __pyx_L1_error) __pyx_ptype_8openTSNE_9quad_tree_QuadTree = &__pyx_type_8openTSNE_9quad_tree_QuadTree; __pyx_vtabptr_array = &__pyx_vtable_array; __pyx_vtable_array.get_memview = (PyObject *(*)(struct __pyx_array_obj *))__pyx_array_get_memview; if (PyType_Ready(&__pyx_type___pyx_array) < 0) __PYX_ERR(1, 105, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_array.tp_print = 0; #endif if (__Pyx_SetVtable(__pyx_type___pyx_array.tp_dict, __pyx_vtabptr_array) < 0) __PYX_ERR(1, 105, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_array) < 0) __PYX_ERR(1, 105, __pyx_L1_error) __pyx_array_type = &__pyx_type___pyx_array; if (PyType_Ready(&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(1, 279, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_MemviewEnum.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_MemviewEnum.tp_dictoffset && __pyx_type___pyx_MemviewEnum.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_MemviewEnum.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(1, 279, __pyx_L1_error) __pyx_MemviewEnum_type = &__pyx_type___pyx_MemviewEnum; __pyx_vtabptr_memoryview = &__pyx_vtable_memoryview; __pyx_vtable_memoryview.get_item_pointer = (char *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_get_item_pointer; __pyx_vtable_memoryview.is_slice = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_is_slice; __pyx_vtable_memoryview.setitem_slice_assignment = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_slice_assignment; __pyx_vtable_memoryview.setitem_slice_assign_scalar = (PyObject *(*)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_setitem_slice_assign_scalar; __pyx_vtable_memoryview.setitem_indexed = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_indexed; __pyx_vtable_memoryview.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryview_convert_item_to_object; __pyx_vtable_memoryview.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryview_assign_item_from_object; if (PyType_Ready(&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(1, 330, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_memoryview.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_memoryview.tp_dictoffset && __pyx_type___pyx_memoryview.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_memoryview.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_SetVtable(__pyx_type___pyx_memoryview.tp_dict, __pyx_vtabptr_memoryview) < 0) __PYX_ERR(1, 330, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(1, 330, __pyx_L1_error) __pyx_memoryview_type = &__pyx_type___pyx_memoryview; __pyx_vtabptr__memoryviewslice = &__pyx_vtable__memoryviewslice; __pyx_vtable__memoryviewslice.__pyx_base = *__pyx_vtabptr_memoryview; __pyx_vtable__memoryviewslice.__pyx_base.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryviewslice_convert_item_to_object; __pyx_vtable__memoryviewslice.__pyx_base.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryviewslice_assign_item_from_object; __pyx_type___pyx_memoryviewslice.tp_base = __pyx_memoryview_type; if (PyType_Ready(&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(1, 965, __pyx_L1_error) #if PY_VERSION_HEX < 0x030800B1 __pyx_type___pyx_memoryviewslice.tp_print = 0; #endif if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_memoryviewslice.tp_dictoffset && __pyx_type___pyx_memoryviewslice.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_memoryviewslice.tp_getattro = __Pyx_PyObject_GenericGetAttr; } if (__Pyx_SetVtable(__pyx_type___pyx_memoryviewslice.tp_dict, __pyx_vtabptr__memoryviewslice) < 0) __PYX_ERR(1, 965, __pyx_L1_error) if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(1, 965, __pyx_L1_error) __pyx_memoryviewslice_type = &__pyx_type___pyx_memoryviewslice; __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; __Pyx_RefNannyFinishContext(); return -1; } static int __Pyx_modinit_type_import_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_type_import_code", 0); /*--- Type import code ---*/ __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_variable_import_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_variable_import_code", 0); /*--- Variable import code ---*/ __Pyx_RefNannyFinishContext(); return 0; } static int __Pyx_modinit_function_import_code(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_modinit_function_import_code", 0); /*--- Function import code ---*/ __Pyx_RefNannyFinishContext(); return 0; } #if PY_MAJOR_VERSION < 3 #ifdef CYTHON_NO_PYINIT_EXPORT #define __Pyx_PyMODINIT_FUNC void #else #define __Pyx_PyMODINIT_FUNC PyMODINIT_FUNC #endif #else #ifdef CYTHON_NO_PYINIT_EXPORT #define __Pyx_PyMODINIT_FUNC PyObject * #else #define __Pyx_PyMODINIT_FUNC PyMODINIT_FUNC #endif #endif #if PY_MAJOR_VERSION < 3 __Pyx_PyMODINIT_FUNC initquad_tree(void) CYTHON_SMALL_CODE; /*proto*/ __Pyx_PyMODINIT_FUNC initquad_tree(void) #else __Pyx_PyMODINIT_FUNC PyInit_quad_tree(void) CYTHON_SMALL_CODE; /*proto*/ __Pyx_PyMODINIT_FUNC PyInit_quad_tree(void) #if CYTHON_PEP489_MULTI_PHASE_INIT { return PyModuleDef_Init(&__pyx_moduledef); } static CYTHON_SMALL_CODE int __Pyx_check_single_interpreter(void) { #if PY_VERSION_HEX >= 0x030700A1 static PY_INT64_T main_interpreter_id = -1; PY_INT64_T current_id = PyInterpreterState_GetID(PyThreadState_Get()->interp); if (main_interpreter_id == -1) { main_interpreter_id = current_id; return (unlikely(current_id == -1)) ? -1 : 0; } else if (unlikely(main_interpreter_id != current_id)) #else static PyInterpreterState *main_interpreter = NULL; PyInterpreterState *current_interpreter = PyThreadState_Get()->interp; if (!main_interpreter) { main_interpreter = current_interpreter; } else if (unlikely(main_interpreter != current_interpreter)) #endif { PyErr_SetString( PyExc_ImportError, "Interpreter change detected - this module can only be loaded into one interpreter per process."); return -1; } return 0; } static CYTHON_SMALL_CODE int __Pyx_copy_spec_to_module(PyObject *spec, PyObject *moddict, const char* from_name, const char* to_name, int allow_none) { PyObject *value = PyObject_GetAttrString(spec, from_name); int result = 0; if (likely(value)) { if (allow_none || value != Py_None) { result = PyDict_SetItemString(moddict, to_name, value); } Py_DECREF(value); } else if (PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Clear(); } else { result = -1; } return result; } static CYTHON_SMALL_CODE PyObject* __pyx_pymod_create(PyObject *spec, CYTHON_UNUSED PyModuleDef *def) { PyObject *module = NULL, *moddict, *modname; if (__Pyx_check_single_interpreter()) return NULL; if (__pyx_m) return __Pyx_NewRef(__pyx_m); modname = PyObject_GetAttrString(spec, "name"); if (unlikely(!modname)) goto bad; module = PyModule_NewObject(modname); Py_DECREF(modname); if (unlikely(!module)) goto bad; moddict = PyModule_GetDict(module); if (unlikely(!moddict)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "loader", "__loader__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "origin", "__file__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "parent", "__package__", 1) < 0)) goto bad; if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "submodule_search_locations", "__path__", 0) < 0)) goto bad; return module; bad: Py_XDECREF(module); return NULL; } static CYTHON_SMALL_CODE int __pyx_pymod_exec_quad_tree(PyObject *__pyx_pyinit_module) #endif #endif { PyObject *__pyx_t_1 = NULL; static PyThread_type_lock __pyx_t_2[8]; __Pyx_RefNannyDeclarations #if CYTHON_PEP489_MULTI_PHASE_INIT if (__pyx_m) { if (__pyx_m == __pyx_pyinit_module) return 0; PyErr_SetString(PyExc_RuntimeError, "Module 'quad_tree' has already been imported. Re-initialisation is not supported."); return -1; } #elif PY_MAJOR_VERSION >= 3 if (__pyx_m) return __Pyx_NewRef(__pyx_m); #endif #if CYTHON_REFNANNY __Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny"); if (!__Pyx_RefNanny) { PyErr_Clear(); __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny"); if (!__Pyx_RefNanny) Py_FatalError("failed to import 'refnanny' module"); } #endif __Pyx_RefNannySetupContext("__Pyx_PyMODINIT_FUNC PyInit_quad_tree(void)", 0); if (__Pyx_check_binary_version() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #ifdef __Pxy_PyFrame_Initialize_Offsets __Pxy_PyFrame_Initialize_Offsets(); #endif __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) __PYX_ERR(0, 1, __pyx_L1_error) __pyx_empty_unicode = PyUnicode_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_unicode)) __PYX_ERR(0, 1, __pyx_L1_error) #ifdef __Pyx_CyFunction_USED if (__pyx_CyFunction_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_FusedFunction_USED if (__pyx_FusedFunction_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_Coroutine_USED if (__pyx_Coroutine_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_Generator_USED if (__pyx_Generator_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_AsyncGen_USED if (__pyx_AsyncGen_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif #ifdef __Pyx_StopAsyncIteration_USED if (__pyx_StopAsyncIteration_init() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif /*--- Library function declarations ---*/ /*--- Threads initialization code ---*/ #if defined(__PYX_FORCE_INIT_THREADS) && __PYX_FORCE_INIT_THREADS #ifdef WITH_THREAD /* Python build with threading support? */ PyEval_InitThreads(); #endif #endif /*--- Module creation code ---*/ #if CYTHON_PEP489_MULTI_PHASE_INIT __pyx_m = __pyx_pyinit_module; Py_INCREF(__pyx_m); #else #if PY_MAJOR_VERSION < 3 __pyx_m = Py_InitModule4("quad_tree", __pyx_methods, __pyx_k_Implements_a_quad_oct_tree_space, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m); #else __pyx_m = PyModule_Create(&__pyx_moduledef); #endif if (unlikely(!__pyx_m)) __PYX_ERR(0, 1, __pyx_L1_error) #endif __pyx_d = PyModule_GetDict(__pyx_m); if (unlikely(!__pyx_d)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_d); __pyx_b = PyImport_AddModule(__Pyx_BUILTIN_MODULE_NAME); if (unlikely(!__pyx_b)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_b); __pyx_cython_runtime = PyImport_AddModule((char *) "cython_runtime"); if (unlikely(!__pyx_cython_runtime)) __PYX_ERR(0, 1, __pyx_L1_error) Py_INCREF(__pyx_cython_runtime); if (PyObject_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) __PYX_ERR(0, 1, __pyx_L1_error); /*--- Initialize various global constants etc. ---*/ if (__Pyx_InitGlobals() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #if PY_MAJOR_VERSION < 3 && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) if (__Pyx_init_sys_getdefaultencoding_params() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif if (__pyx_module_is_main_openTSNE__quad_tree) { if (PyObject_SetAttr(__pyx_m, __pyx_n_s_name_2, __pyx_n_s_main) < 0) __PYX_ERR(0, 1, __pyx_L1_error) } #if PY_MAJOR_VERSION >= 3 { PyObject *modules = PyImport_GetModuleDict(); if (unlikely(!modules)) __PYX_ERR(0, 1, __pyx_L1_error) if (!PyDict_GetItemString(modules, "openTSNE.quad_tree")) { if (unlikely(PyDict_SetItemString(modules, "openTSNE.quad_tree", __pyx_m) < 0)) __PYX_ERR(0, 1, __pyx_L1_error) } } #endif /*--- Builtin init code ---*/ if (__Pyx_InitCachedBuiltins() < 0) goto __pyx_L1_error; /*--- Constants init code ---*/ if (__Pyx_InitCachedConstants() < 0) goto __pyx_L1_error; /*--- Global type/function init code ---*/ (void)__Pyx_modinit_global_init_code(); if (unlikely(__Pyx_modinit_variable_export_code() != 0)) goto __pyx_L1_error; if (unlikely(__Pyx_modinit_function_export_code() != 0)) goto __pyx_L1_error; if (unlikely(__Pyx_modinit_type_init_code() != 0)) goto __pyx_L1_error; (void)__Pyx_modinit_type_import_code(); (void)__Pyx_modinit_variable_import_code(); (void)__Pyx_modinit_function_import_code(); /*--- Execution code ---*/ #if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED) if (__Pyx_patch_abc() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif /* "openTSNE/quad_tree.pyx":36 * * """ * import numpy as np # <<<<<<<<<<<<<< * from cpython.mem cimport PyMem_Malloc, PyMem_Free * */ __pyx_t_1 = __Pyx_Import(__pyx_n_s_numpy, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 36, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) __PYX_ERR(0, 36, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "openTSNE/quad_tree.pyx":1 * # cython: boundscheck=False # <<<<<<<<<<<<<< * # cython: wraparound=False * # cython: cdivision=True */ __pyx_t_1 = __Pyx_PyDict_NewPresized(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":209 * info.obj = self * * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * def __dealloc__(array self): */ __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_array_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 209, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem((PyObject *)__pyx_array_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(1, 209, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; PyType_Modified(__pyx_array_type); /* "View.MemoryView":286 * return self.name * * cdef generic = Enum("") # <<<<<<<<<<<<<< * cdef strided = Enum("") # default * cdef indirect = Enum("") */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__21, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 286, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(generic); __Pyx_DECREF_SET(generic, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":287 * * cdef generic = Enum("") * cdef strided = Enum("") # default # <<<<<<<<<<<<<< * cdef indirect = Enum("") * */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__22, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 287, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(strided); __Pyx_DECREF_SET(strided, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":288 * cdef generic = Enum("") * cdef strided = Enum("") # default * cdef indirect = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__23, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 288, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(indirect); __Pyx_DECREF_SET(indirect, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":291 * * * cdef contiguous = Enum("") # <<<<<<<<<<<<<< * cdef indirect_contiguous = Enum("") * */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__24, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 291, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(contiguous); __Pyx_DECREF_SET(contiguous, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":292 * * cdef contiguous = Enum("") * cdef indirect_contiguous = Enum("") # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__25, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 292, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(indirect_contiguous); __Pyx_DECREF_SET(indirect_contiguous, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; /* "View.MemoryView":316 * * DEF THREAD_LOCKS_PREALLOCATED = 8 * cdef int __pyx_memoryview_thread_locks_used = 0 # <<<<<<<<<<<<<< * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [ * PyThread_allocate_lock(), */ __pyx_memoryview_thread_locks_used = 0; /* "View.MemoryView":317 * DEF THREAD_LOCKS_PREALLOCATED = 8 * cdef int __pyx_memoryview_thread_locks_used = 0 * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [ # <<<<<<<<<<<<<< * PyThread_allocate_lock(), * PyThread_allocate_lock(), */ __pyx_t_2[0] = PyThread_allocate_lock(); __pyx_t_2[1] = PyThread_allocate_lock(); __pyx_t_2[2] = PyThread_allocate_lock(); __pyx_t_2[3] = PyThread_allocate_lock(); __pyx_t_2[4] = PyThread_allocate_lock(); __pyx_t_2[5] = PyThread_allocate_lock(); __pyx_t_2[6] = PyThread_allocate_lock(); __pyx_t_2[7] = PyThread_allocate_lock(); memcpy(&(__pyx_memoryview_thread_locks[0]), __pyx_t_2, sizeof(__pyx_memoryview_thread_locks[0]) * (8)); /* "View.MemoryView":549 * info.obj = self * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 549, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem((PyObject *)__pyx_memoryview_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(1, 549, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; PyType_Modified(__pyx_memoryview_type); /* "View.MemoryView":995 * return self.from_object * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * */ __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 995, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem((PyObject *)__pyx_memoryviewslice_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(1, 995, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; PyType_Modified(__pyx_memoryviewslice_type); /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< * cdef object __pyx_PickleError * cdef object __pyx_result */ __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_15View_dot_MemoryView_1__pyx_unpickle_Enum, NULL, __pyx_n_s_View_MemoryView); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_pyx_unpickle_Enum, __pyx_t_1) < 0) __PYX_ERR(1, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "(tree fragment)":11 * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) * return __pyx_result * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< * __pyx_result.name = __pyx_state[0] * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): */ /*--- Wrapped vars code ---*/ goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); if (__pyx_m) { if (__pyx_d) { __Pyx_AddTraceback("init openTSNE.quad_tree", __pyx_clineno, __pyx_lineno, __pyx_filename); } Py_CLEAR(__pyx_m); } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_ImportError, "init openTSNE.quad_tree"); } __pyx_L0:; __Pyx_RefNannyFinishContext(); #if CYTHON_PEP489_MULTI_PHASE_INIT return (__pyx_m != NULL) ? 0 : -1; #elif PY_MAJOR_VERSION >= 3 return __pyx_m; #else return; #endif } /* --- Runtime support code --- */ /* Refnanny */ #if CYTHON_REFNANNY static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { PyObject *m = NULL, *p = NULL; void *r = NULL; m = PyImport_ImportModule(modname); if (!m) goto end; p = PyObject_GetAttrString(m, "RefNannyAPI"); if (!p) goto end; r = PyLong_AsVoidPtr(p); end: Py_XDECREF(p); Py_XDECREF(m); return (__Pyx_RefNannyAPIStruct *)r; } #endif /* PyObjectGetAttrStr */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { PyTypeObject* tp = Py_TYPE(obj); if (likely(tp->tp_getattro)) return tp->tp_getattro(obj, attr_name); #if PY_MAJOR_VERSION < 3 if (likely(tp->tp_getattr)) return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); #endif return PyObject_GetAttr(obj, attr_name); } #endif /* GetBuiltinName */ static PyObject *__Pyx_GetBuiltinName(PyObject *name) { PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); if (unlikely(!result)) { PyErr_Format(PyExc_NameError, #if PY_MAJOR_VERSION >= 3 "name '%U' is not defined", name); #else "name '%.200s' is not defined", PyString_AS_STRING(name)); #endif } return result; } /* PyErrFetchRestore */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; } #endif /* WriteUnraisableException */ static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno, CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename, int full_traceback, CYTHON_UNUSED int nogil) { PyObject *old_exc, *old_val, *old_tb; PyObject *ctx; __Pyx_PyThreadState_declare #ifdef WITH_THREAD PyGILState_STATE state; if (nogil) state = PyGILState_Ensure(); #ifdef _MSC_VER else state = (PyGILState_STATE)-1; #endif #endif __Pyx_PyThreadState_assign __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); if (full_traceback) { Py_XINCREF(old_exc); Py_XINCREF(old_val); Py_XINCREF(old_tb); __Pyx_ErrRestore(old_exc, old_val, old_tb); PyErr_PrintEx(1); } #if PY_MAJOR_VERSION < 3 ctx = PyString_FromString(name); #else ctx = PyUnicode_FromString(name); #endif __Pyx_ErrRestore(old_exc, old_val, old_tb); if (!ctx) { PyErr_WriteUnraisable(Py_None); } else { PyErr_WriteUnraisable(ctx); Py_DECREF(ctx); } #ifdef WITH_THREAD if (nogil) PyGILState_Release(state); #endif } /* RaiseDoubleKeywords */ static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) { PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION >= 3 "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, PyString_AsString(kw_name)); #endif } /* ParseKeywords */ static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name) { PyObject *key = 0, *value = 0; Py_ssize_t pos = 0; PyObject*** name; PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { name = first_kw_arg; while (*name && (**name != key)) name++; if (*name) { values[name-argnames] = value; continue; } name = first_kw_arg; #if PY_MAJOR_VERSION < 3 if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { while (*name) { if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) && _PyString_Eq(**name, key)) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { if ((**argname == key) || ( (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) && _PyString_Eq(**argname, key))) { goto arg_passed_twice; } argname++; } } } else #endif if (likely(PyUnicode_Check(key))) { while (*name) { int cmp = (**name == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : #endif PyUnicode_Compare(**name, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { int cmp = (**argname == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : #endif PyUnicode_Compare(**argname, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) goto arg_passed_twice; argname++; } } } else goto invalid_keyword_type; if (kwds2) { if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; } else { goto invalid_keyword; } } return 0; arg_passed_twice: __Pyx_RaiseDoubleKeywordsError(function_name, key); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%.200s() keywords must be strings", function_name); goto bad; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%.200s() got an unexpected keyword argument '%.200s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif bad: return -1; } /* RaiseArgTupleInvalid */ static void __Pyx_RaiseArgtupleInvalid( const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found) { Py_ssize_t num_expected; const char *more_or_less; if (num_found < num_min) { num_expected = num_min; more_or_less = "at least"; } else { num_expected = num_max; more_or_less = "at most"; } if (exact) { more_or_less = "exactly"; } PyErr_Format(PyExc_TypeError, "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", func_name, more_or_less, num_expected, (num_expected == 1) ? "" : "s", num_found); } /* PyDictVersioning */ #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { PyObject *dict = Py_TYPE(obj)->tp_dict; return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; } static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { PyObject **dictptr = NULL; Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; if (offset) { #if CYTHON_COMPILING_IN_CPYTHON dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); #else dictptr = _PyObject_GetDictPtr(obj); #endif } return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; } static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { PyObject *dict = Py_TYPE(obj)->tp_dict; if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) return 0; return obj_dict_version == __Pyx_get_object_dict_version(obj); } #endif /* GetModuleGlobalName */ #if CYTHON_USE_DICT_VERSIONS static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) #else static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) #endif { PyObject *result; #if !CYTHON_AVOID_BORROWED_REFS #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } else if (unlikely(PyErr_Occurred())) { return NULL; } #else result = PyDict_GetItem(__pyx_d, name); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } #endif #else result = PyObject_GetItem(__pyx_d, name); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } PyErr_Clear(); #endif return __Pyx_GetBuiltinName(name); } /* PyObjectCall */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; ternaryfunc call = func->ob_type->tp_call; if (unlikely(!call)) return PyObject_Call(func, arg, kw); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = (*call)(func, arg, kw); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif /* PyCFunctionFastCall */ #if CYTHON_FAST_PYCCALL static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { PyCFunctionObject *func = (PyCFunctionObject*)func_obj; PyCFunction meth = PyCFunction_GET_FUNCTION(func); PyObject *self = PyCFunction_GET_SELF(func); int flags = PyCFunction_GET_FLAGS(func); assert(PyCFunction_Check(func)); assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); assert(nargs >= 0); assert(nargs == 0 || args != NULL); /* _PyCFunction_FastCallDict() must not be called with an exception set, because it may clear it (directly or indirectly) and so the caller loses its exception */ assert(!PyErr_Occurred()); if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); } else { return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); } } #endif /* PyFunctionFastCall */ #if CYTHON_FAST_PYCALL static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, PyObject *globals) { PyFrameObject *f; PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject **fastlocals; Py_ssize_t i; PyObject *result; assert(globals != NULL); /* XXX Perhaps we should create a specialized PyFrame_New() that doesn't take locals, but does take builtins without sanity checking them. */ assert(tstate != NULL); f = PyFrame_New(tstate, co, globals, NULL); if (f == NULL) { return NULL; } fastlocals = __Pyx_PyFrame_GetLocalsplus(f); for (i = 0; i < na; i++) { Py_INCREF(*args); fastlocals[i] = *args++; } result = PyEval_EvalFrameEx(f,0); ++tstate->recursion_depth; Py_DECREF(f); --tstate->recursion_depth; return result; } #if 1 || PY_VERSION_HEX < 0x030600B1 static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); PyObject *globals = PyFunction_GET_GLOBALS(func); PyObject *argdefs = PyFunction_GET_DEFAULTS(func); PyObject *closure; #if PY_MAJOR_VERSION >= 3 PyObject *kwdefs; #endif PyObject *kwtuple, **k; PyObject **d; Py_ssize_t nd; Py_ssize_t nk; PyObject *result; assert(kwargs == NULL || PyDict_Check(kwargs)); nk = kwargs ? PyDict_Size(kwargs) : 0; if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { return NULL; } if ( #if PY_MAJOR_VERSION >= 3 co->co_kwonlyargcount == 0 && #endif likely(kwargs == NULL || nk == 0) && co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { if (argdefs == NULL && co->co_argcount == nargs) { result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); goto done; } else if (nargs == 0 && argdefs != NULL && co->co_argcount == Py_SIZE(argdefs)) { /* function called with no arguments, but all parameters have a default value: use default values as arguments .*/ args = &PyTuple_GET_ITEM(argdefs, 0); result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); goto done; } } if (kwargs != NULL) { Py_ssize_t pos, i; kwtuple = PyTuple_New(2 * nk); if (kwtuple == NULL) { result = NULL; goto done; } k = &PyTuple_GET_ITEM(kwtuple, 0); pos = i = 0; while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { Py_INCREF(k[i]); Py_INCREF(k[i+1]); i += 2; } nk = i / 2; } else { kwtuple = NULL; k = NULL; } closure = PyFunction_GET_CLOSURE(func); #if PY_MAJOR_VERSION >= 3 kwdefs = PyFunction_GET_KW_DEFAULTS(func); #endif if (argdefs != NULL) { d = &PyTuple_GET_ITEM(argdefs, 0); nd = Py_SIZE(argdefs); } else { d = NULL; nd = 0; } #if PY_MAJOR_VERSION >= 3 result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, args, (int)nargs, k, (int)nk, d, (int)nd, kwdefs, closure); #else result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, args, (int)nargs, k, (int)nk, d, (int)nd, closure); #endif Py_XDECREF(kwtuple); done: Py_LeaveRecursiveCall(); return result; } #endif #endif /* PyObjectCall2Args */ static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) { PyObject *args, *result = NULL; #if CYTHON_FAST_PYCALL if (PyFunction_Check(function)) { PyObject *args[2] = {arg1, arg2}; return __Pyx_PyFunction_FastCall(function, args, 2); } #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(function)) { PyObject *args[2] = {arg1, arg2}; return __Pyx_PyCFunction_FastCall(function, args, 2); } #endif args = PyTuple_New(2); if (unlikely(!args)) goto done; Py_INCREF(arg1); PyTuple_SET_ITEM(args, 0, arg1); Py_INCREF(arg2); PyTuple_SET_ITEM(args, 1, arg2); Py_INCREF(function); result = __Pyx_PyObject_Call(function, args, NULL); Py_DECREF(args); Py_DECREF(function); done: return result; } /* PyObjectCallMethO */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { PyObject *self, *result; PyCFunction cfunc; cfunc = PyCFunction_GET_FUNCTION(func); self = PyCFunction_GET_SELF(func); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = cfunc(self, arg); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif /* PyObjectCallOneArg */ #if CYTHON_COMPILING_IN_CPYTHON static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_New(1); if (unlikely(!args)) return NULL; Py_INCREF(arg); PyTuple_SET_ITEM(args, 0, arg); result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { #if CYTHON_FAST_PYCALL if (PyFunction_Check(func)) { return __Pyx_PyFunction_FastCall(func, &arg, 1); } #endif if (likely(PyCFunction_Check(func))) { if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { return __Pyx_PyObject_CallMethO(func, arg); #if CYTHON_FAST_PYCCALL } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { return __Pyx_PyCFunction_FastCall(func, &arg, 1); #endif } } return __Pyx__PyObject_CallOneArg(func, arg); } #else static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_Pack(1, arg); if (unlikely(!args)) return NULL; result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } #endif /* MemviewSliceInit */ static int __Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, int ndim, __Pyx_memviewslice *memviewslice, int memview_is_new_reference) { __Pyx_RefNannyDeclarations int i, retval=-1; Py_buffer *buf = &memview->view; __Pyx_RefNannySetupContext("init_memviewslice", 0); if (memviewslice->memview || memviewslice->data) { PyErr_SetString(PyExc_ValueError, "memviewslice is already initialized!"); goto fail; } if (buf->strides) { for (i = 0; i < ndim; i++) { memviewslice->strides[i] = buf->strides[i]; } } else { Py_ssize_t stride = buf->itemsize; for (i = ndim - 1; i >= 0; i--) { memviewslice->strides[i] = stride; stride *= buf->shape[i]; } } for (i = 0; i < ndim; i++) { memviewslice->shape[i] = buf->shape[i]; if (buf->suboffsets) { memviewslice->suboffsets[i] = buf->suboffsets[i]; } else { memviewslice->suboffsets[i] = -1; } } memviewslice->memview = memview; memviewslice->data = (char *)buf->buf; if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { Py_INCREF(memview); } retval = 0; goto no_fail; fail: memviewslice->memview = 0; memviewslice->data = 0; retval = -1; no_fail: __Pyx_RefNannyFinishContext(); return retval; } #ifndef Py_NO_RETURN #define Py_NO_RETURN #endif static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { va_list vargs; char msg[200]; #ifdef HAVE_STDARG_PROTOTYPES va_start(vargs, fmt); #else va_start(vargs); #endif vsnprintf(msg, 200, fmt, vargs); va_end(vargs); Py_FatalError(msg); } static CYTHON_INLINE int __pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, PyThread_type_lock lock) { int result; PyThread_acquire_lock(lock, 1); result = (*acquisition_count)++; PyThread_release_lock(lock); return result; } static CYTHON_INLINE int __pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, PyThread_type_lock lock) { int result; PyThread_acquire_lock(lock, 1); result = (*acquisition_count)--; PyThread_release_lock(lock); return result; } static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) { int first_time; struct __pyx_memoryview_obj *memview = memslice->memview; if (!memview || (PyObject *) memview == Py_None) return; if (__pyx_get_slice_count(memview) < 0) __pyx_fatalerror("Acquisition count is %d (line %d)", __pyx_get_slice_count(memview), lineno); first_time = __pyx_add_acquisition_count(memview) == 0; if (first_time) { if (have_gil) { Py_INCREF((PyObject *) memview); } else { PyGILState_STATE _gilstate = PyGILState_Ensure(); Py_INCREF((PyObject *) memview); PyGILState_Release(_gilstate); } } } static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) { int last_time; struct __pyx_memoryview_obj *memview = memslice->memview; if (!memview ) { return; } else if ((PyObject *) memview == Py_None) { memslice->memview = NULL; return; } if (__pyx_get_slice_count(memview) <= 0) __pyx_fatalerror("Acquisition count is %d (line %d)", __pyx_get_slice_count(memview), lineno); last_time = __pyx_sub_acquisition_count(memview) == 1; memslice->data = NULL; if (last_time) { if (have_gil) { Py_CLEAR(memslice->memview); } else { PyGILState_STATE _gilstate = PyGILState_Ensure(); Py_CLEAR(memslice->memview); PyGILState_Release(_gilstate); } } else { memslice->memview = NULL; } } /* None */ static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); } /* RaiseException */ #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { __Pyx_PyThreadState_declare Py_XINCREF(type); if (!value || value == Py_None) value = NULL; else Py_INCREF(value); if (!tb || tb == Py_None) tb = NULL; else { Py_INCREF(tb); if (!PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } } if (PyType_Check(type)) { #if CYTHON_COMPILING_IN_PYPY if (!value) { Py_INCREF(Py_None); value = Py_None; } #endif PyErr_NormalizeException(&type, &value, &tb); } else { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } value = type; type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } } __Pyx_PyThreadState_assign __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (PyExceptionClass_Check(type)) { PyObject *instance_class = NULL; if (value && PyExceptionInstance_Check(value)) { instance_class = (PyObject*) Py_TYPE(value); if (instance_class != type) { int is_subclass = PyObject_IsSubclass(instance_class, type); if (!is_subclass) { instance_class = NULL; } else if (unlikely(is_subclass == -1)) { goto bad; } else { type = instance_class; } } } if (!instance_class) { PyObject *args; if (!value) args = PyTuple_New(0); else if (PyTuple_Check(value)) { Py_INCREF(value); args = value; } else args = PyTuple_Pack(1, value); if (!args) goto bad; owned_instance = PyObject_Call(type, args, NULL); Py_DECREF(args); if (!owned_instance) goto bad; value = owned_instance; if (!PyExceptionInstance_Check(value)) { PyErr_Format(PyExc_TypeError, "calling %R should have returned an instance of " "BaseException, not %R", type, Py_TYPE(value)); goto bad; } } } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } if (cause) { PyObject *fixed_cause; if (cause == Py_None) { fixed_cause = NULL; } else if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { #if CYTHON_COMPILING_IN_PYPY PyObject *tmp_type, *tmp_value, *tmp_tb; PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); Py_INCREF(tb); PyErr_Restore(tmp_type, tmp_value, tb); Py_XDECREF(tmp_tb); #else PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } #endif } bad: Py_XDECREF(owned_instance); return; } #endif /* ArgTypeTest */ static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } else if (exact) { #if PY_MAJOR_VERSION == 2 if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; #endif } else { if (likely(__Pyx_TypeCheck(obj, type))) return 1; } PyErr_Format(PyExc_TypeError, "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", name, type->tp_name, Py_TYPE(obj)->tp_name); return 0; } /* BytesEquals */ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { #if CYTHON_COMPILING_IN_PYPY return PyObject_RichCompareBool(s1, s2, equals); #else if (s1 == s2) { return (equals == Py_EQ); } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { const char *ps1, *ps2; Py_ssize_t length = PyBytes_GET_SIZE(s1); if (length != PyBytes_GET_SIZE(s2)) return (equals == Py_NE); ps1 = PyBytes_AS_STRING(s1); ps2 = PyBytes_AS_STRING(s2); if (ps1[0] != ps2[0]) { return (equals == Py_NE); } else if (length == 1) { return (equals == Py_EQ); } else { int result; #if CYTHON_USE_UNICODE_INTERNALS Py_hash_t hash1, hash2; hash1 = ((PyBytesObject*)s1)->ob_shash; hash2 = ((PyBytesObject*)s2)->ob_shash; if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { return (equals == Py_NE); } #endif result = memcmp(ps1, ps2, (size_t)length); return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { return (equals == Py_NE); } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { return (equals == Py_NE); } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } #endif } /* UnicodeEquals */ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { #if CYTHON_COMPILING_IN_PYPY return PyObject_RichCompareBool(s1, s2, equals); #else #if PY_MAJOR_VERSION < 3 PyObject* owned_ref = NULL; #endif int s1_is_unicode, s2_is_unicode; if (s1 == s2) { goto return_eq; } s1_is_unicode = PyUnicode_CheckExact(s1); s2_is_unicode = PyUnicode_CheckExact(s2); #if PY_MAJOR_VERSION < 3 if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { owned_ref = PyUnicode_FromObject(s2); if (unlikely(!owned_ref)) return -1; s2 = owned_ref; s2_is_unicode = 1; } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { owned_ref = PyUnicode_FromObject(s1); if (unlikely(!owned_ref)) return -1; s1 = owned_ref; s1_is_unicode = 1; } else if (((!s2_is_unicode) & (!s1_is_unicode))) { return __Pyx_PyBytes_Equals(s1, s2, equals); } #endif if (s1_is_unicode & s2_is_unicode) { Py_ssize_t length; int kind; void *data1, *data2; if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) return -1; length = __Pyx_PyUnicode_GET_LENGTH(s1); if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { goto return_ne; } #if CYTHON_USE_UNICODE_INTERNALS { Py_hash_t hash1, hash2; #if CYTHON_PEP393_ENABLED hash1 = ((PyASCIIObject*)s1)->hash; hash2 = ((PyASCIIObject*)s2)->hash; #else hash1 = ((PyUnicodeObject*)s1)->hash; hash2 = ((PyUnicodeObject*)s2)->hash; #endif if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { goto return_ne; } } #endif kind = __Pyx_PyUnicode_KIND(s1); if (kind != __Pyx_PyUnicode_KIND(s2)) { goto return_ne; } data1 = __Pyx_PyUnicode_DATA(s1); data2 = __Pyx_PyUnicode_DATA(s2); if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { goto return_ne; } else if (length == 1) { goto return_eq; } else { int result = memcmp(data1, data2, (size_t)(length * kind)); #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & s2_is_unicode) { goto return_ne; } else if ((s2 == Py_None) & s1_is_unicode) { goto return_ne; } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } return_eq: #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_EQ); return_ne: #if PY_MAJOR_VERSION < 3 Py_XDECREF(owned_ref); #endif return (equals == Py_NE); #endif } /* GetAttr */ static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { #if CYTHON_USE_TYPE_SLOTS #if PY_MAJOR_VERSION >= 3 if (likely(PyUnicode_Check(n))) #else if (likely(PyString_Check(n))) #endif return __Pyx_PyObject_GetAttrStr(o, n); #endif return PyObject_GetAttr(o, n); } /* GetItemInt */ static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { PyObject *r; if (!j) return NULL; r = PyObject_GetItem(o, j); Py_DECREF(j); return r; } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS Py_ssize_t wrapped_i = i; if (wraparound & unlikely(i < 0)) { wrapped_i += PyList_GET_SIZE(o); } if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { PyObject *r = PyList_GET_ITEM(o, wrapped_i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS Py_ssize_t wrapped_i = i; if (wraparound & unlikely(i < 0)) { wrapped_i += PyTuple_GET_SIZE(o); } if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS if (is_list || PyList_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { PyObject *r = PyList_GET_ITEM(o, n); Py_INCREF(r); return r; } } else if (PyTuple_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, n); Py_INCREF(r); return r; } } else { PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; if (likely(m && m->sq_item)) { if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { Py_ssize_t l = m->sq_length(o); if (likely(l >= 0)) { i += l; } else { if (!PyErr_ExceptionMatches(PyExc_OverflowError)) return NULL; PyErr_Clear(); } } return m->sq_item(o, i); } } #else if (is_list || PySequence_Check(o)) { return PySequence_GetItem(o, i); } #endif return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); } /* ObjectGetItem */ #if CYTHON_USE_TYPE_SLOTS static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { PyObject *runerr; Py_ssize_t key_value; PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence; if (unlikely(!(m && m->sq_item))) { PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name); return NULL; } key_value = __Pyx_PyIndex_AsSsize_t(index); if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); } if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { PyErr_Clear(); PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name); } return NULL; } static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping; if (likely(m && m->mp_subscript)) { return m->mp_subscript(obj, key); } return __Pyx_PyObject_GetIndex(obj, key); } #endif /* decode_c_string */ static CYTHON_INLINE PyObject* __Pyx_decode_c_string( const char* cstring, Py_ssize_t start, Py_ssize_t stop, const char* encoding, const char* errors, PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { Py_ssize_t length; if (unlikely((start < 0) | (stop < 0))) { size_t slen = strlen(cstring); if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { PyErr_SetString(PyExc_OverflowError, "c-string too long to convert to Python"); return NULL; } length = (Py_ssize_t) slen; if (start < 0) { start += length; if (start < 0) start = 0; } if (stop < 0) stop += length; } length = stop - start; if (unlikely(length <= 0)) return PyUnicode_FromUnicode(NULL, 0); cstring += start; if (decode_func) { return decode_func(cstring, length, errors); } else { return PyUnicode_Decode(cstring, length, encoding, errors); } } /* PyErrExceptionMatches */ #if CYTHON_FAST_THREAD_STATE static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { Py_ssize_t i, n; n = PyTuple_GET_SIZE(tuple); #if PY_MAJOR_VERSION >= 3 for (i=0; icurexc_type; if (exc_type == err) return 1; if (unlikely(!exc_type)) return 0; if (unlikely(PyTuple_Check(err))) return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); } #endif /* GetAttr3 */ static PyObject *__Pyx_GetAttr3Default(PyObject *d) { __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) return NULL; __Pyx_PyErr_Clear(); Py_INCREF(d); return d; } static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { PyObject *r = __Pyx_GetAttr(o, n); return (likely(r)) ? r : __Pyx_GetAttr3Default(d); } /* RaiseTooManyValuesToUnpack */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } /* RaiseNeedMoreValuesToUnpack */ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", index, (index == 1) ? "" : "s"); } /* RaiseNoneIterError */ static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } /* ExtTypeTest */ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (likely(__Pyx_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } /* GetTopmostException */ #if CYTHON_USE_EXC_INFO_STACK static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate) { _PyErr_StackItem *exc_info = tstate->exc_info; while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && exc_info->previous_item != NULL) { exc_info = exc_info->previous_item; } return exc_info; } #endif /* SaveResetException */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); *type = exc_info->exc_type; *value = exc_info->exc_value; *tb = exc_info->exc_traceback; #else *type = tstate->exc_type; *value = tstate->exc_value; *tb = tstate->exc_traceback; #endif Py_XINCREF(*type); Py_XINCREF(*value); Py_XINCREF(*tb); } static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = type; exc_info->exc_value = value; exc_info->exc_traceback = tb; #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = type; tstate->exc_value = value; tstate->exc_traceback = tb; #endif Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } #endif /* GetException */ #if CYTHON_FAST_THREAD_STATE static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) #else static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) #endif { PyObject *local_type, *local_value, *local_tb; #if CYTHON_FAST_THREAD_STATE PyObject *tmp_type, *tmp_value, *tmp_tb; local_type = tstate->curexc_type; local_value = tstate->curexc_value; local_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(&local_type, &local_value, &local_tb); #endif PyErr_NormalizeException(&local_type, &local_value, &local_tb); #if CYTHON_FAST_THREAD_STATE if (unlikely(tstate->curexc_type)) #else if (unlikely(PyErr_Occurred())) #endif goto bad; #if PY_MAJOR_VERSION >= 3 if (local_tb) { if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) goto bad; } #endif Py_XINCREF(local_tb); Py_XINCREF(local_type); Py_XINCREF(local_value); *type = local_type; *value = local_value; *tb = local_tb; #if CYTHON_FAST_THREAD_STATE #if CYTHON_USE_EXC_INFO_STACK { _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = local_type; exc_info->exc_value = local_value; exc_info->exc_traceback = local_tb; } #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = local_type; tstate->exc_value = local_value; tstate->exc_traceback = local_tb; #endif Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_SetExcInfo(local_type, local_value, local_tb); #endif return 0; bad: *type = 0; *value = 0; *tb = 0; Py_XDECREF(local_type); Py_XDECREF(local_value); Py_XDECREF(local_tb); return -1; } /* SwapException */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = *type; exc_info->exc_value = *value; exc_info->exc_traceback = *tb; #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = *type; tstate->exc_value = *value; tstate->exc_traceback = *tb; #endif *type = tmp_type; *value = tmp_value; *tb = tmp_tb; } #else static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); PyErr_SetExcInfo(*type, *value, *tb); *type = tmp_type; *value = tmp_value; *tb = tmp_tb; } #endif /* Import */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; #if PY_MAJOR_VERSION < 3 PyObject *py_import; py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); if (!py_import) goto bad; #endif if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if (strchr(__Pyx_MODULE_NAME, '.')) { module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, 1); if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; } #endif if (!module) { #if PY_MAJOR_VERSION < 3 PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, level); #endif } } bad: #if PY_MAJOR_VERSION < 3 Py_XDECREF(py_import); #endif Py_XDECREF(empty_list); Py_XDECREF(empty_dict); return module; } /* FastTypeChecks */ #if CYTHON_COMPILING_IN_CPYTHON static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { while (a) { a = a->tp_base; if (a == b) return 1; } return b == &PyBaseObject_Type; } static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { PyObject *mro; if (a == b) return 1; mro = a->tp_mro; if (likely(mro)) { Py_ssize_t i, n; n = PyTuple_GET_SIZE(mro); for (i = 0; i < n; i++) { if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) return 1; } return 0; } return __Pyx_InBases(a, b); } #if PY_MAJOR_VERSION == 2 static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { PyObject *exception, *value, *tb; int res; __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ErrFetch(&exception, &value, &tb); res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; if (unlikely(res == -1)) { PyErr_WriteUnraisable(err); res = 0; } if (!res) { res = PyObject_IsSubclass(err, exc_type2); if (unlikely(res == -1)) { PyErr_WriteUnraisable(err); res = 0; } } __Pyx_ErrRestore(exception, value, tb); return res; } #else static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; if (!res) { res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); } return res; } #endif static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { Py_ssize_t i, n; assert(PyExceptionClass_Check(exc_type)); n = PyTuple_GET_SIZE(tuple); #if PY_MAJOR_VERSION >= 3 for (i=0; i= 0 || (x^b) >= 0)) return PyInt_FromLong(x); return PyLong_Type.tp_as_number->nb_add(op1, op2); } #endif #if CYTHON_USE_PYLONG_INTERNALS if (likely(PyLong_CheckExact(op1))) { const long b = intval; long a, x; #ifdef HAVE_LONG_LONG const PY_LONG_LONG llb = intval; PY_LONG_LONG lla, llx; #endif const digit* digits = ((PyLongObject*)op1)->ob_digit; const Py_ssize_t size = Py_SIZE(op1); if (likely(__Pyx_sst_abs(size) <= 1)) { a = likely(size) ? digits[0] : 0; if (size == -1) a = -a; } else { switch (size) { case -2: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 2: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case -3: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 3: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case -4: if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; case 4: if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); break; #ifdef HAVE_LONG_LONG } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); goto long_long; #endif } CYTHON_FALLTHROUGH; default: return PyLong_Type.tp_as_number->nb_add(op1, op2); } } x = a + b; return PyLong_FromLong(x); #ifdef HAVE_LONG_LONG long_long: llx = lla + llb; return PyLong_FromLongLong(llx); #endif } #endif if (PyFloat_CheckExact(op1)) { const long b = intval; double a = PyFloat_AS_DOUBLE(op1); double result; PyFPE_START_PROTECT("add", return NULL) result = ((double)a) + (double)b; PyFPE_END_PROTECT(result) return PyFloat_FromDouble(result); } return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); } #endif /* ImportFrom */ static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Format(PyExc_ImportError, #if PY_MAJOR_VERSION < 3 "cannot import name %.230s", PyString_AS_STRING(name)); #else "cannot import name %S", name); #endif } return value; } /* HasAttr */ static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { PyObject *r; if (unlikely(!__Pyx_PyBaseString_Check(n))) { PyErr_SetString(PyExc_TypeError, "hasattr(): attribute name must be string"); return -1; } r = __Pyx_GetAttr(o, n); if (unlikely(!r)) { PyErr_Clear(); return 0; } else { Py_DECREF(r); return 1; } } /* PyObject_GenericGetAttrNoDict */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { PyErr_Format(PyExc_AttributeError, #if PY_MAJOR_VERSION >= 3 "'%.50s' object has no attribute '%U'", tp->tp_name, attr_name); #else "'%.50s' object has no attribute '%.400s'", tp->tp_name, PyString_AS_STRING(attr_name)); #endif return NULL; } static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { PyObject *descr; PyTypeObject *tp = Py_TYPE(obj); if (unlikely(!PyString_Check(attr_name))) { return PyObject_GenericGetAttr(obj, attr_name); } assert(!tp->tp_dictoffset); descr = _PyType_Lookup(tp, attr_name); if (unlikely(!descr)) { return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); } Py_INCREF(descr); #if PY_MAJOR_VERSION < 3 if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) #endif { descrgetfunc f = Py_TYPE(descr)->tp_descr_get; if (unlikely(f)) { PyObject *res = f(descr, obj, (PyObject *)tp); Py_DECREF(descr); return res; } } return descr; } #endif /* PyObject_GenericGetAttr */ #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { return PyObject_GenericGetAttr(obj, attr_name); } return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); } #endif /* SetVTable */ static int __Pyx_SetVtable(PyObject *dict, void *vtable) { #if PY_VERSION_HEX >= 0x02070000 PyObject *ob = PyCapsule_New(vtable, 0, 0); #else PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); #endif if (!ob) goto bad; if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) goto bad; Py_DECREF(ob); return 0; bad: Py_XDECREF(ob); return -1; } /* SetupReduce */ static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { int ret; PyObject *name_attr; name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2); if (likely(name_attr)) { ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); } else { ret = -1; } if (unlikely(ret < 0)) { PyErr_Clear(); ret = 0; } Py_XDECREF(name_attr); return ret; } static int __Pyx_setup_reduce(PyObject* type_obj) { int ret = 0; PyObject *object_reduce = NULL; PyObject *object_reduce_ex = NULL; PyObject *reduce = NULL; PyObject *reduce_ex = NULL; PyObject *reduce_cython = NULL; PyObject *setstate = NULL; PyObject *setstate_cython = NULL; #if CYTHON_USE_PYTYPE_LOOKUP if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; #else if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; #endif #if CYTHON_USE_PYTYPE_LOOKUP object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; #else object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; #endif reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; if (reduce_ex == object_reduce_ex) { #if CYTHON_USE_PYTYPE_LOOKUP object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; #else object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; #endif reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { reduce_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_cython); if (unlikely(!reduce_cython)) goto __PYX_BAD; ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); if (!setstate) PyErr_Clear(); if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { setstate_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate_cython); if (unlikely(!setstate_cython)) goto __PYX_BAD; ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; } PyType_Modified((PyTypeObject*)type_obj); } } goto __PYX_GOOD; __PYX_BAD: if (!PyErr_Occurred()) PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); ret = -1; __PYX_GOOD: #if !CYTHON_USE_PYTYPE_LOOKUP Py_XDECREF(object_reduce); Py_XDECREF(object_reduce_ex); #endif Py_XDECREF(reduce); Py_XDECREF(reduce_ex); Py_XDECREF(reduce_cython); Py_XDECREF(setstate); Py_XDECREF(setstate_cython); return ret; } /* CLineInTraceback */ #ifndef CYTHON_CLINE_IN_TRACEBACK static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) { PyObject *use_cline; PyObject *ptype, *pvalue, *ptraceback; #if CYTHON_COMPILING_IN_CPYTHON PyObject **cython_runtime_dict; #endif if (unlikely(!__pyx_cython_runtime)) { return c_line; } __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); #if CYTHON_COMPILING_IN_CPYTHON cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); if (likely(cython_runtime_dict)) { __PYX_PY_DICT_LOOKUP_IF_MODIFIED( use_cline, *cython_runtime_dict, __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) } else #endif { PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); if (use_cline_obj) { use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; Py_DECREF(use_cline_obj); } else { PyErr_Clear(); use_cline = NULL; } } if (!use_cline) { c_line = 0; PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); } else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { c_line = 0; } __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); return c_line; } #endif /* CodeObjectCache */ static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = start + (end - start) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } /* AddTraceback */ #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, 0, 0, 0, 0, __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; PyThreadState *tstate = __Pyx_PyThreadState_Current; if (c_line) { c_line = __Pyx_CLineForTraceback(tstate, c_line); } py_code = __pyx_find_code_object(c_line ? -c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); } py_frame = PyFrame_New( tstate, /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ __pyx_d, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; __Pyx_PyFrame_SetLineNumber(py_frame, py_line); PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; if (!obj) return; if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } if ((0)) {} view->obj = NULL; Py_DECREF(obj); } #endif /* MemviewSliceIsContig */ static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) { int i, index, step, start; Py_ssize_t itemsize = mvs.memview->view.itemsize; if (order == 'F') { step = 1; start = 0; } else { step = -1; start = ndim - 1; } for (i = 0; i < ndim; i++) { index = start + step * i; if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) return 0; itemsize *= mvs.shape[index]; } return 1; } /* OverlappingSlices */ static void __pyx_get_array_memory_extents(__Pyx_memviewslice *slice, void **out_start, void **out_end, int ndim, size_t itemsize) { char *start, *end; int i; start = end = slice->data; for (i = 0; i < ndim; i++) { Py_ssize_t stride = slice->strides[i]; Py_ssize_t extent = slice->shape[i]; if (extent == 0) { *out_start = *out_end = start; return; } else { if (stride > 0) end += stride * (extent - 1); else start += stride * (extent - 1); } } *out_start = start; *out_end = end + itemsize; } static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, __Pyx_memviewslice *slice2, int ndim, size_t itemsize) { void *start1, *end1, *start2, *end2; __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); return (start1 < end2) && (start2 < end1); } /* Capsule */ static CYTHON_INLINE PyObject * __pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) { PyObject *cobj; #if PY_VERSION_HEX >= 0x02070000 cobj = PyCapsule_New(p, sig, NULL); #else cobj = PyCObject_FromVoidPtr(p, NULL); #endif return cobj; } /* IsLittleEndian */ static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) { union { uint32_t u32; uint8_t u8[4]; } S; S.u32 = 0x01020304; return S.u8[0] == 4; } /* BufferFormatCheck */ static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type) { stack[0].field = &ctx->root; stack[0].parent_offset = 0; ctx->root.type = type; ctx->root.name = "buffer dtype"; ctx->root.offset = 0; ctx->head = stack; ctx->head->field = &ctx->root; ctx->fmt_offset = 0; ctx->head->parent_offset = 0; ctx->new_packmode = '@'; ctx->enc_packmode = '@'; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->is_complex = 0; ctx->is_valid_array = 0; ctx->struct_alignment = 0; while (type->typegroup == 'S') { ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = 0; type = type->fields->type; } } static int __Pyx_BufFmt_ParseNumber(const char** ts) { int count; const char* t = *ts; if (*t < '0' || *t > '9') { return -1; } else { count = *t++ - '0'; while (*t >= '0' && *t <= '9') { count *= 10; count += *t++ - '0'; } } *ts = t; return count; } static int __Pyx_BufFmt_ExpectNumber(const char **ts) { int number = __Pyx_BufFmt_ParseNumber(ts); if (number == -1) PyErr_Format(PyExc_ValueError,\ "Does not understand character buffer dtype format string ('%c')", **ts); return number; } static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { PyErr_Format(PyExc_ValueError, "Unexpected format string character: '%c'", ch); } static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { switch (ch) { case '?': return "'bool'"; case 'c': return "'char'"; case 'b': return "'signed char'"; case 'B': return "'unsigned char'"; case 'h': return "'short'"; case 'H': return "'unsigned short'"; case 'i': return "'int'"; case 'I': return "'unsigned int'"; case 'l': return "'long'"; case 'L': return "'unsigned long'"; case 'q': return "'long long'"; case 'Q': return "'unsigned long long'"; case 'f': return (is_complex ? "'complex float'" : "'float'"); case 'd': return (is_complex ? "'complex double'" : "'double'"); case 'g': return (is_complex ? "'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; case 's': case 'p': return "a string"; case 0: return "end"; default: return "unparseable format string"; } } static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return 2; case 'i': case 'I': case 'l': case 'L': return 4; case 'q': case 'Q': return 8; case 'f': return (is_complex ? 8 : 4); case 'd': return (is_complex ? 16 : 8); case 'g': { PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); return 0; } case 'O': case 'P': return sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(short); case 'i': case 'I': return sizeof(int); case 'l': case 'L': return sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(float) * (is_complex ? 2 : 1); case 'd': return sizeof(double) * (is_complex ? 2 : 1); case 'g': return sizeof(long double) * (is_complex ? 2 : 1); case 'O': case 'P': return sizeof(void*); default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } typedef struct { char c; short x; } __Pyx_st_short; typedef struct { char c; int x; } __Pyx_st_int; typedef struct { char c; long x; } __Pyx_st_long; typedef struct { char c; float x; } __Pyx_st_float; typedef struct { char c; double x; } __Pyx_st_double; typedef struct { char c; long double x; } __Pyx_st_longdouble; typedef struct { char c; void *x; } __Pyx_st_void_p; #ifdef HAVE_LONG_LONG typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_st_float) - sizeof(float); case 'd': return sizeof(__Pyx_st_double) - sizeof(double); case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } /* These are for computing the padding at the end of the struct to align on the first member of the struct. This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': return 'H'; case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } if ((type->typegroup == 'H' || group == 'H') && type->size == size) { } else { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number; int ndim = ctx->head->field->type->ndim; ; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; while (*ts && *ts != ')') { switch (*ts) { case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; default: break; } number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case '\r': case '\n': ++ts; break; case '<': if (!__Pyx_Is_Little_Endian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_Is_Little_Endian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } CYTHON_FALLTHROUGH; case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 'p': if (ctx->enc_type == *ts && got_Z == ctx->is_complex && ctx->enc_packmode == ctx->new_packmode) { ctx->enc_count += ctx->new_count; ctx->new_count = 1; got_Z = 0; ++ts; break; } CYTHON_FALLTHROUGH; case 's': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } /* TypeInfoCompare */ static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) { int i; if (!a || !b) return 0; if (a == b) return 1; if (a->size != b->size || a->typegroup != b->typegroup || a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { if (a->typegroup == 'H' || b->typegroup == 'H') { return a->size == b->size; } else { return 0; } } if (a->ndim) { for (i = 0; i < a->ndim; i++) if (a->arraysize[i] != b->arraysize[i]) return 0; } if (a->typegroup == 'S') { if (a->flags != b->flags) return 0; if (a->fields || b->fields) { if (!(a->fields && b->fields)) return 0; for (i = 0; a->fields[i].type && b->fields[i].type; i++) { __Pyx_StructField *field_a = a->fields + i; __Pyx_StructField *field_b = b->fields + i; if (field_a->offset != field_b->offset || !__pyx_typeinfo_cmp(field_a->type, field_b->type)) return 0; } return !a->fields[i].type && !b->fields[i].type; } } return 1; } /* MemviewSliceValidateAndInit */ static int __pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) { if (buf->shape[dim] <= 1) return 1; if (buf->strides) { if (spec & __Pyx_MEMVIEW_CONTIG) { if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { if (buf->strides[dim] != sizeof(void *)) { PyErr_Format(PyExc_ValueError, "Buffer is not indirectly contiguous " "in dimension %d.", dim); goto fail; } } else if (buf->strides[dim] != buf->itemsize) { PyErr_SetString(PyExc_ValueError, "Buffer and memoryview are not contiguous " "in the same dimension."); goto fail; } } if (spec & __Pyx_MEMVIEW_FOLLOW) { Py_ssize_t stride = buf->strides[dim]; if (stride < 0) stride = -stride; if (stride < buf->itemsize) { PyErr_SetString(PyExc_ValueError, "Buffer and memoryview are not contiguous " "in the same dimension."); goto fail; } } } else { if (spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1) { PyErr_Format(PyExc_ValueError, "C-contiguous buffer is not contiguous in " "dimension %d", dim); goto fail; } else if (spec & (__Pyx_MEMVIEW_PTR)) { PyErr_Format(PyExc_ValueError, "C-contiguous buffer is not indirect in " "dimension %d", dim); goto fail; } else if (buf->suboffsets) { PyErr_SetString(PyExc_ValueError, "Buffer exposes suboffsets but no strides"); goto fail; } } return 1; fail: return 0; } static int __pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) { if (spec & __Pyx_MEMVIEW_DIRECT) { if (buf->suboffsets && buf->suboffsets[dim] >= 0) { PyErr_Format(PyExc_ValueError, "Buffer not compatible with direct access " "in dimension %d.", dim); goto fail; } } if (spec & __Pyx_MEMVIEW_PTR) { if (!buf->suboffsets || (buf->suboffsets[dim] < 0)) { PyErr_Format(PyExc_ValueError, "Buffer is not indirectly accessible " "in dimension %d.", dim); goto fail; } } return 1; fail: return 0; } static int __pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) { int i; if (c_or_f_flag & __Pyx_IS_F_CONTIG) { Py_ssize_t stride = 1; for (i = 0; i < ndim; i++) { if (stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1) { PyErr_SetString(PyExc_ValueError, "Buffer not fortran contiguous."); goto fail; } stride = stride * buf->shape[i]; } } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { Py_ssize_t stride = 1; for (i = ndim - 1; i >- 1; i--) { if (stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1) { PyErr_SetString(PyExc_ValueError, "Buffer not C contiguous."); goto fail; } stride = stride * buf->shape[i]; } } return 1; fail: return 0; } static int __Pyx_ValidateAndInit_memviewslice( int *axes_specs, int c_or_f_flag, int buf_flags, int ndim, __Pyx_TypeInfo *dtype, __Pyx_BufFmt_StackElem stack[], __Pyx_memviewslice *memviewslice, PyObject *original_obj) { struct __pyx_memoryview_obj *memview, *new_memview; __Pyx_RefNannyDeclarations Py_buffer *buf; int i, spec = 0, retval = -1; __Pyx_BufFmt_Context ctx; int from_memoryview = __pyx_memoryview_check(original_obj); __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) original_obj)->typeinfo)) { memview = (struct __pyx_memoryview_obj *) original_obj; new_memview = NULL; } else { memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( original_obj, buf_flags, 0, dtype); new_memview = memview; if (unlikely(!memview)) goto fail; } buf = &memview->view; if (buf->ndim != ndim) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", ndim, buf->ndim); goto fail; } if (new_memview) { __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if ((unsigned) buf->itemsize != dtype->size) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } for (i = 0; i < ndim; i++) { spec = axes_specs[i]; if (!__pyx_check_strides(buf, i, ndim, spec)) goto fail; if (!__pyx_check_suboffsets(buf, i, ndim, spec)) goto fail; } if (buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)) goto fail; if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, new_memview != NULL) == -1)) { goto fail; } retval = 0; goto no_fail; fail: Py_XDECREF(new_memview); retval = -1; no_fail: __Pyx_RefNannyFinishContext(); return retval; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 2, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* MemviewDtypeToObject */ static CYTHON_INLINE PyObject *__pyx_memview_get_double(const char *itemp) { return (PyObject *) PyFloat_FromDouble(*(double *) itemp); } static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *obj) { double value = __pyx_PyFloat_AsDouble(obj); if ((value == (double)-1) && PyErr_Occurred()) return 0; *(double *) itemp = value; return 1; } /* MemviewSliceCopyTemplate */ static __Pyx_memviewslice __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, const char *mode, int ndim, size_t sizeof_dtype, int contig_flag, int dtype_is_object) { __Pyx_RefNannyDeclarations int i; __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; struct __pyx_memoryview_obj *from_memview = from_mvs->memview; Py_buffer *buf = &from_memview->view; PyObject *shape_tuple = NULL; PyObject *temp_int = NULL; struct __pyx_array_obj *array_obj = NULL; struct __pyx_memoryview_obj *memview_obj = NULL; __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); for (i = 0; i < ndim; i++) { if (from_mvs->suboffsets[i] >= 0) { PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " "indirect dimensions (axis %d)", i); goto fail; } } shape_tuple = PyTuple_New(ndim); if (unlikely(!shape_tuple)) { goto fail; } __Pyx_GOTREF(shape_tuple); for(i = 0; i < ndim; i++) { temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); if(unlikely(!temp_int)) { goto fail; } else { PyTuple_SET_ITEM(shape_tuple, i, temp_int); temp_int = NULL; } } array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); if (unlikely(!array_obj)) { goto fail; } __Pyx_GOTREF(array_obj); memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( (PyObject *) array_obj, contig_flag, dtype_is_object, from_mvs->memview->typeinfo); if (unlikely(!memview_obj)) goto fail; if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) goto fail; if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, dtype_is_object) < 0)) goto fail; goto no_fail; fail: __Pyx_XDECREF(new_mvs.memview); new_mvs.memview = NULL; new_mvs.data = NULL; no_fail: __Pyx_XDECREF(shape_tuple); __Pyx_XDECREF(temp_int); __Pyx_XDECREF(array_obj); __Pyx_RefNannyFinishContext(); return new_mvs; } /* CIntFromPyVerify */ #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) #define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) #define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ {\ func_type value = func_value;\ if (sizeof(target_type) < sizeof(func_type)) {\ if (unlikely(value != (func_type) (target_type) value)) {\ func_type zero = 0;\ if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ return (target_type) -1;\ if (is_unsigned && unlikely(value < zero))\ goto raise_neg_overflow;\ else\ goto raise_overflow;\ }\ }\ return (target_type) value;\ } /* CIntFromPy */ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(int) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (int) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (int) 0; case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) case 2: if (8 * sizeof(int) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; case 3: if (8 * sizeof(int) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; case 4: if (8 * sizeof(int) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (int) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(int) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (int) 0; case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) case -2: if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 2: if (8 * sizeof(int) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case -3: if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 3: if (8 * sizeof(int) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case -4: if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 4: if (8 * sizeof(int) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; } #endif if (sizeof(int) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else int val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (int) -1; } } else { int val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (int) -1; val = __Pyx_PyInt_As_int(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to int"); return (int) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } /* CIntFromPy */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(long) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (long) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) case 2: if (8 * sizeof(long) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; case 3: if (8 * sizeof(long) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; case 4: if (8 * sizeof(long) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (long) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) case -2: if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 2: if (8 * sizeof(long) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case -3: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 3: if (8 * sizeof(long) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case -4: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 4: if (8 * sizeof(long) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; } #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (long) -1; } } else { long val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to long"); return (long) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(int) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(int), little, !is_unsigned); } } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(long) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(long), little, !is_unsigned); } } /* CIntFromPy */ static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { const char neg_one = (char) ((char) 0 - (char) 1), const_zero = (char) 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(char) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (char) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (char) 0; case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0]) case 2: if (8 * sizeof(char) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) { return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; case 3: if (8 * sizeof(char) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) { return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; case 4: if (8 * sizeof(char) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) { return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (char) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(char) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (char) 0; case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0]) case -2: if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 2: if (8 * sizeof(char) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case -3: if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 3: if (8 * sizeof(char) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case -4: if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; case 4: if (8 * sizeof(char) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } break; } #endif if (sizeof(char) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else char val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (char) -1; } } else { char val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (char) -1; val = __Pyx_PyInt_As_char(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to char"); return (char) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to char"); return (char) -1; } /* ObjectToMemviewSlice */ static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *obj, int writable_flag) { __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; __Pyx_BufFmt_StackElem stack[1]; int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; int retcode; if (obj == Py_None) { result.memview = (struct __pyx_memoryview_obj *) Py_None; return result; } retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, PyBUF_RECORDS_RO | writable_flag, 1, &__Pyx_TypeInfo_double, stack, &result, obj); if (unlikely(retcode == -1)) goto __pyx_fail; return result; __pyx_fail: result.memview = NULL; result.data = NULL; return result; } /* CheckBinaryVersion */ static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); return PyErr_WarnEx(NULL, message, 1); } return 0; } /* PyObjectSetAttrStr */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value) { PyTypeObject* tp = Py_TYPE(obj); if (likely(tp->tp_setattro)) return tp->tp_setattro(obj, attr_name, value); #if PY_MAJOR_VERSION < 3 if (likely(tp->tp_setattr)) return tp->tp_setattr(obj, PyString_AS_STRING(attr_name), value); #endif return PyObject_SetAttr(obj, attr_name, value); } #endif /* VoidPtrExport */ static int __Pyx_ExportVoidPtr(PyObject *name, void *p, const char *sig) { PyObject *d; PyObject *cobj = 0; d = PyDict_GetItem(__pyx_d, __pyx_n_s_pyx_capi); Py_XINCREF(d); if (!d) { d = PyDict_New(); if (!d) goto bad; if (__Pyx_PyObject_SetAttrStr(__pyx_m, __pyx_n_s_pyx_capi, d) < 0) goto bad; } #if PY_VERSION_HEX >= 0x02070000 cobj = PyCapsule_New(p, sig, 0); #else cobj = PyCObject_FromVoidPtrAndDesc(p, (void *)sig, 0); #endif if (!cobj) goto bad; if (PyDict_SetItem(d, name, cobj) < 0) goto bad; Py_DECREF(cobj); Py_DECREF(d); return 0; bad: Py_XDECREF(cobj); Py_XDECREF(d); return -1; } /* FunctionExport */ static int __Pyx_ExportFunction(const char *name, void (*f)(void), const char *sig) { PyObject *d = 0; PyObject *cobj = 0; union { void (*fp)(void); void *p; } tmp; d = PyObject_GetAttrString(__pyx_m, (char *)"__pyx_capi__"); if (!d) { PyErr_Clear(); d = PyDict_New(); if (!d) goto bad; Py_INCREF(d); if (PyModule_AddObject(__pyx_m, (char *)"__pyx_capi__", d) < 0) goto bad; } tmp.fp = f; #if PY_VERSION_HEX >= 0x02070000 cobj = PyCapsule_New(tmp.p, sig, 0); #else cobj = PyCObject_FromVoidPtrAndDesc(tmp.p, (void *)sig, 0); #endif if (!cobj) goto bad; if (PyDict_SetItemString(d, name, cobj) < 0) goto bad; Py_DECREF(cobj); Py_DECREF(d); return 0; bad: Py_XDECREF(cobj); Py_XDECREF(d); return -1; } /* InitStrings */ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; if (PyObject_Hash(*t->p) == -1) return -1; ++t; } return 0; } static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); } static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { Py_ssize_t ignore; return __Pyx_PyObject_AsStringAndSize(o, &ignore); } #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT #if !CYTHON_PEP393_ENABLED static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { char* defenc_c; PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); if (!defenc) return NULL; defenc_c = PyBytes_AS_STRING(defenc); #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII { char* end = defenc_c + PyBytes_GET_SIZE(defenc); char* c; for (c = defenc_c; c < end; c++) { if ((unsigned char) (*c) >= 128) { PyUnicode_AsASCIIString(o); return NULL; } } } #endif *length = PyBytes_GET_SIZE(defenc); return defenc_c; } #else static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII if (likely(PyUnicode_IS_ASCII(o))) { *length = PyUnicode_GET_LENGTH(o); return PyUnicode_AsUTF8(o); } else { PyUnicode_AsASCIIString(o); return NULL; } #else return PyUnicode_AsUTF8AndSize(o, length); #endif } #endif #endif static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT if ( #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII __Pyx_sys_getdefaultencoding_not_ascii && #endif PyUnicode_Check(o)) { return __Pyx_PyUnicode_AsStringAndSize(o, length); } else #endif #if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) if (PyByteArray_Check(o)) { *length = PyByteArray_GET_SIZE(o); return PyByteArray_AS_STRING(o); } else #endif { char* result; int r = PyBytes_AsStringAndSize(o, &result, length); if (unlikely(r < 0)) { return NULL; } else { return result; } } } static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { int retval; if (unlikely(!x)) return -1; retval = __Pyx_PyObject_IsTrue(x); Py_DECREF(x); return retval; } static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { #if PY_MAJOR_VERSION >= 3 if (PyLong_Check(result)) { if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, "__int__ returned non-int (type %.200s). " "The ability to return an instance of a strict subclass of int " "is deprecated, and may be removed in a future version of Python.", Py_TYPE(result)->tp_name)) { Py_DECREF(result); return NULL; } return result; } #endif PyErr_Format(PyExc_TypeError, "__%.4s__ returned non-%.4s (type %.200s)", type_name, type_name, Py_TYPE(result)->tp_name); Py_DECREF(result); return NULL; } static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { #if CYTHON_USE_TYPE_SLOTS PyNumberMethods *m; #endif const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x) || PyLong_Check(x))) #else if (likely(PyLong_Check(x))) #endif return __Pyx_NewRef(x); #if CYTHON_USE_TYPE_SLOTS m = Py_TYPE(x)->tp_as_number; #if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = m->nb_int(x); } else if (m && m->nb_long) { name = "long"; res = m->nb_long(x); } #else if (likely(m && m->nb_int)) { name = "int"; res = m->nb_int(x); } #endif #else if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { res = PyNumber_Int(x); } #endif if (likely(res)) { #if PY_MAJOR_VERSION < 3 if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { #else if (unlikely(!PyLong_CheckExact(res))) { #endif return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject *x; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(b))) { if (sizeof(Py_ssize_t) >= sizeof(long)) return PyInt_AS_LONG(b); else return PyInt_AsSsize_t(b); } #endif if (likely(PyLong_CheckExact(b))) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)b)->ob_digit; const Py_ssize_t size = Py_SIZE(b); if (likely(__Pyx_sst_abs(size) <= 1)) { ival = likely(size) ? digits[0] : 0; if (size == -1) ival = -ival; return ival; } else { switch (size) { case 2: if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -2: if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case 3: if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -3: if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case 4: if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -4: if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; } } #endif return PyLong_AsSsize_t(b); } x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { return PyInt_FromSize_t(ival); } #endif /* Py_PYTHON_H */ openTSNE-0.6.1/openTSNE/quad_tree.pxd000066400000000000000000000011311413546205200172700ustar00rootroot00000000000000# cython: profile=True # cython: boundscheck=False # cython: wraparound=False # cython: cdivision=True # cython: language_level=3 import numpy as np cdef double EPSILON = np.finfo(np.float64).eps ctypedef struct Node: Py_ssize_t n_dims double *center double length bint is_leaf Node *children double *center_of_mass Py_ssize_t num_points cdef bint is_duplicate(Node * node, double * point, double duplicate_eps=*) nogil cdef class QuadTree: cdef Node root cpdef void add_points(self, double[:, ::1] points) cpdef void add_point(self, double[::1] point) openTSNE-0.6.1/openTSNE/quad_tree.pyx000066400000000000000000000133201413546205200173200ustar00rootroot00000000000000# cython: boundscheck=False # cython: wraparound=False # cython: cdivision=True # cython: language_level=3 """Implements a quad/oct-tree space partitioning algorithm primarily used in efficiently estimating the t-SNE negative gradient. Lowers the time complexity from the naive O(n^2) to O(n * log(n)). Notes ----- I list here several implementation details. Many of these improve efficiency. - Allocating memory is slow, especially if it has to be done millions of times, therefore avoid allocation whereever possible and use buffers. Allocation should be done through the use of `PyMem_Malloc` as this is the fastest method of allocation. Use this over `libc.stdlib.malloc` because, despite requiring the GIL to allocate, it gets tracked in the Python virtual environment (which is desirable) and includes some minor optimizations. Also, since we need the GIL to allocate, this can warn us of any needless memory allocations. - Structs do not support memoryviews, therefore pointers must be used. - Prefer pointers over memoryviews where speed is essential. Memoryview indexing and slicing is slow compared to raw memory access. We can easily convert a memory view to a pointer like so: `&mv[0]` however care must be taken to ensure the memoryview is a C contigous array. This can be ensured by the type declaration `double[:, ::1]` for 2d arrays. References ---------- .. [1] Van Der Maaten, Laurens. "Accelerating t-SNE using tree-based algorithms." Journal of machine learning research 15.1 (2014): 3221-3245. """ import numpy as np from cpython.mem cimport PyMem_Malloc, PyMem_Free cdef extern from "math.h": double fabs(double x) nogil cdef void init_node(Node * node, Py_ssize_t n_dim, double * center, double length): node.n_dims = n_dim node.center = PyMem_Malloc(node.n_dims * sizeof(double)) node.center_of_mass = PyMem_Malloc(node.n_dims * sizeof(double)) if not node.center or not node.center_of_mass: raise MemoryError() cdef Py_ssize_t i for i in range(node.n_dims): node.center[i] = center[i] node.center_of_mass[i] = 0 node.length = length node.is_leaf = True node.num_points = 0 cdef Py_ssize_t get_child_idx_for(Node * node, double * point) nogil: cdef Py_ssize_t idx = 0, d for d in range(node.n_dims): idx |= (point[d] > node.center[d]) << d return idx cdef inline void update_center_of_mass(Node * node, double * point) nogil: cdef Py_ssize_t d for d in range(node.n_dims): node.center_of_mass[d] = (node.center_of_mass[d] * node.num_points + point[d]) \ / (node.num_points + 1) node.num_points += 1 cdef void add_point_to(Node * node, double * point): # If the node is a leaf node and empty, we"re done if node.is_leaf and node.num_points == 0 or is_duplicate(node, point): update_center_of_mass(node, point) return # Otherwise, we have to split the node and sink the previous, existing # point into the appropriate child node cdef Py_ssize_t child_index if node.is_leaf: split_node(node) child_index = get_child_idx_for(node, node.center_of_mass) update_center_of_mass(&node.children[child_index], node.center_of_mass) update_center_of_mass(node, point) # Finally, once the node is properly split, insert the new point into the # corresponding child child_index = get_child_idx_for(node, point) add_point_to(&node.children[child_index], point) cdef void split_node(Node * node): cdef double new_length = node.length / 2 cdef Py_ssize_t num_children = 1 << node.n_dims node.is_leaf = False node.children = PyMem_Malloc(num_children * sizeof(Node)) if not node.children: raise MemoryError() cdef Py_ssize_t i, d cdef double * new_center = PyMem_Malloc(node.n_dims * sizeof(double)) if not new_center: raise MemoryError() for i in range(num_children): for d in range(node.n_dims): if i & (1 << d): new_center[d] = node.center[d] + new_length / 2 else: new_center[d] = node.center[d] - new_length / 2 init_node(&node.children[i], node.n_dims, new_center, new_length) PyMem_Free(new_center) cdef inline bint is_duplicate(Node * node, double * point, double duplicate_eps=1e-6) nogil: cdef Py_ssize_t d for d in range(node.n_dims): if fabs(node.center_of_mass[d] - point[d]) >= duplicate_eps: return False return True cdef void delete_node(Node * node): PyMem_Free(node.center) PyMem_Free(node.center_of_mass) if node.is_leaf: return cdef Py_ssize_t i for i in range(1 << node.n_dims): delete_node(&node.children[i]) PyMem_Free(node.children) cdef class QuadTree: def __init__(self, double[:, ::1] data): cdef: Py_ssize_t n_dim = data.shape[1] double[:] x_min = np.min(data, axis=0) double[:] x_max = np.max(data, axis=0) double[:] center = np.zeros(n_dim) double length = 0 Py_ssize_t d for d in range(n_dim): center[d] = (x_max[d] + x_min[d]) / 2 if x_max[d] - x_min[d] > length: length = x_max[d] - x_min[d] self.root = Node() init_node(&self.root, n_dim, ¢er[0], length) self.add_points(data) cpdef void add_points(self, double[:, ::1] points): cdef Py_ssize_t i for i in range(points.shape[0]): add_point_to(&self.root, &points[i, 0]) cpdef void add_point(self, double[::1] point): add_point_to(&self.root, &point[0]) def __dealloc__(self): delete_node(&self.root) openTSNE-0.6.1/openTSNE/sklearn.py000066400000000000000000000025441413546205200166240ustar00rootroot00000000000000import openTSNE import numpy as np class TSNE(openTSNE.TSNE): __doc__ = openTSNE.TSNE.__doc__ def fit(self, X, y=None): """Fit X into an embedded space. Parameters ---------- X: np.ndarray The data matrix to be embedded. y : ignored """ self.fit_transform(X, y) return self def fit_transform(self, X, y=None): """Fit X into an embedded space and return that transformed output. Parameters ---------- X: np.ndarray The data matrix to be embedded. y : ignored Returns ------- np.ndarray Embedding of the training data in low-dimensional space. """ embedding = super().fit(X) self.embedding_ = embedding return self.embedding_.view(np.ndarray) def transform(self, X, *args, **kwargs): """Apply dimensionality reduction to X. See :meth:`openTSNE.TSNEEmbedding.transform` for additional parameters. Parameters ---------- X: np.ndarray The data matrix to be embedded. Returns ------- np.ndarray Embedding of the training data in low-dimensional space. """ embedding = self.embedding_.transform(X, *args, **kwargs) return embedding.view(np.ndarray) openTSNE-0.6.1/openTSNE/tsne.py000066400000000000000000002153221413546205200161360ustar00rootroot00000000000000import inspect import logging import multiprocessing from collections.abc import Iterable from types import SimpleNamespace from time import time import numpy as np from sklearn.base import BaseEstimator from openTSNE import _tsne from openTSNE import initialization as initialization_scheme from openTSNE.affinity import Affinities, PerplexityBasedNN from openTSNE.quad_tree import QuadTree from openTSNE import utils EPSILON = np.finfo(np.float64).eps log = logging.getLogger(__name__) def _check_callbacks(callbacks): if callbacks is not None: # If list was passed, make sure all of them are actually callable if isinstance(callbacks, Iterable): if any(not callable(c) for c in callbacks): raise ValueError("`callbacks` must contain callable objects!") # The gradient descent method deals with lists elif callable(callbacks): callbacks = (callbacks,) else: raise ValueError("`callbacks` must be a callable object!") return callbacks def _handle_nice_params(embedding: np.ndarray, optim_params: dict) -> None: """Convert the user friendly params into something the optimizer can understand.""" n_samples = embedding.shape[0] # Handle callbacks optim_params["callbacks"] = _check_callbacks(optim_params.get("callbacks")) optim_params["use_callbacks"] = optim_params["callbacks"] is not None # Handle negative gradient method negative_gradient_method = optim_params.pop("negative_gradient_method") # Handle `auto` negative gradient method if isinstance(negative_gradient_method, str) and negative_gradient_method == "auto": if n_samples < 10_000: negative_gradient_method = "bh" else: negative_gradient_method = "fft" log.info( f"Automatically determined negative gradient method `{negative_gradient_method}`" ) if callable(negative_gradient_method): negative_gradient_method = negative_gradient_method elif negative_gradient_method in {"bh", "BH", "barnes-hut"}: negative_gradient_method = kl_divergence_bh elif negative_gradient_method in {"fft", "FFT", "interpolation"}: negative_gradient_method = kl_divergence_fft else: raise ValueError( "Unrecognized gradient method. Please choose one of " "the supported methods or provide a valid callback." ) # `gradient_descent` uses the more informative name `objective_function` optim_params["objective_function"] = negative_gradient_method # Handle number of jobs n_jobs = optim_params.get("n_jobs", 1) if n_jobs < 0: n_cores = multiprocessing.cpu_count() # Add negative number of n_jobs to the number of cores, but increment by # one because -1 indicates using all cores, -2 all except one, and so on n_jobs = n_cores + n_jobs + 1 # If the number of jobs, after this correction is still <= 0, then the user # probably thought they had more cores, so we'll default to 1 if n_jobs <= 0: log.warning( "`n_jobs` receieved value %d but only %d cores are available. " "Defaulting to single job." % (optim_params["n_jobs"], n_cores) ) n_jobs = 1 optim_params["n_jobs"] = n_jobs # Determine learning rate if requested if optim_params.get("learning_rate", "auto") == "auto": optim_params["learning_rate"] = max(200, n_samples / 12) def __check_init_num_samples(num_samples, required_num_samples): if num_samples != required_num_samples: raise ValueError( "The provided initialization contains a different number " "of points (%d) than the data provided (%d)." % (num_samples, required_num_samples) ) def __check_init_num_dimensions(num_dimensions, required_num_dimensions): if num_dimensions != required_num_dimensions: raise ValueError( "The provided initialization contains a different number " "of components (%d) than the embedding (%d)." % (num_dimensions, required_num_dimensions) ) init_checks = SimpleNamespace( num_samples=__check_init_num_samples, num_dimensions=__check_init_num_dimensions, ) class OptimizationInterrupt(InterruptedError): """Optimization was interrupted by a callback. Parameters ---------- error: float The KL divergence of the embedding. final_embedding: Union[TSNEEmbedding, PartialTSNEEmbedding] Is either a partial or full embedding, depending on where the error was raised. """ def __init__(self, error, final_embedding): super().__init__() self.error = error self.final_embedding = final_embedding class PartialTSNEEmbedding(np.ndarray): """A partial t-SNE embedding. A partial embedding is created when we take an existing :class:`TSNEEmbedding` and embed new samples into the embedding space. It differs from the typical embedding in that it is not possible to add new samples to a partial embedding and would generally be a bad idea. Please see the :ref:`parameter-guide` for more information. Parameters ---------- embedding: np.ndarray Initial positions for each data point. reference_embedding: TSNEEmbedding The embedding into which the new samples are to be added. P : array_like An :math:`N \\times M` affinity matrix containing the affinities from each new data point :math:`n` to each data point in the existing embedding :math:`m`. learning_rate: Union[str, float] The learning rate for t-SNE optimization. When ``learning_rate="auto"`` the appropriate learning rate is selected according to max(200, N / 12) as determined in Belkina et al. (2019), Nature Communications. Note that this should *not* be used when adding samples into existing embeddings, where the learning rate often needs to be much lower to obtain convergence. exaggeration: float The exaggeration factor is used to increase the attractive forces of nearby points, producing more compact clusters. momentum: float Momentum accounts for gradient directions from previous iterations, resulting in faster convergence. negative_gradient_method: str Specifies the negative gradient approximation method to use. For smaller data sets, the Barnes-Hut approximation is appropriate and can be set using one of the following aliases: ``bh``, ``BH`` or ``barnes-hut``. For larger data sets, the FFT accelerated interpolation method is more appropriate and can be set using one of the following aliases: ``fft``, ``FFT`` or ``ìnterpolation``. Alternatively, you can use ``auto`` to approximately select the faster method. theta: float This is the trade-off parameter between speed and accuracy of the tree approximation method. Typical values range from 0.2 to 0.8. The value 0 indicates that no approximation is to be made and produces exact results also producing longer runtime. n_interpolation_points: int Only used when ``negative_gradient_method="fft"`` or its other aliases. The number of interpolation points to use within each grid cell for interpolation based t-SNE. It is highly recommended leaving this value at the default 3. min_num_intervals: int Only used when ``negative_gradient_method="fft"`` or its other aliases. The minimum number of grid cells to use, regardless of the ``ints_in_interval`` parameter. Higher values provide more accurate gradient estimations. random_state: Union[int, RandomState] The random state parameter follows the convention used in scikit-learn. If the value is an int, random_state is the seed used by the random number generator. If the value is a RandomState instance, then it will be used as the random number generator. If the value is None, the random number generator is the RandomState instance used by `np.random`. n_jobs: int The number of threads to use while running t-SNE. This follows the scikit-learn convention, ``-1`` meaning all processors, ``-2`` meaning all but one, etc. callbacks: Callable[[int, float, np.ndarray] -> bool] Callbacks, which will be run every ``callbacks_every_iters`` iterations. callbacks_every_iters: int How many iterations should pass between each time the callbacks are invoked. optimizer: gradient_descent Optionally, an existing optimizer can be used for optimization. This is useful for keeping momentum gains between different calls to :func:`optimize`. Attributes ---------- kl_divergence: float The KL divergence or error of the embedding. """ def __new__( cls, embedding, reference_embedding, P, optimizer=None, **gradient_descent_params, ): init_checks.num_samples(embedding.shape[0], P.shape[0]) obj = np.asarray(embedding, dtype=np.float64, order="C").view( PartialTSNEEmbedding ) obj.reference_embedding = reference_embedding obj.P = P obj.gradient_descent_params = gradient_descent_params if optimizer is None: optimizer = gradient_descent() elif not isinstance(optimizer, gradient_descent): raise TypeError( "`optimizer` must be an instance of `%s`, but got `%s`." % (gradient_descent.__class__.__name__, type(optimizer)) ) obj.optimizer = optimizer obj.kl_divergence = None return obj def optimize( self, n_iter, inplace=False, propagate_exception=False, **gradient_descent_params, ): """Run optmization on the embedding for a given number of steps. Parameters ---------- n_iter: int The number of optimization iterations. learning_rate: Union[str, float] The learning rate for t-SNE optimization. When ``learning_rate="auto"`` the appropriate learning rate is selected according to max(200, N / 12), as determined in Belkina et al. "Automated optimized parameters for t-distributed stochastic neighbor embedding improve visualization and analysis of large datasets", 2019. Note that this should *not* be used when adding samples into existing embeddings, where the learning rate often needs to be much lower to obtain convergence. exaggeration: float The exaggeration factor is used to increase the attractive forces of nearby points, producing more compact clusters. momentum: float Momentum accounts for gradient directions from previous iterations, resulting in faster convergence. negative_gradient_method: str Specifies the negative gradient approximation method to use. For smaller data sets, the Barnes-Hut approximation is appropriate and can be set using one of the following aliases: ``bh``, ``BH`` or ``barnes-hut``. For larger data sets, the FFT accelerated interpolation method is more appropriate and can be set using one of the following aliases: ``fft``, ``FFT`` or ``ìnterpolation``. Alternatively, you can use ``auto`` to approximately select the faster method. theta: float This is the trade-off parameter between speed and accuracy of the tree approximation method. Typical values range from 0.2 to 0.8. The value 0 indicates that no approximation is to be made and produces exact results also producing longer runtime. n_interpolation_points: int Only used when ``negative_gradient_method="fft"`` or its other aliases. The number of interpolation points to use within each grid cell for interpolation based t-SNE. It is highly recommended leaving this value at the default 3. min_num_intervals: int Only used when ``negative_gradient_method="fft"`` or its other aliases. The minimum number of grid cells to use, regardless of the ``ints_in_interval`` parameter. Higher values provide more accurate gradient estimations. inplace: bool Whether or not to create a copy of the embedding or to perform updates inplace. propagate_exception: bool The optimization process can be interrupted using callbacks. This flag indicates whether we should propagate that exception or to simply stop optimization and return the resulting embedding. n_jobs: int The number of threads to use while running t-SNE. This follows the scikit-learn convention, ``-1`` meaning all processors, ``-2`` meaning all but one, etc. callbacks: Callable[[int, float, np.ndarray] -> bool] Callbacks, which will be run every ``callbacks_every_iters`` iterations. callbacks_every_iters: int How many iterations should pass between each time the callbacks are invoked. Returns ------- PartialTSNEEmbedding An optimized partial t-SNE embedding. Raises ------ OptimizationInterrupt If a callback stops the optimization and the ``propagate_exception`` flag is set, then an exception is raised. """ # Typically we want to return a new embedding and keep the old one intact if inplace: embedding = self else: embedding = PartialTSNEEmbedding( np.copy(self), self.reference_embedding, self.P, optimizer=self.optimizer.copy(), **self.gradient_descent_params, ) # If optimization parameters were passed to this funciton, prefer those # over the defaults specified in the TSNE object optim_params = dict(self.gradient_descent_params) optim_params.update(gradient_descent_params) optim_params["n_iter"] = n_iter _handle_nice_params(embedding, optim_params) try: # Run gradient descent with the embedding optimizer so gains are # properly updated and kept error, embedding = embedding.optimizer( embedding=embedding, reference_embedding=self.reference_embedding, P=self.P, **optim_params, ) except OptimizationInterrupt as ex: log.info("Optimization was interrupted with callback.") if propagate_exception: raise ex error, embedding = ex.error, ex.final_embedding embedding.kl_divergence = error return embedding class TSNEEmbedding(np.ndarray): """A t-SNE embedding. Please see the :ref:`parameter-guide` for more information. Parameters ---------- embedding: np.ndarray Initial positions for each data point. affinities: Affinities An affinity index which can be used to compute the affinities of new points to the points in the existing embedding. The affinity index also contains the affinity matrix :math:`P` used during optimization. learning_rate: Union[str, float] The learning rate for t-SNE optimization. When ``learning_rate="auto"`` the appropriate learning rate is selected according to max(200, N / 12), as determined in Belkina et al. "Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets", 2019. exaggeration: float The exaggeration factor is used to increase the attractive forces of nearby points, producing more compact clusters. dof: float Degrees of freedom as described in Kobak et al. "Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations", 2019. momentum: float Momentum accounts for gradient directions from previous iterations, resulting in faster convergence. negative_gradient_method: str Specifies the negative gradient approximation method to use. For smaller data sets, the Barnes-Hut approximation is appropriate and can be set using one of the following aliases: ``bh``, ``BH`` or ``barnes-hut``. For larger data sets, the FFT accelerated interpolation method is more appropriate and can be set using one of the following aliases: ``fft``, ``FFT`` or ``ìnterpolation``.A lternatively, you can use ``auto`` to approximately select the faster method. theta: float This is the trade-off parameter between speed and accuracy of the tree approximation method. Typical values range from 0.2 to 0.8. The value 0 indicates that no approximation is to be made and produces exact results also producing longer runtime. n_interpolation_points: int Only used when ``negative_gradient_method="fft"`` or its other aliases. The number of interpolation points to use within each grid cell for interpolation based t-SNE. It is highly recommended leaving this value at the default 3. min_num_intervals: int Only used when ``negative_gradient_method="fft"`` or its other aliases. The minimum number of grid cells to use, regardless of the ``ints_in_interval`` parameter. Higher values provide more accurate gradient estimations. random_state: Union[int, RandomState] The random state parameter follows the convention used in scikit-learn. If the value is an int, random_state is the seed used by the random number generator. If the value is a RandomState instance, then it will be used as the random number generator. If the value is None, the random number generator is the RandomState instance used by `np.random`. n_jobs: int The number of threads to use while running t-SNE. This follows the scikit-learn convention, ``-1`` meaning all processors, ``-2`` meaning all but one, etc. callbacks: Callable[[int, float, np.ndarray] -> bool] Callbacks, which will be run every ``callbacks_every_iters`` iterations. callbacks_every_iters: int How many iterations should pass between each time the callbacks are invoked. optimizer: gradient_descent Optionally, an existing optimizer can be used for optimization. This is useful for keeping momentum gains between different calls to :func:`optimize`. Attributes ---------- kl_divergence: float The KL divergence or error of the embedding. """ def __new__( cls, embedding, affinities, dof=1, n_interpolation_points=3, min_num_intervals=50, ints_in_interval=1, negative_gradient_method="auto", random_state=None, optimizer=None, **gradient_descent_params, ): init_checks.num_samples(embedding.shape[0], affinities.P.shape[0]) obj = np.asarray(embedding, dtype=np.float64, order="C").view(TSNEEmbedding) obj.affinities = affinities # type: Affinities obj.gradient_descent_params = gradient_descent_params # type: dict obj.gradient_descent_params.update({ "negative_gradient_method": negative_gradient_method, "n_interpolation_points": n_interpolation_points, "min_num_intervals": min_num_intervals, "ints_in_interval": ints_in_interval, "dof": dof, }) obj.random_state = random_state if optimizer is None: optimizer = gradient_descent() elif not isinstance(optimizer, gradient_descent): raise TypeError( "`optimizer` must be an instance of `%s`, but got `%s`." % (gradient_descent.__class__.__name__, type(optimizer)) ) obj.optimizer = optimizer obj.kl_divergence = None # Interpolation grid variables obj.interp_coeffs = None obj.box_x_lower_bounds = None obj.box_y_lower_bounds = None return obj def optimize( self, n_iter, inplace=False, propagate_exception=False, **gradient_descent_params, ): """Run optmization on the embedding for a given number of steps. Please see the :ref:`parameter-guide` for more information. Parameters ---------- n_iter: int The number of optimization iterations. learning_rate: Union[str, float] The learning rate for t-SNE optimization. When ``learning_rate="auto"`` the appropriate learning rate is selected according to max(200, N / 12), as determined in Belkina et al. "Automated optimized parameters for t-distributed stochastic neighbor embedding improve visualization and analysis of large datasets", 2019. exaggeration: float The exaggeration factor is used to increase the attractive forces of nearby points, producing more compact clusters. dof: float Degrees of freedom as described in Kobak et al. "Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations", 2019. momentum: float Momentum accounts for gradient directions from previous iterations, resulting in faster convergence. negative_gradient_method: str Specifies the negative gradient approximation method to use. For smaller data sets, the Barnes-Hut approximation is appropriate and can be set using one of the following aliases: ``bh``, ``BH`` or ``barnes-hut``. For larger data sets, the FFT accelerated interpolation method is more appropriate and can be set using one of the following aliases: ``fft``, ``FFT`` or ``ìnterpolation``. Alternatively, you can use ``auto`` to approximately select the faster method. theta: float This is the trade-off parameter between speed and accuracy of the tree approximation method. Typical values range from 0.2 to 0.8. The value 0 indicates that no approximation is to be made and produces exact results also producing longer runtime. n_interpolation_points: int Only used when ``negative_gradient_method="fft"`` or its other aliases. The number of interpolation points to use within each grid cell for interpolation based t-SNE. It is highly recommended leaving this value at the default 3. min_num_intervals: int Only used when ``negative_gradient_method="fft"`` or its other aliases. The minimum number of grid cells to use, regardless of the ``ints_in_interval`` parameter. Higher values provide more accurate gradient estimations. inplace: bool Whether or not to create a copy of the embedding or to perform updates inplace. propagate_exception: bool The optimization process can be interrupted using callbacks. This flag indicates whether we should propagate that exception or to simply stop optimization and return the resulting embedding. max_grad_norm: float Maximum gradient norm. If the norm exceeds this value, it will be clipped. This is most beneficial when adding points into an existing embedding and the new points overlap with the reference points, leading to large gradients. This can make points "shoot off" from the embedding, causing the interpolation method to compute a very large grid, and leads to worse results. max_step_norm: float Maximum update norm. If the norm exceeds this value, it will be clipped. This prevents points from "shooting off" from the embedding. n_jobs: int The number of threads to use while running t-SNE. This follows the scikit-learn convention, ``-1`` meaning all processors, ``-2`` meaning all but one, etc. callbacks: Callable[[int, float, np.ndarray] -> bool] Callbacks, which will be run every ``callbacks_every_iters`` iterations. callbacks_every_iters: int How many iterations should pass between each time the callbacks are invoked. Returns ------- TSNEEmbedding An optimized t-SNE embedding. Raises ------ OptimizationInterrupt If a callback stops the optimization and the ``propagate_exception`` flag is set, then an exception is raised. """ # Typically we want to return a new embedding and keep the old one intact if inplace: embedding = self else: embedding = TSNEEmbedding( np.copy(self), self.affinities, random_state=self.random_state, optimizer=self.optimizer.copy(), **self.gradient_descent_params, ) # If optimization parameters were passed to this funciton, prefer those # over the defaults specified in the TSNE object optim_params = dict(self.gradient_descent_params) optim_params.update(gradient_descent_params) optim_params["n_iter"] = n_iter _handle_nice_params(embedding, optim_params) try: # Run gradient descent with the embedding optimizer so gains are # properly updated and kept error, embedding = embedding.optimizer( embedding=embedding, P=self.affinities.P, **optim_params ) except OptimizationInterrupt as ex: log.info("Optimization was interrupted with callback.") if propagate_exception: raise ex error, embedding = ex.error, ex.final_embedding embedding.kl_divergence = error return embedding def transform( self, X, perplexity=5, initialization="median", k=25, learning_rate=0.1, early_exaggeration=4, early_exaggeration_iter=0, exaggeration=1.5, n_iter=250, initial_momentum=0.5, final_momentum=0.8, max_grad_norm=0.25, max_step_norm=None, ): """Embed new points into the existing embedding. This procedure optimizes each point only with respect to the existing embedding i.e. it ignores any interactions between the points in ``X`` among themselves. Please see the :ref:`parameter-guide` for more information. Parameters ---------- X: np.ndarray The data matrix to be added to the existing embedding. perplexity: float Perplexity can be thought of as the continuous :math:`k` number of nearest neighbors, for which t-SNE will attempt to preserve distances. However, when transforming, we only consider neighbors in the existing embedding i.e. each data point is placed into the embedding, independently of other new data points. initialization: Union[np.ndarray, str] The initial point positions to be used in the embedding space. Can be a precomputed numpy array, ``median``, ``weighted`` or ``random``. In all cases, ``median`` of ``weighted`` should be preferred. k: int The number of nearest neighbors to consider when initially placing the point onto the embedding. This is different from ``perpelxity`` because perplexity affects optimization while this only affects the initial point positions. learning_rate: Union[str, float] The learning rate for t-SNE optimization. When ``learning_rate="auto"`` the appropriate learning rate is selected according to max(200, N / 12), as determined in Belkina et al. "Automated optimized parameters for t-distributed stochastic neighbor embedding improve visualization and analysis of large datasets", 2019. Note that this should *not* be used when adding samples into existing embeddings, where the learning rate often needs to be much lower to obtain convergence. early_exaggeration_iter: int The number of iterations to run in the *early exaggeration* phase. early_exaggeration: float The exaggeration factor to use during the *early exaggeration* phase. Typical values range from 12 to 32. n_iter: int The number of iterations to run in the normal optimization regime. exaggeration: float The exaggeration factor to use during the normal optimization phase. This can be used to form more densely packed clusters and is useful for large data sets. initial_momentum: float The momentum to use during the *early exaggeration* phase. final_momentum: float The momentum to use during the normal optimization phase. max_grad_norm: float Maximum gradient norm. If the norm exceeds this value, it will be clipped. This is most beneficial when adding points into an existing embedding and the new points overlap with the reference points, leading to large gradients. This can make points "shoot off" from the embedding, causing the interpolation method to compute a very large grid, and leads to worse results. max_step_norm: float Maximum update norm. If the norm exceeds this value, it will be clipped. This prevents points from "shooting off" from the embedding. Returns ------- PartialTSNEEmbedding The positions of the new points in the embedding space. """ # We check if the affinity `to_new` methods takes the `perplexity` # parameter and raise an informative error if not. This happes when the # user uses a non-standard affinity class e.g. multiscale, then attempts # to add points via `transform`. These classes take `perplexities` and # fail affinity_signature = inspect.signature(self.affinities.to_new) if "perplexity" not in affinity_signature.parameters: raise TypeError( "`transform` currently does not support non `%s` type affinity " "classes. Please use `prepare_partial` and `optimize` to add " "points to the embedding." % PerplexityBasedNN.__name__ ) # Center the current embedding self -= (np.max(self, axis=0) + np.min(self, axis=0)) / 2 embedding = self.prepare_partial( X, perplexity=perplexity, initialization=initialization, k=k ) try: embedding.optimize( n_iter=early_exaggeration_iter, learning_rate=learning_rate, exaggeration=early_exaggeration, momentum=initial_momentum, inplace=True, propagate_exception=True, max_grad_norm=max_grad_norm, max_step_norm=max_step_norm, ) embedding.optimize( n_iter=n_iter, learning_rate=learning_rate, exaggeration=exaggeration, momentum=final_momentum, inplace=True, propagate_exception=True, max_grad_norm=max_grad_norm, max_step_norm=max_step_norm, ) except OptimizationInterrupt as ex: log.info("Optimization was interrupted with callback.") embedding = ex.final_embedding return embedding def prepare_partial(self, X, initialization="median", k=25, **affinity_params): """Prepare a partial embedding which can be optimized. Parameters ---------- X: np.ndarray The data matrix to be added to the existing embedding. initialization: Union[np.ndarray, str] The initial point positions to be used in the embedding space. Can be a precomputed numpy array, ``median``, ``weighted`` or ``random``. In all cases, ``median`` of ``weighted`` should be preferred. k: int The number of nearest neighbors to consider when initially placing the point onto the embedding. This is different from ``perpelxity`` because perplexity affects optimization while this only affects the initial point positions. **affinity_params: dict Additional params to be passed to the ``Affinities.to_new`` method. Please see individual :class:`~openTSNE.affinity.Affinities` implementations as the parameters differ between implementations. Returns ------- PartialTSNEEmbedding An unoptimized :class:`PartialTSNEEmbedding` object, prepared for optimization. """ P, neighbors, distances = self.affinities.to_new( X, return_distances=True, **affinity_params ) # If initial positions are given in an array, use a copy of that if isinstance(initialization, np.ndarray): init_checks.num_samples(initialization.shape[0], X.shape[0]) init_checks.num_dimensions(initialization.shape[1], self.shape[1]) embedding = np.array(initialization) # Random initialization with isotropic normal distribution elif initialization == "random": embedding = initialization_scheme.random( X, self.shape[1], self.random_state ) elif initialization == "weighted": embedding = initialization_scheme.weighted_mean( X, self, neighbors[:, :k], distances[:, :k] ) elif initialization == "median": embedding = initialization_scheme.median(self, neighbors[:, :k]) else: raise ValueError(f"Unrecognized initialization scheme `{initialization}`.") return PartialTSNEEmbedding( embedding, self, P=P, **self.gradient_descent_params, ) def prepare_interpolation_grid(self, padding=0.25): """Evaluate and save the interpolation grid coefficients. Parameters ---------- padding: float During standard optimization, the grid hugs the embedding points as closely as possible, but this is not what we want when performing transform. This paraemter specifies how much empty space should be appended in each dimension. The values are given in percentages. """ # Center embedding into our grid self -= (np.max(self, axis=0) + np.min(self, axis=0)) / 2 if self.shape[1] == 1: f = _tsne.prepare_negative_gradient_fft_interpolation_grid_1d elif self.shape[1] == 2: f = _tsne.prepare_negative_gradient_fft_interpolation_grid_2d else: raise RuntimeError("Cannot prepare interpolation grid for >2d embeddings") result = f( self.ravel() if self.shape[1] == 1 else self, self.gradient_descent_params["n_interpolation_points"], self.gradient_descent_params["min_num_intervals"], self.gradient_descent_params["ints_in_interval"], self.gradient_descent_params["dof"], padding=padding, ) if len(result) == 2: # 1d case self.interp_coeffs, self.box_x_lower_bounds = result elif len(result) == 3: # 2d case self.interp_coeffs, self.box_x_lower_bounds, self.box_y_lower_bounds = result else: raise RuntimeError( "Prepare interpolation grid function returned >3 values!" ) def __reduce__(self): state = super().__reduce__() new_state = state[2] + ( self.optimizer, self.affinities, self.gradient_descent_params, self.random_state, self.kl_divergence, self.interp_coeffs, self.box_x_lower_bounds, self.box_y_lower_bounds, ) return state[0], state[1], new_state def __setstate__(self, state): self.box_y_lower_bounds = state[-1] self.box_x_lower_bounds = state[-2] self.interp_coeffs = state[-3] self.kl_divergence = state[-4] self.random_state = state[-5] self.gradient_descent_params = state[-6] self.affinities = state[-7] if len(state) == 12: # backwards compat (when I forgot optimizer) self.optimizer = gradient_descent() super().__setstate__(state[:-7]) else: self.optimizer = state[-8] super().__setstate__(state[:-8]) class TSNE(BaseEstimator): """t-Distributed Stochastic Neighbor Embedding. Please see the :ref:`parameter-guide` for more information. Parameters ---------- n_components: int The dimension of the embedding space. This deafults to 2 for easy visualization, but sometimes 1 is used for t-SNE heatmaps. t-SNE is not designed to embed into higher dimension and please note that acceleration schemes break down and are not fully implemented. perplexity: float Perplexity can be thought of as the continuous :math:`k` number of nearest neighbors, for which t-SNE will attempt to preserve distances. learning_rate: Union[str, float] The learning rate for t-SNE optimization. When ``learning_rate="auto"`` the appropriate learning rate is selected according to max(200, N / 12), as determined in Belkina et al. "Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets", 2019. early_exaggeration_iter: int The number of iterations to run in the *early exaggeration* phase. early_exaggeration: float The exaggeration factor to use during the *early exaggeration* phase. Typical values range from 12 to 32. n_iter: int The number of iterations to run in the normal optimization regime. exaggeration: float The exaggeration factor to use during the normal optimization phase. This can be used to form more densely packed clusters and is useful for large data sets. dof: float Degrees of freedom as described in Kobak et al. "Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations", 2019. theta: float Only used when ``negative_gradient_method="bh"`` or its other aliases. This is the trade-off parameter between speed and accuracy of the tree approximation method. Typical values range from 0.2 to 0.8. The value 0 indicates that no approximation is to be made and produces exact results also producing longer runtime. Alternatively, you can use ``auto`` to approximately select the faster method. n_interpolation_points: int Only used when ``negative_gradient_method="fft"`` or its other aliases. The number of interpolation points to use within each grid cell for interpolation based t-SNE. It is highly recommended leaving this value at the default 3. min_num_intervals: int Only used when ``negative_gradient_method="fft"`` or its other aliases. The minimum number of grid cells to use, regardless of the ``ints_in_interval`` parameter. Higher values provide more accurate gradient estimations. ints_in_interval: float Only used when ``negative_gradient_method="fft"`` or its other aliases. Indicates how large a grid cell should be e.g. a value of 3 indicates a grid side length of 3. Lower values provide more accurate gradient estimations. initialization: Union[np.ndarray, str] The initial point positions to be used in the embedding space. Can be a precomputed numpy array, ``pca``, ``spectral`` or ``random``. Please note that when passing in a precomputed positions, it is highly recommended that the point positions have small variance (std(Y) < 0.0001), otherwise you may get poor embeddings. metric: Union[str, Callable] The metric to be used to compute affinities between points in the original space. metric_params: dict Additional keyword arguments for the metric function. initial_momentum: float The momentum to use during the *early exaggeration* phase. final_momentum: float The momentum to use during the normal optimization phase. max_grad_norm: float Maximum gradient norm. If the norm exceeds this value, it will be clipped. This is most beneficial when adding points into an existing embedding and the new points overlap with the reference points, leading to large gradients. This can make points "shoot off" from the embedding, causing the interpolation method to compute a very large grid, and leads to worse results. max_step_norm: float Maximum update norm. If the norm exceeds this value, it will be clipped. This prevents points from "shooting off" from the embedding. n_jobs: int The number of threads to use while running t-SNE. This follows the scikit-learn convention, ``-1`` meaning all processors, ``-2`` meaning all but one, etc. neighbors: str Specifies the nearest neighbor method to use. Can be ``exact``, ``annoy``, ``pynndescent``, ``hnsw``, ``approx``, or ``auto`` (default). ``approx`` uses Annoy if the input data matrix is not a sparse object and if Annoy supports the given metric. Otherwise it uses Pynndescent. ``auto`` uses exact nearest neighbors for N<1000 and the same heuristic as ``approx`` for N>=1000. negative_gradient_method: str Specifies the negative gradient approximation method to use. For smaller data sets, the Barnes-Hut approximation is appropriate and can be set using one of the following aliases: ``bh``, ``BH`` or ``barnes-hut``. For larger data sets, the FFT accelerated interpolation method is more appropriate and can be set using one of the following aliases: ``fft``, ``FFT`` or ``ìnterpolation``. Alternatively, you can use ``auto`` to approximately select the faster method. callbacks: Union[Callable, List[Callable]] Callbacks, which will be run every ``callbacks_every_iters`` iterations. callbacks_every_iters: int How many iterations should pass between each time the callbacks are invoked. random_state: Union[int, RandomState] If the value is an int, random_state is the seed used by the random number generator. If the value is a RandomState instance, then it will be used as the random number generator. If the value is None, the random number generator is the RandomState instance used by `np.random`. verbose: bool """ def __init__( self, n_components=2, perplexity=30, learning_rate="auto", early_exaggeration_iter=250, early_exaggeration=12, n_iter=500, exaggeration=None, dof=1, theta=0.5, n_interpolation_points=3, min_num_intervals=50, ints_in_interval=1, initialization="pca", metric="euclidean", metric_params=None, initial_momentum=0.5, final_momentum=0.8, max_grad_norm=None, max_step_norm=5, n_jobs=1, neighbors="auto", negative_gradient_method="auto", callbacks=None, callbacks_every_iters=50, random_state=None, verbose=False, ): self.n_components = n_components self.perplexity = perplexity self.learning_rate = learning_rate self.early_exaggeration = early_exaggeration self.early_exaggeration_iter = early_exaggeration_iter self.n_iter = n_iter self.exaggeration = exaggeration self.dof = dof self.theta = theta self.n_interpolation_points = n_interpolation_points self.min_num_intervals = min_num_intervals self.ints_in_interval = ints_in_interval # Check if the number of components match the initialization dimension if isinstance(initialization, np.ndarray): init_checks.num_dimensions(initialization.shape[1], n_components) self.initialization = initialization self.metric = metric self.metric_params = metric_params self.initial_momentum = initial_momentum self.final_momentum = final_momentum self.max_grad_norm = max_grad_norm self.max_step_norm = max_step_norm self.n_jobs = n_jobs self.neighbors = neighbors self.negative_gradient_method = negative_gradient_method self.callbacks = callbacks self.callbacks_every_iters = callbacks_every_iters self.random_state = random_state self.verbose = verbose def fit(self, X=None, affinities=None, initialization=None): """Fit a t-SNE embedding for a given data set. Runs the standard t-SNE optimization, consisting of the early exaggeration phase and a normal optimization phase. This function call be called in two ways. 1. We can call it in the standard way using a ``np.array``. This will compute the affinity matrix and initialization, and run the optimization as usual. 2. We can also pass in a precomputed ``affinity`` object, which will override the affinity-related paramters specified in the constructor. This is useful when you wish to use custom affinity objects. Please note that some initialization schemes require ``X`` to be specified, e.g. PCA. If the initilization is not able to be computed, we default to using spectral initilization calculated from the affinity matrix. Parameters ---------- X: Optional[np.ndarray} The data matrix to be embedded. affinities: Optional[openTSNE.affinity.Affinities] A precomputed affinity object. If specified, other affinity-related parameters are ignored e.g. `perplexity` and anything nearest-neighbor search related. initialization: Optional[np.ndarray] The initial point positions to be used in the embedding space. Can be a precomputed numpy array, ``pca``, ``spectral`` or ``random``. Please note that when passing in a precomputed positions, it is highly recommended that the point positions have small variance (std(Y) < 0.0001), otherwise you may get poor embeddings. Returns ------- TSNEEmbedding A fully optimized t-SNE embedding. """ if self.verbose: print("-" * 80, repr(self), "-" * 80, sep="\n") embedding = self.prepare_initial(X, affinities, initialization) try: # Early exaggeration with lower momentum to allow points to find more # easily move around and find their neighbors embedding.optimize( n_iter=self.early_exaggeration_iter, exaggeration=self.early_exaggeration, momentum=self.initial_momentum, inplace=True, propagate_exception=True, ) # Restore actual affinity probabilities and increase momentum to get # final, optimized embedding embedding.optimize( n_iter=self.n_iter, exaggeration=self.exaggeration, momentum=self.final_momentum, inplace=True, propagate_exception=True, ) except OptimizationInterrupt as ex: log.info("Optimization was interrupted with callback.") embedding = ex.final_embedding return embedding def prepare_initial(self, X=None, affinities=None, initialization=None): """Prepare the initial embedding which can be optimized as needed. This function call be called in two ways. 1. We can call it in the standard way using a ``np.array``. This will compute the affinity matrix and initialization as usual. 2. We can also pass in a precomputed ``affinity`` object, which will override the affinity-related paramters specified in the constructor. This is useful when you wish to use custom affinity objects. Please note that some initialization schemes require ``X`` to be specified, e.g. PCA. If the initilization is not able to be computed, we default to using spectral initilization calculated from the affinity matrix. Parameters ---------- X: Optional[np.ndarray} The data matrix to be embedded. affinities: Optional[openTSNE.affinity.Affinities] A precomputed affinity object. If specified, other affinity-related parameters are ignored e.g. `perplexity` and anything nearest-neighbor search related. initialization: Optional[np.ndarray] The initial point positions to be used in the embedding space. Can be a precomputed numpy array, ``pca``, ``spectral`` or ``random``. Please note that when passing in a precomputed positions, it is highly recommended that the point positions have small variance (std(Y) < 0.0001), otherwise you may get poor embeddings. Returns ------- TSNEEmbedding An unoptimized :class:`TSNEEmbedding` object, prepared for optimization. """ # Either `X` or `affinities` must be specified if X is None and affinities is None and initialization is None: raise ValueError( "At least one of the parameters `X` or `affinities` must be specified!" ) # If precomputed affinites are given, use those, otherwise proceed with # standard perpelxity-based affinites if affinities is None: affinities = PerplexityBasedNN( X, self.perplexity, method=self.neighbors, metric=self.metric, metric_params=self.metric_params, n_jobs=self.n_jobs, random_state=self.random_state, verbose=self.verbose, ) else: if not isinstance(affinities, Affinities): raise ValueError( "`affinities` must be an instance of `openTSNE.affinity.Affinities`" ) log.info( "Precomputed affinities provided. Ignoring perplexity-related parameters." ) # If a precomputed initialization was specified, use that, otherwise # use the parameters specified in the constructor if initialization is None: initialization = self.initialization log.info( "Precomputed initialization provided. Ignoring initalization-related " "parameters." ) # If only the affinites have been specified, and the initialization depends # on `X`, switch to spectral initalization if X is None and isinstance(initialization, str) and initialization == "pca": log.warning( "Attempting to use `pca` initalization, but no `X` matrix specified! " "Using `spectral` initilization instead, which doesn't need access " "to the data matrix" ) initialization = "spectral" # Same spiel for precomputed distance matrices if self.metric == "precomputed" and isinstance(initialization, str) and initialization == "pca": log.warning( "Attempting to use `pca` initalization, but using precomputed " "distance matrix! Using `spectral` initilization instead, which " "doesn't need access to the data matrix." ) initialization = "spectral" # Determine the number of samples in the input data set if X is not None: n_samples = X.shape[0] else: n_samples = affinities.P.shape[0] # If initial positions are given in an array, use a copy of that if isinstance(initialization, np.ndarray): init_checks.num_samples(initialization.shape[0], n_samples) init_checks.num_dimensions(initialization.shape[1], self.n_components) embedding = np.array(initialization) stddev = np.std(embedding, axis=0) if any(stddev > 1e-2): log.warning( "Standard deviation of embedding is greater than 0.0001. Initial " "embeddings with high variance may have display poor convergence." ) elif initialization == "pca": embedding = initialization_scheme.pca( X, self.n_components, random_state=self.random_state, verbose=self.verbose, ) elif initialization == "random": embedding = initialization_scheme.random( n_samples, self.n_components, random_state=self.random_state, verbose=self.verbose, ) elif initialization == "spectral": embedding = initialization_scheme.spectral( affinities.P, self.n_components, random_state=self.random_state, verbose=self.verbose, ) else: raise ValueError( f"Unrecognized initialization scheme `{initialization}`." ) gradient_descent_params = { "dof": self.dof, "negative_gradient_method": self.negative_gradient_method, "learning_rate": self.learning_rate, # By default, use the momentum used in unexaggerated phase "momentum": self.final_momentum, # Barnes-Hut params "theta": self.theta, # Interpolation params "n_interpolation_points": self.n_interpolation_points, "min_num_intervals": self.min_num_intervals, "ints_in_interval": self.ints_in_interval, "max_grad_norm": self.max_grad_norm, "max_step_norm": self.max_step_norm, "n_jobs": self.n_jobs, "verbose": self.verbose, # Callback params "callbacks": self.callbacks, "callbacks_every_iters": self.callbacks_every_iters, } return TSNEEmbedding( embedding, affinities=affinities, random_state=self.random_state, **gradient_descent_params, ) def kl_divergence_bh( embedding, P, dof, bh_params, reference_embedding=None, should_eval_error=False, n_jobs=1, **_, ): gradient = np.zeros_like(embedding, dtype=np.float64, order="C") # In the event that we wish to embed new points into an existing embedding # using simple optimization, we compute optimize the new embedding points # w.r.t. the existing embedding. Otherwise, we want to optimize the # embedding w.r.t. itself. We've also got to make sure that the points' # interactions don't interfere with each other pairwise_normalization = reference_embedding is None if reference_embedding is None: reference_embedding = embedding # Compute negative gradient tree = QuadTree(reference_embedding) sum_Q = _tsne.estimate_negative_gradient_bh( tree, embedding, gradient, **bh_params, dof=dof, num_threads=n_jobs, pairwise_normalization=pairwise_normalization, ) del tree # Compute positive gradient sum_P, kl_divergence_ = _tsne.estimate_positive_gradient_nn( P.indices, P.indptr, P.data, embedding, reference_embedding, gradient, dof, num_threads=n_jobs, should_eval_error=should_eval_error, ) # Computing positive gradients summed up only unnormalized q_ijs, so we # have to include normalziation term separately if should_eval_error: kl_divergence_ += sum_P * np.log(sum_Q + EPSILON) return kl_divergence_, gradient def kl_divergence_fft( embedding, P, dof, fft_params, reference_embedding=None, should_eval_error=False, n_jobs=1, **_, ): gradient = np.zeros_like(embedding, dtype=np.float64, order="C") # Compute negative gradient. if embedding.ndim == 1 or embedding.shape[1] == 1: if reference_embedding is not None: sum_Q = _tsne.estimate_negative_gradient_fft_1d_with_grid( embedding.ravel(), gradient.ravel(), reference_embedding.interp_coeffs, reference_embedding.box_x_lower_bounds, fft_params["n_interpolation_points"], dof=dof, ) else: sum_Q = _tsne.estimate_negative_gradient_fft_1d( embedding.ravel(), gradient.ravel(), **fft_params, dof=dof ) elif embedding.shape[1] == 2: if reference_embedding is not None: sum_Q = _tsne.estimate_negative_gradient_fft_2d_with_grid( embedding, gradient, reference_embedding.interp_coeffs, reference_embedding.box_x_lower_bounds, reference_embedding.box_y_lower_bounds, fft_params["n_interpolation_points"], dof=dof, ) else: sum_Q = _tsne.estimate_negative_gradient_fft_2d( embedding, gradient, **fft_params, dof=dof ) else: raise RuntimeError( "Interpolation based t-SNE for >2 dimensions is currently " "unsupported (and generally a bad idea)" ) # The positive gradient function needs a reference embedding always if reference_embedding is None: reference_embedding = embedding # Compute positive gradient sum_P, kl_divergence_ = _tsne.estimate_positive_gradient_nn( P.indices, P.indptr, P.data, embedding, reference_embedding, gradient, dof, num_threads=n_jobs, should_eval_error=should_eval_error, ) if should_eval_error: kl_divergence_ += sum_P * np.log(sum_Q + EPSILON) return kl_divergence_, gradient class gradient_descent: def __init__(self): self.gains = None def copy(self): optimizer = self.__class__() if self.gains is not None: optimizer.gains = np.copy(self.gains) return optimizer def __call__( self, embedding, P, n_iter, objective_function, learning_rate=200, momentum=0.5, exaggeration=None, dof=1, min_gain=0.01, max_grad_norm=None, max_step_norm=5, theta=0.5, n_interpolation_points=3, min_num_intervals=50, ints_in_interval=1, reference_embedding=None, n_jobs=1, use_callbacks=False, callbacks=None, callbacks_every_iters=50, verbose=False, ): """Perform batch gradient descent with momentum and gains. Parameters ---------- embedding: np.ndarray The embedding :math:`Y`. P: array_like Joint probability matrix :math:`P`. n_iter: int The number of iterations to run for. objective_function: Callable[..., Tuple[float, np.ndarray]] A callable that evaluates the error and gradient for the current embedding. learning_rate: Union[str, float] The learning rate for t-SNE optimization. When ``learning_rate="auto"`` the appropriate learning rate is selected according to max(200, N / 12), as determined in Belkina et al. "Automated optimized parameters for t-distributed stochastic neighbor embedding improve visualization and analysis of large datasets", 2019. momentum: float Momentum accounts for gradient directions from previous iterations, resulting in faster convergence. exaggeration: float The exaggeration factor is used to increase the attractive forces of nearby points, producing more compact clusters. dof: float Degrees of freedom of the Student's t-distribution. min_gain: float Minimum individual gain for each parameter. max_grad_norm: float Maximum gradient norm. If the norm exceeds this value, it will be clipped. This is most beneficial when adding points into an existing embedding and the new points overlap with the reference points, leading to large gradients. This can make points "shoot off" from the embedding, causing the interpolation method to compute a very large grid, and leads to worse results. max_step_norm: float Maximum update norm. If the norm exceeds this value, it will be clipped. This prevents points from "shooting off" from the embedding. theta: float This is the trade-off parameter between speed and accuracy of the tree approximation method. Typical values range from 0.2 to 0.8. The value 0 indicates that no approximation is to be made and produces exact results also producing longer runtime. n_interpolation_points: int Only used when ``negative_gradient_method="fft"`` or its other aliases. The number of interpolation points to use within each grid cell for interpolation based t-SNE. It is highly recommended leaving this value at the default 3. min_num_intervals: int Only used when ``negative_gradient_method="fft"`` or its other aliases. The minimum number of grid cells to use, regardless of the ``ints_in_interval`` parameter. Higher values provide more accurate gradient estimations. ints_in_interval: float Only used when ``negative_gradient_method="fft"`` or its other aliases. Indicates how large a grid cell should be e.g. a value of 3 indicates a grid side length of 3. Lower values provide more accurate gradient estimations. reference_embedding: np.ndarray If we are adding points to an existing embedding, we have to compute the gradients and errors w.r.t. the existing embedding. n_jobs: int The number of threads to use while running t-SNE. This follows the scikit-learn convention, ``-1`` meaning all processors, ``-2`` meaning all but one, etc. use_callbacks: bool callbacks: Callable[[int, float, np.ndarray] -> bool] Callbacks, which will be run every ``callbacks_every_iters`` iterations. callbacks_every_iters: int How many iterations should pass between each time the callbacks are invoked. Returns ------- float The KL divergence of the optimized embedding. np.ndarray The optimized embedding Y. Raises ------ OptimizationInterrupt If the provided callback interrupts the optimization, this is raised. """ assert isinstance(embedding, np.ndarray), ( "`embedding` must be an instance of `np.ndarray`. Got `%s` instead" % type(embedding) ) if reference_embedding is not None: assert isinstance(reference_embedding, np.ndarray), ( "`reference_embedding` must be an instance of `np.ndarray`. Got " "`%s` instead" % type(reference_embedding) ) # If the interpolation grid has not yet been evaluated, do it now if reference_embedding is not None and reference_embedding.interp_coeffs is None: reference_embedding.prepare_interpolation_grid() # If we're running transform and using the interpolation scheme, then we # should limit the range where new points can go to should_limit_range = False if reference_embedding is not None: if reference_embedding.box_x_lower_bounds is not None: should_limit_range = True lower_limit = reference_embedding.box_x_lower_bounds[0] upper_limit = reference_embedding.box_x_lower_bounds[-1] update = np.zeros_like(embedding) if self.gains is None: self.gains = np.ones_like(embedding).view(np.ndarray) bh_params = {"theta": theta} fft_params = { "n_interpolation_points": n_interpolation_points, "min_num_intervals": min_num_intervals, "ints_in_interval": ints_in_interval, } # Lie about the P values for bigger attraction forces if exaggeration is None: exaggeration = 1 if exaggeration != 1: P *= exaggeration # Notify the callbacks that the optimization is about to start if isinstance(callbacks, Iterable): for callback in callbacks: # Only call function if present on object getattr(callback, "optimization_about_to_start", lambda: ...)() timer = utils.Timer( "Running optimization with exaggeration=%.2f, lr=%.2f for %d iterations..." % ( exaggeration, learning_rate, n_iter ), verbose=verbose, ) timer.__enter__() if verbose: start_time = time() for iteration in range(n_iter): should_call_callback = use_callbacks and (iteration + 1) % callbacks_every_iters == 0 # Evaluate error on 50 iterations for logging, or when callbacks should_eval_error = should_call_callback or \ (verbose and (iteration + 1) % 50 == 0) error, gradient = objective_function( embedding, P, dof=dof, bh_params=bh_params, fft_params=fft_params, reference_embedding=reference_embedding, n_jobs=n_jobs, should_eval_error=should_eval_error, ) # Clip gradients to avoid points shooting off. This can be an issue # when applying transform and points are initialized so that the new # points overlap with the reference points, leading to large # gradients if max_grad_norm is not None: norm = np.linalg.norm(gradient, axis=1) coeff = max_grad_norm / (norm + 1e-6) mask = coeff < 1 gradient[mask] *= coeff[mask, None] # Correct the KL divergence w.r.t. the exaggeration if needed if should_eval_error and exaggeration != 1: error = error / exaggeration - np.log(exaggeration) if should_call_callback: # Continue only if all the callbacks say so should_stop = any( (bool(c(iteration + 1, error, embedding)) for c in callbacks) ) if should_stop: # Make sure to un-exaggerate P so it's not corrupted in future runs if exaggeration != 1: P /= exaggeration raise OptimizationInterrupt(error=error, final_embedding=embedding) # Update the embedding using the gradient grad_direction_flipped = np.sign(update) != np.sign(gradient) grad_direction_same = np.invert(grad_direction_flipped) self.gains[grad_direction_flipped] += 0.2 self.gains[grad_direction_same] = ( self.gains[grad_direction_same] * 0.8 + min_gain ) update = momentum * update - learning_rate * self.gains * gradient # Clip the update sizes if max_step_norm is not None: update_norms = np.linalg.norm(update, axis=1, keepdims=True) mask = update_norms.squeeze() > max_step_norm update[mask] /= update_norms[mask] update[mask] *= max_step_norm embedding += update # Zero-mean the embedding only if we're not adding new data points, # otherwise this will reset point positions if reference_embedding is None: embedding -= np.mean(embedding, axis=0) # Limit any new points within the circle defined by the interpolation grid if should_limit_range: if embedding.shape[1] == 1: mask = (embedding < lower_limit) | (embedding > upper_limit) np.clip(embedding, lower_limit, upper_limit, out=embedding) elif embedding.shape[1] == 2: r_limit = max(abs(lower_limit), abs(upper_limit)) embedding, mask = utils.clip_point_to_disc(embedding, r_limit, inplace=True) # Zero out the momentum terms for the points that hit the boundary self.gains[~mask] = 0 if verbose and (iteration + 1) % 50 == 0: stop_time = time() print("Iteration %4d, KL divergence %6.4f, 50 iterations in %.4f sec" % ( iteration + 1, error, stop_time - start_time)) start_time = time() timer.__exit__() # Make sure to un-exaggerate P so it's not corrupted in future runs if exaggeration != 1: P /= exaggeration # The error from the loop is the one for the previous, non-updated # embedding. We need to return the error for the actual final embedding, so # compute that at the end before returning error, _ = objective_function( embedding, P, dof=dof, bh_params=bh_params, fft_params=fft_params, reference_embedding=reference_embedding, n_jobs=n_jobs, should_eval_error=True, ) return error, embedding openTSNE-0.6.1/openTSNE/utils.py000066400000000000000000000027731413546205200163310ustar00rootroot00000000000000from functools import wraps from time import time import warnings import numpy as np class Timer: def __init__(self, message, verbose=False): self.message = message self.start_time = time() self.verbose = verbose def __enter__(self): if self.verbose: print("===>", self.message) def __exit__(self, *args): end_time = time() if self.verbose: print(" --> Time elapsed: %.2f seconds" % (end_time - self.start_time)) def deprecate_parameter(parameter): def wrapper(f): @wraps(f) def func(*args, **kwargs): if parameter in kwargs: warnings.warn( f"The parameter `{parameter}` has been deprecated and will be " f"removed in future versions", category=FutureWarning, ) return f(*args, **kwargs) return func return wrapper def is_package_installed(libname): """Check whether a python package is installed.""" import importlib try: importlib.import_module(libname) return True except ImportError: return False def clip_point_to_disc(points, radius, inplace=False): if not inplace: points = points.copy() r = np.linalg.norm(points, axis=1) phi = np.arctan2(points[:, 0], points[:, 1]) mask = r > radius np.clip(r, 0, radius, out=r) points[:, 0] = r * np.sin(phi) points[:, 1] = r * np.cos(phi) return points, mask openTSNE-0.6.1/openTSNE/version.py000066400000000000000000000000261413546205200166430ustar00rootroot00000000000000__version__ = "0.6.1" openTSNE-0.6.1/readthedocs.yml000066400000000000000000000002351413546205200161630ustar00rootroot00000000000000build: image: latest sphinx: configuration: docs/conf.py python: version: 3.8 setup_py_install: true requirements_file: docs/requirements-doc.txt openTSNE-0.6.1/setup.py000066400000000000000000000253671413546205200147020ustar00rootroot00000000000000import distutils import os import platform import sys import tempfile import warnings from distutils import ccompiler from distutils.command.build_ext import build_ext from distutils.errors import CompileError, LinkError from distutils.sysconfig import customize_compiler from os.path import join import setuptools from setuptools import setup, Extension class ConvertNotebooksToDocs(distutils.cmd.Command): description = "Convert the example notebooks to reStructuredText that will" \ "be available in the documentation." user_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): import nbconvert from os.path import join exporter = nbconvert.RSTExporter() writer = nbconvert.writers.FilesWriter() files = [ join("examples", "01_simple_usage.ipynb"), join("examples", "02_advanced_usage.ipynb"), join("examples", "03_preserving_global_structure.ipynb"), join("examples", "04_large_data_sets.ipynb"), ] target_dir = join("docs", "source", "examples") for fname in files: self.announce(f"Converting {fname}...") directory, nb_name = fname.split("/") nb_name, _ = nb_name.split(".") body, resources = exporter.from_file(fname) writer.build_directory = join(target_dir, nb_name) writer.write(body, resources, nb_name) class get_numpy_include: """Helper class to determine the numpy include path The purpose of this class is to postpone importing numpy until it is actually installed, so that the ``get_include()`` method can be invoked. """ def __str__(self): import numpy return numpy.get_include() def get_include_dirs(): """Get include dirs for the compiler.""" return ( os.path.join(sys.prefix, "include"), os.path.join(sys.prefix, "Library", "include"), ) def get_library_dirs(): """Get library dirs for the compiler.""" return ( os.path.join(sys.prefix, "lib"), os.path.join(sys.prefix, "Library", "lib"), ) def has_c_library(library, extension=".c"): """Check whether a C/C++ library is available on the system to the compiler. Parameters ---------- library: str The library we want to check for e.g. if we are interested in FFTW3, we want to check for `fftw3.h`, so this parameter will be `fftw3`. extension: str If we want to check for a C library, the extension is `.c`, for C++ `.cc`, `.cpp` or `.cxx` are accepted. Returns ------- bool Whether or not the library is available. """ with tempfile.TemporaryDirectory(dir=".") as directory: name = join(directory, "%s%s" % (library, extension)) with open(name, "w") as f: f.write("#include <%s.h>\n" % library) f.write("int main() {}\n") # Get a compiler instance compiler = ccompiler.new_compiler() # Configure compiler to do all the platform specific things customize_compiler(compiler) # Add conda include dirs for inc_dir in get_include_dirs(): compiler.add_include_dir(inc_dir) assert isinstance(compiler, ccompiler.CCompiler) try: # Try to compile the file using the C compiler compiler.link_executable(compiler.compile([name]), name) return True except (CompileError, LinkError): return False class CythonBuildExt(build_ext): def build_extensions(self): # Automatically append the file extension based on language. # ``cythonize`` does this for us automatically, so it's not necessary if # that was run for extension in extensions: for idx, source in enumerate(extension.sources): base, ext = os.path.splitext(source) if ext == ".pyx": base += ".cpp" if extension.language == "c++" else ".c" extension.sources[idx] = base extra_compile_args = [] extra_link_args = [] # Optimization compiler/linker flags are added appropriately compiler = self.compiler.compiler_type if compiler == "unix": extra_compile_args += ["-O3"] elif compiler == "msvc": extra_compile_args += ["/Ox", "/fp:fast"] if compiler == "unix" and platform.system() == "Darwin": # For some reason fast math causes segfaults on linux but works on mac extra_compile_args += ["-ffast-math", "-fno-associative-math"] # Annoy specific flags annoy_ext = None for extension in extensions: if "annoy.annoylib" in extension.name: annoy_ext = extension assert annoy_ext is not None, "Annoy extension not found!" if compiler == "unix": annoy_ext.extra_compile_args += ["-std=c++14"] annoy_ext.extra_compile_args += ["-DANNOYLIB_MULTITHREADED_BUILD"] elif compiler == "msvc": annoy_ext.extra_compile_args += ["/std:c++14"] # Set minimum deployment version for MacOS if compiler == "unix" and platform.system() == "Darwin": extra_compile_args += ["-mmacosx-version-min=10.12"] extra_link_args += ["-stdlib=libc++", "-mmacosx-version-min=10.12"] # We don't want the compiler to optimize for system architecture if # we're building packages to be distributed by conda-forge, but if the # package is being built locally, this is desired if not ("AZURE_BUILD" in os.environ or "CONDA_BUILD" in os.environ): if platform.machine() == "ppc64le": extra_compile_args += ["-mcpu=native"] if platform.machine() == "x86_64": extra_compile_args += ["-march=native"] # We will disable openmp flags if the compiler doesn"t support it. This # is only really an issue with OSX clang if has_c_library("omp"): print("Found openmp. Compiling with openmp flags...") if platform.system() == "Darwin" and compiler == "unix": extra_compile_args += ["-Xpreprocessor", "-fopenmp"] extra_link_args += ["-lomp"] elif compiler == "unix": extra_compile_args += ["-fopenmp"] extra_link_args += ["-fopenmp"] elif compiler == "msvc": extra_compile_args += ["/openmp"] extra_link_args += ["/openmp"] else: warnings.warn( "You appear to be using a compiler which does not support " "openMP, meaning that the library will not be able to run on " "multiple cores. Please install/enable openMP to use multiple " "cores." ) for extension in self.extensions: extension.extra_compile_args += extra_compile_args extension.extra_link_args += extra_link_args # Add numpy and system include directories for extension in self.extensions: extension.include_dirs.extend(get_include_dirs()) extension.include_dirs.append(get_numpy_include()) # Add numpy and system include directories for extension in self.extensions: extension.library_dirs.extend(get_library_dirs()) super().build_extensions() # Prepare the Annoy extension # Adapted from annoy setup.py # Various platform-dependent extras extra_compile_args = [] extra_link_args = [] annoy_path = "openTSNE/dependencies/annoy/" annoy = Extension( "openTSNE.dependencies.annoy.annoylib", [annoy_path + "annoymodule.cc"], depends=[annoy_path + f for f in ["annoylib.h", "kissrandom.h", "mman.h"]], language="c++", extra_compile_args=extra_compile_args, extra_link_args=extra_link_args, ) # Other extensions extensions = [ Extension("openTSNE.quad_tree", ["openTSNE/quad_tree.pyx"], language="c++"), Extension("openTSNE._tsne", ["openTSNE/_tsne.pyx"], language="c++"), Extension("openTSNE.kl_divergence", ["openTSNE/kl_divergence.pyx"], language="c++"), annoy, ] # Check if we have access to FFTW3 and if so, use that implementation if has_c_library("fftw3"): print("FFTW3 header files found. Using FFTW implementation of FFT.") extension_ = Extension( "openTSNE._matrix_mul.matrix_mul", ["openTSNE/_matrix_mul/matrix_mul_fftw3.pyx"], libraries=["fftw3"], language="c++", ) extensions.append(extension_) else: print("FFTW3 header files not found. Using numpy implementation of FFT.") extension_ = Extension( "openTSNE._matrix_mul.matrix_mul", ["openTSNE/_matrix_mul/matrix_mul_numpy.pyx"], language="c++", ) extensions.append(extension_) try: from Cython.Build import cythonize extensions = cythonize(extensions) except ImportError: pass def readme(): with open("README.rst", encoding="utf-8") as f: return f.read() # Read in version __version__: str = "" # This is overridden by the next line exec(open(os.path.join("openTSNE", "version.py")).read()) setup( name="openTSNE", description="Extensible, parallel implementations of t-SNE", long_description=readme(), version=__version__, license="BSD-3-Clause", author="Pavlin Poličar", author_email="pavlin.g.p@gmail.com", url="https://github.com/pavlin-policar/openTSNE", project_urls={ "Documentation": "https://opentsne.readthedocs.io/", "Source": "https://github.com/pavlin-policar/openTSNE", "Issue Tracker": "https://github.com/pavlin-policar/openTSNE/issues", }, classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Science/Research", "Intended Audience :: Developers", "Topic :: Software Development", "Topic :: Scientific/Engineering", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX", "Operating System :: Unix", "Operating System :: MacOS", "License :: OSI Approved", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Visualization", "Topic :: Software Development :: Libraries :: Python Modules", ], packages=setuptools.find_packages(include=["openTSNE", "openTSNE.*"]), python_requires=">=3.6", install_requires=[ "numpy>=1.16.6", "scikit-learn>=0.20", "scipy", ], extras_require={ "hnsw": "hnswlib~=0.4.0", "pynndescent": "pynndescent~=0.5.0", }, ext_modules=extensions, cmdclass={"build_ext": CythonBuildExt, "convert_notebooks": ConvertNotebooksToDocs}, ) openTSNE-0.6.1/tests/000077500000000000000000000000001413546205200143155ustar00rootroot00000000000000openTSNE-0.6.1/tests/__init__.py000066400000000000000000000000001413546205200164140ustar00rootroot00000000000000openTSNE-0.6.1/tests/quad_tree_debug.pyx000066400000000000000000000041761413546205200202060ustar00rootroot00000000000000from openTSNE.quad_tree cimport QuadTree, Node import numpy as np def print_tree(QuadTree tree): _print_tree(&tree.root) cdef _print_tree(Node * node, name=None, indent=0): """Print the quad tree in a readable textual format.""" if not node.num_points: return directions = {0: 'SW', 1: 'NW', 2: 'SE', 3: 'NE'} # Print the correct indentation print('\t' * indent + '%s: %s (%d) %s' % ( 'Root' if name is None else name, ['', '[+]'][not node.is_leaf], node.num_points, _str_point(node.center_of_mass), )) if not node.is_leaf: for sector in range(1 << node.n_dims): _print_tree(&node.children[sector], directions[sector], indent + 1) def _str_point(double[:] point): return '(%s)' % ', '.join('%.4f' % point[i] for i in range(point.shape[0])) def plot_tree(QuadTree tree, data): assert isinstance(data, np.ndarray), '`data` must be np.ndarray' if not data.dtype == np.float64: data = data.astype(np.float64) _plot_tree(&tree.root, data) cdef _plot_tree(Node * root, double[:, :] data): import matplotlib.pyplot as plt fig = plt.figure(figsize=(8, 8)) ax = fig.add_subplot(111) ax.set_xticks([]), ax.set_yticks([]), ax.axis('off') centers = [] _add_patch(ax, root, centers) centers = list(zip(*centers)) xs = [p[0] for p in data] ys = [p[1] for p in data] plt.scatter(xs, ys, s=20) # plt.scatter(centers[0], centers[1], edgecolors="r", facecolors="none", s=10, linewidths=1) plt.savefig("quadtree.png", dpi=80, rasterize=True, transparent=True, bbox_inches="tight") plt.show() cdef _add_patch(ax, Node * node, centers): import matplotlib.patches as patches min_bounds = np.asarray(node.center) - node.length / 2 ax.add_patch(patches.Rectangle( min_bounds, node.length, node.length, fill=False )) if not node.is_leaf: for i in range(1 << node.n_dims): _add_patch(ax, &node.children[i], centers) if node.num_points > 0: centers.append([node.center_of_mass[0], node.center_of_mass[1]]) openTSNE-0.6.1/tests/test_affinities.py000066400000000000000000000254111413546205200200520ustar00rootroot00000000000000import logging import unittest from functools import partial import numpy as np from scipy.spatial.distance import squareform, pdist from sklearn import datasets from sklearn.model_selection import train_test_split from openTSNE import affinity, nearest_neighbors affinity.log.setLevel(logging.ERROR) Multiscale = partial(affinity.Multiscale, method="exact") MultiscaleMixture = partial(affinity.MultiscaleMixture, method="exact") PerplexityBasedNN = partial(affinity.PerplexityBasedNN, method="exact") FixedSigmaNN = partial(affinity.FixedSigmaNN, method="exact") Uniform = partial(affinity.Uniform, method="exact") AFFINITY_CLASSES = [ ("PerplexityBasedNN", PerplexityBasedNN), ("FixedSigmaNN", partial(FixedSigmaNN, sigma=1)), ("MultiscaleMixture", partial(MultiscaleMixture, perplexities=[10, 20])), ("Multiscale", partial(Multiscale, perplexities=[10, 20])), ("Uniform", partial(Uniform, k_neighbors=5)), ] class TestPerplexityBased(unittest.TestCase): @classmethod def setUpClass(cls): cls.x = np.random.normal(100, 50, (91, 4)) def test_properly_reduces_large_perplexity(self): aff = PerplexityBasedNN(self.x, perplexity=140) self.assertEqual(aff.perplexity, 30) def test_handles_reducing_perplexity_value(self): perplexity = 20 k_neighbors = perplexity * 3 aff = PerplexityBasedNN(self.x, perplexity=perplexity) self.assertEqual(aff.perplexity, perplexity) # Check that the initial `P` matrix is allright n_samples = self.x.shape[0] original_P = aff.P.copy() # Can't check for equality because the matrix is symmetrized therefore # each point may have non-zero values in more than just the k neighbors self.assertTrue(original_P.nnz >= n_samples * k_neighbors) # Check that lowering the perplexity properly changes affinity matrix perplexity = 10 k_neighbors = perplexity * 3 aff.set_perplexity(perplexity) self.assertEqual(aff.perplexity, perplexity) reduced_P = aff.P.copy() self.assertTrue(reduced_P.nnz >= n_samples * k_neighbors) self.assertTrue(reduced_P.nnz < original_P.nnz, "Lower perplexities should consider less neighbors, " "resulting in a sparser affinity matrix") # Check that increasing the perplexity works (with a warning) perplexity = 40 aff.set_perplexity(perplexity) self.assertEqual(aff.perplexity, perplexity) # Raising the perplexity above the number of neighbors in the kNN graph # would need to recompute the nearest neighbors, so it should raise an error with self.assertRaises(RuntimeError): aff.set_perplexity(70) def test_set_perplexity_respects_symmetrization(self): # Apply symmetrization symmetrize = True aff = PerplexityBasedNN(self.x, perplexity=30, symmetrize=symmetrize) self.assertAlmostEqual(np.sum(aff.P - aff.P.T), 0, delta=1e-16) aff.set_perplexity(20) self.assertAlmostEqual(np.sum(aff.P - aff.P.T), 0, delta=1e-16) # Skip symmetrization symmetrize = False aff = PerplexityBasedNN(self.x, perplexity=30, symmetrize=symmetrize) self.assertAlmostEqual(np.sum(aff.P - aff.P.T), 0, delta=1e-16) aff.set_perplexity(20) self.assertAlmostEqual(np.sum(aff.P - aff.P.T), 0, delta=1e-16) class TestMultiscale(unittest.TestCase): @classmethod def setUpClass(cls): cls.x = np.random.normal(100, 50, (91, 4)) def test_handles_too_large_perplexities(self): # x has 91 samples, this means that the max perplexity that we allow is # (91 - 1) / 3 = 30. -1 because we don't consider ith point. Anything # above that should be ignored or corrected ms = Multiscale(self.x, perplexities=[20]) np.testing.assert_array_equal( ms.perplexities, [20], "Incorrectly changed perplexity that was within a valid range", ) ms = Multiscale(self.x, perplexities=[20, 40]) np.testing.assert_array_equal( ms.perplexities, [20, 30], "Did not lower large perplexity." ) ms = Multiscale(self.x, perplexities=[20, 40, 60]) np.testing.assert_array_equal( ms.perplexities, [20, 30], "Did not drop large perplexities when more than one was too large." ) ms = Multiscale(self.x, perplexities=[20, 30, 40, 60]) np.testing.assert_array_equal( ms.perplexities, [20, 30], "Did not drop duplicate corrected perplexity." ) def test_handles_changing_perplexities(self): perplexities = [15, 25] k_neighbors = perplexities[-1] * 3 ms = Multiscale(self.x, perplexities=perplexities) np.testing.assert_equal(ms.perplexities, perplexities) # Check that the initial `P` matrix is allright n_samples = self.x.shape[0] original_P = ms.P.copy() # Can't check for equality because the matrix is symmetrized therefore # each point may have non-zero values in more than just the k neighbors self.assertTrue(original_P.nnz >= n_samples * k_neighbors) # Check that lowering the perplexity properly changes affinity matrix new_perplexities = [10, 20] k_neighbors = new_perplexities[-1] * 3 ms.set_perplexities(new_perplexities) np.testing.assert_equal(ms.perplexities, new_perplexities) reduced_P = ms.P.copy() self.assertTrue(reduced_P.nnz >= n_samples * k_neighbors) self.assertTrue(reduced_P.nnz < original_P.nnz, "Lower perplexities should consider less neighbors, " "resulting in a sparser affinity matrix") # Raising the perplexity above the initial value would need to recompute # the nearest neighbors, so it should raise an error with self.assertRaises(RuntimeError): ms.set_perplexities([20, 30]) def test_set_perplexities_respects_symmetrization(self): # Apply symmetrization symmetrize = True aff = Multiscale(self.x, perplexities=[10, 20], symmetrize=symmetrize) self.assertAlmostEqual(np.sum(aff.P - aff.P.T), 0, delta=1e-16) aff.set_perplexities([5, 10]) self.assertAlmostEqual(np.sum(aff.P - aff.P.T), 0, delta=1e-16) # Skip symmetrization symmetrize = False aff = Multiscale(self.x, perplexities=[10, 20], symmetrize=symmetrize) self.assertAlmostEqual(np.sum(aff.P - aff.P.T), 0, delta=1e-16) aff.set_perplexities([5, 10]) self.assertAlmostEqual(np.sum(aff.P - aff.P.T), 0, delta=1e-16) class TestUniform(unittest.TestCase): @classmethod def setUpClass(cls): cls.x = np.random.normal(100, 50, (91, 4)) cls.y = np.random.normal(100, 50, (31, 4)) def test_all_unsymmetrized_values_the_same(self): aff = Uniform(self.x, k_neighbors=10, symmetrize=False) values = aff.P.data np.testing.assert_allclose(values, values[0]) def test_to_new_all_equal(self): aff = Uniform(self.x, k_neighbors=10, symmetrize=False) new_p = aff.to_new(self.y) values = new_p.data np.testing.assert_allclose(values, values[0]) new_p = new_p.toarray() np.testing.assert_allclose(new_p.sum(axis=1), np.ones(self.y.shape[0])) class TestAffinityMatrixCorrectness(unittest.TestCase): @classmethod def setUpClass(cls): cls.iris = datasets.load_iris().data def test_that_regular_matrix_sums_to_one(self): for method_name, cls in AFFINITY_CLASSES: aff: affinity.Affinities = cls(self.iris) self.assertAlmostEqual(np.sum(aff.P), 1, msg=method_name) def test_that_to_new_transform_matrix_treats_each_datapoint_separately(self): x_train, x_test = train_test_split(self.iris, test_size=0.33, random_state=42) for method_name, cls in AFFINITY_CLASSES: aff: affinity.Affinities = cls(x_train) P = aff.to_new(x_test) np.testing.assert_allclose( np.asarray(np.sum(P, axis=1)).ravel(), np.ones(len(x_test)), err_msg=method_name, ) def test_handles_precomputed_distance_matrices(self): x = np.random.normal(0, 1, (200, 5)) d = squareform(pdist(x)) for method_name, cls in AFFINITY_CLASSES: aff = cls(d, metric="precomputed") self.assertIsInstance( aff.knn_index, nearest_neighbors.PrecomputedDistanceMatrix, msg=method_name ) def test_affinity_matrix_matches_precomputed_distance_affinity_matrix_random(self): x = np.random.normal(0, 1, (200, 5)) d = squareform(pdist(x)) for method_name, cls in AFFINITY_CLASSES: aff1 = cls(d, metric="precomputed") aff2 = cls(x, metric="euclidean") np.testing.assert_almost_equal( aff1.P.toarray(), aff2.P.toarray(), err_msg=method_name ) def test_affinity_matrix_matches_precomputed_distance_affinity_matrix_iris(self): x = self.iris + np.random.normal(0, 1e-3, self.iris.shape) # iris contains duplicate rows d = squareform(pdist(x)) for method_name, cls in AFFINITY_CLASSES: aff1 = cls(d, metric="precomputed") aff2 = cls(x, metric="euclidean") np.testing.assert_almost_equal( aff1.P.toarray(), aff2.P.toarray(), err_msg=method_name ) class TestAffinityAcceptsKnnIndexAsParameter(unittest.TestCase): @classmethod def setUpClass(cls): cls.iris = datasets.load_iris().data cls.iris += np.random.normal(0, 1e-3, cls.iris.shape) def test_fails_if_neither_data_nor_index_specified(self): for method_name, cls in AFFINITY_CLASSES: with self.assertRaises(ValueError, msg=method_name): cls(data=None, knn_index=None) def test_fails_if_both_data_and_index_specified(self): knn_index = nearest_neighbors.Sklearn(self.iris, k=30) for method_name, cls in AFFINITY_CLASSES: with self.assertRaises(ValueError, msg=method_name): cls(data=self.iris, knn_index=knn_index) def test_accepts_knn_index(self): knn_index = nearest_neighbors.Sklearn(self.iris, k=30) for method_name, cls in AFFINITY_CLASSES: aff = cls(knn_index=knn_index) self.assertIs(aff.knn_index, knn_index, msg=method_name) self.assertEqual(aff.n_samples, self.iris.shape[0]) def test_to_new(self): knn_index = nearest_neighbors.Sklearn(self.iris, k=30) for method_name, cls in AFFINITY_CLASSES: aff = cls(knn_index=knn_index) aff.to_new(self.iris) openTSNE-0.6.1/tests/test_correctness.py000066400000000000000000000271771413546205200202760ustar00rootroot00000000000000import unittest from functools import partial from scipy.spatial.distance import pdist, squareform import openTSNE import openTSNE.affinity import openTSNE.initialization import numpy as np from openTSNE.callbacks import VerifyExaggerationError from sklearn import datasets from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier TSNE = partial(openTSNE.TSNE, neighbors="exact", negative_gradient_method="bh") class TestTSNECorrectness(unittest.TestCase): @classmethod def setUpClass(cls): cls.tsne = TSNE(early_exaggeration_iter=20, n_iter=100) # Set up two modalities, if we want to viually inspect test results random_state = np.random.RandomState(0) cls.x = np.vstack( (random_state.normal(+1, 1, (100, 4)), random_state.normal(-1, 1, (100, 4))) ) cls.x_test = random_state.normal(0, 1, (25, 4)) cls.iris = datasets.load_iris() def test_basic_flow(self): """Verify that the basic flow does not crash.""" embedding = self.tsne.fit(self.x) self.assertFalse(np.any(np.isnan(embedding))) partial_embedding = embedding.transform(self.x_test, n_iter=20) self.assertFalse(np.any(np.isnan(partial_embedding))) def test_advanced_flow(self): """Verify that the advanced flow does not crash.""" embedding = self.tsne.prepare_initial(self.x) embedding = embedding.optimize(20, exaggeration=12) embedding = embedding.optimize(20) # type: openTSNE.TSNEEmbedding self.assertFalse(np.any(np.isnan(embedding))) partial_embedding = embedding.prepare_partial(self.x_test) partial_embedding = partial_embedding.optimize(20, exaggeration=2) partial_embedding = partial_embedding.optimize(20) self.assertFalse(np.any(np.isnan(partial_embedding))) def test_error_exaggeration_correction(self): embedding = self.tsne.prepare_initial(self.x) # The callback raises if the KL divergence does not match the true one embedding.optimize( 50, exaggeration=5, callbacks=[VerifyExaggerationError(embedding)], callbacks_every_iters=1, inplace=True, ) def test_iris(self): x, y = self.iris.data, self.iris.target # Evaluate t-SNE optimization using a KNN classifier knn = KNeighborsClassifier(n_neighbors=10) tsne = TSNE(perplexity=30, initialization="random", random_state=0) # Prepare a random initialization embedding = tsne.prepare_initial(x) # KNN should do poorly on a random initialization knn.fit(embedding, y) predictions = knn.predict(embedding) self.assertLess(accuracy_score(predictions, y), 0.5) # Optimize the embedding for a small number of steps so tests run fast embedding.optimize(250, inplace=True) # Similar points should be grouped together, therefore KNN should do well knn.fit(embedding, y) predictions = knn.predict(embedding) self.assertGreater(accuracy_score(predictions, y), 0.95) def test_iris_with_precomputed_distance_matrices(self): x, y = self.iris.data, self.iris.target distances = squareform(pdist(x)) # Evaluate t-SNE optimization using a KNN classifier knn = KNeighborsClassifier(n_neighbors=10) tsne = TSNE( perplexity=30, initialization="random", random_state=0, metric="precomputed" ) # Prepare a random initialization embedding = tsne.prepare_initial(distances) # KNN should do poorly on a random initialization knn.fit(embedding, y) predictions = knn.predict(embedding) self.assertLess(accuracy_score(predictions, y), 0.5) # Optimize the embedding for a small number of steps so tests run fast embedding.optimize(250, inplace=True) # Similar points should be grouped together, therefore KNN should do well knn.fit(embedding, y) predictions = knn.predict(embedding) self.assertGreater(accuracy_score(predictions, y), 0.95) def test_iris_bh_transform_equivalency_with_one_by_one(self): """Compare one by one embedding vs all at once using BH gradients.""" x_train, x_test = train_test_split( self.iris.data, test_size=0.1, random_state=42 ) # Set up the initial embedding embedding = openTSNE.TSNE( early_exaggeration_iter=0, n_iter=50, neighbors="exact", negative_gradient_method="bh", ).fit(x_train) params = dict(n_iter=100, perplexity=5) # Build up an embedding by adding points one by one new_embedding_1 = np.vstack( [embedding.transform(np.atleast_2d(point), **params) for point in x_test] ) # Add new points altogether new_embedding_2 = embedding.transform(x_test, **params) # Verify that the embedding has actually been optimized self.assertRaises( AssertionError, np.testing.assert_almost_equal, embedding.prepare_partial(x_test, perplexity=params["perplexity"]), new_embedding_1, ) # Check that both methods produced the same embedding np.testing.assert_almost_equal(new_embedding_1, new_embedding_2) def test_iris_fft_transform_equivalency_with_one_by_one(self): """Compare one by one embedding vs all at once using FFT gradients. Note that this won't return the exact same embedding both times because the grid placed over the embedding will differ when placing points one by one vs. when placing them at once. The min/max coords will differ, thus changing the overall approximation. They should be quite similar though. """ x_train, x_test = train_test_split( self.iris.data, test_size=0.1, random_state=42 ) # Set up the initial embedding embedding = openTSNE.TSNE( early_exaggeration_iter=0, n_iter=50, neighbors="exact", negative_gradient_method="fft", ).fit(x_train) # Changing the gradients using clipping changes how the points move # sufficiently so that the interpolation grid is shifted. This test is # more reliable when we don't do gradient clipping and reduce the # learning rate. We increase the number of iterations so that the points # have time to move around params = dict(perplexity=5) # Build up an embedding by adding points one by one new_embedding_1 = np.vstack( [embedding.transform(np.atleast_2d(point), **params) for point in x_test] ) # Add new points altogether new_embedding_2 = embedding.transform(x_test, **params) # Verify that the embedding has actually been optimized self.assertRaises( AssertionError, np.testing.assert_almost_equal, embedding.prepare_partial(x_test, perplexity=params["perplexity"]), new_embedding_1, ) # Check that both methods produced the same embedding np.testing.assert_almost_equal(new_embedding_1, new_embedding_2, decimal=2) def test_iris_bh_transform_correctness(self): x_train, x_test, y_train, y_test = train_test_split( self.iris.data, self.iris.target, test_size=0.33, random_state=42 ) # Set up the initial embedding embedding = openTSNE.TSNE( neighbors="exact", negative_gradient_method="bh", early_exaggeration_iter=0, n_iter=50, random_state=0, ).fit(x_train) # Evaluate t-SNE optimization using a KNN classifier knn = KNeighborsClassifier(n_neighbors=10) knn.fit(embedding, y_train) new_embedding = embedding.transform(x_test, n_iter=100) predictions = knn.predict(new_embedding) self.assertGreater(accuracy_score(predictions, y_test), 0.95) def test_iris_fft_transform_correctness(self): x_train, x_test, y_train, y_test = train_test_split( self.iris.data, self.iris.target, test_size=0.33, random_state=42 ) # Set up the initial embedding embedding = openTSNE.TSNE( neighbors="exact", negative_gradient_method="fft", early_exaggeration_iter=0, n_iter=50, random_state=0, ).fit(x_train) # Evaluate t-SNE optimization using a KNN classifier knn = KNeighborsClassifier(n_neighbors=10) knn.fit(embedding, y_train) new_embedding = embedding.transform(x_test, n_iter=100) predictions = knn.predict(new_embedding) self.assertGreater(accuracy_score(predictions, y_test), 0.95) def test_bh_transform_with_point_subsets_using_perplexity_nn(self): x_train, x_test = train_test_split( self.iris.data, test_size=0.33, random_state=42 ) # Set up the initial embedding init = openTSNE.initialization.pca(x_train) affinity = openTSNE.affinity.PerplexityBasedNN(x_train, method="exact") embedding = openTSNE.TSNEEmbedding( init, affinity, negative_gradient_method="bh", random_state=42 ) embedding.optimize(n_iter=50, inplace=True) # The test set contains 50 samples, so let's verify on half of those transform_params = dict(n_iter=0, early_exaggeration_iter=0) new_embedding_1 = embedding.transform(x_test, **transform_params)[:25] new_embedding_2 = embedding.transform(x_test[:25], **transform_params) np.testing.assert_equal(new_embedding_1, new_embedding_2) def test_fft_transform_with_point_subsets_using_perplexity_nn(self): x_train, x_test = train_test_split( self.iris.data, test_size=0.33, random_state=42 ) # Set up the initial embedding init = openTSNE.initialization.pca(x_train) affinity = openTSNE.affinity.PerplexityBasedNN(x_train, method="exact") embedding = openTSNE.TSNEEmbedding( init, affinity, negative_gradient_method="fft", random_state=42 ) embedding.optimize(n_iter=100, inplace=True) # The test set contains 50 samples, so let's verify on half of those transform_params = dict(n_iter=0, early_exaggeration_iter=0) new_embedding_1 = embedding.transform(x_test, **transform_params)[:25] new_embedding_2 = embedding.transform(x_test[:25], **transform_params) np.testing.assert_equal(new_embedding_1, new_embedding_2) class TestTSNECorrectnessUsingNonStandardDof(TestTSNECorrectness): @classmethod def setUpClass(cls): cls.tsne = TSNE(early_exaggeration_iter=20, n_iter=100, dof=0.8) # Set up two modalities, if we want to viually inspect test results random_state = np.random.RandomState(0) cls.x = np.vstack( (random_state.normal(+1, 1, (100, 4)), random_state.normal(-1, 1, (100, 4))) ) cls.x_test = random_state.normal(0, 1, (25, 4)) cls.iris = datasets.load_iris() class TestTSNECorrectnessUsingPrecomputedDistanceMatrix(unittest.TestCase): def test_iris(self): x = datasets.load_iris().data x += np.random.normal(0, 1e-3, x.shape) # iris contains duplicate rows distances = squareform(pdist(x)) params = dict(initialization="random", random_state=0) embedding1 = TSNE(metric="precomputed", **params).fit(distances) embedding2 = TSNE(metric="euclidean", **params).fit(x) np.testing.assert_almost_equal(embedding1, embedding2) openTSNE-0.6.1/tests/test_different_usages.py000066400000000000000000000155601413546205200212520ustar00rootroot00000000000000import unittest from functools import partial import numpy as np from scipy.spatial.distance import pdist, squareform from sklearn import datasets from sklearn.metrics import accuracy_score from sklearn.neighbors import KNeighborsClassifier, NearestNeighbors import openTSNE from openTSNE import affinity, initialization, nearest_neighbors Multiscale = partial(affinity.Multiscale, method="exact") MultiscaleMixture = partial(affinity.MultiscaleMixture, method="exact") PerplexityBasedNN = partial(affinity.PerplexityBasedNN, method="exact") FixedSigmaNN = partial(affinity.FixedSigmaNN, method="exact") Uniform = partial(affinity.Uniform, method="exact") tsne_params = dict( early_exaggeration_iter=25, n_iter=50, neighbors="exact", negative_gradient_method="bh", ) TSNE = partial(openTSNE.TSNE, **tsne_params) class TestUsage(unittest.TestCase): @classmethod def setUpClass(cls): cls.iris = datasets.load_iris() cls.x = cls.iris.data + np.random.normal(0, 1e-3, cls.iris.data.shape) cls.y = cls.iris.target def eval_embedding(self, embedding, method_name=None): knn = KNeighborsClassifier(n_neighbors=10) knn.fit(embedding, self.y) predictions = knn.predict(embedding) self.assertGreater(accuracy_score(predictions, self.y), 0.95, msg=method_name) class TestUsageSimple(TestUsage): def test_simple(self): embedding = TSNE().fit(self.x) self.eval_embedding(embedding) new_embedding = embedding.transform(self.x) self.eval_embedding(new_embedding, "transform") def test_with_precomputed_distances(self): d = squareform(pdist(self.x)) embedding = TSNE(metric="precomputed").fit(d) self.eval_embedding(embedding) # No transform, precomputed distances can't be queried class TestUsageLowestLevel(TestUsage): def test_1(self): init = initialization.pca(self.x) aff = affinity.PerplexityBasedNN(self.x, perplexity=30) embedding = openTSNE.TSNEEmbedding(init, aff) embedding.optimize(25, exaggeration=12, momentum=0.5, inplace=True) embedding.optimize(50, exaggeration=1, momentum=0.8, inplace=True) self.eval_embedding(embedding) new_embedding = embedding.transform(self.x) self.eval_embedding(new_embedding, f"transform") class TestUsageWithCustomAffinity(TestUsage): def test_affinity(self): init = initialization.random(self.x, random_state=0) for aff in [ affinity.PerplexityBasedNN(self.x, perplexity=30), affinity.Uniform(self.x, k_neighbors=30), affinity.FixedSigmaNN(self.x, sigma=1), affinity.Multiscale(self.x, perplexities=[10, 20]), affinity.MultiscaleMixture(self.x, perplexities=[10, 20]), ]: # Without initilization embedding = TSNE().fit(affinities=aff) self.eval_embedding(embedding, aff.__class__.__name__) new_embedding = embedding.prepare_partial(self.x) new_embedding.optimize(10, learning_rate=0.1, inplace=True) self.eval_embedding(new_embedding, f"transform::{aff.__class__.__name__}") # With initilization embedding = TSNE().fit(affinities=aff, initialization=init) self.eval_embedding(embedding, aff.__class__.__name__) new_embedding = embedding.prepare_partial(self.x) new_embedding.optimize(10, learning_rate=0.1, inplace=True) self.eval_embedding(new_embedding, f"transform::{aff.__class__.__name__}") class TestUsageWithCustomAffinityAndCustomNeighbors(TestUsage): def test_affinity_with_queryable_knn_index(self): knn_index = nearest_neighbors.Sklearn(self.x, k=30) init = initialization.random(self.x, random_state=0) for aff in [ affinity.PerplexityBasedNN(knn_index=knn_index, perplexity=30), affinity.Uniform(knn_index=knn_index, k_neighbors=30), affinity.FixedSigmaNN(knn_index=knn_index, sigma=1), affinity.Multiscale(knn_index=knn_index, perplexities=[10, 20]), affinity.MultiscaleMixture(knn_index=knn_index, perplexities=[10, 20]), ]: # Without initilization embedding = TSNE().fit(affinities=aff) self.eval_embedding(embedding, aff.__class__.__name__) new_embedding = embedding.prepare_partial(self.x) new_embedding.optimize(50, learning_rate=1, inplace=True) self.eval_embedding(new_embedding, f"transform::{aff.__class__.__name__}") # With initilization embedding = TSNE().fit(affinities=aff, initialization=init) self.eval_embedding(embedding, aff.__class__.__name__) new_embedding = embedding.prepare_partial(self.x) new_embedding.optimize(50, learning_rate=1, inplace=True) self.eval_embedding(new_embedding, f"transform::{aff.__class__.__name__}") def test_affinity_with_precomputed_distances(self): d = squareform(pdist(self.x)) knn_index = nearest_neighbors.PrecomputedDistanceMatrix(d, k=30) init = initialization.random(self.x, random_state=0) for aff in [ affinity.PerplexityBasedNN(knn_index=knn_index, perplexity=30), affinity.Uniform(knn_index=knn_index, k_neighbors=30), affinity.FixedSigmaNN(knn_index=knn_index, sigma=1), affinity.Multiscale(knn_index=knn_index, perplexities=[10, 20]), affinity.MultiscaleMixture(knn_index=knn_index, perplexities=[10, 20]), ]: # Without initilization embedding = TSNE().fit(affinities=aff) self.eval_embedding(embedding, aff.__class__.__name__) # With initilization embedding = TSNE().fit(affinities=aff, initialization=init) self.eval_embedding(embedding, aff.__class__.__name__) def test_affinity_with_precomputed_neighbors(self): nn = NearestNeighbors(n_neighbors=30) nn.fit(self.x) distances, neighbors = nn.kneighbors(n_neighbors=30) knn_index = nearest_neighbors.PrecomputedNeighbors(neighbors, distances) init = initialization.random(self.x, random_state=0) for aff in [ affinity.PerplexityBasedNN(knn_index=knn_index, perplexity=30), affinity.Uniform(knn_index=knn_index, k_neighbors=30), affinity.FixedSigmaNN(knn_index=knn_index, sigma=1), affinity.Multiscale(knn_index=knn_index, perplexities=[10, 20]), affinity.MultiscaleMixture(knn_index=knn_index, perplexities=[10, 20]), ]: # Without initilization embedding = TSNE().fit(affinities=aff) self.eval_embedding(embedding, aff.__class__.__name__) # With initilization embedding = TSNE().fit(affinities=aff, initialization=init) self.eval_embedding(embedding, aff.__class__.__name__) openTSNE-0.6.1/tests/test_nearest_neighbors.py000066400000000000000000000376411413546205200214420ustar00rootroot00000000000000import pickle import platform import tempfile import unittest from os import path from unittest.mock import patch, MagicMock import numpy as np import scipy.sparse as sp from scipy.spatial.distance import pdist, cdist, squareform from sklearn import datasets from sklearn.utils import check_random_state from openTSNE import nearest_neighbors from openTSNE.utils import is_package_installed from .test_tsne import check_mock_called_with_kwargs class KNNIndexTestMixin: knn_index = NotImplemented def __init__(self, *args, **kwargs): self.x1 = np.random.normal(100, 50, (150, 50)) self.x2 = np.random.normal(100, 50, (100, 50)) self.iris = datasets.load_iris().data super().__init__(*args, **kwargs) def test_returns_correct_number_neighbors_query_train(self): ks = [1, 5, 10, 30, 50] n_samples = self.x1.shape[0] for k in ks: index: nearest_neighbors.KNNIndex = self.knn_index(self.x1, k, "euclidean") indices, distances = index.build() self.assertEqual(indices.shape, (n_samples, k)) self.assertEqual(distances.shape, (n_samples, k)) def test_returns_proper_distances_query_train(self): index: nearest_neighbors.KNNIndex = self.knn_index(self.iris, 30, "euclidean") indices, distances = index.build() self.assertTrue(np.isfinite(distances).all()) def test_returns_correct_number_neighbors_query(self): ks = [1, 5, 10, 30, 50] n_samples = self.x2.shape[0] index: nearest_neighbors.KNNIndex = self.knn_index(self.x1, 30, "euclidean") index.build() for k in ks: indices, distances = index.query(self.x2, k) self.assertEqual(indices.shape, (n_samples, k)) self.assertEqual(distances.shape, (n_samples, k)) def test_query_train_same_result_with_fixed_random_state(self): knn_index1 = self.knn_index(self.x1, 20, "euclidean", random_state=1) indices1, distances1 = knn_index1.build() knn_index2 = self.knn_index(self.x1, 20, "euclidean", random_state=1) indices2, distances2 = knn_index2.build() np.testing.assert_equal(indices1, indices2) np.testing.assert_equal(distances1, distances2) def test_query_same_result_with_fixed_random_state(self): knn_index1 = self.knn_index(self.x1, 30, "euclidean", random_state=1) indices1, distances1 = knn_index1.build() knn_index2 = self.knn_index(self.x1, 30, "euclidean", random_state=1) indices2, distances2 = knn_index2.build() np.testing.assert_equal(indices1, indices2) np.testing.assert_equal(distances1, distances2) def test_query_same_result_with_fixed_random_state_instance(self): random_state = np.random.RandomState(42) knn_index1 = self.knn_index(self.x1, 30, "euclidean", random_state=random_state) indices1, distances1 = knn_index1.build() random_state = np.random.RandomState(42) knn_index2 = self.knn_index(self.x1, 30, "euclidean", random_state=random_state) indices2, distances2 = knn_index2.build() np.testing.assert_equal(indices1, indices2) np.testing.assert_equal(distances1, distances2) class TestAnnoy(KNNIndexTestMixin, unittest.TestCase): knn_index = nearest_neighbors.Annoy @unittest.skipIf(platform.system() == "Windows", "Files locked on Windows") def test_pickle_without_built_index(self): knn_index = nearest_neighbors.Annoy(self.iris, k=30) self.assertIsNone(knn_index.index) with tempfile.TemporaryDirectory() as dirname: with open(path.join(dirname, "index.pkl"), "wb") as f: pickle.dump(knn_index, f) with open(path.join(dirname, "index.pkl"), "rb") as f: loaded_obj = pickle.load(f) self.assertIsNone(loaded_obj.index) @unittest.skipIf(platform.system() == "Windows", "Files locked on Windows") def test_pickle_without_built_index_cleans_up_fname(self): knn_index = nearest_neighbors.Annoy(self.iris, k=30) with tempfile.TemporaryDirectory() as dirname: with open(path.join(dirname, "index.pkl"), "wb") as f: pickle.dump(knn_index, f) with open(path.join(dirname, "index.pkl"), "rb") as f: loaded_obj = pickle.load(f) self.assertIsNone(loaded_obj.index) @unittest.skipIf(platform.system() == "Windows", "Files locked on Windows") def test_pickle_with_built_index(self): knn_index = nearest_neighbors.Annoy(self.iris, k=30) knn_index.build() self.assertIsNotNone(knn_index.index) with tempfile.TemporaryDirectory() as dirname: with open(path.join(dirname, "index.pkl"), "wb") as f: pickle.dump(knn_index, f) with open(path.join(dirname, "index.pkl"), "rb") as f: loaded_obj = pickle.load(f) load_idx, load_dist = loaded_obj.query(self.iris, 15) orig_idx, orig_dist = knn_index.query(self.iris, 15) np.testing.assert_array_equal(load_idx, orig_idx) np.testing.assert_array_almost_equal(load_dist, orig_dist) class TestSklearn(KNNIndexTestMixin, unittest.TestCase): knn_index = nearest_neighbors.Sklearn def test_cosine_distance(self): k = 15 # Compute cosine distance nearest neighbors using ball tree knn_index = self.knn_index(self.x1, k, "cosine") indices, distances = knn_index.build() # Compute the exact nearest neighbors as a reference true_distances = squareform(pdist(self.x1, metric="cosine")) true_indices_ = np.argsort(true_distances, axis=1)[:, 1:k + 1] true_distances_ = np.vstack([d[i] for d, i in zip(true_distances, true_indices_)]) np.testing.assert_array_equal( indices, true_indices_, err_msg="Nearest neighbors do not match" ) np.testing.assert_array_equal( distances, true_distances_, err_msg="Distances do not match" ) def test_cosine_distance_query(self): k = 15 # Compute cosine distance nearest neighbors using ball tree knn_index = self.knn_index(self.x1, k, "cosine") knn_index.build() indices, distances = knn_index.query(self.x2, k=k) # Compute the exact nearest neighbors as a reference true_distances = cdist(self.x2, self.x1, metric="cosine") true_indices_ = np.argsort(true_distances, axis=1)[:, :k] true_distances_ = np.vstack([d[i] for d, i in zip(true_distances, true_indices_)]) np.testing.assert_array_equal( indices, true_indices_, err_msg="Nearest neighbors do not match" ) np.testing.assert_array_equal( distances, true_distances_, err_msg="Distances do not match" ) def test_uncompiled_callable_metric_same_result(self): k = 15 knn_index = self.knn_index(self.x1, k, "manhattan", random_state=1) knn_index.build() true_indices_, true_distances_ = knn_index.query(self.x2, k=k) def manhattan(x, y): result = 0.0 for i in range(x.shape[0]): result += np.abs(x[i] - y[i]) return result knn_index = self.knn_index(self.x1, k, manhattan, random_state=1) knn_index.build() indices, distances = knn_index.query(self.x2, k=k) np.testing.assert_array_equal( indices, true_indices_, err_msg="Nearest neighbors do not match" ) np.testing.assert_allclose( distances, true_distances_, err_msg="Distances do not match" ) @unittest.skipIf(not is_package_installed("hnswlib"), "`hnswlib`is not installed") class TestHNSW(KNNIndexTestMixin, unittest.TestCase): knn_index = nearest_neighbors.HNSW @classmethod def setUpClass(cls): global hnswlib import hnswlib @unittest.skipIf(platform.system() == "Windows", "Files locked on Windows") def test_pickle_without_built_index(self): knn_index = nearest_neighbors.HNSW(self.iris, k=30) self.assertIsNone(knn_index.index) with tempfile.TemporaryDirectory() as dirname: with open(path.join(dirname, "index.pkl"), "wb") as f: pickle.dump(knn_index, f) with open(path.join(dirname, "index.pkl"), "rb") as f: loaded_obj = pickle.load(f) self.assertIsNone(loaded_obj.index) @unittest.skipIf(platform.system() == "Windows", "Files locked on Windows") def test_pickle_without_built_index_cleans_up_fname(self): knn_index = nearest_neighbors.HNSW(self.iris, k=30) with tempfile.TemporaryDirectory() as dirname: with open(path.join(dirname, "index.pkl"), "wb") as f: pickle.dump(knn_index, f) with open(path.join(dirname, "index.pkl"), "rb") as f: loaded_obj = pickle.load(f) self.assertIsNone(loaded_obj.index) @unittest.skipIf(platform.system() == "Windows", "Files locked on Windows") def test_pickle_with_built_index(self): knn_index = nearest_neighbors.HNSW(self.iris, k=30) knn_index.build() self.assertIsNotNone(knn_index.index) with tempfile.TemporaryDirectory() as dirname: with open(path.join(dirname, "index.pkl"), "wb") as f: pickle.dump(knn_index, f) with open(path.join(dirname, "index.pkl"), "rb") as f: loaded_obj = pickle.load(f) load_idx, load_dist = loaded_obj.query(self.iris, 15) orig_idx, orig_dist = knn_index.query(self.iris, 15) np.testing.assert_array_equal(load_idx, orig_idx) np.testing.assert_array_almost_equal(load_dist, orig_dist) @unittest.skipIf(not is_package_installed("pynndescent"), "`pynndescent`is not installed") class TestNNDescent(KNNIndexTestMixin, unittest.TestCase): knn_index = nearest_neighbors.NNDescent @classmethod def setUpClass(cls): global pynndescent, njit, CPUDispatcher import pynndescent from numba import njit from numba.core.registry import CPUDispatcher def test_random_state_being_passed_through(self): random_state = 1 with patch("pynndescent.NNDescent", wraps=pynndescent.NNDescent) as nndescent: knn_index = nearest_neighbors.NNDescent( self.x1, 30, "euclidean", random_state=random_state ) knn_index.build() nndescent.assert_called_once() check_mock_called_with_kwargs(nndescent, {"random_state": random_state}) def test_uncompiled_callable_is_compiled(self): knn_index = nearest_neighbors.NNDescent(self.x1, 30, "manhattan") def manhattan(x, y): result = 0.0 for i in range(x.shape[0]): result += np.abs(x[i] - y[i]) return result compiled_metric = knn_index.check_metric(manhattan) self.assertTrue(isinstance(compiled_metric, CPUDispatcher)) def test_uncompiled_callable_metric_same_result(self): k = 15 knn_index = self.knn_index(self.x1, k, "manhattan", random_state=1) knn_index.build() true_indices_, true_distances_ = knn_index.query(self.x2, k=k) def manhattan(x, y): result = 0.0 for i in range(x.shape[0]): result += np.abs(x[i] - y[i]) return result knn_index = self.knn_index(self.x1, k, manhattan, random_state=1) knn_index.build() indices, distances = knn_index.query(self.x2, k=k) np.testing.assert_array_equal( indices, true_indices_, err_msg="Nearest neighbors do not match" ) np.testing.assert_allclose( distances, true_distances_, err_msg="Distances do not match" ) def test_numba_compiled_callable_metric_same_result(self): k = 15 knn_index = self.knn_index(self.x1, k, "manhattan", random_state=1) knn_index.build() true_indices_, true_distances_ = knn_index.query(self.x2, k=k) @njit(fastmath=True) def manhattan(x, y): result = 0.0 for i in range(x.shape[0]): result += np.abs(x[i] - y[i]) return result knn_index = self.knn_index(self.x1, k, manhattan, random_state=1) knn_index.build() indices, distances = knn_index.query(self.x2, k=k) np.testing.assert_array_equal( indices, true_indices_, err_msg="Nearest neighbors do not match" ) np.testing.assert_allclose( distances, true_distances_, err_msg="Distances do not match" ) def test_building_with_lt15_builds_proper_graph(self): with patch("pynndescent.NNDescent", wraps=pynndescent.NNDescent) as nndescent: knn_index = nearest_neighbors.NNDescent(self.x1, 10, "euclidean") indices, distances = knn_index.build() self.assertEqual(indices.shape, (self.x1.shape[0], 10)) self.assertEqual(distances.shape, (self.x1.shape[0], 10)) self.assertFalse(np.all(indices[:, 0] == np.arange(self.x1.shape[0]))) # Should be called with 11 because nearest neighbor in pynndescent is itself check_mock_called_with_kwargs(nndescent, dict(n_neighbors=11)) def test_building_with_gt15_calls_query(self): with patch("pynndescent.NNDescent", wraps=pynndescent.NNDescent) as nndescent: nndescent.query = MagicMock(wraps=nndescent.query) knn_index = nearest_neighbors.NNDescent(self.x1, 30, "euclidean") indices, distances = knn_index.build() self.assertEqual(indices.shape, (self.x1.shape[0], 30)) self.assertEqual(distances.shape, (self.x1.shape[0], 30)) self.assertFalse(np.all(indices[:, 0] == np.arange(self.x1.shape[0]))) # The index should be built with 15 neighbors check_mock_called_with_kwargs(nndescent, dict(n_neighbors=15)) # And subsequently queried with the correct number of neighbors. Check # for 31 neighbors because query will return the original point as well, # which we don't consider. check_mock_called_with_kwargs(nndescent.query, dict(k=31)) def test_runs_with_correct_njobs_if_dense_input(self): with patch("pynndescent.NNDescent", wraps=pynndescent.NNDescent) as nndescent: knn_index = nearest_neighbors.NNDescent(self.x1, 5, "euclidean", n_jobs=2) knn_index.build() check_mock_called_with_kwargs(nndescent, dict(n_jobs=2)) def test_runs_with_correct_njobs_if_sparse_input(self): with patch("pynndescent.NNDescent", wraps=pynndescent.NNDescent) as nndescent: x_sparse = sp.csr_matrix(self.x1) knn_index = nearest_neighbors.NNDescent(x_sparse, 5, "euclidean", n_jobs=2) knn_index.build() check_mock_called_with_kwargs(nndescent, dict(n_jobs=2)) def test_random_cluster_when_invalid_indices(self): class MockIndex: def __init__(self, data, n_neighbors, **_): n_samples = data.shape[0] rs = check_random_state(0) indices = rs.randint(0, n_samples, size=(n_samples, n_neighbors)) distances = rs.exponential(5, (n_samples, n_neighbors)) # Set some of the points to have invalid indices indices[:10] = -1 distances[:10] = -1 self.neighbor_graph = indices, distances with patch("pynndescent.NNDescent", wraps=MockIndex): knn_index = nearest_neighbors.NNDescent(self.x1, 5, "euclidean", n_jobs=2) indices, distances = knn_index.build() # Check that indices were replaced by something self.assertTrue(np.all(indices[:10] != -1)) # Check that that "something" are all indices of failed points self.assertTrue(np.all(indices[:10] < 10)) # And check that the distances were set to something positive self.assertTrue(np.all(distances[:10] > 0)) openTSNE-0.6.1/tests/test_sklearn_wrapper.py000066400000000000000000000020141413546205200211220ustar00rootroot00000000000000import unittest import numpy as np from openTSNE.sklearn import TSNE class TestTSNECorrectness(unittest.TestCase): @classmethod def setUpClass(cls): cls.tsne = TSNE( early_exaggeration_iter=20, n_iter=100, neighbors="exact", negative_gradient_method="bh", ) # Set up two modalities, if we want to viually inspect test results random_state = np.random.RandomState(0) cls.x = np.vstack( (random_state.normal(+1, 1, (100, 4)), random_state.normal(-1, 1, (100, 4))) ) cls.x_test = random_state.normal(0, 1, (25, 4)) def test_fit(self): retval = self.tsne.fit(self.x) self.assertIs(type(retval), TSNE) def test_fit_transform(self): retval = self.tsne.fit_transform(self.x) self.assertIs(type(retval), np.ndarray) def test_transform(self): self.tsne.fit(self.x) retval = self.tsne.transform(self.x_test) self.assertIs(type(retval), np.ndarray) openTSNE-0.6.1/tests/test_tsne.py000066400000000000000000001076571413546205200167170ustar00rootroot00000000000000import inspect import logging import pickle import unittest from functools import wraps, partial from typing import Callable, Any, Tuple, Optional from unittest.mock import patch, MagicMock import numpy as np from scipy.spatial.distance import pdist, squareform from sklearn import datasets from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier import openTSNE from openTSNE import affinity from openTSNE import initialization from openTSNE.affinity import PerplexityBasedNN from openTSNE.nearest_neighbors import NNDescent from openTSNE.tsne import kl_divergence_bh, kl_divergence_fft from openTSNE.utils import is_package_installed np.random.seed(42) affinity.log.setLevel(logging.ERROR) TSNE = partial(openTSNE.TSNE, neighbors="exact", negative_gradient_method="bh") def check_params(params: dict) -> Callable: """Run a series of parameterized tests to check tSNE parameter flow.""" def _decorator(test_case: Callable) -> Callable: @wraps(test_case) def _wrapper(self): for param_name in params: for param_value in params[param_name]: test_case(self, param_name, param_value) return _wrapper return _decorator def check_call_contains_kwargs( call: Tuple, params: dict, param_mapping: Optional[dict] = None, ) -> None: """Check whether a `call` object was called with some params, but also some others we don't care about""" _param_mapping = {"negative_gradient_method": "objective_function", "early_exaggeration_iter": "n_iter", "late_exaggeration_iter": "n_iter", "early_exaggeration": "exaggeration", "late_exaggeration": "exaggeration", "initial_momentum": "momentum", "final_momentum": "momentum"} if param_mapping is not None: _param_mapping.update(param_mapping) name, args, kwargs = call for key in params: # If a parameter isn't named the same way in the call if key in _param_mapping: kwargs_key = _param_mapping[key] else: kwargs_key = key expected_value = params[key] actual_value = kwargs.get(kwargs_key, None) if expected_value != actual_value: raise AssertionError( "Mock not called with `%s=%s`. Called with `%s`" % (key, expected_value, actual_value) ) def check_mock_called_with_kwargs(mock: MagicMock, params: dict) -> None: """Check whether a mock was called with kwargs, but also some other params we don't care about.""" for call in mock.mock_calls: check_call_contains_kwargs(call, params) class TestTSNEParameterFlow(unittest.TestCase): """Test that the optimization parameters get properly propagated.""" grad_descent_params = { "negative_gradient_method": [kl_divergence_bh, kl_divergence_fft], "learning_rate": [1, 10, 100], "dof": [0.5, 1, 1.5], "theta": [0.2, 0.5, 0.8], "n_interpolation_points": [3, 5], "min_num_intervals": [10, 20, 30], "ints_in_interval": [1, 2, 5], "max_grad_norm": [None, 0.5, 1], "max_step_norm": [None, 1, 5], "n_jobs": [1, 2, 4], "callbacks": [None, [lambda *args, **kwargs: ...]], "callbacks_every_iters": [25, 50], } @classmethod def setUpClass(cls): cls.x = np.random.randn(100, 4) cls.x_test = np.random.randn(25, 4) @check_params({**grad_descent_params, **{ "early_exaggeration_iter": [50, 100], "early_exaggeration": [4, 12], "initial_momentum": [0.2, 0.5, 0.8], "n_iter": [50, 100], "exaggeration": [None, 2], "final_momentum": [0.2, 0.5, 0.8], }}) @patch("openTSNE.tsne.gradient_descent.__call__") def test_constructor(self, param_name, param_value, gradient_descent): # type: (str, Any, MagicMock) -> None # Make sure mock still conforms to signature gradient_descent.return_value = (1, MagicMock()) # Early exaggeration training loop if param_name in ("early_exaggeration_iter", "early_exaggeration", "initial_momentum"): call_idx = 0 # Main training loop elif param_name in ("n_iter", "exaggeration", "final_momentum"): call_idx = 1 # If general parameter, should be applied to every call else: call_idx = 0 TSNE(**{param_name: param_value}).fit(self.x) self.assertEqual(2, gradient_descent.call_count) check_call_contains_kwargs( gradient_descent.mock_calls[call_idx], {param_name: param_value}, ) @check_params({**grad_descent_params, **{ "n_iter": [50, 100, 150], "exaggeration": [None, 2, 5], "momentum": [0.2, 0.5, 0.8], }}) @patch("openTSNE.tsne.gradient_descent.__call__") def test_embedding_optimize(self, param_name, param_value, gradient_descent): # type: (str, Any, MagicMock) -> None # Make sure mock still conforms to signature gradient_descent.return_value = (1, MagicMock()) # `optimize` requires us to specify the `n_iter` params = {"n_iter": 50, param_name: param_value} tsne = TSNE() embedding = tsne.prepare_initial(self.x) embedding.optimize(**params, inplace=True) self.assertEqual(1, gradient_descent.call_count) check_call_contains_kwargs(gradient_descent.mock_calls[0], params) @check_params({ "early_exaggeration_iter": [50, 100], "early_exaggeration": [None, 2, 4], "n_iter": [50, 100], "exaggeration": [None, 1, 2], "initial_momentum": [0.2, 0.5, 0.8], "final_momentum": [0.2, 0.5, 0.8], "max_grad_norm": [None, 0.5, 1], "max_step_norm": [None, 1, 5], }) @patch("openTSNE.tsne.gradient_descent.__call__") def test_embedding_transform(self, param_name, param_value, gradient_descent): # type: (str, Any, MagicMock) -> None # Make sure mock still conforms to signature gradient_descent.return_value = (1, MagicMock()) # Perform initial embedding - this is tested above tsne = TSNE() embedding = tsne.fit(self.x) gradient_descent.reset_mock() embedding.transform(self.x_test, **{param_name: param_value}) if "early" in param_name or "initial" in param_name: call_idx = 0 else: call_idx = 1 self.assertEqual(2, gradient_descent.call_count) check_call_contains_kwargs( gradient_descent.mock_calls[call_idx], {param_name: param_value}, ) @check_params({**grad_descent_params, **{ "n_iter": [50, 100, 150], "exaggeration": [None, 2, 5], "momentum": [0.2, 0.5, 0.8], "max_grad_norm": [None, 0.5, 1], "max_step_norm": [None, 1, 5], }}) @patch("openTSNE.tsne.gradient_descent.__call__") def test_partial_embedding_optimize(self, param_name, param_value, gradient_descent): # type: (str, Any, MagicMock) -> None # Make sure mock still conforms to signature gradient_descent.return_value = (1, MagicMock()) # Perform initial embedding - this is tested above tsne = TSNE() embedding = tsne.fit(self.x) gradient_descent.reset_mock() # `optimize` requires us to specify the `n_iter` params = {"n_iter": 50, param_name: param_value} partial_embedding = embedding.prepare_partial(self.x_test) partial_embedding.optimize(**params, inplace=True) self.assertEqual(1, gradient_descent.call_count) check_call_contains_kwargs(gradient_descent.mock_calls[0], params) @check_params({"metric": set(NNDescent.VALID_METRICS) - {"mahalanobis"}}) @unittest.skipIf(not is_package_installed("pynndescent"), "`pynndescent`is not installed") @patch("pynndescent.NNDescent") def test_nndescent_distances(self, param_name, metric, nndescent: MagicMock): """Distance metrics should be properly passed down to NN descent""" assert param_name == "metric" tsne = TSNE(metric=metric, neighbors="pynndescent") # We don't care about what happens later, just that the NN method is # properly called nndescent.side_effect = InterruptedError() try: # Haversine distance only supports two dimensions tsne.prepare_initial(self.x[:, :2]) except InterruptedError: pass self.assertEqual(nndescent.call_count, 1) check_call_contains_kwargs(nndescent.mock_calls[0], {"metric": metric}) @unittest.skipIf(not is_package_installed("pynndescent"), "`pynndescent`is not installed") @patch("pynndescent.NNDescent") def test_nndescent_mahalanobis_distance(self, nndescent: MagicMock): """Distance metrics and additional params should be correctly passed down to NN descent""" metric = "mahalanobis" C = np.cov(self.x) tsne = TSNE(metric=metric, metric_params={"V": C}, neighbors="pynndescent") # We don't care about what happens later, just that the NN method is # properly called nndescent.side_effect = InterruptedError() try: tsne.prepare_initial(self.x) except InterruptedError: pass self.assertEqual(nndescent.call_count, 1) check_call_contains_kwargs(nndescent.mock_calls[0], {"metric": metric}) def test_raises_error_on_unrecognized_metric(self): """Unknown distance metric should raise error""" tsne = TSNE(metric="imaginary", neighbors="exact") with self.assertRaises(ValueError): tsne.prepare_initial(self.x) tsne = TSNE(metric="imaginary", neighbors="approx") with self.assertRaises(ValueError): tsne.prepare_initial(self.x) class TestTSNEInplaceOptimization(unittest.TestCase): @classmethod def setUpClass(cls): cls.tsne = TSNE() cls.x = np.random.randn(100, 4) cls.x_test = np.random.randn(25, 4) def test_embedding_inplace_optimization(self): embedding1 = self.tsne.prepare_initial(self.x) embedding2 = embedding1.optimize(n_iter=5, inplace=True) embedding3 = embedding2.optimize(n_iter=5, inplace=True) self.assertIs(embedding1.base, embedding2.base) self.assertIs(embedding2.base, embedding3.base) def test_embedding_not_inplace_optimization(self): embedding1 = self.tsne.prepare_initial(self.x) embedding2 = embedding1.optimize(n_iter=5, inplace=False) embedding3 = embedding2.optimize(n_iter=5, inplace=False) self.assertFalse(embedding1.base is embedding2.base) self.assertFalse(embedding2.base is embedding3.base) self.assertFalse(embedding1.base is embedding3.base) def test_partial_embedding_inplace_optimization(self): # Prepare reference embedding embedding = self.tsne.prepare_initial(self.x) embedding.optimize(10, inplace=True) partial_embedding1 = embedding.prepare_partial(self.x_test) partial_embedding2 = partial_embedding1.optimize(5, inplace=True) partial_embedding3 = partial_embedding2.optimize(5, inplace=True) self.assertIs(partial_embedding1.base, partial_embedding2.base) self.assertIs(partial_embedding2.base, partial_embedding3.base) def test_partial_embedding_not_inplace_optimization(self): # Prepare reference embedding embedding = self.tsne.prepare_initial(self.x) embedding.optimize(10, inplace=True) partial_embedding1 = embedding.prepare_partial(self.x_test) partial_embedding2 = partial_embedding1.optimize(5, inplace=False) partial_embedding3 = partial_embedding2.optimize(5, inplace=False) self.assertFalse(partial_embedding1.base is partial_embedding2.base) self.assertFalse(partial_embedding2.base is partial_embedding3.base) self.assertFalse(partial_embedding1.base is partial_embedding3.base) class TestTSNECallbackParams(unittest.TestCase): @classmethod def setUpClass(cls): cls.tsne = TSNE() cls.x = np.random.randn(100, 4) cls.x_test = np.random.randn(25, 4) def test_can_pass_callbacks_to_tsne_object(self): callback = MagicMock() callback2 = MagicMock() # We don't want individual callbacks to be iterable del callback.__iter__ del callback2.__iter__ # Should be able to pass a single callback TSNE(callbacks=callback, callbacks_every_iters=1, early_exaggeration_iter=0, n_iter=1).fit(self.x) self.assertEqual(callback.call_count, 1) # Should be able to pass a list callbacks callback.reset_mock() TSNE(callbacks=[callback], callbacks_every_iters=1, early_exaggeration_iter=0, n_iter=1).fit(self.x) self.assertEqual(callback.call_count, 1) # Should be able to change the callback on the object callback.reset_mock() tsne = TSNE(callbacks=callback, callbacks_every_iters=1, early_exaggeration_iter=0, n_iter=1) tsne.callbacks = callback2 tsne.fit(self.x) callback.assert_not_called() self.assertEqual(callback2.call_count, 1) def test_can_pass_callbacks_to_embedding_optimize(self): embedding = self.tsne.prepare_initial(self.x) # We don't the callback to be iterable callback = MagicMock() del callback.__iter__ # Should be able to pass a single callback embedding.optimize(1, callbacks=callback, callbacks_every_iters=1) self.assertEqual(callback.call_count, 1) # Should be able to pass a list callbacks callback.reset_mock() embedding.optimize(1, callbacks=[callback], callbacks_every_iters=1) self.assertEqual(callback.call_count, 1) def test_can_pass_callbacks_to_partial_embedding_optimize(self): embedding = self.tsne.prepare_initial(self.x) # We don't the callback to be iterable callback = MagicMock() del callback.__iter__ # Should be able to pass a single callback partial_embedding = embedding.prepare_partial(self.x_test) partial_embedding.optimize(1, callbacks=callback, callbacks_every_iters=1) self.assertEqual(callback.call_count, 1) # Should be able to pass a list callbacks callback.reset_mock() partial_embedding.optimize(1, callbacks=[callback], callbacks_every_iters=1) self.assertEqual(callback.call_count, 1) class TestAlternativeFitUsageWithAffinityAndInitialization(unittest.TestCase): @classmethod def setUpClass(cls): cls.x = np.random.normal(100, 50, (25, 4)) cls.init = np.random.normal(0, 1e-4, (25, 2)) def test_fails_if_no_parameters_specified(self): tsne = TSNE() with self.assertRaises(ValueError): tsne.fit() def test_precomputed_affinity_is_passed_to_embedding_object(self): aff = affinity.PerplexityBasedNN(self.x, 5, method="exact") embedding = TSNE( early_exaggeration_iter=0, n_iter=0, initialization=self.init ).fit(affinities=aff) self.assertIs(embedding.affinities, aff) def test_fails_if_affinities_parameter_is_not_correct_class(self): aff = "definitely not an affinity object" with self.assertRaises(ValueError): TSNE(initialization=self.init).fit(affinities=aff) def test_precomputed_initialization_is_passed_to_embedding_object(self): embedding = TSNE(early_exaggeration_iter=0, n_iter=0) \ .fit(self.x, initialization=self.init) np.testing.assert_array_equal(embedding, self.init) def test_string_initialization(self): # This should not crash TSNE(early_exaggeration_iter=0, n_iter=0).fit(self.x, initialization="pca") def test_parameter_init_takes_precendence_over_constructor_init(self): constructor_init = np.random.normal(1, 1e-4, self.init.shape) embedding = TSNE( early_exaggeration_iter=0, n_iter=0, initialization=constructor_init ).fit(self.x, initialization=self.init) np.testing.assert_array_equal(embedding, self.init) def test_pca_init_with_only_affinities_passed(self): aff = affinity.PerplexityBasedNN(self.x, 5, method="exact") desired_init = initialization.spectral(aff.P) embedding = TSNE( early_exaggeration_iter=0, n_iter=0, initialization="pca" ).fit(affinities=aff) np.testing.assert_array_equal(embedding, desired_init) class TSNEInitialization(unittest.TestCase): transform_initializations = ["random", "median", "weighted"] @classmethod def setUpClass(cls): # It would be nice if the initial data were not nicely behaved to test # for low variance cls.x = np.random.normal(100, 50, (25, 4)) cls.iris = datasets.load_iris()["data"] def test_low_variance(self): """Low variance of the initial embedding is very important for the convergence of tSNE.""" # Cycle through various initializations initializations = ["random", "pca"] allowed = 1e-3 for init in initializations: tsne = TSNE(initialization=init, perplexity=2) embedding = tsne.prepare_initial(self.x) np.testing.assert_array_less(np.var(embedding, axis=0), allowed, "using the `%s` initialization" % init) def test_mismatching_embedding_dimensions_simple_api(self): # Fit tsne = TSNE(n_components=2, initialization=self.x[:10, :2]) with self.assertRaises(ValueError, msg="fit::incorrect number of points"): tsne.fit(self.x[:25]) with self.assertRaises(ValueError, msg="fit::incorrect number of dimensions"): TSNE(n_components=2, initialization=self.x[:10, :4]) # Transform tsne = TSNE(n_components=2, initialization="random") embedding = tsne.fit(self.x) with self.assertRaises(ValueError, msg="transform::incorrect number of points"): embedding.transform(X=self.x[:5], initialization=self.x[:10, :2]) with self.assertRaises(ValueError, msg="transform::incorrect number of dimensions"): embedding.transform(X=self.x, initialization=self.x[:, :4]) def test_same_unoptimized_initializations_for_transform(self): """Initializations should be deterministic.""" x_train, x_test = train_test_split(self.iris, test_size=0.33, random_state=42) embedding = openTSNE.TSNE( early_exaggeration_iter=50, n_iter=50, neighbors="exact", negative_gradient_method="bh", random_state=42, ).fit(x_train) for init in self.transform_initializations: new_embedding_1 = embedding.prepare_partial(x_test, initialization=init) new_embedding_2 = embedding.prepare_partial(x_test, initialization=init) np.testing.assert_equal(new_embedding_1, new_embedding_2, init) def test_same_bh_optimized_median_initializations_for_transform(self): """Transform with Barnes-Hut optimization should be deterministic.""" x_train, x_test = train_test_split(self.iris, test_size=0.33, random_state=42) embedding = openTSNE.TSNE( early_exaggeration_iter=10, n_iter=10, neighbors="exact", negative_gradient_method="bh", random_state=42, ).fit(x_train) for init in self.transform_initializations: new_embedding_1 = embedding.transform( x_test, initialization=init, n_iter=10 ) new_embedding_2 = embedding.transform( x_test, initialization=init, n_iter=10 ) np.testing.assert_equal(new_embedding_1, new_embedding_2, init) def test_same_fft_optimized_median_initializations_for_transform(self): """Transform with interpolation based optimization should be deterministic.""" x_train, x_test = train_test_split(self.iris, test_size=0.33, random_state=42) embedding = openTSNE.TSNE( early_exaggeration_iter=10, n_iter=10, neighbors="exact", negative_gradient_method="fft", random_state=42, ).fit(x_train) for init in self.transform_initializations: new_embedding_1 = embedding.transform( x_test, initialization=init, n_iter=10, learning_rate=10 ) new_embedding_2 = embedding.transform( x_test, initialization=init, n_iter=10, learning_rate=10 ) np.testing.assert_equal(new_embedding_1, new_embedding_2, init) class TestRandomState(unittest.TestCase): @classmethod def setUpClass(cls): # It would be nice if the initial data were not nicely behaved to test # for low variance cls.x = np.random.normal(10000, 50, (25, 4)) cls.x_test = np.random.normal(100, 50, (25, 4)) def test_same_results_on_fixed_random_state_random_init(self): """Results should be exactly the same if we provide a random state.""" tsne1 = TSNE(random_state=1, initialization="random") embedding1 = tsne1.fit(self.x) tsne2 = TSNE(random_state=1, initialization="random") embedding2 = tsne2.fit(self.x) np.testing.assert_array_equal( embedding1, embedding2, "Same random state produced different initial embeddings", ) def test_same_results_on_fixed_random_state_pca_init(self): """Results should be exactly the same if we provide a random state.""" tsne1 = TSNE(random_state=1, initialization="pca") embedding1 = tsne1.fit(self.x) tsne2 = TSNE(random_state=1, initialization="pca") embedding2 = tsne2.fit(self.x) np.testing.assert_array_equal( embedding1, embedding2, "Same random state produced different initial embeddings", ) def test_same_partial_embedding_on_fixed_random_state(self): tsne = TSNE(random_state=1, initialization="random") embedding = tsne.fit(self.x) partial1 = embedding.prepare_partial(self.x_test, initialization="random") partial2 = embedding.prepare_partial(self.x_test, initialization="random") np.testing.assert_array_equal( partial1, partial2, "Same random state produced different partial embeddings", ) @patch("openTSNE.initialization.random", wraps=openTSNE.initialization.random) @patch("openTSNE.nearest_neighbors.Sklearn", wraps=openTSNE.nearest_neighbors.Sklearn) def test_random_state_parameter_is_propagated_random_init_exact(self, init, neighbors): random_state = 1 tsne = openTSNE.TSNE( neighbors="exact", initialization="random", random_state=random_state, ) tsne.prepare_initial(self.x) # Verify that `random_state` was passed init.assert_called_once() check_mock_called_with_kwargs(init, {"random_state": random_state}) neighbors.assert_called_once() check_mock_called_with_kwargs(neighbors, {"random_state": random_state}) @patch("openTSNE.initialization.pca", wraps=openTSNE.initialization.pca) @patch("openTSNE.nearest_neighbors.Annoy", wraps=openTSNE.nearest_neighbors.Annoy) def test_random_state_parameter_is_propagated_pca_init_approx(self, init, neighbors): random_state = 1 tsne = openTSNE.TSNE( neighbors="approx", initialization="pca", random_state=random_state, ) tsne.prepare_initial(self.x) # Verify that `random_state` was passed init.assert_called_once() check_mock_called_with_kwargs(init, {"random_state": random_state}) neighbors.assert_called_once() check_mock_called_with_kwargs(neighbors, {"random_state": random_state}) class TestDefaultParameterSettings(unittest.TestCase): def test_default_params_simple_vs_complex_flow(self): # Relevant affinity parameters are passed to the affinity object mismatching = get_mismatching_default_values( openTSNE.TSNE, PerplexityBasedNN, {"neighbors": "method"}, ) self.assertEqual(mismatching, []) assert len( get_shared_parameters(openTSNE.TSNE, openTSNE.tsne.gradient_descent.__call__) ) > 0, \ "`TSNE` and `gradient_descent` have no shared parameters. Have you " \ "changed the signature or usage?" # The relevant gradient descent parameters are passed down directly to # `gradient_descent` mismatching = get_mismatching_default_values( openTSNE.TSNE, openTSNE.tsne.gradient_descent.__call__, ) # Some default parameters should be different between TSNE and gradient_descent allowed_mismatches = ("n_iter", "learning_rate") mismatching = list(filter(lambda x: x[0] not in allowed_mismatches, mismatching)) self.assertEqual(mismatching, []) def get_shared_parameters(f1, f2): """Get the names of shared parameters from two function signatures.""" params1 = inspect.signature(f1).parameters params2 = inspect.signature(f2).parameters return set(params1.keys()) & set(params2.keys()) def get_mismatching_default_values(f1, f2, mapping=None): """Check that two functions have the same default values for shared parameters.""" # Additional mappings from f1 parameters to f2 parameters may be provided if mapping is None: mapping = {} params1 = inspect.signature(f1).parameters params2 = inspect.signature(f2).parameters mismatch = [] for f1_param_name in params1: # If the param is named differently in f2, rename f2_param_name = mapping[f1_param_name] if f1_param_name in mapping else f1_param_name # If the parameter does not appear in the signature of f2, there"s # nothing to do if f2_param_name not in params2: continue val1 = params1[f1_param_name].default val2 = params2[f2_param_name].default if val1 != val2: mismatch.append((f1_param_name, val1, f2_param_name, val2)) return mismatch class TestGradientDescentOptimizer(unittest.TestCase): @classmethod def setUpClass(cls): cls.tsne = TSNE() random_state = np.random.RandomState(42) cls.x = random_state.randn(100, 4) cls.x_test = random_state.randn(25, 4) def test_optimizer_being_passed_to_subsequent_embeddings(self): embedding = self.tsne.prepare_initial(self.x) self.assertIsNone( embedding.optimizer.gains, "Optimizer should be initialized with no gains" ) # Check the switch from no gains to some gains embedding1 = embedding.optimize(10) self.assertIsNone( embedding.optimizer.gains, "Gains changed on initial optimizer even though we did not do " "inplace optimization.", ) self.assertIsNotNone( embedding1.optimizer.gains, "Gains were not properly set in new embedding." ) self.assertIsNot( embedding.optimizer, embedding1.optimizer, "The embedding and new embedding optimizer are the same instance " "even we did not do inplace optimization.", ) # Check switch from existing gains to new gains embedding2 = embedding1.optimize(10) self.assertIsNot( embedding1.optimizer, embedding2.optimizer, "The embedding and new embedding optimizer are the same instance " "even we did not do inplace optimization.", ) self.assertFalse( np.allclose(embedding1.optimizer.gains, embedding2.optimizer.gains), "The gains in the new embedding did not change at all from the old " "embedding.", ) def test_optimizer_being_passed_to_partial_embeddings(self): embedding = self.tsne.prepare_initial(self.x) embedding.optimize(10, inplace=True) # Partial embeddings get their own optimizer instance partial = embedding.prepare_partial(self.x_test) self.assertIsNot( embedding.optimizer, partial.optimizer, "Embedding and partial embedding optimizers are the same instance.", ) self.assertIsNone( partial.optimizer.gains, "Partial embedding was not initialized with no gains", ) # Check the switch from no gains to some gains partial1 = partial.optimize(10) self.assertIsNone( partial.optimizer.gains, "Gains on initial optimizer changed even though we did not do " "inplace optimization.", ) self.assertIsNotNone( partial1.optimizer.gains, "Gains were not properly set in new partial embedding.", ) # Check switch from existing gains to new gains partial2 = partial1.optimize(10) self.assertIsNot( partial1.optimizer, partial2.optimizer, "The embedding and new embedding optimizer are the same instance " "even we did not do inplace optimization.", ) self.assertFalse( np.allclose(partial1.optimizer.gains, partial2.optimizer.gains), "The gains in the new embedding did not change at all from the old " "embedding.", ) def test_embedding_optimizer_inplace(self): embedding0 = self.tsne.prepare_initial(self.x) # Assign only so the references are clear embedding1 = embedding0.optimize(10, inplace=True) embedding2 = embedding1.optimize(10, inplace=True) self.assertIs(embedding0.optimizer, embedding1.optimizer) self.assertIs(embedding1.optimizer, embedding2.optimizer) def test_partial_embedding_optimizer_inplace(self): embedding = self.tsne.prepare_initial(self.x) embedding.optimize(10, inplace=True) partial0 = embedding.prepare_partial(self.x_test) # Assign only so the references are clear partial1 = partial0.optimize(10, inplace=True) partial2 = partial1.optimize(10, inplace=True) self.assertIs(partial0.optimizer, partial1.optimizer) self.assertIs(partial1.optimizer, partial2.optimizer) def test_pickling(self): obj = openTSNE.tsne.gradient_descent() obj.gains = np.ones(5) loaded_obj = pickle.loads(pickle.dumps(obj)) np.testing.assert_array_equal(loaded_obj.gains, np.ones(5)) def test_gains_is_always_numpy_array(self): embedding = self.tsne.prepare_initial(self.x) self.assertIsInstance(embedding.optimizer.gains, (type(None), np.ndarray)) self.assertNotIsInstance(embedding.optimizer.gains, openTSNE.TSNEEmbedding) embedding = embedding.optimize(10) self.assertIsInstance(embedding.optimizer.gains, (type(None), np.ndarray)) self.assertNotIsInstance(embedding.optimizer.gains, openTSNE.TSNEEmbedding) embedding.optimize(10, inplace=True) self.assertIsInstance(embedding.optimizer.gains, (type(None), np.ndarray)) self.assertNotIsInstance(embedding.optimizer.gains, openTSNE.TSNEEmbedding) def test_pickling_via_embedding(self): embedding = self.tsne.prepare_initial(self.x) # Before optimization loaded_embedding = pickle.loads(pickle.dumps(embedding)) np.testing.assert_equal( embedding.optimizer.gains, loaded_embedding.optimizer.gains, "Failed loading without any optimization", ) # After optimization loaded_embedding = pickle.loads(pickle.dumps(embedding)) np.testing.assert_equal( embedding.optimizer.gains, loaded_embedding.optimizer.gains, "Failed loading after optimization (differing gains)", ) class TestAffinityIntegration(unittest.TestCase): @classmethod def setUpClass(cls): # It would be nice if the initial data were not nicely behaved to test # for low variance cls.x = np.random.normal(100, 50, (25, 4)) cls.x_test = np.random.normal(100, 50, (25, 4)) def test_transform_with_standard_affinity(self): init = openTSNE.initialization.random(self.x) aff = openTSNE.affinity.PerplexityBasedNN(self.x, 5, method="exact") embedding = openTSNE.TSNEEmbedding(init, aff, negative_gradient_method="bh") embedding.optimize(100, inplace=True) # This should not raise an error embedding.transform(self.x_test) def test_transform_with_nonstandard_affinity(self): """Should raise an informative error when a non-standard affinity is used with `transform`.""" init = openTSNE.initialization.random(self.x) aff = openTSNE.affinity.Multiscale(self.x, [2, 5], method="exact") embedding = openTSNE.TSNEEmbedding(init, aff, negative_gradient_method="bh") embedding.optimize(100, inplace=True) with self.assertRaises(TypeError): embedding.transform(self.x_test) class TestTSNEEmebedding(unittest.TestCase): def test_pickling(self): tsne = TSNE(random_state=4) embedding = tsne.fit(np.random.randn(100, 4)) loaded_obj = pickle.loads(pickle.dumps(embedding)) self.assertIsInstance(loaded_obj, openTSNE.TSNEEmbedding) self.assertIsInstance(loaded_obj.affinities, openTSNE.affinity.Affinities) self.assertEqual(4, loaded_obj.random_state) def test_pickling_with_transform(self): tsne = TSNE(random_state=4) embedding: openTSNE.TSNEEmbedding = tsne.fit(np.random.randn(100, 4)) loaded_obj: openTSNE.TSNEEmbedding = pickle.loads(pickle.dumps(embedding)) loaded_obj.transform(np.random.randn(100, 4)) class TestPrecomputedDistanceMatrices(unittest.TestCase): def test_precomputed_dist_matrix_via_affinities_uses_spectral_init(self): x = np.random.normal(0, 1, (200, 5)) d = squareform(pdist(x)) aff = affinity.PerplexityBasedNN(d, metric="precomputed") desired_init = initialization.spectral(aff.P) embedding = TSNE(early_exaggeration_iter=0, n_iter=0).fit(affinities=aff) np.testing.assert_array_equal(embedding, desired_init) def test_precomputed_dist_matrix_via_tsne_interface_uses_spectral_init(self): x = np.random.normal(0, 1, (200, 5)) d = squareform(pdist(x)) aff = affinity.PerplexityBasedNN(d, metric="precomputed") desired_init = initialization.spectral(aff.P) embedding = TSNE(metric="precomputed", early_exaggeration_iter=0, n_iter=0) \ .fit(d) np.testing.assert_array_equal(embedding, desired_init) def test_precomputed_dist_matrix_doesnt_override_valid_inits(self): iris = datasets.load_iris() x, y = iris.data, iris.target d = squareform(pdist(x)) embedding = TSNE( initialization="random", metric="precomputed", early_exaggeration_iter=0, n_iter=0 ).fit(d) knn = KNeighborsClassifier(n_neighbors=10) knn.fit(embedding, y) predictions = knn.predict(embedding) self.assertLess(accuracy_score(predictions, y), 0.55) class TestMisc(unittest.TestCase): def test_very_large_affinity_matrices(self): x = np.random.normal(0, 1, (50, 10)) aff = PerplexityBasedNN(x, perplexity=30) # Super large affinity matrices have so many indices, it needs to be # stored as long aff.P.indptr = aff.P.indptr.astype(np.int64) aff.P.indices = aff.P.indices.astype(np.int64) TSNE().fit(x, affinities=aff) # The old version should still work aff.P.indptr = aff.P.indptr.astype(np.int32) aff.P.indices = aff.P.indices.astype(np.int32) TSNE().fit(x, affinities=aff) openTSNE-0.6.1/tests/test_utils.py000066400000000000000000000020161413546205200170650ustar00rootroot00000000000000import unittest import numpy as np from openTSNE.utils import clip_point_to_disc class TestClipping(unittest.TestCase): def test_circular_clip_do_nothing(self): x = np.array([[0, 1], [1, 0], [0.05, 0.05]], dtype=np.float64) x_clipped, mask = clip_point_to_disc(x, 1) np.testing.assert_almost_equal(x, x_clipped) np.testing.assert_almost_equal(mask, [0, 0, 0]) def test_circular_clip_do_clipping1(self): x = np.array([[0, 1], [5, 0], [0.05, 0.05]], dtype=np.float64) x_clipped, mask = clip_point_to_disc(x, 1) np.testing.assert_almost_equal([[0, 1], [1, 0], [0.05, 0.05]], x_clipped) np.testing.assert_almost_equal(mask, [0, 1, 0]) def test_circular_clip_do_clipping2(self): x = np.array([[0, 1], [1, 0], [1, 1]], dtype=np.float64) x_clipped, mask = clip_point_to_disc(x, 1) v = np.cos(np.radians(45)) np.testing.assert_almost_equal([[0, 1], [1, 0], [v, v]], x_clipped) np.testing.assert_almost_equal(mask, [0, 0, 1])

"4 nS>,B3Xb- #}W@ 1n̿DUH˙ySg˚c 3~D4;>$لRmΟA_`fW!x/A$@,}o@RK8w[ TH@XrC +PR@U!77 H*_˸xe/m}L$Hx(J5}(J^LH ddn~jS^Q#[! iHJk4y3d]uY5:Dgf&chd涭ݗ!Rdͪ7J|ߜ ȡ: }c20R$I`D߼7?s_~}vb ܲov]쳱`3?tcM ;f7O?-Hb;H&v2|& ̋Bg7?ywZ4s .m{79a]Xў U0byu?F9伪x 8Wn>_x OnW|c~wlNkMqt5Zvdt8]-ꎵJ~C' -@5B\DڨJDZWt"cLӐBS<\d:5+<^FV&(IH>G7q=9yr_sh?709`ef,_4#._z=zދ;GFV8ameqZ1<;M[јM.{b0KUTQ;Un~2Z3<8P1H)%iOϝ}"e! cFLw] ƀu'\4eV-8 ,rBVu;կ 9Ww=G1~̷@܋/o`qBT<Ġ]*ǒ} %D>c1O<> o|uOѦRNbL +!ns;3 H̳3! r̕rAQEP"8o@;-B@0uЬsO@-ʀ*ݎn$h[럯 |VA; u2 $euHh1_1KAh*$5x+ʢAkWswF7$ǹqp—=aѺXb_wыdקM+0?njr`rX;)X #`p8};>4Oϝ8R ,_]4SfGM&6mn-R8ۅL۝0iE9*~NȞwM1 '-one* I餂\ĀTN>\P(i.yAR%Of>7CZߟ7d̝dTo8$ۛ3k@R\߀Vt~GC* (mV ,рҦF0k HU> &94jwȈe#J*ʭ #\ ?8dcd>j/Nd dwk/p=佒mkcD-H@3k'Hu}NfAI|/5A/C| YuH`2]Ptqʍ/Ϳ_:%Y͖UtuD\'kEڵq6~EkCy(9ۅ t TS͆< Rušů{N \kСgs|Q}ֱvK:FpiӼ;ǜ{*7)a g散ӽK4;qw98c+ *Q3c={]o? e4=4 ';2LM0íkdGUlgt BOT/Aa+!|A)}N*7D(vw^ː.uaeonOKS ;,~):.FCTH2{FnN9U^]iKUUޠiXUGDܺw~=Hҍnȏuv~S"O$a֟ߏΦyy ӳOxa.&UIScx^ڂm?yՄáA0dPA/:mTz |J/։U5>_)qj"{ZB*ҽrŚy?1աIp;'߭Upsg'RŢ+xHY㴾oKSj#$=8a[PXn=}x*YR i[νFMTפv^V_1 4cF.FuGG2{ot+w,B@%Y@oxrS҂\ "3[@MD Ȩ"Btd WsnFD',l1d!ZAȿB· #A)`.IW~r!#`s X.&F BR<[[$$O]}NB* Tk~@D\~_ Ր.H0PYF]CRp&xT.AJ5&d$+׼؏ ~.=y2*y/$ , ?^:=_Oc=/MmGDϐc!7.$Wr9 jbg׋W5B5\8@q2A aC Vp] dPQP"r)N'~ ff {}#i 7}P͜o>iWƉ*W켝s.ѰŵNqu$RNN1*{ɸFo-'Oөg`O->5qށĢ悪~N0*6,J*va縐g0;˪3@.`bF^0ܲ_VhUveb5Rf3fx{@,kɫa'Lba o7R[kЯX>{=D`j75N6z\[䋰8\JG9]_mv,Q h`w6q‹+SwZ^ï93n>>[%hTjMNYvJ{#ٶS)a$XaA΋ob`Aw֫n, 76Z{H t9_s_ahaeJ ~Ͷl0~0~Ϻ"pkqny,l7c1CǒbA"t9BsfBYتyPͧh-)!2PXRbSJ Lz?kϪ9d݈]PI=r<` 3࿾<?/kl`oʍfF?I r(\ R"Ţ*B΃jPL(.p5|"#~w?zbhn~Ha хg!ļv++/ؼٟ^zx}VW?(QWPߛ3yvxg =w`{؅b] ŚӋNGU?qw{ `ŞPѧP7I5?Y-\pkPBA5(}#M8 ;҈*ZEDu[]X(7"K|.o;|>g%+߆8 d>^{Sʫ+^ss\gq룏V_Pk߮ߜyϹJ1[#/LKnfc=pF;{п.bytZh~2I5mvϙ$_E7K" <=wȫu4M=<,O ]gٮ\< FwGy oUku0o) +x 81z.|''1{A^pŜ1kcbo1 zryS ~Hw /XlTBCڮAi-Al QZmIlqDD!$A.'3~CQG"ѧY̖ S.dA$dNiπYoIȼ @ƆKR-HkOu+ذP!\EPbrPy f!FR8-R2y*D r?f~#RMi]Q(Qg濗]#Ȅ] ^]^A.Ju`SQ():z{V 9e滉hx Me\7/K4 D sjT7{ p?PѾ=$ovvX X !8p@,-Ζ;~o IDATU `I3o"Ge7|otm7?;i:|7cU[oⱤPSk2@EI-ْ<%m 'Ӫ#!:vxI_7V.OQJu}Uk9;(V QoAcc i)'GfЋl>ldp8T! 0~Zgj^;pɱ~Ea˄ǭ|0s3;WU~U|eg:5.&+u {3ӬíDāDfY+Z U Es\o~zlFt9Ev9 {wys[_uN+H a"w쒭hy^_/u|vNVP ,+1F ՚| Gم۔o\Rm顯-͘Ps$Ʋk& s/ : Kx(F\cxUMboߴm.oo +)sg0f~Ss<+~RREڊ@FNoQ^{A XRf)҃2֠Dg @^ 8{(B @>DD M`Sѝ?/AHSDuQgp#){30.Ūm7 =Xv¯;@.AZUzp(}G ܸEۇ0v> KvM?9om>ןBGfU(= rA-Mu"8 < |aoLQp%%ѻ(~of@W\9\ʖ}Ʊq>}ckyDkwR&a!;Fgw?׋tGov3:~=wds;8>:~}]zr޻'T6dsV71Y-".0cڐi d-z ry$EB"A.9F<y'g-ԩA\$ATa0S@.aFUS=&?Z+LvIqZsje񤭫HyVyVMZ;{ﮁ$2k\>e{kCgƿngUu_z_ī:9kn";f, T>|泌'Ze) BTf7…h\TG]ysg>5wF߼JMa;>j+hXw /-A-ŝ0J^&8Qebۣv!,BF0,0mhdgnր"wpGa9=I}q`\D79X>md} ,^+(6- Oh|2uGAy ~\y/p2s2v6)Ȼ:Hj* 6 ##!HHDňa# iaN8C-M5ƋtjbԎd9GT@p`@B64u, z# BUU- vm.,ˣp]7giJ&#T]5t8g;.[ d!gʕk̖7)S0aJW \3߳a-]:ޜt.AkvxÍ%kMjE_v3"2ckSf}WUx<>NJUM» a⒁%{ f n(;Gbќ5pt#`y9 r;UQb.l3ipͶ gB6 /X0gOS(@Dl{[5HD#_NT-S A߻BF14\0 ^䢵݄yE6rZ햌~DDˈhf^T}#AIu3(R V&AF$ Ow,Ђ< M,H7|@Mǃ@& [_G 0h5$ee[}3iH4d> 4ED>P< {,Ȩ%e_CRq60y(Q] 8H< 9 uP#|BпW@%{#scAȈ <>L/gf|{8d_vkqgUzqzvc!,8p1岭+3lAz=69]CYSaIGM4*;Hb^$<^RíeIE'%OL7?Ewؗ7pa!vzWOpU=Wk57?>7)}o(heCci2w]pQZXSEifU4-ۋ!򝮃fIL xVOwkG,AlB0L 0td%\v.,h[윰k3BVGz5'hqO ӏ}_Si2g)CcV>9_r.>BĭSʒ9Uhy򠶗jvکǠsnecxr&o_tQQ3Qt> $LA׍H %됋5'3 [llHD!{c.f~,C r Pc*2$BAFY5?O@рl D!4X<4򐹉j)Ce}o7Ppj"#zo\+ q|$ E=@(Q_1SXo&=dyL2@FE)oq ?G$ vD}*ˍܦ!Vl9 7kyKmY{Smؕ#؅[`Y 8 F<yC@d>LЭQ9Q~ !H7 Łl`׆oV/̼@@g7'}~ %iwn'Ts e\QUf1x (.׵c@Ĭ4(*lmh  `5@4BQo.fdbR\ $`T@= 兪j3]q=^LbU%ҹ76抳9}QǬ"Eх:4nX©8VLf*@7r 7tcK&+`9 *f~ue dUazK+nvUohBۏTY]#[ޤ;wM<_7{Q̹l;Q;hj,doeOR5rOWTh07!7ֿ۪4T" @}ı=! :סXNiNz( I@..h~e/4C[وhoODI>d(⛐#Iu213JD)jsϯru"d"1ThY;ƦJ?幍yrW> JAR4-0\ ^讂c31jC!L> At3LDW@+SQ|q @TJ|rrYH뭐QU%n )ȹ¦kݽ~7_pYP"(C\W[땴7&Tù=N?u3t~7n2H…,?o~?\Z3i0lS;'۶)f͋C୧^^~L"koor޺_k|⼔mC!h.Q2eR.-Ȩ᎐QԱR߹QֶF`tڀJ?f_l=VfOc'gd9d_B܏BB79[P%nҸ7wCnRh `=c@3ϙ6]p;p _0YhϷ$l 3xȶF̅P<'_^"{8e 7PLY;}}|+*';5Tw6QN?3 >7*:F=!p AIPUh $ח"aLxB]2uhڵ}PkCK@e"V _OyDM |sҖ⁴sJ>ΑGsZ߸7:},lc aħf ZpBU'3@Z"B.V!Րqq!sN&Qt72!( XƧcFZ3d[јi" ؽq? Vnvx5$\hIHUI J=6.Zodh7AO 53~.H X Ыkw}\(=pfBzHaA?O${% uȨbp}?G2J: HD'BFNw0F笃܆ A^bGBIGS n(iGQt77Q;_[x4Dt].R4l␁o-{!J嚼=J |s;-x;XMCSt8?`gsoRϮ1C3;^~y/bUzq{ HaxCeܘ@#W܀qyum M,h) e| Ii:\R"9tu E?x?}{CIlxUT ݜ*±coqO nNI;'/̩g{_&WUm9wz2'@BH 2& CbTf Agx̨D@TP@ "]m8㯿/33@mhOYߗ3ߓ_ۓ(o ʉ ac` WJ7[(;!!7Z;ם8|I&h̵!9odٿ:_]ic1wi7?_NK18_VRB2: eD ?v{%Xy6 7Tم81ό'z/Ag"A%@NyJt<ݟtFA;W/Z|"ꃾoEſ0.`>롟? =;dfn&dHH :s5T<8"K<4lEE=,"rQj`K` Q,AIQC~Pr#*?t.$Q 5S{NFi٤i`P ȁ2n鱣)>t+\g?w\S)(b.7|2 ߸l*yZl t93Ny~ΌS'A~$AD:a{d/x!H!"(Iu1ΪCcc9X &A e CĕAHo뱙; }t- F*cژO:<=b!(RAz߫εXrbniw'彮7>㝒h~Ź?ٗ^2;e A@xi7?xi7?QI |b@-eaqd#HXx*@~ (5g0[aAX .ݤvɯRd<+&bI$a{%n B`rQ|Ts'}"0j:C,LfӠ d}5)sTjiR).ۅsǟ <daJS@ {T _E(>r*a$U@l| -cכ=<:/y"`oGVÇDmMۘyE2=|Q(4?bDhÃct)zhBy!MUZt^D"ըLu4= :.Ap}7+$0"%he@tt,*բ90D,1g"4tzhh)p?>3gЊŋK˧C+%ևcps+Q)&|Q61Bā|[mD4AڸpRt 5ЙA?T6 ̷{Rö G8q+퓠D5C\v99x;K8+y qh"['Y|pZl=hRa_I=Fd_͟[@yj32Ab|C]mX{l}&f `C"Ե u߿zQv6ۉ`.``ݦWinp0_MI4bmbyniO) c ~ S(EFNF"8FH+ ;%m؜ܒϮKj4 7 K<aHԡ?/[f u73(޾I6a~ kJ/}!vl3b[0ha[M./aD}F E$bL Km>I1$W<ہYP^Y%͸`8B̰`njm:6O[DžBQl`> DtRNX8*2|.tjOs(2Rz]@DgAӡKWj$qe}"ߪZYtI$;Y9yKCZ9aSG66tTݪ.t9[QQ6}I~Bg3_&o)"ѕ<:^& pR;bhqMODcБ>4ܾj?n _??~Mdp^M`ǡ"X D 9nE!RT;,X2d$6@>{9#\xvGq5fJ2MX$.<~7;I2L)4&z485qq_zngf3\tXLB ?NobsCJfYg&Q,9C`"6~7 6!A֭0%鴯+=sIg6a9>xdX9 NOjwɓd|$o(wlg[bq>hj͍f *7ߴ[0jŦAVA•֦Z凊.e` DGv0̤mBk\Zʌ 0@;tsf}l0p>J !szl~"d -Йݙy;-_Gb Б,4!J6og污} -4ϡox!FEmB7/*ΊUg#T tAg*0*d> ;h"|LC `LS|s7 .,G oitc ^ MNSho(F}Q6ѯVX&o)1o6"]n4Iajؖa_y5j>wi-6o]c0{sVyr92=fbOYʿϴS6w paĂsAx݆LlJwSiCZxr7Ί^ud+˺mRbI4!H@PJlfID|`sw[.RA&XKbp\dtrGL %d,X  Iř$cm<9`4Ʀ޹L3˞k1;[=gqix1WθWmu7> Rl9j{) Qk7mSbϚ힐VWb Z@4`- WIA42po4;d2}6ƀ݀)@BA  JܘnZ#V ߙm`"/h1 c;7b,}3>5l} ?؍ /SFlɯ;]",C 4!>ϠztphR5rE}rՂRyTꢌXD"SMy:D䔪^Wgw:2\D~;KU SPP*"f|-Os\~ *3٫ _+`-k} o, ¿}н'b2QՅ~jyt QQTh`;RC 9FQ>sj#az%6O?bǻ9Ƹיsč:֦݌啝M6A Iݘ&Oduߟ49clz)cPC%`&6vgլ jUE$m>k(Pȉ|WllBG>Ia 0lȑ"|/@)LV)O@2 $OzZ+^ׯ>w`[qMyO*4>v ݹ B7uѬRA*əY8va\8k5aR95ɥ4ZtSUX=4vKFψ퍞[(fcѾDJ(|5mR@ݘ% lvH JiClcd`2TB&ˆЪº|8w.&|.9Aulo||(c֢olh#ډa":_9BD+iAfvR ٣n#0sP˟.BxJ>߁&-C"tڪ!\ B% DĪGYGjТP{A&zu3Wm:xFBFhM}m7*_Tqz8Hg >>|}ХWAED"HMv>wC߇m/@_{7C-p"2ݭ'޺N#RF0}NJn_YY^[7>Eq؛-:=w.UX8>˂x.Fny;)dHCR‰Oڵ7p3v<31:fV>]cɭ#muN?ٹyC7dgS. 2AS*R^0I8:JO&%`@W$ Fȁwɟ5GJoʼR0b#<Z3iƔ{",=q{Y|D;ك*1l\}~ce~'X2b[=lkuQb:2)n;HuAcrÐc l 9nǬ>*iy\EXߛsܠJ'T0RRO>9Ww`ۺ|98|\9},Q 36}sM}U֋!hw3&3?V($p4= sp{u@Jh@[l>] L18bjjZQϋ/@dFvCg>?rU L;43_77GwsE")*۸<~$cAphIU dD{|7$}96 f bmV'ܺ~qt>7/A֑bژZl޺[_~7+]W3IO?uS$bntڷY?'|G5 w`}P2htJ#ئоǝ>iʖ޳-C:Fuvʥ\ '%\_:4&Ъ.dBL Ac0= _J`54TL\(RPqeST~|z 5|Lf&KRa 'uS:+HRlFğ[&^=ȁLs dz(P^:*N8ޙl>qd ni?qE˛`@b#Sϸ'c͛ngt m[nJ n P Xi$cPYm%2E6-F1g9R R ۛ_zl>sؼp>W歺g$tOX3n8%]<}sy3.G5| fXSŐs 3.ˡ*HD&'QLf\dhC=lh PߋPݿvy%TTЙ,4> Z#^d=a: T5Q/tm O3Y0pz-]/b?SLCdCT‡0,ٍd&"LT4鋦1t_s'Q![Ո2~ DE ?#+} ]J ;w4=O??< K.υ:\fN1G$ *b8߆qEKЄjG &h#pϚ}GDjDɜ͜4^ dw!c^/k}[G}^j_y87%njֿ^mj̽Ԙa3k/璺|^0xb\9$E{T IDAT 80!T,Sf``f*   YHK@2\PT Ie.7>nf兖Ae O#iuؘUkƍv>3_Sf]QN.WlHO$dpxӪE/4>ˎ;C?\I[MOpsí3 78e]!Gv.w%[,"͊|邛O?veئ;#=EYTv,ȩ\JPÀQܺUgI ldmnK x(ÿ! (= ݗ7W>YAHm>|uC?8*\VhBf@KBtЙ(kX*Q9f4?^ͦ2&TG:[p|O@")tc$(y*4ω MX_&r_J}sL/49Z@gj*Qf<![PC 0芙:6[#~r 6v}e-6;uHeG޾Ӓɾ wz %V\2QF7l>x9 WV<߽)#>牍qFDԭ1mM-2+wӹ^8X=︣o8RqcL{Ims .4o_6hf6D),~Oeᰎ]_zljak,KoG]݆w"p HcǷ>uM :_&UQ#0Z|%M^#C#|6Tsqr&Rʨ,avV@(2.> Bh*/ǹ~Th+9HYZ@g/.#f^CD/cMcP)Al)s3* #jގ(p:6OARɤUf:Co9tſ,G{F|dLxgzm]JM3b})UC7خIh;`ptL(X >S._Ji)2o3 1` Q<@J0\&d%k KavK1F `X&X980p%Jކ1@*e-zǎᇯ7uuեN땤xnwa7cM] 4 k  cîo?ygx?ˏ5Ă6aBHĘ"0`@@B-6/ɗm@`._F̥0l:?,Rf ZPw"tه] Dt䛦w((h24@_o3CDM:DF c&XЄr9S( Q"TDcoZ rg#\ir̂.-}EН<2\wp{gIV5 Dt9P=soVhϋ~`#PÈ~" /$hhD {-k'K͙wW}`y<睗-VWNs Ͽ0zeuw?ƺ|gQVgȑOcKhLYKšO>vv! hCVHvm#%u92?PSv,%P3=> nMS K*(@J &iK$\חM1|PnS50s"^m ,!a䘯fo:~q k+ۦCi;Dvy[{Ilr2^x<ž ɦsi]3ѥmSābEӍ}|~#wkH*j$ә~ueW>/amSľi3&m,9q&[ φ,z%;IR^DOkaS]|$PC<}3 o3_ζfv}>'@D= ]3kAWn.5] -\]MT"bνM.V+? M|4$*%Q^D#%Ј0UU &hrB8*e7ATdI]"<@gli{@~خʄT [н踀7(,3y>CD_c濿5Pÿ`w^X=3ދ!m/[ElX-z$4I@0/b$b:+wt6n; /*7ZaHF;BY eQp}?"h&QTF3G-/9U_WCȽQD$}6@{6F0@1UlDо^f0x(KCԽja[B隋4Oٿ>Î|2G|k#pi^?cA\ ͗ mg]Z.Q/y/p3nKDpfD;u-H5.C&Qf5!)z'0]C 5lpm {߳ȟP-6ooz M׏ztħ}ś>vt|7-8є+kD˨OE)%󍍛;SN[CcqfCRTV,^UOc˫{3Y0-2{=hnq ͅ=ɬ+lv "&W?~,!u!d)% ;@unQeN,AI>.Q*Y^lk78OI%0T b0v,oB)y1S G m hPǝd47аgdB,W&bb7NU6B5Ý+o^ڴ@ab;6xJ(edOd}O~cyO𳧿 ; gͧ!6H?@ҜB6Ჳ}w?yt^x҂if㆞[ !n_i,ټ$=aď!lG-|5+>E)Wϥ|l SQ P!cfm+2-pM i\@䌄Ȫbq栯OI4Jԃa vLq!h_u{zv.p=ٶ!ݾ)LS].pQ⺞4~}Y.9qSv)^SpY'2|Ok(y1R,^Ұ$þ$Uȥ㮛M)[R@ / 7!JAjނS}ng}eȣ]/o୷QR|dn< 7%?Z t5f> ? )>E.]Mr"@s ^f~ (·߿ *2NFjX0N1I`8";AF [Ma|ОXM "нq64yZhDԄV=LhLT "ʓ.KJp~*[5B<=A݂@5[-.i 7o{iJ#2 }=I} R8665^̹F.Ώ.ڤ =~iPV@Ph]ˀdƲDJ1#max0A vu 87Q#ZZWT S"xbKf|j`˕V7{cc4Z hVE 9y^2,$ڃS'4Ôl=mJ/9lkc:bcuɝߤF-|?ٞN$ұYP0 (C iʢ7H"M2`^_;5f3< z&QD/49|`@ر. &].4i>4!t `Ъ'3}B[sppR#FMT8jĒAJrt!u#q  Zf:ty UGx,>4O,ۗ8FS^Y t0~9}90s>?뎭66c_?1~z Z?JǽTh`vELXS&3Ԍ)i;]?ԤQ]0FuYUP-0 (&H }+׸,\g)Ф)*'4 'hnjUSX.lDDUx 8RWB}s:'3^D4TЊ 4Jvpix_đJ@x#kY` Ry3ИD.b8d !́tו+"$=_OQy⒙L/hHq <7=4ZCs;$nWwO?}mYqǬOwƯ~1%m2 #&]3 P&fyb/T- X,G%\۟&zǒܫvȷhj@>9G[ do}9)exU~i<Rle c`G*YTJ(P M}/4AJA_#'0Y3Rtij$M@[tD$.[&v[{[3s[Z=p}#|EQJT :YhZDυ. BD&A|A(:cYD%SZfއJDgh[tdO8Fkaۂ_Ki_1y0n# GyDq9%H IvJzzR{fB'lhC)X&no~T͍ M-:6gXpe&R^og^ ܂c &B(Š@ <^a,J& So(erwƵϴlD_"#7Kb I/!-%7Xneв特ik܈E]ynK&m7&Ϸ-ݔf" e4a0H8E CFG yaoraho~C:? [r;=')L!&, C_FYjQܺME"j&}w&t1k7If^oV8@3cWCWDp&͟qwB 3NP@LA |E El v#<4sQ!Z#xšKc]OYc]oLTDp ?R꠭=Cc9\&͏B[Df2wuxR[=?M`UYktC7Ͼ]u~E/.y|~^'nQ oQƓZ_)n ʴ+bL~ږ5'\wӿp댒Zn8=y! sΊD=T 0g@̊9 939t63z5o8u]-U9BӼdYy_vt{Yo FDbӶM@@<b6t?vD8*4 ɖAӹ{\49 !B\%lZ?1*;ǘ AZ5Oi1DXPf^@D8$ UA>!BxSg9BPK#)#/OF*J%*ʗ$- ɢMI_?g>uO2< Zw1\ tI^~{MAX6亳b2+u3`|G&eIb%mJWv.[%Jk(26Ull(Z~>$3 [mb\y'^S} glnWEEݚ(:B*?Oy)?ˁ^a,`b "}w?pNgT(!$:LX P"FD=eA;'t$)3Q\ZՄmͯ Mۣ7wbI?P-j!$/ }&,DW A @p2 ] 3.‹m4 &Mٓag͝-O2'v* #xmjs=>`3Ufk#& Gݼ]$;$ՌV/\T{u?Bv~4I*(jmB*T ^Ud˲,CZVBEj @[C^PEF ]LXNk#:R)oAەhuѱBw=z6"$Y4З_:kl^nIWTI,jlA}e΁yI?X_xo2%Xf>`'QT֕d[\uo\T8*wY$ x 0$$I.R :_]m*=1H?lG:c!<*l (rf:7-:Ify$D>iuus6e!lDAd!mj _@th*  o C6,ל@d w@s8Ar 1̼zAՖAذ "WsJ1 He^As'#vFA&/!f<)"Qcq"3Ed KNG$ƾClixu'e|yU!7Vvt-jKŶ-%mxڔ̳a)k>=ĐRzKxQ.tu)|d/#Y;hmcx`@EF\%5[i@VTX) ZuIZH3`6A }(,Q$;5VA@7s[ȍn bX6jzGe b ?9mqG^EVMU^ cxyK$UHH;i/GU(k@1}~iWe*5lcdI+vsx^ bMۗ* 1 56YRdO-o]uWUV=e VٴN6nǵ!sy[muOn5LE=:spE톥У$K?v5)XrIor[w2n6`ekoJ[P^lyzee$q D-J|jڌ#!oa"q=,k)7L3 ]9˄AkGa4McZ^v?QS|_͇9jkOFeݡqmD8m;&Ϣp"H$=r)I$} ݘ(sʞ_.8au#I>5KuzB/gwHŻlF|{t^\R> _`oXfT n#].nkyP1gKsaS7J$xt)l)QV<(Iul2y U1,Zٞ.٩$FNt(?ҳJ%KOẋo eaSw|g߷>.u#7YzE1CekY^V)g $Ӟ_NQ}ye޷QAS$wUGڵAnufM~yHz{j lkmʤO_Ot{ԙgb/튦ۼ cwC]"!%@1x6}Zt8{<Bl89U7Xlʖ0̼Sxh'rCH{]a'Dmi]@7slg2β49lNZEUPN!uaIhΗ<H^@DYfBxR iuׯ!M0 _ !S9[ D)HS.ncQ~" 2`>]mdu֧fmi CM&|bZcI0O\ws^+x{ϼD{tmc! KHϼDӽtaq}X|͘Bb9~k۷["z J'=B!<]'AIu$O"W4ZxS}cf3D4 &2sG 5A>L쯂!">mOa>39F:5ǮeY ٧eXj] g񲜶S>ALd; |}5><;̏89N-Z/f.p3ws Q "<D>uO6~nq}fA;!&MMߤic(Aɮ],GA̬RuxSwǃPMylϷ+yN%k_*Ú48EmN):Wס 2KNgɳ>Y =q,2l^w l/)2(;eUO?v|;4xw:nd yQ}&\=MM ꄐ^9Y&ʆU`E Z/WȚu- = Y5lt+_bW 뾜6M{S )m\A*l3 X޶">y2n[SͳRmw߭)WT^ TC*oL׀g0j\7Xupq#.IP-d}4sS &} DVʵMIZ,;|7M^K߿mIm))ggXͻ0v0T0 A' 򻞆\df~XwIz T/ޝv38_t֏Ħ* ~?umC$4AQX-S?*Mb曶9n R]KUO`9ͮB0EܡNwn}A!4Q78Q{ф Z !"4O$!^Cș 7V76T4{<9?Y({!МsA*""[eC 8!Zl@Mog!;Ah3^# 2ؙᅰK<WQ!=e~;﶐-Uw{QKK%;\1ɻ]^UUq)Czwg5b:Rg澘* # ~w7K7w&ıY[W %lXЯB5s/ֺo'+dZ`B ,eOG\:emn q9DteqGXfs>`|f0+!&DZC5S!ʡlDNvӖA$ lskxj^8Y#=wyzkʤ{m`MYIF &%86$e#F܆a%aE|={=6+8{sd|YZaeْCȑ.`Gx667뀦@H&Äm3& [zL~?[S~4f{j[ ("YcC@<%*]y7?y7/W\3:򩆊{,ݛjNǚZ:K//.5??X=lv@gJ/Q$u 2 ]9 \q4O..wknKUjrZ3LDk!΃ oASكRzL+ZkXu}cy IDATrY^!bMx K^7/7.=yוgwX9 K[qȏ/sšh,%if(\m] ]  }mw=@y`Hu)>` _gpb$y3 B a 8s,` ; -belfS2,ueg~h ˆ!H#dʹ)Aݤ |֓ r -8P:11۽/ĵwx%lfn8H3{[lV̿\;p脘63h5D; 2عpձBySCykgB=f+~m_1ih,]r;/O+K3?m7l2kyv4k\w)b5$2炃6>kA?&ʓgRIp84Fs2J 7d[E@]2}GGv< Ba5ǹ&cV567Mblf\0 6omKIt,Qw Fե-^g}]t;K1qTK\IS}۞ @I#aJ,B>{}oʱjjV]Q˄djEBo\H"k52vUaFYS@n-W*ղ땗C*JMt7w Qe(ߌqʩ&`@bLG澧oBAOiBTW%J7cwq_S  :r!j3byd%tegs!W1 ŧm 1xwA}ʑ Aheo0ze!fxV>[ZdQnuOri~;렄C˚CW?K9KoDRcQg_{*yW6N ڭeuk]If٠A'<ݚZ/ Pdk%e55`5>AvJ(imeHU$~Љ/_ sLBC珼~r቟qKZIċ'&x#{nXojϯN E\S=l9vלŖ8Dŋ߭3] Qr~ Dq^--0ӳ! s!¢ 4qSo<J篥lqL = -!Zu0Z3! UdQ6Ԁ(97Z-{ C 2aK&& 1=rGc"yw(>a"U O$W#=\v.XvmV:[H&4Z"!ɀLV ?tq-;,u^7`H|(2U|Z|@hIJ湚w E`i@>t:-Ƙa|x d8;⪦Mi޵k&.-=rCl=6fHeZ)'.rI5oMo\u2VQ̔ xt6+Ch0exVc=N8yseLJnxabغe&maxejfS*mcWHDAԲ#b S|C;aAA΂1qcBw9xG$$3-l0m-ڼ@[4ܹ!rOG&Di%Eijҽ *f" UKh ᅜ1^y“mBD ɊCNyş^-qMy0j~grjq v^yމ_ovvB/Y`4-u Yن[$\P=ک_2$&  q~]/=b7y a#dE$E$wrsZ,j tҁ)#?d@=덲c@6?"&lCG~NWNCLXUM8hUbrE9צpʴ0;}QQ::xbIqdXrhd 6HnZ ^X2g%x]yk2j^\_ow.ɷ4ޮ76n9k=&㋓ t"|ō_xNF٥iO0P8ϔ7IST=g'3! 5^KpnBG!`Ò 6j&9bBDnwm rƲt`ܢ C!9.°^~*#:~'+Qf.ܚo\@{f^-l "  D!l qAn8 m,IhVUP86IF. ALι'cV!<6D>@rM.Ƚ]84 2qk-XOȬ2eoK)kN9ՍLwP#C*dcBVw S7),'k%z؟;[d1KhѤKWŝ[ %5,MmlwEꬢE\]HI2I%!,򇇒1=as[8-w!l'|˪R;O' >wm4uw|WXx7/~}sP4>ʣۼcC%!C\脚 <43/Q#EB?4Q}!3>$!vN <fUQDқ%1@sXb"o/4+A B s OtGD;<uN_o42DhVz89A[ f!&^LWB,] ] qhIB(V@x"CsqZ{H 6ZlaN 'yrd.cHEh KCˎzU:W T3p7ka[M1\ r{"VJ.W C4rM!G%xWm*|@"ئ:4q7 O>1c_@`&if۬M,sZ6wA "Ao,)8'"x2fR5YCGuaZ J=*Tc] l-S!rV4hl]rS $%\(m4 Z){EPuu|y;$s\@eJY|ðs-ú6y̟]G;u(@(|2̏.=mͱ[:a}39~"=D=nJ0s{":DF g(r!+q"괙D cb:q!4$3K/g+!& a+|'wop¸@ eAZ"9oY{ < z9$j5Yǻm9(^m0& (Q"ƻѬ>3KDC "z"/`6ĻD,2 [jf<`Kynqd{ëyTuys_w~4xpeb{$|c[f[#U%)݌P(eWɊ}]#L0ٛ7rfm/($g|z<*mYf hy›yYO6?U@:n,a-{}/VI@' X@mϼ83l&4T~n)&ϊ`N9.>TT̸>Uxow\wx~.,w`٬7y3ܢlkPwfAۆoPuI*ѳ?Uퟠon ՗Jռv%gmHxqM"{E}+__~ٚʖs㕑ْV]2+1`ǔV]"_ [ ~Q[do=[떥eVN,{/(a"krQyw~h}Q\("kP L\roE}gkP:7T ۼ (a1,@Հ0}Qlݼك[o^aیɳADiY4Y^OGv+*Z⽼$A/ꏵYZ>H>/np!g.dsG|jqĀy1?"̏!f/;Q)i#?(kc41r-%ZA!Qt w/g-9ZdOA|ZeH-B43 % D8YDpŽ"z . ' }m5f#2}=O|Ou`1 ID|eBI. 0dBGp'Vd4G;_#ĠmBHB3kʾY%=nx2zKoI*!ͧ,-?W]eVh<k~4\R9VQ3E8&$l qVPQЌ, @I g9-G2J:4GS^v~?Tk}Fm`C3Dq.Oc):*ĀlᙘQ~ 1N ,K|v!vmV+q&o|u U5GIEޠIs~MniADңز279ڒ0$f7KW̗nfϖAfBtYFs N? Q˰"w[LNv(v󓓷yM׃Bujk.LD|C;|sO"tk A HyAq coL[|; ,ІmEbL`ZtN_w4x33=uUhZd HW+E}RL IDAT7ӁKVoOt=7lh2ԯEd t|Q;:sJ/TDefsƢpS7z>4;hortr|_T?hhwRTs~^Y:(;]m?]1{Q?h5Pv8~FxGG橓qs8 _o넺԰. o y۽ܻ?EW̓`,ޞ1}NYJGBt'u<ґΜ_w20!1/&@B m f a\J,'}~n LQ_u-}^ZiM~f|\ی(99v䃿ǬC߷>◦b|!d6sy7Sl7" K D98FM =@̏=:cbET%'=u<.Rz#ڰh#'h4dAEH6\uP)lmLJ:GcF>Ltht0"mJDd1Zy="0K)BDAhE4n{R ש[*~boCFs O5tZda%C?;*)fiIJR(T>cyU2FAJ2u#J=Z _dYa8YIO8u{ϥ/7Bss^4*Q֯Iڜ2Ѿfc]ksK,M٬K3mmVMyy KLAF@ ",0۔5B.%lxDJ+,-pՊ" Iam㞘sѢ)嵩suŷX=g=_ܺ,/p%QYw9eqr=?qfM: UD" dp:ߩȕFdlhoEe졽>x=A/'y '")|t ėπ'"/#![*(fKbFWRRE =Q0 {)LR1,bġ JDlƵhd{~(6Vs C/Jl_PJo;:6ui`mP~.uPՆZ;~6SY 2=Ey0mQA'~O@\ c#}Q?JR}v52Mg>o~ȟv8;FM>C{a `b- /rYݻmuj\?ԏd,kLN2y[-idw&K]6+B1c-*=!׻,D#;e> q 24z|>}wu-s * m9ubo ~kЧx%-m!`kCfX &w<0j(Ih@aaa`"yhn^`Gv 2sñ[4r|S罺nL|13y @ҥ͙#roW0)gm>F/آsGl#o;TZ5dK0$4Y3oʤCL&JiXeu~wwZ+! cc "dDȸ-f͙En/m7j0瑃?/^< `>Va|mqcf=@?ؤPE|à= 8ˉnRU*^ߎ68Wz/ѤyRjրc UdRC\EhQ;JErt)3JYjz1v;& 9S," w)Iw46Xޯ c@ឆ^8PhC2GU m@Ef ZT鼪wckzk~#L:j1}&?@.+3R% ,W|_wJ)?KLU* <]2"O=I}ot:J ['nh\H,;{r֛pwRoo?K_YmEV]=g{qÿ=I|yկN}P!4{eɂw,e,MN?pQO3¥Ct)bcHX )r!C/9+ۀ~**t)&Ť}l'{6?/af.`#J\H_yZϗDzF]j=tE.Ϭ}Sv )JF.[ʯ#(lWD(m2$`ᤓ6 cI?mpm~Bu% Iy-ۆ} U&љ LCۅqsJ 0aM F8!pSF8OfQ1i_7f _>\|)˟ uϨ`y5|(lb$#p!ZIX9V5m 4Ilc` b*'ID'z^_M4RFJGDjftGP)m슞x PCxbuԁFVl=ÜS UQbat,׿DEUGC` `=:4gN8MxM)QR&heqC?]jRJ ] 5԰V'$c^^[ܹNFn9ԱmñL`%t6" Ţ !W!!A`#(.);5U~E꟬/f}5{i=;}~_]46ec,`"Rf"Qm|66khӁOB 7Ѿzt#G㧣Td(=_ѵH@<&"64PE AXmR)DE  l#3~Z~J'q66͟s?θ'\8mwX[]kwRMuE]iw?2@#l |sǩ&^,#>ob=b^2_g ,1ń~B [ʁr}v@#Ew,6Z0 G? _hKM_2i73kNk(6ŨV%h}й}FcO@#p6!5{C S~BEg/<0wD/"Tu~?0A- ڻ :G'!潆buޘP 1LR1 2}k8D).zZDheTj}C8 VL띋:KD[Q.z RJeWy"J6>$Ѣ5+DdΧ+:"26>EPC )n>E'oM[JH~VM?s+cRI3M4l0ga E^! eR"JT;V]U}KW?Ύ~V> tEЁ  *J\<6}* rt*b\:dc>DvAOM`LEh<^D$kdSa}hge0Yxt缡b Xh|˚W#R 'XDA>SM,,S:<}-?G[yeY <)]6 {&"hr*8XEwq7m0/ Gҭ?跨6m,<  GYXXDs=|Jn B 7O|[0c`! Y(ιz[W^Mj+6r-)s6j} e\<~MXwUn K,k(~d#I5 d `babЋk^Q I%Á0AI6κE0"_g9y?:LK{Nokf AusƎ>Sz3қY;eq5na?w^066PEXզzqS޽EZL3}Edl3S=mXj1GzEtkTX"nh36$P>qcXhUn3Vq zݮj#mB`OD~bU8T*!hQB{E{c6q98xRA)Պfz,_J)$p\Rp]v5P& ?kt2#7N%MNRzi4SU˕(OQ D1I0pZude`I8Mb aRNk[O5i!a)r=/Ү6QD.;eʓ~țu=7㋨C/)!=+?,B&mb$G1nS% !]f,l+7'*h?(|(T{njmch oN>˸9@|{g[NJRe$+Cv,E+ z%,*Lz45A=aϦ mk1\7``x3LmZ]f]~LI 꼰'dӃ[dOu 3\& XtW?].?P0 9>HHkPr-=?*.*ޕ.]&ƈm/~8FX-"'URA((Z s0s ހ4m׏n̰(Sj~6ZGC$n"LUy.+xO} p WЪNWJua#1F0ߎ2orrR:[)5jYXC 9.clH9(OH9%drp vJ)J!!ԑThbtۚԣرjunH?ϳ"X9}ҶӅ苿{-ΠO׼Bu @w5C:urkxo=?m:E\ys5@vBۢ߅zDmC0Ⱥ't\J>r7Q0 _20ޣAM]2m[av|1*娹;t ;9ȖO9j*34:NW<׊u^9ߠܥ蔎5ƔI7a&mcH<͗TP 0v,93H7XnBO?jY]S-=Ӷpu5fJ_a)z/GEvyb)Q1nS&1D 'b`c6jb8]!loW/G\/F8v c_\J$RhcS cY> [hȐRj.Z_2*qR;Zt'̳hCfWC()Z-F ]&:Dy13-tlm߮Wקv PçJ Al479}&w`X)Sbt B☍aed2FR(mqvYo[Қ;4e5Qvr0k%W#e;:hžM(R-iWAiAp@: rq" LԶ:)7|g\")嬺;psPx8,GOCOBj'xq884Ǣ1צRpz1TJ^O:{'gQ1rE$BOsB7\]F!>^\&aC"@$|'(QJ]!?D$ *U5OnA)&"{Ћ qt^`\#q2t+"-xrmEnr,Zi*:*819%@/fڀ }n>~jaBH1 s?&ݲ$,c !{ %2i۞+vm}D&KwdCC IDATega薌Tv99Fp劋q=|QrreS>B؁ CTiП"r%lL,@;mk2r$&LKX#BA4J`]SU@Df61U^M| lFΗ5^5Gs=eqz[6S,Q)Kj3z#q7.遲N)0-}=~ ML}է{x5C`nn17S{9B۟L~sοhe2owwn{lvOf.٦^7Mvp-UV$ c\fMl/beĻh۶ۻ?Zqs gqk<o﷒_UTŌ?&D';R`z=7TFCjA`'Dx>m*kܗ~r@)bѡUU7'Vl"zw-Z f8ZȨ@Xx ܗRyʡ@ I<}-].0Y zaqK~f'^1Ci; m<6OwF/⼋m^5P慌R lKZ`U: 5tbGy%SJ%[t:=|[ʾnXi[}g*505KWbk^󭸯w\i$Ҧ u:&q鷏 Dܬ8,YvJVH"ʤ#n1DaaY;AgF|lIU : lP>.Rթps W/Bn}/znc 9ҖGZt.047CoߖeIG*T2͜^͆X#]/WT*>\UwOyLs?D/v=y3oV;Îxꆣ~٭BwQbNbp*aot1Dز.%d +zZ.kߛ7>и8B-}gQ[n Woٿqs  x?:hU1uI[j:>_@{q()h"F FmE硅AF~-pb=?"IEa2,> [MQ;-]ٮ#dƓJJ@Z)Ո^=M"rUUmLهN)5N)UzC4oGR9+ tNb@D^m9jWthg,vR>چ61v&RA  ylJn=E(&0(0P"fR+}E1{lٚV-hߣ7ytͫzwnw\^(f=Y!"_~]j%hb8S)uYo+R;Gǵ#ý}`ht4Ѣ8MJw:GvX-)_D&z<_}֌GGa' "cj`L QPĐHN+]rI\.I"RwHx>QK›=.kax]:\-']m@.N=v>l/_7x l޺>52 5nP`IH `@ˤl>N`StRq GZkʼnŽlXI`.g1i? oBזS3$݆WpGt:Kgv/S&s,;i{#n{7x 2F[bi 8GQC:^BE/!M[?9gG9y6yiRV *fUoqC7cH5 a^F6Sl=xd%M_1:wecJ4KP^Ż{I5}&å T񂋎ٺ5|dl?]-v6CeѢE?0fo`[T9C Ts3qsP4 Bʶ60SjEߡA\hW/h&BϦsn2 ߾ioM#ږPq0X|ɜ̨81Y0\R*X,kG$=$@)SX4FS[I˪o1:_hu!Z݈=ǜsˮy[/](Eѿd3heJD4|$?. 1gK zb^R*67WJsJ?ɃELF"2-x74X^p(Czn3IF)o=Pwzr-ID!h9I)ul'>](9IT' SD+ߋ٨Yõ(cЦU ح^궏~4:wtP"m+?H4z\u䱥y!~aQ^5,V:6{ީW^ti~;_N* 69cQ)58V!9(hyDB{QJM6;.C׊}Ǔ ]"$9A)u_W/WTjEOuYM;cT D( \Y¸~ "2T)h#)CDvG?SJ2ؓEAumSJTDށx26E]:'r,!Z&~`7M%Tz,p,׋V>e8(#UKU"[>K" [3JzN74u{PR6TǝwBO???e-+nX'J$Y-[v޿߿J]_q}17EW^Z:Y\C/L ~7̘ԏ! E L!oAT[Y 00- `8(1֥ \9m:n~,z,7?ɻJ}g_{7 b:Ʀޚjj2;sv5qsb֜2I5G_)uGqR%p&Ak@^>|e-!ߢЛ*e/hiB3D95eFrwlmO-1.[v¾)-_.HոMXu^"RPE뛁輾ГS޼8zv}!J\DvAO'O>9"B) QQt{*^ )R:԰TJvg)Vȟy x?"/C /o٠o뤖=oEd:ԴENbEYDjۗU3Q7q4$tn^.Eh/Ed?A)-""r :w%j9Gs߸yjpk~*Ey0 f2/wЄcm.;( ǼL$x.&^H(m2UvN^hϐs/L;庥6u Զa:Tеtߔ⟬];B<\- 3RF#[DEQY9m1p|iF:[|-mOvfvl_`*;_s+Tq3^rmYUqa'ϛ7 #vٺg ?RGOϝ euW^7k:nf֜zcߣl*RӪV2eRokHH0T$BVX;)a?K++@2bAB(db!\Q< YPY%U>aQd2-ѽߺ\:~3=j\bSTC]J~:Eh/{7=)]n`hr~+z2RM7(Pih!"gdwv;Dko%ëLz곸OO_4 57݈D["}m}G yx_6mPJ=LnFY:(VfkCL"rqT$U)砽 t}jAE ؍"҅/r25A{ "'s#jl/MovZnlw0(XsȋuKng2 ۆ-[|{Ɓ_m(oxT)3򿛶]l%޼ ;\%H.)vD(JcT%Q*C_+, g`Ʃ |f S$3.}Kwbnr^ϝצ͋317 @6Oؓyp7^;ql챧NI!Mf.ʂW.2BL)qE;-=tT[``5༓&2En϶ 70s,ww$A" ! HNhY3eRW}nfYn{fEySlWi`_]>EFD1c1{0FF%%abņ P:,e3|ˊiyڳ33iE~ᒾ.;<#NdO k&[6h6HbG!}%/<[( /U-HYүٷb*G9m?{O0y`^9=e㑠-loڄb vȖx"r2DtL.q{QiW򆬧g"rhQyvRn& X𸬋Ô s o̩y]k|6<Γ5 φUQvG/(-O>\p)@٥ B4 Aj·WsKr3eۜ **nI&Ǐi_~wײXHgU]yDU/밼楹FGhu){/1/մk "C9eT^#JcpPJ IDAT"E`aS'l M[)Z'"a^v&b[FHz(f1Lti|# ` ;P)ip虘P1.YGjv',#?B糿ͣsKxxJyckٯ ?f*sWȹP/&8[J5BM97q6_[=[g}n3髮a߫ʈr__`l3ԕyK Bⅼ)|̴m^Ɠ*;q#{ΞM_ܐv uK{R cpU4GD.isw/l̟ goSMzoepkls yЉ9NxP"z`f7a}}f./au-q_ߏ}:)]mZӲ_) CRWcCU+VyL0Ʃ}{ե>+&Q?ȭ "&woxR9|U YW|ADYҪ}D3+?DVgJ"LUitTuz#WU qsܾe,&{(&wqRikG|Ųx xąǮvnc.2<`yS/@ġHI\4hD[!12]1^S8?[ݰw_Hʋ\T2HSt?BJH%+7ub%NܙAGAfc2|cx D17[-tNmǜ94ՠABX4:0xnu-B2-KNz͙sB>*6[1ֳIjf myggŜE()}^U`&4~'`kwJW:3(s^6RPՙށ~?WƧ0}?)-{#i3[?FLs_?ӈ)s!F~)9FL^S:b۟b+A#GFQ0-Jb6b:ƙ@!8>$̗@S.;V~k˻] #>؞#x\_~&!ێo{G+Su,o}v+fzyμM }AnnZ0uF1y ۗqpJqѻ#i[m~hA>?%Ͼߢ5[ nO!pf%8Q/Qq(6.Fn(TD/ASx{M>/ԭJiHtWkNdmebq #"SUbEd0Za=DLX z`|S8gw`U:iaKŌń?.bia "rp xҌMbD_/LN>~hi 7#!-On/s@]+BR[$K1ҿa*o6yaX?FP+lmG`D ⍇ǵd"NV@/NB~/#MtM[Jr.ڍ(f=ಃ< l;mgՅMbμiμ1Bggm.d2XU*^lnm < = {yC{TW7x6TnӜX/)ƔDB'gRqL~J>qL)&g'o_z"q?ޫC{+v]ߜz?fo>Q`<-ӌ}SLˁ)?aZ{ʈt NbrNc^Bq!ΘbX#ƙ8zE@@<2K k΋co'u]kt68}+./w &Hj!V.Rkf=d=^"q\QӟKuQBE^kM+C6{kcan:,# H/)$ Y%PG>l@>l?9>{*TN>|H1-k xkQD^'nc瞾,k+,S1wgV0y]0匷c型/9Fp | 1ya*ǕZ;l襜p͹. 5\vH5HycQJH슙H[g 6:?{gaL[44ϩIF*)lrZ~9[znAʖv̧zvbF{"X:DDƄ\/㎥!dK0/U>w;cQcD #fs?/ROչ~"28[UoV La/Cu r;vpr! *lqV.T0oa`B_[ae9O=1JO&F,e啧f%-,:Zp3zeRBABiQOԋ(%l;~6ɗôǰLO.0l*&4ma-/`|y7P0b1O[Eـg>6Lm1U;S)3] b.~EH3M,R7|Ű!Me!SZGq38{osv|6´m֓%g|2vZyTgqԗE{UU.^ң)hGnp3QTCрx&GςP4;[#@5Lz.ݺd; źDSkn1O;d5ˆ C]cB1b7c^E[ryUY90=x,PX/0BgCGD>aπ1}@wkx&9F hƛ)]h&<圇Kb۹X,ˆ{5EvS?pL=Y96{-)X]83Of0p-zHGDM{^ȴʴc;0-~5[4^׆%˃>gG}4|~ DdրNrZB"x~~!i dkiikg0q']TLV l Cil>U_c5'w?e/d.UBUzYNՍ5>G<8?[lY-gq#"vˆwr6'_EM7&qume#"WbUo" 9]Ln>ƣ&6E}QK}a|Z,:bǾ5㪎[l.fq sU# 5?8y8O_G_imFL'yґS& T{>.(bAx:o.B" a$Zg%V#T'yĢ.LFx H&jjgJ]E,hmmO"೺ o,cTB?&iVnm"'`TO,VF D\0YL1[Tu>Sqy`^(x+0^$Y*{}ڄbYICLGڂZ_ZbX6c:PKj}H|=C~9x/ Cwd5QAzex_R<6.۞3isZYmvnWuɠi,\>^ fbc# 6>ծKDHHs7t0L\B2J~ ux"Ai6#)DZc3:b\ɆC_E~娋Vl?HfÅGMrfoXR(S'eOvA\_kFd|qq6:.J2Ko%@=BTR& $WxK]ҹOj^UH2§3f](&n~uM,9akY6<bY[HL_rq&}EA`Uƕ,GzvG1b* ID}/;O0-)ۭji1ڄ<&<70*_[k-*0УF0HS"2Zbld Ran!D"RH9XϗR>[+p9~RPyuqy]K^%[q4" !8*UNL[Fώ|E[,yyiqu}[ʼd*Fyvޮeex,d'/Þ\{Ɓw°7hj6J*u_ 5;oҾsCRG [dyuL6A$wV5O{|nSqi/%NW-{H>='h qP䒑- G\ !\ϴϽ[]HBD4D&.'XlY-gq=@DvT)3ڭ=t0dg`db^/&]/hz`]9fJ.0O3BMoUkK0S^RZ988Kgs})LSuÎ6~u[aBaTس}U=nM^b-d¾M?؎S3Q+l8I4X>3DOՏ⹕\T:ό8D]ʆL=&l\ѓL1K>x>6Oߗ*C[+.Z ˏ \_D^>j>Ņiv9gزs(x=hɼ 8~Uߗ~Ƽ&}N>X9[x"8Qgn=:5~<L-N5e$D(Dk-Y\ǔ^U/L&_0Oih#?>`h W77Ə>wݥ品}KL)5*],S͘6ob.bX!2a߲m^<ܪy.^V9/R|~썿87` ZzTfҶb:>WlŢb` UW氊C^=ty,_]҅hm~}U+=۞ѿzX1nl/R[ C%]V/bNÇk>~,N 2)/,Jż|*UŘ RAbqT"RAa(냿HÏkdE"zc槍8vu:9qVljˆY\PUD>(&ϣ]ⶈdDA9WDny#/ <"-n ܱ$ nb+k)Š,㝌[Vuu-o.D-LkoLkm (soX,kY_/xK:XQP$^׏;g?yտ{g'?jm3KMBɵIH߿Nٺ:7޿էo֥ZdO 6nڔʚ>;c{MYZ8'\T U$gAocj4ArTh7 &煱ؤÆ(ּG"MjGf^Osquu,V,n jo`>0SЦ_0_e6 ov2iU=r]Ovb vE҄Hc[`<*86oaVaۍF[0ڍX,FôwNYS,_.X^*̚ʚ˺՝G6M}g~mъbq2\pJE`yns֋1^늌pB:s^}G~1EL Є jlsAct0cN] o5ޥEX|sX01WEF=uY,el2\@UYDuHx si8sЖSxfʸgb.n{<ҺV;㼃|VL0mDR'D1+Z,FÓU-(~';yϫewkTC۬7$[R"UiuMn{`mlpk"u\%ȸޙ6.~sj*=-3qpuq}KBR5y5G$p D󖺺>|ϦQqӻ`Ҙ/͓1Emۿ3t#G[̲qaŢŲQ˹p &j6O ?mgKC}F#݁4_8 < Im,"cfiUX,e⍿>cO^g0#ǟy҆ƞ9w?"ac:|)YX4->IAl-TS ݟ> n ǝ49ghQQo+\CC'#c.cCp<8n$F$T^lѢyc\a_Pl]hXF1/MĽ$mv#P|YX6Dd?U}YGJ_\ Ll/9D<)p^Urg}kX,eCߟwmi=7޾zP ~axAyqf/k_AB1!@1m֢Ck4Ppz$cUoVTj*^!@Yƕ}I/9^l6s 't-/|mEpg}ߍ 3 t0 U S&LcbX9\ c=$(/Nx̌eX,e 0g5xܯkk:_a*l> 7d`s`OUHg Tt+ a S^&PwE #du lt7ɨ;A z-w1"R&w*">Nx)S0}c`TDȟEdLEA02!mU U큩c%oegL5 ht0!~Kb >5j'PL._O/]Sà L\6ֻ~A{ }A{6-_B]pYIy6S*zLe{Bqt2׹KEG,/ #Uh*oĤ&y7XlJl΢ K=7}bJDFj_[ UЊ)<{GyU7`d<,T^V1kqm0[>vټosb!~ $ԐՈ?P÷͓]8&zԟwy;X]F3#0cAŧjX,w5O`X,5:XbGP(m3omºX$ {hx{VybN%b..h!_lK^apR! D7)47@5jܑ 4ɲL?pl-bbYOQTo7f3 86}0LDxe F(VqmEnȦX,Ųp?6?K,{^Vuݩ q+hn|>'|s  g6ȑ,ֽ"E -}!*Ɋ7"sZ dh2QrM'R aNH.X:WYlY-XX6"T*kC>&hv(0k#G1LQU)S)tS󸢥bX,mL~o`l7mF1O3iU,YGsmUI{R`Dh5x3f h`jE8|x.|mMZE ͭK *XEŭ- 3D4_N{N[²aC, UMD&`GQbH Ør^xJj1N;j*znB:Hc0oeG+*Naf>-~?`BsjG^5vm>{˜q7\0Ճ̓l-^eƊEe#CDĄH#ګ030D)ܿ.3VLCDdNW"ҥ48#$DŽ6kX,z]rWnS$2|dKS`%\%[X];mӦmaBC*S{u3+ Qa y#=3F^>zԤ5yҘ?>]=uӹNgup, V,Z,bIw=D+pP\g1+S0M(٢4.rbX,"zToEruᱧqCoO\abEjkwH|k @._Qw޾k>|[+o?ѣ&z|>?~mRt뻑\up, 6gbـ ٷ)"iNUZ1mSLWQrERˌ0m1cEp$r>0QDNP0"x^U1UsMbX|y;}>+l#~~_W?Rnk{'z H-oh|9sqҴ'xygznp9© !w[HktvĎ5 ?Y8ш,Tz7 ns^8bYV,Z,"r4cBD'i( ){7(ƈf`WM &"G]0"g⥟ocra\U8xGbX,Gy3'MWMQLuFqqC$KwTgj̛\W[=7rS;% j$(&x_b:uGtFo{f{Zf-G+/&x/jz^3Wk-ZD:9g]!"`DZ0l{IU=''r/3H0b1cUUUTD* Vڇn\#@?:6ȥX,忄 m%F,> ]sdӹ:kw?0H/GvbI9@M&"hj+=o_e[5]♙MK"dOܩg7L=cs#Kzh,^Q! mt"}5t>Io~>fjaŢ忒8|]j0#ab3ȩksEdYiŘ"7]0]Ks-&UTՁ8S1UJXPbX,U3cak2wgwe~eY$,b못MAgEmVՑ_vYcOkWVT2mְȭۤo ]XJawk.Ojm<1 wcwEFm6KeGD6ZTu>/C\ΖlzH E@؏zHn4Vb; "RT !;xgɂAOt~וkwgޙIyrӈz"Ђuh !t̶OwOvGAL}^C>@+M|Xc/!z3~ !pU꿿zJw{igX-Tp]8DZHrN=ﹴ\ ЕrpXt?0߭OeBEuX̬5?+}/OÏ,kuZ'{fjy#L՝ik?]9N5T,o=0{(,+!4HZCBDL{؄R&JrҜsnicP}xop-@ B(3mRRLzɅEmVR,E06! Iv?xfALa@DJKwnG0 =R%Ym-FncF/{|ReEP,#6a82I /џ-sKQ0]l?rb4VH~n]np+S%+kUeեk꧿qᄡ:86p7 O 4b̜~[*m FfnX"jE@ȆjBɃl7qp-̬QΒsǘ9>988888p;9>(cw>$q<7r{m=ؓsF3t~NY IDAT_l׏Julr/_醢w _ā&5TvTD)PdEOcm -; Ueey07)8]Pr}w䓉nw$;&<ϩMРDl#_8a-DtDnBf>0 Tr/BP62a^-E tzDݏy^fѿi!E/P a.[i$Tg#3`> N {ϿjfZiyJIv9 [/nTl%\Xp,xHcl$|s,orZڵ؃m߿֛j%~۵,`M6ɈjVoxYjU ʒɭJصzi;@|̼x2]!?6e=єw!lsBp6 w<ݱx|H'mݼ䭥epw$?mUF=G|Eu[(Zq>Ȕ`:q !ZIBw1V@0sQNd:f`+ sa{KDD3i6sQ'% |a|kh =㜑ž45_hX#1s=>}A·ttggfػ3e\6=Kt-E3`75VG|h<2RrZ8E?%{j) fnAx bdD%Bq3ə9FXp.ص.C o`wajt*D!X 6fbQwY۵&!]cn\R]ȵ7_f!+$&b'om5M}SO̿kU>,)ɫzu&T4{OPy㏍ݡ,/Uru!jwyHŮόhHm c=vTPkm]uOzJM'Z|._*K;ǢݳMTV)(zHAACo9~w=W{/nX7֚.ؚFO^ZF;5= }:y/#7}S\=|݊ $8× v \"ɚ`͍'\Pi;aprFTN{|at_n{vb0fҙ@bOBv+r_CDJ[LzR%:DTB\LA!.gW-%&"z{>БQvG?!* u,TG2JZXe˪|Ik]l¢iDLA$0ɅE E0M/ %i557Ҥb'37߾rwnO}Wƣ_el R`eYD!# s_tN]umfzjOv[F?ix}+8k=^ϣ)I{qTt)mv'Xt8谋|a"FD+hG,Y|DK!bN3@9Nx!t@>r=PM=Υ>' gH_\G|*G n} Q@|l? L"z \_2&?~Kޥל˂+[z͉e?}1?7++;'տykVZY.Tw͆ej]"S.>&~ް5TXUw@l3t@y$ls@ܥt3\n;msnz.^/'k ˒ppГ/yj[uM7|11,/;Ȍ2o:TY@|1&p禲:Wmc->K9a8b$)4s0L<!^@&Dς˻`D"}2s;r U@0K{& @}̼ @y=3p%ܗS>f3 q BdCx$}Tjحß;`Ċ[P@GQ[eސ%3M_:qێ5Y.PdŒe~߿m=/xS˒d[ _Q׭H<~#^r 捐o N|ub<3a#S liۜMZ2Sip{ ׺ їyb=:ѩ,ÇZvؐ~'=d1LO6%+#qsHf2JcVxQ8nzh3'3·^ !ekvhz)CMs͉1(A^n1B]  -WLV)*{3mJ1oo #㽊ڠ?fEpX;Q*ҟf=;UU4} 歕W<=F/OKqwҹ#¡oMmaVE-z={c G,: aS! h !0p!x ZO$Jʂ2Uj^{m.8³W1pj BEgBx"i 4炞̳2Wv[!kS?c̕:8888L뭗{<3RSԄ.yqP4Qw_(ʡg>a:99tQrȥht@͛MU {A&&P L_hÍ4g(@_ßG,: Dц[4b--TQt\R(dKS3iQ쁘:ĞKJ6!<\̗Lh rf ྤJKh%y}"2qzoQ^p1m|widBΨe`g^Dʧ|g?~+11, nwZBܲTXgg8@1Sk:MVkB'ʭ{<+Y?apY5# /&F"bϚODaja~ܰOt$e+f# jY,Դÿ=QLEP׍|wlYv~l޲O{LRfH:jꐙƦa^t6=3]$]׵w=!}eO9uTlD%QBC=Te]takCp6D4߶'X̧$ot^9i;AmfcXs62BF$}2[mP"RhבXq/U{~y31I4D.dB,yǷ9vl3PF.z!ğ/4?͈K-33bه{;vO ץRj.QeBV {xjmvUAƨ1I?BF(A\z;mIVl[&eE H BGn͚FiWft'szoǜsR,@+obѸ͖1Dbq|4˴21 ^xЅ/M.UMh4r1^F rm9%8r|HƉv,"^>m;a'g᠂<a`f0"//~H#-AT|R1"z aypSނ(ʮz=. @W_qy$lz 0sZ̼65C@TZ=^O܇~ha0:3y$[|}< 4! Dh'yMF.%%553׫3U;HpZ^0VvI}GuM{toApSx*9FDcsn uպ%qЌWgUov┒Ms5AZJi3&MRRI}c^IeE%Q떛 'S6;nFy}mk'.yx;m_u3(PL-Z=d{6mjuq}Nk[S_.|^W$,˲.e?}Mu/sC>K ۜ Y99ߣϊTUUe[d M*,"$($Q亪\.6;Yt8(sFp3D+1WXp:fޑtO߄\aQzXLq`̄ )+  IDATnI"7̝oL޾^BM {Ǿ^/Djcw&30W!<AxNe Jfߟ\1#H a0sZraoѦiɇphCPeO ;m3Ns%88888jꝶyÔ# i'Ҕngsh#Do5&~Rg|s6rG}ӡ0l7KayYinΖR<χMp̊xW']10-fK"2<^5  lg򉤝ٌjTDto&6>>zK 0MDHo >(;m 3f>=í0M7mkHO2SٲNJ=wUm©gO9bR9S:}|/HV,0 "fLK$I`|n0n7 88inDj|}? PUW9E[v!j NC$gos}L 5f UB[>'ǖCT ܏v.0",E{j9L GqaŮ30c! lgnD"im >Ͼe1F#J4rգ@EGZjM[!PXw%ܥrGq ݞ[:u;{qժםy]2)@.ӢK?jsݲ)cx\X^$:EU\0ey!!- dYfmv+ueXiwp1úF P.Φً7zX&A7+iy~Ӈ*(gepL(X<4Svi-dIr\ {9w|'vcp3?iVL}Qs\׮K~MJ$XP?" 9hn}x$y4ah/kJ <x܉p6DDꡋ xhMDu?p8n.wL ɲ@t7G,:!!8!OB`EOљmTq\ݳB4꘹5Cx,dS3>4 *!(,Bxb q.4 ^gBL-b!Dt}#&f u CȎ-Y y+ DF.} M=JO>HCOa{c% fs;88887YWqKh QDf o'}~ҙ!Cz|׻-m\^z]Nڄ~V #u9g]zܙO.X2dTlgK>tiRZhZeaeKs4 ǭ$rI { 22LT ib."nA7؄K@ݰq%/YhÜ!>ȹ.I1,r%I$)OR4g}~23[zJQ[Rb! F@r{9=-:6)~YOܓ*ɒlZ'H+@5up}nbXiKq ix4ظiS}HHhX f.LE݈k @o8a0T? DJD eV]38M|B(H6v^_ rzQ-h((SDO1܃WCLPLw&*&DT=f;Dul5^ _!3oz} 3MT xp~=>'(ҊQ JD%D$ Dl'| _SSIOOK4-f!H$+h}Nj,̋96/do_ ,'וp<Ɗ[yVXqL22iQ!sK/u r-PWCDafND!jޅD}YƮ@Ĝs a_-0s]fowBT{݊|.n~@=n{ *&<{ѻ@w^7Eh4vm[^hf3f"!t)D{ySAsxiK{n8GAx+QUv[hgE W؛hi&a%˒߇fhB1c >*~®BQî(J{]9 U5aߋ/t.+}B\~?46ipCF*1O:<PAX04BcZvpEB',\ RHvH؛⪈G uF46zSNѺTxݹ`gY&~\WGZ?-m{n^RЧe^So~@q.YG=&D@rQ㩞RRLS|Ǧ!N+I_X`Gq|Ę-]WOjJly);m;avH)4TT?d(i>%|>N\c+eYD,YR[s3m.vSϕS.Wj?nݤl^acLD`"Gm8)pĢ Dԋ^!l1eIe[Nf~=t'@T͂(2!ğa{7{&΃h%H8 B %zN/%qʨCTT0I Zk<<Ⱦ}cOAsu| ! =]Tz/0 cqm״"21Orr' ݾ\,oZ2vYˎp,zk ,[&SeƘ5g$ˡz#dm$}Hg]^լ ͧzIQ]no ~-1= asl+UǍsƨaDTT0a3ZQ=*[,iYH"ۍ--ӌ1nkd}9.SPҖ/]ӹ:ﶔ~‘(R\T.|3}AEw0#!ěBl!Hh.  JE Cc >Wޞr/@!@2.<Y` 08Qf;D{ G!r`=D(irm; !%&"zM fO"Z {!W8c\{?207 Mg4?,}]&F4PZBCQXOؾؕ&㎄Bwu$MD&0SC \*,E>9sX)WmZ3^8KZHktwHnH9?6v{@=۔̌ޓ:v` +KwdH<QDteHJاWYq&q;sX,r52sb;jHF8 U E9lvͶ]BBsIL Iiҕv߃7M*h,vroj髟V6F):7>ー6>sd7acƼe_$Ҙ-W434߳b}c|@cO &%X q.MtI~#n(IRVYna=lI7oz,թ?n)EU7I}[DM}=\DFiߕv XG@/0 D47ٟϰB29D($[hbt<>.`K G+iW3l8<;\<&O!ynƯh 0ʾƇ͗mWcx!} iLy9(/a23W6!3;Cpc?_]@jCZ>|׍bMWrq͕eMf'\<~Gua-}hhjTq{,!Ɉ%ȁ@ B +hY o>J`Vcx'}IiXM{U2a5Ml| Wts^•ӧf=E7B1 <ᔀn9j9!"L4{+!r!Č~"wqDQ>c\6g^wwdl&w.fܠOt\bʊIMK=|?3L03yWID 0" D 1L0 l2 6D" "Iî6I=qwfW~ >ϳtjNWթOn;M"}'Uy3}vة;uncw:qGҿz|bXbG ًJɍ{]SOmjrk<\ukxɗ5衱ilmĦ_~Yt-e?1q=Yz4Uk`ڴG 96 "2рH}A b^Eyؼ.)'AD,4 dMyBO(~:@Z""^LM| R}Spm[zm~YSQp@T u"琑 d "tK  bH!{Z iS)jW 8ߞ'D!B1SQɑQ(%dyF\P9&2$E֦TGnUURT;m;t^yRW 6Qt]/*zіbwѭ*+kI1tCś7e8)|Ϡ!)Ff^7oG~[y|ni XVŵDtۛעc#kP"D:zdւ & IDAT.+Kbq F:5Y&vʙuNMyWUR^dL7 EQ+Q_Ĉ>&sͲ+68NfCu.7IMU0,e2Yꥅښ<**Ft]>S&? &Y6Qy|Ï.H! E~ ;aN|nnV4mbhUEQWrgUw=;uU5}bF1-<{ݏ<>Ey+_Q5<ϋ*fAm%%15uHxߠr}A{Wjmk?kϼosnkmWKLw)ǜp 1o *j" "!{\ODm(9Q<@f "A$' Y'g] R)MH1$AٲcwHwTM;$ԏ,|?xP̃ݫ̥{HC| ɼeH Z_5 k}B_'1yEHC#r: 2ECD8O!B6}DLWc=QTq;?hFT@@r·`jw3GNc%G2(K3)ْՕ};#g{F2|e[KfygGe2yy3ySiѨk*5Sz'7ݻV#?$.=xŽȞ`diEA(n_+(&Љ9@`Hf?HAпx1Shex"Dt6dˈ>dz 2+ZwыD4O1 ȴ6<R3u1ŮG9^ Anq/@lJL5VyȺO|%B"D|x*Eh[٫G$  E`"^\MpPGWdm9{77WwCK+4hkJ: fGxTw7o7'њO$V}nwGsϭ}YѧO]qޭ\r{p׋gtmϕ%eק.?_o6|ws%%f\-tG5El!:jKsbko/>uCNhMwУ E :>xM.X8jg~}j>/U[ZWv6{ʛ~BMjɥcfF,f@ t:ǐNR%n1}# S1dG|t 7;rkBCwZkQ [?@Fvtmu o3]!ۍFX뢐D6zd_ "D!.8b N3_(Żбq})G3lW.x밾~3{?@WDY.@ݰ遍>7eBrH"&[jmatzg_x{͕njD"rّGM}|S2ܛ\tVBuGvxeI4ܝWh53o;Lq~*ខM;s.zm‘C/:7#ҍ߭j$FNٴ^r韹z4ېꌖ:x۔JCQcjdzr"v0`骪g<,iͻ]Q .lײo^[.KV&{X;;SxB!!~0~ F3Ud>)ry`P͐~>?y_NW6e Xlyx^|ggNY$f9n|ʃ.>޽n|nƃ+MG}z9 gƽ j|o)BL&j}׭`Pbnʫ7ؚHjc+Z5mvy|'7˃`S_qc7cjYJM԰O:u5uuC\,e745L'!B%SQ/Xa[(fF.oLચGf/=Όu6 <U! t[;cVˡOB0 5":RTِe: J ~C ~Drk_^ Ok CFrX^BBm7T@LjR.EȿOS!6~Az靋u[9SCH\B _@e!#~Ŧ4(}ܟkH(m(aD4(؊QmuD!z"D!GL~Ml|ixӨE#'*A m|D4TV$xLa`EMm\T$qRܐn#.fd56f\tk @L ~/r͋w٧rD [ٵ_ U&-{7m<R -dW8 1sW+j nsO5wuƏÇC[؅'^V F<Ѭ|ne(1ns{;s ][ģ1t%#$lI#f[*&M`Daf0T"'Z6gy7<׊ %.{$ *vּ/;ӷ>K9'ts6Z"h7!~RШܿ YJw;M]!SSkFs,1\k ]Iw Hq)D } i4?@Hsyae̜`)ޮ!߼>ːD kH`@'?~/* Yc8"5H5 -}P?]t| DL7R@? /|dtV "DjV` 7 PZ؀":A$3t$JVikorTم?L.-T> DyxtH[26tLKo xfKvs3=y?vpsYѓOx㌓˯z__oM7\&t!I qrf#Nםk6l)HyeUb^C˓>ynnYeuq5/'׸mNFuKS;uJggx⽷J;t4E37or!(L왹 "x"Ӡ~u`f^װ)ef]sq" ;:X$S&fqXC;컎 0 5?Dt)8keAsM25r A;mF .>y,nӹ}h/l(C 3 8,e"=0 En3Z:H Ro&϶ "A A F)"uE{9unX  2 H(AwE?6 Q̼w"D!1OYWNQ?"˿ *$p \`xϵ!TT.aeٰ"f`e!'իDi$3u_oZ[ضt& UULmlJ˓1Eq5US86 sxʥк\V È(܌}SmWI<ҡY%(T.8SBnFY }`" ${ ]F7AJ@!BFEmRGOu\YtN:u"rH^ H1t.L߿Л_tѻJ;J ?y8o YȴѠc d:i)Cj`G (?oPDmP )k~X!SvQȿd}e!B>a/Uєb`co`Fz 3_XfwI \,YrH6bw4b.a3ͦє_!VC0Ǧ:+zN/Ѥ Crs$' @Q }n>a=Br`1UOw^vg!93Cqۚf%;DcvDqSGɛX~-.[?gM2i5H:KmX7TAF<-}=te--;YZ(+-mavN;hJ (=G5%e04 ueUSV] gs9DQcsg{|]{34MWbݙ6,%"RESK>9mv]*f69Q6:ESQÄ搏@3F(CAֹ- ȴOк!VAB RF2Er-d!S#-Ha?yin21.g!Lm![[g@8%)߱+ؔwEcbxI_l_BdxaPX` 싂Nt؝R8":xh$},_@Ơ]!B{xX>#\P` :03!x4L܈vssEYmˍrD6>|>O,QĻU-+V<񬴦seKn"ټ9㘝ƮpU]29Emϳ=k7_s̤3:k;Y,ް6[oR-[K&ְqAζO`\ǵ ͊vc>c;sq=^?{/ۗeR ¶6{% SUUT؋}6"`<4 Q]x,G𨚾-e&1C]nyMm)oW#of &,^J8oyn[cPR *u|C=mCAF(C|_hL])<2W_ < AK$+#h q$&h PT߮GCFFB|`.31pH!tp1fCFJD4fQdmf~u@ZORHZEQwUj! V@]lTp)m?iډ8f>Qߢk> ,p#!Elz,D"R"D!Hml^h25g`sҖKti (‚.0OgLxZq#D(ǁC6vZ޸AWtXJT^Pޒܼ/$Dډ8y䔝wOeDQ_Ysny9 P"nWo5m/}ITSsYgzuYsvE"OvUK{f@h͈~P(tE 5\&ei2Y8 A<yvG?t乃+ʫjZB1R9*DMEe\*t;:S Ez`r󔻚Z[S|>O|eEy0 G4571#z+7.#Ѩ8ɪpͷg>_q\|aF.3y SM'KoL;uTbh* H>W:[}s>!Cp$0HE@!4_ R|x2&4] ^WCű@ wPH) LǗA䘹~7ܭlҍt*Om>@F?+:f&}]| `́LmU5 R> doE~Zl"_yS!yŵ:1A(t=&wA ${k*`9 a>;?W"D!8Uy*I@^6ʩ.{׍hsӍv{ec7nh[/;+Jv))I U P=ʼQն9p xoH7/l ^^tfR1 usvssid||E5űcsW@DN;dYml\qYcʫVvd{vhg|* vFe* # "8إԊd$He3fM]WQ%^KNuడQ/ ),[epQ<ʲr*$L+< H8 )4o!B⟋MN-{/h[{|p1E@Z@g:4A )/ uwg27͡{N3AbGcL|:'LjxY[j!8_ IDAT pC)^DU)qʱ?9(=GCKYs ,rbvl—5:K?M$/l73{\yǭC!ОN: m )3l+w%:<"QSV TevX[lΓ3w[:BSU vq$~milUZ]g~P1sֲ#2]Dtg{t]u7$SˆP:;&謲hbzd=LpطS͔=vJ77Ly: 4FP,Hqvd" V !ls@"jFO='Q$=+Vvn$3 &?vo"zQxw8}2 'nlѱf"Yw[mC 9ESCZ Ȉ葐V:^Z z8mc^]XZGfnt !۸'+,3DŽ"D F#pPtW5fErs2SEB U\[ZuM J IKl~x}8YrOd=ߖ,0Dl Ysb)`kߘФ h҄榼}Hr7Mp)@QE4iG€)3)> egB9$d~z̓N;7tW&vcnu3X48\0򚢂#p4BC>HU1bݐ5G%㉤"QT(j*1zpDqK1_U;vuz{"Ԝiz نC A .)4l6,^E$qpݲ7Nߴ~whgć:@q||3%@3B|/#sg7R,!B:O1mȞ@9Jј@'FHԠ~10j \U-ZG *>FٿfUѵ^}AD _,35k((ڃRmLy^k]Pmdi_E|~uHZ[!!EƳB"??;8l<\rOE17E!zrsfӆ]OxUD5s–mjF#7U>,۩:k>LP4EhJ8ќ,Xƒbn #B$(VP~0R+5|qSGE=+m.s.˻t]3<|Lpe=?zd.g>$Te΀dkmCDi, @!c!-J>EUvXA{0- Bp$Sg:C4)Bc{+%˭H,5 Dl6l>g'06l53c ^d݂?;gT]Ag7Vf죜BnFY 񽀙W3P9=S(\(P R^cHuf5dD68zo)l!i#$A-?Qh@P$*R<ډʚ UhBrFAJE4@(2k} : f;\N&C%kkBџ03B1D!_-9޻"\@=Hn g3{sGo6)-˲"z6B$ӊ(7<-6HT@Y!E(CSU/ލCKԕ%\!tMu4O:8Mk0^xri VSbNC(;M6u n޸qWtnq᥯D/y:͈c!ss$SQEt>偩z.Udsf:0bF"d&蓹u 9"if\(/-sL"4]wKIulzSs="TV"=sm-G7|ܜh^g&gJ77T X 񽁈~JD9">9lib {i51V/)3s -0 [yq|B?(A>;2M 3p7.!)<4)k;2gh52̜f>2 `?.E9(DEc=ȧ1la,FZ(|'ۃtf?i*yP5sDDdm!B`\q"7yzb>%(`aɟgs|\s‚ L,s-ڪ_~{/䈪O"YpaT7ZfWv^_9;?esE!yt݁g &D#$v.Zl@Q׉h҄tIL6uʳ3OJYT*R`ڴqSO4'5z=*-p9v/Kz u;\UZ2tWф(`yf62XkREWNv[ޮ\ !.E r"`@m?؃>rH4Q6Zm|zmm[zu}2g&|d u~g}7L!Bb14gY-䀂09@D@\UR]`'H;\叻&~ C $$# el"*E^c03Ok(4R!k3_={z6Yl 41pX&0@F"ӹ4s$<*cQLd[q\ nuA!Bà0ƒg! ؙ۠oXӃ w\Qe4(utn^bŰkM[M9E> XN4!5%W_pv8T8ޡXU^SWË&! x"(ex,6aCӦQml8M=[W&+5?Tôvd!Bb(I#d wz(1SQu!S$/L "}Qh?&c ]R$B6ǧfD!NdOD?X>)k97mt$N?GOb_cdHǐAi j6nnlY^cUneM~Z,Å\ډ(U9e [0kK"D3E'BGc є,e>W p몄ۼnp[ y@)Z&* tnKiw]9o][}jɯ;F}G(r[Br2E2b69KTs%7ϚϚ /s)U$Ɣ`+B4H["D!EeǻD7YH+sV"w)Hm,lYpά_.B?]m>r뚓A۝롸'ٱ;߾(Z; Xel^$+\In@XHn&[#AN"1X<4A˃_'>ۖP|xf|-YU X] ۰!_(~通xNC!P!)2=Rr/~p ~}{B Ǝ![Z1*H14RH :;Z|ٗc\`64)N|%,CI8mj[z;j _q-u"Zi+HќJ llafz{ [l02Vt] v=/c̜(3N!B__ejA*촻\c"6u}|;AaAKP}y3\U)dMbל}nn"pWCnB(C `F"zqp7ȧ1HfHȧ`CS\ڐb-ǐ} }s kd{ Rl5<"o~k?]8M9Ϲy+2udDt<@]F6Ye[{&@9c!Svц"D=-x-89ĮbigÎI6lJ" 2:G*hR1O l۲;AbM'<=eUt9y@C Y(?`m@7*@L$%*掯P Hժ^,"`aY~ɭȲW0{ nUJhLV]9{0wU_UblϪ^1-TV'ʍ8Ԩ 䲇 mP + ʪ U_$?pe6䆛j^ Q@1Гq! Nͺ:db ڸ&w/-+Ӆ2:<]X)긔6¤qS ~Y{O_ɇ|iu&_~{|?XYޗoh#;$'n7J#^12g5_u i vxqRy"XT6DAکB UQ]E}^ر`3/zBeG!B,!?LT #im\H!!K " OB@ VÐbPSH7N0kil%25d e+ȧ)":h"Ë.Gm ӐM7C_x"_ 5@O UѓDf.e#D!B`J@ D7"ؠSaY>m-R"cݞ梏.Zv٢[uv˯1sшVdo/(ps@>]'~axD2R@QA 8yrLE$zu1`WmR̪Zr\.ca!G]=KKow/*0?$^0 iӢ}A %hQkL *ȔIkŏOU>{IBB$H A AD߄EvKE# \AT~\QDAae'h$H왽gzs8U$&,f{?3TUWW7üyJm[I2lf ҔpTn]8XLZޭDsEpR?$tu-7? ߀ZQ(b`xPiGW˞b| +Lo:Lk.-{1`BzIZXXſ{򛿬_>n:ouf Au+z𸅋:^?N$T<{Ģ5[p+Wwڰa7UL+:Dʊ9^[,y>Bm,0bʧ~|"b2=PZC[Tuknj>`S߸'{jX؞ '|}ehMMͻmZoLlnjfy`5[|/Z_tbmj`=?\fITSUU(b^DbJ\T$VRU [,q |łE-y}vyL0,#3ZB$l=$'lŐi0Qcٔ)LK'$1djӬJx)o۱Uc>\HZ܉3}9bqN2,~+$h(N 2< ӏ2F)Yb(}raCZ[xBČ06;JΉ}9_L9)ؑ8_ZƆu]3ĆRr?X}ϟZcpe4:&2h܊'(39|+(A bx2d}_;cLA^=p@}A+ ߴ BSz.ZgmT4?HYZ34"}8/ǴNJG+45,6ÚWmG=~Ănsjlc\z-3N?.n\ɾm+m\v#VDVT:mY(wT&=qt1]P*ibF9!0,N)4,4dJ ÑE0,J}y+^^%L [(q)%IK3>c}(>.$&! :_$0嘵{=BkRj&9aoh{IDATLؠH<\c;&LfI9ZTJYYD1C[+#b'7%+p}G_{ݥK|33}s=VN蕫V ym+jekռ\cִxіtB4nG B ;N~.K($J9i9{%}tjlX7Apl* ;3y3xX[$;֫Ɔy[Fw<:cU\ܘÎJVՓVmͶh2M$;Vܲ,wnc\f焱9s."s㜡WbɚEisSreLnF}Jg9̈+/2`[̙MPYBX߰6B *sCtam.(=ܯb*R1hkա=:axt H$fѨ3[ 9={U56(pۃ~wwlNxOf~ s57̙!ݘ$b~8H)UI`QL☤D G0/P]vJb<8Wp&+bF 1MG}_SJ`_7>:Ĭ {iRiQ>Mim*Zbڃ~;XcoLAfJI  <l1k"OZA3B &\I7 Z V` !؅3NmZ>;,T?D,i[ 9ha|Z{Jk}^p~F؁_[w%K~-E!;?nB > R$D,)m챊]O=ђ{[M56ÂLA|)6;ӉĦ5E 6+A3"}&n)6u-Gb fN~rDzwcs-M*}8qX*/b\ gb{$*v:JŘdj{Ep5UDo41&I\Ei*iR:D Ʊ?뀋Z˔R*{}F4Z4jdRC}D뽏s#1 !k/eZ볶A<4^ַoyB4k E,,]~tZʄ5ZyoduǺ̪|ѮhrQ[REQ@?=f&v9 $ZR"k+m.vM^YsfGwxEIdɰDfnM7LyaoSYUkŷ:/biԪ ctSp0Q75xCqz;t)ǝޑƳ_~g!'wȘԧY +.ZFO7T[qv`—nҐ#XVJ.tvdQt>()_Nu0wI)K֧zXͥA5 ʦ'a;e ^7 zF8 ܀I@Ak]cS"=R1S{ psa"?3iˎ+Wͺ@b.:C>I^- rLщMZ+RP|Ck=38vf͡M5`p0_`^ hp\f43)nxZk8@)҇>1Zǂk<:J)iBZ.U!N@`gM_Z6O?L6:,x{Ej&7pj5^o-هU_LLVo30H 4eKMҝ(5<HԵ"B4n憃 /<~M9ynr҇^El{c!w￯q-x::+6/&#b dރY{7Qk}RtˣGy}1#QLb{ddҴUؼjPI.a"ya`zm|vJ1FWE!{w~ ڷ~ǣz3>9_W8'n<97{<'me?V[퀲 _lFSSy2hT+Ky "([[x]Zz E;Z}PӲ }AbdQԔRS07/Č5GmSJb0,z(A)+:`>xnJarټoT& IVTJ-4ܑS6R0Uޞì=L!B%1SOg]ӟ8=V7=)vvEꬆO|Լ^H}*սhp#u vsAll-, (˯%_$֮ }_+ uf ?WwԵ=$bS ZW*c*| ԖpK$Lpr)&:8LiMP P5Rot3A"(P3xWkqG]B7͚d49I_"uRdb&bG`XU6}sgwg_sUg*W.w|VYA}G]fQҔRI!̈czRjL_`zIILe X*P{V20#`Ou_k;2QMk"kB8ᇗ%m˺[Gx4nHt1,N{3šwll+(?黑2Æ`pQPYtD؜$(NәN=Iռ}|P?/_{e|y%ck,v ,]]Akl{&p@i4.ۗaF; L&W#Sn`NǭZB1kB-U{ b=h3XtŲTG#v]ML& kC<6{149'uq ~c{jz2G>;*  .t2k}|ܟ?"!v2 UR1u~+1lre$ol^ Swa%cAѽ|ԧq<{ޓdfM,,F,p_cfaF L+ntp1$z8t5'P*ӎ zkA!bg03l~: KmUoә1Nu%ւT^Y[PHC&6(*ĉQ[S|ȥz4C8dxۉGuemzT$xYJlY(v#ZfL+ RؼLxlT3*cFE^oO)O}(|XQELw3fixRp]NhfZ,7/~o_!fzs\+c6} ߃D7$bőhW {l:O>^5d.nCeo*b&CV^vOL:`T#XWV{gjV(Y,ݞRhbJ` :fzKX: T'VZ.lga C),?5#M`m !;-PMmCv @xG`BxI1ڬOaĭf>XeyUX[Eec:=||+VQL!!bxA75bYӸmsx`*KD$Z{ҨbNa{6; GTUk[*σ1}cY!vmZ_k3Q 6Q6O|ix\9n?/UQȇm;B!vm~[Hf ,*+[P$_ɹ+bI"(GqTU1i̸XM*T-9bk[b4T!JUaW~Rm-5ֻB81oZ_A\Bذ~#Tl1(\VD+m=N<`ol`'*4lʩTN_poj\f!EQJERw)n!w`?fQF< IM5foZmz!b3b 捘 +g#),`! BhZQŌS9eaTxX%6 ,=Rjf)Z늭O#[aCT_mB6B!1beVbsceLX-t}8D%Y10Y xVT \9x-·©|N]{sw_A_^Z>ܤ4O ptpKZi !{qJܵ"{0 "ancF b]WߕˮOĝ7;Z_,6dQ1֭J[9JLĽ1.Q}G@=f>_PJ5cz@2@!nhՓu4uvd++Oc y)6[gwe3^um ;!5JY)6?m4P{fۓ5b~SkݺC˃Ǘb@s xf㏘B5@; x8 0Sk :M)M巕RD?o;B!v-ozryF/mx=c˓vg]cF$7denPeMD=5}cv"[U3_SNS pvbE;WS{BddQ2ZJ) <z|p0NyXQvPJ`#]c>03+vϝy3>~SP?}'|{fGdQPJ<^' !Cs9gn+:l7YbIJ)SuuiJˀq}B!ĞF56bsSfQshcf!#Y(6Z-YǰB>f@)-x_ddQ!B!DrE!B!D, !B!CE!B!}H(B!IB!B!ɢB!B>$YB!Bч$B!B!dQ!B!D, !B!CE!B!}H(B!IB!B!ɢB!B>$YB!Bч$B!B!dQ!B!D, !B!CE!B!}H(B!IB!B!ɢB!B>$YB!Bч$B!B!dQ!B!Dœ4IENDB`openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_41_0.png000066400000000000000000010602751413546205200254510ustar00rootroot00000000000000PNG  IHDR<. sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxy\}}kU46$. б>NCN65>vG#e[M/!ɐDq'$H tW^o} HAUUw_{M4MӴkniiڕM4MӮy:4M4횧M4MӮy:4M4횧M4MӮy:4M4횧M4MӮy:4M4횧M4MӮy:4M4횧M4MӮy:4M4횧M4MӮy:4M4횧M4MӮy:4M4횧M4MӮy:4M4횧M4MӮy:4M4횧M4MӮy:4M4횧M4MӮy:4M4횧M4MӮy:4M4횧M4MӮy:4M4횧MӴ1hve)} i16jC/Tiz@4&F] }ltih FmV\~:,M!:4횖?[mr?0MQ:4%Fp $/ 8sEJӴwx4M戱77ysl+xx ti5#ofd- lHic隦牱QQW׼L? <DW5M{w iYiCM O$pg0O`GӮ}:4=' t?,3C|60o= 'w4]MSh \5M{פPFbn%q!``g^;] v,3yl{xz-0< zM{iUO翰)G_$ D!xeόl;i vmW!iv=$Xv2KEp ѴhvŤAw?<"1H2oaӏJ,A˶Kd' IHDF@6|r ?lMӮB:4m:y` j{nQ:0`rɝ$P$tjQXn"'+YթhQ"[nz;hviotԻ|LBcwRڸj6%@#rGΎ ~^g'5>}tRKӴ4-KՇQe+# {ORbI$v2OP(æ`&`_4on^2{.B~"iq63Eifs#KNP'LӜ[eb` a9$ piUG<%iE|HƈG}&QL36i &fjβ8ױl5ymrc1/[KN(0飲JPr<0Ӄ{g㇞'5M{ioHfS>JAmywim؎ ITCLiq~u/K"L-#>{$-BYkf8]XgS oގߞ~+bB]5}N<]V)'?r4c(q͎-B4ıa)dU>r _"B IPE2NR۸az9+泙OIJY8Uk#S T}"do}kveG4U*XB;wաfs̭i3xȳ,G8?Uerqa=ǁX-"^I^!PElyrE^[M.] 3m#roYGvau+`g|.JI: /И h2} ok=0?,Nf͞$=}@# mrAg8ܶm Dw . c'軦]}ti3blTu ȽhIݣߜ3KV3UYm__3+5g$w 6|aDd"@$ 03 je#ElN 8* jPFZan h=YXRmVyρT:v*vfhm;FEֽ2iTG;v])ٍ?ڸg?蕒x}՟._jdnufłTY %PT%CH 9Kfp3Bx7S@m$jzm"l(V_!$D En&XI,ץ,~CL>'(#)'i\w3SukVb-C;{W==x|PPCU4J]@SiDU{҆5г!5xZ^qދbJLLfANmv\t$IiE^b b ͨ^GQ2;s(IZXNH"8*WM"0-}dazX DLp;?w{r*u''MǃZwSֽ-e4MtiW灛wmg;=3!`03YQxi$yK&wޑXNuY) 2jrFK@C] ²gDA;Ln64#b%db[-8p p6|BsHr.N^^Hĭ)&*?*k-7U0$}X)r}]$!bZáokT2_4g+V+FZtziWhڕ󋀽g?6س1H3EF8cOe֕m"#=ݰ]\vWg$۲0+>-%0baڞt sK:8=ZaĮJ܈:N໹Nx3g  20NDY/h&ġ@e[11J+t9:SEp 4ȩ,n5*09o^*is}Q)5N6L IѴx4 صg?uTg=j o|=)rv]8,a 8=2csՠ32%̈́Ʊ3w?ъk';'zv%+c.ފ7w-,jŲ]|29 9>QegP8KY M 6Z(0=J?9+ŭy0}ޝC5zQ%7/ɖVck$ۋNnV?ˏX8Y 踨跪IhgQ]!}k*åiU@k C\.@|5e-lQz8'P7вPٚ @%[Yuޝdsɹ\s)1uBvDdKYin0C}lj9NaSZTN?K;E+|2 њtiWH$H׸ ݈T,2 =N60j|-27뀧P}'&5HmV2\#lKP)9`!䀵?;A')=Zm;$E xRvJW{oV_qh(238_x2^Z*K,J?'VrJ,.İ*tXl_''h/..E|LL_!S|Vo=Jߚܶ-ewwr|L_2jl< lT-iohk;7jQ?1pje(ϢB؂ J5O͒EsboXݼv /ųǻqZ0{6MOu_ ݵ`>0;hdjwcU_<+246C<ѥb(i<Td#l77vҊ]nTŚvhf1}\4@TQV4QB>,:RcŖ&mA T00*o-N܂A,AN|Q]f0- k8T/Kk~]fDauf: $nB0a'2u2i  Kc#bl4yŴ'N>TPU)3JTz;P)~m^ٸw4]ya6UvTx Ut%_ iM'#\Ƕu]3__nIʙ;F27.I7FU+b'iX=مhzL8Y[8^؊*e1^|q2kJniQYWr*u?>C@KqcPX}%Fr)FOphLL.3nn(do^4F_b7_l-4Z W0YKZr8A"na)!U*z[ُigXZgiK`:9$5ST?Qwm;_i hk;ɞ#p1Q~.$Ќ0ƈ[-Ma^vpk-yI{M56:u}=\ʙU?t`0gNr10IבU-2Q\_eS^Zg,Mg5Bw³-TQ2>8_>pTSX`1E<&xK/s]͌;JxF~ENC dYqGn4bO3z}kbmbSd.ILALNU]ۦz:5\: U16Ku\i ʍcwDKsÑ*yfM{G? ݗf`y<H ^ `2nj☹f~5Ŋ;ӊyKÙ Jw Kd{*V'9AL͖̒՟tv'k_\#؜nk&88ܿ_gLJMaTgozI=| b =95GH<.P;[ß*m?2__l?uO->-}}kπ^G4xBiuo)'[r3d!!{1C}P6my|$LpGğyTpl8/^vVnq%C M`g'z*S7Z8צ_߳"MӾn:ѴwUե2$ò$%oW$b7nFi!\vB\ {.F6X"T޾@Q(h̙Ke8;Qutwiȋ?􂗴sOȬڵc[Ou^{YD*FOqjɅ9ԉ`yr9VO5TVn W)2pO/qԨ+Wgf 6Se]c[#$9\ +)a#|\G9PԸL/r?Rꢂ]Xb;j^y馗"]ylŦOlj.2,:Ѵwx4QpO:e:`J\X¦$2C_XXӈ$u][seBle|pn-g9$i9+Â]fR<>y.n,S3{Ǔ3@k;u-v-"D5  ،:wP#Yè1cSɓ313 {_3j)}TQOkzoN)%T" }D|\yaHԭbdfY<7)P˒ȯ!WyIgb>;=<ܶCO*#'/*p!ɠC;oT5wϭܕ).ki;Kp_D,Nsv6O9Bmt=c07!D/-Hs|)K&ⵋow0'Įt*(]Gq56"ℍʆDž6jtiWwRws#`eQs a m 3p$  cE4M6 a3T {-89%yh$}Vꝟ[U6Q'ah/E~]QϮIm_!WYOiC9FP$!Acb>><$I<4n6Gɍ$6̊ʨLՙrN!w *h wҠg^)MKa6ch]y7w LW$>o 1 57[n5(Vl OFI"lI<%~r"jq4:|v3/Ε5[ }ٗz&nY)@$Fhj0)}qpuȶ<: :LQX+FP] '_a~ g7}s/(W͵eZ0!W.t ^0l;ILAbYE>d^$D܉ƷxmSt.kl>i;@R/V;s,@Sw5=LtӰhyy\&K 6!+"S8nl!đ!2m&MX{~M (^GG{ti߀] HH3===rK v=5&$CB jReۘFM72n#svr)1 ZQ}7,5L4q];he|W ]9 WQ 0>#tjkx/]WhΖEmoY8Ä[0~ z@,/lyiBaC$#ꐧ$$qTogsk¬S8V{|TY;KĻ_$F͘{3,NՇب WZ7???-׹$P"[ȟow= lq3g3wa]Oqp'wu$R Z 2绠"$5$1#q U1A,Zl*3SrD>Uz´"*_siAhd~ T$pJpdI [9l6P) p2ob%v aQ3r̞CWvn ~¥Ƕk;'K?mz}wc~16:"Fp> Z[Q3LS8udrOih-9Gz;A$°7bᓥ0 %/Wm&!g y&k߮9e{ֽr^gv4OH]ִŞ ?5'oXVvBa 08-:i:&B^21<2IؔӞV]nsgGڦ2} 8Zc',=o{ص^|5}E~#2*D5>>ˠN>D?VIM136ZJ&r" $C 805ED;Yjg5k< Om;O<kL虦Jz)Yk^Ge8|T=<*CwrN?G *k)[z'^/ _A<zõX΍H6#|EbຂwsG[ ,BB 4pn8[NN_.~NE^ iڵEgxN+BheN1/֭O$0)z!qGMwfbʨfajr: *}*՜=}4x{,'ʀ#[^bpQOD@u|#;)VoKZ{i H6,),!( gon%hN⹾a\Kq8]m#qQtCq\4MQNPחҬVF{]q;aNeQÀg?+mG2׻X7r~'s$wب]CP^5k0"<`^oZq88>@m%/~e@Y Ls 28dL 3w*8nH/~nui0 =ryqK0)c`&gN@K.J5<:a>7m)D1)wh?0 lGn '%! _B,c`V`~ vmS=N/*0K?e.mԙ]<=8ͽ;"U<ʊ2}q#}6Rq/^T7…MWE&`Qjc6&Q0Y>0 @^ 2Ch?dP1uM*ffn"]\/DI;_ٱ\XdQى ~lvOMeoXWDo8_Mԯ wAU@vT/nt}cr^9`+I x2߇Q\ RnG& sLzA@U+MH5[5W&Q#WPGiĠS&|ng<OdiKZW7+N?czcȄj6z6Zqwm>2r7TNoOwxv]_@ .9{k{̙^?h'd(_XjYQ_Ia9fYߥȣ!1ȣUـbY2YIyjKnس3P$2/MpIfV %D f%N0=?9s s.f8t~ONs iI~CZY{^ꪺ컿* (Eї|(JH]%6 R&c?;,$- o(+=E E`363 ʆا.xӣ]ƃAv[G>l`/j ky'uBb4JsMi`Mo$1Wfγ[Ȉq8"UT P}?8r5zSȰCSoдr29 5׼?U#;j[}?Ge+W38E00ЎC ]5u= @O*m s֊+> 'g_ f ޵qjr<?DK<`KQ&_Qybe׫k>iΡԗu'-FFpۼGgZe&B-RS=̘,7v S#AбLp_&"AcP' iBFc Eb197QbixDEont0Lz볨4hpOQ̎o(A.Qj4^MN LYuDM=z03 :qԂLƑC*uQ Ge}lԚַ}t5$‘C"Rޣ٥rXp#H/;sO|,͖JiD@UM.=/㹆eE޲>vY=]淽fvW=ʝG.Hu2ԴH3z7ʧOGʀ{&peQiBh~Bx4=bniQF_o:7o/L]GӖ(ڷ!«e=2RQy75Vz. Hc{7FfDB" B~vMc"WL4 T)0@7cd?[P]t.~u)Ө O8B7b6O_4%18mMp9w&O?ic"ʐCk&\E3߻ƙV R-ׂꖖ{p'KejaQw;_+C?ӛ3θa*OHEi IDAT|ZtgڳGjg_!!uaG 'Hm7,LA+y58#fsw;=a_n*jľFs!z2zżKZ\&@NR?\܁RN'-++6|М<(u)2YyY1lh)0D*mȠ]\O[oq_PLo(I8yl33XmdpX=F_<6DrN[2$uV2F2;'2XO DO\r|W A(@WS quX(2|h 5# 5 nzvjsZH߅ Џ$iqq,mm>y.w7m1 vmF߶#].?L#Dv䐘D)&Jjq\K+(zpz/AkY+%x`(ex Ej'3̤k S;R~}v"ܠWtv0fJ[HPCd6$9ܘM" 6W3qV;9/azQ|!SDlmt=jQ Jkz~Ex2}n 0x[ 8L>'p7Ø36@m&ZWQ.;^u\u(17~oYP且ϖe`颋U\߀ZlҬbX˗fHF?j@@k!a2GȊ[<̢<TejQ) a4}W&};?=6ñCl-뭅1SO;=7M=uwyjmE,FQ;i`=.so`l;s6@o\zrsA  CZAW`:ZE+F_/xL=7Ďp7OשVnS/y'R/V굋n1!nOYEh45h Q&S:?Y`9cRsHݴ\(Cƶrw:(ClqeUz,!&U4&W0I> Eo t]t7jI X -˰: Q:y1BƐHBN ,J8>TLU\Yd\7ifکyŽo_W<x&}l9$^ I3z]{|w䐘쐞ډ:\qZt@hq(%5j4B+ׇv%{/zf4]Ѳ6㪖ܨ|+s7׎vH3N؝^FXzcՌŪZ@P(eb;`{2 2-w Aw/ \ H^j/pK}|qiyx7ΞDX'YaT8yz RnϖR9m]nT)5jDR07Ll-2B ʰr5r4E&񉩰43O.KxW}t<>&j1[Fݽ@SwoUo3ֽ-=(M‚J%(=@C)2l#dI&+Jљ.g.^RQ~갫y?0"Bd>ycO?䖝͛MNz=M jo@gAkcZq @K@y`a0z_-fZyBMIkD_6Wk )8Z_7LK+$#J+iqq1%ͫfsÝ^kEYvv!D )4!&LЭ#tm$iPj^@iW,(sј}-!'J`c%q,A<6-n|D +4s8S8|jiXzY!yQdE|82D]ΠBBo)}3$4F7W1(4u͎ xLBɰD)R@$r7Ϩ.xKx$8ؒG-1dp04Y_~ Sv^_1z  \#<240FR`AE&vYZ1# G ^[A2PKTeߥ9Ռb٪kj[|tUl1CASK7qhkgگ.χ_.7znbR7GZO>9Dkmޭ) ~E̷ &t!˝kV4?J[ZեT%yb9*^Z$}<)2r+z;>Kx-.ys!Fy)?G;z\FIRn^9rHQek>@܄f\Y-+4-ڀal`c=.GoMM6B~|fu`"]<:TJNε X+kcsh7 )jEdeCZF RDaLEh#?!VVcKC[B[i =?bZޕO c:~Ԕ5kG|]K׊}UTϋ/x|&þ8y!ЮJ/";i/;c؆uP\GC BCEbbϊpMi &[)zvn"y q˷3CwEoJt ϛ>ʬ:M!s%;ϟpBQUYjE͢Φ68Y HU괲kIsۏJM~ Ҹ[+PGyHQԡ # F ځgZ~*ud?׮z8֋R!@g3I*gP]eہ;ތVl" uE4ԐA p$~2+vo9g7^g+9z2;lcJka7Gǁ&Zs@>[ zz[ZtqOޜYrpU^h }o8 Y+AZEqח4]Uk"6)3%t#5#)ԃւŗFR75; ,[ ¾g_x b=Gڗ]-;ۃCf+]*3j'K1qښe^\qX^1A, F$d?;TAK\^m(%笆ML SsPSko86^cfr}P.̓.B2Lhl͉[2&O8b^o=4. xhFĽl'Fb![nB~g<7c}7?-0Ҷk>kvE׭lEږ}4QsEPorN[C[7 2,}(U ~!+Z{57]I[_- 7}/L++?[FfFκko5b{us~jY2$5 EcE wag{jA5""(-/k 3zW"63FEAPuU$!6y;Z ^ )HMǕP&袋}t :~:>ؒWˊjv㖡5Xi-Yz-7bF=Lav [('G׽qR X0bãA𯱙@Rte+2ΔNs E*GSA]Tkr-M\!T YңQ-oP csF-++u ;RWbkdsg֟|:6>zǧ޿aQrF{ݽ~~kj l>r%}{s|L'dW(ȶ JEe͠ p-X ӍtӅ$gGKeB3t(Xe߈flФ9jJ&w`t\R9X/UO~qs&;ǒ]yY.i(~@fwSB(╛#ui϶ym"ZkRoYulA)^k2B. -# MKGMM8֍4!HJE ?Fۓ kɴR6HoHB(aضSZ$#$2N,8bP,)9h~mfͿ=__vʖ}{^ٯ ksf~^_xu?Q:֋j-4L:X)iv `ܒՁR["4  +uMle|N'dGr$F؟ .颋{ ;!.L롡L[0yKrq, "1K}df+O mِűjhԒi @܉TF҇l n>ƈdJ qEm(D-.pJXnBЀbDt5#,;ix:FflbL$݃f~Qգ>"˝1I {LŇ=Óϵoh~wod 7o<v+\>̗534,u&;2. v\>BIԂO`h@^ {kˊtvcܗ'NoK7袓۱jԗ^B3?|-Ξ(\WqrE\AQ@{˷ "md{N~.;ǶiZQK9]BIΡ (J}ˣa \+⇜x|t[]Bj Gh=wisj|1n:61ƷcҬ -Bi F҂!Q9*Z Fh > @V'!;G-YWɵg$oT` UJӒܕ$nJ>!%eԸڂVT@HHøKuPn0Ӧ2i+눨J{^=.#j,9$8z\akGK9B8swu>Kï7zGUgM|{gpKJ-zknB EQ## V\."nE!D=6 <}w9upf? mu7V?=K~u~Ow>T(Ź˗ŅZY݊R\NZV$('JyӯZҀL)uqE4QdgeN6PD<۞uȍ.xKxDHXHpDU)-GC)ۓWbSg~-ҳVbeӔ[X^ NACUxβ-˭MUWbvKXU:.NPNwoXj:NM4h>_á0QB Ֆfu](" utoѮ \,N`EOO"dF,aj=$Hq͹H6="87[?uXx2z o.V_8{-NǏPCЏnMgH躄 Xf6Vl$&243 QK#QH,E*mV"KCخ "nrE6Jmo=.9rHL׮"ChCick}՞VtD)4s\ kin Rh&]W>1kC+*Z\#<%u(;Yk5~BLZ"t:50NV~«mFLx#QOnU=ެQ _ބ,$g2Q 0RFחPUxoUB4PXE Nl\ϖf rbHѦn8(_4.LZ}>.2_5dli,?Ql_ey7̍Q=rH\~iǤ2>) 7==SjmxǪYު }u- ](A*'%wjO\)הfz }4-Ǧv)- ´aM%vZhꚦR#(3B0,}iSԟxuG0,>_"|nJ_T3Ίm,i4s.99FJ&8&ϡ@)A}ɗ\#;SSp˹SMj)&cM6s"U \c|oknBmguEot ϛ'1N8bIS?uy䐨=.+mD`~zo:=5\^DkYV:J嗶53mnb );mRY v bv:;Kvq]Uڊr`V>;-ױRйʢA*'TCji2 ^mVkim=bE`-z !O!)v44u03|mE! S9+œc>}! GLtY6PV&@ʜ:S 6ݼ*]튇WXm~E:5tV fʷzhaS0aHd퇵HvѤzOFd$`p;Q)`JC;U<ڝJq9c-\:Wc>o@L(%2Zw {R@HVfq/׋i\|>c# e 2lŪ?/AK>MO&b;KEMB؛F4GSɱ݂*վ.͎.y"$ȞpDV@ntsGa>ei473|^3|6kRsk,&VY]| RPB`[`!Ͳ!IQ_qjCLhZa/.Ҡ`kE2E-KD~KnҞٞ?}ѳYߎyΠP4I#-uό,d~Tܔ)rsc?RgCAqGizDA~&Ep^/4M}#ջ?~!2L?g}d뭥-} // s_9MO\^;Ya^m76#|'EX]o5=oJ6 6h+)GZ42u1^kkFesm}zp//ROoM#(yXHԋ0eWo)_4ęaXNNJ zPو4=" JD+zEbjA%\ #k'̘AM NB]tEIq%׾SləXɳRd& ))(;kb:MP6?ļ=fF+)6#0H4FHUIVBU##-n}1l TA@:l $>,׷߻l:eO:sˎw 3k/L 4pDtZ(=џE{׻.^zP7gNמ;?K.Z}gfjY^}~s:KQԗ kR%NjEYݴt:v/o>.Si2KC'[l|-ݛ5ŪfoÌ|5K+n幦X;BUFU9c=;n޼`O~exii5-4G Mx;C@ٰv-PoRv#z!Rmw*rh)*fܨITh`ne[ܽڭ I.GaԽ6I? pm&zꢋ.y:C"OrYYfji L54(DŽ!rSV2-lƱ Rg ^v4m 2Jѱ"IEҠ $Ȕ9*9DB24R!WH0 \㛺 &^T^n4Nyl#Ӣ۔r v[O]:[iO>3bVC!0z}>y ο 5~J'N]Lgr\#ۣ{Qܚnoa͜u}k=) tt{D~аtL]M^rcԤ.V\UoْlVk:a˩@F޽;0xɴLt<.B-EnɌ~tcwe(pP!շ.~=~l-3_Q@X=lٞ0ؙ Ҧ*e|]wƲo" Rܼ*k _^INA nǔZoGU8Á8yL8ؔD6g-:=ki0ې)s-- ]CcCvSMZMڮCJve!rlufy4~E94zfHŹ@Yž=6zs_NoLyᇐ9ga6;R3n ކj=qj)=9 YSYYAq#=C\434r4#;ʺж=յXVͲR'w4KN XQda@,-Sg[/Mo3K(gHxx).nXUQ݊"WP:=*qfE2A"YF^j*NF֎SV@ajO& A^MJ.P'MvIO]u%_1|ph\^8_5䬍Xcm {ran \@J s5C3|<<"D:ڿPOt:q:Y,:-&uv-F#Kp2`ݰx=kQ*ӈLojct!`zl^%л,誠U9ؒ-MW OվuǾk Tګh-]~BVmO3K_"Bƛ\oi{?kk-_ ﺝloz8![)D8nqj>XҲ7yW#N[K{-FqʛX8W}:GV^ws5v~;ĖX?FZP,o-Ͼ2|MMUFBƨ@džDdA0i.c%N«(XeM̗VVZ.il ]חvTHU*QZ&8 蔚o7QYz"wMݳ"*Sa!6i>乧N}ǂ%"'JT{~i+n7f}KrY* c͜_=c 2u/ ۣpp.()h%4=f!}7YBsHM>t(Uqh(Vv>Y^ȝ@YЕ!@kanG=gCr# z;lHn !30]ؤ\n4_#Y*E{KuɈq=L?|\p[[>]('E6KlŌ[D` AB&n;ҪUGZUq!^I#akWp*¥pJhJ\(lA0Y1a zc1B^lEJ(1$bTPVQb JΪ#r cM/W[^MO`c+ ]<mukr۷Jc% hTHH N(xnPE?л{s@^U\ a>-k]/cK-Re7(O?FO,o/>w!4._,yQKD2WNDJgh2Tٲ%q- lْ2`:WGH5Yׂ@%,/%fn]mb"+ϙI;cI5.4IMIԯ!r*qekRڼT*fͭQ mKC m/e83쥞G.XHuy"F;x5n^շfpsM=E;}Me3;33:f?o0TAak @BfR;Mpń8XjYEu^}}{`Hwܻ&5ٌvgf~)?v%@BŊsIe^ xbs]֩m@}=JPj5~R#<7A@mLM3 {0Q_`p$Jb co}go¼+? ݉w Rr W/P'q,+fGL#β хR׿H Q\c屍8K@1WvU̧:&+qHTr&@7u w)՘3YzT=@ 5-TMD"g,DLFS2J{Of[K_ߓٻ>JƘӲ"|#V{Àc,Nt7~;Ș׹ luofS ܑ^Z3c8UL/3(j^.k,(mh6;YI?1Ӻ˱OV/*T[mƾ܅;O7/}PԵ,0kڎ;:)ԛLݑҺSį|Ogn$ֻ̆*$$"<7:zk~6=1&QwaM G4y%vU@)G Sь*7]<[TS>7~1It9"|ʮŰgulE 64BD ϴAWZA\]tߜed;F>" 5b$U`ISo"'kt{TY4*aUdU-F%QQk(^!γH2Kֳ GΙb+MFz}.5n&ӱ* |PU>*s!\ `)P(DRKe#u*DԝsѪwd`W%=К{<ѵȅB]/yyr<,ݿX5cUG3~Cps#w{+~Des\U˛ZleuֲǁB I aOve?] ֑?<$DS  N}ػ]\yrrqq-Vi-f63w[."}a>x(Oo^VX9g!8P!j~aNaܝ*э#{\LPݭCk+-'grNa) f9P,:*[WVH)f \pQ" $*8jBZ*$3IlrĪLHAY/%x9ɌXcX)YUr-kE* [9>DZJ̋Pv%^kr޵ϻ lisΙ)2V<1N+;W/Lx-~we{vb=?Zw/T87NyzqmeTE*7v$َ24;7Zx_^>wÊܞҗ>0K9+{^'vDYq'0eMo֟?q?VtÏ%[@V\Ń@Doב5NJ"$$Bscq5[@][QJL\#vj+C*C]WS)&V (p䣗d*.EOsx TM2k( ;屢W: F:>겧. H.iL2C!Cm##hٸG|7܃ no\M =aeBQY3EK[]- FlVTsL:s~CDa+ҝk枻PW|/}e8k"Sg-P\HgJg׭cE%ވ<`8b^Q ?>kix BӵcqQbۆ%!!!GHOЍܽS s@AV (~6O;/Z|^Tve %PND+0ĉfn%xfD^坞NWå$,v-m$Dj;ک>^)aCAE#YYd>$&b}ąw< \k(ֈjӗz6 uvY tRJ$@Q 񜐲7yH%["짳*)W-[vN*;aWFR}vw?V1;|^W~:x~9U\t:6 " Vu~6*;[ Qfx<@vnTgw8XmBeyCNp:<7(W8pr{8wj5'IBpXڽSPeYb+Y $GsVV)X$i>QB$ux+ eYMИf3Ҝz; ҎBBY3k. yc b &A.w$WWTQQrO&n_~~߻X5^V1A'$$$M0` }Le_hP&_ Ty%j #(qۙ ܒKP\wn)󨼌rKtt ۪))xQlWê@j8HB:W۳ܖY}c3,ShNs4xmh*[%^ī@J\\H]+_MMT5TU<>Gn781J;] #^jͨvp )/:sUo]ܶ&ZQB9l\xɌ`6{2i?+C-šei׾c8X8X! ',iDۋş=H&w)tъ\P;lL32wv:SE f)L $%J+q+wjL(O$X0]8P?A,^aM,KQCqQ;:[}f[\Nj6MU23i>Sʨچ~3$U/b_jI!(ӟ,oCg>oz[<ӟZJUYl- 3ވm+<cQN4֏<2gy\f> ~Pk^5~3rBBBBOcʙG)={hk(uȭ9tMBhX?Gy5UzyG"4_gݕnͯ@ӪRzsL2O'w!o#jvz[)Nwml~] Fvf~lua/`miB9'QV82y6kP7r<9KEAu&A0 L$jo'^^vn}R^~Z ߋ,-]L6uGb+VDC1wQܻ, F_J4Ao~fwBe%}*?6"}(oi KZ78CEDש4Vb!Ms@z^f:*{kYo9 GN?"-Zw('^jLI (,9 K(GhȌ$+}ӉFJ K"8^E)&ݛgXT4(4[~Uk_}ϯ4a Q-鯠TY.Qf@D*R*%ǙEyXS?{hW M2᭛d m[w, ^>"w!K>~d$*F ǮрřM>E^۪݄*}GQ y:<78>)3p#²r/pm#)O>sIr刬"w ('ɠB="{ ~BnI= 3VfCjjģs>&HT\J0,6eJq̖,R^X-ǎ~QUNnB k@Ψ:F v񲉗^[ifl8w8/;_mz]mx/Ee'/ΞYw'\99%*oSq͈̝.MYsDq)*膃4% = o{}_+O_+Z\`vpş!!!!72แٽSāerYჸCUh6zWee`Ht$ ')O 0اndNvwgc|kfM(/'yQ& { ' Zj V1ta l-LgzhlڲYܗICE;E`5GEԀT`6jV˓~trͦܜo-{u/NeҦ_S&l^*'}_~l/_e?=B6ӈ%'z9']dp>d[Z׍KĀ]^~Ʀ*\qm0R4 ~範OJNjqq$,+F:mxPJIԮg} laP) D7ˤSe& 1 @.4ч 3-?VNqPk6G0SL,'s~s/<q~CǾ8vq# Пcfz<6kOM}/?*Ě/yMr0#f{r>7F~jTM y` a ύr[:ZQeе\RT"K 0w>k# 9 jA(Nu-"FkSI$LBOݼ\'۴"h46*]Ků @鬚$Uu}A hb'#cYہ Z, guf^*ĥ '8 :^FصȺB7&O;310toLS{*.t;*(|Oo;gިGJ( Cq7 M܅w? :'vJ9[ɣޓ<=bz#=NٽS$;Dc iʁ!¾~eN! ҔNn$}81.}#4初;)Tb>VpsjsulH֔W0o;Ig5ՇbvGIūЋ5Ѿt' (I再_sJx3GGo}+Q3ZJΜ}SϜ#?'A ݏ;r*(OA*НJ ~!)5ӥx(.xXq&m3yJivLu>ČM(g e6]M "Jy3"h9T+JO#3/&Pao0C?,0y翩Ė86MjB?4msq._ӦԆSI=2F0hNQ @nyc{2b@M7 "Vyv3܋%9)ŻXO9]CTFMW6ޯz+I瀼²-)km@4DD 5Y%$*B1g!&)v'*}/(-zQULtS{Kޟ|ay1*͇Q)cPK혲(!vUѴK{0z+ە5kI@𶿏ӈ*QgW':3 ^,|2dpn|ԩ}2P$ƣG&yxw *UA m %FKS'<[/a G'R.{V i..|k1(VP.jTlp[˕΢Z"ȃ׈_iaVtM|ͫ  mc1r$paYc﷞w{7]K(7%)ETٮ|.:*-QŅ!i9}(=\-(Gk%eGdM$Qv*hA7xx[kyx۷uBBBBW!-8vmzvu ܾMSg7w|fXXcfN֫MVë6PvZHhHz&T:& n6kY)ŅPI:;sL2P΍CTψYbġ6'Did(R$]~;;~9A%Z[4#$+[o(a~P.ܽ1sog?\o?N,|@Gm5+%=$$Pbg%*WETiΞU(waU9ŰELL5er;ykabS֭p6A&ЄA\&İ*$.2^#ug.<,DEb.1'd5bu:;J9}7iUꑩx+<:ju×Qxse壶>^65 Ϝ?SFu^&Xp^.LTVl ip׿@mj߿VQRꏿW?^qBscr)DK%nCܷ/2-#5gg`HkD\WE ^-QnHY/l-ZNh>Xڭܝ=_FHIR~0)a8<#dqf-,+:KQwJ-oDz}`BgwWwt̫kՃQ] lOe+w/N}&vuq58ҭw?Fp Oc_gxx[?H(豍%qއB'$${Pe!-9( 6`%EL9 /2=?--a6 \ nr]dRAoF.TAs(4pI_l!iv\z/K;@9V P䦍w6bnMN$פL+i?C1C1YXڷ)"]ͽky$P\\@9T>iã* ~ T*S) tå.p~5) Oݓ/o⎒E%BvΪN|7mTҶu+}/2@!!!!3!/: vWU8>(a(<-:7sTyRj,5`yGiow_Ep8a_k ^C*S;J2wlׂBgQӍxS#vwSg\6ʩ iw%9l/JM=(8jҎ, (] E.MW߲cpJ(Xjc+{?&X>/x!!!!B(xzPe"j ߷ks`H1{09sj{}h4- -o8axް$ $InHu !`7 ! 7 $`qcEl3}_zs8U,Mg鮪T9mUj٨-F-]c$P .+X2 O'gYr꘢̠<@bTБ6(RԯKvZZD͌܁z8"ճۖ0vdCw٩:;+ED VʿGtQaGw۲̹nU}TPfJ@t/ wpl훳o(4\fy4P $l5PC 5匫] 4#4*'[ڸ+HMn='UىΊB7h$4 |xŕ6Ey)R$QdW-S|q1T%{lZ_ޅ߽"m]U]Je;z .ZÀW{"J---.(™A@C7c ](ʼAǦyqzE8ӱoHC]gr~㧮+VC 5p>Q#} .؀Rp`+475ms0E?GgBŪ8g94GLHQWqBE^R|jY5PK5Ae TE9QD#prC ,iL+ԆS !#dbF2-6$plN<%9cb۟G;4ts\OO9 Vt),.[?Vtmq!$!e验";&:\[EbO9޷o{]yѸN.goKU4~kuD- ?{xy }zt 2w+?!Q+.ZC 5\HD砦9ֆeT7O(BӆG2E/Q'J?tYtt+ZHU4Hq–,bCCdBbTS]5*7y T*݂"puiyG6:%4v2ۖ+L|'x˛^?mm|ZZ(bE) ۖdխ

b <Rv7E*yM3N.0is} W)˞_~;m?O dR.!ylق z  /Pgp/S-N7߿xuD4e^$}i8KQV^h4lhT۶5Z3 {=d=9lrNgR 3$EW{{[VnAajǶ_A |HǶ߹cSV-YxE}~\iH}l#(!,G= $C6L. v72a3$B4̏ Xy=Lи 1掖ϮǷmEych29^(׹,xC3*\mgEpiA!x/lv"A@Rw~o1{=7EEMr]WJ}L*ƘjJΒa~3yk+L'(*c9+&T0I3-T#"W܍|g_co 0DiJs/8#7TpA@fspg3s[7 Bcֳ_dgYk˙|#L'MҶ4Mto<Dz+I(m[a[-hTr>2?a[FC[:4°fC+ޤ/_zφɽw^y_3KVm1G5@SpZ.UL,QR5ʒih hpoئOUV7y7/?8 (o}?x|p׏mۺ8: >'K Nk=Z /T_p9Y~llWym9&[_+-X~RʴEQ˒$Mmu4-idqB4!IA1W(~4%]Q-[Ad+`{Dd$cy})͖(lN.@7MӶCP$ "ΕH!bkS˼j9~,t{aCd.Bd%VRO$ y1&[&PYL#Q*[V0w%Dcd|C̺le_;y[/9/.:7;s@}?~֬>a#XFaXm*Mè2ql4vGw@+ʥCU7"Mn%JvIܞi1$1vݣdJB|󈓜c zloQ܃;E7[2 ƴ)Pjz( ÚZLdY8gݶBԻ;xQ`Qrq끄3NO~>Vfpl϶wS 31W?~9<{Fyw )֑ڼbC/:{#b4WJ?o!-Uއ&Qa爐g)2Zך%" 'W#8 JxyGZ-EsW )`CH[&Pͥh\pBY0N<^n v6m MtkCZU^ F&}Jv73l#v=/N3nKڐCoVcѓO`ƒwwxЭ- pkc\[V'dd0:0ו- l<@1 T]#I˨($qKюX;e'†(0Qnؠ-: zva3lw?]e+KjRql[ iS T'znZ ؊Jfnql&U&{udEx=z=߼c?9?1gf}'?>&o'ŎELY]Q}L(Lܶ [>kxW0ϑjJ*]#N{#2$z$I۰ݠ =aڱk'@Jw{Zk, &6$%SeL:f =whˏ_^Uv;πtd!M(Ͳ1u <ɗfIh"˰!6+ K:>1X%amLF`2CMLJ7/#s2Ո18< C2Z(SEbfcAu]@z l@-A&c-䑿9D"`1:Ƙ5[ZA"pB54{-@[Z!{{FE꧕YbwVـ&М:`b5OGdM38ӯ"U{; q$t!_?NUJ})_+Rˍ1I{RAc EӕRW-Rҳ|JS3dnmC.lLDB۱ƘL)Y 4tQ!¦huR,3{!p1f`9cƙyyTyB!kS>|A+@"ՂwV;m !H9i D.d!n-coznqmJ6K;^feuc._p9F*o=?yvE_rFafz]=M¶SO:~FǫzAWw8{9[բ:c]$IDNʥ2≔wBC XI5H:X(޹{3Q+EUJEl}#ZR*>Ҷm(M.wXYV%2ڵoѐRG\ekQ$p]HBV.g)y"g!.rE\,H?G "F3g7FϹ 8܊D"c8ޥI#p}Zc٥꧙]DTڐ4ǿcS # &eA+^D4syO6{D{ټ:~-m|yRZ$?(j`xM)L*DQ;C;"uC. lc\" '`}1P"i<*}cZ'#Z>329L?`j3T!hہ<ׁ^~F2Rcƛy[9$q:^ߠ˾'YSZ*>/2&'eP1t̅P9 Vaϒ̘8_c 88?LMCrJ1 6;#(qdO*yvd, !ϻ欋y졛-hO#Ce:1zCr~xjucQ:! (SKJɳ8 `BvNH EJ9v٭Ph $ Ww*LjX뉶D[W,Fױ,&V UUDgRJ:YB{WiH3JY*g!ww.vNJ'uV+w#з턯 .tvOZkྣϻ.> l_PrRR7e͐eW- ϸk~sp>4ϺVRemU,^9s%im:pRSnW=ڍٸc(K{&i}x׮rv|ۏPʁb!+Y3rQAv%W'IA[^Q@Zu4g;Zc4ZC7ЈRZ=Vը0MģucP[!l$/׻<-c{9Eo}9#Eqw˵LIM vb2xVx5bw#-ȣET߻&#=X `uJ=1xCöF `@Ty/XB ʵqa$HT i$/An@iCg#]-"H=&#gl9EمO)^Ѡg#I0?Ikew$]g!n̢ d.c@|"#F܍*ud^+Mcj4۬,@]0I@$C@Y)U5ƬWJ^zT˼<ժ.nbU@gԝĂN#Rhłg2)aPxd)+R qj EBk,ڍ_dά3Zadc IDAT8;0Î/4qQS=Hyt_l\rnh=欋ΝI7oamN[jW?c=8dpw5C^I1u5Q.4ac@eYf+ma Uγ0۬m w8y5?ߺu۷ӛsus}JE[ۉɺ} ܒ}dgePWwҶrhAk w,h}6EAjguۃH,R_E)u1&UJu"7^# Pp"=ۣ9VI'Ws?.E.!ƘqR/EB̑|iFusHU.@q^I.`o{]\bMV$b'jC) i<"139cetG;H ؍א1Hb;f5pb +kf8b,p4GTV`G,gv“_}?7[.CD6s)pup'Cb̟[7_~%+RrV[D[g_& d4MIX %oGmJyhl(YĘkNꍪZq+GvA`Mjel]S(wlZ11 X9h$Cmţ1K ƲprF!&3EFQ^D]D}}'8ڂ(TaRj,e ,R{iD!duq<(YjXܵCyaƘP)וوHD7'0IF"nU$xT~t'%L5#D+6ο1&;sU15$%u$g}6f$s'DTJ61_dAq 6t7'MXSp5k'jL`&Fۑ4MI(҂d(n=j jME-ۧ]Go?J vŒ !z45J! DYѯCZ'2^D|5&t;6tI3;Nt4b!o0kdXj #NA*"Hd-(?f$5d{qjZ˼Lj+vg \bm 䩇:@wR ]'2I\?YZݦh;GӅ=nǎLp:K^Ƿ<'|.So,ke}]m6ֺo޷_Ӹ|1u;vڟ1Q|w+N-vOq҆qRH $kj+i/own)hhd q[vJmQTdްcj4&G?n]ѢaxoDv:޲~hGFv<:/7];,XXK !ۮNiyv[eDyO'I"R(Gd[_[G{~q^L 3)>.ۨճc@SwCC}1ҜU}1朿йW7/M!<͈ѓ:  %cˈϻRD7Qo<`0a9z; }s3YE0VN7[v0Nv).ٱ~yW~1T`C{{f.̳Ҙ;YQ0Y7gb$؇}c?fyXF-F:k ~ܑU* 31@Su-tCIS9-]6륞._0s$yE~l>m\Qŷ&_nL$>wpSO[}|0p8c7:1v)\wٕaW02 mV0JmV~旀@j.%DC(`@u/|_qFa"zZWHM$}*7$)xlOb7'Z&"l?"![mߐϫ'[%lYs:uݶiawVNfD&Aeml09gvTĕ"&iA, Cc 2$eϙ/ 3P]WK|W!wܶ[o64t%J !ړ6so%a6@jw?|vhsqn36mrJG[ۦ;r+7%602ҶԼKn5/>C?kKsŕ@=  9f! {LfURY,JcŒ&9j Gj_{z++K&ѝXz=Γow \8j8y~)%ŕ~_WmU恕yҝ]vet(bdoP R`A*@A 楌,NdcmFH+qh9p"@ $_!,,)huD[@͵*CG'VHA[p ߿c zy3*&e;R`lp7Z@5T^pm)>HbmI6' ɦ&42ll\w:;Ny3따mmwFn}*.yXkgnIb_If^8V tS]uA|yٞ= 7[a|17oo mNZ9<@&Sv}{0#Ibm-g̛=;nޑ͋#l׻ [RZS7W? JuG~[ic捽[D+K&Q)55 *e2c\B)o{|9IV o˾pɋKp1ʈ;0^IZJҖ(MvYBߘ y|Y} T}3q-aa"1ыǷ0D{@^o]͞(bcqGxV#+>MW8Y_VD$+}zܥmJw f"C.a0$/CzT5Fk}Cɫ6܁eE`,Z !4> xVk=9a1!e"* v!vٽht 2yazâc(&gf΀J'GַףBZk4=m$)%K犯עhyx&P+M5^TVԦu_KJ>9UŸ*Wit:ro9-? o\ǖX:ʽ*y֕g{JqxN1F©c䗭zKm-xgbZiie~զTIa;u/$*;"`sT- Z;DXAͧU3tZB EVT5Vv"$"Vm [FotyQ8R'O Q9BG=qZs,dO~!X.,6zmwܮYjhtH>@fwΠy9[Ji`tPe! @k=*,`j> >¸{?Apf7 k'؉2Rmo *!x ^rB<+k>^i吁W\T^zXToYֶ߸)`YKp4Cծc[dv\J!kE{nCsȥ犋,?ExR ǟr\|?*~s.qՈzibLeT ӎk5 c@ J61J01D %Q51 m ZE9i VIˇ%5W8ͣ#}V^뙕z'H\v- #tZUvyJ)St5b[;2sUrm67OTjuI=Gkj.B/'g  ;}}j}ko֝w.. ZΐJңZ! 7Pk㸨P5&W5TP~baZO\BūB0C17pD:0D`ObΤ1&Z%yrNǖUD0qb LgG`u1$>L}!8m\z(bl9HblxWRc.gL=Y̽)R!go&e 7b"8e?ttc|q4(Ͽ% ,6YكxPӜQk^ut?pHVPug#,$ б'BST r&M*A-K3䜘D q~䦡Zfw]:N:``S!&lթ㱬.f5BDk~cY&& HL'WQH8vPB>++'ɻOMuN~W o]OCg[ǟ~]v X令)(4Ãl(c+kKȪ8B!h\G9~$'vf7#[ڴe٢Ԍy 9D,I3N)>΢i;v_xV@|MaHG+?=Lm!&E=*{(wG) |kuw{nlhv΄AS&NI !a"0UGwk122rwÎYTtWIy5M&l1 (hP# /`̆msԭ F:S\42P{Fc#bzVZB!DFJ=YES7 !1|50}ߑ`L5YFe}#)иjbcM*"b#u:bH1'U>,+#6]c=&ۜ=%Kz T|S1]#G R6F.˴t ?x.l۟m:yk$nF\uTK-m$5ښ>jU KN bvehh 3}1ReϦʬ B4*-#5 xۜ t@[ 2=#4NQS RB9ڨ{' % *3FRS|849DZ~mlo%koyެQ<ȼ0cGjzQmj5s0dXBВRu}b9IY:tH'`Ǟzd~>֎w\ k"PU RRZ[d<&F\3]D!RFkTk QV!blcY "sWI@}~ 7=Cu^Ѽʥs>y]?g<;[~oֆ;;>.]#{GůO{?0|?5RƵщ;;ZxjO>, )IB2-,'(#`{ch_)lB' Tck1oJ,Jck=!H qRX˺+}eY0)5{6$1k]#1_Hr1f0xddbd8c^&avK0OQyfѸ eo@J.-ӈ |i4.uΒŬ0x0ѾT!kSi_cљF43HkFL~19mgZE}c߽tC01TхvN!8.?F))$0$'8d5ޓE㧥͵}Ba`3Yk?M?7A!ޚ$ЩJk=>`R/=WĘ௷)VcE̜4k/{]J$䑞dFc ևUM_NJLiUm|| ;ǧEeآ#;Ŵo2k])N1"PB)ѐZڇcɠchg  7X&SO@dk>Z9WkqsqAu.V}Ѯ"aQyUa&Z ƒ"GlݽeS6lJͷj*kܕ^잷ҪM~` $&lԆ5%:hM~`Y8 .p 4@Xjrg$DcmYB-PZ-周+Waz *T T~5٭LJ36F0hödݸ2P'kql&s玿ntl0cBxde|m+ZU \K bn*=}sM) 2tb&b߈1zAi_b* 7zv^9d  I~`09Pj,%g0?d.B]38(Lk}T1aL;Bf`1Kmfse͘heDYEUE;z,ݻ`:ֱ_T6}O$|+SHSlSv\wٕbt4l['6y\װdjBJq@YI,?)CC;qE$H8(GjQ)UjN7X@qCPJd<|<C춄XeDe6dlx* /lVb9uDy fߞyG;+gӔđ`kX@y % KE4"'9[R#ӼZh$gBPcĆJb  X[Qk׷JoY?w}bּ'xho:&xlq~S0³-8;޴boZW}hw]vK`ZFűFj$sDMHHJ!eb=;i+4٥|\9cWDZ'OJ,)ĮbQ͋z&͗_{Em~/0kTBHV@WtwFb/h5GYLYwOc3E't0&7L b mz F۩o;Y؀rRL\Pow(\ ,R1g Ul$b>28dZd~,8~)Pܳ~ܓa`%d0e8#ek i1D˩?.o1I n+ ( $/av:#!q"&J6ԋҌO~dDpCpO8wsOcf0L^cYf2!S4/53iˌVyַ݇Os?9l}|}؇ŒZ8ڷֺSj; A*\4. `{>( {~YKw߼ŁJdN<"c_DV5 *v=N9YzD^ Qjq㹉돀Z 51ŢJ{e2b/@"!oE"'U3:\p*#YvFA!#1k08Y6*rd89p}dc\|i!Rv.߂$*X%5qZ.DI1ٕd]8{Zs#[Vzjy-s_K-Nqcr9!DX9 6=hoĨ88|`(E@UxR0`Յk9u]DSi㱳Z⼳W jK[yl6g,filY&=6M=9o~xmkX֕3gtw;ᶵ= wNv)RXF(K9 ,^{B#q}бٮ@:$> g \~5'\~5KsOSut焟(!%_˟v{CS]uAk6ixM<,MqWM=5dXx!jjk0ӑꉶM/Ls1T9~";+CUԫf=6雊M5L0݃18?`ګhd7sc*>&ym̘b,?u />~Pdm" '_Ho,6c_cr|ww 1ۗ1  1^qX\_NĐEI]x'5"` !NZц)!(;gjsZ؇}lꜻo.B Iz&c#,"l"͙-sUdn!~\}RGnZsܜ$TDq<.=W8ȡ^t"Hi|[IX:R8Ja* 6Zk&D}v"E' ǪDK+AYF3#Ai|ٶ="-ODqD )tSRC^'5R"AzH1X <[/sZ\+pEZAk%FOȗ_FsiR-Ӫ-3nWo?gu[d3cܖC'o|Ň#yhˇ=!D\RU-JʹG(&1>`i6A?Y^˥3pZ Ysr8s3J:pMZ3&ZO-I4s;7Oe NvDBKqOW`ݽtv增\9?s>qyצлr]Z"1,5 30 jq`nNwib&(|ApU(>?V%KZf:XT7ajCLMKG5O3gRV^|Lqȏ|32۬X}ۜknz|pRry^Ԙ4#)!+Y\H<4Q e׀KDE Db,"`I(ɚNl8/l퐐!M VH JN@+AzfVJ^$)1$:T ҊjkYfd{7_s/Cf]v~sȟsqebхVKjy'"^8vf*;1syk$F4A3STz2D~ /-6:$ ^9y*X5yiFP8`t`=e}78-(/4`zi x'zxz@әs_{.Ez]C4lW{ !|eDD ,"f| Fiޱs K a &$i"N{4{*vјV&omRK3pLV%7x?&'!&GHNv?g+DP Ōf7{?2C~ѴbXm*ߞ \ƙ8H0mhZgQ!uSN5eƸ.w !\]mi74 !D?`^l} }q0}@s،7li6 }ND1[ٌ܌D;m~6;o[;jhn.1@Pa> [mo]z*Ap:aYƸfCT,!!nKp誆} 4!v6WE% 9Jk5W BH*F7XĖ; C"%":B\7Jǰcd)$iT68:B)[p֮f37 Ӣ}-l"j9Xđ;oXq8Y {v쉵mNR-#낞xi~G;Xlր sje*  І&VC$M"D)zŢ82luUEMkskGutRPnJҞIpzۿ_ $ۏ;wZycQ}SVSOEMオߴz8"ˡ:pb\;M_kIGVQ va Vu, ߿ ܖ[XXӻiN ؞j>F~_ uTMJ@ys6bl7q^5fb#R1F4 TgRN Ԅɧ3\;ۄ:]uwO_e ZIR p ]wٕǾrIx7^k[QlJk} }v7_v oP]glsKi;2|V:QK 9qF-)tF9{6&~L:zrJ&b&L{0$TkY|&`];&q?`) ͤuuOjųfaƽi20ѕL0!o !'ٛK &ǬzfO@4|b Th^fUN3,'iUjeDhQa3E|΂xNG0돧kYk.ڝuyLmjY;,&j42u(*bEhr쐴 (T<0"FHOksBZ2= :QrV߀j*ZQ1qC%]'N-jPb [\JM|J5GnIqڴ9~z7+<F}:{K#+Gڟ>hf^=/Q7[,¯Zѻ%iUr/dT?sKKWmŇ;񁇇mϝÆ ,zũOTX@DQw0S>9]Mg?}_gL!S./'=*>#[K]_~5ǁVe^j{-)($q ٵTbA+zИuI fK`țU߀}]k}sޚn&%{'F֋!`}Sg!Whm\) !VuG`f=e1@rd"fɈJDH44sI+Sf clls"=Rat2'JkhwQ&L4L1=unT-z6Ia{~FLՎY L<*<ǭgMNZ%W0-Bd0GI!yL|7`OTCjT;-k{17r!C;f.V{.Ҭ tֱ wEsi?Q_t{ Ǚ߫??1&i{Wɻ%iQ[ZNkt懆T m(E90 DSkξ ykV7E(6сPΝ`^fJW*؎AͲc0*Fnq CH"!;I/"IBiIFB4PJ$µcje$tKFDKS@4+\ R_YheɄd:fCZՔւ=$7z,BsePݵUXDZsRu>$ bZ!D$ZFsb6Əl9y@._D^3 Q 2[nĎ 5,+m䳢l)6\C \*:*e2whxh!pY8A[Y0b@!O UmWf-J%!#Y-5lVMfW (nbTͷ[бsoa"|7嗞x`PRL}A^7zW'oQ tfvn/]tq r^s꽘Hb+ _(~^dq+< sWbC]ca0?2x0AacO?a[>llF̻h8 TD%,Y; Z%~w f[ptz`}C[1OZH+ֹ>/0o͎Nc_d-9c;9޽EjVK X6vNd8 L& 8aHI&(e!l&x7YZwJ}ݒ}~Ժ]T9}}+>LfM7XH8ZlE`hXձdBB {vG8Jk$Bؖ,+RBk<\#CZ!'* )8-Yei$N5 _vVVA4BXX#ӑD3@냯)LkmyKaL#'d?Ǒ81^? /+xͰLoʛt;rU  حYЦ Xb*ltTiyAK/,$3==GX>:8B%^H$")- r{4 FH#: ~h MM#m(SnFDJ\7m'=l?vsUt3C4xZB|QA|szO8GI~$:b!#~BM[>;bZ]k\c $,$SXgYeR,pLR_b)u9ճbVL~0iÒŶHYmjrkd1!`vl6"%+ݫ] `B8f&;,fmLcs2}g$=WqyҁZ LT*ZY \֭!ӳrVF7Q~_W~GV/$ O-8ݿm\: PIlR x8𢞃 xSၲ ;' l1$iq2qt./}jyFĒ( v@ll G$D]jxfR ȆTu Y_){.ʒ$v,Pv@l7J|bK!gGZXwJrD % Dv+n4ClDO=!$ k-!;% 68&*czwjwۭl3ѿnSߵG$ 2$y)B4fH@N;zh{q̤жkhnB:U"C9bEB(7A,HbtbZ͑~Umʽ D؄ͼCq s_3' 1~~Shu фB*w:\1?e (eTc4xi=N-9z{/M:Q_+;y.$6Q"*H ʖ6=BR|!rQt|WtU,XŢ݉-HXkDNuщÉBJ-kEP49H2k+S.$SVؼkC?Y/q 7ulAS[7=D!gu7NX#X(LO);%8RTUhXIH#\i"$I]#c~R#=\Xgrtבkϳ!#ĢiRW<@0;L }IKc)JtT |ANAqVȥ/Y @? ɹiGjT<wzzmi-8 xe7&aN?l/a= ={GbZl-{1̙p!zP9hOqhkBxdW >5zf_IA6Fb$ !`q@ !"s& n~1kgLY9+?OyL5=($4zP:_8z;.~~UVAU^ -`8BO>SB 1\,] >[Y@^7돾ݙn~%+-C i^u`t9uЎK'Q8,"&Di[-,(hZ͛0C֩} 9|NWZ*ZnU0[ĶHH!u=,|T{&h9<(zOE&;JXia55eA? IDATl\TuKIݒ;S8wfn%"qX~kv i[2VjڲEnL{`y]"=єUH^!(oxpeCEÏYVnZQ`o:~T)[',yZnb V1 @}^V W8Zc}c[QXЦ ^J?ջqx/dbljX/cj춧ߙÀ1%Lm :YULgF ~f0I\)56Y83Oriس#Oa٬y3#kS+|;p0s)D.:m#gK[xR1݁aIk`X܆IGw1k;5;k,Y\^ܠ᧺7P6t$v`vuܠ-&⓫b!u>)km"e63HBUA#X*|^yɏzoxK&1"A:ډɆ\hcC_QAs҅A 甊sd,t\[Ek'd(NܣCZ)ĿUC$Csg B"|WRB(i+G>kOSI(j85_NH6KL=P*dA MABmJc}#i7q-ٷyJņ#ΎкɅr/w]}o4ܩ47!+X h }igwmaqcӿW@&/s*T$L#Aqf%aQŎ#GH*?v"%)<4;Mfzr6ܺu3Nq]"esz2VunC.lrxp=C~L](ZqbVj2RzIB 'nHJX!D a:0 p/dz2GX$J߿f9 c<߈Hrt;š=̳XZӸR4,8e2L}ܷ1vٕ$a1>Dr4kHwdj gK$e ~a6f2F<f X,d~ޕ~Ju4(X jbٽ }68O.Dm0s:]^LgrrV~=—;Ye@&-vF 6Q3rk=g&~Q%ޛ; S.jK.f ڂ\fPE0SډBsmǮW$//+~/>wUuow(.&VdMb,^n'u=rlj?Wɯ?7:H4UG뽕aΡm@ʌNeW^WW! Z碣HK4K.a۱a7\0M%MR >j Ultm%bþ S!D-seB?GPOKܩ5N;m'i5 7˪ټ_` fY?Yo~yc-<;c3D+X$ms/;ܕRf 9OAw~qR fί)?\LoDR. RpdxGƑaH5_+2:;EwV"3Wi39:#G @oCH+} b/1iǨN !*;{1 Ȅ= FCBίDqQDV `J:4p[~/ӏugu5k\snLٳ3i4rsQϖM'3ύ]LM/$puRWu9U?\4p7 +}љr&pҚrVCw ccXf/YI}uorNa70`L򭘺*f?{&bu `|fz9}cb6fHg㣻DO15ë,1s(sflcNSF@kX-EB݄.L k(V15bb/8L|̻jIrV]ˁ6LVis$|/cT.[o}'z͛¹9T7"'71!!0i޿j-؛@U 5-D,+M\k^{oco(6:q"P N_ö*J8Yw?P8^GZ/_|ڑ0\h'$D)XNf uo>k}%_ǥ׳2@$m4#h!q}O['6)k#[sO"1v ^XZ :<۶۶b,admlb=֠jmnyEex9:Iʔ@HJ<kk<_ԫzcn;q"hgvT-!ҷ[Y_eIU;YɡapwyxpGcoRc03)h~("-bJMc3QѩR#mjδ~IQNV=**nmZԱDݱDe9|\z a\3 V>{If1ؖF[1 cVxLB?bkbV{+ihP❎潘nј05\cbzݍ03G1S=bbSx|kz|mgqSǤt,bYa6 y NzY~㉈`e33޳FXlXDT<=,f@ Fz^ϙT9b29FX :V%w ΙdCqwVx?7r*0K2j>WK5މIgIY9+O|¬ b!(8 b v<|~b~;O5mqZ9/$A 9/ޫ`Gh3)Cue+/-ז͏*;OKns&gMeǾx lk"Zar~G#t;ov/?on '\Bci>v[Fcli_AEmwƛ[upFݤauzTXʭV-mR0rŌv'FYX(H$m[IP*@bAk} э KL;F燑mT|#"ѡ8"W@kTqXX -Hbƫ9ooݶh"۱۞>rM+"Ft=y.-GƎ츨_l515r'笾8Ra? Ÿ^Dz撙AsMze%׆<x`t+mƴN%_BG]cj֏+=]9/x|.O۱¢ylp{sL lפ*m4InN :K oˏA pl˱Ƭ8W43O+P_h7[W o_St==)9l_M_~Ȱ=H3mn9W.O8#Sc [+.9(V$ZEDKg11{Z4:&ISO ,>x<uٴmϳz<1t,aË1 4՟O?P/b 0,}XLwY+ٻ)$die$T_=n} 4::w?ܔWbE;{cZKVg"Ohw?ӔÔ[N#d3k+E3YͳwG4D߃IAƤfgZ[B|L?+g)8ʱj@NQ<-Mu H95ЌG кlUxmNF./L7j"[߭ޖos4_:tkuz5" INޗz5˿Q1KNKEVr}.o{d5t'_u׾^qxsԏ&ADx9Ah Yі 'ɑt.>z視l}k*lDk hQ7sEˎd$eliM$l]\uPB)H40ņ Qu_Q !՞aZހԱJ-:R+3d~KJZi4 sI2Rj808l+*C=9ݲcٺ+k'(V#zre1Gf\rmLW'uT/?"}t}O{"?q4gWoBQb!G00=H$~ ;')"- H-ӱbZHRQ^Gfz]. EP1 ˴nVCYQ):hBeq2=SKCv~$XQ7כ-yb>@?;_tȈIXX)7y-.̷N˨zV֔:XL:L(a+E)z0K!DK55^Ƣ'00C<ULb%g=3 G7|@M= vZ>Y}tä+< W1)[8[]ORI-9!EޠY!b$+{V"=)eћՎ9d9b\ H&mx+q/+zt' O'~3D }Xd:=rL"X .4| N`J8^`myd֔JcB晾`˲gy!%TmFtoPʦCgЀ!Con_#nT=k> x6L1#s B|v٬]z!%a``;0 t-N5rT^ L1_1=tѕw !FVRzM&T]\W[ Drib6 lXCΧ`x.dHwhKs&̜rdey1zY+c!铖f&C) VkRƁ2&-9Vb}"rY^6FcއY׃|WFsg,vGiJcĤ% ΁н?|z]5&[(C@˔=_kU6\{f,JZ9IK2kGQ5v&E~UsU=x nwrNůo_{ Z㹹G.W 9b!wE_e$plvݬh%8I~Z-qiARDʶu׎ؠeD A M$B`=*EbЂ$0- ؈dXLo'dTNZ`+܂_' mljI$: B:_"=sȶmOsw3NhsV~+ĩE20{g SnYJ![Go9q{1I(k}񲺘`۸d͌jRP1o߲y#jIXw^M߹RGO8pp73G/|Mw(S*DĎBFZT5}:[H`Z|)$)d4 @calf⼕dʓ¢nIn*pћMk7y*& j&oUIkBG0䞅[0 ;1`ֺ#UE'sYbe9 )u8!0nu=eO11bfN}gea q̦t<YӉEP 0bxHk:^-ye+EHד y&$KR>w[mg?4ݻ>z[i+K#p,@j  IDAT*[kϴ 2QP XgĺoF_H=9.KbK Ah1EWu%͆hNcoYRB!oyȎR:QJ$qOZn2VC됶G%(MTu*JC(ap HclϮtt"MR [T BjmDA %lm5Ȭ(;9E`In2)|3TcK:ܳyk6TOxo󸐖PQKdyv7KJf;a[WtD\y"Kg+VX֒R/*;&zxZQ#ݦyQ2hH6-7ǡQ03fq?'%5_7` Wm!}ei(y.\şYjxK* !=7`K!ݠ0nfiO6ƔbMY41b:j:96d>OuH3&sg$9Ii JL_q9=&0Mn{e,0)?bjY5,eSf}~߀?ƬϜɗҺa>Yt\$N3Hq?+g,xlfL%S_QY};f":,7y3,m$ߣ.—|0YroO#࿌g.#w`1]$w 6XLn5us۵֏t}Vv? fOc1V15*NmB{eɵ`7Yg嬬%7+L)n*$Cop MbjN.z1:m >v("_~- jiU=nF EXk~H7_n3v.B /R^4S^}8<ݖ$\zb!$fyGΰ]W [Ǿ_/+d$iֲ̿p--#ݶ$SBD2j!q[h*V\G0ToA{FHKa& SrȚK,/CeNXe EӞ<=[ "Nq'!a Em<[_tsZ宺۴h R,V>|n;/ɕ3]N$-$H@rRܣ0 #"2_u1SR)t:␉QPZT : ieRFEa NZ=0C2CwEɜ1\ ?)}_|/FD|50͙CF,iDkSS_W_(3>v)X:Ŝ8QTd#n!7u ]{(q,}t'&6m݁W]\|lIfaalS^)f| n2ξ~U}&xߙؕ Uݙan>|&"?(R.a1hrL]߈uiIk}1\6CLtt\ |p:k4@ ! ƻ]a>fF ޡHGΘ(Ea"r &u/`Poa_RxRϧcu}~? 1~{a,ttfn,`6a_ ZĕJW gΆALf!Dc)e"`|SMO#oY fgc?oʽ\30uoHeLT\cY ;1Λ?DZ𢡊Ls ?zgߙܜcEvBL )$f+7_ĖǞYwT=/v̛zjl!,(D!UK[z >a=[ tHcBkazS   8rR^!.Ҫ!Z\??RVP[_2;:ɔ퉾-$<ꊤ&+{Ci [\Pur#9!ڥ@F #E΃gNUЭ&}1* nVO{[vK-4ןvi=zv^:<:wh:9kΕ[]+Gfea\չo'wN4A("kr1&`_ Wp 6 cDFXsIB@M==Iu*]5LOd߼zљs*ڵ~Z]CЗiXfFb8R)BxW?c|Hw,zM*ižy[LhF߲zd; ύD-ߑ]pcwn񎴾_^毝oa-GA>׿j;׉FԓV`t{x/=ӑmn0Z\x9f*t;Qȅm^6rmݿVXX&8J#O_9:b"{0^ i0̖­Vgs~dg|O`eRUC 3[iԑ QR wKߒxq&pUA4%L4 D>8IibfrнᶁS",6^@1jU҉ EV!'vm_ڵ"JnK]s>vxm-rg؍JY5E 0qT@9p؀a;G_FԲY "Sl>R5mzDf K^96/U!}:Y bOlEL0Gh=Nwף CK""wp"CƢ[ncÚW=@σ ~R".6 S,!UQr ڊz.ZSl}u髟Zz[ mL}< UD,O{v]CF)׈5Vʿ)%7 iיٷh9fk͏KRj*H |+:*dڣ },)$q[@S7*HC3;+oC{-aZf."{l~^H>!D cgP@{߆^@h/S @_ZS;|x6:NXlw; x2FfLɎ*wTmL͊-kj濻@c3fϯ{WM/okz=|.}ۯ}Z1$v:EJ{?2o%sIu˪ޅ\v[Of~/vWe6?ڮumB 㕽^tN&0냦*5Fnf:U%\cah@*c؛+%c=9 дE:Phpwa*P(INTrt L%&r%}³rD;kUɝ1 P^Faԧ(gڀ(6 Sqdi)@س f ʯM4.~-mNl\~ڲ\}*3:KKmmGE Ѵcg|4Ztos߉DYCP Ԥl;f<60X"T)gs4LC{ծL)IQ2˃bBy4uATΰlOXGSuH2e ECD)YL!~L);r@x)[@=K4fUZzkܪcT5 $s2c]Hs09WOڀa}o9|y4xM]iQBHNOG{(ht{4Ŝ"GmB,C+ 4Yn| !TJM%iSjNtivhHP=L&B8~Q@F44q:hbtvh"Dt][Çd(IvT,|k1`OT\KN櫯8P.~Á/zڞPمc(*:w4zTv9Zj5LpP>xRꞃ6СB,Y2/a.p,v`Lf w4j7Z-xHBe8:)<$nڬXuDl+nm7~nWjfuOlM/Ӗ?a˸lˤVLh|7ۗnxHOӂ9 WziI=UYx"R,B㺘Ϳ;myLd ܬ@{Ҏ&xo?E?B|X)uvVt0hHEt!"@~ÐT ސNB9d(xvz:xqzx)/VrPct\Fmҕ5t;jGr&x%G"z[Y5&^:Z翲X?C/ow{̠!K]ly\b,Z#WJ|˿E?kmW)`Wg}\/S oQ1 \8(FY>3D@,)B@P,T*ZRޟu Ad"-ae#VlUf'F;AiîUȉF<-^QD(T%8]!kjdhm+00nXV3N.yK0GDb8A3=nOM'&e ï1Kxq!/ؗ+D9HR L8B4iRxNt\M$3v:\@IZyR m}7| o9K\m;l' Ls̓& sޑCyg-ösOF2:1DHtmTe֑:ǂՊŠ2!m'Mqhrs֡Cm UkhTf9cGRPHzHr_x-*t ptMv&agzsW؏-4 IDATl`ts m.4>˝BNqsS\'Sxz|:_ԶW*fQ'nˌIzmԋXF=wkGyϼVR|H}agsуB )="{Lnt*ma+YQc00S6S%p t TUS=P?CʘgNTβ5TMuɰKeg.9bj ax6K4ڴK]40m XFՌ(5Le8Rj3ca|%e}]#aNPXҷeٞ7}T{+?=X~/gB!#vܞ!T=sr\XZHW?ûT˦'l=  d x*&e^8Ce ĵ| 2R4""EGɌ" Y]TGbkT0g@ۂڏJOlhVTdqK|D-afҜ$ l-峙}aku?f=F׸zk|Wqa#䏯:sv00o1S؊sypXͻ?*Lx'#-=v?~ lѠ8wɩr+i!z :&|/*Bu艟#]ܛx.$/MtMg ]O'ۤ$r>jWh(cH7 zSӲM3įl&<~S)M .93tbk4kJGOC!=0Y@fIsURQWF{Ưq(*VЦ`ҏ^:lHR*FvrO4BR*B|d;6| Ao#ݞZ)/~ TВ Y֊h]!lPt@_һӿ\NViw:*2VG^yL{y݆yӟ'mw>fg}O"2j//CE3s_[k2^#*]MD3Q 9fٲh42*nި2Z;wml nZ(E DY`fpoqDnԨtrɎpfrV=[:w§mW%l$&>y+x]_b>|T34 gE0B)#(kVlq`~l# K ˞8͑Ē J8to/˩8L;V9dں&2Xn0wR$M tM^am:<(;R]f-}9| +CG ڋVP5fasREjl $J)s&B[^zdd|(#Lh }y\|"")涻=Lі=?b'*ꦪ$*^٘)FX ر2j?p{͊Bm!գ`m*ߎ^0/^նh+6_(ü9Nkq]pޝk29z1vڃq2b=]cS\*>2| (S9Ix zU'Q9S)77e JDy)M6%?}:lg'!ȣHjhUTZtM h־Zr Z̥d(%}BAth cMCJSzIٖ*-NmЬnrg)9M͹H 1pX';.t^L~ j~#Ћ)%< 3I;T]Uy h1w1RSBgnIڳJ)8·91Dq-p NjY`  RIMB4ǀ@_󮟞~SŋM-|mo_nD8z8 ku$~@9ZzQ |~zʋLaIls= {܊ѽA{[>m; 1̠hζ"xR\D0dO)F[0دYy T$W:^ 1$^DA9z|Qkw9ឋX>Qc[3 c4M%8Ӯ!bֱQX|]n9+,4frN*ٰ1H1=":ʁ2 4!Ln#! b#v eo1E_n^шBKdzlpzK{w96w1tn_\}O#79nb&zgTvn@V#dW;j,;:z)+tNO2ݻ¸6Pcdܥdw'hr\3 ׅ/üג<\}]c^MNtB',v'G xhbmۅ{ <EK;xxgSM$ѓ|Eߣߣeͣarg _E{ K4=z]}hS ^mh1J3Ӂ)K3f]3kLn?\SZdur=!oXh'Dzyrt2-mMlmwQ9>G?Ozs9Au3!왪2F|A"іߟ($ǿ }?Ns9~_VJ)‘a! uKIUτ yw7=Ტ=W/ͲP' ~}/.blbY>SJ]ex/j䞵ymP3Nuz䴢7ڑ-@EtVj0s~Q)٦yanYk03FB"D !4(cҠ$H(Lj;Ϋ=qy- mh`XF@0 `#"T_էˆc"׿Pչ\< C#2'0LaDMC+Ida8ضD`:*(E1bQ`IeMB|AY ЅޓzV$DJjw&m}fS!oCKR5qPȥh/L*rZyMՙ\SyIIR:7pDSKZT&b!h٘w,Mo\&|e3c[Є ω^$lUPho u ,e:LIdOCp[~4.x8s<ii5އ&i|g!~&o/E/R6FϴmIB<tMCձ^c iMb'gp pPO:G.nX!d]f/5ɀP億|挧-11͟ދo3z}W;^!\;UXv?_}lCpM?S"^rBF ɝY 6Fd:*c0 +0R*laںBPr0$p"Ɛ1hAJY& }K ̆6 fI2z)R8X7pn[Vׯܻ.ӲLFfj:jzQ*S.tDpmSmkUz-rUiQRR(+ ۀG)=}dLW*XW"+Zfv}y[J͆+0SK*U:&d6vt1BL\,{3y53xuyF?ӣlYBk_v6Fڽu(g]ao/AőTcqPWh4`}=HSVvmmsH?,ckOT.jLӺjGFm (3;ϼ|`ONπcIl}w}3/||/U*ޓK!ŦJwg~nG'K` k87$-4g-tݛ@ӴB}u"Ks) G2hHo8nA2KX2ދ<8]x, kq1$i@[>B{7UN-Den*f )aS8EV$" 0Wf?MV SHGK-2 eiM;@>do3c|m$>}?u*SS% 6|SaiUjY"l+# &B6Ia Oġ.ױ tBę0 V ϔ&*LJhÝJa'MF ,q|>~KºݗV:2-S[Zv >!^ϴNHھ=蚝}Bɾa0J+"bABo+^+n46TzQʫjFԫSo*K^UZ1D!aћBFn_EV|ߒ2dvr:0<rUu=/0bVg\! G{yJO3dɷKҀ)(qtR L!>`eRdcEgKv2btۼ=|(o[1w~nzG6IUZ=gmu,3r YWҐ-nX%o5yu^OϷ 7(>淨\#$T|FE-=kU[⤑Ds@ vl௔R;M+Kq_!24 Cx%zBE?mt zZ=~-ze kՌ9+r$7-^Emh"- pM'B0^Vv=MR RZҮQr@Ox{ėĽgkLչEܔL$׺Px\}=yWs^wЄtij6 4+_ߜsDEzѠYM`Rꯁn!)-ɿ_nBǪ(.̭;Ϲo8]pY{c۟TlR&#@^I#2 ӐHfm,dKuC*4R(+B hߤt]f=*j-ql)޵kZ1QltiNKc]Qz+xѯ.0O6p潽KT=*&geɕȒQẽד|c?q"#]O\ݫγrϽ2!i_?ۇ͜_ eT@T)yz4%]Ĩlw'=q?Ȫtڟ[/_?h#D>7.{}ZоaqjvFӽ%/^maFe_RFbxB8~0cfkA-;6P3[7:`]K:meڠf FG. IDAT12Vjs9tri]43%q~l%m%8M=`A$2 dv HbSteNLo VonzXMז-Ra tVE9(1 Ѣ M FNdhr:Fb3}hhM'Es8P-t.XWK/\Ԥ\ZWՑnIɔEZY˱ x@{\0|iczhSM=Z64ad-OI[zb>3>Xq\ h v^[\G&F߳ILjh_`⡐3:t?HIm,Ss*ht!:Gy&ɱ*#nDߣ桢i#Xxq=(#r p봿ハ@tpK[[ Jڿd>zWLhL7>\iɻY_f|h.m©LAcw,eQ݋EtGcФS: Ĕ !4Cm3Td LH Bu]Uh Lc4KƯ^ng+#Nmj_WywɏF}ԃ7l-m̶eMQ//d+?fTXTl ˡ0Y̞N= UC6Z^T}*bF2Nsa {V ?0aX5DtXYFF6Xte+{.F-8Ķe`R2oφp"3=]ċA^rֵ\'"s E]Rgo7'Gضt3 " Q ٌizS ^=xt9gt%-Gzn2&~{w]+c>yglޕYڤDP SՉEeWs?_g Zg랿;f|M7v XFaH&I羭"tP ]O`=6=BڋU \#Lml||՞k.oM\ӨўpҠYkn!DiӓZB=mv/BEx} %-SJڹM_~_\Nϙ-K{"3i&d1A? MV#1zqgZD.tn@HozsK >Rzp[сV= }mOU{uIrAh<|ɻ#oW.J=Gx ]w?qUXP{}eP{؅^Í< &5wjdBkde µ@)$%k"qDG0I$2P$" ZH@I:𢢚!/gԬ<}Q*vF2J,PMzUKU-/]=Bq(' w~aOx/X84wlخ'߶3g: 0f&ԎrN|mK:3 +d)8j#`7m~3-i8> ̾%#lo$ )bQl~K6p4_io,/`b,GА$6c6 S"Ld%gl cm=LiYRb͊ie{geIIN"'vH-B3P%C!!\ ISTlLm^ ҥ~L;hʲNϨ_xsYwe6~nmh;99S7E%$ޅl7t/?HQDϹԔ=kmN_|Ͷwp0U<=7=Ϳ~x$񇎓Y@) !֠,^E^JL@{nE{_8mBWB'/&ڻr=-2zx j 0 ʀ~ll_[UGO;$gnl1hD4Ih}M ޽Ϡ+;т%i:{$3EbJ*BY᫿@s`]&h/X 4I^z9&Cjn pVPF|=8*~4>~s:IB}6@A{WWסC^;Oc:9BՇaB8,s5V!ēR6މФX(&˻cüpdO"N`MMykb "ͦ.r2b+Lg;NmF\Rg%ɧ]gm+sh?)Ɵ2gWekfi*\v_3wezCKHիs.,ŒMPk1,aE׃U`x2q)DQ J{ 4],S `bF"m*EDA5" ^2* @);y<'nj[;-GEfF:oˑܶ!Asߛ=?~-߄eۿs$˞ѽc**)ŀ@m)eU).ϐTĐbG_1r=&Um5NfK~ww\6xd:L! r* 4dD9;θ`jŋwDT3!QJ1GU3YHAhDTN(a@7=ér  Sؑ48V1hqlGv+Y绖t>RUJbv_>q{тnkhar)TFbLD/d Oo38FhGLc>2F f-<}7=#. {m_Ngxÿ{͋X,CwT}l#69K}$.iwfl;V\tQ }2FNVGx2?;LҫsXĞI,0i N \k6Akzu k_xm/f} .@$4#MUs=S5F3`;~?a_lak?7/*ĞqUZjrMYLB&+]~h&},QTI(Tqd$mF/}l~o3ߌL{Z45],dngW׿k(!җtۆcP0.㽑] {/cW~G u@k!.jTCOR6dF(\V PՔ.zAf |sGv} o܏]/>;}_Gke+^QF+qk<)=!f;=XK_>΁aL:(մ٢l:(بqn(o L믪xW^v1N5CpۿrK'8՝c1&3ePK޶N6kVT%>tg?o;<Wda~ɼ 9UY~"U'<4cB9 ]jDUS"@O H+Nh&!Bx0QDn;1FH˞G+ȐTz-c#{rPA‹O:K=/:v Ӊ?|hs'CƟxw__xq=6 )@phq#*2L@dئx:4Ȉ,ZR@DGnJmwZ0jA=<:yu>Lo{3ge׻?Z|3#*#ESEWFbX j d=ㅨP(VRӽA_xݒao}xׁL**4X6HC1zS,4S^f:)C|R&n}Sj<0$' ; g&į X?o`-6l^/jښ߸l~{n͟ʭW+] B+ <_ЏZk~xZ4ՀƁ:([ދrls]77q7ogFn8Vz< BZig =yp.wX|}`Ɓ7a*ؐٯx=}^%zs5\:[^ϯasEr۲`?y.  =j`q8/w!bU6`GaL0paΪ6ͪX~#Q 9yJS,YŒ3_/Bet96Gಡ8:O,Z5oV})gnբ_ -\ۿޞx1SB;\v \[BZe-A{?zcNEY;!?y<ָs8J5^&i؊--K JWeŒi[2K{WX9"Q_)'tmʹE1J}#5ŬwR0K|1gB}e)HCQC拽XE(_]{hr{&Uhћx !=HV(0ZDBn + 002sqSYZk~:a]*WYF +)}rjeC >[[/<6S4=NX^U{YG3"zTd93܀ϿYW=WG>x>AHrzݝ(# {́_>즙>ѱO>ٻT\</°pW5|D$ dNhNJP^*;={Y@,I@ -2?B[Ն)+B ۳+1n2f"`RMJ\q:Qi PC-2c򕬻84{hr{=7*<~ [@I]%kXM_`|mЗ;/cm966 UcEYoW S籑PM;}k4ֳ鞁K᧰=݇6F`BAM6,!^MvuqΎ b'^2Iv]ߏ%c WiV;uױ %ۋUI`C?_%з-,ݏ%J#)!(%U>Vz; a\f$.c6m ;>_]zư9^^_vuk0oOfH`󘺼͏B?>-xY1V\]SkAbn;kI`/67r[pkAm`=ms"fe .öl *.!ğaCrBLaKY Ȁ?*! 8W,Wƿxwc 74xkO^ΛEzll'#@ް6`>/R"{|{//hN,qJ8F!f_}b4وiKF;Y5 T7P۵-GZkȺD{`t̥S^:[96O>>~=YT.X26v^ԊO?sϒr` pwlu.ĝwOVqXP F]N)GbR DAEcEuZэZ5ul V1(a:t˜.jZF'IAhs,[De%ۨ r~imӺs7͏ O_=ˏ^ ϯ'wV^>?qtX9xW8(VWc=x[D˟ZoHJ\m{ùc_:( ,Njv:(` 9Y`s]qoՒ3ع[἞ù'مW/c'LqeCQs Olc\[+B] pcAɟZ_5,MR\̥¿ʻ˩;p|E%ګh@||jtUꁜ:jU@Z;uE4DZ0=Te{Ssi6nT^T+#>A\2B"h- aZ?Ë~y;B IDAT(s$gm@e>ɛߞ0 8٣oSjgbOFBe0AH5#4Fdj"SHF0Q((1]ښ܀EB^cAgx ?QPvN7stsNc:Vjݙ/R^0E'^"?Lt Ml~;Bg^油3X,:3O` X=uX6c G?yoL6_ڲu4;l}7uP\9}:(ܛ9S6:"F\X<ܹɵSY|B\oq!m v-DV/X CT[9 fWc "K76tcm+B~gp-MZ-/zb7뫰 6V-. kEiT`dž1hYcɴ3< ߬ʚ/[ G[+i;Q,M9:oѿcQ04Lݣ}|2;Y|pQd?§MQvZ&IGnTm0H@jD[("~e!_kRBV8ƯD\LwtS_4Hs޴'I;=r{q2F;듓i̍2C8 ]W$a&y2 l,1{c}ӯo߼l~o4|-cu2,`eX"y'ָ<,(6o3|sEb=1}:(ư:q,;߁Uɓ#}N!*Atut)?MQyX~^:dsz6.lka ǖ\FH(~.zEymXxY z|hbKbK* ͝Ďk/NF枿#j$=xnq`؅+h38V~ (~/^MJYsƘ5{kǭtgAt]u+q嵷YG!nV[}ܝRUBb \1pYʊ|.|Wc~k_eEds[p PemI|>onIuy!E1eb,% LEiVLA,0сݬ #,/nC |S!@( $J=Y~"O Sͽۦ9\,B\#U4#G̀nK&k/܁Hhf̬ |)k^ޑDzf~/};-Awj/]tKpȕc/$^?ޚ= zcB2ZR\ĩ!HET["XwN$L(<zC+%?GĈ [uO4vjkHz!p Y`Re5@`I 7{` @r)hz~Se翋5es&eFma,}KzXZp4{j߸2uPLau7&N\ͧy`V3Oi8:(Fb7سyU9;TQ`pQsltg)@`jOνXF,iu ºd%кr*؅Qy vH/\85<3]ml;5(cmrbqY$5 炵6s{V] |K{[ WV=2(}]$:rD [< [8_ۃΘưke9B JcW.ڏ-VCHpsb_?OX6O?k'[$u竐$a`!$# %,jHAK8 tW+aGT dbI꼌dPe3#u1Kw}9hcCygr-Ћ+p !Ff!P +H t >s+Qyl~F_:1:~ӜtͦM#V+ O8̻'HýAE K ZRA[T06c3!S#)BG$FES4z,+GqǝA{,?zAiFFt!ȰӍ20xL٬5nŠ.xe\W}B4%%sXXBC~G5׼w_z!￞k5,gGq3مv:k m ^⌹òX}=uP|MjSE.6fGtE6)%O 뼷ц/Z2铄pRVɚ a K_wa76J?/qv{z"cs cc#6[{ӹl߾jSR!A4Gz;:zP :&9/Wq6tf(NtIg{dE,fȩ _zK96M/o I~>M?:գ8{Cǩ]xa^ 3 ѐjȌRy#.,rTPZ0:j>3@HF:wFFJBwH Ay~TO7FEd,K ll{o!l**7'^MH6Aaee3e }x ׸X=esU{lpl,w2Q |d_㽍ꂍ*6qlpn#XK`JIc ¾I*Xˢ"֡9 6p9Ɔ<%D it׼^]Uי>P^pA7> NBI%ʃy8)kүx5O6;` n`,V0L`O D ..+Օ]2>< l fn%%ؓY؜˱5/K!Ѯ8[N[^|"mT_x0K:#-P eSEFvZ6 =t[aps… (|#^( =d.J{%#T%/SI%4PŶWxɍ #"A^}(@E:W25"_T$ a*Ac0A aE\OsDuaFh8VNKTQY'JjUS{AoOQ.ڽ(.^ȢGwc)bsJz=e6}KŸeT=uNT t1gH"_Ը-u?mS3Syb&>Z_OK2>n;O #՘@§Cu$t6rD4Dێ+vWڤ~!POݣl7TKC(cxrw4'{J YO!>#y,DQkE Ih4̸Mkhq:BLsL<\#FSX=i'{f7Ԁ G = ג9lvGP7w)s< [b+/F@_6ֳّ3؂@v&B}XfwyV 9b*_'p66wcO ^rF݋[kyNEwUfT ؃ŵ?A/ᠧυ:H_K?rKD]2;qKds|[﹭ܟ R9'UkOrϕocDZF"7}=loƛo^-M9LCYd,B<iIgËbFKtX|%V}?v<~S]mZ&Vk]f: JG6]'WQaR7x25&0S^'Og.'2O0Zz0w'ޏ͑:̠8[w6$| u@XO-صw cB:lH?K?$1³p/Vl _)rlxLwQFwN:N~BccBEƘ|\U%*$]:iy^C:Ues!#0 B5dGx*4=.%$@>)::S_?ph}j/ώBlD577A7X-XkFN` ;g]Xm]2!v)0Iwog9Y)d 㿟, #[n k=GM0i,vR~9}9;asH`ƓM8W2^ǎs`X6_EwV^f\THW)ZVQl ?æ_B ,-*}P=[,Wa'hKĶ9r繀 ƛE/opppBҥ ET||c1Z5&RyEBܔo !*"Ia#ȅ9b.jk]h\ +ڍ%d߉-q7vrEB$V pRJZ6 V]ZϖU,<0{vLO 8?x*g^[]4j!Q*$uFƎ(BꝋKVy4Rd)'̶:(V2cU_R!M <[n''xءў?qvO'O_tmnlARJZ !*Bʐm].3x*Uà IDATzE<^ ](oF[̊E6sMh *u/51j9'ǯɥ8QaN%5TK.DhqM; X13 R*0=һkU`~ٌjo s"nK#e<'3ritA %ib=Yo^yTPW(y᫰`5jC fb]""߯?r;_~Ǐ(G&-NlOgW[ӷl|^lPzU7'U=O&Æ9h]A5sAa-#}Z=p<9^_<{b#+X7}lZl~ȫ&G_U[X/XtMEGĜuɅ_|{C9o`S~A)YX=޸'7nΰcJq]olY (~l&vލ }l(%XO*!&`L˟rϳZ&p #p}kI܋'Ock=T\VƘ%q{.^S'kRE`D&`D4 EJiи=dmSZA{I S?0"L%Jڑq#J)T,ayc5SqxCL-)Gⅽѻw0a;̧LZGO9y9!TdD<<݀4X2% %ancGJϑn⌖r}O _$]ѩH ˈ #r,DFB“Nv1**!ab|=1ZJdQj`J:VBZiOa/OrPB/T#NgRBdtu4ӑ.dωnm})#[z4 hg*<ߩ4"^TҩQYSW:׾?9Pt+9jL cVf6{GQ4A1:GfkӬ&:gu$`#}~s,:wǡ/ƝNF6Ncex[n6%q\%aR b[^%D\k Zs1\cKm WPlӯV:|.eXOZ>626?U7,Wo1î'ck=_]~1^nyc=[y5Oœds:Hgovz(fiy#]b1%p5!zuTy!K?w` 8}t+`,[*,!Ml蹎˫eG9Ө[;kG>w%!{Yyџ/g [8Ƙ\oldAXv!=ZB+AQe lŠv"e@^mfB fNvۀ( l6@=ݤ֤HS~op~dw="Sūlӽe󰾩!olWXw?W80Va['9(ܻwt+2oI԰d5}>`ƛĽr-OjŧcC7?{ʿ*hC&` g6 ~.`rsAOMr'؞A ֶl| Kx1n vW`=I;^wAvدaA/O3-KO܍# VggXR<9"?Ivw{#o\Oy1`'*_ۤʦk=#owm#X̗\.+c켟_LOV>I` 4PO6\cEOZ/0p+9;_/6P޵^_uc-60=zlo$' )(uHI5heųmN"Sb@K'A jqM-2<3צ\=2gYm,V-Y׻ifix ZꧻRxdR욝#[8IsdҎt-F+Y _xf^gi&=O']e1C1aӑ92rW2ŎZMkgT!{=wai"]Ɉ0[kF.0>nU|zyjGWӾ'H[ r/i4?r9.&|htZ#'#;2 &;d. rGG&$nK᧩`4喯CUxyP&I :HA 3:ڙ&\Iy"и-ۋ  ْ=d\ {Y'(HZ6AĀ) 5 xc]4rY~43pnրwܛE4feTN/c Xld3X%ԛKJb<(=8,0uP{'wy{dOjZO؉%ǎov}JF*5g9$Il9GkUML; !^1KɰMX${a MQaCa V(<ǰcx !VC8{6hqt??:/ȀD=]P[ AݾWCa/=vl?$~pD|>ڿ;{a[Al6}=Qt F814"BLF!D3>Cø՝“HN"ODz2*MrdWxϑԕTG1JBD\}"+^tit;43*})~wOғGBؘ:K5D+͵yFy "Y!$T512/'AB;βOrUwuwԒeI$ؖ fF01D}3<660a a˲-YK*NϮ{becϧ>U>Z\kj26a}+uDW,N6P?hh68 9NP(tsJH _yPILSY{zptp1i$$v:[::?ihhh:bFG)cjM7qGq$!`h4EF 6kl PWTJC0]Hs4dEњJl t3!D`gB.\B[P'1Azs\Ziu]`?@h] w5_ 1rk_V+~N:\ \D?byښlϛ)yse4)XZ1}[9oW\~7f go݁>jЛqfMmt,qq)v E/)h]ZˎA^Kj?iXZ-LNPmo _ގs\S}N`٢T鹤ޅ Y- n#~]ח p'W X7h= /d)1*v'ُϩwIv}w[ݍlV=Pon-][_%lNז?SDOۇpqzu7=)"¥Q7G+;BM5K ՟I/697]{Es3sUj΁]IDʂ˸ -"hi {Ojan\Yzv_?Zڃ.{F8EAFAAFi,ID7o:v4ҋzAWnQRe=#iXԤ>$ GϣMnVvբHq@W4lѦV!/l"Ն6}M_ KY]8Uo6)ʍ`zsK%.!UBMaNvPQ۠el?,"znK8]L|S^i/`oLgjkg!8 i\_ˀ?ۦ&}݋S[R9[k[n';QWx6q렿Nov⴯-+[|Σ1GfT֬|?NKJZRX83;S'p%nn^>KҎtT*D4i!\Eܽ0րdE^ۚ7G#e#攭uN崣nvJm٦9&\7۩ [w}3ؽF\XQQ1qlJN5#AZj8>jҫ*XfzP -@;nR@*IPfVC+kopr.GsOI3#,>}Q;eT89jn)$,ۆǚ+ :-;@acKƉ&{RRSsŁn3bE+F; ]P[uYĶcQ*ʊ,ڄv*F˦FaCEK]&P"] IR32sU%##FD PaHZČ q9nj*X~]D[/MԮluQ ZamAaR{HjP\Ⓛ+#%b4zHCvh$auŷK7>t~ý{'u\wqo]pumD׿ pwxk{&6{2{6ݞ-\7]2 %. k}&pFO7O}=N) }IEr-pv@h4Bz1`<[t ΅p*z!9N[Ӣ[<8R}> ex8Ay[f=|ɲ*4y$A|29Q>S_k܉ve<>^-[ʢRb;zj-b m;vqڭ2D>kp [lmv8k yf{\~g{7(NHg p59 b7BkĶgi3&MG#ň&Ϩ)7k*tOq=f✉Փk)ldXȽբ I ҠQN|/t`RU'W˝?&6480c( SrjUru`D4Z-,ѱyWii*jFR꒦O>Tm±jƢ@+Q80ܕfw/,U>LJ۩jU U.U-(V*kIo#*a!d1&\'<*A#ffl{W1v+ % *X%9&]Ɍb-"9YfNӢ5:k`H:̠a) %*~x󠌘xsCw{֫?ͦ۲3j|Ksqǻ O-ZlD__eX4S8!KOS1w4pBfk۰孏\V3`okӋ;N,WϟK}go0~uݩ~ WL->~?4O[kc" \q MMp_n,Y!km&"?S.a6')Pe {vgs֢g?; RS639n+cxRe ٟ!ks./S=+ 4GY$qNńu"BD͔nJD9[FTUSku9!NԠSxHBݦG>^_|yh>,Y 1 Yz.*jRƟjYA,(2zFbx2ʺСPfWۭJ+ bemjׂOZ֚k vD9'7;nia؛1X D3dK]U#K]VNeaA: i$tL|G:sǶ3^CK,*Ϩ$y"aBiXB$!aBK*DvD:tV Fb1T3A+5RnЊ: :^XZ$EaV,u-\,dE/H~mbLɌsF嗹_b qS,ٽXy&2s>/^^]s׿W?-  u?kx6s$n.fmٛ : IDATv>-zƒyoٛƛ_+䊭pBxW༯g^^O?~ȍ7|xXGq/LH hw; ꓸ [Ǎၾ0kzuW!\0qmR)ӛ6+)[\lY\67}=aܦO 3hp/q:a ^~˝wJP]DׂxuLo~ \7''q"2*ڍdMD={;oΟH"8T?[q.o94ؗ{䳴݊clw#zC1 `qn߅Kk~E,gImNt|Gx5/m/G x@ëkep-Zcm݉,ӜB0bd)ݫ,)^Z:H:b90Qd#VT"br(2)E<+`(&Q`.'0z( X穯&PH "lQɍEd, ƂЪnjBh%4556clz3ZϏ_O;U'*酚"QF.Xx\R{r>Qbm)beE WA,S&V)CS!GV,.vG_&Ik1EI;[I%VC:Y\f-8J*yb!!m[$6"IOWC0&'WP[XϡM-[EUWq I(L3_W@JR5$ a+68RgDEihgZŜ&03dս.HL{W+uW|uH^蚋Cx̜g~3?3:t"ړkvn#N;^Az?k/}@3Wgp/2yy!G3Wɷd-7E~)> )vYܳᲷl^VI܂Ы~KN7_촉3_T/W~A`s^br\pp7qosTl3L,g+^q+YY:spY|]ܡ4ٿ۽:S+`ߊT r8[W,l(ҳWq\L5n׋Y9NwQs{eC!.܏Sx Dp/]:mMqVCXx!ׯ `yp3>K_4|p|n?kޕ40o><8z$} حמ4lpg\83LP$J E:`$_KYKB.)$n\$d(^m,|O< Q،Bc%Q˩\ (Q L(qQ74')ЩMȇ/ w~'>h{m&E>qJI^9]  Z_Sk=!;T؇pYD:̮RjYRx|@06jf"jQ<+16'x^u?mϜ_?p`_8>52p6ZO"N_:^>sdzy88vaV 6'?~ {q/#y/;!Hqi|Q}h[l7U8<)gX|E߇sIK!z%_;op`ۍ8AػAzm/8x1ze@>++]~@2ڷb< Iӳm)kcǽù}WzmVeVc>Ǵ6惠|FZ>dO xk_+|1x<cwY8%AUߣiS4w))'|7=-[<ֶEJb{3<+Ls,/DYqL\Ǘً>1"ؗtY!}⒴e_0"I;5гge2& Ж{5!KrFj͎V\9 J΃өJ#Xw!g=>7z-D}+k2r#RbvѸH)%=n.Z." t(j.B ]j&x2Cwտ3 :|Z4>Ϟ*BI Dj2lQ sM(Ra%@-15&%J E ?HGqEEVy.eN9٪ڞV֕-GJXdY!t8+ҡ3"$Qٝ`p}M'1dNQ%V] 4Szѕ1b9XH;c )\D LKT\1Et-bxU *dC$7z <%j7;Ca*%Xxa\ .B^us_ > Y3 Jh~#g=k'[|\5m}zɛcǯo G2ව*vJ[ A>Ṹt"õBxp=NS0OX H"<}}TJw՝z|)6gݛw#S^:NgX橢3},pq?\5nڄ"Re[e\znn)" z ge߶F5lW[9y9Lł5{I\D Ho'~4j|x"w'/k|TRA~f*{C2^pPuJ -a$*3ZYRz ln(bZl Gqr;vW)%y9n[׿oS\]\HA/1UqasBЯ\WDDcdD/e{,>KTWyί-6kl57x;l}ơ4`\.țkSU†@:&-bc6 EV)Pc$ HvJ2;J{Ҝ`Li uBfNnP`eі" eZz8OlkDZV.'H0Ri-&v:SK__ȩWfdz2<ƪ%r&9xg AKAw\dOn>+#辬W[~cIC8|9©TI!^U$JUͱa^EԈm%صbR᲍U!:y|PB EC~ܬt鬧mmZM :SI *+<2.B5lՄN@QEQt"RlhUXcITb$IXzpyqc'=\'bH <)eU#-ۦ/at; &/ȕ}yoWk`D@e2'Jٛ6,YK_7~TN4*m}vT`wϛ} Թg9y=2lX~0[܏N.{2V<[s {!?vH!tVck5d8Nq2""K}{ہww˷?gr0~ xԯ9_ wXj\pɑvH9zꇞi@t}9]ݗįz>@^Qe+v\Rgˑ~xj|>{4yB7f`mUAvcK!B@E TJ$0@nK鴻*5'5U +vϯH;'VfԶ=s"Ʌb_.5X+jN=^1:Ye!Eo&qoSh2aC8Yƨ]]BtT?xmrT;Վ&oxS功Z&T=OK H聿FHrŠخCsy(kXAr Rb֚@ b\MQVSVBX22*+:,D5r1DeLNP 0 HD^j#In;IUni$0~*cPIuӊ2wZk1+MlʭS}CwP}Hh4<3Ǹ9 Jς/Cz{ /NoY=5pc8eыp wmy $\O0(C ЊƠ0(Hfdeը>:Ut K%˘' ERZ-6#7pʏxm&:7 nDKJi+S(*d]?o~ۍ7ا7?c".ҋp~x+NiA.`﹡czp<ΙYp8"g>}&8KXbD?k+.^߶yv dڙxZhx{|Oگ5٠3dg{1.Ĺ+s8[?Q65,n?H1:Z42qsRywn'N@fEw&mM5v^^D/<랥/q'jӓ^˛x0R5f$JGm͵+wF>Խsƫƪ M֛f 316іAXڸ:\j!Bw(mt6DtS!DjVqmF)E#]lf=$^Y'>G, ${2Z3W(m4(!t\8vAs ocK~aL>"k9EG1CCZl5e@Fֵn"PdHr3LR-Đd=7~},357.Z^q4aR!J'fL,N(*b81t 2EV0?!1ZZ۠XG+HZQ-%WJA6- qdVfUhӢF݄,UV4i1VL>=oY5QW*VԷn^v¯U9_X9Bw_ VQ9PExOOˀJbppcMO\q7j+u\Hpu&b+&ET}eh:LsǛRUz2SFh= s!q~~i?ߗOK&lv6+=~!܇Q,.Ԉ剚__OPSm+dAxn27|?kWg mVڟ.}ow/6Kq@qg%4=Շփ.810&D 53LaQBVͩ5hmMyBx:*d!μ.Bx-*Cz҂$& 4X]LJjS:j's*ኑdtC6L˥ys'Sbg#E )[ߖDd~\amxK >YN[O,p@ۻq +8'߅C>|7w*᾿FeCipڻtpS1#7~LbX3Џipw;N{1NK."ĹnIkqeU8@CX({E}esE8 B,=jLqa*ޖKIƸR} og-(օ&%VU^TCWc/d6B""QeEUש[mT"X95p(<- lm/6;L13tu8p;xA8^CzLas̢MhƘfkX:F <|?mϽz=jqbXL-a`:eY J[Ys'RIO8`t*vT`r@Rd [ux:Q^xf);U8luq}%0͜"SK%:"Fe*hP=FY3#(1D*AK%!CP#ﴊZ31j/|pC,@-jޭ)\P[_5Ԋ[j!pODʤ,DtqW7"ܷګT%dY*ɶlc˻1{1m4,qO)10 4YҘ, xeY%TRUY|"xQ̪,d瓟|۽7n܈9wdI7)M5ŪEN +}\UV 6rw׷uјK!nTteTFg9]`2a}~ SEC(!pt7k7kfOIu/wb'=S?6{ؽfN_`n=w9$k a! :p &yhP<>5ErCSJ]*r/֢'Vv:Shld6Y(zW*ji 9'ԫzTɔ䰈ĺsֳ5/fUZ ]]m/M%'/Wv:1F v^Ռƛ=ZQDLJRٴidoCغE$".~âE pDti\NAy2cYW)UMFCbS2Ce:( J:LjF?ʑ#9Jɗ[${9qN[t (iJ2cq6ϵC)A/1hsW*%gbs7g*?>;lf,f YbK6G¡nw[$փymb5D@G/恻+Y;?~}^9vGϺ։tXk`_οBvWbM){e!cN`96v%x{o.Mhg|.!3WS;a:" M{ q1Tg1, v7_ƀ-&#:Dcj@ 'PSN(<1~ LHS̟h0HDXdSA+E5-Bȣ sꕈ'@/' d:!! @ P)"`}J%۲lUYt@d=1eN!usu5<`ZRD,6"ΧY4#\:D=1|}kݳٙ6h!]ݹ<`Iy[.9g_^ 6\7a{9RN'ߏc1ҡuя_FkvؼX=qRoF;3, e !>]*X+r9{L*4e09WBMG47a؁sY:IU0%ib, 3vjftRM LKl{XKݶHhg=sV@8c r$O R21g+.}3Va6La]=zз_@I!/ e6 v{bݜ5כiIGC~* ;kukןt_Uy}A䧿hr_Qp]`r͎V?9%+@ r0[AIAKT'+su"D{=p:t"H_ofr@tX/T:\FzN1ݹ}F2?-M@Gb Sܤ&O&712g*,zJo?zqZ3eHoՉzB0Z==q tlGY=\H_0yކ_;dQ+=1$aA 0y! l eDZx H /@QhBZ3)AQ#D=TWZWڔD ռ3y_𙿞|<1"mo}~2C":~bLAK( !XbǺ=fқVXϕ Vۀ9mN]v\O`N ٮ|_~lpw\o:>Q C?@t͖g]"18MxM-tzElϿeљz݆)Nf#TM266-2wb-k{v`mu:'zw;t>9~=zSe]?ٰlu+ =:,E!>ki^$,Sx=G[q k(V e3X S,vvk_&|+B5|1J7Wxa_ !~ K^Ss$)n<\23(T0&""u()ÒDuՈՈ&o\ hJ!+qGurmJPJlda MN+ +R$LLvWj IDATT=Y BVa?@q 1z1/f=yػ*|΂f5gq>؆3~v XC}*'vSbcMLW]XMع6,]ս)lۖ)!ğ`z]2]~CG_}:xAq"R7=|GʲVx%lv#ThNjxxf4Ӕ_Nsa4 |)#%R*͋ڏ7>V; WX %x̥t0屘 w 遪B!({EV3 P)cO=*V>+JW$ ;()y̟Zj=z/L'ZLWTCv ^Ko}d zڃچЋPO[q#4-hRЉΛOV\UUh˒K&_N`mЌzba#E0k50CLBf̙@<n C3Jhph iMHB/5ʢՑ+8U)aRLdYV˕(\2ZuN4xYC<_IAgJD<&% 6CSd- yӌ,(`[&%# chᓒ"D "&T- s ܥ-VNDdH0ntw+Ax܄^VLE:Viҳ>n, !t۲X!G_1M7BkqSg\8AvX->/x3u~[Cw?Z]_oX;&ަ8iahSln*)Q?Yh„e{ƸKW S0WR,j25cU'sVLl|E8BL9BӢJ~iK`.*TYZ券/$Z˴;m2*Th 8A**:CKL,:>~?z&߸е}bsWI|Ny,v XkzuHW^ hj:[惵?}rބͲ:kLnR80=zTƧϥ:^*l=[;e )VJ'Vm(u]mv\{6: p}ޙH\/㺅U>}xJmB܏aJJ^gU8<5_<{Xe"16M*!oƙIl 0\ T ^2%o A:R\_o y =}oؼلw\9&JhPGߔZMW(Q(Sa9lH2r#Dŧ~eWG^Tv(](&7g2y`rL(9Uv6I[دRM(h7aC4F"O:ϕ$ZXHT:L\E"MY2y-jL;7aj>Nɓf&FCƦ&_#Q#)H.{c )%(! |EShP$9ƀn|IiN ]kLx2!.WgĢYYzIP2ʊ?vhH޹s})q2avڲ ͊Ln03Z[YSm&). sxJ̘Y&D 8GdSf2a,cEHzrkPTً|&S5ViP0M2vs}g7;( X#TT]$(NEF; 7Y5T^~~y,³vz2: yWْ0lV}l>5/`KRx==;ܢp^M$v/<?Ͱ~e6:ߙar2;‰/uHq}FPʃg|G~^3t(:sU_@Wb}uP`-ӣ>FlTZIr@3Gu?ӽ0[<ҟ)W3ؾ泞*Zw%FB6zavsM9 kY Xk v8\5WkA/=Wb3f#gqs;k(nT^`YB\Gq߂Wtt(=.G|f#.\%_`s.,1@ yG~\ ÈC]V$a?|~/:S*'>}uaw#աG~:IRן?e79ǯs96+z{2]]8fc/ܽʑ)]|m1yF쥍rB\_8¯<ʢ"vuL6T\Y1e?5*yvnW*!)yCz0 I}϶8OmkBOg*favﲸpS[/%,e|C^xCXWR6{ ރvѫBb>7>w)M1\w^2~*4\% \ybg1/0dؼYrMd5EΥLxe*Mǔ5f'@y%ʺʐȔui֛"iL _*$1duR(^ 3H8C%bHNPCaPghDW+K{NŸ:*0%-AЎ27kBJ[Yk1JTv9XcA2aÕą6jʠV&BZoetVvƟ4:!S#b:r,Cc2A[Bd謝LD2^@BGf(` e t/#l)ð@Խʵ׷v3G7^זX\w_/rzֽ_ $9 BT% []lY\ 5|jl!u8D/}cݸIgy0%2K-lro]`8g8q> yA~0 b_Y Lq}UlvYKT(wR$U&#GM5jvR!KBN2f?5dNN@(+ThyJ 21eոr^c ,"A 'AcXD_)?Wī'۴wrt Z!D,fs|+c<,z\f=iȧ0Rw;7%6ȴVGg;(^ptf'RM*yC6 zvA0yFz% %  Ic2!5Zj^f) !PLn0 bDfDgpTZ96#q(?4R-x$X |8y̐YZCȹQӣ)$*qďƳ"J=xY/h0LA !!d!.0N΢n+vD+ѡ!ꦩXb'>J7 Q݊}]ZcM+)usQY\;Ǡg_.4XoFvn݆#\[k4;ŢD/ֳޣ.:=2<ݙs/16uPq~e%rJsaqt}]ul6o?&z?_NTf]^A/ 6Q?K{=w=IUV&z qgVno5S.g$-b;X A[yAay՗^mjY4abj4XE4#1&؁@СD HS"$AjCÔ[eH"0+EY@C`^Bσ3C I ,YA^rޭS/)do<;/ TB4ڠ^. b bA,=FEbs F i^@)|q%h!R)㛔)?Q\NZ,;djlUjG#+&U݇ino ?5⭿f`$P{PXxN+x1jBGNe43BB$_!XfT1%^$) iDsP=sXg'AvT)uh]vzQ$\`U`_3 s3i|[gN>ܾg7J/?;~\lrKo(_!Ux }bKlVXE?-03h2?oe#6Ff;vy0E-MuU}|o+ϕyk0VseBw2X>QۖC=螎x5*ڹծ_Y`|%zi\t{˯>ӽmZwM~"kaOP f,Z٬Kpcy\sb0p-~f+kOKXe?"XL7vB| *ơ@ۨFWv"_OaF|Xz'BHl cƤUFxiGTB.S} dfz88Ovb2/ ɯv-?baؼ]kthcK6*!rr!2"'ǘM6M24%BTENёy" RxB" E{@J$uu=-^-;C$աZ㦝yI.W!V`R +eJfײ/7B&o3xyi&TU h,J HhC 0Tf5o =ga^ /gmv!1"! T@+/:K;!j9d_K̮>;'gcէo8p+=|ojl{rUhԵ[g';:>יKJe#񓢵6E J7JyXCM6C;ˋ P!i씟sP3^K׀eorul.܁6X wz^g uI]\ lRo8yN.Lv{̒e)1MkbWsc7lO`v_NilR6l悘7w|R`kqi$:/KN+Q}=؅Ls씨m}cc4u-fp t XJ>3Xg?>0-{#v5K8w\.c-qF`WQ#`=7pFUؘYb˃s4~ n.)͏a'%ld9+ ߠ-ycqa&BWi1~f0DJG =$$ (SC|b*H< Qtk R #^*dh (9*(Hщ*<#Z TWM.@ti @dV;Z:JڲE:.U؋ IDATT!v^PZ7QEV9^/k$\o,5ʻ35A\G&Dq{~Q SVK$M"! wxQu˜*zkaҧJ):TB Z(*X|}5O V&!-UJMP,;12d9PP7b"[*[\.f4ēBԓQ \c,ZBq_GzY΅uJMkӂΔ*FC]x/12ag0Bi, OE9I ڐHv5j<^Ɛhx"JRPcXd<ɿe b#P=ދXaB>Dd"b:{xt_t3yAmϝٝrE~n4g_*C)oC7 J(m_Rb6C}w?[޾&n;}WGxԯokŏ`k -l2~lv׃g%,69 F,[j+ KJ}CsY X/+}=~\r^gaVǩm&ns߿ >x=gp 2o7~wv^Α!`-A__BoCݶ]vEćʂWy4&-B\M鍇"Ӄnv'͠gg;r=:e %v&1fMq?fako9jg s}XCv_7H0+X *wc5!Ե(V>Xu?e41B~˦762 ϒoyP(גY_,hHR#@h <P$4SEPC*x 3$HL( 2Y';H  T0^I.XƠE`S%,4ѦwR ?(R揮o|Iy~gdﭚdyjBhD)u3Oikcc[6U :FlSD\~&6L e`m%]޹'_lWJJPjGN(Ew!:gIX dhw?ƚ-wXc2M(m(@aNU-3!N VgcdFQd#V> z1 .ʓJy]ȩY6z}ȗ^ ^1BPwaFVyrt¸~dΰ'>`xI2"ݒUg "["ՆqX_lpV aX'1BF`53%{A$ ӻ;(.IZI )u!:ĜahN"HpBsR[_c7#oد;??_6Q c9&6;g+:_yӡ8#?K߶{0oԓ+ҵt_@@t'/d2|_h3E1džG?Ɩw{]9^>mw9;96b aA9Q/B6KyzkQzE6szD`_NⷙWn7y]) 3w{];60x mױ(b v"n6دxW΃t}3ksZcileX^ Քpv& bƘ⏱޼uS8J]j c4ݿoL[y Xef$O zQqy8x;6c !& ۰zbiϻ U> /[<{ܺi[Dm,5.b@A =XS)Ujf*((H&&Etw McHvX%5(_{1m6l 5ĻZiHVm4+%MhN{Q-|RQk2YẌ́1Qݮ|fe,v4RwF}u{o;rv]~E_6fC}EQT@\ZWK/?{ogU~zv]= $A(#sš+h<\r *QA BtT]U]s]{|kU՝$ԧ~Yg=ʭMAWb Wl.n^d-J$6cbtT t,DU9Ya^t@(-Fu]?23.lnrzE6i AqFldI}NJQضGiYioreR T\nKI-J I,I*[_nzr:ˬ1odc+$ avx~B|Za+9y Ǭ6wcR+;866_l]ra֒1.Ёo~4  ;YB~ SG6ug0PvV"NkQ켁.E.'šg0m fo&tsӯ֐ZzYoK^]`&e'VKLS"ԕ^5}5}ӾK/S"M?J¤o!\Nj#Ba n~ (b"Đ;^)1BaH\}gUag|Jf/u8m1I]m&2 w\u\6>p㺾 %/ܿyh[UJ[l,K cJ5'ݼ*J 6. jDdj%|4 XV*J) ,IlM¦ HZZ ʄEg%pXZ* Ρm[4,ı!cJVS|M-h%wGr.~oW/(z$P$k&I XP3&K*,&2,q,X6+u*,[ 2rsH!E`ihBE-!U:LRjbW[ZӜ= I kT? }yVgs ʪ^=$Ntr>a%׭9>H}rOVe4cj4; /X <}Ys|+S$JY3sLj39ęX.Q|IMGe/ϼV֛9ZvDA̰:@Jy3~ݛTE)PvJ831%@|%aXGM="R}N'}6QӓV'KP/3O >s|;8ǗGٓo.%|͡33 }D\GxE;[|e-ZN! Km29?b9-.fVԡ^mULog 2§>{O"8I{DLj?hˍ].e)Ұ<5tބ+t3w':umLэQ@}Ha/a齌c,'ǗTqØE.[0T`R).>ڤ2="~UESJٵJ+5KoawgVBenI!KR`@ރ]6b6qDiLm8-VXHJ[ꮿԵJ]j,!ZŬv"<sn,:▽wp&5PƐdc8lPՠ! %8,*V$'J bQ4r*B(AhŰz}_M?ϖ9(ح]c/DKs+`=V`B+ζ~)Vgm+XKC4Pa=/blz7&s]5)N!v1D񻹸ڕb9,u.;3d:n7fJWI˶I#2k% )IMr K ؕlrx3&'0Mkt^ #{^l̳FGZGBnL%%ȯJ)!LE&oMOsvv6..3kB!!71̏`?=qW['Z[OXRBI6?RȒHDl,\%b+e%ꋳDiT.-!h4ehQЖ"Ybh0O-:AŨ^>?XH 6gWB8fi+RDSN'Ķڵ vUC>?8wK{#_pmm5:z aL-ZM1>+ oSvD=?r@W$eMZ*xyv¶“0ϖ$sIk vI"}fqFDs /xM_mgS$MWQ&`̣#px֍۲T_ٱ]LgFlǓ=NoV8=O-]86˅#57ZD7Flu>ExEJwZa̽1, |&rr>|hKF755#tCӢ=6RNc"kA;#uL:㚡{z-鯜+$r1h6LȒ6pɴI#C&I?J@0-a*,,rD$ >R;:.^S:vKA""(EbڨtmbRZDCH;E"oHBG X  ^z6h1޽gW|g}Fi_+I˜B{j-xDu*ST{B4"Q%V\%I~%c7Nt$eH2uD5FslC&$^cN.hS%1WlQ}އڷ,idyۋ c6ltNnt IDATFf׻iˣkơx}m @w07dS\n3*Nۯh^6z1,WL!X"c;>bvK5iw,Kʎ79!0=r;ҔI C^QLzlic*|zW{hĚ }¤HD^"J-.w;LMޏQJjj\T%uvg^uJC֝CWU2wsBQ*#lnNnlدkѣJp5I֝ u; w wrW+}1O5n ,A9WaZbpJ֋u# Y^?(ͮqЬ©c8:8a3GJۨX%FNA&=Mp6b^t9IL!Ə S(#BP`]$n{YB:ra:'3Уa;䍟pz"_uPqjS.tMhB6W3AA nML&M+I;T|l| ca)`lsp9hPI+ I-O-b*x\c\꿳@'abyyp52VTJdyiJ-{Çj4Feꭘ5/rEJb̃z8Mb )6V>W4ǽV]^>SocUXWxF?Yk= | L~\_A\ [>@Ej.{8\,`j0.ZGB} Swbk/5~l x1ؑqTy~%ۼܶ˒#A$!m tL"B&zqacHBi,U6vhb|O#F1LhIq9,O@m>oFFN ²Ec<+jCf(" efwLƒFbWw/̛O]]jg3qݸh$^!~\Ypmn5#|CϨ{~d}<40߄y79Z:VFXB8N{$J),Znȴ4M>oާ/D_HUfW]վX[d 'ɸu[$4_ $X :N(IȊ:MO+y2 _޺EӘUeة;H)d2ͮ]# n=M)l.T dy&CwWI11DLnx0 n|㵜刳>HCs2zkhIM~C߈_O48̩=z^dM`K';t놻A?5љ|A_ 5=%,DB k=m=wFv9s &8 VtJ5Mgl<KН`hj//͝\Z=L0ik!QE-*-Mz'zte}!I -bTc8;<^8æǀ;ω!6h;&"*m}''5 %q$9ЯϢ$tE&(Cj:Q>fgck2xm=ףc]nW}'?ÐmK]&r>} zXjH4&[') ;]hϒi/!F0|Ru9ȀΒ(~CVObl9k}?9#`͜)1ƧQxf9<:#{ci_ѴY"TiyC_ᵋ]}u-$G:^,5RWD:\`-f݃ix4A|Lmcs`i?"BayJ J">%K$#]L񂕟Z_&y)wwQ"S*D1:$0>O"yjmZoǚU JhFXⲍF({?ۣLjDDbOHz8 : A(>]>#9ء^(6ql?-fq57xIZ62=yy4*{G_s [wG.ĞJt R*H$|vk,}e'$¡`Tlbg4=4r%&XluC9E ͓M^*,(gKO+֛sN8' < VCջjq0*()n);Ut&=wzͥŽ0OuJ½B{1D7b߳[G6ȸ!dȸ8vT{~4Oyi؟BVc'(c0ʶa׀lV/O}ߖm~A(M?Y<&53ǜDjj Cx㳴c0Up\3"+}&\ŸeMDp$q:aP<[0ё`/zӫ!_PɓT[0њn^{b竉KC] [+;myoXZ"uwK'~1Qsb;c.wLߧB AqZ:bx_ϵ'9dFD#?j( Ա}=巪' Mo:ҝg3N6ٿ.91sҒHldӠN:uD8x:KYK{jP,l<QJB%#؉H!BSx! [Ys%e8:\HYPG ϶|hآ [g.;ziB 7`tUo~ϯ~f0/ YP*#,-V zXTRNio~3,KGMB7H坖ۖ IP`  6kSχ &^Ř~9FngAGEqP]= 2j!jvA\`/n4cpLRKI<;BeMkvmVm䇫lr+ܔ;OeB[(TW!2'7Ω;PO!"zdRSEZ^MqKjcT챿Ř3nm#%`Fjq?ghg72oK;#r*~!Kӣ с/o:zUie޸z_l}b ޮ@k]B,Ķ;fA9r_, hK@Y R̝R)IJV[/2WCh4ЕDxl»t,c}Ę,E]ZB2FL$Z]C+K)x)K S<YֺK~ JRGHg=agqyv`&j[4tuB/#ZeKg L'XzZ7o>I}aulmV[k}8!0cC0Z_vĈm> |Ʊx 8gw[ؿ*q!iTQp&T$x49ZmKÝVh=}YUk:UB1D)#ODSD'E<BZh$B-IKe[6'|HvM2vX-;#uq9Q0*;2I/*N(3ĩu]Z{旞aoŽ\ v`v\ x+D)|eso 8 QCM'5t vHp[[dGtuaϰ`>zyMIdDbkkـH0p$O"d iӌ;(qX@al)Y3--!&u\u,?˦ ޾fO3΁ʦblڣ{qt/n6*ЫADDe$2&&C 6GKbkƉDt>y\<"Bl,HH\5^ )*KY9&2Ym-MZẂ*g*emUcA$ѹHF=b"= _}Bkk1޼KObuClde0<ܤ fJK-y)JCfHX˺c[Ɇ-Gxv0s楾VV`²/>+HF[7XZ:L{v Q.,Pjecr8Ѣ(.f. ;R`W=T#V [L GZɘF}UonϵئJyKŽZ=?xB+Wqr#P'hn1KMUEȂmd&OV=Wm蓌'ت of#yQc8ijnC,Ʒ !b i\j\*0󟷣1'+{|p% 1rsmڸRgK*>߄jo nZl$ x\,BBb"Džm9v 1#Py_QZ7r/ij/hMx# gc"3oALYrx"$[B`'ʃlFD^H|%C vr[8g%U~KD @H4P т$D.|Cv.`y d T)TxV/rt|rwR;gЍg Y M;8Z$<Ѷ~TF罡Z9s7L1:\S5xbwOlX~Vm27%ҩX,Fg76E#7?OV-(H h"L@+B+ lO IDATn TPB%o8h$mY OX؜TEꆲԽ*~&c@l-PƓ:SUS戄B3dKa{nn&ǽ{{H1W$" 86ѯgp1zil@ W)L̨fi6G"l#F;.15AP*Jv1E?s=ztU2-iyΙ VyxձQߊzɓ271 wL5|'ޕ^?>WuF]Lpg^py7akJޞރOϖQ!iuҎ} $^}BhvߚЮk!D~mB_Z_Wu??H=+C q7fbӒ;5I$x Nـ h[X8H$`*Bi HE"rb\lx@'[Khdm`6"|g( AD VD5E򥰴JD,=io@)E ]wN;8QulEn='!3|k݌|Ѣ)[!A_Ʊ=QGL"ӠLeO}qD#Z0hZ $[,e.ᰑ[0 +='t V Aa -2$%cB`&$DT: 5Lir'\<츁! KXT1NDTdG&V TnB/s!:@v#8+ĀF*`aY,ЏTM2OL0o`"K.0`pG2dR`Zm`~ bco~*u YO1ΰMum$;M1|,ۏeE iTSX]'\HRv~{=>.-H4bj.N7ꏼņOtKs)~6~PA~eSJ"H"KQO;|qQEhр[{Sίu.8t+A^bm.-L藼k5^tdbhe(L3g01 K̀ Z5,;SOӤ4r~2QL)$1,֫T6 ],Oa"[Y2=Ƙ,j*;ǛF%Ӱ?1Fd ^&򛊧=;hhbiW ihzYLYkOnX}Y.MQ? B6fޚZ;?BEk]Cl%=7=?T;I"j]]qpZ;ё)a}u}6$J%4("K4eX iPC2zZ*qXfXT^7H K A; QnIK6*sOiN䴻% c*n"b./E:Xwl~<xޛ[zUuηn[9TP(* j-v??[& D#LT2VR/]͖5*v,ÖqxYEOVJ RqKa,> N)@U :aSؚCq˲}^?Zi΅  K@!l"M%BRRM06JF,!v|fl%0Jm&e<\6v8Y>3fܛ&W^=@]{#>\gV喢3RZlyׄ}m;7?{^=4zؓ~@SFzط9p蒪 e[C<K!hO;gZFsa5bA?WY-mIJfJKz4w$|24Ffz5I-YZmAGgF>CLc%`qKXm RB+~$v{:hܿQL%\K{d-[OXr{WS'k=8N}ah ~tztr?z,珵?~].tċB^H)MmvɲM.Id(tp҆nwNКӘ$ԳPb:FRfI1gHDt.eeT闕!]>h0Em"0Lq^.gA;RR1*~t;hzփGOmX9*bm!XC_䍪JY}eN6J ׉L!dJE8Kyџ`oz[O;:sb-:V$N Qu@,vt,bdWŴ:A HZYmbcСuXnQ=wGUx^XTX2=h T0l!5 ͌Kj4LD[/oR3یFc@@qszݓ0E>%ՅX6 ϩa{A ^Mq W y'V>C2t)"Z/A }LSLʭQ[䝐BEt|'~fl&,FJ1Q7u"U>yH7aR(W[ͥM4 i+o+q|L|]Mw=>X)کn3*k(SX7坓V7қ;|4v=辬/:}' Rz?9&P?p3:$ 5o?[Cs~R\mVy/r!hch馿=}ö%8mltdjJ TRWcXMXItֵ*;2.4v%Vk&'$[Sw'*UM!I>{q}I7&HZBПM g2^ YgVv=%gK<%^BlD4\m{9LwBRXnEyszb=PcevUpj7G@ݕ?gxԓOWl1\Fd}zdH.xs%1-E:ٮ Df)Gܚ޹wj*~ ]=;h6 w>ł2*5'D4-NS4aMA%7Zx 'V|(͙|g[cRHe(Jv|nrxʎC]'VųN@lLb-`E 7ڹvR;UDv~CpA}=!B|M|"cyw!RR4mL P~ۀOteI&Vgewi*j# ȊnjkNi5HۃT_!_lAd1hFx%Jm\.c31X18*p%ݧb9e4'7F2)̐. %FeXT,@VrX2M6 L fȐvRuP,3jLR^UfsR8Щ܌%cRS >>Nl'ڰL'5g_ 2$ QGȴ3jQd pЂ= !nvO%NB W{s%TҤiwL@KhR<-tۊ\ g -^rф 4VFF/<|jP zz}~%?|tJ|(6M&V[VS6뮅%J]DZ /6H 舵*B>@%"&4T?J f4oA1IWc#0Lj}elV`,S0\J-7TFV#ʹjC6FTzV;g6)iVU0WX㌷Ν] [px gd#m]֧#e5iQ3)'_`xnQJ`HM~IЦe c.N%$h bm0<̺MQ))ZL83lG4CGY.Twz0jV#X'[#6$ ۣ(1eҩxAlM,ndL]kG,`z'%ÉL#!fZNU踫 }FYt:8062 QUU+'ebiSX"$$lwTDx 6m_`c?UEj9 FbtZ4Ȁ5GRb_")d Dg-6c7nJ!-?SPVCV uFΉpYnW}_knF⮽Iy w=|﹓dݟn[)W9t9>NtFҁ{huTp~6'%Y?͟|.bċ,vw=^'d !? mJ"Y7RMtkW֫KImmr~;I0="т#Mbw?h{ُ^=юatyM߈s vD!#DG{$Duy O(Mp9ċZSc yhz.I?$WIPN1mkR!s=*ntNoGFh^Bl} twT)Un;=W9\8=^|=? XD~t5qq+R~Wvh]߫=;@a4X8RB֍o Y3v>ې]x<#0ET &{t]l Y cP̀# g5 GL\s fBH5.#a 3"*vїڲ|cE;ϥƎ(F@WoyTƾ3ըFFgkv*xofvAv`ekUE4 לl9.5SQ؉RO7O6aJ!(,0;"2'jvla ;PL[@l@ 6`{+gf,V;e1n0;`VΜ+ 5PLZLi1/o-2|AqdpP"¶A„PT9ը tSPbz412<ۍщYt.3 DeAtl(B'j)fFIF1q˲@ ]k?&b}3ܵ7S&`s$qRΪz7r/ro?sh~7޾|=|S-YDz@:)jƫЎ\SA<:urY%ȵ.ˍu#BjEu{LzS>OB; H~hv9&X D۞%&!?nB|i$Qs|m,.@"ےVDϑD7! uYqo#iM1" ]g{چ^ :ivhA%=^1˼4VheO ]?< vf.]yJ+?焁 6~@{Ö870X}HϏmh{;k|sP=uӷ朳Kw_~\hɚ9Ufl_1oE3}4Tղ;Ov$±l/?}?]ɞ?Zhow$Ahv^!☁4[R M@mֆ`)x9Y%X7VDҁ0u<EE]&hiaA65;}ԏ̸c"3~ּA}MxЊA,遪@XMXdFsj3# MxzD8|hKɡ#/'u=8M;V;8NI]6>iulY0)5qlŐM((Jx Zs&̯ AL*@ "'!0",K^ 9~ 3qhRQ'm~%B'[FƑ;A73noLȔ9qm]Ҷwn_k'&a8p1IAXX[kwk.a,e6z Xhw1|;ڷx}h\FauarQD߃G_x1Z .!ģݾ}=JRנf秈 ڀ@.'ByYN yY+|ҫxڋK`'qo=M-wГ|=7{ue0js?йzλ& ԅZO$xOѰ -M뽜c~/kOυ]k:kҚ Vy-{^' p%gVJM~4|ڎVY{ߜ%*߅S;!}腏?mGd IDATJ[s祎KJs=y.gMK5Fr=VF,N@ujC*^0!Xijرm$Mw>q\}ol5]OȪW9{=?25kiFja^2 S@@tJWĖ$ƹ4SWaʺ@L,bums@  bs]C=0HIWbEe Uqdd f%ckqZ^>~#7=nLGFeg =m?jߺƭl+SJ7 d>`ËG78Q-O4џz}"5۟l>0y^`7>h ;Ԍ&i#E;T-nf i@C/1D5cuHQv2%H6*}mcBҧUdXe̞BaUUe!)[,b>l LVJ 3cFaظ-~+4R'&s7= 5Ǜs|[v";31;s+oCuoYFw9U(V`H+YRXa(Lk4a;E4$]lZ J#+(Tʔ!Ԯ܀0yYlđpv'7(kY8ҔvOzg–ey/WʟGۚE.akoc,2)1ZSqKl~M'?k=w>?5~!@ IJh79Jh_]2@g6]) Mc_dw_)vk} w@=V ܇^"Mt$w"K9s{'aIdr >4^cYX6U4q̳~M2khd:}z*! tvw?{[O\)c1\NJ}|:^GԲw $A}X[|OiV?e?$+bYeqZa7c D?W(E]:`?:;mCs^y ^v*sW 9 Qm|MO?aѧ|Ta[c ?^x<y4o d+)m1-h/8YY٦(Zڭl\Zx'[XH`Y0zhUG>=еlXr 2τ ּXNvQќ#mTX[HG;*ذ,ǭsKol(3Ue߱sWd}þKOGc,L'^k(id Op68۶|.k 6Ϙ) BؤC[e@PXi|-i.6,:X)rT I">\+K-d@f0Km8fx%oIi1&:&NXPncFT”J_StB8B:4ila PJU,B;XBe[U3Dԑԗj,@SYc0l`M_ղ@@Ũ'@*x}o!S $?)Kb((Ʊ d)m$S7Q3]fGp[.QC,m |\MT ;!U1\(LJͭtQ ^/h|R;-Wzl2j4O6]^ 5`&ڿ밚~zmvG>6ǁ{td:PҞ @QlmVl,~ݯO[e|9uPJ= A 9<Mtj̦ ?)aHop9B'|hf*EťL >:` 6)*_}~t\nva=a41M3{RH! NG/m|߸.m/MqMD::q=oB6b8}HDņk?zF o|B{';*17RBG+0 |\BE T*\7U"_sv{sNѧ| W]#@rg~ޝ;W~7웟]2`sv6Y_ / msP1 `~ĵEvDNAʼR۬Жn'ps X1ڔ1Ę J&RthSX!!qK: ,QSКstܘS0ڔGkcȎsҪ/f_kOgP#^;e'g9}y];}-囇o1W{nי8뚯~7OV?]V%g<3/k\Nj;lx(hVӋSX4)!#<(+eɝ<~Tt0,]TR1j4q>ޓ>oqɚ2v! |V9KСzx^m*Nox)mٰ")|5+dMFl$Ov[VHk| qˌXrHd5hvJfkCкOuM\Ka&<z)k<"i2 QP]?-'|E(( @n6ܖBSmfl;sfM 25EE wU ^*#cdB͔Bh0Fr J.c=Lzj EZ,e!qݳ45C~/ E7-|dm>&M Ջ^۬X6Cܵrp6O,~ ^[0o?m3KĮ6'4x1EVC>cuQ]tˬFwrkLvzWЃc=J8?"%3{1:KV`5vm~BB.F`6zbgz飣_@{)LR{oE2W3l"x<Г1&&ws=qDSFb@ ;z5Bo$P=]+V\gI&Ym=&Co8{ԄTtu?b1A?[UJ|4UJ_!~L2jyszSNRί*aXܖ2 =neg|RM |d'Sb}ʼnP޷RNc`AfLU1]!%˶=kTa2מ]!c6N1@m0L[zQFuZRnb)J2*& ":げ!00@$" %AX@%EhDE1Ypk8T L;;)6R"41A0S[=`H|Eն~4=izQ{e^őNq{Y`n*+ /_uxx#?wd`!~' rSCukя\:eM&:]&6 Y&3@joBh'O6rN`x ۘ/q >J tcU 22:"ȰEhbfhFG5mL=F$QjujWBjѫ64z$fm`"@I-(kГhƭ6[Ϡ#c85mǽ3ۮ%_.YkZD%=8OD.5[Orp%5J|ޅeBKmh$ ܿz_#֏&+4YMzcw !>tME}BIkM_~ޘ灿!_FO7RE] =۴euJ*^(3 c#>t{x1ſ^jeHjӛso(aẉՉQigF<C;,n;15{>[q=ሊ8h7|rm YQY(`5Yqz,U,-/~XWKcyKxRyo`,t–-dݩ #EËjxtDDܞ1Cg}AOXC8QTR?†WbQ'%CU悦ǛrܣM /~H,K?58r5ye_صFDݼe/-Hvl_տ_S-_7I ^ad&+Z[  (\,aC4t20'0~ XM8*X4vdQޑm"b2~N֫rMN B@1f+^6Kj&]TZ[]2%Is~4v傲~vA)cC[ 8QvidΈq_h![}H%:|pYg+##TvElCetkM_4@*nHgJR.ڍ ,FYNuȫ*"9EP/JJD֏-cZUAjЕ\ 1)T4+XOD?[qt|wY.69zf>if)=F4-ji9b`{S'cTegBvG-TD& Zu0@DzOS7٢1@**JfЇĞLi"}\CAO(Fƀi(4hEI1nPPua n4q1fڻOOacL` )OT+&VW 5R&XBw !SG[l;%kN=tֳ۫?O|o֪VA6o/Ujmފ#^{C0@ym;ׁ`P^7qەRH{s^`n~>Ɓ{۷w>W˦]R|k}EE̪dYH۵\czѷ<%RXdU}QA;ݟ;E4c5 Bd&*!%iEDZ8{.iV:GO\+k&DwGzܛ'<ݶLJtato..IH3D*B[ }ߓa^|!"BV\"F'8FzHX<+ t ![)e[\^Ty=lck"mΣR>"CqtB/f~Sw !noB̡c4vً/<}\U\+5{." AbBA/?BYߺmOvvfRDhTa IS !pInU05=q*rFɘQߦDR4p(P)d YJZ@싸zΐ=g56ub32w:~nŐ(V mAˍ4\MU^TD M2W~[h`y9d .(Q#kePus0>Pi36H,[87pѲ\ZX>9~'ol,ݛ^ɾL-m|0[O;qw?wcg UmBR3~ScOAFzt6\-'*"S冑ҝGǞW1k+eFo7ǽ=͘˶q RTЮwRۧ@LH@$?ڙ7qC2#d\RB:@tN PPR\Ew+cUHTIX>\nk¾OC`!rp:P A뼸Mp!VdUg08o-0"fJY Q,5hb"rOFhU3*J 7Ɠ֎"Ss7W_ksmO7W6 ˆ×1_c6h|ewsHj9ޏpj~>41q!0_bx]ɡOw꒝GF@u~86\y:zr]Е> FW8b- ԓVD? dq7MJ!G$DGG>2<M]ۍnCaJBmV_znB^ݰ*tSۺZ$I5hadul@DFBlB !<4A uƪ%O\tޕ'Ԛ''lUXI8Εbz_H6QqIsaq2&х?}~&$Zo㙪f4QlUJёͧ{> y߁N^J厏৻Ϋ FO_~G|z5Z;@Ӈ7l>a"tOp˖RUL;2&ˈfwqM?Qr3FyqѬ(ѦAH'Cc f`*) Ck@e&ml7Zxv[j:㮕2m )v2668DDKZh}+;ln-VD_Wx".gp2>q \H[ /"xfűHî\4ɴM!=rS𾿮իV>L-_}P3So7>ucOkK|zk7[zFtum$c.ODafUXLwQ^ܲr?2^1R#U@zBC9`E2A?3:P\( IDAT9jc '[z릟81R{𩀳KBFtg'FjyC2O]ZyO Gk*3SYTG|΂G"I"Yk"ς(IՔ#JHn[9 'f=|oôJvR-(.@ ;Bj6af XU-L΃6 PkԈxZ "E_Ve:8e G*UQC=܆M_CɑvIcH7vN,z堀&r% K C \aXmF H|}m8[ЩIsJ-B߻Yn#hѡ*&0Ao;/MDr<4E+XS oNi{20W+%R/D-c|+Ω zdMϠhr]PJ5IzǺD=6p~ې3z UH /!D =/~=gm(A?'~^@/R}#gPP|LKbEff:=h&b'_lEQ4q #Wh]una*t޴le PCtms FZxmm)fnz|h2)fYtrymBZIf ̲X}E+-w>S)Ȯ]WoKX>tTHl~M8" [Ls=k$Rgo-,YjN/rwfގBϙj[;nn35Sm:O>;ZGFҭUk=syeUwyQnd[%YO  ādC8Hc /$ā(`!xP{5ؖj[=w<3?=}o[U-zz=O=Uu{wwkugX>;H+sqmSj*GoԖVbTM1Pf-OhYK35ÐauvN(uo^:츅N>v7-}WOݽtѿz׎3r0*ր=a00SbҗF%S+ʳlPEnem=< ih daqSjXJl^W.MB⢠\mO΃U #3fdO,8x ,N ? ۏC(Aw`p_5ƭ*V3}Dۯ*kad֢d.fF'`@m2jda,"J]'u.=.3:aSWᧉkIo߳{SZIجM%Qp|bSKK2-m._PLPdmN*G7"/صo`8?94jg`&eM\ÀaKU0@?zٯfΑGOc@bֺ]n~rx$# O0$yT]bھSt'bƄ-/5߾-q.i_GDqЩ.%c~Y=6REPC m-wp4LΦ+lrp l n L8HaYƣLD<~ܾ}h`ꦞ4 >ߜPf5Bc_iqoÙl ^ c`b(28؜e :CG됞? (~(W-R0i{ eLn6T UW'Y.]9JRI;>&G_teǼ[~˓{]򾟛xى+N/vUIFf ҢR^ ;@T1zEܒWf˩C#S3lyZuUxp(rȎnsGNv,-drXտMvͧKo.㷍O?˥v-ko}aol\ڪڏf)c$Mdڲ0[&[( -*{W1@1NMȁrm Nf) 0TӸǯ0ВW)aY&$ 6zVauG4|U0 ZCX6``lͧV"jER5RtDS8tD!"v>ߧWL ]HbXNVpJ6ΕbWPZ|鉶>wgN">6oeq7{1TDpnz-Wim _< magyn;^<!Ⱦ ƄRZk|C1jsL#Ij 1ݲ_+ `jw:ƘvL0۹8nн-ZQ[bNOs6K=z4\0x3.ch:`ww%FDn\L"^gܷm.1&ΧI$0}mXuq=`g<>v?s !^.mL\:kU|7bMGXky]a"waI^6'zmv|9 Ǚ|Q)/gsg1]?OY.$A tc_x˵7c(o>zqavls[&AR9dEBP|N!5b:29-N(lhFDeaslJFEM8d%W+z\I'_zU'|1=3.E>_ АA I[K:D =3W-ڋe I}"MX K(p5Pst1 "ud'sH *V-s NYlm{e6:srl֙VA{ekhٕЖHsSbV\Lv֖ëR-׋rKۢo;z#S|b}bmn5L 4Q!fwE]M)ϞfBBjq&NNfG A!3j-L՝zto8#Ouk )džSYGw VGKzHB怭uuęөyU:x*=UT>SE#_"p+1; PPɦ֨DcyY>CqVq([䥏3|ը`O-<<\P]8cg:PQn8_Fn"@'0d>`g>qphl,2M~tߜ"fJ&|WR i˜S_^Ҋ+w~/;s> j.7ׁOnr?+NH%By pNW 8^s8|3 ”X-bnR !އݍy(s.a1:#ox=Fp`֫ I} Fc%},?o{-LTic\|^q>~7 js]w٘hIC=s -X`!kj_wГZjc~^w߄^o~ׁPvaq?yWELd?x|:_~?q 6ݿD1:Xk-u |#9ym!;yFϷ>)s=c<)]SU̜N ?s?kc.,n6 oLԵvٞ7wTX?x#ܭlNN[@S S wMokWNiQvwIԣvIve1J[LS@gtq;'`8Y$FPSHoak'~=fU[;5ĭU3~CHA-!ZU#zU]#:ZS˄ kY|:AiitfHm:|TmiCwMq@)ѥJn6O/mKy96Z+rӶi-%Ij4t+zٔ:GJKt)/[*.5M>-ﱖhJ wR $YZ:R ߍlUY񳥁踱vdhM#:N5U}}cLzdcN<3?/;#Nڡ]'nw_Пڛ x&+V櫧>]|Ӊ+cClSv&"oQIWхBQ3]Tn9 HT&M% Axz #K현ki3؇7$:uV+Hg뾈SXvTƛk0TӤTQl8R'Sn+q1wFTefͤ-@B!X!Cg*ySAaFmb~"_e#`10&MDs{Fj{"pÛ<pnUcE#`"E=<'_)k`Cyϕ7֭g]?8M _m]3Tݷ˕־=kŰSadaƁCcY 1ǫYTx4 Bjx: ,9 ;[>s盎/.,fzz{HsdO վKj;b*> ~ ؕ8ђP@yJL8F)2%K-ZHo*%Xu*Ѕf$UKd˓zlʃu19<F$pY" %J9FG׼;x^tmZtLcf9lە^*(FlmV͎Hh++/`f}w WPM*1jʒD##bwҜͮQl,Hµ-qXj,G s['ukzN#T?6Zla&\@yvSd2A2YJʘm6~iEgNhUD 9u8s;!|afE_N,hϏ[SGI r" Ji< 0@BXuFpZP6Ia6;1VANOo:>#2'NL|[U~C{%t1~,Iz,8+7k4M7By 7\֣\ $4^czgS#KED[y~XPs✾O黆`ڧѣ|%?{)Q~ۜ| ~BDvbava(Z_*" ^4xDsJ?!<PcJsr01SW5vc0T_l\ӟz{cl7̾|zx}bՓ~4&'iwzY{A utO*C [ϩ!4QhWcuLGh`XGڤS |3֧4 *Z.HDᑒ+ibP::(ʔmr亱V؅zFa!3!B)MB& bQʗ, haB:$z:Zr3fp)uĀUcyQDgk9"ݫܥEl n 66qEЮ$>W5ٗdB]hTOt{V}._uM-~Dֱϕ}Rc"xlE 3;sm[]F  :mL0g^z|1i"yR|DT_T]5uۮ !> 15&bo4d7mf?xB!֑)JJq.{&El=:FI=~Vy@ ?F̏Luøyd0/vm3s, w驒k(cynѫ?X39?r|ŋ*s=j6t9&f0[E!læ׳Xu0y 6s5@^ 51ߋ׸8&}p-Le) qpx]n7{޻g}:ZwjiU;~O[ A*d !\T]Bڔ`L\A{'y'M?3֧K .䫪. 3 Oc 8a8f5-eOi[m[}QVC:#Voͺsd*E7'GWBs_,.'IKٹ8X|w|PEy2lؓ)b,/AgaB6A*Vg+߸f+rfp@=z" ISZ+ǯ _S[{-ѓT "46 8#a (xPh0BK+H#)5pۚ""  n,ltIQ>]cXa?'TkWUlݲ;E \kTJhL?x̮;26_i^~hn=#{xo}q:p<7 4zm7VW#N ћC}1s0L#o/[ޘs EW?6Cu 0E(L9h.S2p(`G)b( Yż~XӸo~ n^k;FZPmtzt se1K $0y? IDATWbӆBk7y7c"o4fa@`𫺟ߣ ! ؕB f~gm~SݥKe্1hy]Ev^p6~-~7v?)JmcTh"Qd "p4%lf,ePg_z~鳯}vF&&I͘tDY,KBK״Fm\HH\:ttAzJ)dD$DL~F +R042">6kEl7fm{U e{ZFICΤCw*qGo>HcZOxJ i;{a[),Z3ΖupȤohŧ>+7sEZEƟ>'KHa@ݟȔؽ~\t1ouQ}wݍn(g/Zصe OnH}?iz뿀Y0b7Tk/$2=ud۵ˋoP7sszlmΛSDs4M{#tx\/73/s!ˣ~$ӽEg#+[{_/t:Fzb+^ʛZ[&QU=W3ص(k,_c,/.la*7t;f"6FBihn~̒ǵ.eZRim_oifa mY..AJ&%  MEY,#XpD,_38`ul]Ҁei ZHQ^166WN veAV1+#9n:}Yr%ZJoOxJܴ!X>V_.e t(!tʠ]B+0nu]z$A!gWg wd1>RxyyM]/`cB]TPj+ pQsmIEN%:zТ/-X^_Ā^i U{qTf1I٫dMM0}:=جSr8^˨j V N2Dm:ȡy巠Lkȇ᭧xp9[ىz8}ǹegj*D't ǛZ.@>HwߠF) I-hRJ" ԵSұ%( A>UB<2ьScVi/졲4=~DjK/ gtRk-{x]7' {Ø2YSL[1r)@kOovᔻ%Ic7{Yi樽:+Srf6lᎻx @ c/*(pٺu0ч+8vp19&1zLf-n0kY_ݿ 6ѣ'_<&QξcĈz"7 =^"F/<*|)<*rByt> ]oو&O3Ewa߀'o'Z#\o_ `t :v=fd({cTR["}is9M:b4Ԟ[@Yb"]降y|O !uHwAV @D"%ICBuVEQLд8/#%@J,J*)̜kkvPB? @8]% IJ-SյtG VC{짘aW6>}7;jWMub{o_6 _pg뛏c?M6Ho7koKk;zvy|{wbD#ko/X{QEU;ItdE\~1sA z y=`z!ӘhBDpwl9sPl^ f„]~܊OzT Y }?|h!=u|GQ]k\t1-ތ*$żvcŀUsB|8M\3_tUDbع !^S.) Y^= |ks~7葫}O ]MӛHlv|tSMF1HDdq+STC Ě QnuH4:d:1Byigˎؔl-EKL6aKBg7=_4-҂CIR7#,$+ A'QqUC R =܅Um=lYgv֒UKۍlkȎV hPF myD },t<: x'J(8HNr-2D X Qvi!~4=5_#{:{|q)>x-`ճ.3F>H'9vaFU:eeF7;A)lUk!${²i^bY,2 NIA|s`2Q CIt&u0*BHшdfD/ʤZcr6'Cecd;fv}: ~g?A7ݹsӟPhh Swk`Y| !:^aXZBK㎻Wt_:p/e#BX\cV)å3KҺK?I6f,ldskֹ>Z V)QNI, G bDwQMh0ĨV5b`ѩWN:=X JVz<>[[-2un`ф-T+ImG+D DkZQVӒseJM] eY*²BZX$V:D'(eTpJlpaL@VaPiK+u>-2/r-)\(RKAɲm 4)VJ CmM(q#aqpcTCUIX Kb״ ;k!4 ˘P}^bVgN `^l3@jmt_'96й,DpLKik׳I繺~`!rEcOa8b{/D_ˋ8mW%o\GmV%`{S~ckB07/y.e{L=C?/[AxZ4y')RŢ<1$մha1\MŞp@vEbӚ?a׵e0|1LUv[SkI-ę*b4v,48ZYLpicng(Haf"Q(5O9 ԬX xR@)utlPR=VHsk:qF!qxDLeB,C`% =3\iyvyN,5s{@ ˶@((h̶p]fÃ3W]w׀}i-$"I%'@*H֔(|T;e۷v[8riզQs8br0_BNVU[kP?0Wa򥿂a1 };]cl'fQʰߗI fb]߬PH&i0+dqa,2%=cGǸ2nu5iؖzQ}[[y,OtuLY)k,p4N-7RzƗrD%ii'=e=3cY',qC{ȰBUN%yRL%up-߶֒e[F8-a5:#'^Mq.@khbV AfDUD**k铁C3F!g];uel}]-ʻ.EhXo5bTX |C[+/p1sҬ1L4Eo;dw \(F1f]o3rR:]Z״޵P)&^{6D=z!5HL}Hk}0BUQ Bx?ۺcsft11f1Y0㌋?~󾾜_.w)54`ֵԵ '#E/h#rim tb*BZnd~y_4+?$q|dqqd~LYyP?)Nk?ںJ˞Rc\/'GŁ_,}{GK8`Xr:dt5 D3V[CCZB\pߜu~}[qlHES.ZҊ*,dX-%9.]p= -t/#"t;B nNNK?;&bh;l+xR+]Y3hR&BZI[ EU1.1Bb$,m&/9,7qP 2Mv)QlX]m **b5]d2D +;"U*5t S02/^>"z`+w^[egAoXL$H  S&0}^Ve=(%.%H;;;x"첟+2<# d nmjjon_Kdm0zf }Sbm׶+ 8-v=jm}}_{? {o^9/]K-7e( IDATk;sω0Z#^;v~v1ysp~bTQ|f77ŷhU.&FO0Q\D!h}(B@_gsG_މꭵyL$÷y/{mUs{Q/+@H0)&1c^ ! j! &4'-ٽ B4֙bfm;gab Ԃm692SW tjtk+M_AGWv 1t}Ub"O&mfGеyut`G <r2O%ŞG;jv hGGfDoh(ƀ?XQs@Ij=srDz!v)v9w[m [9vJv5 dY9#ϔܺ ~@u=Kʹ{Վ$ƾvҵ}j5lSQƫ !Cwu~;YsX2=_F?jtI#&L*i 黪{ƽ3 7uuͫtՇR3>Q4 nWG<4E;.޺f綦&;k|žv􊪷r԰1x$;/xCˌWVn9bMPN`',SR֔HzJT&i$< wn=O<5Nl|+1D3!&Se'ACxN)ab [ z*!A!BM-;ְqha(6ɴl2)%y ?,bT p@9Lꙴ*(L b fKkh FwX6bM/):#Z92ʛC|S}誉_#pB &Wzԉl;6%SXI I3 .`km 0Tתrj  Adʼn fZ=ϋJ,Ck#Ǻ/xG)|7={BN}?nYh}Rg{1<Dž}>KA עua׭܋NKw9׎OJand{_'YOHmE,D;+OCѡY4L/>1`b-R)[/(|!DzL?q|x;+ڿ#)R!DmHN ! ;^Ƒich#,Dߍ6E^m$!cr~smD/dOi";n'vr*b;:c븧Ȟ5[:ONd\Sޜ.G5+fQJ]%vgqDa,Bt6=~x.!DDR3^q66Ɉ߷6h FsA DOHrh2up7~kz$\7Uk鯵>y2.[Ns M6\U{_)(*]u5=:}sT|7NY9Vy ܺԼn/$VʴG;PI+Wsg]Vk֮}ajze`sokiz=@vφ}W]v;iΘW`jD$* aX$ئEG~-a Mk(IxV4V9/Ii["O $$gxw x%dA76TxH%v~M2 @gك BPH?eQZLS% Bn6Ba@>CڒU{hnKYx AG_ u/b3e9#i$X #wXwd ^vU !RL @+ouԅCP:vI#" Qfp:'HJ-x ga\劙p$ry &[:g{Q}G+Ѐ?gNlނq :<\}k=ZS{#޵;}z;\;6oƜzag7ckoGoguY玈{FГ#DhmN)lVd)N8L.hTk_١^6 joAL@p7}"n,t43"#qxj#aa66Ȩ+/Bxt{ﴣ= }='БJG;H?B6(=^B: !Զc|/N[OQtT@ ўƿF=R|3r:E%X{=Z,4mK70?{Jn%^rذw^rX7T_߼?3UZn_eg('BE+ÅWX{a -wK6w=bC,Քv'vT5g,];.|!`~Rb[W|O`-3#)ZAS{-m N(LV`$u0!*.mU;T\/ o04L_t?f"LWL^n+ x>Gwg?ܹ|q +fw &G5]hcJ!YCZGy- W.@0к{;]7ƃLjn]ú=my h[7hPy %t}Σ}`t\6*]唺oN'%WL[7_ \ >z9YRn.em=|,  D`QDS}w !:Qc23\[bhhٳMSOYL\ aVʎ(^[(Gnq\K5" @KVGO<'\>.I L;ewfs9 E[JpSQBt%q6à)K!񎥔jzJ\Bv')Gwg&jMK%m9+7A:ˢ/u|S`iW ܻ2&ˏ u_OU^x8Y=2] 󦝐\3=Q[W;5Q&tVLGUsW5Re;j+*TMle|w]jLW0㭙u#k+[m`F('xf$!JűۊYDI+]?5[8~صpa.6!VA)IIE (h NXjD$H$  Ea-C*3ALSٱ%:kb$ O$Qd>xXTBcBFRMuĩAQ4![ArAɐ']<勮 $&Xag&LߚUn(,_! "K)T"%A,`RB|`NEK,A,CkaUM隒lť9F[wudzoxBJad:Tַ:kX8{?htSOOnn7:[o`a>wcnvȫnfwU'Lso?" BTJ(fv3~b ! tRF, G@R !~M$9~ɍ#K曎7|׾ M7%R'؁dS; Ԅ<2u L#1Q@K)BEH KB[XG5HXڲgeZ_Z]Cȥ2q6ԨK_ D%.Ū77ZDػQǹ9i4T AY" 1 A Fb?a3%uQ4LJHcBNa`ZsPLSyGh\wٺq.i6=o w]=杴<Jcc2N!MH#\5xnKѻ_% tZ]ͽx6o9nhUZ7S,۳]u3?rrLSߝ5BsR%vHe͞ЀOPEUyAGne-a-s϶_*`C蚪>`FmERY 9jk7ok-T^l_sɣ}{1ZC?ZAGf>HgUtmVwB Ge%`g^2z_tb{m8c]>|wRj`g䌜|Ͽf/~t cEZ,{Ӫ?/^_NOwu;7[~l/OS[MqI5Z)ĕ8V$mi)M;7}ۡm$IlJΣWSIJU9j >QI?ny?%WM;}Vt{:6oYj͵n+OUVI钇XWy$v߹OY/I!:qd)hh$\OQ rcq :x0ɶ쯍^1:" kG7譱]';O7H)ǺFK=KASV]vE;x*tbߑ9l^e'9^!`;RLltTDTOX`v9#')D(aD=|Suݜ_Vz˻?&?.4 J7-8[V-H(HD "Fd&"$(7!JF\1VŞٟ;Cjn`!&ŻɤQbJI$ +ע9i3PMcERKp^=ғ篾1v _@knEz#|vs"E8FnRi3$$rlDQ6R5!}Uzz& /7DˏV&<_ 0i㏣@HDzs;b6̳]Nf/V)ߖ߃ <;cKߓ֮Wl#InNdRj/.BiI64zӾ ,O toB*l  "R?z chR-6TSz:r}_i<7E"z% 6_22 sO|r7/>t>MW=^1xPm+n# 6.\Yőf9!biKI3 PLP#Fh0pPagT;$7yYKp 5S1#Ia3<.G'{Npֹ$W2Ħ0Pj&*C {&xQE NVV ޞ})1OlD)Ls zFq'V `0Ln8"VJz<O$<*m$3j ucf&8|lYlU 1UF0DPS|NE\ Y螀ff-v M"5}̮15Ĥ~{#w@S]B 0&a1gq} sFhWc'-MR IDAT/qTt^FeW.7CB|?2r̓ߺMv_z g/Ra,MW tK$y낋, =2t:@yiJm++^./>v~_zy b`5YҸfcQ7'hȎ%=n~ݙ`Į[`@)u29*"GGk ROOV6xT鼆'* aD;1Nw>4t=D^]nB=/@\|1}!ĵehh``1sn=>5⵻+dvfcx͒qCL7gD7y+~ϛt)u:}ݷHo7Q8иMG:W^l>4ƽ[ѠRN`~*1Vd qq$Yg@i$=\wT(ozeDTs8t  *fΉUBף8 "sfXF"KF љ( St~3 !2IO K;5#RL #K(Q%LRS[U?S1I*eMp` 0ǷDG9{/9Sέ|I]BAI s ;ĭ90Qw>&#r TYns m*cX XWz P6\+$fOwa|G/Y1{_2LuK*ҰvT0oН3O8g/g6%ITi<7ׄ]&eڈ}u8:vQB+Ѭ-r]2qsEYD!YZԐi2f7oϲ}Ջ8ͮsE/=x*),iuw,_8ϲT7Z75e] th-b{ј>SYd\m͎ڦ4 6ϑ}цmSb<{diœt٣ɱHwu:}==v:hhGJ *,:;w-WРn?Pv:C5O7f OY}fJho+ё2m5WNw9#.6U$qUE\qpm)/vy D2RczJP_tn?3roXm8*4aN=) +z\G*& EFaxlI8T)5# fT xM1^bʑ-5Jg 9V!:tyIC!lw6X`2۹q91K-mdyh6W @ok}KwA4SB ]:O]K)#躈Gѩסz| ]PDӞR1AȣS?p$g#u]#JusAȾ|b*)jß?='s̉L- r_zvmܰmwXh|hLv#bwe4;>,cUPTAn~&qgwX2rZॖ;ܽWP1J ie!~wj9]CqT/``s2}ʚ&&:r@(-ڱJCFq"#"N4QD8c}HW?KT ދE.oo9aUT&`Ngk&)P'1%DMp[,AnyPE -ɕIkDdIxiTw"{XT&fe N"s9:>Tk{t@PhB`΋YM?z cšб6\`f:1" 3Hr>/@M6ɷoԩ>@n3Q#¦B /~ 1hNP>,wؕ=Ďmog "k.ShjgCBh/huZDc\n]GD=!Rjig'?kq5|+R!.4i 4Hchxhl|hv.DIovۭ7zYLmm-4(ȱ'Рg (;״'~M,3 O.H>NJ4McK#Zvh~?f7GϱӻgߗA'<릆^cmџJAUG\37W~v繟4 ޮs ks[Xm^M޼~bJ /}z??cEف^7Fw_~5 -7/nO{iÊMwu_6! 䥕۱K s)#ʌQM0QUdD 2(Z1`Y2nuMJ3si|yDa]tΎi5UyO8vߜLv9^ +gFGԚB9Y SE, "i!$J|ZS4'b$V>PY4qncVډKEhӊ 7h=H焝R]钩`MArqp832I눻GUΚZ2qns?Ĝ^y麽H*I2i> 'T=!Z Z7wMk|\Kar@A%e\CR+2%r3,b{sU=p:IsQ9f")V^W:Bk]-f'uC ?uw^OQBs)!zNߎ~} (j"0} r퉳T7_?pҵ>,n `B+q5L秪bqgZ`1#X<= _D BGL>p92-B_Od R/O4=?|lRbz .Eztom|A9v627rX𴽣Y4h>Hs"uOiuS! 8yȢ] ۩ {n^]~ ^G_̎osꮻͣטv\Gc^[@,0u|FyB5ã_<+纗z K[VÓƤ"Ѭh(0(SPo嶏gn#}L>|ndm7Fd? G~B|i?bƎFwk}Zj`^1,JDO!DojҷF\ng fs~}ja([Qkb,;) Q-wV$i`9YuSԘ RBYro4]Pqu7g#ھx$zIg07 2 )¢nLZHY)Hljɥq@%x^* ۥ\G3+P)ZHDWp}0&ة /L ,nHQNY ly$ RTaL<C4rw0JÚ{M"=}خ6rfJ0BpEQ!@ o*ߚb+b W l] B|sh\qZ4{떽DB @^>nL]|:*YD>pV>$K,^v}3Ns͹dXTe+DLs4Yɕ,jzXyRxuW{^)Y&@t9"%^?Ut*4X,17^`H3񌜑 rPTv]S^R,q)R^IZ²M%725sl,i?3Pc>q(~e$>#j^Х k[}u])ʕzNx| CcُflWQ <ۯG& 4:fyaXj}14:WEƠVKzIH"aVI0WFb f䤝33qY^?븏綏JXi:Gh(&azˀ'Q,/79lXuA]Kw\6)(+F8Rq058I'ͺIPm4o`FrИCZ[# % 5& "ELQf B'0n@:X:򸮝,N ! tj$^wY,FϔdƄፆӴD +9:Ԫ5J\ 2HweBX?':+ K埮HB8V?U#$#" Q f` 1ʫP*Ä Kbh܉6> m7oӉ2W碁D) !^Z ?P\*4hl02>#N zQiYe|_ϡ hO4`$: 9?&XNN R;؎hvj DreH}Iӌ{toc4@.<)@;^vV̠8dE M8{Ég6,Z7K̢sM@uF!34 ʏWWBp\`hҠPCrS I,HJ9HȘ2)CܱXU8Q0Q~x俴SбQ&vW~42_wRZ`l`T_3zѼw⼽]=rwrC׽޾)>vO^4^T*3-c#!4"ucpcku(]cVt.p;YIa2So AuHHgڬ}\ҔKjJ0%ȏ1,m.IM`cl.B ?2BMB`!z5tVAuQmtbѭZ,ӭ:$ٔjS:g=HFiEL9[/BT 3y!X1և=] $<(ׇEMPA,͚j!cbATȞ1ht-vFC ?ULiى:Edu$j^5]mWomţ>ʝ;wʷ~ AzgW=i imPQ$JBj7ms9ںy)igI" K̢]I|y 3)\cM.6O*NьM ]'GN'}%lWuES/Chz# t 1ch/'AHy5̬4hh,#I&zn{_Fvj^#y`^KN_x=ohMdnTz*Ӎ/٠!Ef3rFNL뷽R-I2ʘkaZ&y9d7tmWfY-+\ S\#%WZ9ك1bS?>ptuhG+ɿJK$=o[Dj?x<_rLdUgUf[hא%VFrMXSF|,Mz[kN 'j5F#rF9F,X2ma)1*0E%1Tji2~ڽotāh+,6RU,pDSRv"F$ Qx (|(iXG׏DqBdN.nEJl8]0i5ʘ4Du#u$.#kς7 0B'M$J`'1fPF-<+$@"aj)+Pe# W5p(N0 kn@Bl"GӫNkz{13: U*LݕҡYN7<19+'f $@B A-hSQս۫=*6e\.V16 ЌT*RΙ1o9y/_(Bė+Vd޽{;{o;s3e9]L:}J۵jF{9ʶ]Eν'?ErS7K:' h،݆qCjm+$&hCTNhEZ*4Ў`z+^ٌyDq!֓ծczҵY1b9R9Z9q0jEv5V~Q[:b;`]Ɩ2-fJ_hZR!dО3)>nծ񍥾I;W];*P: ͔4Z4O5Ph"ۃ(OUD%Yw9ʼn{^1zE("8lJilmh".)sT*m~1hRFӠIv-z IDATAAJZaYFaRLbK,Ba[`dSu]v$-ab;ZYUaA׋0_`a|?TV-TюkFcie}G:f "w̬u  >ȕZ6,tJv;91/.C֕BfzJu3٠+T`ɀ$2TPU)b“Ůxa?~?6Ws޳mbSiG術_AdoKӤϋK &sOtwGy[wW_V=}阮75^RB,. ?UNR䞨x>ln&>?]'bB* I҆>Z-5:Òw\a]ڶpsʖZʹhUuSg7ϧ̈n>"5L jWI)ki"D8>ӪX[[dyd4$ifQ ;ZZB:8+dl|& G&5-Jc9yڭc͈zS,mą,NQ=Aw,X.B4aæ­9|U0=c* ,J9x]KY J0Z\tgGdaKo0[V5ƴܷk2ލK{B7^DS D"Bv5LA7|yQo@Ւ$'ȅ3M 3`:X HobDXJ(h@hud=^6 L71W} o}⿁r]W}5<'MLbH!^7N}>0(8M8DQ0eZ!Ү!ňptwb;0r1$p ƉSu t9pKK/VV=alSC"Z+U^N=_6-mPrV%ReBԡr+hG[QlLĺUzmhcxE|Z/6ԴW"3~}wR9lOh kX꘸k}Z1W˺Tϼ}Hd ̦9绪J|J\f>tq dPCPb9 i[guKjlӧyh~ܱ'=9>Lώ o|oU|x՗Kw8ⲿ(k'|JF5:*i: bjJ)EMB AĮtq<\{PÓizQ*ϰgDJV [vA8%Z~{Z)Ku4O|V[g]T'<&55$C1=@qGiI.Mٛ,Lm-iXSHHс]AjP*X: &*G+w7=z\5Z܈~j͈ߦY ]Qa3nRrNH,rЇwp7x r0U;'a%qE=ݺl#sXb$1Z#Wg/,~ ee-߼ޮTms!Tq=JuhBCi"-MXh:$2Ŗ]!p\,qNlm[_o|~V18OYZB0B1DR^(J=(p1Q:LdureLdХ]cnWcSIAx#f[05m7`i|Y8˯YajBʼnz"_JTOkӡ%RmMN/)3VFfZ$S@rhfwa̳VŞ[vRM{ad䁏 !TIѮ]p6ɿkɧx_ݞսW"l=|l cZX"ᶽ7P>%~Fz6H# `_Z#!\IжiQ\![޺3Tzm0~/vf" o~Gnއs74fyuM3Q›>YZTo+Zܘe+KmjaԺ‚IQ};Wp ԏ'3MFd!oSOEւy[2^a}gKj"s=<\#Yҝ@8)ldflW@`EX`h?  ? ·'.VD>Qxrv涥K V_dZ,KQ'LsQLWYQ_eE?{a=vVx8-Ln '~jϜ6ڶhr);ȍ-sXgft(J\I:EkH8E!t4*D.ӎ@A ‘+m_n&}d˂p(D&RLb׋#Iۊ> BKk2c"rx&5N;bv^[ES*0EÞlbl'L'o5T87}&)1\P_ʾ/V‹w^OYc3:0$uW#S3-XB`2Ytb`t2VJJl{b%R_8SK712hBh+/ְW$ڞvY] zSw{+mx G':Y|OJ俅}݋dY% E^&!w2i}.CsՕ];: o~+u.ݵ;ﻲnղg3Q].'_-7tg\2k}daR^dniaqB*~|B"<-U}NIN#])T[BhkN^`;uA 3LJZ^,Q"]A њ5^Ywu)lyF d'l4 ~H=#zl=NbA;(0CӸ3n;Vk0>8j0r: Wl:Iiݣ$z/琦EIue/Բ_3G!T!xőE! uln]|jIi"LbFk>`j5ǘc7c"1m5qrzƉ1khcFM><178;E yI8}4Q Z6Die+^*bL|8tǏ0s_)ެ>J2ƈJB"&uKMVËUT^iU`eV:kXO<+|ӺіVp(džR9P%HMB9NSyqMB囝ش9,sGuU:W.0^OZ7N ' i6 ֈ!Ӝq>GRٹܪY̴y`fƱc=oNMŃxu*Ra=_yMo}'Ot\Gn(;0V̿r\ꘟ,Y}ߕ!Q$Х\,1: dXx}vVl,lP5U{tO哳rd=iJ⬾6gz l|j tҌzА IΈ־tO9j,[!3UlRT)rv2%0$.‚XL9AD"t&tXiѱzA?B[k ?OX 2 Iԑ݂շdQ턣a~FMx;bƠ^X@l&zR'." XD(!kn-ג:V8͚+a(T(8bṣyTn/~_m_h˷z3z_cn{2aѭ4AGZ[aR"dp A ֚ΠsHA5P!i:\QkZO+3Ryu]]˶?|߆=xőEL?IS!TŐ :/,aݧ1"2;OjgWyoR(1DVTQcHXlt7-ap,LIe=4q/bDrf۪k߹c.aYI.+RF"W^3 Ҭ$h.ckԉYTgkໜ<46bz^.nZ4_w_Tm#Jc|vMvʻT~Yݼ۸vCn^7W8hTo ;m&}ݕk;la2 rׁۗJqh'h *X3Y$v *I SᬝS!w7;C簼x:-I[ݣsV޴PKpd6~g=0d/.\wĮ>.Cd=cdH2x,)pI7uaDJ-P6(+@Vz FhH $hmaTFXHiEX8Z@jLVqt5~ P%7D6ʤ۩y=D(V,ϒ^0F=xzn0|~KM,Q/$<1<3zuk? HGRC݄7 >B0 SF<Qb8 hd NVzd*@I5zkٜq|FB贪8^215 t LԤxt0)p0]}`Nl=߫݅J[ncp}vXJEhw?8%íJt/RthUFVX2KV#+@ilsvm c Z#8V)jӹ00:/fZOSB!DUk] Ǿ5Ӻ^@VKA h$69[0tYl¨y)BXe;lݳ5fm~n^6Rh+;td14EҠ|AUYz:7p-͖ ( Q׼#~U!k]ٷ'J5qw޿ e\7oʉM[t-=d8`NR E\G͇RU:NHj%(!MWԨSG`ka\w Fk_ к(#m]¨ڞʐӬ Z ԓXBPOZ8vstv%hZ,oK\L^řִ(+ qg{]!.XC>[GޣĪB H*9&!w"N짠kXMcivߎ8x!.zwHj)8x AP })M`O:nXԳu O►sKaxŷ)ɴqrװ% 1ȅ ;sj9^ؐV$̺_xrMQOa櫾 BA*zA!]=in`#I:VOp,̊U⏾UJwLh%^S5MtJ#Ky\j>mۜ66*dSg:i[ng#7|BON/Pȍs+,DEkVA>iN < QDkz!8[0!&ٽgivJDaQ y8VjFsub`}1O"(~s)z"g+7 ڝDbjwk ˏ3w(4f{]Y `EpL\3G!fyܜys1cLm/ɨװo(e 57yDYh^fC X+fA4i,]06y"E~Ɉ{GyB &: @"'h+H>jr:`;Dc&P6g򄵬7B]ٙg{)/N:3yb]йY.e†4x]J6OVvN`&2 5jH|щ E <(iP4T$zMgmKI4$) *%ا$4YFZ{"a+r;9T~Tzϟ@ 'ob"MvV0їa޹Hk~b+7ֆu]@`"Q0QϫD'CŘmќcj;ZL gK _&g\G\:\j W̵s-i̝a^ӫ ̜N/4s#t*ڋ?j,?hz3.S(H8Iְ&nY?:upDƑmz$2DTrcl:| ιasgГS&ބy$TShXP<%\ʾuVP5,т šX&żs.>Z=W8;'x?FL\R\t>tlIwcV}*Na2OgcfU3\!aZɲ,:dDئ{#`TOH x5+Y6T%$XֽG Sz*C96i 'l7&<& =@n/)4 CruF= =)z4KOJM%'sщJmd$7­Z[~ ؼblx/,,J߱pjPw(LDrzBxy @S4tuʗ34'IYxvg?sb`/N~$Hl$Wfwaqַ3\#Nu'I-!lH16) R`&%FpSX会m^@6vGnlf%yĵѿӻy34Epzb>i}Lo\UW0N&\aγ%G~D~k:-} Vp=vsyLq 8iY0KdCr[ı!ѭ̼+MW>hZx98[ˢ4WhJ59c"fճk-& =B^@MC/qF̼a)6?Owyn!r O9>m*kI&Jx[Aı!LLŖY:De*SSɯ^#7=!<cW< ʾЃZ<6wZSoZB@Jz6x$(ZgUa!Jd"k@11@Yh˷Geu!5{,ٖ&^YX3?͠ƁAmf JNPSҙ,RWka`SHMwUtB*k\I˹DJ!IFJx?0&ZDQتt `BwP(RR,RQq4pY'E|$@&z]Qx r p{,JG9C5 64Q>,!.= O"ugjg*"}?'"#{82eP)Rya t?d|09 iBo)ŎKЛGv=zɬRr|$h U Hy~IS}ۋDA HaH)=KbTc5lO p`4BvoK?6` gW4YBzމb Ť9h%7q1a Y>ΞX|Cj[iG'f F cv4Z0Np}SJӅukݐͧiqEzZ%/sYlslJ~ <|Dq^!O5?Zi'ih fz ;;!W`ǧ[[^9xo-foʢ;LɲYH`.$oݟο-3%v 7}<P'QH:&AOTV vpب =Cf][HbBg?I&!Z |`ڥH mYR4dB,2- RLan"&5ۤ"OhMN1QXt246Sl!G`ﻊܨL.(aѕU|(: "^T &wu2*uAifI D쿂s BqB,6wOwO֭if/CϤ3e}J}٭#qe*ȅnJY =!;BSk?]g^o@24羮[n^'*(E`S]2[y .{Dwuܠe&JVp:ι8m1cls#gyWA녎' X(0)nq~,?d3l'/?݊9ɒ~$< FPhG8=B0y` aV +PVk/EvT"О7kR0uzZ?K Uk=#.fLs¨݋!gw#Cfh/Ĥ-#sՂa ?~P;W[.|-1]@r'ީn*iL=3)b^??6t3/DڄTȉ$jB[*OrRN"Ubk}Gv}==7+$U(Fhk O}3ǟLͦ'4Nze; уgeRK{HZ'(|$`נ4)6>q#4XR"V}N~q&)Jj,F+mȅq,-JqL#݀)Dors!-V(}ܞ t\rЉup{xqD~r;FD]0> @yo؋ G 21\~HlG`A ]kb$ԃbD4h`@'2YůZlN,FIwΫoLl˩^QP$s9±H AnJBF Foxϲqcw]X X) ;Cpw;wg WRzwNlXXM_fh8[%lnnOuGn|N5W,Y=al1uygb=|vM,XjQ`0\oJ\.6c{0[abR9i^:1 C:[)+[ev Vrֳ} ?Rݔ@NnTYْ5<.BZO/Aau>{ fAb&]̽Lei ެa kh iO,zK>6F& AItHg}tsK&.R)3g93_}ta&}c6~n|O gRPv.ݎ  RtXLE92y *[?+9X˒GkI׮-тuLԦ/Wf~foUJQ 7t4TX':b!HIDTu: >6r| 4u B3B]3tLB@)-,iBZu*tb)JB5BRkI V&]+N/S)I3s4fR}KO0pa One#0P~d=t(+,:USSFVZpR$#8yї"Ÿ n X!hHmy~80uc֐رm|EuelDrUBP8s ,N#5#RWBXƝo!c|a3ȉ}˵ c#4@qv mZ%c>ɲ;z]0 +-S 2바x8{NggE4xŒEu]zv˙jh̊'0)z<8B܃!;1Oa&S[Õ/';EL0&JP![1iwcH/t't/ƒ 5IvALAl 7lj5D187tDž\zӄ+.Tv~p=T=KC5 44|LNW۶Kb(Ͳ!Pb 8U$ʮHvEj [Ѡtc>BAۤ$GB "\b 3nPCf]Zh7үp:Ryr辰5 G@Bꐬ;#pVLCM~X"kٚ`b#+1܁)ꋝp) dRǥwm9 S8v=cr*d SZ3r(%[ndmb;o;:?GGSEqNZp4CekuaH/ [m &/`v0[7KDt B:؞mzdB[>Р:?a뺚Qӱ%ڂx[@jfq@+4HA}1 |/?{6%^vWEcӥU1?aay"-ufċQܦD߈!_܄ݴ%[3,iGg|WaHE#(r&ck1"#{fiKUeWLclY6?bTAl ŘUhZTxհ>̗GWJ #s&Lک`k|;1QkXu,KT)nL`[Q g0>vg{?~om=;m~k~V4;o\w`/]du'NVӐj*AZ5 ] :B-BYX֒jxPGuNS'{v_.w|Ѷ/}ˏ>8'|%EwF6~1 7TdqwȖunOFƯNXs~v։1L 8f؜9*:‹bR8c[B4&Xf?Fh)mnvҚhP;lD*HѨB4ML*vC[DDX NT3(4J~ @De' O6IN䱓h,`>ۃ'_@9 ::ʘ鶱%5RR)qQsMkY-R{jRVES H``tV\{ 6Wai4%<`qmr 1v<<ٵн [f(*a= ɉ$Jo>!i"K"w ׵"I `u^cw6&Q 3>J"|dc $#g!WE~T$tdcVZ;8ŅDoZ[T}#pv*=yW]"fn.¥T)LbB@c;6rKMJ¸/8o3yC^m^MFJ(Be(y6o"9"āK0Bɂ  !g'ȡY76pݦ,ZޓRQq. >4[> [*- v1-xg)z7~h'.ZqFXݿ8QNG]QAHWJjUL]6\Vd;G(-ejCtdP$m%pFQ["4vW-H )R)#>NWBè;BFiTJuHhAByI:Ԥ#tF"N^8'nYۍ8A* y'jiXSϡ*3$|3IC/n"YՂA@*=v -Z`,P`WhPQiLҘiN*ҦgBZƊp 8]!Mڸ#q]+<hl'2ŮcØ$7q9їVz%!La^hwbºz;߳Nq۸߲d-e IDAT*b^̂^cn{N&V{.df#Pv՗C^i"rҭت^=Kկ1u0Obz M/Uڹ+1 v-(] 111eD3nOa6/r/ͯ%.LOe*6OݥJn]쓀OR^B_s4_?{k>_~&Q³MBXjrO;r;F*<@Dm,eAս6Ζ29CE(-FTƋDzQ ׉%8CMY. <)Hh[ 6=CL-q2p$ʫa5gFSGP'X8`7T|mV~RXLY"fq1=8bMG-bOiA颍L}nHeXHEDJ (5FNغ-!1-iSՍtLC+aǥ/4:y$d?UDRSeQZ"!q#KC$rh91Fq]8:tNX3>$$Tixs16gh:"̓6DŽt9 OpD3O#G6Ьv yNI kj H$C@8=|dǁhWm_ *C“f­D1Szf*S,g >Ql"2t 6f8J.?vRʝa\.;iߴskkK%y[g_O1ufN7/74v?]} 3wu0ukf8I;ލyw1.p fL|گl]Wmlc|Α|'^-/hy9a$-߾)#hoJ;AJ)TC4 21Ajb iu"c3ܪ} +~`yR|ӧY G׃y=ˣk[9xJZ0D+{$4"%ͥjr6#*9zVNsNT{[GC:!+%soMž#9Ł#VeNkQ[rˑu`F:.Z 2 ۤQ47?xX:]oW~~0DSM E D+a 4_G,m~[{I/mlwYZ/ !>J)]EEj3R%i`^[1QzdRq}Q0JOYW%1(91&C$WW:.Kk5ř.,p9d_\.>ۙ^j(.sHz051(fg~볯|L ~g?D9smN"l;~!L⎥L` DhF|SE+"ؔ!;#%CTޗ R:~zDifXud{gͱ6u;GNc+6-.F?/qE.Hn -A5(x.nqOu;OAeLN[TÅh*՘؍YjªZcMq& 9"$iLdk4-tHhSR.FV*d%el')V&6,c=αstQҹhB$MF% ѭT6 K(ށ*V9)VI@:{0'Lm3{~Pހ(b9 n 8]mk[UKX+ ҈H`$2}^kkTG>>lsL#*--DNv >E:N`u+cQ݌Ktr}RHm1?KTIG׵rq |ΈsD(DBH-@ iKxś9+W8,QKUϙTjl?vj/5DՄ=ul_W®n!. x?70߬l1L9K-7gIh4&+#is `C>L/ K|=O(~9Ũ/¾P^Yg W^z]2ff}aHs.&ӵ&f{%Zl<\{u e e <HYFY|SU4&Cv<Λ#YҨ|iokg ^kddõ_xe]OG|.Tb/sܝL|\~ȰV(O=qÄ".%WENM멦-lK0,{Q]ĔrOt.QRĎy#^)50;3op{˷>xw6\+]4Yi.s6^e 5i"$%%&u7?KԁX@Ģy 9d9^H6bP[u#RbR""NHN[80ѪǏah&tAS=-Eָ%`j9u6M@k-\&yDaaD;^ Puo"ڍh7:[D蝰1 -hcqft xG NlAP)>׋70X:kNܭiO%Χ"w嚚LzCk{ۑ?o:+3IZ5YZ܍: eCPEkꭄN^nZY /(n_< \P3w/z铕Hk|No.2JSZĴh*n/mn&q :9;QxӨ~{nuұ/5uӅiH#ެ$i' v% )ʾmn[dk}ɿ do{U⫀lYMEG QߌY;-+'<,5f\G"2[0_8ayf(DT`,-zs%.c'\y2~\M{_ݰK%ccð0Q%N^۬[_bbn^Lj^/&7Mhݥg1DqjS+4K5qv0=#%ck=mlc.m^xO6>Hm(,AT SPp3Հ$7BXic9h d"byAt?uQܶVq!XV;qb[[*9JzjxEk9s2@! A?}T b0x]x6eJښ[UM`rUl6꼭6Ƅ w`RLOTDCIC&v؃QSu&SG+鵒JX&U~e7"ѐhQDAbA﷽x#=3[큂D5:Ai2RzggD-uL3;O~.}b>$h=v[`>M{IX#YCjňEP@h{/*2gn7}>xm VSyA#Ah9T$큀s9I-U>WZ 3@՘;=8l%p*ByqKŹEn2Nur?.e8<2%Uv۝Ͷ-!`$~~w~^cR<Ǩ8R]>2n{o纮ճEf) \h+B1NcC]Cm;K#ש`b%+ݿaSk!g^8mlm'/<ٖSg/hKXMS$g ]#\|1J=` SCf;E}pc,}فR oml @HPJϥn U'N :N1uJ ˡ/˧7ߴ|+-'Hǒ XF!#Z4*Uo\RX:ȋ^U u֢HlgD 3v)a+Άi(2x0pL"ѤVpxUXNI?7 zva9CK]Bʓ];L\H+ΎMPBNՐ cgLNwRJ&t\] j㴮VSJ{), z5@7Ihkyxhփ)F!W; < Sq> ӧρc˳O =_품Bxg>ZΏ.[{ :nw-/Ss#?]#Zx_k>nn='^h?WJ-X6gXKj!|蓟ޗd* Q׀n$="Ïbco%Nf{#F"&|24teJpUqY!PvV~"K)Hq#1D$ !)m A' q 6@QKj(i.tY mFrK:QXLoAhA 1qAjWO⦛H,xEBh ]rsTTS-uYMێloD84&1^e!Z` F dڛa%)-YQ$]XD(M yW~79]ؙnDC,Y̠)MLM=`Wg6!~ZTk EY18UszD96q=6IA0\+%i,~Ӂr1 CMوE,[-B=a͎މIw`b=-<6=arfO4:.u}7YIf$ռd?#U3q.(a/؉㈶t:I9%&r2 ,, I1aL7WX$E踾*\ %* Icw ǑDh8g AdT rxDԉ*8 ѳx1| )[C=nL:WW]/5r~η`jS-'XWFR@Br H\P۽:]FթЫΆ_  5lZ,DF#BMTsjxihs=<lKۼD?|-{K~m~[]X/ߚk]ƛ7ÕmX7۷֩$NڶBىu+6[m΄m\dp0 fzx+T'0cvMIE$f%;}C[galᛑvd%L5{!Zzu)d/U `1sl`c{OaHd,Mc}w+YeX]TdT?9Nʨ 1針 6M|a: IDATB*f-&9ojj]g\15FĸY7! f!z^QoYDf* _w32cS#r~ /uύVXBHl$RulDt3  ܈m7ةĩƒ Yh)bǘ$'*iݨ (LOpf9lVMPu4Z;BDh뢩TM`iCJ H+)hB2(VMOˆ;éWlrg3|=U{VjДiF=jO=<%Ulc'[^lHv%U)X:W(At!:96=td"zFOLsߴ9l];\ic]{BGҚY XBuy,\w߶mXwso(MGqVMbpf/kN/4!Hs)0sva38yЎo|:=dBcĒG-N^Pq;^Tmkx g߾L=u-_{}yg[?^ٟ_R3&o/; *?G) ,Qℶⶈ휫ӄb$MT%zms@\]Pd({ 0_a|입QC.U8=sGb\fA)k2u1˸[T\>~+#!Fsq1̽1oC$:n#W16^ %SX3Ӡ7jWr6 & 3 Q:D1eL_{8<\gl Cҿ߽qzu_- e!ę$"rs/7c6ZS8Aog/[oBm,nc(>@[BeuV u[?ʭ/gf($Mԑ -5B[v΅ʭ:${c7&KĦ= lˮsDD8eX6m*bA>)::ivtVBF(8$F8ʡ͝Ֆ-V$ & (#j?ᓈQn&Y^uB h~+coGa}КCG0͇1{$w"Oݼi-NHFOe7=O93S>^=1 iѳ}UZ3[R*?^Ug,d26o}xy2~k@Q|3?tE?OB@rsC[ƆV{mX!l'bZb( c|\Pn60MlsV*Nletۜ B.s~@Q~^V 9ⷍqGS1vpiT0)1pގ!AYڕlb 3o92 HVwvϓeb#3%.#Y/5Bz/{{hs1G K2I9 zDAuS^wBx9\תLk0p@L)=3? |sOa3Z c_-BYﺬNt8!lǿCӮn_dj붫':3C7rxcg=LCbb^aT(_|_Ǩ;_Z~|Ih5y+Jv8G000.`sbh׻׵?Å6!}u C# 1aStt۔%q6-Szd@kATs 8 T"BX~Mq-R5Y8 -Dbez%ԅl!qc}kh$4(I):\΍iNL+el;Anِ>9vlsZ$yCҨ=\?uķ,!;rD2Ÿ$ vB LRLtk;* g3i['ܽÃg޽8%_Hbxgm\D0h>aU߰SuW36(;I^DN.ƝVLG0qWٱʘ]zr()[^M({O~],zX(|Y"yd$Ÿ?:ݶTw0 d70bVg{_yS3:\rWY_Aowq)jO}C z ɶ1Ә~ 0+)B;1۟ZBdcFcާ&c sidq{SkٸC 1rY3Ìͽ>cHC v{lYt01sY&amm|vMgE5t&qtDhqplHnzƛ[Z~ .i'ƎגYRi k溠")'dO[vP4TkAT_$pJ-)o]fgG"h شOCS lnF!}ۤi%:}g o4<|?sw~#>CݻNc2V۴1~Æn497Xuv81Ӳҽ2SAJW*}ʱEDv cg]=Akp*ҩ1ta@݈7TvpVNS r)vl' 5Ų1W\Ƌ:\ FS[r_[吭wu>9?0pwm~&~Cb[óǾG.K^j//NuFFK;O{9<쩴;qwv|SƶY/Y71!g)-[ZD977X&\)V<Zsy @fCmsV4~߂ xxlcϹ|-`-aZ!sYcۄYf0`+.y,h4詤}ـ^E==~ 6{j`VŐ\̢޽ rsoOw0 Yqdi̞KF^bǾ^In2 fČsc actկ.1 g2u.+B %ڋZ}:0 yC"o`g5 g8:[kgYK-+#'v$qʼn0pHHa`Hn0w.KF! @1(YoI"k_ξtjov@Q귫ު ծ#|&==|%PVaoiueI`+Pt^)r!p@)lF3q2*C.z` #A9 6K]( G! 17Dq5XLS$ܬKWV *i]0#a:>gLiSDU|>zcW8ӍGr,PwJX4aú5u ݾܱ6BnBqic\M+ۅ8`{}B^='eLI[aB"cx2,,xBSm|G;Oc4K'3` ɔاu3} `+akV%6.ugn2=M8A3 MwPG 8囿Eotk` 2w)jƝwF 7J`Aof'uM֭>F8|2zpٰR{.3KuH_y5sMMnY)RyK^`%Du_<|uʻ1UT?͠MhPX)υl[ PP_*$ *s U'8'iQC }׍&QO~35+(w'xl$^6IT:2*=eqJ<FBVSف4磐  )1M̹BǤR 9)r|uT) S= KOOQc aιai ;% fCEka[VH4) lƦC~ߵ f55SF"7"Ct`k[#"R>/d棴5O~Rɼ|Qhdgn~dNgnX:JksO;XƓi ゞq!2 k9TLgJt؞7E$ElԳ)Lgs(?XQ*c~r`chzIuƅ1j"mY`MR{D݌e1RM hԅ]&m7܂޳Q4WF[?vxv Oo~/[w%wui C?|_L~%.ͻQc؏Զ?hyg l<9qC5ۺ~L ,};=$J^[U>J=՟qsO+i~||ͷM02wptċZ_hJ񏠼hQrf%ֲfAm[cf!,QbDC-~9X!ݗzQ !(a\C-:dhyw$⩽e:z.9AU;PD'PQ9Ey<,:[Is_F!*k4"#M( -#F(!K)+B (XJWj\ d"i]#PwZgtAW7gUet_v85$P"0K^is}gYc09c5 P9hme$H<% 9efM5nh+r|{82M#xst MxnF+[F b`!P#$Ƒ\L7/'S^_=|%i 9f5mnJםޑ;r<!j#SsE%RYyO 4--Ԩ#q?sȦVB[23ty&bMS۔[1GXFD4WJi۵FOJB~v(=FZ'ٌkcs9Hfr'Ԫ7~zwX5N 0FP(FмPo>soVyC^]{Lc}_{~,7T|S&+zj-x?>W+?mk9B)2Sasy gWp f5Kђ҂Ǻ:HY8: RV,Nfz}j"@ IDAT[s7kg' !зs}kP)lmMѝ?~&eCw)nW;ݽ𭥷h;g?p%N.=[{?Evv}&{ЪQӋw#wQFo(;XX</%<7m)J -*@ۺ%đE sڽ_I(h*?G+THw/iӱ*h.|1)`GCYb $mAiSiK=rl[7 Jx\BF\ PϣBz$7\ !Db%K%c{ uF<+aZd ){c&Q^~|<%ߵץH)5Oħ]2ʣA\9j2ReMQ6,d4Bvc,QS2mJTSUa/DBrQ\إH+"ti,bܡ')4+D90f +SFR$!3@`yNBd 147SԈ ,t I@zLV ۭSt6 |o< 0ˆO4'BGGtMH,pB'RH1n;fl3G8T֙Y3B$ЬYhR%ۥVWCi$C[FW/Fԋ2_.w3/c\OaYfV')'=0Ɣ2WOZ Yt>OW0F-JFi6_j?U/&7|04#[˩Ț\3D&G{kT=QV>m=3վ[<9Ec~Y^roxk@>,ɫ۽n7'!efb!?x߲ܜNPO}ɇ:B=ϋvaw. E!أ𤔡⻨f܈ZMոmF?ANB@VrbU~B<Ѝ<ʭ}rJA\:Է,4cv0AOZ p,_2=!RB1{kg) TgVtt۳DAKzsݘ[<89d.Xgyn ,BE,i6A  ]"i۸YCflFi $G*Ŧ7'K52B`p"BJED"5"A;2#ۈ DuM;MҔcR }Stfack|]%zqo3?Fәe&ݤkbQ_kz6)fei;z퀧rQ_Mw8,̛UUkNm Xv\ķ'KWjj )D!@f'1O8 7,~YC{ތ4+hkJܨ0ͿȔfy-S;k|[ #kR'{Mk2gn܃8?HwdY)n޵#ص}чD.g>wԱ;wޭ<\YtX.-CZ* J Bxh.rɹE"|&"-?c:n"4:M"Ȅ$/$qIvl3lo'k/4D%:_)$si1TiTHjq=+,\ SRqd*/~eQ-T5(kWP(PcNao?b%0NԗQU['_E-lGRwE85G)B܌Rk/gNt?|̏TFmR"DQSexb^^Z\C='QYQKhxF|rƼ|I=;$aےBAD @{y+nn6 j`HHiPtiMCEh,i|̘1 %µaAB]W7tP'!H.OPTe d6T$\(kT(caFGe(rTҔyLLf=<["UԘk'fU5md+^sD#HBo=krs_ȡqd07=vc>`.9 ِF5D{&ʚI̐>#ęXC*_{r4>l)"B/ a?oh}EM Hr&>OǤ"'EҴk؅xRV9A n!>{ ~8)T(Bfѵj.sߪbQjGAaz&UU q#smVVMΩ"CDB Nʇ0= 1{;k=lyYVHG:Q!.ҭqU!CwΊEvي@#DhmԈ]f lKM3 &pd~V\SzAkl(}= "mx fxռj`3ú'5L~}695D-1k5ǥ_)ZrCauOC1mbT4Ho]H6LQ'o4/~"u߮{-3<0 sqܼkǵ(?&͌ݑ?`>uu~cUho269C?51uD7?>1{Ub׎fsê[_/Mj*+o)eV~]$|bc[^Nf?~Q]tb `<)x3ux1 \BנϣBn@Y2mcuT*q*XJnb)YB!mI|UA iiߞۋbSm ?XZOKlG*l&D՛Qx1)e,K<JLX=IHjyZLڬ$x){`qW﯉c@lL<b6i)eF|2ꡨOJ)]5Yq_*|J쳃:~u?;} B-nhf( .L=nkteP!7ڍ0v,ZPƍlm0Â֫ &3TO؋l) IcE!Fp=&ZE ȇ!C~SKC 0dG^ڢ$&E\C(.(]*S%"B;O\`϶fLb-f̐ik},pm=Oqxg_z8xMlӛcWUƳ]uMskSb$w?>pj=ẃpsAWy>D)̾bE -f'b#WFYgOG>|=x_?cw\*7<7n{9ۻqAyDZ A43x"0knΊҎ^kQ~zS9?lueS_sgv(n}ec#OurD$%2G65^7lo;2sYT{w26*,p#>I%W*0J ~P%.XJ,g#˵^8.$|ڽIKEݥ$;§Ug):G1!꼗1sJ! P"/6ś@c)T.J)B90PD݃w,6^$Xzۋ&UNj`{停Eyk7̢suB^.x%{9vUJa#J䎢څtA?:xxC\Fp lQyKw-Ӈ7RŁHu}\6Zj%+6s3L]"- Mu# t),L"N8>ŢtJC=~+nt,"๪i@h>FWxq~"f'-u%MK"} s^,Za٪+mݼj`Q"9K O!S pdekf(ܖGXl=bljD~u'&0\ƛ*NQWHuWHg!j+e'DB3Q7K33#KSNL^7aT#_t]GT'M3(eʹoC~صc9nnkb~{s?dzRf7{) 00l\Y2@ue5otEO 9g=pܪ`(:5܄sbM7OUĮ_րX7t >̄{rd˳&6aqt24)|Uˇd&M%P kPbP'=W*LecP{ߕ%v+Fۑ[3 qV8v$aK<<Խ`B-BI`ğwQB^~'Pw=ʛem<::sU,q(/EW$l:EYO~zV/34ʏ'#F>[(ny 7Gf'ꕉrgV5&CI`K[%u֌k&o'1Hg?C6l@C;ct(=dZqb GZӼf@JPmEB]2@)Utp@ 4yrE:W/Y!9!iF]nHcQľ8F% mo Ͳ ݣbgBZ5ҠQF{^.Im. IDATMLB/Zq=Wvh{ˑI2&IohP ]bzpB-n"ywQؼ%?Jp_ǖf\4^P Ztu<;޶%& Բj=/P3-RFV}Eqs@]bZ|J)/!hIT$'Hysr=Rg7> ؿ=4oz<@e[a1ԵRb4Q@_(AH-4Dd;4Zc` %lOl&T)e ?]YLi=B##9P:NFXp./:9vn2lN2V,Ak`dTNW99MCbv)I#İ,pʞXﹲS# Tj)5r 41112vpC"\B] f32 玘NY<9[}#W|+Jkw^Zܼ\J߉eARԂ*dߑ*RhYi|ӧa0{o,} cAjcE^ͫ壟hxpOsLF'-7 R"=4_ӲV:GƁ؅{O7CG,.8j\>Tb<OI)!D ׅ"^7Q^?@Ul[*L__XL6BHuXN,%!3/r%X ,E2_7UhUYQx:ZQEU(䭨sm>]|.6*4Usr|'~yT?4:i!=w!\Jy!Bt)WV3Y;}~Q{{\^ͻ97w0{76޴{&>8Pz+pѴ4Ӂ j)cKx2Dm=qd9m| sPbj'EkD2dب9fJɥN)hCClif4v-F<י@=M{}Qp-ݓCQy6se[,MSeuG;izpBx99f_uE]Dz"IBx)1CPQOn}6F->>:|  ʸ<&۠yuO݊Zxu9 ?kBaMt0̋(hTCi̴W30 n?߾Al;L 柫s7Dљ'Fqsv[>.;t%".JT2D"JӴq Gqp !9ٽ/-a`pm6"#;:G r=ytcq(ws!ntK XeC,beD{Ehs$/fAq-jx40cAyW4:`!G eޛ{]{?%?wU~1x v37'm.~\6.B=^^,kh D׍f:Л?dd)f‹ת5SѾ 4E bcM?s^/=S} v &g;\V+@, g !j]V\X*|$W&3Jz.o۹l 4<6l|d[CRjW3&-!Z%~$D^:VV :*&x৤~d)"PJ?oCy,Qs,b{66>vcv#kQ\޻oBTRʙuA?RV@W{nU ٣Q.utIfBf6㢧F+1qn #*E:& 4h+0X6x/ d4h3_6I L݋(-,ۢ+*B%;#՟ǵIG^cedíZ3Ji"G[e"v9VKt Z/%s?Gͣr 3UZ=|n9axY@PYK!{Lz.-Zs%6lXPsmDƳը뙈0~?IO &ui:r(ůcQ-G% ?ҋ$ 㾄CcpewŅdŶ(@+$Uk{o)E}gTkǤ/r>tAy %׳.儨G=R6VO5WY3"b0=X6#籌lETa)+&C:ND3F)f096y?6/vLo|%wDg&raS9SOVS^*0vS DSO_?|hT\ٙ~k"3~TixfF$ bѓOSV\5p (g]uԣF遧 ƚӺ.TVͽgΉ\M!O6x![.7o& %󬄛5mQy/y$UmX) $֤Ҟ*_3h&𣭳r|h`E>"Œ!L7މKW?L@U}X^(•or/\ K\rU`5C(*ZAczh)X~U?>/H70(˜ZLTPmA}'ЪVfq#M1x;*W`aEIHVRMpv8s{.%M7?g% L (_~ UA:AG$=ips?+f[8WsR["8Ca ?~! }aXA HBh(+sFin G#"Ur˥rOȜLײ{;'cMG5I8F~oP }?;|] uolӘ_:wPGZfa?=kns^<$rT]׺.SG;jDz{o_UmtUxA8c_f7ͺOUJ%psp4ëٰwe3QC}s6/q># M,O*ta%^EYpvХ8%+ٗb Pao% 7kv rk1=h)˵:?<@taųeDFCPj1W5]uգW=EA}CgpöNݙ ݟveySsG3|cu ߼~ӿ>̾T'ec߹zpw]f.)E#f+[.d}~%_nZVb!HA + maB4뚆iF;7_*O\V?&MTGdK2<~[3f%y_:b_%(?輳 LZ9p =0u!,|6auzճp LCV ZTyTs$@VPF״BIʋx"9{ *crIgKG\Ц%>@r/InhUZX1WXz%˾tARZaՖ <]]1c}8R:i$hbIMPP@@mzz&ҼZtѕ-wk~`\]>Ryb-8u7>~pع~:-ZNmZ}Н/Pz?g]'5kkGn=gRS[5gTם^clqړfS}O#c=S[dfƌO|y:U{V7oy=yUsLI&=!!P$Vkekɮuu-cCE](QVe#z'FzL/{3LK2 fsϽ3ɴIp`\ D%c {'H'vbĜr㴷D)_WϜݸ9<럽a)nHDmZSvxD\:=ų-ɖK$vE6*3wf{s6kL?eCCOO0<7!8a,@s銻Ƒسoq6m-W $V'7*}_q,#F%7!&_)tu(,5}E3H=^I1 z1~"FMHmG_[Hwۛ(-lEjCl)$ճn$E)xJ$5dP 1~aGj@ރ0 3 IAm.ig51 >$= ==y0^6 2M;7IA.  xhiEQǧ{|_UvWUNZ8B3p>n` |=,fa6~Ia6F3}(8bp_̓h|J(I mq{*TJ^YDSb{Ww{wuMK 殛G>5>h)𦓝tMɮo9_5wdb'TmZdcw>S?9wOwF#ƛ~[>n}躍˛ZrY_ENcYεisw!CȬ YN2g Ƌd'gsx >AksiɅQƎ'rTOP(]* qR;CPZ co! MC 1>YĠV"ƠBЁ|T=$ \kw!ѿ@.DF(Ht!$Hy@L˂c#/G i|1^1&RI9=2"c ~86i`K<¯C"O[kwEQڋb{9cL>RٍAjsq0Cށ1n+ڼW[ߨN`fzҋ 6DgBja-HEJ1u08HR,!Mg^漋NCI ;xM@dD\3؆n~M~̶ I#mn֦"f< 4aS\cDcN#"<_AwT4#=GR p֚\>vAU(p؎q6[qtԱW˃\ x@r؂Ծ3"0hm6Sl{kl^<1ztL+%֦zvn25Ny_,l\W$&{e-kb"|MrtU_жyA3ow/pj'?w+OmyW']XI :ֽpo[f.xSS&Y{fxu)%泺+*,39ї-np^O/pCA43 |1f|`amI;e=dum{cQGDsXc9(2bWwe+>Okl[Nq}[[iS6;I,tB! ِ܅wCfjs_Ÿ{k=1(Ʀ([׍v\*'o NK$k\*xxw}v_:ɞf>qkĤ[ƵVvcoquD{[z2n^f1V./Kve+u'DWUM5㚟'M8e|/WzԮݝAF0 u6_d, [3f'A&z}\8et٣'bqo7 uד[X4~Z5ck#k7B I\۽gDz'DȎgkpb<#p][ĄlG   GqEjSHc]r1RH ?֮ e@*w dcj{+ IDATk֢ދ=r&m7L"#6vcFEN๓ ^nDP-z`v3}dR]1; EQQ^{kDȦImv+7x+b"d4q"Y'tbcsֺqB:jy?mq6@;,jr {}VW3=lﮎd=%ɺ|55M5ϥfQvתibfS䭟 >3R־^z񽟞>7KKPʀ`H͕@Y%{ܽ?\F5|z <mmNnI [?Q˓<]T o%wovEv:.)F;ڜϧfk(/ _sb!~ ҈W|¡fq  ЁȢvrџE dDXD u{zJ#hsz|&8nT6x(P)v|tpn$"UȮ(eq"'D8[k3adZb_'cmz*0Ƅ q)9 dI^~0ؐ<.a-" hBQ+CD"bX1o|!L#nf9ߗ(??<YhW6/[_޿F$Vd k͹xb?$hsʮ[uckGpȡz\܁ AW1xm{grTf*H1X*v3rJ̮MmXI\s{UI7ZtNbgDϼG;Nheny?- mpi7W[Ndi61;_S0ykcx8LD[J_?x‰v bfWmmBV[1|Iu ]oB2TO0,Q0&;1@'"3&"] 4j#߄GcfC";ǃuaH4ADp(V]H]";X 8Գ}ƘG8vy |>(>g gӽmB ۛ1wb#Uqr ϡxő9V,"F{* \MEQj;?/vń'چh3N{Տe+e+kɿa\7=ԉH˽ ɧk3yDbYyǘ1Q`LKIpFR8>m';&ZY^}zmkiqgTMfi.ä򴖘w~UYq'7d{gE+yAWK6\n]}bdHڼdiYu[=3dΔ}7r u1j[t[}YL@YY1YecAE"ưh7 Mr$Z1L|`uƘ $E`+*h"雝nf-b#o? &KE{1uY$5t"|3B} MXM]ƘA!DgM>?o]1 vQ1ӑR|#Kaq21QH Mq)9Bq`w(NJ^7FKb^sj^{/e+1dsoAZSL}խWmeukfيbmLDN$;77.&a/@̒?kAsm!p6l.8yJNW]y'P2a\l8/<93}dBEl璙zbTR`'4a| =oTx3v?c XkƘ3ݼN~ƘۋkꬵXp4Z1FVȂI|+9};u"[!dGmN4݃Ac lQlacU8X* Ƙ-Amd$j #F>043P; g+<͑z^_/y$m"'MEQcr6/[H8^{D~6F57[f6 n/~榖r_~K.h)ؚhI䤪x̊| x %$Ks8{U6Xj7&[;;.wz=$i<}Vهw޹.rbzp"]4cV.0:>? L0[ٲ*.b{kV$F>[(4jr9Fz#Fxzks6OBi51 _Ma.a_n bhF-l2D5"91/ )6;vS~,CFZ$:ΰ Af Q#mAR^ˀcZ_}s=:lSl0BZ08BD#IPOMxl8Ts5ZW(1!=S8؛t%zңwle%=7n gꪁdQIAWSawn$܁4,h.9"}׎Q|hU:6Gƈ55Mqg/!x7nqFAb!"FEL&kmr.F@J"`&x~ LnA1sƘ{Ƿ(&7]y8 I-S~i9=U"0b#[@Vd/hϮ'xL膝ƊQ 93\ۂDWb.Ajp"E}ͰTEy9"2H9I7PYȾUlC&]?.t;&k SJ;jMWm w_BYiW'LU=,g~SuiWviV-kPvROH[R:+ۻhG\y^ kE^F,HfMC6vK@m6\|htH>c 6ehD-0y(4is 4;M=1q>bM/+c5ʰjVcjdaAgRم<;A"'! ,DiX]XM#^cLH 1H 4"(2YAv"yDpI" R}G2xwq9xtX'Jړw=XpEEQ^{uAdgIŁܻowC'ϟȓN9[={تXu3 sg̚ȕl[ZU1;%0oafNzkqgxacV.7ey%ƕ|{>cu$K~ՇZ3{ڿPdb۶5Esw'GS TIME5BU31|t8re p8^6C\̹͑2vPkms?@ѯМ,.f U Ȯ.dT> ycQ0Ra#\.baubIN`X(#FBZS)@![,F#5i6rk(k"ǻk(JkNe+":p" T49oʹYfm3'EMזo/.ZG̾qBͽ"XbV*hg`64Ow ]̭(;IbH%K|})9mW}j?ub1# H͝ 3('#DCp07 xII͝ԀGUǐ2B! cPfXza IckC>TEQ'Fվi6xO'.qM\m_?S׍w2=5o>m3;KZKry"j(D).`;I~ 0Φo u͇v7=t>7}9gێ7Lmn?ys_{2 ٜi3gF9\O6{%uF сAC,A{/r}v9p!};wF\~qBkk E<-}g.y@(EVAmˁ"m?4y1f0Q pԕ%"] !둦.#GL"! ̹UQ$ΥoEvN :*(Cw^P{59ssIW8{+wK7ξy_;9F 2&tI-'zW{ߙk5xmoC56י'c痶{ϔ6i-6EYhDD\H`?CYm`iSm>4ThIF ,dy #Y_CWW,8{ Bwol-OzHĴ~X<> 6IѭηBdT8DJ[UcB("1MnϚ~3afItI~U[wvZȬ\ @y.bƥ,Q+9@-Ƽcʬ6:pu?Ki}[v)c|l/oVw7#iǞvSW ڼ3f6|5ZpynA׸~ wq^p)G kmRk1.T'i1oSw88 ec"wDlCc"S?Ǐqkmw_B3xP;Rr$Z$xHjf)2b1ȎߩHk1"݈!|YJĘ}Xt9|ƘMk,՜)كtd]~ߎ.{5b>@8UR+;z4H=V|"(h/fDSxWMU/#, 1Ww6>]{L\1Q2 `9c(0O:$n,->J`i{7&::JHGX.oItt1}쬩=yzi8ͧ!TC\ˆxÍrm\wu6? EZ5t Vߎ=T6Q/G]G>^5_=i,H߂hlLG5r6'hjntƴk﬚ȸ洔WWpɒ3̶NnI2Q"e{yQ`$|29|icv.!BIe53sZwouqDGCX܃uLĹ=02i&Ѝ|&=9dXY76LPi!4<™HayPurI׌|P I}<"umƘ"QӀƘW#i㑺Y1!ѳ}- S)}0_ |(s.EQc"b@NljNPE{\>)=iwm$23(zopbIJ* \ D"1'#Yx$~ҭi(R:fp P)a_2 uWBwYiSbm>ǟΨ*ShhˌIgÇO:X͐}XCsXYT,Ƙ80j"4$t1bJ{=}}$1O.>,ӌ1OZk;s@gW!z#5ɌSXg?ތD"N$b|E7Ƽ4(d 3.yrpL\7VFNhmN3EI0CU2et~ b p\#ӓ:bg;"L,ed H0 u) [(*ó!<9A _ t[skqkKM6:6z8PZ*џ6~Hd7|gk+eEx!r$x(;k;>y͕H՗1&¾ 1=9ajl_ `(d#'"Ngτ MmVPKsnEQ_3K[+KG;,`!8J}'!MDVuUv^_<鄾ș$3n|ǻ})W3xmm.rp(-.T_֨YT2Hn1t4\d!]XCxG(nU,vV1Lw>u!i-Ì%QEQi ֍r .}Ab @ނ- .%ksOڜA..!Cf(]= M&"%?˓JcƘ 4atCM!5 )E^Ż!ҁHf`6tT aA}X{ɽϿxG24E#EQªkLr5U׌Ly{goMwa=GNxT"'J7H^!/jf51^56]ٓ'"'b"oߧ=m!3#ڼI7>3AEH5dA1$5GaD 3ЃCD ׬\GDe E2Ũ7am}<=9ne3i([ȐOAdw';'/w#zVV%<1ƀ,E'ǣēDb'!=Rv૶X7Q?0zgjsqtw"/s,*G cB#cT4b$]:tx)Hg"p`|AN<8n<9^#kQwB ~(2u v(/\um皙3&$fnd8|7o=Q0>D #]qY>D]\ʒj1ﺓIC^l|wұwo}4g1{US4CfK1eC:Ll;{xjU5垢?sv0Ս,`@/K4ӭֶcn]YX( ; FB"dgCzW[4 }((HƜF;l>m9{Kb{[01\ 'lR=j⥆h ">dKb%Komʲ쒥^Amo<]Ox0~(696[Mm~/[x4Թv)]_O-Vm~rF2;{\ 2y؀4y8<{n1.F CZb#ŝFr- -ӑp1U7#QQE9ɔD0!3\m^vu;dt5 S Bf,Fc58,bē7#_SEQƒט#USϛ}ٻ֑Ϭ\fCYޅh<.>X`惈c&F7X\z/I$n&۽Dyg95UuV5eMX6 ېƝ{zʉX@9A> ܆#Q1fmFr o`,-HW29A6HB1FZ -w"z1犢(13W-뎉->T@ -6<6X Z#DL>!r;5Ӷ/Ū?]K҆"\ZB-.2gE9mp> 5 ^u"9oEtV3ԓ#4c&14}x- 2TD#vSEQ>Z3:x5r'L,yςu)fp]pj@,b1ݞ-3{V2dm4=\ gblt_ 3a<![[ߨڬEx x]u<!u6\ ]3V9ykmN7HD-=ezmprVϺ y5,G>:IQģW3+C" &ش^zd35@3Jʾh/hh$&o#葖pEAYk_Y6`o+'*j km1 +NI M nE͈`,A'p Ai(H0Pu(gqMeع 4{+;f6=Z"EQdiYC7'xs{:0SIDDK4Bi{4ϒ#?1U}3֞J4sX i3b6h܋lf_܎iHITEx$WL8H!ci5у ۀmAhEalőd/D"E_k?REQ~C<_2fjTXlJbvl |{1[S؋EōnɊmqt;@ڵy01(_UH2h[8F 5qGP3cn>`1E4I܉,1`Rc(ڣHHUy$ }DR]3C})02fki+(Gdi{n&! Lp7cE$Kk@&}Qʷ;0&%F]tDPCL! $ČhG܋4zqF:1buq)Pyo=H hZk3ƘjEQN\ݳy_N6ػӶVq]*;Ch.Pqpmj>1C=(ccL)[v[kc1!oGT`65S҉u Vܽl,WZ ?F>mVEQ0 u8QɋRm] o{1ih{w"9iGu#4MA2bӎGmN:f}PY6YT{zc(A=b?[c6!sS :p"NVGz糿uiw Ƙ'0*(X7}4:>I<^I7LCݦ#Y]hY.)iڬ ,*'4Ac 7CGz(1Df:nCFx0j=$:PCy4Kr@+qd,B xZ͢(2P=֘o8y/a9ts)8 h] P3A<6g98XCj=EWG" ssv";~QHF?"lBv2#FEQE( $TWUwO{ONil]4m&nis7vO+?9Yqۏ('8YT1&A! `mlF4#BCR\NE"7k1/[k{9((ʨ`,Id:㟚6ͺnE;MXs1\6G= ǂ6 WF9F(QZ[p?jY|@[),sӑZǓcYl58wk Χ]?EQk?%ۄnnDv;ҋ ģņ6!JGb$2F#r 1 k7ƼkDOFRZG"H kg*(qi+hs}o w @l qR(s$ʨfQQASDi1A"yA ӃEA ) -*hGR #!G2t;֪QTEQND o~kfl4+Gp]Ƒjs 4>Fm4 UQ1Xc̥tdsPD E]4h(CfH%jFgnC o {!pvHޛ(:c憺 <Rэi~ 56C6gF_7SAEE9fkm+j ;="H:$Ȗ^"xxGZDH^QEyYҟQ x (m@iskxk91}ndޅ\D&BJBR9? oOQF8SājA{ڄşip]دea@NEQEQ4ԅj͈.G7!sw^7#Dq$"Y Lr:PSQYTkmp1&.vc@j(V# IqWW(. H݅((767Őv[`@zB5HF.FRW(hs`W]c`sҡ(EQ(;Auő m74SH?A9JQEQeJGm}c k>PׄhY,&6}T#*ˌXͯܯ)(r1 u[n7cިڬ},*clDLj EQEQ"n.M)}PEEQEQEQzWEQEQEQ5(((ʡYTEQEQEQA͢(((rjEQEQEQCP(((EEQEQEQ,*(((fQQEQEQE95(((!YTEQEQEQA͢(((rjEQEQEQCP(((EEQEQEQ,*(((fQQEQEQE95(((!YTEQEQEQA͢(:o= e,0"#,0TG>DlIENDB`openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_18_0.png000066400000000000000000004006351413546205200254520ustar00rootroot00000000000000PNG  IHDRO.@sBIT|d pHYs  ~8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxy]9]k/J-nNMID# @03a =;Cad3thaqBıC˶vJo+dJyʥۄ1p8p8p8p\y8p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p88p8p8xJG!ۀ1zp8;>x>g^ZEc{ ǚ#Wisv!OA vcjx#羸jm~ m u nZ;g9_ +aE@q#yqc~c)K ԥ;p8^[v|W/?=_gx6QMXm!6}wI=gpqfB15n4V #`+p=OcMyR\Zp8ovմ/-{Hw`k'ZM QXv#>O<̢5+[q7 t"lT1F%d6g83][Mk1:X үx7&=Й}*@3i|FcmpfAq;od@^b }p8kq߮WYBA(泌AT> 0`aqS< G>H k!0︰?_εr#yr{:q!Uh"؝6M%]`MH3LVgD<3H-o济ŷ>x̢;y abSbګx?_k!KDJ  1Ze) YMNvucL,Yr?l5OGo;EYt\!i5,fSϺysU!xp8}c3hqqda 40TA" d-2>վߏYgW5<ďc!څ6YM jݶ?l ƐAS̃#xBS>E:aYt\! :3Z7Eq5 B/ !B`;#Wp8o KX}^*R A΃,]J8$$$Ie?ן 6kntt8!$6"]XckxMh{!Ľ2h&Mm1p8ՀofhQrlmTit6Ze2#_iC"6i ^a{;Zf-7G ؍'Z ;1Upnpfubz~q1&^ !Dke.8?Gj"׳cwZZq{| nxl]eແ`9Q q6m6NS 34 oa@!ф% =cBV*atQ0f# 3Pر}O,S!]¦6|{Vvv0?+l)*IjX;_gG4Úx#r ֘ "p8W)qlLv~lv _v$H9yѷ?៪ƀHoN lG*^Jc9*-)E&H24Z.}fywL2tl4s͗ÙBvr;B|czL!"g 7kQש4<^"4M9NZ`׹;=Nkt83ѯeૻON}h}"gAcV)FQCi~}i3Ae(Ȕ 8Nd)tyO/`7rA AeЍߛ n9sgy>"=16P12MX`ΥeqH1%)+P˾:p\Q+=X8>+?`z]#Xm~iq^,A*(zCĤ qzPE`$f8ix>Q9$S/N8˾q`hkl_E6GqMm\*wp~j際~c!s"$ MɓV{ p(OFQW9o8U[fZFaN?FIc/xvc>ŋvcG=}:O@ *aeNэ #Jm6}.TU5g` Sy5( # 4Ӡ_wmjb\~ys+~9h9Mw}iƙU x 3|z|GNsrztq7vB?ό1=L4=ʀW"=/ǵ˃{9[8I350`Gt#, a1BQ |x !<ƶ$!νzsEov8`Ͻt?'#W6+c7Kzv.spNm%)nS~vՕ|lnUu5x~h?w 7c;ͻ6B2aDDۡ AEQg1BڛtA/4(i#PjH h M;сxf];dT͠'rtEӋx6Z#?մYxR δXs3,?j dPBBS0jY4^ƂflXY[nLt,jDݖosNJiTC_mx鉱[S>}ɉ 34O. O-̵JjZ>s6]:E-H,U|)u_f7 ٩G%Fku:Rl &0F3ZSKWC44c7P` FKJ ܵB?dbhr`yRzZ1(?3gil^ί+jzD1(` a/3Z )K^]bQgX{vƻ ^W Ys[.MaVcHmk-RG9Bcf.k8Ǻ_8k?Ys(cׅ=Zm~RdۈMD@Mh%PA5>na%ʗRqHTH M. T&B eisVg[.O5woyc|e0~uDԱtgQ-t 4QkK #S- 4ah@6'a0*unZ5žy16# FX\j]=n3E6k1(,qH=(J<"σN!]Tj8E/}S~+ Y<3#X#b And?Mk9uBQ5qY|EӘ"X$V3lMbf!O4 IDATU6ȯagMK7 geH@Jx1rDK pɺےpiGDDPc#?@mx au%܏;wA"|WVB}ʎh ːf2Yj$dhB=竸/ȂMɦfwd^<֮%`l{[ǎ,ܩM_c.XT_흆Mm ]uο")+G$Ig@tdjiTQ#Aaw#*;&Hcɓ7lB)JJ.Γ ƪ5^=@V<,F–an`uCBH9J^8VVi"$IRIiv)ۄḪpf̴)sF.N_g֟¦ތMA-R75Vc`k+tB+{E:5Q>mla/ak.kMQgYJv(7*hS++.`MEv8+V픛/?33/:|^{ k,[6$7 MI|ɻF9q < B"d lDWvwVgic/@zAOTKITYmm0'zņ?452:4׾X>ts@cGN3Ϊ _&Jo& Eҥ7?ǐ({l/բO+,bFi'@e284s\6㎙>R>'`&*V@ۣ?R{eՇ{9_õq̀Vn]Bjy,}F|:l6E,s $<,~:E%@'wJ!TG;MfK Q[sTuՔ74NeܝN/rK@+ o٤5\B ddSJuI ǥEv+簦hȰ),bߠ,5]St lNkBLgX&;5E]bRElm kClH߿r%Wp\<Å6{o?Кk{6ʵHO̙-&@-`Ĥf@t<5G⸽/V륊Ӣ~Fy@PH"Xނ,J@;^^D3NxHaNqo!ʃwtiޭGo~~ n.;!Aj .GJ*%AXH34ĉI&6)TK[w4Ѫޑ:05`X68rS* ˲ ete$ BGBHf-j~PXT4, j0۵U=Qez!lS/OJrY 󿡐Hu\!}0uoi he;v31쨑5<awNǰ}Ħ~SR{#XA 5Sp P^a>48|&EAQ7(H[uE͡7㱨=~E:0^z.g]p5<۰uX>6Qsf#4(t0{ ڶbf=Clj:QS;*34 tdq @}CKcK2yQV\oL@*lqxZVCό#d(fʝL/b d+Ntu[J+lzf5*1\Z^t >H)׵ TgVHYTa @T}k Z}T$m7f(drq$IR @} 8AM JmhP>:K6Tk68)"JAktگ qKCu\u͢Nv_UiIh1if1oހ^i1^!zmXSSVYiEw:bb5IxN[?XF*fB/ y韟؟FZ4){=yM-5B@BHc%qױ SUņ mMEk{6|w][Ɏl\ Y}aj8t*9A(1Maۜ5~qC. dTքa['M̏[J"smBR2@ c\g19.h nv3ũNe3> 7oľJfr"ӖtRClGҠRlfJc4e^W?ӧn5NJ(H׏oФ&;@T@7{/?k5<5dݔi#iNY10b^:zTR)&j1#ǚNwlDR[gCkj;Σ@䣍A) H㌔$APi1&ʹ̴Oɏ9mv\U"l;-XC1&^B԰&. V`A> ;^©Eg^El*N +]z"YЈb©lѷN6/TD#jib nJr__DkREFŦ)}&K(:L4cx[&w6ofw']nRQz':#$Yr52̯] L]C>C)*B"AX:RQiY;ڜdt*ZڔCO>Gɲ dj!!I` JV^lڜ!$ גMnՂRH4ӄJ rH*(U"2릹0'QMD53X*hͥGgfBy놶 ȥ)eF6q19<¶aN4XThU_~$} ,NPL6:0Y\KT kJc^їQt\͢1fV1Gf ÍZk6`oދmbSE$wM,[陵Iz];/d_a:kѳ"ͣm,ak; bY-RQlQ'[x:k Xsb5uz}h硙Gx{2> _6QI칲դ niVNܤ %fۛ_C= C !kaƎiztlFu6I*! ͐p5F"6zz6{#˃!ãb2^ Ey: [BʼnKTO1?= it0&t+]i_Σ70범ĜІѠֱN:2&|ht[W T òc\&2X<|_uFT{3VN≼:Fa5O t JbRmnd %%Fkt  x. W*y]EXN/(x5")`'-N(m:BTj2XS=j JPⵊ53ukƊT4]-`S5B"Z.LX|R*+}՚~e56zp8W-E <?h{6f'!I^ tP&V4Jf 1TQU>q%Ee2$RI:OY4ϣ6H@C.+yhִ:m"4E)E)%lg ,_~m0o=ǕS JD>X41 +<)*N5lӖ#'Y#ب+(kܲݑ,_W7=݄ʏm_~LEQތX䷧X|fDQ=gJ/ն•Y x6Q/?=nQ|W%}?byl=p8k㟷jyoK-nƩ16i]~4qM  fGoD0U㢗+cԀy;:£hΠ2— [0Ҁ'y6?%>?.k?/r6>q\\/g'j"58Ug`l"]o5)f鍈j[.ESg{kb=ؿ饞`iY!Yl?;K=@HU-"ǰ.og hVԮWe?|6h!ac%bg/:Ǚs`wflr&mƞӾxfϽM$H׬?,\4-)J #ZL)A S َUE<앛eHT =X4@}#qeSD,1>zJA6^?p'ߘwYo~'7PK蔱G'gA+s螡770hqOl7?{|$_\Mgd2)%t#N@|  Iէyғ` De)9h3R2X*}byf[M|)YJfZaŸ H)yH)Q)&RЙ&L#*')%ƇdtpX҃*2:Ie*%JDљ1qJ?]v)WIA7$Za0`gߢRd| z;I'F;S|?3OT_}{[._! |W/]u8..G4{Q6yƘKByJ<~+D';7kۈ2]t<վOl˅#xNb/aHf3^ {E]1Baad#6uk:oÚ;aak5O;m䗛XI.F~\aMz֒N2m8DB߼Xvtb?m4Ω{gspyiϽ;>K{]f Vt3ֶMNzmGFTssoSʠ >:A6M`a- YWwAy:g̓-:9[Z$Ui%oeC A 6M413@DAO1fFLL LkB0L, la[lm cKX[mmzΙ?Nf[*JQq_{2;tO#x'AxC;$=[Ɓ̠Hvf358Q"SXK:>w~js?e- οy=ZS;̻~[FsɃާ/7|JAr@ݱߵƸg99.WNy|i5\AK8Pd 1Y cPMQI3 ;^B%25L&2v*&ꎑrLO :=sZ h#K>ІKK/zcSODI( xV(H$־4(Թ4ϣw~k`3#Ćz:H`;x71m+f6yxUP<)>Z- -KqE,Y9|'=[ IDATcg?5:CoaKN`IYk*]6|j8ѧ2B|Ē%wcXRYuS_Ű('VYWiiE\W;iUHb%Z89u(x sبq5!A..r<.- >m-l))/6[1rWJx^/X1}.7MiI| e@NeEwce.rJ}!?s?niH!7޹T,Lg;ޜy'+Ήgg^y'E?HRH'd'1k=L[/Cb1HI<ֹ0⺙+m45s bǩy>ZvzjSI|TdZy zj-p)gFUѽє(^e7";zίxZ\5ic 4vﭰlLz!yNo~va׸3u}>0]4kC$X0v~y 2x/y}j/Xvyxc6cJn72N E8Sy."t=A 0Fb1H\xW_SEgf&N͍܁b>G]:{Xp[O<2Ofg/kM]Ů{B;nBmeJGlo]*v0\efʼnsY=U;PLxs\k ֙E')ݧVh^ld2r}WQ@/Ƞi#fRD1B)i. ϗS͖5HB~"A0 &H-0ЊBڐ)2VcqB2QҦZ\i)ggj:ZfZ yur1tuc667QJt}0 E!F xUh@>98R @gewLϝ=ߓRlݜ.h헷q6s|Unݽw-~/|-)ͥls]| =}ۼW,:ѯK45Vh :Jg!ɱdÒu ؈6b~kk'b N0g˱}]oK+:<Wڎc\*`y^|*0بP"8 FW¥,*QTP Ij^nBEBbz}5`ۿ `ޅ]!Xދ-Oa|c{.$U::'5BakW# >:0cwCX|)G͊Z9|C]6.3< \ rd 06ET3ݎѿ#Eq͟@wܴrhɅ' d} qz0vT'M5\}3dq>:?r*>~ty eݹ 94v>WLǏjMOʱZ+[Dn2Tfc=m #,]3Z:0p$qLB~z!]ͦ0~#PZZOc#v> KVX7`;] ܊}X2+Ǜ+Qu%=cII'`X}24?Vnq,\(*X5P"/tPfdK[)+ZR UsMƒ}S$y.Wi_OKQX'KP{S}u!%ox܍R9KLJ\8x O̊ #!6-P8 \ȫAB۔Ԥ|ILOv ߟ0Y6z ?MQ[,[kb3 l6d?I~?xf?fC'p 4'})pHFc]OϞM/x4х1ዼRc^V@A5!ֆ4[tH])Cqn5Ipnf]-4y( QF>P~;H%Bǚ5aM[Tks"7tݵL7 ^D"e.o4I]7bn!}+KtD}\j!{fmUxp0 UzNplr RtV'ꞯk@k^ckB~'e6hBiz@mrpG롌fj|qC^AՄDef~a \&[c Dm@?&f֮9yWW,C6p K>>1٫DYW1R +{rz,A !ncB<%11M9$Ƙ=0༆䕦\ At,11JeS6Uo8߶eu\4qt{z,` {ƪ*=wXO`iܦ;Qŧ%1=l}i ¡=*]I UB`kU" F\UD ,A;ŽH[h0>+5[Sm0|?$k$۲27簶8C̙wcn:zwܺ*~|Ga9 gJ6x<ݷϳ\m4k?sp~7:Q+ "cy$:Ó>k9k Y:n_loK{c5dxaƢkM]dؘf 5Z;^3 c! Ǡqj>PPe05/ju3PF$YLwl0qqrz0Цt+E{u~jL K'q.~ٙiGS((r#FipH^ICJIg8B85814Eî.!FqU+ϑizIFs~*9]<.[(c3~Q 2#5Ħm:xIxUb2υAa 6ssY%)y,$B(MhYcL3B(Fv^O=l}0 e]VV:֐f F*gJQ-S0xAeK jWumc  ĨaAltzu \SF?d<Ƙ|->^|>vxp·FNO]ص,gYdϹPxfgͪb. {|L.2H ""+j<dlm.6'/y=w, FY8M&I,XnYlϝ_,aGڃ -7DIQk nLGa r9!oPXKN3Jws&geq^(O4j'G+x뻎6s4 $\ (Fi#A*r_S.^* HGO5k4Pq O]6OΓOtmVu襧 pAJjq]d OBQ?΁339M (M?`GZ(G2hҏRrxbA: $cpz2.YT2m=Xyr}Em<>\41,JRTB p4β$Ǥλ\(>oZ,tKAXrx"XCale !Nb:64Bћcm^)Iꇀʏ\Z%#F߯V]_a]"r*((Σ~IU*vkl KO`ɠ.&η_[Hcxt5jjyTdXb(vm-6`gv+w_%T ` BYǸöꅭTUxx°x;wFc\U/ZD.֝& _u!9<=]1rr)rcmsΙ]Dc}g5H#zIs]#雚9=rhW FIJ"WYNKM_EaR#Bim7U FG[Nފǜ~;ٮ/O!\V'HD@shaȊ1jE $Iau{"RA:`x!lvw`rb,MӔ 'v L o'O?{99(r'VY-35?{L ϯ?vE< XsP1h `RdjMx(SXܧq㼸WR#W{Cqaaw["~^<)s)TuPuO}<}4XY#]#x^@#8va$gnfA<FH ch4j\=14.0H0D^@.9>Maĩm~1v-GoXVϊM|G~<䉟ɟR}/:$;OYϞ^{1HATu_>1W,cB'5p~+E kXƸI&k/-B X}j"n~W(0ų溸B8*Ǧ9!//"ҥҷl0Lj3Pz@"Ǽۜf HZæc,ƘT;cZ'V=Z!1[5cxLcweΛ*5g1*+]%쁮:Ti9P3ۊT~=5,CA,D%1^e, $2 ?{6;ͅR$v_6?Xoo+ɐ#^E5rҕ(EvM &ȳ W:(r$.;b CW[̘6|gde|7wnX8,wvs?ULF!5/0*4ƕ( T*B!4(\8Cc\Bf9F,hFZы1םz[1gV[ngNɉIyit5󂵍U&''C4Beup<)fKA3#ZFxGelBֶ}.Y2=6Ffy}zl{=1'}.c@&s߽S=NIC)@~g{hs߽Swy~>^p>Or_Mؚ?.׍1{#`+A)wQJyy5ǿX c%1^-(+T M2Ī0吷: E:] D'l 6RX=Ƙe8VWYyO{{iD};~$Dr;jr%UQt ҈ /@*)U[8q̅0vHm-bA(uFԐB'y2ц0~BeNNR=}T:pYD95h7<2lo%$s] yy^ޠ Rv 03 Mjҋ B"aV7|GwloƮgKx1DWmUr˻o9sd " ![[5V/qȨ8+ v,z+R*^uXF$=W˿vBћNK06S` EXI{)(bHA`,ۖGe㐎b.B`L^GG%ƊWUziuJj6s*^+Y_G=(+_m/Ur:J* Q!9%zGd1)=6jb݋WL9vj}\κqY,%@ci])۠!A܁C96 xi Äɼ4y;\$SK";a$^vF~6#߭l3zm^!W+}Y(%2&l$Fܒ!B,С&C#)"O3..awmQ8*(do[c%UÓ矠1/p $9(;1lc!jV obQEcW*R)6#j)+z,U]k†48Q84Ct3BScC8|RKBĐ`Y>0h)D9=Q\lUߢa`"3beهD$SwIWk(` g bQ/95Rh, "T TA*6yz3\mGF-V*2m6 l}ĥsK"dG0G rDEjб!\DNEOME\ H،5fĠ:L_Sc;[u.:g&qH2S BME踢REJM{8Rqi$~?bs}ֱ$dں b>8NRa$4n:[h'K7l3A)B7t+AgЗ;iDt`#y{YooC&f_Ro( !vi I-]VeiocY) ḅcLZmmKʈ<2aMMz1FwYZ]ؑLMKNv%  QMF>Ps:Q( *tG{}Z΢wj\D3 I|gg!1izJ)-|ѨR")$NC$ ¦JJ~mRk&W&Ytw|1㭖3'= Ҷ1BK|_Ҭ7lk4i'[;v=v0AĤ0Z{$΍Ry#z^Ů1iBQۍ\zA^-77Q].c !D$6R%%3 1VsSƘ7B{ʉ+9Qqlmhj+T0v450:(錰D%1r{56թvj (ϡSbiKބ}&nn:T2 #+x;pn r9mϧCcxx i#dKok}ɍA԰Lhj]6!$ ܌erHK= :.񐟰su~ ` l's;9.{ƧRZg ܸoj|X;0K6/pQU%aM@S`s\f'T;^83֬8O8x Ȍ%t tDaDf~ 2XݽM7,d64k1Tj a(A5Gbd^(U|Eƀ1獴2j&6T"mwT.2ejFFN Vp]u[ngVV]iG<Ҙ)rק;t(x;Bh벹K5Z*4BZ-*]WE"06ͱVwdN6G^@TQ5>Mke+?yRr0o婚Tjn(>B/z\dha"7^"UBPcȓE‚aJj63}óX% ؇ǒ/F`1~K&{ j׫V!WVZ~ A{w!>#R %;o+\=Ƿ9cw8w3”9HM'HoNruF1p$Odq@PLʑ%1kT7wfǙHbۑf&L8pj~ E`mAp4.+zp}[n%ɫөgdR`gf(mѴN" qRH"%,͖@"O:\Q-*Q8w\iFqL"a_T/<1uq}~Q #$Us5b@(J!C_ƀ#m:7=cB^NNF1+˻q l ď"r!2,,4kԙfSmFOG~'ZPMθ81fpzueBh)[NL˪,lC! U߬3 TEq} o =߾݁o}wmO%mw<|ww2yW YQüE(&$U`0x[,Qp} 9gFWXr܊TƦ[*+BW3c̲}خW>Hj?8bs [z9:"WPaj8 k%2,n {} o>_- 綀<,mqcU,ڏuЍ@*4R,ˁ$'#;3l4R%9>LNv>#d{ Bs~LTs\N OSLT# XPMbͷ3 k5Ȳ?t jZ(M1$vظBzM*5P61\p>ZCfYNeNYzy|x\y˹oWW,6e[+L/p|#1#O3It]c WXi{r"=H;snj8ij讜{arjғAHocx0Ѫh6>qki&RDa@` wvKQoL$NVɋ)hNYqۃ^G3is6.C'+=F_b; `>5{b=﹣g8vEKp};ڷxxMb٢x3ÈOb ܥP&v7 ش``k!C GX+yrEN.{c.ԩ{C ƘGh 5z!dX;bS'/^騊`'m]m8TJWVS)̀dAE帕raǪH~TJUSaw1Bln4J+!>^xfm FuF0C!AIT*4S4ؠ%TC0]i|j!灅w[{QK/:ފ0<cI |3׭)O4zq=g:7 Q Xxذ6uxMȜnaCsRe}d0ќAﶟ;<1Ӽ¼FA3("AiM5{h_x#a8F鰟"}c7Mn$ $c{޿+P8O?pev0c݃撬v?IU dX?~l 987KӨELO !I`@XV^4j?{oeIy >{=UY;U  $˖l=Gأ64shnvr #nn(-Fءk=nyQ7)T(9'Of7"nl|>[\i8GNL\*]20J]JU¼ZkʋfRJLb )8ݱp8佷<߾2vy{vί,6Ƿx S@>a'u2*YE {U KYZw@.I&oI.nAߤ "B 8d['@2A}ˋ1RsQH! cpkXEJ亩!KJ@ Z)j-i4#}6Sl# P6yӖ%T%9~d>\T/YvPqNƘ7T9?!\J=ö@Ds2DIo2dx@4?-\)uDW!VO=Jng?xbw4"T j]=B7 0( 0feZ{meW8[*i/(XҍW+y@rDR#A85GCo tC?1y,\/jen&`FcQ AZLԡ)T[ B>']Gv/z<mT<|&׵y-_/܍y=r/ ?19>P5߇#WǞ?^^Ǒ3q|~"5\" !? W ] IDATm l[/BQ^Q%N,bK 6@+mFC+nաze:tTl[ovzWO\x΍g3}YwyLD1ͪݏ~eIr IÓ2T9uDbu']U['@ziD^RZ$+Rz6] ӠD0]/'MЗڮmFE&ef {"]9^:(rXi Oo\G%гS o pi;sbu $a>z@cxjx!ANIb3>7ls1:*Jwa4.VS,fKpXoz(-2Ő&C UB= aEઁCw= cRbz."yV]aKW1awkqm8ps) J#LQNr%?)!*6b`)ыUlPuOP18F.% y|eKBU*t](PT!\ġA3lP >xA Eë7^3sEV>É;8|tarllĬ7  2$CalbLƀ,=)AdZ5]7'nڸ[~JݍzQuc&h|!"z̀=ˠeAԌ|qWw4-%@䃡5h|db0ƴsUVd>g>/ 8`t+t^:o}bR6M X 7Yks6Kɝ еTZl*MK}]_ H;"πH J8H1y2quP^w&[%ni⦆{)3C ?gΑU6'%~ $%jRP9|3^ z'ps]3"1=x{݇h+iah:iwM F BWuf$Rô Gn*p+:`>5GBH.w[y`N5QIt&]\/E>,6gxxb"w,.>bз߁>XI+}63A7_:%˦5ok&5 0 t'%,*%J@J #>"< :n'@dK.\ ru'@,5Lf@H;*~i&@7Ǔ}w~3o=| yT%>u8 )跑l  $C?;\o@ { t.Rɋ7վZx=8;uN͐!ٌ݃O㓧8o&CF=ݸ$fP&BCq/A5zPSA%+05%):7 X&0Ɗy ˵˂0=l\%0vӠ Z1ʅzZmǎ`VNY¡+&/n/KC'.o+ve?m˓+,-S!Gh 8ǐdU e| mk#Dt= }HE3nH("t%5ӆrp)aMXT rP2\2yK1,Vᱍ[6N,'}}quvm(rtxHU8W0 Ӷ`n vlT`\ǎM3DRh_xvXg<صq3 *0_ FC < +VImCxtzD{ yzZnء1_@ ;[NB*0#Jɱ}6В҃oۈbێ˟VOջ}tz^x32~Z4ρAFD~:Ss4cL C޶K B@qԷn?8j໾q V끁Dj:NC bHo/kXΔf-ˠ~e5|f2Y. g^+aL ^FeD3I zZtI0ND1uMlR2|6 A DP%S;RtbD @;ipDJѳ=3dpІp08jN.g]xpý2'`}TJY )hɀJ/*`Fp0t˛e."9?Bz~-.DiK4.;YkU. N_\<<:0{omx-(/i_-jBIXB7 ; Tn8JGtYW+p57[QV;Š`IƔ1k )b/f(`At}0̓ ܐ$\4Ղ*`Ls7Tzݮ88eڣ8|*Sฮ۶Y&*r{5V.EqƉ*$Mӌpf몈/2+dԺXPW^6[oY@}eX= YP {@Ad4ǙZ+AdR9l P[O:|P@dqtVAda;Hڮ5I3h|_f5@}YLjw cڿ҆fFIQMt39~Zk7&ѿk0|_ݫɌ ɖK}!1Ǝ/ I9 :P8un=}be (Nj'5`Գ4CsHx頇OzxEOs՘AHs!  GI= XN% Y j@ j ^@,0\YV:(fAϭsI F_3MThzu-OT ]Y-4Γ33Sm/C}Ŕe81&T(\ƊDg8dj.۶5 +׵m5:kjrbr:\RC퉝[DqS7_ӻBW5(oHYXivQ-{rbvYVrv˙֐wEȽ \U0ʅB(yۂjE;/T}NckσƑ0|e?R:] 3x~ >{kSG_|:RifȘrGAײ~mIuvUn ǥ1V<,=$SE ӉVeV#ŶQ ?'g3%\u- Zf@ 1fq!a@W2$(kHPQO}5@WU#9>4ݷMlfI,S`:"EMBNUngYWo 1SolO,l+z<{v/u誩ˠqb!ۜYօRҰ*,񥚮.(R7EN%8y.UnVNrJ#RSHEPL5&( *)Aa>ȳn,٥|o*0t=X)q_9N"lo۴Q;p8 va>zn0`jhhI=/n[9G2WtMwMSO1̀r$oC=yoO/u_7_PRN{1fOCɾYQ=nI}\qO > "&'Tb ?0<<0`\nm:W*j3H- @, ckÏEI7XBA@ 6i(7C_7ab3،¿s6Ɗo},.fk]|\ }lޘiwObOIM+8LaA?FB+.,SA~Ҋws|"8w{nR u!0qrT; X$queK-17vap1,tƈUؽ!Bi&#ֵxZrUrVAuqE L 'iyqklzr~q=/ {T{mj1:_Z x#`Pu]ǚ)Bjδj ..ɿyWVownH< +nCǾ3KǖFXl/׎M>sE 3<{@r-R@I1z&3F2([x:BP֥3+(vwL G*GL'S}GԗC Ǡ)1y;?$o} 2id "ܐ|΍q$ UB?ӛH΁2b)id=m9码 "t@RB>i2yb֬-RirޚKYc#OǒDt,H;zˊӑ8{kY9c }lPwjΰ/ s? JP`:z\͐-Y kl&9p E<*t`}iX045P}H08VV*,dO46TuDZ}:XJ ϳMU0 'ʹeи";Uօbc8SPf'TĆ)T#ߥZ-C79crj+ʂ CM52M>bh5[mWz*o*,Z`U0 er>}%cw~Tl>;_ˌ1fXdȶMq q4@ct)[$-@Rt}y&0oHɺ=Yw@'gŵ,W}퀌MxqH ~@,f̀2@8dȐ5϶mWϏ\ZY)Ԅ]p t7:1"OQZ +f&Ԝ,ąajBTҀ@veOmTTPe3R0<,Ygb9o؜/͖iR59ƪgR-ԁ g--[FW D.R ~/Y|WO#߶R[뼝 :揀jkp$ A\ڧ)!&AHHL'|$9Oއd_NSq- 12ֵdqm Im?'1$3I01 t}~,cu>;9c>:&9?tN7i()бAV܂mdȐlf\?ADAC]>Gkv|Tx'龋GaU 3+g. c,l2ACWzQӳҀL_|Yjs%ԸH<۸ʔ{t%hn%k@iƫ_x}tcnLq}c; g(^5UF^_! Р<, Ad~P& Z.ne"2g9) >{D"uD:53 R&~U~_.^h[.W:*h&qT@ M=ZH{iNI+ IDATI^l\n_yO&(IM*0 /] (++@aߋ1V 2R![lzg@hoT}6c@{̓6*,FXBRz^< _5Z3|8`O-Æ6H*7(+B/$6wp`0Z>rLgy0O̱;_9?!G6=W0g {zEVR2Tэ/^wwDG7vH o$Y% ^3q{PS ) >"@'@4>"5 D,nN/ANI[*\=(kCɚ=~}kf$\9ɴ@^ںԨ%X&Q<ej49pHF d_ $OAמV>'YA.APΌi2d8wf_uw}9gU6~Ƿ*JKʖFԧ{\+Wk;ǧ&c[ =a$K<>G ۈr2")6V#;dN7׋(mܭޥe|ZSvA*ݚ U9- X 8M|Fx̘&7L*clT[ _Sqm(@DmPhDG  2`i$ {d3g@".(iۊl4@Y:kg԰h t^HO.YDqMLO4h`tĠԸ'M8"LSx t-uo =P%2!9?ޤV;O9=Sq]k{Qy&Pu͊uжQE)jLJu{h:sk߮7+Av5yY09 CXTL TZmG h`eV^m>xS~z,]#KVBM>žd1kJ<~MS.&s܂[fal3QVR/L/V6rpx+L-;΢I9l6 !7 Wm%M=v;>xfNwUN۟"sѭ7ӟ9K/=g"~rp, FF3x26Hf׀${6tРw@KJ9zlm"v@ wD #D߇~}&e$Qz'hd! "#ɺUl7I}W- :ـ{26AHhUd}hA$0 L!w5A?C 6LRBQx]vĂ"!Czcъھ-(GK'{yKͽoÑy*e+XaBk5;aF|^۴1络hb*>}k߳sG"j*Q3ى㸡k v60XtGLT/ʙy۫Id.5]Tʏ;aTM |j0\mx/ŝڷ|["6/ ]񀴦/k5};?|BSжi;sWg&Pn g:An4,CJ0ƾ ̯E11Q[NA~iۮ'AD0D[{πj^$XN/;@$ "GetI A$/ui':|3͢Tܘl?f%wDމ:.`}(y9qG2d8V5" R_u\PlggX wnL-SP-qQFmC1~s/<&M?ɽ`I,/E*f#zy Dj1=a/.ù\0P)k ,~Jov(^ Js)zN'p3aejF3 +:lu5O/RN-4l1dF҉ؖP'.DJvӍmwܮ|ul>}*6߭eߠwm+Yv=7<2ΆYG7 )Zŋiu@@ Y+jAd+&0jQ՘V(m4~ wl{+(NP ݛ ,k"eR;k 2E7{W26H,=@ D Bضȉ ϵV;O՚ʓ*{g \:levAї;"ǞMbnj|Lgr&WVсcv::5R4Zy2@,?7vg^ooŶ铞$6BڰNo:s#b1pCy Cc 6 ]tУ`!qƐuU{XWlp֛obs oFfRʬ[Rʈ1=>- 7"b^ޙ,m- Pa"MHV@3J^ OgAt]HQ@F^#_wㅲT c} jք>C o=|Yl~a,X_p(]v*wo#iڠyCz%8jQ)c0ཥ h.ì [@.BccVYAgfv(^  ˟6^ yCXOAWs0h (>x(uiϐ!ë@F3,HP8 G҃H~5OeC !@V $U@ s@iPr k D4drɺO$ 9a)%gg@R[|16q2dȐ!C i;h@PU6yTLNs#s? X.r0{`L2!* R5 1h36wVh.\n4il+ZɞGGO{Fj[=r9_@^-'0PB續A812XQ^clA.o{ n}Ð sTKe@ۂ0TEtY'&C"2gAelPfp[5P"l aˠz+ 2@TA: D0> qxRFR R_ vk &21mݣ!C 2}D- ð4v}kse{thi||[(W9 #Wӓr%EApsJϗqΆsK->6xTGB,.=%l+?%sbYVM"¨4l0BUiiT Tƣ[o%[<[>BF](UMr*R6o;ooɐᭇ,fxHz}/K@(yM9=~\N'\D R>it@p $ydQ*;֢gj!|D6~tE<clzː!C $ À&` ™gN.:ahQY9sⴽH1Ԏ*O+FAtP%Q=tQ_p+ͪ;.o;kO _ot 4sGQXXQsW(.lUS{2631P[R?ѵ@p"L %zV;}[o^[C7_uUiB5Pl5)~F3dx )n $ c:w4}< m 극;YK (A.f^$J,[J=A 1SӌvLVJ+*H[ze^2dȐ!ë8ĝDge kԗ .7@nCf|uk>A>yS~t&Ms&9{ޭ54 (J]+j[Ӂ\2C4,У~uqZ5(ꄞ/ X0Ŝ> Wс PW+{ɘG!}])u--[o垳p3d8e3,A3u|acqt'lњ2&ku$b%a&?>tJ"@RQtnͭ$J֟!C 2Dv 'C=bCɒ#Um{;vc'/^͕QίMwVc=P{v`bqr8v}ݻ 9\zmVE`1 5ݸA| eE = [/f6|1W 2dȐ00j3r |Nqa!^ Fe:ʼn_Z:۾ĥF$J]TAi <:GOXE7 +V$Luz9V K1L!Wq"/ UpR*x2E@Ӂ(u06}[oe;hry-^0匁1$_vx 62l>tXo "@ٿ@d& N^"# Ӛ<9k!j s =Mu)b"<ݸAJ^wz:}vؗ^˶2dȐ!CØl !!#BsT ܰ皫/e0mqř% EٚʥР*U0P7(X~u{pKλ{k=ZbɶZ1H!!=! d46yH0:$YM]pЁ0ږ-,s0v(2n.LX%MNX.D I13 x<κ­H MkEqMFҁl_|gs=o,zփmpӌX_w 5 d!Z[կoA;ހ6ل6q{N2me=ۼι"5Aωxx<sF|*b .{f3{q:!o+9A'Q|l*h?;gןtl$-Ӗ a 0:6]FeV7֦'VLQ]L?CP6ÚDl2R@ IDATK}+;<|]wo_'\³hn Fx,Гa$gTXՏcplh^N#Xm WU92ιSmHp͉hgz<ҤKbgrX,uK+Z~ϿhDbޙxg$鴪"X\綪$\.NL,٠2/& ʡՇV㏛3S'd\[.7mS9N@BVHK-`ם;<|]w}?vN'f7)*kд!H4xzRz~Lf4ed5 *"lC;^yZU✻+z.>x<_*Z ՉCv:<2'/ peR/J-E=ދk0M36LA ź2<>vl~frƧcYb"d5P e+ Hn~(jiE-f$&<MⲽD:{_By{x<3X1ι ݗZh/c>j9l FMAYSMYy9gvU='8ގWx<%CIʅƧTaӪbK{_hmbs_dK֏E26.jU dwvtdՙ HŨeMDk:a9[8KŃ23Z)$%J0nCv19{6_}>i3FDRFh,#D1)lj ^X;qZrj~OD@EϞ4cqέk;px'$-ǗMPڡe6 `oBӶ0[]iLL8[, qK0c+uqQy&IAlI;{oaD6 TyNYU$q "Un hq{/lgK3;qru޻,zALh@ tĆC:6iksy*8u ,B 1x."c"g{<sA[Qz3ǦpɄ6"5S;[=p=I+R bs'AX$m*Iu, ôQV6ಕyb IdQ1߁k9&1G]wخ;6{< o,zփ&bZ7Ј]3ǫ-NÙΔȠq+ِL@V#BF>BVaT$ N@vlx!)˯DU8 8֊q')Ʈ躬ƱEET 3$+-yEŽ*a>͸M.#m.is >t6^7gg?94Tϙ2lA}am>dd:FMbU?5_MGSa twfx<l3&˭N4}i 6kcLY$qVкQU{h[aCc&(2>\a>gcbiQQuuMv)u[T@]wU4K)PfӫO=~C<8s4>9S4hFkIENtB=>v~sWo Nh0z<s(4[ Nx|r LZd'HĦ$rBSQ1hN+ #5EJXR"qTq&5HPo"G:!ylx>E;jvۮ;:yԣ')QCu6i:(3h&T ]^PLTC>Yڞ :jYHxq]vc_-7V0Aeʪ($h )PL, XҬWn5[㒔%W[ bAі$ (sWr9bH]fmo&7^gdSDhGPx=q捷~kEϙpC/GF.8F`\3Qo{Pϡ_C ga;. sx<ϩqE^?M@W"DU&*+ʊҔ0I%,DJǵV&6@[ SR ,UܦNF ")e":}Sk IWP!:a &ݨf7ڼWT/Ym6o8ݺ7~я.7>> 9v9ZC1jfHbc0 #aTx6 տF>9{;chGO d^ʼ x<-"g+x"sߕ,ḜjHR0EM2N\A\Ķ؎/G*͆rvPVZ\"9&{T%Sh݆jˉ @ZWDuPCaD6YDhcwx*Z6oE#]B]g<#W-:訡[7BO/,-Dl9/rxWs!,Z];bR9Dy$960^x..n0jsYR"áKq1([THhrhYgnaaLY<v`2/0e*qy?a0KH’@tE a1-G*UJĆ?kW 562^l3o͋'@\@Ei#"7x' MCa4Sptӳ%9jT֭?&`CYڎ ܓhG:ϥCx x<ϯ}gnD?;a⬤UX1dGlUP!amU DiBw|YuV6W3qqf ˶xhY22 K07q,Hղ R8G$4ɝF_D zy=/ym{͗z]}ƢmAqhW^Klj5f_CSPH*>=QocK i EӲqsv[XY("qVCSYyYL"+SHJZ {T6,ǃU:eLH  bEE#*^`Bjp \U`~Wf*Ѭuu^.=燹ͻڌ^Sr Ƣ用SopŚedH5,*vkjכrSDrTcԝu*Ds/mcu O;QA8#m[ґsD/ ;cy]``  N~S V I`OPeHZe'Q^Hb W;6)LeYgŠ陊A KujUa(ĀU1n<.zv)CT_h&ը6ovB3Ns;>o#?Sd#6o"'wyoG6?o=E+""wq}YoM0X `4Y5#E.v_wPVl"~sux{JD ";tz[~ѳ7=3Ll.脌ȳL`_nI&,] Jli,v.7%"ؽUE}|)T戵9}r_\A7#kIyyiם0E Gxm@gçs;?ⱜL<=STs;eq#6fy8Ƣ9)"2 |DM٧B4<1]OV 8ƟBS"rZ9E`UDD#"rRǖ<$"+}զ2O~x$''D$g~#)\|o91uloj?g.y&5V iJaL*%*\ ͣ$\{uPF+LoٴWW%_^nVIږq`[*:r[q\ Tܫz u]Rqtǝj@ZG?VoʮG?G?Is;s;e90H1Sҹ3ùX| J%i9 DہndT\{2\^mB1LoPF$6ƭ U^/?ۀ5Es_B=̟@S%&NhNSuFXFc]> &;z6NzzS$sVTRm+.|l_tB<[daCfDOM.aY8a[cnZdů? *)umۨygǃn=G>]U b4MmLؒךC"gK]FT^WNfGϝ4Tq~y^D4isE\Гf7gc~W6-۳7s"hWm5M|=n>{-zM:$=f&mxB쓯nG/^C]<6CCoAk#/@Ay<Ϻw$k]>% fT( p0TCH*!,adr5KKʇ&fHEVVL4Hoqxl4vys`e4| I 9fCa#\5ly>pn^WC~ z]k[0遵7a'`$&J.?jr<6[FJ|3ף@[3xȢ(jCJoxXӡ@긾o<ճZh7&j$еisgy{Cwe> BCTZJ#DIJ&CrrgXp䂠⡇q{REFoFG>ou2 l~羰<}˪)@1GkNly+9ȩ^5=%& =PmϡM5ko׳"nwRWX&"2Q?̙`PcsQzi^d7m@OQzz H6#4uv;.\p9N&3Ҥ!=|5h&bàɃhZ.t!i !Tpv/CWbNx:^\b<ҖΡ|^ZM; ~/ۺvvUAp#Șb󂲴ڑn L:X& SQ %+"]n[w[.r* c:'+mы[,Ԫܕ IDAT=c`c]2n5Va圖6=l.];;Pm~$\ :8:{ا$x6odlPr^_ݬ{ rFk6/7Ax^>xq׷zA|CKH= (z Mv5 !G/ăz;tDmR "Ό.ڄy]svΡ)>xa"uJs=RV4[QCh_I)Myj=f494ϸ^TP;>_\4.7mn?Ø>mm~]}wKO572_\C>Vsrt<[ B2[#%N\˘pv: LD4s4 rfȊ- EErXHZ0$rPB!-뾧Z# !0T-rQt:"/7sV#}4ɍ]{ޅjWPG루#v-xDK;^ZNE:o)4'GG<~yv?~yXqΕ"7ȟ<{+ jFڎۆt6 =țͅw]@c6)gHlXEEfqP#q$¦Kqŀs.}mTt*s*0x# @/< t_DeԀ } }4oi*Tdg>-?&Mç)s_vRҖCX厰FUXQ^?!$BVjk F"H[ȳp$UNf 6òtFSA$c'TJXSB b ÐPBr*$,!pVQ:Gy;ԱfQcM 羐mA:zgh/ hFۈ^.ux\BM'єu!Uqbmj%7/Rs"7qjc1NM=hb-22vsMMsΉȋgzR2F<<.5澙Q׼QM]j4M]w,Pd!"ZFˇQjx>lx.m<ƯwOPlx?y雤ȋAB!RTB\ - ]C+L LuClbE2NQ+~,v;f{SyQYq*>eq /eP$;r v2eY8nE !hYpʎuGњq0}$͘f1Dhwk}6׏;qHishJ^~Cd|&xc"9wTIƷl GriZ7QǦ;z79\ ,Hx5hB¥m̢i+E:zZ(zVgQ1? 4Uz4<>ub桇OI羵P \hT. eDk ⊱R FL<d̆c)}[KΕWͼkE2G:!A7BE`r2'-Q#CK]f @# |v飊.s;3sS:\5&4Ay^d4jsӣb?Z:nTu߈7kԩFq^/} o j2^ЋcԈ[|M]bt28ϝ- N\ϹM)aYϙޕi(}8NiuCqVQ1FB/L6 M[y *Lͱtf9m=7 Qhs is\@p""R$- q f>F(ii1J#T 56@ADUL1c+αX@J'xU]QeB HR@(ѳZK*NڋfzNYY<0m~P=-xݏ6qTtViKn7i q'86~}:Ko,^H<1j -E8z6)g;eIj nbcƂNImf=ZG=wtFޅK4X~mBMyymzv=|죅C4]D=<%hPGe)v[86;dCC*8 *C9 dT%_` $0)*W}eM3e]ucHQnD,K6LxAr8X WV14!-t PaOVJS *ٻӟ<6)W/Gybn/y\~#)O9H 4hhs;e-؆Ͳ1Z",ϢʵhYz\V >  AٍL\G4ى~sa(6MHUmZņ|WD⦑R;?|9LSۤDXu<6,"2ވzfHj8ЋQEE/@Euk9/{<s(m΁sc( \i[L4*ƎG%1PDȘR'ab lunHH@+ =IDQV%ˮMƇ&ڡT-L4 iqt҂,%@( pa=؀FXkaT"-wWNR%]wJAWϯ9vJp0 5OzchggkGDF+/1Xx+> 'eqspK  [mB!A,µRb@A#b #NHhXʀXNѐrX1XL@;Bkʘ((LAil]*V˦i&@ iP 1Xr縲7 ^YK !Ja6$Ocr@r*%YɃOg.oϥ(sw]QF 6#W@I4]hm~-Yۼ1|p7Xp 9Kϔf3/)D2@]hmhYze=Сcƺ:)Bs5lf212F5$g9}sWM=7 {PAR u0aL!:갰ZيhE uV2N"6j9f*ںh5e{7p0fbA+1kJI XRrzPD$AVJWLKb]jlPf- rT%A;DY$ !CHU!"E^YυN x F͆HrJ O5;;2mhNԐԴGܧK{N7/@D7΄m06COVD9OHD>#b#8XX\ujOf4emltz N?b{3&HlCk JF idu;=Zz#4 AAqIH +,6EY@R!Ҧl-mʶZZM ת-+{fvU7{3e/^fs?@ ThAkJ4ELUJWЁƦp74$ʡMV)k\qbv5 DdQQJjK/EG9.J g/E-k(0ԎTvazgU`vF 1Թ66ߑ;꽙;7S-7e^/ #xGu'5_YRc6xkhI r" J /o572lw_׳\Fkca2/9;/---=:S/stS ǖs IRUi"R8R"GckC4.倩oaĐ !"¥TMA| AP!Ry+aւFUƕ5$=eH"a;fQO qEFCQLH;#WJQ!fFw{~u9$Ux?NN8 qD?=)K "Uv?^ k1~oqqPE|9w}e޾eЊ;KzK@ܡc139XP$=zY>Kwε ---sk1F0N8% )g4֋$ 1΢i3ȲڏJк bb LcӊA)sQX i Δ2C\N"f*n".$!E}R^Ej-LqΗ~\HUL m4@\KL;%ZXO:7^\w{0by1)`'E䷝smF_+^:\b9_s+X---Q>?4U5b"rqc!1%9$ C!XWq-Z9'% V5\^_t;0ti\"AUD* i͹RF2RqX34DX9'!4)`+lwBNEƟšjBx5E:.͍~nܧ|p? 8p4W%Zs+cc_?xwGM_ e"q"yҾK3xB$|G_%a ;w6qιrW.J}K9s8$r@Ӑ z8D1h2cESB*( V$TM->T 2V59\M@L,% U F%D0."\, 1Պie9t8 GЛ @BB2;V)5aI,+'DT&˯zO C݌q9ox|Λw oeS}x1q oB9|lvʢ{_ckSkyiĢ2bl|û?teoݣ[q|8wogafkJ2ԍ GҔuMsoJS..^eԴX&_;["v8 Ɣa)Z1zL744BC#Z@-k]u ։8@(U@l^fJ`ꔆS=%Q2;U0ck.lPy%L8 %YO!{9kaNAB%uHQ^@{+[-FX\)g~|9IN}'wibۀ(D/%6gD !|_@{sӅi΍bsw&'ݓΣw#8/pke6ɊHꜻaa}ADs.{]v(gp2+/z)NrqxG[b{֧NzO6G/Yy4y9wAD.m hL[ZZZ^ 6y;m|~(mP+q"EQkvak a x MHZkJ9 }h82е!,Y`` bKKոQVL! IDATl`z,&URv $ Ô(,ƒ .\SVD`ê\*@8RE1P`R$ Tmo!zjdeoPA?2:*r}Ja ;~2onߟ>;"FBw2X͈Hg𙗄g<, >slXfc)7 {^p-k8na_jA^{xc/.b~9Cu}s""2~%=G% :~s_pc#곂 ĊBB\PbLep-lFVA]kF @5ڸyh"cc1Y@0S`BD9J@ uC9 $hVN;F{c&CMZ)1,pϬw$NCvp5% q1rk[Xqg L jCCdraQP9o&GF/ TGN.:vikoVRo02˽ ›-87=0V?}5?;tݜ᱃>2O\i;|~-7V,FD/?}ߧx~1\sRty)8D3xAn snv`!/npX*|y5w˰9~Z'?ƣ=_9]6<ԪQXrkPԖK ("1 ZcVNDͬ\P90 *ѹ(m,c^Cc Z?J8( E2aBKҁ8hjei"j-2Lbk␝C[3ҾX1ڷZv,teQYX\8r"P+pn1JtgJ3wd}z~ _ڻO?ŏ?o/{aQEr Ue:փY}l4}>{.{Y/L+o"[x?p g\xH|-|uAM/op" p\D>fo)7(}9WdGmoLKK hp=zsމy'V^/u58\eG5;KtQM\0@D͍1YpZ D ЋG@e4yHZ &s7jyy<3s|VX~Zq`? oA^N䍸Qge!YjK7ݯ[0>A0gs""2ѳn^@|W`5'8{}KM}M'ފd:V\Į:yaTQVA= t73AIY,ʙFIut:QJƅ8Jtu0 Rƌjt'&4CK<,խD3 RWv, 7̝Å QYgd M Onacݭ6B$J5"lHYI8Iܧ><љkK9j!7Nyͷ]3/]K=/;/^?vmlK^kY,Dތ~!=ŗ^eeR]/͡c*xQ8(;]fN Zr {k?hZZZ >?'xBG^1~XwMm'~,õ $AvlF~.nq$ݤva/ 35&p"HdpZCGBn =,!AZ)ӫ,`jFeQA( !1-1̜^ e@l[aGfbjd6tj/qOQry8"9;tQ +{|vϥ΍:Šmh80S??_٥*z>LGI ~-_6xXķǗ9h{3=9-\f&aCeVp9j^-={sn9U8v----w韓'O~m#キ8?ϟ}._׿VQ=0sƢ*]GtauR엑>*'GI깑!&p4ƨUuFJ&rN@9NZ|з~DB.5JX #0!qD3D2UN@fg4"[;s rB6 gQhP Z4zzo<{i;T+o-ں0s)>q|wp0Sq)<# C C^1Tﳁ'-"g WKKKǣVguGZ_'ڏnįoVG8{~8N7=dN`W^ueR)]ThWЦnu”$뒦 41@ 5VB'$*jf֏ƈb(Kʛ FSՠD€VwJI5]*٭ MQGĆ-Q;LF]%I3Ņ8MID /QTzO\"֚ Q!uO 3D3NgS <w6vӄvtTCV<8w㽉蕋$:&5x.:4Ibbm%' JEa07PNBf΁ii D@CXt l\^~@t\kjߛ5;s.CLqynbxev(pneY? M/%,JF41;]t+H/' 8ب ӌٗɕY|ݶ.iiyъo5||B0M<[{p)Cw,XB<7i-MkekR,v<]Js' 1B [ZZZ~׶'x>W7S[[;_MF/ng=|s2SD VD 1JպQɭUPjHw$ ͻ\xQ-8R5J95΂8 ݀R`\B;j v'3}%py`"fpqQ3bbzh|/:JxVE"E:˙YEP5lcckD.#/jxLؔ2CM~]`>Z~>ض) h`Ë/RK|^@}eq||)d˥awebɵ"LUs0"v/TeKWk,uߧ{oˬbKKKKkG%Q 7ϛң{_U?{_WO&_ho8r-Յ̦5Us}S][/g}qJ(J~tYU.ۭ 0W"kmS@XJ(g@ "X(hK3Ge1*  +BᠻCLD:Up6Ne--"Z,Db e;`]B!JȵeµKXrPjz `XR,g2.^foŘ "q]liiiiy $|*T.̂ϔ{yYgl㼬>vq{,pe#lmMtHe'N( Qi-5Q؞*$ݞvUZ`=;f 㺘ɥSZnt*kk(#P:h#DòP f*sJC.!pQ9XPTskMwR2MA@] e^kB3L&2  ;qBG_ ekr(ލ%]uKW[Mo%ъoJ0; Ys8|+^! ߃x,nAD$..O񢰻Z|W|v/KZN}8{Jq'.[ZZZZ^<*Iߔ}xcvUiIEbLC٭6pO<, L$jg6dbSDuiUu^G۞wL>2Qa|NWtfed9T6Js`S"pZ2(X%H&*CdTӐ ي^a2eJ.o9b,mŪٕ<1tDA0fU=:}칚#:2J0*>.gqö~w9z;+ L5MKK˫^,.{_JmxSƗ޻5p0|‹ƈ1/Ҍ& +,YfԥZ.7 u'm----2 ACy1<4'!x߿NnoO3N\9ƗOW. #ŖW wX^A|rŀYͲr$EAz};e(d=saxeVqyyY|Lߍ d---/Cs\o-<*Ig|F5yCҠuC|Oѧ+wBC{tYӣIPQSFvviIPo>}YδZm7:@N(g1C%)n9:=D ; Zbkh GӀP5]ܭRga j0r~t:2tbtS_;W%]hV8 ۗ6i7q[Z^Aln}lkB(O`E|B0g.W/–%Kwx4Q8Dz]p/_Bs>W)fZn5Jcp`v56;%Y|`,v]]33`ՅQ5>І@w9NF!Mc\Ytrr\?rvWn}m -$MmE?UNKu]c@ *\n bQ$e! b [haAhBa`Tw=L-ץp̜'|yGk~>oI:ƦЙ}d9Εy٣ذ&d8eٕc:$a;NØ:Ɲc8yN>g<lߪSKk{{?)R,.v 1ٻ#;|"2kK5f6Dq1݊Ϲlii:H́V l͹vE?nEc˭QI4xs|lN`w6X,$AAxVAfm}gS3',b^J5*1PUڊMk*1eur 34 N3*;kl?s,+JCy)aE:P֐Ef\pD$1qEG:KZox֍W/J$"9׊疖AD0p߇wuy|f3 >6_P x2T 9>[,z0 PRұ5{* ]: uOAzdddRԆ+5' dzZX,U°ɒii*k:JYGU#Jsq#Љ@4TL1JٰCmfk]TAE%?,9'|eriȉ#5k<]'{C [V=I8dX:s)p\Zp 1vu7#Tw@3CmmYӣt;L>*>mlniy>Q9݀El>n}Wė|ubQDV?7@"~t6RV ycn$/?n4}K˝hr]x;>Ќ _Y|fMD.Ldw_ĻѱK8omDBֈRTTX{`eYQnDIf1An$N4)`:o蕣]U]g"ܺPdI%ӹjS1:35[P19T(vIBGT;x8b8# р-l4P8u .:ۥX/h  1vP8on6ON dl6bN?1IBtvPۖaVְ>q̆sP{iyjbG>cs a:xw~?@ȭ.UbQD5~ -䰳23t6]>/ek0Nhgܽ~nxysïI\ϣFAkm!$:M@V+x@ jKU5PVe{3F]2Bcv}'g6@ZDwĒAQJ!Q-.DYZ%B#a4SR#>Me sG7DGW)#@9C%eCܫ(8/ǐ0p\:OIA1~flNxʅ7zCubg%e qtDDGUuW_#x!%%mlnks~ D 4],_8 $7r"--ǣ `e"mfYy>#0}"FP% "+泂LIGGv P $bض8{i/2{ ՃYN[PWrI  bϲ20耳,Xy_I7yfXc"dk}GlFzPoWŕ!W$L Aȅ<bLDD&(cFF\q%Ug_֧ IDAT} 0]6LbdC'7J80 `-(u4;UM@뷱n7r\M,$ͯn^]#` wn\R.E2cȡµ¯u=}~s'r73oz{y&^缶 Nc&o?0`>6rz0v;Dd$$lIdQ CpD舠tJQjI-ʺBN> Nո~074TЉ}RٌќCqT&IIF;ԄxݷJ/`P6Pf3v7:4bɢ-8Lqb99sjY 9q2gWpd}Xa5lwK|3iVt 󐢻3N h^1ɀyN eyGE*o?OZjډp4{9_oI,^'v|.p5\,^ rk\rs"4r0UKS?Ko{Zn, W7֗C8asij" "8 q5ʔb{˩KB0.iU"+%tzL,Y(6ṍNqy?sB;bPt '͉beNCqQ&br\ژ3A>Q^PAL5fR=`5aIN$Q]` ÀK+oʋtO7tIl j2WShy>\,e8sM<33#_K倫k%5?fA<=pП|BF/z!(5,\˲j`ԋ5RFL3&b.d hpGnjՔ6KFŜInU|蹜s'n&O# =9Y-xd!ǠTCPŨ8BtKԱLtH5ywChU(X jaxs]_3 dtqXJJ CW؜(Ě.%y“c}؆j=b3( (y anZi$VK늝";0\lP¹I NCieͼp[]7" d<ѷ}_Pmkj^)-(Jk:xq67RF~A);=ۀ^m/j<> WA#X.5"jo yZFQ8eΌ M:tG@NRؠb5sQ̡(\Q^Qvw( PRM (Tp+';"8eNNs}34uj]Mk`*3sod XSVsD~kTOs_bq!*<| RB3aBNFi!sx9T9fTb^ 55`oOJ(ݹ"mhlCf[p'AO{Ҡ]3}2[hƄ! b9ܠ! RM3夀Gh&Rd6[ j[z_ڲ3Ykx yM)\)i=dx8Q( &{ l%8۶"@*zz"jBvm3Š\ߞ_RԼ)|C˿[^avqȓb8ct3ʡ5w7p*uׁz ~;$WNxyf03ND!( \aUP"i {KXyF*9#ƧتȀ8z4ea`@!"zln[2dcEQŚb%zXN%JJL*#K2%Dq@Ҫ"P0.N X{BfL|#Qxaih;®bo"gŽC sQQ`J[`<(ЅȌ'^~bo;! TOSǎw&I5Ҭ ev2nqp5a\GryPphZ'B=0 bJkad ⹌4*6cRST!؆s](-Yse[x=V WEoU涳<8s>0|{m5۾KmaO? :xWzDdhfǀX(Sᛅ;=_In1f$ 8WA8gi0ö{ E!"h^ҵKqnhݱ-B0<04v|ACr[b4R C6MrU: ,\͋!f)a6f"gd\؈zփ%T iقrClxR] ͇CՍhC[(&R*b"F@ Yȍe(B%"óvlJ/}'ig~=y`連{?)(dGN>ǙSm~8⇀Ӄ#MiJ]MesN/pjjG(Aa!qIa 2aE@{7%cAaR2?%4D^!JPCӵT=KVŽ hxH,>Fe.RKX_lBc5][xM ;⼤\2lh=aB4.i*xI0$ %h#Uep]'ٗP33#VYq|S#/T'~57YxyS~k;={*tw>Vsy i܅Lt?,7t'ř"BӝiJMHEgěꦶlLs؀Th=Zi/Hd+&@`*"SK-CdJPk9z+\A1XIv48P0ZBoMᔕ0%+s! U˦Z`"K~`r"@Zؼݰ .i|%d,Vdֆ(%,@S]L}&4\U 4pcTqE:Ɓvm2s$4u5Ʌ~\S暛)ӡGᕿQ\bӒf;$*D'8dy2T/L!mF;^"au+1dBk6"d܊[ ~a+GG)1,ٳCpD.xtcBSRGA;u6Y*H5XuW͓7ffuz6ҀE/[O]@׶榸 WiQ8)?vpu9p΢ֈStRRO׭ qY>788""췖6\Ɖ4s5 -ʣiʫ64(7 NK4]Sp8kSZMJ'% #R&7 q5"2PC<&[+!)lץ̪R7S`Xn48TZFz Q ^E5mHB (8@c r5TqmqޣeAwxOն.Ǿ?/n;]pvo|OK0U('8pt18_gD">eU_b7"O_[ 0n|1Kʩ_XO6ȓ> B Ц@E@K؊0Bʒs.!> Y뀖j"- Ē+&(#1hc/`VOmG%2>⫒#s)F=/g㓬Wh+B<RK0&;]0H'PqMlc}K$V:ք$A `3B]PNαEH^=KA+INbFjć| B$rb7p]Rz Ա>jn Bt>_ib7'O_UEUpEԛWglsVD-e19繍u"Z,ADpN/_|+pk}٦)m~x:Nd # Fkjz~Φ} H w=[>dӒìGKAѡIت"#`Ei :@'cBm+,_4  Qp  IHz#meM#q">};]^"Щng@*vR pmz.؝pfkE7 >k;|O/|=6`eDXCESR a*  m"v򪢇 -rEpZUo٤䣛P,y 1kl%k0WJs% i8$#2`2N l\>^3 ͕/|EϸS\N@ mQd WpP1o i5YO  i@ɀ&ZeEP ~Ƹ Ad!Mi<őb2jQx]J1s آbNsb o΂ZSi2s8۫+%X6/l#"O>mSUYsr6p~pesӸwo=?Ƿ>Ko{ ]AvqلpѸtZvKXN; 5w""}(p5ߛmg^oZQy#'ZZVRI()4Y|2:&J, "(V$َG\LWbΖ jio}='x:؋`͌K{>Ś–u@kryε In 4 =C/L2l# y/BQIxgŌ*"d8{|"B%LpY`yɖFG#Cv.CAg ꒡&~OA YwUBӖ*c2͈[:E^A# ^P_qM`Sڧ>6\*a'O?ӝWl,pwN|fl7pjG>z-/T5\yఈl޺qUPdO_|=&"?\GJfRZ%`gu wGq; w}f<`m~;py 1[T3G@bD)h\Md K<p"tے7 jA TB*ܻ+bK '1[,1x R%CBR2B09AUG.r6mDC sv@!TcMP?"HVh3R^J$B53Pw$GG6ע֒ߢ1zBO+(J,Gq2n26>xe[le}9_* A8E|^J15@>L<)ܶoĥOնĵVL9p%\h}\7~W'8sjxK ;.$}1;-ǃ% qi~inHD;q7$"ud:g)ڍv?#Eí K ofgijeKC$qiskukj9NifmAS2R@a4&͠A%c4DV+* rXZlPK#0,0J>}P ^P@a -,䘡YW.x}Bb ;c-"YWᐚgw/$ÕACcv|v'akMM[Qе?pt%R2Wn-,SVvE|,Dw1dxf3E%ejYMBY"W\"M v5P iFݶ\]{qz_C!< >,AM Nɋu:i7?șS۟3J͜<gN8yz΂@'8ۼ|6s1gNMYp lfQ"3'3,km%"jQ|wr(.a>yzP_sK|%o/׿.2|'9\kx 5mvqkwO`Z.從ǭ_s(۸w0>;żQ̧?>s55w'>7{X1`)HgUjL+Hr|XC#3]j" &/ըT2J@հDu-HRB,okzK vA@T^D[W53Zgl,- ARMv_ȴ0ll%pװ.\Z e7 otz",>&G7)o1#vTL^\y:%zE9V)[F-{W,6 |扔xN0c>rHC&*mJDA~IG-` k*>f,Kpa:[cGFY]:yyRlqum"HM^-BUSazD/F|?#F7wIӋ@gX_z]e/oO5}v gy~8(lbDfΜz 8=.f/wlt56>ව_KiƵ}ʖ[z'"楺CD?sB\ /p^9%;kL;,HqQDp=}]~jKgzQ{8Ÿ~N;16r28  m( cvDK( * ,J/Kѕ@)4Em`] U\2)+BCr= BfJ ÑG^pE aSG\)hlhY%-svd%N_+/’ZDUKqT"?ErbaY3.K%Av D߭ *:_`Lй.GM`c/ xsĭk e %BEsn(Z\E]dj8҈2s\h&Y8G6揯᭮ZoՕ} 'hѯR# (Ꞌw/(>ߣA(_pINI؄^#C鍩BᲸٷͿƾm~oSG80e6=Ӱ1dGHLĢf ]_ BI*$ecK[uW>]Flf:<{0OFN4ir4 *켥uB1<t; <#?\=i#SBR A>ÖhZ]*6崵  &3?dz ^#pg c5n\ C^V8yZAN[ T׵Zu;|@ڤls_|6%C?2~^n7qo3Mytgi?mgbOcSa[|ME)\8G0M1\ Sӏߊ3%n<{}_;3#5;<R5}~;_ϤqY >Y !Լ2O?5W{׿LV5%s0[l#x ("4hEa'=Oa(eM,ģD#pܰ"* K, c=á`G i䨐_(%B/?Nth4Sl؜GS>m pjJ&l-vӘQӐ>"00 %{谠*ex)G-ɋkBz;6-0^@{8vZ)hb[+pVCdܝLgqE7akV_ 7ӯZ)5wi4Y{{97 `wKdQyu> _Ï)XiHCC]4T*H9-cZ:ǚDk*WT#CH;F4T KMF 8BJhyQa,%haL8L@彐դc+}xboUq|q e$ccEW=7Vw|jp> !sC|C x4{hOS.PyNRҤL=MQ|OhHW!<JIsBE a(&-)(IS0#PLK_e ]cA7RN#>6"kQ.Rc%xwZԿ׮yc{7<=x=y0ͳda &拸C_>YGoY6i֋d7ung%^ƩEE`VYk" >ܚ7i$oWxĉ޼ @Djͯb5gqkv(|v9[ /y=lkzd )}T4E69i\@Yi`%vZiQ5O2p5{fB_SLm3'O/߷ܼmnT/v(<UQ/y+8:V1?օky[ -p555o@{_z/ٷ՘-i:,pZFa&cEȢ&,}T2V0DL FAR˞$m rO7;Chh٘Ym챷n1-7hmw)UӰ>p1أT頋f >FQj/-mkC+d- ޚ4fn@H-+ՠ "|o3T2En0v`QߥNh[Fsάj,@y(hoZeB3V15A#p'pͿO.՚qΜZ}m\:kzzĽ,nNva]7?so|TV?=L#ϟNML.ط֎okj,{+ X ء>H^<~an9(:4a R-`py+# -0{P\CMV3\sr #M70> vTTM⨯جQys+ hfӌF>n2P hl& l[nVT!/Z:i>hKll=ݍF;=gmGcYd0!Qd&Ir%kKy䕂␭e0҂v25Rꆼ @ظڵp?c̏R/fSm>`1Nm̩[.O9΢wRG*8㸈bh-.Rz~K}Uk.eM7_|̕܌~E2+zZ S"j*:J $D4%[RAah`hߧY4!GcT:9d9wVKyҐ!¨bcC7agfuF?dI8N8G: #!j Y_3PĶQ,,#{gI'W轘K]Ħ6Qci39``4e`{2!H'a>C0+G9(8{[=8*9BU.~" c(E Ҕ Ke("$Y=[pg+uĚSӿ~+/pTmoY (QD7v5k׊ȧp^+F #o¯l(ΤGYQgt̹IR:}osV3>+.r2_J9= 9% 'F X ! CPaHcT"!īuIzƺo060+i?f"Dl;.^R#D,J" tƉQ ifD k> :Z3`3(3$=aRjx"E%o$xD34zMH>ԊnR~9M'0Ƅ@sXɐdHld2Ȥ5j$\`pFɠCKhzk!Ah3 RSb`2{e!u6jl/1SI`% mHW 6 A4dceZ_L$y9lmp @;7>yInwƕ~jyHī#o8!_x*p899Yow{eU]&N'Ԋ22Q\f ):([eR?@"ãJHM҄P@hj^P RՉd6BX!b1`Xj .ւQ"xdR7uIg0h1r r!RjD'*rjRgӣENhotN$ԏ[̿#f6Q3& :lb@ѿ(ILL̜gΡ2y؆o4o/qSz0Q }jA; R‰ c*hrTbhTMk1U9.Гo'w:3v{[;S'{9Zm7z*Q뻢C#! IDAT-2m 3I!ӈ4TT~HB@B#PƢ1H$> Bi"{`e1 f{eB::G n0VGp>;ԙZܡVf)c26tMRpS*KxU[ĝ ҃`O5 Xmg)q*u|ҟe,ɘ6yDy f>.пE Ӑ"7XR^TQ^}(_Q<vu@UI@Py,"sPM992EMKPrrr^&AnwFX B54[G[tnw\khyujtT^ 3!*LR,!WBt@*h򪰦@Ym  FN u@QP,l0Yio=Fߟg$h}$:e;Ŋ$qa :.þ``Ğɤ\QXlVRm>^T$#eR44~2p~s1>~# (Kz!Dt q*C:U'%9Y1NaJ~jmaH D/JdP+opD;1nW~Ee+[>qnxiI>T6<hj~U o'u#pC]QwP~Eyup8Uv%?uNq% VYƫ]sA#2K02"M`Nz ;;#ٰDR$f UXePg쏣 HZ4}mlv sNN5͌f29:~iD7>+!"&atF4[(# qGɪ%zgu0_O5Lꥀv{rgح%Υ!&RH11(Z:D ~fFCfUy©_rPPx)񏞼zsÈů0{Q[(QTh#(Odx彺kuET _,ABw9wWBh`̹ ,@%տx#pw3'p G4,3+<鎪{-{Jh=2?h;vZ rZ!;NYBCdM"3ZY$1j:ݠk,l(\OKm:raP/ote+ӫ s3i3X6HDIN . J$ɅmfaYbb,3Ud#! O -I\qPgH"8\\a> bHeŅQR>ȁwMfiR%:$Nt{dc@z]gdhQH1S JFN#Hш))%a D"# aaǔzb32YaNa:Nl}*ct,}h' άtф',}7"'*~jwwv9אN,neOR-!D b|rZ v+-lTh\ƜkDyJ9Jk999967V#˲vwf`嫶[?^ݴwث]SY_u.{sk3,2E+9,fv$i긤(2i 1B/D:iu1-x2.J()3J=i)t*fùQFk2)[%F@62L =hG{$%gDŽIJ񳘝Nj9 tzg;!{2gȼ1-dg=?ÞMv of2*X엏2Q&p̹\\)Tѡz* R_yuj Zc߸ Fvf<]{]?$?.>P 1A0h@dZq-"وcT z0HP=3LJc34ۤPM)&)K ujUx߫<[Sp5 CͲ鞲ۺQpx9Qr`lT%۔':T1TWnjUh]*`ke:~eZ}xc8W9s`1۲TŀM!08=xkV/&v>;'ߋT?~ 5>{89.n(n1*l(UvXG [vg(vQ.+oPkG}rJ&wbNN΋s߽zYQ; e=PS\Cm߹\?pc,>7@;FHFW|:S*gc^n PA-g.6m<>=#R!6Qv7,a`1C: 9-mVF]Bkʅ͈V֤pq%XY.#.QVpGF4Ol12VIKYAb_d), Tf *4W 3cO gM]G7c> nEsS 8ݘdCe*R|FZPH႘fhd&zI316Emy 97XDyU$3UR JYyJ(`7]q91/|suQv>yoWg=W akF=jmOt q f$1r~ൺfmSL%M}g t$LP[/y 0ZE6 u2L1D #X:4Rc2*oHaŢtJRK\Jڬ) 1b^ ˒=LA)~L{HEh _fB0&6Zgculq3%^ c ͢[gh%&,?\gQvsG)Q23hsAfBzݝb{ ?m4j=W7X ܌JSh ۬؋j8Z<(4QMP:wesu1P6QϣD+Rʼ嗀~!J)=W< MIwIqx}\|mvƩo%ڝa;Qd[3J jōӆ0AWPAFa:Bόqf=+|*ϼVb/d&KG/bqa1'u*¥W(S#,AZH $[Oi,g=f,ж+9/dTyV"=L{Ll$RG5CƆK%;,O 8wZDuObqƋvfH\@XhƗIKngRkq ca;O'wk=bYm5(W w~M`ϓ?#c2P΋/A9c}S/0bQ{PPmV^J?|6zb`[T@ %Ǹi=WuZ-P7*LXG-0||>]>_}QaIq.}X~R~x6 񑯌3XS?v4aZɬ -N6:ӄABӊ!hEn~(, ʆ![F}X)%I!~ҙܟ k+fL &;CbyFV٤?9OjZw`ukʤ3E81K֘{/buDI |'e^"D. f&Q" m&fL{8d8 -}z?8W:ފmQy^E9/*椥B_)e3@5Y,Xɫ^MRѩmQCr# +{]#Px;0zyQ>,?w~oh |Bwވny\2rOOlBC`:ŝ +R얉~uD[K#Q15 x6 Y 2₴kT(g_Z󘄏k 1& V` &1l]O~;G!.>Dk$ H8gTuL$[3P12cŔ lC&% 3I4Ï!U̢Sp Ca{q$I32lȣx8͞Ji-j۪`{O៾Σyq)/Bh/ŻԛUT5Tr$Mn|y D[gu Bҏ4'''Qe&Z@Љņ9} y99C5]B1{ގYi!1LHKd"\uDXE3+z7[ .IXT#;0uv%;&weo@8̨6vnɚFz2if*M[4OsqZü6:*N9[&5n+c>~YeʈJe+)C-QbWR/#*EB1ɴ%AR,hi5Ȧ-@Tɇ n4;8aBXoմR_/gr|⸐ċ/<ы5"f8Q'P3߆PGyϢPq O{7ІI(xUnN.p ! ]Rzya> C/yp^|.'_Peⷹ~yՉBы&RkoGYyήQ #@ەG> ԢݱIR2ED5RA!㱡ƘHhj& Lf*&A)XdEӣ׍i%N#tYof+luaO3)w2%J"a{mAƈIgXhp,!4% )& x#6q7q1̒W zb%&iBF>!YOd Ý}XY&&Sșlfd/͕~hCƉv) ;7Ư_ _c .0+U)O|~꽺4Zη !Q(9)e|{OP7̡Vp4jEJ`%Fʨ<S*C)e|F*`WUQ 3O]B,Q%%Wr_ XDP@*''VPsvy/mﲗSCa]O'EΆ@ڝnK2CQ3 j%5M tu>JʲļUB 2#N dH=Rzt{ےC.>wh)MԎ|ţV@VC"k0N-1~nnļe`4WhBeexi͕)gHzm+M- -cc1:} y!Dx @lG)Ɩz˚m2B{L6V6ZZ~yMDe|PX$)xɇORaQܭR99Q* T鷅|!D::%G1Aݐ{P1|.%ByZ\@- 61PSGP9E2T+QV!^!,A ! )ec+''^`oSfƿ7={mJNS;O+f/fGF;jl %4(5\ڑE8H86nmC'dYL̈Lj]|کW@L7t I君.gZm8 qfaREPުbXSFh+a>MĜZG b$[87WlOol! Sԧ(6vA9eۊ;IϼNq#OaS`LH,xPrܝX:q۷I})M&K]è,DInTWm2mӏ4~?<>S7}寯O?[4J;ky9s<<8%w ӫyQ!gQ7ϋQq9x%h{~$ %mGwu~75jq7pA IDATv(nT7Bc={RʟaNN+uP6`Gܿ'U[\'.jАŴTs~h(T+mV)&AZǵ,RQ4r?Q`4i-8\/3j]5 gMgJ3츼l1cFtz!vHMl6+}D\,R;hs+jzdqdllOQrl}2O'MЃ"qsg"*Z>" gbm"vf0[i@{bl~4bJ_EdH4&=0'(ߘp5>z<'iTO ˞[3[rb}<Qwe_&"ΔYv͉\9nTٰa46fnc߷}ߋ3?ϾD(js^ɼ"ފ'ߧPP&.v/=ɕvy+G:.*YD=^Rn{" Qb:}Th!ğ&jߢ}/ /y%N'UE](e,"BPLǬ`Y$EV1JጢZD?M$̍[ɘ k "g5&$8s8ײlO  \cn%b7n)܉>b1"B,#t?(%6 X=ڟBcmluB [MؚLC_DZLu*?h@Vr}{j9Yuku [{)Bb$Yx쿬ks,'5Y5t\Մ); ov2-+[ Kah296fw(~{+ _!(/wRg( y=Z1vmD::ʣ%/P^Y\՟RKmbvWݽ߉2SGQ"Q尘ZSd;PߑR>vW<'~9.n1 [>6;U?!}}un?0IW;4Yzb2#&آ~R 1IhtGNm*Ȧ%.Q,m5F#FDsDq~n˘Lŀt!eE86c_#! h&.2Չhz846tۋ|ֹL ~>zfӿ-444f [?旅_Ծ&\4Kb2j3RʅhnhBAD\kg<6kȠj1c}<9k߳oP$?gQׂCr8L_=#CyCyqWQmQ^bo^t%盰 #}~}۫O~4QG*BǘsC+IV ;^yes|[pMlsYCf͒NVV)H}8ZZ2Ä#l20pun ' (ct9E)-aZzd#W%+hvF&]JKuO)#5UgA/蠙7J"t%eofmi *)4JE \U[% +#č`r 1@b 0DS{%m@,Vwc^v p }xv?\K2PIܗQl6jYy6s{N|F׌g|m?/W":PsSKuܿ mOoνP՜׬XBA5lP"w2=fv&J0B ˕kٯu"QB.T:nڡ=2"rEoqY<~U'R3f999 f2?$SO_ҶW6nsۣVucי,`!Ⰷ>cs6ñ1"$CosƸFو9 J@x6cƀTHylϬ99o >h1>N:L hf:Y"*NqIa<d z&3񰓌m#":ʼnDԗCp;U .0(oU4AS^MpKkdt Z7ҁRFf 0">؞dj$ӈ1-u>˝ aARyT_)^m|AǥR>\P7&wms B*oQT*n=*1Jg5P9D ׼ⷎsX&Po_fU`W< *AyM]NNu{>nkRg~}q_揋Op;عFxCfNR"wq$K=;{*qZ`,aJh)J!}$ 5:`dPm +BQ 0̴^)){pd LBO$pr|Q |m~uYΫלXB܆kܰ=A :.~f[qS|3M0WEVHQ1ZbF(q W5rrr^EwdqFI"IS#YFxQgoD%!9cfIw|=難AV4DI^6EFD9c@W':撺SjbhoD*D=A\u.M V)p[wO0n n^MʓsxB*uȬ_ٔtфOS>H8TI$ YRȉ3 "rǿ ԗ`XFEmB X5SCԺ::lBAQG&z+#@~p`+\(ܸ/W+ƬVBdYbA`~5xGm~/Nqh~PѧPK#, t!"ؑMѮiGK^ˤYggETFIØ^BDi}FmY|f}~eV˾&KEb`R*̚%Ddr.,>C$VeI+8˱m'$DBdqK'[h 'Ao#{ܮ:=2el mP/Ճj8Iŭr[xb˞z*rg8|ry͈E[}YDyj+8Q{)V(#9\Y̋|k(b |7* s {@Ji1jw<&LIj 7pOp #W6ىpKN8On Z8"zYS6_TvP+:sCIYkc-slC,eb.N;b12L*iJV%Xi̔fBIG%́hIR-RJ$ M>DPfXx&^&tZ XP Z)$mJ՛|"gLg)&Yè_DL `T4["Cjo}:z5n43* rۜ$~US>5>J]jJnJh7 J,+n/}wPڨQ#9999HLi̘$IidhA:Zv~wpKPx7?" n>y^0 Mhց, &qyqP1ȸ YuV>7<+aeˢS`(1NG Bl_wiF&uԩd^ IbP$c:8M!h1Edʬr8b61 l0a#`X&NYl/"<`KrԇQS3=qȰ :v(vS4 6Ib>XO>ٕ$|:ͭݯ@pZ999׏גXFEc(oW]܋jp+ʋ^G\Ō͋q(=g̓$k_F7 bA$-k A),&yVh.Ļסz_Jj09jF0m. ʘTJ@[NgVՈتds4 "B0z<h1h?ZPX,ցf:ES-C vѭ#Hmϑ7$ 26uyY## m*V m2뇤TU_()Ԋ "~G[TJjBeȼxXPFU+'PAڂEsdghzz*=HbL#-1D,Ua&Aq KVĂ8ùM@eOI^f/;pe,W77SFKh.y8)2o`JQ,bIIIɵǭ^<cK-b[>zn|s!󑻾&6L4SVm? ~ ÔoT-D"*б\74lU|78Rkps4!= Q@~HF,1Qn(h0QT !}ia’[4e|2R1 B(v 4]j7Bd#:G:y~6ЩE(jqz B"H?CA`z=hE*KNBdgĢ !z8#-p{U2#^\O♫$*pbTKgKx6.[;u.Q*))))yA?05(h>6:kqzINJWgυ0;5nMZT{vT0|!6vǷ܈쓷Sºf|&"f$c56!cVtX{$-4S51`R+(<<ϷeDPvZH0la" D~[TzkS(WܙYdZA O>s 359l*,,"/'(O"VZp`]$XoA y0sEDfv@KJ{M\9:\bx.j~!Gq7 XrP \f2ִ1'2XRRRrMsĩ@A@ꢨ nzfz-\ <4xqt284GWԌG_Uąh,[+f (g<&Vli. jsV^V<,h!}``"cv6iˈ<\s@0E0c AZKd&H ޞdÏ33|Ac j9u ,CR|]=FbxpfA{ w3ìΰ3 &2' 헁2XR?SbZn总p北<|_ qTy: lt\SZM7-))))x1GЄi7)gGUf>rאi}}w}ű#j+7z_o~*$RP_υa(dQ$[  XˎoŶ%>]_[K!6leu>ݜ x2Dy:yujC =,Xm@{ ʃz@㡐c)ZN\푐^Au戒yh-#` iw!2b{=x"if VAjl{6g`b jU4 n#Lt;-)'2]܀soW6$^+cg4ot|n@0(pe-_krRt<\x#c;MVj|fw fYN_>'T^745wTٙinV8Mt{X$R#E- /1>+Y1lF.&EZ$ٻaPLh r.rrCrJc5XO,0D+HﶉM! uvC^r7H,T>2UN> ݂7< ~f(&.Q Pz`m߂>3 o,҄Ff6uJIɾbωEkB<OO1BAp^p` ק\·_r9Gkzu{>yō())))B>gY=#̸xe?z+6~.sygnÓc{O?MYl `(j)Ä<`=VB9*cU@i.lw-<rԯzo>_aƋ 'P!Jja%*UZ@z iP@90Y#,zu͠w*L.}GՑw]l~9?x?$yims;SjVpȜgkͥfl($XͰ5DJ! GJQ 0'X-*#S6S{F8ﯲ]c+ijP<$"|}Š2N@"= ( !D pe!DЭ"0 MbM0h=C2 laD..МΈHÞ LAՃ ҧ6=h8EL n7ey7Kd?'h:t( IDAT8gS uJN \y7 #N9ag.~-p܍"2%%%%%{O!@ re""BcdcA&)l93jO$1,$ˇau  V&a+0!'8T4bI; \RؓbZ\ԖREc]Ipb8p?f;qrGq?gw <>Z9  rzʍq[bܱPG^16o,| ]X4^;+|'?pp8(kq晠*9 94 `=hXD=Shrc5ϙ3ޅ"g_Q`aj66XŘ`*QsKaaR[nE-PҠ@cC`IjBՀ`zdotgkPV۔,3$2 хB2 ~fQ?D|'m!>~4@aaljmzϾ/'{g n7p&4?l !>,D+4&mnS>s4'RαcRJ^<:^JzOpv9%%%%%7Rp# 굀Vw,̻Gvbso"ClbJgꋽόw$Qlzԧb(&sxs9Z&zD6ñ!Lzt1O63TxW8Ŕ,>r+gUH 69U""< fz&*;T~0-}`8aT1Yjx2[AcU_U_a&E gP~&KZw>7V'뢒}ž#[e ޞeFs:=3sfwr9˨5|S^#~dG(>3P蒒}DS=Zћo<\l~7΃Jca띺^l7'k߽\9*_2& T88T,K5Z[hJXJ@ClDR = 7DQ2/ B - &IOrDbCȅ% |b+ bn=BDI>|偂ʋ+rRz  Tۈ:\JrPJnvH_5 zLwϕbq}P'cޕR,M6{ɭ /*N&F1F4'@ h-ԭb \h3TԞC̤ Iײ4SM:E Rsp v)$1*fEqmR aub@ P0›L %(oa7P0 7m\UhmW` QPa}N|.dZÙ[)H< OIu0 E+"q!砌%%=/=!ē8ϱ-n]vB '܍+ygd3 xqVpJhy)g t ʳJJJJJ]{+ǟX)a(PW]<Cv+!/2%jE1!80$#Ċd <2R} 5U_c=͡:QARmPKZƨː` |ѣIlIfLx3,H!iKW!Hc1>-؞݅E2nCk3ւy82&*c]rk 76c̝7 Z% 64KK267Xqawh !eqC!N8^-[@B8,t)KJJJwq}Mx26f_4 l!z0Pԑ 번*H@+̳֋$tȇl{)2NYL4 dfx9gN~,|-g)?χe#+is& }4 jD2 ..54BG5 E661 H.&DUS$$čA"Wd*WuӘ'z0 BŃ뻈w}͌<յP,)ٟ'(*pPqZku6i9VpNq}Wf%hC㲶K"Y%%%%%{M}mM `G?~Gې$kds5}R(c:FQ ^:z幠CHp6F*ź4{{l3"X{ԗԭrY$HG?ȉ(:@(I@E(dwNgF݇d hu-s($>\TJs J69k Fcbm$Z:QIENE $AN]~ʻ9BK iSв>OuQQ {!Y`h -ÔjG'3EI j "3aZS!WklZ)8oDgl3qF1&ZIi.%`)6OHkO k(Zv=aZ5ax L#*ɡS0JA~g$ 78僸Tý5r˿ `%%{#?.1%h$⏌mH-LFJe%$kZ􊌤cM,3wo&:Qm!hK 5p!ԴUB¿?mGu$7M?c%f1xZg&ylAor.RRGp_ؕX%>"ꁷ ~Z[pK$,-@wsxQ9 jxj;ѭtem7 s#OjK ?M}qv͑h='ֶ|P \P0TnWty3}!|߫ˎ){xvy&_%%oKpyi\aunv)BHkm_'=taSk %G$2xv%p&+J6ҀG Ø A%3$HZO ;^61cX&r. 6dH0때1 ɺ;|c-R3=*yFm"X|!ޢf<΄]Np.,qfDECrz3^eHQdY87H ̜<\<7XIM穅UҋGQ7NÑ gϽs6ϡ%VRkLNOr))fFU>+F厾Q";מ)/lȀN{@>ɴ ȫ4{)y)p6_ⵔ|#ƿTG+(\Np1_~XRvB!pbQˑP.՛(a,҅`Š5B5ik=9JZU5^ !>#㜈rփ- ]&;u=5}h_m|'C,W#u a"Egx7vPwqm2۠gA ua,):]!j-Ba?MםKA=ʔA FvQUuvǺ~c))&W/.zTܤEnaXVD7^QLIɞ~аp?J!ul@gXC01Rt#6& H Iϐw(7&ʫ30*&F֢FR^8Jl'g5 $9t50cY ."1jk _7<"Y8E|){LLlѫi5F|?aԥczԩb7&:NR !z1a4V/ xZxGjoY4OY\.>7؉yzC9Ґ%2#z}U禺SDTX)(#wp@f%fm;w峻 w.hE0k- 9:}8A?wT ~)~:`@4wHA;T.z'X|M."|/?7}%S42t&AWEk]R (csɵͽpP־Dc)D=kogEfJ_nԝw)u IDAT˔H %{pu>JJv؏o'\/T(aqB10 ;Ԍf)  h4 GVTnĆ_J #'8w~4m-q!˵6QZw rgz|2T c].57\1NbjHJxBuhO.qbb#VY;pbG9^c/^Ha8t±p8"ZJ5 t!d/,1 P:W/x_ZIyScJl<:ZPٞfȐCJ |Pـgy0=E̳FK0v͘4?=FKJD}Y%?j/ckW`^kmqy?biYRvD{e蘽^ ".[Rr#piG,0&Bŧծ7 PX HSYjrxsC6Oqr‘4FyoF4'e?M?barVA~њ)p%U[[a f.Bo oc{+b- &=B?#'KJy}[\~o<6[\˞ū!'8ӊWv-fpΘ̓U㵋n-d p\~xp6{ZRrM!z|?nCw'6w60֚,Xew v-* Pd]k @Ub7zC25X7 @ӌ@N iH8l WvmH͎ސ^6\4V&Uz.oCVoLȣ7?eZ{6%iq"὿2#߮"bH5D62yq yNhm-oJ>ayԑ5`_W{{xOOיXkB5/v1>׶Pj`5m1PnACq9(/rd"[k%%o> 8wu.T)Q"%Yݲ\"Y]+d\"':'2ssN0N5SnX$rPMҖ)Q{n A AY p ,>xkmou*KNZE.v%@Pݘ 'e){]>%jI (QĺD a4= ;5 qRMz"I(Wga[c=>Q5OQȗzqÃq\~Xzc@8:VS_R%wIh2W<պ#C,!SsևeB xƋe QOSCOb\T~mQr]gg:YJ'Im3;x8a*;m4M-[k̙=_&N1d5k1f)N)^SɢKv.6.6>QO#TrU ܐ r[dt 4ר&O? U"(Wi-674@lr^J=Ξ;L==L!Y=74Go龮Zi^c2xCx%\7 vXuA(,u#%-߂fvbi UbccsrmQv#r\l>Py, $19*7+aC|ϕ?.d_5Y&n*ϢkCkm};"s[ƾיPZT4=cLIW:ncگLE.DfneC -nLz4 Vk.Y vW0XZ0 P7n S%2ܸt?C 6̕+DIdA_D =ZS),j vWt:Y.UK-a}"-|CK\j% lm^Bٸ\1H`"2rE\ln_v [ 0HV!c^H\!c6v7sxrLt6b\ӣ?pab\ ,d|.3^2?pwHӵ4ZNs鯍@n/qZ hK͟y' VSe墕/^{ݬn1{Uk1fFTi^>̫" h#=*͘L [3|\|[=y\LThlK9Djsޡ&Uyc;{pxlC=|QOmw4jžKWŽbCaq6y^o2IbscL7ZI˗]s3ضBX^蓧cǧ E H<[2f  ;}a4:zYOUZCIa;5b&?s)n"-q@#E8 32Ɣ ;\l#0m_&pOb_~SbsXH_Y q-zfQk9 |9hɎط8FA=e2Be2XC2l ^y~!}HGwiJ] [D\ޘS`k c3u,Khl _(6>,h<nk?>q-p)AMoq*I3$6 1E.\0)DKkmb1cs6Xkk/\pjQ`Upj^o 'qv ׽K@qJ%%J%d~pˁ/Ox1ƌ>o&2pxj}p&rA0o[qǵF즻~c7}ؼy5~/.5(p)Pm/}꽟m_۶lװ ݜWxdIW^5y٠[ʙᐱlbd홀lË,mol+Tw2E#_)fŸ/]kp7s㪯/bjAcxpC/=Aӥte+-0/Ϝ0F y}<:;;,1Ym>gf*Op|iƫE&EK0 d}O :ϥ⪦9\@Xk{'qc/%E&}=nc|%l8kCmMwGp46;?'v7ؼwvhlߺgܴ|vNqq^bە/7\UXq_|$x.&׀q7uOOOJ)cLH4Č1Y kc N|ĉ+@6>Db&e,]W7:|#[Mz70)㢎JEDγ4Y\k'k\`}j6%""2_J\f}mlkdK!i;^+i78{m-Zgjl,/k֩("2pp+UDDy>\vߔ$ʢdYk;ջ<.IDDd^Ke\6|g6("2c.Y[55{-3IɢL6TBɢLdQDDDDDDP(""""""S(Y),JEDDDDDd %""""""2EBɢLdQDDDDDDP(""""""S(Y),JEDDDDDd %""""""2EBɢLdQDDDDDDDcZ{NZak=Ŋ6;k|Ή'866ɇ]lCP3ʢf 1yc;&cӼ5Ƙ> \O><?pO>L# U1xpp;cø?1@(YkGNr 0k5-|c047[1$k ?Ȝ@cveorWǥW~ ބoŘl#XҦ5~'ͅu/'_woW=܍ܕ@+<`w-b}Mv:N7m/;]MF.RJE\rn %ly CVM{#`ky0x'p)hځ20\K"@.c0ƴ_2Lt-1-? mM7ɢ8 aMC`Иz&=bx ?z7m~fci}bY$с\S\R]xX_9؁^/s 6cꁰî; }W;n8ak0ϸvWSR(2c__ _^4}/_%ucFX> ,v*.qN XKP%54 [l"g͋A}|so=(1GzȘ,4U#U32vԁ:O?B)Yc1un NN*\M@a,k'}#.mõD@0v -d2ϝEDD4ŷεeqwPe$(6a2 zG.ϷRK,`NSw͸N\'N[oV4bM54QuCDT2L[=1 d wa!NVɁa\N.xJE.qۋqyZ\#'q-,.Y]y]>t`ݯ?Ƃ7G =C+ʵxIi$%5L0"^lT%r<\~\l!^>|hhto&)^5z gb?o|{1 UWyV߀(mn-;֯ E9$4 W*o*.\pu$sA\Ҹg1_~k3 zo\ˇ[g;Ӛk\Ҝdf嚟UM2 121I4 b0EZK}3]Wݾ ޥ+V{˚ ~EeXn0O6 ~20Z2h^N.|JEd%\Z\ki:õ^kkDpeo܀%k/F\)""r1Jp7QΤ9h-޸W~5uWi[`P9]nKBfu8S>م5k"kk ,uϰ$~eoЮvuϏo6^M"]x²0d%`ikw%0٨|7\,Ƙ.ۉ>< n@Ƨޞ>Dű-%zplCrD3Z zogW{-ᳯdGfFWg˵Rnqg{vԓmXꛂ]{%6$ ep |[Wu{Fh]? xXb/>("l6:_ L ׯCYEzqxW)q-xwu^XGυ8]ߟj"""f\nF_ܞt(1 -I~Zw}/z5CՁ?z}e,/k5JRuQXyACH˵crdqA<&qhڀX_^f6JEÀ7Ƙnvx(^g j؂"fqUkP''x&*WCJEDd8 {gڴ~Zr{k7&=o276寯ex";3 bմ8zt=1V 4rӏ7RU qRT(Mɢ&aq[kw Akm\4~V]Fk==a5'\.9}k9^SDDdγ~(>ч*!{mޛ^~z-P۴rȚ˟Ғ߽%aYɓm#,,$W|I$6JH$"ٟ?~2팵ԏvRq粸^ķKgq\yxx wVqP ?8܋Z6eիZ 栽X%[+3ٗEMJE.lCsA.ai3 63O+n IDATb9lDӼ\X?嫅>OLHdh;mݖeS.u-_BGvdWO|/fȬ"g!M;⟬=sKqYQc̝c2nu Xk/7nkWݟ>D5k~~==tIu8/\L3oو_ުUwM5/>2Zjα@|eW-9TJ%)]؏ eE(6yʢ2| xq.n.Ub_Һઈ7ZϦ_6Xi&HCx>%/7-Y1bʽ#A9 J6vƺƵKE0\Ըfc o{ʭmh5UW5Džp/< ^`ތ םpKȬPeQ}b4n ^@S֎ºY`nM%O%@vpזga}"""3#ד;2ReZԐXSa}+3mX zoYX!jjˊzwCё8jXmvfS4F Yk?k%k;Hb2m>D õֶۯ%ݯ,$lx'8 vÖ8?ңcb˃ݰ:k9JEN;(Ap[+[k@ nOxoI Z[Xk3"""g޹%Hz! f-KdM\bEC BKR/1Llްeѭ-;k=`kѳ~2gi`^ܴ\EܑǵYkk30km 7fa+cޘBh!9ވGWO.>,~h&2nb\PttY2,;D~;Z,-8z&d'mSh.ZC q j xYg1/џ0|Ҿ∽guٸv"6oآ,sj\qcBcL/pOG80Kij,`P@~  Ia Ƶ]fڅnbyjC9 TӷW<%)KcAnܜv \{=ٵTy}k.%=hu96/j{wwvb-rP(rvB@<&> ,z-""2B2q_%_aH2ҽR +ہ^aˁv9Iɢ qkx 6h6'7dpb6 |j(%\>Eh9| 7d`:{14 WM:vy\K( GZ?9FG×|\V+"xN~eq3^[O_pKд-lk2+=oay$vK ^qlgQ LGӏKU)Y/3wp>Kpp\%r18 ,.\j<>` ܕ>ܙmU[?^GDDw䅗v{q=Ci`cvR{zfbDeX~%:quhT]e%ak{i]}a3ox%2͝^`ዏx/˛Ufe抶2UZO" T6 Wi* Ɯd.iޑ< E*"ni ʸ3Xp5ZHxw3qIc!zWP+ԩ3vIByjtq$y\ڣ#Bv sp5zh\,L:F]d&&F &ԜZKVZ=!""r!3PqC:v:q*\W+Tq_U$Y,`Dų6 ns. JEQڊ:bO vXtR E\*""2/ [\lz`R\l>47y IҢ_(,c$ǶoS<T gQdXk~^܆=7t^ ֪UDD={^Q{g^A67(4X +%.Q;= nآ,%"3nOZU8i&ԀG^E ~`"6g&V[Ó1bb`n!i RcIf(Y! n?bDEqWU,03{ՁTU`7lc`0?J4Zx!PC3bp)zPOܔb WvÖ,̬QgpBpY!^!n!j/nMrɸd1e qD1pcK`,~"Lɢ q'`mAW| ?op}O,>Ȝ'߰u 6^(1B3=uĵ( *@4WtifҐȺ#1Iaf 59Y@' %"Zk1Bk.͸ ZC#:DDDc?9ʢ|%^gr䲐7;C1fLT mǦ[IJEIo7 5MB]8ˈ֞lȼ`6-FE^ \ t096ĸߌp\E14W:|ޡݰEY.(ڳ(2}pCn@nM'wۈ@] ȍQ\Dl85NNƸ4Qzz_m{#Yʢه /kۯ87dOj4HT!þ'8>6GLf19b.7[&}xK=\yr%-ѫ>RdR(2 1!}1ǁ"""sٸ6|dᶆ؜*zH3^)S&WO} 0GɢX;;D1%Y~nh<6ɱma p)nR4%>$^AY.`JEΑ1;uܐ6 lvZkS=XDDbf65s UGx@~0QQ `<0u0g4f ׂKmB7"H>c󋈈wbKE|)"˂nU &)6j3-<vf`)Y9iUoSV%ĵkӘg;?ɚK n9oqf5W1FG=c%_j${f?$^r2VIOܹf7l=5ڳ(r %/(""2PzlOO%yȂxxPy㪋"4_| xZCmDDDb)qDyN^Uw|+*@lCR^&g5F.JEΒ y\£w8(yn2J1ɢMdۮN(zǽI&0d8_{߉ܠ="v f{pt|E,""rٸ6C 5er{?PUQ.,km ,?(w(nRkGH=𼉤l"a*QEs`)f q{ڧ(""2ٸ:` q t [墢dQԁYzmo󉈈\hjNܰ'v}xp% rQўEi` 5Os xAUEWg6] |ĺ.LzT?\,LcLגz=b_"""-qm79&ɿ}X~nog}9Nm"Z;~ 1={G_kvÖQ6ܔ2O^z*Cn/*E.UE&Iˆεˁwԍ@nD2Hd|(M'XkwzDDD.X|8D|s|.64rllN@,6?|nز\#r!P(2%{6ܯ~h YSǞƀ>`2]k|=uYqm+_Zn\n5>1 [e^P(2\V֖gy<\Eq|}kl!""2e>v%hf 196oآ,E1@nYU3M3Pvڪ"rS?Dd)F^_ dR(""r}k ~$Q1`W3{m"ΐys#&n  |Z[=o yww5C``τN~D+Ue> I-]ȼ;[^0|Vz^<7""""""2PEDDDDDd %""""""2EBɢLdQDDDDDDP(""""""S(Y),JEDDDDDd %""""""2EBɢLdQDDDDDDP(""""""S(Y),JEDDDDDd %""""""2EBɢ+;IDATLdQDDDDDDP(""""""S(Y),JEDDDDDdE=u]IENDB`openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_20_0.png000066400000000000000000005733431413546205200254520ustar00rootroot00000000000000PNG  IHDREXsBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxy}}{ٝ=X,NI)RâeE8ʥĩN\*U1NʑZr$jeY\R  {==}{>GxC ꚙ}}y&ZkX,bysbX XX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,eXX,e큜9ts~Nhmo,w3?`U3~N .9} he>n;fCiohU|&Sn/w9sxoxyGFoǾY,d`Zk '< K/_"eޡ3K>>W q}=˛`rX򒂹 @۸{(ș!:y< 0on]s{˻%k &O`ܶ9mܿ3!sy&^ɱ ` ?nnZ/2WiߏJΜv`QX`PPvr=fݳ˝X r?iK[i!Q < #LM+Γ_)^[,.ioh(6 2E(Nʁ'G&XI&A*E0",7>{]ؖ 御ou' p.t#D?I>|qe'o_gE{EΜB>{%Mɟ?#`)7UTـ3o#nr&}[c.Yۊ?fB _o`̅8j]]Aȅ"r4`\əC}}rdw9]7`$0VbF g`,OVT\~+308sj_R %ϓ3>{XޭX 򶡱F`Sm)ɽ*Ik\ .Z yʘQJzA1]d{FopYS"ksFtø=?zNM@G=mgəXy]DCaM'w`oO.Soy` ʬMK7Xgmgca'3)ЊrBvC2 \i ( jsxduoWV7'>RgTCin?SZ#\5ۣ0ۗ&bX9sϞ9s #R=!t`,D$0J#t=r7'`,ƺaܰ#\y&U')spLΜosw-LƤ޾11 PZDJ\D1 s1DsXRJ36d/7p܅!zBJ{l#v+0.69s1'{ 9sL2+#.0dƪ';mS1<@0a㺽c]d`DV)q`z!9s3V0- y"h@+s=V:\׃C0)# JeۻI_HyjrPY#Fkcf-Vo U%+n FN\n$,L2Z#2.`,ɣ 0_ @5y8LZgXQZdP~hW`n,Ki}Cc=S4RCHX#.d=XF*q#1\`Gcڛ]zͻ1zkj#^_;4G'6{C 5}-#}-rat^ɮ2,v4a:< 5T oj2σ؄@jF(9/aR[3Vmp[1$bZ9Ϟ3ƒځpB (BZ&qPBCp@ Fl͊9 w Ny-]~_y3:FWOOs/3G`q:0KYmԹX앮>wri!` G@! LdJׁa]NSL2<|00(ϱߛ`mQzNVX]X|Oy<`a6&™3+)8] RQ)1q9s88ЮF׃B'@٩s,r4M{⩋$=֪W_{# V*XN?4Ѧ_yOrtb}?s艵cFc\ 4Th 2NMQq1㦭¸[(O&1^Ƿ̙ Љyw[k/3/ #V0-k5h0IșIg’M5QBs  ='$Pɖux׉~ie7r7ndQN4ъL\hHH%!C`Ȑ !+8xys9펺;> 0̚(_ sO_Z^;92wZm-/|L #ZZ_ў#Mo* F$xP>@`ܡJ`k/VMŹ{ +#';mw Tn޽W-_{X]XmLkݕ">N|T}E$ȄJM44Y]6xs $ PR$ h-o]4%WFKחGW/:Qtbanz'?x>[Lo2(펻2p>0:S0 @@R%e&5TۀSpdjlSZl3|i$ e`b ^ngݶ2`Ziy[XctOji p),RqR|9lH*:(F-$<^|CI ItDB@j$vx?{ 'K5qWAS zCο].gW0+ȜdLK7W;c(L%=ZKdG(J#$06N&ڀ?9UJr !@HvsoY]r&Zܬ77&9s[}x-+gS଻ E\MOlz3<¥Jy"ǃڶVj{I"T4uS ,Nw8H5r^׎^go'p/كkS#Y>ؠ|!U\n?E_QW^c`  l+3n.;t( X iSdf#SZzʕDuzil{E& @A=_Ng}~L9S. {D$[0s{~f#؎g7od!y!JGHiQ8j^ŏCúՑ\}4kI%2tӅZKЮ`4^jXe`('7_|y5y=M^:+0u5NnF)׀Bci:0TbSV P( \Bg0FvB*val CwZEw;w2LΜez/ V0-&$\pƽX \1,@(!C(i)=:mI+7.Km XաS$wɐ.A4 JT&rJ ra :UY(^>L|3s[W_._!,|n_n~+ζuU( u &We'r`ܸr\2 NS EC@ Y0>e}؂ĮH W,0yuEvHjs\ehݛ~O-O?E )+IqRtD'#Kkf&1#iPS-Ԭނ.aD2yRI+dj,N #u)S* 61$gġI?Em!oO<>bybԁiyi s!/.+}T/v1̶./#@,FF8Lb_| c*Z B&p(LSSO]䎄ndݑ Յs,zG<֜1cCL: OݪyO <}n<,06=] 81ኴ7PdY#j0j-z N!_7jmah(L&gᆲ+o` ǚ\iUr7s]ֳXX|L2ii6\08Ȫm }QG0HڔdB2 eD2xrytEvX'޳t3פ;Z>CH]ϯ9i.s0 X 0:xr*0sT{1>O=t?-{Nϟ}*ZrѪ8Yo<8 v'qTJvB;V[a)t4sݡq½=iͳU.MRJamc[PQYXU(&KnbyaӲo&BYLƅa. h$bR1o,e A Muld7G?MdIW\xrHf:\e*QpAo8Bu<]HM^=gO|?/~#rK8Zpt_SzΕtqN&aGյtE5Tܭ*' FJ{6.79)h=g9\@k)sJ=;2[-MvدϢM|f>vELmrgSr & !L>#E6e1 s-fJ-܊zB ib:eTA &pGSȑL<{%,'b$p gV&w>wh==2wD 䚿&'[Cq]nrYs89Rss9w7ͅȾvWC¡_m\jg-ꛯ_MUg:qwoElQV,`(!9a6֟lx=iB0K936o|­׺{hfK2!GC/0㻜%z4\"no4sbw㟿pB4N}ԃ\{`qVFɹ+]pF|V/5$㢝%R<gۛ.2@2c *(I8`ޥʀ&88X^a7[6ŮXt53S~~~3Nx,wV0-x<}W v6Kab\dr\gfz+Ou9H .%yݬZJ_\[UьpyՠU Ju[ɢ?pfl\M}UgX_fRqN((Љ'Qkhp~Ͽܾ>FS):q`AѪ߀V+ׂu:f'otE(F?lpqķn.OQWR9 dRWŽSC'˕NgǑ@F))@5N8\]Bmuʰv|y.*  :@(XEy@NgqtvV_$U{rR)=byG`-L ~> L\Ν<|q{?S%vvJtV'f(ȄBKlT=Z(lWL0KzDHH&H9TJRFۧCҦ< ='dct-T.dhqv,|  9b#]lf7!XdLSGWN'zbÏ>xr}m*d+(X j!4Lt8oN# dJiTf&n].'!s meц b3#FsU05y=X(0]RKXkCYZ R5t6iy'QDb@+^vM/4 @0ehߩ +fc#pMևJXQ<<BU2=xNƜ`n$i'ÛUˆ%F(P*C9ʴ&C"UP$ (Wq[ T+k'XOjf0 ?}p@v:L@1 EINL3ڳ&-Q$OiꔤA <@;RtTNR%{($ ê~A(t3q%w88%?U<+?gV]XXgTy}?I1vEa}hH &&㦪rhkHdFj=ʽOB$9n/\KtЎ.z#MYIb}F/ndzo^pk˪:5֮p3 6rPb*bϙsqm |^yײA#3(Q3jgNJ]UR;]wiN`qv)oU_ڱ0]v ģhMd#} R) \q^qK.PN`1p7ɭaχ Dݬ\ZG)pZ +=PJdFH{S&peC8-`Zr/$żrmzRHx QIuJc M\;hzaӗI岺ITˣPR5ܹjH茿>6NH z6f-nM̖gPkuehwu L Ìf/P: ѣ#(Z]n$XܨӸiǛ=ou2,;M'tY&wdWtj!bLb)n#%TA JPӨzMK+' 57;x.]R`wcw74=6+Ajd%T@qɓ@wl x{/(=NXc1ɨv3뇚F/7 w3YM(̯w7s0=1А0b\,2dY)㵵M_&㘮Wyt;CZ ؚ{|4z)_^Ybײ;"R=DRt#!wtBfZ냞 }\AԸGS1)bجdu$Õ8kQx1@cHJJf${XqFJDx8@5@T\DGq# A4w* đ#i3'΅ .@q"6ntԒ$Iozj("9K*]ݖK4K:bTj\Q0.)%87E=9~$3u@p8C0@Z0/qFh,V+QmXz`X4b\i{*6(` MMjƤ+p:ALpYHQB镇 !HF# G:eBR18Kr^8̉SZ {3Hl;mLWkiPJLJI6z]5N39Hg{<)ہ Ӵ:_V;mres-y}o-6Zyumun'Q_NΜfəkGO_}-g~鮬ruZX-ك??p#X(؅08.PRe D|.YwV锎i}pM2#z&sc6i<%Wvƃ.N6.wN..\Xn`}`VrcZX,N7vi&zs4^z(QY I,ѮCkRhЙR#TPi@X\:=hqL*98^I)A%@PPAVUʔ"mG#A 3 W)HTL+p@L3E(JNM뒒$zөrJaIH7U*%.`V. 1eꑹ8l` I" *gZk2d;[,* zӌ2m$sɥå4k (˲v4$ݬ{m;V+W.U_j}npʭ,~o_Vp!Eg8p!AgjtMta-L˾3J={ LIAAo1X%#1FBF( .H X @TteF&)ɝaiV Y2 ;(+0A"S(;` E)\'SJpv8g@NiPN$M7FcT:R9z7֤ B 0Mu7MQr25_VڡTE8EHp.(%N>e`Q QP82MERrA\O}Tt qA+Q"s*OrRQŃ\fdT.wZvq><0W]qQsK΃Ss3sZ- !Gp}ZUlǑXtݎ \9H~Z#{yk]hXlRuV^YDg?1 ɇhFiBKq>X~\*ZlQ!"4T9;| D*Q;gz귋2 >? M׊-QHY,EݗR\yN?w]^oߺv{gbo8 ҉1m (^)v3p{q_ 0,.˸ԡ|xU|> Wx{);~aEmLIchݓѰ6][CFy#%ic[/ɮQcڪa;Uk1rڛb9|pt|Uw߻gEP;O'G{UP {~ZNtdn %@LTq58TR|..`[Uk{!UŃDwOF奣2J~VTfLRgDՊ45W$(C&zC;8mZaQ m2B„uJwJF^xZm|ن$ Wo>ySe9V9;9.D|9]S8NH85߸qKfvQљVJwNBm-}e%9fU! ७p0Qco8tÉ1N9 6SI @6;-1+?H2]irg#Ud&3 L3{JA2ᝃJ dĤ:/+~m*^KkogOeg==99tTx30fatcѬβ]nBt-a5=%iu~YDbr-*0uz}ަS9(RdQP'/w`R;e9D3QgCǣ|^(i[;.儵L:gE@K)$SdK?*ȓw>g&&D!zӃAqƉZ*Թz1F[ic @c>.&"Q)J)o tlj:4wa;_qs=ΤGtLR#>^s-##o?Nkk8\x0AZ/swPZp=Mfbw0Y 픃jxT(lUrK q[_h?Ľ߻I\eW~cNi-HH'x4l}b)Rd8 Ob `9Fh5wM(]-X/U,2_{jmD43MF~;~V d;ٝs3kTd~/rV x8Ι3Ιq=3 $R11IBD%BoQ4mm e DG1+$J!iEW 5:(rC`Yh}a2O`}10O,*rl8b +((itha{m8/n[[;p.}eo^'ʵ[K7n駋}UV,/Դ,ꄾwj9Y pgS%13zZr8Be; H!aX65R1.rɥ+!1_hd}Z_ꢸג 4O- ~KWLp8 s")1lkW(jl O?۳zSeבO2Nm7a/)m6 N6s!RH.Akr˚Mmb#4dä2IxTL)Q(n:fu|Jᘼ}!{ڮD*CAj$=^kf^9'R??s?x2~\DJ#:jRg;w,ߟdb]RIza)[?-1f|ȿqvK_7Qp1QgMSy DH$J.H)0LRTIxYvw蝃 De/r-q)_-\+~%wWlAV89ރ .gJL%t:GꆺQNd@1wT{kxڮAB|sS*2NQe?PC$RVxygiҸ9;3Jtg1Ɗe <=؟l#=nl]{zTD)&օZV7@+t֝*ҭkӱz|:ߜ\v#+%[)ܘҔƾ5,ռaeLʲ>()΅Y &7yY]QHIQȅs ÙLÖԔ Nk&[EX*MlXr$dy}0$+'߼{g8Y.nޔwwiޑ {Ƿ^}E\vM%$E*1 $>ƶ^ LJ)!U  P>B& eB'ʯ?/=\dܿx*z)$Amm>v1})nM1P]\$Xk )B$Ol\g6ob\l,%enn-*ՙH}ߤ|9MXp2BIe=9 OV۱7rgJGi"Hud7;itUPWkʄ mܩ4.4 KX >.Kx3Q ?իd(GR)Z=^+> _)?oE'K  s')`+>GY<սA/'rgAO mynN]`pD8FS'.^B1f4 <$q??^ݳr'Af]y%R[>5xny]z],V|UDvL&Wdzv3釧ͪRzpB5Q`w2~M8f4QǤ]DV!EQ=RI1PhF`1x/{*`^+B7%!nM(tM\i۟sJz6ď:s{ƅti4 B7 AHp>DSb/n;w~qeM[BZZX?[ZtQ6}a;O2LKhVlLו2\;RM1K#k!KYMݦq2V[C$Dؾi] /#D*9},4@~\f>H( ҌgԓESz^`k4q1IͺL(מEio8fbo^d!tqw>a 1!vA,I\`Rjll/EczDuN;YYThDbƦlgYM>8c~yt}Σ,+'@Uc\*/-Wϋf" l\W]6P[ù<ʯcLaSLgr'̪M-qNw~lɡ@r8l\l8.rD7\b|t4 tRmTkt_/7߉2^ vxa0kYr0ƧzӖ2II.EkJWJ|Z/mRUSYf):j,s [.?p{xk8P3n6CV(1ܷ=zI%bYnѴXq =o6- JI 2*x Fb(ƍɮPjXw_}Uy^LZu8?C^>D<&hēō a^, (=aԘo zdBY g//ûo:)%buKY`4 -ӅFvw8jfi>>)OewlczT ǻG[\xشZ7XpXlDŝԭ#gYkfYw7mגr]5T-yfy(dV=Xkѵ $  (( !*ld*}TϱG/1PX#M3 E4x-^NS JESv!| p,!%d"# 蝃LD_ggN^BdǙdu DDt},HA+q-_ Wp0xMבoT4xŸ:`Lٝ;i+7ʠj  D.A8@3a?ߚ-KꚐ:mgF[~|r-jėwݽO¨rm~>9^ڹ6?OLFt=N]FT%HZja+BAYKgŖ:iRh#bk:KBn1CXZ LGGFpȰ8o]TF]`0 p,zg#8 ό,Cg lUCB`c=v{@1E aV`2".2@J Ӽ?3"dhL$େ ,MљXo.+~͸ W|DpV .ߝ@)B>tnU}@m /0/WF0v叚jW.AY0@LXd!jՆNҨ kH5f\׳'SHl&8Hjd] rHeh;_6nM'5|)o 'zh[YV=1ȳ͊ϒDWj@6,B3H}t:q\U{8VI?LSA2Ű/۴"eK\D)IŦkQU_59*JcU]&B*z"t.M1BmbI8cly-5bAJ adBˌ<ϰ7!<+">:>;!$#$d$@[鑰D˹+L~!+Ey'>}6L!xM9ƪ>QE,g?l/FB<\ȑRCE ׹g3v fӯ2@(wl|幻h:f~t6閊}2y~v+(dG۳Aś|g0Hwez{g0,IY*/,!=!6$0! >Wt(Bzk.i-R'B03d&hYHbbw'!=zt@=RfL9*EVY"{lU%n (E[)uu1C/"B-*DpQQLt2PH gaOkm]ס 4 C@Z` @ktr_l3xdiY޲o/|ȯkն+>wpk8g+neƩ= -Ɵ.n-4ʭmN$|z܌K k~?Dt[x&fIզ*IN*J gGـbD^ckt][é#{>Gk?`#d9O|f7K=E:@.$7. XkTlFN!iH(o֏ܛ\P<C >s&VtpEb{cL|4U/% Ӻ7//[Y*+6GmoxcM&ku0>R{"Q{ X+CӶQZZfE`A@7}O.D:wMp6pA szҔyxi᥃^M+;Ńj.qgy|Z:v"4}T, ][Ƃ2 i@ J 9 1 }^qikC 3 $A|̐RZh $:k.@ {*`^y30\n/$!PFc*ͶA}+ݟ?x=x2QR۴mO=x_~?~2_^V)91GIX8`34DνA2:Xrf yl8u'Lk#U]g/dE肭B(&҆!APdV3DBh*QbH4EDLN҉PGP=vȓ4Zgmdb::KR-!°M:4Zi-P܊s ,Ck;Gшv3&O8Ifi6dzG2IC}"DA `C`BO @@0'ӋQFZ)$ 0-AA15stm<v~+ƈ=Kt[ao+\+>W^-Ը,w!PG#ºie]4dYaim+s^W["]Tϳrw6uhx{? [[w_6E7ӬM95Z^S$)39HJ^0Dɻ]BP?я:ا͒sn$m @`u~gL9WUpӔ bV`!`rDu`tY@R_fe(|0RjI}Bk[R !aIŊMqG=>kw{:KF2!\fY}4qSb٬_ױ(SV + !荽t#st)! H|' "1B X35R{("'t1r6{]WH<ԈC2!&kcr^?lmX><[*LV_ꃭ;x0<rnA=cY| N2+Ndc xfp ÆWl}ËBڒNClYT DIh:/]@LN*h1);W P˾EIXr։7TC #Hә`L"zy<$/I Mkixϊs&rVUO|Soεv &R(E֘8,r Fbլä*@R1D,1 xV̐ w.]~B ]vCXȗG[nu/qV+  =eRͨV,L6ut}`  , ~e8L;wH{7C(bnB´b5iro=).b 2aGozh%gJ԰q,֫XifHr{PYe]wUE1ӱG}r䧱3!K4M?}JPo|Ke WU槾!e@`ՠ=]_\ܾV[>w&B^j:k.-vɵuW!}fc9P |t` Q v}$61Nt kiuT\q)6<}'T"j~ bO}ӑM5eXt"1FDzDHKb@ HGCp#GHjeV~s&w7''ƵRu$(xf6MNr~1m75\DO/Mg]tQ<1D#ĦT7D3c<1 Qzj 5H0,["U ߝn[h,ԥQ x68d{ X.GU~2 ;2HZ2OsU⯈7Wɇ44Ddyӛ uIƣ*OEj)ONJp,_œ̢9۸wpw h8A.fL(z\Qx*ы <[ZsSN&YҢG&O`Do,bJb,SRF0Y@R2)%$1 T# 5 =,B"`$җu-FhcPlʆ!bf?my7fjϩ:mxyIQ%Y,يDD`%b#H#/FA y_ D@XF'22t{wYlP"%PFUUsW]+ovosf$aY[4e`OXnL|[" ԡ,j!ISpܶ1$q)w0A1BnLhрmOGR ;.5 V0?>¿Lj9/cyŰ-Y`NkS_dH)E U'Vi'(NcY 1.~zmJ"IDrYݺLsf[ٚ=SBz%__Q:fݚ ߃M1Mk,o|YQP o 33FmK)1nJ жKb9G-y C8щh(uicx@Vop'|4Tr4C\EV$ bi姗#tCg|y[N hA {=Hp.?dg YWK$*F&@(Elgk*̓qj) {~M Vn~tc{N<2WϯJ{%-/u^m>xr孟|<ޗ_ýr].rN|GȪ#NJhSڟ8iJW`c,aQa*M%UX9prhQ k !)d l+%k X` 6Bwv9mȍ[q( {IF!uyoEK/v]]UL:Lu3Q9EU|Ϟw{bS]T| ,$&+{|zzX:7E:w~;>lV΅rFkk `X CI=%1滵:/F4%YJ+DAhr`'ۇT o=I1mq1%GEPpQ(!XZQ+O$_Me '7yÏ8Χ{ @`LGE[8 EfJ9?o[߹ EXvv`9 2kp^L("׾{d^rrG ҮtgU{[Uue>vp > ިf^0 w"PP NTMR{q6$: i~;ybs;k,04Oo{J" uSW@`{Lctv*wW{θ'3c\0"z}r#7خ5F SUH+-$3(V$h1YמS3>pJݥ(ã[/y|wWbQRRTkSkXVw2IZFGݮ&T1@s&[bBJr1'@ Ȩ[)1x6``g5i[%@sF#FgbkY-e@U d(!:eIPVǶJFQČyc_L_^5խ [ƩdOȓIhYml+J,_u'ONeNu*|,#/H`DaଇJwڰJ DmE{&$)""S1\b8QK ư)8{y`0B+#2 $>>7p#7^9+F.1VǛցwL^漐s̎fYW/\(02?_. _tY>Kn5~|ܖ~Us> b;YjiTpƋxqYAE2گV%nzSqNж;%,b#nِ2 1JQCa b/OB ѲJcL³ Og"Mgp;Oj`>K;ͪ VV\͝ݝW}*6u \Ш~}nXYJloW0A !VX,6H!0_A02ؚ8FYHd$ .W X<B2F?!gl "g=[xx~~d4Z5|oPD^bM\֜uʜ/gO"A#ɓO0^-~_LgGW糧Q/\7?qoYp>3N5<}8uYI5L$Y59PZ,&m5im⃤M#SU: \z'k8uG\$0y=|XÝw9**G9>DJ:|U LʼX :]d^7޽r'鳳ղԈ7֦A_<WM:{PS"b\yU@ҧDh"ҍ"8@5 8@@Q(Rਫ*G+eU|^fwxGYUDVvsޣQQK m5n+![:sl ln~i+GHpTu !D(5l&Du'eg8k{0x"=yqyyu_ysUL5%MM/QU;M83 zwq nݒIJLヵ7ڷARvt NUmJƪ|\8FV's:k'L@8#q<OYh;#AJ"FRZAHn ,kf*/Nd"5voelqGÔXFݝN?]ќEȈw[򳧧q|G`"gҘ״YVJe X7( Z{y1lw)c "ej3k=_$.# w(QB2k-b9#mT͉]!˧m$>p [ 7}r#7HMxL UpJ3XC|$1>~_$g8m'^؄I&p@i\ '<3""es$qƽWy.1Ɔ䍶>vIFŦ,aF&ð̋(Ƿb LA?NKxZ9<9z=0Α־,*Ja;kq0&0L1_#bg$I@#cpaEQn | 5ZI BpOX瀗=1a0X/i4 gA-R>˼` lOߪb~9G_ɩ&&m^z{ ,s݆*ݝ hzZ xY88>bt60o[^ /kEw_9zb}ToLS^Y犢, ѓaS5/`oL׳! Aܓ'OZ>o[L^6lfـ;aOCV4 #wF8x .}nRBحM3F=kрQA RJ#jTH SrYʇ;{rrY'Lb]| yĵ7 v;e̖PN 6&e\~+yu h`6Ca(Yɓ,!A@BH, ]MOZ!M<<>ƦUb_ 6 XZmԪAAyu`KaF_"_ks BQH;iC)UcGZ:m9 n&¼ᇎ-u<+zS~5M%4yűTS0&Q*N;ۃ~㓽FYsn|mwkNn,ojvf#`OhSL'o/-"p2JLWAZ,t?x(*׳jyAc\{ڼh+9GN)lmMuUvvŒT˥p3)=1fJ$`ܔEeʼt, a9P4Mf\4MlMc^։5VsOAvvss(Fu$!B2x]i%6,XVaP;3k\ϯNR0l6%F׈C !$: 48es0``(S wϧ((hgyNI^;ˢiZk%8{ [< ?(7y+B]Opga-%P7q<#t\To|E`b}=^ZUjV DGJcXux/NUnZIoT`̇(}wVYh28D2{h)bw$w {C`-:մlT]̟=`n.yܺ8ա+F4;e*87Xj:dĹ BTp0H׾zi4?zϋ 5 欿+v$|}8όU6~ػ?\-|$9饋FFg2UD!?}gH<(k.;hx3ўQE_Bkcy),8Xz|rIlD_bX|qy+"^h B#V՞aũÚpΙ N9}j*BJ %D1s"m ]r <U ,1ߊ$*o}.Х{n4Ȳ8<qhЊ煆('O_`}=~xvr|/.F(NK8v#yhSc bϽ p"'xϹĵJ)041XTlX9PA ?;{p. 2Vyƹu30 U~4o=tj{t}Wᇂ?OpUf k^\4}>lYppQqTpIvq2u1qշpX9Wuo:-=-^[+P=cpm`\v(&i+}[x0Yv/fW|)MK׬ }5눪LzS"/a/ GыEYهǿj`^(] :^?˜l y"ySa^ rQu0vZrN v@.TS$!$mrǃaŦ8V;cRr&0_QV% WWYO q1>}6MFfs0aC䕂G{mr.^!}^/fԎbVsc<8ed Ӌ?Y.PzRV;nZQ(s5r=c17h B[7v gqf %ẩe}nm*iô~9-t}WioG֐W~o/4Gl3ApNqޛ:6eAκ;zq7s|R6ݬ;UZ @ߎ(!)?=`]B<>{xpV}>}Y@Djg5W[պ²8 'k#t¬|Lc;`B'if}8g~@֑xZ ,#{.RB&ks^ 4hXQn@^c7펿ً0z:  .!@H#ˆaj>:>8݁Ue b;)f [h`{/.ޡ1U]04_B+˲y yHumzI )YQ~U,y7 o2g3^'Vmۚa赵dqj&^g%cB+!D4߻˼gnMyß}5ƧOK,V>FL^灵ŪyzV%7nonV2{X/UkO;emrev0К^iRa#@@U5y_p}8`zSjuvܽj[6 Ȇ4YThCΕ8|6_Gy ~9dހ VU3-ߙwZrʨ,l,I fiߪvij떢Z-bD)#rul\0ea 5ye?H:vmjn' @Cy iwVA G bMau8ʪ#j0MA`>LRkfج7ϗ!ۇ8;:xvuÝ]ܽbzeUㇽyY\DaHf4pCqW#ln3n}e勺!Fɐ(g B(ռG wE}:šA[aԉfzyf}ϼMyß /?:HF}yL/*#4׏6i ¦j_DxUkHW_ 81 GƵp5c8hc2`zaX^MX.p9\{NTy8;'ӑȢJ;} f9RaRpGQ7&A3ߌO]6Ƶ9ehW]da$Lgx|jpiT`qy@i$E'׊ {D@b[M$%A{4Mց~vr͟^r'\3(@ yC/ lp!"8,9$ͼ6lYyXՊڤ;(7u%:Y/7ˠ+l1♵ήx=DAHdiBa9TP51ZIBkUkg1FT5UE3`EDa;r<<0/& 7 ?_*7:JaޡKuz6P RjN8N4F~Y 6^E-&* +}(oֹ ܼ 2v "{65^8aw|&mNl]+nlxHTY;UVT folNYYy7U|x˪.lTi_m{YE<)U0iQVs$$@.2- [ V E82ͼu^]LrmxҼ8? xx瑥6 V{>N//YaY[ݿŗ%1]vA:l^ ̱G,ϟ)MU;Nuyx1E R|DXm~]mhU|զ@J!/UOȲrSRΝ4_ޒS󞤱8XH4f\:Vw{Xx |ߺußʍ`CB^a]L] E 9XrXK!)s ..Ýy|-v hq1]9^ ]$3:pޅЦ5p:Ue MUty>|| ,WzE8 FW2i0/jⓁֽ|%Uc+`Y5ᄉf7lWFSnm4ɢ,enיzӾDl Fe@ڠE 8kѾQU>Ƚ?rRlXA0&"\nJhcaX(t`R2:AGmVy0Mlg-Wj^.t9 Dń&eirĉx(..dOWk88v{pU6Njex|5L^K7,c|Y,:]U%]d -@ʀ3|5A @د)¹fq#7i ?_jס/ xK%6 BG$px2@E #nTml>oӺx E١m'7$8g  Xlgsv|[w58vCUk\B[ s*k! ƌhjƇln3%\nxf;WwV?LКu|#}4[ƉѝИx88h*r(ڝ:b bƣw֦f1T5׽(DѤYf{C,JQ# [`.!8syU>Xק'w+6bJpH)i4Cue+~ 6yÝI(6RFR)&g0qޙ@y͚3`l^TVnQD;JPΗ+J뷓0^Ak˟[ YFƄV/j~Yz|x)JTfsae KP3h J~p @&|3oF0oW@ۿbѪ,UPJA;L%b NbQ|+lsK? ϶CDc҆aE~ MbA&p6>[APXp7-!!_.NUb aEIE2b [t9#R im01a,CZB)sfhlYGЍXa$WxJag8a؋cʂ1<c` f(9s:_P+؇OOzn[>?٫L79f ]$i3-f6k'4IE;k3NZ13ZlA/MԳlI EYx63ܑY:3Y&(ʖU0u.7fd[<7 xg^'_e+U|UI9M']ۣ]l\5`q\.3d2poLєTJ@kWm?|3|7ח׾J Njh(F! #8%%8a19aul.6Bc;Ub~x } m2B Oa?6lc!~{ @_S~25ͺ Lmq+Y)#1 EX,J c ,t45Ϊ5b04Mll O/ei';6e)80t1zvVzYPNVt6iӕ z[%}y]\2ʒD-*xe׉2,VfYn"_U]|geMieVgz?GGݴU2Ʈprx_#K~7/Ɠs֒eE|gwj@Njlr:}[QfÓvYc.Blx=Y?KōM ]?!/81-\i L :=_~yɾ lEc+)mD6ϴ^`{ #/^^gZ^ӗ}" X<ϛ: ;oao_d5͚*-wδ׭|}>[\_M(v__|"^v۩~}:?ڄzo0hg" j#;]4!gMx7qMUL ݰ؍c e]A ^N׸|?-fTըqaif|+ &<:EKUv'pS3.U EDcZ\4TyQwi9z|{\K @)sj?~?ҫo|we+LFo}Wl[g.\z7ʪZVKUvx' u';>_7|ݗkuI ^`' sÛ3n?_mahFP@f= -]WX*d/; v|w6l7ۊVF{>V4{˟_H&n헯kz)>;Y}Y=v[_Y_9 _8jqbky`k1y=쯝aRL,l@vFDPnE={©]Ⱦxz:w/nOrfT 8kU+U-6R"xmJ!_}ߟYzCOa3Ԍg%0ԖR 4/x0Kn?Vf cHY^#eilN@:9o~j۪7̋xwEz]ƫ=!xt/MkKzjO~c0 Dj8uA(PiWZCpX椥%8đ>؝8esLc%Un}f37h75?N#6pr:OҚڗv[=_ٷ6ڟɭf-_hFBP=>}֘SFC=e@>¯$}ne;=,MSf CLIeݵ$. ͉"O2)iȱEwQ5%ɉe^V7O'6/VrKQ_Yߜf`rb NIwbggyY#H,)1D4Z&+^S~gV6㧓' t*҄3pnw8X~{?T?S¥ԯT/~ģzo]8o)zei7}ZV" t#7^E-% )SУ @Yf00t%hKC#ųv1p>`ST8d/ %͕\pGTLLk^ԢҒyο 1\Jkڞ $K")(T2)ܑvPQ+-H> HCf)4a:l}mMM'oY6;i'[S-k B٠Lt6SgսZ;W7\8H^汐T?I cl0Y^|> O4Dcz=޿9;( (+˫'o<~F3: s/~ZZ4~yF[@ )4Ĩq$qq-8i~g ?!xG;z{é_mS`vB>/Y{ss:M4EI/? +`Das.'yϡa1p=,G-jS՛?[臞2<؄vܳoKdn>WYR~=[{|3kq g.9fߞ{@2[PiCe pe hK;8gwgz-.V"uX[Hx5R~R-Jv yyvnsԣL?$ /iEƁR5ЃF} 3C)X"p/›\R0L/bז緷5ob8eQ#hjʡHm\`c8/9%ť-GQvykčGT{?Ñ=W { s_{~p?LSzXli n08z82]eSAd"8gSzdMi7%Nvpi'mdZTq$|Tm78l힦A)PmiDwvXe17e-낇'*Sܛ&ZZTe \I_ܹfzQ^JQfvkv0d&q#uQyIl7ͭT^w8Y"jSW1yaWEjZzshCB\pѪYhzgyT/׫\ew^yW~ĕr?+ksȌ/7[k|8-^e7lZET vU^?;owo;Wyje#?6?g~=}H@>u+snלZսz=ofa͸/?Ih-]C=LYtRT ]g[ҖpD"OL3p~űiFܠ8fwHsG_ NoS l?J.&K9l?lʶ؇I@(s:he8<)e¾QeX#c ^̛n ;6_]WϞ9w(ىeڞ8n9oՂ@%rAf΅a7y!TI2^,T Ųj3oK!IͧY"n<{QH)0+"#mc@L ="\+&aM͜O}dpZ/>~oj~ٝC8|.L4_jzw? mDJӇSFU?9#[1BOEa=7+c?`]LE< -Xнh[-+R q oWy~f/{&=➻'PsEYi HA\0i,ƲS&@/n#e灃bA* TweeI"z(8#ӏ#ů!&8n))v?OU߉q>̱lY "tJaK 4G-Y%H[N[A#QX+ؑ>Ʋ!XlM\~DYɐaΕ3F}y;LG /^̋'笰ÇRѬQlR _8y^Rkn[Ǎkg?Gοt~ô,% ֨(d b÷E`#tB!pw /9~L9k)⤞|xi]u7ynu}HF/9 /\pS71d?y7vv.}8nogVuUB6lO)oZIl^qyP}ş _,~k*qWyyp7L_xx_<΢c~yoc0p I A ~ȬVp$`b4HIJPQ Mj=eNtRՅ ]f3k83[#alrW0u~T#6{j8X3U~v,Ʀc  !Bժ>jx6EeFj0"C3()a}@96rʖjMՕ/y-ͤxBxۘ%ACNىV(/X@~S@[PũiW_3z[a̠4[a2+%3`ms1 ä4YÏ:`e0ZkKm&CRB)Xi),EY2ɂ#w"TVٍ#CܤI!n.G w~Y}SO _!~?{b[5Sgon dFozm%k~q?yjzJķW8$⵷9,oOE8+臞Kյ}CLzн+V>m_=.PV wqɹ^6 l;-hM@Op33|\81eߨKo[B.WGtgq?qiW". i7lx86#yd k 5(IZRQ(`G` r( !oR/lϔPNvܹ͋+j@MX^9/z~ӋIoRC /j֛#d()F!:wEw;*/L;KOɭnEvg+[ǖ6b!EpPOd=εU^'b]%fn@DY( 1  H/ QW|]O3mH즟gs8oO={rgrS@/$G7?2SrmF[dSKU}$#GA>|36ޓu`Xo з12; B FZ)3 */y恆'o&qrY[;,(!xgVZũӱt8_?!]a$;1딥/q2(&}- A+k!5Х%y?3+Qr&=~`*..9vh{#;O#~-vz.~7*J%oׂ@zƶPQVT60aiqnRMmA'{꺤Ntf95f0Le9լV8Sbќ( 6&,+ک(*:N)aQR1`KWΰ5{!q#s[n݉A\YsO?>6OL&/mnǫqzQԎ__:~bgd8ڧ'ֳb xGL-? <83lG#mD8_1`ṙ>`5.)>K\TXQ^[8{?6Nqjp-qj2 A  PMT rUfpp s:f#vrMřkG7B(OX%՝ߝrJx`,d%B_ηֺ#kV|<.gYV2J׽͞gLb''ׇ.-^}c{{p)[Kdy',^+.YzE/9$H͔Qj7WטeG(5j1uZ^EtG ,I"<͔G)񃦒BuK3׹^jBJqw[.Ij)qI\fLO :u86ݙ]j?tqJW>^VW^bwo63?K~&Y㒎tG8 Er3.Ma_f?#mpdyG(>Ov%pƕWƾq/%>UR .q/w4r˔GsJȺ.5dy]u~9"ű!&wF83ka3J(f[ oJ8'v6<)׃f~@Ei8p[+O wW0ufw 9rkc̋a߬T})Y[koZ'uغ!:jjAD\/÷"{f#d2{892.5sKC(Hӂ]n_P$fS9®l(x߇Wo9lodffo%H cyb,r7W(QWbOHP|/d %%bcW J w(>FB1$5<0ּ(¬j\L$^<4D7ΞU7zFmc. _o^&iFN^:i#<.GbEs iYꗋd.Oτԭ'ѱ"/ɞ͕r(!&g'V#% mO0jkkXytrh4ȇy@x[ei-jTp$3BPW? yލoVT\/㝩퉃[;7̟o܍7í"(b*U^[5?},޼~bi٪u*>=Oо{}Y mE %Ne0pc.}+f0-R>JYTVv*#\:^d\lgN=#F1\= 'vFB,cB~T/H).83'ں:J;J P7yVCG ,r$P |4ʍFtoH" [ Nyk4anGhXȊ'-D{#\i?{զaZ֬/}O6T3i~oҘw;0<~3NKY-^Q}>X!Af.UHP t?+h(ʥaQddҲH˯)L#sգ0=}ԩUpgN~{c8}7']}ԩC 'e~0ӎ,W4ʿxdcD7}Nz썳^"˩kS"69?Z({2N_Dz \@ UzR>{Gp[&s闯'=E=ø`{kc n`ԙ!%ʣIWB5L3vWߠ YiRk@5U\O[)nġX j< K5%\ BXrl7VH5RvYT62t[h(QVZ)FF[oV$ĉPb+mVemMeY7h #;h%Pͬ-Mn}#8չExGF|Or-~#.IV0U-}O4A IDATr?h}sc)BSAMمY$.QBσwB?tFuYYaٚ?r$pN_&fN6qfsO?:uzm ;}nqҩS1i>=av|qx%8SL:\ְUvQQHNjpb 㚮 ytK٤Vfa啝8`(v˼;ovV6؟.l xiI[]x=H;{=+]?>̚wpH83֕`T1^8GQxc7"v5Fo*4#1W&K-p_[%'$7HܥY5R!Q~ZQX_zhSxvhL7la%J$KE٩Ž+qSoRե-u3A݈mIHMAĘn8XFR_Ż'k0 '"hơ;ef3)'%dL 0eV$<zrg<'Fbİ/W[ ╗.鏊Q+ܱzuY8AR$rm0~@ j͜B+؟z:wT 䴃S 1\d͸R2{{rN>Q]{pOQ=|k#2\>L͇7V18j,̠PYkEQ@EA@cleFyJ~0V& ?NJObJA0Ѯt".rp\5Q[˨def]L^UkgI[g(ı{ W=~А69uRhWtհٰ.ؾ45U/e][Z?̵mQB|O !eK [k8Je^0/h0?PV]ݑն<ԑ٩ ,j}8j`BV e04)9@ i➭[RY[/޽7W7GUùR[Xٙ]ܙs;w߾ϼfܡW[KW\ͬ&L0h$M7쬇 A|# RbBQzƿ%J>g~n6pm(2\H_G=wW,S8Dz(%,B ʫ0ƐUJis:0cD4r<݆;JAAǿ惤YJ^r ^H܀/d0.|s2' dVTDD$MJ.#LuZa}MH"[QKGe*eQX /dž;;۶_5]DRxD*ۄxƝ(4|?8KcQi=cl&~ߐfe( ZOlbcgOjkz$ $ojQ0̂8zBfJ@iԐ!X;*$2Ү65yq3ߗp\dڨ?J:uc1w'k\?!Ag~O^E^v_᏿w絩 x[& ;i# ?JB,bg\h7_\M 4JsyࡻλS}l?dwSΌ@& Ba~+RJDQ4vXc>-y:%f1@TJB ǑHIA $t)p1+ 4֥W ƓANhZ~0et5I B@mu9Lz,&[ͥ#M-2SiQAF"D!"v+ΌQg`K /:G#܄+M\m[Ն'd=D \ZiK[HC8h( aZ lk5 H Q]Q65#aRb 4NBîh[Z4 t kWN-梲JWĮa( -%1 (xBQ*B2 c3dX[,A n" K3 =5Jy &B#$0~0WcSa#msfDʤQ"KM5TiY]x22 d=ReKE(ޑX s|A^=zcqWo֍ *ޏ3v}k={Pri%^1Q Ax'4S ǶW#?6V㌠k1{^{[Re|Q{TM*YSVmZZhW'~ n+rプHI̟͟ls!=a~_Wxp?l¸Eh\{28zŠO 131d$/{omYwagsխYRIAL` hdr[ISq;@܎u:$B-bQҴDq66C\$֝=$Y$]ꜽ9g~>C@H1 ES+*ǨLZ0hdĸ*%+ SuP: 21 %j㉒BHgAJжTJJPsʌ ZZj'|- VZS+dZ 8PZ v[=\H0D-T^TY1,C)3ޕVA"RNZhQ[ x|W/R>~-z?k?'X0֗!Ag"2& Əӌntq BDqh?6>cZ؉U>gN' c񡏉tGd':;lldZ)kn,-xQS h8fv ).,7wH!kbJ'1j7w̓_Ńoz1}Tl]`;`~c\<5x};S'3ሆtH埜; T@jiŪ*M}8FJM?雕8b/cvlbć7VUf˱{[ Y')@ }'iL*V ": kMwT~=DU[mSڝU<ٞ/0Ej!VulC[y^[˜ {Miȣ{>F-AWlH#C_K SZlEA$B%CWpF>zUHYbZM[A}=קq 5+iF)uHs_ǯغO8o+ Wyai &ozДz[4I_7xTT{D[b]ƙ-?^)՟0]A t 2Q5pbk`rlL,ZW ,y9O#* E.Ք`#EIEٟg?$ @zE5}ʾZA72퇱0 pJ#~! Kr<\, Ej3mPJX-rd!\ŁשTUVA8o}"gpBЊ|!k'VKRioy-SV̅)i&ԦN[KU4aM-iuV(Jae]!Tѫas,>ڣi so0֌7?j%*QǟVZ]׵B8qӼ%C'2[sY:a;ӈܚkOpJ8כwIز(s9j# =ͪ碆[+7Cci4xh+Xbp0MAaB!"b,]giB"qҭƱ[tUPy[cjk7 G씙몞1E`Fv+Lj:/]ϋ=n !x!R{*G-܊*7QaYYH8NiXaJ)I&ޒ(,v@}~҄;Nz}F1'-֨MjS}kV@<o{ޓV)_BL ac5AVjkڈlek<Bn?C;6M' IDAT^skqW/G_k-<[QCհ,T%ʴs֕f$(i1F*\+6P&q?~Vƈ~f'P!cՃ{sExYs29 ּ`.RGĖ%8^fK"7m9y%Uf•Rʇ{NkMNe^|+b`\FQ*)YVbTA3,T @tͤu^Dz) 9ۓRNap.EJ*]f%]3>x`>?=Ny7:4䜹jWM&͑ecgg6 MOp徛ݫn>]^y&(ny:.,Yo\u;N|;|^6ʰ0[UGwo?w׿BUϝey;%&]E,A /=k˜s][fL$rpc +%6ȳX}KՏ䳗-lEN䳊>gh&F:>Z{9ґ9*^xt$fZձa쨽Uk^A(0#tNwV+WhvZ=|h@6v0^ @4fkCfHl2J-M.ֺX+*zB Ql+o4Tج@ii67^oRܘoV,wz8p`}LM99.dp ?ަ)ϞfmCkd+߬W|Tw?E3BAsQE]?suej`QJy35mc6J\q}w^}Щ'Cg =邤f`z,V1B4{)[_:9K`/V3c!3KmM.tVgj!>qQ9V!XY[RN x%J(k+1Biil('D(ieZ0JtzNΖ%bIJ~Muں֙TFJt0Tv^HLiKf\| 6xRӔg s^u5ksֱ("Ǒ{)B+e9rJVڭV6{վC'^Y9?jS>P+OpQyfn|@ |4O1`#SŞlߺ{E ׭kMŎ3Rq4>r%G{b.o]lg9.'T@ԣǒxko?>;S53W?/'oٱھduJbHbzUć>&y?hkb=]s1ÏMWuŎ9>wVIuǝhl1}tZ/MrIܗ?=ozW/un`;`^ػ40Ǿ^a}S󢷶6Ã׋?dON9qѫrDU䮽E 0@69Re:f̟zS(k>w}RoJY"A+Q L 6nD'%^U& AP[UeWόT%t-\j{E9=HZ*]S_ "('Qaς"k^\IQ],iK.Hdalރ/2gFl3/l]TK6mڎG|meH6j}N~" pSc4[>p=͑>*f euǝ+D{}xrji~ /zץmld/x`bwh4k[lRGjyz,m<^ɎeQ >G؜7l1f~ynciEP9X7!#@Ү QM Qs&۬!-M0Al߳cZkhFjiO##F3LMslm7B[vzz%#E~2=PӠl3rvƺ9Q?BNbkzoAVþ*]v2燎&k"<~W?|v(+F;vqWasl,J}=! x/0yR0p{9 r͑1}$ jX%s+kṲv/zlg9.fN]¹{5ٵ K̭pkf9zl:Z>ϟy8{&_:u~m,z ϜFKū0"ﶘ#8XPunMgiH\yF^juRIȽ҄/uǝoxJqLM͑/MW\2vy<ΞǻR监1sZQ%7V%F*(f./)}]>İODÜ,3Leg"in5q5901PZ9":5:ςLXI?֬ֆW2^T%&p=j_])ˡȴ,M.$Xe:tjޝ/wWN{Wlu%k:YgN1ViKT3P!s%aWJ Nl2a1EkT8ѝ݉|;Z_QCdO)((މk'nﯚa*_O+qr[nhR?ީ~lW+&Z|*E8qFuj5N2& -^NǍwhvUiS[l { E@9r>zN׀y9=F{p48jNx|BZN԰0*-3a˚̲k0mIpBVHz~~ 2~QpHFfB' %V72cw C,i4^{4l(֓ oEnN5%+ Q\l V;+W앲g \6U;z qk|ί{'_ɅM̥؍ѽ)?}xrH a_?:Z,Mw;Xn92z%w_tF'eyb3i]Οz{ߪf C(ⅹ],ƉŴݰOV/fy(o=+sLnpEI S(Ⲧ7R#hH(IٙDˋU2z%FRU,H\Z1/fC)hץ1:S"SA $MJ9B; (kWq\h[(uEa%mgcZ381aT}}O2-1cٶJ U0\2jRz#Uj/RiB􁶀JCFRKf =p2v4(0봇VkG.'»Բ_Wy0EpA#WíԬ޷Nzm'_摝ri6Vl>as17/lxߛS_`ǿ1'+4 A+4eK#?HlA+`O\oc =[#$W,{f^\z5J5N“ JY$FILkubDDp#)r`U uKh[C{$-F_Y[DuQ|Q)<j0EKPՍI[ k#d9"7 K+-n6xkc1BZ i- r-Ǫ8YbfevJ \VѨegWbKkM'ݎT*Z}s!J+R}H9,8-rJ l&k`imB!LД4 /Z\Οޣo:R7;UlL8/7t:n񲞷e/~쵫wO7'"4{ !`uLMpW~Gfڥ?g7Օ Ǫv4"xh˨>:EPOcU!5yLIS;Wa^^fNؽ|(b F66(L[nF -U/a0Y!528fڥQQqGmywԹgy#f&U'` \fqyE@V0!lGd k IǣEK_ *\PBBaΪY /Cb/" 7|;IQNK/xX:۲ȦRu'0V:Ë+(H뢴-m FѠQ١a3:`O]\,fof'ѣ ;ujXH:W'֨io)~|5N<M  I)>qYvn&}V^S7=5:ܽHF`#KHD.2^׾_^WgU/. x]wܹ<+g?/˲m\:l{adNwX` (*e0bij jaY6"4i)iE yj"k8 Dn;[!"Z na;`^>Hiv6Q(řF@HHʱq(GX e#J9S.u=7={zVnfS뒰6DB1LC]I= 讎buEo1CVٶŰze7ȈkKUKek|+QoTB _ T+̢5\uU[|g;ITv;͢*h;j52сw^)l^@+{5- R q.0@.zXwy?7hJ+=~NTJrnfe"4zƜUj{+(øGI#oY{) J_4=qL{Mq)O|ygMwJH~']AA|ُ"v8wͻ"Wc蹗gey H3uBࣄ3;&H ʲYl$yuBgٌ[DβLws cab%<27u5^)l ͈iIFdip*$!Q]LD .*ϙ2\1ȎkVeu#"I 5e! ]UM?R7v.%N>Qj%Y U,\؊nی:ZZ;\!vI(J !gh& А^d%X=QOg9}{W^Qpفp%y]:uK~ ce2P-]7ʾ#mxF9~ Kev\ 識fwRLnv/JX8lѓF8ퟮMjZFZ>'Ab{>ƹ3JF/^2Gُ"&7;ox>47i7F>{~^xU6d7;p12#6O ګs̭!N.)8ϙ{y#}罥5P1`3, ~ qVYd~ -7M}ksϾ?Op>6E] UMX$!l+–!Ȣy,"V i@'Sx 9=rr>0 쓶+t=bef'__h#NKb*& ;w3|jI_Џb*)ETp1^ ˉ;mJTJY!2kUQT^{qҮ_` j_/L}tXz 7ЌQHֽHh*> 'ZZ_|ox󺪪~/?z)m\%Ew8Bl%<+ saJ5ЍRB@ԸݖmDop)j S9cA{,deb90ԂG97Pq nxT?(Jk](6ڶ");.Je07Vo>;f_[S<~q%Gθ2mzg? .~cg\|;@],dqs/yfsuA:9%pɃ~x;}vv9o_eOK}12#}}Nx?^*]z|C/ U:6 =ivMr~s5/`A4(JbϦw91w.dfIg!+\`*[ kE19V<~N9{4A@͠*+'`X):yY: ,TFl0#Q`<ەQMk5'Hv2z,RӋ[ÊJZ^lBH t& 1^*#Rp8$Z"h[GRsQG.m@ua^VjoԮs?lNn>7.{hX]vOFALdhY̝ۜ(9˽@3d0`GF򾛝l}7oڳ/b纝T " : O\љCaGas}=ek=/?aUWryXyszӐ^63v4qwwx!pPjsffTA|c$i~6 #^4 Q 8,k|Yrӣt `8zPm&%;VTk<ў狯};$︒Wb3P[X+Cê&!k:(saϬxt5׫:8T'vfPY]WhxX{UW"u' ThD0 b ]e/7BJO If+(*9Q4*l&hs7E.A$3OcFY,ˇ})m}9wm3YzCūo.On_Z'$zC{᦯l/M$Q?de岎*OʿNrmǥn;.;4dӗ/|Y8KgWx>ُ46dt hڸE';.:%l_6&w3i++3:\= }Д= MVy=pý` cq#RH(mVR2iБPIp s{FnZ>ByZ /;~}|VW;Aie&3v*׭Ͻ>D7].A[|7[UgDzrI2:w8Gzov57Yzv\o;._5zsys 5kWg?^ߞۧBevSTЌ]ruǝ-W\gs0H4v[37Gg^kw%.<㜵IlG7.u˺D>+q$VIY!P1X_C.mv΁`<>js:a_N֍Y1U#Uшz;dї *֚CΡK K^{3d =sZUukc:R>X - \9,Js:PEðV$6Sej'K㪁WKxWZTT9kD2rZE*Vh;!(+ 3>V !dS;td |#nquD'<7W-7]5`k]en]л']+v%JڰS>{6^wIO(8BSg'vwght+ץӓ?;_PkBLQtmܫuq{~H54#* E@s/\|0G^6.lz4 ]'JPz|[/DF C@ Шd3&-|%!IZGA*8qS3TD&`h`'01Fl" _FmZߤL}E#Q?{ofuw~~wkUT/oXed0&eBpH 2L&C 3N40Ò<$L0NbmlceYd]]뭻]9ǹԖd#eyU{=Lf\{l'j}lQҟHw]^2goua7 sC;Lj)Vuc_I]'"*օL[tSI놮+zS Wצ7ٲ"J@9= EXy) \i8"t\lQe|>pSZَy+jdCxQ=pKJp/>bM ]EVä.9cDÄ2C ɧ}1sX87Iq^K=;ʐ}R43K,.eL4STuHN6{]'%2W*a8!dþavBR >YRZ(l KZ  Eº%p6B 9Bs.g ǥT oyչ4}e):*H:k6:h`01;#l\bOMY:qgՁhTMoz-%bm|TQTo>*F{zhPДDV[NKSUV3yo5ރeG֋Q'c<>*S-& = ϔpt>]WJoP- e"Aض| L|Vn?_6pԅ0-?k>: B A@P> yB苀#<5T*{}Q޽ػ~ jw"}FEI BJ?'j;R~c[,+SbL m7T`2Kt#XV6*jk 6_V{ŸTuVþ6YǺ~kA7+MUqcQvF;cCEqH'9J.q8Ojlۂ4UǑ F+IdOVFRgM\Q |!ZM=|h(ٻG~3ڮOxcR+|8 ;EW{ZENӉn*[/aLƵF;½pqI~~C%U|%,\ 7QܭT*{pkFjD/ yT*Dq8H+H%26Uxj6sPψdK^7l.c6K g|vR,3Y=4K3u|ke\?cms G3>qg:zZcN^7KQ U3֪(&|/}*M}퉉5JY鴋hS_(E4C[ s+ #MLYx78[ ^+QJdH-=e' F;xe'Tj˵ {apC;]l^CkvXiUѹ۪w?˰&+|/ `=wz %? {y ҕJ$1:Z쟙曯Υyؕ]ԣX\_X>xU%H.! y1zX Ñ.@dkȲ W@{4P0٢qznocksdh[k4-޺BG(sԇn;q#I.U``,&W\Hg~ЉФ~9VS>l]uOjFO7nUU圳7$DCkjPoH}^ȣZZꉉ/1Faui5Up= ޻Q'Hz(tRk})i G NKnO͝i<ն!vt}gZUs~@'X8Cud>.* K&zW{ D_vrOĀ%q{..IW0ɣ˟HQOf1(zaq-kg7"nCpNF!DkOg𻊯%\0_dHsKU5Z0;n 5`9Qt?z}v]ւ $1 9Wltu-Ze[=&Ϫސk +n5\7-uf$(|Qf/+\]lP+$S.ڟ *Jq[QjKkM*MSDB]*bkU ?QRNٰ$F8V{5a,"= FlX`a#X^RU\9; "tś67dE_Fw=j{W%1֦& PrcU9ȿm(nn=/sQ=m/ug`GWf* S#Z^:ʘh_-4VY$f%S}$|_k:v[fkR˲Z Jֻi-dҎK)cl!Q "pC 7s_R(SxnS 2o(|HV¿Lw?0Mޔ^;} p5a_911UBU?B 8~?ynKl3f듥NҙHc=эcS 1ZU EȺl 䝡( ^h\{3HQ4_%xܫ=H@Qxt)jHQi . lZ)I쀖}j-,VCOOUkLSe[,.bz1ҝ-pI]ij'~Aenj皥5 &[?}FWIKEjX[v485%h"$ֶJi<(֪rJU& C/>/ ZQV9/JQ/xX/ 8!6XXSLs7K7iө=H[ޢ~?$?"V@]oPuzydsI_A)Gߦ}dh+8Թ'>Q|>qVwMuyK!`XH9D[Ǖ߬eµYU*̪P'?*W EHWZvIֿQ):=&XYk-q ƽ!y ikO+0 s8&=T>PTIePw khqM`0!ޮx)Q-fɚH`㏜Y ~#X$Aqvݭ̵Oz'@O2Q;Һwt5e%.ɝ,6n^[eg٤QUTR {.IN"XནT@VGxk(D \OyttIZgĔ+^t&7,tDO%3w 9sp/AF5>Qߡp 5u\7%H!+ۇy֍7oP>goH% 7[F&ua j&:;H#Yo{|E7?RGVOē%!?fWz! ClϴGW Xp! v8J19)zmF Zs9 FYn]Wd&I EC!0H J bH.9h*pE>ݦg"JS9}bju/;*{ʨ|-Mz9]$O}+=tCwxuC[Iw` 7?Un5Jwd8yWPSl"x눽N54#u^ ]+18R: JU,<& 5΁sB**GXx:46bJ{ϷJ?tceU~xm;>Ϯh㮷 !Jg_s~=?@\!>!}L!_7-}sĔ1TM+lmLA4oٓu. TD戾gŖ47$Jh{Kk>lr&đ dy vd2vz< |P4 zeBl:+wѣ}gK|/cgU|0CaR 5Eq"31ObO=^%cX1(.E]gK$ge~[H/\Bء_kkobIzq q]֍pG4 &5-J]ȸr/3yTHoBVY}y (ZyF-dMp &;%;mҐy뷣)qݧkܙsv{G<cǟΔkn{B,Z)rNЪ1!vr.)ʲ7GJ<rN{1% X VUu>RQ%^8Oem%^VW (Ge4"B!2Pt ťJj @ἔxF}Wj|j56Cuc|޸~ x'>vk덇|['*1s߮T#ˉsXg96ߠ4I-"# g:0UA;C ?>#􊐕=Xu`19u}K恉ѥS>sG*DuyDdEUHZS4QŧnOי-.O<8/ғ+)|߶m~ή؆h,5<]Rx׳CmZ~%,m]_p k E %ୋK's|]*a@ˈ? m;q3|ZU!u,ZP#Lg"DV_qό8k͠6( XJWx0"ʈ"< p Y`UMTMkΜ 7&jc_D?Zn=2y`jGs+Nݢ>wc{{f*euEפ덬؉G9v ̽i,<ݟM7Ӝxn/ M}KqM-7d%j)7Q1{rſ^x;.[*Z5'S{w~,؍&'#x} T1z/ĝ;|~*ap5$6}bHS0>=ǁ鵗E Fg[7U2qY%%Bn<3gX\ 5V%GY#* :S4z̲Cc1_'> Jw +傪,M6|^b\lk٢Ygwb2>)ݸ."j)[[RNƔwnV eӨu?$J9:WRqy!,AZ ;UP $(e:0FJ˰xa-1a|(x3!*z}NjImsr`>>d~[IYYuJ(_{S>sԱmlGӯ9Q1р<~L7y᭓[n 9-$+,@%AW* r`[+>e eM|ncVXX~߾x◳K+ ! eFAX{0' !5 V`}zw r O[$ DGG +aM}@֫ofd~]0uv'd9Mx&mIb{Z>cb{ܬFH`3r->!8@pTT._jRbMȆfZ9\4u]uK}tFuX3?{pwW"5jm_\gTSnѭvB㰜]Q(VZQ,QfHZn)F͓,*,}QNLr>53OTUGy$w."bR~XZ]y"<^W%EDQVGc Yz*~jp?Sf=۪wn~L5Iot[s>V~}IқF Ǐ2=O?Vː6G}GX?zө??u_='5ΓbƷ/oejA8y I3s;q{kyS1!T^ sv^!G~m<@$1ڛ؜& cqyk~B[kGFP W,W~ Cr8$߻|W8W-HG,kk7aBLv"H_{9F Ow^ HȮaO㐼cQe$T1T d(("p"qEV8ZOJ&=bOHk:crsJRJloj^_po>eT5ֆs]ƥ^Ȫvo|zKd,vm$VVG5~syo8dv|{Uف6' `rojXFj<iM#r"wlvsX5D{*8GF6K{$9{CT|Ӈ;[/<ߩA^<#:`rH^|p$@OR\/OnCI{yx|y.K_?&aa#\W9xfnsɮ f6aQ",m{/XY`qI\fXJUpgPQuJ9,&a=d7|3"KG[5#)Fdd@nB+/k`{0znn  7bHW 9ph&L9 W% bwwvM 20Z-Xc1`-K'"]f0l{PU VAlMB7Q*[eo6;E:dQPw>Nr]ι2Á+"+L74Uq٭m󫦜sDd5*;;r:&T 6(UJ>My.+ /^c/S0w8;{2d8Qhm#tz-zb­^ 8GV^xJ% aϳZ^\0Ri}&O9nifVm,j̓e;[>I#'6/6/Mo&ܱc@!3i%'װK Eu]=4ApJR4%^' s'clhR*2!y$ k zXycy~=?5v ;O GOE/&Y Z8ck\{ 9tC%"'ɾōK/;rMeIp*..O>sut7ֱBvg}!o# ϕ:%Aઅ#=[ /35"it-,-xnYaԢKBҾ=KRMG0|a/6ye?۱ 5[fʵdy4fzNri]M]6pyH#?ɭj呩Sיot*εOzsN+E&0B^iyLJNr9JZ=y!t"M O\j#Xs3MR;4[bT`hHț_XmINol[tQohϢ :;3GM;"(>t"vƢCZeE1)q<CADi!V1ʒTdGjnˤ#ߺ5vu~=?5H{cAsc٘$X}n'-*VIr â teJ,G_xVyį +si u!SJ^凓NŋX?z#=(p*:w]TX6#l_=I$CRP+pG J[ z]'l=+.1Z'm"X_뀿ʳ;j%y &{fyZsD C gZ6aaP /*d@a ױBLV0:fSQѓ|U,PuH|m(;Wmӛo\tOg]|٪$Kb'ٙ1n6^(J/kЛbv7v۱ݭEtCQ;lwg0.JRe-+Qjgbw  ѱ+P>'Y*]uPYxJ 5Jhc~=?x>;QKl؉DKS{ K|1θfaR7P(ܮp11V_\Շ@JzMO~3g>2u6\ dDEVx|`1~Bka#?' -.ɗb܊H[%a05a3V!4!k <$^>Uox,'!*`!f8|`z` ae- ˏF ,~d) T ]GQl HHN+Xt]*^+QIJrtXF}~ m\9"0ٰ-ogtnMu]ra{RN$^y='7_ [ӃB:'}碸,qSMrJ)\ZqVu^|WI4ηKU? MaON x!rX<+{?`46}@[&sZ;/:ۢ9y)>}| D=;ʙm;h89"e~J\cXo[حՄtnMd6r7v؁\{DO}T%wisJ IDAT]53Qܭ87TUMH}U?;H:{M[TLX`< 1p4P`SrTPZRY+i#Ai ABbB+Pp(i"N@1ffw4]ܗ]5`'2++_w{ںq|?ۭOIODNEF`rNAk-KTNQ{"xq6#B^#+ڸqe =Y//,x/Sy-?!EX@{珼X#/T.O%d/:x=}]ܕs#ZǠb98?e[^S*>>(폾eLx7O@|>wы'[9.>R;oz{-'u/qf.BrFLv.q9K(1 `6x>J m@C >\9.]B9#-ѻ τ;`b+E`ο!0/gMƈ&z̾?ٸ=k'̽g`ɷH5w EQ!tL2U h' ,]Yg1 YoABpFH] )d š! o=fĮvTR1srQSlc'V_-V2'ȯ>DnkzNv_\F/2֣W3;h5cN%Q5AkU$t!<+o('N;U8Zk*+ 8kY)AHVZ9 BဉD ҴC5H,NѐmW=?S/UZ.A>:>ҫ!ѴqC7n9=j%21F)J"k1bKGNbA~PҬ &ԓ&;އ9/ߩp3ytFYiîy^$;&W=?95:B FFY[ْp.iv]]$6 Oq'7J|& Jw cuu>Uw!:J}'ME^TEvt_Vm;mf?=ȊҴܓ0_=a׍g:j XcsE MjDÍ uQCK 9 0U%n.oAvaޡ^c||\s}u^tn̕k69ԯe2?QuH>ӱ~av\RLmSgYvzJvM4lc,> 4q2xbh&YXi-N9TB%hN{N<" IFt`jq at|^2…lڞ2xGoW{!UFC}z\R' ٔ]c~Y^fOǮ+- gngf򫄌 xGOfS3os?5wVt~;Z/})PQ0P@]#Y0fAI%,G3SqCPojl݅J `#t#R1=6tb+4,N$y__/;rZ1[S.ݮp6a'],HuO?mֻյMDdb ]"JPE56wP1n$)ǸQ&E;*\hEyR(kjFY\)S%dbkc\䝳o\:GIy/>l@KQc h|6}'HhH4>~G~ݙx;8ّa1`r3r{ x1 oj$J2]7Pȥ$_6Tp=Vcуew6:v@"¢vB#_zeOMB0:^M/l!+&E!H=t?Iҙ*L#~ׁ{о!dB5^7M2w擥ywjv}mi.&P!i-BuoʸPwWCKnyA8e$aOZ8,bRtRq1T)G䪢H5,vrJ|פצ\3ԛ:T[x.+lYtxMW^/a9ȣBTҕąCKg")T,tߚ62C漏p4G3|\i&DS›J9'+k)cBhF΋s^ZEڴKڃ'h}=c@N8.7}uo'~?}0@' `ypN=jgc'-AO9[ ',gC%?ΣgvEo) mo!ZbH8qziwǽg>[>16i5 +{f'D7Tgb{ה95 H9!2b/4{ϋj/\?|$5v}}&Ak)?3tM9_⅛vSϻt/.3_2P`6J3w쬴:!bGy'pQZK)=~Ӕ;{Efrku`sCQR/w'+E8nqRW7;l>hH3oΤFxtgѝfxzLpN"QNnw`)L*)$qtm1|dUaP~B~e=C3{ _Sq#j8뻦iz!-I<4~wҗ-n<5S\MN<=v%*Y%,iUJ.JY?rBNb6-@?_s5o>@nXn2’Uh;*l97o}6[+-J~0y>XguG%n>>x;Uu_M{%;\}'>N d[~U(QHc!/\en`2N>~䰥C'XmCj<0-AkMt 4UȌ_0tj,֌TCߣEoj:Tde*éOy;hjO\m2< qgǬoiYMG u{j.k}}MۭmI]v*[NeT׾߱Ζ(i R*NYUu{QݴERscr` +ڨqNb >࢖) CX(SO%( ipCX{u4S=Qw/_|#OS`c[tcl|&ԜO+t31_% oߡi29 O/K_~ȫ,38{q:Ǯ^rvos|`;h;.6^szx~0M?w>^]G/ޖZwG?st޷rX\~;I߫9ƺ[4EbPaşcXNi``jrp5icpJ$G0R(gcDe(#bqLj,N7!uj)kV,SZMjuG+Cfj&_u)W.ىobTer{ջ'+vpw\.ѝ~ډ+}咡seqqwb'BMV*#T(rkJD#ڣpLHHԂwyiʴI%Ɛ%56Q6w iA9@[gSCJԟ$̋|?S9Wqok\R}kv5@7bi:zhXu}Qۗt*kRv b2&-5a= h1 YGN97k`B mU3ؿxەY7ʸ7ʩ+pF++yb1co6T-ol^8_dͳWHmkn.lU~c: eޥ8mo'f\݈MF9hh4u)_uQTUU}+7Ds2uݑº΀3jB>w̖)vd6!(0G-}g?gzkϮ "3:R1G(o?8s!BqA88|c~9{lqf?6W_Xak_Ms'nb3_)}_OI>|ayї[]FW(zGRv;9j3Fa2+maɶ'UQeN@~S&jziIG @'&2[u[ ɺIWHqiLvo0eBC0,%Qɱ-ΌR?88/0ȍjDOJY;;ʍ8ZĶ3P.)h|4gET k!` UDԨyV{/()4b֓+ ڃ73MK&:e֭c;P*봩ke)""#/>y0޼~/:LǛoÏ>Enn@+*AVj)͟&ei}kE*6U١3Q#(~O G1J Y)grYwpeeR u7&?p 4-&d[7h~=w_~(;=uf/iyC9pSwjosm:&P4K8J"@֥+)* O8]΋_.kx|f}#ۏq~`~sFè$^z[.g(h}+vrlݾI{eU?է{W;6t숉J=c번4ܟm }X!$TMW %BJ9O^NrzM5 u/ŝcw 칌(P_8s֩צ&%*&W ULSOqݲS*Jv;K+jaoq6^Uk42頇b%R(vEũu.Q , 0/Io¿?5r +.7"lӚi-d.PgMˮ.EVlA0&E:bv/3lw ӧUKvR)묭`ڟ]H&pڟ:㑎)T^-04+qضe2/tb2Hs-[vdOªr 72d@u}ܭ%9^|&&D_qy2!~?4`>} Qc/od= IDAT3^UqQ?-ߐݧӣ(޳oE?ʣAջ،Zi]4}m X}.ճ#Դb3akT@cj<=- DLkI 9:jXQ$,F=3^7&ZkHJ4_آX|LQtkg|^᧎=5;ϜKEtH.Qh?Ь1-<+aBN!+rM2g.uVݏF ;Rl3DGOzL +lf҉8k&;ݎ#2Mw,sQ漒 TynVqSIXEHE{C;A>"QX@EyĉJT[67$K߳r\>Z,[zw#~'X`O'"8QQoCsE|k6m|eUGCAV_IaBL8a`<1Z[d&d47ʥE\=ršhn*)̔qІ-gNЍ6QZ1bٞw,_#'bzu/loegCj>nN!<ؾ7a:wVYv G~ִIN`ؾ&)vz]{^')OïO?M~"l̳&4/r{7#99׾%avΨ]@ՠsCXLkCGڷ$MQP4!:=!Lhx-WuyuMu[disBjCέL>*V:|U߾ =hj dD-jzF^42 !1_iu6Y)5yqyMTF.t8y_LfPRNT}cfZn+eke0m4m 'CףíĴe`4חmw53)~1 hnO'ߝ VrcG=PÝ >sdŠroĄqfhh6!bCોW}$,?6 ` Žx˿\KD?>ue㣿rեU<*Gx//PJNu UTнq9ԙ ֲSFCgIRmq^'ndq8//\ K4:B4"1[Ld{?k@B!7C?~W,AdGJz,&NAg"xELYRZ)guRaZ)َ&KImbz.i-G9d 5DurD K4)-ōʕ1MtV12% #R 1Zƈwʮ>F6in/NsC7G}GN `//5WToHk {1+`;ٝ<:}gUK7tӕX#6ceg8WRu3~he*v\j0D!qiZ5'>P&a;la* [GTSDcr!GO ?Ob"Y i0:=Y:TQůT}Ų)Ԋgtf*YR\X%4l,b0nRl=IkNS)N]x}c: Ij(Wr7K<̙Yj_V㦫~!We4IQT]:J6SYGU5:D^PHODIPŤyxU:e8֤(M]>c0Yf H]"ީ'fD$s,6`ܞ1,S꿶tlI7z#?.|Cl#wSqSOlD/4IK6::ggIʩ2*& pҔBfBVPcb[3j&6mq /S%(',0UZpJ"nUˢا*UM׵@!"%|;<9i 7!}ucU4|0dsy?Oo}+=Yl4Y?y(隣:N-=pAJQ|?{?'2Ո3[$cs_?o|Olۥz;_+ƃ|W$f 4Yzt+l'Aʀ/5"e*&}X+CLuus~-f>}{wd?= ܦ֚#-Wx<{cI8&1oܼ¹z:l0_e4ݏ)c"EW *4u`Պ gL1ZqidTQA]51=7Bc55PIh}i3S 2 vN$g,=iZ1rnO1cغ̓km|a0BC񮣞bb>RDPL68qt ]&9nيk@kEHnbU WG>VY:#:"GDq&RqaޗjZs!($GR(ꮎ7F!k%cWB  ilRƉN ăE4Р9_SzC8g췸n -έŎsiMW SvgC%' K}GP^X7!VĻA6M7vbkc:em&Yتߞy._M{rl¥rwxsKlҰX 20Q̢8YNObv4Um}vEDz%hˋ|/:!ޥ=;'_#Ӈ|5Ɖ6Յ!| =/TX:ʥn恞# 't%CwE1yŠB{3菚tOgÉbFp*앂rx@1n\R*d伅բӴjbzK^$n"!+{$ju'4VU\C;_2+ c K+Pnq tjJt檹0cҚ IDAT2Gp:g[eTԼU` zJbSVT@Ec) `=Kkޓ~cǏZ橤!s[sQَL|)Gs+Gכ;N^:/lQ;\PvUe&bDC;ikWf>z8iK AI2lj94^e0f&tT[M)kyJ̯&M, WFjq\}˥uqi9[jؿsGˢ[ :ڱ#%UVV%R"C>L p7E0CHœL7jov?~R_r;ow ׋)YQ{ݧxz?rdiɏ׳WZ"L`3wzԣ~r׻dnG^UZWuj~3l8`FJ' ,=M{٦߄D4izg73+luhdsš% :\!ٔ;#יDő,{{j5Hgņ SW7$[ r?Hb:͗VH$mzW.H3)bFTDӔ̑eA1'g?aԢfc? n7W:f/-ZUI~U:4Af:)q=4,"JS]^e6b՞f8)G^_NlXhoc P? )KXu>:lFX!6SUY"8FFM}JI~C:;:KG~: R4J% ?wo3wXE(?,gީ~uŭ[6KʽW}'IqͿUo%ѱKjG^L$;e1{oF:*['ukV hLa:Yy5٪tV.$H.-4Sw+Y=R~Wdohby/xP .4sU W TBէWDN059'1nkhx~?=anVfvC>|9[ K@ɏ)xKfK8.}w +_߾u'y®vw;tX]](q4ߥѤj(`z3[Mx;C<Ըuڢ, kMEq"e:٦bG ud;#=&*hMӎZtW9V%5EV"yxwtJ^YiCc!cy%suc: i^c 2ҋKe!X) # )P6Cyn# }Ay. f[PЎ&be9Q˭׮ު 6`z~jؼ*eB F^I1ZT#,s߫TB e qVeUY&WN_z<8}[5"1lj T e̼Ɖ,+XCBa=f Б} *K~S}^@N{M>'xy 5>Xo=OgoNxud.sU/'79? Ε tmy؏~-[{xz D&vRh0f07sつhX̴hF.H=-=B3qLXخUmꏭփVQ$_8䤪ת& _f,W`9ɗdŭ.5-`}yh39/koOҰjG5o/ȏl}Q7bUUC[0^^yF?ٔ{5+dMM.tlMtZLѠ%o+e%4G3 c+x~hN;l"E]5^_(^T>}̱N.v̔و<[oa1voJTQj\b' Q2%<Ԫdϡs2L^x@L2GoX=qGKxg7Z#ϾVմ^ǥPE=(dSү`K%3I¬rG>T+mqӡRj %B3H5HX<˕i<-bnVco(dm#rK_7koSKDNPZ7ѢD&4ƯWmAPP D {(\$W5$Oޖo=O=[这ɏ_QF[0Eh T(;"lKGh=UU~] jF@L=W_s2YTC*C,$!u/_!?ʄ؟0$a}Xҭ8t faʂ\DY&LKW.zĮWiбHh.\'K/UT ~ 屓JłI=1z}"z'(C3IUu:QN[l,%64Hȶ= `PyH`uz.WU,ws7v;3L%97Rz2p&dow(!+.N si8͕D$(cpe.UV"rAP|[or;Q,-t]1@|C0lj`;֘k]Ի[Igc $Y SꔥVᝈ1YitѺP\IRPBW ]f6Yd HsFaPeKyk5+V3a4EEv L(/ĩ0S,/ PRVT²VcfKZAY%-˂.-]΃o{:kX(,(J8yBJf9TW&𑞮d^8EU#@~o k^(xc/E=^򡽪t:/K`S X[ *f)ed/'>r/^}-≟Z|?ĝ? .0'%͒~)_ITu +&Jl"Ȭ:Emon>>/{?QhLn&/fۖV*P>b:3 VBnXϥdˡx -0SraI \Rpɷ|v %^R# 1C}\8\zf{迢uG[<7# a|5U:G&S8˱ְ^L$垡֨텔Ej ITVHUQ[Qǥ ѨY' J'EOʮ)o[ֹ8˝}&Wlj%z}!SGVL3Sz̤aj݀ZZ3BSYkGi5&wcoҼvG|ᬹT p(-2߳E霡0$)~jJ\:l(R!EY)+p`4=DZ埏%nQ/ܽ||[֏oSϞ@'Dc^jKo\۷a I/>qon$N.0Y^kLwE\s@% * ɠ ~HC6q|&q%\JܴwfKi/Y{] ~`q]Ϯ ^v*KLuNZ(_u7"!YeUMßw 7Tݓmü_<<?$YMUK'h}_?U߻9E^zz P ¥dێkB5sWC\-nGeﵷ^kyk&I(OGꅃs/L놗q6Y}h1U,;Aw(]aCf@6!˲*.ufAIqx 2%2]-L' Sp֮(gSUe?ģ>ozoR1/=UcVݫ{y\,*hI<"&#"vqyjwV[$DI5&,NΑ^ګM.;}N4'tMP.or)nKe6e|$XyvI4Z*oYl7f"-hv*T٩Q`=`SWZ̑ӊM0->{2+rSOA$[PY4 C%cV).<tq*<[ݪl7Kdj7몘|-Q\6`(Rk_zpU`*\u$VJ,Ԍjruoo8 l8zm w:ĉQe|x+Q RE)TIEQUѪh«KtD1-T;ƨ()'4(FLs~+ͥSJPi.b QBYgO}ɡMwC^_9ԝUwz5_Wo~RX Y˿G1|P}#l<mN{]*vTmBr+ZDy%x?Üo? ?~: PTQtE%熱%][щzmυF#!+n^t< gN7D_dsk9YJrokDx<%$#BJ+q( a*R"]fܘp"x;$s @SVoL+D9=︩ܴ0Rk"C[0% /bnx]v#ax¢B 6l~"j-៏yv9q_!kUGT {T4Ȇ/(2p+tQ Doh|H~FӾO )uIkܝW(o }4^ zm7ԓ^YO,>? oD_G|m$@eM<7p0g~bSr Cz1"nߜ~6^@ٲ ; IDAT^T`UM7WO,C1 Q8Y$Ѫ˚^pOOu`q3ҭm[m|2H9/+\ #K$qy9_env80ɞftx|19eŽjbgH#zEDl,_lwg@Ӭ<p}z_U@36ɏ?{ᛊ0oboan ?d#Z댎 EVoP"M^SbXN$P-gkVr'l'9 ~V$;In{7pl*ep?7`}21r6>Nu5D:eqdi0bp2epеF3RȲeo[G?x1G?<dFQcaۮvS~@wFĽބr|MVõV0+*tԹm=.X ` =_BJubIRssK2abֈc&Ͽo!4%]$*~-JdPrc]/O|dxu]=p\Mo*ԚSL4 7|$$ vIPyصD.GSu~L28Ah4鰞Jݟڤj7r}u3߳VMg( \$ZQFΗާ9TckJellMJլu fPK'04Z4 ne9BAЎJ*$#Ymy9 :n5ӯS >pYU{wK[$?twKwJ1iu/K)fڿ*b#v?>Tp'Hl`kyfk R:L:7_ɚǸ,_Ob4l>z}/*ZvusuZZ;?=Wϕ ;:-⽭\89ynղ::f#H^~`·?~dc#FE/'bk7y#.ގ#q`>WQ8XvRHub]Eޛ迈*v)H.x{o%uwދ=rϬ}Bh A dmQ-Z2%ʦd ![!d&#ІFQl HWAh]]ݵog H$h4wN:U'}t?G\ KŠ%I{lXhrˈcVNpK\ʔ2l!y&NN gXs,i. d%X=f nBi@M4LmݿVnL219lK~ _>y"齟]@-O  za&Vp2(oa"JR/,Pnh(ȣKSG68}B<X#pO^њ|cx -V%f4*Иؠv^f0&hgKP0.bcg1+.2S`܀1X *KX]anؗ{TF;Oi2@^G*BF6S5jڵh-UF!e )zTXǻFzپv67{l[ylhL2bb+b. N6P_ |d?'5iBZ$I}vĎQYA(J+r 3QR*ܷۙro:ҵ]Yl%lz#ը<1*V/'wXRvDc#z.:Ej#i\Vl^O.>O|[6ӑ[y1EK~sZ py45d;]~~`t0;+72,^(k"+L!?Zۻ~ǿg/ >6HI{^ڗڻ._F~EQ4+OalxJ>71/@ċv%f%>sx?tӒ#i^b}Vȁ EA6/\--(;We82툙q ~'q2!CJ]isb#&uLx 0C51pvhv~1"4n9A䐏o}{4Gat04L,m@ ܄6=@t=rHfnSh$'yfgD:yj]y *iL\K'#E=Vhsh#EL4,rf]PlP(+D%eDG+*9QnBOԅ,{ڡ" )f>yVc$ zIc:k mYrHsMN0C?05؍`nD,Ih{mB.VR 'iȳܲ7ZT(HR])HE h5@tH3ǵd ̖"qz:4ݼm22#r= S,KHjL7+cã~qF 7$,Ill^,l<,$ je.ζwe2}le:isuSjdW :Ssy;[hv{'m=?d||uvpgG`_.f:[Ð44q5rp:HFk~azuD>Qo\iaҶQ2Osmd2#Ѧl\?tl{:\sZ.2ĻJ#zڔgV<ɫ(û]ksTHa'gvLa&{&߳˿&L[]e ;ݏxL2oGYguFiP(&OA*f7v]X-$ Uc^_C,@̞W/v2nzOL5[<"iU ޸AN62+xvK'7א.oo b|e )rFd&$HvA rK /en_q`kMP-Pm।}IR6君dN0Ay"¶ЖeLKa]tdđhE^ozFS,D؝62)R[z~J81+Mb' vwH4^ug7 ӕv+TcT(V%ZfmN.zm{>{;_]sg*[|D /O=QF>|jp[/.,Wũ'V,YFy(go?ߏQګZ7 wd0mVD2Bʁ4ubVp`E_\bUF<_?"uBS\s#ˮy+ŎQ^3YBVNAHp%,n^^,f252Zz)k0MRCX"MfHu?1 G_2Rꦻ `|]unC~p;~ǩ.d?7x+#Gk;?>k.g㷋Wڟxzfj5hU^­`uspLhCQjS[0 &Z4 P0<|>*@䂕Hi DF~zT%JW~iu<+U&_$[M mF"OϓŝXA=giqdKY`ӎ`T3NҎ꬝QL+)” CۭMAs\aUa5+,#0,SNp&3M=AR Ҧ>!)Q B(l Jv`QwSөf5T8$w|-Cue:Q2-WF&Kafֳ ָrMv-aq*ӺIZQ M: rz,=;jq{APǍv΢E4FvdElDEM-s5fJt7 s@9ڕm1m '6p~$y:*Y0W}0Lr+ze G9[dXs?0Z kg_!i=zNl:٬{??4DDe 9I 8{BdYV~l,|UQ)LPʣV*qˆN³vQY7J',e {*7U>\4}K¢ NHڳF/҃@HR6A k ZH!]oM GZ^PqԅuǛx怠&M|meT]23Tҙ ';׌Yqi` љvqJ)9&߭enAƕAb4f z5h\Ӷm#oZX -m-m3F##b5a*kkIKe6\,'#cJ9&ЖqjǰrQ L] S]>7C[;c٬UfjnTX5 Y9h6XVkg?x2~_=&:bécO\{I'Bao"9HʰW0%hQ'~?n?3MopM7A_,~k3TɞbVwqU'hݬ( !RY?fմ݊Mi_,+rVghO/k3>s~I_0|.{C#"%xʺVOsQ1{캭q౉k募*q*cemOU6C-Éj=/ hHk.u^.|k5&];{q>w*$Zp +_;wl-ēfjx mRK~@ ~HnC1KI.?puN91u9mwq)b݋"<龻u~b-aί^ՙ_@_~k,O N鰐/7~K6 wR_Ȇa^,m`nj .8}(@POaKX:btg-r]G6/_4]bl+'X:K1D4,sz0ErMZe~mu=6^Aca|~  ڕ*i.TsW!}ۧDz}QkG[bh0t!:sę[nG"}7_~-m] F7еP2SKb(2.]d|(z 9yaA?Y}ZyX !-J_xq^uxw{? "53` .LţlSSYfIUR^[0 Lc3i[ZOj5}UI岧vn-WjV/=yvV #񎴼lf71ݧ /]xf.{>1 Om^\ߝb58c~}],!yuZ??~]HV o,]Sv\rxE`o?ߧ}a/XDIe,/,+m, !:(LtY=rr=q7r<ѿv-m-EZ|=๿ qnˁb$)ؒl< &N^$RV ^sh*=o$$Mj48{ a*ڂn$cUHBI:y#ӜhCHk%ZLT>>Ak Шg4r͍g C0I!XAf"65˻W8R% lSR/BK$V≉Mc* nLۑY 4oԕNvS[fUH;RGKJՆ$t)WSĐi2Tef^uLQnfJgzvP#zvvi=7+nvWGX:0[O-?׼}ͯmm\Yu)#ݛz_(郵џxyor3{WObhHNԬ䩫0?7Ū7y}4 Cs U_["=Пf*7|O2L9*m0QY1KT&y! ިXJJo,oO'׳Cpn o?No3YcϏH$JpdQbcPWlkq1I<ΎDΥ:Ҭ[[gWѬa5Q__3~Ίq;D ,8jp辡h3Ͱ>&?;#0T)q8G^J!w^~/[7$'GP,Al^[($0\HRQ^ q֘Nς\D$u!(ׁDG荀9>ҀF,p=9pg0 JT\Gm,`y)qmTi-^*emR5f!&> &<휡1; l' !Km0"%z+ :䢇c+x" Z 5kϱD fO . Zɰ&tk^>@iE`prh4v:t ̪ʂrn ˰EiC e*"WEaF.M! VqVy3&zgތU z:ų-ͦ#Z3%f(Y*c4-K|Vćݭŵ6vj`ڱmc#? n>E#\q^z^m [Gqߓ33˖0.]~z/3>u_.W'?*V_79aWw$ D X*M$lOa/ø dvu8 B·*- W+=ٚl֠SX}$3moe7 &4mqܕĭVklܑeJ>ܕˉ*m)v `: $3 r N0\F)yãtIV)k2/K%bNb1[f ɍ.ibcۊJ)64eLrKyIχ,J*e\_Y2ɅʶI+FyvʹF0o)b >s. dTUN L\|gy=gmgn^ :=jپ%76]ɧ__^[!ϐa RZ|3=֐I O_x׮W2ov>G.ϼ-v49۲Hki dV- m aqe_g;b{ͽ["~/wsT߱If RnwOc/[^ze}>L-LZ 't?>yi'y7ȻX־Ie$ ^$uEK-a~}[c'*W_85M4p NiW ."AQz.ig ?Ţ2>+@OjQxL5B"h^0~*Ӑ~c"KfJZUʲZm2V 27ڰ M?zAf8/xG.t4.nmws%`2-\5f\I-6ܟ'u18j]?9X]Y0{䵪w?V۟zvgK &ݫui0yw U:|o = /-CōGĭW uQTN]DkY vW|}ۏ Ӌ@po3$pG?5ܒ*YU;r~31ݰsz6f(Q1,/ᖤLCTªamU[H;~_w a~0LA7q+Vۋ]J1ly(f>÷I3UM!`v=\ kOQ/2$/81s 3gʃ%w#='nx 9w!y K# Iׁ|N/[,9KO?V}vwb2m_,S3i(\vPRn5Lgp6Ρ (vש!@dhafC! F仓FE.}GZ@#]RN`hRH6ʕQkktDȝWŘ/$l-jJjLUktII'(Y}-$IɘidL4v ƳciȄ.S=vm:fk'Ol$FQ*s-msJu\@o E[cLو*bMvad?v#˻i\ֶSvlJ^f&ݭZ-CæQ2 yx-"KiEmyr/ǝ><&fEAቃƢ)cir긾#Kae#D{y,+F雞ct{[Sv ϥ/A\ǦCnSmtpWջ(gFWo&ͻnCڟZ˙0,++oWZeięЬq؞Knҭ$BZG~#5f u\U 0`kZ#]p} t* Lq68[Ҧ >&طInkfS9D8hKv\jfx;edD_Gn,ͫ&>@]#\(<*p|5]HL\zb5zeolJ&TQb$#c )ˁCk $=&mD^BhE#S)Bӷb"MLl%dfySkDyɍNrU;xYԔR1C4$vi{"Qg8alYPUrU1{h4隝||$Wr^`g* m ? :[i.3nw8VTD)Rsckk^6n9HR a9qQ+ι|h!TxC~/zyvd{O=xapywcO>pMɱ&X1OU jb%,ĭmmD@tR|Z1;}ti%:"cal 8..±E}ϫ|O|ϭ^򏏔= }"GD7+Caʗ,m~+f(?kAYO|]7(ovqy1yC|I0ʨݦ_I_ os2# ߍϝ= 4 ^[wyB1Mr9z~ s [`pe`n sPb!_ZjBa,Aa#[?xNRԆTO|3j nރU#^^b31{/1\ʮ ?cq+1Z`aBsV>_D٘ yĸL6c)C67xcEı6TrO~%aE;\i (D3ך+p)GaY\j"&6k9Y\;T*ʒ\g$]׵JEP_*.lRye݁[C+t8Όu3YƂ l"h4]bFR ' :v:jTz(ojy瓰k7]SswѾ팺;9XYZkn'{be_m?'n%1I7 %~#gp;0LšǪoKaO?85tqX(qw>L"r;؛kD)S딵/[%|dQfL Rr Dޗv'ARbt PPiʞt`fw{c;;cX~"e΅/ V쯹oE[MG ;Sj_.cb)7_ E+[(^;$`_>Ϲ~ے֢LFRb+H7]3nCm . (bAyk3@"DBfX*G"bw^~DNp*b$2iͬS{$k+[ÑylUWLc]Oo8y-*/]\=Đn.Y ܎Vc^tf+3mƧ#uG1g&q/fܞ Uh 7n{U4ab Cl B R킽6P0cP(;}M`rk2gjfFZXA+YI=Mժ5HӣP&ݛZ40:.ӗf|&ʑ(S& R< *Op*d-<Jma~:FUTg{a^Vv;<3pzis'ӝ/6۷LVґc2PhhTc}ޗXEѩKXYC8G=L9w(Hu3[vVJ5*GQKwP.C9LuWVSEIFdtĦNN10ѫN|[FNm G\dmI0aZP.wdXTۇ{gw/ʺ 6eԘP_ )AJu0_>lyTT ?B5^ѿȣ2} EˇE]6^iH}"QLzmm:VQl=,\]p wCN`.߄^~]]iWŭdd9}driQo_GHYj,!rX @9"5_ 4FN *(W3,N c"C@iw}zkW@ 86]h+/+a %S*O=q H9uwP;?a1p̗^?~ /K^m_ ͼh0/g{NRuPGM+jU9,:5!L{`*DUE{ /CsU~IUO{an˸`nw6$3o7ɛTmHg~awUkDǰ= .fA:vB1 nǐ̂%lܦKPY(cG<'áЬTtP8md$zHNƶbQ[_S“j,DԽ8<5I-D(%.!SDۮ\TD ;3_VC{Y9^eOVeRhU͝{.櫡=nm*AD(|©?{h[?=oں=l>8o¼`^xK-ه3;{oj~ih]=Ks<ٝԠ.)Z{"82`bSz,Wn,l!8pӰsHKQ/?t?cH7C_q *HbR> K7 W_ڽd!qETآfu64YkC's[vR0<:!X%+ÝjxFrxEYgxL$,쟨:ѾBy[ X+GDymI>L!LqIF0fzh K"2cC:T1%P0#mY2):ᚴ,]DyM8ɋZXglwL.<5Z.ѷ)ں\MUOGyYj͂7ڸJ?S50h{7r?"O#:I$9e32ay旎WeLN9^Z$#:(?ǫ1KofztȹY%Ǒo6[G~j{!s*rg֚C)j)` x;嘯+ HD L"Js!Ic7o[??[ݝx" p)kZ>8Ƿ..'y~L +#[ѮT¢U7YF )KN(IysgG}<*1 h=aSZy a EsQ;~1EJCpU(k4hx=+IAZN~mj!;5 ,jnX',LS5WQbLq8a?C?rmc1C*Kۈ$x2Niy˟+/{tvG8UE %cUb~ʔ ðM`Nh-M-,,B;ꞓW jng՛YYJִۤ)ƅL='lX&1VMNoJҷ\fZaV=J)F仙;+ԣ, [U} Lw,^'cdL(eD2o'7u\Ls3[m/zj]Y;ɴ~2DL\!%|4֣-qoi|S?3uwݗ"9[-Hc,ۊGV~vfCཔJMJ1)kx)__*rha$yFDGtGlk/aZ9'4_~d pzwd˭Z_AW@A&Hq+/$*!S;;q׺nwygp};{뵾׊ɝ?>Pg^ȣc6_ד|x& v7{gq*YZdiLy >_$1H}-d  ys,٫#~r"d\8RF*VqwV@gQMRkc }6~I4hno\ОX g]aqآhq d0E=c+o/sٙ 1N1K5U(Է:݄.B))-0h: & Q5=PL[G5Lq(v5>+O`{X5며L&de<5LjsQ04YTQdH#1ɳp8S[vS8vcGFhlj0yME:5sA(­b)-}!SLz7sBdNFetfL Eޙgۻ8{ýCbj!LxD"0ߟ!א.NlsY}GbR70(I뵠ռkIV(RTXE4AlgvxwsZ1s'1x['b|͚gMH9q*f!?rTy]4dWrbϦCMUqJLrMR!X11n^ܷ2N 殧}Pt,KVa U"we0TJnFJc#t+)S~=H\{sI4 Vm,!le-QHa'3&V¤I&HUAk#03cUYuo'ԩL{^(?4$m 0xxJz3Mn^KݜL+we} 5h( n2vq\<@i9( Fo$c E@;b%VMu U)P)U$MXxBIq#0yBk^YROǺk CocğLjaa?My]v|^r\~`-;Ԕ"JqM_xJRTJ/pyTQԎSNiiR{C@SUtkE×6u5^^~Q?ģq1zdV[At n #0ۅ~aҋ9j'7%/l/B=kve(f"i%d}Eq o;}snFc-`4;uE(*O]'H051HMofqY-tƞukfmX%gRk10BN YDk\'yBO Xb\Diۥ3* RXcxq6ZN|[N|͝R)5pIyӃۧ[>G9~@5{6PF P_Y eq,89XJZ/B7qV/L0OeѺ*e48OIpG8aʹ{/8`E8WTӸ6wP#J/;(I*9=9n|{jjPx݂W$m%tFuܤ`w+%|pl< -iP>xi u—j%^IJʇ_Lm_[[)1_rkG2_SޣiѷT)N1\n IDAT%!7Ouۂl-"02 n"sP(:7`dU{]^nat`(.@# ݅a/O&Akmb 3i% )zn6113/J8 ;T' =:CMt}S]`ENo}w6lQ*NJ>ۋ]>V^p󣳸 O=U gc4ZmdIAEi Ba+c\A *#jZwy !+^ZL1F1W˄LbBӺvрBV-K(UPڻE;Irɷ̒d47R LJPXj!{U_ZKl}a6Q԰ {ifb m̐L Akq02ITIl&rotD pd}Tm=IđQſ9Y|➷u(hbg<gǙz6֏Q.ʩ$۔ ʔ" ʔ׽yJs G91`LƳr7~c I̟ L7?c7} nWwl^~&|d ,)q_ky!憶S0W0u氣LnƖK?kn@X\ L ,߄p4W&v`+>Lڐ0 ݟ_h]K#U1i^mk[z rs1ܞ5E^.C+]M__k,-5oc= n傠h~J ;rt`UMA8kQ5su&/yL׺=t Q6ʱ90!F@aL^HKAߑjihc|I?qyi2geFQy+RGJ,=;YZvPQ!q$rk%8PgK%uQ\G^=(J+cNf*wNp!DBp1I+jm}?# /<6kKѻ%~ F $4q1d:a{MNp쭃q^<|ȑu޹~Sx22jV ˹+brǛlW?ہם0ʏY}.C.}QR+wܺHM6t;8QWp)`o%H嘮s BpX{p{ ę7]G "+#WCU"`nz^5Љ798?Au  1I`:i0[a<7A$NoݙOcrv%;yIP󒷇i]'5T"dO+j5嘍y*u0bl%#mFL݈`bQT͚hb>(8͔d7/OTs-1yn42Ou54aI~vV=qqa'Ջ_~cF_^owǹawbr 3FJz]\L)-]a/ǫ Eؐ6irWiaTF:͍#t\&E$\ǗXbMSj`P3ɇ? #ξa(MʶD)fNtSwmw77nZͭ=qKK|e4=U0u3 :0Ckk `($*xނJ҇>Ap?SEC6'諧P!qNj\Km X/Rbql@bs\/Ù(K'6<`.LO&YHuQc76*toVixΈ^\d@lVc$ӀjZ0M-õ처Ia'RH L~䠫KfgF^ll"D/U'((\f13;@ޞ~05fEnrA&JC+~.Z!947""va?_S'}y7?_yBzDI#%$$sRfgһPAOO %{I#YOۍ0XSޚ7BkyFRIVZeEaMԦ (H${D+qRw[_ P۔W|@Ysoqǹp2ԯ{<*ܙ]ם0;x։~ug]{ͯ&i8H_C8(ߎ߯b| Ga~VZV)yӓ0 f$^u.a}0.`>^HW.C0D x9jwSS@RS(T`"ƨV=,Piܡ9jsoA n6 ϣ:H,@!RCq*YP[rES,3f&GdC3'7"Fy:xk/# ?i  )@9uk'L4pnN5{TT'|Z*uaR&F[ip$NC'XGv_؎*fN&6 %&6bw|(W`Xn 6#˲orF`0ӢXRfnX)8VEfʭ:RL)"QsxA 1%y s?w?V39|*Qu6u-?d_ACftMiq?tR\\(p}7]ʩ3BJ?r ײcK!%jBC(,P!NĐo2]K~^޿3yW#H]16m+fR]@>ó#[NkZ}+(Na.记M.!@G,IT0\ySFSe/F Kwl`+[gHR!!5c0@=D֡PaDm\B\38S4#̰ƶcU$kPe=z.5^E ^#ڭӵ(t*F3DC&_r јVFeEtg{3MnB#܈D(P(-[IR>ڒjJ"]PQ= %ê&J쪲Q^ B !M9okcCH~}oo5d$52vi;z!YslE dc;1%hr4 )47_X#٣.th 3g0c_0"B/UXΑvs ܸIMk͉{ Y;OՆLTlD!(Q( Bw؉^ *D͵\r˖hvc}-\RGuQ g U\md:1qߣ*N' Y,ЎHù*@9 8Fԉ\IjeO o z$V;_Qh=PПϿ2RSWVFۏ}O7^l_U'B~~ro/ UfDуeW_GʰpSW2T?(%͆65x~i}c["[Eeaԏ+O#ӊ%I.+TGF/~:f!ŰdZ1f 6"[ثCGu, ィiN"dž^j.f3lE 8 \t ShZ!Ѹqge98{q'0JZ"s(Q pJ %T~,NNLfgR@nm-˖yk)7{W*h"ws]E6mgSJ lYmZ2Fpp1aciۗ @\/X. mRwLRy2ZMElrS]l:gwp|WlY VXXArkZWg֍";ٛ{;k`Qj?L?{ޕ+]xd"*018ow-I;FRzy 3!L<ɋQ4fdf-b)0\4H‸j^̄,ҟZ>rݎ\0[q7㵆YrJ^ً[ߙ͋\13- C?͟?vov+;o^ogi[w~w~K <5ڲ /|ƫIY_>X:ۇafAP&LmW A=],ȋǰ++'qQPJ,H 0{2DC6Bp _^_}7=gB(=%qqW/5qkV4XF;pBqH!r 3b2tܤT{ {JID9_y`VH.|8?Rfg:o|8i<ƤUF1I+A8]`ߣOp&{rLS}|tN̹+Y[6td϶%w^e6"oM<6р`< ֒dcҰ'f}bL`:*l}A#Q~pفCB8h">68A Y1'6L2UamMl:^$5a Qݡ&pj/2p9¬ZP5p1ڢ(д dgr5*(4³ .w ;iĔޙ'Bt/c"i5`ҡCgC,. /h_3xSc1`Va׺ eGMgA*[8MDɨtBH18ҲTN'!s ~|# BA9S rMuIM v7bhl-ע jMzw-XaRb~Ӻf3#02h, lTle{3_8r`iOmrv9#1@"ZA&TSظ'(qJ\wRN DUM#F fwJHP6E5-׭Mף`a#E^)(S>3O.^%')꘭JˣVqfl3i◪Q*Œ˂3`eu ̣&s{7:&[3ސ2bH_ѺPך1{F%cP3)O)L7BjܯgKjȮQ BԴENaMV2+0 mMRW+r;g{άo I6YgNjӅظsRQ!,2a"0#2;tgZ|L4"/3䖅֎q)by;Ϗ/쭥ƫW:Poy;o)S(}[;91%cK 83/_3_;Z_Q+ /+g } SD] Bw\.ރb!;%nT,w܍LoDoV/㕫J/׾w[`3{>sGYo3gYtE<ٜiL_3o!WҠpZ?KM0ʉW=o=SN_;L o)˔59L-K)iU7l`ze4UdOr&LHbGy0ރEgu"=Wáadkc80@u KO+MS6 t,8gJR8,)<$/V@%a@\n`Oz24eOV,/rr:No3c7(stQH4 fdi|3SO腂`ga9$)},aJ(a 9:(ص0Vx@"Z!hg[f LTmNLMȘ}nZ1s%茘j%ơn ^8Dgu\ZuԧCcE%0H*EE`CiD+pVe'5VSb"*t͕!3%1e U11*m33ٛH1F6=^Hki]NJ3><=(°TvFiugL/|=~k&w#'KˠZvqš-Г8yZA;mRnV=kPQ%*J#0.i'=^94)/C{򶀳ebS?|t=/4Bخ.?f:\|6c?Vo_/?s.Xλ~mo|~߼׽<^XQ9!_/,qg5;~P>~74@oے $zׯ0N̋ I@eSƖPdeWW^\ s5;K kD[K~*]5Nv06dn 3aypb Ag0X6vyL"P`"(o;]w$m׻kֆϟMgj)ٺHbl\M&{.;۬\cڸ]g }FԎ$ce "Mq{&=\6~Lt?6dݝi@_'_?FV8nK/ն~V{e -l.|ӭ\1<[_= O:HRTfuuƣɰmn<طV/s0LNpKL6LI)K0cK^[n9"MBz *m;B l@&]x{av nmXfLLP#}Bxi-^M1~\(!YdS`H?`/ibt7Xu CYB i<""<Q6 /[S^ li/fI!yTcӤ͈-ӉFT>i!"s+kLO^yQRՄVd.ˆpY1{.q=;uj0J CNƺP\5=2r/Bג R$.ѐLθehCC=MQ..Y[S%A\($qũdBQ@UXY l˅αVH_[9Vk[qWk6TfFxg d[aKf̈-'s]_[=ΜZ:=K;|g7qUA06MƩq{)>^2~ 掻ߘ^q̅{JCRr;ǻn_pOj^bb5C0HS8ڟu~FL-.g~WbFK?>L''8t;Ɏ"_$Ԭ໅/Fl_~LŸ^5`Oqw!L=F>3TJν;|,w*Kl4DW* EIINQLL=Rgҕ袤jR`((2߆]?&UHeXCNBD_Tv (j".}rXfC v/P3q՘vЏx|`oRaE>ҬZ]ԑFY5ˌ;tTWYp uRsLI\Y3UJ|^ZLv^Zj9Mx$B A ZZQRJǐga2]F-Cfsdrp6i[9bIli&3-)ia]aq:]dbƈrJj_c/er"qxɇMLK ^*G<ĩɵ ToʟYJcgl?$}GkI>oJo,U{m1"~}{/_xtul^rSNC]ر 4^Z g|OPprV[O rĴ .[ar 8no`^o?g?{p3Ц=~!+:'8}ߝ|;yOĕ/un^*#p?0ܟRmjbOI>3ɪ 'x}w>M;߅7h?Uvk_]oS>4tdVe8_Q~ 2& w|!e$eq잿0;~H4S:{G5UvU _,)$ٙ&+[da{/|LlJ$0{zU31I"@IlRt$hhHHK9wZ26'/ Y{d((2&V˨4,~(Ɇ?F5 5K4q)+9S f0G*%9{DW,4vdS'?zn?y>^\ 7|#*aDfePUa:Хȃ"ڙV^i66Zx,AxnǮ zjqJdogŇ^±=^P99Va]87|Mo!?$|Z8K>o4,q]K1' 0B(p}c IDAToV2@>H*a.7qx'_ >ˏ'4v}x!WLP M4mD&6DF@; 4HDs QP!E3x 9Dr6~Wh! *GuWu8>CD y &lD&0p2H0\pdy<X)SaNM5㋜t>NȰ4²C.ya133!ecduHd% ]ftS8%fZצ53fuFYCGCi?B8#ML**`ұص -md:P򔰮PM# (8] V JvSa1kP%& (@ !L0ҎƑe9Kd)HdAW)҈2m6 buRb*T1@\z+K$Ο|L1e,h|ON{m~k1I~9}DzҡW.UNJt-D~*,^"Oe"1a o?g^ܽmngJ3hƻbUgՎ.~ =`IU{&~fr}坆7x-dwEtxnGn?`^%PZRRM!txI,_5= S&NgK],֟8~߾NZ|w'ofS{]zu}3xtY C!0=" "bX 6؆<̬ WDHE B bR@D00E)*|eSe.4G>jÄ FT2LБ 53K`O'MQ{6A#!tj›A\D )* z.DŽh撴ꑪG%{*Ay–1RXוzG#Q#(rI±Bi2E^,@+H% ! ȫj*#VudCH5MM5(6(0}C$$YCV&Or¬(+Q"@Z6 ATdd~}೸zy @e;e֧(EI`z7{-i0=>vvGir!8{u<7L_-,y\}_+غoQ{"Tܶu2mRmpۯ}KOtdj0ُ_/ qϝ >=}uqO|vg +^ƴjm/c=ߏ.6uvCtnQx vppa\4u 8;W7Cs/;o4rEpk4%x; { qOKHA2"7NZYSic)ĉg~ho@w>z)hmC v$]s8{(` 0*l(`"o@%xRi@T! PGH Z[J!'k\ y dQP30.WuF٤*tž2`{b !WV˴&tv'aƙx2FGf}ƙ"dV|}D. 4Vh,f[Wi">j=*ATaIT B˖Z4(lPH<߂#4FU(E)2,2=:IGP %vͣ#^CjlD΃%K-lDDLfȐM4&S($(Th r=bx8C>'t SVlf#3rShd3@ZFZ~ǦΈ Κ7m*ʇ*?cy7ohUw\O^,㩳7ߍ7jg7cIh{n~m޴E{s?ۮ̞neRËC,OYJ[( %W*bs|zeIbPc šJ* |mc=ô3 }c852m+ZCV B筓,ew`(Lo1יӜ!.dj0%)9< 5 lK mWvaƆ,Y&RȀ[Is%tukSB*-Z3 K<"%">S ]yVh0*NF)C1Be,EČ-#ĆA3 ;IB h2Ő! rrvH}2\j U %v5-6GDLjGDM\t3xCq>zRsc['t4k'ћǻwW5G*q(ַi? &lCn[Q!oҸq?qXx=p{|I_jOT`xo&ݧ dN߸0o|,gS~쎅׬=/}SL>qϝN?J+RĮ?jDNLf>׃_̿]AK.=[B7xli*aqpk/9quS`͆!Jc8bf~dMuzxV)=nzWn yDx-47`4: zfV Qx?Zqr\S oP6)SlkU4!Kx4n_ |+iX^DuE`])kA/*J[g 1cw"Ga),PCe0Dd#=籋h"HבR)Tn5 cJ{xȾцLY"^hi,圸|1`@-6H)2`u227p='mb?'4e!B &52c_2G'?] ]~񩏼—8vh㹪u¯~o\2Ms99{\Hn?U< #gMd8>|{ ?n/TcDs,fe:NwOj|RxIU/"۳B·W_z9p#LIIM QhQIMDϝ3pML7P*I]8()pp<~lle SBZ $ʋ.|?%V"cKSd$G i1`UE(2ynУ 3z((VFP[[ d`WZfuuF=_+YC; ڀIۤaJe #f¾V6)Ds$۹G8bk3JW8ӈSmCг vFZLd6 <2Q`b ¯(X?1$EW>fHEFNENDNW}||2/  >iǭ Sw{Di\ıkAi1ET]>z-\2w~߼] }`u>S~fg7}cyKk}}zLwU6:Uznf{X6z{%ܿM\*I\elmHu 'l* qc\<xc0RR#"H\$/{BE#Veo/g_^Waj22;iI4-0Yӷt(6JG֦X9Ƈ=o}-Gs*fiwID n"h+.vkhF0ڍ yD 0vp#\f4)eAAD5]@R2@,<2YW]6*YRmHu%5|1kiUynxw T:#M#xZ31Ϟh.oӊ53uz~JfmAA#h% ]/\\P42Xd>x$ʌ-|pjr4xY|ӱe8rβALkK0PQb@&yZZNIK|C*f|[#FkD%zt 'ZL 0IJ$=4c<4夿` "h~N!w%Fd4 5 J*VTP8r-S:#>AƩD5^ a9+#Ÿ R7~gL/։mDTTUF KJ/pi_qD:<WaZ <8[@@aqgID!PѮE+nbMصAIػB2<:brڱWn~y0t^V%$fs5`EU 4TN{R%e,n*50:9РIBK+k>m)%5 ̛bL^-r"}<4sjvˌl L|?r# #UVĨTk%SU30ވR"& T% Q4Ɖ%IԆ`sǗ*B0tjJp CD0F v)\ɯF"!Aњ>6"'c1nX5ؒk> FLB*@IA2TI^fЂu7J]HQR71iX[qF6ny|<93?_8!'cMfr(3q>ytR-9kWk^誵sFh;m\c IX/$yTeWI4H:>v L@U#O+"XDLӢĢ @BhrrFz˽Sq;̒T5l{pw}67ǃsҐH97ҰE-n_h{$@*'>}FmJ'Kyc>csOBgſYmOc#Bx{G쾺%7+oySՎv :ދ{H+| H6/5ı\.t&GJ-'8˽DRp/n3**9;+J8nũcBȍ >AQ/Ck^/hnCk[{ Q5`kYF>d1|FH;`#JU2ZUL#'7ȲjCz htçp&~rv0 v5R º[ BJŎE YyxDIx{yUЬ簲0ĪiRK+WbXo`w!YYbJ㾭E3L[>im0Mo`H%=(00@RKɕq"悌S! S8|I>C# ) j(ZSi4&cƠ5(ˌD#h2bbC6o]ǔ~G6;%p4U[Nu[I3HH3Ut*qnUQ U?U{/;S)ո_O=K IDATY_ՏC~xSOٟ_zЈnœY H>KZOԎ6EK_=\$$y Gڋ=wF[w{z`~ޥ9lG4p?jq]k/0cG(MA7BB,}]WaZ ?j>?ȼ<H!S? K ~*u\h_(=!Dg3X/DsS,)vb8igܤ,lw`%(mNU _?hD#O/ =K7pFu'0P\DCpC %y"hCԖ&I._c,N4H4a(m4e=1I98FS!)AYLJ]a*MMJc.rݘe-#ÜQ ؚ7nf5XVێZFe" BYь$JiåtJYm~]]@$ -mc0!Mud8g(ZTPzFx$JdjR`Cg{O|QD4|[lxJ]gMԽ=CfiN([7 D^O_af+kutfV.B]5J$}5q9m#-ڦX!$sQlFg y}>RĘK[7E#DHFa1yIT#BY0BH*MŽ$ʷ6i6iE"JMQFtʜ:m* <:cZLđyg\HƟ L̑n.&ޤ\}3M`g~h{{ 8v_zxM~Q儞!uBS#tl#kȢ;]\Ʈ{g+I@0qj1y~SK!5|;D7a`]a~ @)@~['&nοuP~0;W^VQ D. A+@圠ls{o%m qdl&Fg}DށoJtdص 4)iBWy6dn!mÉ`@Ion5ωi `KЙFxyPI<1{3Y>Vi AqyT.T 4V\Sh*& &ѳ[laMo,]-^zάb/tvU76.SL RbPY5@lva73dhd{$q*<*o(v~Y xj>nYN 71\P b}{(]1x('ɣ9@kESzNe )e 0ךB&w>1&{ynn8/Dzf13O[6 GG JΐjIGdR>)W2w# =uڲߔc5wW{S<..r./fOn5Q\:ڊsBpS \O1ت E-WT &Nᵆi\ kߛYɏ9𩏼Ԃ`hᾹ㽥8vuA(XHy =TW0LVOB4r:)񭬟OWgSe3s9t{8DZ?Kmnб6<|?"e~!>OHETQ-KC^q;B'/?^ _p<+U\H`tX}O.2zuv`xaVl=P7~F!ǻ0KV߷w:ls9+ j_"{K~v#"@kĖ͐:7 扬 c|Y!%%>To P!ۜ.q $ !e5dpF%lʂxO%F)T-b}ո+kMEZy\ aE5!mH兘m!**(@ Z@m:RVIFE~)g! mF9:d$/hJQV9r mUj"_|@czicBQ0BLI%*(/ʂVH$1cFn^0H\NyS5˯;ҝڳ+>TI(g<oXw׊c՚UBXc~O=/wٲT+{+_hLG[*>-<4IMRP †Y!g"+k(O~F0z9R.LW`žLbཫwojH~;иQ(U;:WXso!_*.|EvX=Tr)k7edQWEd)i3vq ݎDB綐Wl gk{(œ2qrPWGcJLHd#L=fF?#`-AF,5| Bm!HM' ƭ5LײY:a(!4bV1U6#!+j̼nh9oibbH/XxS{C΋Mb ΆY[yf?+1%!R1>tl09F&0B`RHoI@U mG pXA>: j*mCTÊQ2N$(DB)|kDi vҮ2D! zlrM-|=9םwHL;Eh`,Slygrp0Z'7^ \=yvמk76l`PT:3H7GS+by+0τGR%=?H2 % Iyּi2t#U{.S% ]nvh:D,[~?BWuGvnP[p1 |UW,܏"fU 8<)'$=z;to{/1.EJOc EO "SR̭4[|س{+{@&ģmCW}C)3wG?x˝۾9a^=룼_OEiRL 98r gKy )ր(d=BbLGU\ t*[񇇍\]`*_ZcTs:CuV؏=EP|s/G"=S7nLxrH}?^Pc[oOwfpGK37 6fȩ?e1MTBuzS׿2~_V"43/Gr)kٴm}V܋*.BQ\[޲͕ 9kǨb k#VwD*`0 E[k̏v,2<-b2Od\"9.PW!Mf S)S8E )C12f-(œ> Rc\%.c&my2[>+smZ9DY+PeԹꩈ WuiI:ZTZY:J]k8&eF%I Nb bF6"-SK^`68>Jf&tT IDATP ЄU= @w+/ <2i5xKdB<)%YU7lِ`͌B!¢Hzd6BSƏvK;{9B|uӇۨv T]Z}90̓ TR\e7h LMJӦỉ3ۄ"4i9NcLH@k"s~+ǡ _*oS.!ONImotZ?O^q}oK__~jPE3mxe~ʵ'/qfD>T"_]oBi-8Gxu;!6#'yaQk8ohb?Ynz(i/q V﬉'x~0/qXNI {efG=X5Uv^ʇ6H_omf{\#7=>E.Uqꦸ#T|voۑx ΁8+>kt2/;js .W-GR_|B_>#]ʅ$wq[RT0EѸ5K4ySS]>?̂a:dd;t1Ki@Q 6hQ Iμ 9sGQёO2v=)Ȣ2ǔcF%v3_$ w?J_tb)HjӲ%J]Ȼ|rTW*.]=t!!NVT>_^; ܡN{8heAi(`Iq](K J93`6!m1ӛAu{*]SF px!FY&@dH!#6u!cEϱ(\1el UY(و̨?ޤ?|gtDMTR{쵷"ΡHw8NVX)\7ZX!,8Fa甶Mi(eu 6-c f`l MÜt{R2O.qC-gb? =ЇYs{]~Ц\.(5b9`؜g/ose==ί)N \^"-g O$ri\Ш"ut6r!j(r"ppbE9J{Ix^,{S&RykV7tXTLPgj49Ӻ@p (58 HPKd\MO#*)'a+% S#)@VMcPbL@n%D2JU%k|6W[މGڦ")/ w}v{{O Q}8 W9e;0r͡4+Q+*uA5\9g^n,g-ƴ)[moBsIs&{۸nGه UWP5i~ ^z;rLp}]Yd,vm2R!oN{uGTcTu5=#-H`Xbw''~*ꍀEҎ|)|u;+> &LkAUGRWp.Sho\I:훋LvO?-6;Ù¡. B gqw8i2">ZJ2  Evu}3-e9TDg) 5Uk 4A15,s#*1! SdWLY7>aBktJoq4q>l5؋ 7%V$΄$U"CՂ7d-@He4p6}Auv+)nQe^4ƚcWmioF6 L;F j˱cs;jՊ 'Kav'j+~xw,) ru*}a]ll9qSg tʌ;}dҺBI}UW̥.7M'Td7%kfZOls{^͈DVǖ ju%][+s,="%Nݥc f+BѦ&\f7seB/딉2nԜ~uqR2Bi*rF*3h6sr1LsӫEWԭ.y&i3CkIL6^5yK3VF8*F3J}GZ 2tԦ"LغoB4n!ǯȽODf_(ꇨZUA9ru T5# QyZgKWSXlx"ć(s8SD3я買JF%שnq_6?Ȅi>b[9 b_nji1[d8.{,rUUҭO6=pJoP0BuطhlOE|Ү]*DR<$]jk:.sRUx ?O#ɱpN0^Orr7l8ru,/g۽++\Zڡ7[zt`}pL[lݓ:ݘ @0G"/zNMǎS:TqELYq8Lg$IBc":{bް,' `ww3uE0 U,ܔ$f>fa^.w";R[L@ē~ #'u9mCjθd8xz#RMEQ4>MU%c ϓa ?kl=sREsޡcNP*Z}j-gfpo|{fe'KbdtSF9-4zbM=~жvĬq*B**篟d| Rio}qk3wP ~NSf%%Obhn.mGQ f0.;aV+:AoE=w>?Zn:+ͩm~;_{+ؙ41S%!ivsj5+e'&"gV KZ3 $-KK[bY-K BsXѧ)|mE?rľx`=VKh. {v>L^y:K;b,v*|6~:60a#~ὓVο`Kk傛{k8w,M8ZtڄuB`88nbNRV='ͷNnGjF1)#1MR^Қ^m)\ z@EAS(όf!$'C%q0v f`2}eRo0?6 L h+g.WOCRYK|ks{AG~чPB]~BU]D2F< @W_Y--¬X1~謍 –lk?5M;\ZZ@]8C ||^䍫kP`@m1[ă|횚R0.9{_r`HsR9ըOfUD|2gK=fYx&j7cS8~VPsɗx];_N"V./ZnN`$}e{!zqМ6H^2mJu2^sáڈ#Q,* ͂"j<'tGtcDbÉǚ=nK, (dYH9D̗'®FtvBrP^p;81;Nj"bul~Dub[|pl kgmk{/]^w=,+#7&Z{G;WiZEO ۓFM!ࣘ}ZFp9oԌ$O6&eByL!B&z"#>#+hLxٙqlr2d ^+=DߔȜ_ΥT30V۲é$0CZG=e]fmN_\jscyEKCi Ѣv5zLR$]'W"tl\U8S:I}/RUųF}I|Gtt1YO\#ۤ<UYJHs8Z?KUS0@KU4aS$ %>=1bK0!d$,𩓑Q΢jL5oM{E4jʣ@OL}Ta_Wlţ=BE<_4?"J}"Lq'S-f<]>m;͌;YvPF:yGy H30|@QmHƯ:ƿ+_Q)^fSTWTA8N}mI?S "-͟(q( e#W޿gFs'G޶O{2Jl%o^Jm87FPƐ)wX BɤkGdJ$ɜORι9ʐ aE9ԴFdžLDIIɅÆ3`Ll+N8Oj䓡Z9T5dTkTjʒI.݁F9'&1҈KsmW]-Vnmm =Yӵlbugv=OML TE>ZI~U#1yd]g/m/l?uv6 IDATɆSr oNjcOӼ+=3٩?1?IE.ꠜҰlcvpCN'C 0a:Kfb@BBjh ;lRR%J J 1]ŔRJ 0ؿhv߉{\\ڌ8}:^TK>mo:w7M;G>{}=:;/<Gqip Ea˯L@B+7Խy{cj i6/܊nGgN5u&~?otht>`@ '+|x!o"IPEϾߌ* 8 yW>UE3<61] <'ے0+XG 46>}~.i͵Lݪbf|rk3v.:sSA)Sx%9FxPN$ ȳ1EH)@q'R z9rZϧ4 P3^ڔ???mf^!5-i U@gihtChg4Dfn~Qn/˝0Oɞ{r9,ɝvdXx,iu&?L\!9:e&1rޖSASSBPKTSI\hMaB ZD"%%*ő%9) }CiTW\quq0n_Ӑ|ZP<Z/ߚ|ܦM ·d"G߫bp8_hRai5F3HGR3 d9D8c,{d*iZ}>F>}/U*֋BϗN J+4ɻ W(3_->z#Bw_ NU8ME9f?szީmT1aB>KZYZI1zdxd}+ Q{^ ʫ[MCݕg?7Fp[2*;Z$3:! 'ȭиfhVrpztt(,ݔA;f2"aMn٭K>]^8f2LQvͥ3Em͡u/|{t?3m;\BX2V>TZ\=(hz$K*؋(lOX-=[ƛ"ݻ>?2w1Kns"WJ?!E* 0>NZrÍ)aY:q+bZǍD߁ll8 f$hlY=$=vQHkgZ)MrJ,v&37?&kz|] ij nM{`s{Eq^}_N%xC>>-}(zǃ~}rOmo\Oʵ͍:;-E$:)X]|Kt*EO1aiF.,ed*í[( uyq]`ť%g4NS54o/3w}ck[R]f]ݙP wPUyQ.Q6sw%y ¹}eOA|Q{iB='^Y k ۣz?M<\ʆm^zB(!h %?!JhkoŒ='-?9׺Jd]R|Lq1;$&=d#LtJK"Sn 9aS]dLkoGd}(8fQ[d!NJO2,*i앎grAn7|)kGki'L_<犧c'p\}חMkոɥN]y>WV/޻+kA~MBcw]sR[ׇySn4 NS2yzz.O8AEŎئFx$B`)貃f&xJb-Vdg"YBjku^}1i9t֑o,}ȣZԺrr\"_h? s᳁"3 ;iR})acT+0 [ E2٢Tik(Pg.ی)Q6uluf׌7(j8xn?ț ేI(f}*#|oUj(psTu.:?'f`*s ;,waZ"]'_vNYˡ7X'ןkGXo:ԧNfZcn_#lYglz):-y3mJWRZĞcP'vGS@Q*jZt7 PxeŽ?n#nRrM&iwB_sI\+ Y=-+NݢY.ԽV[3Q;Nhy&4Z'ȸe"ۗsE-p[Pg」|}|K:`Kݍ-d:#('cJO 8*qj[IQRQJQDdx 8ˉ 2ǰ6ڐnϻ>-XĘ`)KygHۤu__U\Lw T^<%I;V.:mdūQqJtdf=^G<qұA4d2$MIhFG*ZH3R֙қuƤ{yGbz77\J[~ Cֻ41Y3y9p8L=6X3OgL6mYz+W~k垅a'6UvLY56;G‹гEӳFRcQkFڋ,}oVH,6;!2x$m'`3d,K|XQ1|Lk1yςekLLYGV5k*Y}X)U2F Z49';U%avv"3! D/dGx%*NE\TA]:9K\QXKn>N%?'b?=*ݵQ|䥙 a~ .Y5}^2-4R=rrPv/ǻT.Lí 4;k{Jl2ãѓ"uI\"DȉUj3F3BAhhҠTX \c9=''c/t)?GM>5 OTFHTWӳߝ(N17G*ӿv/A[c8VDxҮeU3i0$iՀH`u3}PJSi fr3 =Qƕyi[>vIf3M͵=Iހ܏QR?q9Lh?>R-Z}] iJSIO^ Ѯ:)҇,AA4iJhгČТKxXZl1+v[FDQ}iLQauӒYUb4|"4IՌl}RŃzSɤ=x"N-d;L(=ּWYuW5T.!B_)dBY=%y:2ʓbNt{X94%aXU+WSgūrqwe#}j&)Ը+lڃJfWL,ML7CF*﹢9Ti-=rkY{~P,C~L>9{"ݞߛcGSA|:ulFY[% HR`%-;%JBnb GDvf,ٓ4:xNӈ27??mW(=qf*P3:aPF颸[UQx VqP8"6|OL:"Lq,?JEgi6saoquwI&S]{.,̹Xt@wb5)NcD_0 J4~3լ´ `(Y` [n+qejY9k=KK&C/-de0uV{+Z ^qhbIpuESUm.9:Di*T݅Ae ^Cq)/ѤFj 64# BQiM&u4Zi)9%l!ZY[ѠSxml%3-3nX[<,sb9DֈeDF6ʹ?">e6|OSlF'[Yo(?ܳm{/;LwNbnrO-h-߼2]# hnN rzr= }(:|Tj ˬ_UUk)D*RGb. ZxJe[14uC]BQCMW[*BQm 1 aM\)GKw%gPO0qG=0͠] ?K&pj>o^8IꚊSQxq7>1~S꤀D1ɑ0*L}BL QNb+/Ĝ{ݽۏs,CY9ݕ9{{F/+ݓ[쟹xA|phܽǙK fH %c(glTQ U=w;)dQ0)YiBY[ee^Yهc;כBOUijJ-ըdģX3lC4XnݱdM]o>uWeM%2,k3J7@QnzЮMxbngSOd雮>WWO;_>DMAs\ ms0΃#굗N/d^8>pWg.X3v;ʑ[Co[NPsu+-TҔhjM*Kb943` J#s1F켐H0D R>N'Y@ck6[=g+ܬEC-oDY#PjHptZ;m{cppݓq?4$Fƭ/+?Hw} U|owt}w?m\^7_9W؍a.ϖ7F`#a׍IcRaHUdߋd91Y s&!Ճsk?:5z$~/](VUO\9$1)%\I+h[vÁʇHı`ƎAC 4-ՄtYN{]ذ>Fno(&&T E @  niRP{@"]EZr6l\hK9V0NӅ#u߶[u83w{:VVq2DQS!^>+I〾A ²Ak0FBks4Ct;"$4mD03'~Ѓ3i&1\SQv2t`ĦW43g?8_ɞ o7b =%Ǩ1N6`R9L_kI˾6(}&}MY0`еVAMY~whFR2v'>&N%YK*q(6ZvenJ*4:2QMR쐮NwCQ=Xc怗iAHeCfÈԳS֙Pg^[ed2QqGطUn‹d̪j(zK%^?h{ZUϗŴ ^#Zf?V }u~J3L{߽{L0G=\Hޢt`{[V}{fөwlզGC:FtT2>ۓ^}~|2Z9Rݪㄲ=owd4 ԝUz,<@+rq04WlH]6!3AL_6ƭ:xF6'~Gbq-1Nf(\l3@}n)wMv#wHQҵN]Bc l;4!ƴtu@0evQ{<$^PֺQR6+SJum(ڍi?;h<$=e{%5;KlMKHDȴc@8gݘwF$~V [t!Zs|,Aa<*SE%v30?ԣc؀%@,Ƅ"Xy¾l壝֐&Ssiox9)G_Yb` <:g82NxX,:@ svxwN'~4o+aN{c[;c=x*]s}sayn,lGJX/'N߲f*TGM3)߁}xEGyCnp ̸k+<\IeJh yt Ҹ+n,Re s&^3(!RۦGH&mi,KWAPJ1G?X1ZS}dn ucGMWEbwOW e5- IDATKz CH(*Z}-B49{Мkm>SO<!?Ϝ sѥ+a.¶xI彥8VtE{gki9'BhkrGpEJ'@`kh!h3X%^mZi)^ ۰ vT*} -vjlGk'$*ݤMՌx\O "ٔ4Drt_xӖ ҀĖB&Z bw^7E1_؏Z!Er tDRGtUrg>ZǬzڶ{X-‡j]WȅڇmWhOajB6]:70}pBOTIIN NcvCw>,LK0wnYQѯwrn QjUl?ŤB8+QjS-: yqMQVD$'k199:Lk7W[c[ƤȇLR`tY7aL]#0 ^V1eQOOt*g8 :wÕfg5b}=v@9^A%+ N[@*ʵ$H$tVۖ42|ն _i"CYb;=*ZRq/!Da$vk6ui}Z,(Bc AhV,H=-4E>YLzgF>jo=FϽj&Ķ9@}} s˶%{[f̊]KycPw{%kfBGK򕙌^{*g sm;}|Ihi.mqwƬwb %qHCZ<>W<l4Gy ƨd$ӛ|| % ۽?DŘmE.L,raʺ|~8^jeFmCU:Vf9ґMju &2\|@s9C6jV0vxcꙛav̱iWhi9:[gCa󃃫+~aǪ;yr~&O)v6ZCab{a'iGڎ1AK<Be Kj|"$6NI r4$:9"h:QؗʮYI1SSlq.b@bc㪐a`rE[sDpkzl⿏=ZUᏋO޽-2bm\# p1 f ton-(,u{~`_Z^Biԋ4ھ=l)C)gk]aŤ/}Ћa $v)|u-Z嵴 ~ li,{ shvXư~_b>O-.l|l9g SLuY/ LąkO{>~}5` aObe8G1ِ\Ebi+G=ͷ1 hxA(ma{Xi]}swwn>593T[=Sp.0;ⷓ @hǴD h8MZ;C;fu~,BYI6yR9^b[H-nJzY@ N7 v+;A$ u4cȞ]|P{\FLU튑ߋMw{Rn釮}$~} KnHlP40lF"Ǻw?db̏%a!\xY]ZFW&g.O9ו-/,;1!/ab_>֙L9E.ibFf69-Or~015u1FG8?ƼE#+a|onxx|]k.NF$EV7H٩ɵ: mjiה#JZfvqq =X l!zY)IB-B.WY4f܊¶$M+D@t GkT{"FrpEl񋽁f 0g@{Gk?NKK^}tT0ơ9SoAo_]nmf6%Ӥ B'g[͸Ќi+C~ Gu P;Jp, &'uwDƭ6'<񤂉,c93;S>qP0"2s1(,&|?lS}? 5xyP/ 3qMV1w}GEnt'c$Z ?ơ˥߱[af@la yhCl/0:#`QDp`qvAEBj;v<;"` Dt=T!Mjȶ)9qHb!8*ƥƧI a]8DN$t^Pk¥{/hK]kMj)f~%?رGMWnm V3۶7]oGTh( :ZhC:IS3w}w׾uSL큖$!T;K5 E8t~ q  r1*$1eh[ďfauұwj?WzK0 [W:FO|װ冕;qŖ~ sM{wT#WC{ugX5FJavYmJ{cwh<}}JCzT I˩X a#Q.X^BAR,"G˘VGcT"ZEw=TxJ!vーä¤uz+]>`ufyqv+j}\5Þ8!%Ex+cW T#HDBk]9j}mU'Z/:S!t}JxXU/x;Rq!#5ы.a֕ٓn45GYj̕c8KI"'b[(U:x^2?Bǒپ0JWL'V[;)͖r ;g*9 W|$E;Vm*Xш.v46-ŒK8Lہ9J16%T [H e)p r$5m~]B$re- +i0A/O^{_s}rl1 q NΡs8~'UqG'+ZWJksV].mOx2KpTȨC6l#iu;شf۳TG8H;"$T T($ޑ5XNXKqpͅf{N4ATRD2Olhs}salx2FzλR:i8z׽g|K }UT-n~Yu!=㤞N;GB"w-A;~KTKk;U-73*[nx3uI65$^};CpůއxL w(r &?(m{?ph ~VmǞqL3;'+C˺+c %)3Pe.LJ2C'wcҟuV3<RK3:t8k6yzsG"kLs~N}E٧3"̓B=}&zʃ_a>=`eyF$ Y)1agog}o (%#B |[Dea0o^rdXN>UZ$$xؤX*b&N¥ 3Jdp8*zeݛ/2ۛMmݷnbvS ~PA߳MW_:y'S=VT5R8_crr:k_wc}xَ:C nܚa0F i%P M%ql2! <<:ƻq"n~&O7ש^kxf ~ײ E|蛮RY۶+.tQ´PشtHhm_ MV&HBeU 1Igºe4Ya^ BzIi"(r4q>/&i1dh}sgzk9!fv]Ǒ.۶O_m5ľV/듫D$:4NA k皩-^۪G=%vqiI0FǤ}@vcUHX.h5 &s}!АzkKU#|5'&P)cq.% UD% p2UIҳki4b1P Grnw>CkaS$V&LJ VHO(\,H'`( YI)a,BquWNw_Y*sk3ᣌug@;5\ 4~uѯ op]{~/Ȅݗِws5]K,Mc]y{;wUi9ԞR\J{(qzSGNi#cDɢ sru/zt링1o?׿E;H]b_V`)Q?nj5#jO(9J ? :’>حmBP!`d taѴufDZ<=(l\,2DDFYV?۫l%V?SA7f7]Uж莽F@M釸q7nb?~Gb} L<aX_>ut9jC/-K#pj8a:Ia氙SY<'鬺%$ڌ;qyNcHjJ@1N[b#Il&&'<\I5Q3!E +G` M)*5+:$l1k g;16ts^W@ۻ>ﵙ}whyvLR'kݐ+R wv @pyG:'rЛ"CkK!ijPUyИ`ӉM9pa"FŶEd@ s:ð PBFf< 4cۅMh%.DC) j8@!Zh҅:bq1+跫͢,. Tn0eQkxSJ(s׾ {EoXR)ƯD ved]%8%vWԒA_wG,nܺ xč[!TƇW$_LN-%'0^9S9ў9Y?+گt8y!v%f/z2/JZA\:<ޛu%B.?>G?]#[upHW\1u%/^&M\w/iyLqegC=s9Bt۶.LSމi~0aaQ]a"M2!@zIH$":D;>JGVRdMJT ZJa$/Ե3ߊI"4vvdV,l?ҳ?WW3^Yy޸=?v-gW w8 k^?.wimQRV'U[}֌F4Ouήv 0Ֆ|8%q0-Qҋlr C- OYr2~Vxх N3"]~wN@l^kxƴVǎwҳf-! sqK&*SΗELB4 @ ݔt$V y7 þHr:QVuh"/RTTY/\GQÕ=O/{gM\X֥ ۝ުcZAۓ߿uWlz.s9rIǶZ҅>Ỏ105׼~|8,bq9w &#r&{ s͖0O* aJ  )% M_{ ~N3z0Ƿޑs8us8m{߅QMـ;=ͤѧ"Y|n?T; l#ފ4}+sm |',$.'وh,Dfz0afeױJi;960 ZPa_ٸϩ/oyu{Faq>6h_di|3s]QDVzҲ١n +4_nyYt_ e q7,IDAT> [1ԥTeK}W.}cC!& &L25Ya*,]NJO_w0Y̼;rsm%mwYwR^$[,hyJ#=$4hmb^iJTF[ DbzWTgejP*-+;̐ Q\Kq+*nHse8Kb/ E>V,eŏ83% D*_4&V K^ضvyI\?{+!7#Jei7[PM˻09y,368`/Ә~M񟹸2͵-^J1sx8a7]۶?Iwc0)>i{t{hIXq 2n;WFE)i\hAY4xAoĪ 5bp= ˦ݱ\ ]bb8%+=}AW+bp2rfZC"tT{CY/{%9K_YY݇oX|{)}kw$7o,Ztlah. )NFK0}p/)9Rm89q1)P뙝Y>Kd@;>mAh-@ɗݶ"I)>A:h#hH*C%Kxͥ͹ cZ/7Ks+'ސoOG~pGo`lt"K6Ɉ-+C8}}n_i5 Ovt9Lj0p1ea&4q s^YHqK7X%I_pC"rG m^zePiQlWj? 4Ju4hn{AfF ČPAGДHDb|iZɠL{v'ǚS]Wfy[6׽[?渞ݷ_fXeナqU}sjblS~, qn}'?H Ў2~ 3{`L{ #gVi!.ڋ0cLk,'m1[p/E/0MWĶ_Drc؛8d q{;Ҵ(g3FQXFE(N:Fw1vgǿY:=v^bYn jS0tҨmJa! ZP{@ThQQZ1&iR*I&njljdz,/oJ0@zsX}kBJ /vOٖ`K1qK or79m,Cn ) (k5Ԇ^,#r4+4["8˛ p5 qc[zwKIsv7ou~?9:鶗]8|ؒM ޾99?EEU?:8AS.0% {GkS/rs} Mt|ۊ݅}>dgvxi\xizRH|u8&c^&\Y IGjuVMΪFf٪fW)f$DԈ! b^rUH̥X=[|t)JWE9zw__GNMGlw+xn a"P%IΑ׺Trr6MgPK@vUciJld%hg}w0:4VQ|3Y^rq5hU̯~@ͱdY-;mt#rWxVMOI+dɶjds7 {ucוaze?'S9 YyD>jeiw?Ï25 +wPioṘ6 uPlx+8/<"Hg1KWbZO[n2l˫ACjQHzua[uϵ.~{Xv Lldժ1vO+Bq߭|.eo_۰ F O!Bsv&? | slKoRirx•ݲrw͆bf厅~ s8D mm[lyrb >2>7&nWWw׎g) Z )bw@w@l) ̈z9i,BF3Rzxm[&i9]9jz9r |XŘD"?Qam/+wu/Bd5uVi~v@Sfoe.Eyv˅P6l^vu]Rg2Oi=U2&ҬOOٯ-uq|+| WGDvt >T,Ob ފ-;\I|ce(`nہwckl:5> z)Tzp2s<>D c?;rs0}8= q*K~0#vzU҅pnjM?ͷ^ /U!a}?4S!j>$|=වbi)~XiIa~۶nB1$(\0a'H"]Wƞ( t(}`d! {ÊM[NT{K [ٛnzIV` Ӎn?k{ف=<@IMXPQk!&5[RɮP]m;5ut~QlG˲9YGmRGqKcuGx*q@PlŒstnTr΃pmUG3|ZmK8{oԯeu Oi)F+!ۭ35ެI%  clX+9}S4-:&v@Մj=! t,.TK4iSGƩ]{l',7g IuY>F}^R֓${ !+OQxy*_-vXbԽErٚM\,>i+ʾ,=ܮQoG%Jpb}cp S S?5ԋ"/6% )nͻ__e^ ًUXMIp\} kaY ʳ,pV@2Nz7~罇l 7R6Ȱu;B/Dk|p,fμ7y))`2诱y In+;N>IlE; =E؀1VCe!EȮ:!-']x G1gm##³w!`e TtZs|߁eKdo<- "; ˛ZEk%&= <Vǂ(la4ݥn'r]0ux턵m,a䩟Fjl=X7*b:˜NwۀVBsvk 8gsvl9$/<"5쌨=V8g+#XWɒANJO(+MnmSn햒36(?5 8ދ㻬_@4Ki k9ahKvV&?5i~W :#$Xn {-$N/Qڃ3#eq_IF4d"bycؗekߊ8pu!@)Vowb+~v/{aU|P a^FAn\$Xzw d-9y>X\vO~jfQR6*5.`E5Y-8ɥgeh*Y 3DSWVΫƮg·[]bC!6r62Yl5K9 qIhKvւCMxcIl2as,FV~ʢ4HQ*d/Fgy ^bLۺ߂jP8aY? ٺ 0fh!iA6ͭ>ۭnr:Lm~K9`y~ GYi}!|·[2.q,h>'WwiAv&[#m+,e(v;b7 " rrPfyz3 ^.5"z:_Ⱥ &VWvks7 6u`yeXvB<^ :Q˂eov6~ཬTY)(Bڇ$҄fn=MSpoKzv޹xuCv-ED.2LY`e)V6-`Ƶ}uHHS: }[yvk]ťN?|5ۭb2h?W%"rqi)ϛvK&D䲢)""2mɊ @SDDd """P0EDD)""2Lh bYi70e5@Տw6Dr)ì <td?7G[2|gK_88ucKL.^\n0eC_{ǚf(`ʆk7wۮtnMD3Lvf;|=w}M"ryQ !X}k0lGEe&"rQi0EDD)""2L(` @SDDd """P0EDD)""2L(` @SDDd """P0EDD)""2L(` @SDDd """PvIENDB`openTSNE-0.6.1/docs/source/examples/04_large_data_sets/output_22_0.png000066400000000000000000010642171413546205200254500ustar00rootroot00000000000000PNG  IHDRO.@sBIT|d pHYs  ~8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxy]}nsgѾ!b1f!c8IФim~5$}_~vi&n84&؎"6;VЮz}|ѽ ^< 9޹}f((((J+(((ʅEEQEQEQejEQEQEQYYTEQEQEQffQQEQEQEEEQEQEQejEQEQEQYYTEQEQEQffQQEQEQEEEQEQEQejEQEQEQYYTEQEQEQffQQEQEQEEEQEQEQejEQEQEQYYTEQEQEQffQQEQEQEEEQEQEQejEQEQEQYYTEQEQEQffQQEQEQEEEQEQEQejEQEQEQYYTEQEQEQf;(@l-灠6EQEQ^{3|]wۖ@z(52'QcO 8q((ߗ<l{},8L(oZ,*l6<كd'PEhUREQ*ீeJ#]TzSVmVshF41SHKF@7P~8 \Wk_VEQ7-_p;J6W"|=w_^V7jc6$ u.X !qZ{[EQEys] <:ݏK<&$8 \r]w~ wUQtYT1%.џqW8Bj#+>MZk7*(ʫ+GuD_6" iu(KGGg(t62"O N!+9#c;((`ڼQQ%2vLV!iYSƘ=EQE577z2:1>xj|K,EENB @&7?FEQYL;is<׋(u%8?t6+"DQ,p0;c3|iVEQXH]Dk z\.L_֜U7je:!P[~ +eu&5$((0Y440YL՚^ CjfEYrsn[_@.G"TEQ\dDL2m؋Hs:jASI:E%\9Ɋ,Fe:yjhHJ "U.7tUEQ7?ksKwY̵6y=yZ]ؤ15j,Fe6U6^"P 2Sq+[uh"}SEQs"%g?6ŲLqH[>&WoԷtZxEF/^e:EdXDV#R3 1QQEQ 0&GDqWe6-ȗ?~lﲘh*\7}+EEENoU0t)O_D\ja ^PEQ7qmό- c:67}~&dLUсӧuJţߎ%C(J6+ʼYT&%rD4,+K$t؎D {Ǒ5`ink!co _0 QEQ׉-[h8Mmy޷:O^\zW^";AjРh󎝪EvCU&7&b=p RT_Cf/s)q{ilVEQ׈w:, Z]uJ3_..۷a3:+icVmV.x4(M>= #4EVV ,e$(8 ϝ41u=%@c3ƘZ@(\Ld6{ :hc͛y '?m?XXg?rݪWB;=>}ܔ,T(o(j0Ƹ`MU$t.| .dHr`7b-"Psmo3b.Ŝ6D 4!u1 ֖]EQEyy'DLM=u]w7<Ҵyh6~=; 肆#wo5 q-mhxC5*cvNfC͢Hd1`  +K٘cڴZkF=7!6`-"pJcDz6QEQ.pM zȂ1$tiڼc3fh;mޱsnmtۉ Zį:4,8m?TOl fh6Q.4}O7b(UvD,;<\ |%R(("L:KNNҩt%͹ɳY]a2f6vRWݍCCRlyvfڸ\S6QmV.0,*BBCVoGf9HgfXk%ƴ7x; ._4Hx(L %ג$y&*[(,)y({}GŴG펝yhx'YF9$.$!b)sfy7OABv 5*9JWS86?7FQ^O4M'5v/_x:AF\drд&~zsŴ4f q߳Q<,]#uHQE I'灿jA$^0Pz~$hamޱ3ym_py}o_X ?A1gZ{$QBf4?*\C\^*A*'HI|ZIo0V8hp 4"ns-Xέ9YadVQEQ.D8zL9gbEp3sS6箻mm_ ), 8M#>/! <9tgK:CQEQ+fh n0Cˎðir6`R.2l"/f0OS0qAhhh`tGN~q}]ϫf(5"HE:,C ͳ{Mt̀jV#' :cL)7FRThϹ?Yk'%S6Ǣ((ꖵG6Ɯ64qϫoud. s?"t -C6hDipm~P;vNK jڔgTmV^5 UQk80n!Fρ BV')00fz1 YDzaA'ͥvM-3>.6:P.Ait[kcHEQe!쎝C+N3ucnǑYuU1t}]>tϣ>"y&6.ฐ! S眩0&! /9.{8<:d#ȴhdhs`3m\ |jOݱ3~zfeFhHkt\YkϹg:ۀ"sN#c.@bo"o Y\t> B`IQYckq)Ik9- 䬵{!c6Dc_#Fѣ9x/7}_+EQ 6 i}nOG+Z?m{}.?Ԓ` vY|aD"z=w|D:=SM,")&kQepV3]cGrL!}ZJy<:Ҕ"z;<4؉#ofhrdhKSm~gV%p\y8JyҚH^c.r,31I+!zU~%d𿑕ɥt$M3@,1BvfyY BF-&Zn #+FcT{1 i*zGZK?ߋD_448RQME:|}6a.^34xpcX| >OgZY> m|ڒl}]f@2],Od :XLmC{[ }^I34;U@⛖4M#7;c.d뱋BB3r&'/7Hvdo) V[D:_c&O}=Ngu$J$r c!YJ* <C4崈4 fQk{oMOEQ ˱V x/Ou |-uW:b:fc6m|L:q|Un-kcZ@J3mL*jPV+.UԕTR|$;qp=Ax}_Mda_K)4vw1=*5o^nc~~U_1<ԑEYWh ft3څ^rcک0CV#HDwi+ci n|1WJoDD, SiQ$.f}~ik(\l,{D` G^ӽ&18.|ݧw+#cgu;v^ڼcg1KgH܀djs7p 펝g#dXk}cjgݸ ];uI kқXr,Ι56GlDc>T n|mrw\Q@1'yLT@B\=+:g_bwr_8>5o/~p-+o", 1 'HnĠ\ |Ye{؅B$E҄$8r2#&Z$b1楳8H.#@o)ߙ~ú=ਵvZcARb2SVcsi1'ת B! 1/" K1t!ْ5Ja=k(o:howUaO?OpmmDZ%7{xlHSӋA#ýz= +[{ =t3ڜ jsFL&8/+>0{}sv zG|47g\C j@\Pj\Uj%ʻxGZP0Pp}aXԉry_xgC٬6!}~iv{C}cɳy-̓ŋt !+IEB 1s^c(O=}]Xg9m{MbiPji2D*݈SG_QL~mVgE\ ,14ۉ=hFVHz|8w,&}l)}kgY5ALҟkCA} ƘuEQ^wH.GGOw^wJltbF1xPpx@?G._3wtIwaX{>O酡͒(ڜ6bIvĝ/8}kSp|F˚`(^%&;z0-$ͮ,ǫ6n`fё l릺;zIIsȏaRvW9ϧ=qhǼ}ㅎ|/<osVXr8C? e]y,)G펝ߝf"&x? ΎRB3#s9HGHd,8$5q%rl}ƘN +ۀGcHZn,KߧKYk+s~ 2\g%"l1jc]m8hrzm7( 54٬D>2"&}VNNOH=^Ƙ[k4wb"FDϦb\%1/'zFEQ.唑E3ڜb|\|c0|NXlyGv|welm>ٺ {i^k?+?Wۮz bۗ,kk6q;vVڎ39u]q@ >xM 8X hmO7jgRŴ9{Tv1Nc2cM5S^(- z ^TJԤws|Ĉ <Tc)̎,hK눑{3c^F"G$}|Χ3}8o[i9bcd5 ;hd"&gj/c{# csj=( !38rϷS=ojk"$z*~sپkW|!_ +nJXmvE[X[6i=F>{X'sӡe׏+&oqsryzGD,#QCnhk`rI>8Q "Y|=אE'{￯{}KLSm`p}l#M Uz4 guS'hyw-F:.Vc I/,;J-bmdRa6VGp IDATgY^Prw~뫟|{{c׫׬\Yxn_1K1 & 䄷18iy>`Z[m؂n/b7!+;1"c-f(Iǧ&{P6 cSj-tFEQ.Dn~p@8M8 KHLpFS<F +sF%B6šS(v_ZDULv2LJ>>$7yj|s (/9NH)Hh)ZGIXDFk$.VoFtmڞq &isBtlBP61q$-GkX1t"|gW?rO8= ɑ+;Tm#~9Y\5P6W[?O:qxu>o\Eŋt,gBvHHdD1fk|q"*CEji5,}2bqƘːin){9ziýiZh!!EA W'gD:fQ,s- 8LEftp&x,U+ z3^3"z֍1[^,XtrR]梌wc@>%ף*ܱ!syJMā'iG8ºs[[#ձRomrǷmr.nJΦْish c5zեmoT{T:~8Iqj[z&߽Џv\q]ˍ\zhTϿmXSߺSAMNS׷{#S~d0}}ŪM!%" 2.掿{An\Cl_?q#˔ 5' g|irQnA:68.s4"bDL^ӆ_+w6c̑4i ":b<}Ĉ44kAFs5@3w Bo4 [ΜؚƔ,҃*6}ٌ6O")+|.ϙC}~-yDˑxcND@EQ.~͋+iD:ؠ;y"_vѭ}\uىY;;v9恛 gohE {??zcɻpL.#(;qzeOU~|C|>gvǫ\!ڷ/x|&VlvDp~⪮9}|;8DeL@OTS^Ȋ?{Ap ../WrN\'ɞZrq%,O,M&sȥœx@`+u8554<-q}=/HQ3߈<s,13DlkZj/GN3"[}.W4rGўD:޶x%i y9&H3} Rp*#fHYMV{F٪e%>d. P.K8}ȶ㷤1dt{t?H6 [yϾDC/.|p8rS{ljj4F(kfX.m@$}cWǮYWƭhW&..Qxu;&(Y˙md^naJ؉jļňK|@Wьv"!!-}՛7n:db"&%ॴN={xtϦMuoO3mԦ۷m4ͰON1A{6qVZV!ci|)^-cg㫁O#6bjisːխ\'KQ<1FQ737d3zF:Np\zgW .jY?` oC~|xU~>Zutw޵!C3p||dmݲڛ/l}o?0<>ֹrbƮU]ɩxdr*!6>@׈o?>w5Wjm}:Gv}j][qb?G 6gW{xO<rrs]]S#JzR>o|sy ^E^$V&ܑ\dW[ Χ͎Dδ>n3"'+?X_Ĉ6s Rsu /_MmC92Ɣ*#CW.G-l?^.-{SSko \wßԧT; PxaƘ?]G6B!x45c̳W1` bAR$ڸsi1| G3椏ޏDD//YߘΕ㫈G B|1('(<73Ee)dĴ(q:3|_cb#A10nCއX#7 t7OA(7%]gڧ%')42Lm49ܜ+a<\㰬6ůܱyx偛ƲF*wWoko]5>Sv?0S`N@i+qTʅzR.+֭4#GǢM+Nėػ6;P~DGÿ*vމZxt5@fUqо}zG'w _:3k]_ɻ&s>Xn8Ig[LzZc dDPCsY<1ޅ4lfh;KmTP4C3돍ZV6n,($qb9h- (fNDZW ^qFMŋ4 ,m|d&5q~ )>\$Edb MsnbKwO'eL8ӕ ${Uz\+HĪ??6.Y7fߧlq;3ǐh1^EW!jz1ƴ!#C|}[46/Eߥ cVGNM>4Ȋ(owr|ILygۅzTf}Znc6 u~shYӵb"׽jtM7oӕjen~SS]B5\j˗ۋNN;{EV(Lz׭́#ۋygsߺDό {y05l ulj=+o߸F߲;<֑nX?Q߳WP9ѩW8^ ZpK3gl .({y]*ZOrzۃɣIlmXM1@}6g&%^5i'O5ټΟXuɱLf^yn1^tL< Lxmx\Q/W8vk4s1Z YW.r,^Dc&>GέjvHCsEj3[?mٗ<\J}H߃D:¥y[hs:Ȫ[k̦@?BLO#rGY3i"ȏ k5K2:4e)vu ^D^" я {G{/N""v 1l&dfp&pg>-F+!ܱq_~;)8s9O{39̃ iܱ:yՑ{a}EwW|=r͍osYy|ͽu[7&qZǸVhF\/uVpf^n]=$O<ǻBEčh;5=htgzbxϞ'^r}ʫ;=A22m3}ԴZ<qL1Z.-ml;Kqܓe Tqtnj@>3&|w}.މ[Nې2VmU2N!4$RZ<1$53eja'iÔ& &Nb1bd -r5RaU2blZC=ƛ /1ۀ?F;d֓fi9%ƘQ7<# bP~ I9mCQ'T\ūyOBy H텇F{k<-"]SGy$RCP@);fѸVtYtC] )nz,ۨ!ӓ"át{c=f1km`{/:ΙdSؑlg0zZ*gr=(,K~e]?σ$Ԇ鑹ӪiV:( !!-KJQgh? MHM;v9dMgVFGﯽw^wy WnYU,:s厾v] y6&˿׋ɱCG+A{>$ҋ1FV&v9>:zܻǂɩj_*)9CGQo]{+g &M$h*g!t\7 cSI{2eS7OŖfB  KW`"o90Qk`[We+z;ޣ{<|f,l^ג,j"9EZv<(s bYeDVO3EA@aĬ۴nBF aIUI1=lhβXh "黏"5 1 u|vҠI+Js[G{)c*HDq5Mͺ$[.Hj4d;oBd1Gh(zq٭/;1g%1 (Ɓ\5(7%hm جw]=mr C~V|6 fs\oF5$ONɺS$[ ND=Bcyj j&*7m-N=8~uMنr}8VB$Db$fZ/V]j hx|&ĕz$؝/$!'p敗/|K9ը ㈼ґ/k,@wMރ.LضސWGܐ۬sTN`(ǯVu lr'{Dϥ5ƒ}ȁQ]j $f&փ6PDuMm"DH?C_U^GNsUGf!au#ܗTFW!) $rAskAW +cXS\Ϥ׆Dr]DF((bZǑ8m6vlCKv|kxcZLC wPE_{,B̻Ex@J"f̵YW(fHsGo "(ʒ+n ?+Ŵ8\*Yks>ƌ_/iWW_Z_suos2I189sZn!b})銉4up]FXn>[~ᕑÓ/N8Nq 'OSLWs14Y zBکZk#c9 WKR_# z؊D# ֘np]ZgZhF Y%H C1xo#Q$xiIb,O"/!i\Ad/H|<^C/١6k4$KEEQ^r=-&ߎLZ Ӵkhu05[UŴyhPy膭ԁˢ֭nXys<9yY1Q^& @ɗJM'7BB7 KE:kHlDG۳WvMTNL:F9>=="q;x.K񲒌t{[$&"s<Ȼ^ZŎE/ng r&ؿGGӛLM=÷Ǣ[r::o{e0B[LJ+SY c:]W6.ke姷Z޳Ssr>WѺu\ѠnPvOCzB\}v;,&q0VLcyi~} IDATh[oguk-9ǥ U$3KHQxs5#Ly XAE֜# hc>.GLH;Q<{4kL9Z~[D%E \͵D5u"1}IFV#ن=H%-1Hı|^Sm_֍1YtxԜc8D]dDsfO2F2#W+OBof[6qf EQ^s:N~6Cb9mG4I65AI,:Wm[ )WCSs=)Olk6Wo\0 Fq&Qv -!=NBˆXeIL yIQsq$^1\]ڷ}y7?viuLe˴YC8pIDZe+r6" ~6fYL_V7H^*w=*7L{Ϭ-׬[ҝXfR2m #,$iPq<茀PBx\J"ڲRF),@H fän5vR|_ƞcC&8)Z{Nc]`h2 ! ܔ2QF8rfRiHR cek<bXІ}'JO!8)&D-.@A(uTZБ-δ-[^'֎ 2laO4xWeڗsɩE׬'oo\þd>z6OΓGz#`mUxe.Ҍڰ,[mB\UHP0C˥f!`@DDҁ6]뗯RLCQEX~uΜM\h ǡvD9`fFfx~izʛ ;*qTJ9ۓBCe.|>iW88{l817:qDh/l-H0oD?E_ u/mP<|R DQv." ԅfQ4.{pFxۘۈy=}MJ9g&h qɪ85ZVCj86S T7 Bf~eHN i;PHn(M]k\{:쫝Hf-mf6 O p!@@@)t[Mͮ ߗZd)PȺ A@*Ga=.tbA6Pi e t/޺*~) c#S%'uׄ/#>&D`!5BE5Q"t(`\rԻq PvKzJ& `2*TI"wsx av>Ϙe(SM4OJOпC]:4BW3la!F\BO'"8 ή*P:y;q5s ѱ!狘<7QM4*!vj;۰G fX5It&V5kBr?6dZ7X8-[ߍQ3l 5qGB ҕIH4hѯ"aT,$\SRpXyVΤ$ _xE/ 0i6#ƴLBlv^eO$tmdLoO {K:yRO])çDRH0 J j/q rLs)5Вtt&ӘVf:!d|E9YZ]&&Gi.J8>ҡAm﹓G::o^Tj'h}ʴB KW[n(oL=~q[@ɕ9?fsa P= YpL9l@n5C4rhAglƘ~@$: Mx@J !H&!KPJ=h^3tX`tT|_N z](X$1|IhL2Phx(1 [{}8bA:%q>%,{pjl P5;ʓ 9=b+B#ktE@ =:PKAmn$fC!ع6R[&ho j޿ϭK‰[:G'a6Db2fpQpB ;Ƽ٤?o$i:kDڶ.^)!CrJ^Vt{\\v*FFR 2ЃpC3.c Ӊ RlڅKg'Yh`ppdv"nL6$ǘDf@I1TmuEΞW6 %ӆ3Mv4kE׮bxPĎBIϮ 6VB ^BtPcPBEE},f搶E[>BȾHU>CBmNܲ@bc8Մ8?qvq&{-=.69ZTCm&}@bPn5ž5gjt1gl>N-ɢa9sAg~=G3m`)q  y| 57 ,U;X.&nŐsA0S]G\,t"1!kp<֢5!Q,id/_M+*-"\5MoVr`f0uELhS,l` .% Y@>@5 Q3,&8/3Bƙ@CZmh<$F6me PPp) ݩLicwgUfWk#?z FDq)}4[t>t\s߿lm]w#? kjWxu,@ 96:UmIt$vPsGW/[aZ\4c3lsuQvkm]MI/0H)}AEȜ qbVJyr !P$I QIa"hE={ˠN&4Nq]lNeBP>bwNEk_G٫ai6KKQ`IBh-_.8AArjPHqby>i r)ձ5͢D!g pHp]@$EZP*GW)$-kDtQ(cPB~or*-:llԃ0T-ml3"(Zx<ۓY~kzk@CHPO]w#m-+d9]rͻˣW<6|)[3螑Q"P߈ףc! |4LJa MUˀ$*.Ԃ2t؉9Sx?+jMB4(\%A}R Y*p6ԽwLT\=&EM'Zsc=bOC)*IJ_L g- iI<<Ez럀 rڑk gCtYЃ+,&Q?' 顩^<19qkYqɡ}Ӈ+ɶN3hV"XB6ES0Kԅm.ءBWT"'%B&#RxJ$3Mh!J0T(gx1$T@2Hv+- |!؃+kR !L"QpZ$k0ϰwuضmR}PΘ'\C MB [ߍKM}A5.fnkgn AC"P2`Ř>ERs̾Н*TK2Wfl{wNP( lB² MxB"%yP%+bܰZP/0xNBq"*n"VDЊ~߇=&P]7Ds<&jk ltE(5*z k\D\Q"/A1((Ԥ\Mr @48.jB쎮 FB>4O ;@=$/ji>!=fꙴ@=71 YoqPW]Eǣ>7DMOPpؾEȋfckF[`Yv6Tr.=@D!*!렟F"@uBubX\!ȓx]36F&!~cJJēm٩@Ķ@y9PdomeP)(`f j,~Y3P$0&fe(p(z5աrƢ8zQ)elNBvF:ˣ{++DM4%+H+RfpߛօWA76\̜N@)dT[ia+'enۼhdůwWʌ@nAbJhf%uZ0 7. Pf^Bɴe{3:Zꆵ6 RXolrG :|e{N{k˗?#庖-\^ʴX}ZvLx"b\l6b4:R\C)‘ИoòJ='3D% Z(N'ӭ]Lvud`zZ\;~ - PV_Jy j9Ԫw|U$7$)53@n\]iYN&8>Oi>Y:~y~h[FbԊNEx5[f@pA1)ͮhC'AlteEOe+:~߸bQ.R)JɣNNO时v>+g_GW?h jlCo?iވ7!Pyq1w1.PdP gX19 DŽ.2|D1,.ng}5+Dyʈ(ڭ]:#j?eCP^Y ?q*@MQ;#&E(487#9փz9yP7CsIgׯ 1.P^Amt Ge@ UE΁A6Qy"Bnf0˞ /H'"ni7LH5h6 Dm̴BjZNl9BRTSS:'+Շ;jT7{;+^zWi-ҳMi 7S˜iÀ(XkgiEo&Z];>54̣/rl|tg_BcT 3rܫHn!P#6O\3uΩ83|rTk60t=DrP@ZOVyJPSKL&g%jpcE>Ʈ֘I c $( !ahS-9~hpmVsaa}Fz:Qز2dF$6~ !Pds#·, _=_oM?y?$1<]'A,8׵b(5E8c" 35wgh?נ]ońd%_թ*JU2\ԘDRPFA(Ej,a mmmp$Yht衑r,,}9 'E׺i_-.b#m# 뤾X HՄ] 2&žaM4x:Ӗ "u@%5byHCCu$(<[F6e:Qm+XTA)Am#90mp]RJ8 ݰJ]w /f[iAħ} z, Iv{_?ט'6^d?Rtzݪ!O "ǎOOv4D#(MNV'K90\de=S\dj!:3Bt]MWuDٽ˟yЄ!FKBeOqL^aGyR*s9Z9, j-o}?#{F}[L<~SՋ8rCms4 aiL"rGLɚ"1 8L"/RML ̒o&Phnk"6S Bc@@$m m"LPɰViiI 9IK&``qڃaHCBT& תgHdעdojUԜQU-ScGeqǝ^n.f*Հ[&d֊ows%9G{er`v#Y[>]|O._g]ꮮܪݝ Oh~S(0t@2"ie׉0MePfzZNdE`(?eT[Oxwi뭱m.G,}[7^ִͿ hş3D^CyPD@yPHsJYsRsRܥ;b4a'!14(堞׋3[|>TŨ׻ EB\#P}H)B)~Bԗ*]JlM4>oe(ʍ'a04] 6uCT۷Wjk?Q@E#U`OCy4F Q 9P$P% 3scL@5PڠjA^naAy Q; MF;:: %43 E%HPe36CЊBjPķe E.;!/D}JEc"qΨ/5 *$l(@=`mpA)e9 GKoE=PC inC87RQ 976? un;6h&.hL7A#U]v摫rlx? ?vSboIuD ޲+BpFc HHSN)izk(#mXL;Nju-$% 9=LLIKCglM4Ǩ?v +/BO; j >X۽/\Rd?>??߲+u2Z(cs1~<SӻgHmɁ≉7ṝ?XR۶ݶ =O_R;kI\7+|BVrmhȼ)ȫ2J kβ0$rwhjOQs@ٻ|6A," +@yg;EZWރ. .{*7Aڊ"˃"M#q | EOl0J@DԗPWB l*Ύ1(OZ(9OFAThhʃ{ j|'Hډ ;؃sgX~MU/'YQ4q!G\QCHĞ3k|DLFM4tnI P6cOVmn_:M>%cOUZP\KlqVӯ>6dӰ݁p0L@0t6O"'@Lʤwb`k.5ޒLno[߸5<Ƴ`Տ 5][,Ǐ>%'4& U|tkӋ%rtJ/=?tLd]lz{_;庵LI(Vˆ'x'_{ջ&GOd_Ҧ6ca' 5 H(;ё˙|Sp~g`z"%r~ri Ѷ>mC}ß ú\#PSHIIpOz]0z?PĂ9c=&Qg OMe8De||>ڴM4+%`/ExB)e5FPa,P/"KԄZLe(b *%(Pk^u(Ayft%:|茾Br@vU)!dotn*wqIY:@%>(;ƵxBҨ?9w#U_ !t.ooFc\b#$g\qP1Ϋ<[)P G8WB=#'@DM4qAa1m^vȎno[vVlk WU}'ͷt /ݴo\iԀy!ѪL;ir0E2r|%%J9+ڊ9KV0-9^6zM㻦 c/Zs4)O`X)Zj%˒ھc{`nMo= V^fO.],d-o"Fɧo IoCEKr?=m?i!O+Z?&7C ^8Q-c}bNv^T{ߦґP0niF{0=3ێr:U*0 Й&+Z0Adff|{3Jq)t0XXB"x5m%nYBP &9SBv.sm_Gj/I;n_6I-~1S6z+8&\wMByVC =e"i8Փl~KAyP4eP(@;L!$>vإ"neBH6_E=(B:  oE'~;P;wL2,^0r*98a85XPc瞞 57.ڬ@= "o&h۷-uۼ}8m&ɞ}W/ĂYp,5;FICg6Ւ, {!u5rPeelc7*#=\be/;vPB< Cp_V"ˀ5ZO~J[pyvQ6s'.eZO{'^ߒ&|Q'xgEuF0!d@_|=#_DC>p3;O}_e mdf-JA)GZ6:'AOu۶(]J1،VRiH]0} UGsN9^Nd<J2k hPy*_7[@_D0bI8W0b8PEn"|N cP2AxCXGqڏbٻBK}(@NObtqt3CJ(l79S'OAz*UPx Pe;*t" )|膪}̬@m x_2mR`|Ÿ(5z'^'Q(8#/Ω[M4/, g9f0h$щwOqՃ5Z8b9oEg{+%c:V_+al#z۲|78N{Įw^{wLeV`(/CGofgc+aαvK{墖+d_ t뽴tcKwSfw&E`V,Q$7'^K%4/}/ TMԢLxmLUm3k ͚M^!߱E}hi| q`_(} 'rj9!$~R> (? ~6DMB~g۷e7ڷZ Lg._0l p]RJҝnBUQ؉GS5ǎk?u;Bm&)nommYJi]GaLZ5 IDATcrr2%Mo}-.`*#ݐyu3ťr5dž& ouq\j~m{<4Y|+>__O~ߴV55Ѿ+3ln9ܘ'_Z^)|鋟cר'xgl7cCnjeggܹ뱽~p ls*RG7(Ss&tJ֜JMf)8#i),6^˯;Y ǎWGO\~4{ভZPkwߴ?h&@ o"c%ZE4P$H@M"ʱL@&C @̭"&"8%ӌHPE'HPJ"h8(PAљ" u=_s*Lw@Jk1yrOHkDoƶkg Wǟ7z-P㒄8\jj Q !UgJƪ&h`{M[ֿ:]ulxl#'~{= !rš ) P0(Z1PVc 1A8v3]6 \ J}*$\!yƘ,eeX{WA馏ȥ@=t:  gQK_-d1> `) * DW6\Shg,gǮP;^Y)9˻ 3z3@$K|஻{1]<lx; >c5KV ufd.>1HĥC6<.6ߔKG4{DshʁisCCO95`Y6lφn¯8̱]8_4G6vL`Xl(e`qwfOefƯ|ǿRjt"˫7oͼ5E,6#78 YxVtJ bdGA$qrRj>9<ce"Eӑ8"H+/\a|jjHapW eT+\U~{6<ؼ)]7OXrye{cy)6c1'T@ ;?wqE7??Ulۺo'fA0gV\.7OOe}Ӵ ݈1*w_e^$|eqTt@N*@2ӦͶRSztiX|xC߲D_>qW]Vۮ"dIFDW*{6SYl t0;H:Dd Ek T.XamBXBiX)&9juUX^ ""<cc,"-V׺zg7pf3$>(B҉1b/'5x4|ozmtD< 5U@V2xQwL̀8HIe @iah5hxtCp,6wt3cV]={th'F5U 6V<79{ېZ^"Q*]×Juuy67ZauG2/[s~k_Wb:,kw6MHÙ$8&9Ҽx߾&\a'8ۡzގ֨1YTbW354d'ҷ/C(6O$, !X~?voe{(4%? g˖;yEG0@@v۳QUN'0SڹLݗ*'ypG>1@tE-ޙL]9Z)..6%M͎V?}GFu\w> @?3~m7_ J@Gs+FϾT?31} WNގqgBͅ+.J 5 "c;CH8pA*jPƱ~c4+A3pLL"p?t\ ?hxc|+5oG{o#ӣ;^/lh'Ii+~G&g:_f?<5jKr{z.u^;SkS%ʁW{"5-_#+U=)C* ] pYkٵkұS {Fa^ە_ǿeoz77k鴇?ҙb,(6Ͻy5-%)Huv9g*+lSFhS*UEpQ!?_hd%tH& ob,7;bvMY) HݫV^HTJ.#EF |jDU f<7s2V9E!^C᝭js];ڝjv@ 7^98Y\"6D Z]A ~as ؇S; Ru"\ jUA@m1fAjp<+A[C&[:g@ht3`{Xk[͉s[kk&7+ÿOz 9c=м́r t 4M׆. 5sԝT;@a7HYL-9^K+J2O_o]{h_]֮k梃A$y1|==W<PƧ|4-')5O#21 2W\h'lUZұ&wGm+.v@ceWq.x "`볏-Л{Gt=@3 >}u&Ej ǯbqSbA(X|{_OkzV5yNk}o+RM4iDIi7CTȰUjeLlr=(MڎR\(q=UX:!U[ 6'F Nө { Oh.ݼ~K8k5W@L)[ 0>!Y&aD8z1'.TQhg)BQX>jI= ^:˞o cQ VDP Stk-/$^`@-@*`C $Y0H vz*f 4W:hEH wǛ@״ Nk"~2ih [.#mw(5|X G`(qL}.'"GU/X:v嵊i+3/KbiYɾ6KLЦ>#zYMV\$)1)4YÀ\X/bz<Q.ceΔccL45 "lޤ] dCA!b5kᵫbA8|TCi볏Gfq߃|d(N~n5;'=`|?0tLor'I g~GĈssY!m[C*KєӔݱX>nU"yB j~˃yVc_3`<5-ׅ*WFV|dYuF5oݷ{Ek]rW6;OGͣy288< ]S^`^~ ̨:" ).ypAJR<Y[ RNN P FT1HFDL +{DBS/jf!9ɇ?R S yY[cCq yyB[Sj5\n 5`@z?8D<9)p/X"~ нF0r @S*е S39:<J_]}v idm13b)-k?ZoLesyD |UJ)~\p O+M|$u%1&aY@ bѦL3вlBxR7>GKl{|@OϤ\Z|9~H'*c_žzᇿ!#e 5MNeyLEwh3.y%S)b-e9(& UG"E#mmzknAq[ "kXvT*X4<ԍk Oxw rrDxZ@)"M~bX߯r<P <(u ϫRjPUlwsIеa 2RgGM5-dZ> gǩ{fDL<1OY+'0cG $| b*꫊.@}fù&A7'AYfc@p^DZ R@{kΪdR9ͩG:T3NheV xh~P1}M#$_'fZ;]WʂT cjqpȐ0(4MPZ#]}Ҥ\~Ia~_Kl]Ɇ:u(\Y(,;%́pQ`h N r@+b41I:+fѷGlJeJbnxMǯP"rɽ6o_iI5`R\f/?9ofm7og)dJO.^jN@,Ͻ4d5jt E N`CW/Rv;7)Zd7pӏgGa՝}%b\~Q/D`$t @ 5:;fty#~K Q\8-1 !^;}4@ !&'S7{3d6w]Qcm`ϖsMtI(~toiGeX)iȊeu`(\R2 %y̸"X^|a:Ł\w\.bju ~p0W@zkFrrUp9U.mjR1Պ%eME>H+}ZD`hsP9j2JlhUGnnhT*U`;f1fZ-i3cgw}ف,'T6(FzlGWe+e͋;8{ՑfˮY=`zye ڼ.Y&'($)U T26ь!ɗ}{iZ-EeX1ݓa,0EdIlyP%7Ec-\jާ_I\޷<} i!0nZa4LDp ֬k^MHAYPkRVN\jꛂAo *W~"[e*\16v9 qNzB8~s3(pͨ;^ "p?,H-H({ ӢZZzJ@} T/@ 4Sۼ?qұI` SvG"L)1!!789?k9fd!rGgc!<L(2Eep;՛{Z_-ԡu_e IDATz>{\l0?KP<|۱Gd8(m7%fE%WrP(_jU#bcKw+KVo)Lyg2}e{.a~ٕFM\]-ٓAsu!D)l|СB0$!k ٗWTt +TUqL>=tБ]_0ˮH|p.AS_Bu$ ]enٵG熎Dm7Z"4ba` rqi"',ĩA bdR[؝/v,&]S:c'(He]kp?.@>$)An%Pp\oH{>|?Pw+Stu D4Tj_14!k]]~h]I$-Y}aD@ƣzĊr zFX |σ9tMX!K?bC{ҮV,\}Zˏ|1lE)P+z$ S$^>dj!9#G$)sLOJ/,T罛|G|?汭>R|/1~|`39¬`\9(͋VƇw}ڹX̊u.h?|qr;[TX)U.[etdxwM-;3HftI޼٣MW;59@IWP5ET5} d QqeNP׫|x 1pӥR ٙԠn6@ i еc7ܯ}cg1wO@ jy1 I+!I Q iZrؠs? B1Ǝx1"~il9bt%t)xH\z)Ӷ)x7}[┱7O; 4!A0_, kvR`:k=`X4RPlN2a* )R0H \T #LIncϖV,$=˗^B>162{bI\M=7KYKT+6͢yG4ԓGNlS=7ԳnO?F;jbmZ`&ࣇ'{1yUK<\ZѹC ՘w/K'K,~Ea\_Tq]GL&drBVcFD0!Ϸ|]U PQ< L˂'$󎩌B"5\hCUp]t-HPU{.+okZuo?dXH :A> MJ DPn^ލgC+Tya cAjY4Bٷhw=n{wRA_sk̭%vr n@!Kŏ'wW=Bخ; z(WffUt$FpeLdeA SDgu(­z3[rm<:'`5Y>Kk<'r*3`ylޔ\UHS֜,wzVinϴܯ܍|ldAm (.Ė-w?\?'^p޻yؼ{ nc}׍֦̭_ط{B_rI2xTV2bs$"єy+E،;%NMZ ,@"EJV|cZH13A 8c(3 N dERU`2\#\4p\g}O#6 ` M !Ct /" wܔ  tU7Q(Dj-8X"?:v'@dwDEFc@)1 R&@iBqL BTaN8-O:ADz%c" NE!ıNuv$7|zxj4K RYC"k@ŵ6odXS[ Kkuѹ RǾؗޟ 4@p <SAxnFm2 dۮbш`"]Z+O;t%oZ6dT<,`tx,;2CK+$| 0"BD0$Zݡ9#}UL0">c)T-Y^ s]'l{4zbZUp_r8ѡuT*{~/T4>w[ƫ㓩[}\b zuEݫşj^vmO{3 }cy R0%*)E1IidұLw]Sީb"sVy<O] ȶ7gʥj5A9 >J $IR2θ'@ES1ՇI^qTEE`D(Ufp7>l>;;Rȩ =Fl~A8HR d[sAj䊙٧nmE᠛xD ]q;Jg,^)Zf'(xan5nYk\?76)pcGPOpcYܪZ0qP_U 2hNk _W@NpZ2[)vW@)E/Tb@ clar@ 'E : Z5RIkd#3-8 &7Z\(Aڐs`~W ƦƯ2'"n@NAR͜N<pƁ#|1./m@sSPoT~7?߫W " RYy@1lz.R![P}QUQ+L=*Amĸ신z=`8K4?.Ԟ_.)XP]m[qc]֚J7$fH&!E",@WuQp+FK׵ h!BjMӄU1T|ǂ*ɈE oZp]lAr9dEz`-EQQe bҭyͮ3wΖK94b; ێ0XJcL&^ R<)rH=[n25ͅvcg Q˧'dD`:A/Z_(DP"J utQ/X?RdvPZ8ct3j8Ac ȥ4́d{G>U f4gRkso=]Z[% p;h‚e 4&=KR3@*:5ut]٩Lѹ@: [I nDȀجK`ӅUef꺣VR<TY6GR}WIJygbg&FGT?sXE寕f^P 43zԚEc΄i #}Or&R4 Dd#{ eE%: sFYpVh|'灿zcn9ܲ~~3K?c;N&Ǿv"ha>W$~hǘƲmO''i}0K4ř*,!DqYk̀aW|I2MiɱmΪ"x- HU]IaV qIH8kUC0U"KP$ *Vw<\C*,K(Pd$q35Kjd7b; y#> tcnm~ Dꖁj4]Zzz]"Pb>'@6h*h_2@U T"Zөb bP{2Z, UUCXiP69p913H"y0I56/l3-} %X#ŏ ZyL$>w+!U5Ϟ7%֗u+ՕJ)#ISN !R)!b|$uN*^ܷźe3=j)٬ե()/ TޛX2եrUհ)uk2re J뢎oUBZ,<29=|4 cuo c sI `㙁/ <%F5:.8|isƀd,F骒@y0U! 1F}dN&7C%HL1Dt$ˮ.gNO^݇ow dޛ%'zN1m8dP -n; "H331p,&,U@RPzf7@Ր ΂RNTx}h+n{8n5s!={ {21+%? _hU33;1;3;ox$K¾W)(JHqDc<ܩ"yēb>/ (L/Śve,*qmETLG hժkZg N*z!iUі3{ƍHfݥ9tƒdp߃Zձ'H.8z݂ʳ!@;8KE^\Ub;1C@.aXkrQE4J=j&>oV2dqnvhr-,e _zsDR -sZzrq; i冪&'Ly&\H=$px_ ik{KLȲ*@EUd~`ALpQDc1p!WP2M0χHhGr Ce6!\#(hd`}gV>tǧ6}lE, f@NjJ!|#=-#T*;@Jׅ>$A$+ R~@?)v>78nl?N=> >]>Pzg4(ȵ4 "[k@i-kӥVXW3)f77`M+{QOm %A 7RR0@~̝XwX-E|gH΃Ah?s^92o{K ')Pcc6ozu>?e Y@Rax2Eu2"^̫)?rȒAv|qÒŖc7jJB]Pl>)~ pxG7CCp%!yY*f烪JkKJ#j!cmB*p>!W%kmZ[U3t%rEiD\%ŧT*L18D] HR̵_ZTU%ڝF٭Scl"f6()ESj/QL0]XZR,sBG\1*Iډ|Ο5;+lI`p7= |ôM Kg{y`m2TBH A] <aT.c1^&sB]UHh*tT2t N @D /x>' DXYjO$hw$dDo Ji+HB?$"g 4$[H IDATq@deP` wg X+WqHhHqˢ^g㉣Y zׄM7#|-:ɚnw\7nKZk.k^Nܿq|CV׆Nxܚx:R^{zZthKA|#z˥ph a qSf0}H Ƚm}cs׍LGb]_/0T]]zlAyoTf㎻U"jqbt5(*ȋ6xHcLjdpsdFAAO9돎K2 S-c slCƠw;tY.h1 U'D|-E •)Y c(\Z:z)[ZW䦫>%1ifl٦4۞{v1)𹡪7oyeW}};,TH<cvpڝ(cn'rL:+W+qw!&`c&$&4J5WzgCy*$p[}Ϲoʦ;]ɾN_z/(:svԝc+Kr}3Ԁ04k[H#BEhq}k猲X@Q6cygm8Rs6hZZ!쟙YcLe Now"W]>{s/ &d! 4ǜaKTHxCpb "W6?&$aQk+ru_&XmK=D0<x !jyE>]ݫhްfUsW4&(zg9xdlRm$7+2fDvIWp^7nj߻4׷QT͓{ؼd󞳭kf,KH׵8R}!:kL04xQo,,W'䦨p_w}3=崛?\.olA?7^y?iz=ѿjƙ➺ ^Dw]wu8~O,}WOߺu&}]6KM hz" !5#MI .WZCͱG 7"uO6m6v1~ ! $bTf􊚭ͷ/0C9tZіԵ*DPc`>asdpokj(e[ :~4ZM0v켞PF[9wϣ ϖǾZÿDmzqu%?/7^zNmԾu>gk7‘ڙsU~|ޏjˏλ3k?֊1 pzljپptֹ&eҡݯ+F{ Q[W_2s ZimDO(lm= *.JC V~ECV#ƹSml^ށ9ڊ#^xtV c|AN%HUC4Jeڃ҈%'W^N%Y$+++L9-Q`˪ [jZLݱ}j{jvNG5d%)':QA\Fȳ,Ggu{X+֖gQ;>/m/1jCiA" Y(9UMͶ@[("f|$MΣBDԌQPDP|U3F[[7GKjth`؍gKsq6 O%ymikK@ sjۍlN3:AF9wK/DbDxNκ` &8i)f>XɃ^w*i~v?|~I u?k~WWA{nǿ9{ʝ?ɇf`p+ԱTU)X`.u߹PR;bzfӌ/s!lYT_3uVN)4 ?zf㮯e^'ʜN_;Մnv؄ם**Q*͍K<؝è6VgTTꄹ3&h+lF XCdƒrVs˄(҉2JcmwȎ]*ٻ陴R㗆C:d;.ӝn|j}$UtQ3In [ggY0(T !*y,LJȳ\iEDs~M+ݝʦ:.n&>$Nbo Z+} N뫔+rHa*<*C0}<IlwYttb/RLE>Ÿkꤗ6ދ-MtEvߴ68DGz1͘Tm},\Bh'%ej_DH{k>q5!5/3=kj1!.ngT9 BjdFHsޏ͹Oln\zyTs̖},F P:t߭QRc_ūN0ʇ>OKͽկMdǏ{-WNMNX/Zk/r/5ѧk6w/ouN~m=Z!Z]slyay_:wy2mBUA[] K;ЫI:*'h .'`"4ʡ;j7{~c[VeN8ɁUR wݾ{"o;>p7`oo?$zg5U]G~z}cuH̪Xfݸĉr"LA58:NO&K_‰9gZ8ޕ/rޚi>`}1.yEɝ?XY|̾>h߰ͫMc71r$_83C jjk_u#5X C`z,cFF;Tb2t 441{qD^T)" }#5ˉNS>EdJb+5=/ʪ"KR:\mK^ Y=ڬ!((J"%"V)m$#KS'"4oD)5kJSEQ,gV#B7Zcf%uk !P?(Rj(.8+Au7!xa~X$ozjVۆ: Y+,6xJHA\"x:۶](A$?ߟ<߫6: SY\ȆH_s` .~$õ K3My&S<ě+4Ѓ { _=;~e)lF9o=x-gͯ}7]{חEW^ìH5khi}1u}oZUrN)rΣ"l-QUmqT93.m3̑S ۼ[-[-V68;yz]GTr{+FB)%6DZ 9\X/:5Y #5 /5yl PYJ' h }PJ1D7[UyR=}eW{|aB/ 7Y|.FLڊ%BC!H(_O7s8N J+ ^iT1jdabwmMi.)>O(w1}j^<!Z6< /b w[ᶖuF1/ր}` &F]vs̶kW_VDs_~$SokqId}o=M OG>fk{o>q~wbE9D̨+崔:a&?J-Q.8(Ҡ$tU(=c\~xe)3ܵ0{OnA ?Y-Kywze~r?s;ݟ<|3(;5vk=FspJ)1JX&(#M'@))]`h|7JЭ ^Y,mDA7nQJ [k`0Ȫ^awoDs5nbEy~v`$hCa 3(p$&! ;٠dU%zy8$+7uQˋ잝z"!d顟eD{ɓtQ*{8taMkj#R^ȷ1L„,^Xj|{kU9|/~O Xӧ)}lDߜBZ<7 AXe(|"-u( #%YL !2y5KfL0s?-H!}Y*o=ޯ Ec&txK%'5DNEi(4mgZ\޿ю_?-2}٫n|l~56?7G>sK&pyᶷFo޿k~ z;+Iz|  I؈FڱTVܰ+&5HCa+bG $uϛDij[JsC 9긤SΤ q.C/p$ sPWW]ۗl8ҤHιp8$;^+Rc m3mTLgEQ,u*ъ^#֢-":"Ґ` ?ZpcJ# I5$xФÌz~;>gS_hz2vDDG7_e$3̭FOy6 蝄~ BR {6.}(!F`DX<=Q ^Ζ_(VϠ U#x6F_ pav&`H {/۾ctșӾpZE\3(3?ibgGmoWcOs3ǣo\~t8\y+w:fk06"|nDxe#@4Ayƒu ΅9T~£a-Xu,~ޗ_ZXVwjymf;}҇(Kp(L,N0F~^y-Kv|LmSgS  %q@&Yw)n~w|+oˆmR}˾ե}lg촳vG W3gr-{[VimmEH)jDh XtkAPuI;G*3u|hA]=u[=vd>O07W0 ywG9|pUOé=YeC_&۱u$IvuK'cnUsFוgeD /{ҵ^7$piģ!RF #9˪YKm|>m RoYqD@$65Y༯+cJc|.mqxXʲD4E)*kuE/MPt|}?'DwS"5|#!BiB=ٶ` &M| s5phAqCϝ۠=@FB}։3+'?|?m3ǦH#{R~/EUްTGLEB"pC?Mls[a-#inOʊHZ%xV\ 15km1FZSVw`TujQDz}jv吢PƩ,ILfc0l:>"1qkc48cz$uY5 dzEXl юZ\V"mWQ( E( |0q#&"(\+,qM,%vRCt2eA "wہ0w|O XN@-_usׯ8e, P;rs~4ypXfb]xL粵+ӧOY?3tW%m\(JbƁ( QJA\xH 8KSc1aIJRiމB6Bh$qSm#{.𢼣%Tֺ^,/cNmYć?'D"Ą,h@[G{_kBڜĠd٪팼8[k׾ݹO4i'!lnGu6Eru,\Oxl |: I^ %Bm[1hXSᚷF{ JcZRޗelkv>f;v+{ExMsL0~Clms:$6{|MeqNt:Gs5ݴZ UEPǍW}ԦWZYV$bV4C DPxP؜SNʡ.̫(H;%-k9 )m`;5u|yϊAR\ݝE8ݵr܍SsնL֋W9("\oFIg!dv^ekCX)"&tiAb2fIұ2EXKDM-0pp3.tc/_/族iTח޺M@@-JSm6I^VQpsz,S+GJ"%>NqyY Q!Ҷ3j XH0qB;MXks-j q1*XY.8L'"vyuȾٙRl=E Y|"Kڕ狴$U"z265{&!M<@h1|^A7oH>Bh9V#DE+FbR;m 203j_&W@pkd}4qpp? ADh'#%6lvSKr}ݶ V)); A/k^|]Lʇ>3ח<:$SӨdvjZbX32I {V1m9y-OF (uSxDsjINRGu|\Y:DL\K+cj"׻K`}%GtvVDm7^wxw 2j`y&0YO'M4VX77Lk4wz]]_kŮ/9,{CVq4׏ӐrNN+cdJVٞ_v:F8:bݯ1Vk$Q7ic-ᐢvSe(ɵڰZkf)ZlmQtnXT2VK)% B J/UT{Oa=]H}%{? TDn"o^ xySLpq`B/ Q7w[5@ixH oГ32w i4{!=28иc_ئO.B4RhN\,$r A5!y3!u%jͳmvWl28sf$>I3/:zK_ b=IAC>NnH)$!k׫2uf^ZY[f$Eѭ+Vߺ̷mn^FmǏ;m%QcADZEQ`NY{ݑPȨ3@wĶiC}ko()0xQTet*W[$imv xzYtE(O8Ƅ,^tmqeO`s89=U-mǟAEg]={ 馯fD h{&3rΌ BBӄ?"ښ˔:{R| v1fk#BDVϛL0=c+=QZ֦䜇Sжgȳf s͆4 D52i>ݶc?;RH 5Ih 5ˡ$FŀIvxR&P]D_9>tz=]a7꫷d~sTV:5$6]vJikgN  1ho)C&M w4)n҇$cnl^f_K3G|uWLw?>~]q۹ߵ}`K 8ϋ8 ŐJ seUZ_kzq*R󅢨v @Wߔn0 Z{'!6&51m{;}@z~sEZh@Fi-sh 'o0"K|yN}W}sZ"4Ag9L?D>:?޽~o;vG*ɳ|n%D4KWFZjp]6"Dc@52Bm!)BtPۊ| ADz,:9RXvԦ^"ɦPK #1:vZm(Vk*vQ5f~ݵuzٍSk;v%C+g:qr;IkBP 4H\|M5e4R@e؏vNs]`Stnӗ,EfL4gU^_}rȉrvz++ŵG'rh'N=QߐU[B)]355_Hǎb։_=./f]o|˿h31w~\)Nq8pQt)=XK]U٨om.$U:9Zh_C\URQ eY"@hs-Q 󪫩oLvy?WYxy'\ܘ"P4j0/9WDƹF2BM˭ky@t4A Iy(M $uΖP$/ A~:"hp_ mN.s$?㽄|1yK½:eB4PM)y=7!s>{[{w~g_'9&`~?}`(- qMDQuw(MQg:ޚYߖ($(YyE(1t$P!v4et{JcH%(KX# q(fi- h!KF >P2PWZ$ƚLև6MA%uQ fmfN kgO;go&ܘѼu:{\Wǥ:=M֚TF(Y,jL&HH =@@UXQz& k*DKƀԣJWE;e;{Wf?Cʋn<4:_5P6 4kjg#e8IZ+TQ /9}]Cn\NQ;֖v^/z޵mw`4yyIvTeEJ)qiMm1S[[GZgZ; 0I"JFgdT!Ǫ7_}qI4, "ʷq;Ml+sB3<QOZ(bB蝋7`Ck#_}>QlSWPp-}ϛ[ %w=~xC2A :piF4v<"$`q@[t޵+_C5ӪJfҌ7i1A96t6Q驮zg=l饤Ik&$xTe3g9x{ ^<"%TyѱQ)L*1lx8-l Q Zsva KD5NAЃ‡뙒{DeC+3՚>fsĀxq9(pagۢ~òӱI;czO|iѠ]^Q"z5%cK]D/GƝ gqxJ>>!ꀲBo8BXiGK`Sly'WY8;ީ~Ew/fu__.?L}ɼRQWJ:~Z:R?_/߽//m}o#\g6hmxGr_};:/yS"FiE+$VƉ J14%c|mUOu@B ?¨&2q}y\GuWz5IŖe ^0-DbBHBHba}H& ^ɲmVUƲ%qunݪ:swT !>ًB]q0n?aM}:h8TZЊ2 O~@q\Ù}?A;˲,s. S SNC7=N4~ٹgi]yA(G.9 5cd IDATr Uwf-j낙ЦXʿl T ,Gb!B5gH=`@4t0(Q;ycuܿ+$@dH>ԳM JDD0Qg;La3Rs_HPVH2eB+̚a%Z Ȑ/ c.D(( ($"CO([p\{ѕ{/\QHp;>$0"*QJN.B(%ԞNq p#C!&J:>b=/ld k} k_66oZ]mF[TkFriYe*vԲ);P&4m[{fvޱɽl%[#lK/2S7 $(EelFDC Rx 4D7pV(TA<{-Se냤 ᄂ%dRmJA* Jmalxz˺"`%P8uH[L;@>F|o@3_&a}>Dt(9FL2Sq|Lx;AbK_@6Mp#hq:9n XLQqIhj'a3%I}=vȬ }9 WBCnh4 ~"mz^Bȑ)HBnc=:86HL":Tcj@]^Gc:?{u|:s5BRj uY'OC7y~X7-AH>?[)4 Ŧ *SR&%lIkl fƾh%ٯ @('QjյT3:*b`&Iz95q 3- ҄3vVS=R]BP HE(&TL#jUP*%Bu曮<2(@ZDro1x!fF)>yDi:g[TE)xD%/*,7bܕOc_>Q"O I4E)VV@Qe"=_T",áA{l݆7v74Ki5$癈v{On]}ں ;Hg]alcXY~dvA0%HR|>m[yǎSzvޱcW^#C֦fVZe͍Lq49gAJ J8'dzЩ=1sRNclz222g0^s%)ʕZ1m(SR)";lIPXX393u˺-/Ny |lO4O+`4%дNu!A{x{.ChO ڐ?:XFhh%޽$'Sݧ55 X h hoS\ =ڃ٩>\5L{% QJB܅vj9fRנъ) p48uq4B0U@?6@;<2hԕ2W'af;՚xrk!f?eYY  3`E)%A0Nq" 0)G1H WV)ן8~H1`r @輸%B_č4"eqB<x2R*lI-#RD"bxD(,CUCD VB裯QPJRe5r"W4|j=X #ivJ/-92a>촂QiH Bqc WHIX$TQ_xi#SF0C9BKHQ/aByMVu40]w0vlV rοnauXk\f0k]]]W?̓oic_BL{ o{>y= $ʃڛؖ$S>VO751!+`},+p=q)!DS1pv^Ø?onno;n?DgmY;e,g|0~mhހ.QP.@o4@A@OYɾh{&+!>t=I;T:Zѧ-gICOd&Ѐ'&>~3Chn^= '>A{^tBxH)Bj.eSw+t>T#f] $B^Vmh` !+ b"o!X0KC? x=6XA]2%yrmA84̓~? lo-c ~XR&]q^z 7~>V!w@e `4ZeY(U8ӿ|U=2)TkF-Q]Vt'/> 0B`B0?R3("!%G("4ZeUl 0g-p.+-;w4)80R)b.un1Î j{>BJ - (0Ui-8T$QJ0>ҭ 8!q>検fWS05Q=#jKR2 {1iV>9Hi+D`JefO0H+y(qA0cR咜 IiEWhS-V$fdqasϛ‚iۮI>712z!T7P.uՓztmy87d+8kV;sm,˲,sX~꿶rý"w* *{ jqcayƴ" Ee6X#2mTB*-iI*A1!C+i49n:{$w^8);GQu)C/R L v<2ͦ|0AQ< "l!e@0]S]p1J$(4IN( 0 #W"B0嘢H iSa׳Ya=v`:m)w9(U085Jذh`-jet+U-AJ eȉ>aJ0/eR6FFЁ`Zvߺ4.g,*C-i v6K@2]:DBhw8Jׅ>${v$ o)CRy2cX E<&᨝&u&cfM@dBLIRQA3v,A%ǽ CׂR Q0*胉RJš`A!@I!Č &0`M̔|tw~θ/gI'ͩd:G5˵eY=d4? YW?G5/2M=8S26zF (PHI<*QICȝԇR RPd%{T]_ؖ/!gMY3֎OTfI(aKe%\,.ű>w&-b<:#@lQD蛟JJD䵕"`Z*(Q/IrL.n%: yA",!5iW96U.Q@jT 3߳"`E 83 (vJLBa8HF@@'=$8TSnLdVN5yt~+yjYh!D7_`{(s_FUUCL`s\ǺyGw_u]՞~rG8O_g lLPPJUEwgnnAsbomi6U-l2_GF]RH)! CCBJ0J-GE;[mn2R)g<%J{zR$j: yPBLӀa!Ir򓆳O#.v =f64p$z]x(I:{D@~ =.ڡMBTL y̽ IQѮH@rH)A{IrJq&/:p%k34uO/MhڋZI96GK;R>cйHY6# йakQXhS.]#16h` B{2CaK *;);?U;AZ糌%UפH:v4Ղc@3Q[S$t]8 q<|A 握 =%&̦ zt*#΃4{x" =I-N:o6)Yaş? meЎDZڄ8 &O4HLA|TܿW@qY/Nt%D}v;{ںox-J=|&ߞ+~eG.vFFxcŤADF?r$I‹T **J#Hk0 A M4Y(?n ?w\ˌaw8nɁ2 ! l֕r E*h dR,Dwf[(( )= JJu<,ąRQk2 \w13oPBcځc"r&/3Zi 2$ Bq+Eid g8aƋ"'l8 (@]޲,J! TpɳG"kR)T< Sa|ËjX68g s g6؟ KЅD}oՉT7o%vTCy9Wl01uYe6mv Axdnal1')cY^h5I*"}ke_ q~@D15E#8PF<93EVȤ1`4agsBuq%ȍi?'\aW3* jgVRl.ku,fUؖ"Z5)~K'e9e 1QJ%%40>눍(ڵn6A 4p v :/eF 7:m>І E6XVqBA; N?vmc`hYL$!IIbR( }Rw,n~ cYHAy@fЇ oe=zzB:4>/>yE{Fho2&NPAIf:40.?R[d;ОA\v)NQK^g(.˲<7dd}/ޤ>y=9>y?x}s0]Qw.uueU6@> X\Dᮠ%W)+ KJ! \sw\MVS3M! u/SiEic7[뭯DnڈJ);CAA` qm&zܷ,ڠ/}:h{ݾ C{2h{:3"$Gu pp:KI0ڥB:%ƃV#n|M:-@N }&$h ia0Hg5Pf5pzVAop|ATHك'dqƄ+]N K~F؋Q<&cɺO,~O}YeYFڪuZ7_Klz^s\7aQ.?ӄ}Iy3sG-`ζźw;zHC!)5mfVugYmzs 염HY}T뾿[e\ȷzܲ7K3}y^\4dlO&f@34T2P˸"M!"`"MR`uL әva,J a>0w~řy]32R堮"i+y_{Q̭;<[_2-T=63ϦA,ISJ;"ei T\"Q"I"`M(H$U$J6n|ܾ3WnB=AHF8r+Eͽ X=mn;"T: |jZ E&e#U2A FLLoj F88y}EjFJs]?|tf gXΧ2y@"TR2FiR3\Op;,!І[oBǗ󞟡] L+FzB3myh>2V=Fq: 8'J @#Ѕ^g$r-X\z,PWCZj1P\yIHNe@B{1q8nj%ncz wc 9o_./ m%:D>㪞86B[Hʷn$o065K>{xYf4+zyP.FirBknϔ\:!\& ~" ="NQ XZ$DT=;^'c֩[`3 0{uHȚipKc?#v*Qm~h;GFWfGN*޺@52j(spe>޵P5=LFk6"]*_9gm:]D7DU S,*865( Ct[*ʒb0qu)zL4`8))4Wd  \IxF[9~}1M~]ޅZw|0fCQ:l"1@û@;5)P@sNrzH$)Iy?Q fh;&5gBfƠ[!,sڬ# hz tA?߅?mMBCۄ"&[:O)5~ }R2 G$r:s!K>:%':+Ytm !@d u`Y9%i: ;/"W84`^3kw.V79;1~E!p5g7^y@JTg/6-(>g I*b%egm-`:`V?pI3jgGCw?Uo]MP+^Tu_<.O]j.{Iccf((`&2ַ,ATWZeb!6*VXB)8AEgPyPen'rT;@By\}4 HXush 2P2$ŷ(Kv@WJ> #XvRsE[/!C[+Ldf~ƣX6N)勧/zPVfob0 (NRsͶuщٶe7w/GAAl :pk~m;~ǃ@#n+]bxh grqd|oX+zzG'1=?B rrhnfwYz]s3N+"b'?u;;%܎Ƣ?.hs`wt'E g;PJJИpV2m%}I?lJ 7B eڈIqo@ OL e $a<3Y:!% q3 6!ρ u:|o~XAC@7m0h3SKtrԖ2&O fwBK[D;|5Q$g'iA{|hz\=hc^#,BJT:>/080<*dc:e:Cop=q_`I^g93?Tk>:)Z-ދ[O&==~HwJ 0ylu_`b'PJt OE"E!C`( Fdk@ sٲ (3se[ t.auMΩ {qjEOw!r2MQ#PBI2aC T@R9(m#ѥP 2% ʀ8ĸ7 Y*[Wjeܕ&22#A 5ʃ}Z!!!Tu[2XdmPPBXfj[hN I TwKtKs[{J[${6G$:T ^ߢmNdbe~tqݼ*7W۹m[WYy͛XY@u 0y2ݼK-^ P'T[7G>K}x)/ٹ<#U sՊsG$uP*tC񛟛9c9P oAeB.2\&hل5hc m~; m#2 vh|hB@"KQ+~$h{^h,CFz%~Qh\x\ i%ɝdRh.s ׵g(=I)(Q7B.(躊롟S h=IrevB~dhct^ :yOTJ&k=gz=6 fЬ3j &G2Ws'B~=~A{S[RWq̻c}eamռ߼/+;pheC1eym~;v;9LqtdeH<̤ymhEl$%f W=O{ Cs UfsZWz{zz?v;ڙb>i^힯,V4\&fg1WK`-JptZ10("DrZ//6˝|k~qpn,47rƊ>cvh`YbXvh%Its F} m%IsIs B>wO !hG.0⟻MzS8=Ћ mF/NN˗EhۃK7_~ 5JeR ~;~^ LJ"{p=?B`| yK< fh2~.I<3`Q@B( ςACs54-N]<BVN@3< <#7w:yMu6R@VghɄ>AMMB-?PJ}#!׾Nuϓ}4B$ktǡe,qw^N xdèF>ՅJTҵ]=pVSfZkUotWl_a^ή1yG{z}=rlKK;|lvWg\m#9Hɚݬ =̯mPHJ` B@=rNOer iB$ET`#$)CI/tU95޺:H T=#y0h1b*U?Z$4 ,_X zCZd2 HF"B$ DB R6Ĥ Y PrҲC^>碯WEFQl )8GiG8]Iȍ۫9  {!o3kY mmhZusk ǹf2]C1u8̸Sybfo}IG66o}Co]s^S[[eˆgk @x5ՊrnGrgs=Cp+eՖtͥߠ]c3)PfE%;mo6uݛF\-fVG'6ܵg4\ 'I.EaP"˭ IO+VP~TWDM1TY0OwbN5 N%}Vj]mkM{P}.7> OyÃkD@?z<N?4,ہ.р.j&!d&7 hO6m'I. y!1Kµς>L|8 D|X_dO'|2h\[9}4cuw%ěLRyPBKBf, 80n+3QbWz.sfTwc[o62]㽶+.sRQzsvϝ`#}}BcmhXwAelZnp6/X ԕi# ] {e$#Nd8lbϫ#b*4%6f7g 4@$1"Pt'(-Z6~kQn] 4e `3  l&!Ax!]i%!KJRu5QEYI*IzƗ唅EUS3F MH5sv2sl0^,aZՆS  \q3f@Mr,޽i{^9H-4zuw_q%_l4?tl3\\Pk\0]WC 4hBem`pKbvgl \+ww7| o{۵oypomϮ\5Xo,PZ}}0tMOz0'͖ C1#jS^qլ e40N`q~L3o4#Oo/eSQ%w'"{6}˲흤ZEBAY4+va !0r-4fpB葐VK-FWVf?z/l~{o?=+t^윫DgκeWf%1`0nwqhXIJ0WF)"_l3:8Zl|+D Myy040H1 r3ˀ5$.r4+S6^wZ8rgr+J`g1 05 TĄ8@r MOmAʶE?# ]."KqY7qR۔@$":_ދ6.ťʞ;k8U8¤E'o-s>9=\d2,>;Ed`]B9ތ":Ÿ-r#ak+<$}!~Zz{I.ɓ&7 <1>*MFR"PNMdŬ`$|^Sesm<j;fcg%˗|Q(4vʾ˸gR.D|OoX\_ ;Q|v \*޾ |`7R:R_dg_Fʎ"Mুv7 p{/\!EȺ Z3 ߏM1Z*$.YB֞ճ,$w9Kko3O~[*nݻYmڥH%y&HͿsq; qs_udxP(R{-l~;/yC:b;A㤱nܰ}f:*<gjGOʝXū/xr n|؟uUy2Dz,mvʬ]ҦG֔J@u5$ynn,ӹTxNr$TFqȳ޶&򮟴jnF\cBJ :ƅ\涋5@kYW~U. )XkZHؐ$BжXx5d%Fm&{mzR"O"="V}`ek;g'\#qEjnrhni]›nY}oqs)G׫0@6ALqN t[<@Eahq'z5euzc环_?tz4u`O_{ݻb'{{)f5ӻZ^@P:Çkw]!JkyxweRѿ;]~{HEcԧϪ?=tnnl7`=W|Kl4>k³CS=Fi9PBqq KƼʂ2xe|6K :ZFw͙Ϲ$p .eЮ\TҳM{ b[Xܽvq R,$O)=*K+pCxoE?sU,#w5 qȷy$-4 A1r2R]VTU#ʚ,{2 & hrH YYceWnE;47;bvʿnynwnwDOxl7@W_UБ +D(OSj{9q>mK6"ޙiS }jfGLƄ'c\d+:g6CT ] G&% `7D}/2ݑI5K*ywz)]ϣa,5&2sr2 Z}8jU/Wދzt$a`&"uFۙiá6cs^n֕GhBnR[xROH/9/(Z $Tw#[=Ϳ}y#%!?9t} ʬX`E60ndrضi( E0XsMg3w}5ښogCwswVj33zƟ.|'鿧M NqOY^,6hό`كA3 b׮3/1xZ'ħ8Z#?O?tsQsxJ7u\:(㽔2euZ $gl d"XaAi qRJc0t1j5gǖ.!,:E񺶙X#HU|M֫g(_17}˓@#gq؈|`3#um3KI>fNO8:ukn"g Jʯa@ɐObn~ڙ7>pC'?fڷ%`䵇v߷}uZn>.VEwo)5LVCGgiuדaJ)B<.qt{1wO>lW_m&]_mSz@H(\}q|Ͻ#nok[{{U|鎙4yϷ]v Ws:ڊv[@HQ_꥔a@A)ڥN_}T#ZL*OoƂFyFOvIf i!"W>b@c9p eyÜņ'?=Qvq$ܲ{[~sUܔsn.@Zwz?Xh3{ŻnO|o>)(C =A69)AC%#M$*B@+}x8OW y˨+3[cR2b[h%25րr;m|K`$EUv9k HZթ8NњTN+owEYQ<cRn ՈGWx6U+<"Y00l%Bzehasl#Li"JZ98PщEuSS[r Ĉ?^oLVWNf4[K|L!?ҐWc J>ܺ]"$4B* ucqƁXx+8l>V&S 4Fmq^~r~s h}r:8|H!Hc c1X<dȓo"fP]{c;joHeySRKtusY !$X lG$ytW~O|>甙B:AP'5_N>|˫ۄ܆J<˒󕋱$/>>W;{-pDZ۟Y m!Үt2 &ilPyYv܋7ɯ.f\R]HT@m^u[q>.Wa"4Rr{3=9d璒 ۸~y;?*L\,@1ZRCwƂXMWa~aT *`\ oM1jDoyWOP v34@kP'OrΪq^xAl G8SínNcFhUqF^v9pHǵ(Gn8ӯđ&.,g%X,Hmg?iÓde\7@eW uF 1bF1KSMxs<@CrB.Q^Q2{|=63Q'VYX[2n8\WY Lp,y!͛psEyz60q=8/)qqתqē8"6T]s·|4IvJ`X驗 zY_lqTkq^IN2-ɠ><$IEzvի_{%ޙJ40-OBBX]b:t FEMzxiFRZKaB-/huxqmF9N\M*ϓa$hUE ڛf7Dvjo,!G03 *ۆg*`}a-.[繝@M(kLΌy 샩RjeitZ' *b.Gq(P_ &Y^֒Ϫ?$A!q(199LmK~Ѻٗ]h[=й.6~3. 7ҞL0~kMvi8Eɳ,{xjbYgQDYVF˖>ei >yоetѣ&vb`UF@s. fx {mS9eeW>@sN27/&.Ay\LsdK-AXǀ[wbn3$!+H^nT;'1`t?1>cu ,,8;N'50$C } 煚Ņ[ @fZy!F{ ڲS%)&,fS\n }U+9K<2jQJdm r%Kq| HpԜA5s9p̧:}?\b:l\Wdbs+{ E Z͌>G{/s9m IDATuZV{ tY:[ SIሼwX|OO8䒜ǿٴ6{|uu!rSq8%3reGӥNUSF"XcƴEf5fky8qzt 5*A ,A**5OmAhW~>jK"0t0$riLLt6VIjՖeP3!RV0۳EE0(IB늇$2p%zb[ȭyTGHdhtGD'g3FXcKF QAX}vQN$')*K9\&#*YvOq\ں~nV{}7*"v]YeS"+I VD Gq͋/DX,vhb2?1p@QYQA <,&N:fqCZR` 8Yf1c(Nh껏͛.bdf؄2R`iY{e&do4ϨCi_;'T1\#AۺY}u蔸l'K7_"3N`QQÁRmx6&j&b(ZUApP %0\=\Zar`g pFx`<_Hqegr9` X4elx߳9mt 3թ8"kpK ~86m R{{E*gix.[6EdG/=5\tjᢋ#xs;qxETo W[c2B׳t⬍j szR{ci,ڒLe1{ E0bq≓OakmsIKQ䷾%G&0J9T#D`VV>K=xMN&F'*V#o* 0xԩ[P HzyB\DP EI5lMI;id mPyBm9SS e`#+kYsrA(PXbZ'!}+㾵X+gXq`M)=FQH_r2Mp4'L8ҫN6]Ai%BkcVzݎW+QJ TX E#[HxkSG"Ibr-}^Ǚ*A>?\nzCMөjb-Eoǁ[<Gp[FRs-%GQĽhg4p m'.S05|璔AJgi/`,asoWT*}]<]6gڦ\IJe A8F|SPK q ʝ8?¥Np3 |  ^5?[pdخ?̀hlF`R_(㢠wھOr/~MJ`^s<ࢩEDK&OadQq#Qߢ1,%!G:)ɗ|3>kB9Ok]*'*R_\r˙XpÉKjrE O7ăJ-)BAI)19ocE_I3I-Q22_C/;`8OߪϾ hPþV럺Kۙze% [态nJrnl߼ܭ;}P C=ZX 9"*_'ٕ@3ܨU'aXO)[H%9Pjg!E Ny+󭙫j\epB[_6J%;2 ]؏quY>Wbyɹ?}Sf)@ |gL`}!n2l\lgW&'pD`3M'Q~-Y /PsBu܏1 ,Q8#M8qb=]Ud5|d XG_ X\Y]7lyi%(Xnt~c>݄6I9  #E2u.rN\İ: \d[g(e}e8wxq\q\:PѮ))σ_EmprZz,)oKt EQqۉeOBlåinqj%vl;ps;1K&atnq`8\%C-8熄-%[^A1 >= as<|"5nQxrQz$Tr7y"X71uc&B_0% 6YUP,{pֱɾS}D\mnsjQ &Lzc@z˴#pm!t2He9k-$kQ kPMwr36TPA5A >I*sMad|dqjkDe2qa73ZB(n[{*!Qkm,*XKe87pqQng%yˇ>o> ?4RNbR f0jeJrڬ`ThʼL rФ D&&+h"!ҳuUٮ5 xRve"X)6f?iӳe+F*_srY^yiZՕ4/ӦU4_i5WX!TX_gd/)0^&RNŸ[5~U C x:~D_Rj94Fd2t7f;v\*`1#3GTR l}G7'h@[*'rocLJr֔d<ωLjɱnAŦQm姾D}4!Nⱉ1PyK5&)H)ɮ!U^9/Nㅭiu?*ɉg~{eol쾵կ*زeAvvZ٣;o{M͓?:mqA3jwltdĈ4| ,*&}[ZY}ďgI6c5^5ݤYIQM?qEfӱ4j1+ 9~>/{ߧ?Orڻe՟ `*ݕ!vnae_X4#-eGѰF,"Jζ ݽ?ށnJ-K_^qtg#8|=bt3=#UJU'g>@ޛgtg RO&xݏ܃vqQ e6vl}0 (,AG`-Sl%6ykp t<,Kyw}hޏ@.9Ri7G1.ߏs(_8͑3( ʓXsZ{㬨6h`uڬR\U&Mf86jtvsmnSUVMLepH+7ml$)d3X{Wk$J_=y͞]܁'֦W>wnɷ,4%'w˻1YLtzzvK;zJ\ú9pRhmej["OV|~}Df`Z^`*6 8$>Ʒz-,oz?BtZ ³#74kuB^?HmCkm^LvX}mC@Xo(Gg4XĽ'q @D My,HYKl*m 3bE\Z8w l+SE+o8*…KZ՝r,ǔm?\xЙ\O ݲޯGp'pf̹5ϋE&ť%.p}Ayˊs(.ͱ7@=JK~x56[bZ>8tgRz?$M?EzOy\zʦmQϗ?/ĭ_9nǑ*Mi-Xk.ɳB|/ Hՠ|q# S=|RgcۢLZDladjH tnQVS"N-'Rc6c7]6FWbNW8 m6iMWV 6*ɤ)VJ9lc3G*;bKK茚DFpbbyKז tPL̬֨,RÄe*Qi8e<ޞs+^`Pb<>q4kVFwqww|;rɹmT R\򽤿k닿җǿ8;,l5t4ǮǁZeY.6k!ͩѴ5r .cLa 4?"gg'Fn@[n>q뭷Q͇ߦ*AJi=XK~RC).[e @]$ 4Wnx+"\p$TdN_{e љd x܅VXUYkFoMi!dE79>;٤L<^*\Hqq3<Ύ[b_H)Qh,"S;Θ08V"O&؉qf2'e4VŌ-GN*$Ej@QI$@cFoW?n A6~ݪ;E GތNS1D_ȴ zϜG#ؚ'#wD&2k_ E]2/؜|L,%$J+/:9c<z󟡗nb|Ae)E;As9K {!Ws 6:,@"z (rUZ:gT\8/ݿ˖4ˉNn(B/ Jd:ѵHbU4?NWe}݃VN~Z-Ԍ)N>) X1n:fn TȢ >6Iy,ZuKǥ1w81p.H(sQ@ i]\)YCSwzC+7ɤ >Z ]}Eqb캅+@+-3 OJ0E24۩4 Vb.Q%1ʴ(V30y6QP6Yp.ȑƂӰKbZTD0-xsAT,:"G8Sɱh6Aur,6Y/U=qÏ>LlH*l,eĖC۱Z':DAXdLn"P`BW:NT/8^Wke 7 2gn F[4݆Xn{W`VĞ׿w,w ˏ8PrSQjϹh,yܰ3ǺN'?˕=waa_)͞?b9ZwXY۸M)C&qK|uj(;jAB~溹테NDߍjnW?ea>%aĿFCYk+<=5-4oǏ_O!w \ 8/O(&i$^ؔ*hnvTyAuՙޯTudL !fcFxXD?WR4zr)VD?@h(zB meO@˥7އ.:R-yyfZ3tY4;oF^r}:f;YZϿ+}m)x@f,딥fl4PloI&Ezj !yG^GgЀN4 " E{ȮD&94BtTZ:|rd if6pg?WҲ̂5utK+}Vq:V-7ϯ_f? ;(B;,z*+[;EV2YzxzS5MLH."#fޯmLj~J50%!LX` S ",a(?4v6>b޾syj 澆#OEV>1KtM#]#Š#6 AJL a1WIm2AZ%6d'6kɨ2$΅]o VU4!̡ֆ2e".+ ²[hpYS0b@$&v4ln2 GA4bIJ+:f\a!\N(y5tY+Om?J8*2aul&fX- 0XkQ#;.GngٵL:@ V ђfN $#EѕLI1B`$rru1AY[ T;׬ $J܁>_rape#'{ϯ _Gݺ{;Ŋ=6vj;>BV闞4s3fbcsu*vսnٹvc;flO6Wye tsjA[!Xn+1gO;o";X7O˽М s{VtE뷿Z5^(nYZU1/_Dge6=r:vtskh{L7UJZ^[0M40:/xUK^R^ /DOw`=IBt'K#Fy4+D 5z3PHhBT*mVZE;/L?fYr:̋;ktH結,MvϦ&KMcf*\D1Ft˒I8f|+;i]Bϋ lgcdLZR) !N#s32~S~445Fzk+%࢔Ve; Л =?W,i 1ΠOpR)wS]RqtyjzS0aݬ7h2 фQ>mn/UJ綃$y>] 4pJ6"T^YRʰsru'@]V ˨Du Iil{=޵Dk[87^ C.U[sFc_XRBbY0$2 BdAHbW1" In'VZf^#NI7glj̙T4c{Nш ^h\ZCjeLpr1a.Rh]4?C/W000S,z9h # qc?~~;a+&FR(o]Mݼ)b.n*4 FZ7)}݀͏AKz+Z7oC81aTAz@r%Z)XTJeIJ40a^ͿG56+_^ z졠'Mz :}V艜Zyz,Ӽ4+#ej.2Suk'쏺et̢X@+Ҧ(Y \~fӫ-˾7{VKQΎ+7!7*#)1th^?j =W2R Dv\DRr~4\sV=k$hg_Z/%m2 :r/=4p>kRSM?tߥf r,B#D=U\BϢi:hJ7Mb/|%n z'QcJa!^zmLB As( -$~JʅbEv+Fa8OlInBsgji"8qnqD'lL ,܃* O,oMs$oD֑d mL$:$> ܃X׊_[f!#yTA1ڬemtNsqQ^[#(`y^W"fwO leG2;8|I6Qo0\/ `zM"u.(pu")Yls]'_}F?5v꿖Yu+|>͝ *,Y\8~sN~G>};O;׷ǻׇow3'Jj혿D]4q W֥OmtF qͼ %{^4W.-Crp1 o p+/|?7jNFf>}X?GM|4=~;z=΢s2t,:^uɱ@濱;F{OoBc7OIK98ySNbKy̷{;Xk7ڝ9?(e2x3|hz|nv_N]=~ǀ 4ގ.tZ~y)QtJk^Ӆ=Czѽ]<w=fz(d$KeIR3rcصeƝOeb'YTƲee׽~utK~^4E_{6 :wUiW0 H{ lBߛ;ћi4X@f̕.&ZB:O _)<-c ,KJ&Ri!AI^4^.47G;LzΥנ@_˗ъh ?^#ؼu?K#VzJ4=^v )[y;TZ>(=Ip{*^L2OkhP&1Cthn閛`*?hHjCɮ,ؾ幰>\жBug..yԓf6Kf(4 V#_ZI V4LʝX]Ofk_]`ip̘agmE+LYYVoBd ݌ag9Xo7R=KG*ڿ}ñqa/-Eʲ=WMBy_}h~3?ǀ:MI%9pƮS%{`~e`G>Q\9Y;>SnB7rm>vZ%xO7+S>}ݿc;ֺYs?{g۝+N!x kh66͟HSFAS1}XM%^T&'ũ3IY6A4>)n?N%hz#gw|oݪ\֤&1?ƚx!}E/n{&=f^,RMK;9j4Bo2%yA UA堣m:*`CG mgQ @+<6GI^ڦo%!~$RPx;0;q Ljhˑq*рDNSap* >GIz]?yR2c!DI=zsEBAPJ\5mYRh`B|ǀ44ͮ hp3PFGa=h{ˋNoj@ߔ?#rr|,Qa,[_yب͆IeKɅ@dxq?H(n6)HX[SHH̨;TL# Mn%~tc98#lf.Zu,Džڤ+R5먺 ?JIHpDQԮBq.VN.KH{(b)cUfX1c,a B"\tn0`\e–/T #,ͼߎMtG>闥W{IN';x[ߪQ,w|G>Kz8y=wK}Z7Opnt{:=#h$ Gn{Pm7M}KsJZN[ӇEQ>OlgK=aa-r)9poCgZ^~~ڦo7\>6fo mI^ot6I.*l&Y_ҫS|'j.Ɨ-pyͤ0n#I7.zQ* L#U5e@1#ћZw2t+瘍njF DWit=B)HAϛ)iyРDبѾюGd))(MvS1>8 N#ISOw3hvOv_#yեp+9J0g]9 L QL[l|-{|/:(['1;@My5>)\Tk#Gy|;}:pS66ဗKbZB9F\9dpLr$Lh^׈:& ye%;$zZ̷he4Eh;R741xRaXCIv%qa:*!nz>+!Bb-2uӥV(H JGmtH4]Qւ)jѰ )&,jx>WdjGVװqK$D6vmERᲷlϜQWWɥ%{FN%_fb $VD4,<djON,szcȶ]*rPRk,**0DP@,pm՜xFh%1#3lT6uNM ɭǟgy/zUsC#18y6n<9kNnڨ Y'cuur 9,U:g3Cn IDATyGtܮgǪnsWBe݄?YW[z~|m|y²:}1Hem~/ŌxvGm#$|Pp⦅K{{LK{⦅SswF̖ѽ~.s;?#,c"䆶ٺS 1o|%O(4Z󾯿?UsyAq!oa{,m< twׁӺhݜemf>dMӾW;6Wk%j됑G2L.#Gwlmd/c ~~~ %c5YVB{fU49ȖБ",u;Ka6ыy-=el>jJ-[WP }ɣ z7}`I=|/#(ʥ@R*BE? oglnf}sih^g̃azѳd:9&!D3y5c: =RhGft,B"܂ ټl~g7My5[:;XߩÃ!K"#i.[QxRfa]RFӯd$4$F\#+%[嫎!"1 u}]o նjJRA,8D~Vr8-'ͳt,GX' T+ !dCP^;~nj<4Jaqp:=N^Kփ̡"0PQ*\~l>sߏ]kMAy"aRFF1JO?Zj\~xh wgK-*g;;=A6$7عt u;M9b(^|!MViH(Démى,uk9a3 %;S{oNbΎOkmCcXA9Y&*khmw7|v?ʗ|;'c+Ͻ}`O~,V,܆~Ez[Q/MuQD|?nysL/e{\rG8<8T̵\1:.۞?Z?=ZPr 3Oytw|ϫ,?D|*ߢ8.ڟn܃v˵b(Է߯xH@%yu4H̡.^|asRk/k˲k h-z1:ڻfц~E;&I["gutK2:~V:Jd"G1K ,ӳGk&RrЋ^NH6r_D-kZNk;ڛ6~n"e@lX%ҟZǠzc3˖<.tnzSAaqәyq- hjh(ҭA6gmRfe!ĔRj~cޔ71w-R+^Ŵ.7N m!i8n΁wIÖ((s(5B#BP,fdⰾcWu u{ Ibe@WK}Ķ!I`c[ ^3%aDFIbUHE|Y!EC9I$vȩHDC17w/2o6c8oك$8R0SV:mw wb0S~('v 0?yoo\m?cLn0o@ݖR3FCe3;oI47i fr{޶|0ЍKUJ]/_p3wxN[\UݏQT9ȷ[NSiKUxU'&gpaNt*)33]b~FWٻ<%Jۏcڈ8@c u.Qssf0z[;7EؼE]RwO^9?{nm7Ը@"|}.t'ȇKv&(xEb;-/}jS02 t\f_Jts ]rS"&~f{φn֑E4 ܁6K~J#KQ˹7 ,h@IFh ٦^Y$+ =1cl;.:Ʃ幀a hR{'^+4u'Yuqni=ך&ctCo㥺fK\f>[P^&*ᥩ|%Ҕqgye-/m8VY<G. n:Уk~'::飣i7[n|ݓpMK@7~jKW>2tj:{hҗug:ޚ羬h tD1l藬Ȣ3i$u)$Ѝ~mrLDϙ<cWoʟu9(pno`pI2w6 DV{mZiYK?O7FJ#%Q`li^n vLä$R!RGMi A*V1 WB8.+F=*J؎#[I cb$–_< A7&&Ro`IB6|aQFEH D.;0s*íU~~\;O4[ Xoڐ6sQlY|S#V $BPX |ibkz>X֎A2YQ̝Y/ 'Fzzi~SӲ ;񝭽/$P*VV0^ _>94~[ Z{9Y#G?(~[BuW8JJo{;v)/̛jnOh0m60C䢈Hi_+ev($qΘ2 A,%NFBh,Kɳ8b9L)WydM,Wt(^|XJ^$$ c(yAa#bĻKɿpu+ٿpOwFl7nOJ 13uG_dw{8ؑKΔ-n_{f>=hEۧ裏ߓ:C +:h MAJ/."ov1>LԻm0|.₿Rtmw3 `y1 fQҬVQRLt 鹟@ȃ镪tФ5mR%t,T,rFϟ~ѿi]x s՚$БD.6Cz4~Giq׍oq>{.WX9#n)w3-66zMKInbٮ* nE+2Jr7t.s8Q5dR(Ю Z> *SÔ&1H:J(#X9GY\[J$*J8 rU»jX$@Z YUBu ba`.Įq"%b 'H^;ei9E^ىa`v[qb&PũYq9̭ŔZ3Άq.*oXÌV(3I99I'<7<[b1mF~}o<.Nw/.ZG`fvuWy+v1:%9`3*5#>~h7uڪs />O `!<Mމm_Xm>\WP^iLI3'G1P:aDG\v-M`:FBN \ 7~Z}$L[, `|Yݮ~nV勸ItcC$N3`RZ =^`Y \v:^6}֫z51=gdu& O#`&z<^\O ߆6bwFtY]hQAS0HEZ3 ^ lfGGsn'b#0%ODZ8#gH\)7\5IrOYqvo~_(.d{D7khd(=B&(Weoʟ+;bfƹY] *gL7)֛"!#pak/=iKwWNWɕsͭm@Kl& BHĎEF.wAc[jMI 9giN+%i*A[0,մULȪR΄%%<Yʶ"QBE(-pcF`f(,:Q]ðROA['eNVP} +44R.^ƙUg{'e5ƛOv?':?}GhjEvK=J,)rF3-Cܶim&<2Zm##?҉ż5JhCred*>z +O=#)<[R:V:CԗYl P7miWGh⅙}J7`C 2vؾ0FB2ǜ)SjigKD,ILcݟzđ/[ߝ=p yNn3~4ݏ̣iwVus m y.$"]1zK_@n}lNu(Z7Ѷѥ#oT視zpir$}uAo*]4P"=lY˕~^Y,'cBЬEF/quYhhckSgާl#yeYZәyZdyGŒ D7\p\80K 2 plcc[m˒,YVs>5VZRnMgkZjWo}~űenCjbx8njaONwcY^ wZ-L^gaL_/o;bv !Mf բ4ċiׁ,}ޜ 0;_ᨅ1ruknug?糝.ޛ][ 9|ݜ唥0cA\2rZUP5X934Q R0a&7;5U?6?s&ݠ7-dֆ 6Zd; X7i GzTWN5nWn#|6*h^j#^u6b9my]<՛{ЉB\x؆_m5[ٚ+D]D%*ϲk4%X"leX殃R-B.MK񲔱?`5tk;6HbF^嶃Y9~Wmz۾Ї+v\_V9N48_6/`/mSDGOK\nƯyT۾mٝ/[?`LZγ~DV&=ݹZ_~d"Xpn|˪XE:>l/7dٽtXPް<YVG_#L2yh~ x5msι FGub$^x1b)2cx۾b!)/sOʐ#VUAa`3Ź*IcH,=~D:ƛ~_>b@yIJ:f]-^`jQc0# a/z\o$u0?܅, q*?zZQƘb(mqoE;y 1Ja/jWc ,Q\]6f,[__E!D~3wrAkENaH_} /]h p,'}^s'ID0(=RztZZBu+M|\gUuMw^x"&Gs+8TG/Io\qx9t-yuo/~vfhjĻoԈu;pqPRP[[I˫8jU١F}~fذ702?5'69Vc:Gɼ\hj,*W#Z BrZ@FQ ToH^!U+WiWVRvXO6'h] \"U~l3Yc~SJ_#T*-S)J/^]z(MZXE~f\\)XsBU!6`Ak8oY;fk/L!NyB 8\ɊcHh1(vb}3Ĉ Aik&+ lvv=44Sxͤ\n Gf=A0jr`:9|uxSq՝p7ŶZɉmy駦O>_q߱9cVj[ +m4h63#z3õ'i, )m\{h<ߢqd<"/XpP>m Fx?Wtgo!L/ZW/Z?Ag?;b[|m>>y3,!^ rtq~Y~G^}N"k3PWH֗cPmu}Wi7зwV qnmN:4Rߡ0ާ{bzww͕XA[o}!e=Œ _[|ΟMop\vY>jc}́:<²FP3.![!c< \Ry=XݢMy 3y,m8N}S .M٧-Co$uzaL/bہRDc1cyz]!oG=6pź1f gBr^s^Q"rLm OLk3 sNp>\ pH0_%p HvLIǓ>RAEh49  2+ni)V"RR&lj*#& 2`_,ˎ,G&Nur͗#NjJ+VdUf q>pUxjЩ-ܨ:)gkX2]BVϳ"E)U߷A(ı&z+Fr;X2`B}Ob@?gPO`mHgye^SiKfq \IQXok&D0,7]_Ep.ZNs.[a` \(0C|m'@~n/㋥L Lb՞%̵>e8WceheK8N_uW#C\իXxҳ)!D \t1Ftw)J n>7Kֻ?p<aveGu5q 0׽ f6jI`t0e(Oݢ2t hFzILQ*D*54 :BmPt00gٕԃ ziA5EH@:5)ir2*y6*ϩW}uH&>|5T|/ps2)1?\rDXɬG|-L&IM8hSÊd@:yndiHLZ%u:ҞU8Uw*2-arQ|/ߓw]O-̧2>ל\Ro4]'H]&Bpd)]97UC#ޡNn'*?Coߒ?ѱ /yӯ凞yן`0Nq#Z'ɩl;h/ҷV~>Q6}kOX6].Dslmٝ-uQӊk*@43 yG00^Ր~,}0T_XxUз<_Ɣ!ʹ eZ C,>/ j+=7CL1 h0OGѶ'1$#0}Xz̸maKUe6RL?TdIk,sOuvܴYfs{ey.?>EQhx{{Wy#Z <AcφFtp.MW8gOԷlٲG&2i7K۲ppӥE%jV"j^بn;Î#S_Sd.Qglmji'!MRzH^":ٰFtTő\ =7(4wiKD\T+ Ҹ^OЫ'IYbl(dGkCtu v>DdU5~$2 pu~|zC\yټ7pךg7~L/rjTJ[SϚՎ^4xc#yD(tu3wmMW8&*fjE"\^iWj}:i6wtt?211ێ7]e]m fItmS3mɅY=7:s_IyafdTM.$5I r ƒ,KqEnbxNcD־ qt5̩{_}pa}I5\{[v63Ld%yFdt10]1_u"1ysҁ<R$>e2D|c{_)v>]CA䕹5 C>"& ƫ5l6Y//ʳxuEy\?JNH`>RN0$-Q\z1e F x}hC߂!x aIiLqLH`̦ #EH8 auv.uyo.6~ v{Q_~XcQJ@izr p(mI`iXʉ}prSi] Zz=?~f\~j>evl'}Vsl>) 116QGXHww +Kގٮ@$NiZe,zZ*XV%$ ׵q"\:j!G%k -E IKXbώdj ,KRD;GU/ASwy7]\^YuZk']2Tołg{Ę3&U[T,9O ?:p@w)`lo8OKܔTg,SS*N];h烅Jh <Ӿ~ޖaV$%87mW nSlhJv͞&\=W^f~3Kn\/N?|#GG{ȝ4mUZӞ,G-OќC;4W %Mf (bv! $ ŵz@]4rAu?YSDz)F\8xMc|~j:rѽٶDzǞLl_ʏ?? l#LQ<M? ͛k1HO%ՎPCKӋncH9KKok,~}ϫJ5g~k/v[^xAȢ:BOl"al(^[z^Dz`cb`IvB3Y` %^-ο\]ie6S&DNq]~q̫@>1ls(J_zw0NJObs'?K<%T+vL.b!˘.o[c} 3[}㉿# f௵'u&8zmB[ܝZq=ntKaVk > |) !S7B|KIpH߱*1rԎs3m 0-p "ѧUYwcAfaԊ'3o)E%!"A Q8T\U;tɲ raLt%>"kT Ǘ$ڢp\rȴ&W28A/I4AHH<)T&, %,)j~CI B#e2&VYmn-GO3Y-UV҉߮OgC[jD4jY;_/zNw~kQxs|G2Rժw^&9|4B~`%H?C|tdj< v]4(Vbi-'/,"CrœGq&,qZ_|-{ 3\VzW-9Ag'o0PA3YTfWՓ'E\B3Cd$.-  {Pe^nnCqѩ!|=%f-x/Mlٕ&WK:Zw;ĩM_:Oқ]7Uwo~D/zB(eŵcycX//s`%̒:B|/KxK.|eez|FYjmVI+4PVwצ:*idWS_iM Q(B)$HvHHh|]+,GOemṱAnq3Ѳvꋷw:N\$gZ5Xm.RV m;X8>{7,TD0Y XN :У;tgi(*R SH0c~B)m5K.h!&\u1(BEEG{0c|w4Oj8sbpfL.T !b ѝXi/:KX}c_~d\Ϡr=;-W }<:󡡰}B3cy`XhP>c$H3#Ѥdxm,a՝4\j(왑l+V"!ЍV/c$ нN*mGJ#jB#CwIpL/R*b't]My)];t282l.Y¢c5*QOjiZ\:hi^铍0ncOlub UM!-2 g;^#vWaxݔ,K,-*>z5qdDv8*q z={Z=y|Q;|=2JZ㎵bueN#yIduEWҕRIHKp]Q5Lb)hSF{B@X . ֌٘ɩWD'x):;>jno?x(Qܼ87ݲI|Fq,SDrm^sK,F]z.ī6R(e ;!9Y_ (=}SJe] "0U{.CDq}I.KR,y0VO'e٩b&~F9G{Gy}1"甘c]̮pC`(&Vc /E՚.K„ZB3&vJdxKDe;j'0/ox~n@ 0,;s ]K🗶ׁOQTN  <QD !c54Bvv꩟WSo11!&"&& #@ PB$~B$'$v`gB( Q.YZXԛSqfOEQ7Wb;KʖH(F-V"i=)@yQТS0'茆Iᕮ[DR:D&NWЊ\yc_3:)Vj[`h!u:r;=:emWVKh6iI$RӲO ( =Q.5;W&<"$JԝJzգŴyX w,?+ ` Gp#Qwكʆѱ8>):b9 -JEJ %4®z!'-Zy I^!9e%lFwf_QYĶm7I+{CcY۩l9nVWwM^?{ˍ$k؋́Hw>O}@?b̆eʡO1ܬ|[vCfvoLBhϙSngtYmy׻/4y,e^mK{q[1yGନ8^#CvcTW+OL/ !Z^`y!2SBNj lZ !s/^LEO #&6søՌF9 2l 8H\ :(0i P.9>A&E)oTG;I6viNk@K Y[JOlM1 kHqd vuuHZ N:dV KDb4( ]I*T&dVL-tF[""ZM:}ںc}:B璊7Op#{#j k_.%Xe:^ )DN&*ǹK͗,#x/yDN*IRm[㻾~Utz{7U'\FŠHRh ȕ}P\S~Jr)|M#[UJ0B39:Acm=7HTIpfBeQ[{>gؕC7nO3 }T&*0|kW Wd//8YZ#Q,L-L)8%csܯ[p ґ#O6˻Nxy8>VXýTcR2r R5, qL8x8('EJDŒ.J8gKPdX.IYb#*jKcNaj+\PJw*٭xh^ ]j4N jİ)+#!GfdI#sT,obw v&H&z3h![ ?YLhY5f:zI-8 T%dgrrUlD5ꬦx&][f8%ęQu HrxҢ,bkfVe;ZgV*#^L"vҜ.G*fW~%wFψr)Sh#bee|MڕyNkLlZjQcVkb&V^g NrȋprXK4^RZ;3hSc3ȀE>?>%/k_Do>4ݧۛn Ĭqd3}zm 6PyzbW5ę4M+uUC׸{_?7vj(_|`OFf')|`r! }^Qe~[}[~e,cMHiM \y*h+0{w-n;I!ڼDxċL;Tۣ5uݵtÿ;ey\,G설xr>*P|ek6HnRGOTtGU2RtAIgĬ[C>,'0"QU7},G'6v,vF+{_JڳuMKnZ=\UKv;9:l4WD}#;c4O9-@= @HI8q +%22, >Q|3h,QvnuXW%Y;ZGq~/ p1Ngmsv6D m+dׁުHm']ڷPl? -,ęevڧGq]PmZ8ԝqA-mv/N{hnO{Ϲc F8I{2M"!!Tx/$"R@- ƽ?]˲$ےchܫ}O577y2#;6~2ín&pGt%%".R1l͊onh팀ds yQJW< 1NwX**^NQ Y_`!I);t)2j݋ Ҿ8E(6zB+tɂ҈%5nGSߊ^JG?3cQ)B!tu(AWٷ'p߱vɶq!D,}AGC{M.,#y $ԓ@.$ ! @cB_Z9<q2DH}vR{9;gg}91xpy;_mWOyϦ \\$fUI=TNdL4TM2d9PCICɶ [^$f͚-Kb(2b),1}Lcj!@I40DDQ<%,UC}0"7"סz"BJFˎ2kTkq̖hldȷvѺM?δh5s w.{4vAaDMF׭9oN @bZx"k9tvb;(g bSB;D!DB9322RQlx&3v]2?-Bsir~m-5g#:NΔvNCF\ 2KdJF5WBj)TYPQ }5=_}2'K_]$@?#~{+J#ɋя>Qs]50~ sc~db˦:%?e/(m?XPHj}hp.!C r_LוESnűw7OWNwU{~ b1:aD*G)%PHd&}b4<^ݿŀ32V?V]wLgF ^x:_]>rK@ӴqDVJ%+/{l9;g?Vo}g̍zX` ,ܰ `ߌQRbL 3d&u(SI7]h FQ NbTTr5f&:('"r]"GGױ@d#O6y$fnM}0V-k7Zjd*A+\s%MUQBRqJmvñɵtov;Wܔo~CnKMd#Js=V]rK'<3Ƿm(lDHlhK0NrtuP@DXڇiZOI ,qr~D~7ؗg[|]ȭϺYB,ޢaX=BdT_GbMY i-mVgWP.Sj)'_cК|4T{?j$|LoVtKBыV\[%2|zv;j زMfuoVofso~}1=CGQ!R: |DԯV1rD"1ueڲMuq:s{Cb3=R[S>o6ܲMN~m|"Md`.xpJ FnИ(%sy쌂E! \U4uv"CWUAK W*.޽+qT.@H&ܴǿ h@qK;">zaHɶ;D>^d`}ha (*B-` q $4D1N/$\E=mŅJ_ǺW>5k-`[ zt Mc܇ UnM9x~utuű5?0z䁸+:U4j(n䡱 9j` ;pp(Q& F'5`bCɈbB mZH$!A8m" 0Q, {=D{!D))VhCR04D EBɢ-V5Y#մ +-mۙe.l76(׷Ne_nkN|nLa_P9p0(::vit!ǜ2 e7\NPs:$*Tb d F,B{­գp\1V^}{v)XSg/sfQ#!@IvdzI}.T+pb]xC0l|]SthD+qBhSxfRmA&).'DZ6iȫJ]Kjkw$X6WHQO}߫xZRjza~prS0q ۖ;oO]W] NRGh̷0~=h-PAaj?[UYƒ P$&N>k QY'xk' q#ք`MݠwTBBTm U/ <*;ӕE.tM; |]U-V]B/|bx;tw,ݖVnSLDtf= 4J);X&_z/P:ħɠAP}x<?\ıVqL:Ek?I>T( ,T<ݐV ]Q"Os,X; tZB4zO1kic!hZQ!zSPBq0$;3W^Ԯ]ssZ.1kG.C+W (բ[El2y(D^gUCHj*DCHG EfōEH dG2:XٺR#el*a e"F"|#-Ma "(555͛[o :>xkV\\4 9҅@/o%ag8;mOr~O/7WG`8JP=QTA{&QD]O1& lcQ#EϴB@>ݴݖ^R_д *x5h0iTo>W2SE^40ꠏD@nuHZL[: tbJQ4d?|@R􍢛zZ^;L!+z"+| zUm}}#vCtE[ɢjBFS \58D }ڗo]=` ;0dcn~eQ}Sd5Ry"NO#EuQnL0S$K$M )Z,,#t1ȻDTԸ"_a8>1rR@ѱCb-:S7 7?_2& J DPC殚 ;fND2 ws97Pj)tVCOŠ ygE2,F^ ˴YBJjd e@D6Yڲ.~\ 0D9hv@BdFd51"\5SG[󥽕lᨙCNʂ c äTy|y1 pL==NTYTEBIC8v[I s9sB-<ąBDccO?kg^o+QsǮ<]V|O=f;pC45BJe~⎻F>Vg/QճGDh@0L+7 e7WW&uk/pm?za_%o,v$n? >|i[bZoj#-`[UL[DP\곦rQ4i͘_~ 9Ote1A nG~ÒR!W=sRצ )HkU>K =Dm4DW`\Ã^c4H_lkϋ=>/q,Qh%tfK=Ug| ߀WoJcCS[,hໝnk'kY!Xc7~CDs=Y$ZT>}ly} OrHs'O2w)EGB_$t~M]},_-/VS}Yr.fG/4挂ՏE0ɆE2fOA@ qH`aJDkEAжt܁x7@ #Pp C;a&,Jd2?Cд=ym6#83N֧jEtKKv4-ړeĤ#}طrb)UBc-_oo3DtOkrǚe^!9VccukeE2@,ҷoN P>e4xDANv짶dCYk/6uob {"7`6.R*b%{& a7j x8ige+N~ QO%4=>t:P($TՔ}1>PѡgĻ F iXa>2~ S6XuJSIwtg_A{:n{M Z%=x,ODyBپ9$dC W(% ȕOul=֔2W⛯=_ۚ1̷6^o 1T5ݞ ]Scl:W )kg7=34|V?6!e鷢?O30p5:-Et__iPz*O=j4-nR;p `_3qA.dsvQjMZLB0vGRlUπP~gҳ8 *'R&:Wm8p}'<}iBN+z4Z}]W !&eB8)39AyM:iB xé7$e^ }QLaǿl:5!3s>f!;WgewH8% T%SVP2ZVVZaT6VQC+$鉁Uӏs1TX!kڄuLf|f$q,20tCXV++#TÛ"ݚd40X˂)daud̛N4Vk|YfLMV+[vΘ2yW, `%uLJ\tzRjF-,hWO x0Bm FQ Up+lpΤ Hy΋+g! DI/FB_Di;xʦ:xFzcPW1$/Q6L&XVBmp Nq(L'&A?pt.Zk}C[|蟫esrSS5+׼NFC7n6?ώG3s.LD&N&h)ς|NL$oD VR}8܅`cݸ+KZr/`͇_/;URxn+· Rj6)65RL7sN l͊c=JoXI0^%FD;0=o# ֚l2wlͭ3nMļů'WP6?3XL~*$[* xyISh)mֳ.:'1DU }f|JL_#0NzӢ!ݱнg>Ԧg{IBh@7+N`N1Tpм-@#csB#QQ-ur0Nn Tg?z94<րmtqIU!!ās~BUjqǛZZQG߳k?R̐<KY}?qTUWRꕧo/d7-1顇f'ÃPak=LΡQFgp(0h%s:Ĵ -KZQ]f 3A Л:15K KAu@s1$Xɶ9tj-a2T[X7Dd/ŝ8Kf]f.-$H j?π+X{rvhiȩƚ5TP=>Ѵ-=ڢ /\Z3xʚLScdhC—Ē8Jl`6F^|0,K3N\Aw4@G]8)0Nx/vKe[サ_A+ (߼e\ͽ/7wAr+跄0R^alȘ]<*rc%*lq5:Q 3;2?6mo`Wm 5+rwbޫقFt䂎e,Tt;%nBDǢ !|^N]]7OwtY}ףx2zO)U}w+G&+TXmˠtvtˎsᠻt,y# E٠FY.EEd|;B*}QںyZd&[f8@9:V1nDA<;tdE9L}w6ㇿ7S{/z_guІNfÆ=eȴ]; ÞןAshyu%v؄f/"{phjt|̣ ~=4VZ+ΐ:TpjY1_2tSbz 9@fCY~} {BeF$0JW8 LŊpֶ&PDneM:F %Ug]swi#TG;13vdY6#0;6;fu~)'6_|٘S8TZ;<'[Dž@0.Tzxҕ; fzC4|@iaḷu I|Ex, j%8PiNf5aQF~J9[kפG nlJ;*CƆMH_B gcNGh5a+y.&l]V*>W{ A9~NE k_[f# %J3+=7~m|,ߜ Rzl}]&RYxl#^ C7΢Km_^fr҅KMg >>foU4e3[g%性Wv0F7}v}ރND}Tfi -Qq;sQ@U't.D>Qҗ&QjϣZzkFl:)cs[o9t ~u_w(7P}7->gfUڞɄ4RSQ~:BR#<*$n96B:DST/  *t Ћh$:Y!ǝ@4 q4 ؊>_q3tsh} w*JN~pѝx,NZr @"9 ^9h9 %T),v#ȋ>ŀUSqR Ȧ*;#z8& C]UJ,q]ODa+Ns-`C!`(vP2AߣiM3~o+@5OaDMIp+؋}Gs7UpFb}kԥLo?kww]V^UjnXgTـRϞݞci%_8sLDNji jJF{fN5*gB^5xA\r~@9P1K+Hʱtqډ Bx2XP ,[ W PQN;Su 0Hطz Y*uם;r} ܉z},4{2+M㿯 )듟c?z:Q=<^ hT5[y`~T{Wt4B>최YzY-8ХqynܩXFk&:hǫ٧ס'%4XٷX<%߆9`B 8~|cHYǺ{ Z]h]% 4+@B `lb}](B;>xh1gDjE4'{`#G+)'Gb[wVGS xzs@U* svl=!+% hyeրkRA_xC6Uo#L, w; c#2F :4IB3zÇtAЖDJ燄¥=[ {aUXNew8MU&bm8sti wAyW9 0ykd>{uKOIO`ev.,C1&+=Q +b)up&V,K;$3-.(|VL sۣV&Tҍ|p-pM&vSb04eҕGcx%[IN\KO CQS}v ˳M x& Zq)Z39{?z^ݔƅ2_ӟ5KsKSG[o{?}cj ~qAwWd)-0k{`Qa1FE,PNZovw2#dvyO5G ޘ#;k0K?PYnΘ,;K?릟֌ͱ#Law:r 6F{!x@d*KѲ1bQ2 6vw$cQp#| m,f5}81" lE; {WδœjF骗^,d3(U|tp#HA{S3+Bu3.gPLqŠ?ƜXawuS/ުu>Șu^ . ߆O|k,ÍuDUd|Io+ϒw=23niVT ;&^X tAKql4b#}B7߅~nKE3ʮ.͢::}]ZBW{1a! nidM}-Zj?ӭ;&=&ЕI!'0{~rU'~pאf,&6Ì2,5@`Ȍ1H e`JDH@c gr@L60JC6թc\o+ka$/zWWE^zKgCJs36L1*6hc,u."jŇfԴѴWZ\ 3~ux'\4廳3Z\%*iYyBCOu MbJiٝҥ ,0˗( `3uL CiOEq0kRT(`ZI_*?-T,,ىkbI⸴bJOݕx(#Uiq2$_|brB<<8TkaQjm?DVy.l:ti/ҽ<ȷ#B2ɑٰ|Ww_z0E7+KYdCD!Qv/JAqEԊΡ;iN_on5mZ]WY<_}M\iEozR:qm[ͧ,&}5ע֧by\<KҞTix=Z96<kK-˖>ԦcY[9Lgu0u+}Q~\*x7!q,"#4BP `5[jX:xۀ#~IbO7/}ij«ͫ6ܸ7;?{5FMȶAe0!E:Ð1MV;6i',E8vllc#$[dIT*μw~WJT@s{~a?O@euڦi.dM< AW\%ĉK890&S%ͫ_}H[bz:+6Xe&l5 on8_O+&;8 :Bj1lD/bRF1тeR\SeڴD|k6)XfW*X1Oү`(#_<^`ǀw`y!q H:DdK< ܉Ŭ`TKY0i_ƀڻ0i/&e50t Y`VubHt&Wڃ \9uWq8# ,Y]Haf|O\Q}7Egy֎MD젏')U#.5}F(GDz+Km3Yw8 uv $8FZR@F$ak̥El$QX{?mͤCZZͣ.B!7t&ci{}lЁ&&&k<-#hU)SYF/ď5: F_j}vVU!Y-Eum#ÝE5ٝ&,۵'$[xJlPQak^<+ J+SMM%}fdJ^F^Rv"X#w_+F89GS;>Me#?{Y=A g,̎']{4gN~twE1u9卯z~@R=S)uްVMp y#pn}o|u|ͧ?=vksG@Q1VQi\NuȞ4KYH4BT&b{@{ng'g+h</n~~ݹCU ֔nk 7Dlm߇w`l" ,iZ0WX;Y|ՙCoo9%ƀT+pI ^]qľnc3`Ɓg=t86e7jj?fیI {1طc1@q{RD+vs&la/1YbgyU_}u.EiYңHp6 }Dԑ($Y rfTVW\D8TKpP Jx:T'b+XD a &gZ`b9P"*9,y$M1'Am8aJT3mY _`bv?ȑ堞7x^TS1K+ ժWwDYXc4+w6;rzz\@V( 3]͍lHWǫe\QM5;ڝ#ø[RR A 4 ,UK2^X5,%"S_BP!$BQeD<ۑ2YbQWMMzIS ]]QvL*P(Bmڥ]d pĄoJe=,̷3'߹fc^Nk>&gsg6,_~_!~G^b ~ (h:r 7JkKsz;`s\`b㵾; ;sm2{ǼbY7&Gjo?g47bQk~!-Ue\x~4Ja?mc\Tdn胺Dy}F{WC7A&![ ͘!c/X,n\jwT _dķex^0R(oäc4L+ p €#6(,v ]5 ܦimp~Ε mDs 0k; |81 Kv\ϑ< IDATa7]GS<=&{)Z9c$G0̰L$Y M̘YQ-]i;c:'0kg-\Cfs'1,%DV/0{JY#+w0)B~=ݫj;ɯQս}H5nlfCuh&8^fh"TY*l=RiuOld"6ONA.QxͦZVde}-{#?FQk0a,Z:WP?V>j_95v+sX;ce xk GܯTc}cz v ،` !bORO1`k+uo czb0 Nbk̥ߠ<(fZ{$^6*oMbR[mea<Lu')s֞h/*_AB`eó<3kd_rkՊsM_S@N^"8UjT, Nz0M(1E0 hzp{uIҙݑ0d4 roNF0oN#6nz ϊ5bʄoE*pӌQfXhRTwuDtr@ W+ R;ݖ/v!zTk 'Nyǯ{hlzIm}x-.Z7ۮZ_Rkp֐  "(LJ%۷T(zQ,ͣ@-ʜSIg^sjNu>EB;rAtT(Q\Wh:1b֔o4qdG^4R$F7 IB~uDݘ0 2ex !L Q^Ir-:DڿX8<)A0K\--7uW |Hsom_4?>ܒ# vJxɟ(uxQݼN9t$Gtش:I=ݜ=NNHHp נs1ښ 35f"Z'HJivx~ba*O#uWqՉEJq!9Dӯ\r^B|cހ1pߌV:~c0϶bboŤ *oY0T uFF1Ky!Cq=Ε8Xa`}c2=X6ԏcm$ŢpqᕖazsxD4?^` =męD1Ǥ{Yc_nðD<ϗJ%n0Q`t$V6,R11s}3,!B!]fLԀ=XG/ fJFmDq\ؙ/{VGV^2}-f?l}I3hS3S@*ZUgv=9NiiKA1sCKkWN{xp2/E?Rf.GBU\ћ݈&+:ħ hDC;pt.B,B}p}IRH¶(S2/@&B~ t ,h]R&-U)QV齤BʑF7Ky;+hӿϟz!wK/&>˛*-ERިG`ﶺϨR}qۚ&*}13ʌ375 ȤN„R6UvI .5juXmyS ǜK[]2B24m=◪RUH٪+{nC/Ӵ%%պuK*fQk 8 L}sݘFŠ6ƀF0XH%< plOCa\}pénbay W Ty=0 /jppFF06a@+Ǎ^ ýlo)ێ1i3i|Y|^q؞?_0 Q}(BJ!f^j>_l `|̍)%C45Oolҧ_oRRCr.y仼*W|@iWڏ}Vg7Lmٸl;⶧1WrMED:8@BEj%Xv2đ2laԭS-{: INF"CT, Bh]Tey&|ǏR/#OVȊ\<% jL&HjRK8Nf1~4wiI?|[=٪nΠLz7ˣwi56\vU&ίuN`*$JbYzQc0bdKz bϾ*sgb矤׈E!~按s2Q(IÜ "-V+]mMRdjq); ^L8 M:RO?a?<8&uuyM)' Dk%ANW[uڕ?(y7멕84[w~30B}zz)IT)ژ4#_}ri}%?2q| 2=(c@B5A'0ҊCi'܀B"v|Z Ta Ҿ3(,\aš>PV0;l:+F7g;,s[sZZclKR{P#LL [0VLXuFj} l C`PdeL3`@2hd? Az=n,3H E~ "0)T(q:ɠ«%k]⌵ЂI a@Fi!+)[WXlϧI/ 2 f~}B¤νH jCq#u0ݘb6qaf)9:]l5a]Zw'1͔?ٔGqxs/Z!a҈`/aƯ{mL*rc=Tz WMy չlW5a-$QFħGH|L*TrEot~\/~}֪rL6NWyG~Ae*ny<?BU"%.Jxь^,dDU!ȵ:NN]AnOJknĭUE\5fRu%~D}lI18l%jzlP"GR胻%{fXzd&WiZ[U SBU,,4pYQW+;Qa;]Sc=Oxym"wmG/Is#٨>tKG7tE^_t&1Hq9dto"-RTR{\v5OE]y^M< 8R^c)rϹO>޹뛻go94O-N?~ly䝹'[bS8%Xb Rõ11>=3=`P0 3lj2RAPAR CcG@::U,cE1UD6T6l^$Ylmwz8{5`e˶+B7?pC:_-X-+>p͗,Gi91U[L"v#&zRu`G2q[Y>m氶^P:&OM.ama#muct^I590_j9WT֦T] mLk߳M-`S ^})0H+,J2rx/M138Z1fns,S(OSokxZG(ˑ u3+jKZ`G@&p/FKYSa4oDÿK!/Wryx/3 #4y?B,Ywx"u&E^Z  NmTEVWuMoWGVv˥MC+TMjw3t2JD%8;."1-onKkdqլgI̻@2SE, ~Z+T&~Kȋp9E%HI%'^7ɑ"~AȱpN;H4ũ{=3iR#qVѷfu $(^? ϗ\wġ’*Nj $qntDVGN/VPD;_?Lo- ᦑIHˢA,S!$'G!С޺^,iZ.4R*/|P5E*|Eк 0Iuv5LopCRQ H6m>wY=yGE4t@\[n&n<E08v/k0 Jeº#"s)MkQ^8_fts-9y>H0Ih'GTBVr ._\>i1 Rnr&%:4?6~ jD/.]ޓ[nct>0?tVi?>΁VG Wgm-@u{,.zLl/"ȞKl~Xd-* :E$E?FuC"$0kBk̀܁<0&-og$+ ΕoLSAA^q?_WR:QR\"y"2 >^- xSLj1Mt ss7 ȳ̜< À4H1XM 0\s+Ca jP9f8U2=_Hݥ!!zBa<,x#`b@޿Wkw>t{G!Sn|ytg [Eo+DA4(˙3+zE 7;twk\،B܋gw_l:)3w5?c.i]N *HY -~Xm[[Fe-A8P^FgԣYTV'^ +ѴƔ2z}ڣAu|4;;{Ԃ?xu1!ixb8n~)%X =d}oD`UV5~2q;QmŬ/©MEj.qQh oKOt7c׋.yc:R Oz:')h2$,e^Ur5MfLF 5NkH-,&{Pd"7GyHEW'.~u'-O+|vxfq?'8=M0?JB}ؑk{9?y3nx͖F.?E9x-Ky ?޽P--, "khCQb|E&|D&#[ {D N^d"-J @[{GS>?WroY;UO15Y ER7 A=K IDATժ`P53\>B7Z4OPIpߣŖ7xup}?hB4'REۯy3:pL:q[NWKETܢZ#3QA΀ GqPk!ݔ08'RZoAeF, ˙p CH˧v 흷6ybbаǠ$g揨<{t_?w 7BwϞ12Ǿ p뙳>[;#\wr.-DŽlnSq!u@ ItK 3" aNBAa]]/ Ub-PŠ %}A}E5}Q.:ԝ\FIcDNN1q#0@1:w"oTʇ6mBmuO jLm.`AZ\=qM g^8?o<,wgneP>htKKs z$Mb5MZ0?@J \uLcJZ,#r<_?~ay䁩o<ɕs},NGs)?˕%`7`3Iqs7/-0H&ObȟcdNmko`/52e `5`LcN`m)?w v\= qS^Jd_73$@6kŌCKޏL ܲD.fVf>^2;Gk|f`F Zh܆/X_nW!5c ԛ0J $+\V;lmc8 x n{V ެח4HG*]d`sǤey}Rm\OrbL*L(4]*d] @ QR pI&27?&q7OٿH(u17,-y\N+ؿoNBtuI^vW7eDŽ~S<Ͻ[jzB6{ȁ1W둎)dP8F=wun̋jgxU ?:ڟgMoWwi< 7Ǟ޻ݖ6r\VPJiZWUޘl~'{CF[G9ƸtqTx_%ۅeLIt" O߈T!Iç[x[|smw9N`9ZTzjnƋvn^Z.*vTx|JT \By±B3=xUu]a;V#^YnڕF[9JPޔl&W{{aS?>:c g?g=5ʿ~*>@Ef뼸#sb~ "FXtjQ֥=#F^,b̆{?r+Qn:ѻ~m{h۷c[bZ}dXlrŀEu!8 Bf_k"؋O ;W,Y0:v 3a@Bws;mpjM-3R=@V0ð[߀J^;[i #gic>7zj7b0 .uA>wh|\i yn<#gA S[|UCJˈ[>qUʪX!~v-2³_;ӟ_3_$NT>Ƹb nEt\<¾Rڗ6:էHo|l~wl<>6$MQz<&IpȚaz&ΏddCe-ܾH}*ңΒnLS;&<`l4^Ss+[ѡꉦѣ$x^$wvPrBmٴ$,-ْYkbpEEDmM6qD֞<ЬUjI*ߑ; h"C |דlʶD7$E=H.r_WR :h9=:B?ouU4|:ZPoQcdP/K]3֨4EaRp|P9 ~TĔ2-8 Hu_,iN461Jxp랩55{ qǐnzLLAɯ3 P9#_=ͧp!O_߹#Ϭ0}k_6KBerg$ag@SJܒޒUа˧0\yRO3r\2*?/=)E(ug~cyЫyZW+:*<}0qLZjzCXF x|7'1Ef~I>oʀt ӂٺpN[ )70d[v\ dhN0ncT/eҶZR+62Mjb!!D(I!į !Ť678v qR1lϱ@ }Q̶]x6`㯻Xϰ,c^^^~, ɧ>57ӟ;-.׏=k{򺉬7^!h'+Ceasg^xz%EAXa"wdMγ'ޯ/֊u9s>-V7&CWl('K ӐQ5تWl2nyj譤2>gF{wb6;vz<|Dts4yZS;Ռ%߀ @j(gaEM=Z F„_DVu:rrڮT+־ss*c*q8X_$TΉtyM*cMpG8SBR<]@~hc>5R:R5 4F]HS -K%!9lǕ0sePRYn"`*G!~#:pf0k05lnqm3़#Hu"|No:ݼ4N}qO qǯ qv^+tەVѨ=Еyy_U ~:VʄV0!zK#9rUD]+i gkpK pr mf}34̐$k(Kk)WLdq t(0$0a:ob-H` L^2oƭ;0w< ({9^N[g=ڇ[gAI[bm*s+~͂_{v)Z@k#u5Y{54 dN\bNL+;1(Bf#(gs !> /rlIߏ!yY _ 0rU*_tw,ܬ|̡N_;+"ӡ S3w{Ř]_0B|jhיJȁG>0,z9!2uj! Q Jm",B.@)WHY-kqC+nUS_yTT6lJh~JUNNZSEmyf[^Iqh\/,%+K<_} DjyX#W@ jh$7 q-Ly[b)M3}m +ü^fI#ںhI>S TB+25?& ATd^9rhZ\hB7#p3D4O⚉ҐCzy#?걔"@dl983ꢪ`!% B#xAxIY-QBs2aPà,&Tnh=tCUi6Ή+۶OK|wLcC?Y_f돺αNZb}YG>FE!J|cN<׊olt/Q7_@k 5 W-x~̀ZoUݪUis6LQPqC *g3_+,)hO7+a<3bvØ:& sc0F> ٩-i ^l]]Ny .TFGYb`^Ih ,l$w&d{ NňhBR|hC,ϧ2ٍ`9`6WA'QV <08bƼU6KG6=VpŢ.żGioڹt5vwG{mGkG:SȻ?bT8KˣC=or۲^Y2Gm@+$_\}٩t1x?{vqR4`wm?:0vԁ8#TQAV 5@&T2̨;9\\_U^M5?$F :Ff7*X\!đE<b[Py uhCQދƼ5y?#W ٤[mQS(yef˩ʁK#3k--|HyytWyn8~qr+f5gQsB',t~M0FѴBEgR !=H~T>3gIPj_9  Džs?C7O?_u+&y J: wbS lr[qޖ#FܸZe!qaTǴL}[54V,Y &2!%T[JBy+ !Ed7jZ3Gj=省t8ZJLL88=!<]'8Su P, m:!Uib$=.,0LS?)x#S~%2j=Au!PO*wQ- 12rݿ€TGÁ& x'̗zx7p G\Okj,V#3Ĕ`(+ERf`]H3)g:ˁv Em.*Gyd]:rttZͅ^{nz{Wz۩4m{y+뮩%3Fӛ{ZI¢SvfF" `!JQ2S.a\+b/dZJR|-EED") )BK¦)B L3C"mQHgBbz%&#q DYdE"ijYo>X*^SG/M #P9K֏33b I= >vX\~|~w~?y) !uK_IN%iRJd`wX赻wOY?O{·tn^y͹ڕq y!pyo!JUЄ RwH5"``N rjiFry~aI7Հ'nWz,N2/9jୖ~_q\Je9W%`QJ!g XBVPR̾(u9ikD^=JnNbޖ$&t|}Ay:q"3(=z.#(͙KO W:F@~(9]HO}xJK]ş>?7"j^~E0'dn[q&נ<- F=?_L1D!|dTP,O{$>vH|ΎndQ`mZ#6_TY7jG߻pb$L~BH(|  R  m]5m!h6kěL}cȪז3o[#[cTlmvOW5?q.^\,c'Q<6Z d"4_ݢabt9-$ ޫ!th-KgVCXgƮuvأO}2urm$Bih@)H EFi -t?Hu[mzR^&ZKZċutRDIh^$(R ta@ZUM%Rd vײUc/)4hbˇ0](S4RUڑ)>XhDvlPX` 49v"p6"N~ `]yx/i]+Z7Ϯ[]ԮtB6O{eDg'2ͯbUD1F';Hvc}O$"ե (KX1ml ^h*hYNrXݮ^#;n'u#GxԺd+&VP'zfGQ.AELߠYVP,B(SYaȋhS&oyk<*axIvt(yPޠ"E׵P iTN5mcT<ՔW2ֹگr+ʾR龟|~rŵh׫xՀ'TNcRۓ4ֳ@ќ@9sQU)N^ҝ- R5tzΕW{ @K>Ed@$7 G[#͏ yG鷏]44t[v|J2ׯ{z$]k{k8'wo٠g3'j 1F6q0hD@cb!l /sM zl[IiʅBs]0bD-u}#dMNkrin\\~*``%LG4dFF$p-M"XL\77z#V4k&99R$P_zK l];Y)lhh5a, !t[JT>h p TQC)QP-Rb( KA*4LhհBJ"6a׵B]srĀX.*f'HRkoql\t|\WᓌRWqWh/,f l(M%V:8ap7"|/ ,1Q: < 0hO_D}GiB >~t͢d21q`h{6Z/4&z4LWPS8YRI) ^r1p,N"5 ~B/a#{VDA({8DYNcv:-y ۧi]m'ukQb~ M5QVPэۉ6wt"ey]pE9T>=\j33PQ9:Kk);v+n(7f7MDF6 =][7(Jc/ʀ!zQ]@YPC~zFݒ =0*ΗYe3AUsT@\ (Jv^Ps/8yacQsxy0 :Θ\Mg69PN#i}N"_7|BDžczn}l:9V 鬟]9𾧎>y|7g1eT6Eֿ~L7oXh |K1ziT7p0FaY6@}".yI;!BeԐv)ް(5GD+v8ѫW ՞@, V9MWNEA_m&e<7do,LO8A\jT"xU~.{g'Euɽ>QԎėĵݲAlDAZXCٖ/B D#< PʡS:9^k'-}jٵ> [{Q9_Sn~|<  m{ħﴁwx_;2=ni2hrR`M:'ڰ^7 NjXfoZ1tfh:d9.8bJ nJCD8BbFN d ,S~ˋ9e}FsDꔗ$Q. Ʀl?̏KFiLMf0WtL|i]TpU- |``F-*&؁+2w``f=Q͹!S9oMufƮWKH=#IZňSM⒍7 V6=}6Wg]ENO;[@5k>cJŸzncBƄ!t$pX zjֿ"X|/cS.:&xb{ݳeG<3j$O ,!Qjۉ&\J@j#2'@Ph8RT 5$61$NhZmPXjqF;%F${$o]S{?f~HR)?z6ўo/|7K'0ml8/l8R+۲}F*J7'lm{mM&Q_7H٢>l07 ؝sU  <5Q]k.ٻьN¦-'G\Ц2|ȍ >uK]NbHzχc(0ΝUB}Wsw>n<oZ] APStK,_!hf\Ҋb5&"_n@YtZA}aY^\FoY_p(>ϣNVΓ8Z5c{"w@B]א5WV?<_1=gfGJ#MZ!EwN1 !BEߍ*ɳ]G=>~[QoHJӉgE@yW@ENщPݤZ:*¡/<@2oE/޼8S=YG.S~o }O&gkEcS䂼\ &?~hp0޵2rӛ^|}/xsC~|Njϼk@pNYXRHо(6R 7d4QܖB A Ҍ/KjPեfvU\{nFps`2?d:'g0LV|ٱ_Z$ :4Yv<|~0_]T5|o:c޸1ioscxrt{BUG'v>T7_NjC꺾k[N~Xr% gT ]E]BQtAD@zN:>!5Ma7% $&H0u۽eA&,ϔ3v( e5+%RWkg >ַ"9Ui3OHɤ l3샇p.$K^CcS&RܟI"*Ķi"$BKRtÆ2xGC49ל!JBa/m?Vl>VIz*J'P҉g+iJ )w˾*Dy΂Zm2{7+2ol>fMo^?;j>\e:w9`2Ed6ϭնp';$h.d W`gC5[\Rh3Sh3';?E s"Tn%%21i ,73ܥ"ٱrηn6ޭO6v8n^ ěV7)b9 ! !PTށ2$!D + !ESנc(H6PI9;FM Y\Cp!j tdCEE݃2Q¼+M s0mf7 c 8ɚe:%~nGW9;7Ǒn%y+5'mw؅[P"gB _$h"^c3#F6{QgdǾ5k[YQI f 4%Cy[̮96ZACe_6A{PkA'JGQ&FJ;rYx%Ei_l(x> /W g~}soOc@xGݖngt;3|iݼF/~nꁛkgߪ^ň"{8'O$. 0 v] z 5GډxJj4t#=Ol!W04thd/n JYˠ.A/]A6}vo!yTN>nxmϙi:VjI)4ϴcMD+3oBJ!$ BO O'%2Cj}MNu6'e`PkX ƇO?tmg0m?-͉UCµB÷”ID*K H-'%֤lMեf7K*}[1ZEvym}_:oEg6޴YLJu2dM}t2xm\Jʹ_j>@9~dw_NcyN3G^MTt~]w fKcB4"!Nu (h0A l!cseeH^lyX 1j:aBcp~|p_Aź>҅҇=ѧ+L=V(LEl/6 Emsi-[/ۭRyr2[`E#* CPKE6hmoZ+%?94h:1XidǨ!UA=2f0G.,{|yzk,F~tz[[1sAwjWZb ǝ-p/?l n= g/hdtr? Zk ٹߎ<*{[ya2x^8y }玙9TZXQx̟[x9ev+t>֡ũl=N65\1`:@! HZ@rl]Y8$50EHUP)%qGz^}%Xy.$q!$,0DA&8RW3ޥ Qԩս#Zoխ/ҵOLf۽0"$Ʀ AJFx#j[ih*z"< v}E/"KUͪ22ΗΑ&ޛIGTn'uKΣ&f箻'fկG6^^8ew}r~ߦ }T0fcIl TғzپR}Ѹ-#C^e|m-7 ]At.eܓ<7n_$I#ů6PNE7R0@xMMGr vfieyXEGG7kl9v:)&OE$h.7bnRsນ>Wbx'o&eܥ?y󏦩h0ı3(a(lNљ%)ss^f\9xEN b2ڷecnˮs `ܐ}?Z=ܓ *rxeX@\sC}u'sk1#N9 5/brFY7AԜC>,h՛PsR!zPEfS)O}>'y񾢹<'RJ̀@;~Ei Uq)oQ<Ԙ.}GMTQ rvAJ5@Ǻ(fE3?qԜ!D%(P!/\_rA.yG +nnO;]ëus!bˆ.*;AQ=޲JaӋ=Kk30bh"tʺXSƜ8 \t6K`\`Nty+NӬER c0 - snRaol]ҋ5o}ss"/`uW륱k϶ʥK뒾_Qu5>ǩ';zJZXNi,M$z eN}M\"E@fB&8$sn ,qK DجnMWQHad4mRANO`]ܣI__D~ys@ޯyko_SCx=hO=NY}?qC^o'~I`qzKIFxf,c:~t3 +qc[۲qc[Ʒ,43uGޱW36hzRt6kkof.Ps?%By\qAѩڟD>Xvh:T^s΋6xϱҿĂﺧzK>o8r%1U=C5I {_BZ3ᩕn .D1hQdGX]`WU5crd=Y)Y5hy`\@llSٔmu֯F)hjS(UY=W|?& ýK+Bѝw&(UMALSRZSZ!MVokjnքO:C$ uPU J9Ī"&+b`@zT:'IL\_)fj kԼ-IWچ 3ҪvrG/[K f GhFߒO[Q!M\A+ː&$`x' tGBem@"p ĭ<r6 /s#UXjnZ5c#qCq7:4W(<-k,gKf$3fj~>c>!~S٥}]ڣspfgrʣFk^tfSKɩXȡz/3{ezbHnna|=^|'KQxBls]kT}NF?CAy<.(E:O $C-vyeeu}xSsUWU4Ԁ`7NqxkML ^%I$szoFM\I*P BMU]SWթ:U]4 tO:>{x[g[X4ʣEOףHR:J}5GE;BMI]?D&ܕB}9ޢ2* O;m.VJ7O#)eU u/*m{s gr3|$[- u}Ng'nMe+v9lʙ'RGZ 7kZ+ Bt0d8Ci1LZsm~HS9m~OƪLv B4v1Rr:.yO)j2aTkVۊe fVuywj~ZQ.\}7ω^=[lOl~md>:3 Vj^em ͹&VJta1IZ7bYhFؖjI"U@d>yoȥ"j6CV8G+=">~=\ D icrUGƜ+ϤVd:9a"DNζLeD4 Rɬ ~-APGdAr![`(N]kxgdtAK =RF*5QC>OJMx]&iOkP(RE)_iu9Tס==,.>Bz_n[|IRKsm@*Dj!@z\u6{ҍI=]|_%mb!LݻP$Y '%L[{40&MR#>!sQ)XnA3mKc34c2'(AKX!^q*HDe8XP^QeݏK>MO+e$WC;8{ZV 4:s~(}.'WC̄Ah)"R+z~6q3"ϫ`_#Eө:#ęqK/ª4%+(y`oٖU ȵ fRr0ͤ[Od\oNW͵S2^gxwM[N[_)^cuAN_.t HI ݕ"g?36Kj~=@MZM:zzp[BŁ6 x}ҍ.axɐ BIi(3B#XFzP ݫD3kS<ns"xô|S Q5eS鱶hݮ~0$2}voAiӁJdT+hKUc$DIʮ2fRD1DIMr+)u8DŽw=t=TAfVcw-`D֦|H'1V<ݸ37xCjİM)B 9E :2];{|Ύy11o|]CAuk Cܚ59~S_:IpCmlsy#{Ѩ|nffԆk~u6wxy6P6ݴM믉+<676ec\Si*,d|mmBmmynvPe㜈œ|)iDjPAxTPλ,^%ys/Y +Gp%J.n!XE,&'Q5hI"9(aJ}Q7"N^/-U<=˥|^ \$B{P IDATc?ܽ j`'0+JT.%}'Qbs%H#IB\3ulT{KQMTdITK#ޟGP)x@-nu`Q-r\x*j !JK c,s(IÌoz&8H)]NKj2tT&Z "(Lx9*g#=2MɜSG3z/ǿ4JKbvps*l"M5:sY}f*L?'$"ň2fS4q6 =2[mۯ{F~"}O6=s[pؾƪCԻ׾m:i0+z;Ki~y[??6kDe6 ^_ZJ6sWFTؼ)weGYz6|)-t50x:6oBrvJŗb$7(2f2(y coUŲqm:< ʱB)("Qx(} 8l~%j!J/UWKdg5 5iG]&*܉>J%]3Qc_Q !fQJx{! iM_";Q sqn#BPc,D-X**zIf!4}TԤB|5֫6q3ELQH>F̥zRB/2^)ej?Y|qzv5?Y,SNi7_xhod`րa758_?a_l^nCFՅ_d6i-JBz*iC tHCTy$є=?U5yFI+2ZJFpԊh{.ܳStbnLs~vߦַO oE|y1]i{[@<$ 2eِӑ͐r[00"[YF6)Ѕ5-,AƬh:46t!."#;f8ĬXA ;! -%3Z9¶ym Yoztmp@og6b \q/ӘzfJ`OntA[xY :-&ynfb730)|\irLc[ +E"S%$E z2o/2W[TWL^S*O퐓oL42忽,9o(5rO 5R+<+!O>Q}`rUV5q!XXdn7[yJ-û2%2ͷ ;}\_c`Q̿=|U{r=}k&+'6?'7)μdz Կ;/n4әGG>fw_P[˪w~]Lp~6YYQ=llhR>)6'Dqˁ6O1j]'n 6[,dv)ed v/:rPY~-gxyʢ%5l ILi"EAWEp |4~{Yzq3] EОDM4&Ʈ&፨I,y1jރj"Q UW"IT1TDA׌;Gŕ-wUvOEP\_S>BؼyC҃(yEϓ4uMx攤vN{f}?_J;\ӕeW}^o! uhqi ;У=`d AsL{/AHՄAT.uT{uiBgA P`rZ549St7\sO4Yr::ۊܾ c7οnwlkr0[sMyYƢ'c<)uP|eB,b`LЂ bmΔ mt@VZ@[:GN1&0)>hXz !tƵ!A[6Hkh5%Py[v5]i@Aa392sFہck1Ŵу@` ^5 1ciu[XM@ӳ)1pɠ!Knq,%N'u< c1GTy@G9c5}O\x2Hfo'An4Yفh M[ܮVzvv3ڱPѫɣGDB"TG*4WtNJR[v_why#DaKGGOx'JM8ec)rFM 7XwFҷ*É5%x9EGasZ~S!b,j/V5\\Ťq98f999y)HKgK Pd"+PQELrޯ?=uSJB%Q@&?U)x[sB X] 1Pa X7_O[$3'~R']|!QsZtH)GA`tQ+m-Ȓg#)9Ef-Jq=e!GJCs#?6!vb)ܾ~R?rc5{WF}Nqt X 92b"kTqic2 !, YHyZح0< zAE&2#,1P*ZLYNg\TD}$- Αb?m ߷'ȭǺE;r4=7Kȿu;G\wV: Ӏ!HDg?0Fj.XI֛2aB:0m0IP6`^Y33,ӎ966FL ~AVҠvm9kn&r 2^FMX!Uou&}5kpų3? ={UZ8soā3Fhii鰦AU0R!)2J3ha>$C|Ory*tp M'8Ik I Q qlkGhE7N/|VG{s?>ougnt3>/g>9&`l9,gˡ.}י]KYJ{S; |mkM<{O-;ϛCkm 6e鮌֦7H\ cAiJˎ~{rGjbN8+m2ia#oFɳm/(|>4xXKɦWn;n˩bGcGs#DM>g8EuÛ /%iyäFNM:3mTzQTA3ɞJQGE$D"igR;C*5:@B["h@ E uFMD.u/x_v-!kI*pBKl+PimƑNjٹӊ92G"t0; !ʀbC G "(˜m#kg0 -{B/[4kmd(C7MP efhB401s(EđcsOoYGϭe5JD) ״Q] >iQw"ćl^LG Me c&ѫiL [BVl.S(ݥ|?j}7oL3ߢT`O;bXHBgf.s)B Q2$K:kۖL$1!'y?E hu$ ! JHXFGŁ$Z|N+boW2uu}~oﵝM)&Bn)+/ĭ:~'+ ]1qT)߯eD tErt2LќPnD t3_GH#kck5VSTmAe. TQ2!tpez2-sed찙3Wy1{>~qD={_yYcm<16Au/t[=`p!7U.X/kT#fHmw}Jݥl~DZyE?oAP;O=wg۝>I'y?z2I)sI9O~:/@b[#(2`$q%- (A QNbظxP;LϒRFB%'[yDkB9//"gZSLWKl JwE:Q,QM-:G ˠj"Q"IO$1B;}BWJlmfPw/U,qqXzYzrϗ&ŮrTxQ 㣌Z s56>2Nno5SK;*M_;:9ŨB+ؼ;&aFyv(kaWKϋa/O2Z0I!:5l=CiD rNdd 6NKeLRV`&a`KA95!g yvՕaWu΋WK®^>zź?ɲ}7|tNm%mXߋ5}蚚u8 ǎ44&,@1F4H!=I{cӞ'2;Z.T!)gsHk4 ao@p? G򫘒u{yxamIO86|;EY:V6Vlu{Sz䧇"Y@nm>wϮ?R^X^BNr{\-Q[{x/p5 : Ң (lisӶSNHTD`]ka5~ooO9_$(2F߉CHBO)}^+Imj>JYcISyZĂR~؋QBjEUs-*.V5:PTқh5Q'aRZ0D`'9* L$%eȘPcp5F'( !:QEp(އō%j/fPc8Y I,iRxyԹ@]4jl5%8Oi{9&HZw[05{SuͮHl>w}I{wwZ{#XYxmht1[upD8c!f HEZ@/ӌjR􈳙oS \fy ׭Ӣmqi DPЈtjN-+ddaz}oڪ_yMɇېXΨ~fo1vk- ]٠20ۈdˋk굥&F < Bki!IӤIvhO1yT䣉NT""d@h5;'\|қ%(l&y/x#`Yn9f,8.MrsVi#X6Agh#E fΔd^ X$)jd k#Z;MgZSER3N? rt3I~,@`Hߒ1$6*hFYcH^&v3c ~kmS_^͍(6eڝOc!9*~7&66_E L_Ib_kK+׭ı nڦͷӅգ3_Ž }^gfoJZF A :򷏡| | u[8u7b zl>Ubu1D\ހvB'wB$!xJ?uƿ^<  /Kis%N!eTQ7l 0(Q'+RnJTT}BEP9Yً"0OX)rSl!)CJYBEľ3PQVLoDFz(ћ,upNRO/A;NRW8T{[-"BT먱7(㽨au8,>O!J, |PͿq]۞eE#Y3zpA~cXnKy׺n[_oC*/9;\3sύ˾62(bňPIcI:sFN˩3ߪn8~:.W'c@6ɥڨ:3[A*38/fR_f xPgUњqoYjmXp_9>qeUSB<) ]Є8͚^`&8.[j!"Yotkn@pCtՐ^7Ӡ§`a5Z+MEI.#_N7p 1YTDUM'k#Ja7q؅MVLI̦HwLQ6`10s]UpIc JExL-:ZI.7G):A,݌+xY1GYKhFSrP[N)T|0=lEq3{מ>}4/Zy˼l`.>93 Qm $:`Jym9ZB8nu8um.;7fVVmoB3[{`!^" IDAT~O=!Jliɱm G$G_̹8`̂s;97Ilq;nxUc)EcGWG ը䗰Ї0͉ 0*KnfCE[QkPI$(Q|--BgQuCx?vToPkjEhi+CzOۉc&i޺t= -,㑿gh'S?7J[X>of/bQEuǏT1a!^)1@E7 !PfEBPcQq fZ%pX_N[r.^0P׺f[kPk.p >ٟcҗ'pk7lʎ}@5TʩD?!SC&-!qqf ҄E6F&r`t: E;camF2Bđ&űiڜ*m BIAdKGe~>w9wD݀^[Ra*f19mԢv^+jDKGv5#RVYEn0 QEbe(]uױ^XTa^ژNy8F24`NL3 V? SS{&h.luާK7,(WƎ@asg^qWFD(a䵞޽V?( {ʬ~ރzw_})(6w5bdpw]uI{8sg@BŦkFgyI붼u5Ҋl) o̡^u,`s.y=jam(x77<̊WN ;Rg"uQUGibd !,Ti9RJOV Z(G7iYgB- Nxq#}v6 }VRp}zuMd=N$'?m%ݓC̢qѓ_5~~BZv`)-lKC- fE|VHqPh*ԘC،HމZxَ"7WdQĿE9)-}Ř_(Q^`OLaj#(&\{] "XR\{M~IIJyi;m/nvC?=Vq )ϊd(DZL4C+{ө<+Zm yǟ͍ky{'kvuL>Wnuƨ5:5G1``"bRB#E$H3 @nQI Vd!zАfШ=7;vNƽQpá\l|v۱m}# gqweGqы pX^1Hnx]bGdVFoG3"ϟuw.4A]H+2y*ϡK-BFӰ9h޴fn}ܲ:[ k€2]~]N_Slqy.9@J&,P<":6ϲMai׫N r/Bck91]ȚJ!}8XHT%F mCE,. ML@pَHV& ,Dc'P1i{RVڎ{O7p/|8Ӗ,6(jCEG- pQ9. TBMW"ӜsRX1T"@AzK˜d!S(*ER,\s/ŝUYZ kKPauMr8s'/fOi[b;S p@k-k S-|”OJ")yy6^mw}'sJ /aE"]/jbn MyƞZ ]?rϚ0҆*fa  _*a CvhD C  jY$_o T/Fm5g~\tۺ^wHc2we9kwg/bY֥LՋtd*%@ ZD`LxڒHCkz!11Ff“ŤQ t,!d@"; ZDöIyR]TE7 ;asd:Gz0h;^𰊱V;/B(=ۄ4uV.o _9f/xyl XiLA=֎N `,ώͶ?f~_L~y BsfȮjg=rnͶ?#\̭:|ﯻ [3uggLKm-Ⱦ7{i FaT%VDE.7m;ijIf"2#xn^$6~[mDD< ) 6;(l'6Fy+]AE(_uv4 PɳPN$0'(gXTjO%2PYU9c!bM}X,,8IDi{QL;ٵ^>HuT-%j*\?7~t*= fuTDb(B@EldF>T~B$5u:jOgR~=۾/QDJB[PpR"p*EŚ"΋_:|9ycv<{MW>^a(D BSд)%M@CWFÊ t/wKnv<<_͝f|'`3_R|\>Kׂ=NƢtgֈ*U6Э>W d 52wF |Y"BP@2zIE2LzA:𬕦-W F6:IM\t~zFX_t6Y-K4}l %pY [,.$ev͐`I(uRHTokv MHD-i5x&{ *6fTSX!݌op/Y䊇I t;G>a ]Lj3Z0S2;bdwHO3Z--RûjL?Ǣ^%lhR fpOU3ʲ;u&R::) MXϑ+4h%\! Hm?sQGLJҲE3`zl EmsTTn4o׋OC'bs:AD<:07vɋM3lG̎5`s맬ңs'ovS2׎t/ݞ;ozQ8f4W;u5{iއթщ!'j/.|;[X(u[~[ <rݖ_б.H~\QC:#D;dt̓(n|Ύ^sګ,.JA݊PشIuPQn!)M0.萨QSC'Q,*bb3Ϟ̒j1{PΣ8!ᔤrq(ѹ":ODgaP/ȒB7$eS^TE7`jjُS#u[ 0|TzZ\;~x(k? Q uoPNy95?>o1}(e!ewQ/MD}RքE꓉PG&󳧎gQ/:=紝6n1^gՃCZC3 HcH'u i bZi hef(jsz]Zc QeZku;+-oۛ;{.2>ZcaV6Ҵ"E%!5Y"xg !u,Ņ_Z'1-6AFy'%Txg^2GzsYa7^9ش}^DI% _1D+RPVV@S@U"!☃]>LD{u]߽OzA$DE`UDɖ%*).q8Ě$4'ȟ;آI)YV%(P$F={x@@H&X9Yo7hXrmaq qc_M0)LL=R%k'1=F3 $IMGXZ\D5# 9M{lG)!d%k'MU>G`d~S,<Q8Y!&r۬ga8L@F{U%ՒĞäU 2'DT*i˧hY|xQ]2"d8dX!QVLaɜ;x;qn?S~KA?/BorM_E]3p!h5BzQ_SJ}Mt)=RO !ވ>;DGP3,ξ^kk.uѯU<ע|dmeY&Dsա'0%yt_ m7#KQT#Vnt|5񺁶Sc7tam2:etNp7”x /ͳñ7Z'64aFzO\ovM#}|GSs\{B/æU*6/yS@ B&6%|V{,BT]cŕPeB\0#ˉb L-#ڎѓKw̾od=}n',VJf![2XcXQ$&e!T| !M]0 TʖC ACI X֑" 'l|#'qW`c{-n@`CTvL jlE fš`2K|x g`b{ɍL1Peش%DF5dL-F5FaJ$\rXhA!M KgVQ7a%7IDm&G46S$^2?$8UV=۟`%`y8b)C.v+ 1T@j T2`X1uk IDATmgŖ؉!*xPlqvbTGض(,*B^/o%v;CDQ9KE,>NQC4G,Q&EILg<2;Sg8v|nx^NuY:攗gQQ6~~$k޺xdf58y'fXRtsͭێuַ>hE:Ħm7LُkPĊ|\f7߽%|C⬩#t7NaBHuݒ$biƂrj04foGf0*7s; p(쓉 cJ{l|~1N-w=4:vT?s.$yx/ڣYiޏq$<%ݼ!բג\` w?AWMu!Yks_n}_AEM)=-t~th{}=:XN|FtڨѣM"k^^>&%kKktt_)YEޢ'2syԭe|jW0e4ﯠ#m'2ẨB^F&oDbzޞ wDN+KMlr{勹YR =_=:BV+)x߂~]G)ЀyD)!ލ~>kqKIw*j1rUP͸j$lR:RI0] +Qt"ӌ;pVjǔ/n7ݛdz̾ϛ@糏t} O dh6[9fbekf[:0 r248#"*Bc52 Q"Kْ>"1cKm='I¼ejD6A a՗:$ 8d[&aHj2XAc, LXG(´H%A6+@59-(_5i,8m@#Ǯ)(/q1e& d [D9T}) UJط10{n'Q*6۶nc"Iqr }(A8i6(Q8B yV"$BTfQ6-<6r[mhFb0&M9Uǫur3`ov7N|6h˜E7X$N@&ZebKWN-6(l;>0r˳0~QN%Ԯ=Sה܉܆ M&Ss2fAO0ƔQ_uܼ5FśeE:WmɮՏ'WĮ 6q薁/AeH=ku}h¿sF_#|mIf@g\{ηE7g_.QD~Xb m. Kw&86gۺߥQ4{խ IЇT6QF@GE&sD]u_ S_ko))j 5Cf0p6]oZOZh,^j*P|кϳGߠWӷ&"h6pRgZꦗ`Cί:Fچ^9:9N0K؍0s(Л#$uqB\1S RB<[gBF \WJBw+RS=rU.WRՠ[c$ ^ +ur3`Pe:(%@u+42\w @:`w[&s3ۂ}_8ZnNU]7r#av:eIV#w6ϐ q4}H&Pp)Cپn [5vȉ%Ol:3KjQVʐd`},VFC\1#{vTuwѺ~mhWZRɋWn ~^ ňF .a[^brmF)5w,C/5ט\ƭA|WmE SPX0SR-0=V4ּ!ߥR0Ϲ}y$T۵ǾJxs4uzt߿e}8N&M+ݔm;Ϣ5cuc;뵌>+!M<3v$}i&EaJ,âlSzrvI#Em4%J<n;wxE97Zu3|F껮jV[Zڒ X!LA<-;(UĮϖ;/OAdU4_~e{YhY"$MBC!<؄ bxe+$ -4L$D}"6G[Oˏ^bwd_{j=_pYKLsbf r-c4_M䞢Ws42ڦ{%2αƿxa?VWSlw/|_-'w9 桑̛:irw;S[) [hb #^܀lbG6 anl{4dzo[ʍ>B$(ðmT̘$t,@]m4_^W_A/~t -q(ӳzQ?p?pL؆8Ɔ6B>6Hp|aH$j{WJ^v>οN/b rIm׹4j6bRZ@Fۥ-'4ԥ onB<̜~'@N>} EBgє/v)t}U3؏ݱ)4pF6i)f:hǘ6qm'k'm} <KkIF@Qz]&+=O߈s@+ tDͼ2Iӫm Р/ua6^D}Razb= N :]Lo 0V'RaTJ5饧?<4jC3rU7cwEQkB1װf#4zʤ/cR@+`yjw`:W2IC`~ 8/[Gn>16T|l(Y߾i.{mh>㮯䌍ay5jU:VKn-Nxaq~IdK5h4+#h !H)X#I>7 )݆$UWMшHmá"N-i&IFX6lRB:n=Q`OtJZtÚ Z~{*2of|dmmJs(kԓ3dpj֜41*"V9tQA*di fA8En_!^,Q]nڐ۔0LvH`dLji[++ Ll9%e#<2bzjkU"hdB2iBg:,o..qbi ypM[ǰ1f%NGɻPJA|;|;coiyJm [ 䛄kYVLAI ;V7~xB`t2r˼sQtfl1,;MnXP$K3J:6|Lo:>ӰPdw,W>usjWxxﹼ4xx!Zm;<]QzgT7}G\},6ІܝhR!(^O#'(#gI|!zN.tzw7(V`pN7܌66hEX]KW0Ћ୭M45MKe]]~\tSvzu^-\p6:z-t{m9v@|.@<o_!΍hu[24t2MWl=G3hH ݺb]RѮT1Yhvl}w,5gB(f?wB_~LϣutgGBc!twZ)]h_U#3rU='N'g\K&QLGCA hv:_Kuj濝oe<m4ypwg'~;n~ joO}iyM{õ[S'աHnU/1i쯌}oUGrv6\Je{${5ZB/#y;_]L$N[*:j :ϻ `L1zUnj;4 5%h(FCVtw,סd lA2}xiDСFR@f1mGŵ:=ٮۗMKAI)yHfr#̵׻=7N"AtK VJB 0*F' )FzR*eߝk^T9ѻ,0tbAi(=v f?5!7>cK'^wmv~^de7ۙgf<'[IG4n;b}+/sS4o}5aw$3 S\Q ;}YqfX2ob [KzY#aɬʛgȖ'ʧ%|=,U*~cU -â7,DyV cLpFN$ۉ `Zm'b\bHώnL1Z犍>t#09KV>U^;M(61o#ԌY Gصg[r]+VJ)#]Chg*10wHZȮS'؛0]CۖB!D)m85@*Tr?kI!2'ݱadj^ԕ|{x g7%\DgkCc^܇.Х*U7OAnt,?E"~#"FÇ?AiMB@sg. B 饻-ww ]FsB6nFMn@G7)"Hs9K#~istQ襶:j@qwl-z) Irn.RemKZy=;hM4ѩ!yn-^7z'g^&]:^(B̢Sst_FE=?)~/~bD=%ER.x =?V%/@V`ԾEcaCqi;!Hg~ )6)O- ÿڗ$>>X=?‚g zoMO|=q~HY9 _ZY7|mTdB%Z9tXS-ZJrtfu'2Vx.Y(-U#Q}y-c3p*VgyQ6l(DX2Hl)dGx$cMsfkl+j"Td\O|ȀGw$aHOaTlw Ϫi*Go#F;?+{Mt3tp?>#L <ScQvXpslݱiZޔ>)[}9\61͌X䒰cّiRR2sj4Rd Ag%ɰڜ2+BI4pTAi'X- RPw>^J"q#NjuŚlѰv$4iމV8|"&UpUȷs]o~dcSZg0_a)S27)r-3YػhnSMmeGa-\ؓҧ9e>$ XDqSBV9F'!trX9$ǎ'9yMu$~<'j+,rmr¶Lk$J!2'Mf fGE+rarR`Cr`XMULސ_1Sj`ob?n괳sz#oB,?}j+}w~ZU1g &|3YUtDun·I[{*Hj}ȏ< v(T 0"R>phwSST-01‡y7:cw ~ԮG>߯n~m߿R c蒘usmN@וGh#uU~@`b .th.B%Z))&R.f !rp:8<CH V ]EO0 {,)[ #MwMIj8^Dѣ襧}1$ikA/|$5>PhbZH:j@1WLkG_ѵqgIni~E"l\i@;_: S' хX~\zD)sƋ\q.]#i:y~'R*Ӌe˝$GuZk0B[b{HҴ2 3=d2F+ԉ21XU*֐K)ǭFTy#{59ΣĦDRћVT1XQyjSӧ}JBbJAT&[Wd6?";m8;x1 zsj^ dveFFxVk&j;HxsoFuP̮"$2}N 3N_9;"U#$MIJ\JUJP'?/_Ȉx79{7N˟ڰ~}PF俈gblWguY9F\Wݾk3[WmƵo{SY[ĥ|sܬYsyhsDY~)%.&IB"THE$$6ywX%wepBH=(LwXs׭ ?`ʢ!eG|z4-*J4 =$iP-_z:1WQ8D,D7䉩\'\!Ƅ;:fb%ۖ %By":8yciym-3dMH*'Xcd/[c7 [LD]XZt;x:Ynb7ێu9|zX)+5V"_ #3y) &fg0yC;W(5s.n>)ujF̨ZRbP-NЕO M7Йb(5D왈(U#{Q*a`86. !nvp? m=۽=oOBB_X_}5G^{4,#= 3 PIJ$ihƮFfTqAf:*0=e0$ i\+G9AtRR>i4~5[q;H_JK{N~v>&~{kWЂm/{h|7:pmrG/y]"RjI:-l9ЎChzR'gZt N&6 tU1cM 4bЊf Tü,3 q"_LY=cF3UM,btʭ6Ni [IqVa2!lmņ[rAzh|;}U^RBlUN_*Б zcM_]t:It4߃&[8ow8>!a`;maJM!y1Z T$8nbd9_GK vV'' X h}sk}rꡍ$ߡ5m~}!d}E 8^sDk~捵4\sgM=vCFY~ue{٨Vo

L!8J}_}N0s+n¶n40M֟|kfkHDC#m;pT&r5]QW/GAX<g>wFt}Jt ܛj9Gλ!L<\Ͼ!={ Pٽpe+ff"R_ j YԔisBXv "q(];<b3lb(Uɤ;"Td݄l_r}} Ί}6Ҫ;m + VD/Up"gHyXqF,*7.< $*!,A@e(qXH1w󜢏!Oߑ"hv6zD^ɗjFZڙ ݏ^w;#bKGn VvݵYŮ[y?ͻyA:k?Ʒy*l["=t4!|#`8o?d<">\@,?!Qgή=pu24E䩇W5Et w)nGnl>y~PL#aA2vgy"]Xd"}6+z~pfOl滴G+EkMD H> Sz A; h,Nt+ý6HG!@|3 UE}[Te鈝 q+maz#v' 2SjkÐf_@2|Dk\l紴;6H7fF36VVtEE9ԝ3lS;/&tMF3Zp~D"]}K%a(;yHN@xxɘ m}rk{'1lc 7vs 3EZг0;ވ#d` lVo>A3[}x;{U9< UoFwag|.;E~n< pmj"3 C(W ̷OZ~&X[Q3w̅HF"Q&vh(K&u7P3PNDT9%t5CFq,TZy`vR5vއY9/*6)]-vWKX,Š 'X}_aXAv/@. A[& p2y '! -nvO9lov7BĶcWd MDZ\JmDNH5Q(}b f w[{~p;.6 bo"/g:J9R\Lī򌵲9͑=8v=R-@$mzy/6OE7xqG "c :>w).g? \_9lJQԫm,~O2(nǠ7eGlSMdxe!~=Qdzll Ex~pݛ}^Ehgs#Qvs-JN&ⓚV,B}Հ P!nn)4VSӺ6\E8/"+ mC^e7Xq_Azk-в2zX,hh"nY0=?pCpwdګ)6?{|} L?qB:E9eLE){C +7,dsK)2Cɮ}z?AjdoCw߇R*zB?@@{9a$['ukDuoB)HU5g7auBW`"u'xn~yG}1kw0FўZ!.@qNMHYwFQwY])`s~g{DX=*k-(#vV9znG$ZW"XSl>N6/µB;2DZx@ yנgx4R;ǭ/EFsb[&ki_ĞY_{H/}{:y=rUfysѻ4"Tѻ2lFΛsy)DX;\tl|[OBjE&DqIHGݎB?'̾]I!O~\t D:>F-¿vkCƵE"Yߋ>o˲kUn6KP yҗO9wHTRw+mn$ؘTt"V1$¡.NC=a+E>"lfw6ºJs-M>%LRːGp{0oC,l6Rbv  0m@Z:)❀#ZF pGmȫx#"#/P e`}+G`;R;WU){g&gu3lC|!H k,ZxhS(-NHAA ! qfwn3pp+}ݙ9~?OOYV(`]KމHX6#JB_jsCɈ-"A=Kn{sh'迋,nm_q.xٸ1+m޴ B X FZ5Pm݊ax ¿="sPt pqbj]d}qŝdx2׮M|1?RXg^ %K>ˈ*&OIׅ@]|g%Lψ'ӭdoYȺ,@( %YwAJ%b?C8׀nioHtp}6“Z!ΪB{W!\_\9kp6bnGlm >/DBGh,GƓT"6 7Xzt^@^yu4oonjmCi|3eGeA IDATsWE7CvwtL>!FAg<&toQlkbn#rgw~b`ːZ4Gg.͐ ITpu]n&thi3HkW,b{്q$ \@+Ҁ|\8wCjDN@sYP捰~e? !b7H`BDhcpz#Mߎ%wϺ }{mlD[8{~=q6ˎ tRbD!g?NA ^!biEm=&E*[e2"~̓m]g#" Zi:Y 2a HZ bƙeڂk ذ7x ;Yn:\ڏz'#kN9ŠWr"Z.b:[;[ܝ5:6aU?Ah\zww&ޅOъ-tg#gMCDCt\}sݐ^=-p1ѡ(Dջy7#%nB+޻'ޅ3 ( h=P<'~>Yy$R팬`!ki6;sg^ڗ/p)7.kѣ2~Qlӥ7ouEeO9>P$nʫ ;//}Š˯'ƟN]ׄnY}LNO٥ykٰ+-MsUȫwʩR\Ondy#AarEK"0@ B; *1qKa!>\Ⱥ"\ADb>I3Vg7$4 v!Z#H1# |J61 ]Xg"bF~Hr!wka+6ށ:"#"{'k ͵Dn*1k5tlrL&Tjy\`\l,J"FI`߇@}**{#Mb`}Gh ; 8|DAYǣ}w0"߅ʫ;@nlm97unvJ#3]DPm|֊5.npafp87;0s&>oݰH_LZ=ylozy8DM6GN8UʿaHS{8}~8b9<GK?fG 2}9v);'S=nCL^1D3_DmtAJػ#qϟBVeC" +A;a#wRx&8t8܃;q=^ MS6"u(r<%=O'.4w޵g&33a4]Qlks1%Je2i%R& DB߁p.٭6z )u{:-y]#PJ["ٓ}$8^/ZC_w-X4(^)BV6n ogvD{i$FmS(7Ȳfu6-lgIRP6g{CYɲeT"v_<ѭ͐ELHiTI'lMosyMyq JKWX_ē`{]PѾyxhs۟p߶s_\t~q~nKnHN>pRKUFe1Mȉ'N%bW!b1XĀg{ P5b^D\Ѕ2 &EiǻN+VVmx[G|  !D=%bn4ҹT!_#&{"뿋9޽ w~ٳn+mӈ@B!!|1+CLC=BDµ5fsO\h<,/Wa6l6iCH-1Y 6sq*"Gӄ*KKZYD[2$(h݉ZQnѢLqȖw6d3 $,x>=O.͉T"^f[O"<}hc^2$=h^pH0;XZ#.DlOS R~gD'mY|GNgnƈ%mݷԶVC+!3$/ / S]֓cɓ? aNϲ|%`}*ODĮsy`@*Og f[!('4./lDd"*,mW t85,DWI_Z7^j|:B5KtGY^-AZ YHp=.D@]̎X}c:B$Ƈ< |g}1VHc4ˏ#+YiGCݾ4q#R[)Dl\<i&FB D}m|,R@Q|r4fu1VO"$,M9-"x"- 6m'ӥ)]]L#)sڈ0Ed nGtaq9Rxd$`,û[{#ad‚8´HP9a<#ǝ^xaќ] 'UZ@..[S4wm{]6~ 헥V\om@IֿH@Z zYs9zm#3@6L?jiF>m:$L_a X=E4bē+:1)*FǓױ-(얹e;'!J*W'_PёmhǮttZgۭa-^,Uwzm*vMJ3<__ēwH J^'=9 ێ.l1g# +"a}O#瀺ˋ%GO@$l8"ؔ &x$L#|i;c#EB`rpgG~5"8!mFl$<D˜\H{1:"𮱺zΥ#@/kog A4e@x2WjlǻغlL;u(' -c=}>6h qG-s[YZ11MðiCOm] ߛ"-dy4Q[GEv'1J>9?d:4<|~r) C [D28 g%ؐ`h-Ȏ fّM^wUtJIJ}wǭUs8 )t+v GLV>W< Fgi=:y^Eڻ?;*E71FDڭu6#Z CX,mQG83%cs+κB tdalD֣iql#67Y!:lADK\>$>dk0t"R:+Jdn/B*z" - Q\ RG{)$slAxh n]>o%ljs(ʶd_h@iu/EnLm7o|ߺ1A@۳er9vFJKr-ApwiyٕED)8LH%bK>~ gލFښ;oPwd2PKkUWoO>Z.;,]ַ/k m}3C4dZALH34?% "J"B9͝#9@oH$$m,G<:#gNsqcm&$L9p Fǁ GF>qE?]un~}t[W "d8KW `$;#׌"Wy KmNbD^duakz[O6/6ui7p] mm @يw|U݋ G_8w _hhkr>@|Z f_d}A6{ԏ.kуٶa_Aʃ:`` #[C}dGvrJ<1sUt.!^ڟNaJa`1R5"G͛"%XMDNBZw%nCάLDuh8r:LO ,2o|/E8~ahD4 D{.w/F}>©x" oDʴF>nu! w$l[".hl+/Pom"a4c69 Bjw_{. #>@Bs} R-1CVaƯDx_M6`9CI?oQ?}cҥ܏;JZtf! K,GGM'Ҟ ϟvmՈg%]O?"!ѰmzRz6Lo=|yR+̭Dڶ\^5Tbsx7/]ַ/}|vh"ns|E<~ ϹX!Mp>"j!.E>~ *|!\G"픻[d!!Dx2<" 7akGn[=!&p}rT|0S}{ xVX[BxKhDHsGD7Uq~'-lBl/k+b}?qYX[/#̓Hq!QBB޳pc,d BY&x2}HP-WZ^̵9uIJ񃬣@ϵCC .`@C?. 6gíͭy)KWYXФ|b~\OgISW|v޳ppYBΫ} VPߦu-Uym$ȴ!$,m},LJ\bȺʏ2t?Bf=bnĬ ;X[G sb0b;'0E`g7ٻ!kXd5{art є2d>0سg"6f-Bߌ]KW|}`syXY"٫H ƓT"ֈu\) a5|8/*Ӗd7Ȇ"O!>_[ Omu _dzMb֪Y@[*RoB.ӣ<}k*g>˿S!;í{ė|w+L,d: D `)>I. J54O*D*5|c $p0vD`ӈIp |xdk"G`F,cmAs}&Mgk>-#pKi MU6wgXm%%Kۋ_Cx>E)p 폢ep(D/{47ecVGM۔qP^.ͶmFc^?ukBмun)3OKtV=_rcX9fWHX=RCHAf): afo[).O:= iջ' ܢ#|^g[!Ąo`}ʆu5>K2aK6Aַ{}C/D &xՂ{O8eٖoH1WQiD+ָ#E~<[9γ~mnBU6"= Zߖ#< Y#h_+(XۘABh6W!N ҁZVO$ldmcmtoΉ>2^lL9-91xmm-(m)Hn=}ly_*L"LAtiJ<~`T"…l>ˀr]a7 =N%b_T7-g`&UN3\͏>uoW,X߲FS%LW bQKw "; s "Mȁ!D3,%xE|"HYd!-HpY{! QYVBqE:.>!D Es.f9G6i:4x1P\>*D4#6_γq/LE„LU wAs=? T'9>wql~CSIfGWl~]Dg!: Znβq9P?sbD o/I{ X+gr 1;-Ery{"#>5}[uof 3[3f-81mqV-欑laL%Sڐ;l '\*+ISΘ<}|W2c-\ v] $.jZĬmgϵۻun噦pNku!Qdu]YpoT"ʄFBɯwU"Lpn7H߹V"Eh]|pAWSr9?[ƯF ΁vn_Du?3 %+h?坐5!@5!"> 2P[H@hwF-Q^,ܹg*5L߆X{ /!t(>HlJ菿{\.JĞ67Ũ7)Z$yO_Yʧ#!e4p|[" IDAT 1m'ڸ0 )\~ƕhvGqr>*W6lH[vw Y]jXm5щ֬@ŷau= rcpW>8e/,D,O@7ۮtdrh+}GT9n)4gh<} 0,HE-З?UAY@@-sACg-1َ)sD!\ Ո0i_ڑHĠ_u`#w >Kȵx9!nб^$ɵ7 MXr) r#8 [n\"Apq1PI4bKl dsiG|9l#P^d~ /wsUDBt_R\ r\T᭏YO[TPd칌[Hk} 8Z]`߅n'CJ=D FywGuDD.qfڠ#!Hs֫"y bZ2ʤʩ*8rjۤʩON:MJ o!4'1!9 6@bs$LUY?]ʕzݾ8HF\A{)dc\U1grKS-E(Ey?=TZ~gEz/nM>B4kJsu<>JN@{c>HѱZn%N2wR}H "gUwgrJx%L:eq\ ׾RWq. Sc\x2S.E`= +1J]dAs>H qH蚈@#WW`\  5Z;[=oa˫4v_:FZy}LhmnьܖPyâhy#Zs mcRW:DDH1B壻8K!*Ešs}tyHSzשנ O`s=  7LDJyAp(XhD*Wں\ھ6gٺldkyvwjHq$8 s1m))XveK[78wSgw=O(_aT4YMl]S\Ӄ\ƒC@hE!\тx dm]K͇UG6>~lT?h7%x%|΍S =N+=pe|p(}&K}&$~pt++O$AeotC{sC$(s뢐nn)q#@:g!N}YÊWVhĄI?5bm(Z'{=MmMEt#ANU«ig -G)~YE"b|"!W;$nelB$l e6l56rn?DûNAtu{%6qB 3}S큙cо_hsvvh_uX.**8˕exP80R4ErpY*ᄄ"%O ߇ڎ;Z4K>;7yM_T = OW/)]D[;}V/Sسl#\ݦ&X{ mR?νdxDܮG5728Zb܍EnAڦvU5|Z2Y}F+Mւ^ܯ椼 WF 2ww߲,p5]f?C67.dD'2tg#zYhsUasA[lΜ\D4G""'$DmzX{a[ ||IXvp@$tѝېY`D WWBѽCV,PlfcemO@5_kn+^o#vtkY]_yzq N@n""pQ<-LIޔPfk-NՊgg<i~1F;ZQmHKd(rd[}芚d])1u$U<2LoU^N%bVݍUg )ϻY{87]ndmU; Cx},:%#;‡(yf3Luug3܀0%Y%*1{Y# uH#Hx^SrrSh9ߦ9:?ē"33'|jc|.yϟzm}쏰@Ȏ͉ ˩G "MʳEdT"V,g"_kX$"!W6q.rmz!Z8 hh Y۸7vKs9 #bفYH{ouB Dzde*B('@8gƹ5ZڛXuCLj ꐋo@squ6w!'Z:[O񟏘̓"Xnj,CmR~NDBb19{[F*ˋ'NADƱ,kۅBv@0Һ.B1$uX[kG wyb[k!b$'n> Y^r?Y#AnHnfpaǼ9@h0N9G[&9qip0xdL{yKANpqGS8R ;;K^,wwF$ƓEbú1A{8zG&ot}8¥J2S&>p-:G gBv1 qi•Ñ;T +_5dҺbB!HP(Ƨ{2yemG41-ñܿ@v!kͩ6FGOB4gkx 5 *r'2U1h c(Fz]_KOHX8~qdpaS@{kE(vi3!ù(ϴK![/{y׸ .6oa$ϲԼp+>'-YZp qDQj[ W " {,Gpמ97]>E;mޑpW*;黨L03Kvp6Ck$B.T9ux2}3}Q@o摈v_)8,R,Rbw7ζ6# {rRԧ"3a_dg,g*CW@YĘ:mɺlm0?1ݍԎ<c+"P V"PiʯAڦ㬮֏ZD 8k@1jA([f!9ߢ᦭Ba-m"iVsp+3qȅjĠ^kkP'`@mjC/@X9~?6O} 1A1KmޜhپH:"b_C_9h˂%3o*ݤ)e-l TE"vi׶ĵ]xH\oAX.sHXq a ;!yG̣Zd)8#PD{@H[}[wf? 6'`.\Jt v?HBy+~ \p{dyeRԦî{̼hǏsdsUb|TuC Փ?eRόMG#hFbNinDvJFk63JEP{~lv3pn;݈\޲>LC_sEs ma):W#aa|HPQHy}Ar;A_.9V7 =#3g id 2g.}#"i%įBӽK $ ϹAĴut9qj#!awc2q"dDxi<ε1@}AwS)rᛁ(:.@F#:QdErl;| @D'"Z7”YnD9] f9/! ?2dO&lΊs y< )nқXH88ᕣ1z<.4}_ kzqFڼGr+M G :| eugژVtc݄6#(bCxAo#ZHX@4h>PtdwDq$ baDY?w7iJskqaN)? ǹD>]e%ҳL6탃}tMg i./s'nM>~ʩ5w.+xXe!]OENCՆ\$lGDgPzfz! 𚅄L;/}{R^h~F~p"&'"4 2`T|s0(F 'i=hσ Y0:'lN.V`BځU{{oz[!<1N0uhvri[# bG֔֏+áH;9br\H: 9H8r- u [gKw"t"Ae6i~BjDܜ;zhD@9X4N!ryG~WklM31AN{9z12Z{d`x2}/I2+C2 hYnZBLt{Ν*#h)A+m ߷i x&*j|bƧ5"'3C21~|ֻGtzϹ:~fHu±2)$f 2tood^n Kv#FxD?6# ΰS a y̢~5"qHx)~"hC/Gv'#!#ƿ;+F"ugdGVH)Q,'bBG˺PImš06- O]n:qe7y+c?}(yY"~(|0B̟m,H"b:d+Gv煣$m십OmM7çȨFhu8'ZDm-.J֑xςhH!V@ #eYݏ&سX]NI^h`Y{#6/XNh?6!!˙V,!T"l2dz)`ʩ_T[rt/qH&C5_M}О㷩x+OF= w@bR-,Q<D5L7o%Ѧ^*EĬ'"έs#A'\ Œ.٨1F?$D[@2ԺdC("0{ 6DL }9q̰zScedH!}^j;Zۋz2ksQSmG^wG[bc:{U4 W kJc`k]!YdkSrkOC@V`ϯφZ]Č#asW )7F HXX5`7a[.nss3;nv=$!\oo"%@닶fm]0MlGhDBCH0 5>.׾h9!{g +.H\Bw<'Y౰o=ܐJĮ';"ltx2.?zv^)<] moSdJIJ_ MQ mrC~{_T=p'+~ϻ4.eg>t XQ;V0烆9/y 1%6o#{?軄,96#0FּI#\㺣1OY}s?l}lmw@ĵrA' \"Y}»]G8 6ǿIE$b1֦\*4 y 2$gߴ6vBhW!fb:\!(d-xGY$g{Y-+5׆Ooȫ˶f"SS IDAT|ZCtp|/Lk !-MMU]ЫdtWܒFϾ'bjYG ܞZf6[n?* '!7mndz69h͑7s9ևYn +&ѝnE?ͫƔYۜ, ֆK8_p2kk b/EXN2\Xx2R{nv#PHz̞[`kv)}%>`YBHX Ѣ? YYwZF..|z1b]Z"ݾAr!#?}V|DR$̺܌󐐞EEk BXl{<9dV8gG[}BɅ:o 2Ol [P0:ўh#^H0==,]zA*[4)0zRn~qs*Cl6>QwV gѾ6!bb=P4`Ʒ= Jj)܃2ZP F i#y۹Xl|Cj#jl -\{":e8# N`j_֟3c:px *A!x H8]ks""G4 _];ߋy6lX:_'1-=NwyqcrA2U5͋'g!ahWI [=pz둠].B [ZiH<:{ZA:>+NEXrYAx&eY#,͈8eӑ*D! /묝km~ǯw5 )Dp$F66/huospL{=E!bnX=qDgɮF>hk_ٹZnCgWk =о]WoraDOhhZ՟:ϴuh]g^9[&mW莬m=;.*/];Uh=DvoYk)δDX;+䶰UY]A(j'K̞cf8dR76&UN0kGW\jheEk! x ({2]/Y,~ JĖzYmkm̰vvGZ?! J'|F#rjgV1Yy XY sv1:HsUv"w DD\Ԣͭ:YXߎ{rS[A"Kn rnN*-f2#lޮ>L~swmZ}pP2&"B!M_$ Z?H/GXv"U9  PaߔJ$UODlS>bJ\xPҖ!zҞͳkl-3Arѵ~o"?jhDo"Mq"aZ72KwGQg=taNpm[=;UJ*9_=9}1_'#Lp&|D.FKu啗o{Gd֣܉cW]X(T"vEYY,*ˆeW>[ ~LD'j.?x$M'ӻ,:kz}T͆[]oŠ#"n-ݡIS 2`:׽fw'KB|-k?A C{'J"ܺa֟|{C8R܊֟LH1®iאBk%Y]#m>fqD@4HD#BBOE39P7$֨'xYGL},x>Hq¹)ɜBhsmm/6;oȥf|C1jX4Yݽ кO$\}ؙa}g9L*)\SK[s_7+nÖu+D#-3oVf.>gp̲|l*}Q=MZ*8}d =[.a/y+x2}y's;iwG`=Ho|^$ &=sٛ@i2OC o"-j;bk^jMHibޜo:6g["-Ht7L[v iٖ)@֋703`*v^C\wHM,q}_i@f ,Ѵyᠶq6|'Rؚx2=CtlTRffmCflުqpwzZ&p6m\gdDZu"Y} B5#aVP[ϼt=b267EHH{Fkil}]ԩKơ6'H腅VK}67,'' #*Z…O?;L^leS7!B( BGHQ&JQTFEF@ uK fNyއb(Ayyɹ\N9߻WKC"P3pRnkT.AIE~ 08tW0o}Nk|פV>کW. vDbGģaD0K6Bs~O?_Bօ8K"v.گ;Gb! e.0= %z-,-\Y7yI]fkO03Ӑn\ȞΫmN\$v͹ wHh[; >Y2K_h҉ 7XOAV:Oɒe7U,q?ALHd5ǭw0Vk(f 3|1]x4< K,-kBN܁H}>Z;Xb@E> pٽf=5*bw^O9 #o >)>'mܳ9emFa)< Sl hʅ^v~)I;̓SGG!6CWBK=_dm.vch.iN(o]KAަ5Ǎc"%+X\*uw>] S ).YLh<'۸ߟ0:l0NZԀerNfK m;_brmuOϢݜ!G!^LggduPKgU6}S\\3T,_i@v`y+fN6[lñ\"D?dA@BH,<1\d\,joz-)A8F$.GGVHsC%ۈ ̅.C[+wl;XKH,(Ѻ$Z`#aѪ.tabP̑]y"1QmAykӅIy R~jJ[l|+nuȅK4"&[MH,~:s7x4CnFl~1\Xu럻3'֖S}Fa.x4D$x1:u*mHxk1포Cֳw1:l>]mGÏ| 2TdL f)4 _ Zr}ҕ@}p9]/ +=o"a o f!SفhWHd}~YonT%/s|" ģ?#BdCBhZJw_;A3_Z\!,#`cˠ[{~\ yLjtn%?A8":ks~/E8t6 o< S??uzyD5dk^|X$S\fma4<^` }TG{G@0Le_d}aoM]u6P2v>eFHrw$3%-cd6 ow.-Xu,_?;Gs!~#ZCƟ:<򗣹܄pj{V\앁3Mɼ} [LKqz%KA r+,ːGgO&@we1B 6@(J3H{ȋζ`ϼ/ Yy+&m|*ٝ@aF$#Vdmi YQi?yb #ݍFE]⺮dy(bMuhu)i6o"e6FV6Gk"3Adsw&Y;+o USZ:۾? 16wC”:S jv[P(&om6"`2X̲0!P4}\ctl$xuytW/:MW}4S<1%"Zs_2,p[Y֞+@sw^oJZ(B71a =eOlw•@ad!O|ӌn ^v5[FE8ڊxAa:#ؘ{u4!%iA֟6d<c#,.D4_ΜpUN1ߔI@CeH)ge:jInvǚwO/nEYǥH9[ln2:OgY-63Zn? 6~둡k62!twF!o'>}?3gu#oF3݉uho@_H+ް&>&g(*شk |*Zogw[TA>T}KM]u GUGfUծmr62]|6(YDs鏘fAB\{rwB;J/b"P|Ԣ:.db&R@ ZuX$hEH9 !H|*vx S#J/Z5m nGE\k;"oסa4]ˤ91獖<_bĝH`(Dp#܇W><H BU7gZhi6|zeH9uil}鬓3,7EuFeȚmXۇڳCV RީE3;MC QC'2yE;Ro34(_r"?e~%YE1s.\A4YAh=˃BZy kǢ.^Db,$#vNe1ڣh)G*=aN })hFF R@@X@j"wۑ @<aǕ`Wo9Q`g#vFYHy^Fγ:cMR _Cѹ#6aho:K{o62r.m~ss %+㒩w>+L!lm"v}(ԸH]lh.wnz2<5"#z6k+O︼5܊ԭ(T;Hh5RlN4l!/>V3݉6. )x.nY7ݽgMh}yGfP+hxQ$<%]C{gM~op߹ǎiK$C=J$(ߛ1(Ȯ> F \eښZu!Num߻pmh]H,1)#ph4A 5o}B]xѾ_k4 (Ek1雭Ӑ1}() .P$ E ȅ\]ˑudis"61i e2etȷq6!~@{waqsSyLZ& a#HB]{u'731B#:Z=C.IC)yH9)OD=E K㽞Y >x4K-}_ҞA٥H8BVmPxqcR*W @]%F[mMSH{1HxZH`F tx*Z÷v0 ز&'K"2Ghyh/w*)qK嗷)Eoڧ` ̰) tEx㭆L X"D䱝H,t<nȣ;#x4%F&ƅFG4##R }RW58nц!>Ё|7#~t(RJb]Ft}!` {Ѻ6Vhc,@J;B Clg5? ) ;#u34D!7] IDAT~f#X/@JHE o ) =& -K쎼㐱%$`kGٖy+2olF߻al:򤽍($wH,q⧵7 yomFYx4A$=?w,6:A(e+kLU -Dk3iv>5?Z uX۳/BPv3]0ˑR0ڜfG.kH,q9E6h QBA/6 zC<\l᳕M̓fVU]J,D{6Ѽ p]M]YU/~6zɗwJg/:h͖־5^,2+>O[+\j꪿9DW\|cVU>MrQ+2Yo%`ǣz[k{ !}ǟ} !&@us:RxE 'H91pWx4\oy"tm:pzAb>@"#a1x.T{Cn0v{N9RCWn!Y7Xw>BNFsF\]_kD11.$|=ީ`nAӛ9řӮFϝ6'2Ѿ?O2\[.[c9ַC(ΰ1WSօYos)y;Kк\HXۘ0 A:ց˂|+Gg?>9YߎGËQ""{o5"PFV哐Fke%.]ZHGT*e} wշNj/Ϗ!/k}ȳP>7gt0z)ݏD E6 .Dhx჻/ ( ֣o aqw5ºOy,T ^r~/U=Yw~.:C&$HqpmF HZHa~Ꟃx$P1s?FMDx; dlGF|LJ_f_RQ^-Dn)U(d';Պu 6wH15K22Z>cvTڏ\wNqfc._@kV1yL$#a A.yț9wh} B::ׄr3frnGMk =3W֢5k9>q_nFwϢ}"?N}Qh}=TL` *Et b RA,$46[SwO $ l.j|gn,CXΈou5 `7)x!}- =}?֎ ;)|d7ǟh,q6f+Cr67)zӌֽ R|b7m.i.! ˎ"H#EHy3R\\ҡ~ K܋gbhvlO~ 棵s=^ˣm߁Sɝ>)3 0#~2"_" u x7Kȱqo}nr:ʲ:!atYѻ#Z&}tNY۫=}aDP0 sW:dSތ* Q$;˅NDf?Ab?X_F=)ƣ(`)>D||Bkor#> RXXEƜLFlHwפ֒LZ;z0較r=)D7!pZc !9aL >|/R]4dJ"γ> 9 ޵z{/#ALdlƧٟ䜓htx]kXuWEb_p` 2 &*N&иcK+<ѿ|]$(Y~2zc2L5uCCι;Lܻ׎7/TMYk8ҧ>WYUIw"b ZsF̏Kl|e=X_d1䍑XohZd]:p[$EY#@ "{TwY; !ÿi~9+YT9Gd*wػ1twF RfB 4p./dʎ篾+0SĠy mY[0RV$4hiifws{-Yw|E|~j_ތ峪jkF8"|Ʋ]bJ ٿ{h;KO!_$X2=\(^"t&i6u!!@ Yeɭv`ETRsz/H/Fk{gţ#D YD^)u3 v瑕H0/Ao#516g :S7h"4dH ׉2 U1aJ#&-+֏Sl|T`eþqe>=n/!Hls3@w钇[$d\g}>77|85Ϝ>އSw[RԹ}{1Zs_CJpkK-1*?+D- RƣHA%F]}[[{\Eב`qFYo9gKE dZW/#5x4R] ztkNmMېl"bdXNBLa4mtd#P\ĈJBs,b3l^Jb։ٻ{#51bļLG@s$| 6b#޲MYFшq!l} :B̥/b }CJU{ 1݃l炙ꩢ>xw8>R ,#Sܜ;#Ȟ;Sq՗;YcaSj5eڝ#Y=AK`5d*Y?/Z4d*x睊@pƷBy"P__\]RّhH!༞3/F]N̘ߜWSwĭŲk❷?yǣO8i]㈝r 6tv  _;[񥦮]3`?'DN/UFko&{hUU;n?U+X2j?7K Gr>CVK A \;"@.D;p-'=hGy~<~0KEJ{b󲳺^7]M]=C"D`7;# r[J^T>V{;Y($4o>}\yXSW*O,ve0 mU@ ?5u kVUmV%)NA%dȚՅh p;Ț4 ׻#} Y ס͞/b.eX7K@M$) RF _ \;C MsGY[.d^SD |. o810y fϔB ؀u5\hO}u>H! `$8򑀽ldȘ3 9w叭 Xl&~e0 +oWx[ܙ鍰Bg(MVw]vd0!?Y?F]p\5< amyH`Oc ^9]:z3od<}oL[;iؘzg3)j_V23Q00 /*ZڜkfH8@_>f-ꌆ.ڥ& DbA@elB< >W eVU $QSW^uŅ6o`ZnXeh`he |Z#ѭ cE<Gkw3SF~ tΪ9cMx|&4}N 7;& 2eͦQB@*k*;rņ [4Ȭ4|pX'eg$Cb}7 ɽ}VU:Z)&]󋲶_6k,E2D";PևJ_n@/mμj=%]ێږ,mpw<^oz# \{9³QH4"JЦ.s]l7j "ڝr粚sy)KUoBsY?[p "^S! B *ĘgF'ߍB".x/{bi$/BBM#k$u<$Xt#YO".ĨV-u4ۼ8X/Dj} nWĖX/Ƨa.ytglJ{0Rږڎr{&hϸP)& X=Sօ0SCx|_w݅[,񑡈wˌЎvkl,)ygrrBu>^alFքh+hI<=x记ՕAQ lnD,⑭H [?R.M׼07;BRPm.Dz#+|QHYbu#ޘ cp a&k4L i^D $38ƷoD.r p2玑Xbd$82S% 権xX4mlrѫ[ :] Yb@ 5yMWZ2 ޟ}>/@rYU.AV'QUU |1>vu|ƂI5u'Uήuwp`M]k+?XjkB٩Tv^Ljnn//K/❯ Yi("|>@ uaK]{ښ(99wmy_@gYMTCP]UC5"[->Ԧ"PYsG fs7 ؀f 1N8AxK7;صO0;Uҵbbt@3>ވ< YS_&=Su=#|(l2RfoEbխȶ꫶vss{) !(e4R*g]IHppi"`u3H? @g*[immm6WEJH>RT»vS*m=\;%\{ tHtq&(kwu|fvOF|zxkh-Fbݑ1o4*Db|GJ<ނ?%FX;#>kC{ EPYڟКM4 _Xw@F$wm903i iC{l8 Ȩ1n($"!- N! yGY;Š'^CHtvMFc%)[__r ?"|? ).u/lC}A; \i7!7#@ ?(d,ZQȰ4hw#²3/)CYK9ri(fӸ:mcʡ`!cm6Xg2쌶ai7]!8v}veoo(2x=ýQC'{)o9G}NbA8ApyO_֥hC2v!j:? v6=EP-'`H3ѶYe7 >NDd.Cx%zc_ێ?AK|"˶Bg"en&W H90:oNW{W5ͷ:߲^_٥t']16_Hh@Xΰ"%<w~>; Q )3M,smElYLI{K0'HMm ף݂dHFa "v6[#Dc[ʬ5u K;[sl+wڜ@h\[7xͭW*^u? u%'}罗|Ě/샏 x| O+e2zfaAEś[SyY䐈%;n0sݶ(m+ũ@ l|5XϘeke?DyE2-A }  ?"qQv&H튘و}1k?<g. +s[]wľ;1!ARk]: ߢ ^mΟ6.|d]@Pj!ƍ.E 8&wa8Ac ٸ{K딕F"wf式 :ڍq Hh!VRk @D&#F|-=K iA[YwG5O{pu+0{ލrpţR'hoyw dVҕ@}{]Cr-Z.D^br26OBrӍhO+hG{ ah//!$v#/B ۀa]Bkۖfu7@ƌ딖oZ"B<&ɤd2<۞d2mŋ6Z7^Ȱ^qK",yp2!+~kc"Fw6UH{kR:N(l⯧3 >em#/dtƘRH#k"=t#x7RZ^GDbh MVdftj[G(lkZ7Cíc0s2k/<`ߦ˓W|#h&Ph=R&\ksdDc47HNDkx4C8~s¾+@ޠ|ėC?Z;ksPAzWk [s:o#C<)C-m}N_~f-JCd.[h~)+r9Ɨ^yQysst(\6|k; .]spx~>/hw򝚺jetgv zVUg70{Sƥ*>ɴ)]󣪃}kI]{M ;NImGPPbR)h&do9]bm5O1\vħRddw!HH< si}}4 a;F#\+HjG3ca׿Z8myLwi+w5/GY)+FDW><{64ս0" 2~=ε:z#hEB#+sGZ?XoF2 ar"LJܲ }H˥;eHKD~ȳ)Vň4 o(hH,1h4Zi^DVSE_0t5]a 13u ozW5NJc^!Fu|$Hyx)O!^pH$T_=d.!hԅek }pQ஋lу_.]ilsK{KxpQ bu4k6uhݕ}#H,qZ.QE{09SFyFC&{d,MEbAhxA<d4$Ki[U=qTEU+ʆ),mR\ִ 8uVUk5uՎϪ\SW= ˻`KS`fK^Ζ$,+YQ\.'^bJo;[ta/ZE5uգՋVvE{g,[˅c^ 0j۔+>%*8ܱۄoA{{9L_]f,'չRZ aI>' {y}Q+hѽ.[߂B~;s-r|'%X4 72I;>w-F^WlS_gtB=OƣőXUd K GR r XFeL sYڎbAAM=SA˶s upW;VבR-݅܌‡!S;>|.z,v2(/b>iIٻ [7H#F`lBxOxB4CR5 p1@y cvk |?2^i]yL."KE> ѺnT~LGŮHx4|G$xG},2l%)E{n)>w^+GNHO3wf{9wxϮz-K.r .`feoJh!/BMB @0l qƶmmIV[~g4 s]dg̙s6gj=gWFr;E {ǯ,y)7!q'=0,%۶D̞ꝁK>)ˬ%(ڳ#2 #n"4`OGB{T$ ۬{lkt_pJ FaH쎌@76?Ǘ ɠ+*:7֊g^@XA]ls n"PYHms8>S_*w283skQE:p}z(Ȍi{ Wa;ym)e񎃅H7'6F9OgOI?8C$·m?G]"|=py" _ްrm}\W\¼zmI +kTWֺ_tV9;k`QaV(uNlҙϗFeۺ./{dLT]Yg?63&|9Bڣ/':RSWuړ;Օ_ﯳ}g`}f< ۪~8JI"{1? 6$8& Pˑ}'gs;qtJIA^2$wK.N4,D7f'bs8dP)?A @ގ ՏCGE޶woQ9)O!M3ŸpSy9G!o(jYSd1ƒsR.s3g`6} k__@ѲC\b)\m͒w dP4j\fQaܼ>|sڃ>P/YjȌZDEP8}5 `EwAgzӐנ}hddTh̳#H#|O4rT>+GEqͫ7S.R5ƹeL@A@ ^'4HL6`${2lIۜn`ϞecEWgHw!?_;!ac_ #j 2rZG'#)g|uH#lqEZQ2H[ֶuȐ _tRFgغEaˑ.zgu)s:y,ٹ)[=DV,CX᪶MF8/gHtOa6+q:Y(GW{ECםFZjrNa9;=oϯ m΄BSw5Gȃ_x?K{} |Z{ mioW<+Ts/pE7oOCVWj\1W+k%ޱwhv[ǿN4< )ZR'oCFrm(b7Z h=T#\+wAP`[YHAޝnG@w6oUXΧ2ިE69gQ)[#hW/H IDAT#C 3'b HԼhd߭R_lRpy.D`T>RpabWfxMHZktGԞP{lbyȐlCJ4{9{+E\)F[:f}B)G az=WȈZi}#^:Wosga <!#z=CdxRZ{l+sLin[##sc"\w ӝj =CE eKNjwi}X ']!)!eНiE9f~A.گNNAiv=?D1hdx8d0#|ccp);xY _YY(tJlER.O03s;AH.ٸ3G V%F.5ɨ(y=>!yg=rll..Ÿ5FH~Ξ琉& \TV4e=2+;s{C|wfm!¥6"-C<,] z7i_-ַqb|^kl/6ܙ.[iϝ cg g;x @+@YA"vޫN9RƓ aEɲD,tH x/H? /z$\Nhyʹ^G 01EmnO gTˆ6,dL@֒MSG*+d$ox-H:ox4 M̼UosƳ_R;<Ssl.N>F6!EY,!4a%dd$O,v 2MsVŃʏژ^7- :z[ࡊĆ̨eiSҹ*eCe/A| Q;?)g_ ҁ?(^zDU*s8FG;2Zs#XظdGKwhOC?E\x~OrckqH.es$o@FɱH6F4[|kk|қ{̓H#>2dLC26㑁f5R+`g{7Qqi9o :鮋?;o#xm.@]dss Y'g]'KF6ٳt CQu]>w);"yj69U_1ںH jctsFINB¼vᜃR<~`=h-6bR"C6e􉠳ikN-? BTLχw}yaC=\{n h}[!`=g,E+!>z?LƓ[GnX.1tlGηr_=za^œQAm6 Z::$u/#eɀ]iACu_杂M˲fn:V3HA= k2RK"gw=Ohi/>yHlDr~Y?'׈ RG{5ziT1+lsX~NA7\j3"'Ngӑs {"Yk@p|b{ː]btKkדX`ƓD,.)^ ;mi?oPis[{ν%x+kX]]_SWG{- & ~(;= տl ?vVg:g2~׾@Tt"%W#q?R24#c]\@}#W"I"|Xw6 ; %ay5_Bd{ C>) ?GJ">?Ela!\)PLR2 ,F_"N+yЖ@Gx@p!p[j AKu&ڑ2dU= _hRG` 醀t"m ݁\޴0gʗۺ*lg![fw8/qGO W#a=klm݊u;E*ģNhLqexS@D,r'UWmyA#ds< A:.|u.kNYZShhrIҧ{dg:7iZwGو}]`kCSMݏpNwp z%gCsM_ O#%r o{ W!x| K݉OӪWH,Dr1/:PO)(z})[/@2믐1Xe1r 1dǧT6!Y;Ɋ5( >e#X;V]Y; i_XG1oN_~-ӑ ʺ/j7칳 o'"XzR[6]&C=ׇF} Mmrxr0: $0}) ٌ 1)h#9wMG9x7?;E ( ɯH1Sz?!(;J޽}"%~}֋>EފƓڻw`HQ=k,Fqp5aO$Cs(m/2͞ftBn{z AF hX u)O Rd:H ,y->/GR$CEFȸncz0C\߁% ̊6m{;R.?(=db/Z҂_Q2{! QTk)V"!=dTDhW j B0%T7yMQ+kY[M]U{_m|f|.{0G*:YA6u|h< ,0oR 2o7=돐t6Qx3 E4}l>/i ]SAHAM%b~4G(@!Hx RL/} @~ " ps"9'O y8]i{#!f"A᪽T5&un.ZyXh<[: B=lf!ec82m"w5 |ɕ]oBm= `M"O7WŨ hR @oAMy\4RLsss<p {/#gSO 2ZXHkğS `~k Ax<2Z@Fw;k)UلSyxprנ_s/fnFH; nX+`z:>E6O""BڧК,A d7"C "V(v6Y;VsPtd:|m!CafYlƗwW9gW UL$㶶H"Fɫmk 3e}FΥ!5(Ax"zy]nt;U|(*vx( 99  﫨2۵U E;&f2tB4LWwlVSW5dAu3^ء/|!3e09'f ٣@_嶴,^}@E h%swvf'y[!o{p/>_7}k`Y ܣx~#W)8n "MHD9hޟz =( N4#0oHI}!I"Ϣ? -6nHh>:er =6aH)fX?N`ӊP~Ik~qC/ 2.]$O'uQNT `ŝ !#0ǣt]o-B6h1 _#"  pB[A Ơ%HqwnBCM|kȘd\FR PokZecjGA;"ec0"/tCƧsA O=Gpc"q#ȘO<+ }`ce#<.%0`w@Z{V _QƓD'-@"E4|H~!ce~)KQQK8+ IDATPtkɧ1'mhUOȩ :s/ggȈH#o^}{B<A6"gȸ+D֣όNDl]rt.K6;llG4I")J QJsUT(~|[eo4YomD<1 Gq=jYv$zg]@< {]f;A!-e {%~^G79n+.#ٽ=2'vGGcf*-LmȘZp$U'&[jUd4tkռ8;1O{w4UkꪆUv}jʐ2 淤:^kK̓/V_?sc26"|dY&nhO_]Y8߻?jO~ v[N3b8۷)u0RHY  Ҡh<^ p6-_<Ǿ7{KDz򠛧]6H Tڇ@Vݫ+kϵ>Ɠ?/A5 ڏL眏F`k9,Ɠ 4qoCJK d\ R͞!n:;XlO{O@ |K"Z ֝Wcڞ9攋pWQ) / HpTη)B!FJ' )vH) i9,,x)wHAXguBd})=xm2@|M6|HXOOtb")dҔáZbqdߡhD|p kB e#pFs{јST^`icc8 ;wxu{U/޷5_C2R|I3xrg6U0|둌(@o.u]q}=׈>qw!ހd4M~ɒQDtdEFHaag/YnAt{h9%5HСLbv^ķ+2 |HطܳZۻl,xJ洃ىlmoȱq0 a\'#c݁u#zϫe8 ^zg[ WW/{a;' `q4,g5⓿u?həD,'O^1"+ 矷mD :64mҹ榁5uUTW־/=p#NՕgGV]Y;ZSW5R߯.\0{ a/}}=OA9cL3lCn:s8R_Bhy</0 mWf*:x}[4<߈@1{n9Jq ݐ1f ';æXul5xrh{ )Aߜ%m$jEfYiENnO"#,`m &b ܍(0E6,|ב2f 2>z2t|§ލ둌/ErUux` 烀xK? O$bGa_3vt|V]Y*3zlCsپM9orod4J , `qnM]Օ?ogՕkꪞhjqSnvcNvVg􌃐So7 σF3 ^?r^BBdXB:`;_ZQG/wGEZCw7#y]I߳4D Bx[t>mj""T}X6$lV%bϬE~6so=_9wƢ6v@J%ₜ#,姫7 m[]nc;l}_ \l_Σ}f]F̟{wh2FC6KEH92\!C_4#֯,>u0RXKH90 =I\Xu~"f{{E:Pk/dxe5Zh}E`?hjѾy"O(9fA {tƓwɉXn>cƓ{J+(xtwD,xGxˤWw!gKtC֧@3 25d\*"9)Li$2~]c:[4|)W&b++^Z247r,S@B NHDX$ڇaWF܅ )S5#ET$vWkwZ7Eրe?|x2͸-G6W,AFGja]%' kq4%b[䏑"1p T*LNУty&C[jS`Vvt?k 3zKPT$HCH9)?]"6kl*v @jkrtgD,W+*jhyLӶM/Bx;R̲mSHy݈q3+QTF#ʲ~Xm> p\s):A >EeZWo<Nw#x-އKo>xbC4,'b̠FJ غ6Ɔ?A&\4u$pd:Ż zwAyo;5/)U S7/0viYx-3+,~7F6XA 'MtKh=v0J97dFMƓ!GH]O5F$}3AiH9rӉ"=; Ap:*,JS^E҇ۻAj[dX]N+Qn$I'=Zdk(Hxrh+LӄϨi\=^ݼ/~_{P"z-mțG :aomM]";n5t9=xvv'\ ~-y췱} ,rյ\ ß6vHLCB70{ AGMAyg%7CTE@5XHD|.bzHHAC@]茒ޗ@a^vz`52 t-2NNL=xbG' u'm~!}s.&L#—?Vw+ұ'{݃ f4*ABx&{ X$wGh2A =Ζ=ekx"2j|:m=m|5?#@sl )4 ?)[`m@^63Ǘ@ IH)y )IUHPٿ<˹WZE"<-]eϴ"Ck"fD,*Y^עV&௉X$hguBg~ȶ9Gu4v. WI}{ǯw`]`Rڜ> KR*G#ո xv*N9;ː!m^.o_Eεh/>EaJiW+E_z}ssw~0`#ԎK`˃$n=`F`dl܎[ |$EVqQLL;BaFx=RA$0qH'bWaxRDtwB4bkdoEöhu ܄/ 9"(ڞ??h7.\d*[Zw&ovzGr|+Sъuª6>x|#Ɵ#>G"dDxrrވtilc?]JP݌{ #Uus%{#;_`T4\vj4;‚?hXdLY~zL&3!O>9 H*3@k& %Bg;m'~u}1z Yږɰ:8aկlў!EWC-Ln}UJXȓu30uPnY]UiNNUO hcn4 IӗXnGLSL;b%bh<9$NBnD,)O@BHHpBf0T5(=$C̻V]ꐲA`IƓ;  LƓRULenR}޳F)~Yxк8~C;p[Gg^+Jt桋'<Đksi3#PER]\*6!3;;=H::BB~)-h ޓiU㣮ȣ= _E,,B:)'~{;&}2:<2X_w״z]/"ڞ eAlwy֗cHApQDu7|QuB6B|صvVXAf\]wVi[׳'mlȌZ7m R\C伵&W3/2[EP_ Z=+C]i7WT T?ڐ 9Hug+OBro-2wUHV슜2L.GwG^` + р_}t71Ψw|4ɔJZ˷D2Kd} E@r#؄J`ngoD,UkƓP45ק)GyH~>ex H]gkFz}a-Fs&]as%?@k}~&Xe1ǞM"UƓ" iX9-jZk|_H"lsi3vQ ~"dxAye>؛h߹Þ/G Fc覆y";etd޹'y՗𩉇ڜ[Hݙ]7b+ּ^~VNkנF1Nyv~KzTSWhZ_q1`%вwǎ#? EUUhu pHuemuIM]UɬT:5^l2sstθK_2ؼY]FXV ^'SmkV/zdXᠶ1P6HH. b\%XJx,=q_u)K4 IDAT##an#pB4tU{§u!/No~Fӑ`y%g;F&HFwJ~H @p4^;j\fʀc5"Xe(r$ K_c/ .YEsCr36x+^b4 Q L^GG^Ƞq=w|Յ(ue>2\;CޥHXN6ONyw1^?G4D#>5/Fщ'^A.E) ]0 q]*QH>%W>|+kᒽF<˞j(DF \a|!aFWW]lyxrWXh<@y|'˿_.?Y6`իӭ/kȌz"?9TgbxE8~F4)@Q!xઆ̨7xMBb$[kw} ZmUֈ fRM)}hTQD[B"1E^ND, cՁx UqWT#$S fHGj 'K@."e mN)W鎯@83T:BFɁy}"\>j{ WM"D{lC/{5FϏa` XT;٘[y`E"yˌhWP9EkGE;#H`2Bpˊ0aC}}7,nFi~_!l? I/y?fXf{aй6g& gru ]P9CnrWuEGl mLZҝTdRC(FD,EO;¤6Sp+>?"LՕ'5%K;¡Od OlՐ%R>i =x@Ƽt㚒[?QTq,pA:]R}zOg™-T+⟵5uUg+k?SfY^jMewU ؗiPkjj|^]YWj8o@)|dkҜC[i_r$FF$<.;8dӲ쪼ީ3#Ñfg{6HaI<"cS 򨹪~.t> R!/׮6d!P5H0A]RFhfc|Zy68h03M^YiE;H^HA 'PE0E`A NEk֢4w!3} [!!ht=d(5)W$Ud!eh<2cq N[B@RPDH1gz]d}Zc+hh;_$mħ{3^~kZSͥx{!\$AJˈ:ílڜz+3L48OO MǯӷmI^Μ?xMP4>k~?w[CfԊ~$ErĹ_XLAVζPA-xqK:$o#hp{2Х^ց[Hy&{Ɠ{09*L^ɦ i)K@zPқ N.(DQ H6R\ ) n6{h#>uRʝeGwz$i>`pN'w;t!Rv'ӷ =+"s=3Y/7dz'$ؗ}3L"EV =l ~ C]m~*#'P@|h6cі~k}mm!>Q6][#vg{}hI=ch)Cfocsƒ5[ hƞ^!'!c3:YW4c13^ԅo rO$ )6.} ]u\ON%b_%a=[pB\enk~UI!!oS(k.ѽMjg .I~i}__}pߌ"zsje-кF|D B +(ts[:6&x\&um"giN?B-_*^w %Eܸ3 R͑EL 1T#A7 cpYq {42K7+z0?ƲAڎWo| )&)]#% m#ą GuGeY.Ql>DK?ɒ3[)uۑ<.uJ gJĚ!!i.Rm^Dl E6;!G ٸ҈AEgP\A9"딫]3a IJyU{ MjroXbݘ2H0Xhzb~![m9yriޜLpP7H٬@L$>_iPz;dݮ=ݒ5D? dFB9wԇzw̎'^`5~owGc>hoD3/LN0Ohxklӿ*yW"+AaKK"(h_T;# ~]}/k!!Lhߋ $o0 (3w!잁G| $((< 6W2ke]~yp@: '"oKH2@ B#r{v.h}LPlvb<~up?]vʁO;tA*v1"ƤP4s`<>'ټ|d/R\vzdP= q^~_ > "L?a t5Fؘ\fvXP_!<F<M΍y]/dGWwx ݎT!^άU#-Uhw62ۼ/="fvG[kl+m^B\<(k[LHQvmkC-yvS|<J\/R6_ pydQWą!#ROum(`W][5)5_U ZYqum > |!ݣ+1d~ @7}#![͍z8x庱c6At 'UumU޻锚ڪn+X>FD…\LM(|&Ĵ{ 9L1"G#ɴg%XHZ* Ɗ"w|F'ǓdqB;P;Pi>(]tY"[KLqr-J OwGq#)JN>s5@EqU#NP 7rfo1Za{lSW{_.Qy m<܆&wl_ׄW]o.N0cB^GK|BL3Z40]4pO;J83Lb,D22#ᣑAʍ 6 r%EBqDr׵1Qye;w+"ބ-j) V=k[,zDViu0']\9ۅsrJ]yH? /8㏇{nGEQ<`kE/B4^.@Җ(i}B3*O@aRňk|%ÑQ )VG /iOoAF  rs><'!Cҝ|(*sjx #]-NPv12"EFO~C\D8ߴ,}WwמuAd^^|B<pp*+LYE 3ccۄ#aSxަ !'n뵛]#oE"e6Ưr#80ۮy +RF!#; fTVZY?[0 M \uFSQfOG>c\T][r`ʚ _7(X9Ɯ|8P f?9ȣs(`\nOd!1T=\d9~~F"WF>}& x2=K'Ɠо,T[)<;=¨ %B l<.vL'2m@ΪK6Hxm2n}s`k*{6Lv4ѾCeDΔh<+xޅ}(Gc4'!21!h@;vm񆐐^K:EޗEHPs wx~Oe@c<>]l€ً"a^Odq.F竮x%] g_,eYGָG}txc= Վ He,|VN N1ОyEwد7fPvvu˫v^J>F vZ} pHd ?C8s  CCwXAO^av=pvHۈxGtv6)$ o*;a:h^iQN|pt2 +ADE(!N/\YL>3Y/{ޱ:H$8#6ϽQt c\ 7/;{l8,:GDe6W-A a?B| )xyE8!3^O32nmx2r[mN#~ңEu Q~B; onHڅ@yN[#e(DCJl:/> Co~̝.݊,z3`ABF/H0yFt6d< m kEGl^ 0 Y_K%b5 bZ*αmAsGm\T͖3Cb3v]H0zr:EAA"0FcPAb5H͵y)7 pYj/c Z`Tjk1 oQBo L,Wnő⮋V\!om>YYR<`-_S7~ {On>`9FB 0D=`:/f<.L;.m/#aD> K~ODXu:'(ўEʁK0y^Ar5!A>fT".LBg#BΦp(W7a9hDr2?x#Z֯Qַ!!}6E["Hqpo*H N]_#r3RR@qz- \D] Dp-3hH,~yHdJ̰=ӥ+@ j96F`콮Hɻ{j^y#爦^Gc%0vjk{q*[·<%/L{K2mjm#m8[^$ܕO9c<9յUl- S|ї} R; B4PH/^}H> ?Ip^DG۳]]uӶ6k*C^k{AkQAS+kT[<FUd]>ζͲNz<å[/ӾMdxo>ߜ`E t%Ӎ\e $(v60Aۮ53 #chYvm^\q4ЖJق1=S<}O'!c>vI̓ˎEk7|D!``""(#f;aXrͧyS<"J߱uOܹ>HnB  r9]>@ ]M?j U]{6^LbgUn|(Ŝ=.RDW"m îN]هIvvo ޲VK ±]-P0E~=;>cZ GƦIM.Y]S<4ZY[FAo"|lʐzB<>1dT~Ѣus5h?x׎CX*fBh/B= %C 0 ϠKQm,BCr+"u\XOKWBro aH=Dc!.9KpxR"0LCBӈWAPɥ= R>o@ܤQ{ "OP Tw tAi )CE^N/D6I~ V!UEy"F:K!8JIЅ[?ˆ/mK$~X>;O67mۺsՓK%bs-=/GFȧm~vE2Ugt'׺64O6 e_@'7+li=JZgdRLEjN.[=O{,BF-Y|U(={/05w5 IDATxS+k~S+k\յU=Y3on$qx{Xum˒;P8SZIuɠ;Z_Q7tMcruvT"_|ڞ?5?a%IYxћc杦} Vh Eɥ> z$fb"rqsw|{~j*y`XQzue-)ڞ)1oKd?pYx4P.A-:l?ELyd|^Ո]r݁54B]z?*L߆,zQÐXVk>b A^+h1DL "!{b )Fw:tXȓ ޾]Hh8Cts&9h F1{D?H1),(߅F^? <3DSۍ}Z|xb:ǣ 5h_wčsi=/tBbLP`W %pޏH_JĨ!א Q# Բ=0[a{\S̃(4FDO $LF45ZXBY&S7薴&dTXEo +x">{飽ܙZOt^/Cu]u|f'}!/@vڋXi{M*[OG{r!G5 ®b_#س?CR=vTD^7=t޲<7/9!_8(HDy  ]| Cz yhB7!6;v`8(D3f-$HYkelC޵  t{ )sFg W<'6%pY<'"l#?E|dT3Cٝvku\u@P>}DpNCd:qDx2{ĀEͭ+};RD/"e+xTof# 9GS޷wS90)!e2 ^#-[ _%jյU!W a)\qd^}ƴT"z{?{S+kkN@4UYH /A (wEFK"=ܞ䖾hE%|hlַnCL=H1Rngo?.GtFwfGƁ{JX^_>a5Ck&h=MVyƜw jm~욣о} ˁ/g "ybYH/9As=s*NA m~$N{F!a̅jfEBmEшGo,C{}'d,MDYkg>l2ϣ3o7u|t?qg?轑  vl[WZ`˝X65 NG/[9_ؾSاH*'S /vcH+$C@c-Lѵ Ewߔ'/vuJ^nDƫшsoN=#܇2DO.Y|Z#A2Hsg\ )godz7{*C8<@ӌp wPx]66sQn#|֪R log.U>RH+oNzEDlē!(]nqggdyJ0|iq5|TWG4 syBY2Ua^zg^Q%yOCƓ Oծv{Yb/ڸ\VDWF.^S #RwAR&  %`~ko(,GLaRo-_; ޵x֎F͘GoRԄ,~# gX"`s:RbY,!+dFBV˨tl.FH YnkUh'} 0'R?K mRF vBN!- lmKSXet; nIjv˜7X^iD{2bzl<7.T֓?r]z X;hx۽Mu^;Gvm5dޘ)w$9Եw"L E}Dl 1̂,ËȻ|]vZrl'#X A)/AHN#+n폷Pj}h+Em ѾCؾ] DEyoG"^8Vg=k 2AMCFOSP ?ڸ#r"PmnJmmz#>xx Heo۵&t.ĿV;1?gl>Fvw W[τ{) iO0j2Kt6m {io?/by9g#E#m>5@$b6>H)n}8oS]"į]ҩhLh~?Gt92.\U O"忥}EKP42em}vF]1/D(in^q}aׯnP][7~g&;G^xUFmյUC,e#i{F2Dٞˢ]yQ? <m#7[ kk/Bv N(6ZL$cGYG&Li<[ɴ ښ iz$"bYT"؞_2x2}o*kJ%bw Oիq! %pr/ S>&PV @K#͈99!1/'𐄑7965H!s̤o]<0kbd #FKXG ̙hOD l{KkZzk?{_œ6cӻ {[*cXbD[A@^<>5hJ&w"vHk0*^Κfn}u\YAGC(Lb|esꛄ.ٜGsb"Evpv!( Ǜ0읛<1op͵u=+Ӟ#%\"eb'H#Au R33mnu>f|ǡ6uHqv2Szd/=dwW?BK: sE]6w/!,v6W{~YLЁ \(Sm+ATJH]uyan^w~?2KVؚ_ԦVּdؾo?8$ l3]?}zEo<݁?MW;W-[;x2YP%ZYSsV['Ӄ>۞h/nm Z 7'H%b Hx;0})MdH8){Ɠ-AHy~1W/FHT"Ob5R"],!%YX?QXhmCPec5Ş=Ȟ'jv")*@Fp$LͶ oW`{Y|A6m#F{8 L|r᫷tXP{?Bzl[BD޹W:/Cpɚw-wm\ .D!%'v!a5Dٯ<韕20h>AD\&8kہg92Cg"R\Z 6ڻ'8tNF¿k-+@Yߖ"h0,s"SO]VRѫވA v]ExCBVټ D{6)+p=((?2Sm,VE(cѮDF~hſmmݹхH!fģz۳2V)isg'!v׮@Ǝ h  [eaE+G!vAҤOl'l=#!).(DP&KF!S;2"֗H3|fK%b/YFf{+7?' <9l }hBUx'rg%"6ܷhmD9)?r ,[.()X7kjeͿ_@ y˰26-eZxa{|;vf 0aD_kx |{!ZfYp[x(^diFRF\wil3sS[O%-';:6ScG`F,f )$GI#O'RZ` !) DQՈסп3cnQ Ȓ{x2F*N%b+v?F`>Zb$Wٳg,d>Aͪb־-a}+BiHis)@zSx2}38'ba{Oś#98(^ݾEӁ' |>yS؁dzb;!a +O-KtbӾ"N<3Dlu\Ūwu< SmT*l=lnbA{?|^`A&W `b oF[v!ǵvIGܹ|{n]_J L B =Hloc,^ϮG׾a"FB:yAV9VRy+kk޼P(d8Yax)0b5sozW; ?dѼ0~ ں}S0Œ/_{7,,T"vg]cY~j#|(JĖYH K2ۭ/=Fd45ͿKİ)Xu+E IȺU4\ e#%/A;s(R(EZ%R!0 .,rFF`li~.G8 PNBXډ"0$ ´ `߸ =4s~$yz!0" bmCعم!A8}507헟 tK]weuV>ѾtݾE^>~Ʒ?OBtuE׵ΦжvfGn p(_k38_<ZﮭǴWxNdmi+{#4:wyvK?:j42B^WhsN4TYZY*.<]\\aQfzE]Q4s5S][ǩ5_i߸Ow@ ȢYנ)[xyA8Uִp<{Y{3Ehs[sbh"ͼ`"ȄBqῴsX}z*bx>"6]\d-̷f+a ;{,bm™)E.ddOHz!ŭ5ʝoG B.!AHa[GK6d&{Cw#~~l$Ȝ:<@<~ Hй1a+@Y4kWq᥶O"K^ϐp25<"@Z9w1Fq͇WϧswF^U!o];Y!H@P>'dl ~G]~ ظEēt  f+yv"|7Am:aUY@Vu{6gɶ-ٽ1ttǔT"D;3׆}ښ:kZYӕJCbZi) -j]dY={&ɣ3Uuo78Z!cjG\ߦf,4s}N )p#{PD]db(J"a;`[?wT.E{|8Rw B^Ap/G"l.› {D 2>e2 !~2Xe]G*ijވ'=lL>&~Duˊ =l3h=YKl&"uF=6^ek6Ì!MBcE|o+m][_X &R6,Ka߫{ox?#ЂhlA tVl<ik2Ad=l7T Ɠ#y ,)5^;S+k6{L3)5#lD 1Z}"WWt"yT":eٞ Lm \<$@K`a0Z'ݰy˛s-e|Z/2ք "2Cv=Z$[BPl hڷq`,w@3m,X,f;tw#om|w'? tdy)Lz7ASQz/E(,EkY|v!O#d|lG"b;Y#"s$ IDATN|uH5.c:t=/iHyɌg6#Bb:jk{#rRV Y.5uq\8HDW;!_\D[HoD/nAr@LW;jēKB9!d Y<hgb8@?bPc})GV]WgbOٺwo> LۧjsFޭ$݆29c12bsBB!75|WL+캛Pǣ V̼AL{A>D*W޼ o]$EmxOF|@ad)ƲYb7oxd¡,~6q]=mo1随(u#4r͌I-+#7G) APY @*[>>x(LlZ$A $D/D!}g -gF{{W J^0e;v7)~HqECLK3p<2L'3^ |e w-Fʯ;x'G=2?D~!a6.Y`϶}ets?cy):md]3 *Slh12)!ĘEC6g!F\` fmuټO%ROAclT$M'Ӈ>秢FN@ZBnMsbWe$b7v_=p`*ۣ+?}ъ]7|zDb'$,Bx?y^δ{ Lw|a̅ևtV"~r$px ®Hx"#8Y/D^Wr, Aq!e+oNpˆ=#aW{o6v^`&]u]wewCCe~H9P~Q=' ›BЇ@42NF|v$ٶ~>H:pOwްs5sF"`AԀwPʴT"6T"T"0mյUڪ^յUHQk|eϨUV F~um>Yw;gx'<:Ƃ4f+}״%|pwPP'w ywݷvgJR_Ozj$Whz>}Ck}(q΍bH~rYL]~2.WN:˾|`4{ ) SddhxȂ6Ş R8.FD,&4pzmȲ5E,4ѹK@d֢Cj`]X'2-BZ B4G#Q!ݙbǭkqR^@ ;ػ]o4aSXk<~,OyΣM| r6RHBǻ?qZQ8%|~1Ρf|ԯGMC@Ay~OC! .C Έ!-C4=T"Wq偈)>6Q6*jhG=A=ra2'C~&[5\NBspa>*YOP]hdhG{mRDCy~#޶%Hή" B%_tnYLF76zF;t5FZW欲yp¥"v4h$j+Gֳsm[ͻ ;.o],75)weά]rѼ.]|wC%ޝZ;f:C,|}"#“u5VEv-m>^.@!ue+C< K'T{ iC{>j_ MP )[yt ]_ @JKR^E'hO>R+Eӈhԅ<`-C{g]RdZM|1g{hYh!>RX2L;˶K`q.ˆs'm~nA$Du"%y@ 6Pd{E {yڼZd[JĎXsFJfN0= G>RCʗ _ksʏoݳ)uvl}J#zpEIF; X.qenO02kG f,ym!_ܛ/i*Sѕ m(_gt>{7'ӡx2d L3!\uPC!  8NG/"!feT<*yϿ˝M`Hz;KrәV1;RF mGwk"R2!L"PpwsN 8:1*(Fj '#{5bkY3s? .vL;G{l'BLWr{b~D-c}Z,"텄N*,[UxzWx^A% eƷz^4AA.>ُR9Pwy=-RZ̃'\9`awe6~:{v;;5x}zMյUC쳭 A5G~>_uZV\sB%<!o371t]YU?r[X~6ʚ3^hk&20t~{sAqƂl; _V֜8oJo΃u0"LJތ'I'(Eu%W7() K =dAܧY~F6Yu/D$wqvgϙ3kf&{F "c_UUQ{h)GVZVR[BI$alϜ9sݹ'yBE_y9^\+u6k=*[4e;! c6HH>{J{sP3Hn},GŅŽ_AI|,Qe{FAx`tb@CBg6tVγ£M nhTf[$& o _0s`YiѲu:W7[-juaMX:v ?ѹkfׅ5g.ij;ڠ. |l7`^G=Ε$8܌n,H2# v?`F:a}|>{}'D/A`f( {ďs,in >˦FY jBYDt1D3vDP:m֯/"yaRZ.gwBGC֗%@L[D;o"n^{g}\P=69W'8k 7K\h8s`ߪ߯{O2Ehk.*_mM ? '[WE;m-T/?cUhXF5 w\TX^\ْ5#./vsoshs;{aQbGtr" _nx|H$ ~Ѷr0\[?a1H0} #+̑h>wC Jy$PLmcw=z>YӸi*C'He.D / mvt' #&1 8Z "Vd-Ңՠu[HYD}^m gYL54S-49-ق{ X1!>G"ZI f `!6//AfP>DcWo!wEh2sg"a31?qml/YDs5[{GO@@iR/EBH1ZB=kEF3b xWټboӾ>""^ΑEd1Zfk4/;]\xʋd|y"%RlBH|!5Tf?6)Hc2 W\iu?{y$Ph.KCšAwij]ݺxRjD{@D3?~vݘϾ$( "oશE6.mV Y" H7xҀ?iIBzAn=ϕ]Vg~hK`8ڳ@.KpuȊ{G{2DgjuvQ{_WDCVݿ]b7j&8,Ǣ|}\ zp=e;YE<K%]/@HSXec }mlޮDlgCNDxE!V"31/"~ ʫm,{#wŕww @176w$R7ۘykD{,k;n귣o3So {4_!>ӈDЅhzn6ΒPo|K@gcma-OEyMp)oѹ:˿ 0d~%_Uԭ-.Z=L>NyI_W6li+^ޯ|YѰCZ򮅱H$_p=yOۑy*4K ٻ{]ucKG:#N={U%>̎yh&[SO!ngnTt~CVwf\Gw =ѡ|VD 8!;/m}TY C(t mC4#A/H<~K2?B[Q\[1 sxid@n|΂ր͍ٿ"`@ 1q1Hh#N͈4xSĐN@X#$b!Thc(C̞}ŻED획@Ξ_5{ Km {-E{KA?)ZH}YVwH-I2u>S;P낊c"ހh M6UH#<$P~<ɱds7CDXH,ײ:=є!2ηQ3-LY|bD*3IojDBd5uN{1WDo~jc>8p[NWhP-]Zc}n:+)}K{ţvݟ1߷.~mw@bd|F"9)MK'#h+oaݞr{zX.˹zS[ZlQ'z Ųv~.\F{6Dz ?eh"~"<`ہ\zۗŽ={Bnv:?&]墀0|g%Czݯ{G/"dChϖlWm2nhؚm oHhXA>n9 ]"+ht-D!M~`o&bf .T/D+a"toヷF@䝅/G)@!XGys1||1 Ā_B _^cс4<fnDļ1Kl Bwq!r>p:"mnT#&?KXE.h~ EP!til\FLn$DHE#6?uz@t 2vq^{hY_.u6#u.Ⱦ@L~@X }tA5Ν2֯^V3{ua [-iT1yPD1ݍ&; BuaͻZd]Xfm?L_$ߟNwdw,F|_37 5!^1}^žߌ?Ԏ.l٘f#PG4)E4͕+iEND.Eeh 3Ƿh/D\jlC|.Fډ=ޯ=:Ec~2?A5obs,@zO(ڬ!G z7!kSH/&DAC`NZvqY0C )7؊l 1s@?dGb} :9\ǧ! W!ص!,+Y9)'<`>] [@EhﶤHeʃ A;:cT/Qg<(@;!ޓ'Y3όƢm^{܏ޒ{LT} S7$+s򄩻|^mfL'㭉Tf2̜8Ai!sEP=oSJFD\#U"Oq0%h]@;YlCPAeCګcJ"HeN,px2 \_[~d¸/-D}#p}6iGr&"aBR@ Y#kl5W!r,"{z"7i"CDw$3bo c/BmDH ww#e8L6G.Qlq>`o[7\މv_]֟eH`A=hkE1~6&T@{xe?m6_.7O? I \`T[9\1B$@qMG HNW&R?#:Eȥ$p89v[~xkˀb@)>sh?uu xЅš Dh*xlTnuaO⺰f ڗ_ʹt2 )͵,{H )N$T#@q-9$E`}WLGVY^m{J캕(ƫ ?Eg"ΜjXK#OJDy"Yε>W#98l\ߦ,}.DBq Y,9l~GBYyPꚇO|3 >H8Z=h?[?:V,6Tִݞ-V[U?py2{Ho!إa^w^:ʈ\x~'WGflh|*D;E\DO: 6X_S7#5 y|țۣ=m{A<(, 1yO0,ns[&Oڊ2HN\=ϭmUe9CݡRNCL|Mg:+2s>d}Վ-?_ r}.R@z6_[hdM_ HeiM 3Z3w]|*Wfxb/#Ci,A8-Hֈvq~io@@+{Qm[ı z|j u |Yg Ү [ b9+< $38A{_ĀwWj6%v9<09-+F):1wE$+YӨ}י0!bu0@\$G~ +*Wd=b2mg b<@F$4W.K/,Mس*^%RHQrg:6.zBDD*sh6ژcoPuM:F eY}MA)VߐzshέǷHK?έTw>JppX}+Zh`)>A S=.E`g%Rg_#hOC9.F͹[@<ݕ»iyw ig ٓe};{{K׌5#jkCC: b?^h}}t|WwzD*s+_p!*E4وL Evϱ<Ğw'T=,1Bt7{ ծt>>K+ ;u#<]_cnh>Xh_ cc4(5GF5|P>nRcz'9_{Z7ü9L)^lu6<]TVKm6$ےZAw:LT|*n|I_ `>yUHѶ*M ;}Tyln5<r.ITNUݽq ն")qџŵmTtp)D~4Ur%}[{u_X,`6ĐbH 1訵-&.6947:1fnO!4Y/J#xŻAuA CDԊwwEH@o7cƩ8 ./d}B.!jb#emj3\T/$\ ͎HHp#mnz+w9כ6Y@L D#?6#FFxn";"9_/y5Z%HCiyw[>reB!A-7fw͹[oswsOė(*oК, k}Nֺyu2$բ.y:ݎE@:+4Khq;pG:}v6/I'&R} 2i3@s_,.YA(f}-4rn!K#𵈶A 4=Q A>okDJӭrjZOLͮrD@f R4X]‵xh4ːYnB Ddz63V" UqaD*s>{?DcK<=lnnEd&6`o7#61n P8 C?yV"=ߺ3vӢtsspHW'RunD*d\yԍܶ{ Y6eq~ܸ{$RѮ^"ٻ8V1=哉.8h>银)/\;yX5ξ`(-ޙÞ{5)wZ"SO{ r @D*s!oͶV"x5"x8tlu "֮`=eVdJgF_CƺV/E&_ kq}\?"AOuP k>UĶRH@J(!R<3zGQ{],@VQaJ.)M#eC "%T|duM6 cFk>NDwљ],@m^ 0#^P66i.8W,>\DoPi{!H_ {} C{iR|s1_=m8D\ȳss>ow)+1:,*t~f[G;_mhu90\R.ǚI(ޝ)8He EHt2J&Svl|+a">@[sUOl݅xr9± ѕ-ZTmHY,Lɥ6 hG6cGz䙲-Re]bjz(2eoŬ3fN2d-iǹmM0D*GԬE[V53*_]fL(3E7skKU1%7 YnzӔ?sK"i@N_Dc*JWu@3o)GE‚hnt>)ȺVia^t):N1]dZ}YmmM VB۸.ΡZ#;EW%d_lj)AB?"Х!d-pT)/tg~ĖHe*U'@>}T=Dۑuc "Rvt.b:?֧[^gs]߀J)by/,b]R!Od#wZhC4QT\m׿l|q+F$8mZ?D$#s.x'L8 Pb@it@`䷈iKp0{"Cf|o| iBLO>>=z̮펄yQtYBy$[]_>Ha\mZй/`qz!r*#6h"x [ ODHyO$DU򛴇մH2cf^oD5/Bkv+9K7yO!PY49E~= @ڋF:F X_L"9 'RYq!$dݑ/@Bw%\ n?̕Eg <ĔHKVo[lF:){ODBD'":ܾ.Dbt_Gg# ](RAt`'D^A`,;/ Ǡ}qz|ҖUG3Hsn &ޅzbk{Rw?]9DA|)FKi Zv xl F;ػ6/&RH9#]hB:( v9>5F4hܶI:EkH w1Q{㈆BY#d)[mɞi88[Yr]{#|ݦb~C|\p|_O.) "L?|ِ}p75oʹf :OS칫?oA|i oڀlL!`8 mnh/5#=oaYE>ykG* [S)/]SRUl5>XdՈmIp+GvxUU=v#v\-ǔ'L}m3:7,ZCi*_AF <܈&OڑHez %[GqM9Rw"`95S2݁R$:,$ UͥuȢE9r|lN+@S"9j@֊uTD$Gj"ZW t2M2'"ʔ F1`]_kg>,hni:"c|Wc@F@i텷X400(. g bп8ND̠ Z<  bAHD|_V"mE{0 `ϾSa[ v-6KwiE =$ Ag j6POC»CûQߍY]jt IDAT86o;"@k)D|u|9g͚@Y;\תZ5">r?!%㈖MAV9P)OWdD*shON/rugfowG{{@~mؐ>3Eܹ'LwVV61ϯ<-~Qfڔҹh/uNA,&֥*>Ց+,))lVև&Oj$s,Ck0 >>QRQՐ/.oW‚S}mk'(Em*|LM@a֎z# "p:#H2pH|? A}BWi  /#KK,8Y4$RvDr Z^]\/]AA.eubY{b} Z"-eH,GXq=ܷF5/1t"&,2)4[O _)VZiDĪ1ѾxO{g s,5AuA~ !=ӹ㍲qE@vZ]/bfg"Ƨx𱏏3Ӝl[QSbW6s Og!H0kħ?):,hCkTG#KhO=IaG=X7v` 4$MZDNA%Wk+t2>= C`}7D*SE3-@{@|)b"mk#,{"?uwGbhH0D-N'[Ν@QBg~8:ym(BgʐR=DKE>oG#Ҋh^iU\j}sIxĝ.jsTqܧ\,Cm,N'U8eN_e"o?Enܠ invc\-BoW{%];=ŀ݈Rݴd}YC2M7D;aOұ밿]:|l!'F }z tP!9c.V͵w/D`f֮HeZq'>5|shD*sgvhpY~ M=O0mI#Z;f?il4њ;mٜvf1@{úO0u&w+_= S{O2oaKW-ΏQ6SM~M!Оm+wVfwM|72xT]l^ZT{|-X)ABYpD a4 Z"I;+fg8%7'R @Vn І{8NCȒ0i[F~T A H+4o^OX˖31c7.iEbikD[67-KPеM^7.g߄~a 9|]m~iޞ= ,Cg"֧ t6sZh]-]N6GzLycߝǙH9(ʜ9kC#,m m+ n]5sWAh aι&švY~ΊD.]=D"uŷUׅ5+v <"ݗӼ㿪%R z6}a:xs&!;=/;"B:D z:R惒>m,FUFJ#,UW<ʑWo+?;MvVSP| ǹ#Tx[)jG9@yhsa˞8Wgq e;YBk}?3y%;X#4Ѩ}%e (UAP`?cW^eW=Zk*lnٸ'K2; ._LK2D*sɒÖw D3DVW xݷD*ۻ~kؘFt{} U"ݻMӬ_!RކwAxdէtnqn\ge˾ףr'L!;6m'Lm|b>ߞt޺E'Éȥ~yxIEs\fQ SO .]=[Qyy3OE ry.h$oy5Xs!; !w̲qD,k7ԁ4;OD*;:|G{q"EC[ eZ\c3}F+ i&g"s|Cqπ#T.~b4ZZVgj+DZ2{u."A[o3/Yt#";b/8ێpVes H!FP*lG^d}X\W++ @т˸WeO}f] 泌 d o/hfHuq0ݍ>zYߴg>g+r"AȞUiLJ[BBP:?cq"fj+ZW9wfzeX+ۜzp ۭ,?:4Jk+떼 :3H ZِNڽaA[KD$?Yؾj !<-VМ4.o\_,Tf2$N_^N2NXJ$nt c]({zd9x_!T87D.% AZD9CA(oknGtp_DW ]aaȘ Bil$ It^vG4=[z< Z<|̚KذѠSksGatCʞYsVήgWPٌ}jĞ^jG%R#k.l5џ-v`i:HeC@N A}>ehk$l-_F4I]N&RKmFs.dvYF|)'tqc==Ӯ }M6)pce}}t֕79na8y÷%@r庁x@$]wwL@3ރ tyHeq=OG"H}Ydmy 0WSi%""=%8t`#U;V'_HenABwai@g>#f܎_"o y{Ąw[00!F13yd1?jȚ1#{tc>F@fū|*&ǻng:{vͮ(~i1G}ӆ:g4JP-(R9 м,Ҙot)m!b} &.\qՈiwmerj@ַ#~ raw"asہ'J2~"u`D*}:ÿЙp<>v[6i[s8\bj#:MRO@th=XXf#.c핯Yb׭/AٖkP{.5gK- g j{LO[^ ?ZXK O1\.H Eǵnhɗx!E`d(~wEƝsE.p6&kzw (-.6޾6G!ڟ&;%ER|Gܟ@B4u0޹R0g+[l޳|3EkEl] nNsR5"Zi^⥓0ʸ~]@/L#)Զ}i{?}͞{d"Y!:l e\Gy<^nhC~]>}?CF% ]p÷> V \RZii++onCueh}|S]XUN&O]o7w(u Rq 1 :VFϱwOA&?>D v d833m/Bv?bWi@G鈁/6}dbY{+hXsl]5]V&ݏÓ=4 ӶD*s\r:"nCer@ﺰqLj ޞh~/E)6:>K˷mrjfw`i@B߳ua͚>x.' A4h}ő#Zt2~uP;h ksK2D%хd zkj:޳`:|Joסdj§~Ѡȅ@D A78OF ev-%a+I$hE8a? Ѵw3P*Ո;>S@M7VG4ؚ l-B~<(Ggj|ؿ!. ?:o_Ǧ,dO#;}Tf[7mt2nm([4R)ZloIDtj85NƓT H'OmN 6e(}wUۗjJ'O2*WW_ 9|`f ̋? ,ey_*3 i[?Կ>NP!r g#-+H *GBp'1TWBO (}HM_$\-$Rf"z*MhMa} '[?\WuWv\KQL& aUCL4@֫Hxh?#>!H0 "f2c:r8Fv2oT p>Ş$m^fz֭V[mm\^pED#Mv$ 4MMerH{f#m.>/<Sl"ιvIp6RBa=.zc+ 1ߕ]4l|fѽs}v:uaM:=7к/jqm 6grt$_quYS »6 h7Hک:aϸz-i#!v{ \.@;^@ɛ}WmsoAn]P|Ju{F"!B4 !E9aFl|n =I&eiC(|gd8As[@%=D* OErE~xz5Eʇ^ R,őgѶ:4 e xZ|62|vVǿu<?kEJ SZN'R_z"ϝG-QI9[HemmO9h_#KD_ m- Yη1C/OE07%RJC}" :؞Es wH_eg="`N qD$OCD i|\`zOdٸH @MOl#҇MD*dAqK.s1zHh<'? He#&6$Y-4UMH諊O``ֳ1E:%ep?ݎHCm"U_Rr>@1>Sϡt*C!"t.L}d>@>~6)\v^6;#hEVl.>h CŔ"1-AM 1û6/vM9`|6kѾ[OngHD/iiX9*7ֻm_(u}0+wvE]XYj[}fgY_šV`Ng,[8G{u):6K>a- Z7ќGpCљԪqB}/tA^[Wղp,E1,Y|0WSt2RvHeAt{t.?DAY : 7vB^{^+Z|Fg6DSUV* 3܂hH]l >)F#66-f-uXl<ۜt9jG4;q=s56 # 9).jhӊH{矐Fꇯ1Cr :O3#Dow5p:PF"mL"x=sw$\=V7d!%(qD_Tf{DAKr?h|>Ļ}/=Yߏ s!sQ6_G~?b῰6eOE@CzgU}ڗ2dUda@]/BPD,NE@GL?@׏)1Hx{m x賂z^{Eϯz@Zg |ɫ-Bhm\|V _O gakB.=v~ $8| ,hkCxF7=H6 -CG?V9_hp"rcڑH(x i}kC y b."Pӂ; IDAT}y01vH8  ]pg83o#a#@ĐkEDAB#N+ 1((GC |"3[ڻ4l~ALL!!1u r--ȂT&ʮ# uaMg|ΈV ," {&gY;d=$$ I,R2R>0,H. RD) H ),$H !&6}FyO8Zbï7]мhMɟ39 _PDO[8DFws]Q|‡HsrCvؽ?F1G'I;K=@ -%vvf$ߊ~ bD| ѡG_rDF ;9B~puG/1Ϸw%.D"IΈއ{,bK|V{~T"@<~J.lyL!ց(1f-~\[|>j|\{$wcz# E2ġhOu  k*)K7}b[鯠r)Rcy}wj'Vf=ڳϟW"/'dI 2$,!}pTeujA޹qd$L'gXVRXg+(1+k ҰMmƹ7=8L ZRh^0{806f+?Ek>bHiDh@WemEc}*[gcL OȺ ӐfK`G. 2KEǦ+RأDT"v:b#cr*)bX&(B>\/ksFk5pF"@`T$[ZЭ}_sZEb9t @9L9B 1|U֏[dk1>`kܕ;t d3q|yj$( !F-5$5iGg;a(dpv Ӽg']Y|J^0pGtkuFj?|f@ޕEhv:w9߰_G/,(8m{ HW3w@{iR)_B>;# ߊ\HAuc#4T}qRV|dɵ~b}=SGsgk'V;[pňkH87$AD!@d/h{eȏDy!s:i>3VsQeJފ'#kaZJ"9ץ1(HYLo49lp6lg$Y9ErV#8DP]J! yvĤgY_ ?lڵ3pn,ZثtuEɍ\GZkz[gtˑp r7L_Hwq 3s Ww\'c!`j3lؚ8)xp]tF~H dg4A ! ?lDע= t;f]߆ ٵs;+lmmrWu:ׇlUa" sB&4TWX}XQE9 *-6s"A,'NxB !/j AfO$CY_+Ihޓܹ<ѥ:ko@d_v#chT/ މ,#!s@T6x~kcT"v@*kb3֫2 }lͰ  ߔ\>m+h?YZgoN |t.?gnjX[N a 2=oK/uhMo,$("Wbyh㲲Hhh]8K{uH[ icm>П +Hj^J݃`O$N%bt}q6G{[HȋH !xw$D u47{!sr+@7cߞ#"u|Z7v#= xe_d,o.zS"9$a^3$\ Jv{H3{h*'L?kcs[6#mg =GY5\:YHPp![.*$C|qeֿBk^#z;?G s)rx=,ļ ?G fM[_;$ Dк?̓h[I})cե3!;Bdu1~; ~x|'Ɠn da9{,vs7t+bR K.I{/{߭?U6Z|f|a67"o=o l@b%:߳#^*l0KY`{|=('"^<,@m5os<9Sn}b\wE]l\!^)&tX!*I!7@$LJ%b [CeSZ!p(aԫPV>bmp{}whe}Z Jۛ4 >_1Rmإvb5Uu []j'V?|$f{#=}{6ycnq"b{[%6OQ=YP+I%y iY;!M {GAZ9 eG_0q]=v~~}XRdDB8ٽiК9ǣ} "!{4 \7axm~>: -veN7mf%7:\Ǔ3S_춇BC"Lm>xhS'DjY?)DeQ} .oM˪]9ُ"y\ha{dtOFqXvRe6+w%ps-zF7#hE!(oV/RXЭ~3;ϭϿƒvDg\<;9W :橿t?Fj=tKsNsC\ 1 lNI%b=M$6/ C@T"6/LXJ>'qD{s">8^o}ZSU9݌dޙcv)3|=j'V7Tս`YQVp7r:m=Np(h2^XT"6i%HX>狐{]"zk|{""诠C{VH !zv0b!4J?ҦT B_ ϝ/xW35uɾ/.'7!E.@hYom%\݂!u#MeD$ʍD2&m|`};|7WmN~U$#}8l|2R]kciFΈmgֺ Y)]:xckyHqqtoYmt%BZ^LAV{ $b5x@rZf}{ISS/% r@{}XFEőrFEqueo"v/$d2A}X"|)5 QYkhss{_nd;Zc[\d9o3wSwFtF3wr;)Њa/^my=]OR̔! |ng0U<|oe9 ѻh_hb%C68>_޲*x$dHprD?Ǣq:V}u:d!H PeOy͞G~\6/=x<:Lcs/T r;9߾kA=lmsuM倗MSmG|&Om\G#}Y?F{Źx v<- <쬴nH%bVd*^!T}͔C9jmo;h=;۰4ҔiKhoB{b>t`HdB$6_ynKOFgco഑/tFH0ɝo0,L cZ|!x1:p!T[d=c0'4 X}\7E&DGY+Ҁ/AM}2ߏL ͮEv[$(i:5.E mGkR96;肷]\KDf ^yx1 {51LB\dx2]WwI(>b}ﴈ"Fw>kWbN ;DlF1;6xmc1H hjFAdos |5 #f:?E ?z/A"<^"$AvdBDoD@#]s-mwԇ3޻"p2it\jK[؂ 3x"<4nC`:^)L?+lc\ru2=<OēC`z9J”89^deuA:D!VS0B!*4\xg# hݹV#7q?["'9E`GnI8ob2ܦ+uzvA@{K~*OGg`U6}:ZY~ht7ÞZ2MD[1C }Y\_@ʖxO[hi,C+EwaD/&m|P>{ =:m\ kO#afwqDT5/ G'{/6Q"tE1xwr7T[muaԏ9fnj'|HH'#"JWڷTm1pT?AsK1ˇpWu(ޚ\җ=Wb%9%E2[m5 *|Ю;ClUrVxD3ݕRX<4Wm6Dќ@cVv"7ݎM0:.wb.A+ίpUXEzg$( B_v"mX#4bR޳OO@L`>"wē"Ds83DV7Ydz/T"63LWo-,A=-Y3hKVݟ iF9!BYNF8~F u !4{S]d:kkR\M"8\[[W WeH6*GިtqY^׊>O] L3}MS"|3Mb[E)Bdae9>6 _V{ӎ;~c5gr02t^\6Dw횖5/mY((fUaFk[[*8LOE4 bCn : n,5XcSGP|hD_"z,-"u : _ppuŌ \Jyƞ=w>rs . jZ{a}ښx:{].L'g %_#DL]=DZ$' E6g"z޵9 $ Yn|&7tFwώD|dIꁗU*6Cu{"Z F=}N@PLjSw $Hx0ba\]ux%b:W"E+#qŦ[!P 9{နQyȣs ԇIsZMUݴډÇ]>& | xΎvbuGў~C& QډcӚ:G#qRs%:y_O!9jWucU,k_"⭃FYu^GyGb6¿j#/j\` BH$ ^S0_\ը*Ev\O'tܳc:CC5Vg=`+114""0Sؙda.]UQ9yF8]o?| AHZ:$x?wԍ#Qg"W2aH~a~h/kcp*vc{bW܏o V|YdOJS9ٽ6xS!luƃgnsfxjXAc0>Dg]_Ur|-C +Ê s r*GǁhÇe ƕAf?#Kl?_\2zΕSxbDSA4tί;#A;9,=#Z2D;O-ѕiA Q$tU!5>wT" σ㐢ݳ!2sWnp_,m<>4MC%d0Ki$IvRky0|Q|9\ċUd;5z"v8!NHt͏S@ڻ@WOS IDAT;""d-W4ZȲULThM~Κ<hsX KZAm{O_[eǟ2m ll+OB<%A+xѾc??{6U|GlZý)]?\b++L礝q s~}X鬦D|x5-LB ډњ->,kPL&xӱW=z)0\Dn)茁h≳da ᚨOB;ҹON)5Uu[uI؊H\[&>/DDCW 6 o bȗ6^@GuA<> Yv8 :$LA ˭SsSy²<'C2XwEUkލ 3GDE 7!W?n=GoW8 6$""*nGD}v*'?Bkc3bOgtCdzs#!DmK!D@Kߌv6/{!uID l>-[Z6&dg*[O!^w8tD{>,߾y@D;?a1Ahr ߺr("  S;޷و#8|<+e1ʭ`k Q'gü}P5YYkG[ӑ~le6ZNsUBYLؿwp*)U >> }aҽ7Sؤx2}23O盏6j"B H3S4./:aHE'&8?!<\%B$N@sqVp0Aj\@HtOM%b/X GP D?;-]#"0 nuqj/"Wuiϛj4D-6ϝhjx {Z6vxi|OYbN\ 7ًu< o'&y=o7f p4iz|\C-%3?& 1~k4t2;?أ7WF|aP*Ba+L)rW XPdA R7oH ӷ6SrdM/P,w7H6C0iM}~˙h~]Vn.?,N5>drm/DiJ-DvD43D"Ah]@]V=.EDl)'%֯vQO; YUY3\>Jȫx:\xN RހxH^QxtG|xQy{S0iK_P36Ws"0Qy>@f:;`~g!R]csX/4=M<|y85kk= v oʲ"`o6۾_ HiFh~ )3Zd+Uvk%Uѹ eytwui0oXryoZP?B߁dINPSU7vbus[jR|F"A#fd9m}J\ %ssveis%%klqWѾre_BhT"־{ mtn1iwEދx AѮ/'ӯ DߖJ6Gt9>%aB*=L $$7Ǔ'qj?Yjf_3 Z傗ڐ#sճ9SzYsHx(~x!ZC e| UH6nG$4: u(rg[fnmosmt)m|{ H#7hO9 BZ]]G.`y b.bHH_PdZDx2{vZ0$6m.C]dwB!H9 Qo#|Ql l=~w}w|*I%bRX;[.YH{Ŋ]g["'S7U]G]<kgϼ 3ԇ_$C"-.)/PFs4-bccVV.2?D{5>OS.D!hw}_l\_kDl}8hsqNSӗT$p;d;tNːRe6:#!uf: !DǙOO8)jVOw%xQѩ;{ yb킬jj$LW"7j5^-C X啇֑Sd A6[lJ_]x [[O\Dk2+3 nށfB<^*5k5:!)`w_sS"}щH.xF ص $d<(I%b7?M]W Yh#oWtW0ςo}rpLHW5d\"!eMx2Bֻ #N2CڌN*ίX :ַx2ݭ0sKٖ`/hZ{<{`Շs+̉ %~δaIEZ}Xac("T?#!ޥqw7bwjuvbM˒{>VG3j|5 =ۊhJDB%& _@ַE"D󾃬^5(YJyaAQֿ!O*Ox2]▻sI9&ݻ_-Atz2sNkxN)Q>c^jG_.Bȼe3nm٬~֌ +`גk_ֺ2ȁ 0YYGsKfǓΩDlƎ+jm ;-.)-oX+ډ >%HV(G.9HKۢ9sіsWLPr߅dz,|HGZD\ 5Hifغ]S*[뵠#-U!&{|-:<?F h^\X4O 3vB769j}#Hh,CL-Ĝ6b| 4G3b vK[^D4s9 WC6ww ٳjcE)!b:#_ ^mKm^AB;d:wSBD.A!w i# cp[b}Z /Fʺs'azc]s~*[oo ~(_U('Ѓ .9ڗ6ֿnkfܻT"vEw|O\]gGUb{UADg[m%tq RdfnGL=13υ!j8ĴwLœ3m-ׇQ0x[ٰ Yf"  ="Le kG>lO4#+,|z 5M<3f;]O* yO&d.+, gH>A 䍮eyΠYm-zvbuW#5Wf AkU%?n@,',Uc.LVn Edʟ' A4өR:uh-9C(hsl d)s[],>F%bhFtYKǓɈ8ȪThl3-ln@g5nr$-HAqͶgXisi;<,[6u'Ʉ 6?x#ck8x}lYVؘc(7  š տeCbWģz"l3;#g67Ԣ2)*z=m>'q`ŵol rlܺ8 Z+韞 /{ލnen$ # vKmmW:w{E{ #|)z[Sx׼\dF ~8ڟ[]X},Te[ڊatmr Jfc٘f]81&D"u: .mk)b 9>[ۑ=˞}4"p%0k@` D~(>K!1([y|1m{^r;]x)b"ՋH`Ck[Ąٻ]f{.G{T"Q<Ԗi//A?>%Ȣ( CA|N9pk>L#66|Vdj缁4/  p2>mAW侲;סr6bGil|, ᅨ'Ӈo0q e}vk[孓kkkی*tFVU(=ɍ޺ "̵ة"ޞCs-wWAp=meYNYm)pfeI?A>lH6vMc krrխneP>ׁdT"_Ȣ}/~'A9]ssKщHy">R z؈b.A 'L" _bo Zî]:`)D##w{ؼ@43Ro;KYs7xh++DѼc!ev̼ g'+Qr"G˖#e,G `gT")GEvvX;IYW#WR䕲 :Xsl EpTWNAM~Կ!>p%x L'x2=Kۘ6),e4w$r<%>׾&T"no}p؋FY]sCplHkkC}C/a[$sROיGDs{8lf)~dgL*Le׿]"A/˘`o~ZkG\kڶJ77!_kXSE¬srl.sPJVZLH8==RKZМo4 Lj7| \BH;3<8A$Dw {G]} d5sFg߲T"6=LwA#vuŧ_Z^T &P9Vē?h/@ږ6C,d[)w/.Eq<>tlX#ؼqk|{b\KwV! 3u?޺7$deDlҪO/,FߍDִtHJZoֺ&rܧj[\N(Xf eM斒 ްy'ڜbϺ dYnH0>ܶ4DBkAEsgBp0l@FYFxSF{ Su-L )5J%bX+;| Ay-0@G͈NAǬtV9^CxѬ]!}#@Gw|o.F tI*{ ǻ>Tt^:#Z{1:/#VfdBhC gQY_?+g]C'ץ6s=d=l.٘NėIr( A'^j} ngg~`p IDAThpœ[o'W"vB*{:LFt)H%blOhAEg?*S]DxX|lLl9̺zw4H%bK52J8 Ax dkWeK'!4{/@ לsKBEV b0̲63Ɠi{ތ%ZDln< 7}ti f};Ow <]a>"Ț, C"2 )|&\{Q" R:w"e$sz:6ltPBĐ\]}ք#| +gІ=`+q<>_w X+X;ޥ$ 4 1Glnt/`|LFB+.\ϊ+t/DD  3v& `z$^01N>]qFIBd0=Sh,3b-5?Xae>]+RCslm}96! KQwrzb71#|_]!sC#C]=d}8fs.ݶg[<~oŋ#iX|oo\fIXٮ2(ke߳XǍhAϹ>h=ncj|HYhzod# @:"Wzcmvu :C!](D#LD.BLi_G췈~D;1^5!|+e$kvj%q騝k\ovAoqz1=4c?C{"{ &hBȞP䏞E}6A#z2K!C8i+:"G{ @ bsc"&ބ!A'7"Q #KHa]!p([d:JH%b//Ɠx2'N*kH%bdz[-r;}?8{VQ!!m4pj2}cN_[f5ˢ-/!hO71;! ۃUWcqM%9 djCt "4 D }O"s:SE'@)/!Z ;"w2a]@Aʖ3yBb:' J?$v' :{_7!~\|Pu>yѨvBʐHHT;./"o_;tyH~a2] QE m$ rO^ss'ĉ̍Df x&;<:޷Jҟ 0.⒏ѣH IDx2>!6|b':6C9[$܆ѱӲj^dׇ*w%KG NAvޜ$q! }&]IXEk=򻯶[}-Uw`c}>iS X9G9 SNJ'x2e8b"׎`$<byxHS .F ƽmV#6v@5H3"|0{Q\]DVA|)"ؓ 4=s༜hdhw5dQObX4-H87d [wckگ65Nt'|1;R؄T"6-*ֆaMKs:5 JLfϭ+և+~>\Di$@eHznKms7hvTb}6lsuMlDeCpOY&MD|.Hގyѹ<ň팀IghKWDnA& b#q;R@ JG=xlZ<t"w>sa9g#9$$wFtm1Pܵx]ܥ :n#O4l,.׌R#׊_m>~eBU 7^wwUF#G!2-<ߞs}8)ƥRQ?F&P*kCnk v% >X ]ː± /T}:w YѢ-ǻj+̥ksч`̷' ϪSSUwoMU~cS}Z]K%bǓ飑 u"LڽU ՠIȜ#>`Z<RdTd}"4hK>y9P+͑m"s3b/۸oE(fD(D˳n>Z-c Y?["p.po*;Ӯ-LGLradWZֲ4{$<^]6)#\g1:ftfr*v%J6OjܝguFZjplh{Ow4ÀD~ꐕ~ݳopJ_r-._.:^/#{"v@v<!{1 ÑE+hs(VLCt^/Dq,=ڏgܪ6]n 1`jh-tD I DkBB> B`zsƶ nzt?9ܵc0cJvgݕn9{wfΌt0A<~+ru=!²m .#i_iǯFz­ l? "QU2$D\iaHOB)[<>bwŎ\EWj[r/55J'!"2Cf61"G,uĪcަӀdzT"z}iьig}ˏP1\<+^Ƶ"g h7 tzv8ŶF"ݍbDG Ɛto2,V~Rt{1?yewoُ{"[JɴStyZ6BJ]BM.TH9H}0/su*~BJ2:Чh0z#OS@Ge ֆHM)6oGDlf=>sx!wUw2І]ۚ@ n!G!'ӷP| w% #;.LHvyc=Zt]+_R(qwA;F#ɉ6[dTؘW: 65=Ȁw4st.A*JĒdڳ8ջ?unu O͸+I<I-sJe+LYqN^&՝!ϝ}]\÷(D l,BsF:دnwʺ^s=VƓ T"f̠|5Úp"G;ȫ_HE9ȱՂڎ~tEw\r@W`W [AC3#e`~YJv'Ǡ,#dXEQFhZskzOs(Wo()HܚJ>0 Re_tV"by "{i 㽐mR6'UDףK-(\v"2!~2 &vH!_bE9ޒ:5MUoR(>¯TuȕW5=GU>$Oo=@Y9ދP@fM2h~W"[(^3y0G_Av'iU.u|YLeٯ-^W =ƪvʺ5 7hP꽉q/DbYXMBPuu9Ty>KqFА&6D.C3dG@SD#J)J:7!%vmG!ok8Q6'2jFxzsg'HC+27DQ&tؘ]qtD8G[OL%b.R,"i vN@I%b.T"vW<~ڑ5mjW}8!Mѵ״, yK{Cm`Z*oկ ݯsz> ރ(:-G1mIxeT!] aY߅HUXu ޣЦKVۤ JHssXXd`wAhW@h0z_Az]GN>SQDNlAܥ>tJ+ LC6"Hg҇l,O6#͏{8Ȯ9"tk~9 kJ&|GX}m:/SywE*ghc=.#<9 7A DdOWDF=+""Fxp0|z&|ls_:V6_T"C)A* N~da\vLj^[l@ #]Nr%sȡ(EY?zV>W"^f,0zg{E䶋]Sr>BΫn]Xݰ8;GG MuZZ7 etH~+-'Ӯ> zئE]v JP ".As5RsP<e)IxKj>RL!p8m`L+BPYl6"'q_L%bP''")< d f[wk(AAѪQN|_BF6 HO.H%V@AeHnes)2&6D`<{4_'MDʻa/l;F/+E& ȳ1"W&<imnߍ'/6_n2D0uy͝#(09mZȕ3.S_WPiϪۍ8iє-Gk8 Xc|S)<u֏a֞Rp47rf G] a}K8eQaK%U9ࠬ]m(x2Z.hHGOgXS.w A32 M2O&(E:,gQ$E F(jrҡ?sH ʯឱ1tE$CH-ۮi:†쳡(r3Z3 gTQ ECzE?7vBў8Lw'p4Fķ?݋Q(c:2!:G x#~"\îukN .ݧ*.v-kL>S]ƐFDAqxtczRz!#p;۶oxA`t(v6id1Xk0ٓ{1d1\L?D0Ǣ353~Dc߀z2> 1>exʕ3{{*퍡K(]65W5Ձz=v;C(a 8DvPœmlYDlJ<>EN%b-U"S؜l1{a!6n=ww'x}gg6ft >*n[jD_7;gǂC5Fg'&7wob~˔^rQu:lS@ٹ -vLr>YSSDFJV}yQ8] 'އoCMJ%bW eKMt a)!o_>Kq8 4~@o lC J]k9]k!9ۦ`HÑߎ֣6slGjy)v_B#,& YTޕ\폀'GQ4槔`oGJ݄HP#n5ݮc;+/znG);uT [U"iAAIQM[ϓB䀅~HKUAy|A@g)+>înf1pŲwK)nߥ2׮-~x2zm=2%Y? O"q+G,J"8 ^NP]u9|]5۵<dO*ljLWq kJ t\EPM|Z߷N9r#^yDZgsӶ2Խmex;.Q7 YNL~0d*.{<\S`#]Vo\[PEB@HEx es3R]:_W<}PsmMSȷPW/\؂[ 8)oWן 0zB$C#҂ˁJm_a1|=}+-Vҳݥ65^/#LDqp\lT"63LODFE'*Km\OYDm}Y|k:tzÇMjފ'3S]!քLd-NcDDڐ(Qnk 1A9>ݘ֗6Dg*(r#b)k A'P:L@uGdP-i'!gkȸwp/ »Mh]]hMYOTЦɶZs숢>.Cѩ*"1ȩv"Wv66c$~8!#^t`9*GR{dz/xT]]g "]g,o8\_xevl^'Z#^j;K]ur3Xˣ~7!]K1?%v9=E3/n(ra AVC‚B9]ODbo22P?n)kAT"v[<)@$Dljm'i~a}~/K*`2~8{#ð"' : R}wA)ZsTMˢK&)^()Yh~#Ӷ6"MIǮeEs G9Ϻ#һH+z^g"fQP> QHPbv12ߴq.&hA(hwx2}'JqEP!wݛO D Jww3x2}C!R2$n쟈~wMlQQ3n1( ɥ<Qܣ-T1e j]V2*140e~ܕk:wWKy7?}O'Y?2ߑ{?,ْRLx2Wo(""?Cvbw=49_""\ztaqW0tnE\da83J%b'ӿFJ%b7X5+j{/]Ѡgs1)99[8^ȝ ;dFD0Ǔak(n#S%6Υ0v#X͙v\aѴgE>gu%İÖ`EmS*`(Z"q,E}[~#+_"aǥ Di.E 9>p <`z~ }c#^fS`*ҿOv^]u qh.Yl79\ܽ-Y1M+/ r&,x_rZ%ez ].)()LGFxL\Y5LyW$_4 )ȣ1R#%1*bd G rRVO smOѾ5R.g# 8i^ |?:r 'H؊g0"y>RmHigQd2_F.A!lYo&7.uC7!O_-"fo ;ގ!v?e=ƓPI6VmW^䱆"dDD\'v=}ld?K!g)2fbA {@#R^ Ł2dd5xt{-z[V 5;undAl k[C\as:G?xYhP}W|tEQת7nuq }vkz;t͈鈠~o$G[ѳ)ߢ;jSEHW_L1ܚگAz[;!}r>68)BS@ХU"4~%.)/~p%(GhH?CAkRY!LzUxR<>=K9Di]uHGy1qvJrgqÖ!? bTi(ZEvk9PhAۆ0==NP@^~Cza,(/]pň 0v'dVas-=e){{_ EXje[ܭ-߸O{ tJ[wb=tk7!!و'~k7zw#^fpkìswhv@ەd7"|x JZz?E_d)~4o=eY?.zNB/8LG,]Jw1*fѴ\?{'!x )@J][?d?F: tҹ}mo H/띫++N"efdVjB*,M%b8C~Cώ E 2w&J 9)e*ǰʡ-#H,s]H^6W"doͪZ> ym#B^Va>RN! )q6/#Bi6zǭsm,/_ _"2*9RvK@t5H<ο 5͛7x2=q#LJĞEtCp}t=6Yq'J"^&険=+2z_-Z"y90y\@PŵpkqX}x2ʨw'XG:颇Qҟ"(G:}"f O>F*9o|Piﶚb;ޥH7܀.@u'"qx2}'`kz쳏IJēoTjk'(] o(XX xg!̹*(rw7˜2}_{ B:ȶ{>g|/. eϣm_D` & smv_f yp=ag^NQ54G^ xp\,w~0!=nsQ֐JkSˌFMt@=읫pēp=+}eYx6z rUǴ@65M/S5ef~99L/3zƓ8p—*΂QwY/s[S#$X5دēKz{IvF-2p/ggE9jD^(d# ksdYXaH/@ n ]lOwCП#0,AijTλ)"y96C H\; ˀ>˧ Zg޵5b2bDM\b_>_ʦ^YlP;>T"6#L]뵈,#X1DA>qDdEl+{Jٽ.tŠgiWN~t"36Y?"YS:KSĦ[tAHO<ˑNB]JZbd0D<{b(̶sPPdEmNyx,L R lhT`hhFds#̴s˖Z*)^q$xǠ̔s #R.d`E-o>CkFψkV߀2/Ϫ}&E%oWTtN@U$,ell.Y"O+F;:uS֍־Ik'ݬ?ԾrզܢOG) EHHP4y #-GPDf{ZaiֵhC"^k]:n 3{+= PڧuNe/=ᫌSy`G'Eve7D@x3AT"n#"Dԣ0@`R./&䅜}¾('x+^@!A57X$(  e5RXf;T=yKߊRl y!#׬*.uúœ@xQv^ !W {<l _QhtOg U=z\^4U$L Y+Y? k1?rzf .QJQ:b9fl,}w/s,xm4 hU C- +ѫt$dzOZh_yRvl A"ۿ)z8Ha@Unce/@Df#0FTRA``|=Q,;/PTmClB =$0zo8s""9@c[d)mR* Yu1T">ث/ ms0y\HpO]ȳ]u "K\PH~vl]fs|AZ[l%;tWP|*{5e'0LD_a_ _tNNbNE15Y"2D~LhODFx q=wj@ R;eFE "(Ƴ%ҁ'W}:ۯr~GD&HØװ" H?ڜ#ʥ7|7/)tc+v,I](, Q"nwGs \tm\RX}<&0 K"a VmT"Dl1j LoclS "ZHЋj1Lh0`m(F!#^&*Ռnǒ+O^fd |㲈Y#k,BE Fs1yl&{"s=x#0p mH!|ۮP+0'M'ӓ{q6@EQm,J)i@F#'œD "[!29)Q$lRd3MWˑGy(q#ik93M'ӛ]Vi 2(9`e%A_6 JQMzcaqs'jswNYֲ4gmztϧٵnaζ(=Lo~%}%ϗ辗(T"-SCbQ,QEmӐ>zOoF6FA7u *N#c/£H4(x2M%bƓCPd,݃; -:OٽvQ3u)­M {XEǶ <(ϠlMkrE|z };a޺a+yk׮ic#ȨnF1>/D8`Zg87 Wc]kscDQTivJ?x)0L'PvOO eL\[PTT"ve<~ʶp?JoH\ Juss.Ƌ'Ӯ*ĵBx'-y"\%rz2!,Fa(5v0V dB(9lCkCۮ+>'x2pm*[yvʟsDLY'eUn*6-w"}H&Dx2=ruEk96 SQ ‘q?9HC ;;95 zKqs0&LEhN%bs-|%~㜒ՌlvV%.T^Ze^^_7A+«)Dlg>-F<9)f 6= ҭExLcʿJ#'"hNgDxBپ!<Gփ9x'h͖sեҮ> ћV@D vk[jY{y r!⛐ˀev-ݖʱ^צ^ѫ=r53 /G~ohۇr x'wv{eqa9߄܎l3ݕu+>d׼Пuؿ2euS;$XOv[gHtwD mhUv螭DdHҵȾsku^C?mQ `-2kPH?c۹) ;˿Jw "/GmufW 0]x쏶oW\Ed )޿"kߵ>B ض)FZz6DѼh"_< *">'^"oٶ;#ۈ@ԥټދ }[Bl{z`[vݽv4A:^F^2z==%LJner2JF>aS#(y pQ֏,-5e6fYݲtUfhL''E/t(j-aD p퀰Bs Qn낢!`nǣ(]ck㎱ "0'GgœQu"g}w;,"TO"+A4qDBoqQFDZ9z aw7D`>wxg`kߣj({` ·0Qz;n#"?As .5ē+m,7XcpUpW{T":>b*ض ًZWE5=`K{'"o}6x2Bk/|zYMYp ["Ǖd ȑ]r*8dg^t&J{~҂\˲p=Wz?']"^fC%God: EFo8!;[/H߸t TsI鏀GDb92 J6K"'X:y}ö$kWn#0| ض[~m(UqB`{#hBy]î"b ;n?d .ED@AW\!ezZH6ǓP]n2y^ (A ;+\ogf<6^-B`_>VDRCJ(-`Equ~@(,e~MPnĎ =lUbD6=o.XTs +Fs))c2J!eC[/rʏK&~a:MRؿ Id>^N%b d+ HO@늼Ѕ+kp2^@)ԭvȰw{z>Loeǐp ҿ _b/)rq_ҁU$V7Ǔi$Ft({هѻeq\JN'Ӯ8ض??5œi 1Dlx2)* _"+A4a;O '+½:*b-6d]g=ÕPw.XT"eSf!Gjam}o[^ii=&d[V8debsyo';YdTgmd|jxvG0YE~iVKέ__ē|;y^_wl$XPt)եzr)e"gė oTmӜJĮ'O />*'ц<+ J^о'V>ưx2-'G!"K8kB'ѸǑR+KA/{EkA}-2 2-E?h+nsm!P\*OCJf;J;Ȱd8Nuy(O[>UTOD)'e6(' _ܝmgZ [5wF]Bƹ/"H)Kl'\OGE*'nh;҇Wl:$lY8e$^CDkKSX'T#SjcafBDwFd :@74rt7ASkgƓn(:x2}"px] z/|_px'rhpk/bcU\O(өDEվ񯵟UyAFߜc]K*5@<>&ꕶ.G({Ša#N[6>;3}vsv̯x4*VpU-功x]mAת2%(J3A:m9*SL5YRŌJp{chx{}B*?[oOFCDվ"S 5!'J 8(Mc&.AE,f!5)Qf(uBRlG^P~2zaOCz"/v-˼ GJ_^";PEqe{#.N!"" ơ(~_;s6OE"ұݽoE=ErAF1 JqsY\W[:AD:i lODڜȃx]C R)qmޜyݿ `HœQ z&VՒ/گSē^?#GWHީ3eQ֏|Xj٥H_Kvխ+ZW%]![5EH"o}6)wi1qh]v3QKz;6$(Y~"r*(o ETD<"E= a).A&#2G L;x$aDX/G@T"jbͺj9+3Sx2]\4غ ƕ^[])4yo?Zփ}%eAϛ["ȢhlCw5hl]@U-6njG:À|o>|_\u-z#IFwmtF֝ F/Bտ0y% HA6P-"A%P٥s"#>ȕ NǍ(#A_mKi9!Xz@o}$"?dŶv/(RDV1y{K]"d>@p7D!jEre'!,He/[֣C5hg?{ѳp|"U۽"7g}Q*J;_A!T*w< odT"T"v9iCQuu/ _.]|cÝн+Eu/4G'cē-T"}")["%H/߈n! 7,E#wȌ 0zn ޻a|õq(sK<mǯ$h>8LǫHg7aTA k[EМEq>nHQo=m2gZpvSj6JDܖAE ߇Ǔ([>~ZT?RNEx8JB'ðpR/o2)7/*ܺyaqYÏzy=ꦕ5.FҮpt5 8r kwJ"7#U}-x 'B;z>BN{"^f'd#:IKw&ε7-(Լ`y\ˑxO]2Y{iH'Zwr"J-*'Ñg뷩DiLX: AV^0V}dנyz9Ab?@!Fsœf#p=)(R֎}!znkEPn| J1qo(AOt=N\\i_]l߄-F@;VwRunMvܣQj6(b%X8,pގ֡M_1}HѸ5\ev 567l*{ L_h݁Rn OBXWF-r/BL{%_wUp}C,OQ{+PrzɘNV6~F+_xa1M-e :q$"^欬 |"O[8lAJ#z]ctm"Lx32MtIAĹ 5c'Xg=^>}CM:ε!r#e^/:у!S[Cgۿ9p̬kI֑ȭfTex2}5pG*8(HȊV=4y~&ر棇zG4JB4Erv/֬p 2E܄HF"1ȈEA$j## @ u YvhVƹA7PIR}RyjK%bƓcϱ9®IDA οm3F]Ck%2 o-<şkBZbs*Cu x$LJ^@\JꑲO\oq^CkH%b-dzTweJTS֡d܏"c#X}d)!D,OoA{" YdL#QE"y-mU̥G3Zҽ/"2AΡ(5·8*;3Lm߉k A괫0k 6T-E!NP i\-v ?;OCx8FXX Ɠiý{['o GdMoiSXU.`!r7Z )Zi[|o~֏~5j/39nC7/Sሗ 6X2a 5@Q[A/Ss*}B\'mr"Y?P3V-rDLyY\ٲ[59D6Nk.SJo5ޢC|k%t zPIY9Eܺ ;  %yJӘ|| xny$G!XCR7'RD*3>ʜfsEBOk5t:ɆKl؂ͅyQ"`|utNi/T&9duHaw"]3#HeE O };r aUEu]{*P:7fT;{.A,P6vF59 vv"z:Js59E#gV?(+d7{65(j"x{ Cec>oI u^vh-B~4«hFw(ߕK&Rn6.dw=DW>۞(u4+}0kAk e+s~G>ޱSkcC%[do^]Gk5/i% ZZ*Rl\x;o*Eu]>rp\ EuE^Be  zz &N*~ĕoU֔TBƐ$tWK `U")R"B Q*o$2\)ÐMƮhFF<(EGw"k3gͅ!Pw-Q\,Eо#N Eن/J O'3qa\*Nok^I 2Wcc#6JzPv c}vXڧ8&Ę ;ɱvʷBF:觟s\$R gHOFS5a'\Ҏ?r5۵r?>T}dQ%n(b'9E50  QZa(};ݑw }J׎wFp025GQjAZ|]vGع=AÁ6P#\.|G|XMǍ?q\:k:ozᗯѽ1M Ѽ-"//N1ƎA(49?PtwvR)Bs|3dlww-b~^iE|*9?֐cwr~ly=3"X 4^Ua}o4R)EHUa5ZivN35oW`3UwF췞tnx9E:()=sYH&e" ; z,?#E~"Iq p A:ݕE7"/޻h km3K,C~Jd8^`c=v/6skjoBc""a߱6D*%mܷKMyU5U:`b}}Yֿv!c1)nCgw߮׿qE9D&kR*cM!^KŽ'ȸǩ(P{jtձN7TcjwKTW}@H[H);[#Ma_ߚtvӯDCBN'QپHABF]#r݃Y#ˣ˭mΚ?Ȑsr }"sܿ9F]p_D`J!u!p/p2{a-ҡ!}+ªc># 8 O=w_d#A`菜t%wC;{c59XC?aqOd}ߍ%R/ON2u ~N?/%4`W8umT{_/1?eEw:=cˣ^(T7[ONjE<Ǫ7ИooDpV!{CӪ%UE-M=in ~QvIyi‘GdBFyhΟu(8Pl&dˆ-|0P4*}-r>W/<Įl~?Aayq7>DDCv2?LiNOGQ/"Ys_*ww.z2PQ/{ < >Pp_ب=vS 4'GN -{d @6&FjZkC{r[ޡ4B/{1kף^<+x MLmk69!dǸ+6zl]|uE9Ne%THJKP$|6"냜m| lmՄDz-ϑ+Tae=]? a{gym%eK*/eJgoI]-;̻jP,#ۨd'>3=ӂވ\ҁboy{ BvA/Y-JGϧ)e9rjtANP6>%ՅJ6 ݐy]g$z! C3fs׼D<)T/Z;I"4Mې? e-QV#0(:u]`Z"Rl3wdO$xlGml,sb[@`}jťG!B`#-+PdFHD.Ea=gk݌~Gtv_admu}O݇Y}]#r Joߐ&^k%Ԏm;J"ˮx98-a/O"I#M#XSh_N)$RGSt2h"qoC:Vd"w)<F_bߏb#wF$O{ CۭH? 1tUԔ5  L2.D(Ž=1rA6¡zdȾ *arD#fPMgy/2P_Du?¼2 OG{+'Tfx{в_}+Ӵ4҇ 9ʁwf_nwrOw!ChQ9Jy`>A&zFl Rd=Nyl%{x} QڣPNƿFQ/ 4'تU kg4ٌFr]km;uT0%_D6i(H==lI֣$RQM8BM8$U| } ZTQ&4"r6νw_4l sgo6"N{FFD %<0#G OC`EjE\ bW p9AI,cZmjBQAW u{T%"P=!؏aO y@݆ S9ޏKP-tz&_`үTf0-|oh{ۧqiPoz\%\;S:?%آRh5(Gk+;g5(ťu9tEf"R3m@+!f8)7r]av- i}߾{+MBTpڼky#!Et8RXj9|\oU=?mꛖPUoVܰ"!B{E˾r^v429?q_/*^Ky{S6=1UDKCvRRʑuȓ죴Ð'JkCdHljDmAgy9"_m(-M`깖1Cdlh3PTwZyd|j"9 cm>:ߎlژkn&! v({a>"~gؽ/vG>.AU,nCJƽp񖡉U Ϳgo U^{JΏDm=Zջ8s uGķAc;<~ 0N zKn{l?t}k݄씏+pcw݌'Fl4.D΃k5?U^v{d݂!ydχ]eN,AC(PA/ndN~$X`5nx.} 2hq9E =MJQcv,ZۣI^mYh n6(%e("(z@@v2"q#:/RހHv#""7E4"/lwT6mz>p.߫=W6_(m˻cy(߰wv%"t6FP5ӾX]/E~Y;uP(e'Y'\:,dm'75&0+s(".9HeXW}\tWd"HHe^Bnr:g(WB[g;#B[qԙ~9^7sCPD^e] !7J>9B(w;"xe!>aKOmkuX+~z%2IW }r94l+PZAV8ICX=N=N-ӿ;[J}G(_kop$WIDw'h="̝m܄cm[{iI|<5J4G]%Xg!ɉz=UdӍG~2f~e''x%^DZ'(@ЕQ!Y\m[b+ReGw^zeĥͿw\D.G-Yx!ߧ9 jF@Q/P%ʁD{9?kz?&4}Zdr~u1^s\{ՅC_}}pm:K]BP:n~_|o.M1-t#!>DJ|JD~EJb &k:RT}kFQtطF+9. c^#; E@}r6Os]B+x:3D$)!OrQoqhn9T,i@OzmI0'k3Pt+D#:z'عz1[5lmv)stn")PlcPw;6ZI'=XEέ-H0EN'A&d"OQf8Z"+Hi(Ca(wF5Tb[ (HDBx};DD'nvY M}۳{"}HPQ_j@Nͭn?Շ %È0g{VsW\?^M'6n֠F-=6|ز"exk/#;w1s[d_7]Uіc|ɨTcz=jnk,+/.H. =o섗i6_p;"GD*pyY~,+sYi Tg{NY޷j_`Mjzx!JB"] G*m_B+rN1Pi12^Gz 7I$X_C :)D$]_&.h4Q-o^NK2P$'D@B].PT4r)y)QA0_ljvC` -@А,U b5Ʋ~}Ήv 'V?`aw(*n.ؽ^fj0"#̮y1AkPaTSќHH]FjrDe+ 1hq\Eu0ͅAu~G,Dԧ!@s!c¨PiE/ 3?ԭ_E{=c 6Nq==s 9 \4ӈb uFkh|e \H|CvDk)ӻ 'nv("rv ҙv<7!`=\89 m͏\ث3QdhJ:6lH֥7 ȥp`y5%Ʒ_tQ[#9C-ӞWڳ|O@ IDATM'#¹m2wߡq>nh{ln)f}[ef;8e1\쥪U%K>3W]Q/{*qǎuX2l>qe؇Εc 5_D!MMmEѱ7ފ?& ܷ~>窖.}ǁ^w 5JhK\/x^H:j5MT:~ I27 !@]HEF(R1~-ξț MNу&h#Q:G,2U!`\(k5"{"=]Qg"ȃEJY} ^;f=c<](mzEbyߎ;̞NI'+䉜 h 5A^Sk~!JuvHg^^g2Dzq}n.]QYt2ܒNƿ5*T}G˞L;q1ܥTh X;^7e!;`ZΏ#PL2}<;+G k!ӑu, p)!Cu!X,F\ &Zw A7?-o<{K'ׄ< #=D%H?!z yTN'!ޯX`x[h4<(.Xx!2"9/ 2qh6y$شyS$kuͫ>H'G"OdfDnω'v "PgdG%ȃ@ݰSډq;𫨗D^(Rt26Gj6z(<=J\N} z@F+м !/RzDuڐH˻ώ7{mZ)%.d|g-@h})Ta{#핝WE؍z#r6r|9HsvS(ݞ-Fyg#I 5FJ7_Et;/%t F[n?^/KTIotmk۝c]T2{W]1`A>@<[Dl=)e}us~lyΏcr 9ݻseFȽuǼvЃ º&ezEQ/{eZG?E)Vw|;Ji,tb]AV׍ ZnyCWǁ7)B2p@H2}Dj]7j W1mvY+/ڔt~eI2'\p0}dAZ-7 9GuFF?p \~ 2b!@,C!JF!QݗSrDbD|62Tg"R)"T3k\Tzi堺z ruT @kgD&נ8#lBW<؞B`a.ӚRZU_K]<,"O$IM$R]\0(p=:D|WٳG"hL''WVș}%p_z :f߶K5T-.x_~HK=}d%_W ̗.zZh ٟ2MJ:SX|ːx hBR\G$t2`"t2~cͺ$ʌ"h [W"y0P,/)4Ej ^O=ShN@|D6CX6e,?1WaN޳dK|?ݎH`(Oѭ~K'veAe3ڒc*Zh#Hp{DvEYѳ v*ϫJ˞"}:v]CH羏h'ys4yqLU=lmt/L&9bΏ=mio4rxF{Zlc5_e{~g>kI2#>,={έ}oe3{<0㾓mY^`/RhOK2(*=yq5^;4snmMi.ע^vsKΏ:m֗D*3)JT2 ?5ChEc}! hEFcO(1X=U8yJɹP;yQ7\T E]qli-C%Uxue= d.AQ DRNzٞށ1 ́)hD{)gt>4=DGF0睇5RF +4Ekygy6 (Ny aęT&eok@s[Gۺ䨨@jk.T(SDiu{LφUow Wրj/(bՀyn]LWmFN|ɕS֧ՆzA}נ\tU A]]jU(Rӊt2ސHe'] ʨ.mb'AϽ %GH!wCMWB9NCu|Q/{>nzYΏG {R"Ϣ̆z4z٫mKМ80_y?;6 2-жUmY=QQ/w뒻3kXGcmVXsYr^[ZݜbbW!זzo;dwoΏc|[`} I2}`8Y{=̮hQ-GE#BsRPv]B`vR@ u H)TfxAy5Ǘ l.}jW^G bB^y))m^|<&$ !O߳vbεRJg7}QzGDb툆ۀs(j`-O'{kyZt2~kX:@pv:ݢ\؍YcY'u9no|=zY1s 22ZQT@#A t3ɳfwA[3.EESka> е&ؓj3ܜ+uܦ۞ܝ)м &RP:[ц'RW5@{*2<ZWLljwDiTh-2\ظ 2Q9Ҫu85XΥoOBmH˪_ڣ %= 9CG_GѬӐsl&e^ 4?`?/v9+n.|~e<P.X  e wMoU>SA(Tڨ9_CΏD+]<)J1#bYT"WCF'5x%zؗuօ.K"Cl\.";Е-tq=5,iY~V=]~yE)b|+N'|R[AWPt6r)ay ^DGCiyx!"!B05Ox4u9C "lϠ"PpD#މm{Xt :?NlK@h vA:gpd?BL.OPWpʖprJ_C-S{48!r]c:9lXb㜏"V[2J\kwZydh Ex oT[AX 2@i tsnnsW`^:oqW;G*ҁk[5[疟MG_T;( ͍Y ]^Z 9/gʒcs~l! 巜-٦>zu}|傜{J#J۽N?~Gν_Ŭvf9?zQ٪kT1S'.WvrcQiH|tF8q 2tWZL|wB@&"`rt-jJP~pCT И5%.8[vm|#ZRIGG(2jm'E0&79=e[xdRdD*3-8-DF Hq-gf gD^l:AJOTID\+ݷkYr4A:}dTt2^9۠He6χ}&[_d腯l{TtmaCާahFĵw+yi윮A$!h9+bnvvU7J?\Үy0>{= ' :S6\,"r櫨cKD=M6T;a@kGbYQ3]5¨Bѵ=j8]W)';L.D*s,@j=}Npak3휻> ];H_(t2~++:~e+J7&wO0z­{=^ꣴÀnZ׾_Y/yYIxm}~l.GfQQQ/%"4|ǭOr؊ ݍt2>(c"ۣqz]-2XWֳ_F&_B!ԢE8EO /(t;Q`A^&Gѭvz*S2yα4 o;ul*!AZIЪmt%k쵏`cj9NnwP")AVCXZ3 PV`#mD).Sň'r[]HF#wO0t "ķn3g5לtU.E5C-ހ\kGC]ɥ dLм 2\aol`5|w0gvƻPx՞8c#16g ~ه)>$ 4_ *,>\sB(No x^܁R5jnW:Z]Sp Œ]\ 9r;k2H@"xekxv=wAkwۀhm E!| f'!(n~R;ǥwEu]`&)s(#9Q/ Ej";tIV`{P<@>ld94n=ԇ|re͛_C\؊ݩ~`}E@}6Sj9wFHa2Xk'l"!puQOF-WM9RH} '1C?AVj#hiT vB޻B+U,p!zy| Q1G{TŐ!:̉#dm`dnR yC-6ݒ:{Nu( t2D*Bs_ZLEPr a`WS>^nK7d{'W"(4nț&zݐUJP@#䚷a:pĨ֎_aDz"odRkRTKmr-Ѽ< Vo9aƼV5yVt*5q7Z{9F5##$/Q!v\ ,Aki r6R z`FGt!zBk:9C@za>¸&>Aw"RIPr w ꤈(YviQdeD*sP6M'KwҾISN+/҆KHw$yW*";E~ɾP IDAT߃q{_TWΏ^e,QMH֍ҎXt'kHe&(c0jZ=Av/!0" Ql+;f&.\W~)r{XJ5 2Dcĭ B5]KJ0`s*>azp qXE^&DZTf"֚QEF[QUw[5"ՉTt2>q}Pɻw;%R39d|>=uB%4y兟<hݧ:a?஻N3ybLQ/.>D^k5zÑ=Q-g>85ԵmE;ut3݌ooD.D< T+W*;(GF h.\I !2> y>72H2ϳS:?-d|q"鎜/!r3'v3[GPܞ\sQ+625?r @zcZcgաבNzEC 9,HOݏ4I+\72`"x_A"ؖ 6FN2t٦-'TvZH:u64@7a.y?cӭDfa_Y,pY^r'Qcx옋o}/<~Jgtu0!+A @֮p 6} 7 #ɀ^sN}X5_>?ηHكo7\j*"7X"R!eslBm#w!/ϟA{9"q "6w7c!r?BB mTjDB$ ?y;oгȻ87 ӼO1?OC;(*E^A sJ!-"!E|v/d3ϮY<uuEX^BԍE[@p\ ף|Sρߴ$/W6;t+j]Sd5[߄I7.ݦ@9 SgnN 3syKk棭.qDdέWD+=60WֵS@Ϻ/"Aycϛ;;TM_x9@"1Sh-Y#G{+ZEshMM:ԎwCQ]/Azt2qM"9 2@D* —6BvܿPM 9cv!m(*6(fc"y9?<׊g9EK績H#?AN@ .\})vr݃^폢oP^Ԥuhﭏ1uʷG:vD*ޙǹUo{LfBieGA(Apeg@\@pD( ,"a`˰-HKmoIiLOg2ɽ{"rߩrb_-NWa$C ܁rI9rN s|zGޑŕaw9F=PS 2Hg":JemӶ4uA|(?Ѧt8 Kx7R_!OE3h5X(%<'U?nҁR̭_Ya} wZqG8ku;ce9n3VD(@~%Hw3(Ͱ=c"r\|.mYYO4. f LAi# W>1?9+v =2"v rEݾ ? ă]x=/=uʡ}/\-ӂҴ@+SP}"-==9q.E37Qįо#콇F5fk6*h?7}sFѾ5(}b^n(putzҗ\a^m"٬'F4 6r&/Adٖ1E#z T4(^S8wWP˩=Dj^p)5z+`'֡EQ۲ -oxw4UCsrnA }/Y׈DB)4?"ím 1+ sqZ;7yGehjAhB 6F59̌yJdyd"G}vwgg> wߗ,~nZT;pUgYJB]=4VG!2o,{;V!h|i2T=QKƿ7n_{p[sY{t -kOBFш\9^<݋Ճjmv^-.BΈцcXX%@ W6/5227s윐 =X V=1e̘W澈"sгj<3ѳvr||^ ;v|Ҽ}9퇧* =g-Qh<9 3CHG4=0+Ɠ#PԻ_3Qdi6Z+f،syN@~'"p6rZxM4[edCPI}PPD8 D,Xŋ]2!˗x/ ĸv+dEZ@}r`y i;\o`-OEXd*N9_๴Nv1zJ<5`'PSےí/CEw тԒ8E( $q.6 h\8+،gSM&"Ԃ9JN>""T1^F2s1Bϕz,{Y.nz՜)2ChA(5)=c=b޷V+b\:/Y`.]qJ=.|7Ot;9a|/r=F#p{A>2gfL6lFB%zx̤EӜ}R"o#TxZCҷP*0*0cEG5XGQoa3אsq4lK" ݟǣ߀QmK{613O֧#Qãvۗ+(hlb_[:[ 'w7}A4'd!uP4'oBd ]0r4D wnH>8\FVJ(GI29"ǣKLg|}p"WO2I"[L1Ag~^Ǽ ᦕ%eÛ|~RN `1.Qv K_JWYvT:7%=_~SDP=t폌5h E4cH:鞘22Ei+׽>߽>tNDm9T47/#Ԏmv*F')~6aXx`ss ~Z.Nỷm=xةxXY04 h,9L0jo>ˑ#prPjMm[]6'=7Nb={2Z?gVޑhh 26f(sPs^%OGq|k s2羅 z/{N5Ѧ}o!SY,;d|gi* uhEv4cV*3ƗƓWX+VdI"ҬwKT&ޏ"}fAewi;NJ:LnY i;+UWOB!BLZc1ڟ&!Ґ Wsc=;G}]Xzw9qOJ,-_L΅We'HDh!>}Y{(BF d9޶.4s8YKѺ|PbvvoCJi|Ad&= 7bnAp "WKywKWqKqy-f/k5$bKvnxr-Bj[j$7,\߲|D.k&\ټ_H#gs~^T%mRz#jqL)=27㝇j,g uՁU!J9OS9}/z9FW"O3̵nH")PDI?J\fEV b[>6BV ˑ, ,\2 |-<0PcYo |kRSb5 5ඡz wp gyoJi;6q 977!yѽ96m7l˹J Y*PMꢷ%p?XSv6yY&gODA wڒE>O18FYu 5Cz/EL6_loٸꔲMVN$҆6zZa?[[])!+]d#c Jq1yyQ{߯~kjoWt{+O.u}|NJ`݌6XX^ |Qr\܌s}k[CQK9J!o DJrs%qsx"yj?'1{ז!+5{챍ה^(`/@'n(ط6<TZFk֞$d*=q *tl0?;P-wԋDVg5(#da!RԭY(Kv,;07˻ՉXm8G p=%0웊uO/+imQ>ݍ,agV63/E tH*uQ vW!\`۸*{HPw#CQwQA/ ,DۈDiyhm9T/ Է"n?ՈϬ߮f60|8o߸oнe?}l>ׁG 2~ʼn C5ԡy*[krY[Q6yNZwm41<Taǘ1|7F4_c1г22,\5OMꈛ ȇ6=|vn +ft?o{͸8"O~6lO qo9LGuGQE'k"i^jRʧ?֩+a;=H$݂%@pwZKAih'2-5~\HNGϴ8ANN?݅ ۿ͹W5%Hh ܝE}_@xrD,Ҋ8F>:mBN2J4< 6k9^}C¿ZKDȩr$J[{\HLD,Cb$Ɠ''c1B^f_;:2I+j}Rg9v6<aE=!p^"ECmI=MыnrԟqҠ&嶍eYظMshAQZD$6(_=0yOL"oG/cxr= iEjR#C5~-(jJ FA*קy:y۷s95eԴ[fa3mQ>+@F-d|`0wAmu}x.JkRP B./6>] i;lT8u%n d,BC*{k8ym=+F 2l~עh{9%NEu!C̹pdbF 7 E< IDATȹqޫгpj|*. EK8/{\j>ۉO ,~g@'f9ۧGr*Fɑf QTBrBDg "6Zfy"{Dɿw$b˶fہא}2 k> s'qvevF"m煬(:$:)!9U&K3}{_W3(zt.ǠE7́gY=D"Y'g&bފV;zsx.;맠W=2*#cwX5O>_<"d0 !dU9d3v?TH\;d^iλ !+u&gZvpSo@i;/= -~mK%y-'(Tl}в}UeA"x40Q\9'F Ry^΅'rO0" OSLwGKji([Sej|`}S; yzޤm-XEȀ [-?_C`Cmu}§}C/}Xm폿!+uEc4Rfk~8p8 9~vTtnDd+Hz=n)'C*Ϣv!'8!hxwT4 X@#x`Y,.dDE Rt"C5k/@ڳNsM"V99_)~mI~uʼ Xv"# 4Q} (۷Ҫ־|춉=Mk}[Hxp#N Q% ?*$P@49* 3^XYl/P$'PD.`E$E}$bW俐ay:"`6kA!zjȢh`VnlzR˰`۽sENݗ!AQra#X;Bit!rq}4 /Cn"a7݈vGixо D֢hssCY o|l׺SCd[}|Joj+ _^8<{MƓKQa!YTpSlG"8PpPvh*~qi;\'oB鋯툱(l|MZmIRan]vܢ[+l:=2X6V^q7x0m0^طP$!f^`3}P(+BG*nʛ|9$Vׯك+h? | }7v z_0WzM?bd,_DDn#0,'d%(ekܮFdh".ZT0pTtN'5)kƒ 6dTUH돑a;`2{[ȳ( ٰ` @M짢h| ='2-߻gYqz#ɽx;rȚӯiDM4Ρ4ih9=Spk@Yy=x5#Cshl#52J@r~d\F&bo1g/'i("[D,:/Ig>(Ƛ_&sv)>6_+Vƍ3sU]5뛎='߂OZ{Hޠܓ[-t aD,XϷ!+ ;P }1hY;ۜ*ko". gͲ{|G牐,VTmD颐zkFx@dQ9x2Ao_űņY}aK+Go8mػj.5ٜH5~wfD1~G dݴeZ2pOUtiv_(5u.Μ+нVP~:fcPd J)|Hf^k'H:5L@!WO X>F~ۧSv1pɎV/k9Ewr~fH"?BJ(%]$=2ud2>L!vyfTf58LDia`#,RaD"9y(ty^"Ɠ(hZ*pti;Oam߿-jBVZ%UӍ:%;z0v8vx|ƬЌG`j4WkJ~67a#v7ڗjw׭g\10q]Osmc {){*))Ro繕u~w?ΕA? !o(dt*auꬲk-C&izpB:jƵѼv9kuf_8B6{.,Z-í/0*AiDV6۱pX|C &yjGJoEɯ'b9tM 겓B;HᶐZ͹~ Azc}z< A; Ot!OfGIu 5BHyB/x3۴YsT1Q CؑU>eY]=ŝ݁(x)j @ۖkIɠ~ )Y5MDDF# CѦg9rTZw)dKv:q=^o@QMgi;ܘwG< OZxh+QTfhV;Wf!"xDD Pf'4AmE))4'@sy Pn.l~,E:ɰ'!2tzsr/B92G6#^ `yؙT96RB)!HjtSնlF鐦ͫ+=ekJ5SChqFۂj5H^ /Yl?x|=%K-;ؖ]QZwWŸoğ-6i; mBVlTk{n@޹(e"=!+ebBྴ^7#ʃ[ ,h<9E@~EޯmddJ2 Pe(]+1=y޿ Z{5s/Dvy|D: +4cQTrCQ_+Vi:ʤ#DMC 473،x;|l{,rb ٬{<6_uT.~yI^~hGn-z`=1@@_PÏtT~ "+{ 2@M)L9B>4b9Xd}4EPTd4mlW}>>1h\ 6Tt'aD\YXg~vTD ќIu(4W E~^@αf 2X|p4Zg.DFňUEƓw ԚEQ "x$0RH׳fZ犇/rFCV꬐7 ˶w9j@dME64U@Ox4ТӺkn1"1 7, +|yfYeSvizf?7}:aT[[)+k{Nx?fǾv -co3-v t_< '=x_Q;9&vg2w7WLm:npݨpmjD$k<{d;Ƭ)ݳIg*MG@}EQ G׵jSw~_K"8}g%b;Oو0~9'Ab/ 8^ IDAT,ƓH(D̹ۗEf&c<>)|m{ǿ!d@qRrf\"g9:*|\!+EbҴ桿zw" hc<x z7Cmuzo5j>F2SJ 3ۆ7b8s.Go+h#5f~.}Hh<?=Kσpeyw;p+2nG]Xs '&ܜCmG_Y#Xv:jG R v K MI-y*B$вmr=m߫*[qCQYgg828Jj[A98jܫ9, $bfG4BF 7lf#ys EzP=(:D@Z>#vf0\ve(4|Tuyoʳ_{ϴ,F;Q:SpPDSȐ/(E/c[ZdooP$̇"L)\sQb)Ɠ1T8 5(2hhPp&27Om+7H 8o`FK[ yٞZhY}ߟɐ}sg}Vyh3}ޫ'GJJhH˨l2F4qل1څEJ~DJ!R m=‹`ymQP2 ǥkf0psogu㖘#+-?(Ty9b3}ɕ d2NxԲ,nƓ{MK"kv=xs HQ4@$H؆"'5"J~}J+@ogյ(jy(z?ZW浿"eaPklDa.9WHZZC HvmDuLHrEnƓ9rtU^T25o/<Ֆ(_Zs|6wG;5t&eg!d 3xҔ@w=T=8:/lAȪwbECKӏFa;{[bܰWaW9?91?,1'x=xa <>썉X'$>׾ȏPj1R<`"bV8D&u#"iH}/TEzrPCQ,{熙c@e0ՙC);#Ż͹zw"R/"2dT"9 (\#p6p".!bW:#RVJRYsCVjXJH>|U _}3BVjhJ ̀/aw[E텉}o1V L>҃S}kC˘ם~Ƣ<^jsu 5qbH;Qj>!P>bU6ُj PmI=OF5Rț F>w D+nnB)#IHbD@mۿҧPTj9(nrVu׎CVよ3}9D4G~:}D,RO] ?Í<k(206,Ɠ?EV/KH( A8͇"7"#y*"P'Q[9nŌϝ`5ʜw "!Q /Ɠș&hT?DD,Ch<"Q"Cs؎!RkΞYTc>"Asܩ(ڷ|;# YϠ4_N^#`R Lӎ?v=~Ɏ"C2GCb"gQۈ,u @`oDЂ H‡ ۑ|)"D,ں&bL4 G~E.y?D1(47ҏCƢ˙셈eH`̼v9HE},s}N"2Yq#`}]뉨$Dh(qeۑMQjQH{XߣXx'aucyWPZYtMEuNMC4Qڜ̱Ɠ}6hChPFDDE(e^t"*4=R"|fE31"am`Ye#ʆ7WW|JV=A^˂!rߪ(PmuOu 53Qj۸ۍhrnd\.TtU74e52IEƓP*c|[hG6CF+q=i4JȼmQ lGߢkS9sR#MCt٩ȭ(6;N̚"s|c `y-rW sGU.cA#R@[,i|u 5ÀMxrPu'Ztk̟+Ѣf1Z|=bcpk^zf60{l,d7Z+HrxB4 ֵJ;ztyH &ߣh0qrnDB9Df)ȐH#Qjߠ(Pq^l7 n#2'!uǑAk۲shBd/N[LWŹ=QX$gVDy͡y^R cy;ڜé}Gs$rEZh3Oۙw9=G uM55=\'8jlk=}XŊ3#wm(*ںvύS\q%UwXVo9ځo=8˝ 6 c@,+x^jQ݁J 4ȒR D@Mh㼜% p*g~nڧ8$+H,!ېJē!1<*팈T?-UGy_GG1fi$ ?xE@Y*^{^iqws-G%ʸ؊,}"|oD5ȢwYIEu\#跺?OV1h&X c;f$"3u0Ok0oڇI -n7fs]=Cβ uMox,> d-U;U'Ex+Zjkz}YymW]6jxe+KounR*Dlm!>04q)7W9`.F<Y||ׄhc\="0Du~6ڌ6zx\ߚx-B1; D.\O}wȢCbzދu:g7/RŖ 9I{.h=݀}pWki9 %%Vi#HNVp[.r'BT:̻h\)]t|u2>JA0vL`5IZ6VPxBB`SC]ӝd:YRsQלL=|/~Pt߻HC]ӳPPW^P״GvO}*W˭;я^/f;3mbCEα;0^,ݟ{ oIT3+5 eM_F{ghݣUlrI\CQV|^xŅ6a:sv]2=b\8OqG֚X.bx]Jn>WjP ǓVPE.wޅ8L \|lBwG쉾4Q:?6/rqHGnK+'e L*;`TXAc{8|y޹k>ُF?-pZ4R?F~755qU$G ?2GV3ܛElgflg,wέ9 K=a!Zg;3w8ˢaDed|Yqm\ˑ0Zb6m'L, +Pb m]di&Ҷwm"s/xgZ\yPN@bOD]7U 8l)9?B8]S\+Gثr7A[ӓ?bu8=9ƗAɼY*oAIFbfaU"V'cHP|ܻlTw r+J%baxG)(wtѺ=ʑgT$AI-NB QEݧ -?x Qsȍ~['NK9 ی,^=*S]}+چ_A-G;O BB%HTe ~zAA䪺yq;X,Xv1c^4iNnZ\<;wzMe?{ؐrQYhr,E3κ ͨ&ʵh.sUA'e5׏m(9{k[6f܂6\빜za$ "ÐLdE֙B_K E'QQ_?\6u"SDŞVbmAbdxYAru<޳Nx3bg{x m/t()ihS)Fwrm>&B~4B}L%b/,T"k]:A6Q駩~94v{DrB~6P?ld1e+3n.FθѶͣ^]Xv1wz4OW~v!(]iA?֞5O]mY;d܂'=%{ 4V5666׿,=/=ԳdцoB> 4ٖ곣|h7q#8qT(DeC] uMkkZV?q5O.޷4lgf9Z$~N;#Du\y0 xyDM#H$T"K5(pdp(ˠCHGVP'h/CQqh'M\ X +#^[T(9 (L,d{RA"QӋj1Z}7H߅:bKTjPӲ?VzO!ATY>KljكƦDk#Z'#uOt bY#jkjDg73Ol("hls\6~򇇀[1Bncsh?a9\Ehcu( uM}͝',t*5EGG'c ~R틗suYn^R'3Ȼw>gfsaN<W *{97K_86-ho?>mː`u_6ڭH4"kZ3+KQ\<o6x^H} p"pR R[v IDAT>.Ȃu~$JSǫs{$p| A?#wq޿E3zoE$J@kF~&ȍ4Ã~DȻnA,'>3jIUS;VN~cuX̂el/7IE^eZ1W uMkM:C;7ZҭEQz=8ڵ-h3)ĹV'Uh۟*LsVx{:a+K˗pVnresH@D'n̼j3iJĖ !5md;Q‹Pߡh2/DU=J=mׇ* mG}[T|6, S"M*kGBe`=w#h>ME 2f[9h\"@mw%h=36IC6 ɼKOx2x2P<.dk^{"hэq(_rTwok8#>g8{ӻ߇֥ YJFN>tI8Y,FbT 9HJo2wz`݌yIP`'n uMQmrtr^+'—y4!" )d%~x2=xߑJoG=f^&g+!pŅ!#E dt* Kth*` 0ގQ\+Ā H܉%W(kh{,v"WXq@ ^AVP<߁DhlupuE"4Լ׸$+o+.@fw"$X2?yb@!T̈́ڞFk]+:\<һ·LUd t5$ʯB] 8]򟎔qJ%b{`^h&N9uƼ7]ɼl/k=c^ }ïkFs^jkl75}ޓC50u9q*DlUQ瑋JxCɇ'uJ7 z@Ѧb ^ZUlaےJĞCs.dHt"._XTk/4wr"y=H\-$'!ǠOFg:K!w/GxEhD5V7Z/2vN ,cGM'TԆhA]{y_\B l52)ԯ`ps'6m+τX9.B짷cE]٢/#Nfsjaƻ%-˺֞MHL܄~ h?mqHxUmun+A<RT'P3Gߊ'DǠ;jCr: h[`YdFnMaY-~e)]W4W@y\]BB|P܁UQwS Pz~@(P$\w}{P-2HT/؍0e(*x;cF ֱ%=қBu)\[8=$fL;C*Z#W$ beҳr Y^+H+Gѧzb:З!a|A*kY!z~Ju(TMyNXpDb$P0jƍn-x,QNAhp~Y'&ʦ5g,{jPt ͣ|1X[˧.}q__ė.)-ݚL鷂dža4h3=hH%b_B㫑(A\Z+JP5w!%݀j"%H}` v]^Brq8К@kYߌ{aЗG{#7SPP$~,mY,W#Pxbdٚ|ƻf G1 ! hD"BFQ({:<׻ӉWX=c1 ,BR-F~t!LYѼ$Q6McCŕ}CC]ӝ!+Խk/#Б6ep@~aƎJ VœTY:"17@Osْ<|S$Gv{"O ~YƎN3]xS#uW:bBv$F-ʐXZ?LwP-ȅ-s,L%b3}XP\!wAuӐn4AFEHtb&sP2J8YCIMnFKHEPap؊ 4HDEmN%7t!x`!fkmA̝^gmڂ&UO[FEOWkO9 E&/;zf_VyyG>3zOuw muZeܨax2]dSJĺMPopA [Md|E~[:Gto#K:cQ2Qh39 K݀JĖgǓM%dڈ kBB?_HQ{ Z;#'6ZVղ{{Fߊ4h=c7Q0fk 0[e&AkPz,;9?w#ݛ-i Z _>Y)Bs;9a d#(sߩ(6j2E2!AM`*:{]mE@`QdZ使mV5ǮdL˽v~tTvv 0!L?sF@$H.B}b4CrdQ/ŠP봌C}_62)YA052\{6ܾ]L\q:-"uʆ\ 4_C[5} f䝳cU(z/Pq/x & c'93+pgL?^h}u1vW2naNo+2Ղ6~8~M)D Ⱥӎ3P|M7ڀOC y"jH LAI/N^Ah ˑx$NdPjrӼ@LŷPnӣQQHLB['D*s18?\Bai iw0:L=yh'z]<կ Dƍn ,XNs|"8gť񑈻xҁKE;a?s *B]>a$@{mnyd*GbP6#8{'Ld "qT"fT"x2{zOwb,G{#ԃfoiG-Ⱥuw߻9Ey"}(PdY'ttUy}SoQLHL^ƫ{p mBc8-Gq]@?k݃4 a 0 -o蝗)0qhCDx~r6i;0 cw }_t!p).wMM7 nFDK;A=ch?) ,pWõdz&#*&:MCdr'"aT"A!FuZ>8}L_0gP}W~9H4}_#*v.(i}Uh|xek&bQ3ntY@ƍMq\ c ΏWgg?C4QKPhfPAja3ҧE(J7y݆6+%}«Sx2= EQiH܌f# %o+rY>tynB"/>UF~Ze5 R$|,aW"??GOձYzPz=}jFQgޏ VbI87:hOeDoOџ]UÆ"1U&2—EI3fi6 7",ؼl)Zུ nuQ t)'Yry!+IE<_mO%bm ($$NBj-:; [$4(x%Z_,[xDqeW,ʣoG Z;skCywJl؆,W#ՁPg(h$nGn߃~ v)!; , T=~E# (xdQq[OδajOةzdi@BKpq܃[L1 K Dl!Ѱ1H(R/E֖YHhu#Q0Yf*f$*:s:_}NV ޟ.A,>\T*j a(q8~=wTDc:ޟN`E^%j?CI9 0A@D]@,Z@Ch7ڐjC]ӚAhax2= |h[񗡟.U=cȒqwse;mӁ](E0D̷ӽ܄p҃W¸QȲ5(_4I W#׹Rؠ1NK WpnF}-G}YG}-BN@ꕍC(>kO]琀Zo_˪ȻB׆ߖq cW,X1@46pf#Ͼ6#tTE/GKa)Κڼ!];ChS^%H,B5({xtzkS/DI+zmD|!5`A"ķt!pr} WH=,+k.CY&DP"i( d 3(nDncZABr$Xx>F̓G~]X1@ㄢ|>o8\1ܛaV}39mAVaS4?FkNP7-?Dv%PLXd5qO~p`*+ F<.AYdWNDVp 2m"Hp[(D%Gŗύ'ǓӻijJz Y6#q\Av<&,zW T hA7Hl3}!] (YJ|Y m1Ags; \O8㑅 򆺦kՆatVJǓ! H4 $D(~'-& KQCӚǓ0kd1H>=]Y|qnjHT. b<^Dӻi9+GP*i.G,*irU~e!!.|o 5kwO/}ߐńE A@a[@`,:#w+ 8:Ehy{{ 0 SR|ߛQlۯ WGCyd)G1WT"֞JnEnj  J`{UN'CgN YHД"׽uԏvu?q´ "R:=_9yS 8Zu{܃d+2-07ŽM\>˜!X0~mZG:'aFa_6'"76rv(ɄqT"?Jx2TxOO5| Z{d(!pk"ЅoBmduK%b~7#zJ~ A_l3rkF.{jCWX'|BWQl$VNNQBc[=w#Ōٲ.;1f2ߟ7Vz$KM +Iha7AhaƮh$rk^\@ b(a3d~lVLv.|>r@D[kw)C-g6p9$aW =<7؜1>+W܌/l-NXŊR$&#!Y hȸuNua'8N"_((IW!Bv<WYxsݭf[NTFY ہ3n?F(f2v)G56 ey߽g sS.+B>%ȡ n4CPQ~HC]e1 GRX6v)]`ՊW"G/+}j*{wT4xY[} Wos׽NGƍ7*e3PյNK ͆.>tNیl pW/7bJɜ 5Ȃu{2,.Acsܿ?Z¸ յN76q1?}ڔK;ݗ^H{=X2|mGQ}*my V 0v7j'F]x2qZkPMdͳu:*iUdDd4GF*{nj#ːrQZDbR7 {PխHN܈Z@]yͼ_R^-7zY?v1AcW$LR@eLolnkkZ9P魘ݛ|(u/ޥ[G>⇠I|e2 0_\ TBҟtnYRR,D+h >ݨp`Y\y$[qH,@*(n5\Sb.A)ːl a$[CV\[ϙ <*l XƮ]9h-gh(OD"f > |;fɴj]H,/Xa<4O?J#E1iJCO=~qHx^yeuZAk2n?Cŏ+Q>hM|պ*DeQ (k>x.:L$*bPŕ'?3n;`UhEҁĖ/6@z+uhp|>׵R0 D8 7o' YT@oH8J]$duZBńOG{WzDw5.FbY %8 QbG.$X6e(F_7v,Ʌ$AU"^Gi`\?f@[(2(uiDwxĐEAha1ē)`$Oͨx(UZx)Jȱ@]#׽1H0݋кދM p DzC`ˀWxX.ehl?K&hЩUYFy>R i54aa2qnp`puC]Ӛl0 c%Lډ#QKl*8Xm^,Ð ճ4 =z,LG!Q! E Q""S"N( %>RI+]G.fcK\FI.>@6܏Nik76קQƾ.465 HQA'_R?3 0dz2ZB֘ߦOnZe8yp kCE~9dv%5[ \=b>qP^!k(D$ž0s4v)rnDE'(ŭfwGQpaaI*[j3!E;D'?69Tppŷ(~\]O9?DNC!iYE%o~^x_q 3ng蒌}ĕQ(̂e46 [f4ˑ9ófF{Xq_0 cg!LT-;_Z[]_܉R } au]#$Utu~t-[7<1T&\?~9dokscs}t|{ڡWtՆaNá?}݆+;\9JA9C$}˭ֽ(vC(zWv?^33ntZTG7諱{c..Ics}}}]BiceFk<~0ma"dz'sӁ{(E%/#kQ(  EҼ8lBNܸO" Z4`/=&]$sM/!=tj6 lhݰ).25 0]x2=}Qs?lK87 e=7kT=PBX\䍲YH$eg=ڰjjl%ƨ^&}ƍ^q7zS?v؍1e쪌F(k+s p k7MNLXiDl60 0vA򹥣{FIk7 T[.NY~XdڌSEYǻB{3B5?F{K2vY?7[z䅋mT"OP-T"vƠ50 0vjjho7i)E1W@n7 +2n-/pNƍvzU_q \O 1e46L/%zdH%b| 0 x:-%@$F;Nfܨ%2vL`aaaKraaaQ L`aaaXaaaaaaF0eaaQ L`aaaXaaaaaaF0eaaQ L`aaaXaaaaaaF0eaaQ L`aaaXaaaaaaF0eaaQ L`aaaXaaaaaaF0eaaQ L`aaaXaaa\e8^ IENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_23_0.png000066400000000000000000004362311413546205200301510ustar00rootroot00000000000000PNG  IHDRc%sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxwx[#C U VB ]hdV)Q:--Qv -$@F!@38v%'d@I8}]lsG~aa={aa[3& 0 0caa݈10 0nĘaaF7bb0 0 11faэ3 0 FLaat#& 0 0caa݈10 0nĘaaF7bb0 0 11faэ3 0 FLaat#& 0 0caa݈10 0nĘaaF7bb0 0 11faэ3 0 FB=T{H*&1 06_<{ cp"@% ެka?X06,;7_A#o0 LƆC ֲ&^e00 c3Ęa| Jګ̫*1  ]Uzza捉18a5n0s{y7faͦ4z,^e>XDP>Z p7p"^@_x#1V`al3fH.ouU~M[N,iء\x. xxj2TUT{U{ec\aƖ9cFK̀/8TXֳS%ʾ 䐝tٖ!AW*Í5>0 cǜ1GRW5l$bŸ/U-Rk'ǵ.,[Cҡč560 c=j(T&*z- >Z{C68 0cF$ơzTǬ*@2ixXg{P|CB`:hS0 ؼ~Gra^;m^òE nKdިą.^|c"g0 0g-gPGMu̶^^sE9>REweH|y{; 8ʯ1!fcΘ#|;ve%Fcqdypa^kukjqM=N0 cĘal d)Ke1;/fm&ag? SshOٿ?0 ĘalJ- Kۆz>^ AVZ;aݏ3@4,Hګ ˶e߹@!pj"I}ھ 01DT{%=̗ͮX5Y<_d{B0ͦ4jr4- !a)XnU~M?r01pƫi > \3lPKW5[t|:=O%@>*uG2 0> 4r^.ã ڙ 5wHh5Kc6WWkPG'?mra=cVOWYW_׫jpxwSw=iΆPGCX %Tm}ʯʯ1k0 Kbb0`_{=OdWjr*fm~kԓ+ K>}] yjN&[#^Djګ떓1 3zt*}[4ֿ٧ܾoݎJ_%kNЏ{o{@E`w*ˀ7 a@Lpe}u^kP7aD>0Ǵ1hڮoMauU~^@)rR@;d!1}h6@ﱪiHh<{aFĘa8.kB-寖\Z lt\YZpǪ\UnN"1֌DX! [z'xKWOgItaޙ^*@Ou 06&alN;m}my=/CW/l}WkB$Ė!W,ĀJĿ y.h@?|nAC) 0zVg0pd`i#!CoTr_YA}Kf޷ZPpՏ=nL`//Hٕ@;d`*fPkgkw4V4 g럨tdzZDv@`{R`o.~{\ ПڋB+|[U޹O0 GaΘalT{wd[;750 ؒ1cW.xggojr0rBPb9>*G%`$$Q&4˲.>CU~?{+{쁁*a)X8>Y9[w`Ơpc)rP}90t^meP> TCl,! {պ`_V5'U5w̯f^YgKii)_ 0>wE4vjwhګ5>ܗ*BNWo)_*<xƽ7.[aPj!1rȲ"&gk(}xkW[ߑ9ʯćVojޮan;iFl#n: 0 1[Ɠë[z> ۔.i\{4xf}gqwڇ㼌?;YT5$1[V`7l?։9 dih&QM2J0 %B4 DɇPHw&$؞bCY|Gk&_'k T(W-ǟqZ 2gF;+ׁQqTu pzXkXB)`4cnCbgґRbщ^e(kuUEٜ#=1ѿ6`*C*c:GtZ5c?LGb+T"Ji Nˀg~BM- T{}UJ6 r3D,,O~[EItGQYѱIH"G瀟-_ C杸7QTT񪺯zǠ)s- sYLCE_A +ь 1 A# Wz2~=\4z3 .zp 0/1XKu]j2Tפ66%x2 Y?t>Ö+*swRE;颼z[HXPΡ2rQis6C[ZeNShX}{=\*{Q/aVC2z< S3S%U~@41p.rFEUCN"uj7JzEʯycO Y |5"Dd$<*j2*Ok7؅1 11fhs颥ktɥ_x+c_w ЙE?v{/ a-*1u9^寐J{\ 0m_VW9V5y=Rtj!';۟20rV7V575 0> S=0ޯxcW*1'|}DɽZOk] knGe(FɧQo?+e>rvi^Nf= <Mzx):^U~ͳ^e 0 %z@L8G{=DWoT9j",p?]U;ʯYg0 X?=j@3yH/d/MxgޕXѶ"طaFa[Jk8hm J"~ʾCML_; 0^d(Q~%zʯҝK@B U0dv}oy{\pKxGi&Pa><PH 1\lv~QHtU{yNL~onl&M(EGU;g13בA<'b?O,{M"49G7>/Eɿbr~ܛE^K"h<w?kVU{m"qԈ]\o Q3P!^?lֲL #j 1ꊽA>{;~?oLs]i+mҋCϥ邼?u >5\"3׻%WCY{LP0c ~һ{Hͤ}`Yadߎ~o,)\g֊Pn׫ҡ@ ,f9fEV "c! Yh5 c"pq،Da?4*fs`LP NDX=PqsP직Zk9 V3{uCF>W6h<.o3EzaDS~"I6W1)Z;z7A+ۧxq_-BB-=4_3kEjQ8T}؀X4E{skBe*ZjFbC-٣cdP+pF<Գr3nof3t҄I*GHDyx;TjfP`~V0cVCGhXZܦD,(M"n  Ɠ}#{ 3TaJ|qCjxMެҋ`k}~!? Kۀ]z%wBmn!*@jerD,#+c2עwxE*ics_'ߴ5 468xr(ppi"ydzƓ;E^ 'fz"y˽Ahz$p:{6!J{Geݫ l]3+-ew !ʯQvg̻hM~_6ȅLP74)!>![ L 4[8hμ))mVC"y<d2ty9jծ?lʒ=`עEߨ}iCAeXihK"Sd`orܐh>[ i#ՀDJ)/L{MhAY8sWH_G1HM ]J4C_LP1;vEוZ;aݎ%%c?Õ&MY5uy+zrr`:ލM4  c^HByTDE7󗏜'{i TYZV\^6ɇFR~8D_3ʯIT5i@Gۀ[U~Tއ眼ڃkGq-l2w*R3?y<, d%gThxpqéP pfMP1fXi c"Ol2}8 x[Da@5* %߈6qKֿl<MN&sm5MUO LPrʁ\Cm`Sk}#4 d,LÉƓܰ IDAT@-~pH-d> M~ECXN=Ɠ&blȮ7U1lV6l0P8Xd:$z'DA+p'` S} *k^ Q]ѤyC'ϸxr'xEePWa%<һ"@4,Ɠ(u&|o=bz$,\kkQ kۑ4xv]BH"mKph£w,൓`^}B% tm"QHPݝT:@/׬\ B$ +AaKգp߷k^X$E^B}(@qY:L N]hB0Dɯpd4\&b!ӁC WeER4<XE^u{~*&:hQPP[T@ʅ'~ E)8#\QD,/ko#&oe}8_uulוX+a{;hA x$;`sg҄45qNԍ 06#,LÈƓ#PJprZ'_m8=o嫐8%O.uHP|*d{>z؃AG͏H"8 Q sPw4 vCϭ|̖No H">9O@opu"/4^Ð{#15M6O$β5Z>{ 9e䚂/wlRSkLP1t{̲hzpg}jȅUjC"'|'ƓCxܱQEɱ(lD,rg=/B4<H"ڐ*+39^DQ+k~ֹ (orE3;r]M>ȗY^k7a sz~ioHےQCJTh"<'A. (!<7O^:Wi(+틧 i݅hv_ >vL\fv@!y~Nxgi|?̍cj"y6OԭcB9jh<ٯ.|x2i{w ڦ.uU~GT׼*Z\UU~M=hWD_ja*&0›U'Tm̓4 0֋@ɶckjƓ%hhH"Gw.q'3nL&8 3NGe`Ɠ?BّW!1YoMYax-wUSEﶎe.'1OX=|ō#fQV݇^' _dU~ܵ* b_k.@aaF7abKV@'H"k7oURXҺ|X'|ꁑkP= 5^'E$2OތB~4/8jpk(+ԂQh<|"YsDOkR H -76N _vmTzL1sFxfaa݈F"Y'O_up\"$%bUfGɑVo h< rwG.(A% <'‰{E}t&k&? i(rǝ'u\x$4$bWs~TE'bn娠D,z@%1֞1claHD4<8-Od/xQr,#Ov9X"qt[P|]r]h2(lua}6s +v )^jfBaRTXTb,O|$.Ɠ#z$:PH.T  LE.1Rq +wZ@3׮oac[(.`H4sMK" n%-unyϓ(Xr~B\]ԧpT?,& ܇V r*ݱJ8-kw a!= l(oB!PYr Gu\8A;9ӫ~1~箕| 0 X-t0՛**I-rJ'bױT4D,2"ƨZU7Q$rF +S_`k *q/rJQnZ)*@ +Q H `1H"x2'W4w6} 41ٷ8yw.P 'μ b0 0w!= KkY^)\<vlFC.oP>ݗE^GQC9HH$T= lp*:rM!V{Pr$^A¤Q;J]ܷQԛ`ˑ(GDn[6.%šס64@?Ik%9tJkR 'bT4<]aƆ”[~p`[23^+Ms& !4w(7m_l_ɱ(o\ju 9qU(gPwןPIHέ/!׶;D[+LއrAbE@b6ws7C@ }9O@.Y]yӗEg |5"qhΘa11 |R |tªE9Dc &/?OG7 \C i<O&\˝L^?{b4| 9M (*9,@.4YY$"BC B}/ ! av;lkP N݊6^U帝v 3.x5[TCQX?HD~ˍD,juc?4 0 0=OtF+%`p[P{rf#!NE XV?Mb{-,L5B#m0 2HkG\dizxU(i?\>$ >$Fpx$4w@_;VrNB"w}9_v p읉Xx>OƓu=]v3 0 `b{vyH E/uYo ݫD,R'[upmv,ˑ(LA;3 ;Qb1< ( k^툜 H؜D[V5!,Fe*8!L/6+l[72-C^A"u'wHtyܸ [#C<9ֽ99j'%b\âdu2 0/n Ɠ;!p9<@$`M(HLDNأ5: cPNB :}eI[QV*AF:F\-#ވ! ,fq>JgD|#O&펷7Ǹ?D6$mwFG+Fa]Pȴ+BkMKaa+mэD3nnY1*b@V&bkm!h +Gn\+AP=L$ -$vGi(pA QHt*Ͻ|Tl=;r$ -;9y}G"n=ۓ2@!퐐{ D(wM"?p׭gOj?DCK"aa|Lu#x9Z3]g BHl&Tu;9a>rǦ)5(k ʹz 9U;pky: Bd F}f A̱!LcW"gw+K v$jPFB7[Z h9F({n"׵xՅE7K=aյ:kA&Xa”݄ 3 ӵuU<Ɠ%(y+(lyrvA^eP;ʟ:x2,s8a@%1nE_ƓP~Yj} *cB۞nk5D) @7ӐؚHhqO٤cp7Ynl| ʽaPMHvFqh-('w2uex Μn:|H4̺6!k֎9c݄-ysL"#Rt>Q.[+9MirBlrJB.<~ZG-;n-Wd^D\o*SP2C Ow{CSBA:wE!kG3*ǣٗH5~Q?ta=qmjBB4 #i$`?pc$>W l0+Xe[gHfD>Hջa]$K_M#ԁM@P&2s2FBo(+8  .B:95^H|Bm2"/d~<̇ӇBmB31}pOF]WƓ^4㾰ax (W xTq$#苜lNTO')χ(',2),JuvxABBd'v9Hd0y^ tT<& r/*/! jj391.rNA3@k[x3*~;:;8.mJ-S'r䜲g;*rj~nףF?B.Ǣc[xD,f]lC&g?m䉲Hzݺ1epc6ׂݗRt%bFyHpʜ\j}݇>5=cga9chPS'E:\L"Y(B yxsmϫ׆fH.u$ZvH Ue#;н?rp ܬCPX$v#O=$BX&WT[GL&9s5s M(' l+#UˆA$|j0 I;sȕ{fKɉ E%F<$p`[w݋LQxh<AՉXu/h]{ˢOY6;g/C'!6^v$єE~)'@B}9"Wwcn_7Fȃ 06"&6XE>QGnh<& T![x-Glxy7l( }@yfǠr3Q&430$@aQb>9!#Q WnLʢt 3Ht&'@<:r_qlBMBK;wrϹBbn‰*D,5U}O^~6*.|[8FsӆKh#A['/Fw`?gnNƓ#a6 }6$bp6naaML4.ʝ9=s⨍ύH dKD\ߐK O.Bԁ&,FD\9D,r`432Hn6ȡZ.GWt'M9 @{@zn衻ԝX$<$|!A!;Dwj?(tlB9Byav+ `[o&eu@`k(^Ԅ:tKE(<JH+*9\T"iRo R 9!r9@|{>OG(r7g䚷DgpY"yضpԉ= jƦMUȁDMAKʛY^yH=Dr@䑛Aוu2,(>;q8&;!6F㽀\"$8|$*PN4yBg;n-j!LF.F~ns~^Z{t f/i8z%sƴ?(C.Lۮ͚(mÔFpgw^w}Ov}/[Dn"'wmƓi8tb,ln > ݃ թʖ G{2k{4UZݽƢb|߭Ǎ9?Ɠ%b7L8| cz&\4m'{/jM"lԁ-lMB/H x=lkDK>zep?CH&Wb$0nO,r G!p&;kczC^9ay\7V$(4ƴ9 Vr;g%7C~{٘k!~279ZݱWs{}P(=tyk kq>ۻ:q%׹emL8<8u_fDgVN:qoewM8nFa݄)7Ń,X䊵޿ ?@.@5Xey=ZPN}! At +Phn9Jӭ%ΏCB0]O $Gיn<Jq6ؽ78($"7wNsQn(y=Bm܇Q{m˲ʍy$^e}!!m@3/׸k[w{4Kez xr >P9 #q߂qotZu^^ǬK= IDAT֎Gzy!cȉ,.۶Зx"iqs٘EpWdL8 49 }nBŠs5pg>a?չCHA_*ɞa= Sn$ܔ$b&T=CN9(6(F's(wBrbs9O}.4`x$Š(l׈Br+5kC-Cwj rGBryWoi{;b0>*ee[&#A:Q̮Dbq;BK P88^nC7椑 wʺdٶN a#$` ,ƓG󛞈E1'{̖ u!)z ''` ]l^_^v6E.]/H܋Irв,u/}S!#ODeW!ԉFG9| S'?{E?v,/y4/ 7a,L'FD,rs4*&{JTNoL$\ fz ff A S܍!:#ʗʺG3?MB~BJ/#T"W6=KQ 5n|ݶW"runA(-Ժ;s`w݉rG-;8F!+!g=@hA=8U{hJwrr;Ɠ!!>]ߛ(t ِb}.pFks{߭}vOD\['C[摻h'Gu}p'rv+쿃km}̿UouEjF@4%7"rSƢAH9Av V|9<5l[`Y+D9hE0g}e6/^v'o:qx%pP8ҎFuIL0|Lm*%얅#EƓ0Ds1 /Ы( %V߃NQPzH&\^XElxpMr(B潑s yn7 nya|;^R'd)byIUsfv{Y4)`CcwIL1QQt$5Ĉ&/5D㨱&8VD`.tXezWϳ޹s{ )yvN!h}-D c%HQ#sgZ̸HBVeW4Z"LIX c֮\hXx]RQ'j[>O;mRO%ˡՋoP?7'^ҿ!^*h{hк]kvG>P~,G1h;[sUq5D5ZGiCIy Q'eA@n H`lh^'gTL5.@ӄ1ӞJV͒5Sn> C*{8k9!dV@Xod E4Ŏ#R`A ;CkBgǐ)g'dDSb`+1C;!v=aXf~Rk#HۓlcRk)JdoE``m+7 :foCJ}gt Swc_(a~+fG ZٱݔJ.9X&x2;L7L8vKOǓStMX#0o_EyJH/_3{ߡm*#m4Wy]^b,+)z}@썀X2zBJĂ_-Pt='x}4.Uw cܜJ2^. "6q(KhRK(KT_1jO_DP 6MIU'݀ 5mG{0|; I' )=A=2kLx{^?+YJmZvG >}ēHLN%bs>崃k>l Z/X=hBL؛HK,WZfG/٩BJ0BVPd0@+vi Qju_Ns|J+q)rhoF`ok "^Grc(h7b!e*!G4eWlPm1& _fm܎(CdzK?EGn;ywM׍TYkf+$ˆfQ9eָ&HF|$ 6-#PaA<J^yM5MՑT"j# 9rf-!SyZFcUHX`1㍄~~?`hFh&.",QU| K3{ {;w+`Hy̮dʿ0+@4mWQx}+~q[.= @@ZLin$+Y6&$CT}!pqKߟ+í֯7œx2}kGrXp RwC`"Cs EyY"A ]#11  nw"k@%0Yv x?¨rn^ 8LikYi9ӎй\dz )SPwAJ>k}|t*Qbb⁓F!AȎ5 v(זd-,ڛTh14BVg܌rk}3e8ag<#t PJĦ"p4#p50ap: |/V!FvOر]erMe9>C#}N=!WBEO9&28}bg~Ŀ=nN $a6c;_?+YVJ6f)ً?E!{k^m-]e|v)YV-Rp!vh<KI)B$E x75œ\Ծ]>8jхv ͋'ӽ70?޾hc0g=(}ht4Wy u] U:D~vvFA |v%h09ܾ2G@;L3Q m>ldNh,V2@$zZ/wEWc'>lc'o512k\O:8kܴ8 PP?{ {sG0{F ;n޿ɓuoާsxi>d͔!fXVGwQ 3o fa-RT쎘`}::qe[*$݇9Ǔ'#XB6#(I]? 72@h&sp@uMc|E {'RCNik?lZ_9H["HqNA&٘}}EH >@@d$X]Xk#< D&yx ( 0`sre>{(Cʴj $aW*&`| ze| Y.'1HQ[ؖv?=o *WT#fb o{|fogJ |f vk#llq2k=>#'笭G2LG5!E yڝfm0;7ub1Ek0?=h@hZ{b\_FU D>p~J[p{|4N9&Qym}_,'νYFمDK>.ߎ}A ;T1e?2k߯g9W*/2K;]pӈ.ԶU^ds.ӭ|j]S {9sns ιYι7sCs3s9puufqm78ιιgsrl܌mut{~}DsW9޴Ҏx9793"ꜻx9w֖WAV3(o|n뚢k[9 2C[93>;)&/GwrHAJg3ctbf"AlLĠ@G&Rd!2S!0Pd]ZKcL ^Մ ]ZL_@Fd7F n/)(h,;kлؗwpq.?=\B-Áz:8Fy96k{?9w&oݼ9w~NJ0\PAO})fxw]/Ckӝs9z/799r{~sX^ɧH~G[ůdV)qH)UmviePܒO i&d #eRLXo@I R% Ff8T"-}:A;cP%5Qַ^ G#V!Sjʳް{=5-bPm|npZ*2E &{qCdPsevㅈ |ΰ{A`[gd:kohZP.fJ 6K%bu+@]fcxiS&v4#"T!"tGwN/;HA( h1pzf6oҍ#J{}}ߍ}r<; _ Keι7dV b?bڼfOH`}!-[k\~Cou6inΫ뜻Y8>׆&Y0bC¿6u}l5Hid&XR 9.A!}hgfA5X̕ ppڝB&<`^C,Gg$O @bjR::gcWez̶#P~X ̮'#^œD&dh7/zI+_,z<'?ӫa^bv!6~ڸ*"(zm صƫ7[lDlVI*m[RЦ5xdyXպb߈|~dh>կw5F:vnrʾ7C5mF@!kF~-V5wPZ+K< =kӑYh+& #kYtZC37-UB(H%blxnTN*rO. |W9}rՍ7V~XBv//O -)ƵCc5AXcBʲ ˜i_u^YMV72`?8ڡw0\n;&)-Nϴu!l{i>lY#طo ;|ؖH!- r "O p9~YÏAJ-耀Ϳ")"vh vR4ݑ|z lĶo!%XB<ŏoQ8 =)hJjX=.\ψ>eL'["Dm{ztb `UBhe jEtM<Џ{ 6X{#th ~Pf; `@*Cݿg: hcɼXwt( V=aIwT~I[Iw.wt% &t)B9BL3 oGsy[\*/|gC~) G%,{d PVjt2AXuj<!J1z8-)] IDAT)ֻNկiSXQ,{k3.M;!&;.EXK2-6>@T"OE3b٘Dly׮m.us;`c9ΩE`k!i|sG`J* $L{._jAq|';5 r|rEx2 x789F# 5y9ǎH2SD,؁h_Fk"d_eP~۾шvíA!h-w'LJ:Ɠȼ[ _%)(,;YA[_[GY w QL3mєY3mk{_; ޝgpqEͮ^Ά́[xRlfuU H;~psMk.fA!檥܂6sssqιR4ؘtnsy[߯dmC' iCȺ9mđjB 9h/(7{; %b#!gQ;Z#0RvF6d{) yfBĄ|dlB@fr`HIΰ{l@ xw.FjfH<]WBk:خ} b;A͈QH%budziȔyvoH9?v XocYcq ݀SSb3ic@oG}wʎ_}n yT"> l]X7G_gk7q29E"’?!Pu3b'/GLvNO?Dc~5Z"djJ'ӻMǥyDc5 njGAJ}'܅Dm5ZO.뿎vϼ]wn,ҼK\!ߧ>pbMCe ,ci :B530B{}G]o]@!@Sd&9R`#,<րXOF~RvҡG)C ]v$ߺ} 6i?z rϐqQf56azt=Xh-B6CѺy ZˆZг0kWy͗PPIo|x9ϛwѸF皴B^$?X t2kܕ5Lۮ˿⽯eˣ&-o]{wiѲm΋'mrXA3Zjnmc>$|5# "r,/D R5. :7% 4"%CHЎ~drY+S hD !RA/H]= ,WPBbzBR؟Rt4D t!=`c=EvO  (cr: _T"6)ԡ_"j,nNi 21vC{6uf6TCãh?oP H E07Q1]]_ɥE /Tcl[:Q.C[,LC~55G. c5" @ߜ5l3cE2"8 RX=p{Nꌜ wv,H~4q}>0aH 7ŎDʭ;4 p5 4# «r~|?.5|f8Enbژ\vJH >m='ӿ 4az8cUR@DN<ēSؼT"VOCz"8/2, ɮAʻ1 ؾ JҌkݐJzē|x2]̚"vZ"1J"ݗ"\^B_67q;f]iճ\F 0R$CmJ#hmîWٛ4RydvDm/>Ӌʙн7RRx2=rtk~wΥ,˜is9߿Ƣzڝ Sޜ;eָ;&6w˜iL5# =eָ9L[JV ?qlv-0fڧo'}X.@^*sG#4 ۑ/X ڽ@3kRzOPN+4 5H6+n@uEOQtY4>D}LQ`m#)ڣ9i޲chy Pg'CPz)`iLJ")ܻٽ\m}$U I%b.\]XY/29>|nn@̑Y#5vBQ%,tDl(u@A`;8e4lb@JP4k0gi{[ߩGTM_JĖƓ\%ZO*̚;g"Q"ʞb}:Cʮ%ir$6ѬCmWlN3ׯk")-'/k-q^nNKhM҄'М==h2a˜id%+Y"ɂ dEF L:CO3C1 QRNW#4;#`6ba֠H?! ;z>;9m7;'`"/ـϬHu@RpYi%%/Z߯%t}ho'7ddNt܍Z>>|юF>es2DA`WڸMA^ȱ rBl׺˷~n@J)E2e+@O`|&{+Ekֿ!T"iQC_ 'Ofٷja.]kWNj3alFcCs享on;~I$Ǒx]Ceim;q́,գh,ۢo@ Ysu[zXC '>e{/;=v_sMѳJĶnVB,5h^5g]DEǩde [oܖO@יD>+AJekK.Li=3212u,حG/VH>ro^H\w")N8D@ #(b<~ٞ@Nξ0 ^v3 7RM~>HX%)h®3퀘vh׾{]@@n{rhs0{7L`cWLw#p 2exk'cr@vKt$ӕ/g+Ū9E>ں*".A~:]A?\ܣoAƣ[QE"߰(4'9*?Dw_oꗡ5  FcFL]Sэ伱K4}W}'T"bkaIkGu˄|Ld9 e] =jrMV =3ʭ{Vm,3Ģ,G/Ў>ĔT"%PU!buo"E g.O@ &H3؁kD MU[n",!RX#,_V"`#RAaNj w3b bD;ƥ.k/z>o  m!2߷"L"0b1dٽ%'~X"pp4pCk:7qͮ.' #g[z ٻvUdfڶJ|oE[Y;IKy;8u>Ɋ rAaކ5;wKGU*:JIJ5*?!-R[C6Im;[ ޿{feyo|)8ҟAJ"6 S";jv$+KYTsXF#`P>RPfff6%T}P: FwA;>1PAHI~9nmO!V@/}SWĸG݈v'ڸG@Ȥ2* )HE@eȼ1xeCU @'*@^LZ;7 3K<̌pg$B9Jxֲ.K>y~\4α'Z~LQ5QF4} ˃]clY`L-=xC= =o"Fp,E{f2}rY Ҁ;? ~PL;g〭% "99y͹U]XjZ}Xwz]u :Þ|bC:.BxVZsnt.9M2>t)ι۠㑋877"QhәrI*ēC8|2CJjeW a: 2ĎЍEho{] Tl("YA@ۦUx.mSń ={+P8! .]x7R0B{!Z=2"s5~~W!e_F͹ vqDgk+`]@+#00=0Qœl쮳Cx2 a&*ss|s9L%bp0u @* O'miXS aVu425!VA4L3?wAa| (hNF0Z/Z[4lDaN%|D_: d\ޅdzVlwͷF\/Yث:&9a_꬈'"¯nYGbVLsL@|TM2T\-eȜshøz^dEA$>oY{\`fs{ Gk6QD3~Jp09[ z;b:A֞f{D?g$ ƶeA ,Lg;#&dBa3b{.E ]r{~_5yR"LEbdG`0~}/p?J֟ dnݾȔ: G&4Z"@!n@ 19ݐIwqR\}LSa\w (GE1(5!yMcx'L_yEmZs|\A@ K:,bQly2Áz:8tES;@9ι2?{?9 =#>n&_8"\;}9w^uf{u=nc~=_Cע}ЇEXwcm|PA㑩V\.o=,A*gs@f`~~+^ondj_ι5/ђc[/kQKT"6'LߎgB=BHv*@Q Yx?.ř.SzjGXw#`]wR5ީC* Qխ10!=VuaͻHaJpuwĄ݄@Q/Į->Ghb@~/%hoe ~_!ք| R=1V/m(o503L Mt"ϛnm D1A8OZo̷9(k4lRwZmZ4;ǬTm ߃1U_[n-1},< :{Ky+w;זkVTnl;gXK1f~/W 36 H&e"Udv|4k6"Üs-UMB@\c#MTof} ܾ蝿*!`]}S](}6Qp FSnC'ӭB z 1]ގBe;ڱLŐLbk@n}ޕΈZLeM)#?KN32%sЃ30:^}5ܑy1kx E[A!{>R5˱#Gmhv}G eڊ9[#3ĞT"x2 A&`gmo߹+lZNlʂ0 6G RO 'H#S2)LL'>BvEcr6aC 8mnf<: o>٥9ѹmNLA~͜}?t_zl8sY 1g-s?űg p9w:{?9Wt7ȭ$0SNCWr}h;ezo_D v/s^Bd[M,3bN"LY\O[PwisM}n?>Qú&үBcbV"e3,F"$"@6Z4&4nzЃys*B&j#QDyb횁TbV"E hQf3z#HMl"6 Yvgt>DpT36b}E~j DWƓ+_`/r򬏵HqLq1TEAT3vHˡi:޾yq`zҎedϐx2Gue*ۨ_C \U).XYs3:N`U*b,(*7NA.bt=RXB=r/" Xz~Gh`] B@`A1 JľOڽdjF6^ZHi(6 n9bZ!΄IjK }G4) !Jv z!bcz[ 9lL:#OE:IC =Lk-M  {Y; QC9%ccKhoēhעtb#={o$,]5gk& yzhC틶Eͼ`Bѽ3=$ؗ%ι]חݗodOx2s9V>M@>ϽlxH'=޷#MDl)Xu_aHXx$r87!3^3r\ւ"ԭ I[@JyU`=ɭ',G4(5@x2}hk' 7g"E=hY:ĀnD@\m5v&ք"A"k#p:N(2D eK6/Rz5hdj\ބeE !]Lbs|Zx2}#b@cvk*ˠ\n9ΰK; X f셞)nL]dzoY%Z]r'y'#Q]Ň^̝}r~ 'G51bŞB "& eK?@`RhQ D;?P_(fTT#+Xwv 4܀ŋ6vikFFJV!r-2lE,`#@:V`.A澌q4b > dA 1dࣼ\ *t">0e?洋cg jvL6Zṽ# Qē h܊X'RXP[!fo<4 >{x!-'UZyƫe[ b*d@H]=̈́<ÝAa@l ";y?Auįu6˭_'ٸ_/;SحdcbeQi J_rNJ4"؏h d勐,O?ApL*O!z*{#r?)א?-Os @`^@(lADG`hbێ@fjGih>;Ǚ֏?( VSb{9TlmEuu#4G(t&Jg}h ,&; Fȷ~ y؎ F u1h2^%C g{Γm {o!6Io;T9~ 7]OA\WB ca .B筟.d?+ѸނɝѸ6c:oJĞd[.Y3K,~ >DM[b]T"Z<>)XJVY;c TMT#skHi\,#E_vH/F 'F~st:((Ja}o|xuCɿ|[@ se_?5G#:ԥ\S2|%LE>MG+љOD6 _c$mEx2}\*OV|A*e`lS;col@lӿ F"]r="se_׏Q7U,G-a$e ClL~Wwv;괒>h.!"@)]fʖ͋"<oh@XS~(A-:n4#giHcT@NUk,}[{Vն?m|*Ye@1\lBfz [m` }1k _ېrOfw`R= h|%:}ޡ!+œ^eom B-D?^=9ͷ0+B1dzpYs979wm6;^wvνjžqusΥ/s9s/_e2c@ŋ39r 6T"VL{t*ˤY)u_pd61B}Pf¤רŗDf ߣȹZ6 XV /(.P juvT|"b9{e`A Q{ꑲf?1A!P 9sF $`8w1O1 J֮0'ӗ!P'2Q؎v;}uAlh6 0`cy=/ v!œY ٶ>mN6 x/L!skk>2}x3X_I%bqp*{Osc*'A(b=2cAjV97ڑ?;3Wxz܁Pۢ3{mַXf}lCɂ1`nFf|7dz9=j@(.P72C݁0O%bP"!_<^emT#DN L_B@W4$y"jRbӐI}k)YEXt@%@^ 1M!ԓ-(GDOd-WHy@ǡ@ODFQĴgp)pm<:OEvH)٦b>@ 竄f*~a#z!GY/F6wٵ*90jWVhׄ0)3bx^cصL#z'{}DoWOD,T"Y/BlSt&i\^.Y#`߄/D󓕬|nq8v䪶.IVqTCڈF厜s?BR_l睍3n_a[L{?YZFwޙE <Mv3ιE=*TFk,Y0 'Ӆ9qMS7V&L.Dq\R:|"1#e/ ^+X\~d\i'F:d`VJ6q~RBHE(^i}`Fj_GM;!GY"P DuCCzPE?^hc`xߑI^ĐEPiPJʱR!d]1?L^6Bsoh^T"8Lϱ{XLؽk'bf=z mP4:[[C>^?1huDlē67#ùI'E5Osn.b KCĠyZI*k'edv&.q&b#'ӓSأ_BL5oK11uH8:@,?w"Z;B)\kˈg"2dFi#8tg…uӬ%5 Tz1?)ghs<a SfwG!2wHFx1Z#cvB,Y)\@W֞N$lF!m!_63/<iů |l .=scmzyL2垈RdʼnӔ 3<$+GA4ۣr/FV-4ࠒIŅfFzg14YS͇:L snPA,FP<AywG 8:\K31*>vι|/l9*5%`,)W䝜eZjj58?(:r vG#-hk5"-G"}.R}YV#8B@WHX1(i_ !P2];dLA".D, N2ؑwL h6R!@&͇QjlȮ{/2Ӎ(8`#Е^!G 1'(LA^?1;W%vZC\RYC@Dϕ֏]9YY_Xa#+LІb~7bhe闥eV_f':ɲs7~(/D,G< 1GߊGģ7-FlW*r<^EH_PwHwBDa@L܍?: kYƎMME12]f}QĞ $kZο+]=Km,^\" wC`퟈bF6 y,SXo< @v2x 2WX@rؖ\Ŭͯ f={(r ʚ!V}HOvEȿ#d.k&ҪlcN2L{`V-CˁUk;%zwƣRnJ`ϕu9Ê9W>+s' Om 'BǷP7IJʊ{عhqhӷ7%O0 ^שaRڐm8 drP"ι6q0J sף5z$ Ri ,2 6}2̔yv1ι7zú;DA # LLywէ ȗ]=Df^l<ذy=NEv;bZ <@@qIߡA9Ba'd]>13o!7~݄G#'lFoԣwl.@L[ r΍Xyc29U|\^ä WZR<"sI}_ ~97m q9M2=hE`.Fp]rڼ{FHo"#wtƣ) ^2cڝE`!BcY<v'Zևx,F0 jim: _G/X|bhB[왍][e\$j_&{fDJ*3Hz.b# !ƣ16/J~Ȕd<YdsM<܃_֟罭]#1XUxW4|ޏLZ;XjHF& ĠuCe5ċгүki<'ۈ{u> ,x#{r:ȿXb~.zOx4rϖnƊ3mw-杚\(/.,}e47(LFM4;[4As#Ƌ ȂFD ÎX/柉hOJ;Hz1YH_j2&Ц SM_X:11G#{ *b;.CtdB8C ȜRidγk>tOIRhK2kx8}lP!`{; #3b4o+~+hY63bt^L|jlpU1˽YbXDxf V^/k*qV.JϯYh=o.ldzv#}-HPQP[߳xYK'iZŊEH۱C7/ q vY~" _eliRLMb~/|D9lhA y4$ewaybc K@=2)qbkmȴ8v]c`݄؟v#1 1X\ W4X389ZfɹoCGf$&l #PX?ދ/CX2{([td%C 4"x "=Jf퟈s(zumK|v|RH&=҄ݲfT G[k_.7[y5 "yiu)m mhV>OvNA@UP6#]lӚ姐 F7bp D}!>uhFs1dvI<G#G#4<ً^?IJ+hd W; IDATȸx42 sXTsmvբf!v|IDE@ h)VL MhCF6*zA@Z"Aon{7$O4KlH1sZϋ;+0>b^^, u"bgF!6YKN?@7h:(^$KtAJ~% NyuDem[fؽoB\c!`qյ,]եU'Vw@[͡nV{[_ Sd`a5~ַe>@$CfWo(Qj 5>^B`c95<'؁IbEyIǼ1ϏBs4$[q p‹؎-{ƦE ~D< sѭWt#L1{#ELZ2a4O(Ņu%eE{\0.>}z)nb4K4f0HFްDvkԛ1Ӑ?!p}܅Ar_Ĵ\;HW@ڕdU!T YLG#_X}k e ߕWݤ,{݋@n( 39DJ<U`;o{X;G'bȼ ;o4:!oMIH֮%X{u֟| }o"2gG8LC0 vhu}dj0c7T@nJҝir2h#l7xfJҤ x[_<;#}M]ߤϜVhhfiV͔/#^xƋģhhHFJ.,ҽ )x4"U 3A,l3@M̞z:E ֮w]CcGґc{K`rOB`nJJsÑ#{?, 19 #]@K:z e7H<YFN # (H x422L\- sI O"s8d}1  hOaģQE K l/Aѫ `+z1~.. :eյ^\G?%kޏgr]/ʣH^) +4W3/x>+״`~7K4LuAD dƣjVSuE<)1IEE'ȵC߮1 y$f_ƣr/?|\N!v,)ԓC6{ՈF \ 9?fSL[Uw7kbV9bU!a.b#?|@\ #duhpG!nsg@ePCx4'/_Ғs- ND~`G^̿z=llVbxd@r۰ Q*0&6@olLDfzv~:7H LnGlh>[W?nE-@bSwm,?/G#BoC;.~R))+\UQp¥&Lj&iڢ7z7;s#zOwBz`!pN2ҜglHF^Cx4r16 4 E&8DlԩHGzx4?/w"2o!|L:Ω$7b4tBc5rVE]mmf֕Z멳eg"0F8k4;1 @\ >rd }E-^EQc= c(x1? ֋Pk <CJaN9stACv`0 hN%ZC[L 7 c۠ۋ (Ǯ9 C@ h8^$N o6qխ[eϫȘ;ue KkJʊB@DMqaϾfsn1O/9(gIgOp܄k:>>` ??`e o3o1ERxdod^ %a=mPd\ Ah$rlk;E՝TFR܋[ǣW_r?ۣ\8\b(-±N3A<A 7yhlO!Ʀ5R;EܬWw [lkW){!`-$TXG px9 }ﷶ~[ԋ6KObSbQ?$ G<yۋ#RgcpxM @#3}-eȹ^ՋCs hAhsp ; Q4B/濱!7F/vt{/(ɴ,hޛ:$=22ט{RSk}/ɇ~҅45Uo!ՎLwkZn[O()+*E(eN:cwPRVt+pus.;5=cLї<4&G#׍quDl8P1 >uZAsn4Kncsn(rȵA09w6" σ 8r, 0K(},G#H N|4N_r~% "gB5Jb/W"`s,L#+B&װ@kC= ģ?Hy b~!Z0E^ીY5Rš&oF e9!3lZZ2`iUh=;P}lc'IJߋ)P@le;kF^dţ5^̿8 z~ZZO!YkۏXev^kĘ݄R`8N1_<5Y_u=r?m Ax{ v,kl/m.4IvYkOCg{;=S\XAі&@k]{{Ժ.齼;4V bhb=1[類OoR"% D/ݗ$8Юqb^GmhQb~g:uBߡ:ģ /{10b~nvݯPٛCzp~-iڨ{)u9?dBɚj~^CḢ,R٣Q ]l s F^14/{^ΣE`>i {zdJo%YsnF2툳.6?)hzS'"Ѣym%HG b@x42 a/A v|gfH"|F% s,M}g=6ً̚>lGVuJ< ;w[V̮!%azO;%N.)+Iһ砜R~<A} Nv,9A$&rV2ր68HҪ,?&x4wĞ#,Byw<9ےbZ\cTB2d@5hA,CRShA>Q %@/C;@&giٽ,4)͏}>ĮQnMCcx1+-XJ - h;%|@``/|]o{?E nP虈 |ߜGرOK'5ʿU#?mY=$MԠCXŽhγ\yiTI`<>oLG#"v6Iyʲ""/{1b6Ɖ~ƣ/p?=Hn:~r))+))+w?{HvJOaаa͒ ꥸ4Q\XIqai)о&3Hh6 V6`fI#KO.gӋOFHZhv ϶Ac73& Hxh2EP4 4Rt^R,EKq2Qu@y?,)$KH E#m#o!:2)U"HW!ӕ̰-X7V#PPLaT1~x4=f}¸k_/B T0ρvohQƩ?2#Fַ1vͮ6'a=T!^?x)FY2Ր)x4q^i j1 й\S6B?7yz쩷ϛ M&9SЦsS :ܩA#s "diL9v }\:b"Asem,?fF H r`#XRX)ڝAlvAg1r``V 7HamZK bFٽ3IFY? xbuft/_rjs5X'#Fg5pEB^Ϭ;z]O+"3~L`Jƫd#S2Xd侊Lc2>wkIR 7Oit̔[gd:`Ek4RS۶kohy9%MhlL]y7~c<y1HdX>֎X{4/bbhd]EOCOi`Zy;2nɤ{qȔAc!tc6b?LIv-.,=(,D>s'-Xk.MG>b??_i3o]>^/$<8Mܶ}ZSiBN r΍({1O+90gLhM |1ι @;\ XkAPQιh=Hk z9i` oOE` l<3' \}F u'@&)cԞ۔n\Bb^&#;yTZ! %eEŅl&gw'm@rGۀn]/[{շms=uu@7ZegVS_Am]9%eEŅ- ʺ(rm~0@IYQb>{#FzX\ `s˒G.>Z7;Xi,A 4(@~f_4&b_0jr="̳s@~YhdR<bBa%bFN MZ'}>E\W ; )n|ǡL/GmrϬ=Fo6r %- R &d,LF 2-\!bη/ކ<@ZW/1CњW[oWl<Y.H)@&svJ31x؋/ǣe֧ƣ%^̟`}lҷEbN B艘 AfٙΖHQ䡹6PݽP}1y9}=#OhSFޢK󯺫ob\}zZJPPƼw8(N~Zm'(tBIY@N].]xUtӊwSS.zf13voIiOhL KŤS Mlߠ&7A"V t&vhp t]|~kq#V"Е3ƛ:$"aFtFdȏ@)#Byǣz!)n\} C@6D2 % P׬G!&}u/}WeOTjWԹח~spBkicǣx4@<:E[Xo{دx42ߢ%ٽF7x1u,32 HLȞv.v(T!iyEysOY4h2[lċ--1u-SZ";C ; Gof6Z KW F$))+5a"֡4NEP%ysBZZ9U5dԧ&'Kʊ:/'d.JeV{ma/))+f^wĚY6VXhH3Pv*rؿC)Пh u!E!֛XuQ9#cG L!ȿqVڽ#J|r:Q F 0m&"휆9x42$B)=Xw@YA;/?xɳ%}n,p.`kz1aCF]^oe"6=x t'bMFgS86;Ț_#h[_<W] r%\JAFZѷ9sGo-_,k?eZ˼ESRcY=dVĴk[uDXXRV]qae IX6{oiUHgP`E] TbS%CmZ-KOv}c[RVh]\Xlݗifi _;3v?'+~dVL# e %k,K}($"z/V_#&l)Z,ŸA?= m`JNEv(b#^mfߋ{tStS;6̙ģUHOGJ. ،G#^a'4(@k_Vֶzd mn:zw[v1<1OϑL{''!x@[Jm4db b~D/b4 Xh&o92%%8 =6fa޵6&"Fwg/}ܗ[=fzzn8Rmidڇ7`,6Hbz~UTTpyw'ҦYx{ic_|kS~PaItE4;.-&+3&%#{fw IDAT0ޒ?bV$ٮ05ImN,.,}XURV>T, JqaiuO7%r]O7e̴O~eMv=եA@=\2ELG#7k6"_L8tgdn-}윰2@&r~ Eencvl~CzZr e'45 \\b'.Lϯ݋/3:쀧؃y)]H) D`.K/?F{1|2i K G݇|>'YEC!v0bα}OQc;[" m5?"d3~sJ˯}:v|wA{U}-NŅ3}wko~KR{yh/oZ_Ō̂q>- 1bsu@_jC?حa.+/Be6FX ȆD\U{,R!H< F"5;+ SWT2-@s]C2gJZeA P,lmȝ%#P}:-x [ux4%td*j3XU}!0ڐ-F 77zϦ1},IUx48Z6Z? V@|@h9GC aHy*!R1l|'!GdKg'RR{59+ZdV1.}djvZw6yH @0%})m 2>#ߪ6ֆ1(Ďj_ebߦ"-dY0"۠4߂1tw0MϞNWϧM:ơڋ"6&@ch]!@ ,EV@]6Hżkfdپbij;){OA );']1SNL뒅h>Ӏm<`fWxTl2 6]a,'YC q?C`l;u^WUdnv6 tZjU^;W~_Gblo44Mqa;eihsqc6W\XziIY f{)r:vb"HW[Y_hy×mx&Y )%eE!0ML4yߐ8粑AodιzJr v] M}x1AM{ 8/7_A"j0"u`uG{2¾ۡj8ӐIn.R!Q$#Vz$b"_O"HL_몥TS9۽zeqKkڗv7]$D/㗒L0HFNݐCĮmm_hr `~'v6ccE`eA˝vړG"*;̶GYk FֻX@ P<6(:2 [XO3LoQcA=3Ѣz&Zlfl|KH G``b_\ļA2$tfuLMb)d6o+Ņ%eEc+JIkXf1rXZwXe/X{io]RSAPܝvc9۾ONGŸI*drhM{_2}2w,ߨD X2=Pu6hUE2/G#5=/6k*., Jʊ~m`ݟ8vJ'mLWڧStaιAߌKWA0g3ZmK %=Y*ȯy1#=T9dx40.BI֧#R#Gql|FO)/ȯ,-rACf=ՠ~qY<ozŠE ]zƚ?cĮ֮݅ ƅ&ڧkڡϏVدȌ7eP4`Td샀e嫶ha^H. ;#-)e$3QEOL kGWoK>:KIOOԦ,y;| G@=F >eH=FBNIJ/6{4,GddZڹ)$ʗnd0]^ 9Zx]>d ̿+L4XںR\.%qpUzZݔ44Gaڹ.6}gVBzMlbY渓}^A#&|Mz?xţRnC6[%5z!K\v:ic ;iqu\@U;9>Zgn n;jd%sd{ Ns=&#m-_MІ9Ap}~=JwT9.C:dy?j,~cANF }b/6I,ψҿȋǘoU{Bpʑ|G;ː"=$Hv㷡5 /lלxAzAFzIJz'_ezZF]S77d},^#HA>k[;!`vrV1i!W1aCJu{ S~6]$D,^涩&wUHoxۊCAk[:$9.IM\$7=CǑK[}fgt51R'"5{WllG!517ꃞ[9ǣ[-x1#2}`x4mԠM) CS:{n\6a"a$s": H0/GH\};s p6E)'Hϧ/..ϵo5-}qKޏ{kxIY@d+^sW-F^*UEӽZɟ%eEn(ޟJ@ A+Ҧ36=lsn==Mrq>q΅`PӝsC&tgsνޥIA뜻dUC4eιTu9eXVAp=LqE@ s6q~ukdZ sbƋ3:-KFC3ZH/柎cgvʡv|8i[%L4l|Gh.M!Xx4r uhċ.-'dY!3 :MDsS!~!xzGs]c5v=cQMDeo[$ee# x422iam-:״HJ:{O ?'jܙٝk쎵AJzJ'd#6N)AmzUzR\hcc߿Ff]ޫYϴy^y<ŃSC@׳뿇@{6.l3$ $P9FEMna; wLumNojb ))dan"Od\8wI-gvOr}ݯ۸5-\/6ȕ](JЬ>?deW ^bK߶K\ b{ҩDymTA0~bAPĒeA%~<AykA|0M?';>F(l%ڠ;uW x9G߼Z㈩mYs,q/7URq"ZhEY>~F#Shqy#ݧQcT&#W%3Ro[d۔4FS30Ig"z 2_6~(/R]4 ܀"d# hv6i@o/x1ۙ*HdM-7l}AAx42'='e_=y闩A%["t 8,Kݕiଂ6.!Rnb%AgXv]q'9Ջ;vnsp_[ևol^Zg&$4o`*nzҡdϘgj8 ><#zv.e Kh^]jHTAjJFZnOMezjfNڈf:x֣wu>zk_u}1."ŅKLʹoІ;RRVVRVt\IYyiŅ?z?|3Mϧol oM}s=f CkP"Ք6;s ʯbf'/柏X S?ȩ]K pe<y"6$@GE Z|B4A*9_b/DlG&T EˍDבrfm]̛CZÔ%$tE`PA>ku4BާY?G[F/odz섀yH9@;rMA/>]d'b:.E:dR*GH7RK*/揌G#x*hx4r#Ii&is=J jx4܋" d Sv\bTIAW* <ݹɋ swg (=³ .%nOAAxߋ/d;J3..,x{CRU -RW@RSXQɽz꛹CnSU/cf1'gք;Vմ|a#ږ()+:Bg"{{6?O$bq M?,ޝ'<,@W"7Eâ'1ILc cAιj4N4m>^cXw.Aw}7+ss.hs )ι[o:A\FW(d #u;m@6sp1=Xrr_͈QZ@D܈0OtD~AQ&-/W3YKbb*Yz\Vz1?1-$ˠ̄h# #߰|h_b @u?/׎v8# r7[[_}5Khx3ݿac \kwDБhd.u'bnqqx Ba nCg}SNA"S Ħgt(*:'hV'0:tb-6aEzʅ#\yi:htЪ1C䯋~W^۶kfYmm%Ņcz K+s4qÊ*9'32?|ض5QGq<#?kjsr2VZ_ K甔 (.,]XRVt&5FJUι%c.-M?:3m \AUC;lT̯#`-h)t(?TcNF;}dߧl-H4WE/eu@g:bZzGVNG#,g ֯QdzsmHFos)NE7{& ?9ȶ kش7=Z,]\KݼJTx6RRVtb,/3w})*% B,fk_XWĪ<F6@qc馴jsv5P.Ee>i!8 ~(!fc4ڂS-b{H>rȌBc ZL%Y&vs^c׸1ll& f/477~c?-}x4$|[D~ţZ/柅XZ F~{)]b~=sP~:7G\Q'%eEHI;_lZ4 }jfȯL R@^ܸx4m^/oӛeĆ-C@f2"FLU{Įd̝T_ٸ' V?5*Gʿ;N!$5d|Ɖ\U Y%$k|FL( }XbҐAȲ#C2a֗xWڽ'"i1IvmP{ͳk]2d{o:*)#yơy *=`cᬽYzҊs;mN UX{1b.A&;3wA=]XG"_ac60"0ubZ^q M"!6wKe C"ŒY߽D/\Edޒ;gte>iNWᙈA/CsW=ȯUW!H0@nxtQD,[Ȉ wZ<op̑xvሉޞBr[x! S>_>Yfľ[o]=ĚD.E@Va,MjU8ɞ7E#&+: XV&kWkRsPΥZZHGG䢬G (~G~,|"K[iV.㭍^rsx(8ψ#<1ec4EL]gu:V?0 xV1;%mGIJ\X@ obIhߵvZZ[-d@~4a^@ G!CB #` =>h4a!fk f[ڭ=A."dQ \D\؈ eAN__pg2$HCl bC0+Er@]N"pM=1]#t2~&>F4)z߻wB]3}wB{V> c"( RXO }%{ vO+_M!s,! =g[B[tH\GW&aG e>bkQQBdx D"H{9WkX&H&ˮVnc %.V[dB4{Q(ĠXzS[=ZM yU@EgA~{b X:yG/غap]hM7 _\1"H'JIqF{zh4~lb0ظާXTr馫PN^f3*j_$ц+|.rmk0:]NoUOdɟ)?} OKgmsh|gx:NHUEͭU5#@|EcONW~Ymm78RdxwAhg b>? gbn~g+ݓh]_ؒ7P P]Sj'<~5!C;8",H<P>@Aw61)sWr VŠܶ|\R=yXCˀ8VZ0XYwpu2]5Z>@@%M(@Ո)@Mw4f(+yu6tG 27ș'[ٻZݲޭȍz K }%b g8,ctgy1Uh3}?ʜXsLHm@~V迈OT'"@F 4NO4"~D;s x%3>;k%=c~Aq3pN2Tnjb$G-XӕcgkAҼ'9b>4zx_wuu^ҏyS1adHA&Z +*j~/EF30fڈ9g@}Jm1_8s.>C ԥCy] 6z;ks2.䛸3%<KYꃽPD9r'9vD+'cԚd9%HuAcՏB4.޵߅և;Ɨ-N,E/2fmQߙ}ъ[r{f2mnnˁ=߻oߧ?ogΫKOQka~}GX_4z1܇hقާDfŊ8 r#<{ҫǔ4zLYKKs#9wsnv֢;}k&-'6'9bXqnήe\387{Jmˑhn92'v@8XXeVZ4 s/i^2=X[XlBCY@kb`#hsteq{kds̓v?:]yǗK&Z{R3}.O#}EY7ہstU5/[!qhIu&C3gG`}2dTܢu}1sNєږnуݐ=m}` |sÐd Zs{"C/8ʜs3T@sn ZGUιJ4ZA,gF9F>{dt!%"Y(λSb>Dj{4ȡhwļ ##} X] Vd1FsR&# PbD.#(+F1K*b^E@;#pw8 oF/Fod'"펂s6+~IJee@` BPAp1/"p7&LAT+ӳ6{I|?U`I#eaB PRĄ=؞MW^E.?aq+]C?Ѧӿwk2]VB£@qd><ÕmڴH?@SkDxxyئ3߆' @YI/Ow%џYywc Sg˶ywU;`%н%8Gs'd7M `F6tF,^\j~ޚFd0lCUEMPUQC NW:]y8@u{¶|"V*j^G1n@ ࣩ-2޿CAsju.DncCE֖"o5C#d{^? Vߠozd>%Rcؔi@ubA+ lt& t'MG#ngcCfe>d׾Mׯ[FXm}~j 4:kD ~/K,tȣ !{+۴3L[^E@J>D A,<9ֆlZ= ӕ#cTu:#{x#CB/}2fώ;߬df/m c;XuĞWk"_LuYV+ ku2UUQk݆>>j#XDGW˛^1bEhSN'3׾b_Xf.cKe ιA9رksc xw]8"@ ^t-Rغ$HuF 6 Ȏ%R&ѿ"V{zb)v;E"WOx9@Ff s  H3Ew\ShbG`;<vEVٳ^sCxll}Ϲ֎J¦"Xhe!}ᦆwGH#bJz>Dz)Y xt^,@D"1|X#7Х+:B aӵc4;M$8EhWl;2pkL]>X>VGAo/]EV?X}q?tJ}hL,EYm͝{NMv\<"7Ӏ>3YYyh\/!wB{л|O?Yw8u*~mc F΅V2&t*!NugW.Cni/S ' T+Y9ˬqm:](~KMιpÊsGu)ZZ?%_4ιp.v @p1w8&"[v1sn2;ιIh{ܕιv4'hFf9J<  P0q>rD|KXwBx >Ȉ.E̍CPV|oem)"fXe92h[D} r ]bsKv2s#Cj@y9b*>a~ŮA, yZ>V~@@)vkk6C;z|cԛ\k>(?r"OV6ꏌet1AE:o u)vGl*BD1{yVSK3v BsAƒu)뻇*A\vjw[W1(ֺgː}YڻܱN{-EI_x2 Pk}71=COr>r42τ|>{|X{x{ rB+J} 2Qb"FlĄ]zsA`3ey<} ?FPzĞ݉pwU7{*rG]yA@ as^ wD^E@bz ? £UaupHlojC폀K؜Q"`-\[#m.BVv>9ȭ\s =9 јmm~3F v v,oN(Bk(kCb? >֗ф^gGfa{4NÏ tAtEL/{-PfiE ҡ]1VUԴU+w*juI,wϚE-[=`@Qvлcn~l:6*weUEͿ6p$4N1_QɆU6{F1[YfF. VbX" F?isO|tWs:ǠI٧nhb^O)A.v hʊ}2]KzFTY)EI>C`/(v˭ F`&d|[j؄#F/ A3V e`t{+.+iRн}eV'X΃.PAwxt _;Y= c|Vuf\r|\@oIƣZ@Rvt{+oNR88lKZL7al>A#uIxFm@h{%%\[=,o:s.5]u1}v߇P -Lw>ߋVF_D 2>! 9ڒg:{!y6Hƣrȵd<:)ߠM' @v0*`_??>3?Wmޛյnzc*jӕМ\UElluU5W+A A֝ }ڲDݞ7꜋dZh!ô.n\E6bzyyk n}_*ss-BWt4(59D\MxaB# IDATYېkIk`ƕnfeZbg}{>d cLG&űDj,ڵEǹ+=r>vĹz7yWXy@f8}ߣɭy'ELq%oqvy܅p59F= wZ>ym=rsݲV\ks ށ`;¤H_+NMahlB0z9-YS;UUSv*[Jf,H#29h~ mQCNOd\F} VI9c2>2v7UOZOD)Mܧ"0V+; C!>r=mG,!p 19O!cVX&{6MB.#88%@jG$ccM'ڳsck}1M$dCz]Ēnʽ᯷nh<|vsjs\k{P7||:H;m2.*1-C({ȍx} ֭alOnϮ,@H9<kB@==`Fn~z_ <%7g"pwmsa:b\(HV(NN, 4v cwn( ;sxtC[)R=#"'[+te꫒r3ӕQUQu-3p`fqlV$0VE@d2Pšh"|vFnkDwn ۲;2Narf^ne8Η,!4ttO2ރl4q X}ȧ@~ʆ&]zHSuCﴺU}(G2ɞ 7Z{sEqdCONDG z!`|vgX@r^>ǛDc9b 6K;zʭx)` 1?Fn]|`NWܟuیOP׳mU6A~l; M1"J~3QhBAFhrA@ Ī܍ALJW%҄R9,*8lOhG#&*A7?1|#SD U7 =Vb괡8I3=knNKƣ#dt"Ɉ\mg@dйd#f )_}h4X\梱/vb;Yً H. &.o:hD~k[={,C'p'j@>hd>gtC H+ b v(}Ԍ` kLg;u"kzތXb%ACv];a"? -NEMhLՙǢw~.B sX1> r.a~^NWtgӕ%ӕ?NWFӕy/al=b1X"u L/Oƣla))ַ*H d26sЙWEq; ೀ0j<ɠ ݧ/VC8{1 #þ/b.Fld31*%Pbʜb"FEdOX rMgG"Lgu>hK3usFc6Ǟ]X&NlO3٨>wO}OT;['"&{Q>UFWG >ۋFgco{AgÂcV_؈wg`CxllkKG?]T+f57UWv+g?PUQ#r:Ѣf%PUUQZEcؽ杵84נwԏ675%}JL[ϼr_T8v!پtD,ǒV`o_OuHA Ϙ!?r|& }ʆeqSC.&=XڲK"DjX"cs'i+odAmNȸG{~#}  kgLx1d1\&O cvh,z%Hݏ&4(@Fk X*qlz7# B e?اePi8a̒od|2٦]1G$>.4# vsGm(Ȩ & \lFlz-~#A@q ];#rFZi:h_|VIa F.`~^9]6cn_NVՠ"4VDLr6Z"Rk#ذ?V 0 r4۬;EL(y1a/}v)a_,#FqսsѸXT~lew!/e tKgw-3fh9gۊSZޭNWrӏycWW͚=cE3hn- 7dܵ#ֺb9?Jл~*Ӽ]ϡJ9p\AV-=#î,("ZCι/ާQ<(H~2ιSs7=s:޳ݝss8Acs8$d:usC\9w/w國ߗ:&X9 USk(и3zٛDjwDMDS,U~{G},Cb@gG# GvAFf2H3 Iƣ;Qf!TV>g b@kJ' p+,=׬M%x0r4^!;_ҡ}-=kmnVpdF,K!kZC08Hݽ՜Y??+W2>iD8WZ.E+K7ϧdخ\غXgkv[=~"z^B.tUR[_\t_DO;ʇ Иt-N5#@rf>jт!m#WP4}{!fe? MkԵhj]V=YvMϏ!x*lzc߿b:ݙؖN`(M>!.\9A)s(yl+SE}VI0ZMMʺ.dke;O~aeA@s"=#^\HVfiEk3oijaYaAqEK˙>!x׍E[?f1z~>vf9/sݖ[L+:D4-^眷!hn:97sQ?Cssh[ u ι{hlX2Kƣ(QX"X"2SoELj80~rЪ&d`KT5?@@f[eػW8 ri,w,^2>,䪛^3#Pnm9>EVyh2 kF*iJceF}vWQl@)(NijMǐQڏG`dExEgwd㲳3=<;m=:d"tA{6#u4b_ZKpwV%1 ]b{n,Ď&x;cMOG )֎lĤfтY}"ܔ!U[Is81vYok"ܐPf@hA)Yg;Q ܛieǺ@qq5NWZY󄺦QHAt5KLY2WkCmHhu9}xO>WmwLϡmgz΅iO{WS-p)Z*qNDGGv2^E> ޘA0K>A{Axyg AІJo/A"Vd[6&8ۙ1[edKfHƣ#cahź Ds+IƣkbYhRN#C@xHrQ`dLE.4}_|^$cLEn K!0O"7? vA 1=LMDj{@Rry6j49![sqprkMEm '3yIs$k`̲X9+wb7daKKƣ"72NYEIB r/]=t{ sGReV<&zF"О>X_!E8cwGzkO!Cs] 3'!JF۱I!c^s`] 0m}4?k-9/vB g:p1e (u&L"aф0zkL# cۦg#jHa~okq"4wYڸ wzrNWB>@[{9{.fc'CE8驪YQ!@G =򪊚숧 )3M08 <>+7bZǸe| eɹӕ5]m!ySeG` Z\7UˌC _ 0^GoF.!h23ZտXJ^D/$ rg KnCnY$!24!v]2WekYNCvg2 bTv, }Vh[=Oш O<[c9Bld @@ŸJmz\Ŵ|dE`{(1v3!qqMX^;Эip+kF/12-N\}W U 4] GջnRge}= Zf}n)D@0ۿ0beeu7\`Z`z>t쬿@ J\p4z!TbqUk[wX;*goO w1̧D\W#wSq7x{-:vBFpP.㮛{`Wkouof@Nv+9٭AA^,pۙ03_o xnQx_6|t{@UEOU+Ǣ!L+}QΚ6E+o4^|i2rVw asG汤uZk23m(;_A0i<@8粜s%& +7WqȖLKmQ^˜so [<3/EaځchK~G[X"5̀R &IxA5;筄nF&b"|d!ò2l3ʺ{>UOҞ6d]ܒdԱmog#ʪߊ؎S$}JY]w%\F0 ]+bD99#@a,>2~r'b|NGZhE l(!bu[h9bZVYvimjAwG2dkk rW.[׆撃=AxX"udB!;!ttm,*BAu>2@y<>( `Yc ЄuAʟ! I;"w~E|&GZ_6;p[ %h"k]1ʛG7 CلGA݂&"Ve/u#<o]zG/@`!Z0U:@OLWyVB-ADMh|2ƹ5_NA 6rREhT+تfW3:HQUQ3 fG9݅kwt"I_GA<6huVOӕ7v8<=Ŋ>Bq. Z 3Gd`4Y6awÕ!0b`h{_4f'ureND]b*]I2Т(~fŖ~uPޡ|9h~Ce 5Z|5GO|77yKFw x:]]`u_|n$V@k2^HVEw~[3*(sI΅K"ON|xf냄ߋ*/H{dD::v|%Fخɯ[.sޥa`l|5Y1PEP'bKhp,B}@~ȝX;p;: _ :<;&:bvGd2!{L"=sg^oh; ЎEp4H}Fu>#zbN!wPDԞMSmC_ :Ȯ틀U=kǛ^@[{&h޲A4Xۊd<lHĎ}XA@ ED r5J3!Öޅ}O#v1OdD #uafh}:Ԟug[6p hy߱ 3*Br&-?Eea= aߎVV=7 -8oY_A *mG}hiA;{OFy]Z"v/vC?aUܲ1p-'KrI7<4wO^}Uu=i?.&5웝\܈أg\E*ni߶0O?Vt)^=b1~RG#D}T- ۮwSu&bQwܹO,Ĩ'eb `cci| T;Eh݆&돒X"'bF!.x:N%RsЪ{)A,>=j} ZA:uݎةQީ,l*2*DAg"C/4Ib@> 7Ïgn:YVŏ wD6 c LOXw)B kmGp] >]y?#x 2+;ku959x:n%RW怷 EϺeiȰw;Ls:1]"OdhM?EA =Ğ9Xk}v2SD ]QU,QޠW3մ(mC#9 Fx5Z{GPZC/X]~Bb~ޫ Z [#qNOsTAgs:[Y:9}{ trC+(^]߽ H ؽD*\3sN8GIѢD8Kpg_xg3WfU}& ˧x/7RX[,{!_FV^Gxn>C1oB}_t=ykteOUl`,D]xIM!H BdXZb!P 1Ȁ!\~V)2hw##=<,DlLSX6d}~]̈́'#Ơ1={#bAL() tMXj;0{=DFQ'!ۊ&(FaZyhW۠dlD/b:A%| NVv1Fs_cT` 1ZZ_wx 1r]hyA8#~7 V4fܗ# '38.i^d\~ l8ak߻ށث_@nлb˦"#wGΰrѻSA5 ϧ0T~w~פ_Y b"7yx#IRUQ9i,kD_T\PUQl]b m>oځyuK9haG9٭女]y A@vKkNSnN룽˦?m)|[?7=Yb~1n@G2hl.Էyb4BQU56An~cwotN?mlY,&xy/vS"G~4\@Ʉll,zJ).%ܲ?OEC,S2 w]>, 0t.b| P: rbCw%v>_m:krȝ jبKEfLur.Aw,̩VKcn0 #C%C8Yim1]"H yU\mwWrظXP&JB H!K |PY` PBREE3ܫl=0`xG{̜;9gkc 1+s^kAAb߯ؕZ{|K?(ɴqْ KKVtv^@&m*A,Ń6\)"d|ƴڱX>g&Yv_'S0"mz]46do}uaS;b`FtHfxN,zwhk@=dƇl.ژb6yu+\D+7zjolj44e64Zmhcېr*πe%aye}a!-y#r,~/C+I{wW84mkc]^vMںn3 o&/\>kmc@I/zNs_3` ځ Ues*3'QlK@F2=ȞbZs9RgEE8+vmh-d[hBN[H=9/pK&Oz^i{ X}ڽ}s(R[8`}~A>HIm2?%HyF K鴇Rƃw붴6XLK[س]2OG r.^7s]XL!`/uf߹p<߀ͮuܘajB,Kʹb/@f`bwAUލd<:ۂD{&דX"u!|j\ jmxDwαc.Dcp `mhծA͝ec߂69-քg^֦<V4'\I}89%̸oqA¼s]6,X1?󗎎lte!aKkv,j^tnw9U [rJT4js0o얦ֆƬo=] C`A}acgy59 somA pW] w Pៃ owŀox%R[#АG]݁d< tDj1_!j6r8  `i(R"g~G?|vB>(4XQl#is'Ɋyq,6!H Er<|9@LV0fZE 69 iGMP|خDh|S=."<1|"@Hc2]lc91I7ڳ E9q J'w*.jgMLb[.D c&ߙzF|,dž>T4ևsJ-dYm ALy!+HlIƣm6@z"5Ӑ86!Y枋b ڔ8`>[0\RhNy!2O^u5HP) j MѸFth)3x~ގǁsnIƣ_jR\_),hme~FÐt:L[zȞ9qʡ|8ggOhlkNEK?++O=e%덙60~0F2#Io_\@'fdTM}O ooW1+pGYI}%|/ݳ|e-D m`v ð!}wwƂ ȴr%ې7_6tH"DX"3bԟb&gQ|v5].R/<|8ZA!y}oa!RD/#O .FfHA#`ᓘG۬m!p RjǡŲ)OBlCͅ:p<; KNQuO@4"&Ģ )H:яcubiE,Ȍz?9i8I]9;L9aO4wym1Y"O6{/AY琲va4@sj}R/k y 0^LƣXKIJW:2N5d,Fs?#؈evPaK+h{P|$ih@siB4~9?]V=ƿ i#P=vkٙZ#7'd#a+si@&o\;Ƞ7@$BF[:+GG?_~{s{wvt[ž]t;'9آLJd;mRye<WHY_LhkҔdVa鸸҆OoKGp4>KR[ronz0 Nsٖ*AelNqpeD;eqܘ>}Q %A |}Y[3 :7 x?S݃ x%'hwҶ}'A0# n `:3}AA.?]6 0Gr3"kڗfH\ȇ-[bpF@L-xuRJBX"uK,iW#0 1#?&VO9R.G#%}2R#YHi3X"UVXD1 E @ )U는WX\ğ&N,"`[ |z#&##bn'N!ߠhL]6f"eڗK%RLv= ༬ւ켖LmL?Fltd4Mmeɢ4;Ԉ*:āl,vA')o6{N عϾkUl;!6Y{\Z=Iy3mh #*4W bç2_v,vj>˃  h~h^a~wY[dv=Ѽt)6G\0$\;=o¢g:>ީo\ rTmVЕյ}Z'tXgЗ/כGv$chf54E ? ;&啥cqMr1p|Vv. O4[6qo9qRǂJui?Hdle9A2txD_e0!ߤin }㛔5anғ hm: ao7bwDg8/0ظmr1b8DL@Ͻ蘊ܕC ]jm{;RMHiG0pǢ1#bbZ+~ߣ։H)@dt0>߯ iF %65&ɯ0ɽ`;)} a2=[)s>o@Z#R'bT)z;-cáDM3YYLގYz u%ZL+ = pR:1yA.xz̻,_h4A|Af-e%hͫ+| .X6|M}xqvso+>hm8nڱ wT,1=lpը/[\wo{[׺4fBVS0|خ1eo`cdesqO r x_U&ApvFnd&ڗd<`,z Xc#O?v/R2ӑR)Cxf+BN3TXX3OҎK5vڦ,E FgX[!eRt34I? -kF@kE j||<1Dc)u"t'E,vbf#ftC>iw}#SSLXW"Śk: n<#1r "Bd< H؜>@.`"0나fؽ϶qf3~wd9 Y!w!h.mɮHAβ:޵qg혀@lfmEc6fk;ݓZ{ %h=oC Fk4z7~b׸_D ]s.zkM^ -c>;Yy9 f!m`e :/@@̵:Ǿh{]y6L>Z3n~ϗ^4tvӪN1ADCtnn͙Ԝ̖^WO}3J* 9W>q'4o8C5IjP]hYIwQZ]hf5>RS9 ~}|2΄bx ߈$ |ـ߸w]UWI1d ^\ ^HIuG/Ky(b̶F)؜GvHI~fϟ쀏~XoϞc}hBʬɾߕ 9rN/.AvcHɇ&j#&Ox7L>+Am"@r[#r!XC)/F*@y_خ{=x.Dc<bϽW,g g6;Abb".EDl#:وtr4O\g1\.|OѝH56Wh~fX{g1#bo7.JƣWى]7ϿHG;`2Wyz7<SX\w2#-;޵22ˮ}<㛶JYIŽz[NΟލYhLAp?ٻ ~nֺ# X6fTYVf8/)waN5\6, ^ð%a_VA'n/+x?W6Y0 %1Rn6fN@סLT}"bPJ>LW 519}q-yW-L*FZ|-)d iBf6AL ϳkgz"%/z{o_.B;34 j?1A=hbЋK!夞KLryܨVKQ0p\mhm}ǜꗣ Wս0?n+_l nEk6ϕ-E, '~E yΖhށM )kԄ&9h1k97C |āw:3!9p|E}&ZơìUd,D>3ÑBw'#ӈ7ׇ"&|vBt/@JڐD6r"nu>9XA =+@bd\G%R_s)HY0;#5P^\GP%,c;qShlqG d^4T|BL7 p(GwWA.~/GoYD܎dXԈ=u{fܱgd NT g7o( Zv.z ZLGZ;<֮̋ȗb4h:W{z9yhQ@6]OLL-vb$>>@>/~h>gMƣKϺc6jbMg gf$ѯ >Hѵv@eVf+h @rցF,*NDwJ*[0!h28bc?aր. 6:ئR ( ð= W\|6hv#bV 6L|Hy~-gq;pvG"|C1˞ 8c}8wȸ;MH.tB@DF}Ēd{OI-Ev|d*dZ 1 ӑxsĹ -;ٳ\y,z8'H]G1Pn2u6xt@,z)(սx"/ĂvrHDk \EM9X_d2B,j'; w'ctUd q.Y& <Յp\@F@`ky&n[ul)31ҽ;,mݲ& *^OA`e[-]X\le$”?lL{,;|'’nkn-wԑh>DRKD TJ Dc$oo.QO0v0b]߅-`G eL1ߥ\0F28=f RHt T+yV`E2~bu9`۵!;/@ _K.H^)*k *rRb<KśO@fWjr<X˱%ZXkhiu=`v\ad%RB˪vk^WSe}y 9w@,~.f~L;|!027vA#Dq蠹uE">&Syd݀@hD fw7+cm"z\Z?3BlN`ur366?7֡ 9nVdcp/bFPGoɤ \ӾBİ9s+8e.Z!mVG{SlI3WFtgIO.xaLv={f)啥V;{^rx^}cիkJ)¶ gwhX^yY];,xeV59FHƗ5"6ZZ7Yjs:{cLlZ1i䂦fe^))ZLϩ;)iHy84T(1gYf!%q:2;.O^,FLjF 11Z;zO&хD(T"mkc՟Dq/"s)ߝ<( 1-&È}v;/"zT<3`re(p.hލA"ߞ(! 9k'DžZg_O+ًy2~ޑ[a{&{[k=[@jN[}cml܁4[.0l(jAffb,Dȷr:MD2+K#̾ͽ;1RLm'>1M-jf.A;ܶ_o88[YI\ .xo油Wj]vAVVǶ';wDf,1-z 9yOY.cƎB(ekA'(hfuT-mH@ȁ`q^XVGbJA h:; lef5H* wpB,߂91-[X_3@`¥l@ɩYX"ax_I1;ܔNƣKbTfp!qY@HA)O0T[3ƻHvG5F!8| Vo%@.aȼ 2_"S4\pw 'Ϫ>b7.$8P,ݵS!Frɸ hNmbD\ X3ϳ _lrr"CkcV_" \G}6~ߴTA1\iEљH_UB^$Uv bnoZ9'C>D2얌G;4CN[[piO)|VEdʶ߯.o"dB'&t?mٺڞ}5R!=]X"~b9.|dz`}Rzu;#;d~;Yx |p:Ï>ys'SxB vt`ڜ̷O@6#}s(oA,~v6nXh !&?xsxݟDН(L٥|6#iisМd|} !l\o\X"_߹}S)啥e%uז@ٺk:-WW)\6Ɂ1h e]K)-A c$PA$ ,|dR RܽbԊh3S|)z"}V׍AtDkջ4U@/dV<).rdKOŜC'nb}~O!e{!0. S.*( \`H2u|h8_O15G#݄ s'^@`d->'=&^hRqNN%ȹ @aֆ*kջa,LvoН^Xqz ~G 6tC!b7cp#נۊ@Q .7g7хq]rkcg@V}uY|D] bwكQ|Y%.!%Rc;yE2啥YhmN啥"6u=~/-3'Lqԅ]v|؊ )B[ FF h=i3m5a~!z;u\6lr`!lF! " ;'H!R(v ̜̐.j 0)#" !sNd;l$BVbjm:)h>s>RLi| +cr)<{Fi<HKX"T\ah@ȱ}O4dV=B-IƣѱD.W>DĞ96(OehOcмdPo Gs4Y&"|rkth 3Wn<|<Ȱ1߹ķW36F}+<KuD yDހlcj~1J3ec A"}0kAH\gl u3 7X]fTt8#.%R_5x%Z.Dj$[I^=C?huFsStVe%{:.a]?N˗ "پtA_M `0 7&iC㬎pޠAdax7 d ƾɁucVGt1b}v 3Ӯu)G s})"Lpj7׹O֔ed3tW b/vې2v ŭms$jw!v]lW_Gi|)֎h@Cpi,:A(Q bC)H-k{"p,$*\Ǿ{fbM9hgLpaDZ>L?B/<=c}?!j|>&4@ }ď f4rB!Sq=?gaN@*Y}e&CV6b$Um Ö֎^: [#fR E 8h s[e]X%w7O]}cyK&9rc2]X AW6/d,N徏6JZ_啥R {4Иއ֖1ǻ؜S#ٵ M_Pls |hv ?`6夞 Y_nf#pZmA"齀Ӄ 8+ ÷ z3ZEƇa<wrDFwCaN0D`k0,?Q6Y0KrI2)B4H,A stQwE>WyhWsR 'ū=@&>s0zjj>Y:yhb6NE)·/EtD`?s 2⛊g1E bz;R|>BL_ d[WoY{Hc@fwNA&I#%ѷcԯy._z O]2;% 6{ ܐI="vaZHZΟp=%@DTbu]ёhǸ.?"v;d6,gx˞9roa͚e'cTܮEA4EL^F4;"_Lrwٸ]nwA"/Ǟ*2g݌{ ιT\|6&l'b7L'#ʍUƲƼwgsiû'o'w啥zfmfD=TmF"q`4o}F2} k~WSa^ߖ :~0w=zM}l.VKy)F1t˜C`~_ F5og/ybΝ0 v͖a8'a Z3  0i? v@kdR&oL& ƐBrreEx_b%)2ޫѢV?Bʽ ߣZgm <ퟫEZN0#j8)LvE-Y[^C,H>XɩhxzZ`B2x , ,yALs.i`+ug#,q+RK+}1ο(.xb-6{5>g :XF#gvAiuМGۛ/A;dzb.xDV;}c|/ѾB4'i j{jz1-LkK:#;RR:#eL7K䒴: pd*#@Yb3FK v.vg#@؟n2wAeCyoD`^h++h-qh?BJq+RP.Ax,ă,|?$X"ub~.{B1>D#b  ]DJ3~.xtA,kmio]P @&mloC  %)rM6O(rw.AmW p{b4UgvC)Vo]VǶaݱ h>N@ 6nbiZ{Y,. k|gO8 "f *qs!YCEWĀ^cE,]p"|nwuȋh~hgqDwz2V|XOF'K~Dt7ֶϼ˼斂}8:3S=v?GNYU^yGt-K  L ƗZ .\h~Gct=T ^RVRт{g:7v-; 5}'>;#7H eAo# X6fTYV}y  pY0 j1 )̔n|nC=\ . õA 0 208& d`r"}m@ p=KF;--ئaH6A@)TtHI!bH~S]T8 j3iBA;)F磅t.hQ;fȷ=!&1cE>Bx%p[2=ߜ{](97"ew=?bEdc4i)br 9dB"^F )8GM{vyC{Od>%Rs*R#&j]#UT|X"Ui6S.;d[~qvomWVfeC݉Fdbf"@%T"ߖ=|l:#Eu#" 1ȌK{ !pV˝ju@JP{_Zs=R8֞W2]f ΍p|)X _b5H^퐲̳_ȷ +ЂZGM& \`iB soE 1alFLf-_/6}cz [د?9}E/7FDC-66k_2G:FmjNCL\U/4'!@wg#{I_39g'"0]ޗ;5 @!S{/Eߊ̻u$dtCzfZvYm|߆( dvR2} H)e|˥GKkV՚nA]@C3 U r~YVRHWGJӪJ*jލ.y9ٟ%+F6ͯzר/xjүΪ88c Eڲ#ؘ'vq!F>J׆aݖ))A (,e,:,AXx>TriA_8%@aR }x H]DI'㣒 9Xemjw_R9nv b5I СF`)Hf$b3a5 f\(b$ yZ{!e߆xSK%R[uEֆldrS u@N+QCnjW 6x]z8&Ұ85wA 3[hA,^;ևY>D \kS1+m߷ ^#BcN&{^]4?Ƕ} ;1x &+w$n T3Z-fn/]fǫmD@w~,:Ϟ?nʚ֜mhFИUrGdruyeT୲/}z9;X]1+fDZD"-Uy9`)m]'J*mD}w,\N+uoh*uZYyMT,ɖ0 Sw #eXx'Ɉap">8b-ݒDR"pA-DCJp:u8ž:#?4f#b;~29ծA ֞$SRLn{ BM[o4vMO "Ajtv2Hƣ3bԃHQ>Nbވ|pX3};v|lĮh~kм R̰ù#yCݝtfvOмerA`p K-σ[9ֶ(KL 횵6ݭ!=ޑX`m-z";eG{(WmaH+_3Y{ bͳ‡h-+xY^Y:{i4W.+R:lFOaHMo}{u̠gGw+^BYI}c=:m}9BwYIm.ԙ'B~Z%|RTcnR!:DEhl~:3#m{kzRBvFiHd!sH"12]~s-D@l RHY{sDjZf= S'V'њX"u ~!3 D-5DjDd^iD7YaF!TD!B9t Yk ߝov:3`{,ΎdSDX=DkL~Dj5:g_hk5>XB<-EŽM. 9Y'ojc&2ocɧ)LX6ZCM.i?O.wvj'6{֏<Āc}kc|//As+DC'-rD֞hWc3 N9]̼jc])+o!,ZJ*sO#_o!٣gYK/(d#"}\Kq 223ZFsIʌvo.|٤2Ƙ5_G;>HQ@Q}S^\tHAOMƣsb(C??w̕yH`qϥMs♋O=)J6bBJRng#S ]ڥ #qvFl<Ē͎%RE_c$n5[h<]zH7cMZ8s4iE`BJĮD al}gc-A 3DbW:WE8B'>*O7tY5Zl<#0,*@l/kN&޳WB iwLvç U&?@|6@9X#oǕM>@_&hdRd ~AXmlހX"51y/d<#R^Ys ,&آHn{>A˸fc+K \ Kvr:}45ߨmkXۍmƦ3u?YCC}cYIGfفXaꁙ-+&3lE!e%O}]\6I1~FE|`,Z%RHIxĢ F %<| X"u;),df2 R < Y`Dr-AJg/+")hq; {!]Iϝ|.EHW큔l|̮*B? )Gkf2})HMD>u rR;X "6r,HYgN2'lG Bk߮6>45܂س[(xF@>19j m׸<26[Hm&&jG4_=ɈMu^ B<hc =MI|d>8ƹ⮈I; KmL#u|=d@]xw&M(в?GMVDd<#~44wVt (o >\~CFƔz$ZVښۚ}VWL~b6@yeiн=S;zW6-3ߕWЯ;4f6t[v%o.ҮlR`,b^emq|ĒC;'ص $ [.KI31$L|h|@Dd[%7Ƨ3zF8 "ض 4Zh׼YUH.Au6TɳK"lYY@K[:gZ}SN]}Sg_ ahH;[ۧ{6\mx=2@J/䘉Y6!  = Gd\LHѽȍP@HyMF^ntyg{n!ɟh}kM}H^b}|V }ncvUsG n8?p(m-8GصI_D,1t1~7y:B{D3ѻDsh2qi\F{XiQX INޙIU\mW=00; DmFuhlh;jpQQc&*(02>])Oq(gfo[u[9u7n_\P@Wc_c|\*#%&n]_v ?\X-[F:gSˎx:(IOY3nJ؈6 'M?CyyJ 뾂2 ;&p%55v@C:d d&`Ђ܍yd\N&yҿOZlD&c(OXٳbC={Ƞ#2V!eḋ#cv3Qpt'68b{*mr3>C)E@.TX! 6V [3g*SAͅMl_EsW$.:?'kce1((HT&W?{[oU_{y<:s2NZ#b:hy .6n;"<)m xr(Q!(|~K9wP罿93TcG VVƂdPT&9 bu^%*D%*@LROd\#&&Nƣ?G.6 AT /0"KwF""Py"R>O bl(^\ȧ(FFe#`/:_~UZ{ ѦK-,X@td#~ozB#BcPZG<$n$M'2Ì>;lz#Qؼ!edν+o`qCk>^hnX4%Ms)pں~W\Rյ6;z=$\s.יѝ[Zoh*T-vmgfҹ:Vs[>=-'SqSfO>~>L>~)醾OT~Z_z ʺU~jMzDۣ=Tݿ`tUQKM,j9EI_iW9wڬoBD {yioiV'[-{vȦa%} ]+110P F)UȭZ@qMapF-8B戡~8&Jq)ZnAz{M(~ұv| Ky睚:uӰ3)G;<2Y{BK 1"b?Z_'i3=#CނbBUZTƚol^B<8i]X5dKa y܌ =_QU=+Mo'B4i.M'l5)t 'U$X\uv6s5 Z@!;Fg1 IDAT;2NZknJK;oN 0&7 l=? ;!@u=?Qh&bW#v;bBuL88ɅSOX*+!k^ E]\ys8b<;W 1{C'; 1_G*Ƃ84`fܔCUhN [T^~]bFնr?p2iRɉ!=t -m5:[wq<2_#>Os=Ӛ (:'z9#J$?@kSphjsh{?9wz0>sjdKks}>՛ 2{8S!u3?m Cu %t}sn!ˡ@Q|Zu~snwO9'y#s K7wY[7.Ner=bL&##[ W;! ③"WPS\"yt' D>L03VU:ÉQs//~]4F uu5QlPpL{yĢ}t p9w2|ݞl(,c#{Ag8{?9w3%9@O- c]DHJ+5KdIM]J/TE,ȦEFj:ڽ~ !D'rR k  {x.Z<7 ȨAFv_SQ_2 h:v툝_v$B/W&B=lw#UX_;I?4)oEqX/#QǞ 78D|X,A-B3}Bhtd-"PN 2>NO"P}2[ї q]i}+Ѽf:M'OF֟[[vA [WA '; AX+Q WHZa'k (ZWd4E߉{ Q 6sjVB!(~iy,Fk<'r5Cas-MB {Ƣ?[i5?Ӄ혾_ϡ/|6ybF`-TCh~>GׇqS5㦜\3nXݍ+V=:o^gV48iSZ\Gu 1h \!=^~@C;љʞ 4"@p6MH1릫4d/C,ՁC p%M!g@e6䪭m־Ȑ^hr W"0ю?",@c=WF]ND.cGy2Wǀp1}>3 Z{FRg=QpP@(S#g:9u_SZ6T 3:Đ@񽬟\vtWZ[T&חxބ}qzⵘvby-4m}n>!횱c7:D'+|Gl`Bf h eD@vWOKv߹hSgc)q'qKscQ-:A&ܧ}+9DInA3t״ f{0qk7ys;>>0XϡQ#ֵbq1ÉB*\8{ߝoϝ6}׶WRmSH_2saSAVcsh3y&vihι*_k.ޟx0{t ^\ƢlX6|1`=- QGLP3&p> vE >r YaP8腌d4QY32X=RhQBS3"ȅ8Nvl:vo)@v5)ҾȘl92F`u督NLW<ˈ}d} v 6!V[=w/P81Oː o%QGQ hj:Y౦oeÈd8`ìLG)N۸]om;y lNzE7? +(jfZ?㦃 j}+Fqo#]e}lZڐy=bhKx!s /WE@,|p:Tll:SEL>!oU;mç݅XDb^ qy . >jyͣu'(|͙Zw{/xh׿N\rl:qh;,jn:^:9[ؚ-z4?ιZOvΕ89޾s91l)h-}/y9Wi{\?`r,s]=s{kιv\w 潿h}%[-3F(M'M'W!c_{+ȘRc r͉D.hBW(^| {ᴋ LT&G]{}r 2!] C m7El:ٔM'oͦ~(fܔ8hs[bY_-ۿmmo}'}'E? 76] _e;1\D_6fCRtsM؆xWn&뫷\l(R(Eޮc:Z@1"hXHv.G` bc֟ i~X=[{?om>mbH^F}KlY!`?"ڵ=0fܔ3?n2iah׌I'֔C%ͥhn1"@t7]]Iyx1kG]{Đ!KHv4~ ERKT:` k{B Ѯ:yƬ[BoTlMo9hbvD@P^M׵(v- uP@DUYFOB`&Ft:\bxlG[tCm<:t5@44/ۘ6(ΧeT=0NB*s쇈.Nޑ k*NHerGuUt*YǙi|o7kwI͸)u%e]/*m~yZՇ(c3eX71hNMnCMz<V.h=#hV/[5{y~:!?p7t5wT&w Q*#2kbI m@rJ: MڽڟkE|gdX;B @ V#J+N,#~m[C3hr>@{d6chvhFr@J==$"F)6blmb,؇b^|d[c/"w{V)v1~v7#4 RφD &KТr +@%PD7"M̷gB4܆@C{ ]zP`ļ _({  lE\# @Fd CڬGJPjmCK+s #u\v @sb@4 5Pi bk֮'P,k٣m~M:͍yrY6#@*넀W`G#ow8eޫ/rݏPWә[BiSruX~ B*'UzmDLD;Z]ކdpj4D2q\#qSwăG7e//:;{7kMIw+!h I{hCn 5.c.Z^t+Q{? nȦRx`-N^n6tF.ȽK r6 ]e}6w.C1C@tt=t vMĀޞPʎw@6lCn'tr;ɕw붶_i+*mnQ}ƾDT;]>vtHlOM0cLhdȦǿucF>S;5?X1+G@6d8#ia!N=Nm^5(ި Ȉ D\=%}h7 pBFo09yG4'#mK6^Ytl:JerȦUm[CԴ^37m7g?uHtȧO f 1BeKܜ^P/-֒ecZ'Z~"ʯu" 2G wn8wBF2ڳf!?{!aDIȎAR+b>@O(n(Q;Pmok:dDD`cvxu֟(rދam]v@@u6V2K J8]n{!djB BX[_Cjb!qyX`SUKT&r(X߇=gGsvG t:^CL[pw3}':]܊}O{1z&N@@|>J.En7Ұtn@sçmV:CޯKm11[9׆lXmN޿z loI;:䣗me޻JI K zWZϪ5q~1MTR-ꊀ( I5#C+}zdPWiJ0D,8 #l;<`5NA` D.Z>\d =b!~My@;r ̦XF!#٘&e/@A;Wg,{{); 7Lfڐ?𹜈 ֆНokEqu2F)1ݧtڀW6}?O:v".Cϭml6Ղs[v6-G7ZtRM'q.hST&sČ^>VIWV~Cyʋ7cmO ȝG@z,KXZ`Fu}v={_Her_C}R\?`rMFlgR#*<-!ۗ8J;ʾJ^dLW9~D_DT,P,~὿^bbߤ]*oק5"CTXU;h0s.䈼^:ֳ^9 P9eK![.<I!M"S M/#CCb#bi^Gt21%-l\[<"ö֮}L2Mgbmy1fkXӠVCv}FnWx>,]t<3H#fmCQLUH*GfiD^@1]!S kw#!gM^ŹޯFZ( Affkg?Gu) _F Vބ9ЄB4ɝgGz*T&1ڸE`C- jf4p1|;W!Whd?FP` ?Her[鐏RvUNotYv-w@쇘^ X{ 8o\8;C6C>A :]VUH!V 2^o"oG}&b_#'dBʈhGd92txp-@ 2,E;МN ;[-v":p_C& C;.i. DvMG*B,c1Su CcQYvPzjcˬ!a 1fx_J4?DPLO_D`6׆xD |{,̇q5tA qGT":!=8w$$0Ŗ}f6l_жwT&Wacqc6;]J1PXZ_^y2uMD^ޒյ7 ꇕ\) 8F@ou΍Cka m?-m"T&w ^M'Zڅ>MdQ؅{d >g9n@-$s-G5Pːhl|QUaiF 1zg:崥#֣] P4ߐb.15Y"N >~W[{wE_CN1-4矋@rk @F>3 rC*^vozClR(ROBO!QXq:,Qn| 8.N>oc3dsR\ocvˬ]g'kM6ԋh,5}5"Wl!Q9f@YؕϱͶO ~e_M'uے2a%4iUE/|!Cs%obֶ41\ٔhSy*b滢 |x'!@~s3{C> ٮػI*;?PAm. p:ő"S#2 $ʚ? 9 3NkR\ ;N~mmn6RcB֧P({s#J:1[3T[>CPMw:ͷ bp~i"WEzkvR@y(X RːT־:vp-ZUm.Ar۸^ic7bcKlLZaeJMAPlY4\ e&N/C3G msO -ɻh#r\ɦX(GmEP=gQӘyAe'~M'>vȶ,o?<ѭjdٛ.ҼE)ۃ1hs{ Zn@k=,p%>l:dFf"i(";]v+ IH@4dC)+=X #b~vl]y}~D"z b *2ނGm cc:1%Y[pQB8p՞sQJfA`xoM';bh8DLՌfv7SXPN(G`l:J!$ru*FL$"@l&bjw:p:p6'iQ-EM?4!oykMAb_// |C8jzO٘-DFW]M]}umh>4`mXo;Eɤ~1IW?ɷKGՇV~?x˙ (ˏ!\k[gC_b6+=f?M|,C@bbBZH nLD,2děkz!ڏR(_ dh#p' nEI:SE{ ݆\xmq(x!"펈Mvs$ Lm&$\V"u@aJ1 7BN5z5Doqvbk,kWPYacwχt%_:Za/ ?:bE,(D`z̍4}a?#|7w[+4o׻m~lUxbBF}CDy+au wX`ױ#D47gȰx5VTPGz>ݟ3=ɟ/ \Ol3M^Nǹ9wH%d|zdcLxjLA;@bdcv/ۺ-IA8PYئR?FͦsR܅^M 됑+bA#Ĭ!ÿƞuCF~r Du]׊bA4bv&46mY_"E&^T @a(ZNVZXq~|"d_ؿ5WL8BnƟ/ +ܔD>(k@Q\/޻t5tۆܳ} (~ʞ.gR܉vFE]\ғ,2Ƹ]ks_=$N_^r[cq~sCh=9e=zk8\AetuHt6 R@SM'߉l. B KSޕ0ބCțF@aZ/=h=r@@T PT&7}КT&wWdܩ`\HdO +C5Yrk6 ފm}FM[c:QoC lH`(ٖ(FN(lƞ3=mDnF<hb!Q2r-CWȤfzz1o3 *.FEH2Bw=rȗ7U.0:uDLM+Ũ.dT~=+՚#-S\U`c+uxW*ptHl^e0A_n3qt6$] mLnwv/c1rCiYfb*2/"%IDLD%M+2Pr,PM'JeroSt'#Wֆ#ʈ8uS|{n"!CRt"=~Gqf!x_K|Dj+ !n vVN]Z}u.F5mmJTȻBp/MCl0h!ڃэ6~!,Ot00]K@1p#ShV81/F۵ͦ}glr + on|Źr7^C#@6I5ֿp@)@Xˀ6lt-KLjjr֢9QX ؆vxY浱>M/2Q +K_}xI~9J#+Un>V4}|1oO׌pM5P<5a@[>> 2fܔw{;C>m͂l:9j{'9a~x'JW*3N242{"1ra B:C5!EW (Amb`c*'aibHmaM !cۅ(kG 2"I *G75>_L٦ E]\t~鵟iWyg{ֆ ]6<݊سkntJC-~-g:Rw \XuF,2)veyX!6Gݕ]\hDF#F%n? xIق|`ՆΞq bCtr!@*Ϳl{r pL*;=N.Q% @5vmUـ;P:wYS~oh|R2m*Ѧ(DbCjū;\3Vl`lsM'E !?1$b6!ۈz1OWP!F2bf@ˑQ}vGd^ E[8ȭz<\e P q!тD2> 1w1E@[;cD`-1S{.mktt1 +##3 +CE `B<be!Zbk@sC!sJRL+M?j\l m^7` #W\r1r[?6&Un^ÿ]Bs ;ڄT(m^@soRW 3*7g4vk^gv6iJy}cȭP¢N>}ΡY3n67>iib(oQj #o{{p[xB`~{]?z_\=Ozrgxs4{vR\A6|E|GtEcDT&2 dD0R~1؜(f&"3E |R!fko`pJXUe;Zꈲf}};0B!+&ux,AX?ڮFuD9*ksX< ?(F%Q`bPae|tk@PDt|eXg흉nvXgA%~r/b0ϋx1"@;۞:,DN@E6۠X\>軵+{WO~<2󇝺bAM݋X1 sKNuMk׾Xe}b y|*(ob%;!@s{x?= SVMXqsnK ُڽv,oU✋[=̏S[Oo7`,)]ydȯɦ/k (&jE2IMCȵ 4! ot:o7 i@Zȸ (/9D?"\TDY'"t#@jQY9t"* ϻEI,7&`إk `W"鉈]:(DAo>a®#CQj.D ȥ7t(x{=M'M(Z0Eզf5#! RnnUkl|B9dɋL2PiT&Wl|"Nn*7mpnA2k mF[zlċΝG6anIWSE hlIn("+<,UߺUnΙΏ>Uc"J w 8bڒU;ѷ?6n/+) P QU| D!܇-~+7;q+Gi{*њu\-:s̿Ɂޟeem0?uh>i>3yz9xm@'sٷUx^"#6#;x9{]Kt8{9mp-9_{8{s;~1i_46"o޳YnQSRϭOɦ˫H= d~@gv>bNМM'V \AA}_ (.bk({TkM_m֎hAX7oxto#m Ϭ>XD;M%-%Wh\q"f}xޡqSV'gY^8]k}_,Z 78犑@<˜sgsOy z9"e2 (F<ιKQeF ˦f?$7n~Uk'M?fܔ5LnG5rQ*QS䝩Ln"K?E/h{2 d_OerE >Ew]:{2d"bBu4Z4ٳX@d_v#x8M1六id'ٵ5Ih2"s&2 `F` C u3W@>lnBv@O3 yAvm6~y{?;L~;;20P A@ƟAAD " 0"4 EZCH2 m5fx] }gݝsymuhN@_2((ht%ē釐W+|<>!pI'ÑVu&RN5CM DU|ߠRe^F{o&r0d9G"zNg)T49"D DD$ADx (DGSh%02@.'|C@w"b6R,>B2DTӏ͵h3?l0%po'}S_Ȝw?|9}5-(|zF}NE "פ˝KK%ܧH< h0L)"|t_χk-UJ0)F&"->=-uX/QҿՅ"^s3=^l}M ?z3.(zff>Lf7kS4a WA (z~oߠ}sPTt;`Qaœ5DvED:Z`pēHnW-/#1&#Uk{1"|dSh#s.Af%='ԚmHpQI7Rb5}\Ɉ S 7kƤEu%~soAcSJ%\/zvFt|͌yoFp)Uly%a`tdFq:^˼|Khytz1A׾/]3;G(? cW)Q?y HɡfjmswX`]T.sX 5[JfQ,3Dh8r-d/81SVs.p8!^MU\6;u[gv tj" n%2 0ǰjX|vMцFD:sL_#_QtH7ɘO78\)C\̓=1J۝0٣ƚ\BWԯqBDC$0 2EvԁMh _ɸ\Ýф MuȔw[*ᮎ'Ӷ>'dzD|ad܌Y9xHdzQ's=_vc.%仧SiU\š-~u2Gn; p~֋w]IC¾Vw#vY DA9u2wg&a?ryhyLv͘W:GӌW3R^D=F7#qGn78s>z,kÿ"f^5c}d6`&?,kLGssh6\8)h$5xJNAΡ+d:"A}Kh#2_T[qǪ9s U#"F949 MZ+&-Mtm("0"+:g'~')CfGBRLGfAmod&"ʼeK Sficr<)$ϤdAfw4d³Ќ@?{PFqL7jQ)+}#ݴg!fN7q5R/3VW"5+F5;9gS%!3RUJq9 at"6hA,LC+ѵnw/́c{"9|Cļ=O$ϮWu2l?0%?G;#hL/[_"u2>-@ӮKږ斠㫠/wT︚wKsMELc֋md&gg3\4sL5i]]/HA+DCib8 ʲ(#6 3Ŝgf||~[>W_9wlV+;1/c=\a~~>,e^2tvyu<ﺽxwZ_ld,p/E/ϻͰoˮ؋Aӈ$)'z)#pL5ٷ?zZ'qYr =yv?R2{M)e}( DD\GcM:S,DTъ'zg!21)R hv+zL7!Y0`SP 'O ?޴?"2G6܋ڮL_>NA .Rfɵ  %1 dz"R8̾<&Tcn|M܊Դy!Y}"sMd#Rg9{>R ᯆ7 Nhz\E S[KS%W[=LEӾlAcی71c6z:^u/f|HQ'S:.5$Dl=x" `Gێsu| 8tP[Q=V{5 ]W6wɠ۹'h9"<.㜸6z59&|9Mς at:?BD[^TVXߡdj6 JfUXEkXqB(c79)E'Mh'<?ÛH: C&!6M< aHٝH +/ ?|EJ%)d+(T] ;Bj58f\T2](:lg}] E4PDv,}̝?#34s D{H:71ٶ#x{/1;y+Fҕ@]*`M*ΆNu|<j=Y1gcBH4VM~d蚷 uК )Q'Sjrp=6Q'SW,((/>ug*Rm5'^lz\u2H\ToBeX Mzf"wxV>,η7n^?aSXGA[6lY/><V:!/TPvϫ=ns6C O(D0œ_"Bo4Zg4GȮ"p[jM㑩ɚb#3L8z'g3|'jq(CD7VF/}˴ fl+M>BST#񟢉+HB]Tfl3bS&M4CtEG+F*s((*xִ^3SpDB?){T,l_Q7b.l CY#wyŒQHYHHZ&Պ|ܻWٰW}*#DF)hL'K'5{4oWulH1Z dz\*OI3~obQ'S14gߢ>b&d{W/hUF]˿ r;ːXRThT*tNGzێ6w[i5=\.ͷxyoL{Ci(ei#;wrF~*6}."6 #⳽iKi7Ђ;U͋ ;igk6+GzS^7d&"3o^sibm)u2}ѳbMbrGѱdu2V9;ݟvk&k~MDr&l4YM%ܹdz$JG D`C/o"l""# RWzODXf#BVcWj.Z~_gۄԛ0"a!nH=~H(bt44"$Sw ,3}>݁ԥf3uF#H$q9"Q#Ѵך@,4OFB_|&J 'g#f D&fG F M u.et?Ԡv RE6DNH%&=݈,A$Ìu1x2jɩ[6 sS>[siGr>jB -@Ĵ7d.~ ]h/VlSWLtN%śwBɄ4u}AzHY,;Nf_|NGveE˪wl_~ICA֋ݾ{ @uoè^#3btkbkh1M~oX^E/ C u;jwØL;h!`ηm>݁Yѕ]R7.u~Ka'C̟(o e Gvk;pL[{ͥd-EJ@!~#Є4M#h;l.~Rɩܭ IDATMG >Hԙyh#r29pƇ^ocL fH%&Cĥ?";?Eȿs:R&cH3MhAW:MMDmhDDAb"GRD+rs"zKұ"E6Ch*'7!"vȶTV&d#ZhpyzLkh3+W.Cݪbm>qjӫZ7?8jK8wF؈&6 ̇3:Q =~ Z9ĦspÇ%VA@k_{ӕÿjF*yKaH%;A (PY߸dl>2C(7e6'DJP4[6'H-z #9l k&Ӑv:R;V #❇&" )x3[`iAұfE 2ew= ~ i@1m=Q>Xg롈&H\42s6!Clek֦0Fb]8EZ' ^,iovTKڛ  QEHcRځ zEBfQ'38dth7"61o.Tvć}EfQʜ?wu@g+;W߆Yť&3߭r#W=Դ rw>|̔-PAJ_|ϑ̯l Oi1gU 21O@~N! 9?T)hb[&l$JZVǢD#j"(Cn,")w6͞o\mao|-EA}X/P:s027v1;#m&ĬW=".5i^),ʗV$"I.fVr` xT!jIpִ?I̵HɴMf1JeJqK*:p讻69ylDsA۪`< P+~GJz2߇H֋mJ0d❢4gږSt?EK/kB1+_+{9Wг+T0/T`ʹY Z`HU{yq|ًo ց&ڲWWQ<>)8LVxJ%wj~xHݙT249"$4?8X=" Eg[~RuFg7oBDHZ9!2L@VMEQsD:O?!r;Efomެq7 Q`>鄽 ʆN)(h(מ/(ףfdC>O(7rMY/6ksA@<F`Wbēn7|)C-lw."F-D*M>z7R& V%2D#H?|s+|9Rlf#~AlK^5mO">@6sC[_)1s}#l42N5?<)- 3GJI{{ch] 䠉3c1Ñ:gc|moiSy2S^eχfvE玈7FOL29=J66 8 >2ŖKwP;V/فLDj9j@DĦphBN}g3_d"l{h"D#f7m|ݜ+Vȁzi> xhmP9C@$u ~̸EfJ?mU-4 )>~&s\KnBH,F6pX_}ƿR 'butnSzEQ's *t{d&Ũ 9Ņ ߪb-,%\Znvl.oY\̀F.}bnv-O~fJ`D\ LGքi0 ,7f"DsL]7L61)BZrvR|OG$>QnbD"D.U#Sld"lCb|>bFf7/yBo~M:gJB}~OT3TH鱦?=DYYb{,"-_G'pN-ATό;8.FňxiK3pb*N'}y9DDLaP#?ΰB7! @*O"l?][e3/;­mQ'+2mvd"YT]F@ƄKX*E Bҵ;&[]Dz"f\#񈠶vdƓ4pF{dk]Ecb;p4 DBI"W JɮoJēR/IE^k]g83BO%ܜ ϭFuH7N&fZrlAT#et (  @- LkqZ^~NgԱ|M?B*멄R CO!"-H #r "#u6".yD#"DNv0@ʕHL:x "u@̀M0nTƓsv"R!2AOM%WL޾y9 nl'O b'=TtDB-?/T½#L7x1pt]̳;~Jtw:{aҺ:>]أ 3K{>Ue::Jzvq)DxƓcѾ8p?W %:Z8Nf8ee[ @B@624LM%^ޅ}鲥xLLA^CRS#:Ռ|Fd#kD0z djDfHY:PC{y<M 4xIX8(I%t<ƴ#AEH>f ԽP'^#bA Fo W;87pē齐yv "}:7_/D&HV̝%H9"/H ވT?TjDh!%~DM/FҜ*]#GFDf!4L3pO%GA'p]D@F$r0Rڑzu~bg aε?R0Ց4jgœē ȹ]XĖ(DB 4@1<m5a[Sǯ)xt@ Ո!Ao܌E6-@_,>cS:pē)1ON%ܖ߄4x2CDT?Wg"Sqj1ۄϬxY)cKia~g4"+3vcӦn4?<ppp_*x2kӟIь2ӗ#lO"ܯ kM/I 2>`=[HI()(YDtJZ Yu2E@nWU놺Jw?EmUEHI}n[F:hk -Alsc &QQ'4h,@ XT]N(yH%[1 N*BE~\4|!P=>C+Obv/ BU )HA00LW#?CfF4v'!)'ӕFu:tpy*>"'dzYbUO#F;׌J3pCdQBҌMUrdBf=x7̘.x!^9eA΅K^ͪRBLw.{rt۪pI='ӻm>l(̵?ޤG\T׾{~Q'Su2NѨy݋P12֘m"(22-}72O呹miʑ*pd؍@D._(+ jˆq.&\R㖛sd~ӻT}?= dҴqig yi.r֨s=R3H[rmTϷՇ7 !CmNA>S11p^PT,@0! |’oM ps֋: YCpPT(eyΤ;bTsqcȩx2=/p%{|}ƭ7.@(2J ٘'c3y5R z@<>EE~YOϝUR?Ep41I1yMe!VhB*>OEޣ@K<>!p?va(}MA*.rp?|_wuցIFc9ޒ.]''u;poqWP&s+ sVn-YfWfWP{>rKQ]~̯)|tF~}iz=tۀ CdԽhga|U=}xBkzhT¡GHA ꚷJx9X7?>}j x*p_':h14  @mBnC<>W;}!>Bp\<* r?2D&=:ф;"iKT­Ax2}K%܎x2=/Gj(B| LMkACT8}p[TkAdT}~} i )p@ĥ;,Bc]ηwk5Kj>Z:>˗7UJTm.qb檱-D1ฦoͼϑv{8"WC@D|BnZ )^s|3w(}ďk'ӻ#'_!T}vm"prZô(3fc8=.(-(0fx2SJ56*p?ҁf-EzWf6 N&qj U#9]_^gwFPo@8r|/xH݃-GIMB!+fٳշEs:g4ڒϚQk!3FJ~/| IDAT(vT}ElO;'MG୳}*6vBff:cR:EYof7'FkR,zgTͧGeX{֋yT}21^lA֋]bwb9gcU+_+C|lлZׅWҾtT7Q޽M;Jtv 2#Vd ;\~*}/ OO+6mWEF}>,E#4$+)''"u#<>b-=8 |m5 QT B~ @aU,jъ1;=$t4"1#F`[Mxrqi_#G) iWPx2=8A|5: HҍS~*ᾆ&wEJV?4 )v)?p{tC>o[oJ.&B2dN@@~bP ^H{]뮩[?E.E=బḠxoNq 8$Ŗv֊x2=szU)).{uqQ/D"h"h$!_·I;?nE"OہM󁣲^M|(c{zldS wg<_+wn&sg(٭/#OFj÷(ZKPBc| gkQ6&z pcJdZ>[&D$DLoW}Y/65dwcAiǘG!3u:ވ:0Rg;>?Uq87ź]l$wBxPC>V{72;fz"Vn6tKY- ^lN\CF "rh͍Y(O%y4uhz5Q'SSeؿ7iGm%0$=x2]Ԣb2ԗi3^g wh^'L2ڍ@J9w-Fc["Y!ҳ'r://F_[Q_+eQ <")cYG󨓩BY/?ʵR Lx@|SVy(fE֋=u2^Ha"Y6UE`mWڈRbOv9OK"  2"L;rjE$ JouXw*^_JV32JJQ]jPXD́v  [kC4<_ |dsKખE!% Nzvx2d~~ۥ{{Li/#Ta;=oue+g)qHbP 2.U|$Y9Gr=$Ln$6x2kv*6m ⷥ'HiQLvq "%]z9kSOgm߶9)=G1k l {4~P"v9Rdؕk;_<٬f8A=!N"K–hep ![ XR%} 6C5(p)$dFIi(?̬ݰY?dQ'C5J(Zr*uYQ'3(X652H%tBYSmYjY+R%ZXwcUھXLÐe5t1[8|:D֋\yz_ھ5#P@֋u2wI0k& p򗑪Gzb _gg̔x/r~!N$pY =L@)%nH%ٟ}O{xyƽAcH]ju@K\ -6'^S#^Mdy?|(Hz!:NfR榢#C.4:@xFrbݒꨓ֒ gE@t C.DEmݐB3&pm}iOONme:L@G!byCý "u2].?rXXU r2R.@K>(QEH[GJZ;b!E ng0EEIp?5plkX0^G8Q'S4>=vm\a6H %H-BJ֩ȇ0|ǚ:@0[;Vp p1W4+| 9yb'-B%Щ@i g,@ T0hZeWcMN&dZ)Ta6duT 1\R_8@-̣@fZ+_+gYb Pɍ&vv:⨓-@8e,@U XSÏ,A53+^%oFd|{83ra}| %, dL3]u2zƨyl1e3@8/t E]Jlغa, #D gC9*C)5N6ԯh~"5oz ~@ z5ێY/Ae`c! c_ēo*M #d(?HD, !?02!F:֎19av[%OWf1Q{ {g&WYޗ,$ bDĨ ?$̈0"EpaѠ# DA "HBӝZU;SM&AvSN}Uwɻf_}Uu`6Kg;oedK,ܒkalX͘alLq#7ӧN~o?/g\ry4E.BFJrO`)pFc7p[Ѻr(aƮ) cPjG' >#4B黨 1rpFW#4l"kmȅ䊏 5 cciJL1OܽS>=}G(-x!roQc(:v-y2rɅ(2vV. lu^E|SU3I foal) c32mF3*S'ݿ9\R0}d8^%a#;6=_u?ВxecYJ0 cs11fc{֪.bGSM{,U_ĠBp:^@a +< ƺ;#\| Ym|-p206cD.i7WS{r'\T[hoڧatj=.@2mO:eڌWzKNG5B!hrE&t.wWND>h ѴfoLalՌF\2p'*8֧{U\\=.ilK˶xyʴmե\ p 2,4':m͑o\\CӟxIwX F: E 㕪?7=\OG=iBܲXҐ*b7T<7P 6>^ nnE ud/$F>',-:`!.CH {m(S'w=l1Bwy5΁Î]s65_r3[rh@2HӨV(9 y0 0~ NK>d](rIU8uuԟ@>sP UQ%ma?F|u{L1Ϻ>ߍ"Nf\rɜ=0#NYa=E8Nm{,ߡ hQ}WvA9$:3_ҬOG.;d1Α(rhS"D.Ʌ7bR"A<АF.m!z!8hA/w Wߋ\/% KV>p*DOGv"; {Z^մ/ֹO6V>ְ앻-EyPī I0n2; )q~lM<J0 jƌ7<cETDKr!Uf #ANA5MHȭ++4wK"nEv [] %>\BڪSmDjPjsҾ_(w徹/Cy0O}q4z| .z\9@1п9$G~B[zcki/WvnbbFD.싺$>"LD{2=" wgQQA( z=Pf$̖euMDQvs[;/r[w^zU ]Z FG` Y;6e`et]˻C)mhTH݁ޫ,:֗(Y@@U=+jߵfͫޖa+&ƌݚ%\9C<24^RHZ*:j~!}TOT/;Q1D.E㾋Rn'#w%{8= +v.:)Dfm]Z|ւ/𷬹goG^wʴb۫P_>ur&RoYM\?.WqQj+Wn~2P+>z;\8v8 w*JW^ Gs=PmˏAi!dE'{6ܝvvc& &ƌ]%g= Z"oCG%BB9|? E3STz sR'#P$l(~?u9$FjϩNy,TxלCiڼ8s-K?CQ( MoL}^)ڻÎ/W_^X|:ӓ{RP5@oBDZG9`m#NqA_{#`Jo? XҳgUA>k?ky$؟I}|S)w0 `b%ػwrYd?T&`&b(=5hd|8J?fj@^(5py YϺ,=eƶ(d&%^U ~2E)F"c%e%O>~cQԩUw#}K.H}܊|~j؀s5( '>N_rk c[j@u/߂ޛ+"k'7wAU{ݮ\,~ƢȗWB209}է-թvt. Q&EkF?bbة\2rФ&_}RG(7EQ4%#{߀i(bՄ><\C McET}p`XR<%WG-ZMHeS"X@PmT;6(KH|R\&>"hA枵g\Hvf `?`o$lP|չeHlҿ%n?=+Uޒ-{E{2p?aEJiS*IDAT$$7: D{adU5zZ5rUUs\][j/LEߌ W ČGu`5(ҕE֢zχTL<^mx$GāY!5!ёӊ8#QzJG^-(;MC;ni#rI&=ڹ Ws/x9݊ૈ\r2J~~xFλ gQWj6>h z?CBm52r ׀+S OȕJ;זo(-_W^֧Hj83tal7L;K.4X)[4՝H0xRKo"DH-F3kT:+_GH#\RmHGifXHT4KnHšGawp8TA6pC[7cGuIEu眒{M\{fâPG`^$OeϣVGEx`D[pnXߕ5C{ؑPОǟM>ԥ>nM0 xXѻn탖טF6%uͯRe.úQ:np|9T\?E֥s)f\S'X \jQ(؝Wo0ZbƻJJ2kJ3%Z Ʋ㐰9bUMt灾4 8$cgvޓVCQj1YbdWeF5>QjmQDRL%?JI:T35 Z"jye;.WB®†u"vN갮=(Kpl80<,%{/|iUx㏥>n^6 GH^ZXx6Sg] i5f"![#r+ԅm-_>g-jس zgnal63v:Rw|,ഷp%.y/.k^]X_h(?lC5WE*%(:4X P yE@(;JHD)Jpoˇ3'ya PzV+ס4s2:seC7n_j#|Eۑ> }9JD%e63KKn\Mؙ.BM\`h 03vj"܁RiqD)"1܋ ڃJ^!ԡG^ÐhB"ԑWBvیDۄz Z*WzGIT6E(yrx?HT<(b[NzyK}<Ϟ9$$>^:԰7éǽ P dJv]v{M武bs/va;4vv#<KQQH\@QSoBE/;Y=#{.<2(z ,9 aEͺȬ)jQt./!UKb p*pUZpxz68i+bwmȪ=:YE.|ՎRH)~K 0v ,2fD.)+g>Sc!Uͥg>З< Q)F(xr8muJM@+HT!ag*5^(%y"]F$MD_|xSPtn0F|8|xlb&jñbxLVUDt*sµE %9u~9F 7.ڷ4 /}kQ,dVQivȄH}mN0Ę/P>Bfc=T+6 KuC_P=\0Eh@ƫ-C}1q7yeQTkO[CH`u*dװ75#1[$N"6کԒJB/N"J>R9/>w8oҧk{H* THH`]$V34?;haKSC\9˝zJ,ZVB" |Z,TJcr5Ð_s;_@b0c$eXUFcPq$&"Gѵ1( {g5#!RbЃDX63}G)/%qۜLtBi߅HaYԫһ JSho!6 AX7:F.RrY",》WwR'c;\}ʟT6% ,*r'SON\Ae>a.rɷQ#w4(b(UE/a(J3EB$GQ^=$%[8pNÇ=xeՀby}Z1JS(n[ڪ=]k×]GV+E #ԕWm5a3v%nDѨrHŞ!O_=ᦁݿ !(fkaC^w;NGѪ$rzP#(2ΧD`Տ kB5Q>I$k$ZQfwx:"µaQ2ݘh5qL7f͝)go9eڌmoBp_ |8s ԁR9_B2#4 11f2> ">{yoeýFa(9x>d/[~0k{Q(X0rI5J݉Qb?2amy c=Jxe^^됈 x "uY"k8pht(w~w_wxW^ö."=:~Ւ]˪JTQ#C "GQ0 cҔ.Ec߀Q D0T5EBOu4vTڠ!o^w|4a̦9"$  E!Ѷ3ÑGٗP;YV ,4k.u*DHxAlXxLeP)F o(rɇS/ڒ}>uwq[{Shߖg+E3J uYޛP b%0 'Ӛ 0v(&ƌ]K|DQv$F#ﱣCjP y@d$^DѩR0U ,=ڽ(SIWJm6ms9g?păӷo3"?/U\\6N=Vd p0ps 0/V3fD.藐RB:E`$`މ>|:Htw>Mõ}-ߝ{]r//YmIa|JdiqےV*3, 0v;,MiD.97NHyT>8܊H$-&A>Gk6$ZP.4;HU!!QLT#֍ ^D^T>Y^쇄稸@.sկ~ oD٨; TM@)AeQz@ǙNGOP+jd! Y3hh/H}=< 03QQ+0$%uH@ =Q:H?6k/\C)H D4C,$\5({k5M@.%Xּ 5HpE'Ph}C^/Dbk/bd |QxɶȩȖM1(I{bVXiB03v@ݐ5r#&t ~({u>˚|sWEe x9|?8)Qyc-K~ #qҝH@f3,<$GZAEVF.F̽ mCQCgex~.ACmE8ۿ8{H?0 cabxn!O|kCuY)|EUĻʄV,37u5Pʊۑ0;9ܿ9Biѥz](2~ kբZ}-*EDg6J.C5aGQ]Xz z󮪼/aѢ>gZ~YavĘ˓S_ Ph,=Z:9ע(HP(+"1 o#|CfXDBX$:@C1GX7&I15^4t<}4xD#WBZ^^Nq@/%ŵ91?J?al7-\נyH*#2q38+vRQd+Y'bԽG^$"hWEIJ?%6Վ݁AňE:Ϥ>Vovdʴ&53>^Xr 8<4~jucaXd-I}܎Lyy9#y3/KQU;>E T8Vl <I(p (x[Ȗ#2WLld5_%$R{92fnS<{I_I5X1QOk 0c5(bt4CO#qCRB͞H$ DQjETj"; EN‰=PWc$^AQ ,RcP-vö7ӧN^г5xl뼩Xz9s uav3v[R?xkñ>TYGdZ0y/p Aa 5ڨ#ף~K w'HH=)CTXGQ󁛁\䒋"|8<_F1Z}76eڌb'Ê9k2(=["@#}_|u}ǜjDh 0v;gحI}"m}TlFѭ29DS!]ס(u4գ"d\q6`1#5Μ HBYK[}0gR^l۞rH n|&zlZB]}f7ykˡ+3kacQ;7>5l$CB4vJG(Y ,G.B"+FG"V$JP_Qf-E9 E>vw_0-FBmSۉS'p샏&Uz!+F}C |_l4F E9v.r?EƆzuHp="RCyV'Q< 'Ei,ͷPqͰ| j۽"X2z$$߶ꩺy^V{>j7;r51]F}jc1ƥ(E9v/J)98EE/Y$^|'K4ij҉uȔu`Buf!GQ Wu>}͡sjV@5e}O[w%\K=n]/tP:ߋ3 QϘa"F~՗_@m |_Ҋͨ3"E>|G`AnEHPwaH$k.n[_vrcD.0˩3]%Eryt椯/J0Ęal%9T?%ǡ6@K>"~4>n\RFi aeп3|߂v[Efʽ5UL%aPLfJ4Clp_ A̋{PP`fyK&&}}sOyT\B{=a;cH΀[{d/Q=ԅyVb_^9.>u_a3c>F"m- Wp(׍9jac1 \r21{?;yIaĘa$D.qaaĘaaF?baa10 0~ĘaaF?bb0 0 11faя3 0 GLaa#& 0 0caa10 0~ĘaaF?bb0 0 11faя3 0 GLaa#& 0 0caa10 0~ĘaaF?bb0 0 11faя3 0 GLaa#& 0 0\ }T(IENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_25_0.png000066400000000000000000004660651413546205200301630ustar00rootroot00000000000000PNG  IHDRb6;sBIT|d pHYs  ~8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxgxن]b 6fLJ0} B - %+HHhB"-„fL3` ƽ7ɲյ;ߏ,Mu%iwv쬴[ bX,˶' X,biX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BX!fX,BZzeǠ)G%aه-bp0l5X,R~"퓀eu-BbiX!fX6Rx t]{ 08$,k+X,֏X,J^*Js[# R X,֎beS& :gX,+,˦GM?@O`(P jRR_bnlbޔ:ŝPb 2, 3lDB}$,kX,ֈY,ES< HCj js/bB[<X,ֈbecyf/5?YusYKw{'DJNlbiX!f26폢dÀHN[ӻ}}~Hv[)[,K 1K["c|ք@2ꕻ|oMn.@$ȶ[LjugԵXrC)fn(u#b K׬&3cPWdOX_ Ejڣt]ߝ}>7t|,)u:gbXZ'65ii3eGK?P.P.!{UޘqC%<w&bY+Ȩ%Zװ[g>}{~᲌ۖ:Ńˀ,̈4F]")nNCNnE$;h^Q+0߿DIX]cMA[$X,K+ 1K[i9EꀬV-G/yn2Òl/]Pqg꺟}ө aqhvymMqN)u; ϑ:EgRDRY<~^ndW q?tgPd $, KL`V=ʰBf0#Qj1WF`UP' H}1;6]&ObXZ1VY E\qq;'mlsa"in6Tę)WGB* ˾*uOF=^BY /ثp^}y-~:UҘEȒA9(Z1j䒬aSsX,֎b ԥG# xx$,#wm]"EnXGܾ/M.u DrbK٫Bk=';ɉ]+ V:$2sM.@c{$z#<7T%sX,֊qd)u(N2hJWE )u( 5/Ѿ4! $jnґQs_5M)Z`Kw&i*HeU[Z,YQm^anHlW.H][4- /uoE) 'e+Ⱥ*E.Oh[!4d=sVY,KVYQٝ&A,ZmwUNV#NEn"It*ugQ [ף:Np0#jnx yrT00(O;ݗGP~P@!evbiGl:*0hppPIX~S|`S<`Њ!oH) Հ"[+0BB4"W_!.%a%a=c D]w$tZ=~@Lqo6>We3ۀ})X, J²JWNq[MKϐy Y9zurUS#)EKu@fȼp]Zg#sÑz) uIIA?F(B6՟@|vF}S"N}Ss'~BҦ( &6dW-GSܱH"UBx`IXvZHف(>窢>&+=gz4o4 B$BӼz494(=Kr "GsEFx톦'v4fSde1^%ϽBvBPWcE.Y16;R 5Ӗ.MF_'(;QZ '@9NiSwJ²t!bi0clک$,[k VpYVEvӴxNw7wpn}dd8urڰC(9:QL?h \Z#!."6*ڏۓ4Ec(R#FjtYy4_iSѹ}be쐔:o+=DMm!"w|QѴ}?7>4>aDJ5gU"AVIXv%?u=^eUKݻќrWDQd@" |XYl35܂D%av:X,¦&-;$f:5r^S,wz~rbIۨ>cBkn) #!̈ޚQ B:8)~f\W7wh^GSfrJ" QlHl5J]ͶuH|5"ky'egPgE"YдbI쨬:fNE]ZEQyv%as a$2ݴuU] (])ש5=7-;Œ쇺(QT0ݺgd;E*KMFiF`=i1JM{¡o 1ҦB̲Cd,2`9KVZӵ`$PSݲ5~ /s$C<<vG2Ѣ/_^1KI 󖮉DB9(J(C] JGfn~L泥cHty( uGL 4s2%( W&(L}-=wɩ {o⃍]'~6k_KfB̲.ژmwx#=0iD $>Z=z~Aft%U+:LZtd'V RQ'Rl^T̈́<ENG^b"qu[YWJ}Ps[=c?Rp8Cx8rҬphCS9ՒyB N?f,"gx0ouEoX,6쨬|Zi5x mS\Y9v/ϯI*SQ[@ /p1CkYY(*?XQODQP~JeV4rg"i3ȉEhe!JԧfC01 b e3;8~{X,m 쨔cECRVKH lO6^dN0q:MZ(2,r,ݿߔ.6DcÁ("${B#4d.]irY.JV4?K²k;RWtƝtxMEZ,6YZGIXg:R.8~y~tS]즫?,ݿ_CյaM<p;&7!7J²[G;KHIdQD8:$"Hi~2BC;{ 129D\>b:HNݽ|WnX,m#fiqJL ;!L16_x4O^i` +JvB}{(rv8cI X ȭk={dVտF#'.h}<bXvljҢ@R- ˦R]"؞5%w~Yρ +Vc@Ϗ6UH:}4} ]k[k˟Svs+ND<@u"{~=lfSWE6ҝCG$j9;3N]x~}#8C/9p>qO^s̷Nc!r@q(g}T#Sb`b.{Ck;h꒧֦bF'`V  \0P[r':TFGCAVTo+2`=֧;N$g/ЎXS"Nص%nuisgt,T%p΍E;SSqV8tMZsYŲͱB̲!vCuGow(;S;`ƨc N:{e5[0 Ȧ"ZLqQ޽_!|>=gBѨۀP*Q`b"X{ljE~) + J@MIXkaʣFtuEv1P3vd/ֹX,bbhs?ayG /U*"עfc5&26M.YRTD`Ke}eK6bG_Xd9H( ڃVgXVmx~E>EO'Zta[PѼ^@ٽvrbf7\ƳH"߮'7`[1+Z Lmu|cG8'M>h~|bX6VBUf)bSm ,7-zmfb+6޵ y'+>}bwqwp&x|RN͠zxZ .A7J:-mڒlD|NG$Hl3`3۝"5H4\`F!Y cG&zicGx49z¿ٚ#x8 =~mpb 1Gw=T*.Gٯ{퐫yݐpVz~pY"jt鴱[`;IHBnV}KdBT ^ʛ׻w V"g+{qi6:gi>X 9bi[X!x-DSQc D'0KP8`yl#p[Qe|kj-4h rw3FjV 9;p9 x4uuE57 R1t;z nľZ,ľmcx>2\n=y}5TOo1c&rU}~߭Wkq|C^FX7g͸?)S\Iz nDƱG"n*Dyc,;GήӞgɩ܀Rס-zl:" obX,-b[b_=?, E)2|8wEvHp_1%}3h,`t!9O@PJDMcgh̥ѵ#f`'' /=mQZovۃ B"~C$nwf:iqw)S~zMuG0̯7aH| C cй;EǪo[P:zZYX,KNI)[MꌏT{$29 ^Rg"QGN@t"ˡ։E]vhJ5*ЯC-DE3]9ٚEBYTW?u=?xy]|n$ܬG>mkxlט(>ojŽ\wDs"MCDW, _Ҁ3QJs*ʟfP OAS0|=4j.:p'Oi׭)z }(>=~-HbY6"=x$NIfin2w|cD>^zLPl1\n6h $B 5yBQ/]d"g!#Os_gu Ux(NU5wMo`}cqܶwo\L7؆րKy"jNN];g#;q( vpn.bXց}(/dO(J0X,-bߟ7ູtu=?D%H 'wW{~(~ \j4wuEf"AY ;P4ٻ"uC\T?mx9Fc_6H\v,w6!F{i=GQw|V}C'oHߗ|$g'nf2z±ر#{d~؆0j?`ՙ#ME%WhxoȰXV}OqMxX"c94e(r.:-?Fs;"zdfǨEN=Qo"?3^BD}7`#Qj*󘫚? c_ӑ 1xiA*OscnoF [OG |N>g.+W:K ͎[F,_h>: sMq$GChݏ-Gkac&3a5c&z5bX!1 cz:߻Td)ǐER9; N^,E>D(UR0Qt( )֩BkQ}YpelTEۦH[ ].p>H:vq.Y5/s1 R]Wv:h  Ez6=1P23 #4Ot*K>XA2>bX,+ &JF"~# 8۸DוHjQr(*@B--R;KH`uFcffߗtyH}ju1R= F[`_a-.BX!/34y|m.+Q(4$zCQ'P%H X)J'VTpC&>=h=P'Dܝ HP5MJ)\bP05|`C0{~GI $njqw9}t̺'[[Yl]N#q1\l$8sֳnI~c3ɪ3aT3GLu&1wFL4 $NݝoM'`کJ3tQEI-۞1F?`/XbX!7ʑ@m2a%x5f-@Bk$ZPjpTT Pu}U#a.>E")iI]= |,錼sn:MBD(Lԧֈ!f?Ph0Wz~ PELZsfmm!A:ޜc(w'z~pFﲀr* ˢ(Z߸DܝjFD56?]qwvc(י =;J/OO}Ecl}Y\M\IoX^@KAZ`7dLsT`ﱫQGeAfA*Yf0)dհw"@oQW@-Dil$ 1 Ҋt\d[A,ғuHTBDѶ9M?h`yls6Mj$D?F)Sd&̹05k۬iyN(Vn{sm:m6g p?IݗHqwʇ-0k<(/CY4`LXz7(y"p(v0@gj!zgC]|B4R1sMZc&BtM'Ց6o$X,m6'L;ſ]"N^vFYD(ԸAA8,PU}= Abe:x:hR@+/ףν}hJS27|YN_|9u({~pHVTd*E.Fѷt7gWT<@x~p7r?d-Ch/!$ـ.,!јa8s.{5wWϕ4huA+hsm 﨟yx޸퓑K/E>}&qwIflc(;ǐ`{ R_YTKVNI __, {Ho}Phd[4s7륯-\״^~1F}q [jeUDܽkAhJ mt]Ru:p`}O$tVx~p=3,m'$/*H#$ oQ$gluMJe 90'CM]kG#;)xQTY dHy 톢>Ck$."y+=+:HuCp$s-tpM'PLmC(u]׮ Lݏ=?XeDHiM;2_.;eƆ={:@m2gCjﯜ|]j5iD^$WIU5YsTuio0j0j07EPu'tn!ڜK:{l̅f7@Htf-ïZ;25 u8: D˙HԠhp"L7؏3Л4(Rv>Ihj$7vH@ !qxN`Iw#14y<'H(=DrԱ,5iѮPWdqgQ]BBDܽ=ܣkZ\׉LMw6#DܕHp KF@y"Ƒh._{cR{]_GLjx0m-ˊ3!;wCf1'^4FbXNͺJ/E]f7 Q~ IJo0wq^6,GBC!? Qi`sg2-KK`~S]sGeB 2j!7 B Pz~KdIQCXZ>U櫿9D}HQJPZ0c9#[Rch:^ G!Gs灿%H\"29潨89ӸcPo!1Z.h{'=lC ` 3.wۯ@9k~{y.9feC)( pji'dwi ur"{1jTx"w6 c=?x J1Uףע+pe]6^ٙ]wCx~Xje71FEd< ${,=oZ,mC\\ FYe[Lsg$l҃O" U#݀"[ϢbܟB , \tR__(ԇHXĕ]EoS:=Y iW IDATFB%A1^Uy E\t5ۧ:8h_@󃷑XLg E[}M1kjt`:"In6.5|g:\2hJ>\z~kP!`O!"&8;u+F::4HN[דH9<`Rk6i^zgl:ڴ#W]r̄QfH؆ 1e`#bka.BM 8 E.^@,R~3ѧy(D3рR|{ q(=و<NvdE_Ewp還2,Bf!1t@%HCѴ}Q5cJێ&{Heֶ9R-PhnBR)T32i%4=ff}G$H6y?ڋ!ny;L*,wKPS [a Zwj>{>3@E!f7i;ZkԬeԸpy1JMjֵOx?q5mRqcW7k.1gQ$0:wh87ՈA@8V4>{Z,[8:5Ax "#1௎Ñ8܌2?D *E: (z~'`~'=q.*]#3o99fvTkVhػsLv;k>筨SD=czC yHܗOz~0_K;ڬiG 9 wQS@Z(B6@`5e-^PM^qȗ1=v; ]LCdkۜӷH,D` 1?EjQjuCQ (”@M难WEdK=? wpXT}d/?7/?Y[wcܨD]hF})wc*&r}ƚ~f_ˇCo;pcaVfm:wjXvvH!f"G#rѓԂHfWuf@E}Pj2=7& s/\"A$d!4ΥIJoP׾i}T̬g2v-@ t>&d$GœɌihvQ$n/TaGukg !V>"asDH!CB,s0u 4QmMM5HlvQGzk9#-7zӽh5ș@FSPBR">9v2G]gY9D<r;![˝TQ ]33/۠gKD|#Я@z3W=¯@mXneVoAݯj߇'5gk8*qԖkKJF*acz ;hۮws~Zr2~7 b:ckZ[Cz#x-DC[vQX6 AӭηϦmRÉjϻ5Zzߠltvoqjp:l%e[a8l\B7cwrt%˦-ZX2oyn\uGYJOAyW=^wfPQQk ^ҿ)_6^Dfg£eAQRv6e;!ANG$mSZA Yh"^k0Hʚ!hrn!_dimu@.w7vzW_IJؘ:Fyb! a" Kf,c71yy?b~f_Hl@"I4=B[{"&vFLd#y#R8E!SXb s5svJ(BQn ysI%q[k0Z#sH-heu9p]$޽6kB~c\ 66<=4B :Y& v dmS`2_ysvɓOnn3^ =pX2~ˊ xqS%@vh H- 9jnhBxAhemF #G9SxE*?_ubzy/+vCJGT">9[`̱"5gxȗ ؚam2s 8DylSEw: 7kB~YHy< k#9;f*Zu)pm3 Y[Tn E@mȆ9XY֞6Dgeu By HQ;ڧnʯM .:mD{}v@Wf-{SWPM4kQ]E~ m(?j^kX9~ 9_bV`P^\/)-XB.v pDy7 Z}V.yuҺ.Xobg~EOhlU˪Z/{Rx"aX|ű(X%CP]H]Y%],C#ȹwDh 8 9`"9heR(6n>R3V#؀a}9($ӡ\JZZ9~^CL zA HY`hT";r ~}JSZs )$T_D em,RnFaP18{ItL*  9xV@Y !'e)r"fqEku?/@`9H;P.TG4g"99h5BpPh8b =o0 /w=Ck[UgWYu}W<0zH?P-@g! 1^vʓ |3u %[qg+;oiׂέ[?rPQ0D2e;0 ȹS}C9o#z`KQlDQrDu,R^B>!G701uȱTOY@>D0v{͉Vb੍ݛBgV(d"Me!'pn?]3€sv}Զ^ȫ6nMXk5JkeXlF'Z9KBJ Vϯ^VH \T">KHg7~"g?PSCTbh4)f|zjAE/! &AY}FԴ -E 7Mth%?";\QCIK7m0}ɳ=[}Z%\k|w*_g57{':zH_-Z]-E>@}0_ɖilK ,D 3e;1[hr 9"9ϱS4/{n>C*M !ڊ|[a{lzN֌DmV@v#ZI{w1UVv}xWf {avQW+kY1 7BЖt}JpOl#Zw.PJīQm ~_W#pMl®AT#eS Rtbvod?0P`g|GDy#\r^~y> ! &^c>ԭ-#m59v_7X#ul'4fBG_e 'l6)'({o;Y\ggm|F[5Dc>f+/ojΠ| ڪXn@}t"}k6 [k.8l R43e?7 b6{q.G@19{j,Z D|͚Ea?9(kp'ܚVm(DwRG(.r Ufe9 dFPpB3 AMS$ùAgY>VHAzK':Q-lrfG~ٛ o:! s mhu_cBQHi)R>@ʎAUuA 0"PԨ〫SCVvnADh\/?b=&CUֳB6&N2e Ztի3 a ?  a}vK"/=D!3# vVhn@EߝU5~η碜j[ 3A/P^HplJxI.uUZyFG-=x7&>0աh|f,c6m4>u6v1Sۀbe*nBq`z*?`syI"و@lS|9cD_**!p)DN*sf"xA_JCD}Boyƈf(Ě Fo& 6F Wm.:pYQAx/J~캭aOD'"P$~ x&{mHGT d (R}&a8c'{ H!-@9^!tߥNzODPZ2vg> %q<l!g焓De*46U1;G ˁvowwT"-/nJod+ZIiٵOGpK#EcmX=9xԖ,jWc˧e,cUlޠGWsאN.MYG7 X}fܛM_Y șFt9i"8r Wa> ";g%r 5.muHkݍ rڵ'=i{YO!h; ? kWK9 1G+b !@FS=duL5(+ [$PK{~-β:>h=Q.W(ul뿅: Zrz[+Nap]jGY[yH2e?l]VEeeNPΥỠPQq#8q328k_mT"Z*~ 9Vv( 4R@RGܦZ#cBDؽԜOӛf6kGΨ )@"p$SHY9PSHٛNWcdUk\k,Dó hvgv kVSxAyjq_$(lyD!ElJ' 6G RwQo&(C+ ߅flBLo8 G5F~|7 m6i ng74{mn{! 46f#8YwgXE4õh2Dskpdn5gG/WҲtIi(}%e3QQg%9胍yq9~|A8dחgo'cXƾc۬"ŇЁ C׵ IDATO9^oQsUkcٵ8t%Vhep mRvj!(!iTrr#R"y:r~u z147"X` P[ rZ]'#\Jğ(8kD S *U,qfqNE v/B0Hy@;yIAF#z`{}B#ܮ`}m`" k>oEpuu>+jס "+Rα%mmk]+ܜ},SlGc6Z5 ެ\?? VYD0X^n!Ezˉ}ƒ.FG/)_unߺgl+)-4zH v}CE6in4W!6k3|mֹe^%5!cX6mX}duuc97m/S͚ zmI 5H0)9`9j uԅ#>9eH `^E`1-Q(_%H}..B*M Rf,RV<ouV%rp͈QA=5z$0#~.[YF΃v~_F&aNF\_} @('KVH#:o<حGIk34BMէzIwSٝX׍KNFкUlQC@aWѸl[62v۩D|>]6zHq 㺯ef,cدb4$Z4`T+%Ӗ:8#6%?`ǐ2Qm}4r,AP9S3):'3_pg璋`=r#;9QHmbo '?`xIHa'r+Pvv}'rӑRZ_]"(&W3J.W_ t&+b+'׺^ hx[*_%[,ӽRgGaʵ5#IV^Q(d+RNv `@5ߪ(<=b m؞гh4& QVYVr`stN}#(]m$Q@ ]L jUbM[to߶5TK3D|v'ʗe\AMc4Ngl6--yn1_:UN'>rme'[n^ʊt;.'7bcu_Nwפ$JL8?R/Ǒr C}oF,C8KkDN5L^` Z(*@ QX_A3!ieRCc"ejz!)sPh5Zd4 9(Gh( ua EџQ^xrI*WEOk*2 )By QDXkXw>ZvKpC.+̯6H: k|bV5_L\2v& V'"{AX1R D5Nm* 9 HqA@ˠ\v@/}w(|4>I%⻰yIUpޙJ/￴R\_RZ6֌[w %eZG/{U=~=OX~sSA~_0O-)?kιۃ }x5snpIY&17p?~BP3 9n5@4DYfՅGa9 DHH 9i jJ>(1>B;%mogD ri\Bd}յ=RCB \WiЎ]DXMOE*ЉDK"pBZyIˀcٔԡ}N]5#ZΡ#OVV?YDa~HJ#造9GXbrǠ0Dиjbek6v֞v reV^G ^Lq~15DNB[{Gn  xPnK(ֶ6-\LÎVl+kT" >^\-8?Ҩ[y]< 5)_=&cvSlt9AىɦzG"Y r΅9ףAvεAϓnǙycUι"\Z. 8Fʩ;- V{bD|=OnVV)%/{I^00/p0 &@79ڳv{ah?4r-=!HȳgCb5 DN]gy;E)FN.Z %f勳ȩ_uDV.MýC`v)(enEcE}P`~*K#jem2ZݯH%oY{)D@(n RcV5Xí^Àl/ L%yI?kbe P>[gKW; `]σQzZ"PNfg6zgufFEyeMpVVօRZuWX[@'l[%܎J?!֏=B@/"ZƤc5`k ju -RH5Yf$wfTCO0~>t?!CJJ*ЋZ7k_}/ܹg ;tkS8xL_^l=; ס1Qڻ~2۵cJ}~Al| s_;~n 7)=@/p*lAѳssVX=musAܵb z˝sۣ͢.oι8v9#R}"|\h&ݥ\t4r(w (s,R&] ~[@'Xfo=+\ [%2aގDywEv⶧L*j>;ގֿ"8ڻ0nWf YޯX^{3,/ ן˲+T6h<o}vAvO_KcКr%sPw>zqYM);=&{l!G, YY'TazkrV%N>A3uNF;!`$Rڞ{[VO}.ס|u]/\XȕMԅٯdgV_ sƯAp+gSBПbZA`! 4$/ϴC pؙ(n#ʉmPmD 0rOU)QX"ڡ]T"~B_:DGf?ϱ߻`U `oK8&]ʹ > y]<?at> S;/A~{Sј kqc-Kuk˗e`m '^f _j#yE*)]˽_b"M%7[?]B[JJY9_i5@yQI9UU7cƇuFhCߕuг5݆ )a}< APAk24#t|J|'4s- q3Yιl;=;vrsݿ"ιF@,^E*bH%c7=ٲg!s{/[yK>-뛝O-~}C>H`Rc;9ӮD OkDS 4Eo܅fpaHK#Ƿ#g;9ǐ>(A7M5NF>6͖:ZO%3hmN-ZzсusϥaU ý훏n5Naظ_ssꇃ"%!cY;M}>| ˸x9=t}EcXY(+kڃ( sx s\=;NS9k < m]GK'~e൹c' Zzܯ}Z{ʆطjDKF0tv5EkR]K+BVZTB^g ȘfA*Jmy;4[7r4zc99בb&Ot)zy9L]3@M8 1oKru O6sov>O%ZwA]8L@B(6l,yivbو`T[#^=֬&{?9fl`d1;hF.v Vϴ{r++\m?H5䌛FUn|H"0zBu9D6 EJH5ػZT<4fXLֻ`ʃҶGMC u{ ףhvމ$ ügm8 9!JF9.@y`/ " ds=s?&6HEcS &}$GdFrD uGK4+r@}'Kf9tC x0>b6}@-APJYi4F2fZ='0& s2 M8CUvmt$ %nѽm)^ ފW\wvZ夻.xntwa2j4@oଷ:<`G ^ TֳvYl:];0v+B7)E@w*66?u#_o&,uoF[%z69翶3L~}O.usNgOywY! ܔv%?~ֲtukLP NSUι=ޙP԰q}Bl ܮoF}2۰mcr {ƯXcv#~9T"HGu#Rm !ģ(C3@`B.9ӵy-W"8dʉ)1T"~X7̻-ADt-GHo rChV=F ,@`d8kVpWsvYQ^~y?y ?۵EPrvZ2=h!q2omQ6^$~kWX2.kyH7/쬪-91X߿a+ K=B=7w! |.֯_b'kQ.\{q KYP8slp)9oxI p^ή2R؟ X]AY2O+ϪؘGΫ|f/񏆍{]Sky 1~!/땱t zL-mQ~mن̀mQ>eȡގ@^DśQR$ UG R.D%Er|!MYy4yV !sAJpyI?BvH]Bl 4kn4ZG~W k!J^hmWJ;˼CE9R>jlK(lOAvZ^ )G iA묽2sȾ@&LCjSljKD:N6 Ázm*߽~SV҃>ϨĂe,cmӊY5~ 0W #%rحsD?h]OQ<G3DܥSz90439AHpR"Fܽif|5Btz۽ c1b"]7H(rSDIawG6MSD 9%~wftBə>]J>XTb@BOETe}q'J? /%"T'6m)UǮ}*'P^o6h)v-K! ZCDVYLo** C:*ނp4F!o`u}D`<;sSMb"R+ߑzZ܁)7Khno'# 9%Vf2/EGJbS8/OB!P ~/4NDyl#Qm[od򒾏Z}rް:1Xv@JdhAPu߂ {R71 ʉ܀Bk snDӆh|np{5KjUE1z-n vp%?uS6kD<8:, b9Χ؟p5 pu-,oSUEC74Tƨ_b h%RXv pjVru-E*_X2۱*>(Wv9; H<.{ǽ/Ep =`|"hE8 Q `;9#Q}5pN>Z;!g6BL  z"9Qx)\}!$GjbUXC'%ODHPH!e!;vv{_u]c,MWs]\*ȼGb/"Ū֟]صYND +"gtR%RG6>9OͰk Yi}D3nAN|"+',,& `5_?r_C bFcսiʅ= Fb>I4I/M%XW"oo H/fj5@S/闦F?pܷX9u"=3tmpvX^`1w3<ʂosQؿayh~=la=Ϩͭg%No]RRLuel۳ mg2e XKK,,Gi(rz##iG 9vD0/鿂zwu!8y AK.rg |JqBN: u& "`c RŪ 9ݍ)!P8Ag-;IzkDkCC;RWZ#h;ЧCq8 vmNܷ^6`u/AzG!)B0kvF[1Aq#//8A[I f}SR/ F{Al:jpͳ?Z`OGVh vAc*frX2=\Jh!5pVY8ꇾGG%OFyvmh^^FJvk*%;G3a57cajML!zI^zW/(1.ʯnj-(翵o=ݡ1݇`PQJ%o=2[FCuQJCtnAL Ɵ{-E 2{[J-gll6 #C`99H}-BJV '=h7"h%wB]rF܏(@9Ҧ]} :9R65B= Wo9  r&b"|9UvoC=l+TiMs*.Xޭ[* ^_O HBBmsk#KhB_FPG܂ 9"ZޣhƬNJ/cgoV.;8 Rc A{+RB3Z6 ̡^cQ /oL6x ?6MG-T"Ko1$M~0gq Ug-{#ꗶA}FU  Ԏԡ9MB/eǢgұy}v%91]_^3 _o„5@:Th;X*9Hj ()YMkdYY44 Zç Rj#ubHHYj-Gͽ4= w&9 VhR@NG_H;!蛅{>R`޶4+IFr2TsǁR*IxI -DNFka+cwg))FPw3 PR6Nʽ%s"<)ZvgrQm bmzj!SD^?P`&k=2X6T\ Q(w{Iv/erW}A?Ůw78i-_T"EߏZ`X*%kIJG#_#G2(yn_?&dB9W?zV_cx'ZO197=OB/!@yyA0ܮq z;E= ;E/y(qJks7M{+K^+sLJ%~fV%ˑ" pܜ;Y"ZBs6AB4#*h]mG ȕHMX/p\+\-뼌q%|]8s:rVv~Bи7r૭YZY9E\`^m% 5EЪ{KkvD:8wFх($v%q 7"hOF!x);BE=ș,$ᄋrkvO"թ=`@ ȊT"~#82#%CțDIGa6nDPQ-@6#@ۈXWpV'Պhi(|<}gY;1w08f: n ٺew>{YWrw:17⭳=.q}wCD& yPQofl5\ɟs8`@9>@/ZAjA96B;ι#3gƣE| ;犂 X{8ιA+zA~|8 utks~McXGR Wk~{/obJėRߑ͞{ ,rRCܧ69Bc(,4) {![]cWPAAy t!RNAޓv?LACѾb_ G.h z+F}ێޝUvBRk1!չqYkBUW"E4bG pTV [Cη6b2_– ݩ-0tS4e;Aj'H xGW ~%v\?{!jdxu+"" m@&B6Yn~XԳ;1\;f#uqܣYRwZ[[?4B D0 AST"O* X_֠;'/N%os*j/OiĂ̵Ϫ{l{؁wkumj3H3uX<>9w-#5s ~J `z~"A9_A3ukdY֛ۇu7؏1%$B/9H&? MzIP>| ==/T!g=7i5>냠  PнhX=rg"Y - H\ԡ5v2\Ԉl^B`}9w3=h<.hM7%]{ruMn AY+;pH9TkH᫋t܏rʸ MeO>@*Ktu@ҷgx(Y/@jFec!;(ޙɞ$ "{X5ZR*uϺ[[תܗ(*ADD6}Os}S ₖ%R_^he(1S5@W" d X;?K"-|5X[>Ƒ,[[h}hDc,d}N}$`,'XgO'˷žG;My>7MEtu뗴r%dGcjuCSX"N2.6k.PTQUF54w"saX*F}LO\N@2 <@lL-FV;D 6)n4 ! )fߧp&C#+b"Hه-fC͹H ><Wg&̓ibiYhV1;8HeH ܭGlD hS1Tq*߂>Y6f`@I w#޼ҭmo"Re})P?5!%Rc\x֯|"5)IH삔ŘX:1&"EؒCKފs/gx6HYZb"V,""=R&a0 KP:171AJ5N0l\$:),㭌 ȹ i@j;c-?=?.}ۗ[U3 #4yVXA@ou~9䳲 zZ+\nṵt־^QL1as6G+v 0heXڳ;tև.==<~1k(F@d<)@,:ynk4Wwscsh G'[^Zyhukl&+#LKޚ{67EU;h}pW3w1Akd4//tscjd}AD/S-5X47QiPL8XÈx1,!%ի'z!%Ӄ6 wGosIdb7!?&.| 1˭!@,(EvnH)uFʯ Y^#e? ֎lFl F*kkXGK3Bf{َLuF2!))Ux_(:39 < IDATާ f62e1-iG^vZR\Ԉþ\̃V?k0bY?YƓ 2,4C o?<]js:&\ {G2K'8i%H_XYч/zM~)A,@YEUY;PA$;կ#Sцouyi2c^}<<"g[P5 ;?%?&pu2eKb{E操| RhF;ӑve(&xHd# lh1|ʠ_v'ѵ. s읥|)#badhمHA.iU6sֈNV}P)Yت[SLʺ/o#Z'ζc%b/ZY{}617X{ "Gm(R\~hMDTg cmAb q-U,& b`?@߷<Z'+ڡ1Zk+ìZ.O"@מ+Dc>ELFxmwF"#y% Xv%R&',>2uwZxGX:rpj,:/Ne ڶki ]˛Y{C><[$D+/lAs YϲECMՙM-hFsC9߭U&db?%R-^*+BH.F@bg>G  E:UYɈ!e!2Ė4 & OB jh?@cH)rؠ'dWK"F/y9,'z-be.A/Xg"]ؾ5l)|$wM*DFXvB`g[6}),k=ϺvF&bS_44!`p;bאּ=@βO[;tCqKw'+ǻи{Þ-*ݬ"*E4S"p jA`X|꬏γtz-Gh9I [d#; ce{c-!+xwGFd:[d!w$pw+Cu}<߇P#kvoUu!yKR' 畗VnQ`l([؆$HҘG{gaw-oLw r>#pqb`CHy ?B'.G,ZsxX"CtpwIWC9KvN4Dg#htGJ" Oƣ%R# 1%_̥VW c!2 )HAgZ,wYlKd }=DE赱D;JAGү>z |\aODp3`]p >ke[@LeR8!iOVvͳ6[} ؿre:Xw_]w>!GH~iYX`b2Kسxͭ{W 6֗;2wR7d1&؀:" s:l5.,,*PKFVi?̌ӥx¼eV>]^YJ奕Ue@{1nS-sK:!;d8bYD;ډ%R#=ˆ5XGE""e4E1hk[nW$%RKΉfD꘷l̽?UR"3e[\g!e7)QpKF[9C ,SH!CL1GX^"UE?D]aٹhmm2(mKAF +JǧF rĀGJte{)gjrT[NY gD`t%²c1겧e}̣a@"`*" _̈uLCʐ^WCh'Ĵ݃etiLV3 +g~ ־X1z77h mx&wHkdR!kCb?sQ&4αp#7; |XmWLj-BRdh/CQ +Cv=1YوI 0G5b,{#vկ 7pM}-lr" Z0q7$ZynFs`bZHƣ/%R9xG؜}2>Bx I,:͉[,RQU_Y ڱpr1joӱK+HZ'^ ReKbsr".5SC}L 5 P{`x2m%R""gS .UvwGRf0+2K-FJlAs)w}b\+-H`֪`T!"X4] :s{}ɅK7`:0f|{<qM5:GmހXQh]|1Yw"E2㶳<>B{)660ճֶK5Dd| Cc!Tg#vlg!PT ?B[@^|]} Ϣ12?ގƓVAAL] i9?gݓ[ieZ@[  d15od$P\xًSEUY>:pghwVƼ& }d<1*ʊ#3׬S[н<kh..K+gJ#?1ؿr:[[`2?1< lѦd<: Hw4RF7EX"U h',^R,r'f>!C@LdҪE Z;Ο') a6™" 2gnG8vGf Vsd.gQH ř !|/Ea^~MeVֆ+doq)ZQne_r$4*V^ȗj@zpGwl"63 2ӖR:mkeps|@rK?2ΰ@e]}I1p\Bzd:Fkٸ/e.FYZ%1CʾH9/"0˒-:ha[*4=Ske=)A)푂--1{1)FoX1NQ@K o b뮴 dL=>5dt@wو^ %r!@VYwE` :t.W/< i7 MfTZkוjC."|<}) 2#αkyV=F+0bk=}/^eN.EpQ-nǪ3$S79B@7ZnoeZ')hKp}Q m>ڠ E,KT, RGbԢd<:ߏӫľx_\{c3|*j@yN9Dt;th\^hlPqџ+eFs3#r8Z](})&4^ XyYfX> $m{Ȥ@,2wߛz̆az7m.o|[@?mDfѸ:N}cخ֖N(.?KVh]^~4s'o:͓C*YcTTCGT|_~ҙ{3}>_e_#v@e2]mޏv@,E{u:w9ʍ[놢1N.E w'gfԅ<%Ԝצ[-sh.k> 06" fNAs w$VTEtNxx Z3vB|Zk&;WTan:B~سdV}SdZ5"Lc!?L݄@"v>PcPG?%R=ߧ-nP`8V"α>Y4F#*ߟ &p{Hk[w|Koyc9~v6ZL{Xjv> 09ϙ=뀌pRp>wZ@Z K ͏a4K=$_o,>w~n&ߕAY(ĎF<&4AT5{UeV>הd<: Q*6춮4-9_~껐8|UQUzyiʦz@=4NݦiD~hS0'Qڡ}ZZG<ϻK 6OΞ 5d*޴CkZS~%[[hK,L~S߱D d)g ̋!B3r0)@X6dX{8K#ժ2xLp/ĆЩg2A0Rg#{6m+6=7h&H4۠+DJɝv ?,Xfdzt?V]?40}c2R}!@+N<+ӥֶ0X"bA}X_2u@qچ"FlǦjH^sG| NlfY;ho[ؗ_!vI yhCfv˷Q{ogvN "A܅ADlXMBbA&4 ,jPS:#͟#ba4/| O!nK@xX"XGRߘ@8|XwE 0hCcEUʵ||Ͽq⃫k:'~U{ފ jvc>Ǯ*xe EK/|]}۟w2)/\]QUTT=Y^Z9YiF`LyioV٢)7`BƮY! 2\? ryG$};=Áoehؘom6_gR1Y=/w Rf#3 ѢKSi}Z7Rpmx uU}FFL17"vGh] 3)Į K,HuG y$R!|"|XFʺ;"\LPE @ a0ڦDabY,D@2}ь4!)Qu$:mYc=tiVcP+6)DLbz! '/ 5Vd<΂$t~ 3|慈 vi6RrìE,V-47[Uji~he*Alb4V ,?Ai@>lYW"w0nAcBvFj}g}WkO4_!w (+K ֔GG뀹Dj}.~U0nns<|_ 'we&ǝ- @lP ݾHYhK:y~<6}8c#]ݹ}t+<1'3~aV\QU1Y(A[G.7B;oA?Ϸњ!1aF5.-Gn,@l#Rv:"5xtI,rP3pTa-A6w'"ź#R CR IDAT1?㐏hj{ ,`Dm2I`yBl#Fp=NҭA#dlAi @b/BleH {>pV! CWks??Ӟhw*tB)jpk߭A Ly2gx9_J-Rsc`^Gl]^?UoXamd 'NgCsdYՐNB!B95={{c">+N€'9ՂX6/)Z2_)Q-:˛ 4|L8vDNr7R2{!2)]Ў# ಓ=7)&!l(!> {1ځ#VrvHҟb_eEIZ0g#r{,Z>"~_h DA#8PHoG7 r2=5#I81TS/1H1:SMvg@F ({fdZf# }=b3 w \>rw]9NF#f} V `ֶ3,~.kqb~@GV/'3]ИMX.@ _plχ,h|[ 0Wmz+hͷ>rjglKNk; dHiˈb&2(rG R6yd/Jpj_@Kw$4ٳM4: :[H vBa AJ|rwԵAo"3C"VN(ޭU@δ4F--g~h6sK;Tqb_#?g}>ѸXE̷YVrKg .>/qƢRk~1F"c 2iu8ͷbD kA6#?i`X" %/t,ڮDJEՈ(AIh9}/i+Wvvm:лKJX6hSkO?zEC0"TҞL7KkۇZ[ُck!gA`[i2}TC#UP/E,aFE w[f@j4"3lOKk̫!dzJt(zf[2w2^X4#SW"`)A(xzVֿ}ի z5!s!z4eg~#0wC㹏<&$g"_Nh<9xYN%R;[ . rz+0ژqb d^Nֺ<銪cK+|K 3%dDN0x?wsp/p (8奕k6 ߮ Gc ³lTzyڣ~60a[ߛ(\m*j[V_P;7\d͔CH~voaĞLAL Tk)F$LgU*@'ۺ;"pq6R#_(莱r"1 =#P0@LC "F!żA`n\̀X 總?Z9ܩqhNWYEhގֿ/$/إdwhs:ҧnNg~X4ۻ6 Iƣ-ڊkzzm9(,ʟ{ڣ, s\ϒێ*o on}pSfdH 8pq[elH~0 K#0%RwB1GJT=b(^AfpqJtgcQE [3&hwz/br-jGe$ - 9-g={{b(~h\,Vvd- 2 wRl9R<"w1}S[|k砝P[uiopW$1.AQ:<7>yhd @l5$x]e 9)dt~V&#`׌gmFZbGZa}>Z?ui&bIC콐iἮM>i <[}Olz2;h}"]?imYG͛@i6/Y[0+h쬶GKz[e1Hƣo޽xtD|%.V>`Y6}4ڣ%|k@D~wn-XW1}[;Ma>\{ͼ3>5r\t/F,Gxo[4/泉vfcyik6}ϛdukfe-XrXK>"di+n}IYY[7d.I75t>ʴU|!12g NW8\&wu!ZD\mSj4sծ)IwqPUtYжCSt 0100Mp /n߉qG +;'7]K1R..v!P[oe]mB@;aׄн(v(B @ 0bl-anw1>Aʫ'cwBfLBM6`;'#Fb/>C. ] _~ǘRW;3ĬdREkDJݏ]pH@R|O@fY!c%$A'B`o5MG`:I#`A}mNt _] y@A@f|EX)ˮt16EZ?~:`;j; -шEYL@ }y0IlPߙAf9[8@ÈF`]1 Oƣ DD`@Ƕ3k]WnbŎBarZvէ[fo!0}+Hu<KŹ[Ggkʴ:&M^!ñ3K3NM!%.D/Bcxkm;5-W[GXK w4g|u? KyienFx0/ 'B˕#悇e&PYߣGx^KyKYYT]WeU~F,olnN*xjǙШKoZ+GP{Z0n~ێhm+&WT FQdQGl[_%Bχ#-2<]kyvȜҪzWl}}tł>ellDZDL )ФC^jc"EW1#7Rl +]v˾1!qaׄkg^^C}E">0Y;Xmd ) .b,OGZR>}%br.B NAp(aSom[kmgm; p+[DĞ̰wF v@ICq+؝̘#݂lkxp32/@ 7KϙoN%X"u pqu{oӣAJ>NHy! gggjv -ׄ2|[b04>oBjgPXABd|N<@l9hZ|Z]m æ fhQ) t,ⱖo6!gsKmf.SViuG/+ovBC>SY۬]xA9Ě1'L=)xt @,:1ݒY5nX@\k-"D>_ pG`ϺősG߫~PêEc6 Wn=]p[l?dpED"vcgYYx93971[۟tRkݰ ۣ񝲴B.>jk'31s{?^W<(/geۚkffcϺ#_=`dމ篂w$*]i~޹p²]h`tęSJ;7j͒;|UeiIM@榮m2k;vʉ h}Ȝ.M/Y~ę\v/#G#*_MZ_5a jq9AhY6d{z7̮'ͿK^7xWf|!_CKDH'-%wE)` uoڳ9$)y|o{v*W#E\Cc-B`F'v=|MA8|KX,R:ĺ~j Lh4}yUHI Fv|~+M"Dp ]j<,vt_˻dΙiu#pau_gea}t bD- R+.cv>a X"&+*\@ 5gX-|X.wYdM\hZ׺SV :c6-'3G keΤ7ֶ } h;:s~' o7axInD@ւ/'a#Hn-Wl;q!  15 :ruyiyоrb@dR]CKg|5]~2lECnv+Y=6 -ԟ.X-ElRK+S?/;wgԌ q"_xamrWVw7!P߄̞CF~K|YzEQ&0"p /|k9Z,ţOHgCu]WR^hZo:[, GbRG w j'!vl.RNo ڡBaZm o꽇 o ;xACYO=?,!Ҏ)sӗg1nr?G_v6IƣMDʁ*Af|#w/;@cG>c6V.3ܖm)Ħot-~f {?gA;'7g+E- Xw2ӿjk}\ƴaWĠ>6DC Y}C;gKc%xMX"0 _1[k0I> g|צڥKz5ǀ)奕z"Syi2OfF2Wtק:-8-ԵjHG*ڞֳ4O/+l+~Ѓxt;, 0*6Kg,鼯mh:4A<}Н#&gV@b,Xg,jNM\KcT-,o#%0)eΈr펔xxB~1cOc1B⿣#69|?-!FG`yhg5/b4R~ x&H=g9RQp"Z:[^#y}V @K'@,Q#RaĮ#3)H1:V,å .$BksomSYzz .h7 -`{n]k h01Edt(|{9@cf I,: 9W#p:R8g5kUewDct:.czO?|\umǝ[H=Ǝ[,(uA|"avB&}3ba}س{҅ kD-apx IDATvD'c<nD*Rh%RZZs0_d>|y}dm㽆u9h~K+_U>Ӓ&o^143ȳXg[; ι3={979F ]R67RTsn7>rewy2Py(h?[wO( on(tE2%R-Є*B;K6H7 FP fRPZ'!_; )")3)ю)&_a+L,vtŽg!8v1RXg8ZXR!h2MqڳbG il~'ۡ VVg97y=h?AL vLB h W8DKήw$7 os)3D@guS̴׹UB E jK%AR^BܝGX"OW1D1&h3܈b??Nm+VcO?}V1KXb}?ud6>ƥ-_XVϗRV%X"Eϣ6@~&SoX{q=ڜcm_~Hc}>^R^ZYTz>{?ZS5]F X{_W^ZY{뿹.Sq֓?"_, qƆn/dpsޫBz9A y^s.k}总< ~,@,bT'Kƣifu, >GA\A `Z?B'Aח-"A9]!ſ1(Y{?MOK[ӇHꇂ9C ;)|Bء|cw+hW2jW(' Pcmg2GwSCpb М;x Hԕ4"3 o=~#ȡ9v4w~@B뿶i%<끀Otʵ%RXoey_"|V9bTkL6q[z p:ˣ~G>i$N[Z~+67:,'q뵏.>Tp{vuMhmL~ 7fMHE۵WUӥ lF;/*Bi~h#~ǣb}l.[ʩk i+S6EEH.ED?@K#~\Ӂ@W &[~j؃lR<^ ߤX ȶ%Vzd2<_9LEJ7`?e1Y_OMDj^k>E>TT*A #R GC(GVE!SXLg!)ZCтRcm ^^gcL2%j@nM=B~hCgd[4X A tn z=ACXXѼyB GlT?4}sT.r(NPng}4Y>l>V"V8eݟ{ 3Y_wA"8?`}< 㝝G~ sIyioK"~=GܫI2_,܅l@,HIAbA@(jʄCM>sdEI#pO&奕/mg\d/>ZCuVߑ-=U}(NihB (X'߼b6-E%+O[ ઑ[@HiEǞߑY#xR \d?~V|L9 S{8YFlFA<6h᭳gX []\]_+zGvzHسG!dMƣX"՞ pn>a|C+2(@f@ ẊqD`C,`2]KEb^DL0i觾LQlt @4 &МYADWww vG3"#v=c;]Sle"лc@|AsD,v¦:29EV{~ZkokF`Y2]K 67cX"5y0HBL `6#Dp;S'}ܾá?;G݊Ϛ_z|?!%hCW.@)@ً6Siʦ2{Ϗi]pz곳P9R-.Fqeol*R~KaH\@4H B}ޅحclL|)yI81Y  E2/J/C 1p"؈|@"{b9btB`Td|!߁}fɶ)Y`` #ӞS&X914lQA"@ಅƺKɮhB}= ݁ruqؼeY]9'Lʿ?0%l r)RDw,t\-R6Ayleiזd}b~Nm},л8;懟a}pd,*AK:+h-MŅ+ygum-WھhYu/9R奕'THyOC!/RC-4)[*[eUbH u^|>kXi;Z2A" #%'uHZ#@ @N"hRbҮ#"c#7|~vHy D@ p+FDQ @.?z6RdN2gyA2lV"lӯDžh~ ֓X"'Ft0RX"1o%*e=sn>OgO3d{斊tY1X^Zm_.Ɯ}?X[~Y筲UJ %R!m2]Kz"E9]#SgH!t.LȜ)|jtDJa  im,A0MH<ӑ9(PKPXİ)] e+T~̉ :>]wR=N'ѷ,YtI?sgsp,ꑌGW!#H"2:2H<] aXb/^ R~A δk󬿷%`HƣWQbm/wB^-r8|CiPV 9lѼ8m>sȞVgN{"nN@TU@'ACYxyʿ>HA/Asp+xVV˵BZ䟙6C౅0'&ظ|NXCB~{Lƣ?)1:Ňq`C3Ff*?sa_.uhi|Κ"2 ,GlU/[Bd_Ot5!NƣS_?b!|Zk*`f eQ)TRh bLRGJk*ꓰ~1!]o)5H Aʣ*.@ lB vjU/X}zXD Cl˅1_7Y;wCsX"}"&L5$x4|G@)3R@֞ǍBa~: %~ڊo"}h" %b4W#MD`m} 5pZ,[l)1ѱJ3^xI]c+9$xS"o(W!V4BywxW_@ƹ6}YC`~:+\;d<:uxF+Y^L#S~DSk5?CX?Sݬ_oDT4X@J;L?e6.עw%&[,Νė2~qEwJE,{łv#V/k5}99p  {кs%HG<惕G\[.I\ds,\(CD'߱-#A폐G{us;Ro~RsAl!t>2ϵGP$),\h"H6؜Cjb_<2:_!p=RG#8 Gbb:cG-ZEЯGLo`U R{ )0[~"}HWtA Wi5D2~fC`8/IC#R#z*{n=2 "+EX[k;W8wOEKX"dgEm"^NpwT 7 ~̴Dj_ _l!<-#26:h!KMEFjd^K; rw>;3?aG4ue2K VJ+XH=G؎D wѯ-т1*E<K@~_S-xh r^o#05KCk}l#`0خB^!0TMgAʥB`I2%RwX'VPנ 2eks?k`w2bMG"gXAh8O 8 6R4*{1j_b}'YoeXkB~nu.X [-]gVscAx#L*;tpb ~ z떇;kۮbR.K+!pH[+9GstޏKMfN}4As}@.سC4g.b拾:6`Cl :r# {#d8ob"?3_՘/ֆ|>욃͜z#ź톯@!d)ֈ)^<mR fY?E%\$kO ſΞ}>"; 2d)@ trP/2&p>H~NfBL3Y{5`2SRO"A@!09~&d<:-HS'[?7C_s#4H)Ds;؍A+bY;Y>F`γg|,=]au/)aIxw#\yVMʞ[4~=wݞh}@cb@5b.Es X~Dt"]?]j⪑_?r);h7{ֈ;L7MVqg?s>f"o.x_DjZ nW5>/ְ6zlbtYĀU^Z9"] 'Q.; kaWkvne?P&@'q3W1z ]{EqkqmM=\y~ιW7 8s+ECaGsOɞ}c>8Cx؅"wwHavϩZڽԮp!2β|<|W7Kr 2?xt(ہ]b HuCq&[i/Ggk}2b}17b4ODs`h5R,x݄@\|G 6Aȿ!`GX"AsЮ0@ ^Z[5)y/]mInvLȞ͍%REhA;׬L NpXIĄ܏z`~,t.U}B,e}Mj)z2s ?BDd<:ur~=J1,tH-dkc[kvhxMYE찫dR.|K+7ٙtp4Fk՟z:k=VBTʖ)yι,ZR&AͣfsnGVt'ιy8A= V8ۖrҝ-SyUι3 (<9WV~Fgd<ڀAI2]$Z@(qS^H6 'ؗ dīB1]Ymj@+B{#`Z]{"7;)m0ZmϚخ|@ u"C\G9JھD`b8u"Pu]+k_|K@]jL֮ztje[K@O5!s@|GĊK dq#H 2鞆2@LjYd{ ~NT!l}?9_@@A3`m+buXAhY[mk@283 {.>jH~.~>ؖ ]V߶·%[q(y X+O_ hoIVkQX9~+dh>ggCbT8.Br z_І)E"뜧"o'V~ 54fM_[תhEuV< i *y1?x"]ubR Z.[.;"]wS+/lPZOG tk_JV &k|ʲ ұhg#ps108HFuìцaq7D44 hNOC~>3? '= |b&W_A9O}\4As9xGh} ̲~ÓوXvbX"2MԆ"d$怾"]v pa5\ռVK; 뷵/~d&hWN*/$U^Z9O,XmNmfE8"U pΕumgV:r=k4s~h9wˡ"p8psn;ٖ—H<7t| g lEh2܅LI]FoK-vZ#֫ Ю)Ai},bYV#Y!~)S|g^G3qvG@RM"% vSVXxd<:1sG%WD2΍C%N[v;ytp-B+a"@8 )v Z@g@So146=ɩ->[t#-\z1C;sr/W6FV7?b*Bcfԃ "}?d= b&!Gϲf!x=5( Fw"Ѻ (@VAU97o>dSV?#xt^2]/E/CU(L-NVC7k8c=rE>gP_^z#1&HXR."]MsE,"]-t]yK}%cel1R+q΅k;sh=}߾.-:9mq<{ ~~]NŞBn/ߩ_l1@,Jƣ9~,YΦb>2W Cd2}6ow,C}br ^lB$)ш(YF ۡŴ1WuV泈AvRئ:4!CLPknB`/ pb=>Nƣ(:_2} x/H]j9D̚W&L} a8>LOX]`y%m~Bh@g㒍|jNN!po{2Y'(МGG#gc_)N>8%R}𡵱0$ xF|͵<6;#p~+?Q~ u1K-@,b'H!83ۭ"?/ێ3m|>'Ha$50'[tKܔ?"]+bSO+L.v>_x+-}p@%U%EB44$o5n"]֢M^wHY7*4PsX"bX"cmlVtd_"r#<g_OE;qzmܧ7c^qM@E%ιqh}}97ʸ,k/I6{I,rUiS>F"ۘk¾ -TrS')?}QW!rPFAd{ -ӑBG q"y2 Y"SHqr c1gia_{C M KALX" PG碗b@Bbd<ڈ*&6!61%'cTK'{"֧1K8O!;@i& SαtGp/K5<]6y!و4Tt}77s?/[rbd" ~KEJd<گ}=HGPom/gbN=ޅH7_X[ti̶CwV'/;0pp2]KvG7!E:!%ya6k ydBap":=){hY?ʍS+*Wې{)%HL@J1V7"@{c-)'j`lmelc2ɞ&s>!SXi$R7OŻ3N@աLClR'fGy[DuAh~|bm8#v3~" zcSM墹+A"߼깷3xڞ=14(5dod< D*e{SVMVP\?[Q.Y{ú&I?T\~T^Zy˦{֧+Sa Rի/jdy!/he]%;{zw{?|id`UQ9w/\޿qW 9K\V3ʎS~zxN `m2b``rE4}g ^C[؂10ĥ("f߿@fj{~M2}2HfKD"P@|PE O1"fL+7!߫^6 ZShhF'|i섘(lke?B 9Sey PS` h>xk5b4!fcb %ѷQ\)ݑ^Nފ*Th}@Pq_ Z뷾V_9ڬHle\y *_Zݻ#Fhz}KDɏ֍ cLwC+b'C)|4g" #k2A'kнoͲ6"{kCgBݝ kht4WnGscZ yl5BBTfRBoX|L?ٱOkjS۠w ԗ?,Ct}XJz^SUY[բi^~NwsbT]/Ivi;m]C~mڵZm^o/H]Z^Z94`w<΋3q菞3HuYJSWWr$~e ʦ Ēh }-&PH1y,HM_"ꆔ: )l-) xb= .~ ` ݍvG"p?cP}*R|9i#ȏvaNy\ 8E]bzM,[TK,=6,|Vx4H}GLƣX" ?:)[kB ;V;{y.LsFkgkxy8 ZI4rllD@{KnCxfXvE _@`h 5KFx#%n;Jb[_#Sfg&Lgcu- @)ߛ^MVvԮ/Cf&{4>jγw z4lOנrȷéYY#-ڡ^<(gdu?TOvlxxx՟r6۔BJ:]˂e==,k? rca6~)/HL>h>g]WNjumyfyuqsP&6'x"]yiZ%l3 7f"huD>Ȋt;奕~Ɠow(eECV P)kbEsF.6/*[g-YCK.%RS섞Bl]W4^D$C!b;МήK/C_b8go w LYGt Ϯ_c=X(GY;Z ey)ȿ)0ekHޭCExd<:j@mS8gm@]Iƣk$zu~/sמԔus,B.5wg*e7VNE,bX+)/\69ހcڈ=ǏO<ҭdмԢjkE,\.A蜉hk?}i,r4LqKVk;bb'B]2]]?fVjްU/^h FWw4tH/D h'"TKJѢRpЋFNh+WDه IG p?-H@Hqvo.Hv Z;~?k?K| 2dBԺ)!A`.H]ܬ/@,{k>w{ZC)YĶ#0F\Zezvh:݂|!SpP о {m:T_48o{ lvd{s`-E,Eb4#aRn7Y:|hms[ysh$K|cX=_[;gm G?%v$!w?/ߏDa[˵ͻ~!uUr6PC+@s IrݺU5лBM .ׂN[V^H"]Kܲ5T.kGY \27]_j )/+/K+'#huDkP?C6 ѼzˮMm>鲣&p}H!kMmwrC"]~]b}|+`ޮrIU#4K~e_59__47] \a 3} nw\v}h{mrz0?L/i3hgdB\@ɝHET> cZ!_m b5;#V(}x1%h|;J\D?-0ꀀV׭~~05H vGbծ]v3#}Xidf8EX*d<8H@uD@q+>G8GE .9%G tXN.[{O ̢!9&p?"Xg9 8X_GdZ'&57KmU jmǝ;3;f9{ zu_֧rCo{Cft{m?tG@yڥ|֯8_?,L4|WhMLMhZcH]+/*eahzWfodg^)/>|D~7@5qi'fr EF%;&o?V6X"6!\3; )Q>u׻>'ch} )P?ܽhw81Ǡ#sf2M]{ C' B 0@[ZEaHj8)z#S==;{}H׾ HA hg1S75"Fo~q:_XЂ; gN8cc?$VӀ:)pX!vϱqYtGʫ +rPP s_'r9j+s\n׷Dfi6N?aw+c>,B>}6?d<:Y EL6C4zs!`B@d<[,1O9FjhS }m?yhqs1@Gوly "-*22\{0-@bm7wM%@Em=;yar[|޺^L$UMK6t'?)irC{C8ZihymyiͰEK+WCW# {Lՠޏޫ|4U5xH>]?^xr:gv^}ںX"UYj  {Vf-Co)N8yM?)(%Rˬmc3@lK^Gg(|1#0eL#]X"/:.CA~J!?;һ77e:";;}â}SZZ0+hncmbi l@cs J^].&Cv,@hT^ZH]Y޲`YvIqT-jNޣ9tjgZ/̭jGIm~N46E#Agq+v,(k4\qS\!@llX6+KƣӁcumE ]K"&7o,BlǻxDd g[VvD@X{8z#jgɰG 捈1)[xSC[e 8[cPKjXeuNZݞ@L,?}[7IV|3=.3 =88G惐Y6@j+iA'mz#hw%9 t{[ m(B(烒d< IDATZK@ @_듌;#"0^̉y[2wcc'nDI"`F 9֏؁7 {Bi,{8G9= | [[_,x]qu#Y= <\mag C DAӐ='hޞf}t,>N JEs;ܝ蒛ÄыB_tK?cO(/M/+L urq2K+3V^xLNk*kU:{:4B-\}ack:֮\y9UZ9zD<_55;f2S5ꥭ}빿tqDTZ>Z`}FO_?ٟhBkݙM0}fBk) '#W$y9ڣu]f 7ThL<]fĀoAJd%^clX䧲@ #4%GQE(CO|R/!%MbXEDX"u%0n.3'D򈄲%HųF Neh q_"pq?bNCuV + o$0LE_To嵱DL{?~ 9Gcp{x,6J0ʹ:MG[@oZ?[bԭ'3sm|z]l 2$ _ߢ!s-*:kYM'`בֶ_^;82ex},/Yx##]_ca-G,uUc V/s5{,4oȌo7ڳZ#YLC`d4F%>LHATgr[O+fvS$~^[ܝJ}:}<^R^ZTf =gߟxҀk=t\.[Y^Zy aHhiXwW/ ]#o@Lqs[Qju;oƦ#k[[Fx79wnuv/bE@40r>E~N!LD@?i۞Ծbm͇; eֶl ClTw\Qg-醌h~fU ֿEX;ʮ} ;a瘔js$Nc-;Erc1)&>g> #v'M`E/#)b8W и=pZ꫃g-kfh#J(;7{ Q0K!l@XH=#ڷNH1"pu?BHolwdc4{#z5?itaWqVȴ{ b7 хX 1͡<P#u"$6|}W3[oGMl~c奕k.wxU?dIBT*AѱFwUײ]{ XwAP"E :y}c~4]yShs71S뇘w Jz"3-8Yh61 HhY;E{λP6@ALgW4@`,)?$"?Ոʴ Hl2Jrq.Zi݇[-Cͅ m Gd6syŶc"bc?\iy,ۡ<~xGwh>LEBgC / fp4[0鏻wo7&/{q*Go=iw/..lC7ri} 8yTl޻{ir,zg}b4@fnEyfn 6da <^_YSaa~/P\ *K7t~b>?h,-Z/Eu8a[J8N7S|@k S_͊뺥wgZK8WhiiuvVq9Y=8g3$cAseT$ GH۔0dZ.9X#q9I|dvA)l,Ec"SR$K A-]=t.aW-C&7˰~Z 3L0 ?v<[סXnE a%-.%HLH-סEqok[O2~qb=.@p4~Ybu\XWRd[lU"P=Sb]){Ђ[@hyOߥVnlXF}b%3 x#`kN9u uW > ІxE+w[|ƵzbZ$g̈́yի_v={gZ>ϱ:N6h} KHcg4_t-3)4:w `6꣸hSfD8ozg^H8Ϩ <H ~ ͡ ݋ >-ԗHha8w3g+{wQIAb (%r5-ւ0x&ohnj=zZ&rA* ;h&u5ӘNr]w!o4n6o;.l|ئ2\ 1uAƟAiڧܸNn*5oR"ŵ-2 Nx|^\X$4ۜ= bJ7~1i($̴B}$u}LYtԟCh(EJ. _)h]̑m0ku؀XkdE;ہx2zf!D[RѩiLz;GfϥH Ŀ:d62Y=R>AV"1/ì/i|8|]^ X[s8Z*]VQD.4EB묬ße5RK:u6B QR^\vk2ߚ>S#Zk;aS6Iiyevy'#X$i8ͭZ"=vqoR(^ ݄}1pq5XzSy,@1@p41Ca@, UUNuzfS6~շg3UEBwգnShM|Ϲ9y(%XSTrszh~3mNBIMw uJxQE%c=@ߤ@CᛗY?քJ zM)V1~6E5,k1/2tSPE&W,1W]@` CfHXK)]1]t*v=@E]S%j f4 JONCËR+|s 5 4"3b*Nr^^ae8F& Wزkb:vh7r.rzE 3w!y [h -IH9ϰ|lDsSh'9xqfYt@JIGC2DD'x1B*z~3ǿgj8oam 2 &R4ʬցG(N>mi@,鷱$smjf..n GBLۈrwϰҬ,]gĿ&)Yk`uH~@Y;,h#1ljRTR(//fSZdkPlCϴOxځӀb&Mɔ˻?ihcVbЮ?*3Wyii֣~@A14Wzyi*sUdnLNt(/{uP]TR2/@6Y)VN,dڹQ}@lvS5O_LPg[HI>ſ%QbN_iO@y qQI5E%]STRСbm^_6E_|a{ܗhmN>FW3ќ0xMvʝk҇WffLnđ7.R蟔lOZcPT{3Z^x{kI4׏Eqy..^5`EYeDzxG;xeWl6뚊ޕ?nYeG_ "@^Gi[|ҍD\̉|y#h,bGZ 2:u)4sȹ;1T'-tR6 7)LX$t7&߂REBwcB0R]omЩh{-Ymm{t)% "LSm;HaAtR!Hُ@ ) ZBZXS4{I˱~m>NV!3^olwfG[Qvq?ac\[k#{QLC'R( =) kp4>Xnci:4mDLdL$ث d^?mDNFn5 ͋lkKS ch.1Xۼp#}2~cP5E`{:D4) R`lQI(1]E% 4λ3+sTdPn%h{`0cIuS϶]޸>*|o Ƣ|K4̮WԖO䕽?(/^?!t@|j J]Yܥ7ϙ᰼뀗J ۀw"r!b_1ԣ1@{j>?yw~ЁȤx9 ]϶6x祱HLcYG`&ApF݀;? ͙N ްwK@`0!:Yd6A?g_. گ/!o0`R 3&+]^MږAZ'\ꢒ^h<6 |M~L;e.GNSUf\c}L 9mq݇hKC^"n4ϕ"+1p41-OSbvMG b,^DnCK)b.>:d;̻c+?K h |:ʸĖwc ܀Lm-ݯ |F&Y+9x|8z)0R,B IDATQo@WVfĴu G!珈MwSlwBޘX$/|"Ez1tC f_{!zũ]e=߬*hg}tVМb큘NVI>]X /hح M r'"y;N|e*@]ޙ7z82.|ZN..2k7ϛu2/C˞0=uCF֘s٦G--<a)ue%ƶ<' eO=G~v7Uo%qh,__Wڅ䤺k6fDgK"bꯣ#ޕf  u):+#d':IZY75 ]Փ23hūH>;!*ZDN=R߫p6R^ӡh7W'?|)o"%ꘂηHy ;!6fջ1]; YX9>@ @?`D0Ț 0=A @f"ڢŷ>D2Dm,vF#vhw?hFȇ X#7"V&sz|ii kmmmo?hzA|FӢ_ZS[;h<jW#柬fX,vpOա") k}Z81FcX1?1VF}m`LB js<:!=k/Ȇ-F'X0:cFfOjoIѸ3[2e"6ZzVp.UY$%5p9wmX$Tym_U߮ߊڍi?*H60c$E+:g)_/VYW?|yz\:{s>뱻$n)*)89HqG^jթ n;d`Oihw7/Vڦqxuݓ ]8]׽俍uuI 7eb>tEBMw_k|^Zry).FbOS;?R1O#VbRsS@Jk$-@))qch*J:(3w ?1K ) ~ܬwRnSFF1+At2 6?octx^졓-nOoY;@R1_bn:!py߀C+nB,n佄Lx{9@",$y!#FLZn'0_CqA=Y;h`._ fkXtc,;h:nE|od@sү:a>$/XY He'vwG[V,ABor0`RP_f*}Sڭ[["xUhX$-?p7gԦOuq kEirVHO-O۾tkR)%_}oK2G/4O6ev36$mlei)%ܤ f-Cs(|1+Ek˻vgE%:$q]ֵ݈ޗ뾉 ]A?9$뺇gŗ!%'"M'j R^")h5 G㧣ȏ+b'Š);0G"߯hj$ƹ@ߊɱS2?z,Č\IA`d<>]*b<E`.+xjbкp4~bs) wG+VVUVVm0Kp@J#p*^A@+\hX$4FEϕ+娟F 0͹~ƅ(nc0ぱ, VTRy03x&Zb>G;s%qܜڬ,Of"HL-'pʒ܇7ev+*) hS_vӏ**)HU59w$Wk8-e<3jh ˀ⺢S뻠 Rx_TRh>[_$?,o8GGK]=qviϝ= 8w[\}Ѯ zm\)uc+bHludGbc.x&{-ȭP8Ɨ. G##R &G/:h/8r49 V#؏9X.~D}Zh\𑁰Vj#)h %9"?_\k"E3N%x4Cmii:\]̻Op?t{cNB }M^iWjd6);l\VE9ƧHAw1`G@rı'd=XfҲŊ\p4f]6"f TV[{F=E佬n¦ j~]+EhεG=Ch_ɋ:G >^@kw@#>̲L}h#Q//$M-М(C>xu6M?q34SC_RTRP dfV޾ג"Ǐ5ܱ?Yx:Tk6T_OJ兓 eV:nR;(.*)x7^J 7=Ia~p40Ezi}0xo}tQIhNbϕq3\ֆf:qChOa@>q]hqcbNq]sqEp7qb6mVukI|^q]96Ķ-<~ b4d\XiHyo#xgZDxF#p!s: ew?«g[{g+RvE!g2nr*/Yhـ|w#?EtiF w`z\eCtz`jo3D*tqbd:_=|cmˌqܱIյ-:WllYj}hG[}mKuS]]>z~/{"c[9Qj8^|%_X!< ͹4~w:pY5A3H_`Gle EsAĬseֆmn? Lq|VKQIAUa~m${֮̾:\ץGIIuΐz}ژ4.O8ʔ6==|P8"! &L]4uƱ (hmv^U.bK콀n=,*)HB{C1EBh/tO:)[_\iVm!f|O),Nw;ZBґȇu2qrz?+m^6 q>چ]8w ɹY";r3ڱC_4b@ʮ:K" >OE ݅rE n; ?h@BC)EΩ Stf Z s>~cER=)X$4 BsOChXHo24mQ89V!x~a Vk~]YB/y0A&2Y> ؾnc;t{>?K8qeu9l4Pt̀92zSɱHh}7~st]YwUKfSuw @V8`|ֵR4G ;?|:> +W$?B{QIA2ޚ̷d[o. :I}#Hx&{л q]N,Kl=Kk7 d+`[O}׃'↓/n٦zy_vJKDUMo{`R@Dk׻GoO1 M(/^p,o"woq|n9mr~p4>G=3A8?)c$6h09 HhYh7 Jb{ АXYvAb4`pOzj3̧'"2YE|D'r3ubÏ=?בBrߞ!t:F R\c(F dދ#nG1zF`m{vbH{F A Ĉ[zb|>F걶Q܀pg!wYHhI~Ū0 /rak8~G@Ikh.fZ@Oz:k_ﵾgbp4?ҟ.Dٷ!hNg៎̰1o4Ma6x0#WTۡ;&Qa  _VWd\$DԚ, e/X&/6)ٮKƚlg.mxc^;$4&Ɍ-w*ZbHVdzLO]D"ؽUHh=>ŝ:r P¥qg);x~]3*A꽣ZwFct(;r33Hh7/"O]@ЦCv4Tg; Y bAh ȉfF܎'#}v*S'W"VԦ[hobOg}}e~YJ U֒n[ Zح/-o"EppSL.bvE`'rIg[}iz+=?#f݈H{BEXZ4#s,znCL̡-ngS 1}/Y^a 0biʐ U4˭ ֦jMoBf*\ \w(ZXZ ;b1v:Eh<p8 AZRstCUy{V9t#?-b,pMB7Z]c [=GCV R e k*@AF|V~*O"\L7@\yƽ7wHmc} cPAn dtm9l3/k(/k|+%Y:̝յ&߃OV1gڼC )UhN7%6 8Ҽ&2@RSx.g?&%2M$MͪO:%tCgVO~Xvݽ e,RXg[bgD9XG`#28 XmCjHDe3.S{)$~Vh77͉CI7SX{[9o!nX$Tolŕm6tX HAJoB4<dJxmLD,h};噰ϬGZ{ Oǿ#c/ '- Ƴ2!bwE dﺮƇwaƲ0^G^4soz%) 7@r#OwYf7>EB-fx|#[¦!%OޥץgB⪢SFH2c :}OM2k:1w].S&9'u'+Vr]cӈDuݕmo*=뢍eML8s!=}_t=vnP!v3s1_!'_euwvx/E6{!>laLx-'!1,=Ip$R!v*a1&=TA.ZZڽ)~&?rSHp4Gw[ z٦+́:e5P8`6-(Lb=-1gYn2Dfg˳5 ziy ]NMG 8@ddwr{w)ZX$ɷI{8~nnEB3ne}ؗ ͛/މ Xމ4+wbxi}b34 M"@`.c}_EB|Fo"Q[꣭Db {P0]pmh:u )TwV<}[RQkw"E?nں=m!oV_mvF@[W^H$zgYkZ7܁YZ5.y Ȏ̈LމEBoSq4R^ŧ"t ݋ @`^@J7b@ڻo12@wSD~_Nx -Z;!5ž)| D kfeBJ3{܌X/T̏$|v 6k:YF\ qc|+pZPVTR0;-4?7 ́ {)_v3붶 (~g19'ZC,%V0?g֊+gD14Y)Al)xD,߉[ VrxDxhNл`cR]X<.3#?;!S6;"J1vo C IDAT)b^WG 8ŭJ,u~΅A]+.*n\TR9zZf.OO[xPwX~T>KfzxR]uUWQTR \8ka~򢒂)#6faΡ~vs70)fkʩmq>ǥT|C#y4Ko " h>O. GH܌l{!Ŗ@|7Aj4Զ]-{1'XhX$4|ZJOQĶ͖"S{l9 4 }!S8 #s9AK?)YQ \Pi<E WX=*e/|f<NUgA`X,#϶O=,g#P7; 'Oڪv~njkڲ,[o8]{g2b(A 3d n GhܛJwr5(H5h<&ϴքw|'96.>ľ_Z[7|јlv5|E͕"l[xr͹nir~e9-Vөp{$ul=gֳ6bm7%}A:`j[uZP_^ū˺ &նifip@,_&gޟv_KEB3'!| |d}w;4$x>{?b*OI/?𣲐17Լ9:;6X-1(/n@/&rߍ:#>?oY/'fOhMyI̜y_=~^E3jRW^ꖙni.]ǨW`9KYY~=١M!lX:k_4odb[yf"2]HQݐ1t=XN(Lc!Pr}WvチHAcϧ"VccaNNuU!&2%ރ؁(wT?d;VP#[آȬn!Ix!qp` XnwB@dl$/^S%2hh/PX c%>93_G8Vf[ ~`W^ET[N++&ߺ@ mjXAj`Ǝh|~WZ]V^XP~Ax8Zo@]k}ch'&8uBx4jK6:5! i?֥`W&wQUzJ@ݽ0 OGh;8{8:6*u;AUy-[,O g -97˯';#f£kzXD"s3% sio Okdz#d݇`tH#"AJ/xW"0p4f< BgrtVH$_Cѷ)ݑW8Y,%&& 68Șa!e<:d޻1"_?ޥ:)yr#0D |RȺʻi\Հ@wNeh8dFdl\rK@k11z![7uv,!L˫?u~ňnukzϬcӈL}#k[l}ͣd"= )d6b}F6~[oz>aLHp4^t[Ĉ}ƅ3X$&6sM@̴IgP^Q17:u= 9S g*s@>@jYe۵J= 漭 6%sd{] eq*\ma#u[JwPǹ (v]]oMv(FH8 zRH9lC#v+N{9m ZG]7`/#%⋑RD=iN ꒂG!ʜu  xQg$?Իis25^\H sdN#o֕;/pӂ,pҬO"&;ܒJ0]@T[[D'ݮG ^PRm!|Oڽj?ÚY'* ?쓱H(Eh*Yǻg {nO4g^>EB5m/͔ӿ[osbhnE|ĠDk*~JFYgXl!ߵP[@ un]]gYF8? mJ.¦4ˎ/p]w$f@d9mR@qMm. ۖ"ob#3f:ĚB.;#bnD3վ"BZ!7KV:= Rd@ 7 <bb28¾HY! ]ؐ`#f(8 }s1z)!SCgk@"֫ y1?Z_Hb\d}1u1L" 0̸Y!V0ȍEBZ!ƪXN2{ fp(b߲yYO4/ut]rpLJ(:P[߼X$tY8o􄦑mslloJQIwJk T_B4J@l40uݘ8Dsp뺣!qf\]) fW݁6ѩC>f&݌֧~iR3Phq]&VNqM0+d2MHYm^x:)c/J~/dlHQgس; !M#}_4a#E_d 73%!Rش ߠ|n{w`X$_8>L*OF 2;/z7h"˯%2i]|*gwG݀O-Fs%AtlsazZtnk5q,ڤ4\w#pw _!Y 8e'燣CQ(Ъ@p4# U!'] [= =m'-_D׻ D&d 22vyE,G; 4=iF}4å^hƌ_@Gkȷx6X4v732u; wsLtD=FoNt]&{Yn(whH ܉(EQKȱavE`\4)N߻ '2˲Ͻ@,h8P^،$KS" G BӁ]H)}L_ϙy[ˣcW2qCgwY@ 䝀tS,T{> iXX3܌6n;qOr 1QJ{gd o"l 6dž’bm1Kw#ߴ V$t>ãH/BqFs +Hp^Dq:XW +fbƣSH3Γeh,? ~o{ ofnt<98_"?)VnHzAB=tw!rRUt Dl R" {n7KkĠLG` b,߀A6euקiȔʭ]Q۟l̆Y_"C-ehtL1u-P#pob;hܻX$t04Xpbзh<Mjh]2ZUGb9ht/!3[kX[G=1U~wk8geڡ^;Zyխ'oD~!x84=-9u1;)jmIv(cc |k9FxJAsͿnn7g͛:tPI"$zK\NX h~=_D^@C,K o OAIJ pvǖckw=(uț}n9h$O]@' Z8{=\qƺ[8%q\;ؒY]ki־o yA]?itxNϏ ?;)ր=)5 Fl D,̋w%/C/bG1ޥtw"2! 9C/mgM"d:Ro |P߷C#TzP+~rMdZ9UVdĄ0gTͣXF#1ȻUK81}2ƫ;i;bP dڪGʟ( G㝐wVd eFd^}Y6$>,=Y;9~Ѯr-hl6&ߎpR0.IIK **)XƩ}g46{ xsn"iTC"•wu%h l* ShڭO qJKǹ TZ֝-_8H@@Yo ݍ|l>ja,Ƈ!~, ߟlN#z72aawAH]s4{ )Ӊa IWk@zdjlg`:=e'Iv}S]}ф/?`Mx,hE`5tT2 36 GOF/aS脧v#(((@/lmZ*|+t؝`#A CJ-[h  }g,G^ GaKp41 9&xY HgVv:95&_^mtLxZ)_T {fUu߾ wVޢWMc4bMQ1UcMT46lWEGP ]D@00}xጄ.@f=<3s9vz^kH'-5\6g_һ%;Ix hӉ m{~6ZOާ._Szd^c,D}"$YXbD>6J YiFئtZbV|mH15#?@!V~w S G +"d?܆t꫈) ۡ>4{Lƣn}p2u <Ҝ0G:?yz/JƣsbTKג=}E+_{hpx/} ׎uً7ta#xm {9|H&>b2)d -0GlwG/B}nA:{N5򙛈։s]Yed,CJuh&dabT6RB^Gc إF O":)5#@5 *x5yϞ1D] WT*X:)ƶ{ؚ bb0D`թ(K~L Zf!1!X~JC!afDȇ,ДR\ Uhh:|HeN]:D=<|HѥÂL{XcѺ,:54j:A$M6È$ѿ#tEUh@  h8 >[M9C#GF2l7zGX92>B[BJw7tf2{U$3cTk>G[VjB G!x pzjW8vEdAN`տpuObTX"u"r2%RF;pBcwp(,BeXϵ"@v!ZG.~ 3ZؿESj4&/ ƥV)26 e/w%b.4@۳Z APrw[]_]h~uCznƯaFv9!w@:9u"ȓ xjʬ׾~?ݔ4IlM1YlC/q`99'cDURj:w*n#Qx!ڮuh!& B@r(vBa{0b)f!P0)*)+3ʑNEʌ%C vϝ 870nU6u XKys ӬX# ZAoK#g \ |@~L>?0\d} 9g? 1|7~`n;-"LҮ |̔ȿ ͞Qfsw^yeEԧC U?AXֈi}lo#o"p;2C~em<+f#@i==v;-C`p/-ESq)*%i\Yr_rNi}6lL}q50^ы]:lWG)~/fB Ʀ͍<-ѥÞ߆)lt鰟N}k~&_\&#cAzp퀖M~9`Y.z6Yw1$1tFYKC)ĄT8DfH+K{5RbZr0LcGJ1=}ps5M}}Y#6%RXA@tR_=,@@p}~>G7]C +I70tV{uUNǑLޱq )\d<Ywм֞y.-!f7/[ M Gd!!+" {01$EDꑋv͍; d i>[;>G~D bmk{ R(:q䲭mu> (re1"Pn/rei> {[wה/B E#1#D`ѥv>d&ao!%c uq+32eg4z;sEu5.ֵ,/;&9\:KRM[P 97vs.{?-gedvĐaĢ\xk} V( 4N!9Z2>{fİԠH+0tDJ.W=.ZYIad>7A$:xqY{G мoG`<3A r(F}> ?HݎE4:V#WJ)2[ @@@#Zi}رst>")A\bZ?46h^O8o@k[w /;2Ns#D`lJ2b@og:Zι D@`R8@潯wεFn\;"<9FL\?䃻 t~]Ii 6tɈ-1|Hd<:섃TGx>z!H-AJj2 E+ FHt{1y1`#psoShaZvYYfJm@J`R +`ffVv}0&ѧbTq1Ac?yd|4H]̈>s:ȍ%R}k}Rԝ`սޞ73 G"80)eȔ8ߍv{-(`bG@7H-6UCHG["@  cĵ;-zBOūaímRp桹<ͯ ]x48 g.SERδ~ޕL4/8A$oEU5=NZ>}+-^>5em>v0壑GܿHu3"{Ρ99 g,:vkeYEQSfEwj3>]'tR2duYK{sfwʬ$ߡ͐ц+ hރ -Y1z8GyCJ+If Ĉގu΍s /]G98Nu=`!Es/H/n9]i w"-ӑ"??Nn]#4"%2Gf#]KDog[wa줳@e]uBRh_</{ŪS]ztޭ%@h3]:lk!Z\gK9&k|d'zgP^l m R4Reo2֜J1̅1g$vT$X{J=mh5C 1@t4Էw!6@B݀Djb@F,0D ֞P7V\YY;{|ʪmWZAX":xCxt_gqN%R;@c!bD:A2]c'9"kݻ휫hcxb݂ /=Ӻ)L6d%ѷEd ș4<:y)aq0ŻV=2G~+x0  'sC;c,qh"[# bT;4Iƣ&lEt2ۗvAnUN*#ZQ4+{,kohHg}жŜd%kŢ ֬Șo}@Ѩ_G&fhN:^T?(re-@+'aG9P6 }fvbC.A<,qޝKs[5[jb~ai+Ѻ9a ʟ`:097{s{'npsnl`/w'<Hs.{_swxY}gs3h2;ol@l=r)pc2#=%>!Ύ%RQ|/X*b\L-]0_}-@ j &#%r>pQ̂PHd"R:$WqsQUtD xV{шklza ~a9a>"|_2 LbAOcgh(4aprRA̩yHؖфfU7k>(A,^0--6Ad=G>;B_'Vl<&" '(,\bn돷ت\OpijwM& aYl/@=2+a厲r"pv[$徸ȕBDP NƝjϮ绱@s'Afȣ#NX@YyvYzqf`} 㐢byGe-\H>cOƣv"U@X(ѥnD&7)yo}Q7k1!%uhSȇ$?܀SsA٣7rh;9Wޓw5^s-0ƞW,UlgI~H٦R-1 GkZ"vkdzth5b3>BJ$@/G d&] ]LJEu%]DMvZfYHI7CJ/P !a~{FjOVa1BJ-7'vmx-7ζfup+*A.>w8%WVo식 }G;GX^{-b.AJƞЮB`bhm<ݙrWg볗er0Jk(z  'L4r}ml܂(yUU|}~bXO~})Wa1^9Ec9{7$%EAf;m_h3´FY-T)reAcJ'=a.G.nUFVR9ю\a-sWGgmIbfÇ]:8 ܷͲW\սӎyMKn+G qlX.Z 7p b|<zHƣDgq L[; 3tR\uF~PW(DJo;xHQ}'hG) .S!>{<!@Ub$ݐBʴ:i? @udd>j3g]M$MJdl[K([!ֈd<Ɓ4DEhڢn7! 24Z#3z[y 7R&%#`i 4H%R'!C78;ye{ nF 0,~Q/w/9KXس@yؗ~#2 @܉x7+!y_hcϽظ2-G>G% ϑsi`*g%7cll^FLL]7,“"bu|NBCIƣfd𩛃L?4TGj|zlW7I4lӧ&7UbT[d@xt}LCrXh=0w]?!6U3oX7G))Nș#G;9܍G9aa@21vAi:ZXt)3="h&& Jwud<#am;?ӕT`9 9?e]k&ڠMm~nYSqef!b;gr.E@*뻽ml.E !oGL hkrhceB X"wkJt:ȤzkS  ?nlQ~ܹzi;_ewGZx[b‚oJ0U {C8_9]k' oWGu$w7%Lu)rex]+IHr":ggok۴?Yl=},…ik|k$H c@bT|Ȃt=ɧ2RKs:#bE/٩2vD]5O J"PK"s;bAl(4E'!vs%'1X+PBzd; ?_힎ɫ@@$&'?+?kS%RA 6fO!DjՄfEG/>?1K?yMd<:Ҳ tJ*LX"UNx+럹%R{9m^'?ʶ:@ ̱G֎"徸l瞓gezn.u#8} %xsmnU#f1,B d⏣wp5.re{""W,w|}cr;So~[7P4-kR9B7ph3)?x v#ۃeI7(x0cm& h{nD()(:=,7 ֬Ӏ|"kJiEl35?uWs+?->ADkEJDD ~ RhH9)[1S]@!sK3~̟#@1&6ܡ+3?x lDttZq2bXB9ޤ G'Yk01cԩD*K%R ߡ65h?D(i든=V'Q?{9UdyAQ،:VM4\YKw)b.F+͑9`j=Gg2&t\V|~0Jgsȿ3b6{@]j}K%}?#̑8 XwFl^C)_Y f~\m_ 0S?BWDȤ mF"0pI,gm8D`͍B2i^sPQ~>iz.6?|iEl,S W"Fc>E'QTL&iunګ2 Ax.@fn-T?[;B ף9v!Яzifs|ۉpCx嶐Id]@r^|nAι9-i]b@h}:m> =ws{snb~M.)x[?:G:yCr!; ,J5 _k:;ѺyC\svsch%# sl˾=0b-flKG+5 p52}}ؙoR*5 r ̽N `'|49B~Av8h_OOh=9fnA"H @2H>a}r$d9QfNCNAG}Wj;{ ) ͅU93hjn>LM$ۚ9&u27}\FdyX;ؐOCz+s 7г{v}Z+ ~靵 +Hn}9k;n۝s-͒mTDG!/`Kv'"rbY*}lAڛ}n$Mľ)$RFh]R"_b^[ RÐrj?KF7+HݛGC`9 )hbO%RG"rK,"'K}2}N@osb.FL "_,أ] bQ#p%xtp1;/DZĔ!d+f}q#t%Em8&nBGlfgJԷǗZ5-lkXr_bȕj+f/EDt 4GxfA<ݥ>@r_F/n&{sNXmpIw|'T9W6˜sDk󆞽5 yFx_·ι!19wMF;# 1_FxtA,LM!w m/ 3 ȷ3hY/q=*!isݽ[HYN(uB{!P%OH0tAO]ؒ5cmR쫀g'MsBށK-_ ׏Gzqsn'rmY pu@̷7?9E ɻHge&H܄z:9i"c^9]o6\/T`sn0Ķ[d,<SRo0)} xơO#)yyQ#c#U@L{(:ukɬ^`ͮm$Z}RNS(;OG;\sKn4s6HI>Kv%RbTY/qy=R Nf 3N NjO>F)K?ET'p' 0-'VA/x;Y]s &箞>oɀ^Zme e!2D8.@`M!1*0Vv9uE o]zi Fq2V! x>=C i4^ַiGwþMC/ [o5G@20_x%ԫ> y5ފI,y`ٱ~FNBidjneOhwg4e7,5sQʟOxX"8).m f1EPd i3F ? b97N_|5趯~؅h?.nn]\Y'4f}u]Klq.`msH̶J;om[#Io,,{٬#PE~{4r r\_v΍f s`bjnG4~ D rHYKAf'x1a{rB V$?&щGޖ'QB 왟 V?ڙ;IXJ0_:C -i늭8>ب}3[wEևo!E;1L|]BSۼg],y!o6ݿ!zR5l\qߺ$&kaHy xA@f\ĎB[շͽNζw6pn)_w/Zf}!ᡄ, zo9d%M$Mi֤G_%Rn1R1dz(įjf׍C&  tFANh~ XYa܈uBs^27H݆ˈil_&X";Oa-FL?B)7 GG3nhCĺΝ~[#:e2r9miX.vF\Lz퐟h݀WV- .*9`c_أA Cxr2wD\36>DxFf]79?H͓ǒ舍}.Gm])3rwXU|}-}ۡ91"38{jP 3욶)%4Y>MsR#uD,s:)+s%;[;sОWLìO2\XJ$[YtG(8KR@^F/Y]'"Z@C+0V[A@ ,ϡxmݬf}"~U=J^q6EDj bCRIƣwH : Mѥhn]aULghd#3V/nJdA7ЯD&fĨ Bdr j0U@[ ݂@aK"'{!e+gqf*&P{"vFzHALA%Re O :z >Hi1Z/DLH Кۻ뿏@yHz>i?FHVk'D;We<_ub#+2 ^E = %%1! D,fƓ WN'ޏ[sdjRL3:q9W"-!:W,{$Wa3vp]C||R{!3A#kgmӭԎ Pd zGODcoJP݁|٥W},4Y(ZmidÈE_"D"9~^ ЙLN"[3fwen9OF a*RП3Dx>QYR|L'b9x8=O>&!siK4׹n{9rr/ED,<O>R'tn=V xL̕#@3 nw#fSnsmKLQ-Ĺs#ӧC//H9?Dx xܶ ȋ ]suno4'#}t\MrkS{S"u b<׵s#O"3C^Xh<Ko"XkNx BRTC_KqL.`·*B:mx:ϴy?{ZJ11Cüh~&sZ_\f\ -\˨1[~O\:r`mubɢ<ũZYkm̵ Đʏ""36O1(XdDɏ[ ;MlGe)hW!uĄZTE^w9 O&q#1<'EmcԲ lEv^W'@K5U: ?aʺk7I`kA&i"Xl)w޷s؄c;W /cdܳ}sa(ʟ}!wA4CY:!]ޥg.Y}PɃ0O /[(_D,jvtgR'B&5ն(n Tdd'^Z`w_5)kϰ_m-ؤy]>vN{]\3^!s֒TG>~Q5Y)cTm~XfS-ڒVnL*mz5ۊ/1Ұ9 BckghE}7RfdXsԆ|ΰAY_4 ).32{U"p5+\!nD4xv3gbƓ3];OEऽ_vy"wB褥Wt{""vzJyI3oF&אs 4f 6JAlb:\Se C1U{k(bE#`2D,R瞆I.!!x{_[: 1uEk];}UWO)>5@'e&i^e_"sG%eKU;!l՟Ɠ?Bj`uc ˙(*6/3ZЭ kZXʒ/+ܾ4.;}C&Fi\\I]ǽhꢪ߈!9dPuA9y1cvB g"ȧr(Bs(k sTwc7o%էZpw+2iek0bAbKG1[ PxL3~uxDЀzӝ]!_F#'v;zE zG"_g;׸}[¼b' >B32q^"9 O#_i/w'{#6]ԝlw<'PB~i{gG{h Owh2d%D3ѻ~gm+OY9ʯ~2.\flYh阩7M4܊mҌlTjt`Kl-X/lִ̇@4Ƃh@k `ԟEuȗmZD?_[gǪ:M4Xcߌl*T1d ocF:Źc]oH~0@l=ed^z "\˳Mrs揁μTALO)2y۸||: DD,VX&nrKwwP0)X}1P-ȿ8#QS:!p9`B;^t;bȺkx'Lslhh4x ."f糧y.6XB&?Z@T D&SVٴu;lP9b_/jGl˘T F)5|@g@Sz-cں߽j "?hP?w f5Rhu Rhai&aPd?̟tb+@ȶe: 1[MH"mJqNsVi H#%Ӈ|?A,2 ~&}@b6L,rZ啩kgGdf>]:!^h Ϫ:O^?w/pr"qIWoOD~ Ǧiw]k N{>2宯8OAB&E&{q#AiK>{ ܒgV:B&7A,'ˑ& :f}f>:w-m oRcz:oTIcLSZ?0C=g}ahp mOA6u+",u?ME̒ngh}/#_z1hɶhU=/UhBGA "X?!,w~C42Evw-@F(MBʝc,p!6k?P(U|/V! z< =/~1Q;"xmSP^j[ @(aw=k'Dzq*wO!߯4D,h4 ONkx^Eړ1g05Yc/EfL6|[0m.k7 RUcӲ[Ll-sgl@gɒ/LdN0YWcC&5Mp*dR}BK q|OC Iv;"Snkk9ំKeuG"vC>oE~w V 'G,6mxڱG!V)r>njQI.XdĥhXXq=d]8V-u-ri>rhhިq~f3KlEZI]M2XծAX"ߏ|Ir'mC~@>^bГ.hvF_y>j^Ѡoo)vyݲ@i6. ތZt}|rV&˭+1Мu:pcf"71Vhq{1-oz-/ȪRbk=Z/#`&ЪD4!A 3#>&:I-vv]Y1[7Is:>h}x&.XۣUv{] bҭMO d#9/A \-^ace8ݻt{ۻ+tD w6L8Gg~Gw_kGB'1%7y4 EAJϋ4voCM\NƓ"e՘F; p܈|@,m!siצ=3lS6"Gcb´ ?~ Iܖ{tA #5hQ?-6G){{^F2in mxUYN~/?CнLLbE-%f68dR c9S?dRW:"vK<1P Af S)Rlb6ض]Jx|FE r7`Jk.ZJ9l*Ig ='=rv3y{hE @QЌvvDɼA9Ʋf1vL79:w]ۛQ)1cnr]Ludz@SnvE5cDF EK1r1sơ#U'G)0-OUs+E},'C&qȤvF XYsսi ېIh-@ȍ`gBc [+%\͘Zwe4 ܵˑi4.nCզm8B&5[sɻ;_x:k,EZn^%dRz62kK膗|=Ɠ?K'p!,"ܶ3.i1zr)""p,R}sҩ4 do/b"eH LBoWĜ@^M~'DNshbm^fňiNnc$",ƓGHvwx1}Sk18?@t\w}}GfF)6OF_ю _Hy3=u*D,@4Ե rfF ?ifZ5Gꇃs? & B&eB&#b6'2g"&<߸;&dRQk K?C ѢOqzZ\;8Ue~=_^#G?[M[!˭@0dRgmx>Lq.r Oň1]|ŹJh?>*/MGlm`楹'0wصKh1\qz/)M~@lu'E`;XV)#&uZHr_V!3uG!ףcz΢wX?4χX{C `[ϑH9QסȤr:2m_7#1(?A`=`$ $u{4qp { IDATZri9.A&$up홁L @ޞHtvGCffYZķ3yRD Pwy;i듂!ƓnaNB)}|#@mx9_ȤA(80dR/Ic(x1mׅL'#1L7.Cyzn- O#3-SejowYD,2 SO"K[!R8x8dR#%,B4@K%W ׼`J,)w]Y )ȚV/Kt& ЂɁt4y6~1 Ӎ|\Fg%Mqh֝PB!S®ݏ xhҭEJ^!/""J/hĤD>b Vhtn7~4t_b3hEW6ڵkLB %d;BTr{6|ʀ !MKJf^p㗼_^3HG[!D ;.xfI\gPmF}/c[3L[>,!@⃈]Y':wvچ^eh >h)g^&zp1X@{M22jJZdf-@]JWrflo U7m82ؽ 񕣅3e{iO>7~߁|0@g4/T"  f|]勭~;"Ƙv!G#wq]3$2:9)#xGh?Mdtф= DfUmXd!m3 ^x Ƭ& e1ƝEGS2,G,VG}{qĒ|:{p׆BuwCObtϠ7b@_է w&W-ywGLK2n@os~ߵ~LKO&Ȁ ~݉:&Q˒x?Z(4!%bL/D>qUy"WȤofyhtZH LCJVLIY7@6<;mm5l!Fp4xK5\`3331fI.5\j11ƌ3|b1Ƹ_3jK)7gy1(Ƙckf}c4L5Ƽ7}1ymTc>x1fIƘsVs#1Ϲc.m׆>Ƙ1c4ƼezϕR`6R44| 3 )m/KQ(i(zs (l8,\Vayd) ~&+R!Yt@N^hHj}4}]G҇G)hƓ&b;ݗ "GG+T2)ir{3kמY\߈'HmRSxнH.fDɣܳZ]k Ƙm+]1WXkrmx۶bh)Ƙ!q=rݩF>t<`}sphc32:|3RDFv7cL11S;mG~g!$*G @lMJm@"ݐl3MءaРx16 GB4Y~&÷P:d[Lou Z@Z% @YRZWC ț!L;!YhB{ah<Cu;>@hҨE`׾'RI"iƓ'!6Mvmq>v,rQAosTm_X _<6kGa7/t-QP90^F|2/Gp<轖mh0ӑYkwT"׎|}LМZ 5E{N>PVa[U<~~?34vCɓ;cxZ1 3|أk8f$pd+fVVbxZ[ `kEG Z`>?H~:?S-C1Q Y6>mZk^1V[/)&7R`ODJhpRy[,솘!ON"@N9wpm&}CXDOH"5N@G12zkG{dƓ\{ rמ;>HԺܖ|nەV7'{* ) Tpy/w0A煴 ravsZY"St z-@CȤ66~;Pyr#rV ahapvچmi^@ 7_u dP}ڄ|24FʝhScfl-*[iz~-{W5#_ #Ƙ.t1CȿBƘd$Y]> [um3{2-AV7iՖy1lk)_sRbk੏Ɠ}c&EAc=@^}#g" #x%=ч f7(bp6ZF9E=١=w!`S 2{1}HC@ B\@MK"9µm,#2Np~޻x U4D/!k1S][KѠL#y(UĐtw5R5Ęv[gܳ]ȭiJr ;^?wM`D} =&jJ:q&'B&U2kHȤV}b/zyļ}][}2!LF9`[|};Sچ[5=idžbD[И:cO@sK6#Àg\-?!93-6vtAc1(/RuY!XѷɴR%~1ٺ/`*PВ?x3 -ҽ> dbͳ[k[{Y=f2.G,'Ќ[C ֶvq:?ޅN%p ǚ^4_|٫'q&uJ!Z$O$_x3m9Cɋh98}(:o.2]C`;) Xo(+F C,z b^1R$ FJw24Yv>FhbϝmV` D+!#%%@ KY$rGsʽxy.EFwC/Z" ƝexI"'=Ȫu$s'fB&5 ?A?&8:\?70c \ڍiCȤn@76<~^O,Yw]]QCɠCcȴ ^Ծe.#j{㉽Vt}%OU+smxkPEɞHٶ~P2u֣șŝ]7bƥE-%v䝸sW`52a3Y-G _)uH4 D,Ҵ}|ȉpZ"Yʶ½YѪ Z-`S98g}ˑS  Jujz8#GHek8"-6m쾧x|Ѫ.wp{#klE,4/btuCNƻvV̯0Z׎w;(v`92<[+m'E,HȤ"|^D@T ]RJ/,`Tچ'^%fuE! -&<%XTфLUh'wG},;@mV2]}l{b011'SȤJPIus-&1dے'ycIJ珶Y0Mn@sL^<3?={k$H!~A6F QT?A>PwmS^ޗ#p;ҝ{R)鷐Bx{=m5@kqi_ 푲/CyҐ \nL!*uvsE2eD{_(6r_3Im؋^UvD*+658tyeK8W?#خckj_Թ%T֭hIJi=ʳ]3>?V;L͘:&xU5+~.Ra_*HGl%OB@l)_xRt\@7AS׮B&3GyA_%=ib Z.B`d&oJŔwp{#\[yA} 1W!r:EZ3ܹҬB3\[~'#'/u@_q(_)ݳ@i(癗b<2^wZdN][(YL}]wg4b!wy6J6l6|lچi9mW/O_@h+b/FO8mQR3_?.VI_C>BcaNi~SKs״(2%b#'Zp͘7л̞YܩW+yԲ;5`Lv㗘{#pCnPdzXa)#rzEXD,R<'C[[]&\fmYV4 A8"GJhYow4I1O! !)9qMa˵,E Y8f6]w:x{lX'ϬlW֭4e&Y E @9i"=kߋ"̭t6{yz"! z|?G*׷7y_2P_mzm ^]6.)rYɜ2a/G~&z3Q?̠~(M !6htD+ݫjl_ڼ4iI&2rn&sڊEGR\ B& -kJI"C~µ^'-?Pj2Au-5NT/^vE{B̗627F&-HA )M;81"kqe#j 4ʬ6A_g!r9N"81@NW)>shXۣ[IAx5dR~6EC^|¦Jچ_ iez*-,eG=?e@ dRQtq y W; p6ꛛ}Edb6 \Qѫg3䗇Lb;"j̪'uߔ:vfɁrCyn%w6/ vA̵WR. Xߑh;{Sg HmxExEπ3\^_ Fh"Cud AJd*XK? 1Ϥ)אt!:ov?!ȋ\̟K#CqyъYai{6Җ VR͈Rv@o'"==k2ZG IDAT̴C64G'va"E-p[ͬ#; +VeGr,lL+ʢw"ϐy :V{Ve&;Um^8 o{ZwwmΌeyiI`bV'#X!  ʋt)bB@܍{1zƝghĐO/(8MG~mM(ܹB7!pw9&B b:|>@ Ͼֆ&O3{ EZD,K{0ug%6vq|m؆Lj RlYGyuLKs0Ƈ5~ےmLϢ>GIIyV2i|f4XȤ"߿Ѣ(OvS6aȤr+#uCc:E$b#m!*B;XuȤ^X%ug_vVfb;a^ѩl Y&J"iƓcy/F+TMCSb]&"v+e -mK%2>o[r盁L6ɪ%o+sCAd&y1c9RڸsdPt2 śhY3!X5/:r}[l+@,DX R),Xd.OZ8e6,BWE@d&* F~Xrl3m)b{VX">7)=rw vgs81{!3Ё1͵ 1t2F<}d3XH!=ǃzAkPB}䖆xCMA\{X@EF iom4/3kBbB&A}֫S z=0^P'Ӿ lUqL@l-y R| Ķ܈XS3 x Iˑ?8|z 4-_;W;v;EW@LmF B%-"t)Cܵ d/:"J.[~?WG ȹJ?}_qעD%Wd2>g5^3"`" 'wEA_H"?Iި;h}TO\2u=EݗkE c z_-\X[c&Cyz ԧC&uئp>E~#QQƍ@v \Q[KCVa)wL %!Ɠ \Li;!eV&E,3\;??%)U8d{F׸c#4|_#%ZN>=߻ yRv;6 e')##Bީwo@ѵ*؀ ||7vY"Z9h cXX1Y!(nP_!GrZ]IQ7B&5u,gA@dSF9 )HA7$R$_xr^XQs1FP˻G{B순g"0ly9G Et2gX{t1HsbЪ<ˑv)v܊ 7[Esw݄GwLs*ʯ6̝o 9h=Cަ"'/@WuL|2)/M~(BմKUҺ$#D>2轌;6s[B&u1LoktT+siUp Vd+H}'́djXא)P"(}Y3`ش}j(ٚXd}("S7}ɧ̨I"+^h^"_#;SW>OEpy3GLb W|2>A>|Ł־D,s;9p;GI:Ғ%!,C;2_Wύ lPpԗ;jR3Z}nD`{C4nv )ڤľY9L_ 0n3p pg"' 3{4¼}^AfıH!ClE*@Ϲ|x2 A,Z#bž,İp6?L"qQ0d~!3b (PzJUфؾ[g7]s }ܶD,2 j9|r<#,hȵ=䫵;WO!Sh; (NjDEjd32w~VHI'!p<5Gȳx/L~`z4<E..9r۲eYV[>6uY@DA̦C N{OLe E~]}B&6Ճ1B&UM-yu}c/;[])HA=Rbߠ$bϡKSC{/~";#`S /B4H>֗cTF9oV|@Hp p}E!ظ D,2řNpGe4 Ĩ]ELȿ586رY_tzw=Q)Ku'GɁ[ yʏvV4/|WUO_14FEtj9_b | pzA53Gtnxo 'VzO0YG>)64Y2cQ_9o2 rcӋLgܨ.^{uȀpP聄- ZI@B =% Zh%i`0n¸wo|;5ht9oɺѷ]8s-Lޥמ5=_"~ R-%nխ*A+hX"F~ H i,[}Q,LC;*Z?FWzEϝ`!Z3]ēi GJTU*{;Lec,0(L_69 :={,? 8r: m;"empJck&!ĜST`Uʌj&BvoGQ-os\Ebz;GAG;K) (Bux܀ck&'W l+P" Sa dO%bǓ;S$,LCJ*qdEܵvAC~#!up*@${ ZBP<"ɘ,Gd4R*pqHB+mH0d=@j%h["jkg謈9f^HbRw-xIxt[Xk"N}Ƽ셯 v"|k870ٌ¢ C<^Ww` T?kڈAdO?Dh c ^_ R>DAEuGWP޿Z_DvEˉ Ix2łi "fhC4?^nFW!gf-<" zx2 ҮM%bĀ#Eq,""t߾nlÒP-o Fj7ae6iJ O'v(6&D9ޭD~=Qv*∓C,Ǭ]g%=r ?3;=$x:1nW(׽b󆻷Nc/K"`'"NCD bC g#N%bX82iE _ B,j4G ϳnv[Rz %xpw֍=ߧܗ; n t45KS>3t, D[p(ךu-,,~x5E*j/f9dY>"rsm^/5*3{ٕ`m%՛c7CJ*hR3YH<m SI"`tmA~-Pq2_ Ź)Fkx-,,6|X"a`KD 7#Ѧ<m=FV6Цx:ڤ{!bt<-aDHY:}s!%y͖$FOG_M.H]RӞDZ+hF"Y}1 T~cQlA$fgē  .LӶYQfBbD~?io¥gT"vE<>es6Ɠ".S Q cUfkq\bW+"N#^Cs}{iJ%b|ImzGȪi`%Z> N [35}탈j#ϕgo>a؆W-?"зݑMjieQ0eHQZ*s]VʃET!r9jWlIxe <3a`dd h0F#۱\!K0Qr5h3شz׼ߋH=YDx2=\ OG5][||<㞞tI@ߖZt=zEym2c_ ,jS-t?Z^5*1Yk>Q2yaq͛~Bض{ d=>dhK܃l)}Y\= C>:pE5= rt (I 2i-.E6N5F`7pēѷ䱩D%X% pi݋ ~ymqYyUЖV'@)!"7 ;Q,ҟP^D(D*H)GSgr}jUZnjsy  D3u2RYvA* IDATޯۜkR^FEG"Ui`ׯѬ[ Zd9D5߀t{ըHA-.,t2y? ڸaAܸm{7zEV[ sp[K]jJqYy DrQ # ZAThvLɄPʢ,,~؆h#>6Yy"[o Syi6\瀚|KQeȜ)1H7"3O%bWǓK*@A֩Wa9ǥ(d3.u(@)oS{0c-1cO%bOOēmDD^ٟYDJQh`T"v7uHixuF֍.'.ypTM+l^CNȭjXZ^Эi1T3y6&ծpšlbr1Ic_IaW7[ص#upG3OQ ]WvuQ6o*؃Z+z*LoJf<oSKGQ9iic{1+4"N1xdOBg ֦-1}>0hvpgO¢`T"6kз"Eg I+s`}sQ?0oEv3G6JtTMb\z7>_bBysf t *_1+t0=L߈lQo >eVzw0=\ 7Jː-Ԯ'ӻ!r#Z?bD ݷ f #CAfܽ:1ɷ83[֫tHmQkF릈6 Eb=d}YjTdq2֟Q=6;n_9돈F [G쇉R~lWTP٫y#3@}=~q_)-$"B1 eu}O t7WS*ÜwemD*VHq:L3s;) Ea"rǠ_O@ MD$vU[K;^(4 gK)ܮyzr5ی1f>H)9?X^7V}y1@vG:kp<>g0"eYx?ᓮ1=G˓xuaX'#LēQd`M_c2E#%?<O=25EnD.Y 7oq# _D"bEf*lEf>+o^3c,LtQEZRs<.FrqH  qM/:mT"J8dGMݓU4cEvze|<@ o{l_ߣ"3RrH152IBdlƀx2}d<]ȺqY7H֍N6@'j%4S\t-,,6bX"Yn{>Dh'OU p>o.ŖMDe%H:$L_,Qn M9Hވg#R-"dQ3RU!BDmsq2Rv@e";sRBH;gm@$d&\;t\+5J7sp|*ˠPB"T_G!OP< vC Fw㠬S}7mm$@v 9K[>Ư a "e-/1lXCd}"=A!Qh6Ad̽e,T?AJD~6<Fi*B""a่oݑ=)#rB._7l C*tDP@yHls܁"ax/RC`HyhS rx16[ %R"F\5Ye0&kO^kn*߅x2}KikMP>PC}?Nz%bT"` t!*9>}WP{nW )<$H(ix>eO.ش ܥeN(cM`Q~{pGܗvlJoaa*b.EAnCi7 6P*q(E+-Ae@g{dAb"eV =6ӑ 9)M3u/"G#GJWм&읁Ǝt݄6Ñ/]qv<~u~-@͹{yx+LPx)mzơƋQuQ,P*2{ RE0yEtS/D!Ȑk ?-…Gt{KݶehIɜ0h쪋<.Ӽ<Խ|ׇZ* ՄA+=^ ΁%b(R4:䑂5̏E.]akZG6Y4},'* $)w0);dQV]H2@W K.DG^t7NJѨ:#LPh&}&N/"\lYzsJj{uؼA6wk0%c!EH鴐g9yw'(Pu#NflfȒlzdEG&hCB*2dAMT"v)x9͜v7 AK2W!2y" P Yt"zD QׇȚ|-HikD 5LHW"`^`{߲\#۵=bb͂ #9Z޽YE(x2"F%<D>2^[PRw,=iɎd-KlXw!FƀZ1yΒ0 @q25Y7ꎍ'aTKH$!{oT<3).Fg'"R}f!D Et_uCd L6g5O ;i݈h_k&R*Qˈ؍@ QR5J%b HkLC#/Z6;Pb(fn8ZZUhm"V~kx2 *C2 Q#F߈8ݑ@֍z,,,~D+~80RX×*} Mu~2м"<x ψ|Q|QX9fv1}yN\كQY$@ۏD q:"!"hz,B1p!يX0 矂p*!dzD̮'38dFL׈ Sלxo>ܸ`"N&lWlkM@u/}dR"#D (j1p-"R/b ƾ3v6Fק|RDNEh)"fH*ADa޿y,o"!Eb@3~}Ìq/D"K{ ſ\Y Dx2;Cs2ޖ#;:Tnd_û(6bˑBs(-'Dڟ;ncQm/txqhm7eo}Z==ۊaƃ'eMBdek Jē7PSXؼ["2u( /2σYH9 QY-"vH 8BXe^9 ~?pLd:ο쌀:)sa0FIhF^ݨP< RQ`:"g!i*{ƊPn!ꃬ鉍L~uޯE8"djnwOH*OAxe.oE*C7;v*R<#pd{X+A*E r+AjuhSdAjdyzcn1O2e*T"8DX 2FI!"ׂlHnźU-AkCבx2sDp4=7 D)(dhD3 ?!26)cu'3SntΦƈm<8e7/۬4=dz˦ eDX<'6 )ۡB漵Ȧ,G$"JԠD8/9|̧kv2mlx2=$L{5;d$L?.A㜏/~tDH -47!2"pQ^9d˷ٸGaq B1?Hq|#5HNAT{oQ+˷yl0¢aF![ߏ`d F]U(нd=zDZRd\DnAJD9MxCŖ'ē[yf"Bqz?C%v5{6~IM7dz'dzs\/'ۡ-q:ֿ!4D\T}X,߫F )WwRFWub8RʐFkGEz5*zR}ANE]G8haaa)-,<[NTJa!"Y!U&S~nHupi{ (R1( ٜ?lehY";t"wQYO'Edzp*Oo1=HjDl! C3sl@U}xZEDl9R0hz,Q7+֊h͊{a~Gbv"QQ$؈ֳ+5}E*wÑ"6~eyk)(<)]QB"3EqkPККNFkCթr 8B@7yvT'm$Z0]zVjR hv6g}MX/?>GAIi Y̿6ACV 6WB mU(p: BmWbG*m3ž kv=,E]ōKBG'2&D,JĮM%bsyM)c!0YȢuqz@m*?Ow'[tĿ/"N&q21)OdAZm4a9&`w=49ߧީGN苆WtXDLJ?HmITk"A=HT5\dE2bF"R"ujT_t E;~cˡRGœ3[9)^%Z+ @sʙ߃f窹u;ra|g-@dnʦJ!5 D UJ^VTCEqtrz5"1$Lۮ!J2IGeQ  톒#)ЄVw~m:s;#E1yO!Eٌ#q2ñXO D5Lo K%bóP-s R Q,S "b}Aފ." !n}lS93UW6Ǩb *fT"mu,,,,6FKPͫ# ׫h[6:D9mPŠ}*?_,l݄6"&w#*dD-`7d-o^܊bvl_evA 7?deA0?z"y9Oj}-UtrOQ8?f¢]a1 tD>K%bn<>e=l(kqR-Bd8[O8 D%2bhS8C HzD&"9wR}ND_GT"ֶvz7P3= ʎlF;)`s/Zg!-@³ܖ#Um hE Dv B~"5?]Oٝuї@.W:nvq!87/G#Č#}s'^x!FǵXXXa-@Ǔ_!BЂDjEJkU13/ID 栖3{b^@~I(1`S5  B 4 <}ēS%.h @|H2KEO.0 GtZ\ 4Ϸ"OStCɔ"nw1Oǖu D2s9/k[Q(s/"_:xP@>\7$Q_ߠ;y[Y78'8XkBH#"OHZ<~HEh@D5tmtDC-k5},>HqD$dE"R!eD*e}$~\zE<.A$P}h5RmC{"-yW9kXH] ,W#% e"m3 c qZ0ypzt߼)ƠiWlb`H֍.̺<@֍E_YJĦSL[AO#5` EKЦZHH RF"k )\ n xeB(6ٜLV;!؋Rd 5 )+!P*D4֧kDnf"r!}邔w,"ⅨP Bd:O*CrCb37Y]Z`9y߳jV8Y:y˷՚u5B ɴC?ԦPT"3ѦU0_2$75ǭD1bcJQgkEaL%boƓ}?R"r5"hKT"vrG` ̾fRm!(N)Tg.-rH5J:+_jX^JVuD'kح*t7t%Q|[ߑZv2"W^&w"TmD*a e:H=1chBXD>F~t~{5t){3\?M= vU,+wzՙrD vc 3#hm6=yZdv&ˆ>Z;m,gQ,]eJ.G V""E30 oEI {5W .XnT5&bd?&ozߍuG󝌔(+Rۀʨ)g^!RQ| swsאWa^;)i-GRdj] v޸,5G(D¢C`c, "觩Dl Ec(R8bDEF9~L(,JN%bCJM5ܫ(7j{~ԓ"ix";p>8MHx%=jη+R%Zцx=&?BAId6 Ц23 ,U?!JVƓ_ Gq c~DDe Bpyl"P(#G eDlu툤&Ǔ"9dDL?DˑwZy(~S+[zZh wPQa -Q xQ}^5ڦ۔E Q-,,6X"f:pp=lAV Gl{wT9D4سp,GXODX¼5hORX:?"LR&}CreԌم g3vFJL: g8(?ֲ7OZ1(YFA7ϋ+4sS^pY7ک-,,,~\֤w"MO%ba"a*E =W Y{;dSU!: YHWd*^O ~MlsZƳy!6 {ˡke#=#4޺q&d>8rtmJi\Oi+XJRdG"^dcb*PYCP}( "`Zхs9O~:D^h ѵH~3{[{vqI^G oBod?LD*̭hC} `.碍nw# ~bdEPUJ Ixl@D2T Gtt[ 0TGRؠē Y?`)8 \7x2} r}Uo q[VpB@M!M4$q2nQ|L.ɗQEy}fh~4䌖•HoN@dp"`3Ga*}ᩙ,]vY e!nh:bk cCm3" Q엃Wo^t~v ;ZBd땢8> \Jnd`To9(vi1(׈2۾V%He4>uD̾( `R>467PK Pj6Y l|@ tlnˣ$R9{Z螛H}3 З1eM4`[OǬgh{ź`0ڼ ԀR(flȲ1b#)f!EewAoi0$yjEseMNEg.k'" ْ#ڜޫUzwH:|<6P0Fg,CvjCY:eEkzl:dmi;n~Dl"٠w >> W1~P %b9(Ni7Ta_%yDȼހQH1b3H:?(J%b_クՌe!#HT R_V b }&H#kp:i}#df|#b8e6CHqh&glZ"8m[H<ߊ.A-PYrD*jϝMQQ]6iCKMKO3_mŏ Y5RXx2}RqJ%b "Gld)D+ц]4׻bvlښ+,1_( ڿ $@mj2iʤ| +[O )Y"զ b4D5Ͻ& ȣrn&d"NJh@JW9M&׫H xSnEy? ٛ"*XgQ2Ejž޻ʏ5`،E_VGyGLU֍VYXXX|,>?O" ̹h3"Ft C ڰZQ,Omԯ!nyW8ԧg Uh1ow(ͿZ ىǡXgȂW`[YRz4!h}[n+sܙ{޼7c]V6#{nZ8^u%"k PtsCtx[q23~ͳ"<5|n D+E."H E؊m~C8X?7ڼ,<#Nƈ9x:dh (4f,!b"LJrY @<.S< )4ۚy#"5(AG;nk]JɌE?$Tad#k+7ο8Jg;Z:yNOkF {33 +FkXۓ6?}#fn'=O*^>$X@m= T{kKd5Dٖdgz{#eT"֡L0XՙyEZBՍ=kP\(k?U(,OB2^y/R9uA kC_tʐdY."]>ٱ{JYDf 5p,"T[Qc͹1ieYcm0 6F̢=Q-^zy䙈AshTq϶CdejmT"Eje((h7,ݎ0D%<q2^BK,-v=(^bvjAuUV>^3f܉"ÐJr(8%g\sfK(9aRzP,U7dV E<>u=yR蹭+ne"Bkf| QÁ+= )_ˁq~kQ潈nTDC qDؗfV"r<[k[M¢aIvE<m}Q{m<><.sFezqgGnJCO"Jd7C!fu AJYTRP =4± E|(rXi4S!n٧s(;{7yd37 } 'U~>]B&4no.(}UBoBVzAj= i](%еw.3 UnBs¾ Ju13{kjñirtʧ^ms3 \(}-աe%h!$˗4zFZ|-:ϣPEK$=`}|Co {zHvkQ9 UwĿ]GgnT=s-㎣ -s$ɱIPH^Ctįsmaf6%\)W,pz# fTK*CKW_AT'',B;Qj(Sqb!m_c(݋vJ&GȊO>$4${QHh@sϱּx*j{nĸ-3C/QWR=QUKP\עlhv$,`u|w Ƈv{8YM pUOoGtmF3VRhFNT%A=A/JU5| Kч-=}](|}Xȯ]^TITPz1ɹ׏cs!,%A5(xE7Q0OE׫߶Ouh#"Mcu'h[4ӆՕb]FKY=k]h^Q0\37dz듣hч](tFC ՑYB- j4Xte>Q(@ hym7$5G/+.|b!?a/i0Q8gc(h4n" geMm]=#K8'ߊ鲣ChX? _Ro53\Tx]*L]>FOk⊅޾e(d޾K IDATQw)f1O b(<3S܉FKTw/&S3{xro+'ç7GUO P [?v-eB >vfQ06pu ٔrERQ,'KƣBlF@C>Q>uO|݀נЉP[R{% f0ǨrY^j_\P Px:MUǩtMNjʍ ?dx܂- {zc,&2h(%CZ;o:r+h{ec!Z\;W̬v5կvPE zyti|ǽg~M}#Fcvd9,ǝĮq4u QX@|-걻Kb.(tTs8(|"..Gar͝ a([$;3zBocfUvj7?^Ư}*8AsUG˓B4#٘pϾٱ,M~:`Nk?ݑTO eϕhu VA˔?^4i h}0rnYԺi(k?,T K6%TY4׆v.Z4v8[hA (͋cCc)Z7rΤC̦+bfpmh4hߡDV0Uh 6f݊?zM+WEyffqˁCNfh< V;QO\-%v^񯃖 3(p$/4sνssιZ((tz`k y)PEh"L&<.^#+̫e=ٚhuhcEQvT)ʮC=5;{>Enj 5vȊ,ZS@cH̘3ٚxdk[(IEEK*OXQ{7ǎo5-nvXD٫6e:$?ToNvPEQvZT).V5^2ɏ ju#u}]?_Rx},ؔ_=,``_1d(ۆΚTq-Db.P_Wn.$q7K3o'/pahLqS"`V8l.*S bsrĪ5wI/VCU؇;k(;%TkzG7}Xx { A%[vDh{>#>e1ڎ鎢(΍ 1Eٹ/A8<3U@ீf[o^ ""Y,6|6ĀbT-ϺZA¸@ }+=5f>vuLEQvLT)kmf-XS R͏eAZO?e'ja~"2Sr-3掙_:}lK?PYo>_NZf|۫(;*ee _n;.H@,g]A%+XB |)oQ O$1^, ^}Ϝ3?7e,tKԖgnn+{[9x+ TH0?"r`$7OGęG[X|z`50(0qE]^쿥`9% >+Q}WEPפ~}.͛yX>c$G,`H fY1-g~k)t#3Ҫoy}#KrkVRmSEQbk|h]`a9G|_<#V%dDlE!ւYcͶO2hQx?铼)UE 1Eف٫ߒ>-bVJMЦ_DpqZQ?#(fqfw~|x&[7h("(;6ErF,C˓-JnLHG됅B=q Y\5{hK `;n9t][QeELQv`¡`yM[Wg,@]KgX؊X͈xJZ&q-F֓1} 5ɛe p{nn+4ELQv`&[µ_ryoe|'+b_09}U]%b0K\@L2~~md 5V袑F¡`0Քa-@zWY ,BT=A1 _򚢬516\[2x0<_:ʟmOm:5ȸ(P!(?o؎ks ?]ҳcblS%!<ޜ 22I1}ꂯ}{W6ҜfcT/J꺸2W7Y+m.Ghl1ZfuX[ly VQa(?Y_Q:/+g|M9 ֜g~:ĪE^K^*>,qK.Mض_'QTU nY̚xW׿xj7IޔӁSn~? vҧ((q"/K{[g~X}n\_K~&i%;misi'K]tp(lMX+hvz={߷/|ϟAM&[FQEPt2l-poh d&zX79KV7|1L.x^?,+| 1frW֞Le|ML*89 6n(ʮ5(p(nLv[~$nW(҆ZE؎CIn(ΚT3i! ﶦԈ =ádIQeG@]`r%:z|"\((SqZ~,dWEQb |GSnZC"+gp84mREQP!(GΣьhcn(ʎ 1EQqsYHNZVn(ʎΚTeY9~`]{&EQ bl+9ûd7FQeGD*؎{> (숨SEQEI(($T)($ b((IB((JP!(Jv܀)n(JGBLQc;xx(K ;l;nw9 V'-qS gg*~VP0:7s%1:Cf&t^T)F8LKf;nu> .َs` 2Ks߀E k$ks\ QKl}K8\m k7rYh @Fek׶^ĉB[wHlMD\vMrs Nۭ5z˪A)Hb c-Yji](El0`?qcȬ)P0X8 -K2)ZYm> X0p?v\9C܌fЫIskM x ?[5S2s|My_ӿ:y]t{tIv;ESJ8lDAbjJ kN>UzEն~ >CGlup]iY<<DL[lQ:'孀 qy/KؿIlzTQΎ 1@-?vPv=/@+Ys^Gb@'ulb>v^{޶%/ߺ~Fԣ~Ή9+O.u# l;XX%;nr8}T`2wE2F$n+(IG?Nڏ*Yф dUn˶`8~208- Tmπ׆CfH;}U33"K^(P-<·g2fdFl&ÁL|LI@A4p(z GYEI.(~iq(9QXsgS5ajg"VUHlBGONzȋW펤{Xz۴q]n G"ԇ|{GcE{𙭑X}V޸OQ֞ߚ[=:: zFl\h;@ 2`lٗ)|jdהUΞTe3x>Cݐ؎ĹL /n,Z\7х{6 \e%L\ƈW¡ԭ9@uL>3e%se#bg&05~[K[;6I9"ĖSc^[5S]c `x޸>R v . ~ky0l=qqxJ ,Ce 3N/FVPo)( ܿx~iU=w?߯|xe%$K#FԿs|~u}sk`Gρ?m(ʮ ,:Xb6l et&9yS^E'!?" ̦1RذcáGHb8o5qͷZ$P8PGm Yre`$DMF>^(_|U0)#e1 xzDZvbzzѹ_XNGb &ڂ?Fp: ſS" _'#K!ob& np8lI/(M\Bl,QevܽS{qd'devz-yd42!gPYVQ;[YȬEDy,u|$?؎{Z8l0y3?d ,=ɟX 9|Hʇ_*Ǵ{!y8|ğED,@~KoCU pbI|^oY#1 }+َ{&br&W O0_AQiPqD?v;~~:sU#t)"3 :r',=&fݯ۬hi|ј?-՗ULD#=gác~xH77gXp޻J9#%Z~嫗חwFCBl1oh;np#0- >nkOHאً5,C܂-ڎʲZs[".U+w[j<K R’8[2ˊR(">Bނ7Fo ƾ4&y/Q }U$B "JmҔ}}+>k9=s-0{ TzEA֜9>O[?]"@cE> @b"0*@qI5h~V!;}W b><+ԚWk ^D3D\#3p(w* 2ږ:K_VJ[L^$!yP,e?;nBBy*¶LjEkKǖʥV4G{vD.-̌.j!bd¡Ŷ^gF y gb?osP@6a{ p8{ ={%s"||_` CMX,~*ƲlD #ך V*4 y6yזe}LE4ދ{eYuH|Ee݈F}w#FxSW734m?{\~{- y[nyM\ ?rywnuM6;m~׋Z~qm/xJ󅏬;ƃ"tGBC#7{{jru#xvLdp< ~c-*d MPSqKx6S {¡f{"~e;n^_D4EINjd&5gSm<~&dnH9ȃ5Ӷ!fߧ'b "@<@ܗlKA;4sLk\&ЋJum&̸#cMH\޽@_q_ֶM瘪 瞈+9,so॰dk&ySY=vu @jJ̇LL9LIxD Z$v yK[*J.J+*?{7ϲey^|C[<زK<,:!\ye:= m><*˲nC<#7"NYI=4]?Cc=ϋXUhYV ,<ՖeL:3yhӶVCGrq76m\cIJz߯o [ $cq@:`B mǽ4"6@n aA,.K¡GX1*v:t~81 ¡`Im}-"5Fgc"L؎K[ čvi|j,e"@33F95%{iu&pDBfdw2a42 NisNEZMیx?M9>>g_5m?-y#|"pW!)36#yh!ܪvJ`5яLYf^Z N@a]|>0cod4^&Df*JCߚ=q:"t@,QzdYȘb;eY>.G^Rګ02k!_yȃy/7DU.(0w!27C[̶{ۇX MoGD$ǠD>ۡ.M X X , 7B3&J֦}S!V}V)NzYM@^k$ʲ? /j-2eViqyf |Ӳmx栓Kg1G,y)ݲ,|=˲.3E'6ߋۿdܙmME7󼸡"pbukafz7~k {޲= /ͦgZ~^qsZ؎XMR#"`w \Ebde%h0h4jC'¡L< ݉<33 G!*D(< ҫsbbCD8v¡Ƃv ":n0u<+6%~0הHp~ q ku2w!bG3FӏoU[ΛMib,d~9n7$нȔyI `LG\':z n16bwW zX1L[<3qQ1eb.Ԝp8eYHʀVĪv"7^A\?*YAE>hɈ`?'#U| o'O>+RaɘM=- *xsEvmlbCr#Ԇ!bkD#µO @g8d&gDߘ))Q4ۺ"*af $E|l|V_Avj}XG\Ոx0p(Y aP76؀A֜t"YlY,3 ޞ)(+fUiq((-./=XQZ\ޜP&y=T "aC\yȠ ]{vBiq=m=1?(-._cgƲ,o+ył. 9tCss2ji\U=R/o9yu`rWt)bE퍈 Ģ3ϔ| o!oC/Ҷ6c<D}Knsm m.x&Hqwe9G|xs\`AETqe\|[kBqq@z{R {,rr싈Jdu=S ]_|fզ?om*Ӳ(m؎{ޱ|nq͓ PYEɱ &(e%~|YYEI1"wCsy ܯ]Z\p /a#{_w~$X@>Bf=؜=rŞMߏxXoFϲ[r= \e(S6fW5iX^.?5rJA#s?:%'ká7hҜ8tyH, ~mF' \U4'Ej(6@ceejQ0]xĤ7ڑ P}PiϷ,:o7%$>`C9SrN^~[^H޳rӏC\#"&"xD 'yvܣ;q6?`2q֙L02 piy"-.0bAܸ}ʵ"'q-Mk{q$~y}|9jbGiW9vt؎;qAi=:ea%H8!{[4*J`ɑ{OYl?ʯZ4fwGbh#Au;k";L{ .-A^*jڻ?"Z#GSG (~/{EFy'MFZYYo*mabgMXSekG}-bz|}]qy\_!y^џq{8!7F[ /!¬d' 6؎{CAkfWߔlo+W"׈<C๦M W"+-rH%"َ[e*?r/tCǘe{ܰgY/_N39nG>MB,2!DT|D[ U2p)G&Iوl5u2mV *>S3E)>#2x0|>|ӖCl8ߏ#|r IDAT"3DhW &D?L[G Q󹥚צY=rb g13aVSVQ|ou2.DIyoeXс-6j58k1by;7f(+D}V¹)՗b^,⏭]ʯ#9{QٍvOl=|e򝙃}8^}-ˋ:'ێE|0;^g4w[t%//maBHH{(FaX_Lnu]W;;#1-_zuł.%|~mߥYq2l"kNQgHLۈ;ob;n9`?  3,?SF5_Q3;M$2!SD@eg"<ĭXo<.0mJĺR cjs̹k~`u=Y{c="zێ;!OЈdW\1U,3^5ڴ+\Mkն,T+ĺ;{ U9ln4!V&yݐAh9Anq $%lE\6kX@,יsd!tBqy oc9}>b~XNAӟLj/8馽M\`*ZgoJD r/,A팫Ȭ4dngohv<#7J n3蹰5ӷWul_jcd g^Fœex^/B~ ߥzzF3#*J'һaM{QˠWR?@Rat9Ew/xy+oH^)^a+11f{fOܿ`%Cl&F7>ݳK’5_iJܯ ·O ֫I@.545# 3e8b{f⥈U, Y?{&WUϙe7^vC Y \XX)"*\P,(EPHY"" ͥ/$!ȦMپ3s~|ɝ*}gd{99|&Ҍ@7AHGkq,J=q x;Nz.E X H!_|X/!%t矈L zF ${HhA 12!@w. >!%8$7~+w"Ewm*9 x4ty}}7|vgQ@Ă #0G w]p (M++][˸qlnҌg#<1zRlʚszKuM g8гW&F4׾>Gޣ_eoٖ2Pl#eZwc-v6leG`mxg yEU༃<_oI<¡fqDɢU>_]=ei] PmW{x WL\~k ߁w 䃘mZM#6Lu54c0 c]6#e2Ӵ`ٴ~|lM}'X,fSLP_; )&b.֑KCN3g6]6.y8L,\,1XPfy$؉)h{?F`FM`G `dF9#F w?2Ud b6Bi?Cq# [_iq,1!ύ,ˊ?EtFk97TI*W8Ѓdvݣp JxSNW 0d>iV{w0A `!EWDmм$r̔uxBd~čw-25z'w}}YnveUn-;V;סΫj/yIÀ,l fܴoFh3y̜838"_sls$b/’y \D̼61;;u}.s WG;w 25 \tMu_|ޢ=ܵ H#Urm\ҞrR Ƿ ow〲O¼ f6]V~/G>__p3NErF':K.1Þ+,VZbh$LoWݯ2 i|KT2.dFy@8EHrS.XۖcC osޅ`_n־|`@\#laV>ڧ-[;(uç^wmC}m&Od3-8+7mlrb;M)kٽrΫcķR*DN h;}$E`)#_];s k'>z%h! AkW^ +%HuB$KzDy˗)eswyߨ΂Iv pZw0#k}E34\/7PN%1!ys^Ьc2b>z5po/^UD/06O,l=Hn+'e { 0FQ̸^LFsrT-p<\Wx̦?#rq~J^Zؿ2e|y4ҽe0j&i"voĢҬqÞSଃZ4A!KGoRc8;au8D'bn&D6O༥\,4Q!Gc}?ўtQ{Sw"u5vfӌ'l /[; `;fFlOST~=7eP*-ɤ k:Ck`}}qHѝط8--ǩoD ^IK-x@c0c[L?Cק[߈%ރ0(P.#edXذh,8]3 s":Dg b^qߵ#B=)-@y?@،XSLD zc(Aj $r}ݧ׍g&`: rԈĵہLlw, ϫ7Bt6"@?5\v{^V#h.oetnK&U?]wHy^h4FO/'n߻|W:#r"pEx4]P^k>zʘ{V,>{`͉td%  諾k Ko:4̦%7;⤺ƕ۹Oޅc5`~ >cf#t}GcA.mgwڽ1~7"&cZkV'Ykۍ1W C xZ{7K;|@]Mg_&ߑRzq\5##4v8a}*6sa"R>T?1^CH/A lP Z0)qAJYтEbbC`8F q%5)"ֵuKλ6<Of(LĄu"u C`mnQd+EH@ JI[@-B̟7Q-svyB<bؼlgn `+Uv]qcҽFt@W#8?`JdZ#p p̦GJcZyҜweF|'A35̏SΊ11WZ͋k3Jأkfw=~eg_אyV s;L(t?Po(oqey+=bˆBZUp4|ǥ[zOҹ'ӡC6t ~y_01s OvVZX- ׶Vxϥ^xA_+t6VndfðLa^˴hDA5#w6Ī%{>}A=>Uw0Ωh]E@X׌1-Ƙ)e~k폌1aZ1kƘ*=Z1hkcwXk;1uƘY5#OdM`kZWbi)ZcLه5Y;|,s ?XP_[@L;@efӁ2kYd׼At(BZmQ8߮k; 9/sm$;ӄ0o߫L;X{~tnS[X/zR'ELy,mC,6"n}iG:_W86nM3>7ꑙ!90r.g2|쒵)"+,%\5jgtX`A/9up :hԪAþQ1Θ74ZݞM(GA<[S4vdz4<#'ZNDL-MD)H[)eNjM\Ho|Yk׹к~L7Ƙ >ɒ] 'k<37}A9 (ЋWD-pJ,X6JV<\TQu↡H #e:A&0S/{ qgюw ?[\sAGbqyɧx-sp|'[ѻdFqELANvk&>?/Jx\1xB,XWP$p߈EIH>F~vÐPdS2+aŬ@`"v&4NHW']gJn : 4HR^#d1ύ1B 7ahA%0Mr5O;1Fs4|Sǟ!5 9/C q.JPz$  t|aJww7#0|X9ٴɬ}(SzΔ{>^>B3ԍ`% 3n<#gsE ~K PgOvnN\_=+B;{Y6E9WŖhi~y8L7h]+g2 E:#ߎ57+.r)E}&/Bf> #ZJvw~1[age+q6!hWܘ7k}b!z1XkP q]ٳO 2˝췋 7? Rz!VEP9 Z41 Exa1VY= Nz|~]εADMkTЃeKz4wظ c_"Gv1p\XeP5l4MOy~Oa]Mc7b×Z-w L1ۧc7/cf!]-w!"ZKe] 1&,knAڹ]9 ׹oleb`΅km瑂B_u9|$[o6o[dh:8b f3"E/X#j@?LL6CЏ`(bN 3i Q` ph=Rg]vHQs\`\LB#ZB{6hgT8#5e;'_ Brox ) N;7 1T'U(sn^B=xI?x"RU+{zK=8гl{*psm6~kcGj׎كu&dɞyIJ_,\rח{]%+K\?K\׶WAe}db.SmwIODJlTX@|J7,,D#<14k=Nzxg ȏwفn@`&0zGp¹"i3mfhau(D1A* bG;fg@+ (uO|KC}pyPc M?Qny\gl怢2=pA;NH:i@:|(&A(?/=䱓͐Nzz3ޖEG,~ eJJV_?|ag#jP4e?W"5k #`>^|+̦{# jڤOvP"WHU;$ƘІϷbU ŵ'!]ߋ&qyg`whqJ8 & H--JLM7A{]mF{$"v(ǘac6;CD}5(ǵ uG%r6 &kEn<>{|;)GJ-D?F),_DWO=xa<ځy'{ocs7@ipMp d-BؘMϴ7,hA5w=ry*:mwowi*}A5GOD.V?cSkCK^\_:9N =amۤ'gz_JE:mbf` pO\U} ;Zlל'ޟЀC"Ͽ_jՉ;\5jG=|7#,b z't|bcҩ T#YbOY f6xm^il4r/|w>uw1W;-YvJ *k6)f/bi*m48]h1 dUX1r&wgF ش'HNG`!=ea}HdR= /En+pK(B-İ3_Ƹ3qOdvqs2 9̿v׏#dZ#59ތrP}D_uwCG"3$ z>QpA%n!V^.dHYېCiaڽa>` ܽE+ޢ {ذ$.A& | (Rx6*⭎HJѢwAMH,1iLAzCf`H,>w$u5NoOwtJIu≪,ZKŮ@_@^~)>vW8򀆹@]Һx+WιtbۛEӞ⛥3f AƿlAhd¾yhC Ԡw{ ofӌP͘46wLx%'z AjI6dbx¸HiD;H DW \Du"ߦ7Qf H-т\`9n Z#ڍއ2?mHziF@QZɺOAokb9P$.327."C~.v׎sLE~IEqB˃O/M"K p'(nrQO꙽r\a`h1t>?/c0&LOiU(Z<}:2y2)y{ `#)=9nx%zG+|pC}m6XIC}m720VgC݅uȬgbx̦Ѯ׵ do*UbۙMf4p-?vc'ֲ.^3Փ=#Lxݬ/f%_|Z3O׏kxGL-8?ӆBK%Cw¶;3Gj1;6:O܂pd֙zx1I!)bOG "6 0A__e oI̗L滗 W7f=wtҍ@C?S˵?b Տt!pЌH}bW;_gL\]\PRL[v4͔Fҵ5S1#! hD/BˍpNB8F X Ydy|ςf1u 6/S[Wħ7Q]߿4bjo6gl[ 'Fw큰Ec#x7&VdC^GanE Ks&`{mvʎnUtWN&o1n#[칬iL!&sý`M4]NGc3+J|jfӌ'j[4JO@V;x5ӕ.^6o)Fꓓ1l⯕ Y_]MC跘-O-F;o NIBY=2?7Mnk!TLݼ#'}'1 |EX<C}OBA h"y[AoȉtSڛs3֊PWB}t~@!Dx1(ms^ x!0p]<Ha?W9Znl+܀Loޏ͗o@͐Ih8H$FPG (h|\vпs(ls*qR\[9ȗ<|kWv n kQnwu\|W0'5bm1`Bl wwbϮz֬?{`FAb ~Ơ|dmE~p^NF`≯k7"  6?Fv!};l0$OP9';tC}۴3Xf|԰l&݄6}4'~cQ\~?k.P(3~E˘≗[7/Rcm]gq=h$cuD;|[i8a1v?<Gkݯ!bo:kGq[iٮ!wUv,Kh|&R.>sx6e@fbniG{3`,2]ٳ6R%KI/̧(7fK#^Ɨ9q," !02!ŝrov#؍"^y6My92#igoVW~g;HjTY }\ brkCLwXd6^w(b-C zw?r*G7xֺk=@е)// Ȕס3=o?1Ju&= ؋/?A$fogof\fDmnw c(i7w%>uX<[C}B--%އI`-mh#z˸==jʲ!3l͓myl2lo.oՖd%;d.KBE_,: kw 8k!-Gs/? \5׾RpM6Sacj:{ӹ#VFg`k[cL31f1f~1]cڷ#Ƙ*c\٣iH1b 7G"xC}Fgy)HJ) }R 7*<2P"(]ֵ>-)Bl=9F (YX5՟m^iZg i)bF.&0C>G!FtE(ޟ ;|%*CeĸT"2eg.A 9B/Qڐ#V w<7NC&f!# WBdl2/!r)3"Ε0o>'{3GSǢC:+1dܳͳu@$mnt0홶3 DQr= HyMvP_vk9>Jr.kaކe=m#  !:蛉a M8V뻓Ȭ71NT;K"qڮi4]/aPƚƶW[W3l[?lo=(Y't$  D2+KVCj]IWV!Q6ˑŨ)ov1^eXk:d ȹH6׶}EPB>J7!u:RC""-SS0)rJx z8Aw&~|$Jp$y"soq\w 1U@Q,3LI?H~1AF"K\ 5_+TGϐRt:tZ2Ur!QYqv;}>u~nǸu`q(ܲ.L^egK2ϳ0%P`h(Qez|$ vf=@&P&'pu~\{ϻrSd&kOvTن?3]%*Y%O7׮BTGGf-'B=2>{ә)V.[;*~h֙7ZPcXld[US9푖Qo @vd@ ^5~Ԕ BQl$ڠ=q˺}J ҋcZk'ǣ5m(kyDkﭵWe7h)Bxѳ릢Bֱ+J ^gc9fXk'ީ6#6xK~'*bľHߊUHߏo]tR,$HP#yPޯțL!@'[)ͤH)x+"eU2AT+$ZDjB`*\|6NO3<i)snlwVF  Y˩ iT~㈚kkOXWd!=H.VLt8"P'o~e/g9ٍu"9%~V훓O?xJ"WEDRA0d֋Oϡ7Sg?}^F>삀^_#/Xx pcrPɺYk5ӁکƘض' ܦM@M9!)T FWa=RjF6!ho(gB  b0ۀvW>@PEy5#Qy)wn9w/CQUzܝmS}_%D_67͏g5 ]_X=#|urs_R,\3=1n\E`'Pp>ر\6Z'O&alw: )eR JFy>}5iNVw`jWXfA|.lDg!P|>b3?O-PrnTkwO7_] Wn|/D)shm(YmB"clfXtkÏ[?bJ׃̦ÁV|8fLlj%d, ۲^f,"P_NDu5v}h~;cIir?Ƙ[9/amq݉6ZۑdCEԏ133mg 8`Ykmk0`7c7"0-6 Ƙ[;3y@-oq z/v~,|>O؍/x:)uTwz/E/dʉYl*_h3K ZK Iy:Vk eXh4J#PĠneX! ׏ XSeh.(BI&ݘ<3w~GK/S؜Yح;p y] "Vݷs(v8n^&!p7}lS),|7KC #@!`ºfP0~z#)yt";$E1Ep-Cl-B)G4~XnA_CXYwENU0'J5a%w_[4]3;wϿsUW\~UW(ضdfӌb70k1pT84t&ڄ*3iF)i^zJ[ޖ܊M3Jj[]ʌ\[ u5o1/ڧkU-ko^o6ںK;3[Z{cDdI/#3 asw"ځG{s$rhB^l(3[m>A@B& 7ykh3`3W!03]$4B̸z6A3o`w__i h<0t}>\/ &e닏Opucm7Rd%E`a ):Ƣ]wJ/=F!BPު\{P ׇUc쎀TڱyD\٬E;+}ʠ͡%} kkܳi&(==An<Xwy%z%}uPveHū i3:0fSBTeKV񯺚5\|;""e9>LQE ~ۺ`fӌU]:=o=(vu5ϡ~>W[9'%oK5ƔcQ 'q1SǶX@Ƚc{*0a)6DUcL}?w[m}ZS߁{, {7 b?g:b)f#&Ad)Y=s<Ǔ2d}#@gIn0"bއBRNK-l.fЂ~TB 8}), X{ n䈛t_EnNDs@ke;ai oϡ C̏(o/sySX,R?!H%0{fPkLvv#(D*Ǻ>~Kg6)4n@=>ٺ6k|^ IDATS 鷿d<:fS:.:gtJypa,0u5 #OX4댥xbm\wVjn>yoWF:Tk&L^>gֈ&9aueoc(}6k1/"7W>B-Xo7"pၓ4 m:3(]9%7yPSXw^{k#F OgdzGy&xC`K^ o{SO(ѝS,D,gL!~<r83A|(w 2.@E s~ ZŮݩn,z7! Ķ]ӊ=n:i'ӽOO(@)l7)v,2dRP$_Ƅ¬'01^8ӈ}~%虞|Ş~m_@y̶*`yC}铷X<n}a? //_5t5<ZzOڷ1w 3fXj1(r8i1تe֚E`ok9D}YckU})C&1C9 @|Ŗ"?"o^<0 ! /wfd҄9d.u$"x3] @c8 Ǜ+] j dvC@ =%9A`J3՗sw$rZ@3ȗź^~dy;ԯ'F~5/)K͛qcރzгК[)5jу@~ݼt`<@P@>KІP_{mo9y3bCXG`B#Bࡗ$Z8G3J%#mϲdˈI!|i) \Z94$.=tbׯ>U͐tZ@nȟdٍBe:˳(a bB ;@i}i'zQ_,=(Y7Q `3Wyv\|&0Zwҝ[l  @bFH! ypU)vZo7ʷPbo}Y֢e ]X<ں=,' 6xMtNqUŃ]F?|q;e'iji|]OF2{l,od ۹#5&OO1T)ȴ bW@OoNy;Afu%J#{$oFJ*sBl)BSb hVެ;EΕ5 A|$|bDh@Fg6>=)j)bfDk}U<Ɗ=s<-D~Uebd܉xs3聙ELWKrVE> g !ZA"G} ʴ8vIoR7]b~īX+b&!0812w `41ICybk'P/2=؞!(wˈ)ۍ %| #fh?Bj 5IR|gmkxcK-:A^1z|Pczxexd߈:&AP{PAng!S0xv[H,*'-$OrY['k H7~(mO䃑+ >P_!U`X<ڞj3  4KV=d !2  2;]MS_FoH1o2}fQ A_~n}(g/Ff!yqSh @ W\_=l0:ʍ1u!V ݵ l-CMMwmkZ7us}z>qq boG"P4ݲfwA (^ޥ#]W0,!APFͳ/YG{w";Yͫ"$ݼf[KALiE-m+"/lUygC}mN$O䒱G ߻ GўT>! "kP_6O 3I^yܥ>>/ȇi 5x"P_w1b EA6VΤ< DIHur.G+b+^@ +BTT@M>yWۧs:Xӳ5 #N/1"l#:xSu;։YܿQdP0];EDL 56X<Z/"ݡp~慹?W4B%|E_ڃ ?.Y1MS 7yfA{b'6.Hyt6#._D< XwP׏Qi)͑S >%_Jat\]?ˑa: ?Doy1BށBӈ+# ߱(-Eknns}IV|)ap)փW_=yI!-ϞzpꟋ7)ƵSf]kB{SQrŃ/ K^Kk329yĻ-rgs^,8.!3̆CWZwmOvXgܵkݽǻXyQ>GYAMg/Eؾ@a=P_#b!O`j᪆c^ b'yg\?^G K7t};ȝcrk0C?>[g `,HD9s H8g\y rߘ[Bu>mId܄"pm^9@ ~iG%섣~fNoY$OރKU?^_3;+ݖ{6ؘ."64A $4R!udB !h܄"``&{ǻc| 6AyH:}9kw5%6.8ꪚ~4Co0,=v{ic(kp O69QXmB@ }yAyDx9(\9J*BcaX~ATv¶'swZn`D7-!'np,,fǵ Z㬠=$(Iq  vBg6&83VpQ#{8G*pla<7@ak~${0>z>Xi;Qޘ( y4 ]b]vvgadmizaD0ιыYILZQAn5eYߨmH5䨔{}9CQgr,I='V=%CNi3Aa&4]0X22(iv4;0smy:kȊd}sz6v;[im*{,!_$s2_G@߀,$:Ui0)s!l1 –#G׍D>R͒þ"I 7&-g(5aMP+ۋ"V0o0'3 efyY}weayY8_1₼my}HNFCqC9yP#&g` o>DʰDsuHH,:Md%m|1J݄a'.!ydb^X}+ַ(]t Gen2sc ݷDo~Itoۆ0qzT~.z]t` LAaީQ< <ӸGbQߠYnƖ!/<`x90qzV6rJpĐ ǎ =';@Ϯm@~@O9 9@JdIι?\A{ lچQ| ?{wjRe(?ځFǚkmHF` dʟId0R( !J 84_IteSرw]e)-$ñ^@,kߖ_Idu#eT hcуtպP L)_@163_B[K'fr#&a5DDH#@0aN8^s2;p"0w Ȯ0g/d}DC- I, Z5vfAOzp*tn%|S~69KηjDخw D@NBߡTC[ϐe w2^|]7v7e ڞm Bw₶b`wʆu,[9j͈ɴ;GCcK5@~]U;il>诫j~-?A{}ߌeZzEo:bAb>S[ֲP}GmCOu]ې5bL>x{;d]ܣE2EQY.Lc'b&-;Ti[IJcϬсIL,7 2jc6+}M Y |{C>J_ û0"26f* T6<6߁axB(؁XPe}DMz:X 2H"'cHa,JLˋug3гfI4}b36G6"i`֫\ciMD`+[$'Lw&l@>`AS}= چW{Ė<89 䱱mBb~s^NƖ꿘 cKC;䴫QjC(30bԴA@Nw 6+"xV wZ~N; 7 鉚]Ƅl Q' OS.QZ!pJt~m >Ҵ?뙸 &w+ubkced̓ p5U4 wݎKPGCkק92~؟ O֏# bbʭ-czC\#h;Si]\b!o/3ِQ6@yW `XuKS}چe{x2d{5ƖSsaK^xDF ]K4w9-U5U5Ɩ꯳c8-uUd W3bK.ޯsyseιr< z~\92 { ,So-h4,D#n"('90XFl2e~Q(Gh(Q>/!ZUMC)aLyI¸ohQ  L/r(:Dy=ݴe7{G6yk5sXcI_{9f!gFoy_CAv &B׾ w0hZv²m.]_U=^{H$9\ÎF75Tg`}JcKu>@cK-syxO޿{{~~ a\ }~~1{t8-Xx )H(oȹWʑf:Ca-snE/oA*F#C2!ٺD' -D`͝9LO0M.KބnlF/iGM4g_ л%S>c|z"(k/&&RkĄ Qm4Q?L вo؇[ok4 9lXo} kQc:"?/\2>bB{0|2r2 ~?lj@ϒ{2 IDAT@% Z2f,D=|*qZI$jmA>!5OOg"rj.;`ASiclϵw$sHkȺ/G!NKȲچԈ09a /euU>Vyy]3rɑCVbȜBƖKWޅl.JU>D2:0QgrĺaЬB8I @dS,fM-|73jlVْ!h{@ Ly|D%|+fEh,AܤEt*gbvΑ ;ك|خVbpku!3U cp*dJ 9 AW恫/ܒ>us\W<(1h:Ώ3mn6O C.9O8o:*n;iĖ! cgU7st fpmB95A2  f\eۚv!&bLֲưD}9~[2oEzX>%w< [ޣĪP>[TQ0([a,M:rLF(f&%OZ:O$-s4+?d'xq 8%˹tQ~nwA: Qa\J5- jFy4w%z ñl$ samAa&bsǵ*wMל%o q1H(v 3,|vpoR4yKh{T>_,yz8|jy.κq޲;8|+W.yn136 =Al~G"ԅ& k\3JrDb{~GoϔA`/Ϫڬ-)1 rm,D#9Os,,P#&[iq*s 2ClU->a]FTpC J1|@"ɭ,_xcۄʰM +QQ\6(nF{Oy|X=~s'z? 'bNs-{D ` sb}gg,: b~wk[d4b"^f4?@Z9 n ?cY &siamH[:^5LۏFT]<!91ׯ}#z56T&wOU5_sX\uqֽQx&t:߹9$Qv21[N9p=⍗ml~9JN'7 }ˑRgJ>9Ha b/~ "79&vET7c9>MGb;p0`c16I &RYe09 bMDbzeffUV=7O GL9;r9a?5i윦sLB,ڋ@4&7"pPI?}CNa7 +NGo(|l,@܁B_|'^*I8| [iVn[Gw(䘃@ 1蛏,hrl3*m79YI-/0oGXSaQh7aԅˀ~F#0gB- amHfPUppOaSWd ZtûQ݊/!z :-qچTf<{QlC/k~g|ˍ-ՇӶvGп>6v3SG?} [歍-!ɠt^ny=ǣg_>&Oe$-Mn7h]=HdNv~V7@m+Ec0X伻?6@rQr<x%x}naJ~^f"F9aƄ\ PYBU&QhdzV%g!~1{fQzDB@ú#v&!Q ޚQO °Z?*ZC$#HѪЇ=!&97@\XӍ/_ށta+] H1{Ɍ@I!F-ڄlEzvR7r!+<#2QjIvK剭F!a?jp@=8@*^u P.cmCco `c𺯝v97'o<ݙo)w-2Hfw0]_W|QcKU5g܏QC_)ݒU8"D/GB/L{ k=&0+ڥ~r]κүCr}z ;v{]YmC*s2aD&BzHY$z*, ,N2B" 1zvRW-AdĪ=szmHI;o8°!a|Kx0(d{zXZ8lU9p^m(y<ӊp~B"HGZt:ةA®aLס;DBˡ0'_ cU)ͷ iXG^X6oC r#.f(Baܷ |cDq. ~:Qw!X:g`%D&Cw 1ܹya_B"+Xźz~IgK&"֕7ho3 r [֦MY];+l }JV֬30\ֵ{&S(;50mcCم[N9)zt V'=C 0kQ>:bcxcrS.t/ e{#چT>T_rl[~NK>99\quUͫw{y$;VPېG}f4GƯi-@ohteGאϗ[sP2p1bkRgkvtva;27|{qu49窀6$㜻> |{C9KT_4y/ zKhܷP |9ݥor#~:h݃.ttsBk"6 ])@@rk,A9 2`& VWL"@I@`,Q#:,31XOL߇(c`J&z0m3c}y=Bde "`\ -c ?>#fQ07ؠzl!\1%߆@$b~.pn{>Df%bƄ}@"6ĸ~X`ϰE'}qzjdvD}Xd7ri݃@abfV;݋~#|"yya̿C%ļiy1s]'헢]5~U aw`q-*\ϡ״ek,Qdt#R}Ԟfι珻 ^N ˋG(P?sf1?G;:* |{z>{~)5`9s/c{MkM5#rrVc ~.݄ ,Bz8>ƀ YEB@O_I&XNRvjM QAvgJ5^m- ?F+z"V9~O!2(sh 4PiN.4b߬™[@H~0nTŷ?PcN@W;*8h w9F UGB:ρ.V.zhb (9p܁rz(,p:XaBFՍ[c'l<;f{' ;a.Tu>-[zg B>!QM5;ٞGm:W/O@ ء~9](V`]cKuN d}['U5_W:` g {/t>ݟA{zYw5yO-e!xbCAϒGwzr+dmmg8@ϤιS4ps#jweR'M5cچϚk~ T_[ې1s/6nGmG "qvߍ Zk(Znc(E0l acer4ݤrYrIM gUO*\-="dEy7 ")CocR0b䈏c4:>oAq_a#FkT8w;m#0D8ؾAv JOxܬʄ}-r\3]2SM!S?!r*YasPlWgO3oMkN+Ca;xLdbF,3¸f}X4چԊ'I VZi`ߩwmq=LдaVW[?f պO~q7Zuemjlf$S޻>:tx˳<(mHt+~ުXzi]u~rz>}4|69w~z6>Ӷ^wΕE8FKs `>CzwRxˀXS}ztcdB5<80Sz3ܜ xG+󾸼qh[A]GTCUkԇcdpD W֚щm*7'79}y<gcA;1nfp^7{ׄ9}6,i,QRn~`᷹5TB9@2\ހrjAմpj~MFu[< (Xg-Z.Ci>ot.ޟl+d܍[mݣCJV%놕/}nc!Gt5ʶc(R`G"0Y-aoOP23;msI ʷ=nuY͗_;>?uΕu.޿Aɦچ y9u TzICNl2!!Df*7߉k)1_+;Yd$@Z{ *Q(ξ u|gä.& g Q*1.h3vSQGWySy Omg"i ?>9XZ#L#J{ aNKLh_݋K苟p?cLidgKxzC01ĢW桰l{"в0"V]#[a$ˠz_] E?rr?rv8.GOvO U%ݛʤ^)q*̦| %Cf?bCwoԢ_9pg6N+ [潿=^w;;-[^v1Y*ëyA7;kѮr+h֪mH؉)(WQӾyţ-*$ד 1^hNBN֘'ULGIGeKt0z #`P@@(m*Ox|d|d .b_ydcRS|gyjRF\p j,.V A!y( , E p,W\c~Líy bZPnǕ IDATlN߲֧cy~a3a  QYgWvÆRq%H{@}r i Sc JA!|67a/{a?2kG,GG[mC9e邢1^d;> ɿ 誫jR݋^NvAv&%tq$2 @~n/SF/ܾ!*sö/okYцΞ'B=rH?ߐ[1A{{nb ,T_C0:GDeK((K."V9vllJm@`f,bG}@LnD+F9zs΋ք@C`C(Љ²!=c,QrJOE(|aV0)b^_E6b1"L+{#:q¡0KW<.߂1aL.%"!0Xʼn}]HW*C,I&JF }a_!p.G |%6x1s(zy=QZ[*5"v1J[ ;oB0W"=\}.2$aq bnD@~fy KLq|F~eS}'_47aYv.З!-էU5D~cXkk[p~igʶdMo}I6h:ضh/ۀX4n?4״"͌Ϭ/I3 YC=:@i%Q7lrjF6a!ΕDPz39JbYOL;FؔP3l#?t{5A1R, XʎaDqY g BX>RboDb `J@7m9t ՍF O3;ڍ|%)\M(=w &pf_@6@b*^( u?VҚY>s/̯G@,b- :5t6AZrr xDžsA$dׯ73-@ms|-ټ9.ӗXjN5TOj [GC@@? 5Ti4TOޣln(̹OO̬ey"Fz; E=D#_>#&"2(;hoK-@,|)T_wq Wo^X4}XUWsT_mHAN +˻rșADQUA,Xai@k 4 9 r.|U>\rrJ84G;!@8X}IBǟAy%A tb^s~Zò1D_kF8a#g m.Kø ǰ<g"vb^F!@¦ggC`7j8 ! 1JD(tY:dEvuXa,?B@+wc(g}ĶL) fkdvBGp|H>A1 A}<;0ƻ#.ky$nKw mqB6{mŨ\W\ -C/M%o.&5TX[P&^ۖW.V|8cC)J,% BfL\F!pcr)mK|ߖ={lARkIT˝0QPR#ZjR?c܆2H.c =/Ee#JH[ Qt-wbr9~!PrH0Ư#pEP߮_*uw5׬%cs,{U5R}? ?،O6 =_4yG vC]wEe|ErK'3&Y?\zyJsYR&C$%G$hYGA{[b0I9O6ލ9 F~'< zy5u@Ng/b(eb8E# .MG~a<$:`AV >CXt0) =BbY\LVۈIe1㉕"1L ZQaexLn{g@`vTCl);5[bFP' GӚp6:Rb%L^+OP0W]buUͻjq޻!'sszd23ˏ[QzQVZMp@K&p'm>iCNۊt L/*|woyll9-?exd{~0lЀ⨩fO[Qf`ymCjx(!t?E(gf b PHo1`'*hCN},"c(wZʬm:" t&j9Qpr:,"6[۸0/ntSXk~& 1LE0n_@_1_TX}bȆ# e-wC#< 3(s 6$rhO$)sλFD@DunL߉M-l~hWWBydĜt!][qχ1)Zl-mH6\ېUN&\+P͍^0va8Ȯs|pDpN#^}M=}L"e 9'\!U \]ېΟ ڠQmHMYQ5}E]UMwxo{{7;{*]1 GT,_^Z6nԐ\:dl:4?_W܄h PjG,9t@:pqn0#Fz?oBtMߝs_UWUO]k>sιswH+ɡ=,wHm'pI֧ yWGKݩ$pNmCI<9 L,@5'} 3?3ȉEL)pDp*o X"0P6'7IMKympE40g8(.8j ֙Bkq$PĐkwwirM Lu JCgḿA l+PH7tns[/FIu8TmCg[?mB3Qlbp}C"[MdP8?̻@@6e!<&"hιQ?*P}l EG?7# < r`؊}z'wl^1#KL-sl *mC{4]1|\sendj_I2 (g-E@&i9i9(b73An@Q)ar:¾M"!p Ġ<ضpA_p`KvT f9eӂL3@r +3y3ü[SK/ZUJ"sQ(9GdpI㉂LJmQ&\Wu:}:#8 Vɻ +#5_J?1|Pω)jR˛k4t 7hچZ簪u΄[LW7lu%S|EuU+6>>'~tvuVZqlNd3L|iݷ3[[[')}~#ivB*6k:hfAwo>_T{si ιY ^>9mD6L ⽿ `O JA\Ћ97_bjG`x{מ=vYS}NmC1btCGΜ>v+ -|;Uִ9)"jMG%-},a=3xd2[bɉo&Ff1օu gKU~ƹ!)G`p8T_JmC'!/  iCg*CG8$ҬG%rI ٧W *$w)zhX}tF# (:b!br\XO5<^ېUlfۗ'~ ג0%a[5 FmEVK8Yan,bA6wzpض꠽ kw!借t''p{-lht&w s~R6QuUllE/.놕.)ҧss|HWtrAGmm T_6tOo7Mh;DvgUsg>'{ι3mc~ι#_/8~rd+8{97 ~9ι(pFE~ !ڲk׀^\kCYIe*XT_swmC*]^;!F9X -C7K|rrV䈭ݑ i>M = !2:E9Esɚ{> h@ܴS^nTnUjBNښC[QH#zQ{րu5pFS}͢چT1nVW!U¶!5 1r@Mb; -ў[FՇ>L$C`jb{3(dCfg-نzY  󿐳!b.my!%V-:M_Ǹ0 u$Q`OBXhx b趡SacXuLxx0Ƿe_=hu,`ߓ2 R}V`vuMRfoË>29pJu[molu=;EEg͕D :{o7痢LQmR/`x@R(L=5}5`v>.97cι#6{s9?{ :6.s}\5z Zuqa|17'{gfO:i6t Uېz2RTj@_r`#9ȡG6Gj)ҕg+#\~' K G4=@$VKЗSGo<򇈱ք܅º1eJz ڃ4F"0c`֣amH]}QF0?]LmCDآ2̭#&_doyveb@5g"0zsn #Hg_X |fYmC{Xan 02v*BQ8(a{WI /"fĦ "7*7/_PgmM52!5}BLߠ8'LFm-'>S\gό\pi[;FWo6җqafؕح@ϲ7k P}O]]=|9~w[x#چT5+\mHDlچPY:9qSDr9~s:;{@9q&c 09w"߇X2bS"9ԡ(DWAV_0VVU8e 09œk68Ff̓Ps ݊ފ +i" 7qIX֊[椰~(Ժ%j@eA8kd\3cnF6Vp]օ}7Gq#bEX L٣=v{cE/U.@ pgs8{ӳ:ndaVې HS}Jvm,?*`pĚŵru w,Opwx#_yɦ2H8%cV(#)-KWs:ho̬' g`b1f|}X1Y9!c)ft]X{P4ιLp%sy(A~{Yz) ~$R+2OmCHxG`Zۘ^(Q9[:lk]ۈȱYFS <9R0C"bގþ@lK+sUX_8F>1HGpyu#pR֭)ļnLӅKuT~HmCF `d>Јf#eψƟAyi#Pxο'l C{?d2~􄦆9< Mp%JF|ˉX~k % W VW3QK?bjg1B􆖋X0DžkCE TJC]Xwͮ>A{uqqc-7$z7^Եui -/nM;G{2νDRAS}fŇvܞs#&'U]yι sn"3BNȿ|]HBq_xz99 ӯy:{'WUfRR6AB"dEA@XWk`? -BBM6yNBLak_{vBR\yT8RgH b^1oE0"-x{.Bb@=F6DlAd1!Դ"0qvR"}"1Q# b̶FLc@(t:W}iZgm'HYhmhbeDQ&ٽ@uHYD6 ?@jcKW6Sќ7# 8~= c1g΢fLD Aߝ@YWbrwf-Mtux/t}'ӯڼud D]i[s'-?Hx/ 11kc!m囗;M:>>'9?,_& IDATI(-#@m,Č|E)DZ8و 2N!1pRc2?ڹDjg;m&C@3Z30kR -@``2 ["3sj* ](y~ y#oH]v>laDBl`,i! +:k oy&o] 7+[馬r6PS`݂TF*܉ jcKϣu%σ(|+Rz[e}9h9Bú*#V"aD79;N~R<+{ew%UTYXZU?эeuT}eU<흃{25-YsR2vb<7>teGW͉ ?7}Y(/G/%tryOGMϏQm Dޮ@d`B׻ZK`@H*ĬLv7Ǐy[>oR?f)`#0ӆ;\q;!Ud47XKlĂ8"ZbX])9tPȸ1j:hʥ]]ݽz7:}뻉L[琖[ƗsH;ZΣ_uv39dAS}s26t8ΏOH\Iq#IerRܬT&wN?6HL+M7O0{TEҋlȳ\ "CDjq;zZz-ꔖEe-& ԑm02A<߁^<Q@zd{i۹{]ہ@Lj:|نY_{oov"8x_S6_He- C+T@]az4ރOl8lD)Z;F^//H-鐱{OYGKݴEϺs֎axZtQ :MGVhSlVb%Nw$Avg#(_CaVS6f[! >Ư1x ۈ£Jy;T1E)"waO\߷e,oy _sz:ub,ߓjUrO._gGS<3Ah/wao}Ē=âeeR\b}G`")M fɾT&w$xN6$m]5GvTC` T5 D! `Ē %J쉼#P٬];/s]Іs'tJk x)k*坋1(YF`nm!U?#,XzAw#d$6m6n *#A~K#?5z/{݋@ ̞@#DWDj;Q؄yq*O$cvanކ8'#y3?xO=.[QZA -]MwbҞA*}̮d,ALKQ)xƉ⿅7dzՌFQ Js3F ^0~c<l>{\y&Jdp50:?y@sX{i{SE)^&{Z59 ɦ?LeroϦ> B E* -1dAz+OA5(6f9G@0p֢ 8jbd]y%OcѦxDU}Ϧw6WWZ]F䕹@P"C{Qf{C IH1R 9 [}"pԊSڐg6bUy^M'/Oer_> ^εڴd@L]G+$ (e}Mصε_.d;׊әڝ9BqVw{;3@[jלLnm*iXy[ȚI=:cOΫ /?M'[yx$rt"8ϳ=gQgu:ϝn=ڪR ~ x{6<U-mnxs/x_8΢ǣg2$~#z } @jm3)@-JQɞVM.E7 f{uXЯaȻm\'VB1OcFym& ځ%efǞ62@`BiyWϠo{ZNW9oAmNKZ PՉjQS+98@G%t ,Ch^a PTx vGH}}>h_i%N칰bD߉@g#G3+ \ccLdC1AAi{K7ڊT-DT%A{}O9;,=L_|xҺU%m~Z̐B˦-҅l: ^鳲GxuDW, t8@@1d'f U1c!NŢ?l:r= x Q^ctABڈk %:~J\⥢5({39T&,NȰzGO ƓV6Hu vD r%P(1#5`,]rN|殬&2l Umc[@("wCBn]7 V?F*YhimlDvAB!sJes:.׏pڦue%/bk Aߓg:\#?[\rܑ8<pl[_9f>9W`ko=1SE*0b ؆"!D]B l M@KfO C:mDeӛ@W`2Ā !>0VZ^qVdC- =bBA֖>hʟaG -̭v ~lVZq攪BEӈ={C#l|A۱ch@#km|:쭷[(5a A l q36_v}s\ɓ/ѹdST&71cH=8 cT{g ~k;/ܷ6"g0}6T4o7E)Yo0~gJz{ʁ۹n,nDȬy3bhX"HQ=BMWOerǢ;ѻ(;"MDĻlK97Z e~'28 89w%zy+[,e@^>o޿+H{ђmA'{e&T&$Eq@旈UY 4fթLnDjQ(lA b@ q|;JxjxaSRgshC)v[{D^ɀSfF~`؜;dm@CZhO9`<x\dϳ%Kq.n4ZClbJDyC[=%|!- C $Jh6#6BV%(jƵpGZ=X>_gGݘ#Oi{ hqZO\gEl{n9Z^ALo2(ZS *o0/ Xגl:?oQAIertgOcwd9[g͛oFtp*Ck/_ ^(\Gj3Kg͛Sk9!2hXs6~yq5/?;9 ι9i<^ggr9WateǁX?0}'+%6{X\ٝ5S9bX~6i|,A/z2&%R;힭 1[VgQ:PRE$DkV"u[,0bsB }zxs<5cRkk bCɭv"֭ʜR{<"f[^B>< \uҰnzD,SR;iE@"+VEswbHF'؝c^ѹ61}}k7%FֈTQ`V߿{do*;=yK:os!ވ6 xHD߶9Xϡg2 9kcbS6uN>>M'9}g͛л0 ,D|lTDGM<`m꿢aѬy3B/͜>gwT?ذ)c}hq y2ns7Z?viW97@l7dbtn鍎:mB_F,wxŖrb͔'\qRǝ؅r>F!u!h< k =R\ hCo@ ڷis98ݹ> gt2z;Ҿ"8NF6y;̠|e5@%d𿽭[beM܄6|6\j',uUc{?h Ǣ@O@^+ڨ8z (H0D`(x?Ӯ{KOxCٰX|8"֪Y`wvAPEvr1"W/o 3nQVC/ߣJ`u`HZXiRn-Q"&A*o9z7yRm%K;(Vd A|BxP"ř7QHEx~^zl6 {Jg5؃"W⏽@-Yd< l:@*KetegGOs6kތ :o<ԋoO${Ɯ s>%5o_7sm,-' HFva駚^.u΅o33TpމvҞ`We7eobki^1x)#Y[euUl BlL92> nCrmkЦ& roAhm]荕/p`ϳ9yAd >hF lȎ(Ap @jOd_Te}a?OJ筈A?=|񛉒Q'u:oBiz n+s, ޼ CZVwe5c;Ier#l: N><8o((=ĶT&Wl:zOg7eæ×?`Ҩ+Vliri&ზR̜>q2{;?s]D6 лι[9cy>^J>{i/I٢l'K :z+l::mO1bCB궟"^ x3bEuHgSK)hL݅@"Uaʿ7/$!%0ikzrE^)6 @.dՋ_zhڈ CfUqMvgCj4ЊԼW"08kwo#c۱vnkPG m>N5lC6o' n@`u=Cqj_M'oHero9V?3t,@@dSX?!9*8 }EBb}-V`k ^7`ƮSF$xl:yĝv}h%MzqhK*;}4"Nް۳;2kތ8duSͭ?y>tڬo79w3{˂{Ps6`/}dfIJT&wI])P,@@+~ Mervl2{ PoQJ!  ǣMjns!pA+E%=WP#HR- ^ M:4]#'amp1Q`[Tj$b-H9VieA:F! {"Uc3%?@ekc( @{b B=l&XGnm<]nu_' w]-ɭ_~#&ξ|eH Xke(6rT"`mu'}h}B۰,D {!+39],y3EJ v~' v'Xk] 6\Vrz1bLn}6\r gɻS(ptBu, D@͈mm-@@c&3m8(W5Q;9mHU6NT6FԎf*8uJO ! Pb* ^W V(l|6bcEu)~=0~WD^0%0|وm7ha!I* B^84!vE nDs ; So~E6SoAuؼ>Gpm¢ Mڶ"j4?k 8}|@][h.IuQ3(c6V!d#@*l̦˥5^b]7lܓm+JQR=){##61$ @z2Z@>Z&nBIDJ=DED]"Vc$&yހ] mZِ0z?DHWHo IDAT殓(mR$EޝuDC"Vr7¶@gxүޏٵ#"Q# 2G}wĺ bsmlE [s>?)@1y}9 /& I@ +0t2D(5 :Ѓl W]d_uYZ{ٽ{K3P&7#MPVEIQR&ezX6Ǜ|CqyE6_}T&w.VBvFv8ڴ"[ iAFW@dQ~1TX"É C@#"tDh1oeCFBn"IڤkTEގ A d_s ӄTzp !j7Rzơ[BmٍG'a*|u͇ /t4l* N \Q m 3b]E=DnB1҂cV_Q0Ë:l6rD,1 9z 6v#6v4mehuOE6+7lBf{CԞiQQRzXz|cC>NOer`+ނ6sRT Fm#m |zFh#;#gs L"g>7.9?VEd5!Q2c3 Gxl D*Lˎ"rOZn η#6j)b*XPuGR_k2جkT&7, ֎ (sF4p}_c1=0"vyB i =mRe{@an忀6(vRڠo@*8RG~o "}b,"h6O qzX8lcnБ]6و1Dx0BF!U@ "GB @lM ]DKм5:D@;!QL,{'3`eca]h H͊[A,X>_]CjC*x-bBoVUu ARބ](=i"@6=}Kn@j%H}ZF<:mϷ^ڞG u-T>r!VB\G`IvQ:2veHyRl?Ǡ]^6/3ͩ~p Cڷmcbz"vA"#!6( ձ.}*=bWX[B#C^20DsSS\)JQ89W z?}_윫sӀ{'97 9>c qx6uhQ:&@i>pʛGTUl"K+ SF>$"ZP׳T&N%2fZ@zh<+V+xCmW!5VW` r";O)xL$(E@ e ȃ~B@k`r!kpv"pTX7#zɽ~sF g$ ծ/CPBdvb!8pBe܏Eٺ]`s{芿zn.͏B3KD*ɰNNer>Uc{fLru#zߋ֥2fr"JDd},t}M@{={c[Gw}q+|SܻiXw!B?ӁB{DQگ}f{lg'Aö7W1b(@H нutiѷMv{9m mz/(! }jѦ}2!q1ym]@l& ƚW 􂋑PLrEҐ2'$AkBfvxAd9"BC7I! s'[ԍx <-]kG޵DaP9 |v!N: P![l[=[< ~kSCLl?ɺW}>2IĶʦ_ Ї}{!Իje;=צRޮͨtnힻảM 4(l?;1snߙ \朻{뜛Į޻w$tmtU#.񚒽 m/tqܑF{ftI`s*m#-o.RQg]W:%>bڒx|U x.$zvaO!Èӿ(Od>Mp 6lmb‚RxEck|V7o>lă#f?q]ٌMDl)V>ĊuX؀q3у_6jDngԢkH!$?v0Q?v{_xOQ(kk Fj=bB*¾.o%bj^cDՐr( CvGEP/uV ĽR `e֖EG|(Hm 2Cl.+(Qja61K#` ;_gm8aWCm'Fi<*z*>؊԰_y>l:y!R(yT[LQ-9 ᛾<QUm*~lhh/ 9}N˘C|;Uk{RB;8vo!ޟ?{~z1ļgy޿{ҿΗY]\W{ﯵ?{_1dfv$L}e?k!I6|*}7 uאzC6W+?H l&"[ s4>>v6l_({$۝` :ys4k_}6T&7ݚM'2؟܁@6 m󑊲m_##6Z!vhbmv9}VY[Alԉjn {V(Vk^yv #1PHq/bȎFq6bނfkDHzb V!mC|46K,VQ茗1W:P= OP@tGl|~IerdsA6Nw#eVR%]"p`k7jޔ/RWW9 *ڄJR\%|T ۧAy2zDfkEDdg mA( y$!W%'8-ѺEqCd_b*G@ٲu#0w]%D Cxr Mhv]{ gF ЭBwmDE6"P]GJDq a0fRO`E*cDAm+rdc6T X͓x! j6ʦ+IQ!i @ck!~oD TѺl[M&9dz^辦~'0{| YGՔgֻK|[Qqf+f_M'{,ҵkv=QjobGhs4Qz^]< 05*C 1'8 NJHDIC1BVش8z{d2'>[BkS-G_!d5s ze3 VZKcmN@-vwڸ~־{mr;ڧlNFֶ6f!l؅XMvMbجC Ĭ}mDj2N> NG˦/`l] >Fl:{z8XBW_KxJzKMS[w*;gǀMM%:'|s֌tT&˦>Ʌ$_DlFa6/bl:xE}w4mSF͛JMtoLZ!}\G1\3qZ{oֻƲiߤg+˗vmߺ<ֽ;J9B hcvT&p x)nJK#[{Z 6!Q1ĒԠ q%b0З3st+@9G ڻzdԀs0Fb q hgbmأr-#P"Z=G%; Q,Zg#p5ZqD g @mZce&T[#MA*(d PvyƮ% {*becцXDƏ릠p!x>D'LO2TMpd@Z. CKp#ڰm7h?Ȯ =v ,{mUb^uHx ss]V&Vql'#Fi"l1B ndKD} rkC؊]p7CC-F $ QщBG9,_أ+leD "D!a$k& ~@Zn][ bB,lfMd"ujG |a+&Ď`?1CeVOnR%F,X@`mL Pku+ gx\HUI~=yeߤ2jk f;ۯE۳"K5zzQI* ;к@k<Ѻ89G]ˁ%L#A%M'QWC=Owp>!Cd^k\>BnهК>=HZdkuW<}56?^Qip>1k&5Ꟈ3(aw F}(ZW{{ι ۼ:∹?{6\P/*sOy;E(El! c7zj ;ayc99y1.A%j v^d_UC.+vlڴ3A=8D/ voqZb1!U8THKe[7E,_lGg<G N@QoC Cx ZDa0b=IX#U@j ъG%o: blZSA6ݼlv"f!؃QZlIerdc*Sܱl:ٟZT+PKerX#ؚ6d[*2SS|'uMDjWeNH%4CGv\޹ ܙVf8dp]o׻ =C'гVbwnŭ!V>5 d;;X}Ls ٸ{sL7;DJs! ˏsCx~s~Ld|~ [[_xML̈XnA:!`G-* 46ސ!_"Dyc!@z;Ǘ*O{Q1T*aYF+}j  C:?Hȶn b khxSB U}#"DoD7[?ll"XHY@,U[2+ 7JkoCN (-qX6\䆠)tzX o +;GCy6D NcDku=CF6"眷h Ͽ}8/d|C5bm`Fl[Te/fAs]m7tԻ }7-.,DɱDIH{}P7/Ls~7m~S_#FAQ{@$I۵[xs9DT܄B$ܕH˱.@lw;(J]b ݐ^sj!b>l:Y0[ny+z݁&|ot!؝Qj."d{12(Ǩ|,lt8-/ DA>%<>?KIsB4uD^MH/Kv!]T2(jhm]\o߆b=-h}O6ͦ|_~/E(]X-;xeOHt9zEdXK}~vǏA׿#7!.j:ϼsuyפIW;^H}x6ɝ`?J3ڄؖ!ؔOjC"o%!/Vے{XDIq4' Q!V[ۊ6'}ڭmy|ʎ=:FF쫑 >/BCl%2?ffdkWg}A EtC'䑼 ebceu0RXhe"b!wv )]axWOpwջF +9!lAHerS2l3^yZ Cw17`Dqzgh{`:ݒzxrk|v/ 5H(buty.*{czn/A55_m\Zy1Jb <2mٴENCpKMʼn&1hO_^\ڸH9熅ks?{.WUϚk^'uhB3P:Qx2x-(ذ"Wp(H @Ȑ6~z~|> d99gkf~mƘ& d][ڮ!y H'w$RmYs2_A;n"p+9'F^& 8=Er (Qϼ`s& f W \Ae[`ys{iGr^c#Y_t_A^‡rbOn`&>|3#~7GmNƿIb,a'$JD*D*}m"~+>=w[h] YQ94j2ژg~nܩ>/iW,;fYk{WېC+(+ @͋qmCkm8wE;m|r]5CXӋX&hhCqJCC'fmqT2 6h?:؉ʩm> \,T(>Z#s1r rk\,ww뭵/!u1f.ACΩ/HWd;`, o e4'?KW7'=IkNƯZ}58ԀeKLEd* =P٦ek%(q4CR>);r(C@ÿSؿkE٠Ee Mӻ‘}}taՐ-ePaԯB`[kC&ERލh5Wo'aJck@ Lƹy,e rs8xskYs[7(Xa7d|1?qnМ|cC嶶}Q\H/F@[-ARegqѹ;هwM!4jEEA,Ψ 5iWpt݄ =l!2hm457*^ 2_ʁQh;~fm}kfm&sbtD@)k csA Ë3}"Z/knL"nmح;Խm?T,诞װp];&ljNƟ؎q=(Okcȿr"I+hLp?[@6m%-`;!{g OV^W5bUBhH^@G^E/߁yo-}BpUH]8V#@ T{jF|"ӍHԹLyk(m~8e5ѵqsP:rpg嵅Oa1uwD V_r}0b!0&_E@Z;i{ }!Sϡ3ETC =,s ݎ#G~v KG/yRLGk3Kd`늉M8|`}6: OAM#3ц$:6~#[ys 2Oz&B""Y6|v|Okg"v-1S5ތ; !06Ѻby碚ûW=_J(."N{+ g(}>7t17 eI~qG( #rvw@ȑ!&)Vw^)hwա7s&0GkS+*Gxk 9~įna~ָ^vO@_򁾖Аb*qk tq9Df5 6l(R]#^ps0RG><|{~GI% $J_󳎥e|Տ9(8J-<*uAu<2 1/eZ_:~'qY,]!o<}T@&/˼K]ݼFks$zG|*RSB1oʝ[C`Td>c"ec 9SQx`{nb=ͦYX5m|Z;UU9<’E5]Xݎ-IIJsw WEz?vy8mWO>OMَF~Ԝ)"ÐO_q!Am_@1aM&".64HLF`g,؄ slW#jRw!kb$:mM]|p0zŮclżV_ ;ĥpXƸ9j@rMu}aP?dGQ9CP|Jxddx)H1Ojrà!]>6G"d/6mg9qj0HTM!֒z5g~th= X.wkB me;6U 5)^{_*k¤m K~鮛ܳ[ؾ/SC--<6o|hA_kxUE}qF34)u6,Gj05{Gl6t,͙K7>]FoW)_XIJޕ-#LikibCюd JHB$;ə#g9Nzp@G 6v) !3(D#RnEܹ>uGb@DZB X ׿Vdbiu?u50'"P0>]Xv D mg({W{ s >X7ݘ#Iݜ4]ȧ-ΕUr_HoEJ %RrV#tI"WzEh rl6GZ]18Ӛq}űZCF,!gLF|bU5|5kc@odC,wnAufy#H7@ G66 kcQ Y:\=USX,{F6emE+RM\^l9XVuvlUHߦP`wLE3[y z Om:pm|kNvsT=*[ ֜]8T4?ܱHx!JL\!f:K%keĀ݅P-2 dN !sXZrS]%г>;|p=z#h8*kQ|YAn>UE;b|mrrWvCaHy#SӯCЊ@Ռ,zd6Js<=̌`e]ʑrlD ;7wBjP.cY>!j"^L`@I[7} |%y#i>LY]Mӈc2evpiT<\{0p-c>gk}n"Aj/tc nz|[Q>n r-zuk02~]#hC#XiwƸhrh-j2Oe9En+\zܽT/6Wnm@#- OB`jd~ӃGh-4HyrP] D@5b"1-~?̝@>kc;k7lq\6cqmJ} <6 !fW}"Gʋ,9`5/4uOdf ? =+ft.k"y.J.I_}'c,}S5W(n3E1`1R`_ۺ :8Z[_f36A#x#iN;qHkיHh&]ځ/#E>12{ } Et*C a9zźϫKAO"6h/+6\=d>!s/~!dn)|EWs֓N}Z~9!==_BA0ƞim*eZ$O#(a͛\L%ٵ \>cc?11cbƘC#|1ywεƘc%Ƙ1/cbtcƘƘǍ1SިƘjcƘ1c2>1f1~lރ1iw5ƘtR3Ƽ4ɵ1AwtcmnNc~816\ny3d[q]v<.a/N^HOKҿkNXNAeAwF/ ؀nȬ8`A`I #(}|ǐV1[uHi(BX|}Ipb}~ s!3/ @G@/C2GYiDfUnr!n1 Dž*=-e;1#oqvM_6GQ< j9[.nIv!1ƄU3 ]_[k4܅i-Zo[}3ycQȽoНMݍ1{"ڍcZk}Ő"k폌1Ǣ7o$Zsc&Ax k~sX\UN>cݳZ{1xs[gq}?Z13X?ͥ@[ f VCf; h.DlJWg|"cw cX,C;,q@#8 o3r> g#_l|ʉݺϵks;<*a= FJ"smDF=bPZ^@Pׯs\~Nj7D*-gj%yCqWA=Zk_(ͅ h,# k+GEzWP(ٱ&EMU }&PF%byEfmlAdNvP7tk~1 }>n[>`jחBv[o(.2[^_ ^~T5'5XI8m$NhmM-A?c&!Q k틮1_Dh?ڽ?i; w~Yֶc^BuYkϸܹGS=scA-1Ƙ۬ߤϯ]]_C{bs|:r7.E@dEܝX2mr?ÐI `n}eyF-R.x9]WD Q/3IeSB Grq)N@F̛k!ȸo =λs.GLJ;6J6eн377'V\I@o<ځ ٞ=k)l~[ӻ>lTCƂ BD m&QcY[5NhDtElMƘk \wuS= k?_sPs:^fz=f9Z:.t5NyO_D*}jsX(^%,K!7o!FL[ X;gk1nTÑ``ZVvL:D&1b V^}"nL!XyED/݃BTZ;z:)ЂoC&ڡH"VF n9Ũwϧ$[82uGW:TZlWdfe_g1@.[šDlm]쎼.h| IKszjoS6 p~dn*&s"zNGѶm@M1:=FMf*Z~+2|ZtDg>R@;y;ݯ@E1Y8xPFj)R"Fi81>ykînڗ1֝\GѢq"2)@/Efd_A )* di>9]jQJ!n,UڝӅ?̵F!w*AuI"T,cdw ~F#ShW`nfH[ @љ&!`c)/1UHo)]O%_sAE4{[v?Na-D*c ]dKU!cố=-sIif=R$ ٢!`}jQ4؈> Ūt}C~^+I ۽ݩCʦOkt0_m̼nkP͋8.FҧX&" 8Zl 0Wv_-lyQD}ݿuWuߚ?%:E~m4!%[W3a[o| uI*q93h.V",~tt#գ4O6']QmNߪDM棈Z?EsgǶg4,X_V 9dm,}VdmmW<1#ߋˬ=Ƙ v;K .P`4dWdpu*T|v­qZ"cEӹ噏JZP/"EdFOi~ ]{tFl,c(@@px;w bf# 2?*dA~Zzg4b\ )N"42 |a^ZMqH}@Ϣ;vk!3/27$ȺG#m=ڹٝ#g4%%J7-TAݓ =ԮgC=ȮFXHGMb6#9rBPEFjD63(.hdMN LԜlM;=vY^nip3?Y2%MJlE&R+Uq OHj}G'6!|rⒼ]JD*PC/#0*(܋7lCn 2qo }" r4AMZ$F@f{"@ :jr\#`HAPЍKtRJ)EME`q; i# =);|vEwvp m,lS~ݸDݜLD&]iNsTz>Ѯm=$Rry Ϩ8~_5u3Cŏ:ԕ? 5kcJgm j2r_iӳ\xCYUߵ Q4}`WG^feXz6 Zրyh skF52xY1z6DGMchܟ;&3m6g*ygC)5~]\vŲȜlF:QhBo{ox! }C] `%]llm-D]JJ@l iNS:ٜg`s ˾dgoE&ZpX;/̬ 6l bwk ! 1Uڞ^a EA} r};}ޑH;QDLvUڽ93kŦOmbYuMgyUX5s!MQ\~и%+*fo?Z:g†̣S_@!sRPuf5e=x{Qy2kcpB?@.7EMdĤLbkɸy?M@r4\B.ϠV}D]x> >ֽ{(bί-ľrPPU6C+~ڳd)Lyg`Gn G߭l] .)IIއK:{qQMahuQrGs2~;A`5b~iNƧ&R}mECXEw}Kq@~^7z /xMs0Jb`r,U|UL9_Aٮ?F'>vIl_{r?R7>Bҧ(C@u~#A ̻'"HƧD8}3̿_$j2g!iW,[:OprϪ~Շ|zAb$uXemUm`ެFߍ6k !bh|%`6 ube~+\_-G L%{0 r#hAy2L1t/?)ϯGZ1Q@3b>u"pW ?5_m7C1ϐ DQ!<SگOD``"W k YhnxےX1j2/!z~ܴ׷W*a-ȳɸ_hdƢgu XGp ZwX3fIlk.P5nxe6PyeGP$A)~78ryG)Xۚc }sH~Z]I]f{{̽K]4#Hg[24/vw.RG3 ZB^ы1;EA,R)۵Gu!TC`} |X`|7p +7kc\YAg^[';)qH%kgR(zHF`wb"t>xgm!(ԅK5 ʁ!k(n.' .ߏ!(PF-smB@*kc>D*]ru7'؎C} 4u ɁFSX,ZWggmlid̴+[Q?[> fZbW8j;mDմj:`⨏~9js%1n'۩|`FDvϷoy<@9:<5o~ہXdǜ5CvRƣ gD3d|I5v,\W79"+! PS;/?>7h5 @mV~ $J^g.-ΔXLk-7*7#_ T ߺivRd\L[1 R< ^*uw|稙oH!E{?$U[<HcFk?cqmc(PKs2} {H<lS}l|ϢmCk3Hʸvl5\i Ht!F;QF.A՜;3ȧm >Md:1f#VˁUsRDw"F-Dd[)1 z?>݅gؽ?`Bcnlk%}Lײ %Qt>O[\X/w|Isgx$kc_D6mhRi=wbe{ =̓򬍭@k(DYєLC1Rl ziA$nW{q,ZL#PnƘ#߾.dGĈ$R=YϛG/XhM" A@7LEf+ߣ6}___GW -*#X㎝~[Cl%ROu}p w^'uI^'0Q#ҋNp,"Ŵ7rAT#5E'юLW~sv9GTz:p -N_B4Nt~ėmemlZd"|k=ކ$R郁dɄEObۦW/߸G66 j2h)+/BaU#s;n0Ďm9g vjN{P5b2ƜX_13cL1Gra,3CxRJ@l%)4}]oX[gK~y0)=}@̳P -wG{#aP אu=i5B_=F`CuEE2=yFJ(RsJ?q]Z}S!F@k p;o&sq4`FYCaqP*|2["H?.&3FM,do IǚbeŦLCJlMN~;-nl"B춁9?^w%2%MB.1PRýGJR7klc\IcL5b?Nr!1&ށ$[J 털(j{$pΒGl) ? izxCbB~e[@M>=UpO{*()s[>&/OAϽùlNsG< {+D[ Ceg,Ct}dgmlWJV'@imlM 9ULbON.6ŧG{VΟ6^.p/WRwP L@f HQ=RX5'%;MY܊XY6]λ僊H|d0Й@? /Yw7b;* E;M)kcw7R<Ǫ/tvA0V!sM,G>m#=qC̙ hE@ߡH^p?d||DMEd-B BÞ>{r6'+\;5hm1kcp3a j-tD%ff-"֠m~&S G.CNBŪHM12-ZVȞI BŠۨZ]=vf)Y%@QJK3Rr9Z uyKg pd{ q**+pptv"}6Oy5';TtWج5+Uuxs2m_ͬO}--j2AfOf29 fl>$ Q9'fm,}F]Y5[@xu6ͩ!񒔤$C3d ;2@">;}ĽYp!ڥ!8198gUr~m@Lʼn(i#_Q)HHű}yc:Ukuu\[g㵡AwUT6C ̷\\ SlBm_at$b~W]X x/p. XtZs2"o8H7はDM)hqA}B$Th/=Q뗙@V@X5deQh]Y5,ӺIZ;XgIJRػ T:rhڜ?^OT L rp1sG Eś SF`}"r2mDJ'(2EOK֋ROQTeHCP4gj"@EHq5T6=փ@[*\bx̗1[IA 7ڜEnw7M]^6f&3#b>WwD*m]\kdl,T(ؠ-w|M՛гz5@fm݈"kc&K=G[wULdu›:G|O$@4H"99^#gdO.DX~,ؓkf0A.-KւX#Dr8 )Wt.~]s+3'R4 3KH!#vnhм&Lk{!bt$#*B&yk@іw"inGAhs2M#5$]^K'9o{$R(|g9ڂd^6|h ݍ:W$cۑ|~!oˬwkkY;=Mvt`kڗYk*1fƘ֞Rb02#n;k!w(C8`9G4lg-Hw&r|ޙ0[NQG&}r}|@O9GI5 S;HÍ81Z"`Ն 8#5 4J'`42K_CfIomE7';U"$VD DoB뼟 ;m6xm"vEXruT4DM%~mTJR܈ة`)bXkiNz6;ZV.Hs2oNmN7mq|-݂o@ :.BlN!2~eB ޾6($3 Ae&'_S /}:ȟ&"tp,h+Q,C>WF` 1tÑYqlojmV~pJ`hM_1 6ֻ7D*=["VnМ>?wF1>`~Xlf6ֆUoO%)ɛH3p168#G1Wc53\/0\fy3s;6s1f93b3\e13Oas3k89?_霻l~&8rιou{ˤ\EĶ<"3-J&7/3( K` Z1|9Çz$~Q:gǰ2"^$#R׏D­ %HPH1 /!!U.^EQY@j|os(QGь54,TT]qR!*蟉j9M=x 0>=uT 'gkgT?w.;3_YOױO>`&Ǣ p]ǁs{Փf~S?A(OϯCO s 5r s^n 朻] B7P?>]sΝ$s,}aEsǡ ѨhY:突ǜs# ͧƇPI!madRTTIUz 7%2L*L*ETOF!:e(ʿo5&? QtGH="Nv(JU.f;6#=a*X m. (:׉ҡK1ՠN~;{XzGOAq4{ 4bv| Guk oiro06+azGGsEew`79w  bbrs)K~;Z^s psTRn `Y:=< mtQ}|քq/ONƺh J@E/zaBlIy ѝOʤK2ĕHf> G*t[Q4-WT"7~ݑ(j4a*gZЇe]XhQ]ߎaj6N,]GFOξA42@m fOGK̾b@Q*ЊR׻sѸ{wjpsn?`P9皐QA'k|$=wpD^;3s5(R `Ot[kٶN `o|ue .=E:}i `M%!{xLI$9דHC5d0 DHaK~E(&iv Go@5Vѿ3HMDl$Epx~<۝(*5uZNFQX ?)~?Cٸه 5zL*Bp4p%r#?c$֡T^Lɤ XA,ݓ7EÆeι(s;C% &s9&~In9wOJ?~}BH- ]g_sA93ݙx۴ɤ ї{9Qt $\@gGt2 }Ȇhv$:PFgVK@ $Dta E~oPdTmpwMDw!o J-HK7+!Bؘ$/`&~V 3ۧ4 ݁.T{W"a0JD63kQkyalDMTE([0d9d *s p}gmFKsFsQVa} &;b&=99Q t]w[ܗg7c'ιCqm܀҆ Xw-m(9 ^1>! ̤lGS˿ ?47= ]~:jF1$J{%FZ_-DIHLcEѲ{/݄.b莫J#4mGm1x/!k[\c+>c"~}p?Ҫ9g,inΤ7nяcřTk__WYҊo[[؄ȤE`TH&[9}ČP}𑲅0l h\$&"q6 0a̡s!Addz"dՌϯFѶRǢL]?}.} ݨH Ũ0h$ A MEfÛH؞ߎ |%F޷Nޓ7\X?BTל& SbAPѰ{{KkX\QKnN&h 7}Ys?XLg˻vcſ q,Go"aUBp<{l_?܊b'ǂӪ7kP$̊d`E(dˣfF$e߇nD/ $֒("_gR 7H;]bP~V"iǡA~]/6s+QtnJ9x$C$2FюJt .>d IDAT|;ݎl eR75#ZT:j?%h]_a[?f_OI.guTTx3JSY 7-(q7[:2KHs)Q{y XF*`%jHPDv8բ9EDu]Qܯ3;ܯ"]?DBYeS&4^D>_? E،ɓ㦖Ra GYcGPToqnae]/ H0fCPֱb:/F:4/"^;;Pd!1HD݉ly";8(yJz Q?(Mـ+(ga?H'F⣹dR4/C0^DŽX%+@e& . _*qǎ:($\2HAdZOV"kBBl&sJۨ2TSܝa'`݋ _ÑF]PGH7ӑTԺ8Qrd:L*2il$ZT#/JD.6^@ݍm(2ցma K,j%"\ܯagxu0 lXjr2u^F]ԡX*Hq(zZP%#(Q[ūrjˆ+mO[$Fr;pj P:u+[DV-~xQC&v>tT/haal6Lme$٫PMVRđwQjp JO~DG0lEș"R +h[AĂccHu!17j؎Dt[r']Nvف~ y('eYDK6f 7 4wCҹEx0 06+&tv_ݺwGo2mDG¬1-AB%"^C0x R%$ŽY(YII[擝N߅DU߷0-%Hx9"AXl/B" 4~Hi%Iz|r0 5L?yh QGh~{W J^T;MECѬZ$l\Q1pdaQDESYӨ3'Fb!q]J$*z} S Pto`$ƠX?i NTI%A'$Yg"1pb~*<*J #iS3D!\:"_W EA(L$r~F6$V"!(JVEԍY(Nn֢~CsVh[(v=pQA߇9T=g1XqS_ɤ47dRX?n}'tv6OЫeِː . 7KPQDu]5H;s/U0zEs~J;HG)ӡHC}F YBxaZ!9ծ\EW@k5Q|5eBQׅ꾆HX(YZT4e?gsv(J6 ގ7;j#>%dѧX@2*L }ؙOI3HT}\NH[NMe"{iHDCQT (PDXT" ˣY ͧ<GĐUF IOQooT yfln\ng`nK#5aLHL*mQ{3 +s(XҖau)*_Dґ~]? +PL\ҎDY'1d:Eƣ.ʗ؛(Zr9 \ \I%ژat-Anw̖G.S ~%h~_m}ߜ>5yb}c> &("u 1*F8{r:JI:l_fsQ t .hEb%d6(~.JIEv@k ݉(Uz zkDݖS\rcQQbHr:ξ\qm!$0 0!fE_2tbtE爡HLA) 8EZ8;Rvoh;c7mQl1b?h@k(y8?a?so9I39X"{ VB7 sPp, <%wULgeRH qXXD.fRe!{Wήxa?VUݏR<҈(Fc]F՘5(~{C#..# c=ٓϢ3(uT v=F4?44"kQ|WM}q{,w=؎:cwPd0bƻSsP |uS9DžeՃJU4t/*j DjF'XXNh]F*=oC6Yj< DBO(5VOF44߅";(nej(Ć'9~2?O,xb*&۾ gXjX_ꍨ&{_5M40SHLEB"Wo4$DaX pQ3| ՝u"!5Bb*Ьw7jHzѫ4\ J߃>7_@QPCL+ٝz?}?D]$],>l˓R8)0-&Č$zȤ'jѪBϢeH@(VDP pQ/[7ׄE{:"vY)[VUPg(7 4fln>w~э̽(j{yxxQ? Y\q)j,NE?2ojF?ʖ]AѦϻAH=LTPjC$Fu_wN:. \Ӛ1EV" ;J_gpVD`q&XX@߽j  rU( YuQ^lks)ǀ[ofQm`ٙoMscQ1(XI%LF?„^dRېMDt /Ar)t=TґD/E\q>O }V׍/M/D`zuWL#Pii=BK22.bdxm~{p ]UGBH"ı sTxrfo tI%lc2 cb}cSҒ/;!~,RaC^Qs G͙\X%ja2^epRhQhwH:1w\7M.lr1 2VbZTSa#CCX?;W#vhǂ/I 0,"fl*CPE0^4;yއ"3lEKМk )sm6i M^\hq+u95x}0 cb5bf!~Q=3ڢ]ЅP Hp^(:0oֿ3~,G B/1?jjͤ {a h|}{QnQ}Q$,R^ ? %t>s`>pL4m} ɯ90z 76O X4-]X:Q,tS0 9`Qra(o82;r9 M!߽z"Kb5.x~jxc^eqqKLgǭe;al!Xx◐pz)JP qtaNTTwQud"1TkkȮbgߌ1*1Fw!(ތ Nnb9Uk]5p0cIcLgH } ;Jm(48޽p Յ"iݓ>^_N2iL*c3EuaQ`$† t#Gg0zbf%BVsQ(]a42j*хi ;EG(z{'\6 9Jу4n4\ ꐬ@uQM\/aD9e*𥖠솱cBجBC"*AZR)$ރj>6(8ojlᲰ RX? uN{)4ܙ JkB$ cPD=$`aKм0Ą'xA6j ΤA2x*f@&EأPxuՅ"Ci%w3}^&B2o&&v b(wְBguM[tI%Ƣ[A%*"W --EkJgЅm*2<\I%rtXvE5QȎ{y}ЯǠ% >E-A=uLa,&Č~5`JUs-Qw +q!+L*tUr!QxwAVIR ;ӗ_E?@qmW?ei5Ɩ 1c`]ߥca~]+4w&=Jϫxb#y Ds}31z&;8 xVeHUY]-GTTMwܼk_ 롏cՈ?<޽/ _|ng[nj+~kFi(5te1Ph i A)EY`w/ْz]GP4x&n]u;Vθ}詽tal3 t`יTbU~?x:wr?ݽΫ圫tƽO@MowdRG6A&L$TXQlțC24^s~E˦kѰ0 GQ-A8N060nGw>G>ӿ^s(ڰ-As<Lg\&XkvGO6I.@׽W+wg o}ñEbA)2@ݏ3Wc_W0M"bF{̤ov.̤fvil4t6I%krd[Idk1 Y,JCH%HlFD,쐬 Jeƈғ!JUV"ˀ`.1Ga1=zTyG;¶ȧd: >[ܛFcti #n2jZ(rBaY>X{;J7)a5Hm8006&Čwx߻݀&BՃ%?!61Fa6̉97q_XhA1 Jy&ՖődSKH= ҧ( 0!f ؤ3>c*dZj^jrѤO"<5.1+ T JY}D-%_sFs:CQuei 1c0܄ѓXI%Y4]Ϻ TyFHSĐ+㘲ׄ⬆U1CK&ԡxsE c ŊA2u(}t"p㻍O2M%^æ4`4C~fM\`-NBB|Ɨ;o9^S}Cv}0 ]=з{3phr9rird_KаtJ5M.7x{-B坏š;#ycY] Q46<1k3 0!f $>M.wE˥\ns~+cЀ]m<.:!?H* IDATya~Xwr roBж܂"dE(.⮍,݇*{ F0dRc_b {9r/ l xK浽p؃#1:uWN`E™X(uq(NF4BhalXa \IF/Lgw2ěڢ(5m,]P7&(z 0MF;0p?Gŧ"ۊ_ј3 e\y{]ꑨJ9;NItnɠlld8w ˷ Jo>lj/a=& crnkgTQO䱖Lg뀒l`lWC㑀ڗ>LIQ䫓Ȧ"mad%h~į:`[΋ה 30PL#Msg`+Jǣk~'ًGLnGb+"/k/{]qM.DUy.4(wal؇0RgR|2}2m?:WQ\RYWضƝ8>gנttL*7Gi 74\#QzqVeB/Ùq$JM!1D&al}al\ܗLgNFV=?fTTT6j\ecǕ 'z9$O;l4 [:[X**-X_l]+ 67Z-A-A^>$06+7~z2]/YI188V%WΩW30sQEG)ukAGڦW^[?!sL>I%:16&|얠dM مL9nb+_ZY~40MńacT ,, J8oV/۹R0>VlOXҹ؍mikJF,fdjȧ꫙Tb@;}|[,JQRjrUr߱ٹk6pZ&o0 Ԥac2B d:;_I%3|[|[w];G-~sQEM)O RX8hy;* ot1\݄ JfwSEB1PZr8j;Eިm)35+7~N&(\B&V!3&d쬻^^j>+ޮ4v)!iAbqht*5A=g1yX9d@d:{E6];ne۰HL=oXHx\:!WͽHFĄalEdRs_^ZQ_(T\V=<0hBQfE X(5CWQlo/%rCۀ XV ^;ȯ#АnK3_/oDEa;eNv/BT ^>LCbwDh mVu@ o™U[YL JA0hBbyVKм0RLgG"k è{8_ӑ 8d6UʝcD2~P{i1sV̪*ضB?Qb𩖠o}a3QHX-CZ$*Qԫ}A+0((< r~76oU_IVB>bmWM֋ 1 IE~\;P~Q%x"1`a<ꃨj|$ ~(il{\6|c+F;7?Lg}4 wb}:9EH@"^E4:gőX+df呰pw|&XEMzXlcZjF^za_;s97\_a 1؊H ՇªYݞ"0Jid:[is {_wTőxۆ-S~3tB40+7d:[\ˉ#g[V?c|qc{ 0"V#f"PJ]b3l( ݣX t9GPQ* I C۠ɛX<"GnM/Ð3 06F2L%kZI%JT 6U<(Q(X%0쿄WXU|*Q+]",ye(&Y3 D0<wy&*$ T5p΢с:&(>~Ϊes%{MFi_;^\ #Gl1@alyxl-܂ Bds#CB(ZhX ;3~? 0M+ Lg/@3$AQ(痀灟!ˉ7Tg쫬.D ;=$ I%^2 XD0`l}2="V2u(5u?{P}% p9_ǣpQx7VFþI%Fߢ;aFa1I%H4]ȢNT|?8 /2QB<0ڿeyhoqgb&FaƊ c !^> C:$V8vi8 Pk]h-FˑBl8\ݒJ(9,dR?r0& c^9l4h)dCQh+H=Nj,ZKo#a7͟4 0zKMUtpC# rO vDК"UzD+(xx_ I%a lLF2m~|f N_;&__;X_R@"խ(ŸCK8|#188$J,3 0"F0022gtvHѮX;ϭ,O8dբ DƏ  *!7/"Qw `*څ(2y_[$ݳHȍɤؤ2 0 FSXw+T <z杬+~9(2&aޘ3>$ΎFs%0YL| D$BPox($Wtp3ЙLg'~I%m1akDŽa-"3hBh&1"k8u^a~8K2F[|Ҝ|&JaØ3o9V:Cl'sX_ b|pRd:sTba݃Z6 0 I~DCV D>`C^x0?Ҩm&p]&Xeal$o}(BU'yP>;4q-1hNи}(6 0RG$Qp4vjݯXE` Җ](?Dy(Jv j0 063& 8Eaݟt5G -nR(v W[Ρ-JEȤ$)(]ٵ7O.JX aF/`5bG$١ VΪ\Tz@HگIzbO~04I%Z{`1aTbi2Dܱ;|Ҋ Y2I`nIw\"kP.na0Vo}WgNΘOYnw#C3dd:OLDQo7 !A&$ /~sM;I%߳TTmM5 06 KMFLgF&(e5!3Tb_V'3Cd:+pG$~fg8L*1gsa3d:{yi!y J1I%}3 0֌Ոm(h˝2 0֎ 1Jdl Oz/0 X;& c2 ز10 0>+ 0 0baa} 10 0>„aaFaB0 0 0!faG3 0 #Laa& 0 0baa} 10 0>„aaFaB0 0 0!faG3 0 #Laa& 0 0baa} 10 0>„aaFaB0 0 0!faG3 0 #LaaxsIENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_29_0.png000066400000000000000000004362311413546205200301570ustar00rootroot00000000000000PNG  IHDRc%sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxwx[#C U VB ]hdV)Q:--Qv -$@F!@38v%'d@I8}]lsG~aa={aa[3& 0 0caa݈10 0nĘaaF7bb0 0 11faэ3 0 FLaat#& 0 0caa݈10 0nĘaaF7bb0 0 11faэ3 0 FLaat#& 0 0caa݈10 0nĘaaF7bb0 0 11faэ3 0 FB=T{H*&1 06_<{ cp"@% ެka?X06,;7_A#o0 LƆC ֲ&^e00 c3Ęa| Jګ̫*1  ]Uzza捉18a5n0s{y7faͦ4z,^e>XDP>Z p7p"^@_x#1V`al3fH.ouU~M[N,iء\x. xxj2TUT{U{ec\aƖ9cFK̀/8TXֳS%ʾ 䐝tٖ!AW*Í5>0 cǜ1GRW5l$bŸ/U-Rk'ǵ.,[Cҡč560 c=j(T&*z- >Z{C68 0cF$ơzTǬ*@2ixXg{P|CB`:hS0 ؼ~Gra^;m^òE nKdިą.^|c"g0 0g-gPGMu̶^^sE9>REweH|y{; 8ʯ1!fcΘ#|;ve%Fcqdypa^kukjqM=N0 cĘal d)Ke1;/fm&ag? SshOٿ?0 ĘalJ- Kۆz>^ AVZ;aݏ3@4,Hګ ˶e߹@!pj"I}ھ 01DT{%=̗ͮX5Y<_d{B0ͦ4jr4- !a)XnU~M?r01pƫi > \3lPKW5[t|:=O%@>*uG2 0> 4r^.ã ڙ 5wHh5Kc6WWkPG'?mra=cVOWYW_׫jpxwSw=iΆPGCX %Tm}ʯʯ1k0 Kbb0`_{=OdWjr*fm~kԓ+ K>}] yjN&[#^Djګ떓1 3zt*}[4ֿ٧ܾoݎJ_%kNЏ{o{@E`w*ˀ7 a@Lpe}u^kP7aD>0Ǵ1hڮoMauU~^@)rR@;d!1}h6@ﱪiHh<{aFĘa8.kB-寖\Z lt\YZpǪ\UnN"1֌DX! [z'xKWOgItaޙ^*@Ou 06&alN;m}my=/CW/l}WkB$Ė!W,ĀJĿ y.h@?|nAC) 0zVg0pd`i#!CoTr_YA}Kf޷ZPpՏ=nL`//Hٕ@;d`*fPkgkw4V4 g럨tdzZDv@`{R`o.~{\ ПڋB+|[U޹O0 GaΘalT{wd[;750 ؒ1cW.xggojr0rBPb9>*G%`$$Q&4˲.>CU~?{+{쁁*a)X8>Y9[w`Ơpc)rP}90t^meP> TCl,! {պ`_V5'U5w̯f^YgKii)_ 0>wE4vjwhګ5>ܗ*BNWo)_*<xƽ7.[aPj!1rȲ"&gk(}xkW[ߑ9ʯćVojޮan;iFl#n: 0 1[Ɠë[z> ۔.i\{4xf}gqwڇ㼌?;YT5$1[V`7l?։9 dih&QM2J0 %B4 DɇPHw&$؞bCY|Gk&_'k T(W-ǟqZ 2gF;+ׁQqTu pzXkXB)`4cnCbgґRbщ^e(kuUEٜ#=1ѿ6`*C*c:GtZ5c?LGb+T"Ji Nˀg~BM- T{}UJ6 r3D,,O~[EItGQYѱIH"G瀟-_ C杸7QTT񪺯zǠ)s- sYLCE_A +ь 1 A# Wz2~=\4z3 .zp 0/1XKu]j2Tפ66%x2 Y?t>Ö+*swRE;颼z[HXPΡ2rQis6C[ZeNShX}{=\*{Q/aVC2z< S3S%U~@41p.rFEUCN"uj7JzEʯycO Y |5"Dd$<*j2*Ok7؅1 11fhs颥ktɥ_x+c_w ЙE?v{/ a-*1u9^寐J{\ 0m_VW9V5y=Rtj!';۟20rV7V575 0> S=0ޯxcW*1'|}DɽZOk] knGe(FɧQo?+e>rvi^Nf= <Mzx):^U~ͳ^e 0 %z@L8G{=DWoT9j",p?]U;ʯYg0 X?=j@3yH/d/MxgޕXѶ"طaFa[Jk8hm J"~ʾCML_; 0^d(Q~%zʯҝK@B U0dv}oy{\pKxGi&Pa><PH 1\lv~QHtU{yNL~onl&M(EGU;g13בA<'b?O,{M"49G7>/Eɿbr~ܛE^K"h<w?kVU{m"qԈ]\o Q3P!^?lֲL #j 1ꊽA>{;~?oLs]i+mҋCϥ邼?u >5\"3׻%WCY{LP0c ~һ{Hͤ}`Yadߎ~o,)\g֊Pn׫ҡ@ ,f9fEV "c! Yh5 c"pq،Da?4*fs`LP NDX=PqsP직Zk9 V3{uCF>W6h<.o3EzaDS~"I6W1)Z;z7A+ۧxq_-BB-=4_3kEjQ8T}؀X4E{skBe*ZjFbC-٣cdP+pF<Գr3nof3t҄I*GHDyx;TjfP`~V0cVCGhXZܦD,(M"n  Ɠ}#{ 3TaJ|qCjxMެҋ`k}~!? Kۀ]z%wBmn!*@jerD,#+c2עwxE*ics_'ߴ5 468xr(ppi"ydzƓ;E^ 'fz"y˽Ahz$p:{6!J{Geݫ l]3+-ew !ʯQvg̻hM~_6ȅLP74)!>![ L 4[8hμ))mVC"y<d2ty9jծ?lʒ=`עEߨ}iCAeXihK"Sd`orܐh>[ i#ՀDJ)/L{MhAY8sWH_G1HM ]J4C_LP1;vEוZ;aݎ%%c?Õ&MY5uy+zrr`:ލM4  c^HByTDE7󗏜'{i TYZV\^6ɇFR~8D_3ʯIT5i@Gۀ[U~Tއ眼ڃkGq-l2w*R3?y<, d%gThxpqéP pfMP1fXi c"Ol2}8 x[Da@5* %߈6qKֿl<MN&sm5MUO LPrʁ\Cm`Sk}#4 d,LÉƓܰ IDAT@-~pH-d> M~ECXN=Ɠ&blȮ7U1lV6l0P8Xd:$z'DA+p'` S} *k^ Q]ѤyC'ϸxr'xEePWa%<һ"@4,Ɠ(u&|o=bz$,\kkQ kۑ4xv]BH"mKph£w,൓`^}B% tm"QHPݝT:@/׬\ B$ +AaKգp߷k^X$E^B}(@qY:L N]hB0Dɯpd4\&b!ӁC WeER4<XE^u{~*&:hQPP[T@ʅ'~ E)8#\QD,/ko#&oe}8_uulוX+a{;hA x$;`sg҄45qNԍ 06#,LÈƓ#PJprZ'_m8=o嫐8%O.uHP|*d{>z؃AG͏H"8 Q sPw4 vCϭ|̖No H">9O@opu"/4^Ð{#15M6O$β5Z>{ 9e䚂/wlRSkLP1t{̲hzpg}jȅUjC"'|'ƓCxܱQEɱ(lD,rg=/B4<H"ڐ*+39^DQ+k~ֹ (orE3;r]M>ȗY^k7a sz~ioHےQCJTh"<'A. (!<7O^:Wi(+틧 i݅hv_ >vL\fv@!y~Nxgi|?̍cj"y6OԭcB9jh<ٯ.|x2i{w ڦ.uU~GT׼*Z\UU~M=hWD_ja*&0›U'Tm̓4 0֋@ɶckjƓ%hhH"Gw.q'3nL&8 3NGe`Ɠ?BّW!1YoMYax-wUSEﶎe.'1OX=|ō#fQV݇^' _dU~ܵ* b_k.@aaF7abKV@'H"k7oURXҺ|X'|ꁑkP= 5^'E$2OތB~4/8jpk(+ԂQh<|"YsDOkR H -76N _vmTzL1sFxfaa݈F"Y'O_up\"$%bUfGɑVo h< rwG.(A% <'‰{E}t&k&? i(rǝ'u\x$4$bWs~TE'bn娠D,z@%1֞1claHD4<8-Od/xQr,#Ov9X"qt[P|]r]h2(lua}6s +v )^jfBaRTXTb,O|$.Ɠ#z$:PH.T  LE.1Rq +wZ@3׮oac[(.`H4sMK" n%-unyϓ(Xr~B\]ԧpT?,& ܇V r*ݱJ8-kw a!= l(oB!PYr Gu\8A;9ӫ~1~箕| 0 X-t0՛**I-rJ'bױT4D,2"ƨZU7Q$rF +S_`k *q/rJQnZ)*@ +Q H `1H"x2'W4w6} 41ٷ8yw.P 'μ b0 0w!= KkY^)\<vlFC.oP>ݗE^GQC9HH$T= lp*:rM!V{Pr$^A¤Q;J]ܷQԛ`ˑ(GDn[6.%šס64@?Ik%9tJkR 'bT4<]aƆ”[~p`[23^+Ms& !4w(7m_l_ɱ(o\ju 9qU(gPwןPIHέ/!׶;D[+LއrAbE@b6ws7C@ }9O@.Y]yӗEg |5"qhΘa11 |R |tªE9Dc &/?OG7 \C i<O&\˝L^?{b4| 9M (*9,@.4YY$"BC B}/ ! av;lkP N݊6^U帝v 3.x5[TCQX?HD~ˍD,juc?4 0 0=OtF+%`p[P{rf#!NE XV?Mb{-,L5B#m0 2HkG\dizxU(i?\>$ >$Fpx$4w@_;VrNB"w}9_v p읉Xx>OƓu=]v3 0 `b{vyH E/uYo ݫD,R'[upmv,ˑ(LA;3 ;Qb1< ( k^툜 H؜D[V5!,Fe*8!L/6+l[72-C^A"u'wHtyܸ [#C<9ֽ99j'%b\âdu2 0/n Ɠ;!p9<@$`M(HLDNأ5: cPNB :}eI[QV*AF:F\-#ވ! ,fq>JgD|#O&펷7Ǹ?D6$mwFG+Fa]Pȴ+BkMKaa+mэD3nnY1*b@V&bkm!h +Gn\+AP=L$ -$vGi(pA QHt*Ͻ|Tl=;r$ -;9y}G"n=ۓ2@!퐐{ D(wM"?p׭gOj?DCK"aa|Lu#x9Z3]g BHl&Tu;9a>rǦ)5(k ʹz 9U;pky: Bd F}f A̱!LcW"gw+K v$jPFB7[Z h9F({n"׵xՅE7K=aյ:kA&Xa”݄ 3 ӵuU<Ɠ%(y+(lyrvA^eP;ʟ:x2,s8a@%1nE_ƓP~Yj} *cB۞nk5D) @7ӐؚHhqO٤cp7Ynl| ʽaPMHvFqh-('w2uex Μn:|H4̺6!k֎9c݄-ysL"#Rt>Q.[+9MirBlrJB.<~ZG-;n-Wd^D\o*SP2C Ow{CSBA:wE!kG3*ǣٗH5~Q?ta=qmjBB4 #i$`?pc$>W l0+Xe[gHfD>Hջa]$K_M#ԁM@P&2s2FBo(+8  .B:95^H|Bm2"/d~<̇ӇBmB31}pOF]WƓ^4㾰ax (W xTq$#苜lNTO')χ(',2),JuvxABBd'v9Hd0y^ tT<& r/*/! jj391.rNA3@k[x3*~;:;8.mJ-S'r䜲g;*rj~nףF?B.Ǣc[xD,f]lC&g?m䉲Hzݺ1epc6ׂݗRt%bFyHpʜ\j}݇>5=cga9chPS'E:\L"Y(B yxsmϫ׆fH.u$ZvH Ue#;н?rp ܬCPX$v#O=$BX&WT[GL&9s5s M(' l+#UˆA$|j0 I;sȕ{fKɉ E%F<$p`[w݋LQxh<AՉXu/h]{ˢOY6;g/C'!6^v$єE~)'@B}9"Wwcn_7Fȃ 06"&6XE>QGnh<& T![x-Glxy7l( }@yfǠr3Q&430$@aQb>9!#Q WnLʢt 3Ht&'@<:r_qlBMBK;wrϹBbn‰*D,5U}O^~6*.|[8FsӆKh#A['/Fw`?gnNƓ#a6 }6$bp6naaML4.ʝ9=s⨍ύH dKD\ߐK O.Bԁ&,FD\9D,r`432Hn6ȡZ.GWt'M9 @{@zn衻ԝX$<$|!A!;Dwj?(tlB9Byav+ `[o&eu@`k(^Ԅ:tKE(<JH+*9\T"iRo R 9!r9@|{>OG(r7g䚷DgpY"yضpԉ= jƦMUȁDMAKʛY^yH=Dr@䑛Aוu2,(>;q8&;!6F㽀\"$8|$*PN4yBg;n-j!LF.F~ns~^Z{t f/i8z%sƴ?(C.Lۮ͚(mÔFpgw^w}Ov}/[Dn"'wmƓi8tb,ln > ݃ թʖ G{2k{4UZݽƢb|߭Ǎ9?Ɠ%b7L8| cz&\4m'{/jM"lԁ-lMB/H x=lkDK>zep?CH&Wb$0nO,r G!p&;kczC^9ay\7V$(4ƴ9 Vr;g%7C~{٘k!~279ZݱWs{}P(=tyk kq>ۻ:q%׹emL8<8u_fDgVN:qoewM8nFa݄)7Ń,X䊵޿ ?@.@5Xey=ZPN}! At +Phn9Jӭ%ΏCB0]O $Gיn<Jq6ؽ78($"7wNsQn(y=Bm܇Q{m˲ʍy$^e}!!m@3/׸k[w{4Kez xr >P9 #q߂qotZu^^ǬK= IDAT֎Gzy!cȉ,.۶Зx"iqs٘EpWdL8 49 }nBŠs5pg>a?չCHA_*ɞa= Sn$ܔ$b&T=CN9(6(F's(wBrbs9O}.4`x$Š(l׈Br+5kC-Cwj rGBryWoi{;b0>*ee[&#A:Q̮Dbq;BK P88^nC7椑 wʺdٶN a#$` ,ƓG󛞈E1'{̖ u!)z ''` ]l^_^v6E.]/H܋Irв,u/}S!#ODeW!ԉFG9| S'?{E?v,/y4/ 7a,L'FD,rs4*&{JTNoL$\ fz ff A S܍!:#ʗʺG3?MB~BJ/#T"W6=KQ 5n|ݶW"runA(-Ժ;s`w݉rG-;8F!+!g=@hA=8U{hJwrr;Ɠ!!>]ߛ(t ِb}.pFks{߭}vOD\['C[摻h'Gu}p'rv+쿃km}̿UouEjF@4%7"rSƢAH9Av V|9<5l[`Y+D9hE0g}e6/^v'o:qx%pP8ҎFuIL0|Lm*%얅#EƓ0Ds1 /Ы( %V߃NQPzH&\^XElxpMr(B潑s yn7 nya|;^R'd)byIUsfv{Y4)`CcwIL1QQt$5Ĉ&/5D㨱&8VD`.tXezWϳ޹s{ )yvN!h}-D c%HQ#sgZ̸HBVeW4Z"LIX c֮\hXx]RQ'j[>O;mRO%ˡՋoP?7'^ҿ!^*h{hк]kvG>P~,G1h;[sUq5D5ZGiCIy Q'eA@n H`lh^'gTL5.@ӄ1ӞJV͒5Sn> C*{8k9!dV@Xod E4Ŏ#R`A ;CkBgǐ)g'dDSb`+1C;!v=aXf~Rk#HۓlcRk)JdoE``m+7 :foCJ}gt Swc_(a~+fG ZٱݔJ.9X&x2;L7L8vKOǓStMX#0o_EyJH/_3{ߡm*#m4Wy]^b,+)z}@썀X2zBJĂ_-Pt='x}4.Uw cܜJ2^. "6q(KhRK(KT_1jO_DP 6MIU'݀ 5mG{0|; I' )=A=2kLx{^?+YJmZvG >}ēHLN%bs>崃k>l Z/X=hBL؛HK,WZfG/٩BJ0BVPd0@+vi Qju_Ns|J+q)rhoF`ok "^Grc(h7b!e*!G4eWlPm1& _fm܎(CdzK?EGn;ywM׍TYkf+$ˆfQ9eָ&HF|$ 6-#PaA<J^yM5MՑT"j# 9rf-!SyZFcUHX`1㍄~~?`hFh&.",QU| K3{ {;w+`Hy̮dʿ0+@4mWQx}+~q[.= @@ZLin$+Y6&$CT}!pqKߟ+í֯7œx2}kGrXp RwC`"Cs EyY"A ]#11  nw"k@%0Yv x?¨rn^ 8LikYi9ӎй\dz )SPwAJ>k}|t*Qbb⁓F!AȎ5 v(זd-,ڛTh14BVg܌rk}3e8ag<#t PJĦ"p4#p50ap: |/V!FvOر]erMe9>C#}N=!WBEO9&28}bg~Ŀ=nN $a6c;_?+YVJ6f)ً?E!{k^m-]e|v)YV-Rp!vh<KI)B$E x75œ\Ծ]>8jхv ͋'ӽ70?޾hc0g=(}ht4Wy u] U:D~vvFA |v%h09ܾ2G@;L3Q m>ldNh,V2@$zZ/wEWc'>lc'o512k\O:8kܴ8 PP?{ {sG0{F ;n޿ɓuoާsxi>d͔!fXVGwQ 3o fa-RT쎘`}::qe[*$݇9Ǔ'#XB6#(I]? 72@h&sp@uMc|E {'RCNik?lZ_9H["HqNA&٘}}EH >@@d$X]Xk#< D&yx ( 0`sre>{(Cʴj $aW*&`| ze| Y.'1HQ[ؖv?=o *WT#fb o{|fogJ |f vk#llq2k=>#'笭G2LG5!E yڝfm0;7ub1Ek0?=h@hZ{b\_FU D>p~J[p{|4N9&Qym}_,'νYFمDK>.ߎ}A ;T1e?2k߯g9W*/2K;]pӈ.ԶU^ds.ӭ|j]S {9sns ιYι7sCs3s9puufqm78ιιgsrl܌mut{~}DsW9޴Ҏx9793"ꜻx9w֖WAV3(o|n뚢k[9 2C[93>;)&/GwrHAJg3ctbf"AlLĠ@G&Rd!2S!0Pd]ZKcL ^Մ ]ZL_@Fd7F n/)(h,;kлؗwpq.?=\B-Áz:8Fy96k{?9w&oݼ9w~NJ0\PAO})fxw]/Ckӝs9z/799r{~sX^ɧH~G[ůdV)qH)UmviePܒO i&d #eRLXo@I R% Ff8T"-}:A;cP%5Qַ^ G#V!Sjʳް{=5-bPm|npZ*2E &{qCdPsevㅈ |ΰ{A`[gd:kohZP.fJ 6K%bu+@]fcxiS&v4#"T!"tGwN/;HA( h1pzf6oҍ#J{}}ߍ}r<; _ Keι7dV b?bڼfOH`}!-[k\~Cou6inΫ뜻Y8>׆&Y0bC¿6u}l5Hid&XR 9.A!}hgfA5X̕ ppڝB&<`^C,Gg$O @bjR::gcWez̶#P~X ̮'#^œD&dh7/zI+_,z<'?ӫa^bv!6~ڸ*"(zm صƫ7[lDlVI*m[RЦ5xdyXպb߈|~dh>կw5F:vnrʾ7C5mF@!kF~-V5wPZ+K< =kӑYh+& #kYtZC37-UB(H%blxnTN*rO. |W9}rՍ7V~XBv//O -)ƵCc5AXcBʲ ˜i_u^YMV72`?8ڡw0\n;&)-Nϴu!l{i>lY#طo ;|ؖH!- r "O p9~YÏAJ-耀Ϳ")"vh vR4ݑ|z lĶo!%XB<ŏoQ8 =)hJjX=.\ψ>eL'["Dm{ztb `UBhe jEtM<Џ{ 6X{#th ~Pf; `@*Cݿg: hcɼXwt( V=aIwT~I[Iw.wt% &t)B9BL3 oGsy[\*/|gC~) G%,{d PVjt2AXuj<!J1z8-)] IDAT)ֻNկiSXQ,{k3.M;!&;.EXK2-6>@T"OE3b٘Dly׮m.us;`c9ΩE`k!i|sG`J* $L{._jAq|';5 r|rEx2 x789F# 5y9ǎH2SD,؁h_Fk"d_eP~۾шvíA!h-w'LJ:Ɠȼ[ _%)(,;YA[_[GY w QL3mєY3mk{_; ޝgpqEͮ^Ά́[xRlfuU H;~psMk.fA!檥܂6sssqιR4ؘtnsy[߯dmC' iCȺ9mđjB 9h/(7{; %b#!gQ;Z#0RvF6d{) yfBĄ|dlB@fr`HIΰ{l@ xw.FjfH<]WBk:خ} b;A͈QH%budziȔyvoH9?v XocYcq ݀SSb3ic@oG}wʎ_}n yT"> l]X7G_gk7q29E"’?!Pu3b'/GLvNO?Dc~5Z"djJ'ӻMǥyDc5 njGAJ}'܅Dm5ZO.뿎vϼ]wn,ҼK\!ߧ>pbMCe ,ci :B530B{}G]o]@!@Sd&9R`#,<րXOF~RvҡG)C ]v$ߺ} 6i?z rϐqQf56azt=Xh-B6CѺy ZˆZг0kWy͗PPIo|x9ϛwѸF皴B^$?X t2kܕ5Lۮ˿⽯eˣ&-o]{wiѲm΋'mrXA3Zjnmc>$|5# "r,/D R5. :7% 4"%CHЎ~drY+S hD !RA/H]= ,WPBbzBR؟Rt4D t!=`c=EvO  (cr: _T"6)ԡ_"j,nNi 21vC{6uf6TCãh?oP H E07Q1]]_ɥE /Tcl[:Q.C[,LC~55G. c5" @ߜ5l3cE2"8 RX=p{Nꌜ wv,H~4q}>0aH 7ŎDʭ;4 p5 4# «r~|?.5|f8Enbژ\vJH >m='ӿ 4az8cUR@DN<ēSؼT"VOCz"8/2, ɮAʻ1 ؾ JҌkݐJzē|x2]̚"vZ"1J"ݗ"\^B_67q;f]iճ\F 0R$CmJ#hmîWٛ4RydvDm/>Ӌʙн7RRx2=rtk~wΥ,˜is9߿Ƣzڝ Sޜ;eָ;&6w˜iL5# =eָ9L[JV ?qlv-0fڧo'}X.@^*sG#4 ۑ/X ڽ@3kRzOPN+4 5H6+n@uEOQtY4>D}LQ`m#)ڣ9i޲chy Pg'CPz)`iLJ")ܻٽ\m}$U I%b.\]XY/29>|nn@̑Y#5vBQ%,tDl(u@A`;8e4lb@JP4k0gi{[ߩGTM_JĖƓ\%ZO*̚;g"Q"ʞb}:Cʮ%ir$6ѬCmWlN3ׯk")-'/k-q^nNKhM҄'М==h2a˜id%+Y"ɂ dEF L:CO3C1 QRNW#4;#`6ba֠H?! ;z>;9m7;'`"/ـϬHu@RpYi%%/Z߯%t}ho'7ddNt܍Z>>|юF>es2DA`WڸMA^ȱ rBl׺˷~n@J)E2e+@O`|&{+Ekֿ!T"iQC_ 'Ofٷja.]kWNj3alFcCs享on;~I$Ǒx]Ceim;q́,գh,ۢo@ Ysu[zXC '>e{/;=v_sMѳJĶnVB,5h^5g]DEǩde [oܖO@יD>+AJekK.Li=3212u,حG/VH>ro^H\w")N8D@ #(b<~ٞ@Nξ0 ^v3 7RM~>HX%)h®3퀘vh׾{]@@n{rhs0{7L`cWLw#p 2exk'cr@vKt$ӕ/g+Ū9E>ں*".A~:]A?\ܣoAƣ[QE"߰(4'9*?Dw_oꗡ5  FcFL]Sэ伱K4}W}'T"bkaIkGu˄|Ld9 e] =jrMV =3ʭ{Vm,3Ģ,G/Ў>ĔT"%PU!buo"E g.O@ &H3؁kD MU[n",!RX#,_V"`#RAaNj w3b bD;ƥ.k/z>o  m!2߷"L"0b1dٽ%'~X"pp4pCk:7qͮ.' #g[z ٻvUdfڶJ|oE[Y;IKy;8u>Ɋ rAaކ5;wKGU*:JIJ5*?!-R[C6Im;[ ޿{feyo|)8ҟAJ"6 S";jv$+KYTsXF#`P>RPfff6%T}P: FwA;>1PAHI~9nmO!V@/}SWĸG݈v'ڸG@Ȥ2* )HE@eȼ1xeCU @'*@^LZ;7 3K<̌pg$B9Jxֲ.K>y~\4α'Z~LQ5QF4} ˃]clY`L-=xC= =o"Fp,E{f2}rY Ҁ;? ~PL;g〭% "99y͹U]XjZ}Xwz]u :Þ|bC:.BxVZsnt.9M2>t)ι۠㑋877"QhәrI*ēC8|2CJjeW a: 2ĎЍEho{] Tl("YA@ۦUx.mSń ={+P8! .]x7R0B{!Z=2"s5~~W!e_F͹ vqDgk+`]@+#00=0Qœl쮳Cx2 a&*ss|s9L%bp0u @* O'miXS aVu425!VA4L3?wAa| (hNF0Z/Z[4lDaN%|D_: d\ޅdzVlwͷF\/Yث:&9a_꬈'"¯nYGbVLsL@|TM2T\-eȜshøz^dEA$>oY{\`fs{ Gk6QD3~Jp09[ z;b:A֞f{D?g$ ƶeA ,Lg;#&dBa3b{.E ]r{~_5yR"LEbdG`0~}/p?J֟ dnݾȔ: G&4Z"@!n@ 19ݐIwqR\}LSa\w (GE1(5!yMcx'L_yEmZs|\A@ K:,bQly2Áz:8tES;@9ι2?{?9 =#>n&_8"\;}9w^uf{u=nc~=_Cע}ЇEXwcm|PA㑩V\.o=,A*gs@f`~~+^ondj_ι5/ђc[/kQKT"6'LߎgB=BHv*@Q Yx?.ř.SzjGXw#`]wR5ީC* Qխ10!=VuaͻHaJpuwĄ݄@Q/Į->Ghb@~/%hoe ~_!ք| R=1V/m(o503L Mt"ϛnm D1A8OZo̷9(k4lRwZmZ4;ǬTm ߃1U_[n-1},< :{Ky+w;זkVTnl;gXK1f~/W 36 H&e"Udv|4k6"Üs-UMB@\c#MTof} ܾ蝿*!`]}S](}6Qp FSnC'ӭB z 1]ގBe;ڱLŐLbk@n}ޕΈZLeM)#?KN32%sЃ30:^}5ܑy1kx E[A!{>R5˱#Gmhv}G eڊ9[#3ĞT"x2 A&`gmo߹+lZNlʂ0 6G RO 'H#S2)LL'>BvEcr6aC 8mnf<: o>٥9ѹmNLA~͜}?t_zl8sY 1g-s?űg p9w:{?9Wt7ȭ$0SNCWr}h;ezo_D v/s^Bd[M,3bN"LY\O[PwisM}n?>Qú&үBcbV"e3,F"$"@6Z4&4nzЃys*B&j#QDyb횁TbV"E hQf3z#HMl"6 Yvgt>DpT36b}E~j DWƓ+_`/r򬏵HqLq1TEAT3vHˡi:޾yq`zҎedϐx2Gue*ۨ_C \U).XYs3:N`U*b,(*7NA.bt=RXB=r/" Xz~Gh`] B@`A1 JľOڽdjF6^ZHi(6 n9bZ!΄IjK }G4) !Jv z!bcz[ 9lL:#OE:IC =Lk-M  {Y; QC9%ccKhoēhעtb#={o$,]5gk& yzhC틶Eͼ`Bѽ3=$ؗ%ι]חݗodOx2s9V>M@>ϽlxH'=޷#MDl)Xu_aHXx$r87!3^3r\ւ"ԭ I[@JyU`=ɭ',G4(5@x2}hk' 7g"E=hY:ĀnD@\m5v&ք"A"k#p:N(2D eK6/Rz5hdj\ބeE !]Lbs|Zx2}#b@cvk*ˠ\n9ΰK; X f셞)nL]dzoY%Z]r'y'#Q]Ň^̝}r~ 'G51bŞB "& eK?@`RhQ D;?P_(fTT#+Xwv 4܀ŋ6vikFFJV!r-2lE,`#@:V`.A澌q4b > dA 1dࣼ\ *t">0e?洋cg jvL6Zṽ# Qē h܊X'RXP[!fo<4 >{x!-'UZyƫe[ b*d@H]=̈́<ÝAa@l ";y?Auįu6˭_'ٸ_/;SحdcbeQi J_rNJ4"؏h d勐,O?ApL*O!z*{#r?)א?-Os @`^@(lADG`hbێ@fjGih>;Ǚ֏?( VSb{9TlmEuu#4G(t&Jg}h ,&; Fȷ~ y؎ F u1h2^%C g{Γm {o!6Io;T9~ 7]OA\WB ca .B筟.d?+ѸނɝѸ6c:oJĞd[.Y3K,~ >DM[b]T"Z<>)XJVY;c TMT#skHi\,#E_vH/F 'F~st:((Ja}o|xuCɿ|[@ se_?5G#:ԥ\S2|%LE>MG+љOD6 _c$mEx2}\*OV|A*e`lS;col@lӿ F"]r="se_׏Q7U,G-a$e ClL~Wwv;괒>h.!"@)]fʖ͋"<oh@XS~(A-:n4#giHcT@NUk,}[{Vն?m|*Ye@1\lBfz [m` }1k _ېrOfw`R= h|%:}ޡ!+œ^eom B-D?^=9ͷ0+B1dzpYs979wm6;^wvνjžqusΥ/s9s/_e2c@ŋ39r 6T"VL{t*ˤY)u_pd61B}Pf¤רŗDf ߣȹZ6 XV /(.P juvT|"b9{e`A Q{ꑲf?1A!P 9sF $`8w1O1 J֮0'ӗ!P'2Q؎v;}uAlh6 0`cy=/ v!œY ٶ>mN6 x/L!skk>2}x3X_I%bqp*{Osc*'A(b=2cAjV97ڑ?;3Wxz܁Pۢ3{mַXf}lCɂ1`nFf|7dz9=j@(.P72C݁0O%bP"!_<^emT#DN L_B@W4$y"jRbӐI}k)YEXt@%@^ 1M!ԓ-(GDOd-WHy@ǡ@ODFQĴgp)pm<:OEvH)٦b>@ 竄f*~a#z!GY/F6wٵ*90jWVhׄ0)3bx^cصL#z'{}DoWOD,T"Y/BlSt&i\^.Y#`߄/D󓕬|nq8v䪶.IVqTCڈF厜s?BR_l睍3n_a[L{?YZFwޙE <Mv3ιE=*TFk,Y0 'Ӆ9qMS7V&L.Dq\R:|"1#e/ ^+X\~d\i'F:d`VJ6q~RBHE(^i}`Fj_GM;!GY"P DuCCzPE?^hc`xߑI^ĐEPiPJʱR!d]1?L^6Bsoh^T"8Lϱ{XLؽk'bf=z mP4:[[C>^?1huDlē67#ùI'E5Osn.b KCĠyZI*k'edv&.q&b#'ӓSأ_BL5oK11uH8:@,?w"Z;B)\kˈg"2dFi#8tg…uӬ%5 Tz1?)ghs<a SfwG!2wHFx1Z#cvB,Y)\@W֞N$lF!m!_63/<iů |l .=scmzyL2垈RdʼnӔ 3<$+GA4ۣr/FV-4ࠒIŅfFzg14YS͇:L snPA,FP<AywG 8:\K31*>vι|/l9*5%`,)W䝜eZjj58?(:r vG#-hk5"-G"}.R}YV#8B@WHX1(i_ !P2];dLA".D, N2ؑwL h6R!@&͇QjlȮ{/2Ӎ(8`#Е^!G 1'(LA^?1;W%vZC\RYC@Dϕ֏]9YY_Xa#+LІb~7bhe闥eV_f':ɲs7~(/D,G< 1GߊGģ7-FlW*r<^EH_PwHwBDa@L܍?: kYƎMME12]f}QĞ $kZο+]=Km,^\" wC`퟈bF6 y,SXo< @v2x 2WX@rؖ\Ŭͯ f={(r ʚ!V}HOvEȿ#d.k&ҪlcN2L{`V-CˁUk;%zwƣRnJ`ϕu9Ê9W>+s' Om 'BǷP7IJʊ{عhqhӷ7%O0 ^שaRڐm8 drP"ι6q0J sף5z$ Ri ,2 6}2̔yv1ι7zú;DA # LLywէ ȗ]=Df^l<ذy=NEv;bZ <@@qIߡA9Ba'd]>13o!7~݄G#'lFoԣwl.@L[ r΍Xyc29U|\^ä WZR<"sI}_ ~97m q9M2=hE`.Fp]rڼ{FHo"#wtƣ) ^2cڝE`!BcY<v'Zևx,F0 jim: _G/X|bhB[왍][e\$j_&{fDJ*3Hz.b# !ƣ16/J~Ȕd<YdsM<܃_֟罭]#1XUxW4|ޏLZ;XjHF& ĠuCe5ċгүki<'ۈ{u> ,x#{r:ȿXb~.zOx4rϖnƊ3mw-杚\(/.,}e47(LFM4;[4As#Ƌ ȂFD ÎX/柉hOJ;Hz1YH_j2&Ц SM_X:11G#{ *b;.CtdB8C ȜRidγk>tOIRhK2kx8}lP!`{; #3b4o+~+hY63bt^L|jlpU1˽YbXDxf V^/k*qV.JϯYh=o.ldzv#}-HPQP[߳xYK'iZŊEH۱C7/ q vY~" _eliRLMb~/|D9lhA y4$ewaybc K@=2)qbkmȴ8v]c`݄؟v#1 1X\ W4X389ZfɹoCGf$&l #PX?ދ/CX2{([td%C 4"x "=Jf퟈s(zumK|v|RH&=҄ݲfT G[k_.7[y5 "yiu)m mhV>OvNA@UP6#]lӚ姐 F7bp D}!>uhFs1dvI<G#G#4<ً^?IJ+hd W; IDATȸx42 sXTsmvբf!v|IDE@ h)VL MhCF6*zA@Z"Aon{7$O4KlH1sZϋ;+0>b^^, u"bgF!6YKN?@7h:(^$KtAJ~% NyuDem[fؽoB\c!`qյ,]եU'Vw@[͡nV{[_ Sd`a5~ַe>@$CfWo(Qj 5>^B`c95<'؁IbEyIǼ1ϏBs4$[q p‹؎-{ƦE ~D< sѭWt#L1{#ELZ2a4O(Ņu%eE{\0.>}z)nb4K4f0HFްDvkԛ1Ӑ?!p}܅Ar_Ĵ\;HW@ڕdU!T YLG#_X}k e ߕWݤ,{݋@n( 39DJ<U`;o{X;G'bȼ ;o4:!oMIH֮%X{u֟| }o"2gG8LC0 vhu}dj0c7T@nJҝir2h#l7xfJҤ x[_<;#}M]ߤϜVhhfiV͔/#^xƋģhhHFJ.,ҽ )x4"U 3A,l3@M̞z:E ֮w]CcGґc{K`rOB`nJJsÑ#{?, 19 #]@K:z e7H<YFN # (H x422L\- sI O"s8d}1  hOaģQE K l/Aѫ `+z1~.. :eյ^\G?%kޏgr]/ʣH^) +4W3/x>+״`~7K4LuAD dƣjVSuE<)1IEE'ȵC߮1 y$f_ƣr/?|\N!v,)ԓC6{ՈF \ 9?fSL[Uw7kbV9bU!a.b#?|@\ #duhpG!nsg@ePCx4'/_Ғs- ND~`G^̿z=llVbxd@r۰ Q*0&6@olLDfzv~:7H LnGlh>[W?nE-@bSwm,?/G#BoC;.~R))+\UQp¥&Lj&iڢ7z7;s#zOwBz`!pN2ҜglHF^Cx4r16 4 E&8DlԩHGzx4?/w"2o!|L:Ω$7b4tBc5rVE]mmf֕Z멳eg"0F8k4;1 @\ >rd }E-^EQc= c(x1? ֋Pk <CJaN9stACv`0 hN%ZC[L 7 c۠ۋ (Ǯ9 C@ h8^$N o6qխ[eϫȘ;ue KkJʊB@DMqaϾfsn1O/9(gIgOp܄k:>>` ??`e o3o1ERxdod^ %a=mPd\ Ah$rlk;E՝TFR܋[ǣW_r?ۣ\8\b(-±N3A<A 7yhlO!Ʀ5R;EܬWw [lkW){!`-$TXG px9 }ﷶ~[ԋ6KObSbQ?$ G<yۋ#RgcpxM @#3}-eȹ^ՋCs hAhsp ; Q4B/濱!7F/vt{/(ɴ,hޛ:$=22ט{RSk}/ɇ~҅45Uo!ՎLwkZn[O()+*E(eN:cwPRVt+pus.;5=cLї<4&G#׍quDl8P1 >uZAsn4Kncsn(rȵA09w6" σ 8r, 0K(},G#H N|4N_r~% "gB5Jb/W"`s,L#+B&װ@kC= ģ?Hy b~!Z0E^ીY5Rš&oF e9!3lZZ2`iUh=;P}lc'IJߋ)P@le;kF^dţ5^̿8 z~ZZO!YkۏXev^kĘ݄R`8N1_<5Y_u=r?m Ax{ v,kl/m.4IvYkOCg{;=S\XAі&@k]{{Ժ.齼;4V bhb=1[類OoR"% D/ݗ$8Юqb^GmhQb~g:uBߡ:ģ /{10b~nvݯPٛCzp~-iڨ{)u9?dBɚj~^CḢ,R٣Q ]l s F^14/{^ΣE`>i {zdJo%YsnF2툳.6?)hzS'"Ѣym%HG b@x42 a/A v|gfH"|F% s,M}g=6ً̚>lGVuJ< ;w[V̮!%azO;%N.)+Iһ砜R~<A} Nv,9A$&rV2ր68HҪ,?&x4wĞ#,Byw<9ےbZ\cTB2d@5hA,CRShA>Q %@/C;@&giٽ,4)͏}>ĮQnMCcx1+-XJ - h;%|@``/|]o{?E nP虈 |ߜGرOK'5ʿU#?mY=$MԠCXŽhγ\yiTI`<>oLG#"v6Iyʲ""/{1b6Ɖ~ƣ/p?=Hn:~r))+))+w?{HvJOaаa͒ ꥸ4Q\XIqai)о&3Hh6 V6`fI#KO.gӋOFHZhv ϶Ac73& Hxh2EP4 4Rt^R,EKq2Qu@y?,)$KH E#m#o!:2)U"HW!ӕ̰-X7V#PPLaT1~x4=f}¸k_/B T0ρvohQƩ?2#Fַ1vͮ6'a=T!^?x)FY2Ր)x4q^i j1 й\S6B?7yz쩷ϛ M&9SЦsS :ܩA#s "diL9v }\:b"Asem,?fF H r`#XRX)ڝAlvAg1r``V 7HamZK bFٽ3IFY? xbuft/_rjs5X'#Fg5pEB^Ϭ;z]O+"3~L`Jƫd#S2Xd侊Lc2>wkIR 7Oit̔[gd:`Ek4RS۶kohy9%MhlL]y7~c<y1HdX>֎X{4/bbhd]EOCOi`Zy;2nɤ{qȔAc!tc6b?LIv-.,=(,D>s'-Xk.MG>b??_i3o]>^/$<8Mܶ}ZSiBN r΍({1O+90gLhM |1ι @;\ XkAPQιh=Hk z9i` oOE` l<3' \}F u'@&)cԞ۔n\Bb^&#;yTZ! %eEŅl&gw'm@rGۀn]/[{շms=uu@7ZegVS_Am]9%eEŅ- ʺ(rm~0@IYQb>{#FzX\ `s˒G.>Z7;Xi,A 4(@~f_4&b_0jr="̳s@~YhdR<bBa%bFN MZ'}>E\W ; )n|ǡL/GmrϬ=Fo6r %- R &d,LF 2-\!bη/ކ<@ZW/1CњW[oWl<Y.H)@&svJ31x؋/ǣe֧ƣ%^̟`}lҷEbN B艘 AfٙΖHQ䡹6PݽP}1y9}=#OhSFޢK󯺫ob\}zZJPPƼw8(N~Zm'(tBIY@N].]xUtӊwSS.zf13voIiOhL KŤS Mlߠ&7A"V t&vhp t]|~kq#V"Е3ƛ:$"aFtFdȏ@)#Byǣz!)n\} C@6D2 % P׬G!&}u/}WeOTjWԹח~spBkicǣx4@<:E[Xo{دx42ߢ%ٽF7x1u,32 HLȞv.v(T!iyEysOY4h2[lċ--1u-SZ";C ; Gof6Z KW F$))+5a"֡4NEP%ysBZZ9U5dԧ&'Kʊ:/'d.JeV{ma/))+f^wĚY6VXhH3Pv*rؿC)Пh u!E!֛XuQ9#cG L!ȿqVڽ#J|r:Q F 0m&"휆9x42$B)=Xw@YA;/?xɳ%}n,p.`kz1aCF]^oe"6=x t'bMFgS86;Ț_#h[_<W] r%\JAFZѷ9sGo-_,k?eZ˼ESRcY=dVĴk[uDXXRV]qae IX6{oiUHgP`E] TbS%CmZ-KOv}c[RVh]\Xlݗifi _;3v?'+~dVL# e %k,K}($"z/V_#&l)Z,ŸA?= m`JNEv(b#^mfߋ{tStS;6̙ģUHOGJ. ،G#^a'4(@k_Vֶzd mn:zw[v1<1OϑL{''!x@[Jm4db b~D/b4 Xh&o92%%8 =6fa޵6&"Fwg/}ܗ[=fzzn8Rmidڇ7`,6Hbz~UTTpyw'ҦYx{ic_|kS~PaItE4;.-&+3&%#{fw IDAT0ޒ?bV$ٮ05ImN,.,}XURV>T, JqaiuO7%r]O7e̴O~eMv=եA@=\2ELG#7k6"_L8tgdn-}윰2@&r~ Eencvl~CzZr e'45 \\b'.Lϯ݋/3:쀧؃y)]H) D`.K/?F{1|2i K G݇|>'YEC!v0bα}OQc;[" m5?"d3~sJ˯}:v|wA{U}-NŅ3}wko~KR{yh/oZ_Ō̂q>- 1bsu@_jC?حa.+/Be6FX ȆD\U{,R!H< F"5;+ SWT2-@s]C2gJZeA P,lmȝ%#P}:-x [ux4%td*j3XU}!0ڐ-F 77zϦ1},IUx48Z6Z? V@|@h9GC aHy*!R1l|'!GdKg'RR{59+ZdV1.}djvZw6yH @0%})m 2>#ߪ6ֆ1(Ďj_ebߦ"-dY0"۠4߂1tw0MϞNWϧM:ơڋ"6&@ch]!@ ,EV@]6Hżkfdپbij;){OA );']1SNL뒅h>Ӏm<`fWxTl2 6]a,'YC q?C`l;u^WUdnv6 tZjU^;W~_Gblo44Mqa;eihsqc6W\XziIY f{)r:vb"HW[Y_hy×mx&Y )%eE!0ML4yߐ8粑AodιzJr v] M}x1AM{ 8/7_A"j0"u`uG{2¾ۡj8ӐIn.R!Q$#Vz$b"_O"HL_몥TS9۽zeqKkڗv7]$D/㗒L0HFNݐCĮmm_hr `~'v6ccE`eA˝vړG"*;̶GYk FֻX@ P<6(:2 [XO3LoQcA=3Ѣz&Zlfl|KH G``b_\ļA2$tfuLMb)d6o+Ņ%eEc+JIkXf1rXZwXe/X{io]RSAPܝvc9۾ONGŸI*drhM{_2}2w,ߨD X2=Pu6hUE2/G#5=/6k*., Jʊ~m`ݟ8vJ'mLWڧStaιAߌKWA0g3ZmK %=Y*ȯy1#=T9dx40.BI֧#R#Gql|FO)/ȯ,-rACf=ՠ~qY<ozŠE ]zƚ?cĮ֮݅ ƅ&ڧkڡϏVدȌ7eP4`Td샀e嫶ha^H. ;#-)e$3QEOL kGWoK>:KIOOԦ,y;| G@=F >eH=FBNIJ/6{4,GddZڹ)$ʗnd0]^ 9Zx]>d ̿+L4XںR\.%qpUzZݔ44Gaڹ.6}gVBzMlbY渓}^A#&|Mz?xţRnC6[%5z!K\v:ic ;iqu\@U;9>Zgn n;jd%sd{ Ns=&#m-_MІ9Ap}~=JwT9.C:dy?j,~cANF }b/6I,ψҿȋǘoU{Bpʑ|G;ː"=$Hv㷡5 /lלxAzAFzIJz'_ezZF]S77d},^#HA>k[;!`vrV1i!W1aCJu{ S~6]$D,^涩&wUHoxۊCAk[:$9.IM\$7=CǑK[}fgt51R'"5{WllG!517ꃞ[9ǣ[-x1#2}`x4mԠM) CS:{n\6a"a$s": H0/GH\};s p6E)'Hϧ/..ϵo5-}qKޏ{kxIY@d+^sW-F^*UEӽZɟ%eEn(ޟJ@ A+Ҧ36=lsn==Mrq>q΅`PӝsC&tgsνޥIA뜻dUC4eιTu9eXVAp=LqE@ s6q~ukdZ sbƋ3:-KFC3ZH/柎cgvʡv|8i[%L4l|Gh.M!Xx4r uhċ.-'dY!3 :MDsS!~!xzGs]c5v=cQMDeo[$ee# x422iam-:״HJ:{O ?'jܙٝk쎵AJzJ'd#6N)AmzUzR\hcc߿Ff]ޫYϴy^y<ŃSC@׳뿇@{6.l3$ $P9FEMna; wLumNojb ))dan"Od\8wI-gvOr}ݯ۸5-\/6ȕ](JЬ>?deW ^bK߶K\ b{ҩDymTA0~bAPĒeA%~<AykA|0M?';>F(l%ڠ;uW x9G߼Z㈩mYs,q/7URq"ZhEY>~F#Shqy#ݧQcT&#W%3Ro[d۔4FS30Ig"z 2_6~(/R]4 ܀"d# hv6i@o/x1ۙ*HdM-7l}AAx42'='e_=y闩A%["t 8,Kݕiଂ6.!Rnb%AgXv]q'9Ջ;vnsp_[ևol^Zg&$4o`*nzҡdϘgj8 ><#zv.e Kh^]jHTAjJFZnOMezjfNڈf:x֣wu>zk_u}1."ŅKLʹoІ;RRVVRVt\IYyiŅ?z?|3Mϧol oM}s=f CkP"Ք6;s ʯbf'/柏X S?ȩ]K pe<y"6$@GE Z|B4A*9_b/DlG&T EˍDבrfm]̛CZÔ%$tE`PA>ku4BާY?G[F/odz섀yH9@;rMA/>]d'b:.E:dR*GH7RK*/揌G#x*hx4r#Ii&is=J jx4܋" d Sv\bTIAW* <ݹɋ swg (=³ .%nOAAxߋ/d;J3..,x{CRU -RW@RSXQɽz꛹CnSU/cf1'gք;Vմ|a#ږ()+:Bg"{{6?O$bq M?,ޝ'<,@W"7Eâ'1ILc cAιj4N4m>^cXw.Aw}7+ss.hs )ι[o:A\FW(d #u;m@6sp1=Xrr_͈QZ@D܈0OtD~AQ&-/W3YKbb*Yz\Vz1?1-$ˠ̄h# #߰|h_b @u?/׎v8# r7[[_}5Khx3ݿac \kwDБhd.u'bnqqx Ba nCg}SNA"S Ħgt(*:'hV'0:tb-6aEzʅ#\yi:htЪ1C䯋~W^۶kfYmm%Ņcz K+s4qÊ*9'32?|ض5QGq<#?kjsr2VZ_ K甔 (.,]XRVt&5FJUι%c.-M?:3m \AUC;lT̯#`-h)t(?TcNF;}dߧl-H4WE/eu@g:bZzGVNG#,g ֯QdzsmHFos)NE7{& ?9ȶ kش7=Z,]\KݼJTx6RRVtb,/3w})*% B,fk_XWĪ<F6@qc馴jsv5P.Ee>i!8 ~(!fc4ڂS-b{H>rȌBc ZL%Y&vs^c׸1ll& f/477~c?-}x4$|[D~ţZ/柅XZ F~{)]b~=sP~:7G\Q'%eEHI;_lZ4 }jfȯL R@^ܸx4m^/oӛeĆ-C@f2"FLU{Įd̝T_ٸ' V?5*Gʿ;N!$5d|Ɖ\U Y%$k|FL( }XbҐAȲ#C2a֗xWڽ'"i1IvmP{ͳk]2d{o:*)#yơy *=`cᬽYzҊs;mN UX{1b.A&;3wA=]XG"_ac60"0ubZ^q M"!6wKe C"ŒY߽D/\Edޒ;gte>iNWᙈA/CsW=ȯUW!H0@nxtQD,[Ȉ wZ<op̑xvሉޞBr[x! S>_>Yfľ[o]=ĚD.E@Va,MjU8ɞ7E#&+: XV&kWkRsPΥZZHGG䢬G (~G~,|"K[iV.㭍^rsx(8ψ#<1ec4EL]gu:V?0 xV1;%mGIJ\X@ obIhߵvZZ[-d@~4a^@ G!CB #` =>h4a!fk f[ڭ=A."dQ \D\؈ eAN__pg2$HCl bC0+Er@]N"pM=1]#t2~&>F4)z߻wB]3}wB{V> c"( RXO }%{ vO+_M!s,! =g[B[tH\GW&aG e>bkQQBdx D"H{9WkX&H&ˮVnc %.V[dB4{Q(ĠXzS[=ZM yU@EgA~{b X:yG/غap]hM7 _\1"H'JIqF{zh4~lb0ظާXTr馫PN^f3*j_$ц+|.rmk0:]NoUOdɟ)?} OKgmsh|gx:NHUEͭU5#@|EcONW~Ymm78RdxwAhg b>? gbn~g+ݓh]_ؒ7P P]Sj'<~5!C;8",H<P>@Aw61)sWr VŠܶ|\R=yXCˀ8VZ0XYwpu2]5Z>@@%M(@Ո)@Mw4f(+yu6tG 27ș'[ٻZݲޭȍz K }%b g8,ctgy1Uh3}?ʜXsLHm@~V迈OT'"@F 4NO4"~D;s x%3>;k%=c~Aq3pN2Tnjb$G-XӕcgkAҼ'9b>4zx_wuu^ҏyS1adHA&Z +*j~/EF30fڈ9g@}Jm1_8s.>C ԥCy] 6z;ks2.䛸3%<KYꃽPD9r'9vD+'cԚd9%HuAcՏB4.޵߅և;Ɨ-N,E/2fmQߙ}ъ[r{f2mnnˁ=߻oߧ?ogΫKOQka~}GX_4z1܇hقާDfŊ8 r#<{ҫǔ4zLYKKs#9wsnv֢;}k&-'6'9bXqnήe\387{Jmˑhn92'v@8XXeVZ4 s/i^2=X[XlBCY@kb`#hsteq{kds̓v?:]yǗK&Z{R3}.O#}EY7ہstU5/[!qhIu&C3gG`}2dTܢu}1sNєږnуݐ=m}` |sÐd Zs{"C/8ʜs3T@sn ZGUιJ4ZA,gF9F>{dt!%"Y(λSb>Dj{4ȡhwļ ##} X] Vd1FsR&# PbD.#(+F1K*b^E@;#pw8 oF/Fod'"펂s6+~IJee@` BPAp1/"p7&LAT+ӳ6{I|?U`I#eaB PRĄ=؞MW^E.?aq+]C?Ѧӿwk2]VB£@qd><ÕmڴH?@SkDxxyئ3߆' @YI/Ow%џYywc Sg˶ywU;`%н%8Gs'd7M `F6tF,^\j~ޚFd0lCUEMPUQC NW:]y8@u{¶|"V*j^G1n@ ࣩ-2޿CAsju.DncCE֖"o5C#d{^? Vߠozd>%Rcؔi@ubA+ lt& t'MG#ngcCfe>d׾Mׯ[FXm}~j 4:kD ~/K,tȣ !{+۴3L[^E@J>D A,<9ֆlZ= ӕ#cTu:#{x#CB/}2fώ;߬df/m c;XuĞWk"_LuYV+ ku2UUQk݆>>j#XDGW˛^1bEhSN'3׾b_Xf.cKe ιA9رksc xw]8"@ ^t-Rغ$HuF 6 Ȏ%R&ѿ"V{zb)v;E"WOx9@Ff s  H3Ew\ShbG`;<vEVٳ^sCxll}Ϲ֎J¦"Xhe!}ᦆwGH#bJz>Dz)Y xt^,@D"1|X#7Х+:B aӵc4;M$8EhWl;2pkL]>X>VGAo/]EV?X}q?tJ}hL,EYm͝{NMv\<"7Ӏ>3YYyh\/!wB{л|O?Yw8u*~mc F΅V2&t*!NugW.Cni/S ' T+Y9ˬqm:](~KMιpÊsGu)ZZ?%_4ιp.v @p1w8&"[v1sn2;ιIh{ܕιv4'hFf9J<  P0q>rD|KXwBx >Ȉ.E̍CPV|oem)"fXe92h[D} r ]bsKv2s#Cj@y9b*>a~ŮA, yZ>V~@@)vkk6C;z|cԛ\k>(?r"OV6ꏌet1AE:o u)vGl*BD1{yVSK3v BsAƒu)뻇*A\vjw[W1(ֺgː}YڻܱN{-EI_x2 Pk}71=COr>r42τ|>{|X{x{ rB+J} 2Qb"FlĄ]zsA`3ey<} ?FPzĞ݉pwU7{*rG]yA@ as^ wD^E@bz ? £UaupHlojC폀K؜Q"`-\[#m.BVv>9ȭ\s =9 јmm~3F v v,oN(Bk(kCb? >֗ф^gGfa{4NÏ tAtEL/{-PfiE ҡ]1VUԴU+w*juI,wϚE-[=`@Qvлcn~l:6*weUEͿ6p$4N1_QɆU6{F1[YfF. VbX" F?isO|tWs:ǠI٧nhb^O)A.v hʊ}2]KzFTY)EI>C`/(v˭ F`&d|[j؄#F/ A3V e`t{+.+iRн}eV'X΃.PAwxt _;Y= c|Vuf\r|\@oIƣZ@Rvt{+oNR88lKZL7al>A#uIxFm@h{%%\[=,o:s.5]u1}v߇P -Lw>ߋVF_D 2>! 9ڒg:{!y6Hƣrȵd<:)ߠM' @v0*`_??>3?Wmޛյnzc*jӕМ\UElluU5W+A A֝ }ڲDݞ7꜋dZh!ô.n\E6bzyyk n}_*ss-BWt4(59D\MxaB# IDATYېkIk`ƕnfeZbg}{>d cLG&űDj,ڵEǹ+=r>vĹz7yWXy@f8}ߣɭy'ELq%oqvy܅p59F= wZ>ym=rsݲV\ks ށ`;¤H_+NMahlB0z9-YS;UUSv*[Jf,H#29h~ mQCNOd\F} VI9c2>2v7UOZOD)Mܧ"0V+; C!>r=mG,!p 19O!cVX&{6MB.#88%@jG$ccM'ڳsck}1M$dCz]Ēnʽ᯷nh<|vsjs\k{P7||:H;m2.*1-C({ȍx} ֭alOnϮ,@H9<kB@==`Fn~z_ <%7g"pwmsa:b\(HV(NN, 4v cwn( ;sxtC[)R=#"'[+te꫒r3ӕQUQu-3p`fqlV$0VE@d2Pšh"|vFnkDwn ۲;2Narf^ne8Η,!4ttO2ރl4q X}ȧ@~ʆ&]zHSuCﴺU}(G2ɞ 7Z{sEqdCONDG z!`|vgX@r^>ǛDc9b 6K;zʭx)` 1?Fn]|`NWܟuیOP׳mU6A~l; M1"J~3QhBAFhrA@ Ī܍ALJW%҄R9,*8lOhG#&*A7?1|#SD U7 =Vb괡8I3=knNKƣ#dt"Ɉ\mg@dйd#f )_}h4X\梱/vb;Yً H. &.o:hD~k[={,C'p'j@>hd>gtC H+ b v(}Ԍ` kLg;u"kzތXb%ACv];a"? -NEMhLՙǢw~.B sX1> r.a~^NWtgӕ%ӕ?NWFӕy/al=b1X"u L/Oƣla))ַ*H d26sЙWEq; ೀ0j<ɠ ݧ/VC8{1 #þ/b.Fld31*%Pbʜb"FEdOX rMgG"Lgu>hK3usFc6Ǟ]X&NlO3٨>wO}OT;['"&{Q>UFWG >ۋFgco{AgÂcV_؈wg`CxllkKG?]T+f57UWv+g?PUQ#r:Ѣf%PUUQZEcؽ杵84נwԏ675%}JL[ϼr_T8v!پtD,ǒV`o_OuHA Ϙ!?r|& }ʆeqSC.&=XڲK"DjX"cs'i+odAmNȸG{~#}  kgLx1d1\&O cvh,z%Hݏ&4(@Fk X*qlz7# B e?اePi8a̒od|2٦]1G$>.4# vsGm(Ȩ & \lFlz-~#A@q ];#rFZi:h_|VIa F.`~^9]6cn_NVՠ"4VDLr6Z"Rk#ذ?V 0 r4۬;EL(y1a/}v)a_,#FqսsѸXT~lew!/e tKgw-3fh9gۊSZޭNWrӏycWW͚=cE3hn- 7dܵ#ֺb9?Jл~*Ӽ]ϡJ9p\AV-=#î,("ZCι/ާQ<(H~2ιSs7=s:޳ݝss8Acs8$d:usC\9w/w國ߗ:&X9 USk(и3zٛDjwDMDS,U~{G},Cb@gG# GvAFf2H3 Iƣ;Qf!TV>g b@kJ' p+,=׬M%x0r4^!;_ҡ}-=kmnVpdF,K!kZC08Hݽ՜Y??+W2>iD8WZ.E+K7ϧdخ\غXgkv[=~"z^B.tUR[_\t_DO;ʇ Иt-N5#@rf>jт!m#WP4}{!fe? MkԵhj]V=YvMϏ!x*lzc߿b:ݙؖN`(M>!.\9A)s(yl+SE}VI0ZMMʺ.dke;O~aeA@s"=#^\HVfiEk3oijaYaAqEK˙>!x׍E[?f1z~>vf9/sݖ[L+:D4-^眷!hn:97sQ?Cssh[ u ι{hlX2Kƣ(QX"X"2SoELj80~rЪ&d`KT5?@@f[eػW8 ri,w,^2>,䪛^3#Pnm9>EVyh2 kF*iJceF}vWQl@)(NijMǐQڏG`dExEgwd㲳3=<;m=:d"tA{6#u4b_ZKpwV%1 ]b{n,Ď&x;cMOG )֎lĤfтY}"ܔ!U[Is81vYok"ܐPf@hA)Yg;Q ܛieǺ@qq5NWZY󄺦QHAt5KLY2WkCmHhu9}xO>WmwLϡmgz΅iO{WS-p)Z*qNDGGv2^E> ޘA0K>A{Axyg AІJo/A"Vd[6&8ۙ1[edKfHƣ#cahź Ds+IƣkbYhRN#C@xHrQ`dLE.4}_|^$cLEn K!0O"7? vA 1=LMDj{@Rry6j49![sqprkMEm '3yIs$k`̲X9+wb7daKKƣ"72NYEIB r/]=t{ sGReV<&zF"О>X_!E8cwGzkO!Cs] 3'!JF۱I!c^s`] 0m}4?k-9/vB g:p1e (u&L"aф0zkL# cۦg#jHa~okq"4wYڸ wzrNWB>@[{9{.fc'CE8驪YQ!@G =򪊚숧 )3M08 <>+7bZǸe| eɹӕ5]m!ySeG` Z\7UˌC _ 0^GoF.!h23ZտXJ^D/$ rg KnCnY$!24!v]2WekYNCvg2 bTv, }Vh[=Oш O<[c9Bld @@ŸJmz\Ŵ|dE`{(1v3!qqMX^;Эip+kF/12-N\}W U 4] GջnRge}= Zf}n)D@0ۿ0beeu7\`Z`z>t쬿@ J\p4z!TbqUk[wX;*goO w1̧D\W#wSq7x{-:vBFpP.㮛{`Wkouof@Nv+9٭AA^,pۙ03_o xnQx_6|t{@UEOU+Ǣ!L+}QΚ6E+o4^|i2rVw asG汤uZk23m(;_A0i<@8粜s%& +7WqȖLKmQ^˜so [<3/EaځchK~G[X"5̀R &IxA5;筄nF&b"|d!ò2l3ʺ{>UOҞ6d]ܒdԱmog#ʪߊ؎S$}JY]w%\F0 ]+bD99#@a,>2~r'b|NGZhE l(!bu[h9bZVYvimjAwG2dkk rW.[׆撃=AxX"udB!;!ttm,*BAu>2@y<>( `Yc ЄuAʟ! I;"w~E|&GZ_6;p[ %h"k]1ʛG7 CلGA݂&"Ve/u#<o]zG/@`!Z0U:@OLWyVB-ADMh|2ƹ5_NA 6rREhT+تfW3:HQUQ3 fG9݅kwt"I_GA<6huVOӕ7v8<=Ŋ>Bq. Z 3Gd`4Y6awÕ!0b`h{_4f'ureND]b*]I2Т(~fŖ~uPޡ|9h~Ce 5Z|5GO|77yKFw x:]]`u_|n$V@k2^HVEw~[3*(sI΅K"ON|xf냄ߋ*/H{dD::v|%Fخɯ[.sޥa`l|5Y1PEP'bKhp,B}@~ȝX;p;: _ :<;&:bvGd2!{L"=sg^oh; ЎEp4H}Fu>#zbN!wPDԞMSmC_ :Ȯ틀U=kǛ^@[{&h޲A4Xۊd<lHĎ}XA@ ED r5J3!Öޅ}O#v1OdD #uafh}:Ԟug[6p hy߱ 3*Br&-?Eea= aߎVV=7 -8oY_A *mG}hiA;{OFy]Z"v/vC?aUܲ1p-'KrI7<4wO^}Uu=i?.&5웝\܈أg\E*ni߶0O?Vt)^=b1~RG#D}T- ۮwSu&bQwܹO,Ĩ'eb `cci| T;Eh݆&돒X"'bF!.x:N%RsЪ{)A,>=j} ZA:uݎةQީ,l*2*DAg"C/4Ib@> 7Ïgn:YVŏ wD6 c LOXw)B kmGp] >]y?#x 2+;ku959x:n%RW怷 EϺeiȰw;Ls:1]"OdhM?EA =Ğ9Xk}v2SD ]QU,QޠW3մ(mC#9 Fx5Z{GPZC/X]~Bb~ޫ Z [#qNOsTAgs:[Y:9}{ trC+(^]߽ H ؽD*\3sN8GIѢD8Kpg_xg3WfU}& ˧x/7RX[,{!_FV^Gxn>C1oB}_t=ykteOUl`,D]xIM!H BdXZb!P 1Ȁ!\~V)2hw##=<,DlLSX6d}~]̈́'#Ơ1={#bAL() tMXj;0{=DFQ'!ۊ&(FaZyhW۠dlD/b:A%| NVv1Fs_cT` 1ZZ_wx 1r]hyA8#~7 V4fܗ# '38.i^d\~ l8ak߻ށث_@nлb˦"#wGΰrѻSA5 ϧ0T~w~פ_Y b"7yx#IRUQ9i,kD_T\PUQl]b m>oځyuK9haG9٭女]y A@vKkNSnN룽˦?m)|[?7=Yb~1n@G2hl.Էyb4BQU56An~cwotN?mlY,&xy/vS"G~4\@Ʉll,zJ).%ܲ?OEC,S2 w]>, 0t.b| P: rbCw%v>_m:krȝ jبKEfLur.Aw,̩VKcn0 #C%C8Yim1]"H yU\mwWrظXP&JB H!K |PY` PBREE3ܫl=0`xG{̜;9gkc 1+s^kAAb߯ؕZ{|K?(ɴqْ KKVtv^@&m*A,Ń6\)"d|ƴڱX>g&Yv_'S0"mz]46do}uaS;b`FtHfxN,zwhk@=dƇl.ژb6yu+\D+7zjolj44e64Zmhcېr*πe%aye}a!-y#r,~/C+I{wW84mkc]^vMںn3 o&/\>kmc@I/zNs_3` ځ Ues*3'QlK@F2=ȞbZs9RgEE8+vmh-d[hBN[H=9/pK&Oz^i{ X}ڽ}s(R[8`}~A>HIm2?%HyF K鴇Rƃw붴6XLK[س]2OG r.^7s]XL!`/uf߹p<߀ͮuܘajB,Kʹb/@f`bwAUލd<:ۂD{&דX"u!|j\ jmxDwαc.Dcp `mhծA͝ec߂69-քg^֦<V4'\I}89%̸oqA¼s]6,X1?󗎎lte!aKkv,j^tnw9U [rJT4js0o얦ֆƬo=] C`A}acgy59 somA pW] w Pៃ owŀox%R[#АG]݁d< tDj1_!j6r8  `i(R"g~G?|vB>(4XQl#is'Ɋyq,6!H Er<|9@LV0fZE 69 iGMP|خDh|S=."<1|"@Hc2]lc91I7ڳ E9q J'w*.jgMLb[.D c&ߙzF|,dž>T4ևsJ-dYm ALy!+HlIƣm6@z"5Ӑ86!Y枋b ڔ8`>[0\RhNy!2O^u5HP) j MѸFth)3x~ގǁsnIƣ_jR\_),hme~FÐt:L[zȞ9qʡ|8ggOhlkNEK?++O=e%덙60~0F2#Io_\@'fdTM}O ooW1+pGYI}%|/ݳ|e-D m`v ð!}wwƂ ȴr%ې7_6tH"DX"3bԟb&gQ|v5].R/<|8ZA!y}oa!RD/#O .FfHA#`ᓘG۬m!p RjǡŲ)OBlCͅ:p<; KNQuO@4"&Ģ )H:яcubiE,Ȍz?9i8I]9;L9aO4wym1Y"O6{/AY琲va4@sj}R/k y 0^LƣXKIJW:2N5d,Fs?#؈evPaK+h{P|$ih@siB4~9?]V=ƿ i#P=vkٙZ#7'd#a+si@&o\;Ƞ7@$BF[:+GG?_~{s{wvt[ž]t;'9آLJd;mRye<WHY_LhkҔdVa鸸҆OoKGp4>KR[ronz0 Nsٖ*AelNqpeD;eqܘ>}Q %A |}Y[3 :7 x?S݃ x%'hwҶ}'A0# n `:3}AA.?]6 0Gr3"kڗfH\ȇ-[bpF@L-xuRJBX"uK,iW#0 1#?&VO9R.G#%}2R#YHi3X"UVXD1 E @ )U는WX\ğ&N,"`[ |z#&##bn'N!ߠhL]6f"eڗK%RLv= ༬ւ켖LmL?Fltd4Mmeɢ4;Ԉ*:āl,vA')o6{N عϾkUl;!6Y{\Z=Iy3mh #*4W bç2_v,vj>˃  h~h^a~wY[dv=Ѽt)6G\0$\;=o¢g:>ީo\ rTmVЕյ}Z'tXgЗ/כGv$chf54E ? ;&啥cqMr1p|Vv. O4[6qo9qRǂJui?Hdle9A2txD_e0!ߤin }㛔5anғ hm: ao7bwDg8/0ظmr1b8DL@Ͻ蘊ܕC ]jm{;RMHiG0pǢ1#bbZ+~ߣ։H)@dt0>߯ iF %65&ɯ0ɽ`;)} a2=[)s>o@Z#R'bT)z;-cáDM3YYLގYz u%ZL+ = pR:1yA.xz̻,_h4A|Af-e%hͫ+| .X6|M}xqvso+>hm8nڱ wT,1=lpը/[\wo{[׺4fBVS0|خ1eo`cdesqO r x_U&ApvFnd&ڗd<`,z Xc#O?v/R2ӑR)Cxf+BN3TXX3OҎK5vڦ,E FgX[!eRt34I? -kF@kE j||<1Dc)u"t'E,vbf#ftC>iw}#SSLXW"Śk: n<#1r "Bd< H؜>@.`"0나fؽ϶qf3~wd9 Y!w!h.mɮHAβ:޵qg혀@lfmEc6fk;ݓZ{ %h=oC Fk4z7~b׸_D ]s.zkM^ -c>;Yy9 f!m`e :/@@̵:Ǿh{]y6L>Z3n~ϗ^4tvӪN1ADCtnn͙Ԝ̖^WO}3J* 9W>q'4o8C5IjP]hYIwQZ]hf5>RS9 ~}|2΄bx ߈$ |ـ߸w]UWI1d ^\ ^HIuG/Ky(b̶F)؜GvHI~fϟ쀏~XoϞc}hBʬɾߕ 9rN/.AvcHɇ&j#&Ox7L>+Am"@r[#r!XC)/F*@y_خ{=x.Dc<bϽW,g g6;Abb".EDl#:وtr4O\g1\.|OѝH56Wh~fX{g1#bo7.JƣWى]7ϿHG;`2Wyz7<SX\w2#-;޵22ˮ}<㛶JYIŽz[NΟލYhLAp?ٻ ~nֺ# X6fTYVf8/)waN5\6, ^ð%a_VA'n/+x?W6Y0 %1Rn6fN@סLT}"bPJ>LW 519}q-yW-L*FZ|-)d iBf6AL ϳkgz"%/z{o_.B;34 j?1A=hbЋK!夞KLryܨVKQ0p\mhm}ǜꗣ Wս0?n+_l nEk6ϕ-E, '~E yΖhށM )kԄ&9h1k97C |āw:3!9p|E}&ZơìUd,D>3ÑBw'#ӈ7ׇ"&|vBt/@JڐD6r"nu>9XA =+@bd\G%R_s)HY0;#5P^\GP%,c;qShlqG d^4T|BL7 p(GwWA.~/GoYD܎dXԈ=u{fܱgd NT g7o( Zv.z ZLGZ;<֮̋ȗb4h:W{z9yhQ@6]OLL-vb$>>@>/~h>gMƣKϺc6jbMg gf$ѯ >Hѵv@eVf+h @rցF,*NDwJ*[0!h28bc?aր. 6:ئR ( ð= W\|6hv#bV 6L|Hy~-gq;pvG"|C1˞ 8c}8wȸ;MH.tB@DF}Ēd{OI-Ev|d*dZ 1 ӑxsĹ -;ٳ\y,z8'H]G1Pn2u6xt@,z)(սx"/ĂvrHDk \EM9X_d2B,j'; w'ctUd q.Y& <Յp\@F@`ky&n[ul)31ҽ;,mݲ& *^OA`e[-]X\le$”?lL{,;|'’nkn-wԑh>DRKD TJ Dc$oo.QO0v0b]߅-`G eL1ߥ\0F28=f RHt T+yV`E2~bu9`۵!;/@ _K.H^)*k *rRb<KśO@fWjr<X˱%ZXkhiu=`v\ad%RB˪vk^WSe}y 9w@,~.f~L;|!027vA#Dq蠹uE">&Syd݀@hD fw7+cm"z\Z?3BlN`ur366?7֡ 9nVdcp/bFPGoɤ \ӾBİ9s+8e.Z!mVG{SlI3WFtgIO.xaLv={f)啥V;{^rx^}cիkJ)¶ gwhX^yY];,xeV59FHƗ5"6ZZ7Yjs:{cLlZ1i䂦fe^))ZLϩ;)iHy84T(1gYf!%q:2;.O^,FLjF 11Z;zO&хD(T"mkc՟Dq/"s)ߝ<( 1-&È}v;/"zT<3`re(p.hލA"ߞ(! 9k'DžZg_O+ًy2~ޑ[a{&{[k=[@jN[}cml܁4[.0l(jAffb,Dȷr:MD2+K#̾ͽ;1RLm'>1M-jf.A;ܶ_o88[YI\ .xo油Wj]vAVVǶ';wDf,1-z 9yOY.cƎB(ekA'(hfuT-mH@ȁ`q^XVGbJA h:; lef5H* wpB,߂91-[X_3@`¥l@ɩYX"ax_I1;ܔNƣKbTfp!qY@HA)O0T[3ƻHvG5F!8| Vo%@.aȼ 2_"S4\pw 'Ϫ>b7.$8P,ݵS!Frɸ hNmbD\ X3ϳ _lrr"CkcV_" \G}6~ߴTA1\iEљH_UB^$Uv bnoZ9'C>D2얌G;4CN[[piO)|VEdʶ߯.o"dB'&t?mٺڞ}5R!=]X"~b9.|dz`}Rzu;#;d~;Yx |p:Ï>ys'SxB vt`ڜ̷O@6#}s(oA,~v6nXh !&?xsxݟDН(L٥|6#iisМd|} !l\o\X"_߹}S)啥e%uז@ٺk:-WW)\6Ɂ1h e]K)-A c$PA$ ,|dR RܽbԊh3S|)z"}V׍AtDkջ4U@/dV<).rdKOŜC'nb}~O!e{!0. S.*( \`H2u|h8_O15G#݄ s'^@`d->'=&^hRqNN%ȹ @aֆ*kջa,LvoН^Xqz ~G 6tC!b7cp#נۊ@Q .7g7хq]rkcg@V}uY|D] bwكQ|Y%.!%Rc;yE2啥YhmN啥"6u=~/-3'Lqԅ]v|؊ )B[ FF h=i3m5a~!z;u\6lr`!lF! " ;'H!R(v ̜̐.j 0)#" !sNd;l$BVbjm:)h>s>RLi| +cr)<{Fi<HKX"T\ah@ȱ}O4dV=B-IƣѱD.W>DĞ96(OehOcмdPo Gs4Y&"|rkth 3Wn<|<Ȱ1߹ķW36F}+<KuD yDހlcj~1J3ec A"}0kAH\gl u3 7X]fTt8#.%R_5x%Z.Dj$[I^=C?huFsStVe%{:.a]?N˗ "پtA_M `0 7&iC㬎pޠAdax7 d ƾɁucVGt1b}v 3Ӯu)G s})"Lpj7׹O֔ed3tW b/vې2v ŭms$jw!v]lW_Gi|)֎h@Cpi,:A(Q bC)H-k{"p,$*\Ǿ{fbM9hgLpaDZ>L?B/<=c}?!j|>&4@ }ď f4rB!Sq=?gaN@*Y}e&CV6b$Um Ö֎^: [#fR E 8h s[e]X%w7O]}cyK&9rc2]X AW6/d,N徏6JZ_啥R {4Иއ֖1ǻ؜S#ٵ M_Pls |hv ?`6夞 Y_nf#pZmA"齀Ӄ 8+ ÷ z3ZEƇa<wrDFwCaN0D`k0,?Q6Y0KrI2)B4H,A stQwE>WyhWsR 'ū=@&>s0zjj>Y:yhb6NE)·/EtD`?s 2⛊g1E bz;R|>BL_ d[WoY{Hc@fwNA&I#%ѷcԯy._z O]2;% 6{ ܐI="vaZHZΟp=%@DTbu]ёhǸ.?"v;d6,gx˞9roa͚e'cTܮEA4EL^F4;"_Lrwٸ]nwA"/Ǟ*2g݌{ ιT\|6&l'b7L'#ʍUƲƼwgsiû'o'w啥zfmfD=TmF"q`4o}F2} k~WSa^ߖ :~0w=zM}l.VKy)F1t˜C`~_ F5og/ybΝ0 v͖a8'a Z3  0i? v@kdR&oL& ƐBrreEx_b%)2ޫѢV?Bʽ ߣZgm <ퟫEZN0#j8)LvE-Y[^C,H>XɩhxzZ`B2x , ,yALs.i`+ug#,q+RK+}1ο(.xb-6{5>g :XF#gvAiuМGۛ/A;dzb.xDV;}c|/ѾB4'i j{jz1-LkK:#;RR:#eL7K䒴: pd*#@Yb3FK v.vg#@؟n2wAeCyoD`^h++h-qh?BJq+RP.Ax,ă,|?$X"ub~.{B1>D#b  ]DJ3~.xtA,kmio]P @&mloC  %)rM6O(rw.AmW p{b4UgvC)Vo]VǶaݱ h>N@ 6nbiZ{Y,. k|gO8 "f *qs!YCEWĀ^cE,]p"|nwuȋh~hgqDwz2V|XOF'K~Dt7ֶϼ˼斂}8:3S=v?GNYU^yGt-K  L ƗZ .\h~Gct=T ^RVRт{g:7v-; 5}'>;#7H eAo# X6fTYV}y  pY0 j1 )̔n|nC=\ . õA 0 208& d`r"}m@ p=KF;--ئaH6A@)TtHI!bH~S]T8 j3iBA;)F磅t.hQ;fȷ=!&1cE>Bx%p[2=ߜ{](97"ew=?bEdc4i)br 9dB"^F )8GM{vyC{Od>%Rs*R#&j]#UT|X"Ui6S.;d[~qvomWVfeC݉Fdbf"@%T"ߖ=|l:#Eu#" 1ȌK{ !pV˝ju@JP{_Zs=R8֞W2]f ΍p|)X _b5H^퐲̳_ȷ +ЂZGM& \`iB soE 1alFLf-_/6}cz [د?9}E/7FDC-66k_2G:FmjNCL\U/4'!@wg#{I_39g'"0]ޗ;5 @!S{/Eߊ̻u$dtCzfZvYm|߆( dvR2} H)e|˥GKkV՚nA]@C3 U r~YVRHWGJӪJ*jލ.y9ٟ%+F6ͯzר/xjүΪ88c Eڲ#ؘ'vq!F>J׆aݖ))A (,e,:,AXx>TriA_8%@aR }x H]DI'㣒 9Xemjw_R9nv b5I СF`)Hf$b3a5 f\(b$ yZ{!e߆xSK%R[uEֆldrS u@N+QCnjW 6x]z8&Ұ85wA 3[hA,^;ևY>D \kS1+m߷ ^#BcN&{^]4?Ƕ} ;1x &+w$n T3Z-fn/]fǫmD@w~,:Ϟ?nʚ֜mhFИUrGdruyeT୲/}z9;X]1+fDZD"-Uy9`)m]'J*mD}w,\N+uoh*uZYyMT,ɖ0 Sw #eXx'Ɉap">8b-ݒDR"pA-DCJp:u8ž:#?4f#b;~29ծA ֞$SRLn{ BM[o4vMO "Ajtv2Hƣ3bԃHQ>Nbވ|pX3};v|lĮh~kм R̰ù#yCݝtfvOмerA`p K-σ[9ֶ(KL 횵6ݭ!=ޑX`m-z";eG{(WmaH+_3Y{ bͳ‡h-+xY^Y:{i4W.+R:lFOaHMo}{u̠gGw+^BYI}c=:m}9BwYIm.ԙ'B~Z%|RTcnR!:DEhl~:3#m{kzRBvFiHd!sH"12]~s-D@l RHY{sDjZf= S'V'њX"u ~!3 D-5DjDd^iD7YaF!TD!B9t Yk ߝov:3`{,ΎdSDX=DkL~Dj5:g_hk5>XB<-EŽM. 9Y'ojc&2ocɧ)LX6ZCM.i?O.wvj'6{֏<Āc}kc|//As+DC'-rD֞hWc3 N9]̼jc])+o!,ZJ*sO#_o!٣gYK/(d#"}\Kq 223ZFsIʌvo.|٤2Ƙ5_G;>HQ@Q}S^\tHAOMƣsb(C??w̕yH`qϥMs♋O=)J6bBJRng#S ]ڥ #qvFl<Ē͎%RE_c$n5[h<]zH7cMZ8s4iE`BJĮD al}gc-A 3DbW:WE8B'>*O7tY5Zl<#0,*@l/kN&޳WB iwLvç U&?@|6@9X#oǕM>@_&hdRd ~AXmlހX"51y/d<#R^Ys ,&آHn{>A˸fc+K \ Kvr:}45ߨmkXۍmƦ3u?YCC}cYIGfفXaꁙ-+&3lE!e%O}]\6I1~FE|`,Z%RHIxĢ F %<| X"u;),df2 R < Y`Dr-AJg/+")hq; {!]Iϝ|.EHW큔l|̮*B? )Gkf2})HMD>u rR;X "6r,HYgN2'lG Bk߮6>45܂س[(xF@>19j m׸<26[Hm&&jG4_=ɈMu^ B<hc =MI|d>8ƹ⮈I; KmL#u|=d@]xw&M(в?GMVDd<#~44wVt (o >\~CFƔz$ZVښۚ}VWL~b6@yeiн=S;zW6-3ߕWЯ;4f6t[v%o.ҮlR`,b^emq|ĒC;'ص $ [.KI31$L|h|@Dd[%7Ƨ3zF8 "ض 4Zh׼YUH.Au6TɳK"lYY@K[:gZ}SN]}Sg_ ahH;[ۧ{6\mx=2@J/䘉Y6!  = Gd\LHѽȍP@HyMF^ntyg{n!ɟh}kM}H^b}|V }ncvUsG n8?p(m-8GصI_D,1t1~7y:B{D3ѻDsh2qi\F{XiQX INޙIU\mW=00; DmFuhlh;jpQQc&*(02>])Oq(gfo[u[9u7n_\P@Wc_c|\*#%&n]_v ?\X-[F:gSˎx:(IOY3nJ؈6 'M?CyyJ 뾂2 ;&p%55v@C:d d&`Ђ܍yd\N&yҿOZlD&c(OXٳbC={Ƞ#2V!eḋ#cv3Qpt'68b{*mr3>C)E@.TX! 6V [3g*SAͅMl_EsW$.:?'kce1((HT&W?{[oU_{y<:s2NZ#b:hy .6n;"<)m xr(Q!(|~K9wP罿93TcG VVƂdPT&9 bu^%*D%*@LROd\#&&Nƣ?G.6 AT /0"KwF""Py"R>O bl(^\ȧ(FFe#`/:_~UZ{ ѦK-,X@td#~ozB#BcPZG<$n$M'2Ì>;lz#Qؼ!edν+o`qCk>^hnX4%Ms)pں~W\Rյ6;z=$\s.יѝ[Zoh*T-vmgfҹ:Vs[>=-'SqSfO>~>L>~)醾OT~Z_z ʺU~jMzDۣ=Tݿ`tUQKM,j9EI_iW9wڬoBD {yioiV'[-{vȦa%} ]+110P F)UȭZ@qMapF-8B戡~8&Jq)ZnAz{M(~ұv| Ky睚:uӰ3)G;<2Y{BK 1"b?Z_'i3=#CނbBUZTƚol^B<8i]X5dKa y܌ =_QU=+Mo'B4i.M'l5)t 'U$X\uv6s5 Z@!;Fg1 IDAT;2NZknJK;oN 0&7 l=? ;!@u=?Qh&bW#v;bBuL88ɅSOX*+!k^ E]\ys8b<;W 1{C'; 1_G*Ƃ84`fܔCUhN [T^~]bFնr?p2iRɉ!=t -m5:[wq<2_#>Os=Ӛ (:'z9#J$?@kSphjsh{?9wz0>sjdKks}>՛ 2{8S!u3?m Cu %t}sn!ˡ@Q|Zu~snwO9'y#s K7wY[7.Ner=bL&##[ W;! ③"WPS\"yt' D>L03VU:ÉQs//~]4F uu5QlPpL{yĢ}t p9w2|ݞl(,c#{Ag8{?9w3%9@O- c]DHJ+5KdIM]J/TE,ȦEFj:ڽ~ !D'rR k  {x.Z<7 ȨAFv_SQ_2 h:v툝_v$B/W&B=lw#UX_;I?4)oEqX/#QǞ 78D|X,A-B3}Bhtd-"PN 2>NO"P}2[ї q]i}+Ѽf:M'OF֟[[vA [WA '; AX+Q WHZa'k (ZWd4E߉{ Q 6sjVB!(~iy,Fk<'r5Cas-MB {Ƣ?[i5?Ӄ혾_ϡ/|6ybF`-TCh~>GׇqS5㦜\3nXݍ+V=:o^gV48iSZ\Gu 1h \!=^~@C;љʞ 4"@p6MH1릫4d/C,ՁC p%M!g@e6䪭m־Ȑ^hr W"0ю?",@c=WF]ND.cGy2Wǀp1}>3 Z{FRg=QpP@(S#g:9u_SZ6T 3:Đ@񽬟\vtWZ[T&חxބ}qzⵘvby-4m}n>!횱c7:D'+|Gl`Bf h eD@vWOKv߹hSgc)q'qKscQ-:A&ܧ}+9DInA3t״ f{0qk7ys;>>0XϡQ#ֵbq1ÉB*\8{ߝoϝ6}׶WRmSH_2saSAVcsh3y&vihι*_k.ޟx0{t ^\ƢlX6|1`=- QGLP3&p> vE >r YaP8腌d4QY32X=RhQBS3"ȅ8Nvl:vo)@v5)ҾȘl92F`u督NLW<ˈ}d} v 6!V[=w/P81Oː o%QGQ hj:Y౦oeÈd8`ìLG)N۸]om;y lNzE7? +(jfZ?㦃 j}+Fqo#]e}lZڐy=bhKx!s /WE@,|p:Tll:SEL>!oU;mç݅XDb^ qy . >jyͣu'(|͙Zw{/xh׿N\rl:qh;,jn:^:9[ؚ-z4?ιZOvΕ89޾s91l)h-}/y9Wi{\?`r,s]=s{kιv\w 潿h}%[-3F(M'M'W!c_{+ȘRc r͉D.hBW(^| {ᴋ LT&G]{}r 2!] C m7El:ٔM'oͦ~(fܔ8hs[bY_-ۿmmo}'}'E? 76] _e;1\D_6fCRtsM؆xWn&뫷\l(R(Eޮc:Z@1"hXHv.G` bc֟ i~X=[{?om>mbH^F}KlY!`?"ڵ=0fܔ3?n2iah׌I'֔C%ͥhn1"@t7]]Iyx1kG]{Đ!KHv4~ ERKT:` k{B Ѯ:yƬ[BoTlMo9hbvD@P^M׵(v- uP@DUYFOB`&Ft:\bxlG[tCm<:t5@44/ۘ6(ΧeT=0NB*s쇈.Nޑ k*NHerGuUt*YǙi|o7kwI͸)u%e]/*m~yZՇ(c3eX71hNMnCMz<V.h=#hV/[5{y~:!?p7t5wT&w Q*#2kbI m@rJ: MڽڟkE|gdX;B @ V#J+N,#~m[C3hr>@{d6chvhFr@J==$"F)6blmb,؇b^|d[c/"w{V)v1~v7#4 RφD &KТr +@%PD7"M̷gB4܆@C{ ]zP`ļ _({  lE\# @Fd CڬGJPjmCK+s #u\v @sb@4 5Pi bk֮'P,k٣m~M:͍yrY6#@*넀W`G#ow8eޫ/rݏPWә[BiSruX~ B*'UzmDLD;Z]ކdpj4D2q\#qSwăG7e//:;{7kMIw+!h I{hCn 5.c.Z^t+Q{? nȦRx`-N^n6tF.ȽK r6 ]e}6w.C1C@tt=t vMĀޞPʎw@6lCn'tr;ɕw붶_i+*mnQ}ƾDT;]>vtHlOM0cLhdȦǿucF>S;5?X1+G@6d8#ia!N=Nm^5(ި Ȉ D\=%}h7 pBFo09yG4'#mK6^Ytl:JerȦUm[CԴ^37m7g?uHtȧO f 1BeKܜ^P/-֒ecZ'Z~"ʯu" 2G wn8wBF2ڳf!?{!aDIȎAR+b>@O(n(Q;Pmok:dDD`cvxu֟(rދam]v@@u6V2K J8]n{!djB BX[_Cjb!qyX`SUKT&r(X߇=gGsvG t:^CL[pw3}':]܊}O{1z&N@@|>J.En7Ұtn@sçmV:CޯKm11[9׆lXmN޿z loI;:䣗me޻JI K zWZϪ5q~1MTR-ꊀ( I5#C+}zdPWiJ0D,8 #l;<`5NA` D.Z>\d =b!~My@;r ̦XF!#٘&e/@A;Wg,{{); 7Lfڐ?𹜈 ֆНokEqu2F)1ݧtڀW6}?O:v".Cϭml6Ղs[v6-G7ZtRM'q.hST&sČ^>VIWV~Cyʋ7cmO ȝG@z,KXZ`Fu}v={_Her_C}R\?`rMFlgR#*<-!ۗ8J;ʾJ^dLW9~D_DT,P,~὿^bbߤ]*oק5"CTXU;h0s.䈼^:ֳ^9 P9eK![.<I!M"S M/#CCb#bi^Gt21%-l\[<"ö֮}L2Mgbmy1fkXӠVCv}FnWx>,]t<3H#fmCQLUH*GfiD^@1]!S kw#!gM^ŹޯFZ( Affkg?Gu) _F Vބ9ЄB4ɝgGz*T&1ڸE`C- jf4p1|;W!Whd?FP` ?Her[鐏RvUNotYv-w@쇘^ X{ 8o\8;C6C>A :]VUH!V 2^o"oG}&b_#'dBʈhGd92txp-@ 2,E;МN ;[-v":p_C& C;.i. DvMG*B,c1Su CcQYvPzjcˬ!a 1fx_J4?DPLO_D`6׆xD |{,̇q5tA qGT":!=8w$$0Ŗ}f6l_жwT&Wacqc6;]J1PXZ_^y2uMD^ޒյ7 ꇕ\) 8F@ou΍Cka m?-m"T&w ^M'Zڅ>MdQ؅{d >g9n@-$s-G5Pːhl|QUaiF 1zg:崥#֣] P4ߐb.15Y"N >~W[{wE_CN1-4矋@rk @F>3 rC*^vozClR(ROBO!QXq:,Qn| 8.N>oc3dsR\ocvˬ]g'kM6ԋh,5}5"Wl!Q9f@YؕϱͶO ~e_M'uے2a%4iUE/|!Cs%obֶ41\ٔhSy*b滢 |x'!@~s3{C> ٮػI*;?PAm. p:ő"S#2 $ʚ? 9 3NkR\ ;N~mmn6RcB֧P({s#J:1[3T[>CPMw:ͷ bp~i"WEzkvR@y(X RːT־:vp-ZUm.Ar۸^ic7bcKlLZaeJMAPlY4\ e&N/C3G msO -ɻh#r\ɦX(GmEP=gQӘyAe'~M'>vȶ,o?<ѭjdٛ.ҼE)ۃ1hs{ Zn@k=,p%>l:dFf"i(";]v+ IH@4dC)+=X #b~vl]y}~D"z b *2ނGm cc:1%Y[pQB8p՞sQJfA`xoM';bh8DLՌfv7SXPN(G`l:J!$ru*FL$"@l&bjw:p:p6'iQ-EM?4!oykMAb_// |C8jzO٘-DFW]M]}umh>4`mXo;Eɤ~1IW?ɷKGՇV~?x˙ (ˏ!\k[gC_b6+=f?M|,C@bbBZH nLD,2děkz!ڏR(_ dh#p' nEI:SE{ ݆\xmq(x!"펈Mvs$ Lm&$\V"u@aJ1 7BN5z5Doqvbk,kWPYacwχt%_:Za/ ?:bE,(D`z̍4}a?#|7w[+4o׻m~lUxbBF}CDy+au wX`ױ#D47gȰx5VTPGz>ݟ3=ɟ/ \Ol3M^Nǹ9wH%d|zdcLxjLA;@bdcv/ۺ-IA8PYئR?FͦsR܅^M 됑+bA#Ĭ!ÿƞuCF~r Du]׊bA4bv&46mY_"E&^T @a(ZNVZXq~|"d_ؿ5WL8BnƟ/ +ܔD>(k@Q\/޻t5tۆܳ} (~ʞ.gR܉vFE]\ғ,2Ƹ]ks_=$N_^r[cq~sCh=9e=zk8\AetuHt6 R@SM'߉l. B KSޕ0ބCțF@aZ/=h=r@@T PT&7}КT&wWdܩ`\HdO +C5Yrk6 ފm}FM[c:QoC lH`(ٖ(FN(lƞ3=mDnF<hb!Q2r-CWȤfzz1o3 *.FEH2Bw=rȗ7U.0:uDLM+Ũ.dT~=+՚#-S\U`c+uxW*ptHl^e0A_n3qt6$] mLnwv/c1rCiYfb*2/"%IDLD%M+2Pr,PM'JeroSt'#Wֆ#ʈ8uS|{n"!CRt"=~Gqf!x_K|Dj+ !n vVN]Z}u.F5mmJTȻBp/MCl0h!ڃэ6~!,Ot00]K@1p#ShV81/F۵ͦ}glr + on|Źr7^C#@6I5ֿp@)@Xˀ6lt-KLjjr֢9QX ؆vxY浱>M/2Q +K_}xI~9J#+Un>V4}|1oO׌pM5P<5a@[>> 2fܔw{;C>m͂l:9j{'9a~x'JW*3N242{"1ra B:C5!EW (Amb`c*'aibHmaM !cۅ(kG 2"I *G75>_L٦ E]\t~鵟iWyg{ֆ ]6<݊سkntJC-~-g:Rw \XuF,2)veyX!6Gݕ]\hDF#F%n? xIق|`ՆΞq bCtr!@*Ϳl{r pL*;=N.Q% @5vmUـ;P:wYS~oh|R2m*Ѧ(DbCjū;\3Vl`lsM'E !?1$b6!ۈz1OWP!F2bf@ˑQ}vGd^ E[8ȭz<\e P q!тD2> 1w1E@[;cD`-1S{.mktt1 +##3 +CE `B<be!Zbk@sC!sJRL+M?j\l m^7` #W\r1r[?6&Un^ÿ]Bs ;ڄT(m^@soRW 3*7g4vk^gv6iJy}cȭP¢N>}ΡY3n67>iib(oQj #o{{p[xB`~{]?z_\=Ozrgxs4{vR\A6|E|GtEcDT&2 dD0R~1؜(f&"3E |R!fko`pJXUe;Zꈲf}};0B!+&ux,AX?ڮFuD9*ksX< ?(F%Q`bPae|tk@PDt|eXg흉nvXgA%~r/b0ϋx1"@;۞:,DN@E6۠X\>軵+{WO~<2󇝺bAM݋X1 sKNuMk׾Xe}b y|*(ob%;!@s{x?= SVMXqsnK ُڽv,oU✋[=̏S[Oo7`,)]ydȯɦ/k (&jE2IMCȵ 4! ot:o7 i@Zȸ (/9D?"\TDY'"t#@jQY9t"* ϻEI,7&`إk `W"鉈]:(DAo>a®#CQj.D ȥ7t(x{=M'M(Z0Eզf5#! RnnUkl|B9dɋL2PiT&Wl|"Nn*7mpnA2k mF[zlċΝG6anIWSE hlIn("+<,UߺUnΙΏ>Uc"J w 8bڒU;ѷ?6n/+) P QU| D!܇-~+7;q+Gi{*њu\-:s̿Ɂޟeem0?uh>i>3yz9xm@'sٷUx^"#6#;x9{]Kt8{9mp-9_{8{s;~1i_46"o޳YnQSRϭOɦ˫H= d~@gv>bNМM'V \AA}_ (.bk({TkM_m֎hAX7oxto#m Ϭ>XD;M%-%Wh\q"f}xޡqSV'gY^8]k}_,Z 78犑@<˜sgsOy z9"e2 (F<ιKQeF ˦f?$7n~Uk'M?fܔ5LnG5rQ*QS䝩Ln"K?E/h{2 d_OerE >Ew]:{2d"bBu4Z4ٳX@d_v#x8M1六id'ٵ5Ih2"s&2 `F` C u3W@>lnBv@O3 yAvm6~y{?;L~;;20P A@ƟAAD " 0"4 EZCH2 m5fx] }gݝsymuhN@_2((ht%ē釐W+|<>!pI'ÑVu&RN5CM DU|ߠRe^F{o&r0d9G"zNg)T49"D DD$ADx (DGSh%02@.'|C@w"b6R,>B2DTӏ͵h3?l0%po'}S_Ȝw?|9}5-(|zF}NE "פ˝KK%ܧH< h0L)"|t_χk-UJ0)F&"->=-uX/QҿՅ"^s3=^l}M ?z3.(zff>Lf7kS4a WA (z~oߠ}sPTt;`Qaœ5DvED:Z`pēHnW-/#1&#Uk{1"|dSh#s.Af%='ԚmHpQI7Rb5}\Ɉ S 7kƤEu%~soAcSJ%\/zvFt|͌yoFp)Uly%a`tdFq:^˼|Khytz1A׾/]3;G(? cW)Q?y HɡfjmswX`]T.sX 5[JfQ,3Dh8r-d/81SVs.p8!^MU\6;u[gv tj" n%2 0ǰjX|vMцFD:sL_#_QtH7ɘO78\)C\̓=1J۝0٣ƚ\BWԯqBDC$0 2EvԁMh _ɸ\Ýф MuȔw[*ᮎ'Ӷ>'dzD|ad܌Y9xHdzQ's=_vc.%仧SiU\š-~u2Gn; p~֋w]IC¾Vw#vY DA9u2wg&a?ryhyLv͘W:GӌW3R^D=F7#qGn78s>z,kÿ"f^5c}d6`&?,kLGssh6\8)h$5xJNAΡ+d:"A}Kh#2_T[qǪ9s U#"F949 MZ+&-Mtm("0"+:g'~')CfGBRLGfAmod&"ʼeK Sficr<)$ϤdAfw4d³Ќ@?{PFqL7jQ)+}#ݴg!fN7q5R/3VW"5+F5;9gS%!3RUJq9 at"6hA,LC+ѵnw/́c{"9|Cļ=O$ϮWu2l?0%?G;#hL/[_"u2>-@ӮKږ斠㫠/wT︚wKsMELc֋md&gg3\4sL5i]]/HA+DCib8 ʲ(#6 3Ŝgf||~[>W_9wlV+;1/c=\a~~>,e^2tvyu<ﺽxwZ_ld,p/E/ϻͰoˮ؋Aӈ$)'z)#pL5ٷ?zZ'qYr =yv?R2{M)e}( DD\GcM:S,DTъ'zg!21)R hv+zL7!Y0`SP 'O ?޴?"2G6܋ڮL_>NA .Rfɵ  %1 dz"R8̾<&Tcn|M܊Դy!Y}"sMd#Rg9{>R ᯆ7 Nhz\E S[KS%W[=LEӾlAcی71c6z:^u/f|HQ'S:.5$Dl=x" `Gێsu| 8tP[Q=V{5 ]W6wɠ۹'h9"<.㜸6z59&|9Mς at:?BD[^TVXߡdj6 JfUXEkXqB(c79)E'Mh'<?ÛH: C&!6M< aHٝH +/ ?|EJ%)d+(T] ;Bj58f\T2](:lg}] E4PDv,}̝?#34s D{H:71ٶ#x{/1;y+Fҕ@]*`M*ΆNu|<j=Y1gcBH4VM~d蚷 uК )Q'Sjrp=6Q'SW,((/>ug*Rm5'^lz\u2H\ToBeX Mzf"wxV>,η7n^?aSXGA[6lY/><V:!/TPvϫ=ns6C O(D0œ_"Bo4Zg4GȮ"p[jM㑩ɚb#3L8z'g3|'jq(CD7VF/}˴ fl+M>BST#񟢉+HB]Tfl3bS&M4CtEG+F*s((*xִ^3SpDB?){T,l_Q7b.l CY#wyŒQHYHHZ&Պ|ܻWٰW}*#DF)hL'K'5{4oWulH1Z dz\*OI3~obQ'S14gߢ>b&d{W/hUF]˿ r;ːXRThT*tNGzێ6w[i5=\.ͷxyoL{Ci(ei#;wrF~*6}."6 #⳽iKi7Ђ;U͋ ;igk6+GzS^7d&"3o^sibm)u2}ѳbMbrGѱdu2V9;ݟvk&k~MDr&l4YM%ܹdz$JG D`C/o"l""# RWzODXf#BVcWj.Z~_gۄԛ0"a!nH=~H(bt44"$Sw ,3}>݁ԥf3uF#H$q9"Q#Ѵך@,4OFB_|&J 'g#f D&fG F M u.et?Ԡv RE6DNH%&=݈,A$Ìu1x2jɩ[6 sS>[siGr>jB -@Ĵ7d.~ ]h/VlSWLtN%śwBɄ4u}AzHY,;Nf_|NGveE˪wl_~ICA֋ݾ{ @uoè^#3btkbkh1M~oX^E/ C u;jwØL;h!`ηm>݁Yѕ]R7.u~Ka'C̟(o e Gvk;pL[{ͥd-EJ@!~#Є4M#h;l.~Rɩܭ IDATMG >Hԙyh#r29pƇ^ocL fH%&Cĥ?";?Eȿs:R&cH3MhAW:MMDmhDDAb"GRD+rs"zKұ"E6Ch*'7!"vȶTV&d#ZhpyzLkh3+W.Cݪbm>qjӫZ7?8jK8wF؈&6 ̇3:Q =~ Z9ĦspÇ%VA@k_{ӕÿjF*yKaH%;A (PY߸dl>2C(7e6'DJP4[6'H-z #9l k&Ӑv:R;V #❇&" )x3[`iAұfE 2ew= ~ i@1m=Q>Xg롈&H\42s6!Clek֦0Fb]8EZ' ^,iovTKڛ  QEHcRځ zEBfQ'38dth7"61o.Tvć}EfQʜ?wu@g+;W߆Yť&3߭r#W=Դ rw>|̔-PAJ_|ϑ̯l Oi1gU 21O@~N! 9?T)hb[&l$JZVǢD#j"(Cn,")w6͞o\mao|-EA}X/P:s027v1;#m&ĬW=".5i^),ʗV$"I.fVr` xT!jIpִ?I̵HɴMf1JeJqK*:p讻69ylDsA۪`< P+~GJz2߇H֋mJ0d❢4gږSt?EK/kB1+_+{9Wг+T0/T`ʹY Z`HU{yq|ًo ց&ڲWWQ<>)8LVxJ%wj~xHݙT249"$4?8X=" Eg[~RuFg7oBDHZ9!2L@VMEQsD:O?!r;Efomެq7 Q`>鄽 ʆN)(h(מ/(ףfdC>O(7rMY/6ksA@<F`Wbēn7|)C-lw."F-D*M>z7R& V%2D#H?|s+|9Rlf#~AlK^5mO">@6sC[_)1s}#l42N5?<)- 3GJI{{ch] 䠉3c1Ñ:gc|moiSy2S^eχfvE玈7FOL29=J66 8 >2ŖKwP;V/فLDj9j@DĦphBN}g3_d"l{h"D#f7m|ݜ+Vȁzi> xhmP9C@$u ~̸EfJ?mU-4 )>~&s\KnBH,F6pX_}ƿR 'butnSzEQ's *t{d&Ũ 9Ņ ߪb-,%\Znvl.oY\̀F.}bnv-O~fJ`D\ LGքi0 ,7f"DsL]7L61)BZrvR|OG$>QnbD"D.U#Sld"lCb|>bFf7/yBo~M:gJB}~OT3TH鱦?=DYYb{,"-_G'pN-ATό;8.FňxiK3pb*N'}y9DDLaP#?ΰB7! @*O"l?][e3/;­mQ'+2mvd"YT]F@ƄKX*E Bҵ;&[]Dz"f\#񈠶vdƓ4pF{dk]Ecb;p4 DBI"W JɮoJēR/IE^k]g83BO%ܜ ϭFuH7N&fZrlAT#et (  @- LkqZ^~NgԱ|M?B*멄R CO!"-H #r "#u6".yD#"DNv0@ʕHL:x "u@̀M0nTƓsv"R!2AOM%WL޾y9 nl'O b'=TtDB-?/T½#L7x1pt]̳;~Jtw:{aҺ:>]أ 3K{>Ue::Jzvq)DxƓcѾ8p?W %:Z8Nf8ee[ @B@624LM%^ޅ}鲥xLLA^CRS#:Ռ|Fd#kD0z djDfHY:PC{y<M 4xIX8(I%t<ƴ#AEH>f ԽP'^#bA Fo W;87pē齐yv "}:7_/D&HV̝%H9"/H ވT?TjDh!%~DM/FҜ*]#GFDf!4L3pO%GA'p]D@F$r0Rڑzu~bg aε?R0Ց4jgœē ȹ]XĖ(DB 4@1<m5a[Sǯ)xt@ Ո!Ao܌E6-@_,>cS:pē)1ON%ܖ߄4x2CDT?Wg"Sqj1ۄϬxY)cKia~g4"+3vcӦn4?<ppp_*x2kӟIь2ӗ#lO"ܯ kM/I 2>`=[HI()(YDtJZ Yu2E@nWU놺Jw?EmUEHI}n[F:hk -Alsc &QQ'4h,@ XT]N(yH%[1 N*BE~\4|!P=>C+Obv/ BU )HA00LW#?CfF4v'!)'ӕFu:tpy*>"'dzYbUO#F;׌J3pCdQBҌMUrdBf=x7̘.x!^9eA΅K^ͪRBLw.{rt۪pI='ӻm>l(̵?ޤG\T׾{~Q'Su2NѨy݋P12֘m"(22-}72O呹miʑ*pd؍@D._(+ jˆq.&\R㖛sd~ӻT}?= dҴqig yi.r֨s=R3H[rmTϷՇ7 !CmNA>S11p^PT,@0! |’oM ps֋: YCpPT(eyΤ;bTsqcȩx2=/p%{|}ƭ7.@(2J ٘'c3y5R z@<>EE~YOϝUR?Ep41I1yMe!VhB*>OEޣ@K<>!p?va(}MA*.rp?|_wuցIFc9ޒ.]''u;poqWP&s+ sVn-YfWfWP{>rKQ]~̯)|tF~}iz=tۀ CdԽhga|U=}xBkzhT¡GHA ꚷJx9X7?>}j x*p_':h14  @mBnC<>W;}!>Bp\<* r?2D&=:ф;"iKT­Ax2}K%܎x2=/Gj(B| LMkACT8}p[TkAdT}~} i )p@ĥ;,Bc]ηwk5Kj>Z:>˗7UJTm.qb檱-D1ฦoͼϑv{8"WC@D|BnZ )^s|3w(}ďk'ӻ#'_!T}vm"prZô(3fc8=.(-(0fx2SJ56*p?ҁf-EzWf6 N&qj U#9]_^gwFPo@8r|/xH݃-GIMB!+fٳշEs:g4ڒϚQk!3FJ~/| IDAT(vT}ElO;'MG୳}*6vBff:cR:EYof7'FkR,zgTͧGeX{֋yT}21^lA֋]bwb9gcU+_+C|lлZׅWҾtT7Q޽M;Jtv 2#Vd ;\~*}/ OO+6mWEF}>,E#4$+)''"u#<>b-=8 |m5 QT B~ @aU,jъ1;=$t4"1#F`[Mxrqi_#G) iWPx2=8A|5: HҍS~*ᾆ&wEJV?4 )v)?p{tC>o[oJ.&B2dN@@~bP ^H{]뮩[?E.E=బḠxoNq 8$Ŗv֊x2=szU)).{uqQ/D"h"h$!_·I;?nE"OہM󁣲^M|(c{zldS wg<_+wn&sg(٭/#OFj÷(ZKPBc| gkQ6&z pcJdZ>[&D$DLoW}Y/65dwcAiǘG!3u:ވ:0Rg;>?Uq87ź]l$wBxPC>V{72;fz"Vn6tKY- ^lN\CF "rh͍Y(O%y4uhz5Q'SSeؿ7iGm%0$=x2]Ԣb2ԗi3^g wh^'L2ڍ@J9w-Fc["Y!ҳ'r://F_[Q_+eQ <")cYG󨓩BY/?ʵR Lx@|SVy(fE֋=u2^Ha"Y6UE`mWڈRbOv9OK"  2"L;rjE$ JouXw*^_JV32JJQ]jPXD́v  [kC4<_ |dsKખE!% Nzvx2d~~ۥ{{Li/#Ta;=oue+g)qHbP 2.U|$Y9Gr=$Ln$6x2kv*6m ⷥ'HiQLvq "%]z9kSOgm߶9)=G1k l {4~P"v9Rdؕk;_<٬f8A=!N"K–hep ![ XR%} 6C5(p)$dFIi(?̬ݰY?dQ'C5J(Zr*uYQ'3(X652H%tBYSmYjY+R%ZXwcUھXLÐe5t1[8|:D֋\yz_ھ5#P@֋u2wI0k& p򗑪Gzb _gg̔x/r~!N$pY =L@)%nH%ٟ}O{xyƽAcH]ju@K\ -6'^S#^Mdy?|(Hz!:NfR榢#C.4:@xFrbݒꨓ֒ gE@t C.DEmݐB3&pm}iOONme:L@G!byCý "u2].?rXXU r2R.@K>(QEH[GJZ;b!E ng0EEIp?5plkX0^G8Q'S4>=vm\a6H %H-BJ֩ȇ0|ǚ:@0[;Vp p1W4+| 9yb'-B%Щ@i g,@ T0hZeWcMN&dZ)Ta6duT 1\R_8@-̣@fZ+_+gYb Pɍ&vv:⨓-@8e,@U XSÏ,A53+^%oFd|{83ra}| %, dL3]u2zƨyl1e3@8/t E]Jlغa, #D gC9*C)5N6ԯh~"5oz ~@ z5ێY/Ae`c! c_ēo*M #d(?HD, !?02!F:֎19av[%OWf1Q{ {g&WYޗ,$ bDĨ ?$̈0"EpaѠ# DA "HBӝZU;SM&AvSN}Uwɻf_}Uu`6Kg;oedK,ܒkalX͘alLq#7ӧN~o?/g\ry4E.BFJrO`)pFc7p[Ѻr(aƮ) cPjG' >#4B黨 1rpFW#4l"kmȅ䊏 5 cciJL1OܽS>=}G(-x!roQc(:v-y2rɅ(2vV. lu^E|SU3I foal) c32mF3*S'ݿ9\R0}d8^%a#;6=_u?ВxecYJ0 cs11fc{֪.bGSM{,U_ĠBp:^@a +< ƺ;#\| Ym|-p206cD.i7WS{r'\T[hoڧatj=.@2mO:eڌWzKNG5B!hrE&t.wWND>h ѴfoLalՌF\2p'*8֧{U\\=.ilK˶xyʴmե\ p 2,4':m͑o\\CӟxIwX F: E 㕪?7=\OG=iBܲXҐ*b7T<7P 6>^ nnE ud/$F>',-:`!.CH {m(S'w=l1Bwy5΁Î]s65_r3[rh@2HӨV(9 y0 0~ NK>d](rIU8uuԟ@>sP UQ%ma?F|u{L1Ϻ>ߍ"Nf\rɜ=0#NYa=E8Nm{,ߡ hQ}WvA9$:3_ҬOG.;d1Α(rhS"D.Ʌ7bR"A<АF.m!z!8hA/w Wߋ\/% KV>p*DOGv"; {Z^մ/ֹO6V>ְ앻-EyPī I0n2; )q~lM<J0 jƌ7<cETDKr!Uf #ANA5MHȭ++4wK"nEv [] %>\BڪSmDjPjsҾ_(w徹/Cy0O}q4z| .z\9@1п9$G~B[zcki/WvnbbFD.싺$>"LD{2=" wgQQA( z=Pf$̖euMDQvs[;/r[w^zU ]Z FG` Y;6e`et]˻C)mhTH݁ޫ,:֗(Y@@U=+jߵfͫޖa+&ƌݚ%\9C<24^RHZ*:j~!}TOT/;Q1D.E㾋Rn'#w%{8= +v.:)Dfm]Z|ւ/𷬹goG^wʴb۫P_>ur&RoYM\?.WqQj+Wn~2P+>z;\8v8 w*JW^ Gs=PmˏAi!dE'{6ܝvvc& &ƌ]%g= Z"oCG%BB9|? E3STz sR'#P$l(~?u9$FjϩNy,TxלCiڼ8s-K?CQ( MoL}^)ڻÎ/W_^X|:ӓ{RP5@oBDZG9`m#NqA_{#`Jo? XҳgUA>k?ky$؟I}|S)w0 `b%ػwrYd?T&`&b(=5hd|8J?fj@^(5py YϺ,=eƶ(d&%^U ~2E)F"c%e%O>~cQԩUw#}K.H}܊|~j؀s5( '>N_rk c[j@u/߂ޛ+"k'7wAU{ݮ\,~ƢȗWB209}է-թvt. Q&EkF?bbة\2rФ&_}RG(7EQ4%#{߀i(bՄ><\C McET}p`XR<%WG-ZMHeS"X@PmT;6(KH|R\&>"hA枵g\Hvf `?`o$lP|չeHlҿ%n?=+Uޒ-{E{2p?aEJiS*IDAT$$7: D{adU5zZ5rUUs\][j/LEߌ W ČGu`5(ҕE֢zχTL<^mx$GāY!5!ёӊ8#QzJG^-(;MC;ni#rI&=ڹ Ws/x9݊ૈ\r2J~~xFλ gQWj6>h z?CBm52r ׀+S OȕJ;זo(-_W^֧Hj83tal7L;K.4X)[4՝H0xRKo"DH-F3kT:+_GH#\RmHGifXHT4KnHšGawp8TA6pC[7cGuIEu眒{M\{fâPG`^$OeϣVGEx`D[pnXߕ5C{ؑPОǟM>ԥ>nM0 xXѻn탖טF6%uͯRe.úQ:np|9T\?E֥s)f\S'X \jQ(؝Wo0ZbƻJJ2kJ3%Z Ʋ㐰9bUMt灾4 8$cgvޓVCQj1YbdWeF5>QjmQDRL%?JI:T35 Z"jye;.WB®†u"vN갮=(Kpl80<,%{/|iUx㏥>n^6 GH^ZXx6Sg] i5f"![#r+ԅm-_>g-jس zgnal63v:Rw|,ഷp%.y/.k^]X_h(?lC5WE*%(:4X P yE@(;JHD)Jpoˇ3'ya PzV+ס4s2:seC7n_j#|Eۑ> }9JD%e63KKn\Mؙ.BM\`h 03vj"܁RiqD)"1܋ ڃJ^!ԡG^ÐhB"ԑWBvیDۄz Z*WzGIT6E(yrx?HT<(b[NzyK}<Ϟ9$$>^:԰7éǽ P dJv]v{M武bs/va;4vv#<KQQH\@QSoBE/;Y=#{.<2(z ,9 aEͺȬ)jQt./!UKb p*pUZpxz68i+bwmȪ=:YE.|ՎRH)~K 0v ,2fD.)+g>Sc!Uͥg>З< Q)F(xr8muJM@+HT!ag*5^(%y"]F$MD_|xSPtn0F|8|xlb&jñbxLVUDt*sµE %9u~9F 7.ڷ4 /}kQ,dVQivȄH}mN0Ę/P>Bfc=T+6 KuC_P=\0Eh@ƫ-C}1q7yeQTkO[CH`u*dװ75#1[$N"6کԒJB/N"J>R9/>w8oҧk{H* THH`]$V34?;haKSC\9˝zJ,ZVB" |Z,TJcr5Ð_s;_@b0c$eXUFcPq$&"Gѵ1( {g5#!RbЃDX63}G)/%qۜLtBi߅HaYԫһ JSho!6 AX7:F.RrY",》WwR'c;\}ʟT6% ,*r'SON\Ae>a.rɷQ#w4(b(UE/a(J3EB$GQ^=$%[8pNÇ=xeՀby}Z1JS(n[ڪ=]k×]GV+E #ԕWm5a3v%nDѨrHŞ!O_=ᦁݿ !(fkaC^w;NGѪ$rzP#(2ΧD`Տ kB5Q>I$k$ZQfwx:"µaQ2ݘh5qL7f͝)go9eڌmoBp_ |8s ԁR9_B2#4 11f2> ">{yoeýFa(9x>d/[~0k{Q(X0rI5J݉Qb?2amy c=Jxe^^됈 x "uY"k8pht(w~w_wxW^ö."=:~Ւ]˪JTQ#C "GQ0 cҔ.Ec߀Q D0T5EBOu4vTڠ!o^w|4a̦9"$  E!Ѷ3ÑGٗP;YV ,4k.u*DHxAlXxLeP)F o(rɇS/ڒ}>uwq[{Shߖg+E3J uYޛP b%0 'Ӛ 0v(&ƌ]K|DQv$F#ﱣCjP y@d$^DѩR0U ,=ڽ(SIWJm6ms9g?păӷo3"?/U\\6N=Vd p0ps 0/V3fD.藐RB:E`$`މ>|:Htw>Mõ}-ߝ{]r//YmIa|JdiqےV*3, 0v;,MiD.97NHyT>8܊H$-&A>Gk6$ZP.4;HU!!QLT#֍ ^D^T>Y^쇄稸@.sկ~ oD٨; TM@)AeQz@ǙNGOP+jd! Y3hh/H}=< 03QQ+0$%uH@ =Q:H?6k/\C)H D4C,$\5({k5M@.%Xּ 5HpE'Ph}C^/Dbk/bd |QxɶȩȖM1(I{bVXiB03v@ݐ5r#&t ~({u>˚|sWEe x9|?8)Qyc-K~ #qҝH@f3,<$GZAEVF.F̽ mCQCgex~.ACmE8ۿ8{H?0 cabxn!O|kCuY)|EUĻʄV,37u5Pʊۑ0;9ܿ9Biѥz](2~ kբZ}-*EDg6J.C5aGQ]Xz z󮪼/aѢ>gZ~YavĘ˓S_ Ph,=Z:9ע(HP(+"1 o#|CfXDBX$:@C1GX7&I15^4t<}4xD#WBZ^^Nq@/%ŵ91?J?al7-\נyH*#2q38+vRQd+Y'bԽG^$"hWEIJ?%6Վ݁AňE:Ϥ>Vovdʴ&53>^Xr 8<4~jucaXd-I}܎Lyy9#y3/KQU;>E T8Vl <I(p (x[Ȗ#2WLld5_%$R{92fnS<{I_I5X1QOk 0c5(bt4CO#qCRB͞H$ DQjETj"; EN‰=PWc$^AQ ,RcP-vö7ӧN^г5xl뼩Xz9s uav3v[R?xkñ>TYGdZ0y/p Aa 5ڨ#ף~K w'HH=)CTXGQ󁛁\䒋"|8<_F1Z}76eڌb'Ê9k2(=["@#}_|u}ǜjDh 0v;gحI}"m}TlFѭ29DS!]ס(u4գ"d\q6`1#5Μ HBYK[}0gR^l۞rH n|&zlZB]}f7ykˡ+3kacQ;7>5l$CB4vJG(Y ,G.B"+FG"V$JP_Qf-E9 E>vw_0-FBmSۉS'p샏&Uz!+F}C |_l4F E9v.r?EƆzuHp="RCyV'Q< 'Ei,ͷPqͰ| j۽"X2z$$߶ꩺy^V{>j7;r51]F}jc1ƥ(E9v/J)98EE/Y$^|'K4ij҉uȔu`Buf!GQ Wu>}͡sjV@5e}O[w%\K=n]/tP:ߋ3 QϘa"F~՗_@m |_Ҋͨ3"E>|G`AnEHPwaH$k.n[_vrcD.0˩3]%Eryt椯/J0Ęal%9T?%ǡ6@K>"~4>n\RFi aeп3|߂v[Efʽ5UL%aPLfJ4Clp_ A̋{PP`fyK&&}}sOyT\B{=a;cH΀[{d/Q=ԅyVb_^9.>u_a3c>F"m- Wp(׍9jac1 \r21{?;yIaĘa$D.qaaĘaaF?baa10 0~ĘaaF?bb0 0 11faя3 0 GLaa#& 0 0caa10 0~ĘaaF?bb0 0 11faя3 0 GLaa#& 0 0caa10 0~ĘaaF?bb0 0 11faя3 0 GLaa#& 0 0\ }T(IENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_33_0.png000066400000000000000000003220341413546205200301450ustar00rootroot00000000000000PNG  IHDRc%sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxw|տE]%wc@tQJ`A` KIo 8$$!"C,UTˀ Ƹeܻ-YmqfrG`ܹ3{֢((O;((+bLQEQ%SEQE!*EQEQr1EQEQbLQEQ%SEQE!*EQEQr1EQEQbLQEQ%SEQE!*EQEQr1EQEQbLQEQ%SEQE!*EQEQr1EQEQbLQEQ%SEQE!*EQEQr1EQEQbLQEQ%SEQE!*EQEQr1EIh\CQE<|([;jYu/0^ο#םREQue)]o轰x麶`8ks epl)(@Ř$DBz`H*NċmNחk\SkK[(0\AQ-lFBW+/eI^ىCEQEse'$ X`@{XX~o%MMQeΘ(_ SEQ2*e'"FBsEQe~EIyչ(SH(GorEQe0((JQgLQo3~3~`(Θ(3 ى9혢(1EQ63 ,[g'vN0=O*}/\ |8:;o껢(ʎ1Eم%o"R0=Sᢣ*.ڳrڒzGc\r ?jg^{'m;=i[ Vg'Ʒѡ)h~Eٵ%@E0(FS#@Ar\DEڧ2s͘~f.k~vM f|uǨ(CbLQvh-~{ɊY΍o; \|ݍg.;y^yۏu/xҬk Ta2Rv9tr3J|Y+es vp3:;q<^EQ S*NN0{Y^=pQslPte3Y_Y5cV!Xy-銙kwo@4VVpO-4/],?!I4 K~A9uvbp(ʎ:csނFTG^5@{XUv-qu5pn^Kgokl7M'rRy(˧uh=.j&G(bLQvR&8E n٭G _g[}0 (-G#;&Q׌"o=5Ev/b}luĆ}(;2*e' vGFtEQS*yw©@8$ROVw?&/^Z_nXSS>C֔Ifu@8-,@ƈo98NO(N1Eف>7f/?a3K1iW>ۑ#C19]\#ɬ\L4B^!б<"lGBYͭo BnR0"-מL?uv-pZEQv:T)vF05)p"mx+Wכ;bn=nߝ͞/΢qo&dԗ6`Dq$_[g-O1 < $oɈ3r}1 LY/YCW;|o":mYg'ƶRE0l`SD+} cI'8d2t2䭋(H&_ !ZSзؑO[b+/5kEMƋMR&CԒqo;0= &7= <<, ,MQeDŘlGѾ`S YOb 4I//&5N^GKL[2٢>tYql3||+ лKWܜ ax5{Mj[uOŸHr/`Uh<[.EQc}1+0pYk'K[u%{!mRW(4M2oc<_̟K{7\'*\m P/v͢N8]>< /"@ EQMW`8~$OFBF/$l-g,flc+,V}:p\07InnnRG쨼Sz|^yxN[SQefc9!H(:OӁGBH(p.u'"\;O 9lX1n@z{?ge*n d9" HY939"ޏ^oiG@滼h`Wm*촨3(;pt/W}ZHLF1 f/hؿ31xAN%d•!K"bl5Pl!g,\A9м۳,}Z='/Fwى}WEQgLQvFCF]^MV`0Iw| JZmh$i_)[7?n[Quvw(;7*e'#H(`Ϻ?M2}m8o?$ފA殊{,S?{ #7H~ ]N[f< \R4ߧK-1EQe3:c ,=qm0p%X- ?maCa֚}z0J%}=F}G^{$Z~E-ɖ>}%8VEQue#[Mϓ9ŕSMmRy/C&!R^U >=gg!nڹP`MIQeGFŘbTX#c8sXᢟ{_9'Y}gzىYpdDmq,(;*e q٫V<@xhW}OÁ #-iEQc QefV"h#ؒmB< ,ޒ*SEQE!ZUQEQ%SEQE!*EQEQr1EQEQbLQEQ%SEQE!*EQEQr1EQEQbLQEQ%SEQE!*EQEQr1EQEQbLQEQ%SEQE!*EQEQr1EQEQbLQEQ%rEQv|&}WX(#Θ([=sEQ uE<,0h`0M0{紗()Te1?n jg:;qN(]Θ([:;1 xHX0`U.(=bLQ-@5bU4뚢(Pe :U2pr% X[>m\䇑޻+ѫ͂e{ᬫ(YhJE5[v>3lyžrݡ:yGuM[oM?] 8u%2C#9(;$((p ǎH}cI[%WܣۂӁ Ks7EQvQeFÔʮ[0  >>E/rEQvT) u>0) |ۜls^$X=uβ=4`0gKRXS |T[ݐ~`0Y$(EŘ= eX0,nG/~MzÝ $Y`8 mQv-Dߓk9엢(9ZBn ,P\ 5|j0E9 8 BG7oG}cͱ_kH#`V(;8)ۜ`8:8 c$XY$쫶Ios|`0voh~&;Mqo ZH#L-|l /Pllyc @$\(_1% GdOk,?O'M12 ZO@3`(aa}c _[QeP1l5( LH lsp -'i#gI C&KlSfBֶG /px,-pN 1EQZgL^HHiP EZ oS; 3VFBσh)KgN HJH(0۝EQE4Spq@M@1lH(0zɎ!2@` sqxO'8 !+ĩw@Fp.#0EQo1ec A#"8>!S|'VGB4 Nqc?N_._38[\fEk9٭pt r=QN{GlѸ?{RH(0+6#NݻDo X<y0(kP`8z8Ι-@ܰBM8G(%4gL"Á[e9/X05+ ݶyw<Dy:WxWOk'GN,?"-#N;H,Z%CM6\kW*$~vT=dnʫ߯8` v/j#N}p0 ,ڲgVQEQgLRLkE$HQWJC+Y{WUS_i!5,~۫y1Q\z3d3HA"Z0ediReH[H׈j nݻHcHD4m=w/!! L"jWџEBV9.I}cM%n:(;*Ɣ-B$X ɩl0} E+F.|Ki 1JGrABW 9{u`wkD2D8䞕!I'##\VDBeAY aFܲnH͞EהF32H9@\a 8XO0-GB[D+"H#xzke蘢([ S*.'G q>AG{&;LmNt KFc n8$HݮH#ėH=bN[z #N4dd q!֫Hds:y:,E v~>_39= ?CJw\ tKP` qPyC7{mu߻|vҞ=yUs!ᄒuBQo:cf G{"apW;HmOI}3Bwj,Qz5"jEF.~K %!i☁LO"bg$ڏL HɈ'ӕBROd1+`g BBnD*_yH|k)prD#l+ÑP^$X<#bp 8 Ӷ##@Ͼ3rsy*WaƚKbdV7o/M ke&r?Nm8>ךsyE23h#ohoDq+gGDX)zpm*~vKtc$q^@Kp57ejDD٣ k='%sD|<~ZZeq@`E6ZAW:Y x0=`!25 n/s8Ճ b&ztQɼz̮o9qva*X|n=?J._S hrNؚwnxEf5u+a+u! o:NL$7)O[ٰظuI$xY07z8-GΧ3.Q6" !ʒ`lNAB= [/Dpu 3|бؿwDǹwpqDFpEFs!jH!!!8loNeё9݋$w.D0-2g||ɂ{zy=˛Oo1"g#NkO$2UXs*zݭU.Z&3t$|EQvT\ <8W$\7`C+ѳR1ݛoʼy8湾T9kEu"bd/D,; l͈*B E*Dxc|("@F_u[ն D~Dq^ vӿW1\$ $G qBܸrCPVo;Q"`8:9ǧDBZ_ixn7){r A&ΏD+SÑPe_tvi$a(S"!3|~V$NyLр}6M~l}~hl?Lo7/Y^C\W !}Qs#u"v|ȉ9ā#bqp<u>N_ϭn<"?3sv~$N"j`־;CκkF׊ K֯ǧ?>跁x$X G{!#P.A{. ,?Rf^@H(0}SQOkOn머g{r\,rMM8߀.>i e 2EQS<_kTIOHfEyl:)FJO!7-tY|e~/v> 9 k9"^ D@-A1H ǛՏ$_ րA2/"px_dx<솈~Hy:!2rSnCc aݑ@}i0Bcg]g_ohMY9TM[`8O0f GsOxfœ%^ƬpIg]!)F'WQeۣa](a*g݁YKvyσϜu/<="F\ATBg4q!" n^੮E{k 0guVܽdKy˚%jfu=CgɹS=^B@cmuC'@$X-*[cy*ۭx涹3Z'cN͟ 8M0xljۉopoRl=܊3YysGmP4ّc+k{%H_g]\EHX?"Zܼ$"$dDX勢#+N wq~GJL1D<:{ƒ&<}!a$*:/u:q52!c79Ý?@ p !nO 2hsV" t"TW#G |ksN{Ş Շ?;毯CAQꆘ~sq7G zm75 \dmuCƇ\#k/V7Qr10/巌,Jwq{|:dƘɑG %|"AMW!ӬcNCU9eNw!7 1cL+p^nǕr>r_Nu1Oou9/sĩ^(WFnňЊ#C&`HhНh-QlE`^2hy:v$K*|Ŷ"7~tݹH;"QB&'}Ig#5ʖ9pr9$<-> V!HR[oxH9xWf4z<\XXd 5~o=qÞ@JV7a>>syq#/OKf|w`+U5t$p*v1f1f_kg{#ȋ}jcUȽM@k5\XfkƘrb`6,K1ZcK譈@Mc .q)rkMc*1~Dj]a9 )tFXvӷ|CT\|Ttv6ّ 5!Tݐp<$֕ݑяY{/z+RÑ/^tOC4r:6EU 1K!Z70D6[Fg 2cL5kc?[kVc ӣζ^b.0s Wݣbl'I$\Q"HJ%pԏ8^Hɱ8 ܂ޓP=ȈqHXrS|;<|ݐ<|`#.{8 S Wh ""ͭܿAi$_,gϷQ~Id{n9D}y&SvsNV_S?6?_'_wEӑy}uYJbdF!u$_gݸϨ-e{>5uc*{cx!E4SZ*ga7 |q^dۑJx]~|縿N3b3~_hTԧ)f ' /[CʕsG`.?qۑA!Aĭ>fAgzM>kvÜ:tJx|{^_Eӌ$]^7 k`kk@:l3膼4KD_”߄ .3u*ɞƘe~cۉ1>mn uƶhwm?ӥ HyO$yiH(ˑeHhn,측7 qBLHHyuDf$nt!Δ~)dI!ٶg"DDZ36KOAF2#;mQ_wx*Fvg~=bJd4}'_L@/gNF=ڳ=^5^^bY\?B^|C7#/AS )3ɔ&gOm%Xk;1/T*ػ[ۚf&qߢH{69m2$Ƙ p1xlco Go΋6qJdqH(pFmp2:H(ξH(3G"_6"BiCLODB)dB)D#$N&uu댹/lnq8RK>fWyy}bWn[ °2zEܰN2iӑHX֝|s<"7ǝjP s?r ;&ى_ 8%-PȰ($'EAB7"2DGr~V7s3ؾNs1N~FBw!+IV˹E);'m?<$o32c+8> ѽH(0/#o!G3AZ ٿnd0<_/! [|hdde307I'0/ 4Yy0-[<-Sο qغ:i{S١) 1<ιnAru^ Z9"$#a42tg6+^x[z~^Y^) =stN?HjX3cD0\Tܠbls)2kotJT{SDB[멈 "h"ة~'@0qtbpwi؆Dr@m~;V"k8"r# '"[v=8cÑ AVw@2E)&`Dt\l2B-&|[`68vn?wٙ #3{;3]㴳9^7v 2Mi$h W se @[F2NvGnE"AU7=k.w:u~KzC}cͯ|kڻ6RefoQ7]#0WRB%~ϛXǐ}aLy/yS ¯YC[53 س^s;)r1`N&>ϙ(@0-@꽢=R1׋1`XO>o$* Gku=1.E۶qd^dQ _d{qёNnF@2K~$軣$[~dpq\̋}m=< RaDM.hT7|V+_cyrv}2/S)oM~a|%␞XˁP z|~5QGdp|'ks 2g-9Q|#TmIwś Ĩnh9/}t3P!-ᬹ p/g$J$1 sTVTK5t},pmѕ\j/ 1c_=z k袥W==cצ9)/e==^ASԮrZU1sջuO8 /Tk*j^r'W%7 DB|Mv Uff 5K==37X 38 ,F$X' ɫ"`8r&N"VV5/ZtV;G Ƨ$sq:]k!icM;y Oǐ_u"eο S1LBJŬ4- #@ę;]ov!=?dB!@Wԗrݐddo u?Bܲ^,bspH3to:<޳9cʷ$nK{-[_*yׯADƃ)ޚho)~s~婄/_\XBmu. L׿|Uɣ7^R9pyRxےջ/_ykΖFs3CK*.k9͛qxA&0䞸EJ[cNC+eZoMlk'_hmOu6pwbU'z&QW|$u"~HY>ZWÑQ"P̮O0 Izw©SH(09^:R2[H-'0.R~^ı*AĈ+xH", gIg}d)+DK6בHqWc~g>7o IDAT"r/ l.}| XGT;x Kw%/ z5iOH(pa0ILDȞZ}gPw"Jgb`Ѷk:<]>;reP.ZH ^ ίߌ]K yHƓ㉢k حO~Q8ֿl: K H6ّoʱ7xA6!u+1f?_E&-dy"A8kokk_^KS2Xku)c̃ȷmkWGmdju}ArY2Ÿ,{E\l ;^+#@qH֛"`8 /9umTƙ2mAf9#6!;e)hNMԌݑч:uܑx-OwBs~ɐx_2#"# %@̦i2ozɮ;=13drDzk>2#+0pnHQ|9|q*i@|ƚx]} ١H[uƬX;( q #?E^ i)] /1wRfc%c9QM/}/-,LÕFl~,sz1_8^p ƚ@a]muBla1~|/(9K>f.D_01>LMw3qO 1SkIH-1@]Ykx+N7\9D?4|)hsAض9$ji$H܇)wD-l$WGP~$`8z( 7##<k;N=?  G " gP`8:@lID@".ȍ AnLVWNnS[Zyu;8ϔ%{OHҗHSP@"6"_*+ ea % DIH^v7wg9e#Buk_;3s=2]Lg'ǴG ڈnCp(VF4o5~EqJv^79H1K1K.*4t~@߀h`:c΀HfbgXkcvcf?0Ƅ"kƘYk0ƌF/Zk6 EYs9>Pe3f1D/ 7"~1c=0cƘˍ1GᓐkcLnY^s sb}N%b?'FMDKO?6P\WƓ[_4ē~Dlq<޶"鈩O|ΫO"R\#!:^@brՍ DU_H!9 Ɛf7h+ b醻c+޽ى\=h,p}W()ʎ!)Foc(!ԼunuY43Dz$%{';d,zȅ iT֬C5'g^IPz|@mNer2P][էiHzS&GۏJ8HӋ?ZTQC1|Kb2yD#NeB!eF"6B$ȁp8EO.HhPʖc߱@T9F"fs=S[u7!=f:؞Jp|~cLZ>]cen0܎3@Ze~^nD¿޸cF/Ͼ1ak^E昏$02L?D(DvސJ6I!6 iYYT瓈N̫;4{dQX1gk7 W >$A2ػP+f5 e"K7f釡=,z>ά?.9 ƋBfK6vcJ-<A/\gvpAl,9>@Vݹ"uWS_JDzJ46BW2D~K^ 'd@)"@M%(迀 CqQɠR\XKY Ba ӁP9g︵i2@f mC  H螕+ye;|i-!|R07n+k/dsc@<v6RX;z+̽C ?()sbpnaP? XR-y8w"޳~#.Pf]˚{o0Pvt.te!U NtoU8(0j3] ٳC!=fǡ{%;9 9?s*kVkzʚи\ 2,9N\ewg YEr!X϶.~`y 3y:1QkmOJO1cO-Ƙ19nJώ =zn\`km1!k_] kc8k<`14t^/9 ^œ[ЛN7%s9OV5 W!f\|j}e-t^p}yS'b(T)!8y1[T",LA 3Hfa,b6t+\{0(A,fшMz܃r zA.닖>n-󽕀I-tn;/#B*Gk^J=+?58Ȩ ٹc v-/6+=~g_:4}̣o@j]ϝy@8KGGV}<ʚTV݀APPu X][>v @ϞM^?v{Ykی1T~p8"A6.MC\m6tБOe^0G}d%c.t[ A\c]@-_\gocadz:z~\NbuАJV9_T"} {C8[`"&İ݊&PVZ!mF}iY&kݾ@Є4%MDoaN@oKdίG`mCz=q^{lP95-o k!` r+rG"i߂&/}FXhuC܅K?\y{p}~ʋ 2V=HJ F?s32se=<`uatDɈ~55Y}pa6׷gkxI}u}<엲l;sOY:vTdxjJܗN#0p@'%l<93oOo=b\yD`NeͣdzR8T7}J6_;zsy%p[1]MC˶/uGgKg̩'w8? ׾Xum'<=CZJʳYVWV] ެYz``'QDlkOE&([l*k'?DG!F|31?AvC-^ 7|K1xkSF|cNeNb&"uD^O `T"vs}9 `<݌f[~ktחlv.:hV[@Ѕ!hWr]C2+)]ճY[j؍1> v|zN-<:g;\ Й p&Pز) _㑡0"5&(AT"]vbs p4Д2f&#`!iFT4b Ng8uRks2 ޱ&;[Y 1t!7T" `|#µ}Vr,^c7fʌڕgl /|lfxGMsx2}1TN?vn޹{Ȕ J^tluv<ڪڪi:ۙWf7SY'ݣUV SYOl0Z.-4iwcuTumGT^Z{ zd`g;rŭ8b~R ē#ؓx23Zv_)6 椮LD[.I` zf ` ָ~PBL!(~l+@˩Dx2=e`桸S"w)Ly jvpT\p Ҋ@2>4 &jbY? S}"g_ޝ&-qcx Eʳ9m|MEJ!5"@W@ׁ-#Ϭ$c \R[]1Hb ulڗzś]2-\'+5ͣ(;mkNy%tbXE 65ŗ(k1)@@,L{񫰟ႼK:ʾ@07/\x <wIy4 4;uco2IF3@D/N݁XWa/n;l,ݰmʤh #7kciIaH8wNeGW:7OCē#v+b_-"-qw,Dv1r 6u).F!L<=Gv_V"Ck95?'Ee_.]Wu7:f?':븫EI4TVg6}La4fk#k>\[,?|Ed2 € w< ۄk?Å>kNeNMam--[e̶Qkԏ|tωmm5V0rFzfS`.[1$t*[ثn"!|"C< AZmHx'L#ּ݈6oEЎ3c7sS\cՙź\_>dGIbThE @6Y-E1/5؆Q:~e!jvM'Plm< X;Nj[vYĢs*[rQSzzAF [|Vumէl>!UthYfcϾs?w z_3p\3CEmO}䝏_D78z-f*,}̡T"Jt!]GQlAHRW(x2= e],,vH%bu"l>r^D^,wrށ4x dӐ,  -v@n ꑒL+l-=,=rcCs#P\Xĵ8iZJ>8ߏ@d;*U[+[hwK"mxw Eqq "8!arsmƀ(<k!1cA(sA~U臀Lwl7g7+t%J(ē3WinD{駷{j ,{ٵ1Kw?uA϶S͎̐hXWheO}иxyiu+ji* ʾ%p߈'C>mWwCs*k6,)Ac IDATy-3V­PJ7fAumUzȋ5 p֎eY?kmm/*a9 떶0di>jMZv/Fyv1Rkތ}8kl|} 'c됺}`bhF#v7uT"6?L8k[ ha$$߂خ' }HxNڑ8t烈mZb/j.@R>Oc 2<^9(e+#eBO^w9q*6ă5Ƚ)8|\4Eh? n0N\? Qm8}ބS;/MD &ٌش>JqDqn=|o3!|E0eWm؜!ekIȭnyN%b'_qcvA*wMGq'DЗ:;{W+`NN?nYx25*f7Lq9S-{_8 vjWo9xЄaϬ%bյUIWtw"uvr6\dz 695^7[^~п OC`ƘBCqE'KZxm\1f-U>6rnjح%(+'C}ZwhH59|L%~f}e9g؜ʚzDP3'5&L_=TA(vT"VJ'5D+g/ʸ2=O#ȍ5Co:Gw[ElG@h~6L/C@ܲ9-h^l h)A7,(!W.׿~\܏&sp!Vt>@nMiXwLO $B U~[1pG{ ݮ.(UT"d046o*0q y2Wn}qթ_\uE@{iNO9 jkΟGb~ 9ʹKr~jFr7\hoܾSY[@ ayn/9v)p  50DV]):1x&ɳ{RpY2h碳KGw^&P\ᖜ!f̗RX˘*:還~(/2ɥnNLEZ_gInϺ"%F=qK,܎-<6iFښ@B7&u喢Cur~b 0(sߜqS[+ c#SSB +4&,m-Auׯd$6|a<-N7@<^DL{!wMivLjP8Zg#b`cW Rس(i1;=]ҷbZ#:CnyȽ/aKie={W{1K\&"lT"h@wYf"xB>J\| M: ʯȻŜ~\'F < bZ]}W=,GkEf9b~1֭9놲yWƇ@_Kܘc,$`r @kD]v"m0lp!n= vQnw%nL'7g\ۗĉ_1?D,Ƕ%xd+(id3d.I3 m.toF7tt',wͶ}CKn:M%QU:6BoITkk97A`a78M&<^"\wQPW8EnȡnF٤߃pTC@JTlZDy@p ` z؝-X< =ٲ7L>_ӟRЭAྯFwEwE/R7,:;y_AēRض}/΋ע{Kݧ'C}aL!F&S[ŖIY2pّP)oX_Tǯ95ny+6@umA(\Ov;+_+Yܜ?^{k*[xv d3@ 1/z}tgcAϪPH[;y^ɖC1;7eg1&xIw<㬵//M~ ui<ګB٫6Mw8:gT|]8s/**m;@h5/G4|} bFVy9 :ږ7#P5!#7c4} Mn'O# l*B ʫܢ> ݨO!w ڎ@yAewe 䲩Cov>#(|b4i5]̗϶,@B/a 28PӸk \QV"0;=>Dm̥Knǘ%{nz‚-B͚WZMߖX|Ĕ}aٺ'no_ҫ# Ud8₋o5[֖m/ZssJ0!]wUՏńC/cx%SYck` ȭkoYkی1l)67wv 1cȹڌZ0-v0ƼBZ^ Yk;1~>#ȥX܊s>}a,^9}o@`"r%z9#춙߯o}6-FCQܩgq@FPWnr*BW6ه?#P<o&Pϕؕn xD\1ؿAeY7nzMH~X"z_')?~Uy\^Y9k.45t_Ng[v?z6{#K?ܒJĖǓ-/T2F~x xI,_I%bK/Z,cHrc"pfh7nG#WJ ]G ׳vg3 `Ř=I^_3d.L56v[҆&ڛo]y"z1ήS6|WK{X_2{/}sӑΗN_* X[qDXum95SlwݔvA+&J4P@0#;r-eOH~hN"9-1PF-Gq:jB2KKw*@n8bV,cǭA Cs\Jw jT3ksȥX\n^i;΅Ϸ!`|]ͳz>辩PP6`va#:e]2`Xgo1=qoG!%sݔa}"bD?쎹@x2} p7|ĚT"Y=bc@d: 50zG7p׿4?I4X_),fzd*f .x2=~JVߦ#P3fk4~ Ņ1ȗyŐ"]*Ĕݣ[tFE |-tEF%Acȝy/!&IMY@CkbW6, (x1\لS_}W/\}=}?Ab#A@f;b}w}l0:pu1۵ݳwc5O~E}}5%4'+ҸЈx2[tVlYRqu.3ٯEzncƾ̌'gح2}(b1Z+ 0eI]! A,Za'3$L_@^_~=< I:nb4:?"b!B4ru zPKHRdF)B@)fpD~7 pQS[ˊ!E4Elֹ%V~}BՊ2s1<~' )s( 1[C@cc{!(حh??O'nKō ~|MM⾍\7 Ըcyy;_s Es> $<˵zzٔvVpB!ĤZ;5`́*_ڻnDDd+oT&۶GH6[>x2IIέY]D0x!7h5 İj4EqBniע { T-hMB`M(x> q(nd)!X~ȕ^A?!0IjG,TĵIe iv}AV0t<E]?DqlZΓYmLaEC:{::;3n|۶l%.pN7.'!crw'WT"v/bzx*L/>O*///<ߋ|#mNeMkum~"MWLыQ1TUJkuuor-ơuK[c7"G=zv_%zbX0O" D+ex2=m t\O.F,~5vH`2LI; G1(XB@oTWc݊سo8LhoGdc4"l/- x & ``H@(3BPI0^->ϸq(#?A!W)n|Q|f3nԬG 2@kkrq;ʭ8@>W,]֍FExa01!KtY#Htdoz1e91Qa#-vR]hssums*kS׻x2R'ˊ~ FMư]oGêk6Lq´;=z51p4L/Qy׺7?g%ufm&z1cp{Gx2=G b xp4 /ypT30&(ry&4&b7/3Q&޳;\t6CDw6cʺG^Q0׿-A᠛;cфoѤIU 'p6xݗMu6c\fu*u?KLC16KԵ=ŖePT;.ԝMy&>7.NA VrK)wwXf(CVm ӫ{'0/F| *M.; |)Pq-?čAFt7L^&"@tڛddtV~+*g G ~}Vz8@P|PxӸhc^߲&$HW&rљ;͋7g҈'^\iƘp4ʳQ1t璍 4 ODZ{ZȮC$o1؎<Xk1f"J󬵛1_@^.`};C1A kLľP6 IDAT@I;ε1M |CaB(}~b|\@3v:MDxkgW~7a2XBy%IxxPT76!wH;x(V:Q|s(h<޼KQB zdby77۝1H_lq' v>_gw#ցBBݸ}mr[7Fwsx0$Ѹ[̻HG ՏمH?5-_7vh.f${kJĶ@o`_k\\9C&vh(;jYJ[ڢl95;vf{GWmV# ;Laq6dfBZO]۳k[R RF 8\?w C?0)nG ~܊wlstnHʹL(e#ыFm_~6]܅*f/8ҍz^JľƓ~ t'kos 3VkUVR][UZK%b+Sߜ|MHmP?;Zwofv `C=xa(x޵{h^o3Wq9v,'kmZZk[kP^(iWCsz6ڛdi&LuH}x<.FqܕG!H %4vm=\}X t"ȆBX&R\VI[a:/ɾ_zvgX V!b]ZN^t|8>;p%s >A7.07-jnj2 }֣wށܥ 7rǷе,/{=.#!✍\!g!wdGbVGu[6TqYSj(D>kόlrc\9V:,F{[ ]/O%bK->س8'b.b 恚MuI3|tAA7NQE=p,I>(Yu#@aZK6fb|q/U[ݶ+]vBez> n$H An6Z:7د .=@`nmX " 3,$,vXJ5ĆF9nARZog;Ʉ)@_|wm(0zqεp> Ԣ\b<([m^e>x]+bwT"x2}0z m_յU@.>={fPY[g y C3z.:fyN f)G6vŌtvW̘ r4/9W{}nݛݲj)cL=^FZkWS>{[x2]d"=3~ \JVqA'jaͨF|Z/Iw\PAG uǢIs'zPp@B/_<. `-0*ɮP>߶dXvNEϺ>wovP#͚Fঽw2v\(cpN@@ 閸1ҍJa$v>ֺرrx.GNW柂vNAׇG [[q R/(uq}R]bÊH[o#+c FTM GS鵷Gٜʚ%w6!w g?Vnۻw յU3ˋ/<8OJյU>Ieʚڪ|^V-ǬmƘSjo"L+Qy]ΎSWޟhz.FsJ Ƙh~buoD)t'@ͳ53OO@/D6g0  ՕGAB|4>4~Mr'(@]j4[L$.:oJ vlm(#׎&  m++>Zܱde)F7VaSeV~0L:X]\_'/BF`yNCNb1}x#d}s;[t<1Ucy%EamGL)?ZGg?y"7ݸC@Vo z#bރΓr_DzVv鋼~C:h'(~E箟rc9azmvCCdn|C<.ڸA@^*[3SJí%D;։GQL臁#nUSZ_DYumՇ/_}_eΩ͸rtƢuڻr\B;dziyMP1ώm!p9pm|nu 2/vFF * g'ҷy-b.u}ٍ@JƸeFW'n K=4+j E[C> n `os^k e6 pfw^_!Hq]__qG#QM)ݮFo[K1,fh-}!b e}]誑Z^2S+ DЙKx+LGkVV= nNeMtHֶY?hok0JZPL dӈqvPvw!M!M됫vyrۮM|kPS%\3" ֿo|{ga:vڊE\Cb#bCQR;dgg "B?w_bOE "=tI-tߛ[9r܆E@t 2ƚPfR܇ ODl xB ع AgWG]߷ ׳)kqE=mg =Q԰ Ӑ;x=}\{\ߖkd;8A\ *9W5ƣ ]׻1eB9^m~#ѯx2?d>?uK=sAY`Rs=_De:P/T"H<~}G^;8ϔ-eۦ@+",uDH,K A@ "b[Ҥ -K/Nzg; zkw{9޹s?|H3V[׈dIa HE=bX?ɟx= vL7 "kCOT o% T}iCS]7?6h6ՍGWnjpۺ)1d$@s#>q*gv7 o#bkanY1v͈:AAV!`pZj͋"W7Z\Di;#k#%kbzO rkGxx-=ҏe_31bb"(?ĶMCq^!j *32vκ-S~FKP>l ]W?>s0wqOy{ 1ˊ6LW@_?-.D-vG.>[ݵ b z~Nד+ssx)b\oȜ$/oSO ҆[DMwZ;5[M'Ώ<>?Y6wҕEr-) ^hcW9>0gͦ?FϻkgR=L\{eu+w&xDn®#g K$$/؍k^'ndirny@+؍g 6tT}@dQ~257"'̵ĝҗU` {z9[a:\g͎q}F[%|vF"irnq1IoԺQ0= L#+ mvi@̫%Uۆ:{|MQA=qC}m<Ĝ|#Ϻ|bŽ^9 PDr\}}U=b6A Y#,z{o9f" ufj^hљw-[_ʦ 2JP;>Ϣ`%hA@qݻ}j&( c*|eWR]㏶PgQ`$pEY}lp݅ VR>H|2\b뾏 L#Vo#uc@lJwlkb|14-sd. `XflqῙTH1O}=%c?;3:#%6 @N=o2Rx2]2rlv(-KPxקd (J= F=]>/r0/]kX[:o0~}gr0>%X܍"7_-(>kw}~w]"{nŃk/  5dzP(@.om/jUǺ!RXhlCS]|>|zz2;c3z~nd}mm|z޵s3cotFSl7.z^ͬ1橼IƘ1v7ƜZJ1;,ohXc7ۧ 4fl6`oQ"wcDѥX۹ c־yʷnk}޾EKֱYxΝ3[B/c64OXnK*"z|>ܘlj֘KC3`wPLZHص_cVC /cB)vOFTz n,n>0&vnQU˫A _Ql;y6PZ׶w#µE#ݙ5H'-G yǖJT o/F;΋ܖb=&۱<2fDl[+A2kx/J -H> M5bZ X>7>!jx̻G,nn ~ A wg_1&iw{ --}Ϟc*254Սw*Q(\R.p"@CS]nĎOyH~3O_qW>*@FH7q5cLqȝĆ|ʎ`_+'6$Jη!|cnhcƘEƘ/3s3c1<~;>b1cLSvf11c|cy_dy1Rc~(M1Ɯo\6ИConn^D &}$p{̏Egí@XlD|u b9+ mJbxB,Ydݍ%e(b~$.wM#ܣ]*CWOz\>җSZ" Aܒ\.˔2%( =[ :U߉\6w7^f[N#w& L[ u>ݾ?G ׻J?r7'!wxM(^@ԗA+]:8{ 1A>}n "{fT;OQo|ݏ췚Sf !{g.o9 IDAT`KշVZ(>T7hmO%bdklh;v<2om|5R ڳG\2'CЋ`N r8FDg1yg1qTGx޼l1)2kq;gw[ 6zF49TE}l<\?#x2{>O3Gdgݳ&HTpcɇ#wt\ThA*f[xz&++ӻy;objƷu/>>kv2_4\{ycC7L21_Bn4?K%bR^'qCɫV 8 = k O)44q5''䄗YCSih;y؇C/7 |۔F0}>yG^u[kt?;b7Yk _Zkhv=m[i쿹}Oc a<W=1$nmo7ƜB*ޯ6{yj1®Dx+Z8q#/񙒛^"OE&7W!)ܔ l[?E5A }4Q{Z=pym$ `sO7 Q!ՁhEXTv~C4ID_+ At g ܱy,FU/(1PFXΗ"1`5nw>F@E@qǑBmnHׯ< `ԉbA g[L ݞފqiG`l,AɭèFK͒5XK@}ݗM2m__Xz.Ԡ\?JKopnUګ|=wd%fLh<|Nj-Cˊ7^?]Ȗ,cm6ϗߞ9Oox15HDPkhm)rqh(hb#; 뽀G1C@Sz)POg%ȅEB.x * /<o+.0)`))Eu(AdUho'>s NkOX.l /]ߧ!pNPK$xVozX| ܳ}~%DL%Hp=b¡n?+SA$;֝9՝׋/QƳ_q}17"%X"Wy"Blvת]&( ms>c[2^9 A*(i}6\}tQe:fc5-Pk;Q2r64+y@1|Z攗lkċ=xb?b~Ul0OG=_cc?c6q"6Wqa둼.(X_D&k\vc^ brLGoU}>Wrb<Ϻ͵ tD7{]_A (1'ؚB7Gs<1#Ir;˵ͧlbB׮ qޙJĮd,LGNɔOjYV;VUlx|qdD}m}omyM.AƘ:GcL=үoWݫ7͌U,E nH0tJxw\@ǻ۝Fgh)F"b=H1Ƅa[0Z=!C8 ->sy;w؍ .A`e7}ۖq|Ҽ6p"<7gUekt)%U{M_ L2M nln\n~:\"Tw2rs>k'H`?e=/w\~n^@ԋHz`/ׇJ'#@e]; Ȼԍ7:F &w+7^w6}OfzblnϚs3~gz\G.W1hSO?X^շ.;u1}'{B/MoaT.{med2;7k(?Y5{MO>6kh;]=eeU% ^>u}3hWe2Pgܘm\VN11NK 3{jPx󲧗u?Z F%݂Y(f巢س0Yn(`DJY%G1[_A,QXR4k`Um}miEL3jЃ4BVx(NĝW#A?D})Z'1"0{RO^zQh.P0$S)b;Ak~kh7C0Q6mPtwļ,;6"p5ypAV\-n3`3 `s5%1_VHCl{*{)Lʫכךzz+hzIe;~} 1_Y: ݧ-mhS읏X_t裵,i1'}#W.x|篁44 |mH8ܙ,F}=}66d(vfrX8-Ů8(m@759h3PX<\=UZEoܰ{='BݮV % 9,U0Y=*ۇ ljCgA,E !Cx>׮O42>)QlS;A\[ C@)i8A/rpǾ@K{gq,uc\fAA͸~W!fv!|nFj<8nD { P>Q'niwK0ػfĜ~XFŢ{BrC$r>B:$ J' Fp7͈ͮ_;ZԼe(J׏5n~ޥM/>SG1>he6I%bĵUCf/#ĢcW͠t\UQ\OK+>iƄWRX^:"B>|j}ʕѐvs O,xqGO)c1L+6)-<`ٱAk-r /YQܰI%b(f3A٠U@k'_J/:4! [YXc4#Ћx|ѷ]}>mk$3;}6gU#P _bԌrcxKݏaw"g E`o|(+&o@`dz=S~>`5# z"c9X4GJ'MD* B*]Q۾C@0Gv6z^oDb>vs<>etX!n*l1Iq<\{"pyArGn. /ЬwANQzb쌕{Y셫>1 &՘_1 ^I'F=ݽeuY; {S}LC7+rEĸGhyqsiyɦ;FTEϜB!&浟=taj7YA{m@0cF'ۄ\S=[+p=*/c#6kX%>#1k!p旙ɲm2ˆ;@t5!o'pĎ !p[݂j۶5,]~WmBִw V !@PJ*pH5ysu?>o#^*#iF ,e- ?f7w5zy]=۵w7I$=!"ܺŌ]tdv>_ج_8w}2 ćr^شmC([iCy`5n'X`y*{"6hfdzz\)Ek UDn&7O5}E_Az%rBCSCvro_s7_k W\&yw2k ZTM%zўg֤(,ZQ_Π o%fd4܎bg"E<-@7K]^&lz ݵhxi -x(_ F 2BMAsqa.?Ͷ -of.עyoWTUW"؂ZɛbPó(6€XtfSA>npuٗ &n2b߼xwsן T"vY<!t:7jDb.Dr~Zϡ1f'+7n/h,j\wCvE(Cf.A{+VEpnKتRg-޲ja6}緧 n-|fΘ˚fTW,(F2c_3zlKǘP(WK:k֢8 sl" z~A Aœ(iA_b,L +>;3CH}Y Eq8~V6yM GwxmcCZpێ;5_ĨdU6m"`<`0Ky]&Ęxٴ*>s !PR 9kFf}! C`g~zB6oCV!:D"p8y,zkG.[ Eb't米hL%b[k}'ӛтpFY-'Eٞt|?K%bx2=e 3֝/v~h_nbiAA{X*[Ohc1G҄G-ӆO4̿\xG~i̶O/jyf. GsS?Ya:)n$DJZr%-/e9gL(D z]^Cό="Z zV?t$C//@c cO x"Pe q@[a*kCQHdF}FЍAFb7wK]OvE{KAЕqsMb.Ei6hoRXK*˥'O!VVQp.حʂk1Xko^66[ [wMO'#]y"wR9ys 6 +3v,V gڀ3b*/ێ@Uag(л|Шg|Ll ҢڂJ?'Cܥ^',_,0&p-Fh9 9` JQ,; M; e * ]`\bEy}ZP"3Vow=sYK  7lGо!g} h'ӿ@S ]CX?!t0,:&TwJ7Y;t!vdRX ;~G7%1}=L(I%bq3Nŷ>|'v\AtK˸(+n=o1g M U~# D3H(T~r_RP0~8pg 9;2` ŹWZS&߿])4JDN}kMSы֞bF_+`6B S>cU0r~կmnqdz9Z"[B4Ay7 J^,I `-LNjlAY b^G=M-)nnvĆB=ږ0pE +Pw 柌@ii|xg7bL(5=XIno&pfEMrb,rC߃@/}YEs;/t \JǓi_Dn6!T"ؚ8r\?EqǖT"v!v8c=O7wg;!Rd^}y]zB\*5*>-^bLfϘXn Mu'6-3.˻/_cɕ"#\=dr3='CB$_4 GL6DGX폖ƴo\#|ܴh`aEL++o;7/[v@k럭6|I(붍f1[k IDAT0| >'cA/{Cf vx;;;+p3xs}cn(6x?k&cpcy@J+mf ]U}> C(`G@ #c$ڍ=a{ C C xS%(/_}߻+=qR 6g6 O|؍ك P-d1}h݈(b_b\rw/D $^n.{ d'c[8Ysmv! ֈ]lF@h [PAh +}=0]<6YTǻu=(b:2v1pf<~M/ DQdzm*&T"uw' ??s.A@'L_@ptDB?qc+#(CK%b9x2+b,1 %vVcevNOǔfeJ(k)*1EUup^<.@DG_<~;\͆WK2قM(nQ7\ls9KoYqRcfϟ2zs9~\2`l̊mY-7-A(~ =A^ED1fzfl7^cfq~Yk{^cIgƘ:l{:mr/3MpUbd7İ3ٻ Ɯ4?vy<ZXWfYĊ]w^0'HJцX@Lӻ6}Rr (]3 Ͻ/;2~Ĥ S<lz*N. 3_u/"H\ew|>7W.pyooXWvP"ZdcC7ӂةkw7q?ޤ fLAzs#Pn /WvF wsd1d }̹!#?B.+C}ؔJn'C]6*ĸ]%Z򽪹`]*k?hYsowt-vjgOã-˭6ʘLH8 CKk sh-A~`;u}լ>LcD/xi,=bT"q!.]s`qigA$b"M%=̜ʹ-y`}jϷwdh7/qb*LĒ.CqVߏ>nїr7}oFGɮOxqg})ժni{pŮm碤-}~(V<(z8ͩDT"c ^{Us1ah NdeC|`%뾼s +H+˖Q3qI$Eq/6o7'o]sϥk7rյK ^ Z}mc7w>qk{6E,է~g>VAݰۆ=E}\{'!k?YksZ*mu[%1Djvp1f;wSޅ ~[o%U0<=˳bHH%b􉈑qhBSe)! 2b?A&e>Z@g2L1ޥkemEZ=` Jy`lO[d@'2T6 E*(cÜtvB{j݇jh27;PA>=m#?rCҍ3l6:g 6modHڍ'lX-m_'Ӆ f.YyvxCSbe͗m!^s% VVY-}sQG_/D𾳯2|j;c&L. t>p BW4kfc̃ƘP-ƘgsUC*1?7d3%N} 3T-vށfx2BlT"O!wb@1i "*Bf۬~/]d M}v%(gjOZ׬kgc8Խ1 Od @$8 >lCFՏ\Eq^3 Bd?F@ ljx{C3 e>V0rkq}\[C u"m׍׾Ϡ|G!`OD_Db&6#&!D*ۦ]Ypn2sBYMﬞ эk f.3zt1 ÙcDUo'd\JZ>; J6h\SѢZ|.b~{2練}Κs>]މ>44Օ&v 3lyt;m'w;䕔8s = Q@REKJf[ۿ@ t;=t]^&c1[A`(\74k!Ҍw?ǷdoBF܀@AD_ E`"V(&!w^?}ݍy=b d=d%[;/RK#0=xsnqmWX=ݹ4sz+AR(lBgJC=b'D@).=lRxrnn>r?ŝe<^r=pg*۴6hخD1zRXĮmhrw.wK wsnQD5 F7PBNan|䠽]cd6%orK 0eChant:LcE 2^Z+P*T3 ,e*G> zM e`J:{G6 ݾm왗yF/of(~@TwwO.F^j\,H?J57r. z FHXk@G mK>C,v AȻ,GYvwfkIbŝ~2^-O *' eݾgܜ Е2fO%<¶qXQp.V7L jʯ7Y@&"- w#PXƶ/H a1MOeREyeGPFܵ V~62Q׷'P=8ѵ};{/lLy'b>JV Ƶ-kZrW\b^ Oulv'p[w!=ג0Y ڠ Dc(d^gDW^bACSݰKsEseA}mkޮڠ 4{7ݔg{CVݵr hN^9&4$LW#AED!+^tZMhKPmqd^ՋZzCYԇ]1by̆OH薺1!e;nbw!6F1S^Gy{/npٓ1xa|_ tc)Dqw[!&jEpk QTб(1jEߑl *˻&ALk?A qc's@얖m#j_Yonwj}89L&5=ܷO0ukQfˬ,0z[g|3 O̯~1LBY3ѳw꠽/]cDls<}~NP o{$L2PW+XhB[F#v;EFnN_l ƻ3L"P߇5[>+@`3"/ A@(wL$o{.*LF/ 8 -@Ik}F\}z^ `wעƵVzmTuw]ݜv닆.޷%=+<ܾQjͦ->6PB.G_rk{brT8D'Ǔ/c|+}w5pmmr17{]ew } L9'xT"ho-Z%ME軶aVI'mOr27y9h6`l@eSēE&~h͝D[G,Ex2!r/D`J O <, 2xv+eمbƺE"x= 7µC.>YQ_L?cJ\}ſ!h1d\eN#$F;b,buƸypF {iFo@%%e:j^O]C&^X1/t7n,AI]\_tdZ7^9/ߥ%ek*e"f-{ PLT>%,ȡyd]l1rC{S ,l*TL,'OP'gb^کELq+@2mtoA'Q sAnN=@km-E )A,#bl]ͻqWFus  YFp[,CA'p@E8,LɩD\g>?귽@b/'ǡ CfQߔ@2;㒷ĵѽevƀҤ_evEY($rӝ-η,ma߭'##0Z{1`Wk-|Z{1@/6EA7|簵 O[yCv2;0&\ѢM_Ũ(޽E`+З|$RrmA8^u?G Nq}K=xrm֡.SHQԵq Ѽnaw?@/=CY& (#Q]@:iAvA*XtZZ b 6mj?.&(fz yP2@`Uw^:.㪟đO^2,bRD,JJ%b,&d"O{[ ƓWFHR<O_aclsBY`ĵ| 'Udg 2c>Q"ks!̹1WF1f9AMa7^ fdo Xk80*-̹t`"{H~&ZjPG,J[&'8@8G{6:0AVE a"'9{ă=sFgF#0c(GF-Qcs}m6]$1xdI)L$`D܈h Qn\ *3wO(ri~zyv. =̵Y@HW*scr>%|%(͈ͺеK$z8U,rx2}}lܑp_яo/Ar]dc@sulwV'3vd"N g-/Zΰ5fNi5f8 }/+٪gfRY g,3nٮ ҲWVf #%rg=!Z9@Nc̘Ȑ=S~JbZ8ZUcH ;e1s#1ksP z~u^ jMc~֪b^|8l3OM<.'ἏO'3PVWt#ALL-}@> 0w 83R#ض\k8-،@ob9VԍZ~vCdGircG_0 j_gصK>U!|-VHk]AYX7͋l',+]V!&(F.R_15 HWnB0縱|NKCk܏lsv]BiMtc|ɵlx2}tv zF@ˡ5f5fk̂"'{r*>hFq1f1qTGK[OcW=PR|4 k/kw1R's1f Z{EGrk>Zxf,LWǓ!VGctZ?{gWUezf{BP.ʀ Pv*/(}-+TDT`InzO&>ssB3$0'w=e}=ZY9lmun:|bN>& &BɊst"R)5C6\MܔcSфhH, IXL%W7>\S&>3l8`mc&.TXnC?FߝwW,ծ$%JV`: c@H7=a瘉v_!X`"}3PZ~bchnmez%Do`w vOs bTWoPݜn6N(;h٘t&w7TBG> ԍ- (Ac5cvTTiڈ?n91]t%wя8bDLcU njL d[[{mz9h;MmffV͌ϳl}J{sn n}]*xKz0`߶t*oѻoh%h^ߦ󠽈m+7eHDT&e@:[lk=j!FPߍ4BǑh K=Tr~>ͱ={-t! X 5'$)E5~(!ᅬG]ucrB1x!:~QIWF/#d${/b1:Ծ[e~ސ+b3OAυGn!6>UFlWgA1D`T>Qz~mmy>}{{Le[[6L*$z|QO>EZpmq&1'.{Bp IDATV]>-{kln'/Mz;Ԕ.@L&HX`c~^D,R[.W)+y# &e־(`]ʾ{1h F툑־vB+ʽf5.D,0VHven{.w55ڋBu/hTjk 4!6 P{Q;A|jm.C@$>bOFl-zޮ/_}IKgrG3n:dNO^=j *7 ī{weҙ\M @nȯy ʟffm=㋮MRO=!eas9HZ3nt&wf,lkݯq?mdz0k5y,fUGo]\%+pŝsM!(H\m(0b RA&b, ] sJ$jmZIĪ, :YJBDt2ߍ3/AYГbYY(AWۧ7mLn;ڲ V}}}ƆV"h<q{PlHPΟ&$*C`)@h(2(xGJ+M[_zک~]\-O)/ғem& e '+0xtu 1=V9ū#F`]oXذ >mmڽKҐ3f/bYN#o&ʾ@qg+Sy>ſ`cF#;n6W!྇Ljz+bNF/=+"51 7wǿd3l[:k@\]=;neZʹmLQ;3Wb[XB-5,C ]cыv0 M(AؠC ~w##wO7e?^m1ekCϭ"UbzbbV#  ì7cb܎GLe/PTQviM#mXfX{7Fs4f!ؾo!bwCkvmq6S]>"*^ȶTmnAؚ]ەԅLn^=IX஼Ow1= Oi&kg^4i7=;jyAA-m]t+Pk@@x54y/B՗lb슀A= QPyȤ @%†h\]Bn;dE J?Xd$rbcU3#ԋ&}PlXk92&F&a^Goo@vf|㈭Hfwq=rbcs+ܝBӀ >֮S ;.r)r`່|e#/Rr^n%8{ 7NB,u~-SkcTrmIlk3@_ާھGx71JPi~;rw4={Yk{ߧ4mG望$Á53$ι[xn`/0=K/i3{[=oTnJRD M h݃ؕ z]#DѶ!ڡJ͢hس2QFc & ^HňSo bZ 5Џ H-~}"LF7~%y옎k_Cl}َ c"qcMm}GC;p1rk0"M'g!@ޠcwrDe(n 8_ ]t x' _@Ti'k kD 0bm=f̲16Fa,2fpZtcXާ{]%ͮ'|؟Ϻ=b XMU#rLx<\1D ~~k_nA{9W$qٴ ۋ 5f1⯷ lGص tyﯰg'yH[h;轿97-ܻ&}ˬC~'7-sR(E Jellۅ?@:@#ʞAKe[[n@03r]} #AG gUNEc`^ DY6V%'m+o˱ڶ5?Y.ŒRݸbK =MU\H "T'pW"-X,A/vpC$(zP\?s{w{~/G;Ѣ ;+>I#@uzϲ7+5Ö8 m W7 s;9w #zUvƤ3y q[ t&C Ln I4! r9g PC(ݍOB18ٶ 'm Ў\b1-VJ<;!ۇC \Ī / Q$`z"`55ipY vG=~߰390D^ v@hdt=Ocdg>!v:,Qhto2fs{"8CLk(v Sҙܙ(L/֖yM2+%ͭٵFl)P=*)Nqk'%jYH@ G'/=KY\U̥cs, X62>jI@`})mrs,q "c϶8fCivm_aeCT'\!6Q=UkpY )+jUvV|"n8TCڌh&iDȃ*~`J"Q*< D Gy h,(k3^3kQZm$J^(S~XkX?aPPC|m~U[+I1&uH,9k1mk *ܫu!bB'*>zѳ1wCP-S\-HgrӀ*$>h3of^4)w>ٵbY%bP6v#(8w)P?YuROV%9w ǝswy ιx+st^Bڇxv59/2xXMKr|!Mq]<\SsNDk+Ac,A|Pw'@@@~b(S@K;DZE8j=A-@ XKA ^(sTpPhJ8h5_DZmH-d :"Ӡ!S@`g];0r!VLeFE+я46U!ب'ľn1Gie͜X$z1M61FaŽG@@>0CYg&k(_L*- ^}A{d`?+J_&w>@l`\Ք/梜`1HL'$U?fvfVr 跲Xә72hۇys'\OJk{1'la~b6Ix#>har?Csƣ9޻zk/q}-"/Ħsy#07hv>u7 }Cn(Nx4A~!!DLSbKF6~b]Glp;1"F#0 IDATk(DR  BGu A?LFgm{kV2TI:R(3Z&7QD@(k]{G"n ǮSlan욡Dz0jRt({/I;(Īj(;재s*TM[}۽YmՅ֏Qhr<=w&*E/}L݌|kxʼ 8?jXt&wWsƎ.'>ī|1 ]{U( 1 ^z֏ovmwP#/1[S9`yZ{snBeZ<-b> E(Eʽs-ο"v'&{Gk@B."]MIܒmmY ܝG$`~1Ú̶D Xh=32!#SyXxB(0;pl 93gi&F)1`GTt#U(*"LQa򟌘,}!9B";/st+r_;ErDkַD*,UD,QBF-ze %%eWZbPS=<%"0\F@1ĦVkm,$^V wKmm66ݽKN_H)BgjF.Z~_}Mu*cTݕl(ճ3z o{p\MX;o=@ٴZ[|ښ(krk ۳~!ڲ\=clkKw:Ъǿ+ @f U\q!nj5QBn\zUA @gwBrbhuu.E}e|T  /ddNصlp{rL ѡ(ɡ Fo]+wJ<ԕ|'F@=h(t8ƈ:?`cDǭ!;VO وRٚRH2i2 ;~ 0C믠{bNCvG ѳqE qO2ȝT ڶ;-5Ҟ}kwP`9=2K]bjW#CAz;Kb/RWD˃y iuhmjL(ĒtИ|W"W<&>En8c)7;B,S Xw=#kYR@MD!Rl"2k؎$rW>& F:݊r?4UH8G\Gnr,#SFDEdЏ.ֶ>" ֏@(~(k;!uv!I%:h@5vͣ6X({ߐ;L.@1vI5v?ڑz: שDZppkRc}N$rOAF?wǞbܯpa>t&Wmm Bf6yڮ&O]5pYbuKnf~-tέQ#~סXң  YPD cfaFgLn py ڠ k`%ׯ3QmYCV@ƯѤtǺs0igɺ/gO 4B9E'JxѶ&uDE' a>\q#8,6馍CmƇЋ<8Xbm;x& :Cp"_J=hkO#%4ThVqEJC$|[އJBM ,:Q:q (:eT!ք^jD=DC s;`l2ZJlԇ / C^1>|\;4%_=Hgr!W7ldƲi;!ە}!tfg}^vfáhV?$܊!C !l2j_ !CQ8@ {zyZ~ۛt&wEpko%[ VA{ mc/ef`ŦQur}P@9hr&7$\b&9,hbO9C Pyʺ;1W$3~N =_R?k!3oOL1w@`-g!>-DR 4Nkx#F"n!D`r"nY20kuI=v' :&򭡺=٭A]Rmm>7_6UXNGP!Z%@XSpգI?YV?#vp2btCV謁O}ٵnvm߲d>Z!pGbk9WB&"OK[8BI{Asgϼ jWn; 35믠IMSd;Ď\VqEn$x 1ZM@HWw%4 s$\d<ʆc}T= z=\]˔V=аjݥd#hD`pDM ;[e[M>5h;ֶE?:k6 V?%T܀2  @l49Wy'ݗہo[V#=G,l;ID\Ʒ"`ظTM``bC +X2A Rh7OUU -Nuhמ5֖665֖ewykvmH yKk~#.;Tu퉵ju<_ ʸ}-D ='-GlJkA>72S_lArsMO}"8Noi~?o!WҦu߅ƒ`5hj4e"w* 2PYX"h2OZlD1EA4FTH!'b^D\JVB !N zW=6kdCCDZcZOKv/r@pȆ PGY[YX&*jڟdN!:ks!w`7*؆8xuo睃&v?[uD1N9@^oq(ǡIv([.ԩ,I1&{X>]@ZeEõ sq{v~ҙ\Ճ֖ ;hۇ$bC5*G-rOG~/~dZ\JO-jvmfm kvmEoY[ߍ~/ }Rk;/S+=h[hι$ϦeXE9Yzr;o ~$ ]dOxKι.=89u"#ιg+[;UιZ|nq*Fwy64)Ar^b/vctxMA\`~MpD:;)PkJEl D)SA\Z@E0^J0c;|RE}mDݏFpw'jY2}k'CnGFǢ>)o%IH&-jT.  Q>ܰmM`Up=ةg/#D1g#6(l%Jjxݳ ҉aPfCA$0 cxh (4p/놪Ǡ[+3gLO{[sh 195MC$Z`h^`ͮ-چ"F(`vkkЦAS'2:YY`/vf"Hg̔< kVBqw}8~Z8Sqq{Qay"OyD5v̥ vwni?LÀ1 n}+z܀D*7˶b!&sV/r>ҙ܄t&wI:۲^ҿ'l|{Ni><Ю!~ڰsY XFĪ~~tN~3fvnkqa_>OO=MwX \ށ5Ѷͮq+jSW/lΛZsqӻ;F ljoAv:ICb[z!sVA.C? 洠z+Q?X`rM+$v}Gd?zv}A#! hksH~`-E i?PE2^, M"<\"6&~Sn%bkuD[#bk[CmH#HgW*ھ D DG$T#`a}$cKFjFXslkL ϶쐚R/b"i&:rQWߗb ?ٵ8sg`LkLާovm N[ |MߌܷoeNxGk2,F_B ]ۅ@,S~9^HKJ]lvm(v-e67ϲА f۷$gCX0+/lf>}镞{99w R"xfv6m1!89w~+v(0f@l^LgrG"wlZ'9oE+]=G[: !4D?zхK\g)e)D+6Zos-E(>L" t"\Qe5 ;Ts8Qg"w4\Z%!Bѳo&ٶl\@h'4%slc1}\Gr&B׬}eׇTp:=vݻB:,FY_j*I]\G툾oG9o-&ovV6쉯LRާpk#ʅXCTWLfv>LjܻV$chs=Zx*;ovm.S> std6f)\5l>g\ܯXx~Wi7-ff1JW,汘UE~vsޯt x_*5ιf}97rW(ιs ?᜛x9v4g W`;%l5&ʯ#P4i(@=s~Dソc؄XeY6۶?@c}x h_[s!0 W3*=04GnPq?(k LHArW"׃@켵(#r}c:Кe: +"ram_\+|ބ^&l~6@/ +,xP׳{!~%α/Apg(-$mLE/5 C@3NGYglt&w"mԦ3Qk_[@Ԟ64ɺiFU[;P >$đ-7Wia16QݷيOWk EL)-D~5|9樮[-oj(p=k6(_Mדk/P5RA| %-{snBeY3rMnΘOl 6~Fۈ*m_V│Xm37mәglk@2 *F,ٿ-ә\ MubEJ(&({%PLSMym@ M961.B8X-N ^WڹQDЋ}8Qx>Ͳc&#^Ľȍ6s-#F(ӱج*d/I\5\#UDzB+ C˶ZɭDBȾ\Rq?;1s2#>fG֖= ry0{ ܕmm9 Kgr_CͶlqҙ\U{/eտBmrKn`c』g[[~=ZZkPMZjr{@[0>-N$'yZ6 A>&UO^{bޜsG${+/CKpCeG2v| -mLn4Py o'Q : MYcrA/ ю[$e`} w4"`8`9 IDAT/h"\YI^F6:j2!Ku [OM >oh*C1c0/wX[Ani7=p6vϝl̆~)W8hdV'5Kgr$x4bBCHcf#6iSW²-h 6t&7c*t&mx})^5G~7Z,8>a2vd;=!7㔗; X$n`M"hk*ߎ܉ I%пYz][bi0\MFE$}KC2 %"vEnصAہ{m >hmث1ujȶt (Xv!zM&" CdJ$Ev5r]|ig$B`KHCe453h u(-d:EƖ *^ .F!GL^glm_۹O#P7"*ϧY^G`u;${YYۮsٱ(&n{#wI [g?ZX@glDJФC@P|Y[Hl{zn@p=V+ud[[VX:;e"?R-X%hrb j1p>g Ln'y׵3[j~'ot3wzf\ oa5X K r쌊m's~gnԍͮ- @2n:ۜAlYZd>=UGy`܍7iOͱp/fLtPAl2~)@:k{{d[[fU-N[x6ۋhb@` bE ++{2xk xP]]QYGPJH@`a4DD.a \Iw`Bԟ$ʘ^ȗ馝Hp<Ոؽ"0Y_'Xh rϜLT٘@<\N@%("y!&(`֏UX-FV]'n}vT!{ >}#%.<eRرҙܥLnŒ3ˣLGFL}_]O&XF.Yٚ][m䅫ppb Ҋ[Z+ĒA`hB{'Y J]\ջCc (+S qͮwͮ4v}>Ն e3Zi @y Ԫʡ&W9L3PYAct&w^%L-Ȯ K#0MYR믎?$94F tiH! SDB aJ^,tl$[^|Bn  Mw±! ZG%شI5<2QaDYgOe_6ŁN$7{]qw>JĂF,H-mHûV5dC뀝cߋ0 `xN,pm]Հt8K;J$qrrA@,ǩA.=_Yz;"6!]=rH/&[~g=O]W%6 '3ì_>lTokld/Vkvmp㠳3D/L!$J? `vy귀[@lpkgq+1/kx=m}4 / ڠ"{htR ug{̰lk˭L!-^G;sDٿDp(w EnGC!hr%GlX?dBPp`0RuoYgB#b~b8"kFnUW.z%uc6q'1t#^GcY!6  O6`Bm pakןA0 Mí]:^xmp3c\ {7(`5"<@9QBٛڽ6w]{XDjZt&W\r]~^,hpbG}ֻXbCʼgY?ss:;&ڲñb>e;n,Ϝv/9vY< Ҭ&B#]R{7󎓳{\\.^ P HhAJ[A?ˢ?~( YDKQT!dC@*w~;?>3y6G $p^wyfg3o{r9(؉{"]K|44vo@cviO=-f ^vˤ{:mC2_bI%Vc!n?B#hnB/r bb>\ 2xN"FMp[т]v>1u̕Sx S2TcK6)B% ͓OQ3P*te"u6 JE -B d[jjΕȻrh=#XC[uZ r(T H5= 1ڋ=tBa~8O`~~v^E@ h+VA Ϙ/fعvn.Ӯ!)~I%L36XNVLDǗvGlj%hġ9ᘲwz5ЙI%}1[〩y_󑼍׎hmFlOm{F`"+Ѧ8uk;C3!DP{=,Hb&7md:Ŵ7J*H}ėLg@6#+q;̤]G4I%^M J4 -2uYRTS(S؄& @b1k ƄB!)@@f{G*iQ[kBC@[bBEТꙿYG Zw~Ah뿡(գ"Ѯ"໫+yQ~P"5!ˎyH"sw.(kg=9@1 Y[E`w{!d_vОLg"5ݙT! x[63>7w I{3dQ'?t9L*TeRwkH&ͼ*Tiʒkm4)Gс%.q- V -HK-RfNj*R&L93;16xnyz*`ah4_yO#Ƙ=*Ȋα+1ZkلפC2dGWXk""akfe 2T^=ũ -EHp+!%[ Ͽ@ ,Ԣh@ :vA tNwj4]GX Bі0ABBî^5N ׻vdIB;L#j|~zh(7> ##^YdkD*l9rR.GmR~(%T0[NUq9+BH-<mtѺ 1nr )1Ph {E-;%10fr?ܐ o?6prIJM^X3mWP9;WD-u_R =8BCir"p\*غ{HLgI%΅mmgW\1&tL@*_nFvcmSZX* RcLE тZ@ a{f7MTfkE`T!fgO\16TGd~+f?ArCUa< w\b}]{p{^Gg#p2uuyX^Oȃ!ͮݻo{ 1jU]Evצ\>Bjj.E}]D`=0]q9+lFLg?q)dذ왚'׆j9XV>\I zpø9%|N-UV"MX-c&ӼkW|Ѓ\(݁G`Ԯh38h줙;=|ߕǖ"S9ZはB›D\6)g"51 ,oWyzګGC[fwZBM #'c#+SvYk:_h1ƄIuC? }7UǗi{5͋]co[kW<`Z;}=uBj\[N)ڂ1`[߳ќqQfTrϾ8Z1&2knƘA&*4q8EZK1' q}NINW G955n2ĩ&zKFy1Z}?dR;睚+@܅zO-Dh =%;o@:]Eͳg˲N-A=]{fjw e-3C +돘}]v9R٭j} >] ҂BCZ$Bz"ޡX+PM>Ӷĕ{TVra3.>Y1Wvk v.wmO-'C4Y;XPyFgR"l`4` a#о=XR0c8S>{e*&_sC-Wh^\W3fr ű+7zo@p=3Jk+#͝4f>i4w@h 7#g]5 N`fkեUl4؝c:]cuJ1yg#f4A,?ܖo<`m0ByƘ: m 6и1f[,z@aWOZ6[|Rh-WYk GdI(7Yq`yc^2Jd7;wo*(ĊNr]TWp ^Q<}W#P'b|:Wd x(Y` T05cqOP8sF3BI3&mj0"8nw`iiSM$[@X} ;O%9v>_G|A /B"u}'G`%\]獕j{fQw{e4QW1ǕÚ,C Ȥ/$ٓ\}}ǝ=F q֒d:;ΤO&wp&XLg'O`u͗W<Ꞛ=Fcgkj:ō15W;qe<ΞI%6LVb&h:A Cz[3S {vybgƞG3ћx2c;?m37Lf㏜X#ӀӜh`.趼M̉\R/ܧ6V*D6лƏ:=Ufm̘ƅa`A 6v-/u0a<ZcbEZojBAk1.m ƻ)3{]킾wC|:hQ93\e\&pJB`idѮt;fR(t>,ȧ` w(5]+b1TG AzdSB041F n#5`e+&/5` Pbb|bQ(k-PT Ř 7\=C?Vu#Pye |iB;w2D)ret"P1 "[$ʷUzu '~- P4^'u [/fS,kE+kW\U3WC^Os.vPl+;4˲"&[T[ _CY}m3o|kGm[yYUIa6[5#zٹQcK (Lz-oWL.C"Bcjba Fsڕ c&y)?Lˢ1>FLᾴJܑ}ţ3)WU>:[bm7Ɯe 17[k[1#\׎w#>$P1ffs34Z;y{i pf;x3ƘFTXF٣1Xk_AT,5c̃^MdZS[ng1nkƘ9h^cTbE2řTb}w$[o̗\X)z6&ENZߧ Ҁ:.~mBp%_[-xhpw!`0Izع@5'w#4 ʄe\`Uԏ0F"5ܪFX<$0?٣-ЧtC#QI4GAq}->7w1)G6[?\_.@M>S7DCA7jouϟVZj>h؁h!*J$J\(4 eE=}~lhCj-V-.Ep b뽷e-Ы y1/ mF.{/J+RIo0oj d 2Ӂ7b&7sgy@z#۪:ͽμ_3Q@c-{XDӮ /#\uvnU=C $Nc̡ܲȊN+kgj.tC75EscS'Yk_5 iޮ1з>ngocLT^.FjyힽS]^k_jg_v)kt1 p1 {]2{_#8cV\ږH]mcЂ}4dǡG VR> O"?b Cwgq>(mN 1aNc7w, _ =Ƭ1IuEt|B̏"|y #SvJ{3\۶AjUAZX"3RϬMGǼ!X@Y5+ޮ 3"IĒ?kA b=9dݠuGHˤ$c73ī|bdA3Gh>lkFt@{m1JHI ;ڐ*+ 4[zBq<O#߀ MFom|{OԳG}6`+1Gټb94 GǢoךh) tٞ.b]8j/^y5o}egl Z69*Ƙ:4l&A;-]>vX2QDr2'J܆bO7  IDATLg?%\(û ȿԡIMo"A(Z,-A|BXxUQM D^=mO^>!47ٶMq4.@v bؕ1܄&{:5Al*\Y!:Ni[<5~`˽5hqaw}{{ف A|{z F6^{> hv1vthJ?p}v@1;LHOlUpm{hg=[ P5VC!6W)%x*ǀKMNgR1Ew)od*fr m@g/Zjc&ׅf9VԖhGe|1}w.i{Br@ jb`& 14WOc2<;*6lPQDw)+zu;3L#xM\_s}iTbv&nvCZ/! ޥHg`"R 0QHuj\ Ό!Oma!(xL>HT?MCdb3f+w,p+@  |էq\_$ Ca0tm1xnA `5YWχ;kF8dvW3Gw݆^s/D!D>27%·)T⟙TϙT◽;GSGnVgH8 g|,RCto#w?\%ӈ!w_7yח ׵yFx{+tLDjHf}̪{Юw_d{$=x v&Ea~{.]yMz ^@AoU*8- iDF +(M\A&L-RM>Gˬ Ԡ1][nC!w]+mϽ4pMu"}k_{GG^ jsփ=g)ɤ~lcX7gh!AR%oㅼ?,@T0緃b&;gyY1y_ewW}o +_&A/)k#3[.r*Ij]]&lL͈~uٶU#uW?#ГsҊ~Cmjܥck}=_܊>>Y|,m6$΃ms.CbwDlH%w bO6ѵ"t biF$>ѢB=qG,V=* E fkee=U!" B@1Ww!r RLvuiqA@Se^CvpmV? tz6['ZA"ʙCwm>feRM6O$d8Vm|-[ޯNt<6>;fdYz:6 <̃6>p[2m]r`KcQ7/i:U.οsXàR|ju(Pp$j^^#OdcN2D)J<铈;t Wס~ib`0=ha+!qbTs4c`^,0 ]ZC6pY>M+R=@`R@Q:W!ear/? ]Uho(tGys\}jЂ3'^b sp;]R:Shձ6ٍ0rڳ"[l|̞Z\EO|2);]x`>i|96xQYBYؘɍ͵_~n=c&5`XN`#I%f;|lH>?gQ1B[oj- =I Xt_hRG4BGtp%RS9ghcQ]vYN>vglz0@&XLgeDO@PBu6=T[P9ؗdB d(~R^O~Z HnD;9}J"Mva Tm=[SjUluH S[7ŋha3][Aųxb\]^TC%߇Ⱥw(0dxӵk>b[dy >b%~@gʼycuudޙ>Xob[ ܻ\1c]f4z(ɢ]7 kB<o-Fb&;b/Za/6Gb&wQƗ NuW_<{q,pO˳MM悽Gd:{}[/.ο+ *j3f35f M{_5$s$5F|s-F>>ـ@@U adC ood:#cL*љLg"6@EbBDF z'yY{XQ0*zg[~.@j1];}/!&mb[hQTK:ZoTBBls(L0@^=3]v"P?z/^2w800\9/:Eݹ|"r)>g5m_vu$R>Onw>wTbd:{=bZ]|Ȳ.ulbbc5޹DVB;ϷD)wa BӠ}=o8PE#;v 3*w~Ⅷ8ٹ!81;+oL.66Gq.!frkFueF&w ,ݫhziRCR{OKx-ZφU(UPX[iR8>6ܯ Z 淋Qc`C[chAs6|ZclJ=佗>0ȣ3т K'gRoyt#^t OphroBĵ*"UV;͋F2!"ю?U̘7i `'dv ޯ15`@zBDPTys)p}F`e7w%B*zW,EG܇ iѽÐ=q7xX{>9g^A @x[ݽ>H}p Lgo0X$LgOAlSP{/T!@]>).WC4TϏ?1ԿCwײ^6XG1{(b[7Լ?w6ŭĆ^۸g ׃b{1fr_,iᤳjPʦ`@ƟyX5f5fsŕ/w.f(Cԅ5 ScecA1S7x]WL1V8jhSl|m+͹s/aQ(OޓJɇ*}d:{ F#H&5~!L*d:{8ْ݁3gR_&1Cq^Bl.FMha[I }w@HV-E@hĊvkp @ujOq^9E] 9HEX_#Wf!E@.J> nWvzX_p4 ^I%`Lg9Zv%!-5 gw:P?oMUCȎYK䆡ݳ~Ԣނg'~igϛ9zkN^Tܚ߬ZWhsMT-\ +Ҕ4H0pm!CViD߯w~-?Ylmqah(ŀ܏cъ>n{9y柮,tXjsllbE {y{Z1k>k>Tc6,W UL*q?Lg؉/bCNB68[Uh }d:3RWGJ=&iUNCo Q ycskmE`sWA`g8<g2G ].@>i<[GZP.IKhqk>5|_E>HEWNV(يLDcg5x1hQH#yhd61fa~`LxMR/BwX.? e6/(IH#L#A64Ft|gU7 A?1m~y5 =H\?Wp|h</mi_^uk+F]Ui5zw"59hTqcGm-bM#3ec:wX g1=TAځ1Mǭӟ>Pm@2\z';G߁ ] 8w!vC Bg5 &tW wي,:sEl}0V9FfCo 1#]^ĵɧ:MRg1WE:4lFV=@zv+`G(I!J,5)B(>u(* Xe@}#D4u$. c-'JʿaO5plѼ64Dq b щ66CK6fCq;aܴ'Z_3h{MڈZ IS9fD1hƪ6391ĥ3^3Llƒ{jOd_iLgOBc}M1W_i֒mh3a`A_G^Jc$Į%فP>D3a9֢p3L*q? N`eZ]OLAq5ZBD5ޅvv-~ ]"g1<;=@b[ԩ;G8Ēx& 9WY*W~Dמ~?_MZ>yѪAԎ=1MKBj]: P_w.@‡iux_&;<SнSP~l%Jh:|=ˀbwsܟG y!ˋuޚftd:{~2 ޛLgEqvL*q͠A[*b4ABԇOӊ6*w#W}} qzx9/^qYau=W-~ɽHkIƟE 񕽎]hmgR%CPX9oN_g9_Q \BG1ac G|rS4XkT=~佑>flr 0 JXL*_ D웰L*t&x!~ŝajU(bE@uWޛ&_O{=\vEK||AlC)JBӊ@]y>Ƕ! tC-oݮ& @іhBkk{nDln>mnZ}O۫I;]&0bk|P݈-B5t;zu(A߃, ?bCsOn&[*ۻ_Ɩ!#;mj W [xDjFʢqKdt)ژT1C6V(%ڧb&w @={L{e w쮯@̃3]6h DY$}Czf۾N|ibǗG={ÏH_3644o @&XLg}=$.Nc̡O{GniXgS_D {>ZXl"ӧ*!L^WaG?„"kFmkwHz.!uq'eп@%WjwJQ_8#N\ ' 2x /%4}:/[Y`#rG&2$`=ݝ5jEnnTb =riFϧ=f G*` ?*\e+=jLϣMhǢGn'kGTϺdB["4݄%Qˉy8缍|O:OV⌍AM3']c9W$ٟgR[XlLg# 3oˑ+TĨXsh,[ AW},,#jՖm=-+v\y#]=:]#6!"+X*cB,we/G7;"AYU%[ =@(b"vv4!Vݵb7G+V1]{(yH>Zx_?bg~'^2w76E_"HU:w]>m|i bO=m3~Յ\` /D ,֓+ ]&i2iEơ1mQ#,ѦB!PTDjKKGt4u!h3x1h]bY{$t&Lg3D{&^ng͕Nc\w^0 7#'t6t#bjɤBwZI-xEGpE УH=/ MXksZLZDŝɡ;0: gȩwemAklRgCjWlD`#RCBݮ!t(b+=󠴙 %DW뫨@8n J\F];ya໻bޭ-3l6B |d?рpԆ<{߫%קj#ջ[\g87b^\eE19GՁӀqZ\98z4"&|0OEa۽<iڵ7D`c2o\}I{#}jN PdRoy/!W)Eu3\{=C22F#A"| HYhgeТcyw+wb^w}zТ!T!;?#s{"REwH wunwg!:'}<"6J{#vgdRsDNB biuTvݗAcpB4yȠ7΅f:XP_,Eeɲ2yA7o3]cB/rW!1` Z4[X=_, =y'msf Com|@I?;y E1nP0EL*щ} /D T^F D;" @E`$f#0։@g:ȃ(8FL'^'>~֟wu}qcqN]Ca 3!3@}UދrZî >R&.THMsRQ"RE`_7ruy ~MCGgpeZ*X6yf n|I|HMH2=1`L*19> |4CCz#F(dC@_*< =#ˮ^^UmBH˫a.B4 vcnK+kZ \bj/{1PJO n.; |]@C bȌkj\| QU5TEvȤk6'IF |v#xosѻ"R*Ac\x+Ilf1ۡiRs+O E7;T| *+y2v[m^腈^wDӞ-W)̿@1 ӎ]&wm}'}H3hQ^vfR򀮷$sPI%fk~B*? `Y|ihFosR@ݶgĎD· "[wU* e@d@+wvߧ5FlPѕ,R#ԝ>ݪ&a ڵe_2jvm> 6V nbjӇ[8z硅^l>vWP9UE)q Uenѻ,bG3@㉓Xf.y=j>" =E#;UDŝwlt[|-tyOl լBvmRezl %*Z}޽\NʔK,Jf'Tbu&ȡ~ .FP,%Gj d:J;&ܔWdAϫ]@cqMBhL4=Eb&Mфwmh,slwm\y' Ұɭ+F|b#JdAW b :Jd1fM(Vd[.u7+q?h7YW}@ S|P AUh/vOȤ%JRBZr/{#:Eꣀ1tvmLG@"`0o:R\١,AU%kC%[K-ԀԤHոB>Mѣvs׸ޅ{~3Wd\6=@ʽT=Y ;< r6j3Nm`x8g]98=L*Lg`]Qh_ S=[+Abq2yމY1 D~[{? v%؁䐚!=@LE9Mse@P'!{rEC+_eP}FǦӰf֝~O> 1TG{" ^"q {17!c*Ƙzԥ7O3ԢMH^h́3ۅlo1CXkվR\foE(ceE 4~ZsW CQ怌1Ơ Zmt:Ŝ 3:6xZ>9ZuGYkt颼0Vk%ƘcZkG;&hׇۺ:U .pk;&nj}HI%Τ_s rQgRh.7~ XMUGk}وM%!Ȇe [ϡH"O.J:nj x۱n,%p0hD ͬHnF]wuA`w6U+rrȖn)bƺ֣ m%Rwκz듇BLXhA}ڜ=qoeDOӧhy2=6pPI%^f3d:[Lg@Vp]Rq R."`O{? E1 Mt!s 'B@kg R/_:x6"EKCmyxw9Xe׺]Z_fowcF28ZF2(2kq [Ċ8Z?v0`vkxƘ vM/[k'"u1ƇKrmjq pl΃: 6MHcq2nsnN3y\}tm|E _[k@w2= h})fR,'Q?Do>1G?CN9 P څ9JEh4:}_@%$kC6xUaA/s&HM%6~4TD_>ݣ]uH{Zp,usܚ xlu!z(A&m?baľruVt@#7\)e@&Ċp!vqz_AlFe4\lꁽCjˆ-:oI{5Ոn32}mFH&XLg!Db&ws% IUKݳ#Om'2Uw}1䶥jurSXsqhR?>wC3cغ16Ϣ1:ū*~7O1ޡkbWg ` ds1f'`km ;1ǸG!Vh9رv{-{Fyֻ%`1f;p,4[k_rph suyZXmi&`gM;>0JvmnᯈEz -,1.heeh}$pU22bDj!P2 ZĎ>৑ҠE ? j8b 0jA4U|B[L=GnТ:ĠuK]|0m\ W]QFќft\ٮO|\]wAc.AD֕ݳ\} 9;|k^f 64NFv= z{j@]] b@A+"``Z#P5t!|=1;%o픥\ ?itq; 1/#Lfaay% }%< 32`Y2@W[&X RD L/"d E7yYl͖^`AtqDEԣ ( vP8?,CQTV# ;H&$ ^vlߙlBH\^ww枧Z͗1q҅]}1}D*_&D![Mdâl0|;1qvcu{bQnjwq.wͣ=8 (M J"s=5m] ^AϰE5sUa| p;Fw/p۟cQ˳? Û]t0 l` vp.V.^>ރ] 0|n#뿊>)&Hmg:츑Lʦ`xX;O9ɦ,5LM?bW3$7T3)'yK5b-y*u^'l"lcK':, 3^\aorI,Nw̳DDѫ.;k7(H;uűpw Ha,DcRh,wEF~8Ke|ۙI/j~mU'WϪ4ߐEabf*Ln/〗.pz7ž[_ܹS:zba޺ɽN|ﯶݕKAs#0¥&`YolOۃ xpB. g?\7:׮rK p컀A0 Fm9"s?/5ߏb%3/cº{{ ׺m~-,3k x{z7AUg !F*;R7f5Fbo 1z;JU`GTDƮE,7HTG/мӿh*`[OBMnҎ<`n 1-K&i'M᷸ko:>N] _ XpX_,~E^|Ta5 d' Fm6L>i,M'ĂvZA++jcUf+Ct,E>cbFǏH>bM'l:?\ADl:y-baj՘P𭻾rƛ.ͦa*ĄV#\1ic m㺰O޶çj:?V &VbX-s!n#r>07h໻}qE7qa.,L@L%ׁ%z?f/Ђ ߴEp, 7JFRkDQӓO2ӳzS߆<D NޚƮmWz 10,v}h_潰N*~݌eꂮ慘; |j /^^ɚ=ϴk6$>vJ0omDblq!F^7Ua桫SܯS7b6|db*;koB7c1,|H,}u.&j&25X^MLT5Bqܴq/Tuvׇ;L\E1jFQX|5.+1QDjǪXݻk~`=Xx{C"vGyT`Q<ٌ֙.kpy;w!Ѥ_+{A?@*",,C^X 5C'a.|, gʦnlga2~ /o"B r$Ɔta.7az,R {SiIer`5c=tOL0GSX ,y4eW tCٹval &`B&O"Lޤ=#^DQ*/V| :X'hX,NQML:o(rn>ZZ/$jF]5 KQy{Ͽ]qR;c;LXJ`n6l^JerqL-`gLerSܡXcؘWqX:L,%9#`k%&.?+%*{/Ĭ2:|L  c%ޅ}pցjwnD~LKuM&]jb(Lz.LnLL.pz?﫜0!6@< ݅u.i)tlA7`#&B6˦ ]ݍ>mGY;*AHerc)oMwt`K]~87a0bN"psq8-G"`emXǨ!aՑՕ,\{)DJ Mwah:jR#1K0|O[9s]>:&^sJ{,N~?Ln77NuW~-$ w1+_O̤2dOG1x86U9*Y~+ui DEϺ7[il"RXc IDAT8ci]0ц-Ɗ&~[ )QG,#YXA8LxMnD^\L?c۶,5 cQ0<_X ȵ1z{~!&X* RaٽDT&_*4ygjg-9DR}'Jov`i ,2_}Gh'&&a Ks}< j=b 4!`Lj\o,bklI+v{nb'^=9V}kKpe'N%_1 JSMEb>գu <46L5`b,=x$р}KU%IZM4@$wH012j,6Un;^/fKƉ׹u|onw*Lܵak;4 \ඝ,Z _e#ʙKerS@?ɽabs,&Eu6ȹ&z.7Duca$[W#O#*ߕlW) h{_Fb),W9bBn8}4EpLTb`uL k)0=9\_" 1g@|v/#,Qlƶۊ9Wu !?JSq؜&`^ACN^<60=9vUSQ:0$`]ʋ1_V kRÈy}F>[(QՒcA]my^w^Z(X6f~wWK30F!Gbl3 Kn-J*͘Φݲ,V cEaERc"OXXw t*&&cƧ$+!>cѤL-Ĭ$:;k1t V }QtLy7(>|6S7 x -XDw\HGslu.uvoy;6xL{!xH qY>PEJkN>| x .o`e6 P.p0a"izƧ bY6`YK3&bc&>5 1ÛBTkg,7-v8Prdpx:zvkQBlcbM'oJer7Dx5uT&w&bbC͋Ln[L}Fclff5&*z1qSa\C̼wN(9|e!:K՜ڹsv6̳bXgi:<}ګ 0ˑ vNgɵW6{,!Md$,qXNW0I~6>1Pc/13׋t0! A%}YCej/ݛtϼź*K}ʼ+}Q7W@$>P?fc)BbK)ĺS"ۚX x BJLDB}=P_mC4N\=Zm6lOercBe&IPweQ}kǢf>2m.|:Ǧajkm}QuD3Cw[8IldvKߌfn !P@bLl*}RɍPz")T>.5 b/Nab+_y^ǢR31gpލ ZL8-DŽS1GN`3fKzV᮫m_*Ts90e%cݽ۝Aڰ1B~gL RbEtW e6ߺ~| {" "1vDwtXOKb4?) (!jm.Ezj"!XtNzkTOz߃ X}{dB!ʀ"cbúEvRkKWr1R|3;{i:a%uVL aEbn۽K޳v(F8 ~X̧H{F`mGّDni02fo[;:1Dd~oG2S9R!!1&t%Z}چ};/fT&w]6 -,vmLМ}l8V#Yq8QHdQkj2}m5}am4&*9k *ލՉ KW@*Rwd51ًmE0bWB ;n^Cx7T&Xi!6+q60|"&Y 2o&}_VA,u5dŒ/ߕNdi "_իc&?vއ݀^dBkYe#ʇA1:I!D"cbÍYZKdT&w &"L`1Ѵac㗼MF*lݖ`bkV6h  R#m SqQJHa6 @͆XSyB09:B!T&N2 M+XzϔEbݛ՘z=7ƹS *Za1A&x68}1p,v}泱Sab[/b{U2#[\<ߋuSy [RU3~5Q (nua "-2ڈfS.q_iȍQ[qjfykZ~ݐFq2IbBQ; ޳ۮVNer`ݍwb/aiCZL6ǹM~#H-:4Ƣc^$yMe&=9][;R.vIerͦÄeBQ$³Qj]B7q%7cl:H:ӱZY0&0CL= }i=Xm&VA~/isSĚuzcM *BAĘ)ȦR܉o6(~f:sl ,JvҜ9|ǺaSFaLˁ kT`oYpkuo؟BAĘi(TnQ*3mL!7Rk1K=07D.} wc/ b7ɋU5cne`_78S!DQ7/#QOciðA`"l0fCM4벑h JF Sܹ, X;=vpz6)h1|(,;gm{!bnJ!6X:llYl.w2(~2V6 x3=Kv`yc@Or`a kgaa6ts5p@BsnZ7@!G1!T&0:N>~0>88 l:Ierl:Z*6ۧP -ث{U7ut.fl:ٶ]/T!6CbLH*KUY 1XSY%KU`kuEEϋtdIPKAB br=p!+ֿ9}jcfpfO^}ewaK`l:[*Nޱ/D!AݔBlEop/[G_x@btセ-Mnj͞sBQ^b);oR!AbLAB*; J~ėB0)0FkabD!ĎĘbG`Qx-[\E*:2<&mMA0 ]9w]{_TkEtL̽: `%&glBa !',Φ-(Y,2v&:3KlAq̣,ص* ,.@-f>LyqnҚW/}~ճ|n*11VR"VKbʅֵXY[!6cB!E* ez)h>$&D }-DW pi>LcB!E*-Vl:4uE&cjo,8%~[qInj H CbL1HerYM]Ֆ&JGV=D LmHy?f7amseB !LDl:Y`w:cS {=v`oo%J3_S6m_>4Ol !lnB!L̘r{@0ís'u{M)`[JE0a`.ak|ʄb!1&lB6ϋl:{Uݘ ^âXD7>#^#a>/BBiJ!Ġ"Nnƍ{'_v?(t/6oO5,0F$b!1&2Il:[Qk5p~dn@7ź2l2n<DŽb4b('/4{w,__t˪蛦5xW\bs-em!H !˦ob`iC?.b@4(FQa%۵cM"dBUϘb4ktfz19;( s`Vf#cXם= -[;5 !>jbc0& zM#$KI`lf_1 KUͥ^BH !9MA`$|(RbL1apU` \cE` &|t 6:G2mrBuS !;wa5b@REVDn>EJׇc13lA{a­yBEƄ|x85ͻ`ݍS~U1 )gaBjf61L=E:c{ہn B_1dh OÄV{GDb|ȭ(^@= y3fZzBlUB j&lV$$k feqX}Ly XٍXDl)&JS>R6+oŚj0た !ĘbP*ھժ!V5 K)zG0[ |6rm3K}D_&΄b"1&4\2jѕ# - | Kú)ZMT-%n^Fa3DBFbL1hi °=Oq~ jJ=SĉDCǧۦ8ftd*&_1(IerSpw+v^]tKbnKY+UXזEe  ժw,e抅;+B J^ewc5j'c.(R΀>D.DE]D C)ݺy0љ{~cY !VEiJ!Ġdⵅ|Ecn"/JӒyy՘w J {E!ḋt7}K!*JS !%MA( ȇMAH\X)>5*Ė/5d~e 4^ 05'#3o 3U/RB Jabb1̨up*GyDVtQ`#+N_sR>\ B-BiJ!`ۘH-/0_~Dd9['3o,«>oQkE}[I'cBN{lvߗZQP}@TfKQ$yT56>bX5?zʇE_7zcBt`ѰbϾ>&fz> *&{?`BMQbP4πw U(#$g^ňn!\~=fC Bl/T/ق•£+p$V`5\~Pxc_,YLxOcc^{b{4b4= ֎ޣ `GF۰Qm[VbMpѫN(cBA͌7>uߋ)?gwI|FJ}գz^=pö !Ębвo>q a~cbfu􏫁,qˏ6=:_{ރmH!G5cBAI*W>ֳg_ol`ŘyB?l.WӀٰ\:eGVR\5BQdL1X) uNaʵج" uDOğa5c17l:ٵ/J!#k !Đ)h dIDAT'{sT\n۳BMGiJ!Pif'Dn|'&ĆcƱm3B@bL1$ȇ73;,*֊ׁ1WEauPhqA>L]O\!1!Đ UN@;JC.Rg 55oUsf|uwBi_1T9s=J /R Y 0mFU! *B R\gO908YqU0TDwNV!61!Đ)h~pIPQU{:bċV|Ѷy5ĪrBl*JS !/⤏/O[jҋsJON!.cB!E>L4͗vP_Qk- !nJ!Đ#&=lDU0p>LB1$B ?7{bCïfbPR1!Ude>!bM)R4́RB 1!B21!B2"1&BQF$ƄB!ʈĘB!DB!(#cB!eDbL!H !B1!B2"1&BQF$ƄB!ʈĘB!DB!(#cB!eDbL!H !B1!B2"1&BQF$ƄB!ʈĘB!DB!(#cB!eDbL!H !B3ZIENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_34_0.png000066400000000000000000004261051413546205200301520ustar00rootroot00000000000000PNG  IHDRb6;sBIT|d pHYs  ~8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxgxՆ٦.YrEE]jPЫ+| B( " ,$PhFbWܻ-kwqEBȶdץKwfg9s,PEQEQ?((ʮ 1EQEQB((JBLQEQP!((ASEQE T)(t*EQEQ:b(( 1EQEQB((JBLQEQP!((ASEQE T)(t*EQEQ:b(( 1EQEQB((JBLQEQP!((ASEQE T)(t*EQEQ:@G@Q*J~ (),:z l#%VQx@-Jȱ)(GS DY*r:r`(ш줔XEpp"N28RvC"c!bm>0+}#Ƭ(SR?"֘@ !b, 1EQMM*NJWT՝hX٦I[Nn߾#UEQ6 1EٹY LLXT礲np'*(-1E)R-Bb=w(on| D뽾Ս}6Ie~ZbUL.9Rh$WEمԤXu]}52!uaBU V> 7!VwG'_, d;nF](.Fe'* |UuW]deDҁ{+TY705kF^5EQ 1EyXׯO  Q8R)i)H2 =i}e@U48gWڰ=/HQeWCe'Ŵ50X\D,`-R7j~!VKe`$pP{Ygnw~rm.EQ] )K (GjBDx`"ʁKH>JGZ Iq \`Y+;v"EQ] bfnGןtEV«FDW_8m`d "21J`a{yq#w}ne4m⇶*EQb21}lL?"j(RC61{{zd!S҉f;K߽pqwe2 ;SEQPkUWUd5t(K/4$<dq;D\56;\T.*DKZ7ׂI*EQbsqp/ WfY3p$ IS<|d:A_Mj..~1{ؽޭ )zhjRQv.,u%Voty' _e#iəH2< VQ;Mky¹u|M(,j_(;%VDĄu(~y>}-A!HW%R?Drc닽ҜzA( IEחYWW#_GUON@Y1[#iHݘ4EQFe* N.9ᒌ~݆D&uMjcEh$*@>lU"d*RxI_~ d{SU*hDLQvPJˁWٔ[V>}eM>GfCC]@Vf[($3_^ s\?uUѼx,p p\o(hjRQvl=xxj0VO~3k2C/WTQ^Z bL$5 8 1}_1$hYD;?mHFEQvBScp|ix1#S,nNϛ'iK $uh0C؞HDDET~D% $X)}.7$q($*e%_n:SCxzή Vէvx-uEHYg?v}4eͨs~9o5JOT?^z"*ȩꑹWO"i4NC1W!J2K3ԕs}EQvE4"(HU_{5-Y)ꇤ.HqR2"i˪T'z:~,?";3oIَz{K+HhDLQ:B\>S%'S}&Қ(] AW,W)BH+LC]ƏD~ӏ&^7J>17V%z`C(P!(_?0cp"~lcVW-l?)3b9Ҹk,`6)yc8+_zKrP\h״h{^ky5ƘуQET`ѡC8ìlKD"B"i>Cfb^4 #5 u6`o,vyL EQ] l/.?oNmjeC@:/зNcuّӁ 5cF}qX,_,~!Ӓ:Dt,iy4o-<',="؎$p] EQ] -WȢ#_,Yn-H]ٸ 5H ,D@! )_X6(_%֮ܫokFFu*mumcCvPe}%ƮݐBC"NHk"ii!"+x%?`H,fߤSC+REQvFl_kf̙dH!X=CK ɝ$^yH+HXWG,()n>Ff5z4 'G#=&#B,<| +l?45(ۗg~n@ ,8rD(Z;`em#8 1]} $Xz0}Q^32`p0\8-h%9۷=PEQFLQ q{"6He9=<|go ,b(9r^Q)b賟%;8^a'~+Xb%p-+LU\ds}oEQA" ,hn|mϬ .?ki%g`ΜH=KN F+mw}gy*IH:cYU/\g? Օsc:Yg-gD.:dE "]>[t)(J*xjɸT/Xe_,W2-^q3|v[CHxE?]7,.9" /lyh$/፟qHac]O?U#ufIJoqLOٿ7"pe"N ˫;td(D3`Kf-^ێ|y!>3ee?YE<G{p][i7|B[LDf^ǥ(jGLiΩKGcgv\kHO;u`Y4G|$p%pg ĔH$JEM H,@S'Ņ;s훢(kRQq^EҋqHmO"šDu}`Yو/ɨlI4llwD([Fe?Fd!v8? 'v̭k|OٳKCz`PVQlGST?(ʖBLQ6M&RԷ5ێ{62c/ (/ߩEP!(  Ϸ4ZQg;nRוx~fDwҿbݶp?xvV؎g;nڎ?o>)z9bp" ǐe1s~E4Vz#ZdbD6"@G1@)C1MF•-u6$FDL`9͍kvzk>Oe!(}ߦS_&U,A\gA'0DZx g%{_tؠEQ6 1Z^|1sp9}ےہh$I֌N#*xdGڎx`ADXk;nv4l@AFkHhfOXV̳,<^EQv(TD.f~oo龶Hlmu"ȃq;#D, kx=[y!&q~aI@b \kh%r?,?/+CB'?>Jj;7؎ڈ{#U+[& o;C@\ <1F?5;ǥH@e4v\?R 6[ 8 '2s1Z"QE@1m`?e > %xܷb0xe UQe PUe%{NoZf"2"`*zlF X쾩mX= Ӫs?@Fy@e9@$m6D \nd|b׵veU@RۖiҞM}*ME DBLtag^y{{eW(B'EGL[D4lj2 ) kwlǝfY&6غEr tG'3[l:f!G '-ێ MDj$*([;yȬeHīwr;Ploa?UE,kR.؎@,*>F-U[r?RFE؎ېX-(La~C-%л* XlFseg 2>)k , o)fKVZvm*Ic;n6p3W}o1ȃegE v)P۔gpEzhѱ~?u@J£ïOgU^xL4UQeDMw"t߻~b IDAT<dzݖ@h$-!Z3֌{ &{_&(}㴡-ZB}hG#u5]3O-S[Ea~y0uUf =]g[U} U3?&(Fv"Hxg('E w pqocȵ~K1#r[FC?{:spC'_0q|ݲ 8NWYi+RB`s(2",ƎƭVQ X]۳-߷sf~o<7~=F rK>"ܖsK5@C1UE*TDrVO__9~wG'1,ks f xslSy+vܣ3mǵBsb=~/ѷ!T5' 6H𼂗7Aҳ; nlYAKRmv?)?H9}- _#{ |C'# ˷~)Bl'տ<^hSL!4D4F#V]n;HG3`#PF#s7{DHdej(?AT) F7<#esJC/.qtЛ_X}b0dcoE$3 ϴ" __c$f,Z3 ,}َDҚG#G7qc{bRspo %VQ)MoFGj3pĢ#3O@Z9=[]z[_" 0я:Iqa:4lȉF_؎{z> PXېj{Y+3mwik ^ Kh$|FYEA6+~o)ky)@\ D1ů;i4?us;#T*Rc536k$Pn뢑-==|YDLM9g=D4lD^DX_A?ߣryN5קfH+#N,Đ5׌&-;l?xXW\m-F5 Hxuk:vۣp=%VSn-G&iED#7K_<"m=me[o O}Իk?/뾎I(( /FF1rc~ =`F|Q =qF oˁ ӁTq_F+Lˆffɺ^G/W?fzsHJ;'iL(;?s4$ #ϣvڎ; Ri{e5$},z,n1vcVnɅx֚ݶEԵ+-vfZf~goH~ϖƽ\<, FjE Bw_B>a^RJGlH )g;o"#do5B!o4DM\2mHYiF f,Vk#QH+qȽdڒʩ~{cl28eVc,ob~AO$x> k EqP!ֱd :fy877vw0vz`T4^ՊohUWsa9DN"Q525l?j3/ݳ!k]zNjCf4 \UVQ=XEQ5 Lua{߽E+FXzQƫ(;7-9v`k=3l$3 e$v6SVQ`X%V~ < }E cT}Yw}({QyHgҎ(M+hdbo+G"7 ԍ=|s\熲-='$9^Fm]a]紆ykc }3ڸ6\oCJ~ԵO磽{|ca~yhq$u钳pH9=rgpלٟOo^7nK-"vδ D|~Dy:C:t =l\yFn󗏺ٚڎFy/X$Ҕ{"n?ĂF%FT} d^5К 8vHu"r![7!Q}~Cىc9l82iHx:0-۷jl]Wߧii"25%x!uNh3l}vӷ8s{><1:  . ieSQMMe@hڼr3SW~#wx"Mq@zLm蜽sxvah|?+/yFj)R1y#0ߟi޻*MmHԨ"j6uc=~ŽYg%ldG Z%Hh$zD$ ৛͌oBƖTȟ` fsqC9@&F|mSJ^P 0JAu;:G>Gl.IC_]f#svZM!m;yY#,k+tGxi;.RKnwb* !!+JKm! ^C-m=Xa~K-~*(x["VVQ my2^zUS_nE2TmoɭTTߘ{$܂W-W-]neM`.wYm,"4{6F?">z{ȴy`G#*q@\cf:qFģ̍F5f].N9'kO%g\#F!L H>y[HJȄFN k@M\70˞Dj!Q=̹nG&bH`4\ Lr3< dOYBf~&Ga|"*#rOA&l,G^" O>뿶9 ˟]Y! ~IBQDfX]3;𤴔oyN悜+*FljˏxbzYE8` {wl· c4Zx;T51B]cvܥfHlf_)D/ l],aIl?jrRWo;FdE#Jc@}DGHD.YGne-Gj>BnYp"4#; :1)Htvs;~g㦛3G"r2ǹT$tظ+.23/~> }z3 H0Ŭ?mbJ)( )l4(P! {ǥwkȪolL M OOm8I 5QK̲axA-1[G3服8vV:)BD-XC؎o ؾHkO EGփ{`U 5%\C\؎g(`^44xvs#Eo BV7PD‚{/} b&; T~Tb]7$EU}IG iCv "1H?漝ѝh}ذƟHDfـ FD)i~%fᷛ%Tbu-J;'ͩeHoRo,";d(F&蒽~GmCE\ˣpvs%g<ޚZ Tx߶Dg;h$ VOSZr=YC(FQ! v9L?>1x}O򼂗 }D|l)m2>LI[[3N*op 2S+dCCB&c… jW^y俾o⽑~HM$-RLELӏ{2 %ZX<#-sPb5րXoz$"#Q!HU:"!b) yh5"s"jGf("3A!.cfvK4z]e:l$p7#;#xM-j1?eu͡HgH^D<'EE%VjeyS[,jDD[ ˫( M`"C\˗xv jxoLϏzDq|@d J4btu{eq[<{]05|+g&giS5O  $:#DHdj`3 f6b~wui_eU?),pvK!Wg?H]ƳsH$D@7_'D#GYH Icl}OAqwG"YHH8RbXE#!I݂Ԉ%#J!q Z!AHS$!3[#}Hi 0 JC昘'b"$֛CfeC bzzRsv?R`60 Xŀ'Z~!omwoƭ힕 )ZɵOSȵk,;*e)eU\[xw!9I(΃FĶɦVa3) vsÿ;>q#~nF`D#m[=A"=q2{xI%Sb) v뢑"Ф&ێ[ I/nclzy~i7( 'l= I/c+-3{ZZxl1בa`e[ z8$y921rUz.0 nIV RC{w#)͟cVrV0D^Ǒ(x, Lj{f}S}"ZGl"(}H2v7ٲ I{H#[#Qt$UDCf}fc'}.@^:$J[ҋ;63Igk)ߤ IDATq$|0@ҏK A"_I>Fj>o1Q]F6^6~?wykdxk|a}s9PPVQp ^_ qN7&U\*//(8ePE+>W;bb$1i}/Xd͓eOHdk-"NDjFA>&YoD_2 /5uV_E#oZ9XCO"ߋF—4.jF¥G\nna["IHv>y)nD.DDHDv]7Z_#3l>*Y8&J IOD썈PET:"Ė}C1fL^H 2[m~ߝ1chiwGC,u՚s^DK6mzʊߛ↋{.85,X4pM~qH"} a /thk7 I5~lyx D#x g#_4;ȳy*ahD AҎADLF·sa;H$$m;$me;n7!_ND"7L@|DC"B_^KMi#-*E$#Z߱ZjqEUH##nMֆ?s @qYD-NE>$MEg j~k!© ZgOɧe$ry Rsg3߬ A dgx?W}=^i+(HG^KeO 뷓'3j.ww* 9s/5T[5)=u}|e )Xd((?y!Mlǝ?5m$VtGjtC":30O0?<^D:^BCDr(sCmIYHk%NĨdėHqggF#d1Ȍz)Hdhh$|RIe;=?X1"!"Od:oh$sl}8G!3I%ۏX Y>JReFێ{/M]" b,0D#QT+9qY"}\H][O$$"!B.4"B%f7c\wqUG]iU,[{64chF R&B J`B Bl BYJ T4lcpMeuw.W8yHڽ3g fcyf:He т T&nt29ʴZ߶?yw ^}z~]}kG }dß3 $ ȸSU5XZwS2pwhaY6G5[)1h>9GлA-.)ՔSK a.}܄4O"FHkz Z|;2 FgY%2壐 '!!#UXUeKt2 8棅{Pt2>X NG`;LC>n[xQƾaX4n|vETs ES#>X|1F' Pu b$R=;>"89ܷ+kyB_ BC͉~7 k{[h̖X#eV N^USqw&]4.w.9H8ϰ2w|@xm:V:M$W5,VRF}>_5cMRCnZUS=e@WUME;6J7z݀뼧{c^~=@sKeyu 33܂6FUWwS:Sg6FD*aÿtA:M2 t1+ H \kq5E~;wC{N0;ʼXHtGfWnhNH2M'&R{%R_;uJ"H2&k) Dҭ֎G'8ɹ?1,Ec7e#ϮgO2eӶo!f; YgDNbdbUL%?n bvE){1SѼNwnm솀s|MvO)aWZ=G Ƣ9snsJ'Y, 4s_|_M,قl9௹Xޠlt2G}.3dxBȌuCϪf7mlfⷧ^iW$/% |gG/8byA=w}WUSq[[ :S:,@CNM?gO27 %XLSЎd: X+ahvAQ됲b30[#0soD;6ns/S쉔G*̩m+/Ar]8<)1~i%_T/2 jexʚk/"& `ؿ#58&TE@n8/@Wb*cu)xdoX"}{Ƣ]eYi?"<an_mEwC1t2Ϻs'$WZygZ݇F̗/ %jD* h-ʿxSP#dc۫]W~{79\Q,Ξ5cZ |aaUj*&";\ Fp4^R;S:&@$LpC,1mL W q|DEw-:1RC )pP"N3h_BV8$y/7K{H/V?ݏR&ͳ!&gt CNٸ/bsnnK2?CD`T"FX80gk T{uTf<89/E^4ech  t0@h=b wm\!PI͕a,n!2R1,r@LCB@y'^DtS9e@Y"9vPR8/`ɝ8[Bx>@+ Ƀc#:1>swj*~g|1'UWWUS1h,|)oX"Heڧ9I2sXpBo3~wc 40@E vİdKF #3 &w 4[ع Hf?B.[t2~ 0{$:Qy'&%͂z&Ra'B`Z_@q,d]g7ߞ{nLepSY;'R;~$d%2m[v0_[ߋً'DsH`t]Z YTY|>^EU5VT|VY^݌_ No|ޖ>'آDHyNkwCZ!6g5Q-('_o"w4 j([X{f@tÑq|m?xkjC;{O >J/w !l?g?tI'O@ėZ{a-KطyZ[W3\a`$k{B*`Ω b > ?L!ȧ72J}܅#s~ι5&׎.w P@S5da݄Kh-uD C;#roy>aZ~!Eۮ6U'P%{(s"9vA(bpN@f72 _ fmg{\~ #s;bʭ2_ء묍#qR8 bLg9R/##=B|4#Vo=gZCfn-2f}rS1R͈5g]ml8=gϾͯQVdx{U#1oM:6qoaK˺.&~K `B #3^U5TWwJWz9/b#{?&9~)sZo5hSm{ιj xYWuxvέG_8~Q!VVo2w{"ݺ/}ztm6%lnV|ӯ_|GJD*529ţ\gf]]zЄ!PE0WvwI_leF/tZrX}Reh>bHehoCv_d"д(11Eg#!d 0y=itM"9jFLQ;J'㓭Ob]w_.E/h)F'"EV UqBiLKOYv R9-5"8f}lTH#VWh,qZ'Vfq־S:yMVMA``N qݞCs(+{}92:(C_-YѦcZ[ oBأvs,Fk7HBBԧ V5H#>eyt^=bDĊ䷀*˫;*gSrw݉]N,:K.{Vp dj9ZSxf$f*~[i oϠ6 9uoTu{?:hOtۜs~;Y#b<0{~+QJ׎3⁵MG`x?5ԊM!#=(քɠ1 Rׄ~ol19z!Fh,EL#*DJu +'#Ůx!"e %bTALq["@SC+\%1:ꚵ>p<"aT`nDYt*60 GPlT=+yۓ4 dzʞ S ѩmloE;P\@FF`35(KCѸl\#Lb9] 2;m]dmͳknFs4j@pJ` -o~@a,i3\.rA$[RУ˳ҵxUuyZdjY4^wSeyEU5e1i 7 zsfczwz~?ZUME`&z:疽Yޝ&H|~r$:H)|L8Z pGG0F`]"w Nnu+ksg9Jݻ7w}=osnKg(d609w%"kbd|`֠]2T"| "D~ '2| )0 p8l^%hfR'"eЋ1)Ь15O"d ) !@;lFSnjB "0lv_#C/xOr1bR] t۵[85,<ʏ#~vj@/c=Cor_Dl$BʛqSa({{澄8cu[mc30Θ2ndya6:$;X +B'pH?{ZK''V:T=3F\ۈȵYd=u JV>2Qk|͹K L| |z YZIh _,̓t Vn6gywD#mwMJey轡=V#᪚ӕOTTtDZgX.+ ܡɡKbz`&γE'4](DJi(kC@i;†ص' `:-9BL (.DWEh}3) `QO"87o$R$.Fmi7d"IX"p6nO EfrlqV4G'uFڳWV{ZE <`ꬼg-7!h,B oؾY%1)^1kNjcuAcnvCfzoG) IDAT,b E'"E[7u1,nCĘ ͋fH߿v?#Ϲ󜭼qC9f%[$?>94:uIp#>s-kNJ}Zyˬ 7xܓ63ιϙrCM\/|fw+)8 |pG@,z/0ZBXk/B/rP qټHӚ\qO~@Cs%V!h/`cI(:"*boȾ~=b>3D REV9[Yt?{n/ ~am3~ F!?ޞ@h䯓K2Zy'ؙGHwb޲Xkt5akRm> 1w) z^mEcз,p&_hg=Ý.B;&,R#Nwf+m.F}lw~h蝞XP8gp7c08Ǐ]Q}ZI2GIvZ2N廻^b;o[OzK=t}0 w3zD]'sj*,۾‚Wf GQ!reyu+˫}UMќ!uZ܀ d=xuG崗>e5vmn)X\Dӊ=B#&m҃7r"1>psM\,#'[Y#Y]{{;ZUι\p 1H7:_od}pq!yxFw]ߟ%R;FE:k/OO'O'R GǶHOFh皇"v2%2Эxɔu eePb]Am@LG_G>8)ߖHeE=b4B UTG) M,]ЏK2j MgSdTED"No0M ހ ,t*RPhɳg B,H[?NA`Y<K)\o@]"G?+6$ ́6f!: ->}# +e׭nv^cz;@h> dJş!p9w@t2ޖHeF#@C dhFvdC;(q7Ǐ={6V̱;\ :}KkGmeN8CEVTtA}ХzRpTѴ{w{ع=…@ eN|=;?]={gVW ƪh,C/bkj* gx XQ>m+TfvN;}qoՕ/#PT_7F CbgH'3}69j_!&"$ce?ʜ|qra>؆$_G}0Xr]ÀDGp ! w=)YZ"4qͷ61 }cLV1"2Gs.ц~A +d2t׮0D* R 1P)K: &!3bJpkEq?ׇ0W` =#um*b2&"` GswO (|V|kK=X'dA փ/xEll3@r?kW 2"P ) ɱȉ|5ў=k/=p 0W Lwn:ܯn,yr[6'~uv2qJUMSoI~ъ_I T}{wyGy30S'K 7>CX>s2]ҖͿ*:hcS_Y^& u>F BtHKt吝t2O$,߯},Vo">IX#u[|K򲭻{Ogz+D* e2/%R{~ĕTf0RAH(Rd"uE_Bq2E$Gl4?amcevE*XTRXۮd"2 ')UĸY7H:LsQ[- k%Zi?E$HA b+{<dR-W߷rN@ZO(dm?op]{tDfyNwYsV# 7w\y~vozۍ0B=!l@`s_{1K]x<ʌXÑD+TF6V[WH{1Pyi=?HeK'MT&ض[mIu~@gcq>oi2{8R _rݪ-syyťnX[33a)zWXTZWI'He\â]3}^3!y{]}ޘf,A4 m>Eh)hiH2xG.GsSJ")̋6kk{r;?z|3mQl|LF,=ˏ)d|7b@quN2IUĸ43sWlEr>b2#2 d]֣E> )d :,G k6-ȣs?Rڃ?@~0Y=i}c6 z}'bW됉Vיt`c] eA|kx$HeC(PEX\x?#V0Dr!2y!h\JtBQ r?ރ|,D,hEh AA N.C,L"{ Tgc8BmrC'}6^S".D3@w`v}/,Xge̴~>Ľ cG&Rݳd owݷW[dc#o'Rt2٦ (@@͎@GܷՎMA^Au<8v/Y=vR/ߌ˶dõdϣYثΆ%=u.&bdcs :xU52k~I]Wz<̘k;vyS?jzIUMEE+ٻ,lEuC-߶q`?4jbeNgY|$N%ӂ|?$R3ŷ(7qxG|vsl6_ҖŢrKI교ڵ+ٲ6ZꎞUY^^UMš;oy궪D'V}eg~ZUS1ع{І'U]=eyBdxc#-q8}`>B8etJ||-O~AmQ <h=w-::@E>ǮMwk++f[F{NrA+qHm])GT`h^F~hq|+`"90\dF`q-bz D!bw# @ b5C1+hQ=He k!\GڽLYCLۻBVk9kEV~k:=OZrH̱>+!l$Ӭ[[ͅ@X`=78EgA O 13A #L6wI[OdnB bkݬqD*=O'L; w90; 8on>Es1_es! Eȋym6hh})1b~t2~EnB yh ;=Mg1RWWd"׾U2k^[N)?䐢0|9!`Qع W.D B6C !S Z2@`eXmh' Aa C$=Ps,X 1^B@>j(~ٮ`oec #Sb" p='uCM HȞEJ4gz3V"6i: C *=0a|QBW-Wg')G}j=kW vr{2+/I2'b:2oyH%]A]YuI?;ܗi/LQNKkQI~- 8G4/ |<9;ЖN6湕 j*Dd(,)2ˁ @\x;p;~삂L CH>D&Ð_[կ6gA[MkX{zZ_ 7aE|]fo4 Z A6B@1ϐvuN=]om 'l&q3t%4k@.`!dJ+AoWY][PV|!` 84z4Sd30bO/n"4V1cZw5u/HM-ټhs[a`C^Snͣ+˫o0Kz]3\">tE}>QX68V[ш>]L@~W aN⥍l^wW,X0q}{?;~c K}/g~ PGD*A'тDH ֢+!Vl /ƚH\~vGS5 Dx=bf_HaN{Rz9d8f"s$rz3sB~):NZN[V]2lK@֮.@~J51*G*oA8o;OٹmEibec~>h,i%b!F*lY{9G2'w`ohN:pirȴ-2im}Jic7ZWB/̐-0/C&%;g?΋|Y'pdAT7Z|;{ܼwMtc+xs4T۬e wO;ۇ0I2Ow]iCO֕^7N[պjݠ3t2~_!Rqȷ廻^AE(ro}\UC gWW?VUSQ΋W];~ceyzS>(9 Fs݁'u%|q:ss47MɅ;:j\RW3bhA͋Uvc {.Zd[|m4mHiCJ1,="/EC l* GfOE,Ҟ {2o-ssnׅ0,E7" :s,bJb}׊h=̽U#ˀ}+IW#0,!8%bR^qh1QHԦD*]ׄ&VYWXFw#=atg"0}0a j4qw-"qYk2@esN]9C6h BOD#UJȔ-"œP_hz_e6x,>/`޲q-o9yM_ #sfD*S|֥v݆qjܦ梾 4_ ͉ё\KQ}n͋67DLD)F#&6/w8;hkԷY+Jxu3񗎙~>g4sw lWqA eWAӐ)}(B yf$|SZb=Q}6AJNs=y%"K'۽Do9)qͫy +}fNƛj*ۖr}SY뮕sH2he;EFrѡx4shl` 䇥u$'MSp9x{snsn;z{s. {8Nޏp C.$??ZAk4\}v9{9]7ztΝ_i;9w5ҭc97rJ1 {&w$ BG)[ xfbano]E8Wy$6ZQ p<SNN-h"@; 'A XdlElE fcȾ/Bíf@ vm@'o^Ѐ} L71Y{j*vܯok{lHTV@˫j*ީ,/6Heyu# c9nOn$u`;yIY IDAT`%X/}:GɆ#>#'1Y֝swSɗ%RqmM2;r"N'zk{v #D#oC,̱h៉條Ds:Xz"{ꎀR)60&ȴQ1}2bNh]%|2o > h-@.a Pa)@4#L6"Fhq.2}nhu M ok$W vCٸ2hqvϚѦ"ǪD*3͕-Vwn3d(َy-z%C]VZ:hcN#F?_eo\[7Tܯ6+l+7jACxbQ;UUS{dphNWUS}}Aq}J#yVN/t8)nF5b?Nsڸm4 Mι?yo~B':Խm3k' 9HM'pXމT}7Bw :1{a>CC3)9 ˰?! RϑR"PA{ !YG#36V9)(Dw#2slL}Q<ݭ [Fb+E]ץV 6.m~[`AD֎ gW뫅lEfm̂Z/k2VhkZ ttJmk\IJ`\HXg\Bsd!PPh;-`q2YWd]7o9bkBl.CiCd(GaDKȤTt2>[=`C9o[% BlrI't2~׮of_@MZZK[JxOk6ml.۩w/F} (u ĸsԾ9oYdS7WT+˫α~2͸)fJYNʑ(tO{wιVx92{Z{/v΍^tεMvmh?ڮk#_ι@o֍; ;qFk+_4?9),C;z c/!}>ļlâ1ָ(s5 -FJ)6ĆHiE`]vCm beЮ"HTo; "0O:$R[`;Z˜gvG 0R Wػxw#ǣoXx\'H\ b{U"~ȧPEGHOEk Zް6w\"s7 b7o}=J7ouX;[^(0V1oCh}saM`-'d5Mh_7H'_He((_ˆNcs3b&F[v9R&} cLt2>9\LU`(r&B6.-Hdz>hDoB`Ś.{N%\LYp C`eD6ڈL17/P3s킰/Com==蜵{!q2Z@b#`>jϘ4=yVR4wv@L!@C:oM2"\h}X6%#&#s(m[>x5@ϩ {}s7Y\UMş~^٦368xeU5?qRUSq32fц/|] xs?zcTTs9@n|6#k#QM%ι{Kψm(xFLJ}.Bd RY}(ZXG ŷй>z1 G!#miC6!;}#RS4xnԞ*lDuI S_uի1&j 1]Agy-f}r}*2A 0Bl_|N&4eދVK6 A`k[of5bV#*`~F d Mhal߭'d@`+afk[7{Έپ9ȶA$O?"t1b΂b2c6Zhz1RAk64'tnf?1) (h>kbc@Csz'~Nk 2a}|vs_zٰalB%=j69錹?;kWt{lcoܫ c c9!?,jlf,FB^֑\6Y0{7lm9dh9uJt7TX ThߒHenO']h: )5HI!!R:߃\dz),ቹZdy˭;#e;:YAJ}3{^;k#$L#rs@@7!hiC_E@=+Gp>N*HBpڂCN~29>L1S=Cḻ:UXgooϴ~cm 7|Nyݵ!{ k{pUG w[fE6;ZCs@n֎R!ٜ1v߭(@sl(a̬ӕG8?@ t ^I'S{@w 3Bv|3^6l?_@k~3Q^~ypov{l{^+%? {"-MњؕkƏό#fxj1֞mZOE]8#ɽ}ٵxeW4Fį7A[>TTl!O,L)W!&Ռ҅U:u H  R`+P)G+j>HaEAO-^3׀̍G y!? f13#0BaF3nF&,!4ȳF 8цIȱ8[ =w!I" {"]JdNCU aRZ'[9sAHfg R~GZ}x̓S:;[{_ќNm@/"M`xk+.alHPxTIlmnE)v(YVF#Ѽ)dD#e=ޙ,Q~@,Őv6݇Z]cDo3wO;𸪣Kr\0.thFॄjX$A!/b P^* Ƹ{-Y]wpM1Ƽ<I{Μs|wsH\es+;4/ߏng XXHPwz3_@ r z˯@d`~<Үs]8WYߊM2[Y-+ӯLWP}BBǮ K./dIWxbr4 p>rMG,k|<@#l,# 㳎%ܹz-rz/ߓl$|1I^v"̬ڎa51m{2]h]GfZ&yL9ȖR.aZĦxt|pe.Zyh#d0{q8>i%v *ˣuxb2ZS{UGd<n^ec.tw|_ `fϊԸmX{'K?QfzC[GTǦX<56+KB e%UUTNA/8[VRWi7O@ɬ7Zi鉘J^.ĭ9f7ppBYIՃ_r}a|\NY$+ˣ3cč(=y =xCXZ"v]ȨLXHY õȝm/90S 82%ފe5X(FƼb|O'cQfmGww< xt&r @`:bZ|_"=1A3Qpfst,b#7Ͽ#7\d(JYĪu7=>c @듴FMy6E(k1XM>qZ$bXc&ӲOYuwN@1[ˬ=U}ޙp@^@TFik,vVΓ\ g}5(Aktgs? y})/+AqO5 0b[@"ARGf\S!}b ৕Q_}-@ପ, '"c tĚ>N'ddk Lk1q>^Յ +BdZ; `4=D >` hZ!P# f}7 1B <ݣm(ٱC?ňe@?\ϰ@:!qG´ KJ31P<[%>RwYsiZuӦ~0Of{g1?#17lM}l._ASWPk%GhJv>f@Tb! A]bvBʷrmkL;CŻK9h?h Z3o!b*ˣg_UNJ߻]_EiLZ׿>9hm#ƖܜdCU_L_F*KfJ6 JgREub6{ʎ\OcHG|~?7z ٙ #JZ+K 4`QN gEu$Fg1W2k {sνAz\k9#X's;A09wָ* f9K]1}q;]s^~ U4%(Fc–!Ô} 6c2(+P },>Ⱦt s0[eȈv6Jp I>^Gmj񎲱ڱ>Xnw8v(vB~)4>}yRӅg ~fLOV׶bLIx/\>bN@`JFi#si`)&?7r\@N%D,e`: O;1wKϦ9_[ !It{# 1 /_˰9jC~cZO'=ku)#e% fZEiQة<%fQg{H_>&M~ҭu;䮅#&೟lN}~Ȩ.jN07!btm(ыm/ц/8o 869ni!ipqkr  X_kٲ/hC{ilW`>w-Fߺ*$8Xnv^vrbm#C(p7VbGLn: ]eB,6B2>dH۹ ~ ^6aB7w7 DC]1AlaXjChaO<ظImmEL@X5=֮g<5_Y=$O .(0zwh|GkDۥIhq S_B,V6gXf.<"_zh7C@];Ƴ[>d4Gւr4!jC_]#0N:}+ "6/i6g.{VG?;:*^,~ٵAz}g!݃<O]%֍ ~믗X<̅+^^VRl}/%o}|Tm vA=8/l ؿ {}jv;Mγy)ntȷ&!tZw辺9wKdnu}rD09,znFǧЁw5Y$?h0AOXι7 w1~%*MC]QI fXzRT$LA Ië ewdX dV! "XGX(ǯq@7#O (ϱ1ނoF@ Ag}_ 7B~(t`cPFe*B`1}*3x<0k]?t3_Kt]g0mzߕhYDU1X74vZl^|ځ\} m}v `bsrQ-Ck31zr;|¤} w d}_n\hWtKͷxꌀoK7?DC-1͟5ybp4wKCsꘋ_519 ux&cJ^ʹcd#5u=k saDo20w̯<(b,*Hp+M2.ݽvȷ/9ALG7_`=k x=#NGx3Wu IDATsڝLpA x#SsoP9Vhm ua%^ 9 V9z߇%x!v2f XaȈ pѝlϱBX'aHJ"MPvEF0dhNNhR{-և5 6ر>3ՈI9醀\~bB6Ht?|~1Ȩ7t\wJ42Smk&PXav04b}5(E̼R/3xZ{7 hMsC gͧO a\9#{hĜ}ht-GLݍY!úpwhMas ST.њF䕈nsyZ?smK7t8tM2|7!&p0jRnk YlY8Yr [X.qvvōK mEϠ1A-m9K>7~MM]9wVwF.ιy 7snsWc?8s>$e =rї0ѧ0%ιsι6|9ŀFn\ρ"kBWŊ`AY @.4?Dz-ZTEASO^kuG /I~`CB&]!>ȐFg` Mje`bٖ(c3mXzڸn'#`DpDa/hj}nF3eW Lu 2 B6x0an._U`Z߻!&1&ߜ2C@f7t+xl|Q]4/X/{iky>]w8ZlzݭOì@K٣/b&cGQ_K˘'!`ˮ7о Z {oޗXzA&;u EkozY qνo1g mAY dGu#eeԓIr2X<'B7"RO!ah: 6.AFOwI¸Ylm:º!Xk~cgl?\jnoD@9کLUGϱs *ˣO efCwc.ч7{ |80+'M j r~E`'~Gq>>YrǴ̌O2Zߵ ե;x%8Բ3'``R9T}J|- }Z=k6՟#® = צm67?@oKX<#^'hN2vcz\gieRȥE,kvaRY1ͿJ wMu'.t /ţ;XrcD(J' αY;"_}_y s3Z 1|>yn?_F3@*e%U5եk8%Ek:kI@ֲ/5 l~cXBwd[\T&ffĂ% kcCe|ꇽ5^>7(nf_2\nIzjG0"ӻ/ v;v;y~@SJ-dhm-]h rW<Ïnmd@1Oc)eܘ ! ޣ/-<bj93ӛ+o?̗A6 p<W> 9,%d-+ZMtu&3!oKEȶP9wK'|[pU,ZYm>R;/ja={ WG+Q:{b3 $/Dח\m Wd}fF´sށM<;nܹ͜˺֘ 0M5g])%, fV VH8Q\Ik"i- 2|-Swܺk=t4KowTƖU>īYZwӸ4y<崵F֭,sGRsҚ岳AkHcUe%U} UGg^9=#1K^.2 %z3 |>Kށ߄ y#b-&^Qby|^m#^_ i~Fa_9~*~ `2S2@32D`p2GqaW6r.EO-YY4,ipa-^ؖQ h<P5"0]]LKS.+w $nAq^B o>ZqYFN!oq@nv="l6gڵ}a~6+ Lm P~lLo_otKwZO~F &sa ;6w휫мuM˲~g кoeygKi{?mkOÒ.iT0>HmCymd$D,dȻ[,^l _YSQ]Z8K-!<=SKYIU \ե'!bl^φ|xz.=kx+dHYIU=SKW5ed7?^E/1om>uHt;3w x^ .M .Zvڝ6OtcO&6c7yP:m/ѹKA^Y觢mF"Vd|p/2?.\\XrVokAп|FF#܈ݒ Fǯ9V]0cpN5ZeZ I"346+(!-E@3r۞a3W 2GP0nNVVobE6'60%83ش}l.=K.y4-e: Tt(B)vMg>syĻ#`m Cx;bhj>ab`3+q5 7 @`ڻ(=sٛۉzZC >H, |1_E*ˣoh>'Xٜ&}3>> 2#3K)ogUGkWim~}-)+z5^Im`K?9ӵ4fw־=.͍1* nYE׎>)+jDC:C#&7t}TG,h,=Y|Bb\#;Nm*@%u|U(Pd|L2Y5׎ֿEt,peRȈ.@l~p4&g7W"j7><bLf &h%ʌ}gRipk`ӣ/ ]0@^ 2ALKY !e?9 AN6OCYMg'"pR\:xkCn]>ҧzu=Rk@lR]l[c'ms==oDs(a<4&WG5|0b"efûaswb猵kM#惉bĭ2G!0Q?0am4-#[DHvdvyئFdyx)כNt|_d~ܸ{"`A]9=h퉀d?oP0 D@$x%КyVm\w[Qbо泛7sdjB>,7X{;O!zaWz/tk i L R>XKN&c..; ?]? (%і?m*XB:5X;췕эo6xbq힝mnɝְ(IJ ˧oz){{wYIMV5JttT[Kc|.4sXkʵ>y a RCOW8iU0f͌H\zycp0ynzQYm%ȝ ϸgCwY{ۜ7Aウfu]sWt=M8DdSz>} ?X@F*JPLM֟b;{$N܉ľDz1dRY]_$OF玺셛ma?Hg#F%᱄ @cX14wD͈뎌\;b"Av}VMX0esPq v~WgZSh\ؒw !Uo#`bo> gS֘kJ"M|DGSeC1 y8m6wl=[Z"7swK'<܄X pm\7f?"2ژO %c _D`7L#sz%(8v;=λbdG' tBk.5h]9XHa-і.=3W~GI)QVK{L_E M6; ^|My l.HZk#WguM sI1 )+z ~89w9n HJA%زz9 ^ӜsKOMvebVX<1;N[R-tfb|IX&ĖEY!b]n@c-uh ȨNS5=H0>K[j=I1F R%\ IDATPtcz&] |JnLd엣wmQ\֩r^֧an|6.=;-i:=Ӑ}N;>ֆO%'j~]ac8k,6-LFnkh Z:mlm>F&Ǯ7/7H\es/tgV;=ZXFLBF \* w7Z{'`u6eG*22|EZ{n '/@mh/[SUTKYI&s$#֥~j αd![[hg oF^#r2g c%<ڜئ6?\jmL@3!UEKt/.KpzvGbLj~{Ÿ"4a `u$G!xZ߾FO+w?]hע5uf>OBynX<(˶+rE^֋%h^WY]iq/lbMOARQ]z|YI2}O_;n-Z/!ۢK˂ x=kjBQY^wV2por vȷ/$Ȉ|.&jܭ\dˑ$ EK^[fAF;A.§qh@jborq{P)F+ZH|rqv*yŐ2rpKXrod#b >yE`a5Jq;,#̟Vl\ t4R 2F2^~E~֗7QNYlBov骿}?12 ZH_q6CЃa$a$rdSЮ42~@,(!C m͙=Zg:ʲ9U #BMy Jl[SvF_ΰcD/ "轋|kgc2  ҝ#=L -Y,@F^XYPf'泱w<80ĉ(l9K}+K_xϛ3Θ'7Ftw!Wv' |{#GAsnS*:d VbxX!uUiYMHa[wMOyȳ&({q1_§?aBS F f#| (bF/hA,-Һ" 6,, @Oz>? hf"? 91˷.@BMHy~'4GuxmǿG!}$2څZ@ق#`Ǣ9_`}S=6k+ˣ|bb#U֧+mgdcg_ӑ3 GO89(鯄qLY~h]E[LͷV>tV;WGkP=wM'TG[brk]sp/ai/К9x0ϿnXDaamNB{#v #W#Xaٱxo@dm܄\IĮ s'ڵlp`3N?;؜|bzRH۸>CƢ92`́?u֦eP? 42'Ea6lcm}GyxdX_bs^6ٺYw"_xЋl&+tw!d?._|Br- `@9A >B\=z\ 5~ŨsbX^Y=g[3'QL2&/ Jwsk5KkVUgTId|tx\`OEJr ҝY) a9>i6y[wg5h^i2=b}suWlcݥ{ ȟAi3tlk?n+{&? jP6NvtOEZ;':'ҍz9O|֡UͰ߆\dt)X\wY? CN~f;(odK!ߡ8 "i˴; iX:ۓ*ˣa"/FSL d`pS&b@71e:xQ([pBy+Q=µlS!"9;mD_!9l&̼tkt!˔.@6~:!4USSЛN5=I8sz+&L&{>o\uA l ]8B7Uv81?1kB@4Fn-jc, }9߾bF/v[" ȧhWXz#?1rTGTG lkHeyAE۵4?€^^i׺_=Ա3CD3}dmfK|8][]bDޗ>ؒG\_#}'\Cw9\P3VB9p)]ǧe'~tEʿ~` eZm*sX{Ǡ *wFrdA gCyNGT NsF VſnE ȕ îKC:,룑KreZ.G z E}u,2Ҟjc;8RƮ^7$܁[Xl4#kɬMcKױZܧ ?I.zL&'^oDɧ!M?9Gt7"ٵkQ,k0'L4gzIXҩ Y]3Mwgxp 9٦볭߿0PE$O B'},_z9(H^R27q\Ts!-<`s1bO#ßBDr!d2"t?a; R8F'@ Ewd`s W;[~tG 5{x]cq>bb)Bz2J} !1E!]e}B[/d6^;>A;.vw@lsjpCl%x+ڬ;dAa! Q,1EkHFh k6)!܌@Z鰻ug;kocOƢ1n%998 hdӥO1Nu+N}h`nҰҖ| ;UYmvz3[ZüߪTTեe?=VTv ;g޾dFICo}Q|L,(ķQ̱X ">';v2~}$.IӲ80v(1ABt^#+WdY>A ׀\n##ZD"F.?Dİ1~'\k5Ƒilz!k_dsh}0/Db9>YwGs^1MpMDY S\a,6$s'vX<X.뜉1߆KEuiZʂW'Xs*5VިC:C!΁Թӊ+G|Ǝ#퍌sPFxy _0!c4u!Y]ziO A*G(d"#ֈ͹vܞYRD{"b%JkpabƢkQV!2 ?[? h ',=ծB7,m~VHlKh-65v6aT/n8zhݝjm!2d@L4K@ф 0p=נ5uGĦaz1ѷD*K;DYIƲ;^c֔ӊz{-lI&[g^VRt@!;O蚕ќ) a>B2xA, &U5?@< Ƞe#&ӎGaF𷑻+ v\12(Ѯk}d50}rVd@= ؜( N4!H*!0 ZwB;C;f!cLJ"|FA7  2m^^XLz>]2R~-OG$\>ch11s7fA]OG(/9ԵI_:˲)kxNGIR}b(nJo WP2g:66sϱhY?G{{kx;f'G| 2(u0 뎞i+K?2J6Z_^SKыӃe5~_s|+/m#`tҖ^[Owf|Z:(+ak7qX cvGLX0]\isvD@w2rȨ~A#}C:¸%SI!FbbDnBTa!-~u&qv@7l#2+h˳B Jz׿ rOXvLN@RȠ6s/ ?$d]zX2 އ֠b*yI,v8el.l%{GWpĮE.w 0a6\j9Q ,bHwۘ`7B*-a,YWhbh@x:Nck8'MYыBj͕KEuifUYIՖr' Dl,k =Ee%Ume%Us+KOV&[Ӯ]Q^=_'mA'#/ Ֆvo|9nY1 ^ T8":mO8vw/[o6f9:rmBs vi~z<1O,NÁ5f5ȐLBnO䲺^ 2I!?^Y' Մ܈?%,<q]Ya\XyE|%r"t'yb޺KJƱ <䲫A<"p1i10@xdOv PX,8~~LM鍀/P>q(@ A0n.@T)"i9W pQdc}db!2կ@Iﲾ5!F~ЮL?وD ((khnr Ӭ +z>3"`kR4\gn:1FkB|0= g^: wVv h'V{;`&R_1h^IMwGcFU*7vG{5dɔR={C :`WFUTUVR j1ǎ꜕߸ǽvSaEiG4߇54߹)I.tĉ}9U Rn 9`vMwľ@~Qx +ˣ.Ot"LQ B\X:o!4/0ME ъX<kAi&P U\D>Z|0 p)a-][\ĢdX0Ft r!,hbmc<{5Hd?@H>㡦V3Υ4!aC@FB,v6&G"lg#v Lx"ec59A,dl#^3E6W@4uO֎y}Wd_:$bz q9VO2}܅o SEYaTљes1;n= +$,l3]F5w 3[{XK,\qS~kM,ȣ-t`UN)+u֤'Z}޷/ZpU A#YQ]0* Pؽf>?hu'״ߍق|x;;t?%AsۿAvO8:4€s2sXxEJg1T 7Micڍ0Z١Ѝs\ϴ~v%:tTb\A۶ Z-ws; ?Dkj]g!(] 2b1_؜!;sк2` B)\IO k pn@~+x6?G[c15w(A-1n(SMi@NX=(~I(-0PD- !A9nuODzEf7^>cۭQVBe]Xx腬3dCSU?*}rbEuXt_c"i\3gYIe}.p} [ <7߅ :NBGW>Dι1y90[z];K*[J>F_#맧4BJQT"vߎňXRtm  )ε;$V+vt84Ch}XLȾkG;Yim [{G/U}8X]P~ YLC]@fXY~n߹~ s/Y?G )NȴZh"|G T"hUfeM Pز*?KrOAH)3?w oCG \|Blov!u!RXk凰OFB tG~J ȜTn#fn*5lC`j]j~( .O !1f#ȿAϙH;ǶVbzx!6uM%SL1a) S2x2}-'LW~7,@Z'Y݀Ek^%f=R0u6ucTȬy`i<jr֞ro_{qȄ m4rWAi1#柺icx]nO@ދ'?DJsI@WSuso'y7LWFx63g! mVMW~AS|:}crKrChza8e4ɋQg='*N5eG&+-֛{@MUEM bZ1pv'zփ>mb@/;K9~ʬ>G:] Ks zFy ||ι<ϛ۔ -B:gszO_py={>*p΅=~ 5X<"%ףF]{G*[`/hA&hHq?ga/w{#Gg 2Օ"@#BH"3-A؇vH EF '89 4cZ~:|ˈFqbv|{\nE@aT@BHX:/ ~z,9Qk˹4L_j,ͽ_&.~%'}HGg!{G2nV^֦++ ~X;}~B(>#3 [[#@G 'ӕ!֏X/ENY9. !3Ђ]76 1"~>ӟ@1j7ͥW]sn}v,v0^L_ qCSv@4z@>/և Mz'Yh}GzRUQ3rbY*jk+q_VJnⱍ~]Q:G\=qьUm;(wh <{9w;"5@o[ș27޸:F,|Å#9߽Wι39Wl\y<. H]F'~` 6G7LwCJhR6 S[A`Xk?bHF"bx)m(("fzIO~y>ڲ# N3֏oſb75oY*TS}4>'[M|Z] `[{?܅gmB@4l%m/b={l}3[[wCCs4'ΰkG&[?2/kM?^DOF,0{d | ǰϳ>L -6, {sl,Cs6Eڞ9퐥;x5}4n{'dOnm?gɴc@h*n_g{ǠE޷oK(1Kӎ֑chYaKt|y l"wygO lx^>vpo6G6q|+@,LA+Ei?`3<QSإV`w@Cnd!Ey(bw KǓ(RP=oFa.M wF@͏'Ϸ6PoZW݇9})Rq! X9{ ~b]!pA|̖ 1k`uDmC |)R*W[G"и A A4|={!:Ю9.;++ -Ck{nxIL ߴ{D`*v'۠ n,Y i6v76MV^u+vC *R}O gnO# de~ڐn~=&IAcދ?߆ ڍx~&plV嬡8U #wm5to]imQUQfoXz!;-%SW-j1pJϜbYS<m Vxe ƢχյVU4TVGdW2aF/"Z#79"{m9럈LCJ7rm)R?AI[<~.L??œD_U R:#SG;NȞu VRL+lR?s7O+%G91Ȟh^V՝h6!%|9#PZWZY"#yH):Ͳ2#>8O%f#So);?ڗ:|j4!ˬ7g)m#l|\4wFYh31Ą#G G(?3zC h^ q%"A s{Nz+Y9oX_羊 gN6[FK'́<2 u-h>ژ@`#T"ͯ[Hņ'6)g{ׯ=ۻ~g]JĚέg׮`6'|q_.ߤ#BπUU5{>ttDHK3lT׿`KW޽ 1|I%bsb cNŐ1+YC첣M! D#RzA[{Ճ\b?3JCSKoky-鍶Hg+eA0n+3Utx )ã`uha 6"U+W!W!6d32Om-hG ):N&G>@~֏YLF1q{#?~;v7YuvV"}/7;o}=7Q HퟮW 1JOZ]rMwuH#%/:? G(Y(Lb WXD"4 R8d:y֧ o/ghx # ! ky6I'] &L+g:@3"iu-fndߍG!z/ځ_Ɠx2= ݝJ'Ӄt(-H%bgēx2YG返wF޼x؆лaOb8mo$`umevume\v9;PLh#{R*$߹ઊS'ӾUI%bƍ}._Zs府Ƶь̣u@G;-'kNjVmݽ潟̃d "yMdzoĐCdA`Ut' R$bBpb)=2MC&:$+R 0(͑+2\ vH὆@`-#)`X'[u B_63ϳd2Ȍl" Ro!vw@&L?H1r!1kw>R»Xg.Zm|GJ3y `m Vb  EpWY~@teg6CQ{u`!8-D8 PcG06w=#iu[PKU`u<5]fY4+?#LD{Ѽ~Z!j?=hq};7P.vO+A~_Cn&b˞;͔<>oEze@|H4VQ4.D,_BzUVP*j͠/(<5-]=i@;љ v[4fpMp}cKQSskU?PWlF}Ҽ. 2us֮0o9; {ˉֵ:Gv[=»'EZKoA[d|;-Hhj*k'Q؂y?DJKwF(fXR|}vO7("PDuuUaa~@,G!n8g9_BLO/ԮCJvUkh&vrs> )` e2vMQbAmx 0sɪg8}%88kMaA ۑI 2(tb'[A@aڷO^m^(BYȺ/FU-[f//@q݂i>0ZjĚؼ5+Jx3p$o?DJ92=G  Aџ'XAoA=~Ki9_Dse~6F#,XͧmEVvǕ-8x)v<< mTƢ10ߗ4eƒo 5g*ԣ 쪊e&fD&ꪊ[7GݿCDuK>]Խxnnϒ9O.spέϹܖ}Z95k{>1Ce2Y+93M*8dVJg!| !Z:g1{gPk]Cg9={Ͻyކ_ q y/Q!(Dy%@yo,Ǘ|ޱXs}CD]Q8b_D K%bu? 82L"SV12)[R@i5-3联zk4^BH]s H ~R&"SH@ciF`@ )F B`\V׽X(~@J<[, o0G< hxy${plqx jdzqj@8t2@p E VmV^#bUhjrl|[Zv9;BL#4}4B` k%3 b}F2_?o7LE6ìw!$q4ArT"[^.7. )Z'+GcaNf8[3a IDAT#XIy6c^Dk݋U񨼜5KwZZsN9`NέwyEYᖉ=Ueͫ+>}‰tr!k?kM%vC ٯHF ߠ< ,)X2Xî*ιy5ksI Z/>mKbdz7!5pFzdJꃀ9Ht N"eӌHi1Ar_"f ЕN.RQFv^HG~Y>>#sW~;6@ȑz-bbF"|6kߏ2/BIC"y=!ŴS**L߈ؤ)aV!`^)BMUEx}\dk dATX3'Lz!6+!?9| UP4#PLd m lf!*s0b5A LdFj]ka5(TrkL˜guoA CdIpPyd\nŒx2ݟ U7)2œǕ_?A@ڗ!qswh߶9Q][YE+}oPwfny3+#2oFn,D66Be zK\7hx߲DB"Cnmmsu hG9hhXWz/.h؏O(/94 @UE͂C. whbq>Zcs# XZ][{UE͗c(Zy7Ϣ7#s0J]#]7 ['yOQ?VqbǶ={b`NB: Ɖqyx2S7 MASASDrfBݗDE śؓk+sBIUEB`“m]nj._9tɶmYeۭ+[٘f]S[5Jg-y=zYYep;XgS][9 5?F^,-~ n?q~<[<ιs}yfs +h{Üs5W3YY ιQ1ʊ{W{ۄsn49?_k<ϛ|oG~Fߜ <̥r@ zBb>Rȉ}p|veA2)=Ћ] >Ek_{Br`eUo@UнeEnY)Fp?~b*C twg?"E<xq毁}ge"xC5&R6. &9(GR ZTZT4VmNKb-8!F" `w}G ZW3Mߥ̽\>鈽a<գŁ7$|iB@jon~ݭ- `öx6zA[͑Oah.6Hig? *]=R!k+0w'¹Yu `ݳ\̨<$vxt!]yYEoi+7+sxhaySC_=wfLrk4."{oˀO=0+A4)w74_A;ylDsC_ޞ9!K={<hq-LpιJlMXV3C|[Pd7%w;tE 7d Z`߅~/VēiE7?؀6Do!FJ~AY-z~V{ Gc4̺Ê܉@P?>Vvht&F>_ؘӑ^:áhB^lj,9k{. r>14mVah+}.@@f8(Q^^J%bmdT;1>oO=D&&?ukFm|#0f7 #e8E lΞQk@b!@ ~VNۛg?b7b6@<̹X$Htpq*/L'S=Mw!м]4o@sn:;!~@sm2$lE \H8kK~4ϧzkwƫNOwB `o]k+fe09ˮލZw 6%u(Q@$X][+m4iWx2=b:z /{ɼ?<Lyy:RGsqj@/+^YQ][ZV3@p071{q3?}d)74lC6'z ~}hO >]=q6=89yդT""L!en*[ ,'ӹH_xR& p2zI!RZ1FdY" -4vA*G.;#pp r/rX#s,2ӝr NdCJkbnH%bXPۣ[tt>ZZ=3sc1HgЮ'{?wV~@n*0߀\a0#Ê}?6&X=#ݐJ~OoG%cXd 8fcA`gC[a~c%#K`jT"0L _;3?FǓI-RRkK@ꝅغbͰ>.6nRػnv,}Dצ$T"A`mC9[j,~9؞﷿OKF6o ' Ȕx%O߷#`?<р|wb|G<}óТ>[nOANad~\Ah'h9xA,당Dd .[x2}3 9g'vw_Jx#Y dB3AY|F}^v@_;#6 Vd{ ;z? TV.6'[ u_J,c ߡuٟ 4縡{]C/_;xf199wY;uyt؅$s\ι;Ucιih9s9wsd֮)(& +F}'@t8bvT 6)тyS*)L_N?#vRXHiCB`f}3v-9HDFlEG{!V| /g SqI*{(LOwOGhbWܵNФK#۞V8-W!y}?#3`+bF Dx2E/6>X"XSx2=?/A/g^I'l%؉Ps]t g#sbs`\ixkueݿPDDw6T"v TH܇4O YlC@g3h~ih0~0oB P4>B3ַGY+M?nK%bltd>9ވ 1b f R`Ǣ1m6C|.UUu߈ēRLN%blr͔\,x'?TU|OumҪlF,Cf*jy,f;ˆC$R t R O!b9!)bͶE^GR!E{#eRM ~vgg]mr@DGSE8S؎w@2-G@a1b<p& iS1E!|ŞFJh81BN~z$01=O'YڞRfSkܖMw;^38V,z"0C 픪lg+X]C#ͭldص]Mk+# EVLyO Pw Y~k@,Fs+Kume&czΣ>u6gUמ3A[4w*:A&z7TUԬ~R][6/E?_(7Q-ݐĢH%e0Ǔ )޽ !Rpq,B"1@iR911)>W} R GЃ :GR :Ԟ8L=[;X{{"J?)q`~8dz} Hl } @*k'ӗ 欟-Á?>I8z{Q`GQ "MMYC دl[{|iB@{/dBY@N#,Qs|Z?7Y[,gyT"@<^ȡDlWo֧YhH@q6X9k A\k{; !deCg=KOZ_\uhr5^W3[񪊚 t(/Pgc,Afqykc S粗'յ=DKz^uwnk6[yMpDkrЕOnI[Et rߨE<2yC Wx2.bvD-#бyy<:G#2kD; b!5Z B4'-ZYVw5d:lutH?g}؀~#%1O;gcbB+ zNY%sX|iZ?nc|p@<'i"J̬9?P{[\vl‘fuumWu"ZO֠͋GЏ懔g檊E_k?u6 CT"OD@mt=X!A4T6 $G.RJw#eZx[0RR֏bGS{f^sKY +s)x<IfcqS.&D1̼"s7F x֧"5v? H̉'Z?8磰/#08Lwf[J%bt@X՘ 6?BHQb;<9Vt$I!o0:w?A*okĀ+}gZѢ1]P;{.Dh]yhѳ'>77{rf5w2!!67zs وKI^ͥ^Kk^Eumevy#^b1〾e{sQz=E+ZڳZGdG#9 E:9jA&km-"]YĎ؏C nA hO>y@ːrQ?!hG=ĎP:1at5b#$X` f s؛ P4Yr߻Y{!ן -ֿt @D㟜̆+RتSne%sؘF X=[eDC3%{dYĬ^e!H2v`$ _Cdܔܟm}W vD E;ୀ?t8Ms?RHyImB^҇wֱ"鷣y2 xf$K%LGs=_V8[?yivy˶}͙މGnܕ9fB7B1i|4wn"_R#)wg bٛ"Ow! V<>FxIYk@6 а)_R{ Zo 79RRDy1LoLjEqC`✅lD'?Ɩґyn*ē!%!gfxQ*;R΍tV5?'$(~7HW"6Ix2+ T"{1B]9/a# Ն@I1]$K{+hF <@5v "`UwL^Ot>v.ҩDl1{#\߮v,02yD!0ͳ15Ax'yNkl'dayf[Ϣ9Yw} &]KUEM3@umRČKIֶu#nSsC<; w} "gpQu/wGmY}u%t|9>Bxi׾şl]œ+' IDATb:n}"r̮J%bd}d:k9fuGwTWl鑈9)@ϾFf1~}R5ȼ@ʿ#beζ߅ۏ~+-yD{PlƓ+ViSKO rg홈lO!ߢs/b"HY  G_h p]cZ)? d:l4Z>v^ݑscj}?\Ggx$ z돁osA/r oɁEuk矴~@~c7"}1r L?[ݟ!!>19Qv,CMvM| 7"vx#5_e<0?KUEMbAKцW rW~-m{W97\598+}hcSu|ks+I;i{9|'spyGyAιCQ}mslp=T}bvuE2@ cw"ErCG&C;b3qlvҖUe._5A̳7R&6#s?XhTkR:P@pn?mDdo&b>F #b!>I%b/YZ"p*I~L૷mfu8zڛN[]|Poe0~Fkn.Ad[PGX=o8Zߔ`n~C]3+P߳+z,Y{XkMEư~NE`q4wZyMDtkh!ȫZ4sUADx2D*{T"!{"_^fG=ǘOk,/rK n ̊6P}aVVvG%_@~ytw(`q[&8X2f [`s.X@*`wFfio 1k"ؤg ӟrfBz#ǭ>T"n!+EOJ*OFdzHc,N|"ڀ@H'uݳ?~,/НS#PVآCkn\& RB"gBr~(pf+75t$܊@ cQY8Cd SE.z黻y(H%bœ{9hz{"bۘJJ{oϑ5% [rHAvǂ# ~~>iKzpS[k|Sk+lYƒ"69^P~;[׷ B{к> ys᫭S{ ;szw h8TXɝ=[aɶ/4O K%b zi!'Ng{~q/[3xk$}aj 5gkԀQH턔{!kCkR>CQa Ź7QFV#~ Po9u3ː)x,C &'G"кi d9ods '?K%buW{#usSUGJPu2E#7bs@֏o o/FvYmf#n2ZlsChQۚzϟ֞Ñ9rַ~0[?D T.Es=j})_@LK@9D-6'Ibw v <} xh7[ҷ&wLݧWntG׫tVA$E16lq}(Eh>MBljc|7(6O|ƥ <:Jl8~sE~0_jg8#}@l:R I"s~̀ozx5hF)uHFlEO4$-/jShg~$R;LΪ;3[7$a!CA -TE (JTPAD`$ZR¦'MefﹼK`쳻3{{{=Gfkiz/y@/_Rxq 2}ph! ٞe2Cg ^ï &KZ :xR?6;{iHiK."fl: ih<`=;0퇲$49\3|@j~"0kfވzĚ F&J-E,ֆ=l#Zϟ-]_ˀ^fXcys8ds{A?D%" 2iG`VMj F{ؘ a,ArUhlogl7@-mD愫wFf긕uژ<:\nIJF-}bbcV,ɝM'^_>I͝*|4쐱'<{XecGOȏ41٬3M>? O}mwޯ2򏽩MޗMe;h.NvϘ3o{+b -<ӑ"zC 0DGMDEH#@^ge<)qo6rs_yT&|)2X%fg!~2S@4_A0mvj_@ dB "kI"x|]zJ'TNnPM'Ker K~p`Yb]#3k @#: izlC;n#F[#hڰ.EVא#⠒ m<#*Lws*za|n//-^=kl:7ӑQ'?n>2>LtRw.?XDRcϱqR4Þv[6b+Ӝo87=RsNk@W;z’[9:GG_7i[!zVh3o*r{#hV5 D'֐ntEDF|n@L7o|u0sdKϟ0<7;"%Xh HyAg9,qݑ½ 1*[!Tx\KKr8!BS.y+ӣ3zBӳnUD>a%媰TՈ[@Z *-puD`fOS)d*e&+BqnԱݎ% CRt@(Fuڢ ň}DD7~}V̅!6-C')Y:M~=Yu%t?91s|!8bt8;SrMeuC~d}@>|!Uַ;^9y)X[І# )_a}ԄNނ6#X6DɟCw< +]9\9ѷPk>jdܤ1.InCs'bv²gY=6~j}:܉h87ZG^AԏswMaH"Z[>JNuν3sm!'#Vxa];v*5ߐ.vu$JkG [&Qp -WX"ŶR`F (;" 9Gv㭞?FLkˑ m:RU#J r ZpЋ5;bJ Kт!T:eemLer3佩LPM'X$Y!]yV=1-kSjT" 5,W!|뇝mD~=Y47EJhQȜd} 47.Dk&:>Zڱxְز"bTT&7Sء/!#3]묾oǜm3P 6-hdc6(6h<Ϯ٦T&7xKݻQ uEspz/FasMSyxc1 [߲I؀֪6{!V{Zc߄\޿5ex{=b~FE9\5Z_huV jsbtrQ*A `C%A?eTCH!5gUD3"1$g!fh!_سI sh=5qt.w96F r 'z!;__aTK{bD72j("4Ak@(eīR30IerΦ!$6{"Sc)N-Y u> 9Ϸa 4 ]޽yhNt@@^/qE6\BvL__t\pf2YWYX{!pyReɗi2#wbDB;3jBl]o͑3T&geɥVKPs`d!h+P?>+{ѥƎkIc_C[` / esZ/޷d5i}'D4K)U)o9E7 ve0 IzMvEΈjAX)F"VuBTK|)ܗ0 B ;m h,EKq^8}$bUC'"%>VCjN떡E?Rjw!7M'?nY_4Bl:N*ڱpCwտIԜMmd>|!?i}3} ͓A6sm̊?ִkn2S\i%b˦3S\W۳GsmjW XI6V&8{YʭM3мx*Nΰ1z}Vl:A$7quY?/Ar ftA,n<r~ثм#zEO c1mr^dm&m9˦sN\i~ݕ!,L.(7D.6ٞڏj6i]_#@6, [B\KAW4]g?a~rr܀oFj]~'E@U'"?vhx 4)׭9 r G\~Q@p{"hRmuoH ||b֗#^t7!%քk.~<H.#JT @y?1ȧ t*9+糈!;bfڸ=X$R2<|2ɆܚӭNp! x[;>ߝ: JercᏓxMXt:-#i6<rЎ@fJD@I[ݬM'ge8~ՕN.C[|@s~"\!|wUYj-+gg!L/Merjz oEDk2.%zTIJO4fиIc:o3zd;4sffۤMʲI&%fj9|>+~_+Z5옇۝(GRwt;Eh@ ctEQ'!%Xy8G14 t DH1>L]5'xUiR{F G̑vP-?AbF)D Cj&ވ-@sr(|Nӭo>k (lϱdL$R!Zg-Y-EA\"!04[ lo7T+y2~5ai:@fޞR}R }v=6mL h~e?hC(Ȕt64t& Rz!;} 5n X@whxlװ^+UM'Her}{tS6|S,wO^]H ,ipEhnMDH MN?彩}w_<&m ʦj2@l:9OrgkdK*(V"Ed+S[#UDLae/Av 9#319WLDˈ1`CT؏ WR,w W7? V'眂HV"N9hwb.AJ+bVSW@ vwCUcv%2e܏%9- #6$8['"6o R!i~?@aȡbk/`u +E )ÉNV#p8C A(ztT&jC꬟ -rKWbQ0G,E)h}Ǯ]@ֆ6?wS[?jlhU 냙6Zz!ۗdWS) 6df4΋ DY LEs.䚍]ƅ_ت机ul:9OUGH_6?ӈwE"&ceܤ1șt@Z%)~_ԩM6}dX*XZkMerۯñX83C`i:R\2-JeA Vw"5ˉT=d4ت^ΦRt++xػR IDATAi Q؏EPuFaщ*E&~kQѩv+հpsUKk\ݞpm]/2﷠w K *6XhCxb!y =.u^>'+&9{@ȳqo#7~ +s&xᅭ-6D6 |ND;'l2-l:׵' pbf @9R\W" Z"HqNFf$Hՠ~2=@^R=RR5*'vG#%߈LgN-"oeh1 ) d- +G9!Sa?["rjpoGJIFLX3ځ/X0uQymxys'  * t:e":Ey]1=])f,yneN;Z{!в]?P @*lA`5a~f2"bՖw:b!1q-wrZLT?+NaɹwG@M{;47;"`^߱8ekNܭiQ/v9ui4?5/qu݇Rۼ Qvh2 DHi|yqI;YNF jkݗh_\#1u8IJN5mp\~5QLs  >|eSb!_ɦ|3tUv$2]CFTc#s\{iA@_O0됲l\gĆL#w" WBC`UĦe vjgJtگbe#vvvEYtHӭ-omVV摭6.leYBr_rfvh7Y[quǢ ׁIvu%S$~[иs|w2v JVIMXbUVu^f</[}6bcGOhU`6>"F6#eOO[[A7hW{gKt1DW}'"qmoknB[9wڬV EtPdbBB,~ȯ* FxZM'Ler7#e)-Q*7#1\|V#EZywgK kv1Ѿ{:3D=HW5A:7L!UD1VY?n{Df/{f"_` a顈[s}אY_~y)#%g:Sp"Cm j-P? F? sѺl,ޏrΕO:(o}s7rC"0q/[1pFK' @[ӑz1XG $=h*AJlD_v{-\Heڞi'K>=ыC'~m ?CX;6@Lo;QR腼ǁVrM+^@`:u@ QbڿDV 7H[]CIHk["1E#:Y (/!p{gMB ӭOdH h>ɕ! w+/"j_(۳K{KV~9P_ŦF,ٳt_C0-Gs%eS5A):h⟚Ըt/|8f5*z_WCsyJq-|W+us%<乥;lXqs!ڏA,He1j o&DQ|#s)#ri& LF{8B|Ώ=s?"Jm:m@}BtT&C,T_ֹR\3˼Ӓg +BszϦ+RAT @fӝ1tT&7 )mX`ء DdBwHGM^5!$C7|m#@~ˬ]J0썀U3Qn_"0ieF`4xKnH)gTdҜ2FgoDuF nd\l3yQ͓߯b.o:e#/دhZD=: %.b3?<_nS }(<ʼh{Kt`%ڿZڣw7ԳتΞ.M'CؐOMj\]wtKgcu (H Et#?kYpL,/84FeeȎS~X:lףs﬑)s_fuлUJ(u_WM>A !H:q?Lib\9s{gt]J& ݈C6<{)ì~p?F 4^G>H}hd1CJ{ b:#EwqT&Wt5cP h2[e2~L o㐏T32{Jz"*a?!D8>ئ"EI\4s bL[[D%H_g#5~ fr܅xE!0S1b!6eQyտR"41f D@h~DW$ [ HI?-q'xul& ilݛl5V @ 9Al '#@tmy!:1#3v ~,e%qYk&"2\ڧ>A B5>&&"WԷWO}fq̨?b<[TzΑ7iL/5o 468E@A4ܣP;\ggiks  ˮ_)$׾Ḣ' HlE^D٭rLad;^H#[| ZoEl)ZW!\QREs۞7xB\̢ф!&!veOZZX߳z8~wZ^0y)}ε>냀Y[䝊f!|[!S̉U}` GVzXA@z]_m r_^Ɩ} \=b:[cހa#;&v \Mtz4[du=~p`LOccksM<~i՞P#g(vb!!HmBV|.+͹K#bfyz}YfhlquvCGZR"vЪ29=Z_\ê_9[v]!л}n/έkT%_,Ю|<3pg))i{ ٻ=6v4"3Qq ;z¢ϲm{? 5N8a]ףSȤqϞ휻m##]y:NC@MZڻ뀉s\ut*/N^NerӐwpF`cn*Kd|*;-W!Vg/ pU! NG=ȇ5"Hyn44/= _\sQ#Fk{O@.X;@|kf Fsa=#=Rzt8ioj\ͥZV7-L<ŇTx|z9UlS۵;=]hmەQ#ÃChjz[Li3@mV"t>x[BdluPK}@m]Hv r #s5b':/$:[t ^{)b{Z | ya1Y@jA,XT&H6|J6,ԸПCa ocƮ4zw|M-ؽKVǗ r-/:t%gW:=}K [yneu.|h4]nA|-k\s(JJ@ظIc!Dǽ =Np?wgQ$>ZĿ/t΅;a@M6N>/ v/ hwnzfɏ[E- ,B^E: )h|h6sb2BwאoA&!1L8K-mwG` yF]wkBVPd.e}[@f[@yK`'oB̛a!Mh}'GlsT*dbǮM'G20> y܋),E+Qf"!?6 h~䲟wN(&\h3q2>I7܁Lh\bim_P5qR~YUܲu2PXE/"#̧A|2_{4,fiu퇒s׸Cz_>"h>tF"!^On9o|V~|ǎp-pIcF6Th.o|FW%mϻ_&&._5p(\_V],y 8(N.[`75)6B (2>ף(v=(FU]sO") h=YBi2>>O!vVj\(4gqמu lD- |8(z셾h8n-Y:Ӭ=fL֍ h1[K_ʊo䊞-yNwx 4n-&̳@~:'^OcGOx^Ill뒝 K%훈Uy(L1ĺChF܈+inkl:9AC@Ci#!;tg.vj:"HZE56n!Xd~-yjaZ<"*kC3=Z.޲1 [{nG#~6VXo@:o 5XY_[ˈ“`Uw&f.).޾ǹpzrĴ,uFK K^1Kbw/!nlډL4/8 mCy>s;7EhcՑD%`Pe{Ýws=$ IDATϻZEn%d= ﳊs}s=+yv}k\vFod EļDv 2Yy+EdZQ:JN; _5.Tء6ل%O圱d9W@}1{+w刼{Z\%z_|!2@u6u1䤾RKѮgh!G`%t`W!XL'; Qf4GNq(zG o ֬ٮJh8NC j O_ou)ì_UHQ.W! g%qu?C42_?ڣ;0+sEpࢄh̅-(DD듥Wl]Ln ߱jEphLCj֖74Dq[ јb8LCBl<V n8 OT)4>;׆ܹm sί'{ *!sx{:n9o4mKlݗl21gצ2^k\C_Ls["sDa&?G8pE,b RG#EBJX{[kOQYتY%G ?FÈd/k Pr !lGHa3Զ("@G@yzLvlUJkRDDjQs )HG`,P!v_ 2'ZˬMo"?iDa1^Ȧ xN! B;F+ږ _ k`ݓK^/}i^A {bV!@ͣt72:]=J۪eX?]6%w o++bE ܄;*T9jFNxgkֹ{ko?kZz˖/ h@o/C*j|yhtIt-ovsnQ>}8ztIth͚ E^@;*thh i$RKHoAHؚR_`: A^M'G!qb[>iV puEr#Pv$ʇg(24V#sbz[[ANѸKerg5M-E~{{d_Ziu/ϦK= Xeg-hDVdm\̃䫩Lg{:NQšz_l * v~T4~@vi\P[:bA㼒p9c ۢMH9qu"ГGIm}k\k .^9ǻeL^-5n5b'7d+Zz_[WDuh GhxohS*]x0?Vwz|lYÌҖUG,~t8x{\'`sat[`~sn`"NFّs ru/@8i],?z:{?ܮYIɏT&w:w|6\kܳR0ˀ^!E* l:y%>-7"y(|8e˥}&yp+@Y[Kma=İBHH;H%{F8m2Qk׾Ȍ4 M tT& fYk`J8Ĉ\ z1"d ,iE?p+*7m@,/< V[]C aMGX-~u%Rҫ#\t'c -T9/kj[8}Mj4/G#f~\ܼl:Ը]͛${/2IuB ^B%lb<ڨu)b;h5Hqu{󗸸[bLz5~<ލG Q h NkU츅!={,XnDdسroZ|~(U|qd[' s^#>[lLDV!NyzNSnƻ-o/䤚sZ%bo]svWLXW"jtP%͗#_kK}5cMRU9"Nn"E_%FQ/~_kkjt~:zcc}zAqu_Gl9v'iW*s=~U#E2]~bS+2Ev"2GBĤ,OkDӝZ5=^^gzsnKι߳1b ϑ4_l݃X}ogKf⽟ofh}n/w n]4}܁k PLer ܕ"2[|pf#00)玈r}eoTRJC,hgSnD=+CTCULAl]SX "oBD ])Msɦ :bޮAlG֞ SpXcdA ڱ j}_bؖDS@v@ƞP` mf!7kkoo^J~o5o5ގXވ]v]D&۬M'7v`I˦sv9/NNbrbHJ*K95}cb5nQ~Q@W76oе  IpՇXueZ,O+hWhIT +=n >! ,d(hSPf׷ϯHcG~#Jefv˦6rhs.֑q9m:ADBLk_;xȞׂMC@N@~}F6h{nH!;"`8Ch)R,&dݼM͛F)>hF/(MBzE~5'c9q4,Ee, xlY}g 2xU AHer%tPbu1vLer碝yHnB6\D~c~#W:g} r 8FJEN?BY>t5@Aě׭^mLtP%̬gc rZ 3p_6ELh9j^ck&kKDQ1`dWo!ɦ 𞳖NՍz_;8+Q?>eLCqu y}L {6SfnIձgyw/6j-xyae|ͻc@s.GDg嗭G yi|lmLϻ mޯFš߄֖46슀YBD[˶lD|HTT&7)?ʌcBekM*@f'u*ڍ_UP@-`G t e+l?i)F bIEJ>I=t=#E "3۟ɫhGGmT'#б7b l:T&w6AL&90F"U֏h6tGJ8%b p [ODp.yތ@fH44g)ƇM'ccdH:Bm 5p}s䋩Li\W.T&7D& T&~>]jZ%ΪZe׸/#o4gCTW:Xz_GUEf f:WkR#MKcMkX < +z6=> VwrwF'32gB5ԑ(_ιLz7:+Gl|P聺T&71gfVYw 퐫GGRܮDrS6ݔH9y`U6Wٲ)G -CQ<ش-{u2vB+ĩz, "_į&[Wu-ytM@轹 )HVtr>2GZXnrP> GOS󎓫*M62!HQd&"VD ED!DAABzQ! !!Lz!%:繹KH%y3wνsϽ~fy5 G&VkXܬ](D=e}?N95 [9Fx$r]yo36pllkk/II7SpƒꎅS+kuq%;"c:v#%&$Fݦ7 Q>oW#]ߪ([h@&MC tD>9)β+zK}޾wG Gbʆ ۱HWee}ZG&P7".D~2EyF"FR,"R1$ME13HF]J`d:{{&XLgG<;]8FYAXbah)F1OMgyssd׏GX=ABgήBڿٜ7"fo r/5F+ڬ_hq@5$Jm -̮tx lAykd ʤ@a#L |W>lh+u紵WOh &I4d r}#/r( W^ѷӁ!ʚ/Ek v۹Hs]>t>cA?U \ϾLgϤl8R|bT)xD&}2̄Tb<1se_sSC/G9zzv?] dH>fJLE5{"&l8O@v9r Ƹ>CLzd:)?=LgDpDL*qk2-J̐fRW%s@U,0'ؼ,yd4y6"27b-Fsv#Q-iZ(T{ǂ%ks~j3&3ĵmMr.+!mtЋ@삘9yo}s?CQ:wZ}u-ོ?Nm3$\Ei-J >s;qD(õ{~ܲ} )U '~.CJ*ZGP@ 41[&ETh0RL!f)#HaաhrNt@KhsbE=x1l)CEBĺ e߶"瞰mgRlZl펀66*Ą`۟ͤC5vv[nc9L&+#?;>qhIn@&חC`.dkoElC)c)2'N!,a5HBŴhI[uyi ֕*amSѯok󂲿uZKК'%rg|hM]tv c.W^ L* d)ֈݻ9J|,Z$z *tlۢ 1kqq1KD ޶JJd7Ŏe@'k2} 1S7鄍eĆу=`x.v0D=QA pT IDATm@ 4;,F^b Z(Ktv{Jt^'u[ށ(0!Jw hل~DAH}.6&m 3<ҕ'nX>}L2Ĵ5mdW`v/=HVLu@IZ **cmy_>hnvXc2K[uBcE!ތQZ[F݅=M%o",@i7Wח|@¨z ?U-u1nO_Q4ݯY,Ɋ^DkUhNEȌp: 9!%=)z$#6b^'L~aYѓ~13; 0L&G#َ!4֮r  _+2m d3Ut6*b~د rf}.C ݂lmm@`1aoGdbO]oڜ"P<#7t6b{" b_&\?ͤߴug!9>y Ub|3Ļms.[KxvDkBs9ZSy-AM ה1]Vغ9m}lE 2Huq̎G,v^p5Ď!W}<a}|Yڽ՚'MZ1dQeߖfڽ ELd#cżҲe%lPٶwHkCnfI:8gir LO^{mXxחS sn}3>P„ N遮[ZAʒ~`66ډ0=R0!0\Lo6 Z,<@t];]G䝥3[62``"0>JM\/`z7q(y~YesNGpϭ~檰Q*^|J=vZ ծOs಑њi^uiw"N{F+_\hYkgߦ#\ 4bK^X9_Ai^QܲđsoȲu*L_ST{Xv\F 'םsB•@{KgmNJLU wݐ>={?;^_ncFPkP:<{߰k/[RQNGG:k;rXlYBm KøsyhZ2$W ]2QB湳#w;bzzEtųFÐ9t:uAL*d:{IOu>+#cњ v,Z" }RA1 Tbi9I'&N-CvmcؼvT= *ln u>fhGf!PDcH%}Jk #Tcև'CXkc@f!sqJI%Nb2q'`d2킂rsvv_[1O"oնW\_ ]C WĤe/t2?E/C}2ѯ=A[S6۝,sOd:UJ֗Ԑ\4_d x236:){Ёݛ"p<sagmsU h{2J4q-"63h@mƦ(_HtνE5`'U{O@ ,oιst=cs:M Kxm{k;~ ZFksDYrdUs 0{?_F:g2d;ot*/ˤ+1M8G tpOfRw G9V"QkAV~L tC-0-.# XNtն3и%lmC@&d#]⏬oA_)ƑQ3YGw.Uoۘ>=%Lx[kwC`jK?B5zoع=ߍa5P9`ע EQ[]?ZXߞ㲏2VI%\bt>Cmn۷vo[>||ݷENuQ]h0-T~ShV*'uE tAZ Zc5Y@ﳙTZ#~}|f5d_\e/qQ"Ý FN`Y;~1 8t9C/л{:$LղsoNr1p9g>Qs1_{ s{skj6BM3 mL7@P碋8%yz" U"D W; H1U:2%95OFlp&Erj} (^D ^Oyw[ۯj>L5և\Jhy 1{ܧQL*qc?>ll3d:{*EJ.Cf۷:]~gq>ĵ^d:{=R!'Guyp\̠N1E!%z>Z̵9JSYi@JPUsvvmf܅_ Et11v@N[ 3Lu'#'d:>p1$]N|t!LZ 0n#yszvŶuA7֧6|J _AhPb`>bȎy=?{od:{X&x$JgmKZ#ÚE+_hw+* =ic7|!3AwUom^X]%26JByUE@X SLyJps 6ghQD̀'#0's/E/m64Zdfk?=oA>V;޴mZ\ ; Y* }s969޷couΝ蜫D 0?o]t$KB^t*tC=ݑ.CCxB&=-{N~1>uJu,ioTHĐ>>>Qٷ>['WzW߲x\(8-/|Urdb篷aQI5}?#~N u |{?53Zod{5_빑=FfkWcg fS}^kYw:Xl.簉b "iy}_0݄EdRggO]g l$W{!v7qn8 m> iZblld{ 6Ϗ#Une۟@w!] u;:~k{iIҿ{g{v|Ҧ|%fAk=r#TPcqnJ.Wx[ճȡ7Yy7,- ʧ8^B{/3;7xm?FiT7pã1 xfW/E IZu_:U,$:{I}\8}B~m_LI.Pi9,̤w& [g? 1#5Љ}}r~֛"¶G|B;e+Ա9*cd؎0PT p̡o[C*(}f1teR-Ɉ c+st?v$dE UoBTz۵TOt: v^8ziےh/B@&;,B[XX DslX `뎀>O?D=W ~-5A>?1X2yomb~Ս*viֶ"ҳhMڎximoT@%cgDV2U? "84u1͉yyRe KD&>tЉNҲeeߎeR6`QS W?H.Ak ۟p3'>~QR|16T fR'R7 FYxbXf#% 1I{#[2{U8J\H.R嬨m/8ؠG 1;_wF%z1vE&lmajq2ƿ(bR=bѾXˀt!HB aa>{7)66ض-8:X@[xB浩F%vlA`XwZ/Xs6í]/i׹J D{b=OE#.?g;f3MΣݽ 0.AD5Zs k~%ӳ=m,K]c.WV޻-C[csPlĸ.F'kr'Y9;x56Re ,0:nc -H?~#*@쁘rĬ"0(C^X< %@sԵ7{| N#=p8"dbF,зl ?8bNN?CG\Fh> "P&v  ج "6mG"yDDm^ȕF vژCr"q a5(ZH_:)I;Ƽq9_\jflg,T-~},M1ǖ{%ZG+WuC6$,4?Ƃ"tv䮣xmmo,eE9v^E)JQ1Z@Jz_֠!$3ĿlIX^j>!`4 Z<'T"ǕХ!IG a%hTh(A@q6A%+Z/+ii_2c&T>A dl9w'^FaQO֮;s^wEY9V}aSB;H%5"_ڱ7d튲Nl9ds5Aŋ`@F] z]q63Ī(d:{L*Pv5@!@NFP_$kG4ZhT4˷mA螺e3+'O:,ycyoCk\noo@IWpn(5Ζ,.mEium;t4a%'\nL%4UpZF'o,JQRebSlG:oBnwVyY2=>Jd:{3RoBL;)#oK3 )>,з>T P"2 So}G#" +' W>T"rLw[b: yQ}:2 yJK>G_+Zj3k> hpt$odZ 7Sh (0Q5։UچMUާ}ܾ*8ClW;r]]yGf w#&_=gܘD1 !ɪ*})_^@Gisc n1}3(fx IDAT/(Y?-[SQK笿6ICy ۛR=R v&hv; aM&NBlPP oR{ 6)v@+[Bsf=bJ[w]`/jpn$FX p gRQذ_WБ{{_Y@ڵv ͕Ȭz$YIzbkGf yOE7:/mA <*&:;Ƿ=8J,eTbE2M#e;O1M-m<0?Jn4_}$Jt' D  +q>I .B/2BH}<9A%ZGolTWAN~Gе˖-],v{!z'u>묶s(ZK}4ٯqʎ Tk݊koq'w4DvlwΗ׿[ut*}8l9(8v]mvu(>9!ŊeoyJdMLg{l$o'ٝPL*)(ہCI K'V@Mr1e}MFvtAU4Szs8zE7I%4 mܧڟG&C1d:Ld:{k2=-Ffٗ K> G`q~:zA-͡d!u82VF si~W~6_TzĊ!؊KFޚ +N:L%]G2= E6}+ejrl5Z+n~$?}6}|nxKlBѺk#N.b zn\YuI2׼(Z#'re1+C&}(D6N/gzա{=D/ KIP޲t@ݫ5skjTUi}&2!ՒBE /thp1G庣H (++;Z^B?.E &i@5  _q5 R\'#QsHԆѥ~?-v-C,J=G#iGdfi!!,/ 9;Y Hgm'"%q+o܉<}0Cȹ{)Ƽ} LEpeр0B_g h kZkDCg}qg۷ ֶ@dOvh><dK,H[w^ +mJkYYe6śs_zS,;n_2wܟc.*!e{W~1~NGs#푀-Ef?-,Is9g9nNyxK cT07Z{ZťOBIܧjW,T Z1b.msY*wt?("A8vrν✛{9ݶꜛ蜛H'9s-ggE" J"b9Q`P2Ҝ\I% 2pEpXeVu9OB!> Eؾeے0/2~L~b"@51QgR t>. ~>o_m֎!B=+Kj*hB-xtreyQv;k=z$H{2h= iCf Gɞ=}[Ǻlb.@ сWX8[L!rpaSBk7mw#Pv t1%|w#(E8Jгqtp~$z!mqC `wC|NR[M 2GȤ|SHr:I%F'ٓQȴ2}Qx) a%b}"py9 }nc@-H#BRi_̓L*jB)dZ~fAı@ Qmq-'LonۇM+2fBjooZ_?@JHǠhwi{9 =Ȣ.PAAoZժi]b?=lmDfCIF{G8NA.aۗl>))&T x19>"|"~I%^[{#5Co"R g3z@cFL .F7_ ;DWj!3lc᳄i0Z{S-AoN#@7) $5jnC,!Yf/XߜlvD>عKmC&(mkWjkD6"%>>߉Q.jkF(z͉>5I%4;U(p x9c[y&jd?mX锣};_GFSR~vmJb.=t=c.wG-0]#uBw5=.-1@h? xRi~`{|X(VpQĤַ5-V;\PB&}MsU/mc{YSv{fkQ6PԦd:[e!L*1)Jpp )]Um{ѾЍRFrۿ=wAg+u$"0p25BjXbm=KWRX&dV<d3*2@Fd:{.*D|qw"PQOL*!򍚌r!'L5v*ۯ)2^̉u9&#|}? 1l/#u'M[L^̨;cmP8ĆٸoFA%v=!e] ZdQ) i vˁͷENjo,S:sJn/'Lr>Wc筶 B=}k9%mK]F @k$|UUYd{h 3b.WLZ"O6R\P4y\϶LЌ:_ɗ-?ΞZIu;5JiH/UHV!6zgߩ{o!7;5;j #;YHB@yqU%]#2weǫks(bC% ̮<̍j5f+GǼwy Z;9hmޏݝKkҁWy\.9{---;  wD;=!^tOv:ד09.L/)JQ.7sЋ-ƔMGZ9O Frs =z^^C}m P})3lf-PdY pm2# bJ01Zmس@@5 Zz~Pgߦf@6acbkr)!3d.c)x޼"'O~[/}R0gVW~>tvb;vDh>MA@1޶o7|#;2u bdmB ]g"%,t:yz]:]."~jc]3Ѓ*C׽13^yl `ήfwhku/UĶ L~ 5rfV.p]/vZC!,#њ}_u;>(r=qn~IEKe߶ no n>oHC5݇ҩXP>jg\6[FQ>]D>tW6(gџoȗmGi*GZ),b3ġtv+Rл#]CXe5z#^vވ1dZ~t)d%T7!P7XLgRSoYy[E܃KJݾpط,Ai/0IAW=lNdvRm,B7W@~q?@VpyH:d^ohm:(HX` Q#V+j}h'!x9< I c ;)J<ŧ#Lgw|s@}~L^R?(Ve~՚__%O |%=aB>7mcö +#RXLс^ hSBUWSc.{c(E)W9OL{tY(9+h=I%f7<ɶA0o#54{!9ޮ#Z~ȬAoY߈A쯈: xH%TB (_;ưY ?a쭭uA_yEjvE;͈xтmCA4¬-6G mVZ_F =7Kb+;v߷樂&ΞlѶDtmW~wk۵DU_ŻDl?>6Gub.7W>>NfwKdih~"ݚl}x MHu $l m?}m@Y^UF?}9rg5is}2=ky[+~yx8w+0wUC]2\Cc\7(E)z$!T̜aEe6R,A]RN L*p2=12Q>3} %:d |fH1D3\%#P! )Bv_!kw&i%ȌD|2S@S=YC `mí+l d\Eu[ge6/۸*\˭T]\lϱcٱ"ns)jdS$t M'y ~l`zlb~iukޮ-Ks.]1㏯P?B`[h|V (/! " 2~;:m4qc[s>>["nmc#=RѧmmplMETnaYrUUQ\`R|yY=Lg`XWߠQ#1&bw[ A$Bː1_ع0 rz]@PQ5[̚G# C\\lۿD{ !y1|[!2U.O1v3bDjȶe|>J6WRg,AG (b̀n$+xN[%AXdɄL~1G/[\*(NGk iDscktv{8̧u;nX`~} \}1qsn7nb7XL*,JdR:fRg+ (d7%/FtBGR]mTg؏wP ro[}YDf~L"{b"𕙅L O!F(Ub Osށ^rMA4vTC0̮tH?@Tv%,H~MpS:ew)[2k3#9m^hhbD:$c3Ie)U[-2Y .m z%QssշztAKEl B?b.Ә˭2XP/~sk3kolsSaE=[;IFss,9aPb,_;`$8 ޏLldRL*z&h$G|‚IOF ȌFHyEd),M@&JB֠;aߏ_W(bsX?}[ʗ{u҇x#`75"_+2D/(3t "*C`aRxF laAc{G/Ğ튘#$b)H IDAT$hH{<9gxkmmek8J^O@>dݓd:I_cVNoYRb`OF&˙بGI1g]~'r>7׎|Ć[zQ:W zQEcI%3ՙTb}|y/f//ʦHs V8srν{9wes$(9snꜛi sWιιι[sζu]{wާ9ws5ܛι8v9sN᜛{ a u=gy979J9ts-+zpsjP9W✻09 ܓ ~l}9;97Ʋ\UR4MnudyM&-𘋢$f IĄD&(z1UeM^47r?J!Qs}hÌf:\K-[:AD%Y=b?~P\ػvƣMݗ"DRa}IhLHub4"h=@T`uRCE@0bPXlfټYJm.:6ξ9چu 7yb?F;vN$tfӮw̮˓|ByC\ng =lgO\Qm~LAM!zkc%FJo݊?]Ey'rh4~-F#yk5D9^sm #D˾(_bH= x{s%ιHĭM7;P-} zm{+( {x{]kιyU{ffM6!1Y5t @0(/ (JaBE(2`{)"(2ta!!LBzO6[e!$a̹>)sγs_ۓN ߢ̟(MAyo 8>5~X*Ay^9yOn3#Q!$dFkz!N)u2 o3Pؚʤvsc1BB<_Aekd!V3)7tNDLvCP-(nywGIEg!G̎#"a0I 6;);; #R1Ȑ' "Z3?ٸ4!{O™cs]G:Dz"b afn6jryAHň<@F{-&, ԵILoL;5^(惆UT>éXDBk }^/]jep{^2^jѵ_ܘ䃆'0W=螝\!P}^zTFù';yg=]GSxB`ykJľy(|7 qDO.RaSA,<=[<|{}g߉čO{<^wc󼤽vV)A]KyK*[cDDLg&eu}IwvvgTqHhqC.=ݧP@{J2큞HCF-bW+폫Y5PnMG`$Rk&9:gD\]*D&(%lbHORP~ "w.rDUHI'W#bzW&w&٫?=1R F.>D|r"+Dd r3rr Oq@cDqCO-{QssQlcMGF}!@_ G\CEpQQkH6S\KS?g/|o'+qnF_y!l xokCFы3џ گFOZ5@~a 1+om^ѭ{jNIˏ |DMxand:{P2M2]#{͠,xqDppM=|:kD-.@Ft_o@̵z e2m~A4[ kR8<ϫzA?7]TO=>[U,#! Ul*<%l`K)nwx0adrbLSrFyXa#lΈn@&76^%kZHgR~k2}XR6[ ,l܅Hm!CĎe€=Z2?3~[@Dn6rq=ms~Ŏ1](D*i<~eYTdR~ ВLgBV "d/8yg{ln-@HBV]I%HTٹ'FGxd"eԽw#} ''֎n[O=݊M/*/Z{DC8yvOB$ܳmNxA[~G "jZsvǠ5G/EAg$ y8rDA;TyENfzx xÈ|Vup_a\Z'ۗ!u>R4ˤUlr}>@i!}H.EO G 6s ZO3-^(sG 88KEN "2TdbDjݨv{ŏ6Ͽ!"~IlODDG2WV}YDdL4  xP)s n6%{(BIbG+R1^'{'m뼷gv"ElLwI݂7"ƹ5cc: ֒[e= C[fMB_!ctX`䛗luxg*aw].˦)o. ^cF*xa1 ؠ6]Dڣ )&kڴ>5UogR2)d:;e]x(o+DHA$g2Q$D"t] 2v!:U aizNȸjBwPHTffD.B/7۱q/J]b%nD<D&Oށlo | >odR{*ZqW#k1Ut~:r9Ff82_A2dT[s62-7h^!7@*NArH*!Dd y-EF}8"Cm{6m"Sd迡Xo; )IM`W6|)@#C[LÝS_^lV~+ l?ńX(]`;9% Eu(ٮ"ߌb:X 7&\"OㅈaX+\"~)^U$ )_4rxC0+2ϕVѣuǦE=}.`-MX%U;[A Ix~h>"DXgDm" ""H;U{/צ:Ո4uEyKWtvws j4 C[@DesW%L v.D"}1rM^H &"hr~/2;r{Xg~Ȥ%t`(z7Yg_vqBu<~2fC+tu6ǂB]Xjsb1ן oQ|D>\@ewVĮzEꗕEDg "ud̹a]Sz螺㖱"+ $nBq][NDJs֝M\v f?DJ_m|?|?(R+dx_EdDnylotWjx"wd'/XزLZ%~#~6H6=e㞍aSh] 9B.vʞ.MM= t//]3yg>hX'Mju?B64"E3HX2ɤ5:cYbV(6Dq}#?D0\ nW#E?rBPzHhzhX. ?oz޸>[pv\"DfnB&_bvyVDϤIVIbt?C uԎ\Ia#"%q5Gr;sw"5smۿ3)Lgr0ZP0Ơ?}0f h|XEx,}oѵ< 84I\d/m.Hx\L_rD|0u(Ȥ ǥ)G0d_"DX'DDlA&/KJÃCfI26a+'SbvEqg/C Rۮ'29Pk*W b.EJ+9ڶs/$a\u.rQ~ )_%] WopkyTm"2{-rރavJD:'"7 HX3"E6ٵ9 )ht2)d:ٸzmקq;U/JLg]3)*;Tظ>L"cP<ܽ6zt_Ap2*FHKL䃆^nt? okx$AC3a|:0lHxQab >xv[̕M /7o:!B""algj]\v^2/̤5? )mQ)lcc[X (̶ziBJLD(DDPH و9Ahj7T\7Fc(>n^bǪC2)z2_γ9mKm"E(u epX2 Is,[WvȤvH}XI8[Wnjs}ic n-U$fR6oA50*lC_I :;LKw 0B؆݈ܶ @n4BꋃR;Pׇȥx 5reCkY^gQ۠Q ru∐dI~V?D,O ?[)+!n!RblD&"NAu՚O2Fq_`#lwQ,T=jG'y#C[JUb};r9x΂qE LgʤYlZxg2|,'!]2!Vʹ@oQ ^!a @Gm. dRc#DDDl /D i{]]Px[2} 㪐Ԃ6iQqڧ?"HpK!(˕gHȝy'alDF"Wb\HTzjGlzgx8rDA/5]^݁⼪m]MCbA%a}wƯ.Fh0X1%w:lף[8}@zX!䄗3|7]d.Ox (KHT6;DDl3E&?LlL_]@%vFO_¶77P'n "E5hݜ CŨ [U w `2tmc(뛐 q\s? aMtLʿ3zt6A@͉d-!lR>pEDg!Evrz( ]^a`"+vF5$@>hXdup$Zw&z893: =\&Hxߡ.Ժci 26Kl"Dy "b>/Ԫ6bcPVFs][HDRȀr%x^fc^rGDB*Df 5~zSP&2C%[6RNr6+m8>kD'X휝Ժydcok"@K2Br;'<º2bzO %\!Iܕ>*k2%w@ES@k0 `OƥF |Ij=Wm=mlMHum{脽Ƚl\.HaZ,"5 "j?Bj|Dvlc!BZoǞb&O'+OV.º^nׄ{@u[ʃ Ko#?郔v8塶ZgV'L'3Q~ >o,$ٛ*5 ^\(]I`HCQ&KȽR P g8$K+ 񻈀ԂHrͣT ͞j/@eKP-בzuRGF=j&l*s.2Du#"WoظcT"{mއ "|&zf qڜr(v uP֏kKXkCIqT#&烆jߛGm"Dؤ&?'ї| E/#gÑtU&7"B0ܔC}݈]8|bF yDڐ h%:=z{zo#PȽ7r7ضd쾀HH{8ri^c=~>BjƅuDTIOٵXoźBͩ /WxDoGjVw$zɣ'ǣR^oCYBVlG\Aû?WIü cSm"lG x3)@&7' "f(pUFMDU!iD}Bt8"J&!}q|Ѽ竇 :dYIIUpH2}ȐDCQsH*Gl8RNF.ćoքllD.#Ld$wCHTׁ2)V2}ŀ'}ͮcmzH4Q\Qi)Bz}?tMjqK{ȕ]nwut脗;k{KQ=WR&FaSG oqld: ) Ejd<;gkw|P6Dm R~E3I[ذ>ŖXNDc ⯐5 2D'٨4G;bfa cڑѽao 7IH]P#EqT) 2$H#7H=yٮx\vGE{]Ґr1к*Z+NJyĊXY"%DFw9@g糉~-V^\ڱd6ǫ ZACT"B-"9 lc#/TyIUs;|>m{JT`-Do+ B56;2ҶȅzL %l)"C?@˹fDׯr9r]v8R,F{Kl.=P.xp E.xwR؀%1vGnv#b< kl^˃oFeBq]wkn+)/=W>+" rrk24t4)Yc}[NFa F5 '#pW&I#uLʟ Oܶ"\/2)e NEFzT] iHU8Լiޏr9[lͷVTVv#80Ϝ*kǸ \^>ԌWmw8r[ u e@d² ݅5Vt4@1到O Գm[ne|޳ގDŒwGkH%{Go;W Kgߴ"D!rMF ̹V_V724G"E(@~G.T82heRv_$-Vo{AȍE;s!ޛI_O-[܌#!U԰7F_Q #[նHz  .r ;L7!6i/fR[^?CjGEBdI dFJj5ZϠ52 PrYˀo僆FasC`Ƽ2ˑ3Oz.I2)d: rչJw%Vv,W1=/"FDGrdF[3jAi1՟0z'd$AGٶF]6 b?NZXV4 I SPf*ݧQ䃆e< /W{n;eueZ$ue朋Kyv !5Rd:\T{(>DLʟpMAf(az/B]y+Y=R"CosSHzT6ڎ72g`v%=*u G+E%9Eˁ^"ch rO*w vJޝr}ߞIt횰8'cڌpήEs״>@]Qw RXs.n#l6넗ӽS!Hp= a,WWaeRtv(r~0J?)X.p?N$7(-͕S3XֳRF֥Of)NZ̃(9dk]]Cjo'>/Am>Rki/ERW(!B-6r;.\Շ.Xy̤V{7ˤ%tjdz"ö+r`g+)GqZ&l#av Ș|oD$jm8R>BnMl!)p#[ԾG c>܊֠ǡ.<*EcΕϕ?XYoY ă-==\\Ơ2#' U$̎{}sn=TdW~/DD~,D1gGa@DĶ0dR~l\孛d:Gg}ətv{da+/ʾr@sNSDDhoD\M5#,wZܩz[}"m3Z']~_; 6qM't"%mu3`5D>\R5Z'Zq."ZG[6lv&ZvjoCE)5DN\;[/!f(k2Z#b:PE(>\98#Ո s8#6Q rCiӑ6iq;FwW' HZYTm !x52!#= EjE!LgsFgRmYr99z"Z uX,"aŽF6`]@q?R`ϵJz$^Pe [4I!(X?º=u7s( O@ٙn?P7!@qW/#7d%Nsd"2t8"fJ50^9e=ap̊xqԏ6Tټ\{Mr19r%ΞTڼ"y##J^n"TcAvFHؗPذѺ: |ueaVE%hAX߭AsU;r!k!BNDF#ZjW'.I'!3)K}'"CPoR" _܃2|*:kA ! YQmo:`3krۉ2#c(8窐rrD Hi|q#t޺~OGX"}jA KO܍b ӶO밟^mPb@DP\k"d[<""aX1׳Z8eR~R@Ve*w} `9xW k)ӆ v\8@ԋ/6 ՉHXVTtwFdlKP&;^LNCjuH%rO}̤ ނb6Y9b[W!R>hhIx%6RPoBdj"mz a+RǶ_#mo`vIuv^Ksmߐb\i\j lr^>hC"D؂ňEXtPTpɤp:ۯa!d\s߽ X`O3 _(f2v?!,AmDz~Jk4񧢠8"zE?ve!r5mlD^BJCd"نv6(z]k2\aWdR kr^nHW~ḻl$\<4^kX߉rX]'6>GG#hnr/G먭-lgzgPRG*r8"Ee#R"HZ Uu M sX(dxDD.ADLoߣ $"mHXhǛiR o:;6olm#rXA^.2DLg$RQ?\Q}QFClt$8ՇF>hp uۀz^A?t<8{N./%tu b5*h׶L/b+Ѻу(۾80!B-,+vEA'zxd:329 2^D$h'DjFs5#%7r"8)asPfl'x*d\fuYrg@dl.Rmy냝Z#B0""aM1後Ǣ7Ȥ!w"c G eG@R&&4*yގgTF_#b#šQ߰sEF)^gP|Y%Rz 5-Bn%m+R: I%ٻ2)ߵvh03d4 S7w4 #nGh.E ևhmUͳH!=lC {(FZ֏kԈH?DWmU_ˌ!M68¦Loɤ\p( D P$+Gqa()hmpMEgV>w8R= X=Xg)R~^CjFo`\)]8U]3r_`cz6: [tvl2=ay8"M Ix^n :\W "du W W.ZqAꋨh(Cw^>nTqWm\~ "D)b #5Z$d&eRt `x ,hUHmxȠ:O"QsP >(s02""|d)ȠFc'獨vw)Z=InHtx/^2)Rk٭c]W$(u a NX3l#Ƚ|RFmȸH1HUmH~;RRwp:"0p>hXEa G5`qFG|%5`z&忐Lgkg 2@6T-o(ې"rdP'"90(S2̻!Wh+RzyCݷ#9HRoD%Ȥ%rl ȝ<^{ ]׾}jTKe^^<~ܐ`ٗr^yvFqBе](D_EASTh4>Ah, sz=R/GA&f"D#S1ICW!HUꍌb;2p}QlֽkT뷨^WeR~ȝ6)Vbe5T@D+ʾGXp^D%)\\@,Ef+Zktvo^ YycEߎW,|SC*{F*Uyq<at1h(wE!*"vRo] ͤψ/"CلJH,Fʅ+~b RF;v z)bPV!R7H hzn^kd:2"g9B׬gEnqh5(4Zѵ]K읙"Fu>hw_NlIjz 7(ۮ{-DUTëzHm[IpC׽h<&v#D߃X b,7D2=β- jr%9*{h@ UoD*@BwGi_ EsaBtH(>jB nJG7EM]e'~fb'aؘ;خ}iBkT&, Wq}^jSyEVILٹ$ŀPB+Vz@jTu}>"ac,ZI!3.d4B:#wyyHx>wU{Iס@dtD1g-(`wd;Kkd_',qTŒl9 ` લ^cA2/F=P+75{Ǹb[a9 -`D.]{ 5"p5a&Hsqa[Xذ&4z%w>hXi/GZbg#`s5#̵׺) /C}Ժ"DX"a 2VͿ crbFVڗ0U?_d/CjLMGd\FΎY@qBDPODZm#ε!vb_7\%^*g񖭎\ʅjCvr6o-A朄nA~F셊 trUW#uy$B>hx*NG rnPDjZ "gp!Rd#DDD,'Xj2J ܄X]\vZ21v]XnyqMC}lx)%5T"7(N Pxx%v&P?lިDJ^Bd%ܷ/C{ VBtݯ?n@2p!CK|ф։ܒgp9_2rKxPG\AX/ DvPrΕ#*!B+ "b64N@h؎F}6 ]HAkD "leTbյ9hW#3rUA$>DGő%6r`g&巡$/yU( nFDe_DFG1^g#YC1iEר"w{!%5,gӨ)vBWxzxfP?ԭ24 Dҵj]) /9Z֘;ّ޻ȕl[ vvzk2=};MC6.""aCõO  nLPQjDUCO@F7n+rPv(l1vːQu&ERTcc<LʟLg'#? Fso'T H "~TٶO FB2-Agrg׻ʶĚ*)׊HQS+] t ֽ ܺ"6_jcE*\@ؽapn>h($OCLE!?UTϤ9-sC\k="m78"M[zD\Ȉ!∄,@$m1R0$,"7cmܼܒ1WD ?A2Z6A2)I&ٞ4{X ,꫈l4W^^ZR>Ą?2)d:{RgK%b^"^'!XޝB'(س)6q#=xmBb>@E빕;{4SVWd>RQ@l{cLboḐGꇮg;"烆Ae(1Lɤ?[9B>AET!>Lg s=0ԣLш(MC.T)RBEΛ w #U/A$íH:>+GNH-?]l8l\NmqzD $kGaʁGKwӨr{}b`.KNWj+3Qj1*zzO*Ya?u"E١к a 2 r+g2߀ۗ+- Hp"/.}HᙃBj5),1 R(U_Hhj'?H%+*.;9e&HeR]+(Y R@f3ہ`OuGؘHvkG7\>h8 E y G|,F$yh=,FqDd=nZ7&V=_eR /68!¦5aSi$d# ^LC)DT츭Q-"u(ΈővPC0QQ amp$J#Y_8"R{G!(+!=AXDzOGFG&^Z͟H-;biѶ 4rOs7='Q}a (r$T6%BV@ElLgAUο'CFq R*Qi DD\fHI [F3|jP6g|Y90?fR;q8j3j]#QݱU\\#EC9K ו .6:d "8"> ]z̼\Z+#BA-xđ.&!̘烆{m 4lHu6D1b6)Xlm{.!a|sR^F.} K lⵞG$, dAƐkP@h.wz֎*ǪI$1q7\Ua>"U#onE`y="T?B(䚂; 5(m2N3N. '\%RAPt{;JYHܕHm o>*yU#¾kqY B?"dM "7+24Zc.-F2wP d]rDj2"zeF3SQse#< )$ݎd:{oݝ&yI $eD,`4 QAA*PPZڣXkG*V Ep5 /iD^bB,@Bޓn/ܿKn?5dΜ9{~gf3~_/y$B#: ƅ(3?孨;t+Aɕd_נ/HTKtp&k E*~'pAGl/pND[P@fjHDa!qH>2!]>ycGW^ !֊#h qh,kA.^$~Ux 6TCv9=љv?Zc^3cRRD@'q̐@o4QZJn尓o*!\STsv"@XwH\JΩu@\Lm?D5FyrG)Co {WGˆwE/Š&"zB# H[ 7tr'<\GQĴ7v8zEȞ^XqqkXߌeCfT,@bE1DbDm=۾|N~?EѦ/Ui2r2t#ёю~֘EGnTxݖ%HlD'ʕSFllQ3z6_PTd%9Rh&aMHeL&#sV\N?|3X3l4Xc31x,Υ˳d!4+0fC^OoD} 99 rx4 q-b3 (ײt7ߍҬD$F/&n35Ql2+Km(Fd㑒g3& 4JKf{%J]~g>;, cX1KH .(+@nTzLC>0'w}{/,+/q P7WD)֯(4ruZ=^cnghq 5Ana_Df=2{Nr'3P-_{;dEc4{;*ks09uB"$~ۙv|:yh/:ӎck̘%D"Ey]uʕEѣdB#Zr ll KJ+1tMȋW!15[nkP(2 ǚ^_`ϓ8zpˈZIkG+/9uO>j@WڰE fδgRwٙv J10cPz$Dv!+HWBO2.Cg/6"֎ ٟ /ggʕ(tANV`3JMB«  [D\JDy4DJJTT*W$Bbuu("ZEd"zD5cZtdz}EMδcNgqE1`!f2Oo$qt'POL.p~׳qe܇FT}i[F.Wl'B7l$qЏrKh=lL_-G 7"+[(͖ Q\d0}Z H"A:4L; +z$btJM'Q1azuRhLӔ0Xh7__r%I=:V!w߈QEB[ñK{IH(x FDRo8HDD?#цRf3ÚCK !p8L$J7'd$>_53Hl6eVyqޮg g"H TcQ$1fB<䠾7r1/ʺ~'apR M9.4%qP-OT#{o$qEn Owf)NT7QZ AѫkQsFط'Qa PT$@{Q3tk +@By޳[l=l~,EF<cjSeM`W?x'8ڲ}_Eѯ˧jNKhIΩT"/YQa~ 6Fc?A)kZ $[P.$ĊᘫQ-HxL@iu;"ojM{aEJG^oWW >lmkfYVޗSuc̋ƚM\ע߾I ?ͫn fC?,+SkxN ~-}H4t#p $ɻ [QmgHP4hJI (6n Ⱥc=I3*`KI4܌: 321֏ҼƁpAY$Rvv:ӎZcjSe/Z*W.Fr3XL1+END_͵:`q)Q*T݄[H8OʕӑWo 1E"$ZQm <xJ>4$LR䛖qQ4jQHЁ^CM CPqδ[ IDAT=#vƘ%NMGR*WhT\$=l֪kT܂oDj1J}(}4Ω"i(8$T߅"es( ;Zk$io\ƶVMpiVxC3bX"$qt\jMx?':=` y(,zF])p/$eѻ*nAH DS;8z楬tzs~(Ah@zBδ]ƘũIc^C6"cʕ`RG[xl4xI-.+ͥrDdь&M=hV8${aٰAuq JrdQ灧8 ; = G\[,jlpo܁Z0<}ʟ>mo38"fS*WC!ԚQg\jOMՖ"!q- עtb2lwڰ=p!7-WV\ѵ\QW$>> J` BZ0,[}̚1#bƌ ;}jvc∘1uTF)m݅6.me;ohb2J FG aU@b[EBt bbx<@nPZE]&q|Xn13fD)+Gӓ8wȶN`:Jv"L (M19Ջ-Gix]3\k?ch$R x!F.e,xcljL )+ʕNǽq pe\i>৐md eU_v"YYWd?2l݅~g |~j4 g\6c1cj˗ʕ'q0&BT\>"\.nT_"s!!5 ǐY#0; Ȣ2Z?bc3c1##bԖQ(.tWtỀwm ϯZDZQ!H8!c֟#ԅRUrˊA4ܺ%jkXA,-ًR!gIN}M\y 8sRr $<L7c1cwM3 Ho5d-K{8 c85i(TuSѪ:1Ƙ∘1$7lc33c.7fQ*WNB.Lz1ƘéIcF6185i(T4$q4X03Xc1 &1cꄅ1cL3cbc1uBc1NXc1 1c1Ƙ:a!f1S',Č1cꄅ1cL3cbc1uBc1NXc1 1c1Ƙ:a!f1S',Č1cꄅ1cL3cbc1uBc1NXc1 1c1Ƙ:a!f1S'S8nIENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_40_0.png000066400000000000000000004046061413546205200301510ustar00rootroot00000000000000PNG  IHDRb6;sBIT|d pHYs  ~8tEXtSoftwarematplotlib version3.2.1, http://matplotlib.org/: IDATxw|Ն٢,ɽl`1@` L$Q*$4I@$Oh 0c{dvEkYbvs(N&e>[Y]la5.D<4[mIaEzap}Ux?Gѧ(lue7S~4礽',=AY|kHH"b)HxV nu梜N91EQb{qP\6u5.=l{t}[* ϴEW$mY)o(c!0EQMDeql/fUeUtKZL[{8 Kg `$ Q 0) EQ6b ReU쉸UOS5Fd>S*. |+l\d# YM@$7 Ɩ!$E2l ux_TEd}EŨ*E 37'W{1;a|Djц2!ڦ6Z$#"{ S!ڒ@KL> \^G׫q>r(;9#(Grמ툁ozNW<ʧ|z52W/,-$S9_ÑHr~môM&Sɬ`GG|1SEQ6&eqsW}X0 f";flx}(~ePݾԅ(S]3hMs6-yh7"!ˑ&r"D H",>儁3is\:QEQ]S]kg{P"@_$Dh=F Am5J3`JR5U5UQ)20۬ )g(8oFJI,BBֿqe!YW?~iA%$_DJWD+6 h1Zq1H2|VTTY!dʠz>EQSF}%_,8Fp'4p⒍BZ0дh{ ߀C3I Br ө+hhRQvb \Dq8Y^O 4E\٦qR(G[qÀ*f>SEQ6:b PeUQ~|l(pe'|~`ު<$k"F#ybK]C ^@1VO*`nQ5S8y}dQ= f̭]gִ| RlL)`f>SeT6sx?eڕ-8"[yEQv*4GLQv0xap8ڔ/:bC_.=.gqՈT VoCT._J[NX=hoڦ5JI 5Ⱦ$KŪx~;"uAuv ()C@pCs8\] XgCHm]Y-O&F]2_nfGR(mlm ֮E{+?9;gܒv{j(h$TIтpZb;ݲ[E‰u'$aoIvCB y"ʔK>pdqͥOC$ً/J-] *(A1E u\2<4-*$@\0 LG&g6ExteQUH` v)Qm8V1_x^]7ߪG2-t}}Ϣm۫(BLQvp[^(`hK<+>$"! hI-+{al$/,U,;Eu(V+6>m,fzA_}TG"muEQvET)ʎ_:Se&R*%RON IyXTd TlJXlJ[~O Tqג]r 9+춼>ߞ TE1Ebvݞ]35=|@>!WRf`FSINm2+ԈeiL"}Iy4DdEY@H$5Cz|Wm ( +eqA_'ԱD$e9C iɲÁVͳdHcք1'ܷ"n*2Ix=~]>zŘH9{ƋTE M*ʎ{\*jE(] $`I D#VzX*/,=?FX{ ϻqo5?8$[^(JS{J}PׂV%9=|W%h@۵qrbD|:,E*.%RydIp``dB)AN-MEQT)H ȉ,j i)SWS6d,#VEZVnNDΡJ&#B+k^9HWzNɰ5Whm㒜拀Wn>݆EQeC4O ɪi)gfg ,ˉ5roPA d5يHUmqK{w 1DY Т1ȎI%"%ƲB'?1|`P¡x |u-7xJADZ"wF8iⳂ%(#(;,{2{UCeyK2`~¬5V`X( 9G͍{su/ܝ!O|4 x8i%h$%'DZknjKB@C&3|{(DBJQeALk)ȞW;57:X5OfqD$rHI!ŹjW4+UT|fz1uUQEք2Eq\?l*_ D>o{1{5p5p3BJTSEPGLQ2\$`)7G]ySEQ:b! Qg?Eju5?*( M*JbqVKf k%pEj=kTEQv!T)J?D݌𐼱,5g(vBC9rׁ$L5bvsQ@?EQ 1E^̞FJYoN)( 1E :PEQ1:ő((Jd}EQ 񖊢(J uE&Tu^E]9,=fȁ^0EQ6)nUt/gu}3$d(luE*~"jM6X \˽LKQeg@sE*^MCE(ʦBLQ-gc~$C֐DjiYx5Aw3'EQ bl[Ie ުUEQ6MWe1O^9g$xtEQvFT) Q[3Ǿ^Nd3(;#:jRQ-qr bLEQegE((JФ(;<}%O/f(l+tԤ(;H03P@Qe@1EB׏ |`_A@OY/fk&~n5m~ |)lST)~_M]  x]< xXӹi88Xb5GVBD7C(T-Eo7X9%5&x1[Eئ4s\ RtRƼ}i_]eU.*lS-K}7&*|l1_IU8yDB}ԼXeUܴ~V֊t!(֢+e;1hy}Ktvt(pcܓ?gՀy+B$rnd6.W gӊ([:bh vZ5Tm{7zVͭ9a|Rrz?Z&c=UEFSv+̈;*M^^-5+ߖՙ#O[2_@ehօ0+av'vkOs=P\9nDڊl1n|8b!fz92Q u^G@`*H_β*K'4_(N:bƃ۲/0X]SM, ߋ vtʂJ?^d5E^D$ XT;Ka˚c; Gf7#([&+IcV %8/fmWaBJͪ"֘ EYk߬ 3vMOs(G ?=Q\E($lW b6܀_bvvfz1{v<ԥ٫^\ P ؎8UVE8'mu &G[*UMOnGpi@A)lbOObiC&$޳a`?lZva%$/f׎4~j:ny_<|jצ }`1yϐ)us_=QE( 1e{4aHn#^nKHؿy`8[1Rό /fQ I6 z {[z? tjמ`F$"2wj@QUToO'rcGv@#Ǎ>*BL|Ծ"#w׷ĩL:d##5q?e9=ƍ0~Ҙ뀕@kZ<+ڲۮ9+?C(~Z^Tʪπi^̰j}P(BLTM"vtNABbW .7wbZEGy1ͮAB{|bٗL<~#\y1{Lo^y-Aگ N^eU?cH?iL)p~8m䄵"ߟa+cBL.8 G["`501SL,bvFpLbvXz8q#=%}0#7=yH$C) ->`c**>$\HePESl)k-2{{1cƍEP!Ty/fF< 5 !aӼ=k p*p;#z2<>ymB;,ԃy1S$ iXo #䞹7 4z1CbB/fﰡt*2uq']صW, qwi{HQey4EP! H$+@B4x1Mu*le~ a%[+!: 9p"Ë7Tj_nX޿,iҋlJ?⿮w@!t.0a=`7qSܦ3*A T ݶ IDATnoOYI_.8>}3YV (. B?x* D%# @Fm/f "H{HU;l_b8z\w`8?qqvז\ʍbtOv&Rc*~pCY{`vyK3|GyMˢ_6p\ŋsǑd3+TzR>*GrΩ uv_|o/^x否=LH$BA@h xaWEQvnT$$roj)rjVbT-kqal ܋٩"`ږ}nHqm|[h:p׏"2>bQ/fp\?(ݸ ad?u=r`崷ڏZt\o`"v?@FDmGds4M EoK'曼&]x1/8_Lܹǀ˦. rz 9YIcFrg B+E>$'Z{n"ӚSpgSQ]b; dMX&. gCkN7/t5q|bBd[gbv#zUabv{;g"ao;_u)\Cwܗ׻hIbĚѧ^u\?)L:X׍a2y@^V-DC9]FŧhZ,v\;g- Y< vcׂ8,N[ ?$|yrA%ﳲyfhŞg4 VLudNwD,֔/[r?}(Ώ /f?Lt:2ђMܬ+k=O0<$.o \up\?$N=}_ @ABL0$E+zֳqáGs]nvûFO SQ]ƛ(לsi3Y܀L뇐u|TE{5 霬5$r@F"Np\?'})I8R\֭Z?x[|>a^vJ?%~spݹb3] 0 o:?eŽ @9MqgKb^̾ވy5Cʛv<!~|7=^=R2 5EQ uĔM¸60p挈y#m?bj/f'+3%i9g\{= 7vU') ɛJ珈{?D|!?~xЋ_ v" *Qy}8#Pd+]'Bc"o)w\B#5i- 8#6|Nl`Piv*KMٴAͅ+ h;(Ώ Nq.H[ZO+CV8q~ gظ=ˍt0Ջ36tS"%M duT{̋ٓҶ;G$z#{SO"yVx'uY{ׯG&.3Ifg+p9sW#N_f{1."t*Qwir/&z1 :y'tY-9{_p\+R$cuR!_h_E5ds&rë9q÷v?rz]Xdێq ޼?-?P{mp^݈A8ׯg>O~ܫl1ؼq̮DJnBBL}aXLG&qYZC-+l#bv:"x#ϐ 'Cӳ3Nv\h5^A#YQEنFy4@Mhև7aۅd #;rnB9vs ^y#mxCL~G^ً̾K&Ã8tFOe6yX|uhSNjѨۋ #(|M' _!9|E럌N<׌2"#$ IoB8 " E4ෑ /x3ֈO]噾?;)/}T qf9/f/u\ ">f_QEQ:bێ$:ڕZ08qKhq;r]k b Տg6!Hfsug%+7oSj?qIP ,BW~[0FOq- _u#a:lI* I@\ ?ZfւBd` } ,@}{}s!0qˀA0yE8p ‹S}5EQCضc2"\_R*!٠#fFe]51ZbDl~@F=־,p\ePyҋ〱輧#@C?n"j.@Bo/s74W${1ދ:Mxȸj6R¡L'r R ^ĵ늄.HXFj"lF9"lDaR/#"t "È{\37 a nDH({9H؞?EQG6Œ#z1C5mHM؊lڏZx1vbW!6 .NwẄ́kB%q䨙>ނbܬD3=R {Hj"FT}T\MHN__FFLcՈXdK$'l&"@Bݐ\gB WPYF>`^{gVQ]uĶbv|IѦ1z*CB]k%{1+Vqa28Ӌ-fy9"Hz#nȧ$s݆^2a:]~Gl_SbUi~d=Bn 2rtvڹF*DrQb&&u1Bxku$R9"x"DtGDX ~e/Ӿy3.1(0}K"3arn< Y!Dzߎ'bvIchMD~?~< .fb!9}&k^r7 $" UV4/+^ ^Xכ(b[_fDښّW82OWDn u<[wTe>4o*ddD 7QdmKێBnpX*!0{z1{YX\nf (HH}R=Hau텮#-5z1)r\?ϜwoI~j]鯈HHaS55DuA}"[jʮeH+je#\9癎v$1TX77~iMDpLj=^+ż#Ȟ}^<7n@tË[k -7'G3b[GW&u4aMиDW;&9=;ÐPL!v[89^^iS>Co7#'"8ƥd͈K'0ʋ٭)Y!e$u&2o)2މ/CrRת~B׿؋3Aq0> Iq2EG ,D|B^Fm.LD #lD蕙mpFYMu8};ڬ-Gܪ~,˜zuYyGS`oD0"e2tg4sx(Ic]zu(}\H$b56g[N/76xnoO~ē{ȍ;7*ևeY" h_pbYփ lq'ϲW )QBl31V3/f] G=M7 I~ϴ_HI\z'i#Sx1{W"Cku"HHsp+F(:!"4RF8މ/5W?Os~%g/fKFӏwpqHnV:kgRk:&i+p\$\Ds]DBg$?o vAĠ$߂*AXdͲVӗ\Lr3 Q4 «e*ęrW#靖Y}i{o,o*i;y"RQL4%mDhπPnV]"$rjhe P^~sO^8׸ש천<eY .N3ܧ U7e~bv=޴R$B7λq vAn#j82ч 7~k\JSmKǩ(\H{`5mĥzyqd^.ELqsu`n_\Fo;Wd=basor\cD4 ל[>T_0S(R Q(Bѥ`A^ۅv"Zg B M}i-ϫiW]ilqG `ڥA^(݀u+bc"f#_$'$4D$}Ic:mkxl1SsF>  &j3o h OK^neYw1HfHjdRhOS ʲԽ C[*ƍADܲfw/<#;4F> N7ǜQV/fߎZ G&ݮGV!uM7'}Ʃ|d*!D8S%8x ȗQ9zɫkFFG.@nVTδOn k9VyESaZ #Ow .a%%) A`E(EDyj4f?c24^3i: efy^~dm9: eΦ-^=,+d5g{eDfVg V_HNnGi968`4VdYVAڎA9J˲E6 ,;4et?iLϧ2[&I$ٺ)1y`k]k8ybO#ySՄ"bE$?ygz1;6ɓȌ,G"R|$q{s\FDƷ}y]X8D/8 )2e4cSqI dc@z,Q@;9_8Y^xDl}=EI܋vK"{w.0G_$_!y`#T(^]׭%EFD -J BX|%iW\AM4nDE6I1{\fY\"_<5VE < |0iDd|dL! _r\?ܾƘ:&Nu#؟ַۧMjnvO6׶IYfO}]%RCZ;_ޯ8+{oY*˲J-˚<0i)ɴ.uZ H/i!ӻ[05mAF,Tu]iKV. h~ _y<q@ݐ$1? x:r 8qŃwd3Q/[8y%V:/f ׏F>l 9FH2j IDATkԔqtj;;R4 .+]~O^z#3zTW !OYӁ ""Ods)?Lu1}H9C:ÓI-āq6|,m)C.0ul\8 Ϛ8)b|Tc\cg#$/"/N[`".dd‹qijDWQh~h;{k "B1@l> ő]nJMTdB&gs#Xz~M%KǍzēȍq}#?EeH-Cg&82-qO]j %_;  uז͞{~<x˩}n`Ϗ>C^W}y #IHhjB+z<N [sIA tUҹL;62 TX*V漴>{o4Wls8w}vm|2˲^I&GZuhoPAt82زr nrپ -zgcb+z4 V5tm_r""r\RsR MEna6mB(|ENk dzJYqP^G`󐧃aRn^ԑBVմ2/|xeeE rW.YW`_nv\$$9xq"~~327HRha@n?7} V!ODrADMz`DqӮFMfcy"@^̎S&7냌|'"6w&-AB'y rcܼ'B.@(mڢlmi@DT` )5 CD\L[2gIHG''$})""̾>GD'tDEPŃF!:zd59yO;'ymFWf;ar+цۆGGE_ 'm@/ο,/+_{ mz̒Y6n$/P}2^ )őn*z y1nM>,E}NNCTA"8܋o_3~Ҙ2IFOR&H{:O}ӲUC_jᛁ{-vsoW}}&"U:"߯[Ղ$W[w[r/eYFGN἟Ls\?_~nIW9․k}Cz;/FDS哈['3*L&K&6N l%P`Q`DEUT"AqaEE5BZҖBӅ[4yNoZ& =W^If=s=| yN.]m@yQ{"%SkQ vk=$"vLe|xmhS!)c@vD77W]Ѽ_CӀlwNLwu'Qy"Ͼ 'd*s0:iݗNt'ǫgC* vRb1fwv!X=e2 Gѷ",r460&6"q"!0vc{b,78Dlkaʥ"T!lE ާXh}oF BwƲy]S6nc{Չʼ`nGF 6>5/Tf4G{4^钢ކA{ͽly.-YpP~Ϯm<e@]4ǗIlto>]H2gsesM]Zڊ[; /-붾]v92xy=| f_w.wA܁/v2yN HH,{gDswY}Sw Ub6-![ՉwD&S[Mu9m!*RoƠS H=؈݀TFSZ$ G@g=R}7XV1H䓐> E,']x5H?f1E?Nl4bZHL  [:bUP~  ՉlRAhN6~6!6qbOW'>eC?@ T: 0nvj m޺!lby{/gsm^f[]ݗjzڸ A*`6sll[ц7y, 6hm62s{Ye;}FG54xF1aۀA_rej' ҖNt^vWVeRiȅt^,ҽhC_SW9(}qE3P7`fH} 8r|^!3C_:)ےNܝT9Hn榡O/:.}s2߸ufb[ܿ=`s[ىn'{46KERUQ<(݁Ǘ}9"kdpLn~Ϥ_o[BOl@:"n@Fw R[fD|3 =UC`e0}FsaP;0wқr(˥MgEHw)b>nGV=7!{8WM'0*X0O]//)t٫HW'ZL r!Ipe4 C*Ь0lPOҟMВ w";-j | T'#;q!09 " m47yLe\Ht7ٕ6_ y%1y! >"PBzʫ 7#6P%xyX!\˚X. Ͷ#b$x~? f7lu_b>(de3Չ'uYBĒCm:-|" NbS?~N\T"i_;=9|mU@ ĊOխͽ \uiu ^P%ƞfɠ}vi0=k*سZUwfdSK/>6Wc𣪊ץe]vY5fhe:d*sPRݯG`QފND H2˃Wm7%NȰ6ʇ:a0i/ul!meHgIx P۞gb!y]7Y|DHކj4N2i $_DfƚA,[+!6"#ՉgoG2! @7d*,bNC `R1j6"pe=Y5#Aͳ"5fd8cWq'H=81v_ 6f[j^N ew!ďbZ?K7o_R~x^=L(?v;ir4zwo}ۙS| AVU.rsC̼[ޫ袛{s5dX?FtmGй۞lwGtu⹤n$#%\6ozs't- ߈PJ_ قNeHh S?g"PXHh= V^1qhF3bZ{Mڡa2Ts(mG\=dמhuC'히%ڂCBlecNهAβ6BL=)t"# AFʵ*k;Ѿ{Юl(l\{J0ޗbY9 ``#8fئbsTZB-h=d n>xk3Y^F"мܞ.IJ܄@dbf$8Wd*sK:qM:4E*q`{2TO 5u!h۰}=7A0eiclNW6w~b/'fRSW9.FňAYM]91vvFwN=ȁhX_mG{f҄< s9ذ+*imEjЏM$oxfkoTsA6 x"ιAٗo x_-i0`ܡw'9 mPLĴ$胄`s8+q)my7>T8 HEJhӋИܧEMDhs4)G >ͷM/t*^)$Z_(a֗#0(Dehz|2|yt),ClT_Yfc1@[67tut6@wHMBP20.*uyZӐ>$;-ځ[PAkcuyB(ݣٜu"}bN>?hqdbg#V̋7>MN)-5+CCrMK:,܂{uEI2.tՓ5>욇>c[fnKD"Hk ttvD#yKm6pgPttxeUEO٫+4To¨*vmon/5uǢwH{GeH9|S#M w+KŏMF;h}ߨ|Dw(A^x~Yػ(I f! UįFHjD_kHVr$l  H-DWa~ o^ڗ0@_$\Cb< a^؍=ϔc}0H 8{?F3pW[:} t{[?^Fù|#m,$|{X1T}X[G#t[ic0o4O"s, LSc,Qln}d}/v?w9Dr@,<+d*3tWZV<[.Go p\:q Ihg?zkG 1ىHpw#NBv-vE\Vv9Da0|NJb%<>X k@_$oG/{Mpcw]s*5I{\m!NF1~C'k&с@H7n@*:W[{ڼ6V"Ai̊W#F/b 0f6ϡcƣmW0[Hlcju~]e۵dDZ/Qk}6IoW7Zmu\~`QA)1 [;1]?Gyޖݻ;3^S {uLhe{ᇛ]d*`]^ɝ.^⡙o=iydO>XY~7ˋڙxh?>޽+1O}G훻]ޑݜGZv0G^^QV撂xTxmXk0㐓sw~RUQ^SWY1<xj';HKp_M]]h*jۀ_Ʋx󼚺@YUE5uؗEU;;.o|;ιιuO8@Q8 ` s _ q :OO8jOA&\CAܹ 6X à f\|A+o G|ɰ7O4|(.[YK ̶ɛ)l5yd$ &Ϧ-cGеahބvTHuF"mlA\+rkg#!K'2n Ꞔ6d mvo5 \"(8%cAE۹B@5OJG4*R){=آbA'HN3"O'xzں![=G"l7 IDATߵ{ks;:ho#){a!Z_Z{ h-jenvN=|" [ovRgڳz Zc)B%!zSrv$S쓻eL8{$OX旃i ;[Dblltzr[6mﹰ3Nﲕثa> '_ֹ=VaD֝V\iъݘ^׷>`|aֳQګj* 뺹hah&5ur#b7C՚ë*j-RBYW*j̩hUSW$R{cкnIyd{%.l,_ k-y>h.6_Axǟk x}w.Q3bCp݉LCv.#,P.Ķd* yؗق#>\|F4tl#5 FDz1H(F IBہl7AZ"$_C@#,0i7B.:0<^#cq5 {( XL@wA-BVX%܅{Ⱦ 3Zh e'wE ˑ dp2Gw"CtH蕸CОQ?" AXۮYW)g UB-瑀9ɏoҽpՖ DB{@VYm6{ i}+Fea\|4lNXYk;vl%TQGg.!;t 7m<&6_.{!UG3"t:7h_H<4gV@esΔ}JQsQ<ޫW]9HpgՉƝmp Fk,XXvf ; ڻE*jj*ʑ@Np5}3`lȼ+Gc:Z]2<~<w`(puM]7*jט򞚺Wh!SM] AUEpǠ5UL%.QqD$7 [sAp.x7acnE@ 6d xs'ա/bl] -H:hŁ{]Q|κ⇣$3Ȧ{ aߎg_\1۲HMC Z]ff]ӊl< v2 ؈@B}<İl:G!uH;B U{(hl ?W?:QA{-EV#/ͣ_#0b<М} mF pYg۳.^T0܍-gӓ;+X&2L1Z jo/l18Wճm$!;  oV`m{h׿"vrZC{wh =FGl AtwLۼ搓zZc#)BfCQϬmkяeC"|fsf]c*m_{ݏg؅gN{z ܴaD[SK8`Wæq!= @A$Ɩ6\glL3V E࢚qV tqİ଻rx@WƘ-0Y2i"~]޺$ 8`LV%0';;# `WG3sDkvs 9wJzⳉ97*%ιnhZԥw9%]|$d*s,bγ4([ӑF cXqy9yhϧMqJ?B̐}癋{@-fH@a,WjEltIJ#oW D)7ۧ5#ۇ%~cf 9H6]CB>sTգ ڙ GB{=RzodD'bi~e0؞7LN.|o|\EH]ɮ9A^nlヌ&{>x /  6p^Ϯ_hvfq. V0F{x/ҫl͹%Oz8ű|sf4}f ?RUQ[oQ/Va׿ǒl:p a=yn^͍ԑӿ}auwh=.E7r%] ~\+ǀϣCSr+79|Ssιݵ܎d|4G ` psn siBoɨ{3Ye1@2[ k-T_HOW'_Fj8D*8Z\Т3LBqHuErYdՑ܋"!]1 uJOſ > jkG*qF,?lD/ 7apROuδgʮܩvnB'qo~%b^Wnx)N,4?A'ꛭ=m|^C㋐WЉvƹԞ0&Buga֌铑or_u" ֆ6~ x˷Gal~ƯaRzxf>f!3:FA,0g9KgvL5u@vfcl:|۱mc1 ِyo,&"a~$O>C a($d;-n}l<ӂ&Z|9uѣWmu މm6Zg a@/AHR=:@Lg |+k4˧zͳ*B6$(# \N{]vZ{^Uyv.lcc A!"VOpaZq{uK)\{1YDsx+nJ{t.a}!͢_ `FBz:X)^HUK_2˿Ed͙U3|PSWy:z/{ ehgX3p`X4mҟ?SSA%=:(4VUԾ`!*Xuv.KwR*jU^n{Pѵo'Xkb[~svzG Oωxbz:J|Fn)B,˷g$T!#wbB` 0tЉf0)@F]6  .B6\eTy" P$tJf+:' Ǣw4al!oFct[5@,kLje7}gXrٶ){ܒ+{t.7qm,`guO@L2Rt"9Jf܁, ?:}o:H3o-vOq5p#nњm>[Yձ(ϐ>[;s8Hn3ag{#G(B}L2|{vV/&Sn@!t.˜YEȆNȀUh,ge+᎜˧_}5prϬ{~2uz.{J6z*1h:k^4Ꟁ*j_M]~ͭbAC<񤪊ڇ.N0w]v|$1;]xhPEɛ|>Vu:"2H>38ރ{GK 4M+8! -ҏoa;ۑ/!+ b${2'F pkgUs=cwnp=cAHq{9TolzXh.]$[bSҲ4$S}XdhA۾-|٣u{ m/\6!EϚBs0kRH?`Uĵ٬dunAHs87 \yg ܒLe .@ZڱA3Q#?V܏CZ!(CeXѡi{I2'7'?p- :ޮoY*j?\qGy=˸Ү[OK7 [b݄ _=ѡ5@qžTUQs[UW/ ƾ|DX:<٥\7tud*SlEj "EeH ֬bbƥj"'){"q!bWZ SC@j0[x OVfPQt>: ֞գjǛd_>mu5x`uMW~ʼnvøc >jDo4R>@h;aVLIV,GBK`zČvF!VaJAzQl:|kmN`]lm#" oU`Ab0<ȲEHxI֏cWkvMb^Fl]Pڕ-JrAAъ{,fhnx)kضx%<@l5 @y|'#CpN4hc{2DO ~As݀Y'_6!__ U a=]3CUahmTvc\UkoxQM*j'SE\æ"5^fmOVUԾÓ/>VΞbTfo2ݺz!"bKT FZl$SW_ 5y:zƐ  Dr62z`Kc#0G Ȇj)lxAhm| :} M )m`܄X{?UE햷2,]kkxرK/G;ϬYU{*%लՃ xs4Ox5u1d/f_){ewyʇ%S8\hz׳@٩mg!PR˧,k}摈 1#LYmW叉wN+ֹ :Qԋ HH!!7!U kˇH:O^ C=?Y$d}8*G۔݇l&vD"g^y"aAϑ=ʞeLe[GpG#=3HMb3{Y=G#P=Şqd*YE#6}ɷ9?-2}+M"" # $kfsIsۿsǕaSl~#wlqu$B>}0z)r{Bky{+Ifnh:ߴܕLe"qK?z?Akק?+ MC@;K wm&,^VWѡxr^UQU:tVcQ؀*j_u7" <"h];:=9Gy1mtsmyEZnF]}wb}G_4{%;P-W&-ksAknw(z 6цB/Չd!8 EHUm0פ%SkTCH}/5m=ZEMG|t}/:k}vC7~TT_C Z7f< MфQ]ԜE*sJPؤȝvGBofaG+ƾ7r}?Edh`cBa;>; }#H "Pv"2_߶)3{$~!khƭ-6VQ[ cH5þJO )<1dꠥooD_Bϫr=uxuRHD7EgPGgU/>ӂ0hU9+lѻW~W|w3Ks蔫~Йeh.(\[<u1 H%4zw:{ţ-k,^D"A%:܈';ȏ?%vº<OU6t4?r9F"l̝O hWKoy]=8hMx : ArV9pgsApk%l1f"2ΥꁅeC,c^+qWFv}"AHD>i>PBa$!&ߏ0:dl F$֣t!@$" mw梌# Ah=s1ap|t>r@lg?3QI#<="a:7wiAپo< &t o@4I><>s"7#f*aH @H BU8uʝ@K^"lD0i+mz6b<#ۺAHWY컃AT9鋼=aSJ*UHy A{ :T#Ci q1@7*Չ'Չ!P{ aqYǗc#[))]ǰn9n+ݾDTՉdU8f(u44hو}3p7bmq.W2 A4 nZe7}LlțSZ]"w;ǐv j\6 --AL `2:\ T>T> A)ߔ>lRhCI'S{ uK H܋XKXa'uHxwyHx/CBwzG6Z6M(Xhjn$NgD(@ MI$0D9{MRĘLAlZ^z &g)!ӱ B.}[dmH;t_g aZ@($ >ln1rAX+G Pk*B;Hp{<"Tec l"awV6O1{~<7صSlrV'"d4}7〪~=^xf9f}PA+Rk}*F,Q"#ۼ4l# 0A SlʦZ_[W EZs =րp~+ZdՉ]y2ʱL"[1^ΡTU6U*b}UEmgM]ȶ|BbXtSh<ƽy-a#=E@m:q-2}f<`ޜYWOp\i/✙==a_ܴlls`׷aވCm 8Xv<5{S"mс{gz5E@?1Ce׭:x,ܫkj)ιgAko`o3s_h8 g{8ڦV sǝsF_ 9( x9ɯ Ac)u*Q3P>@mOM5%7NW''SjD $@ (ĐFȫ$ zoa4sl,ݐ6%h{B#2M@g5A/ 2^E.^X$^%40$ qa?;q=V{" B6p6Ej'Y G:qDv/z0JK(iqxr!X@!GF_eϷ%tE yrk|Z#g R+znm& < }N~:@`t J.G`@XOۇ1buƤTfH2"hB׹ꄷA@/͙U~;&,^=b33vga-0ulMOD 9~Q /j{8W WgI݋@l5$檕. s9[(^m,ڹKZan}UE? JKSs7:&LېC.Ďf?s0ܑg[32XA4Ztyi)swQh9g v,zѦ0]XLeȧ(W#2Iye &dD6M>lA7V}ހ_ {MbB mW8j} qLsNIn=KmL%4oC`bD/#m4+ Yn "pxbG}|fӟ W&(A x֞OH{S hУ'2Lj&dV=ï5jkD Z uI<ȵwzbBRQis8nH &S})bZocGVebaLW'uE>sf 5]Zڄr䵵w+F*xq|kNZY,(t.w~3i߫*jmWRq| 6#ȫi}K+*Wm?O_˷I=+Au%sIĞwG|`@, EĬq( n~0|eVAJBl q<={.DUZg1%VB aNR$[FB.2S@'h@Ex+b<ɷ{uGՉ̽I66>MV*a,"2j5#S`g(|kiH5kCH'|Ƴ%ļ}ZCkBv8YNCȒbz=|)9J O KgTvZ[m*ȻVW3o0cބGMQ8S[^tE>YşsERMޫsme5Ol/,#l&L3뿈0gVb#pgTV݆H2?|B}7.]v̱\2c*jf %C{6ݿG EmtAe8{7C}!zf1 wm z>vڌ ZCXscѻׇbI8缙gJ93WaR 36 UY pckkι=gd fs6|ɈBĮn/T$#!Xg{29 +l{`“h: C4Fx&$4F<&]X Xap9>ث8ĠLRWuܴ 90@ >g\G$V#aܽc܃ !9(F[׻,A$˺>bj'VkemCBr5RMlBlD i M($S~lŹ Gs2C7Y "ACUnGڳAH3ݎC[K}ĒY_6'$Sll6 1 AL2kğDr}m^9`べg4#`3m,61@^Ol@.v0Rvo; SOEZp ah#=6 k;҂ʟ"gՉ5|P:߽QI2#ߜN+*c<@dά<~zWrCapUp񁋨xyN/ՉwvZ;+?@vc]vwx^_;͟uA*jj*{c02n`%Nw n6\6{ԇS 8wyj#N{u QNu}w>$S: :~K/H`Bm aaQJ 2i| f R!AꟂTA^}%  rٞ.=Y]5`CAe;{#R)o{>$\Eu}slA>c*)놌>k)HEGYƷgw@lȇTb> ]H 7$S(;0ޙٞl齒HR'RFbQQ,(a  l@"-$JzOn"\^;s{vsig!VT| 1:@b&O JD>:zc:Ⓧ6.ĂT=:"gG`֩w6}is1ܧiʦ25hv.7R\m6\cv(Į$U#"_79wqqq> i,llטbo}ݗ 5XO&_edGT)T@/^q0#Sr\}؊fn~E!o8 @{77֍f6]8Jּd5oEz!gӐ2lɦ[T&woHEo}sj@5 CJk0b"s;R%ȟŢM(_9-{O'2 E}v|51,!%@{T)y)B;UA3BBfнafϳy(91!d1 `qzGlώ_t bJHkWE#l "70;8e}.E`f'VDg,6B-P/Y8m:N;Nx0~>g%h?mB(9)>8aG{kmhla@roY_Bocm vy } T&wR6{*-6W^^B|B%Ǝ62.JŁe~ -ƺ1Cj8b++{ϠoGX2xpzچ7}wK z?b)蹸l:@W|5q>UX7QX}]lQEסy;ȾX tT&W#vbpGoU:].hFS9?Efy' ݂'6@tE ˗>k@5$۬]ۮV1FBm4R=gsRE(j| Ęw==, CgON#| R?Z?[>̆KlBϷ 2 kWgmkh~+zOhg%"oo%;l]?l wf&[ljߝH`mq'sx/1Wl|J6 3OZEaz=jP Y'# .`U/d_:b@,N$)oM'W*>bպQMz'2-v6*<1h(ϝ'-sW\"nϦ-/r72hDet?bSQɵ|FmC 09K7;E>Kga3ڗ_b |1̨m9\M,x̨cXzCzkJ9PHtzK3ٌr3jꚤ=#o@,FmM'_9.Px$N6_@,γD=NTk(W"\PTή7(kC@?RڋC),GADr#y8Q ]uqGcv 0νdXmvUvmH 彺{9RteHj!Ogdosh(A`s+2-NCfܧ@rtI;, ~–LĴ/ږG ?[l9>hfN(ޏA>M(#j>w0~iʦLerAf@u*%2_>Rh~OZ~U׈&N).tt;[h'~b;;3~ZcRܜTk먼qGjVmܭ!?or֝ ̭o[@Q}cXB{!_w'}Z[gpܿ>L^nn;t~?l߭o|J6ܒ>ޙ/k%[^j^r/y=y'(8p\* E6|M'[Q4 boEuy),M_o}lhG/H`^nCHɌGʬ;{_b~i%bņup kc #]ddm#+B# 5L}Kry@ m)2=@= ayMX PԎ̠z5_q-+m}Q]=E @Zkm=&KT&w RExP3cy=QsbkC&CcO\ 80&9iy>{O!dsՈ,u) ~Ӟ VdȔ} Ď]M(1=ؾ|t?=T>Bx#иzb$"Nε?msMc;C[8ӆ)M >ED-wVR}МOBwɦRǁ'(ݼgMXe#vc>3gۍ_BD*ryfѼ^Y1<(uK/>C7OLJ{S/ks/ݎR4܀؜,l+$J !d=l!pXO$}aH3eL"𴖐2B|?2Rz>ߪ#Ջ7Eʸ!6wT<̴@/IergjZCt/3m@dOk_V~XDlNL‹Wg!VP87#C{!peH} 3 )фDSo0=hH(Q |bֱv¿@Ei0n lWsV7kiQMer̦[Pd(;_FEM'[$=[>wL]haO(mߗ~ 6c}7X>w9-N-5lXΨmݱԵ6}v_6M_w}P OK1a#PuΎo=/Z7Ա1+fZĪ別x"UhFHiQT>3O"(vT@ `9b?kgc׫@`'WcJ pG IDATUk X]߳v {Vs™"|2 b؎fg\e}I-@:y7:E|3NMer@KɦT&nWl\vi{}QT6N5~(`FmCGP}cޏq޷ )AR\M&/%g:#$VR\D[zHawo[b1ި]Rw <{^[6eڋsnrkDQA=y5D`N6|;`~m,CCP{l!v!tgPTn b /4} 3>Yiy:m>e}%^a;ma T;m%y@nɫl=%/QnMEa{KyXo @6=2e!trv6\" 5t12t4^wDA>g/&B|*o@_Eճ|'ꐂ ?ụ>B2=(ydFkP? \4Aw~ kY.bU7z`o@"qbqQ!7ϒ͛UFt iSd ^hؼ@U#g`_i?C̋sXmw<;m XLbOD{m!(x $^7O?c_G:.~)q_ZZoߗ61VEQu*$QǢOzZIM1 G#9W!/^\m\?k\N(6Vp͝{̵ijAs4x•&\iǞ=ӅN~Ѿ}9K㺺q[Dy/s?7y^\d4jJ$3Kܲo~5Gp3_(zx9cSaιsY|MpMs{9wsn}~snsnso ߋ0b/! t;{Rf}}D |!ޱy%! BLFkg,޹ߧX'Bȍ#@|2(8)vNg"sHQHȐ~FCʽ]EHVMH jeRX8dT&=TlD{vgoGη*ai=bOjF-qLD>P/[o9/ye*Ȧl:XT&7hΦkP xOW[?R1WjT&Gn1tدaB'u}%ٸm6n~Q~3Z0|l`QX׉^nQp]̴_| C{˞Sѽs=sWιY(pYfFGQ~SG#/ZH*+EqdȦ(̓O1F|<)/#ӊY)b֣s;-@fB>1_&NHG1)gNg!퓰zRpuc׹sW7Ј'|(a9RhkL:pUD1Py= M| iϡB!i~+AۃjFBЍ@bh9#x*kߣ9[oGQ^dMāUQMw݅EQb)z FEGUU'kkӪvt}CV+ѦN0SEHgCoxG`1#GGr5u"Ҋܯc"T@e3;H7#pR7̎ )U(9`Fȼ@3}>J M^߽ZvE8'ۆnbψaULpw Est_G+OVps[Ei CoCXE|0!VĆg}/HJ1J}c]%rW8چmV;T&Ner^a''I2E0QA~`y5}bۺDk+R݋O/^a?U*vHn<9coGHķnn=gڹgߋ +&!7Ʈwe0Kl # boޏT jܽ:@@Ɨ@(_\nw"m>)""@Be8!M \g,@v6ٺxk[z ;c6R|o1ڏq/}ZW?RP|7]o dT- RY>`+X:_ou9bm]M\V_Ai{*;:Nz F7rJTQZچ+_g{sj{sz2Xƣ`imVTm>Noci 7r?46`~9D] RBO6R[ 9_mh_zB[mlPi [ެۊZ(?®!Î/s< ͈)~kgϞ"d@.k0kIwˁ-Lnr6T&w2߉\ Sr< Z{aZӭkeެBk@ysJ+[jb1-gFl+&$;7^(s'?u},A޻NDף(Z{Q EQs.\w}+(ao>b֞M'T&w>PM'/(_!#fx@XŐ2iA!Ėx@AqDRn%R>VۓP(B,])WϢ" #~NA7ST/koZV܎JP-;Uy&@rh kGSiڊ!ei:⾄>z?E`1r6;@ /B~D2/"|&}mG &wcM i>ʶSA!}8fkt 47Iho|u%ֳ{zZ:mE`[E,_ÏMtGH72ߐM'ᅨh^k;VEzߎNWG.{~l}{`ucGߐyIer}-/ ڭʎ-M5}VXp(c P7aIKfȻWv9 f΢fqHQ S^5R$yHYLG!>HqnA@PуpaY@Xs|_{f<+2ޮ/!9lDS`"LP>_f4-6v @_E*'MZ;ڜ~P;> ˼]v!d݌pvK[|Eȁ~ _%lelEm3b>mM6~khk>5G) Q#;wroڷF؁}h/Elib@}k.BΛmoZL`_/iEKMjl~vLn$y}ƺϭ2b8Gyg6C*؜M'_~^T5w ViKvCy+U{%ҿzo˭{^z2k]1}HerV![{xA{])BϏގknB B\ʿjCLЕWX8iDo#݌_"Fy BoǑ -4Btej 5HkF9Zk/`oB a Y[¼bv@{8־φ&j7ڭZ#(ё6 }fB6]GuZ FѢ-]: >rD=zV[˃P-֭EL8 J{ͯwYBP=2h8n֮L 媼D|Vص6F`Z)c!W c{<fC\tcc?RޗM'g\yt}H*K"o>hnTV&k`>{__MWz _Vf6l릡??c#zW{h3m'{@Xp#EvE 6 )["R/վ;.lĒmDnGN>O)RH^z10_AP(Be RS +c|ބ6C,NX(/Ծ0V"(oCrR| nGHAcLCll_A)ALY|"f"~2JSЂ@W)G[ zmC 94GxBi*_fsX~gDl\ߟC|3l=mbO0_'by#]L4ﷹx3MY_[f[qXF[bd\kc;6)C`[_m x?m`mTޔ^XѺ{fsE $ɦK,0g<"/+p9l6ɫ=-Kf6<Ck|a,ce\^IuG=ۈrp⢶%@Bˑ#k RdL{A6c̓ :=#TC\v5b2$^JEJWXljߌ@7 Bv"2/ C"lG }yn&R"@2 )/#~!Z, `G1t+mn6wNʸyp>lw 1/hO >'ظOюLj"RdB"r2dDf6GGL^ 9.@{Lh̗P枈ZN6p/c>)A{ckcWq} GcLKl~*m=G{fJPd,"TbmZL:BUuap|By | N62k a'ѩC/D2[֛%nͦ槵HibnC0lYgl%8RoPO!{_dbc&d":m!l'7ϞtZ>!nc'qp=eClCgd|/ky9 s#ol~syg)2>hs6 y{][/5#FS!ѰíK "B)h/h@A0!b!ɛL; "|vxkۛ}T_OGeϣC Ah8P~{+}yhKg6̮o oywr@,N^vj)G)eyY>VZi7(m(R*#.3<[NI1?HkB B)P?G+#d/f'W;U1A,˧ Pxe0Wڜ}U60(r{2̚>_(}?Kjq:Yy26au C|9Ĝ~kx}yvמ`,??nuW!i1ڱmM߇}c? 9Cq>`kmb;p鍀WO`<}RmLcٸN,r9~7C S/^9BT&C/@ tr.o4o+f>~-jx|ע/r_HV-J] kcv|juf>zX 0zRt~(O#.ļvGO@Q IDAT>N f#.o_޴+7N!Vȧ"MH(:Q<[P.zޗsbBڑ2k@ft<|siR4_>D&d;D)J&R-hĸX@ frxBMD4!}͍OQmͰdc?ܮ=2$ޘ 5}ijVn ՌI1Gq!J|\;Jv2&D"leؚM'gOM}֧O@h-1Elg/C{h/; hnB;h;9k)KDϮ߽//!0Qds}ޓ ;t@/ͮP+`2wy5K*RuWoSk^|I}cݷb.mtc+esXrwXl:55x~FmCNkFyQyk_A5wu=z"PEAιÀEQtmh>k;~2m,]s߽T&W teɮ:n0n.[}3GQ=åbygzo~YeR`u3NHD %& @ F>RIXX 4# /Fr=!/ӳg,\ILx;F`<x >N̵k@gK0yM(։@4W۵GJ|}8 !FN|_ mERtۼrÞgs_AL譾#nvLFa)ֿ=滕8A{hBκ~_SnKw02NEZN&@E # 8EbqzG0Xm){$4 } esgVunm%feI6Ȯ/CqY8%]$Ѳ;0u6t/tpmR\ PM'_o+>>M'c'B~Ɍچ@d GvMih>;E܉8Q~DB^B2 ~ T&w1zߙM'_,(h \ Q6o*>DŹacu*;B:Jd[ e/A ܟMl|iT!0gk̈́LE !3sxy"v W/yk?HA#t(`}eu;Bg%}v!sl+6|g;g"v*b.Fg%8{ABvt'd.i)/VJ"g b7gzPL۾a}{EzsRe' X,]}cu3jrT&ٷ3@_E6n6&+oKǪmYuNgFmË..ι(|*)p/( ι&; s=/w9w}8#ғXKs6 !w<077'f&o%#ZIBFSzwu!p2)dɇl˺]BHT ЁL RXx ^HAgsWR  ]m}=#܊m"G>6zoB LξQ+1F0lv8Rz޿'AYjc)Ľ_5#:?i@<蜄61NAk'Zn##66Q_eቩLS֟!: ݻXy$N6*\.E>O'߈ko`Z~Ί5kr>݀V}c]9> lq4}';CʜsOߋ(:ik R;(ιiB?g9Gό YQ=蜻=.ߎhs.]@|bhsvd8FGQ~-o˦2oި?K)G=m'#pC*(R{#s)b[N( GG-EJg$bj"b h%RBC hkE(o}ʑ⛊LcXi'B]dj>PRBvdN a,&w2!z F 4 `Ya9x"bnOidsG&3o"D(_[bm`gk" p_kZS_Z+~.0̮_ؚ# u*F })"V)s$rnf[Dsh/,DN ;D;0Hڳ9 KL\w_dm 8ClF^Tɗvlo#6WG*2l$>jv\e2s!5E*zSeFmìƺ'P|#̡/|U2oTUmga{ߪˢ3gwֆ|bq> ,꿩k>{gؖ#tr=ybE"8ty([(KȍjG vs3ss,1 䜻 1=[#_%|M M'VA>gKS R1Hw!%}b>|J _;hRؠ |2-Cn. s+z>b>*kg"|}b!% # 9|:vMvMiBjoZ=lF# ĒFB.w8|ggw TxC|s8Jk 6P~~/{ Pڱdɿڼ LC.X2tʊ?;wa:Es܇ ]XppR/֝qwnzrɭiBJ{dG>+ 8F#(䜻((o#3lQCo;&GQje.'63Hf#ePC@达a-%HyF;M(n A}} JBl"9qi-۱.d !9P)Rvw9N j!cHCz bO|Da7JDOAJt)TÑ=xtټyΡ)5LwOH=+AlXvg&76B2߷2*�=AxBo! ߎ@[`E5ȼ3z{תBlaF MB{BZ+R|K1>Gh'P&I#QU3N` whRڀ@Ωַń1~,mv®ф@,B&h8 lNjDk\h㚉 U|ﻐi"bĎ"7!@|kl D #6Tud@Y;Ͷ.ОXEH[r#2e{y >x /8C`Lgg"7}}c4~Q ÷Zp%:ϨmUKB9DQ^mcvY>g9eg}K_q\cQtO_a,AHzw#KD)z\EιQQ;Q%E] }e߂LD/t2RHGȜxPo湍OWX>Ђ6؅Ħݓ !=@֏w3Ѓn02}M}j㤯7FE&@='h׫c7XJ{}|n}G gX'XKŷDo8\isr2WNyV@(SF>tsO3_yF#am1X2ǭo>k#2ʬ8nXع!/B)#C 3hݍV$El}p +옃캫P0lB6|~`Gh_VgssqUe.bU=P!;!=KBqeO'D|bWG?#BDJBc{5y7gn*_psZ/ڰex|.h"b-]QΟ#e݂ :c~}ci.zAۥ>3ϻ/l #NLw!= )gS\\jb1P87x8Ah/x b@A,YH9 $$:\s0Vmԋteyir}Gp ߛ*"[O'QfvR~|5?FL_+r$H\5!pfQf.>AqG7+F,MIRblC``[4sqB ΣF sI[^qܣ5|F p5!xPr]lIMgja6#T&4<Һ kA`t/yhq>JY@` ўrhcA4g]oN `;γ`6C~(A XAJB)ahM'}|JI;;JF`[ ρⵛFrDW/WF:\"  |D<Y[j}cB.OY}c] 8Ο:yи|go l|`5$Yk?=ΐ] eɕLgqD,gt4RƓQj:Gv~R :18EHazw<H^riDLGzdV kB_݊֗ބ+k&vN>X|*1#!$_{"&s8"HOɪsC/Y)qHK`!PYlqͫO!&E(P`e*KVm7['M-+s/]ޯuCQϺWX#0_ETPt 9N?ayϿ*ۄ(d>y Wg~9IW/t;zuolFmC Emͥ?;ƺ ozd(=ez.d7ϧ2Gl:} f#]@&tRM;su [Ѓb_Z{kPA}bB`3m )F4+uL Aʳ$b|eý`>EmBerunc?(;~ͫ rݎ/#*ȧ>vx%|7>̽둃 WfsS6W"{m%C&D#2)fg=~>akПPRkc/2<~^Tk |%ThsQeOxS醭WaQ,߅]-ޅa5Z}*_3믰5P~4Tlg[U@uhOE`VTHl:B@T&w(VG^G/4Ӏ ]u'5uubс2@9rN/QD!c-0|Fmwhbo -j-+s? 姾Aj;|͢UT7ݫ; GzG^r@lGɦȗt֎@HQ0[؝H4#u'RCY#ؾHCLJHp ܩ4KO/B`%R|U@e ō1<.։sF :HG(4Zg!DBHE_G0n gH#pT))>bnY%>e끸q̸O]ivـ޻\u=بX}@vX_BNW/`#˄"7E{V!v9^a6bȫ~ uKKq{G)X%byJ b 5eŭ=9@ϻI.x:NΪoa_l*Q}SX_2I}z,N@{SI5{kRJGz"?dדT&7Q c{-OcP gqXoBd z\ZЧ ttB@F!Y@EH6!G DD] tKpbwk?=f)r%7dž#koHp} f/W"s3X@ȁrKL|#0ԧX@:m}WdsZa,A{#xb Wl.܉~3^Mh"81h56g!P(|@`TQek~0Uz#!uD%ppIhZNֱk6}~oD='Y^(@ho@{֎2!kGQzڷS(GwUGQUvk[vC0tIU;%wΨmچo 7/+3s.?mm(B#nE~K({,a䞋\Ye(H+]F{B؂3Ha6)(l~,R#r;z|)Hi&8iF~>FJ>W-CX`I-pjC`jľ#1> K)Q$wxj f)R#yD|@$dRc?O^6w|&!fZcsq5?Y?m(Yhq(Pf6!;ޮUesB:cyٵF^2x {##Y@B 'G<}N 9؏P&i5٘nB&%^ IDAT9l:y<:m]!֟#Ky?{WuߙuY{ocLC5}I!/@M! %\H) `Z*Lnx€LlwĔn}qa;NsT&Xl"l IIumUq))r?DaumnLT}Ģy3?\f}6`GtJ!晱9=vy":9ҳX_~f㺇tWZd5ʜjc=TI=6^>hQو?5bϸ~}׎@wFlzfI*SU-eG=J2d|}"9hV@IJiKf-Ƨ:oD KO}EH2e{6Mڛ''X5L8;{,io{˖]f띊XL;۔ʚյU'.[=渲.)+lww&M-EO{^i$k5GxB},Xۜl&ة9V /99-q>Ne@ss/l@R$bDDlBa"IuR#e2 1=Y="6H'L$]G>a]w a. !ų Ek!$dz,b98]\շ/ J:ꗇÐ%|2N5 Ǭ~i݌L!dORڐH劭m7Y9G 0 b Aא?1\kM isl\|We $=>XS݋tY@i}?mژ#s:9dX={ľ?!\ƵZa?om}7i~;/!0мK>ވ]hn7 /^cZ$<1Oռ]HeEbi"n:5 I2Uht2)ŊÁEd|>eVqSaE]:?Ӗ[Ab%Rѫ;@umU>/mph\Ǽ k[{л%{mt꛺NxʚhRY ]V)ڱ]>G {1[K*К@ll^ ^됂)v6.-#b|p$hwG'Jb&>/"dH9O` S^_MZ8N#"f {FoMӬ~'F? -1QIc2:WGҞs9EN皂RsZe<Uc?57'R{^k?Rlv"׊^#n.2G`bm]7ڎBZ`̑#`q 2x`]j}> l\#z1b+ى0dF'd$4ȿS7*[jf6>GU1a0b0&]}d?d\#Vz(Z;OC92,Cf8=gO!7LsuWSݲ5յUhXcf CDkIaյUM%BB%r4P?߈EbZFC;o|҇$R<t2~gXd[ڿ snEoF A&!ԑuzgpνi |+ܯ.feeZ~dYqιoAك:$$\mFځs'صۚl@L8"g !@ <!ZDΛ؃GlR)V6&ha܍n-#+1P>0Ơ0| C6PC/N qCay$yu"دw K~[^ !'HenC'}ڛoZ[#@Rرt<>obRBТ0SXYA % n| W,g.] Ȕclu#di{<0utf/b] `ʭEjhsl]6 t)]5Gk@4ehA7\}ss18tCLH_X{G? xGb烹춆!?lԼ'7u 0?ݧNwiK>$S*kTVCCsh8s}kX6°$TDLӟڝe?=_,RYӆޣK?sz!1R AEֽs sӂ ַ17Cs-f.6 yHY?XI(IrCkO@jR#'=-Q#9vv!Erۍ0h^^u}djKHtՆR|hGPO{pq1)`-! OBG" -g!0棈x?wHSs1d:"j`BNX)bB&QkWk8(  /F s'Q bt؋Yyh\^r°>@zO;wDJ pA{H+t1Ğ5! ^KaExCQCc2ٶ"WvMV7>VN?E6&"֕09}֜*!ɕy.9Nq{ZY 6#hcp?ps%vF̢nPut21vρh=ќ@ Hn4 L R\ )_=Eq)UhB &^s-kúY_m/_bJCJۛ|. OȭFlBPzJ^:"F!c#Iȼ2܀LT}o~MHZݏC3W,.'`{9X["`s}>AH~8vG̜hGJo@)CyoCJ-hiF|̋r=YXt!azo[mubߕ Ş=mh!폔\WX2[gm$̟sY\cQ4^B>Vu9 YF\h~82vm6NwY[&"VG:2߆@n 8/M'+_+w:/I@8ZZޡ|dF]k} 6m~_GvX'r?Ģ!6{{ ՟%L'7O@zk7\w[p}^)h6!xONA`h~vuwl+|)$OuolS<ڐЖ[ۜs}'ι2x^ʁ t0y81Lo'"w̽09Z>R#pR!eQiE'v}4"eHDlgB.ҥH!V3ֿ1#.yS`<ܳ)B1"]ӀR GEW#pE s2;MD9f#E"qe~[h}͐C 1; 6&%IG`AKoPtwr@f#WkOeA7"/cep˒.)P6h@+~,\eϝiShvnO(X? |4'# BF~e WN)ɀMIMUw_Ng~uR,P WC'N)5[{7-aC09N?osۂ s |#ͧ dɸ ՜ƇsnMA0^ < ȌιApL87Y=b()1 $`)ݕh';9ބ@bO0h鍀]R_6T=X@@'Y=||2zi$`?g&Z0DI&78H4OnW~^::jA}+2c.{wFʮH1lK38عyV+d|dNݾoB1@ h3EAd}B<@im [dvgh8SPgu!Xh~о_حN# $ބoV?ƮYX>vaB2a4ĞXN[v ãtow&O[s~Í4wCrJumU)SJHeʁVtPu/fN~_\ He"/Z:W9nb:u=~=||;ι:St|a1NFNu og>,c")C9HevH'@1j(D~6"x OI}HyEk Q eU&R{b𚭬y!0Za"o<1nF>_>gCqq"9ʺɮ ň>oL2ڽmș"t`c ֡CF!b#gUiȴ"1ih'}>XoX}lllõOftC %n//.> ʼLD*]ݺ!tE:tpKik]KAbqTf>Y?\+YLy͹wn?$v_3{}мayyb sd*CS]hz#;Mͩs'LS'{ r}q-*F@hz9^֡ŋS*k>Q؊O"7\ ~?*pں>]#O2C zf53>Ϻ|A %_?&%?Š- ֚Lvm"|xLH^) AB} ) )oZkd:qYi* lwĞ<@B琩1C'sGGwBF*70>Y)Hi8cLg pe:O'Rzb^O2~W`X:?IDS_i,3(klhl)\_k!b7 /__#y%dwFN׽l.B[ .XtA 0>q M'z}oԾоSqF G zeB)b{m ہ}OemN싀0?G <2]O|46ޗ{Pb-^10k:6"-}y~FMtxϒ֊G[*(+^9>AjvpS[ s,p#A$2v3u{M}on0 cx(L'#Zw9r!յX2f<=mv4-gxu3~dL2MkH9tG& G! 6@ El^lDf""D ) \u4C|žs2B+fdgoEMh>C9]v5sl}칭< EE/s&Wܣ:\cc_u"eTC<d2^eM`8"|?L}F/Dk2K?6"d{"Y\6ҥ.?Zt bg!'U1wڍT30sZ9„Bo_3⌝fRȎC`G1>F0gfݐb{hEh>4~h=~a$T&O[?Nd2A΢|a_#@nq%wda䕕>v_S77l`",6Ǣ- .ec_wy47>Rx~6ZbX5..\I" پwAJi2PE;u- HMtM=syve}8Sm5BJ8+u)FvAGX]"#0z_|H %: )fg=kᡂ:sv Ȅ5KG" ymLJh8-a< F ӝщf$[]@]bVu>rs3{x+{):؊cJw 9K%-!4-G e$R!"@rܘN/OkC:4 BaLJ#Vg3e85W,b"bDGy8 X1T߶z@`BSv=R2{gsyl.o&M;V$&^#P2 3rSFH[yMce ӏ7oy>XV˚ 6Nlu1R Az?o 4mB&HHeFZf,XlLzZ? [KB4XW(.2%G}9ZGoDN䀿1Qb4A ľ2Zfm阢}3N_3}Xeo{nյ7A֦dޏ0a߭.ӾC^A 2¬K:k?78Ex"tյUzڗߧjHĭEߧTl:0.*XW'KWٻr ]VC;\{)5w4w*L9m{F |fM4>+u_/8OX/% apYH dsKQI$/-o'1+\v|@,ʜ I'?Y>RD*)˯'M-m]{=練˫A{# :)ʕHV"wZ|b*J!F8*G}EٳNd_l]G%b†s@ dZfˁvHa/؊X{_@ "t1(:<:o஫[rlsYȿ(y<듥VBγdxxA4RТY)ƛ ,bB?#7}S4#-dr|2?He^#d/$RˀTf b75} O&&= zѽ vkOµGCs7#L7Sϧ/ES[Wֱs>4Ԋ >4f]/^yl^J~ūBym[4vE1,_3*:zs?)+Yڪ@ʚ'~oɱk*wh]H넅v>,m){[$յU}a\^ZW9`]wxM-[Z!nbC7sil.4hSk)-m;AKSEι&gXiI?R s9w_]>NH ;ˆe,0GN239׉TEt2YrRB@fsWdɣj@ļ8)eXFH"k6!#嬌 !U/D6.0thFÞHF4~ڑR.=B6+g#a;b/FL ik}#yhmC'GsHe#06.|X瑂oCʧI >ӈ=yZ? A>y? 4A:קYPDY"s|,*Hdg:PI2y|)7΃y=@#d-ʜ@OмL̗YY4#^Kh%~]}4Or6EX?u~?V:elѓ vp|G?*GmC zw/[s$[{ͭey ͥa} ׾ZEkյUo#1([z)yhCYy`ccסյUעS*k1+! jś#}VG\k4ڶdytՆ&96^5s`6!A\ ?v-Bk˭ιtNJX ιAO>6ײַHe~y;YqIܵ]d_B?)Dڇyȩ?OEL4)f]d%auF~x#끛?]ZRvњ_]sC^YlX}^2wDlľmDJd~\^ D^E懑" {%H-9k@BК^dJG[=H.A w~UmFKѮYN ʓ'U Vh}Xqogzy(K%RL7"a؛БvB7u]*@<2D,B%m9nCBHZ1BuT5&e jBr4Ef]bGEI7NO8L6,9"eors:XW Sڻi@YoFdFD1~1pC<žM>- .UkD#go#_24Qf Sm *#<^HeAs[]Կe/-ݡܾhx;0/k4O{?IHq^o9qٻϴv<52P7[ZŮ|nJ'K2#H[*3'r9 fڼ(.AAYTOK'9;3vSg*OEeقZ#_FQ|OGx$s"E!D:ls}ѺҮ)5յUp셓v9pE hzWNG>6h {"?O^s{MTwr+97@ v4MvgApUh_|>_fR366݀⠏t2B"̗ykD/"=)E` @doI2'!6`T~Jh*vlQ˞!!Ea9!|.RvX90QbHvEI{!͞QZ  |tcбhG^9v!3nV\p19 Gw Mi1K2udt2^HeD#ֆA^H`z]]^Mևf;xvQ?g "݈fNoB@*B'f>lul:%Hk}܊̨t(9($k_>}͟ <]4'&v]w"I28o`|$Jvh5۽J:Z.Nyu "SBsDZ_Cg|`Y ӉյU〃{1wfv*гe[QAΈz5.Bn0 hZo,/YO}y9鎏 ۨBrڬ{ ᷐~&gA0 ?I5 b9j6e")@NѼ odD*s*bs~Yȴr P=XHe&!Cd-Rb4ɽ !,AElUFE E!@gP Kx\[PD\.b* v k 襺-jDZN[Ov܉Yp,3@ e00Q4OD*^htE. zﶿ' pd[hIu{\bϳ$yY~@ki\J"=`e|o'R~(#>B D!X)7CʛE6D %RK&;>m"v dlGohD"r+3g}2<ϵD'Rw:N'RhhiaEg!_(r#N 49X$@zuumU!6},Ќڪ ߎ4F"{tstCSп 1۵p٢[b΀s^yrY]Qڭڑh h\_0fk#/q^'ؔӨNq}r=\2pݎd[tEg٩ý1`u*3ι #}2þۿuC:v'nv1 f#?ϽIH\&ɷ^Sk4ayHɬϵO b3|SO{G}sq9б}f\;HODY3cy Pk[.k#byj<ʟ@HLRm@fsq6VnOy )PG'Fk0^oaܛY3-Ж-#m b7d`~w}bNomPݏBnދlߴ~㱤'-Bl0t27;9;vcƍxmM(o @x7: K'n2/t)wE8ꑹx[ګWw*F7h;Np.#&xȗ'hvyUw1ZKhEO?+:vC"kX~F GGN!I6R6.@Ybz8onTQXX?9"mƌfv`z 8c"5/>k|,ʌF=hܓHe IDATO'b;]oD y͗69zZ5Yߔ!vC:>o޵&R6l61ő}YA5 1W][U9־O*ZbdsyM/)%k{m'8~RPT:W<0/?W,E{' %{ud+dP E? I2_`d|k!t`N"7 )EdrZ,Dc}6)|nI|bh縒0jnu0Q:{#+cwߣ$ɧg<)ju%8X\V.cЂ)vWmh!3c\Xc%qP`Y" m!w~NGB +:qA(ȌF_kVC >iWG~l;%sYӃvE,yd/Tfg`YϽ+]şXH:_He64A͞T Efh|ZK'D*]4aEJQh\ZCLq!%CՓ-OweVœ^|A;5 hi+ZR>9ABew۔ʚ >L3qң(F fd{1Zg]=ኳJ7rZvg;-΍;v.?Fp fʽ#nu.*B)L# bJD&܃L8ӑ)o'ĔY8L/%<ٌ߹Y=-vuv7F]qK!-y4Пl42܂P@K4 B>>m]>>Vn)u?iՆ@[ D̷v̟XFc}lidD*ss(XnG9:2T[ۑЏ-3~ vvKݪ8ʎ-Yܟ|ւ^&sTК2isHa>?tΔu!r_ 1_h#ڼUk_<;#S=?Xvj!vZ]:ĐM2{u1)WD*31]g H-Gʮ1"kWH`z+#<*}*[@c%)Blz14hq[3۵gvZC ׯie@+n@_ ]=R 15Qg!p!"v&:EfFldBǴj}W?ĶL"L 4xl5CY1VFWqa0 ӜSȍdD*mO"\b-'t);>)Vh"K2]-񷗯d E He"0tcX/F?sEw`i|?vٶdt$;`yaIZlּ})5|aa~}'絶\\? M>BR]sO=ޒ ++t򐗶Jol%/HD*S0CY-TfR8c;b@js R 1ZƸGʹ' kDJt5ha,+ʳ{_D Ȍ׎o!zY;'+YMV0<[{UvO=Z|۵]wG =.\CZh8b n2 F7>7jAr7g44g X2!<䭳&bkL<2Gqj@&_# ~ ;yo"6\`m*ʌkSL'4NNJ2DBB]g"}y#Vfs 0x޳6 ܸmn[t#d"y[!l\UxÆ_tww,uN4UVu83ހքJ0/w{svZ{b"UW~ów%:K n _F bn?vlNrr쏳9t݌.Xb{r_J2$vș~aeH!pҎD;YiF& ubANAi4!e8D"l-5`msv}!497cwΕZzι>OYˀzܵAq=&K'ْklH{He#sZ7tU:_HevG~>ˑ2oGڟ H9!eEJt1*EFݾ-@lĦلa7A1 @@vD~Jr`2b.yx X!eD$xS#Gf ɳ1h:rEeU8}`d" 3w1Ww[buhuC V]kVNY6[b\eu>7`c;89 e %Ԅ (14:^E}T&E #R EC  nH}}<ò$ {>3Ows"vhBlL,Z#jfGl7b1Qu[y78KѸI- Hʿ)i4VTnuB3A)z<4F,WbfɦR\yj~:wZLn21:$@R 侅̆Ƀ k6/2 -S NdV؏h?4Nڬavl22͖ ֞ńv^gw,0nĖ S(!1&Y4ߨG>+LBHCSD=Uw>=i;n~?.\,r#+Z迏xwv#K}+WZ {Mgh(/3;s b޲ιk9vfd (Wtr҇PLhe6RѪ/25[ b8<}ȴV̇!l92$}a bB[<@MHێt<{z#Wf`dhd׆t63[#p+lN(koVŬmC/mDivG@vb5 'cx+h".뻹>bϛQlunB`_"wcqs5_* Hhq~&.G~bgeɅXcXIJD,kD*;n42 #d މ'3L"6htMB&h,xyvLk&ɝL"@>6M~ RLW?-BW%@^ FS"X8T8~{|5zJMXemwY6,{;Cd}y~ܘN[n=jw7fݨ?kC_:O3?oy(\ʾwG:/pAƬ9w{w3 X*s >)IOP:YbRm6 ml:9{._Df)4JH{ۑ #ЎR{v9RǷRBwh~ drz)<[@1 |VHO!4A@ gZ[#/+9ʓ2:d m5G =A~! Kzib*r*U3ae}}ڸ72 r^ޓxO5l4Yv%Fbժ@9/B` UOkԡ(䕩Lno4Q֣qq p\* ̢sdWLk3X+tT&#J#Vϐ֏o"F'+_2cQ٧;%bETƍJqc&z9q}y6sEFgJK>܃co,Z8,C;(g~;ʐNf>][TA1Uj]K{6^P ZXx0O"2s N.CI/C f7CCό#{eȌv{_S'@!@4!8!1)'i)Ӱs?25M pWRfuYl}38(WvGӯ 6*jBqؔb"t?sj2l@"wP Lcy|u?佗7sWGGqVf_O kېo|Ю6|X[טW@`~̩!BT&w2)vo$T2c |.zw67=e@nY./-v[d+z WLBydÓϼ_G&6O/X@SȪsn4|<|997;? 9Ga ?DZ䜫B;kyƗJ6 f>^ھ1_#CpU8h1Lof< _EgRZ fh&R![ R.ڮ{ F1!.:)SDA{R3)vt+[!8b@@e$Rh::w1ĞuI|T&7 ! fRNC!f[ )j"/r+@ nLer!%ߏo8ĔԞ݊SF#"ثΗ6 PزV7ߞGku1cX 1}t PXcb)9h.2qOerS?'">(C4Nrkb4*ڱZD[{Ϭ\9_=VS\6*i׀X…Ll2n S>+KoB ιWn7;9PnD 9#ι_"a rX/s@+XoqƅT&79dɇ ~s(o(DrE06Ni?CNAJ ̶GLō: )HYr䄽);Њ~JG}![gc#C|wtNv!${޾HyFmtN!Hf+([]@ LyR_,z3@2d8(bX-"ʳ긻=k}_t2pkhu81n^qDvoa1=mȹ}.wykRĠnQ* U|w)u~D!l:m`^cCJʲ1Mwk U3xԀV8c҆VF/Cn>?I""gN!`Vn v7ƋbKC;qD(f9W"&MD}G-{#cX_MC\7뗉/v{2]G p4b#Z!eqBm?GIqxwޱ2?eYL/]_k>v5|Hs#@V={ l~5on1g>OYn)L+hk*dbvc1?g@f Ġܓ Zbϕ"Dgr[]Bs(c=M'Ner!f[ذ.6&F+0+jչ/L$rv%pubkVլv ˒{33u&LnOf!X8=\N~A =./ ,&HRېs!&uz9!V g^hXY-"U=[*6ZBOh!v+$MȬ:AnC i}tZ8jsm5Z;4!0sf=bc|k@̿!h]m .!l:Y_k@tĘ[@k{dƪB]P IDAT~buش;R[VLkD2BDȯnO[[{NF`u|lX0.'tIȷ^t SDrqc&~h]%]ebLnGh7ܳ( #FaV@2d;(Z(IG -G~%'Js[!1MD1kUHnYxlDR{#@0j4S&o#VY xn!1z)!Vp _^ݭa^X} _۽Chhzh[꯰z߈\O֏+n!+W`:(;Br_?Z޽Jwb6sG#E9ک?i;x-#ݿdxk=ΫYBw|Q܄.2+.U4:\ B/p]%_(X*+GlN4i' 2݈;"G`9MNoi.RDA:OCWHD5;7}]y8®]r_=N~A檗3M7dj@P>ԞsO6ꉂB{[o]hZ̼H qklf_du 2Î<[fߏD`j_'Z>Z$&r#,T#Ós(??pbEVv|pz! G=xȧmGxX)^v~ UZ=@$ ;oZ1 nEZs1|aÀ>VR}W.ޕ/1SFXiE]%]_ zפ톬CT& 1Eh+m̸x*{0Rd3b#@+Hqo'P$r)>l-E MzEx)- &~(D=V!/pC{5T lqtT&whB^+-N.g#p[Cb}1PpӰ{VY˭][YG`9A03B;/As4bFYO@ׁDZաY{l];K{RXXX#XTM'_g%mgٱì\aVߩt5<.\_ذϺa WWh/sl6c M'B |RѾN_.YlPX*;W#M&=;~7wF+ . qނL=3q>(1Y{@vtF@>dCEK&d6ZoؐrA;5RjJeЕ0{">oj]}7+"+EfP[d(vKerutT& KX7Lergf2NB`0{pc߯DbRX1)}$@OF"k5po!n(6«!CmkЄZ3};rظ"/S y]$01EM}L`wS;\Rhη>?~p;zE OY&|@]׶'\]zD_zҪomio˱1I}1|ZZg h|~:^;{Z -Q#E\#F1ݣg.F)X #=<JwԂ@v{O&=)b+C_vFl3qۛop4^B%(LG>mh^LnkYoXbui!."P!+h@>):վ#߿BqZϳMB9,'}GF,L]̡Evjq5d!s?CPc.V΀X1{`6\ /INEȕ3g "jsP'\A{}YŐPd\:xφű7~7o݌. Wwu"r"Ո_ƻ$|n柟yvΝwU@9wp~k\~;7 {#sI4;^^;-K8tZyϻ6٠X6| xJsE*8N^o Mp?$ &"0PV *mQѿ ю7P@0pSvHFLv0p`x I+vP6C8RD-Da4r #=̐W7[=Mer1&^+=b@%J!4 C+ RY@d8DLOH~ rgWXyAӂ6I|/DX@!)P+7m[4q=b +6*_V碕j %6d\`s;u`~snX-NEmι^h9{?zuΝYčDv WWglLAT&W4wf:Her#E 2\6\LLAE4'V IHg%1,1>gAXSGl>&"&m,R!|;b?؟UHYEJ(& "vCc(n@IZG ȱOMk|sN/!e@aW'bN6胀TRB~|o~~[{,sߩ%2݊Xz fĽ!wVWR\^[Qn̕tr}Kd{6tLAfY= ׉pu5h,wemT.0ȍhA]{M<F'L\/v:K`@Mhn?9&9N?}@89'8hf'Vz51/6u|7&` 鋶GEtiG1E((_RqBz"@eI" Hi}. KH1`"E6Qܪy~9bglx݋@Xo!uD%A n> #G*vSm^D</DQ+Hj;!%z3!+B+qڪ׷QMCcP };՛7yu>@a6DEvm{#Sȏp&q[]cϡmm%Jhl);#?Ϟ%:=N#C(0+2cW`B6,č(A~tڦ}kI]vIH݈X`jm]=pu r,c%qnI1Ry7-iAfD Es Wm}n.钏!k|ĂXot83Q6uh@8V?s"wТ/l@د)eD)l:0whNDlE0Ul熈G!?)ޭP̱[DVbVn,jzH퐦 6%R2ED7XGRTg#mZ @Qϲa?G#pSGmFXlǎB-s~P2=xO"4{7pu"ːb3#bQU0 4֚/l[]0U/DnXY~p=nEN+.ƲdnusҢ h .3Pt'sgysn{ k&{<&KsssٿV\-v]_`f.s KA6H j]ʦa]b>1T&w0JUDyނf:WkbsQB0^=F3+VKQȐ.4pK cц=:n$ gDDijGfwd:i~{)ğ VV׻{ N:[ \ƅ{2!`[ъ% 6ބ[L;>A`74 P@+D/>9qqkxڧ'SO\ؾwu{Y[ 6RKuGcdz+tGr'n{ oy֎r @+f +ӯB}RNĂ[>?N60\K1w]ypWԲ,~ l YlE˂Xuix<5^\i~p+z_4]%J.F4\ -$;ͻ\ 7ms-vg/ax;՜/"2َLl@,N6Aʾ\sQ#e9)sYdX6A*4!5 zDqO7;sSUDgjsW)vn[hW߹<| x `9;18bފNJW6/v9q>f 2B&sP(Da Yi$パ`cK@Po4Fw"w,|'KL;X9zZ;߉̕Ev|?ƀ!h|ˎhGЎV`b NfmFREh8Nl:Vm:+v{U-k,hjC W7^Xe%ۏ{󒒟4*.jY?ގoWڌ_ b.7 =2YnbHֆ]chv<~ )hve{%ڈ>vZ.lБ&|+Rw;3侎|A}!{X=Hvl,nR~]]v{^c@Qv,E[/[%bCX }y"abq bМM'LerB' ,v KK0މ 5"f xYY? FdZ 2k2]#=Q+g oEv{+0VcR%bt1ͦ2}yS6|8ɍ qtat{=01]IerO}3z- WX!MKz-C,F>4v}9kddk[uI?P.V0toeMhJ4oniEQ[7# >1 {?ぇIymuɺˆY9<% !!U@PŁT&W|ê )82뼁|Ay |JszymE{k@N0ocE&X)HVfsU K[=bBu!V'ؘ; v=&vnNs2RL'2s22 D73n} \P["_c͵ fP7=A <&d|}e8(v])bFZisҊKe}ooJer5tqb-, @*;-|.%3[=7ve3| \0߇ 2e )庀0mSpuOG. {Uoj^n,.d}WG;~e2Jll8NIerT&W M'"ߔs-E27߰d=HI솔$Ў@7zHIh$(_=C r]PAo({c<؎=v.N9uLwg(߾YMjA>9c 2Nw@l8F")2OX@gmHYXc?chwdw}2+$b3μvTEO[ތף}߾1W! :S*xf+ZۮFYvͦgSYv֨CdА[uCz_-4 cܣ-t! [ puuy^Us=[NڐHE7.(oshi%G#:M5f[d pEÌGAMKm+.RL!ttrq6|$N*8\ |Ler?2Pϳ5Z߉j菔w9=15Dݬl$r@vj j\GWAokGC.Ǖ!:Ԯ{AfP،Y>V-)/V`M'BqX66E;O7'JP;\/?ehWY@~U{ И}9gdžZاmɈU<1WQHA-VQA"NΦ #froXkD69Չj,28OZ|VE 5&l:B6|cpuHOPqw?be8L3K&л 5~MLA h\T%\]+$\hau,'s Wl*<~\]pu%8ꎴ2U/mO[<>vIt(il:N,f]z T&wRg#hU(R x5B&DyA̧G[0)3|3;F /%NJerO shT`>zrpKXi: co qmbOXfƎDApDk*C|23ހѝ(2bڣڤ(n/o S "|:ZJd|Y04n@d4n?Q/g_ 9ZƧQ'x0 +BB7%" W7uN mYZ֯"ׁ}Yxs!@xT kи}N]2_ke)ED\e/XHJcs~mؼ/]FkPRhxu6:_8.D~ou)Rmh2R̳ #gd41i `7ŽSЮL1'"h; YBl].(C>XBO b˦2=ЮRiwG6 jf˵D#PwL 6"vbOv'bBj;/={S+>v]wY6 '"bZͰ2?L1ĤB?k }OE d:ޞqC6)IUM'2ZMt::T&Wu C=Vҫ}l|%' -Gܫ~I6F܇Q>Y+,mo-bP_@,Oؐ[|wD˿9>w13m Du "o g-.<=~ۑ /޿fL?a|ĽkQsn97{/?Q|mQ1bkNHerLnlC`9ڂ(-vI/R; ҂v #a Zga՞ݎ@CMY/z%Ĵ3!6).o{vW 7`񴲲f D;캕:|(T&w !vGUptg![VrE@g;=\} 2Xj{sEW UbV.u -<#0LPH~i;!G"tIrST&77 +Kz=a WW'!tj`81ߥ)z":]@5y}ؒ䆧2q[!}8ޏȾhK19#8/'6OYb%zQrν?$ l}6~|A72H14'#=D>Ҁ{Meh+E]XhK:מ Znj׼KoiWBz#pi*e=ۺyO) o}ۑB؁#Px wg#Մ&Yxs2!-rKFMd(%`d+kvQ|2sGX3vFP@xXmJrmVv+A%EKKV.-+YsĦ@2no53d>ȕ/m'h7o Ln;VG`׾ 4wX00kbļ-bIP{r5z/A-)68׆`/"}=Rb! T` x3[ݡItk[G Mao2e P6Kk***ѼՏUS}Y}2}Y%Sl{9sns9/sνhK5ӝs9:^v1ιs/8tmQpU8nwνsN۵ιs:.*Þ9otνyÜseι/8':6y9w釔}%\s.9snsr;cok u~g9X5+c*˙XwDlȟ`@+Ȍ}3-y+` 4 2[Vum&ƅhR-"k;#-e;"fko_/ E2zz[X9f"0-EʦͩL.leT&WQtS\\GN"b4nD$OB5C)}p]بHނv$/_}؅Wȱ9}Mj!3r!Bl:y;d2[Woi]!-[-YT[-l:^IEy+xŅfB@hBljq aQLp!=}+ִhkgޑMЂ'zjvG@imNy?X8R;{cs}-?J~s;Fo-p{? Kuιnh9X+&;惙9OvUpZgNd[rVNJer;"1͂oERi#0Պz D V#0|Tzi b Fp&FX ڮ&bpLn?4mbƠAD' i%b@)trQ*k((yx*}1S#(4܇LwpNK4KcM9TRaߛ(^[X Őd*=1Nα)Fdz0bw+צ29ļ=M$.zoҙTffC67(tʹ!ɟ1RwTEl7}mǜ"`|)zg:~cW6׾y&Ә~kX #ā,~^kWD};fٴlz!w5w'F`۱>n:pA],]tlZUnvFC{%;^Dcu g7f;h.[s!Pqg+ιi;1-$?[G؂{_{@Gs!2-gvEQL&W8齟2@@+@u6\\xP &Ifw4 19oW#jB-!^H q4kG hz"%+hѪ=OB0)loW]8( Rj bY#w2!hG& E&476E#ߨ!P  Ռ0L_D1vmI՝Q'׍(:fL÷}}鿟?6dC"#Lw nz7b#z_Jmv]l0Njv⧐ӑIT72޷!6m*if*_tv8z74zsU 73E`~Op` ;AI99@lhR-?{Fg,py?jWι>9w#>ɦt Bzo*!mM'C(N =YKZ+0S1u? xW̐O#GLA,"Ҧ!S&VHDOt,Dqlض {]3)͛oNpQO#'4Tjض k i!fYYBl7 =kb-VLSVWWXvF#[3FQ"V2ɶk"4)`aZ2l(Rk/x|E6q<` W|Tq?}mSw v+G]0/YhbzqLTQ>a"&(C?8L ȝPT՞ CԄ3K"`Pie@ x\^ҕhaL{K>gޯF1$H;$ک!Ǒh!d2ҏ8$ J ˝s}Y7f^0 DIr`w΍뫝sE~9W뜫8{_YjkKu+0#N^}Yl:y6[=*+ga8RJP'vz)V:yf/fh~Le#:^F<4o)t;y*Bl:; nmHBԝcD𢡄`5(xjV5,S1W0bFQG!xoEȴw!.mccͭˊ[÷gG.RDSoZ7kMbr  /e?(tLdgɛmdGlՎD;R]Gkhj* )YVlw 4bxUːIB6!xa׆ݫnȬ4Y֗ٽz'gɹG؎h/2Mf/zwZ E6.ʯz[{C̗iΒUY9J6jo\KgQrƻyuyh r x%=]]K<.r]XkXGChVmԅ#WS;5?%*~ =)}и(͔?Eyء~frm͗;;'Z}3!㈭b&\cg1v(+p'4nPbL@{6<!QBl@E6|t?{&WUϙٝ-f{ A t"E (XA "6)4wB'&oe=Y7$t"}}vSs{osIqN4nzn8P@]hH ߑO]7 ml3&hVF0n7"XޕC6t]yYљ+4I  ZY!Wxd@ܷ=cec"<[s:; P`T!\@ h"rSp#Λ 0րi[3KTK E[ʇVv4E,]?Z||vlRm1|^ַy@_w\=k31k_um̠{)h]h^[]]| pk)Z(3W%l#)$0*we"hJ%b3I<2m[/)Zȷv[蟻݄#Y|_n޲ڇI464뼚? UCk^,Nloh~j7gmNy*VR_m_>.o&*N7ɆlAk(7@h̅*; Ex3k+b2' ϳӫ6zS/GGhwkݾgmk@`dY]ע:q'`13k[B5 4=ίرq V9he]V]28`-Rg-#ۃ?್ @A>OM<1WE3{EnzC57OJ dz'<œ_ŁKGdWo~'2߮-\M3_4h@b fvm/-'j׾bpSĎk'v ,M2oPfs|5MhS 8 Ƴ!Dcܳ9kjx 6hqy<˯w<611?>'CO@mdصi6y987b2Kݳ -@efϗ4Uݢ~FX1i.gRv}܁+ʶXLi昦YU󭁷Il}QIJI98?L[NľxѸ&F+LgdrhR&{т+^&"{vf1냟$z(" ?+#޵a RnTj^Elݮ=SXoy^k6H]ʳȊ$d IDAT4 gXYսb&@P?3' -tCyv]jvb=l GlJ+vwe hyORXK<>{ ̉y KI<>XJfS7.-Ӳ`յu[o7T"ֆ T:;+M72W333ck[kt;W/,%rGYYm1DEcizg^EkhLX6ZuO;?цɜl =RbxN'ڰz,XO6vfrlِ4)h q RX!ԵPcnMxG#Vn)t(53Q0ELfyFmD[KRMNIx2%`( Zbhqv#Σ- Ͼ*4E*a9;m&?!pKnghM̞a5rV׺s=>z 1k6y'fy);֕ن [ذo՝8 :][M{;-;qw^v܊$BhM} ' .'w.V9rCvSxXa*/,n 慏] ۳auŎ =%PQmM[gΈ?Z >]<2X6Wh|JbYDt!'"<$7v 0K4m>wVUE}%X^wmX?PV0/G/#BU,7z~ D<>˺;Ҏ&9h❋&~0W/k6GZ'i ƻkkфـK}J-G;~k4vsuH1j+H1b|޿ѤQw hQ#sd^ Ը{y W#Mo#051tٶ H=x&b@,uiqu_݇hq}-H{t-bj"$JAw|9<8wSwz^d7`dGvhlu!ӄpz7|Mְ.@;E~^qm"#wD s@>R14@ 5[:dXPF&X,֝<}ݮ,/)hN bdm41ܹ;PmYXgqp!~*s8{l1Ŏ QGi^}cc= 8k~*|16ew;ZQ.2Iޒ|bO'l| l8 1{q `z c_Ay[PLbhl#h!D&5u} 2Ke/MŢN MXs΂ mZPow9 Sb̲s8W𒍯Fl3l dmtFd&Q1+vBh%׌@bvR~5/U/91ގPW U଍v\>n$LMFTTO"fwlzov@n.PO%bO#`JKJ3Аo \St`Lrхk^ 4p>(<1¼@en۶h8p"{G#Hم7/ p19}FpV.4EL0za?<Ƙmf ͕'S ݼ1[!r4a-GI>BT"KgM5OAv Dx2=MST"N<> Mu+*XT*KkϢ}6I+ ,{((kMz& 닯<ֵ!Jvcx`r@:E̋ZvE`q1{yݕ-2Q_=8{Cz&.@ob hU![=dӎγy- 14&RX>Lߕ-0}?(L+\ $dmj}{l.YC͌4Z̎Dq?kw fW*bg{SY?1OB…X4Lh<^wlCr}^,>϶crh W~v F8?bgs[VKSJ{E;JT1'δ>iU"N~c+g$_(  3>ǡK-aUv4=cR}}d,LGRyn֠ɻXyoH7aN-w6Z `-VOq'Rލ^wzBDoELڠ@5haGu!ngy⏈i(+L[|TK h-s㧰.XMƢqҟu֣YKd $Tc^C@S >᮹?s1#;Jlg+_8 z3QKb|fCv\hb`_F)ᆱ;m#`Ў&l@~vFhg4~GA/ge-pcAMZ&ѭ\;W"Qhʠ8;}#{ovtlvm\E6]"P֊( S79w`rOw(Eȵ][F+R]d< :Wv-ޢ8@/|Έ| ij5<6M݃l?@Q?b~+67ވDRf"#*R_p5=yÝ1d ߈'_PhCkd.F*{zK{zo:mvm:ѡ$e[V6Ϭ .m1Uh^Z{>Xko1Ƽց1YkZ&^V3Y8;@ ,@EXz@A[?'e+~0R1GoW45Edk>FĖ-B[l(bnr?];}螏v)bƢM~5x^lT1wz.CCBqRsx'=\@Ym rOD*dn\X]ؓtxf{؅])υtT\=8?b2!{4} mи̻ u = ' pU|6=1hDRQfmyfo D4US+ mKBa//zOiFsqi@}?m ?'6chh0n} GOXk1ƘݬO9cX]ko1v>#)(.lAu"KsK[<ҝ_ tB  x EXoB?"%ܛ"OKZoCNudږ}>x]Â&"uA8[dt׭E%3Aޮ?/ s RaVk!@O*k'ߍ'GFw!q;,@QLXC@Z'KoH.Oيd=@,m"q{ĕu!bp#uEν>(w MK+P(^ڴ ҇\8*3L xxv"kϫ3Luj&hni9ZNB^0G[x(^ʩȶyjf-G4'<; w'_E)T">ƫœ`4H%tdE0lSOܾ[؁ ѹ{.L kwg-z#?9c  4Ff@/]U{cC{i[mwn5u-Z lxc5_9KF\цiQ5=wߩXk_F'?G|>E=ؾ7'ӗ!6'&3/E7yHz&W., !P.)9h8G`r ןN"lޭA[r}1suh"Y}a2{?CH#W'tzV",Gk=0rqbOC,-<okB*)>wMrWn ,A_# puߝJ dN9`qyVF_y.\cEW^bhmrLzDƈ${W6="467(wF39xΊefWysMpת׬zhL TwXK5AZk CFc]І5-6ۤAX|ſQ?dӐ/\d[]ЈT"qBj¹(BY(҅@ "ԿJĎ'ӗsy 9jwHb{Db2@vuϦ1r4"sc lvs3lk-@HM#bXzHݰ?>y{RNp Zp~w<2:ٴu""`s0osD1 Qٻ-sm6:F![[]r4bvq/J%bLo$vS_ Zx;v>[ϗDLlc=q@3y/N-觾Ǔ}_sD߽.4:OR4}bHWaāM]+96;>> L$_H)1bQ]PۡUr!fjTd:TLE _e9l@HdurG"C=#P|[k׭GC@ ޓш { rµwKh_ miFLbWDFc]B\|Fl<Mcۺ<9/dx "<B|\9셀߹(bw0ק2dO|\;m> |~710 k6t[uyiKsM%b84DLf7u~&׿ήw*+!5YH>6sMYûԆ5rXs-N`zFo_lJRb<.O%bNēSAދŽ_ >$dBÈrJ+ ،Tu(lís0b@e굈b"݈ԋ^appY\]}ןoB"ZEXw`Wofq˹.ރ]?!F@mo6A6g f@^K-n1*$DLf3\Ջ^FІ'='5|kw + ^ yZVq- `&œ` f4<[; -X拏*IIRb|7=J@hM%bOœK-Rq ݆QZ@yYs#p:Ƶݵ5\~XSs1U"nqeԤ2[^H%~~G=.boB GhB^T7#Ylrj@`p-br}R/0bAx2?P6Dv1\{-fQȋ >h.\]v˳N\/ wކ' 2bdumT?)Ym6έ@ӿ_ymP^i5𽬍>O fRͳ/O%)&!% ˃h۱[6mO+h"[&֗RX"L߆vȠ>"hrG@b_ļ=@L,uG#lU"0b&#psR-6\9- Ҹv_Tryy6vIzR{8~vms׶ݱfw&A/چr~wEq_ww/(NyFP2uoy ~T]{ ໟ{Vw:Fv0$;{U:!6PY_D +Yl IDATL:mxp>8eٵx]I%b6b2ɑ陰em2k$|1И#2%xەOyo[wS)[Ac2+r6?hSI>RbJ.^da<|o#&K s1< !wodt5(\N*Gsu!O `/`eܕ5RNZiϷymQm6=r wT!yAﴗ^eF6 #]1W"k9x Ļ<ǹw>nj ޙk ;W}^P>*kߕ-[ ylsĜTgFEo8KM5m3}#X 10#k~XMIlZO1<Z`cg|>@֦6t'T7g5ǁڗ6P'cn=#6}>I%bn<> XJtbcRجx2=\gXy"cZ reͷEvÀ10b*yhRCތjOt;R#g Ӯ/9b:vb"y`n Fܵ{!D-!`dc\݃F g/b)־o֊O@Cų>jAm>ē֩Ds*{וcpR-q,F#=:q7Qi>z!qfA%uC@Z8T|B .\'}b>(HUybHئ~/k>G` ytT'(?8dbNA[Cuz6k^>t}X2Sp$T!Wrĺu"w߁8U 9wR/dm>b25$b2e}fmnǟ@9| 5QEKV[XsrK 1ZXSƘkH%^`9Z7CƘ!HK3֕{R'kXk/cDYkc^Cӭ@=($4^j1];Qi=s"ZGT;B=1?Hi'[k2Qt={ku='b6\ ^ν2uyd"fcgFJ }xyON%brc<@Sh6]Jvt[S\#ۍHm"ŃNk+5KQhC\YMv/ w}Ν_쾇&Gxז@E,Vrd6 <0qϥO%bœوe;?YMhcG#@ں1Z9p};HeO/Ut6U-t[ggwh%ٴ$b2xuR=1 6eWOQ>2kmt7B w_b@PI>֮1@FFmНc&p -Ey#g 1FJΕ[kZkOrmay؝;w1C9Mz~`s[kaƘ{>5Ykw46M13Ƙ=7ds$ĎE@P19lc */wu6myO\D<fKՅ<!ȎyP^phېWԯ:K+}yse+k-uF9 #囮!p8R=QB" Nn.F/{`OA*\95@ @[ko?ԧkmavOpm@֑lœ;v1%/YLwn*b2c녁Z9Rg1)]~*$J<Nvǿf9м; ́6܋6 {ۯXk @1fc^Ƙ?zW= ܳpc@#&c,|ǭ ;#h-edfe1&עuo>Zkg6b13շQRbRR|xo[S<֏a~-z~Ѐ[@pnEc+P#3r/GEpys]hUhF ވ6~Ąsޝ@4"ԯ) Jw9Drd=_Ǐv6ǻz稝JȅtEw"H[(ּ-|磝S{A4 cT@{"۱O89LФ2ǽG%y@? TwY}7b2ǻs= rj44qƶ#L9W ڵ\6@%&/wWcUڗ1iZڼ1fG0xgO=/0ƄvSu=ޭurck]OFk;#L?(%lӗzbç]= ̵Y]?Lr-=7n~V6Um>\=%о v b@sM+bqcL kcA@ n`c4/>!]qd!y8sc־>׿Ƶi^wEӍH{;ނ4Ӗ31ZksƘ-{80~#Q{ xr_OcśXI(M#k4hV#,[oX+d?[b9c&A`?|`څ# vF  y1G\F#i!7B (dl}]{=jLpdP9ۈB@@6];3\kg9lڵs-M ,A >r79'Adߗ`>e_CQ(RUgXHˑqhQ6w؆LxFu[ϊ#phºP˜J% /Rވl5p̲+=x&%VhkƘWutݎ2h3 GchSy9cOܷ^TFcOƘڌ7gvc۞C׺> 1<|c}Fzc OW V\T/TROXLH%bO/CzI,:LW"0WS@ Fq@R(Ek^1/FY zD}UZ}7v'R9ڔsmntmj@\pmwݵ^ |㮟 [mku(]R̃)[ӑ>4܏WxQp[@& RT"y0x2=y,SF>h1btW+^Zr_IϏï{e0n~'ˣ6z_ P(/TUXۦ-,nٿ;ڱRquoQQݶ㔃}pK،MZ1v#a5$_)1bxs6ç5܏@'9\_ R b|[75yA*}]2# (o}*y)}@l٢YZz,z/^-|栝&.zY}/#md+peUwW:yvA>6s[Ŋ DxL\de%I'$'/{c֧2I%bK%dzڽ>;XJwФ,' H}7 {0o";y_wD\wLSF P=9֕ |W r8D@, ȓg j8rKzjyҳժC i BL_е>׆@@k'îUfRQMCre mK*_mU*|{/u,uaN@ /h˯ +ժCŷW [\ٲ|'`ݚm-:_dMa#Ex6B"&s"/7$HI59g? #{%yMx喣UdK֊1X@AlUi]pr@yqVLr17ɵ@e?Gem7pBc'3E]]4bR=0 . إׅv3!0^\yЮ-R^q_ݓo M69ٕэo%)II6NJW8YG:MӐbR,ʉFyxvS#FH YVb&Ţhw #cx;?wM1 F9*ܓ Q[kg+c7g߸`\uE _[(cKFg61=bt׵ ݳ<ZMWnȶ:boTOc ŪF"CIG 0p䟩Dx2d;~vd_v ~ɒHRM{^Fd$fmsܓvO\b݇ m|4zl]/Q6hLOй:dzg~VY}kC/䓓D[h⭩D]. ALN1H#{K΋4ߎgeܽtǼ<#P`dZ;-`*wlN%bWǓo"Z: v(t#[;:ԧ6YhaZU+x"0&L\"gk{n۹{%a @))rĂ10lL@໌S`hynz}9XF[#&sW>Q;>,Ecc;T<"&l 1)Y}/EIJROWJ@lx222=OD(cdrtpZԌTuel(*w]5C ! ?~=2*;S"Rpz1Fqzw9ȃTY ߄:@N 9*@Ht-2zAl@Dk9"ގ܊z9I%5D'_'g|FU?) ?WOˋ]3a 1GLf`aF]oK)֔.۫sey -ֈ׷pgFseU!FnkJ:{YhRg^:RYzE$6wL yܥ؄پI*^袅wDSJP"Z,By/e߈'ϡ}NzkGFpin :ׄ*.{`~mp ZF1úF^x]7Vl4{,IIJ)K }%U\=565N*œ<7T./}b6 ۬0_-hQH5ӆ[XBX#٩#*KG,ս#Kwx2hB /]A7#ǵ6\{Fr (Ey5rjd$WkSX+pGdDgx2}k߳8oRO :r`;rLXDLf;㿵qSvPᏠ1mzNB "cA|d?˅njoY]1mJʕkЈTOxɸm~Rl~{4QuY0p].`OvsLÂ}rBsyo6j^D1J%)g#=GK J*5pzذڍad,LOd@ R-r߫Xj gp e4y9.=w&fǕSl"hQ+ U(ZJnDK-B g~o91WD,C#b߭{kn޲oXgHhzE.߷zNY'ָ~C <>O; &rʗ:@d)LuypͨI[s@v2(ƁA_0؂u7LË ưܽAd c0!ϦB| \P '!y5~%bD(7H!v;r ܱ՚C}WAH\pX%A $e_MFB^|;Y- k uX^^c;8ϡgyD drQ?hH_6M/Ol^^rHhDr{Qw]|m}Cźc>\* \2wƝWo|i*{mz~u%bV[|AqjgϠ_trW[OrzacӔt~ka܁gӋ%(Lf6bA#s9rrԤt"KTF"e7$ƻE9UYU-ԻǏCUZuH|d+u@NHH_!QPEKPaPw>`GNu Dӑ؈Ǐ \+\Jr=UZzMlzD L Zz޻'bG{UVݳa5%P[ )A윌y3V?2΄V׿jpKD)Lɠ8?L AqKſ$_.\W{dr}6!D5끩V!we=kk~iJskQ0!Ϧ|)cM-Xv}J$"D?* ӃFUANZ?<51B -a5K=VqC7|<%@7D9e9$dNH܏BsPH3Lw_Qئ]Ar5[C. *8/MX&g~C&W 7?Fr`iKrt¬׶.+>eܢ8<<\'ݡcH]ݖ4Tm0u|)L.*/lpt"}_Oʼnӑfg~к#y}75}vۀǃp=*aP]s;_Ź-lzf%_y qqC,z(ork tӈc(_oʂ3A6 (?g$vvE$UGCg#1'ލB-(,?4 r?.D($nABBh7Yl2+|*Mlf)\ag$TFb2N~A W!,yƃ8Ꙍ'?g3 3r3;ӒϦɠXAEg|K9֢(坮=6r鞣&υ$KadP<A#٥︮v_y1aiBAqvr>_]e;USC LBTQ85lCnHU~}9rnF"+˵g?C3\wUk~] Dw@m.qHpͮٽ7$6(i&r8B ܵ,އBa :?FYŭ/P@7뻀 Y_oj aW]Sx]rHXX3 H |cir GNip1ѾmrIUKaU<}-EɠxsPkQma;8dr\7ſP S,8!H Ǎh8Q֮(~T}#K$BbnOt#ܱh6m;D f3@7y$ڐKӑX{CPPgK)ח 7xKu=*47fq+zoV-ɫ1+1_sO3 @,i(s(y"z[!q#7$AdPYAa²R*ե0=t$g()0Q сݚ&mnL1ZDqH(@Tw! Hn 7ifG< =kA!}Dq(,Ǡnv|73 D1w !89p~ҹ膷?h{{0sa;MNņ0e\!4 3mJJ\/# p&rCWQwE(e w?"G!@=O48(wEm&r H4z$uLCЦֹ>"Oi,%pT$$ S\b\͚8Xܺ٭ޯN=srPEkՅֲ P^ȽGnb?h-6ͮŹ6;L{P S5?ߚ NC.vbal%8qp?88$MBnUϦFyBWBrFRqt+SQn^&ށ\9n[PSwt$NB(v rbHL]W]H1Oqkek$ g*⾃zd'wUkO0o 9c]U;'+=G.ޛ~v:o\6]LՇ"8qr}'QEDwqh {uË<S>^. \arggdr$'3H U}ԕ{z9(x-r: hjGm;~ZC$ރ<7!GX(^w;Hw4?sʋZ Gnj^ۨs-iC 98 +W'WsP8Tq Q.D+ D]c@SswM$Z+ꎹ;;Hӆ^l|;\;mc1D#fl)ȁ=('5NYH|Qݔh ~.)^;sHLFj\7 ;ESyNCBɏI=,r#ryo:x/<\$H~ {SXxA)H~Ag3jp[.\u:wzφ}9a" `&Wxa^n;ya#flKjZ/ܡD$FEN\t#O* CBTt.󣅪hjz$v&qx#Tc %=1~龾D w~: ܱ~ſ!׃~Sq{?vc> 9qJ>t6frA!e'c7僭"VF{L,ilT(nB9^č$Vy|`C0fO0 ÄY\FtӽJ75,V2 bL@'@aDߏʋϦo0 ,#fz} %lϦAIqt?D"6AZV@"9d&ގKy_YFW}ˍƮcPe>qe rfr{Y; z')\-ޑ. _lmA>ȩ2Z#еz&<K"1ݎD2DkZDq 0-`BBA^y} GuwCN>@Z]uH5%PwMQ *h9OskBC$@ jYw?YD(FN$Dغ ~*3o ȵhJJ_b&!jD6\)(;1VsE!c(kqǫucȉ<X>^haaDXhxMdrյ( : -ݐ8Սm("jr.ނB@XBiIwl?Ff}(l5Q)և: ^wjd fs݁h\R̝KOkJlխ'+LGªUKN!>Wj&~>\5!g7nڣhnEٙ\!aZL 7x"at wKnCbi7{[TMKZ\|rOԶK/lHq]sMHu"'.N>}|UϦoe⚽ރgJ GZֵ 6xCӗ7'#GlZa`IX܌cPoڋU='D=vFf"tQ?_#ЁZ$ B.DHL -!AՉ$ m@oV\DaBbleՒp=H\[}eoۋ^Hާ9H *;cO鿢B53 X 5Ϧ{2ǁ|6}{L:tSnbp>A #1MY*F#Kk汾{#HvUuWY?A.R$Gm7Բ9p12n9_mN G=*elCUhzDMskՓ>䪽9+T:waDXhxdrz,ɺn'Uf{k>^@ag_!!, 7 Aԇ\(4cg n!~5_#h{ǁ 9sQݨNh:#)p˽hMzJX ߝ@mBHo!PQ>]6 0 ,4ilGG3B ݱaG"u(49{ AƐ; Ў" Kv r| pǐvZ} +Pk+x(2BK&WXN|8drFNQԷh'Z$b۽hA3Q~o* C=8ia8L[NT%Qv-ww :׏aP!~TS$ Z3㛄USND9Xe$~UCUHsܯ|6P!Ψg'P. mW*PCx3 Qn9Q TǢ߈Nxgi${׌.2۝Cz(zg 0/L[kt+pc&Why!9cD @|@A]ǹm{i9^3 cVn–Bw}<ʽzFB*7[3Lp9v+x 9H>fRdr=PF~uC+}Uـ#i?GkR;{cx}&Wx*;#oaưc9bV%+QQo#au=_D:$A""7~խ%+JH4=D[$ޖh7N[(0?Er$#M迈(|6}ﶸN0 46kэzKur~Ϧ\dԙ'姐tO~-E7I(Ϧ_pSYYj$L+ݿ1}ac6'8e`Upr#GB( N 9' c]ԙH 10MФUg>kG2G7*Fb $P/wߓjJT .*Y gs_D!=f j< IDATOa/iIV2xT5$ غZukKgEDk$tMD}~8$FbxG_G} 3b 033Ql=+nu(L: Bbi JrNCB-HNfuM>DbGX_1LԳT1QuMۋhX"R%X1ܤPhoQ+PPs*]n??;܂EiTB"+^$ kO$zPs Pc(vOoyO\^#H( ?"Hh:P|$b}7t]נ|6=RD!ݥ0UylG)L]6l'g61cq_7H(dNA7H}%7}PnD 17pt P^"N!G ʽ ׳H lj +6vUƻ >1|6',+8Y>^;D= 5.?ӥ0=]Aa 3\2po>.4r!UnяCmH %w?7#a;oP^H̅uz8lwGvQ'tF,.DS}]H#a:,]D\Reiy}77i?k3ύlݫ; dP֖Ja{al+Lے rnBpatw9X>)"1Њr#iT!+ Y;UA-.@HDP^tVLA"}k |)|yo<@[/saaPA, tKɠHE?0F#f\.(GMmDYG uH`fQ P 9$NEBs*HbLD(]5mI1hCKa<70ח1ldrX&WHgr_ׅkH쇄Lẏ7kԡ1H7$q?qTBgoNBnQ/H -k5qs/ ;}XjٛϦ/F]A9a,JHzg<mDac3T?vZˍ菇o$b}'?}N9{ۃkd&̻85|rގaaq(oWyU>>Y~$V#ס Dcggyuc gKzl-ZQgrZIDTI{}~$؃w(7gw<#16C2(GThɝi=}4z 1!f 'KFa,\>\a rՀW7K 5k{95> t{(a4{{'ynfݳ s.qlz&F"uz\v$suk!\$/U%Hb'D{H hC~e$P2F.4($7Аk{j\a^\avB{#j oGBj۽Wvc$5>܈FT u'~Ϧ/+~Iz$FkZ@k~ܽ{1vDǗva # $nH 3B 7ccguDfumۈO/!4[Ț}Uz'Ir)uH^Q xoA3+@S {~'OCA?ÿU%Pb>^s#r{m/$`\M0JajUM30՝ Wo[sטѱ&Л )[f 1cшvC7OOE)覶7 ݍjheaZ܇6>Ysy{.{yȩ۔MR$\-lM~>^Q0x m>*h]vcpU$Dܐ! $(giR&Wx;~a>ԦdydPh)L2B oXrR~շ~ւtSTdq0]aC 1cDϦ{2H܇DV?*A E7k3u.وT3'-_a  NX'[5surȩer#QFx"+ibEa\#cfの0?L!j+$עqGh[P)ມ7ʨ 9װ qX,^{ׯZ; :BkG.a;&ČG>~xҵ6=+| jGq?ߎDC/PN$he>Gx~BT!XM&W8Ϧ %'ߜHnϠ4|b 'TGT9XDdž{|&W͝(9*$T}u:D[ 󣥦kţWֻGvC2(Ck.@.7?Gby$ Eؖбz(WRͣ c1cQH0 (7J>^ !rĭf؄nzAc;Wv>IcD3)ՍgO K+VBx"0h=:>l#lm Vnn@uq~Zxm}/uBvb>^*.}$>Գm5Jϣ <yHHiH BNd3c7 6c ' 0e} c1pW>pOA SSNT!Xŗs4\" PU~a@.A}tv7 >r.D{~H_+D/ZHz亝rƼhK 1Nt&W8;M? /y$r!p/r*E(LހYۿ!l`&zHlo.&;Moa#5ihto>S+P\!݀$>FW{. }svF\-\(0hTozr'GZG$|Oʯ|O"v%3>>rAΠ|1E_bwI _%OB4 ?! ow}BAbw ?LL)M3+}sb9bvK&Ws[3<"άqcDNO%7!`gBpq 1{;ϦLE7|6}foF&^jnBbjQ_{-_%>8:Mp!ޭ*|6qܡ"wF侦K*^z׵ ;Qү/X}5cc{gW;>ualGXhnq[Q~ rB=ދuPsp15@(o@BhvXXϦ~ ?>(Mg_%DԛQ1lw'jZFPw]^v!׫bpg][5^3ɠnZ=(!棵!U;>?BYa|L5hީ܃&A|ͽf:X;PFal'3k3ZӂnNAЮn;Llj8)Q(Ef__|6ݑ> ||{tkJ&W$݈`p,akï^uvT&Wg>~앞+L^#I%NH7"AT b 8ϞgD4_\\?}vD}*Ǿ5Rzp^a M=aih0xpp>^궻xQS KHGJM"rjj|nnQ@~>~%ιqk C.$$FIU P>f`'SF| Y\ϦOy<Ϧk$(n tgr6`v>\&!Q#r*R+Gڭ"jxqw6'j]J!gs~  + /X_ SKʕ13vND"I@>mQ> %}aH2x+QysCb8r%B!D}\`tӞ#+Yŗ} j{>dr?drִX.޺8 E GչsQ!Q)D&G.[n*}1e}Pb _M9=>㶞13>Թֽ:v%ɠ*) yDŽݓϦ}ߦJՖ 9~&}x?%_sϘś633F\!:_;[դޏ P)T$쾟PX|6m{\00dlum=# r6뚁 |  ٴAqo4E`2gZ,ǣuըWȢ#"zo{`3+Ӊ;?Z%bCC_NS]0bpyW(/dw:t v*ʫJ;E&WأKjGp>J!:_P~HPl@|6}ֶ=dPG-ـ,gGn:3|Ox z< Q \zvg)Lu0m 1cTs=@݉6vZ;2&O(\XZ \Ϧ+zdr@{>bncX}M mYg/ SИԐ T0H<Z{6fC0p{( ~;2Z S7n5 cbB\zgSN<[k*W4+LEy]7^ɾ:Q~/j{ *Mw4AQ^׾(7X/J@Msks@-#r}YK]5?bjz]|60K7F;ϣ_Kv$clLЀª$nF'OD5LmB2(:Ja\Q( #|+ ?M_DHՆ01sBq&gԍ|enw'E)LUbB՜xmy0q39/q(EL/jnnD9b#?G?vF߇T9[ێ"@ʇ$ InDp&DծM( f|Mr{uS=qRyR)H4aB0v nEfbDlzވh= P~m<Nu s MIOP=}!C!qB$g|f/=zW%m0 c[c9bAsϦ7y6D>q3$ : [ #rXmB0hI!JR nC1Um3hnoY -ȉ[BQH'@r\ijy1.01ؾޯDN-_ݼG%\!Bm?SdPLi Q%d x)6~".\8}O qՇŠ!*MJm >v??M|GSVF†al#f1\ qu8 땀Aq!KFu6 !.E"iHO#1s_=Hܯc':=H=ٙ鼮m2 cX0!fM&W|U=Skua+0@ݬn@b-U;ފȅ( -  >QőZ)5CT;n&Uh6>];[q L1* ͕~})L!(r,g4fF=TmHTu!wUi9ba*Aq2[j-4S$~$;|$IDATrEBDyfnvyg 28ic Gx['ϔ[}! #fƨV'gQu@ JND$p'݇\ ]z9=~^Z Sɠ| h()m;01*I\ H4-Ak{UEBf'3)_;!,DBXBSP5}t~U SW1Rg0F+$vvA"i*Tn>`12Їg 7 䂍q5ɩDUqn&4si$pD!N0vp3 cT ? JJ/ P^m{ [O$DXnnP&()(1JajP]a#s ,@Ze䀭apec=jQr~0baWK>C8B%V45r]B7.5fK7 c/aM#@«=߆H\[0u X˜ygX4<;?v& 칐Z9l10F+סkPۉ| 5\T?P>TXA@0hwW7 a޳w6-CzaH,G0Qk UK2g>+ rͼH X{v V;o &o5lk_a#ba*A>ZH\DWm?UF^fBέ9|m⎱RAV֕Cua|L1HB."6l9 SCXͤ . XadP W"CN;QD"$.Ey`sxوDYg=5TW8*hKaj )Xnajj0iIH\AnW=k6p =%OD\HUЀHF1$.r;1[\˜ 51±ФadP<˩ Q@0_uct|G'4e#o$ݍE*!7nK# #6 XQ SA1()O0F14 cpwPC8~@?r`#m)kAaĉ=PT*NA@N.@hFe?'LaI0Fuc*=zV&k@swb}+h"q5(~_EM(d4p{)Lu#ɠx(B#_Ca4 ct00j0; niќ& u 0kSh.K/nARZ-0badr0'K׼P/8\e\GԊ[[Dw=a{޷g;m!r~_ So4 cBa mem墎q`--(XA_w7\e筼ޕ" Qv ca(1j ?]]wirЄ@u/mG2ԞX|UQ~Rn$b#0P S 0^s M|_O<3,Ǣ_Ӏ] 8X)t` oF$T)dP* %/RaƋbadPCA:+?oTYϧ?6̧h(„a(h`_|6mΖaC& 0|6] Mk56 c1!f1 MWD Фaa0aֻaa0aB0 0 c0!fa1L3 0 &Laa & 0 0 baaÄ 10 0a„aa0aB0 0 c0!fa1L3 0 &Laa & 0 0 baaÄ 10 0a„aa0aB0 0 c0!fa1L3 0 &Laa & 0 0 baaÄ 10 0a$m)ASIENDB`openTSNE-0.6.1/docs/source/examples/03_preserving_global_structure/output_41_0.png000066400000000000000000001327131413546205200301470ustar00rootroot00000000000000PNG  IHDRc%sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 3.0.3, http://matplotlib.org/ IDATxyS߹Ifa7AQQPwjWV]o ھ}kwIZ[bk]j71ZwPAٓܐ0 [8g>3d2'9y91c)0c9Y0f1SDc1cc1Ed1cLY0f1SDc1cc1Ed1cLY0f1SDc1cc1Ed1cLY0f1SDc1cc1Ed1cLY0f1SDc1cc1Ed1cLY0f1SDc1cc1Ed1w{a1\`ArdE2l8once1,3fZ{>ҕʷ0M3Sd6Mi>7o*@o2!(1eƌb 癖wWt;n'ռ^KV5W\k7e1Ƙ̘1{S\T>\_HsU'ǕE{i[xld ]1c)ˌ}ۏʪ~MBY\wn+HU|AE[vɷcx3c9X0f^rWǂUa>a` ?ovǀY}@9>Uc)p` H}ʒ/=ilw}KUmU=@ +8V^~ D2xc1Ee1{(H] G"}VonJ雀MD*,V'Հ3ƘCMSbE_«ԏBM-">UMn;;4L>' CȻ1f1eƌ ȱ_}H57̆ͽʶ4-sPCz![l1 ƌ餏zޭoEqd}meYEk> mmzdYCũnfNAE5+GG-em14bЊ/57WץB ƽB_qgcMIaϿ{6߃T7sc) 1fÁWWUotKom C@ @,HM 44~^1Ƙ.ʂ1cv"H{bJsfNi[F|zuݙՓAir}]8ou}irs1Ƙc@,ԌnQ*[etۓ._?fk& lHh K/>OFcF_Q0G`vۦ:1g5ct`\3F,9@y;zxW[?MzP:+}^Լ'aԢsV5䈚-_ݚrcl51pZv 5gVez?z$}M#{2xl1d:c0,3f KN~b/²sGkFd<.;p.H2]o1e)Q_MƣPnƩn˿ȭNw Nƣ˃ @<^X>G9c1e̡5߁?-x4מpWZ+w{ cˌCF,*A Iƣ[ gE1ƘCeA/H Z58Ja  ,3s̘9hw龫рq;jl/t2`2mc5s̘9&Od<+WQSh}7ˌc1eƌ1c /Hs8G[1Ƙ̘9| 8X"-HU9BilcY0f/~,%Rp'0ͱߟ4c:bӔ怗Gbfmͱ뀇 1ƘX0f_HƣyQGd1kma1SD3]Z,::vc1+7]@Yb1 3]ׁdKF_jJc1f{6Mibclk{ cLWdӔc1EdӔc1Ed1cLY0f1SDc1cc1Ed1cLY0f1SDc1cc1Ed H%Rbc9X0f%R]c1 fbc9Fc1Ed1c1Ƙ"`c1,3c)p`X"$W Dۻc1eBD* |C7'Ac1];8e?[:f`~1ce-1cȦ)1cȂ1c1Ƙ"`c1,3c)" ƌ1cZ[bT0hIƣ.x1,;)~6p X0f1@,;8^~Sc1skK*dc1u]u[ ;Fohc[l%RxtK! Gc1{ M9xt p*p%<07Ȑ]c1fo` %RSgc cT-p9dppU2]KJed/@( qR2}cc1d^KDWNmyDd,xADfH,BAlA0wXfl׽ lkgSкgXgI0),FKAdgsns ALD>p܉s/~i|B5K@ WN\&4) `Y ;D|tKevoҦKvj'E o8z0H7B]U+ݒx1ƘhhzDppTLp"rSpH.{;83Mιl]7V}kjEιw`F \uE,ہX"M4k7|hXoP Re~XcD 40[G[ :cd%")E@o99L0mysna{^@s/"nK`A}weG-B.U&a}düm48G9m47 m %dK}ׁ?[^;~xl31)&5b 39ˌmo T#h0nrPvlc]먫/O} PH9]x\roӋѕ4i81Ƙp5&!Ƃ@H;u~\QiUg`뛞v/Y3 ͝c( X |cd<@||\nNc1{(K2~7 |Pz+\|xx7f:xnQ~;穞/X dЍgLuc)C~2Ȇ\r%UXX +)r̭rl[ uY &ቷmJmu-K~xxuO}( Vz'sLeEc>rcDlShV{mƺASa6r͢]>ЕZ傹_:A0$H}LuwCϧoW 2Q/Ӂovz1cv!YhVׅ` r}6JhՒRHp\#Zu$DD []Lw읏~l&?SFi1<8M_ѠmٓN>7e⬆<cy-CwL+-D}_#s/9羽ۅ{e_^Pp(c}i꿶;mX3W(EW-h wwh5p` ^Z^g}G;4aW37<>,&׏C{t唉6. \;c5"2!Bx Dӿr +뗳f眛 t#_ D+9oN{?c{ bT_t?RZF/Eӑrߋ#9dǭG7. F&"zF##_'޸2@;EBǖvأ{g#Y/_=nE38x+Ϙ=ILe)9hU X R3EdfȂ OG;wCn\\n/ <.SЀM:\|4 VE8_G] !"_ 'hjk"4psv`,HXe+v X7!ZP)C.XUg@_%hf(SϵTl Esm~./Ve{$R|VɆȓSvH=黴bS'R0 wx +/e^<{]D֋xL`sV ι'Dz8nܳ"2Zi?#"b<2X, L$1"a$ ~GI@]|CMRVQHYA8 o;(ߎ" AEX̺,ASc6 /%S=HEnv%Rh:H GܮEk=e-q;m1co-ae=PŻsRf\ɌnB\=1v9HUxtoti6cL`,Hŋ_E[GG ?S85W߯[fC^|p>~(B3 GZZW5<=M5%٣/ȭ,+N6-tC^-'(`w`|ϡٸ^h |Kn /be$c1ՔD*6û]e+ hʡS}#XDyvlS6Rʃ:l=0>TBylc8\' \4y!B^Ĺ^r]3֡P):%Y?|͈5_<7@7&xLnYr:8t[e>xc` }.c'kAJ>̄D[%rGzѩpԤ"z5niI_{R.  R׸m5 5Z?A O&?嘛vl Q!xTfCsMw~b2Ƙ%R?Kƣ+=c?a4]'۝um>S=,*"޶itz5 [y% Bs|#R=E ]h;\1͐E3Y:h u~p_ZKV_[#^։]uyދbY4=lp*`s2m/cL0 X"R."SDssfE%'"s;"܃CDȂ3E]%RyhoqTnk(ȢAɜlH _*_onx 44wo"NAph`TfZj`4Z fjL Qyf4z^6=]ٸ ϣZ#k;cgh1fO-X'MN8ێLl q BD_ -,."9 6>psntuxRs&/:`X"F3F!l{>۾-X@L2{T*C84呐\-e%u{8 \?fN)WІs+L׳Yu%hGhPw2|̿_)mM/Xfۆc CٓGѝGn+o{NDʅV[ٹxL)"RnÀ8ƈG5;yt]ιی <ιDGK`cc9]V/ԷwGs}*sw{w\o|&3|97.{sE0,Gh;yځ=4uf,| n[>l ӌу-F-m ahٿ&] :F+F%cc|2c٧U建pypu-k1{tZlMcWu_ 7S {r9$"eƄ8-,8\GsYFgh=}/yܽEd>sED.r4;{4 \mGgkLbTZvfr94#VD>"lʦR_UW.ֲ1ܿĺunZd w Z< x}8o lDzn_?hUoe۫rqpޫȷ_Fhxb2o}d1X"uk7z>t+[&˅'"@S&I 0.ZA`5 ι%ۊ%Rc o6\ YJs<*CD<\{a*Ѭh-هРn : )H.Dkľ}eh]ڝA迎N{/& M4C֢Sw$u@K,܎FiKƣiD }}'MֺDS#dQȔW/iO|c\0[{kzrjnNްn^l|R^cs]ww\#jb|s-]NzT/ {΅S.M#| 4|팿 rX+ h07zbehkZWVYW!KIp+[(nDW :BWǴc:+:Iw> 84./c,pkc}|ro\)ܝlٓ-0o3.|+._`y/ux~ElUGJw9ι{,+vӔD? N9wN*L; sǻ,^"l4{B]3n$:o~` wϠB]U?t?hMxt MtW+%Щ21V8%d<՗cX"U^~gZe` GK1&_?aBRxեҒ1gz+WSK"46 Zy$T#6TeYl3o'WE 8HF^$_՘NԄ@'PZVYd*g.d C3Ra>lM,<\4WR90FPNcwgv{aiIUqM#16?hql}+K-D~qB- e{?p]2Nj1d<4HMAm7Q%R+,GeaDd2}=8iqGٲ;r5kDug kܐT؟-+һ8"2\0OM r:l^HNZG٪FBq-3^;%K+g? #-!4A?n9,00>\uo6(8i4U1+E8VhѺ${c /SѕЅcЅW$3fO t2qVႠ?/M }k8%]1O ƒ%Ro9g^.jiQPM}vL «-&_]LF[[fT0KA;}E3^ lFhWeAnD}MGu86D֡mn~](xr+zeD Zތ}/a[w,B#bԯЩϳͩ vt\kF7Nn%RdR9nm6iu 5`ɲ#-z׬뒯i-CkC_'oWsuŐ9@t` U$e mh0ԠYܵhp{ln2'p2f9m֣jglcىh]X"5-kט)g9cӚ KJn ,_;f x"~MSK-x3J&}911-)uJB3-4'3"2!)Y;Hc*/YPq٢"r^n8~㜻59t˽+׏Yb?w8^tm͑pKsT.]}%C6>96l'Nç2[Hy(R+T;`)n_iftˀɐYלs.9G9`[) sNDƣ39Uuέkѿ;>uXP'6vi?pmY P"xmGCK/!qhpѬU.k nI\>c`4vI^H$T;  3}ͤ}7X  7oA3l#>g݀ ɏc:;kLy,ꛌGl{ٓ&L8,w͒s,[sTv+: ^VmTIѕ/ǭBjьۃh- \n6:h}Cn@Zx T2@[Q,%R2ķV0tCCUoCwLi{Lz+7.{rU5G蝵Yndfk[ MMŻ46UW7簞ǴT+7{i9ιhʄι9mq4 ;0H\~Z]tq|]nncDj,:j !]GK#{(4c?R4끦yChnTgAvCS_8o9aS] o?k:8?*{[>WORtk~oFbڑxc̎fhGۭ Z`,%R7lmW۾6e'LU_>bnli]ZqkToᕥ)-P^ {YS[,oK֣uc!3 fD sわcs\8ͰuٳCAQX"Uټg?xw(l_VMBugq~d=2h: [љ˂yhv,M~sqWBa+!FSǓ=N ƄB̓n6w ,D 13h`W^A s h-obT$H M~2쥧)H4(|;69 7d<tGzϢ{[ Ge%hږ @ NDӞS+Mߋ׳܋y#?xǠ˝@֦mxBcF7&D3C̻w,[Ӕ}=\^w?vo5e+9njyXڷr=6߇% {qBuc?9D~{f#=]~evEPx?]D-BKdpsn<-"~,"5hp;}p?vmڝ+Z0K> }L&•-ixɎf-sV>Ven#q$(iA/ ^ r2w 4` IDAT+-Dp#۫ Z#N$ngt::V4_vN"rHڲFnhmUxDj50`&:]0 }s{2]Ocvn.Z>9|4 %>;6PPdçF[sn \duK@{g9miMGᱏ%-/%4i{2>"HSv%8)]nS.3,̒钴R(P :Vt@ *  ʈ" ""_FUPJJ%{fotI۴}4|γ|1Iۘ=F?%VcZkZШ21jEӄa]>m5rݧs?4v9څ4#eA`а3{ =>fG'xpe&&SL*zs,dz0c4R)?bMA-2SeR02せO;qN?zjԘᯉs4\pΉlt'ӟZ%DgnE\4,lZo y*$N,mx!-^1| :NpPv}ЈT`:Nͮ4h mU:Z%iZo*X5m$:{-*&kP!6s-NOJ!-(H x